Mercurial > hg > camir-aes2014
changeset 0:e9a9cd732c1e tip
first hg version after svn
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/.hg_archival.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +repo: a5a30010dc3b5bb7616bc6036dcb15dc75b4260b +node: 3d980aa87d2b0f914ae772c8dab357eb27fb7e61 +branch: AES2013 +latesttag: null +latesttagdistance: 679
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.TXT Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +This page allows you to reproduce the experiments from our paper "Feature Preprocessing using RBM for Music Similarity Learning". This includes the newest release of the CAMIR system for modelling music similarity. + +MLR and SVMLIGHT were tested using a framework developed by Daniel Wolff (daniel.wolff.1@city.ac.uk) at the MIRG group. +This version of CAMIR contains the RBM toolbox of Son Tran (son.tran.1@city.ac.uk). +You can download the code using subversion from the following repository. It is licensed under the GNU GPL v3. + +user: anonymous +pass: citymirg +http://chivm.soi.city.ac.uk/svn/camir/branches/code_publications/AES2013/ + +For reproducing the results in the paper using Matlab on a Windows machine follow these steps: + +-Download the code from above using a subversion client such as collabnet svn and the above password +-Set the working directories in the editme_startup.m file of the downloaded code to the location of the downloaded code +-Include the code directory with all subdirectories in your matlab path +-Start Matlab, change the working directory to the location of the downloaded code +-Edit and execute the editme_startup file, this initialises the dataset and could take a few minutes. +-Run the experiments using the scripts in the folder reproduce_AES53rd. +-Most of the results are output as figures. Otherwise values are printed in the Matlab console + +If you use this code please cite our paper. We are more than welcoming your additional bits of functionality to be included in the package. \ No newline at end of file
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/AnnotDB.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,412 @@ +% The thesaurus class is a basic component of all +% genre and tag information managing the whole Vocabulary. + +classdef AnnotDB < handle + + % public properties + properties (SetAccess = private) + lexicon = {}; + + end + + properties(Hidden, Access = private) + + annotsdb; % a numowners x numannots sparse binary / prob matrix + annots_oid; % ownerid to pos in annots conversion + + binary = 0; % indicator whether the db contains binary or scored annots + end + + methods + + % --- + % simple constructor + % --- + function db = AnnotDB(lexicon, annots, ids) + % db = AnnotDB(lexicon, annots, annotation_ids) + % lexicon: the list of all individual annotation elements or + % clips_by_annot or + % lexids_by_clip + + % annots: either a clips x numel(lexicon) binary matrix or + % ... + % + % annotation_ids: clip ids for the binary case, + + if nargin >= 1 + + % --- + % NOTE: two ways of supplying the annots are allowed: + % 1. clip ids for each lexical element + % 2. binary matrix + % --- + if ischar(lexicon) + + if strcmp(lexicon, 'clips_by_annot') + + % --- + % preset the lexicon and hash ids + % --- + db.lexicon = unique(lower(annots)); + + if iscell(ids) + db.annots_oid = unique([ids{:}]); + else + db.annots_oid = unique(ids); + end + + db.annotsdb = sparse(numel(db.annots_oid),... + numel( db.lexicon)); + +% for all annotations + for i = 1:numel(annots) + +% for all ids in set + % is this a cell or just a single index< + if iscell(ids) + for j = 1:numel(ids{i}) + + db.add_pair(ids{i}(j), annots{i}); + end + else + % single ndex case + db.add_pair(ids(i), annots{i}); + end + end + + elseif strcmp(lexicon, 'annots_by_clip') + + end + % this is the binary case + else + + db.lexicon = lexicon; + db.annotsdb = sparse(0,0); + if nargin >= 2 + + db.annotsdb = sparse(annots); + db.annots_oid = ids; + else + db.annotsdb = sparse(0, numel(db.lexicon)); + end + end + end + end + + % --- + % retrieve annot-substructure for given clip ids, + % collecting std = [or = all] ,[and = common] + % annots for these + % --- + function new_db = subset(db, ownerids, mode) + % new_db = subset(db, ownerids, {'and', ['or']}) + + if nargin < 3 + mode = 'or'; + end + + % --- + % create new DB + % we make sure the tag id index keeps + % the same for subsets by copying the whole + % lexicon + % --- + new_db = AnnotDB(db.lexicon); + + switch lower(mode) + case 'and' + + % --- + % TODO: implement this and + % improve speed below + % --- + case 'or' + + % successively fill with given annots + for i = 1:numel(ownerids) + + % --- + % we retrieve annots for each clip + % and add them to the new database + % --- + [annot, score] = annots(db, ownerids(i)); + for j = 1:numel(annot) + + new_db.add_pair(ownerids(i), annot{j}, score(j)); + end + end + otherwise + error 'illegal owner id combination mode. possibly forgot brackets'; + end + end + + % retrieve annot-substructure for complement + % of given clip ids + function [new_db] = exclude(db, ownerids) + + % get complement of clip ids + ownerids = setdiff(db.annots_oid, ownerids); + + new_db = subset(db, ownerids); + end + + % --- + % retrieve clip by annot. + % if multiple annots are given, the clips + % containing all of them (logical and) are + % returned + % --- + function oids = owner(db, annotstr, mode) + + if nargin < 3 + mode = 'and'; + end + + if ~iscell(annotstr) + annotstr = {annotstr}; + end + + annotid = []; + for i = 1:numel(annotstr) + + annotid = [annotid strcellfind(db.lexicon, annotstr{i})]; + end + + oids = owner_for_annotid(db, annotid, mode); + end + + % retrieve owner ids by clip + function ownerids = owner_for_annotid(db, annotid, mode) + % ownerids = ownerids_for_annotid(db, annotid, {['and'], 'or'}) + + if isempty(annotid) + ownerids = []; + return + end + if nargin < 3 + mode = 'and'; + end + + switch lower(mode) + case 'or' + % search for all appearing owners + candidates = sum(db.annotsdb(:, annotid), 2) > 0; + + case 'and' + % search for the common owners + candidates = sum(db.annotsdb(:, annotid), 2) == ... + numel(annotid); + otherwise + error 'illegal tag combination mode'; + end + + + % get positions in database + pos = find(candidates); + + % return owner ids + ownerids = db.annots_oid(pos); + end + + % retrieve annotid by clip + function [aid, score] = annotids_for_owner(db, ownerid, mode) + + % single query case + if numel(ownerid) == 1 + + pos = owner_pos(db, ownerid); + + % get positions in database + aid = find(db.annotsdb(pos, :) > 0); + + score = db.annotsdb(pos, aid); + + + % sort ids for output + if ~db.binary + + [score, idx] = sort(score, 'descend'); + aid = aid(idx); + end + else + if nargin < 3 + mode = 'or'; + end + + % --- + % the query contained multiple ids + % + % we dont return the single results but + % the statistics for this subset of clips + % --- + new_db = db.subset(ownerid, mode); + [null, score, aid] = new_db.stats_count(); + + % cut off at score > 0 to abandon unused tags + u = find(score > 0,1,'last'); + score = score(1:u); + aid = aid(1:u); + end + end + + % retrieve annotation by clip + function [out, score, aid] = annots(db, ownerid) + + [aid, score] = db.annotids_for_owner( ownerid); + + out = db.get_annot_name(aid); + end + + + % retrieve annot name given a annot id + function out = get_annot_name(db, annotid) + + out = {}; + for i = 1:numel(annotid) + + out{i} = db.lexicon{annotid(i)}; + end + end + + % return annotation id for annotation string + function aid = get_annot_id(db, annotstr) + + if ~iscell(annotstr) + + % expensive search within annot list + aid = strcellfind(db.lexicon, annotstr); + else + + % search seperately for each annot + for i = 1:numel(annotstr) + aid(i) = strcellfind(db.lexicon, annotstr{i}); + end + end + end + + % --- + % return statistics on saved annotations. + % = returns the sum of the scores and + % sortec lexicon + % --- + function [labels, score, annotids] = stats(db) + + % out = zeros(1, size(db.annotsdb,2)); + score = full(sum(db.annotsdb, 1)); + [score, annotids] = sort(score,'descend'); + + % prepare labels + labels = db.lexicon(annotids); + end + + % --- + % return statistics on saved annotations. + % = returns the number of annotations and + % sortec lexicon + % --- + function [labels, score, annotids] = stats_count(db) + + % out = zeros(1, size(db.annotsdb,2)); + score = full(sum(db.annotsdb > 0, 1)); + [score, annotids] = sort(score,'descend'); + + % prepare labels + labels = db.lexicon(annotids); + end + + % this is a stub for a tag cloud-like output + function [out] = annots_cloud(db, ownerid) + + % --- + % TODO: actually output tag-cloud + % this output is aimed at input into a web interface + % we successfully used http://www.wordle.net/ + % --- + + if nargin > 1 + db2 = db.subset(ownerid); + else + db2 = db; + end + + [labels, score, annotids] = stats(db2); + + % --- + % Note: for performance issues we compress this data + % to a maximum value of 1001 + % --- + score = ceil((score./max(score))*100); + + out = ''; + for i = 1:numel(annotids) + + % repeat the tag according to score + annot = strrep(labels{i},' ','-'); + for j = 1:score(i) + out = sprintf('%s; %s',annot, out); + end + end + end + + + function out = size(db) + % returns the size of this db + + out = numel(db.lexicon); + end + + function add_pair(db, ownerid, annot, score) +% add_pair(db, owner, annot) adds an annot and owner and can +% increase the lexicon size + + if nargin < 4 + score = 1; + end + + aid = strcellfind(db.lexicon, annot); + + % create new position for annotation if neccesary + if isempty(aid) + + aid = numel(db.lexicon) + 1; + + % add to lexicon + db.lexicon = {db.lexicon{:}, annot}; + + % enhance annotation matrix + db.annotsdb = [db.annotsdb, ... + sparse(size(db.annotsdb,1), 1)]; + end + + + % create new position for clip if neccesary + pos = owner_pos(db, ownerid); + if isempty(pos) + + pos = numel(db.annots_oid) +1; + + % add to oid + db.annots_oid = [db.annots_oid, ownerid]; + + % enhance annotation matrix + db.annotsdb = [db.annotsdb; ... + sparse(1, size(db.annotsdb, 2))]; + end + + % save data to database + db.annotsdb(pos, aid) = score; + end + + end + + + methods(Hidden) + + function pos = owner_pos(db, ownerid) + + % returns database position for owner id + pos = find(db.annots_oid == ownerid); + end + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/AudioFeatureRAW.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,158 @@ +% -- +% This class loads and hanles the aufdio features included with the MTT +% Library +% --- + + +classdef AudioFeatureRAW < MTTAudioFeature & handle + + + properties(Constant = true) + + end + + properties + % --- + % Set default parameters + % --- + my_params = struct([]); + + end + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = AudioFeatureRAW(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + + end + + function [a1, a2, a3] = visualise(feature) + % --- + % plots the different data types collected in this feature + % --- + for i = 1:numel(feature) + clip = feature(i).data.info.owner; + + % --- + % labels for chroma data + % --- + chroma_labels = {'c', 'c#', 'd','d#', 'e', 'f','f#', 'g','g#', 'a','a#', 'b'}; + mode_labels = {'minor', 'major'}; + + % hange labels to reflect detected mode + chroma_labels{feature(i).data.key + 1} = ... + sprintf('(%s) %s',mode_labels{feature(i).data.mode + 1}, chroma_labels{feature(i).data.key + 1}); + + + secs = [feature(i).data.sections_start; feature(i).data.sections_duration]; + + h = figure; + + % number of subplots + n = 3; + + % --- + % chroma feature display + % --- + subplot(n,1,1); + + % get segment times and fix for same lengths for all plots + % --- + % NOTE: Last segment will appear longer + % --- + segments = [feature(i).data.segments_start]; + segments(end) = feature(i).data.duration; + + % display chroma vectors + uimagesc(segments, 0:11, [feature(i).data.segments_pitches]); + + set(gca,'YTick',[0:11], 'YTickLabel', chroma_labels); + + axis xy + colormap(hot) + %colorbar; + ylabel('chroma class'); + title(sprintf('clip %d: %s by %s, chromagram', ... + clip.id, clip.title(),clip.artist())); + + % added sections + axis([0 feature(i).data.duration -1 11.5]); + hl = line([secs(1,:); sum(secs,1)],ones(2, size(secs,2)) * -0.8); + set(hl,'LineWidth',6); + + a1 = gca; + + % --- + % mfcc feature display + % + % NOTE: the first position of timbre is reduced in energy, + % as this seems to introduce some corruption in lots of data + % --- + timbre = feature(i).data.segments_timbre(:,1); + timbre = timbre/ max(max(abs(timbre))) * ... + mean( mean( abs( feature(i).data.segments_timbre(:,2:min(end,5))))); + + subplot(n,1,2); + uimagesc(segments, 0:11, [timbre feature(i).data.segments_timbre(:,2:end)]); + + axis xy + %colorbar; + xlabel('time[s]'); + ylabel('mfcc coeff'); + title(sprintf('mfcc timbre features')); + + a2 = gca; + + % --- + % beats and sections + % --- + + subplot(n,1,3); + axis([0 feature(i).data.duration -0.6 1.2]); + + hl = line([0 feature(i).data.duration],[0 0]); + set(hl, 'Color','g'); + + a3 = gca; + + if ~isfield(feature.data,'bars'); + return; + end + + beats = feature.data.beats; + bars = feature.data.bars; + tatums = feature.data.tatums; + % tatums + hl = line([tatums(1,:); tatums(1,:)],[ones(1, size(tatums,2)) * -0.2; max(-0.1,tatums(2,:))]); + set(hl,'LineWidth',1); + set(hl, 'Color','k'); + + % beats + hl = line([beats(1,:); beats(1,:)],[ones(1, size(beats,2)) * -0.4; max(-0.1,beats(2,:))]); + set(hl,'LineWidth',2); + set(hl, 'Color','b'); + + % bars + hl = line([bars(1,:); bars(1,:)],[ones(1, size(bars,2)) * -0.5; max(-0.1,bars(2,:))]); + set(hl,'LineWidth',4); + set(hl, 'Color','r'); + + % sections + hl = line([secs(1,:); sum(secs,1)],ones(2, size(secs,2)) * -0.5); + set(hl,'LineWidth',6); + + ylabel('confidence'); + title(sprintf('rhythmic features @%3.1f BPM, %d/4 meter',... + feature(i).data.tempo, feature(i).data.timeSignature)); + + end + end + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/Clip.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,312 @@ +% --- +% This is the class for magnatagatune clips +% --- + +classdef Clip < handle + + % --- + % here come the internal clip properties. + % the database is stored as a global variable + % --- + properties (SetAccess = private) + + % magnatagatune clip id + my_id; + end + + % do not save whole db into mat file + properties (Hidden, Transient) + + my_db; + end + + properties (Hidden) + + my_dbpos; + end + + % --- + % here come the clip functions. + % --- + methods + + % --- + % simple constructor + % --- + function clip = Clip(varargin) + + % usual or empty constructor ? + if (nargin > 0) && isnumeric(varargin{1}) + id = varargin{1}; + db_type = varargin{2}; + + % --- + % check for magnatagatunedatabase + % and load it if not present + % --- + evalc(sprintf('global %s;',db_type)); + evalc(sprintf('thisdb = %s;',db_type)); + + if ~isempty(thisdb)% exist('clip_info_proper') + clip.my_db = thisdb; + else + + error 'db not found'; + % dbload; + end + + % --- + % recursive call for creating multiple clips + % --- + if numel(id) > 1 + + % multi-id case + + % --- + % @todo: use class names as db names + % --- + if strcmp(db_type,'db_MTTClip') + clip = MTTClip(); + for i = 1:numel(id) + clip(i) = MTTClip(id(i)); + end + elseif strcmp(db_type,'db_MSDClip') + clip = MSDClip(); + for i = 1:numel(id) + clip(i) = MSDClip(id(i)); + end + elseif strcmp(db_type,'db_CASIMIRClip') + clip = CASIMIRClip(); + for i = 1:numel(id) + clip(i) = CASIMIRClip(id(i)); + end + end + + + elseif ~isempty(id) + % --- + % actual constructor + % --- + clip.my_id = id; + clip.my_dbpos = clip.dbpos(); + + if isempty(clip.my_dbpos) + error ('Clip %d not found in DB', full(id)) + end + end + else + + % --- + % TODO: deal with empty constructor + % --- + end + end + + % --- + % member functions + % --- + + % returns the id (function neccessary for + % multi-clip environments) + function out = id(this) + + out = [this.my_id]; + end + + function out = comparison_id(this) + + out = [this(1).my_db.comparison_ids(this)]; + end + + function out = title(this) + % returns name strings for given genre position ids + + out = this.my_db.clip_info_proper{this.my_dbpos,3}; + end + + function out = album(this) + % returns name strings for given genre position ids + + out = this.my_db.clip_info_proper{this.my_dbpos,5}; + end + + function out = artist(this) + % returns name strings for given genre position ids + + out = this(1).my_db.artistdb.annots(this.id); + out = out{1}; + end + + function out = artist_id(this) + % returns name strings for given genre position ids + + out = this(1).my_db.artistdb.annotids_for_owner(this.id); + end + + function my_tag_ids = tag_ids(this) + % returns clip tag posids for given clip id + + my_tag_ids = this.my_db.tagdb.annotids_for_owner(this.id); + end + + function out = tags(this) + % returns name strings for given genre position ids + + out = this.my_db.tagdb.annots(this.id); + end + + + % this is for ocompability with + function out = raw_annots(this) + + out = this.my_db.clip_info_proper(this.my_dbpos,:); + end + + function [out] = isrc(this) + % returns name strings for given genre position ids + out = this(1).my_db.clip_info_proper{this.my_dbpos,11}; + end + + function [out] = artist_mbid(this) + % returns name strings for given genre position ids + out = this(1).my_db.clip_info_proper{this.my_dbpos,12}; + end + + function [out] = mbtags(this) + % returns nmusicbrainz tags for the artist related to this clip + + [out, score] = this(1).my_db.mbtagdb.annots(this(1).artist_id()); + end + + % @todo: generalise mg tags + function [out, score] = mbtag_ids(this) + + [out, score] = this(1).my_db.mbtagdb.annotids_for_owner(this(1).artist_id()); + end + + + function len = play(clips, plen) + % len = play(clips) + % + % plays magnatune clip given by clip id, and + % returns full playback length + + if nargin <2 + plen = 0; %seconds play + end + + len = 0; + for i = 1:numel(clips) + + % get sample rate + [null,sr] = mp3read(clips(i).mp3file_full(),0); + + if plen > 0 + + % read mp3 file + [src,sr,NBITS,OPTS] = mp3read(clips(i).mp3file_full(), plen*sr); + else + % read full mp3 file + [src,sr,NBITS,OPTS] = mp3read(clips(i).mp3file_full()); + end + + % --- + % NOTE: sound() seems to pause the system when trying to + % play a clip while still playing another one + % --- + sound(src,sr); + + fprintf('\n--- now playing ---\n'); + clips(i).print(); + + % add clip lengths + len = len + length(src) / sr; + end + end + + function skip(clips) + % skips through given clips + + clips.play(5); + end + + function out = dbpos(this) + % returns matrix position for given clip id + + out = find(this.my_db.annots_ids == this.id, 1 ,'first'); + end + + % --- + % Generic Features + % --- + function feature = features(clip, type, varargin) + % feature = features(clip, type, varargin) + % + % returns the features of type "type" for given clips and + % parameters + % e.g. feature = clip.features('MTTAudioFeatureRAW') + + db_name = MTTAudioFeatureDBgen.db_name(type); + + % global database + eval( sprintf( 'global %s;', db_name)); + + % create database if neccesary + if eval(sprintf('isempty(%s);', db_name)); + + eval(sprintf('%s = MTTAudioFeatureDBgen(''%s'');', db_name, type)); + end + + % retrieve features from db + feature = eval(sprintf('%s.get_features(clip, varargin{:});', db_name)); + end + + % --- + % Audio Features Section + % --- + function features = audio_features_raw(clip) + % get the features from the global database + + features = clip.features('MTTAudioFeatureRAW'); + end + + function features = audio_features_basicsm(clip, varargin) + % get the features from the global database + + features = clip.features('MTTAudioFeatureBasicSm', varargin{:}); + end + + function features = genre_features_basic(clip, varargin) + % get the features from the global database + + features = clip.features('MTTTagFeatureGenreBasic', varargin{:}); + + end + + function features = mixed_features_genrebasicsm(clip, varargin) + % get the features from the global database + + features = clip.features('MTTMixedFeatureGenreBasicSm', varargin{:}); + end + + function features = mixed_features_genrebasicsm_pca(clip, varargin) + % get the features from the global database + + features = clip.features('MTTMixedFeatureGenreBasicSmPCA', varargin{:}); + end + + function features = random_features(clip, varargin) + % get the features from the global database + + features = clip.features('MTTRandomFeature', varargin{:}); + end + + end + + % --- + % static methods + % --- + methods(Static = true) + + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ClipComparedGraph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +% --- +% class ClipComparedGraph +% Directed graph representing comparative clip similarities. +% +% each node represents a clip +% an edge a -> b is present if clipas a and b cooccur +% in a comparison triplet +% +% --- + +classdef ClipComparedGraph < Graph + +% --- +% the methods +% --- +methods + + +% constructor of the graph +function G = ClipComparedGraph(comparison, comparison_ids) + + if nargin == 2 + % --- + % handle automatic or manual input + % --- + for i = 1:size(comparison,1) + + % get clips and votes + clips = comparison_ids(comparison(i,1:3)); + + % edges + G.add_edge(clips(1), clips(2), 1); + G.add_edge(clips(1), clips(3), 1); + G.add_edge(clips(2), clips(3), 1); +% +% % symmectric edges +% G.add_edge(clips(2), clips(1), 1); +% G.add_edge(clips(3), clips(1), 1); +% G.add_edge(clips(3), clips(2), 1); + end + + elseif nargin == 1 + + % --- + % Add the input graph to the empty one + % --- + G.add_graph(comparison); + + end + +% end constructor function +end + +end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ClipPairGraph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,499 @@ +% --- +% mother class for the directed clip graph classes +% contains general purpose and graph-theorethical +% functions +% --- +% we consider the similarity between clips +% as symmetric, thus sim(a,b) = sim(b,a) +% this makes the set of nodes much smaller +% --- + +% --- +% TODO: change structure to +% avoid using Vclips, mapping clip ids +% into a node number 00id1..00id2, +% +% Enables full integration into Digraph / Graph class +% --- + +classdef ClipPairGraph < DiGraph + + properties(Hidden) + + max_clipid = 10^6; + vid_map; + vid_last = 0; + end + +methods + +% --- +% Constructor +% --- +function G = ClipPairGraph(comparison) + + if nargin == 0 + + G.vid_map = containers.Map('KeyType', 'double', 'ValueType', 'double'); + + elseif nargin == 1 + + G = ClipPairGraph(); + + % --- + % Add the input graph to the empty one + % --- + G.add_graph(comparison); + + clas = cat(1, class(comparison), superclasses(comparison)); + if strcellfind(clas, 'ClipPairGraph') + + G.vid_map = comparison.vid_map; + end + end +end + +% --- +% Node id by clip +% --- +function Vid = node(G, a, b) + + % did we get just one clip? + if nargin == 2 + % --- + % TODO: this is terribly ineffective + % create all possible pairs and search if they exist + % --- + Vid = []; + for b = G.clips()'; + + sm = min([a b]); + bg = max([a b]); + hash = G.max_clipid *bg + sm; + + % --- + % does this pair exist? if so: return Vid + % --- + if G.vid_map.isKey(hash) + Vid(end+1) = G.vid_map(hash); + end + end + + % Ah no, we got the 2 clips + else + + % --- + % hash function for clip pair + % --- + % sort clip ids + sm = min([a b]); + bg = max([a b]); + + % --- + % get node code: + % max_clipid * bg + sm + % --- + hash = G.max_clipid *bg + sm; + + % --- + % table lookup + % --- + if G.vid_map.isKey(hash) + + Vid = G.vid_map(hash); + else + + % --- + % create new psition in hash table and + % assign this ID to the node + % --- + G.vid_last = G.vid_last + 1; + Vid = G.vid_last; + + G.vid_map(hash) = Vid; + + % --- + % NOTE: this is intended for maybe swithcingto + % cell descriptions in future. + % The node ids will be stored as well + % --- + G.Vlbl{Vid} = G.label(Vid); + end + end +end + +% --- +% Returns all clip ids referenced in the Graph +% --- +function [a] = clips(G) + + [sm, bg] = G.clipsN(G.nodes); + a = unique([sm; bg]); +end + + +% --- +% Clip ids for Node +% --- +function [sm, bg] = clipsN(G, Vid) + + % get all clips referenced in this graph + if numel(Vid) > 1 + sm = []; + bg = []; + for i = 1:numel(Vid); + [sm(end+1), bg(end+1)] = G.clipsN(Vid(i)); + end + else + + % --- + % TODO: There must be a more efficient way to do this + % --- + % get all hash data + + all_hashs = G.vid_map.values(); + all_keys = G.vid_map.keys(); + % --- + % search for this Node ID and return hash value + % --- + hash = all_keys{cell2mat(all_hashs) == Vid}; + + sm = mod(hash, G.max_clipid); + bg = div(hash, G.max_clipid); + end +end + + +% --- +% Edge weight by clip id +% --- +function [weight, V1, V2] = edge(G, a, b, c) + + if nargin == 4 + V1 = add_node(G, a, b); + V2 = add_node(G, a, c); + + elseif nargin == 3 + V1 = a; + V2 = b; + end + + weight = edge@DiGraph(G, V1, V2); +end + +function [weight, varargout] = edges(G,a,b) + + % all edges or from specified node ? + if nargin >= 2 + + % is there a clip pair or node number input? + if nargin == 3 + V1 = add_node(G, a, b); + + elseif nargin == 2 + V1 = a; + end + + [weight, V1, V2] = edges@DiGraph(G, V1); + + else + % --- + % ok, get ALL edges + % --- + [weight, V1, V2] = edges@DiGraph(G); + end + + % how to represent the output + if nargout <= 3 + varargout{1} = V1; + varargout{2} = V2; + else + % --- + % get all the clips from the edges + % --- + ao = zeros(1,numel(V1)); + bo = zeros(1,numel(V1)); + co = zeros(1,numel(V1)); + for i =1:numel(V1) + [ao(i), bo(i), co(i)] = G.clipsE(V1(i), V2(i)); + end + varargout{1} = ao; + varargout{2} = bo; + varargout{3} = co; + end +end + +% --- +% add an edge saying sim(a,b) > sim(a,c) +% --- +function add_edge(G, a, b, c, weight) + + if nargin == 5 + V1 = add_node(G, a, b); + V2 = add_node(G, a, c); + + elseif nargin == 4 + V1 = a; + V2 = b; + weight = c; + end + + % --- + % call superclass + % --- + add_edge@DiGraph(G, V1, V2, weight); + +end + +function Vid = add_node(G, a, b) + + if nargin == 3 + Vid = G.node(a,b); + else + Vid = a; + end + + % --- + % call superclass + % --- + add_node@DiGraph(G, Vid); +end + +% --- +% the pairgraph variant of add_graph also updates the +% clip id hashmap +% ---- +function add_graph(G, G2) + + % determine if symmetric edges have to be added + clas = cat(1, class(G2), superclasses(G2)); + if strcellfind(clas, 'Graph') + add_symm = 1; + else + add_symm = 0; + end + + % --- + % add all nodes and edges in G2 + % --- + for V = G2.nodes(); + + [a, count] = sscanf(G2.label(V),'%u:%u'); + + if count == 2 + b = a(2); + a = a(1); + + G.add_node(a,b); + else + G.add_node(V); + end + end + + % --- + % NOTE / TODO: + % this LABEL inheritance is far too expensive when + % creating many copiesof the same graph + % Its also unnessecary for lots of them + % except for debugging :(. + % --- + G.Vlbl = cell(1, numel(G2.V)); + if G2.cardinality > 1 + G.Vlbl(G.nodes()) = G2.label(G2.nodes()); + else + G.Vlbl(G.nodes()) = {G2.label(G2.nodes())}; + end + + % --- + % get all edges in G2 + % CAVE: if we added the edges above using + % label clip indices, we have to address them + % using these below! + % --- + [val, V1, V2] = edges(G2); + + for i = 1:numel(val) + + % --- + % add edges to graph + % NOTE: we assume either all or no edges + % are labeled with clip indices + % --- + if count == 2 + % --- + % ok, they were labeled above, + % so index by clips. + % + % TODO: + % 1. get rid of this sscanf stuff and use add_edge for + % creating the nodes first, the only add single nodes + % 2. use cell labels globally instead of hashmap here + + [u, count] = sscanf(G2.label(V1(i)),'%u:%u'); + [v, count] = sscanf(G2.label(V2(i)),'%u:%u'); + + a = intersect(u,v); + b = setdiff(u,a); + c = setdiff(v,a); + + G.add_edge(a, b, c, val(i)); + + if add_symm + % --- + % add symmetric edges to graph + % --- + G.add_edge(a, c, b, val(i)); + end + + else + G.add_edge(V1(i), V2(i), val(i)); + + if add_symm + % --- + % add symmetric edges to graph + % --- + G.add_edge(V2(i), V1(i), val(i)); + end + + end + + + end + +end + +function remove_node(G, a, b) + + if nargin == 3 + Vid = G.node(a,b); + else + Vid = a; + end + + % --- + % call superclass + % --- + remove_node@DiGraph(G, Vid); +end + +% --- +% Clip ids for Edge +% --- +function [a,b,c] = clipsE(G, V1, V2) + + [c1, c2] = clipsN(G, V1); + [c3, c4] = clipsN(G, V2); + + % common clip + a = intersect([c1 c2],[c3 c4]); + + % nearer (similar) clip + b = setdiff([c1 c2],a); + + % far clip + c = setdiff([c3 c4],a); + + if isempty(a) + error 'clip similarity graph inconsistent' + end +end + +% --- +function out = label(G, Vid) + + if nargin == 1 + out = cell(numel(G.V), 1); + Vid = 1:numel(G.V); + end + + for i = 1:numel(Vid) + if (numel(G.Vlbl) < Vid(i)) || isempty(G.Vlbl{Vid(i)}) + + [sm, bg] = G.clipsN(Vid(i)); + + if numel(Vid) > 1 + out{i} = sprintf('%d:%d', sm, bg); + else + out = sprintf('%d:%d', sm, bg); + end + else + if numel(Vid) > 1 + out{i} = G.Vlbl{Vid(i)}; + else + out = G.Vlbl{Vid(i)}; + end + end + end +end + +% --- +% determines if Edges in G2 are the same as in G +% --- +function out = le(a,b) + out = isSubgraph(a, b); +end + +function [out] = isSubgraph(G, G2) + + [val, V1, V2] = G2.edges(); + + i = 1; + while i <= numel(V1) + % --- + % Test if edge exists in other graph, + % using clips as identifier + % --- + [a,b,c] = G2.clipsE(V1(i), V2(i)); + + if G.edge(a,b,c) ~= val(i) + out = 0; + return, + end + i = i + 1; + end + out = 1; +end + + +function [Gout] = minus(a, b) + Gout = feval(class(a),a); + Gout.remove_graph(b); +end + +function remove_graph(G, G2) + + % --- + % Get Edges in G2 and remove them + % --- + [~, V1, V2] = G2.edges(); + for i = 1:numel(V1) + % --- + % Test if edge exists in other graph, + % using clips as identifier + % --- + [a,b,c] = G2.clipsE(V1(i), V2(i)); + + G.remove_edge(a,b,c); + end + + % --- + % Note : we only remove nodes with no + % remaining incoming edges + % --- + V = G2.nodes(); + for i = 1:numel(V) + + % --- + % get corresponding node in G via clips + % --- + [a,b] = G2.clipsN(V(i)); + + Vid = node(G, a, b); + if G.degree(Vid) == 0 + + G.remove_node(Vid); + end + end +end +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ClipSimGraph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,191 @@ +% --- +% class ClipSimGraph +% Directed graph representing comparative clip similarities. +% +% each node represents a pair of clips, +% an edge (a,b) -> (a,c) is present if there is at least one +% users judging clip a more similar to clip b than to clip c +% +% The Edges thus represent the (are nearer to each othen than) +% expression +% --- + +classdef ClipSimGraph < ClipPairGraph + +% --- +% the methods +% --- +methods + + +% constructor of the graph +function G = ClipSimGraph(comparison, comparison_ids) + + % --- + % handle manual input + % --- + + if nargin == 1 + % --- + % this uses an imput grpaph + % to create a new MD graph + % --- + + G = ClipSimGraph(); + + % --- + % Add the input graph to the empty one + % --- + G.add_graph(comparison); + + % --- + % TODO: Copy Clip Pair Information if neccessary + % this is by now a handle copy + % --- + clas = cat(1, class(comparison), superclasses(comparison)); + if strcellfind(clas, 'ClipPairGraph') + + G.vid_map = comparison.vid_map; + end + + elseif nargin == 2 + + % --- + % handle automatic or manual input + % --- + for i = 1:size(comparison,1) + + % get clips and votes + clips = comparison_ids(comparison(i,1:3)); + votes = comparison(i,4:6); + + % for each triplet position create an edge reflecting the + % absolute and relative voting for this position + + % --- + % NOTE: we index 0 - 2 to ease the mod + % calculaton for the remaining indices + % --- + for v = 0:2 + + % --- + % has at least one user voted this clip out? + % If so, treat it as an outlier and determine score + % --- + if votes(v+1) > 0 + + a = mod(v+1, 3)+1; + b = mod(v+2, 3)+1; + c = v+1; + + % --- + % TODO: how to determine the weight? + % There are two options: relative in the triple + % and counting absolute hits. + % first we go for the relative ones. + % The other is implemented in ClipSimGraphMulti + % --- + + % --- + % add an edge saying sim(a,b) > sim(a,c) + % --- + G.add_edge(clips(a), clips(b), clips(c), votes(c) / (sum(votes))); + + % --- + % every outlier vote results in two + % dissimilarity equations + % --- + + % edge 2 + G.add_edge(clips(b), clips(a), clips(c), votes(c) / (sum(votes))); + + end + + end + end + end + +end + +% end constructor function + + +function [weights, a, b, c] = similarities(G) + % --- + % returns the weights of edges meaning sim(a,b) > sim(a,c) + % --- + % get edge weights + [weights, a, b, c] = edges(G); +end + +% --- +% add an edge saying sim(a,b) > sim(a,c) +% --- +% function add_edge(G, a, b, c, weight) + +function remove_edge(G, a, b, c) + + if nargin == 4 + V1 = add_node(G, a, b); + V2 = add_node(G, a, c); + + elseif nargin == 3 + V1 = a; + V2 = b; + end + + % --- + % call superclass + % --- + remove_edge@DiGraph(G, V1, V2); + end + +function set_edge(G, a, b, c, weight) + if nargin == 5 + V1 = add_node(G, a, b); + V2 = add_node(G, a, c); + + elseif nargin == 4 + V1 = a; + V2 = b; + weight = c; + end + + % --- + % call superclass + % --- + set_edge@DiGraph(G, V1, V2, weight); +end + +% --- +% simple mix of two sets of info about the same +% nodes: mean. +% NOTE: if there's more than two infos, the earlier +% information will loose influence +% --- +function join_edge(G, a, b, c, weight) + + if nargin == 5 + V1 = G.node(a, b); + V2 = G.node(a, c); + elseif nargin == 4 + V1 = a; + V2 = b; + weight = c; + end + + if G.edge(V1, V2) ~= 0 + + % set Edge to weight + G.E(V1, V2) = (G.E(V1, V2) + weight) /2; + + cprint(4, 'edge weight %d %d %d updated \n',a ,b , c) ; + else + + error 'nodes not found'; + end +end +end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ClipSimGraphMD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,265 @@ +% --- +% class ClipSimGraphMD +% Directed graph representing comparative clip similarities. +% EACH pair of vertices has just ONE directed edge connecting them +% +% each node represents a pair of clips, +% an edge (a,b) -> (a,c) is present if there is at least one +% users judging clip a more similar to clip b than to clip c +% +% The Edges thus represent the (are nearer to each othen than) +% expression +% --- + +classdef ClipSimGraphMD < ClipSimGraph & handle + + +properties + + % --- + % History of edge weights, is a nodes x nodes cell. + % + % E_hist(i,j) = m x 2 Matrix containing + % Pair of information for each set [votes votes_complete] + hE; % History of edge weights +end + +% --- +% the methods +% --- +methods + +% constructor of the graph +function G = ClipSimGraphMD(comparison, comparison_ids) + + if nargin == 0 + % --- + % empty constructor + % --- + + elseif nargin == 1 + % todo: copy graph + + elseif nargin == 2 + + + % --- + % handle automatic or manual input + % --- + for i = 1:size(comparison,1) + + % get clips and votes + clips = comparison_ids(comparison(i,1:3)); + votes = comparison(i,4:6); + + % for each triplet position create an edge reflecting the + % absolute and relative voting for this position + + % --- + % NOTE: we index 0 - 2 to ease the mod + % calculaton for the remaining indices + % --- + for v = 0:2 + + % --- + % has at least one user voted this clip out? + % If so, treat it as an outlier and determine score + % --- + if votes(v+1) > 0 + + a = mod(v+1, 3)+1; + b = mod(v+2, 3)+1; + c = v+1; + + + % --- + % every outlier vote results in two + % dissimilarity equations, favored by + % the people who voted for the outlier + % --- + + % edge 1 + add_edge(G, clips(a), clips(b), clips(c), votes(v+1), sum(votes)); + + % edge 2 + add_edge(G, clips(b), clips(a), clips(c), votes(v+1), sum(votes)); + end + end + + end + end + +% end constructor function +end + +% adds a node stating clip a is more near % +% to clip b then clip c +function Vid = add_node(G, a, b) + + Vid = add_node@ClipSimGraph(G, a, b); + + G.hE{Vid,Vid} = []; + +end + +function remove_node(G, a, b) + + if nargin == 3 + + V1 = G.node(a, b); + elseif nargin == 2 + + V1 = a; + end + + if ~isempty(Vid) + + % --- + % look for edges connected to Vid + % and delete their histograms + % --- + G.hE(:,Vid) = []; + G.hE(Vid,:) = []; + + % --- + % TODO: Efficiently Remove the actual node from the + % GRAPH + % --- + G.Vclips(Vid,:) = []; + + end +end + +% --- +% add an edge saying sim(a,b) > sim(a,c) +% --- +function add_edge(G, a, b, c, votes, allvotes) + + V1 = add_node(G, a, b); + V2 = add_node(G, a, c); + + % --- + % save new weight values into histogram + % --- + if isempty(G.hE(V1, V2)) + + G.hE{V1,V2} = [votes allvotes]; + else + G.hE{V1,V2} = [G.hE{V1,V2}; votes allvotes]; + end + + % --- + % update Edges + % --- + G.update_E(a, b, c); + +end + +function remove_edge(G, a, b, c) + + if nargin == 4 + + V1 = G.node(a, b); + V2 = G.node(a, c); + elseif nargin == 3 + + V1 = a; + V2 = b; + end + + if ~isempty(V1) && ~isempty(V2) + + % set Edge to zero + G.hE{V1, V2} = []; + else + + error 'nodes not found'; + end +end + +% --- +% updates the edge weight given 3 clip ids or +% two nodes, based on the edges history +% +% The specified (V1,V2) and the symmetric edges' (V2,V1) weights +% are evaluated and the stronger edge will get +% the excessive weight while the loosing edge +% will be deleted +% --- +function update_E(G, a, b, c) +% update_E(G, a, b, c) +% update_E(G, V1, V2) + + % determine the type of input parameters + if nargin == 4 + + V1 = G.node(a, b); + V2 = G.node(a, c); + elseif nargin == 3 + + V1 = a; + V2 = b; + end + + % --- + % calculate weighted sum for specified edge + % and for symmetric edge + % --- + thisw = unbal_edge_weight(G, V1, V2); + symmw = unbal_edge_weight(G, V2, V1); + + % --- + % the two //competing// weights are now balanced + % --- + w = thisw - symmw; + + % --- + % set both edges + % --- + G.E(V1,V2) = max(w,0); + G.E(V2,V1) = -min(w,0); + +% if w >= 0 +% G.E(V1,V2) = w; +% G.E(V2,V1) = 0; +% elseif w < 0 +% G.E(V1,V2) = 0; +% G.E(V2,V1) = -w; +% end +end + +end + +methods (Hidden) + + % --- + % This is the core function of this Graph. + % it allows for the calculation of a single Edge, + % before it is balanced with its symmetrical counteredge + % + % --- + function thisw = unbal_edge_weight(G, V1, V2) + % --- + % trivial cases + % --- + if isempty(G.hE(V1, V2)) || isempty(G.hE{V1, V2}) + thisw = 0; + return; + end + + % --- + % Evaluate the single historical entries + % and sum up + % --- + thisw = sum(G.hE{V1, V2},1); + + % --- + % now divide the single votes by the number of + % votes totally made with the option to improve + % this edge + % --- + thisw = thisw(1) / thisw(2); + + end +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ClipSimGraphMulti.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,176 @@ +% --- +% class ClipSimGraphMulti +% Directed MultiGraph representing comparative clip similarities. +% EACH pair of vertices has multiple directed edges connecting them +% +% each node represents a pair of clips, +% an edge (a,b) -> (a,c) is present if there is at least one +% users judging clip a more similar to clip b than to clip c +% +% Each edge thus represent a single (are nearer to each othen than) +% expression +% --- + +classdef ClipSimGraphMulti < ClipSimGraph & handle + + +properties + +end + +% --- +% the methods +% --- +methods + +% constructor of the graph +function G = ClipSimGraphMulti(comparison, comparison_ids) + + if nargin == 0 + % --- + % empty constructor + % --- + + elseif nargin == 1 + % todo: copy graph if same class + + % --- + % this uses an imput grpaph + % to create a new MD graph + % --- + + G = ClipSimGraphMulti(); + + % --- + % Add the input graph to the empty one + % --- + G.add_graph(comparison); + + elseif nargin == 2 + + + % --- + % handle automatic or manual input + % --- + for i = 1:size(comparison,1) + + % get clips and votes + clips = comparison_ids(comparison(i,1:3)); + votes = comparison(i,4:6); + + % for each triplet position create an edge reflecting the + % absolute and relative voting for this position + + % --- + % NOTE: we index 0 - 2 to ease the mod + % calculaton for the remaining indices + % --- + for v = 0:2 + + % --- + % has at least one user voted this clip out? + % If so, treat it as an outlier and determine score + % --- + if votes(v+1) > 0 + + a = mod(v+1, 3)+1; + b = mod(v+2, 3)+1; + c = v+1; + + + % --- + % every outlier vote results in two + % dissimilarity equations, favored by + % the people who voted for the outlier + % --- + + % edge 1 + add_edge(G, clips(a), clips(b), clips(c), votes(v+1)); + + % edge 2 + add_edge(G, clips(b), clips(a), clips(c), votes(v+1)); + end + end + + end + end + +% end constructor function +end + +function out = order(G) + + out = sum(G.V); +end + +function out = num_edges_multi(G) + out = sum(sum(G.E)); +end + +function out = num_edges(G) + out = sum(sum(G.E ~= 0)); +end + + + % --- +% NOTE: the weight explains the multiplicity of each +% edge, to correctily describe this, +% we just count the votes +% --- +function join_edge(G, a, b, c, weight) + + if nargin == 5 + V1 = G.node(a, b); + V2 = G.node(a, c); + elseif nargin == 4 + V1 = a; + V2 = b; + weight = c; + end + + if G.edge(V1, V2) ~= 0 + + % set add number to Edge index + G.E(V1, V2) = (G.E(V1, V2) + weight); + + cprint(3, 'edge weight %d %d %d updated \n',a ,b , c) ; + else + + error 'nodes not found'; + end +end + +% --- +% This eliminates any order-2 cycles, +% by substracting opposing edge counts +% --- +function remove_cycles_length2(G) + + G.E = max(0, G.E - G.E'); +end +% --- +% TODO: this back-and forth transform is a workaround +% avoiding some costly other transformation which would +% happen during the direct digraph call +% --- + +function [Gs, s, id] = connected_components(G, varargin) + % --- + % get all connected subgraphs: + % --- + G2 = DiGraph(G); + + [GsDiGraph, s, id] = connected_components@DiGraph(G2, varargin{:}); + + for i = 1:numel(GsDiGraph) + Gs(i) = ClipSimGraphMulti(GsDiGraph(i)); + end +end + + +end + +methods (Hidden) + +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/DistMeasureMahal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,202 @@ +% --- +% The DistMeasureMahal class states a wrapper for +% special Mahalanobis similarity distance, and is compatible with the +% DistMeasure class +% --- +classdef DistMeasureMahal < handle + + properties (SetAccess = private) + + mahalW; + + featX; + + ids; + ret_ids; + + % special delta parameters + deltafun; + deltafun_params; + end + + methods + + % --- + % constructor + % --- + function m = DistMeasureMahal(clips, mahalW, featX, deltafun, deltafun_params) + + if nargin < 4 && (size(featX, 2) ~= numel(clips) || size(featX, 1) ~= length(mahalW)) + error 'wrong input format' + end + + % fill index and generate matrix; + m.ids = [clips.id]; + + % reverse index + m.ret_ids = sparse(numel(m.ids),1); + m.ret_ids(m.ids) = 1:numel(m.ids); + + % --- + % save mahal Matrix and lazy-copy features + % --- + if size(mahalW, 1) ~= size(mahalW, 2) + + m.mahalW = diag(mahalW); + else + + m.mahalW = mahalW; + end + + m.featX = featX; + + % --- + % special deltas + % --- + if nargin > 3 + m.deltafun = deltafun; + m.deltafun_params = deltafun_params; + else + m.deltafun = []; + end + end + + + % --- + % this compability function returns the + % mahalanobis similarity of two clip indices + % --- + function out = mat(m, idxa, idxb) + + if nargin == 1 + idxa = 1:numel(m.ids); + idxb = 1:numel(m.ids); + end + + % --- + % account for different delta functions + % --- + if ~isempty(m.deltafun) + out = zeros(numel(idxa),numel(idxb)); + for i=1:numel(idxa) + for j=1:numel(idxb) + + % calculate new distance + tmp = m.deltafun(m.featX(:,idxa), m.featX(:,idxb),m.deltafun_params{:}); + out(i,j) = tmp' * m.mahalW * tmp; + end + end + else + % Standard Mahaldist is much faster to calculate + out = sqdist( m.featX(:,idxa), m.featX(:,idxb), m.mahalW); + end + end + + % --- + % returns the distance for the two input clips + % --- + function out = distance(m, clipa, clipb) + posa = m.get_clip_pos(clipa); + posb = m.get_clip_pos(clipb); + + out = m.mat(posa, posb); + end + + % --- + % returns a list of n (default = 10) clips most + % similar to the input + % --- + function [clips, dist] = get_nearest(m, clip, n) + % list = get_nearest(m, clip, n) + % + % returns a list of n (default = 10) clips most + % similar to the input + + % default number of results + if nargin == 2 + + n = 10; + end + + % return all clips in case n = 0 + if n == 0; n = numel(m.ids); end + + % get clip positions + pos = m.get_clip_pos(clip); + + % sort according to distance + [sc, idx] = sort( m.mat(pos, 1:numel(m.ids)), 'ascend'); + + % we only output relevant data + idx = idx(sc < inf); + + if numel(idx) > 0 + % create clips form best ids + clips = MTTClip( m.ids( idx(1:min(n, end)))); + dist = m.mat(pos, idx(1:min(n, end))); + + else + clips = []; + dist = []; + end + end + + + + function [clips, dist] = present_nearest(m, clip, n) + % plays and shows the n best hits for a given clip + + % default number of results + if nargin == 2 + + n = 3; + end + + % get best list + [clips, dist] = get_nearest(m, clip, n); + + clip.audio_features_basicsm.visualise(); + for i = 1:numel(clips) + fprintf('\n\n\n- Rank %d, distance: %1.4f \n\n',i, dist(i)); + + clips(i).audio_features_basicsm.visualise(); + h = gcf(); + t = clips(i).play(20); + pause(t); + close(h); + end + end + + function a = visualise(m) + + figure; + + % plot data + + imagesc(m.mat); + + a = gca; + set(a,'YTick',[1:numel(m.ids)], 'YTickLabel',m.ids); + set(a,'XTick',[1:numel(m.ids)], 'XTickLabel', m.ids); + + axis xy; + colormap(hot); + end + + % end methods + end + + % --- + % private methods + % --- + methods(Access = private) + + function out = get_clip_pos(m, clip) + % returns position in mat for given clip + + out = m.ret_ids(clip.id); + end + + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/DistMeasureNNet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,179 @@ +% --- +% The DistMeasureMahal class states a wrapper for +% special Mahalanobis similarity distance, and is compatible with the +% DistMeasure class +% --- +classdef DistMeasureNNet < handle + + properties (SetAccess = private) + + net; + + featX; + + ids; + ret_ids; + end + + methods + + % --- + % constructor + % --- + function m = DistMeasureNNet(clips, net, featX) + + if size(featX, 2) ~= numel(clips) + error 'wrong input format' + end + + % fill index and generate matrix; + m.ids = [clips.id]; + + % reverse index + m.ret_ids = sparse(numel(m.ids),1); + m.ret_ids(m.ids) = 1:numel(m.ids); + + % --- + % save neural net and lazy-copy features + % --- + m.net = net; + + m.featX = featX; + + end + + + % --- + % this function returns the + % similarity of two clip indices + % --- + function out = mat(m, idxa, idxb) + + if nargin == 1 + idxa = 1:numel(m.ids); + idxb = 1:numel(m.ids); + end + + % cycle through all index combinations + out = zeros(numel(idxa), numel(idxb)); + for i = 1:numel(idxa) + for j = 1:numel(idxb) + + % calculate distance vector + deltas = m.featX(:,idxa(i)) - m.featX(:,idxb(j)); + + % return distance from net + out(i,j) = m.net.calcValue(deltas); + end + end + + end + + % --- + % returns the distance for the two input clips + % --- + function out = distance(m, clipa, clipb) + posa = m.get_clip_pos(clipa); + posb = m.get_clip_pos(clipb); + + out = m.mat(posa, posb); + end + + % --- + % returns a list of n (default = 10) clips most + % similar to the input + % --- + function [clips, dist] = get_nearest(m, clip, n) + % list = get_nearest(m, clip, n) + % + % returns a list of n (default = 10) clips most + % similar to the input + + % default number of results + if nargin == 2 + + n = 10; + end + + % return all clips in case n = 0 + if n == 0; n = numel(m.ids); end + + % get clip positions + pos = m.get_clip_pos(clip); + + % sort according to distance + [sc, idx] = sort( m.mat(pos, 1:numel(m.ids)), 'ascend'); + + % we only output relevant data + idx = idx(sc < inf); + + if numel(idx) > 0 + % create clips form best ids + clips = MTTClip( m.ids( idx(1:min(n, end)))); + dist = m.mat(pos, idx(1:min(n, end))); + + else + clips = []; + dist = []; + end + end + + + + function [clips, dist] = present_nearest(m, clip, n) + % plays and shows the n best hits for a given clip + + % default number of results + if nargin == 2 + + n = 3; + end + + % get best list + [clips, dist] = get_nearest(m, clip, n); + + clip.audio_features_basicsm.visualise(); + for i = 1:numel(clips) + fprintf('\n\n\n- Rank %d, distance: %1.4f \n\n',i, dist(i)); + + clips(i).audio_features_basicsm.visualise(); + h = gcf(); + t = clips(i).play(20); + pause(t); + close(h); + end + end + + function a = visualise(m) + + figure; + + % plot data + + imagesc(m.mat); + + a = gca; + set(a,'YTick',[1:numel(m.ids)], 'YTickLabel',m.ids); + set(a,'XTick',[1:numel(m.ids)], 'XTickLabel', m.ids); + + axis xy; + colormap(hot); + end + + % end methods + end + + % --- + % private methods + % --- + methods(Access = private) + + function out = get_clip_pos(m, clip) + % returns position in mat for given clip + + out = m.ret_ids(clip.id); + end + + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/LFMTagsDB.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,129 @@ +% --- +% This is the wrapper and loader class for Last.Fm db +% and related data +% --- + +classdef LFMTagsDB < handle + + properties + + % connects the last.fm artists to MTTartist ids + matching_artists; + + % saves the last fm tags for last.fm artist ids + tagdb; + end + + properties(Dependent) + + lexicon; + end + + + methods + function db = LFMTagsDB(artistdb) + + % load data + load last.fm.mat; + + % --- + % NOTE: here, we save the corresponding magnatagatune + % artists. There make sure that the last.fm artists + % are properly associated! + % + % finally, the central last.fm artist index will refer + % to both the MTT artist database and the tags + % ---- + + % get all the clips artists + artists = artistdb.lexicon; + + % assign artists to Last.Fm artists + lexicon = []; + annots = []; + + justfmartists = fmartists(:,2); + for i = 1:numel(justfmartists) + + %get matching MTT for each Last.fm artist + pos = strcellfind(artists, justfmartists{i}); + + % save its id + if ~isempty(pos) + associds(i) = artistdb.get_annot_id(artists{pos}); + end + end + + % --- + % TODO: assert wheather if the fmartist id actually coincide + % justfmartists_ids = matching_artists.get_annotid(justfmartists); + % --- + + % call superclass constructor + db.tagdb = AnnotDB(fmartist_annots_names, ... + fmartist_annots, associds); + + % save the mathing artists + db.matching_artists = AnnotDB('clips_by_annot', ... + justfmartists, associds); + + end + end + + + methods + + % retrieve annot by clip + function [out, score, aid] = annots(db, clip) + + % --- + % NOTE: we retrieve the tags for the artist matching the clips + % one + % --- + + fma_id = db.matching_artists.annotids_for_owner(clip.artist_id()); + + artist = clip.artist(); + + if ~isempty(fma_id) + + [out, score, aid] = db.tagdb.annots(fma_id); + else + warning('Artist %s not found in Last.fm tags DB', artist); + end + end + + function out = annotids_for_owner(db, clip) + + % --- + % NOTE: we retrieve the tags for the artist matching the clips + % one + % --- + + fma_id = db.matching_artists.annotids_for_owner(clip.artist_id()); + + artist = clip.artist(); + + if ~isempty(fma_id) + + out = db.tagdb.annotids_for_owner( fma_id ); + else + warning('Artist %s not found in Last.fm tags DB', artist); + end + end + + function out = get.lexicon(db) + % gives back all the tags + + out = db.tagdb.lexicon; + end + end + + methods(Static) + + + end +end + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTAudioFeature.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,362 @@ +% --- +% Mother of allMTT Audio Features +% --- + +classdef MTTAudioFeature < handle + + % common properties + properties (Dependent) + + data; + end + + properties (Hidden) + + % --- + % TODO: get clip features by id and not by pos + % --- + + % position in db + db_pos; + end + + % do not save whole db into mat file + properties (Hidden, Transient) + + % database + my_db; + end + + + % common methods + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTAudioFeature(db, varargin) + % feature = MTTAudioFeatureBasicSm(db, varargin) + + if nargin >= 1 + % save database handle; + feature.my_db = db; + + if nargin >= 2 + if isnumeric(varargin{1}) + + feature.db_pos = varargin{1}; + + % --- + % read parameters from data, which should not be + % overwritten by the manual imputs ? + % --- + feature.set_params(feature.data.info.params); + + if numel(varargin) > 1 + warning('Specified parameters cant be applied with preexisting data'); + end + + else + + % set parameters for feature extraction + feature.set_params(varargin{:}); + end + end + + else + % --- + % TODO: empty constructor + % --- + end + end + + % --- + % get and set methods, as the lazy matlab referencing + % is of no use when the feature has to be adjusted + % during the progess of finalising + % --- + function data = get.data(feature) + + data = feature.my_db.featuredb(feature.db_pos); + end + + function set.data(feature, data) + + feature.my_db.featuredb(feature.db_pos) = data; + end + + function id = owner_id(feature) + % returns associated clip(s) + + for i = 1:numel(feature) + id(i) = feature(i).data.info.owner_id; + end + end + + function out = dim(feature) + % returns dimension of single feature vector + + out = feature(1).data.final.dim; + end + + function label = labels(features) + label = []; + if isfield(features(1).data.final, 'vector_info') + + % --- + % TODO: these labels should be stored at a central point, + % as this is a big waste of memory + % --- + label = features(1).data.final.vector_info.labels; + end + end + + function out = vector(features) + % returns the feature vector(s) + % if multiple features are input, the vectors are + % concatenated to a single matrix of size + % (feature.dim x numel(features) + + % finalise features if not done yet + if ~isfield(features(1).data, 'final') || features(1).data.final.dim == 0 + + features.finalise(); + end + + %shortcut for empty features + if features(1).data.final.dim < 1 + out =[]; + return + end + + out = zeros(features(1).data.final.dim, numel(features)); + for i = 1:numel(features) + + % finalise single feature vectors + if ~isfield(features(i).data, 'final') || features(i).data.final.dim == 0 + + features(i).finalise(); + end + + out(:,i) = features(i).data.final.vector; + end + end + + function out = common(feature) + % returns common feature values and params + + out = feature(1).my_db.commondb; + end + + % visualises all the finalised feature vectors at once + function a1 = visualise_vectors(features) + + % Collect source description data + ids = zeros( numel( features, 1)); + + for i = 1: numel(features) + + ids(i) = features(i).owner_id(); + end +% clips = MTTClip(ids); + + % get collective feature data + vec = features.vector(); + + % feature description data + labels = features(1).labels; + + % plot data + h = figure; + imagesc(vec); + a1 = gca(); + axis xy; + + set(a1,'XTick',1:numel(features), 'XTickLabel', ids); + + if ~isempty(labels) + set(a1,'YTick', [1:features(1).data.final.dim], 'YTickLabel', labels); + end + + xlabel('Clip ID'); + end + + + % --- + % compares the params of the feature with the + % provided param struct or feature + % --- + function out = eq_params(this, in) + % function out = eq_params(this, in) + % + % resolves if a feature with params(in) would be + % the same as the given feature + + type = this.my_db.featuredb_type; + + % --- + % construct dummy feature to get the params parsing right + % --- + if isstruct(in) + + % call constructor for this feature + % type without valid db + in = feval(type, 0, in); + end + + % return whether the strings are equal or not + out = strcmp(this.param_hash(), in.param_hash()); + end + + function unused = set_params(this, varargin) + % copies the parameters within the struct to the featrue instance, + % or + % uses the process_options function to set the parameters. + % this is used for initialisation / constructor of features + + unused = {}; + if numel(this) > 1 + + for i = 1:numel(this) + set_params(this(i), varargin{:}); + end + else + + if isstruct(varargin{1}) + + % ok, just copy the information + % this.my_params = varargin{1}; + + % --- + % NOTE: this is a compability script + % set fields succesively to keep new / nonset field names + % we do not add parameters!! + % --- + fields = fieldnames(varargin{1}); + for i = 1:numel(fields) + + % test whether the parameter is supported + if isfield(this.my_params, fields{i}) + + this.my_params.(fields{i}) = varargin{1}.(fields{i}); + end + end + else + + % get all parameter lines + fields = fieldnames(this.my_params); + + outputstring = ''; + inputstring = ''; + + % we collect the string for all the fields + for i = 1:numel(fields) + + % generate left- hand side of param collection + outputstring = sprintf('%s this.my_params.%s',... + outputstring, fields{i}); + + % right-hand-side + inputstring = sprintf('%s ''%s'', this.my_params.%s',... + inputstring, fields{i}, fields{i}); + + if i < numel(fields) + + % put comma behind last argument + inputstring = sprintf('%s,',inputstring); + outputstring = sprintf('%s,',outputstring); + end + + end + + % evaluate set + eval(sprintf('[%s, unused] = process_options(varargin, %s);', ... + outputstring, inputstring)); + end + end + end %fun + + % --- + % saveto function + % --- + function saveto(features, matfile) + % saves (multiple features to a .mat file) + + % get clip ids + c = zeros(numel(features), 1); + for i = 1:numel(features) + c(i) = features(i).owner_id(); + end + + % build clips + clips = MTTClip(c); + + % let db export the corresponding clips + features(1).my_db.export(matfile, clips); + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + end + + methods (Hidden = true) + + function assign(feature, db_pos) + % sets the feature data link + + feature.db_pos = db_pos; + end + end + + methods (Static) + + function ph = param_hash(type, varargin) + % loads the params for a feature type and adds the + % given parameter values to it. + + % this function can be static or dynamic + if nargin > 1 + % static case + dummy = feval(type,[], varargin{:}); + else + % dynamic case + dummy = type; + end + ph = hash(xml_format(dummy.my_params),'MD5'); + end + + function params = inherited_params(type, varargin) + % loads the params for a feature type and adds the + % given parameter values to it. + + dummy = feval(type); + + params = dummy.my_params; + + % basic check if the input is correct + if mod(numel(varargin), 2) ~= 0 + error('number of params does not match number of values'); + end + + % add the fields to the struct + for i = 1:2:(numel(varargin)-1); + + params.(varargin{i}) = varargin{i+1}; + end + end + end +end + + + + + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTAudioFeatureBasicSm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,832 @@ +classdef MTTAudioFeatureBasicSm < MTTAudioFeature & handle + % --- + % the MTTAudioFeatureBasicSm Class contains + % a basic summary of chroma, mfcc and tempo features + % a few common chroma and mfcc vectors are concatenated + % along with some clip-wide variance + % a metric / rhythm fingerprint is added + % + % The usual workflow for these features consists of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev$', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = struct(... + 'nchromas', 4, ... % 4 chroma vectors + 'chroma_var', 0, ... % chroma variance + 'norm_chromas', 0, ... % not implemented, chromas already rel. + 'min_kshift_chromas', 0.1, ... % treshold for key shift. set to 1 for no shift (0-1) + ... + 'ntimbres', 4, ... + 'timbre_var', 0, ... % timbre variance + 'norm_timbres', 1, ... + 'clip_timbres', 0.85, ... % percentile of data which has to be inside 0-1 bounds + ... + 'norm_weights',0, ... % globally norm weights for chroma times? + 'norm_interval',1, ... + 'max_iter',100, ... % max iterations for chroma and timbre knn + ... + 'nrhythms', 0, ... + 'nints', 11, ... + 'energy_sr', 1000, ... % sample rate for energy curve + 'norm_acorr', 1 ... % normalise arcorr locally-> shape imp... energy is normalised anyways + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTAudioFeatureBasicSm(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + + end + % --- + % extract feature data from raw audio features + % --- + function data = extract(feature, clip) + % --- + % get Basic Summary audio features. this includes possible + % local normalisations + % --- + + global globalvars; + + % --- + % get casimir child clip if available + % --- + if isa(clip, 'CASIMIRClip') + baseclip = clip.child_clip(); + else + baseclip = clip; + end + if isa(baseclip, 'MTTClip') + rawf = baseclip.audio_features_raw(); + elseif isa(baseclip, 'MSDClip') + rawf = baseclip.features('MSDAudioFeatureRAW'); + end + + % --- + % now extract the features + % first step: chroma clustering + % --- + weights = [rawf.data.segments_duration]; + + % normalise weights + weights = weights / rawf.data.duration; + + chroma = [rawf.data.segments_pitches]'; + + % --- + % get most present chroma vectors. + % the weighted k-means should return the four most prominent + % chroma vectors and their weight + % --- + % display error values + + op = foptions(); + op(1) = 0; + op(14) = feature.my_params.max_iter; + + % check for trivial case + if feature.my_params.nchromas == 0 + + chromas = []; + cwght = []; + + elseif feature.my_params.nchromas == 1 + + chromas = mean(chroma, 1); + chroma_var = var(chroma, 0, 1); + cwght = 1; + + elseif numel(weights) > feature.my_params.nchromas + + % --- + % there may be few chromas, try kmeans several (20) times + % --- + cont = 0; + cwght = []; + while (numel(cwght) ~= feature.my_params.nchromas) && (cont < 20); + + [chromas, cwght, post] = ... + weighted_kmeans(feature.my_params.nchromas, chroma, weights, op); + + cont = cont + 1; + end + + if (numel(cwght) ~= feature.my_params.nchromas) + + error('cannot find enough chroma centres'); + end + + % --- + % Calculate the weighted variance of the chroma clusters + % --- + if feature.my_params.chroma_var >= 1 + + chroma_var = zeros(size(chromas)); + for i = 1:size(chroma_var,1) + + % get distance from cluster centroid + tmp_var = (chroma(post(:,i),:) - repmat(chromas(i,:), sum(post(:,i)),1)).^2; + + % add up the weighted differences and normalise by sum + % of weights + chroma_var(i,:) = (weights(post(:,i)) * tmp_var) ./... + (sum(weights(post(:,i)))); + end + end + else + % --- + % odd case: less than nchroma data points. + % we repeat the mean vector at the end + % --- + chromas = [chroma; repmat(mean(chroma, 1),... + feature.my_params.nchromas - numel(weights), 1 )]; + + cwght = weights; + cwght( end + 1:feature.my_params.nchromas ) = 0; + + % --- + % TODO: get a variance for odd case : + % replicate the complete data variance? + % NO: every vector is a clsuter => zero variance + % --- + end + + % trivial case: no variance requested + if ~exist('chroma_var','var') + chroma_var = zeros(size(chromas)); + end + + % sort by associated time + [cwght, idx] = sort(cwght, 'descend'); + chromas = chromas(idx,:); + chroma_var = chroma_var(idx,:); + + % --- + % shift according to detected key, but only if + % the confidencee is high enough + % --- + shift = 0; + if rawf.data.keyConfidence > feature.my_params.min_kshift_chromas; + + shift = -rawf.data.key; + chromas = circshift(chromas, [0 shift]); + chroma_var = circshift(chroma_var, [0 shift]); + end + + % --- + % get mfcc centres: + % the same for mfccs + % --- + mfcc = [rawf.data.segments_timbre]'; + if feature.my_params.ntimbres == 0 + + mfccs = []; + mwght = []; + + elseif feature.my_params.ntimbres == 1 + + mfccs = mean(mfcc, 1); + timbre_var = var(mfccs, 0, 1); + mwght = 1; + + elseif numel(weights) > feature.my_params.ntimbres + + % --- + % there may be few mfccs, try kmeans several times + % --- + cont = 0; + mwght = []; + while (numel(mwght) ~= feature.my_params.ntimbres) && (cont < 20); + + [mfccs, mwght, post] = ... + weighted_kmeans(feature.my_params.ntimbres, mfcc, weights, op); + cont = cont + 1; + end + + if (numel(mwght) ~= feature.my_params.ntimbres) + + error('cannot find enough mfcc centres'); + end + + % --- + % Calculate the weighted variance of the chroma clusters + % --- + if feature.my_params.timbre_var >= 1 + + timbre_var = zeros(size(mfccs)); + for i = 1:size(timbre_var,1) + + % get distance from cluster centroid + tmp_var = (mfcc(post(:,i),:) - repmat(mfccs(i,:), sum(post(:,i)),1)).^2; + + % add up the weighted differences and normalise by sum + % of weights + timbre_var(i,:) = (weights(post(:,i)) * tmp_var) ./... + (sum(weights(post(:,i)))); + end + end + + else + % --- + % odd case: less than nchroma data points. + % we repeat the mean vector at the end + % --- + mfccs = [mfcc; repmat(mean(mfcc, 1),... + feature.my_params.ntimbres - numel(weights), 1)]; + mwght = weights; + mwght( end + 1:feature.my_params.ntimbres) = 0; + end + + % trivial case: no variance requested + if ~exist('timbre_var','var') + timbre_var = zeros(size(mfccs)); + end + + % sort by associated time + [mwght, idx] = sort(mwght, 'descend'); + mfccs = mfccs(idx,:); + timbre_var = timbre_var(idx,:); + + % --- + % get beat features: + % the autocorrelation curve over n quarters of length + % + % alternative: how about using the n=8 quarters relative + % volumes from the start of a sure measure? + % --- + if feature.my_params.nrhythms >= 1 + bars = rawf.data.bars; + beats = rawf.data.beats; + tatums = rawf.data.tatums; + % --- + % NOTE: the beat and tatum markers seem to have an offset :( + % --- + offset = 0.118; %seconds + + [envelope, time] = energy_envelope(feature, clip); + + % we offset the energy curve + time = time + offset; + + % --- + % we try to start at the best beat confidence more + % than sixteen eights from the end + % --- + + if rawf.data.tempo > 0 + + eightl = 30 / rawf.data.tempo; + else + % --- + % odd case: no rhythm data. assume 100 bpm + % --- + + eightl = 0.3; + end + + if isempty(beats) + % --- + % odd case: no beats detected. -> use best tatum + % --- + if ~isempty(tatums) + + beats = tatums; + else + + % ok, just take the beginning + beats = [0; 1]; + end + end + + last_valid = find(beats(1,:) < ... + (rawf.data.duration - feature.my_params.nints * eightl),1, 'last'); + + % find the best valid beat postition + [null, max_measure] = max( beats(2, 1:last_valid)); + max_mtime = beats(1,max_measure); + + % --- + % the correlation is calculated for the estimated eights lenght + % and for the 16th intervals, respectively. + % --- + + % calculate the EIGHTS correlation for the following segment + [acorr8, eight_en, eightt] = ... + beat_histogram(feature, max_mtime, eightl, envelope, time); + + % calculate the SIXTEENTHS correlation for the following segment + [acorr16, six_en, sixt] = ... + beat_histogram(feature, max_mtime, eightl / 2, envelope, time); + + % --- + % save the various features + % --- + % save rythm feature data + + data.rhythm.acorr8 = acorr8; + data.rhythm.acorr8_lag = eightt(1:end/2)-eightt(1); + + data.rhythm.energy8 = eight_en(1:end/2); + data.rhythm.energy8_time = eightt(1:end/2); + + % -- + % the interval is normed locally up to a max value + % associated to 30bpm + % --- + if feature.my_params.norm_interval + + % 1 second max value + data.rhythm.interval8 = eightl / 2; + else + data.rhythm.interval8 = eightl / 2; + end + + if feature.my_params.nrhythms >= 2 + + data.rhythm.acorr16 = acorr16; + data.rhythm.acorr16_lag = data.rhythm.acorr8_lag / 2; + + data.rhythm.energy16 = six_en(1:end/2); + data.rhythm.energy16_time = sixt(1:end/2); + + + % save beat interval / tempo + if feature.my_params.norm_interval + + % 1 second max value + data.rhythm.interval16 = eightl / 2; + else + data.rhythm.interval16 = eightl / 2; + end + + end + else + +% % save empty rythm struct +% data.rhythm = struct([]); + end + + % chroma feature data + for i = 1:size(chromas,1) + data.chroma(i).means = chromas(i,:)'; + data.chroma(i).means_weight = cwght(i); + data.chroma(i).vars = chroma_var(i,:)'; + data.chroma(i).shift = shift; + end + + % mfcc feature data + for i = 1:size(mfccs,1) + data.timbre(i).means = mfccs(i,:)'; + data.timbre(i).means_weight = mwght(i); + data.timbre(i).vars = timbre_var(i,:)'; + end + + % prepare field for final features + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = 'MTTAudioFeatureBasicSm'; + data.info.owner = clip; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % here, we only need to define the post-normalisation + % --- + + % --- + % get chroma variance data NORMALISATION Factors + % TODO: transport chroma variance to finalise step + % --- + if features(1).my_params.chroma_var >= 1 + allfeat = abs(cat(2, features(1).data.chroma(:).vars)); + for i = 2:numel(features) + + allfeat = cat(2 , allfeat, abs(abs(cat(2, features(i).data.chroma(:).vars)))); + end + [~, common.post_normf.chroma_var] = mapminmax(allfeat,0,1); + end + + % --- + % get timbre variance data NORMALISATION Factors + % TODO: transport chroma variance to finalise step + % --- + if features(1).my_params.timbre_var >= 1 + allfeat = abs(cat(2, features(1).data.timbre(:).vars)); + for i = 2:numel(features) + + allfeat = cat(2 , allfeat, abs(abs(cat(2, features(i).data.timbre(:).vars)))); + end + [~, common.post_normf.timbre_var] = mapminmax(allfeat,0,1); + end + + % --- + % derive normalisation for timbre features: + % MFCC's are actually special filter outputs + % (see developer.echonest.com/docs/v4/_static/AnalyzeDocumentation_2.2.pdf + % they are unbounded, so just the relative information will be + % used here. + % We normalise each bin independently + % --- + if features(1).my_params.ntimbres > 0 + + allfeat = abs(cat(2, features(1).data.timbre(:).means)); + for i = 2:numel(features) + + allfeat = cat(2 , allfeat, abs(cat(2, features(i).data.timbre(:).means))); + end + + % --- + % get normalisation factors + % NOTE: the values will later be clipped to [0,1] + % anyways + % --- + if (features(1).my_params.clip_timbres ~= 0 ) || ... + (features(1).my_params.clip_timbres ~= 1 ) + + common.post_normf.timbre = 1 ./ prctile(allfeat, features(1).my_params.clip_timbres * 100, 2); + + else + % just use the maximum + common.post_normf.timbre = 1/max(allfeat, 2); + end + + % set common feature values + features(1).my_db.set_common(common); + + else + + features(1).my_db.set_common([1]); + end + end + + + function finalise(feature) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + if feature(1).my_params.ntimbres > 0 + % --- + % normalise features + % --- + % norm timbre features if neccesary + timbren = []; + if feature(i).my_params.norm_timbres + for j = 1:numel(feature(i).data.timbre) + + timbren = cat(1, timbren, ... + MTTAudioFeatureBasicSm.norm_timbre... + (feature(i).data.timbre(j).means, feature(i).my_db.commondb.post_normf.timbre)); + end + else + + timbren = cat(1, timbren, feature(i).data.timbre(:).means); + end + end + + % --- + % construct resulting feature vector out of features + % --- + vec = []; + info = {}; + if feature(i).my_params.nchromas > 0 + + info{numel(vec)+ 1} = 'chroma'; + vec = cat(1, vec, feature(i).data.chroma(:).means); + + info{numel(vec)+ 1} = 'chroma weights'; + vec = cat(1, vec, [feature(i).data.chroma(:).means_weight]'); + + % --- + % NORMALISE Chroma variance + % --- + if feature(i).my_params.chroma_var >= 1 + + info{numel(vec)+ 1} = 'chroma variance'; + + % normalise this pack of variance vectors + tmp_var = mapminmax('apply', [feature(i).data.chroma(:).vars],... + feature(i).common.post_normf.chroma_var); + + % concatenate normalised data to vector + for vari = 1:size(tmp_var,2) + + vec = cat(1, vec, tmp_var(:, vari)); + end + end + end + + + if feature(i).my_params.ntimbres > 0 + + info{numel(vec)+ 1} = 'timbre'; + vec = cat(1, vec, timbren); + + info{numel(vec)+ 1} = 'timbre weights'; + vec = cat(1, vec, [feature(i).data.timbre(:).means_weight]'); + + % --- + % NORMALISE timbre variance + % --- + if feature(i).my_params.timbre_var >= 1 + + info{numel(vec)+ 1} = 'timbre variance'; + + % normalise this pack of variance vectors + tmp_var = mapminmax('apply', [feature(i).data.timbre(:).vars],... + feature(i).common.post_normf.timbre_var); + + % concatenate normalised data to vector + for vari = 1:size(tmp_var,2) + + vec = cat(1, vec, tmp_var(:, vari)); + end + end + end + + if feature(i).my_params.nrhythms > 0 + + info{numel(vec)+ 1} = 'rhythm 8'; + vec = cat(1, vec, feature(i).data.rhythm.acorr8); + + info{numel(vec)+ 1} = 'int 8'; + vec = cat(1, vec, feature(i).data.rhythm.interval8); + + if feature(i).my_params.nrhythms >= 2 + + info{numel(vec)+ 1} = 'rhythm 16'; + vec = cat(1, vec, feature(i).data.rhythm.acorr16); + + info{numel(vec)+ 1} = 'int 16'; + vec = cat(1, vec, feature(i).data.rhythm.interval16); + end + end + + feature(i).data.final.vector = vec; + feature(i).data.final.dim = numel(feature(i).data.final.vector); + + % fill up info struct and append to feature + + info(end+1: feature(i).data.final.dim) = ... + cell(feature(i).data.final.dim - numel(info),1); + + feature(i).data.final.vector_info.labels = info; + end + + % --- + % TODO: Maybe delete more basic features again at this point? + % --- + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + + + function visualise(feature) + % --- + % plots the different data types collected in this feature + % --- + for i = 1:numel(feature) + clip = feature(i).data.info.owner; + + % display raw features + if isa(clip, 'CASIMIRClip') + baseclip = clip.child_clip(); + else + baseclip = clip; + end + if isa(baseclip, 'MTTClip') + rawf = baseclip.audio_features_raw(); + elseif isa(baseclip, 'MSDClip') + rawf = baseclip.features('MSDAudioFeatureRAW'); + end + + % --- + % @todo: implement MSD feature visualisation + % --- + [a1, a2, a3] = rawf.visualise(); + + % --- + % Display chroma features + % --- + if isfield(feature(i).data, 'chroma') + + chroma_labels = {'c', 'c#', 'd','d#', 'e', 'f','f#', 'g','g#', 'a', 'a#', 'h'}; + mode_labels = {'minor', 'major'}; + + % change labels to reflect detected mode + chroma_labels{rawf.data.key + 1} = ... + sprintf('(%s) %s',mode_labels{rawf.data.mode + 1}, chroma_labels{rawf.data.key + 1}); + + % transpose labels and data + chroma_labels = circshift(chroma_labels, [0, feature(i).data.chroma(1).shift]); + chromar = circshift([rawf.data.segments_pitches], [feature(i).data.chroma(1).shift, 0]); + + % image transposed chromas again + segments = [rawf.data.segments_start]; + segments(end) = rawf.data.duration; + + hold(a1); + uimagesc(segments, 0:11, chromar, 'Parent', a1); + set(a1,'YTick',[0:11], 'YTickLabel', chroma_labels); + + % enlarge plot and plot new data after the old ones + ax = axis(a1); + ax(2) = ax(2) + 2*feature(i).my_params.nchromas + 0.5; + axis(a1, 'xy'); + axis(a1, ax); + + imagesc(rawf.data.duration + (1:feature(i).my_params.nchromas), (-1:11), ... + [ feature(i).data.chroma(:).means_weight; feature(i).data.chroma(:).means],... + 'Parent', a1); + % variance calculated? + if isfield(feature(i).data.chroma, 'vars') + + imagesc(rawf.data.duration + feature(i).my_params.nchromas + (1:feature(i).my_params.nchromas), (-1:11), ... + [feature(i).data.chroma(:).vars],... + 'Parent', a1); + end + end + + % --- + % Display timbre features + % --- + if isfield(feature(i).data, 'timbre') + + % enlarge plot and plot new data after the old ones + hold(a2); + ax = axis(a2); + ax(2) = ax(2) + 2*feature(i).my_params.ntimbres + 0.5; + + axis(a2, ax); + imagesc(rawf.data.duration + (1:feature(i).my_params.ntimbres), (-1:11), ... + [ feature(i).data.timbre(:).means_weight; feature(i).data.timbre(:).means],... + 'Parent', a2); + if isfield(feature(i).data.timbre, 'vars') + + imagesc(rawf.data.duration + feature(i).my_params.ntimbres + (1:feature(i).my_params.ntimbres), (-1:11), ... + [feature(i).data.timbre(:).vars],... + 'Parent', a1); + end + end + + % --- + % Display rhythm features + % --- + if isfield(feature(i).data, 'rhythm') + % data.rhythm.interval + % get timecode + eightt = feature(i).data.rhythm.energy8_time; + sixt = feature(i).data.rhythm.energy16_time; + + hold(a3); + % plot sixteens acorr and energy + plot(sixt, feature(i).data.rhythm.energy16, 'bx') + + plot(sixt, feature(i).data.rhythm.acorr16, 'b') + + % plot eights acorr and energy + plot(eightt, feature(i).data.rhythm.energy8, 'rx') + + plot(eightt, feature(i).data.rhythm.acorr8, 'r') + + % broaden view by fixed 4 seconds + ax = axis(a3); + axis(a3, [max(0, eightt(1)-( eightt(end) - eightt(1) + 4 )) ... + min(rawf.data.duration, eightt(end) +4) ... + ax(3:4)]); + end + end + end + end + + + methods (Hidden = true) + + function [env, time] = energy_envelope(feature, clip) + % extracts the envelope of energy for the given clip + + % --- + % TODO: externalise envelope etc in external audio features + % --- + + [null, src] = evalc('miraudio(clip.mp3file_full())'); + [null, env] = evalc('mirenvelope(src, ''Sampling'', feature.my_params.energy_sr)'); + + time = get(env,'Time'); + time = time{1}{1}; + env = mirgetdata(env); + end + + function [acorr, base_sig, base_t] = beat_histogram(feature, startt, interval, signal, signal_t) + % acorr = beat_histogram(feature, startt, interval, signal, time) + % + % compute correlation for beats of specified length in energy curve + + % get corresponding energy values + dt = signal_t(2) - signal_t(1); + base_t = startt:interval:(startt + (feature.my_params.nints*2-1) * interval); + base_sig = signal( min( numel(signal), max(1,round((base_t - signal_t(1))/dt)))); + + % normalise energy + acbase_sig = base_sig./max(base_sig); + + % calculate their cyclic autocorrelation + acorr = circshift(xcorr(acbase_sig,acbase_sig(1:end/2)),... + [numel(acbase_sig) 0]); + + % cut acorr to relevant points, normalise and square + acorr = (acorr(1:feature.my_params.nints)./feature.my_params.nints).^2; + + % --- + % NOTE: we normalise the autocorrelation locally, to compare the + % (rhythmic) shape + % --- + if feature.my_params.norm_acorr; + + acorr = acorr - min(acorr); + acorr = acorr/max(acorr); + end + end + end + + methods(Static) + + function timbre = norm_timbre(in, normfs) + % returns normed timbre data + + % --- + % individually scale the data using + % the dimensions factors + % --- + timbre = zeros(size(in)); + for i = 1:size(in,2) + + timbre(:,i) = normfs .* in(:,i); + end + + % shift to positive values + timbre = (1 + timbre) /2; + + % clip features to [0,1] + timbre = min(1, max(timbre, 0)); + end + + % --- + % returns parameter md5 hash for comparison + % --- + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTAudioFeatureDBgen.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,514 @@ +classdef MTTAudioFeatureDBgen < handle + + % --- + % the database is stored as a global variable + % --- + + properties (Hidden) + + % feature databases + featuredb; + + commondb; + + feature_type; + feature_params; + + % unfortunately we just can have one clip type at a time + clip_type; + end + + + properties (Hidden) + + % --- + % We use a "db_magnaaudiofeat_xxx" class pointer for all the loaded features, + % which is supposed to work like a cache. + % my_dbpos links to the position of this feature in the db. + % --- + + % database hash + featuredb_hash; + end + + % --- + % member functions + % --- + methods + + % constructor + function db = MTTAudioFeatureDBgen(type, matfile) + + if nargin >= 1 + + % --- + % we test for correct type by + % using an call to the empty arg constructor function + % --- + try + feval(type); + + catch err + fprintf('%s\n', err.message); + error('The specified class does not provide an constructor') + end + db.feature_type = type; + + if nargin >=2 && ~isempty(dir(matfile)) + + + % import database if filename given + db.import(matfile); + end + + end + + % try to load a standard db from mat file? + end + + % --- + % database retrieval. + % this should return a handle to a feature class pointing in this + % global feature database. + % + % parameters can be passed via the varargin parameter + % --- + function features = get_features(db, clip, varargin) + + % --- + % TODO: error checking and processing + % --- + + % --- + % prepare for multiple clips + % --- + if numel(clip) > 1 + + % iterate for all the found clips + features = feval(db.feature_type); + for i = 1:numel(clip) + features(i) = get_features(db, clip(i), varargin{:}); + end + else + % --- + % single feature extraction + % --- + + pos = db.get_clip_pos(clip); + if ~pos + + % assign new database position + pos = db.get_next_pos(); + + % call feature constructor + features = feval(db.feature_type, db, varargin{:}); + + % extract / load feature data + + cprint(2 ,'Extracting %s for clip %d \n', db.feature_type, clip.id()); + + if isempty( db.featuredb) + + db.featuredb = features.extract(clip); + else + + db.featuredb(pos) = features.extract(clip); + end + % --- + % NOTE: + % IF WE ARE SURE THAT EVERYTHING HAS WORKED OUT FINE: + % assign new database position in cache + % --- + db.set_pos(pos, clip); + + % --- + % NOTE: feature objects are directly linked to DB + % positions + % --- + features.assign(pos); + else + + % just return the cached link + features = feval(db.feature_type, db, pos); + end + + % --- + % finalise features if possible (commondb not empty) + % --- + if ~isempty(db.commondb) && isempty(features.data.final) && ... + ismethod(features,'finalise') + + % call finalise + features.finalise(); + end + + % set clip type of the db + db.clip_type = class(clip(1)); + + % get last params + db.feature_params = features(1).my_params; + end + end + + function set_common(db, common) + % sets the common database to input + + if isempty(db.commondb) + + cprint(2, 'Setting common feature values\n'); + else + + cprint(1, 'Common feature values changed'); + end + + db.commondb = common; + end + + function export(db, matfile, clips) + % saves featuredb to matlab data file + + global globalvars; + % save revision for later version and compability control + info.creatorrev = globalvars.camir.revision; + + cprint(2, 'Exporting %s database to %s ...\n', db.feature_type, matfile); + if nargin == 3 + % --- + % TODO: create new hash + % --- + for i = 1:numel(clips) + pos(i) = db.get_clip_pos(clips(i)); + + if pos(i) == 0 + error('Corrupted database'); + end + end + + % get specific data set + featuredb = db.featuredb(pos); + + featuredb_hash = db.featuredb_hash(pos); + + else + featuredb = db.featuredb; + + featuredb_hash = db.featuredb_hash; + end + + commondb = db.commondb; + + feature_type = db.feature_type; + + % --- + % save clip type as well + % NOTE: this only works if all clips have the same type (e.g. + % CASIMIRClip or MTTclip + % --- + clip_type = class(clips(1)); + + save(matfile, 'featuredb', 'commondb', 'feature_type', 'featuredb_hash', 'clip_type'); + end + + function [features, clips] = import(db, matfile, type) + % gets featuredb from matlab data file + + cprint(2, 'importing features from %s', matfile) + load(matfile,'-MAT'); + + if ~strcmp(feature_type, db.feature_type) + + error('feature type of db to import does not match'); + end + + % --- + % TODO / FIXME: check parameter hash before importing + % --- + +% if db.size() > 0 +% +% % get a feature param from the db; +% last_pos = db.get_last_pos; +% dummyparams = db.featuredb(last_pos).info.params; +% +% % --- +% % construct a dummy feature and compare parameters to +% % the params in the database +% % --- +% dummyclip = MTTClip(db.featuredb_hash(last_pos)); +% dummyfeat = db.get_features(dummyclip, dummyparams); +% +% if ~dummybsm.eq_params(fparams(i)) +% +% db_magnaaudiofeat_basicsm.reset; +% end +% +% end + + if exist('clip_type','var') + clips = feval(clip_type,featuredb_hash); + db.clip_type = clip_type; + else + clips = MTTClip(featuredb_hash); + db.clip_type = 'MTTClip'; + end + + % --- + % import features individually into db + % --- + + for i = 1:numel(clips) + + % test if database already contains clip + if ~db.get_clip_pos(clips(i)); + + % get position for this database + pos = db.get_next_pos(); + + % copy values + if ~isempty(db.featuredb) + + db.featuredb(pos) = featuredb(i); + elseif pos == 1; + + db.featuredb = featuredb(i); + else + + error ('Corrupted database'); + end + % update hash + db.set_pos(pos, clips(i)); + end + end + +% Set common features + db.set_common(commondb); + + if nargout > 0 + % retrieve features; + features = get_features(db, clips); + end + + end + + function [matfile] = load(db, featuretype, fparams, clips) + % this is the new implementation of MTTAudiofeature_load + + % get filename + matfile = MTTAudioFeatureDBgen.get_db_filename(featuretype, fparams,clips); + + % does it exist? + if exist(matfile,'file') == 2 + import(db, matfile); + else + matfile= []; + end + end + + function [matfile] = save(db) + % this is the new implementation of MTTAudiofeature_load + + % get clips + clips = feval(db.clip_type,db.featuredb_hash); + + % get filename + matfile = MTTAudioFeatureDBgen.get_db_filename(db.feature_type, db.feature_params ,clips); + + % does it exist? + if exist(matfile,'file') == 2 + warning 'overwriting feature file for this config' + end + + % save params in xml + xml_save(sprintf('%s.xml', substr(matfile, 0, -4)),db.feature_params); + + %export features + export(db, matfile, clips); + end + + function remove_features(db, clip) + % weakly deletes clip features from db + + clear_pos(clip); + end + + function delete(db) + % --- + % probably not anything to do here, as we want to + % keep the db! + % see static method destroy + % --- + end + + function reset(db) + % --- + % deletes all the cached data and destroys the + % global feature database + % --- + db.commondb = []; + db.featuredb = []; + db.featuredb_hash = []; + end + + function out = size(db) + % returns the number of features saved in this db + + out = sum(db.featuredb_hash > 0); + end + + function memory(db) + % returns size of whole db in bytes + + % --- + % TODO: Make this work + % --- + + fprintf(' \n This db contains feature sets for %d clips\n ',numel(db.featuredb_hash)) + % get local copies of data + featuredb = db.featuredb; + featuredb_hash = db.featuredb_hash; + commondb = db.commondb; + + whos('featuredb', 'featuredb_hash', 'commondb') + end + end + + % --- + % private functions + % --- + methods (Hidden) + + % --- + % Hash functions + % --- + function out = get_clip_pos(db, clip) + % should become database hashing function + + out = find(db.featuredb_hash == clip.id); + if isempty(out) + out = 0; + end + end + + function out = get_next_pos(db) + % return index for the new clip features + + out = numel(db.featuredb_hash) + 1; + end + + function last_pos = get_last_pos(db) + % return index the last valid db entry + + last_pos = find(db.featuredb_hash > 0, 1, 'last'); + end + + + function set_pos(db, pos, clip) + % set index for the new clip features + + db.featuredb_hash(pos) = clip.id; + end + + function clear_pos(db, clip) + % remove index of the clip features + + db.featuredb_hash(get_clip_pos(db, clip)) = 0; + end + end + + methods (Static) + + % --- + % this resets all feature dbs except the one exluded in the + % 'exclude', {''} cell + % --- + function reset_feature_dbs(varargin) + + [exclude] = process_options(varargin,'exclude',{}); + % --- + % resets all feature dbs except raw features + % --- + vars = whos ('*','global','-regexp', '^db_*'); + + % --- + % check if each is class of DBgen. + % if not in exclude variable, reset + % --- + for i = 1:numel(vars) + + % import global variable + eval(sprintf('global %s',vars(i).name)); + + if strcmp(eval(sprintf('class(%s)',vars(i).name)), 'MTTAudioFeatureDBgen') ... + && isempty(strcellfind(exclude, vars(i).name)) + + eval(sprintf('%s.reset',vars(i).name)); + end + end + end + + function feature_type = import_type(matfile) + % function feature_type = import_type(matfile) + % + % returns the type of the saved feature db. + + load(matfile, 'feature_type', '-MAT'); + end + + function db_nameo = db_name(type) + % returns the standard global var name for a db of given type + + switch type + case 'MTTAudioFeatureRAW' + db_nameo = 'db_magnaaudiofeat'; + + case 'MTTAudioFeatureBasicSm' + db_nameo = 'db_magnaaudiofeat_basicsm'; + + case 'MTTTagFeatureGenreBasic' + db_nameo = 'db_magnatagfeat_genrebasic'; + + case 'MTTMixedFeatureGenreBasicSm' + db_nameo = 'db_magnamixedfeat_genrebasicsm'; + + otherwise + db_nameo = sprintf('db_%s', type); + end + end + + function matfile = get_db_filename(featuretype, fparams,clips) + % get the paramhash + paramhash = MTTAudioFeatureDBgen.param_hash(featuretype, fparams); + + % add the clip hash bit + cliphash = MTTAudioFeatureDBgen.clip_hash(clips); + + % determine the filename + matfile = sprintf('f%s.%s.mat', paramhash,cliphash); + end + + function ph = param_hash(type, varargin) + % loads the params for a feature type and adds the + % given parameter values to it. + + % this function can be static or dynamic + if nargin > 1 + % static case + dummy = feval(type,[], varargin{:}); + else + % dynamic case + dummy = type; + end + ph = hash(xml_format(dummy.my_params),'MD5'); + end + + function ch = clip_hash(clips) + + % returns the hash of a number of clips + ch = hash([class(clips(1)) mat2str(sort(clips.id))],'MD5'); + end + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTAudioFeatureHMM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,452 @@ +classdef MTTAudioFeatureHMM < MTTAudioFeature & handle + % --- + % the MTTAudioFeatureBasicSm Class contains + % a basic summary of chroma, mfcc and tempo features + % a few common chroma and mfcc vectors are concatenated + % along with some clip-wide variance + % a metric / rhythm fingerprint is added + % + % The usual workflow for these features consists of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev: 2332 $', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = struct(... + 'nstates', 4 ... % predefined number of states + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTAudioFeatureHMM(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + + end + % --- + % extract feature data from raw audio features + % --- + function data = extract(feature, clip) + % --- + % get Basic Summary audio features. this includes possible + % local normalisations + % --- + + global globalvars; + + % --- + % get casimir child clip if available + % --- + if isa(clip, 'CASIMIRClip') + baseclip = clip.child_clip(); + else + baseclip = clip; + end + if isa(baseclip, 'MTTClip') + rawf = baseclip.audio_features_raw(); + elseif isa(baseclip, 'MSDClip') + rawf = baseclip.features('MSDAudioFeatureRAW'); + end + + % --- + % now extract the features + % first step: chroma clustering + % --- + weights = [rawf.data.segments_duration]; + + % normalise weights + weights = weights / rawf.data.duration; + + % get the chroma features + chroma = [rawf.data.segments_pitches]'; + + % --- + % TODO: train hmm + % --- + + + % save hmm into data variable + data.mu = mu1 + data.transmat1 = mu1 + + + + + + + % prepare field for final features + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = 'MTTAudioFeatureBasicSm'; + data.info.owner = clip; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + + + end + + + function finalise(feature) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + if feature(1).my_params.ntimbres > 0 + % --- + % normalise features + % --- + % norm timbre features if neccesary + timbren = []; + if feature(i).my_params.norm_timbres + for j = 1:numel(feature(i).data.timbre) + + timbren = cat(1, timbren, ... + MTTAudioFeatureBasicSm.norm_timbre... + (feature(i).data.timbre(j).means, feature(i).my_db.commondb.post_normf.timbre)); + end + else + + timbren = cat(1, timbren, feature(i).data.timbre(:).means); + end + end + + % --- + % construct resulting feature vector out of features + % --- + vec = []; + info = {}; + if feature(i).my_params.nchromas > 0 + + info{numel(vec)+ 1} = 'chroma'; + vec = cat(1, vec, feature(i).data.chroma(:).means); + + info{numel(vec)+ 1} = 'chroma weights'; + vec = cat(1, vec, [feature(i).data.chroma(:).means_weight]'); + + % --- + % NORMALISE Chroma variance + % --- + if feature(i).my_params.chroma_var >= 1 + + info{numel(vec)+ 1} = 'chroma variance'; + + % normalise this pack of variance vectors + tmp_var = mapminmax('apply', [feature(i).data.chroma(:).vars],... + feature(i).common.post_normf.chroma_var); + + % concatenate normalised data to vector + for vari = 1:size(tmp_var,2) + + vec = cat(1, vec, tmp_var(:, vari)); + end + end + end + + + if feature(i).my_params.ntimbres > 0 + + info{numel(vec)+ 1} = 'timbre'; + vec = cat(1, vec, timbren); + + info{numel(vec)+ 1} = 'timbre weights'; + vec = cat(1, vec, [feature(i).data.timbre(:).means_weight]'); + + % --- + % NORMALISE timbre variance + % --- + if feature(i).my_params.timbre_var >= 1 + + info{numel(vec)+ 1} = 'timbre variance'; + + % normalise this pack of variance vectors + tmp_var = mapminmax('apply', [feature(i).data.timbre(:).vars],... + feature(i).common.post_normf.timbre_var); + + % concatenate normalised data to vector + for vari = 1:size(tmp_var,2) + + vec = cat(1, vec, tmp_var(:, vari)); + end + end + end + + if feature(i).my_params.nrhythms > 0 + + info{numel(vec)+ 1} = 'rhythm 8'; + vec = cat(1, vec, feature(i).data.rhythm.acorr8); + + info{numel(vec)+ 1} = 'int 8'; + vec = cat(1, vec, feature(i).data.rhythm.interval8); + + if feature(i).my_params.nrhythms >= 2 + + info{numel(vec)+ 1} = 'rhythm 16'; + vec = cat(1, vec, feature(i).data.rhythm.acorr16); + + info{numel(vec)+ 1} = 'int 16'; + vec = cat(1, vec, feature(i).data.rhythm.interval16); + end + end + + feature(i).data.final.vector = vec; + feature(i).data.final.dim = numel(feature(i).data.final.vector); + + % fill up info struct and append to feature + + info(end+1: feature(i).data.final.dim) = ... + cell(feature(i).data.final.dim - numel(info),1); + + feature(i).data.final.vector_info.labels = info; + end + + % --- + % TODO: Maybe delete more basic features again at this point? + % --- + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + + + function visualise(feature) + % --- + % plots the different data types collected in this feature + % --- + for i = 1:numel(feature) + clip = feature(i).data.info.owner; + + % display raw features + if isa(clip, 'CASIMIRClip') + baseclip = clip.child_clip(); + else + baseclip = clip; + end + if isa(baseclip, 'MTTClip') + rawf = baseclip.audio_features_raw(); + elseif isa(baseclip, 'MSDClip') + rawf = baseclip.features('MSDAudioFeatureRAW'); + end + + % --- + % @todo: implement MSD feature visualisation + % --- + [a1, a2, a3] = rawf.visualise(); + + % --- + % Display chroma features + % --- + if isfield(feature(i).data, 'chroma') + + chroma_labels = {'c', 'c#', 'd','d#', 'e', 'f','f#', 'g','g#', 'a', 'a#', 'h'}; + mode_labels = {'minor', 'major'}; + + % change labels to reflect detected mode + chroma_labels{rawf.data.key + 1} = ... + sprintf('(%s) %s',mode_labels{rawf.data.mode + 1}, chroma_labels{rawf.data.key + 1}); + + % transpose labels and data + chroma_labels = circshift(chroma_labels, [0, feature(i).data.chroma(1).shift]); + chromar = circshift([rawf.data.segments_pitches], [feature(i).data.chroma(1).shift, 0]); + + % image transposed chromas again + segments = [rawf.data.segments_start]; + segments(end) = rawf.data.duration; + + hold(a1); + uimagesc(segments, 0:11, chromar, 'Parent', a1); + set(a1,'YTick',[0:11], 'YTickLabel', chroma_labels); + + % enlarge plot and plot new data after the old ones + ax = axis(a1); + ax(2) = ax(2) + 2*feature(i).my_params.nchromas + 0.5; + axis(a1, 'xy'); + axis(a1, ax); + + imagesc(rawf.data.duration + (1:feature(i).my_params.nchromas), (-1:11), ... + [ feature(i).data.chroma(:).means_weight; feature(i).data.chroma(:).means],... + 'Parent', a1); + % variance calculated? + if isfield(feature(i).data.chroma, 'vars') + + imagesc(rawf.data.duration + feature(i).my_params.nchromas + (1:feature(i).my_params.nchromas), (-1:11), ... + [feature(i).data.chroma(:).vars],... + 'Parent', a1); + end + end + + % --- + % Display timbre features + % --- + if isfield(feature(i).data, 'timbre') + + % enlarge plot and plot new data after the old ones + hold(a2); + ax = axis(a2); + ax(2) = ax(2) + 2*feature(i).my_params.ntimbres + 0.5; + + axis(a2, ax); + imagesc(rawf.data.duration + (1:feature(i).my_params.ntimbres), (-1:11), ... + [ feature(i).data.timbre(:).means_weight; feature(i).data.timbre(:).means],... + 'Parent', a2); + if isfield(feature(i).data.timbre, 'vars') + + imagesc(rawf.data.duration + feature(i).my_params.ntimbres + (1:feature(i).my_params.ntimbres), (-1:11), ... + [feature(i).data.timbre(:).vars],... + 'Parent', a1); + end + end + + % --- + % Display rhythm features + % --- + if isfield(feature(i).data, 'rhythm') + % data.rhythm.interval + % get timecode + eightt = feature(i).data.rhythm.energy8_time; + sixt = feature(i).data.rhythm.energy16_time; + + hold(a3); + % plot sixteens acorr and energy + plot(sixt, feature(i).data.rhythm.energy16, 'bx') + + plot(sixt, feature(i).data.rhythm.acorr16, 'b') + + % plot eights acorr and energy + plot(eightt, feature(i).data.rhythm.energy8, 'rx') + + plot(eightt, feature(i).data.rhythm.acorr8, 'r') + + % broaden view by fixed 4 seconds + ax = axis(a3); + axis(a3, [max(0, eightt(1)-( eightt(end) - eightt(1) + 4 )) ... + min(rawf.data.duration, eightt(end) +4) ... + ax(3:4)]); + end + end + end + end + + + methods (Hidden = true) + + function [env, time] = energy_envelope(feature, clip) + % extracts the envelope of energy for the given clip + + % --- + % TODO: externalise envelope etc in external audio features + % --- + + [null, src] = evalc('miraudio(clip.mp3file_full())'); + [null, env] = evalc('mirenvelope(src, ''Sampling'', feature.my_params.energy_sr)'); + + time = get(env,'Time'); + time = time{1}{1}; + env = mirgetdata(env); + end + + function [acorr, base_sig, base_t] = beat_histogram(feature, startt, interval, signal, signal_t) + % acorr = beat_histogram(feature, startt, interval, signal, time) + % + % compute correlation for beats of specified length in energy curve + + % get corresponding energy values + dt = signal_t(2) - signal_t(1); + base_t = startt:interval:(startt + (feature.my_params.nints*2-1) * interval); + base_sig = signal( min( numel(signal), max(1,round((base_t - signal_t(1))/dt)))); + + % normalise energy + acbase_sig = base_sig./max(base_sig); + + % calculate their cyclic autocorrelation + acorr = circshift(xcorr(acbase_sig,acbase_sig(1:end/2)),... + [numel(acbase_sig) 0]); + + % cut acorr to relevant points, normalise and square + acorr = (acorr(1:feature.my_params.nints)./feature.my_params.nints).^2; + + % --- + % NOTE: we normalise the autocorrelation locally, to compare the + % (rhythmic) shape + % --- + if feature.my_params.norm_acorr; + + acorr = acorr - min(acorr); + acorr = acorr/max(acorr); + end + end + end + + methods(Static) + + function timbre = norm_timbre(in, normfs) + % returns normed timbre data + + % --- + % individually scale the data using + % the dimensions factors + % --- + timbre = zeros(size(in)); + for i = 1:size(in,2) + + timbre(:,i) = normfs .* in(:,i); + end + + % shift to positive values + timbre = (1 + timbre) /2; + + % clip features to [0,1] + timbre = min(1, max(timbre, 0)); + end + + % --- + % returns parameter md5 hash for comparison + % --- + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTAudioFeatureRAW.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,127 @@ +% -- +% This class loads and hanles the aufdio features included with the MTT +% Library +% --- + + +classdef MTTAudioFeatureRAW < AudioFeatureRAW & handle + + + properties(Constant = true) + + my_revision = str2double(substr('$Rev: 2284 $', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + + end + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTAudioFeatureRAW(varargin) + + feature = feature@AudioFeatureRAW(varargin{:}); + + end + + % --- + % load feature data from xml file + % --- + function data = extract(feature, clip) + % load feature data by parsing xml + + global globalvars; + + % fprintf('parsing features for clip %d \n',clip.id()); + + % parse feature + data = xml_parse_mtt(clip.featurefile_full()); + + % precombine the segmentst information for better + % interoperability with MSD data later + + data.segments_pitches = [data.segments(:).pitches]; + data.segments_timbre = [data.segments(:).timbre]; + + data.segments_duration = [data.segments(:).duration]; + data.segments_start = [data.segments(:).start]; + + data.sections_start = [data.sections(:).start]; + data.sections_duration = [data.sections(:).duration]; + + data.segments_duration = [data.segments(:).duration]; + + data.segments_loudness_max_time = [data.segments(:).loudness_max_time]; + data.segments_loudness_max = [data.segments(:).loudness_max]; + data.segments_loudness = [data.segments(:).loudness]; + + % --- + % TODO: feature.data.bars + % --- + [data.bars, data.beats, data.tatums] = MTTAudioFeatureRAW.infer_bar_times(data.bars); + + % save info data + data.info.type = 'MTTAudioFeatureRAW'; + data.info.owner = clip; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + data.info.params = feature.my_params; + end + end + + methods(Static) + function [bars, beats, tatums] = infer_bar_times(bar) + % [bars, beats, tatums] = infer_bar_times(feature) + % + % extract bar and beat starting times from tatums + beats = []; + bars = []; + tatums = []; + + % bars + for i = 1:numel(bar) + % beats + for j = 1:numel(bar(i).beat) + % tatums + for k = 1:numel(bar(i).beat(j).tatum) + tatum = bar(i).beat(j).tatum(k); + + % collect tatums and confidence + tatums(1:2,end+1) = [tatum.time,tatum.confidence]; + + + % --- + % save beat if this is the first tatum in it + % --- + if k == 1 + beats(1:2,end+1) = [tatum.time,... + bar(i).beat(j).confidence]; + + % --- + % save bar if this is the first tatum in it + % --- + if j == 1 + bars(1:2,end+1) = [tatum.time,... + bar(i).confidence]; + end + end + + + end + end + end + + % end function + end + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTAudioFeatureSlaney08.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,377 @@ +classdef MTTAudioFeatureSlaney08 < MTTAudioFeature & handle + % --- + % This Class contains + % a basic summary of MTT features complementary to those in + % MTTAudioFeatureBasicSm, features are extracted + % as described in Slaney 08 - LEARNING A METRIC FOR MUSIC SIMILARITY + % + % The usual workflow for these features constist of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev$', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = struct(... + 'norm_mttstats', 1, ... % + 'whiten_mttstats', 0, ... % NOTE: whitening as in slaney?? + 'select_mttstats', 1 ...% TODO: way to select certain features + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTAudioFeatureSlaney08(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + + end + % --- + % extract feature data from raw audio features + % --- + function data = extract(feature, clip) + % --- + % get features. this includes possible + % local normalisations + % --- + + global globalvars; + + % --- + % get casimir child clip if available + % --- + if isa(clip, 'CASIMIRClip') + baseclip = clip.child_clip(); + else + baseclip = clip; + end + if isa(baseclip, 'MTTClip') + rawf = baseclip.audio_features_raw(); + elseif isa(baseclip, 'MSDClip') + rawf = baseclip.features('MSDAudioFeatureRAW'); + end + + + % --- + % TODO: implement time_weighted version of the statistical + % evaluations below + % --- + +% segmentDurationMean: mean segment duration (sec.). + data.mttstats.segmentDurationMean = mean(rawf.data.segments_duration); + +% segmentDurationVariance: variance of the segment duration + data.mttstats.segmentDurationVariance = var(rawf.data.segments_duration); + +% timeLoudnessMaxMean: mean time to the segment maximum, or attack duration (sec.). + data.mttstats.timeLoudnessMaxMean = mean(rawf.data.segments_loudness_max_time); + +% loudnessMaxMean: mean of segments’ maximum loudness(dB). + data.mttstats.loudnessMaxMean = mean(rawf.data.segments_loudness_max); + +% loudnessMaxVariance: variance of the segments’ maximum loudness (dB). + data.mttstats.loudnessMaxVariance = var(rawf.data.segments_loudness_max); + +% loudnessBeginMean: average loudness at the start of segments (dB) + data.mttstats.loudnessBeginMean = mean(rawf.data.segments_loudness); + +% loudnessBeginVariance: variance of the loudness at the startof segments (dB2). Correlated with loudnessMaxVariance + data.mttstats.loudnessBeginVariance = var(rawf.data.segments_loudness); + +% loudnessDynamicsMean: average of overall dynamic rangein the segments (dB). +% loudnessDynamicsVariance: segment dynamic range variance +% (dB). Higher variances suggest more dynamics ineach segment. + % --- + % NOTE: the above information cannot be extracted from the MTT + % Features, maybe more recent echonest features allow for this + % --- + +% loudness: overall loudness estimate of the track (dB). + data.mttstats.loudness = rawf.data.loudness; + + % --- + % TODO: get these from the beat loundesses? + % --- + +% tempo: overall track tempo estimate (in beat per minute,BPM). Doubling and halving errors are possible. + data.mttstats.tempo = rawf.data.tempo; + +% tempoConfidence: a measure of the con?dence of the tempo estimate (beween 0 and 1). + %data.mttstats.tempoConfidence = rawf.data.tempoConfidence; + + beats = rawf.data.beats; + tatums = rawf.data.tatums; + +% beatVariance: ameasure of the regularity of the beat (secs). + if numel(beats) > 0 + bdiff = diff(beats(1,:)); + data.mttstats.beatVariance = var(bdiff); + else + + % --- + % This is a facke repolacement variance + % --- + data.mttstats.beatVariance = 0; + end + + +% tatum: estimated overall tatum duration (in seconds). Tatums are subdivisions of the beat. + % --- + % note: the tatum length could be also + % accessed by comparison with the global bpm estimate + % --- + if numel(tatums) > 0 + tdiff = diff(tatums(1,:)); + data.mttstats.tatum = median(tdiff); + + % tatumConfidence: a measure of the con?dence of the tatum estimate (beween 0 and 1). + data.mttstats.tatumConfidence = mean(tatums(2,:)); + + % numTatumsPerBeat: number of tatums per beat + data.mttstats.numTatumsPerBeat = median(bdiff) / data.mttstats.tatum; + else + % --- + % This is a facke replacement tatum + % TODO: maybe set confidence to -1? + % --- + + data.mttstats.tatum = 0; + + % tatumConfidence: a measure of the con?dence of the tatum estimate (beween 0 and 1). + + data.mttstats.tatumConfidence = 0; + + % numTatumsPerBeat: number of tatums per beat + data.mttstats.numTatumsPerBeat = 2; + end + + + % --- + % TODO: beat analysis + % --- + +% timeSignature: estimated time signature (number of beats per measure). (0-7 / 7) + data.mttstats.timeSignature = rawf.data.timeSignature; + +% timeSignatureStability: a rough estimate of the stability of the time signature throughout the track + data.mttstats.timeSignatureStability = rawf.data.timeSignatureConfidence; + + % --- + % prepare field for final features + % --- + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = 'MTTAudioFeatureSlaney08'; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + for i = 1:numel(features) + data = features(i).data.mttstats; + + final(:,i) = [data.segmentDurationMean; ... + data.segmentDurationVariance; ... + data.timeLoudnessMaxMean; ... + data.loudnessMaxMean; ... + data.loudnessMaxVariance; ... + data.loudnessBeginMean; ... + data.loudnessBeginVariance; ... + data.loudness; ... + data.tempo; ... + ... % data.tempoConfidence; ... + data.beatVariance; ... + data.tatum; ... + data.tatumConfidence; ... + data.numTatumsPerBeat; ... + data.timeSignature; ... + data.timeSignatureStability]; + end + + if features(1).my_params.norm_mttstats + if numel(features) == 1 + error ('Insert feature array for this method, or set normalisation to 0'); + end + + % --- + % here, we only need to define the post-normalisation + % --- + [final, pstd] = mapminmax(final,0,1); + common.mttstats.pre_norm = pstd; + + % --- + % NOTE: whitening as in slaney?? + % Would make reading the + % mahal matrices really hard + % --- + + features(1).my_db.set_common(common); + + else + + features(1).my_db.set_common([1]); + end + + % save the normalised features straight away! + features.finalise(final); + end + + + function finalise(features, final) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + % check if features have been finalised already + + % --- + % check for dummy feature + % --- + if isfield(features(1).my_params,'select_mttstats') && ... + isnumeric(features(1).my_params.select_mttstats) && ... + features(1).my_params.select_mttstats == 0 + + % if no information needed just fill everything 0 + for i = 1:numel(features) + features(i).data.final.vector = []; + features(i).data.final.dim = 0; + + % fill up info struct and append to feature + features(i).data.final.vector_info.labels = {}; + end + + return; + end + + % --- + % set feature labelling + % --- + info = {'segmentDurationMean', ... + 'segmentDurationVariance', ... + 'timeLoudnessMaxMean', ... + 'loudnessMaxMean', ... + 'loudnessMaxVariance', ... + 'loudnessBeginMean', ... + 'loudnessBeginVariance', ... + 'loudness', ... + 'tempo', ... + ...% 'tempoConfidence', ... + 'beatVariance', ... + 'tatum', ... + 'tatumConfidence', ... + 'numTatumsPerBeat', ... + 'timeSignature', ... + 'timeSignatureStability'}; + + % --- + % construct resulting feature vector out of features + % --- + if nargin == 2 && isempty(final) + + % the final vector etc already are set to zero; + return; + + elseif nargin == 2 && (numel(features) == size(final, 2)) + for i = 1:numel(features) + + % check for neccesary parameters + if isempty(features(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + features(i).data.final.vector = final(:,i); + features(i).data.final.dim = size(final,1); + + % fill up info struct and append to feature + features(i).data.final.vector_info.labels = info; + end + else + % --- + % if features have been added after gettin gnormalisation + % parameters, ther should be still an option to include + % them + % --- + + for i = 1:numel(features) + + % check for neccesary parameters + if isempty(features(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + data = features(i).data.mttstats; + final = [data.segmentDurationMean; ... + data.segmentDurationVariance; ... + data.timeLoudnessMaxMean; ... + data.loudnessMaxMean; ... + data.loudnessMaxVariance; ... + data.loudnessBeginMean; ... + data.loudnessBeginVariance; ... + data.loudness; ... + data.tempo; ... + ... % data.tempoConfidence; ... + data.beatVariance; ... + data.tatum; ... + data.tatumConfidence; ... + data.numTatumsPerBeat; ... + data.timeSignature; ... + data.timeSignatureStability]; + + if features(1).my_params.norm_mttstats == 1 + + [final] = mapminmax('apply', final, features(1).common.mttstats.pre_norm); + end + + features(i).data.final.vector = final; + features(i).data.final.dim = size(final,1); + + % fill up info struct and append to feature + features(i).data.final.vector_info.labels = info; + end + + end + + % --- + % TODO: Maybe delete more basic features again at this point? + % --- + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTClip.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,98 @@ +% --- +% This is the class for magnatagatune clips +% --- + +classdef MTTClip < Clip + + % --- + % here come the internal clip properties. + % the database is stored as a global variable + % --- + + % --- + % simple constructor + % --- + methods + function clip = MTTClip(varargin) + clip = clip@Clip(varargin{:},'db_MTTClip'); + end + + function out = mp3file(this) + % returns filename for given genre id + + out = this.my_db.clip_info_proper{this.my_dbpos,10}; + end + + + function filename = mp3file_full(this) + % returns mp3 filename for given clip id + + global globalvars; + filename = strcat(globalvars.tstaudiopath,this.mp3file()); + filename = strrep(filename,'\',globalvars.systemslash); + filename = strrep(filename,'/',globalvars.systemslash); + end + + function filename = featurefile_full(this) + % returns xml filename for given clip id + + % --- + % NOTE: the xml files are supposed to be in the folder + % "xml" as subfolder of the mp3 file path + % --- + global globalvars; + filename = strcat(globalvars.tstaudiopath,'xml\',... + this.mp3file,'.xml'); + + filename = strrep(filename,'/',globalvars.systemslash); + filename = strrep(filename,'\',globalvars.systemslash); + end + + + % caution. last.fm dbs take a clip + function [out, score, annotids] = fmtags(this) + + [out, score, annotids] = this.my_db.fmtagdb.annots(this); + end + + % caution. last.fm dbs take a clip + % TODO: revert this to general annotdb for compability + function [out, score] = fmtag_ids(this) + + [out, score] = this.my_db.fmtagdb.annotids_for_owner(this); + end + + function [out, score] = genre_ids(this) + % returns clip genre for given clip id + + [out, score] = this(1).my_db.genredb.annotids_for_owner([this.id]); + end + + function [out, score, annotids] = genres(this) + % returns name strings for given genre position ids + + [out, score, annotids] = this(1).my_db.genredb.annots([this.id]); + end + + function print(this) + % prints out the album info + + fprintf('clip %d: %s by %s,\n on %s \n',this.id, ... + this.title(), this.artist(), this.album()); + + v = strcat(this.genres(),', '); + fprintf(' genres: %s\n', strcat(v{:})); + + v = strcat(this.tags(),', '); + fprintf(' tags: %s\n', strcat(v{:})); + + v = strcat(this.fmtags(),', '); + fprintf(' last.fm tags: %s\n', strcat(v{:})); + + % if nargout == 0 + % fprintf(out) + % end + end + + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTClipDB.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,141 @@ +% --- +% This is the wrapper and loader class for magnatagatune db +% and related data +% --- + +classdef MTTClipDB < handle + + properties (SetAccess = 'private') + + % --- + % TODO: create global "db_magnaclips" class pointer + % --- + + annots; + annots_filenames; + annots_ids; + annots_names; + + % inverse ids for comparison + comparison_ids; + + clip_info_proper; + clip_info_proper_names; + +% magnagenre_childof; + + % genre database + genredb; + + % artist database + artistdb; + + % tag databses + tagdb; + + fmtagdb; + + % @todo: get data + mbtagdb; + end + + properties (Hidden) + % hidden properties + + end + + methods + + % --- + % constructore + % --- + function db = MTTClipDB() + + % --- + % TODO: restructure data / db.mat + % --- + + load 'db.mat'; + + db.annots = annots; + db.annots_ids = annots_ids; + db.annots_names = annots_names; + db.annots_filenames = annots_filenames; + + % comparison ids + db.comparison_ids = sparse(comparison_ids,1, 1:numel(comparison_ids)); + + db.clip_info_proper = clip_info_proper; + db.clip_info_proper_names = clip_info_proper_names; + + % Genre in new structure for textual annotations + db.genredb = AnnotDB(magnagenres, clip_magnagenres, annots_ids); + + % Artist for each clip + db.artistdb = artistdb; %AnnotDB('clips_by_annot', db.clip_info_proper(:, 4), annots_ids); + + % Magnatagatune Tags + db.tagdb = tagdb; % AnnotDB(annots_names, annots, annots_ids); + + % Last Fm Tags + db.fmtagdb = fmtagdb; % LFMTagsDB(db.artistdb); + + % musicbrainz tags + db.mbtagdb = mbtagdb; % LFMTagsDB(db.artistdb); + +% db.clip_magnagenres = clip_magnagenres; +% db.magnagenres = magnagenres; +% db.magnagenre_childof = magnagenre_childof; + + end + + % --- + % member functions + % --- + function out = genres(db) + % returns the magnatune genre list + + out = db.genredb.lexicon; + end + + function clips = clips_by_genre_name(db, name) + % returns all clips having the assigned genre + + clips = MTTClip( db.genredb.owner( name)); + end + + function out = tags(db) + % returns the magnatune artist list + + out = db.tagdb.lexicon; + end + + + function out = artists(db) + % returns the magnatune artist list + + out = db.artistdb.lexicon; + end + + function clips = clips_by_artist_name(db, name) + % returns all clips having the assigned artist + + clips = MTTClip( db.artistdb.owner( name)); + end + end + + % --- + % Hidden Methods + % --- + methods (Hidden = true, Access = private) + + function out = clips_by_genre(db, genre_id) + % returns clip ids given a genre id + + pos = (db.clip_magnagenres(:,genre_id) == 1); + + % return clip ids, not pos + out = db.annots_ids(pos); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureGenreBasicSm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,175 @@ +classdef MTTMixedFeatureGenreBasicSm < MTTAudioFeature & handle + % --- + % + % The usual worklow for these features constist of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev$', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = MTTAudioFeature.inherited_params(... + 'MTTAudioFeatureBasicSm', ... + ... % --- + ... % following are GenreBasic parameters + ... % --- + 'pct_genres', 1 ... % 1/100 percentile genre tags used + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureGenreBasicSm(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % extract feature data by combining genre adn basicsm features + % --- + function data = extract(feature, clip) + + % --- + % get Basic Summary audio features. this includes possible + % local normalisations + % --- + basicsm = clip.audio_features_basicsm(feature.my_params); + + % --- + % get genre tag features + % --- + + genrebasic = clip.genre_features_basic(feature.my_params); + + % save to features data field + data.audio = basicsm; + data.tags = genrebasic; + + % prepare field for final features + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = 'MTTMixedFeatureGenreBasicSm'; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant basicsm + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + basicsm(i) = features(i).data.audio; + end + + % call the features own transsform function + basicsm.define_global_transform(); + + % --- + % We collect all the relevant genretag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + genrebasic(i) = features(i).data.tags; + end + + % call the features own transsform function + genrebasic.define_global_transform(); + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common([1]); %trivial common + end + + + function finalise(feature) + % applies a final transformation and collects the + % information of this feature within a single vector + % see info for types in specific dimensions + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first'); + end + + % --- + % finalise audio feature and get vector + % --- + basicsm = feature(i).data.audio; + basicsm.finalise(); + + % finalise tag features + genrebasic = feature(i).data.tags; + genrebasic.finalise; + + % --- + % final data assembly + % --- + + % concatenate vectors + feature(i).data.final.vector = ... + [basicsm.vector() ; genrebasic.vector()]; + + % add feature dimensions + feature(i).data.final.dim = basicsm.dim + genrebasic.dim; + + % concatenate labels + feature(i).data.final.vector_info.labels = ... + {basicsm.data.final.vector_info.labels{:}, ... + genrebasic.data.final.vector_info.labels{:}}; + end + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + + function visualise(feature) + % --- + % plots the different data types collected in this feature + % --- + for i = 1:numel(feature) + clip = MTTClip(feature(i).owner_id()); + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureGenreBasicSmPCA.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,265 @@ +% -- +% This class loads and hanles the aufdio features included with the MTT +% Library +% --- +classdef MTTMixedFeatureGenreBasicSmPCA < MTTAudioFeature & handle + + properties(Constant = true) + + my_revision = str2double(substr('$Rev$', 5, -1)); + end + + properties + + % --- + % Set default parameters + % --- + my_basetype = 'MTTMixedFeatureGenreBasicSm'; + + my_params = MTTAudioFeature.inherited_params(... + 'MTTMixedFeatureGenreBasicSm', ... + 'min_pca_var', 0, ... % % fraction of variance to keep + 'max_pca_coeffs', 0, ...% max. number of final coefficients + 'norm_pre_pca', 1, ...% normalise pca coefficients after transformation + 'norm_post_pca', 1 ... + ); + end + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureGenreBasicSmPCA(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % load feature data from xml file + % --- + function data = extract(feature, clip) + % load feature data by parsing xml + + global globalvars; + + % --- + % we extract the base features, and save + % the pointers to these. + % the main work is then done in the define_global_transf + % and finalise functions. + % --- + data.basefeat = clip.features(feature.my_basetype,... + feature.my_params); + + % save info data + data.info.type = 'MTTMixedFeatureGenreBasicSmPCA'; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save param data + data.info.params = feature.my_params; + + % prepare field for final features + data.final.vector = []; + data.final.dim = 0; + data.final.vector_info.labels = {}; + end + + function define_global_transform(features) + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant genretag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + basef(i) = features(i).data.basefeat; + end + + % call the features own transsform function + basef.define_global_transform(); + + % --- + % finalise the basic features, and + % get the feature vectors; + % --- + basef.finalise(); + X = basef.vector(); + + % check dataset dimension + if numel(features) < basef.dim; + + error ('Not enough feature vectors for PCA calculation. need %d samples', ... + basef.dim); + end + + % --- + % NOTE: should the data be normalised and scaled to -1:1 + % instead of being in a range of 0-1 AND max-min = 1 + % --- + if features(1).my_params.norm_pre_pca == 1 + + [X, pstd] = mapminmax(X,-1,1); + common.pca.pre_norm = pstd; + elseif features(1).my_params.norm_pre_pca == 2 + + [X, pstd] = mapstd(X,0,1); + common.pca.pre_norm = pstd; + end + + + % --- + % get and apply the principal component analysis + % NOTE: the variance percentile is applied here, too. + % --- + + [Y, ps] = processpca(X, 0); + common.pca.transform = ps; + + % --- + % get cumulative sum of variance, and decide on cutoff + % point + % --- + v = cumsum(var(Y')); + v = v / max(v); + common.pca.transform.varpart = v; + + if features(1).my_params.min_pca_var > 0 + + min_pca_idx = find(v >= features(1).my_params.min_pca_var,1, 'first'); + + % save into pca structure + common.pca.transform.yrows = min_pca_idx; + + end + + % normalise pca values after processing + if features(1).my_params.norm_post_pca + + [Y,pmm] = mapminmax(Y,0,1); + common.pca.post_norm = pmm; + + end + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common(common); + + % save the transformed features straight away! + features.finalise(Y); + end + + function finalise(feature, final) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + + % determine size + if feature(1).my_params.max_pca_coeffs > 0 + + max_size = min(feature(1).common.pca.transform.yrows, ... + feature(1).my_params.max_pca_coeffs); + else + + max_size = feature(1).common.pca.transform.yrows; + end + + + % prepare information + info = {'PCA'}; + if isfield(feature(1).common.pca.transform, 'varpart') + info(2:max_size) = num2cell(feature(1).common.pca.transform.varpart(2:max_size)); + else + info(2:max_size) = num2cell(2:max_size); + end + + + % check if features have been finalised already + if nargin == 2 && isempty(final) + + % the final vector etc already are set to zero; + return; + + elseif nargin == 2 && (numel(feature) == size(final, 2)) + + for i = 1:numel(feature) + + % save final vector and description + feature(i).data.final.vector = final(1:max_size,i); + feature(i).data.final.dim = max_size; + feature(i).data.final.vector_info.labels = info; + end + + else + % features have to be transformed first + % --- + % TODO: this code remains untested + % --- + + % check for neccesary parameters + if isempty(feature(1).my_db.commondb) + + error('Define the global transformation first') + return; + end + + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first') + end + + % --- + % get feature vector and apply transformation + % --- + X = feature(i).data.basefeat.vector(); + + % --- + % apply normalisation used for removing mean + % in training data + % --- + if feature(1).my_params.norm_pre_pca == 1 + + X = mapminmax('apply', X, feature(1).common.pca.pre_norm); + elseif feature(1).my_params.norm_pre_pca == 2 + + X = mapstd('apply', X, feature(1).common.pca.pre_norm); + end + + % apply PCA transform + vec = processpca('apply', X, feature(1).common.pca.transform); + + % normalise pca values after transformation + if feature(1).my_params.norm_post_pca + + vec = mapminmax('apply', vec,... + feature(1).common.pca.post_norm); + end + + % --- + % cut vector to final size. + % NOTE: this should be done before + % transformation to reduce computation time + % --- + vec = vec(1:max_size); + + % save final vector and description + feature(i).data.final.vector = vec; + feature(i).data.final.dim = numel(vec); + feature(i).data.final.vector_info.labels = info; + end + end + end + + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureGenreRandom.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,148 @@ +classdef MTTMixedFeatureGenreRandom < MTTAudioFeature & handle + % --- + % + % The usual worklow for these features constist of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev: 455 $', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = MTTAudioFeature.inherited_params(... + 'MTTTagFeatureGenreBasic', ... + ... % --- + ... % following are Random parameters + ... % --- + 'nrandoms', 100 ... + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureGenreRandom(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % extract feature data by combining genre adn basicsm features + % --- + function data = extract(feature, clip) + + % --- + % get MTTMixedFeatureGenreBasicSm this includes possible + % local normalisations + % --- + data.tags = clip.features('MTTTagFeatureGenreBasic',feature.my_params); + + % --- + % get genre tag features + % --- + + data.random = clip.features('MTTRandomFeature',feature.my_params); + + + % prepare field for final features + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = class(feature); + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant tag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + tags(i) = features(i).data.tags; + end + + % call the features own transsform function + tags.define_global_transform(); + + % --- + % Random features have no global transform + % --- + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common([1]); %trivial common + end + + + function finalise(feature) + % applies a final transformation and collects the + % information of this feature within a single vector + % see info for types in specific dimensions + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first'); + end + + % --- + % final data assembly + % --- + % concatenate vectors + feature(i).data.final.vector = ... + [feature(i).data.tags.vector() ;... + feature(i).data.random.vector()]; + + % add up feature dimensions + feature(i).data.final.dim = feature(i).data.tags.dim... + + feature(i).data.random.dim; + + % concatenate labels + lbl1 = feature(i).data.tags.labels(); + lbl2 = feature(i).data.random.labels(); + feature(i).data.final.vector_info.labels = ... + {lbl1{:}, lbl2{:}}; + end + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureSlaney08GenreBasicSm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,159 @@ +classdef MTTMixedFeatureSlaney08GenreBasicSm < MTTAudioFeature & handle + % --- + % + % + % The usual worklow for these features constist of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev$', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = MTTAudioFeature.inherited_params(... + 'MTTMixedFeatureGenreBasicSm', ... + ... % --- + ... % following are Slaney08 parameters + ... % --- + 'norm_mttstats', 1, ... % + 'whiten_mttstats', 0, ... % NOTE: whitening as in slaney?? + 'select_mttstats', 1 ...% select certain features + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureSlaney08GenreBasicSm(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % extract feature data by combining genre adn basicsm features + % --- + function data = extract(feature, clip) + + % --- + % get MTTMixedFeatureGenreBasicSm this includes possible + % local normalisations + % --- + data.genrebasicsm = clip.features('MTTMixedFeatureGenreBasicSm',feature.my_params); + + % --- + % get genre tag features + % --- + + data.mttstats = clip.features('MTTAudioFeatureSlaney08',feature.my_params); + + + % prepare field for final features + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = class(feature); + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant genrebasicsm + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + genrebasicsm(i) = features(i).data.genrebasicsm; + end + + % call the features own transsform function + genrebasicsm.define_global_transform(); + + % --- + % We collect all the relevant mttstats + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + mttstats(i) = features(i).data.mttstats; + end + + % call the features own transsform function + mttstats.define_global_transform(); + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common([1]); %trivial common + end + + + function finalise(feature) + % applies a final transformation and collects the + % information of this feature within a single vector + % see info for types in specific dimensions + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first'); + end + + % --- + % final data assembly + % --- + % concatenate vectors + feature(i).data.final.vector = ... + [feature(i).data.genrebasicsm.vector() ;... + feature(i).data.mttstats.vector()]; + + % add up feature dimensions + feature(i).data.final.dim = feature(i).data.genrebasicsm.dim... + + feature(i).data.mttstats.dim; + + % concatenate labels + lbl1 = feature(i).data.genrebasicsm.labels(); + lbl2 = feature(i).data.mttstats.labels(); + feature(i).data.final.vector_info.labels = ... + {lbl1{:}, lbl2{:}}; + end + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureSlaney08GenreBasicSmPCA.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,264 @@ +% -- +% This class loads and hanles the aufdio features included with the MTT +% Library +% --- +classdef MTTMixedFeatureSlaney08GenreBasicSmPCA < MTTAudioFeature & handle + + properties(Constant = true) + + my_revision = str2double(substr('$Rev$', 5, -1)); + end + + properties + + % --- + % Set default parameters + % --- + my_basetype = 'MTTMixedFeatureSlaney08GenreBasicSm'; + + my_params = MTTAudioFeature.inherited_params(... + 'MTTMixedFeatureSlaney08GenreBasicSm', ... + 'min_pca_var', 0, ... % fraction of variance to keep + 'max_pca_coeffs', 0, ...% max. number of final coefficients + 'norm_pre_pca', 1, ...% normalise pca coefficients after transformation + 'norm_post_pca', 1 ... + ); + end + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureSlaney08GenreBasicSmPCA(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % load feature data from xml file + % --- + function data = extract(feature, clip) + % load feature data by parsing xml + + global globalvars; + + % --- + % we extract the base features, and save + % the pointers to these. + % the main work is then done in the define_global_transf + % and finalise functions. + % --- + data.basefeat = clip.features(feature.my_basetype,... + feature.my_params); + + % save info data + data.info.type = class(feature); + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save param data + data.info.params = feature.my_params; + + % prepare field for final features + data.final.vector = []; + data.final.dim = 0; + data.final.vector_info.labels = {}; + end + + function define_global_transform(features) + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant genretag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + basef(i) = features(i).data.basefeat; + end + + % call the features own transsform function + basef.define_global_transform(); + + % --- + % finalise the basic features, and + % get the feature vectors; + % --- + X = basef.vector(); + + % check dataset dimension + if numel(features) < basef.dim; + + error ('Not enough feature vectors for PCA calculation. need %d samples', ... + basef.dim); + end + + % --- + % NOTE: should the data be normalised and scaled to -1:1 + % instead of being in a range of 0-1 AND max-min = 1 + % --- + if features(1).my_params.norm_pre_pca == 1 + + [X, pstd] = mapminmax(X,-1,1); + common.pca.pre_norm = pstd; + elseif features(1).my_params.norm_pre_pca == 2 + + [X, pstd] = mapstd(X,0,1); + common.pca.pre_norm = pstd; + end + + + % --- + % get and apply the principal component analysis + % NOTE: the variance percentile is applied here, too. + % --- + + [Y, ps] = processpca(X, 0); + common.pca.transform = ps; + + % --- + % get cumulative sum of variance, and decide on cutoff + % point + % --- + v = cumsum(var(Y')); + v = v / max(v); + common.pca.transform.varpart = v; + + if features(1).my_params.min_pca_var > 0 + + min_pca_idx = find(v >= features(1).my_params.min_pca_var,1, 'first'); + + % save into pca structure + common.pca.transform.yrows = min_pca_idx; + + end + + % normalise pca values after processing + if features(1).my_params.norm_post_pca + + [Y,pmm] = mapminmax(Y,0,1); + common.pca.post_norm = pmm; + + end + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common(common); + + % save the transformed features straight away! + features.finalise(Y); + end + + function finalise(feature, final) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + + % determine size + if feature(1).my_params.max_pca_coeffs > 0 + + max_size = min(feature(1).common.pca.transform.yrows, ... + feature(1).my_params.max_pca_coeffs); + else + + max_size = feature(1).common.pca.transform.yrows; + end + + + % prepare information + info = {'PCA'}; + if isfield(feature(1).common.pca.transform, 'varpart') + info(2:max_size) = num2cell(feature(1).common.pca.transform.varpart(2:max_size)); + else + info(2:max_size) = num2cell(2:max_size); + end + + + % check if features have been finalised already + if nargin == 2 && isempty(final) + + % the final vector etc already are set to zero; + return; + + elseif nargin == 2 && (numel(feature) == size(final, 2)) + + for i = 1:numel(feature) + + % save final vector and description + feature(i).data.final.vector = final(1:max_size,i); + feature(i).data.final.dim = max_size; + feature(i).data.final.vector_info.labels = info; + end + + else + % features have to be transformed first + % --- + % TODO: this code remains untested + % --- + + % check for neccesary parameters + if isempty(feature(1).my_db.commondb) + + error('Define the global transformation first') + return; + end + + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first') + end + + % --- + % get feature vector and apply transformation + % --- + X = feature(i).data.basefeat.vector(); + + % --- + % apply normalisation used for removing mean + % in training data + % --- + if feature(1).my_params.norm_pre_pca == 1 + + X = mapminmax('apply', X, feature(1).common.pca.pre_norm); + elseif feature(1).my_params.norm_pre_pca == 2 + + X = mapstd('apply', X, feature(1).common.pca.pre_norm); + end + + % apply PCA transform + vec = processpca('apply', X, feature(1).common.pca.transform); + + % normalise pca values after transformation + if feature(1).my_params.norm_post_pca + + vec = mapminmax('apply', vec,... + feature(1).common.pca.post_norm); + end + + % --- + % cut vector to final size. + % NOTE: this should be done before + % transformation to reduce computation time + % --- + vec = vec(1:max_size); + + % save final vector and description + feature(i).data.final.vector = vec; + feature(i).data.final.dim = numel(vec); + feature(i).data.final.vector_info.labels = info; + end + end + end + + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureSlaney08GenreBasicSmRBM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,263 @@ +% -- +% This class loads and hanles the aufdio features included with the MTT +% Library +% +% Features are extracted and then porcessed by son's RBM toolbox +% --- +classdef MTTMixedFeatureSlaney08GenreBasicSmRBM < MTTAudioFeature & handle + + properties(Constant = true) + + my_revision = str2double(substr('$Rev: 457 $', 5, -1)); + end + + properties + + % --- + % Set default parameters + % --- + my_basetype = 'MTTMixedFeatureSlaney08GenreBasicSm'; + + my_params = MTTAudioFeature.inherited_params(... + 'MTTMixedFeatureSlaney08GenreBasicSm', ... + ... % --- + ... % Following: RBM params + ... % --- + 'norm_pre_rbm', 0, ... % norm before RBM? + 'norm_post_rbm', 0, ... % norm before RBM? + 'rbm_hidNum',200, ... % number of hidden units + 'rbm_eNum', 100, ... + 'rbm_bNum', 1, ... + 'rbm_gNum', 1, ... + 'rbm_lrate1' , 0.05, ... % initial learning rate + 'rbm_lrate2', 0.1, ... % learning rate + 'rbm_momentum', 0.5, ... + 'rbm_cost', 0.00002, ... % cost function + 'rbm_N', 50, ... + 'rbm_MAX_INC', 10 ... + ); + end + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureSlaney08GenreBasicSmRBM(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % load feature data from xml file + % --- + function data = extract(feature, clip) + % load feature data by parsing xml + + global globalvars; + + % --- + % we extract the base features, and save + % the pointers to these. + % the main work is then done in the define_global_transf + % and finalise functions. + % --- + data.basefeat = clip.features(feature.my_basetype,... + feature.my_params); + + % save info data + data.info.type = class(feature); + data.info.owner = clip; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save param data + data.info.params = feature.my_params; + + % prepare field for final features + data.final.vector = []; + data.final.dim = 0; + data.final.vector_info.labels = {}; + end + + function define_global_transform(features) + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant genretag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + basef(i) = features(i).data.basefeat; + end + + % call the features own transsform function + basef.define_global_transform(); + + % --- + % finalise the basic features, and + % get the feature vectors; + % --- + X = basef.vector(); + + % check dataset dimension + if numel(features) < basef.dim; + + error ('Not enough feature vectors for RBM calculation. need %d samples', ... + basef.dim); + end + + % --- + % NOTE: should the data be normalised and scaled to -1:1 + % instead of being in a range of 0-1 AND max-min = 1 + % --- + if features(1).my_params.norm_pre_rbm == 1 + + [X, pstd] = mapminmax(X,-1,1); + common.rbm.pre_norm = pstd; + elseif features(1).my_params.norm_pre_rbm == 2 + + [X, pstd] = mapstd(X,0,1); + common.rbm.pre_norm = pstd; + end + + + % --- + % TRAIN RBM + % --- + conf.sNum = size(X,2); + conf.eNum = features(1).my_params.rbm_eNum; + conf.bNum = features(1).my_params.rbm_bNum; + conf.gNum = features(1).my_params.rbm_gNum; + conf.hidNum = features(1).my_params.rbm_hidNum; + conf.MAX_INC = features(1).my_params.rbm_MAX_INC; + conf.N = features(1).my_params.rbm_N; + conf.params = [ features(1).my_params.rbm_lrate1 features(1).my_params.rbm_lrate2 ... + features(1).my_params.rbm_momentum features(1).my_params.rbm_cost]; + W1 = zeros(0,0); + [common.rbm.W1 common.rbm.vB1 common.rbm.hB1] = training_rbm_(conf,W1,X'); + + % --- + % This transforms the features using the learnt RBM + % --- + Y = logistic(X' * common.rbm.W1 + repmat(common.rbm.hB1,conf.sNum,1))'; + + + % normalise values after processing + if features(1).my_params.norm_post_rbm + + [Y,pmm] = mapminmax(Y,0,1); + common.rbm.post_norm = pmm; + end + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common(common); + + % save the transformed features straight away! + features.finalise(Y); + end + + function finalise(feature, final) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + + max_size = feature(1).my_params.rbm_hidNum; + + % prepare information + info = {'RBM'}; + info(2:max_size) = num2cell(2:max_size); + + % check if features have been finalised already + if nargin == 2 && isempty(final) + + % the final vector etc already are set to zero; + return; + + elseif nargin == 2 && (numel(feature) == size(final, 2)) + + for i = 1:numel(feature) + + % save final vector and description + feature(i).data.final.vector = final(:,i); + feature(i).data.final.dim = max_size; + feature(i).data.final.vector_info.labels = info; + end + + else + % features have to be transformed first + % --- + % TODO: this code remains untested + % --- + + % check for neccesary parameters + if isempty(feature(1).my_db.commondb) + + error('Define the global transformation first') + return; + end + + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first') + end + + % --- + % get feature vector and apply transformation + % --- + X = feature(i).data.basefeat.vector(); + + % --- + % apply normalisation used for removing mean + % in training data + % --- + if feature(1).my_params.norm_pre_rbm == 1 + + X = mapminmax('apply', X, feature(1).common.rbm.pre_norm); + elseif feature(1).my_params.norm_pre_rbm == 2 + + X = mapstd('apply', X, feature(1).common.rbm.pre_norm); + end + + % --- + % RBM: This transforms the features using the learnt RBM + % --- + conf.sNum = size(X,1); + vec = logistic(X * common.rbm.W1 + repmat(common.rbm.hB1,conf.sNum,1)); + + + % normalise pca values after transformation + if feature(1).my_params.norm_post_rbm + + vec = mapminmax('apply', vec,... + feature(1).common.rbm.post_norm); + end + + % --- + % cut vector to final size. + % NOTE: this should be done before + % transformation to reduce computation time + % --- + vec = vec(1:max_size); + + % save final vector and description + feature(i).data.final.vector = vec; + feature(i).data.final.dim = numel(vec); + feature(i).data.final.vector_info.labels = info; + end + end + end + + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureSon.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,217 @@ +classdef MTTMixedFeatureSon < MTTAudioFeature & handle + % --- + % This Class contains a wrapper for sons feature extraction + % + % The usual workflow for these features constist of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev: 741 $', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = struct( ... + 'son_filename','features_rbm_50x1010', ... + 'son_conf', '' ... + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureSon(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + + end + % --- + % extract feature data from raw audio features + % --- + function data = extract(feature, clip) + % --- + % get features. this includes possible + % local normalisations + % --- + + global globalvars; + global comparison_ids; + global sonfeatbase; + global db_MTTClip; + + if isempty(sonfeatbase); + sonfeatbase = load(feature.my_params.son_filename); + end + % + + % --- + % note: this should reference clip.my_db + % get the actual clip id + idx = db_MTTClip.comparison_ids(clip.id); + + % --- + % NOTE: we just copy everything in a big matrix and then + % normalise the data later + % --- + if isfield(sonfeatbase,'nfvec') + rawf = sonfeatbase.nfvec; + elseif isfield(sonfeatbase,'sonfeatures') + rawf = sonfeatbase.sonfeatures'; + end + + if idx <= size(rawf,2) + + % get the vector from loaded data + data.sonraw = rawf(:,idx); + + else + % --- + % CAVE: Clip indices outside + % the range of the supplied mat file + % are filled up with zeros + % --- + data.sonraw = zeros(size(rawf,1),1); + end + + + + data.vector_info = {'Sonfeat'}; + % padd further info struct + data.vector_info(end+1:size(data.sonraw,1)) =... + cell(size(data.sonraw,1) - numel(data.vector_info) , 1); + + % --- + % prepare field for final features + % --- + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = 'MTTMixedFeatureSon'; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + final = zeros(size(features(1).data.sonraw,1), numel(features)); + for i = 1:numel(features) + if ~isempty(features(i).data.sonraw) + final(:,i) = features(i).data.sonraw; + end + end + + % set common to 1 to tell normalisation is done + features(1).my_db.set_common([1]); + + % save the normalised features straight away! + features.finalise(final); + end + + + function finalise(features, final) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + % check if features have been finalised already + + % --- + % set feature labelling + % --- + + info = {}; + + % --- + % construct resulting feature vector out of features + % --- + if nargin == 2 && isempty(final) + + % the final vector etc already are set to zero; + return; + + elseif nargin == 2 && (numel(features) == size(final, 2)) + % the features have already been preassembled + + for i = 1:numel(features) + + % check for neccesary parameters + if isempty(features(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + features(i).data.final.vector = final(:,i); + features(i).data.final.dim = size(final,1); + + % fill up info struct and append to feature + features(i).data.final.vector_info.labels = ... + features(i).data.vector_info; + end + else + % --- + % if features have been added after gettin gnormalisation + % parameters, ther should be still an option to include + % them + % --- + + for i = 1:numel(features) + + % check for neccesary parameters + if isempty(features(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + final = zeros(numel(features(1).data.sonraw), numel(features)); + for i = 1:numel(features) + if ~isempty(features(i).data.sonraw) + final(:,i) = features(i).data.sonraw; + end + end + features(i).data.final.vector = final; + features(i).data.final.dim = size(final,1); + + % fill up info struct and append to feature + features(i).data.final.vector_info.labels = ... + features(i).data.vector_info; + end + + end + + % --- + % TODO: Maybe delete more basic features again at this point? + % --- + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureSonRBM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,262 @@ +% -- +% This class loads and hanles the aufdio features included with the MTT +% Library +% +% Features are extracted and then porcessed by son's RBM toolbox +% --- +classdef MTTMixedFeatureSonRBM < MTTAudioFeature & handle + + properties(Constant = true) + + my_revision = str2double(substr('$Rev: 457 $', 5, -1)); + end + + properties + + % --- + % Set default parameters + % --- + my_basetype = 'MTTMixedFeatureSon'; + + my_params = MTTAudioFeature.inherited_params(... + 'MTTMixedFeatureSon', ... + ... % --- + ... % Following: RBM params + ... % --- + 'norm_pre_rbm', 0, ... % norm before RBM? + 'norm_post_rbm', 0, ... % norm before RBM? + 'rbm_hidNum',200, ... % number of hidden units + 'rbm_eNum', 100, ... + 'rbm_bNum', 1, ... + 'rbm_gNum', 1, ... + 'rbm_lrate1' , 0.05, ... % initial learning rate + 'rbm_lrate2', 0.1, ... % learning rate + 'rbm_momentum', 0.5, ... + 'rbm_cost', 0.00002, ... % cost function + 'rbm_N', 50, ... + 'rbm_MAX_INC', 10 ... + ); + end + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureSonRBM(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % load feature data from xml file + % --- + function data = extract(feature, clip) + % load feature data by parsing xml + + global globalvars; + + % --- + % we extract the base features, and save + % the pointers to these. + % the main work is then done in the define_global_transf + % and finalise functions. + % --- + data.basefeat = clip.features(feature.my_basetype,... + feature.my_params); + + % save info data + data.info.type = class(feature); + data.info.owner = clip; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save param data + data.info.params = feature.my_params; + + % prepare field for final features + data.final.vector = []; + data.final.dim = 0; + data.final.vector_info.labels = {}; + end + + function define_global_transform(features) + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant genretag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + basef(i) = features(i).data.basefeat; + end + + % call the features own transsform function + basef.define_global_transform(); + + % --- + % finalise the basic features, and + % get the feature vectors; + % --- + X = basef.vector(); + + % check dataset dimension + if numel(features) < basef.dim; + + error ('Not enough feature vectors for RBM calculation. need %d samples', ... + basef.dim); + end + + % --- + % NOTE: should the data be normalised and scaled to -1:1 + % instead of being in a range of 0-1 AND max-min = 1 + % --- + if features(1).my_params.norm_pre_rbm == 1 + + [X, pstd] = mapminmax(X,-1,1); + common.rbm.pre_norm = pstd; + elseif features(1).my_params.norm_pre_rbm == 2 + + [X, pstd] = mapstd(X,0,1); + common.rbm.pre_norm = pstd; + end + + + % --- + % TRAIN RBM + % --- + conf.sNum = size(X,2); + conf.eNum = features(1).my_params.rbm_eNum; + conf.bNum = features(1).my_params.rbm_bNum; + conf.gNum = features(1).my_params.rbm_gNum; + conf.hidNum = features(1).my_params.rbm_hidNum; + conf.MAX_INC = features(1).my_params.rbm_MAX_INC; + conf.N = features(1).my_params.rbm_N; + conf.params = [ features(1).my_params.rbm_lrate1 features(1).my_params.rbm_lrate2 ... + features(1).my_params.rbm_momentum features(1).my_params.rbm_cost]; + W1 = zeros(0,0); + [common.rbm.W1 common.rbm.vB1 common.rbm.hB1] = training_rbm_(conf,W1,X'); + + % --- + % This transforms the features using the learnt RBM + % --- + Y = logistic(X' * common.rbm.W1 + repmat(common.rbm.hB1,conf.sNum,1))'; + + + % normalise values after processing + if features(1).my_params.norm_post_rbm + + [Y,pmm] = mapminmax(Y,0,1); + common.rbm.post_norm = pmm; + end + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common(common); + + % save the transformed features straight away! + features.finalise(Y); + end + + function finalise(feature, final) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + + max_size = feature(1).my_params.rbm_hidNum; + + % prepare information + info = {'RBM'}; + info(2:max_size) = num2cell(2:max_size); + + % check if features have been finalised already + if nargin == 2 && isempty(final) + + % the final vector etc already are set to zero; + return; + + elseif nargin == 2 && (numel(feature) == size(final, 2)) + + for i = 1:numel(feature) + + % save final vector and description + feature(i).data.final.vector = final(:,i); + feature(i).data.final.dim = max_size; + feature(i).data.final.vector_info.labels = info; + end + + else + % features have to be transformed first + % --- + % TODO: this code remains untested + % --- + + % check for neccesary parameters + if isempty(feature(1).my_db.commondb) + + error('Define the global transformation first') + return; + end + + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first') + end + + % --- + % get feature vector and apply transformation + % --- + X = feature(i).data.basefeat.vector(); + + % --- + % apply normalisation used for removing mean + % in training data + % --- + if feature(1).my_params.norm_pre_rbm == 1 + + X = mapminmax('apply', X, feature(1).common.rbm.pre_norm); + elseif feature(1).my_params.norm_pre_rbm == 2 + + X = mapstd('apply', X, feature(1).common.rbm.pre_norm); + end + + % --- + % RBM: This transforms the features using the learnt RBM + % --- + conf.sNum = size(X,1); + vec = logistic(X * common.rbm.W1 + repmat(common.rbm.hB1,conf.sNum,1)); + + + % normalise pca values after transformation + if feature(1).my_params.norm_post_rbm + + vec = mapminmax('apply', vec,... + feature(1).common.rbm.post_norm); + end + + % --- + % cut vector to final size. + % NOTE: this should be done before + % transformation to reduce computation time + % --- + vec = vec(1:max_size); + + % save final vector and description + feature(i).data.final.vector = vec; + feature(i).data.final.dim = numel(vec); + feature(i).data.final.vector_info.labels = info; + end + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureStober11.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,273 @@ +classdef MTTMixedFeatureStober11 < MTTAudioFeature & handle + % --- + % This Class contains + % features are extracted + % as described in Slaney 08 - LEARNING A METRIC FOR MUSIC SIMILARITY + % + % The usual workflow for these features constist of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev$', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = struct(... + 'stob_lowaudio', 1, ... + 'stob_highaudio', 1, ... % + 'stob_tags', 1, ... % + 'stob_norm', 1 ... + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureStober11(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + + end + % --- + % extract feature data from raw audio features + % --- + function data = extract(feature, clip) + % --- + % get features. this includes possible + % local normalisations + % --- + + global globalvars; + global stobbase; + + if isempty(stobbase); + stobbase = load('features_stober'); + end + + % --- + % NOTE: we define feature sets which are included / + % excluded according to the specified parameters + % --- + + lowAudio = {'pitchMean', 'pitchSdev', 'timbreMean', 'timbreSdev'}; + + % highAudio features more or less correspond to slaney 08 features + hiAudio = {'energy','key','loudness','timeSignature',... + 'danceability', 'mode', 'tempo'}; + + metadat = {'tags'}; + + allowedFeat = {}; + + % Select the features to keep + if feature(1).my_params.stob_lowaudio + allowedFeat = {allowedFeat{:}, lowAudio{:}}; + end + if feature(1).my_params.stob_highaudio + allowedFeat = {allowedFeat{:}, hiAudio{:}}; + end + if feature(1).my_params.stob_tags + allowedFeat = {allowedFeat{:}, metadat{:}}; + end + + % get the actual clip id + idx = find(stobbase.clipIds == clip.id); + + % --- + % NOTE: we just copy everything in a big matrix and then + % normalise the data later + % --- + data.vector_info = {}; + data.stobraw = []; + fields = fieldnames(stobbase); + for i = 1:numel(fields) + + % skip clip ID field + if strcmp(fields{i},'clipIds'); + continue; + end + + % skip unwanted features + if isempty(strcellfind(allowedFeat, fields{i})) + continue; + end + + % --- + % TODO: special case for tag features, including + % the tag names + % --- + + % put field info into right position + data.vector_info{numel(data.stobraw)+1} = fields{i}; + + % add data to feature + if size(stobbase.(fields{i}),1) == 1 + data.stobraw(end+1) = stobbase.(fields{i})(idx); + else + % concatenate vector + tmpdat = stobbase.(fields{i})(idx,:); + data.stobraw(end+1:end+numel(tmpdat)) = tmpdat; + end + end + % padd further info struct + data.vector_info(end+1:numel(data.stobraw)) =... + cell(numel(data.stobraw) - numel(data.vector_info) , 1); + + % --- + % prepare field for final features + % --- + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = 'MTTMixedFeatureStober11'; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + final = zeros(numel(features(1).data.stobraw), numel(features)); + for i = 1:numel(features) + if ~isempty(features(i).data.stobraw) + final(:,i) = features(i).data.stobraw'; + end + end + + if features(1).my_params.stob_norm + if numel(features) == 1 + error ('Insert feature array for this method, or set normalisation to 0'); + end + + % --- + % here, we only need to define the post-normalisation + % --- + [final, pstd] = mapminmax(final,0,1); + common.stobstats.post_norm = pstd; + + % --- + % NOTE: whitening as in slaney?? + % Would make reading the + % mahal matrices really hard + % --- + + features(1).my_db.set_common(common); + + else + + features(1).my_db.set_common([1]); + end + + % save the normalised features straight away! + features.finalise(final); + end + + + function finalise(features, final) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + % check if features have been finalised already + + % --- + % set feature labelling + % --- + + info = {}; + + % --- + % construct resulting feature vector out of features + % --- + if nargin == 2 && isempty(final) + + % the final vector etc already are set to zero; + return; + + elseif nargin == 2 && (numel(features) == size(final, 2)) + % the features have already been preassembled + + for i = 1:numel(features) + + % check for neccesary parameters + if isempty(features(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + features(i).data.final.vector = final(:,i); + features(i).data.final.dim = size(final,1); + + % fill up info struct and append to feature + features(i).data.final.vector_info.labels = ... + features(i).data.vector_info; + end + else + % --- + % if features have been added after gettin gnormalisation + % parameters, ther should be still an option to include + % them + % --- + + for i = 1:numel(features) + + % check for neccesary parameters + if isempty(features(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + final = features(i).data.stobraw'; + + if features(1).my_params.stob_norm == 1 + + [final] = mapminmax('apply', final, features(1).common.stobstats.post_norm); + end + + features(i).data.final.vector = final; + features(i).data.final.dim = size(final,1); + + % fill up info struct and append to feature + features(i).data.final.vector_info.labels = ... + features(i).data.vector_info; + end + + end + + % --- + % TODO: Maybe delete more basic features again at this point? + % --- + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureStober11Genre.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,182 @@ +classdef MTTMixedFeatureStober11Genre < MTTAudioFeature & handle + % --- + % + % The usual worklow for these features constist of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev$', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = struct(... + ... % --- + ... % these are Stober11 parameters + ... % --- + 'stob_lowaudio', 1, ... + 'stob_highaudio', 1, ... % + 'stob_tags', 1, ... + 'stob_norm', 1, ... + ... % --- + ... % following are GenreBasic parameters + ... % --- + 'pct_genres', 1, ... % 1/100 percentile genre tags used + 'empty_genres', 1 ... % allow empty genres to persist + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureStober11Genre(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % extract feature data by combining genre adn stob features + % --- + function data = extract(feature, clip) + + % --- + % get Basic Summary audio features. this includes possible + % local normalisations + % --- + stob = clip.features('MTTMixedFeatureStober11',feature.my_params); + + % --- + % get genre tag features + % --- + + genrebasic = clip.genre_features_basic(feature.my_params); + + % save to features data field + data.stob = stob; + data.tags = genrebasic; + + % prepare field for final features + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = 'MTTMixedFeatureStober11Genre'; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant stob + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + stob(i) = features(i).data.stob; + end + + % call the features own transsform function + stob.define_global_transform(); + + % --- + % We collect all the relevant genretag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + genrebasic(i) = features(i).data.tags; + end + + % call the features own transsform function + genrebasic.define_global_transform(); + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common([1]); %trivial common + end + + + function finalise(feature) + % applies a final transformation and collects the + % information of this feature within a single vector + % see info for types in specific dimensions + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first'); + end + + % --- + % finalise audio feature and get vector + % --- + stob = feature(i).data.stob; + stob.finalise(); + + % finalise tag features + genrebasic = feature(i).data.tags; + genrebasic.finalise; + + % --- + % final data assembly + % --- + + % concatenate vectors + feature(i).data.final.vector = ... + [stob.vector() ; genrebasic.vector()]; + + % add feature dimensions + feature(i).data.final.dim = stob.dim + genrebasic.dim; + + % concatenate labels + feature(i).data.final.vector_info.labels = ... + {stob.data.final.vector_info.labels{:}, ... + genrebasic.data.final.vector_info.labels{:}}; + end + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + + function visualise(feature) + % --- + % plots the different data types collected in this feature + % --- + for i = 1:numel(feature) + clip = MTTClip(feature(i).owner_id()); + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureStober11GenrePCA.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,264 @@ +% -- +% This class loads and hanles the aufdio features included with the MTT +% Library +% --- +classdef MTTMixedFeatureStober11GenrePCA < MTTAudioFeature & handle + + properties(Constant = true) + + my_revision = str2double(substr('$Rev: 457 $', 5, -1)); + end + + properties + + % --- + % Set default parameters + % --- + my_basetype = 'MTTMixedFeatureStober11Genre'; + + my_params = MTTAudioFeature.inherited_params(... + 'MTTMixedFeatureStober11Genre', ... + 'min_pca_var', 0, ... % [0-1], def. 0 fraction of variance to keep + 'max_pca_coeffs', 0, ...% max. number of final coefficients + 'norm_pre_pca', 1, ... + 'norm_post_pca', 1 ...% normalise pca coefficients after transformation + ); + end + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureStober11GenrePCA(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % load feature data from xml file + % --- + function data = extract(feature, clip) + % load feature data by parsing xml + + global globalvars; + + % --- + % we extract the base features, and save + % the pointers to these. + % the main work is then done in the define_global_transf + % and finalise functions. + % --- + data.basefeat = clip.features(feature.my_basetype,... + feature.my_params); + + % save info data + data.info.type = class(feature); + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save param data + data.info.params = feature.my_params; + + % prepare field for final features + data.final.vector = []; + data.final.dim = 0; + data.final.vector_info.labels = {}; + end + + function define_global_transform(features) + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant genretag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + basef(i) = features(i).data.basefeat; + end + + % call the features own transsform function + basef.define_global_transform(); + + % --- + % finalise the basic features, and + % get the feature vectors; + % --- + X = basef.vector(); + + % check dataset dimension + if numel(features) < basef.dim; + + error ('Not enough feature vectors for PCA calculation. need %d samples', ... + basef.dim); + end + + % --- + % NOTE: should the data be normalised and scaled to -1:1 + % instead of being in a range of 0-1 AND max-min = 1 + % --- + if features(1).my_params.norm_pre_pca == 1 + + [X, pstd] = mapminmax(X,-1,1); + common.pca.pre_norm = pstd; + elseif features(1).my_params.norm_pre_pca == 2 + + [X, pstd] = mapstd(X,0,1); + common.pca.pre_norm = pstd; + end + + + % --- + % get and apply the principal component analysis + % NOTE: the variance percentile is applied here, too. + % --- + + [Y, ps] = processpca(X, 0); + common.pca.transform = ps; + + % --- + % get cumulative sum of variance, and decide on cutoff + % point + % --- + v = cumsum(var(Y')); + v = v / max(v); + common.pca.transform.varpart = v; + + if features(1).my_params.min_pca_var > 0 + + min_pca_idx = find(v >= features(1).my_params.min_pca_var,1, 'first'); + + % save into pca structure + common.pca.transform.yrows = min_pca_idx; + + end + + % normalise pca values after processing + if features(1).my_params.norm_post_pca + + [Y,pmm] = mapminmax(Y,0,1); + common.pca.post_norm = pmm; + + end + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common(common); + + % save the transformed features straight away! + features.finalise(Y); + end + + function finalise(feature, final) + % applies a final transformation and + % collects the information of this feature within a single vector + % see info for types in specific dimensions + + % determine size + if feature(1).my_params.max_pca_coeffs > 0 + + max_size = min(feature(1).common.pca.transform.yrows, ... + feature(1).my_params.max_pca_coeffs); + else + + max_size = feature(1).common.pca.transform.yrows; + end + + + % prepare information + info = {'PCA'}; + if isfield(feature(1).common.pca.transform, 'varpart') + info(2:max_size) = num2cell(feature(1).common.pca.transform.varpart(2:max_size)); + else + info(2:max_size) = num2cell(2:max_size); + end + + + % check if features have been finalised already + if nargin == 2 && isempty(final) + + % the final vector etc already are set to zero; + return; + + elseif nargin == 2 && (numel(feature) == size(final, 2)) + + for i = 1:numel(feature) + + % save final vector and description + feature(i).data.final.vector = final(1:max_size,i); + feature(i).data.final.dim = max_size; + feature(i).data.final.vector_info.labels = info; + end + + else + % features have to be transformed first + % --- + % TODO: this code remains untested + % --- + + % check for neccesary parameters + if isempty(feature(1).my_db.commondb) + + error('Define the global transformation first') + return; + end + + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first') + end + + % --- + % get feature vector and apply transformation + % --- + X = feature(i).data.basefeat.vector(); + + % --- + % apply normalisation used for removing mean + % in training data + % --- + if feature(1).my_params.norm_pre_pca == 1 + + X = mapminmax('apply', X, feature(1).common.pca.pre_norm); + elseif feature(1).my_params.norm_pre_pca == 2 + + X = mapstd('apply', X, feature(1).common.pca.pre_norm); + end + + % apply PCA transform + vec = processpca('apply', X, feature(1).common.pca.transform); + + % normalise pca values after transformation + if feature(1).my_params.norm_post_pca + + vec = mapminmax('apply', vec,... + feature(1).common.pca.post_norm); + end + + % --- + % cut vector to final size. + % NOTE: this should be done before + % transformation to reduce computation time + % --- + vec = vec(1:max_size); + + % save final vector and description + feature(i).data.final.vector = vec; + feature(i).data.final.dim = numel(vec); + feature(i).data.final.vector_info.labels = info; + end + end + end + + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTMixedFeatureStober11Slaney08GenreBasicSm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,178 @@ +classdef MTTMixedFeatureStober11Slaney08GenreBasicSm < MTTAudioFeature & handle + % --- + % + % The usual worklow for these features constist of three steps + % 1. extract: extracts the basic single-file dependent features + % 2. define_global_transform: calculates the global feature + % transformation parameters + % 3. finalise: applies the common transformations to a specific feature + % --- + + properties(Constant = true) + + % svn hook + my_revision = str2double(substr('$Rev: 638 $', 5, -1)); + end + + properties + % --- + % Set default parameters + % --- + my_params = MTTAudioFeature.inherited_params(... + 'MTTMixedFeatureSlaney08GenreBasicSm', ... + ... % --- + ... % these are Stober11 parameters + ... % --- + 'stob_lowaudio', 1, ... + 'stob_highaudio', 1, ... % + 'stob_tags', 1, ... + 'stob_norm', 1 ... + ... % --- + ); + end + + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTMixedFeatureStober11Slaney08GenreBasicSm(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % extract feature data by combining genre adn stob features + % --- + function data = extract(feature, clip) + + % --- + % get Basic Summary audio features. this includes possible + % local normalisations + % --- + stob = clip.features('MTTMixedFeatureStober11',feature.my_params); + + % --- + % get genre tag features + % --- + genrebasicsm = clip.features('MTTMixedFeatureSlaney08GenreBasicSm',feature.my_params); + + % save to features data field + data.stob = stob; + data.tags = genrebasicsm; + + % prepare field for final features + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + + % save info data + data.info.type = 'MTTMixedFeatureStober11Slaney08GenreBasicSm'; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + end + + function define_global_transform(features) + % calculate and set normalization factors from the group of + % input features. These features will be set for the full database + + if numel(features) == 1 + error ('Insert feature array for this method'); + end + + % --- + % We collect all the relevant stob + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + stob(i) = features(i).data.stob; + end + + % call the features own transsform function + stob.define_global_transform(); + + % --- + % We collect all the relevant genretag + % features and get the transform on this basis. + % --- + for i = 1:numel(features) + genrebasicsm(i) = features(i).data.tags; + end + + % call the features own transsform function + genrebasicsm.define_global_transform(); + + % --- + % set common feature values for mixed features + % --- + features(1).my_db.set_common([1]); %trivial common + end + + + function finalise(feature) + % applies a final transformation and collects the + % information of this feature within a single vector + % see info for types in specific dimensions + + for i = 1:numel(feature) + + % check for neccesary parameters + if isempty(feature(i).my_db.commondb) + + error('Define the global transformation first'); + end + + % --- + % finalise audio feature and get vector + % --- + stob = feature(i).data.stob; + stob.finalise(); + + % finalise tag features + genrebasicsm = feature(i).data.tags; + genrebasicsm.finalise(); + + % --- + % final data assembly + % --- + + % concatenate vectors + feature(i).data.final.vector = ... + [stob.vector() ; genrebasicsm.vector()]; + + % add feature dimensions + feature(i).data.final.dim = stob.dim + genrebasicsm.dim; + + % concatenate labels + feature(i).data.final.vector_info.labels = ... + {stob.data.final.vector_info.labels{:}, ... + genrebasicsm.data.final.vector_info.labels{:}}; + end + end + + % --- + % destructor: do we really want to remove this + % from the database? No, but + % TODO: create marker for unused objects in db, and a cleanup + % function + % --- + function delete(feature) + + end + + function visualise(feature) + % --- + % plots the different data types collected in this feature + % --- + for i = 1:numel(feature) + clip = MTTClip(feature(i).owner_id()); + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTRandomFeature.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +% -- +% This class loads and hanles the aufdio features included with the MTT +% Library +% --- +classdef MTTRandomFeature < MTTAudioFeature & handle + + properties(Constant = true) + + my_revision = str2double(substr('$Rev: 167 $', 5, -1)); + end + + properties + + % --- + % Set default parameters + % --- + my_params = struct(... + 'nrandoms', 100 ... + ); + end + % --- + % member functions + % --- + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTRandomFeature(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + % --- + % load feature data from xml file + % --- + function data = extract(feature, clip) + % load feature data by parsing xml + + global globalvars; + + % fprintf('parsing features for clip %d \n',clip.id()); + + % parse feature + data.random = rand(feature.my_params.nrandoms, 1); + + % save info data + data.info.type = 'MTTRandomFeature'; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save param data + data.info.params = feature.my_params; + + % prepare field for final features + data.final.vector = data.random; + data.final.dim = numel(data.final.vector); + + info = {'random'}; + info(2:data.final.dim) = num2cell(2:data.final.dim); + data.final.vector_info.labels = info; + end + + function visualise(feature) + % --- + % plots the different data types collected in this feature + % --- + + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/MTTTagFeatureGenreBasic.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,218 @@ +classdef MTTTagFeatureGenreBasic < MTTAudioFeature & handle + + + properties(Constant = true) + + my_revision = str2double(substr('$Rev: 99 $', 5, -1)); + end + + % --- + % here come the internal clip properties. + % the database is stored as a global variable + % --- + properties + + my_params = struct(... + ... % --- + ... % these are GenreBasic parameters + ... % --- + 'pct_genres', 1, ... % 1/100 percentile genre tags used + 'empty_genres', 0 ... % allow empty genres to persist + ); + end + + methods + + % --- + % constructor: pointer to feature in database + % --- + function feature = MTTTagFeatureGenreBasic(varargin) + + feature = feature@MTTAudioFeature(varargin{:}); + end + + function data = extract(feature, clip) + % process tag information and build the tag vector + + % --- + % get total number of tags. + % + % NOTE: the highest tag id should match the size of the + % tagdb lexicon + % --- +% num_tags = clip.my_db.genredb.size(); + + if feature.my_params.pct_genres ~= 0 + % no tags, f.e. for parameter experiments in + % higher level features + [tagids, score] = clip.my_db.genredb.annotids_for_owner(clip.id); + + % save lexicon + data.lexicon = clip.my_db.genredb.lexicon; + + % save to data structure + data.tags.ids = tagids; + data.tags.scores = score; + end + + % save info data + data.info.type = 'MTTTagFeatureGenreBasic'; + data.info.owner = clip; + data.info.owner_id = clip.id; + data.info.creatorrev = feature.my_revision; + + % save parameters + data.info.params = feature.my_params; + + data.final.vector = []; + data.final.vector_info = struct(); + data.final.dim = 0; + end + + + % --- + % NOTE: the following transforms are just stated fro + % future use by now + % --- + function define_global_transform(features) + % --- + % compute the relevant tags, and save + % them in the commom place + % --- + + % compute extreme cases + if features(1).my_params.pct_genres == 1 && features(1).my_params.empty_genres + % all tags allowed + + common.rel_dimensions.tags.ids = 1:numel(features(1).data.lexicon); + common.rel_dimensions.tags.id_pos = 1:numel(features(1).data.lexicon); + + % set common feature values + features(1).my_db.set_common(common); + return; + + elseif features(1).my_params.pct_genres == 0 + % no tags, f.e. for parameter experiments in + % higher level features + + common.rel_dimensions.tags.ids = []; + common.rel_dimensions.tags.id_pos = []; + + % set common feature values + features(1).my_db.set_common(common); + return; + end + + + allids = sparse(numel(features), numel(features(1).data.lexicon)); + for i = 1:numel(features) + + allids(i,features(i).data.tags.ids) = ... + allids(i,features(i).data.tags.ids) + features(i).data.tags.scores; + end + + % --- + % get usage of tags and filter not-used tags + % --- + tagsum = sum(allids, 1); + nonzero = tagsum > 0; + + % --- + % NOTE: We remove the empty genres, then + % sort by weight / number of appearance and cut off + ids = find(nonzero); + [null, idx] = sort(tagsum(ids),'descend'); + ids = ids(idx); + + % cutoff + num_tags = ceil( features(1).my_params.pct_genres * numel(ids)); + valid_ids = ids(1:min(end, num_tags)); + + % --- + % NOTE: make sure that the positions for the tags + % stay correctly assigned / consistent + % --- + id_pos = sparse(size(valid_ids)); + id_pos(valid_ids) = 1:num_tags; + + % save to common data structure + common.rel_dimensions.tags.ids = valid_ids; + common.rel_dimensions.tags.id_pos = id_pos; + + % set common feature values + features(1).my_db.set_common(common); + end + + function finalise(features) + + for i = 1:numel(features) + + % check for neccesary parameters + if isempty(features(i).my_db.commondb) + + error('Define the global transformation first') + return; + end + + if features(i).my_params.pct_genres == 0 + vector_info.labels = {}; + + % save into feature struct + features(i).data.final.dim = 0; + features(i).data.final.vector = []; + features(i).data.final.vector_info = vector_info; + return; + end + % get valid tag ids + valid_ids = features(i).common.rel_dimensions.tags.ids; + num_tags = numel(valid_ids); + + % --- + % get vector positions for contined ids, + % and sort out the non_allowed id's + % --- + id_pos = features(i).common.rel_dimensions.tags.id_pos; + + [has_ids, has_pos] = intersect(features(i).data.tags.ids, valid_ids); + + score = features(i).data.tags.scores; + + % create feature vector + vector = zeros(num_tags,1); + vector(id_pos(has_ids)) = score(has_pos); + + % --- + % NOTE: this labelling costs to much space + % --- + vector_info.labels = features(i).data.lexicon(valid_ids); + + % save into feature struct + features(i).data.final.dim = num_tags; + features(i).data.final.vector = vector; + features(i).data.final.vector_info = vector_info; + end + end + + function [a1] = visualise(feature) + + % get tag descriptions form first clip + tags = MTTClip(feature(1).owner_id()).my_db.genres; + + % works for multiple feature instances + for i = 1:numel(feature) + + clip = MTTClip(feature(i).owner_id()); + + % plot feature data + h = bar(feature(i).data.final.vector); + a1 = gca; + + set(a1,'XTick', 1:feature(i).data.final.dim,... + 'XTickLabel', tags); + title(sprintf('clip %d: %s by %s, genres', ... + clip.id, clip.title(),clip.artist())); + end + end + + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_corresponding_albums.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function [fmalbums, fmalbum_names] = fm_corresponding_albums(albums) +%fmalbums = fm_corresponding_albums(albums) +% +% searches last.fm for the artist given in a cell +% array of strings and saves the first hit into the output. +% +% fmalbum_names = {magnatagatune_artist, fm_artist}; +% +% returns -1 of no matches found +maxtry = 3; + +albums = unique(albums); +i = 1; +numtry = 0; +while i < numel(albums) + + % --- + % as sometimes this fails due to connection problems + % or other problems not yet identified, we TRY + % + % for fails, we may try one time again! + % --- + fprintf('%d percent: %s\n',floor(i*100/numel(albums)),char(albums{i})); + try + [tmp, tmpid] = fm_retrieve_album(char(albums{i})); + fmalbums(i,:) = {albums{i}, tmp{1}, tmpid{1}}; + catch err + + % no work :( + warning(err.message); + fmalbums{i} = '-1'; + + numtry = numtry + 1; + + % --- + % NOTE: we try again in case it fails for < maxtry times + % --- + if numtry < maxtry + i = i - 1; + else + numtry = 0; + end + end + i = i + 1; +end + +fmalbum_names = {'magnatagatune_album', 'fm_album', 'mbid'};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_corresponding_artists.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function [fmartist_name, fmartist_name_names] = fm_corresponding_artists(artists) +%[artists, fmartists] = fm_corresponding_artists(artists) +% +% searches last.fm for the artist given in a cell +% array of strings and saves the first hit into the output. +% +% returns -1 of no matches found +maxtry = 10; + +% artists = unique(artists); +i = 1; +numtry = 0; +while i <= numel(artists) + + % --- + % as sometimes this fails due to connection problems + % or other problems not yet identified, we TRY + % + % for fails, we may try one time again! + % --- + fprintf('%d percent: %s\n',floor(i*100/numel(artists)),char(artists{i})); + try + [tmp, tmpid] = fm_retrieve_album(char(artists{i})); + fmartist_name(i,:) = {artists{i}, tmp{1}, tmpid{1}}; + catch err + + % no work :( + warning(err.message); + fmartist_name(i,:) = {artists{i}, '-1', '-1'}; + + numtry = numtry + 1; + + % --- + % NOTE: we try gain in case it fails for < maxtry times + % --- + if numtry < maxtry + i = i - 1; + else + numtry = 0; + end + end + i = i + 1; +end + +fmartist_name_names = {'magnatagatune_album', 'fm_album', 'mbid'}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_network.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +import pylast +import sys + +def get_network(): + # You have to have your own unique two values for API_KEY and API_SECRET + # Obtain yours from http://www.last.fm/api/account for Last.fm + API_KEY = "6fae93d29ad1ace84159d5e264114be7" + API_SECRET = "e04dd6c372461fb2de4eb4908fb35ae3" + + # In order to perform a write operation you need to authenticate yourself + # username = "megaoetzwuchst" + # password_hash = "5c15b1b51d73b8a623fef29ca5035abb" + + network = pylast.LastFMNetwork(api_key = API_KEY, api_secret = + API_SECRET) + + # network = pylast.LastFMNetwork(api_key = API_KEY, api_secret = + # API_SECRET, username = username, password_hash = password_hash) + + return network
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_retrieve_album.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function [album, mbids] = fm_retrieve_album(album_name) +%[album, mbids] = fm_retrieve_album(album_name) +% +% returns last.fm artist tags and tag weights for given artist +% this uses a lastpy python script to handle the REST data exchange + +% call python function +str = python('fm_retrieve_album.py',album_name); + +% parse text function output +album = textscan(str,'%s %s','Delimiter',','); +mbids = album{2}; +album = album{1}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_retrieve_album.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +# gets data from the last.fm webservice and + +import pylast +import sys +import fm_network + +network = fm_network.get_network() + +# now you can use that object every where +srch = network.search_for_artist(sys.argv[1]) + + +# did we find any ? +if (int(srch.get_total_result_count()) == False): + print('-1, None') + +else: + + #print first 200 results + albums = srch.get_next_page() + for album in albums: + mbid = unicode(album.get_mbid()) + print(album.get_name().encode('utf-8','ignore') + ', ' + mbid.encode('utf-8','ignore'))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_retrieve_artist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function [artists, mbids] = fm_retrieve_artist(artist_name) +%[tags, tag_weight] = fm_get_artist_tags(artist_name) +% +% returns last.fm artist tags and tag weights for given artist +% this uses a lastpy python script to handle the REST data exchange + +% call python function +str = python('fm_retrieve_artist.py',artist_name); + +% parse text function output +artists = textscan(str,'%s %s','Delimiter',','); +mbids = artists{2}; +artists = artists{1}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_retrieve_artist.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +# gets data from the last.fm webservice and + +import pylast +import sys +import fm_network + +network = fm_network.get_network() + +# now you can use that object every where +artistsrch = network.search_for_artist(sys.argv[1]) + +# did we find any ? +if int(artistsrch.get_total_result_count()) == False: + print('-1, None') + +else: + + #print alternatives + artists = artistsrch.get_next_page() + for artist in artists: + mbid = unicode(artist.get_mbid()) + print(artist.get_name().encode('utf-8','ignore') + ', ' + mbid.encode('utf-8','ignore')) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_retrieve_artist_similarity.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function [tags, tag_weight] = fm_retrieve_artist_similarity(artist1,artist2) +%[tags, tag_weight] = fm_get_artist_tags(artist_name) +% +% returns last.fm artist tags and tag weights for given artist +% this uses a lastpy python script to handle the REST data exchange + +% call python function +tagstr = python('fm_retrieve_artist_similarity.py', artist1, artist2) + +% parse text function output +%tags = textscan(tagstr,'%s %d','Delimiter',','); +%tag_weight = tags{2}; +%tags = tags{1}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_retrieve_artist_similarity.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +# gets data from the last.fm webservice and + +import pylast +import sys +import fm_network + +network = fm_network.get_network() + +# now you can use that object every where +artist = network.get_artist(sys.argv[1]) + +(score, sharedas) = artist.compare_with_artist(sys.argv[2]) + +print(score) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_retrieve_artist_tags.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [tags, tag_weight] = fm_retrieve_artist_tags(artist_name,bymbid) +%[tags, tag_weight] = fm_get_artist_tags(artist_name,bymbid) +% +% artist_name: either artist name string, or +% musicbrainz artist id with bymbid == 1 +% +% returns last.fm artist tags and tag weights for given artist +% this uses a lastpy python script to handle the REST data exchange + +if nargin < 2 + bymbid = '0'; +else + bymbid = num2str(bymbid); +end + +% call python function +tagstr = python('fm_retrieve_artist_tags.py',artist_name,bymbid); + +% parse text function output +tags = textscan(tagstr,'%s %d','Delimiter',','); +tag_weight = tags{2}; +tags = tags{1}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/fm_retrieve_artist_tags.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +# gets data from the last.fm webservice and + +import pylast +import sys +import fm_network + +network = fm_network.get_network() + +# now you can use that object every where +if sys.argv[2] == '0': + artist = network.get_artist(sys.argv[1]) +else: + artist = network.get_artist_by_mbid(sys.argv[1]) + +# retrieve artist tags +tags = artist.get_top_tags() + +# put in comma separated list +for tag in tags[:-1]: + print(tag.item.get_name().encode('utf-8','ignore')+ ', ' + tag.weight.encode('utf-8','ignore')) + +if len(tags) > 0: + print(tags[-1].item.get_name().encode('utf-8','ignore') + ', ' + tags[-1].weight.encode('utf-8','ignore')) + +else: + print(-1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/genre_stats.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function out = genre_stats(tagged, names, freqs, childof) +% out = genre_stats(names, freqs, childof) +% +% calculates statistics for genre distributions +% +% + +% get overall genre frequency and sort accordingly +allapp = sum(freqs,2); +allapp = allapp/max(allapp); + +[null, idx] = sort(allapp,'descend'); + +% get root potential +rootpot = 1 - sum(childof,2); + +figure; +bar(1:numel(names),[allapp(idx) rootpot(idx)]) +set(gca,'XTick',1:numel(names)); +set(gca,'XTickLabel',names(idx)); +legend('#appearances','root genre possibility'); +title 'genre statistics sorted by frequency of appearances' + +% --- +% determine genres that include x% of the whole dataset +% --- +pctl = 0.98; % 80 percent included + +% --- +% re-sort by appearance and root potential. +% using the multiplication, we can filter out subgenres +% --- +[null, idxrt] = sort(rootpot.*allapp,'descend'); + +% iteratively add 'best' genre according to root potential +gotclips = []; +numclips = []; +num_included = 0; +i = 1; +while i <= numel(names) && num_included < pctl * length(tagged) + + % count clips found for this genre + fprintf('%s \n', char(names{idxrt(i)})); + newclips = setdiff(find(tagged(:,idxrt(i)))', gotclips); + + gotclips = [gotclips newclips]; + numclips(i) = numel(newclips); + + num_included = num_included + numclips(i); + i = i + 1; +end + +figure; +pie(numclips(numclips > 0) / length(tagged)); +legend(names{idxrt(numclips > 0)}); + +out = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/get_comparison_stats.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,136 @@ +function [num_compares] = get_comparison_stats() +% [num_compares] = get_comparison_stats() +% +% - retrieves number of comparisons for each pair of data clips +% - extracts genre associations and statistics + +global db_magnaclips; +global comparison; +global comparison_ids; + +num_compares = sparse(numel(comparison_ids), numel(comparison_ids)); + +for i = 1:size(comparison,1) + abc = sort(comparison(i,1:3)); + a = abc(1); + b = abc(2); + c = abc(3); + num_compares(a,b) = num_compares(a,b) +1; + num_compares(b,c) = num_compares(b,c) +1; + num_compares(a,c) = num_compares(a,c) +1; +end + +% --- +% check for repeated pairs +% --- +% [i,j] = ind2sub(size(num_compares), find(num_compares > 1)) +% num = 300; +% a = find((comparison(:,1) == i(num) | comparison(:,2) == i(num) | comparison(:,3) == i(num))...) +% & (comparison(:,1) == j(num) | comparison(:,2) == j(num) | comparison(:,3) == j(num))) +% +% comparison(a,:) + + +% ------------------------------------------------------------ +% compare genre frequency +% --- +clips = MTTClip(comparison_ids); + +% comparison genres +[genres, scores, gid] = clips.genres(); + +% all genres +[genres2, scores2, gid2] = db_magnaclips.genredb.stats; + +% --- +% plot top genres +% --- + +figure; +bar(scores(1:20)) +set(gca, 'XTick',1:20,'XTickLabel',genres(1:20)) + +figure; +subplot(2,1,1) +bar(scores(1:10) / max(scores)) +set(gca, 'XTickLabel',genres) +title 'comparison subset' + +subplot(2,1,2) +bar(scores2(1:10) / max(scores2)) +set(gca, 'XTickLabel',genres2,'FontSize', 8) +title 'Full MTT data set' + +% --- +% evaluate differences in distribution for all genres +% shown are changes in relation to the whole database(genres2) +% ( relative to the summed tags ) +% --- +genrediff = zeros(numel(genres2),1); +for i=1:numel(genres2) + +% mgen = strcellfind(genres, genres2(i)); + mgen = find(gid == gid2(i)); + + if ~isempty(mgen) + % genrediff(i,1) = scores(mgen)/sum(scores) - scores2(i)/sum(scores2); + genrediff(i,1) = 1 - ( (scores2(i)/sum(scores2)) / (scores(mgen)/sum(scores))); + + else +% genrediff(i) = inf; + error 'genre ids not consistent' + end +end +genrediff = genrediff * 100; + +% visualise difference +figure +bar(genrediff); +set(gca, 'XTick',1:44,'XTickLabel',genres2) +axis([0 45 -200 100]); +title 'relative loss for each genre considering comparison as an excerpt from MTT' +ylabel 'loss(%)' + +% --- +% get distinc genre music sets: +% we try the following sets of 'similar' genre tags +% --- +cgdb = db_magnaclips.genredb.subset(clips.id); + +cids = cgdb.owner({'Classical', 'Baroque'}, 'or'); +cgdb = cgdb.exclude(cids); + +eids = cgdb.owner({'Electronica', 'New Age', 'Ambient'}, 'or'); +cgdb = cgdb.exclude(eids); + +rids = cgdb.owner({'Rock', 'Alt Rock', 'Hard Rock', 'Metal'}, 'or'); +cgdb = cgdb.exclude(rids); + +% get reverse indexing for comparisons +rev_compid = sparse(comparison_ids, 1, 1:numel(comparison_ids)); + +% 8 triples entirely classical +cfit = get_comparison_linfits(comparison, rev_compid(cids)); + +% 43 triples entirely electronic +efit = get_comparison_linfits(comparison, rev_compid(eids)); + +% 6 triples entirely rock +rfit = get_comparison_linfits(comparison, rev_compid(rids)); + +cgdb.stats + +end +% this function returns for each comparison line +% the num. of appearance of given selection of clip ids +function out = get_comparison_linfits(comparison, goodset) + + out = zeros(1, size(comparison,1)); + % for each line + for i = 1:size(comparison,1) + + out(i) = numel(intersect(comparison(i,1:3), goodset')); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/get_magnagenre_numeric.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,108 @@ +function [clip_magnagenres, magnagenres, magnagenre_freqs, magnagenre_childof] = get_magnagenre_numeric(clip_info_magnagenres); +% +% uses the clip_info magnagenres_final db to get a numerical genre +% representation ofthe database +% +% genre_freqs: frequency of genre x in position y +% genre_childof: percentage of genre x being a successor of genre y +% +% reimports the text - based representation of magnatunes and tries to +% determine an underlying structure. +% + +data = clip_info_magnagenres(:,3); + +% --- +% genre and genre posfrequency list: +% --- + +magnagen_id = {}; + +genres = {}; + +max_genres = 50; +max_simul_genres = 4; +genre_freqs = []; +genre_childof = []; + +% for each of the genre tags +for i = 1:length(data) + + % separate genres; + tmp = explode(',', data{i}); + + % find and save correspnding genre indices + for j = 1:length(tmp) + genidx = strcellfind(genres,tmp(j)); + + % add genre to genre list if not existent + if genidx < 1 + genidx = size(genres, 1) + 1; + genres = cat(1, genres, tmp(j)); + + genre_freqs(genidx,:) = zeros(1, max_simul_genres); + genre_childof(genidx,:) = zeros(1, max_genres); + end + + % --- + % here, we save the index to a new genre structure + % --- + if j == 1 + magnagen_id{i} = genidx; + else + magnagen_id{i} = [magnagen_id{i}, genidx]; + end + % --- + % further genre statistics, perhaps its a hierarchy + % --- + + % save frequency by position + genre_freqs(genidx, j) = genre_freqs(genidx, j) + 1; + + % save parent genre if applicable + if j == 1 + + % remember parent index + paridx = genidx; + else + + % count index for this parent + genre_childof(genidx, paridx) = genre_childof(genidx, paridx) + 1; + end + end + +% --- +% - save preceeding first genre for this into another table +% --- +end + +% --- +% this should output quite generic data, to ease +% comparison with other genre hierarchies. +% +% thus, we set the parental relation relative to overall +% appearance of the child genre +% --- + +% remove overlapping columns +idx = find(sum(genre_childof,1) > 0,1, 'last'); +idx = max(size(genre_childof,1),idx); + +genre_childof = genre_childof(:, 1:idx); + +% make values relative to total occurrence of child +for i = 1: size(genre_childof,1) + genre_childof(i, :) = genre_childof(i, :) ./ sum(genre_freqs(i,:)); +end + +% --- +% reformat genre attribute table as sparse matrix +% --- +clip_magnagenres = sparse(length(magnagen_id),length(genres)); +for i = 1:length(magnagen_id) + clip_magnagenres(i,magnagen_id{i}) = 1; +end + +magnagenres = genres; +magnagenre_freqs = genre_freqs; +magnagenre_childof = genre_childof;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/get_similar_clips.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function [clips, score] = get_similar_clips(extid, sim, sim_ids) +% clips = get_similar_clips(id, sim, sim_id) +% +% searches for similar clips for clip with base_id id +% + +if nargin < 3 + warning 'no similarity id codebook given' + sim_ids = 1:size(sim,1); +end + +% get internal clip id +id = find(sim_ids == extid); + +if isempty(id) + error 'no similarity data available' +end + +clips = [find(sim(id,:) ~= 0)]; + +% get clip scores +score = sim(id,clips); + +clips = sim_ids(clips);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/macro_sim_graph_analysis.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,146 @@ + +% Results: +Gcomp = ClipComparedGraph(comparison, comparison_ids) +[Gcompcomps] = Gcomp.connected_components(); +% only 2graphs with 7 nodes, all subtriplets just connect via +% one node -> no "infestation" of other triplets + +G = ClipSimGraph(comparison, comparison_ids) +[Gs,s, id] = G.all_strongly_connected_components(); +u = hist(s); +% 179 cycled triplets in unprepared graph + +Gmd = ClipSimGraphMD(comparison, comparison_ids) +[Gsmd, smd, id] = Gmd.all_strongly_connected_components(); +Gsmd(1).order; +% Not a single triple cycle found + +% --- +% analysis on compliance with stobers preprocessing +% trying to find out the differing ? Graph structures +% for that sake analysis with connected (no cycles) components +% --- +Graw = ClipSimGraph(comparison, comparison_ids); +[Gsraw, sraw] = connected_components(Graw) +hist(sraw) +% in the unedited graph, there are 346 connected triplets + +Gm = ClipSimGraphMulti(comparison, comparison_ids); +[Gsraw2, sraw2] = connected_components(Gm) +u = hist(sraw2) +% in this unedited graph, there are still 346 connected triplets + +Gm.remove_cycles_length2; +[Gs, s, id] = connected_components(DiGraph(Gm)); +u = hist(s) +% 27 single nodes, 337 three-node graphs. why not two-nodes + +% which clips do these belong to? +soloid = find(s == 1); +soloclips = []; +for i = 1:numel(soloid) + tmpG = ClipSimGraph(Gs(soloid(i))); + + % get the clisp of the single node + [sm, bg] = tmpG.clipsN(tmpG.nodes); + soloclips = [soloclips sm bg]; +end +soloclips = unique(soloclips) +% exactly 27 clips lost + +% now with our graph processing +Gmd = ClipSimGraphMD(comparison, comparison_ids) +[Gsmd, smd] = connected_components(Gmd) +% same result, + +% --- +% now which are the orphaned nodes ? +% --- +Go = Gm.unconnected_subgraph(); +Go.label(Go.nodes) + +% --- +% how many clips are completely lost? +% --- +Go = ClipSimGraphMulti(Gm.unconnected_subgraph()); +Go.clips +numel(ans) +Gm.remove_graph(Go) % +lastClip = intersect(Go.clips,Gm.clips) + +% --- +% ok lets chase the last clip +% --- +Gm.node(21950) +Gs = Gm.connected_components(283); +Gs.visualise +DiGraph(Go).visualise + + +% --- +% validation with comparison data +% --- +Gm = ClipSimGraphMulti(comparison, comparison_ids); +num_compares = sparse(numel(comparison_ids), numel(comparison_ids)); +for i = 1:size(comparison,1) +abc = sort(comparison(i,1:3)); +a = abc(1); +b = abc(2); +c = abc(3); +num_compares(a,b) = num_compares(a,b) +1; +num_compares(b,c) = num_compares(b,c) +1; +num_compares(a,c) = num_compares(a,c) +1; +end + +[i,j] = ind2sub(find(num_compares > 2)) +[i,j] = ind2sub(size(num_compares), find(num_compares > 2)) +[i,j] = ind2sub(size(num_compares), find(num_compares > 1)), +i(1) +j(1) +find(comparison(:,1) == i(1) || comparison(:,2) == i(1) || comparison(:,3) == i(1)...) +&& comparison(:,1) == j(1) || comparison(:,2) == j(1) || comparison(:,3) == j(1)) +find((comparison(:,1) == i(1) | comparison(:,2) == i(1) | comparison(:,3) == i(1))...) +& (comparison(:,1) == j(1) | comparison(:,2) == j(1) | comparison(:,3) == j(1))) + +num = 2; +a = find((comparison(:,1) == i(num) | comparison(:,2) == i(num) | comparison(:,3) == i(num))...) +& (comparison(:,1) == j(num) | comparison(:,2) == j(num) | comparison(:,3) == j(num))) +comparison(a,:) +[i,j] = ind2sub(size(num_compares), find(num_compares > 1)) +num = 3; +a = find((comparison(:,1) == i(num) | comparison(:,2) == i(num) | comparison(:,3) == i(num))...) +& (comparison(:,1) == j(num) | comparison(:,2) == j(num) | comparison(:,3) == j(num))) +comparison(a,:) +[i,j] = ind2sub(size(num_compares), find(num_compares > 1)) +num = 18; +a = find((comparison(:,1) == i(num) | comparison(:,2) == i(num) | comparison(:,3) == i(num))...) +& (comparison(:,1) == j(num) | comparison(:,2) == j(num) | comparison(:,3) == j(num))) +comparison(a,:) +[i,j] = ind2sub(size(num_compares), find(num_compares > 1)) +num = 300; +a = find((comparison(:,1) == i(num) | comparison(:,2) == i(num) | comparison(:,3) == i(num))...) +& (comparison(:,1) == j(num) | comparison(:,2) == j(num) | comparison(:,3) == j(num))) +comparison(a,:) + +% --- +% other simulated data +% --- +ClipSimGraph([1 2 3 5 1 0;], 1:100).visualise +ClipSimGraph([1 2 3 5 1 0; 1 2 4 6 0 0], 1:100).visualise +ClipSimGraph([1 2 3 5 1 0; 1 2 4 6 0 0], 1:100).max_degree +G = ClipSimGraph(comparison, comparison_ids); + +% --- +% scc algorithm tests +% --- +V = ones(1,5) +E = zeros(5) +for i=1:4 +E(i+1,i) = 1 +end +E(1,4) = 1 +gtest = DiGRaph(V,E) + +gtest.strongly_connected_components(4) +gtest.strongly_connected_components(5) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/macro_validate_cycle_finder.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ + +% profile clear; +% profile on; + +clear eval; +N = 20; +for ci = 1:N + order = ci; + % connectivity = ceil(rand(1) * order) + % E = mk_rnd_dag(order, connectivity); + E = rand(order) > exp(1) * rand(1); + + G = DiGraph(ones(order,1), E); + eval.G(ci) = G; + + eval.res_naive(ci) = acyclic(E); + + eval.res_myfun(ci) = G.isAcyclic; + + Gstob = ClipSimGraphStober(G); + + eval.res_stober(ci) = Gstob.isAcyclic; +end +eval + + +figure +plot([eval.res_naive' eval.res_myfun'+0.1 eval.res_stober'+0.2],'*') +legend naive myfun stober +axis([0 N -1 2 ]) +% profile viewer; + +%% +% --- +% Comparison of implementations on magnatagatune dataset +% --- + +% Stober Graph +Gstob = ClipSimGraphStober(); + +% Cast into matlab Graph +GstobG = Gstob.to_DiGraph; + +% My Multigraph reimplementation +Gm = ClipSimGraphMulti(comparison, comparison_ids); + +GstobG == Gm % TRUE - the converted Graph exactly resembles my extracted one + +Gm.isAcyclic % FALSE +GstobG.isAcyclic % FALSE + +Gstob.isAcyclic % TRUE (this is wrong, there are a lot of length-2-cycles) + +% --- +% Ok, now remove length 2 cycles +% --- +Gm.remove_cycles_length2 % matlab cycle remover +Gstob.remove_cycles_length2 % stober cycle remover + +GstobGnocycles = Gstob.to_DiGraph(); + +GstobGnocycles == Gm % TRUE - we remove the same edges + +% --- +% NOTE: There are no cycles left +% after removing length 2 cycles +% --- +GstobGnocycles.isAcyclic % TRUE + +Gstob.isAcyclic % FALSE inconsistent wrong result(compare with above) + + +%% Finds the actual defective cycle + +Gstob = ClipSimGraphStober(); +Gstob.remove_cycles_length2 + +GstobG = Gstob.to_DiGraph(); +Gstob.isAcyclic + +N = GstobG.node('227:45936'); + +[Gs] = GstobG.connected_components(N); +Gs.visualise + +% --- +% Search same node in my Multigraph reimplementation +% --- +Gm = ClipSimGraphMulti(comparison, comparison_ids); +Gm.remove_cycles_length2 +Nm = Gm.node(227,45936); + +[Gsm] = Gm.connected_components(Nm); +Gsm.visualise \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/macro_validate_stober_randomsets.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ + +nruns = 50; +weights = zeros(674, nruns); +for i = 1:50; + Gstob = ClipSimGraphStober(); + Gstob.random_all_constraints_graph; + G = ClipSimGraphMulti(Gstob.to_DiGraph()); + + % --- + % derive similarity information: this + % returns the weights of edges meaning sim(a,b) > sim(a,c) + % --- + [weights(:,i), a, b, c] = G.similarities(); +end + +% --- +% lets see if the siilarity data is the same +% it is NOT +% --- +diff = sum(sum(abs(diff(weights)))) + +% --- +% ok, my theory is that +% a. all the three-edge components are erased one edge. +% b. if not all, then the ones with two edges +% being directed at the same node. +% --- + +% --- +% have to convert, as for some reason +% this does not work yet on multigraphs +% --- +Gdi = DiGraph(G); +[Gs, s, id] = Gdi.connected_components(); + +for i = 1:numel(Gs) + edges(i) = Gs(i).num_edges(); +end + +% only 2-edge graphs +max(edges) + +% --- +% test if graphs are subgraphs of 860 full ne +% --- +Gm = ClipSimGraphMulti(comparison, comparison_ids); +Gm.remove_cycles_lenght2; + +Gm.isSubgraph(G) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_ISMIR12_simdata.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +trainpart = logspace(-1.8, 0,20) + +[partBinTrn, partBinTst, partBinNoTrn] = sim_from_comparison_fair_components(comparison, comparison_ids, 10, trainpart); + +[partBinTrn, partBinTst, partBinNoTrn] = sim_from_comparison_UNfair_components(comparison, comparison_ids, 10, trainpart); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_camir_upgrade_paper_simdata.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +trainpart = logspace(-1.8, 0,20) + +for i = 1:10 + filename = sprintf('comp_partBinData_cupaper_%02.0f',i) + sim_from_comparison_fair_components(comparison, comparison_ids, 10, trainpart, filename); + + filename = sprintf('comp_partBinData_unclustered_cupaper_%02.0f', i) + sim_from_comparison_UNfair_components(comparison, comparison_ids, 10, trainpart, filename); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_cupaper12_get_significance.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,351 @@ +% makro_get_significance_cupaper +%% +% ------------------------------ Algorithms Compared id-sampling +% --- +'mlr vs euclidean, mlr all average feat, ID-sampling' +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1a58077f1232c33b787b661039df107d_finalresults'; + +% euclidean average feat - TODO: which sampling is this ? +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults'; +% --- +% NOTE: all 4 test-set runs have exactly the same result +% --- +run1 = -1; +run2 = -1; +mode = 'join_datasets'; % '', 'avgbase', 'join_datasets' +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run1,0,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + + +%% +% --- +'SVM vs euclidean, SVM all average feat, ID-sampling' +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1841892e9df07039bbe4c3a55d11026a_finalresults'; + +% euclidean average feat - TODO: which sampling is this ? +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults'; + +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2,0,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +% ------------------------------ Algorithms Compared TD-sampling +% --- +'mlr unclustered vs euclidean unclustered, mlr all average feat TD-sampling' +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_c364cb0803822d55f2940656c44b184d_finalresults'; + +% euclidean average feat TD-sampling +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_72303bfa642aad872665dee7a3b1e28c_finalresults'; + +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2,0,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +% --- +'SVM unclustered vs euclidean unclustered, SVM all average feat TD-sampling' +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_3434f0534fa910b26bbf927c65a7fb74_finalresults'; + +% euclidean average feat TD-sampling +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_72303bfa642aad872665dee7a3b1e28c_finalresults'; +% --- + +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2,0,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + + +%% +% ------------------------------ Features Compared +'All 12-dim PCA Features compared' +% --- +namePCA12 = {'AcousticPCA12', ... + 'Slaney08PCA12', ... + 'TimbrePCA12', ... + 'ChromaPCA12', ... + 'AllPCA12', ... + 'GenrePCA12'}; +% this is the index how the feature types appear in the paper +paperidx = [4 3 2 6 1 5]; + +filePCA12 = {'runlog_a18bd2111694ac59c9ba0a6810121796_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_37e47c187886f73ec9a7d8dc24a84a52_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_4c6787b403a07f5faf1ec26e891da4fa_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_c5566f74e6a0d00b50f5eea05fdacfee_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_e2c22696e7af9e7eea1fa1fd10a1f785_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_efc6e5e9c56291cd1744092a1c59a293_1841892e9df07039bbe4c3a55d11026a_finalresults'}; + + +namePCA12 = namePCA12(paperidx); +filePCA12 = filePCA12(paperidx); + +p = zeros(numel(namePCA12),numel(namePCA12)); +med = zeros(numel(namePCA12),numel(namePCA12)); +avg = zeros(numel(namePCA12),numel(namePCA12)); +for i=1:numel(name) + for j = 1:i-1 + [p(i,j), med(i,j), avg(i,j)] = test_generic_significance_signrank(filePCA12{i},-1,filePCA12{j},-1,0,mode); + end + % p(i,i) = 0.5; +end +% p = p + p'; +imagesc(p); +colormap(hot) +axis xy +set(gca,'XTick',1:numel(namePCA12), 'XTickLabel', namePCA12); +set(gca,'YTick',1:numel(namePCA12), 'YTickLabel', namePCA12); +matrix2latex(p,'%1.3f') + +%% +'All 52-dim PCA Features compared' +% --- +namePCA52 = {'GenrePCA52', ... + 'AllPCA52', ... + 'TimbrePCA52', ... + 'AcousticPCA52', ... + 'ChromaPCA52' ... + }; +% this is the index how the feature types appear in the paper +paperidx = [5 3 1 4 2]; + +filePCA52 = {'runlog_3cbf4759cf58af0728aaab0b5f2660e3_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_7d5fafec0dc504215acc8cb7a9202a56_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_a3c2c0a5742a42fd54497e69b8f44e8d_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_c7164074206998aa184538bedcfdcf2f_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_efbf7c8e75ae154c2f192acd08fbdcbc_1841892e9df07039bbe4c3a55d11026a_finalresults' ... + }; + + +namePCA52 = namePCA52(paperidx); +filePCA52 = filePCA52(paperidx); + +p = zeros(numel(namePCA52),numel(namePCA52)); +med = zeros(numel(namePCA52),numel(namePCA52)); +avg = zeros(numel(namePCA52),numel(namePCA52)); +for i=1:numel(name) + for j = 1:i-1 + [p(i,j), med(i,j), avg(i,j)] = test_generic_significance_signrank(... + filePCA52{i},run1,filePCA52{j},run2,0,mode); + end + % p(i,i) = 0.5; +end +% p = p + p'; +imagesc(p); +colormap(hot) +axis xy +set(gca,'XTick',1:numel(namePCA52), 'XTickLabel', namePCA52); +set(gca,'YTick',1:numel(namePCA52), 'YTickLabel', namePCA52); +matrix2latex(p,'%1.3f') + +%% +'All full-dim Features compared (SVM)' +% --- +name = {'4Chroma', ... + '1Chroma', ... + '4Timbre', ... + '1Timbre', ... + '1Acoustic', ... + 'Genre', ... + 'Slaney08', ... + '1All', ... + '4All', ... + '4Acoustic' ... + }; + +% this is the index how the feature types appear in the paper +% Features & Chroma(1/4) & Timbre(1/4) & Slaney08 & Genre & Comb. Audio (1/4) & Comb. All(1/4) \\ +paperidx = [2 1 4 3 7 6 5 10 8 9]; + +file = {'runlog_20a2f6a0f20f488e9386ebb8c5026fcf_85c439d9ef3d135936e7645ebc0efe36_finalresults', ... + 'runlog_20a2f6a0f20f488e9386ebb8c5026fcf_85c439d9ef3d135936e7645ebc0efe36_finalresults', ... + 'runlog_3154f36c34c18f60218c5d3f0c0b5931_85c439d9ef3d135936e7645ebc0efe36_finalresults', ... + 'runlog_3154f36c34c18f60218c5d3f0c0b5931_85c439d9ef3d135936e7645ebc0efe36_finalresults', ... + 'runlog_31981d48dd0d25564ef3c2b3ca650b3b_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_37867d3b5bd4c74b7b548732b80fb947_85c439d9ef3d135936e7645ebc0efe36_finalresults', ... + 'runlog_f52d37439805ac4edc70b0432281abc3_85c439d9ef3d135936e7645ebc0efe36_finalresults', ... + 'runlog_0b506247a68167addf97fcb0296650eb_1841892e9df07039bbe4c3a55d11026a_finalresults', ... + 'runlog_800d97be9ef6274dc3bbe6b9be2406a6_1a58077f1232c33b787b661039df107d_finalresults', ... + 'runlog_cf5a61cca09e2a3182b794b70ee1ab91_1841892e9df07039bbe4c3a55d11026a_finalresults' ... + }; + +sets2join = {[1:4],[5:8],[1:4],[5:8],[],[],[],[],[],[]}; + +name = name(paperidx); +file = file(paperidx); + +p = zeros(numel(name),numel(name)); +med = zeros(numel(name),numel(name)); +avg = zeros(numel(name),numel(name)); +for i=1:numel(name) + for j = 1:i-1 + [p(i,j), med(i,j), avg(i,j)] = test_generic_significance_signrank(... + file{i},sets2join{i},file{j},sets2join{j},0,mode); + end + % p(i,i) = 0.5; +end +p = flipud(p); +% p = p + p'; +imagesc(p); +colormap(hot) +axis ij +set(gca,'XTick',1:numel(name), 'XTickLabel', name); +set(gca,'YTick',1:numel(name), 'YTickLabel', fliplr(name)); +matrix2latex(p,'%1.3f') + +%% +'Comparing Feature dimensions and PCA effect on combined all features' +% --- +% 'SVM all average feat, ID-sampling' +allavg = 'runlog_0b506247a68167addf97fcb0296650eb_1841892e9df07039bbe4c3a55d11026a_finalresults'; + +% 'SVM 4 cluster feat, ID-sampling' +allfourcluster = 'runlog_800d97be9ef6274dc3bbe6b9be2406a6_1a58077f1232c33b787b661039df107d_finalresults'; + +% 'SVM 12dim feat, ID-sampling' +pca12 = 'runlog_e2c22696e7af9e7eea1fa1fd10a1f785_1841892e9df07039bbe4c3a55d11026a_finalresults'; + +% 'SVM 52dim feat, ID-sampling' +pca52 = 'runlog_7d5fafec0dc504215acc8cb7a9202a56_1841892e9df07039bbe4c3a55d11026a_finalresults'; +% --- +% NOTE: all 4 test-set runs have exactly the same result +% --- +run1 = -1; +run2 = -1; +mode = 'join_datasets'; % '', 'avgbase', 'join_datasets' + +'AllAvg vs Pca12' +[p, med, avg] = test_generic_significance_signrank(allavg,run1,pca12,run2,0,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +'AllAvg vs pca52' +[p, med, avg] = test_generic_significance_signrank(allavg,run1,pca52,run2,0,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +'Pca12 vs pca52' +[p, med, avg] = test_generic_significance_signrank(pca12,run1,pca52,run2,0,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + + +%% +% ------------------------------ Algorithms Weighted Training Weighted Performance Compared +'MLR t:w,e:w vs euclidean t:w,e:w' +% --- +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_3cdcca7596fed97f87b0ec051cb8bba0_finalresults'; + +% euclidean baseline, file same as the unweighted above +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +'DMLR t:w,e:w vs euclidean t:w,e:w' +% --- +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1cc76d534804229cbdec8b20f8b75dba_finalresults'; + +% euclidean baseline, file same as the unweighted above +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +'SVM t:w,e:w vs euclidean t:w,e:w' +% --- +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_9bd9ccddb2e4e622e2ba9826466442ba_finalresults'; + +% euclidean baseline, file same as the unweighted above +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +% ------------------------------ Algorithms Weighted Training UNWEIGHTED Performance Compared +'MLR t:w,e:uw vs euclidean t:w,e:uw' +% --- +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_3cdcca7596fed97f87b0ec051cb8bba0_finalresults'; + +% euclidean baseline, file same as the unweighted above +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 0 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +'DMLR t:w,e:uw vs euclidean t:w,e:uw' +% --- +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1cc76d534804229cbdec8b20f8b75dba_finalresults'; + +% euclidean baseline, file same as the unweighted above +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 0 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +'SVM t:w,e:uw vs euclidean t:w,e:uw' +% --- +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_9bd9ccddb2e4e622e2ba9826466442ba_finalresults'; + +% euclidean baseline, file same as the unweighted above +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a9788c5e14f30e23ed2a05dbf513c9f_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 0 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + + +%% +% ------------------------------ Algorithms w / uw Training WEIGHTED Performance Compared +'MLR t:w,e:w vs MLR t:uw,e:w' +% --- +% MLR t:w,e:w +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_3cdcca7596fed97f87b0ec051cb8bba0_finalresults'; + +% MLR t:uw,e:w +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1a58077f1232c33b787b661039df107d_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +% --- +'SVM t:w,e:w vs SVM t:uw,e:w' +% --- +% SVM t:w,e:w +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_9bd9ccddb2e4e622e2ba9826466442ba_finalresults'; + +% SVM t:uw,e:w +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_1841892e9df07039bbe4c3a55d11026a_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg); + +%% +% ------------------------------ Algorithms w / uw Training WEIGHTED Performance Compared +'DMLR t:w,e:w vs DMLR t:uw,e:w' +% --- +% DMLR t:w,e:w +file1 = 'runlog_0b506247a68167addf97fcb0296650eb_1cc76d534804229cbdec8b20f8b75dba_finalresults'; + +% DMLR t:uw,e:w +file2 = 'runlog_0b506247a68167addf97fcb0296650eb_bf08b16f453683d96ddffc31c6439730_finalresults'; +% --- +% NOTE: using the "weighted" parameter here +% --- +[p, med, avg] = test_generic_significance_signrank(file1,run1,file2,run2, 1 ,mode); +fprintf('p = %3.4f, median = %3.4f, avg = %3.4f \n\n',p,med,avg);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_export_db_mtttags.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ + + +tag_names = [db_magnaclips.tagdb.lexicon]; +for i = 1:numel(tag_names) + tag_names2(i,[2 1]) = {tag_names{i}, i}; +end +CELL2CSV('tag_names.csv',tag_names2, ',') ; + +for i = 1:numel(u) + file_names(i,1:2) = {u(i), db_magnaclips.annots_filenames{i,2}}; +end +CELL2CSV('file_names.csv',file_names, ',') ; + +for i = 1:numel(clips) + clip_tags(i,1:2) = {u(i), implode(', ', clips(i).tag_ids)}; +end +CELL2CSV('clip_tags.csv',clip_tags, ';') ; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_growingk_simdata.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ + +trainpart = logspace(-1.8, 0,20) +k = [2 5 10]; +for i = 1:numel(k) + filename = sprintf('comp_partBinData_growingk_%02.0f',i) + sim_from_comparison_fair_components(comparison, comparison_ids, k(i), trainpart, filename); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_import_magnatagatune.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,154 @@ +% makro_import_magnatatatune + +csv2cell('annotations_final.csv','fromfile') +clip_info_final = csv2cell('clip_info_final.csv','fromfile'); +comparisons_final = csv2cell('comparisons_final.csv','fromfile'); + +% tag annotations +annots = strcell2matrix(annotations_final,[1 0],[0 1]); +annots_descripts = {annotations_final{1,:}}; +annots_filenames = cat(1,{annotations_final{:,1}},{annotations_final{:,end}})'; + +% comparison measures +comp = strcell2matrix(comparisons_final,[0 0], [0 3]); +comp_descripts = {comparisons_final{1,:}}; + +%% +% ----------------------------------------------------------- +% --- +% part two: extracting and associating Genre +% +% at first we filter out the relevant positions in clip_info_final- +% --- + +% prepare clip_info +info_ids = {clip_info_final{:,1}}; +info_ids = strcell2matrix(info_ids); + +% --- +% CAUTION: a offset because of table header is added +% --- +info_ids = info_ids(2:end); + +% prepare file annots +file_ids = {annots_filenames{:,1}}; +file_ids = strcell2matrix(file_ids); +file_ids = file_ids(2:end); + +% intersect ids +[c, ia, ib] = intersect(file_ids, info_ids); + +% and save into proper +clip_info_proper_names = clip_info_final(1,:); +clip_info_proper = clip_info_final(ib+1,:); + +% clean proper for mysterious beginnings +for i = 1:size(clip_info_proper,1) + % leave out first row; + for j = 2:size(clip_info_proper,2) + clip_info_proper{i,j} = clip_info_proper{i,j}(2:end); + end +end + +% --- +% % GENRE extraction +% +% now, we load the new file and search for the information on the +% actual excerpts we have +% --- +tmp = csv2cell('song_info.csv','fromfile'); + +%% +% these are for keeping book of missing items +not_found = []; +man_album_name = {}; + +rel_cols = [6,7]; +% make header +clip_info_extra_names = {'clip_id',tmp{1,rel_cols}}; +clip_info_extra = {}; + +for i = 1:size(clip_info_proper,1) + % --- + % search by url + % --- + % convert search string + s = char(clip_info_proper{i,9}); + idx = strcellfind(tmp(:,9),s); + + % we'll have to loosen the search + if idx < 1 + warning(sprintf('! %s, album %s, artist %s!',clip_info_proper{i,1},... + clip_info_proper{i,5},clip_info_proper{i,4})); + + % make note ... + not_found = cat(1,not_found,[str2num(clip_info_proper{i,1}), 0]); + + not_found(end,2) = 1; + % --- + % ok, no problem, lets look for the album! + % --- + s = char(clip_info_proper{i,5}); + idx = strcellfind(tmp(:,3),s); + + if idx < 1 + + not_found(end,2) = 2; + % --- + % search for artist + % --- + s = char(clip_info_proper{i,4}); + idx = strcellfind(tmp(:,1),s); + end + + if idx < 1 + + not_found(end,2) = 3; + % --- + % this is the last try to get hold of such artists + % they may be noted as a trackname or description substring in a compilation + % --- + s = char(clip_info_proper{i,4}); + idx = substrcellfind(tmp(:,2),s); + end + + if idx < 1 + + warning(sprintf(' - %s, %s \n',clip_info_proper{i,1},clip_info_proper{i,4})); + newinfo = {''}; + + % reset suspected success + not_found(end,2) = 0; + else + + warning(sprintf(' + associated album %s, artist %s \n',tmp{idx,3},tmp{idx,1})); + + % get relevant data + newinfo = {tmp{idx,[3 1 12]}}; + + % --- + % save genre + % --- + clip_info_extra = cat(1,clip_info_extra,{clip_info_proper{i,1},tmp{idx,rel_cols}}); + end + + % report new location/info of album + s = char(clip_info_proper{i,5}); + if isempty(man_album_name) || strcellfind(man_album_name(:,1),s) == -1 ; + man_album_name = cat(1, man_album_name, {clip_info_proper{i,5},newinfo{:}}); + end + + else + % --- + % save genre + % --- + clip_info_extra = cat(1,clip_info_extra,{clip_info_proper{i,1},tmp{idx,rel_cols}}); + end +end + +clear('newinfo','i','j','idx','s','ia','ib','rel_cols') + + + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_import_magnatagatune_isrc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,83 @@ +% --- +% import isrcs via albums for magnatune clips + +% now, we load the new file and search for the information on the +% actual excerpts we have +% --- +clip_info_proper = db_MTTClip.clip_info_proper; + + + db = adodb_connect('Driver={MySQL ODBC 5.1 Driver}; Server=10.2.165.196; Database=test; UID=daniel; PWD=wucity;',2000); + sql = 'SELECT download_mp3, artist, albumname, isrc, download_mp3lofi FROM `magnatune_song_info` where 1'; % 401 times not found + [res,resformat] = adodb_query(db, sql); + + +% these are for keeping book of missing items +not_found = []; +isrc = cell(numel(db_MTTClip.annots_ids),1); +for i = 1:numel(db_MTTClip.annots_ids) + % --- + % identify the clip by url + % --- + % convert search string + s = db_MTTClip.clip_info_proper{i,9}; + + idx = strcellfind(res.download_mp3,s); + if isempty(idx) + idx = strcellfind(res.download_mp3lofi,s); + end + + % we'll have to loosen the search + if isempty(idx) | idx < 1 +% warning(sprintf('! %s, album %s, artist %s!',clip_info_proper{i,1},... +% clip_info_proper{i,5},clip_info_proper{i,4})); + + % make note ... + not_found = cat(1,not_found,[str2num(clip_info_proper{i,1}), 0]); + + not_found(end,2) = 1; + % --- + % ok, no problem, lets look for the album and artist! + % --- + s = char(clip_info_proper{i,5}); + idx = strcellfind(res.albumname,s); + end + + + if isempty(idx) | idx < 1 + warning(sprintf('-- Album not found %s, album %s, artist %s!',clip_info_proper{i,1},... + clip_info_proper{i,5},clip_info_proper{i,4})); + + not_found(end,2) = 2; + % --- + % search for artist + % --- + s = char(clip_info_proper{i,4}); + idx = strcellfind(res.artist,s); + end + + if isempty(idx) | idx < 0 + warning(sprintf(' ---- Artist not found %s, album %s, artist %s!',clip_info_proper{i,1},... + clip_info_proper{i,5},clip_info_proper{i,4})); + not_found(end,2) = 3; +% % --- +% % this is the last try to get hold of such artists +% % they may be noted as a trackname or description substring in a compilation +% % --- +% s = char(clip_info_proper{i,4}); +% idx = substrcellfind(tmp(:,2),s); + else + isrc{i} = char(res.isrc{idx}); + end + + + % report new location/info of album + + % --- + % save genre + % --- + %clip_info_extra = cat(1,clip_info_extra,{clip_info_proper{i,1},tmp{idx,rel_cols}}); + +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_import_magnatagatune_mbid_cleanupisrc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +function out = makro_import_magnatagatune_mbid_cleanupisrc(); +load db.mat +clip_info_proper_names +artists_mbid_map = containers.Map('KeyType', 'char', 'ValueType', 'char'); +artists_matched = containers.Map('KeyType', 'char', 'ValueType', 'char'); +cnt_workd = 0; +for i = 1:size(clip_info_proper,1) + if ~isempty(clip_info_proper{i,12}) + continue; + end + % --- + % ok, we have the isrc, lets get the artist id + % --- + % clip_info_proper{i,11} = annots_isrc{i}; + + if artists_mbid_map.isKey(clip_info_proper{i,4}) + % did we already process this artist? + if ~strcmp(artists_mbid_map(clip_info_proper{i,4}), '-'); + artist_mbid = artists_mbid_map(clip_info_proper{i,4}); + end + else + try + % no we didnt, get the artist mbid via album and artist name + [artist_mbid,artist_found, out] = mb_retrieve_artist_id_from_album_info(clip_info_proper{i,4},clip_info_proper{i,5}); + + if ~isempty(artist_mbid) + cprint(0,'loaded info for %s: found %s, %s',clip_info_proper{i,4},artist_found,artist_mbid); + + artists_matched(clip_info_proper{i,4}) = artist_found; + artists_mbid_map(clip_info_proper{i,4}) = artist_mbid; + cnt_workd = cnt_workd + 1; + else + artists_mbid_map(clip_info_proper{i,4}) = '-'; + end + pause(1) + catch + end + end + clip_info_proper{i,12} = artist_mbid; + +end + +% clip_info_proper_names{11} = 'isrc'; +clip_info_proper_names{12} = 'artist_mbid'; +clear annots_isrc; +clear artists_mbid_map; +clear i; +clear cnt_workd; +clear out; +save db_new.mat
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_import_magnatagatune_mbtags.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function out = makro_import_magnatagatune_mbtags(); +load db.mat +clip_info_proper_names +cnt_workd = 0; + +mbtagdb = AnnotDB(); + +failedartists = []; + +for i = 1:size(clip_info_proper,1) + % --- + % get internal artist id + % --- + artistintid = artistdb.get_annot_id(lower(clip_info_proper{i,4})); + + % do we have an mbid if not skip? + if isempty(clip_info_proper{i,12}) || strcmp(clip_info_proper{i,12}, '-') + continue; + end + + % do we have tags for this artist? + if ~isempty( mbtagdb.annotids_for_owner(artistintid)) || sum(failedartists== artistintid)> 0; + continue + end + + try + % --- + % ok, lets get the tags + % --- + [tags,count, out] = mb_retrieve_tags_for_artist_id(clip_info_proper{i,12}); + + if ~isempty(tags) + cprint(0,'loaded info for %s: found %s, %s',clip_info_proper{i,4},artist_found,artist_mbid); + + % --- + % TODO: save tags in map + % --- + for i=1:numel(tags) + mbtagdb.add_pair(artistintid, tags{i}, count(i)); + end + cnt_workd = cnt_workd + 1; + + else + cprint(0,'no tags received for %s:%s',clip_info_proper{i,4}); + % TODO: mark no tags received for this + failedartists(end+1) = artistintid; + end + pause(1) + catch + end + +end + +failedartists +numel(failedartists); +clear failedartists +save ('db.mat','mbtagdb','-append')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_insert_mtt_allmusic_genres.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,125 @@ +function [amginfo, notFound]= makro_insert_mtt_allmusic_genres() +% +% gets isrc number of (MSD) tracks and then retrieves genre from allmusic + + +% db connection + db = adodb_connect('Driver={MySQL ODBC 5.1 Driver}; Server=chivm.soi.city.ac.uk; Database=camir_ooo_0.1; UID=daniel; PWD=11a11a;',2000); +% db = adodb_connect('Driver={MySQL ODBC 5.2w Driver}; Server=chivm.soi.city.ac.uk; Database=camir_ooo_0.1; UID=daniel; PWD=11a11a;',2000); + +% --- +% 1. get all / a song from song table +% NOTE: we start ftom the last annotated song +% --- + +% sql = ['SELECT max(songId) FROM linksonggenre_amg']; +% [~, id] = adodb_query(db, sql); +% id = id{1} + +% select only mtt songs +sql = sprintf('SELECT * FROM songlibrary where id < 1020'); +[a] = adodb_query(db, sql); + +sevenapikey = '7drb8p23ggb7'; +roviapikey = 'mwtpx4bjc6wkz4j6u5t2ktr8'; +roviapisecret = 'UMfYTxg6qT'; + +for i=1:numel(a.id) +% --- +% 3. Where isrc code exists: get all genre tags from allmusic/robe +% Optional: get style tags ? +% --- + clip = CASIMIRClip(a.id{i}); + if isempty(clip.child_clip.isrc()) + continue; + end + + % --- + % we update the isrc's and mbids along the way + % --- + sql = ['UPDATE songlibrary SET isrcId=' sprintf('"%s"',clip.child_clip.isrc()) ', artistMBid=' sprintf('"%s"',clip.child_clip.artist_mbid()) ' WHERE (id =' sprintf('%i',a.id{i}) ')']; + [~, id] = adodb_query(db, sql); + + % --- + % have we had this song before? then lets avoid duplicates + % --- + sql = ['SELECT songId FROM linksonggenre_amg WHERE (songId =' sprintf('%i',a.id{i}) ')']; + [~, id] = adodb_query(db, sql); + if ~isempty(id) + continue; + end + % --- + % NOTE: this key is valid for 5 minutes only. + % the sharedsecret is never given to the API :) + % --- + sig = amg_rovi_signature(roviapikey, roviapisecret); + try + % song info API + url = sprintf('http://api.rovicorp.com/data/v1/song/info?apikey=%s&sig=%s&isrcid=%s',roviapikey,sig,clip.child_clip.isrc()); + [tmp, status] = curl(url); + + % after curl we can directly decode + tmpstr = json_decode(tmp); + + if strcmp(tmpstr.status,'error') + notFound(i) = tmpstr; + cprint(1,'%i of %i - %s',i,numel(a.id),notFound(i).messages.text); + pause(rand(2)); + continue; + end + amginfo(i) = tmpstr; + + % .song.genres id: 'MA0000002692' + % name: 'Latin' + % weight: 10 + + % --- + % 4. for each genre: + % - save into genre tagstable + % - associate with song id in linksonggenre + % --- + for j = 1:numel(amginfo(i).song.genres) + + % first insert the genre + genreId = insertGenre(db, amginfo(i).song.genres(j).name, amginfo(i).song.genres(j).id); + + sql = 'REPLACE INTO linksonggenre_amg (songId, genreId) VALUES (%i, %i)'; + sql = sprintf(sql, a.id{i}, genreId); + [res] = adodb_query(db, sql); + end + cprint(1,'%i of %i - saved %i genres',i,numel(a.id),numel( amginfo(i).song.genres)); + pause(rand(10)); + catch + cprint(1,'%i of %i - error response: %s',i,numel(a.id), tmp); + pause(rand(10)); + end + +end +end + +function id = insertGenre(db, name, locId) + + % --- + % check if genre also exists, + % return id if so + % --- + sql = ['SELECT id FROM `genre_amg` WHERE (musicDbLocId ="' locId '")']; + [~, id] = adodb_query(db, sql); + + if ~isempty(id) + id = id{1}; + return; + else + % insert genre into table + sql = [ 'INSERT INTO genre_amg (name,musicDbLocId) ' ... + 'VALUES ("' name '","' locId '")']; + [res] = adodb_query(db, sql); + + sql = 'SELECT max(id) FROM genre_amg'; + [~, id] = adodb_query(db, sql); + id = id{1}; + cprint(1,'inserted genre %i:%s',name) + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_merge_last.fm_data.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,110 @@ +% makro_merge_last.fm_data + +% --- +% make sure we just search once for each artist +% --- +[artists,null, idx] = unique(clip_info_proper(:,4)); + + +% --- +% this is were we search for our artists in last.fm +% --- +[fmartist, fmartist_names] = fm_corresponding_artists(artists); + +%% +% --- +% add Id's to artist structure +% --- + +% collect clip ids +for i = 1:numel(artists) + clip_ids{i} = annots_ids(idx == i); +end +fmartists = cat(2,clip_ids', fmartist); +fmartist_names = {'clip_ids',fmartist_names{:}}; + +clear fmartist; + +[a, b] = fm_retrieve_artist('Mijo'); +fmartist(strcellfind(fmartist(:,1),'Mijo'),2:3) = {a{1}, b{1}}; + +% --- +% TODO: code manual sorting out of bad associations here, for better +% reproducibility +% --- + +% +bad_artiidx = find(strcmp('-1', fmartist(:,2))); + +% --- +% we remove the magnatune compilation artist, +% as tags are not really descriptive for this +% --- +bad_artiidx = [bad_artiidx substrcellfind(fmartist(:,1), 'Magna', 1)]; + +bad_artists = zeros(size(fmartist,1),1); +bad_artists(bad_artiidx) = 1; + + +% --- +% NOTE: as we have two categories of reasons for non-existing tags +% (exclusion above and failure), there is two different data entries fur +% such: '-1' for "artist not found in last.fm database" and +% [] for excluded items. +% --- + +fmartist_tags = {}; +for i = 1:size(fmartist,1) + + if ~bad_artists(i) + fprintf('%d percent: %s\n',floor(i*100/size(fmartist,1)),fmartist{i,2}); + [a, b] = fm_retrieve_artist_tags(fmartist{i,2}); + fmartist_tags(i,:) = {a, b}; + end +end +%% +% --- +% now, we access the frequency of all tags, trying to establish an +% vocabulary suitable for defining similarity measurements +% --- +% --- +% collect all tags and number of occurence +% the tag array is allocated beforeghand to save time +% --- + +fmartist_annots = sparse(size(fmartist_tags,1),2000); +fmartist_annots_names = {}; +for i = 1:size(fmartist_tags,1) + + % --- + % FIXME: obviously some tags get into the names table but dont get any + % score associated. + % --- + for j = 1:numel(fmartist_tags{i,1}) + if isempty(fmartist_tags{i,1}) || strcmp(fmartist_tags{i,1}{j},'-1'); + continue; + end + + % find tag in new tag array + tagidx = strcellfind(fmartist_annots_names, fmartist_tags{i,1}{j}); + if tagidx ~= -1 + + % --- + % NOTE: the fmartist_annots array saves the tag popularities in + % an INT structure. this has to be converted to double before + % using it in any other circumstances + % --- + + % save tag domination + fmartist_annots(i,tagidx) = double(fmartist_tags{i,2}(j))./100; + else + tagidx = numel(fmartist_annots_names)+1; + + % create new tag field + fmartist_annots_names{tagidx} = fmartist_tags{i,1}{j}; + + % save tag domination + fmartist_annots(i,tagidx) = double(fmartist_tags{i,2}(j))./100; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/makro_prepare_comparison.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +% makro_prepare_comparison + +% --- +% re_index the comparison song ids +% --- +comparison_ids = unique([comparison(:,1); comparison(:,2); comparison(:,3)]); + +for i = 1:3 + for j = 1:numel(comparison_ids) + + comparison(comparison(:,i) == comparison_ids(j),i) = j; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/mb_retrieve_artist_id_from_album_info.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function [artist_mbid,artist_found, out] = mb_retrieve_artist_id_from_album_info(artist,album) + +% http://www.musicbrainz.org/ws/2/recording?query=%22we%20will%20rock%20you%22%20AND%20arid:0383dadf-2a4e-4d10-a46a-e9e041da8eb3 +% call python function +str = python('mbid_from_artist_album.py',artist,album) + +% parse text function output +artists = textscan(str,'%s %s','Delimiter','\n'); +mbids = artists{2}; +artist_found = artists{1}{1}; + +% song info API +% url = sprintf('http://www.musicbrainz.org/ws/2/recording?query=%s',urlencode(sprintf('%s AND artist:%s',album,artist))); +url = sprintf('http://www.musicbrainz.org/ws/2/release/%s?inc=artists',urlencode(mbids)); + +[status] = wget(url,' --no-verbose -U firefox -O tmp.xml'); +out = xml2struct('tmp.xml'); + +isrc = []; +artist_mbid = []; + +try +artist_mbid = out.metadata.release.artist_dash_credit.name_dash_credit.artist.Attributes.id; +catch +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/mb_retrieve_artist_id_from_isrc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function [artist_mbid, out] = mb_retrieve_artist_id_from_isrc(isrc) + +% song info API +url = sprintf('http://www.musicbrainz.org/ws/2/isrc/%s',urlencode(isrc)); +[status] = wget(url,'-t 10 -U firefox -O tmp.xml'); + +try + out = xml2struct('tmp.xml'); + % album name for verification + albumname = out.metadata.isrc.recording_dash_list.recording.title + artist_mbid = out.metadata.isrc.recording_dash_list.recording.Attributes.id; +catch + artist_mbid = []; + out = []; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/mb_retrieve_tags_for_artist_id.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function [tags,count, out] = mb_retrieve_tags_for_artist_id(artistid) + +% song info API +url = sprintf('http://www.musicbrainz.org/ws/2/artist/%s?inc=tags',urlencode(artistid)); +[status] = wget(url,'--tries=10 -U firefox -O tmp.xml'); + +try + out = xml2struct('tmp.xml'); + tags = cell(numel(out.metadata.artist.tag_dash_list.tag),1); + count = zeros(numel(out.metadata.artist.tag_dash_list.tag),1); + for i=1:numel(out.metadata.artist.tag_dash_list.tag) + tags{i} = out.metadata.artist.tag_dash_list.tag{i}.name.Text; + count(i) = str2double(out.metadata.artist.tag_dash_list.tag{i}.Attributes.count); + end +catch + out = []; + tags = {}; + count = []; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/mbid_from_artist_album.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +#!/usr/bin/env python +"""A simple script that searches for a release in the MusicBrainz +database and prints out a few details about the first matching release. + + $ ./releasesearch.py "the beatles" revolver + Revolver, by The Beatles + Released 1966-08-08 (Official) + MusicBrainz ID: b4b04cbf-118a-3944-9545-38a0a88ff1a2 +""" +from __future__ import print_function +from __future__ import unicode_literals +import musicbrainzngs +import sys + +musicbrainzngs.set_useragent( + "python-musicbrainz-ngs-example", + "0.1", + "https://github.com/alastair/python-musicbrainz-ngs/", +) + +def show_release_details(rel): + """Print some details about a release dictionary to stdout. + """ + # "artist-credit-phrase" is a flat string of the credited artists + # joined with " + " or whatever is given by the server. + # You can also work with the "artist-credit" list manually. + print("{}, by {}".format(rel['title'], rel["artist-credit-phrase"])) + print("{}".format(rel['id'])) + + + +def fail(message): + """Print a message to stderr and then exit with an error status. + """ + print(message, file=sys.stderr) + sys.exit(1) + +if __name__ == '__main__': + args = sys.argv[1:] + if len(args) != 2: + fail("usage: {} ARTIST ALBUM".format(sys.argv[0])) + artist, album = args + + # Keyword arguments to the "search_*" functions limit keywords to + # specific fields. The "limit" keyword argument is special (like as + # "offset", not shown here) and specifies the number of results to + # return. + result = musicbrainzngs.search_releases(artist=artist, release=album, + limit=1) + # On success, result is a dictionary with a single key: + # "release-list", which is a list of dictionaries. + if not result['release-list']: + fail("no release found") + for (idx, release) in enumerate(result['release-list']): + # print("match #{}:".format(idx+1)) + show_release_details(release)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/metric_fulfills_comparison.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +function out = metric_fulfills_comparison(dist, comparison, comparison_ids) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/metric_fulfills_ranking.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +function [out, equal, singles, valid] = metric_fulfills_ranking(met, Y, clips) +% out = sim_fulfills_ranking(met, Y, ids) +% +% outputs (0-1) percentage of ranking constraints +% completely fulfilled by the given distance measure + +% --- +% for each valid query, check if the ranking is fulfilled +% we also count eeventswhere the distance is equal +% --- +valid = ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2))); + +equal = 0; +singles = false(size(Y,1),1); +for i = 1:size(Y,1) + + if valid(i) + + if numel(Y{i,1}) == 1 && numel(Y{i,2}) == 1 + % --- + % Efficient singular ranking comparison + % using met.distance(clipa,clipb) + % --- + % NOTE: by using the < here, we loose half of the + % examples which have been same distance and + % randomly positioned in earlier runs :( + % --- + distgood = met.distance(clips(i), clips(Y{i,1})); + distbad = met.distance(clips(i), clips(Y{i,2})); + + singles(i) = distgood < distbad; + if distgood == distbad + equal = equal + 1; + end + else + % --- + % NOTE: Only reactivated for testing of + % outdated similarity data + % --- + + warning 'code only for outdated similarity data\n'; + + % --- + % NOTE: this code is analogous to the above + % --- + singles(i) = 1; + for j=1:numel(Y{i,1}) + for k = 1:numel(Y{i,2}) + + % --- + % All the entries in Y(i,1) have to come before + % the ones in Y(i,2) + % --- + distgood = met.distance(clips(i), clips(Y{i,1}(j))); + distbad = met.distance(clips(i), clips(Y{i,2}(k))); + singles(i) = singles(i) && (distgood < distbad); + + % count equalities + if distgood == distbad + equal = equal + 1; + end + if ~singles(i) + break; + end + end + end + + + end + else + end +end + + +out(1) = mean(singles(valid)); +if size(Y,2) == 3 + % --- + % weighted sum of singles: count satisfied votes + % --- + weights = cell2mat( Y( valid, 3)); + out(2) = sum(weights(singles(valid))) / sum(weights); +else + out(2) = -1; +end +out = out'; + +% markup invalid rankings +% singles(~valid) = -1; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/mlr_unittest.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function mlr_unittest(X, Yrel) + +% Loss values to test + LOSS = {'AUC', 'Prec@k', 'MAP', 'MRR', 'NDCG'}; + + + % Regularization values to test + REG = [0,1,2,3]; + + % Batch sizes to test + BATCH = [0 1 5]; + + % Diagonal settings + DIAG = [0 1]; + + figure(1); + for l = 1:length(LOSS) + display(['Testing ', LOSS{l}]); + for r = 1:length(REG) + display(sprintf('\tREG=%d', REG(r))); + for b = 1:length(BATCH) + display(sprintf('\tB=%d', BATCH(b))); + for d = 1:length(DIAG) + display(sprintf('\tDiagonal=%d', DIAG(d))); + [W, Xi, D] = mlr_train(X, Yrel, 10e5, LOSS{l}, REG(r), DIAG(d), BATCH(b)); + imagesc(W); drawnow; +% [W, Xi, D] = mlr_train(X, Yclass, 10e5, LOSS{l}, REG(r), DIAG(d), BATCH(b)); +% imagesc(W); drawnow; + end + end + end + end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/play_comparison.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function out = generic_play_comparison(index, compari, compari_ids, cliptype); +% out = play_comparison(index, comparison, comparison_ids, cliptype); + +if nargin < 2 + global comparison; + compari = comparison; + + global comparison_ids; + compari_ids = comparison_ids; +end + +if nargin < 4 + cliptype = 'MTTClip'; +end + +for i = 1:length(index) + + fprintf('\n \n-- comparison nr.%d ---\n', index(i)); + fprintf(' result: %d - %d - %d', compari(index(i),4:6)); + + tmpclips = feval(cliptype,compari_ids(compari(index(i),1:3))); + tmpclips.skip(); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ranking_from_comparison_ClipSimGraphMulti.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,98 @@ +function [r, valididx, clip_ids] = ranking_from_comparison_ClipSimGraphMulti(comparison, comparison_ids) +% returns rankings for compared songs. +% rankings are genrerated using a Multigraph analysis of the comparison +% data +% +% r(idx,1) = clip being similar to clip idx +% r(idx,2) = clip being less similar to clip idx +% r(idx,3) = weight of this ranking +% +% the indices in clipids correspond to the indices in comparison_ids + +% create similarity graph +% G = ClipSimGraphMulti(comparison, comparison_ids); +% G.remove_cycles_length2; +% save('comp_SimGraphMulti.mat', 'G'); +cprint(2, 'loading Multigraph for Similarity Constraints') +load('comp_SimGraphMulti.mat', 'G'); + + + +% inverse comparison index +inv_comparison = sparse(comparison_ids,1, 1:numel(comparison_ids)); + +% --- +% derive similarity information: this +% returns the weights of edges meaning sim(a,b) > sim(a,c) +% --- +[weights, a, b, c] = G.similarities(); +% --- +% NOTE: it is yet unclear which index +% (the comparison or the mtt clip index) +% is best to use +% +% The lines below transform the MTT ids from the graph into comparison ids +% --- +a = inv_comparison(a); +b = inv_comparison(b); +c = inv_comparison(c); + +% --- +% reformat into ranking: the first 1019 clips are ordered +% as in comparison_ids. then, clips mentioned more than once +% are attached. +% --- +[a, idx] = sort(a); +b = b(idx); +c = c(idx); + + +clip_ids = zeros(numel(a), 1); + +% --- +% NOTE: r may grow over its allocated size +% --- +r = cell(numel(a),1 ); + +% keep track of constraints which have been attached +visited = zeros(1, numel(a)); +for i = 1:numel(comparison_ids) + + % --- + % get first occurence of this index, + % and save the data into ranking + % --- + tmp_idx = find(a == i, 1, 'first'); + + clip_ids(i) = i; % == a(tmp_idx) + + if ~isempty(tmp_idx) + visited(tmp_idx) = true; + + r{i,1} = b(tmp_idx); + r{i,2} = c(tmp_idx); + r{i,3} = weights(tmp_idx); + + valididx(i) = true; + else + % invalid ranking + valididx(i) = false; + r{i,3} = 0; + end +end + +% --- +% Now we attach the remaining constraints +% --- +remaining = find(~visited); +clip_ids( numel(comparison_ids)+1 : numel(comparison_ids)+numel(remaining)) = a(remaining); + +for i = 1:numel(remaining) + r{ numel(comparison_ids)+i, 1} = b(remaining(i)); + r{ numel(comparison_ids)+i , 2} = c(remaining(i)); + r{ numel(comparison_ids)+i , 3} = weights(remaining(i)); +end + +valididx(numel(comparison_ids)+1 : ... + numel(comparison_ids)+numel(remaining)) = true; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ranking_from_comparison_Stober_all_constraints.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,98 @@ +function [r, valididx, clip_ids] = ranking_from_comparison_Stober_all_constraints(comparison, comparison_ids) +% returns rankings for compared songs. +% rankings are genrerated using a Multigraph analysis of the comparison +% data +% +% r(idx,1) = clip being similar to clip idx +% r(idx,2) = clip being less similar to clip idx +% r(idx,3) = weight of this ranking +% +% the indices in clipids correspond to the indices in comparison_ids + +% create similarity graph + +cprint(2, 'Generating Stober Multigraph for Similarity Constraints') +% Gstob = ClipSimGraphStober(); +% Gstob.random_all_constraints_graph; +% G = ClipSimGraphMulti(Gstob.to_DiGraph()); +load('comp_SimGraphStob_ac1.mat', 'G'); + + +% inverse comparison index +inv_comparison = sparse(comparison_ids,1, 1:numel(comparison_ids)); + +% --- +% derive similarity information: this +% returns the weights of edges meaning sim(a,b) > sim(a,c) +% --- +[weights, a, b, c] = G.similarities(); +% --- +% NOTE: it is yet unclear which index +% (the comparison or the mtt clip index) +% is best to use +% +% The lines below transform the MTT ids from the graph into comparison ids +% --- +a = inv_comparison(a); +b = inv_comparison(b); +c = inv_comparison(c); + +% --- +% reformat into ranking: the first 1019 clips are ordered +% as in comparison_ids. then, clips mentioned more than once +% are attached. +% --- +[a, idx] = sort(a); +b = b(idx); +c = c(idx); + + +clip_ids = zeros(numel(a), 1); + +% --- +% NOTE: r may grow over its allocated size +% --- +r = cell(numel(a),1 ); + +% keep track of constraints which have been attached +visited = zeros(1, numel(a)); +for i = 1:numel(comparison_ids) + + % --- + % get first occurence of this index, + % and save the data into ranking + % --- + tmp_idx = find(a == i, 1, 'first'); + + clip_ids(i) = i; % == a(tmp_idx) + + if ~isempty(tmp_idx) + visited(tmp_idx) = true; + + r{i,1} = b(tmp_idx); + r{i,2} = c(tmp_idx); + r{i,3} = weights(tmp_idx); + + valididx(i) = true; + else + % invalid ranking + valididx(i) = false; + r{i,3} = 0; + end +end + +% --- +% Now we attach the remaining constraints +% --- +remaining = find(~visited); +clip_ids( numel(comparison_ids)+1 : numel(comparison_ids)+numel(remaining)) = a(remaining); + +for i = 1:numel(remaining) + r{ numel(comparison_ids)+i, 1} = b(remaining(i)); + r{ numel(comparison_ids)+i , 2} = c(remaining(i)); + r{ numel(comparison_ids)+i , 3} = weights(remaining(i)); +end + +valididx(numel(comparison_ids)+1 : ... + numel(comparison_ids)+numel(remaining)) = true; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ranking_from_comparison_Stober_diffs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function [r, valididx, clip_ids, P] = ranking_from_comparison_Stober_diffs(nDatasets, nRuns) + +global globalvars; +global comparison; +global comparison_ids; + +% --- +% get the standard test set +% NOTE: we will keep the r and valididx, and just use +% the rest of the data for determining the issing edges etc +% --- +load('comp_SimGraphMulti_SimDataDump', 'G', 'r', 'valididx', 'clip_ids'); +nData = sum(valididx > 0); + +% --- +% initialise the new partition +% --- +P = cvpartition_alltrain(nData, nDatasets * nRuns); + + +for i = 1:nDatasets + + % --- + % create new Stober graph + % --- + Gstob = ClipSimGraphStober(); + Gstob.random_all_constraints_graph; + Gdiff = G - ClipSimGraphMulti(Gstob.to_DiGraph()); + + % --- + % get missing entries in stober similarity + % --- + idx = find_graph_in_valid(r, comparison_ids(clip_ids),valididx, Gdiff); + lostData = sum(idx); + if lostData ~= Gdiff.num_edges + error ('Inconsistent Data: edges in gdiff could not be found'); + end + + % --- + % save entries in DataPartition (remove from valid sets) + % NOTE: this is manhual intervention into Class territory :( + % --- + for j = 1:nRuns + jd = j + (i-1) * nRuns; + P.mtraining{jd} = ones(P.N, 1) - idx(valididx)'; + P.mtest{jd} = ones(P.N, 1) - idx(valididx)'; + P.TrainSize(jd) = P.N - lostData; + P.TestSize(jd) = P.N - lostData; + end +end +end + +% --- +% This finds all graphs edges in the ranking +% --- +function idx = find_graph_in_valid(r, clip_ids, valididx, G) + + idx = zeros(1,size(r,1)); + for i = find(valididx) + + a = clip_ids(i); + b = clip_ids(r{i,1}); + c = clip_ids(r{i,2}); + if G.edge(a,b,c) > 0 + idx(i) = 1; + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/ranking_from_comparison_trivial.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +function [r, valididx, clip_ids] = ranking_from_comparison_trivial(comparison, comparison_ids) +% returns a ranking for each of the compared songs +% +% r(idx,1) = all indexes which are similar to clip idx +% r(idx,2) = all indexes which are dissimilar to clip idx +% +% if onlyvalid is set to true, only the transitive hull of +% clips having sim and dissim values will be output + +% --- +% in comparison, the outlying piece is highlighted. +% thus, we naively consider that +% a. both of the remaining pieces are more similar to each other. +% b. the outlier is dissimilar to both of the other pieces +% --- + +[outsort, outidx] = sort(comparison(:,4:6),2,'ascend'); + +r = cell(numel(comparison_ids), 2); +s = zeros(numel(comparison_ids), 2); % score for rankings +for i = 1:size(comparison, 1) + + % get the three relevant comparison ids + + pos = comparison(i, outidx(i,1:3)); + vals = outsort(i,:); + + % --- + % each of the three clips will be evaluated based on its position + % the last clip is the potential outlyier + % --- + + % --- + % usual case: two small values and a last outlie + % --- + if vals(2) ~= vals(3) + + + % --- + % first (most common) clip: + % the second clip is more similar than the third one + % --- + r{pos(1),1} = cat(1, r{pos(1),1}, pos(2)); % similar + r{pos(1),2} = cat(1, r{pos(1),2}, pos(3)); % dissimilar +% else +% +% % ok, both seem more dissimilar to clip 1 +% r{pos(1),2} = cat(1, r{pos(1),1}, pos(2)); % both dissimilar +% r{pos(1),2} = cat (1, r{pos(1),2}, pos(3)); % dissimilar + end + + if vals(3) ~= vals(1) + % --- + % the second clip is more similar to the first than + % the third + % --- + r{pos(2),1} = cat(1, r{pos(2),1}, pos(1)); + r{pos(2),2} = cat(1, r{pos(2),2}, pos(3)); + + % --- + % the third clip is not similar to any of the former + % NOTE: we avoid this information as it possibly leads + % to some contradictionary statements + % --- + r{pos(3),2} = cat(1, r{pos(3),2}, pos(1)); + r{pos(3),2} = cat(1, r{pos(3),2}, pos(2)); + end + + +end + +% --- +% we cleanup r and +% run basic checks on emptiness and paradox assumptions +% --- +valididx = zeros(size(r,1), 1); +for i = 1:size(r, 1) + + % make unique + r{i,1} = unique(r{i,1}); + r{i,2} = unique(r{i,2}); + + % check + valididx(i) = ~isempty(r{i,1}) && ~isempty(r{i,2}) && ... + isempty(intersect(r{i,1}, r{i,2})); +end + +valididx = logical(valididx); +clip_ids = 1:numel(comparison_ids); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/sim_from_comparison_UNfair_components.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,91 @@ +function [partBinTrn, partBinTst, partBinNoTrn] = sim_from_comparison_UNfair_components(comparison, comparison_ids, k, trainpart, filename) +% +% FOR ISMIR 2012 +% +% creates a cross-validation partitioning of the +% similarity data in "multiG", NOT PRESERVING the +% connected components in it during partitioning + +% --- +% get the similarity multigraph and remove cycles +% --- +cprint(2, 'creating graph') +% Gm = ClipSimGraphMulti(comparison, comparison_ids); +% Gm.remove_cycles_length2; +cprint(2, 'loading Multigraph for Similarity Constraints') +load('comp_SimGraphMulti.mat', 'G'); + + +% get similarity data +[weights, a, b, c] = G.similarities(); + +% --- +% We randomise the constraint succession +% --- +datPermu = randperm(numel(a)); +a = a(datPermu); +b = b(datPermu); +c = c(datPermu); +weights = weights(datPermu); + +% --- +% NOTE: we try the easy route: partition the graphs +% and look at which constraints balance we end up with +% --- +P = cvpartition(numel(a), 'k', k); + +% --- +% here we export similarity test sets +% --- +cprint(2, 'export test similarity') +partBinTst = {}; +for i = 1:P.NumTestSets % test runs + + partBinTst{i} = [a(P.test(i))' b(P.test(i))' c(P.test(i))' weights(P.test(i))]; +end + + +% --- +% Note: This uses a "truly" increasing training set +% to do the partial training partition +% --- +cprint(2, 'export train similarity') +for m = 1:numel(trainpart) + + % save test indices + Ptrain(m) = cvpartition_trunctrain_incsubsets(P, trainpart(m)); +end + +% --- +% here we export similarity training sets +% --- +partBinTrn = {}; +for i = 1:P.NumTestSets % train runs + + for m = 1:numel(trainpart) % increasing training sets + + % get training indices + idxB = Ptrain(m).training(i); + + % save into cell + partBinTrn{i,m} = [a(idxB)' b(idxB)' c(idxB)' weights(idxB)]; + end +end + +partBinNoTrn = {}; +for i = 1:P.NumTestSets % train runs + + for m = 1:numel(trainpart) % increasing training sets + + % get training indices + idxB = ~Ptrain(m).training(i) & ~Ptrain(m).test(i); + + % save into cell + partBinNoTrn{i,m} = [a(idxB)' b(idxB)' c(idxB)' weights(idxB)]; + end +end + +if nargin == 5 + save(filename, 'partBinTrn', 'partBinTst', 'partBinNoTrn') +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/sim_from_comparison_attach_weights.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function sim_from_comparison_attach_weights(filen) + +global comparison; +global comparison_ids; +% load data from mat file +load(filen); + +% --- +% get the similarity multigraph and remove cycles +% --- +cprint(2, 'creating graph') +Gm = ClipSimGraphMulti(comparison, comparison_ids); +Gm.remove_cycles_length2; + +nTestSets = size(partBinTst, 2); % num cv bins +ntrainsizes = size(partBinTrn, 2); % num increases of training + +% --- +% test sets +% --- +cprint(2, 'doing test sets') + +% for all validations +for k = 1:size(partBinTst, 2); + + % for each constraint tiplet + for i = 1:size(partBinTst{k},1) + + % get weight for this edge from graph + [weight, ~, ~] = Gm.edge(partBinTst{k}(i,1), partBinTst{k}(i,2),... + partBinTst{k}(i,3)); + + if isempty(weight) || weight == 0 + error 'inconsistent similarity data'; + end + + % save into same data structure + partBinTst{k}(i,4) = weight; + end +end + +% --- +% train sets +% --- + +cprint(2, 'doing train sets') +% for all validations +for k = 1:size(partBinTrn, 1); + + % for all training sizes + for m = 1:size(partBinTrn, 2); + + % for each constraint tiplet + for i = 1:size(partBinTrn{k,m},1) + + % get weight for this edge from graph + [weight, ~, ~] = Gm.edge(partBinTrn{k,m}(i,1), partBinTrn{k,m}(i,2),... + partBinTrn{k,m}(i,3)); + + if isempty(weight) || weight == 0 + error 'inconsistent similarity data'; + end + + % save into same data structure + partBinTrn{k,m}(i,4) = weight; + end + end +end + +% --- +% Notrain sets +% --- +cprint(2, 'doing Notrain sets') +% for all validations +for k = 1:size(partBinNoTrn, 1); + + % for all training sizes + for m = 1:size(partBinNoTrn, 2); + + % for each constraint tiplet + for i = 1:size(partBinNoTrn{k,m},1) + + % get weight for this edge from graph + [weight, ~, ~] = Gm.edge(partBinNoTrn{k,m}(i,1), partBinNoTrn{k,m}(i,2),... + partBinNoTrn{k,m}(i,3)); + + if isempty(weight) || weight == 0 + error 'inconsistent similarity data'; + end + % save into same data structure + partBinNoTrn{k,m}(i,4) = weight; + end + end +end + +% save results to mat file +save(sprintf('%s_weighted.mat',filen), 'partBinTst', 'partBinTrn', 'partBinNoTrn');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/sim_from_comparison_fair_components.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,156 @@ +function [partBinTrn, partBinTst, partBinNoTrn] = sim_from_comparison_fair_components(comparison, comparison_ids, k, trainpart, filename) +% +% [partBinTrn, partBinTst, partBinNoTrn] = +% sim_from_comparison_fair_components(comparison, comparison_ids, k, trainpart, [filename]) + +% creates a cross-validation partitioning of the +% similarity data in "multiG", PRESERVING the +% connected components in it during partitioning + +% --- +% get the similarity multigraph and remove cycles +% --- +cprint(2, 'creating graph') +% Gm = ClipSimGraphMulti(comparison, comparison_ids); +% Gm.remove_cycles_length2; +cprint(2, 'loading Multigraph for Similarity Constraints') +load('comp_SimGraphMulti.mat', 'G'); + +% --- +% Note: we get the connected components in the graph +% and filter out those who have only one node +% --- +cprint(2, 'extracting connected components') +[Gs, s, id] = connected_components(G); + +valid = find(s > 1); +Gsv = Gs(valid); + +% --- +% We randomise the graph triplet order, +% as well as the in-component +% constraint succession be randomised here. +% --- +datPermu = randperm(numel(Gsv)); +Gsv = Gsv(datPermu); + +conPermu = zeros(numel(Gsv),3); +for i = 1:numel(Gsv) + conPermu(i,:) = randperm(3); +end + +% --- +% NOTE: we try the easy route: partition the graphs +% and look at which constraints balance we end up with +% --- +P = cvpartition(numel(Gsv), 'k', k); + +% --- +% here we export the graphs similarity test sets +% --- +cprint(2, 'export test similarity') +partBinTst = {}; +for i = 1:P.NumTestSets % test runs + partBinTst{i} = zeros(0, 3); + + tmp_idx = find(P.test(i)); + for j = 1:numel(tmp_idx); % componens + + % --- + % get the graphs which are associated + % to this set and save them into a new bin. + % --- + [weights, a, b, c] = Gsv(tmp_idx(j)).similarities(); + partBinTst{i} = [partBinTst{i}; [a' b' c' weights]]; + end +end + + +% --- +% Note: This uses a "truly" increasing training set +% to do the partial training partition +% --- +cprint(2, 'export train similarity') +for m = 1:numel(trainpart) + + Ptrain(m) = cvpartition_trunctrain_incsubsets(P, trainpart(m)); +end + +% --- +% here we export the graph's similarity training sets +% --- +partBinTrn = {}; +for i = 1:P.NumTestSets % train runs + + for m = 1:numel(trainpart) % increasing training sets + partBinTrn{i,m} = zeros(0, 3); + + tmp_idx = find(Ptrain(m).training(i)); + for j = 1:numel(tmp_idx); % components + + % --- + % get the graphs which are associated + % to this set and save them into a new bin. + % --- + [weights, a, b, c] = Gsv(tmp_idx(j)).similarities(); + + % --- + % NOTE: WE apply the inner-triplet permutation, + % and truncate it where necessary + % --- + tmp_permu = conPermu(tmp_idx(j),:); + if numel(a) < 3 + tmp_permu = tmp_permu(tmp_permu <= numel(a)); + end + + a = a(tmp_permu); + b = b(tmp_permu); + c = c(tmp_permu); + weights = weights(tmp_permu); + + % save the clips + partBinTrn{i,m} = [partBinTrn{i,m}; [a' b' c' weights]]; + end + end +end + +partBinNoTrn = {}; +for i = 1:P.NumTestSets % train runs + + for m = 1:numel(trainpart) % increasing training sets + partBinNoTrn{i,m} = zeros(0, 3); + + tmp_idx = find(~Ptrain(m).training(i) & ~Ptrain(m).test(i)); + for j = 1:numel(tmp_idx); % components + + % --- + % get the graphs which are associated + % to this set and save them into a new bin. + % --- + [weights, a, b, c] = Gsv(tmp_idx(j)).similarities(); + + % --- + % NOTE: WE apply the inner-triplet permutation, + % and truncate it where necessary + % --- + tmp_permu = conPermu(tmp_idx(j),:); + if numel(a) < 3 + tmp_permu = tmp_permu(tmp_permu <= numel(a)); + end + + a = a(tmp_permu); + b = b(tmp_permu); + c = c(tmp_permu); + weights = weights(tmp_permu); + + % save the clips + partBinNoTrn{i,m} = [partBinNoTrn{i,m}; [a' b' c' weights]]; + end + end +end + +if nargin == 5 + save(filename, 'partBinTrn', 'partBinTst', 'partBinNoTrn') +end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/sim_from_comparison_naive.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +function [sim, dissim, confidence] = sim_from_comparison_naive(comparison, comparison_ids, symmetrical) +% +% [sim, dissim, confidence] = sim_from_comparison_naive(comparison) +% +% derives symmetric, absolute similarity measurements +% from relative magnatagatune comparisons +% naive implementation for first tests of the ITML algorithm +% + +% reindex comparison for more simple evaluation +% makro_prepare_comparison + +% --- +% analyse the number of comparisons for each pair of songs +% --- +[num_compares] = get_comparison_stats(comparison, comparison_ids); + +% --- +% in comparison, the outlying piece is highlighted. +% thus, we naively consider that +% a. both of the remaining pieces are more similar to each other. +% b. the outlier is dissimilar to both of the other pieces +% --- +[outsort, outidx] = sort(comparison(:,4:6),2,'ascend'); + +% --- +% similarity of the two non-outliers a, b +% they are similar if both of them have scores way smaller +% than the outlier c: +% score (a,b) = 1 - (max(a,b)/c) +% +% dissimilarity: clip b is considered more different to clip c than +% a, as clip a seems to share some properties with both songs +% dissim(b,c) = 0.5 + b/(2c) +% --- + +sim = sparse(numel(comparison_ids),numel(comparison_ids)); +dissim = sparse(numel(comparison_ids),numel(comparison_ids)); +for i = 1:size(comparison,1) + + % get the outlier votes + simpair = comparison(i,outidx(i,1:2)); + c = comparison(i,outidx(i,3)); + + % we want a triangular similarity matrix + [simpair, simidx] = sort(simpair); + outsort(i,1:2) = outsort(i,simidx); + + % --- + % save the distance between the second biggest vote and the max vote. + % NOTE: we bias the vote by dividing through the number of total + % comparisons for the particular pair of clips + % --- + sim(simpair(1), simpair(2)) = sim(simpair(1), simpair(2)) + ... + (1 - outsort(i,2) / outsort(i,3)) * (1 / num_compares(simpair(1),simpair(2))); + + dissim(simpair(1:2), c) = 0.5 + (outsort(i,1:2) ./ (2 * outsort(i,3))); +end + +% --- +% mirror to make matrix symmetrical +% --- +if nargin == 3 && symmetrical + sim = sim + sim'; + dissim = dissim + dissim'; +end + +% --- +% TODO: use number of votes and std or similar to +% rate the confidence for each similarity mesurement +% --- +confidence = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tag_stats.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function out = tag_stats(annots, names, max_plot) +% out = tag_stats(annots, names, max_plot) +% +% calculates statistics for tag distributions +% and plots them + +if nargin < 3 + max_plot = 25; +end + +% get overall frequency and sort accordingly +allapp = sum(annots > 0, 1); +% allapp = allapp/max(allapp); + +[null, idx] = sort(allapp,'descend'); + +% --- +% visualize only the fist top 200 +% --- +max_plot = min(numel(names), max_plot); + +figure; +bar(1:max_plot,allapp(idx(1:max_plot))) +set(gca,'XTick',1:max_plot); +set(gca,'XTickLabel',names(idx(1:max_plot))); +axis([1 max_plot 0 max(allapp)]) + +legend('#appearances'); +title 'tag statistics sorted by frequency of appearances' + +out = []; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/do_test_rounds.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,255 @@ +function [out]= do_test_rounds(trainfun, X, simdata, trainparams, fparams,... + paramhash, paramhash_train, clips) + +% --- +% DEBUG: we mix up the training set +% --- +% simdata = mixup(simdata); + +if isfield(simdata, 'clip_type'); + clip_type = simdata.clip_type; +else + clip_type = 'MTTClip'; +end + +nTestSets = size(simdata.partBinTst, 2); % num cv bins +ntrainsizes = size(simdata.partBinTrn, 2); % num increases of training + +for m = 1:ntrainsizes + + ok_train = zeros(2, nTestSets); + ok_test = zeros(2, nTestSets); + equal_test = zeros(1, nTestSets); + ok_notin_train = zeros(2, nTestSets); + +% A = cell(nTestSets,1); +% dout = cell(nTestSets,1); +% clips_train = cell(nTestSets,1); +% clips_test = cell(nTestSets,1); +% clips_notin_train = cell(nTestSets,1); +% Y_notin_train = cell(nTestSets,1); +% Ytrain = cell(nTestSets,1); +% Ytest = cell(nTestSets,1); + % parfor + for k = 1:nTestSets + + + % runlog mlr + try + + % --- + % Get the training constraints and features for this round + % --- + % DEBUG: the similarity data in Ytrain and Ytest seems correct. + [clips_train{k}, Xtrain, Ytrain{k}] ... + = get_data_compact(clips, X, simdata.partBinTrn{k,m}); + Ytest{k}={}; + + % training step + [A{k}, dout{k}] = feval(trainfun, Xtrain, Ytrain{k}, trainparams); + + % --- + % test step + % TODO: the distmeasure object could be created by the wrapper! + % --- + if isfield(dout{k},'interpreter'); + interpreter = str2func(dout{k}.interpreter); + else + % only for backward compability + % warning ('legacy implementation of dist measure'); + if isnumeric(A{k}) + % mahalanobis case + + % special delta mahalanobis + interpreter = str2func('DistMeasureMahal'); + else + % neural network case: A{k} is a neural net object + interpreter = str2func('DistMeasureGeneric'); + end + end + + if isfield(trainparams,'deltafun') + % special delta + diss = feval(interpreter,clips, A{k}, X, str2func(trainparams.deltafun), trainparams.deltafun_params); + else + % standard + % --- + % TODO: the default delta is different between + % similarity measures. except for the mahalmeasure + % this should be specified + % --- + diss = feval(interpreter, clips, A{k}, X); + end + + % test training data + [ok_train(:,k)] = metric_fulfills_ranking... + (diss, Ytrain{k}, feval(clip_type,clips_train{k})); + + % get test data + [clips_test{k}, Xtest, Ytest{k}] ... + = get_data_compact(clips, X, simdata.partBinTst{k}); + + % diss = DistMeasureMahal(feval(clip_type,clips_test{k}), A{k}, Xtest); + % test test data + [ok_test(:,k), equal_test(k)] = metric_fulfills_ranking... + (diss, Ytest{k}, feval(clip_type,clips_test{k})); + cprint(3,'%2.2f %2.2f fold performance', ok_test(:,k)); + + % --- + % extra diag for MLR + % TODO: make this wrappeable + % --- + if isequal(trainfun, @mlr_wrapper) + dout{k}.mprperf = mlr_test(A{k}, 0, Xtrain, Ytrain{k}(:,1:2), Xtest, Ytest{k}(:,1:2)) ; + end + + % --- + % this gives data for the unused training set remainders + % --- + if isfield(simdata,'partBinNoTrn') + if ~isempty(simdata.partBinNoTrn{k,m}) + [clips_notin_train{k}, X_notin_train, Y_notin_train{k}] ... + = get_data_compact(clips, X, simdata.partBinNoTrn{k,m}); + + % test unused training data + [ok_notin_train(:,k), equal_test(k)] = metric_fulfills_ranking... + (diss, Y_notin_train{k}, feval(clip_type,clips_notin_train{k})); + + % what to do if there is no data ? + else + ok_notin_train(:,k) = -1; + end + else + ok_notin_train(:,k) = -1; + end + + catch err + + % --- + % in case training or test fails + % --- + print_error(err); + + A{k} = []; + dout{k} = -1; + + ok_test(:,k) = -1; + ok_train(:,k) = -1; + ok_notin_train(:,k) = -1; + equal_test(k) = -1; + + % --- + % save feature, system and data configuration + % and indicate failure + % --- + xml_save(sprintf('runlog_%s.%s_trainparam.xml',... + paramhash, paramhash_train), trainparams); + xml_save(sprintf('runlog_%s.%s_err.xml',... + paramhash, paramhash_train), print_error(err)); + end + end + + if ~(ntrainsizes == 1) + + % save elaborate testing data + size_sum = 0; + for i = 1:nTestSets + size_sum = size_sum + size(simdata.partBinTrn{i,m}) / size(simdata.partBinTrn{i,end}); + end + size_sum = size_sum / nTestSets; + + out.inctrain.trainfrac(:, m) = size_sum; + out.inctrain.dataPartition(:, m) = 0; + + % --- + % NOTE: the max value is important for debugging, + % especially when the maximal training success is reached + % in the middle of the data set + % --- +% out.inctrain.max_ok_test(:, m) = max(ok_test, 2); + out.inctrain.mean_ok_test(:, m) = mean(ok_test(:, ok_test(1,:) >=0), 2); + out.inctrain.var_ok_test(:, m) = var(ok_test(:, ok_test(1,:) >=0), 0, 2); + out.inctrain.equal_test(m) = median(equal_test); + + out.inctrain.mean_ok_train(:, m) = mean(ok_train(:, ok_train(1,:) >=0), 2); + out.inctrain.var_ok_train(:, m) = var(ok_train(:, ok_train(1,:) >=0), 0, 2); + + % --- + % TODO: DEBUG: this does not work correctly + % maybe thats also true for the above? + % --- + out.inctrain.mean_ok_notin_train(:, m) = mean(ok_notin_train(:, ok_notin_train(1,:) >=0), 2); + out.inctrain.var_ok_notin_train(:, m) = var(ok_notin_train(:, ok_notin_train(1,:) >=0), 0, 2); + + diag.inctrain(m).ok_train = ok_train; + diag.inctrain(m).ok_test = ok_test; + diag.inctrain(m).ok_notin_train = ok_notin_train; + diag.inctrain(m).equal_test = equal_test; + end + + % --- + % save traditional information for full training set + % --- + if size(simdata.partBinTrn{1,m}) == size(simdata.partBinTrn{1,end}); + +% out.max_ok_test = max(ok_test, 2); + out.mean_ok_test = mean(ok_test(:, ok_test(1,:) >=0), 2); + out.var_ok_test = var(ok_test(:, ok_test(1,:) >=0), 0, 2); + out.equal_test = median(equal_test); + + out.mean_ok_train = mean(ok_train(:, ok_train(1,:) >=0), 2); + out.var_ok_train = var(ok_train(:, ok_train(1,:) >=0), 0, 2); + + % --- + % TODO: DEBUG: this does not work correctly + % --- + out.mean_ok_notin_train = mean(ok_notin_train(:, ok_notin_train(1,:) >=0), 2); + out.var_ok_notin_train = var(ok_notin_train(:, ok_notin_train(1,:) >=0), 0, 2); + + % --- + % get winning measure + % we use the weighted winning measure if possible + % --- + if max(ok_test(2,:)) > 0 + [~, best] = max(ok_test(2,:)); + else + [~, best] = max(ok_test(1,:)); + end + + diag.A = A; + diag.diag = dout; + + diag.ok_test = ok_test; + diag.equal_test = equal_test; + diag.ok_train = ok_train; + diag.ok_notin_train = ok_notin_train; + + % save some metric matrices + out.best_A = A{best}; + out.best_diag = dout{best}; + out.best_idx = best; + + end +end + +% save parameters +out.camirrev = camirversion(); +out.fparams = fparams; +out.trainfun = trainfun; +out.trainparams = trainparams; +out.clip_ids = clips.id(); +out.dataPartition = []; +out.Y = size(simdata); +% --- +% NOTE: this takes A LOT OF DISC SPACE +% --- +% out.Ytrain = Ytrain{end}; +% out.Ytest = Ytest{end}; + +% --- +% save the diagostics data to disk +% --- +save(sprintf('runlog_%s.%s_results.mat',... + paramhash, paramhash_train),... + 'out', 'diag'); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/euclidean_wrapper.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function [A, diag] = euclidean_wrapper(X, Y, trainparams) +% dummy wrapper to plug the euclidean distance measure into the +% testing set + +% --- +% call main training function +% --- +A = eye(size(X,1)); + +diag.trainfun = 'euclidean'; +diag.interpreter = 'DistMeasureMahal'; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/get_data_compact.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,96 @@ +function [clip_dict, X, Y, valididx] = get_data_compact(clips, featureVec, simdata) + % --- + % instead of creating a new three feature vectors + % for each constraint, this function links back to the + % FIRST occurrence of a distinct clip and its features + % + % TODO: try to always link ALL occurences of a feature + % / clip in the rankings + % --- + + global comparison_ids; + global comparison_invids; + + % --- + % build the clip dictionary + % --- + uniclip = unique(reshape( simdata, 1, [])); + last_clip_pos = zeros(1, numel(uniclip)); + + clip_dict = []; + Y = {}; + idx_newclip = 1; + valididx = logical([]); + + % cycle all constraints of one cv bin + for j = 1:size(simdata,1) + + + % save the first clip + constraint = simdata(j,:); + + % --- + % check where the clip is and if there + % is space to write cosntraint data + % + % If not, a position is created + a = find_clip(constraint(1), 1); + b = find_clip(constraint(2), 0); + c = find_clip(constraint(3), 0); + + % [a,b,c] -> clip a more similar to clip b than to clip c + + % adress weightings + if numel(constraint) == 4 + Y(a,:) = {b, c, (constraint(4))}; + else + Y(a,:) = {b, c}; + end + end + + % --- + % get feature data + % NOTE: this uses an index of the comparison clips + % --- + X = featureVec(:,(comparison_invids(clip_dict))); + + for i = find(~valididx) + if size(Y,2) == 3 + Y(i,:) = {[], [], 0}; + else + Y(i,:) = {[], []}; + end + end + + function clip_idx = find_clip(clip, write) + uc_idx = find(uniclip == clip); + + % --- + % do we have a place of this clip? + % + % NOTE: last_clip_pos has to be reset after every write + % --- + if (last_clip_pos(uc_idx) == 0) || ... + write && (numel(valididx) >= last_clip_pos(uc_idx)) &&... + (valididx(last_clip_pos(uc_idx)) == 1) + + % if not, create one + clip_idx = idx_newclip; + clip_dict(clip_idx) = clip; + valididx(clip_idx) = false; + + % and save into last pos + last_clip_pos(uc_idx) = clip_idx; + + idx_newclip = idx_newclip + 1; + else + clip_idx = last_clip_pos(uc_idx); + end + + if write + valididx(clip_idx) = true; + end + + end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/mahalmat_wrapper.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function [A, diag] = mahalmat_wrapper(X, Y, trainparams) +% dummy wrapper to plug any fixed mahalanobis matrix into the +% testing set + +% use deltafun to check for correct similarity +if isfield(trainparams,'deltafun') + X = feval(trainparams.deltafun, X(:,1), X(:,2), trainparams.deltafun_params{:}); + diag.deltafun = trainparams.deltafun; +end + +A = trainparams.mahalA; +if size(A,1) ~= size(X,1) + error('wrong mahal matrix size'); +end + +diag.trainfun = 'mahalmat'; +% --- +% intorducing model tyopology: +% mahal, nnet, gbrtree +% --- +diag.interpreter = 'DistMeasureMahal'; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/random_diag_wrapper.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function [A, dout] = random_diag_wrapper(X, Y, trainparams) +% dummy wrapper to plug a random distance measure into the +% testing process + +% --- +% call main training function +% --- +A = diag(trainparams.std *(rand(1, size(X,1))-0.5) + trainparams.mean); + +dout.trainfun = 'random_diag'; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/Exp_SVMLight.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,131 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Experiment code templat % +% Project: sub-euclidean distance for music similarity % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Load features +feature_file = 'rel_music_raw_features.mat'; +vars = whos('-file', feature_file); +A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); +raw_features = A.(vars(1).name); +indices = A.(vars(2).name); +tst_inx = A.(vars(3).name); +trn_inx = A.(vars(4).name); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Define directory to save parameters & results +% dir = '/home/funzi/Documents/'; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +dmr = [0 5 10 20 30 50]; % dimension reduction by PCA +ws = [0 5 10 20 30 50 70]; % window size +% parameters of rbm (if it is used for extraction) +hidNum = [30 50 100 500]; +lr_1 = [0.01 0.05 0.1 0.5]; +lr_2 = [0.05 0.1 0.5 0.7]; +mmt = [0.02 0.05 0.1 0.5]; +cost = [0.00002 0.01 0.1]; + +%% Select parameters (if grid-search is not applied) +di = 1; +wi = 1; +hi = 4; +l1i = 1; +l2i = 1; +mi = 4; +ci = 1; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% If grid search is define +% log_file = strcat(dir,'exp_.mat'); +% inx = resume_from_grid(log_file,8); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Feature extraction +EXT_TYPE = 2; +switch (EXT_TYPE) + case 1 % Using PCA + assert(~exist('OCTAVE_VERSION'),'This script cannot run in octave'); + coeff = princomp(raw_features); + coeff = coeff(:,1:end-dmr(di)); % Change value of dmr(di) to reduce the dimensionality + features = raw_features*coeff; + % normalizing + mm = minmax(features')'; + inn= (find(mm(1,:)~=mm(2,:))); + mm = mm(:,inn); + features = features(:,inn); + features = (features-repmat(mm(1,:),size(features,1),1))./(repmat(mm(2,:),size(features,1),1)-repmat(mm(1,:),size(features,1),1)); + + % describe config + s_conf = xml_format(dmr(di)); + case 2 % Using rbm + conf.hidNum = hidNum(hi); + conf.eNum = 100; + conf.sNum = size(raw_features,1); + conf.bNum = 1; + conf.gNum = 1; + conf.params = [lr_1(l1i) lr_2(l2i) mmt(mi) cost(ci)]; + conf.N = 50; + conf.MAX_INC = 10; + W1 = zeros(0,0); + [W1 vB1 hB1] = training_rbm_(conf,W1,raw_features); + features = logistic(raw_features*W1 + repmat(hB1,conf.sNum,1)); + + % describe config + s_conf = xml_format(conf); +end + +correct = 0; % correct rate +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% CODE HERE %% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +nfvec = features'; +feature_file = 'son_out_files.mat'; +save(feature_file,'nfvec'); + + +% the calling of daniels script +ftype = 'MTTMixedFeatureSon'; +global sonfeatbase; +sonfeatbase = []; + +global db_MTTMixedFeatureSon +db_MTTMixedFeatureSon.reset + +global globalvars; +globalvars.debug = 2 + +fparams_all.son_conf = {s_conf}; +fparams_all.son_filename = {feature_file}; + +% --- +% vary parameters for svmlight +% --- + +trainparams_all = struct(... + 'C', [0.05 0.1 3 10 100], ... %you just want to try this parameter approx (0.01..........1000) (3-100 is nice for daniel) + ... + 'weighted', [0], ... + 'dataset',{{'comp_partBinData_cupaper_01'}}, ... + 'inctrain', 0, ... + 'deltafun', {{'conv_subspace_delta'}}, ... + 'deltafun_params', {{{[0],[1]},{[5],[1]},{[20],[1]},{[40],[1]}}} ... % normalisation improves results + ); + +% set training function +trainfun = @svmlight_wrapper; + + +% create test dirxectory +akt_dir = migrate_to_test_dir(); + +% call eval +result = test_generic_features_parameters_crossval... + (fparams_all, trainparams_all, trainfun, ftype); + +% correct = result.mean_ok_test(1,1); +% fprintf('Correct = %f\n',correct); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Using the logging function to save paramters +% and the result for plotting or in grid search +% logging(log_file,[i1 i2 i3 i4 i5 correct]); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/Exp_grad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,170 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Experiment with gradient ascent % +% Project: sub-euclidean distance for music similarity % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Load features +%feature_file = 'rel_music_raw_features.mat'; +feature_file = 'rel_music_raw_features+simdata_ISMIR12.mat'; + +vars = whos('-file', feature_file); +A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); +raw_features = A.(vars(1).name); +indices = A.(vars(2).name); +tst_inx = A.(vars(3).name); +trn_inx = A.(vars(4).name); +%% Params setting +dmr = [0 5 10 20 30 50]; % dimension reduction by PCA +ws = [0 5 10 20 30 50 70]; % window size +% parameters of rbm (if it is used for extraction) +hidNum = 0; +lr_1 = 0; +lr_2 = 0; +mmt = 0; +cost = 0; +%% Select parameters (if grid-search is not applied) + di = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% If grid search is define +% Define directory to save parameters & results +if ~isempty(findstr('WIN',computer())) + dir = 'C:\Pros\Experiments\ISMIR_2013\grad\'; % In windows platform + dlm = '\'; +elseif ~isempty(findstr('linux',computer())) || ~isempty(findstr('LNX',computer())) + dir = '/home/funzi/Documents/Experiments/ISMIR_2013/grad/'; % In lunix platform + dlm = '/'; +end + +EXT_TYPE = 2; +switch (EXT_TYPE) + case 1 + dir = strcat(dir,'pca',dlm); + case 2 + dir = strcat(dir,'rbm',dlm); + + hidNum = [100 500 1000 1200]; + lr_1 = [0.5 0.7]; + lr_2 = [0.7]; + mmt = [0.1]; + cost = [0.00002]; + otherwise + dir = strcat(dir,'none',dlm); +end + +w_num = size(ws,2); + +for iiii = 1:200 % set the higher range to search for better features in case of ext using rbm + log_file = strcat(dir,'exp',num2str(iiii),'.mat') + inx = resume_from_grid(log_file,8 + w_num); + if inx(end-w_num+1:end)==ones(1,w_num) + max_= zeros(1,w_num); + else + max_ = inx(end-w_num+1:end); + end + + results = zeros(1,w_num); + W_max = cell(1,w_num); + vB_max = cell(1,w_num); + hB_max = cell(1,w_num); + Ws_max = cell(1,w_num); + +for hi = inx(1):size(hidNum,2) +for l1i = inx(2):size(lr_1,2) +% for l1i = inx(3):size(lr_2,2) +for mi = inx(4):size(mmt,2) +for ci = inx(5):size(cost,2) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Feature extraction +features = raw_features; +switch (EXT_TYPE) + case 1 % Using PCA + assert(~exist('OCTAVE_VERSION'),'This script cannot run in octave'); + coeff = princomp(raw_features); + coeff = coeff(:,1:6); % best = 6 + features = raw_features*coeff; + % normalizing + mm = minmax(features')'; + inn= (find(mm(1,:)~=mm(2,:))); + mm = mm(:,inn); + features = features(:,inn); + features = (features-repmat(mm(1,:),size(features,1),1))./(repmat(mm(2,:),size(features,1),1)-repmat(mm(1,:),size(features,1),1)); + case 2 % Using rbm + conf.hidNum = hidNum(hi); + conf.eNum = 100; + conf.sNum = size(raw_features,1); + conf.bNum = 1; + conf.gNum = 1; + conf.params = [lr_1(l1i) lr_1(l1i) mmt(mi) cost(ci)]; + conf.N = 50; + conf.MAX_INC = 10; + W1 = zeros(0,0); + [W1 vB1 hB1] = training_rbm_(conf,W1,raw_features); + features = logistic(raw_features*W1 + repmat(hB1,conf.sNum,1)); + otherwise + % normalizing +% mm = minmax(features')'; +% inn= (find(mm(1,:)~=mm(2,:))); +% mm = mm(:,inn); +% features = features(:,inn); +% features = (features-repmat(mm(1,:),size(features,1),1))./(repmat(mm(2,:),size(features,1),1)-repmat(mm(1,:),size(features,1),1)); +end + +for wi = inx(6):w_num +%% Sub-euclidean computation +w = ws(wi); % w = subspace window size +num_case = size(trn_inx,1); +[trnd_12 trnd_13] = subspace_distances(trn_inx,features,indices,w,0); +[tstd_12 tstd_13] = subspace_distances(tst_inx,features,indices,w,0); +cr_ = 0; % correct rate for training +cr = 0; % correct rate for testing +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% CODE HERE %% +[Ws cr_] = gradient_ascent(trnd_12,trnd_13,0.1,0.1,0.00002); + +for i = 1:num_case + cr = cr + sum((tstd_13{i}-tstd_12{i})*Ws{i}' > 0, 1)/size(tstd_12{i},1); +end +cr = cr/num_case; +if cr_>max_(wi) + max_(wi) = cr_; + results(wi) = cr; + if EXT_TYPE==2 + W_max{wi} = W1; + vB_max{wi} = vB1; + hB_max{wi} = hB1; + Ws_max{wi} = Ws; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +fprintf('[window|train|test]= %2d |%f |%f\n',w,cr_,cr); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Using the logging function to save paramters +% and the result for plotting or in grid search +switch EXT_TYPE + case 1 + % logging(log_file,[100 100 100 100 100 wi cr_ cr max_]); + case 2 + logging(log_file,[hi l1i l1i mi ci wi cr_ cr max_ conf.hidNum conf.eNum conf.params]); + otherwise + logging(log_file,[100 100 100 100 100 wi cr_ cr max_]); +end +end +inx(6)=1; +end +inx(5) = 1; +end +inx(4) = 1; +end +inx(2) = 1; +end +inx(1) = 1; +%% Test on best features + +save(strcat(dir,'res_',num2str(iiii),'.mat'),'max_','results','W_max','vB_max','hB_max','Ws_max','ws'); +[dummy pos] = max(max_); +fprintf('Accuracy (RBM best fts): w = %d train = %f test = %f\n',ws(pos),max_(pos),results(pos)); +clc; +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +clear;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/Exp_normalise_deltas.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,125 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Experiment code templat % +% Project: sub-euclidean distance for music similarity, +% in the last part all the +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Load features +feature_file = 'rel_music_raw_features.mat'; +vars = whos('-file', feature_file); +A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); +raw_features = A.(vars(1).name); +indices = A.(vars(2).name); +tst_inx = A.(vars(3).name); +trn_inx = A.(vars(4).name); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Define directory to save parameters & results +% dir = '/home/funzi/Documents/'; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +dmr = [0 5 10 20 30 50]; % dimension reduction by PCA +ws = [0 5 10 20 30 50 70]; % window size +% parameters of rbm (if it is used for extraction) +hidNum = [30 50 100 500]; +lr_1 = [0.05 0.1 0.5]; +lr_2 = [0.1 0.5 0.7]; +mmt = [0.02 0.05 0.1]; +cost = [0.00002 0.01 0.1]; + +%% Select parameters (if grid-search is not applied) +di = 1; +wi = 1; +hi = 1; +l1i = 1; +l2i = 1; +mi = 1; +ci = 1; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% If grid search is define +% log_file = strcat(dir,'exp_.mat'); +% inx = resume_from_grid(log_file,8); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Feature extraction +EXT_TYPE = 2; +switch (EXT_TYPE) + case 1 % Using PCA + assert(~exist('OCTAVE_VERSION'),'This script cannot run in octave'); + coeff = princomp(raw_features); + coeff = coeff(:,1:end-dmr(di)); % Change value of dmr(di) to reduce the dimensionality + features = raw_features*coeff; + % normalizing + mm = minmax(features')'; + inn= (find(mm(1,:)~=mm(2,:))); + mm = mm(:,inn); + features = features(:,inn); + features = (features-repmat(mm(1,:),size(features,1),1))./(repmat(mm(2,:),size(features,1),1)-repmat(mm(1,:),size(features,1),1)); + case 2 % Using rbm + conf.hidNum = hidNum(hi); + conf.eNum = 100; + conf.sNum = size(raw_features,1); + conf.bNum = 1; + conf.gNum = 1; + conf.params = [lr_1(l1i) lr_2(l2i) mmt(mi) cost(ci)]; + conf.N = 50; + conf.MAX_INC = 10; + W1 = zeros(0,0); + [W1 vB1 hB1] = training_rbm_(conf,W1,raw_features); + features = raw_features*W1 + repmat(hB1,conf.sNum,1); +end + +%% Sub-euclidean computation +num_case = size(trn_inx,1); +trnd_12 = cell(1,num_case); +trnd_13 = cell(1,num_case); +tstd_12 = cell(1,num_case); +tstd_13 = cell(1,num_case); + +w = ws(wi); + +% w = subspace window size +if w == 0 % trnd_12 = d(a,b) , trnd_13= d(a,c) + for i = 1:num_case % over all cross-validation folds (num_case) + [trnd_12{i} trnd_13{i}] = simple_dist(trn_inx{i},features,indices); + [tstd_12{i} tstd_13{i}] = simple_dist(tst_inx{i},features,indices); + end +else + for i = 1:num_case % for w > 1 + [trnd_12{i} trnd_13{i}] = conv_euclidean_dist(trn_inx{i},features,indices,w,1); %% normalize is better than no normalize + [tstd_12{i} tstd_13{i}] = conv_euclidean_dist(tst_inx{i},features,indices,w,1); + end +end +%% Data preparation +trn_dat1 = cell(1,num_case); +trn_dat2 = cell(1,num_case); +tst_dat1 = cell(1,num_case); +tst_dat2 = cell(1,num_case); + +for i=1:num_case + %=> Compute hypothesis + trn_dat1{i} = trnd_13{i} - trnd_12{i}; + trn_dat2{i} = trnd_12{i} - trnd_13{i}; + tst_dat1{i} = tstd_13{i} - tstd_12{i}; + tst_dat2{i} = tstd_12{i} - tstd_13{i}; + + + % --- + % Cheat: Normalize over all training and test delta values using min-max + % Son reports this can give about 95% accuracy + % --- + + mm = minmax([trn_dat1{i};tst_dat1{i}]')'; + inn= find(mm(1,:)~=mm(2,:)); + mm = mm(:,inn); + trn_dat1{i} = + (trn_dat1{i}(:,inn)-repmat(mm(1,:),size(trn_dat1{i},1),1))./repmat(mm(2,:)-mm(1,:),size(trn_dat1{i},1),1); + tst_dat1{i} = (tst_dat1{i}(:,inn)-repmat(mm(1,:),size(tst_dat1{i},1),1))./repmat(mm(2,:)-mm(1,:),size(tst_dat1{i},1),1); + + mm = minmax([trn_dat2{i};tst_dat2{i}]'); + inn= find(mm(1,:)~=mm(2,:)); + mm = mm(:,inn); + trn_dat2{i} = + (trn_dat2{i}(:,inn)-repmat(mm(1,:),size(trn_dat2{i},1),1))./repmat(mm(2,:)-mm(1,:),size(trn_dat2{i},1),1); + tst_dat2{i} = (tst_dat2{i}(:,inn)-repmat(mm(1,:),size(tst_dat2{i},1),1))./repmat(mm(2,:)-mm(1,:),size(tst_dat2{i},1),1); + + + + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/Exp_template.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,146 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Experiment code templat % +% Project: sub-euclidean distance for music similarity % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Load features +feature_file = 'rel_music_raw_features.mat'; +vars = whos('-file', feature_file); +A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); +raw_features = A.(vars(1).name); +indices = A.(vars(2).name); +tst_inx = A.(vars(3).name); +trn_inx = A.(vars(4).name); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Define directory to save parameters & results +% dir = '/home/funzi/Documents/'; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +dmr = [0 5 10 20 30 50]; % dimension reduction by PCA +ws = [0 5 10 20 30 50 70]; % window size +% parameters of rbm (if it is used for extraction) +hidNum = [30 50 100 500]; +lr_1 = [0.05 0.1 0.5]; +lr_2 = [0.1 0.5 0.7]; +mmt = [0.02 0.05 0.1]; +cost = [0.00002 0.01 0.1]; + +%% Select parameters (if grid-search is not applied) +di = 1; +wi = 1; +hi = 1; +l1i = 1; +l2i = 1; +mi = 1; +ci = 1; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% If grid search is define +% log_file = strcat(dir,'exp_.mat'); +% inx = resume_from_grid(log_file,8); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Feature extraction +EXT_TYPE = 2; +switch (EXT_TYPE) + case 1 % Using PCA + assert(~exist('OCTAVE_VERSION'),'This script cannot run in octave'); + coeff = princomp(raw_features); + coeff = coeff(:,1:end-dmr(di)); % Change value of dmr(di) to reduce the dimensionality + features = raw_features*coeff; + % normalizing + mm = minmax(features')'; + inn= (find(mm(1,:)~=mm(2,:))); + mm = mm(:,inn); + features = features(:,inn); + features = (features-repmat(mm(1,:),size(features,1),1))./(repmat(mm(2,:),size(features,1),1)-repmat(mm(1,:),size(features,1),1)); + case 2 % Using rbm + conf.hidNum = hidNum(hi); + conf.eNum = 100; + conf.sNum = size(raw_features,1); + conf.bNum = 1; + conf.gNum = 1; + conf.params = [lr_1(l1i) lr_2(l2i) mmt(mi) cost(ci)]; + conf.N = 50; + conf.MAX_INC = 10; + W1 = zeros(0,0); + [W1 vB1 hB1] = training_rbm_(conf,W1,raw_features); + features = raw_features*W1 + repmat(hB1,conf.sNum,1); +end + +%% Sub-euclidean computation +num_case = size(trn_inx,1); +trnd_12 = cell(1,num_case); +trnd_13 = cell(1,num_case); +tstd_12 = cell(1,num_case); +tstd_13 = cell(1,num_case); + +w = ws(wi); + +% w = subspace window size +if w == 0 % trnd_12 = d(a,b) , trnd_13= d(a,c) + for i = 1:num_case % over all cross-validation folds (num_case) + [trnd_12{i} trnd_13{i}] = simple_dist(trn_inx{i},features,indices); + [tstd_12{i} tstd_13{i}] = simple_dist(tst_inx{i},features,indices); + end +else + for i = 1:num_case % for w > 1 + [trnd_12{i} trnd_13{i}] = conv_euclidean_dist(trn_inx{i},features,indices,w,1); %% normalize is better than no normalize + [tstd_12{i} tstd_13{i}] = conv_euclidean_dist(tst_inx{i},features,indices,w,1); + end +end +%% Data preparation +trn_dat1 = cell(1,num_case); +trn_dat2 = cell(1,num_case); +tst_dat1 = cell(1,num_case); +tst_dat2 = cell(1,num_case); + +for i=1:num_case + %=> Compute hypothesis + trn_dat1{i} = trnd_13{i} - trnd_12{i}; + trn_dat2{i} = trnd_12{i} - trnd_13{i}; + tst_dat1{i} = tstd_13{i} - tstd_12{i}; + tst_dat2{i} = tstd_12{i} - tstd_13{i}; + + % => Normalize using logistic (lost the range) +% trn_dat1{i} = logistic(trn_dat1{i}); +% trn_dat2{i} = logistic(trn_dat2{i}); +% tst_dat1{i} = logistic(tst_dat1{i}); +% tst_dat2{i} = logistic(tst_dat2{i}); + + %=> Normalize using min-max +% mm = minmax(trn_dat1{i}')'; +% inn= find(mm(1,:)~=mm(2,:)); +% mm = mm(:,inn); +% trn_dat1{i} = +% (trn_dat1{i}(:,inn)-repmat(mm(1,:),size(trn_dat1{i},1),1))./repmat(mm(2,:)-mm(1,:),size(trn_dat1{i},1),1); +% mm = minmax(tst_dat1{i}')'; +% inn= find(mm(1,:)~=mm(2,:)); +% mm = mm(:,inn); +% tst_dat1{i} = (tst_dat1{i}(:,inn)-repmat(mm(1,:),size(tst_dat1{i},1),1))./repmat(mm(2,:)-mm(1,:),size(tst_dat1{i},1),1); +% +% mm = minmax(trn_dat2{i}')'; +% inn= find(mm(1,:)~=mm(2,:)); +% mm = mm(:,inn); +% trn_dat2{i} = (trn_dat2{i}(:,inn)-repmat(mm(1,:),size(trn_dat2{i},1),1))./repmat(mm(2,:)-mm(1,:),size(trn_dat2{i},1),1); + +% mm = minmax(tst_dat2{i}')'; +% inn= find(mm(1,:)~=mm(2,:)); +% mm = mm(:,inn); +% tst_dat2{i} = (tst_dat2{i}(:,inn)-repmat(mm(1,:),size(tst_dat2{i},1),1))./repmat(mm(2,:)-mm(1,:),size(tst_dat2{i},1),1); + + % => normalize from [-1 1] to [0 1] + trn_dat1{i} = (trn_dat1{i}+1)/2; + trn_dat2{i} = (trn_dat2{i}+1)/2; + tst_dat1{i} = (tst_dat1{i}+1)/2; + tst_dat2{i} = (tst_dat2{i}+1)/2; +end +correct = 0; % correct rate +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% CODE HERE %% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +fprintf('Correct = %f\n',correct); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Using the logging function to save paramters +% and the result for plotting or in grid search +% logging(log_file,[i1 i2 i3 i4 i5 correct]); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clear; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/conv_euclidean_dist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function [dis_s12 dis_s13] = conv_euclidean_dist(index_list,vector_list,index_map,w,norm) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% compute the distance vector from 2 vector using convolution euclidean % +% w = convolution window % +% sontran2013 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +s_num = size(index_list,1); +s_len = size(vector_list,2); + +dis_s12 = zeros(s_num,s_len); +dis_s13 = zeros(s_num,s_len); + +% Convert list of assigned indices to ordered indices +[checker index_list] = ismember(index_list,index_map); +cv_vec = ones(1,w); +if all(all(checker(:,1:3))) + dis_s12 = sqrt(conv2([1],cv_vec,(vector_list(index_list(:,1),:) - vector_list(index_list(:,2),:)).^2)); + dis_s13 = sqrt(conv2([1],cv_vec,(vector_list(index_list(:,1),:) - vector_list(index_list(:,3),:)).^2)); + % dis_s12 = dis_s12(:,w:end-w); + % dis_s13 = dis_s13(:,w:end-w); + if norm + dis_s12 = dis_s12./(conv2([1],cv_vec,ones(s_num,s_len))); + dis_s13 = dis_s13./(conv2([1],cv_vec,ones(s_num,s_len))); + end +else + fprintf('Some indices are not mapped\n'); +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/conv_subspace_delta.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function out = conv_subspace_delta(a, b, w, norm) +% out = conv_subspace_delta(a, b, w, norm) +% compute the distance vector from 2 vectors a,b using convolution euclidean % +% w = convolution window +% +% DW adapted from sontran2013 + +% window for convolution + +s_len = numel(a); +if w == 0 + w = s_len; +end +cv_vec = ones(w,1); + +out = sqrt(conv(cv_vec,(a - b).^2)); +% out = out(:,w:end-w); +if norm + out = out./(conv(cv_vec,ones(s_len,1))); +end + +% how to get to 1 dimension? +% out = sum(out); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/gradient_ascent.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function [Ws cr_] = gradient_ascent(d_12,d_13,lr,mm,cost) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% gradient descent % +% sontran2013 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +pd_num = size(d_12,2); +Ws = cell(1,pd_num); +DWs = cell(1,pd_num); + +for i = 1:pd_num +Ws{i} = 0.01*ones(1,size(d_12{i},2)); +DWs{i} = zeros(size(Ws{i})); +end +plot_ = 0; +if plot_, h = plot(nan); end +max_ = 0; +eNum = 50; +for j=1:eNum + cr_ = 0; + for i=1:pd_num + if j>1 + if sum(sum(isinf(Ws{i}))) ~=0 || sum(sum(isnan(Ws{i}))) ~=0, break, end + diff = sum(d_13{i}-d_12{i},1)/size(d_12{i},1); + DWs{i} = lr*(diff-cost*Ws{i}) + mm*DWs{i}; + Ws{i} = Ws{i} + DWs{i}; + end + cr_ = cr_ + sum(((d_13{i}-d_12{i})*Ws{i}' > 0))/size(d_12{i},1); + end + cr_ = cr_/pd_num; + if cr_ > max_ + max_ = cr_; + W_max = Ws; + end + if plot_ + cr_plot(j) = cr_; + axis([0 (eNum+1) 0 1]); + set(h,'YData',cr_plot); + drawnow; + end +end +%max +Ws = W_max; +cr_ = max_; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/logging.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function logging( file,log_dat) +% Log all values in data to the file +% sontran2013 +stt = exist(file,'file'); +if stt == 0 + data = zeros(0,0); +else + load(file); +end +data = [data;log_dat]; +if exist('OCTAVE_VERSION') + save('-mat-binary',file,'data'); +else + save(file,'data'); +end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/logistic.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function A = logistic( A ) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Logistic function % +% sontran2012 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +A = 1./(1+exp(-A)); +end +
Binary file core/magnatagatune/tests_evals/rbm_subspace/rel_music_raw_features+simdata_ISMIR12.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/resume_from_grid.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function inx = resume_from_grid(log_file,inx_size); +% The function read the log file and resume the grid search for params +% sontran 2013 +stt = exist(log_file,'file'); +if stt == 0 + inx = ones(1,inx_size); +else + load(log_file); + inx = data(end,:); + inx = inx(1:inx_size); +end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/simple_dist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function [dis_s12,dis_s13] = simple_dist(index_list,vector_list,index_map) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% compute the distance vector from 2 vector % +% sontran2013 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +s_num = size(index_list,1); +s_len = size(vector_list,2); + +dis_s12 = zeros(s_num,s_len); +dis_s13 = zeros(s_num,s_len); + +% Convert list of assigned indices to ordered indices +[checker index_list] = ismember(index_list,index_map); + +if all(all(checker(:,1:3))) + dis_s12 = abs(vector_list(index_list(:,1),:)-vector_list(index_list(:,2),:)); + dis_s13 = abs(vector_list(index_list(:,1),:)-vector_list(index_list(:,3),:)); +else + fprintf('Some indices are not mapped\n'); +end + +end +% simple_dist(sontest{1},sonfeatures,sonindex) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/subspace_distances.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function [d_12 d_13] = subspace_distances(f_inx,features,indices,w,norm) + num_case = max(size(f_inx)); + d_12 = cell(1,num_case); + d_13 = cell(1,num_case); + if w == 0 % d_12 = d(a,b) , d_13= d(a,c) + for i = 1:num_case % over all cross-validation folds (num_case) + [d_12{i} d_13{i}] = simple_dist(f_inx{i},features,indices); + end + else + for i = 1:num_case % for w > 1 + [d_12{i} d_13{i}] = conv_euclidean_dist(f_inx{i},features,indices,w,norm); %% normalize is better than no normalize in almost the case + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/training_rbm_.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function [W visB hidB] = training_rbm_(conf,W,data) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Training RBM % +% conf: training setting % +% W: weights of connections % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +assert(~isempty(data),'[KBRBM] Data is empty'); +%% initialization +visNum = size(data,2); +hidNum = conf.hidNum; +sNum = conf.sNum; +lr = conf.params(1); +N = conf.N; % Number of epoch training with lr_1 +W = 0.1*randn(visNum - size(W,1),size(W,2)); +W = 0.1*randn(size(W,1),hidNum-size(W,2)); + +DW = zeros(size(W)); +visB = zeros(1,visNum); +DVB = zeros(1,visNum); +hidB = zeros(1,hidNum); +DHB = zeros(1,hidNum); +visP = zeros(sNum,visNum); +visN = zeros(sNum,visNum); +visNs = zeros(sNum,visNum); +hidP = zeros(sNum,hidNum); +hidPs = zeros(sNum,hidNum); +hidN = zeros(sNum,hidNum); +hidNs = zeros(sNum,hidNum); + +plot_ = 0; +%% Reconstruction error & evaluation error & early stopping +mse = 0; +omse = 0; +inc_count = 0; +MAX_INC = conf.MAX_INC; % If the error increase MAX_INC times continuously, then stop training +%% Average best settings +n_best = 1; +aW = size(W); +aVB = size(visB); +aHB = size(hidB); +%% Plotting +if plot_, h = plot(nan); end +%% ==================== Start training =========================== %% +for i=1:conf.eNum + if i== N+1 + lr = conf.params(2); + end + omse = mse; + mse = 0; + for j=1:conf.bNum + visP = data((j-1)*conf.sNum+1:j*conf.sNum,:); + %up + hidP = logistic(visP*W + repmat(hidB,sNum,1)); + hidPs = 1*(hidP >rand(sNum,hidNum)); + hidNs = hidPs; + for k=1:conf.gNum + % down + visN = logistic(hidNs*W' + repmat(visB,sNum,1)); + visNs = 1*(visN>rand(sNum,visNum)); + % up + hidN = logistic(visNs*W + repmat(hidB,sNum,1)); + hidNs = 1*(hidN>rand(sNum,hidNum)); + end + % Compute MSE for reconstruction +% rdiff = (visP - visN); +% mse = mse + sum(sum(rdiff.*rdiff))/(sNum*visNum); + % Update W,visB,hidB + diff = (visP'*hidP - visNs'*hidN)/sNum; + DW = lr*(diff - conf.params(4)*W) + conf.params(3)*DW; + W = W + DW; + DVB = lr*sum(visP - visN,1)/sNum + conf.params(3)*DVB; + visB = visB + DVB; + DHB = lr*sum(hidP - hidN,1)/sNum + conf.params(3)*DHB; + hidB = hidB + DHB; + end + %% + if plot_ + mse_plot(i) = mse; + axis([0 (conf.eNum+1) 0 5]); + set(h,'YData',mse_plot); + drawnow; + end +% % save(strcat('C:\Pros\Data\XOR\plot_',num2str(conf.params(2),3),'.mat'),'mse_plot'); +% % plot(mse_plot,'XDataSource','real(mse_plot)','YDataSource','imag(mse_plot)') +% % linkdata on; + + if mse > omse + inc_count = inc_count + 1; + else + inc_count = 0; + end + if inc_count> MAX_INC, break; end; +% fprintf('Epoch %d : MSE = %f\n',i,mse); +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/write_mat_results_ISMIR13RBM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,197 @@ +function [out, stats] = write_mat_results_ISMIR13RBM(dirin,fileout) +% combine the test results from the directories supplied, +% and recombine the bins to a coherent dataset +% +% allows for distinguishing different hidden State numbers + +% folders = {'130505_svm_disttest_subsconv_rbm_largegrid_21_r2385','130505_svm_disttest_subsconv_rbm_largegrid_22_r2385','130505_svm_disttest_subsconv_rbm_largegrid_23_r2385','130505_svm_disttest_subsconv_rbm_largegrid_24_r2385','130506_svm_disttest_subsconv_rbm_largegrid_25_r2385'}; + + + +features = []; +show = 1; + +if nargin == 0 + dirin{1} = './'; +end + +global comparison; +global comparison_ids; + +newout = []; +thisdir = pwd; +% loop through al lthe result directories and +for diri = 1:numel(dirin) + + % --- + % go to directory and locate file + % --- + cd(dirin{diri}); + + u = dir(); + u = {u.name}; + [idx, strpos] = substrcellfind(u, '_finalresults.mat', 1); + + if numel(idx) < 1 + error 'This directory contains no valid test data'; + end + + % just one or more tests in this folder? + if exist('file','var') && isnumeric(file) + cprint(1, 'loading one result file'); + file = u{idx(file)}; + data = load(file); + sappend(out,data.out); + else + for filei = 1:numel(idx) + cprint(1, 'loading result file %i of %i',filei, numel(idx)); + file = u{idx(filei)}; + data = load(file); + newout = sappend(newout,data.out); + end + end + % reset act directory + cd(thisdir); +end + + +% --- +% filter C values! +% --- +allcs = zeros(numel(newout),1); +for i=1:numel(newout) + allcs(i) = newout(i).trainparams.C; +end +valididx = find(allcs == 1); % select C! +newout = newout(valididx); + + +% bundle all datasets +fout = sameparamsubset(newout, 'dataset',''); +out = []; +for ci=1:numel(fout) + filteredout = fout{ci}; + + + ok_test = zeros(2, numel(filteredout)); + ok_train = zeros(2, numel(filteredout)); + ok_config = []; + + + tmpout = filteredout(1); + % cycle over all test sets and get new means + for i=1:numel(filteredout) + ok_test(:,i) = filteredout(i).mean_ok_test; + ok_train(:,i) = filteredout(i).mean_ok_train; + end + + % save the stuff + tmpout.mean_ok_test = mean(ok_test,2); + tmpout.var_ok_test = var(ok_test,0,2); + tmpout.mean_ok_train = mean(ok_train,2); + tmpout.var_ok_train = var(ok_train,0,2); + + tmpout.ok_test = ok_test; + tmpout.ok_train = ok_train; + + % put it in output structure + out = sappend(out,tmpout); +end + +% --- +% show results +% --- +if numel([out.mean_ok_test]) > 1 && show + + % plot means % plot std = sqrt(var) % plot training results + figure; + boxplot([out.mean_ok_test], sqrt([out.var_ok_test]), [out.mean_ok_train]); + title (sprintf('Performance for all configs')); +end + + + +% --- +% write max. test success +% --- +mean_ok_test = [out.mean_ok_test]; +[val, idx] = max(mean_ok_test(1,:)); +if show + fprintf(' --- Maximal test set success: nr. %d, %3.2f percent. train result %3.2f percent. --- \n', idx, val * 100,out(idx).mean_ok_train(1,:)*100) +end + + +% --- +% write max. test/train success +% --- +mean_ok_train = [out.mean_ok_train]; +[val, idx] = max(mean_ok_train(1,:)); +if show + fprintf(' --- Maximal train set success: nr. %d, %3.2f percent, test result %3.2f percent. --- \n', idx, val * 100,out(idx).mean_ok_test(1,:)*100) +end + +% save this summary +save([hash(strcat(dirin{:}),'md5') '_summary'], 'out'); + + + +% --- +% build index of these feature configurations without num_hid +% --- +[fout2,param_hash,idxhash] = sameparamsubset(out, '','rbm_hidNum'); +for ci = 1:numel(fout2) + + % search for the training param index + found = find(idxhash{ci}== idx); + if ~isempty(found) + out_withsameconfig = fout2{ci}; + break; + end +end + + +end + +function [out, param_hash,idx] = sameparamsubset(in, ignoret,ignoref) + % --- + % build index of all existing configurations + % --- + param_hash = cell(numel(in),1); + for i=1:numel(in) + params = struct('trainparams',in(i).trainparams, ... + 'fparams',in(i).fparams); + + % remove the dataset param + if ~isempty(ignoret) + params.trainparams = rmfield(params.trainparams,ignoret); + end + if ~isempty(ignoref) + params.fparams = rmfield(params.fparams,ignoref); + end + + phash = hash(xml_format(params),'md5'); + param_hash{i} = phash; + end + + % --- + % recombine the data for different datasets! + % --- + cvals = unique(param_hash); + + out = {}; + for ci=1:numel(cvals) + idx{ci} = strcellfind(param_hash,cvals(ci),1); + out{ci} = in(idx{ci}); + end + +end + +function boxplot(mean, std, train); + + bar([train; mean]', 1.5); + hold on; + errorbar(1:size(mean,2), mean(1,:), std(1,:),'.'); +% plot(train,'rO'); + colormap(spring); + axis([0 size(mean,2)+1 max(0, min(min([train mean] - 0.1))) max(max([train mean] + 0.1))]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/write_mat_results_ISMIR13RBM_singletraining.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,136 @@ +function [out, stats] = write_mat_results_ISMIR13RBM_singletraining(dirin,fileout) +% combine the test results from the directories supplied, +% group them according to dataset parameter values +% combine the test results from the directories supplied, +% group them according to dataset parameter values + +features = []; +show = 1; + +if nargin == 0 + dirin{1} = './'; +end + +global comparison; +global comparison_ids; + +newout = []; +thisdir = pwd; +% loop through al lthe result directories and +for diri = 1:numel(dirin) + + % --- + % go to directory and locate file + % --- + cd(dirin{diri}); + + u = dir(); + u = {u.name}; + [idx, strpos] = substrcellfind(u, '_finalresults.mat', 1); + + if numel(idx) < 1 + error 'This directory contains no valid test data'; + end + + % just one or more tests in this folder? + if exist('file','var') && isnumeric(file) + cprint(1, 'loading one result file'); + file = u{idx(file)}; + data = load(file); + sappend(out,data.out); + else + for filei = 1:numel(idx) + cprint(1, 'loading result file %i of %i',filei, numel(idx)); + file = u{idx(filei)}; + data = load(file); + newout = sappend(newout,data.out); + end + end + % reset act directory + cd(thisdir); +end + +% --- +% filter according to training parameter C +% +% NOTE :if we don't filter by C, we get strong overfitting with training +% success > 96 % and test set performance aorund 65 % +% --- +cs = zeros(numel(newout),1); +for i=1:numel(newout) + cs(i) = newout(i).trainparams.C; +end +cvals = unique(cs); + +for ci=1:numel(cvals) + valididx = find(cs == cvals(ci)); + filteredout = newout(valididx); + + % --- + % get parameter statistics + % --- + stats = test_generic_display_param_influence(filteredout, show); + + % get maximal values for each test set bin + % --- + % trainparams.dataset contains sets which have each only one bin of the + % ismir testsets + % --- + max_idx = [stats.trainparams.dataset.mean_ok_train.max_idx]; + ok_test = zeros(2, numel(max_idx)); + ok_train = zeros(2, numel(max_idx)); + ok_config = []; + % cycle over all test sets and save best result + for i=1:numel(max_idx) + ok_test(:,i) = filteredout(max_idx(i)).mean_ok_test; + ok_train(:,i) = filteredout(max_idx(i)).mean_ok_train; + ok_config = sappend(ok_config,struct('trainparams',filteredout(max_idx(i)).trainparams, ... + 'fparams',filteredout(max_idx(i)).fparams)); + end + % save the stuff + out(ci).mean_ok_test = mean(ok_test,2); + out(ci).var_ok_test = var(ok_test,0,2); + out(ci).mean_ok_train = mean(ok_train,2); + out(ci).var_ok_train = var(ok_train,0,2); + out(ci).trainparams.C = cvals(ci); + out(ci).ok_config = ok_config; + out(ci).ok_test = ok_test; + out(ci).ok_train = ok_train; +end + +% --- +% show results for different C +% --- +if numel([out.mean_ok_test]) > 1 && show + + % plot means % plot std = sqrt(var) % plot training results + figure; + boxplot([out.mean_ok_test], sqrt([out.var_ok_test]), [out.mean_ok_train]); + title (sprintf('Performance for all configs')); +end + +% --- +% write max. test success +% --- +mean_ok_test = [out.mean_ok_test]; +[val, idx] = max(mean_ok_test(1,:)); +if show + fprintf(' --- Maximal test set success: nr. %d, %3.2f percent. --- \n', idx, val * 100) +end + +% save +save([hash(strcat(dirin{:}),'md5') '_summary'], 'out'); + +end + + + +function boxplot(mean, std, train); + + bar([train; mean]', 1.5); + hold on; + errorbar(1:size(mean,2), mean(1,:), std(1,:),'.'); +% plot(train,'rO'); + colormap(spring); + axis([0 size(mean,2)+1 max(0, min(min([train mean] - 0.1))) max(max([train mean] + 0.1))]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/rbm_subspace/write_mat_results_ISMIR13RBM_windows.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,178 @@ +function [out, stats] = write_mat_results_ISMIR13RBM_windows(dirin,fileout) +% combine the test results from the directories supplied, +% and recombine the bins to a coherent dataset + +% folders_win1 = {'130507_svm_disttest_subsconv_rbm_largegrid_21_r2385','130507_svm_disttest_subsconv_rbm_largegrid_22_r2385','130507_svm_disttest_subsconv_rbm_largegrid_23_r2385','130507_svm_disttest_subsconv_rbm_largegrid_24_r2385','130508_svm_disttest_subsconv_rbm_largegrid_25_r2391'}; +% folders_win2 = {'130511_svm_disttest_subsconv_rbm_largegrid_21_r2391','130511_svm_disttest_subsconv_rbm_largegrid_22_r2391','130511_svm_disttest_subsconv_rbm_largegrid_23_r2391','130511_svm_disttest_subsconv_rbm_largegrid_24_r2391','130513_svm_disttest_subsconv_rbm_largegrid_25_r2391'}; +%folders = {folders_win1{:}, folders_win2{:}}; + +features = []; +show = 1; + +if nargin == 0 + dirin{1} = './'; +end + +global comparison; +global comparison_ids; + +newout = []; +thisdir = pwd; +% loop through al lthe result directories and +for diri = 1:numel(dirin) + + % --- + % go to directory and locate file + % --- + cd(dirin{diri}); + + u = dir(); + u = {u.name}; + [idx, strpos] = substrcellfind(u, '_finalresults.mat', 1); + + if numel(idx) < 1 + error 'This directory contains no valid test data'; + end + + % just one or more tests in this folder? + if exist('file','var') && isnumeric(file) + cprint(1, 'loading one result file'); + file = u{idx(file)}; + data = load(file); + sappend(out,data.out); + else + for filei = 1:numel(idx) + cprint(1, 'loading result file %i of %i',filei, numel(idx)); + file = u{idx(filei)}; + data = load(file); + newout = sappend(newout,data.out); + end + end + % reset act directory + cd(thisdir); +end + +% --- +% filter C values! +% --- +allcs = zeros(numel(newout),1); +for i=1:numel(newout) + allcs(i) = newout(i).trainparams.C; +end +valididx = find(allcs == 1); % select C! +newout = newout(valididx); + +% --- +% filter according to training parameter deltafun_params(windows) +% --- +wind = cell(numel(newout),1); +for i=1:numel(newout) + wind{i} = cell2str(newout(i).trainparams.deltafun_params); +end +windu = unique(wind); + +% save the combined out +newout2 = newout; + +% do the loop as in write_mat_results_ISMIR13RBM +for wi=1:numel(windu) + newout = newout2(strcellfind(wind, windu{wi},1)); + + % bundle all datasets + fout = sameparamsubset(newout, 'dataset',''); + out = []; + for ci=1:numel(fout) + filteredout = fout{ci}; + + ok_test = zeros(2, numel(filteredout)); + ok_train = zeros(2, numel(filteredout)); + ok_config = []; + + + tmpout = filteredout(1); + % cycle over all test sets and get new means + for i=1:numel(filteredout) + ok_test(:,i) = filteredout(i).mean_ok_test; + ok_train(:,i) = filteredout(i).mean_ok_train; + end + + % save the stuff + tmpout.mean_ok_test = mean(ok_test,2); + tmpout.var_ok_test = var(ok_test,0,2); + tmpout.mean_ok_train = mean(ok_train,2); + tmpout.var_ok_train = var(ok_train,0,2); + + tmpout.ok_test = ok_test; + tmpout.ok_train = ok_train; + + % put it in output structure + out = sappend(out,tmpout); + end + +% % --- +% % show results +% % --- +% if numel([out.mean_ok_test]) > 1 && show +% +% % plot means % plot std = sqrt(var) % plot training results +% figure; +% boxplot([out.mean_ok_test], sqrt([out.var_ok_test]), [out.mean_ok_train]); +% title (sprintf('Performance for all configs')); +% end + + % --- + % write max. test/train success + % --- + mean_ok_train = [out.mean_ok_train]; + [val, idx] = max(mean_ok_train(1,:)); + if show + fprintf(' --- Maximal train set success for %s: nr. %d, %3.2f percent, test result %3.2f percent. --- \n', windu{wi}, idx, val * 100,out(idx).mean_ok_test(1,:)*100) + end + +end + +end + +function [out, param_hash,idx] = sameparamsubset(in, ignoret,ignoref) + % --- + % build index of all existing configurations + % --- + param_hash = cell(numel(in),1); + for i=1:numel(in) + params = struct('trainparams',in(i).trainparams, ... + 'fparams',in(i).fparams); + + % remove the dataset param + if ~isempty(ignoret) + params.trainparams = rmfield(params.trainparams,ignoret); + end + if ~isempty(ignoref) + params.fparams = rmfield(params.fparams,ignoref); + end + + phash = hash(xml_format(params),'md5'); + param_hash{i} = phash; + end + + % --- + % recombine the data for different datasets! + % --- + cvals = unique(param_hash); + + out = {}; + for ci=1:numel(cvals) + idx{ci} = strcellfind(param_hash,cvals(ci),1); + out{ci} = in(idx{ci}); + end + +end + +function boxplot(mean, std, train); + + bar([train; mean]', 1.5); + hold on; + errorbar(1:size(mean,2), mean(1,:), std(1,:),'.'); +% plot(train,'rO'); + colormap(spring); + axis([0 size(mean,2)+1 max(0, min(min([train mean] - 0.1))) max(max([train mean] + 0.1))]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/svm_light/svmlight_wrapper.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function [A, diag] = svmlight_wrapper(X, Ytrain, trainparams) +% wrapper to make ITML accessible via a general interface + +% define model file +modelfile = 'tmp_model.dat'; +% define constraints file +trainfile = 'tmp_train.dat'; + +% --- +% include weighting from Y +% --- +if isfield(trainparams,'weighted') && trainparams.weighted + if trainparams.weighted > 1 + + % --- + % NOTE: this is to maintain a tradeoff between weighting + % and space usage : + % + % scale the rating if the "weighted" parameter + % gives a maximum weight + % + % TODO: try logarithmic weight scaling + % --- + Ytrain = scale_ratings(Ytrain, trainparams.weighted); + end + + % --- + % get squared pointwise distance for labeled features + % --- + if isfield(trainparams,'deltafun') + [lhs, rhs, c] = get_svmlight_inequalities_from_ranking(Ytrain, X, str2func(trainparams.deltafun), trainparams.deltafun_params); + else + [lhs, rhs, c] = get_svmlight_inequalities_from_ranking(Ytrain, X); + end + + % save to data file to disk; + success = save_svmlight_inequalities(lhs, rhs, c, trainfile); +else + % --- + % get squared pointwise distance for labeled features + % --- + if isfield(trainparams,'deltafun') + [lhs, rhs, dim] = get_svmlight_inequalities_from_ranking(Ytrain, X, str2func(trainparams.deltafun), trainparams.deltafun_params); + else + [lhs, rhs, dim] = get_svmlight_inequalities_from_ranking(Ytrain, X); + end + success = save_svmlight_inequalities(lhs, rhs, trainfile); + +end + +if ~success + error 'cannot write svm training file' +end + +% call svmlight solver +% D = dos(sprintf('svm_learn -z o -c %d %s %s', C, trainfile, modelfile)); +[diag, ~] = evalc('dos(sprintf(''svm_learn -z o -c %d %s %s'', trainparams.C, trainfile, modelfile));'); + +% Strip some dots from the display +diag = strrep(diag,'.......',''); +cprint(2, diag) + +% --- +% get dual weight vector +% TODO: check whether this the actual w in Schultz2003 +% --- +w = svmlight2weight(modelfile); + +% prolong w to feature dimension +w = [w; zeros(dim - numel(w),1)]; + +% --- +% matrix from weights +% --- +A = spdiags(w, 0, numel(w), numel(w)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/test_generic_display_param_influence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,238 @@ +function stats = test_generic_display_param_influence(results, show) +% returns the mean accuracy influence of each feature and training parameter +% +% the influence is measured by comparing the mean +% achieved accuracy for all tries with each specific +% parameter being constant +% +% TODO: evaluate how the comparisons of all configuration +% tuples twith just the specific analysed parameter +% changing differ from the above approach + +% get statistics for feature parameters +stats.fparams = gen_param_influence(results, 'fparams'); + +% get statistics for feature parameters +if isfield(results, 'trainparams') + + stats.trainparams = gen_param_influence(results, 'trainparams'); + + % the following case is for backwards compability +elseif isfield(results, 'mlrparams') + + stats.trainparams = gen_param_influence(results, 'mlrparams'); +end + +if show + % display results + + if ~isempty(stats.fparams) + figure; + % subplot(2,1,1); + display_param_influence(stats.fparams); + end + + if ~isempty(stats.trainparams) + figure; + % subplot(2,1,2); + display_param_influence(stats.trainparams); + end +end + +end + +% --- +% gen_param_influence +% --- +function stats = gen_param_influence(results, paramname) +% generates statistics given results and parameter type as string. + +% get individual fields of this parameter set +ptypes = fieldnames(results(1).(paramname)); + +for i = 1:numel(ptypes) + % --- + % get all individual configurations of this parameter. + % --- + allvals = [results.(paramname)]; + + % take care of string args + if ~ischar(allvals(1).(ptypes{i})) + if ~iscell(allvals(1).(ptypes{i})) + + % parameter array of chars + allvals = [allvals.(ptypes{i})]; + else + % complex parameter array of cells + for j=1:numel(allvals) + tmpvals{j} = cell2str(allvals(j).(ptypes{i})); + end + allvals = tmpvals; + end + else + % parameter array of numbers + allvals = {allvals.(ptypes{i})}; + end + + % save using original parameter name + tmp = param_influence(results, allvals); + + if ~isempty(tmp) + stats.(ptypes{i}) = tmp; + end +end + +if ~exist('stats','var') + stats = []; +end + +end + + +% --- +% param_influence +% --- +function out = param_influence(results, allvals) +% give the influence (given results) for the parameter settings +% given in allvals. +% +% numel(results) = numel(allvals) + + % --- + % get all different settings of this parameter. + % NOTE: this might also work results-of the box for strings. + % not tested, below has to be changed ot cell / matrix notations + % --- + entries = unique(allvals); + + % just calculate for params with more than one option + if numel(entries) < 2 || ischar(entries) + + out = []; + return; + end + + % calculate statstics for this fixed parameter + for j = 1:numel(entries) + + % care for string parameters + if ~(iscell(allvals) && ischar(allvals{1})) + valid_idx = (allvals == entries(j)); + + % mean_ok_test + valid_ids = find(valid_idx); + else + valid_ids = strcellfind(allvals, entries{j}, 1); + end + + % --- + % get the relevant statistics over the variations + % of the further parameters + % --- + mean_ok_testval = []; + for i = 1:numel(valid_ids) + mean_ok_testval = [mean_ok_testval results(valid_ids(i)).mean_ok_test(1,:)]; + end + + [ma,maidx] = max(mean_ok_testval); + [mi,miidx] = min(mean_ok_testval); + [me] = mean(mean_ok_testval); + mean_ok_test(j) = struct('max',ma , ... + 'max_idx',valid_ids(maidx) , ... + 'min',mi , ... + 'min_idx',valid_ids(miidx) , ... + 'mean',me); + + % --- + % get the training statistics over the variations + % of the further parameters + % --- + mean_ok_trainval = []; + for i = 1:numel(valid_ids) + mean_ok_trainval = [mean_ok_trainval results(valid_ids(i)).mean_ok_train(1,:)]; + end + + [ma,maidx] = max(mean_ok_trainval); + % --- + % NOTE :this allowed for accesment of improvement by RBM selection +% warning testing random idx instead of best one +% maidx = max(1, round(rand(1)* numel(valid_ids))); +% % --- + + [mi,miidx] = min(mean_ok_trainval); + [me] = mean(mean_ok_trainval); + mean_ok_train(j) = struct('max',ma , ... + 'max_idx',valid_ids(maidx) , ... + 'min',mi , ... + 'min_idx',valid_ids(miidx) , ... + 'mean',me); + end + + % --- + % get the statistics over the different values + % this parameter can hold + % + % CAVE/TODO: the idx references are relative to valid_idx + % --- + [best, absolute.best_idx] = max([mean_ok_test.max]); + [worst, absolute.worst_idx] = min([mean_ok_test.max]); + + % --- + % get differences: + difference.max = max([mean_ok_test.max]) - min([mean_ok_test.max]); + + % format output + out.entries = entries; + out.mean_ok_test = mean_ok_test; + out.mean_ok_train = mean_ok_train; + out.difference = difference; + out.absolute = absolute; +end + + +% --- +% display +% --- +function a = display_param_influence(stats) + +if isempty(stats) + return; +end + +ptypes = fieldnames(stats); + +dmean = []; +dmax = []; +best_val = {}; +for i = 1:numel(ptypes) + + % serialise the statistics +% dmean = [dmean stats.(ptypes{i}).difference.mean]; + dmax = [dmax stats.(ptypes{i}).difference.max]; + best_val = {best_val{:} stats.(ptypes{i}).entries( ... + stats.(ptypes{i}).absolute.best_idx) }; + + % take care of string args + if isnumeric(best_val{i}) + lbl{i} = sprintf('%5.2f' ,best_val{i}); + else + lbl{i} = best_val{i}; + end +end + + +bar([dmax]'* 100); +colormap(1-spring); +% legend({'maximal effect on mean correctness'}) +xlabel('effect on max. correctness for best + worst case of other parameters'); +ylabel('correctness (0-100%)'); +a = gca; +set(a,'XTick', 1:numel(ptypes), ... + 'XTickLabel', ptypes); + +% display best param results +for i = 1:numel(ptypes) + text(i,0,lbl{i}, 'color','k'); +end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/test_generic_display_results.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,209 @@ +function [out, stats, features, individual] = test_generic_display_results(file, get_features, show) +% [out, stats] = test_generic_display_results([file], get_features) +% +% [out, stats, features, individual] = test_generic_display_results([file], get_features) +% +% displays the finalresults mat file and enables +% further analysis and duiagnostics of the individual runs + +features = []; + +if nargin < 3 + show = 1; +end +if nargin < 2 + get_features = 1; +end + +global comparison; +global comparison_ids; + +if nargin < 1 || isempty(file) || isnumeric(file) + u = dir(); + u = {u.name}; + [idx, strpos] = substrcellfind(u, '_finalresults.mat', 1); + + if numel(idx) < 1 + error 'This directory contains no valid test data'; + end + + if exist('file','var') && isnumeric(file) + file = u{idx(file)}; + else + if numel(idx) > 1 + file = u{idx(ask_dataset())}; + else + file = u{idx(1)}; + end + end +end + + +% --- +% LOAD THE RESULT DATA +% We have: +% Y +% out.fparams +% trainparams +% dataPartition +% mean_ok_test +% var_ok_test +% mean_ok_train +% --- +load(file); + +% compability +if isfield(out, 'mlrparams') + for i = 1:numel(out) + out(i).trainparams = out(i).mlrparams; + end +end + + +% --- +% % get statistics for feature parameters +% Visualise the accuracy and variance +% --- +if isfield(out, 'inctrain') && show + for i = 1:numel(out) + + figure; + boxplot([out(i).inctrain.mean_ok_test], sqrt([out(i).inctrain.var_ok_test]), [out(i).inctrain.mean_ok_train]); + set(gca,'XTick',1:numel(out(i).inctrain.trainfrac), ... + 'XTickLabel', out(i).inctrain.trainfrac* 100); + + xlabel ('fraction of training data'); + title (sprintf('increasing training size test, config %d',i)); + legend('train', 'train weighted', 'test', 'test weighted'); + + end +end + + +if numel([out.mean_ok_test]) > 1 && show + + % plot means % plot std = sqrt(var) % plot training results + figure; + boxplot([out.mean_ok_test], sqrt([out.var_ok_test]), [out.mean_ok_train]); + title (sprintf('Performance for all configs')); +end + + +% --- +% write max. test success +% --- + mean_ok_test = [out.mean_ok_test]; + [val, idx] = max(mean_ok_test(1,:)); +if show + fprintf(' --- Maximal test set success: nr. %d, %3.2f percent. --- \n', idx, val * 100) +end + +% --- +% display parameter statistics +% --- +stats = test_generic_display_param_influence(out, show); + + +if nargout < 3 + return; +end +% --- +% display statistics and get features +% for run with best test success +% --- +[resfile, featfile] = get_res_filename(out, idx); + +% --- +% import features: +% 1. reset databse +% 2. import features +% 3. assign to clip ids as in ranking +% --- +if get_features + type = MTTAudioFeatureDBgen.import_type(featfile); + db_name = MTTAudioFeatureDBgen.db_name(type); + eval(sprintf('global %s', db_name)); + eval(sprintf('%s.reset();', db_name)); + eval(sprintf('features = %s.import(featfile);', db_name)); + + if isfield(out,'clip_ids') + clips = MTTClip(out(1).clip_ids); + features = clips.features(type); + end +end + +% --- +% Display Metric Stats +% tmp = test_mlr_display_metric_stats(individual.out, individual.diag, features); +% --- + +if nargout < 4 + return; +end +individual = load(resfile); +for i = 1:numel(out) + + [resfile, featfile] = get_res_filename(out, i); + + if get_features + % reset db and load testing features + eval(sprintf('global %s', db_name)); + eval(sprintf('%s.reset();', db_name)); + eval(sprintf('%s.import(featfile);', db_name)); + end + + % load individual results + if i == 1; + + individual = load(resfile); + else + + individual(i) = load(resfile); + end +end +end + +function out = ask_dataset() +% --- +% displays the parameters of the datasets, +% and asks for the right one to display +% --- +clc; +u = dir(); +u = {u.name}; +[idx, strpos] = substrcellfind(u, '_params.mat', 1); + +for i = 1:numel(idx) + file = u{idx(i)}; + fprintf('------------ Dataset nr. %d --------------\n',i); + fprintf('Filename: %s\n',file); + type(file); +end + +out = (input('Please choose the dataset number: ')); +end + + +function [resfile, featfile] = get_res_filename(out, i) +% get filename given test results and index + + paramhash = hash(xml_format(out(i).fparams),'MD5'); + + paramhash_mlr = hash(xml_format(out(i).trainparams),'MD5'); + + featfile = sprintf('runlog_%s_feat.mat', paramhash); + + resfile = sprintf('runlog_%s.%s_results.mat',... + paramhash, paramhash_mlr); +end + + +function boxplot(mean, std, train); + + bar([train; mean]', 1.5); + hold on; + errorbar(1:size(mean,2), mean(1,:), std(1,:),'.'); +% plot(train,'rO'); + colormap(spring); + axis([0 size(mean,2)+1 max(0, min(min([train mean] - 0.1))) max(max([train mean] + 0.1))]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/test_generic_display_results_absviolated.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,99 @@ +function out = test_generic_display_results_absviolated(file) +% out = test_generic_display_results_absviolated(file) +% +% displays the finalresults mat file and enables +% further analysis and duiagnostics of the individual runs + +global db_MTTAudioFeatureSlaney08; +global db_magnamixedfeat_genrebasicsm; +global db_MTTMixedFeatureGenreBasicSmPCA; + + +global comparison; +global comparison_ids; + +if nargin < 1 || isempty(file) || isnumeric(file) + u = dir(); + u = {u.name}; + [idx, strpos] = substrcellfind(u, '_finalresults.mat', 1); + + if exist('file','var') && isnumeric(file) + file = u{idx(file)}; + else + file = u{idx(1)}; + end +end + +load(file); + +% --- +% % get statistics for feature parameters +% Visualise the accuracy and variance +% --- +if isfield(out, 'inctrain') + for i = 1:numel(out) + + % --- + % get training and test sample sizes + % --- + nData = []; + n_train_data = zeros(1,numel(out(i).inctrain.dataPartition)); + n_test_data = zeros(1,numel(out(i).inctrain.dataPartition)); + for j = 1:numel(out(i).inctrain.dataPartition) + n_train_data(j) = mean(out(i).inctrain.dataPartition(j).TrainSize); + n_test_data(j) = mean(out(i).inctrain.dataPartition(j).TestSize); + end + + % --- + % get lost percentages + % --- + mean_lost_test = 1 - [out(i).inctrain.mean_ok_test]; + mean_lost_test = mean_lost_test(1,:).* n_test_data; + + var_ok_test = sqrt([out(i).inctrain.var_ok_test]); + + mean_lost_train = 1 - [out(i).inctrain.mean_ok_train]; + mean_lost_train = mean_lost_train(1,:).* n_train_data; + + % plot test results + figure; + subplot(2,1,1) + plot(n_train_data, mean_lost_test,'r'); + hold; + % plot training results + plot(n_train_data, mean_lost_train,'m'); + + xlabel ('# training constraints'); + ylabel ('# constraints violated'); + legend ('test','training'); + + plot(n_train_data, mean_lost_test + var_ok_test(1,:).* n_test_data,'r:'); + plot(n_train_data, mean_lost_test - (var_ok_test(1,:).* n_test_data),'r:'); + + % --- + % get percentage of unknown data examples learnt + % --- + lost_test_not_in_train = mean_lost_test - mean_lost_train; + ntest_not_in_train = n_test_data - n_train_data; + + lost_test_not_in_train = lost_test_not_in_train ./ ntest_not_in_train; + + lost_test_not_in_train(isnan(lost_test_not_in_train)) = 0; + + subplot(2,1,2) + plot(n_train_data, lost_test_not_in_train); + + xlabel ('# training constraints'); + ylabel ('% unknown constraints violated'); + end +end + + +% --- +% write max. training success +% --- +mean_ok_test = [out.mean_ok_test]; +[val, idx] = max(mean_ok_test(1,:)); +fprintf(' --- Maximal training success: nr. %d, %3.2f percent. --- \n', idx, val * 100) + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/tests_evals/test_generic_features_parameters_crossval.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,228 @@ +function out = test_generic_features_parameters_crossval... + (fparams_all, trainparams_all, trainfun, featuretype) +% +% this is a special function designed for the ISMIR 2012 +% publication. A fixed and partitioned similarity dataset is used, +% and the clip set as well as the features are SPECIFICALLY SELECTED +% to each combination of training and test sets. + +% svn hook +my_revision = str2double(substr('$Rev$', 5, -1)); +[ST,I] = dbstack(); +testscript = ST(end).name; + +global globalvars; +global comparison_ids; +global comparison_invids; + +eval(sprintf('global %s', MTTAudioFeatureDBgen.db_name(featuretype))); + +% --- +% get all combinations resulting from the above parameter set +% descriptions +% --- +fparams = param_combinations(fparams_all); + +trainparams = param_combinations(trainparams_all); + +% --- +% the clips: sorted by comparison_id +% +% Now the clip type can be specified in the first training set +% --- +tmp_simdata = load(trainparams(1).dataset); +if isfield(tmp_simdata, 'comparison_ids'); + comparison_ids = tmp_simdata.comparison_ids; + %comparison_invids = tmp_simdata.comparison_invids; + clip_type = tmp_simdata.clip_type; +else + load ('db.mat', 'comparison','comparison_ids','comparison_names'); + + clip_type = 'MTTClip'; +end +comparison_invids = sparse(comparison_ids,1,[1:numel(comparison_ids)]); +clips = feval(clip_type,comparison_ids); + +% --- +% provide some timing information +% --- +nruns = numel(fparams); +tatic = []; +tatoc = []; +ftoc = []; +mlrtic = []; +mlrtoc = []; +runs = 0; + +res = []; +% TEST +for i = 1:numel(fparams) + + % TIMING start + runs = runs + 1; + tatic(end+1) = cputime(); + + % warning ('disabled feature cache reset'); + MTTAudioFeatureDBgen.reset_feature_dbs('exclude',{'db_magnaaudiofeat'}); + eval('%s.reset;', MTTAudioFeatureDBgen.db_name(featuretype)); + + % extract features + try + % --- + % try loading old features with the same parameter hash. + % we use the md5 hash to distinguish between features + % --- + %paramhash = MTTAudioFeature.param_hash(featuretype, fparams(i)); + %featfile = sprintf('runlog_%s_feat.mat', paramhash); + %if exist(featfile,'file') == 2 + % eval(sprintf('%s.import(featfile);', MTTAudioFeatureDBgen.db_name(featuretype))); + + % load db + paramhash=eval(sprintf('%s.load(featuretype, fparams(i), clips);', MTTAudioFeatureDBgen.db_name(featuretype))); + paramhash=substr(paramhash, 0, -4); + if ~isempty(paramhash) + % --- + % Here, we make sure the clips are + % associated to the feature values + % --- + features = clips.features(featuretype, fparams(i)); + X = features.vector(); + + else + % --- + % we extract the mixed features, but leave the option of + % using no tags + % --- + features = clips.features(featuretype, fparams(i)); + features.define_global_transform(); + + % get the feature vector + X = features.vector(); + +% % save features with specific filename to disc +% xml_save(sprintf('runlog_%s_param.xml', paramhash), fparams(i)); +% features.saveto(featfile); + + paramhash=eval(sprintf('%s.save();', MTTAudioFeatureDBgen.db_name(featuretype))); + paramhash=substr(paramhash, 0, -4); + end + + + % TIMING + ftoc(end+1) = cputime - tatic(end); + fprintf('Got features. took %2.2f minutes\n', ftoc(end) / 60); + catch err + + print_error(err); + + % --- + % TODO: save feature configuration and indicate faliure + % --- + if ~exist('paramhash') + paramhash = hash(xml_format(fparams(i)),'MD5'); + end + xml_save(sprintf('runlog_%s_param.xml', paramhash), fparams(i)); + xml_save(sprintf('runlog_%s_err.xml', paramhash), print_error(err)); + + continue; + end + + + % skip empty feature set; + if isempty(X) + continue; + end + % iterate over trainparams + for j = 1:numel(trainparams) + + % TIMING + mlrtic(end+1) = cputime; + paramhash_mlr = hash(xml_format(trainparams(j)),'MD5'); + + % --- + % Note: here we load the similarity data. + % this data is trunated if inctrain + % --- + simdata = load(trainparams(j).dataset); + if isfield(trainparams(j),'inctrain') && (trainparams(j).inctrain == 0) + + simdata.partBinTrn = simdata.partBinTrn(:,end); + + if isfield(simdata, 'partBinNoTrn') + simdata.partBinNoTrn = simdata.partBinNoTrn(:,end); + end + end + + if isfield(trainparams(j),'notintrain') && (trainparams(j).notintrain == 0) + + if isfield(simdata, 'partBinNoTrn') + simdata = rmfield(simdata, 'partBinNoTrn'); + end + end + + % --- + % NOTE: THIS IS TRAINING + % call training function + % --- + [tmp] = do_test_rounds(trainfun, X, simdata, trainparams(j), fparams(i),... + paramhash, paramhash_mlr, clips); + + tmp.finfo = features(1).data.info; + tmp.fparams = features(1).my_params; + tmp.fparamhash = paramhash; + tmp.script = testscript; + + % TIMING + mlrtoc(end+1) = cputime - mlrtic(end); + tmp.timeused = mlrtoc(end); + + % save result to result struct; + if ~isempty(tmp) + if isempty(res) + + res = tmp; + else + + res(end+1) = tmp; + end + + if size(tmp.mean_ok_test,1) == 2 + maxperf = max(max(tmp.mean_ok_test(1,:))); + else + maxperf = max(max(tmp.mean_ok_test)); + end + fprintf('Learned something: %2.2f perc. Took %2.2f minutes \n',... + maxperf * 100, mlrtoc(end) / 60 ); + pause(0.5); + else + warning('Learned nothing \n'); + end + + % save output + out = res; + + % save final results + save(sprintf('runlog_%s_%s_finalresults.mat',... + hash(xml_format(fparams),'MD5'),... + hash(xml_format(trainparams),'MD5')... + ), 'out'); + + xml_save(sprintf('runlog_%s_%s_params.mat',... + hash(xml_format(fparams),'MD5'),... + hash(xml_format(trainparams),'MD5')... + ), struct('fparams', fparams_all, 'trainparams', trainparams_all) ); + + end + + % TIMING + clc; + tatoc(end+1) = cputime - tatic(end); + cprint(0,'%3.2f percent done, %2.2fh spent, %2.2fh to go. \n mlr / feature: %3.3f \n',... + (runs / nruns) *100 , sum(tatoc) / 3600,... + ((sum(tatoc) / runs) * (nruns - runs)) / 3600, mean(mlrtoc) / mean(ftoc) ); + cprint(0,'\nGo get a coffee, the next round will take %3.0f minutes \n', ... + (mean(mlrtoc) * numel(trainparams) + mean(ftoc)) / 60); +end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/xml_parse_mtt.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ +function features = xml_parse_mtt(file) +% features = xml_parse_mtt(file) +% +% parses the magnatagatune xml file to retrieve the audio +% analysis results. The file isexpected to use the deprecated +% EchoNest XML format +% +% time always travels an the horizontal axis + +% --- +% TODO: Add Error checking to certan tags as chroma number, +% loudness max etc. +% --- + +fdata = fileread(file); +try + % --- + % TODO: check for other than the p-code exceptions + % --- + + data = xml_parseany(fdata); +catch exception +end +%% ------------------------------------------------------------------------ +% --- +% copy general track data +% --- + +fields = fieldnames(data.track{1}.ATTRIBUTE); +for i = 1:length(fields) + features.(fields{i}) = str2double(data.track{1}.ATTRIBUTE.(fields{i})); +end + +%% ------------------------------------------------------------------------ +% --- +% get harmonic analysis. this is stored in segments +% --- +dsegments = data.track{1}.segments{1}.segment; + +for i = 1:numel(dsegments) + + % get start and duration + segments(i).start = str2double(dsegments{i}.ATTRIBUTE.start); + segments(i).duration = str2double(dsegments{i}.ATTRIBUTE.duration); + + % --- + % NOTE: for the chroma and mfcc features, we assume that the classes + % are always saved and parsed in correct order, thus we can afford to + % refrain from saving the class number with the class + % --- + + % assemble chroma features + segments(i).pitches = zeros(12,1); + for j = 1:12 + + segments(i).pitches(j) = str2double(... + dsegments{i}.pitches{1}.pitch{j}.CONTENT); + end + + % assemble mfcc features; + segments(i).timbre = zeros(numel(dsegments{i}.timbre{1}.coeff),1); + for j = 1:numel(dsegments{i}.timbre{1}.coeff) + + segments(i).timbre(j) = str2double(... + dsegments{i}.timbre{1}.coeff{j}.CONTENT); + end + + % get loudness measurements in dB and time + segments(i).loudness = str2double(... + dsegments{i}.loudness{1}.dB{1}.CONTENT); + + segments(i).loudness_time = str2double(... + dsegments{i}.loudness{1}.dB{1}.ATTRIBUTE.time); + + segments(i).loudness_max = str2double(... + dsegments{i}.loudness{1}.dB{2}.CONTENT); + + segments(i).loudness_max_time = str2double(... + dsegments{i}.loudness{1}.dB{2}.ATTRIBUTE.time); +end + +features.segments = segments; + +%% ------------------------------------------------------------------------ +% --- +% get sections +% --- +dsections = data.track{1}.sections{1}.section; + +secstart = zeros(1,numel(dsections)); +secduration = zeros(1,numel(dsections)); +for i = 1:numel(dsections) + sections(i).start = str2double(dsections{i}.ATTRIBUTE.start); + sections(i).duration = str2double(dsections{i}.ATTRIBUTE.duration); +end + +features.sections = sections; + +%% ------------------------------------------------------------------------ +% --- +% get beat and rythm data. the metric data is structured +% hierarchically, as each bar contains several beats, +% which contaisn several tatums. +% NOTE: Although the metrum and tempo have been evaluated and fixed +% on a global scale, the number of bars and tatum vary greatly. +% --- +dbars = data.track{1}.meter{1}.bar; + +for i = 1:numel(dbars) + + % get bar information + bars(i).confidence = str2double(dbars{i}.ATTRIBUTE.conf); + for j = 1:numel(dbars{i}.beat) + + % get beat information + bars(i).beat(j).confidence = str2double(dbars{i}.beat{j}.ATTRIBUTE.conf); + + for k = 1:numel(dbars{i}.beat{j}.tatum) + + % get tatum information + if ~isempty(dbars{i}.beat{j}.tatum{k}.ATTRIBUTE) + bars(i).beat(j).tatum(k).time = str2double(dbars{i}.beat{j}.tatum{k}.CONTENT); + bars(i).beat(j).tatum(k).confidence = str2double(dbars{i}.beat{j}.tatum{k}.ATTRIBUTE.conf); + + else + % save empty struct + bars(i).beat(j).tatum = struct([]); + end + + end + end +end + +features.bars = bars; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/magnatagatune/xml_write_tags.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +% xmloutput_tags + +% Create a sample XML document. +docNode = com.mathworks.xml.XMLUtils.createDocument... + ('tags') +docRootNode = docNode.getDocumentElement; +for i=1:length(annots_descripts) + thisElement = docNode.createElement('tagname'); + thisElement.appendChild... + (docNode.createTextNode(annots_descripts{i}(1:end))); + docRootNode.appendChild(thisElement); +end +%docNode.appendChild(docNode.createComment('this is a comment')); + +% Save the sample XML document. +xmlFileName = ['test','.xml']; +xmlwrite(xmlFileName,docRootNode); +edit(xmlFileName); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/startup_music_research.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,96 @@ +% --- +% startup.m +% this shall become the general camir startup +% --- + +% global globalvars; + +% use this revision numbers for figures etc! +% globalvars.camir.revision = str2double(substr('$Rev$', 5, -1)); +% rev = textscan(evalc('!svnversion'),'%d%s'); +% globalvars.camir.revision = double(rev{1}); + +globalvars.camir.path = pwd; +globalvars.camir.revision = camirversion; + +clear rev +globalvars.debug = 0; + +% --- +% load MTT database +% --- +global comparison; +global comparison_ids; +load ('db.mat', 'comparison','comparison_ids','comparison_names'); + + +% --- +% load MSD database +% --- +global MillionSong MSDsubset +MillionSong = globalvars.camir.msd.basepath; +MSDsubset='subset_'; % or '' for full set + +globalvars.camir.msd.data_path= [MillionSong,'/data']; +globalvars.camir.msd.addf_path= [MillionSong,'/AdditionalFiles']; +globalvars.camir.msd.addf_prefix= [globalvars.camir.msd.addf_path,'/',MSDsubset]; +% Check that we can actually read the dataset + + +warning off MATLAB:class:cannotUpdateClass + +% initialize clip and feature databases + +global db_MTTClip; +db_MTTClip = MTTClipDB(); + +%global db_MSDClip; +%db_MSDClip = MSDClipDB(); + +% global db_CASIMIRClip; +% db_CASIMIRClip = CASIMIRClipDB(); + +global db_magnaaudiofeat; +db_magnaaudiofeat = MTTAudioFeatureDBgen('MTTAudioFeatureRAW'); + +global db_magnaaudiofeat_basicsm; +db_magnaaudiofeat_basicsm = MTTAudioFeatureDBgen('MTTAudioFeatureBasicSm'); + +global db_magnatagfeat_genrebasic; +db_magnatagfeat_genrebasic = MTTAudioFeatureDBgen('MTTTagFeatureGenreBasic'); + +global db_magnamixedfeat_genrebasicsm; +db_magnamixedfeat_genrebasicsm = MTTAudioFeatureDBgen('MTTMixedFeatureGenreBasicSm'); + +global db_MTTMixedFeatureGenreBasicSmPCA; +db_MTTMixedFeatureGenreBasicSmPCA = MTTAudioFeatureDBgen('MTTMixedFeatureGenreBasicSmPCA'); + +global db_MTTAudioFeatureSlaney08; +db_MTTAudioFeatureSlaney08 = MTTAudioFeatureDBgen('MTTAudioFeatureSlaney08'); + +global db_MTTMixedFeatureSlaney08GenreBasicSm; +db_MTTMixedFeatureSlaney08GenreBasicSm = MTTAudioFeatureDBgen('MTTMixedFeatureSlaney08GenreBasicSm'); + +global db_MTTMixedFeatureSlaney08GenreBasicSmPCA; +db_MTTMixedFeatureSlaney08GenreBasicSmPCA = MTTAudioFeatureDBgen('MTTMixedFeatureSlaney08GenreBasicSmPCA'); + +global db_MTTMixedFeatureGenreRandom; +db_MTTMixedFeatureGenreRandom = MTTAudioFeatureDBgen('MTTMixedFeatureGenreRandom'); + +global db_MTTMixedFeatureStober11Genre; +db_MTTMixedFeatureStober11Genre = MTTAudioFeatureDBgen('MTTMixedFeatureStober11Genre'); + +global db_MTTMixedFeatureStober11Slaney08GenreBasicSm; +db_MTTMixedFeatureStober11Slaney08GenreBasicSm = MTTAudioFeatureDBgen('MTTMixedFeatureStober11Slaney08GenreBasicSm'); + +global db_MTTMixedFeatureSon; +db_MTTMixedFeatureSon = MTTAudioFeatureDBgen('MTTMixedFeatureSon'); + +global db_AudioFeatureRAW; +db_AudioFeature = MTTAudioFeatureDBgen('AudioFeatureRAW'); + +%global db_MSDAudioFeatureRAW; +%db_MSDAudioFeatureRAW = MTTAudioFeatureDBgen('MSDAudioFeatureRAW'); + +global db_MTTMixedFeatureSonRBM; +db_MTTMixedFeatureSonRBM = MTTAudioFeatureDBgen('MTTMixedFeatureSonRBM');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/CQueue.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,160 @@ +classdef CQueue < handle +% CQueue define a queue data strcuture +% +% It likes java.util.Queue, however, it could use CQueue.content() to +% return all the data (in cells) of the Queue, and it is a litter faster +% than java's Queue. +% +% q = CQueue(c); c is a cells, and could be omitted +% s.size() return the numble of element +% s.isempty() return true when the queue is empty +% s.empty() delete all the elements in the queue. +% s.push(el) push el to the top of qeueu +% s.pop() pop out the the beginning of queue, and return the element +% s.front() return the the the beginning element of the qeueu +% s.back() return the the the rear element of the qeueu +% s.remove() remove all data from the queue +% s.content() return all the data of the queue (in the form of a +% cells with size [s.size(), 1] +% +% See also CList, CStack +% Copyright: zhang@zhiqiang.org, 2010. +% url: http://zhiqiang.org/blog/it/matlab-data-structures.html + + properties (Access = private) + buffer % a cell, to maintain the data + beg % the start position of the queue + rear % the end position of the queue + % the actually data is buffer(beg:rear-1) + end + + properties (Access = public) + capacity % Õ»µÄÈÝÁ¿£¬µ±ÈÝÁ¿²»¹»Ê±£¬ÈÝÁ¿À©³äΪ2±¶¡£ + end + + methods + function obj = CQueue(c) % ³õʼ»¯ + if nargin >= 1 && iscell(c) + obj.buffer = [c(:); cell(numel(c), 1)]; + obj.beg = 1; + obj.rear = numel(c) + 1; + obj.capacity = 2*numel(c); + elseif nargin >= 1 + obj.buffer = cell(100, 1); + obj.buffer{1} = c; + obj.beg = 1; + obj.rear = 2; + obj.capacity = 100; + else + obj.buffer = cell(100, 1); + obj.capacity = 100; + obj.beg = 1; + obj.rear = 1; + end + end + + function s = size(obj) % ¶ÓÁг¤¶È + if obj.rear >= obj.beg + s = obj.rear - obj.beg; + else + s = obj.rear - obj.beg + obj.capacity; + end + end + + function b = isempty(obj) % return true when the queue is empty + b = ~logical(obj.size()); + end + + function s = empty(obj) % clear all the data in the queue + s = obj.size(); + obj.beg = 1; + obj.rear = 1; + end + + function push(obj, el) % ѹÈëÐÂÔªËص½¶Óβ + if obj.size >= obj.capacity - 1 + sz = obj.size(); + if obj.rear >= obj.front + obj.buffer(1:sz) = obj.buffer(obj.beg:obj.rear-1); + else + obj.buffer(1:sz) = obj.buffer([obj.beg:obj.capacity 1:obj.rear-1]); + end + obj.buffer(sz+1:obj.capacity*2) = cell(obj.capacity*2-sz, 1); + obj.capacity = numel(obj.buffer); + obj.beg = 1; + obj.rear = sz+1; + end + obj.buffer{obj.rear} = el; + obj.rear = mod(obj.rear, obj.capacity) + 1; + end + + function el = front(obj) % ·µ»Ø¶ÓÊ×ÔªËØ + if obj.rear ~= obj.beg + el = obj.buffer{obj.beg}; + else + el = []; + warning('CQueue:NO_DATA', 'try to get data from an empty queue'); + end + end + + function el = back(obj) % ·µ»Ø¶ÓβԪËØ + + if obj.rear == obj.beg + el = []; + warning('CQueue:NO_DATA', 'try to get data from an empty queue'); + else + if obj.rear == 1 + el = obj.buffer{obj.capacity}; + else + el = obj.buffer{obj.rear - 1}; + end + end + + end + + function el = pop(obj) % µ¯³ö¶ÓÊ×ÔªËØ + if obj.rear == obj.beg + error('CQueue:NO_Data', 'Trying to pop an empty queue'); + else + el = obj.buffer{obj.beg}; + obj.beg = obj.beg + 1; + if obj.beg > obj.capacity, obj.beg = 1; end + end + end + + function remove(obj) % Çå¿Õ¶ÓÁÐ + obj.beg = 1; + obj.rear = 1; + end + + function display(obj) % ÏÔʾ¶ÓÁÐ + if obj.size() + if obj.beg <= obj.rear + for i = obj.beg : obj.rear-1 + disp([num2str(i - obj.beg + 1) '-th element of the stack:']); + disp(obj.buffer{i}); + end + else + for i = obj.beg : obj.capacity + disp([num2str(i - obj.beg + 1) '-th element of the stack:']); + disp(obj.buffer{i}); + end + for i = 1 : obj.rear-1 + disp([num2str(i + obj.capacity - obj.beg + 1) '-th element of the stack:']); + disp(obj.buffer{i}); + end + end + else + disp('The queue is empty'); + end + end + + function c = content(obj) % È¡³ö¶ÓÁÐÔªËØ + if obj.rear >= obj.beg + c = obj.buffer(obj.beg:obj.rear-1); + else + c = obj.buffer([obj.beg:obj.capacity 1:obj.rear-1]); + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/CStack.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,108 @@ +classdef CStack < handle +% CStack define a stack data strcuture +% +% It likes java.util.Stack, however, it could use CStack.content() to +% return all the data (in cells) of the Stack, and it is a litter faster +% than java's Stack. +% +% s = CStack(c); c is a cells, and could be omitted +% s.size() return the numble of element +% s.isempty() return true when the stack is empty +% s.empty() delete the content of the stack +% s.push(el) push el to the top of stack +% s.pop() pop out the top of the stack, and return the element +% s.top() return the top element of the stack +% s.remove() remove all the elements in the stack +% s.content() return all the data of the stack (in the form of a +% cells with size [s.size(), 1] +% +% See also CList, CQueue +% +% Copyright: zhang@zhiqiang.org, 2010. +% url: http://zhiqiang.org/blog/it/matlab-data-structures.html + + properties (Access = private) + buffer % Ò»¸öcellÊý×飬±£´æÕ»µÄÊý¾Ý + cur % µ±Ç°ÔªËØλÖÃ, or the length of the stack + capacity % Õ»µÄÈÝÁ¿£¬µ±ÈÝÁ¿²»¹»Ê±£¬ÈÝÁ¿À©³äΪ2±¶¡£ + end + + methods + function obj = CStack(c) + if nargin >= 1 && iscell(c) + obj.buffer = c(:); + obj.cur = numel(c); + obj.capacity = obj.cur; + elseif nargin >= 1 + obj.buffer = cell(100, 1); + obj.cur = 1; + obj.capacity =100; + obj.buffer{1} = c; + else + obj.buffer = cell(100, 1); + obj.capacity = 100; + obj.cur = 0; + end + end + + function s = size(obj) + s = obj.cur; + end + + function remove(obj) + obj.cur = 0; + end + + function b = empty(obj) + b = obj.cur; + obj.cur = 0; + end + + function b = isempty(obj) + b = ~logical(obj.cur); + end + + function push(obj, el) + if obj.cur >= obj.capacity + obj.buffer(obj.capacity+1:2*obj.capacity) = cell(obj.capacity, 1); + obj.capacity = 2*obj.capacity; + end + obj.cur = obj.cur + 1; + obj.buffer{obj.cur} = el; + end + + function el = top(obj) + if obj.cur == 0 + el = []; + warning('CStack:No_Data', 'trying to get top element of an emtpy stack'); + else + el = obj.buffer{obj.cur}; + end + end + + function el = pop(obj) + if obj.cur == 0 + el = []; + warning('CStack:No_Data', 'trying to pop element of an emtpy stack'); + else + el = obj.buffer{obj.cur}; + obj.cur = obj.cur - 1; + end + end + + function display(obj) + if obj.cur + for i = 1:obj.cur + disp([num2str(i) '-th element of the stack:']); + disp(obj.buffer{i}); + end + else + disp('The stack is empty'); + end + end + + function c = content(obj) + c = obj.buffer(1:obj.cur); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/DiGraph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,853 @@ + +% GENERAL GRAPH THEORY CLASS: DiGraph +% +% CAVE: Any special application oriented stuff +% should be outside here + +classdef DiGraph < handle + +properties + + V = sparse(0,1); % boolean for nodes + Vlbl = {}; + + E = sparse(0,0); % sparse weighted edge graph +end + +methods + +% --- +% Constructor +% --- +function G = DiGraph(V, E, Vlbl) + + if nargin == 1 + if ~isnumeric(V) + + G = DiGraph.copy(V); + else + % build graph from Edge Adjacency Matrix + G.V = sparse(find(sum(V + V')) > 0); + G.E = V; + end + end + + if nargin >= 2 + G = DiGraph(); + + if numel(V) ~= max(size(E)) + error 'wrong dimensions'; + end + + % --- + % We copy existent nodes and edges + % --- + if numel(V) == size(V,2) + G.V = V; + else + G.V = V'; + end + G.E = E; + + % --- + % We copy the labels for existent nodes, + % if supplied. + % NOTE: the FIND call is neccessary, as logical indexing + % does not work in this case + % TODO: WHY NOT? + % --- + if (nargin == 3) && ~isempty(Vlbl) + G.Vlbl = cell(numel(G.V),1); + G.Vlbl(find(G.V)) = Vlbl(find(G.V)); + end + + end +end + +% get list of node ids +function out = nodes(G) + out = find(G.V); +end + +% get list of node ids +function out = last_node(G) + out = find(G.V,1,'last'); +end + +function w = edge(G, V1, V2) + + w = G.E(V1, V2); +end + +% get edge list representation +function [val, V1, V2] = edges(G, V1) + + if nargin == 1 + + % get all edges of the graph + [V1, V2] = find(G.E); + + val = zeros(numel(V1), 1); + + for i = 1:numel(V1) + val(i) = G.E(V1(i), V2(i)); + end + + elseif nargin == 2 + + % get all edges coming from or entering this node + V2 = find(G.E(V1, :)); + val = G.E(V1, V2); + + V2 = [ V2 find(G.E(:,V1))']; + val = [ val G.E(V2, V1)']; + end +end + +% compare two graphs +function out = eq(a,b) + + % --- + % compare graph stats + % necessary + % --- + if a.order ~= b.order + out = false; + cprint(3, 'eq: graph node numbers do not match'); + return + end + + if a.num_edges ~= b.num_edges + out = false; + cprint(3, 'eq: graph edge numbers do not match'); + return + end + + + % --- + % compare labels and reindex graph b if + % necessary + % --- + lbla = a.label(); + lblb = b.label(); + + for i = 1:numel(lbla) + if ~isempty(lbla{i}) + tmpidx = strcellfind(lblb, lbla{i}); + + % check for substring problems + for j = 1:numel(tmpidx) + if strcmp(lblb{tmpidx(j)}, lbla{i}) + idx(i) = tmpidx(j); + break; + end + end + end + end + + % test on found labels + num_matching_lbl = sum(idx > 0); + if ~isempty(lbla) && (num_matching_lbl < a.order) + out = false; + cprint(3, 'eq: graph labels do not match'); + return + end + + % --- + % reindex edges and nodes: only replace valid indexes + % --- + val_idx = idx > 0; + idx = idx(val_idx); + + bE(val_idx,val_idx) = b.E(idx,idx); + bV(val_idx) = b.V(idx); + + if sum(a.V ~= bV) > 0 + out = false; + cprint(3, 'eq: nodes do not match'); + return + end + + if sum(sum(a.E ~= bE)) > 0 + out = false; + cprint(3, 'eq: edges do not match'); + return + end + + % --- + % OK, seems these Graphs are the same!!! + % --- + out = true; + +end + +% find node via its label +function Vid = node(G, label) + lbl = G.label(); + + Vid = strcellfind(lbl, label,1); +end + +% add a node +function add_node(G,V) + + % set node indicator + G.V(V) = 1; + + G.E(V,V) = 0; +end + +% remove a node +function remove_node(G,V) + + % remove node + G.V(V) = 0; + + % remove all edges + G.E(V,:) = 0; + G.E(:,V) = 0; +end + +% remove an edge +function remove_edge(G, V1, V2) + + G.E(V1, V2) = 0; +end + +% add an edge +function add_edge(G, V1, V2, weight) + + G.add_node(V1); + G.add_node(V2); + + if G.E(V1,V2) == 0 + + % save weight + set_edge(G, V1, V2, weight); + else + + join_edge(G, V1, V2, weight) + end +end + +% --- +% implementation of edge joining, +% to be overwritten for several +% purposes +% --- +function join_edge(G, V1, V2, weight) + + set_edge(G, V1, V2, G.edge(V1, V2) + weight); +end + +% --- +% sets the edge without considering earlier weights +% --- +function set_edge(G, V1, V2, weight) + + G.E(V1, V2) = weight; +end + +% --- +% Graph-specific functions +% --- +function c = children(G, Vid) + + c = find(G.E(Vid,:) > 0); +end + +function p = parents(G, Vid) + + p = find(G.E(:,Vid) > 0)'; +end + +% --- +% Vertex Degree +% --- +function out = degree(G, V) + + out = sum(G.E(V,:) > 0) + sum(G.E(:,V) > 0); +end + +% --- +% Vertex Degree +% --- +function out = degree_in(G, V) + + out = sum(G.E(V,:) > 0); +end + +% --- +% Max Degree In +% --- +function out = max_degree_in(G) + + out = max(sum(G.E > 0, 2)); +end + +% --- +% Vertex Degree +% --- +function out = degree_out(G, V) + + out = sum(G.E(:,V) > 0); +end + +% --- +% Max Degree In +% --- +function out = max_degree_out(G) + + out = max(sum(G.E > 0, 1)); +end + + +% --- +% Max Degree +% --- +function out = max_degree(G) + + out = max(sum(G.E > 0, 1) + sum(G.E > 0, 2)'); +end + +% --- +% Max weight +% --- +function out = max_weight(G) + + out = max(max(G.E)); +end + +% --- +% Number of Vertices in Graph +% --- +function out = order(G) + out = sum(G.V); +end + +% --- +% Number of Edges in Graph +% --- +function out = num_edges(G) + out = sum(sum(G.E ~= 0)); +end + +% --- +% Number of Vertices in Graph +% --- +function out = cardinality(G) + out = order(G); +end + +function Gs = unconnected_subgraph(G) + Vtmp = (sum(G.E + G.E') == 0); + Etmp = sparse(size(G.E, 1), size(G.E, 1)); + + Gs = DiGraph(Vtmp, Etmp, G.label()); +end + +% --- +% return string labels for a (set of) node(S) +% --- +function out = label(G, Vid) + + out = {}; + if nargin == 1 + % maybe much faster for whole graph + if (numel(G.Vlbl) < G.order() || isempty(G.Vlbl)) + + out = cell(numel(G.V), 1); + for i = 1:numel(Vid) + out{i} = sprintf('%d', Vid(i)); + end + else + out = G.Vlbl; + end + + elseif nargin == 2 + for i = 1:numel(Vid) + + if (numel(G.Vlbl) < Vid(i)) || isempty(G.Vlbl{Vid(i)}) + + if numel(Vid) > 1 + out{i} = sprintf('%d', Vid(i)); + else + out = sprintf('%d', Vid(i)); + end + else + if numel(Vid) > 1 + out{i} = G.Vlbl{Vid(i)}; + else + out = G.Vlbl{Vid(i)}; + end + end + end + end +end + + +% ----------------------------------------------------------------- +% Graph theory algorithms +% --- + + +% --- +% sets all the main diagonal edges to zero +% --- +function remove_cycles_length1(G) + + for i = G.nodes(); + G.E(i,i) = 0; + end +end + + +% --- +% Returns whether a given graph has cycles or not, +% using the SCC search +% --- +function out = isAcyclic(G) + % --- + % We get all the sccs in the DiGraph, and + % return true if none of the sccs has more than + % one node + % --- + + % check for self-loops + if sum(abs(diag(G.E))) > 0 + out = 0; + return; + end + + [~, s, ~] = strongly_connected_components(G); + + if max(s) < 2 + out = 1; + else + out = 0; + end +end + +% --- +% All SCC's ordered by number of nodes in graph +% +% this should also be able to return +% the SCC of just one node +% --- +function [Gs, s, id] = strongly_connected_components(G, Vin) + + % marking + marked = zeros(size(G.V)); + + % --- + % two stacks, + % one: has not been assigned to scc + % two: unclear if in same scc + % --- + stack1 = CStack(); + stack2 = CStack(); + + % initialise scc ids + id = zeros(size(G.V)) - 1; % assigned scc? + idctr = 1; + + % initialise graph ordering + preorder = zeros(G.order, 1); + prectr = 0; + + for v = G.nodes(); + if ~marked(v) + dfs(G, v); + end + end + + % --- + % create subgraphs (DiGraph here) for sccs + % --- + if nargin == 1 + + s = zeros(idctr-1,1); + for idctr2 = 1:idctr-1 + Vtmp = (id == idctr2); + Emask = sparse(size(G.E, 1), size(G.E, 1)); + Emask(Vtmp,Vtmp) = 1; + Etmp = G.E .* Emask; + + Gs(idctr2) = DiGraph(Vtmp, Etmp, G.Vlbl); + s(idctr2) = Gs(idctr2).order(); + end + + % --- + % order by number of nodes + % --- + [s, idx] = sort(s,'descend'); + Gs = Gs(idx); + Gmax = Gs(1); + + else + % --- + % get just the scc for the questioned node + % --- + Vtmp = (id == id(Vin)); + Emask = sparse(size(G.E, 1), size(G.E, 1)); + Emask(Vtmp,Vtmp) = 1; + Etmp = G.E .* Emask; + + Gs = DiGraph(Vtmp, Etmp); + s = Gs.order(); + end + + % --- + % NOTE: THIS IS A NESTED DFS BASED GRAPH ORDERING + % --- + function dfs(G, v) + + % mark this node as visited + marked(v) = 1; + + preorder(v) = prectr; + prectr = prectr + 1; + + % push into both stacks + stack1.push(v); + stack2.push(v); + + % --- + % dfs + % --- + for w = G.children(v) + if ~marked(w) + % --- + % traverse into dfs if not yet visited + % --- + dfs(G, w); + + elseif id(w) == -1 + + % --- + % w has not yet been assigned to a strongly connected + % component + % --- + while ((preorder(stack2.top()) > preorder(w))) + stack2.pop(); + end + end + end + + % --- + % found scc containing v + % --- + if (stack2.top() == v) + stack2.pop(); + + w = -1; + while (w ~= v) + + % --- + % collect all the nodes of this scc + % --- + w = stack1.pop(); + id(w) = idctr; + end + idctr = idctr + 1; + end + + end % function dfs +end + +function [Gs, s, id] = connected_components(G, varargin) + % --- + % get all connected subgraphs: + % --- + + % make edge matrix undirected + G2 = DiGraph(Graph(G)); + + [GsGraph, s, id] = strongly_connected_components(G2, varargin{:}); + + % get the actual subgraps + + for i =1:numel(GsGraph) + Gs(i) = G.subgraph(GsGraph(i).nodes); + end +end + +% --- +% creates new graph just containing the +% specified nodes and optionally specified edges +% nodes can be specified using +% a. the binary G.V structure +% b. or node indices +% --- +function G2 = subgraph(G, V, E) + if nargin == 2 + E = []; + end + + % --- + % create new graph as copy ofthe old + % --- + + G2 = feval(class(G), G); + + % --- + % reset nodes and edges + % NOTE: we determine the input format first + % --- + if (max(V) == 1 && numel(V) > 1) || max(V) == 0 + + G2.remove_node(find(~V)); + else + + G2.remove_node(setdiff(1:numel(G.V), V)); + end + if ~isempty(E) + G2.E = E; + end +end + +% --- +% joins the information of graph G2 with +% this GRaph, not duplicating nodes. +% --- +function add_graph(G, G2) + + % determine if symmetric edges have to be added + clas = cat(1, class(G2), superclasses(G2)); + if strcellfind(clas, 'Graph') + add_symm = 1; + else + add_symm = 0; + end + + % --- + % add all nodes and edges in G2 + % --- + for V = G2.nodes(); + + G.add_node(V); + end + + % --- + % NOTE / TODO: + % this LABEL inheritance is far too expensive when + % creating many copiesof the same graph + % Its also unnessecary for lots of them + % except for debugging :(. + % --- + G.Vlbl = cell(1,numel(G2.V)); + G.Vlbl(G2.nodes()) = G2.label(G2.nodes()); + + % --- + % get all edges in G2 + % --- + [val, V1, V2] = edges(G2); + + for i = 1:numel(val) + + % --- + % add edges to graph + % --- + G.add_edge(V1(i), V2(i), val(i)); + + if add_symm + % --- + % add symmetric edges to graph + % --- + G.add_edge(V2(i), V1(i), val(i)); + end + + end +end + +% --- +% substracts the edges in G2 from +% this GRaph, removing not connected nodes. +% --- +function Gout = minus(a, b) + Gout = feval(class(a),a); + Gout.remove_graph(b); +end + +function remove_graph(G, G2) + + % determine if symmetric edges have to be added + clas = cat(1, class(G2), superclasses(G2)); + if strcellfind(clas, 'Graph') + symm = 1; + else + symm = 0; + end + + % remove edges + [val, V1, V2] = G2.edges(); + for j = 1:numel(val) + + % remove specified edges + G.remove_edge(V1(j), V2(j)); + + % remove symmetric edges if subtracting symm graph + if symm + + G.remove_edge(V2(j), V1(j)); + end + end + + % --- + % Note : we only remove nodes with no + % remaining incoming edges + % --- + V = G2.nodes(); + for j = 1:numel(V) + + if G.degree(V(j)) == 0 + + G.remove_node(V(j)); + end + end + +end + +% --- +% compact graph representation +% --- +function [E, labels] = compact(G) + + % --- + % get nodes and create a reverse index + % --- + Vidx = sparse(G.nodes,1,1:G.order()); + [w, V1, V2] = G.edges(); + + % create compact adjacency matrix + E = sparse(Vidx(V1), Vidx(V2),w, G.order(), G.order()); + + labels = G.label(G.nodes()); +end + +% --- +% determines if Edges in G2 are the same as in G +% --- +function [out] = isSubgraph(G, G2) + + [val, V1, V2] = G2.edges(); + validE = false(numel(V1), 1); + + i = 1; + while i <= numel(V1) + + % --- + % Test if edge exists in other graph + % --- + if G.edge(V1(i),V2(i)) == val(i) + out = 0; + return; + end + i = i +1 ; + end + + % --- + % Test labels + % --- + if ~isempty(G.Vlbl) && ~isempty(G2.Vlbl) + + V = G2.nodes(); + i = 1; + while i <= numel(V) + if strcmp(G.label(V(i)), G2.label(V(i))) ~= 0 + out = 0; + return; + end + i = i + 1; + end + end + + out = 1; +end + +% --- +% Visualise the Similarity Graph +% --- +function visualise(G) + + % --- + % get colormap for edge weights + % --- + cmap = jet(100); + + % --- + % NOTE: we now get the weight colors for all edges + % get maximum weight and all edges + % --- + [colors, V1, V2] = G.edges(); + w = G.max_weight(); + + % normalise colors + colors = max(1,round((colors / w) * 100)); + + % get node labels + V1lbl = G.label(V1); + V2lbl = G.label(V2); + + % --- + % compose edgecolor matrix + % --- + ec = cell(numel(V1), 3); + for i = 1:numel(V1) + ec(i,:) = {V1lbl{i}, V2lbl{i}, cmap(colors(i),:)}; + end + + % --- + % For Performance Issues + % We get the compact Graph and + % !hope! for the labels to correspond to those above + % --- + [E, labels] = compact(G); + + % determine if symmetric Graph + clas = cat(1, class(G), superclasses(G)); + if strcellfind(clas, 'Graph') + symm = 1; + else + symm = 0; + end + + graphViz4Matlab('-adjMat',E, ... + '-nodeLabels',labels, ... + '-edgeColors', ec, ... + '-undirected', symm); +end +end + + +methods (Static) + function G = copy(Gin) + + if strcmp(class(Gin), 'DiGraph') + + G = DiGraph(Gin.V, Gin.E); + G.Vlbl = Gin.Vlbl; + + else + warning ('cannot copy graph, casting instead') + G = DiGraph.cast(Gin); + end + + end + + function G = cast(Gin) + + % --- + % this uses an imput grpaph + % to create a new digraph + % --- + G = DiGraph(); + + % --- + % Add the input graph to the empty one + % --- + G.add_graph(Gin); + + end +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/Graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,154 @@ +% GENERAL GRAPH THEORY CLASS +% +% CAVE: Any special application oriented stuff +% should be outside here + +classdef Graph < DiGraph + +methods + + % --- +% Constructor +% --- +function G = Graph(V, E) + + if nargin == 1 + % --- + % this uses an imput grpaph + % to create a new graph + % --- + G = Graph.copy(V); + + elseif nargin == 2 + + if numel(V) == size(V,2) + G.V = V; + else + G.V = V'; + end + + G.E = E; + end +end +function w = edge(G, V1, V2) + + sm = min([V1, V2]); + bg = max([V1, V2]); + + w = G.E(sm, bg); +end + +% --- +% add an edge +% the undirected case: G.E is triangular +% --- +function add_edge(G, V1, V2, weight) + + G.add_node(V1); + G.add_node(V2); + + sm = min([V1, V2]); + bg = max([V1, V2]); + + if G.E(sm, bg) == 0 + + % save weight + G.E(sm, bg) = weight; + else + + join_edge(G, V1, V2, weight); + end +end + +% remove an edge +function remove_edge(G, V1, V2) + + sm = min([V1, V2]); + bg = max([V1, V2]); + + G.E(sm,bg) = 0; +end + +% --- +% sets the edge without considering earlier weights +% --- +function set_edge(G, V1, V2, weight) + + sm = min([V1, V2]); + bg = max([V1, V2]); + + G.E(sm, bg) = weight; + +end + +% --- +% Graph-specific functions +% in the undirected case, these are the same +% --- +function c = children(G, Vid) + + c = [find(G.E(Vid,:) > 0) find(G.E(:,Vid) > 0)']; +end + +function p = parents(G, Vid) + + p = [find(G.E(Vid,:) > 0) find(G.E(:,Vid) > 0)']; +end + +% --- +% Vertex Degree +% --- +function out = degree(G, V) + + out = sum(G.E(V,:) > 0) + sum(G.E(:,V) > 0); +end + +% --- +% Vertex Degree +% --- +function out = degree_in(G, V) + + out = sum(G.E(V,:) > 0) + sum(G.E(:,V) > 0); +end + +% --- +% Vertex Degree +% --- +function out = degree_out(G, V) + + out = sum(G.E(:,V) > 0) + sum(G.E(:,V) > 0); +end + +end + + +methods (Static) + function G = copy(Gin) + + if strcmp(class(Gin), 'Graph') + + G = Graph(Gin.V, Gin.E); + G.Vlbl = Gin.Vlbl; + + else + warning ('cannot copy graph, casting instead') + G = Graph.cast(Gin); + end + + end + + function G = cast(Gin) + + % --- + % this uses an imput grpaph + % to create a new digraph + % --- + G = Graph(); + + % --- + % Add the input graph to the empty one + % --- + G.add_graph(Gin); + end +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/NaiveHash.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +classdef NaiveHash < handle + + properties + + last_key = 0; + map; + end + +% --- +% the methods +% --- +methods + function ht = NaiveHash(data) + + ht.Map = containers.Map('KeyType', 'int32', 'ValueType', 'int32'); + end + + function put(ht, data) + + % get new key + last_key = last_key + 1; + ticketMap(data) = last_key; + end + + function data(ht, data) + + end + + function out = key(ht, data) + + out = ht.Map(data); + end + + function iskey(ht, key) + + end + +end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/addTopXAxis.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,140 @@ +function [nAxis] = addTopXAxis (varargin) + +% [nAxis] = addTopXAxis (axisH, properties ...) +% Add an X-axis on top (additional to the one on bottom) of the figure, +% with its own ticks labels and axis label (allow double scale). +% Ticks, minor ticks positions and x limits are set to be identical for top and bottom +% axis. Moreover, they are linked, i.e. a modification of one of them, even after execution +% of this function, will result in modification of the other one. +% +% Parameters: +% axisH = handle for axes (dafault = current axes, given by |gca|) +% properties: syntax is two-folded arguments for each property, +% on the form: 'name of property' (case insensitive), value of property +% and properties can be +% xLabStr : the label for the top X-axis (string) +% default = '' +% expression or exp : expression for transforming bottom X-tick labels to +% top X-tick labels. In the expression, |argu| will refer +% to the value of the bottom X-tick labels. +% eg. going from |log10(x)| to linear |x| values will be done with +% the string '10.^argu' for expression. +% default = '' +% Note : if expression needs to use some variables, that are not accessible to this function, they need to be evaluated +% before being passed to this function (see exemples below). +% xTickLabelFormat : display format, used for converting numerical values for tick labels in strings. (string) +% The computation of top axis tick labels using |expression| can lead to numbers taking a lot of space +% because of the precision of the display. Reducing the precision will reduce the size of the labels. +% default = '%4.1f' +% +% Exemples: +% +% axisH = axes % create new axes, and save the handle in axisH +% addTopXAxis(axisH, 'expression', '-argu') % change the label of the ticks by their opposite +% +% +% addTopXAxis('xticklabel', '(k0.*10.^argu)', 'xlabel', '$\lambda_{up}$ (cm)') +% will use the current axis (handle is not passed to the function), and will compute the new X-tick values according to +% x' = k0.*10.^argu; +% where |k0| is a variable whose value has to be set in the 'base' workspace. + +% +% V2 - 11/27/2005 +% Modifs for V2: +% * - now evaluates expression in 'base' workspace. +% Therefore, variables (like 'k0' in the second example) do not need to be evaluated anymore before being passed, +% as long as they already exist in 'base' workspace. It should allow more complex expressions to be passed than previously. +% Example 2 then becomes +% addTopXAxis('expression', '(k0.*10.^argu)', 'xLabStr', '$\lambda_{up}$ (cm)') +% instead of +% addTopXAxis('expression', ['(', num2str(k0),'.*10.^argu)'], 'xLabStr', '$\lambda_{up}$ (cm)' +% Drawback: the function create/assign a variable called 'axisH' in base workspace. Potential conflicts here ... +% * - properties are not case sensitive anymore +% * - 'exp' can be used instead of 'expression' for property name (following John D'Errico comment, if I understood it well ....) +% +% +% Author : Emmanuel P. Dinnat +% Date : 09/2005 +% Contact: emmanueldinnat@yahoo.fr + +global hlink % make the properties link global + +%% Default values for properties +axisH = gca; +xTickLabelFormat = '%4.1f'; +xLabStr = ''; +expression = ''; + +%% Process input parameters (if they exist) +% if input parameters +if length(varargin) > 0 + if isstr(varargin{1}) % if no axes handle is passed ... + axisH = gca; + if (length(varargin) > 1) + properties = varargin(1:end); + end + else % else deal with passed axes handle + if ishandle(varargin{1}) + axisH = varargin{1}; + else + error('addTopXAxis : handle for axes invalid.') + end + properties = varargin(2:end); + end + if ~mod(length(properties),2) + for iArg = 1:length(properties)/2 + % switch properties{2*iArg-1} + switch lower(properties{2*iArg-1}) % modif V2 - suppress case sensitivity + case {'xticklabel','expression' , 'exp'} + expression = properties{2*iArg}; + % case 'xLabStr' + case {'xlabel','xlabstr'} % V2 + xLabStr = properties{2*iArg}; + % case 'xTickLabelFormat' + case 'xticklabelformat' % V2 + xTickLabelFormat = properties{2*iArg}; + otherwise + error(['addTopXAxis : property ''', properties{2*iArg-1},''' does not exist.']) + end + end + else + error('addTopXAxis : arguments number for proporties should be even.') + end +end % if input parameters +%% replace |argu| by x-tick labels in the computation expression for new x-tick labels +% newXtickLabel_command = regexprep(expression, 'argu', 'get(axisH, ''xTick'')'''); + +%% Get paramters of figures to be modified (other parameters to be copied on the new axis are not extracted) + set(axisH, 'units', 'normalized'); + cAxis_pos = get(axisH, 'position'); +%% shift downward original axis a little bit + cAxis_pos(2) = cAxis_pos(2)*0.8; + set(axisH, 'position', cAxis_pos); +%% Make new axis + nAxis = subplot('position', [cAxis_pos(1), (cAxis_pos(2)+cAxis_pos(4))*1.007, cAxis_pos(3), 0.0001]); +%% put new Xaxis on top + set(nAxis, 'xaxisLocation', 'top'); +%% Improve readability + %% delete Y label on new axis + set(nAxis, 'yTickLabel', []); + % remove box for original axis +% set(axisH, 'box', 'off'); + % remove grids + set(nAxis, 'yGrid', 'off'); + set(nAxis, 'xGrid', 'off'); +%% Set new Xaxis limits, ticks and subticks the same as original ones (by link) ... + set(nAxis, 'xlim', get(axisH, 'xlim')); + set(nAxis, 'XTick', get(axisH, 'XTick')); + set(nAxis, 'XMinorTick', get(axisH, 'XMinorTick')); + hlink = linkprop([nAxis, axisH], {'xLim','XTick','XMinorTick'}); +%% ... but replace ticks labels by new ones !!! + assignin('base', 'axisH', axisH) + % set(nAxis, 'xtickLabel', num2str(evalin('base', newXtickLabel_command), xTickLabelFormat)); + set(nAxis, 'xtickLabel', expression); + %% but label for new axis + if exist('xLabStr') + xlabel(xLabStr) + end +%% return current axis to original one (for further modification affecting original axes) + axes(axisH); +% hlink = linkprop([nAxis, gca], {'xLim','XTick','XMinorTick'}) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/camircommit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function camircommit() +% returns the global version of the camir project + +global globalvars; +curpath = pwd; + +% go to camir directory +cd (globalvars.camir.path); +tmp = textscan(evalc('!svnversion'),'%s'); + +% svn commit +svn commit; + +% return to current directory +cd (curpath); + +rev = tmp{1};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/camirversion.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function [rev, latest] = camirversion() +% returns the global version of the camir project + +global globalvars; +curpath = pwd; + +% go to camir directory +cd (globalvars.camir.path); +tmp = textscan(evalc('!svnversion'),'%s'); + +% return to current directory +cd (curpath); + +rev = tmp{1}; +latest = textscan(rev{1},'%d','Delimiter',':'); +if ~isempty(latest{1}) + latest = double(latest{end}(end)); +else + latest='-1'; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/cell2csv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,85 @@ +function cell2csv(fileName, cellArray, separator, excelYear, decimal) +% Writes cell array content into a *.csv file. +% +% CELL2CSV(fileName, cellArray, separator, excelYear, decimal) +% +% fileName = Name of the file to save. [ i.e. 'text.csv' ] +% cellArray = Name of the Cell Array where the data is in +% separator = sign separating the values (default = ';') +% excelYear = depending on the Excel version, the cells are put into +% quotes before they are written to the file. The separator +% is set to semicolon (;) +% decimal = defines the decimal separator (default = '.') +% +% by Sylvain Fiedler, KA, 2004 +% updated by Sylvain Fiedler, Metz, 06 +% fixed the logical-bug, Kaiserslautern, 06/2008, S.Fiedler +% added the choice of decimal separator, 11/2010, S.Fiedler + +%% Checking für optional Variables +if ~exist('separator', 'var') + separator = ','; +end + +if ~exist('excelYear', 'var') + excelYear = 1997; +end + +if ~exist('decimal', 'var') + decimal = '.'; +end + +%% Setting separator for newer excelYears +if excelYear > 2000 + separator = ';'; +end + +%% Write file +datei = fopen(fileName, 'w'); + +for z=1:size(cellArray, 1) + for s=1:size(cellArray, 2) + + var = eval(['cellArray{z,s}']); + % If zero, then empty cell + if size(var, 1) == 0 + var = ''; + end + % If numeric -> String + if isnumeric(var) + var = num2str(var); + % Conversion of decimal separator (4 Europe & South America) + % http://commons.wikimedia.org/wiki/File:DecimalSeparator.svg + if decimal ~= '.' + var = strrep(var, '.', decimal); + end + end + % If logical -> 'true' or 'false' + if islogical(var) + if var == 1 + var = 'TRUE'; + else + var = 'FALSE'; + end + end + % If newer version of Excel -> Quotes 4 Strings + if excelYear > 2000 + var = ['"' var '"']; + end + + % OUTPUT value + fprintf(datei, '%s', var); + + % OUTPUT separator + if s ~= size(cellArray, 2) + fprintf(datei, separator); + end + end + if z ~= size(cellArray, 1) % prevent a empty line at EOF + % OUTPUT newline + fprintf(datei, '\n'); + end +end +% Closing file +fclose(datei); +% END \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/cell2str.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +%CELL2STR Convert 2-D cell array into evaluable string. +% B = CELL2STR(C) returns a B such that C = EVAL(B), under the +% following contraits: +% - C contains only numeric, logic, string or cell arrays, not +% structs nor objects. +% - NDIMS(C) = 2. +% +% B = CELL2STR(C,N) uses N digits of precision for numeric +% arrays. N defaults to 15. +% +% B = CELL2STR(C,'class') and B = CELL2STR(C,N,'class') also +% include the class string for the numeric arrays +% +% See also MAT2STR, EVAL + +% (C) Copyright 2010-2012, All rights reserved. +% Cris Luengo, Uppsala, 12 August 2010. +% 13 July 2012: extended to work with cell arrays. + +function str = cell2str(c,n,mode) + +if nargin==2 + if isnumeric(n) + mode = ''; + else + mode = n; + n = 15; + end +elseif nargin==1 + mode = ''; + n = 15; +end + +if ~isnumeric(n) || numel(n)~=1 + error('Input argument N should be a scalar.') +end +if ~ischar(mode) || ~any(strcmp(mode,{'','class'})) + error('Mode string can only be ''class''.') +end + +if iscell(c) + N = size(c); + if length(N)~=2 + error('Cell array must be 2-dimensional.') + end + str = '{'; + for ii=1:N(1) + if ii>1 + str = [str,';']; + end + for jj=1:N(2) + if jj>1 + str = [str,',']; + end + str = [str,cell2str(c{ii,jj})]; + end + end + str = [str,'}']; +elseif isnumeric(c) + if ~isempty(mode) + str = mat2str(c,n,'class'); + else + str = mat2str(c,n); + end +elseif ischar(c) || islogical(c) + str = mat2str(c); +else + error('Illegal array in input.') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/centeraxes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,168 @@ +function out = centeraxes(ax,opt) +% out = centeraxes(ax,opt) +%========================================================================== +% Center the coordinate axes of a plot so that they pass through the +% origin. +% +% Input: 0, 1 or 2 arguments +% 0: Moves the coordinate axes of 'gca', i.e. the currently active +% axes. +% 1: ax, a handle to the axes which should be moved. +% 2: ax, opt, where opt is a struct with further options +% opt.fontsize = 'size of axis tick labels' +% opt.fontname = name of axis tick font. +% +% If the opt struct is ommitted, the current values for the axes are used. +% +% Output: stuct out containing handles to all line objects created by this +% function. +% +%========================================================================== +% Version: 1.1 +% Created: October 1, 2008, by Johan E. Carlson +% Last modified: November 21, 2009, by Johan E. Carlson +%========================================================================== + +if nargin < 2, + fontsize = get(ax,'FontSize'); + fontname = get(ax,'FontName'); +end +if nargin < 1, + ax = gca; +end + +if nargin == 2, + if isfield(opt,'fontsize'), + fontsize = opt.fontsize; + else + fontsize = get(ax,'FontSize'); + end; + if isfield(opt,'fontname'), + fontname = opt.fontname; + else + fontname = get(ax,'FontName'); + end +end + +axes(ax); +set(gcf,'color',[1 1 1]); +xtext = get(get(ax,'xlabel'),'string'); +ytext = get(get(ax,'ylabel'),'string'); + +%-------------------------------------------------------------------------- +% Check if the current coordinate system include the origin. If not, change +% it so that it does! +%-------------------------------------------------------------------------- +xlim = 1.1*get(ax,'xlim'); +ylim = 1.1*get(ax,'ylim'); +set(ax,'xlim',xlim); +set(ax,'ylim',ylim); + +if xlim(1)>0, + xlim(1) = 0; +end + +if ylim(1)>0, + ylim(1) = 0; +end + +if xlim(2) < 0, + xlim(2) = 0; +end + +if ylim(2) < 0, + ylim(2) = 0; +end; + +set(ax,'xlim',xlim,'ylim',ylim); + + +% ------------------------------------------------------------------------- +% Caculate size of the "axis tick marks" +% ------------------------------------------------------------------------- +axpos = get(ax,'position'); +figpos = get(gcf,'position'); +aspectratio = axpos(4) / (axpos(3)); +xsize = xlim(2) - xlim(1); +ysize = ylim(2) - ylim(1); +xticksize = ysize/figpos(4)*12; +yticksize = xsize*aspectratio/figpos(3)*12; + +% ------------------------------------------------------------------------- +% Store old tick values and tick labels +% ------------------------------------------------------------------------- +ytick = get(ax,'YTick'); +xtick = get(ax,'XTick'); +xticklab = get(ax,'XTickLabel'); +yticklab = get(ax,'YTickLabel'); + +% ------------------------------------------------------------------------- +% Draw new coordinate system +% ------------------------------------------------------------------------- + +yax = line([0; 0],[ylim(1)-1; ylim(2)]); +xax = line([xlim(1); xlim(2)],[0; 0]); +set(xax,'color',[0 0 0]) +set(yax,'color',[0 0 0]) + +% Draw x-axis ticks +for k = 1:length(xtick), + newxtick(k) = line([xtick(k); xtick(k)],[-xticksize/2; xticksize/2]); + if (xtick(k)~=0), + newxticklab(k) = text(xtick(k),-1.5*xticksize, strtrim(xticklab(k,:))); + set(newxticklab(k),'HorizontalAlignment','center',... + 'Fontsize',fontsize,'FontName',fontname); + end +end +set(newxtick,'color',[0 0 0]); + +% Draw y-axis ticks +for k = 1:length(ytick), + newytick(k) = line([-yticksize/2; yticksize/2],[ytick(k); ytick(k)]); + if (ytick(k)~=0), + newyticklab(k) = text(-.8*yticksize,ytick(k), yticklab(k,:)); + set(newyticklab(k),'HorizontalAlignment','right',... + 'FontSize',fontsize,'FontName',fontname); + end +end +set(newytick,'color',[0 0 0]); + +%-------------------------------------------------------------------------- +% Move xlabels +%-------------------------------------------------------------------------- +newxlabel = text(xlim(2),-1.5*xticksize,xtext); +set(newxlabel,'HorizontalAlignment','center',... + 'FontWeight','demi','FontSize',fontsize+2,'FontName',fontname); + +newylabel = text(-yticksize,ylim(2)*1.02,ytext); +set(newylabel,'HorizontalAlignment','right','VerticalAlignment','top',... + 'FontWeight','demi','FontSize',fontsize+2,'FontName',fontname); + +%-------------------------------------------------------------------------- +% Create arrowheads +%-------------------------------------------------------------------------- +x = [0; -yticksize/4; yticksize/4]; +y = [ylim(2); ylim(2)-xticksize; ylim(2)-xticksize]; +patch(x,y,[0 0 0]) + +x = [xlim(2); xlim(2)-yticksize; xlim(2)-yticksize]; +y = [0; xticksize/4; -xticksize/4]; +patch(x,y,[0 0 0]) + +axis off; +box off; + +%-------------------------------------------------------------------------- +% Create output struct +%-------------------------------------------------------------------------- + +if nargout > 0, + out.xaxis = xax; + out.yaxis = yax; + out.xtick = newxtick; + out.ytick = newytick; + out.xticklabel = newxticklab; + out.yticklabel = newyticklab; + out.newxlabel = newxlabel; + out.newylabel = newylabel; +end; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/centeraxes_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +% Script for testing the centeraxes.m function + +clear +clf +clc +%subplot(211) +plot(-8:8, sqrt(5:21)); +set(gca,'ylim',[-4 5],'xlim',[-8 8],'xtick',-8:8,'ytick',-4:5) +xlabel('t'); +ylabel('y(t)') +thicklines(2); + +opt.fontname = 'helvetica'; +opt.fontsize = 8; + +%centeraxes(gca); +centeraxes(gca,opt); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/cprint.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function cprint(l, varargin) +% --- +% function cprint(l, varargin) +% +% sprintfs statement varargin if verbosity level +% globalvars.debug >= l +% +% lvl 1: error / very important info +% lvl 2 : warning, mid-level runtime info +% lvl 3 : high-frequency runtime / debug info +% +% --- + + global globalvars; + + if isfield(globalvars, 'debug') && globalvars.debug >= l + display(sprintf(varargin{:})); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/csv2cell.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,152 @@ +function data = csv2cell(varargin) +% CSV2CELL - parses a Windows CSV file into an NxM cell array, where N is +% the number of lines in the CSV text and M is the number of fields in the +% longest line of the CSV file. Lines are delimited by carriage returns +% and/or newlines. +% +% A Windows CSV file format allows for commas (,) and double quotes (") to +% be contained within fields of the CSV file. Regular fields are just text +% separated by commas (e.g. foo,bar,hello world). Fields containing commas +% or double quotes are surrounded by double quotes (e.g. +% foo,bar,"item1,item2,item3",hello world). In the previous example, +% "item1,item2,item3" is one field in the CSV text. For double quotes to be +% represented, they are written in pairs in the file, and contained within +% a quoted field, (e.g. foo,"this field contains ""quotes""",bar). Spaces +% within fields (even leading and trailing) are preserved. +% +% All fields from the CSV file are returned as strings. If the CSV text +% contains lines with different numbers of fields, then the "missing" +% fields with appear as empty arrays, [], in the returned data. You can +% easily convert the data you expect to be numeric using str2num() and +% num2cell(). +% +% Examples: +% >> csv2cell('foo.csv','fromfile') % loads and parses entire file +% >> csv2cell(',,,') % returns cell array {'','','',''} +% >> csv2cell(',,,','text') % same as above, declaring text input +% >> csv2cell(sprintf('%s\r\n%s',... +% '"Ten Thousand",10000,,"10,000","""It''s ""10 Grand"", baby",10k',... +% ',foo,bar,soo')) +% ans = +% 'Ten Thousand' '10000' '' '10,000' [1x22 char] '10k' +% '' 'foo' 'bar' 'soo' [] [] +% >> % note the two empty [] cells, because the second line has two fewer +% >> % fields than the first. The empty field '' at the beginning of the +% >> % second line is due to the leading comma on that line, which is +% >> % correct behavior. A trailing comma will do the same to the end of a +% >> % line. +% +% Limitations/Exceptions: +% * This code is untested on large files. It may take a long time due to +% variables growing inside loops (yes, poor practice, but easy coding). +% * This code has been minimally tested to work with a variety of weird +% Excel files that I have. +% * Behavior with improperly formatted CSV files is untested. +% * Technically, CSV files from Excel always separate lines with the pair +% of characters \r\n. This parser will also separate lines that have only +% \r or \n as line terminators. +% * Line separation is the first operation. I don't think the Excel CSV +% format has any allowance for newlines or carriage returns within +% fields. If it does, then this parser does not support it and would not +% return bad output. +% +% Copyright 2008 Arthur Hebert + +% Process arguments +if nargin == 1 + text = varargin{1}; +elseif nargin == 2 + switch varargin{2} + case 'fromfile' + filename = varargin{1}; + fid = fopen(filename); + text = char(fread(fid))'; + fclose(fid); + case 'text' + text = varargin{1}; + otherwise + error('Invalid 2nd argument %s. Valid options are ''fromfile'' and ''text''',varargin{2}) + end +else + error('CSV2CELL requires 1 or 2 arguments.') +end + + +% First split it into lines +lines = regexp(text,'(\r\n|[\r\n])','split'); % lines should now be a cell array of text split by newlines + +% a character is either a delimiter or a field +inField = true; +inQuoteField = false; +% if inField && ~inQuoteField --> then we're in a raw field + +skipNext = false; +data = {}; +field = ''; +for lineNumber = 1:length(lines) + nChars = length(lines{lineNumber}); % number of characters in this line + fieldNumber = 1; + for charNumber = 1:nChars + if skipNext + skipNext = false; + continue + end + thisChar = lines{lineNumber}(charNumber); + if thisChar == ',' + if inField + if inQuoteField % this comma is part of the field + field(end+1) = thisChar; + else % this comma is the delimiter marking the end of the field + data{lineNumber,fieldNumber} = field; + field = ''; + fieldNumber = fieldNumber + 1; + end + else % we are not currently in a field -- this is the start of a new delimiter + inField = true; + end + if charNumber == nChars % this is a hanging comma, indicating the last field is blank + data{lineNumber,fieldNumber} = ''; + field = ''; + fieldNumber = fieldNumber + 1; + end + elseif thisChar == '"' + if inField + if inQuoteField + if charNumber == nChars % it's the last character, so this must be the closing delimiter? + inField = false; + inQuoteField = false; + data{lineNumber,fieldNumber} = field; + field = ''; + fieldNumber = fieldNumber + 1; + else + if lines{lineNumber}(charNumber+1) == '"' % this is translated to be a double quote in the field + field(end+1) = '"'; + skipNext = true; + else % this " is the delimiter ending this field + data{lineNumber,fieldNumber} = field; + field = ''; + inField = false; + inQuoteField = false; + fieldNumber = fieldNumber + 1; + end + end + else % this is a delimiter and we are in a new quote field + inQuoteField = true; + end + else % we are not in a field. This must be an opening quote for the first field? + inField = true; + inQuoteField = true; + end + else % any other character ought to be added to field + field(end+1) = thisChar; + if charNumber == nChars + data{lineNumber,fieldNumber} = field; + field = ''; + fieldNumber = fieldNumber + 1; + elseif charNumber == 1 % we are starting a new raw field + inField = true; + end + end + end +end + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/hash.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +function h = hash(inp,meth) +% HASH - Convert an input variable into a message digest using any of +% several common hash algorithms +% +% USAGE: h = hash(inp,'meth') +% +% inp = input variable, of any of the following classes: +% char, uint8, logical, double, single, int8, uint8, +% int16, uint16, int32, uint32, int64, uint64 +% h = hash digest output, in hexadecimal notation +% meth = hash algorithm, which is one of the following: +% MD2, MD5, SHA-1, SHA-256, SHA-384, or SHA-512 +% +% NOTES: (1) If the input is a string or uint8 variable, it is hashed +% as usual for a byte stream. Other classes are converted into +% their byte-stream values. In other words, the hash of the +% following will be identical: +% 'abc' +% uint8('abc') +% char([97 98 99]) +% The hash of the follwing will be different from the above, +% because class "double" uses eight byte elements: +% double('abc') +% [97 98 99] +% You can avoid this issue by making sure that your inputs +% are strings or uint8 arrays. +% (2) The name of the hash algorithm may be specified in lowercase +% and/or without the hyphen, if desired. For example, +% h=hash('my text to hash','sha256'); +% (3) Carefully tested, but no warranty. Use at your own risk. +% (4) Michael Kleder, Nov 2005 +% +% EXAMPLE: +% +% algs={'MD2','MD5','SHA-1','SHA-256','SHA-384','SHA-512'}; +% for n=1:6 +% h=hash('my sample text',algs{n}); +% disp([algs{n} ' (' num2str(length(h)*4) ' bits):']) +% disp(h) +% end + +inp=inp(:); +% convert strings and logicals into uint8 format +if ischar(inp) || islogical(inp) + inp=uint8(inp); +else % convert everything else into uint8 format without loss of data + inp=typecast(inp,'uint8'); +end + +% verify hash method, with some syntactical forgiveness: +meth=upper(meth); +switch meth + case 'SHA1' + meth='SHA-1'; + case 'SHA256' + meth='SHA-256'; + case 'SHA384' + meth='SHA-384'; + case 'SHA512' + meth='SHA-512'; + otherwise +end +algs={'MD2','MD5','SHA-1','SHA-256','SHA-384','SHA-512'}; +if isempty(strmatch(meth,algs,'exact')) + error(['Hash algorithm must be ' ... + 'MD2, MD5, SHA-1, SHA-256, SHA-384, or SHA-512']); +end + +% create hash +x=java.security.MessageDigest.getInstance(meth); +x.update(inp); +h=typecast(x.digest,'uint8'); +h=dec2hex(h)'; +if(size(h,1))==1 % remote possibility: all hash bytes < 128, so pad: + h=[repmat('0',[1 size(h,2)]);h]; +end +h=lower(h(:)'); +clear x +return \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/implode.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function out = implode(divider, input); +% out = implode(divider, input) + +out = []; +for i = 1:numel(input) + + % only put divider inbetween data + if i > 1 + + out = sprintf('%s%s%d', out, divider, input(i)); + else + + out = sprintf('%d', input(i)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/kldiv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +function KL = kldiv(varValue,pVect1,pVect2,varargin) +%KLDIV Kullback-Leibler or Jensen-Shannon divergence between two distributions. +% KLDIV(X,P1,P2) returns the Kullback-Leibler divergence between two +% distributions specified over the M variable values in vector X. P1 is a +% length-M vector of probabilities representing distribution 1, and P2 is a +% length-M vector of probabilities representing distribution 2. Thus, the +% probability of value X(i) is P1(i) for distribution 1 and P2(i) for +% distribution 2. The Kullback-Leibler divergence is given by: +% +% KL(P1(x),P2(x)) = sum[P1(x).log(P1(x)/P2(x))] +% +% If X contains duplicate values, there will be an warning message, and these +% values will be treated as distinct values. (I.e., the actual values do +% not enter into the computation, but the probabilities for the two +% duplicate values will be considered as probabilities corresponding to +% two unique values.) The elements of probability vectors P1 and P2 must +% each sum to 1 +/- .00001. +% +% A "log of zero" warning will be thrown for zero-valued probabilities. +% Handle this however you wish. Adding 'eps' or some other small value +% to all probabilities seems reasonable. (Renormalize if necessary.) +% +% KLDIV(X,P1,P2,'sym') returns a symmetric variant of the Kullback-Leibler +% divergence, given by [KL(P1,P2)+KL(P2,P1)]/2. See Johnson and Sinanovic +% (2001). +% +% KLDIV(X,P1,P2,'js') returns the Jensen-Shannon divergence, given by +% [KL(P1,Q)+KL(P2,Q)]/2, where Q = (P1+P2)/2. See the Wikipedia article +% for "Kullback–Leibler divergence". This is equal to 1/2 the so-called +% "Jeffrey divergence." See Rubner et al. (2000). +% +% EXAMPLE: Let the event set and probability sets be as follow: +% X = [1 2 3 3 4]'; +% P1 = ones(5,1)/5; +% P2 = [0 0 .5 .2 .3]' + eps; +% +% Note that the event set here has duplicate values (two 3's). These +% will be treated as DISTINCT events by KLDIV. If you want these to +% be treated as the SAME event, you will need to collapse their +% probabilities together before running KLDIV. One way to do this +% is to use UNIQUE to find the set of unique events, and then +% iterate over that set, summing probabilities for each instance of +% each unique event. Here, we just leave the duplicate values to be +% treated independently (the default): +% KL = kldiv(X,P1,P2); +% KL = +% 19.4899 +% +% Note also that we avoided the log-of-zero warning by adding 'eps' +% to all probability values in P2. We didn't need to renormalize +% because we're still within the sum-to-one tolerance. +% +% REFERENCES: +% 1) Cover, T.M. and J.A. Thomas. "Elements of Information Theory," Wiley, +% 1991. +% 2) Johnson, D.H. and S. Sinanovic. "Symmetrizing the Kullback-Leibler +% distance." IEEE Transactions on Information Theory (Submitted). +% 3) Rubner, Y., Tomasi, C., and Guibas, L. J., 2000. "The Earth Mover's +% distance as a metric for image retrieval." International Journal of +% Computer Vision, 40(2): 99-121. +% 4) <a href="matlab:web('http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence','-browser')">Kullback–Leibler divergence</a>. Wikipedia, The Free Encyclopedia. +% +% See also: MUTUALINFO, ENTROPY + +if ~isequal(unique(varValue),sort(varValue)), + warning('KLDIV:duplicates','X contains duplicate values. Treated as distinct values.') +end +if ~isequal(size(varValue),size(pVect1)) || ~isequal(size(varValue),size(pVect2)), + error('All inputs must have same dimension.') +end +% Check probabilities sum to 1: +if (abs(sum(pVect1) - 1) > .00001) || (abs(sum(pVect2) - 1) > .00001), + error('Probablities don''t sum to 1.') +end + +if ~isempty(varargin), + switch varargin{1}, + case 'js', + logQvect = log2((pVect2+pVect1)/2); + KL = .5 * (sum(pVect1.*(log2(pVect1)-logQvect)) + ... + sum(pVect2.*(log2(pVect2)-logQvect))); + + case 'sym', + KL1 = sum(pVect1 .* (log2(pVect1)-log2(pVect2))); + KL2 = sum(pVect2 .* (log2(pVect2)-log2(pVect1))); + KL = (KL1+KL2)/2; + + otherwise + error(['Last argument' ' "' varargin{1} '" ' 'not recognized.']) + end +else + KL = sum(pVect1 .* (log2(pVect1)-log2(pVect2))); +end + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/cut_Y.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function r = cut_Y(r, valid_idx) +% disables the invalid parts of the ranking structure Y + + % get indices not mentioned + u = find(~valid_idx); + + % clear bad data + for i = 1 : numel(u) + + r{u(i), 1} = []; + r{u(i), 2} = []; + end + + % also clear weights if applicable + if size(r,2) == 3 + for i = 1 : numel(u) + r{u(i), 3} = 0; + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/cvpartition_alltrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +% --- +% fake partitioning for comparison +% to stober08: +% --- +classdef cvpartition_alltrain + +properties (Hidden) + + mtest; + mtraining; +end +properties + N; + NumTestSets; + TrainSize; + TestSize; +end + + +methods + +% --- +% constuctor: directly calculates the truncated testset +% --- +function P = cvpartition_alltrain(nData, nRuns) + + P.NumTestSets = nRuns; + P.N = nData; + + % build training and test sets + for i = 1:P.NumTestSets + P.TrainSize(i) = nData; + P.TestSize(i) = nData; + P.mtraining{i} = ones(P.N, 1); + P.mtest{i} = ones(P.N, 1); + end +end + +function out = test(P, i) + + out = P.mtest{i}; +end + +function out = training(P, i) + + out = P.mtraining{i}; +end + +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/cvpartition_trunctrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +% --- +% class cvpartition_trunctrain +% NOTE: this is a fake cvpartition double for +% using cvpartitions in truncated-training size experiments +% --- +classdef cvpartition_trunctrain + +properties (Hidden) + + mtest; + mtraining; +end +properties + N; + NumTestSets; + TrainSize; + TestSize; +end + + +methods + +% --- +% constuctor: directly calculates the truncated testset +% --- +function P = cvpartition_trunctrain(Pin, perctrain) + + P.N = Pin.N; + P.NumTestSets = Pin.NumTestSets; + + for i = 1:Pin.NumTestSets + + % copy testing data + P.TestSize(i) = Pin.TestSize(i); + P.mtest{i} = Pin.test(i); + + % calculate new training size + P.TrainSize(i) = ceil(perctrain * Pin.TrainSize(i)); + + % get actual training indices + idx = find(Pin.training(i)); + + % --- + % TODO: save the permutation in a global variable, + % tomake the same smaller set available + % for all further experiments. + % moreover, it would be great if the smaller training sets + % are subsets of the bigger ones + % --- + tokeep = randperm(numel(idx)); + tokeep = tokeep(1:P.TrainSize(i)); + + % get indices to keep + idx = idx(tokeep); + + % build truncated training set + P.mtraining{i} = false(P.N, 1); + P.mtraining{i}(idx) = true; + end +end + +function out = test(P, i) + + out = P.mtest{i}; +end + +function out = training(P, i) + + out = P.mtraining{i}; +end + +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/cvpartition_trunctrain_incsubsets.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,128 @@ +% --- +% class cvpartition_trunctrain +% NOTE: this is a fake cvpartition double for +% using cvpartitions in truncated-training size experiments +% +% differently from cvpartition_trunctrain, we take all the training sizes +% at once and generate training partitions where the smaller ones are subsets +% of the bigger ones +% --- +classdef cvpartition_trunctrain_incsubsets + +properties (Hidden) + + mtest; + mtraining; +end +properties + N; + NumTestSets; + TrainSize; + TestSize; +end + + +methods + +% --- +% constuctor: directly calculates the truncated testset +% --- +function P = cvpartition_trunctrain_incsubsets(Pin, perctrain) + + % --- + % NOTE: we use a different permutation for each cv-Buun (testset), + % as otherwise the very small training sets will have about the same + % data + % --- + if ~cvpartition_trunctrain_incsubsets.exists_permutation(Pin) + cvpartition_trunctrain_incsubsets.renew_permutation(Pin); + end + + P.N = Pin.N; + P.NumTestSets = Pin.NumTestSets; + + for i = 1:Pin.NumTestSets + + % copy testing data + P.TestSize(i) = Pin.TestSize(i); + P.mtest{i} = Pin.test(i); + + % calculate new training size + P.TrainSize(i) = ceil(perctrain * Pin.TrainSize(i)); + + % get actual training indices + idx = find(Pin.training(i)); + + % --- + % NOTE: the Test-Set-Specific permutation is applied + % we only extract as many indices as fit in Pin + % --- + permu = cvpartition_trunctrain_incsubsets.get_permutation(i,Pin.TrainSize(i)); + + % truncate the indices + idx = idx(permu(1:P.TrainSize(i))); + + % build truncated training set + P.mtraining{i} = false(P.N, 1); + P.mtraining{i}(idx) = true; + end +end +function out = test(P, i) + + out = P.mtest{i}; +end + +function out = training(P, i) + + out = P.mtraining{i}; +end +end + +methods (Static) + + % --- + % TODO: save the permutation in a global variable, + % tomake the same smaller set available + % for all further experiments. + % moreover, it would be great if the smaller training sets + % are subsets of the bigger ones + % --- + function renew_permutation(P) + global globalvars; + + if isfield(globalvars.camir, ... + 'cvpartition_trunctrain_incsubsets'); + warning 'renwewing permutations for train sets'; + end + + for i = 1:P.NumTestSets + globalvars.camir.cvpartition_trunctrain_incsubsets.permutation(i).data = ... + randperm(P.N); + end + end + + function idx = get_permutation(testId, trainSize) + % returns the permutation for specific test set + global globalvars; + + idx = globalvars.camir.cvpartition_trunctrain_incsubsets.permutation(testId).data; + + % cut the permutation to contain no exxcess numbers + idx = idx(idx <= trainSize); + end + + function out = exists_permutation(P) + global globalvars; + if isfield(globalvars.camir, ... + 'cvpartition_trunctrain_incsubsets'); + + out = (numel(globalvars.camir.cvpartition_trunctrain_incsubsets.permutation) == P.NumTestSets) ... + && (numel(globalvars.camir.cvpartition_trunctrain_incsubsets.permutation(1).data) == P.N); + + + else out = false; + end + end + +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/display_mahalanobis_metric.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +function display_mahalanobis_metric(A, labels) +% display a mala matrix and its stats + +if nargin < 2 + labels = num2cell(1:size(A,1)); + +elseif ~iscell(labels) + + features = labels; + labels = features.labels; +end + + + +figure; + +% plot matrix +imagesc(A); +axis xy; + +% set labels +set(gca,'YTick', 1:numel(labels), ... + 'YTickLabel', labels); +set(gca,'XTick',1:numel(labels), ... + 'XTickLabel', labels); + +% --- +% approximate parameter weights: +% diagonal and sum(abs(row)) +% TODO: make nshow dependend on percentile +% --- + +nshow = min(numel(labels), 50); +figure; + +% get diagonal values of the Matrix +diagw = abs(diag(A)); + +% --- +% weight with feature values if possible +% --- +if exist('features','var') + + diagw = diagw.* mean(features.vector(),2); +end + + +[diagw, idx] = sort(diagw, 'descend'); + +% normalise +alld = sum(diagw); + +% plot +bar(diagw(1:nshow)./ alld); +set(gca,'XTick',1:nshow, ... + 'XTickLabel', labels(idx(1:nshow))); + +ylabel ('relevance factor'); + +if exist('features','var') + xlabel 'normalised weight' +else + xlabel 'matrix factors' +end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/get_fo_deltas.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function [deltas, idx, weights] = get_fo_deltas(r, X, rectify) +% [deltas, idx, (weights)] = get_fo_deltas(r, X) +% +% returns the difference vectors (dist(a,c) > dist(a,b)) +% for a given ranking: +% deltas{i}(:,1) = a - b, deltas{i}(:,2) = a - c; +% +% set rectify to output absolute values of training examples + +if nargin < 3 + rectify = 0; +end + +% --- +% NOTE: this preallocation is not complete +% --- +deltas = {}; +idx = {}; +weights = []; +for i = 1:size(r,1) + + % feature index + a = i; + + % check if ranking is valid + if ~isempty(r{i,1}) && ~isempty(r{i,2})&& ... + isempty(intersect(r{i,1}, r{i,2})); + + % --- + % NOTE / TODO: the follwing is intended for compability + % both sides of the ranking may have more than one entry. + % for the MTT database, the ranking may be correct, but the + % inequalities build from non-singular rankings are not + % based on the actual data + % --- + for j = 1:numel(r{i,1}) + b = r{i,1}(j); + + for k = 1:numel(r{i,2}) + c = r{i,2}(k); + + % --- + % get vector deltas, + % NOTE: first: dissimilar, then similar pair + % --- + [dac] = X(:,a) - X(:,c); + [dab] = X(:,a) - X(:,b); + + if ~rectify + deltas{end+1} = [dac dab]; + else + % --- + % rectify the data for training + % --- + deltas{end+1} = abs([dac dab]); + end + + idx{end+1} = {[a c],[a b]}; + + % save weights + if size(r,2) == 3 + weights(end+1) = r{i,3}(1); + end + end + end + end +end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/get_itml_deltas.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,85 @@ +function [X, C, idx] = get_itml_deltas(r, in) +% [X, C, idx] = get_itml_deltas(r, in) + +%ITML Specs +% C: 4 column matrix +% column 1, 2: index of constrained points. Indexes between 1 and n +% column 3: 1 if points are similar, -1 if dissimilar +% column 4: right-hand side (lower or upper bound, depending on +% whether points are similar or dissimilar) +% +% X: (n x m) data matrix - each row corresponds to a single instance +% --- +% NOTE: X is thus input in transposed shape for the ITML algorithm +% --- + +% --- +% NOTE: this preallocation is not complete +% --- +X = zeros(size(in,1), 0); +C = zeros(0,4); +idx = zeros(0,2); + +for i = 1:size(r,1) + + % feature indexing + a = i; + + % check if ranking is valid + if ~isempty(r{i,1}) && ~isempty(r{i,2})&& ... + isempty(intersect(r{i,1}, r{i,2})); + + % --- + % NOTE / TODO: the follwing is intended for compability + % both sides of the ranking may have more than one entry. + % for the MTT database, the ranking may be correct, but the + % inequalities build from non-singular rankings are not + % based on the actual data + % --- + for j = 1:numel(r{i,1}) + b = r{i,1}(j); + + for k = 1:numel(r{i,2}) + c = r{i,2}(k); + + % --- + % get vector deltas + % --- + [dab] = in(:,a) - in(:,b); + [dac] = in(:,a) - in(:,c); + + % --- + % save deltas in new feature matrix + % TODO: this method has duplicate entries + % if the pairs appear more than once + % index the data set and use more efficiently!!! + % --- + X = [X dab]; + idx(end+1,:) = [a b]; + iab = size(idx, 1); + + X = [X dac]; + idx(end+1,:) = [a c]; + iac = size(idx, 1); + + % --- + % NOTE: + % in terms of the constraint, + % this should mean: dac - dab >= 1 + % + % 4th position cannot be 0, converges to Inf if > 1 + % -1,-1 learns the opposite of what constraitns say + % --- + C(end+1, :) = [iab iac -1 -1]; + end + end + end +end + +% % --- +% % NOTE: here, we transpose the X for usage i nthe training +% % --- +% X = X'; + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/get_svmlight_inequalities_from_ranking.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +function [lhs, rhs, dim, factors, invalid] = get_svmlight_inequalities_from_ranking(r, X, delta_fun,delta_params) +% [lhs, rhs] = get_svmlight_inequalities_from_ranking(r, features) +% +% delta_fun.fun function pointer returning distance / delta(a, b) +% for feature vectors a,b, default: delta(a, b) = (a - b).^2; +% delta_fun.args extra arguments +% +% prepares ranking data to be used with the svmlight implementation +% of Schultz & Joachims 2003 + +% --- +% CAVE: this file still allows arbitrary rankings +% this could be good but also introducing +% confusion in the future. Especially the +% weightings are only defined per query +% --- + +% --- +% variable function to get vector deltas +% --- +if nargin < 3 + delta_fun = @get_delta; + delta_params = {[]}; +end + +% --- +% fix right hand side to one +% (after Schultz & Joachims 2003) +% --- +fix_rhs = 1; +fix_weight = 1; + +% --- +% NOTE: this preallocation is not complete +% --- +lhs = cell(0,2); +factors = []; +invalid = []; +for i = 1:size(r,1) + + % feature index + a = i; + + % check if ranking is valid + if ~isempty(r{i,1}) && ~isempty(r{i,2})&& ... + isempty(intersect(r{i,1}, r{i,2})); + + % --- + % NOTE / TODO: the follwing is intended for compability + % both sides of the ranking may have more than one entry. + % for the MTT database, the ranking may be correct, but the + % inequalities build from non-singular rankings are not + % based on the actual data + % --- + for j = 1:numel(r{i,1}) + b = r{i,1}(j); + + for k = 1:numel(r{i,2}) + c = r{i,2}(k); + + % --- + % get vector deltas + % --- + [dab] = delta_fun(X(:,a), X(:,b), delta_params{:}); + [dac] = delta_fun(X(:,a), X(:,c), delta_params{:}); + + % get the delta difference vector + ddiff = dac - dab; + if i == 1 + dim = numel(ddiff); + end + + % --- + % save the non-empty differences row by row + % NOTE: it is not clear whether the indexing for + % \omega starts a 0 or 1. + % ---- + xgzero = find( ddiff ~= 0); + + if ~isempty(xgzero) + lhs = cat(1,lhs,{xgzero, ddiff(xgzero)}); + else + invalid = [invalid i]; + end + + % save factors + if (nargout > 2) + if (size(r,2) > 2) + factors = [factors, r{i,3}]; % / max_weight + else + factors = [factors, fix_weight]; + end + end + + end + end + end +end + +% --- +% determine right hand side +% --- +rhs = ones(size(lhs,1), 1) .* fix_rhs; +cprint(2, 'SVMLight data: %d invalid rankings excluded from training set', numel(invalid)); +end + + +function out = get_delta(a, b, A) +% returns the pointwise (transformed) feature vector differences + +if nargin == 2 || isempty(A) + + % --- + % return squared factors as in euclidean distance + % --- + out = (a - b).^2; + +else + + % transform vectors before accessing difference + out = (A' * a - A' * b).^2; +end + +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/mlr_repeat_YX_by_rating.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [Yout, Xout] = mlr_repeat_YX_by_rating(Y, X, weights) +% [Yout, Xout] = mlr_repeat_YX_by_rating(Y, X, weights) +% +% repeats the data in X and Y according to the weights specified +% in weights or Y(:,3) + +cprint(2, 'Repeating data for weighted learning'); +if nargin == 2 + weights = cell2mat(Y(:,3)); +end + +Yout = Y(:,1:2); + +valid = ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2))); + +for i = 1:size(Y, 1) + if valid(i) + for j = 2:weights(i) + Yout(end + 1, :) = Y(i, 1:2); + end + end +end + +Xout = zeros(size(X,1), size(Y,1)); +Xout(:,1:size(X,2)) = X; +for i = 1:size(Y, 1) + if valid(i) + for j = 2:weights(i) + Xout(:,end+1) = X(:,i); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/save_svmlight_inequalities.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function success = save_svmlight_inequalities(lhs, rhs, factors, file) +% success = save_svmlight_inequalities(lhs, rhs, file) +% +% saves the optimisation problem given by lhs and rhs to +% a svmlight data file. the individual equations can +% be weighted using +% +% success = save_svmlight_inequalities(lhs, rhs, factors, file) + +if nargin == 3 + file = factors; + factors = []; +end + +% open file +fid = fopen(file, 'w+'); +if fid < 1 + success = 0; + return; +end + +try + % write individual constraint rows + for i = 1:size(lhs,1) + + % --- + % print rows:" rhs #fnum:#fval #fnum:#fval #fnum:#fval ..." + % --- + + % print right hand side + fprintf(fid,'%d ', rhs(i)); + + % print cost factors if availablefactor + if (numel(lhs{i,1}) > 0) && (numel(factors) >= i) + + fprintf(fid,'cost:%f ', factors(i)); + end + + % print left hand side + for j = 1:numel(lhs{i,1}) + + fprintf(fid,'%d:%2.16f ', lhs{i,1}(j), lhs{i,2}(j)); + end + + % finish line + fprintf(fid,'\n'); + end +catch + success = 0; + fclose(fid); + return; +end +success = 1; +fclose(fid); + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/scale_ratings.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function Y = scale_ratings(Y, max_weight) +% [Yout, Xout] = scale_ratings(Y, max_weight) + + +% get maximal weight +weights = cell2mat(Y(:,3)); +max_dataweight = max(weights); + +valid = ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2))); + +% scale weights to a maximal value of max_weight +for i = 1:size(Y, 1) + if valid(i) && Y{i,3} > 0 + Y{i,3} = min(max_weight, max(1, round((Y{i,3} / max_dataweight) * max_weight))); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/sim_get_traintest_clip_overlap.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function [relative, absolute, test_cn, train_cn] = sim_get_traintest_clip_overlap(datafile) +% +% get_traintest_clip_overlap(datafile) +% +% returns the percentage of overlapping constraints +% with the corresponding training set for each test set +% +% how many percent of the test set are reappearing in the training set + + +% simdata = load(datafile); +if nargin < 1 + simdata = load('comp_partBinData_unclustered_cupaper_01'); +else + simdata = load(datafile); +end +nTestSets = size(simdata.partBinTst, 2); % num cv bins +ntrainsizes = size(simdata.partBinTrn, 2); % num increases of training + + +absolute = zeros(nTestSets, ntrainsizes); +relative = zeros(nTestSets, ntrainsizes); +for k = 1:nTestSets % all test/training combinatios + + % get clips of this test set + test_clips = unique([simdata.partBinTst{k}(:,1); simdata.partBinTst{k}(:,2); simdata.partBinTst{k}(:,3)]); + test_cn(k) = numel(test_clips); + for m = 1:ntrainsizes + + % get clips of this training set + train_clips = unique([simdata.partBinTrn{k,m}(:,1); simdata.partBinTrn{k,m}(:,2); simdata.partBinTrn{k,m}(:,3)]); + + % intersect both clip sets + same = intersect(train_clips, test_clips); + + % get stats + absolute(k,m) = numel(same); + relative(k,m) = absolute(k,m) / numel(test_clips); + end + train_cn(k) = numel(train_clips); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/svmlight2weight.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function w = svmlight2weight(modelfile) +% returns the dual weight vecor for a given svm model +% +% All the script does is compute the weighted sum of the support vectors +% (first element in line is alpha*y, what follows is the feature vector). +% For further info, see the comment in the model file for its format. + +tagstr = python('svmlight2weight.py', modelfile); + +[firstidx] = strfind(tagstr, '1 :'); + +%get start of vector description +tagstr = tagstr(firstidx:end); + +% parse text function output +vecs = textscan(tagstr,'%d %f','Delimiter',':'); + +% initialise +w = zeros(max(vecs{1}),1); + +% set values +w(vecs{1}) = vecs{2}; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/svmlight2weight.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +# Compute the weight vector of linear SVM based on the model file +# Original Perl Author: Thorsten Joachims (thorsten@joachims.org) +# Python Version: Ori Cohen (orioric@gmail.com) +# Call: python svm2weights.py svm_model + +import sys +from operator import itemgetter + +try: + import psyco + psyco.full() +except ImportError: + print 'Psyco not installed, the program will just run slower' + +def sortbyvalue(d,reverse=True): + ''' proposed in PEP 265, using the itemgetter this function sorts a dictionary''' + return sorted(d.iteritems(), key=itemgetter(1), reverse=True) + +def sortbykey(d,reverse=True): + ''' proposed in PEP 265, using the itemgetter this function sorts a dictionary''' + return sorted(d.iteritems(), key=itemgetter(0), reverse=False) + +def get_file(): + """ + Tries to extract a filename from the command line. If none is present, it + assumes file to be svm_model (default svmLight output). If the file + exists, it returns it, otherwise it prints an error message and ends + execution. + """ + # Get the name of the data file and load it into + if len(sys.argv) < 2: + # assume file to be svm_model (default svmLight output) + print "Assuming file as svm_model" + filename = 'svm_model' + #filename = sys.stdin.readline().strip() + else: + filename = sys.argv[1] + + + try: + f = open(filename, "r") + except IOError: + print "Error: The file '%s' was not found on this system." % filename + sys.exit(0) + + return f + + + + +if __name__ == "__main__": + f = get_file() + i=0 + lines = f.readlines() + printOutput = True + w = {} + for line in lines: + if i>10: + features = line[:line.find('#')-1] + comments = line[line.find('#'):] + alpha = features[:features.find(' ')] + feat = features[features.find(' ')+1:] + for p in feat.split(' '): # Changed the code here. + a,v = p.split(':') + if not (int(a) in w): + w[int(a)] = 0 + for p in feat.split(' '): + a,v = p.split(':') + w[int(a)] +=float(alpha)*float(v) + elif i==1: + if line.find('0')==-1: + print 'Not linear Kernel!\n' + printOutput = False + break + elif i==10: + if line.find('threshold b')==-1: + print "Parsing error!\n" + printOutput = False + break + + i+=1 + f.close() + + #if you need to sort the features by value and not by feature ID then use this line intead: + #ws = sortbyvalue(w) + + ws = sortbykey(w) + if printOutput == True: + for (i,j) in ws: + print i,':',j + i+=1 + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/machine_learning/weighted_kmeans.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,163 @@ +function [centres, cweights, post, errlog, options] = weighted_kmeans(centres, data, weights, options) +%[centres, cweights, post, errlog, options] = weighted_kmeans(centres,data, weights, options) +% +% weighted_kmeans Trains a k means cluster model on weighted input vectors +% +% Adapted from the Netlab Toolbox by Daniel Wolff, +% This function takes a WEIGHTS vector, containing weights for the +% different data points. This can be used for training with varying +% discretisation intervals. +% +% Description +% CENTRES = weighted_kmeans(NCENTRES, DATA, WEIGHTS, OPTIONS) or +% CENTRES = weighted_kmeans(CENTRES, DATA, WEIGHTS, OPTIONS) uses the batch K-means +% algorithm to set the centres of a cluster model. The matrix DATA +% represents the data which is being clustered, with each row +% corresponding to a vector. The sum of squares error function is used. +% The point at which a local minimum is achieved is returned as +% CENTRES. The error value at that point is returned in OPTIONS(8). +% +% +% POST and ERRLOG +% also return the cluster number (in a one-of-N encoding) for each +% data point in POST and a log of the error values after each cycle in +% ERRLOG. The optional parameters have the following +% interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG. If OPTIONS(1) is set to 0, then +% only warning messages are displayed. If OPTIONS(1) is -1, then +% nothing is displayed. +% +% OPTIONS(2) is a measure of the absolute precision required for the +% value of CENTRES at the solution. If the absolute difference between +% the values of CENTRES between two successive steps is less than +% OPTIONS(2), then this condition is satisfied. +% +% OPTIONS(3) is a measure of the precision required of the error +% function at the solution. If the absolute difference between the +% error functions between two successive steps is less than OPTIONS(3), +% then this condition is satisfied. Both this and the previous +% condition must be satisfied for termination. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% See also +% GMMINIT, GMMEM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[ndata, data_dim] = size(data); +[ncentres, dim] = size(centres); + +if dim ~= data_dim + if dim == 1 && ncentres == 1 && centres > 1 + + if ndata == numel(weights) + + % --- + % allow for number of centres specification + % --- + dim = data_dim; + ncentres = centres; + + options(5) = 1; + else + error('Data dimension does not match number of weights') + end + + else + error('Data dimension does not match dimension of centres') + end +end + +if (ncentres > ndata) + error('More centres than data') +end + +% Sort out the options +if (options(14)) + niters = options(14); +else + niters = 100; +end + +store = 0; +if (nargout > 3) + store = 1; + errlog = zeros(1, niters); +end + +% Check if centres and posteriors need to be initialised from data +if (options(5) == 1) + % Do the initialisation + perm = randperm(ndata); + perm = perm(1:ncentres); + + % Assign first ncentres (permuted) data points as centres + centres = data(perm, :); +end +% Matrix to make unit vectors easy to construct +id = eye(ncentres); + +% save accumulated weight for a center +cweights = zeros(ncentres, 1); + +% Main loop of algorithm +for n = 1:niters + + % Save old centres to check for termination + old_centres = centres; + + % Calculate posteriors based on existing centres + d2 = dist2(data, centres); + % Assign each point to nearest centre + [minvals, index] = min(d2', [], 1); + post = logical(id(index,:)); + + % num_points = sum(post, 1); + % Adjust the centres based on new posteriors + for j = 1:ncentres + if (sum(weights(post(:,j))) > 0) + % --- + % NOTE: this is edited to include the weights. + % Instead of summing the vectors directly, the vectors are weighted + % and then the result is divided by the sum of the weights instead + % of the number of vectors for this class + % --- + cweights(j) = sum(weights(post(:,j))); + + centres(j,:) = sum(diag(weights(post(:,j))) * data(post(:,j),:), 1)... + /cweights(j); + end + end + + % Error value is total squared distance from cluster centres + % edit: weighted by the vectors weight + e = sum(minvals .* weights); + if store + errlog(n) = e; + end + if options(1) > 0 + fprintf(1, 'Cycle %4d Error %11.6f\n', n, e); + end + + if n > 1 + % Test for termination + if max(max(abs(centres - old_centres))) < options(2) & ... + abs(old_e - e) < options(3) + options(8) = e; + return; + end + end + old_e = e; +end + +% If we get here, then we haven't terminated in the given number of +% iterations. +options(8) = e; +if (options(1) >= 0) + disp(maxitmess); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/matrix2latex.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,133 @@ +function s = latex(M,varargin) +%LATEX Print a matrix in LaTeX tabular format. +% LATEX(M) prints out the numeric matrix M in a LaTeX tabular +% format. The '&' character appears between entries in a row, '\\' +% is appended to the ends of rows, and each entry is set in math +% mode. Complex numbers are understood, and exponentials will be +% converted to a suitable format. +% +% LATEX(M,'nomath') does not include the $$ needed to put each +% entry in math mode (e.g., for use with the amsmath matrix modes). +% +% LATEX(M,FMT) uses a format specifier FMT of the SPRINTF type for +% each entry. +% +% LATEX(M,FMT1,FMT2,...) works through the given format specifiers +% on each row of M. If fewer are given than the column size of M, +% the last is used repeatedly for the rest of the row. +% +% S = LATEX(M,...) does not display output but returns a character +% array S. +% +% Examples: +% latex( magic(4) ) +% latex( magic(4), '%i', 'nomath' ) +% latex( magic(4), '%i', '%.2f' ) +% +% See also SPRINTF, SYM/LATEX. + +% Copyright 2002 by Toby Driscoll. Last updated 12/06/02. + +if ~isa(M,'double') + error('Works only for arrays of numbers.') +elseif ndims(M) > 2 + error('Works only for 2D arrays.') +end + +if nargin < 2 + fmt = {'%#.5g'}; + mathstr = '$'; +else + fmt = varargin; + idx = strmatch('nomath',fmt); + if isempty(idx) + mathstr = '$'; + else + mathstr = ''; + fmt = fmt([1:idx-1 idx+1:end]); + if isempty(fmt), fmt = {'%#.5g'}; end + end +end + +% Extend the format specifiers. +[m,n] = size(M); +if n > length(fmt) + [fmt{end:n}] = deal(fmt{end}); +end + +% Create one format for a row. +rowfmt = ''; +for p = 1:n + % Remove blanks. + thisfmt = deblank(fmt{p}); + + % Add on imaginary part if needed. + if ~isreal(M(:,p)) + % Use the same format as for the real part, but force a + sign for + % positive numbers. + ifmt = thisfmt; + j = findstr(ifmt,'%'); + if ~any(strcmp(ifmt(j+1),['-';'+';' ';'#'])) + ifmt = [ifmt(1:j) '+' ifmt(j+1:end)]; + else + ifmt(j+1) = '+'; + end + ifmt = [ifmt 'i']; + thisfmt = [thisfmt ifmt]; + end + + % Add to row. + rowfmt = [rowfmt mathstr thisfmt mathstr ' & ']; +end + +% After last column, remove column separator and put in newline. +rowfmt(end-1:end) = []; +rowfmt = [rowfmt '\\\\\n']; + +% Use it. +A = M.'; +if isreal(M) + S = sprintf(rowfmt,A); +else + S = sprintf(rowfmt,[real(A(:)) imag(A(:))].'); +end + +% Remove extraneous imaginary part for real entries. +if ~isreal(M) + zi = sprintf(ifmt,0); + S = strrep(S,zi,blanks(length(zi))); +end + +% Remove NaNs. +S = strrep(S,'$NaN$','--'); +S = strrep(S,'NaN','--'); + +% Convert 'e' exponents to LaTeX form. This is probably really slow, but +% what can you do without regular expressions? +S = strrep(S,'e','E'); +ex = min(findstr(S,'E')); +while ~isempty(ex) + % Find first non-digit character. Where is ISDIGIT? + j = ex+2; + while ~isempty(str2num(S(j))) & ~strcmp(S(j),'i') + j = j+1; + end + + % This strips off leading '+' and zeros. + num = sprintf('%i',str2num(S(ex+1:j-1))); + + ee = ['\times 10^{' num '}']; + S = [S(1:ex-1) ee S(j:end)]; + + ex = ex + min(findstr(S(ex+1:end),'E')); +end + +% For good form, remove that last '\\'. +S(end-2:end-1) = ' '; + +% Display or output? +if nargout==0 + disp(S) +else + s = S; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/migrate_to_test_dir.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function out = migrate_to_test_dir(varargin) +% +% out = migrate_to_test_dir('testname', 'this is a test description') +% +% or +% +% out = migrate_to_test_dir('fixedtestdir', 'dirname') +% +% create test run directory, add current dir to path and move +% into test run dir +% +% NOTE: the test directory is created in "globalvars.testdatadir" if set +% + +global globalvars; + +[sTest,fixedtestdir, unused] = process_options(varargin, ... + 'testname', '','fixedtestdir', ''); + +% use starting script name as description if none is given +if isempty(sTest) + [ST, I] = dbstack(); + sTest = ST(end).name; +end + + +% switch to test directory, and add this or current dir to path +if ~isempty(globalvars.tstoutputpath) && ~isempty(dir(globalvars.tstoutputpath)); + addpath(globalvars.tstoutputpath); + cd(globalvars.tstoutputpath); +else + addpath(pwd); +end + +% get camir version +[~,cv] = camirversion(); + +% get current date +sDate = datestr(now,'yymmdd'); + +if isempty(fixedtestdir) + newdir = sprintf('%s_%s_r%d',sDate,sTest,cv); +else + newdir = fixedtestdir; +end + +% create dir if not existent +if isempty(dir(newdir)); + mkdir(newdir); +end + +cd(newdir); +if ~isempty(strfind(pwd, newdir)) + out = newdir; +else + warning 'cannot migrate to specified test directory, Ill dump right here'; + out = -1; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/param_combinations.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function combis = param_combinations(params, pos) +% given a param struct with multiple options for diverse +% parameters, param_combinations(params) returns all +% valid combinations of the param sets + +if nargin < 2 + pos = 1; +end + +% get available fields +fields = fieldnames(params); + +nparams = params; + if pos <= numel(fields) + + for j = 1:numel(params.(fields{pos})) + + % --- + % successively ralter the params struct, + % choosing one of the options + % --- + if ~iscell(params.(fields{pos})) + + nparams.(fields{pos}) = params.(fields{pos})(j); + else + + nparams.(fields{pos}) = params.(fields{pos}){j}; + end + + if j == 1 + combis = param_combinations(nparams, pos + 1); + + else + + % gather the resulting configurations, in reverse order + % regarding the recursion + combis = cat(1, param_combinations(nparams, pos + 1), combis); + end + end + else + % we have reached the leaves, containing single combinations + combis = nparams; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/print_error.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function out = print_error(err) +% --- +% print error messages +% and following lines: stack +% --- +out.msg = sprintf('%s\n',err.message); +if nargout < 1 + fprintf(out.msg); +end + +for e = 1:length(err.stack) + out.line{e} = sprintf('%s at %i\n',err.stack(e).name, err.stack(e).line); + if nargout < 1 + fprintf(out.line{e}); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/python.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +function [result status] = python(varargin) +%python Execute python command and return the result. +% python(pythonFILE) calls python script specified by the file pythonFILE +% using appropriate python executable. +% +% python(pythonFILE,ARG1,ARG2,...) passes the arguments ARG1,ARG2,... +% to the python script file pythonFILE, and calls it by using appropriate +% python executable. +% +% RESULT=python(...) outputs the result of attempted python call. If the +% exit status of python is not zero, an error will be returned. +% +% [RESULT,STATUS] = python(...) outputs the result of the python call, and +% also saves its exit status into variable STATUS. +% +% See also SYSTEM, JAVA, MEX. + +% Copyright 1990-2007 The MathWorks, Inc. +% $Revision: 1.1.4.8 $ + +cmdString = ''; + +% Add input to arguments to operating system command to be executed. +% (If an argument refers to a file on the MATLAB path, use full file path.) +for i = 1:nargin + thisArg = varargin{i}; + if isempty(thisArg) || ~ischar(thisArg) + error('MATLAB:perl:InputsMustBeStrings', 'All input arguments must be valid strings.'); + end + if i==1 + if exist(thisArg, 'file')==2 + % This is a valid file on the MATLAB path + if isempty(dir(thisArg)) + % Not complete file specification + % - file is not in current directory + % - OR filename specified without extension + % ==> get full file path + thisArg = which(thisArg); + end + else + % First input argument is pythonFile - it must be a valid file + error('MATLAB:perl:FileNotFound', 'Unable to find python file: %s', thisArg); + end + end + + % Wrap thisArg in double quotes if it contains spaces + if any(thisArg == ' ') + thisArg = ['"', thisArg, '"']; + end + + % Add argument to command string + cmdString = [cmdString, ' ', thisArg]; +end + +% Execute python script +errTxtNoPerl = 'Unable to find python executable.'; + +if isempty(cmdString) + error('MATLAB:perl:NoPerlCommand', 'No python command specified'); +elseif ispc + % PC + perlCmd = []; % fullfile('C:\Program Files (x86)\Python3x'); + cmdString = ['python' cmdString]; + perlCmd = ['set PATH=',perlCmd, ';%PATH%&' cmdString]; + [status, result] = dos(perlCmd); +else + [status ignore] = unix('which python'); %#ok + if (status == 0) + cmdString = ['python', cmdString]; + [status, result] = unix(cmdString); + else + error('MATLAB:python:NoExecutable', errTxtNoPython); + end +end + +% Check for errors in shell command +if nargout < 2 && status~=0 + error('MATLAB:perl:ExecutionError', ... + 'System error: %sCommand executed: %s', result, cmdString); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/reset_now.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +% reset_now.m +global globalvars; +cd(globalvars.camir.path) +cd .. +cd .. +clear; +clear global; +clear all; +clear global; +clear; + +startup; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/sappend.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function target = sappend(target, source) +% target = sappend(target, source) +% +% appends source to target +% target = [] or struct(), source = struct() +% +% updates the srtuct target by adding the source strut at its end +% if target = [], it is initialised by the first struct in source + + +if isempty(target) + target = source(1); + starti = 2; +else + starti = 1; +end + +for i = starti:numel(source) + target(end+1) = source(i); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/sqlescape.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function out = sqlescape(in) + +out = strrep(in, '''', ''''''); + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/strcell2matrix.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [out] = strcell2matrix(in, start, finish) +% +% out = strcell2matrix(in,start) +% +% in: cell array containing string numbers (from csv2cell) +% start: start offset of first number [row, column] +% finish: end offset + +% e.g. annots = strcell2matrix(annotations_final,[1 0], [0 0]) + +if nargin == 1 + start = [0 0]; + finish = [0 0]; +end + +if nargin == 2 + finish = [0 0]; +end + + +% --- +% Get the data and ignore the rest +% --- +out = zeros(size(in) - start - finish); + + +for i = (1 + start(1)):size(in,1) - finish(1) + for j = (1 + start(1)):size(in,2) - finish(2) + + out(i - start(1), j - start(2)) = str2double(in{i,j}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/strcellfind.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function out = strcellfind(strdb, str, findAll) +% out = strcellfind(strdb, str) +% +% finds a string within an cell array of strings +% only outputs the first occurence, unless +% findAll is set to true +% +% strcellfind is NOT CASE sensitive + +if nargin < 3 + findAll = 0; +end + +out = []; +for i = 1:length(strdb) + if strcmpi( lower(char(strdb{i})), lower(char(str))) == 1; + if ~findAll + out = i; + return; + else + out(end+1) = i; + end + end +end +% --- +% NOTE: Bad backwards compability +% --- +% if isempty(out) +% out = -1; +% end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/substrcellfind.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function [idx, strpos] = substrcellfind(strdb, str, findAll) +% [idx, strpos] = substrcellfind(strdb, str) +% +% finds a string within an cell array of strings +% only outputs the first occurence, unless +% findAll is set to true +% +% substrcellfind is NOT CASE sensitive + +if nargin < 3 + findAll = 0; +end + +idx = []; +strpos = []; +for i = 1:length(strdb) + + % search string in cell db + tpos = strfind(lower(char(strdb{i})), lower(str)); + + if ~isempty(tpos) + + strpos(end+1) = tpos; + + if ~findAll + + idx = i; + return; + else + idx(end+1) = i; + end + end +end + +% if isempty(idx) +% idx = -1; +% end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/test_generic_significance_signrank.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +function [p, med, avg, stats] = test_generic_significance_signrank(file1,run1,file2,run2, weighted, mode) +% +% [p, med] = test_generic_get_signrank(input1, run1, input2, run2, weighted, mode) +% +% @param mode: string determining the way results are preprocessed: +% '', 'avgbase', {'join_datasets',[sets2join]} +% +% get wilcoxon signed rank of two test runs + +if nargin < 5 + weighted = 0; +end +if nargin < 6 + avgbase = 0; +end + + + +% get individual cross-validation results +[out1, ~, ~, indi1] = test_generic_display_results(file1, 0, 0); + +[out2, ~, ~, indi2] = test_generic_display_results(file2, 0, 0); +switch mode + case 'avgbase' + + %out2(run2).mean_ok_test + perf1 = indi1(run1).diag.ok_test(weighted+1,:); + perf2 = indi2(run2).diag.ok_test(weighted+1,:); + p2avg = mean(perf2); + perf_diffs = perf1 - p2avg; + + + case 'join_datasets' + %out2(run2).mean_ok_test + + % get the dataset indices which are to join + if isempty(run1) | (run1 < 1) + sets2join1 = 1:numel(indi1); + else + sets2join1 = run1; + end + if isempty(run2) | (run2 < 1) + sets2join2 = 1:numel(indi2); + else + sets2join2 = run2; + end + + perf1 = join_datasets(indi1,weighted,sets2join1); + perf2 = join_datasets(indi2,weighted,sets2join2); + perf_diffs = perf1 - perf2; + + otherwise + % get difference of individual results + perf_diffs = indi1(run1).diag.ok_test(weighted+1,:) - indi2(run2).diag.ok_test(weighted+1,:); +end + +[p, h, stats] = signrank(perf_diffs ); +% [p, h] = signtest(perf1,perf2); +% [h, p, stats] = kstest2(perf1,perf2); +% [h2,p2] = ttest(perf_diffs); + +% get median of performance difference +med = median(perf_diffs); + +% get median of performance difference +avg = mean(perf_diffs); + +% output interpretation +if h && med > 0 + fprintf('input 1 >> input 2 :)\n'); +elseif h && med < 0 + fprintf('input 2 >> input 1 ;)\n'); +else + fprintf('no significant difference :( \n'); +end + + +% --- +% joins the test data to a single performance vector +% --- +function perf = join_datasets(indi,weighted, sets2join) +perf = []; + +for i = sets2join + perf = [perf indi(i).diag.ok_test(weighted+1,:)]; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/uimage.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,132 @@ +function h = uimage(varargin) +%UIMAGE Display image with uneven axis. +% UIMAGE(X,Y,C) displays matrix C as an image, using the vectors X and +% Y to specify the X and Y coordinates. X and Y may be unevenly spaced +% vectors, but must be increasing. The size of C must be LENGTH(Y)* +% LENGTH(X). (Most probably you'll want to display C' instead of C). +% +% Contrary to Matlab's original IMAGE function, here the vectors X and Y +% do not need to be linearly spaced. Whereas IMAGE linearly interpolates +% the X-axis between X(1) and X(end), ignoring all other values (idem +% for Y), UIMAGE allows for X and/or Y to be unevenly spaced vectors, by +% locally stretching the matrix C (ie, by duplicating some elements of C) +% for larger X and/or Y intervals. +% +% The syntax for UIMAGE(X,Y,C,...) is the same as IMAGE(X,Y,C,...) +% (all the remaining arguments, eg 'PropertyName'-PropertyValue pairs, +% are passed to IMAGE). See IMAGE for details. +% +% Use UIMAGESC to scale the data using the full colormap. The syntax for +% UIMAGESC(X,Y,C,...) is the same as IMAGESC(X,Y,C,...). +% +% Typical uses: +% - Plotting a spatio-temporal diagram (T,X), with unevenly spaced +% time intervals for T (eg, when some values are missing, or when +% using a non-constant sampling rate). +% - Plotting a set of power spectra with frequency in log-scale. +% +% h = UIMAGE(X,Y,C,...) returns a handle to the image. +% +% Example: +% c = randn(50,20); % Random 50x20 matrix +% x = logspace(1,3,50); % log-spaced X-axis, between 10 and 1000 +% y = linspace(3,8,20); % lin-spaced Y-axis, between 3 and 8 +% uimagesc(x,y,c'); % displays the matrix +% +% F. Moisy +% Revision: 1.03, Date: 2006/06/14. +% +% See also IMAGE, IMAGESC, UIMAGESC. + + +% History: +% 2006/06/12: v1.00, first version. +% 2006/06/14: v1.03, minor bug fixed; works in ML6. + +error(nargchk(3,inf,nargin)); + +% maximum number of matrix elements to interpolate the uneven axis +% (typically between 500 and 5000): +nmax = 2000; + +x = varargin{1}; +y = varargin{2}; +c = varargin{3}; + +if any(diff(x)<=0) || any(diff(y)<=0) + error('The X and Y axis should be increasing.'); +end + +dx = min(diff(x)); % smallest interval for X +dy = min(diff(y)); % smallest interval for Y + +% test if X and Y are linearly spaced (to within 10^-12): +evenx = all(abs(diff(x)/dx-1)<1e-12); % true if X is linearly spaced +eveny = all(abs(diff(y)/dy-1)<1e-12); % true if Y is linearly spaced + + +if evenx && eveny % X and Y both evenly spaced + + xe = x; + ye = y; + ce = c; + +elseif evenx && ~eveny % X even and Y uneven + + nx = length(x); + xe = x; + + ny = ceil(1 + (y(end) - y(1))/dy); % number of points for Y + ny = min(ny, nmax); + ye = linspace(y(1), y(end), ny); + + ce = zeros(ny,nx); + + for j=1:ny + indj = find(y<=ye(j)); + ce(j,1:nx) = c(indj(end), 1:nx); + end; + +elseif ~evenx && eveny % X uneven and Y even + + nx = ceil(1 + (x(end) - x(1))/dx); % number of points for X + nx = min(nx, nmax); + xe = linspace(x(1), x(end), nx); + + ny = length(y); + ye = y; + + ce = zeros(ny,nx); + + for i=1:nx + indi = find(x<=xe(i)); + ce(1:ny,i) = c(1:ny, indi(end)); + end; + +elseif ~evenx && ~eveny % X and Y both uneven + + nx = ceil(1 + (x(end) - x(1))/dx); % number of points for X + nx = min(nx, nmax); + xe = linspace(x(1), x(end), nx); + + ny = ceil(1 + (y(end) - y(1))/dy); % number of points for Y + ny = min(ny, nmax); + ye = linspace(y(1), y(end), ny); + + ce = zeros(ny,nx); + + for i=1:nx + for j=1:ny + indi = find(x<=xe(i)); + indj = find(y<=ye(j)); + ce(j,i) = c(indi(end), indj(end)); + end; + end; + +end + +hh = image(xe, ye, ce, varargin{4:end}); + +if nargout>0 + h = hh; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/uimagesc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function h = uimagesc(varargin) +%UIMAGESC Display scaled image with uneven axis. +% UIMAGESC(...) is the same as UIMAGE(...) except the data is scaled +% to use the full colormap. See UIMAGE for details. +% +% Note: UIMAGESC is based on Matlab's original IMAGESC, Revision 5.11.4.5. +% UIMAGESC simply calls UIMAGE with a scaled colormap. +% +% F. Moisy - adapted from TMW +% Revision: 1.01, Date: 2006/06/13. +% +% See also IMAGE, IMAGESC, UIMAGE. + +% History: +% 2006/06/12: v1.00, first version. + +clim = []; +switch (nargin), + case 0, + hh = uimage('CDataMapping','scaled'); + case 1, + hh = uimage(varargin{1},'CDataMapping','scaled'); + case 3, + hh = uimage(varargin{:},'CDataMapping','scaled'); + otherwise, + + % Determine if last input is clim + if isequal(size(varargin{end}),[1 2]) + str = false(length(varargin),1); + for n=1:length(varargin) + str(n) = ischar(varargin{n}); + end + str = find(str); + if isempty(str) || (rem(length(varargin)-min(str),2)==0), + clim = varargin{end}; + varargin(end) = []; % Remove last cell + else + clim = []; + end + else + clim = []; + end + hh = uimage(varargin{:},'CDataMapping','scaled'); +end + +% Get the parent Axes of the image +cax = ancestor(hh,'axes'); + +if ~isempty(clim), + set(cax,'CLim',clim) +elseif ~ishold(cax), + set(cax,'CLimMode','auto') +end + +if nargout > 0 + h = hh; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/uplot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,195 @@ +function han = uplot(varargin) +% UPLOT Plot of double and FRD data. +% +% UPLOT([plot_type],SYS1,SYS2,SYS3, ...) +% UPLOT([plot_type],[1 10],[.1 5],SYS1, ...) +% UPLOT([plot_type],SYS1,'linetype1',SYS2,'linetype2',...) +% +% Plot double and FRD objects. The syntax is the same as the MATLAB +% plot command except that all data is contained in SYSi, and the +% axes are specified by PLOT_TYPE. +% +% The (optional) plot_type argument must be one of: +% +% 'iv,d' matin .vs. independent variable (default option) +% 'iv,m' magnitude .vs. independent variable +% 'iv,lm' log(magnitude) .vs. independent variable +% 'iv,p' phase .vs. independent variable +% 'liv,d' matin .vs. log(independent variable) +% 'liv,m' magnitude .vs. log(independent variable) +% 'liv,lm' log(magnitude) .vs. log(independent variable) +% 'liv,p' phase .vs. log(independent variable) +% 'nyq' real .vs. imaginary (parametrized by indep variable) +% 'nic' Nichols chart +% 'bode' Bode magnitude and phase plots +% +%See also: BODE, LOGLOG, PLOT, NICHOLS, NYQUIST, SEMILOGX, SEMILOGY, SIGMA. + +% Copyright 2004 The MathWorks, Inc. + +nin = nargin; +if isa(varargin{1},'char') + plottype = varargin{1}; + sidx = 2; +else + plottype = 'iv,d'; + sidx = 1; +end + +argcell = cell(0,1); +cnt = 1; +cflag = 0; +dflag = 0; +ydataloc = []; +for i=sidx:nin + arg = varargin{i}; + switch class(arg) + case 'frd' + if dflag==1 + error('Double data must come in pairs'); + else + cflag = 0; + szm = size(arg); + if length(szm)==2 + npts = length(arg.Frequency); + ydata = reshape(arg.ResponseData,[szm(1)*szm(2) npts]).'; + xdata = arg.Frequency; + argcell = [argcell;{xdata};{ydata}]; + ydataloc = [ydataloc;cnt+1]; + cnt = cnt + 2; + else + nad = length(szm) - 2; + npts = length(arg.Frequency); + tmp = permute(arg.ResponseData,[1 2 4:4+nad-1 3]); + ydata = reshape(tmp,[prod(szm) npts]).'; + xdata = arg.Frequency; + argcell = [argcell;{xdata};{ydata}]; + ydataloc = [ydataloc;cnt+1]; + cnt = cnt + 2; + end + end + case 'char' + if dflag==1 + error('Double data must come in pairs'); + else + if cflag==0 + argcell = [argcell;{arg}]; + cnt = cnt + 1; + cflag = 1; + else + error('Never have 2 chars in a row'); + end + end + case 'double' + cflag = 0; + if dflag==0 % think xdata + argcell = [argcell;{arg}]; + cnt = cnt + 1; + dflag = 1; + elseif dflag==1 % think ydata + argcell = [argcell;{arg}]; + ydataloc = [ydataloc;cnt]; + cnt = cnt + 1; + dflag = 0; + end + otherwise + if isuncertain(arg) + error('Cannot plot uncertain matrices or systems'); + else + error('Cannot plot this type of data'); + end + end +end +xmin = inf; +xmax = -inf; +for i=1:length(ydataloc) + xmin = min([xmin min(argcell{ydataloc(i)-1})]); + xmax = max([xmax max(argcell{ydataloc(i)-1})]); +end +for i=1:length(ydataloc) + if length(argcell{ydataloc(i)})==1 + argcell{ydataloc(i)} = [argcell{ydataloc(i)} argcell{ydataloc(i)}]; + argcell{ydataloc(i)-1} = [xmin xmax]; + end +end + +switch plottype +case 'iv,d' + h = plot(argcell{:}); +case 'iv,m' + for i=1:length(ydataloc) + argcell{ydataloc(i)} = abs(argcell{ydataloc(i)}); + end + h = plot(argcell{:}); +case 'iv,lm' + for i=1:length(ydataloc) + argcell{ydataloc(i)} = abs(argcell{ydataloc(i)}); + end + h = semilogy(argcell{:}); +case 'iv,p' + for i=1:length(ydataloc) + argcell{ydataloc(i)} = (180/pi)*angle(argcell{ydataloc(i)}); + end + h = plot(argcell{:}); +case 'liv,d' + h = semilogx(argcell{:}); +case 'liv,ld' + h = loglog(argcell{:}); +case 'liv,m' + for i=1:length(ydataloc) + argcell{ydataloc(i)} = abs(argcell{ydataloc(i)}); + end + h = semilogx(argcell{:}); +case 'liv,lm' + for i=1:length(ydataloc) + argcell{ydataloc(i)} = abs(argcell{ydataloc(i)}); + end + h = loglog(argcell{:}); +case 'liv,p' + for i=1:length(ydataloc) + argcell{ydataloc(i)} = (180/pi)*angle(argcell{ydataloc(i)}); + end + h = semilogx(argcell{:}); +case {'nyq'} + for i=1:length(ydataloc) + %x-data, real part + argcell{ydataloc(i)-1} = real(argcell{ydataloc(i)}); + argcell{ydataloc(i)} = imag(argcell{ydataloc(i)}); + end + h = plot(argcell{:}); +case {'ri'} + for i=1:length(ydataloc) + %x-data, real part + argcell{ydataloc(i)-1} = real(argcell{ydataloc(i)}); + %y-data, imag part + argcell{ydataloc(i)} = imag(argcell{ydataloc(i)}); + end + h = plot(argcell{:}); +case {'nic'} + for i=1:length(ydataloc) + %x-data, imag part + argcell{ydataloc(i)-1} = 360/(2*pi)*negangle(argcell{ydataloc(i)}); + %y-data, real part + argcell{ydataloc(i)} = 20*log10(abs(argcell{ydataloc(i)})); + end + h = plot(argcell{:}); +case 'bode' + subplot(2,1,1) + magcell = argcell; + for i=1:length(ydataloc) + magcell{ydataloc(i)} = abs(magcell{ydataloc(i)}); + end + hm = loglog(magcell{:}); + subplot(2,1,2) + for i=1:length(ydataloc) + argcell{ydataloc(i)} = (180/pi)*angle(argcell{ydataloc(i)}); + end + hp = semilogx(argcell{:}); + h = [hm;hp]; +otherwise + error('invalid plot type'); +end + +if nargout==1 + han = h; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/write_mat_results_ismir12.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function write_mat_results_ismir12(filein, subrun, fileout) +% write_mat_resutls_ismir12(filein, subrun, fileout) +% +% write results from result file into ismir12 generic format. +% if subrun is a vector, the results are averaged over the +% runs specified + + +[out, stats, features, individual] = test_generic_display_results(filein, 0); + +% --- +% we get the single individual values and +% --- +for i = 1:numel(subrun) + individual = individual(subrun); + + % number of inctrain cycles + n_inctrain = numel(individual.diag.inctrain); + + % --- + % ok_train_unused + % --- + values_ok_train_unused{i} = reshape([individual.diag.inctrain.ok_notin_train], [],n_inctrain); + values_ok_train_unused{i} = values_ok_train_unused(1:2:end,:).*100; + + mean_ok_train_unused{i} = mean(values_ok_train_unused, 1); + var_ok_train_unused{i} = var(values_ok_train_unused,[], 1); + + % --- + % ok_train + % --- + values_ok_train{i} = reshape([individual.diag.inctrain.ok_train], [],n_inctrain); + values_ok_train{i} = values_ok_train(1:2:end,:).*100; + + mean_ok_train{i} = mean(values_ok_train, 1); + var_ok_train{i} = var(values_ok_train,[], 1); + + % --- + % ok_test + % --- + values_ok_test{i} = reshape([individual.diag.inctrain.ok_test], [],n_inctrain); + values_ok_test{i} = values_ok_test(1:2:end,:).*100; + + mean_ok_test{i} = mean(values_ok_test, 1); + var_ok_test{i} = var(values_ok_test,[], 1); +end + + + +clear ('out', 'stats', 'features', 'individual'); +save(fileout)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/core/tools/xml2struct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,190 @@ +function [ s ] = xml2struct( file ) +%Convert xml file into a MATLAB structure +% [ s ] = xml2struct( file ) +% +% A file containing: +% <XMLname attrib1="Some value"> +% <Element>Some text</Element> +% <DifferentElement attrib2="2">Some more text</Element> +% <DifferentElement attrib3="2" attrib4="1">Even more text</DifferentElement> +% </XMLname> +% +% Will produce: +% s.XMLname.Attributes.attrib1 = "Some value"; +% s.XMLname.Element.Text = "Some text"; +% s.XMLname.DifferentElement{1}.Attributes.attrib2 = "2"; +% s.XMLname.DifferentElement{1}.Text = "Some more text"; +% s.XMLname.DifferentElement{2}.Attributes.attrib3 = "2"; +% s.XMLname.DifferentElement{2}.Attributes.attrib4 = "1"; +% s.XMLname.DifferentElement{2}.Text = "Even more text"; +% +% Please note that the following characters are substituted +% '-' by '_dash_', ':' by '_colon_' and '.' by '_dot_' +% +% Written by W. Falkena, ASTI, TUDelft, 21-08-2010 +% Attribute parsing speed increased by 40% by A. Wanner, 14-6-2011 +% Added CDATA support by I. Smirnov, 20-3-2012 +% +% Modified by X. Mo, University of Wisconsin, 12-5-2012 + + if (nargin < 1) + clc; + help xml2struct + return + end + + if isa(file, 'org.apache.xerces.dom.DeferredDocumentImpl') || isa(file, 'org.apache.xerces.dom.DeferredElementImpl') + % input is a java xml object + xDoc = file; + else + %check for existance + if (exist(file,'file') == 0) + %Perhaps the xml extension was omitted from the file name. Add the + %extension and try again. + if (isempty(strfind(file,'.xml'))) + file = [file '.xml']; + end + + if (exist(file,'file') == 0) + error(['The file ' file ' could not be found']); + end + end + %read the xml file + xDoc = xmlread(file); + end + + %parse xDoc into a MATLAB structure + s = parseChildNodes(xDoc); + +end + +% ----- Subfunction parseChildNodes ----- +function [children,ptext,textflag] = parseChildNodes(theNode) + % Recurse over node children. + children = struct; + ptext = struct; textflag = 'Text'; + if hasChildNodes(theNode) + childNodes = getChildNodes(theNode); + numChildNodes = getLength(childNodes); + + for count = 1:numChildNodes + theChild = item(childNodes,count-1); + [text,name,attr,childs,textflag] = getNodeData(theChild); + + if (~strcmp(name,'#text') && ~strcmp(name,'#comment') && ~strcmp(name,'#cdata_dash_section')) + %XML allows the same elements to be defined multiple times, + %put each in a different cell + if (isfield(children,name)) + if (~iscell(children.(name))) + %put existsing element into cell format + children.(name) = {children.(name)}; + end + index = length(children.(name))+1; + %add new element + children.(name){index} = childs; + textFieldNames = fieldnames(text); + for t = 1:length(textFieldNames) + textFieldName = textFieldNames{t}; + children.(name){index}.(textFieldName) = text.(textFieldName); + end + if(~isempty(attr)) + children.(name){index}.('Attributes') = attr; + end + else + %add previously unknown (new) element to the structure + children.(name) = childs; + if(~isempty(text) && ~isempty(fieldnames(text))) + textFieldNames = fieldnames(text); + numTextFieldNames = length( textFieldNames ); + for i = 1:numTextFieldNames + thisFieldName = textFieldNames{i}; + children.(name).(thisFieldName) = text.(thisFieldName); + end + end + if(~isempty(attr)) + children.(name).('Attributes') = attr; + end + end + else + ptextflag = 'Text'; + if (strcmp(name, '#cdata_dash_section')) + ptextflag = 'CDATA'; + elseif (strcmp(name, '#comment')) + ptextflag = 'Comment'; + end + + %this is the text in an element (i.e., the parentNode) + if (~isempty(regexprep(text.(textflag),'[\s]*',''))) + if (~isfield(ptext,ptextflag) || isempty(ptext.(ptextflag))) + ptext.(ptextflag) = text.(textflag); + else + %what to do when element data is as follows: + %<element>Text <!--Comment--> More text</element> + + %put the text in different cells: + % if (~iscell(ptext)) ptext = {ptext}; end + % ptext{length(ptext)+1} = text; + + %just append the text + ptext.(ptextflag) = [ptext.(ptextflag) text.(textflag)]; + end + end + end + + end + end +end + +% ----- Subfunction getNodeData ----- +function [text,name,attr,childs,textflag] = getNodeData(theNode) + % Create structure of node info. + + %make sure name is allowed as structure name + name = toCharArray(getNodeName(theNode))'; + name = strrep(name, '-', '_dash_'); + name = strrep(name, ':', '_colon_'); + name = strrep(name, '.', '_dot_'); + + attr = parseAttributes(theNode); + if (isempty(fieldnames(attr))) + attr = []; + end + + %parse child nodes + [childs,text,textflag] = parseChildNodes(theNode); + + if (isempty(fieldnames(childs)) && isempty(fieldnames(text))) + %get the data of any childless nodes + % faster than if any(strcmp(methods(theNode), 'getData')) + % no need to try-catch (?) + % faster than text = char(getData(theNode)); + text.(textflag) = toCharArray(getTextContent(theNode))'; + end + +end + +% ----- Subfunction parseAttributes ----- +function attributes = parseAttributes(theNode) + % Create attributes structure. + + attributes = struct; + if hasAttributes(theNode) + theAttributes = getAttributes(theNode); + numAttributes = getLength(theAttributes); + + for count = 1:numAttributes + %attrib = item(theAttributes,count-1); + %attr_name = regexprep(char(getName(attrib)),'[-:.]','_'); + %attributes.(attr_name) = char(getValue(attrib)); + + %Suggestion of Adrian Wanner + str = toCharArray(toString(item(theAttributes,count-1)))'; + k = strfind(str,'='); + attr_name = str(1:(k(1)-1)); + attr_name = strrep(attr_name, '-', '_dash_'); + attr_name = strrep(attr_name, ':', '_colon_'); + attr_name = strrep(attr_name, '.', '_dot_'); + attributes.(attr_name) = str((k(1)+2):(end-1)); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/editme_startup.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +% The CAMIR framework for music similarity experiments +% Copyright (C) {2013} {Daniel Wolff} (wolffd.mail [at] gmail [dot] com) +% +% This program is free software: you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation, either version 3 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program. If not, see [http://www.gnu.org/licenses/]. +% +% This software has been made possible by many other projects included +% in the /toolboxes folder. Please consider their further licenses. + +% --- +% startup.m local +% this is the place to set system-specific parameters and paths +% +% please run the scripts in the folder 'reproduce_AES53rd' for reproducing +% experimental results of +% Feature Preprocessing with RBMs for Music Similarity Learning +% Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd conference +% --- + +global globalvars; + +% --- +% @TODO: PLEASE insert your absolute path to the folder containing this +% script +% --- + my_path = pwd;%'C:\_danielwolff\_documents\coding\matlab\music_research_branches\ISMIR2013'; +% --- +% Add Toolboxes +% --- +addpath(genpath(my_path)); + +addpath(genpath([my_path '/toolboxes/bioakustik_tools'])) +addpath(genpath([my_path '/toolboxes/graph_visualisation/graphViz4Matlab/util'])) + +addpath(pathsdiff(genpath([my_path]),... + '.svn')); + +% smv-light +addtosystempath([my_path '/toolboxes/SVM-light']); + +globalvars.camir.msd.basepath = ''; +globalvars.tstaudiopath = [my_path '/features']; +globalvars.mfilepath = ''; +globalvars.tstoutputpath = [my_path '/features']; +globalvars.systemslash = filesep; + +try + cd core +catch +end + +startup_music_research; + +try + cd .. + cd reproduce_AES53rd +catch +end
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Use with the GNU Affero General Public License. + + Notwithstanding any other provision of this License, you have +permission to link or combine any covered work with a work licensed +under version 3 of the GNU Affero General Public License into a single +combined work, and to convey the resulting work. The terms of this +License will continue to apply to the part which is the covered work, +but the special requirements of the GNU Affero General Public License, +section 13, concerning interaction through a network will apply to the +combination as such. + + 14. Revised Versions of this License. + + The Free Software Foundation may publish revised and/or new versions of +the GNU General Public License from time to time. Such new versions will +be similar in spirit to the present version, but may differ in detail to +address new problems or concerns. + + Each version is given a distinguishing version number. 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Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + {one line to give the program's name and a brief idea of what it does.} + Copyright (C) {year} {name of author} + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see [http://www.gnu.org/licenses/]. + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + {project} Copyright (C) {year} {fullname} + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +[http://www.gnu.org/licenses/]. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +[http://www.gnu.org/philosophy/why-not-lgpl.html]. + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +% --- +% This script plots 50-dimensional RBM features as in figure 2 of +% +% Feature Preprocessing with RBMs for Music Similarity Learning +% Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd +% conference +% --- + +% cr_: training correct +% cr : testing correct +feature_file = 'rel_music_raw_features+simdata_ISMIR12'; +vars = whos('-file', feature_file); +A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); +raw_features = A.(vars(1).name); +indices = A.(vars(2).name); +tst_inx = A.(vars(3).name); +trn_inx = A.(vars(4).name); +% +figure(1); imagesc(raw_features);colorbar; +title 'Original Features'; + +% load pregenerated RBM features +mod = load('rbm_50'); + +% --- +% uncomment the following line to use newly calculated RBM features +% mod = new_rbm(50,'grad'); +% --- + +features = logistic(raw_features*mod.W_max{1} + repmat(mod.hB_max{1},size(raw_features,1),1)); +figure(2); imagesc(features);colorbar; +title 'RBM Features'; + +num_case = size(trn_inx,1); +[trnd_12 trnd_13] = subspace_distances(trn_inx,features,indices,1,1); +[tstd_12 tstd_13] = subspace_distances(tst_inx,features,indices,1,1); +cr_ = 0; % correct rate for training +cr = 0; % correct rate for testing +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +% --- +% This script trains similarity measures and evaluates the +% impact of the number of hidden states as displayed in figure 3 of +% % Feature Preprocessing with RBMs for Music Similarity Learning +% Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd +% conference +% +% The output is printed in the console and plotted afterwards +% +% please note that the RBM training is a probabilistic process, and +% thus the papers' results can only be reproduced approximately with +% large numbers iterations of this script, and selection of RBMs according to +% their training set performance. +% --- + +% this version reproduces the figure approximately using precomputed RBM +[test(1), train(1)] = rbm_fig3('rbm_h30'); +[test(2), train(2)] = rbm_fig3('rbm_h50'); +[test(3), train(3)] = rbm_fig3('rbm_h100'); +[test(4), train(4)] = rbm_fig3('rbm_h500'); +[test(5), train(5)] = rbm_fig3('rbm_h1000'); + +% optionally, in order to test new RBMs, use the code below +% [test(1), train(1)] = rbm_fig3(30); +% [test(2), train(2)] = rbm_fig3(50); +% [test(3), train(3)] = rbm_fig3(100); +% [test(4), train(4)] = rbm_fig3(500); +% [test(5), train(5)] = rbm_fig3(1000); + +hidNum = [30 50 100 500 1000]; +hFig = figure; +set(hFig,'Units','centimeters'); +set(hFig, 'Position', [10 10 10 6]); +plot(hidNum,train*100,'--rx'); +hold on +plot(hidNum,test*100,'-bo'); +lg = legend('Training','Test'); +set(lg,'Location','SouthEast'); +title ('Figure 4: GRADIENT results for different hidNum');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure3/new_rbm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [mod] = new_rbm(hidNum,type) +% computes a new RBM that can be used instead of the saved ones + +conf.hidNum = hidNum; +conf.eNum = 150; +conf.bNum = 1; +conf.gNum = 1; +conf.N = 80; +conf.MAX_INC = 10; + +% use the specific parameters from the paper +switch type + + case 'svm' + conf.params = [0.7 0.7 0.1 2.0e-5]; + case 'grad' + conf.params = [0.05 0.1 0.1 2.0e-5]; + otherwise +end + +% load data +feature_file = 'rel_music_raw_features+simdata_ISMIR12'; +vars = whos('-file', feature_file); +A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); +data = A.(vars(1).name); +conf.sNum = size(data,1); + +mod = struct(); +[mod.W_max{1} mod.vB_max{1} mod.hB_max{1}] = training_rbm(conf,zeros(0,0),feature_file); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure3/rbm_fig3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function [cr, cr_] = rbm_fig3(preloaded) + +% cr_: training correct +% cr : testing correct +feature_file = 'rel_music_raw_features+simdata_ISMIR12'; +vars = whos('-file', feature_file); +A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); +raw_features = A.(vars(1).name); +indices = A.(vars(2).name); +tst_inx = A.(vars(3).name); +trn_inx = A.(vars(4).name); +% +%figure(1); imagesc(raw_features);colorbar; + +% load the RBM, or compute a new one +if ischar(preloaded) + mod = load(preloaded); +else + if isstruct(preloaded) + mod = preloaded; + else + % create new RBM + mod = new_rbm(preloaded,'grad'); + end +end +features = logistic(raw_features*mod.W_max{1} + repmat(mod.hB_max{1},size(raw_features,1),1)); +%figure(2); imagesc(features);colorbar; + +num_case = size(trn_inx,1); +[trnd_12 trnd_13] = subspace_distances(trn_inx,features,indices,1,1); +[tstd_12 tstd_13] = subspace_distances(tst_inx,features,indices,1,1); +cr_ = 0; % correct rate for training +cr = 0; % correct rate for testing +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% CODE HERE %% +%[Ws cr_] = gradient_ascent(trnd_12,trnd_13,0.1,0.1,0.00002); %eNum = 10 +[Ws cr_] = gradient_ascent(trnd_12,trnd_13,0.05,0.01,0.00002); + +for i = 1:num_case + cr = cr + sum((tstd_13{i}-tstd_12{i})*Ws{i}' > 0, 1)/size(tstd_12{i},1); +end +cr = cr/num_case; +%% Check the result +fprintf('Gradient RBM Test / Train Result=%f / %f\n',cr*100,cr_*100); +%fprintf('Training=%f Testing=%f\n',cr_,cr); + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure4.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,103 @@ +% --- +% This script trains similarity measures and evaluates the +% impact of the number of hidden states as displayed in figure 4 of +% +% Feature Preprocessing with RBMs for Music Similarity Learning +% Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd +% conference +% +% please note that the RBM training is a probabilistic process, and +% thus the papers' results can only be reproduced approximately with +% some iterations of this script, and selection of RBMs according to +% their training set performance. +% This takes considerable time, and for the sake of usability of this script +% is left our. % It is exemplarily done for hidnum = 1000 in the script "table 1" in the +% correpsonding folder. +% +% % The train and test performances are plotted in a figure +% +% For convenicence, The precomputed RBM features are stored in the files +% accompaining this script. +% In order to compute new SVM features, delete these files. +% --- + +global globalvars; +globalvars.debug = 3; + +% --- +% vary feature parameters of mixed features +% --- + +ftype = 'MTTMixedFeatureSonRBM'; %'MTTMixedFeatureStober11Genre'; + +fparams_all = struct(... + ... % --- + ... % these are SONfeatRaw parameters + ... % --- + 'son_filename',{{'rel_music_raw_features+simdata_ISMIR12.mat'}}, ... + 'son_conf', 2, ... + ... % --- + ... % Following: RBM params + ... % --- + 'norm_pre_rbm', 0, ... % norm before RBM? + 'norm_post_rbm',0, ... % norm before RBM? + 'rbm_hidNum',[30 50 100 500 1000], ... % number of hidden units % 500 + 'rbm_eNum', 100, ... + 'rbm_bNum', 1, ... + 'rbm_gNum', 1, ... + 'rbm_lrate1' , [0.05], ... % initial learning rate % 0.01 + 'rbm_lrate2', [0.17], ... % learning rate, %0.05 + 'rbm_momentum', [0.1], ... % 0.5 + 'rbm_cost', [0.00002], ... % cost function + 'rbm_N', 50, ... + 'rbm_MAX_INC', 10 ... + ); + +% --- +% vary parameters for svmlight +% --- + +trainparams_all = struct(... + 'C', [1], ...% + 'weighted', [0], ... + 'dataset', {{'comp_partBinData_ISMIR12_01.mat'}}, ... + 'inctrain', 0 ... + ... % this optional + ... %'deltafun', {{'conv_subspace_delta'}}, ... + ... %'deltafun_params', {{{[1],[0]},{[5],[1]},{[10],[1]},{[20],[1]},{[30],[1]},{[50],[1]},{[70],[1]}}} ... % normalisation improves results + ); + +% set training function +trainfun = @svmlight_wrapper; + + +% create test directory +akt_dir = migrate_to_test_dir(); + + +% call eval +out = test_generic_features_parameters_crossval... + (fparams_all, trainparams_all, trainfun, ftype); + +% get number of Hidden Units +fparams = [out(:).fparams]; +hidNum = [fparams(:).rbm_hidNum]; +[hidNum,idx] =sort(hidNum); + +% get sorted test data results +svm_test_results = [out(idx).mean_ok_test]; +svm_test_results = svm_test_results(1,:); + +% get sorted train data results +svm_train_results = [out(idx).mean_ok_train]; +svm_train_results = svm_train_results(1,:); + +% --- +% plot results +% --- +figure; +plot(hidNum,svm_train_results,'r-'); +hold on +plot(hidNum,svm_test_results,'b'); +legend('Training','Test'); +title ('Figure 4: SVM results for different hidNum');
Binary file reproduce_AES53rd/rerun_figure_4/f1bed60c9cfd463391c392be29df3aa40.fcd3e694f837cc092ac6e86432bdaac7.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/f1bed60c9cfd463391c392be29df3aa40.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 10:58:41 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">1</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">30</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/f30f27fa409951e4a6338c6b8e033f315.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 11:26:36 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">2</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">50</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/f4146a16625be1f961cd1f47add7d2a0a.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 11:24:12 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">2</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">100</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/f41a184d2b727e46307dc49dc7d60286d.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 11:19:32 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">2</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">500</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
Binary file reproduce_AES53rd/rerun_figure_4/f46b983e00a37d5109fb1148f4e915dde.fcd3e694f837cc092ac6e86432bdaac7.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/f46b983e00a37d5109fb1148f4e915dde.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 10:49:35 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">1</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">500</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
Binary file reproduce_AES53rd/rerun_figure_4/f51c27ac475045cb4ff410deca9d0ccb4.fcd3e694f837cc092ac6e86432bdaac7.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/f51c27ac475045cb4ff410deca9d0ccb4.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 11:28:38 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">2</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">30</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
Binary file reproduce_AES53rd/rerun_figure_4/f549ec90427cf6dcc6f9f12c9c3853912.fcd3e694f837cc092ac6e86432bdaac7.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/f549ec90427cf6dcc6f9f12c9c3853912.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 10:54:21 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">1</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">100</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
Binary file reproduce_AES53rd/rerun_figure_4/f7a4e67d3dce99eed94c26ad193351927.fcd3e694f837cc092ac6e86432bdaac7.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/f7a4e67d3dce99eed94c26ad193351927.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 11:10:08 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">2</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">1000</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
Binary file reproduce_AES53rd/rerun_figure_4/fc1fb454d81b345c55adf4d3c068f2901.fcd3e694f837cc092ac6e86432bdaac7.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/fc1fb454d81b345c55adf4d3c068f2901.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 10:40:09 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">1</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">1000</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
Binary file reproduce_AES53rd/rerun_figure_4/ff1a207e56413639c2328fe4ff9076260.fcd3e694f837cc092ac6e86432bdaac7.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_figure_4/ff1a207e56413639c2328fe4ff9076260.fcd3e694f837cc092ac6e86432bdaac7.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +<?xml version="1.0"?> +<!-- Written on 09-Oct-2013 10:56:41 using the XML Toolbox for Matlab --> +<root xml_tb_version="3.1" idx="1" type="struct" size="1 1"> + <son_filename idx="1" type="char" size="1 42">rel_music_raw_features+simdata_ISMIR12.mat</son_filename> + <son_conf idx="1" type="double" size="1 1">1</son_conf> + <norm_pre_rbm idx="1" type="double" size="1 1">0</norm_pre_rbm> + <norm_post_rbm idx="1" type="double" size="1 1">0</norm_post_rbm> + <rbm_hidNum idx="1" type="double" size="1 1">50</rbm_hidNum> + <rbm_eNum idx="1" type="double" size="1 1">100</rbm_eNum> + <rbm_bNum idx="1" type="double" size="1 1">1</rbm_bNum> + <rbm_gNum idx="1" type="double" size="1 1">1</rbm_gNum> + <rbm_lrate1 idx="1" type="double" size="1 1">0.05</rbm_lrate1> + <rbm_lrate2 idx="1" type="double" size="1 1">0.17</rbm_lrate2> + <rbm_momentum idx="1" type="double" size="1 1">0.1</rbm_momentum> + <rbm_cost idx="1" type="double" size="1 1">2e-005</rbm_cost> + <rbm_N idx="1" type="double" size="1 1">50</rbm_N> + <rbm_MAX_INC idx="1" type="double" size="1 1">10</rbm_MAX_INC> +</root>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_gradient_table3/gradient_table3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +% cr_: training correct +% cr : testing correct +feature_file = 'rel_music_raw_features+simdata_ISMIR12'; +vars = whos('-file', feature_file); +A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name); +raw_features = A.(vars(1).name); +indices = A.(vars(2).name); +tst_inx = A.(vars(3).name); +trn_inx = A.(vars(4).name); +% +% figure(1); imagesc(raw_features);colorbar; +mod = load('res_48'); +features = logistic(raw_features*mod.W_max{1} + repmat(mod.hB_max{1},size(raw_features,1),1)); +% figure(2); imagesc(features);colorbar; + +num_case = size(trn_inx,1); +[trnd_12 trnd_13] = subspace_distances(trn_inx,features,indices,1,1); +[tstd_12 tstd_13] = subspace_distances(tst_inx,features,indices,1,1); +cr_ = 0; % correct rate for training +cr = 0; % correct rate for testing +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% CODE HERE %% +%[Ws cr_] = gradient_ascent(trnd_12,trnd_13,0.1,0.1,0.00002); %eNum = 10 +[Ws cr_] = gradient_ascent(trnd_12,trnd_13,0.05,0.01,0.00002); + +for i = 1:num_case + cr = cr + sum((tstd_13{i}-tstd_12{i})*Ws{i}' > 0, 1)/size(tstd_12{i},1); +end +cr = cr/num_case; +%% Check the result +fprintf('Gradient RBM Test / Train Result=%f / %f\n',cr*100,cr_*100); +%fprintf('Training=%f Testing=%f\n',cr_,cr); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_svm_table3/svm_table3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +% --- +% this script trains similarity measures using RBM and SVM as in Table 3 +% please note that the RBM training is a probabilistic process. +% Here, training is done on 20 random initialisations of RBM features , +% the test results corresponding to the RBM with the best training result are then +% returned. +% --- + +% --- +% vary feature parameters of mixed features +% --- + +global globalvars; +globalvars.debug = 3; + +ftype = 'MTTMixedFeatureSonRBM'; %'MTTMixedFeatureStober11Genre'; + +fparams_all = struct(... + ... % --- + ... % these are SONfeatRaw parameters + ... % --- + 'son_filename',{{'rel_music_raw_features+simdata_ISMIR12.mat'}}, ... + 'son_conf', 1:5, ... + ... % --- + ... % Following: RBM params + ... % --- + 'norm_pre_rbm', 0, ... % norm before RBM? + 'norm_post_rbm',0, ... % norm before RBM? + 'rbm_hidNum',[1000], ... % number of hidden units % 500 + 'rbm_eNum', 100, ... + 'rbm_bNum', 1, ... + 'rbm_gNum', 1, ... + 'rbm_lrate1' , [0.05], ... % initial learning rate % 0.01 + 'rbm_lrate2', [0.10], ... % learning rate, %0.05 + 'rbm_momentum', [0.1], ... % 0.5 + 'rbm_cost', [0.00002], ... % cost function + 'rbm_N', 50, ... + 'rbm_MAX_INC', 10 ... + ); + +% --- +% vary parameters for svmlight +% --- + +trainparams_all = struct(... + 'C', [1], ...% + 'weighted', [0], ... + 'dataset', {{'comp_partBinData_ISMIR12_01.mat'}}, ... + 'inctrain', 0 ... + ... % this optional + ... %'deltafun', {{'conv_subspace_delta'}}, ... + ... %'deltafun_params', {{{[1],[0]},{[5],[1]},{[10],[1]},{[20],[1]},{[30],[1]},{[50],[1]},{[70],[1]}}} ... % normalisation improves results + ); + +% set training function +trainfun = @svmlight_wrapper; + + +% create test directory +akt_dir = migrate_to_test_dir(); + + +% call eval +out = test_generic_features_parameters_crossval... + (fparams_all, trainparams_all, trainfun, ftype); + +% --- +% check training results and select best RBM according to trainign data +% --- +svm_train_performances = [out(:).mean_ok_train]; +[bestTrain, idx] = max(svm_train_performances(1,:)); +result = out(idx); + +% get corresponding test performance +svm_test_performance = result.mean_ok_test(1,:); +fprintf('SVM RBM Test/Train Result=%f / %f\n',svm_test_performance*100,bestTrain*100);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/reproduce_AES53rd/rerun_table3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +% --- +% This script trains similarity measures and shows the +% results regarding RBM of table 3 +% +% Feature Preprocessing with RBMs for Music Similarity Learning +% Son N. Tran, Daniel Wolff, Tillman Weyde, Artur Garcez, AES53rd +% conference +% +% please note that the RBM training is a probabilistic process, and +% thus the papers' results can only be reproduced approximately with +% large numbers of iterations of this script, and selection of RBMs according to +% their training set performance. +% Here, training is done on 20 random initialisations of RBM features , +% the test results corresponding to the RBM with the best training result are then +% returned. +% +% The train and test performances are output in the console +% +% For convenicence, The precomputed RBM features are stored in the files +% accompaining this script. +% In order to compute new SVM features, delete these files. +% --- + +% --- +% get svm results for RBM +% --- +svm_table3 +% svm_test_performance +%fprintf('SVM Original Test Result (Wolff etal. 2012)=71.20 / 83.54\n'); + +% --- +% get gradient results for RBM +% --- +gradient_table3 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/ChangeLog.Sourceforge.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4436 @@ + + +2007-02-11 17:12 nsaunier + + * BNT/learning/learn_struct_pdag_pc.m: Bug submitted by Imme Ebert-Uphoff (ebert@tree.com) (see Thu Feb 8, 2007 email on the BNT mailing list). + +2005-11-26 12:12 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/fwdback_twoslice.m: merged + fwdback_twoslice.m to release branch + +2005-11-25 17:24 nsaunier + + * BNT/inference/dynamic/@hmm_inf_engine/fwdback_twoslice.m: adding + old missing fwdback_twoslice.m + +2005-11-25 17:24 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/fwdback_twoslice.m: file + fwdback_twoslice.m was added on branch release-1_0 on 2005-11-26 + 20:12:05 +0000 + +2005-09-25 15:54 yozhik + + * BNT/add_BNT_to_path.m: fix paths + +2005-09-25 15:30 yozhik + + * BNT/add_BNT_to_path.m: Restored directories to path. + +2005-09-25 15:29 yozhik + + * HMM/fwdback_twoslice.m: added missing fwdback_twoslice + +2005-09-17 11:14 yozhik + + * ChangeLog, BNT/add_BNT_to_path.m, BNT/test_BNT.m, + BNT/examples/static/cmp_inference_static.m, + BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m: Merged + bug fixes from HEAD. + +2005-09-17 11:11 yozhik + + * ChangeLog: added change log + +2005-09-17 11:11 yozhik + + * ChangeLog: file ChangeLog was added on branch release-1_0 on + 2005-09-17 18:14:47 +0000 + +2005-09-17 10:00 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m: Temporary + rollback to fix error, per Kevin. + +2005-09-17 09:59 yozhik + + * BNT/examples/static/cmp_inference_static.m: Commented out + erroneous line, per Kevin. + +2005-09-17 09:58 yozhik + + * BNT/add_BNT_to_path.m: Changed to require BNT_HOME to be + predefined. + +2005-09-17 09:56 yozhik + + * BNT/test_BNT.m: Commented out problematic tests. + +2005-09-17 09:38 yozhik + + * BNT/test_BNT.m: renable tests + +2005-09-12 22:18 yozhik + + * KPMtools/pca_kpm.m: Initial import of code base from Kevin + Murphy. + +2005-09-12 22:18 yozhik + + * KPMtools/pca_kpm.m: Initial revision + +2005-08-29 10:44 yozhik + + * graph/: README.txt, Old/best_first_elim_order.m, + Old/dag_to_jtree.m, Old/dfs.m, Old/dsep_test.m, + Old/mk_2D_lattice_slow.m, acyclic.m, assignEdgeNums.m, + best_first_elim_order.m, check_jtree_property.m, + check_triangulated.m, children.m, cliques_to_jtree.m, + cliques_to_strong_jtree.m, connected_graph.m, + dag_to_essential_graph.m, dfs.m, dfs_test.m, dijkstra.m, + family.m, graph_separated.m, graph_to_jtree.m, + min_subtree_con_nodes.m, minimum_spanning_tree.m, minspan.m, + mk_2D_lattice.m, mk_2D_lattice_slow.m, mk_adj_mat.m, + mk_adjmat_chain.m, mk_all_dags.m, mk_nbrs_of_dag.m, + mk_nbrs_of_digraph.m, mk_nbrs_of_digraph_broken.m, + mk_nbrs_of_digraph_not_vectorized.m, mk_rnd_dag.m, + mk_rnd_dag_given_edge_prob.m, mk_rooted_tree.m, mk_undirected.m, + moralize.m, neighbors.m, parents.m, pred2path.m, + reachability_graph.m, scc.m, strong_elim_order.m, test.m, + test_strong_root.m, topological_sort.m, trees.txt, triangulate.c, + triangulate.m, triangulate_2Dlattice_demo.m, triangulate_test.m: + Initial import of code base from Kevin Murphy. + +2005-08-29 10:44 yozhik + + * graph/: README.txt, Old/best_first_elim_order.m, + Old/dag_to_jtree.m, Old/dfs.m, Old/dsep_test.m, + Old/mk_2D_lattice_slow.m, acyclic.m, assignEdgeNums.m, + best_first_elim_order.m, check_jtree_property.m, + check_triangulated.m, children.m, cliques_to_jtree.m, + cliques_to_strong_jtree.m, connected_graph.m, + dag_to_essential_graph.m, dfs.m, dfs_test.m, dijkstra.m, + family.m, graph_separated.m, graph_to_jtree.m, + min_subtree_con_nodes.m, minimum_spanning_tree.m, minspan.m, + mk_2D_lattice.m, mk_2D_lattice_slow.m, mk_adj_mat.m, + mk_adjmat_chain.m, mk_all_dags.m, mk_nbrs_of_dag.m, + mk_nbrs_of_digraph.m, mk_nbrs_of_digraph_broken.m, + mk_nbrs_of_digraph_not_vectorized.m, mk_rnd_dag.m, + mk_rnd_dag_given_edge_prob.m, mk_rooted_tree.m, mk_undirected.m, + moralize.m, neighbors.m, parents.m, pred2path.m, + reachability_graph.m, scc.m, strong_elim_order.m, test.m, + test_strong_root.m, topological_sort.m, trees.txt, triangulate.c, + triangulate.m, triangulate_2Dlattice_demo.m, triangulate_test.m: + Initial revision + +2005-08-26 18:08 yozhik + + * KPMtools/fullfileKPM.m: Initial import of code base from Kevin + Murphy. + +2005-08-26 18:08 yozhik + + * KPMtools/fullfileKPM.m: Initial revision + +2005-08-21 13:00 yozhik + + * + BNT/inference/static/@pearl_inf_engine/private/parallel_protocol.m: + Initial import of code base from Kevin Murphy. + +2005-08-21 13:00 yozhik + + * + BNT/inference/static/@pearl_inf_engine/private/parallel_protocol.m: + Initial revision + +2005-07-11 12:07 yozhik + + * KPMtools/plotcov2New.m: Initial import of code base from Kevin + Murphy. + +2005-07-11 12:07 yozhik + + * KPMtools/plotcov2New.m: Initial revision + +2005-07-06 12:32 yozhik + + * KPMtools/montageKPM2.m: Initial import of code base from Kevin + Murphy. + +2005-07-06 12:32 yozhik + + * KPMtools/montageKPM2.m: Initial revision + +2005-06-27 18:35 yozhik + + * KPMtools/montageKPM3.m: Initial import of code base from Kevin + Murphy. + +2005-06-27 18:35 yozhik + + * KPMtools/montageKPM3.m: Initial revision + +2005-06-27 18:30 yozhik + + * KPMtools/cell2matPad.m: Initial import of code base from Kevin + Murphy. + +2005-06-27 18:30 yozhik + + * KPMtools/cell2matPad.m: Initial revision + +2005-06-15 14:13 yozhik + + * BNT/CPDs/@gaussian_CPD/gaussian_CPD.m: Initial import of code + base from Kevin Murphy. + +2005-06-15 14:13 yozhik + + * BNT/CPDs/@gaussian_CPD/gaussian_CPD.m: Initial revision + +2005-06-08 18:56 yozhik + + * Kalman/testKalman.m: Initial import of code base from Kevin + Murphy. + +2005-06-08 18:56 yozhik + + * Kalman/testKalman.m: Initial revision + +2005-06-08 18:25 yozhik + + * HMM/: testHMM.m, fixed_lag_smoother_demo.m: Initial import of + code base from Kevin Murphy. + +2005-06-08 18:25 yozhik + + * HMM/: testHMM.m, fixed_lag_smoother_demo.m: Initial revision + +2005-06-08 18:22 yozhik + + * HMM/: README.txt, dhmm_em.m: Initial import of code base from + Kevin Murphy. + +2005-06-08 18:22 yozhik + + * HMM/: README.txt, dhmm_em.m: Initial revision + +2005-06-08 18:17 yozhik + + * HMM/fwdback.m: Initial import of code base from Kevin Murphy. + +2005-06-08 18:17 yozhik + + * HMM/fwdback.m: Initial revision + +2005-06-05 11:46 yozhik + + * KPMtools/: rectintLoopC.c, rectintLoopC.dll: Initial import of + code base from Kevin Murphy. + +2005-06-05 11:46 yozhik + + * KPMtools/: rectintLoopC.c, rectintLoopC.dll: Initial revision + +2005-06-01 12:39 yozhik + + * KPMtools/montageKPM.m: Initial import of code base from Kevin + Murphy. + +2005-06-01 12:39 yozhik + + * KPMtools/montageKPM.m: Initial revision + +2005-05-31 21:49 yozhik + + * KPMtools/initFigures.m: Initial import of code base from Kevin + Murphy. + +2005-05-31 21:49 yozhik + + * KPMtools/initFigures.m: Initial revision + +2005-05-31 11:19 yozhik + + * KPMstats/unidrndKPM.m: Initial import of code base from Kevin + Murphy. + +2005-05-31 11:19 yozhik + + * KPMstats/unidrndKPM.m: Initial revision + +2005-05-30 15:08 yozhik + + * KPMtools/filepartsLast.m: Initial import of code base from Kevin + Murphy. + +2005-05-30 15:08 yozhik + + * KPMtools/filepartsLast.m: Initial revision + +2005-05-29 23:01 yozhik + + * KPMtools/plotBox.m: Initial import of code base from Kevin + Murphy. + +2005-05-29 23:01 yozhik + + * KPMtools/plotBox.m: Initial revision + +2005-05-25 18:31 yozhik + + * KPMtools/plotColors.m: Initial import of code base from Kevin + Murphy. + +2005-05-25 18:31 yozhik + + * KPMtools/plotColors.m: Initial revision + +2005-05-25 12:11 yozhik + + * KPMtools/genpathKPM.m: Initial import of code base from Kevin + Murphy. + +2005-05-25 12:11 yozhik + + * KPMtools/genpathKPM.m: Initial revision + +2005-05-23 17:03 yozhik + + * netlab3.3/demhmc1.m: Initial import of code base from Kevin + Murphy. + +2005-05-23 17:03 yozhik + + * netlab3.3/demhmc1.m: Initial revision + +2005-05-23 16:44 yozhik + + * netlab3.3/gmminit.m: Initial import of code base from Kevin + Murphy. + +2005-05-23 16:44 yozhik + + * netlab3.3/gmminit.m: Initial revision + +2005-05-23 16:07 yozhik + + * netlab3.3/metrop.m: Initial import of code base from Kevin + Murphy. + +2005-05-23 16:07 yozhik + + * netlab3.3/metrop.m: Initial revision + +2005-05-22 23:23 yozhik + + * netlab3.3/demmet1.m: Initial import of code base from Kevin + Murphy. + +2005-05-22 23:23 yozhik + + * netlab3.3/demmet1.m: Initial revision + +2005-05-22 16:32 yozhik + + * KPMstats/: dirichletrnd.m, dirichletpdf.m, test_dir.m, + multirnd.m, multipdf.m: Initial import of code base from Kevin + Murphy. + +2005-05-22 16:32 yozhik + + * KPMstats/: dirichletrnd.m, dirichletpdf.m, test_dir.m, + multirnd.m, multipdf.m: Initial revision + +2005-05-13 13:52 yozhik + + * KPMtools/: asort.m, dirKPM.m: Initial import of code base from + Kevin Murphy. + +2005-05-13 13:52 yozhik + + * KPMtools/: asort.m, dirKPM.m: Initial revision + +2005-05-09 18:32 yozhik + + * netlab3.3/dem2ddat.m: Initial import of code base from Kevin + Murphy. + +2005-05-09 18:32 yozhik + + * netlab3.3/dem2ddat.m: Initial revision + +2005-05-09 15:20 yozhik + + * KPMtools/: mkdirKPM.m, optimalMatching.m, optimalMatchingTest.m, + subsets1.m: Initial import of code base from Kevin Murphy. + +2005-05-09 15:20 yozhik + + * KPMtools/: mkdirKPM.m, optimalMatching.m, optimalMatchingTest.m, + subsets1.m: Initial revision + +2005-05-09 09:47 yozhik + + * KPMtools/bipartiteMatchingDemo.m: Initial import of code base + from Kevin Murphy. + +2005-05-09 09:47 yozhik + + * KPMtools/bipartiteMatchingDemo.m: Initial revision + +2005-05-08 22:25 yozhik + + * KPMtools/bipartiteMatchingIntProg.m: Initial import of code base + from Kevin Murphy. + +2005-05-08 22:25 yozhik + + * KPMtools/bipartiteMatchingIntProg.m: Initial revision + +2005-05-08 21:45 yozhik + + * KPMtools/bipartiteMatchingDemoPlot.m: Initial import of code base + from Kevin Murphy. + +2005-05-08 21:45 yozhik + + * KPMtools/bipartiteMatchingDemoPlot.m: Initial revision + +2005-05-08 19:55 yozhik + + * KPMtools/subsetsFixedSize.m: Initial import of code base from + Kevin Murphy. + +2005-05-08 19:55 yozhik + + * KPMtools/subsetsFixedSize.m: Initial revision + +2005-05-08 15:48 yozhik + + * KPMtools/centeringMatrix.m: Initial import of code base from + Kevin Murphy. + +2005-05-08 15:48 yozhik + + * KPMtools/centeringMatrix.m: Initial revision + +2005-05-08 10:51 yozhik + + * netlab3.3/demgmm1.m: Initial import of code base from Kevin + Murphy. + +2005-05-08 10:51 yozhik + + * netlab3.3/demgmm1.m: Initial revision + +2005-05-06 18:09 yozhik + + * BNT/add_BNT_to_path.m: Initial import of code base from Kevin + Murphy. + +2005-05-06 18:09 yozhik + + * BNT/add_BNT_to_path.m: Initial revision + +2005-05-03 21:35 yozhik + + * KPMstats/standardize.m: Initial import of code base from Kevin + Murphy. + +2005-05-03 21:35 yozhik + + * KPMstats/standardize.m: Initial revision + +2005-05-03 13:18 yozhik + + * KPMstats/histCmpChi2.m: Initial import of code base from Kevin + Murphy. + +2005-05-03 13:18 yozhik + + * KPMstats/histCmpChi2.m: Initial revision + +2005-05-03 12:01 yozhik + + * KPMtools/strsplit.m: Initial import of code base from Kevin + Murphy. + +2005-05-03 12:01 yozhik + + * KPMtools/strsplit.m: Initial revision + +2005-05-02 13:19 yozhik + + * KPMtools/hsvKPM.m: Initial import of code base from Kevin Murphy. + +2005-05-02 13:19 yozhik + + * KPMtools/hsvKPM.m: Initial revision + +2005-04-27 11:34 yozhik + + * BNT/potentials/@dpot/: subsasgn.m, subsref.m: Initial import of + code base from Kevin Murphy. + +2005-04-27 11:34 yozhik + + * BNT/potentials/@dpot/: subsasgn.m, subsref.m: Initial revision + +2005-04-27 10:58 yozhik + + * KPMtools/mahal2conf.m, nethelp3.3/conffig.htm, + nethelp3.3/confmat.htm, nethelp3.3/conjgrad.htm, + nethelp3.3/consist.htm, nethelp3.3/convertoldnet.htm, + nethelp3.3/datread.htm, nethelp3.3/datwrite.htm, + nethelp3.3/dem2ddat.htm, nethelp3.3/demard.htm, + nethelp3.3/demev1.htm, nethelp3.3/demev2.htm, + nethelp3.3/demev3.htm, nethelp3.3/demgauss.htm, + nethelp3.3/demglm1.htm, nethelp3.3/demglm2.htm, + nethelp3.3/demgmm1.htm, nethelp3.3/demgmm2.htm, + nethelp3.3/demgmm3.htm, nethelp3.3/demgmm4.htm, + nethelp3.3/demgmm5.htm, nethelp3.3/demgp.htm, + nethelp3.3/demgpard.htm, nethelp3.3/demgpot.htm, + nethelp3.3/demgtm1.htm, nethelp3.3/demgtm2.htm, + nethelp3.3/demhint.htm, nethelp3.3/demhmc1.htm, + nethelp3.3/demhmc2.htm, nethelp3.3/demhmc3.htm, + nethelp3.3/demkmn1.htm, nethelp3.3/demknn1.htm, + nethelp3.3/demmdn1.htm, nethelp3.3/demmet1.htm, + nethelp3.3/demmlp1.htm, nethelp3.3/demmlp2.htm, + nethelp3.3/demnlab.htm, nethelp3.3/demns1.htm, + nethelp3.3/demolgd1.htm, nethelp3.3/demopt1.htm, + nethelp3.3/dempot.htm, nethelp3.3/demprgp.htm, + nethelp3.3/demprior.htm, nethelp3.3/demrbf1.htm, + nethelp3.3/demsom1.htm, nethelp3.3/demtrain.htm, + nethelp3.3/dist2.htm, nethelp3.3/eigdec.htm, + nethelp3.3/errbayes.htm, nethelp3.3/evidence.htm, + nethelp3.3/fevbayes.htm, nethelp3.3/gauss.htm, + nethelp3.3/gbayes.htm, nethelp3.3/glm.htm, + nethelp3.3/glmderiv.htm, nethelp3.3/glmerr.htm, + nethelp3.3/glmevfwd.htm, nethelp3.3/glmfwd.htm, + nethelp3.3/glmgrad.htm, nethelp3.3/glmhess.htm, + nethelp3.3/glminit.htm, nethelp3.3/glmpak.htm, + nethelp3.3/glmtrain.htm, nethelp3.3/glmunpak.htm, + nethelp3.3/gmm.htm, nethelp3.3/gmmactiv.htm, + nethelp3.3/gmmem.htm, nethelp3.3/gmminit.htm, + nethelp3.3/gmmpak.htm, nethelp3.3/gmmpost.htm, + nethelp3.3/gmmprob.htm, nethelp3.3/gmmsamp.htm, + nethelp3.3/gmmunpak.htm, nethelp3.3/gp.htm, + nethelp3.3/gpcovar.htm, nethelp3.3/gpcovarf.htm, + nethelp3.3/gpcovarp.htm, nethelp3.3/gperr.htm, + nethelp3.3/gpfwd.htm, nethelp3.3/gpgrad.htm, + nethelp3.3/gpinit.htm, nethelp3.3/gppak.htm, + nethelp3.3/gpunpak.htm, nethelp3.3/gradchek.htm, + nethelp3.3/graddesc.htm, nethelp3.3/gsamp.htm, + nethelp3.3/gtm.htm, nethelp3.3/gtmem.htm, nethelp3.3/gtmfwd.htm, + nethelp3.3/gtminit.htm, nethelp3.3/gtmlmean.htm, + nethelp3.3/gtmlmode.htm, nethelp3.3/gtmmag.htm, + nethelp3.3/gtmpost.htm, nethelp3.3/gtmprob.htm, + nethelp3.3/hbayes.htm, nethelp3.3/hesschek.htm, + nethelp3.3/hintmat.htm, nethelp3.3/hinton.htm, + nethelp3.3/histp.htm, nethelp3.3/hmc.htm, nethelp3.3/index.htm, + nethelp3.3/kmeans.htm, nethelp3.3/knn.htm, nethelp3.3/knnfwd.htm, + nethelp3.3/linef.htm, nethelp3.3/linemin.htm, + nethelp3.3/maxitmess.htm, nethelp3.3/mdn.htm, + nethelp3.3/mdn2gmm.htm, nethelp3.3/mdndist2.htm, + nethelp3.3/mdnerr.htm, nethelp3.3/mdnfwd.htm, + nethelp3.3/mdngrad.htm, nethelp3.3/mdninit.htm, + nethelp3.3/mdnpak.htm, nethelp3.3/mdnpost.htm, + nethelp3.3/mdnprob.htm, nethelp3.3/mdnunpak.htm, + nethelp3.3/metrop.htm, nethelp3.3/minbrack.htm, + nethelp3.3/mlp.htm, nethelp3.3/mlpbkp.htm, + nethelp3.3/mlpderiv.htm, nethelp3.3/mlperr.htm, + nethelp3.3/mlpevfwd.htm, nethelp3.3/mlpfwd.htm, + nethelp3.3/mlpgrad.htm, nethelp3.3/mlphdotv.htm, + nethelp3.3/mlphess.htm, nethelp3.3/mlphint.htm, + nethelp3.3/mlpinit.htm, nethelp3.3/mlppak.htm, + nethelp3.3/mlpprior.htm, nethelp3.3/mlptrain.htm, + nethelp3.3/mlpunpak.htm, nethelp3.3/netderiv.htm, + nethelp3.3/neterr.htm, nethelp3.3/netevfwd.htm, + nethelp3.3/netgrad.htm, nethelp3.3/nethelp3.3.zip, + nethelp3.3/nethess.htm, nethelp3.3/netinit.htm, + nethelp3.3/netopt.htm, nethelp3.3/netpak.htm, + nethelp3.3/netunpak.htm, nethelp3.3/olgd.htm, nethelp3.3/pca.htm, + nethelp3.3/plotmat.htm, nethelp3.3/ppca.htm, + nethelp3.3/quasinew.htm, nethelp3.3/rbf.htm, + nethelp3.3/rbfbkp.htm, nethelp3.3/rbfderiv.htm, + nethelp3.3/rbferr.htm, nethelp3.3/rbfevfwd.htm, + nethelp3.3/rbffwd.htm, nethelp3.3/rbfgrad.htm, + nethelp3.3/rbfhess.htm, nethelp3.3/rbfjacob.htm, + nethelp3.3/rbfpak.htm, nethelp3.3/rbfprior.htm, + nethelp3.3/rbfsetbf.htm, nethelp3.3/rbfsetfw.htm, + nethelp3.3/rbftrain.htm, nethelp3.3/rbfunpak.htm, + nethelp3.3/rosegrad.htm, nethelp3.3/rosen.htm, + nethelp3.3/scg.htm, nethelp3.3/som.htm, nethelp3.3/somfwd.htm, + nethelp3.3/sompak.htm, nethelp3.3/somtrain.htm, + nethelp3.3/somunpak.htm, netlab3.3/Contents.m, netlab3.3/LICENSE, + netlab3.3/conffig.m, netlab3.3/confmat.m, netlab3.3/conjgrad.m, + netlab3.3/consist.m, netlab3.3/convertoldnet.m, + netlab3.3/datread.m, netlab3.3/datwrite.m, netlab3.3/demard.m, + netlab3.3/demev1.m, netlab3.3/demev2.m, netlab3.3/demev3.m, + netlab3.3/demgauss.m, netlab3.3/demglm1.m, netlab3.3/demglm2.m, + netlab3.3/demgmm2.m, netlab3.3/demgmm3.m, netlab3.3/demgmm4.m, + netlab3.3/demgmm5.m, netlab3.3/demgp.m, netlab3.3/demgpard.m, + netlab3.3/demgpot.m, netlab3.3/demgtm1.m, netlab3.3/demgtm2.m, + netlab3.3/demhint.m, netlab3.3/demhmc2.m, netlab3.3/demhmc3.m, + netlab3.3/demkmn1.m, netlab3.3/demknn1.m, netlab3.3/demmdn1.m, + netlab3.3/demmlp1.m, netlab3.3/demmlp2.m, netlab3.3/demnlab.m, + netlab3.3/demns1.m, netlab3.3/demolgd1.m, netlab3.3/demopt1.m, + netlab3.3/dempot.m, netlab3.3/demprgp.m, netlab3.3/demprior.m, + netlab3.3/demrbf1.m, netlab3.3/demsom1.m, netlab3.3/demtrain.m, + netlab3.3/dist2.m, netlab3.3/eigdec.m, netlab3.3/errbayes.m, + netlab3.3/evidence.m, netlab3.3/fevbayes.m, netlab3.3/gauss.m, + netlab3.3/gbayes.m, netlab3.3/glm.m, netlab3.3/glmderiv.m, + netlab3.3/glmerr.m, netlab3.3/glmevfwd.m, netlab3.3/glmfwd.m, + netlab3.3/glmgrad.m, netlab3.3/glmhess.m, netlab3.3/glminit.m, + netlab3.3/glmpak.m, netlab3.3/glmtrain.m, netlab3.3/glmunpak.m, + netlab3.3/gmm.m, netlab3.3/gmmactiv.m, netlab3.3/gmmem.m, + netlab3.3/gmmpak.m, netlab3.3/gmmpost.m, netlab3.3/gmmprob.m, + netlab3.3/gmmsamp.m, netlab3.3/gmmunpak.m, netlab3.3/gp.m, + netlab3.3/gpcovar.m, netlab3.3/gpcovarf.m, netlab3.3/gpcovarp.m, + netlab3.3/gperr.m, netlab3.3/gpfwd.m, netlab3.3/gpgrad.m, + netlab3.3/gpinit.m, netlab3.3/gppak.m, netlab3.3/gpunpak.m, + netlab3.3/gradchek.m, netlab3.3/graddesc.m, netlab3.3/gsamp.m, + netlab3.3/gtm.m, netlab3.3/gtmem.m, netlab3.3/gtmfwd.m, + netlab3.3/gtminit.m, netlab3.3/gtmlmean.m, netlab3.3/gtmlmode.m, + netlab3.3/gtmmag.m, netlab3.3/gtmpost.m, netlab3.3/gtmprob.m, + netlab3.3/hbayes.m, netlab3.3/hesschek.m, netlab3.3/hintmat.m, + netlab3.3/hinton.m, netlab3.3/histp.m, netlab3.3/hmc.m, + netlab3.3/kmeansNetlab.m, netlab3.3/knn.m, netlab3.3/knnfwd.m, + netlab3.3/linef.m, netlab3.3/linemin.m, netlab3.3/maxitmess.m, + netlab3.3/mdn.m, netlab3.3/mdn2gmm.m, netlab3.3/mdndist2.m, + netlab3.3/mdnerr.m, netlab3.3/mdnfwd.m, netlab3.3/mdngrad.m, + netlab3.3/mdninit.m, netlab3.3/mdnnet.mat, netlab3.3/mdnpak.m, + netlab3.3/mdnpost.m, netlab3.3/mdnprob.m, netlab3.3/mdnunpak.m, + netlab3.3/minbrack.m, netlab3.3/mlp.m, netlab3.3/mlpbkp.m, + netlab3.3/mlpderiv.m, netlab3.3/mlperr.m, netlab3.3/mlpevfwd.m, + netlab3.3/mlpfwd.m, netlab3.3/mlpgrad.m, netlab3.3/mlphdotv.m, + netlab3.3/mlphess.m, netlab3.3/mlphint.m, netlab3.3/mlpinit.m, + netlab3.3/mlppak.m, netlab3.3/mlpprior.m, netlab3.3/mlptrain.m, + netlab3.3/mlpunpak.m, netlab3.3/netderiv.m, netlab3.3/neterr.m, + netlab3.3/netevfwd.m, netlab3.3/netgrad.m, netlab3.3/nethess.m, + netlab3.3/netinit.m, netlab3.3/netlab3.3.zip, + netlab3.3/netlogo.mat, netlab3.3/netopt.m, netlab3.3/netpak.m, + netlab3.3/netunpak.m, netlab3.3/oilTrn.dat, netlab3.3/oilTst.dat, + netlab3.3/olgd.m, netlab3.3/pca.m, netlab3.3/plotmat.m, + netlab3.3/ppca.m, netlab3.3/quasinew.m, netlab3.3/rbf.m, + netlab3.3/rbfbkp.m, netlab3.3/rbfderiv.m, netlab3.3/rbferr.m, + netlab3.3/rbfevfwd.m, netlab3.3/rbffwd.m, netlab3.3/rbfgrad.m, + netlab3.3/rbfhess.m, netlab3.3/rbfjacob.m, netlab3.3/rbfpak.m, + netlab3.3/rbfprior.m, netlab3.3/rbfsetbf.m, netlab3.3/rbfsetfw.m, + netlab3.3/rbftrain.m, netlab3.3/rbfunpak.m, netlab3.3/rosegrad.m, + netlab3.3/rosen.m, netlab3.3/scg.m, netlab3.3/som.m, + netlab3.3/somfwd.m, netlab3.3/sompak.m, netlab3.3/somtrain.m, + netlab3.3/somunpak.m, netlab3.3/xor.dat, netlabKPM/README.txt, + netlabKPM/demgmm1_movie.m, netlabKPM/evidence_weighted.m, + netlabKPM/glmerr_weighted.m, netlabKPM/glmgrad_weighted.m, + netlabKPM/glmhess_weighted.m, netlabKPM/glmtrain_weighted.m, + netlabKPM/gmm1.avi, netlabKPM/gmmem2.m, + netlabKPM/gmmem_multi_restart.m, netlabKPM/kmeans_demo.m, + netlabKPM/mlperr_weighted.m, netlabKPM/mlpgrad_weighted.m, + netlabKPM/mlphdotv_weighted.m, netlabKPM/mlphess_weighted.m, + netlabKPM/neterr_weighted.m, netlabKPM/netgrad_weighted.m, + netlabKPM/nethess_weighted.m, netlabKPM/netopt_weighted.m, + netlabKPM/process_options.m: Initial import of code base from + Kevin Murphy. + +2005-04-27 10:58 yozhik + + * KPMtools/mahal2conf.m, nethelp3.3/conffig.htm, + nethelp3.3/confmat.htm, nethelp3.3/conjgrad.htm, + nethelp3.3/consist.htm, nethelp3.3/convertoldnet.htm, + nethelp3.3/datread.htm, nethelp3.3/datwrite.htm, + nethelp3.3/dem2ddat.htm, nethelp3.3/demard.htm, + nethelp3.3/demev1.htm, nethelp3.3/demev2.htm, + nethelp3.3/demev3.htm, nethelp3.3/demgauss.htm, + nethelp3.3/demglm1.htm, nethelp3.3/demglm2.htm, + nethelp3.3/demgmm1.htm, nethelp3.3/demgmm2.htm, + nethelp3.3/demgmm3.htm, nethelp3.3/demgmm4.htm, + nethelp3.3/demgmm5.htm, nethelp3.3/demgp.htm, + nethelp3.3/demgpard.htm, nethelp3.3/demgpot.htm, + nethelp3.3/demgtm1.htm, nethelp3.3/demgtm2.htm, + nethelp3.3/demhint.htm, nethelp3.3/demhmc1.htm, + nethelp3.3/demhmc2.htm, nethelp3.3/demhmc3.htm, + nethelp3.3/demkmn1.htm, nethelp3.3/demknn1.htm, + nethelp3.3/demmdn1.htm, nethelp3.3/demmet1.htm, + nethelp3.3/demmlp1.htm, nethelp3.3/demmlp2.htm, + nethelp3.3/demnlab.htm, nethelp3.3/demns1.htm, + nethelp3.3/demolgd1.htm, nethelp3.3/demopt1.htm, + nethelp3.3/dempot.htm, nethelp3.3/demprgp.htm, + nethelp3.3/demprior.htm, nethelp3.3/demrbf1.htm, + nethelp3.3/demsom1.htm, nethelp3.3/demtrain.htm, + nethelp3.3/dist2.htm, nethelp3.3/eigdec.htm, + nethelp3.3/errbayes.htm, nethelp3.3/evidence.htm, + nethelp3.3/fevbayes.htm, nethelp3.3/gauss.htm, + nethelp3.3/gbayes.htm, nethelp3.3/glm.htm, + nethelp3.3/glmderiv.htm, nethelp3.3/glmerr.htm, + nethelp3.3/glmevfwd.htm, nethelp3.3/glmfwd.htm, + nethelp3.3/glmgrad.htm, nethelp3.3/glmhess.htm, + nethelp3.3/glminit.htm, nethelp3.3/glmpak.htm, + nethelp3.3/glmtrain.htm, nethelp3.3/glmunpak.htm, + nethelp3.3/gmm.htm, nethelp3.3/gmmactiv.htm, + nethelp3.3/gmmem.htm, nethelp3.3/gmminit.htm, + nethelp3.3/gmmpak.htm, nethelp3.3/gmmpost.htm, + nethelp3.3/gmmprob.htm, nethelp3.3/gmmsamp.htm, + nethelp3.3/gmmunpak.htm, nethelp3.3/gp.htm, + nethelp3.3/gpcovar.htm, nethelp3.3/gpcovarf.htm, + nethelp3.3/gpcovarp.htm, nethelp3.3/gperr.htm, + nethelp3.3/gpfwd.htm, nethelp3.3/gpgrad.htm, + nethelp3.3/gpinit.htm, nethelp3.3/gppak.htm, + nethelp3.3/gpunpak.htm, nethelp3.3/gradchek.htm, + nethelp3.3/graddesc.htm, nethelp3.3/gsamp.htm, + nethelp3.3/gtm.htm, nethelp3.3/gtmem.htm, nethelp3.3/gtmfwd.htm, + nethelp3.3/gtminit.htm, nethelp3.3/gtmlmean.htm, + nethelp3.3/gtmlmode.htm, nethelp3.3/gtmmag.htm, + nethelp3.3/gtmpost.htm, nethelp3.3/gtmprob.htm, + nethelp3.3/hbayes.htm, nethelp3.3/hesschek.htm, + nethelp3.3/hintmat.htm, nethelp3.3/hinton.htm, + nethelp3.3/histp.htm, nethelp3.3/hmc.htm, nethelp3.3/index.htm, + nethelp3.3/kmeans.htm, nethelp3.3/knn.htm, nethelp3.3/knnfwd.htm, + nethelp3.3/linef.htm, nethelp3.3/linemin.htm, + nethelp3.3/maxitmess.htm, nethelp3.3/mdn.htm, + nethelp3.3/mdn2gmm.htm, nethelp3.3/mdndist2.htm, + nethelp3.3/mdnerr.htm, nethelp3.3/mdnfwd.htm, + nethelp3.3/mdngrad.htm, nethelp3.3/mdninit.htm, + nethelp3.3/mdnpak.htm, nethelp3.3/mdnpost.htm, + nethelp3.3/mdnprob.htm, nethelp3.3/mdnunpak.htm, + nethelp3.3/metrop.htm, nethelp3.3/minbrack.htm, + nethelp3.3/mlp.htm, nethelp3.3/mlpbkp.htm, + nethelp3.3/mlpderiv.htm, nethelp3.3/mlperr.htm, + nethelp3.3/mlpevfwd.htm, nethelp3.3/mlpfwd.htm, + nethelp3.3/mlpgrad.htm, nethelp3.3/mlphdotv.htm, + nethelp3.3/mlphess.htm, nethelp3.3/mlphint.htm, + nethelp3.3/mlpinit.htm, nethelp3.3/mlppak.htm, + nethelp3.3/mlpprior.htm, nethelp3.3/mlptrain.htm, + nethelp3.3/mlpunpak.htm, nethelp3.3/netderiv.htm, + nethelp3.3/neterr.htm, nethelp3.3/netevfwd.htm, + nethelp3.3/netgrad.htm, nethelp3.3/nethelp3.3.zip, + nethelp3.3/nethess.htm, nethelp3.3/netinit.htm, + nethelp3.3/netopt.htm, nethelp3.3/netpak.htm, + nethelp3.3/netunpak.htm, nethelp3.3/olgd.htm, nethelp3.3/pca.htm, + nethelp3.3/plotmat.htm, nethelp3.3/ppca.htm, + nethelp3.3/quasinew.htm, nethelp3.3/rbf.htm, + nethelp3.3/rbfbkp.htm, nethelp3.3/rbfderiv.htm, + nethelp3.3/rbferr.htm, nethelp3.3/rbfevfwd.htm, + nethelp3.3/rbffwd.htm, nethelp3.3/rbfgrad.htm, + nethelp3.3/rbfhess.htm, nethelp3.3/rbfjacob.htm, + nethelp3.3/rbfpak.htm, nethelp3.3/rbfprior.htm, + nethelp3.3/rbfsetbf.htm, nethelp3.3/rbfsetfw.htm, + nethelp3.3/rbftrain.htm, nethelp3.3/rbfunpak.htm, + nethelp3.3/rosegrad.htm, nethelp3.3/rosen.htm, + nethelp3.3/scg.htm, nethelp3.3/som.htm, nethelp3.3/somfwd.htm, + nethelp3.3/sompak.htm, nethelp3.3/somtrain.htm, + nethelp3.3/somunpak.htm, netlab3.3/Contents.m, netlab3.3/LICENSE, + netlab3.3/conffig.m, netlab3.3/confmat.m, netlab3.3/conjgrad.m, + netlab3.3/consist.m, netlab3.3/convertoldnet.m, + netlab3.3/datread.m, netlab3.3/datwrite.m, netlab3.3/demard.m, + netlab3.3/demev1.m, netlab3.3/demev2.m, netlab3.3/demev3.m, + netlab3.3/demgauss.m, netlab3.3/demglm1.m, netlab3.3/demglm2.m, + netlab3.3/demgmm2.m, netlab3.3/demgmm3.m, netlab3.3/demgmm4.m, + netlab3.3/demgmm5.m, netlab3.3/demgp.m, netlab3.3/demgpard.m, + netlab3.3/demgpot.m, netlab3.3/demgtm1.m, netlab3.3/demgtm2.m, + netlab3.3/demhint.m, netlab3.3/demhmc2.m, netlab3.3/demhmc3.m, + netlab3.3/demkmn1.m, netlab3.3/demknn1.m, netlab3.3/demmdn1.m, + netlab3.3/demmlp1.m, netlab3.3/demmlp2.m, netlab3.3/demnlab.m, + netlab3.3/demns1.m, netlab3.3/demolgd1.m, netlab3.3/demopt1.m, + netlab3.3/dempot.m, netlab3.3/demprgp.m, netlab3.3/demprior.m, + netlab3.3/demrbf1.m, netlab3.3/demsom1.m, netlab3.3/demtrain.m, + netlab3.3/dist2.m, netlab3.3/eigdec.m, netlab3.3/errbayes.m, + netlab3.3/evidence.m, netlab3.3/fevbayes.m, netlab3.3/gauss.m, + netlab3.3/gbayes.m, netlab3.3/glm.m, netlab3.3/glmderiv.m, + netlab3.3/glmerr.m, netlab3.3/glmevfwd.m, netlab3.3/glmfwd.m, + netlab3.3/glmgrad.m, netlab3.3/glmhess.m, netlab3.3/glminit.m, + netlab3.3/glmpak.m, netlab3.3/glmtrain.m, netlab3.3/glmunpak.m, + netlab3.3/gmm.m, netlab3.3/gmmactiv.m, netlab3.3/gmmem.m, + netlab3.3/gmmpak.m, netlab3.3/gmmpost.m, netlab3.3/gmmprob.m, + netlab3.3/gmmsamp.m, netlab3.3/gmmunpak.m, netlab3.3/gp.m, + netlab3.3/gpcovar.m, netlab3.3/gpcovarf.m, netlab3.3/gpcovarp.m, + netlab3.3/gperr.m, netlab3.3/gpfwd.m, netlab3.3/gpgrad.m, + netlab3.3/gpinit.m, netlab3.3/gppak.m, netlab3.3/gpunpak.m, + netlab3.3/gradchek.m, netlab3.3/graddesc.m, netlab3.3/gsamp.m, + netlab3.3/gtm.m, netlab3.3/gtmem.m, netlab3.3/gtmfwd.m, + netlab3.3/gtminit.m, netlab3.3/gtmlmean.m, netlab3.3/gtmlmode.m, + netlab3.3/gtmmag.m, netlab3.3/gtmpost.m, netlab3.3/gtmprob.m, + netlab3.3/hbayes.m, netlab3.3/hesschek.m, netlab3.3/hintmat.m, + netlab3.3/hinton.m, netlab3.3/histp.m, netlab3.3/hmc.m, + netlab3.3/kmeansNetlab.m, netlab3.3/knn.m, netlab3.3/knnfwd.m, + netlab3.3/linef.m, netlab3.3/linemin.m, netlab3.3/maxitmess.m, + netlab3.3/mdn.m, netlab3.3/mdn2gmm.m, netlab3.3/mdndist2.m, + netlab3.3/mdnerr.m, netlab3.3/mdnfwd.m, netlab3.3/mdngrad.m, + netlab3.3/mdninit.m, netlab3.3/mdnnet.mat, netlab3.3/mdnpak.m, + netlab3.3/mdnpost.m, netlab3.3/mdnprob.m, netlab3.3/mdnunpak.m, + netlab3.3/minbrack.m, netlab3.3/mlp.m, netlab3.3/mlpbkp.m, + netlab3.3/mlpderiv.m, netlab3.3/mlperr.m, netlab3.3/mlpevfwd.m, + netlab3.3/mlpfwd.m, netlab3.3/mlpgrad.m, netlab3.3/mlphdotv.m, + netlab3.3/mlphess.m, netlab3.3/mlphint.m, netlab3.3/mlpinit.m, + netlab3.3/mlppak.m, netlab3.3/mlpprior.m, netlab3.3/mlptrain.m, + netlab3.3/mlpunpak.m, netlab3.3/netderiv.m, netlab3.3/neterr.m, + netlab3.3/netevfwd.m, netlab3.3/netgrad.m, netlab3.3/nethess.m, + netlab3.3/netinit.m, netlab3.3/netlab3.3.zip, + netlab3.3/netlogo.mat, netlab3.3/netopt.m, netlab3.3/netpak.m, + netlab3.3/netunpak.m, netlab3.3/oilTrn.dat, netlab3.3/oilTst.dat, + netlab3.3/olgd.m, netlab3.3/pca.m, netlab3.3/plotmat.m, + netlab3.3/ppca.m, netlab3.3/quasinew.m, netlab3.3/rbf.m, + netlab3.3/rbfbkp.m, netlab3.3/rbfderiv.m, netlab3.3/rbferr.m, + netlab3.3/rbfevfwd.m, netlab3.3/rbffwd.m, netlab3.3/rbfgrad.m, + netlab3.3/rbfhess.m, netlab3.3/rbfjacob.m, netlab3.3/rbfpak.m, + netlab3.3/rbfprior.m, netlab3.3/rbfsetbf.m, netlab3.3/rbfsetfw.m, + netlab3.3/rbftrain.m, netlab3.3/rbfunpak.m, netlab3.3/rosegrad.m, + netlab3.3/rosen.m, netlab3.3/scg.m, netlab3.3/som.m, + netlab3.3/somfwd.m, netlab3.3/sompak.m, netlab3.3/somtrain.m, + netlab3.3/somunpak.m, netlab3.3/xor.dat, netlabKPM/README.txt, + netlabKPM/demgmm1_movie.m, netlabKPM/evidence_weighted.m, + netlabKPM/glmerr_weighted.m, netlabKPM/glmgrad_weighted.m, + netlabKPM/glmhess_weighted.m, netlabKPM/glmtrain_weighted.m, + netlabKPM/gmm1.avi, netlabKPM/gmmem2.m, + netlabKPM/gmmem_multi_restart.m, netlabKPM/kmeans_demo.m, + netlabKPM/mlperr_weighted.m, netlabKPM/mlpgrad_weighted.m, + netlabKPM/mlphdotv_weighted.m, netlabKPM/mlphess_weighted.m, + netlabKPM/neterr_weighted.m, netlabKPM/netgrad_weighted.m, + netlabKPM/nethess_weighted.m, netlabKPM/netopt_weighted.m, + netlabKPM/process_options.m: Initial revision + +2005-04-25 19:29 yozhik + + * KPMstats/KLgauss.m, KPMstats/README.txt, KPMstats/beta_sample.m, + KPMstats/chisquared_histo.m, KPMstats/chisquared_prob.m, + KPMstats/chisquared_readme.txt, KPMstats/chisquared_table.m, + KPMstats/clg_Mstep.m, KPMstats/clg_Mstep_simple.m, + KPMstats/clg_prob.m, KPMstats/condGaussToJoint.m, + KPMstats/cond_indep_fisher_z.m, + KPMstats/condgaussTrainObserved.m, KPMstats/condgauss_sample.m, + KPMstats/convertBinaryLabels.m, KPMstats/cwr_demo.m, + KPMstats/cwr_em.m, KPMstats/cwr_predict.m, KPMstats/cwr_prob.m, + KPMstats/cwr_readme.txt, KPMstats/cwr_test.m, + KPMstats/dirichlet_sample.m, KPMstats/distchck.m, + KPMstats/eigdec.m, KPMstats/est_transmat.m, + KPMstats/fit_paritioned_model_testfn.m, + KPMstats/fit_partitioned_model.m, KPMstats/gamma_sample.m, + KPMstats/gaussian_prob.m, KPMstats/gaussian_sample.m, + KPMstats/linear_regression.m, KPMstats/logist2.m, + KPMstats/logist2Apply.m, KPMstats/logist2ApplyRegularized.m, + KPMstats/logist2Fit.m, KPMstats/logist2FitRegularized.m, + KPMstats/logistK.m, KPMstats/logistK_eval.m, + KPMstats/marginalize_gaussian.m, KPMstats/matrix_T_pdf.m, + KPMstats/matrix_normal_pdf.m, KPMstats/mc_stat_distrib.m, + KPMstats/mixgauss_Mstep.m, KPMstats/mixgauss_classifier_apply.m, + KPMstats/mixgauss_classifier_train.m, KPMstats/mixgauss_em.m, + KPMstats/mixgauss_init.m, KPMstats/mixgauss_prob.m, + KPMstats/mixgauss_prob_test.m, KPMstats/mixgauss_sample.m, + KPMstats/mkPolyFvec.m, KPMstats/mk_unit_norm.m, + KPMstats/multinomial_prob.m, KPMstats/multinomial_sample.m, + KPMstats/normal_coef.m, KPMstats/partial_corr_coef.m, + KPMstats/parzen.m, KPMstats/parzenC.c, KPMstats/parzenC.dll, + KPMstats/parzenC.mexglx, KPMstats/parzenC_test.m, + KPMstats/parzen_fit_select_unif.m, KPMstats/pca.m, + KPMstats/rndcheck.m, KPMstats/sample.m, + KPMstats/sample_discrete.m, KPMstats/sample_gaussian.m, + KPMstats/student_t_logprob.m, KPMstats/student_t_prob.m, + KPMstats/unif_discrete_sample.m, KPMstats/weightedRegression.m, + KPMtools/README.txt, KPMtools/approx_unique.m, + KPMtools/approxeq.m, KPMtools/argmax.m, KPMtools/argmin.m, + KPMtools/assert.m, KPMtools/assignEdgeNums.m, + KPMtools/assign_cols.m, KPMtools/axis_pct.m, KPMtools/block.m, + KPMtools/cell2num.m, KPMtools/chi2inv.m, KPMtools/choose.m, + KPMtools/collapse_mog.m, KPMtools/colmult.c, + KPMtools/colmult.mexglx, KPMtools/computeROC.m, + KPMtools/compute_counts.m, KPMtools/conf2mahal.m, + KPMtools/cross_entropy.m, KPMtools/div.m, KPMtools/draw_circle.m, + KPMtools/draw_ellipse.m, KPMtools/draw_ellipse_axes.m, + KPMtools/em_converged.m, KPMtools/entropy.m, + KPMtools/exportfig.m, KPMtools/extend_domain_table.m, + KPMtools/factorial.m, KPMtools/find_equiv_posns.m, + KPMtools/hash_add.m, KPMtools/hash_del.m, KPMtools/hash_lookup.m, + KPMtools/hungarian.m, KPMtools/image_rgb.m, + KPMtools/imresizeAspect.m, KPMtools/ind2subv.c, + KPMtools/ind2subv.m, KPMtools/installC_KPMtools.m, + KPMtools/is_psd.m, KPMtools/is_stochastic.m, + KPMtools/isemptycell.m, KPMtools/isposdef.m, KPMtools/isscalar.m, + KPMtools/isvector.m, KPMtools/junk.c, KPMtools/loadcell.m, + KPMtools/logb.m, KPMtools/logdet.m, KPMtools/logsum.m, + KPMtools/logsum_simple.m, KPMtools/logsum_test.m, + KPMtools/logsumexp.m, KPMtools/logsumexpv.m, + KPMtools/marg_table.m, KPMtools/marginalize_table.m, + KPMtools/matprint.m, KPMtools/max_mult.c, KPMtools/max_mult.m, + KPMtools/mexutil.c, KPMtools/mexutil.h, + KPMtools/mk_multi_index.m, KPMtools/mk_stochastic.m, + KPMtools/mult_by_table.m, KPMtools/myintersect.m, + KPMtools/myismember.m, KPMtools/myones.m, KPMtools/myplot.m, + KPMtools/myrand.m, KPMtools/myrepmat.m, KPMtools/myreshape.m, + KPMtools/mysetdiff.m, KPMtools/mysize.m, KPMtools/mysubset.m, + KPMtools/mysymsetdiff.m, KPMtools/bipartiteMatchingHungarian.m, + KPMtools/myunion.m, KPMtools/nchoose2.m, KPMtools/ncols.m, + KPMtools/nonmaxsup.m, KPMtools/normalise.m, + KPMtools/normaliseC.c, KPMtools/normaliseC.dll, + KPMtools/normalize.m, KPMtools/nrows.m, KPMtools/num2strcell.m, + KPMtools/partitionData.m, KPMtools/partition_matrix_vec.m, + KPMtools/pca_netlab.m, KPMtools/pick.m, KPMtools/plotROC.m, + KPMtools/plotROCkpm.m, KPMtools/plot_axis_thru_origin.m, + KPMtools/plot_ellipse.m, KPMtools/plot_matrix.m, + KPMtools/plot_polygon.m, KPMtools/plotcov2.m, + KPMtools/plotcov3.m, KPMtools/plotgauss1d.m, + KPMtools/plotgauss2d.m, KPMtools/plotgauss2d_old.m, + KPMtools/polygon_area.m, KPMtools/polygon_centroid.m, + KPMtools/polygon_intersect.m, KPMtools/previewfig.m, + KPMtools/process_options.m, KPMtools/rand_psd.m, + KPMtools/rectintC.m, KPMtools/rectintLoopC.mexglx, + KPMtools/rectintSparse.m, KPMtools/rectintSparseC.m, + KPMtools/rectintSparseLoopC.c, KPMtools/rectintSparseLoopC.dll, + KPMtools/repmatC.c, KPMtools/repmatC.dll, + KPMtools/repmatC.mexglx, KPMtools/rgb2grayKPM.m, + KPMtools/rnd_partition.m, KPMtools/rotate_xlabel.m, + KPMtools/safeStr.m, KPMtools/sampleUniformInts.m, + KPMtools/sample_discrete.m, KPMtools/set_xtick_label.m, + KPMtools/set_xtick_label_demo.m, KPMtools/setdiag.m, + KPMtools/softeye.m, KPMtools/sort_evec.m, + KPMtools/splitLongSeqIntoManyShort.m, KPMtools/sprintf_intvec.m, + KPMtools/sqdist.m, KPMtools/strmatch_multi.m, + KPMtools/strmatch_substr.m, KPMtools/subplot2.m, + KPMtools/subplot3.m, KPMtools/subsets.m, KPMtools/subv2ind.c, + KPMtools/subv2ind.m, KPMtools/sumv.m, KPMtools/suptitle.m, + KPMtools/unaryEncoding.m, KPMtools/wrap.m, + KPMtools/xticklabel_rotate90.m, KPMtools/zipload.m, + KPMtools/zipsave.m: Initial import of code base from Kevin + Murphy. + +2005-04-25 19:29 yozhik + + * KPMstats/KLgauss.m, KPMstats/README.txt, KPMstats/beta_sample.m, + KPMstats/chisquared_histo.m, KPMstats/chisquared_prob.m, + KPMstats/chisquared_readme.txt, KPMstats/chisquared_table.m, + KPMstats/clg_Mstep.m, KPMstats/clg_Mstep_simple.m, + KPMstats/clg_prob.m, KPMstats/condGaussToJoint.m, + KPMstats/cond_indep_fisher_z.m, + KPMstats/condgaussTrainObserved.m, KPMstats/condgauss_sample.m, + KPMstats/convertBinaryLabels.m, KPMstats/cwr_demo.m, + KPMstats/cwr_em.m, KPMstats/cwr_predict.m, KPMstats/cwr_prob.m, + KPMstats/cwr_readme.txt, KPMstats/cwr_test.m, + KPMstats/dirichlet_sample.m, KPMstats/distchck.m, + KPMstats/eigdec.m, KPMstats/est_transmat.m, + KPMstats/fit_paritioned_model_testfn.m, + KPMstats/fit_partitioned_model.m, KPMstats/gamma_sample.m, + KPMstats/gaussian_prob.m, KPMstats/gaussian_sample.m, + KPMstats/linear_regression.m, KPMstats/logist2.m, + KPMstats/logist2Apply.m, KPMstats/logist2ApplyRegularized.m, + KPMstats/logist2Fit.m, KPMstats/logist2FitRegularized.m, + KPMstats/logistK.m, KPMstats/logistK_eval.m, + KPMstats/marginalize_gaussian.m, KPMstats/matrix_T_pdf.m, + KPMstats/matrix_normal_pdf.m, KPMstats/mc_stat_distrib.m, + KPMstats/mixgauss_Mstep.m, KPMstats/mixgauss_classifier_apply.m, + KPMstats/mixgauss_classifier_train.m, KPMstats/mixgauss_em.m, + KPMstats/mixgauss_init.m, KPMstats/mixgauss_prob.m, + KPMstats/mixgauss_prob_test.m, KPMstats/mixgauss_sample.m, + KPMstats/mkPolyFvec.m, KPMstats/mk_unit_norm.m, + KPMstats/multinomial_prob.m, KPMstats/multinomial_sample.m, + KPMstats/normal_coef.m, KPMstats/partial_corr_coef.m, + KPMstats/parzen.m, KPMstats/parzenC.c, KPMstats/parzenC.dll, + KPMstats/parzenC.mexglx, KPMstats/parzenC_test.m, + KPMstats/parzen_fit_select_unif.m, KPMstats/pca.m, + KPMstats/rndcheck.m, KPMstats/sample.m, + KPMstats/sample_discrete.m, KPMstats/sample_gaussian.m, + KPMstats/student_t_logprob.m, KPMstats/student_t_prob.m, + KPMstats/unif_discrete_sample.m, KPMstats/weightedRegression.m, + KPMtools/README.txt, KPMtools/approx_unique.m, + KPMtools/approxeq.m, KPMtools/argmax.m, KPMtools/argmin.m, + KPMtools/assert.m, KPMtools/assignEdgeNums.m, + KPMtools/assign_cols.m, KPMtools/axis_pct.m, KPMtools/block.m, + KPMtools/cell2num.m, KPMtools/chi2inv.m, KPMtools/choose.m, + KPMtools/collapse_mog.m, KPMtools/colmult.c, + KPMtools/colmult.mexglx, KPMtools/computeROC.m, + KPMtools/compute_counts.m, KPMtools/conf2mahal.m, + KPMtools/cross_entropy.m, KPMtools/div.m, KPMtools/draw_circle.m, + KPMtools/draw_ellipse.m, KPMtools/draw_ellipse_axes.m, + KPMtools/em_converged.m, KPMtools/entropy.m, + KPMtools/exportfig.m, KPMtools/extend_domain_table.m, + KPMtools/factorial.m, KPMtools/find_equiv_posns.m, + KPMtools/hash_add.m, KPMtools/hash_del.m, KPMtools/hash_lookup.m, + KPMtools/hungarian.m, KPMtools/image_rgb.m, + KPMtools/imresizeAspect.m, KPMtools/ind2subv.c, + KPMtools/ind2subv.m, KPMtools/installC_KPMtools.m, + KPMtools/is_psd.m, KPMtools/is_stochastic.m, + KPMtools/isemptycell.m, KPMtools/isposdef.m, KPMtools/isscalar.m, + KPMtools/isvector.m, KPMtools/junk.c, KPMtools/loadcell.m, + KPMtools/logb.m, KPMtools/logdet.m, KPMtools/logsum.m, + KPMtools/logsum_simple.m, KPMtools/logsum_test.m, + KPMtools/logsumexp.m, KPMtools/logsumexpv.m, + KPMtools/marg_table.m, KPMtools/marginalize_table.m, + KPMtools/matprint.m, KPMtools/max_mult.c, KPMtools/max_mult.m, + KPMtools/mexutil.c, KPMtools/mexutil.h, + KPMtools/mk_multi_index.m, KPMtools/mk_stochastic.m, + KPMtools/mult_by_table.m, KPMtools/myintersect.m, + KPMtools/myismember.m, KPMtools/myones.m, KPMtools/myplot.m, + KPMtools/myrand.m, KPMtools/myrepmat.m, KPMtools/myreshape.m, + KPMtools/mysetdiff.m, KPMtools/mysize.m, KPMtools/mysubset.m, + KPMtools/mysymsetdiff.m, KPMtools/bipartiteMatchingHungarian.m, + KPMtools/myunion.m, KPMtools/nchoose2.m, KPMtools/ncols.m, + KPMtools/nonmaxsup.m, KPMtools/normalise.m, + KPMtools/normaliseC.c, KPMtools/normaliseC.dll, + KPMtools/normalize.m, KPMtools/nrows.m, KPMtools/num2strcell.m, + KPMtools/partitionData.m, KPMtools/partition_matrix_vec.m, + KPMtools/pca_netlab.m, KPMtools/pick.m, KPMtools/plotROC.m, + KPMtools/plotROCkpm.m, KPMtools/plot_axis_thru_origin.m, + KPMtools/plot_ellipse.m, KPMtools/plot_matrix.m, + KPMtools/plot_polygon.m, KPMtools/plotcov2.m, + KPMtools/plotcov3.m, KPMtools/plotgauss1d.m, + KPMtools/plotgauss2d.m, KPMtools/plotgauss2d_old.m, + KPMtools/polygon_area.m, KPMtools/polygon_centroid.m, + KPMtools/polygon_intersect.m, KPMtools/previewfig.m, + KPMtools/process_options.m, KPMtools/rand_psd.m, + KPMtools/rectintC.m, KPMtools/rectintLoopC.mexglx, + KPMtools/rectintSparse.m, KPMtools/rectintSparseC.m, + KPMtools/rectintSparseLoopC.c, KPMtools/rectintSparseLoopC.dll, + KPMtools/repmatC.c, KPMtools/repmatC.dll, + KPMtools/repmatC.mexglx, KPMtools/rgb2grayKPM.m, + KPMtools/rnd_partition.m, KPMtools/rotate_xlabel.m, + KPMtools/safeStr.m, KPMtools/sampleUniformInts.m, + KPMtools/sample_discrete.m, KPMtools/set_xtick_label.m, + KPMtools/set_xtick_label_demo.m, KPMtools/setdiag.m, + KPMtools/softeye.m, KPMtools/sort_evec.m, + KPMtools/splitLongSeqIntoManyShort.m, KPMtools/sprintf_intvec.m, + KPMtools/sqdist.m, KPMtools/strmatch_multi.m, + KPMtools/strmatch_substr.m, KPMtools/subplot2.m, + KPMtools/subplot3.m, KPMtools/subsets.m, KPMtools/subv2ind.c, + KPMtools/subv2ind.m, KPMtools/sumv.m, KPMtools/suptitle.m, + KPMtools/unaryEncoding.m, KPMtools/wrap.m, + KPMtools/xticklabel_rotate90.m, KPMtools/zipload.m, + KPMtools/zipsave.m: Initial revision + +2005-04-03 18:39 yozhik + + * BNT/learning/score_dags.m: Initial import of code base from Kevin + Murphy. + +2005-04-03 18:39 yozhik + + * BNT/learning/score_dags.m: Initial revision + +2005-03-31 11:20 yozhik + + * BNT/installC_BNT.m: Initial import of code base from Kevin + Murphy. + +2005-03-31 11:20 yozhik + + * BNT/installC_BNT.m: Initial revision + +2005-03-30 11:59 yozhik + + * KPMtools/asdemo.html: Initial import of code base from Kevin + Murphy. + +2005-03-30 11:59 yozhik + + * KPMtools/asdemo.html: Initial revision + +2005-03-26 18:51 yozhik + + * KPMtools/asdemo.m: Initial import of code base from Kevin Murphy. + +2005-03-26 18:51 yozhik + + * KPMtools/asdemo.m: Initial revision + +2005-01-15 18:27 yozhik + + * BNT/CPDs/@tabular_CPD/: get_field.m, set_fields.m, tabular_CPD.m: + Initial import of code base from Kevin Murphy. + +2005-01-15 18:27 yozhik + + * BNT/CPDs/@tabular_CPD/: get_field.m, set_fields.m, tabular_CPD.m: + Initial revision + +2004-11-24 12:12 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/junk: Initial import of + code base from Kevin Murphy. + +2004-11-24 12:12 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/junk: Initial revision + +2004-11-22 14:41 yozhik + + * BNT/examples/dynamic/orig_water1.m: Initial import of code base + from Kevin Murphy. + +2004-11-22 14:41 yozhik + + * BNT/examples/dynamic/orig_water1.m: Initial revision + +2004-11-22 14:15 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/cbk_inf_engine.m: Initial + import of code base from Kevin Murphy. + +2004-11-22 14:15 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/cbk_inf_engine.m: Initial + revision + +2004-11-06 13:52 yozhik + + * BNT/examples/static/StructLearn/model_select2.m: Initial import + of code base from Kevin Murphy. + +2004-11-06 13:52 yozhik + + * BNT/examples/static/StructLearn/model_select2.m: Initial revision + +2004-11-06 12:55 yozhik + + * BNT/examples/static/StructLearn/model_select1.m: Initial import + of code base from Kevin Murphy. + +2004-11-06 12:55 yozhik + + * BNT/examples/static/StructLearn/model_select1.m: Initial revision + +2004-10-22 18:18 yozhik + + * HMM/viterbi_path.m: Initial import of code base from Kevin + Murphy. + +2004-10-22 18:18 yozhik + + * HMM/viterbi_path.m: Initial revision + +2004-09-29 20:09 yozhik + + * BNT/inference/static/@var_elim_inf_engine/marginal_nodes.m: + Initial import of code base from Kevin Murphy. + +2004-09-29 20:09 yozhik + + * BNT/inference/static/@var_elim_inf_engine/marginal_nodes.m: + Initial revision + +2004-09-12 20:21 yozhik + + * BNT/examples/limids/amnio.m: Initial import of code base from + Kevin Murphy. + +2004-09-12 20:21 yozhik + + * BNT/examples/limids/amnio.m: Initial revision + +2004-09-12 19:27 yozhik + + * BNT/examples/limids/oil1.m: Initial import of code base from + Kevin Murphy. + +2004-09-12 19:27 yozhik + + * BNT/examples/limids/oil1.m: Initial revision + +2004-09-12 14:01 yozhik + + * BNT/examples/static/sprinkler1.m: Initial import of code base + from Kevin Murphy. + +2004-09-12 14:01 yozhik + + * BNT/examples/static/sprinkler1.m: Initial revision + +2004-08-29 05:41 yozhik + + * HMM/transmat_train_observed.m: Initial import of code base from + Kevin Murphy. + +2004-08-29 05:41 yozhik + + * HMM/transmat_train_observed.m: Initial revision + +2004-08-05 08:25 yozhik + + * BNT/potentials/: @dpot/divide_by_pot.m, Tables/divide_by_table.m: + Initial import of code base from Kevin Murphy. + +2004-08-05 08:25 yozhik + + * BNT/potentials/: @dpot/divide_by_pot.m, Tables/divide_by_table.m: + Initial revision + +2004-08-04 12:59 yozhik + + * BNT/potentials/@dpot/: marginalize_pot.m, multiply_by_pot.m: + Initial import of code base from Kevin Murphy. + +2004-08-04 12:59 yozhik + + * BNT/potentials/@dpot/: marginalize_pot.m, multiply_by_pot.m: + Initial revision + +2004-08-04 12:36 yozhik + + * BNT/@assocarray/subsref.m: Initial import of code base from Kevin + Murphy. + +2004-08-04 12:36 yozhik + + * BNT/@assocarray/subsref.m: Initial revision + +2004-08-04 08:54 yozhik + + * BNT/potentials/@dpot/normalize_pot.m: Initial import of code base + from Kevin Murphy. + +2004-08-04 08:54 yozhik + + * BNT/potentials/@dpot/normalize_pot.m: Initial revision + +2004-08-04 08:51 yozhik + + * BNT/potentials/Tables/: marg_table.m, mult_by_table.m, + extend_domain_table.m: Initial import of code base from Kevin + Murphy. + +2004-08-04 08:51 yozhik + + * BNT/potentials/Tables/: marg_table.m, mult_by_table.m, + extend_domain_table.m: Initial revision + +2004-08-02 15:23 yozhik + + * BNT/CPDs/@noisyor_CPD/CPD_to_CPT.m: Initial import of code base + from Kevin Murphy. + +2004-08-02 15:23 yozhik + + * BNT/CPDs/@noisyor_CPD/CPD_to_CPT.m: Initial revision + +2004-08-02 15:05 yozhik + + * BNT/general/noisyORtoTable.m: Initial import of code base from + Kevin Murphy. + +2004-08-02 15:05 yozhik + + * BNT/general/noisyORtoTable.m: Initial revision + +2004-06-29 10:46 yozhik + + * BNT/learning/learn_struct_pdag_pc.m: Initial import of code base + from Kevin Murphy. + +2004-06-29 10:46 yozhik + + * BNT/learning/learn_struct_pdag_pc.m: Initial revision + +2004-06-15 10:50 yozhik + + * GraphViz/graph_to_dot.m: Initial import of code base from Kevin + Murphy. + +2004-06-15 10:50 yozhik + + * GraphViz/graph_to_dot.m: Initial revision + +2004-06-11 14:16 yozhik + + * BNT/CPDs/@tabular_CPD/log_marg_prob_node.m: Initial import of + code base from Kevin Murphy. + +2004-06-11 14:16 yozhik + + * BNT/CPDs/@tabular_CPD/log_marg_prob_node.m: Initial revision + +2004-06-09 18:56 yozhik + + * BNT/README.txt: Initial import of code base from Kevin Murphy. + +2004-06-09 18:56 yozhik + + * BNT/README.txt: Initial revision + +2004-06-09 18:53 yozhik + + * BNT/CPDs/@generic_CPD/learn_params.m: Initial import of code base + from Kevin Murphy. + +2004-06-09 18:53 yozhik + + * BNT/CPDs/@generic_CPD/learn_params.m: Initial revision + +2004-06-09 18:42 yozhik + + * BNT/examples/static/nodeorderExample.m: Initial import of code + base from Kevin Murphy. + +2004-06-09 18:42 yozhik + + * BNT/examples/static/nodeorderExample.m: Initial revision + +2004-06-09 18:33 yozhik + + * BNT/: learning/score_family.m, test_BNT.m: Initial import of code + base from Kevin Murphy. + +2004-06-09 18:33 yozhik + + * BNT/: learning/score_family.m, test_BNT.m: Initial revision + +2004-06-09 18:28 yozhik + + * BNT/: learning/learn_params.m, CPDs/@gaussian_CPD/learn_params.m, + examples/static/gaussian2.m: Initial import of code base from + Kevin Murphy. + +2004-06-09 18:28 yozhik + + * BNT/: learning/learn_params.m, CPDs/@gaussian_CPD/learn_params.m, + examples/static/gaussian2.m: Initial revision + +2004-06-09 18:25 yozhik + + * BNT/CPDs/@tabular_CPD/learn_params.m: Initial import of code base + from Kevin Murphy. + +2004-06-09 18:25 yozhik + + * BNT/CPDs/@tabular_CPD/learn_params.m: Initial revision + +2004-06-09 18:17 yozhik + + * BNT/general/sample_bnet.m: Initial import of code base from Kevin + Murphy. + +2004-06-09 18:17 yozhik + + * BNT/general/sample_bnet.m: Initial revision + +2004-06-07 12:45 yozhik + + * BNT/examples/static/discrete1.m: Initial import of code base from + Kevin Murphy. + +2004-06-07 12:45 yozhik + + * BNT/examples/static/discrete1.m: Initial revision + +2004-06-07 12:04 yozhik + + * BNT/: inference/static/@global_joint_inf_engine/marginal_nodes.m, + inference/static/@global_joint_inf_engine/enter_evidence.m, + examples/dynamic/mk_bat_dbn.m: Initial import of code base from + Kevin Murphy. + +2004-06-07 12:04 yozhik + + * BNT/: inference/static/@global_joint_inf_engine/marginal_nodes.m, + inference/static/@global_joint_inf_engine/enter_evidence.m, + examples/dynamic/mk_bat_dbn.m: Initial revision + +2004-06-07 08:53 yozhik + + * BNT/examples/limids/asia_dt1.m: Initial import of code base from + Kevin Murphy. + +2004-06-07 08:53 yozhik + + * BNT/examples/limids/asia_dt1.m: Initial revision + +2004-06-07 08:48 yozhik + + * BNT/general/: solve_limid.m, compute_joint_pot.m: Initial import + of code base from Kevin Murphy. + +2004-06-07 08:48 yozhik + + * BNT/general/: solve_limid.m, compute_joint_pot.m: Initial + revision + +2004-06-07 07:39 yozhik + + * Kalman/README.txt: Initial import of code base from Kevin Murphy. + +2004-06-07 07:39 yozhik + + * Kalman/README.txt: Initial revision + +2004-06-07 07:33 yozhik + + * GraphViz/README.txt: Initial import of code base from Kevin + Murphy. + +2004-06-07 07:33 yozhik + + * GraphViz/README.txt: Initial revision + +2004-05-31 15:19 yozhik + + * HMM/dhmm_sample.m: Initial import of code base from Kevin Murphy. + +2004-05-31 15:19 yozhik + + * HMM/dhmm_sample.m: Initial revision + +2004-05-25 17:32 yozhik + + * HMM/mhmm_sample.m: Initial import of code base from Kevin Murphy. + +2004-05-25 17:32 yozhik + + * HMM/mhmm_sample.m: Initial revision + +2004-05-24 15:26 yozhik + + * HMM/mc_sample.m: Initial import of code base from Kevin Murphy. + +2004-05-24 15:26 yozhik + + * HMM/mc_sample.m: Initial revision + +2004-05-18 07:50 yozhik + + * BNT/installC_graph.m: Initial import of code base from Kevin + Murphy. + +2004-05-18 07:50 yozhik + + * BNT/installC_graph.m: Initial revision + +2004-05-13 18:13 yozhik + + * BNT/inference/static/@gaussian_inf_engine/gaussian_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2004-05-13 18:13 yozhik + + * BNT/inference/static/@gaussian_inf_engine/gaussian_inf_engine.m: + Initial revision + +2004-05-11 12:23 yozhik + + * BNT/examples/dynamic/mk_chmm.m: Initial import of code base from + Kevin Murphy. + +2004-05-11 12:23 yozhik + + * BNT/examples/dynamic/mk_chmm.m: Initial revision + +2004-05-11 11:45 yozhik + + * BNT/examples/dynamic/mk_water_dbn.m: Initial import of code base + from Kevin Murphy. + +2004-05-11 11:45 yozhik + + * BNT/examples/dynamic/mk_water_dbn.m: Initial revision + +2004-05-05 06:32 yozhik + + * GraphViz/draw_dot.m: Initial import of code base from Kevin + Murphy. + +2004-05-05 06:32 yozhik + + * GraphViz/draw_dot.m: Initial revision + +2004-03-30 09:18 yozhik + + * BNT/: general/mk_named_CPT.m, + CPDs/@softmax_CPD/convert_to_table.m: Initial import of code base + from Kevin Murphy. + +2004-03-30 09:18 yozhik + + * BNT/: general/mk_named_CPT.m, + CPDs/@softmax_CPD/convert_to_table.m: Initial revision + +2004-03-22 14:32 yozhik + + * GraphViz/draw_graph.m: Initial import of code base from Kevin + Murphy. + +2004-03-22 14:32 yozhik + + * GraphViz/draw_graph.m: Initial revision + +2004-03-12 15:21 yozhik + + * GraphViz/dot_to_graph.m: Initial import of code base from Kevin + Murphy. + +2004-03-12 15:21 yozhik + + * GraphViz/dot_to_graph.m: Initial revision + +2004-03-04 14:34 yozhik + + * BNT/examples/static/burglary.m: Initial import of code base from + Kevin Murphy. + +2004-03-04 14:34 yozhik + + * BNT/examples/static/burglary.m: Initial revision + +2004-03-04 14:27 yozhik + + * BNT/examples/static/burglar-alarm-net.lisp.txt: Initial import of + code base from Kevin Murphy. + +2004-03-04 14:27 yozhik + + * BNT/examples/static/burglar-alarm-net.lisp.txt: Initial revision + +2004-02-28 09:25 yozhik + + * BNT/examples/static/learn1.m: Initial import of code base from + Kevin Murphy. + +2004-02-28 09:25 yozhik + + * BNT/examples/static/learn1.m: Initial revision + +2004-02-22 11:43 yozhik + + * BNT/examples/static/brainy.m: Initial import of code base from + Kevin Murphy. + +2004-02-22 11:43 yozhik + + * BNT/examples/static/brainy.m: Initial revision + +2004-02-20 14:00 yozhik + + * BNT/CPDs/@discrete_CPD/convert_to_pot.m: Initial import of code + base from Kevin Murphy. + +2004-02-20 14:00 yozhik + + * BNT/CPDs/@discrete_CPD/convert_to_pot.m: Initial revision + +2004-02-18 17:12 yozhik + + * + BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_soft_evidence.m: + Initial import of code base from Kevin Murphy. + +2004-02-18 17:12 yozhik + + * + BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_soft_evidence.m: + Initial revision + +2004-02-13 18:06 yozhik + + * HMM/mhmmParzen_train_observed.m: Initial import of code base from + Kevin Murphy. + +2004-02-13 18:06 yozhik + + * HMM/mhmmParzen_train_observed.m: Initial revision + +2004-02-12 15:08 yozhik + + * HMM/gausshmm_train_observed.m: Initial import of code base from + Kevin Murphy. + +2004-02-12 15:08 yozhik + + * HMM/gausshmm_train_observed.m: Initial revision + +2004-02-12 04:57 yozhik + + * BNT/examples/static/HME/hmemenu.m: Initial import of code base + from Kevin Murphy. + +2004-02-12 04:57 yozhik + + * BNT/examples/static/HME/hmemenu.m: Initial revision + +2004-02-07 20:52 yozhik + + * HMM/mhmm_em.m: Initial import of code base from Kevin Murphy. + +2004-02-07 20:52 yozhik + + * HMM/mhmm_em.m: Initial revision + +2004-02-04 15:53 yozhik + + * BNT/examples/dynamic/mk_orig_bat_dbn.m: Initial import of code + base from Kevin Murphy. + +2004-02-04 15:53 yozhik + + * BNT/examples/dynamic/mk_orig_bat_dbn.m: Initial revision + +2004-02-03 23:42 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/enter_soft_evidence.m: + Initial import of code base from Kevin Murphy. + +2004-02-03 23:42 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/enter_soft_evidence.m: + Initial revision + +2004-02-03 09:15 yozhik + + * GraphViz/Old/graphToDot.m: Initial import of code base from Kevin + Murphy. + +2004-02-03 09:15 yozhik + + * GraphViz/Old/graphToDot.m: Initial revision + +2004-01-30 18:57 yozhik + + * BNT/examples/dynamic/mk_orig_water_dbn.m: Initial import of code + base from Kevin Murphy. + +2004-01-30 18:57 yozhik + + * BNT/examples/dynamic/mk_orig_water_dbn.m: Initial revision + +2004-01-27 13:08 yozhik + + * GraphViz/: my_call.m, editGraphGUI.m: Initial import of code base + from Kevin Murphy. + +2004-01-27 13:08 yozhik + + * GraphViz/: my_call.m, editGraphGUI.m: Initial revision + +2004-01-27 13:01 yozhik + + * GraphViz/Old/: dot_to_graph.m, draw_graph.m: Initial import of + code base from Kevin Murphy. + +2004-01-27 13:01 yozhik + + * GraphViz/Old/: dot_to_graph.m, draw_graph.m: Initial revision + +2004-01-27 12:47 yozhik + + * GraphViz/Old/pre_pesha_graph_to_dot.m: Initial import of code + base from Kevin Murphy. + +2004-01-27 12:47 yozhik + + * GraphViz/Old/pre_pesha_graph_to_dot.m: Initial revision + +2004-01-27 12:42 yozhik + + * GraphViz/Old/draw_dot.m: Initial import of code base from Kevin + Murphy. + +2004-01-27 12:42 yozhik + + * GraphViz/Old/draw_dot.m: Initial revision + +2004-01-14 17:06 yozhik + + * BNT/examples/static/Models/mk_hmm_bnet.m: Initial import of code + base from Kevin Murphy. + +2004-01-14 17:06 yozhik + + * BNT/examples/static/Models/mk_hmm_bnet.m: Initial revision + +2004-01-12 12:53 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/enter_evidence.m: Initial + import of code base from Kevin Murphy. + +2004-01-12 12:53 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/enter_evidence.m: Initial + revision + +2004-01-04 17:23 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/bp_mrf2.m: Initial + import of code base from Kevin Murphy. + +2004-01-04 17:23 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/bp_mrf2.m: Initial + revision + +2003-12-15 22:17 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/marginal_nodes.m: Initial + import of code base from Kevin Murphy. + +2003-12-15 22:17 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/marginal_nodes.m: Initial + revision + +2003-10-31 14:37 yozhik + + * BNT/inference/static/@jtree_inf_engine/jtree_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-10-31 14:37 yozhik + + * BNT/inference/static/@jtree_inf_engine/jtree_inf_engine.m: + Initial revision + +2003-09-05 07:06 yozhik + + * BNT/learning/learn_struct_mcmc.m: Initial import of code base + from Kevin Murphy. + +2003-09-05 07:06 yozhik + + * BNT/learning/learn_struct_mcmc.m: Initial revision + +2003-08-18 14:50 yozhik + + * BNT/learning/learn_params_dbn_em.m: Initial import of code base + from Kevin Murphy. + +2003-08-18 14:50 yozhik + + * BNT/learning/learn_params_dbn_em.m: Initial revision + +2003-07-30 06:37 yozhik + + * BNT/potentials/: @mpot/set_domain_pot.m, + @cgpot/Old/set_domain_pot.m, @cgpot/set_domain_pot.m: Initial + import of code base from Kevin Murphy. + +2003-07-30 06:37 yozhik + + * BNT/potentials/: @mpot/set_domain_pot.m, + @cgpot/Old/set_domain_pot.m, @cgpot/set_domain_pot.m: Initial + revision + +2003-07-28 19:44 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/: dbn_init_bel.m, + dbn_marginal_from_bel.m, dbn_update_bel.m, dbn_update_bel1.m, + marginal_family.m, update_engine.m: Initial import of code base + from Kevin Murphy. + +2003-07-28 19:44 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/: dbn_init_bel.m, + dbn_marginal_from_bel.m, dbn_update_bel.m, dbn_update_bel1.m, + marginal_family.m, update_engine.m: Initial revision + +2003-07-28 15:44 yozhik + + * GraphViz/: approxeq.m, process_options.m: Initial import of code + base from Kevin Murphy. + +2003-07-28 15:44 yozhik + + * GraphViz/: approxeq.m, process_options.m: Initial revision + +2003-07-24 06:41 yozhik + + * BNT/CPDs/@hhmmQ_CPD/update_ess.m: Initial import of code base + from Kevin Murphy. + +2003-07-24 06:41 yozhik + + * BNT/CPDs/@hhmmQ_CPD/update_ess.m: Initial revision + +2003-07-22 15:55 yozhik + + * BNT/CPDs/@gaussian_CPD/update_ess.m: Initial import of code base + from Kevin Murphy. + +2003-07-22 15:55 yozhik + + * BNT/CPDs/@gaussian_CPD/update_ess.m: Initial revision + +2003-07-06 13:57 yozhik + + * BNT/inference/static/@pearl_inf_engine/bethe_free_energy.m: + Initial import of code base from Kevin Murphy. + +2003-07-06 13:57 yozhik + + * BNT/inference/static/@pearl_inf_engine/bethe_free_energy.m: + Initial revision + +2003-05-21 06:49 yozhik + + * BNT/potentials/@scgpot/: complement_pot.m, normalize_pot.m, + recursive_combine_pots.m: Initial import of code base from Kevin + Murphy. + +2003-05-21 06:49 yozhik + + * BNT/potentials/@scgpot/: complement_pot.m, normalize_pot.m, + recursive_combine_pots.m: Initial revision + +2003-05-20 07:10 yozhik + + * BNT/CPDs/@gaussian_CPD/maximize_params.m: Initial import of code + base from Kevin Murphy. + +2003-05-20 07:10 yozhik + + * BNT/CPDs/@gaussian_CPD/maximize_params.m: Initial revision + +2003-05-13 09:11 yozhik + + * HMM/mhmm_em_demo.m: Initial import of code base from Kevin + Murphy. + +2003-05-13 09:11 yozhik + + * HMM/mhmm_em_demo.m: Initial revision + +2003-05-13 07:35 yozhik + + * BNT/examples/dynamic/viterbi1.m: Initial import of code base from + Kevin Murphy. + +2003-05-13 07:35 yozhik + + * BNT/examples/dynamic/viterbi1.m: Initial revision + +2003-05-11 16:31 yozhik + + * BNT/CPDs/@gaussian_CPD/convert_to_table.m: Initial import of code + base from Kevin Murphy. + +2003-05-11 16:31 yozhik + + * BNT/CPDs/@gaussian_CPD/convert_to_table.m: Initial revision + +2003-05-11 16:13 yozhik + + * BNT/CPDs/@gaussian_CPD/gaussian_CPD_params_given_dps.m: Initial + import of code base from Kevin Murphy. + +2003-05-11 16:13 yozhik + + * BNT/CPDs/@gaussian_CPD/gaussian_CPD_params_given_dps.m: Initial + revision + +2003-05-11 08:39 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/README: Initial + import of code base from Kevin Murphy. + +2003-05-11 08:39 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/README: Initial + revision + +2003-05-04 15:31 yozhik + + * BNT/uninstallC_BNT.m: Initial import of code base from Kevin + Murphy. + +2003-05-04 15:31 yozhik + + * BNT/uninstallC_BNT.m: Initial revision + +2003-05-04 15:23 yozhik + + * BNT/examples/dynamic/: dhmm1.m, ghmm1.m, mhmm1.m: Initial import + of code base from Kevin Murphy. + +2003-05-04 15:23 yozhik + + * BNT/examples/dynamic/: dhmm1.m, ghmm1.m, mhmm1.m: Initial + revision + +2003-05-04 15:11 yozhik + + * HMM/mhmm_logprob.m: Initial import of code base from Kevin + Murphy. + +2003-05-04 15:11 yozhik + + * HMM/mhmm_logprob.m: Initial revision + +2003-05-04 15:01 yozhik + + * HMM/: dhmm_logprob.m, dhmm_em_online.m, dhmm_em_online_demo.m: + Initial import of code base from Kevin Murphy. + +2003-05-04 15:01 yozhik + + * HMM/: dhmm_logprob.m, dhmm_em_online.m, dhmm_em_online_demo.m: + Initial revision + +2003-05-04 14:58 yozhik + + * HMM/: pomdp_sample.m, dhmm_sample_endstate.m, dhmm_em_demo.m: + Initial import of code base from Kevin Murphy. + +2003-05-04 14:58 yozhik + + * HMM/: pomdp_sample.m, dhmm_sample_endstate.m, dhmm_em_demo.m: + Initial revision + +2003-05-04 14:47 yozhik + + * + BNT/inference/online/@hmm_2TBN_inf_engine/private/mk_hmm_obs_lik_vec.m: + Initial import of code base from Kevin Murphy. + +2003-05-04 14:47 yozhik + + * + BNT/inference/online/@hmm_2TBN_inf_engine/private/mk_hmm_obs_lik_vec.m: + Initial revision + +2003-05-04 14:42 yozhik + + * + BNT/inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_matrix.m: + Initial import of code base from Kevin Murphy. + +2003-05-04 14:42 yozhik + + * + BNT/inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_matrix.m: + Initial revision + +2003-04-22 14:00 yozhik + + * BNT/CPDs/@tabular_CPD/display.m: Initial import of code base from + Kevin Murphy. + +2003-04-22 14:00 yozhik + + * BNT/CPDs/@tabular_CPD/display.m: Initial revision + +2003-03-28 09:22 yozhik + + * BNT/examples/dynamic/ho1.m: Initial import of code base from + Kevin Murphy. + +2003-03-28 09:22 yozhik + + * BNT/examples/dynamic/ho1.m: Initial revision + +2003-03-28 09:12 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-03-28 09:12 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m: + Initial revision + +2003-03-28 08:35 yozhik + + * GraphViz/arrow.m: Initial import of code base from Kevin Murphy. + +2003-03-28 08:35 yozhik + + * GraphViz/arrow.m: Initial revision + +2003-03-25 16:06 yozhik + + * BNT/examples/static/Models/mk_asia_bnet.m: Initial import of code + base from Kevin Murphy. + +2003-03-25 16:06 yozhik + + * BNT/examples/static/Models/mk_asia_bnet.m: Initial revision + +2003-03-20 07:07 yozhik + + * BNT/potentials/@scgpot/README: Initial import of code base from + Kevin Murphy. + +2003-03-20 07:07 yozhik + + * BNT/potentials/@scgpot/README: Initial revision + +2003-03-14 01:45 yozhik + + * + BNT/inference/dynamic/@stable_ho_inf_engine/stable_ho_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-03-14 01:45 yozhik + + * + BNT/inference/dynamic/@stable_ho_inf_engine/stable_ho_inf_engine.m: + Initial revision + +2003-03-12 02:38 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/enter_evidence.m: + Initial import of code base from Kevin Murphy. + +2003-03-12 02:38 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/enter_evidence.m: + Initial revision + +2003-03-11 10:07 yozhik + + * BNT/potentials/@scgpot/reduce_pot.m: Initial import of code base + from Kevin Murphy. + +2003-03-11 10:07 yozhik + + * BNT/potentials/@scgpot/reduce_pot.m: Initial revision + +2003-03-11 09:49 yozhik + + * BNT/potentials/@scgpot/combine_pots.m: Initial import of code + base from Kevin Murphy. + +2003-03-11 09:49 yozhik + + * BNT/potentials/@scgpot/combine_pots.m: Initial revision + +2003-03-11 09:37 yozhik + + * BNT/potentials/@scgcpot/reduce_pot.m: Initial import of code base + from Kevin Murphy. + +2003-03-11 09:37 yozhik + + * BNT/potentials/@scgcpot/reduce_pot.m: Initial revision + +2003-03-11 09:06 yozhik + + * BNT/potentials/@scgpot/marginalize_pot.m: Initial import of code + base from Kevin Murphy. + +2003-03-11 09:06 yozhik + + * BNT/potentials/@scgpot/marginalize_pot.m: Initial revision + +2003-03-11 06:04 yozhik + + * BNT/potentials/@scgpot/scgpot.m: Initial import of code base from + Kevin Murphy. + +2003-03-11 06:04 yozhik + + * BNT/potentials/@scgpot/scgpot.m: Initial revision + +2003-03-09 15:03 yozhik + + * BNT/CPDs/@gaussian_CPD/convert_to_pot.m: Initial import of code + base from Kevin Murphy. + +2003-03-09 15:03 yozhik + + * BNT/CPDs/@gaussian_CPD/convert_to_pot.m: Initial revision + +2003-03-09 14:44 yozhik + + * BNT/CPDs/@tabular_CPD/maximize_params.m: Initial import of code + base from Kevin Murphy. + +2003-03-09 14:44 yozhik + + * BNT/CPDs/@tabular_CPD/maximize_params.m: Initial revision + +2003-02-21 03:20 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_difclq_nodes.m: + Initial import of code base from Kevin Murphy. + +2003-02-21 03:20 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_difclq_nodes.m: + Initial revision + +2003-02-21 03:13 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_nodes.m: + Initial import of code base from Kevin Murphy. + +2003-02-21 03:13 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_nodes.m: + Initial revision + +2003-02-19 01:52 yozhik + + * BNT/inference/dynamic/@stable_ho_inf_engine/: enter_evidence.m, + marginal_family.m, marginal_nodes.m, test_ho_inf_enginge.m, + update_engine.m: Initial import of code base from Kevin Murphy. + +2003-02-19 01:52 yozhik + + * BNT/inference/dynamic/@stable_ho_inf_engine/: enter_evidence.m, + marginal_family.m, marginal_nodes.m, test_ho_inf_enginge.m, + update_engine.m: Initial revision + +2003-02-10 07:38 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/push.m: Initial + import of code base from Kevin Murphy. + +2003-02-10 07:38 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/push.m: Initial + revision + +2003-02-06 18:25 yozhik + + * KPMtools/checkpsd.m: Initial import of code base from Kevin + Murphy. + +2003-02-06 18:25 yozhik + + * KPMtools/checkpsd.m: Initial revision + +2003-02-05 19:16 yozhik + + * GraphViz/draw_hmm.m: Initial import of code base from Kevin + Murphy. + +2003-02-05 19:16 yozhik + + * GraphViz/draw_hmm.m: Initial revision + +2003-02-01 16:23 yozhik + + * BNT/: general/dbn_to_hmm.m, learning/learn_params_dbn.m: Initial + import of code base from Kevin Murphy. + +2003-02-01 16:23 yozhik + + * BNT/: general/dbn_to_hmm.m, learning/learn_params_dbn.m: Initial + revision + +2003-02-01 11:42 yozhik + + * BNT/general/mk_dbn.m: Initial import of code base from Kevin + Murphy. + +2003-02-01 11:42 yozhik + + * BNT/general/mk_dbn.m: Initial revision + +2003-01-30 16:13 yozhik + + * BNT/CPDs/@gaussian_CPD/maximize_params_debug.m: Initial import of + code base from Kevin Murphy. + +2003-01-30 16:13 yozhik + + * BNT/CPDs/@gaussian_CPD/maximize_params_debug.m: Initial revision + +2003-01-30 14:38 yozhik + + * BNT/CPDs/@gaussian_CPD/Old/maximize_params.m: Initial import of + code base from Kevin Murphy. + +2003-01-30 14:38 yozhik + + * BNT/CPDs/@gaussian_CPD/Old/maximize_params.m: Initial revision + +2003-01-29 03:23 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_singleclq_nodes.m: + Initial import of code base from Kevin Murphy. + +2003-01-29 03:23 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_singleclq_nodes.m: + Initial revision + +2003-01-24 11:36 yozhik + + * Kalman/sample_lds.m: Initial import of code base from Kevin + Murphy. + +2003-01-24 11:36 yozhik + + * Kalman/sample_lds.m: Initial revision + +2003-01-24 04:52 yozhik + + * BNT/potentials/@scgpot/extension_pot.m: Initial import of code + base from Kevin Murphy. + +2003-01-24 04:52 yozhik + + * BNT/potentials/@scgpot/extension_pot.m: Initial revision + +2003-01-23 10:49 yozhik + + * BNT/: general/convert_dbn_CPDs_to_tables1.m, + inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_vec.m: + Initial import of code base from Kevin Murphy. + +2003-01-23 10:49 yozhik + + * BNT/: general/convert_dbn_CPDs_to_tables1.m, + inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_vec.m: + Initial revision + +2003-01-23 10:44 yozhik + + * BNT/general/convert_dbn_CPDs_to_tables.m: Initial import of code + base from Kevin Murphy. + +2003-01-23 10:44 yozhik + + * BNT/general/convert_dbn_CPDs_to_tables.m: Initial revision + +2003-01-22 13:38 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m: Initial + import of code base from Kevin Murphy. + +2003-01-22 13:38 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m: Initial + revision + +2003-01-22 12:32 yozhik + + * HMM/mc_sample_endstate.m: Initial import of code base from Kevin + Murphy. + +2003-01-22 12:32 yozhik + + * HMM/mc_sample_endstate.m: Initial revision + +2003-01-22 09:56 yozhik + + * HMM/fixed_lag_smoother.m: Initial import of code base from Kevin + Murphy. + +2003-01-22 09:56 yozhik + + * HMM/fixed_lag_smoother.m: Initial revision + +2003-01-20 08:56 yozhik + + * GraphViz/draw_graph_test.m: Initial import of code base from + Kevin Murphy. + +2003-01-20 08:56 yozhik + + * GraphViz/draw_graph_test.m: Initial revision + +2003-01-18 15:10 yozhik + + * BNT/general/dsep_test.m: Initial import of code base from Kevin + Murphy. + +2003-01-18 15:10 yozhik + + * BNT/general/dsep_test.m: Initial revision + +2003-01-18 15:00 yozhik + + * BNT/copyright.txt: Initial import of code base from Kevin Murphy. + +2003-01-18 15:00 yozhik + + * BNT/copyright.txt: Initial revision + +2003-01-18 14:49 yozhik + + * Kalman/tracking_demo.m: Initial import of code base from Kevin + Murphy. + +2003-01-18 14:49 yozhik + + * Kalman/tracking_demo.m: Initial revision + +2003-01-18 14:22 yozhik + + * BNT/: examples/dummy, inference/dummy, inference/dynamic/dummy, + inference/online/dummy, inference/static/dummy: Initial import of + code base from Kevin Murphy. + +2003-01-18 14:22 yozhik + + * BNT/: examples/dummy, inference/dummy, inference/dynamic/dummy, + inference/online/dummy, inference/static/dummy: Initial revision + +2003-01-18 14:16 yozhik + + * BNT/examples/dynamic/: ehmm1.m, jtree_clq_test.m: Initial import + of code base from Kevin Murphy. + +2003-01-18 14:16 yozhik + + * BNT/examples/dynamic/: ehmm1.m, jtree_clq_test.m: Initial + revision + +2003-01-18 14:11 yozhik + + * BNT/inference/static/: + @jtree_sparse_inf_engine/jtree_sparse_inf_engine.m, + @jtree_mnet_inf_engine/jtree_mnet_inf_engine.m: Initial import of + code base from Kevin Murphy. + +2003-01-18 14:11 yozhik + + * BNT/inference/static/: + @jtree_sparse_inf_engine/jtree_sparse_inf_engine.m, + @jtree_mnet_inf_engine/jtree_mnet_inf_engine.m: Initial revision + +2003-01-18 13:17 yozhik + + * GraphViz/draw_dbn_test.m: Initial import of code base from Kevin + Murphy. + +2003-01-18 13:17 yozhik + + * GraphViz/draw_dbn_test.m: Initial revision + +2003-01-11 10:53 yozhik + + * BNT/inference/static/@pearl_inf_engine/pearl_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-01-11 10:53 yozhik + + * BNT/inference/static/@pearl_inf_engine/pearl_inf_engine.m: + Initial revision + +2003-01-11 10:48 yozhik + + * BNT/examples/dynamic/HHMM/Map/learn_map.m: Initial import of code + base from Kevin Murphy. + +2003-01-11 10:48 yozhik + + * BNT/examples/dynamic/HHMM/Map/learn_map.m: Initial revision + +2003-01-11 10:41 yozhik + + * BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_evidence.m: + Initial import of code base from Kevin Murphy. + +2003-01-11 10:41 yozhik + + * BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_evidence.m: + Initial revision + +2003-01-11 10:13 yozhik + + * BNT/inference/dynamic/@bk_inf_engine/enter_soft_evidence.m: + Initial import of code base from Kevin Murphy. + +2003-01-11 10:13 yozhik + + * BNT/inference/dynamic/@bk_inf_engine/enter_soft_evidence.m: + Initial revision + +2003-01-07 08:25 yozhik + + * BNT/CPDs/@softmax_CPD/softmax_CPD.m: Initial import of code base + from Kevin Murphy. + +2003-01-07 08:25 yozhik + + * BNT/CPDs/@softmax_CPD/softmax_CPD.m: Initial revision + +2003-01-03 14:01 yozhik + + * + BNT/inference/static/@belprop_mrf2_inf_engine/belprop_mrf2_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-01-03 14:01 yozhik + + * + BNT/inference/static/@belprop_mrf2_inf_engine/belprop_mrf2_inf_engine.m: + Initial revision + +2003-01-02 09:49 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/find_mpe.m: Initial + import of code base from Kevin Murphy. + +2003-01-02 09:49 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/find_mpe.m: Initial + revision + +2003-01-02 09:28 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/: set_params.m, + enter_soft_evidence.m: Initial import of code base from Kevin + Murphy. + +2003-01-02 09:28 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/: set_params.m, + enter_soft_evidence.m: Initial revision + +2002-12-31 14:06 yozhik + + * BNT/general/mk_mrf2.m: Initial import of code base from Kevin + Murphy. + +2002-12-31 14:06 yozhik + + * BNT/general/mk_mrf2.m: Initial revision + +2002-12-31 13:24 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/marginal_nodes.m: + Initial import of code base from Kevin Murphy. + +2002-12-31 13:24 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/marginal_nodes.m: + Initial revision + +2002-12-31 11:00 yozhik + + * BNT/inference/static/@belprop_inf_engine/belprop_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2002-12-31 11:00 yozhik + + * BNT/inference/static/@belprop_inf_engine/belprop_inf_engine.m: + Initial revision + +2002-12-16 11:16 yozhik + + * BNT/examples/dynamic/HHMM/remove_hhmm_end_state.m: Initial import + of code base from Kevin Murphy. + +2002-12-16 11:16 yozhik + + * BNT/examples/dynamic/HHMM/remove_hhmm_end_state.m: Initial + revision + +2002-12-16 09:57 yozhik + + * BNT/general/unroll_set.m: Initial import of code base from Kevin + Murphy. + +2002-12-16 09:57 yozhik + + * BNT/general/unroll_set.m: Initial revision + +2002-11-26 14:14 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram3.m: Initial import of code + base from Kevin Murphy. + +2002-11-26 14:14 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram3.m: Initial revision + +2002-11-26 14:04 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram2.m: Initial import of code + base from Kevin Murphy. + +2002-11-26 14:04 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram2.m: Initial revision + +2002-11-22 16:44 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/Old/mgram2.m: Initial import of + code base from Kevin Murphy. + +2002-11-22 16:44 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/Old/mgram2.m: Initial revision + +2002-11-22 15:59 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram1.m: Initial import of code + base from Kevin Murphy. + +2002-11-22 15:59 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram1.m: Initial revision + +2002-11-22 15:51 yozhik + + * BNT/inference/dynamic/@jtree_dbn_inf_engine/marginal_nodes.m: + Initial import of code base from Kevin Murphy. + +2002-11-22 15:51 yozhik + + * BNT/inference/dynamic/@jtree_dbn_inf_engine/marginal_nodes.m: + Initial revision + +2002-11-22 15:07 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/: num2letter.m, letter2num.m: + Initial import of code base from Kevin Murphy. + +2002-11-22 15:07 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/: num2letter.m, letter2num.m: + Initial revision + +2002-11-22 14:35 yozhik + + * BNT/general/convert_dbn_CPDs_to_pots.m: Initial import of code + base from Kevin Murphy. + +2002-11-22 14:35 yozhik + + * BNT/general/convert_dbn_CPDs_to_pots.m: Initial revision + +2002-11-22 13:45 yozhik + + * HMM/mk_rightleft_transmat.m: Initial import of code base from + Kevin Murphy. + +2002-11-22 13:45 yozhik + + * HMM/mk_rightleft_transmat.m: Initial revision + +2002-11-14 12:33 yozhik + + * BNT/examples/dynamic/water2.m: Initial import of code base from + Kevin Murphy. + +2002-11-14 12:33 yozhik + + * BNT/examples/dynamic/water2.m: Initial revision + +2002-11-14 12:07 yozhik + + * BNT/examples/dynamic/water1.m: Initial import of code base from + Kevin Murphy. + +2002-11-14 12:07 yozhik + + * BNT/examples/dynamic/water1.m: Initial revision + +2002-11-14 12:02 yozhik + + * BNT/inference/: online/@hmm_2TBN_inf_engine/marginal_nodes.m, + dynamic/@hmm_inf_engine/marginal_nodes.m, + online/@hmm_2TBN_inf_engine/hmm_2TBN_inf_engine.m, + dynamic/@hmm_inf_engine/hmm_inf_engine.m, + dynamic/@hmm_inf_engine/marginal_family.m, + online/@hmm_2TBN_inf_engine/marginal_family.m: Initial import of + code base from Kevin Murphy. + +2002-11-14 12:02 yozhik + + * BNT/inference/: online/@hmm_2TBN_inf_engine/marginal_nodes.m, + dynamic/@hmm_inf_engine/marginal_nodes.m, + online/@hmm_2TBN_inf_engine/hmm_2TBN_inf_engine.m, + dynamic/@hmm_inf_engine/hmm_inf_engine.m, + dynamic/@hmm_inf_engine/marginal_family.m, + online/@hmm_2TBN_inf_engine/marginal_family.m: Initial revision + +2002-11-14 08:31 yozhik + + * BNT/inference/: + online/@jtree_2TBN_inf_engine/jtree_2TBN_inf_engine.m, + dynamic/@jtree_dbn_inf_engine/jtree_dbn_inf_engine.m: Initial + import of code base from Kevin Murphy. + +2002-11-14 08:31 yozhik + + * BNT/inference/: + online/@jtree_2TBN_inf_engine/jtree_2TBN_inf_engine.m, + dynamic/@jtree_dbn_inf_engine/jtree_dbn_inf_engine.m: Initial + revision + +2002-11-13 17:01 yozhik + + * BNT/examples/: static/qmr2.m, dynamic/arhmm1.m: Initial import of + code base from Kevin Murphy. + +2002-11-13 17:01 yozhik + + * BNT/examples/: static/qmr2.m, dynamic/arhmm1.m: Initial revision + +2002-11-03 08:44 yozhik + + * BNT/examples/static/Models/mk_alarm_bnet.m: Initial import of + code base from Kevin Murphy. + +2002-11-03 08:44 yozhik + + * BNT/examples/static/Models/mk_alarm_bnet.m: Initial revision + +2002-11-01 16:32 yozhik + + * Kalman/kalman_forward_backward.m: Initial import of code base + from Kevin Murphy. + +2002-11-01 16:32 yozhik + + * Kalman/kalman_forward_backward.m: Initial revision + +2002-10-23 08:17 yozhik + + * Kalman/learning_demo.m: Initial import of code base from Kevin + Murphy. + +2002-10-23 08:17 yozhik + + * Kalman/learning_demo.m: Initial revision + +2002-10-18 13:05 yozhik + + * BNT/inference/static/@pearl_inf_engine/marginal_family.m: Initial + import of code base from Kevin Murphy. + +2002-10-18 13:05 yozhik + + * BNT/inference/static/@pearl_inf_engine/marginal_family.m: Initial + revision + +2002-10-10 16:45 yozhik + + * BNT/examples/dynamic/jtree_clq_test2.m: Initial import of code + base from Kevin Murphy. + +2002-10-10 16:45 yozhik + + * BNT/examples/dynamic/jtree_clq_test2.m: Initial revision + +2002-10-10 16:14 yozhik + + * BNT/examples/dynamic/: mk_mildew_dbn.m, mk_uffe_dbn.m: Initial + import of code base from Kevin Murphy. + +2002-10-10 16:14 yozhik + + * BNT/examples/dynamic/: mk_mildew_dbn.m, mk_uffe_dbn.m: Initial + revision + +2002-10-09 13:36 yozhik + + * BNT/examples/dynamic/mk_ps_from_clqs.m: Initial import of code + base from Kevin Murphy. + +2002-10-09 13:36 yozhik + + * BNT/examples/dynamic/mk_ps_from_clqs.m: Initial revision + +2002-10-07 06:26 yozhik + + * BNT/CPDs/@deterministic_CPD/deterministic_CPD.m: Initial import + of code base from Kevin Murphy. + +2002-10-07 06:26 yozhik + + * BNT/CPDs/@deterministic_CPD/deterministic_CPD.m: Initial revision + +2002-10-02 08:39 yozhik + + * BNT/potentials/Tables/marg_tableC.c: Initial import of code base + from Kevin Murphy. + +2002-10-02 08:39 yozhik + + * BNT/potentials/Tables/marg_tableC.c: Initial revision + +2002-10-02 08:28 yozhik + + * BNT/potentials/Tables/: mult_by_tableM.m, mult_by_table2.m: + Initial import of code base from Kevin Murphy. + +2002-10-02 08:28 yozhik + + * BNT/potentials/Tables/: mult_by_tableM.m, mult_by_table2.m: + Initial revision + +2002-10-01 14:33 yozhik + + * BNT/potentials/Tables/mult_by_tableC.c: Initial import of code + base from Kevin Murphy. + +2002-10-01 14:33 yozhik + + * BNT/potentials/Tables/mult_by_tableC.c: Initial revision + +2002-10-01 14:23 yozhik + + * BNT/potentials/Tables/mult_by_table.c: Initial import of code + base from Kevin Murphy. + +2002-10-01 14:23 yozhik + + * BNT/potentials/Tables/mult_by_table.c: Initial revision + +2002-10-01 14:20 yozhik + + * BNT/potentials/Tables/repmat_and_mult.c: Initial import of code + base from Kevin Murphy. + +2002-10-01 14:20 yozhik + + * BNT/potentials/Tables/repmat_and_mult.c: Initial revision + +2002-10-01 12:04 yozhik + + * BNT/potentials/@dpot/dpot.m: Initial import of code base from + Kevin Murphy. + +2002-10-01 12:04 yozhik + + * BNT/potentials/@dpot/dpot.m: Initial revision + +2002-10-01 11:21 yozhik + + * BNT/examples/static/Belprop/belprop_polytree_discrete.m: Initial + import of code base from Kevin Murphy. + +2002-10-01 11:21 yozhik + + * BNT/examples/static/Belprop/belprop_polytree_discrete.m: Initial + revision + +2002-10-01 11:16 yozhik + + * BNT/examples/static/cmp_inference_static.m: Initial import of + code base from Kevin Murphy. + +2002-10-01 11:16 yozhik + + * BNT/examples/static/cmp_inference_static.m: Initial revision + +2002-10-01 10:39 yozhik + + * BNT/potentials/Tables/marg_tableM.m: Initial import of code base + from Kevin Murphy. + +2002-10-01 10:39 yozhik + + * BNT/potentials/Tables/marg_tableM.m: Initial revision + +2002-09-29 03:21 yozhik + + * BNT/potentials/Tables/mult_by_table_global.m: Initial import of + code base from Kevin Murphy. + +2002-09-29 03:21 yozhik + + * BNT/potentials/Tables/mult_by_table_global.m: Initial revision + +2002-09-26 01:39 yozhik + + * BNT/learning/learn_struct_K2.m: Initial import of code base from + Kevin Murphy. + +2002-09-26 01:39 yozhik + + * BNT/learning/learn_struct_K2.m: Initial revision + +2002-09-24 15:43 yozhik + + * BNT/: CPDs/@hhmm2Q_CPD/update_ess.m, + CPDs/@hhmm2Q_CPD/maximize_params.m, + examples/dynamic/HHMM/Map/disp_map_hhmm.m: Initial import of code + base from Kevin Murphy. + +2002-09-24 15:43 yozhik + + * BNT/: CPDs/@hhmm2Q_CPD/update_ess.m, + CPDs/@hhmm2Q_CPD/maximize_params.m, + examples/dynamic/HHMM/Map/disp_map_hhmm.m: Initial revision + +2002-09-24 15:34 yozhik + + * BNT/CPDs/@hhmm2Q_CPD/: hhmm2Q_CPD.m, reset_ess.m: Initial import + of code base from Kevin Murphy. + +2002-09-24 15:34 yozhik + + * BNT/CPDs/@hhmm2Q_CPD/: hhmm2Q_CPD.m, reset_ess.m: Initial + revision + +2002-09-24 15:13 yozhik + + * BNT/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m: Initial import + of code base from Kevin Murphy. + +2002-09-24 15:13 yozhik + + * BNT/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m: Initial revision + +2002-09-24 06:10 yozhik + + * BNT/CPDs/@hhmmQ_CPD/maximize_params.m: Initial import of code + base from Kevin Murphy. + +2002-09-24 06:10 yozhik + + * BNT/CPDs/@hhmmQ_CPD/maximize_params.m: Initial revision + +2002-09-24 06:02 yozhik + + * BNT/examples/dynamic/HHMM/Map/sample_from_map.m: Initial import + of code base from Kevin Murphy. + +2002-09-24 06:02 yozhik + + * BNT/examples/dynamic/HHMM/Map/sample_from_map.m: Initial revision + +2002-09-24 05:46 yozhik + + * BNT/CPDs/@hhmm2Q_CPD/CPD_to_CPT.m: Initial import of code base + from Kevin Murphy. + +2002-09-24 05:46 yozhik + + * BNT/CPDs/@hhmm2Q_CPD/CPD_to_CPT.m: Initial revision + +2002-09-24 03:49 yozhik + + * BNT/examples/dynamic/HHMM/Map/mk_map_hhmm.m: Initial import of + code base from Kevin Murphy. + +2002-09-24 03:49 yozhik + + * BNT/examples/dynamic/HHMM/Map/mk_map_hhmm.m: Initial revision + +2002-09-24 00:02 yozhik + + * BNT/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m: Initial import + of code base from Kevin Murphy. + +2002-09-24 00:02 yozhik + + * BNT/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m: Initial revision + +2002-09-23 21:19 yozhik + + * BNT/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m: Initial import of code base from + Kevin Murphy. + +2002-09-23 21:19 yozhik + + * BNT/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m: Initial revision + +2002-09-23 19:58 yozhik + + * BNT/CPDs/@hhmmQ_CPD/update_CPT.m: Initial import of code base + from Kevin Murphy. + +2002-09-23 19:58 yozhik + + * BNT/CPDs/@hhmmQ_CPD/update_CPT.m: Initial revision + +2002-09-23 19:30 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_CPT.m: Initial import of code base + from Kevin Murphy. + +2002-09-23 19:30 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_CPT.m: Initial revision + +2002-09-21 14:37 yozhik + + * BNT/examples/dynamic/HHMM/abcd_hhmm.m: Initial import of code + base from Kevin Murphy. + +2002-09-21 14:37 yozhik + + * BNT/examples/dynamic/HHMM/abcd_hhmm.m: Initial revision + +2002-09-21 13:58 yozhik + + * BNT/examples/dynamic/HHMM/mk_hhmm.m: Initial import of code base + from Kevin Murphy. + +2002-09-21 13:58 yozhik + + * BNT/examples/dynamic/HHMM/mk_hhmm.m: Initial revision + +2002-09-10 10:44 yozhik + + * BNT/CPDs/@gaussian_CPD/log_prob_node.m: Initial import of code + base from Kevin Murphy. + +2002-09-10 10:44 yozhik + + * BNT/CPDs/@gaussian_CPD/log_prob_node.m: Initial revision + +2002-07-28 16:09 yozhik + + * BNT/learning/: learn_struct_pdag_pc_constrain.m, CovMat.m: + Initial import of code base from Kevin Murphy. + +2002-07-28 16:09 yozhik + + * BNT/learning/: learn_struct_pdag_pc_constrain.m, CovMat.m: + Initial revision + +2002-07-24 07:48 yozhik + + * BNT/general/hodbn_to_bnet.m: Initial import of code base from + Kevin Murphy. + +2002-07-24 07:48 yozhik + + * BNT/general/hodbn_to_bnet.m: Initial revision + +2002-07-23 06:17 yozhik + + * BNT/general/mk_higher_order_dbn.m: Initial import of code base + from Kevin Murphy. + +2002-07-23 06:17 yozhik + + * BNT/general/mk_higher_order_dbn.m: Initial revision + +2002-07-20 18:25 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/set_fields.m: Initial + import of code base from Kevin Murphy. + +2002-07-20 18:25 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/set_fields.m: Initial + revision + +2002-07-20 17:32 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/back_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-07-20 17:32 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/back_mpe.m: Initial + revision + +2002-07-02 15:56 yozhik + + * BNT/examples/dynamic/HHMM/Motif/learn_motif_hhmm.m: Initial + import of code base from Kevin Murphy. + +2002-07-02 15:56 yozhik + + * BNT/examples/dynamic/HHMM/Motif/learn_motif_hhmm.m: Initial + revision + +2002-06-27 13:34 yozhik + + * BNT/general/add_ev_to_dmarginal.m: Initial import of code base + from Kevin Murphy. + +2002-06-27 13:34 yozhik + + * BNT/general/add_ev_to_dmarginal.m: Initial revision + +2002-06-24 16:54 yozhik + + * BNT/CPDs/@hhmmF_CPD/update_ess.m: Initial import of code base + from Kevin Murphy. + +2002-06-24 16:54 yozhik + + * BNT/CPDs/@hhmmF_CPD/update_ess.m: Initial revision + +2002-06-24 16:38 yozhik + + * BNT/CPDs/@hhmmF_CPD/hhmmF_CPD.m: Initial import of code base from + Kevin Murphy. + +2002-06-24 16:38 yozhik + + * BNT/CPDs/@hhmmF_CPD/hhmmF_CPD.m: Initial revision + +2002-06-24 15:45 yozhik + + * BNT/CPDs/@hhmmF_CPD/update_CPT.m: Initial import of code base + from Kevin Murphy. + +2002-06-24 15:45 yozhik + + * BNT/CPDs/@hhmmF_CPD/update_CPT.m: Initial revision + +2002-06-24 15:35 yozhik + + * BNT/CPDs/@hhmmF_CPD/Old/: hhmmF_CPD.m, log_prior.m, + maximize_params.m, reset_ess.m, update_CPT.m, update_ess.m: + Initial import of code base from Kevin Murphy. + +2002-06-24 15:35 yozhik + + * BNT/CPDs/@hhmmF_CPD/Old/: hhmmF_CPD.m, log_prior.m, + maximize_params.m, reset_ess.m, update_CPT.m, update_ess.m: + Initial revision + +2002-06-24 15:23 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess4.m: Initial import of code + base from Kevin Murphy. + +2002-06-24 15:23 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess4.m: Initial revision + +2002-06-24 15:08 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess3.m: Initial import of code + base from Kevin Murphy. + +2002-06-24 15:08 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess3.m: Initial revision + +2002-06-24 14:20 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess2.m: Initial import of code + base from Kevin Murphy. + +2002-06-24 14:20 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess2.m: Initial revision + +2002-06-24 11:56 yozhik + + * BNT/: general/mk_fgraph_given_ev.m, + CPDs/mk_isolated_tabular_CPD.m: Initial import of code base from + Kevin Murphy. + +2002-06-24 11:56 yozhik + + * BNT/: general/mk_fgraph_given_ev.m, + CPDs/mk_isolated_tabular_CPD.m: Initial revision + +2002-06-24 11:19 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/: hhmmQ_CPD.m, log_prior.m, + maximize_params.m, reset_ess.m, update_ess.m: Initial import of + code base from Kevin Murphy. + +2002-06-24 11:19 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/: hhmmQ_CPD.m, log_prior.m, + maximize_params.m, reset_ess.m, update_ess.m: Initial revision + +2002-06-20 13:30 yozhik + + * BNT/examples/dynamic/mildew1.m: Initial import of code base from + Kevin Murphy. + +2002-06-20 13:30 yozhik + + * BNT/examples/dynamic/mildew1.m: Initial revision + +2002-06-19 17:18 yozhik + + * BNT/: inference/dynamic/@hmm_inf_engine/find_mpe.m, + examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m: Initial + import of code base from Kevin Murphy. + +2002-06-19 17:18 yozhik + + * BNT/: inference/dynamic/@hmm_inf_engine/find_mpe.m, + examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m: Initial + revision + +2002-06-19 17:03 yozhik + + * BNT/examples/static/fgraph/fg1.m: Initial import of code base + from Kevin Murphy. + +2002-06-19 17:03 yozhik + + * BNT/examples/static/fgraph/fg1.m: Initial revision + +2002-06-19 16:59 yozhik + + * BNT/: examples/static/softev1.m, + inference/static/@belprop_fg_inf_engine/find_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-06-19 16:59 yozhik + + * BNT/: examples/static/softev1.m, + inference/static/@belprop_fg_inf_engine/find_mpe.m: Initial + revision + +2002-06-19 15:11 yozhik + + * BNT/inference/static/@var_elim_inf_engine/find_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-06-19 15:11 yozhik + + * BNT/inference/static/@var_elim_inf_engine/find_mpe.m: Initial + revision + +2002-06-19 15:08 yozhik + + * BNT/: inference/static/@belprop_inf_engine/find_mpe.m, + examples/static/mpe1.m, examples/static/mpe2.m: Initial import of + code base from Kevin Murphy. + +2002-06-19 15:08 yozhik + + * BNT/: inference/static/@belprop_inf_engine/find_mpe.m, + examples/static/mpe1.m, examples/static/mpe2.m: Initial revision + +2002-06-19 15:04 yozhik + + * BNT/inference/static/@var_elim_inf_engine/: + var_elim_inf_engine.m, enter_evidence.m: Initial import of code + base from Kevin Murphy. + +2002-06-19 15:04 yozhik + + * BNT/inference/static/@var_elim_inf_engine/: + var_elim_inf_engine.m, enter_evidence.m: Initial revision + +2002-06-19 14:56 yozhik + + * BNT/inference/static/@global_joint_inf_engine/find_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-06-19 14:56 yozhik + + * BNT/inference/static/@global_joint_inf_engine/find_mpe.m: Initial + revision + +2002-06-17 16:49 yozhik + + * BNT/inference/online/: @jtree_2TBN_inf_engine/back1_mpe.m, + @smoother_engine/find_mpe.m: Initial import of code base from + Kevin Murphy. + +2002-06-17 16:49 yozhik + + * BNT/inference/online/: @jtree_2TBN_inf_engine/back1_mpe.m, + @smoother_engine/find_mpe.m: Initial revision + +2002-06-17 16:46 yozhik + + * BNT/inference/online/: @jtree_2TBN_inf_engine/fwd.m, + @jtree_2TBN_inf_engine/fwd1.m, @smoother_engine/enter_evidence.m: + Initial import of code base from Kevin Murphy. + +2002-06-17 16:46 yozhik + + * BNT/inference/online/: @jtree_2TBN_inf_engine/fwd.m, + @jtree_2TBN_inf_engine/fwd1.m, @smoother_engine/enter_evidence.m: + Initial revision + +2002-06-17 16:38 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/backT_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-06-17 16:38 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/backT_mpe.m: Initial + revision + +2002-06-17 16:34 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/: back.m, backT.m, + back1.m: Initial import of code base from Kevin Murphy. + +2002-06-17 16:34 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/: back.m, backT.m, + back1.m: Initial revision + +2002-06-17 16:14 yozhik + + * BNT/inference/static/@jtree_inf_engine/: find_mpe.m, + find_max_config.m: Initial import of code base from Kevin Murphy. + +2002-06-17 16:14 yozhik + + * BNT/inference/static/@jtree_inf_engine/: find_mpe.m, + find_max_config.m: Initial revision + +2002-06-17 14:58 yozhik + + * BNT/general/Old/calc_mpe.m: Initial import of code base from + Kevin Murphy. + +2002-06-17 14:58 yozhik + + * BNT/general/Old/calc_mpe.m: Initial revision + +2002-06-17 13:59 yozhik + + * BNT/inference/static/@jtree_inf_engine/: enter_evidence.m, + distribute_evidence.m: Initial import of code base from Kevin + Murphy. + +2002-06-17 13:59 yozhik + + * BNT/inference/static/@jtree_inf_engine/: enter_evidence.m, + distribute_evidence.m: Initial revision + +2002-06-17 13:29 yozhik + + * BNT/inference/static/@jtree_mnet_inf_engine/: find_mpe.m, + enter_evidence.m: Initial import of code base from Kevin Murphy. + +2002-06-17 13:29 yozhik + + * BNT/inference/static/@jtree_mnet_inf_engine/: find_mpe.m, + enter_evidence.m: Initial revision + +2002-06-16 13:01 yozhik + + * BNT/general/is_mnet.m: Initial import of code base from Kevin + Murphy. + +2002-06-16 13:01 yozhik + + * BNT/general/is_mnet.m: Initial revision + +2002-06-16 12:52 yozhik + + * BNT/general/mk_mnet.m: Initial import of code base from Kevin + Murphy. + +2002-06-16 12:52 yozhik + + * BNT/general/mk_mnet.m: Initial revision + +2002-06-16 12:34 yozhik + + * BNT/inference/static/@jtree_inf_engine/init_pot.m: Initial import + of code base from Kevin Murphy. + +2002-06-16 12:34 yozhik + + * BNT/inference/static/@jtree_inf_engine/init_pot.m: Initial + revision + +2002-06-16 12:06 yozhik + + * BNT/potentials/@dpot/find_most_prob_entry.m: Initial import of + code base from Kevin Murphy. + +2002-06-16 12:06 yozhik + + * BNT/potentials/@dpot/find_most_prob_entry.m: Initial revision + +2002-05-31 03:25 yozhik + + * BNT/general/unroll_higher_order_topology.m: Initial import of + code base from Kevin Murphy. + +2002-05-31 03:25 yozhik + + * BNT/general/unroll_higher_order_topology.m: Initial revision + +2002-05-29 08:59 yozhik + + * BNT/@assocarray/assocarray.m, + BNT/CPDs/@boolean_CPD/boolean_CPD.m, + BNT/CPDs/@discrete_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@discrete_CPD/CPD_to_pi.m, + BNT/CPDs/@discrete_CPD/CPD_to_scgpot.m, + BNT/CPDs/@discrete_CPD/README, + BNT/CPDs/@discrete_CPD/convert_CPD_to_table_hidden_ps.m, + BNT/CPDs/@discrete_CPD/convert_obs_CPD_to_table.m, + BNT/CPDs/@discrete_CPD/convert_to_sparse_table.c, + BNT/CPDs/@discrete_CPD/convert_to_table.m, + BNT/CPDs/@discrete_CPD/discrete_CPD.m, + BNT/CPDs/@discrete_CPD/dom_sizes.m, + BNT/CPDs/@discrete_CPD/log_prob_node.m, + BNT/CPDs/@discrete_CPD/prob_node.m, + BNT/CPDs/@discrete_CPD/sample_node.m, + BNT/CPDs/@discrete_CPD/Old/convert_to_pot.m, + BNT/CPDs/@discrete_CPD/Old/convert_to_table.m, + BNT/CPDs/@discrete_CPD/Old/prob_CPD.m, + BNT/CPDs/@discrete_CPD/Old/prob_node.m, + BNT/CPDs/@discrete_CPD/private/prod_CPT_and_pi_msgs.m, + BNT/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@gaussian_CPD/CPD_to_pi.m, + BNT/CPDs/@gaussian_CPD/CPD_to_scgpot.m, + BNT/CPDs/@gaussian_CPD/adjustable_CPD.m, + BNT/CPDs/@gaussian_CPD/convert_CPD_to_table_hidden_ps.m, + BNT/CPDs/@gaussian_CPD/display.m, + BNT/CPDs/@gaussian_CPD/get_field.m, + BNT/CPDs/@gaussian_CPD/reset_ess.m, + BNT/CPDs/@gaussian_CPD/sample_node.m, + BNT/CPDs/@gaussian_CPD/set_fields.m, + BNT/CPDs/@gaussian_CPD/Old/CPD_to_lambda_msg.m, + BNT/CPDs/@gaussian_CPD/Old/gaussian_CPD.m, + BNT/CPDs/@gaussian_CPD/Old/log_prob_node.m, + BNT/CPDs/@gaussian_CPD/Old/update_ess.m, + BNT/CPDs/@gaussian_CPD/Old/update_tied_ess.m, + BNT/CPDs/@gaussian_CPD/private/CPD_to_linear_gaussian.m, + BNT/CPDs/@generic_CPD/README, + BNT/CPDs/@generic_CPD/adjustable_CPD.m, + BNT/CPDs/@generic_CPD/display.m, + BNT/CPDs/@generic_CPD/generic_CPD.m, + BNT/CPDs/@generic_CPD/log_prior.m, + BNT/CPDs/@generic_CPD/set_clamped.m, + BNT/CPDs/@generic_CPD/Old/BIC_score_CPD.m, + BNT/CPDs/@generic_CPD/Old/CPD_to_dpots.m, + BNT/CPDs/@gmux_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@gmux_CPD/convert_to_pot.m, + BNT/CPDs/@gmux_CPD/CPD_to_pi.m, BNT/CPDs/@gmux_CPD/display.m, + BNT/CPDs/@gmux_CPD/gmux_CPD.m, BNT/CPDs/@gmux_CPD/sample_node.m, + BNT/CPDs/@gmux_CPD/Old/gmux_CPD.m, + BNT/CPDs/@hhmmF_CPD/log_prior.m, + BNT/CPDs/@hhmmF_CPD/maximize_params.m, + BNT/CPDs/@hhmmF_CPD/reset_ess.m, BNT/CPDs/@hhmmQ_CPD/log_prior.m, + BNT/CPDs/@hhmmQ_CPD/reset_ess.m, + BNT/CPDs/@mlp_CPD/convert_to_table.m, + BNT/CPDs/@mlp_CPD/maximize_params.m, BNT/CPDs/@mlp_CPD/mlp_CPD.m, + BNT/CPDs/@mlp_CPD/reset_ess.m, BNT/CPDs/@mlp_CPD/update_ess.m, + BNT/CPDs/@noisyor_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@noisyor_CPD/CPD_to_pi.m, + BNT/CPDs/@noisyor_CPD/noisyor_CPD.m, + BNT/CPDs/@noisyor_CPD/private/sum_prod_CPD_and_pi_msgs.m, + BNT/CPDs/@root_CPD/CPD_to_pi.m, + BNT/CPDs/@root_CPD/convert_to_pot.m, + BNT/CPDs/@root_CPD/log_marg_prob_node.m, + BNT/CPDs/@root_CPD/log_prob_node.m, + BNT/CPDs/@root_CPD/root_CPD.m, BNT/CPDs/@root_CPD/sample_node.m, + BNT/CPDs/@root_CPD/Old/CPD_to_CPT.m, + BNT/CPDs/@softmax_CPD/convert_to_pot.m, + BNT/CPDs/@softmax_CPD/display.m, + BNT/CPDs/@softmax_CPD/get_field.m, + BNT/CPDs/@softmax_CPD/maximize_params.m, + BNT/CPDs/@softmax_CPD/reset_ess.m, + BNT/CPDs/@softmax_CPD/sample_node.m, + BNT/CPDs/@softmax_CPD/set_fields.m, + BNT/CPDs/@softmax_CPD/update_ess.m, + BNT/CPDs/@softmax_CPD/private/extract_params.m, + BNT/CPDs/@tabular_CPD/CPD_to_CPT.m, + BNT/CPDs/@tabular_CPD/bayes_update_params.m, + BNT/CPDs/@tabular_CPD/log_nextcase_prob_node.m, + BNT/CPDs/@tabular_CPD/log_prior.m, + BNT/CPDs/@tabular_CPD/reset_ess.m, + BNT/CPDs/@tabular_CPD/update_ess.m, + BNT/CPDs/@tabular_CPD/update_ess_simple.m, + BNT/CPDs/@tabular_CPD/Old/BIC_score_CPD.m, + BNT/CPDs/@tabular_CPD/Old/bayesian_score_CPD.m, + BNT/CPDs/@tabular_CPD/Old/log_marg_prob_node_case.m, + BNT/CPDs/@tabular_CPD/Old/mult_CPD_and_pi_msgs.m, + BNT/CPDs/@tabular_CPD/Old/prob_CPT.m, + BNT/CPDs/@tabular_CPD/Old/prob_node.m, + BNT/CPDs/@tabular_CPD/Old/sample_node.m, + BNT/CPDs/@tabular_CPD/Old/sample_node_single_case.m, + BNT/CPDs/@tabular_CPD/Old/tabular_CPD.m, + BNT/CPDs/@tabular_CPD/Old/update_params.m, + BNT/CPDs/@tabular_decision_node/CPD_to_CPT.m, + BNT/CPDs/@tabular_decision_node/display.m, + BNT/CPDs/@tabular_decision_node/get_field.m, + BNT/CPDs/@tabular_decision_node/set_fields.m, + BNT/CPDs/@tabular_decision_node/tabular_decision_node.m, + BNT/CPDs/@tabular_decision_node/Old/tabular_decision_node.m, + BNT/CPDs/@tabular_kernel/convert_to_pot.m, + BNT/CPDs/@tabular_kernel/convert_to_table.m, + BNT/CPDs/@tabular_kernel/get_field.m, + BNT/CPDs/@tabular_kernel/set_fields.m, + BNT/CPDs/@tabular_kernel/tabular_kernel.m, + BNT/CPDs/@tabular_kernel/Old/tabular_kernel.m, + BNT/CPDs/@tabular_utility_node/convert_to_pot.m, + BNT/CPDs/@tabular_utility_node/display.m, + BNT/CPDs/@tabular_utility_node/tabular_utility_node.m, + BNT/CPDs/@tree_CPD/display.m, + BNT/CPDs/@tree_CPD/evaluate_tree_performance.m, + BNT/CPDs/@tree_CPD/get_field.m, + BNT/CPDs/@tree_CPD/learn_params.m, BNT/CPDs/@tree_CPD/readme.txt, + BNT/CPDs/@tree_CPD/set_fields.m, BNT/CPDs/@tree_CPD/tree_CPD.m, + BNT/CPDs/Old/@linear_gaussian_CPD/linear_gaussian_CPD.m, + BNT/CPDs/Old/@linear_gaussian_CPD/log_marg_prob_node.m, + BNT/CPDs/Old/@linear_gaussian_CPD/update_params_complete.m, + BNT/CPDs/Old/@root_gaussian_CPD/log_marg_prob_node.m, + BNT/CPDs/Old/@root_gaussian_CPD/root_gaussian_CPD.m, + BNT/CPDs/Old/@root_gaussian_CPD/update_params_complete.m, + BNT/CPDs/Old/@tabular_chance_node/CPD_to_upot.m, + BNT/CPDs/Old/@tabular_chance_node/tabular_chance_node.m, + BNT/examples/dynamic/bat1.m, BNT/examples/dynamic/bkff1.m, + BNT/examples/dynamic/chmm1.m, + BNT/examples/dynamic/cmp_inference_dbn.m, + BNT/examples/dynamic/cmp_learning_dbn.m, + BNT/examples/dynamic/cmp_online_inference.m, + BNT/examples/dynamic/fhmm_infer.m, + BNT/examples/dynamic/filter_test1.m, + BNT/examples/dynamic/kalman1.m, + BNT/examples/dynamic/kjaerulff1.m, + BNT/examples/dynamic/loopy_dbn1.m, + BNT/examples/dynamic/mk_collage_from_clqs.m, + BNT/examples/dynamic/mk_fhmm.m, BNT/examples/dynamic/reveal1.m, + BNT/examples/dynamic/scg_dbn.m, + BNT/examples/dynamic/skf_data_assoc_gmux.m, + BNT/examples/dynamic/HHMM/add_hhmm_end_state.m, + BNT/examples/dynamic/HHMM/hhmm_jtree_clqs.m, + BNT/examples/dynamic/HHMM/mk_hhmm_topo.m, + BNT/examples/dynamic/HHMM/mk_hhmm_topo_F1.m, + BNT/examples/dynamic/HHMM/pretty_print_hhmm_parse.m, + BNT/examples/dynamic/HHMM/Motif/fixed_args_mk_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Motif/mk_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Motif/sample_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Old/mk_abcd_hhmm.m, + BNT/examples/dynamic/HHMM/Old/mk_arrow_alpha_hhmm3.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm2.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm3.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm3_args.m, + BNT/examples/dynamic/HHMM/Old/motif_hhmm.m, + BNT/examples/dynamic/HHMM/Old/remove_hhmm_end_state.m, + BNT/examples/dynamic/HHMM/Square/get_square_data.m, + BNT/examples/dynamic/HHMM/Square/hhmm_inference.m, + BNT/examples/dynamic/HHMM/Square/is_F2_true_D3.m, + BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m, + BNT/examples/dynamic/HHMM/Square/mk_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/plot_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_cts.m, + BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_discrete.m, + BNT/examples/dynamic/HHMM/Square/square4.mat, + BNT/examples/dynamic/HHMM/Square/square4_cases.mat, + BNT/examples/dynamic/HHMM/Square/test_square_fig.m, + BNT/examples/dynamic/HHMM/Square/test_square_fig.mat, + BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m, + BNT/examples/dynamic/Old/chmm1.m, + BNT/examples/dynamic/Old/cmp_inference.m, + BNT/examples/dynamic/Old/kalman1.m, + BNT/examples/dynamic/Old/old.water1.m, + BNT/examples/dynamic/Old/online1.m, + BNT/examples/dynamic/Old/online2.m, + BNT/examples/dynamic/Old/scg_dbn.m, + BNT/examples/dynamic/SLAM/mk_gmux_robot_dbn.m, + BNT/examples/dynamic/SLAM/mk_linear_slam.m, + BNT/examples/dynamic/SLAM/slam_kf.m, + BNT/examples/dynamic/SLAM/slam_offline_loopy.m, + BNT/examples/dynamic/SLAM/slam_partial_kf.m, + BNT/examples/dynamic/SLAM/slam_stationary_loopy.m, + BNT/examples/dynamic/SLAM/Old/offline_loopy_slam.m, + BNT/examples/dynamic/SLAM/Old/paskin1.m, + BNT/examples/dynamic/SLAM/Old/skf_data_assoc_gmux2.m, + BNT/examples/dynamic/SLAM/Old/slam_kf.m, + BNT/examples/limids/id1.m, BNT/examples/limids/pigs1.m, + BNT/examples/static/cg1.m, BNT/examples/static/cg2.m, + BNT/examples/static/discrete2.m, BNT/examples/static/discrete3.m, + BNT/examples/static/fa1.m, BNT/examples/static/gaussian1.m, + BNT/examples/static/gibbs_test1.m, BNT/examples/static/lw1.m, + BNT/examples/static/mfa1.m, BNT/examples/static/mixexp1.m, + BNT/examples/static/mixexp2.m, BNT/examples/static/mixexp3.m, + BNT/examples/static/mog1.m, BNT/examples/static/qmr1.m, + BNT/examples/static/sample1.m, BNT/examples/static/softmax1.m, + BNT/examples/static/Belprop/belprop_loop1_discrete.m, + BNT/examples/static/Belprop/belprop_loop1_gauss.m, + BNT/examples/static/Belprop/belprop_loopy_cg.m, + BNT/examples/static/Belprop/belprop_loopy_discrete.m, + BNT/examples/static/Belprop/belprop_loopy_gauss.m, + BNT/examples/static/Belprop/belprop_polytree_cg.m, + BNT/examples/static/Belprop/belprop_polytree_gauss.m, + BNT/examples/static/Belprop/bp1.m, + BNT/examples/static/Belprop/gmux1.m, + BNT/examples/static/Brutti/Belief_IOhmm.m, + BNT/examples/static/Brutti/Belief_hmdt.m, + BNT/examples/static/Brutti/Belief_hme.m, + BNT/examples/static/Brutti/Sigmoid_Belief.m, + BNT/examples/static/HME/HMEforMatlab.jpg, + BNT/examples/static/HME/README, BNT/examples/static/HME/fhme.m, + BNT/examples/static/HME/gen_data.m, + BNT/examples/static/HME/hme_class_plot.m, + BNT/examples/static/HME/hme_reg_plot.m, + BNT/examples/static/HME/hme_topobuilder.m, + BNT/examples/static/HME/test_data_class.mat, + BNT/examples/static/HME/test_data_class2.mat, + BNT/examples/static/HME/test_data_reg.mat, + BNT/examples/static/HME/train_data_class.mat, + BNT/examples/static/HME/train_data_reg.mat, + BNT/examples/static/Misc/mixexp_data.txt, + BNT/examples/static/Misc/mixexp_graddesc.m, + BNT/examples/static/Misc/mixexp_plot.m, + BNT/examples/static/Misc/sprinkler.bif, + BNT/examples/static/Models/mk_cancer_bnet.m, + BNT/examples/static/Models/mk_car_bnet.m, + BNT/examples/static/Models/mk_ideker_bnet.m, + BNT/examples/static/Models/mk_incinerator_bnet.m, + BNT/examples/static/Models/mk_markov_chain_bnet.m, + BNT/examples/static/Models/mk_minimal_qmr_bnet.m, + BNT/examples/static/Models/mk_qmr_bnet.m, + BNT/examples/static/Models/mk_vstruct_bnet.m, + BNT/examples/static/Models/Old/mk_hmm_bnet.m, + BNT/examples/static/SCG/scg1.m, BNT/examples/static/SCG/scg2.m, + BNT/examples/static/SCG/scg3.m, + BNT/examples/static/SCG/scg_3node.m, + BNT/examples/static/SCG/scg_unstable.m, + BNT/examples/static/StructLearn/bic1.m, + BNT/examples/static/StructLearn/cooper_yoo.m, + BNT/examples/static/StructLearn/k2demo1.m, + BNT/examples/static/StructLearn/mcmc1.m, + BNT/examples/static/StructLearn/pc1.m, + BNT/examples/static/StructLearn/pc2.m, + BNT/examples/static/Zoubin/README, + BNT/examples/static/Zoubin/csum.m, + BNT/examples/static/Zoubin/ffa.m, + BNT/examples/static/Zoubin/mfa.m, + BNT/examples/static/Zoubin/mfa_cl.m, + BNT/examples/static/Zoubin/mfademo.m, + BNT/examples/static/Zoubin/rdiv.m, + BNT/examples/static/Zoubin/rprod.m, + BNT/examples/static/Zoubin/rsum.m, + BNT/examples/static/dtree/test_housing.m, + BNT/examples/static/dtree/test_restaurants.m, + BNT/examples/static/dtree/test_zoo1.m, + BNT/examples/static/dtree/tmp.dot, + BNT/examples/static/dtree/transform_data_into_bnt_format.m, + BNT/examples/static/fgraph/fg2.m, + BNT/examples/static/fgraph/fg3.m, + BNT/examples/static/fgraph/fg_mrf1.m, + BNT/examples/static/fgraph/fg_mrf2.m, + BNT/general/bnet_to_fgraph.m, + BNT/general/compute_fwd_interface.m, + BNT/general/compute_interface_nodes.m, + BNT/general/compute_minimal_interface.m, + BNT/general/dbn_to_bnet.m, + BNT/general/determine_elim_constraints.m, + BNT/general/do_intervention.m, BNT/general/dsep.m, + BNT/general/enumerate_scenarios.m, BNT/general/fgraph_to_bnet.m, + BNT/general/log_lik_complete.m, + BNT/general/log_marg_lik_complete.m, BNT/general/mk_bnet.m, + BNT/general/mk_fgraph.m, BNT/general/mk_limid.m, + BNT/general/mk_mutilated_samples.m, + BNT/general/mk_slice_and_half_dbn.m, + BNT/general/partition_dbn_nodes.m, + BNT/general/sample_bnet_nocell.m, BNT/general/sample_dbn.m, + BNT/general/score_bnet_complete.m, + BNT/general/unroll_dbn_topology.m, + BNT/general/Old/bnet_to_gdl_graph.m, + BNT/general/Old/calc_mpe_bucket.m, + BNT/general/Old/calc_mpe_dbn.m, + BNT/general/Old/calc_mpe_given_inf_engine.m, + BNT/general/Old/calc_mpe_global.m, + BNT/general/Old/compute_interface_nodes.m, + BNT/general/Old/mk_gdl_graph.m, GraphViz/draw_dbn.m, + GraphViz/make_layout.m, BNT/license.gpl.txt, + BNT/general/add_evidence_to_gmarginal.m, + BNT/inference/@inf_engine/bnet_from_engine.m, + BNT/inference/@inf_engine/get_field.m, + BNT/inference/@inf_engine/inf_engine.m, + BNT/inference/@inf_engine/marginal_family.m, + BNT/inference/@inf_engine/set_fields.m, + BNT/inference/@inf_engine/update_engine.m, + BNT/inference/@inf_engine/Old/marginal_family_pot.m, + BNT/inference/@inf_engine/Old/observed_nodes.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/bk_ff_hmm_inf_engine.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_init_bel.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_marginal_from_bel.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_predict_bel.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_update_bel.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_update_bel1.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/marginal_family.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/bk_ff_fb.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/combine_marginals_into_joint.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/dbn_to_hmm.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_mat.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_vec.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_vec1.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/project_joint_onto_marginals.m, + BNT/inference/dynamic/@bk_inf_engine/bk_inf_engine.m, + BNT/inference/dynamic/@bk_inf_engine/dbn_init_bel.m, + BNT/inference/dynamic/@bk_inf_engine/dbn_marginal_from_bel.m, + BNT/inference/dynamic/@bk_inf_engine/dbn_update_bel.m, + BNT/inference/dynamic/@bk_inf_engine/dbn_update_bel1.m, + BNT/inference/dynamic/@bk_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@bk_inf_engine/marginal_family.m, + BNT/inference/dynamic/@bk_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@bk_inf_engine/update_engine.m, + BNT/inference/dynamic/@ff_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@ff_inf_engine/enter_soft_evidence.m, + BNT/inference/dynamic/@ff_inf_engine/ff_inf_engine.m, + BNT/inference/dynamic/@ff_inf_engine/filter_evidence.m, + BNT/inference/dynamic/@ff_inf_engine/marginal_family.m, + BNT/inference/dynamic/@ff_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@ff_inf_engine/smooth_evidence.m, + BNT/inference/dynamic/@ff_inf_engine/Old/enter_soft_evidence.m, + BNT/inference/dynamic/@ff_inf_engine/Old/enter_soft_evidence1.m, + BNT/inference/dynamic/@ff_inf_engine/Old/marginal_family.m, + BNT/inference/dynamic/@frontier_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@frontier_inf_engine/enter_soft_evidence.m, + BNT/inference/dynamic/@frontier_inf_engine/frontier_inf_engine.m, + BNT/inference/dynamic/@frontier_inf_engine/marginal_family.m, + BNT/inference/dynamic/@frontier_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@frontier_inf_engine/set_fwdback.m, + BNT/inference/dynamic/@hmm_inf_engine/update_engine.m, + BNT/inference/dynamic/@hmm_inf_engine/Old/dhmm_inf_engine.m, + BNT/inference/dynamic/@hmm_inf_engine/Old/marginal_family.m, + BNT/inference/dynamic/@hmm_inf_engine/Old/marginal_nodes.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/marginal_family.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence1.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence2.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence3.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence4.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/marginal_nodes.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/enter_soft_evidence_nonint.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/enter_soft_evidence_trans.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine1.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine2.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/jtree_unrolled_dbn_inf_engine.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/marginal_family.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/update_engine.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/marginal_family.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/marginal_nodes.m, + BNT/inference/dynamic/@kalman_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@kalman_inf_engine/kalman_inf_engine.m, + BNT/inference/dynamic/@kalman_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@kalman_inf_engine/update_engine.m, + BNT/inference/dynamic/@kalman_inf_engine/private/dbn_to_lds.m, + BNT/inference/dynamic/@kalman_inf_engine/private/extract_params_from_gbn.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/enter_soft_ev.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/pearl_dbn_inf_engine.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/correct_smooth.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/enter_evidence.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/filter_evidence.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/filter_evidence_obj_oriented.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/smooth_evidence.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/smooth_evidence_fast.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/Old/wrong_smooth.m, + BNT/inference/dynamic/@pearl_dbn_inf_engine/private/init_pearl_dbn_ev_msgs.m, + BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/marginal_family.m, + 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BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_family.m, + BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_nodes_SS.m, + BNT/inference/static/@jtree_sparse_inf_engine/cliques_from_engine.m, + BNT/inference/static/@jtree_sparse_inf_engine/clq_containing_nodes.m, + BNT/inference/static/@jtree_sparse_inf_engine/collect_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/distribute_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/enter_evidence.m, + BNT/inference/static/@jtree_sparse_inf_engine/enter_soft_evidence.m, + BNT/inference/static/@jtree_sparse_inf_engine/init_pot.c, + BNT/inference/static/@jtree_sparse_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_sparse_inf_engine/marginal_nodes.m, + BNT/inference/static/@jtree_sparse_inf_engine/set_fields.m, + BNT/inference/static/@jtree_sparse_inf_engine/old/collect_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/distribute_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot1.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot1.m, + BNT/inference/static/@likelihood_weighting_inf_engine/enter_evidence.m, + BNT/inference/static/@likelihood_weighting_inf_engine/likelihood_weighting_inf_engine.m, + BNT/inference/static/@likelihood_weighting_inf_engine/marginal_nodes.m, + BNT/inference/static/@pearl_inf_engine/enter_evidence.m, + BNT/inference/static/@pearl_inf_engine/loopy_converged.m, + BNT/inference/static/@pearl_inf_engine/marginal_nodes.m, + BNT/inference/static/@pearl_inf_engine/private/compute_bel.m, + BNT/inference/static/@pearl_inf_engine/private/prod_lambda_msgs.m, + BNT/inference/static/@pearl_inf_engine/private/tree_protocol.m, + BNT/inference/static/@quickscore_inf_engine/enter_evidence.m, + BNT/inference/static/@quickscore_inf_engine/marginal_nodes.m, + BNT/inference/static/@quickscore_inf_engine/quickscore_inf_engine.m, + BNT/inference/static/@quickscore_inf_engine/private/C_quickscore.c, + BNT/inference/static/@quickscore_inf_engine/private/nr.h, + BNT/inference/static/@quickscore_inf_engine/private/nrutil.c, + BNT/inference/static/@quickscore_inf_engine/private/nrutil.h, + BNT/inference/static/@quickscore_inf_engine/private/quickscore.m, + BNT/learning/bayes_update_params.m, + BNT/learning/bic_score_family.m, + BNT/learning/compute_cooling_schedule.m, + BNT/learning/dirichlet_score_family.m, + BNT/learning/kpm_learn_struct_mcmc.m, + BNT/learning/learn_params_em.m, + BNT/learning/learn_struct_dbn_reveal.m, + BNT/learning/learn_struct_pdag_ic_star.m, + BNT/learning/mcmc_sample_to_hist.m, BNT/learning/mk_schedule.m, + BNT/learning/mk_tetrad_data_file.m, + BNT/learning/score_dags_old.m, HMM/dhmm_logprob_brute_force.m, + HMM/dhmm_logprob_path.m, HMM/mdp_sample.m, Kalman/AR_to_SS.m, + Kalman/SS_to_AR.m, Kalman/convert_to_lagged_form.m, + Kalman/ensure_AR.m, Kalman/eval_AR_perf.m, + Kalman/kalman_filter.m, Kalman/kalman_smoother.m, + Kalman/kalman_update.m, Kalman/learn_AR.m, + Kalman/learn_AR_diagonal.m, Kalman/learn_kalman.m, + Kalman/smooth_update.m, + BNT/general/convert_dbn_CPDs_to_tables_slow.m, + BNT/general/dispcpt.m, BNT/general/linear_gaussian_to_cpot.m, + BNT/general/partition_matrix_vec_3.m, + BNT/general/shrink_obs_dims_in_gaussian.m, + BNT/general/shrink_obs_dims_in_table.m, + BNT/potentials/CPD_to_pot.m, BNT/potentials/README, + BNT/potentials/check_for_cd_arcs.m, + BNT/potentials/determine_pot_type.m, + BNT/potentials/mk_initial_pot.m, + BNT/potentials/@cgpot/cg_can_to_mom.m, + BNT/potentials/@cgpot/cg_mom_to_can.m, + BNT/potentials/@cgpot/cgpot.m, BNT/potentials/@cgpot/display.m, + BNT/potentials/@cgpot/divide_by_pot.m, + BNT/potentials/@cgpot/domain_pot.m, + BNT/potentials/@cgpot/enter_cts_evidence_pot.m, + BNT/potentials/@cgpot/enter_discrete_evidence_pot.m, + BNT/potentials/@cgpot/marginalize_pot.m, + BNT/potentials/@cgpot/multiply_by_pot.m, + BNT/potentials/@cgpot/multiply_pots.m, + BNT/potentials/@cgpot/normalize_pot.m, + BNT/potentials/@cgpot/pot_to_marginal.m, + BNT/potentials/@cgpot/Old/normalize_pot.m, + BNT/potentials/@cgpot/Old/simple_marginalize_pot.m, + BNT/potentials/@cpot/cpot.m, BNT/potentials/@cpot/cpot_to_mpot.m, + BNT/potentials/@cpot/display.m, + BNT/potentials/@cpot/divide_by_pot.m, + BNT/potentials/@cpot/domain_pot.m, + BNT/potentials/@cpot/enter_cts_evidence_pot.m, + BNT/potentials/@cpot/marginalize_pot.m, + BNT/potentials/@cpot/multiply_by_pot.m, + BNT/potentials/@cpot/multiply_pots.m, + BNT/potentials/@cpot/normalize_pot.m, + BNT/potentials/@cpot/pot_to_marginal.m, + BNT/potentials/@cpot/rescale_pot.m, + BNT/potentials/@cpot/set_domain_pot.m, + BNT/potentials/@cpot/Old/cpot_to_mpot.m, + BNT/potentials/@cpot/Old/normalize_pot.convert.m, + BNT/potentials/@dpot/approxeq_pot.m, + BNT/potentials/@dpot/display.m, + BNT/potentials/@dpot/domain_pot.m, + BNT/potentials/@dpot/dpot_to_table.m, + BNT/potentials/@dpot/get_fields.m, + BNT/potentials/@dpot/multiply_pots.m, + BNT/potentials/@dpot/pot_to_marginal.m, + BNT/potentials/@dpot/set_domain_pot.m, + BNT/potentials/@mpot/display.m, + BNT/potentials/@mpot/marginalize_pot.m, + BNT/potentials/@mpot/mpot.m, BNT/potentials/@mpot/mpot_to_cpot.m, + BNT/potentials/@mpot/normalize_pot.m, + BNT/potentials/@mpot/pot_to_marginal.m, + BNT/potentials/@mpot/rescale_pot.m, + BNT/potentials/@upot/approxeq_pot.m, + BNT/potentials/@upot/display.m, + BNT/potentials/@upot/divide_by_pot.m, + BNT/potentials/@upot/marginalize_pot.m, + BNT/potentials/@upot/multiply_by_pot.m, + BNT/potentials/@upot/normalize_pot.m, + BNT/potentials/@upot/pot_to_marginal.m, + BNT/potentials/@upot/upot.m, + BNT/potentials/@upot/upot_to_opt_policy.m, + BNT/potentials/Old/comp_eff_node_sizes.m, + BNT/potentials/Tables/divide_by_sparse_table.c, + BNT/potentials/Tables/divide_by_table.c, + BNT/potentials/Tables/marg_sparse_table.c, + BNT/potentials/Tables/marg_table.c, + BNT/potentials/Tables/mult_by_sparse_table.c, + BNT/potentials/Tables/rep_mult.c, HMM/mk_leftright_transmat.m: + Initial import of code base from Kevin Murphy. + +2002-05-29 08:59 yozhik + + * BNT/@assocarray/assocarray.m, + BNT/CPDs/@boolean_CPD/boolean_CPD.m, + BNT/CPDs/@discrete_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@discrete_CPD/CPD_to_pi.m, + BNT/CPDs/@discrete_CPD/CPD_to_scgpot.m, + BNT/CPDs/@discrete_CPD/README, + BNT/CPDs/@discrete_CPD/convert_CPD_to_table_hidden_ps.m, + BNT/CPDs/@discrete_CPD/convert_obs_CPD_to_table.m, + BNT/CPDs/@discrete_CPD/convert_to_sparse_table.c, + BNT/CPDs/@discrete_CPD/convert_to_table.m, + BNT/CPDs/@discrete_CPD/discrete_CPD.m, + BNT/CPDs/@discrete_CPD/dom_sizes.m, + BNT/CPDs/@discrete_CPD/log_prob_node.m, + BNT/CPDs/@discrete_CPD/prob_node.m, + BNT/CPDs/@discrete_CPD/sample_node.m, + BNT/CPDs/@discrete_CPD/Old/convert_to_pot.m, + BNT/CPDs/@discrete_CPD/Old/convert_to_table.m, + BNT/CPDs/@discrete_CPD/Old/prob_CPD.m, + BNT/CPDs/@discrete_CPD/Old/prob_node.m, + BNT/CPDs/@discrete_CPD/private/prod_CPT_and_pi_msgs.m, + BNT/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@gaussian_CPD/CPD_to_pi.m, + BNT/CPDs/@gaussian_CPD/CPD_to_scgpot.m, + BNT/CPDs/@gaussian_CPD/adjustable_CPD.m, + BNT/CPDs/@gaussian_CPD/convert_CPD_to_table_hidden_ps.m, + BNT/CPDs/@gaussian_CPD/display.m, + BNT/CPDs/@gaussian_CPD/get_field.m, + BNT/CPDs/@gaussian_CPD/reset_ess.m, + BNT/CPDs/@gaussian_CPD/sample_node.m, + BNT/CPDs/@gaussian_CPD/set_fields.m, + BNT/CPDs/@gaussian_CPD/Old/CPD_to_lambda_msg.m, + BNT/CPDs/@gaussian_CPD/Old/gaussian_CPD.m, + BNT/CPDs/@gaussian_CPD/Old/log_prob_node.m, + BNT/CPDs/@gaussian_CPD/Old/update_ess.m, + BNT/CPDs/@gaussian_CPD/Old/update_tied_ess.m, + BNT/CPDs/@gaussian_CPD/private/CPD_to_linear_gaussian.m, + BNT/CPDs/@generic_CPD/README, + BNT/CPDs/@generic_CPD/adjustable_CPD.m, + BNT/CPDs/@generic_CPD/display.m, + BNT/CPDs/@generic_CPD/generic_CPD.m, + BNT/CPDs/@generic_CPD/log_prior.m, + BNT/CPDs/@generic_CPD/set_clamped.m, + BNT/CPDs/@generic_CPD/Old/BIC_score_CPD.m, + BNT/CPDs/@generic_CPD/Old/CPD_to_dpots.m, + BNT/CPDs/@gmux_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@gmux_CPD/convert_to_pot.m, + BNT/CPDs/@gmux_CPD/CPD_to_pi.m, BNT/CPDs/@gmux_CPD/display.m, + BNT/CPDs/@gmux_CPD/gmux_CPD.m, BNT/CPDs/@gmux_CPD/sample_node.m, + BNT/CPDs/@gmux_CPD/Old/gmux_CPD.m, + BNT/CPDs/@hhmmF_CPD/log_prior.m, + BNT/CPDs/@hhmmF_CPD/maximize_params.m, + BNT/CPDs/@hhmmF_CPD/reset_ess.m, BNT/CPDs/@hhmmQ_CPD/log_prior.m, + BNT/CPDs/@hhmmQ_CPD/reset_ess.m, + BNT/CPDs/@mlp_CPD/convert_to_table.m, + BNT/CPDs/@mlp_CPD/maximize_params.m, BNT/CPDs/@mlp_CPD/mlp_CPD.m, + BNT/CPDs/@mlp_CPD/reset_ess.m, BNT/CPDs/@mlp_CPD/update_ess.m, + BNT/CPDs/@noisyor_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@noisyor_CPD/CPD_to_pi.m, + BNT/CPDs/@noisyor_CPD/noisyor_CPD.m, + BNT/CPDs/@noisyor_CPD/private/sum_prod_CPD_and_pi_msgs.m, + BNT/CPDs/@root_CPD/CPD_to_pi.m, + BNT/CPDs/@root_CPD/convert_to_pot.m, + BNT/CPDs/@root_CPD/log_marg_prob_node.m, + BNT/CPDs/@root_CPD/log_prob_node.m, + BNT/CPDs/@root_CPD/root_CPD.m, BNT/CPDs/@root_CPD/sample_node.m, + BNT/CPDs/@root_CPD/Old/CPD_to_CPT.m, + BNT/CPDs/@softmax_CPD/convert_to_pot.m, + BNT/CPDs/@softmax_CPD/display.m, + BNT/CPDs/@softmax_CPD/get_field.m, + BNT/CPDs/@softmax_CPD/maximize_params.m, + BNT/CPDs/@softmax_CPD/reset_ess.m, + BNT/CPDs/@softmax_CPD/sample_node.m, + BNT/CPDs/@softmax_CPD/set_fields.m, + BNT/CPDs/@softmax_CPD/update_ess.m, + BNT/CPDs/@softmax_CPD/private/extract_params.m, + BNT/CPDs/@tabular_CPD/CPD_to_CPT.m, + BNT/CPDs/@tabular_CPD/bayes_update_params.m, + BNT/CPDs/@tabular_CPD/log_nextcase_prob_node.m, + BNT/CPDs/@tabular_CPD/log_prior.m, + BNT/CPDs/@tabular_CPD/reset_ess.m, + BNT/CPDs/@tabular_CPD/update_ess.m, + BNT/CPDs/@tabular_CPD/update_ess_simple.m, + BNT/CPDs/@tabular_CPD/Old/BIC_score_CPD.m, + BNT/CPDs/@tabular_CPD/Old/bayesian_score_CPD.m, + BNT/CPDs/@tabular_CPD/Old/log_marg_prob_node_case.m, + BNT/CPDs/@tabular_CPD/Old/mult_CPD_and_pi_msgs.m, + BNT/CPDs/@tabular_CPD/Old/prob_CPT.m, + BNT/CPDs/@tabular_CPD/Old/prob_node.m, + BNT/CPDs/@tabular_CPD/Old/sample_node.m, + BNT/CPDs/@tabular_CPD/Old/sample_node_single_case.m, + BNT/CPDs/@tabular_CPD/Old/tabular_CPD.m, + BNT/CPDs/@tabular_CPD/Old/update_params.m, + BNT/CPDs/@tabular_decision_node/CPD_to_CPT.m, + BNT/CPDs/@tabular_decision_node/display.m, + BNT/CPDs/@tabular_decision_node/get_field.m, + BNT/CPDs/@tabular_decision_node/set_fields.m, + BNT/CPDs/@tabular_decision_node/tabular_decision_node.m, + BNT/CPDs/@tabular_decision_node/Old/tabular_decision_node.m, + BNT/CPDs/@tabular_kernel/convert_to_pot.m, + BNT/CPDs/@tabular_kernel/convert_to_table.m, + BNT/CPDs/@tabular_kernel/get_field.m, + BNT/CPDs/@tabular_kernel/set_fields.m, + BNT/CPDs/@tabular_kernel/tabular_kernel.m, + BNT/CPDs/@tabular_kernel/Old/tabular_kernel.m, + BNT/CPDs/@tabular_utility_node/convert_to_pot.m, + BNT/CPDs/@tabular_utility_node/display.m, + BNT/CPDs/@tabular_utility_node/tabular_utility_node.m, + BNT/CPDs/@tree_CPD/display.m, + BNT/CPDs/@tree_CPD/evaluate_tree_performance.m, + BNT/CPDs/@tree_CPD/get_field.m, + BNT/CPDs/@tree_CPD/learn_params.m, BNT/CPDs/@tree_CPD/readme.txt, + BNT/CPDs/@tree_CPD/set_fields.m, BNT/CPDs/@tree_CPD/tree_CPD.m, + BNT/CPDs/Old/@linear_gaussian_CPD/linear_gaussian_CPD.m, + BNT/CPDs/Old/@linear_gaussian_CPD/log_marg_prob_node.m, + BNT/CPDs/Old/@linear_gaussian_CPD/update_params_complete.m, + BNT/CPDs/Old/@root_gaussian_CPD/log_marg_prob_node.m, + BNT/CPDs/Old/@root_gaussian_CPD/root_gaussian_CPD.m, + BNT/CPDs/Old/@root_gaussian_CPD/update_params_complete.m, + BNT/CPDs/Old/@tabular_chance_node/CPD_to_upot.m, + BNT/CPDs/Old/@tabular_chance_node/tabular_chance_node.m, + BNT/examples/dynamic/bat1.m, BNT/examples/dynamic/bkff1.m, + BNT/examples/dynamic/chmm1.m, + BNT/examples/dynamic/cmp_inference_dbn.m, + BNT/examples/dynamic/cmp_learning_dbn.m, + BNT/examples/dynamic/cmp_online_inference.m, + BNT/examples/dynamic/fhmm_infer.m, + BNT/examples/dynamic/filter_test1.m, + BNT/examples/dynamic/kalman1.m, + BNT/examples/dynamic/kjaerulff1.m, + BNT/examples/dynamic/loopy_dbn1.m, + BNT/examples/dynamic/mk_collage_from_clqs.m, + BNT/examples/dynamic/mk_fhmm.m, BNT/examples/dynamic/reveal1.m, + BNT/examples/dynamic/scg_dbn.m, + BNT/examples/dynamic/skf_data_assoc_gmux.m, + BNT/examples/dynamic/HHMM/add_hhmm_end_state.m, + BNT/examples/dynamic/HHMM/hhmm_jtree_clqs.m, + BNT/examples/dynamic/HHMM/mk_hhmm_topo.m, + BNT/examples/dynamic/HHMM/mk_hhmm_topo_F1.m, + BNT/examples/dynamic/HHMM/pretty_print_hhmm_parse.m, + BNT/examples/dynamic/HHMM/Motif/fixed_args_mk_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Motif/mk_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Motif/sample_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Old/mk_abcd_hhmm.m, + BNT/examples/dynamic/HHMM/Old/mk_arrow_alpha_hhmm3.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm2.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm3.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm3_args.m, + BNT/examples/dynamic/HHMM/Old/motif_hhmm.m, + BNT/examples/dynamic/HHMM/Old/remove_hhmm_end_state.m, + BNT/examples/dynamic/HHMM/Square/get_square_data.m, + BNT/examples/dynamic/HHMM/Square/hhmm_inference.m, + BNT/examples/dynamic/HHMM/Square/is_F2_true_D3.m, + BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m, + BNT/examples/dynamic/HHMM/Square/mk_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/plot_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_cts.m, + BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_discrete.m, + BNT/examples/dynamic/HHMM/Square/square4.mat, + BNT/examples/dynamic/HHMM/Square/square4_cases.mat, + BNT/examples/dynamic/HHMM/Square/test_square_fig.m, + BNT/examples/dynamic/HHMM/Square/test_square_fig.mat, + BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m, + BNT/examples/dynamic/Old/chmm1.m, + BNT/examples/dynamic/Old/cmp_inference.m, + BNT/examples/dynamic/Old/kalman1.m, + BNT/examples/dynamic/Old/old.water1.m, + BNT/examples/dynamic/Old/online1.m, + BNT/examples/dynamic/Old/online2.m, + BNT/examples/dynamic/Old/scg_dbn.m, + BNT/examples/dynamic/SLAM/mk_gmux_robot_dbn.m, + BNT/examples/dynamic/SLAM/mk_linear_slam.m, + BNT/examples/dynamic/SLAM/slam_kf.m, + BNT/examples/dynamic/SLAM/slam_offline_loopy.m, + BNT/examples/dynamic/SLAM/slam_partial_kf.m, + BNT/examples/dynamic/SLAM/slam_stationary_loopy.m, + BNT/examples/dynamic/SLAM/Old/offline_loopy_slam.m, + BNT/examples/dynamic/SLAM/Old/paskin1.m, + BNT/examples/dynamic/SLAM/Old/skf_data_assoc_gmux2.m, + BNT/examples/dynamic/SLAM/Old/slam_kf.m, + BNT/examples/limids/id1.m, BNT/examples/limids/pigs1.m, + BNT/examples/static/cg1.m, BNT/examples/static/cg2.m, + BNT/examples/static/discrete2.m, BNT/examples/static/discrete3.m, + BNT/examples/static/fa1.m, BNT/examples/static/gaussian1.m, + BNT/examples/static/gibbs_test1.m, BNT/examples/static/lw1.m, + BNT/examples/static/mfa1.m, BNT/examples/static/mixexp1.m, + BNT/examples/static/mixexp2.m, BNT/examples/static/mixexp3.m, + BNT/examples/static/mog1.m, BNT/examples/static/qmr1.m, + BNT/examples/static/sample1.m, BNT/examples/static/softmax1.m, + BNT/examples/static/Belprop/belprop_loop1_discrete.m, + BNT/examples/static/Belprop/belprop_loop1_gauss.m, + BNT/examples/static/Belprop/belprop_loopy_cg.m, + BNT/examples/static/Belprop/belprop_loopy_discrete.m, + BNT/examples/static/Belprop/belprop_loopy_gauss.m, + BNT/examples/static/Belprop/belprop_polytree_cg.m, + BNT/examples/static/Belprop/belprop_polytree_gauss.m, + BNT/examples/static/Belprop/bp1.m, + BNT/examples/static/Belprop/gmux1.m, + BNT/examples/static/Brutti/Belief_IOhmm.m, + BNT/examples/static/Brutti/Belief_hmdt.m, + BNT/examples/static/Brutti/Belief_hme.m, + BNT/examples/static/Brutti/Sigmoid_Belief.m, + BNT/examples/static/HME/HMEforMatlab.jpg, + BNT/examples/static/HME/README, BNT/examples/static/HME/fhme.m, + BNT/examples/static/HME/gen_data.m, + BNT/examples/static/HME/hme_class_plot.m, + BNT/examples/static/HME/hme_reg_plot.m, + BNT/examples/static/HME/hme_topobuilder.m, + BNT/examples/static/HME/test_data_class.mat, + BNT/examples/static/HME/test_data_class2.mat, + BNT/examples/static/HME/test_data_reg.mat, + BNT/examples/static/HME/train_data_class.mat, + BNT/examples/static/HME/train_data_reg.mat, + BNT/examples/static/Misc/mixexp_data.txt, + BNT/examples/static/Misc/mixexp_graddesc.m, + BNT/examples/static/Misc/mixexp_plot.m, + BNT/examples/static/Misc/sprinkler.bif, + BNT/examples/static/Models/mk_cancer_bnet.m, + BNT/examples/static/Models/mk_car_bnet.m, + BNT/examples/static/Models/mk_ideker_bnet.m, + BNT/examples/static/Models/mk_incinerator_bnet.m, + BNT/examples/static/Models/mk_markov_chain_bnet.m, + BNT/examples/static/Models/mk_minimal_qmr_bnet.m, + BNT/examples/static/Models/mk_qmr_bnet.m, + BNT/examples/static/Models/mk_vstruct_bnet.m, + BNT/examples/static/Models/Old/mk_hmm_bnet.m, + BNT/examples/static/SCG/scg1.m, BNT/examples/static/SCG/scg2.m, + BNT/examples/static/SCG/scg3.m, + BNT/examples/static/SCG/scg_3node.m, + BNT/examples/static/SCG/scg_unstable.m, + BNT/examples/static/StructLearn/bic1.m, + BNT/examples/static/StructLearn/cooper_yoo.m, + BNT/examples/static/StructLearn/k2demo1.m, + BNT/examples/static/StructLearn/mcmc1.m, + BNT/examples/static/StructLearn/pc1.m, + BNT/examples/static/StructLearn/pc2.m, + BNT/examples/static/Zoubin/README, + BNT/examples/static/Zoubin/csum.m, + BNT/examples/static/Zoubin/ffa.m, + BNT/examples/static/Zoubin/mfa.m, + BNT/examples/static/Zoubin/mfa_cl.m, + BNT/examples/static/Zoubin/mfademo.m, + BNT/examples/static/Zoubin/rdiv.m, + BNT/examples/static/Zoubin/rprod.m, + 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BNT/inference/static/@quickscore_inf_engine/private/nrutil.c, + BNT/inference/static/@quickscore_inf_engine/private/nrutil.h, + BNT/inference/static/@quickscore_inf_engine/private/quickscore.m, + BNT/learning/bayes_update_params.m, + BNT/learning/bic_score_family.m, + BNT/learning/compute_cooling_schedule.m, + BNT/learning/dirichlet_score_family.m, + BNT/learning/kpm_learn_struct_mcmc.m, + BNT/learning/learn_params_em.m, + BNT/learning/learn_struct_dbn_reveal.m, + BNT/learning/learn_struct_pdag_ic_star.m, + BNT/learning/mcmc_sample_to_hist.m, BNT/learning/mk_schedule.m, + BNT/learning/mk_tetrad_data_file.m, + BNT/learning/score_dags_old.m, HMM/dhmm_logprob_brute_force.m, + HMM/dhmm_logprob_path.m, HMM/mdp_sample.m, Kalman/AR_to_SS.m, + Kalman/SS_to_AR.m, Kalman/convert_to_lagged_form.m, + Kalman/ensure_AR.m, Kalman/eval_AR_perf.m, + Kalman/kalman_filter.m, Kalman/kalman_smoother.m, + Kalman/kalman_update.m, Kalman/learn_AR.m, + Kalman/learn_AR_diagonal.m, Kalman/learn_kalman.m, + Kalman/smooth_update.m, + BNT/general/convert_dbn_CPDs_to_tables_slow.m, + BNT/general/dispcpt.m, BNT/general/linear_gaussian_to_cpot.m, + BNT/general/partition_matrix_vec_3.m, + BNT/general/shrink_obs_dims_in_gaussian.m, + BNT/general/shrink_obs_dims_in_table.m, + BNT/potentials/CPD_to_pot.m, BNT/potentials/README, + BNT/potentials/check_for_cd_arcs.m, + BNT/potentials/determine_pot_type.m, + BNT/potentials/mk_initial_pot.m, + BNT/potentials/@cgpot/cg_can_to_mom.m, + BNT/potentials/@cgpot/cg_mom_to_can.m, + BNT/potentials/@cgpot/cgpot.m, BNT/potentials/@cgpot/display.m, + BNT/potentials/@cgpot/divide_by_pot.m, + BNT/potentials/@cgpot/domain_pot.m, + BNT/potentials/@cgpot/enter_cts_evidence_pot.m, + BNT/potentials/@cgpot/enter_discrete_evidence_pot.m, + BNT/potentials/@cgpot/marginalize_pot.m, + BNT/potentials/@cgpot/multiply_by_pot.m, + BNT/potentials/@cgpot/multiply_pots.m, + BNT/potentials/@cgpot/normalize_pot.m, + BNT/potentials/@cgpot/pot_to_marginal.m, + BNT/potentials/@cgpot/Old/normalize_pot.m, + BNT/potentials/@cgpot/Old/simple_marginalize_pot.m, + BNT/potentials/@cpot/cpot.m, BNT/potentials/@cpot/cpot_to_mpot.m, + BNT/potentials/@cpot/display.m, + BNT/potentials/@cpot/divide_by_pot.m, + BNT/potentials/@cpot/domain_pot.m, + BNT/potentials/@cpot/enter_cts_evidence_pot.m, + BNT/potentials/@cpot/marginalize_pot.m, + BNT/potentials/@cpot/multiply_by_pot.m, + BNT/potentials/@cpot/multiply_pots.m, + BNT/potentials/@cpot/normalize_pot.m, + BNT/potentials/@cpot/pot_to_marginal.m, + BNT/potentials/@cpot/rescale_pot.m, + BNT/potentials/@cpot/set_domain_pot.m, + BNT/potentials/@cpot/Old/cpot_to_mpot.m, + BNT/potentials/@cpot/Old/normalize_pot.convert.m, + BNT/potentials/@dpot/approxeq_pot.m, + BNT/potentials/@dpot/display.m, + BNT/potentials/@dpot/domain_pot.m, + BNT/potentials/@dpot/dpot_to_table.m, + BNT/potentials/@dpot/get_fields.m, + BNT/potentials/@dpot/multiply_pots.m, + BNT/potentials/@dpot/pot_to_marginal.m, + BNT/potentials/@dpot/set_domain_pot.m, + BNT/potentials/@mpot/display.m, + BNT/potentials/@mpot/marginalize_pot.m, + BNT/potentials/@mpot/mpot.m, BNT/potentials/@mpot/mpot_to_cpot.m, + BNT/potentials/@mpot/normalize_pot.m, + BNT/potentials/@mpot/pot_to_marginal.m, + BNT/potentials/@mpot/rescale_pot.m, + BNT/potentials/@upot/approxeq_pot.m, + BNT/potentials/@upot/display.m, + BNT/potentials/@upot/divide_by_pot.m, + BNT/potentials/@upot/marginalize_pot.m, + BNT/potentials/@upot/multiply_by_pot.m, + BNT/potentials/@upot/normalize_pot.m, + BNT/potentials/@upot/pot_to_marginal.m, + BNT/potentials/@upot/upot.m, + BNT/potentials/@upot/upot_to_opt_policy.m, + BNT/potentials/Old/comp_eff_node_sizes.m, + BNT/potentials/Tables/divide_by_sparse_table.c, + BNT/potentials/Tables/divide_by_table.c, + BNT/potentials/Tables/marg_sparse_table.c, + BNT/potentials/Tables/marg_table.c, + BNT/potentials/Tables/mult_by_sparse_table.c, + BNT/potentials/Tables/rep_mult.c, HMM/mk_leftright_transmat.m: + Initial revision + +2002-05-29 04:59 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/: + clq_containing_nodes.m, problems.txt, push_pot_toclique.m, + Old/initialize_engine.m: Initial import of code base from Kevin + Murphy. + +2002-05-29 04:59 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/: + clq_containing_nodes.m, problems.txt, push_pot_toclique.m, + Old/initialize_engine.m: Initial revision + +2002-05-19 15:11 yozhik + + * BNT/potentials/: @scgcpot/marginalize_pot.m, + @scgcpot/normalize_pot.m, @scgcpot/rescale_pot.m, + @scgcpot/scgcpot.m, @scgpot/direct_combine_pots.m, + @scgpot/pot_to_marginal.m: Initial import of code base from Kevin + Murphy. + +2002-05-19 15:11 yozhik + + * BNT/potentials/: @scgcpot/marginalize_pot.m, + @scgcpot/normalize_pot.m, @scgcpot/rescale_pot.m, + @scgcpot/scgcpot.m, @scgpot/direct_combine_pots.m, + @scgpot/pot_to_marginal.m: Initial revision + +2001-07-28 08:43 yozhik + + * BNT/potentials/genops.c: Initial import of code base from Kevin + Murphy. + +2001-07-28 08:43 yozhik + + * BNT/potentials/genops.c: Initial revision +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/ChangeLog.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +- 7 May 2010 wsun +* revised \BNT\inference\static\@jtree_inf_engine\jtree_inf_engine.m: It is proved that the last clique created in the elimination process is not necessarily to be a strong root for CLG model. Instead, we use a clique called interface clique, that contains all discrete parents and at least one continuous node from a connected continuous component, as a guaranteed strong root. + +* added one function \graph\findroot.m: this is to find the interface cliqeu with max number of discrete parent nodes as the guaranteed strong root. + +- 16 Feb 2010 mdunham +* Minor bug fixes to make MATLAB 2009 compatible + +- 19 Oct 97 murphyk + +* BNT\CPDs\@noisyor_CPD\CPD_to_CPT.m: 2nd half of the file is a repeat +of the first half and was deleted (thanks to Karl Kuschner) + +* KPMtools\myismember.m should return logical for use in "assert" so add line at end + p=logical(p); this prevents "assert" from failing on an integer input. +(thanks to Karl Kuschner) + + + +- 17 Oct 07 murphyk + +* Updated subv2ind and ind2subv in KPMtools to Tom Minka's implementation. +His ind2subv is faster (vectorized), but I had to modify it so it +matched the behavior of my version when called with siz=[]. +His subv2inv is slightly simpler than mine because he does not treat +the siz=[2 2 ... 2] case separately. +Note: there is now no need to ever use the C versions of these +functions (or any others, for that matter). + +* removed BNT/add_BNT_to_path since no longer needed. + + + +- 4 Oct 07 murphyk + +* moved code from sourceforge to UBC website, made version 1.0.4 + +* @pearl_inf_engine/pearl_inf_engine line 24, default +argument for protocol changed from [] to 'parallel'. +Also, changed private/parallel_protocol so it doesn't write to an +empty file id (Matlab 7 issue) + +* added foptions (Matlab 7 issue) + +* changed genpathKPM to exclude svn. Put it in toplevel directory to +massively simplify the installation process. + + +-------------------------------------- +For changes to the sourceforge version, +culminating in version 1.0.3, see ChangeLog.Sourceforge.txt.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +/README.txt/1.1.1.1/Mon Jun 7 14:33:20 2004// +/approxeq.m/1.1.1.1/Mon Jul 28 22:44:46 2003// +/arrow.m/1.1.1.1/Fri Mar 28 16:35:06 2003// +/dot_to_graph.m/1.1.1.1/Fri Mar 12 23:21:08 2004// +/draw_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/draw_dbn_test.m/1.1.1.1/Sat Jan 18 21:17:18 2003// +/draw_dot.m/1.1.1.1/Wed May 5 13:32:32 2004// +/draw_graph.m/1.1.1.1/Mon Mar 22 22:32:04 2004// +/draw_graph_test.m/1.1.1.1/Mon Jan 20 16:56:02 2003// +/draw_hmm.m/1.1.1.1/Thu Feb 6 03:16:32 2003// +/editGraphGUI.m/1.1.1.1/Tue Jan 27 21:08:48 2004// +/graph_to_dot.m/1.1.1.1/Tue Jun 15 17:50:40 2004// +/make_layout.m/1.1.1.1/Wed May 29 15:59:54 2002// +/my_call.m/1.1.1.1/Tue Jan 27 21:08:38 2004// +/process_options.m/1.1.1.1/Mon Jul 28 22:44:46 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/GraphViz
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/dot_to_graph.m/1.1.1.1/Tue Jan 27 21:01:54 2004// +/draw_dot.m/1.1.1.1/Tue Jan 27 20:42:50 2004// +/draw_graph.m/1.1.1.1/Tue Jan 27 21:03:56 2004// +/graphToDot.m/1.1.1.1/Tue Feb 3 17:15:18 2004// +/pre_pesha_graph_to_dot.m/1.1.1.1/Tue Jan 27 20:47:40 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/GraphViz/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/Old/dot_to_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +function [Adj, labels, x, y] = dot_to_graph(filename) + +% [Adj, labels, x, y] = dot_to_graph(filename) +% Extract a matrix representation, node labels, and node position coordinates +% from a file in GraphViz format http://www.research.att.com/sw/tools/graphviz +% +% INPUTS: +% 'filename' - the file in DOT format containing the graph layout. +% OUTPUT: +% 'Adj' - an adjacency matrix representation of the graph in 'filename'; +% 'labels' - a character array with the names of the nodes of the graph; +% 'x' - a row vector with the x-coordinates of the nodes in 'filename'; +% 'y' - a row vector with the y-coordinates of the nodes in 'filename'. +% +% WARNINGS: not guaranted to parse ANY GraphViz file. Debugged on undirected +% sample graphs from GraphViz(Heawood, Petersen, ER, ngk10_4, process). +% Complaines about RecursionLimit set only to 500 on huge graphs. +% Ignores singletons (disjoint nodes). +% Sample DOT code "ABC.dot", read by [Adj, labels, x, y] = dot_to_graph('ABC.dot') +% digraph G { +% A [pos="28,31"]; +% B [pos="74,87"]; +% A -- B [pos="e,61,71 41,47 46,53 50,58 55,64"]; +% } +% last modified: Jan 2004 +% by Alexi Savov: asavov @wustl.edu | http://artsci.wustl.edu/~azsavov +% Leon Peshkin: pesha @ai.mit.edu | http://www.ai.mit.edu/~pesha + +if ~exist(filename) % Checks whether the specified file exists. + error('* * * File does not exist or could not be found. * * *'); return; +end; + +lines = textread(filename,'%s','delimiter','\n','commentstyle','c'); % Read file into cell array +dot_lines = strvcat(lines); % of lines, ignoring C-style comments + +if findstr(dot_lines(1,:), 'graph ') == [] % Is this a DOT file ? + error('* * * File does not appear to be in valid DOT format. * * *'); return; +end; + +Nlns = size(dot_lines,1); % The number of lines; +labels = {}; +unread = 1:Nlns; % 'unread' list of lines which has not been examined yet +edge_id = 1; +for line_ndx = 1:Nlns % This section sets the adjacency matrix A(Lnode,Rnode) = edge_id. + line = dot_lines(line_ndx,:); + Ddash_pos = strfind(line, ' -- ') + 1; % double dash positions + arrow_pos = strfind(line, ' -> ') + 1; % arrow dash positions + tokens = strread(line,'%s','delimiter',' "'); + left_bound = 1; + for dash_pos = [Ddash_pos arrow_pos]; % if empty - not a POS line + Lnode = sscanf(line(left_bound:dash_pos -2), '%s'); + Rnode = sscanf(line(dash_pos +3 : length(line)-1),'%s',1); + Lndx = strmatch(Lnode, labels, 'exact'); + Rndx = strmatch(Rnode, labels, 'exact'); + if isempty(Lndx) % extend our list of labels + labels{end+1} = Lnode; + Lndx = length(labels); + end + if isempty(Rndx) + labels{end+1} = Rnode; + Rndx = length(labels); + end + Adj(Lndx, Rndx) = edge_id;; + if ismember(dash_pos, Ddash_pos) % The edge is undirected, A(Rndx,LndxL) is also set to 1; + Adj(Rndx, Lndx) = edge_id; + end + edge_id = edge_id + 1; + left_bound = dash_pos + 3; + unread = setdiff(unread, line_ndx); + end +end +Nvrt = length(labels); % number of vertices we found [Do we ever have singleton vertices ???] +% labels = strvcat(labels); % convert to the searchable array +x = zeros(1, Nvrt); +y = zeros(1, Nvrt); +lst_node = 0; + % Find node's position coordinates if they are contained in 'filename'. +for line_ndx = unread % Look for node's coordiantes among the 'unread' lines. + line = dot_lines(line_ndx,:); + bra_pos = strfind(line, '['); % has to have "[" if it has the lable + pos_pos = strfind(line, 'pos'); % position of the "pos" + for node = 1:Nvrt % look through the list of labels + % THE NEXT STATEMENT we assume no label is substring of any other label + lbl_pos = strfind(line, labels{node}); + if (~isempty(lbl_pos) & ~isempty(bra_pos) & (x(node) == 0)) % make sure we have not seen it + if (lbl_pos(1) < bra_pos(1)) % label has to be to the left of braket + lst_node = node; + end + end + end + if (~isempty(pos_pos) & lst_node) % this line contains SOME position + [node_pos] = sscanf(line(pos_pos:length(line)), ' pos = "%d,%d"')'; + x(lst_node) = node_pos(1); + y(lst_node) = node_pos(2); + lst_node = 0; % not to assign position several times + end +end + +if (isempty(find(x)) & (nargout > 2)) % If coordinates were requested, but not found in 'filename'. + warning('File does not contain node coordinates.'); +end; +if ~(size(Adj,1)==size(Adj,2)) % Make sure Adj is a square matrix. ? + Adj = eye(max(size(Adj)),size(Adj,1))*Adj*eye(size(Adj,2),max(size(Adj))); +end; +x = .9*(x-min(x))/range(x)+.05; % normalise and push off margins +y = .9*(y-min(y))/range(y)+.05; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/Old/draw_dot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function draw_dot(adj); +% +% draw_dot(name) +% +% Sample code illustrating use of dot_to_graph.m function +% Leon Peshkin +if ispc, shell = 'dos'; else, shell = 'unix'; end % Which OS ? + +cmdline = strcat(shell,'(''neato -V'')'); +status = eval(cmdline); +[status, result] = dos('neato -V'); % request version to check NEATO +if status == 1, fprintf('Complaining \n'); exit, end + +tmpDOTfile = '_GtDout.dot'; % to be platform independant no use of directories +tmpLAYOUT = '_LAYout.dot'; +directed = 0; % assume UN-directed graph +graph_to_dot(adj > 0, 'directed', directed, 'filename', tmpDOTfile); % save in file + +cmdline = strcat([shell '(''neato -Tdot ' tmpDOTfile ' -o ' tmpLAYOUT ''')']); % preserve trailing spaces +status = eval(cmdline); % get NEATO todo layout + +[adj, labels, x, y] = dot_to_graph(tmpLAYOUT); % load layout +delete(tmpLAYOUT); delete(tmpDOTfile); % clean up temporary files + +figure(1); clf; axis square % now plot +[x, y, h] = draw_graph(adj>0, labels, zeros(size(x,2),1), x, y); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/Old/draw_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,310 @@ +function [x, y, h] = draw_graph(adj, labels, node_t, x, y) +% DRAW_LAYOUT Draws a layout for a graph +% +% [<X, Y>] = DRAW_LAYOUT(ADJ, <LABELS, ISBOX, X, Y>) +% +% Inputs : +% ADJ : Adjacency matrix (source, sink) +% LABELS : Cell array containing labels <Default : '1':'N'> +% ISBOX : 1 if node is a box, 0 if oval <Default : zeros> +% X, Y, : Coordinates of nodes on the unit square <Default : calls make_layout> +% +% Outputs : +% X, Y : Coordinates of nodes on the unit square +% H : Object handles +% +% Usage Example : [x, y] = draw_layout([0 1;0 0], {'Hidden','Visible'}, [1 0]'); +% +% h(i,1) is the text handle - color +% h(i,2) is the circle handle - facecolor +% +% Note : +% See also MAKE_LAYOUT + +% Uses : + +% Change History : +% Date Time Prog Note +% 13-Apr-2000 9:06 PM ATC Created under MATLAB 5.3.1.29215a (R11.1) + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl +adj = double(adj); +N = size(adj,1); +if nargin<2, +% labels = cellstr(char(zeros(N,1)+double('+'))); + labels = cellstr(int2str((1:N)')); +end; + +if nargin<3, + node_t = zeros(N,1); +% node_t = rand(N,1) > 0.5; +else + node_t = node_t(:); +end; + +axis([0 1 0 1]); +set(gca,'XTick',[],'YTick',[],'box','on'); +% axis('square'); +%colormap(flipud(gray)); + +if nargin<4, + [x y] = make_layout(adj); +end; + +idx1 = find(node_t==0); wd1=[]; +if ~isempty(idx1), +[h1 wd1] = textoval(x(idx1), y(idx1), labels(idx1)); +end; + +idx2 = find(node_t~=0); wd2 = []; +if ~isempty(idx2), +[h2 wd2] = textbox(x(idx2), y(idx2), labels(idx2)); +end; + +wd = zeros(size(wd1,1)+size(wd2,1),2); +if ~isempty(idx1), wd(idx1, :) = wd1; end; +if ~isempty(idx2), wd(idx2, :) = wd2; end; + +for i=1:N, + j = find(adj(i,:)==1); + for k=j, + if x(k)-x(i)==0, + sign = 1; + if y(i)>y(k), alpha = -pi/2; else alpha = pi/2; end; + else + alpha = atan((y(k)-y(i))/(x(k)-x(i))); + if x(i)<x(k), sign = 1; else sign = -1; end; + end; + dy1 = sign.*wd(i,2).*sin(alpha); dx1 = sign.*wd(i,1).*cos(alpha); + dy2 = sign.*wd(k,2).*sin(alpha); dx2 = sign.*wd(k,1).*cos(alpha); + if adj(k,i)==0, % if directed edge + arrow([x(i)+dx1 y(i)+dy1],[x(k)-dx2 y(k)-dy2]); + else + line([x(i)+dx1 x(k)-dx2],[y(i)+dy1 y(k)-dy2],'color','k'); + adj(k,i)=-1; % Prevent drawing lines twice + end; + end; +end; + +if nargout>2, + h = zeros(length(wd),2); + if ~isempty(idx1), + h(idx1,:) = h1; + end; + if ~isempty(idx2), + h(idx2,:) = h2; + end; +end; + +%%%%% + +function [t, wd] = textoval(x, y, str) +% TEXTOVAL Draws an oval around text objects +% +% [T, WIDTH] = TEXTOVAL(X, Y, STR) +% [..] = TEXTOVAL(STR) % Interactive +% +% Inputs : +% X, Y : Coordinates +% TXT : Strings +% +% Outputs : +% T : Object Handles +% WIDTH : x and y Width of ovals +% +% Usage Example : [t] = textoval('Visit to Asia?'); +% +% +% Note : +% See also TEXTBOX + +% Uses : + +% Change History : +% Date Time Prog Note +% 15-Jun-1998 10:36 AM ATC Created under MATLAB 5.1.0.421 + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +temp = []; + +switch nargin, + case 1, + str = x; + if ~isa(str,'cell') str=cellstr(str); end; + N = length(str); + wd = zeros(N,2); + for i=1:N, + [x, y] = ginput(1); + tx = text(x,y,str{i},'HorizontalAlignment','center','VerticalAlign','middle'); + [ptc wx wy] = draw_oval(tx, x, y); + wd(i,:) = [wx wy]; + delete(tx); + tx = text(x,y,str{i},'HorizontalAlignment','center','VerticalAlign','middle'); + temp = [temp ; tx ptc]; + end; + case 3, + if ~isa(str,'cell') str=cellstr(str); end; + N = length(str); + wd = zeros(N,2); + for i=1:N, + tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle'); + [ptc wx wy] = draw_oval(tx, x(i), y(i)); + wd(i,:) = [wx wy]; + delete(tx); + tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle'); + temp = [temp; tx ptc]; + end; + otherwise, +end; + +if nargout>0, t = temp; end; + +%%%%%%%%% + + +function [ptc, wx, wy] = draw_oval(tx, x, y) +% Draws an oval box around a tex object +sz = get(tx,'Extent'); +wy = sz(4); +wx = max(2/3*sz(3), wy); +wx = 0.5*wx; % KPM +wy = 0.5*wy; +ptc = ellipse(x, y, wx, wy); +set(ptc, 'FaceColor','w'); + + +%%%%%%%%%%%%% + +function [p] = ellipse(x, y, rx, ry, c) +% ELLIPSE Draws Ellipse shaped patch objects +% +% [<P>] = ELLIPSE(X, Y, Rx, Ry, C) +% +% Inputs : +% X : N x 1 vector of x coordinates +% Y : N x 1 vector of y coordinates +% Rx, Ry : Radii +% C : Color index +% +% +% Outputs : +% P = Handles of Ellipse shaped path objects +% +% Usage Example : [] = ellipse(); +% +% +% Note : +% See also + +% Uses : + +% Change History : +% Date Time Prog Note +% 27-May-1998 9:55 AM ATC Created under MATLAB 5.1.0.421 + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +if (nargin < 2) error('Usage Example : e = ellipse([0 1],[0 -1],[1 0.5],[2 0.5]); '); end; +if (nargin < 3) rx = 0.1; end; +if (nargin < 4) ry = rx; end; +if (nargin < 5) c = 1; end; + +if length(c)==1, c = ones(size(x)).*c; end; +if length(rx)==1, rx = ones(size(x)).*rx; end; +if length(ry)==1, ry = ones(size(x)).*ry; end; + +n = length(x); +p = zeros(size(x)); +t = 0:pi/30:2*pi; +for i=1:n, + px = rx(i)*cos(t)+x(i); + py = ry(i)*sin(t)+y(i); + p(i) = patch(px,py,c(i)); +end; + +if nargout>0, pp = p; end; + +%%%%% + +function [t, wd] = textbox(x,y,str) +% TEXTBOX Draws A Box around the text +% +% [T, WIDTH] = TEXTBOX(X, Y, STR) +% [..] = TEXTBOX(STR) +% +% Inputs : +% X, Y : Coordinates +% TXT : Strings +% +% Outputs : +% T : Object Handles +% WIDTH : x and y Width of boxes +%% +% Usage Example : t = textbox({'Ali','Veli','49','50'}); +% +% +% Note : +% See also TEXTOVAL + +% Uses : + +% Change History : +% Date Time Prog Note +% 09-Jun-1998 11:43 AM ATC Created under MATLAB 5.1.0.421 + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +% See +temp = []; + +switch nargin, + case 1, + str = x; + if ~isa(str,'cell') str=cellstr(str); end; + N = length(str); + wd = zeros(N,2); + for i=1:N, + [x, y] = ginput(1); + tx = text(x,y,str{i},'HorizontalAlignment','center','VerticalAlign','middle'); + [ptc wx wy] = draw_box(tx, x, y); + wd(i,:) = [wx wy]; + delete(tx); + tx = text(x,y,str{i},'HorizontalAlignment','center','VerticalAlign','middle'); + temp = [temp; tx ptc]; + end; + case 3, + if ~isa(str,'cell') str=cellstr(str); end; + N = length(str); + for i=1:N, + tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle'); + [ptc wx wy] = draw_box(tx, x(i), y(i)); + wd(i,:) = [wx wy]; + delete(tx); + tx = text(x(i),y(i),str{i},'HorizontalAlignment','center','VerticalAlign','middle'); + temp = [temp; tx ptc]; + end; + + otherwise, + +end; + +if nargout>0, t = temp; end; + + +function [ptc, wx, wy] = draw_box(tx, x, y) +% Draws a box around a tex object + sz = get(tx,'Extent'); + wy = 2/3*sz(4); + wx = max(2/3*sz(3), wy); + ptc = patch([x-wx x+wx x+wx x-wx], [y+wy y+wy y-wy y-wy],'w'); + set(ptc, 'FaceColor','w'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/Old/graphToDot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,84 @@ +function graphToDot(adj, varargin) +% GRAPHTODOT Makes a GraphViz (AT&T) ile representing an adjacency matrix +% function graphToDot(adj, ...) +% Optional arguments should be passed as name/value pairs [default] +% +% 'filename' - if omitted, writes to 'tmp.dot' +% 'arc_label' - arc_label{i,j} is a string attached to the i-j arc [""] +% 'node_label' - node_label{i} is a string attached to the node i ["i"] +% 'width' - width in inches [10] +% 'height' - height in inches [10] +% 'leftright' - 1 means layout left-to-right, 0 means top-to-bottom [0] +% 'directed' - 1 means use directed arcs, 0 means undirected [1] +% +% For details on graphviz, See http://www.research.att.com/sw/tools/graphviz +% +% See also dot_to_graph and draw_dot +% +% First version written by Kevin Murphy 2002. +% Modified by Leon Peshkin, Jan 2004. + +node_label = []; arc_label = []; % set default args +width = 10; height = 10; +leftright = 0; directed = 1; filename = 'tmp.dot'; + +for i = 1:2:nargin-1 % get optional args + switch varargin{i} + case 'filename', filename = varargin{i+1}; + case 'node_label', node_label = varargin{i+1}; + case 'arc_label', arc_label = varargin{i+1}; + case 'width', width = varargin{i+1}; + case 'height', height = varargin{i+1}; + case 'leftright', leftright = varargin{i+1}; + case 'directed', directed = varargin{i+1}; + end +end + +fid = fopen(filename, 'w'); +if directed + fprintf(fid, 'digraph G {\n'); + arctxt = '->'; + if isempty(arc_label) + labeltxt = ''; + else + labeltxt = '[label="%s"]'; + end +else + fprintf(fid, 'graph G {\n'); + arctxt = '--'; + if isempty(arc_label) + labeltxt = '[dir=none]'; + else + labeltext = '[label="%s",dir=none]'; + end +end +edgeformat = strcat(['%d ',arctxt,' %d ',labeltxt,';\n']); +fprintf(fid, 'center = 1;\n'); +fprintf(fid, 'size=\"%d,%d\";\n', width, height); +if leftright + fprintf(fid, 'rankdir=LR;\n'); +end +Nnds = length(adj); +for node = 1:Nnds % process nodes + if isempty(node_label) + fprintf(fid, '%d;\n', node); + else + fprintf(fid, '%d [ label = "%s" ];\n', node, +node_label{node}); + end +end +for node1 = 1:Nnds % process edges + if directed + arcs = find(adj(node1,:)); % children(adj, node); + else + arcs = find(adj(node1,node1+1:Nnds)); % remove duplicate arcs + end + for node2 = arcs + fprintf(fid, edgeformat, node1, node2); + end +end +fprintf(fid, '}'); +fclose(fid); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/Old/pre_pesha_graph_to_dot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,166 @@ +function graph_to_dot(G, varargin) +% DAG_TO_DOT Make a file representing the directed graph in dotty format. +% dag_to_dot(G, ...) +% +% Optional arguments should be passed as name/value pairs [default] +% +% 'filename' - if omitted, we write to 'tmp.dot', convert this to 'tmp.ps', +% and then call ghostview automatically +% 'arc_label' - arc_label{i,j} is a string attached to the i->j arc. [""] +% 'node_label' - node_label{i} is a string attached to node i. ["i"] +% 'width' - width in inches [10] +% 'height' - height in inches [10] +% 'leftright' - 1 means layout left-to-right, 0 means top-to-bottom [0] +% 'directed' - 1 means use directed arcs, 0 means undirected [1] +% +% For details on dotty, See http://www.research.att.com/sw/tools/graphviz +% +% Example: +% G = rand(5,5); +% names = cell(5,5); +% names{1,2} = 'arc 1-2'; +% graph_to_dot(G, 'arc_label', names) +% or graph_to_dot(G, 'arc_label', 'numbers') % prints value of G(i,j) on i->j arc + +% Kevin Murphy, 1998 + +% set default args +filename = []; +node_label = []; +arc_label = []; +width = 10; +height = 10; +leftright = 0; +directed = 1; +% get optional args +args = varargin; +for i=1:2:length(args) + switch args{i} + case 'filename', filename = args{i+1}; + case 'node_label', node_label = args{i+1}; + case 'arc_label', arc_label = args{i+1}; + case 'width', width = args{i+1}; + case 'height', height = args{i+1}; + case 'leftright', leftright = args{i+1}; + case 'directed', directed = args{i+1}; + end +end + +if isstr(arc_label) & strcmp(arc_label, 'numbers') + N = length(G); + arc_label = cell(N,N); + for i=1:N + for j=1:N + arc_label{i,j} = sprintf('%4.2f', G(i,j)); + end + end +end + +if isempty(filename) + make_file(G, 'tmp.dot', node_label, arc_label, width, height, leftright, directed); + if isunix + !dot -Tps tmp.dot -o tmp.ps + + !gs tmp.ps & + else + dos('dot -Tps tmp.dot -o tmp.ps'); + dos('gsview32 tmp.ps &'); + end +else + + + make_file(G, filename, node_label, arc_label, width, height, leftright, directed); +end + + +%%%%%% + +function make_file(G, filename, node_label, arc_label, width, height, leftright, directed) + +n = length(G); +fid = fopen(filename, 'w'); +if directed + fprintf(fid, 'digraph G {\n'); +else + fprintf(fid, 'graph G {\n'); +end +fprintf(fid, 'center = 1;\n'); +fprintf(fid, 'size=\"%d,%d\";\n', width, height); +if leftright + fprintf(fid, 'rankdir=LR;\n'); +end +for i=1:n + if isempty(node_label) + fprintf(fid, '%d;\n', i); + else + fprintf(fid, '%d [ label = "%s" ];\n', i, node_label{i}); + end +end +if directed + for i=1:n + cs = children(G,i); + for j=1:length(cs) + c = cs(j); + if isempty(arc_label) + fprintf(fid, '%d -> %d;\n', i, c); + else + fprintf(fid, '%d -> %d [label="%s"];\n', i, c, arc_label{i,c}); + end + end + end +else + for i=1:n + ns = intersect(neighbors(G,i), i+1:n); % remove duplicate arcs + for j=1:length(ns) + c = ns(j); + if isempty(arc_label) + fprintf(fid, '%d -- %d [dir=none];\n', i, c); + else + fprintf(fid, '%d -- %d [label="%s",dir=none];\n', i, c, arc_label{i,c}); + end + end + end +end +fprintf(fid, '\n}'); +fclose(fid); + + + +%%%%%%%%%%%%%%% + +function cs = children(adj_mat, i, t) +% CHILDREN Return the indices of a node's children in sorted order +% c = children(adj_mat, i, t) +% +% t is an optional argument: if present, dag is assumed to be a 2-slice DBN + +if nargin < 3 + cs = find(adj_mat(i,:)); +else + if t==1 + cs = find(adj_mat(i,:)); + else + ss = length(adj_mat)/2; + j = i+ss; + cs = find(adj_mat(j,:)) + (t-2)*ss; + end +end + +%%%%%%%%%%%% + +function ps = parents(adj_mat, i) +% PARENTS Return the list of parents of node i +% ps = parents(adj_mat, i) + +ps = find(adj_mat(:,i))'; + +%%%%%%%%%%%%% + +function ns = neighbors(adj_mat, i) +% NEIGHBORS Find the parents and children of a node in a graph. +% ns = neighbors(adj_mat, i) + +ns = union(children(adj_mat, i), parents(adj_mat, i)); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/README.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +Graph visualization (automatic layout) functions +------------------------------------------------ + +This directory contains code to automatically layout and visualize +graphs. It provides a matlab interface to the graphviz program: + http://www.research.att.com/sw/tools/graphviz +Written by Kevin Murphy, Leon Peshkin, Tom Minka. + +draw_graph was written by Ali Taylan Cemgil, and is entirely +self-contained matlab: it does not need graphviz, but produces lower +quality results. +http://www.mbfys.kun.nl/~cemgil/matlab/layout.html + +See also the following URLs for other graph layout programs: + http://www.ics.uci.edu/~eppstein/gina/gdraw.html + http://www.cwi.nl/InfoVisu/
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/approxeq.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function p = approxeq(a, b, tol, rel) +% APPROXEQ Are a and b approximately equal (to within a specified tolerance)? +% p = approxeq(a, b, thresh) +% 'tol' defaults to 1e-3. +% p(i) = 1 iff abs(a(i) - b(i)) < thresh +% +% p = approxeq(a, b, thresh, 1) +% p(i) = 1 iff abs(a(i)-b(i))/abs(a(i)) < thresh + +if nargin < 3, tol = 1e-2; end +if nargin < 4, rel = 0; end + +a = a(:); +b = b(:); +d = abs(a-b); +if rel + p = ~any( (d ./ (abs(a)+eps)) > tol); +else + p = ~any(d > tol); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/arrow.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1333 @@ +function [h,yy,zz] = arrow(varargin) +% ARROW Draw a line with an arrowhead. +% +% ARROW(Start,Stop) draws a line with an arrow from Start to Stop (points +% should be vectors of length 2 or 3, or matrices with 2 or 3 +% columns), and returns the graphics handle of the arrow(s). +% +% ARROW uses the mouse (click-drag) to create an arrow. +% +% ARROW DEMO & ARROW DEMO2 show 3-D & 2-D demos of the capabilities of ARROW. +% +% ARROW may be called with a normal argument list or a property-based list. +% ARROW(Start,Stop,Length,BaseAngle,TipAngle,Width,Page,CrossDir) is +% the full normal argument list, where all but the Start and Stop +% points are optional. If you need to specify a later argument (e.g., +% Page) but want default values of earlier ones (e.g., TipAngle), +% pass an empty matrix for the earlier ones (e.g., TipAngle=[]). +% +% ARROW('Property1',PropVal1,'Property2',PropVal2,...) creates arrows with the +% given properties, using default values for any unspecified or given as +% 'default' or NaN. Some properties used for line and patch objects are +% used in a modified fashion, others are passed directly to LINE, PATCH, +% or SET. For a detailed properties explanation, call ARROW PROPERTIES. +% +% Start The starting points. B +% Stop The end points. /|\ ^ +% Length Length of the arrowhead in pixels. /|||\ | +% BaseAngle Base angle in degrees (ADE). //|||\\ L| +% TipAngle Tip angle in degrees (ABC). ///|||\\\ e| +% Width Width of the base in pixels. ////|||\\\\ n| +% Page Use hardcopy proportions. /////|D|\\\\\ g| +% CrossDir Vector || to arrowhead plane. //// ||| \\\\ t| +% NormalDir Vector out of arrowhead plane. /// ||| \\\ h| +% Ends Which end has an arrowhead. //<----->|| \\ | +% ObjectHandles Vector of handles to update. / base ||| \ V +% E angle||<-------->C +% ARROW(H,'Prop1',PropVal1,...), where H is a |||tipangle +% vector of handles to previously-created arrows ||| +% and/or line objects, will update the previously- ||| +% created arrows according to the current view -->|A|<-- width +% and any specified properties, and will convert +% two-point line objects to corresponding arrows. ARROW(H) will update +% the arrows if the current view has changed. Root, figure, or axes +% handles included in H are replaced by all descendant Arrow objects. +% +% A property list can follow any specified normal argument list, e.g., +% ARROW([1 2 3],[0 0 0],36,'BaseAngle',60) creates an arrow from (1,2,3) to +% the origin, with an arrowhead of length 36 pixels and 60-degree base angle. +% +% The basic arguments or properties can generally be vectorized to create +% multiple arrows with the same call. This is done by passing a property +% with one row per arrow, or, if all arrows are to have the same property +% value, just one row may be specified. +% +% You may want to execute AXIS(AXIS) before calling ARROW so it doesn't change +% the axes on you; ARROW determines the sizes of arrow components BEFORE the +% arrow is plotted, so if ARROW changes axis limits, arrows may be malformed. +% +% This version of ARROW uses features of MATLAB 5 and is incompatible with +% earlier MATLAB versions (ARROW for MATLAB 4.2c is available separately); +% some problems with perspective plots still exist. + +% Copyright (c)1995-1997, Erik A. Johnson <johnsone@uiuc.edu>, 8/14/97 + +% Revision history: +% 8/14/97 EAJ Added workaround for MATLAB 5.1 scalar logical transpose bug. +% 7/21/97 EAJ Fixed a few misc bugs. +% 7/14/97 EAJ Make arrow([],'Prop',...) do nothing (no old handles) +% 6/23/97 EAJ MATLAB 5 compatible version, release. +% 5/27/97 EAJ Added Line Arrows back in. Corrected a few bugs. +% 5/26/97 EAJ Changed missing Start/Stop to mouse-selected arrows. +% 5/19/97 EAJ MATLAB 5 compatible version, beta. +% 4/13/97 EAJ MATLAB 5 compatible version, alpha. +% 1/31/97 EAJ Fixed bug with multiple arrows and unspecified Z coords. +% 12/05/96 EAJ Fixed one more bug with log plots and NormalDir specified +% 10/24/96 EAJ Fixed bug with log plots and NormalDir specified +% 11/13/95 EAJ Corrected handling for 'reverse' axis directions +% 10/06/95 EAJ Corrected occasional conflict with SUBPLOT +% 4/24/95 EAJ A major rewrite. +% Fall 94 EAJ Original code. + +% Things to be done: +% - segment parsing, computing, and plotting into separate subfunctions +% - change computing from Xform to Camera paradigms +% + this will help especially with 3-D perspective plots +% + if the WarpToFill section works right, remove warning code +% + when perpsective works properly, remove perspective warning code +% - add cell property values and struct property name/values (like get/set) +% - get rid of NaN as the "default" data label +% + perhaps change userdata to a struct and don't include (or leave +% empty) the values specified as default; or use a cell containing +% an empty matrix for a default value +% - add functionality of GET to retrieve current values of ARROW properties + +% Many thanks to Keith Rogers <kerog@ai.mit.edu> for his many excellent +% suggestions and beta testing. Check out his shareware package MATDRAW. +% He has permission to distribute ARROW with MATDRAW. + +% global variable initialization +global ARROW_PERSP_WARN ARROW_STRETCH_WARN ARROW_AXLIMITS +if isempty(ARROW_PERSP_WARN ), ARROW_PERSP_WARN =1; end; +if isempty(ARROW_STRETCH_WARN), ARROW_STRETCH_WARN=1; end; + +% Handle callbacks +if (nargin>0 & isstr(varargin{1}) & strcmp(lower(varargin{1}),'callback')), + arrow_callback(varargin{2:end}); return; +end; + +% Are we doing the demo? +c = sprintf('\n'); +if (nargin==1 & isstr(varargin{1})), + arg1 = lower(varargin{1}); + if strncmp(arg1,'prop',4), arrow_props; + elseif strncmp(arg1,'demo',4) + clf reset + demo_info = arrow_demo; + if ~strncmp(arg1,'demo2',5), + hh=arrow_demo3(demo_info); + else, + hh=arrow_demo2(demo_info); + end; + if (nargout>=1), h=hh; end; + elseif strncmp(arg1,'fixlimits',3), + arrow_fixlimits(ARROW_AXLIMITS); + ARROW_AXLIMITS=[]; + elseif strncmp(arg1,'help',4), + disp(help(mfilename)); + else, + error([upper(mfilename) ' got an unknown single-argument string ''' deblank(arg1) '''.']); + end; + return; +end; + +% Check # of arguments +if (nargout>3), error([upper(mfilename) ' produces at most 3 output arguments.']); end; + +% find first property number +firstprop = nargin+1; +for k=1:length(varargin), if ~isnumeric(varargin{k}), firstprop=k; break; end; end; +lastnumeric = firstprop-1; + +% check property list +if (firstprop<=nargin), + for k=firstprop:2:nargin, + curarg = varargin{k}; + if ~isstr(curarg) | sum(size(curarg)>1)>1, + error([upper(mfilename) ' requires that a property name be a single string.']); + end; + end; + if (rem(nargin-firstprop,2)~=1), + error([upper(mfilename) ' requires that the property ''' ... + varargin{nargin} ''' be paired with a property value.']); + end; +end; + +% default output +if (nargout>0), h=[]; end; +if (nargout>1), yy=[]; end; +if (nargout>2), zz=[]; end; + +% set values to empty matrices +start = []; +stop = []; +len = []; +baseangle = []; +tipangle = []; +wid = []; +page = []; +crossdir = []; +ends = []; +ax = []; +oldh = []; +ispatch = []; +defstart = [NaN NaN NaN]; +defstop = [NaN NaN NaN]; +deflen = 16; +defbaseangle = 90; +deftipangle = 16; +defwid = 0; +defpage = 0; +defcrossdir = [NaN NaN NaN]; +defends = 1; +defoldh = []; +defispatch = 1; + +% The 'Tag' we'll put on our arrows +ArrowTag = 'Arrow'; + +% check for oldstyle arguments +if (firstprop==2), + % assume arg1 is a set of handles + oldh = varargin{1}(:); + if isempty(oldh), return; end; +elseif (firstprop>9), + error([upper(mfilename) ' takes at most 8 non-property arguments.']); +elseif (firstprop>2), + s = str2mat('start','stop','len','baseangle','tipangle','wid','page','crossdir'); + for k=1:firstprop-1, eval([deblank(s(k,:)) '=varargin{k};']); end; +end; + +% parse property pairs +extraprops={}; +for k=firstprop:2:nargin, + prop = varargin{k}; + val = varargin{k+1}; + prop = [lower(prop(:)') ' ']; + if strncmp(prop,'start' ,5), start = val; + elseif strncmp(prop,'stop' ,4), stop = val; + elseif strncmp(prop,'len' ,3), len = val(:); + elseif strncmp(prop,'base' ,4), baseangle = val(:); + elseif strncmp(prop,'tip' ,3), tipangle = val(:); + elseif strncmp(prop,'wid' ,3), wid = val(:); + elseif strncmp(prop,'page' ,4), page = val; + elseif strncmp(prop,'cross' ,5), crossdir = val; + elseif strncmp(prop,'norm' ,4), if (isstr(val)), crossdir=val; else, crossdir=val*sqrt(-1); end; + elseif strncmp(prop,'end' ,3), ends = val; + elseif strncmp(prop,'object',6), oldh = val(:); + elseif strncmp(prop,'handle',6), oldh = val(:); + elseif strncmp(prop,'type' ,4), ispatch = val; + elseif strncmp(prop,'userd' ,5), %ignore it + else, + % make sure it is a valid patch or line property + eval('get(0,[''DefaultPatch'' varargin{k}]);err=0;','err=1;'); errstr=lasterr; + if (err), eval('get(0,[''DefaultLine'' varargin{k}]);err=0;','err=1;'); end; + if (err), + errstr(1:max(find(errstr==setstr(13)|errstr==setstr(10)))) = ''; + error([upper(mfilename) ' got ' errstr]); + end; + extraprops={extraprops{:},varargin{k},val}; + end; +end; + +% Check if we got 'default' values +start = arrow_defcheck(start ,defstart ,'Start' ); +stop = arrow_defcheck(stop ,defstop ,'Stop' ); +len = arrow_defcheck(len ,deflen ,'Length' ); +baseangle = arrow_defcheck(baseangle,defbaseangle,'BaseAngle' ); +tipangle = arrow_defcheck(tipangle ,deftipangle ,'TipAngle' ); +wid = arrow_defcheck(wid ,defwid ,'Width' ); +crossdir = arrow_defcheck(crossdir ,defcrossdir ,'CrossDir' ); +page = arrow_defcheck(page ,defpage ,'Page' ); +ends = arrow_defcheck(ends ,defends ,'' ); +oldh = arrow_defcheck(oldh ,[] ,'ObjectHandles'); +ispatch = arrow_defcheck(ispatch ,defispatch ,'' ); + +% check transpose on arguments +[m,n]=size(start ); if any(m==[2 3])&(n==1|n>3), start = start'; end; +[m,n]=size(stop ); if any(m==[2 3])&(n==1|n>3), stop = stop'; end; +[m,n]=size(crossdir); if any(m==[2 3])&(n==1|n>3), crossdir = crossdir'; end; + +% convert strings to numbers +if ~isempty(ends) & isstr(ends), + endsorig = ends; + [m,n] = size(ends); + col = lower([ends(:,1:min(3,n)) ones(m,max(0,3-n))*' ']); + ends = NaN*ones(m,1); + oo = ones(1,m); + ii=find(all(col'==['non']'*oo)'); if ~isempty(ii), ends(ii)=ones(length(ii),1)*0; end; + ii=find(all(col'==['sto']'*oo)'); if ~isempty(ii), ends(ii)=ones(length(ii),1)*1; end; + ii=find(all(col'==['sta']'*oo)'); if ~isempty(ii), ends(ii)=ones(length(ii),1)*2; end; + ii=find(all(col'==['bot']'*oo)'); if ~isempty(ii), ends(ii)=ones(length(ii),1)*3; end; + if any(isnan(ends)), + ii = min(find(isnan(ends))); + error([upper(mfilename) ' does not recognize ' deblank(endsorig(ii,:)) ' as a valid Ends value.']); + end; +else, + ends = ends(:); +end; +if ~isempty(ispatch) & isstr(ispatch), + col = lower(ispatch(:,1)); + patchchar='p'; linechar='l'; defchar=' '; + mask = col~=patchchar & col~=linechar & col~=defchar; + if any(mask) + error([upper(mfilename) ' does not recognize ' deblank(ispatch(min(find(mask)),:)) ' as a valid Type value.']); + end; + ispatch = (col==patchchar)*1 + (col==linechar)*0 + (col==defchar)*defispatch; +else, + ispatch = ispatch(:); +end; +oldh = oldh(:); + +% check object handles +if ~all(ishandle(oldh)), error([upper(mfilename) ' got invalid object handles.']); end; + +% expand root, figure, and axes handles +if ~isempty(oldh), + ohtype = get(oldh,'Type'); + mask = strcmp(ohtype,'root') | strcmp(ohtype,'figure') | strcmp(ohtype,'axes'); + if any(mask), + oldh = num2cell(oldh); + for ii=find(mask)', + oldh(ii) = {findobj(oldh{ii},'Tag',ArrowTag)}; + end; + oldh = cat(1,oldh{:}); + if isempty(oldh), return; end; % no arrows to modify, so just leave + end; +end; + +% largest argument length +[mstart,junk]=size(start); [mstop,junk]=size(stop); [mcrossdir,junk]=size(crossdir); +argsizes = [length(oldh) mstart mstop ... + length(len) length(baseangle) length(tipangle) ... + length(wid) length(page) mcrossdir length(ends) ]; +args=['length(ObjectHandle) '; ... + '#rows(Start) '; ... + '#rows(Stop) '; ... + 'length(Length) '; ... + 'length(BaseAngle) '; ... + 'length(TipAngle) '; ... + 'length(Width) '; ... + 'length(Page) '; ... + '#rows(CrossDir) '; ... + '#rows(Ends) ']; +if (any(imag(crossdir(:))~=0)), + args(9,:) = '#rows(NormalDir) '; +end; +if isempty(oldh), + narrows = max(argsizes); +else, + narrows = length(oldh); +end; +if (narrows<=0), narrows=1; end; + +% Check size of arguments +ii = find((argsizes~=0)&(argsizes~=1)&(argsizes~=narrows)); +if ~isempty(ii), + s = args(ii',:); + while ((size(s,2)>1)&((abs(s(:,size(s,2)))==0)|(abs(s(:,size(s,2)))==abs(' ')))), + s = s(:,1:size(s,2)-1); + end; + s = [ones(length(ii),1)*[upper(mfilename) ' requires that '] s ... + ones(length(ii),1)*[' equal the # of arrows (' num2str(narrows) ').' c]]; + s = s'; + s = s(:)'; + s = s(1:length(s)-1); + error(setstr(s)); +end; + +% check element length in Start, Stop, and CrossDir +if ~isempty(start), + [m,n] = size(start); + if (n==2), + start = [start NaN*ones(m,1)]; + elseif (n~=3), + error([upper(mfilename) ' requires 2- or 3-element Start points.']); + end; +end; +if ~isempty(stop), + [m,n] = size(stop); + if (n==2), + stop = [stop NaN*ones(m,1)]; + elseif (n~=3), + error([upper(mfilename) ' requires 2- or 3-element Stop points.']); + end; +end; +if ~isempty(crossdir), + [m,n] = size(crossdir); + if (n<3), + crossdir = [crossdir NaN*ones(m,3-n)]; + elseif (n~=3), + if (all(imag(crossdir(:))==0)), + error([upper(mfilename) ' requires 2- or 3-element CrossDir vectors.']); + else, + error([upper(mfilename) ' requires 2- or 3-element NormalDir vectors.']); + end; + end; +end; + +% fill empty arguments +if isempty(start ), start = [Inf Inf Inf]; end; +if isempty(stop ), stop = [Inf Inf Inf]; end; +if isempty(len ), len = Inf; end; +if isempty(baseangle ), baseangle = Inf; end; +if isempty(tipangle ), tipangle = Inf; end; +if isempty(wid ), wid = Inf; end; +if isempty(page ), page = Inf; end; +if isempty(crossdir ), crossdir = [Inf Inf Inf]; end; +if isempty(ends ), ends = Inf; end; +if isempty(ispatch ), ispatch = Inf; end; + +% expand single-column arguments +o = ones(narrows,1); +if (size(start ,1)==1), start = o * start ; end; +if (size(stop ,1)==1), stop = o * stop ; end; +if (length(len )==1), len = o * len ; end; +if (length(baseangle )==1), baseangle = o * baseangle ; end; +if (length(tipangle )==1), tipangle = o * tipangle ; end; +if (length(wid )==1), wid = o * wid ; end; +if (length(page )==1), page = o * page ; end; +if (size(crossdir ,1)==1), crossdir = o * crossdir ; end; +if (length(ends )==1), ends = o * ends ; end; +if (length(ispatch )==1), ispatch = o * ispatch ; end; +ax = o * gca; + +% if we've got handles, get the defaults from the handles +if ~isempty(oldh), + for k=1:narrows, + oh = oldh(k); + ud = get(oh,'UserData'); + ax(k) = get(oh,'Parent'); + ohtype = get(oh,'Type'); + if strcmp(get(oh,'Tag'),ArrowTag), % if it's an arrow already + if isinf(ispatch(k)), ispatch(k)=strcmp(ohtype,'patch'); end; + % arrow UserData format: [start' stop' len base tip wid page crossdir' ends] + start0 = ud(1:3); + stop0 = ud(4:6); + if (isinf(len(k))), len(k) = ud( 7); end; + if (isinf(baseangle(k))), baseangle(k) = ud( 8); end; + if (isinf(tipangle(k))), tipangle(k) = ud( 9); end; + if (isinf(wid(k))), wid(k) = ud(10); end; + if (isinf(page(k))), page(k) = ud(11); end; + if (isinf(crossdir(k,1))), crossdir(k,1) = ud(12); end; + if (isinf(crossdir(k,2))), crossdir(k,2) = ud(13); end; + if (isinf(crossdir(k,3))), crossdir(k,3) = ud(14); end; + if (isinf(ends(k))), ends(k) = ud(15); end; + elseif strcmp(ohtype,'line')|strcmp(ohtype,'patch'), % it's a non-arrow line or patch + convLineToPatch = 1; %set to make arrow patches when converting from lines. + if isinf(ispatch(k)), ispatch(k)=convLineToPatch|strcmp(ohtype,'patch'); end; + x=get(oh,'XData'); x=x(~isnan(x(:))); if isempty(x), x=NaN; end; + y=get(oh,'YData'); y=y(~isnan(y(:))); if isempty(y), y=NaN; end; + z=get(oh,'ZData'); z=z(~isnan(z(:))); if isempty(z), z=NaN; end; + start0 = [x(1) y(1) z(1) ]; + stop0 = [x(end) y(end) z(end)]; + else, + error([upper(mfilename) ' cannot convert ' ohtype ' objects.']); + end; + ii=find(isinf(start(k,:))); if ~isempty(ii), start(k,ii)=start0(ii); end; + ii=find(isinf(stop( k,:))); if ~isempty(ii), stop( k,ii)=stop0( ii); end; + end; +end; + +% convert Inf's to NaN's +start( isinf(start )) = NaN; +stop( isinf(stop )) = NaN; +len( isinf(len )) = NaN; +baseangle( isinf(baseangle)) = NaN; +tipangle( isinf(tipangle )) = NaN; +wid( isinf(wid )) = NaN; +page( isinf(page )) = NaN; +crossdir( isinf(crossdir )) = NaN; +ends( isinf(ends )) = NaN; +ispatch( isinf(ispatch )) = NaN; + +% set up the UserData data (here so not corrupted by log10's and such) +ud = [start stop len baseangle tipangle wid page crossdir ends]; + +% Set Page defaults +%page = (~isnan(page))&(page); +if isnan(page) + page = 0; +end + +% Get axes limits, range, min; correct for aspect ratio and log scale +axm = zeros(3,narrows); +axr = zeros(3,narrows); +axrev = zeros(3,narrows); +ap = zeros(2,narrows); +xyzlog = zeros(3,narrows); +limmin = zeros(2,narrows); +limrange = zeros(2,narrows); +oldaxlims = zeros(narrows,7); +oneax = all(ax==ax(1)); +if (oneax), + T = zeros(4,4); + invT = zeros(4,4); +else, + T = zeros(16,narrows); + invT = zeros(16,narrows); +end; +axnotdone = logical(ones(size(ax))); +while (any(axnotdone)), + ii = min(find(axnotdone)); + curax = ax(ii); + curpage = page(ii); + % get axes limits and aspect ratio + axl = [get(curax,'XLim'); get(curax,'YLim'); get(curax,'ZLim')]; + oldaxlims(min(find(oldaxlims(:,1)==0)),:) = [curax reshape(axl',1,6)]; + % get axes size in pixels (points) + u = get(curax,'Units'); + axposoldunits = get(curax,'Position'); + really_curpage = curpage & strcmp(u,'normalized'); + if (really_curpage), + curfig = get(curax,'Parent'); + pu = get(curfig,'PaperUnits'); + set(curfig,'PaperUnits','points'); + pp = get(curfig,'PaperPosition'); + set(curfig,'PaperUnits',pu); + set(curax,'Units','pixels'); + curapscreen = get(curax,'Position'); + set(curax,'Units','normalized'); + curap = pp.*get(curax,'Position'); + else, + set(curax,'Units','pixels'); + curapscreen = get(curax,'Position'); + curap = curapscreen; + end; + set(curax,'Units',u); + set(curax,'Position',axposoldunits); + % handle non-stretched axes position + str_stretch = { 'DataAspectRatioMode' ; ... + 'PlotBoxAspectRatioMode' ; ... + 'CameraViewAngleMode' }; + str_camera = { 'CameraPositionMode' ; ... + 'CameraTargetMode' ; ... + 'CameraViewAngleMode' ; ... + 'CameraUpVectorMode' }; + notstretched = strcmp(get(curax,str_stretch),'manual'); + manualcamera = strcmp(get(curax,str_camera),'manual'); + if ~arrow_WarpToFill(notstretched,manualcamera,curax), + % give a warning that this has not been thoroughly tested + if 0 & ARROW_STRETCH_WARN, + ARROW_STRETCH_WARN = 0; + strs = {str_stretch{1:2},str_camera{:}}; + strs = [char(ones(length(strs),1)*sprintf('\n ')) char(strs)]'; + warning([upper(mfilename) ' may not yet work quite right ' ... + 'if any of the following are ''manual'':' strs(:).']); + end; + % find the true pixel size of the actual axes + texttmp = text(axl(1,[1 2 2 1 1 2 2 1]), ... + axl(2,[1 1 2 2 1 1 2 2]), ... + axl(3,[1 1 1 1 2 2 2 2]),''); + set(texttmp,'Units','points'); + textpos = get(texttmp,'Position'); + delete(texttmp); + textpos = cat(1,textpos{:}); + textpos = max(textpos(:,1:2)) - min(textpos(:,1:2)); + % adjust the axes position + if (really_curpage), + % adjust to printed size + textpos = textpos * min(curap(3:4)./textpos); + curap = [curap(1:2)+(curap(3:4)-textpos)/2 textpos]; + else, + % adjust for pixel roundoff + textpos = textpos * min(curapscreen(3:4)./textpos); + curap = [curap(1:2)+(curap(3:4)-textpos)/2 textpos]; + end; + end; + if ARROW_PERSP_WARN & ~strcmp(get(curax,'Projection'),'orthographic'), + ARROW_PERSP_WARN = 0; + warning([upper(mfilename) ' does not yet work right for 3-D perspective projection.']); + end; + % adjust limits for log scale on axes + curxyzlog = [strcmp(get(curax,'XScale'),'log'); ... + strcmp(get(curax,'YScale'),'log'); ... + strcmp(get(curax,'ZScale'),'log')]; + if (any(curxyzlog)), + ii = find([curxyzlog;curxyzlog]); + if (any(axl(ii)<=0)), + error([upper(mfilename) ' does not support non-positive limits on log-scaled axes.']); + else, + axl(ii) = log10(axl(ii)); + end; + end; + % correct for 'reverse' direction on axes; + curreverse = [strcmp(get(curax,'XDir'),'reverse'); ... + strcmp(get(curax,'YDir'),'reverse'); ... + strcmp(get(curax,'ZDir'),'reverse')]; + ii = find(curreverse); + if ~isempty(ii), + axl(ii,[1 2])=-axl(ii,[2 1]); + end; + % compute the range of 2-D values + curT = get(curax,'Xform'); + lim = curT*[0 1 0 1 0 1 0 1;0 0 1 1 0 0 1 1;0 0 0 0 1 1 1 1;1 1 1 1 1 1 1 1]; + lim = lim(1:2,:)./([1;1]*lim(4,:)); + curlimmin = min(lim')'; + curlimrange = max(lim')' - curlimmin; + curinvT = inv(curT); + if (~oneax), + curT = curT.'; + curinvT = curinvT.'; + curT = curT(:); + curinvT = curinvT(:); + end; + % check which arrows to which cur corresponds + ii = find((ax==curax)&(page==curpage)); + oo = ones(1,length(ii)); + axr(:,ii) = diff(axl')' * oo; + axm(:,ii) = axl(:,1) * oo; + axrev(:,ii) = curreverse * oo; + ap(:,ii) = curap(3:4)' * oo; + xyzlog(:,ii) = curxyzlog * oo; + limmin(:,ii) = curlimmin * oo; + limrange(:,ii) = curlimrange * oo; + if (oneax), + T = curT; + invT = curinvT; + else, + T(:,ii) = curT * oo; + invT(:,ii) = curinvT * oo; + end; + axnotdone(ii) = zeros(1,length(ii)); +end; +oldaxlims(oldaxlims(:,1)==0,:)=[]; + +% correct for log scales +curxyzlog = xyzlog.'; +ii = find(curxyzlog(:)); +if ~isempty(ii), + start( ii) = real(log10(start( ii))); + stop( ii) = real(log10(stop( ii))); + if (all(imag(crossdir)==0)), % pulled (ii) subscript on crossdir, 12/5/96 eaj + crossdir(ii) = real(log10(crossdir(ii))); + end; +end; + +% correct for reverse directions +ii = find(axrev.'); +if ~isempty(ii), + start( ii) = -start( ii); + stop( ii) = -stop( ii); + crossdir(ii) = -crossdir(ii); +end; + +% transpose start/stop values +start = start.'; +stop = stop.'; + +% take care of defaults, page was done above +ii=find(isnan(start(:) )); if ~isempty(ii), start(ii) = axm(ii)+axr(ii)/2; end; +ii=find(isnan(stop(:) )); if ~isempty(ii), stop(ii) = axm(ii)+axr(ii)/2; end; +ii=find(isnan(crossdir(:) )); if ~isempty(ii), crossdir(ii) = zeros(length(ii),1); end; +ii=find(isnan(len )); if ~isempty(ii), len(ii) = ones(length(ii),1)*deflen; end; +ii=find(isnan(baseangle )); if ~isempty(ii), baseangle(ii) = ones(length(ii),1)*defbaseangle; end; +ii=find(isnan(tipangle )); if ~isempty(ii), tipangle(ii) = ones(length(ii),1)*deftipangle; end; +ii=find(isnan(wid )); if ~isempty(ii), wid(ii) = ones(length(ii),1)*defwid; end; +ii=find(isnan(ends )); if ~isempty(ii), ends(ii) = ones(length(ii),1)*defends; end; + +% transpose rest of values +len = len.'; +baseangle = baseangle.'; +tipangle = tipangle.'; +wid = wid.'; +page = page.'; +crossdir = crossdir.'; +ends = ends.'; +ax = ax.'; + +% given x, a 3xN matrix of points in 3-space; +% want to convert to X, the corresponding 4xN 2-space matrix +% +% tmp1=[(x-axm)./axr; ones(1,size(x,1))]; +% if (oneax), X=T*tmp1; +% else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T.*tmp1; +% tmp2=zeros(4,4*N); tmp2(:)=tmp1(:); +% X=zeros(4,N); X(:)=sum(tmp2)'; end; +% X = X ./ (ones(4,1)*X(4,:)); + +% for all points with start==stop, start=stop-(verysmallvalue)*(up-direction); +ii = find(all(start==stop)); +if ~isempty(ii), + % find an arrowdir vertical on screen and perpendicular to viewer + % transform to 2-D + tmp1 = [(stop(:,ii)-axm(:,ii))./axr(:,ii);ones(1,length(ii))]; + if (oneax), twoD=T*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,ii).*tmp1; + tmp2=zeros(4,4*length(ii)); tmp2(:)=tmp1(:); + twoD=zeros(4,length(ii)); twoD(:)=sum(tmp2)'; end; + twoD=twoD./(ones(4,1)*twoD(4,:)); + % move the start point down just slightly + tmp1 = twoD + [0;-1/1000;0;0]*(limrange(2,ii)./ap(2,ii)); + % transform back to 3-D + if (oneax), threeD=invT*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT(:,ii).*tmp1; + tmp2=zeros(4,4*length(ii)); tmp2(:)=tmp1(:); + threeD=zeros(4,length(ii)); threeD(:)=sum(tmp2)'; end; + start(:,ii) = (threeD(1:3,:)./(ones(3,1)*threeD(4,:))).*axr(:,ii)+axm(:,ii); +end; + +% compute along-arrow points +% transform Start points + tmp1=[(start-axm)./axr;ones(1,narrows)]; + if (oneax), X0=T*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T.*tmp1; + tmp2=zeros(4,4*narrows); tmp2(:)=tmp1(:); + X0=zeros(4,narrows); X0(:)=sum(tmp2)'; end; + X0=X0./(ones(4,1)*X0(4,:)); +% transform Stop points + tmp1=[(stop-axm)./axr;ones(1,narrows)]; + if (oneax), Xf=T*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T.*tmp1; + tmp2=zeros(4,4*narrows); tmp2(:)=tmp1(:); + Xf=zeros(4,narrows); Xf(:)=sum(tmp2)'; end; + Xf=Xf./(ones(4,1)*Xf(4,:)); +% compute pixel distance between points + D = sqrt(sum(((Xf(1:2,:)-X0(1:2,:)).*(ap./limrange)).^2)); +% compute and modify along-arrow distances + len1 = len; + len2 = len - (len.*tan(tipangle/180*pi)-wid/2).*tan((90-baseangle)/180*pi); + slen0 = zeros(1,narrows); + slen1 = len1 .* ((ends==2)|(ends==3)); + slen2 = len2 .* ((ends==2)|(ends==3)); + len0 = zeros(1,narrows); + len1 = len1 .* ((ends==1)|(ends==3)); + len2 = len2 .* ((ends==1)|(ends==3)); + % for no start arrowhead + ii=find((ends==1)&(D<len2)); + if ~isempty(ii), + slen0(ii) = D(ii)-len2(ii); + end; + % for no end arrowhead + ii=find((ends==2)&(D<slen2)); + if ~isempty(ii), + len0(ii) = D(ii)-slen2(ii); + end; + len1 = len1 + len0; + len2 = len2 + len0; + slen1 = slen1 + slen0; + slen2 = slen2 + slen0; + % note: the division by D below will probably not be accurate if both + % of the following are true: + % 1. the ratio of the line length to the arrowhead + % length is large + % 2. the view is highly perspective. +% compute stoppoints + tmp1=X0.*(ones(4,1)*(len0./D))+Xf.*(ones(4,1)*(1-len0./D)); + if (oneax), tmp3=invT*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1; + tmp2=zeros(4,4*narrows); tmp2(:)=tmp1(:); + tmp3=zeros(4,narrows); tmp3(:)=sum(tmp2)'; end; + stoppoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm; +% compute tippoints + tmp1=X0.*(ones(4,1)*(len1./D))+Xf.*(ones(4,1)*(1-len1./D)); + if (oneax), tmp3=invT*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1; + tmp2=zeros(4,4*narrows); tmp2(:)=tmp1(:); + tmp3=zeros(4,narrows); tmp3(:)=sum(tmp2)'; end; + tippoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm; +% compute basepoints + tmp1=X0.*(ones(4,1)*(len2./D))+Xf.*(ones(4,1)*(1-len2./D)); + if (oneax), tmp3=invT*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1; + tmp2=zeros(4,4*narrows); tmp2(:)=tmp1(:); + tmp3=zeros(4,narrows); tmp3(:)=sum(tmp2)'; end; + basepoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm; +% compute startpoints + tmp1=X0.*(ones(4,1)*(1-slen0./D))+Xf.*(ones(4,1)*(slen0./D)); + if (oneax), tmp3=invT*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1; + tmp2=zeros(4,4*narrows); tmp2(:)=tmp1(:); + tmp3=zeros(4,narrows); tmp3(:)=sum(tmp2)'; end; + startpoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm; +% compute stippoints + tmp1=X0.*(ones(4,1)*(1-slen1./D))+Xf.*(ones(4,1)*(slen1./D)); + if (oneax), tmp3=invT*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1; + tmp2=zeros(4,4*narrows); tmp2(:)=tmp1(:); + tmp3=zeros(4,narrows); tmp3(:)=sum(tmp2)'; end; + stippoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm; +% compute sbasepoints + tmp1=X0.*(ones(4,1)*(1-slen2./D))+Xf.*(ones(4,1)*(slen2./D)); + if (oneax), tmp3=invT*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=invT.*tmp1; + tmp2=zeros(4,4*narrows); tmp2(:)=tmp1(:); + tmp3=zeros(4,narrows); tmp3(:)=sum(tmp2)'; end; + sbasepoint = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)).*axr+axm; + +% compute cross-arrow directions for arrows with NormalDir specified +if (any(imag(crossdir(:))~=0)), + ii = find(any(imag(crossdir)~=0)); + crossdir(:,ii) = cross((stop(:,ii)-start(:,ii))./axr(:,ii), ... + imag(crossdir(:,ii))).*axr(:,ii); +end; + +% compute cross-arrow directions +basecross = crossdir + basepoint; +tipcross = crossdir + tippoint; +sbasecross = crossdir + sbasepoint; +stipcross = crossdir + stippoint; +ii = find(all(crossdir==0)|any(isnan(crossdir))); +if ~isempty(ii), + numii = length(ii); + % transform start points + tmp1 = [basepoint(:,ii) tippoint(:,ii) sbasepoint(:,ii) stippoint(:,ii)]; + tmp1 = (tmp1-axm(:,[ii ii ii ii])) ./ axr(:,[ii ii ii ii]); + tmp1 = [tmp1; ones(1,4*numii)]; + if (oneax), X0=T*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,[ii ii ii ii]).*tmp1; + tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:); + X0=zeros(4,4*numii); X0(:)=sum(tmp2)'; end; + X0=X0./(ones(4,1)*X0(4,:)); + % transform stop points + tmp1 = [(2*stop(:,ii)-start(:,ii)-axm(:,ii))./axr(:,ii);ones(1,numii)]; + tmp1 = [tmp1 tmp1 tmp1 tmp1]; + if (oneax), Xf=T*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=T(:,[ii ii ii ii]).*tmp1; + tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:); + Xf=zeros(4,4*numii); Xf(:)=sum(tmp2)'; end; + Xf=Xf./(ones(4,1)*Xf(4,:)); + % compute perpendicular directions + pixfact = ((limrange(1,ii)./limrange(2,ii)).*(ap(2,ii)./ap(1,ii))).^2; + pixfact = [pixfact pixfact pixfact pixfact]; + pixfact = [pixfact;1./pixfact]; + [dummyval,jj] = max(abs(Xf(1:2,:)-X0(1:2,:))); + jj1 = ((1:4)'*ones(1,length(jj))==ones(4,1)*jj); + jj2 = ((1:4)'*ones(1,length(jj))==ones(4,1)*(3-jj)); + jj3 = jj1(1:2,:); + Xp = X0; + Xp(jj2) = X0(jj2) + ones(sum(jj2(:)),1); + Xp(jj1) = X0(jj1) - (Xf(jj2)-X0(jj2))./(Xf(jj1)-X0(jj1)) .* pixfact(jj3); + % inverse transform the cross points + if (oneax), Xp=invT*Xp; + else, tmp1=[Xp;Xp;Xp;Xp]; tmp1=invT(:,[ii ii ii ii]).*tmp1; + tmp2=zeros(4,16*numii); tmp2(:)=tmp1(:); + Xp=zeros(4,4*numii); Xp(:)=sum(tmp2)'; end; + Xp=(Xp(1:3,:)./(ones(3,1)*Xp(4,:))).*axr(:,[ii ii ii ii])+axm(:,[ii ii ii ii]); + basecross(:,ii) = Xp(:,0*numii+(1:numii)); + tipcross(:,ii) = Xp(:,1*numii+(1:numii)); + sbasecross(:,ii) = Xp(:,2*numii+(1:numii)); + stipcross(:,ii) = Xp(:,3*numii+(1:numii)); +end; + +% compute all points +% compute start points + axm11 = [axm axm axm axm axm axm axm axm axm axm axm]; + axr11 = [axr axr axr axr axr axr axr axr axr axr axr]; + st = [stoppoint tippoint basepoint sbasepoint stippoint startpoint stippoint sbasepoint basepoint tippoint stoppoint]; + tmp1 = (st - axm11) ./ axr11; + tmp1 = [tmp1; ones(1,size(tmp1,2))]; + if (oneax), X0=T*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[T T T T T T T T T T T].*tmp1; + tmp2=zeros(4,44*narrows); tmp2(:)=tmp1(:); + X0=zeros(4,11*narrows); X0(:)=sum(tmp2)'; end; + X0=X0./(ones(4,1)*X0(4,:)); +% compute stop points + tmp1 = ([start tipcross basecross sbasecross stipcross stop stipcross sbasecross basecross tipcross start] ... + - axm11) ./ axr11; + tmp1 = [tmp1; ones(1,size(tmp1,2))]; + if (oneax), Xf=T*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[T T T T T T T T T T T].*tmp1; + tmp2=zeros(4,44*narrows); tmp2(:)=tmp1(:); + Xf=zeros(4,11*narrows); Xf(:)=sum(tmp2)'; end; + Xf=Xf./(ones(4,1)*Xf(4,:)); +% compute lengths + len0 = len.*((ends==1)|(ends==3)).*tan(tipangle/180*pi); + slen0 = len.*((ends==2)|(ends==3)).*tan(tipangle/180*pi); + le = [zeros(1,narrows) len0 wid/2 wid/2 slen0 zeros(1,narrows) -slen0 -wid/2 -wid/2 -len0 zeros(1,narrows)]; + aprange = ap./limrange; + aprange = [aprange aprange aprange aprange aprange aprange aprange aprange aprange aprange aprange]; + D = sqrt(sum(((Xf(1:2,:)-X0(1:2,:)).*aprange).^2)); + Dii=find(D==0); if ~isempty(Dii), D=D+(D==0); le(Dii)=zeros(1,length(Dii)); end; %should fix DivideByZero warnings + tmp1 = X0.*(ones(4,1)*(1-le./D)) + Xf.*(ones(4,1)*(le./D)); +% inverse transform + if (oneax), tmp3=invT*tmp1; + else, tmp1=[tmp1;tmp1;tmp1;tmp1]; tmp1=[invT invT invT invT invT invT invT invT invT invT invT].*tmp1; + tmp2=zeros(4,44*narrows); tmp2(:)=tmp1(:); + tmp3=zeros(4,11*narrows); tmp3(:)=sum(tmp2)'; end; + pts = tmp3(1:3,:)./(ones(3,1)*tmp3(4,:)) .* axr11 + axm11; + +% correct for ones where the crossdir was specified +ii = find(~(all(crossdir==0)|any(isnan(crossdir)))); +if ~isempty(ii), + D1 = [pts(:,1*narrows+ii)-pts(:,9*narrows+ii) ... + pts(:,2*narrows+ii)-pts(:,8*narrows+ii) ... + pts(:,3*narrows+ii)-pts(:,7*narrows+ii) ... + pts(:,4*narrows+ii)-pts(:,6*narrows+ii) ... + pts(:,6*narrows+ii)-pts(:,4*narrows+ii) ... + pts(:,7*narrows+ii)-pts(:,3*narrows+ii) ... + pts(:,8*narrows+ii)-pts(:,2*narrows+ii) ... + pts(:,9*narrows+ii)-pts(:,1*narrows+ii)]/2; + ii = ii'*ones(1,8) + ones(length(ii),1)*[1:4 6:9]*narrows; + ii = ii(:)'; + pts(:,ii) = st(:,ii) + D1; +end; + + +% readjust for reverse directions +iicols=(1:narrows)'; iicols=iicols(:,ones(1,11)); iicols=iicols(:).'; +tmp1=axrev(:,iicols); +ii = find(tmp1(:)); if ~isempty(ii), pts(ii)=-pts(ii); end; + +% readjust for log scale on axes +tmp1=xyzlog(:,iicols); +ii = find(tmp1(:)); if ~isempty(ii), pts(ii)=10.^pts(ii); end; + +% compute the x,y,z coordinates of the patches; +ii = narrows*(0:10)'*ones(1,narrows) + ones(11,1)*(1:narrows); +ii = ii(:)'; +x = zeros(11,narrows); +y = zeros(11,narrows); +z = zeros(11,narrows); +x(:) = pts(1,ii)'; +y(:) = pts(2,ii)'; +z(:) = pts(3,ii)'; + +% do the output +if (nargout<=1) + % % create or modify the patches + if isnan(ispatch), ispatch =0; end + newpatch = ispatch & (isempty(oldh)|~strcmp(get(oldh,'Type'),'patch')); + newline = ~ispatch & (isempty(oldh)|~strcmp(get(oldh,'Type'),'line')); + if isempty(oldh), H=zeros(narrows,1); else, H=oldh; end; +% % make or modify the arrows + for k=1:narrows, + if all(isnan(ud(k,[3 6])))&arrow_is2DXY(ax(k)), zz=[]; else, zz=z(:,k); end; + xyz = {'XData',x(:,k),'YData',y(:,k),'ZData',zz,'Tag',ArrowTag}; + if newpatch(k)|newline(k), + if newpatch(k), + H(k) = patch(xyz{:}); + else, + H(k) = line(xyz{:}); + end; + if ~isempty(oldh), arrow_copyprops(oldh(k),H(k)); end; + else, + if ispatch(k), xyz={xyz{:},'CData',[]}; end; + set(H(k),xyz{:}); + end; + end; + if ~isempty(oldh), delete(oldh(oldh~=H)); end; +% % additional properties + set(H,'Clipping','off'); + set(H,{'UserData'},num2cell(ud,2)); + if (length(extraprops)>0), set(H,extraprops{:}); end; + % handle choosing arrow Start and/or Stop locations if unspecified + [H,oldaxlims,errstr] = arrow_clicks(H,ud,x,y,z,ax,oldaxlims); + if ~isempty(errstr), error([upper(mfilename) ' got ' errstr]); end; + % set the output + if (nargout>0), h=H; end; + % make sure the axis limits did not change + if isempty(oldaxlims), + ARROW_AXLIMITS = []; + else, + lims = get(oldaxlims(:,1),{'XLim','YLim','ZLim'})'; + lims = reshape(cat(2,lims{:}),6,size(lims,2)); + %mask = arrow_is2DXY(oldaxlims(:,1)); + %oldaxlims(mask,6:7) = lims(5:6,mask)'; + ARROW_AXLIMITS = oldaxlims(find(any(oldaxlims(:,2:7)'~=lims)),:); + if ~isempty(ARROW_AXLIMITS), + warning(arrow_warnlimits(ARROW_AXLIMITS,narrows)); + end; + end; +else, + % don't create the patch, just return the data + h=x; + yy=y; + zz=z; +end; + + + +function out = arrow_defcheck(in,def,prop) +% check if we got 'default' values + out = in; + if ~isstr(in), return; end; + if size(in,1)==1 & strncmp(lower(in),'def',3), + out = def; + elseif ~isempty(prop), + error([upper(mfilename) ' does not recognize ''' in(:)' ''' as a valid ''' prop ''' string.']); + end; + + + +function [H,oldaxlims,errstr] = arrow_clicks(H,ud,x,y,z,ax,oldaxlims) +% handle choosing arrow Start and/or Stop locations if necessary + errstr = ''; + if isempty(H)|isempty(ud)|isempty(x), return; end; + % determine which (if any) need Start and/or Stop + needStart = all(isnan(ud(:,1:3)'))'; + needStop = all(isnan(ud(:,4:6)'))'; + mask = any(needStart|needStop); + if ~any(mask), return; end; + ud(~mask,:)=[]; ax(:,~mask)=[]; + x(:,~mask)=[]; y(:,~mask)=[]; z(:,~mask)=[]; + % make them invisible for the time being + set(H,'Visible','off'); + % save the current axes and limits modes; set to manual for the time being + oldAx = gca; + limModes=get(ax(:),{'XLimMode','YLimMode','ZLimMode'}); + set(ax(:),{'XLimMode','YLimMode','ZLimMode'},{'manual','manual','manual'}); + % loop over each arrow that requires attention + jj = find(mask); + for ii=1:length(jj), + h = H(jj(ii)); + axes(ax(ii)); + % figure out correct call + if needStart(ii), prop='Start'; else, prop='Stop'; end; + [wasInterrupted,errstr] = arrow_click(needStart(ii)&needStop(ii),h,prop,ax(ii)); + % handle errors and control-C + if wasInterrupted, + delete(H(jj(ii:end))); + H(jj(ii:end))=[]; + oldaxlims(jj(ii:end),:)=[]; + break; + end; + end; + % restore the axes and limit modes + axes(oldAx); + set(ax(:),{'XLimMode','YLimMode','ZLimMode'},limModes); + +function [wasInterrupted,errstr] = arrow_click(lockStart,H,prop,ax) +% handle the clicks for one arrow + fig = get(ax,'Parent'); + % save some things + oldFigProps = {'Pointer','WindowButtonMotionFcn','WindowButtonUpFcn'}; + oldFigValue = get(fig,oldFigProps); + oldArrowProps = {'EraseMode'}; + oldArrowValue = get(H,oldArrowProps); + set(H,'EraseMode','background'); %because 'xor' makes shaft invisible unless Width>1 + global ARROW_CLICK_H ARROW_CLICK_PROP ARROW_CLICK_AX ARROW_CLICK_USE_Z + ARROW_CLICK_H=H; ARROW_CLICK_PROP=prop; ARROW_CLICK_AX=ax; + ARROW_CLICK_USE_Z=~arrow_is2DXY(ax)|~arrow_planarkids(ax); + set(fig,'Pointer','crosshair'); + % set up the WindowButtonMotion so we can see the arrow while moving around + set(fig,'WindowButtonUpFcn','set(gcf,''WindowButtonUpFcn'','''')', ... + 'WindowButtonMotionFcn',''); + if ~lockStart, + set(H,'Visible','on'); + set(fig,'WindowButtonMotionFcn',[mfilename '(''callback'',''motion'');']); + end; + % wait for the button to be pressed + [wasKeyPress,wasInterrupted,errstr] = arrow_wfbdown(fig); + % if we wanted to click-drag, set the Start point + if lockStart & ~wasInterrupted, + pt = arrow_point(ARROW_CLICK_AX,ARROW_CLICK_USE_Z); + feval(mfilename,H,'Start',pt,'Stop',pt); + set(H,'Visible','on'); + ARROW_CLICK_PROP='Stop'; + set(fig,'WindowButtonMotionFcn',[mfilename '(''callback'',''motion'');']); + % wait for the mouse button to be released + eval('waitfor(fig,''WindowButtonUpFcn'','''');','wasInterrupted=1;'); + if wasInterrupted, errstr=lasterr; end; + end; + if ~wasInterrupted, feval(mfilename,'callback','motion'); end; + % restore some things + set(gcf,oldFigProps,oldFigValue); + set(H,oldArrowProps,oldArrowValue); + +function arrow_callback(varargin) +% handle redrawing callbacks + if nargin==0, return; end; + str = varargin{1}; + if ~isstr(str), error([upper(mfilename) ' got an invalid Callback command.']); end; + s = lower(str); + if strcmp(s,'motion'), + % motion callback + global ARROW_CLICK_H ARROW_CLICK_PROP ARROW_CLICK_AX ARROW_CLICK_USE_Z + feval(mfilename,ARROW_CLICK_H,ARROW_CLICK_PROP,arrow_point(ARROW_CLICK_AX,ARROW_CLICK_USE_Z)); + drawnow; + else, + error([upper(mfilename) ' does not recognize ''' str(:).' ''' as a valid Callback option.']); + end; + +function out = arrow_point(ax,use_z) +% return the point on the given axes + if nargin==0, ax=gca; end; + if nargin<2, use_z=~arrow_is2DXY(ax)|~arrow_planarkids(ax); end; + out = get(ax,'CurrentPoint'); + out = out(1,:); + if ~use_z, out=out(1:2); end; + +function [wasKeyPress,wasInterrupted,errstr] = arrow_wfbdown(fig) +% wait for button down ignoring object ButtonDownFcn's + if nargin==0, fig=gcf; end; + errstr = ''; + % save ButtonDownFcn values + objs = findobj(fig); + buttonDownFcns = get(objs,'ButtonDownFcn'); + mask=~strcmp(buttonDownFcns,''); objs=objs(mask); buttonDownFcns=buttonDownFcns(mask); + set(objs,'ButtonDownFcn',''); + % save other figure values + figProps = {'KeyPressFcn','WindowButtonDownFcn'}; + figValue = get(fig,figProps); + % do the real work + set(fig,'KeyPressFcn','set(gcf,''KeyPressFcn'','''',''WindowButtonDownFcn'','''');', ... + 'WindowButtonDownFcn','set(gcf,''WindowButtonDownFcn'','''')'); + lasterr(''); + wasInterrupted=0; eval('waitfor(fig,''WindowButtonDownFcn'','''');','wasInterrupted=1;'); + wasKeyPress = ~wasInterrupted & strcmp(get(fig,'KeyPressFcn'),''); + if wasInterrupted, errstr=lasterr; end; + % restore ButtonDownFcn and other figure values + set(objs,'ButtonDownFcn',buttonDownFcns); + set(fig,figProps,figValue); + + + +function [out,is2D] = arrow_is2DXY(ax) +% check if axes are 2-D X-Y plots + % may not work for modified camera angles, etc. + out = zeros(size(ax)); % 2-D X-Y plots + is2D = out; % any 2-D plots + views = get(ax(:),{'View'}); + views = cat(1,views{:}); + out(:) = abs(views(:,2))==90; + is2D(:) = out(:) | all(rem(views',90)==0)'; + +function out = arrow_planarkids(ax) +% check if axes descendents all have empty ZData (lines,patches,surfaces) + out = logical(ones(size(ax))); + allkids = get(ax(:),{'Children'}); + for k=1:length(allkids), + kids = get([findobj(allkids{k},'flat','Type','line') + findobj(allkids{k},'flat','Type','patch') + findobj(allkids{k},'flat','Type','surface')],{'ZData'}); + for j=1:length(kids), + if ~isempty(kids{j}), out(k)=logical(0); break; end; + end; + end; + + + +function arrow_fixlimits(axlimits) +% reset the axis limits as necessary + if isempty(axlimits), disp([upper(mfilename) ' does not remember any axis limits to reset.']); end; + for k=1:size(axlimits,1), + if any(get(axlimits(k,1),'XLim')~=axlimits(k,2:3)), set(axlimits(k,1),'XLim',axlimits(k,2:3)); end; + if any(get(axlimits(k,1),'YLim')~=axlimits(k,4:5)), set(axlimits(k,1),'YLim',axlimits(k,4:5)); end; + if any(get(axlimits(k,1),'ZLim')~=axlimits(k,6:7)), set(axlimits(k,1),'ZLim',axlimits(k,6:7)); end; + end; + + + +function out = arrow_WarpToFill(notstretched,manualcamera,curax) +% check if we are in "WarpToFill" mode. + out = strcmp(get(curax,'WarpToFill'),'on'); + % 'WarpToFill' is undocumented, so may need to replace this by + % out = ~( any(notstretched) & any(manualcamera) ); + + + +function out = arrow_warnlimits(axlimits,narrows) +% create a warning message if we've changed the axis limits + msg = ''; + switch (size(axlimits,1)==1) + case 1, msg=''; + case 2, msg='on two axes '; + otherwise, msg='on several axes '; + end; + msg = [upper(mfilename) ' changed the axis limits ' msg ... + 'when adding the arrow']; + if (narrows>1), msg=[msg 's']; end; + out = [msg '.' sprintf('\n') ' Call ' upper(mfilename) ... + ' FIXLIMITS to reset them now.']; + + + +function arrow_copyprops(fm,to) +% copy line properties to patches + props = {'EraseMode','LineStyle','LineWidth','Marker','MarkerSize',... + 'MarkerEdgeColor','MarkerFaceColor','ButtonDownFcn', ... + 'Clipping','DeleteFcn','BusyAction','HandleVisibility', ... + 'Selected','SelectionHighlight','Visible'}; + lineprops = {'Color', props{:}}; + patchprops = {'EdgeColor',props{:}}; + patch2props = {'FaceColor',patchprops{:}}; + fmpatch = strcmp(get(fm,'Type'),'patch'); + topatch = strcmp(get(to,'Type'),'patch'); + set(to( fmpatch& topatch),patch2props,get(fm( fmpatch& topatch),patch2props)); %p->p + set(to(~fmpatch&~topatch),lineprops, get(fm(~fmpatch&~topatch),lineprops )); %l->l + set(to( fmpatch&~topatch),lineprops, get(fm( fmpatch&~topatch),patchprops )); %p->l + set(to(~fmpatch& topatch),patchprops, get(fm(~fmpatch& topatch),lineprops) ,'FaceColor','none'); %l->p + + + +function arrow_props +% display further help info about ARROW properties + c = sprintf('\n'); + disp([c ... + 'ARROW Properties: Default values are given in [square brackets], and other' c ... + ' acceptable equivalent property names are in (parenthesis).' c c ... + ' Start The starting points. For N arrows, B' c ... + ' this should be a Nx2 or Nx3 matrix. /|\ ^' c ... + ' Stop The end points. For N arrows, this /|||\ |' c ... + ' should be a Nx2 or Nx3 matrix. //|||\\ L|' c ... + ' Length Length of the arrowhead (in pixels on ///|||\\\ e|' c ... + ' screen, points on a page). [16] (Len) ////|||\\\\ n|' c ... + ' BaseAngle Angle (degrees) of the base angle /////|D|\\\\\ g|' c ... + ' ADE. For a simple stick arrow, use //// ||| \\\\ t|' c ... + ' BaseAngle=TipAngle. [90] (Base) /// ||| \\\ h|' c ... + ' TipAngle Angle (degrees) of tip angle ABC. //<----->|| \\ |' c ... + ' [16] (Tip) / base ||| \ V' c ... + ' Width Width of the base in pixels. Not E angle ||<-------->C' c ... + ' the ''LineWidth'' prop. [0] (Wid) |||tipangle' c ... + ' Page If provided, non-empty, and not NaN, |||' c ... + ' this causes ARROW to use hardcopy |||' c ... + ' rather than onscreen proportions. A' c ... + ' This is important if screen aspect --> <-- width' c ... + ' ratio and hardcopy aspect ratio are ----CrossDir---->' c ... + ' vastly different. []' c... + ' CrossDir A vector giving the direction towards which the fletches' c ... + ' on the arrow should go. [computed such that it is perpen-' c ... + ' dicular to both the arrow direction and the view direction' c ... + ' (i.e., as if it was pasted on a normal 2-D graph)] (Note' c ... + ' that CrossDir is a vector. Also note that if an axis is' c ... + ' plotted on a log scale, then the corresponding component' c ... + ' of CrossDir must also be set appropriately, i.e., to 1 for' c ... + ' no change in that direction, >1 for a positive change, >0' c ... + ' and <1 for negative change.)' c ... + ' NormalDir A vector normal to the fletch direction (CrossDir is then' c ... + ' computed by the vector cross product [Line]x[NormalDir]). []' c ... + ' (Note that NormalDir is a vector. Unlike CrossDir,' c ... + ' NormalDir is used as is regardless of log-scaled axes.)' c ... + ' Ends Set which end has an arrowhead. Valid values are ''none'',' c ... + ' ''stop'', ''start'', and ''both''. [''stop''] (End)' c... + ' ObjectHandles Vector of handles to previously-created arrows to be' c ... + ' updated or line objects to be converted to arrows.' c ... + ' [] (Object,Handle)' c ]); + + + +function out = arrow_demo + % demo + % create the data + [x,y,z] = peaks; + [ddd,out.iii]=max(z(:)); + out.axlim = [min(x(:)) max(x(:)) min(y(:)) max(y(:)) min(z(:)) max(z(:))]; + + % modify it by inserting some NaN's + [m,n] = size(z); + m = floor(m/2); + n = floor(n/2); + z(1:m,1:n) = NaN*ones(m,n); + + % graph it + clf('reset'); + out.hs=surf(x,y,z); + out.x=x; out.y=y; out.z=z; + xlabel('x'); ylabel('y'); + +function h = arrow_demo3(in) + % set the view + axlim = in.axlim; + axis(axlim); + zlabel('z'); + %set(in.hs,'FaceColor','interp'); + view(viewmtx(-37.5,30,20)); + title(['Demo of the capabilities of the ARROW function in 3-D']); + + % Normal blue arrow + h1 = feval(mfilename,[axlim(1) axlim(4) 4],[-.8 1.2 4], ... + 'EdgeColor','b','FaceColor','b'); + + % Normal white arrow, clipped by the surface + h2 = feval(mfilename,axlim([1 4 6]),[0 2 4]); + t=text(-2.4,2.7,7.7,'arrow clipped by surf'); + + % Baseangle<90 + h3 = feval(mfilename,[3 .125 3.5],[1.375 0.125 3.5],30,50); + t2=text(3.1,.125,3.5,'local maximum'); + + % Baseangle<90, fill and edge colors different + h4 = feval(mfilename,axlim(1:2:5)*.5,[0 0 0],36,60,25, ... + 'EdgeColor','b','FaceColor','c'); + t3=text(axlim(1)*.5,axlim(3)*.5,axlim(5)*.5-.75,'origin'); + set(t3,'HorizontalAlignment','center'); + + % Baseangle>90, black fill + h5 = feval(mfilename,[-2.9 2.9 3],[-1.3 .4 3.2],30,120,[],6, ... + 'EdgeColor','r','FaceColor','k','LineWidth',2); + + % Baseangle>90, no fill + h6 = feval(mfilename,[-2.9 2.9 1.3],[-1.3 .4 1.5],30,120,[],6, ... + 'EdgeColor','r','FaceColor','none','LineWidth',2); + + % Stick arrow + h7 = feval(mfilename,[-1.6 -1.65 -6.5],[0 -1.65 -6.5],[],16,16); + t4=text(-1.5,-1.65,-7.25,'global mininum'); + set(t4,'HorizontalAlignment','center'); + + % Normal, black fill + h8 = feval(mfilename,[-1.4 0 -7.2],[-1.4 0 -3],'FaceColor','k'); + t5=text(-1.5,0,-7.75,'local minimum'); + set(t5,'HorizontalAlignment','center'); + + % Gray fill, crossdir specified, 'LineStyle' -- + h9 = feval(mfilename,[-3 2.2 -6],[-3 2.2 -.05],36,[],27,6,[],[0 -1 0], ... + 'EdgeColor','k','FaceColor',.75*[1 1 1],'LineStyle','--'); + + % a series of normal arrows, linearly spaced, crossdir specified + h10y=(0:4)'/3; + h10 = feval(mfilename,[-3*ones(size(h10y)) h10y -6.5*ones(size(h10y))], ... + [-3*ones(size(h10y)) h10y -.05*ones(size(h10y))], ... + 12,[],[],[],[],[0 -1 0]); + + % a series of normal arrows, linearly spaced + h11x=(1:.33:2.8)'; + h11 = feval(mfilename,[h11x -3*ones(size(h11x)) 6.5*ones(size(h11x))], ... + [h11x -3*ones(size(h11x)) -.05*ones(size(h11x))]); + + % series of magenta arrows, radially oriented, crossdir specified + h12x=2; h12y=-3; h12z=axlim(5)/2; h12xr=1; h12zr=h12z; ir=.15;or=.81; + h12t=(0:11)'/6*pi; + h12 = feval(mfilename, ... + [h12x+h12xr*cos(h12t)*ir h12y*ones(size(h12t)) ... + h12z+h12zr*sin(h12t)*ir],[h12x+h12xr*cos(h12t)*or ... + h12y*ones(size(h12t)) h12z+h12zr*sin(h12t)*or], ... + 10,[],[],[],[], ... + [-h12xr*sin(h12t) zeros(size(h12t)) h12zr*cos(h12t)],... + 'FaceColor','none','EdgeColor','m'); + + % series of normal arrows, tangentially oriented, crossdir specified + or13=.91; h13t=(0:.5:12)'/6*pi; + locs = [h12x+h12xr*cos(h13t)*or13 h12y*ones(size(h13t)) h12z+h12zr*sin(h13t)*or13]; + h13 = feval(mfilename,locs(1:end-1,:),locs(2:end,:),6); + + % arrow with no line ==> oriented downwards + h14 = feval(mfilename,[3 3 .100001],[3 3 .1],30); + t6=text(3,3,3.6,'no line'); set(t6,'HorizontalAlignment','center'); + + % arrow with arrowheads at both ends + h15 = feval(mfilename,[-.5 -3 -3],[1 -3 -3],'Ends','both','FaceColor','g', ... + 'Length',20,'Width',3,'CrossDir',[0 0 1],'TipAngle',25); + + h=[h1;h2;h3;h4;h5;h6;h7;h8;h9;h10;h11;h12;h13;h14;h15]; + +function h = arrow_demo2(in) + axlim = in.axlim; + dolog = 1; + if (dolog), set(in.hs,'YData',10.^get(in.hs,'YData')); end; + shading('interp'); + view(2); + title(['Demo of the capabilities of the ARROW function in 2-D']); + hold on; [C,H]=contour(in.x,in.y,in.z,20,'-'); hold off; + for k=H', + set(k,'ZData',(axlim(6)+1)*ones(size(get(k,'XData'))),'Color','k'); + if (dolog), set(k,'YData',10.^get(k,'YData')); end; + end; + if (dolog), axis([axlim(1:2) 10.^axlim(3:4)]); set(gca,'YScale','log'); + else, axis(axlim(1:4)); end; + + % Normal blue arrow + start = [axlim(1) axlim(4) axlim(6)+2]; + stop = [in.x(in.iii) in.y(in.iii) axlim(6)+2]; + if (dolog), start(:,2)=10.^start(:,2); stop(:,2)=10.^stop(:,2); end; + h1 = feval(mfilename,start,stop,'EdgeColor','b','FaceColor','b'); + + % three arrows with varying fill, width, and baseangle + start = [-3 -3 10; -3 -1.5 10; -1.5 -3 10]; + stop = [-.03 -.03 10; -.03 -1.5 10; -1.5 -.03 10]; + if (dolog), start(:,2)=10.^start(:,2); stop(:,2)=10.^stop(:,2); end; + h2 = feval(mfilename,start,stop,24,[90;60;120],[],[0;0;4],'Ends',str2mat('both','stop','stop')); + set(h2(2),'EdgeColor',[0 .35 0],'FaceColor',[0 .85 .85]); + set(h2(3),'EdgeColor','r','FaceColor',[1 .5 1]); + h=[h1;h2];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/dot_to_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,121 @@ +function [Adj, labels, x, y] = dot_to_graph(filename) +% [Adj, labels, x, y] = dot_to_graph(filename) +% Extract a matrix representation, node labels, and node position coordinates +% from a file in GraphViz format http://www.research.att.com/sw/tools/graphviz +% +% INPUTS: +% 'filename' - the file in DOT format containing the graph layout. +% OUTPUT: +% 'Adj' - an adjacency matrix representation of the graph in 'filename'; +% 'labels' - a character array with the names of the nodes of the graph; +% 'x' - a row vector with the x-coordinates of the nodes in 'filename'; +% 'y' - a row vector with the y-coordinates of the nodes in 'filename'. +% +% WARNINGS: not guaranted to parse ANY GraphViz file. Debugged on undirected +% sample graphs from GraphViz(Heawood, Petersen, ER, ngk10_4, process). +% Complaines about RecursionLimit set only to 500 on huge graphs. +% Ignores singletons (disjoint nodes). +% Sample DOT code "ABC.dot", read by [Adj, labels, x, y] = dot_to_graph('ABC.dot') +% digraph G { +% A [pos="28,31"]; +% B [pos="74,87"]; +% A -- B [pos="e,61,71 41,47 46,53 50,58 55,64"]; +% } +% last modified: Jan 2004 +% by Alexi Savov: asavov @wustl.edu | http://artsci.wustl.edu/~azsavov +% Leon Peshkin: pesha @ai.mit.edu | http://www.ai.mit.edu/~pesha +% Tom Minka + +if ~exist(filename) % Checks whether the specified file exists. + error('* * * File does not exist or could not be found. * * *'); +end; + +lines = textread(filename,'%s','delimiter','\n','commentstyle','c'); % Read file into cell array +dot_lines = strvcat(lines); % of lines, ignoring C-style comments + +if findstr(dot_lines(1,:), 'graph ') == [] % Is this a DOT file ? + error('* * * File does not appear to be in valid DOT format. * * *'); +end; + +Nlns = size(dot_lines,1); % The number of lines; +nodes = {}; +unread = 1:Nlns; % 'unread' list of lines which has not been examined yet +edge_id = 1; +Adj = []; +for line_ndx = 1:Nlns % This section sets the adjacency matrix A(Lnode,Rnode) = edge_id. + line = dot_lines(line_ndx,:); + Ddash_pos = strfind(line, ' -- ') + 1; % double dash positions + arrow_pos = strfind(line, ' -> ') + 1; % arrow dash positions + tokens = strread(line,'%s','delimiter',' "'); + left_bound = 1; + for dash_pos = [Ddash_pos arrow_pos]; % if empty - not a POS line + Lnode = sscanf(line(left_bound:dash_pos -2), '%s'); + Rnode = sscanf(line(dash_pos +3 : length(line)-1),'%s',1); + Lndx = strmatch(Lnode, nodes, 'exact'); + Rndx = strmatch(Rnode, nodes, 'exact'); + if isempty(Lndx) % extend our list of nodes + nodes{end+1} = Lnode; + Lndx = length(nodes); + end + if isempty(Rndx) + nodes{end+1} = Rnode; + Rndx = length(nodes); + end + Adj(Lndx, Rndx) = edge_id; + if ismember(dash_pos, Ddash_pos) % The edge is undirected, A(Rndx,LndxL) is also set to 1; + Adj(Rndx, Lndx) = edge_id; + end + edge_id = edge_id + 1; + left_bound = dash_pos + 3; + unread = setdiff(unread, line_ndx); + end +end +Nvrt = length(nodes); % number of vertices we found [Do we ever have singleton vertices ???] +% nodes = strvcat(nodes); % convert to the searchable array +x = zeros(1, Nvrt); +y = zeros(1, Nvrt); +labels = nodes; +% Find node's position coordinates if they are contained in 'filename'. +for line_ndx = unread % Look for node's coordinates among the 'unread' lines. + line = dot_lines(line_ndx,:); + bra_pos = strfind(line, '['); % has to have "[" if it has the label + lst_node = 0; + for node = 1:Nvrt % look through the list of nodes + % THE NEXT STATEMENT we assume no node is substring of any other node + lbl_pos = strfind(line, nodes{node}); + if (~isempty(lbl_pos) & ~isempty(bra_pos) & (x(node) == 0)) % make sure we have not seen it + if (lbl_pos(1) < bra_pos(1)) % label has to be to the left of bracket + lst_node = node; + end + end + end + if lst_node + pos_pos = strfind(line, 'pos'); % position of the "pos" + if ~isempty(pos_pos) % this line contains SOME position + [node_pos] = sscanf(line(pos_pos:end), ' pos = "%d,%d"')'; + x(lst_node) = node_pos(1); + y(lst_node) = node_pos(2); + end + % minka + label_pos = strfind(line, 'label'); % position of the "label" + if ~isempty(label_pos) + label_end = strfind(line(label_pos:end),','); + labels{lst_node} = unquote(line(label_pos+(6:label_end(1)-2))); + end + end +end + +if (isempty(find(x)) & (nargout > 2)) % If coordinates were requested, but not found in 'filename'. + warning('File does not contain node coordinates.'); +end; +if ~(size(Adj,1)==size(Adj,2)) % Make sure Adj is a square matrix. ? + Adj = eye(max(size(Adj)),size(Adj,1))*Adj*eye(size(Adj,2),max(size(Adj))); +end; +x = .9*(x-min(x))/range(x)+.05; % normalise and push off margins +y = .9*(y-min(y))/range(y)+.05; + + + +function s = unquote(s) + +s = strrep(s,'"','');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/draw_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,99 @@ +function [x, y, h] = draw_dbn(adj, inter, flip_intra, K, labels, node_t, x, y) +% DRAW_LAYOUT_DBN Draws a layout for a Dynamical Belief Network +% +% [<X1, Y1, X2, Y2>] = DRAW_LAYOUT_DBN(INTRA, INTER, <FLIP_FLAG, K, LABELS, ISBOX, X1, Y1>) +% +% Inputs : +% INTRA, INTER : Adjacency matrices +% FLIP_FLAG : Transposes the DAG layout obtained from INTRA connections +% If X1, Y1 are specified, FLIP_FLAG has no effect. +% K : Unfold K times <Default = 2> +% LABELS - if -1, we use 1:N*K +% Rest : See DRAW_LAYOUT +% +% Outputs : +% Xi, Yi : Coordinates of nodes (for i'th timeslice) on the unit square +% H : Object Handles +% +% Usage Example : draw_layout_dbn(intra, inter, 1); +% draw_layout_dbn(intra, inter); +% +% Note : +% See also DRAW_GRAPH + +% Uses : DRAW_GRAPH + +% Change History : +% Date Time Prog Note +% 17-Apr-2000 1:02 PM ATC Created under MATLAB 5.3.1.29215a (R11.1) + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +N = size(adj,1); +if nargin<3, + flip_intra = 0; +end; + +if nargin<4, + K = 2; +end; + +if K<2 | K>7, error('2<=K<=7 must hold..'); end; + + +if nargin<5 +% labels = cellstr(char(zeros(N,1)+double('+'))); +% labels = cellstr(int2str((1:N)')); + labels = cellstr(char((0:N-1)'+double('a'))); +end; + +if nargin<6, + node_t = zeros(N,1); +% node_t = rand(N,1) > 0.5; +end; + +if nargin<7, + [x1 y1] = make_layout(adj); + if flip_intra, tmp = x1; x1 = y1; y1 = tmp; end; +end; + +mid = round(K/2); + + +xi = x1(:)-1; +x = []; +y = repmat(y1(:), [K 1]); +node_t2 = repmat(node_t(:), [K 1]); + +if isa(labels,'double') & labels==-1 % KPM + lb = num2strcell(1:N*K); +else + lb = {}; + for i=1:K, + labels1 = labels(:); + if i==mid, str = ''; else str = sprintf('%+d',i-mid); end; + for i=1:N, + labels1{i} = [labels1{i} '(t' str ')']; + end; + lb = [lb; labels1(:)]; + end; +end + +dag = zeros(N*K); + +for i=1:K, + xi = xi+1; + x = [x; xi]; + + idx = ((i-1)*N+1):i*N; + dag(idx,idx) = adj; + if i<K, + dag(idx,idx+N) = inter; + end; +end; + +[x, y, h] = draw_graph(dag, lb, node_t2, x/K, y); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/draw_dbn_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +% TEST_LAYOUT_DBN Script to test some DBN layouts +% + +% Change History : +% Date Time Prog Note +% 17-Apr-2000 2:40 PM ATC Created under MATLAB 5.3.1.29215a (R11.1) + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +disp('draw mhmm1') +clf +set(gcf, 'pos', [0 0 1024 600]); + +intra = zeros(3); +intra(1,[2 3]) = 1; +intra(2,3) = 1; +inter = zeros(3); +inter(1,1) = 1; +n = 3; +dnodes = [1 2]; +isbox = zeros(n,1); isbox(dnodes) = 1; +unfold = 4; +draw_dbn(intra, inter, 0, unfold, {'Q', 'M', 'Y'}, isbox); + + +pause +clf +disp('draw water1') +bnet = mk_water_dbn; +unfold = 3; +flip = 1; +[dummyx, dummyy, h] = draw_dbn(bnet.intra, bnet.inter, flip, unfold); + +col = rand(size(h,1),3); +for i=1:length(h), + col = rand(1,3); + % patches + set(h(i,2),'facecolor', col); drawnow; + % text + set(h(i,1),'color', 1-col); drawnow; +end; + + +pause +clf +disp('draw BAT static') +% This requires BNT + +%sz = get(0, 'ScreenSize'); +%sv = get(gcf, 'pos'); +%set(gcf, 'units','pix','pos', sz); +[bnet, names] = mk_bat_dbn; +N = size(bnet.intra,1); +G = [bnet.intra bnet.inter; + zeros(N,N) bnet.intra]; +names2 = names; +for i=1:N + names2{i} = sprintf('%s(1)', names{i}); + names2{i+N} = sprintf('%s(2)', names{i}); +end +draw_graph(G, names2); + +pause +disp('draw bat dbn') +clf +draw_dbn(bnet.intra, bnet.inter, 0, 2, names2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/draw_dot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,124 @@ +function draw_dot(adj,varargin); +%DRAW_DOT Draw a graph. +% DRAW_DOT(ADJ) plots the graph ADJ in the current figure window, using +% 'neato' to optimize the layout. +% +% Optional arguments can be passed as name/value pairs: [default] +% +% 'isbox' - a vector specifying which nodes should be boxed [0] +% 'rotate' - rotate the graph so that nodes are vertically aligned [1] +% 'tolerance' - alignment tolerance for 'rotate' [0.001] +% 'start' - a random seed (to select different solutions) +% 'options' - a string of command-line options for 'neato' [''] +% All of the optional arguments to graph_to_dot are also supported, such as +% 'node_label'. +% +% See also GRAPH_TO_DOT. +% +% Example: +% size=15; Adj = rand(size) > .8; +% Adj2 = triu(Adj,1)+ triu(Adj,1)' + diag(zeros(size,1)); +% draw_dot(Adj2) + +% Original: Leon Peshkin +% Modified by Tom Minka + +% minka +N = size(adj,1); +unique_labels = cellstr(num2str((1:N)','%-1d')); +labels = unique_labels; +isbox = zeros(N,1); +rotate_flag = 1; +tolerance = 0.001; +options = ''; +for i = 1:2:length(varargin) + switch varargin{i} + case 'node_label', labels = varargin{i+1}; + % replace with unique labels + varargin{i+1} = unique_labels; + case 'isbox', isbox = varargin{i+1}; + case 'rotate', rotate_flag = varargin{i+1}; + case 'tolerance', tolerance = varargin{i+1}; + case 'start', start = varargin{i+1}; + options = [options ' -Gstart=' num2str(start)]; + case 'options', options = [options ' ' varargin{i+1}]; + end +end + +if ispc, shell = 'dos'; else, shell = 'unix'; end % Which OS ? + +cmdline = strcat(shell,'(''neato -V'')'); +status = eval(cmdline); +%[status, result] = dos('neato -V'); % request version to check NEATO +if status == 1, fprintf('Complaining \n'); exit, end + +tmpDOTfile = '_GtDout.dot'; % to be platform independant no use of directories +tmpLAYOUT = '_LAYout.dot'; +graph_to_dot(adj > 0, 'filename', tmpDOTfile, 'node_label', unique_labels, varargin{:}); % save in file + +cmdline = strcat([shell '(''neato -Tdot ' tmpDOTfile options ' -o ' tmpLAYOUT ''')']); % preserve trailing spaces +status = eval(cmdline); % get NEATO todo layout + +[adj, permuted_labels, x, y] = dot_to_graph(tmpLAYOUT); % load layout +delete(tmpLAYOUT); delete(tmpDOTfile); % clean up temporary files + +% permute the original arguments to match permuted_labels. +order = []; +for i = 1:length(permuted_labels) + j = strmatch(permuted_labels{i},unique_labels,'exact'); + order(i) = j(1); +end +labels = labels(order); +isbox = isbox(order); +if rotate_flag + [x,y] = best_rotation(x,y,tolerance); +end + +figure(1); clf; axis square % now plot +[x, y, h] = draw_graph(adj>0, labels, isbox, x, y, varargin{:}); + + +function [x,y] = best_rotation(x,y,h) +% Rotate the points to maximize the horizontal and vertical alignment. +% Written by Tom Minka. + +xm = mean(x); +ym = mean(y); +xr = max(x)-min(x); +yr = max(y)-min(y); +x = (x-xm)/xr; +y = (y-ym)/yr; + +xy = [x(:) y(:)]; +if 1 + angle = fminbnd(@rotation_cost,-pi/4,pi/4,[],xy,h); +else + angles = linspace(-pi/4,pi/4,40); + e = []; + for i = 1:length(angles) + e(i) = rotation_cost(angles(i),xy,h); + end + %figure(2) + %plot(angles*180/pi,e) + angle = angles(argmin(e)); +end +%angle*180/pi +c = cos(angle); s = sin(angle); +xy = xy*[c s; -s c]; + +x = xy(:,1)*xr+xm; +y = xy(:,2)*yr+ym; + + +function e = rotation_cost(angle,xy,h) +% xy is 2-column matrix. +% e is small if many x's and y's are aligned. + +c = cos(angle); s = sin(angle); +xy = xy*[c s; -s c]; +dx = sqdist(xy(:,1)',xy(:,1)'); +dy = sqdist(xy(:,2)',xy(:,2)'); +dx = setdiag(dx,Inf); +dy = setdiag(dy,Inf); +e = sum(exp(-dx(:)/h))+sum(exp(-dy(:)/h)); +e = -e;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/draw_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,332 @@ +function [x, y, h] = draw_graph(adj, labels, node_t, x, y, varargin) +% DRAW_LAYOUT Draws a layout for a graph +% +% [X, Y, H] = DRAW_LAYOUT(ADJ, <LABELS, ISBOX, X, Y>) +% +% Inputs : +% ADJ : Adjacency matrix (source, sink) +% LABELS : Cell array containing labels <Default : '1':'N'> +% ISBOX : 1 if node is a box, 0 if oval <Default : zeros> +% X, Y, : Coordinates of nodes on the unit square <Default : calls make_layout> +% +% Outputs : +% X, Y : Coordinates of nodes on the unit square +% H : Object handles +% +% Usage Example : [x, y] = draw_layout([0 1;0 0], {'Hidden','Visible'}, [1 0]'); +% +% h(i,1) is the text handle - color +% h(i,2) is the circle handle - facecolor +% +% See also MAKE_LAYOUT + +% Change History : +% Date Time Prog Note +% 13-Apr-2000 9:06 PM ATC Created under MATLAB 5.3.1.29215a (R11.1) +% +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +adj = double(adj); +N = size(adj,1); +if nargin<2, + labels = cellstr(int2str((1:N)')); +end + +if nargin<3, + node_t = zeros(N,1); +else + node_t = node_t(:); +end; + +axis([0 1 0 1]); +set(gca,'XTick',[],'YTick',[],'box','on'); +% axis('square'); +%colormap(flipud(gray)); + +if nargin<4, + [x y] = make_layout(adj); +end; + +idx1 = find(node_t==0); h1 = []; wd1=[]; +if ~isempty(idx1) + [h1 wd1] = textoval(x(idx1), y(idx1), labels(idx1), varargin{:}); +end; + +idx2 = find(node_t~=0); h2 = []; wd2 = []; +if ~isempty(idx2) + [h2 wd2] = textbox(x(idx2), y(idx2), labels(idx2), varargin{:}); +end; + +wd = zeros(size(wd1,1)+size(wd2,1),2); +if ~isempty(idx1), wd(idx1, :) = wd1; end; +if ~isempty(idx2), wd(idx2, :) = wd2; end; + +% bug: this code assumes [x y] is the center of each box and oval, which +% isn't exactly true. +h_edge = []; +for i=1:N, + j = find(adj(i,:)==1); + for k=j, + if x(k)-x(i)==0, + sign = 1; + if y(i)>y(k), alpha = -pi/2; else alpha = pi/2; end; + else + alpha = atan((y(k)-y(i))/(x(k)-x(i))); + if x(i)<x(k), sign = 1; else sign = -1; end; + end; + dy1 = sign.*wd(i,2).*sin(alpha); dx1 = sign.*wd(i,1).*cos(alpha); + dy2 = sign.*wd(k,2).*sin(alpha); dx2 = sign.*wd(k,1).*cos(alpha); + if adj(k,i)==0, % if directed edge + h = arrow([x(i)+dx1 y(i)+dy1],[x(k)-dx2 y(k)-dy2],'BaseAngle',30); + else + h = line([x(i)+dx1 x(k)-dx2],[y(i)+dy1 y(k)-dy2]); + adj(k,i)=-1; % Prevent drawing lines twice + end; + h_edge = [h_edge h]; + end; +end; + +color.box = 'black'; +color.text = color.box; +color.edge = [1 1 1]*3/4; +%color.edge = 'green'; +if ~isempty(idx1) + set(h1(:,1),'Color',color.text) + set(h1(:,2),'EdgeColor',color.box) +end +if ~isempty(idx2) + set(h2(:,1),'Color',color.text) + set(h2(:,2),'EdgeColor',color.box) +end +set(h_edge,'Color',color.edge) + +if nargout>2, + h = zeros(length(wd),2); + if ~isempty(idx1), + h(idx1,:) = h1; + end; + if ~isempty(idx2), + h(idx2,:) = h2; + end; +end; + +%%%%% + +function [t, wd] = textoval(x, y, str, varargin) +% TEXTOVAL Draws an oval around text objects +% +% [T, WIDTH] = TEXTOVAL(X, Y, STR) +% [..] = TEXTOVAL(STR) % Interactive +% +% Inputs : +% X, Y : Coordinates +% TXT : Strings +% +% Outputs : +% T : Object Handles +% WIDTH : x and y Width of ovals +% +% Usage Example : [t] = textoval('Visit to Asia?'); +% +% +% Note : +% See also TEXTBOX + +% Uses : + +% Change History : +% Date Time Prog Note +% 15-Jun-1998 10:36 AM ATC Created under MATLAB 5.1.0.421 +% 12-Mar-2004 10:00 AM minka Changed placement/sizing. +% +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +temp = []; +textProperties = {'BackgroundColor','Color','FontAngle','FontName','FontSize','FontUnits','FontWeight','Rotation'}; +varargin = argfilter(varargin,textProperties); + +if nargin == 1 + str = x; +end +if ~isa(str,'cell') str=cellstr(str); end; +N = length(str); +wd = zeros(N,2); +for i=1:N, + if nargin == 1 + [x, y] = ginput(1); + end + tx = text(x(i),y(i),str{i},'HorizontalAlignment','center',varargin{:}); + % minka + [ptc wx wy] = draw_oval(tx); + wd(i,:) = [wx wy]; + % draw_oval will paint over the text, so need to redraw it + delete(tx); + tx = text(x(i),y(i),str{i},'HorizontalAlignment','center',varargin{:}); + temp = [temp; tx ptc]; +end +if nargout>0, t = temp; end; + +%%%%%%%%% + + +function [ptc, wx, wy] = draw_oval(tx, x, y) +% Draws an oval box around a tex object +sz = get(tx,'Extent'); +% minka +wy = 2/3*sz(4); +wx = 2/3*sz(3); +x = sz(1)+sz(3)/2; +y = sz(2)+sz(4)/2; +ptc = ellipse(x, y, wx, wy); +set(ptc, 'FaceColor','w'); + + +%%%%%%%%%%%%% + +function [p] = ellipse(x, y, rx, ry, c) +% ELLIPSE Draws Ellipse shaped patch objects +% +% [<P>] = ELLIPSE(X, Y, Rx, Ry, C) +% +% Inputs : +% X : N x 1 vector of x coordinates +% Y : N x 1 vector of y coordinates +% Rx, Ry : Radii +% C : Color index +% +% +% Outputs : +% P = Handles of Ellipse shaped path objects +% +% Usage Example : [] = ellipse(); +% +% +% Note : +% See also + +% Uses : + +% Change History : +% Date Time Prog Note +% 27-May-1998 9:55 AM ATC Created under MATLAB 5.1.0.421 + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +if (nargin < 2) error('Usage Example : e = ellipse([0 1],[0 -1],[1 0.5],[2 0.5]); '); end; +if (nargin < 3) rx = 0.1; end; +if (nargin < 4) ry = rx; end; +if (nargin < 5) c = 1; end; + +if length(c)==1, c = ones(size(x)).*c; end; +if length(rx)==1, rx = ones(size(x)).*rx; end; +if length(ry)==1, ry = ones(size(x)).*ry; end; + +n = length(x); +p = zeros(size(x)); +t = 0:pi/30:2*pi; +for i=1:n, + px = rx(i)*cos(t)+x(i); + py = ry(i)*sin(t)+y(i); + p(i) = patch(px,py,c(i)); +end; + +if nargout>0, pp = p; end; + +%%%%% + +function [t, wd] = textbox(x,y,str,varargin) +% TEXTBOX Draws A Box around the text +% +% [T, WIDTH] = TEXTBOX(X, Y, STR) +% [..] = TEXTBOX(STR) +% +% Inputs : +% X, Y : Coordinates +% TXT : Strings +% +% Outputs : +% T : Object Handles +% WIDTH : x and y Width of boxes +%% +% Usage Example : t = textbox({'Ali','Veli','49','50'}); +% +% +% Note : +% See also TEXTOVAL + +% Uses : + +% Change History : +% Date Time Prog Note +% 09-Jun-1998 11:43 AM ATC Created under MATLAB 5.1.0.421 +% 12-Mar-2004 10:00 AM minka Changed placement/sizing. +% +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +temp = []; +textProperties = {'BackgroundColor','Color','FontAngle','FontName','FontSize','FontUnits','FontWeight','Rotation'}; +varargin = argfilter(varargin,textProperties); + +if nargin == 1 + str = x; +end +if ~isa(str,'cell') str=cellstr(str); end; +N = length(str); +wd = zeros(N,2); +for i=1:N, + if nargin == 1 + [x, y] = ginput(1); + end + tx = text(x(i),y(i),str{i},'HorizontalAlignment','center',varargin{:}); + % minka + [ptc wx wy] = draw_box(tx); + wd(i,:) = [wx wy]; + % draw_box will paint over the text, so need to redraw it + delete(tx); + tx = text(x(i),y(i),str{i},'HorizontalAlignment','center',varargin{:}); + temp = [temp; tx ptc]; +end; + +if nargout>0, t = temp; end; + + +function [ptc, wx, wy] = draw_box(tx) +% Draws a box around a text object +sz = get(tx,'Extent'); +% minka +wy = 1/2*sz(4); +wx = 1/2*sz(3); +x = sz(1)+sz(3)/2; +y = sz(2)+sz(4)/2; +ptc = patch([x-wx x+wx x+wx x-wx], [y+wy y+wy y-wy y-wy],'w'); +set(ptc, 'FaceColor','w'); + + + +function args = argfilter(args,keep) +%ARGFILTER Remove unwanted arguments. +% ARGFILTER(ARGS,KEEP), where ARGS = {'arg1',value1,'arg2',value2,...}, +% returns a new argument list where only the arguments named in KEEP are +% retained. KEEP is a character array or cell array of strings. + +% Written by Tom Minka + +if ischar(keep) + keep = cellstr(keep); +end +i = 1; +while i < length(args) + if ~ismember(args{i},keep) + args = args(setdiff(1:length(args),[i i+1])); + else + i = i + 2; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/draw_graph_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +% TEST_LAYOUT Script to test some bayesian net layouts +% + +% Change History : +% Date Time Prog Note +% 13-Apr-2000 10:40 PM ATC Created under MATLAB 5.3.1.29215a (R11.1) + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +%bnet = mk_asia_bnet; +%draw_graph(bnet.dag); + +% Make the following network (from Jensen (1996) p84 fig 4.17) +% 1 +% / | \ +% 2 3 4 +% | | | +% 5 6 7 +% \/ \/ +% 8 9 +% where all arcs point downwards + +disp('plot directed') +clf; + +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + +draw_graph(dag); + +pause +clf +disp('plot undirected') +udag = [dag+dag']; +draw_graph(udag); + +pause +clf +disp('plot mixed') +mg = [dag]; +mg(2,1) = 1; mg(8,5) = 1; +draw_graph(mg); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/draw_hmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,117 @@ +function draw_hmm(A, varargin) +% DRAW_HMM Make a picture of the HMM using dotty +% function draw_hmm(A, ...) +% +% For details on dotty, see http://www.research.att.com/sw/tools/graphviz +% +% If A(i,j) > thresh, we draw and arc from state i to state j. +% +% Optional arguments (name/value pairs) [default] +% +% thresh - [1e-1] +% obsprob - If B(i,o) > 0, we include "o" in the name of state i. +% e.g., if state 5 emits 1,3,7, its label becomes "5: 1 3 7". +% startprob - ifstartprob(i) > 0, the state name will be prefixed with "+". +% endprob - if endprob(i) > 0, the state name will be appended with "-". +% filename - if [], we write to 'tmp.dot', convert this to 'tmp.ps' +% using 'dot -Tps tmp.dot -o tmp.ps', and then call ghostview to display the result. +% dot and gv must be on your system path. +% If filename ~= [], we just generate the dot file, and do not +% convert it to postscript or call ghostview. + +[thresh, B, startprob, endprob, filename] = ... + process_options(varargin, 'thresh', 1e-1, 'obsprob', [], 'startprob', [], 'endprob', [], ... + 'filename', []); + +Q = length(A); + +arclabel = cell(Q,Q); +G = zeros(Q,Q); +for i=1:Q + for j=1:Q + if A(i,j) < thresh + arclabel{i,j} = ''; + else + G(i,j) = 1; + arclabel{i,j} = sprintf('%5.3f', A(i,j)); + end + end +end + + +nodelabel = cell(1,Q); +for i=1:Q + % annotate start/stop states + if ~isempty(startprob) & ~approxeq(startprob(i), 0) + start = '+'; + else + start = ''; + end + if ~isempty(endprob) & ~approxeq(hmm.endprob(i), 0) + stop = '-'; + else + stop = ''; + end + label = sprintf('%s%d%s :', start, i, stop); + + if ~isempty(B) + output_label = mk_output_label(B); + label = strcat(label, output_label); + end + + nodelabel{i} = label; +end + + +if isempty(filename) + filename = 'tmp.dot'; + %mkdot(G, filename, arclabel, nodelabel) + graph_to_dot(G, 'filename', filename, 'arc_label', arclabel, 'node_label', nodelabel); + fprintf('converting from .ps to .dot\n') + !dot -Tps tmp.dot -o tmp.ps + !gv tmp.ps & +else + graph_to_dot(G, 'filename', filename, 'arc_label', arclabel, 'node_label', nodelabel); + %mkdot(G, filename, arclabel, nodelabel) +end + + +%%%%%%%%% + +function label = mk_output_label(B) + +[Q O] = size(B); +label = ''; + +if 0 + % print most probable symbols + for i=1:Q + m = max(B(i,:)); + ndx = find(abs(B(i,:) - repmat(m,1,O)) < 1e-2); + %ndx = find(B(i,:)==m); + %label = sprintf('%d,', ndx); + end +end + +if 0 + % print prob distrib over all symbols + for o=1:O + if approxeq(B(i,o), 0) + % + else + label = strcat(label, sprintf('%d(%3.2f),', o, B(i,o))); + end + end +end + +if 1 + % print all non-zero symbols + chars = ['a' 'b' 'c']; + for o=1:O + if approxeq(B(i,o), 0) + % + else + label = strcat(label, sprintf('%s', chars(o))); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/editGraphGUI.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function g +%here is how one creates a function ("callback") which does something +%(prints the node label) when you click on the node's text in Matlab figure. +% +% Leon Peshkin http://www.ai.mit.edu/~pesha +% +%draw_graph(...) + + % "gca" is the current "axes" object, parent of all objects in figure + % "gcbo" is the handle of the object whose callback is being executed + % "findall" gives handles to all elements of a given type in the figure +text_elms = findall(gca,'Type','text'); +for ndx = 1:length(text_elms) + callbk = 'my_call(str2num(get(gcbo,''String'')))'; + set(text_elms(ndx), 'ButtonDownFcn', callbk); % assume the node label is a number +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/graph_to_dot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function graph_to_dot(adj, varargin) +%GRAPH_TO_DOT Makes a GraphViz (AT&T) file representing an adjacency matrix +% graph_to_dot(adj, ...) writes to the specified filename. +% +% Optional arguments can be passed as name/value pairs: [default] +% +% 'filename' - if omitted, writes to 'tmp.dot' +% 'arc_label' - arc_label{i,j} is a string attached to the i-j arc [""] +% 'node_label' - node_label{i} is a string attached to the node i ["i"] +% 'width' - width in inches [10] +% 'height' - height in inches [10] +% 'leftright' - 1 means layout left-to-right, 0 means top-to-bottom [0] +% 'directed' - 1 means use directed arcs, 0 means undirected [1] +% +% For details on graphviz, See http://www.research.att.com/sw/tools/graphviz +% +% See also dot_to_graph and draw_dot. + +% First version written by Kevin Murphy 2002. +% Modified by Leon Peshkin, Jan 2004. +% Bugfix by Tom Minka, Mar 2004. + +node_label = []; arc_label = []; % set default args +width = 10; height = 10; +leftright = 0; directed = 1; filename = 'tmp.dot'; + +for i = 1:2:nargin-1 % get optional args + switch varargin{i} + case 'filename', filename = varargin{i+1}; + case 'node_label', node_label = varargin{i+1}; + case 'arc_label', arc_label = varargin{i+1}; + case 'width', width = varargin{i+1}; + case 'height', height = varargin{i+1}; + case 'leftright', leftright = varargin{i+1}; + case 'directed', directed = varargin{i+1}; + end +end +% minka +if ~directed + adj = triu(adj | adj'); +end + +fid = fopen(filename, 'w'); +if directed + fprintf(fid, 'digraph G {\n'); + arctxt = '->'; + if isempty(arc_label) + labeltxt = ''; + else + labeltxt = '[label="%s"]'; + end +else + fprintf(fid, 'graph G {\n'); + arctxt = '--'; + if isempty(arc_label) + labeltxt = '[dir=none]'; + else + labeltext = '[label="%s",dir=none]'; + end +end +edgeformat = strcat(['%d ',arctxt,' %d ',labeltxt,';\n']); +fprintf(fid, 'center = 1;\n'); +fprintf(fid, 'size=\"%d,%d\";\n', width, height); +if leftright + fprintf(fid, 'rankdir=LR;\n'); +end +Nnds = length(adj); +for node = 1:Nnds % process nodes + if isempty(node_label) + fprintf(fid, '%d;\n', node); + else + fprintf(fid, '%d [ label = "%s" ];\n', node, node_label{node}); + end +end +for node1 = 1:Nnds % process edges + arcs = find(adj(node1,:)); % children(adj, node); + for node2 = arcs + if ~isempty(arc_label) + fprintf(fid, edgeformat,node1,node2,arc_label{node1,node2}); + else + fprintf(fid, edgeformat, node1, node2); + end + end +end +fprintf(fid, '}'); +fclose(fid);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/make_layout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,170 @@ +function [x, y] = layout_dag(adj) +% MAKE_LAYOUT Creates a layout from an adjacency matrix +% +% [X, Y] = MAKE_LAYOUT(ADJ) +% +% Inputs : +% ADJ = adjacency matrix (source, sink) +% +% Outputs : +% X, Y : Positions of nodes +% +% Usage Example : [X, Y] = make_layout(adj); +% +% +% Note : Uses some very simple heuristics, so any other +% algorithm would create a nicer layout +% +% See also + +% Uses : + +% Change History : +% Date Time Prog Note +% 13-Apr-2000 8:25 PM ATC Created under MATLAB 5.3.1.29215a (R11.1) + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +N = size(adj,1); +tps = toposort(adj); + +if ~isempty(tps), % is directed ? + level = zeros(1,N); + for i=tps, + idx = find(adj(:,i)); + if ~isempty(idx), + l = max(level(idx)); + level(i)=l+1; + end; + end; +else + level = poset(adj,1)'-1; +end; + +y = (level+1)./(max(level)+2); +y = 1-y; +x = zeros(size(y)); +for i=0:max(level), + idx = find(level==i); + offset = (rem(i,2)-0.5)/10; + x(idx) = (1:length(idx))./(length(idx)+1)+offset; +end; + +%%%%%%% + +function [depth] = poset(adj, root) +% POSET Identify a partial ordering among the nodes of a graph +% +% [DEPTH] = POSET(ADJ,ROOT) +% +% Inputs : +% ADJ : Adjacency Matrix +% ROOT : Node to start with +% +% Outputs : +% DEPTH : Depth of the Node +% +% Usage Example : [depth] = poset(adj,12); +% +% +% Note : All Nodes must be connected +% See also + +% Uses : + +% Change History : +% Date Time Prog Note +% 17-Jun-1998 12:01 PM ATC Created under MATLAB 5.1.0.421 + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +adj = adj+adj'; + +N = size(adj,1); +depth = zeros(N,1); +depth(root) = 1; +queue = root; + +while 1, + if isempty(queue), + if all(depth), break; + else + root = find(depth==0); + root = root(1); + depth(root) = 1; + queue = root; + end; + end; + r = queue(1); queue(1) = []; + idx = find(adj(r,:)); + idx2 = find(~depth(idx)); + idx = idx(idx2); + queue = [queue idx]; + depth(idx) = depth(r)+1; +end; + +%%%%%%%%% + +function [seq] = toposort(adj) +% TOPOSORT A Topological ordering of nodes in a directed graph +% +% [SEQ] = TOPOSORT(ADJ) +% +% Inputs : +% ADJ : Adjacency Matrix. +% ADJ(i,j)==1 ==> there exists a directed edge +% from i to j +% +% Outputs : +% SEQ : A topological ordered sequence of nodes. +% empty matrix if graph contains cycles. +% +% Usage Example : +% N=5; +% [l,u] = lu(rand(N)); +% adj = ~diag(ones(1,N)) & u>0.5; +% seq = toposort(adj); +% +% +% Note : +% See also + +% Uses : + +% Change History : +% Date Time Prog Note +% 18-May-1998 4:44 PM ATC Created under MATLAB 5.1.0.421 + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +N = size(adj); +indeg = sum(adj,1); +outdeg = sum(adj,2); +seq = []; + +for i=1:N, + % Find nodes with indegree 0 + idx = find(indeg==0); + % If can't find than graph contains a cycle + if isempty(idx), + seq = []; + break; + end; + % Remove the node with the max number of connections + [dummy idx2] = max(outdeg(idx)); + indx = idx(idx2); + seq = [seq, indx]; + indeg(indx)=-1; + idx = find(adj(indx,:)); + indeg(idx) = indeg(idx)-1; +end; + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/my_call.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function my_call(value) +fprintf('%d \n', value); % might check here whether this is a label at all + % since we get here by clicking on ANY text in figure
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/GraphViz/process_options.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,132 @@ +% PROCESS_OPTIONS - Processes options passed to a Matlab function. +% This function provides a simple means of +% parsing attribute-value options. Each option is +% named by a unique string and is given a default +% value. +% +% Usage: [var1, var2, ..., varn[, unused]] = ... +% process_options(args, ... +% str1, def1, str2, def2, ..., strn, defn) +% +% Arguments: +% args - a cell array of input arguments, such +% as that provided by VARARGIN. Its contents +% should alternate between strings and +% values. +% str1, ..., strn - Strings that are associated with a +% particular variable +% def1, ..., defn - Default values returned if no option +% is supplied +% +% Returns: +% var1, ..., varn - values to be assigned to variables +% unused - an optional cell array of those +% string-value pairs that were unused; +% if this is not supplied, then a +% warning will be issued for each +% option in args that lacked a match. +% +% Examples: +% +% Suppose we wish to define a Matlab function 'func' that has +% required parameters x and y, and optional arguments 'u' and 'v'. +% With the definition +% +% function y = func(x, y, varargin) +% +% [u, v] = process_options(varargin, 'u', 0, 'v', 1); +% +% calling func(0, 1, 'v', 2) will assign 0 to x, 1 to y, 0 to u, and 2 +% to v. The parameter names are insensitive to case; calling +% func(0, 1, 'V', 2) has the same effect. The function call +% +% func(0, 1, 'u', 5, 'z', 2); +% +% will result in u having the value 5 and v having value 1, but +% will issue a warning that the 'z' option has not been used. On +% the other hand, if func is defined as +% +% function y = func(x, y, varargin) +% +% [u, v, unused_args] = process_options(varargin, 'u', 0, 'v', 1); +% +% then the call func(0, 1, 'u', 5, 'z', 2) will yield no warning, +% and unused_args will have the value {'z', 2}. This behaviour is +% useful for functions with options that invoke other functions +% with options; all options can be passed to the outer function and +% its unprocessed arguments can be passed to the inner function. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [varargout] = process_options(args, varargin) + +% Check the number of input arguments +n = length(varargin); +if (mod(n, 2)) + error('Each option must be a string/value pair.'); +end + +% Check the number of supplied output arguments +if (nargout < (n / 2)) + error('Insufficient number of output arguments given'); +elseif (nargout == (n / 2)) + warn = 1; + nout = n / 2; +else + warn = 0; + nout = n / 2 + 1; +end + +% Set outputs to be defaults +varargout = cell(1, nout); +for i=2:2:n + varargout{i/2} = varargin{i}; +end + +% Now process all arguments +nunused = 0; +for i=1:2:length(args) + found = 0; + for j=1:2:n + if strcmpi(args{i}, varargin{j}) + varargout{(j + 1)/2} = args{i + 1}; + found = 1; + break; + end + end + if (~found) + if (warn) + warning(sprintf('Option ''%s'' not used.', args{i})); + args{i} + else + nunused = nunused + 1; + unused{2 * nunused - 1} = args{i}; + unused{2 * nunused} = args{i + 1}; + end + end +end + +% Assign the unused arguments +if (~warn) + if (nunused) + varargout{nout} = unused; + else + varargout{nout} = cell(0); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +/README.txt/1.1.1.1/Thu Jun 9 01:22:48 2005// +/dhmm_em.m/1.1.1.1/Thu Jun 9 01:25:04 2005// +/dhmm_em_demo.m/1.1.1.1/Sun May 4 22:01:12 2003// +/dhmm_em_online.m/1.1.1.1/Sun May 4 22:02:58 2003// +/dhmm_em_online_demo.m/1.1.1.1/Sun May 4 22:04:10 2003// +/dhmm_logprob.m/1.1.1.1/Sun May 4 22:01:34 2003// +/dhmm_logprob_brute_force.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dhmm_logprob_path.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dhmm_sample.m/1.1.1.1/Mon May 31 22:19:50 2004// +/dhmm_sample_endstate.m/1.1.1.1/Sun May 4 22:00:34 2003// +/fixed_lag_smoother.m/1.1.1.1/Wed Jan 22 17:56:04 2003// +/fixed_lag_smoother_demo.m/1.1.1.1/Thu Jun 9 01:27:20 2005// +/fwdback.m/1.1.1.1/Thu Jun 9 01:17:50 2005// +/gausshmm_train_observed.m/1.1.1.1/Thu Feb 12 23:08:22 2004// +/mc_sample.m/1.1.1.1/Mon May 24 22:26:34 2004// +/mc_sample_endstate.m/1.1.1.1/Wed Jan 22 20:32:28 2003// +/mdp_sample.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mhmmParzen_train_observed.m/1.1.1.1/Sat Feb 14 02:06:30 2004// +/mhmm_em.m/1.1.1.1/Sun Feb 8 04:52:42 2004// +/mhmm_em_demo.m/1.1.1.1/Tue May 13 16:11:22 2003// +/mhmm_logprob.m/1.1.1.1/Sun May 4 22:11:54 2003// +/mhmm_sample.m/1.1.1.1/Wed May 26 00:32:28 2004// +/mk_leftright_transmat.m/1.1.1.1/Wed May 29 15:59:58 2002// +/mk_rightleft_transmat.m/1.1.1.1/Fri Nov 22 21:45:52 2002// +/pomdp_sample.m/1.1.1.1/Sun May 4 21:58:20 2003// +/testHMM.m/1.1.1.1/Thu Jun 9 01:25:50 2005// +/transmat_train_observed.m/1.1.1.1/Sun Aug 29 12:41:52 2004// +/viterbi_path.m/1.1.1.1/Sat Oct 23 01:18:22 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/HMM
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/README.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +Hidden Markov Model (HMM) Toolbox written by Kevin Murphy (1998). +See http://www.ai.mit.edu/~murphyk/Software/hmm.html for details. + +Models +------ + +dhmm = HMM with discrete output +mhmm = HMM with mixture of Gaussians output; + Use mhmm with M=1 components to simulate an HMM with a single Gaussian output. + +Demos +----- + +mhmm_em_demo +dhmm_em_demo +dhmm_em_online_demo +fixed_lag_smoother_demo + +References +----------- + +See "A tutorial on Hidden Markov Models and selected applications in speech recognition", + L. Rabiner, 1989, Proc. IEEE 77(2):257--286.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_em.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,124 @@ +function [LL, prior, transmat, obsmat, nrIterations] = ... + dhmm_em(data, prior, transmat, obsmat, varargin) +% LEARN_DHMM Find the ML/MAP parameters of an HMM with discrete outputs using EM. +% [ll_trace, prior, transmat, obsmat, iterNr] = learn_dhmm(data, prior0, transmat0, obsmat0, ...) +% +% Notation: Q(t) = hidden state, Y(t) = observation +% +% INPUTS: +% data{ex} or data(ex,:) if all sequences have the same length +% prior(i) +% transmat(i,j) +% obsmat(i,o) +% +% Optional parameters may be passed as 'param_name', param_value pairs. +% Parameter names are shown below; default values in [] - if none, argument is mandatory. +% +% 'max_iter' - max number of EM iterations [10] +% 'thresh' - convergence threshold [1e-4] +% 'verbose' - if 1, print out loglik at every iteration [1] +% 'obs_prior_weight' - weight to apply to uniform dirichlet prior on observation matrix [0] +% +% To clamp some of the parameters, so learning does not change them: +% 'adj_prior' - if 0, do not change prior [1] +% 'adj_trans' - if 0, do not change transmat [1] +% 'adj_obs' - if 0, do not change obsmat [1] +% +% Modified by Herbert Jaeger so xi are not computed individually +% but only their sum (over time) as xi_summed; this is the only way how they are used +% and it saves a lot of memory. + +[max_iter, thresh, verbose, obs_prior_weight, adj_prior, adj_trans, adj_obs] = ... + process_options(varargin, 'max_iter', 10, 'thresh', 1e-4, 'verbose', 1, ... + 'obs_prior_weight', 0, 'adj_prior', 1, 'adj_trans', 1, 'adj_obs', 1); + +previous_loglik = -inf; +loglik = 0; +converged = 0; +num_iter = 1; +LL = []; + +if ~iscell(data) + data = num2cell(data, 2); % each row gets its own cell +end + +while (num_iter <= max_iter) & ~converged + % E step + [loglik, exp_num_trans, exp_num_visits1, exp_num_emit] = ... + compute_ess_dhmm(prior, transmat, obsmat, data, obs_prior_weight); + + % M step + if adj_prior + prior = normalise(exp_num_visits1); + end + if adj_trans & ~isempty(exp_num_trans) + transmat = mk_stochastic(exp_num_trans); + end + if adj_obs + obsmat = mk_stochastic(exp_num_emit); + end + + if verbose, fprintf(1, 'iteration %d, loglik = %f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + LL = [LL loglik]; +end +nrIterations = num_iter - 1; + +%%%%%%%%%%%%%%%%%%%%%%% + +function [loglik, exp_num_trans, exp_num_visits1, exp_num_emit, exp_num_visitsT] = ... + compute_ess_dhmm(startprob, transmat, obsmat, data, dirichlet) +% COMPUTE_ESS_DHMM Compute the Expected Sufficient Statistics for an HMM with discrete outputs +% function [loglik, exp_num_trans, exp_num_visits1, exp_num_emit, exp_num_visitsT] = ... +% compute_ess_dhmm(startprob, transmat, obsmat, data, dirichlet) +% +% INPUTS: +% startprob(i) +% transmat(i,j) +% obsmat(i,o) +% data{seq}(t) +% dirichlet - weighting term for uniform dirichlet prior on expected emissions +% +% OUTPUTS: +% exp_num_trans(i,j) = sum_l sum_{t=2}^T Pr(X(t-1) = i, X(t) = j| Obs(l)) +% exp_num_visits1(i) = sum_l Pr(X(1)=i | Obs(l)) +% exp_num_visitsT(i) = sum_l Pr(X(T)=i | Obs(l)) +% exp_num_emit(i,o) = sum_l sum_{t=1}^T Pr(X(t) = i, O(t)=o| Obs(l)) +% where Obs(l) = O_1 .. O_T for sequence l. + +numex = length(data); +[S O] = size(obsmat); +exp_num_trans = zeros(S,S); +exp_num_visits1 = zeros(S,1); +exp_num_visitsT = zeros(S,1); +exp_num_emit = dirichlet*ones(S,O); +loglik = 0; + +for ex=1:numex + obs = data{ex}; + T = length(obs); + %obslik = eval_pdf_cond_multinomial(obs, obsmat); + obslik = multinomial_prob(obs, obsmat); + [alpha, beta, gamma, current_ll, xi_summed] = fwdback(startprob, transmat, obslik); + + loglik = loglik + current_ll; + exp_num_trans = exp_num_trans + xi_summed; + exp_num_visits1 = exp_num_visits1 + gamma(:,1); + exp_num_visitsT = exp_num_visitsT + gamma(:,T); + % loop over whichever is shorter + if T < O + for t=1:T + o = obs(t); + exp_num_emit(:,o) = exp_num_emit(:,o) + gamma(:,t); + end + else + for o=1:O + ndx = find(obs==o); + if ~isempty(ndx) + exp_num_emit(:,o) = exp_num_emit(:,o) + sum(gamma(:, ndx), 2); + end + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_em_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +O = 3; +Q = 2; + +% "true" parameters +prior0 = normalise(rand(Q,1)); +transmat0 = mk_stochastic(rand(Q,Q)); +obsmat0 = mk_stochastic(rand(Q,O)); + +% training data +T = 5; +nex = 10; +data = dhmm_sample(prior0, transmat0, obsmat0, T, nex); + +% initial guess of parameters +prior1 = normalise(rand(Q,1)); +transmat1 = mk_stochastic(rand(Q,Q)); +obsmat1 = mk_stochastic(rand(Q,O)); + +% improve guess of parameters using EM +[LL, prior2, transmat2, obsmat2] = dhmm_em(data, prior1, transmat1, obsmat1, 'max_iter', 5); +LL + +% use model to compute log likelihood +loglik = dhmm_logprob(data, prior2, transmat2, obsmat2) +% log lik is slightly different than LL(end), since it is computed after the final M step
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_em_online.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function [transmat, obsmat, exp_num_trans, exp_num_emit, gamma, ll] = dhmm_em_online(... + prior, transmat, obsmat, exp_num_trans, exp_num_emit, decay, data, ... + act, adj_trans, adj_obs, dirichlet, filter_only) +% ONLINE_EM Adjust the parameters using a weighted combination of the old and new expected statistics +% +% [transmat, obsmat, exp_num_trans, exp_num_emit, gamma, ll] = online_em(... +% prior, transmat, obsmat, exp_num_trans, exp_num_emit, decay, data, act, ... +% adj_trans, adj_obs, dirichlet, filter_only) +% +% 0 < decay < 1, with smaller values meaning the past is forgotten more quickly. +% (We need to decay the old ess, since they were based on out-of-date parameters.) +% The other params are as in learn_hmm. +% We do a single forwards-backwards pass on the provided data, initializing with the specified prior. +% (If filter_only = 1, we only do a forwards pass.) + +if ~exist('act'), act = []; end +if ~exist('adj_trans'), adj_trans = 1; end +if ~exist('adj_obs'), adj_obs = 1; end +if ~exist('dirichlet'), dirichlet = 0; end +if ~exist('filter_only'), filter_only = 0; end + +% E step +olikseq = multinomial_prob(data, obsmat); +if isempty(act) + [alpha, beta, gamma, ll, xi] = fwdback(prior, transmat, olikseq, 'fwd_only', filter_only); +else + [alpha, beta, gamma, ll, xi] = fwdback(prior, transmat, olikseq, 'fwd_only', filter_only, ... + 'act', act); +end + +% Increment ESS +[S O] = size(obsmat); +if adj_obs + exp_num_emit = decay*exp_num_emit + dirichlet*ones(S,O); + T = length(data); + if T < O + for t=1:T + o = data(t); + exp_num_emit(:,o) = exp_num_emit(:,o) + gamma(:,t); + end + else + for o=1:O + ndx = find(data==o); + if ~isempty(ndx) + exp_num_emit(:,o) = exp_num_emit(:,o) + sum(gamma(:, ndx), 2); + end + end + end +end + +if adj_trans & (T > 1) + if isempty(act) + exp_num_trans = decay*exp_num_trans + sum(xi,3); + else + % act(2) determines Q(2), xi(:,:,1) holds P(Q(1), Q(2)) + A = length(transmat); + for a=1:A + ndx = find(act(2:end)==a); + if ~isempty(ndx) + exp_num_trans{a} = decay*exp_num_trans{a} + sum(xi(:,:,ndx), 3); + end + end + end +end + + +% M step + +if adj_obs + obsmat = mk_stochastic(exp_num_emit); +end +if adj_trans & (T>1) + if isempty(act) + transmat = mk_stochastic(exp_num_trans); + else + for a=1:A + transmat{a} = mk_stochastic(exp_num_trans{a}); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_em_online_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +% Example of online EM applied to a simple POMDP with fixed action seq + +clear all + +% Create a really easy model to learn +rand('state', 1); +O = 2; +S = 2; +A = 2; +prior0 = [1 0]'; +transmat0 = cell(1,A); +transmat0{1} = [0.9 0.1; 0.1 0.9]; % long runs of 1s and 2s +transmat0{2} = [0.1 0.9; 0.9 0.1]; % short runs +obsmat0 = eye(2); + +%prior0 = normalise(rand(S,1)); +%transmat0 = mk_stochastic(rand(S,S)); +%obsmat0 = mk_stochastic(rand(S,O)); + +T = 10; +act = [1*ones(1,25) 2*ones(1,25) 1*ones(1,25) 2*ones(1,25)]; +data = pomdp_sample(prior0, transmat0, obsmat0, act); +%data = sample_dhmm(prior0, transmat0, obsmat0, T, 1); + +% Initial guess of params +rand('state', 2); % different seed! +transmat1 = cell(1,A); +for a=1:A + transmat1{a} = mk_stochastic(rand(S,S)); +end +obsmat1 = mk_stochastic(rand(S,O)); +prior1 = prior0; % so it labels states the same way + +% Uniformative Dirichlet prior (expected sufficient statistics / pseudo counts) +e = 0.001; +ess_trans = cell(1,A); +for a=1:A + ess_trans{a} = repmat(e, S, S); +end +ess_emit = repmat(e, S, O); + +% Params +w = 2; +decay_sched = [0.1:0.1:0.9]; + +% Initialize +LL1 = zeros(1,T); +t = 1; +y = data(t); +data_win = y; +act_win = [1]; % arbitrary initial value +[prior1, LL1(1)] = normalise(prior1 .* obsmat1(:,y)); + +% Iterate +for t=2:T + y = data(t); + a = act(t); + if t <= w + data_win = [data_win y]; + act_win = [act_win a]; + else + data_win = [data_win(2:end) y]; + act_win = [act_win(2:end) a]; + prior1 = gamma(:, 2); + end + d = decay_sched(min(t, length(decay_sched))); + [transmat1, obsmat1, ess_trans, ess_emit, gamma, ll] = dhmm_em_online(... + prior1, transmat1, obsmat1, ess_trans, ess_emit, d, data_win, act_win); + bel = gamma(:, end); + LL1(t) = ll/length(data_win); + %fprintf('t=%d, ll=%f\n', t, ll); +end + +LL1(1) = LL1(2); % since initial likelihood is for 1 slice +plot(1:T, LL1, 'rx-'); + + +% compare with offline learning + +if 0 +rand('state', 2); % same seed as online learner +transmat2 = cell(1,A); +for a=1:A + transmat2{a} = mk_stochastic(rand(S,S)); +end +obsmat2 = mk_stochastic(rand(S,O)); +prior2 = prior0; +[LL2, prior2, transmat2, obsmat2] = dhmm_em(data, prior2, transmat2, obsmat2, .... + 'max_iter', 10, 'thresh', 1e-3, 'verbose', 1, 'act', act); + +LL2 = LL2 / T + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_logprob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [loglik, errors] = dhmm_logprob(data, prior, transmat, obsmat) +% LOG_LIK_DHMM Compute the log-likelihood of a dataset using a discrete HMM +% [loglik, errors] = log_lik_dhmm(data, prior, transmat, obsmat) +% +% data{m} or data(m,:) is the m'th sequence +% errors is a list of the cases which received a loglik of -infinity + +if ~iscell(data) + data = num2cell(data, 2); +end +ncases = length(data); + +loglik = 0; +errors = []; +for m=1:ncases + obslik = multinomial_prob(data{m}, obsmat); + [alpha, beta, gamma, ll] = fwdback(prior, transmat, obslik, 'fwd_only', 1); + if ll==-inf + errors = [errors m]; + end + loglik = loglik + ll; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_logprob_brute_force.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function logp = enumerate_HMM_loglik(prior, transmat, obsmat) +% ENUMERATE_HMM_LOGLIK Compute the log likelihood of a sequence by exhaustive (O(Q^T)) enumeration. +% logp = enumerate_HMM_loglik(prior, transmat, obsmat) +% +% Inputs: +% prior(i) = Pr(Q(1) = i) +% transmat(i,j) = Pr(Q(t+1)=j | Q(t)=i) +% obsmat(i,t) = Pr(y(t) | Q(t)=i) + +Q = length(prior); +T = size(obsmat, 2); +sizes = repmat(Q, 1, T); + +psum = 0; +for i=1:Q^T + qs = ind2subv(sizes, i); % make the state sequence + psum = psum + prob_path(prior, transmat, obsmat, qs); +end +logp = log(psum) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_logprob_path.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function [ll, p] = prob_path(prior, transmat, obsmat, qs) +% PROB_PATH Compute the prob. of a specific path (state sequence) through an HMM. +% [ll, p] = prob_path(prior, transmat, obsmat, states) +% +% ll = log prob path +% p(t) = Pr(O(t)) * Pr(Q(t) -> Q(t+1)) for 1<=t<T, p(T) = Pr(O(T)) + +T = size(obsmat, 2); +p = zeros(1,T); +p(1) = prior(qs(1)) * obsmat(qs(1),1); +for t=2:T + p(t) = transmat(qs(t-1), qs(t)) * obsmat(qs(t),t); +end + +ll = sum(log(p));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function [obs, hidden] = dhmm_sample(initial_prob, transmat, obsmat, numex, len) +% SAMPLE_DHMM Generate random sequences from a Hidden Markov Model with discrete outputs. +% +% [obs, hidden] = sample_dhmm(initial_prob, transmat, obsmat, numex, len) +% Each row of obs is an observation sequence of length len. + +hidden = mc_sample(initial_prob, transmat, len, numex); +obs = multinomial_sample(hidden, obsmat); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/dhmm_sample_endstate.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function [obs, hidden] = dhmm_sample_endstate(startprob, transmat, obsmat, endprob, numex) +% SAMPLE_DHMM Generate random sequences from an HMM with discrete outputs. +% function [obs, hidden] = sample_dhmm_endstate(startprob, transmat, obsmat, endprob, numex) +% +% We sample until we have have entered the end state +% obs{m} and hidden{m} are the m'th sequence + +hidden = cell(1,numex); +obs = cell(1,numex); + +for m=1:numex + hidden{m} = mc_sample_endstate(startprob, transmat, endprob); + T = length(hidden{m}); + obs{m} = zeros(1,T); + for t=1:T + h = hidden{m}(t); + obs{m}(t) = sample_discrete(obsmat(h,:)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/fixed_lag_smoother.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [alpha, obslik, gamma, xi] = fixed_lag_smoother(d, alpha, obslik, obsvec, transmat, act) +% FIXED_LAG_SMOOTHER Computed smoothed posterior estimates within a window given previous filtered window. +% [alpha, obslik, gamma, xi] = fixed_lag_smoother(d, alpha, obslik, obsvec, transmat, act) +% +% d >= 2 is the desired window width. +% Actually, we use d=min(d, t0), where t0 is the current time. +% +% alpha(:, t0-d:t0-1) - length d window, excluding t0 (Columns indexed 1..d) +% obslik(:, t0-d:t0-1) - length d window +% obsvec - likelihood vector for current observation +% transmat - transition matrix +% If we specify the optional 'act' argument, transmat{a} should be a cell array, and +% act(t0-d:t0) - length d window, last column = current action +% +% Output: +% alpha(:, t0-d+1:t0) - last column = new filtered estimate +% obslik(:, t0-d+1:t0) - last column = obsvec +% xi(:, :, t0-d+1:t0-1) - 2 slice smoothed window +% gamma(:, t0-d+1:t0) - smoothed window +% +% As usual, we define (using T=d) +% alpha(i,t) = Pr(Q(t)=i | Y(1:t)) +% gamma(i,t) = Pr(Q(t)=i | Y(1:T)) +% xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | Y(1:T)) +% +% obslik(i,t) = Pr(Y(t) | Q(t)=i) +% transmat{a}(i,j) = Pr(Q(t)=j | Q(t-1)=i, A(t)=a) + +[S n] = size(alpha); +d = min(d, n+1); +if d < 2 + error('must keep a window of length at least 2'); +end + +if ~exist('act') + act = ones(1, n+1); + transmat = { transmat }; +end + +% pluck out last d-1 components from the history +alpha = alpha(:, n-d+2:n); +obslik = obslik(:, n-d+2:n); + +% Extend window by 1 +t = d; +obslik(:,t) = obsvec; +xi = zeros(S, S, d-1); +xi(:,:,t-1) = normalise((alpha(:,t-1) * obslik(:,t)') .* transmat{act(t)}); +alpha(:,t) = sum(xi(:,:,t-1), 1)'; + +% Now smooth backwards inside the window +beta = ones(S, d); +T = d; +%fprintf('smooth from %d to 1, i.e., %d to %d\n', d, t0, t0-d+1); +gamma(:,T) = alpha(:,T); +for t=T-1:-1:1 + b = beta(:,t+1) .* obslik(:,t+1); + beta(:,t) = normalise(transmat{act(t)} * b); + gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); + xi(:,:,t) = normalise((transmat{act(t)} .* (alpha(:,t) * b'))); +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/fixed_lag_smoother_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +% Example of fixed lag smoothing + +rand('state', 1); +S = 2; +O = 2; +T = 7; +data = sample_discrete([0.5 0.5], 1, T); +transmat = mk_stochastic(rand(S,S)); +obsmat = mk_stochastic(rand(S,O)); +obslik = multinomial_prob(data, obsmat); +prior = [0.5 0.5]'; + + +[alpha0, beta0, gamma0, ll0, xi0] = fwdback(prior, transmat, obslik); + +w = 3; +alpha1 = zeros(S, T); +gamma1 = zeros(S, T); +xi1 = zeros(S, S, T-1); +t = 1; +b = obsmat(:, data(t)); +olik_win = b; % window of conditional observation likelihoods +alpha_win = normalise(prior .* b); +alpha1(:,t) = alpha_win; +for t=2:T + [alpha_win, olik_win, gamma_win, xi_win] = ... + fixed_lag_smoother(w, alpha_win, olik_win, obsmat(:, data(t)), transmat); + alpha1(:,max(1,t-w+1):t) = alpha_win; + gamma1(:,max(1,t-w+1):t) = gamma_win; + xi1(:,:,max(1,t-w+1):t-1) = xi_win; +end + +e = 1e-1; +%assert(approxeq(alpha0, alpha1, e)); +assert(approxeq(gamma0(:, T-w+1:end), gamma1(:, T-w+1:end), e)); +%assert(approxeq(xi0(:,:,T-w+1:end), xi1(:,:,T-w+1:end), e)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/fwdback.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,197 @@ +function [alpha, beta, gamma, loglik, xi_summed, gamma2] = fwdback(init_state_distrib, ... + transmat, obslik, varargin) +% FWDBACK Compute the posterior probs. in an HMM using the forwards backwards algo. +% +% [alpha, beta, gamma, loglik, xi, gamma2] = fwdback(init_state_distrib, transmat, obslik, ...) +% +% Notation: +% Y(t) = observation, Q(t) = hidden state, M(t) = mixture variable (for MOG outputs) +% A(t) = discrete input (action) (for POMDP models) +% +% INPUT: +% init_state_distrib(i) = Pr(Q(1) = i) +% transmat(i,j) = Pr(Q(t) = j | Q(t-1)=i) +% or transmat{a}(i,j) = Pr(Q(t) = j | Q(t-1)=i, A(t-1)=a) if there are discrete inputs +% obslik(i,t) = Pr(Y(t)| Q(t)=i) +% (Compute obslik using eval_pdf_xxx on your data sequence first.) +% +% Optional parameters may be passed as 'param_name', param_value pairs. +% Parameter names are shown below; default values in [] - if none, argument is mandatory. +% +% For HMMs with MOG outputs: if you want to compute gamma2, you must specify +% 'obslik2' - obslik(i,j,t) = Pr(Y(t)| Q(t)=i,M(t)=j) [] +% 'mixmat' - mixmat(i,j) = Pr(M(t) = j | Q(t)=i) [] +% +% For HMMs with discrete inputs: +% 'act' - act(t) = action performed at step t +% +% Optional arguments: +% 'fwd_only' - if 1, only do a forwards pass and set beta=[], gamma2=[] [0] +% 'scaled' - if 1, normalize alphas and betas to prevent underflow [1] +% 'maximize' - if 1, use max-product instead of sum-product [0] +% +% OUTPUTS: +% alpha(i,t) = p(Q(t)=i | y(1:t)) (or p(Q(t)=i, y(1:t)) if scaled=0) +% beta(i,t) = p(y(t+1:T) | Q(t)=i)*p(y(t+1:T)|y(1:t)) (or p(y(t+1:T) | Q(t)=i) if scaled=0) +% gamma(i,t) = p(Q(t)=i | y(1:T)) +% loglik = log p(y(1:T)) +% xi(i,j,t-1) = p(Q(t-1)=i, Q(t)=j | y(1:T)) - NO LONGER COMPUTED +% xi_summed(i,j) = sum_{t=}^{T-1} xi(i,j,t) - changed made by Herbert Jaeger +% gamma2(j,k,t) = p(Q(t)=j, M(t)=k | y(1:T)) (only for MOG outputs) +% +% If fwd_only = 1, these become +% alpha(i,t) = p(Q(t)=i | y(1:t)) +% beta = [] +% gamma(i,t) = p(Q(t)=i | y(1:t)) +% xi(i,j,t-1) = p(Q(t-1)=i, Q(t)=j | y(1:t)) +% gamma2 = [] +% +% Note: we only compute xi if it is requested as a return argument, since it can be very large. +% Similarly, we only compute gamma2 on request (and if using MOG outputs). +% +% Examples: +% +% [alpha, beta, gamma, loglik] = fwdback(pi, A, multinomial_prob(sequence, B)); +% +% [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat); +% [alpha, beta, gamma, loglik, xi, gamma2] = fwdback(pi, A, B, 'obslik2', B2, 'mixmat', mixmat); + +if nargout >= 5, compute_xi = 1; else compute_xi = 0; end +if nargout >= 6, compute_gamma2 = 1; else compute_gamma2 = 0; end + +[obslik2, mixmat, fwd_only, scaled, act, maximize, compute_xi, compute_gamma2] = ... + process_options(varargin, ... + 'obslik2', [], 'mixmat', [], ... + 'fwd_only', 0, 'scaled', 1, 'act', [], 'maximize', 0, ... + 'compute_xi', compute_xi, 'compute_gamma2', compute_gamma2); + +[Q T] = size(obslik); + +if isempty(obslik2) + compute_gamma2 = 0; +end + +if isempty(act) + act = ones(1,T); + transmat = { transmat } ; +end + +scale = ones(1,T); + +% scale(t) = Pr(O(t) | O(1:t-1)) = 1/c(t) as defined by Rabiner (1989). +% Hence prod_t scale(t) = Pr(O(1)) Pr(O(2)|O(1)) Pr(O(3) | O(1:2)) ... = Pr(O(1), ... ,O(T)) +% or log P = sum_t log scale(t). +% Rabiner suggests multiplying beta(t) by scale(t), but we can instead +% normalise beta(t) - the constants will cancel when we compute gamma. + +loglik = 0; + +alpha = zeros(Q,T); +gamma = zeros(Q,T); +if compute_xi + xi_summed = zeros(Q,Q); +else + xi_summed = []; +end + +%%%%%%%%% Forwards %%%%%%%%%% + +t = 1; +alpha(:,1) = init_state_distrib(:) .* obslik(:,t); +if scaled + %[alpha(:,t), scale(t)] = normaliseC(alpha(:,t)); + [alpha(:,t), scale(t)] = normalise(alpha(:,t)); +end +assert(approxeq(sum(alpha(:,t)),1)) +for t=2:T + %trans = transmat(:,:,act(t-1))'; + trans = transmat{act(t-1)}; + if maximize + m = max_mult(trans', alpha(:,t-1)); + %A = repmat(alpha(:,t-1), [1 Q]); + %m = max(trans .* A, [], 1); + else + m = trans' * alpha(:,t-1); + end + alpha(:,t) = m(:) .* obslik(:,t); + if scaled + %[alpha(:,t), scale(t)] = normaliseC(alpha(:,t)); + [alpha(:,t), scale(t)] = normalise(alpha(:,t)); + end + if compute_xi & fwd_only % useful for online EM + %xi(:,:,t-1) = normaliseC((alpha(:,t-1) * obslik(:,t)') .* trans); + xi_summed = xi_summed + normalise((alpha(:,t-1) * obslik(:,t)') .* trans); + end + assert(approxeq(sum(alpha(:,t)),1)) +end +if scaled + if any(scale==0) + loglik = -inf; + else + loglik = sum(log(scale)); + end +else + loglik = log(sum(alpha(:,T))); +end + +if fwd_only + gamma = alpha; + beta = []; + gamma2 = []; + return; +end + +%%%%%%%%% Backwards %%%%%%%%%% + +beta = zeros(Q,T); +if compute_gamma2 + M = size(mixmat, 2); + gamma2 = zeros(Q,M,T); +else + gamma2 = []; +end + +beta(:,T) = ones(Q,1); +%gamma(:,T) = normaliseC(alpha(:,T) .* beta(:,T)); +gamma(:,T) = normalise(alpha(:,T) .* beta(:,T)); +t=T; +if compute_gamma2 + denom = obslik(:,t) + (obslik(:,t)==0); % replace 0s with 1s before dividing + gamma2(:,:,t) = obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]) ./ repmat(denom, [1 M]); + %gamma2(:,:,t) = normaliseC(obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M])); % wrong! +end +for t=T-1:-1:1 + b = beta(:,t+1) .* obslik(:,t+1); + %trans = transmat(:,:,act(t)); + trans = transmat{act(t)}; + if maximize + B = repmat(b(:)', Q, 1); + beta(:,t) = max(trans .* B, [], 2); + else + beta(:,t) = trans * b; + end + if scaled + %beta(:,t) = normaliseC(beta(:,t)); + beta(:,t) = normalise(beta(:,t)); + end + %gamma(:,t) = normaliseC(alpha(:,t) .* beta(:,t)); + gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); + if compute_xi + %xi(:,:,t) = normaliseC((trans .* (alpha(:,t) * b'))); + xi_summed = xi_summed + normalise((trans .* (alpha(:,t) * b'))); + end + if compute_gamma2 + denom = obslik(:,t) + (obslik(:,t)==0); % replace 0s with 1s before dividing + gamma2(:,:,t) = obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]) ./ repmat(denom, [1 M]); + %gamma2(:,:,t) = normaliseC(obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M])); + end +end + +% We now explain the equation for gamma2 +% Let zt=y(1:t-1,t+1:T) be all observations except y(t) +% gamma2(Q,M,t) = P(Qt,Mt|yt,zt) = P(yt|Qt,Mt,zt) P(Qt,Mt|zt) / P(yt|zt) +% = P(yt|Qt,Mt) P(Mt|Qt) P(Qt|zt) / P(yt|zt) +% Now gamma(Q,t) = P(Qt|yt,zt) = P(yt|Qt) P(Qt|zt) / P(yt|zt) +% hence +% P(Qt,Mt|yt,zt) = P(yt|Qt,Mt) P(Mt|Qt) [P(Qt|yt,zt) P(yt|zt) / P(yt|Qt)] / P(yt|zt) +% = P(yt|Qt,Mt) P(Mt|Qt) P(Qt|yt,zt) / P(yt|Qt)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/gausshmm_train_observed.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function [initState, transmat, mu, Sigma] = gausshmm_train_observed(obsData, hiddenData, ... + nstates, varargin) +% GAUSSHMM_TRAIN_OBSERVED Estimate params of HMM with Gaussian output from fully observed sequences +% [initState, transmat, mu, Sigma] = gausshmm_train_observed(obsData, hiddenData, nstates,...) +% +% INPUT +% If all sequences have the same length +% obsData(:,t,ex) +% hiddenData(ex,t) - must be ROW vector if only one sequence +% If sequences have different lengths, we use cell arrays +% obsData{ex}(:,t) +% hiddenData{ex}(t) +% +% Optional argumnets +% dirichletPriorWeight - for smoothing transition matrix counts +% +% Optional parameters from mixgauss_Mstep: +% 'cov_type' - 'full', 'diag' or 'spherical' ['full'] +% 'tied_cov' - 1 (Sigma) or 0 (Sigma_i) [0] +% 'clamped_cov' - pass in clamped value, or [] if unclamped [ [] ] +% 'clamped_mean' - pass in clamped value, or [] if unclamped [ [] ] +% 'cov_prior' - Lambda_i, added to YY(:,:,i) [0.01*eye(d,d,Q)] +% +% Output +% mu(:,q) +% Sigma(:,:,q) + +[dirichletPriorWeight, other] = process_options(... + varargin, 'dirichletPriorWeight', 0); + +[transmat, initState] = transmat_train_observed(hiddenData, nstates, ... + 'dirichletPriorWeight', dirichletPriorWeight); + +% convert to obsData(:,t*nex) +if ~iscell(obsData) + [D T Nex] = size(obsData); + obsData = reshape(obsData, D, T*Nex); +else + obsData = cat(2, obsData{:}); + hiddenData = cat(2,hiddenData{:}); +end +[mu, Sigma] = condgaussTrainObserved(obsData, hiddenData(:), nstates, varargin{:}); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mc_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function S = mc_sample(prior, trans, len, numex) +% SAMPLE_MC Generate random sequences from a Markov chain. +% STATE = SAMPLE_MC(PRIOR, TRANS, LEN) generates a sequence of length LEN. +% +% STATE = SAMPLE_MC(PRIOR, TRANS, LEN, N) generates N rows each of length LEN. + +if nargin==3 + numex = 1; +end + +S = zeros(numex,len); +for i=1:numex + S(i, 1) = sample_discrete(prior); + for t=2:len + S(i, t) = sample_discrete(trans(S(i,t-1),:)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mc_sample_endstate.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function S = sample_mc_endstate(startprob, trans, endprob) +% SAMPLE_MC_ENDSTATE Generate a random sequence from a Markov chain until enter the endstate. +% seq = sample_mc(startprob, trans, endprob) + +% add an end state +Q = size(trans,1); +transprob = zeros(Q,Q+1); +end_state = Q+1; +for i=1:Q + for j=1:Q + transprob(i,j) = (1-endprob(i)) * trans(i,j); + end + transprob(i,end_state) = endprob(i); + %assert(approxeq(sum(transprob(i,:)), 1)) +end + +S = []; +S(1) = sample_discrete(startprob); +t = 1; +p = endprob(S(t)); +stop = (S(1) == end_state); +while ~stop + S(t+1) = sample_discrete(transprob(S(t),:)); + stop = (S(t+1) == end_state); + t = t + 1; +end +S = S(1:end-1); % don't include end state
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mdp_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function state = sample_mdp(prior, trans, act) +% SAMPLE_MDP Sample a sequence of states from a Markov Decision Process. +% state = sample_mdp(prior, trans, act) +% +% Inputs: +% prior(i) = Pr(Q(1)=i) +% trans{a}(i,j) = Pr(Q(t)=j | Q(t-1)=i, A(t)=a) +% act(a) = A(t), so act(1) is ignored +% +% Output: +% state is a vector of length T=length(act) + +len = length(act); +state = zeros(1,len); +state(1) = sample_discrete(prior); +for t=2:len + state(t) = sample_discrete(trans{act(t)}(state(t-1),:)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mhmmParzen_train_observed.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function [initState, transmat, mu, Nproto, pick] = mhmmParzen_train_observed(obsData, hiddenData, ... + nstates, maxNproto, varargin) +% mhmmParzentrain_observed with mixture of Gaussian outputs from fully observed sequences +% function [initState, transmat, mu, Nproto] = mhmm_train_observed_parzen(obsData, hiddenData, ... +% nstates, maxNproto) +% +% +% INPUT +% If all sequences have the same length +% obsData(:,t,ex) +% hiddenData(ex,t) - must be ROW vector if only one sequence +% If sequences have different lengths, we use cell arrays +% obsData{ex}(:,t) +% hiddenData{ex}(t) +% +% Optional argumnets +% dirichletPriorWeight - for smoothing transition matrix counts +% mkSymmetric +% +% Output +% mu(:,q) +% Nproto(q) is the number of prototypes (mixture components) chosen for state q + +[transmat, initState] = transmat_train_observed(... + hiddenData, nstates, varargin{:}); + +% convert to obsData(:,t*nex) +if ~iscell(obsData) + [D T Nex] = size(obsData); + obsData = reshape(obsData, D, T*Nex); +else + obsData = cat(2, obsData{:}); + hiddenData = cat(2, hiddenData{:}); +end +[mu, Nproto, pick] = parzen_fit_select_unif(obsData, hiddenData(:), maxNproto); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mhmm_em.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +function [LL, prior, transmat, mu, Sigma, mixmat] = ... + mhmm_em(data, prior, transmat, mu, Sigma, mixmat, varargin); +% LEARN_MHMM Compute the ML parameters of an HMM with (mixtures of) Gaussians output using EM. +% [ll_trace, prior, transmat, mu, sigma, mixmat] = learn_mhmm(data, ... +% prior0, transmat0, mu0, sigma0, mixmat0, ...) +% +% Notation: Q(t) = hidden state, Y(t) = observation, M(t) = mixture variable +% +% INPUTS: +% data{ex}(:,t) or data(:,t,ex) if all sequences have the same length +% prior(i) = Pr(Q(1) = i), +% transmat(i,j) = Pr(Q(t+1)=j | Q(t)=i) +% mu(:,j,k) = E[Y(t) | Q(t)=j, M(t)=k ] +% Sigma(:,:,j,k) = Cov[Y(t) | Q(t)=j, M(t)=k] +% mixmat(j,k) = Pr(M(t)=k | Q(t)=j) : set to [] or ones(Q,1) if only one mixture component +% +% Optional parameters may be passed as 'param_name', param_value pairs. +% Parameter names are shown below; default values in [] - if none, argument is mandatory. +% +% 'max_iter' - max number of EM iterations [10] +% 'thresh' - convergence threshold [1e-4] +% 'verbose' - if 1, print out loglik at every iteration [1] +% 'cov_type' - 'full', 'diag' or 'spherical' ['full'] +% +% To clamp some of the parameters, so learning does not change them: +% 'adj_prior' - if 0, do not change prior [1] +% 'adj_trans' - if 0, do not change transmat [1] +% 'adj_mix' - if 0, do not change mixmat [1] +% 'adj_mu' - if 0, do not change mu [1] +% 'adj_Sigma' - if 0, do not change Sigma [1] +% +% If the number of mixture components differs depending on Q, just set the trailing +% entries of mixmat to 0, e.g., 2 components if Q=1, 3 components if Q=2, +% then set mixmat(1,3)=0. In this case, B2(1,3,:)=1.0. + +if ~isstr(varargin{1}) % catch old syntax + error('optional arguments should be passed as string/value pairs') +end + +[max_iter, thresh, verbose, cov_type, adj_prior, adj_trans, adj_mix, adj_mu, adj_Sigma] = ... + process_options(varargin, 'max_iter', 10, 'thresh', 1e-4, 'verbose', 1, ... + 'cov_type', 'full', 'adj_prior', 1, 'adj_trans', 1, 'adj_mix', 1, ... + 'adj_mu', 1, 'adj_Sigma', 1); + +previous_loglik = -inf; +loglik = 0; +converged = 0; +num_iter = 1; +LL = []; + +if ~iscell(data) + data = num2cell(data, [1 2]); % each elt of the 3rd dim gets its own cell +end +numex = length(data); + + +O = size(data{1},1); +Q = length(prior); +if isempty(mixmat) + mixmat = ones(Q,1); +end +M = size(mixmat,2); +if M == 1 + adj_mix = 0; +end + +while (num_iter <= max_iter) & ~converged + % E step + [loglik, exp_num_trans, exp_num_visits1, postmix, m, ip, op] = ... + ess_mhmm(prior, transmat, mixmat, mu, Sigma, data); + + + % M step + if adj_prior + prior = normalise(exp_num_visits1); + end + if adj_trans + transmat = mk_stochastic(exp_num_trans); + end + if adj_mix + mixmat = mk_stochastic(postmix); + end + if adj_mu | adj_Sigma + [mu2, Sigma2] = mixgauss_Mstep(postmix, m, op, ip, 'cov_type', cov_type); + if adj_mu + mu = reshape(mu2, [O Q M]); + end + if adj_Sigma + Sigma = reshape(Sigma2, [O O Q M]); + end + end + + if verbose, fprintf(1, 'iteration %d, loglik = %f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + LL = [LL loglik]; +end + + +%%%%%%%%% + +function [loglik, exp_num_trans, exp_num_visits1, postmix, m, ip, op] = ... + ess_mhmm(prior, transmat, mixmat, mu, Sigma, data) +% ESS_MHMM Compute the Expected Sufficient Statistics for a MOG Hidden Markov Model. +% +% Outputs: +% exp_num_trans(i,j) = sum_l sum_{t=2}^T Pr(Q(t-1) = i, Q(t) = j| Obs(l)) +% exp_num_visits1(i) = sum_l Pr(Q(1)=i | Obs(l)) +% +% Let w(i,k,t,l) = P(Q(t)=i, M(t)=k | Obs(l)) +% where Obs(l) = Obs(:,:,l) = O_1 .. O_T for sequence l +% Then +% postmix(i,k) = sum_l sum_t w(i,k,t,l) (posterior mixing weights/ responsibilities) +% m(:,i,k) = sum_l sum_t w(i,k,t,l) * Obs(:,t,l) +% ip(i,k) = sum_l sum_t w(i,k,t,l) * Obs(:,t,l)' * Obs(:,t,l) +% op(:,:,i,k) = sum_l sum_t w(i,k,t,l) * Obs(:,t,l) * Obs(:,t,l)' + + +verbose = 0; + +%[O T numex] = size(data); +numex = length(data); +O = size(data{1},1); +Q = length(prior); +M = size(mixmat,2); +exp_num_trans = zeros(Q,Q); +exp_num_visits1 = zeros(Q,1); +postmix = zeros(Q,M); +m = zeros(O,Q,M); +op = zeros(O,O,Q,M); +ip = zeros(Q,M); + +mix = (M>1); + +loglik = 0; +if verbose, fprintf(1, 'forwards-backwards example # '); end +for ex=1:numex + if verbose, fprintf(1, '%d ', ex); end + %obs = data(:,:,ex); + obs = data{ex}; + T = size(obs,2); + if mix + [B, B2] = mixgauss_prob(obs, mu, Sigma, mixmat); + [alpha, beta, gamma, current_loglik, xi, gamma2] = ... + fwdback(prior, transmat, B, 'obslik2', B2, 'mixmat', mixmat); + else + B = mixgauss_prob(obs, mu, Sigma); + [alpha, beta, gamma, current_loglik, xi] = fwdback(prior, transmat, B); + end + loglik = loglik + current_loglik; + if verbose, fprintf(1, 'll at ex %d = %f\n', ex, loglik); end + + exp_num_trans = exp_num_trans + sum(xi,3); + exp_num_visits1 = exp_num_visits1 + gamma(:,1); + + if mix + postmix = postmix + sum(gamma2,3); + else + postmix = postmix + sum(gamma,2); + gamma2 = reshape(gamma, [Q 1 T]); % gamma2(i,m,t) = gamma(i,t) + end + for i=1:Q + for k=1:M + w = reshape(gamma2(i,k,:), [1 T]); % w(t) = w(i,k,t,l) + wobs = obs .* repmat(w, [O 1]); % wobs(:,t) = w(t) * obs(:,t) + m(:,i,k) = m(:,i,k) + sum(wobs, 2); % m(:) = sum_t w(t) obs(:,t) + op(:,:,i,k) = op(:,:,i,k) + wobs * obs'; % op(:,:) = sum_t w(t) * obs(:,t) * obs(:,t)' + ip(i,k) = ip(i,k) + sum(sum(wobs .* obs, 2)); % ip = sum_t w(t) * obs(:,t)' * obs(:,t) + end + end +end +if verbose, fprintf(1, '\n'); end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mhmm_em_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +if 1 + O = 4; + T = 10; + nex = 50; + M = 2; + Q = 3; +else + O = 8; %Number of coefficients in a vector + T = 420; %Number of vectors in a sequence + nex = 1; %Number of sequences + M = 1; %Number of mixtures + Q = 6; %Number of states +end +cov_type = 'full'; + +data = randn(O,T,nex); + +% initial guess of parameters +prior0 = normalise(rand(Q,1)); +transmat0 = mk_stochastic(rand(Q,Q)); + +if 0 + Sigma0 = repmat(eye(O), [1 1 Q M]); + % Initialize each mean to a random data point + indices = randperm(T*nex); + mu0 = reshape(data(:,indices(1:(Q*M))), [O Q M]); + mixmat0 = mk_stochastic(rand(Q,M)); +else + [mu0, Sigma0] = mixgauss_init(Q*M, data, cov_type); + mu0 = reshape(mu0, [O Q M]); + Sigma0 = reshape(Sigma0, [O O Q M]); + mixmat0 = mk_stochastic(rand(Q,M)); +end + +[LL, prior1, transmat1, mu1, Sigma1, mixmat1] = ... + mhmm_em(data, prior0, transmat0, mu0, Sigma0, mixmat0, 'max_iter', 5); + + +loglik = mhmm_logprob(data, prior1, transmat1, mu1, Sigma1, mixmat1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mhmm_logprob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [loglik, errors] = mhmm_logprob(data, prior, transmat, mu, Sigma, mixmat) +% LOG_LIK_MHMM Compute the log-likelihood of a dataset using a (mixture of) Gaussians HMM +% [loglik, errors] = log_lik_mhmm(data, prior, transmat, mu, sigma, mixmat) +% +% data{m}(:,t) or data(:,t,m) if all cases have same length +% errors is a list of the cases which received a loglik of -infinity +% +% Set mixmat to ones(Q,1) or omit it if there is only 1 mixture component + +Q = length(prior); +if size(mixmat,1) ~= Q % trap old syntax + error('mixmat should be QxM') +end +if nargin < 6, mixmat = ones(Q,1); end + +if ~iscell(data) + data = num2cell(data, [1 2]); % each elt of the 3rd dim gets its own cell +end +ncases = length(data); + +loglik = 0; +errors = []; +for m=1:ncases + obslik = mixgauss_prob(data{m}, mu, Sigma, mixmat); + [alpha, beta, gamma, ll] = fwdback(prior, transmat, obslik, 'fwd_only', 1); + if ll==-inf + errors = [errors m]; + end + loglik = loglik + ll; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mhmm_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function [obs, hidden] = mhmm_sample(T, numex, initial_prob, transmat, mu, Sigma, mixmat) +% SAMPLE_MHMM Generate random sequences from an HMM with (mixtures of) Gaussian output. +% [obs, hidden] = sample_mhmm(T, numex, initial_prob, transmat, mu, Sigma, mixmat) +% +% INPUTS: +% T - length of each sequence +% numex - num. sequences +% init_state_prob(i) = Pr(Q(1) = i) +% transmat(i,j) = Pr(Q(t+1)=j | Q(t)=i) +% mu(:,j,k) = mean of Y(t) given Q(t)=j, M(t)=k +% Sigma(:,:,j,k) = cov. of Y(t) given Q(t)=j, M(t)=k +% mixmat(j,k) = Pr(M(t)=k | Q(t)=j) : set to ones(Q,1) or omit if single mixture +% +% OUTPUT: +% obs(:,t,l) = observation vector at time t for sequence l +% hidden(t,l) = the hidden state at time t for sequence l + +Q = length(initial_prob); +if nargin < 7, mixmat = ones(Q,1); end +O = size(mu,1); +hidden = zeros(T, numex); +obs = zeros(O, T, numex); + +hidden = mc_sample(initial_prob, transmat, T, numex)'; +for i=1:numex + for t=1:T + q = hidden(t,i); + m = sample_discrete(mixmat(q,:), 1, 1); + obs(:,t,i) = gaussian_sample(mu(:,q,m), Sigma(:,:,q,m), 1); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mk_leftright_transmat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function transmat = mk_leftright_transmat(Q, p) +% MK_LEFTRIGHT_TRANSMAT Q = num states, p = prob on (i,i), 1-p on (i,i+1) +% function transmat = mk_leftright_transmat(Q, p) + +transmat = p*diag(ones(Q,1)) + (1-p)*diag(ones(Q-1,1),1); +transmat(Q,Q)=1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/mk_rightleft_transmat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function transmat = mk_rightleft_transmat(Q, p) +% MK_RIGHTLEFT_TRANSMAT Q = num states, p = prob on (i,i), 1-p on (i,i+1) +% function transmat = mk_rightleft_transmat(Q, p) + +transmat = p*diag(ones(Q,1)) + (1-p)*diag(ones(Q-1,1),-1); +transmat(1,1)=1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/pomdp_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function [obs, hidden] = pomdp_sample(initial_prob, transmat, obsmat, act) +% SAMPLE_POMDP Generate a random sequence from a Partially Observed Markov Decision Process. +% [obs, hidden] = sample_pomdp(prior, transmat, obsmat, act) +% +% Inputs: +% prior(i) = Pr(Q(1)=i) +% transmat{a}(i,j) = Pr(Q(t)=j | Q(t-1)=i, A(t)=a) +% obsmat(i,k) = Pr(Y(t)=k | Q(t)=i) +% act(a) = A(t), so act(1) is ignored +% +% Output: +% obs and hidden are vectors of length T=length(act) + + +len = length(act); +hidden = mdp_sample(initial_prob, transmat, act); +obs = zeros(1, len); +for t=1:len + obs(t) = sample_discrete(obsmat(hidden(t),:)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/testHMM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +% Run all the demos, to check everything is "syntactically correct" +mhmm_em_demo +dhmm_em_demo +dhmm_em_online_demo +fixed_lag_smoother_demo
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/transmat_train_observed.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function [transmat, initState] = transmat_train_observed(labels, nstates, varargin) +% transmat_train_observed ML estimation from fully observed data +% function [transmat, initState] = transmat_train_observed(labels, nstates, varargin) +% +% If all sequences have the same length +% labels(ex,t) +% If sequences have different lengths, we use cell arrays +% labels{ex}(t) + +[dirichletPriorWeight, mkSymmetric, other] = process_options(... + varargin, 'dirichletPriorWeight', 0, 'mkSymmetric', 0); + +if ~iscell(labels) + [numex T] = size(labels); + if T==1 + labels = labels'; + end + %fprintf('T=%d, numex=%d\n', T, numex); + labels = num2cell(labels,2); % each row gets its own cell +end +numex = length(labels); + +counts = zeros(nstates, nstates); +counts1 = zeros(nstates,1); +for s=1:numex + labs = labels{s}; labs = labs(:)'; + dat = [labs(1:end-1); labs(2:end)]; + counts = counts + compute_counts(dat, [nstates nstates]); + q = labs(1); + counts1(q) = counts1(q) + 1; +end +pseudo_counts = dirichletPriorWeight*ones(nstates, nstates); +if mkSymmetric + counts = counts + counts'; +end +transmat = mk_stochastic(counts + pseudo_counts); +initState = normalize(counts1 + dirichletPriorWeight*ones(nstates,1)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/HMM/viterbi_path.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function path = viterbi_path(prior, transmat, obslik) +% VITERBI Find the most-probable (Viterbi) path through the HMM state trellis. +% path = viterbi(prior, transmat, obslik) +% +% Inputs: +% prior(i) = Pr(Q(1) = i) +% transmat(i,j) = Pr(Q(t+1)=j | Q(t)=i) +% obslik(i,t) = Pr(y(t) | Q(t)=i) +% +% Outputs: +% path(t) = q(t), where q1 ... qT is the argmax of the above expression. + + +% delta(j,t) = prob. of the best sequence of length t-1 and then going to state j, and O(1:t) +% psi(j,t) = the best predecessor state, given that we ended up in state j at t + +scaled = 1; + +T = size(obslik, 2); +prior = prior(:); +Q = length(prior); + +delta = zeros(Q,T); +psi = zeros(Q,T); +path = zeros(1,T); +scale = ones(1,T); + + +t=1; +delta(:,t) = prior .* obslik(:,t); +if scaled + [delta(:,t), n] = normalise(delta(:,t)); + scale(t) = 1/n; +end +psi(:,t) = 0; % arbitrary value, since there is no predecessor to t=1 +for t=2:T + for j=1:Q + [delta(j,t), psi(j,t)] = max(delta(:,t-1) .* transmat(:,j)); + delta(j,t) = delta(j,t) * obslik(j,t); + end + if scaled + [delta(:,t), n] = normalise(delta(:,t)); + scale(t) = 1/n; + end +end +[p, path(T)] = max(delta(:,T)); +for t=T-1:-1:1 + path(t) = psi(path(t+1),t+1); +end + +% If scaled==0, p = prob_path(best_path) +% If scaled==1, p = Pr(replace sum with max and proceed as in the scaled forwards algo) +% Both are different from p(data) as computed using the sum-product (forwards) algorithm + +if 0 +if scaled + loglik = -sum(log(scale)); + %loglik = prob_path(prior, transmat, obslik, path); +else + loglik = log(p); +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +/KLgauss.m/1.1.1.1/Tue Apr 26 02:29:16 2005// +/README.txt/1.1.1.1/Tue Apr 26 02:29:18 2005// +/beta_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/chisquared_histo.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/chisquared_prob.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/chisquared_readme.txt/1.1.1.1/Tue Apr 26 02:29:18 2005// +/chisquared_table.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/clg_Mstep.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/clg_Mstep_simple.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/clg_prob.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/condGaussToJoint.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cond_indep_fisher_z.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/condgaussTrainObserved.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/condgauss_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/convertBinaryLabels.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_demo.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_em.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_predict.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_prob.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_readme.txt/1.1.1.1/Tue Apr 26 02:29:18 2005// +/cwr_test.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/dirichlet_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/dirichletpdf.m/1.1.1.1/Sun May 22 23:32:18 2005// +/dirichletrnd.m/1.1.1.1/Sun May 22 23:32:12 2005// +/distchck.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/eigdec.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/est_transmat.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/fit_paritioned_model_testfn.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/fit_partitioned_model.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/gamma_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/gaussian_prob.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/gaussian_sample.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/histCmpChi2.m/1.1.1.1/Tue May 3 20:18:16 2005// +/linear_regression.m/1.1.1.1/Tue Apr 26 02:29:18 2005// +/logist2.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logist2Apply.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logist2ApplyRegularized.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logist2Fit.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logist2FitRegularized.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logistK.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/logistK_eval.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/marginalize_gaussian.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/matrix_T_pdf.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/matrix_normal_pdf.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mc_stat_distrib.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_Mstep.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_classifier_apply.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_classifier_train.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_em.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_init.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_prob.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_prob_test.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mixgauss_sample.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mkPolyFvec.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/mk_unit_norm.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/multinomial_prob.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/multinomial_sample.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/multipdf.m/1.1.1.1/Sun May 22 23:32:42 2005// +/multirnd.m/1.1.1.1/Sun May 22 23:32:38 2005// +/normal_coef.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/partial_corr_coef.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzen.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzenC.c/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzenC.dll/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzenC.mexglx/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzenC_test.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/parzen_fit_select_unif.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/pca.m/1.1.1.1/Tue Apr 26 02:29:20 2005// +/rndcheck.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/sample.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/sample_discrete.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/sample_gaussian.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/standardize.m/1.1.1.1/Wed May 4 04:35:36 2005// +/student_t_logprob.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/student_t_prob.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/test_dir.m/1.1.1.1/Sun May 22 23:32:20 2005// +/unidrndKPM.m/1.1.1.1/Tue May 31 18:19:24 2005// +/unif_discrete_sample.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +/weightedRegression.m/1.1.1.1/Tue Apr 26 02:29:22 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/KPMstats
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/KLgauss.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function kl = KLgauss(P, Q) +%The following computes D(P||Q), the KL divergence between two zero-mean +%Gaussians with covariance P and Q: +% klDiv = -0.5*(log(det(P*inv(Q))) + trace(eye(N)-P*inv(Q))); + +R = P*inv(Q); +kl = -0.5*(log(det(R))) + trace(eye(length(P))-R); + +%To get MI, just set P=cov(X,Y) and Q=blockdiag(cov(X),cov(Y)).
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/README.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +KPMstats is a directory of miscellaneous statistics functions written by +Kevin Patrick Murphy and various other people (see individual file headers). + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/beta_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function r = betarnd(a,b,m,n); +%BETARND Random matrices from beta distribution. +% R = BETARND(A,B) returns a matrix of random numbers chosen +% from the beta distribution with parameters A and B. +% The size of R is the common size of A and B if both are matrices. +% If either parameter is a scalar, the size of R is the size of the other +% parameter. Alternatively, R = BETARND(A,B,M,N) returns an M by N matrix. + +% Reference: +% [1] L. Devroye, "Non-Uniform Random Variate Generation", +% Springer-Verlag, 1986 + +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +if nargin < 2, + error('Requires at least two input arguments'); +end + +if nargin == 2 + [errorcode rows columns] = rndcheck(2,2,a,b); +end + +if nargin == 3 + [errorcode rows columns] = rndcheck(3,2,a,b,m); +end + +if nargin == 4 + [errorcode rows columns] = rndcheck(4,2,a,b,m,n); +end + +if errorcode > 0 + error('Size information is inconsistent.'); +end + +r = zeros(rows,columns); + +% Use Theorem 4.1, case A (Devroye, page 430) to derive beta +% random numbers as a ratio of gamma random numbers. +if prod(size(a)) == 1 + a1 = a(ones(rows,1),ones(columns,1)); + g1 = gamrnd(a1,1); +else + g1 = gamrnd(a,1); +end +if prod(size(b)) == 1 + b1 = b(ones(rows,1),ones(columns,1)); + g2 = gamrnd(b1,1); +else + g2 = gamrnd(b,1); +end +r = g1 ./ (g1 + g2); + +% Return NaN if b is not positive. +if any(any(b <= 0)); + if prod(size(b) == 1) + tmp = NaN; + r = tmp(ones(rows,columns)); + else + k = find(b <= 0); + tmp = NaN; + r(k) = tmp(ones(size(k))); + end +end + +% Return NaN if a is not positive. +if any(any(a <= 0)); + if prod(size(a) == 1) + tmp = NaN; + r = tmp(ones(rows,columns)); + else + k = find(a <= 0); + tmp = NaN; + r(k) = tmp(ones(size(k))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/chisquared_histo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function s = chisquared_histo(h1, h2) +% Measure distance between 2 histograms (small numbers means more similar) +denom = h1 + h2; +denom = denom + (denom==0); +s = sum(((h1 - h2) .^ 2) ./ denom);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/chisquared_prob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function P = chisquared_prob(X2,v) +%CHISQUARED_PROB computes the chi-squared probability function. +% P = CHISQUARED_PROB( X2, v ) returns P(X2|v), the probability +% of observing a chi-squared value <= X2 with v degrees of freedom. +% This is the probability that the sum of squares of v unit-variance +% normally-distributed random variables is <= X2. +% X2 and v may be matrices of the same size size, or either +% may be a scalar. +% +% e.g., CHISQUARED_PROB(5.99,2) returns 0.9500, verifying the +% 95% confidence bound for 2 degrees of freedom. This is also +% cross-checked in, e.g., Abramowitz & Stegun Table 26.8 +% +% See also CHISQUARED_TABLE +% +%Peter R. Shaw, WHOI + +% References: Press et al., Numerical Recipes, Cambridge, 1986; +% Abramowitz & Stegun, Handbook of Mathematical Functions, Dover, 1972. + +% Peter R. Shaw, Woods Hole Oceanographic Institution +% Woods Hole, MA 02543 +% (508) 457-2000 ext. 2473 pshaw@whoi.edu +% March, 1990; fixed Oct 1992 for version 4 + +% Computed using the Incomplete Gamma function, +% as given by Press et al. (Recipes) eq. (6.2.17) + +% Following nonsense is necessary from Matlab version 3 -> version 4 +versn_str=version; eval(['versn=' versn_str(1) ';']); +if versn<=3, %sigh + P = gamma(v/2, X2/2); +else + P = gammainc(X2/2, v/2); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/chisquared_readme.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +From ftp://ftp.mathworks.com/pub/contrib/v4/stats/chisquared/ + +Chi-squared probability function. + +Abstract +m-files to compute the Chi-squared probability function, and +the percentage points of the probability function. + +P = CHISQUARED_PROB( X2, v ) returns P(X2|v), the probability +of observing a chi-squared value <= X2 with v degrees of freedom. +This is the probability that the sum of squares of v unit-variance +normally-distributed random variables is <= X2. + +Conversely: +X2 = CHISQUARED_TABLE( P, v ) returns the X2, the value of +chi-squared corresponding to v degrees of freedom and probability P. + +In reference textbooks, what is normally tabulated are the +percentage points of the chi-squared distribution; thus, one +would use CHISQUARED_TABLE rather than interpolate such a table. + +References: Press et al., Numerical Recipes, Cambridge, 1986; +Abramowitz & Stegun, Handbook of Mathematical Functions, +Dover, 1972; Table 26.8 + +Peter R. Shaw +Woods Hole Oceanographic Institution, Woods Hole, MA 02543 +(508) 457-2000 ext. 2473 +pshaw@whoi.edu + +--FILES GENERATED-- +README_chisq +chisquared_prob.m (function) -- chi-squared probability function +chisquared_table.m (function) -- "percentage points" (i.e., inverse) + of chi-squared probability function +chiaux.m (function) -- auxiliary fn. used by chisquared_table
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/chisquared_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,63 @@ +function X2 = chisquared_table(P,v) +%CHISQUARED_TABLE computes the "percentage points" of the +%chi-squared distribution, as in Abramowitz & Stegun Table 26.8 +% X2 = CHISQUARED_TABLE( P, v ) returns the value of chi-squared +% corresponding to v degrees of freedom and probability P. +% P is the probability that the sum of squares of v unit-variance +% normally-distributed random variables is <= X2. +% P and v may be matrices of the same size size, or either +% may be a scalar. +% +% e.g., to find the 95% confidence interval for 2 degrees +% of freedom, use CHISQUARED_TABLE( .95, 2 ), yielding 5.99, +% in agreement with Abramowitz & Stegun's Table 26.8 +% +% This result can be checked through the function +% CHISQUARED_PROB( 5.99, 2 ), yielding 0.9500 +% +% The familiar 1.96-sigma confidence bounds enclosing 95% of +% a 1-D gaussian is found through +% sqrt( CHISQUARED_TABLE( .95, 1 )), yielding 1.96 +% +% See also CHISQUARED_PROB +% +%Peter R. Shaw, WHOI +%Leslie Rosenfeld, MBARI + +% References: Press et al., Numerical Recipes, Cambridge, 1986; +% Abramowitz & Stegun, Handbook of Mathematical Functions, Dover, 1972. + +% Peter R. Shaw, Woods Hole Oceanographic Institution +% Woods Hole, MA 02543 pshaw@whoi.edu +% Leslie Rosenfeld, MBARI +% Last revision: Peter Shaw, Oct 1992: fsolve with version 4 + +% ** Calls function CHIAUX ** +% Computed using the Incomplete Gamma function, +% as given by Press et al. (Recipes) eq. (6.2.17) + +[mP,nP]=size(P); +[mv,nv]=size(v); +if mP~=mv | nP~=nv, + if mP==1 & nP==1, + P=P*ones(mv,nv); + elseif mv==1 & nv==1, + v=v*ones(mP,nP); + else + error('P and v must be the same size') + end +end +[m,n]=size(P); X2 = zeros(m,n); +for i=1:m, + for j=1:n, + if v(i,j)<=10, + x0=P(i,j)*v(i,j); + else + x0=v(i,j); + end +% Note: "old" and "new" calls to fsolve may or may not follow +% Matlab version 3.5 -> version 4 (so I'm keeping the old call around...) +% X2(i,j) = fsolve('chiaux',x0,zeros(16,1),[v(i,j),P(i,j)]); %(old call) + X2(i,j) = fsolve('chiaux',x0,zeros(16,1),[],[v(i,j),P(i,j)]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/clg_Mstep.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,203 @@ +function [mu, Sigma, B] = clg_Mstep(w, Y, YY, YTY, X, XX, XY, varargin) +% MSTEP_CLG Compute ML/MAP estimates for a conditional linear Gaussian +% [mu, Sigma, B] = Mstep_clg(w, Y, YY, YTY, X, XX, XY, varargin) +% +% We fit P(Y|X,Q=i) = N(Y; B_i X + mu_i, Sigma_i) +% and w(i,t) = p(M(t)=i|y(t)) = posterior responsibility +% See www.ai.mit.edu/~murphyk/Papers/learncg.pdf. +% +% See process_options for how to specify the input arguments. +% +% INPUTS: +% w(i) = sum_t w(i,t) = responsibilities for each mixture component +% If there is only one mixture component (i.e., Q does not exist), +% then w(i) = N = nsamples, and +% all references to i can be replaced by 1. +% Y(:,i) = sum_t w(i,t) y(:,t) = weighted observations +% YY(:,:,i) = sum_t w(i,t) y(:,t) y(:,t)' = weighted outer product +% YTY(i) = sum_t w(i,t) y(:,t)' y(:,t) = weighted inner product +% You only need to pass in YTY if Sigma is to be estimated as spherical. +% +% In the regression context, we must also pass in the following +% X(:,i) = sum_t w(i,t) x(:,t) = weighted inputs +% XX(:,:,i) = sum_t w(i,t) x(:,t) x(:,t)' = weighted outer product +% XY(i) = sum_t w(i,t) x(:,t) y(:,t)' = weighted outer product +% +% Optional inputs (default values in []) +% +% 'cov_type' - 'full', 'diag' or 'spherical' ['full'] +% 'tied_cov' - 1 (Sigma) or 0 (Sigma_i) [0] +% 'clamped_cov' - pass in clamped value, or [] if unclamped [ [] ] +% 'clamped_mean' - pass in clamped value, or [] if unclamped [ [] ] +% 'clamped_weights' - pass in clamped value, or [] if unclamped [ [] ] +% 'cov_prior' - added to Sigma(:,:,i) to ensure psd [0.01*eye(d,d,Q)] +% +% If cov is tied, Sigma has size d*d. +% But diagonal and spherical covariances are represented in full size. + +[cov_type, tied_cov, ... + clamped_cov, clamped_mean, clamped_weights, cov_prior, ... + xs, ys, post] = ... + process_options(varargin, ... + 'cov_type', 'full', 'tied_cov', 0, 'clamped_cov', [], 'clamped_mean', [], ... + 'clamped_weights', [], 'cov_prior', [], ... + 'xs', [], 'ys', [], 'post', []); + +[Ysz Q] = size(Y); + +if isempty(X) % no regression + %B = []; + B2 = zeros(Ysz, 1, Q); + for i=1:Q + B(:,:,i) = B2(:,1:0,i); % make an empty array of size Ysz x 0 x Q + end + [mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY, varargin{:}); + return; +end + + +N = sum(w); +if isempty(cov_prior) + cov_prior = 0.01*repmat(eye(Ysz,Ysz), [1 1 Q]); +end +%YY = YY + cov_prior; % regularize the scatter matrix + +% Set any zero weights to one before dividing +% This is valid because w(i)=0 => Y(:,i)=0, etc +w = w + (w==0); + +Xsz = size(X,1); +% Append 1 to X to get Z +ZZ = zeros(Xsz+1, Xsz+1, Q); +ZY = zeros(Xsz+1, Ysz, Q); +for i=1:Q + ZZ(:,:,i) = [XX(:,:,i) X(:,i); + X(:,i)' w(i)]; + ZY(:,:,i) = [XY(:,:,i); + Y(:,i)']; +end + + +%%% Estimate mean and regression + +if ~isempty(clamped_weights) & ~isempty(clamped_mean) + B = clamped_weights; + mu = clamped_mean; +end +if ~isempty(clamped_weights) & isempty(clamped_mean) + B = clamped_weights; + % eqn 5 + mu = zeros(Ysz, Q); + for i=1:Q + mu(:,i) = (Y(:,i) - B(:,:,i)*X(:,i)) / w(i); + end +end +if isempty(clamped_weights) & ~isempty(clamped_mean) + mu = clamped_mean; + % eqn 3 + B = zeros(Ysz, Xsz, Q); + for i=1:Q + tmp = XY(:,:,i)' - mu(:,i)*X(:,i)'; + %B(:,:,i) = tmp * inv(XX(:,:,i)); + B(:,:,i) = (XX(:,:,i) \ tmp')'; + end +end +if isempty(clamped_weights) & isempty(clamped_mean) + mu = zeros(Ysz, Q); + B = zeros(Ysz, Xsz, Q); + % Nothing is clamped, so we must estimate B and mu jointly + for i=1:Q + % eqn 9 + if rcond(ZZ(:,:,i)) < 1e-10 + sprintf('clg_Mstep warning: ZZ(:,:,%d) is ill-conditioned', i); + % probably because there are too few cases for a high-dimensional input + ZZ(:,:,i) = ZZ(:,:,i) + 1e-5*eye(Xsz+1); + end + %A = ZY(:,:,i)' * inv(ZZ(:,:,i)); + A = (ZZ(:,:,i) \ ZY(:,:,i))'; + B(:,:,i) = A(:, 1:Xsz); + mu(:,i) = A(:, Xsz+1); + end +end + +if ~isempty(clamped_cov) + Sigma = clamped_cov; + return; +end + + +%%% Estimate covariance + +% Spherical +if cov_type(1)=='s' + if ~tied_cov + Sigma = zeros(Ysz, Ysz, Q); + for i=1:Q + % eqn 16 + A = [B(:,:,i) mu(:,i)]; + %s = trace(YTY(i) + A'*A*ZZ(:,:,i) - 2*A*ZY(:,:,i)) / (Ysz*w(i)); % wrong! + s = (YTY(i) + trace(A'*A*ZZ(:,:,i)) - trace(2*A*ZY(:,:,i))) / (Ysz*w(i)); + Sigma(:,:,i) = s*eye(Ysz,Ysz); + + %%%%%%%%%%%%%%%%%%% debug + if ~isempty(xs) + [nx T] = size(xs); + zs = [xs; ones(1,T)]; + yty = 0; + zAAz = 0; + yAz = 0; + for t=1:T + yty = yty + ys(:,t)'*ys(:,t) * post(i,t); + zAAz = zAAz + zs(:,t)'*A'*A*zs(:,t)*post(i,t); + yAz = yAz + ys(:,t)'*A*zs(:,t)*post(i,t); + end + assert(approxeq(yty, YTY(i))) + assert(approxeq(zAAz, trace(A'*A*ZZ(:,:,i)))) + assert(approxeq(yAz, trace(A*ZY(:,:,i)))) + s2 = (yty + zAAz - 2*yAz) / (Ysz*w(i)); + assert(approxeq(s,s2)) + end + %%%%%%%%%%%%%%% end debug + + end + else + S = 0; + for i=1:Q + % eqn 18 + A = [B(:,:,i) mu(:,i)]; + S = S + trace(YTY(i) + A'*A*ZZ(:,:,i) - 2*A*ZY(:,:,i)); + end + Sigma = repmat(S / (N*Ysz), [1 1 Q]); + end +else % Full/diagonal + if ~tied_cov + Sigma = zeros(Ysz, Ysz, Q); + for i=1:Q + A = [B(:,:,i) mu(:,i)]; + % eqn 10 + SS = (YY(:,:,i) - ZY(:,:,i)'*A' - A*ZY(:,:,i) + A*ZZ(:,:,i)*A') / w(i); + if cov_type(1)=='d' + Sigma(:,:,i) = diag(diag(SS)); + else + Sigma(:,:,i) = SS; + end + end + else % tied + SS = zeros(Ysz, Ysz); + for i=1:Q + A = [B(:,:,i) mu(:,i)]; + % eqn 13 + SS = SS + (YY(:,:,i) - ZY(:,:,i)'*A' - A*ZY(:,:,i) + A*ZZ(:,:,i)*A'); + end + SS = SS / N; + if cov_type(1)=='d' + Sigma = diag(diag(SS)); + else + Sigma = SS; + end + Sigma = repmat(Sigma, [1 1 Q]); + end +end + +Sigma = Sigma + cov_prior; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/clg_Mstep_simple.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function [mu, B] = clg_Mstep_simple(w, Y, YY, YTY, X, XX, XY) +% CLG_MSTEP_SIMPLE Same as CLG_MSTEP, but doesn;t estimate Sigma, so is slightly faster +% function [mu, B] = clg_Mstep_simple(w, Y, YY, YTY, X, XX, XY) +% +% See clg_Mstep for details. +% Unlike clg_Mstep, there are no optional arguments, which are slow to process +% if this function is inside a tight loop. + +[Ysz Q] = size(Y); + +if isempty(X) % no regression + %B = []; + B2 = zeros(Ysz, 1, Q); + for i=1:Q + B(:,:,i) = B2(:,1:0,i); % make an empty array of size Ysz x 0 x Q + end + [mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY); + return; +end + +N = sum(w); +%YY = YY + cov_prior; % regularize the scatter matrix + +% Set any zero weights to one before dividing +% This is valid because w(i)=0 => Y(:,i)=0, etc +w = w + (w==0); + +Xsz = size(X,1); +% Append 1 to X to get Z +ZZ = zeros(Xsz+1, Xsz+1, Q); +ZY = zeros(Xsz+1, Ysz, Q); +for i=1:Q + ZZ(:,:,i) = [XX(:,:,i) X(:,i); + X(:,i)' w(i)]; + ZY(:,:,i) = [XY(:,:,i); + Y(:,i)']; +end + +mu = zeros(Ysz, Q); +B = zeros(Ysz, Xsz, Q); +for i=1:Q + % eqn 9 + if rcond(ZZ(:,:,i)) < 1e-10 + sprintf('clg_Mstep warning: ZZ(:,:,%d) is ill-conditioned', i); + %probably because there are too few cases for a high-dimensional input + ZZ(:,:,i) = ZZ(:,:,i) + 1e-5*eye(Xsz+1); + end + %A = ZY(:,:,i)' * inv(ZZ(:,:,i)); + A = (ZZ(:,:,i) \ ZY(:,:,i))'; + B(:,:,i) = A(:, 1:Xsz); + mu(:,i) = A(:, Xsz+1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/clg_prob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function p = eval_pdf_clg(X,Y,mu,Sigma,W) +% function p = eval_pdf_clg(X,Y,mu,Sigma,W) +% +% p(c,t) = N(Y(:,t); mu(:,c) + W(:,:,c)*X(:,t), Sigma(:,:,c)) + +[d T] = size(Y); +[d nc] = size(mu); +p = zeros(nc,T); +for c=1:nc + denom = (2*pi)^(d/2)*sqrt(abs(det(Sigma(:,:,c)))); + M = repmat(mu(:,c), 1, T) + W(:,:,c)*X; + mahal = sum(((Y-M)'*inv(Sigma(:,:,c))).*(Y-M)',2); + p(c,:) = (exp(-0.5*mahal) / denom)'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/condGaussToJoint.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [muXY, SigmaXY] = condGaussToJoint(muX, SigmaX, muY, SigmaY, WYgivenX) + +% Compute P(X,Y) from P(X) * P(Y|X) where P(X)=N(X;muX,SigmaX) +% and P(Y|X) = N(Y; WX + muY, SigmaY) + +% For details on how to compute a Gaussian from a Bayes net +% - "Gaussian Influence Diagrams", R. Shachter and C. R. Kenley, Management Science, 35(5):527--550, 1989. + +% size(W) = dy x dx +dx = length(muX); +dy = length(muY); +muXY = [muX(:); WYgivenX*muX(:) + muY]; + +W = [zeros(dx,dx) WYgivenX'; + zeros(dy,dx) zeros(dy,dy)]; +D = [SigmaX zeros(dx,dy); + zeros(dy,dx) SigmaY]; + +U = inv(eye(size(W)) - W')'; +SigmaXY = U' * D * U; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/cond_indep_fisher_z.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,142 @@ +function [CI, r, p] = cond_indep_fisher_z(X, Y, S, C, N, alpha) +% COND_INDEP_FISHER_Z Test if X indep Y given Z using Fisher's Z test +% CI = cond_indep_fisher_z(X, Y, S, C, N, alpha) +% +% C is the covariance (or correlation) matrix +% N is the sample size +% alpha is the significance level (default: 0.05) +% +% See p133 of T. Anderson, "An Intro. to Multivariate Statistical Analysis", 1984 + +if nargin < 6, alpha = 0.05; end + +r = partial_corr_coef(C, X, Y, S); +z = 0.5*log( (1+r)/(1-r) ); +z0 = 0; +W = sqrt(N - length(S) - 3)*(z-z0); % W ~ N(0,1) +cutoff = norminv(1 - 0.5*alpha); % P(|W| <= cutoff) = 0.95 +%cutoff = mynorminv(1 - 0.5*alpha); % P(|W| <= cutoff) = 0.95 +if abs(W) < cutoff + CI = 1; +else % reject the null hypothesis that rho = 0 + CI = 0; +end +p = normcdf(W); +%p = mynormcdf(W); + +%%%%%%%%% + +function p = normcdf(x,mu,sigma) +%NORMCDF Normal cumulative distribution function (cdf). +% P = NORMCDF(X,MU,SIGMA) computes the normal cdf with mean MU and +% standard deviation SIGMA at the values in X. +% +% The size of P is the common size of X, MU and SIGMA. A scalar input +% functions as a constant matrix of the same size as the other inputs. +% +% Default values for MU and SIGMA are 0 and 1 respectively. + +% References: +% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical +% Functions", Government Printing Office, 1964, 26.2. + +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +if nargin < 3, + sigma = 1; +end + +if nargin < 2; + mu = 0; +end + +[errorcode x mu sigma] = distchck(3,x,mu,sigma); + +if errorcode > 0 + error('Requires non-scalar arguments to match in size.'); +end + +% Initialize P to zero. +p = zeros(size(x)); + +% Return NaN if SIGMA is not positive. +k1 = find(sigma <= 0); +if any(k1) + tmp = NaN; + p(k1) = tmp(ones(size(k1))); +end + +% Express normal CDF in terms of the error function. +k = find(sigma > 0); +if any(k) + p(k) = 0.5 * erfc( - (x(k) - mu(k)) ./ (sigma(k) * sqrt(2))); +end + +% Make sure that round-off errors never make P greater than 1. +k2 = find(p > 1); +if any(k2) + p(k2) = ones(size(k2)); +end + +%%%%%%%% + +function x = norminv(p,mu,sigma); +%NORMINV Inverse of the normal cumulative distribution function (cdf). +% X = NORMINV(P,MU,SIGMA) finds the inverse of the normal cdf with +% mean, MU, and standard deviation, SIGMA. +% +% The size of X is the common size of the input arguments. A scalar input +% functions as a constant matrix of the same size as the other inputs. +% +% Default values for MU and SIGMA are 0 and 1 respectively. + +% References: +% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical +% Functions", Government Printing Office, 1964, 7.1.1 and 26.2.2 + +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +if nargin < 3, + sigma = 1; +end + +if nargin < 2; + mu = 0; +end + +[errorcode p mu sigma] = distchck(3,p,mu,sigma); + +if errorcode > 0 + error('Requires non-scalar arguments to match in size.'); +end + +% Allocate space for x. +x = zeros(size(p)); + +% Return NaN if the arguments are outside their respective limits. +k = find(sigma <= 0 | p < 0 | p > 1); +if any(k) + tmp = NaN; + x(k) = tmp(ones(size(k))); +end + +% Put in the correct values when P is either 0 or 1. +k = find(p == 0); +if any(k) + tmp = Inf; + x(k) = -tmp(ones(size(k))); +end + +k = find(p == 1); +if any(k) + tmp = Inf; + x(k) = tmp(ones(size(k))); +end + +% Compute the inverse function for the intermediate values. +k = find(p > 0 & p < 1 & sigma > 0); +if any(k), + x(k) = sqrt(2) * sigma(k) .* erfinv(2 * p(k) - 1) + mu(k); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/condgaussTrainObserved.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function [mu, Sigma] = mixgaussTrainObserved(obsData, hiddenData, nstates, varargin); +% mixgaussTrainObserved Max likelihood estimates of conditional Gaussian from raw data +% function [mu, Sigma] = mixgaussTrainObserved(obsData, hiddenData, nstates, ...); +% +% Input: +% obsData(:,i) +% hiddenData(i) - this is the mixture component label for example i +% Optional arguments - same as mixgauss_Mstep +% +% Output: +% mu(:,q) +% Sigma(:,:,q) - same as mixgauss_Mstep + +[D numex] = size(obsData); +Y = zeros(D, nstates); +YY = zeros(D,D,nstates); +YTY = zeros(nstates,1); +w = zeros(nstates, 1); +for q=1:nstates + ndx = find(hiddenData==q); + w(q) = length(ndx); % each data point has probability 1 of being in this cluster + data = obsData(:,ndx); + Y(:,q) = sum(data,2); + YY(:,:,q) = data*data'; + YTY(q) = sum(diag(data'*data)); +end +[mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY, varargin{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/condgauss_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function x = mixgauss_sample(mu, Sigma, labels) +% MIXGAUSS_SAMPLE Sample from a mixture of Gaussians given known mixture labels +% function x = mixgauss_sample(mu, Sigma, labels) + +T = length(labels); +[D Q] = size(mu); +x = zeros(D,T); +for q=1:Q + ndx = find(labels==q); + x(:,ndx) = gaussian_sample(mu(:,q)', Sigma(:,:,q), length(ndx))'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/convertBinaryLabels.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +% labels01 = (labelsPM+1)/2; % maps -1->0, +1->1 +% labelsPM = (2*labels01)-1; % maps 0,1 -> -1,1 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/cwr_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,124 @@ +% Compare my code with +% http://www.media.mit.edu/physics/publications/books/nmm/files/index.html +% +% cwm.m +% (c) Neil Gershenfeld 9/1/97 +% 1D Cluster-Weighted Modeling example +% +clear all +figure; +seed = 0; +rand('state', seed); +randn('state', seed); +x = (-10:10)'; +y = (x > 0); +npts = length(x); +plot(x,y,'+') +xlabel('x') +ylabel('y') +nclusters = 4; +nplot = 100; +xplot = 24*(1:nplot)'/nplot - 12; + +mux = 20*rand(1,nclusters) - 10; +muy = zeros(1,nclusters); +varx = ones(1,nclusters); +vary = ones(1,nclusters); +pc = 1/nclusters * ones(1,nclusters); +niterations = 5; +eps = 0.01; + + +I = repmat(eye(1,1), [1 1 nclusters]); +O = repmat(zeros(1,1), [1 1 nclusters]); +X = x(:)'; +Y = y(:)'; + +cwr = cwr_em(X, Y, nclusters, 'muX', mux, 'muY', muy, 'SigmaX', I, ... + 'cov_typeX', 'spherical', 'SigmaY', I, 'cov_typeY', 'spherical', ... + 'priorC', pc, 'weightsY', O, 'create_init_params', 0, ... + 'clamp_weights', 1, 'max_iter', niterations, ... + 'cov_priorX', eps*ones(1,1,nclusters), ... + 'cov_priorY', eps*ones(1,1,nclusters)); + + +% Gershenfeld's EM code +for step = 1:niterations + pplot = exp(-(kron(xplot,ones(1,nclusters)) ... + - kron(ones(nplot,1),mux)).^2 ... + ./ (2*kron(ones(nplot,1),varx))) ... + ./ sqrt(2*pi*kron(ones(nplot,1),varx)) ... + .* kron(ones(nplot,1),pc); + plot(xplot,pplot,'k'); + pause(0); + px = exp(-(kron(x,ones(1,nclusters)) ... + - kron(ones(npts,1),mux)).^2 ... + ./ (2*kron(ones(npts,1),varx))) ... + ./ sqrt(2*pi*kron(ones(npts,1),varx)); + py = exp(-(kron(y,ones(1,nclusters)) ... + - kron(ones(npts,1),muy)).^2 ... + ./ (2*kron(ones(npts,1),vary))) ... + ./ sqrt(2*pi*kron(ones(npts,1),vary)); + p = px .* py .* kron(ones(npts,1),pc); + pp = p ./ kron(sum(p,2),ones(1,nclusters)); + pc = sum(pp)/npts; + yfit = sum(kron(ones(npts,1),muy) .* p,2) ... + ./ sum(p,2); + mux = sum(kron(x,ones(1,nclusters)) .* pp) ... + ./ (npts*pc); + varx = eps + sum((kron(x,ones(1,nclusters)) ... + - kron(ones(npts,1),mux)).^2 .* pp) ... + ./ (npts*pc); + muy = sum(kron(y,ones(1,nclusters)) .* pp) ... + ./ (npts*pc); + vary = eps + sum((kron(y,ones(1,nclusters)) ... + - kron(ones(npts,1),muy)).^2 .* pp) ... + ./ (npts*pc); +end + + +% Check equal +cwr_pc = cwr.priorC'; +assert(approxeq(cwr_pc, pc)) +cwr_mux = cwr.muX; +assert(approxeq(mux, cwr_mux)) +cwr_SigmaX = squeeze(cwr.SigmaX)'; +assert(approxeq(varx, cwr_SigmaX)) +cwr_muy = cwr.muY; +assert(approxeq(muy, cwr_muy)) +cwr_SigmaY = squeeze(cwr.SigmaY)'; +assert(approxeq(vary, cwr_SigmaY)) + + +% Prediction + +X = xplot(:)'; +[cwr_mu, Sigma, post] = cwr_predict(cwr, X); +cwr_ystd = squeeze(Sigma)'; + +% pplot(t,c) +pplot = exp(-(kron(xplot,ones(1,nclusters)) ... + - kron(ones(nplot,1),mux)).^2 ... + ./ (2*kron(ones(nplot,1),varx))) ... + ./ sqrt(2*pi*kron(ones(nplot,1),varx)) ... + .* kron(ones(nplot,1),pc); +yplot = sum(kron(ones(nplot,1),muy) .* pplot,2) ... + ./ sum(pplot,2); +ystdplot = sum(kron(ones(nplot,1),(muy.^2+vary)) .* pplot,2) ... + ./ sum(pplot,2) - yplot.^2; + + +% Check equal +assert(approxeq(yplot(:)', cwr_mu(:)')) +assert(approxeq(ystdplot, cwr_ystd)) +assert(approxeq(pplot ./ repmat(sum(pplot,2), 1, nclusters),post') ) + +plot(xplot,yplot,'k'); +hold on +plot(xplot,yplot+ystdplot,'k--'); +plot(xplot,yplot-ystdplot,'k--'); +plot(x,y,'k+'); +axis([-12 12 -1 1.1]); +plot(xplot,.8*pplot/max(max(pplot))-1,'k') +hold off +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/cwr_em.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,161 @@ +function cwr = cwr_em(X, Y, nc, varargin) +% CWR_LEARN Fit the parameters of a cluster weighted regression model using EM +% function cwr = cwr_learn(X, Y, ...) +% +% X(:, t) is the t'th input example +% Y(:, t) is the t'th output example +% nc is the number of clusters +% +% Kevin Murphy, May 2003 + +[max_iter, thresh, cov_typeX, cov_typeY, clamp_weights, ... + muX, muY, SigmaX, SigmaY, weightsY, priorC, create_init_params, ... +cov_priorX, cov_priorY, verbose, regress, clamp_covX, clamp_covY] = process_options(... + varargin, 'max_iter', 10, 'thresh', 1e-2, 'cov_typeX', 'full', ... + 'cov_typeY', 'full', 'clamp_weights', 0, ... + 'muX', [], 'muY', [], 'SigmaX', [], 'SigmaY', [], 'weightsY', [], 'priorC', [], ... + 'create_init_params', 1, 'cov_priorX', [], 'cov_priorY', [], 'verbose', 0, ... + 'regress', 1, 'clamp_covX', 0, 'clamp_covY', 0); + +[nx N] = size(X); +[ny N2] = size(Y); +if N ~= N2 + error(sprintf('nsamples X (%d) ~= nsamples Y (%d)', N, N2)); +end +%if N < nx +% fprintf('cwr_em warning: dim X (%d) > nsamples X (%d)\n', nx, N); +%end +if (N < nx) & regress + fprintf('cwr_em warning: dim X = %d, nsamples X = %d\n', nx, N); +end +if (N < ny) + fprintf('cwr_em warning: dim Y = %d, nsamples Y = %d\n', ny, N); +end +if (nc > N) + error(sprintf('cwr_em: more centers (%d) than data', nc)) +end + +if nc==1 + % No latent variable, so there is a closed-form solution + w = 1/N; + WYbig = Y*w; + WYY = WYbig * Y'; + WY = sum(WYbig, 2); + WYTY = sum(diag(WYbig' * Y)); + cwr.priorC = 1; + cwr.SigmaX = []; + if ~regress + % This is just fitting an unconditional Gaussian + cwr.weightsY = []; + [cwr.muY, cwr.SigmaY] = ... + mixgauss_Mstep(1, WY, WYY, WYTY, ... + 'cov_type', cov_typeY, 'cov_prior', cov_priorY); + % There is a much easier way... + assert(approxeq(cwr.muY, mean(Y'))) + assert(approxeq(cwr.SigmaY, cov(Y') + 0.01*eye(ny))) + else + % This is just linear regression + WXbig = X*w; + WXX = WXbig * X'; + WX = sum(WXbig, 2); + WXTX = sum(diag(WXbig' * X)); + WXY = WXbig * Y'; + [cwr.muY, cwr.SigmaY, cwr.weightsY] = ... + clg_Mstep(1, WY, WYY, WYTY, WX, WXX, WXY, ... + 'cov_type', cov_typeY, 'cov_prior', cov_priorY); + end + if clamp_covY, cwr.SigmaY = SigmaY; end + if clamp_weights, cwr.weightsY = weightsY; end + return; +end + + +if create_init_params + [cwr.muX, cwr.SigmaX] = mixgauss_init(nc, X, cov_typeX); + [cwr.muY, cwr.SigmaY] = mixgauss_init(nc, Y, cov_typeY); + cwr.weightsY = zeros(ny, nx, nc); + cwr.priorC = normalize(ones(nc,1)); +else + cwr.muX = muX; cwr.muY = muY; cwr.SigmaX = SigmaX; cwr.SigmaY = SigmaY; + cwr.weightsY = weightsY; cwr.priorC = priorC; +end + + +if clamp_covY, cwr.SigmaY = SigmaY; end +if clamp_covX, cwr.SigmaX = SigmaX; end +if clamp_weights, cwr.weightsY = weightsY; end + +previous_loglik = -inf; +num_iter = 1; +converged = 0; + +while (num_iter <= max_iter) & ~converged + + % E step + + [likXandY, likYgivenX, post] = cwr_prob(cwr, X, Y); + loglik = sum(log(likXandY)); + % extract expected sufficient statistics + w = sum(post,2); % post(c,t) + WYY = zeros(ny, ny, nc); + WY = zeros(ny, nc); + WYTY = zeros(nc,1); + + WXX = zeros(nx, nx, nc); + WX = zeros(nx, nc); + WXTX = zeros(nc, 1); + WXY = zeros(nx,ny,nc); + %WYY = repmat(reshape(w, [1 1 nc]), [ny ny 1]) .* repmat(Y*Y', [1 1 nc]); + for c=1:nc + weights = repmat(post(c,:), ny, 1); + WYbig = Y .* weights; + WYY(:,:,c) = WYbig * Y'; + WY(:,c) = sum(WYbig, 2); + WYTY(c) = sum(diag(WYbig' * Y)); + + weights = repmat(post(c,:), nx, 1); % weights(nx, nsamples) + WXbig = X .* weights; + WXX(:,:,c) = WXbig * X'; + WX(:,c) = sum(WXbig, 2); + WXTX(c) = sum(diag(WXbig' * X)); + WXY(:,:,c) = WXbig * Y'; + end + + % M step + % Q -> X is called Q->Y in Mstep_clg + [cwr.muX, cwr.SigmaX] = mixgauss_Mstep(w, WX, WXX, WXTX, ... + 'cov_type', cov_typeX, 'cov_prior', cov_priorX); + for c=1:nc + assert(is_psd(cwr.SigmaX(:,:,c))) + end + + if clamp_weights % affects estimate of mu and Sigma + W = cwr.weightsY; + else + W = []; + end + [cwr.muY, cwr.SigmaY, cwr.weightsY] = ... + clg_Mstep(w, WY, WYY, WYTY, WX, WXX, WXY, ... + 'cov_type', cov_typeY, 'clamped_weights', W, ... + 'cov_prior', cov_priorY); + %'xs', X, 'ys', Y, 'post', post); % debug + %a = linspace(min(Y(2,:)), max(Y(2,:)), nc+2); + %cwr.muY(2,:) = a(2:end-1); + + cwr.priorC = normalize(w); + + for c=1:nc + assert(is_psd(cwr.SigmaY(:,:,c))) + end + + if clamp_covY, cwr.SigmaY = SigmaY; end + if clamp_covX, cwr.SigmaX = SigmaX; end + if clamp_weights, cwr.weightsY = weightsY; end + + if verbose, fprintf(1, 'iteration %d, loglik = %f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/cwr_predict.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function [mu, Sigma, weights, mask] = cwr_predict(cwr, X, mask_data) +% CWR_PREDICT cluster weighted regression: predict Y given X +% function [mu, Sigma] = cwr_predict(cwr, X) +% +% mu(:,t) = E[Y|X(:,t)] = sum_c P(c | X(:,t)) E[Y|c, X(:,t)] +% Sigma(:,:,t) = Cov[Y|X(:,t)] +% +% [mu, Sigma, weights, mask] = cwr_predict(cwr, X, mask_data) +% mask(i) = sum_t sum_c p(mask_data(:,i) | X(:,t), c) P(c|X(:,t)) +% This evaluates the predictive density on a set of points +% (This is only sensible if T=1, ie. X is a single vector) + +[nx T] = size(X); +[ny nx nc] = size(cwr.weightsY); +mu = zeros(ny, T); +Sigma = zeros(ny, ny, T); + +if nargout == 4 + comp_mask = 1; + N = size(mask_data,2); + mask = zeros(N,1); +else + comp_mask = 0; +end + +if nc==1 + if isempty(cwr.weightsY) + mu = repmat(cwr.muY, 1, T); + Sigma = repmat(cwr.SigmaY, [1 1 T]); + else + mu = repmat(cwr.muY, 1, T) + cwr.weightsY * X; + Sigma = repmat(cwr.SigmaY, [1 1 T]); + %for t=1:T + % mu(:,t) = cwr.muY + cwr.weightsY*X(:,t); + % Sigma(:,:,t) = cwr.SigmaY; + %end + end + if comp_mask, mask = gaussian_prob(mask_data, mu, Sigma); end + weights = []; + return; +end + + +% likX(c,t) = p(x(:,t) | c) +likX = mixgauss_prob(X, cwr.muX, cwr.SigmaX); +weights = normalize(repmat(cwr.priorC, 1, T) .* likX, 1); +for t=1:T + mut = zeros(ny, nc); + for c=1:nc + mut(:,c) = cwr.muY(:,c) + cwr.weightsY(:,:,c)*X(:,t); + if comp_mask + mask = mask + gaussian_prob(mask_data, mut(:,c), cwr.SigmaY(:,:,c)) * weights(c); + end + end + %w = normalise(cwr.priorC(:) .* likX(:,t)); + [mu(:,t), Sigma(:,:,t)] = collapse_mog(mut, cwr.SigmaY, weights(:,t)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/cwr_prob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function [likXandY, likYgivenX, post] = cwr_prob(cwr, X, Y); +% CWR_EVAL_PDF cluster weighted regression: evaluate likelihood of Y given X +% function [likXandY, likYgivenX, post] = cwr_prob(cwr, X, Y); +% +% likXandY(t) = p(x(:,t), y(:,t)) +% likXgivenY(t) = p(x(:,t)| y(:,t)) +% post(c,t) = p(c | x(:,t), y(:,t)) + +[nx N] = size(X); +nc = length(cwr.priorC); + +if nc == 1 + [mu, Sigma] = cwr_predict(cwr, X); + likY = gaussian_prob(Y, mu, Sigma); + likXandY = likY; + likYgivenX = likY; + post = ones(1,N); + return; +end + + +% likY(c,t) = p(y(:,t) | c) +likY = clg_prob(X, Y, cwr.muY, cwr.SigmaY, cwr.weightsY); + +% likX(c,t) = p(x(:,t) | c) +[junk, likX] = mixgauss_prob(X, cwr.muX, cwr.SigmaX); +likX = squeeze(likX); + +% prior(c,t) = p(c) +prior = repmat(cwr.priorC(:), 1, N); + +post = likX .* likY .* prior; +likXandY = sum(post, 1); +post = post ./ repmat(likXandY, nc, 1); +%loglik = sum(log(lik)); +%loglik = log(lik); + +likX = sum(likX .* prior, 1); +likYgivenX = likXandY ./ likX;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/cwr_readme.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +This directory implements Cluster Weighted Regression, as described in +Neil Gershenfeld, "The nature of mathematical modelling", p182. +(See also http://www.media.mit.edu/physics/publications/books/nmm/files/index.html) + +Written by K. Murphy, 2 May 2003 + +The model is as follows: + +X<--| +| Q +v | +Y<-- + +where Q is a discrete latent mixture variable. + +A mixture of experts has an X->Q arc instead of a Q->X arc; +the X->Q arc is modelled by a softmax, which is slightly harder to fit than a +mixture of Gaussians. + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/cwr_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +% Verify that my code gives the same results as the 1D example at +% http://www.media.mit.edu/physics/publications/books/nmm/files/cwm.m + +seed = 0; +rand('state', seed); +randn('state', seed); +x = (-10:10)'; +y = double(x > 0); +npts = length(x); +plot(x,y,'+') + +nclusters = 4; +nplot = 100; +xplot = 24*(1:nplot)'/nplot - 12; + +mux = 20*rand(1,nclusters) - 10; +muy = zeros(1,nclusters); +varx = ones(1,nclusters); +vary = ones(1,nclusters); +pc = 1/nclusters * ones(1,nclusters); + + +I = repmat(eye(1,1), [1 1 nclusters]); +O = repmat(zeros(1,1), [1 1 nclusters]); +X = x(:)'; +Y = y(:)'; + +% Do 1 iteration of EM + +%cwr = cwr_em(X, Y, nclusters, 'muX', mux, 'muY', muy, 'SigmaX', I, 'cov_typeX', 'spherical', 'SigmaY', I, 'cov_typeY', 'spherical', 'priorC', pc, 'weightsY', O, 'init_params', 0, 'clamp_weights', 1, 'max_iter', 1, 'cov_priorX', zeros(1,1,nclusters), 'cov_priorY', zeros(1,1,nclusters)); + +cwr = cwr_em(X, Y, nclusters, 'muX', mux, 'muY', muy, 'SigmaX', I, 'cov_typeX', 'spherical', 'SigmaY', I, 'cov_typeY', 'spherical', 'priorC', pc, 'weightsY', O, 'create_init_params', 0, 'clamp_weights', 1, 'max_iter', 1); + + +% Check this matches Gershenfeld's code + +% E step +% px(t,c) = prob(x(t) | c) +px = exp(-(kron(x,ones(1,nclusters)) ... + - kron(ones(npts,1),mux)).^2 ... + ./ (2*kron(ones(npts,1),varx))) ... + ./ sqrt(2*pi*kron(ones(npts,1),varx)); +py = exp(-(kron(y,ones(1,nclusters)) ... + - kron(ones(npts,1),muy)).^2 ... + ./ (2*kron(ones(npts,1),vary))) ... + ./ sqrt(2*pi*kron(ones(npts,1),vary)); +p = px .* py .* kron(ones(npts,1),pc); +pp = p ./ kron(sum(p,2),ones(1,nclusters)); + +% M step +eps = 0.01; +pc2 = sum(pp)/npts; + +mux2 = sum(kron(x,ones(1,nclusters)) .* pp) ... + ./ (npts*pc2); +varx2 = eps + sum((kron(x,ones(1,nclusters)) ... + - kron(ones(npts,1),mux2)).^2 .* pp) ... + ./ (npts*pc2); +muy2 = sum(kron(y,ones(1,nclusters)) .* pp) ... + ./ (npts*pc2); +vary2 = eps + sum((kron(y,ones(1,nclusters)) ... + - kron(ones(npts,1),muy2)).^2 .* pp) ... + ./ (npts*pc2); + + +denom = (npts*pc2); +% denom(c) = N*pc(c) = w(c) = sum_t pp(c,t) +% since pc(c) = sum_t pp(c,t) / N + +cwr_mux = cwr.muX; +assert(approxeq(mux2, cwr_mux)) +cwr_SigmaX = squeeze(cwr.SigmaX)'; +assert(approxeq(varx2, cwr_SigmaX)) + +cwr_muy = cwr.muY; +assert(approxeq(muy2, cwr_muy)) +cwr_SigmaY = squeeze(cwr.SigmaY)'; +assert(approxeq(vary2, cwr_SigmaY)) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/dirichlet_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function theta = dirichlet_sample(alpha, N) +% SAMPLE_DIRICHLET Sample N vectors from Dir(alpha(1), ..., alpha(k)) +% theta = sample_dirichlet(alpha, N) +% theta(i,j) = i'th sample of theta_j, where theta ~ Dir + +% We use the method from p. 482 of "Bayesian Data Analysis", Gelman et al. + +assert(alpha > 0); +k = length(alpha); +theta = zeros(N, k); +scale = 1; % arbitrary +for i=1:k + %theta(:,i) = gamrnd(alpha(i), scale, N, 1); + theta(:,i) = gamma_sample(alpha(i), scale, N, 1); +end +%theta = mk_stochastic(theta); +S = sum(theta,2); +theta = theta ./ repmat(S, 1, k);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/dirichletpdf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function p = dirichletpdf(x, alpha) +%DIRICHLETPDF Dirichlet probability density function. +% p = dirichletpdf(x, alpha) returns the probability of vector +% x under the Dirichlet distribution with parameter vector +% alpha. +% +% Author: David Ross + +%------------------------------------------------- +% Check the input +%------------------------------------------------- +error(nargchk(2,2,nargin)); + +% enusre alpha is a vector +if min(size(alpha)) ~= 1 | ndims(alpha) > 2 | length(alpha) == 1 + error('alpha must be a vector'); +end + +% ensure x is is a vector of the same size as alpha +if any(size(x) ~= size(alpha)) + error('x and alpha must be the same size'); +end + + +%------------------------------------------------- +% Main +%------------------------------------------------- +if any(x < 0) + p = 0; +elseif sum(x) ~= 1 + disp(['dirichletpdf warning: sum(x)~=1, but this may be ' ... + 'due to numerical issues']); + p = 0; +else + z = gammaln(sum(alpha)) - sum(gammaln(alpha)); + z = exp(z); + + p = z * prod(x.^(alpha-1)); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/dirichletrnd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function x = dirichletrnd(alpha) +%DIRICHLETRND Random vector from a dirichlet distribution. +% x = dirichletrnd(alpha) returns a vector randomly selected +% from the Dirichlet distribution with parameter vector alpha. +% +% The algorithm used is the following: +% For each alpha(i), generate a value s(i) with distribution +% Gamma(alpha(i),1). Now x(i) = s(i) / sum_j s(j). +% +% The above algorithm was recounted to me by Radford Neal, but +% a reference would be appreciated... +% Do the gamma parameters always have to be 1? +% +% Author: David Ross +% $Id: dirichletrnd.m,v 1.1.1.1 2005/05/22 23:32:12 yozhik Exp $ + +%------------------------------------------------- +% Check the input +%------------------------------------------------- +error(nargchk(1,1,nargin)); + +if min(size(alpha)) ~= 1 | length(alpha) < 2 + error('alpha must be a vector of length at least 2'); +end + + +%------------------------------------------------- +% Main +%------------------------------------------------- +gamma_vals = gamrnd(alpha, ones(size(alpha)), size(alpha)); +denom = sum(gamma_vals); +x = gamma_vals / denom; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/distchck.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +function [errorcode,out1,out2,out3,out4] = distchck(nparms,arg1,arg2,arg3,arg4) +%DISTCHCK Checks the argument list for the probability functions. + +% B.A. Jones 1-22-93 +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +errorcode = 0; + +if nparms == 1 + out1 = arg1; + return; +end + +if nparms == 2 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + if scalararg1 + out1 = arg1(ones(r2,1),ones(c2,1)); + else + out1 = arg1; + end + if scalararg2 + out2 = arg2(ones(r1,1),ones(c1,1)); + else + out2 = arg2; + end +end + +if nparms == 3 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); + [r3 c3] = size(arg3); + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 + out1 = arg1; + end + if ~scalararg2 + out2 = arg2; + end + if ~scalararg3 + out3 = arg3; + end + rows = max([r1 r2 r3]); + columns = max([c1 c2 c3]); + + if scalararg1 + out1 = arg1(ones(rows,1),ones(columns,1)); + end + if scalararg2 + out2 = arg2(ones(rows,1),ones(columns,1)); + end + if scalararg3 + out3 = arg3(ones(rows,1),ones(columns,1)); + end + out4 =[]; + +end + +if nparms == 4 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); + [r3 c3] = size(arg3); + [r4 c4] = size(arg4); + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + scalararg4 = (prod(size(arg4)) == 1); + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg4 + if r1 ~= r4 | c1 ~= c4 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg4 & ~scalararg2 + if r4 ~= r2 | c4 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg4 + if r3 ~= r4 | c3 ~= c4 + errorcode = 1; + return; + end + end + + + if ~scalararg1 + out1 = arg1; + end + if ~scalararg2 + out2 = arg2; + end + if ~scalararg3 + out3 = arg3; + end + if ~scalararg4 + out4 = arg4; + end + + rows = max([r1 r2 r3 r4]); + columns = max([c1 c2 c3 c4]); + if scalararg1 + out1 = arg1(ones(rows,1),ones(columns,1)); + end + if scalararg2 + out2 = arg2(ones(rows,1),ones(columns,1)); + end + if scalararg3 + out3 = arg3(ones(rows,1),ones(columns,1)); + end + if scalararg4 + out4 = arg4(ones(rows,1),ones(columns,1)); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/eigdec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function [evals, evec] = eigdec(x, N) +%EIGDEC Sorted eigendecomposition +% +% Description +% EVALS = EIGDEC(X, N computes the largest N eigenvalues of the +% matrix X in descending order. [EVALS, EVEC] = EIGDEC(X, N) also +% computes the corresponding eigenvectors. +% +% See also +% PCA, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(x, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the eigenvalues of the data covariance matrix +if evals_only + % Use eig function as always more efficient than eigs here + temp_evals = eig(x); +else + % Use eig function unless fraction of eigenvalues required is tiny + if (N/size(x, 2)) > 0.04 + fprintf('netlab pca: using eig\n'); + [temp_evec, temp_evals] = eig(x); + else + options.disp = 0; + fprintf('netlab pca: using eigs\n'); + [temp_evec, temp_evals] = eigs(x, N, 'LM', options); + end + temp_evals = diag(temp_evals); +end + +% Eigenvalues nearly always returned in descending order, but just +% to make sure..... +[evals perm] = sort(-temp_evals); +evals = -evals(1:N); +%evec=temp_evec(:,1:N); +if ~evals_only + if evals == temp_evals(1:N) + % Originals were in order + evec = temp_evec(:, 1:N); + return + else + fprintf('netlab pca: sorting evec\n'); + % Need to reorder the eigenvectors + for i=1:N + evec(:,i) = temp_evec(:,perm(i)); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/est_transmat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function [A,C] = est_transmat(seq) +% ESTIMATE_TRANSMAT Max likelihood of a Markov chain transition matrix +% [A,C] = estimate_transmat(seq) +% +% seq is a vector of positive integers +% +% e.g., seq = [1 2 1 2 3], C(1,2)=2, C(2,1)=1, C(2,3)=1, so +% A(1,:)=[0 1 0], A(2,:) = [0.5 0 0.5], +% all other entries are 0 + +% Use a trick with sparse matrices to count the number of each transition. +% From http://www.mathworks.com/company/newsletter/may03/dna.shtml + +C = full(sparse(seq(1:end-1), seq(2:end),1)); +A = mk_stochastic(C);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/fit_paritioned_model_testfn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function model = foo(inputs, outputs, varargin) + +model.inputs = inputs; +model.outputs = outputs; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/fit_partitioned_model.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function [model, partition_size] = fit_partitioned_model(... + inputs, outputs, selectors, sel_sizes, min_size, partition_names, fn_name, varargin) +%function [models, partition_sizes] = fit_partitioned_model(... +% inputs, outputs, selectors, sel_sizes, min_size, partition_names, fn_name, varargin) +% +% Fit models to different subsets (columns) of the input/output data, +% as chosen by the selectors matrix. If there is only output data, set input=[]. +% If there is less than min_size data in partition i, +% we set model{i} = [] +% +% Example: +% selectors = [1 2 1 1 1 +% 1 2 2 1 2] +% sel_sizes = [2 2] so there are 4 models: (1,1), (2,1), (1,2), (2,2) +% We fit model{1} to data from columns 1,4 +% We fit model{2} to no data +% We fit model{3} to data from column 3,5 +% We fit model{4} to data from column 2 (assuming min_size <= 1) +% +% For each partition, we call the specified function with the specified arguments +% as follows: +% model{i} = fn(input(:,cols{i}), output(:,cols{i}), args) +% (We omit input if []) +% partition_size(i) is the amount of data in the i'th partition. +% +% Example use: row 1 of selectors is whether an object is present/absent +% and row 2 is the location. +% +% Demo: +% inputs = 1:5; outputs = 6:10; selectors = as above +% fn = 'fit_partitioned_model_testfn'; +% [model, partition_size] = fit_partitioned_model(inputs, outputs, selectors, [2 2], fn) +% should produce +% model{1}.input = [1 4], model{1}.output = [6 9] +% model{2} = [] +% model{3}.input = [3 5], model{3}.output = [8 10], +% model{4}.input = [2], model{3}.output = [7], +% partition_size = [2 0 2 1] + + +sel_ndx = subv2ind(sel_sizes, selectors'); +Nmodels = prod(sel_sizes); +model = cell(1, Nmodels); +partition_size = zeros(1, Nmodels); +for m=1:Nmodels + ndx = find(sel_ndx==m); + partition_size(m) = length(ndx); + if ~isempty(partition_names) % & (partition_size(m) < min_size) + fprintf('partition %s has size %d, min size = %d\n', ... + partition_names{m}, partition_size(m), min_size); + end + if partition_size(m) >= min_size + if isempty(inputs) + model{m} = feval(fn_name, outputs(:, ndx), varargin{:}); + else + model{m} = feval(fn_name, inputs(:,ndx), outputs(:, ndx), varargin{:}); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/gamma_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +function r = gamrnd(a,b,m,n); +%GAMRND Random matrices from gamma distribution. +% R = GAMRND(A,B) returns a matrix of random numbers chosen +% from the gamma distribution with parameters A and B. +% The size of R is the common size of A and B if both are matrices. +% If either parameter is a scalar, the size of R is the size of the other +% parameter. Alternatively, R = GAMRND(A,B,M,N) returns an M by N matrix. +% +% Some references refer to the gamma distribution +% with a single parameter. This corresponds to GAMRND +% with B = 1. (See Devroye, pages 401-402.) + +% GAMRND uses a rejection or an inversion method depending on the +% value of A. + +% References: +% [1] L. Devroye, "Non-Uniform Random Variate Generation", +% Springer-Verlag, 1986 + +% B.A. Jones 2-1-93 +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:18 $ + +if nargin < 2, + error('Requires at least two input arguments.'); +end + + +if nargin == 2 + [errorcode rows columns] = rndcheck(2,2,a,b); +end + +if nargin == 3 + [errorcode rows columns] = rndcheck(3,2,a,b,m); +end + +if nargin == 4 + [errorcode rows columns] = rndcheck(4,2,a,b,m,n); +end + +if errorcode > 0 + error('Size information is inconsistent.'); +end + +% Initialize r to zero. +lth = rows*columns; +r = zeros(lth,1); +a = a(:); b = b(:); + +scalara = (length(a) == 1); +if scalara + a = a*ones(lth,1); +end + +scalarb = (length(b) == 1); +if scalarb + b = b*ones(lth,1); +end + +% If a == 1, then gamma is exponential. (Devroye, page 405). +k = find(a == 1); +if any(k) + r(k) = -b(k) .* log(rand(size(k))); +end + + +k = find(a < 1 & a > 0); +% (Devroye, page 418 Johnk's generator) +if any(k) + c = zeros(lth,1); + d = zeros(lth,1); + c(k) = 1 ./ a(k); + d(k) = 1 ./ (1 - a(k)); + accept = k; + while ~isempty(accept) + u = rand(size(accept)); + v = rand(size(accept)); + x = u .^ c(accept); + y = v .^ d(accept); + k1 = find((x + y) <= 1); + if ~isempty(k1) + e = -log(rand(size(k1))); + r(accept(k1)) = e .* x(k1) ./ (x(k1) + y(k1)); + accept(k1) = []; + end + end + r(k) = r(k) .* b(k); +end + +% Use a rejection method for a > 1. +k = find(a > 1); +% (Devroye, page 410 Best's algorithm) +bb = zeros(size(a)); +c = bb; +if any(k) + bb(k) = a(k) - 1; + c(k) = 3 * a(k) - 3/4; + accept = k; + count = 1; + while ~isempty(accept) + m = length(accept); + u = rand(m,1); + v = rand(m,1); + w = u .* (1 - u); + y = sqrt(c(accept) ./ w) .* (u - 0.5); + x = bb(accept) + y; + k1 = find(x >= 0); + if ~isempty(k1) + z = 64 * (w .^ 3) .* (v .^ 2); + k2 = (z(k1) <= (1 - 2 * (y(k1) .^2) ./ x(k1))); + k3 = k1(find(k2)); + r(accept(k3)) = x(k3); + k4 = k1(find(~k2)); + k5 = k4(find(log(z(k4)) <= (2*(bb(accept(k4)).*log(x(k4)./bb(accept(k4)))-y(k4))))); + r(accept(k5)) = x(k5); + omit = [k3; k5]; + accept(omit) = []; + end + end + r(k) = r(k) .* b(k); +end + +% Return NaN if a or b is not positive. +r(b <= 0 | a <= 0) = NaN; + +r = reshape(r,rows,columns);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/gaussian_prob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function p = gaussian_prob(x, m, C, use_log) +% GAUSSIAN_PROB Evaluate a multivariate Gaussian density. +% p = gaussian_prob(X, m, C) +% p(i) = N(X(:,i), m, C) where C = covariance matrix and each COLUMN of x is a datavector + +% p = gaussian_prob(X, m, C, 1) returns log N(X(:,i), m, C) (to prevents underflow). +% +% If X has size dxN, then p has size Nx1, where N = number of examples + +if nargin < 4, use_log = 0; end + +if length(m)==1 % scalar + x = x(:)'; +end +[d N] = size(x); +%assert(length(m)==d); % slow +m = m(:); +M = m*ones(1,N); % replicate the mean across columns +denom = (2*pi)^(d/2)*sqrt(abs(det(C))); +mahal = sum(((x-M)'*inv(C)).*(x-M)',2); % Chris Bregler's trick +if any(mahal<0) + warning('mahal < 0 => C is not psd') +end +if use_log + p = -0.5*mahal - log(denom); +else + p = exp(-0.5*mahal) / (denom+eps); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/gaussian_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function x = gsamp(mu, covar, nsamp) +%GSAMP Sample from a Gaussian distribution. +% +% Description +% +% X = GSAMP(MU, COVAR, NSAMP) generates a sample of size NSAMP from a +% D-dimensional Gaussian distribution. The Gaussian density has mean +% vector MU and covariance matrix COVAR, and the matrix X has NSAMP +% rows in which each row represents a D-dimensional sample vector. +% +% See also +% GAUSS, DEMGAUSS +% + +% Copyright (c) Ian T Nabney (1996-2001) + +d = size(covar, 1); + +mu = reshape(mu, 1, d); % Ensure that mu is a row vector + +[evec, eval] = eig(covar); + +coeffs = randn(nsamp, d)*sqrt(eval); + +x = ones(nsamp, 1)*mu + coeffs*evec';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/histCmpChi2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function d = histCmpChi2(h1, h2) +% Compare two histograms using chi-squared +% function d = histCmpChi2(h1, h2) +% +% d(i,j) = chi^2(h1(i,:), h2(j,:)) = sum_b (h1(i,b)-h2(j,b)^2 / (h1(i,b) + h2(j,b)) + +[N B] = size(h1); +d = zeros(N,N); +for i=1:N + h1i = repmat(h1(i,:), N, 1); + numer = (h1i - h2).^2; + denom = h1i + h2 + eps; % if denom=0, then numer=0 + d(i,:) = sum(numer ./ denom, 2); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/linear_regression.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function [muY, SigmaY, weightsY] = linear_regression(X, Y, varargin) +% LINEAR_REGRESSION Fit params for P(Y|X) = N(Y; W X + mu, Sigma) +% +% X(:, t) is the t'th input example +% Y(:, t) is the t'th output example +% +% Kevin Murphy, August 2003 +% +% This is a special case of cwr_em with 1 cluster. +% You can also think of it as a front end to clg_Mstep. + +[cov_typeY, clamp_weights, muY, SigmaY, weightsY,... + cov_priorY, regress, clamp_covY] = process_options(... + varargin, ... + 'cov_typeY', 'full', 'clamp_weights', 0, ... + 'muY', [], 'SigmaY', [], 'weightsY', [], ... + 'cov_priorY', [], 'regress', 1, 'clamp_covY', 0); + +[nx N] = size(X); +[ny N2] = size(Y); +if N ~= N2 + error(sprintf('nsamples X (%d) ~= nsamples Y (%d)', N, N2)); +end + +w = 1/N; +WYbig = Y*w; +WYY = WYbig * Y'; +WY = sum(WYbig, 2); +WYTY = sum(diag(WYbig' * Y)); +if ~regress + % This is just fitting an unconditional Gaussian + weightsY = []; + [muY, SigmaY] = ... + mixgauss_Mstep(1, WY, WYY, WYTY, ... + 'cov_type', cov_typeY, 'cov_prior', cov_priorY); + % There is a much easier way... + assert(approxeq(muY, mean(Y'))) + assert(approxeq(SigmaY, cov(Y') + 0.01*eye(ny))) +else + % This is just linear regression + WXbig = X*w; + WXX = WXbig * X'; + WX = sum(WXbig, 2); + WXTX = sum(diag(WXbig' * X)); + WXY = WXbig * Y'; + [muY, SigmaY, weightsY] = ... + clg_Mstep(1, WY, WYY, WYTY, WX, WXX, WXY, ... + 'cov_type', cov_typeY, 'cov_prior', cov_priorY); +end +if clamp_covY, SigmaY = SigmaY; end +if clamp_weights, weightsY = weightsY; end + +if nx==1 & ny==1 & regress + P = polyfit(X,Y); % Y = P(1) X^1 + P(2) X^0 = ax + b + assert(approxeq(muY, P(2))) + assert(approxeq(weightsY, P(1))) +end + +%%%%%%%% Test +if 0 + c1 = randn(2,100); c2 = randn(2,100); + y = c2(1,:); X = [ones(size(c1,2),1) c1']; + b = regress(y(:), X); % stats toolbox + [m,s,w] = linear_regression(c1, y); + assert(approxeq(b(1),m)) + assert(approxeq(b(2), w(1))) + assert(approxeq(b(3), w(2))) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/logist2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,115 @@ +function [beta,p,lli] = logist2(y,x,w) +% [beta,p,lli] = logist2(y,x) +% +% 2-class logistic regression. +% +% INPUT +% y Nx1 colum vector of 0|1 class assignments +% x NxK matrix of input vectors as rows +% [w] Nx1 vector of sample weights +% +% OUTPUT +% beta Kx1 column vector of model coefficients +% p Nx1 column vector of fitted class 1 posteriors +% lli log likelihood +% +% Class 1 posterior is 1 / (1 + exp(-x*beta)) +% +% David Martin <dmartin@eecs.berkeley.edu> +% April 16, 2002 + +% Copyright (C) 2002 David R. Martin <dmartin@eecs.berkeley.edu> +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as +% published by the Free Software Foundation; either version 2 of the +% License, or (at your option) any later version. +% + +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA +% 02111-1307, USA, or see http://www.gnu.org/copyleft/gpl.html. + +error(nargchk(2,3,nargin)); + +% check inputs +if size(y,2) ~= 1, + error('Input y not a column vector.'); +end +if size(y,1) ~= size(x,1), + error('Input x,y sizes mismatched.'); +end + +% get sizes +[N,k] = size(x); + +% if sample weights weren't specified, set them to 1 +if nargin < 3, + w = 1; +end + +% normalize sample weights so max is 1 +w = w / max(w); + +% initial guess for beta: all zeros +beta = zeros(k,1); + +% Newton-Raphson via IRLS, +% taken from Hastie/Tibshirani/Friedman Section 4.4. +iter = 0; +lli = 0; +while 1==1, + iter = iter + 1; + + % fitted probabilities + p = 1 ./ (1 + exp(-x*beta)); + + % log likelihood + lli_prev = lli; + lli = sum( w .* (y.*log(p+eps) + (1-y).*log(1-p+eps)) ); + + % least-squares weights + wt = w .* p .* (1-p); + + % derivatives of likelihood w.r.t. beta + deriv = x'*(w.*(y-p)); + + % Hessian of likelihood w.r.t. beta + % hessian = x'Wx, where W=diag(w) + % Do it this way to be memory efficient and fast. + hess = zeros(k,k); + for i = 1:k, + wxi = wt .* x(:,i); + for j = i:k, + hij = wxi' * x(:,j); + hess(i,j) = -hij; + hess(j,i) = -hij; + end + end + + % make sure Hessian is well conditioned + if (rcond(hess) < eps), + error(['Stopped at iteration ' num2str(iter) ... + ' because Hessian is poorly conditioned.']); + break; + end; + + % Newton-Raphson update step + step = hess\deriv; + beta = beta - step; + + % termination criterion based on derivatives + tol = 1e-6; + if abs(deriv'*step/k) < tol, break; end; + + % termination criterion based on log likelihood +% tol = 1e-4; +% if abs((lli-lli_prev)/(lli+lli_prev)) < 0.5*tol, break; end; +end; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/logist2Apply.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function p = logist2Apply(beta, x) +% LOGIST2APPLY 2 class logistic regression: compute posterior prob of class 1 +% function p = logist2Apply(beta, x) +% +% x(:,i) - each COLUMN is a test case; we append 1s automatically, if appropriate + +[D Ncases] = size(x); +if length(beta)==D+1 + F = [x; ones(1,Ncases)]; +else + F = x; +end +p = 1./(1+exp(-beta(:)'*F));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/logist2ApplyRegularized.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function prob = logist2ApplyRegularized(net, features) + +prob = glmfwd(net, features')';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/logist2Fit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [beta, p] = logist2Fit(y, x, addOne, w) +% LOGIST2FIT 2 class logsitic classification +% function beta = logist2Fit(y,x, addOne) +% +% y(i) = 0/1 +% x(:,i) = i'th input - we optionally append 1s to last dimension +% w(i) = optional weight +% +% beta(j)- regression coefficient + +if nargin < 3, addOne = 1; end +if nargin < 4, w = 1; end + +Ncases = size(x,2); +if Ncases ~= length(y) + error(sprintf('size of data = %dx%d, size of labels=%d', size(x,1), size(x,2), length(y))) +end +if addOne + x = [x; ones(1,Ncases)]; +end +[beta, p] = logist2(y(:), x', w(:)); +beta = beta(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/logist2FitRegularized.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function [net, niter] = logist2FitRegularized(labels, features, maxIter) + +if nargin < 3, maxIter = 100; end + +[D N] = size(features); +weightPrior = 0.5; +net = glm(D, 1, 'logistic', weightPrior); +options = foptions; +options(14) = maxIter; +[net, options] = glmtrain(net, options, features', labels(:)); +niter = options(14); +%w = logist2Fit(labelsPatches(jValidPatches), features(:, jValidPatches)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/logistK.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,287 @@ +function [beta,post,lli] = logistK(x,y,w,beta) +% [beta,post,lli] = logistK(x,y,beta,w) +% +% k-class logistic regression with optional sample weights +% +% k = number of classes +% n = number of samples +% d = dimensionality of samples +% +% INPUT +% x dxn matrix of n input column vectors +% y kxn vector of class assignments +% [w] 1xn vector of sample weights +% [beta] dxk matrix of model coefficients +% +% OUTPUT +% beta dxk matrix of fitted model coefficients +% (beta(:,k) are fixed at 0) +% post kxn matrix of fitted class posteriors +% lli log likelihood +% +% Let p(i,j) = exp(beta(:,j)'*x(:,i)), +% Class j posterior for observation i is: +% post(j,i) = p(i,j) / (p(i,1) + ... p(i,k)) +% +% See also logistK_eval. +% +% David Martin <dmartin@eecs.berkeley.edu> +% May 3, 2002 + +% Copyright (C) 2002 David R. Martin <dmartin@eecs.berkeley.edu> +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as +% published by the Free Software Foundation; either version 2 of the +% License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA +% 02111-1307, USA, or see http://www.gnu.org/copyleft/gpl.html. + +% TODO - this code would be faster if x were transposed + +error(nargchk(2,4,nargin)); + +debug = 0; +if debug>0, + h=figure(1); + set(h,'DoubleBuffer','on'); +end + +% get sizes +[d,nx] = size(x); +[k,ny] = size(y); + +% check sizes +if k < 2, + error('Input y must encode at least 2 classes.'); +end +if nx ~= ny, + error('Inputs x,y not the same length.'); +end + +n = nx; + +% make sure class assignments have unit L1-norm +sumy = sum(y,1); +if abs(1-sumy) > eps, + sumy = sum(y,1); + for i = 1:k, y(i,:) = y(i,:) ./ sumy; end +end +clear sumy; + +% if sample weights weren't specified, set them to 1 +if nargin < 3, + w = ones(1,n); +end + +% normalize sample weights so max is 1 +w = w / max(w); + +% if starting beta wasn't specified, initialize randomly +if nargin < 4, + beta = 1e-3*rand(d,k); + beta(:,k) = 0; % fix beta for class k at zero +else + if sum(beta(:,k)) ~= 0, + error('beta(:,k) ~= 0'); + end +end + +stepsize = 1; +minstepsize = 1e-2; + +post = computePost(beta,x); +lli = computeLogLik(post,y,w); + +for iter = 1:100, + %disp(sprintf(' logist iter=%d lli=%g',iter,lli)); + vis(x,y,beta,lli,d,k,iter,debug); + + % gradient and hessian + [g,h] = derivs(post,x,y,w); + + % make sure Hessian is well conditioned + if rcond(h) < eps, + % condition with Levenberg-Marquardt method + for i = -16:16, + h2 = h .* ((1 + 10^i)*eye(size(h)) + (1-eye(size(h)))); + if rcond(h2) > eps, break, end + end + if rcond(h2) < eps, + warning(['Stopped at iteration ' num2str(iter) ... + ' because Hessian can''t be conditioned']); + break + end + h = h2; + end + + % save lli before update + lli_prev = lli; + + % Newton-Raphson with step-size halving + while stepsize >= minstepsize, + % Newton-Raphson update step + step = stepsize * (h \ g); + beta2 = beta; + beta2(:,1:k-1) = beta2(:,1:k-1) - reshape(step,d,k-1); + + % get the new log likelihood + post2 = computePost(beta2,x); + lli2 = computeLogLik(post2,y,w); + + % if the log likelihood increased, then stop + if lli2 > lli, + post = post2; lli = lli2; beta = beta2; + break + end + + % otherwise, reduce step size by half + stepsize = 0.5 * stepsize; + end + + % stop if the average log likelihood has gotten small enough + if 1-exp(lli/n) < 1e-2, break, end + + % stop if the log likelihood changed by a small enough fraction + dlli = (lli_prev-lli) / lli; + if abs(dlli) < 1e-3, break, end + + % stop if the step size has gotten too small + if stepsize < minstepsize, brea, end + + % stop if the log likelihood has decreased; this shouldn't happen + if lli < lli_prev, + warning(['Stopped at iteration ' num2str(iter) ... + ' because the log likelihood decreased from ' ... + num2str(lli_prev) ' to ' num2str(lli) '.' ... + ' This may be a bug.']); + break + end +end + +if debug>0, + vis(x,y,beta,lli,d,k,iter,2); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% class posteriors +function post = computePost(beta,x) + [d,n] = size(x); + [d,k] = size(beta); + post = zeros(k,n); + bx = zeros(k,n); + for j = 1:k, + bx(j,:) = beta(:,j)'*x; + end + for j = 1:k, + post(j,:) = 1 ./ sum(exp(bx - repmat(bx(j,:),k,1)),1); + end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% log likelihood +function lli = computeLogLik(post,y,w) + [k,n] = size(post); + lli = 0; + for j = 1:k, + lli = lli + sum(w.*y(j,:).*log(post(j,:)+eps)); + end + if isnan(lli), + error('lli is nan'); + end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% gradient and hessian +%% These are computed in what seems a verbose manner, but it is +%% done this way to use minimal memory. x should be transposed +%% to make it faster. +function [g,h] = derivs(post,x,y,w) + + [k,n] = size(post); + [d,n] = size(x); + + % first derivative of likelihood w.r.t. beta + g = zeros(d,k-1); + for j = 1:k-1, + wyp = w .* (y(j,:) - post(j,:)); + for ii = 1:d, + g(ii,j) = x(ii,:) * wyp'; + end + end + g = reshape(g,d*(k-1),1); + + % hessian of likelihood w.r.t. beta + h = zeros(d*(k-1),d*(k-1)); + for i = 1:k-1, % diagonal + wt = w .* post(i,:) .* (1 - post(i,:)); + hii = zeros(d,d); + for a = 1:d, + wxa = wt .* x(a,:); + for b = a:d, + hii_ab = wxa * x(b,:)'; + hii(a,b) = hii_ab; + hii(b,a) = hii_ab; + end + end + h( (i-1)*d+1 : i*d , (i-1)*d+1 : i*d ) = -hii; + end + for i = 1:k-1, % off-diagonal + for j = i+1:k-1, + wt = w .* post(j,:) .* post(i,:); + hij = zeros(d,d); + for a = 1:d, + wxa = wt .* x(a,:); + for b = a:d, + hij_ab = wxa * x(b,:)'; + hij(a,b) = hij_ab; + hij(b,a) = hij_ab; + end + end + h( (i-1)*d+1 : i*d , (j-1)*d+1 : j*d ) = hij; + h( (j-1)*d+1 : j*d , (i-1)*d+1 : i*d ) = hij; + end + end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% debug/visualization +function vis (x,y,beta,lli,d,k,iter,debug) + + if debug<=0, return, end + + disp(['iter=' num2str(iter) ' lli=' num2str(lli)]); + if debug<=1, return, end + + if d~=3 | k>10, return, end + + figure(1); + res = 100; + r = abs(max(max(x))); + dom = linspace(-r,r,res); + [px,py] = meshgrid(dom,dom); + xx = px(:); yy = py(:); + points = [xx' ; yy' ; ones(1,res*res)]; + func = zeros(k,res*res); + for j = 1:k, + func(j,:) = exp(beta(:,j)'*points); + end + [mval,ind] = max(func,[],1); + hold off; + im = reshape(ind,res,res); + imagesc(xx,yy,im); + hold on; + syms = {'w.' 'wx' 'w+' 'wo' 'w*' 'ws' 'wd' 'wv' 'w^' 'w<'}; + for j = 1:k, + [mval,ind] = max(y,[],1); + ind = find(ind==j); + plot(x(1,ind),x(2,ind),syms{j}); + end + pause(0.1); + +% eof
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/logistK_eval.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,83 @@ +function [post,lik,lli] = logistK_eval(beta,x,y) +% [post,lik,lli] = logistK_eval(beta,x,y) +% +% Evaluate logistic regression model. +% +% INPUT +% beta dxk model coefficients (as returned by logistK) +% x dxn matrix of n input column vectors +% [y] kxn vector of class assignments +% +% OUTPUT +% post kxn fitted class posteriors +% lik 1xn vector of sample likelihoods +% lli log likelihood +% +% Let p(i,j) = exp(beta(:,j)'*x(:,i)), +% Class j posterior for observation i is: +% post(j,i) = p(i,j) / (p(i,1) + ... p(i,k)) +% The likelihood of observation i given soft class assignments +% y(:,i) is: +% lik(i) = prod(post(:,i).^y(:,i)) +% The log-likelihood of the model given the labeled samples is: +% lli = sum(log(lik)) +% +% See also logistK. +% +% David Martin <dmartin@eecs.berkeley.edu> +% May 7, 2002 + +% Copyright (C) 2002 David R. Martin <dmartin@eecs.berkeley.edu> +% +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License as +% published by the Free Software Foundation; either version 2 of the +% License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA +% 02111-1307, USA, or see http://www.gnu.org/copyleft/gpl.html. + +error(nargchk(2,3,nargin)); + +% check sizes +if size(beta,1) ~= size(x,1), + error('Inputs beta,x not the same height.'); +end +if nargin > 3 & size(y,2) ~= size(x,2), + error('Inputs x,y not the same length.'); +end + +% get sizes +[d,k] = size(beta); +[d,n] = size(x); + +% class posteriors +post = zeros(k,n); +bx = zeros(k,n); +for j = 1:k, + bx(j,:) = beta(:,j)'*x; +end +for j = 1:k, + post(j,:) = 1 ./ sum(exp(bx - repmat(bx(j,:),k,1)),1); +end +clear bx; + +% likelihood of each sample +if nargout > 1, + y = y ./ repmat(sum(y,1),k,1); % L1-normalize class assignments + lik = prod(post.^y,1); +end + +% total log likelihood +if nargout > 2, + lli = sum(log(lik+eps)); +end; + +% eof
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/marginalize_gaussian.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function [muX, SXX] = marginalize_gaussian(mu, Sigma, X, Y, ns) +% MARGINALIZE_GAUSSIAN Compute Pr(X) from Pr(X,Y) where X and Y are jointly Gaussian. +% [muX, SXX] = marginalize_gaussian(mu, Sigma, X, Y, ns) + +[muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/matrix_T_pdf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function p = matrix_T_pdf(A, M, V, K, n) +% MATRIX_T_PDF Evaluate the density of a matrix under a Matrix-T distribution +% p = matrix_T_pdf(A, M, V, K, n) + +% See "Bayesian Linear Regression", T. Minka, MIT Tech Report, 2001 + +[d m] = size(K); +is = 1:d; +c1 = prod(gamma((n+1-is)/2)) / prod(gamma((n-m+1-is)/2)); +c2 = det(K)^(d/2) / det(pi*V)^(m/2); %% pi or 2pi? +p = c1 * c2 * det((A-M)'*inv(V)*(A-M)*K + eye(m))^(-n/2); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/matrix_normal_pdf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function p = matrix_normal_pdf(A, M, V, K) +% MATRIX_NORMAL_PDF Evaluate the density of a matrix under a Matrix-Normal distribution +% p = matrix_normal_pdf(A, M, V, K) + +% See "Bayesian Linear Regression", T. Minka, MIT Tech Report, 2001 + +[d m] = size(K); +c = det(K)^(d/2) / det(2*pi*V)^(m/2); +p = c * exp(-0.5*tr((A-M)'*inv(V)*(A-M)*K));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mc_stat_distrib.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function pi = mc_stat_distrib(P) +% MC_STAT_DISTRIB Compute stationary distribution of a Markov chain +% function pi = mc_stat_distrib(P) +% +% Each row of P should sum to one; pi is a column vector + +% Kevin Murphy, 16 Feb 2003 + +% The stationary distribution pi satisfies pi P = pi +% subject to sum_i pi(i) = 1, 0 <= pi(i) <= 1 +% Hence +% (P' 0n (pi = (pi +% 1n 0) 1) 1) +% or P2 pi2 = pi2. +% Naively we can solve this using (P2 - I(n+1)) pi2 = 0(n+1) +% or P3 pi2 = 0(n+1), i.e., pi2 = P3 \ zeros(n+1,1) +% but this is singular (because of the sum-to-one constraint). +% Hence we replace the last row of P' with 1s instead of appending ones to create P2, +% and similarly for pi. + +n = length(P); +P4 = P'-eye(n); +P4(end,:) = 1; +pi = P4 \ [zeros(n-1,1);1]; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_Mstep.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,106 @@ +function [mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY, varargin) +% MSTEP_COND_GAUSS Compute MLEs for mixture of Gaussians given expected sufficient statistics +% function [mu, Sigma] = Mstep_cond_gauss(w, Y, YY, YTY, varargin) +% +% We assume P(Y|Q=i) = N(Y; mu_i, Sigma_i) +% and w(i,t) = p(Q(t)=i|y(t)) = posterior responsibility +% See www.ai.mit.edu/~murphyk/Papers/learncg.pdf. +% +% INPUTS: +% w(i) = sum_t w(i,t) = responsibilities for each mixture component +% If there is only one mixture component (i.e., Q does not exist), +% then w(i) = N = nsamples, and +% all references to i can be replaced by 1. +% YY(:,:,i) = sum_t w(i,t) y(:,t) y(:,t)' = weighted outer product +% Y(:,i) = sum_t w(i,t) y(:,t) = weighted observations +% YTY(i) = sum_t w(i,t) y(:,t)' y(:,t) = weighted inner product +% You only need to pass in YTY if Sigma is to be estimated as spherical. +% +% Optional parameters may be passed as 'param_name', param_value pairs. +% Parameter names are shown below; default values in [] - if none, argument is mandatory. +% +% 'cov_type' - 'full', 'diag' or 'spherical' ['full'] +% 'tied_cov' - 1 (Sigma) or 0 (Sigma_i) [0] +% 'clamped_cov' - pass in clamped value, or [] if unclamped [ [] ] +% 'clamped_mean' - pass in clamped value, or [] if unclamped [ [] ] +% 'cov_prior' - Lambda_i, added to YY(:,:,i) [0.01*eye(d,d,Q)] +% +% If covariance is tied, Sigma has size d*d. +% But diagonal and spherical covariances are represented in full size. + +[cov_type, tied_cov, clamped_cov, clamped_mean, cov_prior, other] = ... + process_options(varargin,... + 'cov_type', 'full', 'tied_cov', 0, 'clamped_cov', [], 'clamped_mean', [], ... + 'cov_prior', []); + +[Ysz Q] = size(Y); +N = sum(w); +if isempty(cov_prior) + %cov_prior = zeros(Ysz, Ysz, Q); + %for q=1:Q + % cov_prior(:,:,q) = 0.01*cov(Y(:,q)'); + %end + cov_prior = repmat(0.01*eye(Ysz,Ysz), [1 1 Q]); +end +%YY = reshape(YY, [Ysz Ysz Q]) + cov_prior; % regularize the scatter matrix +YY = reshape(YY, [Ysz Ysz Q]); + +% Set any zero weights to one before dividing +% This is valid because w(i)=0 => Y(:,i)=0, etc +w = w + (w==0); + +if ~isempty(clamped_mean) + mu = clamped_mean; +else + % eqn 6 + %mu = Y ./ repmat(w(:)', [Ysz 1]);% Y may have a funny size + mu = zeros(Ysz, Q); + for i=1:Q + mu(:,i) = Y(:,i) / w(i); + end +end + +if ~isempty(clamped_cov) + Sigma = clamped_cov; + return; +end + +if ~tied_cov + Sigma = zeros(Ysz,Ysz,Q); + for i=1:Q + if cov_type(1) == 's' + % eqn 17 + s2 = (1/Ysz)*( (YTY(i)/w(i)) - mu(:,i)'*mu(:,i) ); + Sigma(:,:,i) = s2 * eye(Ysz); + else + % eqn 12 + SS = YY(:,:,i)/w(i) - mu(:,i)*mu(:,i)'; + if cov_type(1)=='d' + SS = diag(diag(SS)); + end + Sigma(:,:,i) = SS; + end + end +else % tied cov + if cov_type(1) == 's' + % eqn 19 + s2 = (1/(N*Ysz))*(sum(YTY,2) + sum(diag(mu'*mu) .* w)); + Sigma = s2*eye(Ysz); + else + SS = zeros(Ysz, Ysz); + % eqn 15 + for i=1:Q % probably could vectorize this... + SS = SS + YY(:,:,i)/N - mu(:,i)*mu(:,i)'; + end + if cov_type(1) == 'd' + Sigma = diag(diag(SS)); + else + Sigma = SS; + end + end +end + +if tied_cov + Sigma = repmat(Sigma, [1 1 Q]); +end +Sigma = Sigma + cov_prior;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_classifier_apply.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function [classHatTest, probPos] = mixgauss_classifier_apply(mixgauss, testFeatures) + +Bpos = mixgauss_prob(testFeatures, mixgauss.pos.mu, mixgauss.pos.Sigma, mixgauss.pos.prior); +Bneg = mixgauss_prob(testFeatures, mixgauss.neg.mu, mixgauss.neg.Sigma, mixgauss.neg.prior); +prior_pos = mixgauss.priorC(1); +prior_neg = mixgauss.priorC(2); +post = normalize([Bpos * prior_pos; Bneg * prior_neg], 1); +probPos = post(1,:)'; +[junk, classHatTest] = max(post); +classHatTest(find(classHatTest==2))=0; +classHatTest = classHatTest(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_classifier_train.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +function mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nc, varargin) +% function mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nclusters, varargin) +% trainFeatures(:,i) for i'th example +% trainLabels should be 0,1 +% To evaluate performance on a tets set, use +% mixgauss = mixgauss_classifier_train(trainFeatures, trainLabels, nc, 'testFeatures', tf, 'testLabels', tl) + +[testFeatures, testLabels, max_iter, thresh, cov_type, mu, Sigma, priorC, method, ... + cov_prior, verbose, prune_thresh] = process_options(... + varargin, 'testFeatures', [], 'testLabels', [], ... + 'max_iter', 10, 'thresh', 0.01, 'cov_type', 'diag', ... + 'mu', [], 'Sigma', [], 'priorC', [], 'method', 'kmeans', ... + 'cov_prior', [], 'verbose', 0, 'prune_thresh', 0); + +Nclasses = 2; % max([trainLabels testLabels]) + 1; + +pos = find(trainLabels == 1); +neg = find(trainLabels == 0); + +if verbose, fprintf('fitting pos\n'); end +[mixgauss.pos.mu, mixgauss.pos.Sigma, mixgauss.pos.prior] = ... + mixgauss_em(trainFeatures(:, pos), nc, varargin{:}); + +if verbose, fprintf('fitting neg\n'); end +[mixgauss.neg.mu, mixgauss.neg.Sigma, mixgauss.neg.prior] = ... + mixgauss_em(trainFeatures(:, neg), nc, varargin{:}); + + +if ~isempty(priorC) + mixgauss.priorC = priorC; +else + mixgauss.priorC = normalize([length(pos) length(neg)]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_em.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +function [mu, Sigma, prior] = mixgauss_em(Y, nc, varargin) +% MIXGAUSS_EM Fit the parameters of a mixture of Gaussians using EM +% function [mu, Sigma, prior] = mixgauss_em(data, nc, varargin) +% +% data(:, t) is the t'th data point +% nc is the number of clusters + +% Kevin Murphy, 13 May 2003 + +[max_iter, thresh, cov_type, mu, Sigma, method, ... + cov_prior, verbose, prune_thresh] = process_options(... + varargin, 'max_iter', 10, 'thresh', 1e-2, 'cov_type', 'full', ... + 'mu', [], 'Sigma', [], 'method', 'kmeans', ... + 'cov_prior', [], 'verbose', 0, 'prune_thresh', 0); + +[ny T] = size(Y); + +if nc==1 + % No latent variable, so there is a closed-form solution + mu = mean(Y')'; + Sigma = cov(Y'); + if strcmp(cov_type, 'diag') + Sigma = diag(diag(Sigma)); + end + prior = 1; + return; +end + +if isempty(mu) + [mu, Sigma, prior] = mixgauss_init(nc, Y, cov_type, method); +end + +previous_loglik = -inf; +num_iter = 1; +converged = 0; + +%if verbose, fprintf('starting em\n'); end + +while (num_iter <= max_iter) & ~converged + % E step + probY = mixgauss_prob(Y, mu, Sigma, prior); % probY(q,t) + [post, lik] = normalize(probY .* repmat(prior, 1, T), 1); % post(q,t) + loglik = log(sum(lik)); + + % extract expected sufficient statistics + w = sum(post,2); % w(c) = sum_t post(c,t) + WYY = zeros(ny, ny, nc); % WYY(:,:,c) = sum_t post(c,t) Y(:,t) Y(:,t)' + WY = zeros(ny, nc); % WY(:,c) = sum_t post(c,t) Y(:,t) + WYTY = zeros(nc,1); % WYTY(c) = sum_t post(c,t) Y(:,t)' Y(:,t) + for c=1:nc + weights = repmat(post(c,:), ny, 1); % weights(:,t) = post(c,t) + WYbig = Y .* weights; % WYbig(:,t) = post(c,t) * Y(:,t) + WYY(:,:,c) = WYbig * Y'; + WY(:,c) = sum(WYbig, 2); + WYTY(c) = sum(diag(WYbig' * Y)); + end + + % M step + prior = normalize(w); + [mu, Sigma] = mixgauss_Mstep(w, WY, WYY, WYTY, 'cov_type', cov_type, 'cov_prior', cov_prior); + + if verbose, fprintf(1, 'iteration %d, loglik = %f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + +end + +if prune_thresh > 0 + ndx = find(prior < prune_thresh); + mu(:,ndx) = []; + Sigma(:,:,ndx) = []; + prior(ndx) = []; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_init.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type, method) +% MIXGAUSS_INIT Initial parameter estimates for a mixture of Gaussians +% function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type. method) +% +% INPUTS: +% data(:,t) is the t'th example +% M = num. mixture components +% cov_type = 'full', 'diag' or 'spherical' +% method = 'rnd' (choose centers randomly from data) or 'kmeans' (needs netlab) +% +% OUTPUTS: +% mu(:,k) +% Sigma(:,:,k) +% weights(k) + +if nargin < 4, method = 'kmeans'; end + +[d T] = size(data); +data = reshape(data, d, T); % in case it is data(:, t, sequence_num) + +switch method + case 'rnd', + C = cov(data'); + Sigma = repmat(diag(diag(C))*0.5, [1 1 M]); + % Initialize each mean to a random data point + indices = randperm(T); + mu = data(:,indices(1:M)); + weights = normalise(ones(M,1)); + case 'kmeans', + mix = gmm(d, M, cov_type); + options = foptions; + max_iter = 5; + options(1) = -1; % be quiet! + options(14) = max_iter; + mix = gmminit(mix, data', options); + mu = reshape(mix.centres', [d M]); + weights = mix.priors(:); + for m=1:M + switch cov_type + case 'diag', + Sigma(:,:,m) = diag(mix.covars(m,:)); + case 'full', + Sigma(:,:,m) = mix.covars(:,:,m); + case 'spherical', + Sigma(:,:,m) = mix.covars(m) * eye(d); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_prob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,133 @@ +function [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat, unit_norm) +% EVAL_PDF_COND_MOG Evaluate the pdf of a conditional mixture of Gaussians +% function [B, B2] = eval_pdf_cond_mog(data, mu, Sigma, mixmat, unit_norm) +% +% Notation: Y is observation, M is mixture component, and both may be conditioned on Q. +% If Q does not exist, ignore references to Q=j below. +% Alternatively, you may ignore M if this is a conditional Gaussian. +% +% INPUTS: +% data(:,t) = t'th observation vector +% +% mu(:,k) = E[Y(t) | M(t)=k] +% or mu(:,j,k) = E[Y(t) | Q(t)=j, M(t)=k] +% +% Sigma(:,:,j,k) = Cov[Y(t) | Q(t)=j, M(t)=k] +% or there are various faster, special cases: +% Sigma() - scalar, spherical covariance independent of M,Q. +% Sigma(:,:) diag or full, tied params independent of M,Q. +% Sigma(:,:,j) tied params independent of M. +% +% mixmat(k) = Pr(M(t)=k) = prior +% or mixmat(j,k) = Pr(M(t)=k | Q(t)=j) +% Not needed if M is not defined. +% +% unit_norm - optional; if 1, means data(:,i) AND mu(:,i) each have unit norm (slightly faster) +% +% OUTPUT: +% B(t) = Pr(y(t)) +% or +% B(i,t) = Pr(y(t) | Q(t)=i) +% B2(i,k,t) = Pr(y(t) | Q(t)=i, M(t)=k) +% +% If the number of mixture components differs depending on Q, just set the trailing +% entries of mixmat to 0, e.g., 2 components if Q=1, 3 components if Q=2, +% then set mixmat(1,3)=0. In this case, B2(1,3,:)=1.0. + + + + +if isvectorBNT(mu) & size(mu,2)==1 + d = length(mu); + Q = 1; M = 1; +elseif ndims(mu)==2 + [d Q] = size(mu); + M = 1; +else + [d Q M] = size(mu); +end +[d T] = size(data); + +if nargin < 4, mixmat = ones(Q,1); end +if nargin < 5, unit_norm = 0; end + +%B2 = zeros(Q,M,T); % ATB: not needed allways +%B = zeros(Q,T); + +if isscalarBNT(Sigma) + mu = reshape(mu, [d Q*M]); + if unit_norm % (p-q)'(p-q) = p'p + q'q - 2p'q = n+m -2p'q since p(:,i)'p(:,i)=1 + %avoid an expensive repmat + disp('unit norm') + %tic; D = 2 -2*(data'*mu)'; toc + D = 2 - 2*(mu'*data); + tic; D2 = sqdist(data, mu)'; toc + assert(approxeq(D,D2)) + else + D = sqdist(data, mu)'; + end + clear mu data % ATB: clear big old data + % D(qm,t) = sq dist between data(:,t) and mu(:,qm) + logB2 = -(d/2)*log(2*pi*Sigma) - (1/(2*Sigma))*D; % det(sigma*I) = sigma^d + B2 = reshape(exp(logB2), [Q M T]); + clear logB2 % ATB: clear big old data + +elseif ndims(Sigma)==2 % tied full + mu = reshape(mu, [d Q*M]); + D = sqdist(data, mu, inv(Sigma))'; + % D(qm,t) = sq dist between data(:,t) and mu(:,qm) + logB2 = -(d/2)*log(2*pi) - 0.5*logdet(Sigma) - 0.5*D; + %denom = sqrt(det(2*pi*Sigma)); + %numer = exp(-0.5 * D); + %B2 = numer/denom; + B2 = reshape(exp(logB2), [Q M T]); + +elseif ndims(Sigma)==3 % tied across M + B2 = zeros(Q,M,T); + for j=1:Q + % D(m,t) = sq dist between data(:,t) and mu(:,j,m) + if isposdef(Sigma(:,:,j)) + D = sqdist(data, permute(mu(:,j,:), [1 3 2]), inv(Sigma(:,:,j)))'; + logB2 = -(d/2)*log(2*pi) - 0.5*logdet(Sigma(:,:,j)) - 0.5*D; + B2(j,:,:) = exp(logB2); + else + error(sprintf('mixgauss_prob: Sigma(:,:,q=%d) not psd\n', j)); + end + end + +else % general case + B2 = zeros(Q,M,T); + for j=1:Q + for k=1:M + %if mixmat(j,k) > 0 + B2(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma(:,:,j,k)); + %end + end + end +end + +% B(j,t) = sum_k B2(j,k,t) * Pr(M(t)=k | Q(t)=j) + +% The repmat is actually slower than the for-loop, because it uses too much memory +% (this is true even for small T). + +%B = squeeze(sum(B2 .* repmat(mixmat, [1 1 T]), 2)); +%B = reshape(B, [Q T]); % undo effect of squeeze in case Q = 1 + +B = zeros(Q,T); +if Q < T + for q=1:Q + %B(q,:) = mixmat(q,:) * squeeze(B2(q,:,:)); % squeeze chnages order if M=1 + B(q,:) = mixmat(q,:) * permute(B2(q,:,:), [2 3 1]); % vector * matrix sums over m + end +else + for t=1:T + B(:,t) = sum(mixmat .* B2(:,:,t), 2); % sum over m + end +end +%t=toc;fprintf('%5.3f\n', t) + +%tic +%A = squeeze(sum(B2 .* repmat(mixmat, [1 1 T]), 2)); +%t=toc;fprintf('%5.3f\n', t) +%assert(approxeq(A,B)) % may be false because of round off error
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_prob_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,111 @@ +function test_eval_pdf_cond_mixgauss() + +%Q = 10; M = 100; d = 20; T = 500; +Q = 2; M = 3; d = 4; T = 5; + +mu = rand(d,Q,M); +data = randn(d,T); +%mixmat = mk_stochastic(rand(Q,M)); +mixmat = mk_stochastic(ones(Q,M)); + +% tied scalar +Sigma = 0.01; + +mu = rand(d,M,Q); +weights = mixmat'; +N = M*ones(1,Q); +tic; [B, B2, D] = parzen(data, mu, Sigma, N, weights); toc +tic; [BC, B2C, DC] = parzenC(data, mu, Sigma, N); toc +approxeq(B,BC) +B2C = reshape(B2C,[M Q T]); +approxeq(B2,B2C) +DC = reshape(DC,[M Q T]); +approxeq(D,DC) + + +return + +tic; [B, B2] = eval_pdf_cond_mixgauss(data, mu, Sigma, mixmat); toc +tic; C = eval_pdf_cond_parzen(data, mu, Sigma); toc +approxeq(B,C) + +return; + + +mu = reshape(mu, [d Q*M]); + +data = mk_unit_norm(data); +mu = mk_unit_norm(mu); +tic; D = 2 -2*(data'*mu); toc % avoid an expensive repmat +tic; D2 = sqdist(data, mu); toc +approxeq(D,D2) + + +% D(t,m) = sq dist between data(:,t) and mu(:,m) +mu = reshape(mu, [d Q*M]); +D = dist2(data', mu'); +%denom = (2*pi)^(d/2)*sqrt(abs(det(C))); +denom = (2*pi*Sigma)^(d/2); % sqrt(det(2*pi*Sigma)) +numer = exp(-0.5/Sigma * D'); +B2 = numer / denom; +B2 = reshape(B2, [Q M T]); + +tic; B = squeeze(sum(B2 .* repmat(mixmat, [1 1 T]), 2)); toc + +tic +A = zeros(Q,T); +for q=1:Q + A(q,:) = mixmat(q,:) * squeeze(B2(q,:,:)); % sum over m +end +toc +assert(approxeq(A,B)) + +tic +A = zeros(Q,T); +for t=1:T + A(:,t) = sum(mixmat .* B2(:,:,t), 2); % sum over m +end +toc +assert(approxeq(A,B)) + + + + +mu = reshape(mu, [d Q M]); +B3 = zeros(Q,M,T); +for j=1:Q + for k=1:M + B3(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma*eye(d)); + end +end +assert(approxeq(B2, B3)) + +logB4 = -(d/2)*log(2*pi*Sigma) - (1/(2*Sigma))*D; % det(sigma*I) = sigma^d +B4 = reshape(exp(logB4), [Q M T]); +assert(approxeq(B4, B3)) + + + + +% tied cov matrix + +Sigma = rand_psd(d,d); +mu = reshape(mu, [d Q*M]); +D = sqdist(data, mu, inv(Sigma))'; +denom = sqrt(det(2*pi*Sigma)); +numer = exp(-0.5 * D); +B2 = numer / denom; +B2 = reshape(B2, [Q M T]); + +mu = reshape(mu, [d Q M]); +B3 = zeros(Q,M,T); +for j=1:Q + for k=1:M + B3(j,k,:) = gaussian_prob(data, mu(:,j,k), Sigma); + end +end +assert(approxeq(B2, B3)) + +logB4 = -(d/2)*log(2*pi) - 0.5*logdet(Sigma) - 0.5*D; +B4 = reshape(exp(logB4), [Q M T]); +assert(approxeq(B4, B3))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [data, indices] = mixgauss_sample(mu, Sigma, mixweights, Nsamples) +% mixgauss_sample Sample data from a mixture of Gaussians +% function [data, indices] = mixgauss_sample(mu, Sigma, mixweights, Nsamples) +% +% Model is P(X) = sum_k mixweights(k) N(X; mu(:,k), Sigma(:,:,k)) or Sigma(k) for scalar +% data(:,i) is the i'th sample from P(X) +% indices(i) is the component from which sample i was drawn + +[D K] = size(mu); +data = zeros(D, Nsamples); +indices = sample_discrete(mixweights, 1, Nsamples); +for k=1:K + if ndims(Sigma) < 3 + sig = Sigma(k); + else + sig = Sigma(:,:,k); + end + ndx = find(indices==k); + if length(ndx) > 0 + data(:,ndx) = sample_gaussian(mu(:,k), sig, length(ndx))'; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mkPolyFvec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function p = mkPolyFvec(x) +% MKPOLYFVEC Make feature vector by constructing 2nd order polynomial from input data +% function p = mkPolyFvec(x) +% +% x(:,i) for example i +% p(:,i) = [x(1,i) x(2,i) x(3,i) x(1,i)^2 x(2,i)^2 x(3,i)^2 .. +% x(1,i)*x(2,i) x(1,i)*x(3,i) x(2,i)*x(3,i)]' +% +% Example +% x = [4 5 6]' +% p = [4 5 6 16 25 36 20 24 30]' + +fvec = x; +fvecSq = x.*x; +[D N] = size(x); +fvecCross = zeros(D*(D-1)/2, N); +i = 1; +for d=1:D + for d2=d+1:D + fvecCross(i,:) = x(d,:) .* x(d2,:); + i = i + 1; + end +end +p = [fvec; fvecSq; fvecCross];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/mk_unit_norm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function B = mk_unit_norm(A) +% MK_UNIT_NORM Make each column be a unit norm vector +% function B = mk_unit_norm(A) +% +% We divide each column by its magnitude + + +[nrows ncols] = size(A); +s = sum(A.^2); +ndx = find(s==0); +s(ndx)=1; +B = A ./ repmat(sqrt(s), [nrows 1]); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/multinomial_prob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function B = eval_pdf_cond_multinomial(data, obsmat) +% EVAL_PDF_COND_MULTINOMIAL Evaluate pdf of conditional multinomial +% function B = eval_pdf_cond_multinomial(data, obsmat) +% +% Notation: Y = observation (O values), Q = conditioning variable (K values) +% +% Inputs: +% data(t) = t'th observation - must be an integer in {1,2,...,K}: cannot be 0! +% obsmat(i,o) = Pr(Y(t)=o | Q(t)=i) +% +% Output: +% B(i,t) = Pr(y(t) | Q(t)=i) + +[Q O] = size(obsmat); +T = prod(size(data)); % length(data); +B = zeros(Q,T); + +for t=1:T + B(:,t) = obsmat(:, data(t)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/multinomial_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function Y = sample_cond_multinomial(X, M) +% SAMPLE_MULTINOMIAL Sample Y(i) ~ M(X(i), :) +% function Y = sample_multinomial(X, M) +% +% X(i) = i'th sample +% M(i,j) = P(Y=j | X=i) = noisy channel model +% +% e.g., if X is a binary image, +% Y = sample_multinomial(softeye(2, 0.9), X) +% will create a noisy version of X, where bits are flipped with probability 0.1 + +if any(X(:)==0) + error('data must only contain positive integers') +end + +Y = zeros(size(X)); +for i=min(X(:)):max(X(:)) + ndx = find(X==i); + Y(ndx) = sample_discrete(M(i,:), length(ndx), 1); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/multipdf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function p = multipdf(x,theta) +%MULTIPDF Multinomial probability density function. +% p = multipdf(x,theta) returns the probabilities of +% vector x, under the multinomial distribution +% with parameter vector theta. +% +% Author: David Ross + +%-------------------------------------------------------- +% Check the arguments. +%-------------------------------------------------------- +error(nargchk(2,2,nargin)); + +% make sure theta is a vector +if ndims(theta) > 2 | all(size(theta) > 1) + error('theta must be a vector'); +end + +% make sure x is of the appropriate size +if ndims(x) > 2 | any(size(x) ~= size(theta)) + error('columns of X must have same length as theta'); +end + + +%-------------------------------------------------------- +% Main... +%-------------------------------------------------------- +p = prod(theta .^ x); +p = p .* factorial(sum(x)) ./ prod(factorial_v(x)); + + +%-------------------------------------------------------- +% Function factorial_v(x): computes the factorial function +% on each element of x +%-------------------------------------------------------- +function r = factorial_v(x) + +if size(x,2) == 1 + x = x'; +end + +r = []; +for y = x + r = [r factorial(y)]; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/multirnd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function r = multirnd(theta,k) +%MULTIRND - Random vector from multinomial distribution. +% r = multirnd(theta,k) returns a vector randomly selected +% from the multinomial distribution with parameter vector +% theta, and count k (i.e. sum(r) = k). +% +% Note: if k is unspecified, then it is assumed k=1. +% +% Author: David Ross +% + +%-------------------------------------------------------- +% Check the arguments. +%-------------------------------------------------------- +error(nargchk(1,2,nargin)); + +% make sure theta is a vector +if ndims(theta) > 2 | all(size(theta) > 1) + error('theta must be a vector'); +end + +% if theta is a row vector, convert it to a column vector +if size(theta,1) == 1 + theta = theta'; +end + +% make sure k is a scalar? + +% if the number of samples has not been provided, set +% it to one +if nargin == 1 + k = 1; +end + + +%-------------------------------------------------------- +% Main... +%-------------------------------------------------------- +n = length(theta); +theta_cdf = cumsum(theta); + +r = zeros(n,1); +random_vals = rand(k,1); + +for j = 1:k + index = min(find(random_vals(j) <= theta_cdf)); + r(index) = r(index) + 1; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/normal_coef.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function c = normal_coef (Sigma) +% NORMAL_COEF Compute the normalizing coefficient for a multivariate gaussian. +% c = normal_coef (Sigma) + +n = length(Sigma); +c = (2*pi)^(-n/2) * det(Sigma)^(-0.5); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/partial_corr_coef.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function [r, c] = partial_corr_coef(S, i, j, Y) +% PARTIAL_CORR_COEF Compute a partial correlation coefficient +% [r, c] = partial_corr_coef(S, i, j, Y) +% +% S is the covariance (or correlation) matrix for X, Y, Z +% where X=[i j], Y is conditioned on, and Z is marginalized out. +% Let S2 = Cov[X | Y] be the partial covariance matrix. +% Then c = S2(i,j) and r = c / sqrt( S2(i,i) * S2(j,j) ) +% + +% Example: Anderson (1984) p129 +% S = [1.0 0.8 -0.4; +% 0.8 1.0 -0.56; +% -0.4 -0.56 1.0]; +% r(1,3 | 2) = 0.0966 +% +% Example: Van de Geer (1971) p111 +%S = [1 0.453 0.322; +% 0.453 1.0 0.596; +% 0.322 0.596 1]; +% r(2,3 | 1) = 0.533 + +X = [i j]; +i2 = 1; % find_equiv_posns(i, X); +j2 = 2; % find_equiv_posns(j, X); +S2 = S(X,X) - S(X,Y)*inv(S(Y,Y))*S(Y,X); +c = S2(i2,j2); +r = c / sqrt(S2(i2,i2) * S2(j2,j2));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/parzen.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +function [B,B2,dist] = parzen(data, mu, Sigma, N) +% EVAL_PDF_COND_PARZEN Evaluate the pdf of a conditional Parzen window +% function B = eval_pdf_cond_parzen(data, mu, Sigma, N) +% +% B(q,t) = Pr(data(:,t) | Q=q) = sum_{m=1}^{N(q)} w(m,q)*K(data(:,t) - mu(:,m,q); sigma) +% where K() is a Gaussian kernel with spherical variance sigma, +% and w(m,q) = 1/N(q) if m<=N(q) and = 0 otherwise +% where N(q) is the number of mxiture components for q +% +% B2(m,q,t) = K(data(:,t) - mu(:,m,q); sigma) for m=1:max(N) + +% This is like eval_pdf_cond_parzen, except mu is mu(:,m,q) instead of mu(:,q,m) +% and we use 1/N(q) instead of mixmat(q,m) + +if nargout >= 2 + keep_B2 = 1; +else + keep_B2 = 0; +end + +if nargout >= 3 + keep_dist = 1; +else + keep_dist = 0; +end + +[d M Q] = size(mu); +[d T] = size(data); + +M = max(N(:)); + +B = zeros(Q,T); +const1 = (2*pi*Sigma)^(-d/2); +const2 = -(1/(2*Sigma)); +if T*Q*M>20000000 % not enough memory to call sqdist + disp('eval parzen for loop') + if keep_dist, + dist = zeros(M,Q,T); + end + if keep_B2 + B2 = zeros(M,Q,T); + end + for q=1:Q + D = sqdist(mu(:,1:N(q),q), data); % D(m,t) + if keep_dist + dist(:,q,:) = D; + end + tmp = const1 * exp(const2*D); + if keep_B2, + B2(:,q,:) = tmp; + end + if N(q) > 0 + %B(q,:) = (1/N(q)) * const1 * sum(exp(const2*D), 2); + B(q,:) = (1/N(q)) * sum(tmp,1); + end + end +else + %disp('eval parzen vectorized') + dist = sqdist(reshape(mu(:,1:M,:), [d M*Q]), data); % D(mq,t) + dist = reshape(dist, [M Q T]); + B2 = const1 * exp(const2*dist); % B2(m,q,t) + if ~keep_dist + clear dist + end + + % weights(m,q) is the weight of mixture component m for q + % = 1/N(q) if m<=N(q) and = 0 otherwise + % e.g., N = [2 3 1], M = 3, + % weights = [1/2 1/3 1 = 1/2 1/3 1/1 2 3 1 1 1 1 + % 1/2 1/3 0 1/2 1/3 1/1 .* 2 3 1 <= 2 2 2 + % 0 1/3 0] 1/2 1/3 1/1 2 3 1 3 3 3 + + Ns = repmat(N(:)', [M 1]); + ramp = 1:M; + ramp = repmat(ramp(:), [1 Q]); + n = N + (N==0); % avoid 1/0 by replacing with 0* 1/1m where 0 comes from mask + N1 = repmat(1 ./ n(:)', [M 1]); + mask = (ramp <= Ns); + weights = N1 .* mask; + B2 = B2 .* repmat(mask, [1 1 T]); + + % B(q,t) = sum_m B2(m,q,t) * P(m|q) = sum_m B2(m,q,t) * weights(m,q) + B = squeeze(sum(B2 .* repmat(weights, [1 1 T]), 1)); + B = reshape(B, [Q T]); % undo effect of squeeze in case Q = 1 +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/parzenC.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,116 @@ +/* C mex version of parzen.m +[B,B2] = parzen(feat, mu, Sigma, Nproto); +*/ +#include "mex.h" +#include <stdio.h> +#include <math.h> + +#define PI 3.141592654 + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int D, M, Q, T, d, m, q, t; + double *data, *mu, *SigmaPtr, *N, Sigma; + double *B, *dist, *B2, tmp; + const int* dim_mu; + double const1, const2, sum_m, sum_d, diff; + int Dt, DMq, Dm, MQt, Mq; + int dims_B2[3]; + + int ndim_mu, i, save_B2; + + data = mxGetPr(prhs[0]); + mu = mxGetPr(prhs[1]); + SigmaPtr = mxGetPr(prhs[2]); + Sigma = *SigmaPtr; + N = mxGetPr(prhs[3]); + + D = mxGetM(prhs[0]); + T = mxGetN(prhs[0]); + + ndim_mu = mxGetNumberOfDimensions(prhs[1]); + dim_mu = mxGetDimensions(prhs[1]); + D = dim_mu[0]; + M = dim_mu[1]; + /* printf("parzenC: nlhs=%d, D=%d, M=%d, T=%d\n", nlhs, D, M, T); */ + + /* If mu is mu(d,m,o,p), then [d M Q] = size(mu) in matlab sets Q=o*p, + i.e.. the size of all conditioning variabeles */ + Q = 1; + for (i = 2; i < ndim_mu; i++) { + /* printf("dim_mu[%d]=%d\n", i, dim_mu[i]); */ + Q = Q*dim_mu[i]; + } + + /* M = max(N) */ + M = -1000000; + for (i=0; i < Q; i++) { + /* printf("N[%d]=%d\n", i, (int) N[i]); */ + if (N[i] > M) { + M = (int) N[i]; + } + } + + /* printf("parzenC: nlhs=%d, D=%d, Q=%d, M=%d, T=%d\n", nlhs, D, Q, M, T); */ + + plhs[0] = mxCreateDoubleMatrix(Q,T, mxREAL); + B = mxGetPr(plhs[0]); + + if (nlhs >= 2) + save_B2 = 1; + else + save_B2 = 0; + + if (save_B2) { + /* printf("parzenC saving B2\n"); */ + /*plhs[1] = mxCreateDoubleMatrix(M*Q*T,1, mxREAL);*/ + dims_B2[0] = M; + dims_B2[1] = Q; + dims_B2[2] = T; + plhs[1] = mxCreateNumericArray(3, dims_B2, mxDOUBLE_CLASS, mxREAL); + B2 = mxGetPr(plhs[1]); + } else { + /* printf("parzenC not saving B2\n"); */ + } + /* + plhs[2] = mxCreateDoubleMatrix(M*Q*T,1, mxREAL); + dist = mxGetPr(plhs[2]); + */ + const1 = pow(2*PI*Sigma, -D/2.0); + const2 = -(1/(2*Sigma)); + + for (t=0; t < T; t++) { + /* printf("t=%d!\n",t); */ + Dt = D*t; + MQt = M*Q*t; + for (q=0; q < Q; q++) { + sum_m = 0; + DMq = D*M*q; + Mq = M*q; + + for (m=0; m < (int)N[q]; m++) { + sum_d = 0; + Dm = D*m; + for (d=0; d < D; d++) { + /* diff = data(d,t) - mu(d,m,q) */ + /*diff = data[d + D*t] - mu[d + D*m + D*M*q]; */ + diff = data[d + Dt] - mu[d + Dm + DMq]; + sum_d = sum_d + diff*diff; + } + /* dist[m,q,t] = dist[m + M*q + M*Q*t] = dist[m + Mq + MQt] = sum_d */ + tmp = const1 * exp(const2*sum_d); + sum_m = sum_m + tmp; + if (save_B2) + B2[m + Mq + MQt] = tmp; + } + + if (N[q]>0) { + B[q + Q*t] = (1.0/N[q]) * sum_m; + } else { + B[q + Q*t] = 0.0; + } + } + } +} + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/parzenC_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +d = 2; M = 3; Q = 4; T = 5; Sigma = 10; +N = sample_discrete(normalize(ones(1,M)), 1, Q); +data = randn(d,T); +mu = randn(d,M,Q); + +[BM, B2M] = parzen(data, mu, Sigma, N); +[B, B2] = parzenC(data, mu, Sigma, N); + +approxeq(B,BM) +approxeq(B2,B2M)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/parzen_fit_select_unif.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function [mu, N, pick] = parzen_fit_select_unif(data, labels, max_proto, varargin) +% PARZEN_FIT_SELECT_UNIF Fit a parzen density estimator by selecting prototypes uniformly from data +% [mu, N, pick] = parzen_fit_select_unif(data, max_proto, labels, ...) +% +% We partition the data into different subsets based on the labels. +% We then choose up to max_proto columns from each subset, chosen uniformly. +% +% INPUTS +% data(:,t) +% labels(t) - should be in {1,2,..,Q} +% max_proto - max number of prototypes per partition +% +% Optional args +% partition_names{m} - for debugging +% boundary - do not choose prototypes which are within 'boundary' of the label transition +% +% OUTPUTS +% mu(:, m, q) for label q, prototype m for 1 <= m <= N(q) +% N(q) = number of prototypes for label q +% pick{q} = identity of the prototypes + +nclasses = max(labels); +[boundary, partition_names] = process_options(... + varargin, 'boundary', 0, 'partition_names', []); + +[D T] = size(data); +mu = zeros(D, 1, nclasses); % dynamically determine num prototypes (may be less than K) +mean_feat = mean(data,2); +pick = cell(1,nclasses); +for c=1:nclasses + ndx = find(labels==c); + if isempty(ndx) + %fprintf('no training images have label %d (%s)\n', c, partition_names{c}) + fprintf('no training images have label %d\n', c); + nviews = 1; + mu(:,1,c) = mean_feat; + else + foo = linspace(boundary+1, length(ndx-boundary), max_proto); + pick{c} = ndx(unique(floor(foo))); + nviews = length(pick{c}); + %fprintf('picking %d views for class %d=%s\n', nviews, c, class_names{c}); + mu(:,1:nviews,c) = data(:, pick{c}); + end + N(c) = nviews; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/pca.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +function [PCcoeff, PCvec] = pca(data, N) +%PCA Principal Components Analysis +% +% Description +% PCCOEFF = PCA(DATA) computes the eigenvalues of the covariance +% matrix of the dataset DATA and returns them as PCCOEFF. These +% coefficients give the variance of DATA along the corresponding +% principal components. +% +% PCCOEFF = PCA(DATA, N) returns the largest N eigenvalues. +% +% [PCCOEFF, PCVEC] = PCA(DATA) returns the principal components as well +% as the coefficients. This is considerably more computationally +% demanding than just computing the eigenvalues. +% +% See also +% EIGDEC, GTMINIT, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin == 1 + N = size(data, 2); +end + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(data, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the sorted eigenvalues of the data covariance matrix +if evals_only + PCcoeff = eigdec(cov(data), N); +else + [PCcoeff, PCvec] = eigdec(cov(data), N); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/rndcheck.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,294 @@ +function [errorcode, rows, columns] = rndcheck(nargs,nparms,arg1,arg2,arg3,arg4,arg5) +%RNDCHECK error checks the argument list for the random number generators. + +% B.A. Jones 1-22-93 +% Copyright (c) 1993-98 by The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:29:22 $ + +sizeinfo = nargs - nparms; +errorcode = 0; + +if nparms == 3 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); + [r3 c3] = size(arg3); +end + +if nparms == 2 + [r1 c1] = size(arg1); + [r2 c2] = size(arg2); +end + +if sizeinfo == 0 + if nparms == 1 + [rows columns] = size(arg1); + end + + if nparms == 2 + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + if ~scalararg1 + [rows columns] = size(arg1); + elseif ~scalararg2 + [rows columns] = size(arg2); + else + [rows columns] = size(arg1); + end + end + + if nparms == 3 + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + if ~scalararg1 + [rows columns] = size(arg1); + elseif ~scalararg2 + [rows columns] = size(arg2); + else + [rows columns] = size(arg3); + end + end +end + +if sizeinfo == 1 + scalararg1 = (prod(size(arg1)) == 1); + if nparms == 1 + if prod(size(arg2)) ~= 2 + errorcode = 2; + return; + end + if ~scalararg1 & arg2 ~= size(arg1) + errorcode = 3; + return; + end + if (arg2(1) < 0 | arg2(2) < 0 | arg2(1) ~= round(arg2(1)) | arg2(2) ~= round(arg2(2))), + errorcode = 4; + return; + end + rows = arg2(1); + columns = arg2(2); + end + + if nparms == 2 + if prod(size(arg3)) ~= 2 + errorcode = 2; + return; + end + scalararg2 = (prod(size(arg2)) == 1); + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + if (arg3(1) < 0 | arg3(2) < 0 | arg3(1) ~= round(arg3(1)) | arg3(2) ~= round(arg3(2))), + errorcode = 4; + return; + end + if ~scalararg1 + if any(arg3 ~= size(arg1)) + errorcode = 3; + return; + end + [rows columns] = size(arg1); + elseif ~scalararg2 + if any(arg3 ~= size(arg2)) + errorcode = 3; + return; + end + [rows columns] = size(arg2); + else + rows = arg3(1); + columns = arg3(2); + end + end + + if nparms == 3 + if prod(size(arg4)) ~= 2 + errorcode = 2; + return; + end + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + + if (arg4(1) < 0 | arg4(2) < 0 | arg4(1) ~= round(arg4(1)) | arg4(2) ~= round(arg4(2))), + errorcode = 4; + return; + end + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + if ~scalararg1 + if any(arg4 ~= size(arg1)) + errorcode = 3; + return; + end + [rows columns] = size(arg1); + elseif ~scalararg2 + if any(arg4 ~= size(arg2)) + errorcode = 3; + return; + end + [rows columns] = size(arg2); + elseif ~scalararg3 + if any(arg4 ~= size(arg3)) + errorcode = 3; + return; + end + [rows columns] = size(arg3); + else + rows = arg4(1); + columns = arg4(2); + end + end +end + +if sizeinfo == 2 + if nparms == 1 + scalararg1 = (prod(size(arg1)) == 1); + if ~scalararg1 + [rows columns] = size(arg1); + if rows ~= arg2 | columns ~= arg3 + errorcode = 3; + return; + end + end + if (arg2 < 0 | arg3 < 0 | arg2 ~= round(arg2) | arg3 ~= round(arg3)), + errorcode = 4; + return; + end + rows = arg2; + columns = arg3; + end + + if nparms == 2 + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + if ~scalararg1 + [rows columns] = size(arg1); + if rows ~= arg3 | columns ~= arg4 + errorcode = 3; + return; + end + elseif ~scalararg2 + [rows columns] = size(arg2); + if rows ~= arg3 | columns ~= arg4 + errorcode = 3; + return; + end + else + if (arg3 < 0 | arg4 < 0 | arg3 ~= round(arg3) | arg4 ~= round(arg4)), + errorcode = 4; + return; + end + rows = arg3; + columns = arg4; + end + end + + if nparms == 3 + scalararg1 = (prod(size(arg1)) == 1); + scalararg2 = (prod(size(arg2)) == 1); + scalararg3 = (prod(size(arg3)) == 1); + + if ~scalararg1 & ~scalararg2 + if r1 ~= r2 | c1 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 & ~scalararg3 + if r1 ~= r3 | c1 ~= c3 + errorcode = 1; + return; + end + end + + if ~scalararg3 & ~scalararg2 + if r3 ~= r2 | c3 ~= c2 + errorcode = 1; + return; + end + end + + if ~scalararg1 + [rows columns] = size(arg1); + if rows ~= arg4 | columns ~= arg5 + errorcode = 3; + return; + end + elseif ~scalararg2 + [rows columns] = size(arg2); + if rows ~= arg4 | columns ~= arg5 + errorcode = 3; + return; + end + elseif ~scalararg3 + [rows columns] = size(arg3); + if rows ~= arg4 | columns ~= arg5 + errorcode = 3; + return; + end + else + if (arg4 < 0 | arg5 < 0 | arg4 ~= round(arg4) | arg5 ~= round(arg5)), + errorcode = 4; + return; + end + rows = arg4; + columns = arg5; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function x = sample(p, n) +% SAMPLE Sample from categorical distribution. +% Returns a row vector of integers, sampled according to the probability +% distribution p. +% Uses the stick-breaking algorithm. +% Much faster algorithms are also possible. + +if nargin < 2 + n = 1; +end + +cdf = cumsum(p(:)); +for i = 1:n + x(i) = sum(cdf < rand) + 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/sample_discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function M = sample_discrete(prob, r, c) +% SAMPLE_DISCRETE Like the built in 'rand', except we draw from a non-uniform discrete distrib. +% M = sample_discrete(prob, r, c) +% +% Example: sample_discrete([0.8 0.2], 1, 10) generates a row vector of 10 random integers from {1,2}, +% where the prob. of being 1 is 0.8 and the prob of being 2 is 0.2. + +n = length(prob); + +if nargin == 1 + r = 1; c = 1; +elseif nargin == 2 + c == r; +end + +R = rand(r, c); +M = ones(r, c); +cumprob = cumsum(prob(:)); + +if n < r*c + for i = 1:n-1 + M = M + (R > cumprob(i)); + end +else + % loop over the smaller index - can be much faster if length(prob) >> r*c + cumprob2 = cumprob(1:end-1); + for i=1:r + for j=1:c + M(i,j) = sum(R(i,j) > cumprob2)+1; + end + end +end + + +% Slower, even though vectorized +%cumprob = reshape(cumsum([0 prob(1:end-1)]), [1 1 n]); +%M = sum(R(:,:,ones(n,1)) > cumprob(ones(r,1),ones(c,1),:), 3); + +% convert using a binning algorithm +%M=bindex(R,cumprob);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/sample_gaussian.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function M = sample_gaussian(mu, Sigma, N) +% SAMPLE_GAUSSIAN Draw N random row vectors from a Gaussian distribution +% samples = sample_gaussian(mean, cov, N) + +if nargin==2 + N = 1; +end + +% If Y = CX, Var(Y) = C Var(X) C'. +% So if Var(X)=I, and we want Var(Y)=Sigma, we need to find C. s.t. Sigma = C C'. +% Since Sigma is psd, we have Sigma = U D U' = (U D^0.5) (D'^0.5 U'). + +mu = mu(:); +n=length(mu); +[U,D,V] = svd(Sigma); +M = randn(n,N); +M = (U*sqrt(D))*M + mu*ones(1,N); % transform each column +M = M'; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/standardize.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function [S, mu, sigma2] = standardize(M, mu, sigma2) +% function S = standardize(M, mu, sigma2) +% Make each column of M be zero mean, std 1. +% Thus each row is scaled separately. +% +% If mu, sigma2 are omitted, they are computed from M + +M = double(M); +if nargin < 2 + mu = mean(M,2); + sigma2 = std(M,0,2); + sigma2 = sigma2 + eps*(sigma2==0); +end + +[nrows ncols] = size(M); +S = M - repmat(mu(:), [1 ncols]); +S = S ./ repmat(sigma2, [1 ncols]); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/student_t_logprob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function L = log_student_pdf(X, mu, lambda, alpha) +% LOG_STUDENT_PDF Evaluate the log of the multivariate student-t distribution at a point +% L = log_student_pdf(X, mu, lambda, alpha) +% +% Each column of X is evaluated. +% See Bernardo and Smith p435. + +k = length(mu); +assert(size(X,1) == k); +[k N] = size(X); +logc = gammaln(0.5*(alpha+k)) - gammaln(0.5*alpha) - (k/2)*log(alpha*pi) + 0.5*log(det(lambda)); +middle = (1 + (1/alpha)*(X-mu)'*lambda*(X-mu)); % scalar version +L = logc - ((alpha+k)/2)*log(middle);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/student_t_prob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function p = student_t_pdf(X, mu, lambda, alpha) +% STUDENT_T_PDF Evaluate the multivariate student-t distribution at a point +% p = student_t_pdf(X, mu, lambda, alpha) +% +% Each column of X is evaluated. +% See Bernardo and Smith p435. + +k = length(mu); +assert(size(X,1) == k); +[k N] = size(X); +numer = gamma(0.5*(alpha+k)); +denom = gamma(0.5*alpha) * (alpha*pi)^(k/2); +c = (numer/denom) * det(lambda)^(0.5); +p = c*(1 + (1/alpha)*(X-mu)'*lambda*(X-mu))^(-(alpha+k)/2); % scalar version +%m = repmat(mu(:), 1, N); +%exponent = sum((X-m)'*lambda*(X-m), 2); % column vector +%p = c*(1 + (1/alpha)*exponent).^(-(alpha+k)/2); + +keyboard
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/test_dir.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +% # of sample points +n_samples = 1000; + +p = ones(3,1)/3; + +% Low Entropy +alpha = 0.5*p; + +% High Entropy +%alpha = 10*p; + +% draw n_samples random points from the 3-d dirichlet(alpha), +% and plot the results +points = zeros(3,n_samples); +for i = 1:n_samples + points(:,i) = dirichletrnd(alpha); +end + +scatter3(points(1,:)', points(2,:)', points(3,:)', 'r', '.', 'filled'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/unidrndKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function R = unidrndKPM(min, max, nr, nc) + +if nargin < 3 + nr = 1; nc = 1; +end + +R = unidrnd(max-min+1, nr, nc) + (min-1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/unif_discrete_sample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function r = unif_discrete_sample(n, nrows, ncols) +% UNIF_DISCRETE_SAMPLE Generate random numbers uniformly from {1,2,..,n} +% function r = unif_discrete_sample(n, nrows, ncols) +% Same as unidrnd in the stats toolbox. + +r = ceil(n .* rand(nrows,ncols));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMstats/weightedRegression.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function [a, b, error] = weightedRegression(x, z, w) +% [a , b, error] = fitRegression(x, z, w); +% % Weighted scalar linear regression +% +% Find a,b to minimize +% error = sum(w * |z - (a*x + b)|^2) +% and x(i) is a scalar + +if nargin < 3, w = ones(1,length(x)); end + +w = w(:)'; +x = x(:)'; +z = z(:)'; + +W = sum(w); +Y = sum(w .* z); +YY = sum(w .* z .* z); +YTY = sum(w .* z .* z); +X = sum(w .* x); +XX = sum(w .* x .* x); +XY = sum(w .* x .* z); + +[b, a] = clg_Mstep_simple(W, Y, YY, YTY, X, XX, XY); +error = sum(w .* (z - (a*x + b)).^2 ); + +if 0 + % demo + seed = 1; + rand('state', seed); randn('state', seed); + x = -10:10; + N = length(x); + noise = randn(1,N); + aTrue = rand(1,1); + bTrue = rand(1,1); + z = aTrue*x + bTrue + noise; + + w = ones(1,N); + [a, b, err] = weightedRegression(x, z, w); + + b2=regress(z(:), [x(:) ones(N,1)]); + assert(approxeq(b,b2(2))) + assert(approxeq(a,b2(1))) + + % Make sure we go through x(15) perfectly + w(15) = 1000; + [aW, bW, errW] = weightedRegression(x, z, w); + + figure; + plot(x, z, 'ro') + hold on + plot(x, a*x+b, 'bx-') + plot(x, aW*x+bW, 'gs-') + title(sprintf('a=%5.2f, aHat=%5.2f, aWHat=%5.3f, b=%5.2f, bHat=%5.2f, bWHat=%5.3f, err=%5.3f, errW=%5.3f', ... + aTrue, a, aW, bTrue, b, bW, err, errW)) + legend('truth', 'ls', 'wls') + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,176 @@ +/README.txt/1.1.1.1/Tue Apr 26 02:30:30 2005// +/approx_unique.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/approxeq.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/argmax.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/argmin.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/asdemo.html/1.1.1.1/Wed Mar 30 19:59:00 2005// +/asdemo.m/1.1.1.1/Sun Mar 27 02:51:24 2005// +/asort.m/1.1.1.1/Fri May 13 20:52:22 2005// +/assert.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/assignEdgeNums.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/assign_cols.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/axis_pct.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/bipartiteMatchingDemo.m/1.1.1.1/Mon May 9 16:47:32 2005// +/bipartiteMatchingDemoPlot.m/1.1.1.1/Mon May 9 04:45:10 2005// +/bipartiteMatchingHungarian.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/bipartiteMatchingIntProg.m/1.1.1.1/Mon May 9 05:25:10 2005// +/block.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/cell2matPad.m/1.1.1.1/Tue Jun 28 01:30:44 2005// +/cell2num.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/centeringMatrix.m/1.1.1.1/Sun May 8 22:48:48 2005// +/checkpsd.m/1.1.1.1/Fri Feb 7 02:25:30 2003// +/chi2inv.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/choose.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/collapse_mog.m/1.1.1.1/Tue Apr 26 02:30:30 2005// +/colmult.c/1.1.1.1/Tue Apr 26 02:30:30 2005// +/colmult.mexglx/1.1.1.1/Tue Apr 26 02:30:30 2005// +/computeROC.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/compute_counts.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/conf2mahal.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/cross_entropy.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/dirKPM.m/1.1.1.1/Fri May 13 20:52:22 2005// +/div.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/draw_circle.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/draw_ellipse.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/draw_ellipse_axes.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/em_converged.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/entropy.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/exportfig.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/extend_domain_table.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/factorial.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/filepartsLast.m/1.1.1.1/Mon May 30 22:08:06 2005// +/find_equiv_posns.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/fullfileKPM.m/1.1.1.1/Sat Aug 27 01:08:50 2005// +/genpathKPM.m/1.1.1.1/Wed May 25 19:11:42 2005// +/hash_add.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/hash_del.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/hash_lookup.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/hsvKPM.m/1.1.1.1/Mon May 2 20:19:00 2005// +/hungarian.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/image_rgb.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/imresizeAspect.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/ind2subv.c/1.1.1.1/Tue Apr 26 02:30:32 2005// +/ind2subv.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/initFigures.m/1.1.1.1/Wed Jun 1 04:49:22 2005// +/installC_KPMtools.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/is_psd.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/is_stochastic.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/isemptycell.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/isposdef.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/isscalar.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/isvector.m/1.1.1.1/Tue Apr 26 02:30:32 2005// +/junk.c/1.1.1.1/Tue Apr 26 02:30:32 2005// +/loadcell.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logb.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logdet.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsum.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsum_simple.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsum_test.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsumexp.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/logsumexpv.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mahal2conf.m/1.1.1.1/Wed Apr 27 17:58:32 2005// +/marg_table.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/marginalize_table.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/matprint.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/max_mult.c/1.1.1.1/Tue Apr 26 02:30:34 2005// +/max_mult.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mexutil.c/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mexutil.h/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mk_multi_index.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mk_stochastic.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mkdirKPM.m/1.1.1.1/Mon May 9 22:20:22 2005// +/montageKPM.m/1.1.1.1/Wed Jun 1 19:39:54 2005// +/montageKPM2.m/1.1.1.1/Wed Jul 6 19:32:54 2005// +/montageKPM3.m/1.1.1.1/Tue Jun 28 01:35:44 2005// +/mult_by_table.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myintersect.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myismember.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myones.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myplot.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myrand.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myrepmat.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myreshape.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mysetdiff.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mysize.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mysubset.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/mysymsetdiff.m/1.1.1.1/Tue Apr 26 02:30:34 2005// +/myunion.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/nchoose2.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/ncols.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/nonmaxsup.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/normalise.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/normaliseC.c/1.1.1.1/Tue Apr 26 02:30:36 2005// +/normaliseC.dll/1.1.1.1/Tue Apr 26 02:30:36 2005// +/normalize.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/nrows.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/num2strcell.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/optimalMatching.m/1.1.1.1/Mon May 9 22:20:22 2005// +/optimalMatchingTest.m/1.1.1.1/Mon May 9 22:20:22 2005// +/partitionData.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/partition_matrix_vec.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/pca_kpm.m/1.1.1.1/Tue Sep 13 05:18:28 2005// +/pca_netlab.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/pick.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotBox.m/1.1.1.1/Mon May 30 06:01:56 2005// +/plotColors.m/1.1.1.1/Thu May 26 01:31:22 2005// +/plotROC.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotROCkpm.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plot_axis_thru_origin.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plot_ellipse.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plot_matrix.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plot_polygon.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotcov2.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotcov2New.m/1.1.1.1/Mon Jul 11 19:07:28 2005// +/plotcov3.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotgauss1d.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotgauss2d.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/plotgauss2d_old.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/polygon_area.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/polygon_centroid.m/1.1.1.1/Tue Apr 26 02:30:36 2005// +/polygon_intersect.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/previewfig.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/process_options.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rand_psd.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintC.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintLoopC.c/1.1.1.1/Sun Jun 5 18:46:40 2005// +/rectintLoopC.dll/1.1.1.1/Sun Jun 5 18:46:40 2005// +/rectintLoopC.mexglx/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintSparse.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintSparseC.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintSparseLoopC.c/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rectintSparseLoopC.dll/1.1.1.1/Tue Apr 26 02:30:38 2005// +/repmatC.c/1.1.1.1/Tue Apr 26 02:30:38 2005// +/repmatC.dll/1.1.1.1/Tue Apr 26 02:30:38 2005// +/repmatC.mexglx/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rgb2grayKPM.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rnd_partition.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/rotate_xlabel.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/safeStr.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sampleUniformInts.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sample_discrete.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/set_xtick_label.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/set_xtick_label_demo.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/setdiag.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/softeye.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sort_evec.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/splitLongSeqIntoManyShort.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sprintf_intvec.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/sqdist.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/strmatch_multi.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/strmatch_substr.m/1.1.1.1/Tue Apr 26 02:30:38 2005// +/strsplit.m/1.1.1.1/Tue May 3 19:01:46 2005// +/subplot2.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/subplot3.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/subsets.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/subsets1.m/1.1.1.1/Mon May 9 22:20:22 2005// +/subsetsFixedSize.m/1.1.1.1/Mon May 9 02:55:36 2005// +/subv2ind.c/1.1.1.1/Tue Apr 26 02:30:40 2005// +/subv2ind.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/sumv.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/suptitle.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/unaryEncoding.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/wrap.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/xticklabel_rotate90.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/zipload.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +/zipsave.m/1.1.1.1/Tue Apr 26 02:30:40 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/KPMtools
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/README.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +KPMtools is a directory of miscellaneous matlab functions written by +Kevin Patrick Murphy and various other people (see individual file headers). +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/approx_unique.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function [B, keep] = approx_unique(A, thresh, flag) +% APPROX_UNIQUE Return elements of A that differ from the rest by less than thresh +% B = approx_unique(A, thresh) +% B = approx_unique(A, thresh, 'rows') + +keep = []; + +if nargin < 3 | isempty(flag) + A = sort(A) + B = A(1); + for i=2:length(A) + if ~approxeq(A(i), A(i-1), thresh) + B = [B A(i)]; + keep = [keep i]; + end + end +else +% A = sortrows(A); +% B = A(1,:); +% for i=2:size(A,1) +% if ~approxeq(A(i,:), A(i-1,:), thresh) +% B = [B; A(i,:)]; +% keep = [keep i]; +% end +% end + B = []; + for i=1:size(A,1) + duplicate = 0; + for j=i+1:size(A,1) + if approxeq(A(i,:), A(j,:), thresh) + duplicate = 1; + break; + end + end + if ~duplicate + B = [B; A(i,:)]; + keep = [keep i]; + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/approxeq.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function p = approxeq(a, b, tol, rel) +% APPROXEQ Are a and b approximately equal (to within a specified tolerance)? +% p = approxeq(a, b, thresh) +% 'tol' defaults to 1e-3. +% p(i) = 1 iff abs(a(i) - b(i)) < thresh +% +% p = approxeq(a, b, thresh, 1) +% p(i) = 1 iff abs(a(i)-b(i))/abs(a(i)) < thresh + +if nargin < 3, tol = 1e-2; end +if nargin < 4, rel = 0; end + +a = a(:); +b = b(:); +d = abs(a-b); +if rel + p = ~any( (d ./ (abs(a)+eps)) > tol); +else + p = ~any(d > tol); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/argmax.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function indices = argmax(v) +% ARGMAX Return as a subscript vector the location of the largest element of a multidimensional array v. +% indices = argmax(v) +% +% Returns the first maximum in the case of ties. +% Example: +% X = [2 8 4; 7 3 9]; +% argmax(X) = [2 3], i.e., row 2 column 3 + +[m i] = max(v(:)); +indices = ind2subv(mysize(v), i);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/argmin.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function indices = argmin(v) +% ARGMIN Return as a subscript vector the location of the smallest element of a multidimensional array v. +% indices = argmin(v) +% +% Returns the first minimum in the case of ties. +% Example: +% X = [2 8 4; 7 3 9]; +% argmin(X) = [1 1], i.e., row 1 column 1 + +[m i] = min(v(:)); +indices = ind2subv(mysize(v), i); +%indices = ind2subv(size(v), i);
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create some data</span></pre><pre style='margin-left:22.5pt'><b +style='mso-bidi-font-weight:normal'><span style='mso-tab-count:2'>         </span>d = {<o:p></o:p></b></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>strings with one valid alphanumeric number</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>sorted <b style='mso-bidi-font-weight:normal'>numerically</b></span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'-<span class=SpellE><span class=GramE>inf</span></span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=GramE>x-3.2e4y</span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=GramE>f-1.4</span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'-.1'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'+ <span +class=GramE>.1d-2</span>'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'.1'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'f.1'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'f -+1.4'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'f.2'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'f.3'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'f.10'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'f.11'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'+<span class=SpellE>inf</span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=GramE><span class=string>' -</span></span><span class=SpellE><span +class=string>nan</span></span><span class=string>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'+ <span class=SpellE><span class=GramE>nan</span></span>'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=SpellE><span class=GramE>nan</span></span>'</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>strings with many numbers or invalid/ambiguous numbers</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>sorted in <span class=SpellE><span class=GramE><b +style='mso-bidi-font-weight:normal'>ascii</b></span></span><b style='mso-bidi-font-weight: +normal'> dictionary order</b></span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=GramE><span +class=string>' <span class=SpellE>nan</span></span></span><span class=string> <span +class=SpellE>nan</span>'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'+ .1e-.2'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'-1 2'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'Z12e12ez'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=SpellE><span class=GramE>inf</span></span> -<span +class=SpellE>inf</span>'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'s.3TT.4'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'z12e12ez'</span></pre><pre style='margin-left:22.5pt'><span +class=comment>%<span style='mso-tab-count:2'>        </span>strings without numbers</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>sorted in <span class=SpellE><span class=GramE><b +style='mso-bidi-font-weight:normal'>ascii</b></span></span><b style='mso-bidi-font-weight: +normal'> dictionary order</b></span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>' . .. '</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'.'</span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:3'>                 </span><span class=string>'...'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=GramE><span class=string>'.b a.'</span></span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'<span class=GramE>a</span> string'</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:3'>                 </span><span +class=string>'a. .b'</span></pre><pre style='margin-left:22.5pt'><b +style='mso-bidi-font-weight:normal'><span style='mso-tab-count:2'>         </span>};<o:p></o:p></b></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span>... and scramble it...</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span><span +class=GramE>rand(</span><span class=string>'seed'</span>,10);</pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>d=<span +class=GramE>d(</span><span class=SpellE>randperm</span>(<span class=SpellE>numel</span>(d)));</pre><pre +style='margin-left:22.5pt'><o:p> </o:p></pre><pre style='margin-left:22.5pt'><span +class=comment>% - run <b style='mso-bidi-font-weight:normal'>ASORT</b> with<o:p></o:p></span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><b +style='mso-bidi-font-weight:normal'>verbose output</b>:<span style='mso-tab-count: +2'>             </span><-v></span></pre><pre style='margin-left:22.5pt'><span +class=comment>%<span style='mso-spacerun:yes'>  </span><span class=GramE><b +style='mso-bidi-font-weight:normal'>keep</b></span><b style='mso-bidi-font-weight: +normal'> additional results</b>:<span style='mso-tab-count:1'>    </span><-d></span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE><span class=GramE>asort</span></span><span class=GramE>(</span>d,<span +class=string>'-v'</span>,<span class=string>'-d'</span>);</pre><pre +style='margin-left:22.5pt'><span class=comment>% - or</span></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-tab-count: +2'>        </span>p=<span class=SpellE><span class=GramE>asort</span></span><span +class=GramE>(</span>char(d),</span><span class=string>'-v'</span>,<span +class=string>'-d'</span>);</pre><pre style='margin-left:7.5pt'><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>      </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'...'<span style='mso-spacerun:yes'>        </span><span +class=SpellE><span class=GramE>'</span></span><span class=GramE> -</span><span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>        </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'+ .1e-.2'<span style='mso-spacerun:yes'>   </span><span +class=GramE>' .</span> .. '<span style='mso-spacerun:yes'>       </span>'-<span +class=SpellE><span class=GramE>inf</span></span>'<span style='mso-spacerun:yes'>        </span><span style='mso-spacerun:yes'>        </span>[<span style='mso-spacerun:yes'>            </span>-<span +class=SpellE>Inf</span>]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'.1'<span style='mso-spacerun:yes'>         </span>' <span +class=SpellE>nan</span> <span class=SpellE>nan</span>'<span style='mso-spacerun:yes'>     </span>'x-3.2e4y'<span style='mso-spacerun:yes'>            </span>[<span style='mso-spacerun:yes'>          </span>-32000]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'.b a.'<span style='mso-spacerun:yes'>      </span>'+ .1d-2'<span style='mso-spacerun:yes'>      </span>'f-1.4'<span style='mso-spacerun:yes'>               </span>[<span style='mso-spacerun:yes'>            </span>-1.4]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>       </span>'+ .1e-.2'<span style='mso-spacerun:yes'>     </span>'-.1'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>            </span>-0.1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.1'<span style='mso-spacerun:yes'>        </span>'+ <span +class=SpellE><span class=GramE>nan</span></span>'<span style='mso-spacerun:yes'>        </span>'+ .1d-2'<span style='mso-spacerun:yes'>             </span>[<span style='mso-spacerun:yes'>           </span>0.001]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span class=GramE>' -</span><span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>      </span>'+<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>         </span>'.1'<span style='mso-spacerun:yes'>                  </span>[<span style='mso-spacerun:yes'>             </span>0.1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-1 2'<span style='mso-spacerun:yes'>       </span>'-.1'<span style='mso-spacerun:yes'>          </span>'f.1'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=SpellE><span class=GramE>nan</span></span>'<span style='mso-spacerun:yes'>        </span>'-1 2'<span style='mso-spacerun:yes'>         </span>'f -+1.4'<span style='mso-spacerun:yes'>             </span>[<span style='mso-spacerun:yes'>             </span>1.4]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=GramE>a</span> string'<span style='mso-spacerun:yes'>   </span>'-<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>         </span>'f.2'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>2]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span class=GramE>'f.3'<span style='mso-spacerun:yes'>        </span>'.'</span><span style='mso-spacerun:yes'>            </span>'f.3'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>3]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'+ <span +class=GramE>.1d-2</span>'<span style='mso-spacerun:yes'>    </span>'...'<span style='mso-spacerun:yes'>          </span>'f.10'<span style='mso-spacerun:yes'>  </span><span style='mso-spacerun:yes'>              </span>[<span style='mso-spacerun:yes'>              </span>10]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'a. .b'<span style='mso-spacerun:yes'>      </span>'.1'<span style='mso-spacerun:yes'>           </span>'f.11'<span style='mso-spacerun:yes'>                </span>[<span style='mso-spacerun:yes'>    </span><span style='mso-spacerun:yes'>          </span>11]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'s.3TT.4'<span style='mso-spacerun:yes'>    </span>'.b a.'<span style='mso-spacerun:yes'>    </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span>'+<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>                </span>[<span style='mso-spacerun:yes'>             </span><span +class=SpellE><span class=GramE>Inf</span></span>]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span>'+<span +class=SpellE>inf</span>'<span style='mso-spacerun:yes'>       </span>'Z12e12ez'<span style='mso-spacerun:yes'>     </span>' -<span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>               </span>[<span style='mso-spacerun:yes'>         </span><span style='mso-spacerun:yes'>    </span></span></i><st1:place><span + class=SpellE><i><span style='color:gray'>NaN</span></i></span></st1:place><i><span +style='color:gray'>]<o:p></o:p></span></i></pre><pre><i><span style='color: +gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>nan</span></span> <span class=SpellE>nan</span>'<span style='mso-spacerun:yes'>   </span>'a string'<span style='mso-spacerun:yes'>     </span>'+ <span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>               </span>[<span style='mso-spacerun:yes'>             </span></span></i><st1:place><span + class=SpellE><i><span style='color:gray'>NaN</span></i></span></st1:place><i><span +style='color:gray'>]<o:p></o:p></span></i></pre><pre><i><span style='color: +gray'><span style='mso-spacerun:yes'>   </span>'<span class=GramE>f-1.4</span>'<span style='mso-spacerun:yes'>      </span>'a. .<span +class=GramE>b</span>'<span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>    </span>'<span +class=SpellE>nan</span>'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>             </span></span></i><st1:place><span + class=SpellE><i><span style='color:gray'>NaN</span></i></span></st1:place><i><span +style='color:gray'>]<o:p></o:p></span></i></pre><pre><i><span style='color: +gray'><span style='mso-spacerun:yes'>   </span>'x-3.2e4y'<span style='mso-spacerun:yes'>   </span>'f -+1.4'<span style='mso-spacerun:yes'>    </span><span style='mso-spacerun:yes'>  </span>'--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=SpellE><span class=GramE>inf</span></span> -<span class=SpellE>inf</span>'<span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>  </span>'f-1.4'<span style='mso-spacerun:yes'>        </span>' <span +class=SpellE>nan</span> <span class=SpellE>nan</span>'<span style='mso-spacerun:yes'>            </span>' <span +class=SpellE>nan</span> <span class=SpellE>nan</span>'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'+ <span +class=SpellE><span class=GramE>nan</span></span>'<span style='mso-spacerun:yes'>      </span>'f.1'<span style='mso-spacerun:yes'>          </span>'+ .1e-.2'<span style='mso-spacerun:yes'>            </span>'+ .1e-.2'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'f.2'<span style='mso-spacerun:yes'>        </span>'f.10'<span style='mso-spacerun:yes'>         </span>'-1 2'<span style='mso-spacerun:yes'>                </span>'-1 2'<span style='mso-spacerun:yes'>            </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'f.11'<span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'f.11'</span><span style='mso-spacerun:yes'>         </span>'Z12e12ez'<span style='mso-spacerun:yes'>            </span><span +class=SpellE>'Z12e12ez'</span><span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'Z12e12ez'<span style='mso-spacerun:yes'>   </span>'f.2'<span style='mso-spacerun:yes'>     </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>    </span>'<span +class=SpellE>inf</span> -<span class=SpellE>inf</span>'<span style='mso-spacerun:yes'>            </span>'<span +class=SpellE>inf</span> -<span class=SpellE>inf</span>'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'z12e12ez'<span style='mso-spacerun:yes'>   </span>'f.3'<span style='mso-spacerun:yes'>          </span>'s.3TT.4'<span style='mso-spacerun:yes'>             </span><span +class=SpellE>'s.3TT.4'</span><span style='mso-spacerun:yes'>         </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'f -+1.4'<span style='mso-spacerun:yes'>    </span>'<span +class=SpellE>inf</span> -<span class=SpellE>inf</span>'<span style='mso-spacerun:yes'>     </span>'z12e12ez'<span style='mso-spacerun:yes'>            </span><span +class=SpellE>'z12e12ez'</span><span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>' . .. '<span style='mso-spacerun:yes'>     </span>'<span +class=SpellE><span class=GramE>nan</span></span>'<span style='mso-spacerun:yes'>          </span>'--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.10'<span style='mso-spacerun:yes'>       </span>'s.3TT.4'<span style='mso-spacerun:yes'>      </span><span +class=GramE>' .</span> .. '<span style='mso-spacerun:yes'>        </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span> . .. '<span style='mso-spacerun:yes'>          </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'.'<span style='mso-spacerun:yes'>          </span><span +class=GramE>'x-3.2e4y'<span style='mso-spacerun:yes'>     </span>'.'</span><span style='mso-spacerun:yes'>                   </span><span +class=SpellE>'.'</span><span style='mso-spacerun:yes'>               </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'-.1'<span style='mso-spacerun:yes'>    </span><span style='mso-spacerun:yes'>    </span>'z12e12ez'<span style='mso-spacerun:yes'>     </span>'...'<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'</span>...'<span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'.b a.'<span style='mso-spacerun:yes'>               </span>'.b a.'<span style='mso-spacerun:yes'>           </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span><span +class=GramE>' <span class=SpellE>'</span></span><span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'a string'<span style='mso-spacerun:yes'>            </span>'a string'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span><span +class=GramE>' <span class=SpellE>'</span></span><span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'a. .b'<span style='mso-spacerun:yes'>               </span>'a. .<span +class=GramE>b</span>'<span style='mso-spacerun:yes'>           </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>% - show results</span></pre><pre style='margin-left: +22.5pt'><span style='mso-tab-count:2'>         </span><span class=GramE>o</span></pre><pre><i><span +style='color:gray'>o = <o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>          </span><span +class=GramE>magic</span>: 'ASORT'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>     </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE><span class=GramE>ver</span></span>: '</span></i><st1:date +Year="2005" Day="30" Month="3"><i><span style='color:gray'>30-Mar-2005</span></i></st1:date><i><span +style='color:gray'> </span></i><st1:time Minute="57" Hour="11"><i><span + style='color:gray'>11:57:07</span></i></st1:time><i><span style='color:gray'>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>          </span>time: '30-Mar-2005 11:57:17'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>       </span><span +class=GramE>runtime</span>: 0.047<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>input_class</span>: 'cell'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>input_msize</span>: [29 1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>input_bytes</span>: 2038<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>strng_class</span>: 'char'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>strng_msize</span>: [29 8]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span +class=SpellE>strng_bytes</span>: 464<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>           </span><span +class=SpellE><span class=GramE>anr</span></span>: {16x1 cell}<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>     </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE><span class=GramE>snr</span></span>: {7x1 cell}<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>     </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE><span class=GramE>str</span></span>: {6x1 cell}<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>             </span><span +class=GramE>c</span>: [29x12 char]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>             </span><span +class=GramE>t</span>: [29x12 logical]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>             </span><span +class=GramE>n</span>: [16x12 char]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>           </span><span +class=GramE>d</span>: [16x1 double]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span style='mso-tab-count:2'>         </span><span class=SpellE>o.anr</span></pre><pre><span +class=SpellE><span class=GramE><i><span style='color:gray'>ans</span></i></span></span><i><span +style='color:gray'> = <o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span>'-<span +class=SpellE><span class=GramE>inf</span></span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=GramE>x-3.2e4y</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=GramE>f-1.4</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-.1'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'+ <span +class=GramE>.1d-2</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'.1'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.1'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f -+1.4'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.2'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.3'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.10'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'f.11'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span>'+<span +class=SpellE>inf</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span><span class=GramE>' -</span><span +class=SpellE>nan</span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>  </span>'+ <span +class=SpellE><span class=GramE>nan</span></span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'<span +class=SpellE><span class=GramE>nan</span></span>'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>% - run <b style='mso-bidi-font-weight:normal'>ASORT</b> with <b +style='mso-bidi-font-weight:normal'>no-space</b>/<b style='mso-bidi-font-weight: +normal'>template</b> options</span></pre><pre style='margin-left:22.5pt'><span +class=comment>%<span style='mso-spacerun:yes'>  </span><span class=GramE>NOTE</span> the impact of -w/-t order!</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>s<span +class=GramE>={</span><span class=string>'ff - 1'</span>,<span class=string>'ff + 1'</span>,<span +class=string>'- 12'</span>};</pre><pre><i><span style='color:gray'><o:p> </o:p></span></i></pre><pre +style='margin-left:22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span>RAW</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE>asort</span>(s,<span class=string>'-v'</span>);</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>    </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'<span +class=GramE>ff</span> - 1'<span style='mso-spacerun:yes'>   </span>'- 12'<span style='mso-spacerun:yes'>         </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'<span +class=GramE>ff</span> + 1'<span style='mso-spacerun:yes'>   </span>'ff + 1'<span style='mso-spacerun:yes'>       </span>'ff + 1'<span style='mso-spacerun:yes'>              </span>[<span style='mso-spacerun:yes'>               </span>1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'- 12'<span style='mso-spacerun:yes'>     </span>'ff - 1'<span style='mso-spacerun:yes'>       </span>'ff - 1'<span style='mso-spacerun:yes'>              </span>[<span style='mso-spacerun:yes'>               </span>1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>        </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>- 12'<span style='mso-spacerun:yes'>                </span>[<span style='mso-spacerun:yes'>              </span>12]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>        </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span>--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>        </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><span +class=GramE>remove</span> <span class=SpellE><b style='mso-bidi-font-weight: +normal'>SPACE</b>s</span></span></pre><pre style='margin-left:22.5pt'><span +style='mso-tab-count:2'>         </span>o=<span class=SpellE>asort</span>(s,<span +class=string>'-v'</span>,<span class=string>'-w'</span>);</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>   </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff-1' <span style='mso-spacerun:yes'>    </span>'-12'<span style='mso-spacerun:yes'>          </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'<span +class=GramE>ff+</span>1'<span style='mso-spacerun:yes'>    </span><span +class=SpellE>'ff+1'</span><span style='mso-spacerun:yes'>         </span>'-12'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>             </span>-12]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-12'<span style='mso-spacerun:yes'>     </span>'ff-1'<span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'ff-1'</span><span style='mso-spacerun:yes'>                </span>[<span style='mso-spacerun:yes'>              </span>-1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff+1'<span style='mso-spacerun:yes'>       </span><span style='mso-spacerun:yes'>         </span>[<span style='mso-spacerun:yes'>               </span>1]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><span +class=GramE>remove</span> <b style='mso-bidi-font-weight:normal'>TEMPLATE</b>(s)</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE>asort</span>(s,<span class=string>'-v'</span>,<span class=string>'-t'</span><span +class=GramE>,{</span><span class=string>'ff'</span>,<span class=string>'1'</span>});</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>   </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span><span class=GramE>' -</span> ' <span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span> + '<span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span>--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span><span class=GramE>' +</span> ' <span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span> - '<span style='mso-spacerun:yes'>          </span><span +class=SpellE>'</span>- 2'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>2]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'- 2'<span style='mso-spacerun:yes'>     </span>'- 2'<span style='mso-spacerun:yes'>          </span>'--- ASCII NUMBERS'<span style='mso-spacerun:yes'>  </span><span style='mso-spacerun:yes'> </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span>' <span +class=SpellE>'</span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span> + '<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'</span> + '<span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span>' <span +class=SpellE>'</span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span> - '<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'</span> - '<span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><span +class=GramE>remove</span> <b style='mso-bidi-font-weight:normal'>TEMPLATE</b>(s) than <span +class=SpellE><b style='mso-bidi-font-weight:normal'>SPACE</b>s</span></span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE>asort</span>(s,<span class=string>'-v'</span>,<span class=string>'-t'</span>,<span +class=string>'1'</span>,<span class=string>'-w'</span>);</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>   </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff-'<span style='mso-spacerun:yes'>     </span>'-2'<span style='mso-spacerun:yes'>           </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>       </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff+' <span style='mso-spacerun:yes'>     </span><span +class=SpellE>'ff+'</span><span style='mso-spacerun:yes'>          </span>'-2'<span style='mso-spacerun:yes'>               </span><span style='mso-spacerun:yes'>   </span>[<span style='mso-spacerun:yes'>              </span>-2]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'-2'<span style='mso-spacerun:yes'>      </span>'ff-'<span style='mso-spacerun:yes'>          </span>'--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff+'<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'ff+'</span><span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'>    </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff-'<span style='mso-spacerun:yes'>                 </span><span +class=SpellE>'ff-'</span><span style='mso-spacerun:yes'>             </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre style='margin-left: +22.5pt'><span class=comment>%<span style='mso-spacerun:yes'>  </span><span +class=GramE>remove</span> <span class=SpellE><b style='mso-bidi-font-weight: +normal'>SPACE</b>s</span> than <b style='mso-bidi-font-weight:normal'>TEMPLATE</b>(s)</span></pre><pre +style='margin-left:22.5pt'><span style='mso-tab-count:2'>         </span>o=<span +class=SpellE>asort</span>(s,<span class=string>'-v'</span>,<span class=string>'-w'</span>,<span +class=string>'-t'</span>,<span class=string>'1'</span>);</pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'INPUT'<span style='mso-spacerun:yes'>   </span>'ASCII SORT'<span style='mso-spacerun:yes'>   </span>'NUM SORT'<span style='mso-spacerun:yes'>            </span>'NUM READ'<span style='mso-spacerun:yes'>        </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff- '<span style='mso-spacerun:yes'>    </span><span +class=SpellE>'</span>- 2'<span style='mso-spacerun:yes'>          </span>'--- NUMERICAL'<span style='mso-spacerun:yes'>  </span><span style='mso-spacerun:yes'>     </span>'--- NUMBERS'<span style='mso-spacerun:yes'>     </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>    </span>'ff+ '<span style='mso-spacerun:yes'>    </span>'ff+ '<span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span>- 2'<span style='mso-spacerun:yes'>                 </span>[<span style='mso-spacerun:yes'>               </span>2]<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'>   </span>'- 2'<span style='mso-spacerun:yes'>     </span>'ff- '<span style='mso-spacerun:yes'>   </span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>     </span><span +class=SpellE>'</span>--- ASCII NUMBERS'<span style='mso-spacerun:yes'>   </span>'--- ASCII NUMBERS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span><span +class=SpellE>'</span>--- ASCII STRINGS'<span style='mso-spacerun:yes'>   </span>'--- ASCII STRINGS'<o:p></o:p></span></i></pre><pre><i><span +style='color:gray'> <span style='mso-spacerun:yes'>   </span><span class=GramE>' <span +class=SpellE>'</span></span><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>      </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff+ '<span style='mso-spacerun:yes'>                </span>'ff+ '<span style='mso-spacerun:yes'>            </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><span style='mso-spacerun:yes'> </span><span style='mso-spacerun:yes'>   </span><span +class=GramE>' <span class=SpellE>'</span></span><span style='mso-spacerun:yes'>       </span><span +class=SpellE>'</span> <span class=SpellE>'</span><span style='mso-spacerun:yes'>            </span>'ff- '<span style='mso-spacerun:yes'>                </span>'ff- '<span style='mso-spacerun:yes'>            </span><o:p></o:p></span></i></pre><pre><i><span +style='color:gray'><o:p> </o:p></span></i></pre> + +<p class=footer style='tab-stops:45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2pt 458.0pt 503.8pt 549.6pt 595.4pt 641.2pt 687.0pt 732.8pt'><br> +Published with MATLAB® 7.0.4<o:p></o:p></p> + +</div> + +<!-- +##### SOURCE BEGIN ##### +% ASORT +% a pedestrian NUMERICAL SORTER of ALPHANUMERIC data + +% - create some data + d = { +% strings with one valid alphanumeric number +% sorted numerically + '-inf' + 'x-3.2e4y' + 'f-1.4' + '-.1' + '+ .1d-2' + '.1' + 'f.1' + 'f -+1.4' + 'f.2' + 'f.3' + 'f.10' + 'f.11' + '+inf' + ' -nan' + '+ nan' + 'nan' +% strings with many numbers or invalid/ambiguous numbers +% sorted in ascii dictionary order + ' nan nan' + '+ .1e-.2' + '-1 2' + 'Z12e12ez' + 'inf -inf' + 's.3TT.4' + 'z12e12ez' +% strings without numbers +% sorted in ascii dictionary order + ' . .. ' + '.' + '...' + '.b a.' + 'a string' + 'a. .b' + }; +% ... and scramble it... + rand('seed',10); + d=d(randperm(numel(d))); + +% - run ASORT with verbose output +% and keep additional results + o=asort(d,'-v','-d'); +% - or +% p=asort(char(d)); + +% - show results + o + o.anr + +% - run ASORT with no-space/template options +% NOTE the impact of -t/-w order! + s={'ff - 1','ff + 1','- 12'}; +% RAW + o=asort(s,'-v'); +% remove SPACEs + o=asort(s,'-v','-w'); +% remove TEMPLATE(s) + o=asort(s,'-v','-t',{'ff','1'}); +% remove TEMPLATE(s) than SPACEs + o=asort(s,'-v','-t','1','-w'); +% remove SPACEs than TEMPLATE(s) + o=asort(s,'-v','-w','-t','1'); + + +##### SOURCE END ##### +--> +</body> + +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/asdemo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +% ASORT +% a pedestrian NUMERICAL SORTER of ALPHANUMERIC data + +% - create some data + d = { +% strings with one valid alphanumeric number +% sorted numerically + '-inf' + 'x-3.2e4y' + 'f-1.4' + '-.1' + '+ .1d-2' + '.1' + 'f.1' + 'f -+1.4' + 'f.2' + 'f.3' + 'f.10' + 'f.11' + '+inf' + ' -nan' + '+ nan' + 'nan' +% strings with many numbers or invalid/ambiguous numbers +% sorted in ascii dictionary order + ' nan nan' + '+ .1e-.2' + '-1 2' + 'Z12e12ez' + 'inf -inf' + 's.3TT.4' + 'z12e12ez' +% strings without numbers +% sorted in ascii dictionary order + ' . .. ' + '.' + '...' + '.b a.' + 'a string' + 'a. .b' + }; +% ... and scramble it... + rand('seed',10); + d=d(randperm(numel(d))); + +% - run ASORT with +% verbose output: <-v> +% keep additional results: <-d> + o=asort(d,'-v','-d'); +% - or +% p=asort(char(d),'-v','-d'); + +% - show results + o + o.anr + +% - run ASORT with no-space/template options +% NOTE the impact of -w/-t order! + s={'ff - 1','ff + 1','- 12'}; +% RAW + o=asort(s,'-v'); +% remove SPACEs + o=asort(s,'-v','-w'); +% remove TEMPLATE(s) + o=asort(s,'-v','-t',{'ff','1'}); +% remove TEMPLATE(s) than SPACEs + o=asort(s,'-v','-t','1','-w'); +% remove SPACEs than TEMPLATE(s) + o=asort(s,'-v','-w','-t','1'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/asort.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,376 @@ +%[ANR,SNR,STR] = ASORT(INP,'OPT',...); +% S = ASORT(INP,'OPT',...); +% to sort alphanumeric strings numerically if +% they contain one properly formatted number +% otherwise, ascii dictionary sorting is applied +% +% INP unsorted input: +% - a char array +% - a cell array of strings +% OPT options +% -s - sorting option +% '-s','ascend' [def] +% '-s','descend' +% -st - force output form S [def: nargout dependent] +% -t - replace matching template(s) with one space +% prior to sorting +% '-t','template' +% '-t',{'template1','template2',...} +% -w - remove space(s) prior to sorting +% +% NOTE -t/-w options are processed in the +% order that they appear in +% the command line +% +% -v - verbose output [def: quiet] +% -d - debug mode +% save additional output in S +% .c: lex parser input +% .t: lex parser table +% .n: lex parser output +% .d: numbers read from .n +% +% ANR numerically sorted alphanumeric strings [eg, 'f.-1.5e+2x.x'] +% - contain one number that can be read by +% <strread> | <sscanf> +% SNR ascii dict sorted alphanumeric strings +% http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=7212# +% +% - contain more than one number [eg, 'f.-1.5e +2.x'] +% - contain incomplete|ambiguous numbers [eg, 'f.-1.5e+2.x'] +% STR ascii dict sorted strings +% - contain no numbers [eg, 'a test'] +% +% S structure with fields +% .anr +% .srn +% .str + +% created: +% us 03-Mar-2002 +% modified: +% us 30-Mar-2005 11:57:07 / TMW R14.sp2 + +%-------------------------------------------------------------------------------- +function varargout=asort(inp,varargin) + +varargout(1:nargout)={[]}; +if ~nargin + help(mfilename); + return; +end + +% - common parameters/options +n=[]; +ds=[]; +anr={}; +snr={}; +str={}; +smod='ascend'; % sorting option +tmpl={}; % template(s) +sflg=false; % output mode: structure +tflg=false; % remove template(s) +dflg=false; % debug mode +vflg=false; % verbose output +wflg=false; % remove spaces + +if nargin > 1 + ix=find(strcmp('-s',varargin)); + if ~isempty(ix) && nargin > ix(end)+1 + smod=varargin{ix(end)+1}; + end + ix=find(strcmp('-t',varargin)); + if ~isempty(ix) && nargin > ix(end)+1 + tflg=ix(end); + tmpl=varargin{ix(end)+1}; + end + if find(strcmp('-d',varargin)); + dflg=true; + end + if find(strcmp('-st',varargin)); + sflg=true; + end + if find(strcmp('-v',varargin)); + vflg=true; + end + ix=find(strcmp('-w',varargin)); + if ~isempty(ix) + wflg=ix(end); + end +end +% spec numbers +ntmpl={ + ' inf ' + '+inf ' + '-inf ' + ' nan ' + '+nan ' + '-nan ' + }; +% spec chars +ctmpl={ + '.' % decimal point + 'd' % exponent + 'e' % exponent + }; + +if nargout <= 3 + varargout{1}=inp; +else + disp(sprintf('ASORT> too many output args [%-1d/%-1d]\n',nargout,3)); + help(mfilename); + return; +end +if isempty(inp) + disp(sprintf('ASORT> input is empty')); + return; +end + +ti=clock; +winp=whos('inp'); +switch winp.class + case 'cell' + if ~iscellstr(inp) + disp(sprintf('ASORT> cell is not an array of strings')); + return; + end + inp=inp(:); + [ins,inx]=sort(inp); + case 'char' + % [ins,inx]=sortrows(inp); + inp=cstr(inp); + otherwise + disp(sprintf('ASORT> does not sort input of class <%s>',winp.class)); + return; +end + +inp=inp(:); +inp=setinp(inp,tmpl,[tflg wflg]); +[ins,inx]=sort(inp); +if strcmp(smod,'descend') + ins=ins(end:-1:1,:); + inx=inx(end:-1:1); +end +ins=inp(inx); +c=lower(char(ins)); +wins=whos('c'); +[cr,cc]=size(c); + +% - LEXICAL PARSER +%-------------------------------------------------------------------------------- +% - extend input on either side for search +c=[' '*ones(cr,2) c ' '*ones(cr,2)]; + +% - search for valid alphanumeric items in strings +% numbers/signs +t=(c>='0'&c<='9'); +t=t|c=='-'; +t=t|c=='+'; +[tr,tc]=size(t); +% decimal points +% note: valid numbers with dec points must follow these templates +% nr.nr +% sign.nr +% nr.<SPACE> +% <SPACE>.nr +ix1= t(:,1:end-2) & ... + ~isletter(c(:,1:end-2)) & ... + c(:,2:end-1)=='.'; +t(:,2:end-1)=t(:,2:end-1)|ix1; +ix1= (t(:,3:end) & ... + (~isletter(c(:,3:end)) & ... + ~isletter(c(:,1:end-2))) | ... + (c(:,3:end)=='e' | ... + c(:,3:end)=='d')) & ... + c(:,2:end-1)=='.'; +t(:,2:end-1)=t(:,2:end-1)|ix1; +% t(:,3:end)=t(:,3:end)|ix1; +% signs +t(c=='-')=false; +t(c=='+')=false; +ix1= t(:,3:end) & ... + (c(:,2:end-1)=='-' | ... + c(:,2:end-1)=='+'); +t(:,2:end-1)=t(:,2:end-1)|ix1; +% exponents +ix1= t(:,1:end-2) & ... + (c(:,2:end-1)=='e' | ... + c(:,2:end-1)=='d'); +t(:,2:end-1)=t(:,2:end-1)|ix1; +% spec numbers +c=reshape(c.',1,[]); +t=t'; +ic=[]; +for j=1:numel(ntmpl) + ic=[ic,strfind(c,ntmpl{j})]; +end +ic=sort(ic); +for i=1:numel(ic) + ix=ic(i)+0:ic(i)+4; + t(ix)=true; +end +t=t'; +c=reshape(c.',[tc,tr]).'; +t(c==' ')=false; +%-------------------------------------------------------------------------------- + +% - only allow one number per string +il=~any(t,2); +ib=strfind(reshape(t.',1,[]),[0 1]); +if ~isempty(ib) + ixe=cell(3,1); + n=reshape(char(t.*c).',1,[]); + for i=1:numel(ctmpl) + id=strfind(n,ctmpl{i}); + if ~isempty(id) + [dum,dum,ixu{i},ixe{i}]=dupinx(id,tc); + end + end + in=false(tr,1); + im=in; + % must check for anomalous cases like <'.d'> + id=sort(... + [find(n>='0' & n<='9'),... + strfind(n,'inf'),... + strfind(n,'nan')]); + % [ibu,ibd,ixbu,ixe{i+1}]=dupinx(id,tc); + [ibu,ibd,ixbu,ixbd]=dupinx(id,tc); + in(ixbu)=true; + in(ixbd)=true; + [ibu,ibd,ixbu,ixbd]=dupinx(ib,tc); + im(ixbu)=true; + in=in&im; + in([ixe{:}])=false; + il=~any(t,2); + ia=~(in|il); + + % - read valid strings + n=t(in,:).*c(in,:); + n(n==0)=' '; + n=char(n); + dn=strread(n.','%n'); + if numel(dn) ~= numel(find(in)) + %disp(sprintf('ASORT> unexpected fatal error reading input!')); + if nargout + s.c=c; + s.t=t; + s.n=n; + s.d=dn; + varargout{1}=s; + end + return; + end + + % - sort numbers + [ds,dx]=sort(dn,1,smod); + in=find(in); + anr=ins(in(dx)); + snr=ins(ia); +end +str=ins(il); +to=clock; + +% - prepare output +if nargout < 3 || sflg + s.magic='ASORT'; + s.ver='30-Mar-2005 11:57:07'; + s.time=datestr(clock); + s.runtime=etime(to,ti); + s.input_class=winp.class; + s.input_msize=winp.size; + s.input_bytes=winp.bytes; + s.strng_class=wins.class; + s.strng_msize=wins.size; + s.strng_bytes=wins.bytes; + s.anr=anr; + s.snr=snr; + s.str=str; + if dflg + s.c=c; + s.t=t; + s.n=n; + s.d=ds; + end + varargout{1}=s; +else + s={anr,snr,str}; + for i=1:nargout + varargout{i}=s{i}; + end +end + +if vflg + inp=cstr(inp); + an=[{'--- NUMERICAL'}; anr]; + as=[{'--- ASCII NUMBERS'}; snr]; + at=[{'--- ASCII STRINGS'}; str]; + nn=[{'--- NUMBERS'}; num2cell(ds)]; + ag={' ';' ';' '}; + u=[{'INPUT'}; inp;ag]; + v=[{'ASCII SORT'}; ins;ag]; + w=[{'NUM SORT'}; an;as;at]; + x=[{'NUM READ'}; nn;as;at]; + w=[u,v,w,x]; + disp(w); +end + +return; +%-------------------------------------------------------------------------------- +function c=cstr(s) +% - bottleneck waiting for a good <cellstr> replacement +% it consumes ~75% of <asort>'s processing time! + +c=s; +if ischar(s) + sr=size(s,1); + c=cell(sr,1); + for i=1:sr + c{i}=s(i,:); % no deblanking! + end +end +return; +%-------------------------------------------------------------------------------- +function [idu,idd,ixu,ixd]=dupinx(ix,nc) +% - check for more than one entry/row in a matrix of column size <nc> +% unique indices: idu / ixu +% duplicate indices: idd / ixd + +if isempty(ix) + idu=[]; + idd=[]; + ixu=[]; + ixd=[]; + return; +end +id=fix(ix/nc)+1; +idi=diff(id)~=0; +ide=[true idi]; +idb=[idi true]; +idu=idb & ide; +idd=idb==1 & ide==0; +ixu=id(idu); +ixd=id(idd); +return; +%-------------------------------------------------------------------------------- +function inp=setinp(inp,tmpl,flg) +% - remove space(s) and/or templates + +if isempty(inp) || ~any(flg) + return; +end + +for i=sort(flg) + switch i + case flg(1) + if ischar(tmpl) + tmpl={tmpl}; + end + for i=1:numel(tmpl) + inp=strrep(inp,tmpl{i},' '); + end + case flg(2) + inp=strrep(inp,' ',''); + end +end +return; +%--------------------------------------------------------------------------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/assert.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function assert(pred, str) +% ASSERT Raise an error if the predicate is not true. +% assert(pred, string) + +if nargin<2, str = ''; end + +if ~pred + s = sprintf('assertion violated: %s', str); + error(s); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/assertBNT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function assertBNT(pred, str) +% ASSERT Raise an error if the predicate is not true. +% assert(pred, string) + +if nargin<2, str = ''; end + +if ~pred + s = sprintf('assertion violated: %s', str); + error(s); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/assignEdgeNums.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [edge_id, nedges] = assignEdgeNums(adj_mat) +% give each edge a unique number +% we number (i,j) for j>i first, in row, column order. +% Then we number the reverse links + +nnodes = length(adj_mat); +edge_id = zeros(nnodes); +e = 1; +for i=1:nnodes + for j=i+1:nnodes + if adj_mat(i,j) + edge_id(i,j) = e; + e = e+1; + end + end +end + +nedges = e-1; +tmp = edge_id; +ndx = find(tmp); +tmp(ndx) = tmp(ndx)+nedges; +edge_id = edge_id + triu(tmp)'; + + +if 0 +ndx = find(adj_mat); +nedges = length(ndx); +nnodes = length(adj_mat); +edge_id = zeros(1, nnodes*nnodes); +edge_id(ndx) = 1:nedges; +edge_id = reshape(edge_id, nnodes, nnodes); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/assign_cols.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function M = assign_cols(cols, vals, M) +% ASSIGN_COLS Assign values to columns of a matrix +% function M = assign_cols(M, cols, vals, M) +% +% Example: +% M = assign_cols(data, ones(1,N)) +% will construct a 1-of-K encoding of the data, where K=ncols=max(data) and N=nrows=length(data) +% +% Example: +% M = zeros(3,2); +% M = assign_cols([1 2 1], [10 20 30], M) +% is equivalent to +% M(1, 1) = 10 +% M(2, 2) = 20 +% M(3, 1) = 30 +% + +if nargin < 3 + nr = length(cols); + nc = max(cols); + M = zeros(nr, nc); +else + [nr nc] = size(M); +end + +if 0 +for r=1:nr + M(r, cols(r)) = vals(r); +end +end + +if 1 +rows = 1:nr; +ndx = subv2ind([nr nc], [rows(:) cols(:)]); +M(ndx) = vals; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/axis_pct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +function ax = axis_pct(pct) +% AXIS_PCT Set reasonable axis limits. +% AXIS_PCT(pct) sets axis limits to extend pct% beyond limits of plotted +% objects. Default is 5%. +% Works for linear or log scale. +% Unfortunately, the axes won't change when new points are plotted. + +if nargin < 1 + pct = 0.05; +end +ax = [Inf -Inf Inf -Inf Inf -Inf]; + +% find bounding box of plotted objects +children = get(gca,'children'); +for child = children' + if strcmp(get(child,'type'),'text') + xyz = get(child,'position'); + % need to determine bounding box of the text + c([1 2]) = xyz(1); + c([3 4]) = xyz(2); + c([5 6]) = xyz(3); + else + x = get(child,'xdata'); + c(1) = min(x); + c(2) = max(x); + y = get(child,'ydata'); + c(3) = min(y); + c(4) = max(y); + z = get(child,'zdata'); + if isempty(z) + c([5 6]) = 0; + else + c(5) = min(z); + c(6) = max(z); + end + end + ax([1 3 5]) = min(ax([1 3 5]), c([1 3 5])); + ax([2 4 6]) = max(ax([2 4 6]), c([2 4 6])); +end +if strcmp(get(gca,'xscale'), 'log') + ax([1 2]) = log(ax([1 2])); +end +if strcmp(get(gca,'yscale'), 'log') + ax([3 4]) = log(ax([3 4])); +end +dx = ax(2)-ax(1); +if dx == 0 + dx = 1; +end +dy = ax(4)-ax(3); +if dy == 0 + dy = 1; +end +dz = ax(6)-ax(5); +if dz == 0 + dz = 1; +end +ax = ax + [-dx dx -dy dy -dz dz]*pct; +if strcmp(get(gca,'xscale'), 'log') + ax([1 2]) = exp(ax([1 2])); +end +if strcmp(get(gca,'yscale'), 'log') + ax([3 4]) = exp(ax([3 4])); +end +% clip for 2D +ax = ax(1:length(axis)); +axis(ax); +if nargout < 1 + clear ax +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/bipartiteMatchingDemo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,112 @@ +% Consider matching sources to detections + +% s1 d2 +% s2 d3 +% d1 + +%a = bipartiteMatchingHungarian([52;0.01]) + +% sources(:,i) = [x y] coords +sources = [0.1 0.7; 0.6 0.4]'; +detections = [0.2 0.2; 0.2 0.8; 0.7 0.1]'; +dst = sqdist(sources, detections); + +% a = [2 3] which means s1-d2, s2-d3 +a = bipartiteMatchingHungarian(dst); +a2 = bipartiteMatchingIntProg(dst); +assert(isequal(a(:),a2(:))) + + +figure(1); clf +bipartiteMatchingDemoPlot(sources, detections, a) + + + + +%%%% Flip roles of sources and detections + +%dst = dst'; +dst = sqdist(detections, sources); +% a = [0 1 2] which means d1-0, d2-s1, d3-s2 +a = bipartiteMatchingHungarian(dst); + +a2 = bipartiteMatchingIntProg(dst); +assert(isequal(a(:),a2(:))) + +figure(2); clf +bipartiteMatchingDemoPlot(detections, sources, a) % swapped args + + + + +%%%%%%%%%% Move s1 nearer to d1 +% d2 +% s2 d3 +% s1 d1 + +sources = [0.1 0.3; 0.6 0.4]'; +detections = [0.2 0.2; 0.2 0.8; 0.7 0.1]'; +dst = sqdist(sources, detections); + +% a = [2 3] which means s1-d2, s2-d3 +a = bipartiteMatchingHungarian(dst); +[a2, ass] = bipartiteMatchingIntProg(dst); +assert(isequal(a(:),a2(:))) + + +figure(3); clf +bipartiteMatchingDemoPlot(sources, detections, a) + + + +%%%%%%%%%% + +% Use random points + +% Generate 2D data from a mixture of 2 Gaussians (from netlab demgmm1) +randn('state', 0); rand('state', 0); +gmix = gmm(2, 2, 'spherical'); +ndat1 = 10; ndat2 = 10; ndata = ndat1+ndat2; +%gmix.centres = [0.3 0.3; 0.7 0.7]; +%gmix.covars = [0.01 0.01]; +gmix.centres = [0.5 0.5; 0.5 0.5]; +gmix.covars = [0.1 0.01]; +[x, label] = gmmsamp(gmix, ndata); + +ndx = find(label==1); +sources = x(ndx,:)'; +ndx = find(label==2); +detections = x(ndx,:)'; +dst = sqdist(sources, detections); + +[a, ass] = bipartiteMatchingIntProg(dst); +[a2] = bipartiteMatchingHungarian(dst); +assert(isequal(a(:), a2(:))) + +figure(4); clf +bipartiteMatchingDemoPlot(sources, detections, a) + +% only match 80% of points +p1 = size(sources, 2); +p2 = size(detections, 2); +nmatch = ceil(0.8*min(p1,p2)); +a2 = bipartiteMatchingIntProg(dst, nmatch); +figure(5); clf +bipartiteMatchingDemoPlot(sources, detections, a2) + + +%%% swap roles + +ndx = find(label==2); +sources = x(ndx,:)'; +ndx = find(label==1); +detections = x(ndx,:)'; +dst = sqdist(sources, detections); + +% only match 80% of points +p1 = size(sources, 2); +p2 = size(detections, 2); +nmatch = ceil(0.8*min(p1,p2)); +a2 = bipartiteMatchingIntProg(dst, nmatch); +figure(6); clf +bipartiteMatchingDemoPlot(sources, detections, a2)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/bipartiteMatchingDemoPlot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function bipartiteMatchingDemoPlot(sources, detections, a) + +hold on +p1 = size(sources,2); +p2 = size(detections,2); +for i=1:p1 + h=text(sources(1,i), sources(2,i), sprintf('s%d', i)); + set(h, 'color', 'r'); +end +for i=1:p2 + h=text(detections(1,i), detections(2,i), sprintf('d%d', i)); + set(h, 'color', 'b'); +end + +if nargin < 3, return; end + +for i=1:p1 + j = a(i); + if j==0 % i not matched to anything + continue + end + line([sources(1,i) detections(1,j)], [sources(2,i) detections(2,j)]) +end +axis_pct;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/bipartiteMatchingHungarian.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +% MATCH - Solves the weighted bipartite matching (or assignment) +% problem. +% +% Usage: a = match(C); +% +% Arguments: +% C - an m x n cost matrix; the sets are taken to be +% 1:m and 1:n; C(i, j) gives the cost of matching +% items i (of the first set) and j (of the second set) +% +% Returns: +% +% a - an m x 1 assignment vector, which gives the +% minimum cost assignment. a(i) is the index of +% the item of 1:n that was matched to item i of +% 1:m. If item i (of 1:m) was not matched to any +% item of 1:n, then a(i) is zero. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [a] = optimalMatching(C) + +% Trivial cases: +[p, q] = size(C); +if (p == 0) + a = []; + return; +elseif (q == 0) + a = zeros(p, 1); + return; +end + + +if 0 +% First, reduce the problem by making easy optimal matches. If two +% elements agree that they are the best match, then match them up. +[x, a] = min(C, [], 2); +[y, b] = min(C, [], 1); +u = find(1:p ~= b(a(:))); +a(u) = 0; +v = find(1:q ~= a(b(:))'); +C = C(u, v); +if (isempty(C)) return; end +end + +% Get the (new) size of the two sets, u and v. +[m, n] = size(C); + +%mx = realmax; +mx = 2*max(C(:)); +mn = -2*min(C(:)); +% Pad the affinity matrix to be square +if (m < n) + C = [C; mx * ones(n - m, n)]; +elseif (n < m) + C = [C, mx * ones(m, m - n)]; +end + +% Run the Hungarian method. First replace infinite values by the +% largest (or smallest) finite values. +C(find(isinf(C) & (C > 0))) = mx; +C(find(isinf(C) & (C < 0))) = mn; +%fprintf('running hungarian\n'); +[b, cost] = hungarian(C'); + +% Extract only the real assignments +ap = b(1:m)'; +ap(find(ap > n)) = 0; + +a = ap; +%% Incorporate this sub-assignment into the complete assignment +% k = find(ap); +% a(u(k)) = v(ap(k)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/bipartiteMatchingIntProg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function [a,ass] = bipartiteMatchingIntProg(dst, nmatches) +% BIPARTITEMATCHINGINTPROG Use binary integer programming (linear objective) to solve for optimal linear assignment +% function a = bipartiteMatchingIntProg(dst) +% a(i) = best matching column for row i +% +% This gives the same result as bipartiteMatchingHungarian. +% +% function a = bibpartiteMatchingIntProg(dst, nmatches) +% only matches the specified number (must be <= min(size(dst))). +% This can be used to allow outliers in both source and target. +% +% For details, see Marciel & Costeira, "A global solution to sparse correspondence +% problems", PAMI 25(2), 2003 + +if nargin < 2, nmatches = []; end + +[p1 p2] = size(dst); +p1orig = p1; p2orig = p2; +dstorig = dst; + +if isempty(nmatches) % no outliers allowed (modulo size difference) + % ensure matrix is square + m = max(dst(:)); + if p1<p2 + dst = [dst; m*ones(p2-p1, p2)]; + elseif p1>p2 + dst = [dst m*ones(p1, p1-p2)]; + end +end +[p1 p2] = size(dst); + + +c = dst(:); % vectorize cost matrix + +% row-sum: ensure each column sums to 1 +A2 = kron(eye(p2), ones(1,p1)); +b2 = ones(p2,1); + +% col-sum: ensure each row sums to 1 +A3 = kron(ones(1,p2), eye(p1)); +b3 = ones(p1,1); + +if isempty(nmatches) + % enforce doubly stochastic + A = [A2; A3]; + b = [b2; b3]; + Aineq = zeros(1, p1*p2); + bineq = 0; +else + nmatches = min([nmatches, p1, p2]); + Aineq = [A2; A3]; + bineq = [b2; b3]; % row and col sums <= 1 + A = ones(1,p1*p2); + b = nmatches; % total num matches = b (otherwise get degenerate soln) +end + + +ass = bintprog(c, Aineq, bineq, A, b); +ass = reshape(ass, p1, p2); + +a = zeros(1, p1orig); +for i=1:p1orig + ndx = find(ass(i,:)==1); + if ~isempty(ndx) & (ndx <= p2orig) + a(i) = ndx; + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/block.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function sub = block(blocks, block_sizes) +% BLOCK Return a vector of subscripts corresponding to the specified blocks. +% sub = block(blocks, block_sizes) +% +% e.g., block([2 5], [2 1 2 1 2]) = [3 7 8]. + +blocks = blocks(:)'; +block_sizes = block_sizes(:)'; +skip = [0 cumsum(block_sizes)]; +start = skip(blocks)+1; +fin = start + block_sizes(blocks) - 1; +sub = []; +for j=1:length(blocks) + sub = [sub start(j):fin(j)]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/cell2matPad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function data2 = cell2matPad(data) +% data{f}(y,x,b) - each frame can have a different size (can can even be empty) +% data2(y,x,b,f) = zero padded version + +Nframes = length(data); +Nbands = -inf; +nr = -inf; nc = -inf; +for f=1:Nframes + if isempty(data{f}), continue; end + nr = max(nr, size(data{f},1)); + nc = max(nc, size(data{f},2)); + Nbands = max(Nbands, size(data{f},3)); +end +data2 = zeros(nr, nc, Nbands, Nframes); +for f=1:Nframes + if isempty(data{f}), continue; end + data2(1:size(data{f},1), 1:size(data{f},2), :, f) = data{f}; +end +if Nbands == 1 + data2 = squeeze(data2); % reshape(data2, [size(data2,1), size(data2,2), Nframes]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/cell2num.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function N = cell2num(C) +% CELL2NUM Convert a 2D cell array to a 2D numeric array +% N = cell2num(C) +% If the cells contain column vectors, they must have the same number of rows in each row of C. +% Each column will be concatenated. +% +% Example 1: +% C = num2cell(rand(2,2)) +% [0.4565] [0.8214] +% [0.0185] [0.4447] +% N = cell2num(C) +% 0.4565 0.8214 +% 0.0185 0.4447 +% +% Example 2: +% C = cell(2, 3); +% for i=1:2 +% for j=1:3 +% C{i,j} = rand(i, 1); +% end +% end +% C = +% [ 0.8998] [ 0.8216] [ 0.6449] +% [2x1 double] [2x1 double] [2x1 double] +% C{2,1} = +% 0.8180 +% 0.6602 +% N=cell2num(C) +% 0.8998 0.8216 0.6449 +% 0.8180 0.3420 0.3412 +% 0.6602 0.2897 0.5341 + + +% error('use cell2mat in matlab 7') + + +if isempty(C) + N = []; + return; +end + +if any(cellfun('isempty', C)) %any(isemptycell(C)) + error('can''t convert cell array with empty cells to matrix') +end + +[nrows ncols] = size(C); +%N = reshape(cat(1, C{:}), [nrows ncols]); % this only works if C only contains scalars +r = 0; +for i=1:nrows + r = r + size(C{i,1}, 1); +end +c = 0; +for j=1:ncols + c = c + size(C{1,j}, 2); +end +N = reshape(cat(1, C{:}), [r c]); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/centeringMatrix.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +N = 3; +x = rand(N,2); % each row is a feature vector +m = mean(x,1); +xc = x-repmat(m, N, 1); + +C = eye(N) - (1/N)*ones(N,N); +xc2 = C*x; +assert(approxeq(xc, xc2))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/checkpsd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function s = checkpsd(s) + +if (any(isnan(s) | isinf(s) | ~isreal(s))) + warning('S contains complex numbers, Inf, or NaN'); +end +% Drop any negative eigenvalues. +[V, D] = eig(full(s)); +d = real(diag(D)); +if (any(d < 0)) + warning(sprintf(['S is not positive semidefinite (min. eig. =' ... + ' %0.5g); projecting.'], min(d))); + d(find(d < 0)) = 0; + D = diag(d); + s = V * D * V'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/chi2inv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function x = chi2inv(p,v); +%CHI2INV Inverse of the chi-square cumulative distribution function (cdf). +% X = CHI2INV(P,V) returns the inverse of the chi-square cdf with V +% degrees of freedom at the values in P. The chi-square cdf with V +% degrees of freedom, is the gamma cdf with parameters V/2 and 2. +% +% The size of X is the common size of P and V. A scalar input +% functions as a constant matrix of the same size as the other input. + +% References: +% [1] M. Abramowitz and I. A. Stegun, "Handbook of Mathematical +% Functions", Government Printing Office, 1964, 26.4. +% [2] E. Kreyszig, "Introductory Mathematical Statistics", +% John Wiley, 1970, section 10.2 (page 144) + +% Copyright 1993-2002 The MathWorks, Inc. +% $Revision: 1.1.1.1 $ $Date: 2005/04/26 02:30:30 $ + +if nargin < 2, + error('Requires two input arguments.'); +end + +[errorcode p v] = distchck(2,p,v); + +if errorcode > 0 + error('Requires non-scalar arguments to match in size.'); +end + +% Call the gamma inverse function. +x = gaminv(p,v/2,2); + +% Return NaN if the degrees of freedom is not positive. +k = (v <= 0); +if any(k(:)) + x(k) = NaN; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/choose.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function c = choose(n,k) +% CHOOSE The number of ways of choosing k things from n +% c = choose(n,k) + +c = factorial(n)/(factorial(k) * factorial(n-k));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/collapse_mog.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [new_mu, new_Sigma, new_Sigma2] = collapse_mog(mu, Sigma, coefs) +% COLLAPSE_MOG Collapse a mixture of Gaussians to a single Gaussian by moment matching +% [new_mu, new_Sigma] = collapse_mog(mu, Sigma, coefs) +% +% coefs(i) - weight of i'th mixture component +% mu(:,i), Sigma(:,:,i) - params of i'th mixture component + +% S = sum_c w_c (S_c + m_c m_c' + m m' - 2 m_c m') +% = sum_c w_c (S_c + m_c m_c') + m m' - 2 (sum_c m_c) m' +% = sum_c w_c (S_c + m_c m_c') - m m' + +new_mu = sum(mu * diag(coefs), 2); % weighted sum of columns + +n = length(new_mu); +new_Sigma = zeros(n,n); +new_Sigma2 = zeros(n,n); +for j=1:length(coefs) + m = mu(:,j) - new_mu; + new_Sigma = new_Sigma + coefs(j) * (Sigma(:,:,j) + m*m'); + new_Sigma2 = new_Sigma2 + coefs(j) * (Sigma(:,:,j) + mu(:,j)*mu(:,j)'); +end +%assert(approxeq(new_Sigma, new_Sigma2 - new_mu*new_mu'))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/colmult.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +#include <stdio.h> +#include "mex.h" + +/* +out = colop(M, v) + +Apply binary operator to a vector v and to each column of M in turn +to produce a matrix the same size as M. + +This is equivalent to + +out = zeros(size(M)); +for col=1:size(M,2) + out(:,col) = op(M(:,col), v); +end + +The code needs to be modified for each different operator 'op'. +eg op = '.*' + +In vectorized form: + +out = M .* repmat(v(:), 1, size(M,2)) + +(This function was formerly called repmat_and_mult.c) + +*/ + +/* M(i,j) = M(i + nrows*j) since Matlab uses Fortran layout. */ + + +#define INMAT(i,j) M[(i)+nrows*(j)] +#define OUTMAT(i,j) out[(i)+nrows*(j)] + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] + ) +{ + double *out, *M, *v; + int nrows, ncols, r, c; + + /* read the input args */ + M = mxGetPr(prhs[0]); + nrows = mxGetM(prhs[0]); + ncols = mxGetN(prhs[0]); + + v = mxGetPr(prhs[1]); + + plhs[0] = mxCreateDoubleMatrix(nrows, ncols, mxREAL); + out = mxGetPr(plhs[0]); + + for (c=0; c < ncols; c++) { + for (r=0; r < nrows; r++) { + OUTMAT(r,c) = INMAT(r,c) * v[r]; + /* printf("r=%d, c=%d, M=%f, v=%f\n", r, c, INMAT(r,c), v[r]); */ + } + } + +} + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/computeROC.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +function [FPrate, TPrate, AUC, thresholds] = computeROC(confidence, testClass) +% function [FPrate, TPrate, AUC, thresholds] = computeROC(confidence, testClass) +% +% computeROC computes the data for an ROC curve based on a classifier's confidence output. +% It returns the false positive rate and the true positive rate along with +% the area under the ROC curve, and the list of thresholds. +% +% Inputs: +% - confidence(i) is proportional to the probability that +% testClass(i) is positive +% +% testClass = 0 => target absent +% testClass = 1 => target present +% +% Based on algorithms 2 and 4 from Tom Fawcett's paper "ROC Graphs: Notes and +% Practical Considerations for Data Mining Researchers" (2003) +% http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf" +% +% Vlad Magdin, 21 Feb 2005 + +% break ties in scores +S = rand('state'); +rand('state',0); +confidence = confidence + rand(size(confidence))*10^(-10); +rand('state',S) +[thresholds order] = sort(confidence, 'descend'); +testClass = testClass(order); + +%%% -- calculate TP/FP rates and totals -- %%% +AUC = 0; +faCnt = 0; +tpCnt = 0; +falseAlarms = zeros(1,size(thresholds,2)); +detections = zeros(1,size(thresholds,2)); +fPrev = -inf; +faPrev = 0; +tpPrev = 0; + +P = max(size(find(testClass==1))); +N = max(size(find(testClass==0))); + +for i=1:length(thresholds) + if thresholds(i) ~= fPrev + falseAlarms(i) = faCnt; + detections(i) = tpCnt; + + AUC = AUC + polyarea([faPrev faPrev faCnt/N faCnt/N],[0 tpPrev tpCnt/P 0]); + + fPrev = thresholds(i); + faPrev = faCnt/N; + tpPrev = tpCnt/P; + end + + if testClass(i) == 1 + tpCnt = tpCnt + 1; + else + faCnt = faCnt + 1; + end +end + +AUC = AUC + polyarea([faPrev faPrev 1 1],[0 tpPrev 1 0]); + +FPrate = falseAlarms/N; +TPrate = detections/P;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/compute_counts.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function count = compute_counts(data, sz) +% COMPUTE_COUNTS Count the number of times each combination of discrete assignments occurs +% count = compute_counts(data, sz) +% +% data(i,t) is the value of variable i in case t +% sz(i) : values for variable i are assumed to be in [1:sz(i)] +% +% Example: to compute a transition matrix for an HMM from a sequence of labeled states: +% transmat = mk_stochastic(compute_counts([seq(1:end-1); seq(2:end)], [nstates nstates])); + +assert(length(sz) == size(data, 1)); +P = prod(sz); +indices = subv2ind(sz, data'); % each row of data' is a case +%count = histc(indices, 1:P); +count = hist(indices, 1:P); +count = myreshape(count, sz); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/conf2mahal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +% CONF2MAHAL - Translates a confidence interval to a Mahalanobis +% distance. Consider a multivariate Gaussian +% distribution of the form +% +% p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C))) +% +% where MD(x, m, P) is the Mahalanobis distance from x +% to m under P: +% +% MD(x, m, P) = (x - m) * P * (x - m)' +% +% A particular Mahalanobis distance k identifies an +% ellipsoid centered at the mean of the distribution. +% The confidence interval associated with this ellipsoid +% is the probability mass enclosed by it. Similarly, +% a particular confidence interval uniquely determines +% an ellipsoid with a fixed Mahalanobis distance. +% +% If X is an d dimensional Gaussian-distributed vector, +% then the Mahalanobis distance of X is distributed +% according to the Chi-squared distribution with d +% degrees of freedom. Thus, the Mahalanobis distance is +% determined by evaluating the inverse cumulative +% distribution function of the chi squared distribution +% up to the confidence value. +% +% Usage: +% +% m = conf2mahal(c, d); +% +% Inputs: +% +% c - the confidence interval +% d - the number of dimensions of the Gaussian distribution +% +% Outputs: +% +% m - the Mahalanobis radius of the ellipsoid enclosing the +% fraction c of the distribution's probability mass +% +% See also: MAHAL2CONF + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function m = conf2mahal(c, d) + +m = chi2inv(c, d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/cross_entropy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function kl = cross_entropy(p, q, symmetric) +% CROSS_ENTROPY Compute the Kullback-Leibler divergence between two discrete prob. distributions +% kl = cross_entropy(p, q, symmetric) +% +% If symmetric = 1, we compute the symmetric version. Default: symmetric = 0; + +tiny = exp(-700); +if nargin < 3, symmetric = 0; end +p = p(:); +q = q(:); +if symmetric + kl = (sum(p .* log((p+tiny)./(q+tiny))) + sum(q .* log((q+tiny)./(p+tiny))))/2; +else + kl = sum(p .* log((p+tiny)./(q+tiny))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/dirKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,98 @@ +function filenames = dirKPM(dirname, ext, varargin) +% dirKPM Like the built-in dir command, but returns filenames as a cell array instead of a struct +% +% filenames = dirKPM(dirname) +% returns all files, except '.' and '..' +% +% filenames = dirKPM('images', '*.jpg') +% returns files with this extension +% eg filenames{1} = 'foo.jpg' etc +% +% OPTIONAL ARGUMENTS [default in brackets] +% filenames = dirKPM('images', '', param1, val1, param2, val2, ...) +% +% 'fileType'='image' ['all'] means return files with extension .jpg, .png, .bmp +% +% 'prepend'=1 [0] means preprend folder name to filename +% eg filenames{1} = 'images/foo.jpg' +% +% 'doSort'=1 [1] means sort filenames in ascending alphanumerical order (where possible) +% +% 'doRecurse'=1 [0] recursive dir, apply the same dirKPM call on all +% subfolders (decrease MAXDEPTH option to prevent recursion from branching +% too explosively) + +if nargin < 1, dirname = '.'; end + +if nargin < 2, ext = ''; end + +[fileType, prepend, doSort, doRecurse, MAXDEPTH, DEPTH] = process_options(... + varargin, 'fileType', 'all', 'prepend', 0, 'doSort', 1, 'doRecurse', 0,... + 'MAXDEPTH', 3, 'DEPTH', 0); + +tmp = dir(fullfile(dirname, ext)); +[filenames I] = setdiff({tmp.name}, {'.', '..'}); +tmp = tmp(I); + +if doRecurse && sum([tmp.isdir])>0 && DEPTH<MAXDEPTH + for fi=1:length(tmp) + subDirFilenames = {}; + + if tmp(fi).isdir + varargin = change_option( varargin, 'prepend', false ); + varargin = change_option( varargin, 'doSort', false ); + varargin = change_option( varargin, 'DEPTH', DEPTH+1 ); + subDirFilenames = dirKPM( fullfile(dirname,tmp(fi).name), ext, varargin{:} ); + + for sdfi=1:length(subDirFilenames) + subDirFilenames{sdfi} = fullfile(tmp(fi).name, subDirFilenames{sdfi}); + end + end + + + nfilenames = length(filenames); + if length(subDirFilenames)>0 + filenames(nfilenames+1:nfilenames+length(subDirFilenames)) = subDirFilenames; + end + end +end + +nfiles = length(filenames); +if nfiles==0 return; end + +switch fileType + case 'image', + for fi=1:nfiles + good(fi) = isImage(filenames{fi}); + end + filenames = filenames(find(good)); + case 'all', + % no-op + otherwise + error(sprintf('unrecognized file type %s', fileType)); +end + +if doSort +% % sort filenames alphanumerically (if possible) +% DJE, buggy, MUST save tmp.anr/snr/str or else we potentially lose +% filenames +% tmp = asort(filenames, '-s', 'ascend'); +% if ~isempty(tmp.anr) +% filenames = tmp.anr'; +% else +% filenames = tmp.str'; +% end + % if names could not be sorted, return original order + + filenames=sort(filenames); + +end + + +if prepend + nfiles = length(filenames); + for fi=1:nfiles + filenames{fi} = fullfile(dirname, filenames{fi}); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/div.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function d = div(a,b) +% DIV Integer division +% d = div(a,b) + +d = floor(a / b);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/draw_circle.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function h = draw_circle(x, r, outline_color, fill_color) +% draw filled circles at centers x with radii r. +% x is a matrix of columns. r is a row vector. + +n = 40; % resolution +radians = [0:(2*pi)/(n-1):2*pi]; +unitC = [sin(radians); cos(radians)]; + +% extend r if necessary +if length(r) < cols(x) + r = [r repmat(r(length(r)), 1, cols(x)-length(r))]; +end + +h = []; +% hold is needed for fill() +held = ishold; +hold on +for i=1:cols(x) + y = unitC*r(i) + repmat(x(:, i), 1, n); + if nargin < 4 + h = [h line(y(1,:), y(2,:), 'Color', outline_color)]; + else + h = [h fill(y(1,:), y(2,:), fill_color, 'EdgeColor', outline_color)]; + end +end +if ~held + hold off +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/draw_ellipse.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function h = draw_ellipse(x, c, outline_color, fill_color) +% DRAW_ELLIPSE(x, c, outline_color, fill_color) +% Draws ellipses at centers x with covariance matrix c. +% x is a matrix of columns. c is a positive definite matrix. +% outline_color and fill_color are optional. + +n = 40; % resolution +radians = [0:(2*pi)/(n-1):2*pi]; +unitC = [sin(radians); cos(radians)]; +r = chol(c)'; + +if nargin < 3 + outline_color = 'g'; +end + +h = []; +for i=1:cols(x) + y = r*unitC + repmat(x(:, i), 1, n); + if nargin < 4 + h = [h line(y(1,:), y(2,:), 'Color', outline_color)]; + else + h = [h fill(y(1,:), y(2,:), fill_color, 'EdgeColor', outline_color)]; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/draw_ellipse_axes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function h = draw_ellipse_axes(x, c, linespec) +% DRAW_ELLIPSE_AXES(x, c, linespec) +% Draws the major and minor axes of ellipses. +% Ellipses are centered at x with covariance matrix c. +% x is a matrix of columns. c is a positive definite matrix. +% linespec is optional. + +[v,e] = eig(c); +v = v*sqrt(e); + +h = []; +for j = 1:cols(v) + x1 = repmat(x(1,:),2,1) + repmat([-1;1]*v(1,j),1,cols(x)); + x2 = repmat(x(2,:),2,1) + repmat([-1;1]*v(2,j),1,cols(x)); + h = [h line(x1,x2)]; +end +if nargin > 2 + set_linespec(h,linespec); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/em_converged.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [converged, decrease] = em_converged(loglik, previous_loglik, threshold, check_increased) +% EM_CONVERGED Has EM converged? +% [converged, decrease] = em_converged(loglik, previous_loglik, threshold) +% +% We have converged if the slope of the log-likelihood function falls below 'threshold', +% i.e., |f(t) - f(t-1)| / avg < threshold, +% where avg = (|f(t)| + |f(t-1)|)/2 and f(t) is log lik at iteration t. +% 'threshold' defaults to 1e-4. +% +% This stopping criterion is from Numerical Recipes in C p423 +% +% If we are doing MAP estimation (using priors), the likelihood can decrase, +% even though the mode of the posterior is increasing. + +if nargin < 3, threshold = 1e-4; end +if nargin < 4, check_increased = 1; end + +converged = 0; +decrease = 0; + +if check_increased + if loglik - previous_loglik < -1e-3 % allow for a little imprecision + fprintf(1, '******likelihood decreased from %6.4f to %6.4f!\n', previous_loglik, loglik); + decrease = 1; +converged = 0; +return; + end +end + +delta_loglik = abs(loglik - previous_loglik); +avg_loglik = (abs(loglik) + abs(previous_loglik) + eps)/2; +if (delta_loglik / avg_loglik) < threshold, converged = 1; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/entropy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function H = entropy(v, scale) +% ENTROPY Entropy log base 2 +% H = entropy(v) +% If v is a matrix, we compute the entropy of each column +% +% % H = entropy(v,1) means we scale the result so that it lies in [0,1] + +if nargin < 2, scale = 0; end + +v = v + (v==0); +H = -1 * sum(v .* log2(v), 1); % sum the rows + +if scale + n = size(v, 1); + unif = normalise(ones(n,1)); + H = H / entropy(unif); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/exportfig.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,991 @@ +function varargout = exportfig(varargin) +%EXPORTFIG Export a figure. +% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is +% a figure handle and FILENAME is a string that specifies the +% name of the output file. +% +% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME +% with options initially specified by the structure OPTIONS. The +% field names of OPTIONS must be legal parameters listed below +% and the field values must be legal values for the corresponding +% parameter. Default options can be set in releases prior to R12 +% by storing the OPTIONS structure in the root object's appdata +% with the command +% setappdata(0,'exportfigdefaults', OPTIONS) +% and for releases after R12 by setting the preference with the +% command +% setpref('exportfig', 'defaults', OPTIONS) +% +% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies +% parameters that control various characteristics of the output +% file. Any parameter value can be the string 'auto' which means +% the parameter uses the default factory behavior, overriding +% any other default for the parameter. +% +% Format Paramter: +% 'Format' a string +% specifies the output format. Defaults to 'eps'. For a +% list of export formats type 'help print'. +% 'Preview' one of the strings 'none', 'tiff' +% specifies a preview for EPS files. Defaults to 'none'. +% +% Size Parameters: +% 'Width' a positive scalar +% specifies the width in the figure's PaperUnits +% 'Height' a positive scalar +% specifies the height in the figure's PaperUnits +% 'Bounds' one of the strings 'tight', 'loose' +% specifies a tight or loose bounding box. Defaults to 'tight'. +% 'Reference' an axes handle or a string +% specifies that the width and height parameters +% are relative to the given axes. If a string is +% specified then it must evaluate to an axes handle. +% +% Specifying only one dimension sets the other dimension +% so that the exported aspect ratio is the same as the +% figure's or reference axes' current aspect ratio. +% If neither dimension is specified the size defaults to +% the width and height from the figure's or reference +% axes' size. Tight bounding boxes are only computed for +% 2-D views and in that case the computed bounds enclose all +% text objects. +% +% Rendering Parameters: +% 'Color' one of the strings 'bw', 'gray', 'cmyk' +% 'bw' specifies that lines and text are exported in +% black and all other objects in grayscale +% 'gray' specifies that all objects are exported in grayscale +% 'rgb' specifies that all objects are exported in color +% using the RGB color space +% 'cmyk' specifies that all objects are exported in color +% using the CMYK color space +% 'Renderer' one of 'painters', 'zbuffer', 'opengl' +% specifies the renderer to use +% 'Resolution' a positive scalar +% specifies the resolution in dots-per-inch. +% 'LockAxes' one of 0 or 1 +% specifies that all axes limits and ticks should be fixed +% while exporting. +% +% The default color setting is 'bw'. +% +% Font Parameters: +% 'FontMode' one of the strings 'scaled', 'fixed' +% 'FontSize' a positive scalar +% in 'scaled' mode multiplies with the font size of each +% text object to obtain the exported font size +% in 'fixed' mode specifies the font size of all text +% objects in points +% 'DefaultFixedFontSize' a positive scalar +% in 'fixed' mode specified the default font size in +% points +% 'FontSizeMin' a positive scalar +% specifies the minimum font size allowed after scaling +% 'FontSizeMax' a positive scalar +% specifies the maximum font size allowed after scaling +% 'FontEncoding' one of the strings 'latin1', 'adobe' +% specifies the character encoding of the font +% 'SeparateText' one of 0 or 1 +% specifies that the text objects are stored in separate +% file as EPS with the base filename having '_t' appended. +% +% If FontMode is 'scaled' but FontSize is not specified then a +% scaling factor is computed from the ratio of the size of the +% exported figure to the size of the actual figure. +% +% The default 'FontMode' setting is 'scaled'. +% +% Line Width Parameters: +% 'LineMode' one of the strings 'scaled', 'fixed' +% 'LineWidth' a positive scalar +% 'DefaultFixedLineWidth' a positive scalar +% 'LineWidthMin' a positive scalar +% specifies the minimum line width allowed after scaling +% 'LineWidthMax' a positive scalar +% specifies the maximum line width allowed after scaling +% The semantics of 'Line' parameters are exactly the +% same as the corresponding 'Font' parameters, except that +% they apply to line widths instead of font sizes. +% +% Style Map Parameter: +% 'LineStyleMap' one of [], 'bw', or a function name or handle +% specifies how to map line colors to styles. An empty +% style map means styles are not changed. The style map +% 'bw' is a built-in mapping that maps lines with the same +% color to the same style and otherwise cycles through the +% available styles. A user-specified map is a function +% that takes as input a cell array of line objects and +% outputs a cell array of line style strings. The default +% map is []. +% +% Examples: +% exportfig(gcf,'fig1.eps','height',3); +% Exports the current figure to the file named 'fig1.eps' with +% a height of 3 inches (assuming the figure's PaperUnits is +% inches) and an aspect ratio the same as the figure's aspect +% ratio on screen. +% +% opts = struct('FontMode','fixed','FontSize',10,'height',3); +% exportfig(gcf, 'fig2.eps', opts, 'height', 5); +% Exports the current figure to 'fig2.eps' with all +% text in 10 point fonts and with height 5 inches. +% +% See also PREVIEWFIG, APPLYTOFIG, RESTOREFIG, PRINT. + +% Copyright 2000 Ben Hinkle +% Email bug reports and comments to bhinkle@mathworks.com + +if (nargin < 2) + error('Too few input arguments'); +end + +% exportfig(H, filename, [options,] ...) +H = varargin{1}; +if ~LocalIsHG(H,'figure') + error('First argument must be a handle to a figure.'); +end +filename = varargin{2}; +if ~ischar(filename) + error('Second argument must be a string.'); +end +paramPairs = {varargin{3:end}}; +if nargin > 2 + if isstruct(paramPairs{1}) + pcell = LocalToCell(paramPairs{1}); + paramPairs = {pcell{:}, paramPairs{2:end}}; + end +end +verstr = version; +majorver = str2num(verstr(1)); +defaults = []; +if majorver > 5 + if ispref('exportfig','defaults') + defaults = getpref('exportfig','defaults'); + end +elseif exist('getappdata') + defaults = getappdata(0,'exportfigdefaults'); +end +if ~isempty(defaults) + dcell = LocalToCell(defaults); + paramPairs = {dcell{:}, paramPairs{:}}; +end + +% Do some validity checking on param-value pairs +if (rem(length(paramPairs),2) ~= 0) + error(['Invalid input syntax. Optional parameters and values' ... + ' must be in pairs.']); +end + +auto.format = 'eps'; +auto.preview = 'none'; +auto.width = -1; +auto.height = -1; +auto.color = 'bw'; +auto.defaultfontsize=10; +auto.fontsize = -1; +auto.fontmode='scaled'; +auto.fontmin = 8; +auto.fontmax = 60; +auto.defaultlinewidth = 1.0; +auto.linewidth = -1; +auto.linemode=[]; +auto.linemin = 0.5; +auto.linemax = 100; +auto.fontencoding = 'latin1'; +auto.renderer = []; +auto.resolution = []; +auto.stylemap = []; +auto.applystyle = 0; +auto.refobj = -1; +auto.bounds = 'tight'; +explicitbounds = 0; +auto.lockaxes = 1; +auto.separatetext = 0; +opts = auto; + +% Process param-value pairs +args = {}; +for k = 1:2:length(paramPairs) + param = lower(paramPairs{k}); + if ~ischar(param) + error('Optional parameter names must be strings'); + end + value = paramPairs{k+1}; + + switch (param) + case 'format' + opts.format = LocalCheckAuto(lower(value),auto.format); + if strcmp(opts.format,'preview') + error(['Format ''preview'' no longer supported. Use PREVIEWFIG' ... + ' instead.']); + end + case 'preview' + opts.preview = LocalCheckAuto(lower(value),auto.preview); + if ~strcmp(opts.preview,{'none','tiff'}) + error('Preview must be ''none'' or ''tiff''.'); + end + case 'width' + opts.width = LocalToNum(value, auto.width); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.width) + error('Width must be a numeric scalar > 0'); + end + end + case 'height' + opts.height = LocalToNum(value, auto.height); + if ~ischar(value) | ~strcmp(value,'auto') + if(~LocalIsPositiveScalar(opts.height)) + error('Height must be a numeric scalar > 0'); + end + end + case 'color' + opts.color = LocalCheckAuto(lower(value),auto.color); + if ~strcmp(opts.color,{'bw','gray','rgb','cmyk'}) + error('Color must be ''bw'', ''gray'',''rgb'' or ''cmyk''.'); + end + case 'fontmode' + opts.fontmode = LocalCheckAuto(lower(value),auto.fontmode); + if ~strcmp(opts.fontmode,{'scaled','fixed'}) + error('FontMode must be ''scaled'' or ''fixed''.'); + end + case 'fontsize' + opts.fontsize = LocalToNum(value,auto.fontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontsize) + error('FontSize must be a numeric scalar > 0'); + end + end + case 'defaultfixedfontsize' + opts.defaultfontsize = LocalToNum(value,auto.defaultfontsize); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultfontsize) + error('DefaultFixedFontSize must be a numeric scalar > 0'); + end + end + case 'fontsizemin' + opts.fontmin = LocalToNum(value,auto.fontmin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmin) + error('FontSizeMin must be a numeric scalar > 0'); + end + end + case 'fontsizemax' + opts.fontmax = LocalToNum(value,auto.fontmax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.fontmax) + error('FontSizeMax must be a numeric scalar > 0'); + end + end + case 'fontencoding' + opts.fontencoding = LocalCheckAuto(lower(value),auto.fontencoding); + if ~strcmp(opts.fontencoding,{'latin1','adobe'}) + error('FontEncoding must be ''latin1'' or ''adobe''.'); + end + case 'linemode' + opts.linemode = LocalCheckAuto(lower(value),auto.linemode); + if ~strcmp(opts.linemode,{'scaled','fixed'}) + error('LineMode must be ''scaled'' or ''fixed''.'); + end + case 'linewidth' + opts.linewidth = LocalToNum(value,auto.linewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linewidth) + error('LineWidth must be a numeric scalar > 0'); + end + end + case 'defaultfixedlinewidth' + opts.defaultlinewidth = LocalToNum(value,auto.defaultlinewidth); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.defaultlinewidth) + error(['DefaultFixedLineWidth must be a numeric scalar >' ... + ' 0']); + end + end + case 'linewidthmin' + opts.linemin = LocalToNum(value,auto.linemin); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemin) + error('LineWidthMin must be a numeric scalar > 0'); + end + end + case 'linewidthmax' + opts.linemax = LocalToNum(value,auto.linemax); + if ~ischar(value) | ~strcmp(value,'auto') + if ~LocalIsPositiveScalar(opts.linemax) + error('LineWidthMax must be a numeric scalar > 0'); + end + end + case 'linestylemap' + opts.stylemap = LocalCheckAuto(value,auto.stylemap); + case 'renderer' + opts.renderer = LocalCheckAuto(lower(value),auto.renderer); + if ~ischar(value) | ~strcmp(value,'auto') + if ~strcmp(opts.renderer,{'painters','zbuffer','opengl'}) + error(['Renderer must be ''painters'', ''zbuffer'' or' ... + ' ''opengl''.']); + end + end + case 'resolution' + opts.resolution = LocalToNum(value,auto.resolution); + if ~ischar(value) | ~strcmp(value,'auto') + if ~(isnumeric(value) & (prod(size(value)) == 1) & (value >= 0)); + error('Resolution must be a numeric scalar >= 0'); + end + end + case 'applystyle' % means to apply the options and not export + opts.applystyle = 1; + case 'reference' + if ischar(value) + if strcmp(value,'auto') + opts.refobj = auto.refobj; + else + opts.refobj = eval(value); + end + else + opts.refobj = value; + end + if ~LocalIsHG(opts.refobj,'axes') + error('Reference object must evaluate to an axes handle.'); + end + case 'bounds' + opts.bounds = LocalCheckAuto(lower(value),auto.bounds); + explicitbounds = 1; + if ~strcmp(opts.bounds,{'tight','loose'}) + error('Bounds must be ''tight'' or ''loose''.'); + end + case 'lockaxes' + opts.lockaxes = LocalToNum(value,auto.lockaxes); + case 'separatetext' + opts.separatetext = LocalToNum(value,auto.separatetext); + otherwise + error(['Unrecognized option ' param '.']); + end +end + +% make sure figure is up-to-date +drawnow; + +allLines = findall(H, 'type', 'line'); +allText = findall(H, 'type', 'text'); +allAxes = findall(H, 'type', 'axes'); +allImages = findall(H, 'type', 'image'); +allLights = findall(H, 'type', 'light'); +allPatch = findall(H, 'type', 'patch'); +allSurf = findall(H, 'type', 'surface'); +allRect = findall(H, 'type', 'rectangle'); +allFont = [allText; allAxes]; +allColor = [allLines; allText; allAxes; allLights]; +allMarker = [allLines; allPatch; allSurf]; +allEdge = [allPatch; allSurf]; +allCData = [allImages; allPatch; allSurf]; + +old.objs = {}; +old.prop = {}; +old.values = {}; + +% Process format +if strncmp(opts.format,'eps',3) & ~strcmp(opts.preview,'none') + args = {args{:}, ['-' opts.preview]}; +end + +hadError = 0; +oldwarn = warning; +try + + % lock axes limits, ticks and labels if requested + if opts.lockaxes + old = LocalManualAxesMode(old, allAxes, 'TickMode'); + old = LocalManualAxesMode(old, allAxes, 'TickLabelMode'); + old = LocalManualAxesMode(old, allAxes, 'LimMode'); + end + + % Process size parameters + figurePaperUnits = get(H, 'PaperUnits'); + oldFigureUnits = get(H, 'Units'); + oldFigPos = get(H,'Position'); + set(H, 'Units', figurePaperUnits); + figPos = get(H,'Position'); + refsize = figPos(3:4); + if opts.refobj ~= -1 + oldUnits = get(opts.refobj, 'Units'); + set(opts.refobj, 'Units', figurePaperUnits); + r = get(opts.refobj, 'Position'); + refsize = r(3:4); + set(opts.refobj, 'Units', oldUnits); + end + aspectRatio = refsize(1)/refsize(2); + if (opts.width == -1) & (opts.height == -1) + opts.width = refsize(1); + opts.height = refsize(2); + elseif (opts.width == -1) + opts.width = opts.height * aspectRatio; + elseif (opts.height == -1) + opts.height = opts.width / aspectRatio; + end + wscale = opts.width/refsize(1); + hscale = opts.height/refsize(2); + sizescale = min(wscale,hscale); + old = LocalPushOldData(old,H,'PaperPositionMode', ... + get(H,'PaperPositionMode')); + set(H, 'PaperPositionMode', 'auto'); + newPos = [figPos(1) figPos(2)+figPos(4)*(1-hscale) ... + wscale*figPos(3) hscale*figPos(4)]; + set(H, 'Position', newPos); + set(H, 'Units', oldFigureUnits); + + % process line-style map + if ~isempty(opts.stylemap) & ~isempty(allLines) + oldlstyle = LocalGetAsCell(allLines,'LineStyle'); + old = LocalPushOldData(old, allLines, {'LineStyle'}, ... + oldlstyle); + newlstyle = oldlstyle; + if ischar(opts.stylemap) & strcmpi(opts.stylemap,'bw') + newlstyle = LocalMapColorToStyle(allLines); + else + try + newlstyle = feval(opts.stylemap,allLines); + catch + warning(['Skipping stylemap. ' lasterr]); + end + end + set(allLines,{'LineStyle'},newlstyle); + end + + % Process rendering parameters + switch (opts.color) + case {'bw', 'gray'} + if ~strcmp(opts.color,'bw') & strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + end + args = {args{:}, ['-d' opts.format]}; + + %compute and set gray colormap + oldcmap = get(H,'Colormap'); + newgrays = 0.30*oldcmap(:,1) + 0.59*oldcmap(:,2) + 0.11*oldcmap(:,3); + newcmap = [newgrays newgrays newgrays]; + old = LocalPushOldData(old, H, 'Colormap', oldcmap); + set(H, 'Colormap', newcmap); + + %compute and set ColorSpec and CData properties + old = LocalUpdateColors(allColor, 'color', old); + old = LocalUpdateColors(allAxes, 'xcolor', old); + old = LocalUpdateColors(allAxes, 'ycolor', old); + old = LocalUpdateColors(allAxes, 'zcolor', old); + old = LocalUpdateColors(allMarker, 'MarkerEdgeColor', old); + old = LocalUpdateColors(allMarker, 'MarkerFaceColor', old); + old = LocalUpdateColors(allEdge, 'EdgeColor', old); + old = LocalUpdateColors(allEdge, 'FaceColor', old); + old = LocalUpdateColors(allCData, 'CData', old); + + case {'rgb','cmyk'} + if strncmp(opts.format,'eps',3) + opts.format = [opts.format 'c']; + args = {args{:}, ['-d' opts.format]}; + if strcmp(opts.color,'cmyk') + args = {args{:}, '-cmyk'}; + end + else + args = {args{:}, ['-d' opts.format]}; + end + otherwise + error('Invalid Color parameter'); + end + if (~isempty(opts.renderer)) + args = {args{:}, ['-' opts.renderer]}; + end + if (~isempty(opts.resolution)) | ~strncmp(opts.format,'eps',3) + if isempty(opts.resolution) + opts.resolution = 0; + end + args = {args{:}, ['-r' int2str(opts.resolution)]}; + end + + % Process font parameters + if ~isempty(opts.fontmode) + oldfonts = LocalGetAsCell(allFont,'FontSize'); + oldfontunits = LocalGetAsCell(allFont,'FontUnits'); + set(allFont,'FontUnits','points'); + switch (opts.fontmode) + case 'fixed' + if (opts.fontsize == -1) + set(allFont,'FontSize',opts.defaultfontsize); + else + set(allFont,'FontSize',opts.fontsize); + end + case 'scaled' + if (opts.fontsize == -1) + scale = sizescale; + else + scale = opts.fontsize; + end + newfonts = LocalScale(oldfonts,scale,opts.fontmin,opts.fontmax); + set(allFont,{'FontSize'},newfonts); + otherwise + error('Invalid FontMode parameter'); + end + old = LocalPushOldData(old, allFont, {'FontSize'}, oldfonts); + old = LocalPushOldData(old, allFont, {'FontUnits'}, oldfontunits); + end + if strcmp(opts.fontencoding,'adobe') & strncmp(opts.format,'eps',3) + args = {args{:}, '-adobecset'}; + end + + % Process line parameters + if ~isempty(opts.linemode) + oldlines = LocalGetAsCell(allMarker,'LineWidth'); + old = LocalPushOldData(old, allMarker, {'LineWidth'}, oldlines); + switch (opts.linemode) + case 'fixed' + if (opts.linewidth == -1) + set(allMarker,'LineWidth',opts.defaultlinewidth); + else + set(allMarker,'LineWidth',opts.linewidth); + end + case 'scaled' + if (opts.linewidth == -1) + scale = sizescale; + else + scale = opts.linewidth; + end + newlines = LocalScale(oldlines, scale, opts.linemin, opts.linemax); + set(allMarker,{'LineWidth'},newlines); + end + end + + % adjust figure bounds to surround axes + if strcmp(opts.bounds,'tight') + if (~strncmp(opts.format,'eps',3) & LocalHas3DPlot(allAxes)) | ... + (strncmp(opts.format,'eps',3) & opts.separatetext) + if (explicitbounds == 1) + warning(['Cannot compute ''tight'' bounds. Using ''loose''' ... + ' bounds.']); + end + opts.bounds = 'loose'; + end + end + warning('off'); + if ~isempty(allAxes) + if strncmp(opts.format,'eps',3) + if strcmp(opts.bounds,'loose') + args = {args{:}, '-loose'}; + end + old = LocalPushOldData(old,H,'Position', oldFigPos); + elseif strcmp(opts.bounds,'tight') + oldaunits = LocalGetAsCell(allAxes,'Units'); + oldapos = LocalGetAsCell(allAxes,'Position'); + oldtunits = LocalGetAsCell(allText,'units'); + oldtpos = LocalGetAsCell(allText,'Position'); + set(allAxes,'units','points'); + apos = LocalGetAsCell(allAxes,'Position'); + oldunits = get(H,'Units'); + set(H,'units','points'); + origfr = get(H,'position'); + fr = []; + for k=1:length(allAxes) + if ~strcmpi(get(allAxes(k),'Tag'),'legend') + axesR = apos{k}; + r = LocalAxesTightBoundingBox(axesR, allAxes(k)); + r(1:2) = r(1:2) + axesR(1:2); + fr = LocalUnionRect(fr,r); + end + end + if isempty(fr) + fr = [0 0 origfr(3:4)]; + end + for k=1:length(allAxes) + ax = allAxes(k); + r = apos{k}; + r(1:2) = r(1:2) - fr(1:2); + set(ax,'Position',r); + end + old = LocalPushOldData(old, allAxes, {'Position'}, oldapos); + old = LocalPushOldData(old, allText, {'Position'}, oldtpos); + old = LocalPushOldData(old, allText, {'Units'}, oldtunits); + old = LocalPushOldData(old, allAxes, {'Units'}, oldaunits); + old = LocalPushOldData(old, H, 'Position', oldFigPos); + old = LocalPushOldData(old, H, 'Units', oldFigureUnits); + r = [origfr(1) origfr(2)+origfr(4)-fr(4) fr(3:4)]; + set(H,'Position',r); + else + args = {args{:}, '-loose'}; + old = LocalPushOldData(old,H,'Position', oldFigPos); + end + end + + % Process text in a separate file if needed + if opts.separatetext & ~opts.applystyle + % First hide all text and export + oldtvis = LocalGetAsCell(allText,'visible'); + set(allText,'visible','off'); + oldax = LocalGetAsCell(allAxes,'XTickLabel',1); + olday = LocalGetAsCell(allAxes,'YTickLabel',1); + oldaz = LocalGetAsCell(allAxes,'ZTickLabel',1); + null = cell(length(oldax),1); + [null{:}] = deal([]); + set(allAxes,{'XTickLabel'},null); + set(allAxes,{'YTickLabel'},null); + set(allAxes,{'ZTickLabel'},null); + print(H, filename, args{:}); + set(allText,{'Visible'},oldtvis); + set(allAxes,{'XTickLabel'},oldax); + set(allAxes,{'YTickLabel'},olday); + set(allAxes,{'ZTickLabel'},oldaz); + % Now hide all non-text and export as eps in painters + [path, name, ext] = fileparts(filename); + tfile = fullfile(path,[name '_t.eps']); + tfile2 = fullfile(path,[name '_t2.eps']); + foundRenderer = 0; + for k=1:length(args) + if strncmp('-d',args{k},2) + args{k} = '-deps'; + elseif strncmp('-zbuffer',args{k},8) | ... + strncmp('-opengl', args{k},6) + args{k} = '-painters'; + foundRenderer = 1; + end + end + if ~foundRenderer + args = {args{:}, '-painters'}; + end + allNonText = [allLines; allLights; allPatch; ... + allImages; allSurf; allRect]; + oldvis = LocalGetAsCell(allNonText,'visible'); + oldc = LocalGetAsCell(allAxes,'color'); + oldaxg = LocalGetAsCell(allAxes,'XGrid'); + oldayg = LocalGetAsCell(allAxes,'YGrid'); + oldazg = LocalGetAsCell(allAxes,'ZGrid'); + [null{:}] = deal('off'); + set(allAxes,{'XGrid'},null); + set(allAxes,{'YGrid'},null); + set(allAxes,{'ZGrid'},null); + set(allNonText,'Visible','off'); + set(allAxes,'Color','none'); + print(H, tfile2, args{:}); + set(allNonText,{'Visible'},oldvis); + set(allAxes,{'Color'},oldc); + set(allAxes,{'XGrid'},oldaxg); + set(allAxes,{'YGrid'},oldayg); + set(allAxes,{'ZGrid'},oldazg); + %hack up the postscript file + fid1 = fopen(tfile,'w'); + fid2 = fopen(tfile2,'r'); + line = fgetl(fid2); + while ischar(line) + if strncmp(line,'%%Title',7) + fprintf(fid1,'%s\n',['%%Title: ', tfile]); + elseif (length(line) < 3) + fprintf(fid1,'%s\n',line); + elseif ~strcmp(line(end-2:end),' PR') & ... + ~strcmp(line(end-1:end),' L') + fprintf(fid1,'%s\n',line); + end + line = fgetl(fid2); + end + fclose(fid1); + fclose(fid2); + delete(tfile2); + + elseif ~opts.applystyle + drawnow; + print(H, filename, args{:}); + end + warning(oldwarn); + +catch + warning(oldwarn); + hadError = 1; +end + +% Restore figure settings +if opts.applystyle + varargout{1} = old; +else + for n=1:length(old.objs) + if ~iscell(old.values{n}) & iscell(old.prop{n}) + old.values{n} = {old.values{n}}; + end + set(old.objs{n}, old.prop{n}, old.values{n}); + end +end + +if hadError + error(deblank(lasterr)); +end + +% +% Local Functions +% + +function outData = LocalPushOldData(inData, objs, prop, values) +outData.objs = {objs, inData.objs{:}}; +outData.prop = {prop, inData.prop{:}}; +outData.values = {values, inData.values{:}}; + +function cellArray = LocalGetAsCell(fig,prop,allowemptycell); +cellArray = get(fig,prop); +if nargin < 3 + allowemptycell = 0; +end +if ~iscell(cellArray) & (allowemptycell | ~isempty(cellArray)) + cellArray = {cellArray}; +end + +function newArray = LocalScale(inArray, scale, minv, maxv) +n = length(inArray); +newArray = cell(n,1); +for k=1:n + newArray{k} = min(maxv,max(minv,scale*inArray{k}(1))); +end + +function gray = LocalMapToGray1(color) +gray = color; +if ischar(color) + switch color(1) + case 'y' + color = [1 1 0]; + case 'm' + color = [1 0 1]; + case 'c' + color = [0 1 1]; + case 'r' + color = [1 0 0]; + case 'g' + color = [0 1 0]; + case 'b' + color = [0 0 1]; + case 'w' + color = [1 1 1]; + case 'k' + color = [0 0 0]; + end +end +if ~ischar(color) + gray = 0.30*color(1) + 0.59*color(2) + 0.11*color(3); +end + +function newArray = LocalMapToGray(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if ~isempty(color) + color = LocalMapToGray1(color); + end + if isempty(color) | ischar(color) + newArray{k} = color; + else + newArray{k} = [color color color]; + end +end + +function newArray = LocalMapColorToStyle(inArray); +inArray = LocalGetAsCell(inArray,'Color'); +n = length(inArray); +newArray = cell(n,1); +styles = {'-','--',':','-.'}; +uniques = []; +nstyles = length(styles); +for k=1:n + gray = LocalMapToGray1(inArray{k}); + if isempty(gray) | ischar(gray) | gray < .05 + newArray{k} = '-'; + else + if ~isempty(uniques) & any(gray == uniques) + ind = find(gray==uniques); + else + uniques = [uniques gray]; + ind = length(uniques); + end + newArray{k} = styles{mod(ind-1,nstyles)+1}; + end +end + +function newArray = LocalMapCData(inArray); +n = length(inArray); +newArray = cell(n,1); +for k=1:n + color = inArray{k}; + if (ndims(color) == 3) & isa(color,'double') + gray = 0.30*color(:,:,1) + 0.59*color(:,:,2) + 0.11*color(:,:,3); + color(:,:,1) = gray; + color(:,:,2) = gray; + color(:,:,3) = gray; + end + newArray{k} = color; +end + +function outData = LocalUpdateColors(inArray, prop, inData) +value = LocalGetAsCell(inArray,prop); +outData.objs = {inData.objs{:}, inArray}; +outData.prop = {inData.prop{:}, {prop}}; +outData.values = {inData.values{:}, value}; +if (~isempty(value)) + if strcmp(prop,'CData') + value = LocalMapCData(value); + else + value = LocalMapToGray(value); + end + set(inArray,{prop},value); +end + +function bool = LocalIsPositiveScalar(value) +bool = isnumeric(value) & ... + prod(size(value)) == 1 & ... + value > 0; + +function value = LocalToNum(value,auto) +if ischar(value) + if strcmp(value,'auto') + value = auto; + else + value = str2num(value); + end +end + +%convert a struct to {field1,val1,field2,val2,...} +function c = LocalToCell(s) +f = fieldnames(s); +v = struct2cell(s); +opts = cell(2,length(f)); +opts(1,:) = f; +opts(2,:) = v; +c = {opts{:}}; + +function c = LocalIsHG(obj,hgtype) +c = 0; +if (length(obj) == 1) & ishandle(obj) + c = strcmp(get(obj,'type'),hgtype); +end + +function c = LocalHas3DPlot(a) +zticks = LocalGetAsCell(a,'ZTickLabel'); +c = 0; +for k=1:length(zticks) + if ~isempty(zticks{k}) + c = 1; + return; + end +end + +function r = LocalUnionRect(r1,r2) +if isempty(r1) + r = r2; +elseif isempty(r2) + r = r1; +elseif max(r2(3:4)) > 0 + left = min(r1(1),r2(1)); + bot = min(r1(2),r2(2)); + right = max(r1(1)+r1(3),r2(1)+r2(3)); + top = max(r1(2)+r1(4),r2(2)+r2(4)); + r = [left bot right-left top-bot]; +else + r = r1; +end + +function c = LocalLabelsMatchTicks(labs,ticks) +c = 0; +try + t1 = num2str(ticks(1)); + n = length(ticks); + tend = num2str(ticks(n)); + c = strncmp(labs(1),t1,length(labs(1))) & ... + strncmp(labs(n),tend,length(labs(n))); +end + +function r = LocalAxesTightBoundingBox(axesR, a) +r = []; +atext = findall(a,'type','text','visible','on'); +if ~isempty(atext) + set(atext,'units','points'); + res=LocalGetAsCell(atext,'extent'); + for n=1:length(atext) + r = LocalUnionRect(r,res{n}); + end +end +if strcmp(get(a,'visible'),'on') + r = LocalUnionRect(r,[0 0 axesR(3:4)]); + oldunits = get(a,'fontunits'); + set(a,'fontunits','points'); + label = text(0,0,'','parent',a,... + 'units','points',... + 'fontsize',get(a,'fontsize'),... + 'fontname',get(a,'fontname'),... + 'fontweight',get(a,'fontweight'),... + 'fontangle',get(a,'fontangle'),... + 'visible','off'); + fs = get(a,'fontsize'); + + % handle y axis tick labels + ry = [0 -fs/2 0 axesR(4)+fs]; + ylabs = get(a,'yticklabels'); + yticks = get(a,'ytick'); + maxw = 0; + if ~isempty(ylabs) + for n=1:size(ylabs,1) + set(label,'string',ylabs(n,:)); + ext = get(label,'extent'); + maxw = max(maxw,ext(3)); + end + if ~LocalLabelsMatchTicks(ylabs,yticks) & ... + strcmp(get(a,'xaxislocation'),'bottom') + ry(4) = ry(4) + 1.5*ext(4); + end + if strcmp(get(a,'yaxislocation'),'left') + ry(1) = -(maxw+5); + else + ry(1) = axesR(3); + end + ry(3) = maxw+5; + r = LocalUnionRect(r,ry); + end + + % handle x axis tick labels + rx = [0 0 0 fs+5]; + xlabs = get(a,'xticklabels'); + xticks = get(a,'xtick'); + if ~isempty(xlabs) + if strcmp(get(a,'xaxislocation'),'bottom') + rx(2) = -(fs+5); + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + 2*fs; + rx(2) = rx(2) - 2*fs; + end + else + rx(2) = axesR(4); + % exponent is still below axes + if ~LocalLabelsMatchTicks(xlabs,xticks); + rx(4) = rx(4) + axesR(4) + 2*fs; + rx(2) = -2*fs; + end + end + set(label,'string',xlabs(1,:)); + ext1 = get(label,'extent'); + rx(1) = -ext1(3)/2; + set(label,'string',xlabs(size(xlabs,1),:)); + ext2 = get(label,'extent'); + rx(3) = axesR(3) + (ext2(3) + ext1(3))/2; + r = LocalUnionRect(r,rx); + end + set(a,'fontunits',oldunits); + delete(label); +end + +function c = LocalManualAxesMode(old, allAxes, base) +xs = ['X' base]; +ys = ['Y' base]; +zs = ['Z' base]; +oldXMode = LocalGetAsCell(allAxes,xs); +oldYMode = LocalGetAsCell(allAxes,ys); +oldZMode = LocalGetAsCell(allAxes,zs); +old = LocalPushOldData(old, allAxes, {xs}, oldXMode); +old = LocalPushOldData(old, allAxes, {ys}, oldYMode); +old = LocalPushOldData(old, allAxes, {zs}, oldZMode); +set(allAxes,xs,'manual'); +set(allAxes,ys,'manual'); +set(allAxes,zs,'manual'); +c = old; + +function val = LocalCheckAuto(val, auto) +if ischar(val) & strcmp(val,'auto') + val = auto; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/extend_domain_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function B = extend_domain_table(A, smalldom, smallsz, bigdom, bigsz) +% EXTEND_DOMAIN_TABLE Expand an array so it has the desired size. +% B = extend_domain_table(A, smalldom, smallsz, bigdom, bigsz) +% +% A is the array with domain smalldom and sizes smallsz. +% bigdom is the desired domain, with sizes bigsz. +% +% Example: +% smalldom = [1 3], smallsz = [2 4], bigdom = [1 2 3 4], bigsz = [2 1 4 5], +% so B(i,j,k,l) = A(i,k) for i in 1:2, j in 1:1, k in 1:4, l in 1:5 + +if isequal(size(A), [1 1]) % a scalar + B = A; % * myones(bigsz); + return; +end + +map = find_equiv_posns(smalldom, bigdom); +sz = ones(1, length(bigdom)); +sz(map) = smallsz; +B = myreshape(A, sz); % add dimensions for the stuff not in A +sz = bigsz; +sz(map) = 1; % don't replicate along A's dimensions +B = myrepmat(B, sz(:)'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/factorial.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function x = factorial(n) +% FACTORIAL Compute n! +% x = factorial(n) + +if n == 0 + x = 1; +else + x = n*factorial(n-1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/filepartsLast.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function [last] = filepartsLast(fname) +% filepartsLast Return the last part of a filename (strip off directory and suffix) +% function filepartsLast(fname) +% +% Examples +% filepartsLast('C:/foo/bar') = 'bar' +% filepartsLast('C:/foo/bar.mat') = 'bar' +% filepartsLast('C:/foo/bar.mat.gz') = 'bar.mat' +% filepartsLast('bar.mat') = 'bar' + +[pathstr,name,ext,versn] = fileparts(fname); +last = name;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/find_equiv_posns.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function p = find_equiv_posns(vsmall, vlarge) +% FIND_EQUIV_POSNS p[i] = the place where vsmall[i] occurs in vlarge. +% p = find_equiv_posns(vsmall, vlarge) +% THE VECTORS ARE ASSUMED TO BE SORTED. +% +% e.g., vsmall=[2,8], vlarge=[2,7,8,4], p=[1,3] +% +% In R/S, this function is called 'match' + +%if ~mysubset(vsmall, vlarge) +% error('small domain must occur in large domain'); +%end + +if isempty(vsmall) | isempty(vlarge) + p = []; + return; +end + +bitvec = sparse(1, max(vlarge)); +%bitvec = zeros(1, max(vlarge)); +bitvec(vsmall) = 1; +p = find(bitvec(vlarge)); + +%p = find(ismember(vlarge, vsmall)); % slower
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/fullfileKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function f = fullfileKPM(varargin) +% fullfileKPM Concatenate strings with file separator, then convert it to a/b/c +% function f = fullfileKPM(varargin) + +f = fullfile(varargin{:}); +f = strrep(f, '\', '/');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/genpathKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function p = genpathKPM(d) +% genpathKPM Like built-in genpath, but omits directories whose name is 'Old', 'old' or 'CVS' +% function p = genpathKPM(d) + +if nargin==0, + p = genpath(fullfile(matlabroot,'toolbox')); + if length(p) > 1, p(end) = []; end % Remove trailing pathsep + return +end + +% initialise variables +methodsep = '@'; % qualifier for overloaded method directories +p = ''; % path to be returned + +% Generate path based on given root directory +files = dir(d); +if isempty(files) + return +end + +% Add d to the path even if it is empty. +p = [p d pathsep]; + +% set logical vector for subdirectory entries in d +isdir = logical(cat(1,files.isdir)); +% +% Recursively descend through directories which are neither +% private nor "class" directories. +% +dirs = files(isdir); % select only directory entries from the current listing + +for i=1:length(dirs) + dirname = dirs(i).name; + if ~strcmp( dirname,'.') & ... + ~strcmp( dirname,'..') & ... + ~strncmp( dirname,methodsep,1)& ... + ~strcmp( dirname,'private') & ... + ~strcmp( dirname, 'old') & ... % KPM + ~strcmp( dirname, 'Old') & ... % KPM + ~strcmp( dirname, 'CVS') % KPM + p = [p genpathKPM(fullfile(d,dirname))]; % recursive calling of this function. + end +end + +%------------------------------------------------------------------------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/hash_add.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function hash_add(key, val, fname) +% HASH_ADD Append key,value pair to end of hashtable stored in a file +% function hash_add(key, val, filename) +% +% See hash_lookup for an example + +if ~exist(fname, 'file') + % new hashtable + hashtable.key{1} = key; + hashtable.value{1} = val; +else + %hashtable = importdata(fname); + %hashtable = load(fname, '-mat'); + load(fname, '-mat'); + Nentries = length(hashtable.key); + hashtable.key{Nentries+1} = key; + hashtable.value{Nentries+1} = val; +end +save(fname, 'hashtable', '-mat');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/hash_del.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function ndx = hash_del(key, fname) +% HASH_DEL Remove all entries that match key from hashtable stored in a file +% ndx = hash_del(key, fname) +% +% Returns indices of matching entries (if any) +% See hash_lookup for an example + +ndx = []; + +if ~exist(fname, 'file') + % new hashtable - no op +else + %hashtable = importdata(fname); + %hashtable = load(fname, '-mat'); + load(fname, '-mat'); + Nentries = length(hashtable.key); + for i=1:Nentries + if isequal(hashtable.key{i}, key) + ndx = [ndx i]; + end + end + hashtable.key(ndx) = []; + hashtable.value(ndx) = []; + save(fname, 'hashtable', '-mat'); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/hash_lookup.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function [val, found, Nentries] = hash_lookup(key, fname) +% HASH_LOOKUP Lookup a key in a hash table stored in a file using linear search +% function [val, found, Nentries] = hash_lookup(key, filename) +% +% Example: +% If htbl.mat does not exist, +% [val,found,N] = hash_lookup('foo', 'htbl') +% returns found val = [], found = 0, N = 0 +% hash_add('foo', 42, 'htbl') +% hash_add('bar', [1:10], 'htbl') +% [val,found,N] = hash_lookup('foo', 'htbl') +% now returns val = 42, found = 1, N = 2 +% +% Type 'delete htbl' to delete the file/ reset the hashtable + + +val = []; +found = 0; + +if exist(fname, 'file')==0 + % new hashtable + Nentries = 0; +else + %hashtable = importdata(fname); + load(fname); + Nentries = length(hashtable.key); + for i=1:Nentries + if isequal(hashtable.key{i}, key) + val = hashtable.value{i}; + found = 1; + break; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/hsvKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function colors = hsvKPM(N) +% hsvKPM Like built-in HSV, except it randomizes the order, so that adjacent colors are dis-similar +% function colors = hsvKPM(N) + +colors = hsv(N); +perm = randperm(N); +colors = colors(perm,:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/hungarian.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,464 @@ +function [C,T]=hungarian(A) +%HUNGARIAN Solve the Assignment problem using the Hungarian method. +% +%[C,T]=hungarian(A) +%A - a square cost matrix. +%C - the optimal assignment. +%T - the cost of the optimal assignment. + +% Adapted from the FORTRAN IV code in Carpaneto and Toth, "Algorithm 548: +% Solution of the assignment problem [H]", ACM Transactions on +% Mathematical Software, 6(1):104-111, 1980. + +% v1.0 96-06-14. Niclas Borlin, niclas@cs.umu.se. +% Department of Computing Science, Umeå University, +% Sweden. +% All standard disclaimers apply. + +% A substantial effort was put into this code. If you use it for a +% publication or otherwise, please include an acknowledgement or at least +% notify me by email. /Niclas + +[m,n]=size(A); + +if (m~=n) + error('HUNGARIAN: Cost matrix must be square!'); +end + +% Save original cost matrix. +orig=A; + +% Reduce matrix. +A=hminired(A); + +% Do an initial assignment. +[A,C,U]=hminiass(A); + +% Repeat while we have unassigned rows. +while (U(n+1)) + % Start with no path, no unchecked zeros, and no unexplored rows. + LR=zeros(1,n); + LC=zeros(1,n); + CH=zeros(1,n); + RH=[zeros(1,n) -1]; + + % No labelled columns. + SLC=[]; + + % Start path in first unassigned row. + r=U(n+1); + % Mark row with end-of-path label. + LR(r)=-1; + % Insert row first in labelled row set. + SLR=r; + + % Repeat until we manage to find an assignable zero. + while (1) + % If there are free zeros in row r + if (A(r,n+1)~=0) + % ...get column of first free zero. + l=-A(r,n+1); + + % If there are more free zeros in row r and row r in not + % yet marked as unexplored.. + if (A(r,l)~=0 & RH(r)==0) + % Insert row r first in unexplored list. + RH(r)=RH(n+1); + RH(n+1)=r; + + % Mark in which column the next unexplored zero in this row + % is. + CH(r)=-A(r,l); + end + else + % If all rows are explored.. + if (RH(n+1)<=0) + % Reduce matrix. + [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR); + end + + % Re-start with first unexplored row. + r=RH(n+1); + % Get column of next free zero in row r. + l=CH(r); + % Advance "column of next free zero". + CH(r)=-A(r,l); + % If this zero is last in the list.. + if (A(r,l)==0) + % ...remove row r from unexplored list. + RH(n+1)=RH(r); + RH(r)=0; + end + end + + % While the column l is labelled, i.e. in path. + while (LC(l)~=0) + % If row r is explored.. + if (RH(r)==0) + % If all rows are explored.. + if (RH(n+1)<=0) + % Reduce cost matrix. + [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR); + end + + % Re-start with first unexplored row. + r=RH(n+1); + end + + % Get column of next free zero in row r. + l=CH(r); + + % Advance "column of next free zero". + CH(r)=-A(r,l); + + % If this zero is last in list.. + if(A(r,l)==0) + % ...remove row r from unexplored list. + RH(n+1)=RH(r); + RH(r)=0; + end + end + + % If the column found is unassigned.. + if (C(l)==0) + % Flip all zeros along the path in LR,LC. + [A,C,U]=hmflip(A,C,LC,LR,U,l,r); + % ...and exit to continue with next unassigned row. + break; + else + % ...else add zero to path. + + % Label column l with row r. + LC(l)=r; + + % Add l to the set of labelled columns. + SLC=[SLC l]; + + % Continue with the row assigned to column l. + r=C(l); + + % Label row r with column l. + LR(r)=l; + + % Add r to the set of labelled rows. + SLR=[SLR r]; + end + end +end + +% Calculate the total cost. +T=sum(orig(logical(sparse(C,1:size(orig,2),1)))); + + +function A=hminired(A) +%HMINIRED Initial reduction of cost matrix for the Hungarian method. +% +%B=assredin(A) +%A - the unreduced cost matris. +%B - the reduced cost matrix with linked zeros in each row. + +% v1.0 96-06-13. Niclas Borlin, niclas@cs.umu.se. + +[m,n]=size(A); + +% Subtract column-minimum values from each column. +colMin=min(A); +A=A-colMin(ones(n,1),:); + +% Subtract row-minimum values from each row. +rowMin=min(A')'; +A=A-rowMin(:,ones(1,n)); + +% Get positions of all zeros. +[i,j]=find(A==0); + +% Extend A to give room for row zero list header column. +A(1,n+1)=0; +for k=1:n + % Get all column in this row. + cols=j(k==i)'; + % Insert pointers in matrix. + A(k,[n+1 cols])=[-cols 0]; +end + + +function [A,C,U]=hminiass(A) +%HMINIASS Initial assignment of the Hungarian method. +% +%[B,C,U]=hminiass(A) +%A - the reduced cost matrix. +%B - the reduced cost matrix, with assigned zeros removed from lists. +%C - a vector. C(J)=I means row I is assigned to column J, +% i.e. there is an assigned zero in position I,J. +%U - a vector with a linked list of unassigned rows. + +% v1.0 96-06-14. Niclas Borlin, niclas@cs.umu.se. + +[n,np1]=size(A); + +% Initalize return vectors. +C=zeros(1,n); +U=zeros(1,n+1); + +% Initialize last/next zero "pointers". +LZ=zeros(1,n); +NZ=zeros(1,n); + +for i=1:n + % Set j to first unassigned zero in row i. + lj=n+1; + j=-A(i,lj); + + % Repeat until we have no more zeros (j==0) or we find a zero + % in an unassigned column (c(j)==0). + + while (C(j)~=0) + % Advance lj and j in zero list. + lj=j; + j=-A(i,lj); + + % Stop if we hit end of list. + if (j==0) + break; + end + end + + if (j~=0) + % We found a zero in an unassigned column. + + % Assign row i to column j. + C(j)=i; + + % Remove A(i,j) from unassigned zero list. + A(i,lj)=A(i,j); + + % Update next/last unassigned zero pointers. + NZ(i)=-A(i,j); + LZ(i)=lj; + + % Indicate A(i,j) is an assigned zero. + A(i,j)=0; + else + % We found no zero in an unassigned column. + + % Check all zeros in this row. + + lj=n+1; + j=-A(i,lj); + + % Check all zeros in this row for a suitable zero in another row. + while (j~=0) + % Check the in the row assigned to this column. + r=C(j); + + % Pick up last/next pointers. + lm=LZ(r); + m=NZ(r); + + % Check all unchecked zeros in free list of this row. + while (m~=0) + % Stop if we find an unassigned column. + if (C(m)==0) + break; + end + + % Advance one step in list. + lm=m; + m=-A(r,lm); + end + + if (m==0) + % We failed on row r. Continue with next zero on row i. + lj=j; + j=-A(i,lj); + else + % We found a zero in an unassigned column. + + % Replace zero at (r,m) in unassigned list with zero at (r,j) + A(r,lm)=-j; + A(r,j)=A(r,m); + + % Update last/next pointers in row r. + NZ(r)=-A(r,m); + LZ(r)=j; + + % Mark A(r,m) as an assigned zero in the matrix . . . + A(r,m)=0; + + % ...and in the assignment vector. + C(m)=r; + + % Remove A(i,j) from unassigned list. + A(i,lj)=A(i,j); + + % Update last/next pointers in row r. + NZ(i)=-A(i,j); + LZ(i)=lj; + + % Mark A(r,m) as an assigned zero in the matrix . . . + A(i,j)=0; + + % ...and in the assignment vector. + C(j)=i; + + % Stop search. + break; + end + end + end +end + +% Create vector with list of unassigned rows. + +% Mark all rows have assignment. +r=zeros(1,n); +rows=C(C~=0); +r(rows)=rows; +empty=find(r==0); + +% Create vector with linked list of unassigned rows. +U=zeros(1,n+1); +U([n+1 empty])=[empty 0]; + + +function [A,C,U]=hmflip(A,C,LC,LR,U,l,r) +%HMFLIP Flip assignment state of all zeros along a path. +% +%[A,C,U]=hmflip(A,C,LC,LR,U,l,r) +%Input: +%A - the cost matrix. +%C - the assignment vector. +%LC - the column label vector. +%LR - the row label vector. +%U - the +%r,l - position of last zero in path. +%Output: +%A - updated cost matrix. +%C - updated assignment vector. +%U - updated unassigned row list vector. + +% v1.0 96-06-14. Niclas Borlin, niclas@cs.umu.se. + +n=size(A,1); + +while (1) + % Move assignment in column l to row r. + C(l)=r; + + % Find zero to be removed from zero list.. + + % Find zero before this. + m=find(A(r,:)==-l); + + % Link past this zero. + A(r,m)=A(r,l); + + A(r,l)=0; + + % If this was the first zero of the path.. + if (LR(r)<0) + ...remove row from unassigned row list and return. + U(n+1)=U(r); + U(r)=0; + return; + else + + % Move back in this row along the path and get column of next zero. + l=LR(r); + + % Insert zero at (r,l) first in zero list. + A(r,l)=A(r,n+1); + A(r,n+1)=-l; + + % Continue back along the column to get row of next zero in path. + r=LC(l); + end +end + + +function [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR) +%HMREDUCE Reduce parts of cost matrix in the Hungerian method. +% +%[A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR) +%Input: +%A - Cost matrix. +%CH - vector of column of 'next zeros' in each row. +%RH - vector with list of unexplored rows. +%LC - column labels. +%RC - row labels. +%SLC - set of column labels. +%SLR - set of row labels. +% +%Output: +%A - Reduced cost matrix. +%CH - Updated vector of 'next zeros' in each row. +%RH - Updated vector of unexplored rows. + +% v1.0 96-06-14. Niclas Borlin, niclas@cs.umu.se. + +n=size(A,1); + +% Find which rows are covered, i.e. unlabelled. +coveredRows=LR==0; + +% Find which columns are covered, i.e. labelled. +coveredCols=LC~=0; + +r=find(~coveredRows); +c=find(~coveredCols); + +% Get minimum of uncovered elements. +m=min(min(A(r,c))); + +% Subtract minimum from all uncovered elements. +A(r,c)=A(r,c)-m; + +% Check all uncovered columns.. +for j=c + % ...and uncovered rows in path order.. + for i=SLR + % If this is a (new) zero.. + if (A(i,j)==0) + % If the row is not in unexplored list.. + if (RH(i)==0) + % ...insert it first in unexplored list. + RH(i)=RH(n+1); + RH(n+1)=i; + % Mark this zero as "next free" in this row. + CH(i)=j; + end + % Find last unassigned zero on row I. + row=A(i,:); + colsInList=-row(row<0); + if (length(colsInList)==0) + % No zeros in the list. + l=n+1; + else + l=colsInList(row(colsInList)==0); + end + % Append this zero to end of list. + A(i,l)=-j; + end + end +end + +% Add minimum to all doubly covered elements. +r=find(coveredRows); +c=find(coveredCols); + +% Take care of the zeros we will remove. +[i,j]=find(A(r,c)<=0); + +i=r(i); +j=c(j); + +for k=1:length(i) + % Find zero before this in this row. + lj=find(A(i(k),:)==-j(k)); + % Link past it. + A(i(k),lj)=A(i(k),j(k)); + % Mark it as assigned. + A(i(k),j(k))=0; +end + +A(r,c)=A(r,c)+m;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/image_rgb.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function image_rgb(M) +% Show a matrix of integers as a color image. +% This is like imagesc, except we know what the mapping is from integer to color. +% If entries of M contain integers in {1,2,3}, we map +% this to red/green/blue + +cmap = [1 0 0; % red + 0 1 0; % green + 0 0 1; % blue + 127/255 1 212/255]; % aquamarine +image(M) +set(gcf,'colormap', cmap); + +if 1 + % make dummy handles, one per object type, for the legend + str = {}; + for i=1:size(cmap,1) + dummy_handle(i) = line([0 0.1], [0 0.1]); + set(dummy_handle(i), 'color', cmap(i,:)); + set(dummy_handle(i), 'linewidth', 2); + str{i} = num2str(i); + end + legend(dummy_handle, str, -1); +end + +if 0 +[nrows ncols] = size(M); +img = zeros(nrows, ncols, 3); +for r=1:nrows + for c=1:ncols + q = M(r,c); + img(r,c,q) = 1; + end +end +image(img) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/imresizeAspect.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function img = imresizeAspect(img, maxSize) +% function img = imresizeAspect(img, maxSize) +% If image is larger than max size, reduce size, preserving aspect ratio of input. +% +% If size(input) = [y x] and maxSize = [yy xx], +% then size(output) is given by the following (where a=y/x) +% if y<yy & x<xx then [y x] +% else if a<1 then [a*xx xx] +% else [yy yy/a] + +% For why we use bilinear, +% See http://nickyguides.digital-digest.com/bilinear-vs-bicubic.htm + +if isempty(maxSize), return; end + +[y x c] = size(img); +a= y/x; +yy = maxSize(1); xx = maxSize(2); +if y <= yy & x <= xx + % no-op +else + if a < 1 + img = imresize(img, ceil([a*xx xx]), 'bilinear'); + else + img = imresize(img, ceil([yy yy/a]), 'bilinear'); + end + fprintf('resizing from %dx%d to %dx%d\n', y, x, size(img,1), size(img,2)); +end + + +%test +if 0 + maxSize = [240 320]; + %img = imread('C:\Images\Wearables\web_static_office\8.jpg'); + %img = imread('C:\Images\Wearables\web_static_office\billandkirsten.jpg'); + %img = imread('C:\Images\Wearables\web_static_street_april\p5.jpg'); + img = imread('C:\Images\Wearables\Database_static_street\art11.jpg'); + img2 = imresizeAspect(img, maxSize); + figure(1); clf; imshow(img) + figure(2); clf; imshow(img2) + fprintf('%dx%d (%5.3f) to %dx%d (%5.3f)\n', ... + size(img,1), size(img,2), size(img,1)/size(img,2), ... + size(img2,1), size(img2,2), size(img2,1)/size(img2,2)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/ind2subv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function sub = ind2subv(siz,index) +%IND2SUBV Subscript vector from linear index. +% IND2SUBV(SIZ,IND) returns a vector of the equivalent subscript values +% corresponding to a single index into an array of size SIZ. +% If IND is a vector, then the result is a matrix, with subscript vectors +% as rows. + +%sub = ind2subvTest(siz, index); +n = length(siz); +if n==0, sub = index; return; end % added by KPM 17 Nov 07 +cum_size = cumprod(siz(:)'); +prev_cum_size = [1 cum_size(1:end-1)]; +index = index(:) - 1; +sub = rem(repmat(index,1,n),repmat(cum_size,length(index),1)); +sub = floor(sub ./ repmat(prev_cum_size,length(index),1))+1; + +% slow way +%for dim = n:-1:1 +% sub(:,dim) = floor(index/cum_size(dim))+1; +% index = rem(index,cum_size(dim)); +%end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/ind2subvKPM.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,176 @@ +/* C mex version of ind2subv.m in misc directory */ +/* 2 input, 1 output */ +/* siz, ndx */ +/* sub */ + +#include "mex.h" +#include <math.h> + +void rbinary(int num, int n, double *rbits){ + int i, mask; + num = num - 1; + + mask = 1 << (n-1); /* mask = 00100...0 , where the 1 is in column n (rightmost = col 1) */ + for (i = 0; i < n; i++) { + rbits[n-i-1] = ((num & mask) == 0) ? 1 : 2; + num <<= 1; + } +} + +void ind_subv(int num, const double *sizes, int n, double *rbits){ + int i; + int *cumprod; + + cumprod = malloc(n * sizeof(int)); + num = num - 1; + cumprod[0] = 1; + for (i = 0; i < n-1; i++) + cumprod[i+1] = cumprod[i] * (int)sizes[i]; + for (i = n-1; i >= 0; i--) { + rbits[i] = ((int)floor(num / cumprod[i])) + 1; + num = num % cumprod[i]; + } + free(cumprod); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, k, nCol, nRow, nnRow, binary, count, temp, temp1, start; + double *pSize, *pNdx, *pr; + double ndx; + int *subv, *cumprod, *templai; + + pSize = mxGetPr(prhs[0]); + pNdx = mxGetPr(prhs[1]); + nCol = mxGetNumberOfElements(prhs[0]); + nnRow = mxGetNumberOfElements(prhs[1]); + + nRow = 1; + for(i=0; i<nCol; i++){ + nRow *= (int)pSize[i]; + } + + if(nCol == 0){ + plhs[0] = mxDuplicateArray(prhs[1]); + return; + } + + binary = 2; + for (i = 0; i < nCol; i++){ + if (pSize[i] > 2.0){ + binary = 0; + break; + } + else if((int)pSize[i] == 1){ + binary = 1; + } + } + + if(nnRow == 1){ + ndx = mxGetScalar(prhs[1]); + plhs[0] = mxCreateDoubleMatrix(1, nCol, mxREAL); + pr = mxGetPr(plhs[0]); + if(binary == 2)rbinary((int)ndx, nCol, pr); + else ind_subv((int)ndx, pSize, nCol, pr); + return; + } + + plhs[0] = mxCreateDoubleMatrix(nnRow, nCol, mxREAL); + pr = mxGetPr(plhs[0]); + + subv = malloc(nRow * nCol * sizeof(int)); + + if (binary == 2) { + for(j=0; j<nCol; j++){ + temp = (1 << j); + temp1 = j * nRow; + for(i=0; i<nRow; i++){ + subv[temp1 + i] = ((i & temp) == 0) ? 1 : 2; + } + } + } + else if(binary == 1){ + cumprod = (int *)malloc(nCol * sizeof(int)); + templai = (int *)malloc(nCol * sizeof(int)); + cumprod[0] = 1; + templai[0] = nRow; + for(i=1; i<nCol; i++){ + k = (int)pSize[i-1] - 1; + cumprod[i] = cumprod[i-1] << k; + templai[i] = templai[i-1] >> k; + } + for(j=0; j<nCol; j++){ + temp1 = j * nRow; + if(pSize[j] == 1.0){ + for(i=0; i<nRow; i++){ + subv[temp1 + i] = 1; + } + } + else{ + temp = 1; + count = 0; + for(i=0; i<templai[j]; i++){ + if(temp > 2) temp = 1; + for(k=0; k<cumprod[j]; k++){ + subv[temp1 + count] = temp; + count++; + } + temp++; + } + } + } + free(templai); + free(cumprod); + } + else { + cumprod = (int *)malloc(nCol * sizeof(int)); + templai = (int *)malloc(nCol * sizeof(int)); + cumprod[0] = 1; + for(i=1; i<nCol; i++){ + cumprod[i] = (int)(cumprod[i-1] * pSize[i-1]); + } + templai[0] = nRow; + for(i=1; i<nCol; i++){ + templai[i] = nRow / cumprod[i]; + } + for(j=0; j<nCol; j++){ + temp1 = j * nRow; + if(pSize[j] == 1.0){ + for(i=0; i<nRow; i++){ + subv[temp1 + i] = 1; + } + } + else{ + temp = 1; + count = 0; + for(i=0; i<templai[j]; i++){ + if(temp > (int)pSize[j]) temp = 1; + for(k=0; k<cumprod[j]; k++){ + subv[temp1 + count] = temp; + count++; + } + temp++; + } + } + } + free(cumprod); + free(templai); + } + + count = 0; + for(j=0; j<nCol; j++){ + temp1 = j * nRow; + for(i=0; i<nnRow; i++){ + start = (int)pNdx[i] - 1; + pr[count] = subv[temp1 + start]; + count++; + } + } + free(subv); +} + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/ind2subvKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function sub = ind2subvKPM(siz, ndx) +% IND2SUBV Like the built-in ind2sub, but returns the answer as a row vector. +% sub = ind2subv(siz, ndx) +% +% siz and ndx can be row or column vectors. +% sub will be of size length(ndx) * length(siz). +% +% Example +% ind2subv([2 2 2], 1:8) returns +% [1 1 1 +% 2 1 1 +% ... +% 2 2 2] +% That is, the leftmost digit toggle fastest. +% +% See also SUBV2IND + +n = length(siz); + + +if n==0 + sub = ndx; + return; +end + +if all(siz==2) + sub = dec2bitv(ndx-1, n); + sub = sub(:,n:-1:1)+1; + return; +end + +cp = [1 cumprod(siz(:)')]; +ndx = ndx(:) - 1; +sub = zeros(length(ndx), n); +for i = n:-1:1 % i'th digit + sub(:,i) = floor(ndx/cp(i))+1; + ndx = rem(ndx,cp(i)); +end + + + +%%%%%%%%%% + +function bits = dec2bitv(d,n) +% DEC2BITV Convert a decimal integer to a bit vector. +% bits = dec2bitv(d,n) is just like the built-in dec2bin, except the answer is a vector, not a string. +% n is an optional minimum length on the bit vector. +% If d is a vector, each row of the output array will be a bit vector. + + +if (nargin<2) + n=1; % Need at least one digit even for 0. +end +d = d(:); + +[f,e]=log2(max(d)); % How many digits do we need to represent the numbers? +bits=rem(floor(d*pow2(1-max(n,e):0)),2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/ind2subvMinka.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function sub = ind2subvMinka(siz,index) +%IND2SUBV Subscript vector from linear index. +% IND2SUBV(SIZ,IND) returns a vector of the equivalent subscript values +% corresponding to a single index into an array of size SIZ. +% If IND is a vector, then the result is a matrix, with subscript vectors +% as rows. + +% Written by Tom Minka +% (c) Microsoft Corporation. All rights reserved. + +n = length(siz); +if n==0, sub = index; return; end % added by KPM 17 Nov 07 +cum_size = cumprod(siz(:)'); +prev_cum_size = [1 cum_size(1:end-1)]; +index = index(:) - 1; +sub = rem(repmat(index,1,n),repmat(cum_size,length(index),1)); +sub = floor(sub ./ repmat(prev_cum_size,length(index),1))+1; + +% slow way +%for dim = n:-1:1 +% sub(:,dim) = floor(index/cum_size(dim))+1; +% index = rem(index,cum_size(dim)); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/ind2subvTest.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function sub = ind2subvTest(siz,index) +%IND2SUBV Subscript vector from linear index. +% IND2SUBV(SIZ,IND) returns a vector of the equivalent subscript values +% corresponding to a single index into an array of size SIZ. +% If IND is a vector, then the result is a matrix, with subscript vectors +% as rows. + +sub = ind2subvMinka(siz, index); +subKPM = ind2subvKPM(siz, index); +assert(isequal(sub, subKPM))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/initFigures.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +% initFigures +% Position 6 figures on the edges of the screen. +% [xmin ymin w h] where (0,0) = bottom left +% Numbers assume screen resolution is 1024 x 1280 + +global FIGNUM NUMFIGS +FIGNUM = 1; NUMFIGS = 6; + +screenMain = true; % set to false if initializing figures for second screen +%screenMain = false; + +if screenMain + xoff = 0; +else + %xoff = 1280; + xoff = -1280; +end + +% 2 x 3 design +w = 400; h = 300; +xs = [10 450 875] + xoff; +ys = [650 40]; + +if 0 +% 3x3 design +w = 350; h = 250; +xs = [10 380 750]+xoff; +ys = [700 350 10]; +end + + +Nfigs = length(xs)*length(ys); +if screenMain + fig = 1; +else + fig = Nfigs + 1; +end + +for yi=1:length(ys) + for xi=1:length(xs) + figure(fig); + set(gcf, 'position', [xs(xi) ys(yi) w h]); + fig = fig + 1; + end +end + +% To plot something on the next available figure (with wrap around), use +% sfigure(FIGNUM); clf; FIGNUM = wrap(FIGNUM+1, NUMFIGS);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/installC_KPMtools.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +mex ind2subv.c +mex subv2ind.c +mex normalise.c +mex -c mexutil.c +if ~isunix + mex repmatC.c mexutil.obj +else + mex repmatC.c mexutil.o +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/is_psd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function b = positive_semidefinite(M) +% +% Return true iff v M v' >= 0 for any vector v. +% We do this by checking that all the eigenvalues are non-negative. + +E = eig(M); +if length(find(E>=0)) == length(E) + b = 1; +else + b = 0; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/is_stochastic.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function p = is_stochastic(T) +% IS_STOCHASTIC Is the argument a stochastic matrix, i.e., the sum over the last dimension is 1. +% p = is_stochastic(T) + +p = approxeq(T, mk_stochastic(T));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/isemptycell.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function E = isemptycell(C) +% ISEMPTYCELL Apply the isempty function to each element of a cell array +% E = isemptycell(C) +% +% This is equivalent to E = cellfun('isempty', C), +% where cellfun is a function built-in to matlab version 5.3 or newer. + +if 0 % all(version('-release') >= 12) + E = cellfun('isempty', C); +else + E = zeros(size(C)); + for i=1:prod(size(C)) + E(i) = isempty(C{i}); + end + E = logical(E); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/isequalKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function p = isequalKPM(a,b) + +if isempty(a) & isempty(b) + p = 1; +else + p = isequal(a,b); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/isposdef.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function b = isposdef(a) +% ISPOSDEF Test for positive definite matrix. +% ISPOSDEF(A) returns 1 if A is positive definite, 0 otherwise. +% Using chol is much more efficient than computing eigenvectors. + +% From Tom Minka's lightspeed toolbox + +[R,p] = chol(a); +b = (p == 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/isscalarBNT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function p = isscalarBNT(v) +% ISSCALAR Returns 1 if all dimensions have size 1. +% p = isscalar(v) + +p = (prod(size(v))==1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/isvectorBNT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function p = isvectorBNT(v) +% ISVECTOR Returns 1 if all but one dimension have size 1. +% p = isvector(v) +% +% Example: isvector(rand(1,2,1)) = 1, isvector(rand(2,2)) = 0. + +s=size(v); +p = (ndims(v)<=2) & (s(1) == 1 | s(2) == 1); +%p = sum( size(v) > 1) <= 1; % Peter Acklam's solution
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/junk.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ + + m = mxGetM(prhs[0]); + n = mxGetN(prhs[0]); + pr = mxGetPr(prhs[0]); + pi = mxGetPi(prhs[0]); + cmplx = (pi == NULL ? 0 : 1); + + /* Allocate space for sparse matrix. + * NOTE: Assume at most 20% of the data is sparse. Use ceil + * to cause it to round up. + */ + + percent_sparse = 0.2; + nzmax = (int)ceil((double)m*(double)n*percent_sparse); + + plhs[0] = mxCreateSparse(m,n,nzmax,cmplx); + sr = mxGetPr(plhs[0]); + si = mxGetPi(plhs[0]); + irs = mxGetIr(plhs[0]); + jcs = mxGetJc(plhs[0]); + + /* Copy nonzeros. */ + k = 0; + isfull = 0; + for (j = 0; (j < n); j++) { + int i; + jcs[j] = k; + for (i = 0; (i < m); i++) { + if (IsNonZero(pr[i]) || (cmplx && IsNonZero(pi[i]))) { + + /* Check to see if non-zero element will fit in + * allocated output array. If not, increase + * percent_sparse by 10%, recalculate nzmax, and augment + * the sparse array. + */ + if (k >= nzmax) { + int oldnzmax = nzmax; + percent_sparse += 0.1; + nzmax = (int)ceil((double)m*(double)n*percent_sparse); + + /* Make sure nzmax increases atleast by 1. */ + if (oldnzmax == nzmax) + nzmax++; + + mxSetNzmax(plhs[0], nzmax); + mxSetPr(plhs[0], mxRealloc(sr, nzmax*sizeof(double))); + if (si != NULL) + mxSetPi(plhs[0], mxRealloc(si, nzmax*sizeof(double))); + mxSetIr(plhs[0], mxRealloc(irs, nzmax*sizeof(int))); + + sr = mxGetPr(plhs[0]); + si = mxGetPi(plhs[0]); + irs = mxGetIr(plhs[0]); + } + sr[k] = pr[i]; + if (cmplx) { + si[k] = pi[i]; + } + irs[k] = i; + k++; + } + } + pr += m; + pi += m; + } + jcs[n] = k; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/loadcell.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +function [lc,dflag,dattype]=loadcell(fname,delim,exclusions,options); +%function [lc,dflag,numdata]=loadcell(fname,delim,exclusions); +% +% loadcell loads a cell array with character delimited +% data, which can have variable length lines and content. +% Numeric values are converted from string to double +% unless options is a string containing 'string'. +% +% loadcell is for use with small datasets. It is not optimised +% for large datasets. +% +% fname is the filename to be loaded +% +% delim is/are the relevant delimiter(s). If char(10) is included +% newlines are simply treated as delimiters and a 1-d array is created. +% +% exclusions are the set of characters to be treated as paired +% braces: line ends or delimiters within braces are ignored. +% braces are single characters and any brace can pair with +% any other brace: no type pair checking is done. +% +% options can be omitted or can contain 'string' if no numeric +% conversion is required, 'single' if multiple adjacent seperators +% should not be treated as one, 'free' if all linefeeds should be stripped +% first and 'empty2num' if empty fields are to be treated as numeric +% zeros rather than an empty character set. Combine options using +% concatenation. +% +% lc is a cell array containing the loaded data. +% +% dflag is a set of flags denoting the (i,j) values where data was entered +% dflag(i,j)=1 implies lc(i,j) was loaded from the data, and not just set +% to empty, say, by default. +% +% numdata is an array numdata(i,j)=NaN implies +% lc(i,j) is a string, otherwise it stores the number at i,j. +% This will occur regardless of whether the 'string' option is set. +% +% lc will return -1 if the file is not found or could not be +% opened. +% +% Hint: numdata+(1/dflag-1) provides a concise descriptor for the numeric data +% Inf=not loaded +% NaN=was string or empty set. +% otherwise numeric +% +% EXAMPLE +% +%[a,b]=loadcell('resultsfile',[',' char(9)],'"','single-string'); +% will load file 'resultsfile' into variable a, treating any of tab or +% comma as delimiters. Delimiters or carriage returns lying +% between two double inverted commas will be ignored. Two adjacent delimiters +% will count twice, and all data will be kept as a string. +% +% Note: in space-separated data 'single' would generally be omitted, +% wheras in comma-seperated data it would be included. +% +% Note the exclusion characters will remain in the final data, and any data +% contained within or containing exclusion characters will not be +% converted to numerics. +% +% (c) Amos Storkey 2002 +% v b160702 + +% MATLAB is incapable of loading variable length lines or variable type values +% with a whole file command under the standard library sets. This mfile +% fills that gap. +if (nargin<4) + options=' '; +end; +dflag = []; +%Open file +fid=fopen(fname,'rt'); +%Cannot open: return -1 +if (fid<0) + lc=-1; +else + fullfile=fread(fid,'uchar=>char')'; + %Strip LF if free is set + if ~isempty(findstr(options,'free')) + fullfile=strrep(fullfile,char(10),''); + end; + %Find all delimiters + delimpos=[]; + for s=1:length(delim) + delimpos=[delimpos find(fullfile==delim(s))]; + end + %Find all eol + endpos=find(fullfile==char(10)); + endpos=setdiff(endpos,delimpos); + %find all exclusions + xclpos=[]; + for s=1:length(exclusions); + xclpos=[xclpos find(fullfile==exclusions(s))]; + end + sort(xclpos); + xclpos=[xclpos(1:2:end-1);xclpos(2:2:end)]; + %Combine eol and delimiters + jointpos=union(delimpos,endpos); + t=1; + %Remove delim/eol within exclusion pairs + removedelim=[]; + for s=1:length(jointpos) + if any((jointpos(s)>xclpos(1,:)) & (jointpos(s)<xclpos(2,:))) + removedelim(t)=jointpos(s); + t=t+1; + end; + + end + %and add start point + jointpos=[0 setdiff(jointpos,removedelim)]; + i=1; + j=1; + posind=1; + multflag=isempty(findstr(options,'single')); + stringflag=~isempty(findstr(options,'string')); + emptnum=~isempty(findstr(options,'empty2num')); + %Run through + while (posind<(length(jointpos))) + %Get current field + tempstr=fullfile(jointpos(posind)+1:jointpos(posind+1)-1); + %If empty only continue if adjacent delim count. + if ~(isempty(tempstr) & multflag); + %This ij is set + dflag(i,j)=1; + %Convert to num + tempno=str2double([tempstr]); + %If emptystring convert to zero if emptnum set + if (isempty(tempstr) & emptnum) + tempno=0; + end; + %Set dattype to no (or NaN if not a num + dattype(i,j)=tempno; + %If NaN set lc to string else to num if stringflag not set + if (isnan(tempno) | stringflag) + lc{i,j}=tempstr; + else + lc{i,j}=tempno; + end; + %Next j + j=j+1; + end; + %If eol inc i and reset j + if ismember(jointpos(posind+1),endpos) + i=i+1; + j=1; + end; + %Inc to next delim + posind=posind+1; + end; +end; +%Logicalise dflag +dflag=logical(dflag);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/logb.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function x = logb(y, base) +% logb - logarithm to base b +% function x = logb(y, base) + +x = log(y) ./ log(base);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/logdet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function y = logdet(A) +% log(det(A)) where A is positive-definite. +% This is faster and more stable than using log(det(A)). + +% From Tom Minka's lightspeed toolbox + +U = chol(A); +y = 2*sum(log(diag(U)));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/logsum.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function y=logsum(x,d) +%LOGSUM logsum(x,d)=log(sum(exp(x),d)) +% d gives dimension to sum along + +% Copyright (C) Mike Brookes 1998 +% +% Last modified Mon Oct 12 15:47:25 1998 +% +% VOICEBOX is a MATLAB toolbox for speech processing. Home page is at +% http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html +% + +if nargin==1 + d=[find(size(x)-1) 1]; + d=d(1); +end +n=size(x,d); +if n<=1, y=x; return; end +s=size(x); +p=[d:ndims(x) 1:d-1]; +z=reshape(permute(x,p),n,prod(s)/n); + +y=max(z); +y=y+log(sum(exp(z-y(ones(n,1),:)))); + +s(d)=1; +y=ipermute(reshape(y,s(p)),p); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/logsum_simple.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function result = logsum(logv) + +len = length(logv); +if (len<2); + error('Subroutine logsum cannot sum less than 2 terms.'); +end; + +% First two terms +if (logv(2)<logv(1)), + result = logv(1) + log( 1 + exp( logv(2)-logv(1) ) ); +else, + result = logv(2) + log( 1 + exp( logv(1)-logv(2) ) ); +end; + +% Remaining terms +for (i=3:len), + term = logv(i); + if (result<term), + result = term + log( 1 + exp( result-term ) ); + else, + result = result + log( 1 + exp( term-result ) ); + end; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/logsum_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +p1 = log(1e-5); +p2 = log(5*1e-6); +p3 = log(sum(exp([p1 p2]))) +p4 = logsumexp([p1 p2],2) +p5 = logsum([p1 p2]) +p6 = logsum([p1 p2])
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/logsumexp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function s = logsumexp(a, dim) +% Returns log(sum(exp(a),dim)) while avoiding numerical underflow. +% Default is dim = 1 (rows) or dim=2 for a row vector +% logsumexp(a, 2) will sum across columns instead of rows + +% Written by Tom Minka, modified by Kevin Murphy + +if nargin < 2 + dim = 1; + if ndims(a) <= 2 & size(a,1)==1 + dim = 2; + end +end + +% subtract the largest in each column +[y, i] = max(a,[],dim); +dims = ones(1,ndims(a)); +dims(dim) = size(a,dim); +a = a - repmat(y, dims); +s = y + log(sum(exp(a),dim)); +%i = find(~finite(y)); +%if ~isempty(i) +% s(i) = y(i); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/logsumexpv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function s = logsumexpv(a) +% Returns log(sum(exp(a))) while avoiding numerical underflow. +% +% e.g., log(e^a1 + e^a2) = a1 + log(1 + e^(a2-a1)) if a1>a2 +% If a1 ~ a2, and a1>a2, then e^(a2-a1) is exp(small negative number), +% which can be computed without underflow. + +% Same as logsumexp, except we assume a is a vector. +% This avoids a call to repmat, which takes 50% of the time! + +a = a(:)'; % make row vector +m = max(a); +b = a - m*ones(1,length(a)); +s = m + log(sum(exp(b))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mahal2conf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +% MAHAL2CONF - Translates a Mahalanobis distance into a confidence +% interval. Consider a multivariate Gaussian +% distribution of the form +% +% p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C))) +% +% where MD(x, m, P) is the Mahalanobis distance from x +% to m under P: +% +% MD(x, m, P) = (x - m) * P * (x - m)' +% +% A particular Mahalanobis distance k identifies an +% ellipsoid centered at the mean of the distribution. +% The confidence interval associated with this ellipsoid +% is the probability mass enclosed by it. +% +% If X is an d dimensional Gaussian-distributed vector, +% then the Mahalanobis distance of X is distributed +% according to the Chi-squared distribution with d +% degrees of freedom. Thus, the confidence interval is +% determined by integrating the chi squared distribution +% up to the Mahalanobis distance of the measurement. +% +% Usage: +% +% c = mahal2conf(m, d); +% +% Inputs: +% +% m - the Mahalanobis radius of the ellipsoid +% d - the number of dimensions of the Gaussian distribution +% +% Outputs: +% +% c - the confidence interval, i.e., the fraction of +% probability mass enclosed by the ellipsoid with the +% supplied Mahalanobis distance +% +% See also: CONF2MAHAL + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function c = mahal2conf(m, d) + +c = chi2cdf(m, d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/marg_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) +% MARG_TABLE Marginalize a table +% smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) + +if nargin < 5, maximize = 0; end + + +smallT = myreshape(bigT, bigsz); % make sure it is a multi-dim array +sum_over = mysetdiff(bigdom, onto); +ndx = find_equiv_posns(sum_over, bigdom); +if maximize + for i=1:length(ndx) + smallT = max(smallT, [], ndx(i)); + end +else + for i=1:length(ndx) + smallT = sum(smallT, ndx(i)); + end +end + + +ns = zeros(1, max(bigdom)); +%ns(bigdom) = mysize(bigT); % ignores trailing dimensions of size 1 +ns(bigdom) = bigsz; + +smallT = squeeze(smallT); % remove all dimensions of size 1 +smallT = myreshape(smallT, ns(onto)); % put back relevant dims of size 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/marginalize_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +function smallT = marginalize_table(bigT, bigdom, bigsz, onto, maximize) +% MARG_TABLE Marginalize a table +% function smallT = marginalize_table(bigT, bigdom, bigsz, onto, maximize) + +% Like marg_table in BNT, except we do not assume the domains are sorted + +if nargin < 5, maximize = 0; end + + +smallT = myreshape(bigT, bigsz); % make sure it is a multi-dim array +sum_over = mysetdiff(bigdom, onto); +ndx = find_equiv_posns(sum_over, bigdom); +if maximize + for i=1:length(ndx) + smallT = max(smallT, [], ndx(i)); + end +else + for i=1:length(ndx) + smallT = sum(smallT, ndx(i)); + end +end + + +ns = zeros(1, max(bigdom)); +%ns(bigdom) = mysize(bigT); % ignores trailing dimensions of size 1 +ns(bigdom) = bigsz; + +% If onto has a different ordering than bigdom, the following +% will produce the wrong results + +%smallT = squeeze(smallT); % remove all dimensions of size 1 +%smallT = myreshape(smallT, ns(onto)); % put back relevant dims of size 1 + +% so permute dimensions to match desired ordering (as specified by onto) + + +% like find_equiv_posns, but keeps ordering +outdom = [onto sum_over]; +for i=1:length(outdom) + j = find(bigdom==outdom(i)); + match(i) = j; +end +outdom = [onto sum_over]; +for i=1:length(outdom) + j = find(bigdom==outdom(i)); + match(i) = j; +end +if match ~= 1 + smallT = permute(smallT, match); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/matprint.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +% MATPRINT - prints a matrix with specified format string +% +% Usage: matprint(a, fmt, fid) +% +% a - Matrix to be printed. +% fmt - C style format string to use for each value. +% fid - Optional file id. +% +% Eg. matprint(a,'%3.1f') will print each entry to 1 decimal place + +% Peter Kovesi +% School of Computer Science & Software Engineering +% The University of Western Australia +% pk @ csse uwa edu au +% http://www.csse.uwa.edu.au/~pk +% +% March 2002 + +function matprint(a, fmt, fid) + + if nargin < 3 + fid = 1; + end + + [rows,cols] = size(a); + + % Construct a format string for each row of the matrix consisting of + % 'cols' copies of the number formating specification + fmtstr = []; + for c = 1:cols + fmtstr = [fmtstr, ' ', fmt]; + end + fmtstr = [fmtstr '\n']; % Add a line feed + + fprintf(fid, fmtstr, a'); % Print the transpose of the matrix because + % fprintf runs down the columns of a matrix. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/max_mult.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +/* C mex version of max_mult.m in BPMRF2 directory */ +/* gcc -Wall -I/mit/matlab_v6.5/distrib/bin/glnx86 -c max_mult.c */ + +#include <math.h> +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + int rows,cols,common,m,n,p; + double y1, y2; + double *arr1, *arr2, *arr3; + + + if (nrhs!=2 || nlhs>1) + mexErrMsgTxt("max_mult requires two inputs and one output"); + if (mxIsChar(prhs[0]) || mxIsClass(prhs[0], "sparse") || mxIsComplex(prhs[0]) + || mxIsChar(prhs[1]) || mxIsClass(prhs[1], "sparse") || mxIsComplex(prhs[1])) + mexErrMsgTxt("Inputs must be real, full, and nonstring"); + if (mxGetN(prhs[0])!=mxGetM(prhs[1])) + mexErrMsgTxt("The number of columns of A must be the same as the number of rows of x"); + + + arr1=mxGetPr(prhs[0]); + arr2=mxGetPr(prhs[1]); + p=mxGetN(prhs[0]); + m=mxGetM(prhs[0]); + n=mxGetN(prhs[1]); + plhs[0]=mxCreateDoubleMatrix(m, n, mxREAL); + arr3=mxMalloc(m*n*sizeof(double)); + + for (rows=0; rows<m ; rows++) + for (cols=0; cols<n ; cols++) + { + y1=arr1[rows]*arr2[cols*p]; + for (common=1; common<p; common++) + { + y2=arr1[rows+common*m]*arr2[common+cols*p]; + if (y2>y1) + y1=y2; + } + arr3[rows+cols*m]=y1; + } + + mxSetPr(plhs[0], arr3); + +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/max_mult.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function y=max_mult(A,x) +% MAX_MULT Like matrix multiplication, but sum gets replaced by max +% function y=max_mult(A,x) y(i) = max_j A(i,j) x(j) + +%X=ones(size(A,1),1) * x(:)'; % X(j,i) = x(i) +%y=max(A.*X, [], 2); + +% This is faster +if size(x,2)==1 + X=x*ones(1,size(A,1)); % X(i,j) = x(i) + y=max(A'.*X)'; +else +%this works for arbitrarily sized A and x (but is ugly, and slower than above) + X=repmat(x, [1 1 size(A,1)]); + B=repmat(A, [1 1 size(x,2)]); + C=permute(B,[2 3 1]); + y=permute(max(C.*X),[3 2 1]); +% this is even slower, as is using squeeze instead of permute +% Y=permute(X, [3 1 2]); +% y=permute(max(Y.*B, [], 2), [1 3 2]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mexutil.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +#include "mexutil.h" + +/* Functions to create uninitialized arrays. */ + +mxArray *mxCreateNumericArrayE(int ndim, const int *dims, + mxClassID class, mxComplexity ComplexFlag) +{ + mxArray *a; + int i, *dims1 = mxMalloc(ndim*sizeof(int)); + size_t sz = 1; + for(i=0;i<ndim;i++) { + sz *= dims[i]; + dims1[i] = 1; + } + a = mxCreateNumericArray(ndim,dims1,class,ComplexFlag); + sz *= mxGetElementSize(a); + mxSetDimensions(a, dims, ndim); + mxFree(dims1); + mxSetData(a, mxRealloc(mxGetData(a), sz)); + if(ComplexFlag == mxCOMPLEX) { + mxSetPi(a, mxRealloc(mxGetPi(a),sz)); + } + return a; +} +mxArray *mxCreateNumericMatrixE(int m, int n, mxClassID class, + mxComplexity ComplexFlag) +{ + size_t sz = m*n*sizeof(double); + mxArray *a = mxCreateNumericMatrix(1, 1, class, ComplexFlag); + mxSetM(a,m); + mxSetN(a,n); + mxSetPr(a, mxRealloc(mxGetPr(a),sz)); + if(ComplexFlag == mxCOMPLEX) { + mxSetPi(a, mxRealloc(mxGetPi(a),sz)); + } + return a; +} +mxArray *mxCreateDoubleMatrixE(int m, int n, + mxComplexity ComplexFlag) +{ + return mxCreateNumericMatrixE(m,n,mxDOUBLE_CLASS,ComplexFlag); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mexutil.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +#include "mex.h" + +mxArray *mxCreateNumericArrayE(int ndim, const int *dims, + mxClassID class, mxComplexity ComplexFlag); +mxArray *mxCreateNumericMatrixE(int m, int n, mxClassID class, + mxComplexity ComplexFlag); +mxArray *mxCreateDoubleMatrixE(int m, int n, + mxComplexity ComplexFlag);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mk_multi_index.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function index = mk_multi_index(n, dims, vals) +% MK_MULTI_INDEX Compute the indices of the submatrix where dims(i)=vals(i). +% index = mk_multi_index(n, dims, vals) +% +% Example: +% index = mk_multi_index(3, [1 3], [3 2]) +% gives index = {3, ':', 2}, which will select out dim 1 = 3 and dim 3 = 2 +% So if A(:,:,1)=[1 2;3 4; 5 6]; A(:,:,2)=[7 8; 9 10; 11 12] +% then A(index{:}) = [11 12]: + +if n==0 + index = { 1 }; + return; +end + +index = cell(1,n); +for i=1:n + index{i} = ':'; +end +for i=1:length(dims) + index{dims(i)} = vals(i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mk_stochastic.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function [T,Z] = mk_stochastic(T) +% MK_STOCHASTIC Ensure the argument is a stochastic matrix, i.e., the sum over the last dimension is 1. +% [T,Z] = mk_stochastic(T) +% +% If T is a vector, it will sum to 1. +% If T is a matrix, each row will sum to 1. +% If T is a 3D array, then sum_k T(i,j,k) = 1 for all i,j. + +% Set zeros to 1 before dividing +% This is valid since S(j) = 0 iff T(i,j) = 0 for all j + +if (ndims(T)==2) & (size(T,1)==1 | size(T,2)==1) % isvector + [T,Z] = normalise(T); +elseif ndims(T)==2 % matrix + Z = sum(T,2); + S = Z + (Z==0); + norm = repmat(S, 1, size(T,2)); + T = T ./ norm; +else % multi-dimensional array + ns = size(T); + T = reshape(T, prod(ns(1:end-1)), ns(end)); + Z = sum(T,2); + S = Z + (Z==0); + norm = repmat(S, 1, ns(end)); + T = T ./ norm; + T = reshape(T, ns); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mkdirKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function mkdirKPM(dname) +% function mkdirKPM(dname) +% If directory does not exist, make it +% +% mkdirKPM('foo\bar\baz') makes subdirectories bar and baz +% mkdirKPM('foo\bar\baz.txt') makes subdirectories bar + +% convert foo\bar\baz to pathstr=foo\bar, name=baz +% convert foo\bar\baz.txt to pathstr=foo\bar, name=baz +[pathstr, name, ext, versn] = fileparts(dname); +if ~isempty(ext) % we stripped off something after final period + % convert foo\bar to pathstr=foo, name=bar + % convert foo\bar.bad to pathstr=foo, name=bar.bad + [pathstr, name, ext, versn] = fileparts(pathstr); + name = sprintf('%s%s', name, ext); % in case there is a period in the directory name +end + +dname = fullfile(pathstr, name); +if ~exist(dname, 'dir') + %fprintf('mkdirKPM: making %s, %s \n', pathstr, name); + mkdir(pathstr, name) +else + %fprintf('mkdirKPM: %s already exists\n', dname) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/montageKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,102 @@ +function h = montageKPM(arg) +% montageKPM is like the built-in montage, but assumes input is MxNxK or filenames +% +% Converts patches (y,x,i) into patches(y,x,1,i) +% Also, adds a black border aroudn them + +if iscell(arg) + h= montageFilenames(arg); +else + nr = size(arg,1); nc = size(arg,2); Npatches = size(arg,3); + patchesColor = reshape(arg, [nr nc 1 Npatches]); + patchesColor = patchesColor ./ max(patchesColor(:)); + + if 1 + %put a black border around them for display purposes + border = 5; + bgColor = ones(1,1,class(patchesColor)); + patchesColorBig = bgColor*ones(nr+2*border, nc+2*border, 1, Npatches, class(patchesColor)); + %patchesColorBig = zeros(nr+2*border, nc+2*border, 1, Npatches, class(patchesColor)); + patchesColorBig(border+1:end-border, border+1:end-border, :, :) = patchesColor; + else + patchesColorBig = patchesColor; + end + montage(patchesColorBig) + +end + +%%%%%%%%%%%%% + +function h = montageFilenames(filenames) + +%[nRows, nCols, nBands, nFrames] = size(a); + +% Estimate nMontageColumns and nMontageRows given the desired ratio of +% Columns to Rows to be one (square montage). +aspectRatio = 1; +nMontageCols = sqrt(aspectRatio * nRows * nFrames / nCols); + +% Make sure montage rows and columns are integers. The order in the adjustment +% matters because the montage image is created horizontally across columns. +nMontageCols = ceil(nMontageCols); +nMontageRows = ceil(nFrames / nMontageCols); + +% Create the montage image. +b = a(1,1); % to inherit type +b(1,1) = 0; % from a +b = repmat(b, [nMontageRows*nRows, nMontageCols*nCols, nBands, 1]); + +rows = 1 : nRows; +cols = 1 : nCols; + +for i = 0:nMontageRows-1 + for j = 0:nMontageCols-1, + k = j + i * nMontageCols + 1; + if k <= nFrames + b(rows + i * nRows, cols + j * nCols, :) = a(:,:,:,k); + else + break; + end + end +end + +if isempty(cm) + hh = imshow(b); +else + hh = imshow(b,cm); +end + +if nargout > 0 + h = hh; +end + +%-------------------------------------------------------------- +%Parse Inputs Function + +function [I,map] = parse_inputs(varargin) + +% initialize variables +map = []; + +iptchecknargin(1,2,nargin,mfilename); +iptcheckinput(varargin{1},{'uint8' 'double' 'uint16' 'logical' 'single' ... + 'int16'},{},mfilename, 'I, BW, or RGB',1); +I = varargin{1}; + +if nargin==2 + if isa(I,'int16') + eid = sprintf('Images:%s:invalidIndexedImage',mfilename); + msg1 = 'An indexed image can be uint8, uint16, double, single, or '; + msg2 = 'logical.'; + error(eid,'%s %s',msg1, msg2); + end + map = varargin{2}; + iptcheckinput(map,{'double'},{},mfilename,'MAP',1); + if ((size(map,1) == 1) && (prod(map) == numel(I))) + % MONTAGE(D,[M N P]) OBSOLETE + eid = sprintf('Images:%s:obsoleteSyntax',mfilename); + msg1 = 'MONTAGE(D,[M N P]) is an obsolete syntax.'; + msg2 = 'Use multidimensional arrays to represent multiframe images.'; + error(eid,'%s\n%s',msg1,msg2); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/montageKPM2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function montageKPM2(data) +% data(y,x,b,f) or data(y,x,f) +% can be double - uses imagesc to display, not imshow +% based on imaqmontage + +if ndims(data)==3 + nr = size(data,1); nc = size(data,2); Npatches = size(data,3); Nbands = 1; + data = reshape(data, [nr nc Nbands Npatches]); +else + nr = size(data,1); nc = size(data,2); Nbands = size(data,3); Npatches = size(data,4); +end +nativeVal = data(1, 1); +dataOrig = data; + +%put a black border around them for display purposes +border = 5; +bgColor = min(data(:)); +%bgColor = max(data(:)); +data = bgColor*ones(nr+2*border, nc+2*border, Nbands, Npatches, class(data)); +data(border+1:end-border, border+1:end-border, :, :) = dataOrig; + +[width, height, bands, nFrames] = size(data); + +% Determine the number of axis rows and columns. +axCols = sqrt(nFrames); +if (axCols<1) + % In case we have a slim image. + axCols = 1; +end +axRows = nFrames/axCols; +if (ceil(axCols)-axCols) < (ceil(axRows)-axRows), + axCols = ceil(axCols); + axRows = ceil(nFrames/axCols); +else + axRows = ceil(axRows); + axCols = ceil(nFrames/axRows); +end + +% Size the storage to hold all frames. +storage = repmat(nativeVal, [axRows*width, axCols*height, bands, 1]); + +% Fill the storage up with data pixels. +rows = 1:width; +cols = 1:height; +for i=0:axRows-1, + for j=0:axCols-1, + k = j+i*axCols+1; + if k<=nFrames, + storage(rows+i*width, cols+j*height, :) = data(:,:,:,k); + else + break; + end + end +end + + +% Display the tiled frames nicely and +% pop the window forward. +im = imagesc(storage); + +ax = get(im, 'Parent'); +fig = get(ax, 'Parent'); +set(ax, 'XTick', [], 'YTick', []) +figure(fig) + +% If working with single band images, update the colormap. +if 0 % bands==1, + colormap(gray); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/montageKPM3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function montageKPM3(data) +% data{f}(y,x,b) - each frame can have a different size (can can even be empty) + +data2 = cell2matPad(data); +montageKPM2(data2)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mult_by_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% MULT_BY_TABLE +% bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +bigT(:) = bigT(:) .* Ts(:); % must have bigT(:) on LHS to preserve shape
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/myintersect.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function C = myintersect(A,B) +% MYINTERSECT Intersection of two sets of positive integers (much faster than built-in intersect) +% C = myintersect(A,B) + +A = A(:)'; B = B(:)'; + +if isempty(A) + ma = 0; +else + ma = max(A); +end + +if isempty(B) + mb = 0; +else + mb = max(B); +end + +if ma==0 | mb==0 + C = []; +else + %bits = sparse(1, max(ma,mb)); + bits = zeros(1, max(ma,mb)); + bits(A) = 1; + C = B(logical(bits(B))); +end + +%sum( bitget( bitand( cliquesb(i), cliquesb(j) ), 1:52 ) );
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/myismember.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function p = myismember(a,A) +% MYISMEMBER Is 'a' an element of a set of positive integers? (much faster than built-in ismember) +% p = myismember(a,A) + +%if isempty(A) | a < min(A) | a > max(A) % slow + +if length(A)==0 + p = 0; +elseif a < min(A) + p = 0; +elseif a > max(A) + p = 0; +else + bits = zeros(1, max(A)); + bits(A) = 1; + p = bits(a); +end +p = logical(p);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/myones.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function T = myones(sizes) +% MYONES Like the built-in ones, except myones(k) produces a k*1 vector instead of a k*k matrix, +% T = myones(sizes) + +if length(sizes)==0 + T = 1; +elseif length(sizes)==1 + T = ones(sizes, 1); +else + T = ones(sizes(:)'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/myplot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +colors = ['r' 'b' 'k' 'g' 'c' 'y' 'm' ... + 'r' 'b' 'k' 'g' 'c' 'y' 'm']; +symbols = ['o' 'x' 's' '>' '<' '^' 'v' ... + '*' 'p' 'h' '+' 'd' 'o' 'x']; +for i=1:length(colors) + styles{i} = sprintf('-%s%s', colors(i), symbols(i)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/myrand.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function T = myrand(sizes) +% MYRAND Like the built-in rand, except myrand(k) produces a k*1 vector instead of a k*k matrix, +% T = myrand(sizes) + +if length(sizes)==0 + warning('myrand[]'); + T = rand(1,1); +elseif length(sizes)==1 + T = rand(sizes, 1); +else + T = rand(sizes); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/myrepmat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function T = myrepmat(T, sizes) +% MYREPMAT Like the built-in repmat, except myrepmat(T,n) == repmat(T,[n 1]) +% T = myrepmat(T, sizes) + +if length(sizes)==1 + T = repmat(T, [sizes 1]); +else + T = repmat(T, sizes(:)'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/myreshape.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function T = myreshape(T, sizes) +% MYRESHAPE Like the built-in reshape, except myreshape(T,n) == reshape(T,[n 1]) +% T = myreshape(T, sizes) + +if length(sizes)==0 + return; +elseif length(sizes)==1 + T = reshape(T, [sizes 1]); +else + T = reshape(T, sizes(:)'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mysetdiff.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function C = mysetdiff(A,B) +% MYSETDIFF Set difference of two sets of positive integers (much faster than built-in setdiff) +% C = mysetdiff(A,B) +% C = A \ B = { things in A that are not in B } +% +% Original by Kevin Murphy, modified by Leon Peshkin + +if isempty(A) + C = []; + return; +elseif isempty(B) + C = A; + return; +else % both non-empty + bits = zeros(1, max(max(A), max(B))); + bits(A) = 1; + bits(B) = 0; + C = A(logical(bits(A))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mysize.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function sz = mysize(M) +% MYSIZE Like the built-in size, except it returns n if M is a vector of length n, and 1 if M is a scalar. +% sz = mysize(M) +% +% The behavior is best explained by examples +% - M = rand(1,1), mysize(M) = 1, size(M) = [1 1] +% - M = rand(2,1), mysize(M) = 2, size(M) = [2 1] +% - M = rand(1,2), mysize(M) = 2, size(M) = [1 2] +% - M = rand(2,2,1), mysize(M) = [2 2], size(M) = [2 2] +% - M = rand(1,2,1), mysize(M) = 2, size(M) = [1 2] + +if isvectorBNT(M) + sz = length(M); +else + sz = size(M); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mysubset.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function p=mysubset(small,large) +% MYSUBSET Is the small set of +ve integers a subset of the large set? +% p = mysubset(small, large) + +% Surprisingly, this is not built-in. + +if isempty(small) + p = 1; % isempty(large); +else + p = length(myintersect(small,large)) == length(small); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/mysymsetdiff.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function C = mysymsetdiff(A,B) +% MYSYMSETDIFF Symmetric set difference of two sets of positive integers (much faster than built-in setdiff) +% C = mysetdiff(A,B) +% C = (A\B) union (B\A) = { things that A and B don't have in common } + +if isempty(A) + ma = 0; +else + ma = max(A); +end + +if isempty(B) + mb = 0; +else + mb = max(B); +end + +if ma==0 + C = B; +elseif mb==0 + C = A; +else % both non-empty + m = max(ma,mb); + bitsA = sparse(1, m); + bitsA(A) = 1; + bitsB = sparse(1, m); + bitsB(B) = 1; + C = find(xor(bitsA, bitsB)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/myunion.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function C = myunion(A,B) +% MYUNION Union of two sets of positive integers (much faster than built-in union) +% C = myunion(A,B) + +if isempty(A) + ma = 0; +else + ma = max(A); +end + +if isempty(B) + mb = 0; +else + mb = max(B); +end + +if ma==0 & mb==0 + C = []; +elseif ma==0 & mb>0 + C = B; +elseif ma>0 & mb==0 + C = A; +else + %bits = sparse(1, max(ma,mb)); + bits = zeros(1, max(ma,mb)); + bits(A) = 1; + bits(B) = 1; + C = find(bits); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/nchoose2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function c = nchoose2(v, f) +%NCHOOSE2 All combinations of N elements taken two at a time. +% +% NCHOOSE2(1:N) or NCHOOSEK(V) where V is a vector of length N, +% produces a matrix with N*(N-1)/2 rows and K columns. Each row of +% the result has K of the elements in the vector V. +% +% NCHOOSE2(N,FLAG) is the same as NCHOOSE2(1:N) but faster. +% +% NCHOOSE2(V) is much faster than NCHOOSEK(V,2). +% +% See also NCHOOSEK, PERMS. + +% Author: Peter J. Acklam +% Time-stamp: 2000-03-03 13:03:59 +% E-mail: jacklam@math.uio.no +% URL: http://www.math.uio.no/~jacklam + + nargs = nargin; + if nargs < 1 + error('Not enough input arguments.'); + elseif nargs == 1 + v = v(:); + n = length(v); + elseif nargs == 2 + n = v; + else + error('Too many input arguments.'); + end + + [ c(:,2), c(:,1) ] = find( tril( ones(n), -1 ) ); + + if nargs == 1 + c = v(c); + end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/ncols.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function c = cols(x) +% COLS The number of columns. +% COLS is a more readable alternative to size(x,2). +c = size(x,2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/nonmaxsup.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +% NONMAXSUP - Non-maximal Suppression +% +% Usage: cim = nonmaxsup(im, radius) +% +% Arguments: +% im - image to be processed. +% radius - radius of region considered in non-maximal +% suppression (optional). Typical values to use might +% be 1-3. Default is 1. +% +% Returns: +% cim - image with pixels that are not maximal within a +% square neighborhood zeroed out. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function cim = nonmaxsup(m, radius) + if (nargin == 1) radius = 1; end + % Extract local maxima by performing a grey scale morphological + % dilation and then finding points in the corner strength image that + % match the dilated image and are also greater than the threshold. + sze = 2 * radius + 1; % Size of mask. + mx = ordfilt2(m, sze^2, ones(sze)); % Grey-scale dilate. + cim = sparse(m .* (m == mx)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/normalise.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function [M, z] = normalise(A, dim) +% NORMALISE Make the entries of a (multidimensional) array sum to 1 +% [M, c] = normalise(A) +% c is the normalizing constant +% +% [M, c] = normalise(A, dim) +% If dim is specified, we normalise the specified dimension only, +% otherwise we normalise the whole array. + +if nargin < 2 + z = sum(A(:)); + % Set any zeros to one before dividing + % This is valid, since c=0 => all i. A(i)=0 => the answer should be 0/1=0 + s = z + (z==0); + M = A / s; +elseif dim==1 % normalize each column + z = sum(A); + s = z + (z==0); + %M = A ./ (d'*ones(1,size(A,1)))'; + M = A ./ repmatC(s, size(A,1), 1); +else + % Keith Battocchi - v. slow because of repmat + z=sum(A,dim); + s = z + (z==0); + L=size(A,dim); + d=length(size(A)); + v=ones(d,1); + v(dim)=L; + %c=repmat(s,v); + c=repmat(s,v'); + M=A./c; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/normaliseC.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +/* C mex version of normalise.m in misc directory */ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + double *T, *sum_ptr, sum; + int i, N; + + plhs[0] = mxDuplicateArray(prhs[0]); + T = mxGetPr(plhs[0]); + if(mxIsSparse(plhs[0])) N = mxGetJc(plhs[0])[mxGetN(plhs[0])]; + else N = mxGetNumberOfElements(plhs[0]); + + plhs[1] = mxCreateDoubleMatrix(1, 1, mxREAL); + sum_ptr = mxGetPr(plhs[1]); + + sum = 0; + for (i = 0; i < N; i++) { + sum += *T++; + } + T = mxGetPr(plhs[0]); + if (sum > 0) { + for (i = 0; i < N; i++) { + *T++ /= sum; + } + } + *sum_ptr = sum; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/normalize.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function [M, z] = normalise(A, dim) +% NORMALISE Make the entries of a (multidimensional) array sum to 1 +% [M, c] = normalise(A) +% c is the normalizing constant +% +% [M, c] = normalise(A, dim) +% If dim is specified, we normalise the specified dimension only, +% otherwise we normalise the whole array. + +if nargin < 2 + z = sum(A(:)); + % Set any zeros to one before dividing + % This is valid, since c=0 => all i. A(i)=0 => the answer should be 0/1=0 + s = z + (z==0); + M = A / s; +elseif dim==1 % normalize each column + z = sum(A); + s = z + (z==0); + %M = A ./ (d'*ones(1,size(A,1)))'; + M = A ./ repmatC(s, size(A,1), 1); +else + % Keith Battocchi - v. slow because of repmat + z=sum(A,dim); + s = z + (z==0); + L=size(A,dim); + d=length(size(A)); + v=ones(d,1); + v(dim)=L; + %c=repmat(s,v); + c=repmat(s,v'); + M=A./c; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/nrows.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function r = rows(x) +% ROWS The number of rows. +% ROWS is a more readable alternative to size(x,1). +r = size(x,1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/num2strcell.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function c = num2strcell(n, format) +% num2strcell Convert vector of numbers to cell array of strings +% function c = num2strcell(n, format) +% +% If format is omitted, we use +% c{i} = sprintf('%d', n(i)) + +if nargin < 2, format = '%d'; end + +N = length(n); +c = cell(1,N); +for i=1:N + c{i} = sprintf(format, n(i)); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/optimalMatching.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +% MATCH - Solves the weighted bipartite matching (or assignment) +% problem. +% +% Usage: a = match(C); +% +% Arguments: +% C - an m x n cost matrix; the sets are taken to be +% 1:m and 1:n; C(i, j) gives the cost of matching +% items i (of the first set) and j (of the second set) +% +% Returns: +% +% a - an m x 1 assignment vector, which gives the +% minimum cost assignment. a(i) is the index of +% the item of 1:n that was matched to item i of +% 1:m. If item i (of 1:m) was not matched to any +% item of 1:n, then a(i) is zero. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [a] = optimalMatching(C) + +% Trivial cases: +[p, q] = size(C); +if (p == 0) + a = []; + return; +elseif (q == 0) + a = zeros(p, 1); + return; +end + + +if 0 +% First, reduce the problem by making easy optimal matches. If two +% elements agree that they are the best match, then match them up. +[x, a] = min(C, [], 2); +[y, b] = min(C, [], 1); +u = find(1:p ~= b(a(:))); +a(u) = 0; +v = find(1:q ~= a(b(:))'); +C = C(u, v); +if (isempty(C)) return; end +end + +% Get the (new) size of the two sets, u and v. +[m, n] = size(C); + +%mx = realmax; +mx = 2*max(C(:)); +mn = -2*min(C(:)); +% Pad the affinity matrix to be square +if (m < n) + C = [C; mx * ones(n - m, n)]; +elseif (n < m) + C = [C, mx * ones(m, m - n)]; +end + +% Run the Hungarian method. First replace infinite values by the +% largest (or smallest) finite values. +C(find(isinf(C) & (C > 0))) = mx; +C(find(isinf(C) & (C < 0))) = mn; +%fprintf('running hungarian\n'); +[b, cost] = hungarian(C'); + +% Extract only the real assignments +ap = b(1:m)'; +ap(find(ap > n)) = 0; + +a = ap; +%% Incorporate this sub-assignment into the complete assignment +% k = find(ap); +% a(u(k)) = v(ap(k)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/optimalMatchingTest.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +% Consider matching sources to detections + +% s1 d2 +% s2 d3 +% d1 + +a = optimalMatching([52;0.01]) + +% sources(:,i) = [x y] coords +sources = [0.1 0.7; 0.6 0.4]'; +detections = [0.2 0.2; 0.2 0.8; 0.7 0.1]'; +dst = sqdist(sources, detections) + +% a = [2 3] which means s1-d2, s2-d3 +a = optimalMatching(dst) + +% a = [0 1 2] which means d1-0, d2-s1, d3-s2 +a = optimalMatching(dst')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/partitionData.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function varargout = partitionData(Ndata, varargin) +% PARTITIONDATA Partition a vector of indices into random sets +% [a,b,c,...] = partitionData(N, 0.3, 0.2, 0.5, ...) +% +% Examples: +% [a,b,c]=partitionData(105,0.3,0.2,0.5); +% a= 1:30, b=32:52, c=52:105 (last bin gets all the left over) + +Npartitions = length(varargin); +perm = randperm(Ndata); +%perm = 1:Ndata; +ndx = 1; +for i=1:Npartitions + pc(i) = varargin{i}; + Nbin(i) = fix(Ndata*pc(i)); + low(i) = ndx; + if i==Npartitions + high(i) = Ndata; + else + high(i) = low(i)+Nbin(i)-1; + end + varargout{i} = perm(low(i):high(i)); + ndx = ndx+Nbin(i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/partition_matrix_vec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function [m1, m2, K11, K12, K21, K22] = partition_matrix_vec(m, K, n1, n2, bs) +% PARTITION_MATRIX_VEC Partition a vector and matrix into blocks. +% [m1, m2, K11, K12, K21, K22] = partition_matrix_vec(m, K, blocks1, blocks2, bs) +% +% bs(i) = block size of i'th node +% +% Example: +% n1 = [6 8], n2 = [5], bs = [- - - - 2 1 - 2], where - = don't care +% m = [0.1 0.2 0.3 0.4 0.5], K = some 5*5 matrix, +% So E[X5] = [0.1 0.2], E[X6] = [0.3], E[X8] = [0.4 0.5] +% m1 = [0.3 0.4 0.5], m2 = [0.1 0.2]; + +dom = myunion(n1, n2); +n1i = block(find_equiv_posns(n1, dom), bs(dom)); +n2i = block(find_equiv_posns(n2, dom), bs(dom)); +m1 = m(n1i); +m2 = m(n2i); +K11 = K(n1i, n1i); +K12 = K(n1i, n2i); +K21 = K(n2i, n1i); +K22 = K(n2i, n2i);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/pca_kpm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [pc_vec]=pca_kpm(features,N, method); +% PCA_KPM Compute top N principal components using eigs or svd. +% [pc_vec]=pca_kpm(features,N) +% +% features(:,i) is the i'th example - each COLUMN is an observation +% pc_vec(:,j) is the j'th basis function onto which you should project the data +% using pc_vec' * features + +[d ncases] = size(features); +fm=features-repmat(mean(features,2), 1, ncases); + + +if method==1 % d*d < d*ncases + fprintf('pca_kpm eigs\n'); + options.disp = 0; + C = cov(fm'); % d x d matrix + [pc_vec, evals] = eigs(C, N, 'LM', options); +else + % [U,D,V] = SVD(fm), U(:,i)=evec of fm fm', V(:,i) = evec of fm' fm + fprintf('pca_kpm svds\n'); + [U,D,V] = svds(fm', N); + pc_vec = V; +end + +if 0 +X = randn(5,3); +X = X-repmat(mean(X),5,1); +C = X'*X; +C2=cov(X) +[U,D,V]=svd(X); +[V2,D2]=eig(X) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/pca_netlab.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +function [PCcoeff, PCvec] = pca(data, N) +%PCA Principal Components Analysis +% +% Description +% PCCOEFF = PCA(DATA) computes the eigenvalues of the covariance +% matrix of the dataset DATA and returns them as PCCOEFF. These +% coefficients give the variance of DATA along the corresponding +% principal components. +% +% PCCOEFF = PCA(DATA, N) returns the largest N eigenvalues. +% +% [PCCOEFF, PCVEC] = PCA(DATA) returns the principal components as well +% as the coefficients. This is considerably more computationally +% demanding than just computing the eigenvalues. +% +% See also +% EIGDEC, GTMINIT, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin == 1 + N = size(data, 2); +end + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(data, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the sorted eigenvalues of the data covariance matrix +if evals_only + PCcoeff = eigdec(cov(data), N); +else + [PCcoeff, PCvec] = eigdec(cov(data), N); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/pick.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function [i,j] = pick(ndx) +% PICK Pick an entry at random from a vector +% function [i,j] = pick(ndx) +% +% i = ndx(j) for j ~ U(1:length(ndx)) + +dist = normalize(ones(1,length(ndx))); +j = sample_discrete(dist); +i = ndx(j);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotBox.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [h, ht] =plotBox(box, col, str) +% function h=plotBox(box, col, str) +% +% box = [xlow xhigh ylow yhigh] +% col = color (default - red) +% str = string printed at center (default '') + +if nargin < 2, col = 'r'; end +if nargin < 3, str = ''; end + +box = double(box); % fails on single + +h = plot([box(1) box(2) box(2) box(1) box(1)], [ box(3) box(3) box(4) box(4) box(3)]); +set(h, 'color', col); +set(h, 'linewidth', 2); +if ~isempty(str) + xc = mean(box(1:2)); + yc = mean(box(3:4)); + ht = text(xc, yc, str); +else + ht = []; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotColors.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function styles = plotColors() + +colors = ['r' 'b' 'k' 'g' 'c' 'y' 'm' ... + 'r' 'b' 'k' 'g' 'c' 'y' 'm']; +symbols = ['o' 'x' '+' '>' '<' '^' 'v' ... + '*' 'p' 'h' 's' 'd' 'o' 'x']; +for i=1:length(colors) + styles{i} = sprintf('-%s%s', colors(i), symbols(i)); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotROC.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function [falseAlarmRate, detectionRate, area, th] = plotROC(confidence, testClass, col, varargin) +% function [falseAlarmRate, detectionRate, area, th] = plotroc(confidence, testClass, color) + +if nargin < 3, col = []; end + +[scale01] = process_options(varargin, 'scale01', 1); + +[falseAlarmRate detectionRate area th] = computeROC(confidence, testClass); + +if ~isempty(col) + h=plot(falseAlarmRate, detectionRate, [col '-']); + %set(h, 'linewidth', 2); + ex = 0.05*max(falseAlarmRate); + ey = 0.05; + if scale01 + axis([0-ex max(falseAlarmRate)+ex 0-ey 1+ey]) + else + % zoom in on the top left corner + axis([0-ex max(falseAlarmRate)*0.5+ex 0.5-ey 1+ey]) + end + grid on + ylabel('detection rate') + %xlabel('# false alarms') + xlabel('false alarm rate') +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotROCkpm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function [falseAlarmRate, detectionRate, area, th] = plotROC(confidence, testClass, col, varargin) +% You pass the scores and the classes, and the function returns the false +% alarm rate and the detection rate for different points across the ROC. +% +% [faR, dR] = plotROC(score, class) +% +% faR (false alarm rate) is uniformly sampled from 0 to 1 +% dR (detection rate) is computed using the scores. +% +% class = 0 => target absent +% class = 1 => target present +% +% score is the output of the detector, or any other measure of detection. +% There is no plot unless you add a third parameter that is the color of +% the graph. For instance: +% [faR, dR] = plotROC(score, class, 'r') +% +% faR, dR are size 1x1250 + +if nargin < 3, col = []; end +[scale01] = process_options(varargin, 'scale01', 1); + +S = rand('state'); +rand('state',0); +confidence = confidence + rand(size(confidence))*10^(-10); +rand('state',S) + +ndxAbs = find(testClass==0); % absent +ndxPres = find(testClass==1); % present + +[th, j] = sort(confidence(ndxAbs)); +th = th(fix(linspace(1, length(th), 1250))); + +cAbs = confidence(ndxAbs); +cPres = confidence(ndxPres); +for t=1:length(th) + if length(ndxPres) == 0 + detectionRate(t) = 0; + else + detectionRate(t) = sum(cPres>=th(t)) / length(ndxPres); + end + if length(ndxAbs) == 0 + falseAlarmRate(t) = 0; + else + falseAlarmRate(t) = sum(cAbs>=th(t)) / length(ndxAbs); + end + + %detectionRate(t) = sum(confidence(ndxPres)>=th(t)) / length(ndxPres); + %falseAlarmRate(t) = sum(confidence(ndxAbs)>=th(t)) / length(ndxAbs); + %detections(t) = sum(confidence(ndxPres)>=th(t)); + %falseAlarms(t) = sum(confidence(ndxAbs)>=th(t)); +end + +area = sum(abs(falseAlarmRate(2:end) - falseAlarmRate(1:end-1)) .* detectionRate(2:end)); + +if ~isempty(col) + h=plot(falseAlarmRate, detectionRate, [col '-']); + %set(h, 'linewidth', 2); + e = 0.05; + if scale01 + axis([0-e 1+e 0-e 1+e]) + else + % zoom in on the top left corner + axis([0-e 0.5+e 0.5-e 1+e]) + end + grid on + ylabel('detection rate') + xlabel('false alarm rate') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plot_axis_thru_origin.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function plot_axis_thru_origin() + +lnx=line(get(gca,'xlim'),[0 0]); lny=line([0 0],get(gca,'ylim'));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plot_ellipse.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +% PLOT_ELLIPSE +% h=plot_ellipse(x,y,theta,a,b) +% +% This routine plots an ellipse with centre (x,y), axis lengths a,b +% with major axis at an angle of theta radians from the horizontal. + +% +% Author: P. Fieguth +% Jan. 98 +% +%http://ocho.uwaterloo.ca/~pfieguth/Teaching/372/plot_ellipse.m + +function h=plot_ellipse(x,y,theta,a,b) + +np = 100; +ang = [0:np]*2*pi/np; +R = [cos(theta) -sin(theta); sin(theta) cos(theta)]; +pts = [x;y]*ones(size(ang)) + R*[cos(ang)*a; sin(ang)*b]; +h=plot( pts(1,:), pts(2,:) );
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plot_matrix.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function plot_matrix(G, bw) +% PLOT_MATRIX Plot a 2D matrix as a grayscale image, and label the axes +% +% plot_matrix(M) +% +% For 0/1 matrices (eg. adjacency matrices), use +% plot_matrix(M,1) + +if nargin < 2, bw = 0; end + +if 0 + imagesc(G) + %image(G) + %colormap([1 1 1; 0 0 0]); % black squares on white background + %colormap(gray) + grid on + n = length(G); + + % shift the grid lines so they don't intersect the squares + set(gca,'xtick',1.5:1:n); + set(gca,'ytick',1.5:1:n); + + % Turn off the confusing labels, which are fractional + % Ideally we could shift the labels to lie between the axis lines... +% set(gca,'xticklabel', []); +% set(gca,'yticklabel', []); +else + % solution provided by Jordan Rosenthal <jr@ece.gatech.edu> + % You can plot the grid lines manually: + % This uses the trick that a point with a value nan does not get plotted. + imagesc(G); + if bw + colormap([1 1 1; 0 0 0]); + end + n = length(G); + x = 1.5:1:n; + x = [ x; x; repmat(nan,1,n-1) ]; + y = [ 0.5 n+0.5 nan ].'; + y = repmat(y,1,n-1); + x = x(:); + y = y(:); + line(x,y,'linestyle',':','color','k'); + line(y,x,'linestyle',':','color','k'); + set(gca,'xtick',1:n) + set(gca,'ytick',1:n) +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plot_polygon.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function out=plot_polygon(p, args, close_loop) +% PLOT_POLYGON +% function handle=plot_polygon(p, args, close_loop) +% p(1,i), p(2,i) are the x/y coords of point i. +% If non-empty, args are passed thru to the plot command. +% If close_loop = 1, connect the last point to the first + +% All rights reserved. Documentation updated April 1999. +% Matt Kawski. http://math.la.asu.edu/~kawski +% He calls it pplot + +if nargin < 2, args = []; end +if nargin < 3, close_loop = 0; end + +if close_loop + p = [p p(:,1)]; +end + +if isempty(args) + out=plot(p(1,:),p(2,:)); +else + out=plot(p(1,:),p(2,:),args); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotcov2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +% PLOTCOV2 - Plots a covariance ellipse with major and minor axes +% for a bivariate Gaussian distribution. +% +% Usage: +% h = plotcov2(mu, Sigma[, OPTIONS]); +% +% Inputs: +% mu - a 2 x 1 vector giving the mean of the distribution. +% Sigma - a 2 x 2 symmetric positive semi-definite matrix giving +% the covariance of the distribution (or the zero matrix). +% +% Options: +% 'conf' - a scalar between 0 and 1 giving the confidence +% interval (i.e., the fraction of probability mass to +% be enclosed by the ellipse); default is 0.9. +% 'num-pts' - the number of points to be used to plot the +% ellipse; default is 100. +% +% This function also accepts options for PLOT. +% +% Outputs: +% h - a vector of figure handles to the ellipse boundary and +% its major and minor axes +% +% See also: PLOTCOV3 + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function h = plotcov2(mu, Sigma, varargin) + +if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end +if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end + +[p, ... + n, ... + plot_opts] = process_options(varargin, 'conf', 0.9, ... + 'num-pts', 100); +h = []; +holding = ishold; +if (Sigma == zeros(2, 2)) + z = mu; +else + % Compute the Mahalanobis radius of the ellipsoid that encloses + % the desired probability mass. + k = conf2mahal(p, 2); + % The major and minor axes of the covariance ellipse are given by + % the eigenvectors of the covariance matrix. Their lengths (for + % the ellipse with unit Mahalanobis radius) are given by the + % square roots of the corresponding eigenvalues. + if (issparse(Sigma)) + [V, D] = eigs(Sigma); + else + [V, D] = eig(Sigma); + end + % Compute the points on the surface of the ellipse. + t = linspace(0, 2*pi, n); + u = [cos(t); sin(t)]; + w = (k * V * sqrt(D)) * u; + z = repmat(mu, [1 n]) + w; + % Plot the major and minor axes. + L = k * sqrt(diag(D)); + h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ... + [mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:}); + hold on; + h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ... + [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})]; +end + +h = [h; plot(z(1, :), z(2, :), plot_opts{:})]; +if (~holding) hold off; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotcov2New.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,119 @@ +% PLOTCOV2 - Plots a covariance ellipsoid with axes for a bivariate +% Gaussian distribution. +% +% Usage: +% [h, s] = plotcov2(mu, Sigma[, OPTIONS]); +% +% Inputs: +% mu - a 2 x 1 vector giving the mean of the distribution. +% Sigma - a 2 x 2 symmetric positive semi-definite matrix giving +% the covariance of the distribution (or the zero matrix). +% +% Options: +% 'conf' - a scalar between 0 and 1 giving the confidence +% interval (i.e., the fraction of probability mass to +% be enclosed by the ellipse); default is 0.9. +% 'num-pts' - if the value supplied is n, then (n + 1)^2 points +% to be used to plot the ellipse; default is 20. +% 'label' - if non-empty, a string that will label the +% ellipsoid (default: []) +% 'plot-axes' - a 0/1 flag indicating if the ellipsoid's axes +% should be plotted (default: 1) +% 'plot-opts' - a cell vector of arguments to be handed to PLOT3 +% to contol the appearance of the axes, e.g., +% {'Color', 'g', 'LineWidth', 1}; the default is {} +% 'fill-color' - a color specifier; is this is not [], the +% covariance ellipse is filled with this color +% (default: []) +% +% Outputs: +% h - a vector of handles on the axis lines +% +% See also: PLOTCOV3 + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [h, s] = plotcov2New(mu, Sigma, varargin) + +h = []; +s = []; + +if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end +if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end + +Sigma = checkpsd(Sigma); + +[p, ... + n, ... + label, ... + plot_axes, ... + plot_opts, ... + fill_color] = process_options(varargin, 'conf', 0.9, ... + 'num-pts', 20, ... + 'label', [], ... + 'plot-axes', 1, ... + 'plot-opts', {}, ... + 'fill-color', []); +holding = ishold; +% Compute the Mahalanobis radius of the ellipsoid that encloses +% the desired probability mass. +k = conf2mahal(p, 2); +% Scale the covariance matrix so the confidence region has unit +% Mahalanobis distance. +Sigma = Sigma * k; +% The axes of the covariance ellipse are given by the eigenvectors of +% the covariance matrix. Their lengths (for the ellipse with unit +% Mahalanobis radius) are given by the square roots of the +% corresponding eigenvalues. +[V, D] = eig(full(Sigma)); +V = real(V); +D = real(D); +D = abs(D); + +% Compute the points on the boundary of the ellipsoid. +t = linspace(0, 2*pi, n); +u = [cos(t(:))'; sin(t(:))']; +w = (V * sqrt(D)) * u; +z = repmat(mu(:), [1 n]) + w; +h = [h; plot(z(1, :), z(2, :), plot_opts{:})]; +if (~isempty(fill_color)) + s = patch(z(1, :), z(2, :), fill_color); +end + +% Plot the axes. +if (plot_axes) + hold on; + L = sqrt(diag(D)); + h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ... + [mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:}); + h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ... + [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})]; +end + + +if (~isempty(label)) + th = text(mu(1), mu(2), label); + set(th, 'FontSize', 18); + set(th, 'FontName', 'Times'); + set(th, 'FontWeight', 'bold'); + set(th, 'FontAngle', 'italic'); + set(th, 'HorizontalAlignment', 'center'); +end + +if (~holding & plot_axes) hold off; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotcov3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,109 @@ +% PLOTCOV3 - Plots a covariance ellipsoid with axes for a trivariate +% Gaussian distribution. +% +% Usage: +% [h, s] = plotcov3(mu, Sigma[, OPTIONS]); +% +% Inputs: +% mu - a 3 x 1 vector giving the mean of the distribution. +% Sigma - a 3 x 3 symmetric positive semi-definite matrix giving +% the covariance of the distribution (or the zero matrix). +% +% Options: +% 'conf' - a scalar between 0 and 1 giving the confidence +% interval (i.e., the fraction of probability mass to +% be enclosed by the ellipse); default is 0.9. +% 'num-pts' - if the value supplied is n, then (n + 1)^2 points +% to be used to plot the ellipse; default is 20. +% 'plot-opts' - a cell vector of arguments to be handed to PLOT3 +% to contol the appearance of the axes, e.g., +% {'Color', 'g', 'LineWidth', 1}; the default is {} +% 'surf-opts' - a cell vector of arguments to be handed to SURF +% to contol the appearance of the ellipsoid +% surface; a nice possibility that yields +% transparency is: {'EdgeAlpha', 0, 'FaceAlpha', +% 0.1, 'FaceColor', 'g'}; the default is {} +% +% Outputs: +% h - a vector of handles on the axis lines +% s - a handle on the ellipsoid surface object +% +% See also: PLOTCOV2 + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [h, s] = plotcov3(mu, Sigma, varargin) + +if size(Sigma) ~= [3 3], error('Sigma must be a 3 by 3 matrix'); end +if length(mu) ~= 3, error('mu must be a 3 by 1 vector'); end + +[p, ... + n, ... + plot_opts, ... + surf_opts] = process_options(varargin, 'conf', 0.9, ... + 'num-pts', 20, ... + 'plot-opts', {}, ... + 'surf-opts', {}); +h = []; +holding = ishold; +if (Sigma == zeros(3, 3)) + z = mu; +else + % Compute the Mahalanobis radius of the ellipsoid that encloses + % the desired probability mass. + k = conf2mahal(p, 3); + % The axes of the covariance ellipse are given by the eigenvectors of + % the covariance matrix. Their lengths (for the ellipse with unit + % Mahalanobis radius) are given by the square roots of the + % corresponding eigenvalues. + if (issparse(Sigma)) + [V, D] = eigs(Sigma); + else + [V, D] = eig(Sigma); + end + if (any(diag(D) < 0)) + error('Invalid covariance matrix: not positive semi-definite.'); + end + % Compute the points on the surface of the ellipsoid. + t = linspace(0, 2*pi, n); + [X, Y, Z] = sphere(n); + u = [X(:)'; Y(:)'; Z(:)']; + w = (k * V * sqrt(D)) * u; + z = repmat(mu(:), [1 (n + 1)^2]) + w; + + % Plot the axes. + L = k * sqrt(diag(D)); + h = plot3([mu(1); mu(1) + L(1) * V(1, 1)], ... + [mu(2); mu(2) + L(1) * V(2, 1)], ... + [mu(3); mu(3) + L(1) * V(3, 1)], plot_opts{:}); + hold on; + h = [h; plot3([mu(1); mu(1) + L(2) * V(1, 2)], ... + [mu(2); mu(2) + L(2) * V(2, 2)], ... + [mu(3); mu(3) + L(2) * V(3, 2)], plot_opts{:})]; + h = [h; plot3([mu(1); mu(1) + L(3) * V(1, 3)], ... + [mu(2); mu(2) + L(3) * V(2, 3)], ... + [mu(3); mu(3) + L(3) * V(3, 3)], plot_opts{:})]; +end + +s = surf(reshape(z(1, :), [(n + 1) (n + 1)]), ... + reshape(z(2, :), [(n + 1) (n + 1)]), ... + reshape(z(3, :), [(n + 1) (n + 1)]), ... + surf_opts{:}); + +if (~holding) hold off; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotgauss1d.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function h = plotgauss1d(mu, sigma2) +% function h = plotgauss1d(mu, sigma^2) +% Example +% plotgauss1d(0,5); hold on; h=plotgauss1d(0,2);set(h,'color','r') + +sigma = sqrt(sigma2); +x = linspace(mu-3*sigma, mu+3*sigma, 100); +p = gaussian_prob(x, mu, sigma2); +h = plot(x, p, '-');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotgauss2d.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,130 @@ +function h=plotgauss2d(mu, Sigma) +% PLOTGAUSS2D Plot a 2D Gaussian as an ellipse with optional cross hairs +% h=plotgauss2(mu, Sigma) +% + +h = plotcov2(mu, Sigma); +return; + +%%%%%%%%%%%%%%%%%%%%%%%% + +% PLOTCOV2 - Plots a covariance ellipse with major and minor axes +% for a bivariate Gaussian distribution. +% +% Usage: +% h = plotcov2(mu, Sigma[, OPTIONS]); +% +% Inputs: +% mu - a 2 x 1 vector giving the mean of the distribution. +% Sigma - a 2 x 2 symmetric positive semi-definite matrix giving +% the covariance of the distribution (or the zero matrix). +% +% Options: +% 'conf' - a scalar between 0 and 1 giving the confidence +% interval (i.e., the fraction of probability mass to +% be enclosed by the ellipse); default is 0.9. +% 'num-pts' - the number of points to be used to plot the +% ellipse; default is 100. +% +% This function also accepts options for PLOT. +% +% Outputs: +% h - a vector of figure handles to the ellipse boundary and +% its major and minor axes +% +% See also: PLOTCOV3 + +% Copyright (C) 2002 Mark A. Paskin + +function h = plotcov2(mu, Sigma, varargin) + +if size(Sigma) ~= [2 2], error('Sigma must be a 2 by 2 matrix'); end +if length(mu) ~= 2, error('mu must be a 2 by 1 vector'); end + +[p, ... + n, ... + plot_opts] = process_options(varargin, 'conf', 0.9, ... + 'num-pts', 100); +h = []; +holding = ishold; +if (Sigma == zeros(2, 2)) + z = mu; +else + % Compute the Mahalanobis radius of the ellipsoid that encloses + % the desired probability mass. + k = conf2mahal(p, 2); + % The major and minor axes of the covariance ellipse are given by + % the eigenvectors of the covariance matrix. Their lengths (for + % the ellipse with unit Mahalanobis radius) are given by the + % square roots of the corresponding eigenvalues. + if (issparse(Sigma)) + [V, D] = eigs(Sigma); + else + [V, D] = eig(Sigma); + end + % Compute the points on the surface of the ellipse. + t = linspace(0, 2*pi, n); + u = [cos(t); sin(t)]; + w = (k * V * sqrt(D)) * u; + z = repmat(mu, [1 n]) + w; + % Plot the major and minor axes. + L = k * sqrt(diag(D)); + h = plot([mu(1); mu(1) + L(1) * V(1, 1)], ... + [mu(2); mu(2) + L(1) * V(2, 1)], plot_opts{:}); + hold on; + h = [h; plot([mu(1); mu(1) + L(2) * V(1, 2)], ... + [mu(2); mu(2) + L(2) * V(2, 2)], plot_opts{:})]; +end + +h = [h; plot(z(1, :), z(2, :), plot_opts{:})]; +if (~holding) hold off; end + +%%%%%%%%%%%% + +% CONF2MAHAL - Translates a confidence interval to a Mahalanobis +% distance. Consider a multivariate Gaussian +% distribution of the form +% +% p(x) = 1/sqrt((2 * pi)^d * det(C)) * exp((-1/2) * MD(x, m, inv(C))) +% +% where MD(x, m, P) is the Mahalanobis distance from x +% to m under P: +% +% MD(x, m, P) = (x - m) * P * (x - m)' +% +% A particular Mahalanobis distance k identifies an +% ellipsoid centered at the mean of the distribution. +% The confidence interval associated with this ellipsoid +% is the probability mass enclosed by it. Similarly, +% a particular confidence interval uniquely determines +% an ellipsoid with a fixed Mahalanobis distance. +% +% If X is an d dimensional Gaussian-distributed vector, +% then the Mahalanobis distance of X is distributed +% according to the Chi-squared distribution with d +% degrees of freedom. Thus, the Mahalanobis distance is +% determined by evaluating the inverse cumulative +% distribution function of the chi squared distribution +% up to the confidence value. +% +% Usage: +% +% m = conf2mahal(c, d); +% +% Inputs: +% +% c - the confidence interval +% d - the number of dimensions of the Gaussian distribution +% +% Outputs: +% +% m - the Mahalanobis radius of the ellipsoid enclosing the +% fraction c of the distribution's probability mass +% +% See also: MAHAL2CONF + +% Copyright (C) 2002 Mark A. Paskin + +function m = conf2mahal(c, d) + +m = chi2inv(c, d); % matlab stats toolbox
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/plotgauss2d_old.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function h=plotgauss2d_old(mu, Sigma, plot_cross) +% PLOTGAUSS2D Plot a 2D Gaussian as an ellipse with optional cross hairs +% h=plotgauss2(mu, Sigma) +% +% h=plotgauss2(mu, Sigma, 1) also plots the major and minor axes +% +% Example +% clf; S=[2 1; 1 2]; plotgauss2d([0;0], S, 1); axis equal + +if nargin < 3, plot_cross = 0; end +[V,D]=eig(Sigma); +lam1 = D(1,1); +lam2 = D(2,2); +v1 = V(:,1); +v2 = V(:,2); +%assert(approxeq(v1' * v2, 0)) +if v1(1)==0 + theta = 0; % horizontal +else + theta = atan(v1(2)/v1(1)); +end +a = sqrt(lam1); +b = sqrt(lam2); +h=plot_ellipse(mu(1), mu(2), theta, a,b); + +if plot_cross + mu = mu(:); + held = ishold; + hold on + minor1 = mu-a*v1; minor2 = mu+a*v1; + hminor = line([minor1(1) minor2(1)], [minor1(2) minor2(2)]); + + major1 = mu-b*v2; major2 = mu+b*v2; + hmajor = line([major1(1) major2(1)], [major1(2) major2(2)]); + %set(hmajor,'color','r') + if ~held + hold off + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/polygon_area.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function a = polygon_area(x,y) +% AREA Area of a planar polygon. +% AREA(X,Y) Calculates the area of a 2-dimensional +% polygon formed by vertices with coordinate vectors +% X and Y. The result is direction-sensitive: the +% area is positive if the bounding contour is counter- +% clockwise and negative if it is clockwise. +% +% See also TRAPZ. + +% Copyright (c) 1995 by Kirill K. Pankratov, +% kirill@plume.mit.edu. +% 04/20/94, 05/20/95 + + % Make polygon closed ............. +x = [x(:); x(1)]; +y = [y(:); y(1)]; + + % Calculate contour integral Int -y*dx (same as Int x*dy). +lx = length(x); +a = -(x(2:lx)-x(1:lx-1))'*(y(1:lx-1)+y(2:lx))/2; +a = abs(a);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/polygon_centroid.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +function [x0,y0] = centroid(x,y) +% CENTROID Center of mass of a polygon. +% [X0,Y0] = CENTROID(X,Y) Calculates centroid +% (center of mass) of planar polygon with vertices +% coordinates X, Y. +% Z0 = CENTROID(X+i*Y) returns Z0=X0+i*Y0 the same +% as CENTROID(X,Y). + +% Copyright (c) 1995 by Kirill K. Pankratov, +% kirill@plume.mit.edu. +% 06/01/95, 06/07/95 + +% Algorithm: +% X0 = Int{x*ds}/Int{ds}, where ds - area element +% so that Int{ds} is total area of a polygon. +% Using Green's theorem the area integral can be +% reduced to a contour integral: +% Int{x*ds} = -Int{x^2*dy}, Int{ds} = Int{x*dy} along +% the perimeter of a polygon. +% For a polygon as a sequence of line segments +% this can be reduced exactly to a sum: +% Int{x^2*dy} = Sum{ (x_{i}^2+x_{i+1}^2+x_{i}*x_{i+1})* +% (y_{i+1}-y_{i})}/3; +% Int{x*dy} = Sum{(x_{i}+x_{i+1})(y_{i+1}-y_{i})}/2. +% Similarly +% Y0 = Int{y*ds}/Int{ds}, where +% Int{y*ds} = Int{y^2*dx} = +% = Sum{ (y_{i}^2+y_{i+1}^2+y_{i}*y_{i+1})* +% (x_{i+1}-x_{i})}/3. + + % Handle input ...................... +if nargin==0, help centroid, return, end +if nargin==1 + sz = size(x); + if sz(1)==2 % Matrix 2 by n + y = x(2,:); x = x(1,:); + elseif sz(2)==2 % Matrix n by 2 + y = x(:,2); x = x(:,1); + else + y = imag(x); + x = real(x); + end +end + + % Make a polygon closed .............. +x = [x(:); x(1)]; +y = [y(:); y(1)]; + + % Check length ....................... +l = length(x); +if length(y)~=l + error(' Vectors x and y must have the same length') +end + + % X-mean: Int{x^2*dy} ................ +del = y(2:l)-y(1:l-1); +v = x(1:l-1).^2+x(2:l).^2+x(1:l-1).*x(2:l); +x0 = v'*del; + + % Y-mean: Int{y^2*dx} ................ +del = x(2:l)-x(1:l-1); +v = y(1:l-1).^2+y(2:l).^2+y(1:l-1).*y(2:l); +y0 = v'*del; + + % Calculate area: Int{y*dx} .......... +a = (y(1:l-1)+y(2:l))'*del; +tol= 2*eps; +if abs(a)<tol + disp(' Warning: area of polygon is close to 0') + a = a+sign(a)*tol+(~a)*tol; +end + % Multiplier +a = 1/3/a; + + % Divide by area ..................... +x0 = -x0*a; +y0 = y0*a; + +if nargout < 2, x0 = x0+i*y0; end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/polygon_intersect.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function [is,S] = isintpl(x1,y1,x2,y2) +% ISINTPL Check for intersection of polygons. +% [IS,S] = ISINTPL(X1,Y1,X2,Y2) Accepts coordinates +% X1,Y1 and X2, Y2 of two polygons. Returns scalar +% IS equal to 1 if these polygons intersect each other +% and 0 if they do not. +% Also returns Boolean matrix S of the size length(X1) +% by length(X2) so that {ij} element of which is 1 if +% line segments i to i+1 of the first polygon and +% j to j+1 of the second polygon intersect each other, +% 0 if they don't and .5 if they "touch" each other. + +% Copyright (c) 1995 by Kirill K. Pankratov, +% kirill@plume.mit.edu. +% 06/20/95, 08/25/95 + + + % Handle input ................................... +if nargin==0, help isintpl, return, end +if nargin<4 + error(' Not enough input arguments ') +end + + % Make column vectors and check sizes ............ +x1 = x1(:); y1 = y1(:); +x2 = x2(:); y2 = y2(:); +l1 = length(x1); +l2 = length(x2); +if length(y1)~=l1 | length(y2)~=l2 + error('(X1,Y1) and (X2,Y2) must pair-wise have the same length.') +end + + % Quick exit if empty +if l1<1 | l2<1, is = []; S = []; return, end + + % Check if their ranges are intersecting ......... +lim1 = [min(x1) max(x1)]'; +lim2 = [min(x2) max(x2)]'; +isx = interval(lim1,lim2); % X-ranges +lim1 = [min(y1) max(y1)]'; +lim2 = [min(y2) max(y2)]'; +isy = interval(lim1,lim2); % Y-ranges +is = isx & isy; +S = zeros(l2,l1); + +if ~is, return, end % Early exit if disparate limits + + % Indexing ....................................... +[i11,i12] = meshgrid(1:l1,1:l2); +[i21,i22] = meshgrid([2:l1 1],[2:l2 1]); +i11 = i11(:); i12 = i12(:); +i21 = i21(:); i22 = i22(:); + + % Calculate matrix of intersections .............. +S(:) = iscross([x1(i11) x1(i21)]',[y1(i11) y1(i21)]',... + [x2(i12) x2(i22)]',[y2(i12) y2(i22)]')'; + +S = S'; +is = any(any(S)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/previewfig.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function f = previewfig(h,varargin) +%PREVIEWFIG Preview a figure to be exported using EXPORTFIG. +% F = PREVIEWFIG(H) creates a preview of H with the default +% EXPORTFIG options and returns the preview's figure handle in F. +% F = PREVIEWFIG(H,OPTIONS) creates a preview with OPTIONS as +% described in EXPORTFIG. +% PREVIEWFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) creates a preview +% with the specified parameter-value pairs to H as described in +% EXPORTFIG. +% +% See also EXPORTFIG, APPLYTOFIG, RESTOREFIG. + +% Copyright 2000 Ben Hinkle +% Email bug reports and comments to bhinkle@mathworks.com + +filename = [tempname, '.png']; +args = {'resolution',0,'format','png'}; +if nargin > 1 + exportfig(h, filename, varargin{:}, args{:}); +else + exportfig(h, filename, args{:}); +end + +X = imread(filename,'png'); +height = size(X,1); +width = size(X,2); +delete(filename); +f = figure( 'Name', 'Preview', ... + 'Menubar', 'none', ... + 'NumberTitle', 'off', ... + 'Visible', 'off'); +image(X); +axis image; +ax = findobj(f, 'type', 'axes'); +axesPos = [0 0 width height]; +set(ax, 'Units', 'pixels', ... + 'Position', axesPos, ... + 'Visible', 'off'); +figPos = get(f,'Position'); +rootSize = get(0,'ScreenSize'); +figPos(3:4) = axesPos(3:4); +if figPos(1) + figPos(3) > rootSize(3) + figPos(1) = rootSize(3) - figPos(3) - 50; +end +if figPos(2) + figPos(4) > rootSize(4) + figPos(2) = rootSize(4) - figPos(4) - 50; +end +set(f, 'Position',figPos, ... + 'Visible', 'on');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/process_options.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,132 @@ +% PROCESS_OPTIONS - Processes options passed to a Matlab function. +% This function provides a simple means of +% parsing attribute-value options. Each option is +% named by a unique string and is given a default +% value. +% +% Usage: [var1, var2, ..., varn[, unused]] = ... +% process_options(args, ... +% str1, def1, str2, def2, ..., strn, defn) +% +% Arguments: +% args - a cell array of input arguments, such +% as that provided by VARARGIN. Its contents +% should alternate between strings and +% values. +% str1, ..., strn - Strings that are associated with a +% particular variable +% def1, ..., defn - Default values returned if no option +% is supplied +% +% Returns: +% var1, ..., varn - values to be assigned to variables +% unused - an optional cell array of those +% string-value pairs that were unused; +% if this is not supplied, then a +% warning will be issued for each +% option in args that lacked a match. +% +% Examples: +% +% Suppose we wish to define a Matlab function 'func' that has +% required parameters x and y, and optional arguments 'u' and 'v'. +% With the definition +% +% function y = func(x, y, varargin) +% +% [u, v] = process_options(varargin, 'u', 0, 'v', 1); +% +% calling func(0, 1, 'v', 2) will assign 0 to x, 1 to y, 0 to u, and 2 +% to v. The parameter names are insensitive to case; calling +% func(0, 1, 'V', 2) has the same effect. The function call +% +% func(0, 1, 'u', 5, 'z', 2); +% +% will result in u having the value 5 and v having value 1, but +% will issue a warning that the 'z' option has not been used. On +% the other hand, if func is defined as +% +% function y = func(x, y, varargin) +% +% [u, v, unused_args] = process_options(varargin, 'u', 0, 'v', 1); +% +% then the call func(0, 1, 'u', 5, 'z', 2) will yield no warning, +% and unused_args will have the value {'z', 2}. This behaviour is +% useful for functions with options that invoke other functions +% with options; all options can be passed to the outer function and +% its unprocessed arguments can be passed to the inner function. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [varargout] = process_options(args, varargin) + +% Check the number of input arguments +n = length(varargin); +if (mod(n, 2)) + error('Each option must be a string/value pair.'); +end + +% Check the number of supplied output arguments +if (nargout < (n / 2)) + error('Insufficient number of output arguments given'); +elseif (nargout == (n / 2)) + warn = 1; + nout = n / 2; +else + warn = 0; + nout = n / 2 + 1; +end + +% Set outputs to be defaults +varargout = cell(1, nout); +for i=2:2:n + varargout{i/2} = varargin{i}; +end + +% Now process all arguments +nunused = 0; +for i=1:2:length(args) + found = 0; + for j=1:2:n + if strcmpi(args{i}, varargin{j}) + varargout{(j + 1)/2} = args{i + 1}; + found = 1; + break; + end + end + if (~found) + if (warn) + warning(sprintf('Option ''%s'' not used.', args{i})); + args{i} + else + nunused = nunused + 1; + unused{2 * nunused - 1} = args{i}; + unused{2 * nunused} = args{i + 1}; + end + end +end + +% Assign the unused arguments +if (~warn) + if (nunused) + varargout{nout} = unused; + else + varargout{nout} = cell(0); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rand_psd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function M = rand_psd(d, d2, k) +% Create a random positive definite matrix of size d by d by k (k defaults to 1) +% M = rand_psd(d, d2, k) default: d2 = d, k = 1 + +if nargin<2, d2 = d; end +if nargin<3, k = 1; end +if d2 ~= d, error('must be square'); end + +M = zeros(d,d,k); +for i=1:k + A = rand(d); + M(:,:,i) = A*A'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rectintC.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [overlap, normoverlap] = rectintC(A,B) +% +% A(i,:) = [x y w h] +% B(j,:) = [x y w h] +% overlap(i,j) = area of intersection +% normoverlap(i,j) = overlap(i,j) / min(area(i), area(j)) +% +% Same as built-in rectint, but faster and uses less memory (since avoids repmat). + + +leftA = A(:,1); +bottomA = A(:,2); +rightA = leftA + A(:,3); +topA = bottomA + A(:,4); + +leftB = B(:,1)'; +bottomB = B(:,2)'; +rightB = leftB + B(:,3)'; +topB = bottomB + B(:,4)'; + +verbose = 0; +[overlap, normoverlap] = rectintLoopC(leftA, rightA, topA, bottomA, leftB, rightB, topB, bottomB, verbose);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rectintLoopC.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ + +#include "mex.h" +#include <stdio.h> + +#define MAX(x,y) ((x)>(y) ? (x) : (y)) +#define MIN(x,y) ((x)<(y) ? (x) : (y)) + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] + ) +{ + int j,k,m,n,nzmax,*irs,*jcs, *irs2, *jcs2; + double *overlap, *overlap2, tmp, areaA, areaB; + double *leftA, *rightA, *topA, *bottomA; + double *leftB, *rightB, *topB, *bottomB; + double *verbose; + + m = MAX(mxGetM(prhs[0]), mxGetN(prhs[0])); + n = MAX(mxGetM(prhs[4]), mxGetN(prhs[4])); + /* printf("A=%d, B=%d\n", m, n); */ + + leftA = mxGetPr(prhs[0]); + rightA = mxGetPr(prhs[1]); + topA = mxGetPr(prhs[2]); + bottomA = mxGetPr(prhs[3]); + + leftB = mxGetPr(prhs[4]); + rightB = mxGetPr(prhs[5]); + topB = mxGetPr(prhs[6]); + bottomB = mxGetPr(prhs[7]); + + verbose = mxGetPr(prhs[8]); + + plhs[0] = mxCreateDoubleMatrix(m,n, mxREAL); + overlap = mxGetPr(plhs[0]); + + plhs[1] = mxCreateDoubleMatrix(m,n, mxREAL); + overlap2 = mxGetPr(plhs[1]); + + k = 0; + for (j = 0; (j < n); j++) { + int i; + for (i = 0; (i < m); i++) { + tmp = (MAX(0, MIN(rightA[i], rightB[j]) - MAX(leftA[i], leftB[j]) )) * + (MAX(0, MIN(topA[i], topB[j]) - MAX(bottomA[i], bottomB[j]) )); + + if (tmp > 0) { + overlap[k] = tmp; + + areaA = (rightA[i]-leftA[i])*(topA[i]-bottomA[i]); + areaB = (rightB[j]-leftB[j])*(topB[j]-bottomB[j]); + overlap2[k] = tmp/MIN(areaA, areaB); + + if (*verbose) { + printf("j=%d,i=%d,overlap=%5.3f, norm=%5.3f\n", j,i, overlap[k], overlap2[k]); + } + } + + k++; + } + } +} + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rectintSparse.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function [overlap, normoverlap] = rectintSparse(A,B) +% +% A(i,:) = [x y w h] +% B(j,:) = [x y w h] +% overlap(i,j) = area of intersection +% normoverla(i,j) +% +% Same as built-in rectint, but uses less memory. +% Use rectintSparseC for a faster version. +% + +leftA = A(:,1); +bottomA = A(:,2); +rightA = leftA + A(:,3); +topA = bottomA + A(:,4); + +leftB = B(:,1)'; +bottomB = B(:,2)'; +rightB = leftB + B(:,3)'; +topB = bottomB + B(:,4)'; + +numRectA = size(A,1); +numRectB = size(B,1); + +%out = rectintSparseLoopC(leftA, rightA, topA, bottomA, leftB, rightB, topB, bottomB); + +nnz = ceil(0.2*numRectA*numRectB); % guess of number of non-zeroes +overlap = sparse([], [], [], numRectA, numRectB, nnz); +normoverlap = sparse([], [], [], numRectA, numRectB, nnz); +for j=1:numRectB + for i=1:numRectA + tmp = (max(0, min(rightA(i), rightB(j)) - max(leftA(i), leftB(j)) ) ) .* ... + (max(0, min(topA(i), topB(j)) - max(bottomA(i), bottomB(j)) ) ); + if tmp>0 + overlap(i,j) = tmp; + areaA = (rightA(i)-leftA(i))*(topA(i)-bottomA(i)); + areaB = (rightB(j)-leftB(j))*(topB(j)-bottomB(j)); + normoverlap(i,j) = min(tmp/areaA, tmp/areaB); + end + %fprintf('j=%d, i=%d, overlap=%5.3f, norm=%5.3f\n',... + % j, i, overlap(i,j), normoverlap(i,j)); + end +end + + +if 0 +N = size(bboxDense01,2); % 1000; +rect = bboxToRect(bboxDense01)'; +A = rect(1:2,:); +B = rect(1:N,:); + +tic; out1 = rectint(A, B); toc +tic; out2 = rectintSparse(A, B); toc +tic; out3 = rectintSparseC(A, B); toc +tic; out4 = rectintC(A, B); toc +assert(approxeq(out1, out2)) +assert(approxeq(out1, full(out3))) +assert(approxeq(out1, out4)) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rectintSparseC.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [overlap, normoverlap] = rectintSparseC(A,B) +% +% function [area, normarea] = rectintSparseC(A,B) +% A(i,:) = [x y w h] +% B(j,:) = [x y w h] +% out(i,j) = area of intersection +% +% Same as built-in rectint, but uses less memory. +% Also, returns area of overlap normalized by area of patch. +% See rectintSparse + +if isempty(A) | isempty(B) + overlap = []; + normoverlap = []; + return; +end + +leftA = A(:,1); +bottomA = A(:,2); +rightA = leftA + A(:,3); +topA = bottomA + A(:,4); + +leftB = B(:,1)'; +bottomB = B(:,2)'; +rightB = leftB + B(:,3)'; +topB = bottomB + B(:,4)'; + +numRectA = size(A,1); +numRectB = size(B,1); + +verbose = 0; +[overlap, normoverlap] = rectintSparseLoopC(leftA, rightA, topA, bottomA, leftB, rightB, topB, bottomB, verbose);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rectintSparseLoopC.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,147 @@ +/* This is based on +http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_external/ch04cr12.shtml + +See rectintSparse.m for the matlab version of this code. + +*/ + +#include <math.h> /* Needed for the ceil() prototype. */ +#include "mex.h" +#include <stdio.h> + +/* If you are using a compiler that equates NaN to be zero, you + * must compile this example using the flag -DNAN_EQUALS_ZERO. + * For example: + * + * mex -DNAN_EQUALS_ZERO fulltosparse.c + * + * This will correctly define the IsNonZero macro for your C + * compiler. + */ + +#if defined(NAN_EQUALS_ZERO) +#define IsNonZero(d) ((d) != 0.0 || mxIsNaN(d)) +#else +#define IsNonZero(d) ((d) != 0.0) +#endif + +#define MAX(x,y) ((x)>(y) ? (x) : (y)) +#define MIN(x,y) ((x)<(y) ? (x) : (y)) + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] + ) +{ + /* Declare variables. */ + int j,k,m,n,nzmax,*irs,*jcs, *irs2, *jcs2; + double *overlap, *overlap2, tmp, areaA, areaB; + double percent_sparse; + double *leftA, *rightA, *topA, *bottomA; + double *leftB, *rightB, *topB, *bottomB; + double *verbose; + + /* Get the size and pointers to input data. */ + m = MAX(mxGetM(prhs[0]), mxGetN(prhs[0])); + n = MAX(mxGetM(prhs[4]), mxGetN(prhs[4])); + /* printf("A=%d, B=%d\n", m, n); */ + + leftA = mxGetPr(prhs[0]); + rightA = mxGetPr(prhs[1]); + topA = mxGetPr(prhs[2]); + bottomA = mxGetPr(prhs[3]); + + leftB = mxGetPr(prhs[4]); + rightB = mxGetPr(prhs[5]); + topB = mxGetPr(prhs[6]); + bottomB = mxGetPr(prhs[7]); + + verbose = mxGetPr(prhs[8]); + + /* Allocate space for sparse matrix. + * NOTE: Assume at most 20% of the data is sparse. Use ceil + * to cause it to round up. + */ + + percent_sparse = 0.01; + nzmax = (int)ceil((double)m*(double)n*percent_sparse); + + plhs[0] = mxCreateSparse(m,n,nzmax,0); + overlap = mxGetPr(plhs[0]); + irs = mxGetIr(plhs[0]); + jcs = mxGetJc(plhs[0]); + + plhs[1] = mxCreateSparse(m,n,nzmax,0); + overlap2 = mxGetPr(plhs[1]); + irs2 = mxGetIr(plhs[1]); + jcs2 = mxGetJc(plhs[1]); + + + /* Assign nonzeros. */ + k = 0; + for (j = 0; (j < n); j++) { + int i; + jcs[j] = k; + jcs2[j] = k; + for (i = 0; (i < m); i++) { + tmp = (MAX(0, MIN(rightA[i], rightB[j]) - MAX(leftA[i], leftB[j]) )) * + (MAX(0, MIN(topA[i], topB[j]) - MAX(bottomA[i], bottomB[j]) )); + + if (*verbose) { + printf("j=%d,i=%d,tmp=%5.3f\n", j,i,tmp); + } + + if (IsNonZero(tmp)) { + + /* Check to see if non-zero element will fit in + * allocated output array. If not, increase + * percent_sparse by 20%, recalculate nzmax, and augment + * the sparse array. + */ + if (k >= nzmax) { + int oldnzmax = nzmax; + percent_sparse += 0.2; + nzmax = (int)ceil((double)m*(double)n*percent_sparse); + + /* Make sure nzmax increases atleast by 1. */ + if (oldnzmax == nzmax) + nzmax++; + printf("reallocating from %d to %d\n", oldnzmax, nzmax); + + mxSetNzmax(plhs[0], nzmax); + mxSetPr(plhs[0], mxRealloc(overlap, nzmax*sizeof(double))); + mxSetIr(plhs[0], mxRealloc(irs, nzmax*sizeof(int))); + overlap = mxGetPr(plhs[0]); + irs = mxGetIr(plhs[0]); + + mxSetNzmax(plhs[1], nzmax); + mxSetPr(plhs[1], mxRealloc(overlap2, nzmax*sizeof(double))); + mxSetIr(plhs[1], mxRealloc(irs2, nzmax*sizeof(int))); + overlap2 = mxGetPr(plhs[1]); + irs2 = mxGetIr(plhs[1]); + } + + overlap[k] = tmp; + irs[k] = i; + + areaA = (rightA[i]-leftA[i])*(topA[i]-bottomA[i]); + areaB = (rightB[j]-leftB[j])*(topB[j]-bottomB[j]); + overlap2[k] = MIN(tmp/areaA, tmp/areaB); + irs2[k] = i; + + k++; + } /* IsNonZero */ + } /* for i */ + } + jcs[n] = k; + jcs2[n] = k; + +} + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/repmatC.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,149 @@ +/* +mex -c mexutil.c +mex repmat.c mexutil.obj +to check for warnings: +gcc -Wall -I/cygdrive/c/MATLAB6p1/extern/include -c repmat.c +*/ +#include "mexutil.h" +#include <string.h> + +/* repeat a block of memory rep times */ +void memrep(char *dest, size_t chunk, int rep) +{ +#if 0 + /* slow way */ + int i; + char *p = dest; + for(i=1;i<rep;i++) { + p += chunk; + memcpy(p, dest, chunk); + } +#else + /* fast way */ + if(rep == 1) return; + memcpy(dest + chunk, dest, chunk); + if(rep & 1) { + dest += chunk; + memcpy(dest + chunk, dest, chunk); + } + /* now repeat using a block twice as big */ + memrep(dest, chunk<<1, rep>>1); +#endif +} + +void repmat(char *dest, const char *src, int ndim, int *destdimsize, + int *dimsize, const int *dims, int *rep) +{ + int d = ndim-1; + int i, chunk; + /* copy the first repetition into dest */ + if(d == 0) { + chunk = dimsize[0]; + memcpy(dest,src,chunk); + } + else { + /* recursively repeat each slice of src */ + for(i=0;i<dims[d];i++) { + repmat(dest + i*destdimsize[d-1], src + i*dimsize[d-1], + ndim-1, destdimsize, dimsize, dims, rep); + } + chunk = destdimsize[d-1]*dims[d]; + } + /* copy the result rep-1 times */ + memrep(dest,chunk,rep[d]); +} + +void mexFunction(int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) +{ + const mxArray *srcmat; + int ndim, *dimsize, eltsize; + const int *dims; + int ndimdest, *destdims, *destdimsize; + char *src, *dest; + int *rep; + int i,nrep; + int extra_rep = 1; + int empty; + + if(nrhs < 2) mexErrMsgTxt("Usage: xrepmat(A, [N M ...])"); + srcmat = prhs[0]; + if(mxIsSparse(srcmat)) { + mexErrMsgTxt("Sorry, can't handle sparse matrices yet."); + } + if(mxIsCell(srcmat)) { + mexErrMsgTxt("Sorry, can't handle cell arrays yet."); + } + ndim = mxGetNumberOfDimensions(srcmat); + dims = mxGetDimensions(srcmat); + eltsize = mxGetElementSize(srcmat); + + /* compute dimension sizes */ + dimsize = mxCalloc(ndim, sizeof(int)); + dimsize[0] = eltsize*dims[0]; + for(i=1;i<ndim;i++) dimsize[i] = dimsize[i-1]*dims[i]; + + /* determine repetition vector */ + ndimdest = ndim; + if(nrhs == 2) { + nrep = mxGetN(prhs[1]); + if(nrep > ndimdest) ndimdest = nrep; + rep = mxCalloc(ndimdest, sizeof(int)); + for(i=0;i<nrep;i++) { + double repv = mxGetPr(prhs[1])[i]; + rep[i] = (int)repv; + } + if(nrep == 1) { + /* special behavior */ + nrep = 2; + rep[1] = rep[0]; + } + } + else { + nrep = nrhs-1; + if(nrep > ndimdest) ndimdest = nrep; + rep = mxCalloc(ndimdest, sizeof(int)); + for(i=0;i<nrep;i++) { + rep[i] = (int)*mxGetPr(prhs[i+1]); + } + } + for(i=nrep;i<ndimdest;i++) rep[i] = 1; + + /* compute output size */ + destdims = mxCalloc(ndimdest, sizeof(int)); + for(i=0;i<ndim;i++) destdims[i] = dims[i]*rep[i]; + for(;i<ndimdest;i++) { + destdims[i] = rep[i]; + extra_rep *= rep[i]; + } + destdimsize = mxCalloc(ndim, sizeof(int)); + destdimsize[0] = eltsize*destdims[0]; + for(i=1;i<ndim;i++) destdimsize[i] = destdimsize[i-1]*destdims[i]; + + + /* for speed, array should be uninitialized */ + plhs[0] = mxCreateNumericArray(ndimdest, destdims, mxGetClassID(srcmat), + mxIsComplex(srcmat)?mxCOMPLEX:mxREAL); + + /* if any rep[i] == 0, output should be empty array. + Added by KPM 11/13/02. + */ + empty = 0; + for (i=0; i < nrep; i++) { + if (rep[i]==0) + empty = 1; + } + if (empty) + return; + + src = (char*)mxGetData(srcmat); + dest = (char*)mxGetData(plhs[0]); + repmat(dest,src,ndim,destdimsize,dimsize,dims,rep); + if(ndimdest > ndim) memrep(dest,destdimsize[ndim-1],extra_rep); + if(mxIsComplex(srcmat)) { + src = (char*)mxGetPi(srcmat); + dest = (char*)mxGetPi(plhs[0]); + repmat(dest,src,ndim,destdimsize,dimsize,dims,rep); + if(ndimdest > ndim) memrep(dest,destdimsize[ndim-1],extra_rep); + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rgb2grayKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function g = rgb2grayKPM(rgb) +% function g = rgb2grayKPM(rgb) +% rgb2grayKPM Like the built-in function, but if r is already gray, does not cause an error + +[nr nc ncolors] = size(rgb); +if ncolors > 1 + g = rgb2gray(rgb); +else + g = rgb; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rnd_partition.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function [train, test] = rnd_partition(data, train_percent); +% function [train, test] = rnd_partition(data, train_percent); +% +% data(:,i) is the i'th example +% train_percent of these columns get put into train, the rest into test + +N = size(data, 2); +ndx = randperm(N); +k = ceil(N*train_percent); +train_ndx = ndx(1:k); +test_ndx = ndx(k+1:end); +train = data(:, train_ndx); +test = data(:, test_ndx);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/rotate_xlabel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function hText = rotate_xlabel(degrees, newlabels) + +% Posted to comp.soft-sys.matlab on 2003-05-01 13:45:36 PST +% by David Borger (borger@ix.netcom.com) + +xtl = get(gca,'XTickLabel'); +set(gca,'XTickLabel',''); +lxtl = length(xtl); +xtl = newlabels; +if 0 % nargin>1 + lnl = length(newlabels); + if lnl~=lxtl + error('Number of new labels must equal number of old'); + end; + xtl = newlabels; +end; + + +hxLabel=get(gca,'XLabel'); +xLP=get(hxLabel,'Position'); +y=xLP(2); +XTick=get(gca,'XTick'); +y=repmat(y,length(XTick),1); +%fs=get(gca,'fontsize'); +fs = 12; +hText=text(XTick,y,xtl,'fontsize',fs); +set(hText,'Rotation',degrees,'HorizontalAlignment','right'); + +% Modifications by KPM + +ylim = get(gca,'ylim'); +height = ylim(2)-ylim(1); +N = length(hText); +for i=1:N + voffset = ylim(2) - 0*height; + set(hText(i), 'position', [i voffset 0]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/safeStr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function s = safeStr(s) +% Change punctuation characters to they print properly + +s = strrep(s, '\', '/'); +s = strrep(s, '_', '-');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/sampleUniformInts.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function M = sampleUniformInts(N, r, c) + +% M is an rxc matrix of integers in 1..N + +prob = normalize(ones(N,1)); +M = sample_discrete(prob, r, c);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/sample_discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function M = sample_discrete(prob, r, c) +% SAMPLE_DISCRETE Like the built in 'rand', except we draw from a non-uniform discrete distrib. +% M = sample_discrete(prob, r, c) +% +% Example: sample_discrete([0.8 0.2], 1, 10) generates a row vector of 10 random integers from {1,2}, +% where the prob. of being 1 is 0.8 and the prob of being 2 is 0.2. + +n = length(prob); + +if nargin == 1 + r = 1; c = 1; +elseif nargin == 2 + c == r; +end + +R = rand(r, c); +M = ones(r, c); +cumprob = cumsum(prob(:)); + +if n < r*c + for i = 1:n-1 + M = M + (R > cumprob(i)); + end +else + % loop over the smaller index - can be much faster if length(prob) >> r*c + cumprob2 = cumprob(1:end-1); + for i=1:r + for j=1:c + M(i,j) = sum(R(i,j) > cumprob2)+1; + end + end +end + + +% Slower, even though vectorized +%cumprob = reshape(cumsum([0 prob(1:end-1)]), [1 1 n]); +%M = sum(R(:,:,ones(n,1)) > cumprob(ones(r,1),ones(c,1),:), 3); + +% convert using a binning algorithm +%M=bindex(R,cumprob);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/set_xtick_label.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function set_xtick_label(tick_labels, angle, axis_label) +% SET_XTICK_LABEL Print the xtick labels at an angle instead of horizontally +% set_xtick_label(tick_labels, angle, axis_label) +% +% angle default = 90 +% axis_label default = '' +% +% This is derived from Solution Number: 5375 on mathworks.com +% See set_xtick_label_demo for an example + +if nargin < 2, angle = 90; end +if nargin < 3, axis_label = []; end + +% Reduce the size of the axis so that all the labels fit in the figure. +pos = get(gca,'Position'); +%set(gca,'Position',[pos(1), .2, pos(3) .65]) +%set(gca,'Position',[pos(1), 0, pos(3) .45]) +%set(gca,'Position',[pos(1), 0.1, pos(3) 0.5]) + +ax = axis; % Current axis limits +axis(axis); % Fix the axis limits +Yl = ax(3:4); % Y-axis limits + +%set(gca, 'xtick', 1:length(tick_labels)); +set(gca, 'xtick', 0.7:1:length(tick_labels)); +Xt = get(gca, 'xtick'); + +% Place the text labels +t = text(Xt,Yl(1)*ones(1,length(Xt)),tick_labels); +set(t,'HorizontalAlignment','right','VerticalAlignment','top', 'Rotation', angle); + +% Remove the default labels +set(gca,'XTickLabel','') + +% Get the Extent of each text object. This +% loop is unavoidable. +for i = 1:length(t) + ext(i,:) = get(t(i),'Extent'); +end + +% Determine the lowest point. The X-label will be +% placed so that the top is aligned with this point. +LowYPoint = min(ext(:,2)); + +% Place the axis label at this point +if ~isempty(axis_label) + Xl = get(gca, 'Xlim'); + XMidPoint = Xl(1)+abs(diff(Xl))/2; + tl = text(XMidPoint,LowYPoint, axis_label, 'VerticalAlignment','top', ... + 'HorizontalAlignment','center'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/set_xtick_label_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ + +% Generate some test data. Assume that the X-axis represents months. +x = 1:12; +y = 10*rand(1,length(x)); + +% Plot the data. +h = plot(x,y,'+'); + +% Add a title. +title('This is a title') + +% Set the X-Tick locations so that every other month is labeled. +Xt = 1:2:11; +Xl = [1 12]; +set(gca,'XTick',Xt,'XLim',Xl); + +% Add the months as tick labels. +months = ['Jan'; + 'Feb'; + 'Mar'; + 'Apr'; + 'May'; + 'Jun'; + 'Jul'; + 'Aug'; + 'Sep'; + 'Oct'; + 'Nov'; + 'Dec']; + +set_xtick_label(months(1:2:12, :), 90, 'xaxis label'); + + + +if 0 + + +% Generate some test data. Assume that the X-axis represents months. +x = 1:8; +y = 10*rand(1,length(x)); + +% Plot the data. +h = plot(x,y,'+'); + +S = subsets(1:3); +str = cell(1,8); +for i=1:2^3 + str{i} = num2str(S{i}); +end +set_xtick_label(str); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/setdiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function M = setdiag(M, v) +% SETDIAG Set the diagonal of a matrix to a specified scalar/vector. +% M = set_diag(M, v) + +n = length(M); +if length(v)==1 + v = repmat(v, 1, n); +end + +% e.g., for 3x3 matrix, elements are numbered +% 1 4 7 +% 2 5 8 +% 3 6 9 +% so diagnoal = [1 5 9] + + +J = 1:n+1:n^2; +M(J) = v; + +%M = triu(M,1) + tril(M,-1) + diag(v); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/softeye.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function M = softeye(K, p) +% SOFTEYE Make a stochastic matrix with p on the diagonal, and the remaining mass distributed uniformly +% M = softeye(K, p) +% +% M is a K x K matrix. + +M = p*eye(K); +q = 1-p; +for i=1:K + M(i, [1:i-1 i+1:K]) = q/(K-1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/sort_evec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [evec, evals] = sort_evec(temp_evec, temp_evals, N) + +if ~isvectorBNT(temp_evals) + temp_evals = diag(temp_evals); +end + +% Eigenvalues nearly always returned in descending order, but just +% to make sure..... +[evals perm] = sort(-temp_evals); +evals = -evals(1:N); +if evals == temp_evals(1:N) + % Originals were in order + evec = temp_evec(:, 1:N); + return +else + fprintf('sorting evec\n'); + % Need to reorder the eigenvectors + for i=1:N + evec(:,i) = temp_evec(:,perm(i)); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/splitLongSeqIntoManyShort.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function short = splitLongSeqIntoManyShort(long, Tsmall) +% splitLongSeqIntoManyShort Put groups of columns into a cell array of narrower matrices +% function short = splitLongSeqIntoManyShort(long, Tsmall) +% +% long(:,t) +% short{i} = long(:,ndx1:ndx2) where each segment (except maybe the last) is of length Tsmall + +T = length(long); +Nsmall = ceil(T/Tsmall); +short = cell(Nsmall,1); + +t = 1; +for i=1:Nsmall + short{i} = long(:,t:min(T,t+Tsmall-1)); + t = t+Tsmall; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/sprintf_intvec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function s = sprintf_intvec(v) +% SPRINTF_INTVEC Print a vector of ints as comma separated string, with no trailing comma +% function s = sprintf_intvec(v) +% +% e.g., sprintf_intvec(1:3) returns '1,2,3' + +s = sprintf('%d,', v); +s = s(1:end-1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/sqdist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function m = sqdist(p, q, A) +% SQDIST Squared Euclidean or Mahalanobis distance. +% SQDIST(p,q) returns m(i,j) = (p(:,i) - q(:,j))'*(p(:,i) - q(:,j)). +% SQDIST(p,q,A) returns m(i,j) = (p(:,i) - q(:,j))'*A*(p(:,i) - q(:,j)). + +% From Tom Minka's lightspeed toolbox + +[d, pn] = size(p); +[d, qn] = size(q); + +if nargin == 2 + + pmag = sum(p .* p, 1); + qmag = sum(q .* q, 1); + m = repmat(qmag, pn, 1) + repmat(pmag', 1, qn) - 2*p'*q; + %m = ones(pn,1)*qmag + pmag'*ones(1,qn) - 2*p'*q; + +else + + if isempty(A) | isempty(p) + error('sqdist: empty matrices'); + end + Ap = A*p; + Aq = A*q; + pmag = sum(p .* Ap, 1); + qmag = sum(q .* Aq, 1); + m = repmat(qmag, pn, 1) + repmat(pmag', 1, qn) - 2*p'*Aq; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/strmatch_multi.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [posns] = strmatch_multi(keys, strs) +% STRMATCH_MULTI Find where each key occurs in list of strings. +% [pos] = strmatch_multi(key, strs) where key is a string and strs is a cell array of strings +% works like the built-in command sequence pos = strmatch(key, strs, 'exact'), +% except that pos is the first occurrence of key in strs; if there is no occurence, pos is 0. +% +% [posns] = strmatch_multi(keys, strs), where keys is a cell array of strings, +% matches each element of keys. It loops over whichever is shorter, keys or strs. + +if ~iscell(keys), keys = {keys}; end +nkeys = length(keys); +posns = zeros(1, nkeys); +if length(keys) < length(strs) + for i=1:nkeys + %pos = strmatch(keys{i}, strs, 'exact'); + ndx = strcmp(keys{i}, strs); % faster + pos = find(ndx); + if ~isempty(pos) + posns(i) = pos(1); + end + end +else + for s=1:length(strs) + %ndx = strmatch(strs{s}, keys, 'exact'); + ndx = strcmp(strs{s}, keys); + ndx = find(ndx); + posns(ndx) = s; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/strmatch_substr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function ndx = strmatch_substr(str, strs) +% STRMATCH_SUBSTR Like strmatch, except str can match any part of strs{i}, not just prefix. +% ndx = strmatch_substr(str, strs) +% +% Example: +% i = strmatch('max', {'max','minimax','maximum'}) +% returns i = [1; 3] since only 1 and 3 begin with max, but +% i = strmatch_substr('max', {'max','minimax','maximum'}) +% returns i = [1;2;3]; +% +% If str is also a cell array, it is like calling strmatch_substr several times +% and concatenating the results. +% Example: +% +% i = strmatch_substr({'foo', 'dog'}, {'foo', 'hoofoo', 'dog'}) +% returns i = [1;2;3] + +ndx = []; +if ~iscell(str), str = {str}; end +for j=1:length(str) + for i=1:length(strs) + %ind = strfind(strs{i}, str{j}); % not supported in 6.0 + ind = findstr(strs{i}, str{j}); + if ~isempty(ind) + ndx = [ndx; i]; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/strsplit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function parts = strsplit(splitstr, str, option) +%STRSPLIT Split string into pieces. +% +% STRSPLIT(SPLITSTR, STR, OPTION) splits the string STR at every occurrence +% of SPLITSTR and returns the result as a cell array of strings. By default, +% SPLITSTR is not included in the output. +% +% STRSPLIT(SPLITSTR, STR, OPTION) can be used to control how SPLITSTR is +% included in the output. If OPTION is 'include', SPLITSTR will be included +% as a separate string. If OPTION is 'append', SPLITSTR will be appended to +% each output string, as if the input string was split at the position right +% after the occurrence SPLITSTR. If OPTION is 'omit', SPLITSTR will not be +% included in the output. + +% Author: Peter J. Acklam +% Time-stamp: 2004-09-22 08:48:01 +0200 +% E-mail: pjacklam@online.no +% URL: http://home.online.no/~pjacklam + + nargsin = nargin; + error(nargchk(2, 3, nargsin)); + if nargsin < 3 + option = 'omit'; + else + option = lower(option); + end + + splitlen = length(splitstr); + parts = {}; + + while 1 + + k = strfind(str, splitstr); + if isempty(k) + parts{end+1} = str; + break + end + + switch option + case 'include' + parts(end+1:end+2) = {str(1:k(1)-1), splitstr}; + case 'append' + parts{end+1} = str(1 : k(1)+splitlen-1); + case 'omit' + parts{end+1} = str(1 : k(1)-1); + otherwise + error(['Invalid option string -- ', option]); + end + + + str = str(k(1)+splitlen : end); + + end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subplot2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function subplot2(nrows, ncols, i, j) +% function subplot2(nrows, ncols, i, j) + + +sz = [nrows ncols]; +%k = sub2ind(sz, i, j) +k = sub2ind(sz(end:-1:1), j, i); +subplot(nrows, ncols, k); + +if 0 + ncols_plot = ceil(sqrt(Nplots)); + nrows_plot = ceil(Nplots/ncols_plot); + Nplots = nrows_plot*ncols_plot; + for p=1:Nplots + subplot(nrows_plot, ncols_plot, p); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subplot3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function fignum = subplot3(nrows, ncols, fignumBase, plotnumBase) +% function subplot3(nrows, ncols, fignumBase, plotnumBase) +% Choose a subplot number, opening a new figure if necessary +% eg nrows=2, ncols = 2, we plot on (fignum, plotnum) = (1,1), (1,2), (1,3), (1,4), (2,1), ... + +nplotsPerFig = nrows*ncols; +fignum = fignumBase + div(plotnumBase-1, nplotsPerFig); +plotnum = wrap(plotnumBase, nplotsPerFig); +figure(fignum); +if plotnum==1, clf; end +subplot(nrows, ncols, plotnum); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subsets.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function [T, bitv] = subsets(S, U, L, sorted, N) +% SUBSETS Create a set of all the subsets of S which have cardinality <= U and >= L +% T = subsets(S, U, L) +% U defaults to length(S), L defaults to 0. +% So subsets(S) generates the powerset of S. +% +% Example: +% T = subsets(1:4, 2, 1) +% T{:} = 1, 2, [1 2], 3, [1 3], [2 3], 4, [1 4], [2 4], [3 4] +% +% T = subsets(S, U, L, sorted) +% If sorted=1, return the subsets in increasing size +% +% Example: +% T = subsets(1:4, 2, 1, 1) +% T{:} = 1, 2, 3, 4, [1 2], [1 3], [2 3], [1 4], [2 4], [3 4] +% +% [T, bitv] = subsets(S, U, L, sorted, N) +% Row i of bitv is a bit vector representation of T{i}, +% where bitv has N columns (representing 1:N). +% N defaults to max(S). +% +% Example: +% [T,bitv] = subsets(2:4, 2^3, 0, 0, 5) +% T{:} = [], 2, 3, [2 3], 4, [2 4], [3 4], [2 3 4] +% bitv= +% 0 0 0 0 0 +% 0 1 0 0 0 +% 0 0 1 0 0 +% 0 1 1 0 0 +% 0 0 0 1 0 +% 0 1 0 1 0 +% 0 0 1 1 0 +% 0 1 1 1 0 + +n = length(S); + +if nargin < 2, U = n; end +if nargin < 3, L = 0; end +if nargin < 4, sorted = 0; end +if nargin < 5, N = max(S); end + +bits = ind2subv(2*ones(1,n), 1:2^n)-1; +sm = sum(bits,2); +masks = bits((sm <= U) & (sm >= L), :); +m = size(masks, 1); +T = cell(1, m); +for i=1:m + s = S(find(masks(i,:))); + T{i} = s; +end + +if sorted + T = sortcell(T); +end + +bitv = zeros(m, N); +bitv(:, S) = masks;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subsets1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function sub_s=subsets1(s,k) +% SUBSETS1 creates sub-sets of a specific from a given set +% SS = subsets1(S, k) +% +% S is the given set +% k is the required sub-sets size +% +% Example: +% +% >> ss=subsets1([1:4],3); +% >> ss{:} +% ans = +% 1 2 3 +% ans = +% 1 2 4 +% ans = +% 1 3 4 +% ans = +% 2 3 4 +% +% Written by Raanan Yehezkel, 2004 + +if k<0 % special case + error('subset size must be positive'); +elseif k==0 % special case + sub_s={[]}; +else + l=length(s); + ss={}; + if l>=k + if k==1 % Exit condition + for I=1:l + ss{I}=s(I); + end + else + for I=1:l + ss1=subsets1(s([(I+1):l]),k-1); + for J=1:length(ss1) + ss{end+1}=[s(I),ss1{J}]; + end + end + end + end + sub_s=ss; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subsetsFixedSize.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function sub_s=subsets1(s,k) +% SUBSETS1 creates sub-sets of a specific size from a given set +% SS = subsets1(S, k) +% +% S is the given set +% k is the required sub-sets size +% +% Example: +% +% >> ss=subsets1([1:4],3); +% >> ss{:} +% ans = +% 1 2 3 +% ans = +% 1 2 4 +% ans = +% 1 3 4 +% ans = +% 2 3 4 +% +% Written by Raanan Yehezkel, 2004 + +if k<0 % special case + error('subset size must be positive'); +elseif k==0 % special case + sub_s={[]}; +else + l=length(s); + ss={}; + if l>=k + if k==1 % Exit condition + for I=1:l + ss{I}=s(I); + end + else + for I=1:l + ss1=subsets1(s([(I+1):l]),k-1); + for J=1:length(ss1) + ss{end+1}=[s(I),ss1{J}]; + end + end + end + end + sub_s=ss; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subv2ind.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function index = subv2ind(siz,sub) +%SUBV2IND Linear index from subscript vector. +% SUBV2IND(SIZ,SUB) returns an equivalent single index corresponding to a +% subscript vector for an array of size SIZ. +% If SUB is a matrix, with subscript vectors as rows, then the result is a +% column vector. +% +% This is the opposite of IND2SUBV, so that +% SUBV2IND(SIZ,IND2SUBV(SIZ,IND)) == IND. +% +% See also IND2SUBV, SUB2IND. + +%index = subv2indTest(siz,sub); +prev_cum_size = [1 cumprod(siz(1:end-1))]; +%index = (sub-1)*prev_cum_size' + 1; +index = sub*prev_cum_size' - sum(prev_cum_size) + 1; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subv2indKPM.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,89 @@ +/* C mex version of subv2ind*/ +/* 2 inputs, 1 output */ +/* siz, subv */ +/* ndx */ +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, k, nCol, nRow, binary, temp; + double *pSize, *pSubv, *pr; + int *cumprod; + + pSize = mxGetPr(prhs[0]); + pSubv = mxGetPr(prhs[1]); + nCol = mxGetNumberOfElements(prhs[0]); + nRow = mxGetM(prhs[1]); + + + if(mxIsEmpty(prhs[1])){ + plhs[0] = mxCreateDoubleMatrix(0, 0, mxREAL); + return; + } + + if(mxIsEmpty(prhs[0])){ + plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(plhs[0]) = 1; + return; + } + + binary = 2; + for (i = 0; i < nCol; i++){ + if (pSize[i] > 2.0){ + binary = 0; + break; + } + else if(pSize[i] == 1.0){ + binary = 1; + } + } + + plhs[0] = mxCreateDoubleMatrix(nRow, 1, mxREAL); + pr = mxGetPr(plhs[0]); + for(i=0; i<nRow; i++){ + pr[i] = 1.0; + } + + if (binary == 2){ + for(j=0; j<nCol; j++){ + temp = j * nRow; + for(i=0; i<nRow; i++){ + pr[i] += ((int)pSubv[temp + i] - 1) << j; + } + } + } + else if(binary == 1){ + cumprod = malloc(nCol * sizeof(int)); + cumprod[0] = 1; + for(i=1; i<nCol; i++){ + k = (int)pSize[i-1] - 1; + cumprod[i] = cumprod[i-1] << k; + } + for(j=0; j<nCol; j++){ + temp = j * nRow; + for(i=0; i<nRow; i++){ + k = (int)pSubv[temp + i] - 1; + if(k)pr[i] += cumprod[j]; + } + } + free(cumprod); + } + else { + cumprod = malloc(nCol * sizeof(int)); + cumprod[0] = 1; + for(i=1; i<nCol; i++){ + k = (int)pSize[i-1]; + cumprod[i] = cumprod[i-1] * k; + } + for(j=0; j<nCol; j++){ + temp = j * nRow; + for(i=0; i<nRow; i++){ + k = (int)pSubv[temp + i] - 1; + pr[i] += cumprod[j] * k; + } + } + free(cumprod); + } +} + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subv2indKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +function ndx = subv2indKPM(siz, subv) +% SUBV2IND Like the built-in sub2ind, but the subscripts are given as row vectors. +% ind = subv2ind(siz,subv) +% +% siz can be a row or column vector of size d. +% subv should be a collection of N row vectors of size d. +% ind will be of size N * 1. +% +% Example: +% subv = [1 1 1; +% 2 1 1; +% ... +% 2 2 2]; +% subv2ind([2 2 2], subv) returns [1 2 ... 8]' +% i.e., the leftmost digit toggles fastest. +% +% See also IND2SUBV. + + +if isempty(subv) + ndx = []; + return; +end + +if isempty(siz) + ndx = 1; + return; +end + +[ncases ndims] = size(subv); + +%if length(siz) ~= ndims +% error('length of subscript vector and sizes must be equal'); +%end + +if all(siz==2) + %rbits = subv(:,end:-1:1)-1; % read from right to left, convert to 0s/1s + %ndx = bitv2dec(rbits)+1; + twos = pow2(0:ndims-1); + ndx = ((subv-1) * twos(:)) + 1; + %ndx = sum((subv-1) .* twos(ones(ncases,1), :), 2) + 1; % equivalent to matrix * vector + %ndx = sum((subv-1) .* repmat(twos, ncases, 1), 2) + 1; % much slower than ones + %ndx = ndx(:)'; +else + %siz = siz(:)'; + cp = [1 cumprod(siz(1:end-1))]'; + %ndx = ones(ncases, 1); + %for i = 1:ndims + % ndx = ndx + (subv(:,i)-1)*cp(i); + %end + ndx = (subv-1)*cp + 1; +end + +%%%%%%%%%%% + +function d = bitv2dec(bits) +% BITV2DEC Convert a bit vector to a decimal integer +% d = butv2dec(bits) +% +% This is just like the built-in bin2dec, except the argument is a vector, not a string. +% If bits is an array, each row will be converted. + +[m n] = size(bits); +twos = pow2(n-1:-1:0); +d = sum(bits .* twos(ones(m,1),:),2); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subv2indMinka.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function index = subv2indMinka(siz,sub) +%SUBV2IND Linear index from subscript vector. +% SUBV2IND(SIZ,SUB) returns an equivalent single index corresponding to a +% subscript vector for an array of size SIZ. +% If SUB is a matrix, with subscript vectors as rows, then the result is a +% column vector. +% +% This is the opposite of IND2SUBV, so that +% SUBV2IND(SIZ,IND2SUBV(SIZ,IND)) == IND. +% +% See also IND2SUBV, SUB2IND. + +% Written by Tom Minka + +prev_cum_size = [1 cumprod(siz(1:end-1))]; +%index = (sub-1)*prev_cum_size' + 1; +index = sub*prev_cum_size' - sum(prev_cum_size) + 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/subv2indTest.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function ndx = subv2indTest(siz,sub) +%SUBV2IND Linear index from subscript vector. +% SUBV2IND(SIZ,SUB) returns an equivalent single index corresponding to a +% subscript vector for an array of size SIZ. +% If SUB is a matrix, with subscript vectors as rows, then the result is a +% column vector. +% +% This is the opposite of IND2SUBV, so that +% SUBV2IND(SIZ,IND2SUBV(SIZ,IND)) == IND. +% +% See also IND2SUBV, SUB2IND. + +ndx = subv2indMinka(siz,sub); +ndx2 = subv2indKPM(siz,sub); +assert(isequalKPM(ndx,ndx2))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/sumv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function T2 = sumv(T1, sum_over) +% +% Like the built in sum, but will sum over several dimensions and then squeeze the result. + +T2 = T1; +for i=1:length(sum_over) + if sum_over(i) <= ndims(T2) % prevent summing over non-existent dimensions + T2=sum(T2, sum_over(i)); + end +end +T2 = squeeze(T2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/suptitle.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,105 @@ +function hout=suptitle(str, fs) +%SUPTITLE Puts a title above all subplots. +% SUPTITLE('text') adds text to the top of the figure +% above all subplots (a "super title"). Use this function +% after all subplot commands. + +% Drea Thomas 6/15/95 drea@mathworks.com + +% Warning: If the figure or axis units are non-default, this +% will break. + +% Parameters used to position the supertitle. + +% Amount of the figure window devoted to subplots +plotregion = .92; + +% Y position of title in normalized coordinates +titleypos = .95; + +% Fontsize for supertitle +if nargin < 2 + fs = get(gcf,'defaultaxesfontsize')+4; +end + +% Fudge factor to adjust y spacing between subplots +fudge=1; + +haold = gca; +figunits = get(gcf,'units'); + +% Get the (approximate) difference between full height (plot + title +% + xlabel) and bounding rectangle. + + if (~strcmp(figunits,'pixels')), + set(gcf,'units','pixels'); + pos = get(gcf,'position'); + set(gcf,'units',figunits); + else, + pos = get(gcf,'position'); + end + ff = (fs-4)*1.27*5/pos(4)*fudge; + + % The 5 here reflects about 3 characters of height below + % an axis and 2 above. 1.27 is pixels per point. + +% Determine the bounding rectange for all the plots + +% h = findobj('Type','axes'); + +% findobj is a 4.2 thing.. if you don't have 4.2 comment out +% the next line and uncomment the following block. + +h = findobj(gcf,'Type','axes'); % Change suggested by Stacy J. Hills + +% If you don't have 4.2, use this code instead +%ch = get(gcf,'children'); +%h=[]; +%for i=1:length(ch), +% if strcmp(get(ch(i),'type'),'axes'), +% h=[h,ch(i)]; +% end +%end + + + + +max_y=0; +min_y=1; + +oldtitle =0; +for i=1:length(h), + if (~strcmp(get(h(i),'Tag'),'suptitle')), + pos=get(h(i),'pos'); + if (pos(2) < min_y), min_y=pos(2)-ff/5*3;end; + if (pos(4)+pos(2) > max_y), max_y=pos(4)+pos(2)+ff/5*2;end; + else, + oldtitle = h(i); + end +end + +if max_y > plotregion, + scale = (plotregion-min_y)/(max_y-min_y); + for i=1:length(h), + pos = get(h(i),'position'); + pos(2) = (pos(2)-min_y)*scale+min_y; + pos(4) = pos(4)*scale-(1-scale)*ff/5*3; + set(h(i),'position',pos); + end +end + +np = get(gcf,'nextplot'); +set(gcf,'nextplot','add'); +if (oldtitle), + delete(oldtitle); +end +ha=axes('pos',[0 1 1 1],'visible','off','Tag','suptitle'); +ht=text(.5,titleypos-1,str);set(ht,'horizontalalignment','center','fontsize',fs); +set(gcf,'nextplot',np); +axes(haold); +if nargout, + hout=ht; +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/unaryEncoding.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function U = unaryEncoding(data, K) +% unaryEncoding Encode data(s) as a 1-of-K column vector +% function U = unaryEncoding(data, K) +% +% eg. +% If data = [3 2 2] and K=3, +% then U = [0 0 0 +% 0 1 1 +% 1 0 0] + +if nargin < 2, K = max(data); end +N = length(data); +U = zeros(K,N); +ndx = subv2ind([K N], [data(:)'; 1:N]'); +U(ndx) = 1; +U = reshape(U, [K N]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/wrap.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function v = wrap(u,N) +% WRAP Wrap a vector of indices around a torus. +% v = wrap(u,N) +% +% e.g., wrap([-1 0 1 2 3 4], 3) = 2 3 1 2 3 1 + +v = mod(u-1,N)+1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/xticklabel_rotate90.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function xticklabel_rotate90(XTick,varargin) +%XTICKLABEL_ROTATE90 - Rotate numeric Xtick labels by 90 degrees +% +% Syntax: xticklabel_rotate90(XTick) +% +% Input: XTick - vector array of XTick positions & values (numeric) +% +% Output: none +% +% Example 1: Set the positions of the XTicks and rotate them +% figure; plot([1960:2004],randn(45,1)); xlim([1960 2004]); +% xticklabel_rotate90([1960:2:2004]); +% %If you wish, you may set a few text "Property-value" pairs +% xticklabel_rotate90([1960:2:2004],'Color','m','Fontweight','bold'); +% +% Example 2: %Rotate XTickLabels at their current position +% XTick = get(gca,'XTick'); +% xticklabel_rotate90(XTick); +% +% Other m-files required: none +% Subfunctions: none +% MAT-files required: none +% +% See also: TEXT, SET + +% Author: Denis Gilbert, Ph.D., physical oceanography +% Maurice Lamontagne Institute, Dept. of Fisheries and Oceans Canada +% email: gilbertd@dfo-mpo.gc.ca Web: http://www.qc.dfo-mpo.gc.ca/iml/ +% February 1998; Last revision: 24-Mar-2003 + +if ~isnumeric(XTick) + error('XTICKLABEL_ROTATE90 requires a numeric input argument'); +end + +%Make sure XTick is a column vector +XTick = XTick(:); + +%Set the Xtick locations and set XTicklabel to an empty string +set(gca,'XTick',XTick,'XTickLabel','') + +% Define the xtickLabels +xTickLabels = num2str(XTick); + +% Determine the location of the labels based on the position +% of the xlabel +hxLabel = get(gca,'XLabel'); % Handle to xlabel +xLabelString = get(hxLabel,'String'); + +if ~isempty(xLabelString) + warning('You may need to manually reset the XLABEL vertical position') +end + +set(hxLabel,'Units','data'); +xLabelPosition = get(hxLabel,'Position'); +y = xLabelPosition(2); + +%CODE below was modified following suggestions from Urs Schwarz +y=repmat(y,size(XTick,1),1); +% retrieve current axis' fontsize +fs = get(gca,'fontsize'); + +% Place the new xTickLabels by creating TEXT objects +hText = text(XTick, y, xTickLabels,'fontsize',fs); + +% Rotate the text objects by 90 degrees +set(hText,'Rotation',90,'HorizontalAlignment','right',varargin{:}) + +%------------- END OF CODE --------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/zipload.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +%ZIPLOAD Load compressed data file created with ZIPSAVE +% +% [data] = zipload( filename ) +% filename: string variable that contains the name of the +% compressed file (do not include '.zip' extension) +% Use only with files created with 'zipsave' +% pkzip25.exe has to be in the matlab path. This file is a compression utility +% made by Pkware, Inc. It can be dowloaded from: http://www.pkware.com +% Or directly from ftp://ftp.pkware.com/pk250c32.exe, for the Windows 95/NT version. +% This function was tested using 'PKZIP 2.50 Command Line for Windows 9x/NT' +% It is important to use version 2.5 of the utility. Otherwise the command line below +% has to be changed to include the proper options of the compression utility you +% wish to use. +% This function was tested in MATLAB Version 5.3 under Windows NT. +% Fernando A. Brucher - May/25/1999 +% +% Example: +% [loadedData] = zipload('testfile'); +%-------------------------------------------------------------------- + +function [data] = zipload( filename ) + +%--- Decompress data file by calling pkzip (comand line command) --- +% Options used: +% 'extract' = decompress file +% 'silent' = no console output +% 'over=all' = overwrite files + +%eval( ['!pkzip25 -extract -silent -over=all ', filename, '.zip'] ) +eval( ['!pkzip25 -extract -silent -over=all ', filename, '.zip'] ) + + +%--- Load data from decompressed file --- +% try, catch takes care of cases when pkzip fails to decompress a +% valid matlab format file + +try + tmpStruc = load( filename ); + data = tmpStruc.data; +catch, return, end + + +%--- Delete decompressed file --- + +delete( [filename,'.mat'] ) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/KPMtools/zipsave.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +%ZIPSAVE Save data in compressed format +% +% zipsave( filename, data ) +% filename: string variable that contains the name of the resulting +% compressed file (do not include '.zip' extension) +% pkzip25.exe has to be in the matlab path. This file is a compression utility +% made by Pkware, Inc. It can be dowloaded from: http://www.pkware.com +% This function was tested using 'PKZIP 2.50 Command Line for Windows 9x/NT' +% It is important to use version 2.5 of the utility. Otherwise the command line below +% has to be changed to include the proper options of the compression utility you +% wish to use. +% This function was tested in MATLAB Version 5.3 under Windows NT. +% Fernando A. Brucher - May/25/1999 +% +% Example: +% testData = [1 2 3; 4 5 6; 7 8 9]; +% zipsave('testfile', testData); +% +% Modified by Kevin Murphy, 26 Feb 2004, to use winzip +%------------------------------------------------------------------------ + +function zipsave( filename, data ) + +%--- Save data in a temporary file in matlab format (.mat)--- + +eval( ['save ''', filename, ''' data'] ) + + +%--- Compress data by calling pkzip (comand line command) --- +% Options used: +% 'add' = add compressed files to the resulting zip file +% 'silent' = no console output +% 'over=all' = overwrite files + +%eval( ['!pkzip25 -silent -add -over=all ', filename, '.zip ', filename,'.mat'] ) +eval( ['!zip ', filename, '.zip ', filename,'.mat'] ) + +%--- Delete temporary matlab format file --- + +delete( [filename,'.mat'] ) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/AR_to_SS.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function [F,H,Q,R,initx, initV] = AR_to_SS(coef, C, y) +% +% Convert a vector auto-regressive model of order k to state-space form. +% [F,H,Q,R] = AR_to_SS(coef, C, y) +% +% X(i) = A(1) X(i-1) + ... + A(k) X(i-k+1) + v, where v ~ N(0, C) +% and A(i) = coef(:,:,i) is the weight matrix for i steps ago. +% We initialize the state vector with [y(:,k)' ... y(:,1)']', since +% the state vector stores [X(i) ... X(i-k+1)]' in order. + +[s s2 k] = size(coef); % s is the size of the state vector +bs = s * ones(1,k); % size of each block + +F = zeros(s*k); +for i=1:k + F(block(1,bs), block(i,bs)) = coef(:,:,i); +end +for i=1:k-1 + F(block(i+1,bs), block(i,bs)) = eye(s); +end + +H = zeros(1*s, k*s); +% we get to see the most recent component of the state vector +H(block(1,bs), block(1,bs)) = eye(s); +%for i=1:k +% H(block(1,bs), block(i,bs)) = eye(s); +%end + +Q = zeros(k*s); +Q(block(1,bs), block(1,bs)) = C; + +R = zeros(s); + +initx = zeros(k*s, 1); +for i=1:k + initx(block(i,bs)) = y(:, k-i+1); % concatenate the first k observation vectors +end + +initV = zeros(k*s); % no uncertainty about the state (since perfectly observable)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +/AR_to_SS.m/1.1.1.1/Wed May 29 15:59:56 2002// +/README.txt/1.1.1.1/Mon Jun 7 14:39:28 2004// +/SS_to_AR.m/1.1.1.1/Wed May 29 15:59:56 2002// +/convert_to_lagged_form.m/1.1.1.1/Wed May 29 15:59:56 2002// +/ensure_AR.m/1.1.1.1/Wed May 29 15:59:56 2002// +/eval_AR_perf.m/1.1.1.1/Wed May 29 15:59:56 2002// +/kalman_filter.m/1.1.1.1/Wed May 29 15:59:56 2002// +/kalman_forward_backward.m/1.1.1.1/Sat Nov 2 00:32:36 2002// +/kalman_smoother.m/1.1.1.1/Wed May 29 15:59:56 2002// +/kalman_update.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_AR.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_AR_diagonal.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_kalman.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learning_demo.m/1.1.1.1/Wed Oct 23 15:17:42 2002// +/sample_lds.m/1.1.1.1/Fri Jan 24 19:36:02 2003// +/smooth_update.m/1.1.1.1/Wed May 29 15:59:56 2002// +/testKalman.m/1.1.1.1/Thu Jun 9 01:56:34 2005// +/tracking_demo.m/1.1.1.1/Sat Jan 18 22:49:22 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/Kalman
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/README.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +Kalman filter toolbox written by Kevin Murphy, 1998. +See http://www.ai.mit.edu/~murphyk/Software/kalman.html for details. + +Installation +------------ + +1. Install KPMtools from http://www.ai.mit.edu/~murphyk/Software/KPMtools.html +3. Assuming you installed all these files in your matlab directory, In Matlab type + +addpath matlab/KPMtools +addpath matlab/Kalman + + +Demos +----- +See tracking_demo.m for a demo of 2D tracking. +See learning_demo.m for a demo of parameter estimation using EM.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/SS_to_AR.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [coef, C] = SS_to_AR(F, Q, k, diagonal) +% +% Extract the parameters of a vector autoregresssive process of order k from the state-space form. +% [coef, C] = SS_to_AR(F, Q, k, diagonal) + +if nargin<4, diagonal = 0; end + +s = length(Q) / k; +bs = s*ones(1,k); +coef = zeros(s,s,k); +for i=1:k + if diagonal + coef(:,:,i) = diag(diag(F(block(1,bs), block(i,bs)))); + else + coef(:,:,i) = F(block(1,bs), block(i,bs)); + end +end +C = Q(block(1,bs), block(1,bs)); +if diagonal + C = diag(diag(C)); +end +%C = sqrt(Q(block(1,bs), block(1,bs))); % since cov(1,1) of full vector = C C'
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/convert_to_lagged_form.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function yy = convert_to_lagged_form(y, k) +% Create an observation vector yy(:,t) containing the last k values of y, newest first +% e.g., k=2, y = (a1 a2 a3) yy = a2 a3 +% (b1 b2 b3) b2 b2 +% a1 a2 +% b1 b2 + +[s T] = size(y); +bs = s*ones(1,k); +yy = zeros(k*s, T-k+1); +for i=1:k + yy(block(i,bs), :) = y(:, k-i+1:end-i+1); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/ensure_AR.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function [A, C, Q, R, initx, initV] = ensure_AR(A, C, Q, R, initx, initV, k, obs, diagonal) +% +% Ensure that the system matrices have the right form for an autoregressive process. + +ss = length(A); +if nargin<8, obs=ones(ss, 1); end +if nargin<9, diagonal=0; end + +[coef, C] = SS_to_AR(A, Q, k, diagonal); +[A, C, Q, R, initx, initV] = AR_to_SS(coef, C, obs);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/eval_AR_perf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function [ypred, ll, mse] = eval_AR_perf(coef, C, y, model) +% Evaluate the performance of an AR model. +% +% Inputs +% coef(:,:,k,m) - coef. matrix to use for k steps back, model m +% C(:,:,m) - cov. matrix for model m +% y(:,t) - observation at time t +% model(t) - which model to use at time t (defaults to 1 if not specified) +% +% Outputs +% ypred(:,t) - the predicted value of y at t based on the evidence thru t-1. +% ll - log likelihood +% mse - mean squared error = sum_t d_t . d_t, where d_t = pred(y_t) - y(t) + +[s T] = size(y); +k = size(coef, 3); +M = size(coef, 4); + +if nargin<4, model = ones(1, T); end + +ypred = zeros(s, T); +ypred(:, 1:k) = y(:, 1:k); +mse = 0; +ll = 0; +for j=1:M + c(j) = log(normal_coef(C(:,:,j))); + invC(:,:,j) = inv(C(:,:,j)); +end +coef = reshape(coef, [s s*k M]); + +for t=k+1:T + m = model(t-k); + past = y(:,t-1:-1:t-k); + ypred(:,t) = coef(:, :, m) * past(:); + d = ypred(:,t) - y(:,t); + mse = mse + d' * d; + ll = ll + c(m) - 0.5*(d' * invC(:,:,m) * d); +end +mse = mse / (T-k+1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/kalman_filter.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +function [x, V, VV, loglik] = kalman_filter(y, A, C, Q, R, init_x, init_V, varargin) +% Kalman filter. +% [x, V, VV, loglik] = kalman_filter(y, A, C, Q, R, init_x, init_V, ...) +% +% INPUTS: +% y(:,t) - the observation at time t +% A - the system matrix +% C - the observation matrix +% Q - the system covariance +% R - the observation covariance +% init_x - the initial state (column) vector +% init_V - the initial state covariance +% +% OPTIONAL INPUTS (string/value pairs [default in brackets]) +% 'model' - model(t)=m means use params from model m at time t [ones(1,T) ] +% In this case, all the above matrices take an additional final dimension, +% i.e., A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m). +% However, init_x and init_V are independent of model(1). +% 'u' - u(:,t) the control signal at time t [ [] ] +% 'B' - B(:,:,m) the input regression matrix for model m +% +% OUTPUTS (where X is the hidden state being estimated) +% x(:,t) = E[X(:,t) | y(:,1:t)] +% V(:,:,t) = Cov[X(:,t) | y(:,1:t)] +% VV(:,:,t) = Cov[X(:,t), X(:,t-1) | y(:,1:t)] t >= 2 +% loglik = sum{t=1}^T log P(y(:,t)) +% +% If an input signal is specified, we also condition on it: +% e.g., x(:,t) = E[X(:,t) | y(:,1:t), u(:, 1:t)] +% If a model sequence is specified, we also condition on it: +% e.g., x(:,t) = E[X(:,t) | y(:,1:t), u(:, 1:t), m(1:t)] + +[os T] = size(y); +ss = size(A,1); % size of state space + +% set default params +model = ones(1,T); +u = []; +B = []; +ndx = []; + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i} + case 'model', model = args{i+1}; + case 'u', u = args{i+1}; + case 'B', B = args{i+1}; + case 'ndx', ndx = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +x = zeros(ss, T); +V = zeros(ss, ss, T); +VV = zeros(ss, ss, T); + +loglik = 0; +for t=1:T + m = model(t); + if t==1 + %prevx = init_x(:,m); + %prevV = init_V(:,:,m); + prevx = init_x; + prevV = init_V; + initial = 1; + else + prevx = x(:,t-1); + prevV = V(:,:,t-1); + initial = 0; + end + if isempty(u) + [x(:,t), V(:,:,t), LL, VV(:,:,t)] = ... + kalman_update(A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m), y(:,t), prevx, prevV, 'initial', initial); + else + if isempty(ndx) + [x(:,t), V(:,:,t), LL, VV(:,:,t)] = ... + kalman_update(A(:,:,m), C(:,:,m), Q(:,:,m), R(:,:,m), y(:,t), prevx, prevV, ... + 'initial', initial, 'u', u(:,t), 'B', B(:,:,m)); + else + i = ndx{t}; + % copy over all elements; only some will get updated + x(:,t) = prevx; + prevP = inv(prevV); + prevPsmall = prevP(i,i); + prevVsmall = inv(prevPsmall); + [x(i,t), smallV, LL, VV(i,i,t)] = ... + kalman_update(A(i,i,m), C(:,i,m), Q(i,i,m), R(:,:,m), y(:,t), prevx(i), prevVsmall, ... + 'initial', initial, 'u', u(:,t), 'B', B(i,:,m)); + smallP = inv(smallV); + prevP(i,i) = smallP; + V(:,:,t) = inv(prevP); + end + end + loglik = loglik + LL; +end + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/kalman_forward_backward.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +% KALMAN_FORWARD_BACKWARD Forward Backward Propogation in Information Form +% +% +% Note : +% +% M file accompanying my technical note +% +% A Technique for Painless Derivation of Kalman Filtering Recursions +% +% available from http://www.mbfys.kun.nl/~cemgil/papers/painless-kalman.ps +% + +% Uses : + +% Change History : +% Date Time Prog Note +% 07-Jun-2001 2:24 PM ATC Created under MATLAB 5.3.1.29215a (R11.1) + +% ATC = Ali Taylan Cemgil, +% SNN - University of Nijmegen, Department of Medical Physics and Biophysics +% e-mail : cemgil@mbfys.kun.nl + +A = [1 1;0 1]; +C = [1 0]; +Q = eye(2)*0.01^2; +R = 0.001^2; +mu1 = [0;1]; +P1 = 3*Q; + +inv_Q = inv(Q); +inv_R = inv(R); + +y = [0 1.1 2 2.95 3.78]; + +T = length(y); +L = size(Q,1); + +%%%%% Forward message Passing +h_f = zeros(L, T); +K_f = zeros(L, L, T); +g_f = zeros(1, T); +h_f_pre = zeros(L, T); +K_f_pre = zeros(L, L, T); +g_f_pre = zeros(1, T); + + +K_f_pre(:, :, 1) = inv(P1); +h_f_pre(:,1) = K_f_pre(:, :, 1)*mu1; +g_f_pre(1) = -0.5*log(det(2*pi*P1)) - 0.5*mu1'*inv(P1)*mu1; + +for i=1:T, + h_f(:,i) = h_f_pre(:,i) + C'*inv_R*y(:,i); + K_f(:,:,i) = K_f_pre(:,:,i) + C'*inv_R*C; + g_f(i) = g_f_pre(i) -0.5*log(det(2*pi*R)) - 0.5*y(:,i)'*inv_R*y(:,i); + if i<T, + M = inv(A'*inv_Q*A + K_f(:,:,i)); + h_f_pre(:,i+1) = inv_Q*A*M*h_f(:,i); + K_f_pre(:,:,i+1) = inv_Q - inv_Q*A*M*A'*inv_Q; + g_f_pre(i+1) = g_f(i) -0.5*log(det(2*pi*Q)) + 0.5*log(det(2*pi*M)) + 0.5*h_f(:,i)'*M*h_f(:,i); + end; +end + +%%% Backward Message Passing +h_b = zeros(L, T); +K_b = zeros(L, L, T); +g_b = zeros(1, T); + +h_b_post = zeros(L, T); +K_b_post = zeros(L, L, T); +g_b_post = zeros(1, T); + +for i=T:-1:1, + h_b(:,i) = h_b_post(:,i) + C'*inv_R*y(:,i); + K_b(:,:,i) = K_b_post(:,:,i) + C'*inv_R*C; + g_b(i) = g_b_post(i) - 0.5*log(det(2*pi*R)) - 0.5*y(:,i)'*inv_R*y(:,i); + if i>1, + M = inv(inv_Q + K_b(:,:,i)); + h_b_post(:,i-1) = A'*inv(Q)*M*h_b(:,i); + K_b_post(:,:,i-1) = A'*inv_Q*(Q - M)*inv_Q*A; + g_b_post(i-1) = g_b(i) -0.5*log(det(2*pi*Q)) + 0.5*log(det(2*pi*M)) + 0.5*h_b(:,i)'*M*h_b(:,i); + end; +end; + + +%%%% Smoothed Estimates + +mu = zeros(size(h_f)); +Sig = zeros(size(K_f)); +g = zeros(size(g_f)); +lalpha = zeros(size(g_f)); + +for i=1:T, + Sig(:,:,i) = inv(K_b_post(:,:,i) + K_f(:,:,i)); + mu(:,i) = Sig(:,:,i)*(h_b_post(:,i) + h_f(:,i)); + g(i) = g_b_post(i) + g_f(:,i); + lalpha(i) = g(i) + 0.5*log(det(2*pi*Sig(:,:,i))) + 0.5*mu(:,i)'*inv(Sig(:,:,i))*mu(:,i); +end; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/kalman_smoother.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function [xsmooth, Vsmooth, VVsmooth, loglik] = kalman_smoother(y, A, C, Q, R, init_x, init_V, varargin) +% Kalman/RTS smoother. +% [xsmooth, Vsmooth, VVsmooth, loglik] = kalman_smoother(y, A, C, Q, R, init_x, init_V, ...) +% +% The inputs are the same as for kalman_filter. +% The outputs are almost the same, except we condition on y(:, 1:T) (and u(:, 1:T) if specified), +% instead of on y(:, 1:t). + +[os T] = size(y); +ss = length(A); + +% set default params +model = ones(1,T); +u = []; +B = []; + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i} + case 'model', model = args{i+1}; + case 'u', u = args{i+1}; + case 'B', B = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +xsmooth = zeros(ss, T); +Vsmooth = zeros(ss, ss, T); +VVsmooth = zeros(ss, ss, T); + +% Forward pass +[xfilt, Vfilt, VVfilt, loglik] = kalman_filter(y, A, C, Q, R, init_x, init_V, ... + 'model', model, 'u', u, 'B', B); + +% Backward pass +xsmooth(:,T) = xfilt(:,T); +Vsmooth(:,:,T) = Vfilt(:,:,T); +%VVsmooth(:,:,T) = VVfilt(:,:,T); + +for t=T-1:-1:1 + m = model(t+1); + if isempty(B) + [xsmooth(:,t), Vsmooth(:,:,t), VVsmooth(:,:,t+1)] = ... + smooth_update(xsmooth(:,t+1), Vsmooth(:,:,t+1), xfilt(:,t), Vfilt(:,:,t), ... + Vfilt(:,:,t+1), VVfilt(:,:,t+1), A(:,:,m), Q(:,:,m), [], []); + else + [xsmooth(:,t), Vsmooth(:,:,t), VVsmooth(:,:,t+1)] = ... + smooth_update(xsmooth(:,t+1), Vsmooth(:,:,t+1), xfilt(:,t), Vfilt(:,:,t), ... + Vfilt(:,:,t+1), VVfilt(:,:,t+1), A(:,:,m), Q(:,:,m), B(:,:,m), u(:,t+1)); + end +end + +VVsmooth(:,:,1) = zeros(ss,ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/kalman_update.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +function [xnew, Vnew, loglik, VVnew] = kalman_update(A, C, Q, R, y, x, V, varargin) +% KALMAN_UPDATE Do a one step update of the Kalman filter +% [xnew, Vnew, loglik] = kalman_update(A, C, Q, R, y, x, V, ...) +% +% INPUTS: +% A - the system matrix +% C - the observation matrix +% Q - the system covariance +% R - the observation covariance +% y(:) - the observation at time t +% x(:) - E[X | y(:, 1:t-1)] prior mean +% V(:,:) - Cov[X | y(:, 1:t-1)] prior covariance +% +% OPTIONAL INPUTS (string/value pairs [default in brackets]) +% 'initial' - 1 means x and V are taken as initial conditions (so A and Q are ignored) [0] +% 'u' - u(:) the control signal at time t [ [] ] +% 'B' - the input regression matrix +% +% OUTPUTS (where X is the hidden state being estimated) +% xnew(:) = E[ X | y(:, 1:t) ] +% Vnew(:,:) = Var[ X(t) | y(:, 1:t) ] +% VVnew(:,:) = Cov[ X(t), X(t-1) | y(:, 1:t) ] +% loglik = log P(y(:,t) | y(:,1:t-1)) log-likelihood of innovatio + +% set default params +u = []; +B = []; +initial = 0; + +args = varargin; +for i=1:2:length(args) + switch args{i} + case 'u', u = args{i+1}; + case 'B', B = args{i+1}; + case 'initial', initial = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +% xpred(:) = E[X_t+1 | y(:, 1:t)] +% Vpred(:,:) = Cov[X_t+1 | y(:, 1:t)] + +if initial + if isempty(u) + xpred = x; + else + xpred = x + B*u; + end + Vpred = V; +else + if isempty(u) + xpred = A*x; + else + xpred = A*x + B*u; + end + Vpred = A*V*A' + Q; +end + +e = y - C*xpred; % error (innovation) +n = length(e); +ss = length(A); +S = C*Vpred*C' + R; +Sinv = inv(S); +ss = length(V); +loglik = gaussian_prob(e, zeros(1,length(e)), S, 1); +K = Vpred*C'*Sinv; % Kalman gain matrix +% If there is no observation vector, set K = zeros(ss). +xnew = xpred + K*e; +Vnew = (eye(ss) - K*C)*Vpred; +VVnew = (eye(ss) - K*C)*A*V; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/learn_AR.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [coef, C] = learn_AR(data, k) +% Find the ML parameters of a vector autoregressive process of order k. +% [coef, C] = learn_AR(k, data) +% data{l}(:,t) = the observations at time t in sequence l + +warning('learn_AR seems to be broken'); + +nex = length(data); +obs = cell(1, nex); +for l=1:nex + obs{l} = convert_to_lagged_form(data{l}, k); +end + +% The initial parameter values don't matter, since this is a perfectly observable problem. +% However, the size of F must be set correctly. +y = data{1}; +[s T] = size(y); +coef = rand(s,s,k); +C = rand_psd(s); +[F,H,Q,R,initx,initV] = AR_to_SS(coef, C, y); + +max_iter = 1; +fully_observed = 1; +diagQ = 0; +diagR = 0; +[F, H, Q, R, initx, initV, loglik] = ... + learn_kalman(obs, F, H, Q, R, initx, initV, max_iter, diagQ, diagR, fully_observed); + +[coef, C] = SS_to_AR(F, Q, k); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/learn_AR_diagonal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function [coef, C] = learn_AR_diagonal(y, k) +% Find the ML parameters for a collection of independent scalar AR processes. + +% sep_coef(1,1,t,i) is the coefficient to apply to compopnent i of the state vector t steps ago +% eg. consider two components L and R and let A = coef(:,:,1,:), B = coef(:,:,2,:) +% L3 (AL 0 BL 0) (L2) (CL 0 0 0) +% R3 = (0 AR 0 BR) (R2) (0 CR 0 0) +% L2 (1 0 0 0 ) (L1) + (0 0 0 0) +% R2 (0 1 0 0 ) (R1) (0 0 0 0) + +ss = size(y, 1); +sep_coef = zeros(1,1,k,ss); +for i=1:ss + [sep_coef(:,:,:,i), sep_cov(i)] = learn_AR(k, y(i,:)); +end +C = diag(sep_cov); +for t=1:k + x = sep_coef(1,1,t,:); + coef(:,:,t) = diag(x(:)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/learn_kalman.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,182 @@ +function [A, C, Q, R, initx, initV, LL] = ... + learn_kalman(data, A, C, Q, R, initx, initV, max_iter, diagQ, diagR, ARmode, constr_fun, varargin) +% LEARN_KALMAN Find the ML parameters of a stochastic Linear Dynamical System using EM. +% +% [A, C, Q, R, INITX, INITV, LL] = LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0) fits +% the parameters which are defined as follows +% x(t+1) = A*x(t) + w(t), w ~ N(0, Q), x(0) ~ N(init_x, init_V) +% y(t) = C*x(t) + v(t), v ~ N(0, R) +% A0 is the initial value, A is the final value, etc. +% DATA(:,t,l) is the observation vector at time t for sequence l. If the sequences are of +% different lengths, you can pass in a cell array, so DATA{l} is an O*T matrix. +% LL is the "learning curve": a vector of the log lik. values at each iteration. +% LL might go positive, since prob. densities can exceed 1, although this probably +% indicates that something has gone wrong e.g., a variance has collapsed to 0. +% +% There are several optional arguments, that should be passed in the following order. +% LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0, MAX_ITER, DIAGQ, DIAGR, ARmode) +% MAX_ITER specifies the maximum number of EM iterations (default 10). +% DIAGQ=1 specifies that the Q matrix should be diagonal. (Default 0). +% DIAGR=1 specifies that the R matrix should also be diagonal. (Default 0). +% ARMODE=1 specifies that C=I, R=0. i.e., a Gauss-Markov process. (Default 0). +% This problem has a global MLE. Hence the initial parameter values are not important. +% +% LEARN_KALMAN(DATA, A0, C0, Q0, R0, INITX0, INITV0, MAX_ITER, DIAGQ, DIAGR, F, P1, P2, ...) +% calls [A,C,Q,R,initx,initV] = f(A,C,Q,R,initx,initV,P1,P2,...) after every M step. f can be +% used to enforce any constraints on the params. +% +% For details, see +% - Ghahramani and Hinton, "Parameter Estimation for LDS", U. Toronto tech. report, 1996 +% - Digalakis, Rohlicek and Ostendorf, "ML Estimation of a stochastic linear system with the EM +% algorithm and its application to speech recognition", +% IEEE Trans. Speech and Audio Proc., 1(4):431--442, 1993. + + +% learn_kalman(data, A, C, Q, R, initx, initV, max_iter, diagQ, diagR, ARmode, constr_fun, varargin) +if nargin < 8, max_iter = 10; end +if nargin < 9, diagQ = 0; end +if nargin < 10, diagR = 0; end +if nargin < 11, ARmode = 0; end +if nargin < 12, constr_fun = []; end +verbose = 1; +thresh = 1e-4; + + +if ~iscell(data) + N = size(data, 3); + data = num2cell(data, [1 2]); % each elt of the 3rd dim gets its own cell +else + N = length(data); +end + +N = length(data); +ss = size(A, 1); +os = size(C,1); + +alpha = zeros(os, os); +Tsum = 0; +for ex = 1:N + %y = data(:,:,ex); + y = data{ex}; + T = length(y); + Tsum = Tsum + T; + alpha_temp = zeros(os, os); + for t=1:T + alpha_temp = alpha_temp + y(:,t)*y(:,t)'; + end + alpha = alpha + alpha_temp; +end + +previous_loglik = -inf; +loglik = 0; +converged = 0; +num_iter = 1; +LL = []; + +% Convert to inline function as needed. +if ~isempty(constr_fun) + constr_fun = fcnchk(constr_fun,length(varargin)); +end + + +while ~converged & (num_iter <= max_iter) + + %%% E step + + delta = zeros(os, ss); + gamma = zeros(ss, ss); + gamma1 = zeros(ss, ss); + gamma2 = zeros(ss, ss); + beta = zeros(ss, ss); + P1sum = zeros(ss, ss); + x1sum = zeros(ss, 1); + loglik = 0; + + for ex = 1:N + y = data{ex}; + T = length(y); + [beta_t, gamma_t, delta_t, gamma1_t, gamma2_t, x1, V1, loglik_t] = ... + Estep(y, A, C, Q, R, initx, initV, ARmode); + beta = beta + beta_t; + gamma = gamma + gamma_t; + delta = delta + delta_t; + gamma1 = gamma1 + gamma1_t; + gamma2 = gamma2 + gamma2_t; + P1sum = P1sum + V1 + x1*x1'; + x1sum = x1sum + x1; + %fprintf(1, 'example %d, ll/T %5.3f\n', ex, loglik_t/T); + loglik = loglik + loglik_t; + end + LL = [LL loglik]; + if verbose, fprintf(1, 'iteration %d, loglik = %f\n', num_iter, loglik); end + %fprintf(1, 'iteration %d, loglik/NT = %f\n', num_iter, loglik/Tsum); + num_iter = num_iter + 1; + + %%% M step + + % Tsum = N*T + % Tsum1 = N*(T-1); + Tsum1 = Tsum - N; + A = beta * inv(gamma1); + %A = (gamma1' \ beta')'; + Q = (gamma2 - A*beta') / Tsum1; + if diagQ + Q = diag(diag(Q)); + end + if ~ARmode + C = delta * inv(gamma); + %C = (gamma' \ delta')'; + R = (alpha - C*delta') / Tsum; + if diagR + R = diag(diag(R)); + end + end + initx = x1sum / N; + initV = P1sum/N - initx*initx'; + + if ~isempty(constr_fun) + [A,C,Q,R,initx,initV] = feval(constr_fun, A, C, Q, R, initx, initV, varargin{:}); + end + + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; +end + + + +%%%%%%%%% + +function [beta, gamma, delta, gamma1, gamma2, x1, V1, loglik] = ... + Estep(y, A, C, Q, R, initx, initV, ARmode) +% +% Compute the (expected) sufficient statistics for a single Kalman filter sequence. +% + +[os T] = size(y); +ss = length(A); + +if ARmode + xsmooth = y; + Vsmooth = zeros(ss, ss, T); % no uncertainty about the hidden states + VVsmooth = zeros(ss, ss, T); + loglik = 0; +else + [xsmooth, Vsmooth, VVsmooth, loglik] = kalman_smoother(y, A, C, Q, R, initx, initV); +end + +delta = zeros(os, ss); +gamma = zeros(ss, ss); +beta = zeros(ss, ss); +for t=1:T + delta = delta + y(:,t)*xsmooth(:,t)'; + gamma = gamma + xsmooth(:,t)*xsmooth(:,t)' + Vsmooth(:,:,t); + if t>1 beta = beta + xsmooth(:,t)*xsmooth(:,t-1)' + VVsmooth(:,:,t); end +end +gamma1 = gamma - xsmooth(:,T)*xsmooth(:,T)' - Vsmooth(:,:,T); +gamma2 = gamma - xsmooth(:,1)*xsmooth(:,1)' - Vsmooth(:,:,1); + +x1 = xsmooth(:,1); +V1 = Vsmooth(:,:,1); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/learning_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +% Make a point move in the 2D plane +% State = (x y xdot ydot). We only observe (x y). +% Generate data from this process, and try to learn the dynamics back. + +% X(t+1) = F X(t) + noise(Q) +% Y(t) = H X(t) + noise(R) + +ss = 4; % state size +os = 2; % observation size +F = [1 0 1 0; 0 1 0 1; 0 0 1 0; 0 0 0 1]; +H = [1 0 0 0; 0 1 0 0]; +Q = 0.1*eye(ss); +R = 1*eye(os); +initx = [10 10 1 0]'; +initV = 10*eye(ss); + +seed = 1; +rand('state', seed); +randn('state', seed); +T = 100; +[x,y] = sample_lds(F, H, Q, R, initx, T); + +% Initializing the params to sensible values is crucial. +% Here, we use the true values for everything except F and H, +% which we initialize randomly (bad idea!) +% Lack of identifiability means the learned params. are often far from the true ones. +% All that EM guarantees is that the likelihood will increase. +F1 = randn(ss,ss); +H1 = randn(os,ss); +Q1 = Q; +R1 = R; +initx1 = initx; +initV1 = initV; +max_iter = 10; +[F2, H2, Q2, R2, initx2, initV2, LL] = learn_kalman(y, F1, H1, Q1, R1, initx1, initV1, max_iter); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/sample_lds.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [x,y] = sample_lds(F, H, Q, R, init_state, T, models, G, u) +% SAMPLE_LDS Simulate a run of a (switching) stochastic linear dynamical system. +% [x,y] = switching_lds_draw(F, H, Q, R, init_state, models, G, u) +% +% x(t+1) = F*x(t) + G*u(t) + w(t), w ~ N(0, Q), x(0) = init_state +% y(t) = H*x(t) + v(t), v ~ N(0, R) +% +% Input: +% F(:,:,i) - the transition matrix for the i'th model +% H(:,:,i) - the observation matrix for the i'th model +% Q(:,:,i) - the transition covariance for the i'th model +% R(:,:,i) - the observation covariance for the i'th model +% init_state(:,i) - the initial mean for the i'th model +% T - the num. time steps to run for +% +% Optional inputs: +% models(t) - which model to use at time t. Default = ones(1,T) +% G(:,:,i) - the input matrix for the i'th model. Default = 0. +% u(:,t) - the input vector at time t. Default = zeros(1,T) +% +% Output: +% x(:,t) - the hidden state vector at time t. +% y(:,t) - the observation vector at time t. + + +if ~iscell(F) + F = num2cell(F, [1 2]); + H = num2cell(H, [1 2]); + Q = num2cell(Q, [1 2]); + R = num2cell(R, [1 2]); +end + +M = length(F); +%T = length(models); + +if nargin < 7, + models = ones(1,T); +end +if nargin < 8, + G = num2cell(repmat(0, [1 1 M])); + u = zeros(1,T); +end + +[os ss] = size(H{1}); +state_noise_samples = cell(1,M); +obs_noise_samples = cell(1,M); +for i=1:M + state_noise_samples{i} = sample_gaussian(zeros(length(Q{i}),1), Q{i}, T)'; + obs_noise_samples{i} = sample_gaussian(zeros(length(R{i}),1), R{i}, T)'; +end + +x = zeros(ss, T); +y = zeros(os, T); + +m = models(1); +x(:,1) = init_state(:,m); +y(:,1) = H{m}*x(:,1) + obs_noise_samples{m}(:,1); + +for t=2:T + m = models(t); + x(:,t) = F{m}*x(:,t-1) + G{m}*u(:,t-1) + state_noise_samples{m}(:,t); + y(:,t) = H{m}*x(:,t) + obs_noise_samples{m}(:,t); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/smooth_update.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function [xsmooth, Vsmooth, VVsmooth_future] = smooth_update(xsmooth_future, Vsmooth_future, ... + xfilt, Vfilt, Vfilt_future, VVfilt_future, A, Q, B, u) +% One step of the backwards RTS smoothing equations. +% function [xsmooth, Vsmooth, VVsmooth_future] = smooth_update(xsmooth_future, Vsmooth_future, ... +% xfilt, Vfilt, Vfilt_future, VVfilt_future, A, B, u) +% +% INPUTS: +% xsmooth_future = E[X_t+1|T] +% Vsmooth_future = Cov[X_t+1|T] +% xfilt = E[X_t|t] +% Vfilt = Cov[X_t|t] +% Vfilt_future = Cov[X_t+1|t+1] +% VVfilt_future = Cov[X_t+1,X_t|t+1] +% A = system matrix for time t+1 +% Q = system covariance for time t+1 +% B = input matrix for time t+1 (or [] if none) +% u = input vector for time t+1 (or [] if none) +% +% OUTPUTS: +% xsmooth = E[X_t|T] +% Vsmooth = Cov[X_t|T] +% VVsmooth_future = Cov[X_t+1,X_t|T] + +%xpred = E[X(t+1) | t] +if isempty(B) + xpred = A*xfilt; +else + xpred = A*xfilt + B*u; +end +Vpred = A*Vfilt*A' + Q; % Vpred = Cov[X(t+1) | t] +J = Vfilt * A' * inv(Vpred); % smoother gain matrix +xsmooth = xfilt + J*(xsmooth_future - xpred); +Vsmooth = Vfilt + J*(Vsmooth_future - Vpred)*J'; +VVsmooth_future = VVfilt_future + (Vsmooth_future - Vfilt_future)*inv(Vfilt_future)*VVfilt_future; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/testKalman.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +tracking_demo +learning_demo
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/Kalman/tracking_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +% Make a point move in the 2D plane +% State = (x y xdot ydot). We only observe (x y). + +% This code was used to generate Figure 15.9 of "Artificial Intelligence: a Modern Approach", +% Russell and Norvig, 2nd edition, Prentice Hall, 2003. + +% X(t+1) = F X(t) + noise(Q) +% Y(t) = H X(t) + noise(R) + +ss = 4; % state size +os = 2; % observation size +F = [1 0 1 0; 0 1 0 1; 0 0 1 0; 0 0 0 1]; +H = [1 0 0 0; 0 1 0 0]; +Q = 0.1*eye(ss); +R = 1*eye(os); +initx = [10 10 1 0]'; +initV = 10*eye(ss); + +seed = 9; +rand('state', seed); +randn('state', seed); +T = 15; +[x,y] = sample_lds(F, H, Q, R, initx, T); + +[xfilt, Vfilt, VVfilt, loglik] = kalman_filter(y, F, H, Q, R, initx, initV); +[xsmooth, Vsmooth] = kalman_smoother(y, F, H, Q, R, initx, initV); + +dfilt = x([1 2],:) - xfilt([1 2],:); +mse_filt = sqrt(sum(sum(dfilt.^2))) + +dsmooth = x([1 2],:) - xsmooth([1 2],:); +mse_smooth = sqrt(sum(sum(dsmooth.^2))) + + +figure(1) +clf +%subplot(2,1,1) +hold on +plot(x(1,:), x(2,:), 'ks-'); +plot(y(1,:), y(2,:), 'g*'); +plot(xfilt(1,:), xfilt(2,:), 'rx:'); +for t=1:T, plotgauss2d(xfilt(1:2,t), Vfilt(1:2, 1:2, t)); end +hold off +legend('true', 'observed', 'filtered', 3) +xlabel('x') +ylabel('y') + + + +% 3x3 inches +set(gcf,'units','inches'); +set(gcf,'PaperPosition',[0 0 3 3]) +%print(gcf,'-depsc','/home/eecs/murphyk/public_html/Bayes/Figures/aima_filtered.eps'); +%print(gcf,'-djpeg','-r100', '/home/eecs/murphyk/public_html/Bayes/Figures/aima_filtered.jpg'); + + +figure(2) +%subplot(2,1,2) +hold on +plot(x(1,:), x(2,:), 'ks-'); +plot(y(1,:), y(2,:), 'g*'); +plot(xsmooth(1,:), xsmooth(2,:), 'rx:'); +for t=1:T, plotgauss2d(xsmooth(1:2,t), Vsmooth(1:2, 1:2, t)); end +hold off +legend('true', 'observed', 'smoothed', 3) +xlabel('x') +ylabel('y') + + +% 3x3 inches +set(gcf,'units','inches'); +set(gcf,'PaperPosition',[0 0 3 3]) +%print(gcf,'-djpeg','-r100', '/home/eecs/murphyk/public_html/Bayes/Figures/aima_smoothed.jpg'); +%print(gcf,'-depsc','/home/eecs/murphyk/public_html/Bayes/Figures/aima_smoothed.eps');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/@assocarray/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/assocarray.m/1.1.1.1/Wed May 29 15:59:52 2002// +/subsref.m/1.1.1.1/Wed Aug 4 19:36:30 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/@assocarray/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/@assocarray
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/@assocarray/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/@assocarray/assocarray.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function A = assocarray(keys, vals) +% ASSOCARRAY Make an associative array +% function A = assocarray(keys, vals) +% +% keys{i} is the i'th string, vals{i} is the i'th value. +% After construction, A('foo') will return the value associated with foo. + +A.keys = keys; +A.vals = vals; +A = class(A, 'assocarray');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/@assocarray/subsref.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function val = subsref(A, S) +% SUBSREF Subscript reference for an associative array +% A('foo') will return the value associated with foo. +% If there are multiple identicaly keys, the first match is returned. +% Currently the search is sequential. + +i = 1; +while i <= length(A.keys) + if strcmp(S.subs{1}, A.keys{i}) + val = A.vals{i}; + return; + end + i = i + 1; +end +error(['can''t find ' S.subs{1}])
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@boolean_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/boolean_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@boolean_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@boolean_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@boolean_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@boolean_CPD/boolean_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,179 @@ +function CPD = boolean_CPD(bnet, self, ftype, fname, pfail) +% BOOLEAN_CPD Make a tabular CPD representing a (noisy) boolean function +% +% CPD = boolean_cpd(bnet, self, 'inline', f) uses the inline function f +% to specify the CPT. +% e.g., suppose X4 = X2 AND (NOT X3). Then we can write +% bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('(x(1) & ~x(2)')); +% Note that x(1) refers pvals(1) = X2, and x(2) refers to pvals(2)=X3. +% +% CPD = boolean_cpd(bnet, self, 'named', f) assumes f is a function name. +% f can be built-in to matlab, or a file. +% e.g., If X4 = X2 AND X3, we can write +% bnet.CPD{4} = boolean_CPD(bnet, 4, 'named', 'and'); +% e.g., If X4 = X2 OR X3, we can write +% bnet.CPD{4} = boolean_CPD(bnet, 4, 'named', 'any'); +% +% CPD = boolean_cpd(bnet, self, 'rnd') makes a random non-redundant bool fn. +% +% CPD = boolean_CPD(bnet, self, 'inline'/'named', f, pfail) +% will put probability mass 1-pfail on f(parents), and put pfail on the other value. +% This is useful for simulating noisy boolean functions. +% If pfail is omitted, it is set to 0. +% (Note that adding noise to a random (non-redundant) boolean function just creates a different +% (potentially redundant) random boolean function.) +% +% Note: This cannot be used to simulate a noisy-OR gate. +% Example: suppose C has parents A and B, and the +% link of A->C fails with prob pA and the link B->C fails with pB. +% Then the noisy-OR gate defines the following distribution +% +% A B P(C=0) +% 0 0 1.0 +% 1 0 pA +% 0 1 pB +% 1 1 pA * PB +% +% By contrast, boolean_CPD(bnet, C, 'any', p) would define +% +% A B P(C=0) +% 0 0 1-p +% 1 0 p +% 0 1 p +% 1 1 p + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = tabular_CPD(bnet, self); + return; +elseif isa(bnet, 'boolean_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end + +if nargin < 5, pfail = 0; end + +ps = parents(bnet.dag, self); +ns = bnet.node_sizes; +psizes = ns(ps); +self_size = ns(self); + +psucc = 1-pfail; + +k = length(ps); +switch ftype + case 'inline', f = eval_bool_fn(fname, k); + case 'named', f = eval_bool_fn(fname, k); + case 'rnd', f = mk_rnd_bool_fn(k); + otherwise, error(['unknown function type ' ftype]); +end + +CPT = zeros(prod(psizes), self_size); +ndx = find(f==0); +CPT(ndx, 1) = psucc; +CPT(ndx, 2) = pfail; +ndx = find(f==1); +CPT(ndx, 2) = psucc; +CPT(ndx, 1) = pfail; +if k > 0 + CPT = reshape(CPT, [psizes self_size]); +end + +clamp = 1; +CPD = tabular_CPD(bnet, self, CPT, [], clamp); + + + +%%%%%%%%%%%% + +function f = eval_bool_fn(fname, n) +% EVAL_BOOL_FN Evaluate a boolean function on all bit vectors of length n +% f = eval_bool_fn(fname, n) +% +% e.g. f = eval_bool_fn(inline('x(1) & x(3)'), 3) +% returns 0 0 0 0 0 1 0 1 + +ns = 2*ones(1, n); +f = zeros(1, 2^n); +bits = ind2subv(ns, 1:2^n); +for i=1:2^n + f(i) = feval(fname, bits(i,:)-1); +end + +%%%%%%%%%%%%%%% + +function f = mk_rnd_bool_fn(n) +% MK_RND_BOOL_FN Make a random bit vector of length n that encodes a non-redundant boolean function +% f = mk_rnd_bool_fn(n) + +red = 1; +while red + f = sample_discrete([0.5 0.5], 2^n, 1)-1; + red = redundant_bool_fn(f); +end + +%%%%%%%% + + +function red = redundant_bool_fn(f) +% REDUNDANT_BOOL_FN Does a boolean function depend on all its input values? +% r = redundant_bool_fn(f) +% +% f is a vector of length 2^n, representing the output for each bit vector. +% An input is redundant if there is no assignment to the other bits +% which changes the output e.g., input 1 is redundant if u(2:n) s.t., +% f([0 u(2:n)]) <> f([1 u(2:n)]). +% A function is redundant it it has any redundant inputs. + +n = log2(length(f)); +ns = 2*ones(1,n); +red = 0; +for i=1:n + ens = ns; + ens(i) = 1; + U = ind2subv(ens, 1:2^(n-1)); + U(:,i) = 1; + f1 = f(subv2ind(ns, U)); + U(:,i) = 2; + f2 = f(subv2ind(ns, U)); + if isequal(f1, f2) + red = 1; + return; + end +end + + +%%%%%%%%%% + +function [b, iter] = rnd_truth_table(N) +% RND_TRUTH_TABLE Construct the output of a random truth table s.t. each input is non-redundant +% b = rnd_truth_table(N) +% +% N is the number of inputs. +% b is a random bit string of length N, representing the output of the truth table. +% Non-redundant means that, for each input position k, +% there are at least two bit patterns, u and v, that differ only in the k'th position, +% s.t., f(u) ~= f(v), where f is the function represented by b. +% We use rejection sampling to ensure non-redundancy. +% +% Example: b = [0 0 0 1 0 0 0 1] is indep of 3rd input (AND of inputs 1 and 2) + +bits = ind2subv(2*ones(1,N), 1:2^N)-1; +redundant = 1; +iter = 0; +while redundant & (iter < 4) + iter = iter + 1; + b = sample_discrete([0.5 0.5], 1, 2^N)-1; + redundant = 0; + for i=1:N + on = find(bits(:,i)==1); + off = find(bits(:,i)==0); + if isequal(b(on), b(off)) + redundant = 1; + break; + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@deterministic_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/deterministic_CPD.m/1.1.1.1/Mon Oct 7 13:26:36 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@deterministic_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@deterministic_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@deterministic_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@deterministic_CPD/deterministic_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function CPD = deterministic_CPD(bnet, self, fname, pfail) +% DETERMINISTIC_CPD Make a tabular CPD representing a (noisy) deterministic function +% +% CPD = deterministic_CPD(bnet, self, fname) +% This calls feval(fname, pvals) for each possible vector of parent values. +% e.g., suppose there are 2 ternary parents, then pvals = +% [1 1], [2 1], [3 1], [1 2], [2 2], [3 2], [1 3], [2 3], [3 3] +% If v = feval(fname, pvals(i)), then +% CPD(x | parents=pvals(i)) = 1 if x==v, and = 0 if x<>v +% e.g., suppose X4 = X2 AND (NOT X3). Then +% bnet.CPD{4} = deterministic_CPD(bnet, 4, inline('((x(1)-1) & ~(x(2)-1)) + 1')); +% Note that x(1) refers pvals(1) = X2, and x(2) refers to pvals(2)=X3 +% See also boolean_CPD. +% +% CPD = deterministic_CPD(bnet, self, fname, pfail) +% will put probability mass 1-pfail on f(parents), and distribute pfail over the other values. +% This is useful for simulating noisy deterministic functions. +% If pfail is omitted, it is set to 0. +% + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = tabular_CPD(bnet, self); + return; +elseif isa(bnet, 'deterministic_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end + +if nargin < 4, pfail = 0; end + +ps = parents(bnet.dag, self); +ns = bnet.node_sizes; +psizes = ns(ps); +self_size = ns(self); + +psucc = 1-pfail; + +CPT = zeros(prod(psizes), self_size); +pvals = zeros(1, length(ps)); +for i=1:prod(psizes) + pvals = ind2subv(psizes, i); + x = feval(fname, pvals); + %fprintf('%d ', [pvals x]); fprintf('\n'); + if psucc == 1 + CPT(i, x) = 1; + else + CPT(i, x) = psucc; + rest = mysetdiff(1:self_size, x); + CPT(i, rest) = pfail/length(rest); + end +end +CPT = reshape(CPT, [psizes self_size]); + +CPD = tabular_CPD(bnet, self, 'CPT',CPT, 'clamped',1); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/CPD_to_lambda_msg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% CPD_TO_LAMBDA_MSG Compute lambda message (discrete) +% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% Pearl p183 eq 4.52 + +switch msg_type + case 'd', + T = prod_CPT_and_pi_msgs(CPD, n, ps, msg, p); + mysize = length(msg{n}.lambda); + lambda = dpot(n, mysize, msg{n}.lambda); + T = multiply_by_pot(T, lambda); + lam_msg = pot_to_marginal(marginalize_pot(T, p)); + lam_msg = lam_msg.T; + case 'g', + error('discrete_CPD can''t create Gaussian msgs') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/CPD_to_pi.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% COMPUTE_PI Compute pi vector (discrete) +% pi = compute_pi(CPD, msg_type, n, ps, msg, evidence) +% Pearl p183 eq 4.51 + +switch msg_type + case 'd', + T = prod_CPT_and_pi_msgs(CPD, n, ps, msg); + pi = pot_to_marginal(marginalize_pot(T, n)); + pi = pi.T(:); + case 'g', + error('can only convert discrete CPD to Gaussian pi if observed') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/CPD_to_scgpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function pot = CPD_to_scgpot(CPD, domain, ns, cnodes, evidence) +% CPD_TO_SCGPOT Convert a CPD to a CG potential, incorporating any evidence (discrete) +% pot = CPD_to_scgpot(CPD, domain, ns, cnodes, evidence) +% +% domain is the domain of CPD. +% node_sizes(i) is the size of node i. +% cnodes +% evidence{i} is the evidence on the i'th node. + +%odom = domain(~isemptycell(evidence(domain))); + +%vals = cat(1, evidence{odom}); +%map = find_equiv_posns(odom, domain); +%index = mk_multi_index(length(domain), map, vals); +CPT = CPD_to_CPT(CPD); +%CPT = CPT(index{:}); +CPT = CPT(:); +%ns(odom) = 1; +potarray = cell(1, length(CPT)); +for i=1:length(CPT) + %p = CPT(i); + potarray{i} = scgcpot(0, 0, CPT(i)); + %scpot{i} = scpot(0, 0); +end +pot = scgpot(domain, [], [], ns, potarray);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_scgpot.m/1.1.1.1/Wed May 29 15:59:52 2002// +/README/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_CPD_to_table_hidden_ps.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_obs_CPD_to_table.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_to_pot.m/1.1.1.1/Fri Feb 20 22:00:38 2004// +/convert_to_sparse_table.c/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_to_table.m/1.1.1.1/Wed May 29 15:59:52 2002// +/discrete_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/dom_sizes.m/1.1.1.1/Wed May 29 15:59:52 2002// +/log_prob_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +/prob_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +A D/Old//// +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@discrete_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_to_table.m/1.1.1.1/Wed May 29 15:59:52 2002// +/prob_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/prob_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@discrete_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/Old/convert_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a tabular CPD to one or more potentials +% pots = convert_to_pot(CPD, pot_type, domain, evidence) +% +% pots{i} = CPD evaluated using evidence(domain(:,i)) +% If 'domains' is a single row vector, pots will be an object, not a cell array. + +ncases = size(domain,2); +assert(ncases==1); % not yet vectorized + +sz = dom_sizes(CPD); +ns = zeros(1, max(domain)); +ns(domain) = sz; + +local_ev = evidence(domain); +obs_bitv = ~isemptycell(local_ev); +odom = domain(obs_bitv); +T = convert_to_table(CPD, domain, local_ev, obs_bitv); + +switch pot_type + case 'u', + pot = upot(domain, sz, T, 0*myones(sz)); + case 'd', + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + case {'c','g'}, + % Since we want the output to be a Gaussian, the whole family must be observed. + % In other words, the potential is really just a constant. + p = T; + %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1))); + ns(domain) = 0; + pot = cpot(domain, ns(domain), log(p)); + case 'cg', + T = T(:); + ns(odom) = 1; + can = cell(1, length(T)); + for i=1:length(T) + can{i} = cpot([], [], log(T(i))); + end + pot = cgpot(domain, [], ns, can); + otherwise, + error(['unrecognized pot type ' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/Old/convert_to_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function T = convert_to_table(CPD, domain, local_ev, obs_bitv) +% CONVERT_TO_TABLE Convert a discrete CPD to a table +% function T = convert_to_table(CPD, domain, local_ev, obs_bitv) +% +% We convert the CPD to a CPT, and then lookup the evidence on the discrete parents. +% The resulting table can easily be converted to a potential. + + +CPT = CPD_to_CPT(CPD); +obs_child_only = ~any(obs_bitv(1:end-1)) & obs_bitv(end); + +if obs_child_only + sz = size(CPT); + CPT = reshape(CPT, prod(sz(1:end-1)), sz(end)); + o = local_ev{end}; + T = CPT(:, o); +else + odom = domain(obs_bitv); + vals = cat(1, local_ev{find(obs_bitv)}); % undo cell array + map = find_equiv_posns(odom, domain); + index = mk_multi_index(length(domain), map, vals); + T = CPT(index{:}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/Old/prob_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function p = prob_CPD(CPD, domain, ns, cnodes, evidence) +% PROB_CPD Compute prob of a node given evidence on the parents (discrete) +% p = prob_CPD(CPD, domain, ns, cnodes, evidence) +% +% domain is the domain of CPD. +% node_sizes(i) is the size of node i. +% cnodes = all the cts nodes +% evidence{i} is the evidence on the i'th node. + +ps = domain(1:end-1); +self = domain(end); +CPT = CPD_to_CPT(CPD); + +if isempty(ps) + T = CPT; +else + assert(~any(isemptycell(evidence(ps)))); + pvals = cat(1, evidence{ps}); + i = subv2ind(ns(ps), pvals(:)'); + T = reshape(CPT, [prod(ns(ps)) ns(self)]); + T = T(i,:); +end +p = T(evidence{self}); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/Old/prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function [P, p] = prob_node(CPD, self_ev, pev) +% PROB_NODE Compute prod_m P(x(i,m)| x(pi_i,m), theta_i) for node i (discrete) +% [P, p] = prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) +% +% p(m) = P(x(i,m)| x(pi_i,m), theta_i) +% P = prod p(m) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +ncases = length(self_ev); +sz = dom_sizes(CPD); + +nparents = length(sz)-1; +if nparents == 0 + assert(isempty(pev)); +else + assert(isequal(size(pev), [nparents ncases])); +end + +n = length(sz); +dom = 1:n; +p = zeros(1, ncases); +if nparents == 0 + for m=1:ncases + if usecell + evidence = {self_ev{m}}; + else + evidence = num2cell(self_ev(m)); + end + T = convert_to_table(CPD, dom, evidence); + p(m) = T; + end +else + for m=1:ncases + if usecell + evidence = cell(1,n); + evidence(1:n-1) = pev(:,m); + evidence(n) = self_ev(m); + else + evidence = num2cell([pev(:,m)', self_ev(m)]); + end + T = convert_to_table(CPD, dom, evidence); + p(m) = T; + end +end +P = prod(p); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +Any CPD on a discrete child with discrete parents +can be represented as a table (although this might be quite big). +discrete_CPD uses this tabular representation to implement various +functions. Subtypes are free to implement more efficient versions. +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/convert_CPD_to_table_hidden_ps.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function T = convert_CPD_to_table_hidden_ps(CPD, child_obs) +% CONVERT_CPD_TO_TABLE_HIDDEN_PS Convert a discrete CPD to a table +% T = convert_CPD_to_table_hidden_ps(CPD, child_obs) +% +% This is like convert_to_table, except that we are guaranteed that +% none of the parents have evidence on them. +% child_obs may be an integer (1,2,...) or []. + +CPT = CPD_to_CPT(CPD); +if isempty(child_obs) + T = CPT(:); +else + sz = dom_sizes(CPD); + if length(sz)==1 % no parents + T = CPT(child_obs); + else + CPT = reshape(CPT, prod(sz(1:end-1)), sz(end)); + T = CPT(:, child_obs); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/convert_obs_CPD_to_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a discrete CPD to a table +% T = convert_to_table(CPD, domain, evidence) +% +% We convert the CPD to a CPT, and then lookup the evidence on the discrete parents. +% The resulting table can easily be converted to a potential. + +CPT = CPD_to_CPT(CPD); +odom = domain(~isemptycell(evidence(domain))); +vals = cat(1, evidence{odom}); +map = find_equiv_posns(odom, domain); +index = mk_multi_index(length(domain), map, vals); +T = CPT(index{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/convert_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a discrete CPD to a potential +% pot = convert_to_pot(CPD, pot_type, domain, evidence) +% +% pots = CPD evaluated using evidence(domain) + +ncases = size(domain,2); +assert(ncases==1); % not yet vectorized + +sz = dom_sizes(CPD); +ns = zeros(1, max(domain)); +ns(domain) = sz; + +CPT1 = CPD_to_CPT(CPD); +spar = issparse(CPT1); +odom = domain(~isemptycell(evidence(domain))); +if spar + T = convert_to_sparse_table(CPD, domain, evidence); +else + T = convert_to_table(CPD, domain, evidence); +end + +switch pot_type + case 'u', + pot = upot(domain, sz, T, 0*myones(sz)); + case 'd', + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + case {'c','g'}, + % Since we want the output to be a Gaussian, the whole family must be observed. + % In other words, the potential is really just a constant. + p = T; + %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1))); + ns(domain) = 0; + pot = cpot(domain, ns(domain), log(p)); + + case 'cg', + T = T(:); + ns(odom) = 1; + can = cell(1, length(T)); + for i=1:length(T) + if T(i) == 0 + can{i} = cpot([], [], -Inf); % bug fix by Bob Welch 20/2/04 + else + can{i} = cpot([], [], log(T(i))); + end; + end + pot = cgpot(domain, [], ns, can); + + case 'scg' + T = T(:); + ns(odom) = 1; + pot_array = cell(1, length(T)); + for i=1:length(T) + pot_array{i} = scgcpot([], [], T(i)); + end + pot = scgpot(domain, [], [], ns, pot_array); + + otherwise, + error(['unrecognized pot type ' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/convert_to_sparse_table.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,154 @@ +/* convert_to_sparse_table.c convert a sparse discrete CPD with evidence into sparse table */ +/* convert_to_pot.m located in ../CPDs/discrete_CPD call it */ +/* 3 input */ +/* CPD prhs[0] with 1D sparse CPT */ +/* domain prhs[1] */ +/* evidence prhs[2] */ +/* 1 output */ +/* T plhs[0] sparse table */ + +#include <math.h> +#include "mex.h" + +void ind_subv(int index, const int *cumprod, const int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, NS, NZB, count, bdim, match, domain, bindex, sindex, nzCounts=0; + int *observed, *bsubv, *ssubv, *bir, *sir, *bjc, *sjc, *mask, *ssize, *bcumprod, *scumprod; + double *pDomain, *pSize, *bpr, *spr; + mxArray *pTemp; + + pTemp = mxGetField(prhs[0], 0, "CPT"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + pTemp = mxGetField(prhs[0], 0, "sizes"); + pSize = mxGetPr(pTemp); + + pDomain = mxGetPr(prhs[1]); + bdim = mxGetNumberOfElements(prhs[1]); + + mask = malloc(bdim * sizeof(int)); + ssize = malloc(bdim * sizeof(int)); + observed = malloc(bdim * sizeof(int)); + + for(i=0; i<bdim; i++){ + ssize[i] = (int)pSize[i]; + } + + count = 0; + for(i=0; i<bdim; i++){ + domain = (int)pDomain[i] - 1; + pTemp = mxGetCell(prhs[2], domain); + if(pTemp){ + mask[count] = i; + ssize[i] = 1; + observed[count] = (int)mxGetScalar(pTemp) - 1; + count++; + } + } + + if(count == 0){ + pTemp = mxGetField(prhs[0], 0, "CPT"); + plhs[0] = mxDuplicateArray(pTemp); + free(mask); + free(ssize); + free(observed); + return; + } + + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(count * sizeof(int)); + bcumprod = malloc(bdim * sizeof(int)); + scumprod = malloc(bdim * sizeof(int)); + + NS = 1; + for(i=0; i<bdim; i++){ + NS *= ssize[i]; + } + + plhs[0] = mxCreateSparse(NS, 1, NS, mxREAL); + spr = mxGetPr(plhs[0]); + sir = mxGetIr(plhs[0]); + sjc = mxGetJc(plhs[0]); + sjc[0] = 0; + sjc[1] = NS; + + bcumprod[0] = 1; + scumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bcumprod[i+1] = bcumprod[i] * (int)pSize[i]; + scumprod[i+1] = scumprod[i] * ssize[i]; + } + + nzCounts = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bcumprod, bdim, bsubv); + for(j=0; j<count; j++){ + ssubv[j] = bsubv[mask[j]]; + } + match = 1; + for(j=0; j<count; j++){ + if((ssubv[j]) != observed[j]){ + match = 0; + break; + } + } + if(match){ + spr[nzCounts] = bpr[i]; + sindex = subv_ind(bdim, scumprod, bsubv); + sir[nzCounts] = sindex; + nzCounts++; + } + } + + reset_nzmax(plhs[0], NS, nzCounts); + free(mask); + free(ssize); + free(observed); + free(bsubv); + free(ssubv); + free(bcumprod); + free(scumprod); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/convert_to_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a discrete CPD to a table +% T = convert_to_table(CPD, domain, evidence) +% +% We convert the CPD to a CPT, and then lookup the evidence on the discrete parents. +% The resulting table can easily be converted to a potential. + +domain = domain(:); +CPT = CPD_to_CPT(CPD); +odom = domain(~isemptycell(evidence(domain))); +vals = cat(1, evidence{odom}); +map = find_equiv_posns(odom, domain); +index = mk_multi_index(length(domain), map, vals); +T = CPT(index{:}); +T = T(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/discrete_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function CPD = discrete_CPD(clamped, dom_sizes) +% DISCRETE_CPD Virtual constructor for generic discrete CPD +% CPD = discrete_CPD(clamped, dom_sizes) + +CPD.dom_sizes = dom_sizes; +CPD = class(CPD, 'discrete_CPD', generic_CPD(clamped));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/dom_sizes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function sz = dom_sizes(CPD) +% DOM_SIZES Return the size of each node in the domain +% sz = dom_sizes(CPD) + +sz = CPD.dom_sizes;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/log_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute sum_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (discrete) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +[P, p] = prob_node(CPD, self_ev, pev); % P may underflow, so we use p +tiny = exp(-700); +p = p + (p==0)*tiny; % replace 0s by tiny +L = sum(log(p));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/prod_CPT_and_pi_msgs.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@discrete_CPD/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/private/prod_CPT_and_pi_msgs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function T = prod_CPT_and_pi_msgs(CPD, n, ps, msgs, except) +% PROD_CPT_AND_PI_MSGS Multiply the CPD and all the pi messages from parents, perhaps excepting one +% T = prod_CPY_and_pi_msgs(CPD, n, ps, msgs, except) + +if nargin < 5, except = -1; end + +dom = [ps n]; +%ns = sparse(1, max(dom)); +ns = zeros(1, max(dom)); +CPT = CPD_to_CPT(CPD); +ns(dom) = mysize(CPT); +T = dpot(dom, ns(dom), CPT); +for i=1:length(ps) + p = ps(i); + if p ~= except + T = multiply_by_pot(T, dpot(p, ns(p), msgs{n}.pi_from_parent{i})); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +function [P, p] = prob_node(CPD, self_ev, pev) +% PROB_NODE Compute prod_m P(x(i,m)| x(pi_i,m), theta_i) for node i (discrete) +% [P, p] = prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) +% +% p(m) = P(x(i,m)| x(pi_i,m), theta_i) +% P = prod p(m) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +ncases = length(self_ev); +sz = dom_sizes(CPD); + +nparents = length(sz)-1; +if nparents == 0 + assert(isempty(pev)); +else + assert(isequal(size(pev), [nparents ncases])); +end + +n = length(sz); +dom = 1:n; +p = zeros(1, ncases); +if isa(CPD, 'tabular_CPD') + % speed up by looking up CPT using index Zhang Yimin 2001-12-31 + if usecell + if nparents == 0 + data = [cell2num(self_ev)]; + else + data = [cell2num(pev); cell2num(self_ev)]; + end + else + if nparents == 0 + data = [self_ev]; + else + data = [pev; self_ev]; + end + end + + indices = subv2ind(sz, data'); % each row of data' is a case + + CPT=CPD_to_CPT(CPD); + p = CPT(indices); + + %get the prob list + %cpt_size = prod(sz); + %prob_list=reshape(CPT, cpt_size, 1); + %for m=1:ncases %here we assume we get evidence for node and all its parents + % idx=indices(m); + % p(m)=prob_list(idx); + %end + +else % eg. softmax + + for m=1:ncases + if usecell + if nparents == 0 + evidence = {self_ev{m}}; + else + evidence = cell(1,n); + evidence(1:n-1) = pev(:,m); + evidence(n) = self_ev(m); + end + else + if nparents == 0 + evidence = num2cell(self_ev(m)); + else + evidence = num2cell([pev(:,m)', self_ev(m)]); + end + end + T = convert_to_table(CPD, dom, evidence); + p(m) = T; + end +end + +P = prod(p); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@discrete_CPD/sample_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function y = sample_node(CPD, pvals) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (discrete) +% y = sample_node(CPD, parent_evidence) +% +% parent_evidence{i} is the value of the i'th parent + +if 0 +n = length(pvals)+1; +dom = 1:n; +evidence = cell(1,n); +evidence(1:n-1) = pvals; +T = convert_to_table(CPD, dom, evidence); +y = sample_discrete(T); +end + + +CPT = CPD_to_CPT(CPD); +sz = mysize(CPT); +nparents = length(sz)-1; +switch nparents + case 0, T = CPT; + case 1, T = CPT(pvals{1}, :); + case 2, T = CPT(pvals{1}, pvals{2}, :); + case 3, T = CPT(pvals{1}, pvals{2}, pvals{3}, :); + case 4, T = CPT(pvals{1}, pvals{2}, pvals{3}, pvals{4}, :); + otherwise, + pvals = cat(1, pvals{:}); + psz = sz(1:end-1); + ssz = sz(end); + i = subv2ind(psz, pvals(:)'); + T = reshape(CPT, [prod(psz) ssz]); + T = T(i,:); +end +y = sample_discrete(T);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% CPD_TO_LAMBDA_MSG Compute lambda message (gaussian) +% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% Pearl p183 eq 4.52 + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + cps = ps(CPD.cps); + cpsizes = CPD.sizes(CPD.cps); + self_size = CPD.sizes(end); + i = find_equiv_posns(p, cps); % p is n's i'th cts parent + psz = cpsizes(i); + if all(msg{n}.lambda.precision == 0) % no info to send on + lam_msg.precision = zeros(psz, psz); + lam_msg.info_state = zeros(psz, 1); + return; + end + [m, Q, W] = gaussian_CPD_params_given_dps(CPD, [ps n], evidence); + Bmu = m; + BSigma = Q; + for k=1:length(cps) % only get pi msgs from cts parents + pk = cps(k); + if pk ~= p + %bk = block(k, cpsizes); + bk = CPD.cps_block_ndx{k}; + Bk = W(:, bk); + m = msg{n}.pi_from_parent{k}; + BSigma = BSigma + Bk * m.Sigma * Bk'; + Bmu = Bmu + Bk * m.mu; + end + end + % BSigma = Q + sum_{k \neq i} B_k Sigma_k B_k' + %bi = block(i, cpsizes); + bi = CPD.cps_block_ndx{i}; + Bi = W(:,bi); + P = msg{n}.lambda.precision; + if (rcond(P) > 1e-3) | isinf(P) + if isinf(P) % Y is observed + Sigma_lambda = zeros(self_size, self_size); % infinite precision => 0 variance + mu_lambda = msg{n}.lambda.mu; % observed_value; + else + Sigma_lambda = inv(P); + mu_lambda = Sigma_lambda * msg{n}.lambda.info_state; + end + C = inv(Sigma_lambda + BSigma); + lam_msg.precision = Bi' * C * Bi; + lam_msg.info_state = Bi' * C * (mu_lambda - Bmu); + else + % method that uses matrix inversion lemma to avoid inverting P + A = inv(P + inv(BSigma)); + C = P - P*A*P; + lam_msg.precision = Bi' * C * Bi; + D = eye(self_size) - P*A; + z = msg{n}.lambda.info_state; + lam_msg.info_state = Bi' * (D*z - D*P*Bmu); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CPD_to_pi.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% CPD_TO_PI Compute the pi vector (gaussian) +% function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + [m, Q, W] = gaussian_CPD_params_given_dps(CPD, [ps n], evidence); + cps = ps(CPD.cps); + cpsizes = CPD.sizes(CPD.cps); + pi.mu = m; + pi.Sigma = Q; + for k=1:length(cps) % only get pi msgs from cts parents + %bk = block(k, cpsizes); + bk = CPD.cps_block_ndx{k}; + Bk = W(:, bk); + m = msg{n}.pi_from_parent{k}; + pi.Sigma = pi.Sigma + Bk * m.Sigma * Bk'; + pi.mu = pi.mu + Bk * m.mu; % m.mu = u(k) + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CPD_to_scgpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function pot = CPD_to_scgpot(CPD, domain, ns, cnodes, evidence) +% CPD_TO_CGPOT Convert a Gaussian CPD to a CG potential, incorporating any evidence +% pot = CPD_to_cgpot(CPD, domain, ns, cnodes, evidence) + +self = CPD.self; +dnodes = mysetdiff(1:length(ns), cnodes); +odom = domain(~isemptycell(evidence(domain))); +cdom = myintersect(cnodes, domain); +cheaddom = myintersect(self, domain); +ctaildom = mysetdiff(cdom,cheaddom); +ddom = myintersect(dnodes, domain); +cobs = myintersect(cdom, odom); +dobs = myintersect(ddom, odom); +ens = ns; % effective node size +ens(cobs) = 0; +ens(dobs) = 1; + +% Extract the params compatible with the observations (if any) on the discrete parents (if any) +% parents are all but the last domain element +ps = domain(1:end-1); +dps = myintersect(ps, ddom); +dops = myintersect(dps, odom); + +map = find_equiv_posns(dops, dps); +dpvals = cat(1, evidence{dops}); +index = mk_multi_index(length(dps), map, dpvals); + +dpsize = prod(ens(dps)); +cpsize = size(CPD.weights(:,:,1), 2); % cts parents size +ss = size(CPD.mean, 1); % self size +% the reshape acts like a squeeze +m = reshape(CPD.mean(:, index{:}), [ss dpsize]); +C = reshape(CPD.cov(:, :, index{:}), [ss ss dpsize]); +W = reshape(CPD.weights(:, :, index{:}), [ss cpsize dpsize]); + + +% Convert each conditional Gaussian to a canonical potential +pot = cell(1, dpsize); +for i=1:dpsize + %pot{i} = linear_gaussian_to_scgcpot(m(:,i), C(:,:,i), W(:,:,i), cdom, ns, cnodes, evidence); + pot{i} = scgcpot(ss, cpsize, 1, m(:,i), W(:,:,i), C(:,:,i)); +end + +pot = scgpot(ddom, cheaddom, ctaildom, ens, pot); + + +function pot = linear_gaussian_to_scgcpot(mu, Sigma, W, domain, ns, cnodes, evidence) +% LINEAR_GAUSSIAN_TO_CPOT Convert a linear Gaussian CPD to a stable conditional potential element. +% pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence) + +p = 1; +A = mu; +B = W; +C = Sigma; +ns(odom) = 0; +%pot = scgcpot(, ns(domain), p, A, B, C); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_scgpot.m/1.1.1.1/Wed May 29 15:59:52 2002// +/adjustable_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_CPD_to_table_hidden_ps.m/1.1.1.1/Wed May 29 15:59:52 2002// +/convert_to_pot.m/1.1.1.1/Sun Mar 9 23:03:16 2003// +/convert_to_table.m/1.1.1.1/Sun May 11 23:31:54 2003// +/display.m/1.1.1.1/Wed May 29 15:59:52 2002// +/gaussian_CPD.m/1.1.1.1/Wed Jun 15 21:13:06 2005// +/gaussian_CPD_params_given_dps.m/1.1.1.1/Sun May 11 23:13:40 2003// +/get_field.m/1.1.1.1/Wed May 29 15:59:52 2002// +/learn_params.m/1.1.1.1/Thu Jun 10 01:28:10 2004// +/log_prob_node.m/1.1.1.1/Tue Sep 10 17:44:00 2002// +/maximize_params.m/1.1.1.1/Tue May 20 14:10:06 2003// +/maximize_params_debug.m/1.1.1.1/Fri Jan 31 00:13:10 2003// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:52 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:52 2002// +/update_ess.m/1.1.1.1/Tue Jul 22 22:55:46 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +A D/Old//// +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gaussian_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/CPD_to_lambda_msg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p) +% CPD_TO_LAMBDA_MSG Compute lambda message (gaussian) +% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p) +% Pearl p183 eq 4.52 + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + self_size = CPD.sizes(end); + if all(msg{n}.lambda.precision == 0) % no info to send on + lam_msg.precision = zeros(self_size); + lam_msg.info_state = zeros(self_size, 1); + return; + end + cpsizes = CPD.sizes(CPD.cps); + dpval = 1; + Q = CPD.cov(:,:,dpval); + Sigmai = Q; + wmu = zeros(self_size, 1); + for k=1:length(ps) + pk = ps(k); + if pk ~= p + bk = block(k, cpsizes); + Bk = CPD.weights(:, bk, dpval); + m = msg{n}.pi_from_parent{k}; + Sigmai = Sigmai + Bk * m.Sigma * Bk'; + wmu = wmu + Bk * m.mu; % m.mu = u(k) + end + end + % Sigmai = Q + sum_{k \neq i} B_k Sigma_k B_k' + i = find_equiv_posns(p, ps); + bi = block(i, cpsizes); + Bi = CPD.weights(:,bi, dpval); + + if 0 + P = msg{n}.lambda.precision; + if isinf(P) % inv(P)=Sigma_lambda=0 + precision_temp = inv(Sigmai); + lam_msg.precision = Bi' * precision_temp * Bi; + lam_msg.info_state = precision_temp * (msg{n}.lambda.mu - wmu); + else + A = inv(P + inv(Sigmai)); + precision_temp = P + P*A*P; + lam_msg.precision = Bi' * precision_temp * Bi; + self_size = length(P); + C = eye(self_size) + P*A; + z = msg{n}.lambda.info_state; + lam_msg.info_state = C*z - C*P*wmu; + end + end + + if isinf(msg{n}.lambda.precision) + Sigma_lambda = zeros(self_size, self_size); % infinite precision => 0 variance + mu_lambda = msg{n}.lambda.mu; % observed_value; + else + Sigma_lambda = inv(msg{n}.lambda.precision); + mu_lambda = Sigma_lambda * msg{n}.lambda.info_state; + end + precision_temp = inv(Sigma_lambda + Sigmai); + lam_msg.precision = Bi' * precision_temp * Bi; + lam_msg.info_state = Bi' * precision_temp * (mu_lambda - wmu); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:52 2002// +/gaussian_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/log_prob_node.m/1.1.1.1/Wed May 29 15:59:52 2002// +/maximize_params.m/1.1.1.1/Thu Jan 30 22:38:16 2003// +/update_ess.m/1.1.1.1/Wed May 29 15:59:52 2002// +/update_tied_ess.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gaussian_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/gaussian_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,184 @@ +function CPD = gaussian_CPD(varargin) +% GAUSSIAN_CPD Make a conditional linear Gaussian distrib. +% +% To define this CPD precisely, call the continuous (cts) parents (if any) X, +% the discrete parents (if any) Q, and this node Y. Then the distribution on Y is: +% - no parents: Y ~ N(mu, Sigma) +% - cts parents : Y|X=x ~ N(mu + W x, Sigma) +% - discrete parents: Y|Q=i ~ N(mu(i), Sigma(i)) +% - cts and discrete parents: Y|X=x,Q=i ~ N(mu(i) + W(i) x, Sigma(i)) +% +% CPD = gaussian_CPD(bnet, node, ...) will create a CPD with random parameters, +% where node is the number of a node in this equivalence class. +% +% The list below gives optional arguments [default value in brackets]. +% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y) and Q = prod(ns(Q)).) +% +% mean - mu(:,i) is the mean given Q=i [ randn(Y,Q) ] +% cov - Sigma(:,:,i) is the covariance given Q=i [ repmat(eye(Y,Y), [1 1 Q]) ] +% weights - W(:,:,i) is the regression matrix given Q=i [ randn(Y,X,Q) ] +% cov_type - if 'diag', Sigma(:,:,i) is diagonal [ 'full' ] +% tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i [0] +% clamp_mean - if 1, we do not adjust mu(:,i) during learning [0] +% clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning [0] +% clamp_weights - if 1, we do not adjust W(:,:,i) during learning [0] +% cov_prior_weight - weight given to I prior for estimating Sigma [0.01] +% +% e.g., CPD = gaussian_CPD(bnet, i, 'mean', [0; 0], 'clamp_mean', 'yes') +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% CPD = gaussian_CPD(bnet, self, mu, Sigma, W, cov_type, tied_cov, clamp_mean, clamp_cov, clamp_weight) +% +% Sometimes it is useful to create an "isolated" CPD, without needing to pass in a bnet. +% In this case, you must specify the discrete and cts parents (dps, cps) and the family sizes, followed +% by the optional arguments above: +% CPD = gaussian_CPD('self', i, 'dps', dps, 'cps', cps, 'sz', fam_size, ...) + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'gaussian_CPD', generic_CPD(clamp)); + return; +elseif isa(varargin{1}, 'gaussian_CPD') + % This might occur if we are copying an object. + CPD = varargin{1}; + return; +end +CPD = init_fields; + +CPD = class(CPD, 'gaussian_CPD', generic_CPD(0)); + + +% parse mandatory arguments +if ~isstr(varargin{1}) % pass in bnet + bnet = varargin{1}; + self = varargin{2}; + args = varargin(3:end); + ns = bnet.node_sizes; + ps = parents(bnet.dag, self); + dps = myintersect(ps, bnet.dnodes); + cps = myintersect(ps, bnet.cnodes); + fam_sz = ns([ps self]); +else + disp('parsing new style') + for i=1:2:length(varargin) + switch varargin{i}, + case 'self', self = varargin{i+1}; + case 'dps', dps = varargin{i+1}; + case 'cps', cps = varargin{i+1}; + case 'sz', fam_sz = varargin{i+1}; + end + end + ps = myunion(dps, cps); + args = varargin; +end + +CPD.self = self; +CPD.sizes = fam_sz; + +% Figure out which (if any) of the parents are discrete, and which cts, and how big they are +% dps = discrete parents, cps = cts parents +CPD.cps = find_equiv_posns(cps, ps); % cts parent index +CPD.dps = find_equiv_posns(dps, ps); +ss = fam_sz(end); +psz = fam_sz(1:end-1); +dpsz = prod(psz(CPD.dps)); +cpsz = sum(psz(CPD.cps)); + +% set default params +CPD.mean = randn(ss, dpsz); +CPD.cov = 100*repmat(eye(ss), [1 1 dpsz]); +CPD.weights = randn(ss, cpsz, dpsz); +CPD.cov_type = 'full'; +CPD.tied_cov = 0; +CPD.clamped_mean = 0; +CPD.clamped_cov = 0; +CPD.clamped_weights = 0; +CPD.cov_prior_weight = 0.01; + +nargs = length(args); +if nargs > 0 + if ~isstr(args{1}) + % gaussian_CPD(bnet, self, mu, Sigma, W, cov_type, tied_cov, clamp_mean, clamp_cov, clamp_weights) + if nargs >= 1 & ~isempty(args{1}), CPD.mean = args{1}; end + if nargs >= 2 & ~isempty(args{2}), CPD.cov = args{2}; end + if nargs >= 3 & ~isempty(args{3}), CPD.weights = args{3}; end + if nargs >= 4 & ~isempty(args{4}), CPD.cov_type = args{4}; end + if nargs >= 5 & ~isempty(args{5}) & strcmp(args{5}, 'tied'), CPD.tied_cov = 1; end + if nargs >= 6 & ~isempty(args{6}), CPD.clamped_mean = 1; end + if nargs >= 7 & ~isempty(args{7}), CPD.clamped_cov = 1; end + if nargs >= 8 & ~isempty(args{8}), CPD.clamped_weights = 1; end + else + CPD = set_fields(CPD, args{:}); + end +end + +% Make sure the matrices have 1 dimension per discrete parent. +% Bug fix due to Xuejing Sun 3/6/01 +CPD.mean = myreshape(CPD.mean, [ss ns(dps)]); +CPD.cov = myreshape(CPD.cov, [ss ss ns(dps)]); +CPD.weights = myreshape(CPD.weights, [ss cpsz ns(dps)]); + +CPD.init_cov = CPD.cov; % we reset to this if things go wrong during learning + +% expected sufficient statistics +CPD.Wsum = zeros(dpsz,1); +CPD.WYsum = zeros(ss, dpsz); +CPD.WXsum = zeros(cpsz, dpsz); +CPD.WYYsum = zeros(ss, ss, dpsz); +CPD.WXXsum = zeros(cpsz, cpsz, dpsz); +CPD.WXYsum = zeros(cpsz, ss, dpsz); + +% For BIC +CPD.nsamples = 0; +switch CPD.cov_type + case 'full', + ncov_params = ss*(ss-1)/2; % since symmetric (and positive definite) + case 'diag', + ncov_params = ss; + otherwise + error(['unrecognized cov_type ' cov_type]); +end +% params = weights + mean + cov +if CPD.tied_cov + CPD.nparams = ss*cpsz*dpsz + ss*dpsz + ncov_params; +else + CPD.nparams = ss*cpsz*dpsz + ss*dpsz + dpsz*ncov_params; +end + + + +clamped = CPD.clamped_mean & CPD.clamped_cov & CPD.clamped_weights; +CPD = set_clamped(CPD, clamped); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.sizes = []; +CPD.cps = []; +CPD.dps = []; +CPD.mean = []; +CPD.cov = []; +CPD.weights = []; +CPD.clamped_mean = []; +CPD.clamped_cov = []; +CPD.clamped_weights = []; +CPD.init_cov = []; +CPD.cov_type = []; +CPD.tied_cov = []; +CPD.Wsum = []; +CPD.WYsum = []; +CPD.WXsum = []; +CPD.WYYsum = []; +CPD.WXXsum = []; +CPD.WXYsum = []; +CPD.nsamples = []; +CPD.nparams = []; +CPD.cov_prior_weight = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/log_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +use_log = 1; +ncases = length(self_ev); +nparents = length(CPD.sizes)-1; +assert(ncases == size(pev, 2)); + +if ncases == 0 + L = 0; + return; +end + +if length(CPD.dps)==0 % no discrete parents, so we can vectorize + i = 1; + if usecell + Y = cell2num(self_ev); + else + Y = self_ev; + end + if length(CPD.cps) == 0 + L = gaussian_prob(Y, CPD.mean(:,i), CPD.cov(:,:,i), use_log); + else + if usecell + X = cell2num(pev); + else + X = pev; + end + L = gaussian_prob(Y, CPD.mean(:,i) + CPD.weights(:,:,i)*X, CPD.cov(:,:,i), use_log); + end +else % each case uses a (potentially) different set of parameters + L = 0; + for m=1:ncases + if usecell + dpvals = cat(1, pev{CPD.dps, m}); + else + dpvals = pev(CPD.dps, m); + end + i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)'); + y = self_ev{m}; + if length(CPD.cps) == 0 + L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log); + else + if usecell + x = cat(1, pev{CPD.cps, m}); + else + x = pev(CPD.cps, m); + end + L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,147 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian) +% CPD = maximize_params(CPD, temperature) +% +% Temperature is currently only used for entropic prior on Sigma + +% For details, see "Fitting a Conditional Gaussian Distribution", Kevin Murphy, tech. report, +% 1998, available at www.cs.berkeley.edu/~murphyk/papers.html +% Refering to table 2, we use equations 1/2 to estimate the covariance matrix in the untied/tied case, +% and equation 9 to estimate the weight matrix and mean. +% We do not implement spherical Gaussians - the code is already pretty complicated! + +if ~adjustable_CPD(CPD), return; end + +%assert(approxeq(CPD.nsamples, sum(CPD.Wsum))); +assert(~any(isnan(CPD.WXXsum))) +assert(~any(isnan(CPD.WXYsum))) +assert(~any(isnan(CPD.WYYsum))) + +[self_size cpsize dpsize] = size(CPD.weights); + +% Append 1s to the parents, and derive the corresponding cross products. +% This is used when estimate the means and weights simultaneosuly, +% and when estimatting Sigma. +% Let x2 = [x 1]' +XY = zeros(cpsize+1, self_size, dpsize); % XY(:,:,i) = sum_l w(l,i) x2(l) y(l)' +XX = zeros(cpsize+1, cpsize+1, dpsize); % XX(:,:,i) = sum_l w(l,i) x2(l) x2(l)' +YY = zeros(self_size, self_size, dpsize); % YY(:,:,i) = sum_l w(l,i) y(l) y(l)' +for i=1:dpsize + XY(:,:,i) = [CPD.WXYsum(:,:,i) % X*Y + CPD.WYsum(:,i)']; % 1*Y + % [x * [x' 1] = [xx' x + % 1] x' 1] + XX(:,:,i) = [CPD.WXXsum(:,:,i) CPD.WXsum(:,i); + CPD.WXsum(:,i)' CPD.Wsum(i)]; + YY(:,:,i) = CPD.WYYsum(:,:,i); +end + +w = CPD.Wsum(:); +% Set any zeros to one before dividing +% This is valid because w(i)=0 => WYsum(:,i)=0, etc +w = w + (w==0); + +if CPD.clamped_mean + % Estimating B2 and then setting the last column (the mean) to the clamped mean is *not* equivalent + % to estimating B and then adding the clamped_mean to the last column. + if ~CPD.clamped_weights + B = zeros(self_size, cpsize, dpsize); + for i=1:dpsize + if det(CPD.WXXsum(:,:,i))==0 + B(:,:,i) = 0; + else + % Eqn 9 in table 2 of TR + %B(:,:,i) = CPD.WXYsum(:,:,i)' * inv(CPD.WXXsum(:,:,i)); + B(:,:,i) = (CPD.WXXsum(:,:,i) \ CPD.WXYsum(:,:,i))'; + end + end + %CPD.weights = reshape(B, [self_size cpsize dpsize]); + CPD.weights = B; + end +elseif CPD.clamped_weights % KPM 1/25/02 + if ~CPD.clamped_mean % ML estimate is just sample mean of the residuals + for i=1:dpsize + CPD.mean(:,i) = (CPD.WYsum(:,i) - CPD.weights(:,:,i) * CPD.WXsum(:,i)) / w(i); + end + end +else % nothing is clamped, so estimate mean and weights simultaneously + B2 = zeros(self_size, cpsize+1, dpsize); + for i=1:dpsize + if det(XX(:,:,i))==0 % fix by U. Sondhauss 6/27/99 + B2(:,:,i)=0; + else + % Eqn 9 in table 2 of TR + %B2(:,:,i) = XY(:,:,i)' * inv(XX(:,:,i)); + B2(:,:,i) = (XX(:,:,i) \ XY(:,:,i))'; + end + CPD.mean(:,i) = B2(:,cpsize+1,i); + CPD.weights(:,:,i) = B2(:,1:cpsize,i); + end +end + +% Let B2 = [W mu] +if cpsize>0 + B2(:,1:cpsize,:) = reshape(CPD.weights, [self_size cpsize dpsize]); +end +B2(:,cpsize+1,:) = reshape(CPD.mean, [self_size dpsize]); + +% To avoid singular covariance matrices, +% we use the regularization method suggested in "A Quasi-Bayesian approach to estimating +% parameters for mixtures of normal distributions", Hamilton 91. +% If the ML estimate is Sigma = M/N, the MAP estimate is (M+gamma*I) / (N+gamma), +% where gamma >=0 is a smoothing parameter (equivalent sample size of I prior) + +gamma = CPD.cov_prior_weight; + +if ~CPD.clamped_cov + if CPD.cov_prior_entropic % eqn 12 of Brand AI/Stat 99 + Z = 1-temp; + % When temp > 1, Z is negative, so we are dividing by a smaller + % number, ie. increasing the variance. + else + Z = 0; + end + if CPD.tied_cov + S = zeros(self_size, self_size); + % Eqn 2 from table 2 in TR + for i=1:dpsize + S = S + (YY(:,:,i) - B2(:,:,i)*XY(:,:,i)); + end + %denom = max(1, CPD.nsamples + gamma + Z); + denom = CPD.nsamples + gamma + Z; + S = (S + gamma*eye(self_size)) / denom; + if strcmp(CPD.cov_type, 'diag') + S = diag(diag(S)); + end + CPD.cov = repmat(S, [1 1 dpsize]); + else + for i=1:dpsize + % Eqn 1 from table 2 in TR + S = YY(:,:,i) - B2(:,:,i)*XY(:,:,i); + %denom = max(1, w(i) + gamma + Z); % gives wrong answers on mhmm1 + denom = w(i) + gamma + Z; + S = (S + gamma*eye(self_size)) / denom; + CPD.cov(:,:,i) = S; + end + if strcmp(CPD.cov_type, 'diag') + for i=1:dpsize + CPD.cov(:,:,i) = diag(diag(CPD.cov(:,:,i))); + end + end + end +end + + +check_covars = 0; +min_covar = 1e-5; +if check_covars % prevent collapsing to a point + for i=1:dpsize + if min(svd(CPD.cov(:,:,i))) < min_covar + disp(['resetting singular covariance for node ' num2str(CPD.self)]); + CPD.cov(:,:,i) = CPD.init_cov(:,:,i); + end + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,85 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a Gaussian node +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +%if nargin < 6 +% hidden_bitv = zeros(1, max(fmarginal.domain)); +% hidden_bitv(find(isempty(evidence)))=1; +%end + +dom = fmarginal.domain; +self = dom(end); +ps = dom(1:end-1); +hidden_self = hidden_bitv(self); +cps = myintersect(ps, cnodes); +dps = mysetdiff(ps, cps); +hidden_cps = all(hidden_bitv(cps)); +hidden_dps = all(hidden_bitv(dps)); + +CPD.nsamples = CPD.nsamples + 1; +[ss cpsz dpsz] = size(CPD.weights); % ss = self size + +% Let X be the cts parent (if any), Y be the cts child (self). + +if ~hidden_self & (isempty(cps) | ~hidden_cps) & hidden_dps % all cts nodes are observed, all discrete nodes are hidden + % Since X and Y are observed, SYY = 0, SXX = 0, SXY = 0 + % Since discrete parents are hidden, we do not need to add evidence to w. + w = fmarginal.T(:); + CPD.Wsum = CPD.Wsum + w; + y = evidence{self}; + Cyy = y*y'; + if ~CPD.useC + W = repmat(w(:)',ss,1); % W(y,i) = w(i) + W2 = repmat(reshape(W, [ss 1 dpsz]), [1 ss 1]); % W2(x,y,i) = w(i) + CPD.WYsum = CPD.WYsum + W .* repmat(y(:), 1, dpsz); + CPD.WYYsum = CPD.WYYsum + W2 .* repmat(reshape(Cyy, [ss ss 1]), [1 1 dpsz]); + else + W = w(:)'; + W2 = reshape(W, [1 1 dpsz]); + CPD.WYsum = CPD.WYsum + rep_mult(W, y(:), size(CPD.WYsum)); + CPD.WYYsum = CPD.WYYsum + rep_mult(W2, Cyy, size(CPD.WYYsum)); + end + if cpsz > 0 % X exists + x = cat(1, evidence{cps}); x = x(:); + Cxx = x*x'; + Cxy = x*y'; + if ~CPD.useC + CPD.WXsum = CPD.WXsum + W .* repmat(x(:), 1, dpsz); + CPD.WXXsum = CPD.WXXsum + W2 .* repmat(reshape(Cxx, [cpsz cpsz 1]), [1 1 dpsz]); + CPD.WXYsum = CPD.WXYsum + W2 .* repmat(reshape(Cxy, [cpsz ss 1]), [1 1 dpsz]); + else + CPD.WXsum = CPD.WXsum + rep_mult(W, x(:), size(CPD.WXsum)); + CPD.WXXsum = CPD.WXXsum + rep_mult(W2, Cxx, size(CPD.WXXsum)); + CPD.WXYsum = CPD.WXYsum + rep_mult(W2, Cxy, size(CPD.WXYsum)); + end + end + return; +end + +% general (non-vectorized) case +fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes); % slow! + +if dpsz == 1 % no discrete parents + w = 1; +else + w = fullm.T(:); +end + +CPD.Wsum = CPD.Wsum + w; +xi = 1:cpsz; +yi = (cpsz+1):(cpsz+ss); +for i=1:dpsz + muY = fullm.mu(yi, i); + SYY = fullm.Sigma(yi, yi, i); + CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY; + CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y] + if cpsz > 0 + muX = fullm.mu(xi, i); + SXX = fullm.Sigma(xi, xi, i); + SXY = fullm.Sigma(xi, yi, i); + CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX; + CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX'); + CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY'); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/Old/update_tied_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,118 @@ +function CPD = update_tied_ess(CPD, domain, engine, evidence, ns, cnodes) + +if ~adjustable_CPD(CPD), return; end +nCPDs = size(domain, 2); +fmarginal = cell(1, nCPDs); +for l=1:nCPDs + fmarginal{l} = marginal_family(engine, nodes(l)); +end + +[ss cpsz dpsz] = size(CPD.weights); +if const_evidence_pattern(engine) + dom = domain(:,1); + dnodes = mysetdiff(1:length(ns), cnodes); + ddom = myintersect(dom, dnodes); + cdom = myintersect(dom, cnodes); + odom = dom(~isemptycell(evidence(dom))); + hdom = dom(isemptycell(evidence(dom))); + % If all hidden nodes are discrete and all cts nodes are observed + % (e.g., HMM with Gaussian output) + % we can add the observed evidence in parallel + if mysubset(ddom, hdom) & mysubset(cdom, odom) + [mu, Sigma, T] = add_cts_ev_to_marginals(fmarginal, evidence, ns, cnodes); + else + mu = zeros(ss, dpsz, nCPDs); + Sigma = zeros(ss, ss, dpsz, nCPDs); + T = zeros(dpsz, nCPDs); + for l=1:nCPDs + [mu(:,:,l), Sigma(:,:,:,l), T(:,l)] = add_ev_to_marginals(fmarginal{l}, evidence, ns, cnodes); + end + end +end +CPD.nsamples = CPD.nsamples + nCPDs; + + +if dpsz == 1 % no discrete parents + w = 1; +else + w = fullm.T(:); +end +CPD.Wsum = CPD.Wsum + w; +% Let X be the cts parent (if any), Y be the cts child (self). +xi = 1:cpsz; +yi = (cpsz+1):(cpsz+ss); +for i=1:dpsz + muY = fullm.mu(yi, i); + SYY = fullm.Sigma(yi, yi, i); + CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY; + CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y] + if cpsz > 0 + muX = fullm.mu(xi, i); + SXX = fullm.Sigma(xi, xi, i); + SXY = fullm.Sigma(xi, yi, i); + CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX; + CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY'); + CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX'); + end +end + + +%%%%%%%%%%%%% + +function fullm = add_evidence_to_marginal(fmarginal, evidence, ns, cnodes) + + +dom = fmarginal.domain; + +% Find out which values of the discrete parents (if any) are compatible with +% the discrete evidence (if any). +dnodes = mysetdiff(1:length(ns), cnodes); +ddom = myintersect(dom, dnodes); +cdom = myintersect(dom, cnodes); +odom = dom(~isemptycell(evidence(dom))); +hdom = dom(isemptycell(evidence(dom))); + +dobs = myintersect(ddom, odom); +dvals = cat(1, evidence{dobs}); +ens = ns; % effective node sizes +ens(dobs) = 1; +S = prod(ens(ddom)); +subs = ind2subv(ens(ddom), 1:S); +mask = find_equiv_posns(dobs, ddom); +subs(mask) = dvals; +supportedQs = subv2ind(ns(ddom), subs); + +if isempty(ddom) + Qarity = 1; +else + Qarity = prod(ns(ddom)); +end +fullm.T = zeros(Qarity, 1); +fullm.T(supportedQs) = fmarginal.T(:); + +% Now put the hidden cts parts into their right blocks, +% leaving the observed cts parts as 0. +cobs = myintersect(cdom, odom); +chid = myintersect(cdom, hdom); +cvals = cat(1, evidence{cobs}); +n = sum(ns(cdom)); +fullm.mu = zeros(n,Qarity); +fullm.Sigma = zeros(n,n,Qarity); + +if ~isempty(chid) + chid_blocks = block(find_equiv_posns(chid, cdom), ns(cdom)); +end +if ~isempty(cobs) + cobs_blocks = block(find_equiv_posns(cobs, cdom), ns(cdom)); +end + +for i=1:length(supportedQs) + Q = supportedQs(i); + if ~isempty(chid) + fullm.mu(chid_blocks, Q) = fmarginal.mu(:, i); + fullm.Sigma(chid_blocks, chid_blocks, Q) = fmarginal.Sigma(:,:,i); + end + if ~isempty(cobs) + fullm.mu(cobs_blocks, Q) = cvals(:); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/adjustable_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function p = adjustable_CPD(CPD) +% ADJUSTABLE_CPD Does this CPD have any adjustable params? (gaussian) +% p = adjustable_CPD(CPD) + +p = ~CPD.clamped_mean | ~CPD.clamped_cov | ~CPD.clamped_weights;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/convert_CPD_to_table_hidden_ps.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function T = convert_CPD_to_table_hidden_ps(CPD, self_val) +% CONVERT_CPD_TO_TABLE_HIDDEN_PS Convert a Gaussian CPD to a table +% function T = convert_CPD_to_table_hidden_ps(CPD, self_val) +% +% self_val must be a non-empty vector. +% All the parents are hidden. +% +% This is used by misc/convert_dbn_CPDs_to_tables + +m = CPD.mean; +C = CPD.cov; +W = CPD.weights; + +[ssz dpsize] = size(m); + +T = zeros(dpsize, 1); +for i=1:dpsize + T(i) = gaussian_prob(self_val, m(:,i), C(:,:,i)); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/convert_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a Gaussian CPD to one or more potentials +% pot = convert_to_pot(CPD, pot_type, domain, evidence) + +sz = CPD.sizes; +ns = zeros(1, max(domain)); +ns(domain) = sz; + +odom = domain(~isemptycell(evidence(domain))); +ps = domain(1:end-1); +cps = ps(CPD.cps); +dps = ps(CPD.dps); +self = domain(end); +cdom = [cps(:)' self]; +ddom = dps; +cnodes = cdom; + +switch pot_type + case 'u', + error('gaussian utility potentials not yet supported'); + + case 'd', + T = convert_to_table(CPD, domain, evidence); + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + + case {'c','g'}, + [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence); + pot = linear_gaussian_to_cpot(m, C, W, domain, ns, cnodes, evidence); + + case 'cg', + [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence); + % Convert each conditional Gaussian to a canonical potential + cobs = myintersect(cdom, odom); + dobs = myintersect(ddom, odom); + ens = ns; % effective node size + ens(cobs) = 0; + ens(dobs) = 1; + dpsize = prod(ens(dps)); + can = cell(1, dpsize); + for i=1:dpsize + if isempty(W) + can{i} = linear_gaussian_to_cpot(m(:,i), C(:,:,i), [], cdom, ns, cnodes, evidence); + else + can{i} = linear_gaussian_to_cpot(m(:,i), C(:,:,i), W(:,:,i), cdom, ns, cnodes, evidence); + end + end + pot = cgpot(ddom, cdom, ens, can); + + case 'scg', + [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence); + cobs = myintersect(cdom, odom); + dobs = myintersect(ddom, odom); + ens = ns; % effective node size + ens(cobs) = 0; + ens(dobs) = 1; + dpsize = prod(ens(dps)); + cpsize = size(W, 2); % cts parents size + ss = size(m, 1); % self size + cheaddom = self; + ctaildom = cps(:)'; + pot_array = cell(1, dpsize); + for i=1:dpsize + pot_array{i} = scgcpot(ss, cpsize, 1, m(:,i), W(:,:,i), C(:,:,i)); + end + pot = scgpot(ddom, cheaddom, ctaildom, ens, pot_array); + + otherwise, + error(['unrecognized pot_type' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/convert_to_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a Gaussian CPD to a table +% T = convert_to_table(CPD, domain, evidence) + + +sz = CPD.sizes; +ns = zeros(1, max(domain)); +ns(domain) = sz; + +odom = domain(~isemptycell(evidence(domain))); +ps = domain(1:end-1); +cps = ps(CPD.cps); +dps = ps(CPD.dps); +self = domain(end); +cdom = [cps(:)' self]; +ddom = dps; +cnodes = cdom; + +[m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence); + + +ns(odom) = 1; +dpsize = prod(ns(dps)); +self = domain(end); +assert(myismember(self, odom)); +self_val = evidence{self}; +T = zeros(dpsize, 1); +if length(cps) > 0 + assert(~any(isemptycell(evidence(cps)))); + cps_vals = cat(1, evidence{cps}); + for i=1:dpsize + T(i) = gaussian_prob(self_val, m(:,i) + W(:,:,i)*cps_vals, C(:,:,i)); + end +else + for i=1:dpsize + T(i) = gaussian_prob(self_val, m(:,i), C(:,:,i)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('gaussian_CPD object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/gaussian_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,161 @@ +function CPD = gaussian_CPD(bnet, self, varargin) +% GAUSSIAN_CPD Make a conditional linear Gaussian distrib. +% +% CPD = gaussian_CPD(bnet, node, ...) will create a CPD with random parameters, +% where node is the number of a node in this equivalence class. + +% To define this CPD precisely, call the continuous (cts) parents (if any) X, +% the discrete parents (if any) Q, and this node Y. Then the distribution on Y is: +% - no parents: Y ~ N(mu, Sigma) +% - cts parents : Y|X=x ~ N(mu + W x, Sigma) +% - discrete parents: Y|Q=i ~ N(mu(i), Sigma(i)) +% - cts and discrete parents: Y|X=x,Q=i ~ N(mu(i) + W(i) x, Sigma(i)) +% +% The list below gives optional arguments [default value in brackets]. +% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y) and Q = prod(ns(Q)).) +% Parameters will be reshaped to the right size if necessary. +% +% mean - mu(:,i) is the mean given Q=i [ randn(Y,Q) ] +% cov - Sigma(:,:,i) is the covariance given Q=i [ repmat(100*eye(Y,Y), [1 1 Q]) ] +% weights - W(:,:,i) is the regression matrix given Q=i [ randn(Y,X,Q) ] +% cov_type - if 'diag', Sigma(:,:,i) is diagonal [ 'full' ] +% tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i [0] +% clamp_mean - if 1, we do not adjust mu(:,i) during learning [0] +% clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning [0] +% clamp_weights - if 1, we do not adjust W(:,:,i) during learning [0] +% cov_prior_weight - weight given to I prior for estimating Sigma [0.01] +% cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0] +% +% e.g., CPD = gaussian_CPD(bnet, i, 'mean', [0; 0], 'clamp_mean', 1) + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'gaussian_CPD', generic_CPD(clamp)); + return; +elseif isa(bnet, 'gaussian_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +CPD = class(CPD, 'gaussian_CPD', generic_CPD(0)); + +args = varargin; +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +dps = myintersect(ps, bnet.dnodes); +cps = myintersect(ps, bnet.cnodes); +fam_sz = ns([ps self]); + +CPD.self = self; +CPD.sizes = fam_sz; + +% Figure out which (if any) of the parents are discrete, and which cts, and how big they are +% dps = discrete parents, cps = cts parents +CPD.cps = find_equiv_posns(cps, ps); % cts parent index +CPD.dps = find_equiv_posns(dps, ps); +ss = fam_sz(end); +psz = fam_sz(1:end-1); +dpsz = prod(psz(CPD.dps)); +cpsz = sum(psz(CPD.cps)); + +% set default params +CPD.mean = randn(ss, dpsz); +CPD.cov = 100*repmat(eye(ss), [1 1 dpsz]); +CPD.weights = randn(ss, cpsz, dpsz); +CPD.cov_type = 'full'; +CPD.tied_cov = 0; +CPD.clamped_mean = 0; +CPD.clamped_cov = 0; +CPD.clamped_weights = 0; +CPD.cov_prior_weight = 0.01; +CPD.cov_prior_entropic = 0; +nargs = length(args); +if nargs > 0 + CPD = set_fields(CPD, args{:}); +end + +% Make sure the matrices have 1 dimension per discrete parent. +% Bug fix due to Xuejing Sun 3/6/01 +CPD.mean = myreshape(CPD.mean, [ss ns(dps)]); +CPD.cov = myreshape(CPD.cov, [ss ss ns(dps)]); +CPD.weights = myreshape(CPD.weights, [ss cpsz ns(dps)]); + +% Precompute indices into block structured matrices +% to speed up CPD_to_lambda_msg and CPD_to_pi +cpsizes = CPD.sizes(CPD.cps); +CPD.cps_block_ndx = cell(1, length(cps)); +for i=1:length(cps) + CPD.cps_block_ndx{i} = block(i, cpsizes); +end + +%%%%%%%%%%% +% Learning stuff + +% expected sufficient statistics +CPD.Wsum = zeros(dpsz,1); +CPD.WYsum = zeros(ss, dpsz); +CPD.WXsum = zeros(cpsz, dpsz); +CPD.WYYsum = zeros(ss, ss, dpsz); +CPD.WXXsum = zeros(cpsz, cpsz, dpsz); +CPD.WXYsum = zeros(cpsz, ss, dpsz); + +% For BIC +CPD.nsamples = 0; +switch CPD.cov_type + case 'full', + % since symmetric + %ncov_params = ss*(ss-1)/2; + ncov_params = ss*(ss+1)/2; + case 'diag', + ncov_params = ss; + otherwise + error(['unrecognized cov_type ' cov_type]); +end +% params = weights + mean + cov +if CPD.tied_cov + CPD.nparams = ss*cpsz*dpsz + ss*dpsz + ncov_params; +else + CPD.nparams = ss*cpsz*dpsz + ss*dpsz + dpsz*ncov_params; +end + +% for speeding up maximize_params +CPD.useC = exist('rep_mult'); + +clamped = CPD.clamped_mean & CPD.clamped_cov & CPD.clamped_weights; +CPD = set_clamped(CPD, clamped); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.sizes = []; +CPD.cps = []; +CPD.dps = []; +CPD.mean = []; +CPD.cov = []; +CPD.weights = []; +CPD.clamped_mean = []; +CPD.clamped_cov = []; +CPD.clamped_weights = []; +CPD.cov_type = []; +CPD.tied_cov = []; +CPD.Wsum = []; +CPD.WYsum = []; +CPD.WXsum = []; +CPD.WYYsum = []; +CPD.WXXsum = []; +CPD.WXYsum = []; +CPD.nsamples = []; +CPD.nparams = []; +CPD.cov_prior_weight = []; +CPD.cov_prior_entropic = []; +CPD.useC = []; +CPD.cps_block_ndx = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/gaussian_CPD_params_given_dps.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function [m, C, W] = gaussian_CPD_params_given_dps(CPD, domain, evidence) +% GAUSSIAN_CPD_PARAMS_GIVEN_EV_ON_DPS Extract parameters given evidence on all discrete parents +% function [m, C, W] = gaussian_CPD_params_given_ev_on_dps(CPD, domain, evidence) + +ps = domain(1:end-1); +dps = ps(CPD.dps); +if isempty(dps) + m = CPD.mean; + C = CPD.cov; + W = CPD.weights; +else + odom = domain(~isemptycell(evidence(domain))); + dops = myintersect(dps, odom); + dpvals = cat(1, evidence{dops}); + if length(dops) == length(dps) + dpsizes = CPD.sizes(CPD.dps); + dpval = subv2ind(dpsizes, dpvals(:)'); + m = CPD.mean(:, dpval); + C = CPD.cov(:, :, dpval); + W = CPD.weights(:, :, dpval); + else + map = find_equiv_posns(dops, dps); + index = mk_multi_index(length(dps), map, dpvals); + m = CPD.mean(:, index{:}); + C = CPD.cov(:, :, index{:}); + W = CPD.weights(:, :, index{:}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/get_field.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function val = get_params(CPD, name) +% GET_PARAMS Get the parameters (fields) for a gaussian_CPD object +% val = get_params(CPD, name) +% +% The following fields can be accessed +% +% mean - mu(:,i) is the mean given Q=i +% cov - Sigma(:,:,i) is the covariance given Q=i +% weights - W(:,:,i) is the regression matrix given Q=i +% +% e.g., mean = get_params(CPD, 'mean') + +switch name + case 'mean', val = CPD.mean; + case 'cov', val = CPD.cov; + case 'weights', val = CPD.weights; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/learn_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function CPD = learn_params(CPD, fam, data, ns, cnodes) +%function CPD = learn_params(CPD, fam, data, ns, cnodes) +% LEARN_PARAMS Compute the maximum likelihood estimate of the params of a gaussian CPD given complete data +% CPD = learn_params(CPD, fam, data, ns, cnodes) +% +% data(i,m) is the value of node i in case m (can be cell array). +% We assume this node has a maximize_params method. + +ncases = size(data, 2); +CPD = reset_ess(CPD); +% make a fully observed joint distribution over the family +fmarginal.domain = fam; +fmarginal.T = 1; +fmarginal.mu = []; +fmarginal.Sigma = []; +if ~iscell(data) + cases = num2cell(data); +else + cases = data; +end +hidden_bitv = zeros(1, max(fam)); +for m=1:ncases + % specify (as a bit vector) which elements in the family domain are hidden + hidden_bitv = zeros(1, max(fmarginal.domain)); + ev = cases(:,m); + hidden_bitv(find(isempty(ev)))=1; + CPD = update_ess(CPD, fmarginal, ev, ns, cnodes, hidden_bitv); +end +CPD = maximize_params(CPD); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/log_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +use_log = 1; +ncases = length(self_ev); +nparents = length(CPD.sizes)-1; +assert(ncases == size(pev, 2)); + +if ncases == 0 + L = 0; + return; +end + +L = 0; +for m=1:ncases + if isempty(CPD.dps) + i = 1; + else + if usecell + dpvals = cat(1, pev{CPD.dps, m}); + else + dpvals = pev(CPD.dps, m); + end + i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)'); + end + if usecell + y = self_ev{m}; + else + y = self_ev(m); + end + if length(CPD.cps) == 0 + L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log); + else + if usecell + x = cat(1, pev{CPD.cps, m}); + else + x = pev(CPD.cps, m); + end + L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian) +% CPD = maximize_params(CPD, temperature) +% +% Temperature is currently ignored. + +if ~adjustable_CPD(CPD), return; end + + +if CPD.clamped_mean + cl_mean = CPD.mean; +else + cl_mean = []; +end + +if CPD.clamped_cov + cl_cov = CPD.cov; +else + cl_cov = []; +end + +if CPD.clamped_weights + cl_weights = CPD.weights; +else + cl_weights = []; +end + +[ssz psz Q] = size(CPD.weights); + +[ss cpsz dpsz] = size(CPD.weights); % ss = self size = ssz +if cpsz > CPD.nsamples + fprintf('gaussian_CPD/maximize_params: warning: input dimension (%d) > nsamples (%d)\n', ... + cpsz, CPD.nsamples); +end + +prior = repmat(CPD.cov_prior_weight*eye(ssz,ssz), [1 1 Q]); + + +[CPD.mean, CPD.cov, CPD.weights] = ... + clg_Mstep(CPD.Wsum, CPD.WYsum, CPD.WYYsum, [], CPD.WXsum, CPD.WXXsum, CPD.WXYsum, ... + 'cov_type', CPD.cov_type, 'clamped_mean', cl_mean, ... + 'clamped_cov', cl_cov, 'clamped_weights', cl_weights, ... + 'tied_cov', CPD.tied_cov, ... + 'cov_prior', prior); + +if 0 +CPD.mean = reshape(CPD.mean, [ss dpsz]); +CPD.cov = reshape(CPD.cov, [ss ss dpsz]); +CPD.weights = reshape(CPD.weights, [ss cpsz dpsz]); +end + +% Bug fix 11 May 2003 KPM +% clg_Mstep collapses all discrete parents into one mega-node +% but convert_to_CPT needs access to each parent separately +sz = CPD.sizes; +ss = sz(end); + +% Bug fix KPM 20 May 2003: +cpsz = sum(sz(CPD.cps)); +%if isempty(CPD.cps) +% cpsz = 0; +%else +% cpsz = sz(CPD.cps); +%end +dpsz = sz(CPD.dps); +CPD.mean = myreshape(CPD.mean, [ss dpsz]); +CPD.cov = myreshape(CPD.cov, [ss ss dpsz]); +CPD.weights = myreshape(CPD.weights, [ss cpsz dpsz]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/maximize_params_debug.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,189 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian) +% CPD = maximize_params(CPD, temperature) +% +% Temperature is currently ignored. + +if ~adjustable_CPD(CPD), return; end + +CPD1 = struct(new_maximize_params(CPD)); +CPD2 = struct(old_maximize_params(CPD)); +assert(approxeq(CPD1.mean, CPD2.mean)) +assert(approxeq(CPD1.cov, CPD2.cov)) +assert(approxeq(CPD1.weights, CPD2.weights)) + +CPD = new_maximize_params(CPD); + +%%%%%%% +function CPD = new_maximize_params(CPD) + +if CPD.clamped_mean + cl_mean = CPD.mean; +else + cl_mean = []; +end + +if CPD.clamped_cov + cl_cov = CPD.cov; +else + cl_cov = []; +end + +if CPD.clamped_weights + cl_weights = CPD.weights; +else + cl_weights = []; +end + +[ssz psz Q] = size(CPD.weights); + +prior = repmat(CPD.cov_prior_weight*eye(ssz,ssz), [1 1 Q]); +[CPD.mean, CPD.cov, CPD.weights] = ... + Mstep_clg('w', CPD.Wsum, 'YY', CPD.WYYsum, 'Y', CPD.WYsum, 'YTY', [], ... + 'XX', CPD.WXXsum, 'XY', CPD.WXYsum, 'X', CPD.WXsum, ... + 'cov_type', CPD.cov_type, 'clamped_mean', cl_mean, ... + 'clamped_cov', cl_cov, 'clamped_weights', cl_weights, ... + 'tied_cov', CPD.tied_cov, ... + 'cov_prior', prior); + + +%%%%%%%%%%% + +function CPD = old_maximize_params(CPD) + + +if ~adjustable_CPD(CPD), return; end + +%assert(approxeq(CPD.nsamples, sum(CPD.Wsum))); +assert(~any(isnan(CPD.WXXsum))) +assert(~any(isnan(CPD.WXYsum))) +assert(~any(isnan(CPD.WYYsum))) + +[self_size cpsize dpsize] = size(CPD.weights); + +% Append 1s to the parents, and derive the corresponding cross products. +% This is used when estimate the means and weights simultaneosuly, +% and when estimatting Sigma. +% Let x2 = [x 1]' +XY = zeros(cpsize+1, self_size, dpsize); % XY(:,:,i) = sum_l w(l,i) x2(l) y(l)' +XX = zeros(cpsize+1, cpsize+1, dpsize); % XX(:,:,i) = sum_l w(l,i) x2(l) x2(l)' +YY = zeros(self_size, self_size, dpsize); % YY(:,:,i) = sum_l w(l,i) y(l) y(l)' +for i=1:dpsize + XY(:,:,i) = [CPD.WXYsum(:,:,i) % X*Y + CPD.WYsum(:,i)']; % 1*Y + % [x * [x' 1] = [xx' x + % 1] x' 1] + XX(:,:,i) = [CPD.WXXsum(:,:,i) CPD.WXsum(:,i); + CPD.WXsum(:,i)' CPD.Wsum(i)]; + YY(:,:,i) = CPD.WYYsum(:,:,i); +end + +w = CPD.Wsum(:); +% Set any zeros to one before dividing +% This is valid because w(i)=0 => WYsum(:,i)=0, etc +w = w + (w==0); + +if CPD.clamped_mean + % Estimating B2 and then setting the last column (the mean) to the clamped mean is *not* equivalent + % to estimating B and then adding the clamped_mean to the last column. + if ~CPD.clamped_weights + B = zeros(self_size, cpsize, dpsize); + for i=1:dpsize + if det(CPD.WXXsum(:,:,i))==0 + B(:,:,i) = 0; + else + % Eqn 9 in table 2 of TR + %B(:,:,i) = CPD.WXYsum(:,:,i)' * inv(CPD.WXXsum(:,:,i)); + B(:,:,i) = (CPD.WXXsum(:,:,i) \ CPD.WXYsum(:,:,i))'; + end + end + %CPD.weights = reshape(B, [self_size cpsize dpsize]); + CPD.weights = B; + end +elseif CPD.clamped_weights % KPM 1/25/02 + if ~CPD.clamped_mean % ML estimate is just sample mean of the residuals + for i=1:dpsize + CPD.mean(:,i) = (CPD.WYsum(:,i) - CPD.weights(:,:,i) * CPD.WXsum(:,i)) / w(i); + end + end +else % nothing is clamped, so estimate mean and weights simultaneously + B2 = zeros(self_size, cpsize+1, dpsize); + for i=1:dpsize + if det(XX(:,:,i))==0 % fix by U. Sondhauss 6/27/99 + B2(:,:,i)=0; + else + % Eqn 9 in table 2 of TR + %B2(:,:,i) = XY(:,:,i)' * inv(XX(:,:,i)); + B2(:,:,i) = (XX(:,:,i) \ XY(:,:,i))'; + end + CPD.mean(:,i) = B2(:,cpsize+1,i); + CPD.weights(:,:,i) = B2(:,1:cpsize,i); + end +end + +% Let B2 = [W mu] +if cpsize>0 + B2(:,1:cpsize,:) = reshape(CPD.weights, [self_size cpsize dpsize]); +end +B2(:,cpsize+1,:) = reshape(CPD.mean, [self_size dpsize]); + +% To avoid singular covariance matrices, +% we use the regularization method suggested in "A Quasi-Bayesian approach to estimating +% parameters for mixtures of normal distributions", Hamilton 91. +% If the ML estimate is Sigma = M/N, the MAP estimate is (M+gamma*I) / (N+gamma), +% where gamma >=0 is a smoothing parameter (equivalent sample size of I prior) + +gamma = CPD.cov_prior_weight; + +if ~CPD.clamped_cov + if CPD.cov_prior_entropic % eqn 12 of Brand AI/Stat 99 + Z = 1-temp; + % When temp > 1, Z is negative, so we are dividing by a smaller + % number, ie. increasing the variance. + else + Z = 0; + end + if CPD.tied_cov + S = zeros(self_size, self_size); + % Eqn 2 from table 2 in TR + for i=1:dpsize + S = S + (YY(:,:,i) - B2(:,:,i)*XY(:,:,i)); + end + %denom = CPD.nsamples + gamma + Z; + denom = CPD.nsamples + Z; + S = (S + gamma*eye(self_size)) / denom; + if strcmp(CPD.cov_type, 'diag') + S = diag(diag(S)); + end + CPD.cov = repmat(S, [1 1 dpsize]); + else + for i=1:dpsize + % Eqn 1 from table 2 in TR + S = YY(:,:,i) - B2(:,:,i)*XY(:,:,i); + %denom = w(i) + gamma + Z; + denom = w(i) + Z; + S = (S + gamma*eye(self_size)) / denom; + CPD.cov(:,:,i) = S; + end + if strcmp(CPD.cov_type, 'diag') + for i=1:dpsize + CPD.cov(:,:,i) = diag(diag(CPD.cov(:,:,i))); + end + end + end +end + + +check_covars = 0; +min_covar = 1e-5; +if check_covars % prevent collapsing to a point + for i=1:dpsize + if min(svd(CPD.cov(:,:,i))) < min_covar + disp(['resetting singular covariance for node ' num2str(CPD.self)]); + CPD.cov(:,:,i) = CPD.init_cov(:,:,i); + end + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/private/CPD_to_linear_gaussian.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function [mu, Sigma, W] = CPD_to_linear_gaussian(CPD, domain, ns, cnodes, evidence) + +ps = domain(1:end-1); +dnodes = mysetdiff(1:length(ns), cnodes); +dps = myintersect(ps, dnodes); % discrete parents + +if isempty(dps) + Q = 1; +else + assert(~any(isemptycell(evidence(dps)))); + dpvals = cat(1, evidence{dps}); + Q = subv2ind(ns(dps), dpvals(:)'); +end + +mu = CPD.mean(:,Q); +Sigma = CPD.cov(:,:,Q); +W = CPD.weights(:,:,Q); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/CPD_to_linear_gaussian.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gaussian_CPD/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics for a Gaussian CPD. +% CPD = reset_ess(CPD) + +CPD.nsamples = 0; +CPD.Wsum = zeros(size(CPD.Wsum)); +CPD.WYsum = zeros(size(CPD.WYsum)); +CPD.WYYsum = zeros(size(CPD.WYYsum)); +CPD.WXsum = zeros(size(CPD.WXsum)); +CPD.WXXsum = zeros(size(CPD.WXXsum)); +CPD.WXYsum = zeros(size(CPD.WXYsum));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/sample_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function y = sample_node(CPD, pev) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (gaussian) +% y = sample_node(CPD, parent_evidence) +% +% pev{i} is the value of the i'th parent (if there are any parents) +% y is the sampled value (a scalar or vector) + +if length(CPD.dps)==0 + i = 1; +else + dpvals = cat(1, pev{CPD.dps}); + i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)'); +end + +if length(CPD.cps) == 0 + y = gsamp(CPD.mean(:,i), CPD.cov(:,:,i), 1); +else + pev = pev(:); + x = cat(1, pev{CPD.cps}); + y = gsamp(CPD.mean(:,i) + CPD.weights(:,:,i)*x(:), CPD.cov(:,:,i), 1); +end +y = y(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function CPD = set_fields(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a gaussian_CPD object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% +% mean - mu(:,i) is the mean given Q=i +% cov - Sigma(:,:,i) is the covariance given Q=i +% weights - W(:,:,i) is the regression matrix given Q=i +% cov_type - if 'diag', Sigma(:,:,i) is diagonal +% tied_cov - if 1, we constrain Sigma(:,:,i) to be the same for all i +% clamp_mean - if 1, we do not adjust mu(:,i) during learning +% clamp_cov - if 1, we do not adjust Sigma(:,:,i) during learning +% clamp_weights - if 1, we do not adjust W(:,:,i) during learning +% clamp - if 1, we do not adjust any params +% cov_prior_weight - weight given to I prior for estimating Sigma +% cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0] +% +% e.g., CPD = set_params(CPD, 'mean', [0;0]) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'mean', CPD.mean = args{i+1}; + case 'cov', CPD.cov = args{i+1}; + case 'weights', CPD.weights = args{i+1}; + case 'cov_type', CPD.cov_type = args{i+1}; + %case 'tied_cov', CPD.tied_cov = strcmp(args{i+1}, 'yes'); + case 'tied_cov', CPD.tied_cov = args{i+1}; + case 'clamp_mean', CPD.clamped_mean = args{i+1}; + case 'clamp_cov', CPD.clamped_cov = args{i+1}; + case 'clamp_weights', CPD.clamped_weights = args{i+1}; + case 'clamp', clamp = args{i+1}; + CPD.clamped_mean = clamp; + CPD.clamped_cov = clamp; + CPD.clamped_weights = clamp; + case 'cov_prior_weight', CPD.cov_prior_weight = args{i+1}; + case 'cov_prior_entropic', CPD.cov_prior_entropic = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a Gaussian node +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +%if nargin < 6 +% hidden_bitv = zeros(1, max(fmarginal.domain)); +% hidden_bitv(find(isempty(evidence)))=1; +%end + +dom = fmarginal.domain; +self = dom(end); +ps = dom(1:end-1); +cps = myintersect(ps, cnodes); +dps = mysetdiff(ps, cps); + +CPD.nsamples = CPD.nsamples + 1; +[ss cpsz dpsz] = size(CPD.weights); % ss = self size +[ss dpsz] = size(CPD.mean); + +% Let X be the cts parent (if any), Y be the cts child (self). + +if ~hidden_bitv(self) & ~any(hidden_bitv(cps)) & all(hidden_bitv(dps)) + % Speedup for the common case that all cts nodes are observed, all discrete nodes are hidden + % Since X and Y are observed, SYY = 0, SXX = 0, SXY = 0 + % Since discrete parents are hidden, we do not need to add evidence to w. + w = fmarginal.T(:); + CPD.Wsum = CPD.Wsum + w; + y = evidence{self}; + Cyy = y*y'; + if ~CPD.useC + WY = repmat(w(:)',ss,1); % WY(y,i) = w(i) + WYY = repmat(reshape(WY, [ss 1 dpsz]), [1 ss 1]); % WYY(y,y',i) = w(i) + %CPD.WYsum = CPD.WYsum + WY .* repmat(y(:), 1, dpsz); + CPD.WYsum = CPD.WYsum + y(:) * w(:)'; + CPD.WYYsum = CPD.WYYsum + WYY .* repmat(reshape(Cyy, [ss ss 1]), [1 1 dpsz]); + else + W = w(:)'; + W2 = reshape(W, [1 1 dpsz]); + CPD.WYsum = CPD.WYsum + rep_mult(W, y(:), size(CPD.WYsum)); + CPD.WYYsum = CPD.WYYsum + rep_mult(W2, Cyy, size(CPD.WYYsum)); + end + if cpsz > 0 % X exists + x = cat(1, evidence{cps}); x = x(:); + Cxx = x*x'; + Cxy = x*y'; + WX = repmat(w(:)',cpsz,1); % WX(x,i) = w(i) + WXX = repmat(reshape(WX, [cpsz 1 dpsz]), [1 cpsz 1]); % WXX(x,x',i) = w(i) + WXY = repmat(reshape(WX, [cpsz 1 dpsz]), [1 ss 1]); % WXY(x,y,i) = w(i) + if ~CPD.useC + CPD.WXsum = CPD.WXsum + WX .* repmat(x(:), 1, dpsz); + CPD.WXXsum = CPD.WXXsum + WXX .* repmat(reshape(Cxx, [cpsz cpsz 1]), [1 1 dpsz]); + CPD.WXYsum = CPD.WXYsum + WXY .* repmat(reshape(Cxy, [cpsz ss 1]), [1 1 dpsz]); + else + CPD.WXsum = CPD.WXsum + rep_mult(W, x(:), size(CPD.WXsum)); + CPD.WXXsum = CPD.WXXsum + rep_mult(W2, Cxx, size(CPD.WXXsum)); + CPD.WXYsum = CPD.WXYsum + rep_mult(W2, Cxy, size(CPD.WXYsum)); + end + end + return; +end + +% general (non-vectorized) case +fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes); % slow! + +if dpsz == 1 % no discrete parents + w = 1; +else + w = fullm.T(:); +end + +CPD.Wsum = CPD.Wsum + w; +xi = 1:cpsz; +yi = (cpsz+1):(cpsz+ss); +for i=1:dpsz + muY = fullm.mu(yi, i); + SYY = fullm.Sigma(yi, yi, i); + CPD.WYsum(:,i) = CPD.WYsum(:,i) + w(i)*muY; + CPD.WYYsum(:,:,i) = CPD.WYYsum(:,:,i) + w(i)*(SYY + muY*muY'); % E[X Y] = Cov[X,Y] + E[X] E[Y] + if cpsz > 0 + muX = fullm.mu(xi, i); + SXX = fullm.Sigma(xi, xi, i); + SXY = fullm.Sigma(xi, yi, i); + CPD.WXsum(:,i) = CPD.WXsum(:,i) + w(i)*muX; + CPD.WXXsum(:,:,i) = CPD.WXXsum(:,:,i) + w(i)*(SXX + muX*muX'); + CPD.WXYsum(:,:,i) = CPD.WXYsum(:,:,i) + w(i)*(SXY + muX*muY'); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +/README/1.1.1.1/Wed May 29 15:59:52 2002// +/adjustable_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/display.m/1.1.1.1/Wed May 29 15:59:52 2002// +/generic_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/learn_params.m/1.1.1.1/Thu Jun 10 01:53:20 2004// +/log_prior.m/1.1.1.1/Wed May 29 15:59:52 2002// +/set_clamped.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@generic_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/Old/BIC_score_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function score = BIC_score_CPD(CPD, fam, data, ns, cnodes) +% BIC_score_CPD Compute the BIC score of a generic CPD +% score = BIC_score_CPD(CPD, fam, data, ns, cnodes) +% +% We assume this node has a maximize_params method + +ncases = size(data, 2); +CPD = reset_ess(CPD); +% make a fully observed joint distribution over the family +fmarginal.domain = fam; +fmarginal.T = 1; +fmarginal.mu = []; +fmarginal.Sigma = []; +if ~iscell(data) + cases = num2cell(data); +else + cases = data; +end +for m=1:ncases + CPD = update_ess(CPD, fmarginal, cases(:,m), ns, cnodes); +end +CPD = maximize_params(CPD); +self = fam(end); +ps = fam(1:end-1); +L = log_prob_node(CPD, cases(self,:), cases(ps,:)); +score = L - 0.5*CPD.nparams*log(ncases);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/Old/CPD_to_dpots.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function pots = CPD_to_dpots(CPD, domain, ns, cnodes, evidence) +% CPD_TO_DPOTS Convert the CPD to several discrete potentials, for different instantiations (generic) +% pots = CPD_to_dpots(CPD, domain, ns, cnodes, evidence) +% +% domain(:,i) is the domain of the i'th instantiation of CPD. +% node_sizes(i) is the size of node i. +% cnodes = all the cts nodes +% evidence{i} is the evidence on the i'th node. +% +% This just calls CPD_to_dpot for each domain. + +nCPDs = size(domain,2); +pots = cell(1,nCPDs); +for i=1:nCPDs + pots{i} = CPD_to_dpot(CPD, domain(:,i), ns, cnodes, evidence); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/BIC_score_CPD.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_dpots.m/1.1.1.1/Wed May 29 15:59:52 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@generic_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +A generic CPD implements general purpose functions like 'display', +that subtypes can inherit.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/adjustable_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function p = adjustable_CPD(CPD) +% ADJUSTABLE_CPD Does this CPD have any adjustable params? (generic) +% p = adjustable_CPD(CPD) + +p = ~CPD.clamped;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function display(CPD) + +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/generic_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function CPD = generic_CPD(clamped) +% GENERIC_CPD Virtual constructor for generic CPD +% CPD = discrete_CPD(clamped) + +if nargin < 1, clamped = 0; end + +CPD.clamped = clamped; +CPD = class(CPD, 'generic_CPD');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/learn_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function CPD = learn_params(CPD, fam, data, ns, cnodes) +% LEARN_PARAMS Compute the maximum likelihood estimate of the params of a generic CPD given complete data +% CPD = learn_params(CPD, fam, data, ns, cnodes) +% +% data(i,m) is the value of node i in case m (can be cell array). +% We assume this node has a maximize_params method. + +%error('no longer supported') % KPM 1 Feb 03 + +if 1 +ncases = size(data, 2); +CPD = reset_ess(CPD); +% make a fully observed joint distribution over the family +fmarginal.domain = fam; +fmarginal.T = 1; +fmarginal.mu = []; +fmarginal.Sigma = []; +if ~iscell(data) + cases = num2cell(data); +else + cases = data; +end +hidden_bitv = zeros(1, max(fam)); +for m=1:ncases + % specify (as a bit vector) which elements in the family domain are hidden + hidden_bitv = zeros(1, max(fmarginal.domain)); + ev = cases(:,m); + hidden_bitv(find(isempty(evidence)))=1; + CPD = update_ess(CPD, fmarginal, ev, ns, cnodes, hidden_bitv); +end +CPD = maximize_params(CPD); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/log_prior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a generic CPD - we return 0 +% L = log_prior(CPD) + +L = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@generic_CPD/set_clamped.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function CPD = set_clamped(CPD, bit) + +CPD.clamped = bit;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/CPD_to_lambda_msg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% CPD_TO_LAMBDA_MSG Compute lambda message (gmux) +% lam_msg = compute_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% Pearl p183 eq 4.52 + +% Let Y be this node, X1..Xn be the cts parents and M the discrete switch node. +% e.g., for n=3, M=1 +% +% X1 X2 X3 M +% \ +% \ +% Y +% +% So the only case in which we send an informative message is if p=1=M. +% To the other cts parents, we send the "know nothing" message. + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + cps = ps(CPD.cps); + cpsizes = CPD.sizes(CPD.cps); + self_size = CPD.sizes(end); + i = find_equiv_posns(p, cps); % p is n's i'th cts parent + psz = cpsizes(i); + dps = ps(CPD.dps); + M = evidence{dps}; + if isempty(M) + error('gmux node must have observed discrete parent') + end + P = msg{n}.lambda.precision; + if all(P == 0) | (cps(M) ~= p) % if we know nothing, or are sending to a disconnected parent + lam_msg.precision = zeros(psz, psz); + lam_msg.info_state = zeros(psz, 1); + return; + end + % We are sending a message to the only effectively connected parent. + % There are no other incoming pi messages. + Bmu = CPD.mean(:,M); + BSigma = CPD.cov(:,:,M); + Bi = CPD.weights(:,:,M); + if (det(P) > 0) | isinf(P) + if isinf(P) % Y is observed + Sigma_lambda = zeros(self_size, self_size); % infinite precision => 0 variance + mu_lambda = msg{n}.lambda.mu; % observed_value; + else + Sigma_lambda = inv(P); + mu_lambda = Sigma_lambda * msg{n}.lambda.info_state; + end + C = inv(Sigma_lambda + BSigma); + lam_msg.precision = Bi' * C * Bi; + lam_msg.info_state = Bi' * C * (mu_lambda - Bmu); + else + % method that uses matrix inversion lemma + A = inv(P + inv(BSigma)); + C = P - P*A*P; + lam_msg.precision = Bi' * C * Bi; + D = eye(self_size) - P*A; + z = msg{n}.lambda.info_state; + lam_msg.info_state = Bi' * (D*z - D*P*Bmu); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/CPD_to_pi.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% CPD_TO_PI Compute the pi vector (gaussian) +% function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) + +switch msg_type + case 'd', + error('gaussian_CPD can''t create discrete msgs') + case 'g', + dps = ps(CPD.dps); + k = evidence{dps}; + if isempty(k) + error('gmux node must have observed discrete parent') + end + m = msg{n}.pi_from_parent{k}; + B = CPD.weights(:,:,k); + pi.mu = CPD.mean(:,k) + B * m.mu; + pi.Sigma = CPD.cov(:,:,k) + B * m.Sigma * B'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:52 2002// +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:52 2002// +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/gmux_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gmux_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/gmux_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@gmux_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/Old/gmux_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +function CPD = gmux_CPD(bnet, self, varargin) +% GMUX_CPD Make a Gaussian multiplexer node +% +% CPD = gmux_CPD(bnet, node, ...) is used similarly to gaussian_CPD, +% except we assume there is exactly one discrete parent (call it M) +% which is used to select which cts parent to pass through to the output. +% i.e., we define P(Y=y|M=m, X1, ..., XK) = N(y | W*x(m) + mu, Sigma) +% where Y represents this node, and the Xi's are the cts parents. +% All the Xi must have the same size, and the num values for M must be K. +% +% Currently the params for this kind of CPD cannot be learned. +% +% Optional arguments [ default in brackets ] +% +% mean - mu [zeros(Y,1)] +% cov - Sigma [eye(Y,Y)] +% weights - W [ randn(Y,X) ] + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'gmux_CPD', generic_CPD(clamp)); + return; +elseif isa(bnet, 'gmux_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +CPD = class(CPD, 'gmux_CPD', generic_CPD(1)); + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +dps = myintersect(ps, bnet.dnodes); +cps = myintersect(ps, bnet.cnodes); +fam_sz = ns([ps self]); + +CPD.self = self; +CPD.sizes = fam_sz; + +% Figure out which (if any) of the parents are discrete, and which cts, and how big they are +% dps = discrete parents, cps = cts parents +CPD.cps = find_equiv_posns(cps, ps); % cts parent index +CPD.dps = find_equiv_posns(dps, ps); +if length(CPD.dps) ~= 1 + error('gmux must have exactly 1 discrete parent') +end +ss = fam_sz(end); +cpsz = fam_sz(CPD.cps(1)); % in gaussian_CPD, cpsz = sum(fam_sz(CPD.cps)) +if ~all(fam_sz(CPD.cps) == cpsz) + error('all cts parents must have same size') +end +dpsz = fam_sz(CPD.dps); +if dpsz ~= length(cps) + error(['the arity of the mux node is ' num2str(dpsz) ... + ' but there are ' num2str(length(cps)) ' cts parents']); +end + +% set default params +CPD.mean = zeros(ss, 1); +CPD.cov = eye(ss); +CPD.weights = randn(ss, cpsz); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'mean', CPD.mean = args{i+1}; + case 'cov', CPD.cov = args{i+1}; + case 'weights', CPD.weights = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.sizes = []; +CPD.cps = []; +CPD.dps = []; +CPD.mean = []; +CPD.cov = []; +CPD.weights = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/convert_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a gmux CPD to a Gaussian potential +% pot = convert_to_pot(CPD, pot_type, domain, evidence) + +switch pot_type + case {'d', 'u', 'cg', 'scg'}, + error(['can''t convert gmux to potential of type ' pot_type]) + + case {'c','g'}, + % We create a large weight matrix with zeros in all blocks corresponding + % to the non-chosen parents, since they are effectively disconnected. + % The chosen parent is determined by the value, m, of the discrete parent. + % Thus the potential is as large as the whole family. + ps = domain(1:end-1); + dps = ps(CPD.dps); % CPD.dps is an index, not a node number (because of param tying) + cps = ps(CPD.cps); + m = evidence{dps}; + if isempty(m) + error('gmux node must have observed discrete parent') + end + bs = CPD.sizes(CPD.cps); + b = block(m, bs); + sum_cpsz = sum(CPD.sizes(CPD.cps)); + selfsz = CPD.sizes(end); + W = zeros(selfsz, sum_cpsz); + W(:,b) = CPD.weights(:,:,m); + + ns = zeros(1, max(domain)); + ns(domain) = CPD.sizes; + self = domain(end); + cdom = [cps(:)' self]; + pot = linear_gaussian_to_cpot(CPD.mean(:,m), CPD.cov(:,:,m), W, domain, ns, cdom, evidence); + + otherwise, + error(['unrecognized pot_type' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('gmux_CPD object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/gmux_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +function CPD = gmux_CPD(bnet, self, varargin) +% GMUX_CPD Make a Gaussian multiplexer node +% +% CPD = gmux_CPD(bnet, node, ...) is used similarly to gaussian_CPD, +% except we assume there is exactly one discrete parent (call it M) +% which is used to select which cts parent to pass through to the output. +% i.e., we define P(Y=y|M=m, X1, ..., XK) = N(y | W(m)*x(m) + mu(m), Sigma(m)) +% where Y represents this node, and the Xi's are the cts parents. +% All the Xi must have the same size, and the num values for M must be K. +% +% Currently the params for this kind of CPD cannot be learned. +% +% Optional arguments [ default in brackets ] +% +% mean - mu(:,i) is the mean given M=i [ zeros(Y,K) ] +% cov - Sigma(:,:,i) is the covariance given M=i [ repmat(1*eye(Y,Y), [1 1 K]) ] +% weights - W(:,:,i) is the regression matrix given M=i [ randn(Y,X,K) ] + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'gmux_CPD', generic_CPD(clamp)); + return; +elseif isa(bnet, 'gmux_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +CPD = class(CPD, 'gmux_CPD', generic_CPD(1)); + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +dps = myintersect(ps, bnet.dnodes); +cps = myintersect(ps, bnet.cnodes); +fam_sz = ns([ps self]); + +CPD.self = self; +CPD.sizes = fam_sz; + +% Figure out which (if any) of the parents are discrete, and which cts, and how big they are +% dps = discrete parents, cps = cts parents +CPD.cps = find_equiv_posns(cps, ps); % cts parent index +CPD.dps = find_equiv_posns(dps, ps); +if length(CPD.dps) ~= 1 + error('gmux must have exactly 1 discrete parent') +end +ss = fam_sz(end); +cpsz = fam_sz(CPD.cps(1)); % in gaussian_CPD, cpsz = sum(fam_sz(CPD.cps)) +if ~all(fam_sz(CPD.cps) == cpsz) + error('all cts parents must have same size') +end +dpsz = fam_sz(CPD.dps); +if dpsz ~= length(cps) + error(['the arity of the mux node is ' num2str(dpsz) ... + ' but there are ' num2str(length(cps)) ' cts parents']); +end + +% set default params +%CPD.mean = zeros(ss, 1); +%CPD.cov = eye(ss); +%CPD.weights = randn(ss, cpsz); +CPD.mean = zeros(ss, dpsz); +CPD.cov = 1*repmat(eye(ss), [1 1 dpsz]); +CPD.weights = randn(ss, cpsz, dpsz); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'mean', CPD.mean = args{i+1}; + case 'cov', CPD.cov = args{i+1}; + case 'weights', CPD.weights = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.sizes = []; +CPD.cps = []; +CPD.dps = []; +CPD.mean = []; +CPD.cov = []; +CPD.weights = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/sample_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function y = sample_node(CPD, pev) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (gmux) +% y = sample_node(CPD, parent_evidence) +% +% parent_ev{i} is the value of the i'th parent + +dpval = pev{CPD.dps}; +x = pev{CPD.cps(dpval)}; +y = gsamp(CPD.mean(:,dpval) + CPD.weights(:,:,dpval)*x(:), CPD.cov(:,:,dpval), 1); +y = y(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmm2Q_CPD/CPD_to_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function CPT = CPD_to_CPT(CPD) +% Compute the big CPT for an HHMM Q node (including F parents) +% by combining internal transprob and startprob +% function CPT = CPD_to_CPT(CPD) + +Qsz = CPD.Qsz; + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + error('not implemented') + else % no F from self, hence no startprob (top level) + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + % when Fself=1, the CPT(i,j) = delta(i,j) for all k + for k=1:prod(CPD.Qpsizes) + Qps_vals = ind2subv(CPD.Qpsizes, k); + ndx = mk_multi_index(nps+1, [CPD.Fbelow_ndx CPD.Qps_ndx], [1 Qps_vals]); + CPT(ndx{:}) = eye(Qsz); % CPT(:,2,k,:) or CPT(:,k,2,:) etc + end + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 2); + CPT(ndx{:}) = CPD.transprob; % we assume transprob is in topo order + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % bottom level + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 1); + CPT(ndx{:}) = CPD.transprob; + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 2); + CPT(ndx{:}) = CPD.startprob; + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmm2Q_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/CPD_to_CPT.m/1.1.1.1/Tue Sep 24 12:46:46 2002// +/hhmm2Q_CPD.m/1.1.1.1/Tue Sep 24 22:34:40 2002// +/maximize_params.m/1.1.1.1/Tue Sep 24 22:44:36 2002// +/reset_ess.m/1.1.1.1/Tue Sep 24 22:36:16 2002// +/update_ess.m/1.1.1.1/Tue Sep 24 22:43:30 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmm2Q_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmm2Q_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmm2Q_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmm2Q_CPD/hhmm2Q_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function CPD = hhmm2Q_CPD(bnet, self, varargin) +% HHMMQ_CPD Make the CPD for a Q node in a 2 level hierarchical HMM +% CPD = hhmmQ_CPD(bnet, self, ...) +% +% Fself(t-1) Qps +% \ | +% \ v +% Qold(t-1) -> Q(t) +% / +% / +% Fbelow(t-1) +% +% +% optional args [defaults] +% +% Fself - node number <= ss +% Fbelow - node number <= ss +% Qps - node numbers (all <= 2*ss) - uses 2TBN indexing +% transprob - CPT for when Fbelow=2 and Fself=1 +% startprob - CPT for when Fbelow=2 and Fself=2 +% If Fbelow=1, we cannot change state. + +ss = bnet.nnodes_per_slice; +ns = bnet.node_sizes(:); + +% set default arguments +Fself = []; +Fbelow = []; +Qps = []; +startprob = []; +transprob = []; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'Fself', Fself = varargin{i+1}; + case 'Fbelow', Fbelow = varargin{i+1}; + case 'Qps', Qps = varargin{i+1}; + case 'transprob', transprob = varargin{i+1}; + case 'startprob', startprob = varargin{i+1}; + end +end + +ps = parents(bnet.dag, self); +old_self = self-ss; +ndsz = ns(:)'; +CPD.dom_sz = [ndsz(ps) ns(self)]; +CPD.Fself_ndx = find_equiv_posns(Fself, ps); +CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps); +Qps = mysetdiff(ps, [Fself Fbelow old_self]); +CPD.Qps_ndx = find_equiv_posns(Qps, ps); +CPD.old_self_ndx = find_equiv_posns(old_self, ps); + +Qps = ps(CPD.Qps_ndx); +CPD.Qsz = ns(self); +CPD.Qpsizes = ns(Qps); + +CPD.transprob = transprob; +CPD.startprob = startprob; +CPD.start_counts = []; +CPD.trans_counts = []; + +CPD = class(CPD, 'hhmm2Q_CPD', discrete_CPD(0, CPD.dom_sz)); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmm2Q_CPD/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmQ node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +if sum(CPD.start_counts(:)) > 0 + CPD.startprob = mk_stochastic(CPD.start_counts); +end +if sum(CPD.trans_counts(:)) > 0 + CPD.transprob = mk_stochastic(CPD.trans_counts); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmm2Q_CPD/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm2 Q node. +% CPD = reset_ess(CPD) + +domsz = CPD.dom_sz; +domsz(CPD.Fself_ndx) = 1; +domsz(CPD.Fbelow_ndx) = 1; +Qdom_sz = domsz; +Qdom_sz(Qdom_sz==1)=[]; % get rid of dimensions of size 1 + +CPD.start_counts = zeros(Qdom_sz); +CPD.trans_counts = zeros(Qdom_sz);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmm2Q_CPD/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +marg = add_ev_to_dmarginal(fmarginal, evidence, ns); + +nps = length(CPD.dom_sz)-1; % num parents + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + ndx = mk_multi_index(nps+1, [CPD.Fbelow_ndx CPD.Fself_ndx], [2 1]); + CPD.trans_counts = CPD.trans_counts + squeeze(marg.T(ndx{:})); + ndx = mk_multi_index(nps+1, [CPD.Fbelow_ndx CPD.Fself_ndx], [2 2]); + CPD.start_counts = CPD.start_counts + squeeze(marg.T(ndx{:})); + else % no F from self, hence no startprob (top level) + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 2); + CPD.trans_counts = CPD.trans_counts + squeeze(marg.T(ndx{:})); + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % self F (bottom level) + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 1); + CPD.trans_counts = CPD.trans_counts + squeeze(marg.T(ndx{:})); + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 2); + CPD.start_counts = CPD.start_counts + squeeze(marg.T(ndx{:})); + else % no F from self or below + error('no F signal') + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/hhmmF_CPD.m/1.1.1.1/Mon Jun 24 23:38:24 2002// +/log_prior.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_CPT.m/1.1.1.1/Mon Jun 24 22:45:04 2002// +/update_ess.m/1.1.1.1/Mon Jun 24 23:54:30 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmmF_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/hhmmF_CPD.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/log_prior.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/maximize_params.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/reset_ess.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/update_CPT.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +/update_ess.m/1.1.1.1/Mon Jun 24 22:35:06 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmmF_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/hhmmF_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function CPD = hhmmF_CPD(bnet, self, Qnodes, d, D, varargin) +% HHMMF_CPD Make the CPD for an F node at depth D of a D-level hierarchical HMM +% CPD = hhmmF_CPD(bnet, self, Qnodes, d, D, ...) +% +% Q(d-1) +% \ +% \ +% F(d) +% / | +% / | +% Q(d) F(d+1) +% +% We assume nodes are ordered (numbered) as follows: +% Q(1), ... Q(d), F(d+1), F(d) +% +% F(d)=2 means level d has finished. The prob this happens depends on Q(d) +% and optionally on Q(d-1), Q(d=1), ..., Q(1). +% Also, level d can only finish if the level below has finished +% (hence the F(d+1) -> F(d) arc). +% +% If d=D, there is no F(d+1), so F(d) is just a regular tabular_CPD. +% If all models always finish in the same state (e.g., their last), +% we don't need to condition on the state of parent models (Q(d-1), ...) +% +% optional args [defaults] +% +% termprob - termprob(k,i,2) = prob finishing given Q(d)=i and Q(1:d-1)=k [ finish in last state ] +% +% hhmmF_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc. +% +% We create an isolated tabular_CPD with no F parent to learn termprob +% so we can avail of e.g., entropic or Dirichlet priors. +% +% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01. + + +ps = parents(bnet.dag, self); +Qps = myintersect(ps, Qnodes); +F = mysetdiff(ps, Qps); +CPD.Q = Qps(end); % Q(d) +assert(CPD.Q == Qnodes(d)); +CPD.Qps = Qps(1:end-1); % all Q parents except Q(d), i.e., calling context + +ns = bnet.node_sizes(:); +CPD.Qsizes = ns(Qnodes); +CPD.d = d; +CPD.D = D; + +Qsz = ns(CPD.Q); +Qpsz = prod(ns(CPD.Qps)); + +% set default arguments +p = 0.9; +%termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz +termprob = zeros(Qpsz, Qsz, 2); +termprob(:, Qsz, 2) = p; +termprob(:, Qsz, 1) = 1-p; +termprob(:, 1:(Qsz-1), 1) = 1; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'termprob', termprob = varargin{i+1}; + otherwise, error(['unrecognized argument ' varargin{i}]) + end +end + +ps = [CPD.Qps CPD.Q]; +% ns(self) = 2 since this is an F node +CPD.sub_CPD_term = mk_isolated_tabular_CPD(ps, ns([ps self]), {'CPT', termprob}); +S = struct(CPD.sub_CPD_term); +CPD.termprob = S.CPT; + +CPD = class(CPD, 'hhmmF_CPD', tabular_CPD(bnet, self)); + +CPD = update_CPT(CPD); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/log_prior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a hhmm F CPD +% L = log_prior(CPD) + +L = log_prior(CPD.sub_CPD_term);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmF node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +CPD.sub_CPD_term = maximize_params(CPD.sub_CPD_term, temp); +S = struct(CPD.sub_CPD_term); +CPD.termprob = S.CPT; + +CPD = update_CPT(CPD);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm F node. +% CPD = reset_ess(CPD) + +CPD.sub_CPD_term = reset_ess(CPD.sub_CPD_term);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/update_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function CPD = update_CPT(CPD) +% Compute the big CPT for an HHMM F node given internal termprob +% function CPD = update_CPT(CPD) + +Qsz = CPD.Qsizes(CPD.Q); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); + +% P(Q(1:d-1), Q(d), F(d+1), F(d)) +CPT = zeros(Qpsz, Qsz, 2, 2); +CPT(:,:,1,1) = 1; % if F(d+1)=1, then F(d)=1 +CPT(:,:,2,:) = CPD.termprob; + +CPD = set_fields(CPD, 'CPT', CPT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/Old/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmmF node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +% Figure out the node numbers associated with each parent +% so we extract evidence from the right place +dom = fmarginal.domain; % Q(1) .. Q(d) F(d+1) F(d) +Qps = fmarginal.domain(1:end-2); +Q = Qps(end); +Qps = Qps(1:end-1); + +Qsz = CPD.Qsizes(CPD.Q); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); % may be 1 + +% We assume the F node are always hidden, but allow some of the Q nodes +% to be observed. We do case analysis for speed. +%We only extract prob from fmarginal.T when F(d+1)=2 i.e., model below has finished. +% wrong -> % We sum over the possibilities that F(d+1) = 1 or 2 + +obs_self = ~hidden_bitv(Q); +if obs_self + self_val = evidence{Q}; +end + +if isempty(Qps) % independent of parent context + counts = zeros(Qsz, 2); + %fmarginal.T(Q(d), F(d+1), F(d)) + if obs_self + marg = myreshape(fmarginal.T, [1 2 2]); + counts(self_val,:) = marg(1,2,:); + %counts(self_val,:) = marg(1,1,:) + marg(1,2,:); + else + marg = myreshape(fmarginal.T, [Qsz 2 2]); + counts = squeeze(marg(:,2,:)); + %counts = squeeze(marg(:,2,:)) + squeeze(marg(:,1,:)); + end +else + counts = zeros(Qpsz, Qsz, 2); + %fmarginal.T(Q(1:d-1), Q(d), F(d+1), F(d)) + obs_Qps = ~any(hidden_bitv(Qps)); % we assume that all or none of the Q parents are observed + if obs_Qps + Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps})); + end + if obs_self & obs_Qps + marg = myreshape(fmarginal.T, [1 1 2 2]); + counts(Qps_val, self_val, :) = squeeze(marg(1,1,2,:)); + %counts(Qps_val, self_val, :) = squeeze(marg(1,1,2,:)) + squeeze(marg(1,1,1,:)); + elseif ~obs_self & obs_Qps + marg = myreshape(fmarginal.T, [1 Qsz 2 2]); + counts(Qps_val, :, :) = squeeze(marg(1,:,2,:)); + %counts(Qps_val, :, :) = squeeze(marg(1,:,2,:)) + squeeze(marg(1,:,1,:)); + elseif obs_self & ~obs_Qps + error('not yet implemented') + else + marg = myreshape(fmarginal.T, [Qpsz Qsz 2 2]); + counts(:, :, :) = squeeze(marg(:,:,2,:)); + %counts(:, :, :) = squeeze(marg(:,:,2,:)) + squeeze(marg(:,:,1,:)); + end +end + +CPD.sub_CPD_term = update_ess_simple(CPD.sub_CPD_term, counts);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/hhmmF_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +function CPD = hhmmF_CPD(bnet, self, Qself, Fbelow, varargin) +% HHMMF_CPD Make the CPD for an F node in a hierarchical HMM +% CPD = hhmmF_CPD(bnet, self, Qself, Fbelow, ...) +% +% Qps +% \ +% \ +% Fself +% / | +% / | +% Qself Fbelow +% +% We assume nodes are ordered (numbered) as follows: Qps, Q, Fbelow, F +% All nodes numbers should be from slice 1. +% +% If Fbelow if missing, this becomes a regular tabular_CPD. +% Qps may be omitted. +% +% optional args [defaults] +% +% Qps - node numbers. +% termprob - termprob(k,i,2) = prob finishing given Q(d)=i and Q(1:d-1)=k [ finish in last state wp 0.9] +% +% hhmmF_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc. +% +% We create an isolated tabular_CPD with no F parent to learn termprob +% so we can avail of e.g., entropic or Dirichlet priors. +% +% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01. + + + +Qps = []; +% get parents +for i=1:2:length(varargin) + switch varargin{i}, + case 'Qps', Qps = varargin{i+1}; + end +end + +ns = bnet.node_sizes(:); +Qsz = ns(Qself); +Qpsz = prod(ns(Qps)); +CPD.Qsz = Qsz; +CPD.Qpsz = Qpsz; + +ps = parents(bnet.dag, self); +CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps); +CPD.Qps_ndx = find_equiv_posns(Qps, ps); +CPD.Qself_ndx = find_equiv_posns(Qself, ps); + +% set default arguments +p = 0.9; +%termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz +termprob = zeros(Qpsz, Qsz, 2); +termprob(:, Qsz, 2) = p; +termprob(:, Qsz, 1) = 1-p; +termprob(:, 1:(Qsz-1), 1) = 1; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'termprob', termprob = varargin{i+1}; + end +end + +CPD.sub_CPD_term = mk_isolated_tabular_CPD([Qpsz Qsz 2], {'CPT', termprob}); +S = struct(CPD.sub_CPD_term); +CPD.termprob = S.CPT; + +CPD = class(CPD, 'hhmmF_CPD', tabular_CPD(bnet, self)); + +CPD = update_CPT(CPD); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/log_prior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a hhmm F CPD +% L = log_prior(CPD) + +L = log_prior(CPD.sub_CPD_term);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmF node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +CPD.sub_CPD_term = maximize_params(CPD.sub_CPD_term, temp); +S = struct(CPD.sub_CPD_term); +CPD.termprob = S.CPT; + +CPD = update_CPT(CPD);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm F node. +% CPD = reset_ess(CPD) + +CPD.sub_CPD_term = reset_ess(CPD.sub_CPD_term);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/update_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function CPD = update_CPT(CPD) +% Compute the big CPT for an HHMM F node given internal termprob +% function CPD = update_CPT(CPD) + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +% CPT(Qpsz, Q, Fbelow, Fself) +CPT = zeros(Qpsz, Qsz, 2, 2); +CPT(:,:,1,1) = 1; % if Fbelow=1 (off), then Fself=1 (off) +CPT(:,:,2,:) = CPD.termprob; + +CPD = set_fields(CPD, 'CPT', CPT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmF_CPD/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmmF node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% +% We assume the F nodes are always hidden + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +%Fself = dom(end); +%Fbelow = dom(CPD.Fbelow_ndx); +Qself = dom(CPD.Qself_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if all(hidden_bitv(Qps)) % we assume all are hidden or all are observed + k_ndx = 1:Qpsz; + eff_Qpsz = Qpsz; +else + k_ndx = subv2ind(Qpsz, cat(1, evidence{Qps})); + eff_Qpsz = 1; +end + +if hidden_bitv(Qself) + j_ndx = 1:Qsz; + eff_Qsz = Qsz; +else + j_ndx = evidence{Qself}; + eff_Qsz = 1; +end + +% Fmarginal(Qps, Q, Fbelow, F) +fmarg = myreshape(fmarginal.T, [eff_Qpsz eff_Qsz 2 2]); + +counts = zeros(Qpsz, Qsz, 2); +%counts(k_ndx, j_ndx, :) = sum(fmarginal.T(:, :, :, :), 3); % sum over Fbelow +counts(k_ndx, j_ndx, :) = fmarg(:, :, 2, :); % Fbelow = 2 + +CPD.sub_CPD_term = update_ess_simple(CPD.sub_CPD_term, counts);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/hhmmQ_CPD.m/1.1.1.1/Tue Sep 24 04:19:26 2002// +/log_prior.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Tue Sep 24 13:10:18 2002// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_CPT.m/1.1.1.1/Tue Sep 24 02:58:18 2002// +/update_ess.m/1.1.1.1/Thu Jul 24 13:41:34 2003// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmmQ_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +/hhmmQ_CPD.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/log_prior.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/maximize_params.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/reset_ess.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/update_CPT.m/1.1.1.1/Tue Sep 24 02:30:32 2002// +/update_ess.m/1.1.1.1/Mon Jun 24 18:19:00 2002// +/update_ess2.m/1.1.1.1/Mon Jun 24 21:20:52 2002// +/update_ess3.m/1.1.1.1/Mon Jun 24 22:08:08 2002// +/update_ess4.m/1.1.1.1/Mon Jun 24 22:23:32 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@hhmmQ_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/hhmmQ_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +function CPD = hhmmQ_CPD(bnet, self, Qnodes, d, D, varargin) +% HHMMQ_CPD Make the CPD for a Q node at depth D of a D-level hierarchical HMM +% CPD = hhmmQ_CPD(bnet, self, Qnodes, d, D, ...) +% +% Fd(t-1) \ Q1:d-1(t) +% \ | +% \ v +% Qd(t-1) -> Qd(t) +% / +% / +% Fd+1(t-1) +% +% We assume parents are ordered (numbered) as follows: +% Qd(t-1), Fd+1(t-1), Fd(t-1), Q1(t), ..., Qd(t) +% +% The parents of Qd(t) can either be just Qd-1(t) or the whole stack Q1:d-1(t) (allQ) +% In either case, we will call them Qps. +% If d=1, Qps does not exist. Also, the F1(t-1) -> Q1(t) arc is optional. +% If the arc is missing, startprob does not need to be specified, +% since the toplevel is assumed to never reset (F1 does not exist). +% If d=D, Fd+1(t-1) does not exist (there is no signal from below). +% +% optional args [defaults] +% +% transprob - transprob(i,k,j) = prob transition from i to j given Qps = k ['leftright'] +% selfprob - prob of a transition from i to i given Qps=k [0.1] +% startprob - startprob(k,j) = prob start in j given Qps = k ['leftstart'] +% startargs - other args to be passed to the sub tabular_CPD for learning startprob +% transargs - other args will be passed to the sub tabular_CPD for learning transprob +% allQ - 1 means use all Q nodes above d as parents, 0 means just level d-1 [0] +% F1toQ1 - 1 means add F1(t-1) -> Q1(t) arc, 0 means level 1 never resets [0] +% +% For d=1, startprob(1,j) is only needed if F1toQ1=1 +% Also, transprob(i,j) can be used instead of transprob(i,1,j). +% +% hhmmQ_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc. +% +% We create isolated tabular_CPDs with no F parents to learn transprob/startprob +% so we can avail of e.g., entropic or Dirichlet priors. +% In the future, we will be able to represent the transprob using a tree_CPD. +% +% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01. + + +ss = bnet.nnodes_per_slice; +%assert(self == Qnodes(d)+ss); +ns = bnet.node_sizes(:); +CPD.Qsizes = ns(Qnodes); +CPD.d = d; +CPD.D = D; +allQ = 0; + +% find out which parents to use, to get right size +for i=1:2:length(varargin) + switch varargin{i}, + case 'allQ', allQ = varargin{i+1}; + end +end + +if d==1 + CPD.Qps = []; +else + if allQ + CPD.Qps = Qnodes(1:d-1); + else + CPD.Qps = Qnodes(d-1); + end +end + +Qsz = ns(self); +Qpsz = prod(ns(CPD.Qps)); + +% set default arguments +startprob = 'leftstart'; +transprob = 'leftright'; +startargs = {}; +transargs = {}; +CPD.F1toQ1 = 0; +selfprob = 0.1; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'transprob', transprob = varargin{i+1}; + case 'selfprob', selfprob = varargin{i+1}; + case 'startprob', startprob = varargin{i+1}; + case 'startargs', startargs = varargin{i+1}; + case 'transargs', transargs = varargin{i+1}; + case 'F1toQ1', CPD.F1toQ1 = varargin{i+1}; + end +end + +Qps = CPD.Qps + ss; +old_self = self-ss; + +if strcmp(transprob, 'leftright') + LR = mk_leftright_transmat(Qsz, selfprob); + transprob = repmat(reshape(LR, [1 Qsz Qsz]), [Qpsz 1 1]); % transprob(k,i,j) + transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j) +end +transargs{end+1} = 'CPT'; +transargs{end+1} = transprob; +CPD.sub_CPD_trans = mk_isolated_tabular_CPD([old_self Qps], ns([old_self Qps self]), transargs); +S = struct(CPD.sub_CPD_trans); +CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); + + +if strcmp(startprob, 'leftstart') + startprob = zeros(Qpsz, Qsz); + startprob(:,1) = 1; +end + +if (d==1) & ~CPD.F1toQ1 + CPD.sub_CPD_start = []; + CPD.startprob = []; +else + startargs{end+1} = 'CPT'; + startargs{end+1} = startprob; + CPD.sub_CPD_start = mk_isolated_tabular_CPD(Qps, ns([Qps self]), startargs); + S = struct(CPD.sub_CPD_start); + CPD.startprob = myreshape(S.CPT, [Qpsz Qsz]); +end + +CPD = class(CPD, 'hhmmQ_CPD', tabular_CPD(bnet, self)); + +CPD = update_CPT(CPD); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/log_prior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a hhmm CPD +% L = log_prior(CPD) + +L = log_prior(CPD.sub_CPD_trans); +if ~isempty(CPD.sub_CPD_start) + L = L + log_prior(CPD.sub_CPD_start); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmQ node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +Qsz = CPD.Qsizes(CPD.d); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = maximize_params(CPD.sub_CPD_start, temp); + S = struct(CPD.sub_CPD_start); + CPD.startprob = myreshape(S.CPT, [Qpsz Qsz]); + %CPD.startprob = S.CPT; +end + +if 1 + % If we are in a state that can only go the end state, + % we will never see a transition to another (non-end) state, + % so counts(i,k,j)=0 (and termprob(k,i)=1). + % We set counts(i,k,i)=1 in this case. + % This will cause remove_hhmm_end_state to return a + % stochastic matrix, but otherwise has no effect on EM. + counts = get_field(CPD.sub_CPD_trans, 'counts'); + counts = reshape(counts, [Qsz Qpsz Qsz]); + for k=1:Qpsz + for i=1:Qsz + if sum(counts(i,k,:))==0 % never witnessed a transition out of i + counts(i,k,i)=1; % add self loop + %fprintf('CPDQ d=%d i=%d k=%d\n', CPD.d, i, k); + end + end + end + CPD.sub_CPD_trans = set_fields(CPD.sub_CPD_trans, 'counts', counts(:)); +end + +CPD.sub_CPD_trans = maximize_params(CPD.sub_CPD_trans, temp); +S = struct(CPD.sub_CPD_trans); +%CPD.transprob = S.CPT; +CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); + +CPD = update_CPT(CPD);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm Q node. +% CPD = reset_ess(CPD) + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = reset_ess(CPD.sub_CPD_start); +end +CPD.sub_CPD_trans = reset_ess(CPD.sub_CPD_trans);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/update_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +function CPD = update_CPT(CPD) +% Compute the big CPT for an HHMM Q node (including F parents) given internal transprob and startprob +% function CPD = update_CPT(CPD) + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + % Fb(t-1) Fself(t-1) P(Q(t)=j| Q(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,k,j) + % 1 2 impossible + % 2 2 startprob(k,j) + CPT = zeros(Qsz, 2, 2, Qpsz, Qsz); + I = repmat(eye(Qsz), [1 1 Qpsz]); % i,j,k + I = permute(I, [1 3 2]); % i,k,j + CPT(:, 1, 1, :, :) = I; + CPT(:, 2, 1, :, :) = CPD.transprob; + CPT(:, 1, 2, :, :) = I; + CPT(:, 2, 2, :, :) = repmat(reshape(CPD.startprob, [1 Qpsz Qsz]), [Qsz 1 1]); % replicate over i + else % no F from self, hence no startprob + % Fb(t-1) P(Q(t)=j| Q(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 delta(i,j) + % 2 transprob(i,k,j) + + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + %CPT = zeros(Qsz, 2, Qpsz, Qsz); % assumes CPT(Q(t-1), F(t-1), Qps, Q(t)) + % but a member of Qps may preceed Q(t-1) or F(t-1) in the ordering + + I = repmat(eye(Qsz), [1 1 Qpsz]); % i,j,k + I = permute(I, [1 3 2]); % i,k,j + + % the following fails if there is a member of Qps with a lower + % number than F + %CPT(:, 1, :, :) = I; + %CPT(:, 2, :, :) = CPD.transprob; + + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 1); + CPT(ndx{:}) = I; + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 2); + CPT(ndx{:}) = CPD.transprob; + keyboard + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) + % Q(t-1), Fself(t-1), Qps, Q(t) + + % if condition start on previous concrete state (as in map learning), + % CPT(:, 1, :, :, :) = CPD.transprob(Q(t-1), Qps, Q(t)) + % CPT(:, 2, :, :, :) = CPD.startprob(Q(t-1), Qps, Q(t)) + + % Fself(t-1) P(Q(t-1)=i, Qps(t)=k -> Q(t)=j) + % ------------------------------------------------------ + % 1 transprob(i,k,j) + % 2 startprob(k,j) + CPT = zeros(Qsz, 2, Qpsz, Qsz); + I = repmat(eye(Qsz), [1 1 Qpsz]); % i,j,k + I = permute(I, [1 3 2]); % i,k,j + CPT(:, 1, :, :) = CPD.transprob; + if CPD.fullstartprob + CPT(:, 2, :, :) = CPD.startprob; + else + CPT(:, 2, :, :) = repmat(reshape(CPD.startprob, [1 Qpsz Qsz]), [Qsz 1 1]); % replicate over i + end + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + +CPD = set_fields(CPD, 'CPT', CPT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,141 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) + +% Figure out the node numbers associated with each parent +% e.g., D=4, d=3, Qps = all Qs above, so dom = [Q3(t-1) F4(t-1) F3(t-1) Q1(t) Q2(t) Q3(t)]. +% so self = Q3(t), old_self = Q3(t-1), CPD.Qps = [1 2], Qps = [Q1(t) Q2(t)] +dom = fmarginal.domain; +self = dom(end); +old_self = dom(1); +Qps = dom(length(dom)-length(CPD.Qps):end-1); + +Qsz = CPD.Qsizes(CPD.d); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); + +% If some of the Q nodes are observed (which happens during supervised training) +% the counts will only be non-zero in positions +% consistent with the evidence. We put the computed marginal responsibilities +% into the appropriate slots of the big counts array. +% (Recall that observed discrete nodes only have a single effective value.) +% (A more general, but much slower, way is to call add_evidence_to_dmarginal.) +% We assume the F nodes are never observed. + +obs_self = ~hidden_bitv(self); +obs_Qps = (~isempty(Qps)) & (~any(hidden_bitv(Qps))); % we assume that all or none of the Q parents are observed + +if obs_self + self_val = evidence{self}; + oldself_val = evidence{old_self}; +end + +if obs_Qps + Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps})); + if Qps_val == 0 + keyboard + end +end + +if CPD.d==1 % no Qps from above + if ~CPD.F1toQ1 % no F from self + % marg(Q1(t-1), F2(t-1), Q1(t)) + % F2(t-1) P(Q1(t)=j | Q1(t-1)=i) + % 1 delta(i,j) + % 2 transprob(i,j) + if obs_self + hor_counts = zeros(Qsz, Qsz); + hor_counts(oldself_val, self_val) = fmarginal.T(2); + else + marg = reshape(fmarginal.T, [Qsz 2 Qsz]); + hor_counts = squeeze(marg(:,2,:)); + end + else + % marg(Q1(t-1), F2(t-1), F1(t-1), Q1(t)) + % F2(t-1) F1(t-1) P(Qd(t)=j| Qd(t-1)=i) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,j) + % 1 2 impossible + % 2 2 startprob(j) + if obs_self + marg = myreshape(fmarginal.T, [1 2 2 1]); + hor_counts = zeros(Qsz, Qsz); + hor_counts(oldself_val, self_val) = marg(1,2,1,1); + ver_counts = zeros(Qsz, 1); + %ver_counts(self_val) = marg(1,2,2,1); + ver_counts(self_val) = marg(1,2,2,1) + marg(1,1,2,1); + else + marg = reshape(fmarginal.T, [Qsz 2 2 Qsz]); + hor_counts = squeeze(marg(:,2,1,:)); + %ver_counts = squeeze(sum(marg(:,2,2,:),1)); % sum over i + ver_counts = squeeze(sum(marg(:,2,2,:),1)) + squeeze(sum(marg(:,1,2,:),1)); % sum i,b + end + end % F1toQ1 +else % d ~= 1 + if CPD.d < CPD.D % general case + % marg(Qd(t-1), Fd+1(t-1), Fd(t-1), Qps(t), Qd(t)) + % Fd+1(t-1) Fd(t-1) P(Qd(t)=j| Qd(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,k,j) + % 1 2 impossible + % 2 2 startprob(k,j) + if obs_Qps & obs_self + marg = myreshape(fmarginal.T, [1 2 2 1 1]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(oldself_val, Qps_val, self_val) = marg(1, 2,1, k,1); + ver_counts = zeros(Qpsz, Qsz); + %ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1); + ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1) + marg(1, 1,2, k,1); + elseif obs_Qps & ~obs_self + marg = myreshape(fmarginal.T, [Qsz 2 2 1 Qsz]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(:, Qps_val, :) = marg(:, 2,1, k,:); + ver_counts = zeros(Qpsz, Qsz); + %ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1); + ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1) + sum(marg(:, 1,2, k,:), 1); + elseif ~obs_Qps & obs_self + error('not yet implemented') + else % everything is hidden + marg = reshape(fmarginal.T, [Qsz 2 2 Qpsz Qsz]); + hor_counts = squeeze(marg(:,2,1,:,:)); % i,k,j + %ver_counts = squeeze(sum(marg(:,2,2,:,:),1)); % sum over i + ver_counts = squeeze(sum(marg(:,2,2,:,:),1)) + squeeze(sum(marg(:,1,2,:,:),1)); % sum over i,b + end + else % d == D, so no F from below + % marg(QD(t-1), FD(t-1), Qps(t), QD(t)) + % FD(t-1) P(QD(t)=j | QD(t-1)=i, Qps(t)=k) + % 1 transprob(i,k,j) + % 2 startprob(k,j) + if obs_Qps & obs_self + marg = myreshape(fmarginal.T, [1 2 1 1]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(oldself_val, Qps_val, self_val) = marg(1, 1, k,1); + ver_counts = zeros(Qpsz, Qsz); + ver_counts(Qps_val, self_val) = marg(1, 2, k,1); + elseif obs_Qps & ~obs_self + marg = myreshape(fmarginal.T, [Qsz 2 1 Qsz]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(:, Qps_val, :) = marg(:, 1, k,:); + ver_counts = zeros(Qpsz, Qsz); + ver_counts(Qps_val, :) = sum(marg(:, 2, k, :), 1); + elseif ~obs_Qps & obs_self + error('not yet implemented') + else % everything is hidden + marg = reshape(fmarginal.T, [Qsz 2 Qpsz Qsz]); + hor_counts = squeeze(marg(:,1,:,:)); + ver_counts = squeeze(sum(marg(:,2,:,:),1)); % sum over i + end + end +end + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/update_ess2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,178 @@ +function CPD = update_ess2(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +self = dom(end); % by assumption +old_self = dom(CPD.old_self_ndx); +Fself = dom(CPD.Fself_ndx); +Fbelow = dom(CPD.Fbelow_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + + +fmarg = add_ev_to_dmarginal(fmarginal, evidence, ns); + + + +% hor_counts(old_self, Qps, self), +% fmarginal(old_self, Fbelow, Fself, Qps, self) +% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not +% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) +% Since any of i,j,k may be observed, we write +% hor_counts(counts_ndx{:}) = fmarginal(fmarg_ndx{:}) +% where e.g., counts_ndx = {1, ':', 2} if Qps is hidden but we observe old_self=1, self=2. +% To create this counts_ndx, we write counts_ndx = mk_multi_ndx(3, obs_dim, obs_val) +% where counts_obs_dim = [1 3], counts_obs_val = [1 2] specifies the values of dimensions 1 and 3. + +counts_obs_dim = []; +fmarg_obs_dim = []; +obs_val = []; +if hidden_bitv(self) + effQsz = Qsz; +else + effQsz = 1; + counts_obs_dim = [counts_obs_dim 3]; + fmarg_obs_dim = [fmarg_obs_dim 5]; + obs_val = [obs_val evidence{self}]; +end + +% e.g., D=4, d=3, Qps = all Qs above, so dom = [Q3(t-1) F4(t-1) F3(t-1) Q1(t) Q2(t) Q3(t)]. +% so self = Q3(t), old_self = Q3(t-1), CPD.Qps = [1 2], Qps = [Q1(t) Q2(t)] +dom = fmarginal.domain; +self = dom(end); +old_self = dom(1); +Qps = dom(length(dom)-length(CPD.Qps):end-1); + +Qsz = CPD.Qsizes(CPD.d); +Qpsz = prod(CPD.Qsizes(CPD.Qps)); + +% If some of the Q nodes are observed (which happens during supervised training) +% the counts will only be non-zero in positions +% consistent with the evidence. We put the computed marginal responsibilities +% into the appropriate slots of the big counts array. +% (Recall that observed discrete nodes only have a single effective value.) +% (A more general, but much slower, way is to call add_evidence_to_dmarginal.) +% We assume the F nodes are never observed. + +obs_self = ~hidden_bitv(self); +obs_Qps = (~isempty(Qps)) & (~any(hidden_bitv(Qps))); % we assume that all or none of the Q parents are observed + +if obs_self + self_val = evidence{self}; + oldself_val = evidence{old_self}; +end + +if obs_Qps + Qps_val = subv2ind(Qpsz, cat(1, evidence{Qps})); + if Qps_val == 0 + keyboard + end +end + +if CPD.d==1 % no Qps from above + if ~CPD.F1toQ1 % no F from self + % marg(Q1(t-1), F2(t-1), Q1(t)) + % F2(t-1) P(Q1(t)=j | Q1(t-1)=i) + % 1 delta(i,j) + % 2 transprob(i,j) + if obs_self + hor_counts = zeros(Qsz, Qsz); + hor_counts(oldself_val, self_val) = fmarginal.T(2); + else + marg = reshape(fmarginal.T, [Qsz 2 Qsz]); + hor_counts = squeeze(marg(:,2,:)); + end + else + % marg(Q1(t-1), F2(t-1), F1(t-1), Q1(t)) + % F2(t-1) F1(t-1) P(Qd(t)=j| Qd(t-1)=i) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,j) + % 1 2 impossible + % 2 2 startprob(j) + if obs_self + marg = myreshape(fmarginal.T, [1 2 2 1]); + hor_counts = zeros(Qsz, Qsz); + hor_counts(oldself_val, self_val) = marg(1,2,1,1); + ver_counts = zeros(Qsz, 1); + %ver_counts(self_val) = marg(1,2,2,1); + ver_counts(self_val) = marg(1,2,2,1) + marg(1,1,2,1); + else + marg = reshape(fmarginal.T, [Qsz 2 2 Qsz]); + hor_counts = squeeze(marg(:,2,1,:)); + %ver_counts = squeeze(sum(marg(:,2,2,:),1)); % sum over i + ver_counts = squeeze(sum(marg(:,2,2,:),1)) + squeeze(sum(marg(:,1,2,:),1)); % sum i,b + end + end % F1toQ1 +else % d ~= 1 + if CPD.d < CPD.D % general case + % marg(Qd(t-1), Fd+1(t-1), Fd(t-1), Qps(t), Qd(t)) + % Fd+1(t-1) Fd(t-1) P(Qd(t)=j| Qd(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,k,j) + % 1 2 impossible + % 2 2 startprob(k,j) + if obs_Qps & obs_self + marg = myreshape(fmarginal.T, [1 2 2 1 1]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(oldself_val, Qps_val, self_val) = marg(1, 2,1, k,1); + ver_counts = zeros(Qpsz, Qsz); + %ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1); + ver_counts(Qps_val, self_val) = marg(1, 2,2, k,1) + marg(1, 1,2, k,1); + elseif obs_Qps & ~obs_self + marg = myreshape(fmarginal.T, [Qsz 2 2 1 Qsz]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(:, Qps_val, :) = marg(:, 2,1, k,:); + ver_counts = zeros(Qpsz, Qsz); + %ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1); + ver_counts(Qps_val, :) = sum(marg(:, 2,2, k,:), 1) + sum(marg(:, 1,2, k,:), 1); + elseif ~obs_Qps & obs_self + error('not yet implemented') + else % everything is hidden + marg = reshape(fmarginal.T, [Qsz 2 2 Qpsz Qsz]); + hor_counts = squeeze(marg(:,2,1,:,:)); % i,k,j + %ver_counts = squeeze(sum(marg(:,2,2,:,:),1)); % sum over i + ver_counts = squeeze(sum(marg(:,2,2,:,:),1)) + squeeze(sum(marg(:,1,2,:,:),1)); % sum over i,b + end + else % d == D, so no F from below + % marg(QD(t-1), FD(t-1), Qps(t), QD(t)) + % FD(t-1) P(QD(t)=j | QD(t-1)=i, Qps(t)=k) + % 1 transprob(i,k,j) + % 2 startprob(k,j) + if obs_Qps & obs_self + marg = myreshape(fmarginal.T, [1 2 1 1]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(oldself_val, Qps_val, self_val) = marg(1, 1, k,1); + ver_counts = zeros(Qpsz, Qsz); + ver_counts(Qps_val, self_val) = marg(1, 2, k,1); + elseif obs_Qps & ~obs_self + marg = myreshape(fmarginal.T, [Qsz 2 1 Qsz]); + k = 1; + hor_counts = zeros(Qsz, Qpsz, Qsz); + hor_counts(:, Qps_val, :) = marg(:, 1, k,:); + ver_counts = zeros(Qpsz, Qsz); + ver_counts(Qps_val, :) = sum(marg(:, 2, k, :), 1); + elseif ~obs_Qps & obs_self + error('not yet implemented') + else % everything is hidden + marg = reshape(fmarginal.T, [Qsz 2 Qpsz Qsz]); + hor_counts = squeeze(marg(:,1,:,:)); + ver_counts = squeeze(sum(marg(:,2,:,:),1)); % sum over i + end + end +end + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/update_ess3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) +% +% we assume if one of the Qps is observed, all of them are +% We assume the F nodes are already hidden + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +self = dom(CPD.self_ndx); +old_self = dom(CPD.old_self_ndx); +%Fself = dom(CPD.Fself_ndx); +%Fbelow = dom(CPD.Fbelow_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + + +% hor_counts(old_self, Qps, self), +% fmarginal(old_self, Fbelow, Fself, Qps, self) +% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not +% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) +% Since any of i,j,k may be observed, we write +% hor_counts(ndx{:}) = fmarginal(...) +% where e.g., ndx = {1, ':', 2} if Qps is hidden but we observe old_self=1, self=2. + +% ndx{i,k,j} +if hidden_bitv(old_self) + ndx{1} = ':'; +else + ndx{1} = evidence{old_self}; +end +if hidden_bitv(Qps) + ndx{2} = ':'; +else + ndx{2} = subv2ind(Qpsz, cat(1, evidence{Qps})); +end +if hidden_bitv(self) + ndx{3} = ':'; +else + ndx{3} = evidence{self}; +end + +fmarg = add_ev_to_dmarginal(fmarginal, evidence, ns); +% marg(Qold(t-1), Fbelow(t-1), Fself(t-1), Qps(t), Qself(t)) +hor_counts = zeros(Qsz, Qpsz, Qsz); +ver_counts = zeros(Qpsz, Qsz); + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + fmarg.T = myreshape(fmarg.T, [Qsz 2 2 Qpsz Qsz]); + marg_ndx = {ndx{1}, 2, 1, ndx{2}, ndx{3}}; + hor_counts(ndx{:}) = fmarg.T(marg_ndx{:}); + ver_counts(ndx{2:3}) = ... % sum over Fbelow and Qold=i + sum(fmarg.T({ndx{1}, 1, 2, ndx{2}, ndx{3}}),1) + .. + sum(fmarg.T({ndx{1}, 2, 2, ndx{2}, ndx{3}}),1); + else % no F from self, hence no startprob + fmarg.T = myreshape(fmarg.T, [Qsz 2 Qpsz Qsz]); + hor_counts(ndx{:}) = fmarg.T({ndx{1}, 2, ndx{2}, ndx{3}}); + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % self F + fmarg.T = myreshape(fmarg.T, [Qsz 2 Qpsz Qsz]); + hor_counts(ndx{:}) = fmarg.T({ndx{1}, 1, ndx{2}, ndx{3}}); + ver_counts(ndx{2:3}) = ... % sum over Qold=i + sum(fmarg.T({ndx{1}, 2, ndx{2}, ndx{3}}),1); + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/Old/update_ess4.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) +% +% we assume if one of the Qps is observed, all of them are +% We assume the F nodes are already hidden + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +self = dom(CPD.self_ndx); +old_self = dom(CPD.old_self_ndx); +%Fself = dom(CPD.Fself_ndx); +%Fbelow = dom(CPD.Fbelow_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + + +% hor_counts(old_self, Qps, self), +% fmarginal(old_self, Fbelow, Fself, Qps, self) +% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not +% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) +% Since any of i,j,k may be observed, we write +% hor_counts(i_counts_ndx, kndx, jndx) = fmarginal(i_fmarg_ndx...) +% where i_fmarg_ndx = 1 and i_counts_ndx = i if old_self is observed to have value i, +% i_fmarg_ndx = 1:Qsz and i_counts_ndx = 1:Qsz if old_self is hidden, etc. + + +if hidden_bitv(old_self) + i_counts_ndx = 1:Qsz; + i_fmarg_ndx = 1:Qsz; + eff_oldQsz = Qsz; +else + i_counts_ndx = evidence{old_self}; + i_fmarg_ndx = 1; + eff_oldQsz = 1; +end + +if all(hidden_bitv(Qps)) % we assume all are hidden or all are observed + k_counts_ndx = 1:Qpsz; + k_fmarg_ndx = 1:Qpsz; + eff_Qpsz = Qpsz; +else + k_counts_ndx = subv2ind(Qpsz, cat(1, evidence{Qps})); + k_fmarg_ndx = 1; + eff_Qpsz = 1; +end + +if hidden_bitv(self) + j_counts_ndx = 1:Qsz; + j_fmarg_ndx = 1:Qsz; + eff_Qsz = Qsz; +else + j_counts_ndx = evidence{self}; + j_fmarg_ndx = 1; + eff_Qsz = 1; +end + +hor_counts = zeros(Qsz, Qpsz, Qsz); +ver_counts = zeros(Qpsz, Qsz); + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + fmarg.T = myreshape(fmarg.T, [eff_oldQsz 2 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ... + fmarg.T(:, i_fmarg_ndx, 2, 1, k_fmarg_ndx, j_fmarg_ndx); + ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Fbelow and Qold + sum(fmarg.T(:, 1, 2, k_fmarg_ndx, j_fmarg_ndx), 1) + ... + sum(fmarg.T(:, 2, 2, k_fmarg_ndx, j_fmarg_ndx), 1); + else % no F from self, hence no startprob + fmarg.T = myreshape(fmarg.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ... + fmarg.T(i_fmarg_ndx, 2, k_fmarg_ndx, j_fmarg_ndx); + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % self F + fmarg.T = myreshape(fmarg.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ... + fmarg.T(i_fmarg_ndx, 1, k_fmarg_ndx, j_fmarg_ndx); + ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Qold + sum(fmarg.T(:, 2, k_fmarg_ndx, j_fmarg_ndx), 1); + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,132 @@ +function CPD = hhmmQ_CPD(bnet, self, varargin) +% HHMMQ_CPD Make the CPD for a Q node in a hierarchical HMM +% CPD = hhmmQ_CPD(bnet, self, ...) +% +% Fself(t-1) Qps(t) +% \ | +% \ v +% Qold(t-1) -> Q(t) +% / +% / +% Fbelow(t-1) +% +% Let ss = slice size = num. nodes per slice. +% This node is Q(t), and has mandatory parents Qold(t-1) (assumed to be numbered Q(t)-ss) +% and optional parents Fbelow, Fself, Qps. +% We require parents to be ordered (numbered) as follows: +% Qold, Fbelow, Fself, Qps, Q. +% +% If Fself=2, we use the transition matrix, else we use the prior matrix. +% If Fself node is omitted (eg. top level), we always use the transition matrix. +% If Fbelow=2, we may change state, otherwise we must stay in the same state. +% If Fbelow node is omitted (eg., bottom level), we may change state at every step. +% If Qps (Q parents) are specified, all parameters are conditioned on their joint value. +% We may choose any subset of nodes to condition on, as long as they as numbered lower than self. +% +% optional args [defaults] +% +% Fself - node number <= ss +% Fbelow - node number <= ss +% Qps - node numbers (all <= 2*ss) - uses 2TBN indexing +% transprob - transprob(i,k,j) = prob transition from i to j given Qps = k ['leftright'] +% selfprob - prob of a transition from i to i given Qps=k [0.1] +% startprob - startprob(k,j) = prob start in j given Qps = k ['leftstart'] +% startargs - other args to be passed to the sub tabular_CPD for learning startprob +% transargs - other args will be passed to the sub tabular_CPD for learning transprob +% fullstartprob - 1 means startprob depends on Q(t-1) [0] +% hhmmQ_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc. +% +% We create isolated tabular_CPDs with no F parents to learn transprob/startprob +% so we can avail of e.g., entropic or Dirichlet priors. +% In the future, we will be able to represent the transprob using a tree_CPD. +% +% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01. + + +ss = bnet.nnodes_per_slice; +ns = bnet.node_sizes(:); + +% set default arguments +Fself = []; +Fbelow = []; +Qps = []; +startprob = 'leftstart'; +transprob = 'leftright'; +startargs = {}; +transargs = {}; +selfprob = 0.1; +fullstartprob = 0; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'Fself', Fself = varargin{i+1}; + case 'Fbelow', Fbelow = varargin{i+1}; + case 'Qps', Qps = varargin{i+1}; + case 'transprob', transprob = varargin{i+1}; + case 'selfprob', selfprob = varargin{i+1}; + case 'startprob', startprob = varargin{i+1}; + case 'startargs', startargs = varargin{i+1}; + case 'transargs', transargs = varargin{i+1}; + case 'fullstartprob', fullstartprob = varargin{i+1}; + end +end + +CPD.fullstartprob = fullstartprob; + +ps = parents(bnet.dag, self); +ndsz = ns(:)'; +CPD.dom_sz = [ndsz(ps) ns(self)]; +CPD.Fself_ndx = find_equiv_posns(Fself, ps); +CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps); +%CPD.Qps_ndx = find_equiv_posns(Qps+ss, ps); +CPD.Qps_ndx = find_equiv_posns(Qps, ps); +old_self = self-ss; +CPD.old_self_ndx = find_equiv_posns(old_self, ps); + +Qps = ps(CPD.Qps_ndx); +CPD.Qsz = ns(self); +CPD.Qpsz = prod(ns(Qps)); +CPD.Qpsizes = ns(Qps); +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if strcmp(transprob, 'leftright') + LR = mk_leftright_transmat(Qsz, selfprob); + transprob = repmat(reshape(LR, [1 Qsz Qsz]), [Qpsz 1 1]); % transprob(k,i,j) + transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j) +end +transargs{end+1} = 'CPT'; +transargs{end+1} = transprob; +CPD.sub_CPD_trans = mk_isolated_tabular_CPD(ns([old_self Qps self]), transargs); +S = struct(CPD.sub_CPD_trans); +%CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); +CPD.transprob = S.CPT; + + +if strcmp(startprob, 'leftstart') + startprob = zeros(Qpsz, Qsz); + startprob(:,1) = 1; +end +if isempty(CPD.Fself_ndx) + CPD.sub_CPD_start = []; + CPD.startprob = []; +else + startargs{end+1} = 'CPT'; + startargs{end+1} = startprob; + if CPD.fullstartprob + CPD.sub_CPD_start = mk_isolated_tabular_CPD(ns([self Qps self]), startargs); + S = struct(CPD.sub_CPD_start); + %CPD.startprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); + CPD.startprob = S.CPT; + else + CPD.sub_CPD_start = mk_isolated_tabular_CPD(ns([Qps self]), startargs); + S = struct(CPD.sub_CPD_start); + %CPD.startprob = myreshape(S.CPT, [CPD.Qpsizes Qsz]); + CPD.startprob = S.CPT; + end +end + +CPD = class(CPD, 'hhmmQ_CPD', tabular_CPD(bnet, self)); + +CPD = update_CPT(CPD); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/log_prior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a hhmm CPD +% L = log_prior(CPD) + +L = log_prior(CPD.sub_CPD_trans); +if ~isempty(CPD.sub_CPD_start) + L = L + log_prior(CPD.sub_CPD_start); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a hhmmQ node to their ML/MAP values. +% CPD = maximize_params(CPD, temperature) + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = maximize_params(CPD.sub_CPD_start, temp); + S = struct(CPD.sub_CPD_start); + CPD.startprob = myreshape(S.CPT, [Qpsz Qsz]); + %CPD.startprob = S.CPT; +end + +if 1 + % If we are in a state that can only go the end state, + % we will never see a transition to another (non-end) state, + % so counts(i,k,j)=0 (and termprob(k,i)=1). + % We set counts(i,k,i)=1 in this case. + % This will cause remove_hhmm_end_state to return a + % stochastic matrix, but otherwise has no effect on EM. + counts = get_field(CPD.sub_CPD_trans, 'counts'); + counts = reshape(counts, [Qsz Qpsz Qsz]); + for k=1:Qpsz + for i=1:Qsz + if sum(counts(i,k,:))==0 % never witnessed a transition out of i + counts(i,k,i)=1; % add self loop + %fprintf('CPDQ d=%d i=%d k=%d\n', CPD.d, i, k); + end + end + end + CPD.sub_CPD_trans = set_fields(CPD.sub_CPD_trans, 'counts', counts(:)); +end + +CPD.sub_CPD_trans = maximize_params(CPD.sub_CPD_trans, temp); +S = struct(CPD.sub_CPD_trans); +%CPD.transprob = S.CPT; +CPD.transprob = myreshape(S.CPT, [Qsz Qpsz Qsz]); + +CPD = update_CPT(CPD);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a hhmm Q node. +% CPD = reset_ess(CPD) + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = reset_ess(CPD.sub_CPD_start); +end +CPD.sub_CPD_trans = reset_ess(CPD.sub_CPD_trans);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/update_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +function CPD = update_CPT(CPD) +% Compute the big CPT for an HHMM Q node (including F parents) given internal transprob and startprob +% function CPD = update_CPT(CPD) + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + % Fb(t-1) Fself(t-1) P(Q(t)=j| Q(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 1 delta(i,j) + % 2 1 transprob(i,k,j) + % 1 2 impossible + % 2 2 startprob(k,j) + CPT = zeros(Qsz, 2, 2, Qpsz, Qsz); + I = repmat(eye(Qsz), [1 1 Qpsz]); % i,j,k + I = permute(I, [1 3 2]); % i,k,j + CPT(:, 1, 1, :, :) = I; + CPT(:, 2, 1, :, :) = CPD.transprob; + CPT(:, 1, 2, :, :) = I; + CPT(:, 2, 2, :, :) = repmat(reshape(CPD.startprob, [1 Qpsz Qsz]), ... + [Qsz 1 1]); % replicate over i + else % no F from self, hence no startprob + % Fb(t-1) P(Q(t)=j| Q(t-1)=i, Qps(t)=k) + % ------------------------------------------------------ + % 1 delta(i,j) + % 2 transprob(i,k,j) + + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + %CPT = zeros(Qsz, 2, Qpsz, Qsz); % assumes CPT(Q(t-1), F(t-1), Qps, Q(t)) + % but a member of Qps may preceed Q(t-1) or F(t-1) in the ordering + + for k=1:CPD.Qpsz + Qps_vals = ind2subv(CPD.Qpsizes, k); + ndx = mk_multi_index(nps+1, [CPD.Fbelow_ndx CPD.Qps_ndx], [1 Qps_vals]); + CPT(ndx{:}) = eye(Qsz); % CPT(:,2,k,:) or CPT(:,k,2,:) etc + end + ndx = mk_multi_index(nps+1, CPD.Fbelow_ndx, 2); + CPT(ndx{:}) = CPD.transprob; % we assume transprob is in topo order + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) + % Q(t-1), Fself(t-1), Qps, Q(t) + + % Fself(t-1) P(Q(t-1)=i, Qps(t)=k -> Q(t)=j) + % ------------------------------------------------------ + % 1 transprob(i,k,j) + % 2 startprob(k,j) + + nps = length(CPD.dom_sz)-1; % num parents + CPT = 0*myones(CPD.dom_sz); + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 1); + CPT(ndx{:}) = CPD.transprob; + if CPD.fullstartprob + ndx = mk_multi_index(nps+1, CPD.Fself_ndx, 2); + CPT(ndx{:}) = CPD.startprob; + else + for i=1:CPD.Qsz + ndx = mk_multi_index(nps+1, [CPD.Fself_ndx CPD.old_self_ndx], [2 i]); + CPT(ndx{:}) = CPD.startprob; + end + end + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + +CPD = set_fields(CPD, 'CPT', CPT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@hhmmQ_CPD/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a hhmm Q node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, idden_bitv) +% +% we assume if one of the Qps is observed, all of them are +% We assume the F nodes are already hidden + +% Figure out the node numbers associated with each parent +dom = fmarginal.domain; +self = dom(end); +old_self = dom(CPD.old_self_ndx); +%Fself = dom(CPD.Fself_ndx); +%Fbelow = dom(CPD.Fbelow_ndx); +Qps = dom(CPD.Qps_ndx); + +Qsz = CPD.Qsz; +Qpsz = CPD.Qpsz; + + +% hor_counts(old_self, Qps, self), +% fmarginal(old_self, Fbelow, Fself, Qps, self) +% hor_counts(i,k,j) = fmarginal(i,2,1,k,j) % below has finished, self has not +% ver_counts(i,k,j) = fmarginal(i,2,2,k,j) % below has finished, and so has self (reset) +% Since any of i,j,k may be observed, we write +% hor_counts(i_counts_ndx, kndx, jndx) = fmarginal(i_fmarg_ndx...) +% where i_fmarg_ndx = 1 and i_counts_ndx = i if old_self is observed to have value i, +% i_fmarg_ndx = 1:Qsz and i_counts_ndx = 1:Qsz if old_self is hidden, etc. + + +if hidden_bitv(old_self) + i_counts_ndx = 1:Qsz; + eff_oldQsz = Qsz; +else + i_counts_ndx = evidence{old_self}; + eff_oldQsz = 1; +end + +if all(hidden_bitv(Qps)) % we assume all are hidden or all are observed + k_counts_ndx = 1:Qpsz; + eff_Qpsz = Qpsz; +else + k_counts_ndx = subv2ind(Qpsz, cat(1, evidence{Qps})); + eff_Qpsz = 1; +end + +if hidden_bitv(self) + j_counts_ndx = 1:Qsz; + eff_Qsz = Qsz; +else + j_counts_ndx = evidence{self}; + eff_Qsz = 1; +end + +hor_counts = zeros(Qsz, Qpsz, Qsz); +ver_counts = zeros(Qpsz, Qsz); + +if ~isempty(CPD.Fbelow_ndx) + if ~isempty(CPD.Fself_ndx) % general case + fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = fmarg(:, 2, 1, :, :); + ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Fbelow and Qold + sumv(fmarg(:, :, 2, :, :), [1 2]); % require Fself=2 + else % no F from self, hence no startprob + fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = ... + fmarg(:, 2, :, :); % require Fbelow = 2 + end +else % no F signal from below + if ~isempty(CPD.Fself_ndx) % self F + fmarg = myreshape(fmarginal.T, [eff_oldQsz 2 eff_Qpsz eff_Qsz]); + hor_counts(i_counts_ndx, k_counts_ndx, j_counts_ndx) = fmarg(:, 1, :, :); + ver_counts(k_counts_ndx, j_counts_ndx) = ... % sum over Qold + squeeze(sum(fmarg(:, 2, :, :), 1)); % Fself=2 + else % no F from self + error('An hhmmQ node without any F parents is just a tabular_CPD') + end +end + + +CPD.sub_CPD_trans = update_ess_simple(CPD.sub_CPD_trans, hor_counts); + +if ~isempty(CPD.sub_CPD_start) + CPD.sub_CPD_start = update_ess_simple(CPD.sub_CPD_start, ver_counts); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/convert_to_table.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mlp_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@mlp_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/convert_to_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a mlp CPD to a table, incorporating any evidence +% T = convert_to_table(CPD, domain, evidence) + +self = domain(end); +ps = domain(1:end-1); % self' parents +%cps = myintersect(ps, cnodes); % self' continous parents +cnodes = domain(CPD.cpndx); +cps = myintersect(ps, cnodes); +odom = domain(~isemptycell(evidence(domain))); % obs nodes in the net +assert(myismember(cps, odom)); % !ALL the CTS parents must be observed! +ns(cps)=1; +dps = mysetdiff(ps, cps); % self' discrete parents +dobs = myintersect(dps, odom); % discrete obs parents + +% Extract the params compatible with the observations (if any) on the discrete parents (if any) + +if ~isempty(dobs), + dvals = cat(1, evidence{dobs}); + ns_eff= CPD.sizes; % effective node sizes + ens=ns_eff; + ens(dobs) = 1; + S=prod(ens(dps)); + subs = ind2subv(ens(dps), 1:S); + mask = find_equiv_posns(dobs, dps); + for i=1:length(mask), + subs(:,mask(i)) = dvals(i); + end + support = subv2ind(ns_eff(dps), subs)'; +else + ns_eff= CPD.sizes; + support=[1:prod(ns_eff(dps))]; +end + +W1=[]; b1=[]; W2=[]; b2=[]; + +W1 = CPD.W1(:,:,support); +b1= CPD.b1(support,:); +W2 = CPD.W2(:,:,support); +b2= CPD.b2(support,:); +ns(odom) = 1; +dpsize = prod(ns(dps)); % overall size of the self' discrete parents + +x = cat(1, evidence{cps}); +ndata=size(x,2); + +if ~isempty(evidence{self}) % + app=struct(CPD); % + ns(self)=app.mlp{1}.nout; % pump up self to the original dimension if observed + clear app; % +end % + +T =zeros(dpsize, ns(self)); % +for i=1:dpsize % + W1app = W1(:,:,i); % + b1app = b1(i,:); % + W2app = W2(:,:,i); % + b2app = b2(i,:); % for each of the dpsize combinations of self'parents values + z = tanh(x(:)'*W1app + ones(ndata, 1)*b1app); % we tabulate the corrisponding glm model + a = z*W2app + ones(ndata, 1)*b2app; % (element of the cell array CPD.glim) + appoggio = normalise(exp(a)); % + T(i,:)=appoggio; % + W1app=[]; W2app=[]; b1app=[]; b2app=[]; % + z=[]; a=[]; appoggio=[]; % +end % + +if ~isempty(evidence{self}) + appoggio=[]; % + appoggio=zeros(1,ns(self)); % + r = evidence{self}; %...if self is observed => in output there's only the probability of the 'true' class + for i=1:dpsize % + appoggio(i)=T(i,r); % + end + T=zeros(dpsize,1); + for i=1:dpsize + T(i,1)=appoggio(i); + end + clear appoggio; + ns(self) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Find ML params of an MLP using Scaled Conjugated Gradient (SCG) +% CPD = maximize_params(CPD, temperature) +% temperature parameter is ignored + +if ~adjustable_CPD(CPD), return; end +options = foptions; + +% options(1) >= 0 means print an annoying message when the max. num. iter. is reached +if CPD.verbose + options(1) = 1; +else + options(1) = -1; +end +%options(1) = CPD.verbose; + +options(2) = CPD.wthresh; +options(3) = CPD.llthresh; +options(14) = CPD.max_iter; + +dpsz=length(CPD.mlp); + +for i=1:dpsz + mask=[]; + mask=find(CPD.eso_weights(:,:,i)>0); % for adapting the parameters we use only positive weighted example + if ~isempty(mask), + CPD.mlp{i} = netopt_weighted(CPD.mlp{i}, options, CPD.parent_vals(mask',:), CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), 'scg'); + + CPD.W1(:,:,i)=CPD.mlp{i}.w1; % update the parameters matrix + CPD.b1(i,:)=CPD.mlp{i}.b1; % + CPD.W2(:,:,i)=CPD.mlp{i}.w2; % update the parameters matrix + CPD.b2(i,:)=CPD.mlp{i}.b2; % + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/mlp_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,139 @@ +function CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped, max_iter, verbose, wthresh, llthresh) +% MLP_CPD Make a CPD from a Multi Layer Perceptron (i.e., feedforward neural network) +% +% We use a different MLP for each discrete parent combination (if there are any discrete parents). +% We currently assume this node (the child) is discrete. +% +% CPD = mlp_CPD(bnet, self, nhidden) +% will create a CPD with random parameters, where self is the number of this node and nhidden the number of the hidden nodes. +% The params are drawn from N(0, s*I), where s = 1/sqrt(n+1), n = length(X). +% +% CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2) allows you to specify the params, where +% w1 = first-layer weight matrix +% b1 = first-layer bias vector +% w2 = second-layer weight matrix +% b2 = second-layer bias vector +% These are assumed to be the same for each discrete parent combination. +% If any of these are [], random values will be created. +% +% CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped) allows you to prevent the params from being +% updated during learning (if clamped = 1). Default: clamped = 0. +% +% CPD = mlp_CPD(bnet, self, nhidden, w1, b1, w2, b2, clamped, max_iter, verbose, wthresh, llthresh) +% alllows you to specify params that control the M step: +% max_iter - the maximum number of steps to take (default: 10) +% verbose - controls whether to print (default: 0 means silent). +% wthresh - a measure of the precision required for the value of +% the weights W at the solution. Default: 1e-2. +% llthresh - a measure of the precision required of the objective +% function (log-likelihood) at the solution. Both this and the previous condition must +% be satisfied for termination. Default: 1e-2. +% +% For learning, we use a weighted version of scaled conjugated gradient in the M step. + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'mlp_CPD', discrete_CPD(0,[])); + return; +elseif isa(bnet, 'mlp_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +assert(myismember(self, bnet.dnodes)); +ns = bnet.node_sizes; + +ps = parents(bnet.dag, self); +dnodes = mysetdiff(1:length(bnet.dag), bnet.cnodes); +dps = myintersect(ps, dnodes); +cps = myintersect(ps, bnet.cnodes); +dpsz = prod(ns(dps)); +cpsz = sum(ns(cps)); +self_size = ns(self); + +% discrete/cts parent index - which ones of my parents are discrete/cts? +CPD.dpndx = find_equiv_posns(dps, ps); +CPD.cpndx = find_equiv_posns(cps, ps); + +CPD.mlp = cell(1,dpsz); +for i=1:dpsz + CPD.mlp{i} = mlp(cpsz, nhidden, self_size, 'softmax'); + if nargin >=4 & ~isempty(w1) + CPD.mlp{i}.w1 = w1; + end + if nargin >=5 & ~isempty(b1) + CPD.mlp{i}.b1 = b1; + end + if nargin >=6 & ~isempty(w2) + CPD.mlp{i}.w2 = w2; + end + if nargin >=7 & ~isempty(b2) + CPD.mlp{i}.b2 = b2; + end + W1app(:,:,i)=CPD.mlp{i}.w1; + W2app(:,:,i)=CPD.mlp{i}.w2; + b1app(i,:)=CPD.mlp{i}.b1; + b2app(i,:)=CPD.mlp{i}.b2; +end +if nargin < 8, clamped = 0; end +if nargin < 9, max_iter = 10; end +if nargin < 10, verbose = 0; end +if nargin < 11, wthresh = 1e-2; end +if nargin < 12, llthresh = 1e-2; end + +CPD.self = self; +CPD.max_iter = max_iter; +CPD.verbose = verbose; +CPD.wthresh = wthresh; +CPD.llthresh = llthresh; + +% sufficient statistics +% Since MLP is not in the exponential family, we must store all the raw data. +% +CPD.W1=W1app; % Extract all the parameters of the node for handling discrete obs parents +CPD.W2=W2app; % +nparaW=[size(W1app) size(W2app)]; % +CPD.b1=b1app; % +CPD.b2=b2app; % +nparab=[size(b1app) size(b2app)]; % + +CPD.sizes=bnet.node_sizes(:); % used in CPD_to_table to pump up the node sizes + +CPD.parent_vals = []; % X(l,:) = value of cts parents in l'th example + +CPD.eso_weights=[]; % weights used by the SCG algorithm + +CPD.self_vals = []; % Y(l,:) = value of self in l'th example + +% For BIC +CPD.nsamples = 0; +CPD.nparams=prod(nparaW)+prod(nparab); +CPD = class(CPD, 'mlp_CPD', discrete_CPD(clamped, ns([ps self]))); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.mlp = {}; +CPD.self = []; +CPD.max_iter = []; +CPD.verbose = []; +CPD.wthresh = []; +CPD.llthresh = []; +CPD.approx_hess = []; +CPD.W1 = []; +CPD.W2 = []; +CPD.b1 = []; +CPD.b2 = []; +CPD.sizes = []; +CPD.parent_vals = []; +CPD.eso_weights=[]; +CPD.self_vals = []; +CPD.nsamples = []; +CPD.nparams = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics for a CPD (mlp) +% CPD = reset_ess(CPD) + +CPD.W1 = []; +CPD.W2 = []; +CPD.b1 = []; +CPD.b2 = []; +CPD.parent_vals = []; +CPD.eso_weights=[]; +CPD.self_vals = []; +CPD.nsamples = 0; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,131 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a CPD (MLP) +% CPD = update_ess(CPD, family_marginal, evidence, node_sizes, cnodes, hidden_bitv) +% +% fmarginal = overall posterior distribution of self and its parents +% fmarginal(i1,i2...,ik,s)=prob(Pa1=i1,...,Pak=ik, self=s| X) +% +% => 1) prob(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) with prob(Pa1,...,Pak)=sum{s,fmarginal} +% [self estimation -> CPD.self_vals] +% 2) prob(Pa1,...,Pak) [SCG weights -> CPD.eso_weights] +% +% Hidden_bitv is ignored + +% Written by Pierpaolo Brutti + +if ~adjustable_CPD(CPD), return; end + +dom = fmarginal.domain; +cdom = myintersect(dom, cnodes); +assert(~any(isemptycell(evidence(cdom)))); +ns(cdom)=1; + +self = dom(end); +ps=dom(1:end-1); +dpdom=mysetdiff(ps,cdom); + +dnodes = mysetdiff(1:length(ns), cnodes); + +ddom = myintersect(ps, dnodes); % +if isempty(evidence{self}), % if self is hidden in what follow we must + ddom = myintersect(dom, dnodes); % consider its dimension +end % + +odom = dom(~isemptycell(evidence(dom))); +hdom = dom(isemptycell(evidence(dom))); % hidden parents in domain + +dobs = myintersect(ddom, odom); +dvals = cat(1, evidence{dobs}); +ens = ns; % effective node sizes +ens(dobs) = 1; + +dpsz=prod(ns(dpdom)); +S=prod(ens(ddom)); +subs = ind2subv(ens(ddom), 1:S); +mask = find_equiv_posns(dobs, ddom); +for i=1:length(mask), + subs(:,mask(i)) = dvals(i); +end +supportedQs = subv2ind(ns(ddom), subs); + +Qarity = prod(ns(ddom)); +if isempty(ddom), + Qarity = 1; +end +fullm.T = zeros(Qarity, 1); +fullm.T(supportedQs) = fmarginal.T(:); + +% For dynamic (recurrent) net------------------------------------------------------------- +% ---------------------------------------------------------------------------------------- +high=size(evidence,1); % slice height +ss_ns=ns(1:high); % single slice nodes sizes +pos=self; % +slice_num=0; % +while pos>high, % + slice_num=slice_num+1; % find active slice + pos=pos-high; % pos=self posistion into a single slice +end % + +last_dim=pos-1; % +if isempty(evidence{self}), % + last_dim=pos; % +end % last_dim=last reshaping dimension +reg=dom-slice_num*high; +dex=myintersect(reg(find(reg>=0)), [1:last_dim]); % +rs_dim=ss_ns(dex); % reshaping dimensions + +if slice_num>0, + act_slice=[]; past_ancest=[]; % + act_slice=slice_num*high+[1:high]; % recover the active slice nodes + % past_ancest=mysetdiff(ddom, act_slice); + past_ancest=mysetdiff(ps, act_slice); % recover ancestors contained into past slices + app=ns(past_ancest); + rs_dim=[app(:)' rs_dim(:)']; % +end % +if length(rs_dim)==1, rs_dim=[1 rs_dim]; end % +if size(rs_dim,1)~=1, rs_dim=rs_dim'; end % + +fullm.T=reshape(fullm.T, rs_dim); % reshaping the marginal + +% ---------------------------------------------------------------------------------------- +% ---------------------------------------------------------------------------------------- + +% X = cts parent, R = discrete self + +% 1) observations vector -> CPD.parents_vals ------------------------------------------------- +x = cat(1, evidence{cdom}); + +% 2) weights vector -> CPD.eso_weights ------------------------------------------------------- +if isempty(evidence{self}) % R is hidden + sum_over=length(rs_dim); + app=sum(fullm.T, sum_over); + pesi=reshape(app,[dpsz,1]); + clear app; +else + pesi=reshape(fullm.T,[dpsz,1]); +end + +assert(approxeq(sum(pesi),1)); + +% 3) estimate (if R is hidden) or recover (if R is obs) self'value---------------------------- +if isempty(evidence{self}) % R is hidden + app=mk_stochastic(fullm.T); % P(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) + app=reshape(app,[dpsz ns(self)]); % matrix size: prod{j,ns(Paj)} x ns(self) + r=app; + clear app; +else + r = zeros(dpsz,ns(self)); + for i=1:dpsz + if pesi(i)~=0, r(i,evidence{self}) = 1; end + end +end +for i=1:dpsz + if pesi(i) ~=0, assert(approxeq(sum(r(i,:)),1)); end +end + +CPD.nsamples = CPD.nsamples + 1; +CPD.parent_vals(CPD.nsamples,:) = x(:)'; +for i=1:dpsz + CPD.eso_weights(CPD.nsamples,:,i)=pesi(i); + CPD.self_vals(CPD.nsamples,:,i) = r(i,:); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/CPD_to_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the discrete CPD to tabular form (noisyor) +% CPT = CPD_to_CPT(CPD) +% +% CPT(U1,...,Un, X) = Pr(X|U1,...,Un) where the Us are the parents (excluding leak). + +if ~isempty(CPD.CPT) + CPT = CPD.CPT; % remember to flush cache if params change (e.g., during learning) + return; +end + +q = [CPD.leak_inhibit CPD.inhibit(:)']; +% q(i) is the prob. that the i'th parent will be inhibited (flipped from 1 to 0). +% q(1) is the leak inhibition probability, and length(q) = n + 1. + +if length(q)==1 + CPT = [q 1-q]; + return; +end + +n = length(q); +Bn = ind2subv(2*ones(1,n), 1:(2^n))-1; % all n bit vectors, with the left most column toggling fastest (LSB) +CPT = zeros(2^n, 2); +% Pr(X=0 | U_1 .. U_n) = prod_{i: U_i = on} q_i = prod_i q_i ^ U_i = exp(u' * log(q_i)) +% This method is problematic when q contains zeros + +Q = repmat(q(:)', 2^n, 1); +Q(logical(~Bn)) = 1; +CPT(:,1) = prod(Q,2); +CPT(:,2) = 1-CPT(:,1); + +CPT = reshape(CPT(2:2:end), 2*ones(1,n)); % skip cases in which the leak is off + +CPD.CPT = CPT;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/CPD_to_lambda_msg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence) +% CPD_TO_LAMBDA_MSG Compute lambda message (noisyor) +% lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p) +% Pearl p190 top eqn + +switch msg_type + case 'd', + l0 = msg{n}.lambda(1); + l1 = msg{n}.lambda(2); + Pi = sum_prod_CPD_and_pi_msgs(CPD, n, ps, msg, p); + i = find(p==ps); % p is n's i'th parent + q = CPD.inhibit(i); + lam_msg = zeros(2,1); + for u=0:1 + lam_msg(u+1) = l1 - (q^u)*(l1 - l0)*Pi; + end + case 'g', + error('noisyor_CPD can''t create Gaussian msgs') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/CPD_to_pi.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% CPD_TO_PI Compute pi vector (noisyor) +% pi = CPD_to_pi(CPD, msg_type, n, ps, msg) +% Pearl p188 eqn 4.57 + +switch msg_type + case 'd', + pi = sum_prod_CPD_and_pi_msgs(CPD, n, ps, msg); + pi = [pi 1-pi]'; + case 'g', + error('can''t convert noisy-or CPD to Gaussian pi') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/CPD_to_CPT.m/1.1.1.1/Mon Aug 2 22:23:32 2004// +/CPD_to_lambda_msg.m/1.1.1.1/Wed May 29 15:59:54 2002// +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:54 2002// +/noisyor_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@noisyor_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/noisyor_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +function CPD = noisyor_CPD(bnet, self, leak_inhibit, inhibit) +% NOISYOR_CPD Make a noisy-or CPD +% CPD = NOISYOR_CPD(BNET, NODE_NUM, LEAK_INHIBIT, INHIBIT) +% +% A noisy-or node turns on if any of its parents are on, provided they are not inhibited. +% The prob. that the i'th parent gets inhibited (flipped from 1 to 0) is inhibit(i). +% The prob that the leak node (a dummy parent that is always on) gets inhibit is leak_inhibit. +% These params default to random values if omitted. +% +% Example: suppose C has parents A and B, and the +% link of A->C fails with prob pA and the link B->C fails with pB. +% Then the noisy-OR gate defines the following distribution +% +% A B P(C=0) +% 0 0 1.0 +% 1 0 pA +% 0 1 pB +% 1 1 pA * PB +% +% Currently, learning is not supported for noisy-or nodes +% (since the M step is somewhat complicated). +% +% For simple generalizations of the noisy-OR model, see e.g., +% - Srinivas, "A generalization of the noisy-OR model", UAI 93 +% - Meek and Heckerman, "Learning Causal interaction models", UAI 97. + + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'noisyor_CPD', discrete_CPD(1, [])); + return; +elseif isa(bnet, 'noisyor_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + + +ps = parents(bnet.dag, self); +fam = [ps self]; +ns = bnet.node_sizes; +assert(all(ns(fam)==2)); +assert(isempty(myintersect(fam, bnet.cnodes))); + +if nargin < 3, leak_inhibit = rand(1, 1); end +if nargin < 4, inhibit = rand(1, length(ps)); end + +CPD.self = self; +CPD.inhibit = inhibit; +CPD.leak_inhibit = leak_inhibit; + + +% For BIC +CPD.nparams = 0; +CPD.nsamples = 0; + +CPD.CPT = []; % cached copy, to speed up CPD_to_CPT + +clamped = 1; +CPD = class(CPD, 'noisyor_CPD', discrete_CPD(clamped, ns([ps self]))); + + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.inhibit = []; +CPD.leak_inhibit = []; +CPD.nparams = []; +CPD.nsamples = []; +CPD.CPT = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/sum_prod_CPD_and_pi_msgs.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@noisyor_CPD/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@noisyor_CPD/private/sum_prod_CPD_and_pi_msgs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function pi = sum_prod_CPD_and_pi_msgs(CPD, n, ps, msg, except) +% SUM_PROD_CPD_AND_PI_MSGS Compute pi = sum_{u\p} P(n|u) prod_{ui in ps\p} pi_msg(ui->n) +% pi = sum_prod_CPD_and_pi_msgs(CPD, n, ps, msg, p) +% +% pi = prod_i (qi pi_msg(ui->n) + 1 - pi_msg(ui->n)) = prod_i (1 - ci pi_msg(ui->n)) +% is the product of the endorsement withheld (Pearl p188 eqn 4.56) +% We skip p from this product, if specified. + +if nargin < 5, except = -1; end +pi = 1; +for i=1:length(ps) + p = ps(i); + if p ~= except + pi_from_parent = msg{n}.pi_from_parent{i}; + q = CPD.inhibit(i); + c = 1-q; + pi = pi * (1 - c*pi_from_parent(2)); + end +end +% The pi msg that a leak node sends to its child is [0 1] +% since its own pi is [0 1] and its lambda to self is [0 1]. +q = CPD.leak_inhibit; +% 1 - c*pi_from_parent = 1-c*1 = q +pi = pi * q; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/CPD_to_pi.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) +% CPD_TO_PI Compute the pi vector (root) +% function pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence) + +self_ev = evidence{n}; +switch msg_type + case 'd', + error('root_CPD can''t create discrete msgs') + case 'g', + pi.mu = self_ev; + pi.Sigma = zeros(size(self_ev)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/CPD_to_pi.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_marg_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/root_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@root_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/Old/CPD_to_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the CPD to tabular form (root) +% CPT = CPD_to_CPT(CPD) + +CPT = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/CPD_to_CPT.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@root_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/convert_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a root CPD to one or more potentials +% pots = convert_to_pot(CPD, pot_type, domain, evidence) + +assert(length(domain)==1); +assert(~isempty(evidence(domain))); +T = 1; + +sz = CPD.sizes; +ns = zeros(1, max(domain)); +ns(domain) = sz; + +switch pot_type + case 'u', + pot = upot(domain, 1, T, 0); + case 'd', + ns(domain) = 1; + pot = dpot(domain, ns(domain), T); + case {'c','g'}, + ns(domain) = 0; + pot = cpot(domain, ns(domain), 0); + case 'cg', + ddom = []; + cdom = domain; % we assume the root node is cts + %pot = cgpot(ddom, cdom, ns, {cpot([],[],0)}); + pot = cgpot(ddom, cdom, ns); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/log_marg_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function L = log_marg_prob_node(CPD, self_ev, pev) +% LOG_MARG_PROB_NODE Compute prod_m log int_{theta_i} P(x(i,m)| x(pi_i,m), theta_i) for node i (root) +% L = log_marg_prob_node(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m +% pev{i,m} is the evidence on the i'th parent in case m (ignored) +% We always return L = 0. + +L = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/log_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function L = log_prob_node(CPD, self_ev, pev) +% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (root) +% L = log_prob_node(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m +% pev{i,m} is the evidence on the i'th parent in case m (ignored) +% We always return L = 0. + +L = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/root_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function CPD = root_CPD(bnet, self, val) +% ROOT_CPD Make a conditional prob. distrib. which has no parameters. +% CPD = ROOT_CPD(BNET, NODE_NUM, VAL) +% +% The node must not have any parents and is assumed to always be observed. +% It is a way of modelling exogenous inputs to a model. +% VAL is the value to which the root is clamped (default: []) + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'root_CPD', generic_CPD(1)); + return; +elseif isa(bnet, 'root_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + + +if nargin < 3, val = []; end + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +if ~isempty(ps) + error('root CPDs should have no parents') +end + +CPD.self = self; +CPD.val = val; +CPD.sizes = ns(self); + +clamped = 1; +CPD = class(CPD, 'root_CPD', generic_CPD(clamped)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.val = []; +CPD.sizes = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@root_CPD/sample_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function y = sample_node(CPD, pev) +% SAMPLE_NODE Draw a random sample from P(Y|pa(y), theta) (root) +% Y = SAMPLE_NODE(CPD, PEV) +% +% pev{i} is the evidence on the i'th parent. +% Since a root has no parents, we ignore pev, +% and return the value the root was clamped to when it was created. + +y = CPD.val;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_to_table.m/1.1.1.1/Tue Mar 30 17:19:22 2004// +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:54 2002// +/softmax_CPD.m/1.1.1.1/Tue Jan 7 16:25:14 2003// +/update_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@softmax_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/convert_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a softmax CPD to a potential +% pots = convert_to_pot(CPD, pot_type, domain, evidence) +% +% pots = CPD evaluated using evidence(domain) + +ncases = size(domain,2); +assert(ncases==1); % not yet vectorized + +sz = dom_sizes(CPD); +ns = zeros(1, max(domain)); +ns(domain) = sz; + +odom = domain(~isemptycell(evidence(domain))); +T = convert_to_table(CPD, domain, evidence); + +switch pot_type + case 'u', + pot = upot(domain, sz, T, 0*myones(sz)); + case 'd', + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + + case {'c','g'}, + % Since we want the output to be a Gaussian, the whole family must be observed. + % In other words, the potential is really just a constant. + p = T; + %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1))); + ns(domain) = 0; + pot = cpot(domain, ns(domain), log(p)); + + case 'cg', + T = T(:); + ns(odom) = 1; + can = cell(1, length(T)); + for i=1:length(T) + can{i} = cpot([], [], log(T(i))); + end + ps = domain(1:end-1); + dps = ps(CPD.dpndx); + cps = ps(CPD.cpndx); + ddom = [dps CPD.self]; + cdom = cps; + pot = cgpot(ddom, cdom, ns, can); + + case 'scg' + T = T(:); + ns(odom) = 1; + pot_array = cell(1, length(T)); + for i=1:length(T) + pot_array{i} = scgcpot([], [], T(i)); + end + pot = scgpot(domain, [], [], ns, pot_array); + + otherwise, + error(['unrecognized pot type ' pot_type]) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/convert_to_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a softmax CPD to a table, incorporating any evidence +% T = convert_to_table(CPD, domain, evidence) + +self = domain(end); +ps = domain(1:end-1); +cnodes = domain(CPD.cpndx); +cps = myintersect(ps, cnodes); +dps = domain(CPD.dpndx); +dps_as_cps = domain(CPD.dps_as_cps.ndx); +all_dps = union(dps,dps_as_cps); +odom = domain(~isemptycell(evidence(domain))); +if ~isempty(cps), assert(myismember(cps, odom)); end % all cts parents must be observed + +ns = zeros(1, max(domain)); +ns(domain) = CPD.sizes; +ens = ns; % effective node sizes +ens(odom) = 1; + +% dpsize >= glimsz because the glm parameters are tied across the dps_as_cps parents +dpsize = prod(ens(all_dps)); % size of ALL self'discrete parents +dpvals = cat(1, evidence{myintersect(all_dps, odom)}); +cpvals = cat(1, evidence{cps}); +if ~isempty(dps_as_cps), + separator = CPD.dps_as_cps.separator; + dp_as_cpmap = find_equiv_posns(dps_as_cps, all_dps); + dops_map = find_equiv_posns(myintersect(all_dps, odom), all_dps); + puredp_map = find_equiv_posns(dps, all_dps); + subs = ind2subv(ens(all_dps), 1:prod(ens(all_dps))); + if ~isempty(dops_map), subs(:,dops_map) = subs(:,dops_map)+repmat(dpvals(:)',[size(subs,1) 1])-1; end +end + +[w,b] = extract_params(CPD); +T = zeros(dpsize, ns(self)); +for i=1:dpsize, + active_glm = i; + dp_as_cpvals=zeros(1,sum(ns(dps_as_cps))); + if ~isempty(dps_as_cps), + active_glm = max([1,subv2ind(ns(dps), subs(i,puredp_map))]); + % Extract the params compatible with the observations (if any) on the 'pure' discrete parents (if any) + where_one = separator + subs(i,dp_as_cpmap); + % and get in the dp_as_cp parents... + dp_as_cpvals(where_one)=1; + end + T(i,:) = normalise(exp([dp_as_cpvals(:); cpvals(:)]'*w(:,:,active_glm) + b(:,active_glm)')); +end +if myismember(self, odom) + r = evidence{self}; + T = T(:,r); +end + +T = myreshape(T, ens(domain));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('softmax_CPD object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/get_field.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function val = get_params(CPD, name) +% GET_PARAMS Get the parameters (fields) for a softmax_CPD object +% val = get_params(CPD, name) +% +% The following fields can be accessed +% +% weights - W(X,Y,Q) +% offset - b(Y,Q) +% +% e.g., W = get_params(CPD, 'weights') + +[W, b] = extract_params(CPD); +switch name + case 'weights', val = W; + case 'offset', val = b; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (dsoftmax) using IRLS +% CPD = maximize_params(CPD, temperature) +% temperature parameter is ignored + +% Written by Pierpaolo Brutti + +if ~adjustable_CPD(CPD), return; end +options = foptions; + +if CPD.verbose + options(1) = 1; +else + options(1) = -1; +end +%options(1) = CPD.verbose; + +options(2) = CPD.wthresh; +options(3) = CPD.llthresh; +options(5) = CPD.approx_hess; +options(14) = CPD.max_iter; + +dpsize = size(CPD.self_vals,3); +for i=1:dpsize, + mask=find(CPD.eso_weights(:,:,i)>0); % for adapting the parameters we use only positive weighted example + if ~isempty(mask), + if ~isempty(CPD.dps_as_cps.ndx), + puredp_map = find_equiv_posns(CPD.dpndx, union(CPD.dpndx, CPD.dps_as_cps.ndx)); % find the glm structure + subs = ind2subv(CPD.sizes(union(CPD.dpndx, CPD.dps_as_cps.ndx)),i); % that corrisponds to the + active_glm = max([1,subv2ind(CPD.sizes(CPD.dpndx), subs(puredp_map))]); % i-th 'fictitious' example + + CPD.glim{active_glm} = netopt_weighted(CPD.glim{active_glm}, options, CPD.parent_vals(mask',:,i),... + CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), 'scg'); + else + alfa = 0.4; if CPD.solo, alfa = 1; end % learning step = 1 <=> self is all alone in the net + CPD.glim{i} = glmtrain_weighted(CPD.glim{i}, options, CPD.parent_vals(mask',:),... + CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), alfa); + end + end + mask=[]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/extract_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@softmax_CPD/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/private/extract_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function [W, b] = extract_params(CPD) + +% W(X,Y,Q), b(Y,Q) where Y = ns(self), X = ns(cps), Q = prod(ns(dps)) + +glimsz = prod(CPD.sizes(CPD.dpndx)); +ss = CPD.sizes(end); +cpsz = sum(CPD.sizes(CPD.cpndx)); +dp_as_cpsz = sum(CPD.sizes(CPD.dps_as_cps.ndx)); +W = zeros(dp_as_cpsz + cpsz, ss, glimsz); +b = zeros(ss, glimsz); + +for i=1:glimsz + W(:,:,i) = CPD.glim{i}.w1; + b(:,i) = CPD.glim{i}.b1(:); +end + +W = myreshape(W, [dp_as_cpsz + cpsz ss CPD.sizes(CPD.dpndx)]); +b = myreshape(b, [ss CPD.sizes(CPD.dpndx)]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics for a CPD (dsoftmax) +% CPD = reset_ess(CPD) + +CPD.parent_vals = []; +CPD.eso_weights=[]; +CPD.self_vals = []; +CPD.nsamples = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/sample_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function y = sample_node(CPD, pvals) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (discrete) +% y = sample_node(CPD, parent_evidence) +% +% parent_evidence{i} is the value of the i'th parent + +n = length(pvals)+1; +dom = 1:n; +%evidence = cell(1,n); +%evidence(1:n-1) = pvals(:)'; +evidence = pvals; +evidence{end+1} = []; +T = convert_to_table(CPD, dom, evidence); +y = sample_discrete(T);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function CPD = set_params(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a softmax_CPD object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y), Q1=ns(dps(1)), Q2=ns(dps(2)), ... +% where dps are the discrete parents; if there are no discrete parents, we set Q1=1.) +% +% weights - (W(:,j,a,b,...) - W(:,j',a,b,...)) is ppn to dec. boundary +% between j,j' given Q1=a,Q2=b,... [ randn(X,Y,Q1,Q2,...) ] +% offset - (offset(j,a,b,...) - offset(j',a,b,...)) is the offset to dec. boundary +% between j,j' given Q1=a,Q2=b,... [ randn(Y,Q1,Q2,...) ] +% clamped - 'yes' means don't adjust params during learning ['no'] +% max_iter - the maximum number of steps to take [10] +% verbose - 'yes' means print the LL at each step of IRLS ['no'] +% wthresh - convergence threshold for weights [1e-2] +% llthresh - convergence threshold for log likelihood [1e-2] +% approx_hess - 'yes' means approximate the Hessian for speed ['no'] +% +% e.g., CPD = set_params(CPD,'offset', zeros(ns(i),1)); + +args = varargin; +nargs = length(args); +glimsz = prod(CPD.sizes(CPD.dpndx)); +for i=1:2:nargs + switch args{i}, + case 'discrete', str='nothing to do'; + case 'clamped', CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes')); + case 'max_iter', CPD.max_iter = args{i+1}; + case 'verbose', CPD.verbose = strcmp(args{i+1}, 'yes'); + case 'max_iter', CPD.max_iter = args{i+1}; + case 'wthresh', CPD.wthresh = args{i+1}; + case 'llthresh', CPD.llthresh = args{i+1}; + case 'approx_hess', CPD.approx_hess = strcmp(args{i+1}, 'yes'); + case 'weights', for q=1:glimsz, CPD.glim{q}.w1 = args{i+1}(:,:,q); end; + case 'offset', + if glimsz == 1 + CPD.glim{1}.b1 = args{i+1}; + else + for q=1:glimsz, CPD.glim{q}.b1 = args{i+1}(:,q); end; + end + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/softmax_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,187 @@ +function CPD = softmax_CPD(bnet, self, varargin) +% SOFTMAX_CPD Make a softmax (multinomial logit) CPD +% +% To define this CPD precisely, let W be an (m x n) matrix with W(i,:) = {i-th row of B} +% => we can define the following vectorial function: +% +% softmax: R^n |--> R^m +% softmax(z,i-th)=exp(W(i,:)*z)/sum_k(exp(W(k,:)*z)) +% +% (this constructor augments z with a one at the beginning to introduce an offset term (=bias, intercept)) +% Now call the continuous (cts) and always observed (obs) parents X, +% the discrete parents (if any) Q, and this node Y then we use the discrete parent(s) just to index +% the parameter vectors (c.f., conditional Gaussian nodes); that is: +% prob(Y=i | X=x, Q=j) = softmax(x,i-th|j) +% where '|j' means that we are using the j-th (m x n) parameters matrix W(:,:,j). +% If there are no discrete parents, this is a regular softmax node. +% If Y is binary, this is a logistic (sigmoid) function. +% +% CPD = softmax_CPD(bnet, node_num, ...) will create a softmax CPD with random parameters, +% where node is the number of a node in this equivalence class. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y), Q1=ns(dps(1)), Q2=ns(dps(2)), ... +% where dps are the discrete parents; if there are no discrete parents, we set Q1=1.) +% +% discrete - the discrete parents that we want to treat like the cts ones [ [] ]. +% This can be used to define sigmoid belief network - see below the reference. +% For example suppose that Y has one cts parents X and two discrete ones: Q, C1 where: +% -> Q is binary (1/2) and used just to index the parameters of 'self' +% -> C1 is ternary (1/2/3) and treated as a cts node <=> its values appear into the linear +% part of the softmax function +% then: +% prob(Y|X=x, Q=q, C1=c1)= softmax(W(:,:,q)' * y) +% where y = [1 | delta(C1,1) delta(C1,2) delta(C1,3) | x(:)']' and delta(Y,a)=indicator(Y=a). +% weights - (w(:,j,a,b,...) - w(:,j',a,b,...)) is ppn to dec. boundary +% between j,j' given Q1=a,Q2=b,... [ randn(X,Y,Q1,Q2,...) ] +% offset - (b(j,a,b,...) - b(j',a,b,...)) is the offset to dec. boundary +% between j,j' given Q1=a,Q2=b,... [ randn(Y,Q1,Q2,...) ] +% +% e.g., CPD = softmax_CPD(bnet, i, 'offset', zeros(ns(i),1)); +% +% The following fields control the behavior of the M step, which uses +% a weighted version of the Iteratively Reweighted Least Squares (WIRLS) if dps_as_cps=[]; or +% a weighted SCG otherwise, as implemented in Netlab, and modified by Pierpaolo Brutti. +% +% clamped - 'yes' means don't adjust params during learning ['no'] +% max_iter - the maximum number of steps to take [10] +% verbose - 'yes' means print the LL at each step of IRLS ['no'] +% wthresh - convergence threshold for weights [1e-2] +% llthresh - convergence threshold for log likelihood [1e-2] +% approx_hess - 'yes' means approximate the Hessian for speed ['no'] +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% softmax_CPD(bnet, self, w, b, clamped, max_iter, verbose, wthresh, llthresh, approx_hess) +% +% REFERENCE +% For details on the sigmoid belief nets, see: +% - Neal (1992). Connectionist learning of belief networks, Artificial Intelligence, 56, 71-113. +% - Saul, Jakkola, Jordan (1996). Mean field theory for sigmoid belief networks, Journal of Artificial Intelligence Reseach (4), pagg. 61-76. +% +% For details on the M step, see: +% - K. Chen, L. Xu, H. Chi (1999). Improved learning algorithms for mixtures of experts in multiclass +% classification. Neural Networks 12, pp. 1229-1252. +% - M.I. Jordan, R.A. Jacobs (1994). Hierarchical Mixtures of Experts and the EM algorithm. +% Neural Computation 6, pp. 181-214. +% - S.R. Waterhouse, A.J. Robinson (1994). Classification Using Hierarchical Mixtures of Experts. In Proc. IEEE +% Workshop on Neural Network for Signal Processing IV, pp. 177-186 + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'softmax_CPD', discrete_CPD(0, [])); + return; +elseif isa(bnet, 'softmax_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +assert(myismember(self, bnet.dnodes)); +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +dps = myintersect(ps, bnet.dnodes); +cps = myintersect(ps, bnet.cnodes); + +clamped = 0; +CPD = class(CPD, 'softmax_CPD', discrete_CPD(clamped, ns([ps self]))); + +dps_as_cpssz = 0; +dps_as_cps = []; +% determine if any discrete parents are to be treated as cts +if nargin >= 3 & isstr(varargin{1}) % might have passed in 'discrete' + for i=1:2:length(varargin) + if strcmp(varargin{i}, 'discrete') + dps_as_cps = varargin{i+1}; + assert(myismember(dps_as_cps, dps)); + dps = mysetdiff(dps, dps_as_cps); % put out the dps treated as cts + CPD.dps_as_cps.ndx = find_equiv_posns(dps_as_cps, ps); + CPD.dps_as_cps.separator = [0 cumsum(ns(dps_as_cps(1:end-1)))]; % concatenated dps_as_cps dims separators + dps_as_cpssz = sum(ns(dps_as_cps)); + break; + end + end +end +assert(~isempty(union(cps, dps_as_cps))); % It have to be at least a cts or a dps_as_cps parents +self_size = ns(self); +cpsz = sum(ns(cps)); +glimsz = prod(ns(dps)); +CPD.dpndx = find_equiv_posns(dps, ps); % it contains only the indeces of the 'pure' dps +CPD.cpndx = find_equiv_posns(cps, ps); + +CPD.self = self; +CPD.solo = (length(ns)<=2); +CPD.sizes = bnet.node_sizes([ps self]); + +% set default params +CPD.max_iter = 10; +CPD.verbose = 0; +CPD.wthresh = 1e-2; +CPD.llthresh = 1e-2; +CPD.approx_hess = 0; +CPD.glim = cell(1,glimsz); +for i=1:glimsz + CPD.glim{i} = glm(dps_as_cpssz + cpsz, self_size, 'softmax'); +end + +if nargin >= 3 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + % softmax_CPD(bnet, self, w, b, clamped, max_iter, verbose, wthresh, llthresh, approx_hess) + if nargs >= 1 & ~isempty(args{1}), CPD = set_fields(CPD, 'weights', args{1}); end + if nargs >= 2 & ~isempty(args{2}), CPD = set_fields(CPD, 'offset', args{2}); end + if nargs >= 3 & ~isempty(args{3}), CPD = set_clamped(CPD, args{3}); end + if nargs >= 4 & ~isempty(args{4}), CPD.max_iter = args{4}; end + if nargs >= 5 & ~isempty(args{5}), CPD.verbose = args{5}; end + if nargs >= 6 & ~isempty(args{6}), CPD.wthresh = args{6}; end + if nargs >= 7 & ~isempty(args{7}), CPD.llthresh = args{7}; end + if nargs >= 8 & ~isempty(args{8}), CPD.approx_hess = args{8}; end + else + CPD = set_fields(CPD, args{:}); + end +end + +% sufficient statistics +% Since dsoftmax is not in the exponential family, we must store all the raw data. +CPD.parent_vals = []; % X(l,:) = value of cts parents in l'th example +CPD.self_vals = []; % Y(l,:) = value of self in l'th example + +CPD.eso_weights=[]; % weights used by the WIRLS algorithm + +% For BIC +CPD.nsamples = 0; +if ~adjustable_CPD(CPD), + CPD.nparams=0; +else + [W, b] = extract_params(CPD); + CPD.nparams= prod(size(W)) + prod(size(b)); +end + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.glim = {}; +CPD.self = []; +CPD.solo = []; +CPD.max_iter = []; +CPD.verbose = []; +CPD.wthresh = []; +CPD.llthresh = []; +CPD.approx_hess = []; +CPD.sizes = []; +CPD.parent_vals = []; +CPD.eso_weights=[]; +CPD.self_vals = []; +CPD.nsamples = []; +CPD.nparams = []; +CPD.dpndx = []; +CPD.cpndx = []; +CPD.dps_as_cps.ndx = []; +CPD.dps_as_cps.separator = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a softmax node +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% +% fmarginal = overall posterior distribution of self and its parents +% fmarginal(i1,i2...,ik,s)=prob(Pa1=i1,...,Pak=ik, self=s| X) +% +% => 1) prob(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) with prob(Pa1,...,Pak)=sum{s,fmarginal} +% [self estimation -> CPD.self_vals] +% 2) prob(Pa1,...,Pak) [WIRLS weights -> CPD.eso_weights] +% +% Hidden_bitv is ignored + +% Written by Pierpaolo Brutti + +if ~adjustable_CPD(CPD), return; end + +domain = fmarginal.domain; +self = domain(end); +ps = domain(1:end-1); +cnodes = domain(CPD.cpndx); +cps = myintersect(domain, cnodes); +dps = mysetdiff(ps, cps); +dn_use = dps; +if isempty(evidence{self}) dn_use = [dn_use self]; end % if self is hidden we must consider its dimension +dps_as_cps = domain(CPD.dps_as_cps.ndx); +odom = domain(~isemptycell(evidence(domain))); + +ns = zeros(1, max(domain)); +ns(domain) = CPD.sizes; % CPD.sizes = bnet.node_sizes([ps self]); +ens = ns; % effective node sizes +ens(odom) = 1; +dpsize = prod(ns(dps)); + +% Extract the params compatible with the observations (if any) on the discrete parents (if any) +dops = myintersect(dps, odom); +dpvals = cat(1, evidence{dops}); + +subs = ind2subv(ens(dn_use), 1:prod(ens(dn_use))); +dpmap = find_equiv_posns(dops, dn_use); +if ~isempty(dpmap), subs(:,dpmap) = subs(:,dpmap)+repmat(dpvals(:)',[size(subs,1) 1])-1; end +supportedQs = subv2ind(ns(dn_use), subs); subs=subs(1:prod(ens(dps)),1:length(dps)); +Qarity = prod(ns(dn_use)); +if isempty(dn_use), Qarity = 1; end + +fullm.T = zeros(Qarity, 1); +fullm.T(supportedQs) = fmarginal.T(:); +rs_dim = CPD.sizes; rs_dim(CPD.cpndx) = 1; % +if ~isempty(evidence{self}), rs_dim(end)=1; end % reshaping the marginal +fullm.T = reshape(fullm.T, rs_dim); % + +% --------------------------------------------------------------------------------UPDATE-- + +CPD.nsamples = CPD.nsamples + 1; + +% 1) observations vector -> CPD.parents_vals --------------------------------------------- +cpvals = cat(1, evidence{cps}); + +if ~isempty(dps_as_cps), % ...get in the dp_as_cp parents... + separator = CPD.dps_as_cps.separator; + dp_as_cpmap = find_equiv_posns(dps_as_cps, dps); + for i=1:dpsize, + dp_as_cpvals=zeros(1,sum(ns(dps_as_cps))); + possible_vals = ind2subv(ns(dps),i); + ll=find(ismember(subs(:,dp_as_cpmap), possible_vals(dp_as_cpmap), 'rows')==1); + if ~isempty(ll), + where_one = separator + possible_vals(dp_as_cpmap); + dp_as_cpvals(where_one)=1; + end + CPD.parent_vals(CPD.nsamples,:,i) = [dp_as_cpvals(:); cpvals(:)]'; + end +else + CPD.parent_vals(CPD.nsamples,:) = cpvals(:)'; +end + +% 2) weights vector -> CPD.eso_weights ---------------------------------------------------- +if isempty(evidence{self}), % self is hidden + pesi=reshape(sum(fullm.T, length(rs_dim)),[dpsize,1]); +else + pesi=reshape(fullm.T,[dpsize,1]); +end +assert(approxeq(sum(pesi),1)); % check + +% 3) estimate (if R is hidden) or recover (if R is obs) self'value------------------------- +if isempty(evidence{self}) % P(self|Pa1,...,Pak)=fmarginal/prob(Pa1,...,Pak) + r=reshape(mk_stochastic(fullm.T), [dpsize ns(self)]); % matrix size: prod{j,ns(Paj)} x ns(self) +else + r = zeros(dpsize,ns(self)); + for i=1:dpsize, if pesi(i)~=0, r(i,evidence{self}) = 1; end; end +end +for i=1:dpsize, if pesi(i)~=0, assert(approxeq(sum(r(i,:)),1)); end; end % check + +% 4) save the previous values -------------------------------------------------------------- +for i=1:dpsize + CPD.eso_weights(CPD.nsamples,:,i)=pesi(i); + CPD.self_vals(CPD.nsamples,:,i) = r(i,:); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/CPD_to_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the discrete CPD to tabular form (tabular) +% CPT = CPD_to_CPT(CPD) + +CPT = CPD.CPT;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +/CPD_to_CPT.m/1.1.1.1/Wed May 29 15:59:54 2002// +/bayes_update_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/display.m/1.1.1.1/Tue Apr 22 21:00:02 2003// +/get_field.m/1.1.1.1/Sun Jan 16 02:27:30 2005// +/learn_params.m/1.1.1.1/Thu Jun 10 01:25:02 2004// +/log_marg_prob_node.m/1.1.1.1/Fri Jun 11 21:16:00 2004// +/log_nextcase_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_prior.m/1.1.1.1/Wed May 29 15:59:54 2002// +/maximize_params.m/1.1.1.1/Sun Mar 9 22:44:40 2003// +/reset_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Sun Jan 16 02:27:30 2005// +/tabular_CPD.m/1.1.1.1/Sun Jan 16 02:27:32 2005// +/update_ess.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_ess_simple.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/BIC_score_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function score = BIC_score_CPD(CPD, fam, data, ns, cnodes) +% BIC_score_CPD Compute the BIC score of a tabular CPD +% score = BIC_score_CPD(CPD, fam, data, ns, cnodes) + +if iscell(data) + local_data = cell2num(data(fam,:)); +else + local_data = data(fam, :); +end +counts = compute_counts(local_data, CPD.sizes); +CPT = mk_stochastic(counts); % MLE +tiny = exp(-700); +CPT = CPT + (CPT==0)*tiny; % replace 0s by tiny +LL = sum(log(CPT(:)) .* counts(:)); +N = size(data, 2); +score = LL - 0.5*CPD.nparams*log(N); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +/BIC_score_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/bayesian_score_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_marg_prob_node_case.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mult_CPD_and_pi_msgs.m/1.1.1.1/Wed May 29 15:59:54 2002// +/prob_CPT.m/1.1.1.1/Wed May 29 15:59:54 2002// +/prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_node_single_case.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_CPD/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/bayesian_score_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function score = bayesian_score_CPD(CPD, local_ev) +% bayesian_score_CPD Compute the Bayesian score of a tabular CPD using uniform Dirichlet prior +% score = bayesian_score_CPD(CPD, local_ev) +% +% The Bayesian score is the log marginal likelihood + +if iscell(local_ev) + data = num2cell(local_ev); +else + data = local_ev; +end + +score = dirichlet_score_family(compute_counts(data, CPD.sizes));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/log_marg_prob_node_case.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function L = log_marg_prob_node_case(CPD, y, x) +% LOG_MARG_PROB_NODE_CASE Compute prod_m log P(x(i,m)| x(pi_i,m)) for node i (tabular) +% L = log_marg_prob_node_case(CPD, self_ev, parent_ev) +% +% This is a slightly optimised version of log_marg_prob_node. +% We assume we have exactly 1 case, i.e., y is a scalar and x is a vector (not a cell array). + +sz = CPD.sizes; +nparents = length(sz)-1; + +% We assume the CPTs are already set to the mean of the posterior (due to update_params) + +switch nparents + case 0, p = CPD.CPT(y); + case 1, p = CPD.CPT(x(1), y); + case 2, p = CPD.CPT(x(1), x(2), y); + case 3, p = CPD.CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(sz, [x y]); + p = CPD.CPT(ind); +end +L = log(p);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/mult_CPD_and_pi_msgs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function T = mult_CPD_and_pi_msgs(CPD, n, ps, msgs, except) +% MULT_CPD_AND_PI_MSGS Multiply the CPD and all the pi messages from parents, perhaps excepting one +% T = mult_CPD_and_pi_msgs(CPD, n, ps, msgs, except) + +if nargin < 5, except = -1; end + +dom = [ps n]; +%ns = sparse(1, max(dom)); +ns = zeros(1, max(dom)); +ns(dom) = mysize(CPD.CPT); +T = dpot(dom, ns(dom), CPD.CPT); +for i=1:length(ps) + p = ps(i); + if p ~= except + T = multiply_by_pot(T, dpot(p, ns(p), msgs{n}.pi_from_parent{i}.T)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/prob_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function p = prob_CPT(CPD, x) +% PROB_CPT Lookup the prob. of a family value in a tabular CPD +% p = prob_CPT(CPD, x) +% +% This is a version of prob_CPD optimized for tables. + +switch length(x) + case 1, p = CPD.CPT(x); + case 2, p = CPD.CPT(x(1), x(2)); + case 3, p = CPD.CPT(x(1), x(2), x(3)); + case 4, p = CPD.CPT(x(1), x(2), x(3), x(4)); + otherwise, + ind = subv2ind(mysize(CPD.CPT), x); + p = CPD.CPT(ind); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function p = prob_node(CPD, self_ev, pev) +% PROB_NODE Compute P(y|pa(y), theta) (tabular) +% p = prob_node(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m +% pev{i,m} is the evidence on the i'th parent in case m +% If there is a single case, self_ev can be a scalar instead of a cell array + +ncases = size(pev, 2); + +%assert(~any(isemptycell(pev))); % slow +%assert(~any(isemptycell(self_ev))); % slow + +CPT = CPD_to_CPT(CPD); +sz = mysize(CPT); +nparents = length(sz)-1; +assert(nparents == size(pev, 1)); + +if ncases==1 + x = cat(1, pev{:}); + if iscell(y) + y = self_ev{1}; + else + y = self_ev; + end + switch nparents + case 0, p = CPT(y); + case 1, p = CPT(x(1), y); + case 2, p = CPT(x(1), x(2), y); + case 3, p = CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(CPD.sizes, [x y]); + p = CPT(ind); + end +else + x = num2cell(pev)'; % each row is a case + y = cat(1, self_ev{:})'; + ind = subv2ind(CPD.sizes, [x y]); + p = CPT(ind); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/sample_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function y = sample_node(CPD, pev, nsamples) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (tabular) +% Y = SAMPLE_NODE(CPD, PEV, NSAMPLES) +% +% pev(i,m) is the value of the i'th parent in sample m (if there are any parents). +% y(m) is the m'th sampled value (a row vector). +% (If pev is a cell array, so is y.) +% nsamples defaults to 1. + +if nargin < 3, nsamples = 1; end + +%if nargin < 4, usecell = 0; end +if iscell(pev), usecell = 1; else usecell = 0; end + +if nsamples == 1, pev = pev(:); end + +sz = CPD.sizes; +nparents = length(sz)-1; +if nparents==0 + y = sample_discrete(CPD.CPT, 1, nsamples); + if usecell + y = num2cell(y); + end + return; +end + +sz = CPD.sizes; +[nparents nsamples] = size(pev); + +if usecell + pvals = cell2num(pev)'; % each row is a case +else + pvals = pev'; +end + +psz = sz(1:end-1); +ssz = sz(end); +ndx = subv2ind(psz, pvals); +T = reshape(CPD.CPT, [prod(psz) ssz]); +T2 = T(ndx,:); % each row is a distribution selected by the parents +C = cumsum(T2, 2); % sum across columns +R = rand(nsamples, 1); +y = ones(nsamples, 1); +for i=1:ssz-1 + y = y + (R > C(:,i)); +end +y = y(:)'; +if usecell + y = num2cell(y); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/sample_node_single_case.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function y = sample_node(CPD, pev) +% SAMPLE_NODE Draw a random sample from P(Xi | x(pi_i), theta_i) (tabular) +% y = sample_node(CPD, pev) +% +% pev{i} is the value of the i'th parent (if any) + +%assert(~any(isemptycell(pev))); + +%CPT = CPD_to_CPT(CPD); +%sz = mysize(CPT); +sz = CPD.sizes; +nparents = length(sz)-1; +if nparents > 0 + pvals = cat(1, pev{:}); +end +switch nparents + case 0, T = CPD.CPT; + case 1, T = CPD.CPT(pvals(1), :); + case 2, T = CPD.CPT(pvals(1), pvals(2), :); + case 3, T = CPD.CPT(pvals(1), pvals(2), pvals(3), :); + case 4, T = CPD.CPT(pvals(1), pvals(2), pvals(3), pvals(4), :); + otherwise, + psz = sz(1:end-1); + ssz = sz(end); + i = subv2ind(psz, pvals(:)'); + T = reshape(CPD.CPT, [prod(psz) ssz]); + T = T(i,:); +end + +if sz(end)==2 + r = rand(1,1); + if r > T(1) + y = 2; + else + y = 1; + end +else + y = sample_discrete(T); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/tabular_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,186 @@ +function CPD = tabular_CPD(bnet, self, varargin) +% TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT) +% +% CPD = tabular_CPD(bnet, node) creates a random CPT. +% +% The following arguments can be specified [default in brackets] +% +% CPT - specifies the params ['rnd'] +% - T means use table T; it will be reshaped to the size of node's family. +% - 'rnd' creates rnd params (drawn from uniform) +% - 'unif' creates a uniform distribution +% - 'leftright' only transitions from i to i/i+1 are allowed, for each non-self parent context. +% The non-self parents are all parents except oldself. +% selfprob - The prob of transition from i to i if CPT = 'leftright' [0.1] +% old_self - id of the node corresponding to self in the previous slice [self-ss] +% adjustable - 0 means don't adjust the parameters during learning [1] +% prior_type - defines type of prior ['none'] +% - 'none' means do ML estimation +% - 'dirichlet' means add pseudo-counts to every cell +% - 'entropic' means use a prior P(theta) propto exp(-H(theta)) (see Brand) +% dirichlet_weight - equivalent sample size (ess) of the dirichlet prior [1] +% dirichlet_type - defines the type of Dirichlet prior ['BDeu'] +% - 'unif' means put dirichlet_weight in every cell +% - 'BDeu' means we put 'dirichlet_weight/(r q)' in every cell +% where r = self_sz and q = prod(parent_sz) (see Heckerman) +% trim - 1 means trim redundant params (rows in CPT) when using entropic prior [0] +% +% e.g., tabular_CPD(bnet, i, 'CPT', T) +% e.g., tabular_CPD(bnet, i, 'CPT', 'unif', 'dirichlet_weight', 2, 'dirichlet_type', 'unif') +% +% REFERENCES +% M. Brand - "Structure learning in conditional probability models via an entropic prior +% and parameter extinction", Neural Computation 11 (1999): 1155--1182 +% M. Brand - "Pattern discovery via entropy minimization" [covers annealing] +% AI & Statistics 1999. Equation numbers refer to this paper, which is available from +% www.merl.com/reports/docs/TR98-21.pdf +% D. Heckerman, D. Geiger and M. Chickering, +% "Learning Bayesian networks: the combination of knowledge and statistical data", +% Microsoft Research Tech Report, 1994 + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_CPD', discrete_CPD(0, [])); + return; +elseif isa(bnet, 'tabular_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +fam_sz = ns([ps self]); +CPD.sizes = fam_sz; +CPD.leftright = 0; + +% set defaults +CPD.CPT = mk_stochastic(myrand(fam_sz)); +CPD.adjustable = 1; +CPD.prior_type = 'none'; +dirichlet_type = 'BDeu'; +dirichlet_weight = 1; +CPD.trim = 0; +selfprob = 0.1; + +% extract optional args +args = varargin; +% check for old syntax CPD(bnet, i, CPT) as opposed to CPD(bnet, i, 'CPT', CPT) +if ~isempty(args) & ~isstr(args{1}) + CPD.CPT = myreshape(args{1}, fam_sz); + args = []; +end + +% if old_self is specified, read in the value before CPT is created +old_self = []; +for i=1:2:length(args) + switch args{i}, + case 'old_self', old_self = args{i+1}; + end +end + +for i=1:2:length(args) + switch args{i}, + case 'CPT', + T = args{i+1}; + if ischar(T) + switch T + case 'unif', CPD.CPT = mk_stochastic(myones(fam_sz)); + case 'rnd', CPD.CPT = mk_stochastic(myrand(fam_sz)); + case 'leftright', + % we just initialise the CPT to leftright - this structure will + % be maintained by EM, assuming we don't use a prior... + CPD.leftright = 1; + if isempty(old_self) % we assume the network is a DBN + ss = bnet.nnodes_per_slice; + old_self = self-ss; + end + other_ps = mysetdiff(ps, old_self); + Qps = prod(ns(other_ps)); + Q = ns(self); + p = selfprob; + LR = mk_leftright_transmat(Q, p); + transprob = repmat(reshape(LR, [1 Q Q]), [Qps 1 1]); % transprob(k,i,j) + transprob = permute(transprob, [2 1 3]); % now transprob(i,k,j) + CPD.CPT = myreshape(transprob, fam_sz); + otherwise, error(['invalid CPT ' T]); + end + else + CPD.CPT = myreshape(T, fam_sz); + end + + case 'prior_type', CPD.prior_type = args{i+1}; + case 'dirichlet_type', dirichlet_type = args{i+1}; + case 'dirichlet_weight', dirichlet_weight = args{i+1}; + case 'adjustable', CPD.adjustable = args{i+1}; + case 'clamped', CPD.adjustable = ~args{i+1}; + case 'trim', CPD.trim = args{i+1}; + case 'old_self', noop = 1; % already read in + otherwise, error(['invalid argument name: ' args{i}]); + end +end + +switch CPD.prior_type + case 'dirichlet', + switch dirichlet_type + case 'unif', CPD.dirichlet = dirichlet_weight * myones(fam_sz); + case 'BDeu', CPD.dirichlet = dirichlet_weight * mk_stochastic(myones(fam_sz)); + otherwise, error(['invalid dirichlet_type ' dirichlet_type]) + end + case {'entropic', 'none'} + CPD.dirichlet = []; + otherwise, error(['invalid prior_type ' prior_type]) +end + + + +% fields to do with learning +if ~CPD.adjustable + CPD.counts = []; + CPD.nparams = 0; + CPD.nsamples = []; +else + CPD.counts = zeros(size(CPD.CPT)); + psz = fam_sz(1:end-1); + ss = fam_sz(end); + if CPD.leftright + % For each of the Qps contexts, we specify Q elements on the diagoanl + CPD.nparams = Qps * Q; + else + % sum-to-1 constraint reduces the effective arity of the node by 1 + CPD.nparams = prod([psz ss-1]); + end + CPD.nsamples = 0; +end + +fam_sz = CPD.sizes; +psz = prod(fam_sz(1:end-1)); +ssz = fam_sz(end); +CPD.trimmed_trans = zeros(psz, ssz); % must declare before reading + +CPD = class(CPD, 'tabular_CPD', discrete_CPD(~CPD.adjustable, fam_sz)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.sizes = []; +CPD.prior_type = []; +CPD.dirichlet = []; +CPD.adjustable = []; +CPD.counts = []; +CPD.nparams = []; +CPD.nsamples = []; +CPD.trim = []; +CPD.trimmed_trans = []; +CPD.leftright = []; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/Old/update_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function CPD = update_params(CPD, ev, counts) +% UPDATE_PARAMS Update the Dirichlet pseudo counts and compute the new MAP param estimates (tabular) +% +% CPD = update_params(CPD, ev) uses the evidence on the family from a single case. +% +% CPD = update_params(CPD, [], counts) does a batch update using the specified suff. stats. + +if nargin < 3 + n = length(ev); + data = cat(1, ev{:}); % convert to a vector of scalars + counts = compute_counts(data(:)', 1:n, mysize(CPD.CPT)); +end + +CPD.prior = CPD.prior + counts; +CPD.CPT = mk_stochastic(CPD.prior);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/bayes_update_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function CPD = bayes_update_params(CPD, self_ev, pev) +% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (tabular) +% CPD = update_params_complete(CPD, self_ev, pev) +% +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% These can be arrays or cell arrays. +% +% We update the Dirichlet pseudo counts and set the CPT to the mean of the posterior. + +if iscell(self_ev), usecell = 1; else usecell = 0; end + +ncases = length(self_ev); +sz = CPD.sizes; +nparents = length(sz)-1; +assert(nparents == size(pev,1)); + +if ncases == 0 | ~adjustable_CPD(CPD) + return; +elseif ncases == 1 % speedup the sequential learning case by avoiding normalization of the whole array + if usecell + x = cat(1, pev{:})'; + y = self_ev{1}; + else + x = pev(:)'; + y = self_ev; + end + switch nparents + case 0, + CPD.dirichlet(y) = CPD.dirichlet(y)+1; + CPD.CPT = CPD.dirichlet / sum(CPD.dirichlet); + case 1, + CPD.dirichlet(x(1), y) = CPD.dirichlet(x(1), y)+1; + CPD.CPT(x(1), :) = CPD.dirichlet(x(1), :) ./ sum(CPD.dirichlet(x(1), :)); + case 2, + CPD.dirichlet(x(1), x(2), y) = CPD.dirichlet(x(1), x(2), y)+1; + CPD.CPT(x(1), x(2), :) = CPD.dirichlet(x(1), x(2), :) ./ sum(CPD.dirichlet(x(1), x(2), :)); + case 3, + CPD.dirichlet(x(1), x(2), x(3), y) = CPD.dirichlet(x(1), x(2), x(3), y)+1; + CPD.CPT(x(1), x(2), x(3), :) = CPD.dirichlet(x(1), x(2), x(3), :) ./ sum(CPD.dirichlet(x(1), x(2), x(3), :)); + otherwise, + ind = subv2ind(sz, [x y]); + CPD.dirichlet(ind) = CPD.dirichlet(ind) + 1; + CPD.CPT = mk_stochastic(CPD.dirichlet); + end +else + if usecell + data = [cell2num(pev); cell2num(self_ev)]; + else + data = [pev; self_ev]; + end + counts = compute_counts(data, sz); + CPD.dirichlet = CPD.dirichlet + counts; + CPD.CPT = mk_stochastic(CPD.dirichlet); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function display(CPD) + +disp('tabular_CPD object'); +disp(struct(CPD)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/get_field.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function val = get_field(CPD, name) +% GET_PARAMS Get the parameters (fields) for a tabular_CPD object +% val = get_params(CPD, name) +% +% The following fields can be accessed +% +% cpt, counts +% +% e.g., CPT = get_params(CPD, 'cpt') + +switch name + case 'cpt', val = CPD.CPT; + case 'counts', val = CPD.counts; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/learn_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function CPD = learn_params(CPD, fam, data, ns, cnodes) +%function CPD = learn_params(CPD, local_data) +% LEARN_PARAMS Compute the ML/MAP estimate of the params of a tabular CPD given complete data +% CPD = learn_params(CPD, local_data) +% +% local_data(i,m) is the value of i'th family member in case m (can be cell array). + +local_data = data(fam, :); +if iscell(local_data) + local_data = cell2num(local_data); +end +counts = compute_counts(local_data, CPD.sizes); +switch CPD.prior_type + case 'none', CPD.CPT = mk_stochastic(counts); + case 'dirichlet', CPD.CPT = mk_stochastic(counts + CPD.dirichlet); + otherwise, error(['unrecognized prior ' CPD.prior_type]) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/log_marg_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function L = log_marg_prob_node(CPD, self_ev, pev, usecell) +% LOG_MARG_PROB_NODE Compute sum_m log P(x(i,m)| x(pi_i,m)) for node i (tabular) +% L = log_marg_prob_node(CPD, self_ev, pev) +% +% This differs from log_prob_node because we integrate out the parameters. +% self_ev(m) is the evidence on this node in case m. +% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents). +% (These may also be cell arrays.) + +ncases = length(self_ev); +sz = CPD.sizes; +nparents = length(sz)-1; +assert(ncases == size(pev, 2)); + +if nargin < 4 + %usecell = 0; + if iscell(self_ev) + usecell = 1; + else + usecell = 0; + end +end + + +if ncases==0 + L = 0; + return; +elseif ncases==1 % speedup the sequential learning case + CPT = CPD.CPT; + % We assume the CPTs are already set to the mean of the posterior (due to bayes_update_params) + if usecell + x = cat(1, pev{:})'; + y = self_ev{1}; + else + %x = pev(:)'; + x = pev; + y = self_ev; + end + switch nparents + case 0, p = CPT(y); + case 1, p = CPT(x(1), y); + case 2, p = CPT(x(1), x(2), y); + case 3, p = CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(sz, [x y]); + p = CPT(ind); + end + L = log(p); +else + % We ignore the CPTs here and assume the prior has not been changed + + % We arrange the data as in the following example. + % Let there be 2 parents and 3 cases. Let p(i,m) be parent i in case m, + % and y(m) be the child in case m. Then we create the data matrix + % + % p(1,1) p(1,2) p(1,3) + % p(2,1) p(2,2) p(2,3) + % y(1) y(2) y(3) + if usecell + data = [cell2num(pev); cell2num(self_ev)]; + else + data = [pev; self_ev]; + end + %S = struct(CPD); fprintf('log marg prob node %d, ps\n', S.self); disp(S.parents) + counts = compute_counts(data, sz); + L = dirichlet_score_family(counts, CPD.dirichlet); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/log_nextcase_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +function L = log_nextcase_prob_node(CPD, self_ev, pev, test_self_ev, test_pev) +% LOG_NEXTCASE_PROB_NODE compute the joint distribution of a node (tabular) of a new case given +% completely observed data. +% +% The input arguments are mainly similar with log_marg_prob_node(CPD, self_ev, pev, usecell), +% but add test_self_ev, test_pev, and without usecell +% test_self_ev(m) is the evidence on this node in a test case. +% test_pev(i) is the evidence on the i'th parent in the test case (if there are any parents). +% +% Written by qian.diao@intel.com + +ncases = length(self_ev); +sz = CPD.sizes; +nparents = length(sz)-1; +assert(ncases == size(pev, 2)); + +if nargin < 6 + %usecell = 0; + if iscell(self_ev) + usecell = 1; + else + usecell = 0; + end +end + + +if ncases==0 + L = 0; + return; +elseif ncases==1 % speedup the sequential learning case; here need correction!!! + CPT = CPD.CPT; + % We assume the CPTs are already set to the mean of the posterior (due to bayes_update_params) + if usecell + x = cat(1, pev{:})'; + y = self_ev{1}; + else + %x = pev(:)'; + x = pev; + y = self_ev; + end + switch nparents + case 0, p = CPT(y); + case 1, p = CPT(x(1), y); + case 2, p = CPT(x(1), x(2), y); + case 3, p = CPT(x(1), x(2), x(3), y); + otherwise, + ind = subv2ind(sz, [x y]); + p = CPT(ind); + end + L = log(p); +else + % We ignore the CPTs here and assume the prior has not been changed + + % We arrange the data as in the following example. + % Let there be 2 parents and 3 cases. Let p(i,m) be parent i in case m, + % and y(m) be the child in case m. Then we create the data matrix + % + % p(1,1) p(1,2) p(1,3) + % p(2,1) p(2,2) p(2,3) + % y(1) y(2) y(3) + if usecell + data = [cell2num(pev); cell2num(self_ev)]; + else + data = [pev; self_ev]; + end + counts = compute_counts(data, sz); + + % compute the (N_ijk'+ N_ijk)/(N_ij' + N_ij) under the condition of 1_m+1,ijk = 1 + L = predict_family(counts, CPD.prior, test_self_ev, test_pev); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/log_prior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function L = log_prior(CPD) +% LOG_PRIOR Return log P(theta) for a tabular CPD +% L = log_prior(CPD) + +switch CPD.prior_type + case 'none', + L = 0; + case 'dirichlet', + D = CPD.dirichlet(:); + L = sum(log(D + (D==0))); + case 'entropic', + % log-prior = log exp(-H(theta)) = sum_i theta_i log (theta_i) + fam_sz = CPD.sizes; + psz = prod(fam_sz(1:end-1)); + ssz = fam_sz(end); + C = reshape(CPD.CPT, psz, ssz); + L = sum(sum(C .* log(C + (C==0)))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/maximize_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function CPD = maximize_params(CPD, temp) +% MAXIMIZE_PARAMS Set the params of a tabular node to their ML/MAP values. +% CPD = maximize_params(CPD, temp) + +if ~adjustable_CPD(CPD), return; end + +%assert(approxeq(sum(CPD.counts(:)), CPD.nsamples)); % false! +switch CPD.prior_type + case 'none', + counts = reshape(CPD.counts, size(CPD.CPT)); + CPD.CPT = mk_stochastic(counts); + case 'dirichlet', + counts = reshape(CPD.counts, size(CPD.CPT)); + CPD.CPT = mk_stochastic(counts + CPD.dirichlet); + + % case 'entropic', +% % For an HMM, +% % CPT(i,j) = pr(X(t)=j | X(t-1)=i) = transprob(i,j) +% % counts(i,j) = E #(X(t-1)=i, X(t)=j) = exp_num_trans(i,j) +% Z = 1-temp; +% fam_sz = CPD.sizes; +% psz = prod(fam_sz(1:end-1)); +% ssz = fam_sz(end); +% counts = reshape(CPD.counts, psz, ssz); +% CPT = zeros(psz, ssz); +% for i=CPD.entropic_pcases(:)' +% [CPT(i,:), logpost] = entropic_map_estimate(counts(i,:), Z); +% end +% non_entropic_pcases = mysetdiff(1:psz, CPD.entropic_pcases); +% for i=non_entropic_pcases(:)' +% CPT(i,:) = mk_stochastic(counts(i,:)); +% end +% %for i=1:psz +% % [CPT(i,:), logpost] = entropic_map(counts(i,:), Z); +% %end +% if CPD.trim & (temp < 2) % at high temps, we would trim everything! +% % grad(j) = d log lik / d theta(i ->j) +% % CPT(i,j) = 0 => counts(i,j) = 0 +% % so we can safely replace 0s by 1s in the denominator +% denom = CPT(i,:) + (CPT(i,:)==0); +% grad = counts(i,:) ./ denom; +% trim = find(CPT(i,:) <= exp(-(1/Z)*grad)); % eqn 32 +% if ~isempty(trim) +% CPT(i,trim) = 0; +% if all(CPD.trimmed_trans(i,trim)==0) % trimming for 1st time +% disp(['trimming CPT(' num2str(i) ',' num2str(trim) ')']) +% end +% CPD.trimmed_trans(i,trim) = 1; +% end +% end +% CPD.CPT = myreshape(CPT, CPD.sizes); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/reset_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function CPD = reset_ess(CPD) +% RESET_ESS Reset the Expected Sufficient Statistics of a tabular node. +% CPD = reset_ess(CPD) + +%CPD.counts = zeros(size(CPD.CPT)); +CPD.counts = zeros(prod(size(CPD.CPT)), 1); +CPD.nsamples = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function CPD = set_fields(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a tabular_CPD object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% +% CPT, prior, clamped, counts +% +% e.g., CPD = set_params(CPD, 'CPT', 'rnd') + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'CPT', + if ischar(args{i+1}) + switch args{i+1} + case 'unif', CPD.CPT = mk_stochastic(myones(CPD.sizes)); + case 'rnd', CPD.CPT = mk_stochastic(myrand(CPD.sizes)); + otherwise, error(['invalid type ' args{i+1}]); + end + elseif isscalarBNT(args{i+1}) + p = args{i+1}; + k = CPD.sizes(end); + % Bug fix by Hervé Boutrouille 10/1/01 + CPD.CPT = myreshape(sample_dirichlet(p*ones(1,k), prod(CPD.sizes(1:end-1)), CPD.sizes)); + %CPD.CPT = myreshape(sample_dirichlet(p*ones(1,k), prod(CPD.sizes(1:end-1))), CPD.sizes); + else + CPD.CPT = myreshape(args{i+1}, CPD.sizes); + end + + case 'prior', + if ischar(args{i+1}) & strcmp(args{i+1}, 'unif') + CPD.prior = myones(CPD.sizes); + elseif isscalarBNT(args{i+1}) + CPD.prior = args{i+1} * normalise(myones(CPD.sizes)); + else + CPD.prior = myreshape(args{i+1}, CPD.sizes); + end + + %case 'clamped', CPD.clamped = strcmp(args{i+1}, 'yes'); + %case 'clamped', CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes')); + case 'clamped', CPD = set_clamped(CPD, args{i+1}); + + case 'counts', CPD.counts = args{i+1}; + + otherwise, + %error(['invalid argument name ' args{i}]); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/tabular_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +function CPD = tabular_CPD(bnet, self, varargin) +% TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT) +% +% CPD = tabular_CPD(bnet, node) creates a random CPT. +% +% The following arguments can be specified [default in brackets] +% +% CPT - specifies the params ['rnd'] +% - T means use table T; it will be reshaped to the size of node's family. +% - 'rnd' creates rnd params (drawn from uniform) +% - 'unif' creates a uniform distribution +% adjustable - 0 means don't adjust the parameters during learning [1] +% prior_type - defines type of prior ['none'] +% - 'none' means do ML estimation +% - 'dirichlet' means add pseudo-counts to every cell +% - 'entropic' means use a prior P(theta) propto exp(-H(theta)) (see Brand) +% dirichlet_weight - equivalent sample size (ess) of the dirichlet prior [1] +% dirichlet_type - defines the type of Dirichlet prior ['BDeu'] +% - 'unif' means put dirichlet_weight in every cell +% - 'BDeu' means we put 'dirichlet_weight/(r q)' in every cell +% where r = self_sz and q = prod(parent_sz) (see Heckerman) +% trim - 1 means trim redundant params (rows in CPT) when using entropic prior [0] +% entropic_pcases - list of assignments to the parents nodes when we should use +% the entropic prior; all other cases will be estimated using ML [1:psz] +% sparse - 1 means use 1D sparse array to represent CPT [0] +% +% e.g., tabular_CPD(bnet, i, 'CPT', T) +% e.g., tabular_CPD(bnet, i, 'CPT', 'unif', 'dirichlet_weight', 2, 'dirichlet_type', 'unif') +% +% REFERENCES +% M. Brand - "Structure learning in conditional probability models via an entropic prior +% and parameter extinction", Neural Computation 11 (1999): 1155--1182 +% M. Brand - "Pattern discovery via entropy minimization" [covers annealing] +% AI & Statistics 1999. Equation numbers refer to this paper, which is available from +% www.merl.com/reports/docs/TR98-21.pdf +% D. Heckerman, D. Geiger and M. Chickering, +% "Learning Bayesian networks: the combination of knowledge and statistical data", +% Microsoft Research Tech Report, 1994 + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_CPD', discrete_CPD(0, [])); + return; +elseif isa(bnet, 'tabular_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +fam_sz = ns([ps self]); +psz = prod(ns(ps)); +CPD.sizes = fam_sz; +CPD.leftright = 0; +CPD.sparse = 0; + +% set defaults +CPD.CPT = mk_stochastic(myrand(fam_sz)); +CPD.adjustable = 1; +CPD.prior_type = 'none'; +dirichlet_type = 'BDeu'; +dirichlet_weight = 1; +CPD.trim = 0; +selfprob = 0.1; +CPD.entropic_pcases = 1:psz; + +% extract optional args +args = varargin; +% check for old syntax CPD(bnet, i, CPT) as opposed to CPD(bnet, i, 'CPT', CPT) +if ~isempty(args) & ~isstr(args{1}) + CPD.CPT = myreshape(args{1}, fam_sz); + args = []; +end + +for i=1:2:length(args) + switch args{i}, + case 'CPT', + T = args{i+1}; + if ischar(T) + switch T + case 'unif', CPD.CPT = mk_stochastic(myones(fam_sz)); + case 'rnd', CPD.CPT = mk_stochastic(myrand(fam_sz)); + otherwise, error(['invalid CPT ' T]); + end + else + CPD.CPT = myreshape(T, fam_sz); + end + case 'prior_type', CPD.prior_type = args{i+1}; + case 'dirichlet_type', dirichlet_type = args{i+1}; + case 'dirichlet_weight', dirichlet_weight = args{i+1}; + case 'adjustable', CPD.adjustable = args{i+1}; + case 'clamped', CPD.adjustable = ~args{i+1}; + case 'trim', CPD.trim = args{i+1}; + case 'entropic_pcases', CPD.entropic_pcases = args{i+1}; + case 'sparse', CPD.sparse = args{i+1}; + otherwise, error(['invalid argument name: ' args{i}]); + end +end + +switch CPD.prior_type + case 'dirichlet', + switch dirichlet_type + case 'unif', CPD.dirichlet = dirichlet_weight * myones(fam_sz); + case 'BDeu', CPD.dirichlet = (dirichlet_weight/psz) * mk_stochastic(myones(fam_sz)); + otherwise, error(['invalid dirichlet_type ' dirichlet_type]) + end + case {'entropic', 'none'} + CPD.dirichlet = []; + otherwise, error(['invalid prior_type ' prior_type]) +end + + + +% fields to do with learning +if ~CPD.adjustable + CPD.counts = []; + CPD.nparams = 0; + CPD.nsamples = []; +else + %CPD.counts = zeros(size(CPD.CPT)); + CPD.counts = zeros(prod(size(CPD.CPT)), 1); + psz = fam_sz(1:end-1); + ss = fam_sz(end); + if CPD.leftright + % For each of the Qps contexts, we specify Q elements on the diagoanl + CPD.nparams = Qps * Q; + else + % sum-to-1 constraint reduces the effective arity of the node by 1 + CPD.nparams = prod([psz ss-1]); + end + CPD.nsamples = 0; +end + +CPD.trimmed_trans = []; +fam_sz = CPD.sizes; + +%psz = prod(fam_sz(1:end-1)); +%ssz = fam_sz(end); +%CPD.trimmed_trans = zeros(psz, ssz); % must declare before reading + +%sparse CPT +if CPD.sparse + CPD.CPT = sparse(CPD.CPT(:)); +end + +CPD = class(CPD, 'tabular_CPD', discrete_CPD(~CPD.adjustable, fam_sz)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.sizes = []; +CPD.prior_type = []; +CPD.dirichlet = []; +CPD.adjustable = []; +CPD.counts = []; +CPD.nparams = []; +CPD.nsamples = []; +CPD.trim = []; +CPD.trimmed_trans = []; +CPD.leftright = []; +CPD.entropic_pcases = []; +CPD.sparse = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/update_ess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) +% UPDATE_ESS Update the Expected Sufficient Statistics of a tabular node. +% function CPD = update_ess(CPD, fmarginal, evidence, ns, cnodes, hidden_bitv) + +dom = fmarginal.domain; + +if all(hidden_bitv(dom)) + CPD = update_ess_simple(CPD, fmarginal.T); + %fullm = add_ev_to_dmarginal(fmarginal, evidence, ns); + %assert(approxeq(fullm.T(:), fmarginal.T(:))) +else + fullm = add_ev_to_dmarginal(fmarginal, evidence, ns); + CPD = update_ess_simple(CPD, fullm.T); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_CPD/update_ess_simple.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function CPD = update_ess_simple(CPD, counts) +% UPDATE_ESS_SIMPLE Update the Expected Sufficient Statistics of a tabular node. +% function CPD = update_ess_simple(CPD, counts) + +CPD.nsamples = CPD.nsamples + 1; +CPD.counts = CPD.counts + counts(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/CPD_to_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function CPT = CPD_to_CPT(CPD) +% CPD_TO_CPT Convert the tabular_decision_node to a CPT +% CPT = CPD_to_CPT(CPD) + +CPT = CPD.CPT;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/CPD_to_CPT.m/1.1.1.1/Wed May 29 15:59:54 2002// +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_decision_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_decision_node
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/tabular_decision_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_decision_node/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/Old/tabular_decision_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function CPD = tabular_decision_node(sz, CPT) +% TABULAR_DECISION_NODE Represent the randomized policy over a discrete decision/action node as a table +% CPD = tabular_decision_node(sz, CPT) +% +% sz(1:end-1) is the sizes of the parents, sz(end) is the size of this node +% By default, CPT is set to the uniform random policy + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_decision_node'); + return; +elseif isa(sz, 'tabular_decision_node') + % This might occur if we are copying an object. + CPD = sz; + return; +end +CPD = init_fields; + +if nargin < 2 + CPT = mk_stochastic(myones(sz)); +else + CPT = myreshape(CPT, sz); +end + +CPD.CPT = CPT; +CPD.size = sz; + +CPD = class(CPD, 'tabular_decision_node'); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.size = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('tabular decision node object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/get_field.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function vals = get_field(CPD, name) +% GET_PARAMS Get the parameters (fields) for a tabular_decision_node object +% vals = get_params(CPD, name) +% +% The following fields can be accessed +% +% policy - the table containing the policy +% +% e.g., policy = get_params(CPD, 'policy') + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'policy', vals = CPD.CPT; + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function CPD = set_params(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a tabular_decision_node object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% +% policy - the table containing the policy +% +% e.g., CPD = set_params(CPD, 'policy', T) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'policy', CPD.CPT = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_decision_node/tabular_decision_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function CPD = tabular_decision_node(bnet, self, CPT) +% TABULAR_DECISION_NODE Represent a stochastic policy over a discrete decision/action node as a table +% CPD = tabular_decision_node(bnet, self, CPT) +% +% node is the number of a node in this equivalence class. +% CPT is an optional argument (see tabular_CPD for details); by default, it is the uniform policy. + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_decision_node', discrete_CPD(1, [])); + return; +elseif isa(bnet, 'tabular_decision_node') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + +ns = bnet.node_sizes; +fam = family(bnet.dag, self); +ps = parents(bnet.dag, self); +sz = ns(fam); + +if nargin < 3 + CPT = mk_stochastic(myones(sz)); +else + CPT = myreshape(CPT, sz); +end + +CPD.CPT = CPT; +CPD.sizes = sz; + +clamped = 1; % don't update using EM +CPD = class(CPD, 'tabular_decision_node', discrete_CPD(clamped, ns([ps self]))); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.sizes = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_to_table.m/1.1.1.1/Wed May 29 15:59:54 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_kernel.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_kernel
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/tabular_kernel.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_kernel/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/Old/tabular_kernel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function K = tabular_kernel(fg, self) +% TABULAR_KERNEL Make a table-based local kernel (discrete potential) +% K = tabular_kernel(fg, self) +% +% fg is a factor graph +% self is the number of a representative domain +% +% Use 'set_params_kernel' to adjust the following fields +% table - a q[1]xq[2]x... array, where q[i] is the number of values for i'th node +% in this domain [default: random values from [0,1], which need not sum to 1] + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + K = init_fields; + K = class(K, 'tabular_kernel'); + return; +elseif isa(fg, 'tabular_kernel') + % This might occur if we are copying an object. + K = fg; + return; +end +K = init_fields; + +ns = fg.node_sizes; +dom = fg.doms{self}; +% we don't store the actual domain since it may vary due to parameter tieing +K.sz = ns(dom); +K.table = myrand(K.sz); + +K = class(K, 'tabular_kernel'); + + +%%%%%%% + + +function K = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +K.table = []; +K.sz = []; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/convert_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a tabular CPD to one or more potentials +% pot = convert_to_pot(CPD, pot_type, domain, evidence) + +% This is the same as discrete_CPD/convert_to_pot, +% except we didn't want to the kernel to inherit methods like sample_node etc. + +sz = CPD.sz; +ns = zeros(1, max(domain)); +ns(domain) = sz; + +odom = domain(~isemptycell(evidence(domain))); +T = convert_to_table(CPD, domain, evidence); + +switch pot_type + case 'u', + pot = upot(domain, sz, T, 0*myones(sz)); + case 'd', + ns(odom) = 1; + pot = dpot(domain, ns(domain), T); + case 'c', + % Since we want the output to be a Gaussian, the whole family must be observed. + % In other words, the potential is really just a constant. + p = T.p; + %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1))); + ns(domain) = 0; + pot = cpot(domain, ns(domain), log(p)); + case 'cg', + T = T(:); + ns(odom) = 1; + can = cell(1, length(T)); + for i=1:length(T) + can{i} = cpot([], [], log(T(i))); + end + pot = cgpot(domain, [], ns, can); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/convert_to_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function T = convert_to_table(CPD, domain, evidence) +% CONVERT_TO_TABLE Convert a discrete CPD to a table +% T = convert_to_table(CPD, domain, evidence) +% +% We convert the CPD to a CPT, and then lookup the evidence on the discrete parents. +% The resulting table can easily be converted to a potential. + +CPT = CPD.table; +odom = domain(~isemptycell(evidence(domain))); +vals = cat(1, evidence{odom}); +map = find_equiv_posns(odom, domain); +index = mk_multi_index(length(domain), map, vals); +T = CPT(index{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/get_field.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function val = get_params_kernel(K, name) +% GET_PARAMS_KERNEL Accessor function for a field (tabular_kernel) +% val = get_params_kernel(K, name) +% +% e.g., get_params_kernel(K, 'table') + +switch name + case 'table', val = K.table; + otherwise, + error(['invalid field name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function K = set_params_kernel(K, name, val) +% SET_PARAMS_KERNEL Accessor function for a field (table_kernel) +% K = set_params_kernel(K, name, val) +% +% e.g., K = set_params_kernel(K, 'table', rand(2,3,2)) for a kernel on 3 nodes with 2,3,2 values each + +% We should check if the arguments are valid... + +switch name + case 'table', K.table = val; + otherwise, + error(['invalid field name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_kernel/tabular_kernel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function K = tabular_kernel(sz, table) +% TABULAR_KERNEL Make a table-based local kernel (discrete potential) +% K = tabular_kernel(sz, table) +% +% sz(i) is the number of values the i'th member of this kernel can have +% table is an optional array of size sz[1] x sz[2] x... [default: random] + +if nargin==0 + % This occurs if we are trying to load an object from a file. + K = init_fields; + K = class(K, 'tabular_kernel'); + return; +elseif isa(sz, 'tabular_kernel') + % This might occur if we are copying an object. + K = sz; + return; +end +K = init_fields; + +if nargin < 2, table = myrand(sz); end + +K.sz = sz; +K.table = table; + +K = class(K, 'tabular_kernel'); + + +%%%%%%% + + +function K = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +K.sz = []; +K.table = []; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_utility_node/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/convert_to_pot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_utility_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_utility_node/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tabular_utility_node
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_utility_node/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_utility_node/convert_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function pot = convert_to_pot(CPD, pot_type, domain, evidence) +% CONVERT_TO_POT Convert a tabular utility node to one or more potentials +% pot = convert_to_pot(CPD, pot_type, domain, evidence) + +switch pot_type + case 'u', + sz = [CPD.sizes 1]; % the utility node itself has size 1 + pot = upot(domain, sz, 1*myones(sz), myreshape(CPD.T, sz)); + otherwise, + error(['can''t convert a utility node to a ' pot_type ' potential']); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_utility_node/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('tabular utility node object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tabular_utility_node/tabular_utility_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +function CPD = tabular_utility_node(bnet, node, T) +% TABULAR_UTILITY_NODE Represent a utility function as a table +% CPD = tabular_utility_node(bnet, node, T) +% +% node is the number of a node in this equivalence class. +% T is an optional argument (same shape as the CPT in tabular_CPD, but missing the last (child) +% dimension). By default, entries in T are chosen u.a.r. from 0:1 (using 'rand'). + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'tabular_utility_node'); + return; +elseif isa(bnet, 'tabular_utility_node') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + + +ns = bnet.node_sizes; +ps = parents(bnet.dag, node); +sz = ns(ps); + +if nargin < 3 + T = myrand(sz); +else + T = myreshape(T, sz); +end + +CPD.T = T; +CPD.sizes = sz; + +CPD = class(CPD, 'tabular_utility_node'); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.T = []; +CPD.sizes = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +/display.m/1.1.1.1/Wed May 29 15:59:54 2002// +/evaluate_tree_performance.m/1.1.1.1/Wed May 29 15:59:54 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:54 2002// +/learn_params.m/1.1.1.1/Wed May 29 15:59:54 2002// +/readme.txt/1.1.1.1/Wed May 29 15:59:54 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tree_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/@tree_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(CPD) + +disp('dtree_CPD object'); +disp(struct(CPD));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/evaluate_tree_performance.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,82 @@ +function [score,outputs] = evaluate(CPD, fam, data, ns, cnodes) +% Evaluate evaluate the performance of the classification/regression tree on given complete data +% score = evaluate(CPD, fam, data, ns, cnodes) +% +% fam(i) is the node id of the i-th node in the family of nodes, self node is the last one +% data(i,m) is the value of node i in case m (can be cell array). +% ns(i) is the node size for the i-th node in the whold bnet +% cnodes(i) is the node id for the i-th continuous node in the whole bnet +% +% Output +% score is the classification accuracy (for classification) +% or mean square deviation (for regression) +% here for every case we use the mean value at the tree leaf node as its predicted value +% outputs(i) is the predicted output value for case i +% +% Author: yimin.zhang@intel.com +% Last updated: Jan. 19, 2002 + + +if iscell(data) + local_data = cell2num(data(fam,:)); +else + local_data = data(fam, :); +end + +%get local node sizes and node types +node_sizes = ns(fam); +node_types = zeros(1,size(ns,2)); %all nodes are disrete +node_types(cnodes)=1; +node_types=node_types(fam); + +fam_size=size(fam,2); +output_type = node_types(fam_size); + +num_cases=size(local_data,2); +total_error=0; + +outputs=zeros(1,num_cases); +for i=1:num_cases + %class one case using the tree + cur_node=CPD.tree.root; % at the root node of the tree + while (1) + if (CPD.tree.nodes(cur_node).is_leaf==1) + if (output_type==0) %output is discrete + %use the class with max probability as the output + [maxvalue,class_id]=max(CPD.tree.nodes(cur_node).probs); + outputs(i)=class_id; + if (class_id~=local_data(fam_size,i)) + total_error=total_error+1; + end + else %output is continuous + %use the mean as the value + outputs(i)=CPD.tree.nodes(cur_node).mean; + cur_deviation = CPD.tree.nodes(cur_node).mean-local_data(fam_size,i); + total_error=total_error+cur_deviation*cur_deviation; + end + break; + end + cur_attr = CPD.tree.nodes(cur_node).split_id; + attr_val = local_data(cur_attr,i); + if (node_types(cur_attr)==0) %discrete attribute + % goto the attr_val -th child + cur_node = CPD.tree.nodes(cur_node).children(attr_val); + else + if (attr_val <= CPD.tree.nodes(cur_node).split_threshhold) + cur_node = CPD.tree.nodes(cur_node).children(1); + else + cur_node = CPD.tree.nodes(cur_node).children(2); + end + end + if (cur_node > CPD.tree.num_node) + fprintf('Fatal error: Tree structure corrupted.\n'); + return; + end + end + %update the classification error number +end +if (output_type==0) + score=1-total_error/num_cases; +else + score=total_error/num_cases; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/get_field.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function val = get_params(CPD, name) +% GET_PARAMS Get the parameters (fields) for a tabular_CPD object +% val = get_params(CPD, name) +% +% The following fields can be accessed +% +% cpt - the CPT +% +% e.g., CPT = get_params(CPD, 'cpt') + +switch name + case 'cpt', val = CPD.CPT; + case 'tree', val = CPD.tree; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/learn_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,642 @@ +function CPD = learn_params(CPD, fam, data, ns, cnodes, varargin) +% LEARN_PARAMS Construct classification/regression tree given complete data +% CPD = learn_params(CPD, fam, data, ns, cnodes) +% +% fam(i) is the node id of the i-th node in the family of nodes, self node is the last one +% data(i,m) is the value of node i in case m (can be cell array). +% ns(i) is the node size for the i-th node in the whold bnet +% cnodes(i) is the node id for the i-th continuous node in the whole bnet +% +% The following optional arguments can be specified in the form of name/value pairs: +% stop_cases: for early stop (pruning). A node is not split if it has less than k cases. default is 0. +% min_gain: for early stop (pruning). +% For discrete output: A node is not split when the gain of best split is less than min_gain. default is 0. +% For continuous (cts) outpt: A node is not split when the gain of best split is less than min_gain*score(root) +% (we denote it cts_min_gain). default is 0.006 +% %%%%%%%%%%%%%%%%%%%Struction definition of dtree_CPD.tree%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% tree.num_node the last position in tree.nodes array for adding new nodes, +% it is not always same to number of nodes in a tree, because some position in the +% tree.nodes array can be set to unused (e.g. in tree pruning) +% tree.nodes is the array of nodes in the tree plus some unused nodes. +% tree.nodes(1) is the root for the tree. +% +% Below is the attributes for each node +% tree.nodes(i).used; % flag this node is used (0 means node not used, it can be removed from tree to save memory) +% tree.nodes(i).is_leaf; % if 1 means this node is a leaf, if 0 not a leaf. +% tree.nodes(i).children; % children(i) is the node number in tree.nodes array for the i-th child node +% tree.nodes(i).split_id; % the attribute id used to split this node +% tree.nodes(i).split_threshhold; % the threshhold for continuous attribute to split this node +% %%%%%attributes specially for classification tree (discrete output) +% tree.nodes(i).probs % probs(i) is the prob for i-th value of class node +% % For three output class, the probs = [0.9 0.1 0.0] means the probability of +% % class 1 is 0.9, for class 2 is 0.1, for class 3 is 0.0. +% %%%%%attributes specially for regression tree (continuous output) +% tree.nodes(i).mean % mean output value for this node +% tree.nodes(i).std % standard deviation for output values in this node +% +% Author: yimin.zhang@intel.com +% Last updated: Jan. 19, 2002 + +% Want list: +% (1) more efficient for cts attributes: get the values of cts attributes at first (the begining of build_tree function), then doing bi_search in finding threshhold +% (2) pruning classification tree using Pessimistic Error Pruning +% (3) bi_search for strings (used for transform data to BNT format) + +global tree %tree must be global so that it can be accessed in recursive slitting function +global cts_min_gain +tree=[]; % clear the tree +tree.num_node=0; +cts_min_gain=0; + +stop_cases=0; +min_gain=0; + +args = varargin; +nargs = length(args); +if (nargs>0) + if isstr(args{1}) + for i=1:2:nargs + switch args{i}, + case 'stop_cases', stop_cases = args{i+1}; + case 'min_gain', min_gain = args{i+1}; + end + end + else + error(['error in input parameters']); + end +end + +if iscell(data) + local_data = cell2num(data(fam,:)); +else + local_data = data(fam, :); +end +%counts = compute_counts(local_data, CPD.sizes); +%CPD.CPT = mk_stochastic(counts + CPD.prior); % bug fix 11/5/01 +node_types = zeros(1,size(ns,2)); %all nodes are disrete +node_types(cnodes)=1; +%make the data be BNT compliant (values for discrete nodes are from 1-n, here n is the node size) +%trans_data=transform_data(local_data,'tmp.dat',[]); %here no cts nodes + +build_dtree (CPD, local_data, ns(fam), node_types(fam),stop_cases,min_gain); +%CPD.tree=copy_tree(tree); +CPD.tree=tree; %copy the tree constructed to CPD + + +function new_tree = copy_tree(tree) +% copy the tree to new_tree +new_tree.num_node=tree.num_node; +new_tree.root = tree.root; +for i=1:tree.num_node + new_tree.nodes(i)=tree.nodes(i); +end + + +function build_dtree (CPD, fam_ev, node_sizes, node_types,stop_cases,min_gain) +global tree +global cts_min_gain + +tree.num_node=0; %the current number of nodes in the tree +tree.root=1; + +T = 1:size(fam_ev,2) ; %all cases +candidate_attrs = 1:(size(node_sizes,2)-1); %all attributes +node_id=1; %the root node +lastnode=size(node_sizes,2); %the last element in all nodes is the dependent variable (category node) +num_cat=node_sizes(lastnode); + +% get minimum gain for cts output (used in stop splitting) +if (node_types(size(fam_ev,1))==1) %cts output + N = size(fam_ev,2); + output_id = size(fam_ev,1); + cases_T = fam_ev(output_id,:); %get all the output value for cases T + std_T = std(cases_T); + avg_y_T = mean(cases_T); + sqr_T = cases_T - avg_y_T; + cts_min_gain = min_gain*(sum(sqr_T.*sqr_T)/N); % min_gain * (R(root) = 1/N * SUM(y-avg_y)^2) +end + +split_dtree (CPD, fam_ev, node_sizes, node_types, stop_cases,min_gain, T, candidate_attrs, num_cat); + + + +% pruning method +% (1) Restrictions on minimum node size: A node is not split if it has smaller than k cases. +% (2) Threshholds on impurity: a threshhold is imposed on the splitting test score. Threshhold can be +% imposed on local goodness measure (the gain_ratio of a node) or global goodness. +% (3) Mininum Error Pruning (MEP), (no need pruning set) +% Prune if static error<=backed-up error +% Static error at node v: e(v) = (Nc + 1)/(N+k) (laplace estimate, prior for each class equal) +% here N is # of all examples, Nc is # of majority class examples, k is number of classes +% Backed-up error at node v: (Ti is the i-th subtree root) +% E(T) = Sum_1_to_n(pi*e(Ti)) +% (4) Pessimistic Error Pruning (PEP), used in Quilan C4.5 (no need pruning set, efficient because of pruning top-down) +% Probability of error (apparent error rate) +% q = (N-Nc+0.5)/N +% where N=#examples, Nc=#examples in majority class +% Error of a node v (if pruned) q(v)= (Nv- Nc,v + 0.5)/Nv +% Error of a subtree q(T)= Sum_of_l_leaves(Nl - Nc,l + 0.5)/Sum_of_l_leaves(Nl) +% Prune if q(v)<=q(T) +% +% Implementation statuts: +% (1)(2) has been implemented as the input parameters of learn_params. +% (4) is implemented in this function +function pruning(fam_ev,node_sizes,node_types) +% PRUNING prune the constructed tree using PEP +% pruning(fam_ev,node_sizes,node_types) +% +% fam_ev(i,j) is the value of attribute i in j-th training cases (for whole tree), the last row is for the class label (self_ev) +% node_sizes(i) is the node size for the i-th node in the family +% node_types(i) is the node type for the i-th node in the family, 0 for disrete node, 1 for continous node +% the global parameter 'tree' is for storing the input tree and the pruned tree + + +function split_T = split_cases(fam_ev,node_sizes,node_types,T,node_i, threshhold) +% SPLIT_CASES split the cases T according to values of node_i in the family +% split_T = split_cases(fam_ev,node_sizes,node_types,T,node_i) +% +% fam_ev(i,j) is the value of attribute i in j-th training cases (for whole tree), the last row is for the class label (self_ev) +% node_sizes(i) is the node size for the i-th node in the family +% node_types(i) is the node type for the i-th node in the family, 0 for disrete node, 1 for continous node +% node_i is the attribute we need to split + +if (node_types(node_i)==0) %discrete attribute + %init the subsets of T + split_T = cell(1,node_sizes(node_i)); %T will be separated into |node_size of i| subsets according to different values of node i + for i=1:node_sizes(node_i) % here we assume that the value of an attribute is 1:node_size + split_T{i}=zeros(1,0); + end + + size_t = size(T,2); + for i=1:size_t + case_id = T(i); + %put this case into one subset of split_T according to its value for node_i + value = fam_ev(node_i,case_id); + pos = size(split_T{value},2)+1; + split_T{value}(pos)=case_id; % here assumes the value of an attribute is 1:node_size + end +else %continuous attribute + %init the subsets of T + split_T = cell(1,2); %T will be separated into 2 subsets (<=threshhold) (>threshhold) + for i=1:2 + split_T{i}=zeros(1,0); + end + + size_t = size(T,2); + for i=1:size_t + case_id = T(i); + %put this case into one subset of split_T according to its value for node_i + value = fam_ev(node_i,case_id); + subset_num=1; + if (value>threshhold) + subset_num=2; + end + pos = size(split_T{subset_num},2)+1; + split_T{subset_num}(pos)=case_id; + end +end + + + +function new_node = split_dtree (CPD, fam_ev, node_sizes, node_types, stop_cases, min_gain, T, candidate_attrs, num_cat) +% SPLIT_TREE Split the tree at node node_id with cases T (actually it is just indexes to family evidences). +% new_node = split_dtree (fam_ev, node_sizes, node_types, T, node_id, num_cat, method) +% +% fam_ev(i,j) is the value of attribute i in j-th training cases (for whole tree), the last row is for the class label (self_ev) +% node_sizes{i} is the node size for the i-th node in the family +% node_types{i} is the node type for the i-th node in the family, 0 for disrete node, 1 for continous node +% stop_cases is the threshold of number of cases to stop slitting +% min_gain is the minimum gain need to split a node +% T(i) is the index of i-th cases in current decision tree node, we need split it further +% candidate_attrs(i) the node id for the i-th attribute that still need to be considered as split attribute +%%%%% node_id is the index of current node considered for a split +% num_cat is the number of output categories for the decision tree +% output: +% new_node is the new node created +global tree +global cts_min_gain + +size_fam = size(fam_ev,1); %number of family size +output_type = node_types(size_fam); %the type of output for the tree (0 is discrete, 1 is continuous) +size_attrs = size(candidate_attrs,2); %number of candidate attributes +size_t = size(T,2); %number of training cases in this tree node + +%(1)computeFrequenceyForEachClass(T) +if (output_type==0) %discrete output + class_freqs = zeros(1,num_cat); + for i=1:size_t + case_id = T(i); + case_class = fam_ev(size_fam,case_id); %get the class label for this case + class_freqs(case_class)=class_freqs(case_class)+1; + end +else %cts output + N = size(fam_ev,2); + cases_T = fam_ev(size(fam_ev,1),T); %get the output value for cases T + std_T = std(cases_T); +end + +%(2) if OneClass (for discrete output) or same output value (for cts output) or Class With #examples < stop_cases +% return a leaf; +% create a decision node N; + +% get majority class in this node +if (output_type == 0) + top1_class = 0; %the class with the largest number of cases + top1_class_cases = 0; %the number of cases in top1_class + [top1_class_cases,top1_class]=max(class_freqs); +end + +if (size_t==0) %impossble + new_node=-1; + fprintf('Fatal error: please contact the author. \n'); + return; +end + +% stop splitting if needed + %for discrete output: one class + %for cts output, all output value in cases are same + %cases too little +if ( (output_type==0 & top1_class_cases == size_t) | (output_type==1 & std_T == 0) | (size_t < stop_cases)) + %create one new leaf node + tree.num_node=tree.num_node+1; + tree.nodes(tree.num_node).used=1; %flag this node is used (0 means node not used, it will be removed from tree at last to save memory) + tree.nodes(tree.num_node).is_leaf=1; + tree.nodes(tree.num_node).children=[]; + tree.nodes(tree.num_node).split_id=0; %the attribute(parent) id to split this tree node + tree.nodes(tree.num_node).split_threshhold=0; + if (output_type==0) + tree.nodes(tree.num_node).probs=class_freqs/size_t; %the prob for each value of class node + + % tree.nodes(tree.num_node).probs=zeros(1,num_cat); %the prob for each value of class node + % tree.nodes(tree.num_node).probs(top1_class)=1; %use the majority class of parent node, like for binary class, + %and majority is class 2, then the CPT is [0 1] + %we may need to use prior to do smoothing, to get [0.001 0.999] + tree.nodes(tree.num_node).error.self_error=1-top1_class_cases/size_t; %the classfication error in this tree node when use default class + tree.nodes(tree.num_node).error.all_error=1-top1_class_cases/size_t; %no total classfication error in this tree node and its subtree + tree.nodes(tree.num_node).error.all_error_num=size_t - top1_class_cases; + fprintf('Create leaf node(onecla) %d. Class %d Cases %d Error %d \n',tree.num_node, top1_class, size_t, size_t - top1_class_cases ); + else + avg_y_T = mean(cases_T); + tree.nodes(tree.num_node).mean = avg_y_T; + tree.nodes(tree.num_node).std = std_T; + fprintf('Create leaf node(samevalue) %d. Mean %8.4f Std %8.4f Cases %d \n',tree.num_node, avg_y_T, std_T, size_t); + end + new_node = tree.num_node; + return; +end + +%create one new node +tree.num_node=tree.num_node+1; +tree.nodes(tree.num_node).used=1; %flag this node is used (0 means node not used, it will be removed from tree at last to save memory) +tree.nodes(tree.num_node).is_leaf=1; +tree.nodes(tree.num_node).children=[]; +tree.nodes(tree.num_node).split_id=0; +tree.nodes(tree.num_node).split_threshhold=0; +if (output_type==0) + tree.nodes(tree.num_node).error.self_error=1-top1_class_cases/size_t; + tree.nodes(tree.num_node).error.all_error=0; + tree.nodes(tree.num_node).error.all_error_num=0; +else + avg_y_T = mean(cases_T); + tree.nodes(tree.num_node).mean = avg_y_T; + tree.nodes(tree.num_node).std = std_T; +end +new_node = tree.num_node; + +%Stop splitting if no attributes left in this node +if (size_attrs==0) + if (output_type==0) + tree.nodes(tree.num_node).probs=class_freqs/size_t; %the prob for each value of class node + tree.nodes(tree.num_node).error.all_error=1-top1_class_cases/size_t; + tree.nodes(tree.num_node).error.all_error_num=size_t - top1_class_cases; + fprintf('Create leaf node(noattr) %d. Class %d Cases %d Error %d \n',tree.num_node, top1_class, size_t, size_t - top1_class_cases ); + else + fprintf('Create leaf node(noattr) %d. Mean %8.4f Std %8.4f Cases %d \n',tree.num_node, avg_y_T, std_T, size_t); + end + return; +end + + +%(3) for each attribute A +% ComputeGain(A); +max_gain=0; %the max gain score (for discrete information gain or gain ration, for cts node the R(T)) +best_attr=0; %the attribute with the max_gain +best_split = []; %the split of T according to the value of best_attr +cur_best_threshhold = 0; %the threshhold for split continuous attribute +best_threshhold=0; + +% compute Info(T) (for discrete output) +if (output_type == 0) + class_split_T = split_cases(fam_ev,node_sizes,node_types,T,size(fam_ev,1),0); %split cases according to class + info_T = compute_info (fam_ev, T, class_split_T); +else % compute R(T) (for cts output) +% N = size(fam_ev,2); +% cases_T = fam_ev(size(fam_ev,1),T); %get the output value for cases T +% std_T = std(cases_T); +% avg_y_T = mean(cases_T); + sqr_T = cases_T - avg_y_T; + R_T = sum(sqr_T.*sqr_T)/N; % get R(T) = 1/N * SUM(y-avg_y)^2 + info_T = R_T; +end + +for i=1:(size_fam-1) + if (myismember(i,candidate_attrs)) %if this attribute still in the candidate attribute set + if (node_types(i)==0) %discrete attibute + split_T = split_cases(fam_ev,node_sizes,node_types,T,i,0); %split cases according to value of attribute i + % For cts output, we compute the least square gain. + % For discrete output, we compute gain ratio + cur_gain = compute_gain(fam_ev,node_sizes,node_types,T,info_T,i,split_T,0,output_type); %gain ratio + else %cts attribute + %get the values of this attribute + ev = fam_ev(:,T); + values = ev(i,:); + sort_v = sort(values); + %remove the duplicate values in sort_v + v_set = unique(sort_v); + best_gain = 0; + best_threshhold = 0; + best_split1 = []; + + %find the best split for this cts attribute + % see "Quilan 96: Improved Use of Continuous Attributes in C4.5" + for j=1:(size(v_set,2)-1) + mid_v = (v_set(j)+v_set(j+1))/2; + split_T = split_cases(fam_ev,node_sizes,node_types,T,i,mid_v); %split cases according to value of attribute i (<=mid_v) + % For cts output, we compute the least square gain. + % For discrete output, we use Quilan 96: use information gain instead of gain ratio to select threshhold + cur_gain = compute_gain(fam_ev,node_sizes,node_types,T,info_T,i,split_T,1,output_type); + %if (i==6) + % fprintf('gain %8.5f threshhold %6.3f spliting %d\n', cur_gain, mid_v, size(split_T{1},2)); + %end + + if (best_gain < cur_gain) + best_gain = cur_gain; + best_threshhold = mid_v; + %best_split1 = split_T; %here we need to copy array, not good!!! (maybe we can compute after we get best_attr + end + end + %recalculate the gain_ratio of the best_threshhold + split_T = split_cases(fam_ev,node_sizes,node_types,T,i,best_threshhold); + best_gain = compute_gain(fam_ev,node_sizes,node_types,T,info_T,i,split_T,0,output_type); %gain_ratio + if (output_type==0) %for discrete output + cur_gain = best_gain-log2(size(v_set,2)-1)/size_t; % Quilan 96: use the gain_ratio-log2(N-1)/|D| as the gain of this attr + else %for cts output + cur_gain = best_gain; + end + end + + if (max_gain < cur_gain) + max_gain = cur_gain; + best_attr = i; + cur_best_threshhold=best_threshhold; %save the threshhold + %best_split = split_T; %here we need to copy array, not good!!! So we will recalculate in below line 313 + end + end +end + +% stop splitting if gain is too small +if (max_gain==0 | (output_type==0 & max_gain < min_gain) | (output_type==1 & max_gain < cts_min_gain)) + if (output_type==0) + tree.nodes(tree.num_node).probs=class_freqs/size_t; %the prob for each value of class node + tree.nodes(tree.num_node).error.all_error=1-top1_class_cases/size_t; + tree.nodes(tree.num_node).error.all_error_num=size_t - top1_class_cases; + fprintf('Create leaf node(nogain) %d. Class %d Cases %d Error %d \n',tree.num_node, top1_class, size_t, size_t - top1_class_cases ); + else + fprintf('Create leaf node(nogain) %d. Mean %8.4f Std %8.4f Cases %d \n',tree.num_node, avg_y_T, std_T, size_t); + end + return; +end + +%get the split of cases according to the best split attribute +if (node_types(best_attr)==0) %discrete attibute + best_split = split_cases(fam_ev,node_sizes,node_types,T,best_attr,0); +else + best_split = split_cases(fam_ev,node_sizes,node_types,T,best_attr,cur_best_threshhold); +end + +%(4) best_attr = AttributeWithBestGain; +%(5) if best_attr is continuous ???? why need this? maybe the value in the decision tree must appeared in data +% find threshhold in all cases that <= max_V +% change the split of T +tree.nodes(tree.num_node).split_id=best_attr; +tree.nodes(tree.num_node).split_threshhold=cur_best_threshhold; %for cts attribute only + +%note: below threshhold rejust is linera search, so it is slow. A better method is described in paper "Efficient C4.5" +%if (output_type==0) +if (node_types(best_attr)==1) %is a continuous attribute + %find the value that approximate best_threshhold from below (the largest that <= best_threshhold) + best_value=0; + for i=1:size(fam_ev,2) %note: need to search in all cases for all tree, not just in cases for this node + val = fam_ev(best_attr,i); + if (val <= cur_best_threshhold & val > best_value) %val is more clear to best_threshhold + best_value=val; + end + end + tree.nodes(tree.num_node).split_threshhold=best_value; %for cts attribute only +end +%end + +if (output_type == 0) + fprintf('Create node %d split at %d gain %8.4f Th %d. Class %d Cases %d Error %d \n',tree.num_node, best_attr, max_gain, tree.nodes(tree.num_node).split_threshhold, top1_class, size_t, size_t - top1_class_cases ); +else + fprintf('Create node %d split at %d gain %8.4f Th %d. Mean %8.4f Cases %d\n',tree.num_node, best_attr, max_gain, tree.nodes(tree.num_node).split_threshhold, avg_y_T, size_t ); +end + +%(6) Foreach T' in the split_T +% if T' is Empty +% Child of node_id is a leaf +% else +% Child of node_id = split_tree (T') +tree.nodes(new_node).is_leaf=0; %because this node will be split, it is not leaf now +for i=1:size(best_split,2) + if (size(best_split{i},2)==0) %T(i) is empty + %create one new leaf node + tree.num_node=tree.num_node+1; + tree.nodes(tree.num_node).used=1; %flag this node is used (0 means node not used, it will be removed from tree at last to save memory) + tree.nodes(tree.num_node).is_leaf=1; + tree.nodes(tree.num_node).children=[]; + tree.nodes(tree.num_node).split_id=0; + tree.nodes(tree.num_node).split_threshhold=0; + if (output_type == 0) + tree.nodes(tree.num_node).probs=zeros(1,num_cat); %the prob for each value of class node + tree.nodes(tree.num_node).probs(top1_class)=1; %use the majority class of parent node, like for binary class, + %and majority is class 2, then the CPT is [0 1] + %we may need to use prior to do smoothing, to get [0.001 0.999] + tree.nodes(tree.num_node).error.self_error=0; + tree.nodes(tree.num_node).error.all_error=0; + tree.nodes(tree.num_node).error.all_error_num=0; + else + tree.nodes(tree.num_node).mean = avg_y_T; %just use parent node's mean value + tree.nodes(tree.num_node).std = std_T; + end + %add the new leaf node to parents + num_children=size(tree.nodes(new_node).children,2); + tree.nodes(new_node).children(num_children+1)=tree.num_node; + if (output_type==0) + fprintf('Create leaf node(nullset) %d. %d-th child of Father %d Class %d\n',tree.num_node, i, new_node, top1_class ); + else + fprintf('Create leaf node(nullset) %d. %d-th child of Father %d \n',tree.num_node, i, new_node ); + end + + else + if (node_types(best_attr)==0) % if attr is discrete, it should be removed from the candidate set + new_candidate_attrs = mysetdiff(candidate_attrs,[best_attr]); + else + new_candidate_attrs = candidate_attrs; + end + new_sub_node = split_dtree (CPD, fam_ev, node_sizes, node_types, stop_cases, min_gain, best_split{i}, new_candidate_attrs, num_cat); + %tree.nodes(parent_id).error.all_error += tree.nodes(new_sub_node).error.all_error; + fprintf('Add subtree node %d to %d. #nodes %d\n',new_sub_node,new_node, tree.num_node ); + +% tree.nodes(new_node).error.all_error_num = tree.nodes(new_node).error.all_error_num + tree.nodes(new_sub_node).error.all_error_num; + %add the new leaf node to parents + num_children=size(tree.nodes(new_node).children,2); + tree.nodes(new_node).children(num_children+1)=new_sub_node; + end +end + +%(7) Compute errors of N; for doing pruning +% get the total error for the subtree +if (output_type==0) + tree.nodes(new_node).error.all_error=tree.nodes(new_node).error.all_error_num/size_t; +end +%doing pruning, but doing here is not so efficient, because it is bottom up. +%if tree.nodes() +%after doing pruning, need to update the all_error to self_error + +%(8) Return N + + + + +%(1) For discrete output, we use GainRatio defined as below +% Gain(X,T) +% GainRatio(X,T) = ---------- +% SplitInfo(X,T) +% where +% Gain(X,T) = Info(T) - Info(X,T) +% |Ti| +% Info(X,T) = Sum for i from 1 to n of ( ---- * Info(Ti)) +% |T| + +% SplitInfo(D,T) is the information due to the split of T on the basis +% of the value of the categorical attribute D. Thus SplitInfo(D,T) is +% I(|T1|/|T|, |T2|/|T|, .., |Tm|/|T|) +% where {T1, T2, .. Tm} is the partition of T induced by the value of D. + +% Definition of Info(Ti) +% If a set T of records is partitioned into disjoint exhaustive classes C1, C2, .., Ck on the basis of the +% value of the categorical attribute, then the information needed to identify the class of an element of T +% is Info(T) = I(P), where P is the probability distribution of the partition (C1, C2, .., Ck): +% P = (|C1|/|T|, |C2|/|T|, ..., |Ck|/|T|) +% Here I(P) is defined as +% I(P) = -(p1*log(p1) + p2*log(p2) + .. + pn*log(pn)) +% +%(2) For continuous output (regression tree), we use least squares score (adapted from Leo Breiman's book "Classification and regression trees", page 231 +% The original support only binary split, we further extend it to permit multiple-child split +% +% Delta_R = R(T) - Sum for all childe nodes Ti (R(Ti)) +% Where R(Ti)= 1/N * Sum for all cases i in node Ti ((yi - avg_y(Ti))^2) +% here N is the number of all training cases for construct the regression tree +% avg_y(Ti) is the average value for output variable for the cases in node Ti + +function gain_score = compute_gain (fam_ev, node_sizes, node_types, T, info_T, attr_id, split_T, score_type, output_type) +% COMPUTE_GAIN Compute the score for the split of cases T using attribute attr_id +% gain_score = compute_gain (fam_ev, T, attr_id, node_size, method) +% +% fam_ev(i,j) is the value of attribute i in j-th training cases, the last row is for the class label (self_ev) +% T(i) is the index of i-th cases in current decision tree node, we need split it further +% attr_id is the index of current node considered for a split +% split_T{i} is the i_th subset in partition of cases T according to the value of attribute attr_id +% score_type if 0, is gain ratio, 1 is information gain (only apply to discrete output) +% node_size(i) the node size of i-th node in the family +% output_type: 0 means discrete output, 1 means continuous output. +gain_score=0; +% ***********for DISCRETE output******************************************************* +if (output_type == 0) + % compute Info(T) + total_cnt = size(T,2); + if (total_cnt==0) + return; + end; + %class_split_T = split_cases(fam_ev,node_sizes,node_types,T,size(fam_ev,1),0); %split cases according to class + %info_T = compute_info (fam_ev, T, class_split_T); + + % compute Info(X,T) + num_class = size(split_T,2); + subset_sizes = zeros(1,num_class); + info_ti = zeros(1,num_class); + for i=1:num_class + subset_sizes(i)=size(split_T{i},2); + if (subset_sizes(i)~=0) + class_split_Ti = split_cases(fam_ev,node_sizes,node_types,split_T{i},size(fam_ev,1),0); %split cases according to class + info_ti(i) = compute_info(fam_ev, split_T{i}, class_split_Ti); + end + end + ti_ratios = subset_sizes/total_cnt; %get the |Ti|/|T| + info_X_T = sum(ti_ratios.*info_ti); + + %get Gain(X,T) + gain_X_T = info_T - info_X_T; + + if (score_type == 1) %information gain + gain_score=gain_X_T; + return; + end + %compute the SplitInfo(X,T) //is this also for cts attr, only split into two subsets + splitinfo_T = compute_info (fam_ev, T, split_T); + if (splitinfo_T~=0) + gain_score = gain_X_T/splitinfo_T; + end + +% ************for continuous output************************************************** +else + N = size(fam_ev,2); + + % compute R(Ti) + num_class = size(split_T,2); + R_Ti = zeros(1,num_class); + for i=1:num_class + if (size(split_T{i},2)~=0) + cases_T = fam_ev(size(fam_ev,1),split_T{i}); + avg_y_T = mean(cases_T); + sqr_T = cases_T - avg_y_T; + R_Ti(i) = sum(sqr_T.*sqr_T)/N; % get R(Ti) = 1/N * SUM(y-avg_y)^2 + end + end + %delta_R = R(T) - SUM(R(Ti)) + gain_score = info_T - sum(R_Ti); + +end + + +% Definition of Info(Ti) +% If a set T of records is partitioned into disjoint exhaustive classes C1, C2, .., Ck on the basis of the +% value of the categorical attribute, then the information needed to identify the class of an element of T +% is Info(T) = I(P), where P is the probability distribution of the partition (C1, C2, .., Ck): +% P = (|C1|/|T|, |C2|/|T|, ..., |Ck|/|T|) +% Here I(P) is defined as +% I(P) = -(p1*log(p1) + p2*log(p2) + .. + pn*log(pn)) +function info = compute_info (fam_ev, T, split_T) +% COMPUTE_INFO compute the information for the split of T into split_T +% info = compute_info (fam_ev, T, split_T) + +total_cnt = size(T,2); +num_class = size(split_T,2); +subset_sizes = zeros(1,num_class); +probs = zeros(1,num_class); +log_probs = zeros(1,num_class); +for i=1:num_class + subset_sizes(i)=size(split_T{i},2); +end + +probs = subset_sizes/total_cnt; +%log_probs = log2(probs); % if probs(i)=0, the log2(probs(i)) will be Inf +for i=1:size(probs,2) + if (probs(i)~=0) + log_probs(i)=log2(probs(i)); + end +end + +info = sum(-(probs.*log_probs)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/readme.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +Decision/regression tree CPD +Author: Yimin Zhang yimin.zhang@intel.com +21 Jan 2002 + + +See also Paul Bradley's Multisurface Method-Tree matlab code + http://www.cs.wisc.edu/~paulb/msmt/ +http://www.cs.wisc.edu/~olvi/uwmp/msmt.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function CPD = set_fields(CPD, varargin) +% SET_PARAMS Set the parameters (fields) for a tabular_CPD object +% CPD = set_params(CPD, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% +% CPT - the CPT +% prior - the prior +% clamped - 1 means don't adjust during EM +% +% e.g., CPD = set_params(CPD, 'CPT', 'rnd') + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'CPT', + if ischar(args{i+1}) + switch args{i+1} + case 'unif', CPD.CPT = mk_stochastic(myones(CPD.sizes)); + case 'rnd', CPD.CPT = mk_stochastic(myrand(CPD.sizes)); + otherwise, error(['invalid type ' args{i+1}]); + end + elseif isscalarBNT(args{i+1}) + p = args{i+1}; + k = CPD.sizes(end); + % Bug fix by Hervé BOUTROUILLE 10/1/01 + CPD.CPT = myreshape(sample_dirichlet(p*ones(1,k), prod(CPD.sizes(1:end-1)), CPD.sizes)); + %CPD.CPT = myreshape(sample_dirichlet(p*ones(1,k), prod(CPD.sizes(1:end-1))), CPD.sizes); + else + CPD.CPT = myreshape(args{i+1}, CPD.sizes); + end + + case 'prior', + if ischar(args{i+1}) & strcmp(args{i+1}, 'unif') + CPD.prior = myones(CPD.sizes); + elseif isscalarBNT(args{i+1}) + CPD.prior = args{i+1} * normalise(myones(CPD.sizes)); + else + CPD.prior = myreshape(args{i+1}, CPD.sizes); + end + + %case 'clamped', CPD.clamped = strcmp(args{i+1}, 'yes'); + %case 'clamped', CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes')); + case 'clamped', CPD = set_clamped(CPD, args{i+1}); + + otherwise, + %error(['invalid argument name ' args{i}]); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/tree_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function CPD = tree_CPD(varargin) +%DTREE_CPD Make a conditional prob. distrib. which is a decision/regression tree. +% +% CPD =dtree_CPD() will create an empty tree. + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + clamp = 0; + CPD = class(CPD, 'tree_CPD', discrete_CPD(clamp, [])); + return; +elseif isa(varargin{1}, 'tree_CPD') + % This might occur if we are copying an object. + CPD = varargin{1}; + return; +end + +CPD = init_fields; + + +clamped = 0; +fam_sz = []; +CPD = class(CPD, 'tree_CPD', discrete_CPD(clamped, fam_sz)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +%init the decision tree set the root to null +CPD.tree.num_node = 0; +CPD.tree.root=1; +CPD.tree.nodes=[]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/mk_isolated_tabular_CPD.m/1.1.1.1/Mon Jun 24 18:58:32 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +A D/@boolean_CPD//// +A D/@deterministic_CPD//// +A D/@discrete_CPD//// +A D/@gaussian_CPD//// +A D/@generic_CPD//// +A D/@gmux_CPD//// +A D/@hhmm2Q_CPD//// +A D/@hhmmF_CPD//// +A D/@hhmmQ_CPD//// +A D/@mlp_CPD//// +A D/@noisyor_CPD//// +A D/@root_CPD//// +A D/@softmax_CPD//// +A D/@tabular_CPD//// +A D/@tabular_decision_node//// +A D/@tabular_kernel//// +A D/@tabular_utility_node//// +A D/@tree_CPD//// +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@linear_gaussian_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/linear_gaussian_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_marg_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_params_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@linear_gaussian_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/Old/@linear_gaussian_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@linear_gaussian_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@linear_gaussian_CPD/linear_gaussian_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,87 @@ +function CPD = linear_gaussian_CPD(bnet, self, theta, sigma, theta0, n0, alpha0, beta0) +% LINEAR_GAUSSIAN_CPD Make a linear Gaussian distrib. +% +% CPD = linear_gaussian_CPD(bnet, self, theta, lambda) +% This defines the distribution P(Y|X) = N(y | theta'*x, sigma), +% where y (self) is a scalar, theta is a regression vector, and sigma is the variance. +% Pass in [] to generate a default random value for a parameter. +% +% CPD = linear_gaussian_CPD(bnet, self, [], [], theta0, n0, alpha0, beta0) +% defines a Normal-Gamma prior over the parameters: +% P(theta | lambda) = N(theta | theta0, n0*lambda) +% P(lambda) = Gamma(lambda | alpha0, beta0) +% where lambda = 1/sigma is the precision for y. +% n0 is a precision matrix, beta0 is a scale factor. +% Pass in [] to generate a default value for a hyperparameter. +% theta and sigma will be set to their prior expected values. +% See "Bayesian Theory", Bernardo and Smith (2000), p442. + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'linear_gaussian_CPD', generic_CPD(0)); + return; +elseif isa(bnet, 'linear_gaussian_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + + +ns = bnet.node_sizes; +ps = parents(bnet.dag, self); +d = sum(ns(ps)); +assert(ns(self)==1); + + +if nargin < 5, + prior = []; + if isempty(theta), theta = randn(d, 1); end + if isempty(sigma), sigma = 1; end +else + + %if isempty(theta0), theta0 = zeros(d, 1); end + %if isempty(n0), n0 = 0.1*eye(d); end + %if isempty(alpha0), alpha0 = 0.1; end + %if isempty(beta0), beta0 = 0.1; end + + % use non-informative priors + if isempty(theta0), theta0 = zeros(d, 1); end + if isempty(n0), n0 = 0.001*ones(d); end + if isempty(alpha0), alpha0 = -d/2 + 0.001; end + if isempty(beta0), beta0 = 0.001; end + + prior.theta = theta0; + prior.n = n0; + prior.alpha = alpha0; + prior.beta = beta0; + + % set params to their mean + theta = prior.theta; + %sigma = prior.beta/prior.alpha; % mean of Gamma is E[lambda] = alpha/beta +end + + +CPD.self = self; +CPD.theta = theta; +CPD.sigma = sigma; +CPD.prior = prior; + + +clamped = 0; +CPD = class(CPD, 'linear_gaussian_CPD', generic_CPD(clamped)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.theta = []; +CPD.sigma = []; +CPD.prior = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@linear_gaussian_CPD/log_marg_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function L = log_marg_prob_node(CPD, self_ev, pev) +% LOG_MARG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m)) for node i (linear_gaussian) +% L = log_marg_prob_node(CPD, self_ev, pev) +% +% This differs from log_prob_node because we integrate out the parameters. +% self_ev{m} is the evidence on this node in case m. +% pev{i,m} is the evidence on the i'th parent in case m +% We assume there is <= 1 case. + +ncases = length(self_ev); + +if ncases==0 + L = 0; + return; +elseif ncases==1 + y = self_ev{1}; + x = cat(1, pev{:}); % column vector + f = 1-x'*inv(x*x' + CPD.prior.n)*x; + alpha = CPD.prior.alpha; + L = log_student_pdf(y, x'*CPD.prior.theta, f*alpha/CPD.prior.beta, 2*alpha); +else + error('can''t handle batch data'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@linear_gaussian_CPD/update_params_complete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function CPD = update_params_complete(CPD, self_ev, pev) +% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (linear_gaussian) +% CPD = update_params_complete(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m. +% pev{i,m} is the evidence on the i'th parent in case m +% +% We update the hyperparams and set the params to the mean of the posterior. + +y = cat(1, self_ev{:}); +X = cell2num(pev)'; +[N k] = size(X); % each row is a case + +n0 = CPD.prior.n; +th0 = CPD.prior.theta; +CPD.prior.theta = inv(n0 + X'*X)*(n0*th0 + X'*y); +thn = CPD.prior.theta; +CPD.prior.beta = CPD.prior.beta + 0.5*(y-X*thn)'*y + 0.5*(th0-thn)'*n0*th0; +CPD.prior.alpha = CPD.prior.alpha + 0.5*N; +CPD.prior.n = CPD.prior.n + X'*X; + + +% set params to their mean +CPD.theta = CPD.prior.theta; +%CPD.sigma = CPD.prior.beta/CPD.prior.alpha; % mean of Gamma is E[lambda] = alpha/beta
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@root_gaussian_CPD/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/log_marg_prob_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/root_gaussian_CPD.m/1.1.1.1/Wed May 29 15:59:54 2002// +/update_params_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@root_gaussian_CPD/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/Old/@root_gaussian_CPD
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@root_gaussian_CPD/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@root_gaussian_CPD/log_marg_prob_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function L = log_marg_prob_node(CPD, self_ev, pev) +% LOG_MARG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m)) for node i (root_gaussian) +% L = log_marg_prob_node(CPD, self_ev, pev) +% +% This differs from log_prob_node because we integrate out the parameters. +% self_ev{m} is the evidence on this node in case m. +% pev{i,m} is the evidence on the i'th parent in case m (ignored). + +ncases = length(self_ev); + +if ncases==0 + L = 0; + return; +elseif ncases==1 + x = cat(1, self_ev{:}); + k = length(x); + n0 = CPD.prior.n; + mu = CPD.prior.mu; + alpha = CPD.prior.alpha; + beta = CPD.prior.beta; + gamma = 2*alpha - k + 1; + % Bernardo and Smith p441 + L = log_student_pdf(x, mu, n0/(n0+1)*0.5*gamma*inv(beta), gamma); +else + error('can''t handle batch data'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@root_gaussian_CPD/root_gaussian_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +function CPD = root_gaussian_CPD(bnet, self, mu, Sigma, mu0, n0, alpha0, beta0) +% ROOT_GAUSSIAN_CPD Make an unconditional Gaussian distrib. +% +% CPD = root_gaussian_CPD(bnet, self, mu, Sigma) +% This defines the distribution Y ~ N(mu, Sigma), +% Pass in [] to generate a default random value for a parameter. +% +% CPD = root_gaussian_CPD(bnet, self, [], [], mu0, n0, alpha0, beta0) +% defines a Normal-Wishart prior over the parameters: +% P(mu | lambda) = N(mu | mu0, n0*lambda) +% P(lambda) = Wishart(lambda | alpha0, beta0) +% where lambda = inv(Sigma) is the precision matrix of mu. +% n0 is a scale factor, beta0 is a precision matrix. +% Pass in [] to generate a default value for a hyperparameter. +% mu and Sigma will be set to their prior expected values. +% See "Bayesian Theory", Bernardo and Smith (2000), p441. + + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'root_gaussian_CPD', generic_CPD(0)); + return; +elseif isa(bnet, 'root_gaussian_CPD') + % This might occur if we are copying an object. + CPD = bnet; + return; +end +CPD = init_fields; + + +ns = bnet.node_sizes; +d = ns(self); + +if nargin < 5, + prior = []; + if isempty(mu), mu = randn(d, 1); end + if isempty(Sigma), Sigma = eye(d); end +else + if isempty(mu0), mu0 = zeros(d, 1); end + if isempty(n0), n0 = 0.1; end + if isempty(alpha0), alpha0 = (d-1)/2 + 1; end % Wishart requires 2 alpha > d-1 + if isempty(beta0), beta0 = eye(d); end + + prior.mu = mu0; + prior.n = n0; + prior.alpha = alpha0; + prior.beta = beta0; + + % set params to their mean + mu = prior.mu; + Sigma = prior.beta/prior.alpha; % mean of Wishart is E[lambda] = alpha*inv(beta) +end + +CPD.self = self; +CPD.mu = mu; +CPD.Sigma = Sigma; +CPD.prior = prior; + +clamped = 0; +CPD = class(CPD, 'root_gaussian_CPD', generic_CPD(clamped)); + + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.self = []; +CPD.mu = []; +CPD.Sigma = []; +CPD.prior = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@root_gaussian_CPD/update_params_complete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function CPD = update_params_complete(CPD, self_ev, pev) +% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (root_gaussian) +% CPD = update_params_complete(CPD, self_ev, pev) +% +% self_ev{m} is the evidence on this node in case m. +% pev{i,m} is the evidence on the i'th parent in case m (ignored) +% +% We update the hyperparams and set the params to the mean of the posterior. + +X = cell2num(self_ev); +[k N] = size(X); % each column is a case + +one = ones(N,1); +xbar = X*one / N; % = mean(X')' +S = X*(eye(N) - one*one'/N)*X'; + +n0 = CPD.prior.n; +nn = 1/(n0 + N); +mu0 = CPD.prior.mu; +CPD.prior.mu = nn*(n0*mu0 + N*xbar); +CPD.prior.alpha = CPD.prior.alpha + 0.5*N; +CPD.prior.beta = CPD.prior.beta + 0.5*S + 0.5*nn*N*n0*(mu0-xbar)*(mu0-xbar)'; +CPD.prior.n = CPD.prior.n + N; + +% set params to their mean +CPD.mu = CPD.prior.mu; +% E[Cov] = E inv(n lambda) = 1/(n (alpha-(k+1)/2)) beta +CPD.Sigma = CPD.prior.beta /(CPD.prior.n * (CPD.prior.alpha - (k+1)/2)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@tabular_chance_node/CPD_to_upot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function pot = CPD_to_upot(CPD, domain) +% CPD_TO_UPOT Convert a CPD to a utility potential +% pot = CPD_to_upot(CPD, domain) + +sz = CPD.size; % mysize(CPD.CPT); +pot = upot(domain, sz, CPD.CPT, 0*myones(sz));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@tabular_chance_node/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/CPD_to_upot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tabular_chance_node.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@tabular_chance_node/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/Old/@tabular_chance_node
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@tabular_chance_node/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/@tabular_chance_node/tabular_chance_node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function CPD = tabular_chance_node(sz, CPT) +% TABULAR_CHANCE_NODE Like tabular_CPD, but simplified +% CPD = tabular_chance_node(sz, CPT) +% +% sz(1:end-1) is the sizes of the parents, sz(end) is the size of this node +% By default, CPT is a random stochastic matrix. + +if nargin==0 + % This occurs if we are trying to load an object from a file. + CPD = init_fields; + CPD = class(CPD, 'tabular_chance_node'); + return; +elseif isa(sz, 'tabular_chance_node') + % This might occur if we are copying an object. + CPD = sz; + return; +end +CPD = init_fields; + +if nargin < 2, + CPT = mk_stochastic(myones(sz)); +else + CPT = myreshape(CPT, sz); +end + +CPD.CPT = CPT; +CPD.size = sz; + +CPD = class(CPD, 'tabular_chance_node'); + +%%%%%%%%%%% + +function CPD = init_fields() +% This ensures we define the fields in the same order +% no matter whether we load an object from a file, +% or create it from scratch. (Matlab requires this.) + +CPD.CPT = []; +CPD.size = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +A D/@linear_gaussian_CPD//// +A D/@root_gaussian_CPD//// +A D/@tabular_chance_node////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/CPDs/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/mk_isolated_tabular_CPD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function CPD = mk_isolated_tabular_CPD(fam_sz, args) +% function CPD = mk_isolated_tabular_CPD(fam_sz, args) +% function CPD = mk_isolated_tabular_CPD(fam_sz, args) +% Make a single CPD by creating a mini-bnet containing just this one family. +% This is necessary because the CPD constructor requires a bnet. + +n = length(fam_sz); +dag = zeros(n,n); +ps = 1:(n-1); +if ~isempty(ps) + dag(ps,n) = 1; +end +bnet = mk_bnet(dag, fam_sz); +CPD = tabular_CPD(bnet, n, args{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/README.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +The Bayes Net Toolbox for Matlab was written by Kevin Patrick Murphy et al. +This version (FullBNT_1.0.6) was last updated on 5/9/2010, by Wei Sun. + +BNT has moved to bnt.googlecode.com, in Jan. 2010. +To download the latest version, and to get documentation, please go to + http://code.google.com/p/bnt/
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/copyright.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +Written by Kevin Murphy (murphyk@ai.mit.edu), 1997-2003 +Distributed under the GNU Library GPL - see license.gpl for details.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:06 2003// +D/dynamic//// +D/limids//// +D/static////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +/arhmm1.m/1.1.1.1/Thu Nov 14 01:03:34 2002// +/bat1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/bkff1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/chmm1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/cmp_inference_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/cmp_learning_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/cmp_online_inference.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dhmm1.m/1.1.1.1/Sun May 4 22:23:18 2003// +/ehmm1.m/1.1.1.1/Sat Jan 18 22:16:24 2003// +/fhmm_infer.m/1.1.1.1/Wed May 29 15:59:54 2002// +/filter_test1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/ghmm1.m/1.1.1.1/Sun May 4 22:23:32 2003// +/ho1.m/1.1.1.1/Fri Mar 28 17:22:36 2003// +/jtree_clq_test.m/1.1.1.1/Sat Jan 18 22:16:38 2003// +/jtree_clq_test2.m/1.1.1.1/Thu Oct 10 23:45:12 2002// +/kalman1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/kjaerulff1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/loopy_dbn1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mhmm1.m/1.1.1.1/Sun May 4 22:23:40 2003// +/mildew1.m/1.1.1.1/Thu Jun 20 20:30:24 2002// +/mk_bat_dbn.m/1.1.1.1/Mon Jun 7 19:07:18 2004// +/mk_chmm.m/1.1.1.1/Tue May 11 19:23:14 2004// +/mk_collage_from_clqs.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_fhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_mildew_dbn.m/1.1.1.1/Thu Oct 10 23:14:36 2002// +/mk_orig_bat_dbn.m/1.1.1.1/Wed Feb 4 23:53:06 2004// +/mk_orig_water_dbn.m/1.1.1.1/Sat Jan 31 02:57:52 2004// +/mk_ps_from_clqs.m/1.1.1.1/Wed Oct 9 20:36:56 2002// +/mk_uffe_dbn.m/1.1.1.1/Thu Oct 10 23:14:54 2002// +/mk_water_dbn.m/1.1.1.1/Tue May 11 18:45:38 2004// +/orig_water1.m/1.1.1.1/Mon Nov 22 22:41:42 2004// +/reveal1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/scg_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/skf_data_assoc_gmux.m/1.1.1.1/Wed May 29 15:59:54 2002// +/viterbi1.m/1.1.1.1/Tue May 13 14:35:40 2003// +/water1.m/1.1.1.1/Thu Nov 14 20:07:56 2002// +/water2.m/1.1.1.1/Thu Nov 14 20:33:42 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +A D/HHMM//// +A D/Old//// +A D/SLAM////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +/abcd_hhmm.m/1.1.1.1/Sat Sep 21 21:37:54 2002// +/add_hhmm_end_state.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hhmm_jtree_clqs.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_hhmm.m/1.1.1.1/Sat Sep 21 20:58:06 2002// +/mk_hhmm_topo.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_hhmm_topo_F1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/pretty_print_hhmm_parse.m/1.1.1.1/Wed May 29 15:59:54 2002// +/remove_hhmm_end_state.m/1.1.1.1/Mon Dec 16 19:16:50 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +A D/Map//// +A D/Mgram//// +A D/Motif//// +A D/Old//// +A D/Square////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/disp_map_hhmm.m/1.1.1.1/Tue Sep 24 22:45:56 2002// +/learn_map.m/1.1.1.1/Sat Jan 11 18:48:46 2003// +/mk_map_hhmm.m/1.1.1.1/Tue Sep 24 10:49:52 2002// +/mk_rnd_map_hhmm.m/1.1.1.1/Tue Sep 24 22:13:48 2002// +/sample_from_map.m/1.1.1.1/Tue Sep 24 13:02:30 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Map
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/mk_map_hhmm.m/1.1.1.1/Tue Sep 24 07:02:44 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Map/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,156 @@ +function bnet = mk_map_hhmm(varargin) + +% p is the prob of a successful move (defines the reliability of motors) +p = 1; +num_obs_nodes = 1; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'p', p = varargin{i+1}; + case 'numobs', num_obs_node = varargin{i+1}; + end +end + + +q = 1-p; + +% assign numbers to the nodes in topological order +U = 1; A = 2; C = 3; F = 4; O = 5; + +% create graph structure + +ss = 5; % slice size +intra = zeros(ss,ss); +intra(U,F)=1; +intra(A,[C F O])=1; +intra(C,[F O])=1; + +inter = zeros(ss,ss); +inter(U,[A C])=1; +inter(A,[A C])=1; +inter(F,[A C])=1; +inter(C,C)=1; + +% node sizes +ns = zeros(1,ss); +ns(U) = 2; % left/right +ns(A) = 2; +ns(C) = 3; +ns(F) = 2; +ns(O) = 5; % we will assign each state a unique symbol +l = 1; r = 2; % left/right +L = 1; R = 2; + +% Make the DBN +bnet = mk_dbn(intra, inter, ns, 'observed', O); +eclass = bnet.equiv_class; + + + +% Define CPDs for slice 1 +% We clamp all of them, i.e., do not try to learn them. + +% uniform probs over actions (the input could be chosen from a policy) +bnet.CPD{eclass(U,1)} = tabular_CPD(bnet, U, 'CPT', mk_stochastic(ones(ns(U),1)), ... + 'adjustable', 0); + +% uniform probs over starting abstract state +bnet.CPD{eclass(A,1)} = tabular_CPD(bnet, A, 'CPT', mk_stochastic(ones(ns(A),1)), ... + 'adjustable', 0); + +% Uniform probs over starting concrete state, modulo the fact +% that corridor 2 is only of length 2. +CPT = zeros(ns(A), ns(C)); % CPT(i,j) = P(C starts in j | A=i) +CPT(1, :) = [1/3 1/3 1/3]; +CPT(2, :) = [1/2 1/2 0]; +bnet.CPD{eclass(C,1)} = tabular_CPD(bnet, C, 'CPT', CPT, 'adjustable', 0); + +% Termination probs +CPT = zeros(ns(U), ns(A), ns(C), ns(F)); +CPT(r,1,1,:) = [1 0]; +CPT(r,1,2,:) = [1 0]; +CPT(r,1,3,:) = [q p]; +CPT(r,2,1,:) = [1 0]; +CPT(r,2,2,:) = [q p]; +CPT(l,1,1,:) = [q p]; +CPT(l,1,2,:) = [1 0]; +CPT(l,1,3,:) = [1 0]; +CPT(l,2,1,:) = [q p]; +CPT(l,2,2,:) = [1 0]; + +bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', CPT); + + +% Assign each state a unique observation +CPT = zeros(ns(A), ns(C), ns(O)); +CPT(1,1,1)=1; +CPT(1,2,2)=1; +CPT(1,3,3)=1; +CPT(2,1,4)=1; +CPT(2,2,5)=1; +%CPT(2,3,:) undefined + +bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', CPT); + + +% Define the CPDs for slice 2 + +% Abstract + +% Since the top level never resets, the starting distribution is irrelevant: +% A2 will be determined by sampling from transmat(A1,:). +% But the code requires we specify it anyway; we make it all 0s, a dummy value. +startprob = zeros(ns(U), ns(A)); + +transmat = zeros(ns(U), ns(A), ns(A)); +transmat(R,1,:) = [q p]; +transmat(R,2,:) = [0 1]; +transmat(L,1,:) = [1 0]; +transmat(L,2,:) = [p q]; + +% Qps are the parents we condition the parameters on, in this case just +% the past action. +bnet.CPD{eclass(A,2)} = hhmm2Q_CPD(bnet, A+ss, 'Fbelow', F, ... + 'startprob', startprob, 'transprob', transmat); + + + +% Concrete + +transmat = zeros(ns(C), ns(U), ns(A), ns(C)); +transmat(1,r,1,:) = [q p 0.0]; +transmat(2,r,1,:) = [0.0 q p]; +transmat(3,r,1,:) = [0.0 0.0 1.0]; +transmat(1,r,2,:) = [q p 0.0]; +transmat(2,r,2,:) = [0.0 1.0 0.0]; +% +transmat(1,l,1,:) = [1.0 0.0 0.0]; +transmat(2,l,1,:) = [p q 0.0]; +transmat(3,l,1,:) = [0.0 p q]; +transmat(1,l,2,:) = [1.0 0.0 0.0]; +transmat(2,l,2,:) = [p q 0.0]; + +% Add a new dimension for A(t-1), by copying old vals, +% so the matrix is the same size as startprob + + +transmat = reshape(transmat, [ns(C) ns(U) ns(A) 1 ns(C)]); +transmat = repmat(transmat, [1 1 1 ns(A) 1]); + +% startprob(C(t-1), U(t-1), A(t-1), A(t), C(t)) +startprob = zeros(ns(C), ns(U), ns(A), ns(A), ns(C)); +startprob(1,L,1,1,:) = [1.0 0.0 0.0]; +startprob(3,R,1,2,:) = [1.0 0.0 0.0]; +startprob(3,R,1,1,:) = [0.0 0.0 1.0]; +% +startprob(1,L,2,1,:) = [0.0 0.0 010]; +startprob(2,L,2,1,:) = [1.0 0.0 0.0]; +startprob(2,R,2,2,:) = [0.0 1.0 0.0]; + +% want transmat(U,A,C,At,Ct), ie. in topo order +transmat = permute(transmat, [2 3 1 4 5]); +startprob = permute(startprob, [2 3 1 4 5]); +bnet.CPD{eclass(C,2)} = hhmm2Q_CPD(bnet, C+ss, 'Fself', F, ... + 'startprob', startprob, 'transprob', transmat); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/disp_map_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function disp_map_hhmm(bnet) + +eclass = bnet.equiv_class; +U = 1; A = 2; C = 3; F = 4; + +S = struct(bnet.CPD{eclass(A,2)}); +disp('abstract trans') +dispcpt(S.transprob) + +S = struct(bnet.CPD{eclass(C,2)}); +disp('concrete trans for go left') % UAC AC +dispcpt(squeeze(S.transprob(1,:,:,:,:))) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/learn_map.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +seed = 1; +rand('state', seed); +randn('state', seed); + +obs_model = 'unique'; % each cell has a unique label (essentially fully observable) +%obs_model = 'four'; % each cell generates 4 observations, NESW + +% Generate the true network, and a randomization of it +realnet = mk_map_hhmm('p', 0.9, 'obs_model', obs_model); +rndnet = mk_rnd_map_hhmm('obs_model', obs_model); +eclass = realnet.equiv_class; +U = 1; A = 2; C = 3; F = 4; onodes = 5; + +ss = realnet.nnodes_per_slice; +T = 100; +evidence = sample_dbn(realnet, 'length', T); +ev = cell(ss,T); +ev(onodes,:) = evidence(onodes,:); + +infeng = jtree_dbn_inf_engine(rndnet); + +if 0 +% suppose we do not observe the final finish node, but only know +% it is more likely to be on that off +ev2 = ev; +infeng = enter_evidence(infeng, ev2, 'soft_evidence_nodes', [F T], 'soft_evidence', {[0.3 0.7]'}); +end + + +learnednet = learn_params_dbn_em(infeng, {evidence}, 'max_iter', 5); + +disp('real model') +disp_map_hhmm(realnet) + +disp('learned model') +disp_map_hhmm(learnednet) + +disp('rnd model') +disp_map_hhmm(rndnet) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/mk_map_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +function bnet = mk_map_hhmm(varargin) + +% p is the prob of a successful move (defines the reliability of motors) +p = 1; +obs_model = 'unique'; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'p', p = varargin{i+1}; + case 'obs_model', obs_model = varargin{i+1}; + end +end + + +q = 1-p; +unique_obs = strcmp(obs_model, 'unique'); + +% assign numbers to the nodes in topological order +U = 1; A = 2; C = 3; F = 4; +if unique_obs + onodes = 5; +else + N = 5; E = 6; S = 7; W = 8; % north, east, south, west + onodes = [N E S W]; +end + +% create graph structure + +ss = 4 + length(onodes); % slice size +intra = zeros(ss,ss); +intra(U,F)=1; +intra(A,[C F onodes])=1; +intra(C,[F onodes])=1; + +inter = zeros(ss,ss); +inter(U,[A C])=1; +inter(A,[A C])=1; +inter(F,[A C])=1; +inter(C,C)=1; + +% node sizes +ns = zeros(1,ss); +ns(U) = 2; % left/right +ns(A) = 2; +ns(C) = 3; +ns(F) = 2; +if unique_obs + ns(onodes) = 5; % we will assign each state a unique symbol +else + ns(onodes) = 2; +end +l = 1; r = 2; % left/right +L = 1; R = 2; + +% Make the DBN +bnet = mk_dbn(intra, inter, ns, 'observed', onodes); +eclass = bnet.equiv_class; + + + +% Define CPDs for slice 1 +% We clamp all the CPDs that are not tied, +% since we cannot learn them from a single sequence. + +% uniform probs over actions (the input could be chosen from a policy) +bnet.CPD{eclass(U,1)} = tabular_CPD(bnet, U, 'CPT', mk_stochastic(ones(ns(U),1)), ... + 'adjustable', 0); + +% uniform probs over starting abstract state +bnet.CPD{eclass(A,1)} = tabular_CPD(bnet, A, 'CPT', mk_stochastic(ones(ns(A),1)), ... + 'adjustable', 0); + +% Uniform probs over starting concrete state, modulo the fact +% that corridor 2 is only of length 2. +CPT = zeros(ns(A), ns(C)); % CPT(i,j) = P(C starts in j | A=i) +CPT(1, :) = [1/3 1/3 1/3]; +CPT(2, :) = [1/2 1/2 0]; +bnet.CPD{eclass(C,1)} = tabular_CPD(bnet, C, 'CPT', CPT, 'adjustable', 0); + +% Termination probs +CPT = zeros(ns(U), ns(A), ns(C), ns(F)); +CPT(r,1,1,:) = [1 0]; +CPT(r,1,2,:) = [1 0]; +CPT(r,1,3,:) = [q p]; +CPT(r,2,1,:) = [1 0]; +CPT(r,2,2,:) = [q p]; +CPT(l,1,1,:) = [q p]; +CPT(l,1,2,:) = [1 0]; +CPT(l,1,3,:) = [1 0]; +CPT(l,2,1,:) = [q p]; +CPT(l,2,2,:) = [1 0]; + +bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', CPT); + + +% Observation model +if unique_obs + CPT = zeros(ns(A), ns(C), 5); + CPT(1,1,1)=1; % Theo state 4 + CPT(1,2,2)=1; % Theo state 5 + CPT(1,3,3)=1; % Theo state 6 + CPT(2,1,4)=1; % Theo state 9 + CPT(2,2,5)=1; % Theo state 10 + %CPT(2,3,:) undefined + O = onodes(1); + bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', CPT); +else + % north/east/south/west can see wall (1) or opening (2) + CPT = zeros(ns(A), ns(C), 2); + CPT(:,:,1) = q; + CPT(:,:,2) = p; + bnet.CPD{eclass(W,1)} = tabular_CPD(bnet, W, 'CPT', CPT); + bnet.CPD{eclass(E,1)} = tabular_CPD(bnet, E, 'CPT', CPT); + CPT = zeros(ns(A), ns(C), 2); + CPT(:,:,1) = p; + CPT(:,:,2) = q; + bnet.CPD{eclass(S,1)} = tabular_CPD(bnet, S, 'CPT', CPT); + bnet.CPD{eclass(N,1)} = tabular_CPD(bnet, N, 'CPT', CPT); +end + +% Define the CPDs for slice 2 + +% Abstract + +% Since the top level never resets, the starting distribution is irrelevant: +% A2 will be determined by sampling from transmat(A1,:). +% But the code requires we specify it anyway; we make it all 0s, a dummy value. +startprob = zeros(ns(U), ns(A)); + +transmat = zeros(ns(U), ns(A), ns(A)); +transmat(R,1,:) = [q p]; +transmat(R,2,:) = [0 1]; +transmat(L,1,:) = [1 0]; +transmat(L,2,:) = [p q]; + +% Qps are the parents we condition the parameters on, in this case just +% the past action. +bnet.CPD{eclass(A,2)} = hhmm2Q_CPD(bnet, A+ss, 'Fbelow', F, ... + 'startprob', startprob, 'transprob', transmat); + + + +% Concrete + +transmat = zeros(ns(C), ns(U), ns(A), ns(C)); +transmat(1,r,1,:) = [q p 0.0]; +transmat(2,r,1,:) = [0.0 q p]; +transmat(3,r,1,:) = [0.0 0.0 1.0]; +transmat(1,r,2,:) = [q p 0.0]; +transmat(2,r,2,:) = [0.0 1.0 0.0]; +% +transmat(1,l,1,:) = [1.0 0.0 0.0]; +transmat(2,l,1,:) = [p q 0.0]; +transmat(3,l,1,:) = [0.0 p q]; +transmat(1,l,2,:) = [1.0 0.0 0.0]; +transmat(2,l,2,:) = [p q 0.0]; + +% Add a new dimension for A(t-1), by copying old vals, +% so the matrix is the same size as startprob + + +transmat = reshape(transmat, [ns(C) ns(U) ns(A) 1 ns(C)]); +transmat = repmat(transmat, [1 1 1 ns(A) 1]); + +% startprob(C(t-1), U(t-1), A(t-1), A(t), C(t)) +startprob = zeros(ns(C), ns(U), ns(A), ns(A), ns(C)); +startprob(1,L,1,1,:) = [1.0 0.0 0.0]; +startprob(3,R,1,2,:) = [1.0 0.0 0.0]; +startprob(3,R,1,1,:) = [0.0 0.0 1.0]; +% +startprob(1,L,2,1,:) = [0.0 0.0 010]; +startprob(2,L,2,1,:) = [1.0 0.0 0.0]; +startprob(2,R,2,2,:) = [0.0 1.0 0.0]; + +% want transmat(U,A,C,At,Ct), ie. in topo order +transmat = permute(transmat, [2 3 1 4 5]); +startprob = permute(startprob, [2 3 1 4 5]); +bnet.CPD{eclass(C,2)} = hhmm2Q_CPD(bnet, C+ss, 'Fself', F, ... + 'startprob', startprob, 'transprob', transmat); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +function bnet = mk_rnd_map_hhmm(varargin) + +% We copy the deterministic structure of the real HHMM, +% but randomize the probabilities of the adjustable CPDs. +% The key trick is that 0s in the real HHMM remain 0 +% even when multiplied by a randon number. + +obs_model = 'unique'; + +for i=1:2:length(varargin) + switch varargin{i}, + case 'obs_model', obs_model = varargin{i+1}; + end +end + + +unique_obs = strcmp(obs_model, 'unique'); + +psuccess = 0.9; +% must be less than 1, so that pfail > 0 +% otherwise we copy too many 0s +bnet = mk_map_hhmm('p', psuccess, 'obs_model', obs_model); +ns = bnet.node_sizes; +ss = bnet.nnodes_per_slice; + +U = 1; A = 2; C = 3; F = 4; +%unique_obs = (bnet.nnodes_per_slice == 5); +if unique_obs + onodes = 5; +else + north = 5; east = 6; south = 7; west = 8; + onodes = [north east south west]; +end + +eclass = bnet.equiv_class; +S=struct(bnet.CPD{eclass(F,1)}); +CPT = mk_stochastic(rand(size(S.CPT)) .* S.CPT); +bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', CPT); + + +% Observation model +if unique_obs + CPT = zeros(ns(A), ns(C), 5); + CPT(1,1,1)=1; % Theo state 4 + CPT(1,2,2)=1; % Theo state 5 + CPT(1,3,3)=1; % Theo state 6 + CPT(2,1,4)=1; % Theo state 9 + CPT(2,2,5)=1; % Theo state 10 + %CPT(2,3,:) undefined + O = onodes(1); + bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', CPT); +else + for i=[north east south west] + CPT = mk_stochastic(rand(ns(A), ns(C), 2)); + bnet.CPD{eclass(i,1)} = tabular_CPD(bnet, i, 'CPT', CPT); + end +end + +% Define the CPDs for slice 2 + +startprob = zeros(ns(U), ns(A)); +S = struct(bnet.CPD{eclass(A,2)}); +transprob = mk_stochastic(rand(size(S.transprob)) .* S.transprob); +bnet.CPD{eclass(A,2)} = hhmm2Q_CPD(bnet, A+ss, 'Fbelow', F, ... + 'startprob', startprob, 'transprob', transprob); + +S = struct(bnet.CPD{eclass(C,2)}); +transprob = mk_stochastic(rand(size(S.transprob)) .* S.transprob); +startprob = mk_stochastic(rand(size(S.startprob)) .* S.startprob); +bnet.CPD{eclass(C,2)} = hhmm2Q_CPD(bnet, C+ss, 'Fself', F, ... + 'startprob', startprob, 'transprob', transprob); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/sample_from_map.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +if 0 +% Generate some sample paths + +bnet = mk_map_hhmm('p', 1); +% assign numbers to the nodes in topological order +U = 1; A = 2; C = 3; F = 4; O = 5; + + +seed = 0; +rand('state', seed); +randn('state', seed); + +% control policy = sweep right then left +T = 10; +ss = 5; +ev = cell(ss, T); +ev(U,:) = num2cell([R*ones(1,5) L*ones(1,5)]); + +% fix initial conditions to be in left most state +ev{A,1} = 1; +ev{C,1} = 1; +evidence = sample_dbn(bnet, 'length', T, 'evidence', ev) + + +% Now do same but with noisy actuators + +bnet = mk_map_hhmm('p', 0.8); +evidence = sample_dbn(bnet, 'length', T, 'evidence', ev) + +end + +% Now do same but with 4 observations per slice + +bnet = mk_map_hhmm('p', 0.8, 'obs_model', 'four'); +ss = bnet.nnodes_per_slice; + +ev = cell(ss, T); +ev(U,:) = num2cell([R*ones(1,5) L*ones(1,5)]); +ev{A,1} = 1; +ev{C,1} = 1; +evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/letter2num.m/1.1.1.1/Fri Nov 22 23:10:20 2002// +/mgram1.m/1.1.1.1/Fri Nov 22 23:59:00 2002// +/mgram2.m/1.1.1.1/Tue Nov 26 22:04:24 2002// +/mgram3.m/1.1.1.1/Tue Nov 26 22:14:10 2002// +/num2letter.m/1.1.1.1/Fri Nov 22 23:07:40 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Mgram
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/mgram2.m/1.1.1.1/Sat Nov 23 00:44:34 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Mgram/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/Old/mgram2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,191 @@ +% like mgram1, except we use a durational HMM instead of an HHMM2 + +past = 0; + +words = {'the', 't', 'h', 'e'}; +data = 'the'; +nwords = length(words); +word_len = zeros(1, nwords); +word_prob = normalise(ones(1,nwords)); +word_logprob = log(word_prob); +for wi=1:nwords + word_len(wi)=length(words{wi}); +end +D = max(word_len); + + +alphasize = 26*2; +data = letter2num(data); +T = length(data); + +% node numbers +W = 1; % top level state = word id +L = 2; % bottom level state = letter position within word +F = 3; +O = 4; + +ss = 4; +intra = zeros(ss,ss); +intra(W,[F L O])=1; +intra(L,[O F])=1; + +inter = zeros(ss,ss); +inter(W,W)=1; +inter(L,L)=1; +inter(F,[W L O])=1; + +% node sizes +ns = zeros(1,ss); +ns(W) = nwords; +ns(L) = D; +ns(F) = 2; +ns(O) = alphasize; +ns2 = [ns ns]; + +% Make the DBN +bnet = mk_dbn(intra, inter, ns, 'observed', O); +eclass = bnet.equiv_class; + +% uniform start distrib over words, uniform trans mat +Wstart = normalise(ones(1,nwords)); +Wtrans = mk_stochastic(ones(nwords,nwords)); + +% always start in state d = length(word) for each bottom level HMM +Lstart = zeros(nwords, D); +for i=1:nwords + l = length(words{i}); + Lstart(i,l)=1; +end + +% make downcounters +RLtrans = mk_rightleft_transmat(D, 0); % 0 self loop prob +Ltrans = repmat(RLtrans, [1 1 nwords]); + +% Finish when downcoutner = 1 +Fprob = zeros(nwords, D, 2); +Fprob(:,1,2)=1; +Fprob(:,2:end,1)=1; + + +% Define CPDs for slice +bnet.CPD{eclass(W,1)} = tabular_CPD(bnet, W, 'CPT', Wstart); +bnet.CPD{eclass(L,1)} = tabular_CPD(bnet, L, 'CPT', Lstart); +bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', Fprob); + + +% Define CPDs for slice 2 +bnet.CPD{eclass(W,2)} = hhmmQ_CPD(bnet, W+ss, 'Fbelow', F, 'startprob', Wstart, 'transprob', Wtrans); +bnet.CPD{eclass(L,2)} = hhmmQ_CPD(bnet, L+ss, 'Fself', F, 'Qps', W+ss, 'startprob', Lstart, 'transprob', Ltrans); + + +if 0 +% To test it is generating correctly, we create an artificial +% observation process that capitalizes at the start of a new segment +% Oprob(Ft-1,Qt,Dt,Yt) +Oprob = zeros(2,nwords,D,alphasize); +Oprob(1,1,3,letter2num('t'),1)=1; +Oprob(1,1,2,letter2num('h'),1)=1; +Oprob(1,1,1,letter2num('e'),1)=1; +Oprob(2,1,3,letter2num('T'),1)=1; +Oprob(2,1,2,letter2num('H'),1)=1; +Oprob(2,1,1,letter2num('E'),1)=1; +Oprob(1,2,1,letter2num('a'),1)=1; +Oprob(2,2,1,letter2num('A'),1)=1; +Oprob(1,3,1,letter2num('b'),1)=1; +Oprob(2,3,1,letter2num('B'),1)=1; +Oprob(1,4,1,letter2num('c'),1)=1; +Oprob(2,4,1,letter2num('C'),1)=1; + +% Oprob1(Qt,Dt,Yt) +Oprob1 = zeros(nwords,D,alphasize); +Oprob1(1,3,letter2num('t'),1)=1; +Oprob1(1,2,letter2num('h'),1)=1; +Oprob1(1,1,letter2num('e'),1)=1; +Oprob1(2,1,letter2num('a'),1)=1; +Oprob1(3,1,letter2num('b'),1)=1; +Oprob1(4,1,letter2num('c'),1)=1; + +bnet.CPD{eclass(O,2)} = tabular_CPD(bnet, O+ss, 'CPT', Oprob); +bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', Oprob1); + +evidence = cell(ss,T); +%evidence{W,1}=1; +sample = cell2num(sample_dbn(bnet, 'length', T, 'evidence', evidence)); +str = num2letter(sample(4,:)) +end + + + + +[log_obslik, obslik, match] = mk_mgram_obslik(lower(data), words, word_len, word_prob); +% obslik(j,t,d) +softCPDpot = cell(ss,T); +ens = ns; +ens(O)=1; +ens2 = [ens ens]; +for t=2:T + dom = [F W+ss L+ss O+ss]; + % tab(Ft-1, Q2, Dt) + tab = ones(2, nwords, D); + if past + tab(1,:,:)=1; % if haven't finished previous word, likelihood is 1 + tab(2,:,:) = squeeze(obslik(:,t,:)); % otherwise likelihood of this segment + else + for d=1:max(1,min(D,T+1-t)) + tab(2,:,d) = squeeze(obslik(:,t+d-1,d)); + end + end + softCPDpot{O,t} = dpot(dom, ens2(dom), tab); +end +t = 1; +dom = [W L O]; +% tab(Q2, Dt) +tab = ones(nwords, D); +if past + tab = squeeze(obslik(:,t,:)); +else + for d=1:min(D,T-t) + tab(:,d) = squeeze(obslik(:,t+d-1,d)); + end +end +softCPDpot{O,t} = dpot(dom, ens(dom), tab); + + +%bnet.observed = []; +% uniformative observations +%bnet.CPD{eclass(O,2)} = tabular_CPD(bnet, O+ss, 'CPT', mk_stochastic(ones(2,nwords,D,alphasize))); +%bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', mk_stochastic(ones(nwords,D,alphasize))); + +engine = jtree_dbn_inf_engine(bnet); +evidence = cell(ss,T); +% we add dummy data to O to force its effective size to be 1. +% The actual values have already been incorporated into softCPDpot +evidence(O,:) = num2cell(ones(1,T)); +[engine, ll_dbn] = enter_evidence(engine, evidence, 'softCPDpot', softCPDpot); + + +%evidence(F,:) = num2cell(2*ones(1,T)); +%[engine, ll_dbn] = enter_evidence(engine, evidence); + + +gamma = zeros(nwords, T); +for t=1:T + m = marginal_nodes(engine, [W F], t); + gamma(:,t) = m.T(:,2); +end + +gamma + +xidbn = zeros(nwords, nwords); +for t=1:T-1 + m = marginal_nodes(engine, [W F W+ss], t); + xidbn = xidbn + squeeze(m.T(:,2,:)); +end + +% thee +% xidbn(1,4) = 0.9412 the->e +% (2,3)=0.0588 t->h +% (3,4)=0.0588 h-e +% (4,4)=0.0588 e-e + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/letter2num.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function n = letter2num(l) + +% map a-z to 1:26 and A-Z to 27:52 +punct_code = [32:47 58:64 91:96 123:126]; +digits_code = 48:57; +upper_code = 65:90; +lower_code = 97:122; + +c = double(l); +n = c-96; +ndx = find(n <= 0); % upper case +n(ndx) = c(ndx) - 64 + 26;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/mgram1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,116 @@ +% a multigram is a degenerate 2HHMM where the bottom level HMMs emit deterministic strings +% and the the top level abstract states are independent of each other +% cf. HSMM/test_mgram2 + +words = {'the', 't', 'h', 'e'}; +data = 'the'; +nwords = length(words); +word_len = zeros(1, nwords); +word_prob = normalise(ones(1,nwords)); +word_logprob = log(word_prob); +for wi=1:nwords + word_len(wi)=length(words{wi}); +end +D = max(word_len); + +alphasize = 26; +data = letter2num(data); +T = length(data); + +% node numbers +W = 1; % top level state = word id +L = 2; % bottom level state = letter position within word +F = 3; +O = 4; + +ss = 4; +intra = zeros(ss,ss); +intra(W,[F L O])=1; +intra(L,[O F])=1; + +inter = zeros(ss,ss); +inter(W,W)=1; +inter(L,L)=1; +inter(F,[W L])=1; + +% node sizes +ns = zeros(1,ss); +ns(W) = nwords; +ns(L) = D; +ns(F) = 2; +ns(O) = alphasize; + + +% Make the DBN +bnet = mk_dbn(intra, inter, ns, 'observed', O); +eclass = bnet.equiv_class; + + + +% uniform start distrib over words, uniform trans mat +Wstart = normalise(ones(1,nwords)); +Wtrans = mk_stochastic(ones(nwords,nwords)); + +% always start in state 1 for each bottom level HMM +delta1_start = zeros(1, D); +delta1_start(1) = 1; +Lstart = repmat(delta1_start, nwords, 1); +LRtrans = mk_leftright_transmat(D, 0); % 0 self loop prob +Ltrans = repmat(LRtrans, [1 1 nwords]); + +% Finish in the last letter of each word +Fprob = zeros(nwords, D, 2); +Fprob(:,:,1)=1; +for i=1:nwords + Fprob(i,length(words{i}),2)=1; + Fprob(i,length(words{i}),1)=0; +end + +% Each state uniquely emits a letter +Oprob = zeros(nwords, D, alphasize); +for i=1:nwords + for l=1:length(words{i}) + a = double(words{i}(l))-96; + Oprob(i,l,a)=1; + end +end + + +% Define CPDs for slice +bnet.CPD{eclass(W,1)} = tabular_CPD(bnet, W, 'CPT', Wstart); +bnet.CPD{eclass(L,1)} = tabular_CPD(bnet, L, 'CPT', Lstart); +bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', Fprob); +bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', Oprob); + +% Define CPDs for slice 2 +bnet.CPD{eclass(W,2)} = hhmmQ_CPD(bnet, W+ss, 'Fbelow', F, 'startprob', Wstart, 'transprob', Wtrans); +bnet.CPD{eclass(L,2)} = hhmmQ_CPD(bnet, L+ss, 'Fself', F, 'Qps', W+ss, 'startprob', Lstart, 'transprob', Ltrans); + +evidence = cell(ss,T); +evidence{W,1}=1; +sample = cell2num(sample_dbn(bnet, 'length', T, 'evidence', evidence)); +str = lower(sample(4,:)) + +engine = jtree_dbn_inf_engine(bnet); +evidence = cell(ss,T); +evidence(O,:) = num2cell(data); +[engine, ll_dbn] = enter_evidence(engine, evidence); + +gamma = zeros(nwords, T); +for t=1:T + m = marginal_nodes(engine, [W F], t); + gamma(:,t) = m.T(:,2); +end +gamma + +xidbn = zeros(nwords, nwords); +for t=1:T-1 + m = marginal_nodes(engine, [W F W+ss], t); + xidbn = xidbn + squeeze(m.T(:,2,:)); +end + +% thee +% xidbn(1,4) = 0.9412 the->e +% (2,3)=0.0588 t->h +% (3,4)=0.0588 h-e +% (4,4)=0.0588 e-e
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/mgram2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,200 @@ +% Like a durational HMM, except we use soft evidence on the observed nodes. +% Should give the same results as HSMM/test_mgram2. + +past = 1; +% If past=1, P(Yt|Qt=j,Dt=d) = P(y_{t-d+1:t}|j) +% If past=0, P(Yt|Qt=j,Dt=d) = P(y_{t:t+d-1}|j) - future evidence + +words = {'the', 't', 'h', 'e'}; +data = 'the'; +nwords = length(words); +word_len = zeros(1, nwords); +word_prob = normalise(ones(1,nwords)); +word_logprob = log(word_prob); +for wi=1:nwords + word_len(wi)=length(words{wi}); +end +D = max(word_len); + + +alphasize = 26*2; +data = letter2num(data); +T = length(data); + +% node numbers +W = 1; % top level state = word id +L = 2; % bottom level state = letter position within word +F = 3; +O = 4; + +ss = 4; +intra = zeros(ss,ss); +intra(W,[F L O])=1; +intra(L,[O F])=1; + +inter = zeros(ss,ss); +inter(W,W)=1; +inter(L,L)=1; +inter(F,[W L O])=1; + +% node sizes +ns = zeros(1,ss); +ns(W) = nwords; +ns(L) = D; +ns(F) = 2; +ns(O) = alphasize; +ns2 = [ns ns]; + +% Make the DBN +bnet = mk_dbn(intra, inter, ns, 'observed', O); +eclass = bnet.equiv_class; + +% uniform start distrib over words, uniform trans mat +Wstart = normalise(ones(1,nwords)); +Wtrans = mk_stochastic(ones(nwords,nwords)); +%Wtrans = ones(nwords,nwords); + +% always start in state d = length(word) for each bottom level HMM +Lstart = zeros(nwords, D); +for i=1:nwords + l = length(words{i}); + Lstart(i,l)=1; +end + +% make downcounters +RLtrans = mk_rightleft_transmat(D, 0); % 0 self loop prob +Ltrans = repmat(RLtrans, [1 1 nwords]); + +% Finish when downcoutner = 1 +Fprob = zeros(nwords, D, 2); +Fprob(:,1,2)=1; +Fprob(:,2:end,1)=1; + + +% Define CPDs for slice 1 +bnet.CPD{eclass(W,1)} = tabular_CPD(bnet, W, 'CPT', Wstart); +bnet.CPD{eclass(L,1)} = tabular_CPD(bnet, L, 'CPT', Lstart); +bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', Fprob); + + +% Define CPDs for slice 2 +bnet.CPD{eclass(W,2)} = hhmmQ_CPD(bnet, W+ss, 'Fbelow', F, 'startprob', Wstart, 'transprob', Wtrans); +bnet.CPD{eclass(L,2)} = hhmmQ_CPD(bnet, L+ss, 'Fself', F, 'Qps', W+ss, 'startprob', Lstart, 'transprob', Ltrans); + + +if 0 +% To test it is generating correctly, we create an artificial +% observation process that capitalizes at the start of a new segment +% Oprob(Ft-1,Qt,Dt,Yt) +Oprob = zeros(2,nwords,D,alphasize); +Oprob(1,1,3,letter2num('t'),1)=1; +Oprob(1,1,2,letter2num('h'),1)=1; +Oprob(1,1,1,letter2num('e'),1)=1; +Oprob(2,1,3,letter2num('T'),1)=1; +Oprob(2,1,2,letter2num('H'),1)=1; +Oprob(2,1,1,letter2num('E'),1)=1; +Oprob(1,2,1,letter2num('a'),1)=1; +Oprob(2,2,1,letter2num('A'),1)=1; +Oprob(1,3,1,letter2num('b'),1)=1; +Oprob(2,3,1,letter2num('B'),1)=1; +Oprob(1,4,1,letter2num('c'),1)=1; +Oprob(2,4,1,letter2num('C'),1)=1; + +% Oprob1(Qt,Dt,Yt) +Oprob1 = zeros(nwords,D,alphasize); +Oprob1(1,3,letter2num('t'),1)=1; +Oprob1(1,2,letter2num('h'),1)=1; +Oprob1(1,1,letter2num('e'),1)=1; +Oprob1(2,1,letter2num('a'),1)=1; +Oprob1(3,1,letter2num('b'),1)=1; +Oprob1(4,1,letter2num('c'),1)=1; + +bnet.CPD{eclass(O,2)} = tabular_CPD(bnet, O+ss, 'CPT', Oprob); +bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', Oprob1); + +evidence = cell(ss,T); +%evidence{W,1}=1; +sample = cell2num(sample_dbn(bnet, 'length', T, 'evidence', evidence)); +str = num2letter(sample(4,:)) +end + + +if 1 + +[log_obslik, obslik, match] = mk_mgram_obslik(lower(data), words, word_len, word_prob); +% obslik(j,t,d) +softCPDpot = cell(ss,T); +ens = ns; +ens(O)=1; +ens2 = [ens ens]; +for t=2:T + dom = [F W+ss L+ss O+ss]; + % tab(Ft-1, Q2, Dt) + tab = ones(2, nwords, D); + if past + tab(1,:,:)=1; % if haven't finished previous word, likelihood is 1 + %tab(2,:,:) = squeeze(obslik(:,t,:)); % otherwise likelihood of this segment + for d=1:min(t,D) + tab(2,:,d) = squeeze(obslik(:,t,d)); + end + else + for d=1:max(1,min(D,T+1-t)) + tab(2,:,d) = squeeze(obslik(:,t+d-1,d)); + end + end + softCPDpot{O,t} = dpot(dom, ens2(dom), tab); +end +t = 1; +dom = [W L O]; +% tab(Q2, Dt) +tab = ones(nwords, D); +if past + %tab = squeeze(obslik(:,t,:)); + tab(:,1) = squeeze(obslik(:,t,1)); +else + for d=1:min(D,T-t) + tab(:,d) = squeeze(obslik(:,t+d-1,d)); + end +end +softCPDpot{O,t} = dpot(dom, ens(dom), tab); + + +%bnet.observed = []; +% uniformative observations +%bnet.CPD{eclass(O,2)} = tabular_CPD(bnet, O+ss, 'CPT', mk_stochastic(ones(2,nwords,D,alphasize))); +%bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', mk_stochastic(ones(nwords,D,alphasize))); + +engine = jtree_dbn_inf_engine(bnet); +evidence = cell(ss,T); +% we add dummy data to O to force its effective size to be 1. +% The actual values have already been incorporated into softCPDpot +evidence(O,:) = num2cell(ones(1,T)); +[engine, ll_dbn] = enter_evidence(engine, evidence, 'softCPDpot', softCPDpot); + + +%evidence(F,:) = num2cell(2*ones(1,T)); +%[engine, ll_dbn] = enter_evidence(engine, evidence); + + +gamma = zeros(nwords, T); +for t=1:T + m = marginal_nodes(engine, [W F], t); + gamma(:,t) = m.T(:,2); +end + +gamma + +xidbn = zeros(nwords, nwords); +for t=1:T-1 + m = marginal_nodes(engine, [W F W+ss], t); + xidbn = xidbn + squeeze(m.T(:,2,:)); +end + +% thee +% xidbn(1,4) = 0.9412 the->e +% (2,3)=0.0588 t->h +% (3,4)=0.0588 h-e +% (4,4)=0.0588 e-e + + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/mgram3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,235 @@ +% like mgram2, except we unroll the DBN so we can use smaller +% state spaces for the early duration nodes: +% the state spaces are D1 in {1}, D2 in {1,2} + +past = 1; + +words = {'the', 't', 'h', 'e'}; +data = 'the'; +nwords = length(words); +word_len = zeros(1, nwords); +word_prob = normalise(ones(1,nwords)); +word_logprob = log(word_prob); +for wi=1:nwords + word_len(wi)=length(words{wi}); +end +D = max(word_len); + + +alphasize = 26*2; +data = letter2num(data); +T = length(data); + +% node numbers +W = 1; % top level state = word id +L = 2; % bottom level state = letter position within word +F = 3; +O = 4; + +ss = 4; +intra = zeros(ss,ss); +intra(W,[F L O])=1; +intra(L,[O F])=1; + +inter = zeros(ss,ss); +inter(W,W)=1; +inter(L,L)=1; +inter(F,[W L O])=1; + +T = 3; +dag = unroll_dbn_topology(intra, inter, T); + +% node sizes +ns = zeros(1,ss); +ns(W) = nwords; +ns(L) = D; +ns(F) = 2; +ns(O) = alphasize; +ns = repmat(ns(:), [1 T]); +for d=1:D + ns(d,L)=d; % max duration +end +ns = ns(:); + +% Equiv class in brackets for D=3 +% The Lt's are not tied until t>=D, since they have different sizes. +% W1 and W2 are not tied since they have different parent sets. + +% W1 (1) W2 (5) W3 (5) W4 (5) +% L1 (2) L2 (6) L3 (7) L4 (7) +% F1 (3) F2 (3) F3 (4) F3 (4) +% O1 (4) O2 (4) O2 (4) O4 (4) + +% Since we are not learning, we can dispense with tying + +% Make the bnet +Wnodes = unroll_set(W, ss, T); +Lnodes = unroll_set(L, ss, T); +Fnodes = unroll_set(F, ss, T); +Onodes = unroll_set(O, ss, T); + +bnet = mk_bnet(dag, ns); +eclass = bnet.equiv_class; + +% uniform start distrib over words, uniform trans mat +Wstart = normalise(ones(1,nwords)); +Wtrans = mk_stochastic(ones(nwords,nwords)); +bnet.CPD{eclass(Wnodes(1))} = tabular_CPD(bnet, Wnodes(1), 'CPT', Wstart); +for t=2:T +bnet.CPD{eclass(Wnodes(t))} = hhmmQ_CPD(bnet, Wnodes(t), 'Fbelow', Fnodes(t-1), ... + 'startprob', Wstart, 'transprob', Wtrans); +end + +% always start in state d = length(word) for each bottom level HMM +% and then count down +% make downcounters +RLtrans = mk_rightleft_transmat(D, 0); % 0 self loop prob +Ltrans = repmat(RLtrans, [1 1 nwords]); + +for t=1:T + Lstart = zeros(nwords, min(t,D)); + for i=1:nwords + l = length(words{i}); + Lstart(i,l)=1; + if d==1 + bnet.CPD{eclass(Lnodes(1))} = tabular_CPD(bnet, Lnodes(1), 'CPT', Lstart); + else + bnet.CPD{eclass(Lnodes(t))} = hhmmQ_CPD(bnet, Lnodes(t), 'Fself', Fnodes(t-1), 'Qps', Wnodes(t), ... + 'startprob', Lstart, 'transprob', Ltrans); + end + end +end + + +% Finish when downcoutner = 1 +Fprob = zeros(nwords, D, 2); +Fprob(:,1,2)=1; +Fprob(:,2:end,1)=1; + + +% Define CPDs for slice +bnet.CPD{eclass(W,1)} = tabular_CPD(bnet, W, 'CPT', Wstart); +bnet.CPD{eclass(L,1)} = tabular_CPD(bnet, L, 'CPT', Lstart); +bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', Fprob); + + +% Define CPDs for slice 2 +bnet.CPD{eclass(W,2)} = hhmmQ_CPD(bnet, W+ss, 'Fbelow', F, 'startprob', Wstart, 'transprob', Wtrans); +bnet.CPD{eclass(L,2)} = hhmmQ_CPD(bnet, L+ss, 'Fself', F, 'Qps', W+ss, 'startprob', Lstart, 'transprob', Ltrans); + + +if 0 +% To test it is generating correctly, we create an artificial +% observation process that capitalizes at the start of a new segment +% Oprob(Ft-1,Qt,Dt,Yt) +Oprob = zeros(2,nwords,D,alphasize); +Oprob(1,1,3,letter2num('t'),1)=1; +Oprob(1,1,2,letter2num('h'),1)=1; +Oprob(1,1,1,letter2num('e'),1)=1; +Oprob(2,1,3,letter2num('T'),1)=1; +Oprob(2,1,2,letter2num('H'),1)=1; +Oprob(2,1,1,letter2num('E'),1)=1; +Oprob(1,2,1,letter2num('a'),1)=1; +Oprob(2,2,1,letter2num('A'),1)=1; +Oprob(1,3,1,letter2num('b'),1)=1; +Oprob(2,3,1,letter2num('B'),1)=1; +Oprob(1,4,1,letter2num('c'),1)=1; +Oprob(2,4,1,letter2num('C'),1)=1; + +% Oprob1(Qt,Dt,Yt) +Oprob1 = zeros(nwords,D,alphasize); +Oprob1(1,3,letter2num('t'),1)=1; +Oprob1(1,2,letter2num('h'),1)=1; +Oprob1(1,1,letter2num('e'),1)=1; +Oprob1(2,1,letter2num('a'),1)=1; +Oprob1(3,1,letter2num('b'),1)=1; +Oprob1(4,1,letter2num('c'),1)=1; + +bnet.CPD{eclass(O,2)} = tabular_CPD(bnet, O+ss, 'CPT', Oprob); +bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', Oprob1); + +evidence = cell(ss,T); +%evidence{W,1}=1; +sample = cell2num(sample_dbn(bnet, 'length', T, 'evidence', evidence)); +str = num2letter(sample(4,:)) +end + + + + +[log_obslik, obslik, match] = mk_mgram_obslik(lower(data), words, word_len, word_prob); +% obslik(j,t,d) +softCPDpot = cell(ss,T); +ens = ns; +ens(O)=1; +ens2 = [ens ens]; +for t=2:T + dom = [F W+ss L+ss O+ss]; + % tab(Ft-1, Q2, Dt) + tab = ones(2, nwords, D); + if past + tab(1,:,:)=1; % if haven't finished previous word, likelihood is 1 + %tab(2,:,:) = squeeze(obslik(:,t,:)); % otherwise likelihood of this segment + for d=1:min(t,D) + tab(2,:,d) = squeeze(obslik(:,t,d)); + end + else + for d=1:max(1,min(D,T+1-t)) + tab(2,:,d) = squeeze(obslik(:,t+d-1,d)); + end + end + softCPDpot{O,t} = dpot(dom, ens2(dom), tab); +end +t = 1; +dom = [W L O]; +% tab(Q2, Dt) +tab = ones(nwords, D); +if past + %tab = squeeze(obslik(:,t,:)); + tab(:,1) = squeeze(obslik(:,t,1)); +else + for d=1:min(D,T-t) + tab(:,d) = squeeze(obslik(:,t+d-1,d)); + end +end +softCPDpot{O,t} = dpot(dom, ens(dom), tab); + + +%bnet.observed = []; +% uniformative observations +%bnet.CPD{eclass(O,2)} = tabular_CPD(bnet, O+ss, 'CPT', mk_stochastic(ones(2,nwords,D,alphasize))); +%bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', mk_stochastic(ones(nwords,D,alphasize))); + +engine = jtree_dbn_inf_engine(bnet); +evidence = cell(ss,T); +% we add dummy data to O to force its effective size to be 1. +% The actual values have already been incorporated into softCPDpot +evidence(O,:) = num2cell(ones(1,T)); +[engine, ll_dbn] = enter_evidence(engine, evidence, 'softCPDpot', softCPDpot); + + +%evidence(F,:) = num2cell(2*ones(1,T)); +%[engine, ll_dbn] = enter_evidence(engine, evidence); + + +gamma = zeros(nwords, T); +for t=1:T + m = marginal_nodes(engine, [W F], t); + gamma(:,t) = m.T(:,2); +end + +gamma + +xidbn = zeros(nwords, nwords); +for t=1:T-1 + m = marginal_nodes(engine, [W F W+ss], t); + xidbn = xidbn + squeeze(m.T(:,2,:)); +end + +% thee +% xidbn(1,4) = 0.9412 the->e +% (2,3)=0.0588 t->h +% (3,4)=0.0588 h-e +% (4,4)=0.0588 e-e + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Mgram/num2letter.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function l = num2letter(n) + +% map 1:26 to a-z and 27:52 to A-Z +punct_code = [32:47 58:64 91:96 123:126]; +digits_code = 48:57; +upper_code = 65:90; +lower_code = 97:122; + +letters = [char(lower_code) char(upper_code)]; +l = letters(n);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Motif/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/fixed_args_mk_motif_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/learn_motif_hhmm.m/1.1.1.1/Tue Jul 2 22:56:14 2002// +/mk_motif_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_motif_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Motif/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Motif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Motif/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Motif/fixed_args_mk_motif_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,99 @@ +function bnet = fixed_args_mk_motif_hhmm(motif_length, motif_pattern, background_char) +% +% BNET = MK_MOTIF_HHMM(MOTIF_LENGTH) +% Make the following HHMM +% +% S2 <----------------------> S1 +% | | +% | | +% M1 -> M2 -> M3 -> end B1 -> end +% +% where Mi represents the i'th letter in the motif +% and B is the background state. +% Si chooses between running the motif or the background. +% The Si and B states have self loops (not shown). +% +% The transition params are defined to respect the above topology. +% The background is uniform; each motif state has a random obs. distribution. +% +% BNET = MK_MOTIF_HHMM(MOTIF_LENGTH, MOTIF_PATTERN) +% In this case, we make the motif submodel deterministically +% emit the motif pattern. +% +% BNET = MK_MOTIF_HHMM(MOTIF_LENGTH, MOTIF_PATTERN, BACKGROUND_CHAR) +% In this case, we make the background submodel +% deterministically emit the specified character (to make the pattern +% easier to see). + +if nargin < 2, motif_pattern = []; end +if nargin < 3, background_char = []; end + +chars = ['a', 'c', 'g', 't']; +Osize = length(chars); + +motif_length = length(motif_pattern); +Qsize = [2 motif_length]; +Qnodes = 1:2; +D = 2; +transprob = cell(1,D); +termprob = cell(1,D); +startprob = cell(1,D); + +% startprob{d}(k,j), startprob{1}(1,j) +% transprob{d}(i,k,j), transprob{1}(i,j) +% termprob{d}(k,j) + + +% LEVEL 1 + +startprob{1} = zeros(1, 2); +startprob{1} = [1 0]; % always start in the background model + +% When in the background state, we stay there with high prob +% When in the motif state, we immediately return to the background state. +transprob{1} = [0.8 0.2; + 1.0 0.0]; + + +% LEVEL 2 +startprob{2} = 'leftstart'; % both submodels start in substate 1 +transprob{2} = zeros(motif_length, 2, motif_length); +termprob{2} = zeros(2, motif_length); + +% In the background model, we only use state 1. +transprob{2}(1,1,1) = 1; % self loop +termprob{2}(1,1) = 0.2; % prob transition to end state + +% Motif model +transprob{2}(:,2,:) = mk_leftright_transmat(motif_length, 0); % no self loops +termprob{2}(2,end) = 1.0; % last state immediately terminates + + +% OBS LEVEl + +obsprob = zeros([Qsize Osize]); +if isempty(background_char) + % uniform background model + obsprob(1,1,:) = normalise(ones(Osize,1)); +else + % deterministic background model (easy to see!) + m = find(chars==background_char); + obsprob(1,1,m) = 1.0; +end + +if gen_motif + % initialise with true motif (cheating) + for i=1:motif_length + m = find(chars == motif_pattern(i)); + obsprob(2,i,m) = 1.0; + end +else + obsprob(2,:,:) = mk_stochastic(ones(motif_length, Osize)); +end + +Oargs = {'CPT', obsprob}; + +[bnet, Qnodes, Fnodes, Onode] = mk_hhmm('Qsizes', Qsize, 'Osize', Osize, 'discrete_obs', 1, ... + 'Oargs', Oargs, 'Ops', Qnodes(1:2), ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Motif/learn_motif_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,75 @@ + +seed = 0; +rand('state', seed); +randn('state', seed); + +chars = ['a', 'c', 'g', 't']; +motif = 'accca'; +motif_length = length(motif); +motif_code = zeros(1, motif_length); +for i=1:motif_length + motif_code(i) = find(chars == motif(i)); +end + +[bnet_init, Qnodes, Fnodes, Onode] = mk_motif_hhmm('motif_length', length(motif)); +%[bnet_init, Qnodes, Fnodes, Onode] = mk_motif_hhmm('motif_pattern', motif); +ss = bnet_init.nnodes_per_slice; + + + +% We generate a training set by creating uniform sequences, +% and inserting a single motif at a random location. +ntrain = 100; +T = 20; +cases = cell(1, ntrain); + +if 1 + % uniform background + background_dist = normalise(ones(1, length(chars))); +end +if 0 + % use a constant background + background_dist = zeros(1, length(chars)); + m = find(chars=='t'); + background_dist(m) = 1.0; +end +if 0 + % use a background skewed away from the motif + p = 0.01; q = (1-(2*p))/2; + background_dist = [p p q q]; +end + +unif_pos = normalise(ones(1, T-length(motif))); +cases = cell(1, ntrain); +data = zeros(1,T); +for i=1:ntrain + data = sample_discrete(background_dist, 1, T); + L = sample_discrete(unif_pos, 1, 1); + data(L:L+length(motif)-1) = motif_code; + cases{i} = cell(ss, T); + cases{i}(Onode,:) = num2cell(data); +end +disp('sample training cases') +for i=1:5 + chars(cell2num(cases{i}(Onode,:))) +end + +engine_init = hmm_inf_engine(bnet_init); + +[bnet_learned, LL, engine_learned] = ... + learn_params_dbn_em(engine_init, cases, 'max_iter', 100, 'thresh', 1e-2); +% 'anneal', 1, 'anneal_rate', 0.7); + +% extract the learned motif profile +eclass = bnet_learned.equiv_class; +CPDO=struct(bnet_learned.CPD{eclass(Onode,1)}); +fprintf('columns = chars, rows = states\n'); +profile_learned = squeeze(CPDO.CPT(2,:,:)) +[m,ndx] = max(profile_learned, [], 2); +map_motif_learned = chars(ndx) +back_learned = squeeze(CPDO.CPT(1,1,:))' +%map_back_learned = chars(argmax(back_learned)) + +CPDO_init = struct(bnet_init.CPD{eclass(Onode,1)}); +profile_init = squeeze(CPDO_init.CPT(2,:,:)); +back_init = squeeze(CPDO_init.CPT(1,1,:))';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Motif/mk_motif_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,137 @@ +function [bnet, Qnodes, Fnodes, Onode] = mk_motif_hhmm(varargin) +% [bnet, Qnodes, Fnodes, Onode] = mk_motif_hhmm(...) +% +% Make the following HHMM +% +% S2 <----------------------> S1 +% | | +% | | +% M1 -> M2 -> M3 -> end B1 -> end +% +% where Mi represents the i'th letter in the motif +% and B is the background state. +% Si chooses between running the motif or the background. +% The Si and B states have self loops (not shown). +% +% The transition params are defined to respect the above topology. +% The background is uniform; each motif state has a random obs. distribution. +% +% Optional params: +% motif_length - required, unless we specify motif_pattern +% motif_pattern - if specified, we make the motif submodel deterministically +% emit this pattern +% background - if specified, we make the background submodel +% deterministically emit this (makes the motif easier to see!) + + +args = varargin; +nargs = length(args); + +% extract pattern, if any +motif_pattern = []; +for i=1:2:nargs + switch args{i}, + case 'motif_pattern', motif_pattern = args{i+1}; + end +end + +% set defaults +motif_length = length(motif_pattern); +background_char = []; + +% get params +for i=1:2:nargs + switch args{i}, + case 'motif_length', motif_length = args{i+1}; + case 'background', background_char = args{i+1}; + end +end + + +chars = ['a', 'c', 'g', 't']; +Osize = length(chars); + +Qsize = [2 motif_length]; +Qnodes = 1:2; +D = 2; +transprob = cell(1,D); +termprob = cell(1,D); +startprob = cell(1,D); + +% startprob{d}(k,j), startprob{1}(1,j) +% transprob{d}(i,k,j), transprob{1}(i,j) +% termprob{d}(k,j) + + +% LEVEL 1 + +startprob{1} = zeros(1, 2); +startprob{1} = [1 0]; % always start in the background model + +% When in the background state, we stay there with high prob +% When in the motif state, we immediately return to the background state. +transprob{1} = [0.8 0.2; + 1.0 0.0]; + + +% LEVEL 2 +startprob{2} = 'leftstart'; % both submodels start in substate 1 +transprob{2} = zeros(motif_length, 2, motif_length); +termprob{2} = zeros(2, motif_length); + +% In the background model, we only use state 1. +transprob{2}(1,1,1) = 1; % self loop +termprob{2}(1,1) = 0.2; % prob transition to end state + +% Motif model +transprob{2}(:,2,:) = mk_leftright_transmat(motif_length, 0); % no self loops +termprob{2}(2,end) = 1.0; % last state immediately terminates + + +% OBS LEVEl + +obsprob = zeros([Qsize Osize]); +if isempty(background_char) + % uniform background model + %obsprob(1,1,:) = normalise(ones(Osize,1)); + obsprob(1,1,:) = normalise(rand(Osize,1)); +else + % deterministic background model (easy to see!) + m = find(chars==background_char); + obsprob(1,1,m) = 1.0; +end + +if ~isempty(motif_pattern) + % initialise with true motif (cheating) + for i=1:motif_length + m = find(chars == motif_pattern(i)); + obsprob(2,i,m) = 1.0; + end +else + obsprob(2,:,:) = mk_stochastic(rand(motif_length, Osize)); +end + +if 0 + Oargs = {'CPT', obsprob}; +else + % We use a minent prior for the emission distribution for the states in the motif model + % (but not the background model). This encourages nearly deterministic distributions. + % We create an index matrix (where M = motif length) + % [2 1 + % 2 2 + % ... + % 2 M] + % and then convert this to a list of integers, which + % specifies when to use the minent prior (Q1=2 specifies motif model). + M = motif_length; + ndx = [2*ones(M,1) (1:M)']; + pcases = subv2ind([2 motif_length], ndx); + Oargs = {'CPT', obsprob, 'prior_type', 'entropic', 'entropic_pcases', pcases}; +end + + + +[bnet, Qnodes, Fnodes, Onode] = mk_hhmm('Qsizes', Qsize, 'Osize', Osize, 'discrete_obs', 1, ... + 'Oargs', Oargs, 'Ops', Qnodes(1:2), ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Motif/sample_motif_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +%bnet = mk_motif_hhmm('motif_pattern', 'acca', 'background', 't'); +bnet = mk_motif_hhmm('motif_pattern', 'accaggggga', 'background', []); + +chars = ['a', 'c', 'g', 't']; +Tmax = 100; + +for seqi=1:5 + evidence = cell2num(sample_dbn(bnet, 'length', Tmax)); + chars(evidence(end,:)) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +/mk_abcd_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_arrow_alpha_hhmm3.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_hhmm2.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_hhmm3.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_hhmm3_args.m/1.1.1.1/Wed May 29 15:59:54 2002// +/motif_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/remove_hhmm_end_state.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_abcd_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,109 @@ +% Make the HHMM in Figure 1 of the NIPS'01 paper + +Qsize = [2 3 2]; +D = 3; + +% transprob{d}(i,k,j), transprob{1}(i,j) +% termprob{d}(k,j), termprob{1}(1,j) +% startprob{d}(k,j), startprob{1}(1,j) +% obsprob(k, o) for discrete outputs + +% LEVEL 1 +% 1 2 e +A{1} = [0 0 1; + 0 0 1]; +[transprob{1}, termprob{1}] = remove_hhmm_end_state(A{1}); +startprob{1} = [0.5 0.5]; +Q1args = {'startprob', startprob{1}, 'transprob', transprob{1}}; + +% LEVEL 2 +A{2} = zeros(Qsize(2), Qsize(1), Qsize(2)+1); + +% 1 2 3 e +A{2}(:,1,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1]; + +% 1 2 3 e +A{2}(:,2,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1]; + +[transprob{2}, termprob{2}] = remove_hhmm_end_state(A{2}); + +% always enter level 2 in state 1 +startprob{2} = [1 0 0 + 1 0 0]; + +Q2args = {'startprob', startprob{2}, 'transprob', transprob{2}}; +F2args = {'CPT', termprob{2}}; + + +% LEVEL 3 + +A{3} = zeros([Qsize(3) Qsize(1:2) Qsize(3)+1]); +endstate = Qsize(3)+1; +% Qt-1(3) Qt(1) Qt(2) Qt(3) +% 1 2 e +A{3}(1, 1, 1, endstate) = 1.0; +A{3}(:, 1, 2, :) = [0.0 1.0 0.0 + 0.5 0.0 0.5]; +A{3}(1, 1, 3, endstate) = 1.0; + +A{3}(1, 2, 1, endstate) = 1.0; +A{3}(:, 2, 2, :) = [0.0 1.0 0.0 + 0.5 0.0 0.5]; +A{3}(1, 2, 3, endstate) = 1.0; + +A{3} = reshape(A{3}, [Qsize(3) prod(Qsize(1:2)) Qsize(3)+1]); +[transprob{3}, termprob{3}] = remove_hhmm_end_state(A{3}); + +% define the vertical entry points to level 3 +startprob{3} = zeros(Qsize); +% Q1 Q2 Q3 +startprob{3}(1, 1, 1) = 1.0; +startprob{3}(1, 2, 1) = 1.0; +startprob{3}(1, 3, 1) = 1.0; + +startprob{3}(2, 1, 1) = 1.0; +startprob{3}(2, 2, 1) = 1.0; +startprob{3}(2, 3, 1) = 1.0; + +startprob{3} = reshape(startprob{3}, prod(Qsize(1:2)), Qsize(3)); + +chars = ['a', 'b', 'c', 'd', 'x', 'y']; +Osize = length(chars); + +obsprob = zeros([Qsize Osize]); +% 1 2 3 O +obsprob(1,1,1,find(chars == 'a')) = 1.0; + +obsprob(1,2,1,find(chars == 'x')) = 1.0; +obsprob(1,2,2,find(chars == 'y')) = 1.0; + +obsprob(1,3,1,find(chars == 'b')) = 1.0; + +obsprob(2,1,1,find(chars == 'c')) = 1.0; + +obsprob(2,2,1,find(chars == 'x')) = 1.0; +obsprob(2,2,2,find(chars == 'y')) = 1.0; + +obsprob(2,3,1,find(chars == 'd')) = 1.0; + +obsprob = reshape(obsprob, prod(Qsize), Osize); + +[intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo(D); + +hhmm.Qnodes = Qnodes; +hhmm.Fnodes = Fnodes; +hhmm.Onode = Onode; +hhmm.D = D; +hhmm.Qsize = Qsize; +hhmm.Osize = Osize; +hhmm.startprob = startprob; +hhmm.transprob = transprob; +hhmm.termprob = termprob; +hhmm.obsprob = obsprob; +hhmm.A = A; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_arrow_alpha_hhmm3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +% Make the following HHMM +% +% LH RH +% / \ +% / \ +% LR -> UD -> RL -> DU RL -> UD -> LR -> DU +% \ +% \ +% Q1 -> Q2 +% +% where level 1 is fully interconnected (not shown) +% level 2 is left-right +% and each model at level 3 is a 2 state LR shared HMM + +Qsizes = [2 4 2]; +D = 3; + +% LEVEL 1 + +startprob1 = 'ergodic'; +transprob1 = 'ergodic'; + + +% LEVEL 2 + +startprob = zeros(2, 4); +% Q1 Q2 +startprob(1, 1) = 1; +startprob(2, 3) = 1; + +transprob = zeros(2, 4, 4); +transprob(1,:,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1 + 0 0 0 1]; +transprob(2,:,:) = [0 0 0 1 + 1 0 0 0 + 0 1 0 0 + 0 0 0 1]; + +Q2args = {'startprob', startprob, 'transprob', transprob}; + +% always terminate in state 4 (default) +% F2args + +% LEVEL 3 + +% Defaults are fine: always start in state 1, left-right model, finish in state 2 + + +% OBS LEVEl + +chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; +Osize = length(chars); + +obsprob = zeros([4 2 Osize]); +% Q2 Q3 O +obsprob(1, 1, find(chars == 'L')) = 1.0; +obsprob(1, 2, find(chars == 'l')) = 1.0; + +obsprob(2, 1, find(chars == 'U')) = 1.0; +obsprob(2, 2, find(chars == 'u')) = 1.0; + +obsprob(3, 1, find(chars == 'R')) = 1.0; +obsprob(3, 2, find(chars == 'r')) = 1.0; + +obsprob(4, 1, find(chars == 'D')) = 1.0; +obsprob(4, 2, find(chars == 'd')) = 1.0; + +Oargs = {'CPT', obsprob}; + + +bnet = mk_hhmm3('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', 1, 'Oargs', Oargs, 'Q1args', Q1args, 'Q2args', Q2args); + +T = 20; +usecell = 0; +evidence = sample_dbn(bnet, T, usecell); +%chars(evidence(end,:)) + +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; obs = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, obs, chars); + +eclass = bnet.equiv_class; +S=struct(bnet.CPD{eclass(Q2,2)})
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_hhmm2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,111 @@ +function bnet = mk_hhmm2(varargin) +% MK_HHMM2 Make a 2 level Hierarchical HMM +% bnet = mk_hhmm2(...) +% +% 2-layer hierarchical HMM (node numbers in parens) +% +% Q1(1) ---------> Q1(5) +% / | \ / | +% | | v / | +% | | F2(3) --- / | +% | | ^ \ | +% | | / \ | +% | v \ v +% | Q2(2)--------> Q2 (6) +% | | +% \ | +% v v +% O(4) +% +% +% Optional arguments [default] +% +% discrete_obs - 1 means O is tabular_CPD, 0 means O is gaussian_CPD [0] +% obsCPT - CPT(o,q1,q2) params for O ['rnd'] +% mu - mu(:,q1,q2) params for O [ [] ] +% Sigma - Sigma(:,q1,q2) params for O [ [] ] +% +% F2toQ1 - 1 if Q2 is an hhmm_CPD, 0 if F2 -> Q2 arc is absent, so level 2 never resets [1] +% Q1args - arguments to be passed to the constructors for Q1(t=2) [ {} ] +% Q2args - arguments to be passed to the constructors for Q2(t=2) [ {} ] +% +% F2 only turns on (wp 0.5) when Q2 enters its final state. +% Q1 (slice 1) is clamped to be uniform. +% Q2 (slice 1) is clamped to always start in state 1. + +[os nmodels nstates] = size(mu); + +ss = 4; +Q1 = 1; Q2 = 2; F2 = 3; obs = 4; +Qnodes = [Q1 Q2]; +names = {'Q1', 'Q2', 'F2', 'obs'}; +intra = zeros(ss); +intra(Q1, [Q2 F2 obs]) = 1; +intra(Q2, [F2 obs]) = 1; + +inter = zeros(ss); +inter(Q1,Q1) = 1; +inter(F2,Q1) = 1; +if F2toQ2 + inter(F2,Q2)=1; +end +inter(Q2,Q2) = 1; + +ns = zeros(1,ss); + +ns(Q1) = nmodels; +ns(Q2) = nstates; +ns(F2) = 2; +ns(obs) = os; + +dnodes = [Q1 Q2 F2]; +if discrete_obs + dnodes = [dnodes obs]; +end +onodes = [obs]; + +bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'discrete', dnodes, 'names', names); +eclass = bnet.equiv_class; + +% SLICE 1 + +% We clamp untied nodes in the first slice, since their params can't be estimated +% from just one sequence + +% uniform prior on initial model +CPT = normalise(ones(1,nmodels)); +bnet.CPD{eclass(Q1,1)} = tabular_CPD(bnet, Q1, 'CPT', CPT, 'adjustable', 0); + +% each model always starts in state 1 +CPT = zeros(ns(Q1), ns(Q2)); +CPT(:, 1) = 1.0; +bnet.CPD{eclass(Q2,1)} = tabular_CPD(bnet, Q2, 'CPT', CPT, 'adjustable', 0); + +% Termination probability +CPT = zeros(ns(Q1), ns(Q2), 2); +if 1 + % Each model can only terminate in its final state. + % 0 params will remain 0 during EM, thus enforcing this constraint. + CPT(:, :, 1) = 1.0; % all states turn F off ... + p = 0.5; + CPT(:, ns(Q2), 2) = p; % except the last one + CPT(:, ns(Q2), 1) = 1-p; +end +bnet.CPD{eclass(F2,1)} = tabular_CPD(bnet, F2, 'CPT', CPT); + +if discrete_obs + bnet.CPD{eclass(obs,1)} = tabular_CPD(bnet, obs, obs_args{:}); +else + bnet.CPD{eclass(obs,1)} = gaussian_CPD(bnet, obs, obs_args{:}); +end + +% SLICE 2 + + +bnet.CPD{eclass(Q1,2)} = hhmm_CPD(bnet, Q1+ss, Qnodes, 1, D, 'args', Q1args); + +if F2toQ2 + bnet.CPD{eclass(Q2,2)} = hhmmQD_CPD(bnet, Q2+ss, Qnodes, 2, D, Q2args{:}); +else + bnet.CPD{eclass(Q2,2)} = tabular_CPD(bnet, Q2+ss, Q2args{:}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_hhmm3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +function bnet = mk_hhmm3(varargin) +% MK_HHMM3 Make a 3 level Hierarchical HMM +% bnet = mk_hhmm3(...) +% +% 3-layer hierarchical HMM where level 1 only connects to level 2, not 3 or obs. +% This enforces sub-models (which differ only in their Q1 index) to be shared. +% Also, we enforce the fact that each model always starts in its initial state +% and only finishes in its final state. However, the prob. of finishing (as opposed to +% self-transitioning to the final state) can be learned. +% The fact that we always finish from the same state means we do not need to condition +% F(i) on Q(i-1), since finishing prob is indep of calling context. +% +% The DBN is the same as Fig 10 in my tech report. +% +% Q1 ----------> Q1 +% | / | +% | / | +% | F2 ------- | +% | ^ \ | +% | /| \ | +% v | v v +% Q2-| --------> Q2 +% /| | ^ +% / | | /| +% | | F3 ---------/ | +% | | ^ \ | +% | v / v +% | Q3 -----------> Q3 +% | | +% \ | +% v v +% O +% +% +% Optional arguments in name/value format [default] +% +% Qsizes - sizes at each level [ none ] +% Osize - size of O node [ none ] +% discrete_obs - 1 means O is tabular_CPD, 0 means O is gaussian_CPD [0] +% Oargs - cell array of args to pass to the O CPD [ {} ] +% transprob1 - transprob1(i,j) = P(Q1(t)=j|Q1(t-1)=i) ['ergodic'] +% startprob1 - startprob1(j) = P(Q1(t)=j) ['leftstart'] +% transprob2 - transprob2(i,k,j) = P(Q2(t)=j|Q2(t-1)=i,Q1(t)=k) ['leftright'] +% startprob2 - startprob2(k,j) = P(Q2(t)=j|Q1(t)=k) ['leftstart'] +% termprob2 - termprob2(j,f) = P(F2(t)=f|Q2(t)=j) ['rightstop'] +% transprob3 - transprob3(i,k,j) = P(Q3(t)=j|Q3(t-1)=i,Q2(t)=k) ['leftright'] +% startprob3 - startprob3(k,j) = P(Q3(t)=j|Q2(t)=k) ['leftstart'] +% termprob3 - termprob3(j,f) = P(F3(t)=f|Q3(t)=j) ['rightstop'] +% +% leftstart means the model always starts in state 1. +% rightstop means the model always finished in its last state (Qsize(d)). +% +% Q1:Q3 in slice 1 are of type tabular_CPD +% Q1:Q3 in slice 2 are of type hhmmQ_CPD. +% F2 is of type hhmmF_CPD, F3 is of type tabular_CPD. + +ss = 6; D = 3; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; obs = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; +names = {'Q1', 'Q2', 'Q3', 'F3', 'F2', 'obs'}; + +intra = zeros(ss); +intra(Q1, Q2) = 1; +intra(Q2, [F2 Q3 obs]) = 1; +intra(Q3, [F3 obs]) = 1; +intra(F3, F2) = 1; + +inter = zeros(ss); +inter(Q1,Q1) = 1; +inter(Q2,Q2) = 1; +inter(Q3,Q3) = 1; +inter(F2,[Q1 Q2]) = 1; +inter(F3,[Q2 Q3]) = 1; + + +% get sizes of nodes +args = varargin; +nargs = length(args); +Qsizes = []; +Osize = 0; +for i=1:2:nargs + switch args{i}, + case 'Qsizes', Qsizes = args{i+1}; + case 'Osize', Osize = args{i+1}; + end +end +if isempty(Qsizes), error('must specify Qsizes'); end +if Osize==0, error('must specify Osize'); end + +% set default params +discrete_obs = 0; +Oargs = {}; +startprob1 = 'ergodic'; +startprob2 = 'leftstart'; +startprob3 = 'leftstart'; +transprob1 = 'ergodic'; +transprob2 = 'leftright'; +transprob3 = 'leftright'; +termprob2 = 'rightstop'; +termprob3 = 'rightstop'; + + +for i=1:2:nargs + switch args{i}, + case 'discrete_obs', discrete_obs = args{i+1}; + case 'Oargs', Oargs = args{i+1}; + case 'Q1args', Q1args = args{i+1}; + case 'Q2args', Q2args = args{i+1}; + case 'Q3args', Q3args = args{i+1}; + case 'F2args', F2args = args{i+1}; + case 'F3args', F3args = args{i+1}; + end +end + + +ns = zeros(1,ss); +ns(Qnodes) = Qsizes; +ns(obs) = Osize; +ns(Fnodes) = 2; + +dnodes = [Qnodes Fnodes]; +if discrete_obs + dnodes = [dnodes obs]; +end +onodes = [obs]; + +bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'discrete', dnodes, 'names', names); +eclass = bnet.equiv_class; + +if strcmp(startprob1, 'ergodic') + startprob1 = normalise(ones(1,ns(Q1))); +end +if strcmp(startprob2, 'leftstart') + startprob2 = zeros(ns(Q1), ns(Q2)); + starpbrob2(:, 1) = 1.0; +end +if strcmp(startprob3, 'leftstart') + startprob3 = zeros(ns(Q2), ns(Q3)); + starpbrob3(:, 1) = 1.0; +end + +if strcmp(termprob2, 'rightstop') + p = 0.9; + termprob2 = zeros(Qsize(2),2); + termprob2(:, 2) = p; + termprob2(:, 1) = 1-p; + termprob2(1:(Qsize(2)-1), 1) = 1; +end +if strcmp(termprob3, 'rightstop') + p = 0.9; + termprob3 = zeros(Qsize(3),2); + termprob3(:, 2) = p; + termprob3(:, 1) = 1-p; + termprob3(1:(Qsize(3)-1), 1) = 1; +end + + +% SLICE 1 + +% We clamp untied nodes in the first slice, since their params can't be estimated +% from just one sequence + +bnet.CPD{eclass(Q1,1)} = tabular_CPD(bnet, Q1, 'CPT', startprob1, 'adjustable', 0); +bnet.CPD{eclass(Q2,1)} = tabular_CPD(bnet, Q2, 'CPT', startprob2, 'adjustable', 0); +bnet.CPD{eclass(Q3,1)} = tabular_CPD(bnet, Q3, 'CPT', startprob3, 'adjustable', 0); + +bnet.CPD{eclass(F2,1)} = hhmmF_CPD(bnet, F2, Qnodes, 2, D, 'termprob', termprob2); +bnet.CPD{eclass(F3,1)} = tabular_CPD(bnet, F3, 'CPT', termprob3); + +if discrete_obs + bnet.CPD{eclass(obs,1)} = tabular_CPD(bnet, obs, Oargs{:}); +else + bnet.CPD{eclass(obs,1)} = gaussian_CPD(bnet, obs, Oargs{:}); +end + +% SLICE 2 + +bnet.CPD{eclass(Q1,2)} = hhmmQ_CPD(bnet, Q1+ss, Qnodes, 1, D, 'transprob', transprob1, 'startprob', startprob1); +bnet.CPD{eclass(Q2,2)} = hhmmQ_CPD(bnet, Q2+ss, Qnodes, 2, D, 'transprob', transprob2, 'startprob', startprob2); +bnet.CPD{eclass(Q3,2)} = hhmmQ_CPD(bnet, Q3+ss, Qnodes, 3, D, 'transprob', transprob3, 'startprob', startprob3); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/mk_hhmm3_args.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,165 @@ +function bnet = mk_hhmm3(varargin) +% MK_HHMM3 Make a 3 level Hierarchical HMM +% bnet = mk_hhmm3(...) +% +% 3-layer hierarchical HMM where level 1 only connects to level 2, not 3 or obs. +% This enforces sub-models (which differ only in their Q1 index) to be shared. +% Also, we enforce the fact that each model always starts in its initial state +% and only finishes in its final state. However, the prob. of finishing (as opposed to +% self-transitioning to the final state) can be learned. +% The fact that we always finish from the same state means we do not need to condition +% F(i) on Q(i-1), since finishing prob is indep of calling context. +% +% The DBN is the same as Fig 10 in my tech report. +% +% Q1 ----------> Q1 +% | / | +% | / | +% | F2 ------- | +% | ^ \ | +% | /| \ | +% v | v v +% Q2-| --------> Q2 +% /| | ^ +% / | | /| +% | | F3 ---------/ | +% | | ^ \ | +% | v / v +% | Q3 -----------> Q3 +% | | +% \ | +% v v +% O +% +% Q1 (slice 1) is clamped to be uniform. +% Q2 (slice 1) is clamped to always start in state 1. +% Q3 (slice 1) is clamped to always start in state 1. +% F3 by default will only finish if Q3 is in its last state (F3 is a tabular_CPD) +% F2 by default gets the default hhmmF_CPD params. +% Q1:Q3 (slice 2) by default gets the default hhmmQ_CPD params. +% O by default gets the default tabular/Gaussian params. +% +% Optional arguments in name/value format [default] +% +% Qsizes - sizes at each level [ none ] +% Osize - size of O node [ none ] +% discrete_obs - 1 means O is tabular_CPD, 0 means O is gaussian_CPD [0] +% Oargs - cell array of args to pass to the O CPD [ {} ] +% Q1args - args to be passed to constructor for Q1 (slice 2) [ {} ] +% Q2args - args to be passed to constructor for Q2 (slice 2) [ {} ] +% Q3args - args to be passed to constructor for Q3 (slice 2) [ {} ] +% F2args - args to be passed to constructor for F2 [ {} ] +% F3args - args to be passed to constructor for F3 [ {'CPT', finish in last Q3 state} ] +% + +ss = 6; D = 3; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; obs = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; +names = {'Q1', 'Q2', 'Q3', 'F3', 'F2', 'obs'}; + +intra = zeros(ss); +intra(Q1, Q2) = 1; +intra(Q2, [F2 Q3 obs]) = 1; +intra(Q3, [F3 obs]) = 1; +intra(F3, F2) = 1; + +inter = zeros(ss); +inter(Q1,Q1) = 1; +inter(Q2,Q2) = 1; +inter(Q3,Q3) = 1; +inter(F2,[Q1 Q2]) = 1; +inter(F3,[Q2 Q3]) = 1; + + +% get sizes of nodes +args = varargin; +nargs = length(args); +Qsizes = []; +Osize = 0; +for i=1:2:nargs + switch args{i}, + case 'Qsizes', Qsizes = args{i+1}; + case 'Osize', Osize = args{i+1}; + end +end +if isempty(Qsizes), error('must specify Qsizes'); end +if Osize==0, error('must specify Osize'); end + +% set default params +discrete_obs = 0; +Oargs = {}; +Q1args = {}; +Q2args = {}; +Q3args = {}; +F2args = {}; + +% P(Q3, F3) +CPT = zeros(Qsizes(3), 2); +% Each model can only terminate in its final state. +% 0 params will remain 0 during EM, thus enforcing this constraint. +CPT(:, 1) = 1.0; % all states turn F off ... +p = 0.5; +CPT(Qsizes(3), 2) = p; % except the last one +CPT(Qsizes(3), 1) = 1-p; +F3args = {'CPT', CPT}; + +for i=1:2:nargs + switch args{i}, + case 'discrete_obs', discrete_obs = args{i+1}; + case 'Oargs', Oargs = args{i+1}; + case 'Q1args', Q1args = args{i+1}; + case 'Q2args', Q2args = args{i+1}; + case 'Q3args', Q3args = args{i+1}; + case 'F2args', F2args = args{i+1}; + case 'F3args', F3args = args{i+1}; + end +end + +ns = zeros(1,ss); +ns(Qnodes) = Qsizes; +ns(obs) = Osize; +ns(Fnodes) = 2; + +dnodes = [Qnodes Fnodes]; +if discrete_obs + dnodes = [dnodes obs]; +end +onodes = [obs]; + +bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'discrete', dnodes, 'names', names); +eclass = bnet.equiv_class; + +% SLICE 1 + +% We clamp untied nodes in the first slice, since their params can't be estimated +% from just one sequence + +% uniform prior on initial model +CPT = normalise(ones(1,ns(Q1))); +bnet.CPD{eclass(Q1,1)} = tabular_CPD(bnet, Q1, 'CPT', CPT, 'adjustable', 0); + +% each model always starts in state 1 +CPT = zeros(ns(Q1), ns(Q2)); +CPT(:, 1) = 1.0; +bnet.CPD{eclass(Q2,1)} = tabular_CPD(bnet, Q2, 'CPT', CPT, 'adjustable', 0); + +% each model always starts in state 1 +CPT = zeros(ns(Q2), ns(Q3)); +CPT(:, 1) = 1.0; +bnet.CPD{eclass(Q3,1)} = tabular_CPD(bnet, Q3, 'CPT', CPT, 'adjustable', 0); + +bnet.CPD{eclass(F2,1)} = hhmmF_CPD(bnet, F2, Qnodes, 2, D, F2args{:}); + +bnet.CPD{eclass(F3,1)} = tabular_CPD(bnet, F3, F3args{:}); + +if discrete_obs + bnet.CPD{eclass(obs,1)} = tabular_CPD(bnet, obs, Oargs{:}); +else + bnet.CPD{eclass(obs,1)} = gaussian_CPD(bnet, obs, Oargs{:}); +end + +% SLICE 2 + +bnet.CPD{eclass(Q1,2)} = hhmmQ_CPD(bnet, Q1+ss, Qnodes, 1, D, Q1args{:}); +bnet.CPD{eclass(Q2,2)} = hhmmQ_CPD(bnet, Q2+ss, Qnodes, 2, D, Q2args{:}); +bnet.CPD{eclass(Q3,2)} = hhmmQ_CPD(bnet, Q3+ss, Qnodes, 3, D, Q3args{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/motif_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +% Make the following HHMM +% +% S1 <----------------------> S2 +% | | +% | | +% M1 -> M2 -> M3 -> end B1 -> end +% +% where Mi represents the i'th letter in the motif +% and B is the background state. +% Si chooses between running the motif or the background. +% The Si and B states have self loops (not shown). + +if 0 +seed = 0; +rand('state', seed); +randn('state', seed); +end + +chars = ['a', 'c', 'g', 't']; +Osize = length(chars); + +motif_pattern = 'acca'; +motif_length = length(motif_pattern); +Qsize = [2 motif_length]; +Qnodes = 1:2; +D = 2; +transprob = cell(1,D); +termprob = cell(1,D); +startprob = cell(1,D); + +% startprob{d}(k,j), startprob{1}(1,j) +% transprob{d}(i,k,j), transprob{1}(i,j) +% termprob{d}(k,j) + + +% LEVEL 1 + +startprob{1} = zeros(1, 2); +startprob{1} = [1 0]; % always start in the background model + +% When in the background state, we stay there with high prob +% When in the motif state, we immediately return to the background state. +transprob{1} = [0.8 0.2; + 1.0 0.0]; + + +% LEVEL 2 +startprob{2} = 'leftstart'; % both submodels start in substate 1 +transprob{2} = zeros(motif_length, 2, motif_length); +termprob{2} = zeros(2, motif_length); + +% In the background model, we only use state 1. +transprob{2}(1,1,1) = 1; % self loop +termprob{2}(1,1) = 0.2; % prob transition to end state + +% Motif model +transprob{2}(:,2,:) = mk_leftright_transmat(motif_length, 0); +termprob{2}(2,end) = 1.0; % last state immediately terminates + + +% OBS LEVEl + +obsprob = zeros([Qsize Osize]); +if 0 + % uniform background model + obsprob(1,1,:) = normalise(ones(Osize,1)); +else + % deterministic background model (easy to see!) + m = find(chars=='t'); + obsprob(1,1,m) = 1.0; +end +if 1 + % initialise with true motif (cheating) + for i=1:motif_length + m = find(chars == motif_pattern(i)); + obsprob(2,i,m) = 1.0; + end +end + +Oargs = {'CPT', obsprob}; + +[bnet, Qnodes, Fnodes, Onode] = mk_hhmm('Qsizes', Qsize, 'Osize', Osize, 'discrete_obs', 1, ... + 'Oargs', Oargs, 'Ops', Qnodes(1:2), ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); + + +Tmax = 20; +usecell = 0; + +for seqi=1:5 + evidence = sample_dbn(bnet, Tmax, usecell); + chars(evidence(end,:)) + %T = size(evidence, 2) + %pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, chars); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Old/remove_hhmm_end_state.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function [transprob, termprob] = remove_hhmm_end_state(A) +% REMOVE_END_STATE Infer transition and termination probabilities from automaton with an end state +% [transprob, termprob] = remove_end_state(A) +% A(i,k,j) = Pr( i->j | Qps=k), where i in 1:Q, j in 1:(Q+1), and Q+1 is the end state + +if ndims(A)==2 % top level + Q = size(A,1); + transprob = A(:,1:Q); + termprob = A(:,Q+1)'; + + % rescale + for i=1:Q + for j=1:Q + denom = (1-termprob(i)); + denom = denom + (denom==0)*eps; + transprob(i,j) = transprob(i,j) / denom; + end + end +else + Q = size(A,1); + Qk = size(A,2); + transprob = A(:, :, 1:Q); + termprob = A(:,:,Q+1)'; + + % rescale + for k=1:Qk + for i=1:Q + for j=1:Q + denom = (1-termprob(k,i)); + denom = denom + (denom==0)*eps; + transprob(i,k,j) = transprob(i,k,j) / denom; + end + end + end + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +/get_square_data.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hhmm_inference.m/1.1.1.1/Wed May 29 15:59:54 2002// +/is_F2_true_D3.m/1.1.1.1/Wed May 29 15:59:54 2002// +/learn_square_hhmm_cts.m/1.1.1.1/Thu Jun 20 00:19:22 2002// +/learn_square_hhmm_discrete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/plot_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_square_hhmm_cts.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_square_hhmm_discrete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/square4.mat/1.1.1.1/Wed May 29 15:59:54 2002// +/square4_cases.mat/1.1.1.1/Wed May 29 15:59:54 2002// +/test_square_fig.m/1.1.1.1/Wed May 29 15:59:54 2002// +/test_square_fig.mat/1.1.1.1/Wed May 29 15:59:54 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Square
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/learn_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/plot_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_square_hhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/HHMM/Square/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,294 @@ +% Learn a 3 level HHMM similar to mk_square_hhmm + +% Because startprob should be shared for t=1:T, +% but in the DBN is shared for t=2:T, we train using a single long sequence. + +discrete_obs = 0; +supervised = 1; +obs_finalF2 = 0; +% It is not possible to observe F2 if we learn +% because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume +% the F nodes are always hidden (for speed). +% However, for generating, we might want to set the final F2=true +% to force all subroutines to finish. + +ss = 6; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +seed = 1; +rand('state', seed); +randn('state', seed); + +if discrete_obs + Qsizes = [2 4 2]; +else + Qsizes = [2 4 1]; +end + +D = 3; +Qnodes = 1:D; +startprob = cell(1,D); +transprob = cell(1,D); +termprob = cell(1,D); + +startprob{1} = 'unif'; +transprob{1} = 'unif'; + +% In the unsupervised case, it is essential that we break symmetry +% in the initial param estimates. +%startprob{2} = 'unif'; +%transprob{2} = 'unif'; +%termprob{2} = 'unif'; +startprob{2} = 'rnd'; +transprob{2} = 'rnd'; +termprob{2} = 'rnd'; + +leftright = 0; +if leftright + % Initialise base-level models as left-right. + % If we initialise with delta functions, + % they will remain delat funcitons after learning + startprob{3} = 'leftstart'; + transprob{3} = 'leftright'; + termprob{3} = 'rightstop'; +else + % If we want to be able to run a base-level model backwards... + startprob{3} = 'rnd'; + transprob{3} = 'rnd'; + termprob{3} = 'rnd'; +end + +if discrete_obs + % Initialise observations of lowest level primitives in a way which we can interpret + chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + L=find(chars=='L'); l=find(chars=='l'); + U=find(chars=='U'); u=find(chars=='u'); + R=find(chars=='R'); r=find(chars=='r'); + D=find(chars=='D'); d=find(chars=='d'); + Osize = length(chars); + + p = 0.9; + obsprob = (1-p)*ones([4 2 Osize]); + % Q2 Q3 O + obsprob(1, 1, L) = p; + obsprob(1, 2, l) = p; + obsprob(2, 1, U) = p; + obsprob(2, 2, u) = p; + obsprob(3, 1, R) = p; + obsprob(3, 2, r) = p; + obsprob(4, 1, D) = p; + obsprob(4, 2, d) = p; + obsprob = mk_stochastic(obsprob); + Oargs = {'CPT', obsprob}; + +else + % Initialise means of lowest level primitives in a way which we can interpret + % These means are little vectors in the east, south, west, north directions. + % (left-right=east, up-down=south, right-left=west, down-up=north) + Osize = 2; + mu = zeros(2, Qsizes(2), Qsizes(3)); + noise = 0; + scale = 3; + for q3=1:Qsizes(3) + mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1); + end + Sigma = repmat(reshape(scale*eye(2), [2 2 1 1 ]), [1 1 Qsizes(2) Qsizes(3)]); + Oargs = {'mean', mu, 'cov', Sigma, 'cov_type', 'diag'}; +end + +bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs,... + 'Oargs', Oargs, 'Ops', Qnodes(2:3), ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); + +if supervised + bnet.observed = [Q1 Q2 Onode]; +else + bnet.observed = [Onode]; +end + +if obs_finalF2 + engine = jtree_dbn_inf_engine(bnet); + % can't use ndx version because sometimes F2 is hidden, sometimes observed + error('can''t observe F when learning') +else + if supervised + engine = jtree_ndx_dbn_inf_engine(bnet); + else + engine = jtree_hmm_inf_engine(bnet); + end +end + +if discrete_obs + % generate some synthetic data (easier to debug) + cases = {}; + + T = 8; + ev = cell(ss, T); + ev(Onode,:) = num2cell([L l U u R r D d]); + if supervised + ev(Q1,:) = num2cell(1*ones(1,T)); + ev(Q2,:) = num2cell( [1 1 2 2 3 3 4 4]); + end + cases{1} = ev; + cases{3} = ev; + + T = 8; + ev = cell(ss, T); + if leftright % base model is left-right + ev(Onode,:) = num2cell([R r U u L l D d]); + else + ev(Onode,:) = num2cell([r R u U l L d D]); + end + if supervised + ev(Q1,:) = num2cell(2*ones(1,T)); + ev(Q2,:) = num2cell( [3 3 2 2 1 1 4 4]); + end + + cases{2} = ev; + cases{4} = ev; + + if obs_finalF2 + for i=1:length(cases) + T = size(cases{i},2); + cases{i}(F2,T)={2}; % force F2 to be finished at end of seq + end + end + + if 0 + ev = cases{4}; + engine2 = enter_evidence(engine2, ev); + T = size(ev,2); + for t=1:T + m=marginal_family(engine2, F2, t); + fprintf('t=%d\n', t); + reshape(m.T, [2 2]) + end + end + + % [bnet2, LL] = learn_params_dbn_em(engine, cases, 'max_iter', 10); + long_seq = cat(2, cases{:}); + [bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 200); + + % figure out which subsequence each model is responsible for + mpe = calc_mpe_dbn(engine2, long_seq); + pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, chars); + +else + load 'square4_cases' % cases{seq}{i,t} for i=1:ss + %plot_square_hhmm(cases{1}) + %long_seq = cat(2, cases{:}); + train_cases = cases(1:2); + long_seq = cat(2, train_cases{:}); + if ~supervised + T = size(long_seq,2); + for t=1:T + long_seq{Q1,t} = []; + long_seq{Q2,t} = []; + end + end + [bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 100); + + CPDO=struct(bnet2.CPD{eclass(Onode,1)}); + mu = CPDO.mean; + Sigma = CPDO.cov; + CPDO_full = CPDO; + + % force diagonal covs after training + for k=1:size(Sigma,3) + Sigma(:,:,k) = diag(diag(Sigma(:,:,k))); + end + bnet2.CPD{6} = set_fields(bnet.CPD{6}, 'cov', Sigma); + + if 0 + % visualize each model by concatenating means for each model for nsteps in a row + nsteps = 5; + ev = cell(ss, nsteps*prod(Qsizes(2:3))); + t = 1; + for q2=1:Qsizes(2) + for q3=1:Qsizes(3) + for i=1:nsteps + ev{Onode,t} = mu(:,q2,q3); + ev{Q2,t} = q2; + t = t + 1; + end + end + end + plot_square_hhmm(ev) + end + + % bnet3 is the same as the learned model, except we will use it in testing mode + if supervised + bnet3 = bnet2; + bnet3.observed = [Onode]; + engine3 = hmm_inf_engine(bnet3); + %engine3 = jtree_ndx_dbn_inf_engine(bnet3); + else + bnet3 = bnet2; + engine3 = engine2; + end + + if 0 + % segment whole sequence + mpe = calc_mpe_dbn(engine3, long_seq); + pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []); + end + + % segment each sequence + test_cases = cases(3:4); + for i=1:2 + ev = test_cases{i}; + T = size(ev, 2); + for t=1:T + ev{Q1,t} = []; + ev{Q2,t} = []; + end + mpe = calc_mpe_dbn(engine3, ev); + subplot(1,2,i) + plot_square_hhmm(mpe) + %pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []); + q1s = cell2num(mpe(Q1,:)); + h = hist(q1s, 1:Qsizes(1)); + map_q1 = argmax(h); + str = sprintf('test seq %d is of type %d\n', i, map_q1); + title(str) + end + +end + +if 0 +% Estimate gotten by couting transitions in the labelled data +% Note that a self transition shouldnt count if F2=off. +Q2ev = cell2num(ev(Q2,:)); +Q2a = Q2ev(1:end-1); +Q2b = Q2ev(2:end); +counts = compute_counts([Q2a; Q2b], [4 4]); +end + +eclass = bnet2.equiv_class; +CPDQ1=struct(bnet2.CPD{eclass(Q1,2)}); +CPDQ2=struct(bnet2.CPD{eclass(Q2,2)}); +CPDQ3=struct(bnet2.CPD{eclass(Q3,2)}); +CPDF2=struct(bnet2.CPD{eclass(F2,1)}); +CPDF3=struct(bnet2.CPD{eclass(F3,1)}); + + +A=add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2)); +squeeze(A(:,1,:)) +squeeze(A(:,2,:)) +CPDQ2.startprob + +if 0 +S=struct(CPDF2.sub_CPD_term); +S.nsamples +reshape(S.counts, [2 4 2]) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,183 @@ +function bnet = mk_square_hhmm(discrete_obs, true_params, topright) + +% Make a 3 level HHMM described by the following grammar +% +% Square -> CLK | CCK % clockwise or counterclockwise +% CLK -> LR UD RL DU start on top left (1 2 3 4) +% CCK -> RL UD LR DU if start at top right (3 2 1 4) +% CCK -> UD LR DU RL if start at top left (2 1 4 3) +% +% LR = left-right, UD = up-down, RL = right-left, DU = down-up +% LR, UD, RL, DU are sub HMMs. +% +% For discrete observations, the subHMMs are 2-state left-right. +% LR emits L then l, etc. +% +% For cts observations, the subHMMs are 1 state. +% LR emits a vector in the -> direction, with a little noise. +% Since there is no constraint that we remain in the LR state as long as the RL state, +% the sides of the square might have different lengths, +% so the result is not really a square! +% +% If true_params = 0, we use random parameters at the top 2 levels +% (ready for learning). At the bottom level, we use noisy versions +% of the "true" observations. +% +% If topright=1, counter-clockwise starts at top right, not top left +% This example was inspired by Ivanov and Bobick. + +if nargin < 3, topright = 1; end + +if 1 % discrete_obs + Qsizes = [2 4 2]; +else + Qsizes = [2 4 1]; +end + +D = 3; +Qnodes = 1:D; +startprob = cell(1,D); +transprob = cell(1,D); +termprob = cell(1,D); + +% LEVEL 1 + +startprob{1} = 'unif'; +transprob{1} = 'unif'; + +% LEVEL 2 + +if true_params + startprob{2} = zeros(2, 4); + startprob{2}(1, :) = [1 0 0 0]; + if topright + startprob{2}(2, :) = [0 0 1 0]; + else + startprob{2}(2, :) = [0 1 0 0]; + end + + transprob{2} = zeros(4, 2, 4); + + transprob{2}(:,1,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1 + 0 0 0 1]; % 4->e + if topright + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 1 0 0 + 0 0 0 1]; % 4->e + else + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 0 1 0 % 3->e + 0 0 1 0]; + end + + %termprob{2} = 'rightstop'; + termprob{2} = zeros(2,4,2); + pfin = 0.8; + termprob{2}(1,:,2) = [0 0 0 pfin]; % finish in state 4 (DU) + termprob{2}(1,:,1) = 1 - [0 0 0 pfin]; + if topright + termprob{2}(2,:,2) = [0 0 0 pfin]; + termprob{2}(2,:,1) = 1 - [0 0 0 pfin]; + else + termprob{2}(2,:,2) = [0 0 pfin 0]; % finish in state 3 (RL) + termprob{2}(2,:,1) = 1 - [0 0 pfin 0]; + end +else + % In the unsupervised case, it is essential that we break symmetry + % in the initial param estimates. + %startprob{2} = 'unif'; + %transprob{2} = 'unif'; + %termprob{2} = 'unif'; + startprob{2} = 'rnd'; + transprob{2} = 'rnd'; + termprob{2} = 'rnd'; +end + +% LEVEL 3 + +if 1 | true_params + startprob{3} = 'leftstart'; + transprob{3} = 'leftright'; + termprob{3} = 'rightstop'; +else + % If we want to be able to run a base-level model backwards... + startprob{3} = 'rnd'; + transprob{3} = 'rnd'; + termprob{3} = 'rnd'; +end + + +% OBS LEVEl + +if discrete_obs + % Initialise observations of lowest level primitives in a way which we can interpret + chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + L=find(chars=='L'); l=find(chars=='l'); + U=find(chars=='U'); u=find(chars=='u'); + R=find(chars=='R'); r=find(chars=='r'); + D=find(chars=='D'); d=find(chars=='d'); + Osize = length(chars); + + if true_params + p = 1; % makes each state fully observed + else + p = 0.9; + end + + obsprob = (1-p)*ones([4 2 Osize]); + % Q2 Q3 O + obsprob(1, 1, L) = p; + obsprob(1, 2, l) = p; + obsprob(2, 1, U) = p; + obsprob(2, 2, u) = p; + obsprob(3, 1, R) = p; + obsprob(3, 2, r) = p; + obsprob(4, 1, D) = p; + obsprob(4, 2, d) = p; + obsprob = mk_stochastic(obsprob); + Oargs = {'CPT', obsprob}; +else + % Initialise means of lowest level primitives in a way which we can interpret + % These means are little vectors in the east, south, west, north directions. + % (left-right=east, up-down=south, right-left=west, down-up=north) + Osize = 2; + mu = zeros(2, Qsizes(2), Qsizes(3)); + scale = 3; + if true_params + noise = 0; + else + noise = 0.5*scale; + end + for q3=1:Qsizes(3) + mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1); + end + Sigma = repmat(reshape(scale*eye(2), [2 2 1 1 ]), [1 1 Qsizes(2) Qsizes(3)]); + Oargs = {'mean', mu, 'cov', Sigma, 'cov_type', 'diag'}; +end + +if discrete_obs + selfprob = 0.5; +else + selfprob = 0.95; + % If less than this, it won't look like a square + % because it doesn't spend enough time in each state + % Unfortunately, the variance on durations (lengths of each side) + % is very large +end +bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs, ... + 'Oargs', Oargs, 'Ops', Qnodes(2:3), 'selfprob', selfprob, ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/Old/plot_square_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function plot_square_hhmm(ev) +% Plot the square shape implicit in the evidence. +% ev{i,t} is the value of node i in slice t. +% The observed node contains a velocity (delta increment), which is converted +% into a position. +% The Q2 node specifies which model is used; each segment is color-coded +% in the order red, green, blue, black. + +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; + +delta = cell2num(ev(Onode,:)); % delta(:,t) +Q2label = cell2num(ev(Q2,:)); + +T = size(delta, 2); +pos = zeros(2,T+1); +clf +hold on +cols = {'r', 'g', 'b', 'k'}; +boundary = 0; +coli = 1; +for t=2:T+1 + pos(:,t) = pos(:,t-1) + delta(:,t-1); + plot(pos(1,t), pos(2,t), sprintf('%c.', cols{coli})); + if t < T + boundary = (Q2label(t) ~= Q2label(t-1)); + end + if boundary + coli = coli + 1; + coli = mod(coli-1, length(cols)) + 1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/Old/sample_square_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,160 @@ + +seed = 0; +rand('state', seed); +randn('state', seed); + +discrete_obs = 1; +topright = 0; + +Qsizes = [2 4 2]; +D = 3; +Qnodes = 1:D; +startprob = cell(1,D); +transprob = cell(1,D); +termprob = cell(1,D); + +% LEVEL 1 + +startprob{1} = 'ergodic'; +transprob{1} = 'ergodic'; + +% LEVEL 2 + +startprob{2} = zeros(2, 4); +startprob{2}(1, :) = [1 0 0 0]; +if topright + startprob{2}(2, :) = [0 0 1 0]; +else + startprob{2}(2, :) = [0 1 0 0]; +end + +transprob{2} = zeros(4, 2, 4); + +transprob{2}(:,1,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1 + 0 0 0 1]; % 4->e +if topright + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 1 0 0 + 0 0 0 1]; % 4->e +else + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 0 1 0 % 3->e + 0 0 1 0]; +end + +%termprob{2} = 'rightstop'; +termprob{2} = zeros(2,4,2); +pfin = 0.8; +termprob{2}(1,:,2) = [0 0 0 pfin]; % finish in state 4 (DU) +termprob{2}(1,:,1) = 1 - [0 0 0 pfin]; +if topright + termprob{2}(2,:,2) = [0 0 0 pfin]; + termprob{2}(2,:,1) = 1 - [0 0 0 pfin]; +else + termprob{2}(2,:,2) = [0 0 pfin 0]; % finish in state 3 (RL) + termprob{2}(2,:,1) = 1 - [0 0 pfin 0]; +end + +% LEVEL 3 + +startprob{3} = 'leftstart'; +transprob{3} = 'leftright'; +termprob{3} = 'rightstop'; + + +% OBS LEVEl + +if discrete_obs + chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + L=find(chars=='L'); l=find(chars=='l'); + U=find(chars=='U'); u=find(chars=='u'); + R=find(chars=='R'); r=find(chars=='r'); + D=find(chars=='D'); d=find(chars=='d'); + Osize = length(chars); + + obsprob = zeros([4 2 Osize]); + % Q2 Q3 O + obsprob(1, 1, L) = 1.0; + obsprob(1, 2, l) = 1.0; + obsprob(2, 1, U) = 1.0; + obsprob(2, 2, u) = 1.0; + obsprob(3, 1, R) = 1.0; + obsprob(3, 2, r) = 1.0; + obsprob(4, 1, D) = 1.0; + obsprob(4, 2, d) = 1.0; + + Oargs = {'CPT', obsprob}; +else + Osize = 2; + mu = zeros(2, 4, 2); + noise = 0; + scale = 10; + for q3=1:2 + mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1); + end + for q3=1:2 + mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1); + end + for q3=1:2 + mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1); + end + for q3=1:2 + mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1); + end + Sigma = repmat(reshape(0.01*eye(2), [2 2 1 1 ]), [1 1 4 2]); + Oargs = {'mean', mu, 'cov', Sigma}; +end + +bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs, ... + 'Oargs', Oargs, 'Ops', Qnodes(2:3), ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); + +if discrete_obs + Tmax = 30; +else + Tmax = 200; +end +usecell = ~discrete_obs; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +for seqi=1:3 + evidence = sample_dbn(bnet, Tmax, usecell, 'stop_sampling_F2'); + T = size(evidence, 2) + if discrete_obs + pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, chars); + else + pos = zeros(2,T+1); + delta = cell2num(evidence(Onode,:)); + clf + hold on + cols = {'r', 'g', 'k', 'b'}; + boundary = cell2num(evidence(F3,:))-1; + coli = 1; + for t=2:T+1 + pos(:,t) = pos(:,t-1) + delta(:,t-1); + plot(pos(1,t), pos(2,t), sprintf('%c.', cols{coli})); + if boundary(t-1) + coli = coli + 1; + coli = mod(coli-1, length(cols)) + 1; + end + end + %plot(pos(1,:), pos(2,:), '.') + %pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, []); + pause + end +end + +eclass = bnet.equiv_class; +S=struct(bnet.CPD{eclass(Q2,2)}); + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/get_square_data.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +% Let the user draw a square with the mouse, +% and then click on the corners to do a manual segmentation + +ss = 6; +Q1 = 1; Q2 = 2; Q3 = 3; obsvel = 6; +CLOCKWISE = 1; ANTICLOCK = 2; +LR = 1; UD = 2; RL = 3; DU = 4; + +% repeat this block manually incrementing the sequence number +% and setting ori. +% (since I don't know how to call getmouse as a call-return function). +seq = 4; +%ori = CLOCKWISE +ori = ANTICLOCK; +clear xpos ypos +getmouse +% end block + +% manual segmentation with the mouse +startseg(1) = 1; +for i=2:4 + fprintf('click on start of segment %d\n', i); + [x,y] = ginput(1); + plot(x,y,'ro') + d = dist2([xpos; ypos]', [x y]); + startseg(i) = argmin(d); +end + +% plot corners in green +%ti = first point in (i+1)st segment +t1 = startseg(1); t2 = startseg(2); t3 = startseg(3); t4 = startseg(4); +plot(xpos(t2), ypos(t2), 'g*') +plot(xpos(t3), ypos(t3), 'g*') +plot(xpos(t4), ypos(t4), 'g*') + + +xvel = xpos(2:end) - xpos(1:end-1); +yvel = ypos(2:end) - ypos(1:end-1); +speed = [xvel(:)'; yvel(:)']; +pos_data{seq} = [xpos(:)'; ypos(:)']; +vel_data{seq} = [xvel(:)'; yvel(:)']; +T = length(xvel); +Q1label{seq} = num2cell(repmat(ori, 1, T)); +Q2label{seq} = zeros(1, T); +if ori == CLOCKWISE + Q2label{seq}(t1:t2) = LR; + Q2label{seq}(t2+1:t3) = UD; + Q2label{seq}(t3+1:t4) = RL; + Q2label{seq}(t4+1:T) = DU; +else + Q2label{seq}(t1:t2) = RL; + Q2label{seq}(t2+1:t3) = UD; + Q2label{seq}(t3+1:t4) = LR; + Q2label{seq}(t4+1:T) = DU; +end + +% pos_data{seq}(:,t), vel_data{seq}(:,t) Q1label{seq}(t) Q2label{seq}(t) +save 'square4' pos_data vel_data Q1label Q2label + +nseq = 4; +cases = cell(1,nseq); +for seq=1:nseq + T = size(vel_data{seq},2); + ev = cell(ss,T); + ev(obsvel,:) = num2cell(vel_data{seq},1); + ev(Q1,:) = Q1label{seq}; + ev(Q2,:) = num2cell(Q2label{seq}); + cases{seq} = ev; +end +save 'square4_cases' cases
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/hhmm_inference.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +bnet = mk_square_hhmm(1, 1); + +engine = {}; +engine{end+1} = hmm_inf_engine(bnet); +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); + +exact = 1:length(engine); +filter = 0; +single = 0; +maximize = 0; +T = 4; + +[err, inf_time, engine] = cmp_inference(bnet, engine, exact, T, filter, single, maximize);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/is_F2_true_D3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function stop = is_F2_true_D3(vals) +% function stop = is_F2_true_D3(vals) +% +% If vals(F2)=2 then level 2 has finished, so we return stop=1 +% to stop sample_dbn. Otherwise we return stop=0. +% We assume this is for a D=3 level HHMM. + +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +stop = 0; +if (iscell(vals) & vals{F2}==2) | (~iscell(vals) & vals(F2)==2) + stop = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,152 @@ +% Try to learn a 3 level HHMM similar to mk_square_hhmm +% from hand-drawn squares. + +% Because startprob should be shared for t=1:T, +% but in the DBN is shared for t=2:T, we train using a single long sequence. + +discrete_obs = 0; +supervised = 1; +obs_finalF2 = 0; +% It is not possible to observe F2 if we learn +% because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume +% the F nodes are always hidden (for speed). +% However, for generating, we might want to set the final F2=true +% to force all subroutines to finish. + +seed = 1; +rand('state', seed); +randn('state', seed); + +bnet = mk_square_hhmm(discrete_obs, 0); + +ss = 6; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; +Qsizes = [2 4 1]; + +if supervised + bnet.observed = [Q1 Q2 Onode]; +else + bnet.observed = [Onode]; +end + +if obs_finalF2 + engine = jtree_dbn_inf_engine(bnet); + % can't use ndx version because sometimes F2 is hidden, sometimes observed + error('can''t observe F when learning') +else + if supervised + engine = jtree_ndx_dbn_inf_engine(bnet); + else + engine = jtree_hmm_inf_engine(bnet); + end +end + +load 'square4_cases' % cases{seq}{i,t} for i=1:ss +%plot_square_hhmm(cases{1}) +%long_seq = cat(2, cases{:}); +train_cases = cases(1:2); +long_seq = cat(2, train_cases{:}); +if ~supervised + T = size(long_seq,2); + for t=1:T + long_seq{Q1,t} = []; + long_seq{Q2,t} = []; + end +end +[bnet2, LL, engine2] = learn_params_dbn_em(engine, {long_seq}, 'max_iter', 2); + +eclass = bnet2.equiv_class; +CPDO=struct(bnet2.CPD{eclass(Onode,1)}); +mu = CPDO.mean; +Sigma = CPDO.cov; +CPDO_full = CPDO; + +% force diagonal covs after training +for k=1:size(Sigma,3) + Sigma(:,:,k) = diag(diag(Sigma(:,:,k))); +end +bnet2.CPD{6} = set_fields(bnet.CPD{6}, 'cov', Sigma); + +if 0 + % visualize each model by concatenating means for each model for nsteps in a row + nsteps = 5; + ev = cell(ss, nsteps*prod(Qsizes(2:3))); + t = 1; + for q2=1:Qsizes(2) + for q3=1:Qsizes(3) + for i=1:nsteps + ev{Onode,t} = mu(:,q2,q3); + ev{Q2,t} = q2; + t = t + 1; + end + end + end + plot_square_hhmm(ev) +end + +% bnet3 is the same as the learned model, except we will use it in testing mode +if supervised + bnet3 = bnet2; + bnet3.observed = [Onode]; + engine3 = hmm_inf_engine(bnet3); + %engine3 = jtree_ndx_dbn_inf_engine(bnet3); +else + bnet3 = bnet2; + engine3 = engine2; +end + +if 0 + % segment whole sequence + mpe = calc_mpe_dbn(engine3, long_seq); + pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []); +end + +% segment each sequence +test_cases = cases(3:4); +for i=1:2 + ev = test_cases{i}; + T = size(ev, 2); + for t=1:T + ev{Q1,t} = []; + ev{Q2,t} = []; + end + %mpe = calc_mpe_dbn(engine3, ev); + mpe = find_mpe(engine3, ev) + subplot(1,2,i) + plot_square_hhmm(mpe) + %pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, []); + q1s = cell2num(mpe(Q1,:)); + h = hist(q1s, 1:Qsizes(1)); + map_q1 = argmax(h); + str = sprintf('test seq %d is of type %d\n', i, map_q1); + title(str) +end + + +if 0 +% Estimate gotten by couting transitions in the labelled data +% Note that a self transition shouldnt count if F2=off. +Q2ev = cell2num(ev(Q2,:)); +Q2a = Q2ev(1:end-1); +Q2b = Q2ev(2:end); +counts = compute_counts([Q2a; Q2b], [4 4]); +end + +eclass = bnet2.equiv_class; +CPDQ1=struct(bnet2.CPD{eclass(Q1,2)}); +CPDQ2=struct(bnet2.CPD{eclass(Q2,2)}); +CPDQ3=struct(bnet2.CPD{eclass(Q3,2)}); +CPDF2=struct(bnet2.CPD{eclass(F2,1)}); +CPDF3=struct(bnet2.CPD{eclass(F3,1)}); + + +A=add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2)); +squeeze(A(:,1,:)); +CPDQ2.startprob; + +if 0 +S=struct(CPDF2.sub_CPD_term); +S.nsamples +reshape(S.counts, [2 4 2]) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,171 @@ +% Try to learn a 3 level HHMM similar to mk_square_hhmm +% from synthetic discrete sequences + + +discrete_obs = 1; +supervised = 0; +obs_finalF2 = 0; + +seed = 1; +rand('state', seed); +randn('state', seed); + +bnet_init = mk_square_hhmm(discrete_obs, 0); + +ss = 6; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +if supervised + bnet_init.observed = [Q1 Q2 Onode]; +else + bnet_init.observed = [Onode]; +end + +if obs_finalF2 + engine_init = jtree_dbn_inf_engine(bnet_init); + % can't use ndx version because sometimes F2 is hidden, sometimes observed + error('can''t observe F when learning') + % It is not possible to observe F2 if we learn + % because the update_ess method for hhmmF_CPD and hhmmQ_CPD assume + % the F nodes are always hidden (for speed). + % However, for generating, we might want to set the final F2=true + % to force all subroutines to finish. +else + if supervised + engine_init = jtree_ndx_dbn_inf_engine(bnet_init); + else + engine_init = hmm_inf_engine(bnet_init); + end +end + +% generate some synthetic data (easier to debug) +chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; +L=find(chars=='L'); l=find(chars=='l'); +U=find(chars=='U'); u=find(chars=='u'); +R=find(chars=='R'); r=find(chars=='r'); +D=find(chars=='D'); d=find(chars=='d'); + +cases = {}; + +T = 8; +ev = cell(ss, T); +ev(Onode,:) = num2cell([L l U u R r D d]); +if supervised + ev(Q1,:) = num2cell(1*ones(1,T)); + ev(Q2,:) = num2cell( [1 1 2 2 3 3 4 4]); +end +cases{1} = ev; +cases{3} = ev; + +T = 8; +ev = cell(ss, T); +%we start with R then r, even though we are running the model 'backwards'! +ev(Onode,:) = num2cell([R r U u L l D d]); + +if supervised + ev(Q1,:) = num2cell(2*ones(1,T)); + ev(Q2,:) = num2cell( [3 3 2 2 1 1 4 4]); +end + +cases{2} = ev; +cases{4} = ev; + +if obs_finalF2 + for i=1:length(cases) + T = size(cases{i},2); + cases{i}(F2,T)={2}; % force F2 to be finished at end of seq + end +end + + +% startprob should be shared for t=1:T, +% but in the DBN it is shared for t=2:T, +% so we train using a single long sequence. +long_seq = cat(2, cases{:}); +[bnet_learned, LL, engine_learned] = ... + learn_params_dbn_em(engine_init, {long_seq}, 'max_iter', 200); + +% figure out which subsequence each model is responsible for +mpe = calc_mpe_dbn(engine_learned, long_seq); +pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, chars); + + +% The "true" segmentation of the training sequence is +% Q1: 1 2 +% O: L l U u R r D d | R r U u L l D d | etc. +% +% When we learn in a supervised fashion, we recover the "truth". + +% When we learn in an unsupervised fashion with seed=1, we get +% Q1: 2 1 +% O: L l U u R r D d R r | U u L l D d | etc. +% +% This means for model 1: +% starts in state 2 +% transitions 2->1, 1->4, 4->e, 3->2 +% +% For model 2, +% starts in state 1 +% transitions 1->2, 2->3, 3->4 or e, 4->3 + +% examine the params +eclass = bnet_learned.equiv_class; +CPDQ1=struct(bnet_learned.CPD{eclass(Q1,2)}); +CPDQ2=struct(bnet_learned.CPD{eclass(Q2,2)}); +CPDQ3=struct(bnet_learned.CPD{eclass(Q3,2)}); +CPDF2=struct(bnet_learned.CPD{eclass(F2,1)}); +CPDF3=struct(bnet_learned.CPD{eclass(F3,1)}); +CPDO=struct(bnet_learned.CPD{eclass(Onode,1)}); + +A_learned =add_hhmm_end_state(CPDQ2.transprob, CPDF2.termprob(:,:,2)); +squeeze(A_learned(:,1,:)) +squeeze(A_learned(:,2,:)) + + +% Does the "true" model have higher likelihood than the learned one? +% i.e., Does the unsupervised method learn the wrong model because +% we have the wrong cost fn, or because of local minima? + +bnet_true = mk_square_hhmm(discrete_obs,1); + +% examine the params +eclass = bnet_learned.equiv_class; +CPDQ1_true=struct(bnet_true.CPD{eclass(Q1,2)}); +CPDQ2_true=struct(bnet_true.CPD{eclass(Q2,2)}); +CPDQ3_true=struct(bnet_true.CPD{eclass(Q3,2)}); +CPDF2_true=struct(bnet_true.CPD{eclass(F2,1)}); +CPDF3_true=struct(bnet_true.CPD{eclass(F3,1)}); + +A_true =add_hhmm_end_state(CPDQ2_true.transprob, CPDF2_true.termprob(:,:,2)); +squeeze(A_true(:,1,:)) + + +if supervised + engine_true = jtree_ndx_dbn_inf_engine(bnet_true); +else + engine_true = hmm_inf_engine(bnet_true); +end + +%[engine_learned, ll_learned] = enter_evidence(engine_learned, long_seq); +%[engine_true, ll_true] = enter_evidence(engine_true, long_seq); +[engine_learned, ll_learned] = enter_evidence(engine_learned, cases{2}); +[engine_true, ll_true] = enter_evidence(engine_true, cases{2}); +ll_learned +ll_true + + +% remove concatentation artefacts +ll_learned = 0; +ll_true = 0; +for m=1:length(cases) + [engine_learned, ll_learned_tmp] = enter_evidence(engine_learned, cases{m}); + [engine_true, ll_true_tmp] = enter_evidence(engine_true, cases{m}); + ll_learned = ll_learned + ll_learned_tmp; + ll_true = ll_true + ll_true_tmp; +end +ll_learned +ll_true + +% In both cases, ll_learned >> ll_true +% which shows we are using the wrong cost function!
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/mk_square_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,180 @@ +function bnet = mk_square_hhmm(discrete_obs, true_params, topright) + +% Make a 3 level HHMM described by the following grammar +% +% Square -> CLK | CCK % clockwise or counterclockwise +% CLK -> LR UD RL DU start on top left (1 2 3 4) +% CCK -> RL UD LR DU if start at top right (3 2 1 4) +% CCK -> UD LR DU RL if start at top left (2 1 4 3) +% +% LR = left-right, UD = up-down, RL = right-left, DU = down-up +% LR, UD, RL, DU are sub HMMs. +% +% For discrete observations, the subHMMs are 2-state left-right. +% LR emits L then l, etc. +% +% For cts observations, the subHMMs are 1 state. +% LR emits a vector in the -> direction, with a little noise. +% Since there is no constraint that we remain in the LR state as long as the RL state, +% the sides of the square might have different lengths, +% so the result is not really a square! +% +% If true_params = 0, we use random parameters at the top 2 levels +% (ready for learning). At the bottom level, we use noisy versions +% of the "true" observations. +% +% If topright=1, counter-clockwise starts at top right, not top left +% This example was inspired by Ivanov and Bobick. + +if nargin < 3, topright = 1; end + +if 1 % discrete_obs + Qsizes = [2 4 2]; +else + Qsizes = [2 4 1]; +end + +D = 3; +Qnodes = 1:D; +startprob = cell(1,D); +transprob = cell(1,D); +termprob = cell(1,D); + +% LEVEL 1 + +startprob{1} = 'unif'; +transprob{1} = 'unif'; + +% LEVEL 2 + +if true_params + startprob{2} = zeros(2, 4); + startprob{2}(1, :) = [1 0 0 0]; + if topright + startprob{2}(2, :) = [0 0 1 0]; + else + startprob{2}(2, :) = [0 1 0 0]; + end + + transprob{2} = zeros(4, 2, 4); + + transprob{2}(:,1,:) = [0 1 0 0 + 0 0 1 0 + 0 0 0 1 + 0 0 0 1]; % 4->e + if topright + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 1 0 0 + 0 0 0 1]; % 4->e + else + transprob{2}(:,2,:) = [0 0 0 1 + 1 0 0 0 + 0 0 1 0 % 3->e + 0 0 1 0]; + end + + %termprob{2} = 'rightstop'; + termprob{2} = zeros(2,4); + pfin = 0.8; + termprob{2}(1,:) = [0 0 0 pfin]; % finish in state 4 (DU) + if topright + termprob{2}(2,:) = [0 0 0 pfin]; + else + termprob{2}(2,:) = [0 0 pfin 0]; % finish in state 3 (RL) + end +else + % In the unsupervised case, it is essential that we break symmetry + % in the initial param estimates. + %startprob{2} = 'unif'; + %transprob{2} = 'unif'; + %termprob{2} = 'unif'; + startprob{2} = 'rnd'; + transprob{2} = 'rnd'; + termprob{2} = 'rnd'; +end + +% LEVEL 3 + +if 1 | true_params + startprob{3} = 'leftstart'; + transprob{3} = 'leftright'; + termprob{3} = 'rightstop'; +else + % If we want to be able to run a base-level model backwards... + startprob{3} = 'rnd'; + transprob{3} = 'rnd'; + termprob{3} = 'rnd'; +end + + +% OBS LEVEl + +if discrete_obs + % Initialise observations of lowest level primitives in a way which we can interpret + chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + L=find(chars=='L'); l=find(chars=='l'); + U=find(chars=='U'); u=find(chars=='u'); + R=find(chars=='R'); r=find(chars=='r'); + D=find(chars=='D'); d=find(chars=='d'); + Osize = length(chars); + + if true_params + p = 1; % makes each state fully observed + else + p = 0.9; + end + + obsprob = (1-p)*ones([4 2 Osize]); + % Q2 Q3 O + obsprob(1, 1, L) = p; + obsprob(1, 2, l) = p; + obsprob(2, 1, U) = p; + obsprob(2, 2, u) = p; + obsprob(3, 1, R) = p; + obsprob(3, 2, r) = p; + obsprob(4, 1, D) = p; + obsprob(4, 2, d) = p; + obsprob = mk_stochastic(obsprob); + Oargs = {'CPT', obsprob}; +else + % Initialise means of lowest level primitives in a way which we can interpret + % These means are little vectors in the east, south, west, north directions. + % (left-right=east, up-down=south, right-left=west, down-up=north) + Osize = 2; + mu = zeros(2, Qsizes(2), Qsizes(3)); + scale = 3; + if true_params + noise = 0; + else + noise = 0.5*scale; + end + for q3=1:Qsizes(3) + mu(:, 1, q3) = scale*[1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 2, q3) = scale*[0;-1] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 3, q3) = scale*[-1;0] + noise*rand(2,1); + end + for q3=1:Qsizes(3) + mu(:, 4, q3) = scale*[0;1] + noise*rand(2,1); + end + Sigma = repmat(reshape(scale*eye(2), [2 2 1 1 ]), [1 1 Qsizes(2) Qsizes(3)]); + Oargs = {'mean', mu, 'cov', Sigma, 'cov_type', 'diag'}; +end + +if discrete_obs + selfprob = 0.5; +else + selfprob = 0.95; + % If less than this, it won't look like a square + % because it doesn't spend enough time in each state + % Unfortunately, the variance on durations (lengths of each side) + % is very large +end +bnet = mk_hhmm('Qsizes', Qsizes, 'Osize', Osize', 'discrete_obs', discrete_obs, ... + 'Oargs', Oargs, 'Ops', Qnodes(2:3), 'selfprob', selfprob, ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/plot_square_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function plot_square_hhmm(ev) +% Plot the square shape implicit in the evidence. +% ev{i,t} is the value of node i in slice t. +% The observed node contains a velocity (delta increment), which is converted +% into a position. +% The Q2 node specifies which model is used, and hence which color +% to use: 1=red, 2=green, 3=blue, 4=black. + +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; + +delta = cell2num(ev(Onode,:)); % delta(:,t) +Q2label = cell2num(ev(Q2,:)); + +T = size(delta, 2); +pos = zeros(2,T+1); +hold on +cols = {'r', 'g', 'b', 'k'}; +for t=2:T+1 + pos(:,t) = pos(:,t-1) + delta(:,t-1); + plot(pos(1,t), pos(2,t), sprintf('%c.', cols{Q2label(t-1)})); + if (t==2) + text(pos(1,t-1),pos(2,t-1),sprintf('%d',t)) + elseif (mod(t,20)==0) + text(pos(1,t),pos(2,t),sprintf('%d',t)) + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/sample_square_hhmm_cts.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +% Generate samples from the HHMM with the true params. + +seed = 1; +rand('state', seed); +randn('state', seed); + +discrete_obs = 0; + +bnet = mk_square_hhmm(discrete_obs, 1); +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +for seqi=1:1 + evidence = sample_dbn(bnet, 'stop_test', 'is_F2_true_D3'); + clf + plot_square_hhmm(evidence); + %pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, []); + fprintf('sequence %d has length %d; press key to continue\n', seqi, size(evidence,2)) + pause +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/sample_square_hhmm_discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +% Generate samples from the HHMM with the true params. + +seed = 0; +rand('state', seed); +randn('state', seed); + +discrete_obs = 1; + +bnet = mk_square_hhmm(discrete_obs, 1); + +Tmax = 30; +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; +chars = ['L', 'l', 'U', 'u', 'R', 'r', 'D', 'd']; + +for seqi=1:3 + evidence = cell2num(sample_dbn(bnet, 'stop_test', 'is_F2_true_D3')); + T = size(evidence, 2) + pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, chars); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/test_square_fig.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1310 @@ +function fig = test_square_fig() +% This is the machine-generated representation of a Handle Graphics object +% and its children. Note that handle values may change when these objects +% are re-created. This may cause problems with any callbacks written to +% depend on the value of the handle at the time the object was saved. +% +% To reopen this object, just type the name of the M-file at the MATLAB +% prompt. The M-file and its associated MAT-file must be on your path. + +load test_square_fig + +h0 = figure('Color',[0.8 0.8 0.8], ... + 'Colormap',mat0, ... + 'PointerShapeCData',mat1, ... + 'Position',[540 374 476 292]); +h1 = axes('Parent',h0, ... + 'CameraUpVector',[0 1 0], ... + 'Color',[1 1 1], ... + 'ColorOrder',mat2, ... + 'NextPlot','add', ... + 'Position',[0.13 0.11 0.3270231213872832 0.8149999999999998], ... + 'XColor',[0 0 0], ... + 'XLim',[-10 50], ... + 'XLimMode','manual', ... + 'YColor',[0 0 0], ... + 'YLim',[-60 10], ... + 'YLimMode','manual', ... + 'ZColor',[0 0 0]); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.4608294930875587, ... + 'YData',0.2923976608187218); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'String','2'); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',1.152073732718893, ... + 'YData',0.2923976608187218); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.995391705069125, ... + 'YData',0.8771929824561511); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',3.686635944700463, ... + 'YData',0.8771929824561511); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',6.451612903225808, ... + 'YData',0.8771929824561511); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',9.677419354838712, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',10.36866359447005, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',15.43778801843318, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',17.51152073732719, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',19.81566820276498, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',20.50691244239631, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',23.73271889400922, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',25.57603686635945, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',29.95391705069125, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',31.79723502304147, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',35.02304147465438, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',35.71428571428572, ... + 'YData',2.046783625730996); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',38.47926267281106, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',40.3225806451613, ... + 'YData',1.461988304093566); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[40.3225806451613 1.461988304093566 0], ... + 'String','20'); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',42.62672811059908, ... + 'YData',mat3); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.31797235023042, ... + 'YData',mat4); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.31797235023042, ... + 'YData',0.8771929824561511); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.54838709677419, ... + 'YData',0); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.77880184331798, ... + 'YData',-0.5847953216374293); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.47004608294931, ... + 'YData',-2.339181286549703); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.93087557603687, ... + 'YData',-4.385964912280699); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',46.7741935483871, ... + 'YData',-9.064327485380119); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.00460829493088, ... + 'YData',-10.81871345029239); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.69585253456221, ... + 'YData',mat5); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.69585253456221, ... + 'YData',-15.20467836257309); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-19.00584795321637); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-19.88304093567251); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-22.51461988304093); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-23.09941520467836); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-26.02339181286549); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-26.31578947368421); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-27.77777777777777); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-28.3625730994152); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.23502304147466, ... + 'YData',-30.99415204678362); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[47.23502304147466 -30.99415204678362 0], ... + 'String','40'); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-31.57894736842105); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-33.62573099415204); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-34.50292397660818); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-37.42690058479531); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.46543778801843, ... + 'YData',-38.01169590643274); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.00460829493088, ... + 'YData',-42.39766081871344); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.00460829493088, ... + 'YData',-42.98245614035087); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',47.00460829493088, ... + 'YData',-46.49122807017543); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',46.7741935483871, ... + 'YData',-46.78362573099415); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',46.54377880184332, ... + 'YData',-49.41520467836257); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',46.54377880184332, ... + 'YData',-49.70760233918128); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',45.85253456221199, ... + 'YData',-51.46198830409356); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',45.85253456221199, ... + 'YData',-51.75438596491227); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.93087557603687, ... + 'YData',-53.21637426900584); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.70046082949308, ... + 'YData',-53.21637426900584); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',44.00921658986175, ... + 'YData',-54.09356725146198); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',43.77880184331798, ... + 'YData',-54.38596491228069); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',41.93548387096774, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',41.47465437788019, ... + 'YData',-55.26315789473683); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',39.1705069124424, ... + 'YData',-55.55555555555554); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[39.1705069124424 -55.55555555555554 0], ... + 'String','60'); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',38.94009216589862, ... + 'YData',-55.84795321637426); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',36.63594470046083, ... + 'YData',-55.55555555555554); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',36.17511520737327, ... + 'YData',-55.55555555555554); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',32.94930875576037, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',32.48847926267281, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',28.11059907834102, ... + 'YData',-53.80116959064326); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',27.64976958525346, ... + 'YData',-53.50877192982455); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',23.963133640553, ... + 'YData',-53.50877192982455); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',23.27188940092166, ... + 'YData',-53.50877192982455); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',19.5852534562212, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',19.12442396313364, ... + 'YData',-54.97076023391811); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat6, ... + 'YData',-56.14035087719297); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat7, ... + 'YData',-56.14035087719297); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',9.907834101382491, ... + 'YData',-57.30994152046782); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',9.447004608294932, ... + 'YData',-57.30994152046782); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',6.221198156682029, ... + 'YData',-57.30994152046782); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',4.838709677419356, ... + 'YData',-56.7251461988304); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.764976958525345, ... + 'YData',-56.14035087719297); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.534562211981569, ... + 'YData',-56.14035087719297); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.9216589861751174, ... + 'YData',-53.80116959064327); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[0.9216589861751174 -53.80116959064327 0], ... + 'String','80'); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.6912442396313381, ... + 'YData',-53.21637426900584); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.152073732718893, ... + 'YData',-48.24561403508771); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.152073732718893, ... + 'YData',-47.953216374269); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.843317972350228, ... + 'YData',-44.73684210526315); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.843317972350228, ... + 'YData',-44.44444444444444); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.304147465437787, ... + 'YData',-39.76608187134502); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.764976958525345, ... + 'YData',-38.01169590643274); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.225806451612904, ... + 'YData',-30.99415204678362); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.225806451612904, ... + 'YData',-29.82456140350877); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.225806451612904, ... + 'YData',-24.85380116959064); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.225806451612904, ... + 'YData',-24.26900584795321); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.534562211981566, ... + 'YData',-17.5438596491228); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.304147465437787, ... + 'YData',-16.95906432748537); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.612903225806452, ... + 'YData',-11.98830409356725); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.612903225806452, ... + 'YData',-11.40350877192982); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat8, ... + 'YData',-8.47953216374269); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat9, ... + 'YData',-8.187134502923968); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.152073732718893, ... + 'YData',-5.263157894736835); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-1.152073732718893, ... + 'YData',-4.970760233918128); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.9216589861751139, ... + 'YData',-2.923976608187132); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[-0.9216589861751139 -2.923976608187132 0], ... + 'String','100'); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.9216589861751139, ... + 'YData',-2.631578947368411); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.6912442396313345, ... + 'YData',mat10); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.6912442396313345, ... + 'YData',-0.8771929824561369); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.6912442396313345, ... + 'YData',-0.5847953216374293); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[19.80645161290322 12.0675105485232 17.32050807568877], ... + 'VerticalAlignment','bottom'); +set(get(h2,'Parent'),'Title',h2); +h1 = axes('Parent',h0, ... + 'CameraUpVector',[0 1 0], ... + 'Color',[1 1 1], ... + 'ColorOrder',mat11, ... + 'NextPlot','add', ... + 'Position',[0.5779768786127169 0.11 0.3270231213872832 0.8149999999999998], ... + 'XColor',[0 0 0], ... + 'YColor',[0 0 0], ... + 'ZColor',[0 0 0]); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.4608294930875587, ... + 'YData',0); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'String','2'); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-2.764976958525345, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-3.456221198156683, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-7.834101382488477, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-11.52073732718894, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat12, ... + 'YData',-0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-19.35483870967742, ... + 'YData',0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-23.50230414746544, ... + 'YData',0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-24.88479262672811, ... + 'YData',0.8771929824561369); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-28.11059907834102, ... + 'YData',0.8771929824561369); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-29.49308755760369, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-31.10599078341014, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-32.02764976958525, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-33.17972350230414, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-33.6405529953917, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 1], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-34.7926267281106, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.02304147465438, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.48387096774194, ... + 'YData',1.461988304093566); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.71428571428572, ... + 'YData',1.461988304093566); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[-35.71428571428572 1.461988304093566 0], ... + 'String','20'); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.17511520737327, ... + 'YData',mat13); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',0.8771929824561369); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.63594470046083, ... + 'YData',0.2923976608187076); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.63594470046083, ... + 'YData',0); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.63594470046083, ... + 'YData',mat14); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-2.339181286549703); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-2.631578947368425); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-4.67836257309942); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-5.555555555555557); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-8.187134502923982); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.40552995391705, ... + 'YData',-8.771929824561397); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.86635944700461, ... + 'YData',-13.15789473684211); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',mat15); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-16.08187134502924); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-17.54385964912281); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-18.12865497076023); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-19.88304093567251); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-20.17543859649123); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-21.92982456140351); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-22.22222222222222); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[-37.09677419354839 -22.22222222222222 0], ... + 'String','40'); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-23.09941520467836); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.09677419354839, ... + 'YData',-23.39181286549707); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-25.14619883040935); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-25.43859649122807); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-28.3625730994152); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.55760368663595, ... + 'YData',-28.94736842105263); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-31.87134502923976); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-32.16374269005848); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-34.7953216374269); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-35.38011695906432); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-38.88888888888889); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-39.76608187134503); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-43.27485380116958); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-43.5672514619883); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.78801843317973, ... + 'YData',-44.44444444444444); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.55760368663595, ... + 'YData',-45.32163742690058); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.55760368663595, ... + 'YData',-45.61403508771929); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-47.36842105263158); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-37.32718894009217, ... + 'YData',-47.95321637426901); +h2 = line('Parent',h1, ... + 'Color',[0 1 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.86635944700461, ... + 'YData',-49.70760233918129); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[-36.86635944700461 -49.70760233918129 0], ... + 'String','60'); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-36.86635944700461, ... + 'YData',-50); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.71428571428572, ... + 'YData',-50.29239766081872); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-35.25345622119816, ... + 'YData',-50.29239766081872); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-32.02764976958527, ... + 'YData',-50.29239766081872); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-31.33640552995393, ... + 'YData',-50.29239766081872); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-27.88018433179725, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-27.41935483870969, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-18.20276497695854, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-16.82027649769586, ... + 'YData',-51.16959064327486); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-12.21198156682029, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-11.52073732718895, ... + 'YData',-50.58479532163743); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-6.912442396313377, ... + 'YData',-51.16959064327486); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-5.069124423963142, ... + 'YData',-51.75438596491229); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',mat16, ... + 'YData',-52.046783625731); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',-0.9216589861751281, ... + 'YData',-52.33918128654972); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.2304147465437687, ... + 'YData',-52.33918128654972); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',0.4608294930875481, ... + 'YData',-52.33918128654972); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.304147465437776, ... + 'YData',-52.63157894736843); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',2.534562211981548, ... + 'YData',-52.63157894736843); +h2 = line('Parent',h1, ... + 'Color',[1 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',3.917050691244224, ... + 'YData',-52.63157894736843); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[3.917050691244224 -52.63157894736843 0], ... + 'String','80'); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',4.147465437788011, ... + 'YData',-52.63157894736843); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',4.147465437788011, ... + 'YData',-52.33918128654972); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.529953917050673, ... + 'YData',-46.19883040935674); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.990783410138231, ... + 'YData',-44.44444444444446); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',7.834101382488466, ... + 'YData',-28.0701754385965); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',8.294930875576025, ... + 'YData',-22.80701754385966); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',8.755760368663584, ... + 'YData',mat17); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',8.525345622119797, ... + 'YData',-14.9122807017544); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',7.373271889400908, ... + 'YData',-10.23391812865498); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',7.142857142857135, ... + 'YData',-9.94152046783627); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',6.221198156682018, ... + 'YData',-7.602339181286567); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',6.221198156682018, ... + 'YData',-7.309941520467845); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.760368663594459, ... + 'YData',-5.555555555555571); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.529953917050673, ... + 'YData',-5.555555555555571); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.2995391705069, ... + 'YData',-4.093567251462005); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.069124423963128, ... + 'YData',-2.631578947368439); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.069124423963128, ... + 'YData',-2.339181286549717); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.069124423963128, ... + 'YData',mat18); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',5.069124423963128, ... + 'YData',-1.169590643274873); +h2 = line('Parent',h1, ... + 'Color',[0 0 0], ... + 'LineStyle','none', ... + 'Marker','.', ... + 'XData',4.838709677419342, ... + 'YData',-0.2923976608187218); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'Position',[4.838709677419342 -0.2923976608187218 0], ... + 'String','100'); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[-10.38961038961038 12.0675105485232 17.32050807568877], ... + 'VerticalAlignment','bottom'); +set(get(h2,'Parent'),'Title',h2); +if nargout > 0, fig = h0; end
Binary file toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Square/test_square_fig.mat has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/abcd_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +% Make the HHMM in Figure 1 of the NIPS'01 paper + +Qsize = [2 3 2]; +Qnodes = 1:3; +D = 3; +transprob = cell(1,D); +termprob = cell(1,D); +startprob = cell(1,D); +clear A; + +% transprob{d}(i,k,j), transprob{1}(i,j) +% termprob{d}(k,j), termprob{1}(1,j) +% startprob{d}(k,j), startprob{1}(1,j) + + +% LEVEL 1 + +% 1 2 e +A{1} = [0 0 1; + 0 0 1]; +[transprob{1}, termprob{1}] = remove_hhmm_end_state(A{1}); +startprob{1} = [0.5 0.5]; + +% LEVEL 2 +A{2} = zeros(Qsize(2), Qsize(1), Qsize(2)+1); + +% 1 2 3 e +A{2}(:,1,:) = [0 1 0 0 % Q1=1 => model below state 0 + 0 0 1 0 + 0 0 0 1]; + +% 1 2 3 e +A{2}(:,2,:) = [0 1 0 0 % Q1=2 => model below state 1 + 0 0 1 0 + 0 0 0 1]; + +[transprob{2}, termprob{2}] = remove_hhmm_end_state(A{2}); + +% always enter level 2 in state 1 +startprob{2} = [1 0 0 + 1 0 0]; + +% LEVEL 3 + +A{3} = zeros([Qsize(3) Qsize(2) Qsize(3)+1]); +endstate = Qsize(3)+1; +% Qt-1(3) Qt(2) Qt(3) +% 1 2 e +A{3}(1, 1, endstate) = 1.0; % Q2=1 => model below state 2/5 +A{3}(:, 2, :) = [0.0 1.0 0.0 % Q2=2 => model below state 3/6 + 0.5 0.0 0.5]; +A{3}(1, 3, endstate) = 1.0; % Q2=3 => model below state 4/7 + +[transprob{3}, termprob{3}] = remove_hhmm_end_state(A{3}); + +startprob{3} = 'leftstart'; + + + +% OBS LEVEl + +chars = ['a', 'b', 'c', 'd', 'x', 'y']; +Osize = length(chars); + +obsprob = zeros([Qsize Osize]); +% 1 2 3 O +obsprob(1,1,1,find(chars == 'a')) = 1.0; + +obsprob(1,2,1,find(chars == 'x')) = 1.0; +obsprob(1,2,2,find(chars == 'y')) = 1.0; + +obsprob(1,3,1,find(chars == 'b')) = 1.0; + +obsprob(2,1,1,find(chars == 'c')) = 1.0; + +obsprob(2,2,1,find(chars == 'x')) = 1.0; +obsprob(2,2,2,find(chars == 'y')) = 1.0; + +obsprob(2,3,1,find(chars == 'd')) = 1.0; + +Oargs = {'CPT', obsprob}; + +bnet = mk_hhmm('Qsizes', Qsize, 'Osize', Osize, 'discrete_obs', 1, ... + 'Oargs', Oargs, 'Ops', Qnodes(1:3), ... + 'startprob', startprob, 'transprob', transprob, 'termprob', termprob); + + +Q1 = 1; Q2 = 2; Q3 = 3; F3 = 4; F2 = 5; Onode = 6; +Qnodes = [Q1 Q2 Q3]; Fnodes = [F2 F3]; + +for seqi=1:3 + evidence = sample_dbn(bnet, 'stop_test', 'is_F2_true_D3'); + ev = cell2num(evidence); + chars(ev(end,:)) + %T = size(evidence, 2) + %pretty_print_hhmm_parse(evidence, Qnodes, Fnodes, Onode, chars); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/add_hhmm_end_state.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function A = add_hhmm_end_state(transprob, termprob) +% ADD_HMM_END_STATE Combine trans and term probs into transmat for automaton with an end state +% function A = add_hhmm_end_state(transprob, termprob) +% +% A(i,k,j) = Pr( i->j | Qps=k), where i in 1:Q, j in 1:(Q+1), and Q+1 is the end state +% This implements the equation in sec 4.6 of my tech report, where +% transprob(i,k,j) = \tilde{A}_k(i,j), termprob(k,j) = \tau_k(j) +% +% For the top level, the k index is missing. + +Q = size(transprob,1); +toplevel = (ndims(transprob)==2); +if toplevel + Qk = 1; + transprob = reshape(transprob, [Q 1 Q]); + termprob = reshape(termprob, [1 Q]); +else + Qk = size(transprob, 2); +end + +A = zeros(Q, Qk, Q+1); +A(:,:,Q+1) = termprob'; + +for k=1:Qk + for i=1:Q + for j=1:Q + A(i,k,j) = transprob(i,k,j) * (1-termprob(k,i)); + end + end +end + +if toplevel + A = squeeze(A); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/hhmm_jtree_clqs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,143 @@ +% Find out how big the cliques are in an HHMM as a function of depth +% (This is how we get the complexity bound of O(D K^{1.5D}).) + +if 0 +Qsize = []; +Fsize = []; +Nclqs = []; +end + +ds = 1:15; + +for d = ds + allQ = 1; + [intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo(d, allQ); + + N = length(intra); + ns = 2*ones(1,N); + + bnet = mk_dbn(intra, inter, ns); + for i=1:N + bnet.CPD{i} = tabular_CPD(bnet, i); + end + + if 0 + T = 5; + dag = unroll_dbn_topology(intra, inter, T); + engine = jtree_unrolled_dbn_inf_engine(bnet, T, 'constrained', 1); + S = struct(engine); + S1 = struct(S.sub_engine); + end + + engine = jtree_dbn_inf_engine(bnet); + S = struct(engine); + J = S.jtree_struct; + + ss = 2*d+1; + Qnodes2 = Qnodes + ss; + QQnodes = [Qnodes Qnodes2]; + + % find out how many Q nodes in each clique, and how many F nodes + C = length(J.cliques); + Nclqs(d) = 0; + for c=1:C + Qsize(c,d) = length(myintersect(J.cliques{c}, QQnodes)); + Fsize(c,d) = length(myintersect(J.cliques{c}, Fnodes)); + if length(J.cliques{c}) > 1 % exclude observed leaves + Nclqs(d) = Nclqs(d) + 1; + end + end + %pred_max_Qsize(d) = ceil(d+(d+1)/2); + pred_max_Qsize(d) = ceil(1.5*d); + + fprintf('d=%d\n', d); + %fprintf('D=%d, max F = %d. max Q = %d, pred max Q = %d\n', ... + % D, max(Fsize), max(Qsize), ceil(D+(D+1)/2)); + + %histc(Qsize,1:max(Qsize)) % how many of each size? +end % next d + + +Q = 2; +pred_mass = ds.*(Q.^ds) + Q.^(ceil(1.5 * ds)) +pred_mass2 = Q.^(ceil(1.5 * ds)) + +for d=ds + mass(d) = 0; + for c=1:C + mass(d) = mass(d) + Q^Qsize(c,d); + end +end + + +if 0 +%plot(ds, max(Qsize), 'o-', ds, pred_max_Qsize, '*--'); +%plot(ds, max(Qsize), 'o-', ds, 1.5*ds, '*--'); +%plot(ds, mass, 'o-', ds, pred_mass, '*--'); +D = 15; +%plot(ds(1:D), mass(1:D), 'bo-', ds(1:D), pred_mass(1:D), 'g*--', ds(1:D), pred_mass2(1:D), 'k+-.'); +plot(ds(1:D), log(mass(1:D)), 'bo-', ds(1:D), log(pred_mass(1:D)), 'g*--', ds(1:D), log(pred_mass2(1:D)), 'k+-.'); + +grid on +xlabel('depth of hierarchy') +title('max num Q nodes in any clique vs. depth') +legend('actual', 'predicted') + +%previewfig(gcf, 'width', 3, 'height', 1.5, 'color', 'bw'); +%exportfig(gcf, '/home/cs/murphyk/WP/ConferencePapers/HHMM/clqsize2.eps', ... +% 'width', 3, 'height', 1.5, 'color', 'bw'); + +end + + +if 0 +for d=ds + effnumclqs(d) = length(find(Qsize(:,d)>0)); +end +ds = 1:10; +Qs = 2:10; +maxC = size(Qsize, 1); +cost = []; +cost_bound = []; +for qi=1:length(Qs) + Q = Qs(qi); + for d=ds + cost(d,qi) = 0; + for c=1:maxC + if length(Qsize(c,d) > 0) % this clique contains Q nodes + cost(d,qi) = cost(d,qi) + Q^Qsize(c,d)*2^Fsize(c,d); + end + end + %cost_bound(d,qi) = effnumclqs(d) * 8 * Q^(max(Qsize(:,d))); + cost_bound(d,qi) = (effnumclqs(d)*8) + Q^(max(Qsize(:,d))); + end +end + +qi=2; plot(ds, cost(:,qi), 'o-', ds, cost_bound(:,qi), '*--'); +end + + +if 0 +% convert numbers in cliques into names +for d=1:D + Fdecode(Fnodes(d)) = d; +end +for c=8:15 + clqs = J.cliques{c}; + fprintf('clique %d: ', c); + for k=clqs + if myismember(k, Qnodes) + fprintf('Q%d ', k) + elseif myismember(k, Fnodes) + fprintf('F%d ', Fdecode(k)) + elseif isequal(k, Onode) + fprintf('O ') + elseif myismember(k, Qnodes2) + fprintf('Q%d* ', k-ss) + else + error(['unrecognized node ' k]) + end + end + fprintf('\n'); +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/mk_hhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,258 @@ +function [bnet, Qnodes, Fnodes, Onode] = mk_hhmm(varargin) +% MK_HHMM Make a Hierarchical HMM +% function [bnet, Qnodes, Fnodes, Onode] = mk_hhmm(...) +% +% e.g. 3-layer hierarchical HMM where level 1 only connects to level 2 +% and the parents of the observed node are levels 2 and 3. +% (This DBN is the same as Fig 10 in my tech report.) +% +% Q1 ----------> Q1 +% | \ ^ | +% | v / | +% | F2 ------/ | +% | ^ ^ \ | +% | / | \ | +% | / | || +% v | vv +% Q2----| --------> Q2 +% /| \ | ^| +% / | v | / | +% | | F3 --------/ | +% | | ^ \ | +% | v / v v +% | Q3 -----------> Q3 +% | | +% \ | +% v v +% O +% +% +% Optional arguments in name/value format [default value in brackets] +% +% Qsizes - sizes at each level [ none ] +% allQ - 1 means level i connects to all Q levels below, 0 means just to i+1 [0] +% transprob - transprob{d}(i,k,j) = P(Q(d,t)=j|Q(d,t-1)=i,Q(1:d-1,t)=k) ['leftright'] +% startprob - startprob{d}(k,j) = P(Q(d,t)=j|Q(1:d-1,t)=k) ['leftstart'] +% termprob - termprob{d}(k,j) = P(F(d,t)=2|Q(1:d-1,t)=k,Q(d,t)=j) for d>1 ['rightstop'] +% selfprop - prob of a self transition (termprob default = 1-selfprop) [0.8] +% Osize - size of O node +% discrete_obs - 1 means O is tabular_CPD, 0 means gaussian_CPD [0] +% Oargs - cell array of args to pass to the O CPD [ {} ] +% Ops - Q parents of O [Qnodes(end)] +% F1 - 1 means level 1 can finish (restart), else there is no F1->Q1 arc [0] +% clamp1 - 1 means we clamp the params of the Q nodes in slice 1 (Qt1params) [1] +% Note: the Qt1params are startprob, which should be shared with other slices. +% However, in the current implementation, the Qt1params will only be estimated +% from the initial state of each sequence. +% +% For d=1, startprob{1}(1,j) is only used in the first slice and +% termprob{1} is ignored, since we assume the top level never resets. +% Also, transprob{1}(i,j) can be used instead of transprob{1}(i,1,j). +% +% leftstart means the model always starts in state 1. +% rightstop means the model can only finish in its last state (Qsize(d)). +% unif means each state is equally like to reach any other +% rnd means the transition/starting probs are random (drawn from rand) +% +% Q1:QD in slice 1 are of type tabular_CPD +% Q1:QD in slice 2 are of type hhmmQ_CPD. +% F(2:D-1) is of type hhmmF_CPD, FD is of type tabular_CPD. + +args = varargin; +nargs = length(args); + +% get sizes of nodes and topology +Qsizes = []; +Osize = []; +allQ = 0; +Ops = []; +F1 = 0; +for i=1:2:nargs + switch args{i}, + case 'Qsizes', Qsizes = args{i+1}; + case 'Osize', Osize = args{i+1}; + case 'allQ', allQ = args{i+1}; + case 'Ops', Ops = args{i+1}; + case 'F1', F1 = args{i+1}; + end +end +if isempty(Qsizes), error('must specify Qsizes'); end +if Osize==0, error('must specify Osize'); end +D = length(Qsizes); +Qnodes = 1:D; + +if isempty(Ops), Ops = Qnodes(end); end + + +[intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo(D, allQ, Ops, F1); +ss = length(intra); +names = {}; + +if F1 + Fnodes_ndx = Fnodes; +else + Fnodes_ndx = [-1 Fnodes]; % Fnodes(1) is a dummy index +end + +% set default params +discrete_obs = 0; +Oargs = {}; +startprob = cell(1,D); +startprob{1} = 'unif'; +for d=2:D + startprob{d} = 'leftstart'; +end +transprob = cell(1,D); +transprob{1} = 'unif'; +for d=2:D + transprob{d} = 'leftright'; +end +termprob = cell(1,D); +for d=2:D + termprob{d} = 'rightstop'; +end +selfprob = 0.8; +clamp1 = 1; + +for i=1:2:nargs + switch args{i}, + case 'discrete_obs', discrete_obs = args{i+1}; + case 'Oargs', Oargs = args{i+1}; + case 'startprob', startprob = args{i+1}; + case 'transprob', transprob = args{i+1}; + case 'termprob', termprob = args{i+1}; + case 'selfprob', selfprob = args{i+1}; + case 'clamp1', clamp1 = args{i+1}; + end +end + +ns = zeros(1,ss); +ns(Qnodes) = Qsizes; +ns(Onode) = Osize; +ns(Fnodes) = 2; + +dnodes = [Qnodes Fnodes]; +if discrete_obs + dnodes = [dnodes Onode]; +end +onodes = [Onode]; + +bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'discrete', dnodes, 'names', names); +eclass = bnet.equiv_class; + +for d=1:D + if d==1 + Qps = []; + elseif allQ + Qps = Qnodes(1:d-1); + else + Qps = Qnodes(d-1); + end + Qpsz = prod(ns(Qps)); + Qsz = ns(Qnodes(d)); + if isstr(startprob{d}) + switch startprob{d} + case 'unif', startprob{d} = mk_stochastic(ones(Qpsz, Qsz)); + case 'rnd', startprob{d} = mk_stochastic(rand(Qpsz, Qsz)); + case 'leftstart', startprob{d} = zeros(Qpsz, Qsz); startprob{d}(:,1) = 1; + end + end + if isstr(transprob{d}) + switch transprob{d} + case 'unif', transprob{d} = mk_stochastic(ones(Qsz, Qpsz, Qsz)); + case 'rnd', transprob{d} = mk_stochastic(rand(Qsz, Qpsz, Qsz)); + case 'leftright', + LR = mk_leftright_transmat(Qsz, selfprob); + temp = repmat(reshape(LR, [1 Qsz Qsz]), [Qpsz 1 1]); % transprob(k,i,j) + transprob{d} = permute(temp, [2 1 3]); % now transprob(i,k,j) + end + end + if isstr(termprob{d}) + switch termprob{d} + case 'unif', termprob{d} = mk_stochastic(ones(Qpsz, Qsz, 2)); + case 'rnd', termprob{d} = mk_stochastic(rand(Qpsz, Qsz, 2)); + case 'rightstop', + %termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz + stopprob = 1-selfprob; + termprob{d} = zeros(Qpsz, Qsz, 2); + termprob{d}(:,Qsz,2) = stopprob; + termprob{d}(:,Qsz,1) = 1-stopprob; + termprob{d}(:,1:(Qsz-1),1) = 1; + otherwise, error(['unrecognized termprob ' termprob{d}]) + end + elseif d>1 % passed in termprob{d}(k,j) + temp = termprob{d}; + termprob{d} = zeros(Qpsz, Qsz, 2); + termprob{d}(:,:,2) = temp; + termprob{d}(:,:,1) = ones(Qpsz,Qsz) - temp; + end +end + + +% SLICE 1 + +for d=1:D + bnet.CPD{eclass(Qnodes(d),1)} = tabular_CPD(bnet, Qnodes(d), 'CPT', startprob{d}, 'adjustable', clamp1); +end + +if F1 + d = 1; + bnet.CPD{eclass(Fnodes_ndx(d),1)} = hhmmF_CPD(bnet, Fnodes_ndx(d), Qnodes(d), Fnodes_ndx(d+1), ... + 'termprob', termprob{d}); +end +for d=2:D-1 + if allQ + Qps = Qnodes(1:d-1); + else + Qps = Qnodes(d-1); + end + bnet.CPD{eclass(Fnodes_ndx(d),1)} = hhmmF_CPD(bnet, Fnodes_ndx(d), Qnodes(d), Fnodes_ndx(d+1), ... + 'Qps', Qps, 'termprob', termprob{d}); +end +bnet.CPD{eclass(Fnodes_ndx(D),1)} = tabular_CPD(bnet, Fnodes_ndx(D), 'CPT', termprob{D}); + +if discrete_obs + bnet.CPD{eclass(Onode,1)} = tabular_CPD(bnet, Onode, Oargs{:}); +else + bnet.CPD{eclass(Onode,1)} = gaussian_CPD(bnet, Onode, Oargs{:}); +end + +% SLICE 2 + +%for d=1:D +% bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, Qnodes, d, D, ... +% 'startprob', startprob{d}, 'transprob', transprob{d}, ... +% 'allQ', allQ); +%end + +d = 1; +if F1 + bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, 'Fself', Fnodes_ndx(d), ... + 'Fbelow', Fnodes_ndx(d+1), ... + 'startprob', startprob{d}, 'transprob', transprob{d}); +else + bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, ... + 'Fbelow', Fnodes_ndx(d+1), ... + 'startprob', startprob{d}, 'transprob', transprob{d}); +end +for d=2:D-1 + if allQ + Qps = Qnodes(1:d-1); + else + Qps = Qnodes(d-1); + end + Qps = Qps + ss; % since all in slice 2 + bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, 'Fself', Fnodes_ndx(d), ... + 'Fbelow', Fnodes_ndx(d+1), 'Qps', Qps, ... + 'startprob', startprob{d}, 'transprob', transprob{d}); +end +d = D; +if allQ + Qps = Qnodes(1:d-1); +else + Qps = Qnodes(d-1); +end +Qps = Qps + ss; % since all in slice 2 +bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, 'Fself', Fnodes_ndx(d), ... + 'Qps', Qps, ... + 'startprob', startprob{d}, 'transprob', transprob{d});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/mk_hhmm_topo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function [intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo(D, all_Q_to_Qs, Ops, F1) +% MK_HHMM_TOPO Make Hierarchical HMM topology +% function [intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo(D, all_Q_to_Qs, Ops, F1) +% +% D is the depth of the hierarchy +% If all_Q_to_Qs = 1, level i connects to all levels below, else just to i+1 [0] +% Ops are the Q parents of the observed node [Qnodes(end)] +% If F1=1, level 1 can finish (restart), else there is no F1->Q1 arc [0] + +Qnodes = 1:D; + +if nargin < 2, all_Q_to_Qs = 1; end +if nargin < 3, Ops = Qnodes(D); end +if nargin < 4, F1 = 0; end + +if F1 + Fnodes = 2*D:-1:D+1; % must number from bottom to top + Onode = 2*D+1; + ss = 2*D+1; +else + Fnodes = [-1 (2*D)-1:-1:D+1]; % Fnodes(1) is a dummy index + Onode = 2*D; + ss = 2*D; +end + +intra = zeros(ss); +intra(Ops, Onode) = 1; +for d=1:D-1 + if all_Q_to_Qs + intra(Qnodes(d), Qnodes(d+1:end)) = 1; + else + intra(Qnodes(d), Qnodes(d+1)) = 1; + end +end +for d=D:-1:3 + intra(Fnodes(d), Fnodes(d-1)) = 1; +end +if F1 + intra(Fnodes(2), Fnodes(1)) = 1; +end +if all_Q_to_Qs + if F1 + intra(Qnodes(1), Fnodes(1:end)) = 1; + else + intra(Qnodes(1), Fnodes(2:end)) = 1; + end + for d=2:D + intra(Qnodes(d), Fnodes(d:end)) = 1; + end +else + if F1 + intra(Qnodes(1), Fnodes([1 2])) = 1; + else + intra(Qnodes(1), Fnodes(2)) = 1; + end + for d=2:D-1 + intra(Qnodes(d), Fnodes([d d+1])) = 1; + end + intra(Qnodes(D), Fnodes(D)) = 1; +end + + +inter = zeros(ss); +for d=1:D + inter(Qnodes(d), Qnodes(d)) = 1; +end +if F1 + inter(Fnodes(1), Qnodes(1)) = 1; +end +for d=2:D + inter(Fnodes(d), Qnodes([d-1 d])) = 1; +end + +if ~F1 + Fnodes = Fnodes(2:end); % strip off dummy -1 term +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/mk_hhmm_topo_F1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo_F1(D, all_Q_to_Qs, Ops) +% MK_HHMM_TOPO Make Hierarchical HMM topology assuming level 1 can finish +% function [intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo(D, all_Q_to_Qs, Ops, F1) +% +% D is the depth of the hierarchy +% If all_Q_to_Qs = 1, level i connects to all levels below, else just to i+1 [0] +% Ops are the Q parents of the observed node [Qnodes(end)] +% If F1=1, level 1 can finish (restart), else there is no F1->Q1 arc [0] + +Qnodes = 1:D; + +if nargin < 2, all_Q_to_Qs = 1; end +if nargin < 3, Ops = Qnodes(D); end +if nargin < 4, F1 = 0; end + +if F1 + Fnodes = 2*D:-1:D+1; % must number from bottom to top + Onode = 2*D+1; + ss = 2*D+1; +else + Fnodes = (2*D)-1:-1:D+1; + Onode = 2*D; + ss = 2*D; +end + +intra = zeros(ss); +intra(Ops, Onode) = 1; +for d=1:D-1 + if all_Q_to_Qs + intra(Qnodes(d), Qnodes(d+1:end)) = 1; + else + intra(Qnodes(d), Qnodes(d+1)) = 1; + end +end +for d=D:-1:3 + intra(Fnodes(d), Fnodes(d-1)) = 1; +end +if F1 + intra(Fnodes(2), Fnodes(1)) = 1; +end +if all_Q_to_Qs + for d=1:D + intra(Qnodes(d), Fnodes(d:end)) = 1; + end +else + for d=1:D + if d < D + intra(Qnodes(d), Fnodes([d d+1])) = 1; + else + intra(Qnodes(d), Fnodes(d)) = 1; + end + end +end + +inter = zeros(ss); +for d=1:D + inter(Qnodes(d), Qnodes(d)) = 1; +end +for d=1:D + if d==1 + inter(Fnodes(d), Qnodes(d)) = 1; + else + inter(Fnodes(d), Qnodes([d-1 d])) = 1; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/pretty_print_hhmm_parse.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, alphabet) +% function pretty_print_hhmm_parse(mpe, Qnodes, Fnodes, Onode, alphabet) +% +% mpe(i,t) is the most probable value of node i at time t +% Qnodes(1:D), Fnodes = [F2 .. FD], Onode contain the node ids +% alphabet(i) is the i'th output symbol, or [] if don't want displayed + +T = size(mpe,2); +ncols = 20; +t1 = 1; t2 = min(T, t1+ncols-1); +while (t1 < T) + %fprintf('%d:%d\n', t1, t2); + if iscell(mpe) + print_block_cell(mpe(:,t1:t2), Qnodes, Fnodes, Onode, alphabet, t1); + else + print_block(mpe(:,t1:t2), Qnodes, Fnodes, Onode, alphabet, t1); + end + fprintf('\n\n'); + t1 = t2+1; t2 = min(T, t1+ncols-1); +end + +%%%%%% + +function print_block_cell(mpe, Qnodes, Fnodes, Onode, alphabet, start) + +D = length(Qnodes); +T = size(mpe, 2); +fprintf('%3d ', start:start+T-1); fprintf('\n'); +for d=1:D + for t=1:T + if (d > 1) & (mpe{Fnodes(d-1),t} == 2) + fprintf('%3d|', mpe{Qnodes(d), t}); + else + fprintf('%3d ', mpe{Qnodes(d), t}); + end + end + fprintf('\n'); +end +if ~isempty(alphabet) + a = cell2num(mpe(Onode,:)); + %fprintf('%3c ', alphabet(mpe{Onode,:})); + fprintf('%3c ', alphabet(a)) + fprintf('\n'); +end + + +%%%%%% + +function print_block(mpe, Qnodes, Fnodes, Onode, alphabet, start) + +D = length(Qnodes); +T = size(mpe, 2); +fprintf('%3d ', start:start+T-1); fprintf('\n'); +for d=1:D + for t=1:T + if (d > 1) & (mpe(Fnodes(d-1),t) == 2) + fprintf('%3d|', mpe(Qnodes(d), t)); + else + fprintf('%3d ', mpe(Qnodes(d), t)); + end + end + fprintf('\n'); +end +if ~isempty(alphabet) + fprintf('%3c ', alphabet(mpe(Onode,:))); + fprintf('\n'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/remove_hhmm_end_state.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function [transprob, termprob] = remove_hhmm_end_state(A) +% REMOVE_END_STATE Infer transition and termination probabilities from automaton with an end state +% [transprob, termprob] = remove_end_state(A) +% +% A(i,k,j) = Pr( i->j | Qps=k), where i in 1:Q, j in 1:(Q+1), and Q+1 is the end state +% This implements the equation in footnote 3 of my NIPS 01 paper, +% transprob(i,k,j) = \tilde{A}_k(i,j) +% termprob(k,j) = \tau_k(j) +% +% For the top level, the k index is missing. + +Q = size(A,1); +toplevel = (ndims(A)==2); +if toplevel + Qk = 1; + A = reshape(A, [Q 1 Q+1]); +else + Qk = size(A, 2); +end + +transprob = A(:, :, 1:Q); +term = A(:,:,Q+1)'; % term(k,j) = P(Qj -> end | k) +termprob = term; +%termprob = zeros(Qk, Q, 2); +%termprob(:,:,2) = term; +%termprob(:,:,1) = 1-term; + +for k=1:Qk + for i=1:Q + for j=1:Q + denom = (1-termprob(k,i)); + denom = denom + (denom==0)*eps; + transprob(i,k,j) = transprob(i,k,j) / denom; + end + end +end + +if toplevel + termprob = squeeze(termprob); + transprob = squeeze(transprob); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +/chmm1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/cmp_inference.m/1.1.1.1/Wed May 29 15:59:54 2002// +/kalman1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/old.water1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/online1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/online2.m/1.1.1.1/Wed May 29 15:59:54 2002// +/scg_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/chmm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +% Compare the speeds of various inference engines on a coupled HMM + +N = 2; +Q = 2; +rand('state', 0); +randn('state', 0); +discrete = 1; +if discrete + Y = 2; % size of output alphabet +else + Y = 1; +end +coupled = 1; +[bnet, onodes] = mk_chmm(N, Q, Y, discrete, coupled); +ss = N*2; + +T = 3; + + +engine = {}; +tic; engine{end+1} = jtree_dbn_inf_engine(bnet, 'observed', onodes); toc +%tic; engine{end+1} = jtree_ndxSD_dbn_inf_engine(bnet, onodes); toc +%tic; engine{end+1} = jtree_ndxB_dbn_inf_engine(bnet, onodes); toc +engine{end+1} = hmm_inf_engine(bnet, onodes); +%engine{end+1} = dhmm_inf_engine(bnet, onodes); +tic; engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T, onodes); toc + +%engine{end+1} = bk_inf_engine(bnet, 'ff', onodes); +%engine{end+1} = loopy_dbn_inf_engine(bnet, onodes); + +exact = [1 2 3]; + +filter = 0; +single = 0; +maximize = 0; + +[err, time, engine] = cmp_inference(bnet, onodes, engine, exact, T, filter, single, maximize); +%err = cmp_learning(bnet, onodes, engine, exact, T); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/cmp_inference.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,75 @@ +function [err, time, engine] = cmp_inference(bnet, engine, exact, T, filter, singletons, maximize) +% CMP_INFERENCE Compare several inference engines on a DBN +% [err, time, engine] = cmp_inference(bnet, engine, exact, T, filter, singletons, maximize) +% +% engine{i} is the i'th inference engine. +% 'exact' specifies which engines do exact inference - +% we check that these all give the same results. +% 'T' is the length of the random sequence we generate. +% If filter=1, we do filtering, else smoothing (default: smoothing) +% If singletons=1, we compare marginal_nodes, else marginal_family (default: family) +% +% err(e,n,t) = sum_i | Pr_exact(X(n,t)=i) - Pr_e(X(n,t)=i) | +% where Pr_e = prob. according to engine e +% time(e) = elapsed time for doing inference with engine e + +err = []; + +if nargin < 5, filter = 0; end +if nargin < 6, singletons = 0; end +if nargin < 7, maximize = 0; end + +check_ll = 1; + +assert(~maximize); + +E = length(engine); +ref = exact(1); % reference + +ss = length(bnet.intra); +ev = sample_dbn(bnet, 'length', T); +evidence = cell(ss,T); +onodes = bnet.observed; +evidence(onodes,:) = ev(onodes, :); + +assert(~filter); +for i=1:E + tic; + %[engine{i}, ll(i)] = enter_evidence(engine{i}, evidence, 'maximize', maximize); + [engine{i}, ll(i)] = enter_evidence(engine{i}, evidence); + time(i)=toc; + fprintf('engine %d took %6.4f seconds\n', i, time(i)); +end + +cmp = mysetdiff(exact, ref); +if check_ll +for i=cmp(:)' + if ~approxeq(ll(ref), ll(i)) + error(['engine ' num2str(i) ' has wrong ll']) + end +end +end +ll + +hnodes = mysetdiff(1:ss, onodes); +m = cell(1,E); +for t=1:T + for n=hnodes(:)' + for e=1:E + if singletons + m{e} = marginal_nodes(engine{e}, n, t); + else + m{e} = marginal_family(engine{e}, n, t); + end + end + for e=1:E + assert(isequal(m{e}.domain, m{ref}.domain)); + end + for e=cmp(:)' + if ~approxeq(m{ref}.T(:), m{e}.T(:)) + str= sprintf('engine %d is wrong; n=%d, t=%d', e, n, t); + error(str) + end + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/kalman1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,127 @@ +% Make a linear dynamical system +% X1 -> X2 +% | | +% v v +% Y1 Y2 + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +X = 2; % size of hidden state +Y = 2; % size of observable state + +ns = [X Y]; +dnodes = []; +onodes = [2]; +eclass1 = [1 2]; +eclass2 = [3 2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ... + 'observed', onodes); + +x0 = rand(X,1); +V0 = eye(X); +C0 = rand(Y,X); +R0 = eye(Y); +A0 = rand(X,X); +Q0 = eye(X); + +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0, 'cov_prior_weight', 0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0, ... + 'clamp_mean', 1, 'cov_prior_weight', 0); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0, ... + 'clamp_mean', 1, 'cov_prior_weight', 0); + + +T = 5; % fixed length sequences + +clear engine; +engine{1} = kalman_inf_engine(bnet); +engine{2} = jtree_unrolled_dbn_inf_engine(bnet, T); +engine{3} = jtree_dbn_inf_engine(bnet); +N = length(engine); + +% inference + +ev = sample_dbn(bnet, T); +evidence = cell(n,T); +evidence(onodes,:) = ev(onodes, :); + +t = 1; +query = [1 3]; +m = cell(1, N); +ll = zeros(1, N); +for i=1:N + [engine{i}, ll(i)] = enter_evidence(engine{i}, evidence); + m{i} = marginal_nodes(engine{i}, query, t); +end + +% compare all engines to engine{1} +for i=2:N + assert(approxeq(m{1}.mu, m{i}.mu)); + assert(approxeq(m{1}.Sigma, m{i}.Sigma)); + assert(approxeq(ll(1), ll(i))); +end + +if 0 +for i=2:N + approxeq(m{1}.mu, m{i}.mu) + approxeq(m{1}.Sigma, m{i}.Sigma) + approxeq(ll(1), ll(i)) +end +end + +% learning + +ncases = 5; +cases = cell(1, ncases); +for i=1:ncases + ev = sample_dbn(bnet, T); + cases{i} = cell(n,T); + cases{i}(onodes,:) = ev(onodes, :); +end + +max_iter = 2; +bnet2 = cell(1,N); +LLtrace = cell(1,N); +for i=1:N + [bnet2{i}, LLtrace{i}] = learn_params_dbn_em(engine{i}, cases, 'max_iter', max_iter); +end + +for i=1:N + temp = bnet2{i}; + for e=1:3 + CPD{i,e} = struct(temp.CPD{e}); + end +end + +for i=2:N + assert(approxeq(LLtrace{i}, LLtrace{1})); + for e=1:3 + assert(approxeq(CPD{i,e}.mean, CPD{1,e}.mean)); + assert(approxeq(CPD{i,e}.cov, CPD{1,e}.cov)); + assert(approxeq(CPD{i,e}.weights, CPD{1,e}.weights)); + end +end + + +% Compare to KF toolbox + +data = zeros(Y, T, ncases); +for i=1:ncases + data(:,:,i) = cell2num(cases{i}(onodes, :)); +end +[A2, C2, Q2, R2, x2, V2, LL2trace] = learn_kalman(data, A0, C0, Q0, R0, x0, V0, max_iter); + + +e = 1; +assert(approxeq(x2, CPD{e,1}.mean)) +assert(approxeq(V2, CPD{e,1}.cov)) +assert(approxeq(C2, CPD{e,2}.weights)) +assert(approxeq(R2, CPD{e,2}.cov)); +assert(approxeq(A2, CPD{e,3}.weights)) +assert(approxeq(Q2, CPD{e,3}.cov)); +assert(approxeq(LL2trace, LLtrace{1})) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/old.water1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +% Compare the speeds of various inference engines on the water DBN + +[bnet, onodes] = mk_water_dbn; + +T = 3; + +engine = {}; +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T, onodes); +engine{end+1} = hmm_inf_engine(bnet, onodes); +engine{end+1} = frontier_inf_engine(bnet, onodes); +engine{end+1} = jtree_dbn_inf_engine(bnet, onodes); +engine{end+1} = bk_inf_engine(bnet, 'exact', onodes); + +engine{end+1} = bk_inf_engine(bnet, 'ff', onodes); +engine{end+1} = bk_inf_engine(bnet, { [1 2], [3 4 5 6], [7 8] }, onodes); + +N = length(engine); +exact = 1:5; + + +filter = 0; +err = cmp_inference(bnet, onodes, engine, exact, T, filter); + +% elapsed times for enter_evidence (matlab 5.3 on PIII with 256MB running Redhat linux) + +% T = 5, 4/20/00 +% 0.6266 unrolled * +% 0.3490 hmm * +% 1.1743 frontier +% 1.4621 old frontier +% 0.3270 fast frontier * +% 1.3926 jtree +% 1.3790 bk +% 0.4916 fast bk +% 0.4190 fast bk compiled +% 0.3574 fast jtree * + + +err = cmp_learning(bnet, onodes, engine, exact, T); + +% elapsed times for learn_params_dbn_em (matlab 5.3 on PIII with 256MB running Redhat linux) + +% T = 5, 2cases, 2 iter, 4/20/00 +% 3.5750 unrolled +% 3.7475 hmm +% 2.1452 fast frontier +% 2.5724 fast bk compiled +% 2.3387 fast jtree
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/online1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +% Check that online inference gives same results as filtering for various algorithms + +N = 3; +Q = 2; +ss = N*2; + +rand('state', 0); +randn('state', 0); + + +obs_size = 1; +discrete_obs = 0; +bnet = mk_chmm(N, Q, obs_size, discrete_obs); +ns = bnet.node_sizes_slice; + +engine = {}; +engine{end+1} = hmm_inf_engine(bnet); +E = length(engine); + +onodes = (1:N)+N; + +T = 4; +ev = cell(ss,T); +ev(onodes,:) = num2cell(randn(N, T)); + + +filter = 1; +loglik2 = zeros(1,E); +for e=1:E + [engine2{e}, loglik2(e)] = enter_evidence(engine{e}, ev, 'filter', filter); +end + +loglik = zeros(1,E); +marg1 = cell(E,N,T); +for e=1:E + ll = zeros(1,T); + engine{e} = dbn_init_bel(engine{e}); + for t=1:T + [engine{e}, ll(t)] = dbn_update_bel(engine{e}, ev(:,t), t); + for i=1:N + marg1{e,i,t} = dbn_marginal_from_bel(engine{e}, i); + end + end + loglik1(e) = sum(ll); +end + +assert(approxeq(loglik1, loglik2)) + +a = zeros(E,N,T); +for e=1:E + for t=1:T + for i=1:N + marg2{e,i,t} = marginal_nodes(engine2{e}, i, t); + a(e,i,t) = (approxeq(marg2{e,i,t}.T(:), marg1{e,i,t}.T(:))); + end + end +end + +assert(all(a(:)==1))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/online2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +N = 1; % regular HMM +Q = 2; +ss = 2; +hnodes = 1; +onodes = 2; + +rand('state', 0); +randn('state', 0); +O = 2; +discrete_obs = 1; +bnet = mk_chmm(N, Q, O, discrete_obs); +ns = bnet.node_sizes_slice; + +engine = hmm_inf_engine(bnet, onodes); + +T = 4; +ev = cell(ss,T); +ev(onodes,:) = num2cell(sample_discrete([0.5 0.5], N, T)); + + +engine = dbn_init_bel(engine); +for t=1:T + if t==1 + [engine, ll(t)] = dbn_update_bel1(engine, ev(:,t)); + else + [engine, ll(t)] = dbn_update_bel(engine, ev(:,t-1:t)); + end + % one-step ahead prediction + lag = 1; + engine2 = dbn_predict_bel(engine, lag); + marg = dbn_marginal_from_bel(engine2, 1) + marg = dbn_marginal_from_bel(engine2, 2) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/Old/scg_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +% to test whether scg inference engine can handl dynameic BN +% Make a linear dynamical system +% X1 -> X2 +% | | +% v v +% Y1 Y2 + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +X = 2; % size of hidden state +Y = 2; % size of observable state + +ns = [X Y]; +dnodes = []; +onodes = [2]; +eclass1 = [1 2]; +eclass2 = [3 2]; +bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2); + +x0 = rand(X,1); +V0 = eye(X); +C0 = rand(Y,X); +R0 = eye(Y); +A0 = rand(X,X); +Q0 = eye(X); + +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0); +%bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0, 'full', 'untied', 'clamped_mean'); +%bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0, 'full', 'untied', 'clamped_mean'); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0); + + +T = 5; % fixed length sequences + +clear engine; +%engine{1} = kalman_inf_engine(bnet, onodes); +engine{1} = scg_unrolled_dbn_inf_engine(bnet, T, onodes); +engine{2} = jtree_unrolled_dbn_inf_engine(bnet, T); + +N = length(engine); + +% inference + +ev = sample_dbn(bnet, T); +evidence = cell(n,T); +evidence(onodes,:) = ev(onodes, :); + +t = 2; +query = [1 3]; +m = cell(1, N); +ll = zeros(1, N); + +engine{1} = enter_evidence(engine{1}, evidence); +[engine{2}, ll(2)] = enter_evidence(engine{2}, evidence); +m{1} = marginal_nodes(engine{1}, query); +m{2} = marginal_nodes(engine{2}, query, t); + + +% compare all engines to engine{1} +for i=2:N + assert(approxeq(m{1}.mu, m{i}.mu)); + assert(approxeq(m{1}.Sigma, m{i}.Sigma)); +% assert(approxeq(ll(1), ll(i))); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/mk_gmux_robot_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_linear_slam.m/1.1.1.1/Wed May 29 15:59:54 2002// +/slam_kf.m/1.1.1.1/Wed May 29 15:59:54 2002// +/slam_offline_loopy.m/1.1.1.1/Wed May 29 15:59:54 2002// +/slam_partial_kf.m/1.1.1.1/Wed May 29 15:59:54 2002// +/slam_stationary_loopy.m/1.1.1.1/Wed May 29 15:59:54 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/SLAM
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/offline_loopy_slam.m/1.1.1.1/Wed May 29 15:59:54 2002// +/paskin1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/skf_data_assoc_gmux2.m/1.1.1.1/Wed May 29 15:59:54 2002// +/slam_kf.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/dynamic/SLAM/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/Old/offline_loopy_slam.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,231 @@ +% We navigate a robot around a square using a fixed control policy and no noise. +% We assume the robot observes the relative distance to the nearest landmark. +% Everything is linear-Gaussian. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Create toy data set + +seed = 0; +rand('state', seed); +randn('state', seed); + +if 1 + T = 20; + ctrl_signal = [repmat([1 0]', 1, T/4) repmat([0 1]', 1, T/4) ... + repmat([-1 0]', 1, T/4) repmat([0 -1]', 1, T/4)]; +else + T = 5; + ctrl_signal = repmat([1 0]', 1, T); +end + +nlandmarks = 4; +true_landmark_pos = [1 1; + 4 1; + 4 4; + 1 4]'; +init_robot_pos = [0 0]'; + +true_robot_pos = zeros(2, T); +true_data_assoc = zeros(1, T); +true_rel_dist = zeros(2, T); +for t=1:T + if t>1 + true_robot_pos(:,t) = true_robot_pos(:,t-1) + ctrl_signal(:,t); + else + true_robot_pos(:,t) = init_robot_pos + ctrl_signal(:,t); + end + nn = argmin(dist2(true_robot_pos(:,t)', true_landmark_pos')); + %nn = t; % observe 1, 2, 3 + true_data_assoc(t) = nn; + true_rel_dist(:,t) = true_landmark_pos(:, nn) - true_robot_pos(:,t); +end + +figure(1); +%clf; +hold on +%plot(true_landmark_pos(1,:), true_landmark_pos(2,:), '*'); +for i=1:nlandmarks + text(true_landmark_pos(1,i), true_landmark_pos(2,i), sprintf('L%d',i)); +end +for t=1:T + text(true_robot_pos(1,t), true_robot_pos(2,t), sprintf('%d',t)); +end +hold off +axis([-1 6 -1 6]) + +R = 1e-3*eye(2); % noise added to observation +Q = 1e-3*eye(2); % noise added to robot motion + +% Create data set +obs_noise_seq = sample_gaussian([0 0]', R, T)'; +obs_rel_pos = true_rel_dist + obs_noise_seq; +%obs_rel_pos = true_rel_dist; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Create params for inference + +% X(t) = A X(t-1) + B U(t) + noise(Q) + +% [L1] = [1 ] * [L1] + [0] * Ut + [0 ] +% [L2] [ 1 ] [L2] [0] [ 0 ] +% [R ]t [ 1] [R ]t-1 [1] [ Q] + +% Y(t)|S(t)=s = C(s) X(t) + noise(R) +% Yt|St=1 = [1 0 -1] * [L1] + R +% [L2] +% [R ] + +% Create indices into block structure +bs = 2*ones(1, nlandmarks+1); % sizes of blocks in state space +robot_block = block(nlandmarks+1, bs); +for i=1:nlandmarks + landmark_block(:,i) = block(i, bs)'; +end +Xsz = 2*(nlandmarks+1); % 2 values for each landmark plus robot +Ysz = 2; % observe relative location +Usz = 2; % input is (dx, dy) + + +% create block-diagonal trans matrix for each switch +A = zeros(Xsz, Xsz); +for i=1:nlandmarks + bi = landmark_block(:,i); + A(bi, bi) = eye(2); +end +bi = robot_block; +A(bi, bi) = eye(2); +A = repmat(A, [1 1 nlandmarks]); % same for all switch values + +% create block-diagonal system cov + + +Qbig = zeros(Xsz, Xsz); +bi = robot_block; +Qbig(bi,bi) = Q; % only add noise to robot motion +Qbig = repmat(Qbig, [1 1 nlandmarks]); + +% create input matrix +B = zeros(Xsz, Usz); +B(robot_block,:) = eye(2); % only add input to robot position +B = repmat(B, [1 1 nlandmarks]); + +% create observation matrix for each value of the switch node +% C(:,:,i) = (0 ... I ... -I) where the I is in the i'th posn. +% This computes L(i) - R +C = zeros(Ysz, Xsz, nlandmarks); +for i=1:nlandmarks + C(:, landmark_block(:,i), i) = eye(2); + C(:, robot_block, i) = -eye(2); +end + +% create observation cov for each value of the switch node +Rbig = repmat(R, [1 1 nlandmarks]); + +% initial conditions +init_x = zeros(Xsz, 1); +init_v = zeros(Xsz, Xsz); +bi = robot_block; +init_x(bi) = init_robot_pos; +init_V(bi, bi) = 1e-5*eye(2); % very sure of robot posn +for i=1:nlandmarks + bi = landmark_block(:,i); + init_V(bi,bi)= 1e5*eye(2); % very uncertain of landmark psosns + %init_x(bi) = true_landmark_pos(:,i); + %init_V(bi,bi)= 1e-5*eye(2); % very sure of landmark psosns +end + +%%%%%%%%%%%%%%%%%%%%% +% Inference +if 1 +[xsmooth, Vsmooth] = kalman_smoother(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ... + 'model', true_data_assoc, 'u', ctrl_signal, 'B', B); + +est_robot_pos = xsmooth(robot_block, :); +est_robot_pos_cov = Vsmooth(robot_block, robot_block, :); + +for i=1:nlandmarks + bi = landmark_block(:,i); + est_landmark_pos(:,i) = xsmooth(bi, T); + est_landmark_pos_cov(:,:,i) = Vsmooth(bi, bi, T); +end +end + + +if 0 +figure(1); hold on +for i=1:nlandmarks + h=plotgauss2d(est_landmark_pos(:,i), est_landmark_pos_cov(:,:,i)); + set(h, 'color', 'r') +end +hold off + +hold on +for t=1:T + h=plotgauss2d(est_robot_pos(:,t), est_robot_pos_cov(:,:,t)); + set(h,'color','r') + h=text(est_robot_pos(1,t), est_robot_pos(2,2), sprintf('R%d', t)); + set(h,'color','r') +end +hold off +end + + +if 0 +figure(3) +if 0 + for t=1:T + imagesc(inv(Vsmooth(:,:,t))) + colorbar + fprintf('t=%d; press key to continue\n', t); + pause + end +else + for t=1:T + subplot(5,4,t) + imagesc(inv(Vsmooth(:,:,t))) + end +end +end + + + + + +%%%%%%%%%%%%%%%%% +% DBN inference + +if 1 + [bnet, Unode, Snode, Lnodes, Rnode, Ynode, Lsnode] = ... + mk_gmux_robot_dbn(nlandmarks, Q, R, init_x, init_V, robot_block, landmark_block); + engine = pearl_unrolled_dbn_inf_engine(bnet, 'max_iter', 50, 'filename', ... + '/home/eecs/murphyk/matlab/loopyslam.txt'); +else + [bnet, Unode, Snode, Lnodes, Rnode, Ynode] = ... + mk_gmux2_robot_dbn(nlandmarks, Q, R, init_x, init_V, robot_block, landmark_block); + engine = jtree_dbn_inf_engine(bnet); +end + +nnodes = bnet.nnodes_per_slice; +evidence = cell(nnodes, T); +evidence(Ynode, :) = num2cell(obs_rel_pos, 1); +evidence(Unode, :) = num2cell(ctrl_signal, 1); +evidence(Snode, :) = num2cell(true_data_assoc); + + +[engine, ll, niter] = enter_evidence(engine, evidence); +niter + +loopy_est_robot_pos = zeros(2, T); +for t=1:T + m = marginal_nodes(engine, Rnode, t); + loopy_est_robot_pos(:,t) = m.mu; +end + +for i=1:nlandmarks + m = marginal_nodes(engine, Lnodes(i), T); + loopy_est_landmark_pos(:,i) = m.mu; + loopy_est_landmark_pos_cov(:,:,i) = m.Sigma; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/Old/paskin1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,238 @@ +% This is like robot1, except we only use a Kalman filter. +% The goal is to study how the precision matrix changes. + +seed = 1; +rand('state', seed); +randn('state', seed); + +if 0 + T = 20; + ctrl_signal = [repmat([1 0]', 1, T/4) repmat([0 1]', 1, T/4) ... + repmat([-1 0]', 1, T/4) repmat([0 -1]', 1, T/4)]; +else + T = 60; + ctrl_signal = repmat([1 0]', 1, T); +end + +nlandmarks = 6; +if 0 + true_landmark_pos = [1 1; + 4 1; + 4 4; + 1 4]'; +else + true_landmark_pos = 10*rand(2,nlandmarks); +end +if 0 +figure(1); clf +hold on +for i=1:nlandmarks + %text(true_landmark_pos(1,i), true_landmark_pos(2,i), sprintf('L%d',i)); + plot(true_landmark_pos(1,i), true_landmark_pos(2,i), '*') +end +hold off +end + +init_robot_pos = [0 0]'; + +true_robot_pos = zeros(2, T); +true_data_assoc = zeros(1, T); +true_rel_dist = zeros(2, T); +for t=1:T + if t>1 + true_robot_pos(:,t) = true_robot_pos(:,t-1) + ctrl_signal(:,t); + else + true_robot_pos(:,t) = init_robot_pos + ctrl_signal(:,t); + end + nn = argmin(dist2(true_robot_pos(:,t)', true_landmark_pos')); + %true_data_assoc(t) = nn; + %true_data_assoc = wrap(t, nlandmarks); % observe 1, 2, 3, 4, 1, 2, ... + true_data_assoc = sample_discrete(normalise(ones(1,nlandmarks)),1,T); + true_rel_dist(:,t) = true_landmark_pos(:, nn) - true_robot_pos(:,t); +end + +R = 1e-3*eye(2); % noise added to observation +Q = 1e-3*eye(2); % noise added to robot motion + +% Create data set +obs_noise_seq = sample_gaussian([0 0]', R, T)'; +obs_rel_pos = true_rel_dist + obs_noise_seq; +%obs_rel_pos = true_rel_dist; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Create params for inference + +% X(t) = A X(t-1) + B U(t) + noise(Q) + +% [L1] = [1 ] * [L1] + [0] * Ut + [0 ] +% [L2] [ 1 ] [L2] [0] [ 0 ] +% [R ]t [ 1] [R ]t-1 [1] [ Q] + +% Y(t)|S(t)=s = C(s) X(t) + noise(R) +% Yt|St=1 = [1 0 -1] * [L1] + R +% [L2] +% [R ] + +% Create indices into block structure +bs = 2*ones(1, nlandmarks+1); % sizes of blocks in state space +robot_block = block(nlandmarks+1, bs); +for i=1:nlandmarks + landmark_block(:,i) = block(i, bs)'; +end +Xsz = 2*(nlandmarks+1); % 2 values for each landmark plus robot +Ysz = 2; % observe relative location +Usz = 2; % input is (dx, dy) + + +% create block-diagonal trans matrix for each switch +A = zeros(Xsz, Xsz); +for i=1:nlandmarks + bi = landmark_block(:,i); + A(bi, bi) = eye(2); +end +bi = robot_block; +A(bi, bi) = eye(2); +A = repmat(A, [1 1 nlandmarks]); % same for all switch values + +% create block-diagonal system cov + + +Qbig = zeros(Xsz, Xsz); +bi = robot_block; +Qbig(bi,bi) = Q; % only add noise to robot motion +Qbig = repmat(Qbig, [1 1 nlandmarks]); + +% create input matrix +B = zeros(Xsz, Usz); +B(robot_block,:) = eye(2); % only add input to robot position +B = repmat(B, [1 1 nlandmarks]); + +% create observation matrix for each value of the switch node +% C(:,:,i) = (0 ... I ... -I) where the I is in the i'th posn. +% This computes L(i) - R +C = zeros(Ysz, Xsz, nlandmarks); +for i=1:nlandmarks + C(:, landmark_block(:,i), i) = eye(2); + C(:, robot_block, i) = -eye(2); +end + +% create observation cov for each value of the switch node +Rbig = repmat(R, [1 1 nlandmarks]); + +% initial conditions +init_x = zeros(Xsz, 1); +init_v = zeros(Xsz, Xsz); +bi = robot_block; +init_x(bi) = init_robot_pos; +%init_V(bi, bi) = 1e-5*eye(2); % very sure of robot posn +init_V(bi, bi) = Q; % simualate uncertainty due to 1 motion step +for i=1:nlandmarks + bi = landmark_block(:,i); + init_V(bi,bi)= 1e5*eye(2); % very uncertain of landmark psosns + %init_x(bi) = true_landmark_pos(:,i); + %init_V(bi,bi)= 1e-5*eye(2); % very sure of landmark psosns +end + +%k = nlandmarks-1; % exact +k = 3; +ndx = {}; +for t=1:T + landmarks = unique(true_data_assoc(t:-1:max(t-k,1))); + tmp = [landmark_block(:, landmarks) robot_block']; + ndx{t} = tmp(:); +end + +[xa, Va] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ... + 'model', true_data_assoc, 'u', ctrl_signal, 'B', B, ... + 'ndx', ndx); + +[xe, Ve] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ... + 'model', true_data_assoc, 'u', ctrl_signal, 'B', B); + + +if 0 +est_robot_pos = x(robot_block, :); +est_robot_pos_cov = V(robot_block, robot_block, :); + +for i=1:nlandmarks + bi = landmark_block(:,i); + est_landmark_pos(:,i) = x(bi, T); + est_landmark_pos_cov(:,:,i) = V(bi, bi, T); +end +end + + + +nrows = 10; +stepsize = T/(2*nrows); +ts = 1:stepsize:T; + +if 1 % plot + +clim = [0 max(max(Va(:,:,end)))]; + +figure(2) +if 0 + imagesc(Ve(1:2:end,1:2:end, T)) + clim = get(gca,'clim'); +else + i = 1; + for t=ts(:)' + subplot(nrows,2,i) + i = i + 1; + imagesc(Ve(1:2:end,1:2:end, t)) + set(gca, 'clim', clim) + colorbar + end +end +suptitle('exact') + + +figure(3) +if 0 + imagesc(Va(1:2:end,1:2:end, T)) + set(gca,'clim', clim) +else + i = 1; + for t=ts(:)' + subplot(nrows,2,i) + i = i+1; + imagesc(Va(1:2:end,1:2:end, t)) + set(gca, 'clim', clim) + colorbar + end +end +suptitle('approx') + + +figure(4) +i = 1; +for t=ts(:)' + subplot(nrows,2,i) + i = i+1; + Vd = Va(1:2:end,1:2:end, t) - Ve(1:2:end,1:2:end,t); + imagesc(Vd) + set(gca, 'clim', clim) + colorbar +end +suptitle('diff') + +end % all plot + + +for t=1:T + i = 1:2*nlandmarks; + denom = Ve(i,i,t) + (Ve(i,i,t)==0); + Vd =(Va(i,i,t)-Ve(i,i,t)) ./ denom; + Verr(t) = max(Vd(:)); +end +figure(6); plot(Verr) +title('max relative Verr') + +for t=1:T + %err(t)=rms(xa(:,t), xe(:,t)); + err(t)=rms(xa(1:end-2,t), xe(1:end-2,t)); % exclude robot +end +figure(5);plot(err) +title('rms mean pos')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/Old/skf_data_assoc_gmux2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,155 @@ +% This is like skf_data_assoc_gmux, except the objects don't move. +% We are uncertain of their initial positions, and get more and more observations +% over time. The goal is to test deterministic links (0 covariance). +% This is like robot1, except the robot doesn't move and is always at [0 0], +% so the relative location is simply L(s). + +nobj = 2; +N = nobj+2; +Xs = 1:nobj; +S = nobj+1; +Y = nobj+2; + +intra = zeros(N,N); +inter = zeros(N,N); +intra([Xs S], Y) =1; +for i=1:nobj + inter(Xs(i), Xs(i))=1; +end + +Xsz = 2; % state space = (x y) +Ysz = 2; +ns = zeros(1,N); +ns(Xs) = Xsz; +ns(Y) = Ysz; +ns(S) = nobj; + +bnet = mk_dbn(intra, inter, ns, 'discrete', S, 'observed', [S Y]); + +% For each object, we have +% X(t+1) = F X(t) + noise(Q) +% Y(t) = H X(t) + noise(R) +F = eye(2); +H = eye(2); +Q = 0*eye(Xsz); % no noise in dynamics +R = eye(Ysz); + +init_state{1} = [10 10]'; +init_state{2} = [10 -10]'; +init_cov = eye(2); + +% Uncertain of initial state (position) +for i=1:nobj + bnet.CPD{Xs(i)} = gaussian_CPD(bnet, Xs(i), 'mean', init_state{i}, 'cov', init_cov); +end +bnet.CPD{S} = root_CPD(bnet, S); % always observed +bnet.CPD{Y} = gmux_CPD(bnet, Y, 'cov', repmat(R, [1 1 nobj]), 'weights', repmat(H, [1 1 nobj])); +% slice 2 +eclass = bnet.equiv_class; +for i=1:nobj + bnet.CPD{eclass(Xs(i), 2)} = gaussian_CPD(bnet, Xs(i)+N, 'mean', zeros(Xsz,1), 'cov', Q, 'weights', F); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Create LDS params + +% X(t) = A X(t-1) + B U(t) + noise(Q) + +% [L11] = [1 ] * [L1] + [Q ] +% [L2] [ 1] [L2] [ Q] + +% Y(t)|S(t)=s = C(s) X(t) + noise(R) +% Yt|St=1 = [1 0] * [L1] + R +% [L2] + +nlandmarks = nobj; + +% Create indices into block structure +bs = 2*ones(1, nobj); % sizes of blocks in state space +for i=1:nlandmarks + landmark_block(:,i) = block(i, bs)'; +end +Xsz = 2*(nlandmarks); % 2 values for each landmark plus robot +Ysz = 2; % observe relative location + +% create block-diagonal trans matrix for each switch +A = zeros(Xsz, Xsz); +for i=1:nlandmarks + bi = landmark_block(:,i); + A(bi, bi) = eye(2); +end +A = repmat(A, [1 1 nlandmarks]); % same for all switch values + +% create block-diagonal system cov +Qbig = zeros(Xsz, Xsz); +Qbig = repmat(Qbig, [1 1 nlandmarks]); + + +% create observation matrix for each value of the switch node +% C(:,:,i) = (0 ... I ...) where the I is in the i'th posn. +C = zeros(Ysz, Xsz, nlandmarks); +for i=1:nlandmarks + C(:, landmark_block(:,i), i) = eye(2); +end + +% create observation cov for each value of the switch node +Rbig = repmat(R, [1 1 nlandmarks]); + +% initial conditions +init_x = [init_state{1}; init_state{2}]; +init_V = zeros(Xsz, Xsz); +for i=1:nlandmarks + bi = landmark_block(:,i); + init_V(bi,bi) = init_cov; +end + + + +%%%%%%%%%%%%%%%% +% Observe objects at random +T = 10; +evidence = cell(N, T); +data_assoc = sample_discrete(normalise(ones(1,nobj)), 1, T); +evidence(S,:) = num2cell(data_assoc); +evidence = sample_dbn(bnet, 'evidence', evidence); + + +% Inference +ev = cell(N,T); +ev(bnet.observed,:) = evidence(bnet.observed, :); +y = cell2num(evidence(Y,:)); + +engine = pearl_unrolled_dbn_inf_engine(bnet); +engine = enter_evidence(engine, ev); + +loopy_est_pos = zeros(2, nlandmarks); +loopy_est_pos_cov = zeros(2, 2, nlandmarks); +for i=1:nobj + m = marginal_nodes(engine, Xs(i), T); + loopy_est_pos(:,i) = m.mu; + loopy_est_pos_cov(:,:,i) = m.Sigma; +end + + +[xsmooth, Vsmooth] = kalman_smoother(y, A, C, Qbig, Rbig, init_x, init_V, 'model', data_assoc); + +kf_est_pos = zeros(2, nlandmarks); +kf_est_pos_cov = zeros(2, 2, nlandmarks); +for i=1:nlandmarks + bi = landmark_block(:,i); + kf_est_pos(:,i) = xsmooth(bi, T); + kf_est_pos_cov(:,:,i) = Vsmooth(bi, bi, T); +end + + +kf_est_pos +loopy_est_pos + +kf_est_pos_time = zeros(2, nlandmarks, T); +for t=1:T + for i=1:nlandmarks + bi = landmark_block(:,i); + kf_est_pos_time(:,i,t) = xsmooth(bi, t); + end +end +kf_est_pos_time % same for all t since smoothed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/Old/slam_kf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,172 @@ +% This is like robot1, except we only use a Kalman filter. +% The goal is to study how the precision matrix changes. + +seed = 0; +rand('state', seed); +randn('state', seed); + +if 0 + T = 20; + ctrl_signal = [repmat([1 0]', 1, T/4) repmat([0 1]', 1, T/4) ... + repmat([-1 0]', 1, T/4) repmat([0 -1]', 1, T/4)]; +else + T = 12; + ctrl_signal = repmat([1 0]', 1, T); +end + +nlandmarks = 6; +if 0 + true_landmark_pos = [1 1; + 4 1; + 4 4; + 1 4]'; +else + true_landmark_pos = 10*rand(2,nlandmarks); +end +figure(1); clf +hold on +for i=1:nlandmarks + %text(true_landmark_pos(1,i), true_landmark_pos(2,i), sprintf('L%d',i)); + plot(true_landmark_pos(1,i), true_landmark_pos(2,i), '*') +end +hold off + +init_robot_pos = [0 0]'; + +true_robot_pos = zeros(2, T); +true_data_assoc = zeros(1, T); +true_rel_dist = zeros(2, T); +for t=1:T + if t>1 + true_robot_pos(:,t) = true_robot_pos(:,t-1) + ctrl_signal(:,t); + else + true_robot_pos(:,t) = init_robot_pos + ctrl_signal(:,t); + end + %nn = argmin(dist2(true_robot_pos(:,t)', true_landmark_pos')); + nn = wrap(t, nlandmarks); % observe 1, 2, 3, 4, 1, 2, ... + true_data_assoc(t) = nn; + true_rel_dist(:,t) = true_landmark_pos(:, nn) - true_robot_pos(:,t); +end + +R = 1e-3*eye(2); % noise added to observation +Q = 1e-3*eye(2); % noise added to robot motion + +% Create data set +obs_noise_seq = sample_gaussian([0 0]', R, T)'; +obs_rel_pos = true_rel_dist + obs_noise_seq; +%obs_rel_pos = true_rel_dist; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Create params for inference + +% X(t) = A X(t-1) + B U(t) + noise(Q) + +% [L1] = [1 ] * [L1] + [0] * Ut + [0 ] +% [L2] [ 1 ] [L2] [0] [ 0 ] +% [R ]t [ 1] [R ]t-1 [1] [ Q] + +% Y(t)|S(t)=s = C(s) X(t) + noise(R) +% Yt|St=1 = [1 0 -1] * [L1] + R +% [L2] +% [R ] + +% Create indices into block structure +bs = 2*ones(1, nlandmarks+1); % sizes of blocks in state space +robot_block = block(nlandmarks+1, bs); +for i=1:nlandmarks + landmark_block(:,i) = block(i, bs)'; +end +Xsz = 2*(nlandmarks+1); % 2 values for each landmark plus robot +Ysz = 2; % observe relative location +Usz = 2; % input is (dx, dy) + + +% create block-diagonal trans matrix for each switch +A = zeros(Xsz, Xsz); +for i=1:nlandmarks + bi = landmark_block(:,i); + A(bi, bi) = eye(2); +end +bi = robot_block; +A(bi, bi) = eye(2); +A = repmat(A, [1 1 nlandmarks]); % same for all switch values + +% create block-diagonal system cov + + +Qbig = zeros(Xsz, Xsz); +bi = robot_block; +Qbig(bi,bi) = Q; % only add noise to robot motion +Qbig = repmat(Qbig, [1 1 nlandmarks]); + +% create input matrix +B = zeros(Xsz, Usz); +B(robot_block,:) = eye(2); % only add input to robot position +B = repmat(B, [1 1 nlandmarks]); + +% create observation matrix for each value of the switch node +% C(:,:,i) = (0 ... I ... -I) where the I is in the i'th posn. +% This computes L(i) - R +C = zeros(Ysz, Xsz, nlandmarks); +for i=1:nlandmarks + C(:, landmark_block(:,i), i) = eye(2); + C(:, robot_block, i) = -eye(2); +end + +% create observation cov for each value of the switch node +Rbig = repmat(R, [1 1 nlandmarks]); + +% initial conditions +init_x = zeros(Xsz, 1); +init_v = zeros(Xsz, Xsz); +bi = robot_block; +init_x(bi) = init_robot_pos; +init_V(bi, bi) = 1e-5*eye(2); % very sure of robot posn +for i=1:nlandmarks + bi = landmark_block(:,i); + init_V(bi,bi)= 1e5*eye(2); % very uncertain of landmark psosns + %init_x(bi) = true_landmark_pos(:,i); + %init_V(bi,bi)= 1e-5*eye(2); % very sure of landmark psosns +end + +[xsmooth, Vsmooth] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ... + 'model', true_data_assoc, 'u', ctrl_signal, 'B', B); + +est_robot_pos = xsmooth(robot_block, :); +est_robot_pos_cov = Vsmooth(robot_block, robot_block, :); + +for i=1:nlandmarks + bi = landmark_block(:,i); + est_landmark_pos(:,i) = xsmooth(bi, T); + est_landmark_pos_cov(:,:,i) = Vsmooth(bi, bi, T); +end + + + +P = zeros(size(Vsmooth)); +for t=1:T + P(:,:,t) = inv(Vsmooth(:,:,t)); +end + +figure(1) +for t=1:T + subplot(T/2,2,t) + imagesc(P(1:2:end,1:2:end, t)) + colorbar +end + +figure(2) +for t=1:T + subplot(T/2,2,t) + imagesc(Vsmooth(1:2:end,1:2:end, t)) + colorbar +end + + + +% marginalize out robot position and then check structure +bi = landmark_block(:); +V = Vsmooth(bi,bi,T); +P = inv(V); +P(1:2:end,1:2:end)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/mk_gmux_robot_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,85 @@ +function [bnet, Unode, Snode, Lnodes, Rnode, Ynode, Lsnode] = ... + mk_gmux_robot_dbn(nlandmarks, Q, R, init_x, init_V, robot_block, landmark_block) + +% Make DBN + +% S +% | L1 -------> L1' +% | | L2 ----------> L2' +% \ | / +% v v v +% Ls +% | +% v +% Y +% ^ +% | +% R -------> R' +% ^ +% | +% U +% +% +% S is a switch, Ls is a deterministic gmux, Y = Ls-R, +% R(t+1) = R(t) + U(t+1), L(t+1) = L(t) + + +% number nodes topologically +Snode = 1; +Lnodes = 2:nlandmarks+1; +Lsnode = nlandmarks+2; +Unode = nlandmarks+3; +Rnode = nlandmarks+4; +Ynode = nlandmarks+5; + +nnodes = nlandmarks+5; +intra = zeros(nnodes, nnodes); +intra([Snode Lnodes], Lsnode) =1; +intra(Unode,Rnode)=1; +intra([Rnode Lsnode], Ynode)=1; + +inter = zeros(nnodes, nnodes); +inter(Rnode, Rnode)=1; +for i=1:nlandmarks + inter(Lnodes(i), Lnodes(i))=1; +end + +Lsz = 2; % (x y) posn of landmark +Rsz = 2; % (x y) posn of robot +Ysz = 2; % relative distance +Usz = 2; % (dx dy) ctrl +Ssz = nlandmarks; % can switch between any landmark + +ns = zeros(1,nnodes); +ns(Snode) = Ssz; +ns(Lnodes) = Lsz; +ns(Lsnode) = Lsz; +ns(Ynode) = Ysz; +ns(Rnode) = Rsz; +ns(Ynode) = Usz; +ns(Unode) = Usz; + +bnet = mk_dbn(intra, inter, ns, 'discrete', Snode, 'observed', [Snode Ynode Unode]); + + +bnet.CPD{Snode} = root_CPD(bnet, Snode); % always observed +bnet.CPD{Unode} = root_CPD(bnet, Unode); % always observed +for i=1:nlandmarks + bi = landmark_block(:,i); + bnet.CPD{Lnodes(i)} = gaussian_CPD(bnet, Lnodes(i), 'mean', init_x(bi), 'cov', init_V(bi,bi)); +end +bi = robot_block; +bnet.CPD{Rnode} = gaussian_CPD(bnet, Rnode, 'mean', init_x(bi), 'cov', init_V(bi,bi), 'weights', eye(2)); +bnet.CPD{Lsnode} = gmux_CPD(bnet, Lsnode, 'cov', repmat(zeros(Lsz,Lsz), [1 1 nlandmarks]), ... + 'weights', repmat(eye(Lsz,Lsz), [1 1 nlandmarks])); +W = [eye(2) -eye(2)]; % Y = Ls - R, where Ls is the lower-numbered parent +bnet.CPD{Ynode} = gaussian_CPD(bnet, Ynode, 'mean', zeros(Ysz,1), 'cov', R, 'weights', W); + +% slice 2 +eclass = bnet.equiv_class; +W = [eye(2) eye(2)]; % R(t) = R(t-1) + U(t), where R(t-1) is the lower-numbered parent +bnet.CPD{eclass(Rnode,2)} = gaussian_CPD(bnet, Rnode+nnodes, 'mean', zeros(Rsz,1), 'cov', Q, 'weights', W); +for i=1:nlandmarks + bnet.CPD{eclass(Lnodes(i), 2)} = gaussian_CPD(bnet, Lnodes(i)+nnodes, 'mean', zeros(2,1), ... + 'cov', zeros(2,2), 'weights', eye(2)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/mk_linear_slam.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,164 @@ +function [A,B,C,Q,R,Qbig,Rbig,init_x,init_V,robot_block,landmark_block,... + true_landmark_pos, true_robot_pos, true_data_assoc, ... + obs_rel_pos, ctrl_signal] = mk_linear_slam(varargin) + +% We create data from a linear system for testing SLAM algorithms. +% i.e. , new robot pos = old robot pos + ctrl_signal, which is just a displacement vector. +% and observation = landmark_pos - robot_pos, which is just a displacement vector. +% +% The behavior is determined by the following optional arguments: +% +% 'nlandmarks' - num. landmarks +% 'landmarks' - 'rnd' means random locations in the unit sqyare +% 'square' means at [1 1], [4 1], [4 4] and [1 4] +% 'T' - num steps to run +% 'ctrl' - 'stationary' means the robot remains at [0 0], +% 'leftright' means the robot receives a constant contol of [1 0], +% 'square' means we navigate the robot around the square +% 'data-assoc' - 'rnd' means we observe landmarks at random +% 'nn' means we observe the nearest neighbor landmark +% 'cycle' means we observe landmarks in order 1,2,.., 1, 2, ... + +args = varargin; +% get mandatory params +for i=1:2:length(args) + switch args{i}, + case 'nlandmarks', nlandmarks = args{i+1}; + case 'T', T = args{i+1}; + end +end + +% set defaults +true_landmark_pos = rand(2,nlandmarks); +true_data_assoc = []; + +% get args +for i=1:2:length(args) + switch args{i}, + case 'landmarks', + switch args{i+1}, + case 'rnd', true_landmark_pos = rand(2,nlandmarks); + case 'square', true_landmark_pos = [1 1; 4 1; 4 4; 1 4]'; + end + case 'ctrl', + switch args{i+1}, + case 'stationary', ctrl_signal = repmat([0 0]', 1, T); + case 'leftright', ctrl_signal = repmat([1 0]', 1, T); + case 'square', ctrl_signal = [repmat([1 0]', 1, T/4) repmat([0 1]', 1, T/4) ... + repmat([-1 0]', 1, T/4) repmat([0 -1]', 1, T/4)]; + end + case 'data-assoc', + switch args{i+1}, + case 'rnd', true_data_assoc = sample_discrete(normalise(ones(1,nlandmarks)),1,T); + case 'cycle', true_data_assoc = wrap(1:T, nlandmarks); + end + end +end +if isempty(true_data_assoc) + use_nn = 1; +else + use_nn = 0; +end + +%%%%%%%%%%%%%%%%%%%%%%%% +% generate data + +init_robot_pos = [0 0]'; +true_robot_pos = zeros(2, T); +true_rel_dist = zeros(2, T); +for t=1:T + if t>1 + true_robot_pos(:,t) = true_robot_pos(:,t-1) + ctrl_signal(:,t); + else + true_robot_pos(:,t) = init_robot_pos + ctrl_signal(:,t); + end + nn = argmin(dist2(true_robot_pos(:,t)', true_landmark_pos')); + if use_nn + true_data_assoc(t) = nn; + end + true_rel_dist(:,t) = true_landmark_pos(:, nn) - true_robot_pos(:,t); +end + + +R = 1e-3*eye(2); % noise added to observation +Q = 1e-3*eye(2); % noise added to robot motion + +% Create data set +obs_noise_seq = sample_gaussian([0 0]', R, T)'; +obs_rel_pos = true_rel_dist + obs_noise_seq; +%obs_rel_pos = true_rel_dist; + +%%%%%%%%%%%%%%%%%% +% Create params + + +% X(t) = A X(t-1) + B U(t) + noise(Q) + +% [L1] = [1 ] * [L1] + [0] * Ut + [0 ] +% [L2] [ 1 ] [L2] [0] [ 0 ] +% [R ]t [ 1] [R ]t-1 [1] [ Q] + +% Y(t)|S(t)=s = C(s) X(t) + noise(R) +% Yt|St=1 = [1 0 -1] * [L1] + R +% [L2] +% [R ] + +% Create indices into block structure +bs = 2*ones(1, nlandmarks+1); % sizes of blocks in state space +robot_block = block(nlandmarks+1, bs); +for i=1:nlandmarks + landmark_block(:,i) = block(i, bs)'; +end +Xsz = 2*(nlandmarks+1); % 2 values for each landmark plus robot +Ysz = 2; % observe relative location +Usz = 2; % input is (dx, dy) + + +% create block-diagonal trans matrix for each switch +A = zeros(Xsz, Xsz); +for i=1:nlandmarks + bi = landmark_block(:,i); + A(bi, bi) = eye(2); +end +bi = robot_block; +A(bi, bi) = eye(2); +A = repmat(A, [1 1 nlandmarks]); % same for all switch values + +% create block-diagonal system cov + + +Qbig = zeros(Xsz, Xsz); +bi = robot_block; +Qbig(bi,bi) = Q; % only add noise to robot motion +Qbig = repmat(Qbig, [1 1 nlandmarks]); + +% create input matrix +B = zeros(Xsz, Usz); +B(robot_block,:) = eye(2); % only add input to robot position +B = repmat(B, [1 1 nlandmarks]); + +% create observation matrix for each value of the switch node +% C(:,:,i) = (0 ... I ... -I) where the I is in the i'th posn. +% This computes L(i) - R +C = zeros(Ysz, Xsz, nlandmarks); +for i=1:nlandmarks + C(:, landmark_block(:,i), i) = eye(2); + C(:, robot_block, i) = -eye(2); +end + +% create observation cov for each value of the switch node +Rbig = repmat(R, [1 1 nlandmarks]); + +% initial conditions +init_x = zeros(Xsz, 1); +init_v = zeros(Xsz, Xsz); +bi = robot_block; +init_x(bi) = init_robot_pos; +%init_V(bi, bi) = 1e-5*eye(2); % very sure of robot posn +init_V(bi, bi) = Q; % simualate uncertainty due to 1 motion step +for i=1:nlandmarks + bi = landmark_block(:,i); + init_V(bi,bi)= 1e5*eye(2); % very uncertain of landmark psosns + %init_x(bi) = true_landmark_pos(:,i); + %init_V(bi,bi)= 1e-5*eye(2); % very sure of landmark psosns +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/slam_kf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,78 @@ +% Plot how precision matrix changes over time for KF solution + +seed = 0; +rand('state', seed); +randn('state', seed); + +[A,B,C,Q,R,Qbig,Rbig,init_x,init_V,robot_block,landmark_block,... + true_landmark_pos, true_robot_pos, true_data_assoc, ... + obs_rel_pos, ctrl_signal] = mk_linear_slam(... + 'nlandmarks', 6, 'T', 12, 'ctrl', 'leftright', 'data-assoc', 'cycle'); + +figure(1); clf +hold on +for i=1:nlandmarks + %text(true_landmark_pos(1,i), true_landmark_pos(2,i), sprintf('L%d',i)); + plot(true_landmark_pos(1,i), true_landmark_pos(2,i), '*') +end +hold off + + +[x, V] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ... + 'model', true_data_assoc, 'u', ctrl_signal, 'B', B); + +est_robot_pos = x(robot_block, :); +est_robot_pos_cov = V(robot_block, robot_block, :); + +for i=1:nlandmarks + bi = landmark_block(:,i); + est_landmark_pos(:,i) = x(bi, T); + est_landmark_pos_cov(:,:,i) = V(bi, bi, T); +end + + +if 0 +figure(1); hold on +for i=1:nlandmarks + h=plotgauss2d(est_landmark_pos(:,i), est_landmark_pos_cov(:,:,i)); + set(h, 'color', 'r') +end +hold off + +hold on +for t=1:T + h=plotgauss2d(est_robot_pos(:,t), est_robot_pos_cov(:,:,t)); + set(h,'color','r') + h=text(est_robot_pos(1,t), est_robot_pos(2,2), sprintf('R%d', t)); + set(h,'color','r') +end +hold off +end + + +P = zeros(size(V)); +for t=1:T + P(:,:,t) = inv(V(:,:,t)); +end + +if 0 + figure(2) + for t=1:T + subplot(T/2,2,t) + imagesc(P(1:2:end,1:2:end, t)) + colorbar + end +else + figure(2) + for t=1:T + subplot(T/2,2,t) + imagesc(V(1:2:end,1:2:end, t)) + colorbar + end +end + +% marginalize out robot position and then check structure +bi = landmark_block(:); +V = V(bi,bi,T); +P = inv(V); +P(1:2:end,1:2:end)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/slam_offline_loopy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +% Compare Kalman smoother with loopy + +seed = 0; +rand('state', seed); +randn('state', seed); +nlandmarks = 6; +T = 12; + +[A,B,C,Q,R,Qbig,Rbig,init_x,init_V,robot_block,landmark_block,... + true_landmark_pos, true_robot_pos, true_data_assoc, ... + obs_rel_pos, ctrl_signal] = mk_linear_slam(... + 'nlandmarks', nlandmarks, 'T', T, 'ctrl', 'leftright', 'data-assoc', 'cycle'); + +[xsmooth, Vsmooth] = kalman_smoother(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ... + 'model', true_data_assoc, 'u', ctrl_signal, 'B', B); + +est_robot_pos = xsmooth(robot_block, :); +est_robot_pos_cov = Vsmooth(robot_block, robot_block, :); + +for i=1:nlandmarks + bi = landmark_block(:,i); + est_landmark_pos(:,i) = xsmooth(bi, T); + est_landmark_pos_cov(:,:,i) = Vsmooth(bi, bi, T); +end + + +if 1 + [bnet, Unode, Snode, Lnodes, Rnode, Ynode, Lsnode] = ... + mk_gmux_robot_dbn(nlandmarks, Q, R, init_x, init_V, robot_block, landmark_block); + engine = pearl_unrolled_dbn_inf_engine(bnet, 'max_iter', 50, 'filename', ... + '/home/eecs/murphyk/matlab/loopyslam.txt'); +else + [bnet, Unode, Snode, Lnodes, Rnode, Ynode] = ... + mk_gmux2_robot_dbn(nlandmarks, Q, R, init_x, init_V, robot_block, landmark_block); + engine = jtree_dbn_inf_engine(bnet); +end + +nnodes = bnet.nnodes_per_slice; +evidence = cell(nnodes, T); +evidence(Ynode, :) = num2cell(obs_rel_pos, 1); +evidence(Unode, :) = num2cell(ctrl_signal, 1); +evidence(Snode, :) = num2cell(true_data_assoc); + +[engine, ll, niter] = enter_evidence(engine, evidence); +niter + +loopy_est_robot_pos = zeros(2, T); +for t=1:T + m = marginal_nodes(engine, Rnode, t); + loopy_est_robot_pos(:,t) = m.mu; +end + +for i=1:nlandmarks + m = marginal_nodes(engine, Lnodes(i), T); + loopy_est_landmark_pos(:,i) = m.mu; + loopy_est_landmark_pos_cov(:,:,i) = m.Sigma; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/slam_partial_kf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +% See how well partial Kalman filter updates work + +seed = 0; +rand('state', seed); +randn('state', seed); +nlandmarks = 6; +T = 12; + +[A,B,C,Q,R,Qbig,Rbig,init_x,init_V,robot_block,landmark_block,... + true_landmark_pos, true_robot_pos, true_data_assoc, ... + obs_rel_pos, ctrl_signal] = mk_linear_slam(... + 'nlandmarks', nlandmarks, 'T', T, 'ctrl', 'leftright', 'data-assoc', 'cycle'); + +% exact +[xe, Ve] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ... + 'model', true_data_assoc, 'u', ctrl_signal, 'B', B); + + +% approx +%k = nlandmarks-1; % exact +k = 3; +ndx = {}; +for t=1:T + landmarks = unique(true_data_assoc(t:-1:max(t-k,1))); + tmp = [landmark_block(:, landmarks) robot_block']; + ndx{t} = tmp(:); +end + +[xa, Va] = kalman_filter(obs_rel_pos, A, C, Qbig, Rbig, init_x, init_V, ... + 'model', true_data_assoc, 'u', ctrl_signal, 'B', B, ... + 'ndx', ndx); + + + +nrows = 10; +stepsize = T/(2*nrows); +ts = 1:stepsize:T; + +if 1 % plot + +clim = [0 max(max(Va(:,:,end)))]; + +figure(2) +if 0 + imagesc(Ve(1:2:end,1:2:end, T)) + clim = get(gca,'clim'); +else + i = 1; + for t=ts(:)' + subplot(nrows,2,i) + i = i + 1; + imagesc(Ve(1:2:end,1:2:end, t)) + set(gca, 'clim', clim) + colorbar + end +end +suptitle('exact') + + +figure(3) +if 0 + imagesc(Va(1:2:end,1:2:end, T)) + set(gca,'clim', clim) +else + i = 1; + for t=ts(:)' + subplot(nrows,2,i) + i = i+1; + imagesc(Va(1:2:end,1:2:end, t)) + set(gca, 'clim', clim) + colorbar + end +end +suptitle('approx') + + +figure(4) +i = 1; +for t=ts(:)' + subplot(nrows,2,i) + i = i+1; + Vd = Va(1:2:end,1:2:end, t) - Ve(1:2:end,1:2:end,t); + imagesc(Vd) + set(gca, 'clim', clim) + colorbar +end +suptitle('diff') + +end % all plot + + +for t=1:T + %err(t)=rms(xa(:,t), xe(:,t)); + err(t)=rms(xa(1:end-2,t), xe(1:end-2,t)); % exclude robot +end +figure(5);plot(err) +title('rms mean pos') + + +for t=1:T + i = 1:2*nlandmarks; + denom = Ve(i,i,t) + (Ve(i,i,t)==0); + Vd =(Va(i,i,t)-Ve(i,i,t)) ./ denom; + Verr(t) = max(Vd(:)); +end +figure(6); plot(Verr) +title('max relative Verr')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/SLAM/slam_stationary_loopy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,155 @@ +% This is like skf_data_assoc_gmux, except the objects don't move. +% We are uncertain of their initial positions, and get more and more observations +% over time. The goal is to test deterministic links (0 covariance). +% This is like robot1, except the robot doesn't move and is always at [0 0], +% so the relative location is simply L(s). + +nobj = 2; +N = nobj+2; +Xs = 1:nobj; +S = nobj+1; +Y = nobj+2; + +intra = zeros(N,N); +inter = zeros(N,N); +intra([Xs S], Y) =1; +for i=1:nobj + inter(Xs(i), Xs(i))=1; +end + +Xsz = 2; % state space = (x y) +Ysz = 2; +ns = zeros(1,N); +ns(Xs) = Xsz; +ns(Y) = Ysz; +ns(S) = nobj; + +bnet = mk_dbn(intra, inter, ns, 'discrete', S, 'observed', [S Y]); + +% For each object, we have +% X(t+1) = F X(t) + noise(Q) +% Y(t) = H X(t) + noise(R) +F = eye(2); +H = eye(2); +Q = 0*eye(Xsz); % no noise in dynamics +R = eye(Ysz); + +init_state{1} = [10 10]'; +init_state{2} = [10 -10]'; +init_cov = eye(2); + +% Uncertain of initial state (position) +for i=1:nobj + bnet.CPD{Xs(i)} = gaussian_CPD(bnet, Xs(i), 'mean', init_state{i}, 'cov', init_cov); +end +bnet.CPD{S} = root_CPD(bnet, S); % always observed +bnet.CPD{Y} = gmux_CPD(bnet, Y, 'cov', repmat(R, [1 1 nobj]), 'weights', repmat(H, [1 1 nobj])); +% slice 2 +eclass = bnet.equiv_class; +for i=1:nobj + bnet.CPD{eclass(Xs(i), 2)} = gaussian_CPD(bnet, Xs(i)+N, 'mean', zeros(Xsz,1), 'cov', Q, 'weights', F); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Create LDS params + +% X(t) = A X(t-1) + B U(t) + noise(Q) + +% [L11] = [1 ] * [L1] + [Q ] +% [L2] [ 1] [L2] [ Q] + +% Y(t)|S(t)=s = C(s) X(t) + noise(R) +% Yt|St=1 = [1 0] * [L1] + R +% [L2] + +nlandmarks = nobj; + +% Create indices into block structure +bs = 2*ones(1, nobj); % sizes of blocks in state space +for i=1:nlandmarks + landmark_block(:,i) = block(i, bs)'; +end +Xsz = 2*(nlandmarks); % 2 values for each landmark plus robot +Ysz = 2; % observe relative location + +% create block-diagonal trans matrix for each switch +A = zeros(Xsz, Xsz); +for i=1:nlandmarks + bi = landmark_block(:,i); + A(bi, bi) = eye(2); +end +A = repmat(A, [1 1 nlandmarks]); % same for all switch values + +% create block-diagonal system cov +Qbig = zeros(Xsz, Xsz); +Qbig = repmat(Qbig, [1 1 nlandmarks]); + + +% create observation matrix for each value of the switch node +% C(:,:,i) = (0 ... I ...) where the I is in the i'th posn. +C = zeros(Ysz, Xsz, nlandmarks); +for i=1:nlandmarks + C(:, landmark_block(:,i), i) = eye(2); +end + +% create observation cov for each value of the switch node +Rbig = repmat(R, [1 1 nlandmarks]); + +% initial conditions +init_x = [init_state{1}; init_state{2}]; +init_V = zeros(Xsz, Xsz); +for i=1:nlandmarks + bi = landmark_block(:,i); + init_V(bi,bi) = init_cov; +end + + + +%%%%%%%%%%%%%%%% +% Observe objects at random +T = 10; +evidence = cell(N, T); +data_assoc = sample_discrete(normalise(ones(1,nobj)), 1, T); +evidence(S,:) = num2cell(data_assoc); +evidence = sample_dbn(bnet, 'evidence', evidence); + + +% Inference +ev = cell(N,T); +ev(bnet.observed,:) = evidence(bnet.observed, :); +y = cell2num(evidence(Y,:)); + +engine = pearl_unrolled_dbn_inf_engine(bnet); +engine = enter_evidence(engine, ev); + +loopy_est_pos = zeros(2, nlandmarks); +loopy_est_pos_cov = zeros(2, 2, nlandmarks); +for i=1:nobj + m = marginal_nodes(engine, Xs(i), T); + loopy_est_pos(:,i) = m.mu; + loopy_est_pos_cov(:,:,i) = m.Sigma; +end + + +[xsmooth, Vsmooth] = kalman_smoother(y, A, C, Qbig, Rbig, init_x, init_V, 'model', data_assoc); + +kf_est_pos = zeros(2, nlandmarks); +kf_est_pos_cov = zeros(2, 2, nlandmarks); +for i=1:nlandmarks + bi = landmark_block(:,i); + kf_est_pos(:,i) = xsmooth(bi, T); + kf_est_pos_cov(:,:,i) = Vsmooth(bi, bi, T); +end + + +kf_est_pos +loopy_est_pos + +kf_est_pos_time = zeros(2, nlandmarks, T); +for t=1:T + for i=1:nlandmarks + bi = landmark_block(:,i); + kf_est_pos_time(:,i,t) = xsmooth(bi, t); + end +end +kf_est_pos_time % same for all t since smoothed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/arhmm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +% Make an HMM with autoregressive Gaussian observations (switching AR model) +% X1 -> X2 +% | | +% v v +% Y1 -> Y2 + +seed = 0; +rand('state', seed); +randn('state', seed); + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +inter(2,2) = 1; +n = 2; + +Q = 2; % num hidden states +O = 2; % size of observed vector + +ns = [Q O]; +dnodes = 1; +onodes = [2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'observed', onodes); + +bnet.CPD{1} = tabular_CPD(bnet, 1); +bnet.CPD{2} = gaussian_CPD(bnet, 2); +bnet.CPD{3} = tabular_CPD(bnet, 3); +bnet.CPD{4} = gaussian_CPD(bnet, 4); + + +T = 10; % fixed length sequences + +engine = {}; +%engine{end+1} = hmm_inf_engine(bnet); +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); +%engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); +%engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); + +inf_time = cmp_inference_dbn(bnet, engine, T, 'check_ll',1); +learning_time = cmp_learning_dbn(bnet, engine, T, 'check_ll', 1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/bat1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +% Compare the speeds of various inference engines on the BAT DBN +[bnet, names] = mk_bat_dbn; + +T = 3; % fixed length sequence - we make it short just for speed + +USEC = exist('@jtree_C_inf_engine/collect_evidence','file'); + +disp('constructing engines for BAT'); +engine = {}; % time in seconds for inference +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T, 'useC', USEC); % 0.39 +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); % 4.89 +engine{end+1} = jtree_dbn_inf_engine(bnet); % 4.45 +if 0 +engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'SD'); % 2.98 +engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'D'); % 3.52 +engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'B'); % 2.40 +if USEC, engine{end+1} = jtree_C_dbn_inf_engine(bnet); end % 3.54 +%engine{end+1} = hmm_inf_engine(bnet, onodes); % too big +end + +%tic; engine{end+1} = frontier_inf_engine(bnet); toc % very slow +% The frontier engine thrashes badly on the BAT network +%tic; engine{end+1} = bk_inf_engine(bnet, 'exact', onodes); toc % SLOW! + +%tic; engine{end+1} = bk_inf_engine(bnet, 'ff', onodes); toc + +%clusters{1} = [stringmatch({'LeftClr', 'RightClr', 'LatAct', 'Xdot', 'InLane'}, names)]; +%clusters{2} = [stringmatch({'FwdAct', 'Ydot', 'Stopped', 'EngStatus', 'FBStatus'}, names)]; + +%tic; engine{end+1} = bk_inf_engine(bnet, clusters, onodes); toc + +disp('inference') +time = cmp_inference_dbn(bnet, engine, T) + +disp('learning') +time = cmp_learning_dbn(bnet, engine, T) + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/bkff1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +% Compare different implementations of fully factored Boyen Koller + +water = 1; +if water + bnet = mk_water_dbn; +else + N = 5; + Q = 2; + Y = 2; + bnet = mk_chmm(N, Q, Y); +end +ss = length(bnet.intra); + +engine = {}; +engine{end+1} = bk_inf_engine(bnet, 'clusters', 'ff'); +engine{end+1} = bk_ff_hmm_inf_engine(bnet); +E = length(engine); + +T = 5; +time = cmp_inference_dbn(bnet, engine, T, 'singletons_only', 1) + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/chmm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +% Compare the speeds of various inference engines on a coupled HMM + +N = 3; +Q = 2; +rand('state', 0); +randn('state', 0); +discrete = 0; +if discrete + Y = 2; % size of output alphabet +else + Y = 3; % size of observed vectors +end +coupled = 1; +bnet = mk_chmm(N, Q, Y, discrete, coupled); +%bnet = mk_fhmm(N, Q, Y, discrete); % factorial HMM +ss = length(bnet.node_sizes_slice); + +T = 3; + +USEC = exist('@jtree_C_inf_engine/collect_evidence','file'); + +engine = {}; +engine{end+1} = jtree_dbn_inf_engine(bnet); +%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'SD'); +%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'D'); +%engine{end+1} = jtree_ndx_dbn_inf_engine(bnet, 'ndx_type', 'B'); +if USEC, engine{end+1} = jtree_C_dbn_inf_engine(bnet); end +engine{end+1} = hmm_inf_engine(bnet); +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); + +% times in matlab N=4 Q=4 T=5 (* = winner) +% jtree SD B hmm dhmm unrolled +% 0.6266 1.1563 8.3815 0.3069 0.1948* 0.8654 inf +% 0.9057* 2.1522 12.6314 2.6847 2.3107 3.1905 learn + +%engine{end+1} = bk_inf_engine(bnet, 'ff', onodes); +%engine{end+1} = pearl_unrolled_dbn_inf_engine(bnet, T); + +inf_time = cmp_inference_dbn(bnet, engine, T) +learning_time = cmp_learning_dbn(bnet, engine, T) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/cmp_inference_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,100 @@ +function [time, engine] = cmp_inference_dbn(bnet, engine, T, varargin) +% CMP_INFERENCE_DBN Compare several inference engines on a DBN +% function [time, engine] = cmp_inference_dbn(bnet, engine, T, ...) +% +% engine{i} is the i'th inference engine. +% time(e) = elapsed time for doing inference with engine e +% +% The list below gives optional arguments [default value in brackets]. +% +% exact - specifies which engines do exact inference [ 1:length(engine) ] +% singletons_only - if 1, we only call marginal_nodes, else this and marginal_family [0] +% check_ll - 1 means we check that the log-likelihoods are correct [1] + +% set default params +exact = 1:length(engine); +singletons_only = 0; +check_ll = 1; +onodes = bnet.observed; + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'exact', exact = args{i+1}; + case 'singletons_only', singletons_only = args{i+1}; + case 'check_ll', check_ll = args{i+1}; + case 'observed', onodes = args{i+1}; + otherwise, + error(['unrecognized argument ' args{i}]) + end +end + +E = length(engine); +ref = exact(1); % reference + +ss = length(bnet.intra); +ev = sample_dbn(bnet, 'length', T); +evidence = cell(ss,T); +evidence(onodes,:) = ev(onodes, :); + +for i=1:E + tic; + [engine{i}, ll(i)] = enter_evidence(engine{i}, evidence); + time(i)=toc; + fprintf('engine %d took %6.4f seconds\n', i, time(i)); +end + +cmp = mysetdiff(exact, ref); +if check_ll + for i=cmp(:)' + if ~approxeq(ll(ref), ll(i)) + error(['engine ' num2str(i) ' has wrong ll']) + end + end +end +ll + +hnodes = mysetdiff(1:ss, onodes); + +if ~singletons_only + get_marginals(engine, hnodes, exact, 0, T); +end +get_marginals(engine, hnodes, exact, 1, T); + +%%%%%%%%%% + +function get_marginals(engine, hnodes, exact, singletons, T) + +bnet = bnet_from_engine(engine{1}); +N = length(bnet.intra); +cnodes_bitv = zeros(1,N); +cnodes_bitv(bnet.cnodes) = 1; +ref = exact(1); % reference +cmp = exact(2:end); +E = length(engine); +m = cell(1,E); + +for t=1:T + for n=1:N + %for n=hnodes(:)' + for e=1:E + if singletons + m{e} = marginal_nodes(engine{e}, n, t); + else + m{e} = marginal_family(engine{e}, n, t); + end + end + for e=cmp(:)' + assert(isequal(m{e}.domain, m{ref}.domain)); + if cnodes_bitv(n) & isfield(m{e}, 'mu') & isfield(m{ref}, 'mu') + wrong = ~approxeq(m{ref}.mu, m{e}.mu) | ~approxeq(m{ref}.Sigma, m{e}.Sigma); + else + wrong = ~approxeq(m{ref}.T(:), m{e}.T(:)); + end + if wrong + error(sprintf('engine %d is wrong; n=%d, t=%d, fam=%d', e, n, t, ~singletons)) + end + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/cmp_learning_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,89 @@ +function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, varargin) +% CMP_LEARNING_DBN Compare a bunch of inference engines by learning a DBN +% function [time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, exact, T, ncases, max_iter) +% +% engine{i} is the i'th inference engine. +% time(e) = elapsed time for doing inference with engine e +% CPD{e,c} is the learned CPD for eclass c in engine e +% LL{e} is the learning curve for engine e +% cases{i} is the i'th training case +% +% The list below gives optional arguments [default value in brackets]. +% +% exact - specifies which engines do exact inference [ 1:length(engine) ] +% check_ll - 1 means we check that the log-likelihoods are correct [1] +% ncases - num. random training cases [2] +% max_iter - max. num EM iterations [2] + +% set default params +exact = 1:length(engine); +check_ll = 1; +ncases = 2; +max_iter = 2; + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'exact', exact = args{i+1}; + case 'check_ll', check_ll = args{i+1}; + case 'ncases', ncases = args{i+1}; + case 'max_iter', max_iter = args{i+1}; + otherwise, + error(['unrecognized argument ' args{i}]) + end +end + +E = length(engine); +ss = length(bnet.intra); +onodes = bnet.observed; + +cases = cell(1, ncases); +for i=1:ncases + ev = sample_dbn(bnet, 'length', T); + cases{i} = cell(ss,T); + cases{i}(onodes,:) = ev(onodes, :); +end + +LL = cell(1,E); +time = zeros(1,E); +for i=1:E + tic + [bnet2{i}, LL{i}] = learn_params_dbn_em(engine{i}, cases, 'max_iter', max_iter); + time(i) = toc; + fprintf('engine %d took %6.4f seconds\n', i, time(i)); +end + +ref = exact(1); % reference +cmp = mysetdiff(exact, ref); +if check_ll + for i=cmp(:)' + if ~approxeq(LL{ref}, LL{i}) + error(['engine ' num2str(i) ' has wrong ll']) + end + end +end + +nCPDs = length(bnet.CPD); +CPD = cell(E, nCPDs); +tabular = zeros(1, nCPDs); +for i=1:E + temp = bnet2{i}; + for c=1:nCPDs + tabular(c) = isa(temp.CPD{c}, 'tabular_CPD'); + CPD{i,c} = struct(temp.CPD{c}); + end +end + +for i=cmp(:)' + for c=1:nCPDs + if tabular(c) + assert(approxeq(CPD{i,c}.CPT, CPD{ref,c}.CPT)); + else + assert(approxeq(CPD{i,c}.mean, CPD{ref,c}.mean)); + assert(approxeq(CPD{i,c}.cov, CPD{ref,c}.cov)); + assert(approxeq(CPD{i,c}.weights, CPD{ref,c}.weights)); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/cmp_online_inference.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function [time, engine] = cmp_online_inference(bnet, engine, T, varargin) +% CMP_ONLINE_INFERENCE Compare several online inference engines on a DBN +% function [time, engine] = cmp_online_inference(bnet, engine, T, ...) +% +% engine{i} is the i'th inference engine. +% time(e) = elapsed time for doing inference with engine e +% +% The list below gives optional arguments [default value in brackets]. +% +% exact - specifies which engines do exact inference [ 1:length(engine) ] +% singletons_only - if 1, we only call marginal_nodes, else this and marginal_family [0] +% check_ll - 1 means we check that the log-likelihoods are correct [1] + +% set default params +exact = 1:length(engine); +singletons_only = 0; +check_ll = 1; +onodes = bnet.observed; + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'exact', exact = args{i+1}; + case 'singletons_only', singletons_only = args{i+1}; + case 'check_ll', check_ll = args{i+1}; + case 'observed', onodes = args{i+1}; + otherwise, + error(['unrecognized argument ' args{i}]) + end +end + +E = length(engine); +ref = exact(1); % reference +cmp = mysetdiff(exact, ref); + +ss = length(bnet.intra); +hnodes = mysetdiff(1:ss, onodes); +ev = sample_dbn(bnet, 'length', T); +evidence = cell(ss,T); +evidence(onodes,:) = ev(onodes, :); + +time = zeros(1,E); +for t=1:T + for e=1:E + tic; + [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence(:,t), t); + time(e)= time(e) + toc; + end + if check_ll + for e=cmp(:)' + if ~approxeq(ll(ref), ll(e)) + error(['engine ' num2str(e) ' has wrong ll']) + end + end + end + if ~singletons_only + check_marginals(engine, hnodes, exact, 0, t); + end + check_marginals(engine, hnodes, exact, 1, t); +end + + +%%%%%%%%%% + +function check_marginals(engine, hnodes, exact, singletons, t) + +bnet = bnet_from_engine(engine{1}); +N = length(bnet.intra); +cnodes_bitv = zeros(1,N); +cnodes_bitv(bnet.cnodes) = 1; +ref = exact(1); % reference +cmp = exact(2:end); +E = length(engine); +m = cell(1,E); + +for n=1:N + %for n=hnodes(:)' + for e=1:E + if singletons + m{e} = marginal_nodes(engine{e}, n, t); + else + m{e} = marginal_family(engine{e}, n, t); + end + end + for e=cmp(:)' + assert(isequal(m{e}.domain, m{ref}.domain)); + if cnodes_bitv(n) & isfield(m{e}, 'mu') & isfield(m{ref}, 'mu') + wrong = ~approxeq(m{ref}.mu, m{e}.mu) | ~approxeq(m{ref}.Sigma, m{e}.Sigma); + else + wrong = ~approxeq(m{ref}.T(:), m{e}.T(:)); + end + if wrong + error(sprintf('engine %d is wrong; n=%d, t=%d, fam=%d', e, n, t, ~singletons)) + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/dhmm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +% Make an HMM with discrete observations +% X1 -> X2 +% | | +% v v +% Y1 Y2 + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +Q = 2; % num hidden states +O = 2; % num observable symbols + +ns = [Q O]; +dnodes = 1:2; +onodes = [2]; +eclass1 = [1 2]; +eclass2 = [3 2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ... + 'observed', onodes); + +rand('state', 0); +prior1 = normalise(rand(Q,1)); +transmat1 = mk_stochastic(rand(Q,Q)); +obsmat1 = mk_stochastic(rand(Q,O)); +bnet.CPD{1} = tabular_CPD(bnet, 1, prior1); +bnet.CPD{2} = tabular_CPD(bnet, 2, obsmat1); +bnet.CPD{3} = tabular_CPD(bnet, 3, transmat1); + + +T = 5; % fixed length sequences + +engine = {}; +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); +engine{end+1} = hmm_inf_engine(bnet); +engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +if 1 +%engine{end+1} = frontier_inf_engine(bnet); % broken +engine{end+1} = bk_inf_engine(bnet, 'clusters', {[1]}); +engine{end+1} = jtree_dbn_inf_engine(bnet); +end + +inf_time = cmp_inference_dbn(bnet, engine, T); + +ncases = 2; +max_iter = 2; +[learning_time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, 'ncases', ncases, 'max_iter', max_iter); + +% Compare to HMM toolbox + +data = zeros(ncases, T); +for i=1:ncases + %data(i,:) = cat(2, cases{i}{onodes,:}); + data(i,:) = cell2num(cases{i}(onodes,:)); +end +[LL2, prior2, transmat2, obsmat2] = dhmm_em(data, prior1, transmat1, obsmat1, 'max_iter', max_iter); + +e = 1; +assert(approxeq(prior2, CPD{e,1}.CPT)) +assert(approxeq(obsmat2, CPD{e,2}.CPT)) +assert(approxeq(transmat2, CPD{e,3}.CPT)) +assert(approxeq(LL2, LL{e})) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/ehmm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +% make the structure of an embedded HMM with 2 rows and 3 columns + +% 1------------>2 +% |\ \ | \ \ +% 3->4->5 6->7->8 + +n = 8; +dag = zeros(n); +dag(1,[2 3 4 5])=1; +dag(2,[6 7 8])=1; +for i=3:4 + dag(i,i+1)=1; +end +for i=6:7 + dag(i,i+1)=1; +end +ns = 2*ones(1,n); +bnet = mk_bnet(dag,ns); +for i=1:n + bnet.CPD{i}=tabular_CPD(bnet,i); +end +[jtree, root, cliques] = graph_to_jtree(moralize(bnet.dag), ones(1,n), {}, {}); +%[jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges] = dag_to_jtree(bnet); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/fhmm_infer.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,324 @@ +function [loglik, gamma] = fhmm_infer(inter, CPTs_slice1, CPTs, obsmat, node_sizes) +% FHMM_INFER Exact inference for a factorial HMM. +% [loglik, gamma] = fhmm_infer(inter, CPTs_slice1, CPTs, obsmat, node_sizes) +% +% Inputs: +% inter - the inter-slice adjacency matrix +% CPTs_slice1{s}(j) = Pr(Q(s,1) = j) where Q(s,t) = hidden node s in slice t +% CPT{s}(i1, i2, ..., j) = Pr(Q(s,t) = j | Pa(s,t-1) = i1, i2, ...), +% obsmat(i,t) = Pr(y(t) | Q(t)=i) +% node_sizes is a vector with the cardinality of the hidden nodes +% +% Outputs: +% gamma(i,t) = Pr(X(t)=i | O(1:T)) as in an HMM, +% except that i is interpreted as an M digit, base-K number (if there are M chains each of cardinality K). +% +% +% For M chains each of cardinality K, the frontiers (i.e., cliques) +% contain M+1 nodes, and it takes M steps to advance the frontier by one time step, +% so the run time is O(T M K^(M+1)). +% An HMM takes O(T S^2) where S is the size of the state space. +% Collapsing the FHMM to an HMM results in S = K^M. +% For details, see +% "The Factored Frontier Algorithm for Approximate Inference in DBNs", +% Kevin Murphy and Yair Weiss, submitted to NIPS 2000. +% +% The frontier algorithm makes the following topological assumptions: +% +% - All nodes are persistent (connect to the next slice) +% - No connections within a timeslice +% - There is a single observation variable, which depends on all the hidden nodes +% - Each node can have several parents in the previous time slice (generalizes a FHMM slightly) +% + +% The forwards pass of the frontier algorithm can be explained with the following example. +% Suppose we have 3 hidden nodes per slice, A, B, C. +% The goal is to compute alpha(j, t) = Pr( (A_t,B_t,C_t)=j | Y(1:t)) +% We move alpha from t to t+1 one node at a time, as follows. +% We define the following quantities: +% s([a1 b1 c1], 1) = Prob(A(t)=a1, B(t)=b1, C(t)=c1 | Y(1:t)) = alpha(j, t) +% s([a2 b1 c1], 2) = Prob(A(t+1)=a2, B(t)=b1, C(t)=c1 | Y(1:t)) +% s([a2 b2 c1], 3) = Prob(A(t+1)=a2, B(t+1)=b2, C(t)=c1 | Y(1:t)) +% s([a2 b2 c2], 4) = Prob(A(t+1)=a2, B(t+1)=b2, C(t+1)=c2 | Y(1:t)) +% s([a2 b2 c2], 5) = Prob(A(t+1)=a2, B(t+1)=b2, C(t+1)=c2 | Y(1:t+1)) = alpha(j, t+1) +% +% These can be computed recursively as follows: +% +% s([a2 b1 c1], 2) = sum_{a1} P(a2|a1) s([a1 b1 c1], 1) +% s([a2 b2 c1], 3) = sum_{b1} P(b2|b1) s([a2 b1 c1], 2) +% s([a2 b2 c2], 4) = sum_{c1} P(c2|c1) s([a2 b2 c1], 1) +% s([a2 b2 c2], 5) = normalise( s([a2 b2 c2], 4) .* P(Y(t+1)|a2,b2,c2) + + +[kk,ll,mm] = make_frontier_indices(inter, node_sizes); % can pass in as args + +scaled = 1; + +M = length(node_sizes); +S = prod(node_sizes); +T = size(obsmat, 2); + +alpha = zeros(S, T); +beta = zeros(S, T); +gamma = zeros(S, T); +scale = zeros(1,T); +tiny = exp(-700); + + +alpha(:,1) = make_prior_from_CPTs(CPTs_slice1, node_sizes); +alpha(:,1) = alpha(:,1) .* obsmat(:, 1); + +if scaled + s = sum(alpha(:,1)); + if s==0, s = s + tiny; end + scale(1) = 1/s; +else + scale(1) = 1; +end +alpha(:,1) = alpha(:,1) * scale(1); + +%a = zeros(S, M+1); +%b = zeros(S, M+1); +anew = zeros(S,1); +aold = zeros(S,1); +bnew = zeros(S,1); +bold = zeros(S,1); + +for t=2:T + %a(:,1) = alpha(:,t-1); + aold = alpha(:,t-1); + + c = 1; + for i=1:M + ns = node_sizes(i); + cpt = CPTs{i}; + for j=1:S + s = 0; + for xx=1:ns + %k = kk(xx,j,i); + %l = ll(xx,j,i); + k = kk(c); + l = ll(c); + c = c + 1; + % s = s + a(k,i) * CPTs{i}(l); + s = s + aold(k) * cpt(l); + end + %a(j,i+1) = s; + anew(j) = s; + end + aold = anew; + end + + %alpha(:,t) = a(:,M+1) .* obsmat(:, obs(t)); + alpha(:,t) = anew .* obsmat(:, t); + + if scaled + s = sum(alpha(:,t)); + if s==0, s = s + tiny; end + scale(t) = 1/s; + else + scale(t) = 1; + end + alpha(:,t) = alpha(:,t) * scale(t); + +end + + +beta(:,T) = ones(S,1) * scale(T); +for t=T-1:-1:1 + %b(:,1) = beta(:,t+1) .* obsmat(:, obs(t+1)); + bold = beta(:,t+1) .* obsmat(:, t+1); + + c = 1; + for i=1:M + ns = node_sizes(i); + cpt = CPTs{i}; + for j=1:S + s = 0; + for xx=1:ns + %k = kk(xx,j,i); + %m = mm(xx,j,i); + k = kk(c); + m = mm(c); + c = c + 1; + % s = s + b(k,i) * CPTs{i}(m); + s = s + bold(k) * cpt(m); + end + %b(j,i+1) = s; + bnew(j) = s; + end + bold = bnew; + end + % beta(:,t) = b(:,M+1) * scale(t); + beta(:,t) = bnew * scale(t); +end + + +if scaled + loglik = -sum(log(scale)); % scale(i) is finite +else + lik = alpha(:,1)' * beta(:,1); + loglik = log(lik+tiny); +end + +for t=1:T + gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); +end + +%%%%%%%%%%% + +function [kk,ll,mm] = make_frontier_indices(inter, node_sizes) +% +% Precompute indices for use in the frontier algorithm. +% These only depend on the topology, not the parameters or data. +% Hence we can compute them outside of fhmm_infer. +% This saves a lot of run-time computation. + +M = length(node_sizes); +S = prod(node_sizes); + +mns = max(node_sizes); +kk = zeros(mns, S, M); +ll = zeros(mns, S, M); +mm = zeros(mns, S, M); + +for i=1:M + for j=1:S + u = ind2subv(node_sizes, j); + x = u(i); + for xx=1:node_sizes(i) + uu = u; + uu(i) = xx; + k = subv2ind(node_sizes, uu); + kk(xx,j,i) = k; + ps = find(inter(:,i)==1); + ps = ps(:)'; + l = subv2ind(node_sizes([ps i]), [uu(ps) x]); % sum over parent + ll(xx,j,i) = l; + m = subv2ind(node_sizes([ps i]), [u(ps) xx]); % sum over child + mm(xx,j,i) = m; + end + end +end + +%%%%%%%%% + +function prior=make_prior_from_CPTs(indiv_priors, node_sizes) +% +% composite_prior=make_prior(individual_priors, node_sizes) +% Make the prior for the first node in a Markov chain +% from the priors on each node in the equivalent DBN. +% prior{i}(j) = Pr(X_i=j), where X_i is the i'th node in slice 1. +% composite_prior(i) = Pr(slice1 = i). + +n = length(indiv_priors); +S = prod(node_sizes); +prior = zeros(S,1); +for i=1:S + vi = ind2subv(node_sizes, i); + p = 1; + for k=1:n + p = p * indiv_priors{k}(vi(k)); + end + prior(i) = p; +end + + + +%%%%%%%%%%% + +function [loglik, alpha, beta] = FHMM_slow(inter, CPTs_slice1, CPTs, obsmat, node_sizes, data) +% +% Same as the above, except we don't use the optimization of computing the indices outside the loop. + + +scaled = 1; + +M = length(node_sizes); +S = prod(node_sizes); +[numex T] = size(data); + +obs = data; + +alpha = zeros(S, T); +beta = zeros(S, T); +a = zeros(S, M+1); +b = zeros(S, M+1); +scale = zeros(1,T); + +alpha(:,1) = make_prior_from_CPTs(CPTs_slice1, node_sizes); +alpha(:,1) = alpha(:,1) .* obsmat(:, obs(1)); +if scaled + s = sum(alpha(:,1)); + if s==0, s = s + tiny; end + scale(1) = 1/s; +else + scale(1) = 1; +end +alpha(:,1) = alpha(:,1) * scale(1); + +for t=2:T + fprintf(1, 't %d\n', t); + a(:,1) = alpha(:,t-1); + for i=1:M + for j=1:S + u = ind2subv(node_sizes, j); + xnew = u(i); + s = 0; + for xold=1:node_sizes(i) + uold = u; + uold(i) = xold; + k = subv2ind(node_sizes, uold); + ps = find(inter(:,i)==1); + ps = ps(:)'; + l = subv2ind(node_sizes([ps i]), [uold(ps) xnew]); + s = s + a(k,i) * CPTs{i}(l); + end + a(j,i+1) = s; + end + end + alpha(:,t) = a(:,M+1) .* obsmat(:, obs(t)); + + if scaled + s = sum(alpha(:,t)); + if s==0, s = s + tiny; end + scale(t) = 1/s; + else + scale(t) = 1; + end + alpha(:,t) = alpha(:,t) * scale(t); + +end + + +beta(:,T) = ones(S,1) * scale(T); +for t=T-1:-1:1 + fprintf(1, 't %d\n', t); + b(:,1) = beta(:,t+1) .* obsmat(:, obs(t+1)); + for i=1:M + for j=1:S + u = ind2subv(node_sizes, j); + xold = u(i); + s = 0; + for xnew=1:node_sizes(i) + unew = u; + unew(i) = xnew; + k = subv2ind(node_sizes, unew); + ps = find(inter(:,i)==1); + ps = ps(:)'; + l = subv2ind(node_sizes([ps i]), [u(ps) xnew]); + s = s + b(k,i) * CPTs{i}(l); + end + b(j,i+1) = s; + end + end + beta(:,t) = b(:,M+1) * scale(t); +end + + +if scaled + loglik = -sum(log(scale)); % scale(i) is finite +else + lik = alpha(:,1)' * beta(:,1); + loglik = log(lik+tiny); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/filter_test1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +% Compare online filtering algorithms on some DBNs + +seed = 0; +rand('state', seed); +randn('state', seed); + +if 0 + N = 3; + Q = 2; + obs_size = 1; + discrete_obs = 0; + bnet = mk_chmm(N, Q, obs_size, discrete_obs); +else + %bnet = mk_bat_dbn; + bnet = mk_water_dbn; +end + +T = 3; + +engine = {}; +engine{end+1} = filter_engine(hmm_2TBN_inf_engine(bnet)); +engine{end+1} = filter_engine(jtree_2TBN_inf_engine(bnet)); + +time = cmp_online_inference(bnet, engine, T);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/ghmm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +% Make an HMM with Gaussian observations +% X1 -> X2 +% | | +% v v +% Y1 Y2 + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +Q = 2; % num hidden states +O = 2; % size of observed vector +ns = [Q O]; +bnet = mk_dbn(intra, inter, ns, 'discrete', 1, 'observed', 2); + +prior0 = normalise(rand(Q,1)); +transmat0 = mk_stochastic(rand(Q,Q)); +mu0 = rand(O,Q); +Sigma0 = repmat(eye(O), [1 1 Q]); +bnet.CPD{1} = tabular_CPD(bnet, 1, prior0); +%% we set the cov prior to 0 to give same results as HMM toolbox +%bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu0, 'cov', Sigma0, 'cov_prior_weight', 0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu0, 'cov', Sigma0); +bnet.CPD{3} = tabular_CPD(bnet, 3, transmat0); + + +T = 5; % fixed length sequences + +engine = {}; +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); +engine{end+1} = hmm_inf_engine(bnet); +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); +%engine{end+1} = frontier_inf_engine(bnet); +engine{end+1} = bk_inf_engine(bnet, 'clusters', {[1]}); +engine{end+1} = jtree_dbn_inf_engine(bnet); + + +inf_time = cmp_inference_dbn(bnet, engine, T); + +ncases = 2; +max_iter = 2; +[learning_time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, 'ncases', ncases, 'max_iter', max_iter); + +% Compare to HMM toolbox + +data = zeros(O, T, ncases); +for i=1:ncases + data(:,:,i) = cell2num(cases{i}(bnet.observed, :)); +end + +tic +[LL2, prior2, transmat2, mu2, Sigma2] = mhmm_em(data, prior0, transmat0, mu0, Sigma0, [], 'max_iter', max_iter); +t=toc; +disp(['HMM toolbox took ' num2str(t) ' seconds ']) + +e = 1; +assert(approxeq(prior2, CPD{e,1}.CPT)) +assert(approxeq(mu2, CPD{e,2}.mean)) +assert(approxeq(Sigma2, CPD{e,2}.cov)) +assert(approxeq(transmat2, CPD{e,3}.CPT)) +assert(approxeq(LL2, LL{e}))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/ho1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,156 @@ +function ho1() + +% Example of how to create a higher order DBN +% Written by Rainer Deventer <deventer@informatik.uni-erlangen.de> 3/28/03 + +bnet = createBNetNL(); + +%%%%%%%%%%%% + + +function bnet = createBNetNL(varargin) + % Generate a Bayesian network, which is able to model nonlinearities at +% the input. The only input is the order of the dynamic system. If this +% parameter is missing, the an order of two is assumed +if nargin > 0 + order = varargin{1} +else + order = 2; +end + +ss = 6; % For each time slice the following nodes are modeled + % ud(t_k) Discrete node, which decides whether saturation is reached. + % Node number 2 + % uv(t_k) Visible input node with node number 2 + % uh(t_k) Hidden input node with node number 3 + % y(t_k) Modeled output, Number 4 + % z(t_k) Disturbing variable, number 5 + % q(t_k), number6 6 + +intra = zeros(ss,ss); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Within each timeslice ud(t_k) is connected with uv(t_k) and uh(t_k) % +% This part is used to model saturation % +% A connection from uv(t_k) to uh(t_k) is omitted % +% Additionally y(t_k) is connected with q(t_k). To model the disturbing% +% value z(t_k) is connected with q(t_k). % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +intra(1,2:3) = 1; % Connections ud(t_k) -> uv(t_k) and ud(t_k) -> uh(t_k) +intra(4:5,6) = 1; % Connectios y(t_k) -> q(t_k) and z(t_k) -> q(t_k) + + + +inter = zeros(ss,ss,order); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% The Markov assumption is not met as connections from time slice t to t+2 % +% exist. % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +for i = 1:order + if i == 1 + inter(1,1,i) = 1; %Connect the discrete nodes. This is necessary to improve + %the disturbing reaction + inter(3,4,i) = 1; %Connect uh(t_{k-1}) with y(t_k) + inter(4,4,i) = 1; %Connect y(t_{k-1}) with y(t_k) + inter(5,5,i) = 1; %Connect z(t_{k-1}) with z(t_k) + else + inter(3,4,i) = 1; %Connect uh(t_{k-i}) with y(t_k) + inter(4,4,i) = 1; %Connect y(t_{k-i}) with y(t_k) + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Define the dimensions of the discrete nodes. Node 1 has two states % +% 1 = lower saturation reached % +% 2 = Upper saturation reached % +% Values in between are model by probabilities between 0 and 1 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +node_sizes = ones(1,ss); +node_sizes(1) = 2; +dnodes = [1]; + +eclass = [1:6;7 2:3 8 9 6;7 2:3 10 11 6]; +bnet = mk_higher_order_dbn(intra,inter,node_sizes,... + 'discrete',dnodes,... + 'eclass',eclass); + +cov_high = 400; +cov_low = 0.01; +weight1 = randn(1,1); +weight2 = randn(1,1); +weight3 = randn(1,1); +weight4 = randn(1,1); + +numOfNodes = 5 + order; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Nodes of the first time-slice % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Discrete input node, +bnet.CPD{1} = tabular_CPD(bnet,1,'CPT',[1/2 1/2],'adjustable',0); + + +% Modeled visible input +bnet.CPD{2} = gaussian_CPD(bnet,2,'mean',[0 10],'clamp_mean',1,... + 'cov',[10 10],'clamp_cov',1); + +% Modeled hidden input +bnet.CPD{3} = gaussian_CPD(bnet,3,'mean',[0, 10],'clamp_mean',1,... + 'cov',[0.1 0.1],'clamp_cov',1); + +% Modeled output in the first timeslice, thus there are no parents +% Usuallz the output nodes get a low covariance. But in the first +% time-slice a prediction of the output is not possible due to +% missing information +bnet.CPD{4} = gaussian_CPD(bnet,4,'mean',0,'clamp_mean',1,... + 'cov',cov_high,'clamp_cov',1); + +%Disturbance +bnet.CPD{5} = gaussian_CPD(bnet,5,'mean',0,... + 'cov',[4],... + 'clamp_mean',1,... + 'clamp_cov',1); + +%Observed output. +bnet.CPD{6} = gaussian_CPD(bnet,6,'mean',0,... + 'clamp_mean',1,... + 'cov',cov_low,'clamp_cov',1,... + 'weights',[1 1],'clamp_weights',1); + +% Discrete node at second time slice +bnet.CPD{7} = tabular_CPD(bnet,7,'CPT',[0.6 0.4 0.4 0.6],'adjustable',0); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Node for the model output % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +bnet.CPD{8} = gaussian_CPD(bnet,10,'mean',0,... + 'cov',cov_high,... + 'clamp_mean',1,... + 'clamp_cov',1); +% 'weights',[0.0791 0.9578]); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Node for the disturbance % +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +bnet.CPD{9} = gaussian_CPD(bnet,11,'mean',0,'clamp_mean',1,... + 'cov',[4],'clamp_cov',1,... + 'weights',[1],'clamp_weights',1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Node for the model output % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +bnet.CPD{10} = gaussian_CPD(bnet,16,'mean',0,'clamp_mean',1,... + 'cov',cov_low,'clamp_cov',1); +% 'weights',[0.0188 -0.0067 0.0791 0.9578]); + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Node for the disturbance % +%%%%%%%%%%%%%%%%%%%%%%%%%%%% +bnet.CPD{11} = gaussian_CPD(bnet,17,'mean',0,'clamp_mean',1,... + 'cov',[0.2],'clamp_cov',1,... + 'weights',[1],'clamp_weights',1); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/jtree_clq_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,150 @@ +% Construct various DBNs and examine their clique structure. +% This was used to generate various figures in chap 3-4 of my thesis. + +% Examine the cliques in the unrolled mildew net + +%dbn = mk_mildew_dbn; +dbn = mk_chmm(4); +ss = dbn.nnodes_per_slice; +T = 7; +N = ss*T; +bnet = dbn_to_bnet(dbn, T); + +constrained = 0; +if constrained + stages = num2cell(unroll_set(1:ss, ss, T), 1); +else + stages = { 1:N; }; +end +clusters = {}; +%[jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges] = ... +% dag_to_jtree(bnet, bnet.observed, stages, clusters); +[jtree, root, cliques] = graph_to_jtree(moralize(bnet.dag), ones(1,N), stages, clusters); + +flip=1; +clf;[dummyx, dummyy, h] = draw_dbn(dbn.intra, dbn.inter, flip, T, -1); +dir = '/home/eecs/murphyk/WP/Thesis/Figures/Inf/MildewUnrolled'; +mk_ps_from_clqs(dbn, T, cliques, []) +%mk_collage_from_clqs(dir, cliques) + + +% Examine the cliques in the cascade DBN + +% A-A +% \ +% B B +% \ +% C C +% \ +% D D +ss = 4; +intra = zeros(ss); +inter = zeros(ss); +inter(1, [1 2])=1; +for i=2:ss-1 + inter(i,i+1)=1; +end + + +% 2 coupled HMMs 1,3 and 2,4 +ss = 4; +intra = zeros(ss); +inter = zeros(ss); % no persistent edges +%inter = diag(ones(ss,1)); % persitence edges +inter(1,3)=1; inter(3,1)=1; +inter(2,4)=1; inter(4,2)=1; + +%bnet = mk_fhmm(3); +bnet = mk_chmm(4); +intra = bnet.intra; +inter = bnet.inter; + +clqs = compute_minimal_interface(intra, inter); +celldisp(clqs) + + + + +% A A +% \ +% B B +% \ +% C C +% \ +% D-D +ss = 4; +intra = zeros(ss); +inter = zeros(ss); +for i=1:ss-1 + inter(i,i+1)=1; +end +inter(4,4)=1; + + + +ns = 2*ones(1,ss); +dbn = mk_dbn(intra, inter, ns); +for i=2*ss + dbn.CPD{i} = tabular_CPD(bnet, i); +end + +T = 4; +N = ss*T; +bnet = dbn_to_bnet(dbn, T); + +constrained = 1; +if constrained + % elim first 3 slices first in any order + stages = {1:12, 13:16}; + %stages = num2cell(unroll_set(1:ss, ss, T), 1); +else + stages = { 1:N; }; +end +clusters = {}; +%[jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges] = ... +% dag_to_jtree(bnet, bnet.observed, stages, clusters); +[jtree, root, cliques] = graph_to_jtree(moralize(bnet.dag), ones(1,N), stages, clusters); + + + + + +% Examine the cliques in the 1.5 slice DBN + +%dbn = mk_mildew_dbn; +dbn = mk_water_dbn; +%dbn = mk_bat_dbn; +ss = dbn.nnodes_per_slice; +int = compute_fwd_interface(dbn); +bnet15 = mk_slice_and_half_dbn(dbn, int); +N = length(bnet15.dag); +stages = {1:N}; + +% bat +%cl1 = [16 17 19 7 14]; +%cl2 = [27 25 21 23 20]; +%clusters = {cl1, cl2, cl1+ss, cl2+ss}; + +% water +%cl1 = 1:2; cl2 = 3:6; cl3 = 7:8; +%clusters = {cl1, cl2, cl3, cl1+ss, cl2+ss, cl3+ss}; + +%clusters = {}; +clusters = {int, int+ss}; +%[jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges] = ... +% dag_to_jtree(bnet15, bnet.observed, stages, clusters); +[jtree, root, cliques] = graph_to_jtree(moralize(bnet15.dag), ones(1,N), stages, clusters); + +clq_len = []; +for c=1:length(cliques) + clq_len(c) = length(cliques{c}); +end +hist(clq_len, 1:max(clq_len)); +h=hist(clq_len, 1:max(clq_len)); +axis([1 max(clq_len)+1 0 max(h)+1]) +xlabel('clique size','fontsize',16) +ylabel('number','fontsize',16) + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/jtree_clq_test2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ + +%bnet = mk_uffe_dbn; +bnet = mk_mildew_dbn; +ss = length(bnet.intra); + +% construct jtree from 1.5 slice DBN + +int = compute_fwd_interface(bnet.intra, bnet.inter); +bnet15 = mk_slice_and_half_dbn(bnet, int); + +% use unconstrained elimination, +% but force there to be a clique containing both interfaces +clusters = {int, int+ss}; +jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +S=struct(jtree_engine) +in_clq = clq_containing_nodes(jtree_engine, int); +out_clq = clq_containing_nodes(jtree_engine, int+ss) + + +% Also make a jtree from slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice); +jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); +S1=struct(jtree_engine1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/kalman1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +% Make a linear dynamical system +% X1 -> X2 +% | | +% v v +% Y1 Y2 + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +X = 2; % size of hidden state +Y = 2; % size of observable state +ns = [X Y]; +bnet = mk_dbn(intra, inter, ns, 'discrete', [], 'observed', 2); + +x0 = rand(X,1); +V0 = eye(X); +C0 = rand(Y,X); +R0 = eye(Y); +A0 = rand(X,X); +Q0 = eye(X); + +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0, 'cov_prior_weight', 0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0, ... + 'clamp_mean', 1, 'cov_prior_weight', 0); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0, ... + 'clamp_mean', 1, 'cov_prior_weight', 0); + + +T = 5; % fixed length sequences + +clear engine; +engine{1} = kalman_inf_engine(bnet); +engine{2} = jtree_unrolled_dbn_inf_engine(bnet, T); +engine{3} = jtree_dbn_inf_engine(bnet); +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +N = length(engine); + + +inf_time = cmp_inference_dbn(bnet, engine, T); + +ncases = 2; +max_iter = 2; +[learning_time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, 'ncases', ncases, 'max_iter', max_iter); + + +% Compare to KF toolbox + +data = zeros(Y, T, ncases); +for i=1:ncases + data(:,:,i) = cell2num(cases{i}(onodes, :)); +end +[A2, C2, Q2, R2, x2, V2, LL2trace] = learn_kalman(data, A0, C0, Q0, R0, x0, V0, max_iter); + + +e = 1; +assert(approxeq(x2, CPD{e,1}.mean)) +assert(approxeq(V2, CPD{e,1}.cov)) +assert(approxeq(C2, CPD{e,2}.weights)) +assert(approxeq(R2, CPD{e,2}.cov)); +assert(approxeq(A2, CPD{e,3}.weights)) +assert(approxeq(Q2, CPD{e,3}.cov)); +assert(approxeq(LL2trace, LL{1})) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/kjaerulff1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +% Compare the speeds of various inference engines on the DBN in Kjaerulff +% "dHugin: A computational system for dynamic time-sliced {B}ayesian networks", +% Intl. J. Forecasting 11:89-111, 1995. +% +% The intra structure is (all arcs point downwards) +% +% 1 -> 2 +% \ / +% 3 +% | +% 4 +% / \ +% 5 6 +% \ / +% 7 +% | +% 8 +% +% The inter structure is 1->1, 4->4, 8->8 + +seed = 0; +rand('state', seed); +randn('state', seed); + +ss = 8; +intra = zeros(ss); +intra(1,[2 3])=1; +intra(2,3)=1; +intra(3,4)=1; +intra(4,[5 6])=1; +intra([5 6], 7)=1; +intra(7,8)=1; + +inter = zeros(ss); +inter(1,1)=1; +inter(4,4)=1; +inter(8,8)=1; + +ns = 2*ones(1,ss); +onodes = 2; +bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'eclass2', (1:ss)+ss); +for i=1:2*ss + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +T = 4; + +engine = {}; +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); +engine{end+1} = jtree_dbn_inf_engine(bnet); +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +%engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); % observed nodes have children + +inf_time = cmp_inference_dbn(bnet, engine, T) +learning_time = cmp_learning_dbn(bnet, engine, T)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/loopy_dbn1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +N = 1; % single chain = HMM - should give exact answers +Q = 2; +rand('state', 0); +randn('state', 0); +discrete = 1; +if discrete + Y = 2; % size of output alphabet +else + Y = 1; +end +coupled = 1; +bnet = mk_chmm(N, Q, Y, discrete, coupled); +ss = N*2; + +T = 3; + +engine = {}; +engine{end+1} = jtree_dbn_inf_engine(bnet); +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); +engine{end+1} = pearl_unrolled_dbn_inf_engine(bnet, 'protocol', 'tree'); + +inf_time = cmp_inference_dbn(bnet, engine, T) +learning_time = cmp_learning_dbn(bnet, engine, T) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mhmm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,85 @@ +% Make an HMM with mixture of Gaussian observations +% Q1 ---> Q2 +% / | / | +% M1 | M2 | +% \ v \ v +% Y1 Y2 +% where Pr(m=j|q=i) is a multinomial and Pr(y|m,q) is a Gaussian + +%seed = 3; +%rand('state', seed); +%randn('state', seed); + +intra = zeros(3); +intra(1,[2 3]) = 1; +intra(2,3) = 1; +inter = zeros(3); +inter(1,1) = 1; +n = 3; + +Q = 2; % num hidden states +O = 2; % size of observed vector +M = 2; % num mixture components per state + +ns = [Q M O]; +dnodes = [1 2]; +onodes = [3]; +eclass1 = [1 2 3]; +eclass2 = [4 2 3]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ... + 'observed', onodes); + +prior0 = normalise(rand(Q,1)); +transmat0 = mk_stochastic(rand(Q,Q)); +mixmat0 = mk_stochastic(rand(Q,M)); +mu0 = rand(O,Q,M); +Sigma0 = repmat(eye(O), [1 1 Q M]); +bnet.CPD{1} = tabular_CPD(bnet, 1, prior0); +bnet.CPD{2} = tabular_CPD(bnet, 2, mixmat0); +%% we set the cov prior to 0 to give same results as HMM toolbox +%bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', mu0, 'cov', Sigma0, 'cov_prior_weight', 0); +% new version of HMM toolbox uses the same default prior on Gaussians as BNT +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', mu0, 'cov', Sigma0); +bnet.CPD{4} = tabular_CPD(bnet, 4, transmat0); + + + +T = 5; % fixed length sequences + +engine = {}; +engine{end+1} = hmm_inf_engine(bnet); +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); +if 0 +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); +%engine{end+1} = frontier_inf_engine(bnet); +engine{end+1} = bk_inf_engine(bnet, 'clusters', 'exact'); +engine{end+1} = jtree_dbn_inf_engine(bnet); +end + +inf_time = cmp_inference_dbn(bnet, engine, T); + +ncases = 2; +max_iter = 2; +[learning_time, CPD, LL, cases] = cmp_learning_dbn(bnet, engine, T, 'ncases', ncases, 'max_iter', max_iter); + +% Compare to HMM toolbox + +data = zeros(O, T, ncases); +for i=1:ncases + data(:,:,i) = reshape(cell2num(cases{i}(onodes,:)), [O T]); +end +tic; +[LL2, prior2, transmat2, mu2, Sigma2, mixmat2] = ... + mhmm_em(data, prior0, transmat0, mu0, Sigma0, mixmat0, 'max_iter', max_iter); +t=toc; +disp(['HMM toolbox took ' num2str(t) ' seconds ']) + +for e = 1:length(engine) + assert(approxeq(prior2, CPD{e,1}.CPT)) + assert(approxeq(mixmat2, CPD{e,2}.CPT)) + assert(approxeq(mu2, CPD{e,3}.mean)) + assert(approxeq(Sigma2, CPD{e,3}.cov)) + assert(approxeq(transmat2, CPD{e,4}.CPT)) + assert(approxeq(LL2, LL{e})) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mildew1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +bnet = mk_mildew_dbn; + +T = 4; +engine = {}; +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); +engine{end+1} = jtree_dbn_inf_engine(bnet); +%engine{end+1} = hmm_inf_engine(bnet); % 8 is observed but has kids +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +%engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); + +inf_time = cmp_inference_dbn(bnet, engine, T, 'check_ll', 0) +%learning_time = cmp_learning_dbn(bnet, engine, T) + +S = struct(engine{1}); +S1 = struct(S.unrolled_engine); +G = S1.jtree; +%graph_to_dot(G, 'directed', 0, 'leftright', 1, ... +% 'filename', '/home/eecs/murphyk/WP/Thesis/Figures/Inf/Mildew/jtree.dot') +%!dot -Tps jtree.dot -o jtree.ps +% The resulting ps file cannot be converted using ps2pdf. + +N = length(G); +for i=1:N + for j=1:N + if G(i,j) + G(j,i)=1; + end + end +end +draw_graph(G)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_bat_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,63 @@ +function [bnet, names] = mk_bat_dbn() +% MK_BAT_DBN Make the BAT DBN +% [bnet, names] = mk_bat_dbn() +% See +% - Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a Bayesian Automated Taxi", IJCAI 95 +% - Boyen and Koller, "Tractable Inference for Complex Stochastic Processes", UAI98. + +names = {'LeftClr', 'RightClr', 'LatAct', 'Xdot', 'InLane', 'FwdAct', ... + 'Ydot', 'Stopped', 'EngStatus', 'FBStatus', ... + 'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', ... + 'FYdotDiffSens', 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens', ... + 'SensorValid', 'FYdotDiff', 'FcloseSlow', 'Fclr', 'BXdot', 'BcloseFast', 'Bclr', 'BYdotDiff'}; +ss = length(names); + +intrac = {... + 'LeftClr', 'LeftClrSens'; + 'RightClr', 'RightClrSens'; + 'LatAct', 'TurnSignalSens'; 'LatAct', 'Xdot'; + 'Xdot', 'XdotSens'; + 'FwdAct', 'Ydot'; + 'Ydot', 'YdotSens'; 'Ydot', 'Stopped'; + 'EngStatus', 'Ydot'; 'EngStatus', 'FYdotDiff'; 'EngStatus', 'Fclr'; 'EngStatus', 'BXdot'; + 'SensorValid', 'XdotSens'; 'SensorValid', 'YdotSens'; + 'FYdotDiff', 'FYdotDiffSens'; 'FYdotDiff', 'FcloseSlow'; + 'FcloseSlow', 'FBStatus'; + 'Fclr', 'FclrSens'; 'Fclr', 'FcloseSlow'; + 'BXdot', 'BXdotSens'; + 'Bclr', 'BclrSens'; 'Bclr', 'BXdot'; 'Bclr', 'BcloseFast'; + 'BcloseFast', 'FBStatus'; + 'BYdotDiff', 'BYdotDiffSens'; 'BYdotDiff', 'BcloseFast'}; +[intra, names] = mk_adj_mat(intrac, names, 1); + + +interc = {... + 'LeftClr', 'LeftClr'; 'LeftClr', 'LatAct'; + 'RightClr', 'RightClr'; 'RightClr', 'LatAct'; + 'LatAct', 'LatAct'; 'LatAct', 'FwdAct'; + 'Xdot', 'Xdot'; 'Xdot', 'InLane'; + 'InLane', 'InLane'; 'InLane', 'LatAct'; + 'FwdAct', 'FwdAct'; + 'Ydot', 'Ydot'; + 'Stopped', 'Stopped'; + 'EngStatus', 'EngStatus'; + 'FBStatus', 'FwdAct'; 'FBStatus', 'LatAct'}; +inter = mk_adj_mat(interc, names, 0); + +obs = {'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', 'FYdotDiffSens', ... + 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens'}; + +for i=1:length(obs) + onodes(i) = strmatch(obs{i}, names); %stringmatch(obs{i}, names); +end +onodes = sort(onodes); + +dnodes = 1:ss; +ns = 2*ones(1,ss); % binary nodes +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'observed', onodes, 'eclass2', (1:ss)+ss); + +% make rnd params +for i=1:2*ss + bnet.CPD{i} = tabular_CPD(bnet, i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_chmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,103 @@ +function bnet = mk_chmm(N, Q, Y, discrete_obs, coupled, CPD) +% MK_CHMM Make a coupled Hidden Markov Model +% +% There are N hidden nodes, each connected to itself and its two nearest neighbors in the next +% slice (apart from the edges, where there is 1 nearest neighbor). +% +% Example: If N = 3, the hidden backbone is as follows, where all arrows point to the righ+t +% +% X1--X2 +% \/ +% /\ +% X2--X2 +% \/ +% /\ +% X3--X3 +% +% Each hidden node has a "private" observed child (not shown). +% +% BNET = MK_CHMM(N, Q, Y) +% Each hidden node is discrete and has Q values. +% Each observed node is a Gaussian vector of length Y. +% +% BNET = MK_CHMM(N, Q, Y, DISCRETE_OBS) +% If discrete_obs = 1, the observations are discrete (values in {1, .., Y}). +% +% BNET = MK_CHMM(N, Q, Y, DISCRETE_OBS, COUPLED) +% If coupled = 0, the chains are not coupled, i.e., we make N parallel HMMs. +% +% BNET = MK_CHMM(N, Q, Y, DISCRETE_OBS, COUPLED, CPDs) +% means use the specified CPD structures instead of creating random params. +% CPD{i}.CPT, i=1:N specifies the prior +% CPD{i}.CPT, i=2N+1:3N specifies the transition model +% CPD{i}.mean, CPD{i}.cov, i=N+1:2N specifies the observation model if Gaussian +% CPD{i}.CPT, i=N+1:2N if discrete + + +if nargin < 2, Q = 2; end +if nargin < 3, Y = 1; end +if nargin < 4, discrete_obs = 0; end +if nargin < 5, coupled = 1; end +if nargin < 6, rnd = 1; else rnd = 0; end + +ss = N*2; +hnodes = 1:N; +onodes = (1:N)+N; + +intra = zeros(ss); +for i=1:N + intra(hnodes(i), onodes(i))=1; +end + +inter = zeros(ss); +if coupled + for i=1:N + inter(i, max(i-1,1):min(i+1,N))=1; + end +else + inter(1:N, 1:N) = eye(N); +end + +ns = [Q*ones(1,N) Y*ones(1,N)]; + +eclass1 = [hnodes onodes]; +eclass2 = [hnodes+ss onodes]; +if discrete_obs + dnodes = 1:ss; +else + dnodes = hnodes; +end +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ... + 'observed', onodes); + +if rnd + for i=hnodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i); + end + for i=onodes(:)' + if discrete_obs + bnet.CPD{i} = tabular_CPD(bnet, i); + else + bnet.CPD{i} = gaussian_CPD(bnet, i); + end + end + for i=hnodes(:)'+ss + bnet.CPD{i} = tabular_CPD(bnet, i); + end +else + for i=hnodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i, CPD{i}.CPT); + end + for i=onodes(:)' + if discrete_obs + bnet.CPD{i} = tabular_CPD(bnet, i, CPD{i}.CPT); + else + bnet.CPD{i} = gaussian_CPD(bnet, i, CPD{i}.mean, CPD{i}.cov); + end + end + for i=hnodes(:)'+ss + bnet.CPD{i} = tabular_CPD(bnet, i, CPD{i}.CPT); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_collage_from_clqs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function mk_collage_from_clqs(dir, cliques) + +% For use with mk_ps_from_clqs. +% This generates a latex file that glues all the .ps files +% into one big figure. + +cd(dir) +C = length(cliques); + +ncols = 4; +width = 1.5; +fid = fopen('collage.tex', 'w'); +fprintf(fid, '\\documentclass{article}\n'); +fprintf(fid, '\\usepackage{psfig}\n'); +fprintf(fid, '\\begin{document}\n'); +fprintf(fid, '\\centerline{\n'); +fprintf(fid, '\\begin{tabular}{'); +for col=1:ncols, fprintf(fid, 'c'); end +fprintf(fid, '}\n'); +c = 1; +for row = 1:floor(C/ncols) + for col=1:ncols-1 + fname = sprintf('%s/clq%d.ps', dir, c); + fprintf(fid, '\\psfig{file=%s,width=%3fin} & \n', fname, width); + c = c + 1; + end + fname = sprintf('%s/clq%d.ps', dir, c); + fprintf(fid, '\\psfig{file=%s,width=%3fin} \\\\ \n', fname, width); + c = c + 1; +end +% last row +while (c <= C) + fname = sprintf('%s/clq%d.ps', dir, c); + fprintf(fid, '\\psfig{file=%s,width=%3fin} & \n', fname, width); + c = c + 1; +end +fprintf(fid, '\\end{tabular}\n'); +fprintf(fid, '}\n'); +fprintf(fid, '\\end{document}'); +fclose(fid); + +!latex collage.tex & +!dvips -o collage.ps collage.dvi & +!ghostview collage.ps &
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_fhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function bnet = mk_fhmm(N, Q, Y, discrete_obs) +% MK_FHMM Make a factorial Hidden Markov Model +% +% There are N independent parallel hidden chains, each connected to the output +% +% e.g., N = 2 (vertical/diagonal edges point down) +% +% A1--->A2 +% | B1--|->B2 +% | / |/ +% Y1 Y2 +% +% [bnet, onode] = mk_chmm(n, q, y, discrete_obs) +% +% Each hidden node is discrete and has Q values. +% If discrete_obs = 1, each observed node is discrete and has values 1..Y. +% If discrete_obs = 0, each observed node is a Gaussian vector of length Y. + +if nargin < 2, Q = 2; end +if nargin < 3, Y = 2; end +if nargin < 4, discrete_obs = 1; end + +ss = N+1; +hnodes = 1:N; +onode = N+1; + +intra = zeros(ss); +intra(hnodes, onode) = 1; + +inter = eye(ss); +inter(onode,onode) = 0; + +ns = [Q*ones(1,N) Y]; + +eclass1 = [hnodes onode]; +eclass2 = [hnodes+ss onode]; +if discrete_obs + dnodes = 1:ss; +else + dnodes = hnodes; +end +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ... + 'observed', onode); + +for i=hnodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i); +end +i = onode; +if discrete_obs + bnet.CPD{i} = tabular_CPD(bnet, i); +else + bnet.CPD{i} = gaussian_CPD(bnet, i); +end +for i=hnodes(:)'+ss + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_mildew_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function bnet = mk_mildew_dbn() + +% DBN for foreacasting the gross yield of wheat based on climatic data, +% observations of leaf area index (LAI) and extension of mildew, +% and knowledge of amount of fungicides used and time of usage. +% From Kjaerulff '95. + +Fungi=1; Mildew=2; LAI=3; Precip=4; Temp=5; Micro=6; Solar=7; Photo=8; Dry=9; +n = 9; +intra = zeros(n,n); +intra(Mildew, LAI)=1; +intra(LAI,[Micro Photo])=1; +intra(Precip,Micro)=1; +intra(Temp,[Micro Photo])=1; +intra(Solar,Photo)=1; +intra(Photo,Dry)=1; + +inter = zeros(n,n); +inter(Fungi,Mildew)=1; +inter(Mildew,Mildew)=1; +inter(LAI,LAI)=1; +inter(Micro,Mildew)=1; +inter(Dry,Dry)=1; + +ns = 2*ones(1,n); +bnet = mk_dbn(intra, inter, ns, 'observed', [Photo]); + +for e=1:max(bnet.equiv_class(:)) + i = bnet.rep_of_eclass(e); + bnet.CPD{e} = tabular_CPD(bnet,i); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_orig_bat_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,198 @@ +function [bnet, names] = mk_orig_bat_dbn() +% MK_BAT_DBN Make the BAT DBN +% [bnet, names] = mk_bat_dbn() +% See +% - Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a Bayesian Automated Taxi", IJCAI 95 +% - Boyen and Koller, "Tractable Inference for Complex Stochastic Processes", UAI98. + +names = {'LeftClr', 'RightClr', 'LatAct', 'Xdot', 'InLane', 'FwdAct', ... + 'Ydot', 'Stopped', 'EngStatus', 'FBStatus', ... + 'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', ... + 'FYdotDiffSens', 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens', ... + 'SensorValid', 'FYdotDiff', 'FcloseSlow', 'Fclr', 'BXdot', 'BcloseFast', 'Bclr', 'BYdotDiff'}; +ss = length(names); + +intrac = {... + 'LeftClr', 'LeftClrSens'; + 'RightClr', 'RightClrSens'; + 'LatAct', 'TurnSignalSens'; 'LatAct', 'Xdot'; + 'Xdot', 'XdotSens'; + 'FwdAct', 'Ydot'; + 'Ydot', 'YdotSens'; 'Ydot', 'Stopped'; + 'EngStatus', 'Ydot'; 'EngStatus', 'FYdotDiff'; 'EngStatus', 'Fclr'; 'EngStatus', 'BXdot'; + 'SensorValid', 'XdotSens'; 'SensorValid', 'YdotSens'; + 'FYdotDiff', 'FYdotDiffSens'; 'FYdotDiff', 'FcloseSlow'; + 'FcloseSlow', 'FBStatus'; + 'Fclr', 'FclrSens'; 'Fclr', 'FcloseSlow'; + 'BXdot', 'BXdotSens'; + 'Bclr', 'BclrSens'; 'Bclr', 'BXdot'; 'Bclr', 'BcloseFast'; + 'BcloseFast', 'FBStatus'; + 'BYdotDiff', 'BYdotDiffSens'; 'BYdotDiff', 'BcloseFast'}; +[intra, names] = mk_adj_mat(intrac, names, 1); + + +interc = {... + 'LeftClr', 'LeftClr'; 'LeftClr', 'LatAct'; + 'RightClr', 'RightClr'; 'RightClr', 'LatAct'; + 'LatAct', 'LatAct'; 'LatAct', 'FwdAct'; + 'Xdot', 'Xdot'; 'Xdot', 'InLane'; + 'InLane', 'InLane'; 'InLane', 'LatAct'; + 'FwdAct', 'FwdAct'; + 'Ydot', 'Ydot'; + 'Stopped', 'Stopped'; + 'EngStatus', 'EngStatus'; + 'FBStatus', 'FwdAct'; 'FBStatus', 'LatAct'}; +inter = mk_adj_mat(interc, names, 0); + +obs = {'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', 'FYdotDiffSens', ... + 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens'}; + +for i=1:length(obs) + onodes(i) = stringmatch(obs{i}, names); +end +onodes = sort(onodes); + +dnodes = 1:ss; +ns = zeros(1,ss); + +ns(stringmatch('LeftClr', names)) = 2; +ns(stringmatch('RightClr', names)) = 2; +ns(stringmatch('LatAct', names)) = 3; +ns(stringmatch('Xdot', names)) = 7; +ns(stringmatch('InLane', names)) = 2; +ns(stringmatch('FwdAct', names)) = 3; +ns(stringmatch('Ydot', names)) = 11; +ns(stringmatch('Stopped', names)) = 2; +ns(stringmatch('EngStatus', names)) = 2; +ns(stringmatch('FBStatus', names)) = 3; +ns(stringmatch('LeftClrSens', names)) = 2; +ns(stringmatch('RightClrSens', names)) = 2; +ns(stringmatch('TurnSignalSens', names)) = 3; +ns(stringmatch('XdotSens', names)) = 7; +ns(stringmatch('YdotSens', names)) = 11; +ns(stringmatch('FYdotDiffSens', names)) = 8; +ns(stringmatch('FclrSens', names)) = 20; +ns(stringmatch('BXdotSens', names)) = 8; +ns(stringmatch('BclrSens', names)) = 20; +ns(stringmatch('BYdotDiffSens', names)) = 8; +ns(stringmatch('SensorValid', names)) = 2; +ns(stringmatch('FYdotDiff', names)) = 4; +ns(stringmatch('FcloseSlow', names)) = 2; +ns(stringmatch('Fclr', names)) = 3; +ns(stringmatch('BXdot', names)) = 8; +ns(stringmatch('BcloseFast', names)) = 2; +ns(stringmatch('Bclr', names)) = 3; +ns(stringmatch('BYdotDiff', names)) = 4; + +%ns = 2*ones(1,ss); + + +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'observed', onodes, 'eclass2', (1:ss)+ss); + +% make unif params +for i=1:2*ss + bnet.CPD{i} = tabular_CPD(bnet, i, 'CPT', 'unif'); +end + +i = stringmatch('LeftClr', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.99 0.01 0.01 0.99]); + +i = stringmatch('RightClr', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.99 0.01 0.01 0.99]); + +i = stringmatch('LatAct', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.166666666666667 0.975609756097561 0.111111111111111 0.961538461538462 0.00980392156862745 0.0380952380952381 0.00952380952380952 0.037037037037037 0.166666666666667 0.444444444444444 0.0048780487804878 0.0192307692307692 0.0384615384615385 0.888888888888889 0.0344827586206897 0.87719298245614 0.00819672131147541 0.032 0.008 0.03125 0.0384615384615385 0.137931034482759 0.00444444444444444 0.0175438596491228 0.166666666666667 0.975609756097561 0.111111111111111 0.961538461538462 0.166666666666667 0.8 0.04 0.454545454545455 0.166666666666667 0.444444444444444 0.0048780487804878 0.0192307692307692 0.0384615384615385 0.888888888888889 0.0344827586206897 0.87719298245614 0.0384615384615385 0.444444444444444 0.0222222222222222 0.3125 0.0384615384615385 0.137931034482759 0.00444444444444444 0.0175438596491228 0.166666666666667 0.975609756097561 0.111111111111111 0.961538461538462 0.166666666666667 0.8 0.04 0.454545454545455 0.166666666666667 0.444444444444444 0.0048780487804878 0.0192307692307692 0.0384615384615385 0.888888888888889 0.0344827586206897 0.87719298245614 0.0384615384615385 0.444444444444444 0.0222222222222222 0.3125 0.0384615384615385 0.137931034482759 0.00444444444444444 0.0175438596491228 0.666666666666667 0.0195121951219512 0.444444444444444 0.0192307692307692 0.980392156862745 0.952380952380952 0.952380952380952 0.925925925925926 0.666666666666667 0.444444444444444 0.0195121951219512 0.0192307692307692 0.923076923076923 0.106666666666667 0.827586206896552 0.105263157894737 0.983606557377049 0.96 0.96 0.9375 0.923076923076923 0.827586206896552 0.106666666666667 0.105263157894737 0.666666666666667 0.0195121951219512 0.444444444444444 0.0192307692307692 0.666666666666667 0.16 0.16 0.0909090909090909 0.666666666666667 0.444444444444444 0.0195121951219512 0.0192307692307692 0.923076923076923 0.106666666666667 0.827586206896552 0.105263157894737 0.923076923076923 0.533333333333333 0.533333333333333 0.375 0.923076923076923 0.827586206896552 0.106666666666667 0.105263157894737 0.666666666666667 0.0195121951219512 0.444444444444444 0.0192307692307692 0.666666666666667 0.16 0.16 0.0909090909090909 0.666666666666667 0.444444444444444 0.0195121951219512 0.0192307692307692 0.923076923076923 0.106666666666667 0.827586206896552 0.105263157894737 0.923076923076923 0.533333333333333 0.533333333333333 0.375 0.923076923076923 0.827586206896552 0.106666666666667 0.105263157894737 0.166666666666667 0.0048780487804878 0.444444444444444 0.0192307692307692 0.00980392156862745 0.00952380952380952 0.0380952380952381 0.037037037037037 0.166666666666667 0.111111111111111 0.975609756097561 0.961538461538462 0.0384615384615385 0.00444444444444444 0.137931034482759 0.0175438596491228 0.00819672131147541 0.008 0.032 0.03125 0.0384615384615385 0.0344827586206897 0.888888888888889 0.87719298245614 0.166666666666667 0.0048780487804878 0.444444444444444 0.0192307692307692 0.166666666666667 0.04 0.8 0.454545454545455 0.166666666666667 0.111111111111111 0.975609756097561 0.961538461538462 0.0384615384615385 0.00444444444444444 0.137931034482759 0.0175438596491228 0.0384615384615385 0.0222222222222222 0.444444444444444 0.3125 0.0384615384615385 0.0344827586206897 0.888888888888889 0.87719298245614 0.166666666666667 0.0048780487804878 0.444444444444444 0.0192307692307692 0.166666666666667 0.04 0.8 0.454545454545455 0.166666666666667 0.111111111111111 0.975609756097561 0.961538461538462 0.0384615384615385 0.00444444444444444 0.137931034482759 0.0175438596491228 0.0384615384615385 0.0222222222222222 0.444444444444444 0.3125 0.0384615384615385 0.0344827586206897 0.888888888888889 0.87719298245614]); + +i = stringmatch('Xdot', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.497512437810945 0.115207373271889 0.0564971751412429 0.0290697674418605 0.075187969924812 0.0300751879699248 0.0298507462686567 0.0980392156862745 0.0392156862745098 0.043956043956044 0.0294117647058824 0.0283687943262411 0.0392156862745098 0.0392156862745098 0.0373134328358209 0.0300751879699248 0.0300751879699248 0.0232558139534884 0.0225988700564972 0.0184331797235023 0.0199004975124378 0.373134328358209 0.460829493087558 0.282485875706215 0.290697674418605 0.37593984962406 0.075187969924812 0.0373134328358209 0.490196078431373 0.0980392156862745 0.043956043956044 0.0294117647058824 0.0283687943262411 0.0392156862745098 0.0392156862745098 0.0746268656716418 0.037593984962406 0.0300751879699248 0.0232558139534884 0.0225988700564972 0.0184331797235023 0.0199004975124378 0.0497512437810945 0.345622119815668 0.564971751412429 0.581395348837209 0.37593984962406 0.37593984962406 0.0746268656716418 0.245098039215686 0.490196078431373 0.21978021978022 0.0735294117647059 0.0354609929078014 0.0490196078431373 0.0392156862745098 0.373134328358209 0.075187969924812 0.037593984962406 0.0232558139534884 0.0225988700564972 0.0184331797235023 0.0199004975124378 0.0199004975124378 0.0230414746543779 0.0282485875706215 0.0290697674418605 0.075187969924812 0.37593984962406 0.373134328358209 0.0490196078431373 0.245098039215686 0.54945054945055 0.735294117647059 0.709219858156028 0.245098039215686 0.0490196078431373 0.373134328358209 0.37593984962406 0.075187969924812 0.0290697674418605 0.0282485875706215 0.0230414746543779 0.0199004975124378 0.0199004975124378 0.0184331797235023 0.0225988700564972 0.0232558139534884 0.037593984962406 0.075187969924812 0.373134328358209 0.0392156862745098 0.0490196078431373 0.0549450549450549 0.0735294117647059 0.141843971631206 0.490196078431373 0.245098039215686 0.0746268656716418 0.37593984962406 0.37593984962406 0.581395348837209 0.564971751412429 0.345622119815668 0.0497512437810945 0.0199004975124378 0.0184331797235023 0.0225988700564972 0.0232558139534884 0.0300751879699248 0.037593984962406 0.0746268656716418 0.0392156862745098 0.0392156862745098 0.043956043956044 0.0294117647058824 0.0283687943262411 0.0980392156862745 0.490196078431373 0.0373134328358209 0.075187969924812 0.37593984962406 0.290697674418605 0.282485875706215 0.460829493087558 0.373134328358209 0.0199004975124378 0.0184331797235023 0.0225988700564972 0.0232558139534884 0.0300751879699248 0.0300751879699248 0.0373134328358209 0.0392156862745098 0.0392156862745098 0.043956043956044 0.0294117647058824 0.0283687943262411 0.0392156862745098 0.0980392156862745 0.0298507462686567 0.0300751879699248 0.075187969924812 0.0290697674418605 0.0564971751412429 0.115207373271889 0.497512437810945]); + +i = stringmatch('InLane', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.5 0.3 0.5 0.3 0.5 0.3 0.9 0.01 0.5 0.3 0.5 0.3 0.5 0.3 0.5 0.7 0.5 0.7 0.5 0.7 0.1 0.99 0.5 0.7 0.5 0.7 0.5 0.7]); + +i = stringmatch('FwdAct', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.4 0.4 0.4 0.949050949050949 0.949050949050949 0.949050949050949 0.4 0.4 0.4 0.6 0.1 0.1 0.949050949050949 0.949050949050949 0.949050949050949 0.05 0.05 0.05 0.4 0.4 0.4 0.949050949050949 0.949050949050949 0.949050949050949 0.4 0.4 0.4 0.4 0.4 0.4 0.04995004995005 0.04995004995005 0.04995004995005 0.4 0.4 0.4 0.3 0.8 0.3 0.04995004995005 0.04995004995005 0.04995004995005 0.7 0.7 0.7 0.4 0.4 0.4 0.04995004995005 0.04995004995005 0.04995004995005 0.4 0.4 0.4 0.2 0.2 0.2 0.000999000999000999 0.000999000999000999 0.000999000999000999 0.2 0.2 0.2 0.1 0.1 0.6 0.000999000999000999 0.000999000999000999 0.000999000999000999 0.25 0.25 0.25 0.2 0.2 0.2 0.000999000999000999 0.000999000999000999 0.000999000999000999 0.2 0.2 0.2]); + +i = stringmatch('Ydot', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.999880014398272 0.72463768115942 0.595238095238095 0.230414746543779 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.595238095238095 0.230414746543779 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.230414746543779 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.0144927536231884 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 9.99880014398272e-005 0.144927536231884 0.297619047619048 0.460829493087558 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.297619047619048 0.460829493087558 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.460829493087558 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.0144927536231884 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 0.0144927536231884 0.0595238095238095 0.230414746543779 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.0595238095238095 0.230414746543779 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.230414746543779 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.0144927536231884 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 9.99880014398272e-006 0.0144927536231884 0.00595238095238095 0.0460829493087558 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.0460829493087558 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.0460829493087558 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.0144927536231884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0144927536231884 0.00595238095238095 0.00460829493087558 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.00460829493087558 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.00460829493087558 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.0144927536231884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0144927536231884 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00442477876106195 0.00460829493087558 0.00595238095238095 0.0144927536231884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0144927536231884 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00460829493087558 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00460829493087558 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0442477876106195 0.00460829493087558 0.00595238095238095 0.0144927536231884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0144927536231884 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0460829493087558 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0460829493087558 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.221238938053097 0.0460829493087558 0.00595238095238095 0.0144927536231884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0144927536231884 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.230414746543779 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.230414746543779 0.0595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.442477876106195 0.230414746543779 0.0595238095238095 0.0144927536231884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0144927536231884 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.460829493087558 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.460829493087558 0.297619047619048 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.221238938053097 0.460829493087558 0.297619047619048 0.144927536231884 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0144927536231884 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.230414746543779 0.00595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.230414746543779 0.595238095238095 0.00460829493087558 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.00442477876106195 0.0442477876106195 0.230414746543779 0.595238095238095 0.72463768115942]); + +i = stringmatch('Stopped', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]); + +i = stringmatch('EngStatus', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [0.9 1.0e-006 0.1 0.999999]); + +i = stringmatch('FBStatus', names)+ss; +bnet.CPD{i} = tabular_CPD(bnet, i, [1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0]); + +i = stringmatch('SensorValid', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [1.0e-004 0.9999]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [1.0e-004 0.9999]); + +i = stringmatch('FYdotDiff', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.908182726364545 0.238095238095238 0.000908182726364545 0.476190476190476 9.08182726364545e-005 0.238095238095238 0.0908182726364545 0.0476190476190476]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.908182726364545 0.238095238095238 0.000908182726364545 0.476190476190476 9.08182726364545e-005 0.238095238095238 0.0908182726364545 0.0476190476190476]); + +i = stringmatch('FcloseSlow', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [1.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [1.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0]); + +i = stringmatch('Fclr', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.000499750124937531 0.142857142857143 0.499750124937531 0.285714285714286 0.499750124937531 0.571428571428571]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.000499750124937531 0.142857142857143 0.499750124937531 0.285714285714286 0.499750124937531 0.571428571428571]); + +i = stringmatch('BXdot', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.00980392156862745 0.00980392156862745 0.00980392156862745 0.00980392156862745 0.0454545454545455 0.00980392156862745 0.0490196078431373 0.0490196078431373 0.0490196078431373 0.0490196078431373 0.136363636363636 0.0490196078431373 0.196078431372549 0.196078431372549 0.196078431372549 0.196078431372549 0.136363636363636 0.196078431372549 0.392156862745098 0.392156862745098 0.392156862745098 0.392156862745098 0.181818181818182 0.392156862745098 0.196078431372549 0.196078431372549 0.196078431372549 0.196078431372549 0.136363636363636 0.196078431372549 0.0490196078431373 0.0490196078431373 0.0490196078431373 0.0490196078431373 0.136363636363636 0.0490196078431373 0.00980392156862745 0.00980392156862745 0.00980392156862745 0.00980392156862745 0.0454545454545455 0.00980392156862745 0.0980392156862745 0.0980392156862745 0.0980392156862745 0.0980392156862745 0.181818181818182 0.0980392156862745]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.00980392156862745 0.00980392156862745 0.00980392156862745 0.00980392156862745 0.0454545454545455 0.00980392156862745 0.0490196078431373 0.0490196078431373 0.0490196078431373 0.0490196078431373 0.136363636363636 0.0490196078431373 0.196078431372549 0.196078431372549 0.196078431372549 0.196078431372549 0.136363636363636 0.196078431372549 0.392156862745098 0.392156862745098 0.392156862745098 0.392156862745098 0.181818181818182 0.392156862745098 0.196078431372549 0.196078431372549 0.196078431372549 0.196078431372549 0.136363636363636 0.196078431372549 0.0490196078431373 0.0490196078431373 0.0490196078431373 0.0490196078431373 0.136363636363636 0.0490196078431373 0.00980392156862745 0.00980392156862745 0.00980392156862745 0.00980392156862745 0.0454545454545455 0.00980392156862745 0.0980392156862745 0.0980392156862745 0.0980392156862745 0.0980392156862745 0.181818181818182 0.0980392156862745]); + +i = stringmatch('BcloseFast', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]); + +i = stringmatch('Bclr', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.142857142857143 0.285714285714286 0.571428571428571]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.142857142857143 0.285714285714286 0.571428571428571]); + +i = stringmatch('BYdotDiff', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.238095238095238 0.476190476190476 0.238095238095238 0.0476190476190476]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.238095238095238 0.476190476190476 0.238095238095238 0.0476190476190476]); + +i = stringmatch('LeftClrSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.909090909090909 0.0909090909090909 0.0909090909090909 0.909090909090909]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.909090909090909 0.0909090909090909 0.0909090909090909 0.909090909090909]); + +i = stringmatch('RightClrSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.909090909090909 0.0909090909090909 0.0909090909090909 0.909090909090909]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.909090909090909 0.0909090909090909 0.0909090909090909 0.909090909090909]); + +i = stringmatch('TurnSignalSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.75 0.000998003992015968 0.01 0.24 0.998003992015968 0.24 0.01 0.000998003992015968 0.75]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.75 0.000998003992015968 0.01 0.24 0.998003992015968 0.24 0.01 0.000998003992015968 0.75]); + +i = stringmatch('XdotSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.142857142857143 0.897666068222621 0.142857142857143 0.0824402308326463 0.142857142857143 0.00818330605564648 0.142857142857143 0.000818330605564648 0.142857142857143 0.000818330605564648 0.142857142857143 0.000824402308326463 0.142857142857143 0.000897666068222621 0.142857142857143 0.0897666068222621 0.142857142857143 0.824402308326463 0.142857142857143 0.0818330605564648 0.142857142857143 0.00818330605564648 0.142857142857143 0.000818330605564648 0.142857142857143 0.000824402308326463 0.142857142857143 0.000897666068222621 0.142857142857143 0.00897666068222621 0.142857142857143 0.0824402308326463 0.142857142857143 0.818330605564648 0.142857142857143 0.0818330605564648 0.142857142857143 0.00818330605564648 0.142857142857143 0.000824402308326463 0.142857142857143 0.000897666068222621 0.142857142857143 0.000897666068222621 0.142857142857143 0.00824402308326463 0.142857142857143 0.0818330605564648 0.142857142857143 0.818330605564648 0.142857142857143 0.0818330605564648 0.142857142857143 0.00824402308326463 0.142857142857143 0.000897666068222621 0.142857142857143 0.000897666068222621 0.142857142857143 0.000824402308326463 0.142857142857143 0.00818330605564648 0.142857142857143 0.0818330605564648 0.142857142857143 0.818330605564648 0.142857142857143 0.0824402308326463 0.142857142857143 0.00897666068222621 0.142857142857143 0.000897666068222621 0.142857142857143 0.000824402308326463 0.142857142857143 0.000818330605564648 0.142857142857143 0.00818330605564648 0.142857142857143 0.0818330605564648 0.142857142857143 0.824402308326463 0.142857142857143 0.0897666068222621 0.142857142857143 0.000897666068222621 0.142857142857143 0.000824402308326463 0.142857142857143 0.000818330605564648 0.142857142857143 0.000818330605564648 0.142857142857143 0.00818330605564648 0.142857142857143 0.0824402308326463 0.142857142857143 0.897666068222621]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.142857142857143 0.897666068222621 0.142857142857143 0.0824402308326463 0.142857142857143 0.00818330605564648 0.142857142857143 0.000818330605564648 0.142857142857143 0.000818330605564648 0.142857142857143 0.000824402308326463 0.142857142857143 0.000897666068222621 0.142857142857143 0.0897666068222621 0.142857142857143 0.824402308326463 0.142857142857143 0.0818330605564648 0.142857142857143 0.00818330605564648 0.142857142857143 0.000818330605564648 0.142857142857143 0.000824402308326463 0.142857142857143 0.000897666068222621 0.142857142857143 0.00897666068222621 0.142857142857143 0.0824402308326463 0.142857142857143 0.818330605564648 0.142857142857143 0.0818330605564648 0.142857142857143 0.00818330605564648 0.142857142857143 0.000824402308326463 0.142857142857143 0.000897666068222621 0.142857142857143 0.000897666068222621 0.142857142857143 0.00824402308326463 0.142857142857143 0.0818330605564648 0.142857142857143 0.818330605564648 0.142857142857143 0.0818330605564648 0.142857142857143 0.00824402308326463 0.142857142857143 0.000897666068222621 0.142857142857143 0.000897666068222621 0.142857142857143 0.000824402308326463 0.142857142857143 0.00818330605564648 0.142857142857143 0.0818330605564648 0.142857142857143 0.818330605564648 0.142857142857143 0.0824402308326463 0.142857142857143 0.00897666068222621 0.142857142857143 0.000897666068222621 0.142857142857143 0.000824402308326463 0.142857142857143 0.000818330605564648 0.142857142857143 0.00818330605564648 0.142857142857143 0.0818330605564648 0.142857142857143 0.824402308326463 0.142857142857143 0.0897666068222621 0.142857142857143 0.000897666068222621 0.142857142857143 0.000824402308326463 0.142857142857143 0.000818330605564648 0.142857142857143 0.000818330605564648 0.142857142857143 0.00818330605564648 0.142857142857143 0.0824402308326463 0.142857142857143 0.897666068222621]); + +i = stringmatch('YdotSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.0909090909090909 0.894454382826476 0.0909090909090909 0.0821692686935086 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.0894454382826476 0.0909090909090909 0.821692686935086 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.00894454382826476 0.0909090909090909 0.0821692686935086 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.00821692686935086 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 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0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0821692686935086 0.0909090909090909 0.00894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.821692686935086 0.0909090909090909 0.0894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0821692686935086 0.0909090909090909 0.894454382826476]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.0909090909090909 0.894454382826476 0.0909090909090909 0.0821692686935086 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.0894454382826476 0.0909090909090909 0.821692686935086 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.00894454382826476 0.0909090909090909 0.0821692686935086 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.00821692686935086 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.00821692686935086 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.815660685154976 0.0909090909090909 0.0821692686935086 0.0909090909090909 0.00894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0815660685154976 0.0909090909090909 0.821692686935086 0.0909090909090909 0.0894454382826476 0.0909090909090909 0.000894454382826476 0.0909090909090909 0.000821692686935086 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.000815660685154976 0.0909090909090909 0.00815660685154975 0.0909090909090909 0.0821692686935086 0.0909090909090909 0.894454382826476]); + +i = stringmatch('FYdotDiffSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.53191206429753 0.00806445109313635 0.0053191206429753 9.9930048965724e-005 0.265956032148765 0.00806445109313635 0.0053191206429753 9.9930048965724e-005 0.132978016074383 0.0806445109313634 0.0053191206429753 9.9930048965724e-005 0.053191206429753 0.806445109313635 0.053191206429753 9.9930048965724e-005 0.0053191206429753 0.0806445109313634 0.132978016074383 9.9930048965724e-005 0.0053191206429753 0.00806445109313635 0.265956032148765 9.9930048965724e-005 0.0053191206429753 0.00806445109313635 0.53191206429753 9.9930048965724e-005 5.3191206429753e-006 8.06445109313635e-006 5.3191206429753e-006 0.99930048965724]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.53191206429753 0.00806445109313635 0.0053191206429753 9.9930048965724e-005 0.265956032148765 0.00806445109313635 0.0053191206429753 9.9930048965724e-005 0.132978016074383 0.0806445109313634 0.0053191206429753 9.9930048965724e-005 0.053191206429753 0.806445109313635 0.053191206429753 9.9930048965724e-005 0.0053191206429753 0.0806445109313634 0.132978016074383 9.9930048965724e-005 0.0053191206429753 0.00806445109313635 0.265956032148765 9.9930048965724e-005 0.0053191206429753 0.00806445109313635 0.53191206429753 9.9930048965724e-005 5.3191206429753e-006 8.06445109313635e-006 5.3191206429753e-006 0.99930048965724]); + +i = stringmatch('FclrSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.472366556447804 0.0044762757385855 6.20655412115194e-005 0.472366556447804 0.044762757385855 6.20655412115194e-005 0.0472366556447804 0.44762757385855 0.000620655412115194 0.000472366556447804 0.44762757385855 0.00620655412115194 0.000472366556447804 0.044762757385855 0.0620655412115194 0.000472366556447804 0.0044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.472366556447804 0.0044762757385855 6.20655412115194e-005 0.472366556447804 0.044762757385855 6.20655412115194e-005 0.0472366556447804 0.44762757385855 0.000620655412115194 0.000472366556447804 0.44762757385855 0.00620655412115194 0.000472366556447804 0.044762757385855 0.0620655412115194 0.000472366556447804 0.0044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194]); + +i = stringmatch('BXdotSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.689655172413793 0.172413793103448 0.0546448087431694 0.00546448087431694 0.00546448087431694 0.00546448087431694 0.00574712643678161 0.00689655172413793 0.206896551724138 0.574712643678161 0.163934426229508 0.0546448087431694 0.00546448087431694 0.00546448087431694 0.00574712643678161 0.00689655172413793 0.0689655172413793 0.172413793103448 0.546448087431694 0.163934426229508 0.0546448087431694 0.00546448087431694 0.00574712643678161 0.00689655172413793 0.00689655172413793 0.0574712643678161 0.163934426229508 0.546448087431694 0.163934426229508 0.0546448087431694 0.00574712643678161 0.00689655172413793 0.00689655172413793 0.00574712643678161 0.0546448087431694 0.163934426229508 0.546448087431694 0.163934426229508 0.0574712643678161 0.00689655172413793 0.00689655172413793 0.00574712643678161 0.00546448087431694 0.0546448087431694 0.163934426229508 0.546448087431694 0.172413793103448 0.0689655172413793 0.00689655172413793 0.00574712643678161 0.00546448087431694 0.00546448087431694 0.0546448087431694 0.163934426229508 0.574712643678161 0.206896551724138 0.00689655172413793 0.00574712643678161 0.00546448087431694 0.00546448087431694 0.00546448087431694 0.0546448087431694 0.172413793103448 0.689655172413793]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.689655172413793 0.172413793103448 0.0546448087431694 0.00546448087431694 0.00546448087431694 0.00546448087431694 0.00574712643678161 0.00689655172413793 0.206896551724138 0.574712643678161 0.163934426229508 0.0546448087431694 0.00546448087431694 0.00546448087431694 0.00574712643678161 0.00689655172413793 0.0689655172413793 0.172413793103448 0.546448087431694 0.163934426229508 0.0546448087431694 0.00546448087431694 0.00574712643678161 0.00689655172413793 0.00689655172413793 0.0574712643678161 0.163934426229508 0.546448087431694 0.163934426229508 0.0546448087431694 0.00574712643678161 0.00689655172413793 0.00689655172413793 0.00574712643678161 0.0546448087431694 0.163934426229508 0.546448087431694 0.163934426229508 0.0574712643678161 0.00689655172413793 0.00689655172413793 0.00574712643678161 0.00546448087431694 0.0546448087431694 0.163934426229508 0.546448087431694 0.172413793103448 0.0689655172413793 0.00689655172413793 0.00574712643678161 0.00546448087431694 0.00546448087431694 0.0546448087431694 0.163934426229508 0.574712643678161 0.206896551724138 0.00689655172413793 0.00574712643678161 0.00546448087431694 0.00546448087431694 0.00546448087431694 0.0546448087431694 0.172413793103448 0.689655172413793]); + +i = stringmatch('BclrSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.472366556447804 0.0044762757385855 6.20655412115194e-005 0.472366556447804 0.044762757385855 6.20655412115194e-005 0.0472366556447804 0.44762757385855 0.000620655412115194 0.000472366556447804 0.44762757385855 0.00620655412115194 0.000472366556447804 0.044762757385855 0.0620655412115194 0.000472366556447804 0.0044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.472366556447804 0.0044762757385855 6.20655412115194e-005 0.472366556447804 0.044762757385855 6.20655412115194e-005 0.0472366556447804 0.44762757385855 0.000620655412115194 0.000472366556447804 0.44762757385855 0.00620655412115194 0.000472366556447804 0.044762757385855 0.0620655412115194 0.000472366556447804 0.0044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194 0.000472366556447804 0.00044762757385855 0.0620655412115194]); + +i = stringmatch('BYdotDiffSens', names); +%bnet.CPD{i} = tabular_CPD(bnet, i, [0.53191206429753 0.00806445109313635 0.0053191206429753 9.9930048965724e-005 0.265956032148765 0.00806445109313635 0.0053191206429753 9.9930048965724e-005 0.132978016074383 0.0806445109313634 0.0053191206429753 9.9930048965724e-005 0.053191206429753 0.806445109313635 0.053191206429753 9.9930048965724e-005 0.0053191206429753 0.0806445109313634 0.132978016074383 9.9930048965724e-005 0.0053191206429753 0.00806445109313635 0.265956032148765 9.9930048965724e-005 0.0053191206429753 0.00806445109313635 0.53191206429753 9.9930048965724e-005 5.3191206429753e-006 8.06445109313635e-006 5.3191206429753e-006 0.99930048965724]); +bnet.CPD{i+ss} = tabular_CPD(bnet, i+ss, [0.53191206429753 0.00806445109313635 0.0053191206429753 9.9930048965724e-005 0.265956032148765 0.00806445109313635 0.0053191206429753 9.9930048965724e-005 0.132978016074383 0.0806445109313634 0.0053191206429753 9.9930048965724e-005 0.053191206429753 0.806445109313635 0.053191206429753 9.9930048965724e-005 0.0053191206429753 0.0806445109313634 0.132978016074383 9.9930048965724e-005 0.0053191206429753 0.00806445109313635 0.265956032148765 9.9930048965724e-005 0.0053191206429753 0.00806445109313635 0.53191206429753 9.9930048965724e-005 5.3191206429753e-006 8.06445109313635e-006 5.3191206429753e-006 0.99930048965724]); +%BIF2BNT added a bunch of zeros at the end of this cpd. Hopefully the only occurence of this bug! 0 0 0 0 0 0 0 0]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_orig_water_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,121 @@ +function dbn = mk_orig_water_dbn +% Converted by Frank Hutter: +% Provided in Phrog format by Xavier Boyen. +% Manually converted into BIF. +% Converted to BNT from BIF by Web-based bif2bnt (2004-01-30T05:28:10) +% Manually converted into function creating DBN. +% Manually changed the node numbering s.t. A-B-C-D-E-F-G-H from the BK paper correspond to 1-2-3-4-5-6-7-8 + +node = struct('C_NI_12_ANT', 1, ... + 'CKNI_12_ANT', 2, ... + 'CBODD_12_ANT', 3, ... + 'CNOD_12_ANT', 4, ... + 'CBODN_12_ANT', 5, ... + 'CNON_12_ANT', 6, ... + 'CKND_12_ANT', 7, ... + 'CKNN_12_ANT', 8, ... + 'C_NI_12_OBS', 9, ... + 'CKNI_12_OBS', 10, ... + 'CBODD_12_OBS', 11, ... + 'CNOD_12_OBS', 12, ... + 'CBODN_12_OBS', 13, ... + 'CNON_12_OBS', 14, ... + 'CKND_12_OBS', 15, ... + 'CKNN_12_OBS', 16, ... + 'C_NI_12_ULT', 17, ... + 'CKNI_12_ULT', 18, ... + 'CBODD_12_ULT', 19, ... + 'CNOD_12_ULT', 20, ... + 'CBODN_12_ULT', 21, ... + 'CNON_12_ULT', 22, ... + 'CKND_12_ULT', 23, ... + 'CKNN_12_ULT', 24); + +adjacency = zeros(24); +adjacency([node.C_NI_12_ANT], node.C_NI_12_OBS) = 1; +adjacency([node.CKNI_12_ANT], node.CKNI_12_OBS) = 1; +adjacency([node.CBODD_12_ANT], node.CBODD_12_OBS) = 1; +adjacency([node.CKND_12_ANT], node.CKND_12_OBS) = 1; +adjacency([node.CNOD_12_ANT], node.CNOD_12_OBS) = 1; +adjacency([node.CBODN_12_ANT], node.CBODN_12_OBS) = 1; +adjacency([node.CKNN_12_ANT], node.CKNN_12_OBS) = 1; +adjacency([node.CNON_12_ANT], node.CNON_12_OBS) = 1; +adjacency([node.C_NI_12_ANT], node.C_NI_12_ULT) = 1; +adjacency([node.CKNI_12_ANT], node.CKNI_12_ULT) = 1; +adjacency([node.CBODN_12_ANT node.CNOD_12_ANT node.CBODD_12_ANT node.CKNI_12_ANT node.C_NI_12_ANT], node.CBODD_12_ULT) = 1; +adjacency([node.CKNN_12_ANT node.CKND_12_ANT node.CKNI_12_ANT], node.CKND_12_ULT) = 1; +adjacency([node.CNON_12_ANT node.CNOD_12_ANT node.CBODD_12_ANT], node.CNOD_12_ULT) = 1; +adjacency([node.CNON_12_ANT node.CBODN_12_ANT node.CBODD_12_ANT], node.CBODN_12_ULT) = 1; +adjacency([node.CKNN_12_ANT node.CKND_12_ANT], node.CKNN_12_ULT) = 1; +adjacency([node.CNON_12_ANT node.CKNN_12_ANT node.CBODN_12_ANT node.CNOD_12_ANT], node.CNON_12_ULT) = 1; + +ss = 16; +dnodes = 1:ss; +ant = 1:8; +onodes = 9:16; +ult = 17:24; +intra = adjacency(1:ss, 1:ss); +inter_real = adjacency(ant, ult); +inter = zeros(ss); +inter(ant,1:length(ult)) = inter_real; + +eclass1 = 1:16; +eclass2 = [17:24 9:16]; + +value = {{'zz3num'; 'zz4num'; 'zz5num'; 'zz6num'}, ... + {'zz20mgl'; 'zz30mgl'; 'zz40mgl'}, ... + {'zz15mgl'; 'zz20mgl'; 'zz25mgl'; 'zz30mgl'}, ... + {'zz05mgl'; 'zz1mgl'; 'zz2mgl'; 'zz4mgl'}, ... + {'zz5mgl'; 'zz10mgl'; 'zz15mgl'; 'zz20mgl'}, ... + {'zz2mgl'; 'zz4mgl'; 'zz6mgl'; 'zz10mgl'}, ... + {'zz2mgl'; 'zz4mgl'; 'zz6mgl'}, ... + {'zz05mgl'; 'zz1mgl'; 'zz2mgl'}, ... + {'zz3num'; 'zz4num'; 'zz5num'; 'zz6num'}, ... + {'zz20mgl'; 'zz30mgl'; 'zz40mgl'}, ... + {'zz15mgl'; 'zz20mgl'; 'zz25mgl'; 'zz30mgl'}, ... + {'zz05mgl'; 'zz1mgl'; 'zz2mgl'; 'zz4mgl'}, ... + {'zz5mgl'; 'zz10mgl'; 'zz15mgl'; 'zz20mgl'}, ... + {'zz2mgl'; 'zz4mgl'; 'zz6mgl'; 'zz10mgl'}, ... + {'zz2mgl'; 'zz4mgl'; 'zz6mgl'}, ... + {'zz05mgl'; 'zz1mgl'; 'zz2mgl'}, ... + {'zz3num'; 'zz4num'; 'zz5num'; 'zz6num'}, ... + {'zz20mgl'; 'zz30mgl'; 'zz40mgl'}, ... + {'zz15mgl'; 'zz20mgl'; 'zz25mgl'; 'zz30mgl'}, ... + {'zz05mgl'; 'zz1mgl'; 'zz2mgl'; 'zz4mgl'}, ... + {'zz5mgl'; 'zz10mgl'; 'zz15mgl'; 'zz20mgl'}, ... + {'zz2mgl'; 'zz4mgl'; 'zz6mgl'; 'zz10mgl'}, ... + {'zz2mgl'; 'zz4mgl'; 'zz6mgl'}, ... + {'zz05mgl'; 'zz1mgl'; 'zz2mgl'}}; + +ns = zeros(1,24); +for i=1:24 + ns(i) = length(value{i}); +end + +dbn = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ... + 'observed', onodes); + +dbn.CPD{node.C_NI_12_ANT} = tabular_CPD(dbn, node.C_NI_12_ANT, 1/ns(1) * ones(1,ns(1))); +dbn.CPD{node.CKNI_12_ANT} = tabular_CPD(dbn, node.CKNI_12_ANT, 1/ns(2) * ones(1,ns(2))); +dbn.CPD{node.CBODD_12_ANT} = tabular_CPD(dbn, node.CBODD_12_ANT, 1/ns(3) * ones(1,ns(3))); +dbn.CPD{node.CNOD_12_ANT} = tabular_CPD(dbn, node.CNOD_12_ANT, 1/ns(4) * ones(1,ns(4))); +dbn.CPD{node.CBODN_12_ANT} = tabular_CPD(dbn, node.CBODN_12_ANT, 1/ns(5) * ones(1,ns(5))); +dbn.CPD{node.CNON_12_ANT} = tabular_CPD(dbn, node.CNON_12_ANT, 1/ns(6) * ones(1,ns(6))); +dbn.CPD{node.CKND_12_ANT} = tabular_CPD(dbn, node.CKND_12_ANT, 1/ns(7) * ones(1,ns(7))); +dbn.CPD{node.CKNN_12_ANT} = tabular_CPD(dbn, node.CKNN_12_ANT, 1/ns(8) * ones(1,ns(8))); +dbn.CPD{node.C_NI_12_OBS} = tabular_CPD(dbn, node.C_NI_12_OBS, [0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7]); +dbn.CPD{node.CKNI_12_OBS} = tabular_CPD(dbn, node.CKNI_12_OBS, [0.8 0.1 0.1 0.1 0.8 0.1 0.1 0.1 0.8]); +dbn.CPD{node.CBODD_12_OBS} = tabular_CPD(dbn, node.CBODD_12_OBS, [0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7]); +dbn.CPD{node.CKND_12_OBS} = tabular_CPD(dbn, node.CKND_12_OBS, [0.8 0.1 0.1 0.1 0.8 0.1 0.1 0.1 0.8]); +dbn.CPD{node.CNOD_12_OBS} = tabular_CPD(dbn, node.CNOD_12_OBS, [0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7]); +dbn.CPD{node.CBODN_12_OBS} = tabular_CPD(dbn, node.CBODN_12_OBS, [0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7]); +dbn.CPD{node.CKNN_12_OBS} = tabular_CPD(dbn, node.CKNN_12_OBS, [0.8 0.1 0.1 0.1 0.8 0.1 0.1 0.1 0.8]); +dbn.CPD{node.CNON_12_OBS} = tabular_CPD(dbn, node.CNON_12_OBS, [0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.7]); +dbn.CPD{node.C_NI_12_ULT} = tabular_CPD(dbn, node.C_NI_12_ULT, [0.5 0.2 0.1 0 0.4 0.55 0.3 0.15 0.1 0.2 0.5 0.25 0 0.05 0.1 0.6]); +dbn.CPD{node.CKNI_12_ULT} = tabular_CPD(dbn, node.CKNI_12_ULT, [0.48 0.2 0.04 0.48 0.6 0.48 0.04 0.2 0.48]); +dbn.CPD{node.CBODD_12_ULT} = tabular_CPD(dbn, node.CBODD_12_ULT, [1 1 0.9791 0.9473 0.9949 0.9473 0.8997 0.8521 0.9473 0.8838 0.8203 0.7568 0.0903 0.0585 0.0268 0 0.0426 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0.9951 0.9634 1 0.9634 0.9158 0.8681 0.9634 0.8999 0.8364 0.7729 0.109 0.0773 0.0455 0.0138 0.0614 0.0138 0 0 0.0138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0.9762 1 0.9762 0.9286 0.881 0.9762 0.9127 0.8493 0.7858 0.124 0.0923 0.0605 0.0288 0.0764 0.0288 0 0 0.0288 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0.9848 1 0.9848 0.9372 0.8896 0.9848 0.9213 0.8578 0.7943 0.134 0.1023 0.0705 0.0388 0.0864 0.0388 0 0 0.0388 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.9791 0.9473 0.9156 0.9632 0.9156 0.8679 0.8203 0.9156 0.8521 0.7886 0.7251 0.0585 0.0268 0 0 0.0109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.9951 0.9634 0.9316 0.9793 0.9316 0.884 0.8364 0.9316 0.8681 0.8046 0.7412 0.0773 0.0455 0.0138 0 0.0296 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0.9762 0.9445 0.9921 0.9445 0.8969 0.8493 0.9445 0.881 0.8175 0.754 0.0923 0.0605 0.0288 0 0.0446 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0.9848 0.9531 1 0.9531 0.9054 0.8578 0.9531 0.8896 0.8261 0.7626 0.1023 0.0705 0.0388 0.007 0.0546 0.007 0 0 0.007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9791 0.9473 0.9156 0.8838 0.9314 0.8838 0.8362 0.7886 0.8838 0.8203 0.7568 0.6933 0.0268 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9951 0.9634 0.9316 0.8999 0.9475 0.8999 0.8523 0.8046 0.8999 0.8364 0.7729 0.7094 0.0455 0.0138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.9762 0.9445 0.9127 0.9604 0.9127 0.8651 0.8175 0.9127 0.8493 0.7858 0.7223 0.0605 0.0288 0 0 0.0129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0.9848 0.9531 0.9213 0.9689 0.9213 0.8737 0.8261 0.9213 0.8578 0.7943 0.7308 0.0705 0.0388 0.007 0 0.0229 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9473 0.9156 0.8838 0.8521 0.8997 0.8521 0.8045 0.7568 0.8521 0.7886 0.7251 0.6616 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9634 0.9316 0.8999 0.8681 0.9158 0.8681 0.8205 0.7729 0.8681 0.8046 0.7412 0.6777 0.0138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9762 0.9445 0.9127 0.881 0.9286 0.881 0.8334 0.7858 0.881 0.8175 0.754 0.6905 0.0288 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9848 0.9531 0.9213 0.8896 0.9372 0.8896 0.842 0.7943 0.8896 0.8261 0.7626 0.6991 0.0388 0.007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0209 0.0527 0.0051 0.0527 0.1003 0.1479 0.0527 0.1162 0.1797 0.2432 0.9097 0.9415 0.9732 0.995 0.9574 0.995 0.9474 0.8998 0.995 0.9315 0.868 0.8045 0.1362 0.1045 0.0727 0.041 0.0886 0.041 0 0 0.041 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0049 0.0366 0 0.0366 0.0842 0.1319 0.0366 0.1001 0.1636 0.2271 0.891 0.9227 0.9545 0.9862 0.9386 0.9862 0.9662 0.9185 0.9862 0.9503 0.8868 0.8233 0.157 0.1253 0.0935 0.0618 0.1094 0.0618 0.0142 0 0.0618 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0238 0 0.0238 0.0714 0.119 0.0238 0.0873 0.1507 0.2142 0.876 0.9077 0.9395 0.9712 0.9236 0.9712 0.9812 0.9335 0.9712 0.9653 0.9018 0.8383 0.1737 0.142 0.1102 0.0785 0.1261 0.0785 0.0308 0 0.0785 0.015 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0152 0 0.0152 0.0628 0.1104 0.0152 0.0787 0.1422 0.2057 0.866 0.8977 0.9295 0.9612 0.9136 0.9612 0.9912 0.9435 0.9612 0.9753 0.9118 0.8483 0.1848 0.1531 0.1213 0.0896 0.1372 0.0896 0.042 0 0.0896 0.0261 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0209 0.0527 0.0844 0.0368 0.0844 0.1321 0.1797 0.0844 0.1479 0.2114 0.2749 0.9415 0.9732 0.995 0.9633 0.9891 0.9633 0.9157 0.868 0.9633 0.8998 0.8363 0.7728 0.1045 0.0727 0.041 0.0092 0.0568 0.0092 0 0 0.0092 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0049 0.0366 0.0684 0.0207 0.0684 0.116 0.1636 0.0684 0.1319 0.1954 0.2588 0.9227 0.9545 0.9862 0.982 0.9704 0.982 0.9344 0.8868 0.982 0.9185 0.855 0.7916 0.1253 0.0935 0.0618 0.03 0.0777 0.03 0 0 0.03 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0238 0.0555 0.0079 0.0555 0.1031 0.1507 0.0555 0.119 0.1825 0.246 0.9077 0.9395 0.9712 0.997 0.9554 0.997 0.9494 0.9018 0.997 0.9335 0.87 0.8066 0.142 0.1102 0.0785 0.0467 0.0943 0.0467 0 0 0.0467 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0152 0.0469 0 0.0469 0.0946 0.1422 0.0469 0.1104 0.1739 0.2374 0.8977 0.9295 0.9612 0.993 0.9454 0.993 0.9594 0.9118 0.993 0.9435 0.88 0.8166 0.1531 0.1213 0.0896 0.0578 0.1054 0.0578 0.0102 0 0.0578 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0209 0.0527 0.0844 0.1162 0.0686 0.1162 0.1638 0.2114 0.1162 0.1797 0.2432 0.3067 0.9732 0.995 0.9633 0.9315 0.9792 0.9315 0.8839 0.8363 0.9315 0.868 0.8045 0.7411 0.0727 0.041 0.0092 0 0.0251 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0049 0.0366 0.0684 0.1001 0.0525 0.1001 0.1477 0.1954 0.1001 0.1636 0.2271 0.2906 0.9545 0.9862 0.982 0.9503 0.9979 0.9503 0.9027 0.855 0.9503 0.8868 0.8233 0.7598 0.0935 0.0618 0.03 0 0.0459 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0238 0.0555 0.0873 0.0396 0.0873 0.1349 0.1825 0.0873 0.1507 0.2142 0.2777 0.9395 0.9712 0.997 0.9653 0.9871 0.9653 0.9177 0.87 0.9653 0.9018 0.8383 0.7748 0.1102 0.0785 0.0467 0.015 0.0626 0.015 0 0 0.015 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0152 0.0469 0.0787 0.0311 0.0787 0.1263 0.1739 0.0787 0.1422 0.2057 0.2692 0.9295 0.9612 0.993 0.9753 0.9771 0.9753 0.9277 0.88 0.9753 0.9118 0.8483 0.7848 0.1213 0.0896 0.0578 0.0261 0.0737 0.0261 0 0 0.0261 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0527 0.0844 0.1162 0.1479 0.1003 0.1479 0.1955 0.2432 0.1479 0.2114 0.2749 0.3384 0.995 0.9633 0.9315 0.8998 0.9474 0.8998 0.8522 0.8045 0.8998 0.8363 0.7728 0.7093 0.041 0.0092 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0366 0.0684 0.1001 0.1319 0.0842 0.1319 0.1795 0.2271 0.1319 0.1954 0.2588 0.3223 0.9862 0.982 0.9503 0.9185 0.9662 0.9185 0.8709 0.8233 0.9185 0.855 0.7916 0.7281 0.0618 0.03 0 0 0.0142 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0238 0.0555 0.0873 0.119 0.0714 0.119 0.1666 0.2142 0.119 0.1825 0.246 0.3095 0.9712 0.997 0.9653 0.9335 0.9812 0.9335 0.8859 0.8383 0.9335 0.87 0.8066 0.7431 0.0785 0.0467 0.015 0 0.0308 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0152 0.0469 0.0787 0.1104 0.0628 0.1104 0.158 0.2057 0.1104 0.1739 0.2374 0.3009 0.9612 0.993 0.9753 0.9435 0.9912 0.9435 0.8959 0.8483 0.9435 0.88 0.8166 0.7531 0.0896 0.0578 0.0261 0 0.042 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.005 0 0.005 0.0526 0.1002 0.005 0.0685 0.132 0.1955 0.8638 0.8955 0.9273 0.959 0.9114 0.959 0.9933 0.9457 0.959 0.9775 0.914 0.8505 0.1809 0.1491 0.1174 0.0856 0.1333 0.0856 0.038 0 0.0856 0.0221 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0338 0.0815 0 0.0497 0.1132 0.1767 0.843 0.8747 0.9065 0.9382 0.8906 0.9382 0.9858 0.9666 0.9382 0.9983 0.9348 0.8713 0.2034 0.1716 0.1399 0.1081 0.1558 0.1081 0.0605 0.0129 0.1081 0.0446 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0188 0.0665 0 0.0347 0.0982 0.1617 0.8263 0.858 0.8898 0.9215 0.8739 0.9215 0.9692 0.9832 0.9215 0.985 0.9515 0.888 0.2214 0.1896 0.1579 0.1261 0.1738 0.1261 0.0785 0.0309 0.1261 0.0626 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0088 0.0565 0 0.0247 0.0882 0.1517 0.8152 0.8469 0.8787 0.9104 0.8628 0.9104 0.958 0.9943 0.9104 0.9739 0.9626 0.8991 0.2334 0.2016 0.1699 0.1381 0.1858 0.1381 0.0905 0.0429 0.1381 0.0746 0.0112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.005 0.0367 0 0.0367 0.0843 0.132 0.0367 0.1002 0.1637 0.2272 0.8955 0.9273 0.959 0.9908 0.9432 0.9908 0.9616 0.914 0.9908 0.9457 0.8822 0.8187 0.1491 0.1174 0.0856 0.0539 0.1015 0.0539 0.0063 0 0.0539 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.018 0 0.018 0.0656 0.1132 0.018 0.0815 0.145 0.2084 0.8747 0.9065 0.9382 0.97 0.9223 0.97 0.9824 0.9348 0.97 0.9666 0.9031 0.8396 0.1716 0.1399 0.1081 0.0764 0.124 0.0764 0.0288 0 0.0764 0.0129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.003 0 0.003 0.0506 0.0982 0.003 0.0665 0.13 0.1934 0.858 0.8898 0.9215 0.9533 0.9057 0.9533 0.9991 0.9515 0.9533 0.9832 0.9197 0.8562 0.1896 0.1579 0.1261 0.0944 0.142 0.0944 0.0468 0 0.0944 0.0309 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0406 0.0882 0 0.0565 0.12 0.1834 0.8469 0.8787 0.9104 0.9422 0.8946 0.9422 0.9898 0.9626 0.9422 0.9943 0.9308 0.8673 0.2016 0.1699 0.1381 0.1064 0.154 0.1064 0.0588 0.0112 0.1064 0.0429 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.005 0.0367 0.0685 0.0208 0.0685 0.1161 0.1637 0.0685 0.132 0.1955 0.2589 0.9273 0.959 0.9908 0.9775 0.9749 0.9775 0.9298 0.8822 0.9775 0.914 0.8505 0.787 0.1174 0.0856 0.0539 0.0221 0.0698 0.0221 0 0 0.0221 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.018 0.0497 0.0021 0.0497 0.0973 0.145 0.0497 0.1132 0.1767 0.2402 0.9065 0.9382 0.97 0.9983 0.9541 0.9983 0.9507 0.9031 0.9983 0.9348 0.8713 0.8078 0.1399 0.1081 0.0764 0.0446 0.0923 0.0446 0 0 0.0446 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.003 0.0347 0 0.0347 0.0823 0.13 0.0347 0.0982 0.1617 0.2252 0.8898 0.9215 0.9533 0.985 0.9374 0.985 0.9673 0.9197 0.985 0.9515 0.888 0.8245 0.1579 0.1261 0.0944 0.0626 0.1103 0.0626 0.015 0 0.0626 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0247 0 0.0247 0.0723 0.12 0.0247 0.0882 0.1517 0.2152 0.8787 0.9104 0.9422 0.9739 0.9263 0.9739 0.9785 0.9308 0.9739 0.9626 0.8991 0.8356 0.1699 0.1381 0.1064 0.0746 0.1223 0.0746 0.027 0 0.0746 0.0112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.005 0.0367 0.0685 0.1002 0.0526 0.1002 0.1478 0.1955 0.1002 0.1637 0.2272 0.2907 0.959 0.9908 0.9775 0.9457 0.9933 0.9457 0.8981 0.8505 0.9457 0.8822 0.8187 0.7552 0.0856 0.0539 0.0221 0 0.038 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.018 0.0497 0.0815 0.0338 0.0815 0.1291 0.1767 0.0815 0.145 0.2084 0.2719 0.9382 0.97 0.9983 0.9666 0.9858 0.9666 0.9189 0.8713 0.9666 0.9031 0.8396 0.7761 0.1081 0.0764 0.0446 0.0129 0.0605 0.0129 0 0 0.0129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.003 0.0347 0.0665 0.0188 0.0665 0.1141 0.1617 0.0665 0.13 0.1934 0.2569 0.9215 0.9533 0.985 0.9832 0.9692 0.9832 0.9356 0.888 0.9832 0.9197 0.8562 0.7927 0.1261 0.0944 0.0626 0.0309 0.0785 0.0309 0 0 0.0309 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0247 0.0565 0.0088 0.0565 0.1041 0.1517 0.0565 0.12 0.1834 0.2469 0.9104 0.9422 0.9739 0.9943 0.958 0.9943 0.9467 0.8991 0.9943 0.9308 0.8673 0.8039 0.1381 0.1064 0.0746 0.0429 0.0905 0.0429 0 0 0.0429 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0067 0.0543 0 0.0225 0.086 0.1495 0.8191 0.8509 0.8826 0.9144 0.8667 0.9144 0.962 1 0.9144 0.9779 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0334 0 0.0017 0.0652 0.1287 0.7966 0.8284 0.8601 0.8919 0.8442 0.8919 0.9395 0.9871 0.8919 0.9554 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0168 0 0 0.0485 0.112 0.7786 0.8104 0.8421 0.8739 0.8262 0.8739 0.9215 0.9691 0.8739 0.9374 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0057 0 0 0.0374 0.1009 0.7666 0.7984 0.8301 0.8619 0.8142 0.8619 0.9095 0.9571 0.8619 0.9254 0.9888 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0384 0.086 0 0.0543 0.1178 0.1813 0.8509 0.8826 0.9144 0.9461 0.8985 0.9461 0.9937 1 0.9461 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0176 0.0652 0 0.0334 0.0969 0.1604 0.8284 0.8601 0.8919 0.9236 0.876 0.9236 0.9712 1 0.9236 0.9871 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0009 0.0485 0 0.0168 0.0803 0.1438 0.8104 0.8421 0.8739 0.9056 0.858 0.9056 0.9532 1 0.9056 0.9691 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0374 0 0.0057 0.0692 0.1327 0.7984 0.8301 0.8619 0.8936 0.846 0.8936 0.9412 0.9888 0.8936 0.9571 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0225 0 0.0225 0.0702 0.1178 0.0225 0.086 0.1495 0.213 0.8826 0.9144 0.9461 0.9779 0.9302 0.9779 1 1 0.9779 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0017 0 0.0017 0.0493 0.0969 0.0017 0.0652 0.1287 0.1922 0.8601 0.8919 0.9236 0.9554 0.9077 0.9554 1 1 0.9554 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0327 0.0803 0 0.0485 0.112 0.1755 0.8421 0.8739 0.9056 0.9374 0.8897 0.9374 0.985 1 0.9374 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0215 0.0692 0 0.0374 0.1009 0.1644 0.8301 0.8619 0.8936 0.9254 0.8777 0.9254 0.973 1 0.9254 0.9888 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0225 0.0543 0.0067 0.0543 0.1019 0.1495 0.0543 0.1178 0.1813 0.2448 0.9144 0.9461 0.9779 1 0.962 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0017 0.0334 0 0.0334 0.0811 0.1287 0.0334 0.0969 0.1604 0.2239 0.8919 0.9236 0.9554 0.9871 0.9395 0.9871 1 1 0.9871 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0168 0 0.0168 0.0644 0.112 0.0168 0.0803 0.1438 0.2073 0.8739 0.9056 0.9374 0.9691 0.9215 0.9691 1 1 0.9691 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0057 0 0.0057 0.0533 0.1009 0.0057 0.0692 0.1327 0.1961 0.8619 0.8936 0.9254 0.9571 0.9095 0.9571 1 1 0.9571 1 1 1]); +dbn.CPD{node.CKND_12_ULT} = tabular_CPD(dbn, node.CKND_12_ULT, [0.9524 0.9127 0.873 0 0 0 0 0 0 0.9444 0.9048 0.8651 0 0 0 0 0 0 0.9286 0.8889 0.8492 0 0 0 0 0 0 0.0476 0.0873 0.127 0.9921 0.9524 0.9127 0.0317 0 0 0.0556 0.0952 0.1349 0.9841 0.9444 0.9048 0.0238 0 0 0.0714 0.1111 0.1508 0.9683 0.9286 0.8889 0.0079 0 0 0 0 0 0.0079 0.0476 0.0873 0.9683 1 1 0 0 0 0.0159 0.0556 0.0952 0.9762 1 1 0 0 0 0.0317 0.0714 0.1111 0.9921 1 1]); +dbn.CPD{node.CNOD_12_ULT} = tabular_CPD(dbn, node.CNOD_12_ULT, [1 1 1 1 0.3675 0.4905 0.5862 0.6627 0 0 0 0 0 0 0 0 1 1 1 1 0.2405 0.3635 0.4592 0.5358 0 0 0 0 0 0 0 0 0.8893 0.9816 1 1 0.1135 0.2366 0.3322 0.4088 0 0 0 0 0 0 0 0 0.6354 0.7276 0.7994 0.8568 0 0 0.0783 0.1548 0 0 0 0 0 0 0 0 0 0 0 0 0.6325 0.5095 0.4138 0.3373 0.2972 0.3711 0.4285 0.4744 0 0 0 0 0 0 0 0 0.7595 0.6365 0.5408 0.4642 0.2338 0.3076 0.365 0.4109 0 0 0 0 0.1107 0.0184 0 0 0.8865 0.7634 0.6678 0.5912 0.1703 0.2441 0.3015 0.3474 0 0 0 0 0.3646 0.2724 0.2006 0.1432 0.9298 0.9913 0.9217 0.8452 0.0433 0.1171 0.1745 0.2204 0 0 0 0 0 0 0 0 0 0 0 0 0.7028 0.6289 0.5715 0.5256 0.2129 0.2539 0.2858 0.3113 0 0 0 0 0 0 0 0 0.7662 0.6924 0.635 0.5891 0.1812 0.2222 0.2541 0.2796 0 0 0 0 0 0 0 0 0.8297 0.7559 0.6985 0.6526 0.1494 0.1904 0.2223 0.2478 0 0 0 0 0.0702 0.0087 0 0 0.9567 0.8829 0.8255 0.7796 0.0859 0.1269 0.1588 0.1843 0 0 0 0 0 0 0 0 0 0 0 0 0.7871 0.7461 0.7142 0.6887 0 0 0 0 0 0 0 0 0 0 0 0 0.8188 0.7778 0.7459 0.7204 0 0 0 0 0 0 0 0 0 0 0 0 0.8506 0.8096 0.7777 0.7522 0 0 0 0 0 0 0 0 0 0 0 0 0.9141 0.8731 0.8412 0.8157]); +dbn.CPD{node.CBODN_12_ULT} = tabular_CPD(dbn, node.CBODN_12_ULT, [0.9557 0.9067 0.8577 0.8087 0.0406 0 0 0 0 0 0 0 0 0 0 0 0.9561 0.9071 0.8581 0.809 0.0412 0 0 0 0 0 0 0 0 0 0 0 0.9562 0.9072 0.8582 0.8092 0.0414 0 0 0 0 0 0 0 0 0 0 0 0.9564 0.9073 0.8583 0.8093 0.0416 0 0 0 0 0 0 0 0 0 0 0 0.0443 0.0933 0.1423 0.1913 0.9594 0.9916 0.9426 0.8936 0.1152 0.0662 0.0172 0 0 0 0 0 0.0439 0.0929 0.1419 0.191 0.9588 0.9922 0.9432 0.8942 0.116 0.067 0.018 0 0 0 0 0 0.0438 0.0928 0.1418 0.1908 0.9586 0.9924 0.9434 0.8944 0.1163 0.0673 0.0183 0 0 0 0 0 0.0436 0.0927 0.1417 0.1907 0.9584 0.9926 0.9436 0.8946 0.1166 0.0676 0.0185 0 0 0 0 0 0 0 0 0 0 0.0084 0.0574 0.1064 0.8848 0.9338 0.9828 0.9682 0.1835 0.1344 0.0854 0.0364 0 0 0 0 0 0.0078 0.0568 0.1058 0.884 0.933 0.982 0.969 0.1844 0.1354 0.0863 0.0373 0 0 0 0 0 0.0076 0.0566 0.1056 0.8837 0.9327 0.9817 0.9693 0.1847 0.1357 0.0867 0.0377 0 0 0 0 0 0.0074 0.0564 0.1054 0.8834 0.9324 0.9815 0.9695 0.185 0.136 0.087 0.038 0 0 0 0 0 0 0 0 0 0 0 0.0318 0.8165 0.8656 0.9146 0.9636 0 0 0 0 0 0 0 0 0 0 0 0.031 0.8156 0.8646 0.9137 0.9627 0 0 0 0 0 0 0 0 0 0 0 0.0307 0.8153 0.8643 0.9133 0.9623 0 0 0 0 0 0 0 0 0 0 0 0.0305 0.815 0.864 0.913 0.962]); +dbn.CPD{node.CKNN_12_ULT} = tabular_CPD(dbn, node.CKNN_12_ULT, [1 1 0.8234 0.4459 0.2499 0.0538 0 0 0 0 0 0.1766 0.5541 0.7501 0.9462 0.3627 0.2646 0.1666 0 0 0 0 0 0 0.6373 0.7354 0.8334]); +dbn.CPD{node.CNON_12_ULT} = tabular_CPD(dbn, node.CNON_12_ULT, [0.9555 0.9432 0.9187 0.8697 0.9618 0.9495 0.925 0.876 0.9662 0.954 0.9295 0.8804 0.9696 0.9573 0.9328 0.8838 0.9102 0.8979 0.8734 0.8244 0.9164 0.9042 0.8797 0.8306 0.9209 0.9086 0.8841 0.8351 0.9243 0.912 0.8875 0.8385 0.8648 0.8526 0.8281 0.779 0.8711 0.8588 0.8343 0.7853 0.8756 0.8633 0.8388 0.7898 0.8789 0.8667 0.8422 0.7931 0.0056 0 0 0 0.0125 0.0003 0 0 0.0175 0.0052 0 0 0.0212 0.009 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0445 0.0568 0.0813 0.1303 0.0382 0.0505 0.075 0.124 0.0338 0.046 0.0705 0.1196 0.0304 0.0427 0.0672 0.1162 0.0898 0.1021 0.1266 0.1756 0.0836 0.0958 0.1203 0.1694 0.0791 0.0914 0.1159 0.1649 0.0757 0.088 0.1125 0.1615 0.1352 0.1474 0.1719 0.221 0.1289 0.1412 0.1657 0.2147 0.1244 0.1367 0.1612 0.2102 0.1211 0.1333 0.1578 0.2069 0.9944 0.9933 0.9688 0.9198 0.9875 0.9997 0.9758 0.9267 0.9825 0.9948 0.9807 0.9317 0.9788 0.991 0.9845 0.9354 0.9602 0.948 0.9235 0.8744 0.9672 0.9549 0.9304 0.8814 0.9722 0.9599 0.9354 0.8864 0.9759 0.9636 0.9391 0.8901 0.9149 0.9026 0.8781 0.8291 0.9219 0.9096 0.8851 0.8361 0.9268 0.9146 0.8901 0.841 0.9306 0.9183 0.8938 0.8448 0.055 0.0427 0.0182 0 0.0622 0.05 0.0254 0 0.0674 0.0551 0.0306 0 0.0712 0.059 0.0345 0 0.0096 0 0 0 0.0169 0.0046 0 0 0.022 0.0098 0 0 0.0259 0.0137 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0067 0.0312 0.0802 0 0 0.0242 0.0733 0 0 0.0193 0.0683 0 0 0.0155 0.0646 0.0398 0.052 0.0765 0.1256 0.0328 0.0451 0.0696 0.1186 0.0278 0.0401 0.0646 0.1136 0.0241 0.0364 0.0609 0.1099 0.0851 0.0974 0.1219 0.1709 0.0781 0.0904 0.1149 0.1639 0.0732 0.0854 0.1099 0.159 0.0694 0.0817 0.1062 0.1552 0.945 0.9573 0.9818 0.9846 0.9378 0.95 0.9746 0.9882 0.9326 0.9449 0.9694 0.9908 0.9288 0.941 0.9655 0.9927 0.9904 0.9987 0.9864 0.9619 0.9831 0.9954 0.9901 0.9655 0.978 0.9902 0.9926 0.9681 0.9741 0.9863 0.9946 0.9701 0.9822 0.976 0.9638 0.9393 0.9858 0.9796 0.9674 0.9429 0.9884 0.9822 0.97 0.9455 0.9903 0.9842 0.9719 0.9474 0.0767 0.0706 0.0583 0.0338 0.0804 0.0743 0.062 0.0375 0.0831 0.0769 0.0647 0.0402 0.0851 0.0789 0.0667 0.0422 0.054 0.0479 0.0356 0.0111 0.0577 0.0516 0.0394 0.0149 0.0604 0.0543 0.042 0.0175 0.0624 0.0563 0.044 0.0195 0.0313 0.0252 0.013 0 0.0351 0.0289 0.0167 0 0.0377 0.0316 0.0194 0 0.0397 0.0336 0.0214 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0154 0 0 0 0.0118 0 0 0 0.0092 0 0 0 0.0073 0 0.0013 0.0136 0.0381 0 0 0.0099 0.0345 0 0 0.0074 0.0319 0 0 0.0054 0.0299 0.0178 0.024 0.0362 0.0607 0.0142 0.0204 0.0326 0.0571 0.0116 0.0178 0.03 0.0545 0.0097 0.0158 0.0281 0.0526 0.9233 0.9294 0.9417 0.9662 0.9196 0.9257 0.938 0.9625 0.9169 0.9231 0.9353 0.9598 0.9149 0.9211 0.9333 0.9578 0.946 0.9521 0.9644 0.9889 0.9423 0.9484 0.9606 0.9851 0.9396 0.9457 0.958 0.9825 0.9376 0.9437 0.956 0.9805 0.9687 0.9748 0.987 1 0.9649 0.9711 0.9833 1 0.9623 0.9684 0.9806 1 0.9603 0.9664 0.9786 1]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_ps_from_clqs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function mk_ps_from_clqs(dbn, T, cliques, dir) + +% Draw multiple copies of the DBN, +% and indicate the nodes in each clique by shading the nodes. +% Generate a series of color postscript files, +% or, if dir=[], displays them to the screen and pauses. + +if isempty(dir) + print_to_file = 0; +else + print_to_file = 1; +end + +if print_to_file, cd(dir), end +flip = 1; +clf; +[dummyx, dummyy, h] = draw_dbn(dbn.intra, dbn.inter, flip, T, -1); + +C = length(cliques); + +% nodes = []; +% for i=1:C +% cl = cliques{i}; +% nodes = [nodes cl(:)']; +% end +%nodes = unique(nodes); +ss = length(dbn.intra); +nodes = 1:(ss*T); + +for c=1:C + for i=cliques{c} + set(h(i,2), 'facecolor', 'r'); + end + rest = mysetdiff(nodes, cliques{c}); + for i=rest + set(h(i,2), 'facecolor', 'w'); + end + if print_to_file + print(gcf, '-depsc', sprintf('clq%d.ps', c)) + else + disp(['clique ' num2str(c) ' = ' num2str(cliques{c}) '; hit key for next']) + pause + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_uffe_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function bnet = mk_uffe_dbn() + +% Make the Uffe DBN from fig 3.4 p55 of my thesis + +ss = 4; +intra = zeros(ss,ss); +intra(1,[2 3])=1; +intra(2,3)=1; +intra(3,4)=1; +inter = zeros(ss,ss); +inter(1,1)=1; +inter(4,4)=1; +ns = 2*ones(1,ss); +bnet = mk_dbn(intra, inter, ns);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/mk_water_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function bnet = mk_water_dbn(discrete_obs, obs_leaves) +% MK_WATER_DBN +% bnet = mk_water_dbn(discrete_obs, obs_leaves) +% +% If discrete_obs = 1 (default), the leaves are binary, else scalar Gaussians +% If obs_leaves = 1, all the leaves are observed, otherwise rnd nodes are observed +% +% This is a model of the biological processes of a water purification plant, developed +% by Finn V. Jensen, Uffe Kjærulff, Kristian G. Olesen, and Jan Pedersen. +% See http://www-nt.cs.berkeley.edu/home/nir/public_html/Repository/water.htm +% See also Boyen and Koller, "Tractable Inference for Complex Stochastic Processes", UAI98 + +if nargin < 1, discrete_obs = 1; end +if nargin < 1, obs_leaves = 1; end + +ss = 12; +intra = zeros(ss); +intra(1,9) = 1; +intra(3,10) = 1; +intra(4,11) = 1; +intra(8,12) = 1; + +inter = zeros(ss); +inter(1, [1 3]) = 1; +inter(2, [2 3 7]) = 1; +inter(3, [3 4 5]) = 1; +inter(4, [3 4 6]) = 1; +inter(5, [3 5 6]) = 1; +inter(6, [4 5 6]) = 1; +inter(7, [7 8]) = 1; +inter(8, [6 7 8]) = 1; + +if obs_leaves + onodes = 9:12; % leaves +else + onodes = [1 5 9:12]; % throw in some other nodes +end +hnodes = 1:8; +if discrete_obs + ns = 2*ones(1 ,ss); + dnodes = 1:ss; +else + ns = [2*ones(1,length(hnodes)) 1*ones(length(onodes))]; + dnodes = hnodes; +end + +eclass1 = 1:12; +eclass2 = [13:20 9:12]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ... + 'observed', onodes); +if discrete_obs + for i=1:max(eclass2) + bnet.CPD{i} = tabular_CPD(bnet, i); + end +else + for i=hnodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i); + end + for i=onodes(:)' + bnet.CPD{i} = gaussian_CPD(bnet, i); + end + for i=hnodes(:)'+ss + bnet.CPD{i} = tabular_CPD(bnet, i); + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/orig_water1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +% Compare the speeds of various inference engines on the water DBN +seed = 0; +rand('state', seed); +randn('state', seed); + +%bnet = mk_water_dbn; +bnet = mk_orig_water_dbn; + +T = 3; +engine = {}; +%engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +%engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); +%engine{end+1} = jtree_dbn_inf_engine(bnet); +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); +engine{end+1} = cbk_inf_engine(bnet, 'clusters', {[1],[2],[3],[4],[5],[6],[7],[8]}); %ff +engine{end+1} = cbk_inf_engine(bnet, 'clusters', {[1 2],[3 4 5 6],[7 8]}); %manually designed marginally independent by BK +engine{end+1} = cbk_inf_engine(bnet, 'clusters', {[1:5], [3:7], [7:8]}); %manually designed conditionally independent by BK +engine{end+1} = cbk_inf_engine(bnet, 'clusters', {[1 3], [2 3 7], [3 5], [3 4 7], [6 7 8]}); %automatically found using TJTs offline +engine{end+1} = cbk_inf_engine(bnet, 'clusters', {[1 3 5], [2 3 5 7], [3 4 7], [4 6 7], [6 7 8]}); %automatically found using TJTs offline +engine{end+1} = cbk_inf_engine(bnet, 'clusters', {[1 3 4 5], [2 3 4 7 8], [4 6 7 8]}); %automatically found using TJTs offline + +% bk_inf_engine yields exactly the same results for the marginally independent cases. +%engine{end+1} = bk_inf_engine(bnet, 'clusters', 'ff'); +%engine{end+1} = bk_inf_engine(bnet, 'clusters', { [1 2], [3 4 5 6], [7 8] }); + + +inf_time = cmp_inference_dbn(bnet, engine, T, 'exact', 1) +learning_time = cmp_learning_dbn(bnet, engine, T, 'exact', 1) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/reveal1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +% Make a DBN with the following inter-connectivity matrix +% 1 +% / \ +% 2 3 +% \ / +% 4 +% | +% 5 +% where all arcs point down. In addition, there are persistence arcs from each node to itself. +% There are no intra-slice connections. +% Nodes have noisy-or CPDs. +% Node 1 turns on spontaneously due to its leaky source. +% This effect trickles down to the other nodes in the order shown. +% All the other nodes inhibit their leaks. +% None of the nodes inhibit the connection from themselves, so that once they are on, they remain +% on (persistence). +% +% This model was used in the experiments reported in +% - "Learning the structure of DBNs", Friedman, Murphy and Russell, UAI 1998. +% where the structure was learned even in the presence of missing data. +% In that paper, we used the structural EM algorithm. +% Here, we assume full observability and tabular CPDs for the learner, so we can use a much +% simpler learning algorithm. + +ss = 5; + +inter = eye(ss); +inter(1,[2 3]) = 1; +inter(2,4)=1; +inter(3,4)=1; +inter(4,5)=1; + +intra = zeros(ss); +ns = 2*ones(1,ss); + +bnet = mk_dbn(intra, inter, ns); + +% All nodes start out off +for i=1:ss + bnet.CPD{i} = tabular_CPD(bnet, i, [1.0 0.0]'); +end + +% The following params correspond to Fig 4a in the UAI 98 paper +% The first arg is the leak inhibition prob. +% The vector contains the inhib probs from the parents in the previous slice; +% the last element is self, which is never inhibited. +bnet.CPD{1+ss} = noisyor_CPD(bnet, 1+ss, 0.8, 0); +bnet.CPD{2+ss} = noisyor_CPD(bnet, 2+ss, 1, [0.9 0]); +bnet.CPD{3+ss} = noisyor_CPD(bnet, 3+ss, 1, [0.8 0]); +bnet.CPD{4+ss} = noisyor_CPD(bnet, 4+ss, 1, [0.7 0.6 0]); +bnet.CPD{5+ss} = noisyor_CPD(bnet, 5+ss, 1, [0.5 0]); + + +% Generate some training data + +nseqs = 20; +seqs = cell(1,nseqs); +T = 30; +for i=1:nseqs + seqs{i} = sample_dbn(bnet, T); +end + +max_fan_in = 3; % let's cheat a little here + +% computing num. incorrect edges as a fn of the size of the training set +%sz = [5 10 15 20]; +sz = [5 10]; +h = zeros(1, length(sz)); +for i=1:length(sz) + inter2 = learn_struct_dbn_reveal(seqs(1:sz(i)), ns, max_fan_in); + h(i) = sum(abs(inter(:)-inter2(:))); % hamming distance +end +h
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/scg_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +% Test whether stable conditional Gaussian inference works +% Make a linear dynamical system +% X1 -> X2 +% | | +% v v +% Y1 Y2 + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +X = 2; % size of hidden state +Y = 2; % size of observable state + +ns = [X Y]; +bnet = mk_dbn(intra, inter, ns, 'discrete', [], 'observed', 2); + +x0 = rand(X,1); +V0 = eye(X); +C0 = rand(Y,X); +R0 = eye(Y); +A0 = rand(X,X); +Q0 = eye(X); + +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0); + + +T = 5; % fixed length sequences + +engine = {}; +engine{end+1} = kalman_inf_engine(bnet); +engine{end+1} = scg_unrolled_dbn_inf_engine(bnet, T); +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); + +inf_time = cmp_inference_dbn(bnet, engine, T, 'check_ll', 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/skf_data_assoc_gmux.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,139 @@ +% We consider a switching Kalman filter of the kind studied +% by Zoubin Ghahramani, i.e., where the switch node determines +% which of the hidden chains we get to observe (data association). +% e.g., for n=2 chains +% +% X1 -> X1 +% | X2 -> X2 +% \ | +% v +% Y +% ^ +% | +% S +% +% Y is a gmux (multiplexer) node, where S switches in one of the parents. +% We differ from Zoubin by not connecting the S nodes over time (which +% doesn't make sense for data association). +% Indeed, we assume the S nodes are always observed. +% +% +% We will track 2 objects (points) moving in the plane, as in BNT/Kalman/tracking_demo. +% We will alternate between observing them. + +nobj = 2; +N = nobj+2; +Xs = 1:nobj; +S = nobj+1; +Y = nobj+2; + +intra = zeros(N,N); +inter = zeros(N,N); +intra([Xs S], Y) =1; +for i=1:nobj + inter(Xs(i), Xs(i))=1; +end + +Xsz = 4; % state space = (x y xdot ydot) +Ysz = 2; +ns = zeros(1,N); +ns(Xs) = Xsz; +ns(Y) = Ysz; +ns(S) = n; + +bnet = mk_dbn(intra, inter, ns, 'discrete', S, 'observed', [S Y]); + +% For each object, we have +% X(t+1) = F X(t) + noise(Q) +% Y(t) = H X(t) + noise(R) +F = [1 0 1 0; 0 1 0 1; 0 0 1 0; 0 0 0 1]; +H = [1 0 0 0; 0 1 0 0]; +Q = 1e-3*eye(Xsz); +%R = 1e-3*eye(Ysz); +R = eye(Ysz); + +% We initialise object 1 moving to the right, and object 2 moving to the left +% (Here, we assume nobj=2) +init_state{1} = [10 10 1 0]'; +init_state{2} = [10 -10 -1 0]'; + +for i=1:nobj + bnet.CPD{Xs(i)} = gaussian_CPD(bnet, Xs(i), 'mean', init_state{i}, 'cov', 1e-4*eye(Xsz)); +end +bnet.CPD{S} = root_CPD(bnet, S); % always observed +bnet.CPD{Y} = gmux_CPD(bnet, Y, 'cov', repmat(R, [1 1 nobj]), 'weights', repmat(H, [1 1 nobj])); +% slice 2 +eclass = bnet.equiv_class; +for i=1:nobj + bnet.CPD{eclass(Xs(i), 2)} = gaussian_CPD(bnet, Xs(i)+N, 'mean', zeros(Xsz,1), 'cov', Q, 'weights', F); +end + +% Observe objects at random +T = 10; +evidence = cell(N, T); +data_assoc = sample_discrete(normalise(ones(1,nobj)), 1, T); +evidence(S,:) = num2cell(data_assoc); +evidence = sample_dbn(bnet, 'evidence', evidence); + +% plot the data +true_state = cell(1,nobj); +for i=1:nobj + true_state{i} = cell2num(evidence(Xs(i), :)); % true_state{i}(:,t) = [x y xdot ydot]' +end +obs_pos = cell2num(evidence(Y,:)); +figure(1) +clf +hold on +styles = {'rx', 'go', 'b+', 'k*'}; +for i=1:nobj + plot(true_state{i}(1,:), true_state{i}(2,:), styles{i}); +end +for t=1:T + text(obs_pos(1,t), obs_pos(2,t), sprintf('%d', t)); +end +hold off +relax_axes(0.1) + + +% Inference +ev = cell(N,T); +ev(bnet.observed,:) = evidence(bnet.observed, :); + +engines = {}; +engines{end+1} = jtree_dbn_inf_engine(bnet); +%engines{end+1} = scg_unrolled_dbn_inf_engine(bnet, T); +engines{end+1} = pearl_unrolled_dbn_inf_engine(bnet); +E = length(engines); + +inferred_state = cell(nobj,E); % inferred_state{i,e}(:,t) +for e=1:E + engines{e} = enter_evidence(engines{e}, ev); + for i=1:nobj + inferred_state{i,e} = zeros(4, T); + for t=1:T + m = marginal_nodes(engines{e}, Xs(i), t); + inferred_state{i,e}(:,t) = m.mu; + end + end +end +inferred_state{1,1} +inferred_state{1,2} + +% Plot results +figure(2) +clf +hold on +styles = {'rx', 'go', 'b+', 'k*'}; +nstyles = length(styles); +c = 1; +for e=1:E + for i=1:nobj + plot(inferred_state{i,e}(1,:), inferred_state{i,e}(2,:), styles{mod(c-1,nstyles)+1}); + c = c + 1; + end +end +for t=1:T + text(obs_pos(1,t), obs_pos(2,t), sprintf('%d', t)); +end +hold off +relax_axes(0.1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/viterbi1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +% Compute Viterbi path discrete HMM by different methods + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +Q = 2; % num hidden states +O = 2; % num observable symbols + +ns = [Q O]; +dnodes = 1:2; +onodes = [2]; +eclass1 = [1 2]; +eclass2 = [3 2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2, ... + 'observed', onodes); + +for seed=1:10 +rand('state', seed); +prior = normalise(rand(Q,1)); +transmat = mk_stochastic(rand(Q,Q)); +obsmat = mk_stochastic(rand(Q,O)); +bnet.CPD{1} = tabular_CPD(bnet, 1, prior); +bnet.CPD{2} = tabular_CPD(bnet, 2, obsmat); +bnet.CPD{3} = tabular_CPD(bnet, 3, transmat); + + +% Create a sequence +T = 5; +ev = sample_dbn(bnet, T); +evidence = cell(2,T); +evidence(2,:) = ev(2,:); % extract observed component +data = cell2num(ev(2,:)); + +%obslik = mk_dhmm_obs_lik(data, obsmat); +obslik = multinomial_prob(data, obsmat); +path = viterbi_path(prior, transmat, obslik); + +engine = {}; +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); + +mpe = find_mpe(engine{1}, evidence); + +assert(isequal(cell2num(mpe(1,:)), path)) % extract values of hidden nodes +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/water1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +% Compare the speeds of various inference engines on the water DBN +seed = 0; +rand('state', seed); +randn('state', seed); + +bnet = mk_water_dbn; + +T = 3; +engine = {}; +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); +engine{end+1} = jtree_dbn_inf_engine(bnet); +engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); + +%engine{end+1} = bk_inf_engine(bnet, 'ff', onodes); +%engine{end+1} = bk_inf_engine(bnet, { [1 2], [3 4 5 6], [7 8] }, onodes); + +inf_time = cmp_inference_dbn(bnet, engine, T) +learning_time = cmp_learning_dbn(bnet, engine, T) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/water2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +% Compare the speeds of various inference engines on the water DBN +seed = 0; +rand('state', seed); +randn('state', seed); + +bnet = mk_water_dbn; + +T = 3; + +engine = {}; +engine{end+1} = smoother_engine(jtree_2TBN_inf_engine(bnet)); +engine{end+1} = smoother_engine(jtree_sparse_2TBN_inf_engine(bnet)); +engine{end+1} = smoother_engine(hmm_2TBN_inf_engine(bnet)); +engine{end+1} = jtree_dbn_inf_engine(bnet); +%engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T); + +%engine{end+1} = bk_inf_engine(bnet, 'ff', onodes); +%engine{end+1} = bk_inf_engine(bnet, { [1 2], [3 4 5 6], [7 8] }, onodes); + +inf_time = cmp_inference_dbn(bnet, engine, T) +%learning_time = cmp_learning_dbn(bnet, engine, T) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/limids/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/amnio.m/1.1.1.1/Mon Sep 13 03:21:04 2004// +/asia_dt1.m/1.1.1.1/Mon Jun 7 15:53:54 2004// +/id1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/oil1.m/1.1.1.1/Mon Sep 13 02:27:08 2004// +/pigs1.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/limids/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/limids
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/limids/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/limids/amnio.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ + +clear all +B0 = 1; Rtriple = 2; Damnio = 3; +B1 = 4; Ramnio = 5; Dabort = 6; +B2 = 7; U = 8; + +N = 8; +dag = zeros(N,N); +dag(B0, [Rtriple B1 Ramnio]) = 1; +dag(Rtriple, [Damnio Dabort]) = 1; +dag(Damnio, [B1 Ramnio]) = 1; +dag(B1, B2) = 1; +dag(Ramnio, [Dabort U]) = 1; +dag(Dabort, B2) = 1; +dag(B2, U) = 1; + + + +ns = zeros(1,N); +ns(B0) = 2; +ns(B1) = 3; +ns(B2) = 4; +ns(Rtriple) = 2; +ns(Ramnio) = 3; +ns(Damnio) = 2; +ns(Dabort) = 2; +ns(U) = 1; + +limid = mk_limid(dag, ns, 'chance', [B0 B1 B2], ... + 'decision', [Damnio Dabort], 'utility', [U]); + +% states of nature +healthy = 1; downs = 2; miscarry = 3; aborted = 4; +% test results +pos = 1; neg = 2; unk = 3; +% actions +yes = 1; no = 2; + +% Prior probability baby has downs syndrome +tbl = zeros(2,1); +p = 1/1000; % from www.downs-syndrome.org.uk figure +p = 24/10000; % www-personal.umich.edu/~bobwolfe/560/review/Downs.pdf (for women agen 35-40) +tbl(healthy) = 1-p; +tbl(downs) = p; +limid.CPD{B0} = tabular_CPD(limid, B0, tbl); + +% Reliability of triple screen test +% Unreliable sensor +% B0 -> Rtriple +tbl = zeros(2,2); % Rtriple = pos, neg +p = 0.5; % high false positive rate (guess) +tbl(healthy, :) = [p 1-p]; +p = 0.6; % low detection rate (march of dimes figure) +tbl(downs, :) = [p 1-p]; +limid.CPD{Rtriple} = tabular_CPD(limid, Rtriple, tbl); + +limid.CPD{Damnio} = tabular_decision_node(limid, Damnio); + +% Effect of amnio on baby B0,Damnio -> B1 + % 1/200 risk of miscarry +p = 1/200; % (march of dimes figure) +tbl = zeros(2, 2, 3); % B1 = healthy, downs, miscarry +tbl(healthy, no, :) = [1 0 0]; +tbl(downs, no, :) = [0 1 0]; +tbl(healthy, yes, :) = [1-p 0 p]; +tbl(downs, yes, :) = [0 1-p p]; +limid.CPD{B1} = tabular_CPD(limid, B1, tbl); + +% Reliability of amnio B0, Damnio -> Ramnio +% Perfect sensor +tbl = zeros(2,2,3); % Ramnio = pos, neg, unk +tbl(:, no, :) = repmat([0 0 1], 2 ,1); +tbl(healthy, yes, :) = [0 1 0]; +tbl(downs, yes, :) = [1 0 0]; +limid.CPD{Ramnio} = tabular_CPD(limid, Ramnio, tbl); + +limid.CPD{Dabort} = tabular_decision_node(limid, Dabort); + +% Effect of abortion on baby B1, Dabort -> B2 +tbl = zeros(3, 2, 4); % B2 = healthy, downs, miscarry, aborted +tbl(:, yes, :) = repmat([0 0 0 1], 3, 1); +tbl(healthy, no, :) = [1 0 0 0]; +tbl(downs, no, :) = [0 1 0 0]; +tbl(miscarry, no, :) = [0 0 1 0]; +limid.CPD{B2} = tabular_CPD(limid, B2, tbl); + +% Utility U(Ramnio, B2) +tbl = zeros(3, 4); +tbl(:, healthy) = 5000; +tbl(:, downs) = -50000; +tbl(:, miscarry) = -1000; +tbl(:, aborted) = -1000; + +if 0 +%tbl(unk, miscarry) = 0; % this case is impossible +tbl(pos, miscarry) = -1; +tbl(neg, miscarry) = -1000; +if 1 + tbl(unk, aborted) = -100; + tbl(pos, aborted) = -1; + tbl(neg, aborted) = -500; +else % pro-life utility fn + tbl(unk, aborted) = -500000; + tbl(pos, aborted) = -500000; + tbl(neg, aborted) = -500000; +end +end + +limid.CPD{U} = tabular_utility_node(limid, U, tbl); + + + +engine = jtree_limid_inf_engine(limid); +[strategy, MEU] = solve_limid(engine); + +% Rtriple U(Damnio=1=yes) U(Damnio=2=no) +% 1=pos 0 1 +% 2=neg 0 1 +dispcpt(strategy{Damnio}) +if isequal(strategy{Damnio}(1,:), strategy{Damnio}(2,:)) + % Rtriple result irrelevant + doAmnio = argmax(strategy{Damnio}(1,:)) +else + doAmnio = 1; +end + +% Rtriple Ramnio U(Dabort=yes=1) U(Dabort=no=2) +% 1=pos 1=pos 1 0 +% 2=neg 1=pos 1 0 +% 1=pos 2=neg 0 1 +% 2=neg 2=neg 0 1 +% 1=pos 3=unk 0 1 +% 2=neg 3=unk 0 1 +dispcpt(strategy{Dabort}) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/limids/asia_dt1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,100 @@ +% decision theoretic version of asia network +% Cowell et al, p177 +% We explicitely add the no-forgetting arcs. + +Smoking = 1; +VisitToAsia = 2; +Bronchitis = 3; +LungCancer = 4; +TB = 5; +Do_Xray = 6; +TBorCancer = 7; +Util_Xray = 8; +Dys = 9; +posXray = 10; +Do_Hosp = 11; +Util_Hosp = 12; + +n = 12; +dag = zeros(n); +dag(Smoking, [Bronchitis LungCancer]) = 1; +dag(VisitToAsia, [TB Do_Xray Do_Hosp]) = 1; +dag(Bronchitis, Dys) = 1; +dag(LungCancer, [Util_Hosp TBorCancer]) = 1; +dag(TB, [Util_Hosp TBorCancer Util_Xray]) = 1; +dag(Do_Xray, [posXray Util_Xray Do_Hosp]) = 1; +dag(TBorCancer, [Dys posXray]) = 1; +dag(Dys, Do_Hosp) = 1; +dag(posXray, Do_Hosp) = 1; +dag(Do_Hosp, Util_Hosp) = 1; + +dnodes = [Do_Xray Do_Hosp]; +unodes = [Util_Xray Util_Hosp]; +cnodes = mysetdiff(1:n, [dnodes unodes]); % chance nodes +ns = 2*ones(1,n); +ns(unodes) = 1; +limid = mk_limid(dag, ns, 'chance', cnodes, 'decision', dnodes, 'utility', unodes); + +% 1 = yes, 2 = no +limid.CPD{VisitToAsia} = tabular_CPD(limid, VisitToAsia, [0.01 0.99]); +limid.CPD{Bronchitis} = tabular_CPD(limid, Bronchitis, [0.6 0.3 0.4 0.7]); +limid.CPD{Dys} = tabular_CPD(limid, Dys, [0.9 0.7 0.8 0.1 0.1 0.3 0.2 0.9]); +limid.CPD{TBorCancer} = tabular_CPD(limid, TBorCancer, [1 1 1 0 0 0 0 1]); + +limid.CPD{LungCancer} = tabular_CPD(limid, LungCancer, [0.1 0.01 0.9 0.99]); +limid.CPD{Smoking} = tabular_CPD(limid, Smoking, [0.5 0.5]); +limid.CPD{TB} = tabular_CPD(limid, TB, [0.05 0.01 0.95 0.99]); +limid.CPD{posXray} = tabular_CPD(limid, posXray, [0.98 0.5 0.05 0.5 0.02 0.5 0.95 0.5]); + +limid.CPD{Util_Hosp} = tabular_utility_node(limid, Util_Hosp, [180 120 160 15 2 4 0 40]); +limid.CPD{Util_Xray} = tabular_utility_node(limid, Util_Xray, [0 1 10 10]); + +for i=dnodes(:)' + limid.CPD{i} = tabular_decision_node(limid, i); +end + +engines = {}; +engines{end+1} = global_joint_inf_engine(limid); +engines{end+1} = jtree_limid_inf_engine(limid); +%engines{end+1} = belprop_inf_engine(limid); + +exact = [1 2]; +%approx = 3; +approx = []; + + +NE = length(engines); +MEU = zeros(1, NE); +niter = zeros(1, NE); +strategy = cell(1, NE); + +tol = 1e-2; +for e=1:length(engines) + [strategy{e}, MEU(e), niter(e)] = solve_limid(engines{e}); +end + +for e=exact(:)' + assert(approxeq(MEU(e), 47.49, tol)) + assert(isequal(strategy{e}{Do_Xray}(:)', [1 0 0 1])) + + % Check the hosptialize strategy is correct (p180) + % We assume the patient has not been to Asia and therefore did not have an Xray. + % In this case it is optimal not to hospitalize regardless of whether the patient has + % dyspnoea or not (and of course regardless of the value of pos_xray). + asia = 2; + do_xray = 2; + for dys = 1:2 + for pos_xray = 1:2 + assert(argmax(squeeze(strategy{e}{Do_Hosp}(asia, do_xray, dys, pos_xray, :))) == 2) + end + end +end + + +for e=approx(:)' + approxeq(strategy{exact(1)}{Do_Xray}, strategy{e}{Do_Xray}) + approxeq(strategy{exact(1)}{Do_Hosp}, strategy{e}{Do_Hosp}) +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/limids/id1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +% influence diagram with no loops +% +% rv dec +% \ / +% utility + +N = 3; +dag = zeros(N); +X = 1; D = 2; U = 3; +dag([X D], U)=1; + +ns = zeros(1,N); +ns(X) = 2; ns(D) = 2; ns(U) = 1; + +limid = mk_limid(dag, ns, 'chance', X, 'decision', D, 'utility', U); + +% use random params +limid.CPD{X} = tabular_CPD(limid, X); +limid.CPD{D} = tabular_decision_node(limid, D); +limid.CPD{U} = tabular_utility_node(limid, U); + +%fname = '/home/cs/murphyk/matlab/Misc/loopybel.txt'; +global BNT_HOME +fname = sprintf('%s/loopybel.txt', BNT_HOME); + +engines = {}; +engines{end+1} = global_joint_inf_engine(limid); +engines{end+1} = jtree_limid_inf_engine(limid); +%engines{end+1} = belprop_inf_engine(limid, 'max_iter', 2*N, 'filename', fname); +engines{end+1} = belprop_inf_engine(limid, 'max_iter', 2*N); + +exact = [1 2]; +approx = 3; + +E = length(engines); +strategy = cell(1, E); +MEU = zeros(1, E); +for e=1:E + [strategy{e}, MEU(e)] = solve_limid(engines{e}); + MEU +end +MEU + +for e=exact(:)' + assert(approxeq(strategy{exact(1)}{D}, strategy{e}{D})) +end + +for e=approx(:)' + approxeq(strategy{exact(1)}{D}, strategy{e}{D}) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/limids/oil1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,91 @@ +% oil wildcatter influence diagram in Cowell et al p172 + +% T = test for oil? +% UT = utility (negative cost) of testing +% O = amount of oil = Dry, Wet or Soaking +% R = results of test = NoStrucure, OpenStructure, ClosedStructure or NoResult +% D = drill? +% UD = utility of drilling + +% Decision sequence = T R D O + +T = 1; UT = 2; O = 3; R = 4; D = 5; UD = 6; +N = 6; +dag = zeros(N); +dag(T, [UT R D]) = 1; +dag(O, [R UD]) = 1; +dag(R, D) = 1; +dag(D, UD) = 1; + +ns = zeros(1,N); +ns(O) = 3; ns(R) = 4; ns(T) = 2; ns(D) = 2; ns(UT) = 1; ns(UD) = 1; + +limid = mk_limid(dag, ns, 'chance', [O R], 'decision', [T D], 'utility', [UT UD]); + +limid.CPD{O} = tabular_CPD(limid, O, [0.5 0.3 0.2]); +tbl = [0.6 0 0.3 0 0.1 0 0.3 0 0.4 0 0.4 0 0.1 0 0.3 0 0.5 0 0 1 0 1 0 1]; +limid.CPD{R} = tabular_CPD(limid, R, tbl); + +limid.CPD{UT} = tabular_utility_node(limid, UT, [-10 0]); +limid.CPD{UD} = tabular_utility_node(limid, UD, [-70 50 200 0 0 0]); + +if 1 + % start with uniform policies + limid.CPD{T} = tabular_decision_node(limid, T); + limid.CPD{D} = tabular_decision_node(limid, D); +else + % hard code optimal policies + limid.CPD{T} = tabular_decision_node(limid, T, [1.0 0.0]); + a = 0.5; b = 1-a; % arbitrary value + tbl = myreshape([0 a 1 a 1 a a a 1 b 0 b 0 b b b], ns([T R D])); + limid.CPD{D} = tabular_decision_node(limid, D, tbl); +end + +%fname = '/home/cs/murphyk/matlab/Misc/loopybel.txt'; + +engines = {}; +engines{end+1} = global_joint_inf_engine(limid); +engines{end+1} = jtree_limid_inf_engine(limid); +%engines{end+1} = belprop_inf_engine(limid, 'max_iter', 3*N, 'filename', fname); + +exact = [1 2]; +%approx = 3; +approx = []; + +E = length(engines); +strategy = cell(1, E); +MEU = zeros(1, E); +for e=1:E + [strategy{e}, MEU(e)] = solve_limid(engines{e}); + MEU +end +MEU + +for e=exact(:)' + assert(approxeq(MEU(e), 22.5)) + % U(T=yes) U(T=no) + % 1 0 + assert(argmax(strategy{e}{T}) == 1); % test = yes + t = 1; % test = yes + % strategy{D} T R U(D=yes=1) U(D=no=2) + % 1=yes 1=noS 0 1 Don't drill + % 2=no 1=noS 1 0 + % 1=yes 2=opS 1 0 + % 2=no 2=opS 1 0 + % 1=yes 3=clS 1 0 + % 2=no 3=clS 1 0 + % 1=yes 4=unk 1 0 + % 2=no 4=unk 1 0 + + for r=[2 3] % OpS, ClS + assert(argmax(squeeze(strategy{e}{D}(t,r,:))) == 1); % drill = yes + end + r = 1; % noS + assert(argmax(squeeze(strategy{e}{D}(t,r,:))) == 2); % drill = no +end + + +for e=approx(:)' + approxeq(strategy{exact(1)}{T}, strategy{e}{T}) + approxeq(strategy{exact(1)}{D}, strategy{e}{D}) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/limids/pigs1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +% pigs model from Lauritzen and Nilsson, 2001 + +seed = 0; +rand('state', seed); +randn('state', seed); + +% we number nodes down and to the right +h = [1 5 9 13]; +t = [2 6 10]; +d = [3 7 11]; +u = [4 8 12 14]; + +N = 14; +dag = zeros(N); + +% causal arcs +for i=1:3 + dag(h(i), [t(i) h(i+1)]) = 1; + dag(d(i), [u(i) h(i+1)]) = 1; +end +dag(h(4), u(4)) = 1; + +% information arcs +fig = 2; +switch fig + case 0, + % no info arcs + case 1, + % no-forgetting policy (figure 1) + for i=1:3 + dag(t(i), d(i:3)) = 1; + end + case 2, + % reactive policy (figure 2) + for i=1:3 + dag(t(i), d(i)) = 1; + end + case 7, + % omniscient policy (figure 7: di has access to hidden state h(i-1)) + dag(t(1), d(1)) = 1; + for i=2:3 + %dag([h(i-1) t(i-1) d(i-1)], d(i)) = 1; + dag([h(i-1) d(i-1)], d(i)) = 1; % t(i-1) is redundant given h(i-1) + end +end + + +ns = 2*ones(1,N); +ns(u) = 1; + +% parameter tying +params = ones(1,N); +uparam = 1; +final_uparam = 2; +tparam = 3; +h1_param = 4; +hparam = 5; +dparams = 6:8; + +params(u(1:3)) = uparam; +params(u(4)) = final_uparam; +params(t) = tparam; +params(h(1)) = h1_param; +params(h(2:end)) = hparam; +params(d) = dparams; + +limid = mk_limid(dag, ns, 'chance', [h t], 'decision', d, 'utility', u, 'equiv_class', params); + +% h = 1 means healthy, h = 2 means diseased +% d = 1 means don't treat, d = 2 means treat +% t = 1 means test shows healthy, t = 2 means test shows diseased + +if 0 + % use random params + limid.CPD{final_uparam} = tabular_utility_node(limid, u(4)); + limid.CPD{uparam} = tabular_utility_node(limid, u(1)); + limid.CPD{tparam} = tabular_CPD(limid, t(1)); + limid.CPD{h1_param} = tabular_CPD(limid, h(1)); + limid.CPD{hparam} = tabular_CPD(limid, h(2)); +else + limid.CPD{final_uparam} = tabular_utility_node(limid, u(4), [1000 300]); + limid.CPD{uparam} = tabular_utility_node(limid, u(1), [0 -100]); % costs have negative utility! + + % h P(t=1) P(t=2) + % 1 0.9 0.1 + % 2 0.2 0.8 + limid.CPD{tparam} = tabular_CPD(limid, t(1), [0.9 0.2 0.1 0.8]); + + % P(h1) + limid.CPD{h1_param} = tabular_CPD(limid, h(1), [0.9 0.1]); + + % hi di P(hj=1) P(hj=2), j = i+1, i=1:3 + % 1 1 0.8 0.2 + % 2 1 0.1 0.9 + % 1 2 0.9 0.1 + % 2 2 0.5 0.5 + limid.CPD{hparam} = tabular_CPD(limid, h(2), [0.8 0.1 0.9 0.5 0.2 0.9 0.1 0.5]); +end + +% Decision nodes get assigned uniform policies by default +for i=1:3 + limid.CPD{dparams(i)} = tabular_decision_node(limid, d(i)); +end + + +fname = '/home/cs/murphyk/matlab/Misc/loopybel.txt'; + +engines = {}; +engines{end+1} = global_joint_inf_engine(limid); +engines{end+1} = jtree_limid_inf_engine(limid); +%engines{end+1} = belprop_inf_engine(limid, 'max_iter', 1*N, 'filename', fname, 'tol', 1e-3); + +exact = [1 2]; +%approx = 3; +approx = []; + +max_iter = 1; +order = d(end:-1:1); +%order = d(1:end); + +NE = length(engines); +MEU = zeros(1, NE); +niter = zeros(1, NE); +strategy = cell(1, NE); +for e=1:NE + [strategy{e}, MEU(e), niter(e)] = solve_limid(engines{e}, 'max_iter', max_iter, 'order', order); +end +MEU + +% check results match those in the paper (p. 22) +direct_policy = eye(2); % treat iff test is positive +never_policy = [1 0; 1 0]; % never treat +tol = 1e-0; % results in paper are reported to 0dp +for e=exact(:)' + switch fig + case 2, % reactive policy + assert(approxeq(MEU(e), 727, tol)); + assert(approxeq(strategy{e}{d(1)}(:), never_policy(:))) + assert(approxeq(strategy{e}{d(2)}(:), direct_policy(:))) + assert(approxeq(strategy{e}{d(3)}(:), direct_policy(:))) + case 1, assert(approxeq(MEU(e), 729, tol)); + case 7, assert(approxeq(MEU(e), 732, tol)); + end +end + + +for e=approx(:)' + for i=1:3 + approxeq(strategy{exact(1)}{d(i)}, strategy{e}{d(i)}) + dispcpt(strategy{e}{d(i)}) + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +/belprop_loop1_discrete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/belprop_loop1_gauss.m/1.1.1.1/Wed May 29 15:59:54 2002// +/belprop_loopy_cg.m/1.1.1.1/Wed May 29 15:59:54 2002// +/belprop_loopy_discrete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/belprop_loopy_gauss.m/1.1.1.1/Wed May 29 15:59:54 2002// +/belprop_polytree_cg.m/1.1.1.1/Wed May 29 15:59:54 2002// +/belprop_polytree_discrete.m/1.1.1.1/Tue Oct 1 18:21:26 2002// +/belprop_polytree_gauss.m/1.1.1.1/Wed May 29 15:59:54 2002// +/bp1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/gmux1.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/Belprop
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/belprop_loop1_discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +% Compare different loopy belief propagation algorithms on a graph with a single loop. +% LBP should give exact results if it converges. + +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; +ns = 2*ones(1,N); +bnet = mk_bnet(dag, ns); +for i=1:N + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +engines = {}; +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel'); +engines{end+1} = belprop_fg_inf_engine(bnet_to_fgraph(bnet)); +engines{end+1} = belprop_inf_engine(bnet, 'protocol', 'parallel'); + +% belprop_fg does not support marginal_family +% belprop_fg and belprop do not support loglik even on discrete +[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', 2, ... + 'check_ll', 0, 'singletons_only', 1, 'check_converged', 2:4); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/belprop_loop1_gauss.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +% Compare different loopy belief propagation algorithms on a graph with a single loop. +% LBP should give exact results if it converges. + +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; +ns = 2*ones(1,N); +bnet = mk_bnet(dag, ns, 'discrete', []); +for i=1:N + bnet.CPD{i} = gaussian_CPD(bnet, i); +end + +engines = {}; +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel', 'max_iter', 20); +%engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel', 'max_iter', 20, 'filename', ... +% '/home/eecs/murphyk/matlab/gausspearl.txt', 'tol', 1e-5); + +% pearl gaussian does not compute loglik +[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', [2], ... + 'check_ll', 0, 'singletons_only', 0, 'check_converged', [2]); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/belprop_loopy_cg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +% Same as cg1, except we assume all discretes are observed, +% and use loopy for approximate inference. + +ns = 2*ones(1,9); +F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; +n = 9; +dnodes = [B F W]; +cnodes = mysetdiff(1:n, dnodes); + +%bnet = mk_incinerator_bnet(ns); +bnet = mk_incinerator_bnet; + +bnet.observed = [dnodes E]; + +engines = {}; +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel'); +nengines = length(engines); + + +[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'check_ll', 0, ... + 'singletons_only', 0, 'exact', 1, 'check_converged', 2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/belprop_loopy_discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +% Compare different loopy belief propagation algorithms on a graph with many loops + +bnet = mk_asia_bnet('orig'); + +engines = {}; +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel'); +engines{end+1} = belprop_fg_inf_engine(bnet_to_fgraph(bnet)); +engines{end+1} = belprop_inf_engine(bnet, 'protocol', 'parallel'); + +[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', [1 3 5], ... + 'check_ll', 0, 'singletons_only', 1, 'check_converged', 2:4); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/belprop_loopy_gauss.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +% Compare different loopy belief propagation algorithms on a graph with many loops +% If LBP converges, the means should be exact + +bnet = mk_asia_bnet('gauss'); + +engines = {}; +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel'); + +[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1, 'observed', [1 3 5], ... + 'check_ll', 0, 'singletons_only', 0, 'check_converged', 2); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/belprop_polytree_cg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +% Inference on a conditional Gaussian model + +% Make the following polytree, where all arcs point down + +% 1 2 +% \ / +% 3 +% / \ +% 4 5 + +N = 5; +dag = zeros(N,N); +dag(1,3) = 1; +dag(2,3) = 1; +dag(3, [4 5]) = 1; + +ns = [2 1 2 1 2]; + +dnodes = 1; +%onodes = [1 5]; +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', dnodes); + +bnet.CPD{1} = tabular_CPD(bnet, 1); +for i=2:N + bnet.CPD{i} = gaussian_CPD(bnet, i); +end + +engine = {}; +engine{end+1} = jtree_inf_engine(bnet); +engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel'); + +[time, engine] = cmp_inference_static(bnet, engine, 'maximize', 0, 'check_ll', 0, ... + 'singletons_only', 0, 'observed', [1 3]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/belprop_polytree_discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +% Make the following polytree, where all arcs point down + +% 1 2 +% \ / +% 3 +% / \ +% 4 5 + +N = 5; +dag = zeros(N,N); +dag(1,3) = 1; +dag(2,3) = 1; +dag(3, [4 5]) = 1; + +ns = 2*ones(1,N); % binary nodes + +onodes = [1 5]; + +bnet = mk_bnet(dag, ns, 'observed', onodes); + +if 0 +seed = 0; +rand('state', seed); +randn('state', seed); +end + +for i=1:N + %bnet.CPD{i} = tabular_CPD(bnet, i); + bnet.CPD{i} = noisyor_CPD(bnet, i); +end + +engine = {}; +engine{end+1} = jtree_inf_engine(bnet); +engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'tree'); +engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel'); + +[err, time] = cmp_inference_static(bnet, engine, 'maximize', 0, 'check_ll', 1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/belprop_polytree_gauss.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ +% Do the example from Satnam Alag's PhD thesis, UCB ME dept 1996 p46 + +% Make the following polytree, where all arcs point down + +% 1 2 +% \ / +% 3 +% / \ +% 4 5 + +N = 5; +dag = zeros(N,N); +dag(1,3) = 1; +dag(2,3) = 1; +dag(3, [4 5]) = 1; + +ns = [2 1 2 1 2]; + +bnet = mk_bnet(dag, ns, 'discrete', []); + +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', [1 0]', 'cov', [4 1; 1 4]); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', 1, 'cov', 1); +B1 = [1 2; 1 0]; B2 = [2 1]'; +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', [0 0]', 'cov', [2 1; 1 1], ... + 'weights', [B1 B2]); +H1 = [1 1]; +bnet.CPD{4} = gaussian_CPD(bnet, 4, 'mean', 0, 'cov', 1, 'weights', H1); +H2 = [1 0; 1 1]; +bnet.CPD{5} = gaussian_CPD(bnet, 5, 'mean', [0 0]', 'cov', eye(2), 'weights', H2); + +engine = {}; +engine{end+1} = jtree_inf_engine(bnet); +engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'tree'); +engine{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel'); +E = length(engine); + +if 1 +% no evidence +evidence = cell(1,N); +ll = zeros(1,E); +for e=1:E + [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence); + add_ev = 1; + m = marginal_nodes(engine{e}, 3, add_ev); + assert(approxeq(m.mu, [3 2]')) + assert(approxeq(m.Sigma, [30 9; 9 6])) + + m = marginal_nodes(engine{e}, 4, add_ev); + assert(approxeq(m.mu, 5)) + assert(approxeq(m.Sigma, 55)) + + m = marginal_nodes(engine{e}, 5, add_ev); + assert(approxeq(m.mu, [3 5]')) + assert(approxeq(m.Sigma, [31 39; 39 55])) +end +end + +if 1 +% evidence on leaf 5 +evidence = cell(1,N); +evidence{5} = [5 5]'; +for e=1:E + [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence); + add_ev = 1; + m = marginal_nodes(engine{e}, 3, add_ev); + assert(approxeq(m.mu, [4.4022 1.0217]')) + assert(approxeq(m.Sigma, [0.7011 -0.4891; -0.4891 1.1087])) + + m = marginal_nodes(engine{e}, 4, add_ev); + assert(approxeq(m.mu, 5.4239)) + assert(approxeq(m.Sigma, 1.8315)) + + m = marginal_nodes(engine{e}, 1, add_ev); + assert(approxeq(m.mu, [0.3478 1.1413]')) + assert(approxeq(m.Sigma, [1.8261 -0.1957; -0.1957 1.0924])) + + m = marginal_nodes(engine{e}, 2, add_ev); + assert(approxeq(m.mu, 0.9239)) + assert(approxeq(m.Sigma, 0.8315)) + + m = marginal_nodes(engine{e}, 5, add_ev); + assert(approxeq(m.mu, evidence{5})) + assert(approxeq(m.Sigma, zeros(2))) +end +end + +if 1 +% evidence on leaf 4 (non-info-state version is uninvertible) +evidence = cell(1,N); +evidence{4} = 10; +for e=1:E + [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence); + add_ev = 1; + m = marginal_nodes(engine{e}, 3, add_ev); + assert(approxeq(m.mu, [6.5455 3.3636]')) + assert(approxeq(m.Sigma, [2.3455 -1.6364; -1.6364 1.9091])) + + m = marginal_nodes(engine{e}, 5, add_ev); + assert(approxeq(m.mu, [6.5455 9.9091]')) + assert(approxeq(m.Sigma, [3.3455 0.7091; 0.7091 1.9818])) + + m = marginal_nodes(engine{e}, 1, add_ev); + assert(approxeq(m.mu, [1.9091 0.9091]')) + assert(approxeq(m.Sigma, [2.1818 -0.8182; -0.8182 2.1818])) + + m = marginal_nodes(engine{e}, 2, add_ev); + assert(approxeq(m.mu, 1.2727)) + assert(approxeq(m.Sigma, 0.8364)) +end +end + + +if 1 +% evidence on leaves 4,5 and root 2 +evidence = cell(1,N); +evidence{2} = 0; +evidence{4} = 10; +evidence{5} = [5 5]'; +for e=1:E + [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence); + add_ev = 1; + m = marginal_nodes(engine{e}, 3, add_ev); + assert(approxeq(m.mu, [4.9964 2.4444]')); + assert(approxeq(m.Sigma, [0.6738 -0.5556; -0.5556 0.8889])); + + m = marginal_nodes(engine{e}, 1, add_ev); + assert(approxeq(m.mu, [2.2043 1.2151]')); + assert(approxeq(m.Sigma, [1.2903 -0.4839; -0.4839 0.8065])); +end +end + +if 1 + [time, engine] = cmp_inference_static(bnet, engine, 'maximize', 0, 'check_ll', 0, ... + 'singletons_only', 0, 'observed', [1 3 5]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/bp1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +% Compare different loopy belief propagation algorithms on a graph with a single loop. +% LBP should give exact results if it converges. + +seed = 0; +rand('state', seed); +randn('state', seed); + +N = 2; +dag = zeros(N,N); +dag(1,2)=1; +ns = ones(1,N); +bnet = mk_bnet(dag, ns, 'discrete', []); +for i=1:N + %bnet.CPD{i} = gaussian_CPD(bnet, i, 'mean', 0); + bnet.CPD{i} = gaussian_CPD(bnet, i); +end + +engines = {}; +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'tree'); + +[time, engines] = cmp_inference_static(bnet, engines, 'maximize', 0, 'exact', 1:2, 'observed', [2], ... + 'check_ll', 0, 'singletons_only', 1, 'check_converged', []); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Belprop/gmux1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +% Test gmux. +% The following model, where Y is a gmux node, +% and M is set to 1, should be equivalent to X1 -> Y +% +% X1 Xn M +% \ | / +% Y + +n = 3; +N = n+2; +Xs = 1:n; +M = n+1; +Y = n+2; +dag = zeros(N,N); +dag([Xs M], Y)=1; + +dnodes = M; +ns = zeros(1, N); +sz = 2; +ns(Xs) = sz; +ns(M) = n; +ns(Y) = sz; + +bnet = mk_bnet(dag, ns, 'discrete', M, 'observed', [M Y]); + +psz = ns(Xs(1)); +selfsz = ns(Y); + +W = randn(selfsz, psz); +mu = randn(selfsz, 1); +Sigma = eye(selfsz, selfsz); + +bnet.CPD{M} = root_CPD(bnet, M); +for i=Xs(:)' + bnet.CPD{i} = gaussian_CPD(bnet, i, 'mean', zeros(psz, 1), 'cov', eye(psz, psz)); +end +bnet.CPD{Y} = gmux_CPD(bnet, Y, 'mean', mu, 'weights', W, 'cov', Sigma); + +evidence = cell(1,N); +yval = randn(selfsz, 1); +evidence{Y} = yval; +m = 2; +%notm = not(m-1)+1; % only valid for n=2 +notm = mysetdiff(1:n, m); +evidence{M} = m; + +engines = {}; +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = pearl_inf_engine(bnet, 'protocol', 'parallel'); + +for e=1:length(engines) + engines{e} = enter_evidence(engines{e}, evidence); + mXm{e} = marginal_nodes(engines{e}, Xs(m)); + + % Since M=m, only Xm was updated. + % Hence the posterior on Xnotm should equal the prior. + for i=notm(:)' + mXnotm = marginal_nodes(engines{e}, Xs(i)); + assert(approxeq(mXnotm.mu, zeros(psz,1))) + assert(approxeq(mXnotm.Sigma, eye(psz, psz))) + end +end + +% Check that all engines give the same posterior +for e=2:length(engines) + assert(approxeq(mXm{e}.mu, mXm{1}.mu)) + assert(approxeq(mXm{e}.Sigma, mXm{1}.Sigma)) +end + + +% Compute the correct posterior by building Xm -> Y + +N = 2; +dag = zeros(N,N); +dag(1, 2)=1; +ns = [psz selfsz]; +bnet = mk_bnet(dag, ns, 'discrete', [], 'observed', 2); + +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', zeros(psz, 1), 'cov', eye(psz, psz)); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu, 'cov', Sigma, 'weights', W); + +jengine = jtree_inf_engine(bnet); +evidence = {[], yval}; +jengine = enter_evidence(jengine, evidence); % apply Bayes rule to invert the arc +mX = marginal_nodes(jengine, 1); + +for e=1:length(engines) + assert(approxeq(mX.mu, mXm{e}.mu)) + assert(approxeq(mX.Sigma, mXm{e}.Sigma)) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/Belief_IOhmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +% Sigmoid Belief IOHMM +% Here is the model +% +% X \ X \ +% | | | | +% Q-|->Q-|-> ... +% | / | / +% Y Y +% +clear all; +clc; +rand('state',0); randn('state',0); +X = 1; Q = 2; Y = 3; +% intra time-slice graph +intra=zeros(3); +intra(X,[Q Y])=1; +intra(Q,Y)=1; +% inter time-slice graph +inter=zeros(3); +inter(Q,Q)=1; + +ns = [1 3 1]; +dnodes = [2]; +eclass1 = [1 2 3]; +eclass2 = [1 4 3]; +bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2); +bnet.CPD{1} = root_CPD(bnet, 1); +% ========================================================== +bnet.CPD{2} = softmax_CPD(bnet, 2); +bnet.CPD{4} = softmax_CPD(bnet, 5, 'discrete', [2]); +% ========================================================== +bnet.CPD{3} = gaussian_CPD(bnet, 3); + +% make some data +T=20; +cases = cell(3, T); +cases(1,:)=num2cell(round(rand(1,T)*2)+1); +%cases(2,:)=num2cell(round(rand(1,T))+1); +cases(3,:)=num2cell(rand(1,T)); + +engine = bk_inf_engine(bnet, 'exact', [1 2 3]); + +% log lik before learning +[engine, loglik] = enter_evidence(engine, cases); + +% do learning +ev=cell(1,1); +ev{1}=cases; +[bnet2, LL2] = learn_params_dbn_em(engine, ev, 3); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/Belief_hmdt.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +% Sigmoid Belief Hidden Markov Decision Tree (Jordan/Gharhamani 1996) +% +clear all; +%clc; +rand('state',0); randn('state',0); +X = 1; Q1 = 2; Q2 = 3; Y = 4; +% intra time-slice graph +intra=zeros(4); +intra(X,[Q1 Q2 Y])=1; +intra(Q1,[Q2 Y])=1; +intra(Q2, Y)=1; +% inter time-slice graph +inter=zeros(4); +inter(Q1,Q1)=1; +inter(Q2,Q2)=1; + +ns = [1 2 3 1]; +dnodes = [2 3]; +eclass1 = [1 2 3 4]; +eclass2 = [1 5 6 4]; +bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2); + +bnet.CPD{1} = root_CPD(bnet, 1); +% ========================================= +bnet.CPD{2} = softmax_CPD(bnet, 2); +bnet.CPD{3} = softmax_CPD(bnet, 3, 'discrete', [2]); +bnet.CPD{5} = softmax_CPD(bnet, 6); +bnet.CPD{6} = softmax_CPD(bnet, 7, 'discrete', [3 6]); +% ========================================= +bnet.CPD{4} = gaussian_CPD(bnet, 4); + +% make some data +T=20; +cases = cell(4, T); +cases(1,:)=num2cell(round(rand(1,T)*2)+1); +%cases(2,:)=num2cell(round(rand(1,T))+1); +%cases(3,:)=num2cell(round(rand(1,T)*2)+1); +cases(4,:)=num2cell(rand(1,T)); + +engine = bk_inf_engine(bnet, 'exact', [1 2 3 4]); + +% log lik before learning +[engine, loglik] = enter_evidence(engine, cases); + +% do learning +ev=cell(1,1); +ev{1}=cases; +[bnet2, LL2] = learn_params_dbn_em(engine, ev, 10); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/Belief_hme.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +% Sigmoid Belief Hierarchical Mixtures of Experts + +clear all +clc +X = 1; +Q1 = 2; +Q2 = 3; +Y = 4; +dag = zeros(4,4); +dag(X,[Q1 Q2 Y]) = 1; +dag(Q1, [Q2 Y]) = 1; +dag(Q2,Y)=1; +ns = [1 3 4 3]; +dnodes = [2 3 4]; +onodes=[1 2 3 4]; +bnet = mk_bnet(dag,ns, dnodes); + +rand('state',0); randn('state',0); + +bnet.CPD{1} = root_CPD(bnet, 1); +bnet.CPD{2} = softmax_CPD(bnet, 2, 'max_iter', 3); +bnet.CPD{3} = softmax_CPD(bnet, 3, 'discrete', [2], 'max_iter', 3); +bnet.CPD{4} = softmax_CPD(bnet, 4, 'discrete', [2 3], 'max_iter', 3); + +T=5; +cases = cell(4, T); +cases(1,:)=num2cell(rand(1,T)); +%cases(2,:)=num2cell(round(rand(1,T)*2)+1); +%cases(3,:)=num2cell(round(rand(1,T)*3)+1); +cases(4,:)=num2cell(round(rand(1,T)*2)+1); + +engine = jtree_inf_engine(bnet, onodes); + +[engine, loglik] = enter_evidence(engine, cases); + +disp('learning-------------------------------------------') +[bnet2, LL2] = learn_params_em(engine, cases, 4); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/Belief_IOhmm.m/1.1.1.1/Wed May 29 15:59:54 2002// +/Belief_hmdt.m/1.1.1.1/Wed May 29 15:59:54 2002// +/Belief_hme.m/1.1.1.1/Wed May 29 15:59:54 2002// +/Sigmoid_Belief.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/Brutti
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Brutti/Sigmoid_Belief.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +% Sigmoid Belief Net + +clear all +clc +dum1 = 1; +dum2 = 2; +dum3 = 3; +Q1 = 4; +Q2 = 5; +Y = 6; +dag = zeros(6,6); +dag(dum1,[Q1 Y]) = 1; +dag(dum2, Q2)=1; +dag(dum3, [Q1 Q2])=1; +dag(Q1,[Q2 Y]) = 1; +dag(Q2, Y)=1; + +ns = [2 2 3 3 4 3]; +dnodes = [1:6]; +bnet = mk_bnet(dag,ns, dnodes); + +rand('state',0); randn('state',0); +n_iter=10; +clamped=0; + +bnet.CPD{1} = tabular_CPD(bnet, 1); +bnet.CPD{2} = tabular_CPD(bnet, 2); +bnet.CPD{3} = tabular_CPD(bnet, 3); +% CPD = dsoftmax_CPD(bnet, self, dummy_pars, w, b, clamped, max_iter, verbose, wthresh,... +% llthresh, approx_hess) +bnet.CPD{4} = softmax_CPD(bnet, 4, 'discrete', [1 3]); +bnet.CPD{5} = softmax_CPD(bnet, 5, 'discrete', [2 3]); +bnet.CPD{6} = softmax_CPD(bnet, 6, 'discrete', [1 4]); + +T=5; +cases = cell(6, T); +cases(1,:)=num2cell(round(rand(1,T)*1)+1); +%cases(2,:)=num2cell(round(rand(1,T)*1)+1); +cases(3,:)=num2cell(round(rand(1,T)*2)+1); +cases(4,:)=num2cell(round(rand(1,T)*2)+1); +%cases(5,:)=num2cell(round(rand(1,T)*3)+1); +cases(6,:)=num2cell(round(rand(1,T)*2)+1); + +engine = jtree_inf_engine(bnet); + +[engine, loglik] = enter_evidence(engine, cases); + +disp('learning-------------------------------------------') +[bnet2, LL2, eng2] = learn_params_em(engine, cases, n_iter); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +/brainy.m/1.1.1.1/Sun Feb 22 19:43:32 2004// +/burglar-alarm-net.lisp.txt/1.1.1.1/Thu Mar 4 22:27:48 2004// +/burglary.m/1.1.1.1/Thu Mar 4 22:34:14 2004// +/cg1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/cg2.m/1.1.1.1/Wed May 29 15:59:54 2002// +/cmp_inference_static.m/1.2/Sat Sep 17 16:59:57 2005// +/discrete1.m/1.1.1.1/Mon Jun 7 19:45:06 2004// +/discrete2.m/1.1.1.1/Wed May 29 15:59:54 2002// +/discrete3.m/1.1.1.1/Wed May 29 15:59:54 2002// +/fa1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/gaussian1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/gaussian2.m/1.1.1.1/Thu Jun 10 01:31:02 2004// +/gibbs_test1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/learn1.m/1.1.1.1/Sat Feb 28 17:25:40 2004// +/lw1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mfa1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mixexp1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mixexp2.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mixexp3.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mog1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mpe1.m/1.1.1.1/Wed Jun 19 22:08:58 2002// +/mpe2.m/1.1.1.1/Wed Jun 19 22:09:08 2002// +/nodeorderExample.m/1.1.1.1/Thu Jun 10 01:42:04 2004// +/qmr1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/qmr2.m/1.1.1.1/Thu Nov 14 01:01:46 2002// +/sample1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/softev1.m/1.1.1.1/Wed Jun 19 23:59:18 2002// +/softmax1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sprinkler1.m/1.1.1.1/Sun Sep 12 21:01:38 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +A D/Belprop//// +A D/Brutti//// +A D/HME//// +A D/Misc//// +A D/Models//// +A D/SCG//// +A D/StructLearn//// +A D/Zoubin//// +A D/dtree//// +A D/fgraph////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +/HMEforMatlab.jpg/1.1.1.1/Wed May 29 15:59:54 2002// +/README/1.1.1.1/Wed May 29 15:59:54 2002// +/fhme.m/1.1.1.1/Wed May 29 15:59:54 2002// +/gen_data.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hme_class_plot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hme_reg_plot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hme_topobuilder.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hmemenu.m/1.1.1.1/Thu Feb 12 12:57:28 2004// +/test_data_class.mat/1.1.1.1/Wed May 29 15:59:54 2002// +/test_data_class2.mat/1.1.1.1/Wed May 29 15:59:54 2002// +/test_data_reg.mat/1.1.1.1/Wed May 29 15:59:54 2002// +/train_data_class.mat/1.1.1.1/Wed May 29 15:59:54 2002// +/train_data_reg.mat/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/HME
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +This directory contains code for hierarchical mixture of experts, +written by Pierpaolo Brutti (May 2001). Run the file hmemenu to get started.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/fhme.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,109 @@ +function risultati = fhme(net, nodes_info, data, n) +%HMEFWD Forward propagation through an HME model +% +% Each row of the (n x class_num) matrix 'risultati' containes the estimated class posterior prob. +% +% ---------------------------------------------------------------------------------------------------- +% -> pierpaolo_b@hotmail.com or -> pampo@interfree.it +% ---------------------------------------------------------------------------------------------------- +% +ns=net.node_sizes; +if nargin==3 + ndata=n; +else + ndata=size(data, 1); +end +altezza=size(ns,2); +coeff=cell(altezza-1,1); +for m=1:ndata + %- i=2 -------------------------------------------------------------------------------------- + s=struct(net.CPD{2}); + if nodes_info(1,2)==0, + mu=[]; W=[]; predict=[]; + mu=s.mean(:,:); + W=s.weights(:,:,:); + predict=mu(:,:)+W(:,:,:)*data(m,:)'; + coeff{1,1}=predict'; + elseif nodes_info(1,2)==1, + coeff{1,1}=fglm(s.glim{1}, data(m,:)); + else, + coeff{1,1}=fmlp(s.mlp{1}, data(m,:)); + end + %---------------------------------------------------------------------------------------------- + if altezza>3, + for i=3:altezza-1, + s=[]; f=[]; dpsz=[]; + f=family(net.dag,i); f=f(2:end-1); dpsz=prod(ns(f)); + s=struct(net.CPD{i}); + for j=1:dpsz, + if nodes_info(1,i)==1, + coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*fglm(s.glim{j}, data(m,:)); + else + coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*fmlp(s.mlp{j}, data(m,:)); + end + end + app=cat(2, coeff{i-1,1}(:)); coeff{i-1,1}=app'; clear app; + end + end + %- i=altezza ---------------------------------------------------------------------------------- + if altezza>2, + i=altezza; + s=[]; f=[]; dpsz=[]; + f=family(net.dag,i); f=f(2:end-1); dpsz=prod(ns(f)); + s=struct(net.CPD{i}); + if nodes_info(1,i)==0, + mu=[]; W=[]; + mu=s.mean(:,:); + W=s.weights(:,:,:); + end + for j=1:dpsz, + if nodes_info(1,i)==0, + predict=[]; + predict=mu(:,j)+W(:,:,j)*data(m,:)'; + coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*predict'; + elseif nodes_info(1,i)==1, + coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*fglm(s.glim{j}, data(m,:)); + else + coeff{i-1,1}(j,:)=coeff{i-2,1}(1,j)*fmlp(s.mlp{j}, data(m,:)); + end + end + end + %---------------------------------------------------------------------------------------------- + risultati(m,:)=sum(coeff{altezza-1,1},1); + clear coeff; coeff=cell(altezza-1,1); +end +return + +%------------------------------------------------------------------- + +function [y, a] = fglm(net, x) +%GLMFWD Forward propagation through 1-layer net->GLM statistical model + +ndata = size(x, 1); + +a = x*net.w1 + ones(ndata, 1)*net.b1; + +nout = size(a,2); +% Ensure that sum(exp(a), 2) does not overflow +maxcut = log(realmax) - log(nout); +% Ensure that exp(a) > 0 +mincut = log(realmin); +a = min(a, maxcut); +a = max(a, mincut); +temp = exp(a); +y = temp./(sum(temp, 2)*ones(1,nout)); + +%------------------------------------------------------------------- + +function [y, z, a] = fmlp(net, x) +%MLPFWD Forward propagation through 2-layer network. + +ndata = size(x, 1); + +z = tanh(x*net.w1 + ones(ndata, 1)*net.b1); +a = z*net.w2 + ones(ndata, 1)*net.b2; +temp = exp(a); +nout = size(a,2); +y = temp./(sum(temp,2)*ones(1,nout)); + +%-------------------------------------------------------------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/gen_data.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function [data, ndata1, ndata2, targets]=gen_data(ndata, seed) +% Generate data from three classes in 2d +% Setting 'seed' for reproducible results +% OUTPUT +% data : data set +% ndata1, ndata2: separator + +if nargin<1, + error('Missing data size'); +end + +input_dim = 2; +num_classes = 3; + +if nargin==2, + % Fix seeds for reproducible results + randn('state', seed); + rand('state', seed); +end + +% Generate mixture of three Gaussians in two dimensional space +data = randn(ndata, input_dim); +targets = zeros(ndata, 3); + +% Priors for the clusters +prior(1) = 0.4; +prior(2) = 0.3; +prior(3) = 0.3; + +% Cluster centres +c = [2.0, 2.0; 0.0, 0.0; 1, -1]; + +ndata1 = round(prior(1)*ndata); +ndata2 = round((prior(1) + prior(2))*ndata); +% Put first cluster at (2, 2) +data(1:ndata1, 1) = data(1:ndata1, 1) * 0.5 + c(1,1); +data(1:ndata1, 2) = data(1:ndata1, 2) * 0.5 + c(1,2); +targets(1:ndata1, 1) = 1; + +% Leave second cluster at (0,0) +data((ndata1 + 1):ndata2, :) = data((ndata1 + 1):ndata2, :); +targets((ndata1+1):ndata2, 2) = 1; + +data((ndata2+1):ndata, 1) = data((ndata2+1):ndata,1) *0.6 + c(3, 1); +data((ndata2+1):ndata, 2) = data((ndata2+1):ndata,2) *0.6 + c(3, 2); +targets((ndata2+1):ndata, 3) = 1; + +if 0 + ndata = 1; + data = x; + targets = [1 0 0]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/hme_class_plot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,142 @@ +function fh=hme_class_plot(net, nodes_info, train_data, test_data) +% +% Use this function ONLY when the input dimension is 2 +% and the problem is a classification one. +% We assume that each row of 'train_data' & 'test_data' is an example. +% +%------Line Spec------------------------------------------------------------------------ +% +% LineWidth - specifies the width (in points) of the line +% MarkerEdgeColor - specifies the color of the marker or the edge color +% forfilled markers (circle, square, diamond, pentagram, hexagram, and the +% four triangles). +% MarkerFaceColor - specifies the color of the face of filled markers. +% MarkerSize - specifies the size of the marker in points. +% +% Example +% ------- +% plot(t,sin(2*t),'-mo',... +% 'LineWidth',2,... +% 'MarkerEdgeColor','k',... % 'k'=black +% 'MarkerFaceColor',[.49 1 .63],... % RGB color +% 'MarkerSize',12) +%---------------------------------------------------------------------------------------- + +class_num=nodes_info(2,end); +mn_x = round(min(train_data(:,1))); mx_x = round(max(train_data(:,1))); +mn_y = round(min(train_data(:,2))); mx_y = round(max(train_data(:,2))); +if nargin==4, + mn_x = round(min([train_data(:,1); test_data(:,1)])); + mx_x = round(max([train_data(:,1); test_data(:,1)])); + mn_y = round(min([train_data(:,2); test_data(:,2)])); + mx_y = round(max([train_data(:,1); test_data(:,2)])); +end +x = mn_x(1)-1:0.2:mx_x(1)+1; +y = mn_y(1)-1:0.2:mx_y(1)+1; +[X, Y] = meshgrid(x,y); +X = X(:); +Y = Y(:); +num_g=size(X,1); +griglia = [X Y]; +rand('state',1); +if class_num<=6, + colors=['r'; 'g'; 'b'; 'c'; 'm'; 'y']; +else + colors=rand(class_num, 3); % each row is an RGB color +end +fh = figure('Name','Data & decision boundaries', 'MenuBar', 'none', 'NumberTitle', 'off'); +ms=5; % Marker Size +if nargin==4, +% ms=4; % Marker Size + subplot(1,2,1); +end +% Plot of train_set ------------------------------------------------------------------------- +axis([mn_x-1 mx_x+1 mn_y-1 mx_y+1]); +set(gca, 'Box', 'on'); +c_max_train = max(train_data(:,3)); +hold on +for m=1:c_max_train, + app_x=train_data(:,1); + app_y=train_data(:,2); + thisX=app_x(train_data(:,3)==m); + thisY=app_y(train_data(:,3)==m); + if class_num<=6, + str_col=[]; + str_col=['o', colors(m,:)]; + plot(thisX, thisY, str_col, 'MarkerSize', ms); + else + plot(thisX, thisY, 'o',... + 'LineWidth', 1,... + 'MarkerEdgeColor', colors(m,:), 'MarkerSize', ms) + end +end +%---hmefwd_generale(net,data,ndata)----------------------------------------------------------- +Z=fhme(net, nodes_info, griglia, num_g); % forward propagation trougth the HME +%--------------------------------------------------------------------------------------------- +[foo , class] = max(Z'); % 0/1 loss function => we assume that the true class is the one with the + % maximum posterior prob. +class = class'; +for m = 1:class_num, + thisX=[]; thisY=[]; + thisX = X(class == m); + thisY = Y(class == m); + if class_num<=6, + str_col=[]; + str_col=['d', colors(m,:)]; + h=plot(thisX, thisY, str_col); + else + h = plot(thisX, thisY, 'd',... + 'MarkerEdgeColor',colors(m,:),... + 'MarkerFaceColor','w'); + end + set(h, 'MarkerSize', 4); +end +title('Training set and Decision Boundaries (0/1 loss)') +hold off + +% Plot of test_set -------------------------------------------------------------------------- +if nargin==4, + subplot(1,2,2); + axis([mn_x-1 mx_x+1 mn_y-1 mx_y+1]); + set(gca, 'Box', 'on'); + hold on + if size(test_data,2)==3, % we know the classification of the test set examples + c_max_test = max(test_data(:,3)); + for m=1:c_max_test, + app_x=test_data(:,1); + app_y=test_data(:,2); + thisX=app_x(test_data(:,3)==m); + thisY=app_y(test_data(:,3)==m); + if class_num<=6, + str_col=[]; + str_col=['o', colors(m,:)]; + plot(thisX, thisY, str_col, 'MarkerSize', ms); + else + plot(thisX, thisY, 'o',... + 'LineWidth', 1,... + 'MarkerEdgeColor', colors(m,:),... + 'MarkerSize',ms); + end + end + else + plot(test_data(:,1), test_data(:,2), 'ko',... + 'MarkerSize', ms); + end + for m = 1:class_num, + thisX=[]; thisY=[]; + thisX = X(class == m); + thisY = Y(class == m); + if class_num<=6, + str_col=[]; + str_col=['d', colors(m,:)]; + h=plot(thisX, thisY, str_col); + else + h = plot(thisX, thisY, 'd',... + 'MarkerEdgeColor', colors(m,:),... + 'MarkerFaceColor','w'); + end + set(h, 'MarkerSize', 4); + end + title('Test set and Decision Boundaries (0/1 loss)') + hold off +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/hme_reg_plot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function fh=hme_reg_plot(net, nodes_info, train_data, test_data) +% +% Use this function ONLY when the input dimension is 1 +% and the problem is a regression one. +% We assume that each row of 'train_data' & 'test_data' is an example. +% +% ---------------------------------------------------------------------------------------------------- +% -> pierpaolo_b@hotmail.com or -> pampo@interfree.it +% ---------------------------------------------------------------------------------------------------- + +fh=figure('Name','HME based regression', 'MenuBar', 'none', 'NumberTitle', 'off'); + +mn_x_train = round(min(train_data(:,1))); +mx_x_train = round(max(train_data(:,1))); +x_train = mn_x_train(1):0.01:mx_x_train(1); +Z_train=fhme(net, nodes_info, x_train',size(x_train,2)); % forward propagation trougth the HME + +if nargin==4, + subplot(2,1,1); + mn_x_test = round(min(test_data(:,1))); + mx_x_test = round(max(test_data(:,1))); + x_test = mn_x_test(1):0.01:mx_x_test(1); + Z_test=fhme(net, nodes_info, x_test',size(x_test,2)); % forward propagation trougth the HME +end + +hold on; +set(gca, 'Box', 'on'); +plot(x_train', Z_train, 'r'); +plot(train_data(:,1),train_data(:,2),'+k'); +title('Training set and prediction'); +hold off + +if nargin==4, + subplot(2,1,2); + hold on; + set(gca, 'Box', 'on'); + plot(x_train', Z_train, 'r'); + if size(test_data,2)==2, + plot(test_data(:,1),test_data(:,2),'+k'); + end + title('Test set and prediction'); + hold off +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/hme_topobuilder.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function [bnet, onodes]=hme_topobuilder(nodes_info); +% +% HME topology builder +% +% ---------------------------------------------------------------------------------------------------- +% -> pierpaolo_b@hotmail.com or -> pampo@interfree.it +% ---------------------------------------------------------------------------------------------------- + +nodes_num=size(nodes_info,2); +dag = zeros(nodes_num); +list=[1:nodes_num]; +for i=1:(nodes_num-1) + app=[]; + app=list((i+1):end); + dag(i,app) = 1; +end +onodes = [1 nodes_num]; +dnodes = list(2:end-1); +if nodes_info(1,end)>0, + dnodes=[dnodes nodes_num]; +end +ns = nodes_info(2,:); + +bnet = mk_bnet(dag, ns, dnodes); +clamped = 0; + +bnet.CPD{1} = root_CPD(bnet, 1); + +rand('state', 50); +randn('state', 50); + +for i=2:nodes_num, + if (nodes_info(1,i)==0)&(nodes_info(4,i)==1), + bnet.CPD{i} = gaussian_CPD(bnet, i, [], [], [], 'full'); + elseif (nodes_info(1,i)==0)&(nodes_info(4,i)==2), + bnet.CPD{i} = gaussian_CPD(bnet, i, [], [], [], 'diag'); + elseif (nodes_info(1,i)==0)&(nodes_info(4,i)==3), + bnet.CPD{i} = gaussian_CPD(bnet, i, [], [], [], 'full', 'tied'); + elseif (nodes_info(1,i)==0)&(nodes_info(4,i)==4), + bnet.CPD{i} = gaussian_CPD(bnet, i, [], [], [], 'diag', 'tied'); + elseif nodes_info(1,i)==1, + %bnet.CPD{i} = dsoftmax_CPD(bnet, i, [], [], clamped, nodes_info(4,i)); + bnet.CPD{i} = softmax_CPD(bnet, i, 'clamped', clamped, 'max_iter', nodes_info(4,i)); + else + bnet.CPD{i} = mlp_CPD(bnet, i, nodes_info(3,i), [], [], [], [], clamped, nodes_info(4,i)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/hmemenu.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,552 @@ +% dataset -> (1=>user data) or (2=>toy example) +% type -> (1=> Regression model) or (2=>Classification model) +% num_glevel -> number of hidden nodes in the net (gating levels) +% num_exp -> number of experts in the net +% branch_fact -> dimension of the hidden nodes in the net +% cov_dim -> root node dimension +% res_dim -> output node dimension +% nodes_info -> 4 x num_glevel+2 matrix that contain all the info about the nodes: +% nodes_info(1,:) = nodes type: (0=>gaussian)or(1=>softmax)or(2=>mlp) +% nodes_info(2,:) = nodes size: [cov_dim num_glevel x branch_fact res_dim] +% nodes_info(3,:) = hidden units number (for mlp nodes) +% |- optimizer iteration number (for softmax & mlp CPD) +% nodes_info(4,:) =|- covariance type (for gaussian CPD)-> +% | (1=>Full)or(2=>Diagonal)or(3=>Full&Tied)or(4=>Diagonal&Tied) +% fh1 -> Figure: data & decizion boundaries; fh2 -> confusion matrix; fh3 -> LL trace +% test_data -> test data matrix +% train_data -> training data matrix +% ntrain -> size(train_data,2) +% ntest -> size(test_data,2) +% cases -> (cell array) training data formatted for the learning engine +% bnet -> bayesian net before learning +% bnet2 -> bayesian net after learning +% ll -> log-likelihood before learning +% LL2 -> log-likelihood trace +% onodes -> obs nodes in bnet & bnet2 +% max_em_iter -> maximum number of interations of the EM algorithm +% train_result -> prediction on the training set (as test_result) +% +% IMPORTANT: CHECK the loading path (lines 64 & 364) +% ---------------------------------------------------------------------------------------------------- +% -> pierpaolo_b@hotmail.com or -> pampo@interfree.it +% ---------------------------------------------------------------------------------------------------- + +error('this no longer works with the latest version of BNT') + +clear all; +clc; +disp('---------------------------------------------------'); +disp(' Hierarchical Mixtures of Experts models builder '); +disp('---------------------------------------------------'); +disp(' ') +disp(' Using this script you can build both an HME model') +disp('as in [Wat94] and [Jor94] i.e. with ''softmax'' gating') +disp('nodes and ''gaussian'' ( for regression ) or ''softmax''') +disp('( for classification ) expert node, and its variants') +disp('called ''gated nets'' where we use ''mlp'' models in') +disp('place of a number of ''softmax'' ones [Mor98], [Wei95].') +disp(' You can decide to train and test the model on your') +disp('datasets or to evaluate its performance on a toy') +disp('example.') +disp(' ') +disp('Reference') +disp('[Mor98] P. Moerland (1998):') +disp(' Localized mixtures of experts. (http://www.idiap.ch/~perry/)') +disp('[Jor94] M.I. Jordan, R.A. Jacobs (1994):') +disp(' HME and the EM algorithm. (http://www.cs.berkeley.edu/~jordan/)') +disp('[Wat94] S.R. Waterhouse, A.J. Robinson (1994):') +disp(' Classification using HME. (http://www.oigeeza.com/steve/)') +disp('[Wei95] A.S. Weigend, M. Mangeas (1995):') +disp(' Nonlinear gated experts for time series.') +disp(' ') + +if 0 +disp('(See the figure)') +pause(5); +%%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +im_path=which('HMEforMatlab.jpg'); +fig=imread(im_path, 'jpg'); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +figure('Units','pixels','MenuBar','none','NumberTitle','off', 'Name', 'HME model'); +image(fig); +axis image; +axis off; +clear fig; +set(gca,'Position',[0 0 1 1]) +disp('(Press any key to continue)') +pause +end + +clc +disp('---------------------------------------------------'); +disp(' Specify the Architecture '); +disp('---------------------------------------------------'); +disp(' '); +disp('What kind of model do you need?') +disp(' ') +disp('1) Regression ') +disp('2) Classification') +disp(' ') +type=input('1 or 2?: '); +if (isempty(type)|(~ismember(type,[1 2]))), error('Invalid value'); end +clc +disp('----------------------------------------------------'); +disp(' Specify the Architecture '); +disp('----------------------------------------------------'); +disp(' ') +disp('Now you have to set the number of experts and gating') +disp('levels in the net. This script builds only balanced') +disp('hierarchy with the same branching factor (>1)at each') +disp('(gating) level. So remember that: ') +disp(' ') +disp(' num_exp = branch_fact^num_glevel ') +disp(' ') +disp('with branch_fact >=2.') +disp('You can also set to zeros the number of gating level') +disp('in order to obtain a classical GLM model. ') +disp(' ') +disp('----------------------------------------------------'); +disp(' ') +num_glevel=input('Insert the number of gating levels {0,...,20}: '); +if (isempty(num_glevel)|(~ismember(num_glevel,[0:20]))), error('Invalid value'); end +nodes_info=zeros(4,num_glevel+2); +if num_glevel>0, %------------------------------------------------------------------------------------ + for i=2:num_glevel+1, + clc + disp('----------------------------------------------------'); + disp(' Specify the Architecture '); + disp('----------------------------------------------------'); + disp(' ') + disp(['-> Gating network ', num2str(i-1), ' is a: ']) + disp(' ') + disp(' 1) Softmax model'); + disp(' 2) Two layer perceptron model') + disp(' ') + nodes_info(1,i)=input('1 or 2?: '); + if (isempty(nodes_info(1,i))|(~ismember(nodes_info(1,i),[1 2]))), error('Invalid value'); end + disp(' ') + if nodes_info(1,i)==2, + nodes_info(3,i)=input('Insert the number of units in the hidden layer: '); + if (isempty(nodes_info(3,i))|(floor(nodes_info(3,i))~=nodes_info(3,i))|(nodes_info(3,i)<=0)), + error(['Invalid value: ', num2str(nodes_info(3,i)), ' is not a positive integer!']); + end + disp(' ') + end + nodes_info(4,i)=input('Insert the optimizer iteration number: '); + if (isempty(nodes_info(4,i))|(floor(nodes_info(4,i))~=nodes_info(4,i))|(nodes_info(4,i)<=0)), + error(['Invalid value: ', num2str(nodes_info(4,i)), ' is not a positive integer!']); + end + end + clc + disp('---------------------------------------------------------'); + disp(' Specify the Architecture '); + disp('---------------------------------------------------------'); + disp(' ') + disp('Now you have to set the number of experts in the network'); + disp('The value will be adjusted in order to obtain a hierarchy'); + disp('as said above.') + disp(' '); + num_exp=input(['Insert the approximative number of experts (>=', num2str(2^num_glevel), '): ']); + if (isempty(num_exp)|(num_exp<=0)|(num_exp<2^num_glevel)), + error('Invalid value'); + end + app1=0; base=2; + while app1<num_exp, + app1=base^num_glevel; + base=base+1; + end + app2=(base-2)^num_glevel; + branch_fact=base-1; + if app2>=(2^num_glevel)&(abs(app2-num_exp)<abs(app1-num_exp)), + branch_fact=base-2; + end + clear app1 app2 base; + disp(' ') + disp(['The effective number of experts in the net is: ', num2str(branch_fact^num_glevel), '.']) + disp(' '); +else + clc + disp('---------------------------------------------------------'); + disp(' Specify the Architecture (GLM model) '); + disp('---------------------------------------------------------'); + disp(' ') +end % END of: if num_glevel>0------------------------------------------------------------------------- + +if type==2, + disp(['-> Expert node is a: ']) + disp(' ') + disp(' 1) Softmax model'); + disp(' 2) Two layer perceptron model') + disp(' ') + nodes_info(1,end)=input('1 or 2?: '); + if (isempty(nodes_info(1,end))|(~ismember(nodes_info(1,end),[1 2]))), + error('Invalid value'); + end + disp(' ') + if nodes_info(1,end)==2, + nodes_info(3,end)=input('Insert the number of units in the hidden layer: '); + if (isempty(nodes_info(3,end))|(floor(nodes_info(3,end))~=nodes_info(3,end))|(nodes_info(3,end)<=0)), + error(['Invalid value: ', num2str(nodes_info(3,end)), ' is not a positive integer!']); + end + disp(' ') + end + nodes_info(4,end)=input('Insert the optimizer iteration number: '); + if (isempty(nodes_info(4,end))|(floor(nodes_info(4,end))~=nodes_info(4,end))|(nodes_info(4,end)<=0)), + error(['Invalid value: ', num2str(nodes_info(4,end)), ' is not a positive integer!']); + end +elseif type==1, + disp('What kind of covariance matrix structure do you want?') + disp(' ') + disp(' 1) Full'); + disp(' 2) Diagonal') + disp(' 3) Full & Tied'); + disp(' 4) Diagonal & Tied') + + disp(' ') + nodes_info(4,end)=input('1, 2, 3 or 4?: '); + if (isempty(nodes_info(4,end))|(~ismember(nodes_info(4,end),[1 2 3 4]))), + error('Invalid value'); + end +end +clc +disp('----------------------------------------------------'); +disp(' Specify the Input '); +disp('----------------------------------------------------'); +disp(' ') +disp('Do you want to...') +disp(' ') +disp('1) ...use your own dataset?') +disp('2) ...apply the model on a toy example?') +disp(' ') +dataset=input('1 or 2?: '); +if (isempty(dataset)|(~ismember(dataset,[1 2]))), error('Invalid value'); end +if dataset==1, + if type==1, + clc + disp('-------------------------------------------------------'); + disp(' Specify the Input - Regression problem '); + disp('-------------------------------------------------------'); + disp(' ') + disp('Be sure that each row of your data matrix is an example'); + disp('with the covariate values that precede the respond ones') + disp(' ') + disp('-------------------------------------------------------'); + disp(' ') + cov_dim=input('Insert the covariate space dimension: '); + if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)), + error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']); + end + disp(' ') + res_dim=input('Insert the dimension of the respond variable: '); + if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)), + error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']); + end + disp(' '); + elseif type==2 + clc + disp('-------------------------------------------------------'); + disp(' Specify the Input - Classification problem '); + disp('-------------------------------------------------------'); + disp(' ') + disp('Be sure that each row of your data matrix is an example'); + disp('with the covariate values that precede the class labels'); + disp('(integer value >=1). '); + disp(' ') + disp('-------------------------------------------------------'); + disp(' ') + cov_dim=input('Insert the covariate space dimension: '); + if (isempty(cov_dim)|(floor(cov_dim)~=cov_dim)|(cov_dim<=0)), + error(['Invalid value: ', num2str(cov_dim), ' is not a positive integer!']); + end + disp(' ') + res_dim=input('Insert the number of classes: '); + if (isempty(res_dim)|(floor(res_dim)~=res_dim)|(res_dim<=0)), + error(['Invalid value: ', num2str(res_dim), ' is not a positive integer!']); + end + disp(' ') + end + % ------------------------------------------------------------------------------------------------ + % Loading training data -------------------------------------------------------------------------- + % ------------------------------------------------------------------------------------------------ + train_path=input('Insert the complete (with extension) path of the training data file:\n >> ','s'); + if isempty(train_path), error('You must specify a data set for training!'); end + if ~isempty(findstr('.mat',train_path)), + ap=load(train_path); app=fieldnames(ap); train_data=eval(['ap.', app{1,1}]); + clear ap app; + elseif ~isempty(findstr('.txt',train_path)), + train_data=load(train_path, '-ascii'); + else + error('Invalid data format: not a .mat or a .txt file') + end + if (size(train_data,2)~=cov_dim+res_dim)&(type==1), + error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',... + num2str(cov_dim+res_dim),'!']); + elseif (size(train_data,2)~=cov_dim+1)&(type==2), + error(['Invalid data matrix size: ', num2str(size(train_data,2)), ' columns rather than ',... + num2str(cov_dim+1),'!']); + elseif (~isempty(find(ismember(intersect([train_data(:,end)' 1:res_dim],... + train_data(:,end)'),[1:res_dim])==0)))&(type==2), + error('Invalid class label'); + end + ntrain=size(train_data,1); + train_d=train_data(:,1:cov_dim); + if type==2, + train_t=zeros(ntrain, res_dim); + for m=1:res_dim, + train_t((find(train_data(:,end)==m))',m)=1; + end + else + train_t=train_data(:,cov_dim+1:end); + end + disp(' ') + % ------------------------------------------------------------------------------------------------ + % Loading test data ------------------------------------------------------------------------------ + % ------------------------------------------------------------------------------------------------ + disp('(If you don''t want to specify a test-set press ''return'' only)'); + test_path=input('Insert the complete (with extension) path of the test data file:\n >> ','s'); + if ~isempty(test_path), + if ~isempty(findstr('.mat',test_path)), + ap=load(test_path); app=fieldnames(ap); test_data=eval(['ap.', app{1,1}]); + clear ap app; + elseif ~isempty(findstr('.txt',test_path)), + test_data=load(test_path, '-ascii'); + else + error('Invalid data format: not a .mat or a .txt file') + end + if (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+res_dim)&(type==1), + error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',... + num2str(cov_dim+res_dim), ' or ', num2str(cov_dim), '!']); + elseif (size(test_data,2)~=cov_dim)&(size(test_data,2)~=cov_dim+1)&(type==2), + error(['Invalid data matrix size: ', num2str(size(test_data,2)), ' columns rather than ',... + num2str(cov_dim+1), ' or ', num2str(cov_dim), '!']); + elseif (~isempty(find(ismember(intersect([test_data(:,end)' 1:res_dim],... + test_data(:,end)'),[1:res_dim])==0)))&(type==2)&(size(test_data,2)==cov_dim+1), + error('Invalid class label'); + end + ntest=size(test_data,1); + test_d=test_data(:,1:cov_dim); + if (type==2)&(size(test_data,2)>cov_dim), + test_t=zeros(ntest, res_dim); + for m=1:res_dim, + test_t((find(test_data(:,end)==m))',m)=1; + end + elseif (type==1)&(size(test_data,2)>cov_dim), + test_t=test_data(:,cov_dim+1:end); + end + disp(' '); + end +else + clc + disp('----------------------------------------------------'); + disp(' Specify the Input '); + disp('----------------------------------------------------'); + disp(' ') + ntrain = input('Insert the number of examples in training (<500): '); + if (isempty(ntrain)|(floor(ntrain)~=ntrain)|(ntrain<=0)|(ntrain>500)), + error(['Invalid value: ', num2str(ntrain), ' is not a positive integer <500!']); + end + disp(' ') + test_path='toy'; + ntest = input('Insert the number of examples in test (<500): '); + if (isempty(ntest)|(floor(ntest)~=ntest)|(ntest<=0)|(ntest>500)), + error(['Invalid value: ', num2str(ntest), ' is not a positive integer <500!']); + end + + if type==2, + cov_dim=2; + res_dim=3; + seed = 42; + [train_d, ntrain1, ntrain2, train_t]=gen_data(ntrain, seed); + for m=1:ntrain + q=[]; q = find(train_t(m,:)==1); + train_data(m,:)=[train_d(m,:) q]; + end + [test_d, ntest1, ntest2, test_t]=gen_data(ntest); + for m=1:ntest + q=[]; q = find(test_t(m,:)==1); + test_data(m,:)=[test_d(m,:) q]; + end + else + cov_dim=1; + res_dim=1; + global HOME + %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + load([HOME '/examples/static/Misc/mixexp_data.txt'], '-ascii'); + %%%%%WARNING!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + train_data = mixexp_data(1:ntrain, :); + train_d=train_data(:,1:cov_dim); train_t=train_data(:,cov_dim+1:end); + test_data = mixexp_data(ntrain+1:ntrain+ntest, :); + test_d=test_data(:,1:cov_dim); + if size(test_data,2)>cov_dim, + test_t=test_data(:,cov_dim+1:end); + end + end +end +% Set the nodes dimension----------------------------------- +if num_glevel>0, + nodes_info(2,2:num_glevel+1)=branch_fact; +end +nodes_info(2,1)=cov_dim; nodes_info(2,end)=res_dim; +%----------------------------------------------------------- +% Prepare the training data for the learning engine--------- +%----------------------------------------------------------- +cases = cell(size(nodes_info,2), ntrain); +for m=1:ntrain, + cases{1,m}=train_data(m,1:cov_dim)'; + cases{end,m}=train_data(m,cov_dim+1:end)'; +end +%----------------------------------------------------------------------------------------------------- +[bnet onodes]=hme_topobuilder(nodes_info); +engine = jtree_inf_engine(bnet, onodes); +clc +disp('---------------------------------------------------------------------'); +disp(' L E A R N I N G '); +disp('---------------------------------------------------------------------'); +disp(' ') +ll = 0; +for l=1:ntrain + scritta=['example number: ', int2str(l),'---------------------------------------------']; + disp(scritta); + ev = cases(:,l); + [engine, loglik] = enter_evidence(engine, ev); + ll = ll + loglik; +end +disp(' ') +disp(['Log-likelihood before learning: ', num2str(ll)]); +disp(' ') +disp('(Press any key to continue)'); +pause +%----------------------------------------------------------- +clc +disp('---------------------------------------------------------------------'); +disp(' L E A R N I N G '); +disp('---------------------------------------------------------------------'); +disp(' ') +max_em_iter=input('Insert the maximum number of the EM algorithm iterations: '); +if (isempty(max_em_iter)|(floor(max_em_iter)~=max_em_iter)|(max_em_iter<=1)), + error(['Invalid value: ', num2str(ntest), ' is not a positive integer >1!']); +end +disp(' ') +disp(['Log-likelihood before learning: ', num2str(ll)]); +disp(' ') + +[bnet2, LL2] = learn_params_em(engine, cases, max_em_iter); +disp(' ') +fprintf('HME: loglik before learning %f, after %d iters %f\n', ll, length(LL2), LL2(end)); +disp(' ') +disp('(Press any key to continue)'); +pause +%----------------------------------------------------------------------------------- +% Classification problem: plot data & decision boundaries if the input data size = 2 +% Regression problem: plot data & prediction if the input data size = 1 +%----------------------------------------------------------------------------------- +if (type==2)&(nodes_info(2,1)==2)&(~isempty(test_path)), + fh1=hme_class_plot(bnet2, nodes_info, train_data, test_data); + disp(' '); + disp('(See the figure)'); +elseif (type==2)&(nodes_info(2,1)==2)&(isempty(test_path)), + fh1=hme_class_plot(bnet2, nodes_info, train_data); + disp(' '); + disp('(See the figure)'); +elseif (type==1)&(nodes_info(2,1)==1)&(~isempty(test_path)), + fh1=hme_reg_plot(bnet2, nodes_info, train_data, test_data); + disp(' '); + disp('(See the figure)'); +elseif (type==1)&(nodes_info(2,1)==1)&(isempty(test_path)), + fh1=hme_reg_plot(bnet2, nodes_info, train_data); + disp(' ') + disp('(See the figure)'); +end +%----------------------------------------------------------------------------------- +% Classification problem: plot confusion matrix +%----------------------------------------------------------------------------------- +if (type==2) + ztrain=fhme(bnet2, nodes_info, train_d, size(train_d,1)); + [Htrain, trainRate]=confmat(ztrain, train_t); % CM on the training set + fh2=figure('Name','Confusion matrix', 'MenuBar', 'none', 'NumberTitle', 'off'); + if (~isempty(test_path))&(size(test_data,2)>cov_dim), + ztest=fhme(bnet2, nodes_info, test_d, size(test_d,1)); + [Htest, testRate]=confmat(ztest, test_t); % CM on the test set + subplot(1,2,1); + end + plotmat(Htrain,'b','k',12) + tick=[0.5:1:(0.5+nodes_info(2,end)-1)]; + set(gca,'XTick',tick) + set(gca,'YTick',tick) + grid('off') + ylabel('True') + xlabel('Prediction') + title(['Confusion Matrix: training set (' num2str(trainRate(1)) '%)']) + if (~isempty(test_path))&(size(test_data,2)>cov_dim), + subplot(1,2,2) + plotmat(Htest,'b','k',12) + set(gca,'XTick',tick) + set(gca,'YTick',tick) + grid('off') + ylabel('True') + xlabel('Prediction') + title(['Confusion Matrix: test set (' num2str(testRate(1)) '%)']) + end + disp(' ') + disp('(Press any key to continue)'); + pause +end +%----------------------------------------------------------------------------------- +% Regression & Classification problem: calculate the predictions & plot the LL trace +%----------------------------------------------------------------------------------- +train_result=fhme(bnet2,nodes_info,train_d,size(train_d,1)); +if ~isempty(test_path), + test_result=fhme(bnet2,nodes_info,test_d,size(test_d,1)); +end +fh3=figure('Name','Log-likelihood trace', 'MenuBar', 'none', 'NumberTitle', 'off') +plot(LL2,'-ro',... + 'MarkerEdgeColor','k',... + 'MarkerFaceColor',[1 1 0],... + 'MarkerSize',4) +title('Log-likelihood trace') +%----------------------------------------------------------------------------------- +% Regression & Classification problem: save the predictions +%----------------------------------------------------------------------------------- +clc +disp('------------------------------------------------------------------'); +disp(' Save the results '); +disp('------------------------------------------------------------------'); +disp(' ') +%----------------------------------------------------------------------------------- +save_quest_m=input('Do you want to save the HME model (Y/N)? [Y default]: ', 's'); +if isempty(save_quest_m), + save_quest_m='Y'; +end +if ~findstr(save_quest_m, ['Y', 'N']), error('Invalid input'); end +if save_quest_m=='Y', + disp(' '); + m_save=input('Insert the complete path for save the HME model (.mat):\n >> ', 's'); + if isempty(m_save), error('You must specify a path!'); end + save(m_save, 'bnet2'); +end +%----------------------------------------------------------------------------------- +disp(' ') +save_quest=input('Do you want to save the HME predictions (Y/N)? [Y default]: ', 's'); +disp(' ') +if isempty(save_quest), + save_quest='Y'; +end +if ~findstr(save_quest, ['Y', 'N']), error('Invalid input'); end +if save_quest=='Y', + tr_save=input('Insert the complete path for save the training data prediction (.mat):\n >> ', 's'); + if isempty(tr_save), error('You must specify a path!'); end + save(tr_save, 'train_result'); + if ~isempty(test_path), + disp(' ') + te_save=input('Insert the complete path for save the test data prediction (.mat):\n >> ', 's'); + if isempty(te_save), error('You must specify a path!'); end + save(te_save, 'test_result'); + end +end +clc +disp('----------------------------------------------------'); +disp(' B Y E ! '); +disp('----------------------------------------------------'); +pause(2) +%clear +clc
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Misc/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/mixexp_data.txt/1.1.1.1/Wed May 29 15:59:54 2002// +/mixexp_graddesc.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mixexp_plot.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sprinkler.bif/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Misc/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/Misc
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Misc/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Misc/mixexp_data.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1000 @@ + 0.68 0.65 + -0.31 -0.24 + 0.85 0.94 + -0.34 -0.31 + -0.58 0.62 + -0.04 -0.11 + -0.82 0.61 + -0.38 0.16 + 0.71 0.75 + 0.80 0.95 + 0.66 0.64 + -0.11 -0.03 + -0.16 0.11 + -0.47 0.75 + 0.09 0.43 + 0.04 -0.36 + -0.80 0.77 + -0.63 0.79 + -0.13 0.46 + 0.49 0.78 + 0.93 1.12 + 0.08 -0.35 + 0.27 -0.19 + -0.66 0.56 + 0.98 1.02 + 0.13 -0.16 + -0.21 0.10 + 0.41 0.29 + 0.18 0.34 + -0.46 0.21 + 0.03 -0.24 + 0.96 1.42 + 0.96 1.45 + 0.93 1.12 + 0.08 -0.21 + -0.49 0.39 + 0.66 0.21 + -0.70 0.45 + -0.81 0.57 + -0.17 0.10 + -0.22 0.03 + 0.24 0.32 + -0.07 0.31 + -0.47 0.58 + 0.41 0.28 + -0.16 0.03 + 0.97 0.79 + -0.07 -0.09 + 0.22 0.32 + -0.71 0.73 + 0.51 0.22 + 0.21 0.57 + 0.64 0.58 + -0.65 0.74 + 0.05 0.55 + 0.41 0.67 + -0.96 1.36 + -0.35 0.03 + -0.38 -0.15 + 0.61 0.71 + -0.93 0.75 + 0.98 0.99 + 0.58 0.61 + 0.53 0.62 + -0.24 -0.04 + -0.84 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Misc/mixexp_graddesc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ + +%%%%%%%%%% + +function [theta, eta] = mixture_of_experts(q, data, num_iter, theta, eta) +% MIXTURE_OF_EXPERTS Fit a piecewise linear regression model using stochastic gradient descent. +% [theta, eta] = mixture_of_experts(q, data, num_iter) +% +% Inputs: +% q = number of pieces (experts) +% data(l,:) = input example l +% +% Outputs: +% theta(i,:) = regression vector for expert i +% eta(i,:) = softmax (gating) params for expert i + +[num_cases dim] = size(data); +data = [ones(num_cases,1) data]; % prepend with offset +mu = 0.5; % step size +sigma = 1; % variance of noise + +if nargin < 4 + theta = 0.1*rand(q, dim); + eta = 0.1*rand(q, dim); +end + +for t=1:num_iter + for iter=1:num_cases + x = data(iter, 1:dim); + ystar = data(iter, dim+1); % target + % yhat(i) = E[y | Q=i, x] = prediction of i'th expert + yhat = theta * x'; + % gate_prior(i,:) = Pr(Q=i | x) + gate_prior = exp(eta * x'); + gate_prior = gate_prior / sum(gate_prior); + % lik(i) = Pr(y | Q=i, x) + lik = (1/(sqrt(2*pi)*sigma)) * exp(-(0.5/sigma^2) * ((ystar - yhat) .* (ystar - yhat))); + % gate_posterior(i,:) = Pr(Q=i | x, y) + gate_posterior = gate_prior .* lik; + gate_posterior = gate_posterior / sum(gate_posterior); + % Update + eta = eta + mu*(gate_posterior - gate_prior)*x; + theta = theta + mu*(gate_posterior .* (ystar - yhat))*x; + end + + if mod(t,100)==0 + fprintf(1, 'iter %d\n', t); + end + +end +fprintf(1, '\n'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Misc/mixexp_plot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function plot_mixexp(theta, eta, data) +% PLOT_MIXEXP Plot the results for a piecewise linear regression model +% plot_mixexp(theta, eta, data) +% +% data(l,:) = [x y] for example l +% theta(i,:) = regression vector for expert i +% eta(i,:) = softmax (gating) params for expert i + +numexp = size(theta, 1); + +mn = min(data); +mx = max(data); +xa = mn(1):0.01:mx(1); +x = [ones(length(xa),1) xa']; +% pr(i,l) = posterior probability of expert i on example l +pr = exp(eta * x'); +pr = pr ./ (ones(numexp,1) * sum(pr)); +% y(i,l) = prediction of expert i for example l +y = theta * x'; +% yg(l) = weighted prediction for example l +yg = sum(y .* pr)'; + +subplot(3,2,1); +plot(xa, y(1,:)); +title('expert 1'); + +subplot(3,2,2); +plot(xa, y(2,:)); +title('expert 2'); + +subplot(3,2,3); +plot(xa, pr(1,:)); +title('gating 1'); + +subplot(3,2,4); +plot(xa, pr(2,:)); +title('gating 2'); + +subplot(3,2,5); +plot(xa, yg); +axis([-1 1 -1 2]) +title('prediction'); + +subplot(3,2,6); +title('data'); +hold on +plot(data(:,1), data(:,2), '+'); +hold off +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Misc/sprinkler.bif Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +network Grass + {} +variable Cloudy + { type discrete[2] {false true}; } +variable Sprinkler + { type discrete[2] {false true}; } +variable Rain + { type discrete[2] {false true}; } +variable WetGrass + { type discrete[2] {false true}; } +probability (Cloudy) + { table 0.5 0.5; } +probability (Sprinkler | Cloudy) + { table 0.5 0.9 0.5 0.1; } +probability (Rain | Cloudy) + { table 0.8 0.2 0.2 0.8; } +probability (WetGrass | Rain Sprinkler) + { table 1.0 0.1 0.1 0.01 0.0 0.9 0.9 0.99; }
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +/mk_alarm_bnet.m/1.1.1.1/Sun Nov 3 16:44:14 2002// +/mk_asia_bnet.m/1.1.1.1/Wed Mar 26 00:06:42 2003// +/mk_cancer_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_car_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_hmm_bnet.m/1.1.1.1/Thu Jan 15 01:06:12 2004// +/mk_ideker_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_incinerator_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_markov_chain_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_minimal_qmr_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_qmr_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_vstruct_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/Models
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/mk_hmm_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/Models/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/Old/mk_hmm_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function [bnet, onodes] = mk_hmm_bnet(T, Q, O, cts_obs, param_tying) +% MK_HMM_BNET Make a (static( bnet to represent a hidden Markov model +% [bnet, onodes] = mk_hmm_bnet(T, Q, O, cts_obs, param_tying) +% +% T = num time slices +% Q = num hidden states +% O = size of the observed node (num discrete values or length of vector) +% cts_obs - 1 means the observed node is a continuous-valued vector, 0 means it's discrete +% param_tying - 1 means we create 3 CPDs, 0 means we create 1 CPD per node + +N = 2*T; +dag = zeros(N); +for i=1:T-1 + dag(i,i+1)=1; +end +onodes = T+1:N; +for i=1:T + dag(i, onodes(i)) = 1; +end + +if cts_obs + dnodes = 1:T; +else + dnodes = 1:N; +end +ns = [Q*ones(1,T) O*ones(1,T)]; + +if param_tying + eclass = [1 2*ones(1,T-1) 3*ones(1,T)]; +else + eclass = 1:N; +end + +bnet = mk_bnet(dag, ns, dnodes, eclass); + +hnodes = mysetdiff(1:N, onodes); +if ~param_tying + for i=hnodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i); + end + if cts_obs + for i=onodes(:)' + bnet.CPD{i} = gaussian_CPD(bnet, i); + end + else + for i=onodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i); + end + end +else + bnet.CPD{1} = tabular_CPD(bnet, 1); + bnet.CPD{2} = tabular_CPD(bnet, 2); + if cts_obs + bnet.CPD{3} = gaussian_CPD(bnet, 3); + else + bnet.CPD{3} = tabular_CPD(bnet, 3); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_alarm_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,118 @@ +function bnet = mk_alarm_bnet() + +% Written by Qian Diao <qian.diao@intel.com> on 11 Dec 01 + +N = 37; +dag = zeros(N,N); +dag(21,23) = 1 ; +dag(21,24) = 1 ; +dag(1,24) = 1 ; +dag(1,23) = 1 ; +dag(2,26) = 1 ; +dag(2,25) = 1 ; +dag(2,24) = 1 ; +dag(2,13) = 1 ; +dag(2,23) = 1 ; +dag(13,30) = 1 ; +dag(30,31) = 1 ; +dag(3,14) = 1 ; +dag(3,19) = 1 ; +dag(4,36) = 1 ; +dag(14,35) = 1 ; +dag(32,33) = 1 ; +dag(32,35) = 1 ; +dag(32,34) = 1 ; +dag(32,36) = 1 ; +dag(15,21) = 1 ; +dag(5,31) = 1 ; +dag(27,30) = 1 ; +dag(28,31) = 1 ; +dag(28,29) = 1 ; +dag(26,28) = 1 ; +dag(26,27) = 1 ; +dag(16,31) = 1 ; +dag(16,37) = 1 ; +dag(23,26) = 1 ; +dag(23,29) = 1 ; +dag(23,25) = 1 ; +dag(6,15) = 1 ; +dag(7,27) = 1 ; +dag(8,21) = 1 ; +dag(19,20) = 1 ; +dag(19,22) = 1 ; +dag(31,32) = 1 ; +dag(9,14) = 1 ; +dag(9,17) = 1 ; +dag(9,19) = 1 ; +dag(10,33) = 1 ; +dag(10,34) = 1 ; +dag(11,16) = 1 ; +dag(12,13) = 1 ; +dag(12,18) = 1 ; +dag(35,37) = 1 ; + +node_sizes = 2*ones(1,N); +node_sizes(2) = 3; +node_sizes(6) = 3; +node_sizes(14) = 3; +node_sizes(15) = 4; +node_sizes(16) = 3; +node_sizes(18) = 3; +node_sizes(19) = 3; +node_sizes(20) = 3; +node_sizes(21) = 4; +node_sizes(22) = 3; +node_sizes(23) = 4; +node_sizes(24) = 4; +node_sizes(25) = 4; +node_sizes(26) = 4; +node_sizes(27) = 3; +node_sizes(28) = 3; +node_sizes(29) = 4; +node_sizes(30) = 3; +node_sizes(32) = 3; +node_sizes(33) = 3; +node_sizes(34) = 3; +node_sizes(35) = 3; +node_sizes(36) = 3; +node_sizes(37) = 3; + +bnet = mk_bnet(dag, node_sizes); + +bnet.CPD{1} = tabular_CPD(bnet, 1,[0.96 0.04 ]); +bnet.CPD{2} = tabular_CPD(bnet, 2,[0.92 0.03 0.05 ]); +bnet.CPD{3} = tabular_CPD(bnet, 3,[0.8 0.2 ]); +bnet.CPD{4} = tabular_CPD(bnet, 4,[0.95 0.05 ]); +bnet.CPD{5} = tabular_CPD(bnet, 5,[0.8 0.2 ]); +bnet.CPD{6} = tabular_CPD(bnet, 6,[0.01 0.98 0.01 ]); +bnet.CPD{7} = tabular_CPD(bnet, 7,[0.01 0.99 ]); +bnet.CPD{8} = tabular_CPD(bnet, 8,[0.95 0.05 ]); +bnet.CPD{9} = tabular_CPD(bnet, 9,[0.95 0.05 ]); +bnet.CPD{10} = tabular_CPD(bnet, 10,[0.9 0.1 ]); +bnet.CPD{11} = tabular_CPD(bnet, 11,[0.99 0.01 ]); +bnet.CPD{12} = tabular_CPD(bnet, 12,[0.99 0.01 ]); +bnet.CPD{13} = tabular_CPD(bnet, 13,[0.95 0.95 0.05 0.1 0.1 0.01 0.05 0.05 0.95 0.9 0.9 0.99 ]); +bnet.CPD{14} = tabular_CPD(bnet, 14,[0.05 0.95 0.5 0.98 0.9 0.04 0.49 0.01 0.05 0.01 0.01 0.01 ]); +bnet.CPD{15} = tabular_CPD(bnet, 15,[0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 ]); +bnet.CPD{16} = tabular_CPD(bnet, 16,[0.3 0.98 0.4 0.01 0.3 0.01 ]); +bnet.CPD{17} = tabular_CPD(bnet, 17,[0.99 0.1 0.01 0.9 ]); +bnet.CPD{18} = tabular_CPD(bnet, 18,[0.05 0.01 0.9 0.19 0.05 0.8 ]); +bnet.CPD{19} = tabular_CPD(bnet, 19,[0.05 0.98 0.01 0.95 0.9 0.01 0.09 0.04 0.05 0.01 0.9 0.01 ]); +bnet.CPD{20} = tabular_CPD(bnet, 20,[0.95 0.04 0.01 0.04 0.95 0.29 0.01 0.01 0.7 ]); +bnet.CPD{21} = tabular_CPD(bnet, 21,[0.97 0.97 0.01 0.97 0.01 0.97 0.01 0.97 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 ]); +bnet.CPD{22} = tabular_CPD(bnet, 22,[0.95 0.04 0.01 0.04 0.95 0.04 0.01 0.01 0.95 ]); +bnet.CPD{23} = tabular_CPD(bnet, 23,[0.97 0.97 0.97 0.97 0.97 0.97 0.01 0.95 0.97 0.97 0.01 0.95 0.01 0.4 0.97 0.97 0.01 0.5 0.01 0.3 0.97 0.97 0.01 0.3 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.03 0.01 0.01 0.97 0.03 0.01 0.58 0.01 0.01 0.01 0.48 0.01 0.68 0.01 0.01 0.01 0.68 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 0.01 ]); +bnet.CPD{24} = tabular_CPD(bnet, 24,[0.97 0.97 0.97 0.97 0.97 0.97 0.01 0.01 0.4 0.1 0.01 0.01 0.01 0.01 0.2 0.05 0.01 0.01 0.01 0.01 0.2 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.49 0.58 0.84 0.9 0.29 0.01 0.01 0.75 0.25 0.01 0.01 0.01 0.01 0.7 0.15 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.3 0.01 0.05 0.08 0.3 0.97 0.08 0.04 0.25 0.38 0.08 0.01 0.01 0.09 0.25 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.2 0.01 0.01 0.01 0.4 0.01 0.9 0.01 0.45 0.6 0.9 0.97 0.97 0.01 0.59 0.97 0.97 ]); +bnet.CPD{25} = tabular_CPD(bnet, 25,[0.97 0.97 0.97 0.01 0.6 0.01 0.01 0.5 0.01 0.01 0.5 0.01 0.01 0.01 0.01 0.97 0.38 0.97 0.01 0.48 0.01 0.01 0.48 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.01 0.97 ]); +bnet.CPD{26} = tabular_CPD(bnet, 26,[0.97 0.97 0.97 0.01 0.01 0.03 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.95 0.01 0.01 0.94 0.01 0.01 0.88 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.04 0.01 0.01 0.1 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.01 ]); +bnet.CPD{27} = tabular_CPD(bnet, 27,[0.98 0.98 0.98 0.98 0.95 0.01 0.95 0.01 0.01 0.01 0.01 0.01 0.04 0.95 0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.04 0.01 0.98 ]); +bnet.CPD{28} = tabular_CPD(bnet, 28,[0.01 0.01 0.04 0.9 0.01 0.01 0.92 0.09 0.98 0.98 0.04 0.01 ]); +bnet.CPD{29} = tabular_CPD(bnet, 29,[0.97 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.97 0.01 0.01 0.01 0.01 0.97 0.97 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.97 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.97 0.97 0.43 ]); +bnet.CPD{30} = tabular_CPD(bnet, 30,[0.98 0.98 0.01 0.98 0.01 0.69 0.01 0.01 0.98 0.01 0.01 0.3 0.01 0.01 0.01 0.01 0.98 0.01 ]); +bnet.CPD{31} = tabular_CPD(bnet, 31,[0.05 0.01 0.05 0.01 0.05 0.01 0.05 0.01 0.05 0.01 0.05 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.1 0.01 0.95 0.01 0.95 0.05 0.1 0.01 0.95 0.01 0.95 0.05 0.1 0.01 0.3 0.01 0.3 0.01 0.95 0.01 0.99 0.05 0.95 0.05 0.95 0.01 0.99 0.05 0.99 0.05 0.3 0.01 0.99 0.01 0.3 0.01 0.95 0.99 0.95 0.99 0.95 0.99 0.95 0.99 0.95 0.99 0.95 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.9 0.99 0.05 0.99 0.05 0.95 0.9 0.99 0.05 0.99 0.05 0.95 0.9 0.99 0.7 0.99 0.7 0.99 0.05 0.99 0.00999999 0.95 0.05 0.95 0.05 0.99 0.01 0.95 0.01 0.95 0.7 0.99 0.01 0.99 0.7 0.99 ]); +bnet.CPD{32} = tabular_CPD(bnet, 32,[0.1 0.01 0.89 0.09 0.01 0.9 ]); +bnet.CPD{33} = tabular_CPD(bnet, 33,[0.98 0.33333334 0.01 0.33333334 0.01 0.33333334 0.01 0.33333334 0.98 0.33333334 0.01 0.33333334 0.01 0.33333334 0.01 0.33333334 0.98 0.33333334 ]); +bnet.CPD{34} = tabular_CPD(bnet, 34,[0.98 0.33333334 0.01 0.33333334 0.01 0.33333334 0.01 0.33333334 0.98 0.33333334 0.01 0.33333334 0.01 0.33333334 0.01 0.33333334 0.98 0.33333334 ]); +bnet.CPD{35} = tabular_CPD(bnet, 35,[0.98 0.95 0.3 0.95 0.04 0.01 0.8 0.01 0.01 0.01 0.04 0.69 0.04 0.95 0.3 0.19 0.04 0.01 0.01 0.01 0.01 0.01 0.01 0.69 0.01 0.95 0.98 ]); +bnet.CPD{36} = tabular_CPD(bnet, 36,[0.98 0.98 0.01 0.4 0.01 0.3 0.01 0.01 0.98 0.59 0.01 0.4 0.01 0.01 0.01 0.01 0.98 0.3 ]); +bnet.CPD{37} = tabular_CPD(bnet, 37,[0.98 0.98 0.3 0.98 0.1 0.05 0.9 0.05 0.01 0.01 0.01 0.6 0.01 0.85 0.4 0.09 0.2 0.09 0.01 0.01 0.1 0.01 0.05 0.55 0.01 0.75 0.9 ]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_asia_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function bnet = mk_asia_bnet(CPD_type, p, arity) +% MK_ASIA_BNET Make the 'Asia' bayes net. +% +% BNET = MK_ASIA_BNET uses the parameters specified on p21 of Cowell et al, +% "Probabilistic networks and expert systems", Springer Verlag 1999. +% +% BNET = MK_ASIA_BNET('cpt', p) uses random parameters drawn from a Dirichlet(p,p,...) +% distribution. If p << 1, this is nearly deterministic; if p >> 1, this is nearly uniform. +% +% BNET = MK_ASIA_BNET('bool') makes each CPT a random boolean function. +% +% BNET = MK_ASIA_BNET('gauss') makes each CPT a random linear Gaussian distribution. +% +% BNET = MK_ASIA_BNET('orig') is the same as MK_ASIA_BNET. +% +% BNET = MK_ASIA_BNET('cpt', p, arity) can specify non-binary nodes. + + +if nargin == 0, CPD_type = 'orig'; end +if nargin < 3, arity = 2; end + +Smoking = 1; +Bronchitis = 2; +LungCancer = 3; +VisitToAsia = 4; +TB = 5; +TBorCancer = 6; +Dys = 7; +Xray = 8; + +n = 8; +dag = zeros(n); +dag(Smoking, [Bronchitis LungCancer]) = 1; +dag(Bronchitis, Dys) = 1; +dag(LungCancer, TBorCancer) = 1; +dag(VisitToAsia, TB) = 1; +dag(TB, TBorCancer) = 1; +dag(TBorCancer, [Dys Xray]) = 1; + +ns = arity*ones(1,n); +if strcmp(CPD_type, 'gauss') + dnodes = []; +else + dnodes = 1:n; +end +bnet = mk_bnet(dag, ns, 'discrete', dnodes); + +switch CPD_type + case 'orig', + % true is 2, false is 1 + bnet.CPD{VisitToAsia} = tabular_CPD(bnet, VisitToAsia, [0.99 0.01]); + bnet.CPD{Bronchitis} = tabular_CPD(bnet, Bronchitis, [0.7 0.4 0.3 0.6]); + % minka: bug fix + bnet.CPD{Dys} = tabular_CPD(bnet, Dys, [0.9 0.2 0.3 0.1 0.1 0.8 0.7 0.9]); + bnet.CPD{TBorCancer} = tabular_CPD(bnet, TBorCancer, [1 0 0 0 0 1 1 1]); + % minka: bug fix + bnet.CPD{LungCancer} = tabular_CPD(bnet, LungCancer, [0.99 0.9 0.01 0.1]); + bnet.CPD{Smoking} = tabular_CPD(bnet, Smoking, [0.5 0.5]); + bnet.CPD{TB} = tabular_CPD(bnet, TB, [0.99 0.95 0.01 0.05]); + bnet.CPD{Xray} = tabular_CPD(bnet, Xray, [0.95 0.02 0.05 0.98]); + case 'bool', + for i=1:n + bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd'); + end + case 'gauss', + for i=1:n + bnet.CPD{i} = gaussian_CPD(bnet, i, 'cov', 1*eye(ns(i))); + end + case 'cpt', + for i=1:n + bnet.CPD{i} = tabular_CPD(bnet, i, p); + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_cancer_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +function bnet = mk_cancer_bnet(CPD_type, p) +% MK_CANCER_BNET Make the 'Cancer' Bayes net. +% +% BNET = MK_CANCER_BNET uses the noisy-or parameters specified in Fig 4a of the UAI98 paper by +% Friedman, Murphy and Russell, "Learning the Structure of DPNs", p145. +% +% BNET = MK_CANCER_BNET('noisyor', p) makes each CPD a noisy-or, with probability p of +% suppression for each parent; leaks are turned off. +% +% BNET = MK_CANCER_BNET('cpt', p) uses random CPT parameters drawn from a Dirichlet(p,p,...) +% distribution. If p << 1, this is near deterministic; if p >> 1, this is near 1/k. +% p defaults to 1.0 (uniform distribution). +% +% BNET = MK_CANCER_BNET('bool') makes each CPT a random boolean function. +% +% In all cases, the root is set to a uniform distribution. + +if nargin == 0 + rnd = 0; +else + rnd = 1; +end + +n = 5; +dag = zeros(n); +dag(1,[2 3]) = 1; +dag(2,4) = 1; +dag(3,4) = 1; +dag(4,5) = 1; + +ns = 2*ones(1,n); +bnet = mk_bnet(dag, ns); + +if ~rnd + bnet.CPD{1} = tabular_CPD(bnet, 1, [0.5 0.5]); + bnet.CPD{2} = noisyor_CPD(bnet, 2, 1.0, 1-0.9); + bnet.CPD{3} = noisyor_CPD(bnet, 3, 1.0, 1-0.2); + bnet.CPD{4} = noisyor_CPD(bnet, 4, 1.0, 1-[0.7 0.6]); + bnet.CPD{5} = noisyor_CPD(bnet, 5, 1.0, 1-0.5); +else + switch CPD_type + case 'noisyor', + for i=1:n + ps = parents(dag, i); + bnet.CPD{i} = noisyor_CPD(bnet, i, 1.0, p*ones(1,length(ps))); + end + case 'bool', + for i=1:n + bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd'); + end + case 'cpt', + for i=1:n + bnet.CPD{i} = tabular_CPD(bnet, i, p); + end + otherwise + error(['bad CPD type ' CPD_type]); + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_car_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function bnet = mk_car_bnet() +% MK_CAR_BNET Make the car trouble-shooter bayes net. +% +% This network is from p13 of "Troubleshooting under uncertainty", Heckerman, Breese and +% Rommelse, Microsoft Research Tech Report 1994. + + +BatteryAge = 1; +Battery = 2; +Starter = 3; +Lights = 4; +TurnsOver = 5; +FuelPump = 6; +FuelLine = 7; +FuelSubsys =8; +Fuel = 9; +Spark = 10; +Starts = 11; +Gauge = 12; + +n = 12; +dag = zeros(n); +dag(1,2) = 1; +dag(2,[4 5])=1; +dag(3,5) = 1; +dag(6,8) = 1; +dag(7,8) = 1; +dag(8,11) = 1; +dag(9,12) = 1; +dag(10,11) = 1; + +arity = 2; +ns = arity*ones(1,n); +bnet = mk_bnet(dag, ns); +for i=1:n + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_hmm_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying) +% MK_HMM_BNET Make a (static) bnet to represent a hidden Markov model +% bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying) +% +% T = num time slices +% Q = num hidden states +% O = size of the observed node (num discrete values or length of vector) +% cts_obs - 1 means the observed node is a continuous-valued vector, 0 means it's discrete +% param_tying - 1 means we create 3 CPDs, 0 means we create 1 CPD per node + +N = 2*T; +dag = zeros(N); +%hnodes = 1:2:2*T; +hnodes = 1:T; +for i=1:T-1 + dag(hnodes(i), hnodes(i+1))=1; +end +%onodes = 2:2:2*T; +onodes = T+1:2*T; +for i=1:T + dag(hnodes(i), onodes(i)) = 1; +end + +if cts_obs + dnodes = hnodes; +else + dnodes = 1:N; +end +ns = ones(1,N); +ns(hnodes) = Q; +ns(onodes) = O; + +if param_tying + H1class = 1; Hclass = 2; Oclass = 3; + eclass = ones(1,N); + eclass(hnodes(2:end)) = Hclass; + eclass(hnodes(1)) = H1class; + eclass(onodes) = Oclass; +else + eclass = 1:N; +end + +bnet = mk_bnet(dag, ns, 'observed', onodes, 'discrete', dnodes, 'equiv_class', eclass); + +hnodes = mysetdiff(1:N, onodes); +if ~param_tying + for i=hnodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i); + end + if cts_obs + for i=onodes(:)' + bnet.CPD{i} = gaussian_CPD(bnet, i); + end + else + for i=onodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i); + end + end +else + bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior + bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix + if cts_obs + bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1)); + else + bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_ideker_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function bnet = mk_ideker_bnet(CPD_type, p) +% MK_IDEKER_BNET Make the Bayes net in the PSB'00 paper by Ideker, Thorsson and Karp. +% +% BNET = MK_IDEKER_BNET uses the boolean functions specified in the paper +% "Discovery of regulatory interactions through perturbation: inference and experimental design", +% Pacific Symp. on Biocomputing, 2000. +% +% BNET = MK_IDEKER_BNET('root') uses the above boolean functions, but puts a uniform +% distribution on the root nodes. +% +% BNET = MK_IDEKER_BNET('cpt', p) uses random parameters drawn from a Dirichlet(p,p,...) +% distribution. If p << 1, this is nearly deterministic; if p >> 1, this is nearly uniform. +% +% BNET = MK_IDEKER_BNET('bool') makes each CPT a random boolean function. +% +% BNET = MK_IDEKER_BNET('orig') is the same as MK_IDEKER_BNET. + + +if nargin == 0 + CPD_type = 'orig'; +end + +n = 4; +dag = zeros(n); +dag(1,3)=1; +dag(2,[3 4])=1; +dag(3,4)=1; +ns = 2*ones(1,n); +bnet = mk_bnet(dag, ns); + +switch CPD_type + case 'orig', + bnet.CPD{1} = tabular_CPD(bnet, 1, [0 1]); + bnet.CPD{2} = tabular_CPD(bnet, 2, [0 1]); + bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)')); + bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)')); + case 'root', + bnet.CPD{1} = tabular_CPD(bnet, 1, [0.5 0.5]); + bnet.CPD{2} = tabular_CPD(bnet, 2, [0.5 0.5]); + bnet.CPD{3} = boolean_CPD(bnet, 3, 'inline', inline('x(1) & x(2)')); + bnet.CPD{4} = boolean_CPD(bnet, 4, 'inline', inline('x(1) & ~x(2)')); + case 'bool', + for i=1:n + bnet.CPD{i} = boolean_CPD(bnet, i, 'rnd'); + end + case 'cpt', + for i=1:n + bnet.CPD{i} = tabular_CPD(bnet, i, p); + end + otherwise, + error(['unknown type ' CPD_type]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_incinerator_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +function bnet = mk_incinerator_bnet(ns) +% MK_INCINERATOR_BNET The waste incinerator emissions example from Cowell et al p145 +% function bnet = mk_incinerator_bnet(ns) +% +% If ns is omitted, we use the scalars and binary nodes and the original params. +% Otherwise, we use random params of the desired size. +% +% Lauritzen, "Propogation of Probabilities, Means and Variances in Mixed Graphical Association Models", +% JASA 87(420): 1098--1108 +% This example is reprinted on p145 of "Probabilistic Networks and Expert Systems", +% Cowell, Dawid, Lauritzen and Spiegelhalter, 1999, Springer. +% For a picture, see http://www.cs.berkeley.edu/~murphyk/Bayes/usage.html#cg_model + +% node numbers +F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; +names = {'F', 'W', 'E', 'B', 'C', 'D', 'Min', 'Mout', 'L'}; +n = 9; +dnodes = [F W B]; +cnodes = mysetdiff(1:n, dnodes); + +% node sizes - all cts nodes are scalar, all discrete nodes are binary +if nargin < 1 + ns = ones(1, n); + ns(dnodes) = 2; + rnd = 0; +else + rnd = 1; +end + +% topology (p 1099, fig 1) +dag = zeros(n); +dag(F,E)=1; +dag(W,[E Min D]) = 1; +dag(E,D)=1; +dag(B,[C D])=1; +dag(D,[L Mout])=1; +dag(Min,Mout)=1; + +% params (p 1102) +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'names', names); + +if rnd + for i=dnodes(:)' + bnet.CPD{i} = tabular_CPD(bnet, i); + end + for i=cnodes(:)' + bnet.CPD{i} = gaussian_CPD(bnet, i); + end +else + bnet.CPD{B} = tabular_CPD(bnet, B, 'CPT', [0.85 0.15]); % 1=stable, 2=unstable + bnet.CPD{F} = tabular_CPD(bnet, F, 'CPT', [0.95 0.05]); % 1=intact, 2=defect + bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [2/7 5/7]); % 1=industrial, 2=household + bnet.CPD{E} = gaussian_CPD(bnet, E, 'mean', [-3.9 -0.4 -3.2 -0.5], ... + 'cov', [0.00002 0.0001 0.00002 0.0001]); + bnet.CPD{D} = gaussian_CPD(bnet, D, 'mean', [6.5 6.0 7.5 7.0], ... + 'cov', [0.03 0.04 0.1 0.1], 'weights', [1 1 1 1]); + bnet.CPD{C} = gaussian_CPD(bnet, C, 'mean', [-2 -1], 'cov', [0.1 0.3]); + bnet.CPD{L} = gaussian_CPD(bnet, L, 'mean', 3, 'cov', 0.25, 'weights', -0.5); + bnet.CPD{Min} = gaussian_CPD(bnet, Min, 'mean', [0.5 -0.5], 'cov', [0.01 0.005]); + bnet.CPD{Mout} = gaussian_CPD(bnet, Mout, 'mean', 0, 'cov', 0.002, 'weights', [1 1]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_markov_chain_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function bnet = mk_markov_chain_bnet(N, Q) + +dag = zeros(N); +dag(1,2)=1; dag(2,3)=1; +ns = Q*ones(1,N); +bnet = mk_bnet(dag, ns); +for i=1:N + bnet.CPD{i} = tabular_CPD(bnet, i); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_minimal_qmr_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,82 @@ +function [bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, leak, prior, pos, neg, pos_only) +% MK_MINIMAL_QMR_BNET Make a QMR model which only contains the observed findings +% [bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, prior, leak, pos, neg) +% +% Input: +% G(i,j) = 1 iff there is an arc from disease i to finding j +% inhibit(i,j) = inhibition probability on i->j arc +% leak(j) = inhibition prob. on leak->j arc +% prior(i) = prob. disease i is on +% pos = list of leaves that have positive observations +% neg = list of leaves that have negative observations +% pos_only = 1 means only include positively observed leaves in the model - the negative +% ones are absorbed into the prior terms +% +% Output: +% bnet +% vals is their value + +if pos_only + obs = pos; +else + obs = myunion(pos, neg); +end +Nfindings = length(obs); +[Ndiseases maxNfindings] = size(inhibit); +N = Ndiseases + Nfindings; +finding_node = Ndiseases+1:N; + +% j = finding_node(i) means the i'th finding node is the j'th node in the bnet +% k = obs(i) means the i'th observed (positive) finding is the k'th finding overall +% If all findings are observed, and posonly = 0, we have i = obs(i) for all i. + +%dag = sparse(N, N); +dag = zeros(N, N); +dag(1:Ndiseases, Ndiseases+1:N) = G(:,obs); + +ns = 2*ones(1,N); +bnet = mk_bnet(dag, ns, 'observed', finding_node); + +CPT = cell(1, Ndiseases); +for d=1:Ndiseases + CPT{d} = [1-prior(d) prior(d)]; +end + +if pos_only + % Fold in the negative evidence into the prior + for i=1:length(neg) + n = neg(i); + ps = parents(G,n); + for pi=1:length(ps) + p = ps(pi); + q = inhibit(p,n); + CPT{p} = CPT{p} .* [1 q]; + end + % Arbitrarily attach the leak term to the first parent + p = ps(1); + q = leak(n); + CPT{p} = CPT{p} .* [q q]; + end +end + +for d=1:Ndiseases + bnet.CPD{d} = tabular_CPD(bnet, d, CPT{d}'); +end + +for i=1:Nfindings + fnode = finding_node(i); + fid = obs(i); + ps = parents(G, fid); + bnet.CPD{fnode} = noisyor_CPD(bnet, fnode, leak(fid), inhibit(ps, fid)); +end + +obs_nodes = finding_node; +vals = sparse(1, maxNfindings); +vals(pos) = 2; +vals(neg) = 1; +vals = full(vals(obs)); + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_qmr_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function bnet = mk_qmr_bnet(G, inhibit, leak, prior, tabular_findings, onodes) +% MK_QMR_BNET Make a QMR model +% bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% +% G(i,j) = 1 iff there is an arc from disease i to finding j +% inhibit(i,j) = inhibition probability on i->j arc +% leak(j) = inhibition prob. on leak->j arc +% prior(i) = prob. disease i is on +% tabular_findings = 1 means multinomial leaves (ignores leak/inhibit params) +% = 0 means noisy-OR leaves (default = 0) + +if nargin < 5, tabular_findings = 0; end + +[Ndiseases Nfindings] = size(inhibit); +N = Ndiseases + Nfindings; +finding_node = Ndiseases+1:N; +ns = 2*ones(1,N); +dag = zeros(N,N); +dag(1:Ndiseases, finding_node) = G; +if nargin < 6, onodes = finding_node; end +bnet = mk_bnet(dag, ns, 'observed', onodes); + +for d=1:Ndiseases + CPT = [1-prior(d) prior(d)]; + bnet.CPD{d} = tabular_CPD(bnet, d, CPT'); +end + +for i=1:Nfindings + fnode = finding_node(i); + ps = parents(G, i); + if tabular_findings + bnet.CPD{fnode} = tabular_CPD(bnet, fnode); + else + bnet.CPD{fnode} = noisyor_CPD(bnet, fnode, leak(i), inhibit(ps, i)); + end +end + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Models/mk_vstruct_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function oracle = mk_vstruct_bnet() +% MK_VSTRUCT_BNET Make a simple V-structured 3-node noisy-AND Bayes net +% oracle = mk_vstruct_bnet() + +N = 3; +dag = zeros(N); +A = 1; B = 2; C = 3; +dag(A,C)=1; +dag(B,C)=1; +ns = 2*ones(1,N); + +oracle = mk_bnet(dag, ns); +oracle.CPD{1} = tabular_CPD(oracle, 1, [0.5 0.5]); +oracle.CPD{2} = tabular_CPD(oracle, 2, [0.5 0.5]); +pnoise = 0.1; % degree of noise +oracle.CPD{3} = boolean_CPD(oracle, 3, 'named', 'all', pnoise);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/SCG/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/scg1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/scg2.m/1.1.1.1/Wed May 29 15:59:54 2002// +/scg3.m/1.1.1.1/Wed May 29 15:59:54 2002// +/scg_3node.m/1.1.1.1/Wed May 29 15:59:54 2002// +/scg_unstable.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/SCG/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/SCG
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/SCG/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/SCG/scg1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,77 @@ +% Same as cg1, except we call stab_cond_gauss_inf_engine + +bnet = mk_incinerator_bnet; + +engines = {}; +engines{end+1} = stab_cond_gauss_inf_engine(bnet); +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = cond_gauss_inf_engine(bnet); +nengines = length(engines); + +F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; +n = 9; +dnodes = [B F W]; +cnodes = mysetdiff(1:n, dnodes); + +evidence = cell(1,n); % no evidence +ll = zeros(1, nengines); +for e=1:nengines + [engines{e}, ll(e)] = enter_evidence(engines{e}, evidence); +end +%assert(approxeq(ll(1), ll))) +ll + +% Compare to the results in table on p1107. +% These results are printed to 3dp in Cowell p150 + +mu = zeros(1,n); +sigma = zeros(1,n); +dprob = zeros(1,n); +addev = 1; +tol = 1e-2; +for e=1:nengines + for i=cnodes(:)' + m = marginal_nodes(engines{e}, i, addev); + mu(i) = m.mu; + sigma(i) = sqrt(m.Sigma); + end + for i=dnodes(:)' + m = marginal_nodes(engines{e}, i, addev); + dprob(i) = m.T(1); + end + assert(approxeq(mu([E D C L Min Mout]), [-3.25 3.04 -1.85 1.48 -0.214 2.83], tol)) + assert(approxeq(sigma([E D C L Min Mout]), [0.709 0.770 0.507 0.631 0.459 0.860], tol)) + assert(approxeq(dprob([B F W]), [0.85 0.95 0.29], tol)) + %m = marginal_nodes(engines{e}, bnet.names('E'), addev); + %assert(approxeq(m.mu, -3.25, tol)) + %assert(approxeq(sqrt(m.Sigma), 0.709, tol)) +end + +% Add evidence (p 1105, top right) +evidence = cell(1,n); +evidence{W} = 1; % industrial +evidence{L} = 1.1; +evidence{C} = -0.9; + +ll = zeros(1, nengines); +for e=1:nengines + [engines{e}, ll(e)] = enter_evidence(engines{e}, evidence); +end +%assert(all(approxeq(ll(1), ll))) +ll + +for e=1:nengines + for i=cnodes(:)' + m = marginal_nodes(engines{e}, i, addev); + mu(i) = m.mu; + sigma(i) = sqrt(m.Sigma); + end + for i=dnodes(:)' + m = marginal_nodes(engines{e}, i, addev); + dprob(i) = m.T(1); + end + assert(approxeq(mu([E D C L Min Mout]), [-3.90 3.61 -0.9 1.1 0.5 4.11], tol)) + assert(approxeq(sigma([E D C L Min Mout]), [0.076 0.326 0 0 0.1 0.344], tol)) + assert(approxeq(dprob([B F W]), [0.0122 0.9995 1], tol)) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/SCG/scg2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +% Same as cg2, except we call stab_cond_gauss_inf_engine + +ns = 2*ones(1,9); +bnet = mk_incinerator_bnet(ns); + +engines = {}; +engines{end+1} = stab_cond_gauss_inf_engine(bnet); +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = cond_gauss_inf_engine(bnet); +nengines = length(engines); + +[err, time] = cmp_inference_static(bnet, engines, 'singletons_only', 1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/SCG/scg3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +% Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17) +% 1 +% / | \ +% 2 3 4 +% | | | +% 5 6 7 +% \/ \/ +% 8 9 +% where all arcs point downwards + +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + +gauss = 1; +if gauss + ns = ones(1,N); % scalar nodes + ns(1) = 2; + ns(9) = 3; + dnodes = []; +else + ns = 2*ones(1,N); % binary nodes + dnodes = 1:N; +end + +bnet = mk_bnet(dag, ns, 'discrete', dnodes); +% use random params +for i=1:N + if gauss + bnet.CPD{i} = gaussian_CPD(bnet, i); + else + bnet.CPD{i} = tabular_CPD(bnet, i); + end +end + +engines = {}; +engines{1} = jtree_inf_engine(bnet); +engines{2} = stab_cond_gauss_inf_engine(bnet); + +[err, time] = cmp_inference_static(bnet, engines);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/SCG/scg_3node.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +% This example is from Page.143 of "Probabilistic Networks and Expert Systems", +% Cowell, Dawid, Lauritzen and Spiegelhalter, 1999, Springer. + +X = 1; Y = 2; Z = 3; +n = 3; + +dag = zeros(n); +dag(X, Y)=1; +dag(Y, Z)=1; + +ns = ones(1, n); +dnodes = []; + +bnet = mk_bnet(dag, ns, dnodes); +bnet.CPD{X} = gaussian_CPD(bnet, X, 'mean', 0, 'cov', 1); +bnet.CPD{Y} = gaussian_CPD(bnet, Y, 'mean', 0, 'cov', 1, 'weights', 1); +bnet.CPD{Z} = gaussian_CPD(bnet, Z, 'mean', 0, 'cov', 1, 'weights', 1); + +engines = {}; +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = stab_cond_gauss_inf_engine(bnet); +nengines = length(engines); + +evidence = cell(1,n); +evidence{Y} = 1.5; + +for e=1:nengines + engines{e} = enter_evidence(engines{e}, evidence); + margX = marginal_nodes(engines{e}, X); + assert(approxeq(margX.mu, 0.75)) + assert(approxeq(margX.Sigma, 0.5)) + + margZ = marginal_nodes(engines{e}, Z); + assert(approxeq(margZ.mu, 1.5)) + assert(approxeq(margZ.Sigma, 1)) +end + + +evidence = cell(1,n); +evidence{Z} = 1.5; + +for e=1:nengines + engines{e} = enter_evidence(engines{e}, evidence); + margX = marginal_nodes(engines{e}, X); + assert(approxeq(margX.mu, 1/2)) + assert(approxeq(margX.Sigma, 2/3)) + + margY = marginal_nodes(engines{e}, Y); + assert(approxeq(margY.mu, 1)) + assert(approxeq(margY.Sigma, 2/3)) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/SCG/scg_unstable.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,91 @@ +function scg_unstable() + +% the objective of this script is to test if the stable conditonal gaussian +% inference can handle the numerical instability problem described on +% page.151 of 'Probabilistic networks and expert system' by Cowell, Dawid, Lauritzen and +% Spiegelhalter, 1999. + +A = 1; Y = 2; +n = 2; + +ns = ones(1, n); +dnodes = [A]; +cnodes = Y; +ns = [2 1]; + +dag = zeros(n); +dag(A, Y) = 1; + +bnet = mk_bnet(dag, ns, dnodes); + +bnet.CPD{A} = tabular_CPD(bnet, A, [0.5 0.5]'); +bnet.CPD{Y} = gaussian_CPD(bnet, Y, 'mean', [0 1], 'cov', [1e-5 1e-6]); + +evidence = cell(1, n); + +pot_type = 'cg'; +potYgivenA = convert_to_pot(bnet.CPD{Y}, pot_type, [A Y], evidence); +potA = convert_to_pot(bnet.CPD{A}, pot_type, A, evidence); +potYandA = multiply_by_pot(potYgivenA, potA); +potA2 = marginalize_pot(potYandA, A); + +thresh = 1; % 0dp + +[g,h,K] = extract_can(potA); +assert(approxeq(g(:)', [-0.693147 -0.693147], thresh)) + + +[g,h,K] = extract_can(potYgivenA); +assert(approxeq(g(:)', [4.83752 -499994], thresh)) +assert(approxeq(h(:)', [0 1e6])) +assert(approxeq(K(:)', [1e5 1e6])) + +[g,h,K] = extract_can(potYandA); +assert(approxeq(g(:)', [4.14437 -499995], thresh)) +assert(approxeq(h(:)', [0 1e6])) +assert(approxeq(K(:)', [1e5 1e6])) + + +[g,h,K] = extract_can(potA2); +%assert(approxeq(g(:)', [-0.69315 -1])) +g +assert(approxeq(g(:)', [-0.69315 -0.69315])) + + + +if 0 +pot_type = 'scg'; +spotYgivenA = convert_to_pot(bnet.CPD{Y}, pot_type, [A Y], evidence); +spotA = convert_to_pot(bnet.CPD{A}, pot_type, A, evidence); +spotYandA = direct_combine_pots(spotYgivenA, spotA); +spotA2 = marginalize_pot(spotYandA, A); + +spotA=struct(spotA); +spotA2=struct(spotA2); +for i=1:2 + assert(approxeq(spotA2.scgpotc{i}.p, spotA.scgpotc{i}.p)) + assert(approxeq(spotA2.scgpotc{i}.A, spotA.scgpotc{i}.A)) + assert(approxeq(spotA2.scgpotc{i}.B, spotA.scgpotc{i}.B)) + assert(approxeq(spotA2.scgpotc{i}.C, spotA.scgpotc{i}.C)) +end + +end + + +%%%%%%%%%%% + +function [g,h,K] = extract_can(pot) + +pot = struct(pot); +D = length(pot.can); +g = zeros(1, D); +h = zeros(1, D); +K = zeros(1, D); +for i=1:D + S = struct(pot.can{i}); + g(i) = S.g; + if length(S.h) > 0 + h(i) = S.h; + K(i) = S.K; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +/bic1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/cooper_yoo.m/1.1.1.1/Wed May 29 15:59:54 2002// +/k2demo1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mcmc1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/model_select1.m/1.1.1.1/Sat Nov 6 20:55:18 2004// +/model_select2.m/1.1.1.1/Sat Nov 6 21:52:42 2004// +/pc1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/pc2.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/StructLearn
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/bic1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +% compare BIC and Bayesian score + +N = 4; +dag = zeros(N,N); +%C = 1; S = 2; R = 3; W = 4; % topological order +C = 4; S = 2; R = 3; W = 1; % arbitrary order +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; + + +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes +bnet = mk_bnet(dag, ns); +bnet.CPD{C} = tabular_CPD(bnet, C, 'CPT', [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, 'CPT', [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, 'CPT', [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); + + +seed = 0; +rand('state', seed); +randn('state', seed); +ncases = 1000; +data = cell(N, ncases); +for m=1:ncases + data(:,m) = sample_bnet(bnet); +end + +priors = [0.1 1 10]; +P = length(priors); +params = cell(1,P); +for p=1:P + params{p} = cell(1,N); + for i=1:N + %params{p}{i} = {'prior', priors(p)}; + params{p}{i} = {'prior_type', 'dirichlet', 'dirichlet_weight', priors(p)}; + end +end + +%sz = 1000:1000:10000; +sz = 10:10:100; +S = length(sz); +bic_score = zeros(S, 1); +bayes_score = zeros(S, P); +for i=1:S + bic_score(i) = score_dags(data(:,1:sz(i)), ns, {dag}, 'scoring_fn', 'bic', 'params', []); +end +diff = zeros(S,P); +for p=1:P + for i=1:S + bayes_score(i,p) = score_dags(data(:,1:sz(i)), ns, {dag}, 'params', params{p}); + end +end + +for p=1:P + for i=1:S + diff(i,p) = bayes_score(i,p)/ bic_score(i); + %diff(i,p) = abs(bayes_score(i,p) - bic_score(i)); + end +end + +if 0 +plot(sz, diff(:,1), 'g--*', sz, diff(:,2), 'b-.+', sz, diff(:,3), 'k:s'); +title('Relative BIC error vs. size of data set') +legend('BDeu 0.1', 'BDeu 1', 'Bdeu 10', 2) +end + +if 0 +plot(sz, bic_score, 'r-o', sz, bayes_score(:,1), 'g--*', sz, bayes_score(:,2), 'b-.+', sz, bayes_score(:,3), 'k:s'); +legend('bic', 'BDeu 0.01', 'BDeu 1', 'Bdeu 100') +ylabel('score') +title('score vs. size of data set') +end + +%xlabel('num. data cases') + +%previewfig(gcf, 'format', 'png', 'height', 2, 'color', 'rgb') +%exportfig(gcf, '/home/cs/murphyk/public_html/Bayes/Figures/bic.png', 'format', 'png', 'height', 2, 'color', 'rgb')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/cooper_yoo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +% Do the example in Cooper and Yoo, "Causal discovery from a mixture of experimental and +% observational data", UAI 99, p120 + +N = 2; +dag = zeros(N); +A = 1; B = 2; +dag(A,B) = 1; +ns = 2*ones(1,N); + +bnet0 = mk_bnet(dag, ns); +%bnet0.CPD{A} = tabular_CPD(bnet0, A, 'unif', 1); +bnet0.CPD{A} = tabular_CPD(bnet0, A, 'CPT', 'unif', 'prior_type', 'dirichlet'); +bnet0.CPD{B} = tabular_CPD(bnet0, B, 'CPT', 'unif', 'prior_type', 'dirichlet'); + +samples = [2 2; + 2 1; + 2 2; + 1 1; + 1 2; + 2 2; + 1 1; + 2 2; + 1 2; + 2 1; + 1 1]; + +clamped = [0 0; + 0 0; + 0 0; + 0 0; + 0 0; + 1 0; + 1 0; + 0 1; + 0 1; + 0 1; + 0 1]; + +nsamples = size(samples, 1); + +% sequential version +LL = 0; +bnet = bnet0; +for l=1:nsamples + ev = num2cell(samples(l,:)'); + manip = find(clamped(l,:)'); + LL = LL + log_marg_lik_complete(bnet, ev, manip); + bnet = bayes_update_params(bnet, ev, manip); +end +assert(approxeq(exp(LL), 5.97e-7)) % compare with result from UAI paper + + +% batch version +cases = num2cell(samples'); +LL2 = log_marg_lik_complete(bnet0, cases, clamped'); +bnet2 = bayes_update_params(bnet0, cases, clamped'); + +assert(approxeq(LL, LL2)) + +for j=1:N + s1 = struct(bnet.CPD{j}); % violate object privacy + s2 = struct(bnet2.CPD{j}); + assert(approxeq(s1.CPT, s2.CPT)) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/k2demo1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +N = 4; +dag = zeros(N,N); +%C = 1; S = 2; R = 3; W = 4; +C = 4; S = 2; R = 3; W = 1; % arbitrary order +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; + +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +bnet = mk_bnet(dag, ns); +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); + +seed = 0; +rand('state', seed); +randn('state', seed); +ncases = 100; +data = zeros(N, ncases); +for m=1:ncases + data(:,m) = cell2num(sample_bnet(bnet)); +end + +order = [C S R W]; +max_fan_in = 2; + +%dag2 = learn_struct_K2(data, ns, order, 'max_fan_in', max_fan_in, 'verbose', 'yes'); + +sz = 5:5:50; +for i=1:length(sz) + dag2 = learn_struct_K2(data(:,1:sz(i)), ns, order, 'max_fan_in', max_fan_in); + correct(i) = isequal(dag, dag2); +end +correct + +for i=1:length(sz) + dag3 = learn_struct_K2(data(:,1:sz(i)), ns, order, 'max_fan_in', max_fan_in, 'scoring_fn', 'bic', 'params', []); + correct(i) = isequal(dag, dag3); +end +correct + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/mcmc1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +% We compare MCMC structure learning with exhaustive enumeration of all dags. + +N = 3; +%N = 4; +dag = mk_rnd_dag(N); +ns = 2*ones(1,N); +bnet = mk_bnet(dag, ns); +for i=1:N + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +ncases = 100; +data = zeros(N, ncases); +for m=1:ncases + data(:,m) = cell2num(sample_bnet(bnet)); +end + +dags = mk_all_dags(N); +score = score_dags(data, ns, dags); +post = normalise(exp(score)); + +[sampled_graphs, accept_ratio] = learn_struct_mcmc(data, ns, 'nsamples', 100, 'burnin', 10); +mcmc_post = mcmc_sample_to_hist(sampled_graphs, dags); + +if 0 + subplot(2,1,1) + bar(post) + subplot(2,1,2) + bar(mcmc_post) + print(gcf, '-djpeg', '/home/cs/murphyk/public_html/Bayes/Figures/mcmc_post.jpg') + + clf + plot(accept_ratio) + print(gcf, '-djpeg', '/home/cs/murphyk/public_html/Bayes/Figures/mcmc_accept.jpg') +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/model_select1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,121 @@ +% Bayesian model selection demo. + +% We generate data from the model A->B +% and compute the posterior prob of all 3 dags on 2 nodes: +% (1) A B, (2) A <- B , (3) A -> B +% Models 2 and 3 are Markov equivalent, and therefore indistinguishable from +% observational data alone. +% Using the "difficult" params, the true model only gets a higher posterior after 2000 trials! +% However, using the noisy NOT gate, the true model wins after 12 trials. + +% ground truth +N = 2; +dag = zeros(N); +A = 1; B = 2; +dag(A,B) = 1; + +difficult = 0; +if difficult + ntrials = 2000; + ns = 3*ones(1,N); + true_bnet = mk_bnet(dag, ns); + rand('state', 0); + temp = 5; + for i=1:N + %true_bnet.CPD{i} = tabular_CPD(true_bnet, i, temp); + true_bnet.CPD{i} = tabular_CPD(true_bnet, i); + end +else + ntrials = 25; + ns = 2*ones(1,N); + true_bnet = mk_bnet(dag, ns); + true_bnet.CPD{1} = tabular_CPD(true_bnet, 1, [0.5 0.5]); + pfail = 0.1; + psucc = 1-pfail; + true_bnet.CPD{2} = tabular_CPD(true_bnet, 2, [pfail psucc; psucc pfail]); % NOT gate +end + +G = mk_all_dags(N); +nhyp = length(G); +hyp_bnet = cell(1, nhyp); +for h=1:nhyp + hyp_bnet{h} = mk_bnet(G{h}, ns); + for i=1:N + % We must set the CPTs to the mean of the prior for sequential log_marg_lik to be correct + % The BDeu prior is score equivalent, so models 2,3 will be indistinguishable. + % The uniform Dirichlet prior is not score equivalent... + fam = family(G{h}, i); + hyp_bnet{h}.CPD{i}= tabular_CPD(hyp_bnet{h}, i, 'prior_type', 'dirichlet', ... + 'CPT', 'unif'); + end +end +prior = normalise(ones(1, nhyp)); + +% save results before doing sequential updating +init_hyp_bnet = hyp_bnet; +init_prior = prior; + + +rand('state', 0); +hyp_w = zeros(ntrials+1, nhyp); +hyp_w(1,:) = prior(:)'; + +data = zeros(N, ntrials); + +% First we compute the posteriors sequentially + +LL = zeros(1, nhyp); +ll = zeros(1, nhyp); +for t=1:ntrials + ev = cell2num(sample_bnet(true_bnet)); + data(:,t) = ev; + for i=1:nhyp + ll(i) = log_marg_lik_complete(hyp_bnet{i}, ev); + hyp_bnet{i} = bayes_update_params(hyp_bnet{i}, ev); + end + prior = normalise(prior .* exp(ll)); + LL = LL + ll; + hyp_w(t+1,:) = prior; +end + +% Plot posterior model probabilities +% Red = model 1 (no arcs), blue/green = models 2/3 (1 arc) +% Blue = model 2 (2->1) +% Green = model 3 (1->2, "ground truth") + +if 1 + figure; +m = size(hyp_w, 1); +h=plot(1:m, hyp_w(:,1), 'r-', 1:m, hyp_w(:,2), 'b-.', 1:m, hyp_w(:,3), 'g:'); +axis([0 m 0 1]) +title('model posterior vs. time') +%previewfig(gcf, 'format', 'png', 'height', 2, 'color', 'rgb') +%exportfig(gcf, '/home/cs/murphyk/public_html/Bayes/Figures/model_select.png',... +%'format', 'png', 'height', 2, 'color', 'rgb') +drawnow +end + + +% Now check that batch updating gives same result +hyp_bnet2 = init_hyp_bnet; +prior2 = init_prior; + +cases = num2cell(data); +LL2 = zeros(1, nhyp); +for i=1:nhyp + LL2(i) = log_marg_lik_complete(hyp_bnet2{i}, cases); + hyp_bnet2{i} = bayes_update_params(hyp_bnet2{i}, cases); +end + + +assert(approxeq(LL, LL2)) +LL + +for i=1:nhyp + for j=1:N + s1 = struct(hyp_bnet{i}.CPD{j}); + s2 = struct(hyp_bnet2{i}.CPD{j}); + assert(approxeq(s1.CPT, s2.CPT)) + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/model_select2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,83 @@ +% Online Bayesian model selection demo. + +% We generate data from the model A->B +% and compute the posterior prob of all 3 dags on 2 nodes: +% (1) A B, (2) A <- B , (3) A -> B +% Models 2 and 3 are Markov equivalent, and therefore indistinguishable from +% observational data alone. + +% We control the dependence of B on A by setting +% P(B|A) = 0.5 - epislon and vary epsilon +% as in Koller & Friedman book p512 + +% ground truth +N = 2; +dag = zeros(N); +A = 1; B = 2; +dag(A,B) = 1; + +ntrials = 100; +ns = 2*ones(1,N); +true_bnet = mk_bnet(dag, ns); +true_bnet.CPD{1} = tabular_CPD(true_bnet, 1, [0.5 0.5]); + +% hypothesis space +G = mk_all_dags(N); +nhyp = length(G); +hyp_bnet = cell(1, nhyp); +for h=1:nhyp + hyp_bnet{h} = mk_bnet(G{h}, ns); + for i=1:N + % We must set the CPTs to the mean of the prior for sequential log_marg_lik to be correct + % The BDeu prior is score equivalent, so models 2,3 will be indistinguishable. + % The uniform Dirichlet prior is not score equivalent... + fam = family(G{h}, i); + hyp_bnet{h}.CPD{i}= tabular_CPD(hyp_bnet{h}, i, 'prior_type', 'dirichlet', ... + 'CPT', 'unif'); + end +end + +clf +seeds = 1:3; +expt = 1; +for seedi=1:length(seeds) + seed = seeds(seedi); + rand('state', seed); + randn('state', seed); + + es = [0.05 0.1 0.15 0.2]; + for ei=1:length(es) + e = es(ei); + true_bnet.CPD{2} = tabular_CPD(true_bnet, 2, [0.5+e 0.5-e; 0.5-e 0.5+e]); + + prior = normalise(ones(1, nhyp)); + hyp_w = zeros(ntrials+1, nhyp); + hyp_w(1,:) = prior(:)'; + LL = zeros(1, nhyp); + ll = zeros(1, nhyp); + for t=1:ntrials + ev = cell2num(sample_bnet(true_bnet)); + for i=1:nhyp + ll(i) = log_marg_lik_complete(hyp_bnet{i}, ev); + hyp_bnet{i} = bayes_update_params(hyp_bnet{i}, ev); + end + prior = normalise(prior .* exp(ll)); + LL = LL + ll; + hyp_w(t+1,:) = prior; + end + + % Plot posterior model probabilities + % Red = model 1 (no arcs), blue/green = models 2/3 (1 arc) + % Blue = model 2 (2->1) + % Green = model 3 (1->2, "ground truth") + + subplot2(length(seeds), length(es), seedi, ei); + m = size(hyp_w,1); + h=plot(1:m, hyp_w(:,1), 'r-', 1:m, hyp_w(:,2), 'b-.', 1:m, hyp_w(:,3), 'g:'); + axis([0 m 0 1]) + %title('model posterior vs. time') + title(sprintf('e=%3.2f, seed=%d', e, seed)); + drawnow + expt = expt + 1; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/pc1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +% SGS p118 +% Try learning the structure using an oracle for the cond indep tests + +n = 5; + +A = 1; B = 2; C = 3; D = 4; E = 5; + +G = zeros(n); +G(A,B)=1; +G(B,[C D]) = 1; +G(C,E)=1; +G(D,E)=1; + +k = 2; + +pdag = learn_struct_pdag_pc('dsep', n, k, G) + + + + +if 0 +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; + +pdag = learn_struct_pdag_pc('dsep', N, 2, dag) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/StructLearn/pc2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +% SGS p141 (female orgasm data set) + +C = eye(7,7); +C(2,1:1) = [-0.132]; +C(3,1:2) = [0.009 -0.136]; +C(4,1:3) = [0.22 -0.166 0.403]; +C(5,1:4) = [-0.008 0.008 0.598 0.282]; +C(6,1:5) = [0.119 -0.076 0.264 0.514 0.176]; +C(7,1:6) = [0.118 -0.137 0.368 0.414 0.336 0.338]; + +n = 7; +for i=1:n + for j=i+1:n + C(i,j)=C(j,i); + end +end + +max_fan_in = 4; +nsamples = 281; +alpha = 0.05; +pdag = learn_struct_pdag_pc('cond_indep_fisher_z', n, max_fan_in, C, nsamples, alpha)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +/README/1.1.1.1/Wed May 29 15:59:54 2002// +/csum.m/1.1.1.1/Wed May 29 15:59:54 2002// +/ffa.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mfa.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mfa_cl.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mfademo.m/1.1.1.1/Wed May 29 15:59:54 2002// +/rdiv.m/1.1.1.1/Wed May 29 15:59:54 2002// +/rprod.m/1.1.1.1/Wed May 29 15:59:54 2002// +/rsum.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/Zoubin
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +This software was downloaded from + http://www.gatsby.ucl.ac.uk/~zoubin/software.html +with permission of the author. + + +This software was written by + +Zoubin Ghahramani +Dept of Computer Science +University of Toronto +zoubin@cs.toronto.edu + +This software is written in Matlab 4.2c and should run on all platforms +supporting this version of Matlab. Matlab is a commercial software +package available from The MathWorks (http://www.mathworks.com/). + +This software is meant for free non-commercial use and distribution. See the +copyright notice at the bottom of this page. + +If you use it, please refer to the accompanying technical report: + +Ghahramani, Z. and Hinton, G.E. (1996) The EM Algorithm for Mixtures +of Factor Analyzers. University of Toronto Technical Report CRG-TR-96-1. +Available at ftp://ftp.cs.toronto.edu/pub/zoubin/tr-96-1.ps.gz + +If you find bugs, or would like to see if I've implemented any +extensions, please send me email at zoubin@cs.toronto.edu. The +software is provided "as is", and I cannot guarantee I will be able +to fix all problems or answer all inquiries. + +See mfademo.m for a demo. + +Hope you find it useful. Please send me email if you find it useful +and I will put you on a mailing list announcing releases of other +statistical machine learning software in Matlab. + + +---------------------------------------------------------------------- + Copyright (c) 1996 by Zoubin Ghahramani + Toronto, Ontario, Canada. + All Rights Reserved + +Permission to use, copy, modify, and distribute this software and its +documentation for non-commercial purposes only is hereby granted +without fee, provided that the above copyright notice appears in all +copies and that both the copyright notice and this permission notice +appear in supporting documentation, and that my name not be used in +advertising or publicity pertaining to distribution of the software +without specific, written prior permission. I make no representations +about the suitability of this software for any purpose. It is provided +"as is" without express or implied warranty. + +I disclaim all warranties with regard to this software, including all +implied warranties of merchantability and fitness. In no event shall I +be liable for any special, indirect or consequential damages or any +damages whatsoever resulting from loss of use, data or profits, +whether in an action of contract, negligence or other tortious action, +arising out of or in connection with the use or performance of this +software. + +Zoubin Ghahramani Dec 17, 1996
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/csum.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +% column sum +% function Z=csum(X) + +function Z=csum(X) + +N=length(X(:,1)); +if (N>1) + Z=sum(X); +else + Z=X; +end; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/ffa.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,75 @@ +% function [L,Ph,LL]=ffa(X,K,cyc,tol); +% +% Fast Maximum Likelihood Factor Analysis using EM +% +% X - data matrix +% K - number of factors +% cyc - maximum number of cycles of EM (default 100) +% tol - termination tolerance (prop change in likelihood) (default 0.0001) +% +% L - factor loadings +% Ph - diagonal uniquenesses matrix +% LL - log likelihood curve +% +% Iterates until a proportional change < tol in the log likelihood +% or cyc steps of EM +% + +function [L,Ph,LL]=ffa(X,K,cyc,tol); + +if nargin<4 tol=0.0001; end; +if nargin<3 cyc=100; end; + +N=length(X(:,1)); +D=length(X(1,:)); +tiny=exp(-700); + +X=X-ones(N,1)*mean(X); +XX=X'*X/N; +diagXX=diag(XX); + +randn('seed', 0); +cX=cov(X); +scale=det(cX)^(1/D); +L=randn(D,K)*sqrt(scale/K); +Ph=diag(cX); + +I=eye(K); + +lik=0; LL=[]; + +const=-D/2*log(2*pi); + + +for i=1:cyc; + + %%%% E Step %%%% + Phd=diag(1./Ph); + LP=Phd*L; + MM=Phd-LP*inv(I+L'*LP)*LP'; + dM=sqrt(det(MM)); + beta=L'*MM; + XXbeta=XX*beta'; + EZZ=I-beta*L +beta*XXbeta; + + %%%% Compute log likelihood %%%% + + oldlik=lik; + lik=N*const+N*log(dM)-0.5*N*sum(diag(MM*XX)); + fprintf('cycle %i lik %g \n',i,lik); + LL=[LL lik]; + + %%%% M Step %%%% + + L=XXbeta*inv(EZZ); + Ph=diagXX-diag(L*XXbeta'); + + if (i<=2) + likbase=lik; + elseif (lik<oldlik) + disp('VIOLATION'); + elseif ((lik-likbase)<(1+tol)*(oldlik-likbase)||~isfinite(lik)) + break; + end; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/mfa.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol); +% +% Maximum Likelihood Mixture of Factor Analysis using EM +% +% X - data matrix +% M - number of mixtures (default 1) +% K - number of factors in each mixture (default 2) +% cyc - maximum number of cycles of EM (default 100) +% tol - termination tolerance (prop change in likelihood) (default 0.0001) +% +% Lh - factor loadings +% Ph - diagonal uniquenesses matrix +% Mu - mean vectors +% Pi - priors +% LL - log likelihood curve +% +% Iterates until a proportional change < tol in the log likelihood +% or cyc steps of EM + +function [Lh, Ph, Mu, Pi, LL] = mfa(X,M,K,cyc,tol) + +if nargin<5 tol=0.0001; end; +if nargin<4 cyc=100; end; +if nargin<3 K=2; end; +if nargin<2 M=1; end; + +N=length(X(:,1)); +D=length(X(1,:)); +tiny=exp(-700); + +%rand('state',0); + +fprintf('\n'); + +if (M==1) + [Lh,Ph,LL]=ffa(X,K,cyc,tol); + Mu=mean(X); + Pi=1; +else + if N==1 + mX = X; + else + mX=mean(X); + end + cX=cov(X); + scale=det(cX)^(1/D); + randn('state',0); + Lh=randn(D*M,K)*sqrt(scale/K); + Ph=diag(cX)+tiny; + Pi=ones(M,1)/M; + %randn('state',0); + Mu=randn(M,D)*sqrtm(cX)+ones(M,1)*mX; + oldMu=Mu; + I=eye(K); + + lik=0; + LL=[]; + + H=zeros(N,M); % E(w|x) + EZ=zeros(N*M,K); + EZZ=zeros(K*M,K); + XX=zeros(D*M,D); + s=zeros(M,1); + const=(2*pi)^(-D/2); + %%%%%%%%%%%%%%%%%%%% + for i=1:cyc; + + %%%% E Step %%%% + + Phi=1./Ph; + Phid=diag(Phi); + for k=1:M + Lht=Lh((k-1)*D+1:k*D,:); + LP=Phid*Lht; + MM=Phid-LP*inv(I+Lht'*LP)*LP'; + dM=sqrt(det(MM)); + Xk=(X-ones(N,1)*Mu(k,:)); + XM=Xk*MM; + H(:,k)=const*Pi(k)*dM*exp(-0.5*rsum(XM.*Xk)); + EZ((k-1)*N+1:k*N,:)=XM*Lht; + end; + + Hsum=rsum(H); + oldlik=lik; + lik=sum(log(Hsum+(Hsum==0)*exp(-744))); + + Hzero=(Hsum==0); Nz=sum(Hzero); + H(Hzero,:)=tiny*ones(Nz,M)/M; + Hsum(Hzero)=tiny*ones(Nz,1); + + H=rdiv(H,Hsum); + s=csum(H); + s=s+(s==0)*tiny; + s2=sum(s)+tiny; + + for k=1:M + kD=(k-1)*D+1:k*D; + Lht=Lh(kD,:); + LP=Phid*Lht; + MM=Phid-LP*inv(I+Lht'*LP)*LP'; + Xk=(X-ones(N,1)*Mu(k,:)); + XX(kD,:)=rprod(Xk,H(:,k))'*Xk/s(k); + beta=Lht'*MM; + EZZ((k-1)*K+1:k*K,:)=I-beta*Lht +beta*XX(kD,:)*beta'; + end; + + %%%% log likelihood %%%% + + LL=[LL lik]; + fprintf('cycle %g \tlog likelihood %g ',i,lik); + + if (i<=2) + likbase=lik; + elseif (lik<oldlik) + fprintf(' violation'); + elseif ((lik-likbase)<(1 + tol)*(oldlik-likbase)||~isfinite(lik)) + break; + end; + + fprintf('\n'); + + %%%% M Step %%%% + + % means and covariance structure + + Ph=zeros(D,1); + for k=1:M + kD=(k-1)*D+1:k*D; + kK=(k-1)*K+1:k*K; + kN=(k-1)*N+1:k*N; + + T0=rprod(X,H(:,k)); + T1=T0'*[EZ(kN,:) ones(N,1)]; + XH=EZ(kN,:)'*H(:,k); + T2=inv([s(k)*EZZ(kK,:) XH; XH' s(k)]); + T3=T1*T2; + Lh(kD,:)=T3(:,1:K); + Mu(k,:)=T3(:,K+1)'; + T4=diag(T0'*X-T3*T1')/s2; + Ph=Ph+T4.*(T4>0); + end; + + Phmin=exp(-700); + Ph=Ph.*(Ph>Phmin)+(Ph<=Phmin)*Phmin; % to avoid zero variances + + % priors + Pi=s'/s2; + + end; + fprintf('\n'); +end; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/mfa_cl.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +% function [lik, likv]=mfa_cl(X,Lh,Ph,Mu,Pi); +% +% Calculates log likelihoods of a data set under a mixture of factor +% analysis model. +% +% X - data matrix +% Lh - factor loadings +% Ph - diagonal uniquenesses matrix +% Mu - mean vectors +% Pi - priors +% +% lik - log likelihood of X +% likv - vector of log likelihoods +% +% If 0 or 1 output arguments requested, lik is returned. If 2 output +% arguments requested, [lik likv] is returned. + +function [lik, likv]=mfa_cl(X,Lh,Ph,Mu,Pi); + +N=length(X(:,1)); +D=length(X(1,:)); +K=length(Lh(1,:)); +M=length(Pi); + +if (abs(sum(Pi)-1) > 1e-6) + disp('ERROR: Pi should sum to 1'); + return; +elseif ((size(Lh) ~= [D*M K]) | (size(Ph) ~= [D 1]) | (size(Mu) ~= [M D]) ... + | (size(Pi) ~= [M 1] & size(Pi) ~= [1 M])) + disp('ERROR in input matrix sizes'); + return; +end; + +tiny=exp(-744); +const=(2*pi)^(-D/2); + +I=eye(K); +Phi=1./Ph; +Phid=diag(Phi); +for k=1:M + Lht=Lh((k-1)*D+1:k*D,:); + LP=Phid*Lht; + MM=Phid-LP*inv(I+Lht'*LP)*LP'; + dM=sqrt(det(MM)); + Xk=(X-ones(N,1)*Mu(k,:)); + XM=Xk*MM; + H(:,k)=const*Pi(k)*dM*exp(-0.5*sum((XM.*Xk)'))'; +end; + +Hsum=rsum(H); + +likv=log(Hsum+(Hsum==0)*tiny); +lik=sum(likv); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/mfademo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +echo on; + +clc; + +% This is a very basic demo of the mixture of factor analyzer software +% written in Matlab by Zoubin Ghahramani +% Dept of Computer Science +% University of Toronto + +pause; % Hit any key to continue + +% To demonstrate the software we generate a sample data set +% from a mixture of two Gaussians + +pause; % Hit any key to continue + +X1=randn(300,5); % zero mean 5 dim Gaussian data +X2=randn(200,5)+2; % 5 dim Gaussian data with mean [1 1 1 1 1] +X=[X1;X2]; % total 500 data points from mixture + +% Fitting the model is very easy. For example to fit a mixture of 2 +% factor analyzers with three factors each... + +pause; % Hit any key to continue + + +[Lh,Ph,Mu,Pi,LL]=mfa(X,2,3); + +% Lh, Ph, Mu, and Pi are the factor loadings, observervation +% variances, observation means for each mixture, and mixing +% proportions. LL is the vector of log likelihoods (the learning +% curve). For more information type: help mfa + +% to plot the learning curve (log likelihood at each step of EM)... + +pause; % Hit any key to continue + +plot(LL); + +% you get a more informative picture of convergence by looking at the +% log of the first difference of the log likelihoods... + +pause; % Hit any key to continue + +semilogy(diff(LL)); + +% you can look at some of the parameters of the fitted model... + +pause; % Hit any key to continue + +Mu + +Pi + +% ...to see whether they make any sense given that me know how the +% data was generated. + +% you can also evaluate the log likelihood of another data set under +% the model we have just fitted using the mfa_cl (for Calculate +% Likelihood) function. For example, here we generate a test from the +% same distribution. + + +X1=randn(300,5); +X2=randn(200,5)+2; +Xtest=[X1; X2]; + +pause; % Hit any key to continue + +mfa_cl(Xtest,Lh,Ph,Mu,Pi) + +% we should expect the log likelihood of the test set to be lower than +% that of the training set. + +% finally, we can also fit a regular factor analyzer using the ffa +% function (Fast Factor Analysis)... + +pause; % Hit any key to continue + +[L,Ph,LL]=ffa(X,3); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/rdiv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +% function Z=rdiv(X,Y) +% +% row division: Z = X / Y row-wise +% Y must have one column + +function Z=rdiv(X,Y) + +[N M]=size(X); +[K L]=size(Y); +if(N ~= K | L ~=1) + disp('Error in RDIV'); + return; +end + +Z=zeros(N,M); + +if M<N, + for m=1:M + Z(:,m)=X(:,m)./Y; + end +else + for n=1:N + Z(n,:)=X(n,:)/Y(n); + end; +end; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/rprod.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +% row product +% function Z=rprod(X,Y) + +function Z=rprod(X,Y) + +if(length(X(:,1)) ~= length(Y(:,1)) | length(Y(1,:)) ~=1) + disp('Error in RPROD'); + return; +end + +Z=zeros(size(X)); + +for i=1:length(X(1,:)) + Z(:,i)=X(:,i).*Y; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/Zoubin/rsum.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +% row sum +% function Z=rsum(X) + +function Z=rsum(X) + +[N M]=size(X); + +Z=zeros(N,1); + +if M==1, + Z=X; +elseif M<2*N, + for m=1:M, + Z=Z+X(:,m); + end; +else + for n=1:N + Z(n)=sum(X(n,:)); + end; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/brainy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +% Example of explaining away from +% http://www.ai.mit.edu/~murphyk/Bayes/bnintro.html#explainaway +% +% Suppose you have to be brainy or smart to get into college. +% B S P(C=1) P(C=2) 1=false 2=true +% 1 1 1.0 0.0 +% 2 1 0.0 1.0 +% 1 2 0.0 1.0 +% 2 2 0.0 1.0 +% +% +% If we observe that you are in college, you must be either brainy or sporty or both. +% If we observre you are in college and sporty, it is less likely you are brainy, +% since brainy-ness and sporty-ness compete as causal explanations of the effect. + +% B S +% \/ +% C + +B = 1; S = 2; C = 3; +dag = zeros(3,3); +dag([B S], C)=1; +ns = 2*ones(1,3); +bnet = mk_bnet(dag, ns); +bnet.CPD{B} = tabular_CPD(bnet, B, 'CPT', [0.5 0.5]'); +bnet.CPD{S} = tabular_CPD(bnet, S, 'CPT', [0.5 0.5]'); +CPT = zeros(2,2,2); +CPT(1,1,:) = [1 0]; +CPT(2,1,:) = [0 1]; +CPT(1,2,:) = [0 1]; +CPT(2,2,:) = [0 1]; +bnet.CPD{C} = tabular_CPD(bnet, C, 'CPT', CPT); + +engine = jtree_inf_engine(bnet); +ev = cell(1,3); +ev{C} = 2; +engine = enter_evidence(engine, ev); +m = marginal_nodes(engine, B); +fprintf('P(B=true|C=true) = %5.3f\n', m.T(2)) % 0.67 + +ev{S} = 2; +engine = enter_evidence(engine, ev); +m = marginal_nodes(engine, B); +fprintf('P(B=true|C=true,S=true) = %5.3f\n', m.T(2)) % 0.5 = unconditional baseline P(B=true)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/burglar-alarm-net.lisp.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +#| +The following code represents the burglar alarm Bayes network from +Chapter 14 of Russell & Norvig, 2nd Edition. This network representation +is used in the corresponding Bayes net code found in this directory. + +The conditional probability tables consist of the values listed here +(along with the probabilities of the corresponding complementary events): + +P(Burglary = true) = 0.001 (=> P(Burglary = false) = 0.999) +P(Earthquake = true) = 0.002 (=> P(Earthquake = false) = 0.998) + +P(Alarm = true | Burglary = true, Earthquake = true) = 0.95 +P(Alarm = true | Burglary = true, Earthquake = false) = 0.94 +P(Alarm = true | Burglary = false, Earthquake = true) = 0.29 +P(Alarm = true | Burglary = false, Earthquake = false) = 0.001 + +P(JohnCalls = true | Alarm = true) = 0.90 +P(JohnCalls = true | Alarm = false) = 0.05 + +P(MaryCalls = true | Alarm = true) = 0.70 +P(MaryCalls = true | Alarm = false) = 0.01 +|# + +(setf *burglar-alarm-net* + '((MaryCalls (true false) + (Alarm) + ((true) 0.70 0.30) + ((false) 0.01 0.99)) + (JohnCalls (true false) + (Alarm) + ((true) 0.90 0.10) + ((false) 0.05 0.95)) + (Alarm (true false) + (Burglary Earthquake) + ((true true) 0.95 0.05) + ((true false) 0.94 0.06) + ((false true) 0.29 0.71) + ((false false) 0.001 0.999)) + (Burglary (true false) + () + (0.001 0.999)) + (Earthquake (true false) + () + (0.002 0.998)) + ))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/burglary.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +% Burglar alarm example + +N = 5; +dag = zeros(N,N); +E = 1; B = 2; R = 3; A = 4; C = 5; +dag(E,[R A]) = 1; +dag(B,A) = 1; +dag(A,C)=1; + +% true = state 1, false = state 2 +ns = 2*ones(1,N); % binary nodes +bnet = mk_bnet(dag, ns); + +bnet.CPD{E} = tabular_CPD(bnet, E, [0.1 0.9]); +bnet.CPD{B} = tabular_CPD(bnet, B, [0.01 0.99]); +%bnet.CPD{R} = tabular_CPD(bnet, R, [0.65 0.00001 0.35 0.99999]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.65 0.01 0.35 0.99]); +bnet.CPD{A} = tabular_CPD(bnet, A, [0.95 0.8 0.3 0.001 0.05 0.2 0.7 0.999]); +bnet.CPD{C} = tabular_CPD(bnet, C, [0.7 0.05 0.3 0.95]); + + +engine = jtree_inf_engine(bnet); +ev = cell(1,N); +ev{C} = 1; +engine = enter_evidence(engine, ev); +mE = marginal_nodes(engine, E); +mB = marginal_nodes(engine, B); +fprintf('P(E|c)=%5.3f, P(B|c)=%5.3f\n', mE.T(1), mB.T(1)) + +ev{C} = 1; +ev{R} = 1; +engine = enter_evidence(engine, ev); +mE = marginal_nodes(engine, E); +mB = marginal_nodes(engine, B); +fprintf('P(E|c,r)=%5.3f, P(B|c,r)=%5.3f\n', mE.T(1), mB.T(1)) + + +if 0 +nsamples = 100; +samples = zeros(nsamples, 5); +for i=1:nsamples + samples(i,:) = cell2num(sample_bnet(bnet))'; +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/cg1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +% Conditional Gaussian network +% The waste incinerator emissions example from Lauritzen (1992), +% "Propogation of Probabilities, Means and Variances in Mixed Graphical Association Models", +% JASA 87(420): 1098--1108 +% +% This example is reprinted on p145 of "Probabilistic Networks and Expert Systems", +% Cowell, Dawid, Lauritzen and Spiegelhalter, 1999, Springer. +% +% For a picture, see http://www.cs.berkeley.edu/~murphyk/Bayes/usage.html#cg_model + +ns = 2*ones(1,9); +%bnet = mk_incinerator_bnet(ns); +bnet = mk_incinerator_bnet; + +engines = {}; +%engines{end+1} = stab_cond_gauss_inf_engine(bnet); +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = cond_gauss_inf_engine(bnet); +nengines = length(engines); + +F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; +n = 9; +dnodes = [B F W]; +cnodes = mysetdiff(1:n, dnodes); + +evidence = cell(1,n); % no evidence +ll = zeros(1, nengines); +for e=1:nengines + [engines{e}, ll(e)] = enter_evidence(engines{e}, evidence); +end +%assert(approxeq(ll(1), ll))) +ll + +% Compare to the results in table on p1107. +% These results are printed to 3dp in Cowell p150 + +mu = zeros(1,n); +sigma = zeros(1,n); +dprob = zeros(1,n); +addev = 1; +tol = 1e-2; +for e=1:nengines + for i=cnodes(:)' + m = marginal_nodes(engines{e}, i, addev); + mu(i) = m.mu; + sigma(i) = sqrt(m.Sigma); + end + for i=dnodes(:)' + m = marginal_nodes(engines{e}, i, addev); + dprob(i) = m.T(1); + end + assert(approxeq(mu([E D C L Min Mout]), [-3.25 3.04 -1.85 1.48 -0.214 2.83], tol)) + assert(approxeq(sigma([E D C L Min Mout]), [0.709 0.770 0.507 0.631 0.459 0.860], tol)) + assert(approxeq(dprob([B F W]), [0.85 0.95 0.29], tol)) + %m = marginal_nodes(engines{e}, bnet.names('E'), addev); + %assert(approxeq(m.mu, -3.25, tol)) + %assert(approxeq(sqrt(m.Sigma), 0.709, tol)) +end + +% Add evidence (p 1105, top right) +evidence = cell(1,n); +evidence{W} = 1; % industrial +evidence{L} = 1.1; +evidence{C} = -0.9; + +ll = zeros(1, nengines); +for e=1:nengines + [engines{e}, ll(e)] = enter_evidence(engines{e}, evidence); +end +assert(all(approxeq(ll(1), ll))) + +for e=1:nengines + for i=cnodes(:)' + m = marginal_nodes(engines{e}, i, addev); + mu(i) = m.mu; + sigma(i) = sqrt(m.Sigma); + end + for i=dnodes(:)' + m = marginal_nodes(engines{e}, i, addev); + dprob(i) = m.T(1); + end + assert(approxeq(mu([E D C L Min Mout]), [-3.90 3.61 -0.9 1.1 0.5 4.11], tol)) + assert(approxeq(sigma([E D C L Min Mout]), [0.076 0.326 0 0 0.1 0.344], tol)) + assert(approxeq(dprob([B F W]), [0.0122 0.9995 1], tol)) +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/cg2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +% Conditional Gaussian network with vector-valued nodes and random params + +ns = 2*ones(1,9); +bnet = mk_incinerator_bnet(ns); + +engines = {}; +%engines{end+1} = stab_cond_gauss_inf_engine(bnet); +engines{end+1} = jtree_inf_engine(bnet); +engines{end+1} = cond_gauss_inf_engine(bnet); + +[err, time] = cmp_inference_static(bnet, engines, 'singletons_only', 1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/cmp_inference_static.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +function [time, engine] = cmp_inference_static(bnet, engine, varargin) +% CMP_INFERENCE Compare several inference engines on a BN +% function [time, engine] = cmp_inference_static(bnet, engine, ...) +% +% engine{i} is the i'th inference engine. +% time(e) = elapsed time for doing inference with engine e +% +% The list below gives optional arguments [default value in brackets]. +% +% exact - specifies which engines do exact inference [ 1:length(engine) ] +% singletons_only - if 1, we only call marginal_nodes, else this and marginal_family [0] +% maximize - 1 means we do max-propagation, 0 means sum-propagation [0] +% check_ll - 1 means we check that the log-likelihoods are correct [1] +% observed - list of the observed ndoes [ bnet.observed ] +% check_converged - list of loopy engines that should be checked for convergence [ [] ] +% If an engine has converged, it is added to the exact list. + + +% set default params +exact = 1:length(engine); +singletons_only = 0; +maximize = 0; +check_ll = 1; +observed = bnet.observed; +check_converged = []; + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'exact', exact = args{i+1}; + case 'singletons_only', singletons_only = args{i+1}; + case 'maximize', maximize = args{i+1}; + case 'check_ll', check_ll = args{i+1}; + case 'observed', observed = args{i+1}; + case 'check_converged', check_converged = args{i+1}; + otherwise, + error(['unrecognized argument ' args{i}]) + end +end + +E = length(engine); +ref = exact(1); % reference + +N = length(bnet.dag); +ev = sample_bnet(bnet); +evidence = cell(1,N); +evidence(observed) = ev(observed); +%celldisp(evidence(observed)) + +for i=1:E + tic; + if check_ll + [engine{i}, ll(i)] = enter_evidence(engine{i}, evidence, 'maximize', maximize); + else + engine{i} = enter_evidence(engine{i}, evidence, 'maximize', maximize); + end + time(i)=toc; +end + +for i=check_converged(:)' + niter = loopy_converged(engine{i}); + if niter > 0 + fprintf('loopy engine %d converged in %d iterations\n', i, niter); +% exact = myunion(exact, i); + else + fprintf('loopy engine %d has not converged\n', i); + end +end + +cmp = exact(2:end); +if check_ll + for i=cmp(:)' + assert(approxeq(ll(ref), ll(i))); + end +end + +hnodes = mysetdiff(1:N, observed); + +if ~singletons_only + get_marginals(engine, hnodes, exact, 0); +end +get_marginals(engine, hnodes, exact, 1); + +%%%%%%%%%% + +function get_marginals(engine, hnodes, exact, singletons) + +bnet = bnet_from_engine(engine{1}); +N = length(bnet.dag); +cnodes_bitv = zeros(1,N); +cnodes_bitv(bnet.cnodes) = 1; +ref = exact(1); % reference +cmp = exact(2:end); +E = length(engine); + +for n=hnodes(:)' + for e=1:E + if singletons + m{e} = marginal_nodes(engine{e}, n); + else + m{e} = marginal_family(engine{e}, n); + end + end + for e=cmp(:)' + if cnodes_bitv(n) + assert(approxeq(m{ref}.mu, m{e}.mu)) + assert(approxeq(m{ref}.Sigma, m{e}.Sigma)) + else + assert(approxeq(m{ref}.T, m{e}.T)) + end + assert(isequal(m{e}.domain, m{ref}.domain)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/discrete1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +% Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17) +% 1 +% / | \ +% 2 3 4 +% | | | +% 5 6 7 +% \/ \/ +% 8 9 +% where all arcs point downwards + +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + +dnodes = 1:N; +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +onodes = [2 7]; +bnet = mk_bnet(dag, ns, 'observed', onodes); +% use random params +for i=1:N + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +query = [3]; +engine = {}; +engine{end+1} = jtree_inf_engine(bnet); +engine{end+1} = var_elim_inf_engine(bnet); +%engine{end+1} = global_joint_inf_engine(bnet); +% global joint is designed for limids because does not normalize + +%engine{end+1} = enumerative_inf_engine(bnet); +%engine{end+1} = jtree_onepass_inf_engine(bnet, query, onodes); + +maximize = 0; % jtree_ndx crashes on max-prop +[err, time] = cmp_inference_static(bnet, engine, 'maximize', maximize); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/discrete2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +% Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17) +% 1 +% / | \ +% 2 3 4 +% | | | +% 5 6 7 +% \/ \/ +% 8 9 +% where all arcs point downwards +seed = 0; +rand('state', seed); +randn('state', seed); + +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + +dnodes = 1:N; +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +onodes = [2 4]; +bnet = mk_bnet(dag, ns, 'observed', onodes); +% use random params +for i=1:N + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +%USEC = exist('@jtree_C_inf_engine/collect_evidence','file'); +query = [3]; +engine = {}; +engine{end+1} = jtree_inf_engine(bnet); +engine{end+1} = jtree_sparse_inf_engine(bnet); +%engine{end+1} = jtree_ndx_inf_engine(bnet, 'ndx_type', 'SD'); +%engine{end+1} = jtree_ndx_inf_engine(bnet, 'ndx_type', 'B'); +%engine{end+1} = jtree_ndx_inf_engine(bnet, 'ndx_type', 'D'); +%if USEC, engine{end+1} = jtree_C_inf_engine(bnet); end +%engine{end+1} = var_elim_inf_engine(bnet); +%engine{end+1} = enumerative_inf_engine(bnet); +%engine{end+1} = jtree_onepass_inf_engine(bnet, query, onodes); + +maximize = 0; % jtree_ndx crashes on max-prop +[err, time] = cmp_inference_static(bnet, engine, 'maximize', maximize); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/discrete3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +% Compare various inference engines on the following network (from Jensen (1996) p84 fig 4.17) +% 1 +% / | \ +% 2 3 4 +% | | | +% 5 6 7 +% \/ \/ +% 8 9 +% where all arcs point downwards + +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + +dnodes = 1:N; +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +onodes = [1]; +evidence = cell(1,N); +evidence(onodes) = num2cell(1); +bnet = mk_bnet(dag, ns, 'observed', onodes); +% use random params +%for i=1:N +% bnet.CPD{i} = tabular_CPD(bnet, i); +%end +bnet.CPD{1} = tabular_CPD(bnet, 1, 'sparse', 1, 'CPT', [0.8, 0.2]); +bnet.CPD{2} = tabular_CPD(bnet, 2, 'sparse', 1, 'CPT', [1 0 0 1]); +bnet.CPD{3} = tabular_CPD(bnet, 3, 'sparse', 1, 'CPT', [0 1 1 0]); +bnet.CPD{4} = tabular_CPD(bnet, 4, 'sparse', 1, 'CPT', [1 1 0 0]); +bnet.CPD{5} = tabular_CPD(bnet, 5, 'sparse', 1, 'CPT', [0 0 1 1]); +bnet.CPD{6} = tabular_CPD(bnet, 6, 'sparse', 1, 'CPT', [1 0 0 1]); +bnet.CPD{7} = tabular_CPD(bnet, 7, 'sparse', 1, 'CPT', [0 1 1 0]); +bnet.CPD{8} = tabular_CPD(bnet, 8, 'sparse', 1, 'CPT', [1 1 0 0 0 0 1 1]); +bnet.CPD{9} = tabular_CPD(bnet, 9, 'sparse', 1, 'CPT', [0 1 0 1 1 0 1 0]); + +engine = jtree_sparse_inf_engine(bnet); +tic +[engine, ll] = enter_evidence(engine, evidence); +toc +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/test_housing.m/1.1.1.1/Wed May 29 15:59:54 2002// +/test_restaurants.m/1.1.1.1/Wed May 29 15:59:54 2002// +/test_zoo1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/tmp.dot/1.1.1.1/Wed May 29 15:59:54 2002// +/transform_data_into_bnt_format.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/dtree
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/test_housing.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,87 @@ +% Here the training data is adapted from UCI ML repository, 'housing' data +% Input variables: 12 continous, one binary +% Ouput variables: continous +% The testing result trace is in the end of this script, it is same to the graph in page 219 of +% Leo Brieman etc. 1984 book titled "Classification and regression trees". + +dtreeCPD=tree_CPD; + +% load data +fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'housing', 'housing.data'); +data=load(fname); +data=data'; +data=transform_data_into_bnt_format(data,[1:3,5:14]); + +% learn decision tree from data +ns=1*ones(1,14); +ns(4)=2; +dtreeCPD1=learn_params(dtreeCPD,1:14,data,ns,[1:3,5:14],'stop_cases',5,'min_gain',0.006); + +% evaluate on data +[score,outputs]=evaluate_tree_performance(dtreeCPD1,1:14,data,ns,[1:3,5:14]); +fprintf('Mean square deviation (using regression tree to predict) in old training data %6.3f\n',score); + + +% show decision tree using graphpad +% It should be easy, but still not implemented + + + +% >> test_housing +% Create node 1 split at 6 gain 38.2205 Th 6.939000e+000. Mean 22.5328 Cases 506 +% Create node 2 split at 13 gain 14.4503 Th 1.437000e+001. Mean 19.9337 Cases 430 +% Create node 3 split at 8 gain 4.9809 Th 1.358000e+000. Mean 23.3498 Cases 255 +% Create node 4 split at 1 gain 0.7722 Th 1.023300e+001. Mean 45.5800 Cases 5 +% Create leaf node(samevalue) 5. Mean 50.0000 Std 0.0000 Cases 4 +% Add subtree node 5 to 4. #nodes 5 +% Create leaf node(samevalue) 6. Mean 27.9000 Std 0.0000 Cases 1 +% Add subtree node 6 to 4. #nodes 6 +% Add subtree node 4 to 3. #nodes 6 +% Create node 7 split at 6 gain 2.8497 Th 6.540000e+000. Mean 22.9052 Cases 250 +% Create node 8 split at 13 gain 0.5970 Th 7.560000e+000. Mean 21.6297 Cases 195 +% Create leaf node(nogain) 9. Mean 23.9698 Std 1.7568 Cases 43 +% Add subtree node 9 to 8. #nodes 9 +% Create leaf node(nogain) 10. Mean 20.9678 Std 2.8242 Cases 152 +% Add subtree node 10 to 8. #nodes 10 +% Add subtree node 8 to 7. #nodes 10 +% Create leaf node(nogain) 11. Mean 27.4273 Std 3.4512 Cases 55 +% Add subtree node 11 to 7. #nodes 11 +% Add subtree node 7 to 3. #nodes 11 +% Add subtree node 3 to 2. #nodes 11 +% Create node 12 split at 1 gain 2.2467 Th 6.962150e+000. Mean 14.9560 Cases 175 +% Create node 13 split at 5 gain 0.5172 Th 5.240000e-001. Mean 17.1376 Cases 101 +% Create leaf node(nogain) 14. Mean 20.0208 Std 3.0672 Cases 24 +% Add subtree node 14 to 13. #nodes 14 +% Create leaf node(nogain) 15. Mean 16.2390 Std 2.9746 Cases 77 +% Add subtree node 15 to 13. #nodes 15 +% Add subtree node 13 to 12. #nodes 15 +% Create node 16 split at 5 gain 0.6133 Th 6.050000e-001. Mean 11.9784 Cases 74 +% Create leaf node(nogain) 17. Mean 16.6333 Std 4.5052 Cases 12 +% Add subtree node 17 to 16. #nodes 17 +% Create leaf node(nogain) 18. Mean 11.0774 Std 3.0090 Cases 62 +% Add subtree node 18 to 16. #nodes 18 +% Add subtree node 16 to 12. #nodes 18 +% Add subtree node 12 to 2. #nodes 18 +% Add subtree node 2 to 1. #nodes 18 +% Create node 19 split at 6 gain 6.0493 Th 7.420000e+000. Mean 37.2382 Cases 76 +% Create node 20 split at 1 gain 1.9900 Th 7.367110e+000. Mean 32.1130 Cases 46 +% Create node 21 split at 8 gain 0.6273 Th 1.877300e+000. Mean 33.3488 Cases 43 +% Create leaf node(samevalue) 22. Mean 45.6500 Std 6.1518 Cases 2 +% Add subtree node 22 to 21. #nodes 22 +% Create leaf node(nogain) 23. Mean 32.7488 Std 3.5690 Cases 41 +% Add subtree node 23 to 21. #nodes 23 +% Add subtree node 21 to 20. #nodes 23 +% Create leaf node(samevalue) 24. Mean 14.4000 Std 3.7363 Cases 3 +% Add subtree node 24 to 20. #nodes 24 +% Add subtree node 20 to 19. #nodes 24 +% Create node 25 split at 1 gain 1.1001 Th 2.733970e+000. Mean 45.0967 Cases 30 +% Create leaf node(nogain) 26. Mean 45.8966 Std 4.4005 Cases 29 +% Add subtree node 26 to 25. #nodes 26 +% Create leaf node(samevalue) 27. Mean 21.9000 Std 0.0000 Cases 1 +% Add subtree node 27 to 25. #nodes 27 +% Add subtree node 25 to 19. #nodes 27 +% Add subtree node 19 to 1. #nodes 27 +% Mean square deviation (using regression tree to predict) in old training data 9.405 +% + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/test_restaurants.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,98 @@ +% Here the training data is adapted from Russell95 book. See restaurant.names for description. +% (1) Use infomation-gain as the split testing score, we get the the same decision tree as the book Russell 95 (page 537), +% and the Gain(Patrons) is 0.5409, equal to the result in Page 541 of Russell 95. (see below output trace) +% (Note: the dtree in that book has small compilation error, the Type node is from YES of Hungry node, not NO.) +% (2) Use gain-ratio (Quilan 93), the splitting defavorite attribute with more values. (e.g. the Type attribute here) + +dtreeCPD=tree_CPD; + +% load data +fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'restaurant', 'restaurant.data'); +data=load(fname); +data=data'; + +%make the data be BNT compliant (values for discrete nodes are from 1-n, here n is the node size) + % e.g. if the values are [0 1 6], they must be mapping to [1 2 3] +%data=transform_data(data,'tmp.dat',[]); %here no cts nodes + +% learn decision tree from data +ns=2*ones(1,11); +ns(5:6)=3; +ns(9:10)=4; +dtreeCPD1=learn_params(dtreeCPD,1:11,data,ns,[]); + +% evaluate on data +[score,outputs]=evaluate_tree_performance(dtreeCPD1,1:11,data,ns,[]); +fprintf('Accuracy in training data %6.3f\n',score); + +% show decision tree using graphpad + + + +% --------------------------Output trace: using Information-Gain------------------------------ +% The splits are Patron, Hungry, Type, Fri/Sat +% ********************************* +% Create node 1 split at 5 gain 0.5409 Th 0. Class 1 Cases 12 Error 6 +% Create leaf node(onecla) 2. Class 1 Cases 2 Error 0 +% Add subtree node 2 to 1. #nodes 2 +% Create leaf node(onecla) 3. Class 2 Cases 4 Error 0 +% Add subtree node 3 to 1. #nodes 3 +% Create node 4 split at 4 gain 0.2516 Th 0. Class 1 Cases 6 Error 2 +% Create leaf node(onecla) 5. Class 1 Cases 2 Error 0 +% Add subtree node 5 to 4. #nodes 5 +% Create node 6 split at 9 gain 0.5000 Th 0. Class 1 Cases 4 Error 2 +% Create leaf node(nullset) 7. Father 6 Class 1 +% Create node 8 split at 3 gain 1.0000 Th 0. Class 1 Cases 2 Error 1 +% Create leaf node(onecla) 9. Class 1 Cases 1 Error 0 +% Add subtree node 9 to 8. #nodes 9 +% Create leaf node(onecla) 10. Class 2 Cases 1 Error 0 +% Add subtree node 10 to 8. #nodes 10 +% Add subtree node 8 to 6. #nodes 10 +% Create leaf node(onecla) 11. Class 2 Cases 1 Error 0 +% Add subtree node 11 to 6. #nodes 11 +% Create leaf node(onecla) 12. Class 1 Cases 1 Error 0 +% Add subtree node 12 to 6. #nodes 12 +% Add subtree node 6 to 4. #nodes 12 +% Add subtree node 4 to 1. #nodes 12 +% ******************************** +% +% Note: +% ***Create node 4 split at 4 gain 0.2516 Th 0. Class 1 Cases 6 Error 2 +% This mean we create a new node number 4, it is splitting at the attribute 4, and info-gain is 0.2516, +% "Th 0" means threshhold for splitting continous attribute, "Class 1" means the majority class at node 4 is 1, +% and "Cases 6" means it has 6 cases attached to it, "Error 2" means it has two errors if changing the class lable of +% all the cases in it to the majority class. +% *** Add subtree node 12 to 6. #nodes 12 +% It means we add the child node 12 to node 6. +% *** Create leaf node(onecla) 10. Class 2 Cases 1 Error 0 +% here 'onecla' means all cases in this node belong to one class, so no need to split further. +% 'nullset' means no training cases belong to this node, we use its parent node majority class as its class +% +% +% +% ---------------Output trace: using GainRatio----------------------- +% The splits are Patron, Hungry, Fri/Sat, Price +% +% +% Create node 1 split at 5 gain 0.3707 Th 0. Class 1 Cases 12 Error 6 +% Create leaf node(onecla) 2. Class 1 Cases 2 Error 0 +% Add subtree node 2 to 1. #nodes 2 +% Create leaf node(onecla) 3. Class 2 Cases 4 Error 0 +% Add subtree node 3 to 1. #nodes 3 +% Create node 4 split at 4 gain 0.2740 Th 0. Class 1 Cases 6 Error 2 +% Create leaf node(onecla) 5. Class 1 Cases 2 Error 0 +% Add subtree node 5 to 4. #nodes 5 +% Create node 6 split at 3 gain 0.3837 Th 0. Class 1 Cases 4 Error 2 +% Create leaf node(onecla) 7. Class 1 Cases 1 Error 0 +% Add subtree node 7 to 6. #nodes 7 +% Create node 8 split at 6 gain 1.0000 Th 0. Class 2 Cases 3 Error 1 +% Create leaf node(onecla) 9. Class 2 Cases 2 Error 0 +% Add subtree node 9 to 8. #nodes 9 +% Create leaf node(nullset) 10. Father 8 Class 2 +% Create leaf node(onecla) 11. Class 1 Cases 1 Error 0 +% Add subtree node 11 to 8. #nodes 11 +% Add subtree node 8 to 6. #nodes 11 +% Add subtree node 6 to 4. #nodes 11 +% Add subtree node 4 to 1. #nodes 11 +% +%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/test_zoo1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +% Here the training data is adapted from UCI ML repository, 'zoo' data + +dtreeCPD=tree_CPD; + +% load data +fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'zoo', 'zoo1.data') +data=load(fname); +data=data'; + +data=transform_data_into_bnt_format(data, []); + +% learn decision tree from data +ns=2*ones(1,17); +ns(13)=6; +ns(17)=7; +dtreeCPD1=learn_params(dtreeCPD,1:17,data,ns,[],'stop_cases',5); % a node with less than 5 cases will not be splitted + +% evaluate on data +[score,outputs]=evaluate_tree_performance(dtreeCPD1,1:17,data,ns,[]); +fprintf('Accuracy in old training data %6.3f\n',score); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/tmp.dot Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +digraph G { +center = 1; +size="4,4"; +n1 [ label = "1 :" ]; +n2 [ label = "2 :" ]; +n3 [ label = "3 :" ]; +n4 [ label = "4 :" ]; +n5 [ label = "5 :" ]; +n6 [ label = "6 :" ]; +n7 [ label = "7 :" ]; +n8 [ label = "8 :" ]; +n9 [ label = "9 :" ]; +n10 [ label = "10 :" ]; +n1 -> n5 [label="1.000"]; +n2 -> n7 [label="0.800"]; +n2 -> n10 [label="0.200"]; +n3 -> n2 [label="1.000"]; +n4 -> n8 [label="1.000"]; +n5 -> n3 [label="0.143"]; +n5 -> n5 [label="0.571"]; +n5 -> n8 [label="0.286"]; +n6 -> n4 [label="1.000"]; +n7 -> n6 [label="0.333"]; +n7 -> n9 [label="0.667"]; +n8 -> n1 [label="0.333"]; +n8 -> n5 [label="0.333"]; +n8 -> n10 [label="0.333"]; +n9 -> n2 [label="1.000"]; +n10 -> n9 [label="1.000"]; + +} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/dtree/transform_data_into_bnt_format.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +function [bnt_data, old_values] = transform_data_into_bnt_format(data,cnodes) +% TRANSFORM_DATA_TO_BNT_FORMAT Ensures discrete variables have values 1,2,..,k +% e.g., if the values of a discrete are [0 1 6], they must be mapped to [1 2 3] +% +% data(i,j) is the value for i-th node in j-th case. +% bnt_data(i,j) is the new value. +% old_values{i} are the original values for node i. +% cnodes is the list of all continous nodes, e.g. [3 5] means the 3rd and 5th node is continuous +% +% Author: yimin.zhang@intel.com +% Last updated: Jan. 22, 2002 by Kevin Murphy. + +num_nodes=size(data,1); +num_cases=size(data,2); +old_values=cell(1,num_nodes); + +for i=1:num_nodes + if (myismember(i,cnodes)==1) %cts nodes no need to be transformed + %just copy the data + bnt_data(i,:)=data(i,:); + continue; + end + values = data(i,:); + sort_v = sort(values); + %remove the duplicate values in sort_v + v_set = unique(sort_v); + + %transform the values + for j=1:size(values,2) + index = binary_search(v_set,values(j)); + if (index==-1) + fprintf('value not found in tranforming data to bnt format.\n'); + return; + end + bnt_data(i,j)=index; + end + old_values{i}=v_set; +end + + +%%%%%%%%%%%% + +function index=binary_search(vector, value) +% BI_SEARCH do binary search for value in the vector +% Author: yimin.zhang@intel.com +% Last updated: Jan. 19, 2002 + +begin_index=1; +end_index=size(vector,2); +index=-1; +while (begin_index<=end_index) + mid=floor((begin_index+end_index)/2); + if (isstr(vector(mid))) + % need to write a strcmp to return three result (< = >) + else + if (value==vector(mid)) + index=mid; + return; + elseif (value>vector(mid)) + begin_index=mid+1; + else + end_index=mid-1; + end + end +end +return;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fa1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +% Factor analysis +% Z -> X, Z in R^k, X in R^D, k << D (high dimensional observations explained by small source) +% Z ~ N(0,I), X|Z ~ N(L z, Psi), where Psi is diagonal. +% +% We compare to Zoubin Ghahramani's code. + +state = 0; +rand('seed', state); +randn('seed', state); +max_iter = 3; +k = 2; +D = 4; +N = 10; +X = randn(N, D); + +% Initialize as in Zoubin's ffa (fast factor analysis) +X=X-ones(N,1)*mean(X); +XX=X'*X/N; +diagXX=diag(XX); +cX=cov(X); +scale=det(cX)^(1/D); +randn('seed', 0); % must reset seed here so initial params are identical to mfa +L0=randn(D,k)*sqrt(scale/k); +W0 = L0; +Psi0=diag(cX); + +[L1, Psi1, LL1] = ffa(X,k,max_iter); + + +ns = [k D]; +dag = zeros(2,2); +dag(1,2) = 1; +bnet = mk_bnet(dag, ns, 'discrete', [], 'observed', 2); +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', zeros(k,1), 'cov', eye(k), 'cov_type', 'diag', ... + 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(D,1), 'cov', diag(Psi0), 'weights', W0, ... + 'cov_type', 'diag', 'cov_prior_weight', 0, 'clamp_mean', 1); + +engine = jtree_inf_engine(bnet); +evidence = cell(2,N); +evidence(2,:) = num2cell(X', 1); + +[bnet2, LL2] = learn_params_em(engine, evidence, max_iter); + +s = struct(bnet2.CPD{2}); +L2 = s.weights; +Psi2 = s.cov; + + + +% Compare to Zoubin's code +assert(approxeq(LL2, LL1)); +assert(approxeq(Psi2, diag(Psi1))); +assert(approxeq(L2, L1)); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/fg1.m/1.1.1.1/Thu Jun 20 00:03:30 2002// +/fg2.m/1.1.1.1/Wed May 29 15:59:54 2002// +/fg3.m/1.1.1.1/Wed May 29 15:59:54 2002// +/fg_mrf1.m/1.1.1.1/Wed May 29 15:59:54 2002// +/fg_mrf2.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/examples/static/fgraph
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,98 @@ +% make an unrolled HMM, convert to factor graph, and check that +% loopy propagation on the fgraph gives the exact answers. + +seed = 1; +rand('state', seed); +randn('state', seed); + +T = 3; +Q = 3; +O = 3; +cts_obs = 0; +param_tying = 1; +bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying); + +data = sample_bnet(bnet); + +fgraph = bnet_to_fgraph(bnet); +big_bnet = fgraph_to_bnet(fgraph); +% converting factor graph back does not recover the structure of the original bnet + +max_iter = 2*T; + +engine = {}; +engine{1} = jtree_inf_engine(bnet); +engine{2} = belprop_inf_engine(bnet, 'max_iter', max_iter); +engine{3} = belprop_fg_inf_engine(fgraph, 'max_iter', max_iter); +engine{4} = jtree_inf_engine(big_bnet); +nengines = length(engine); + +big_engine = 4; +fgraph_engine = 3; + + +N = 2*T; +evidence = cell(1,N); +onodes = bnet.observed; +evidence(onodes) = data(onodes); +hnodes = mysetdiff(1:N, onodes); + +bigN = length(big_bnet.dag); +big_evidence = cell(1, bigN); +big_evidence(onodes) = data(onodes); +big_evidence(N+1:end) = {1}; % factors are observed to be 1 + +ll = zeros(1, nengines); +for i=1:nengines + if i==big_engine + tic; [engine{i}, ll(i)] = enter_evidence(engine{i}, big_evidence); toc + else + tic; [engine{i}, ll(i)] = enter_evidence(engine{i}, evidence); toc + end +end + +% compare all engines to engine{1} + +% the log likelihood values may be bogus... +for i=2:nengines + %assert(approxeq(ll(1), ll(i))); +end + + +marg = zeros(T, nengines, Q); % marg(t,e,:) +for t=1:T + for e=1:nengines + m = marginal_nodes(engine{e}, t); + marg(t,e,:) = m.T; + end +end +marg + + +m = cell(nengines, T); +for i=1:T + for e=1:nengines + m{e,i} = marginal_nodes(engine{e}, hnodes(i)); + end + for e=2:nengines + assert(approxeq(m{e,i}.T, m{1,i}.T)); + end +end + +mpe = {}; +ll = zeros(1, nengines); +for e=1:nengines + if e==big_engine + mpe{e} = find_mpe(engine{e}, big_evidence); + mpe{e} = mpe{e}(1:N); % chop off dummy nodes + else + mpe{e} = find_mpe(engine{e}, evidence); + end +end + +% fgraph can't compute loglikelihood for software reasons +% jtree on the big_bnet gives the wrong ll +for e=2:nengines + %assert(approxeq(ll(1), ll(e))); + assert(approxeq(cell2num(mpe{1}), cell2num(mpe{e}))) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +% make a factor graph corresponding to an HMM, where we absorb the evidence up front, +% and then eliminate the observed nodes. +% Compare this with not absorbing the evidence. + +seed = 1; +rand('state', seed); +randn('state', seed); + +T = 3; +Q = 3; +O = 2; +cts_obs = 0; +param_tying = 1; +bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying); +N = 2*T; +onodes = bnet.observed; +hnodes = mysetdiff(1:N, onodes); + +data = sample_bnet(bnet); + +init_factor = bnet.CPD{1}; +obs_factor = bnet.CPD{3}; +edge_factor = bnet.CPD{2}; % trans matrix + +nfactors = T; +nvars = T; % hidden only +G = zeros(nvars, nfactors); +G(1,1) = 1; +for t=1:T-1 + G(t:t+1, t+1)=1; +end + +node_sizes = Q*ones(1,T); + +% We tie params as follows: +% the first hidden node use init_factor (number 1) +% all hidden nodes on the backbone use edge_factor (number 2) +% all observed nodes use the same factor, namely obs_factor + +small_fg = mk_fgraph_given_ev(G, node_sizes, {init_factor, edge_factor}, {obs_factor}, data(onodes), ... + 'equiv_class', [1 2*ones(1,T-1)], 'ev_equiv_class', ones(1,T)); + +small_bnet = fgraph_to_bnet(small_fg); + +% don't pre-process evidence +big_fg = bnet_to_fgraph(bnet); +big_bnet = fgraph_to_bnet(big_fg); + + + +engine = {}; +engine{1} = jtree_inf_engine(bnet); +engine{2} = belprop_fg_inf_engine(small_fg, 'max_iter', 2*T); +engine{3} = jtree_inf_engine(small_bnet); +engine{4} = belprop_fg_inf_engine(big_fg, 'max_iter', 3*T); +engine{5} = jtree_inf_engine(big_bnet); +nengines = length(engine); + + +% on BN, use the original evidence +evidence = cell(1, 2*T); +evidence(onodes) = data(onodes); +tic; [engine{1}, ll(1)] = enter_evidence(engine{1}, evidence); toc + + +% on small_fg, we have already included the evidence +evidence = cell(1,T); +tic; [engine{2}, ll(2)] = enter_evidence(engine{2}, evidence); toc + + +% on small_bnet, we must add evidence to the dummy nodes +V = small_fg.nvars; +dummy = V+1:V+small_fg.nfactors; +N = max(dummy); +evidence = cell(1, N); +evidence(dummy) = {1}; +tic; [engine{3}, ll(3)] = enter_evidence(engine{3}, evidence); toc + + +% on big_fg, use the original evidence +evidence = cell(1, 2*T); +evidence(onodes) = data(onodes); +tic; [engine{4}, ll(4)] = enter_evidence(engine{4}, evidence); toc + + +% on big_bnet, we must add evidence to the dummy nodes +V = big_fg.nvars; +assert(V == 2*T); +dummy = V+1:V+big_fg.nfactors; +N = max(dummy); +evidence = cell(1, N); +evidence(onodes) = data(onodes); +evidence(dummy) = {1}; +tic; [engine{5}, ll(5)] = enter_evidence(engine{5}, evidence); toc + + +marg = zeros(T, nengines, Q); % marg(t,e,:) +for t=1:T + for e=1:nengines + m = marginal_nodes(engine{e}, t); + marg(t,e,:) = m.T; + end +end +marg(:,:,1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,83 @@ +% make a factor graph corresponding to an HMM with Gaussian outputs, where we absorb the +% evidence up front + +seed = 1; +rand('state', seed); +randn('state', seed); + +T = 3; +Q = 3; +O = 2; +cts_obs = 1; +param_tying = 1; +bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying); +N = 2*T; +onodes = bnet.observed; +hnodes = mysetdiff(1:N, onodes); + +data = sample_bnet(bnet); + +init_factor = bnet.CPD{1}; +obs_factor = bnet.CPD{3}; +edge_factor = bnet.CPD{2}; % trans matrix + +nfactors = T; +nvars = T; % hidden only +G = zeros(nvars, nfactors); +G(1,1) = 1; +for t=1:T-1 + G(t:t+1, t+1)=1; +end + +node_sizes = Q*ones(1,T); + +% We tie params as follows: +% the first hidden node use init_factor (number 1) +% all hidden nodes on the backbone use edge_factor (number 2) +% all observed nodes use the same factor, namely obs_factor + +small_fg = mk_fgraph_given_ev(G, node_sizes, {init_factor, edge_factor}, {obs_factor}, data(onodes), ... + 'equiv_class', [1 2*ones(1,T-1)], 'ev_equiv_class', ones(1,T)); + +small_bnet = fgraph_to_bnet(small_fg); + +% don't pre-process evidence +% big_fg = bnet_to_fgraph(bnet); % can't handle Gaussian node + + +engine = {}; +engine{1} = jtree_inf_engine(bnet); +engine{2} = belprop_fg_inf_engine(small_fg, 'max_iter', 2*T); +engine{3} = jtree_inf_engine(small_bnet); +nengines = length(engine); + + +% on BN, use the original evidence +evidence = cell(1, 2*T); +evidence(onodes) = data(onodes); +tic; [engine{1}, ll(1)] = enter_evidence(engine{1}, evidence); toc + + +% on small_fg, we have already included the evidence +evidence = cell(1,T); +tic; [engine{2}, ll(2)] = enter_evidence(engine{2}, evidence); toc + + +% on small_bnet, we must add evidence to the dummy nodes +V = small_fg.nvars; +dummy = V+1:V+small_fg.nfactors; +N = max(dummy); +evidence = cell(1, N); +evidence(dummy) = {1}; +tic; [engine{3}, ll(3)] = enter_evidence(engine{3}, evidence); toc + + + +marg = zeros(T, nengines, Q); % marg(t,e,:) +for t=1:T + for e=1:nengines + m = marginal_nodes(engine{e}, t); + marg(t,e,:) = m.T; + end +end +marg(:,:,1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg_mrf1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,113 @@ +seed = 0; +rand('state', seed); +randn('state', seed); + +nrows = 3; +ncols = 3; +npixels = nrows*ncols; + +% we number pixels in transposed raster scan order (top to bottom, left to right) + +% hidden var +HV = reshape(1:npixels, nrows, ncols); +% observed var +OV = reshape(1:npixels, nrows, ncols) + length(HV(:)); + +% observed factor +OF = reshape(1:npixels, nrows, ncols); +% vertical edge factor VEF(i,j) is the factor for edge HV(i,j) - HV(i+1,j) +VEF = reshape((1:(nrows-1)*ncols), nrows-1, ncols) + length(OF(:)); +% horizontal edge factor HEF(i,j) is the factor for edge HV(i,j) - HV(i,j+1) +HEF = reshape((1:nrows*(ncols-1)), nrows, ncols-1) + length(OF(:)) + length(VEF(:)); + +nvars = length(HV(:))+length(OV(:)); +assert(nvars == 2*npixels); +nfac = length(OF(:)) + length(VEF(:)) + length(HEF(:)); + +K = 2; % number of discrete values for the hidden vars +%O = 1; % each observed pixel is a scalar +O = 2; % each observed pixel is binary + +factors = cell(1,3); + +% hidden states generate observed 0 or 1 plus noise +%factors{2} = cond_gauss1_kernel(K, O, 'mean', [0 1], 'cov', [0.1 0.1]); +pnoise = 0.2; +factors{1} = tabular_kernel([K O], [1-pnoise pnoise; pnoise 1-pnoise]); +ofactor = 1; + +% encourage compatibility between neighboring vertical pixels +factors{2} = tabular_kernel([K K], [0.8 0.2; 0.2 0.8]); +vedge_factor = 2; + +%% no constraint between neighboring horizontal pixels +%factors{3} = tabular_kernel([K K], [0.5 0.5; 0.5 0.5]); + +factors{3} = tabular_kernel([K K], [0.8 0.2; 0.2 0.8]); +hedge_factor = 3; + + + +factor_ndx = zeros(1, 3); +G = zeros(nvars, nfac); +ns = [K*ones(1,length(HV(:))) O*ones(1,length(OV(:)))]; + +N = length(ns); +%cnodes = OV(:); +cnodes = []; +dnodes = 1:N; + +for i=1:nrows + for j=1:ncols + G([HV(i,j), OV(i,j)], OF(i,j)) = 1; + factor_ndx(OF(i,j)) = ofactor; + + if i < nrows + G(HV(i:i+1,j), VEF(i,j)) = 1; + factor_ndx(VEF(i,j)) = vedge_factor; + end + + if j < ncols + G(HV(i,j:j+1), HEF(i,j)) = 1; + factor_ndx(HEF(i,j)) = hedge_factor; + end + + end +end + + +fg = mk_fgraph(G, ns, factors, 'discrete', dnodes, 'equiv_class', factor_ndx); + +if 1 + % make image with vertical stripes + I = zeros(nrows, ncols); + for j=1:2:ncols + I(:,j) = 1; + end +else + % make image with square in middle + I = zeros(nrows, ncols); + I(3:6,3:6) = 1; +end + + +% corrupt image +O = mod(I + (rand(nrows,ncols)> (1-pnoise)), 2); + +maximize = 1; +engine = belprop_fg_inf_engine(fg, 'maximize', maximize, 'max_iter', npixels*5); + +evidence = cell(1, nvars); +onodes = OV(:); +evidence(onodes) = num2cell(O+1); % values must be in range {1,2} + +engine = enter_evidence(engine, evidence); + +for i=1:nrows + for j=1:ncols + m = marginal_nodes(engine, HV(i,j)); + Ihat(i,j) = argmax(m.T)-1; + end +end + +Ihat
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/fgraph/fg_mrf2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,150 @@ +seed = 0; +rand('state', seed); +randn('state', seed); + +nrows = 5; +ncols = 5; +npixels = nrows*ncols; + +% we number pixels in transposed raster scan order (top to bottom, left to right) + +% H(i,j) is the number of the hidden node at (i,j) +H = reshape(1:npixels, nrows, ncols); +% O(i,j) is the number of the obsevred node at (i,j) +O = reshape(1:npixels, nrows, ncols) + length(H(:)); + + +% Make a Bayes net where each hidden pixel generates an observed pixel +% but there are no connections between the hidden pixels. +% We use this just to generate noisy versions of known images. +N = 2*npixels; +dag = zeros(N); +for i=1:nrows + for j=1:ncols + dag(H(i,j), O(i,j)) = 1; + end +end + + +K = 2; % number of discrete values for the hidden vars +ns = ones(N,1); +ns(H(:)) = K; +ns(O(:)) = 1; + + +% make image with vertical stripes +I = zeros(nrows, ncols); +for j=1:2:ncols + I(:,j) = 1; +end + +% each "hidden" node will be instantiated to the pixel in the known image +% each observed node has conditional Gaussian distribution +eclass = ones(1,N); +%eclass(H(:)) = 1; +%eclass(O(:)) = 2; +eclass(H(:)) = 1:npixels; +eclass(O(:)) = npixels+1; +bnet = mk_bnet(dag, ns, 'discrete', H(:), 'equiv_class', eclass); + + +%bnet.CPD{1} = tabular_CPD(bnet, H(1), 'CPT', normalise(ones(1,K))); +for i=1:nrows + for j=1:ncols + bnet.CPD{H(i,j)} = root_CPD(bnet, H(i,j), I(i,j) + 1); + end +end + +% If H(i,j)=1, O(i,j)=+1 plus noise +% If H(i,j)=2, O(i,j)=-1 plus noise +sigma = 0.5; +bnet.CPD{eclass(O(1,1))} = gaussian_CPD(bnet, O(1,1), 'mean', [1 -1], 'cov', reshape(sigma*ones(1,K), [1 1 K])); +ofactor = bnet.CPD{eclass(O(1,1))}; +%ofactor = gaussian_CPD('self', 2, 'dps', 1, 'cps', [], 'sz', [K O], 'mean', [1 -1], 'cov', reshape(sigma*ones(1,K), [1 1 K))); + + +data = sample_bnet(bnet); +img = reshape(data(O(:)), nrows, ncols) + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Now create MRF represented as a factor graph to try and recover the scene + +% VEF(i,j) is the number of the factor for the vertical edge between HV(i,j) - HV(i+1,j) +VEF = reshape((1:(nrows-1)*ncols), nrows-1, ncols); +% HEF(i,j) is the number of the factor for the horizontal edge between HV(i,j) - HV(i,j+1) +HEF = reshape((1:nrows*(ncols-1)), nrows, ncols-1) + length(VEF(:)); + +nvars = npixels; +nfac = length(VEF(:)) + length(HEF(:)); + +G = zeros(nvars, nfac); +N = length(ns); +eclass = zeros(1, nfac); % eclass(i)=j means factor i gets its params from factors{j} +vfactor_ndx = 1; % all vertcial edges get their params from factors{1} +hfactor_ndx = 2; % all vertcial edges get their params from factors{2} +for i=1:nrows + for j=1:ncols + if i < nrows + G(H(i:i+1,j), VEF(i,j)) = 1; + eclass(VEF(i,j)) = vfactor_ndx; + end + if j < ncols + G(H(i,j:j+1), HEF(i,j)) = 1; + eclass(HEF(i,j)) = hfactor_ndx; + end + end +end + + +% "kitten raised in cage" prior - more likely to see continguous vertical lines +vfactor = tabular_kernel([K K], softeye(K, 0.9)); +hfactor = tabular_kernel([K K], softeye(K, 0.5)); +factors = cell(1,2); +factors{vfactor_ndx} = vfactor; +factors{hfactor_ndx} = hfactor; + +ev_eclass = ones(1,N); % every observation factor gets is params from ofactor +ns = K*ones(1,nvars); +%fg = mk_fgraph_given_ev(G, ns, factors, {ofactor}, num2cell(img), 'equiv_class', eclass, 'ev_equiv_class', ev_eclass); +fg = mk_fgraph_given_ev(G, ns, factors, {ofactor}, img, 'equiv_class', eclass, 'ev_equiv_class', ev_eclass); + +bnet2 = fgraph_to_bnet(fg); + +% inference + + +maximize = 1; + +engine = {}; +engine{1} = belprop_fg_inf_engine(fg, 'max_iter', npixels*2); +engine{2} = jtree_inf_engine(bnet2); +nengines = length(engine); + +% on fg, we have already included the evidence +evidence = cell(1,npixels); +tic; [engine{1}, ll(1)] = enter_evidence(engine{1}, evidence, 'maximize', maximize); toc + + +% on bnet2, we must add evidence to the dummy nodes +V = fg.nvars; +dummy = V+1:V+fg.nfactors; +N = max(dummy); +evidence = cell(1, N); +evidence(dummy) = {1}; +tic; [engine{2}, ll(2)] = enter_evidence(engine{2}, evidence); toc + + +Ihat = zeros(nrows, ncols, nengines); +for e=1:nengines + for i=1:nrows + for j=1:ncols + m = marginal_nodes(engine{e}, H(i,j)); + Ihat(i,j,e) = argmax(m.T)-1; + end + end +end +Ihat
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/gaussian1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +% Make the following network (from Jensen (1996) p84 fig 4.17) +% 1 +% / | \ +% 2 3 4 +% | | | +% 5 6 7 +% \/ \/ +% 8 9 +% where all arcs point downwards + + +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + +ns = [5 4 3 2 2 1 2 2 2]; % vector-valued nodes +%ns = ones(1,9); % scalar nodes +dnodes = []; + +bnet = mk_bnet(dag, ns, 'discrete', []); +rand('state', 0); +randn('state', 0); +for i=1:N + bnet.CPD{i} = gaussian_CPD(bnet, i); +end + +clear engine; +engine{1} = gaussian_inf_engine(bnet); +engine{2} = jtree_inf_engine(bnet); + +[err, time] = cmp_inference_static(bnet, engine); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/gaussian2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +% Make the following network (from Jensen (1996) p84 fig 4.17) +% 1 +% / | \ +% 2 3 4 +% | | | +% 5 6 7 +% \/ \/ +% 8 9 +% where all arcs point downwards + + +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + +ns = [5 4 3 2 2 1 2 2 2]; % vector-valued nodes +%ns = ones(1,9); % scalar nodes +dnodes = []; + +bnet = mk_bnet(dag, ns, 'discrete', []); +rand('state', 0); +randn('state', 0); +for i=1:N + bnet.CPD{i} = gaussian_CPD(bnet, i); +end + +clear engine; +engine{1} = gaussian_inf_engine(bnet); +engine{2} = jtree_inf_engine(bnet); + +[err, time] = cmp_inference_static(bnet, engine); + +Nsamples = 100; +samples = cell(N, Nsamples); +for s=1:Nsamples + samples(:,s) = sample_bnet(bnet); +end +bnet2 = learn_params(bnet, samples);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/gibbs_test1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +function gibbs_test1() + +disp('gibbs test 1') + +rand('state', 0); +randn('state', 0); + +%[bnet onodes hnodes qnodes] = gibbs_ex_1; +[bnet onodes hnodes qnodes] = gibbs_ex_2; + +je = jtree_inf_engine(bnet); +ge = gibbs_sampling_inf_engine (bnet, 'T', 50, 'burnin', 0, ... + 'order', [2 2 1 2 1]); + +ev = sample_bnet(bnet); + +evidence = cell(length(bnet.dag), 1); +evidence(onodes) = ev(onodes); +[je lj] = enter_evidence(je, evidence); +[ge lg] = enter_evidence(ge, evidence); + + +mj = marginal_nodes(je, qnodes); + +[mg ge] = marginal_nodes (ge, qnodes); +for t = 1:100 + [mg ge] = marginal_nodes (ge, qnodes, 'reset_counts', 0); + diff = mj.T - mg.T; + err(t) = norm (diff(:), 1); +end +clf +plot(err); +%title('error vs num. Gibbs samples') + + +%%%%%%% + +function [bnet, onodes, hnodes, qnodes] = gibbs_ex_1 +% bnet = gibbs_ex_1 +% a simple network to test the gibbs sampling engine +% 1 +% / | \ +% 2 3 4 +% | | | +% 5 6 7 +% \/ \/ +% 8 9 +% where all arcs point downwards + +N = 9; +dag = zeros(N,N); +dag(1,2)=1; dag(1,3)=1; dag(1,4)=1; +dag(2,5)=1; dag(3,6)=1; dag(4,7)=1; +dag(5,8)=1; dag(6,8)=1; dag(6,9)=1; dag(7,9) = 1; + +onodes = 8:9; +hnodes = 1:7; +qnodes = [1 2 6]; +ns = [2 3 4 3 5 2 4 3 2]; + +eclass = [1 2 3 2 4 5 6 7 8]; + +bnet = mk_bnet (dag, ns, 'equiv_class', eclass); + +for i = 1:3 + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +for i = 4:8 + bnet.CPD{i} = tabular_CPD(bnet, i+1); +end + + + +%%%%%%% + +function [bnet, onodes, hnodes, qnodes] = gibbs_ex_2 +% bnet = gibbs_ex_2 +% a very simple network +% +% 1 2 +% \ / +% 3 + +N = 3; +dag = zeros(N,N); +dag(1,3)=1; dag(2,3)=1; + +onodes = 3; +hnodes = 1:2; +qnodes = 1:2; +ns = [2 4 3]; + +eclass = [1 2 3]; + +bnet = mk_bnet (dag, ns, 'equiv_class', eclass); + +for i = 1:3 + bnet.CPD{i} = tabular_CPD(bnet, i); +end + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/learn1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +% Lawn sprinker example from Russell and Norvig p454 +% See www.cs.berkeley.edu/~murphyk/Bayes/usage.html for details. + +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; + +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +bnet = mk_bnet(dag, ns); +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); + +CPT = cell(1,N); +for i=1:N + s=struct(bnet.CPD{i}); % violate object privacy + CPT{i}=s.CPT; +end + +% Generate training data +nsamples = 50; +samples = cell(N, nsamples); +for i=1:nsamples + samples(:,i) = sample_bnet(bnet); +end +data = cell2num(samples); + +% Make a tabula rasa +bnet2 = mk_bnet(dag, ns); +seed = 0; +rand('state', seed); +bnet2.CPD{C} = tabular_CPD(bnet2, C, 'clamped', 1, 'CPT', [0.5 0.5], ... + 'prior_type', 'dirichlet', 'dirichlet_weight', 0); +bnet2.CPD{R} = tabular_CPD(bnet2, R, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); +bnet2.CPD{S} = tabular_CPD(bnet2, S, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); +bnet2.CPD{W} = tabular_CPD(bnet2, W, 'prior_type', 'dirichlet', 'dirichlet_weight', 0); + + +% Find MLEs from fully observed data +bnet4 = learn_params(bnet2, samples); + +% Bayesian updating with 0 prior is equivalent to ML estimation +bnet5 = bayes_update_params(bnet2, samples); + +CPT4 = cell(1,N); +for i=1:N + s=struct(bnet4.CPD{i}); % violate object privacy + CPT4{i}=s.CPT; +end + +CPT5 = cell(1,N); +for i=1:N + s=struct(bnet5.CPD{i}); % violate object privacy + CPT5{i}=s.CPT; + assert(approxeq(CPT5{i}, CPT4{i})) +end + + +if 1 +% Find MLEs from partially observed data + +% hide 50% of the nodes +samplesH = samples; +hide = rand(N, nsamples) > 0.5; +[I,J]=find(hide); +for k=1:length(I) + samplesH{I(k), J(k)} = []; +end + +engine = jtree_inf_engine(bnet2); +max_iter = 5; +[bnet6, LL] = learn_params_em(engine, samplesH, max_iter); + +CPT6 = cell(1,N); +for i=1:N + s=struct(bnet6.CPD{i}); % violate object privacy + CPT6{i}=s.CPT; +end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/lw1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +% Evaluate effectiveness of likelihood weighting on the lawn sprinkler example + +N = 4; +dag = zeros(N,N); +C = 1; R = 2; S = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; + +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +bnet = mk_bnet(dag, ns); +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); + + +clear engine; +engine{1} = jtree_inf_engine(bnet); +engine{2} = likelihood_weighting_inf_engine(bnet); + +nengines = length(engine); +m = cell(1, nengines); +ll = zeros(1, nengines); + +evidence = cell(1,N); +%evidence{C} = true; % evidence at the top is the easiest +evidence{W} = true; % evidence at the bottom is the hardets + +query = [R]; + +i=1; +engine{i} = enter_evidence(engine{i}, evidence); +exact_m = marginal_nodes(engine{i}, query); + +i=2; +samples = 100:100:500; +err = zeros(1, length(samples)); +for j=1:length(samples) + nsamples = samples(j); + engine{i} = enter_evidence(engine{i}, evidence, nsamples); + approx_m = marginal_nodes(engine{i}, query); + a1=approxeq(approx_m.T,exact_m.T,1e-1); + a2=approxeq(approx_m.T,exact_m.T,1e-2); + a3=approxeq(approx_m.T,exact_m.T,1e-3); + e = sum(abs(approx_m.T(:) - exact_m.T(:))); + fprintf('%d samples, 1dp %d, 2dp %d, 3dp %d, err %f\n', nsamples, a1, a2, a3, e); + err(j) = e; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/mfa1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +% Factor analysis +% Z -> X, Z in R^k, X in R^D, k << D (high dimensional observations explained by small source) +% Z ~ N(0,I), X|Z ~ N(L z, Psi), where Psi is diagonal. +% +% Mixtures of FA +% Now X|Z,W=i ~ N(mu(i) + L(i) Z, Psi(i)) +% +% We compare to Zoubin Ghahramani's code. + +randn('state', 0); +max_iter = 3; +M = 2; +k = 3; +D = 5; + +n = 5; +X1 = randn(n, D); +X2 = randn(n, D) + 2; % move the mean to (2,2,2...) +X = [X1; X2]; +N = size(X, 1); + +% initialise as in mfa +tiny=exp(-700); +mX = mean(X); +cX=cov(X); +scale=det(cX)^(1/D); +randn('state',0); % must reset seed here so initial params are identical to mfa +L0=randn(D*M,k)*sqrt(scale/k); +W0 = permute(reshape(L0, [D M k]), [1 3 2]); % use D,K,M +Psi0=diag(cX)+tiny; +Pi0=ones(M,1)/M; +Mu0=randn(M,D)*sqrtm(cX)+ones(M,1)*mX; + +[Lh1, Ph1, Mu1, Pi1, LL1] = mfa(X,M,k,max_iter); +Lh1 = permute(reshape(Lh1, [D M k]), [1 3 2]); % use D,K,M + + +ns = [M k D]; +dag = zeros(3); +dag(1,3) = 1; +dag(2,3) = 1; +dnodes = 1; +onodes = 3; + +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', onodes); +bnet.CPD{1} = tabular_CPD(bnet, 1, Pi0); + +%bnet.CPD{2} = gaussian_CPD(bnet, 2, zeros(k, 1), eye(k), [], 'diag', 'untied', 'clamp_mean', 'clamp_cov'); + +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(k, 1), 'cov', eye(k), 'cov_type', 'diag', ... + 'cov_prior_weight', 0, 'clamp_mean', 1, 'clamp_cov', 1); + +%bnet.CPD{3} = gaussian_CPD(bnet, 3, Mu0', repmat(diag(Psi0), [1 1 M]), W0, 'diag', 'tied'); + +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', Mu0', 'cov', repmat(diag(Psi0), [1 1 M]), ... + 'weights', W0, 'cov_type', 'diag', 'cov_prior_weight', 0, 'tied_cov', 1); + +engine = jtree_inf_engine(bnet); +evidence = cell(3, N); +evidence(3,:) = num2cell(X', 1); + +[bnet2, LL2, engine2] = learn_params_em(engine, evidence, max_iter); + +s = struct(bnet2.CPD{1}); +Pi2 = s.CPT(:); +s = struct(bnet2.CPD{3}); +Mu2 = s.mean; +W2 = s.weights; +Sigma2 = s.cov; + + +% Compare to Zoubin's code +assert(approxeq(LL1,LL2)); +for i=1:M + assert(approxeq(W2(:,:,i), Lh1(:,:,i))); + assert(approxeq(Sigma2(:,:,i), diag(Ph1))); + assert(approxeq(Mu2(:,i), Mu1(i,:))); + assert(approxeq(Pi2(:), Pi1(:))); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/mixexp1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +% Fit a piece-wise linear regression model. +% Here is the model +% +% X \ +% | | +% Q | +% | / +% Y +% +% where all arcs point down. +% We condition everything on X, so X is a root node. Q is a softmax, and Y is a linear Gaussian. +% Q is hidden, X and Y are observed. + +X = 1; +Q = 2; +Y = 3; +dag = zeros(3,3); +dag(X,[Q Y]) = 1; +dag(Q,Y) = 1; +ns = [1 2 1]; % make X and Y scalars, and have 2 experts +dnodes = [2]; +onodes = [1 3]; +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', onodes); + + +w = [-5 5]; % w(:,i) is the normal vector to the i'th decisions boundary +b = [0 0]; % b(i) is the offset (bias) to the i'th decisions boundary + +mu = [0 0]; +sigma = 1; +Sigma = repmat(sigma*eye(ns(Y)), [ns(Y) ns(Y) ns(Q)]); +W = [-1 1]; +W2 = reshape(W, [ns(Y) ns(X) ns(Q)]); + +bnet.CPD{1} = root_CPD(bnet, 1); +bnet.CPD{2} = softmax_CPD(bnet, 2, w, b); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', mu, 'cov', Sigma, 'weights', W2); + + + +% Check inference + +x = 0.1; +ystar = 1; + +engine = jtree_inf_engine(bnet); +[engine, loglik] = enter_evidence(engine, {x, [], ystar}); +Qpost = marginal_nodes(engine, 2); + +% eta(i,:) = softmax (gating) params for expert i +eta = [b' w']; + +% theta(i,:) = regression vector for expert i +theta = [mu' W']; + +% yhat(i) = E[y | Q=i, x] = prediction of i'th expert +x1 = [1 x]'; +yhat = theta * x1; + +% gate_prior(i,:) = Pr(Q=i | x) +gate_prior = normalise(exp(eta * x1)); + +% cond_lik(i) = Pr(y | Q=i, x) +cond_lik = (1/(sqrt(2*pi)*sigma)) * exp(-(0.5/sigma^2) * ((ystar - yhat) .* (ystar - yhat))); + +% gate_posterior(i,:) = Pr(Q=i | x, y) +[gate_posterior, lik] = normalise(gate_prior .* cond_lik); + +assert(approxeq(gate_posterior(:), Qpost.T(:))); +assert(approxeq(log(lik), loglik)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/mixexp2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +% Fit a piece-wise linear regression model. +% Here is the model +% +% X \ +% | | +% Q | +% | / +% Y +% +% where all arcs point down. +% We condition everything on X, so X is a root node. Q is a softmax, and Y is a linear Gaussian. +% Q is hidden, X and Y are observed. + +X = 1; +Q = 2; +Y = 3; +dag = zeros(3,3); +dag(X,[Q Y]) = 1; +dag(Q,Y) = 1; +ns = [1 2 1]; % make X and Y scalars, and have 2 experts +dnodes = [2]; +onodes = [1 3]; +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', onodes); + +IRLS_iter = 10; +clamped = 0; + +bnet.CPD{1} = root_CPD(bnet, 1); + +if 0 + % start with good initial params + w = [-5 5]; % w(:,i) is the normal vector to the i'th decisions boundary + b = [0 0]; % b(i) is the offset (bias) to the i'th decisions boundary + + mu = [0 0]; + sigma = 1; + Sigma = repmat(sigma*eye(ns(Y)), [ns(Y) ns(Y) ns(Q)]); + W = [-1 1]; + W2 = reshape(W, [ns(Y) ns(X) ns(Q)]); + + bnet.CPD{2} = softmax_CPD(bnet, 2, w, b, clamped, IRLS_iter); + bnet.CPD{3} = gaussian_CPD(bnet, 3, mu, Sigma, W2); +else + % start with rnd initial params + rand('state', 0); + randn('state', 0); + bnet.CPD{2} = softmax_CPD(bnet, 2, 'clamped', clamped, 'max_iter', IRLS_iter); + bnet.CPD{3} = gaussian_CPD(bnet, 3); +end + + + +load('/examples/static/Misc/mixexp_data.txt', '-ascii'); +% Just use 1/10th of the data, to speed things up +data = mixexp_data(1:10:end, :); +%data = mixexp_data; + +%plot(data(:,1), data(:,2), '.') + + +s = struct(bnet.CPD{2}); % violate object privacy +%eta0 = [s.glim.b1; s.glim.w1]'; +eta0 = [s.glim{1}.b1; s.glim{1}.w1]'; +s = struct(bnet.CPD{3}); % violate object privacy +W = reshape(s.weights, [1 2]); +theta0 = [s.mean; W]'; + +%figure(1) +%mixexp_plot(theta0, eta0, data); +%suptitle('before learning') + +ncases = size(data, 1); +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); + +engine = jtree_inf_engine(bnet); + +% log lik before learning +ll = 0; +for l=1:ncases + ev = cases(:,l); + [engine, loglik] = enter_evidence(engine, ev); + ll = ll + loglik; +end + +% do learning +max_iter = 5; +[bnet2, LL2] = learn_params_em(engine, cases, max_iter); + +s = struct(bnet2.CPD{2}); +%eta2 = [s.glim.b1; s.glim.w1]'; +eta2 = [s.glim{1}.b1; s.glim{1}.w1]'; +s = struct(bnet2.CPD{3}); +W = reshape(s.weights, [1 2]); +theta2 = [s.mean; W]'; + +%figure(2) +%mixexp_plot(theta2, eta2, data); +%suptitle('after learning') + +fprintf('mixexp2: loglik before learning %f, after %d iters %f\n', ll, length(LL2), LL2(end)); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/mixexp3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +% Fit a piece-wise linear regression model. +% Here is the model +% +% X \ +% | | +% Q | +% | / +% Y +% +% where all arcs point down. +% We condition everything on X, so X is a root node. Q is a softmax, and Y is a linear Gaussian. +% Q is hidden, X and Y are observed. + +X = 1; +Q = 2; +Y = 3; +dag = zeros(3,3); +dag(X,[Q Y]) = 1; +dag(Q,Y) = 1; +ns = [1 2 1]; % make X and Y scalars, and have 2 experts +dnodes = [2]; +onodes = [1 3]; +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', onodes); + +IRLS_iter = 10; +clamped = 0; + +bnet.CPD{1} = root_CPD(bnet, 1); + +% start with good initial params +w = [-5 5]; % w(:,i) is the normal vector to the i'th decisions boundary +b = [0 0]; % b(i) is the offset (bias) to the i'th decisions boundary + +mu = [0 0]; +sigma = 1; +Sigma = repmat(sigma*eye(ns(Y)), [ns(Y) ns(Y) ns(Q)]); +W = [-1 1]; +W2 = reshape(W, [ns(Y) ns(X) ns(Q)]); + +bnet.CPD{2} = softmax_CPD(bnet, 2, w, b, clamped, IRLS_iter); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', mu, 'cov', Sigma, 'weights', W2); + + +engine = jtree_inf_engine(bnet); + +evidence = cell(1,3); +evidence{X} = 0.68; + +engine = enter_evidence(engine, evidence); + +m = marginal_nodes(engine, Y); +m.mu
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/mog1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +% Fit a mixture of Gaussians using netlab and BNT + +rand('state', 0); +randn('state', 0); + +% Q -> Y +ncenters = 2; dim = 2; +cov_type = 'full'; + +% Generate the data from a mixture of 2 Gaussians +%mu = randn(dim, ncenters); +mu = zeros(dim, ncenters); +mu(:,1) = [-1 -1]'; +mu(:,1) = [1 1]'; +Sigma = repmat(0.1*eye(dim),[1 1 ncenters]); +ndat1 = 8; ndat2 = 8; +%ndat1 = 2; ndat2 = 2; +ndata = ndat1+ndat2; +x1 = gsamp(mu(:,1), Sigma(:,:,1), ndat1); +x2 = gsamp(mu(:,2), Sigma(:,:,2), ndat2); +data = [x1; x2]; +%plot(x1(:,1),x1(:,2),'ro', x2(:,1),x2(:,2),'bx') + +% Fit using netlab +max_iter = 3; +mix = gmm(dim, ncenters, cov_type); +options = foptions; +options(1) = 1; % verbose +options(14) = max_iter; + +% extract initial params +%mix = gmminit(mix, x, options); % Initialize with K-means +mu0 = mix.centres'; +pi0 = mix.priors(:); +Sigma0 = mix.covars; % repmat(eye(dim), [1 1 ncenters]); + +[mix, options] = gmmem(mix, data, options); + +% Final params +ll1 = options(8); +mu1 = mix.centres'; +pi1 = mix.priors(:); +Sigma1 = mix.covars; + + + + +% BNT + +dag = zeros(2); +dag(1,2) = 1; +node_sizes = [ncenters dim]; +discrete_nodes = 1; +onodes = 2; + +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes, 'observed', onodes); +bnet.CPD{1} = tabular_CPD(bnet, 1, pi0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', mu0, 'cov', Sigma0, 'cov_type', cov_type, ... + 'cov_prior_weight', 0); + +engine = jtree_inf_engine(bnet); + +evidence = cell(2, ndata); +evidence(2,:) = num2cell(data', 1); + +[bnet2, LL] = learn_params_em(engine, evidence, max_iter); + +ll2 = LL(end); +s1 = struct(bnet2.CPD{1}); +pi2 = s1.CPT(:); + +s2 = struct(bnet2.CPD{2}); +mu2 = s2.mean; +Sigma2 = s2.cov; + +% assert(approxeq(ll1, ll2)); % gmmem returns the value after the final M step, GMT before +assert(approxeq(mu1, mu2)); +assert(approxeq(Sigma1, Sigma2)) +assert(approxeq(pi1, pi2)) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/mpe1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +seed = 1; +rand('state', seed); +randn('state', seed); + +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; + +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +bnet = mk_bnet(dag, ns); +if 0 + bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); + bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); + bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); + bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +else + for i=1:N, bnet.CPD{i} = tabular_CPD(bnet, i); end +end + + + +evidence = cell(1,N); +onodes = [1 3]; +data = sample_bnet(bnet); +evidence(onodes) = data(onodes); + +clear engine; +engine{1} = belprop_inf_engine(bnet); +engine{2} = jtree_inf_engine(bnet); +engine{3} = global_joint_inf_engine(bnet); +engine{4} = var_elim_inf_engine(bnet); +E = length(engine); + +clear mpe; +for e=1:E + mpe{e} = find_mpe(engine{e}, evidence); +end +for e=2:E + assert(isequal(mpe{1}, mpe{e})) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/mpe2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +% Computing most probable explanation. + +% If you don't break ties consistently, loopy can give wrong mpe +% even though the graph has no cycles, and even though the max-marginals are the same. +% This example was contributed by Wentau Yih <wtyih@yahoo.com> 29 Jan 02. + +% define loop-free graph structure (all edges point down) +% +% Xe1 Xe2 +% | | +% E1 E2 +% \ / +% R1 +% | +% Xr1 + +N = 6; +dag = zeros(N,N); +Xe1 = 1; Xe2 = 2; E1 = 3; E2 = 4; R1 = 5; Xr1 = 6; +dag(Xe1, E1) = 1; +dag(Xe2, E2) = 1; +dag([E1 E2], R1) = 1; +dag(R1, Xr1) = 1; + +node_sizes = [ 1 1 2 2 2 1 ]; + +% create BN + +bnet = mk_bnet(dag, node_sizes, 'observed', [Xe1 Xe2 Xr1]); + +% fill in CPT + +bnet.CPD{Xe1} = tabular_CPD(bnet, Xe1, [1]); +bnet.CPD{Xe2} = tabular_CPD(bnet, Xe2, [1]); +bnet.CPD{E1} = tabular_CPD(bnet, E1, [0.2 0.8]); +bnet.CPD{E2} = tabular_CPD(bnet, E2, [0.3 0.7]); +bnet.CPD{R1} = tabular_CPD(bnet, R1, [1 1 1 0.8 0 0 0 0.2]); +bnet.CPD{Xr1} = tabular_CPD(bnet, Xr1, [0.15 0.85]); + +clear engine; +engine{1} = belprop_inf_engine(bnet); +engine{2} = jtree_inf_engine(bnet); +engine{3} = global_joint_inf_engine(bnet); +engine{4} = var_elim_inf_engine(bnet); + +evidence = cell(1,N); +evidence{Xe1} = 1; evidence{Xe2} = 1; evidence{Xr1} = 1; + +mpe = find_mpe(engine{1}, evidence, 'break_ties', 0) % gives wrong results +mpe = find_mpe(engine{1}, evidence) +for i=2:4 + mpe = find_mpe(engine{i}, evidence) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/nodeorderExample.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +% example to illustrate why nodes must be numbered topologically. +% Due to Shinya OHTANI <ohtani@pdp.crl.sony.co.jp> +% 9 June 2004 + +%%%%%%%%% WRONG RESULTS because 2 -> 1 +% should have P(parent|no evidence) = prior = [03. 0.7] + +node = struct('ChildNode', 1, ... + 'ParentNode', 2); + +adjacency = zeros(2); +adjacency([node.ParentNode], node.ChildNode) = 1; + +value = {{'TRUE'; 'FALSE'}, ... + {'TRUE'; 'FALSE'}}; + +bnet = mk_bnet(adjacency, [2 2]); +bnet.CPD{node.ChildNode} = tabular_CPD(bnet, node.ChildNode, [0.2 0.4 0.8 0.6]); +bnet.CPD{node.ParentNode} = tabular_CPD(bnet, node.ParentNode, [0.3 0.7]); + +evidence = cell(1,2); +% evidence{node.ChildNode} = 1; +% evidence{node.ParentNode} = 1; + +engine = jtree_inf_engine(bnet); +[engine, loglik] = enter_evidence(engine, evidence); + + +marg = marginal_nodes(engine, node.ChildNode); +disp(sprintf(' ChildNode : %8.6f %8.6f',marg.T(1),marg.T(2)) ); +marg = marginal_nodes(engine, node.ParentNode); +disp(sprintf(' ParentNode : %8.6f %8.6f',marg.T(1),marg.T(2)) ); + +% +% ChildNode : 0.534483 0.465517 +% ParentNode : 0.155172 0.844828 +% loglik = 0.15 + + + +%%%%%%%%% RIGHT RESULTS because 1 -> 2 + +node = struct('ChildNode', 2, ... + 'ParentNode', 1); + + +adjacency = zeros(2); +adjacency([node.ParentNode], node.ChildNode) = 1; + +value = {{'TRUE'; 'FALSE'}, ... + {'TRUE'; 'FALSE'}}; + +bnet = mk_bnet(adjacency, [2 2]); +bnet.CPD{node.ChildNode} = tabular_CPD(bnet, node.ChildNode, [0.2 0.4 0.8 0.6]); +bnet.CPD{node.ParentNode} = tabular_CPD(bnet, node.ParentNode, [0.3 0.7]); + +evidence = cell(1,2); +% evidence{node.ChildNode} = 1; +% evidence{node.ParentNode} = 1; + +engine = jtree_inf_engine(bnet); +[engine, loglik] = enter_evidence(engine, evidence); + + +marg = marginal_nodes(engine, node.ChildNode); +disp(sprintf(' ChildNode : %8.6f %8.6f',marg.T(1),marg.T(2)) ); +marg = marginal_nodes(engine, node.ParentNode); +disp(sprintf(' ParentNode : %8.6f %8.6f',marg.T(1),marg.T(2)) );
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/qmr1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,112 @@ +% Make a QMR-like network +% This is a bipartite graph, where the top layer contains hidden disease nodes, +% and the bottom later contains observed finding nodes. +% The diseases have Bernoulli CPDs, the findings noisy-or CPDs. +% See quickscore_inf_engine for references. + +pMax = 0.01; +Nfindings = 10; +Ndiseases = 5; +%Nfindings = 20; +%Ndiseases = 10; + +N=Nfindings+Ndiseases; +findings = Ndiseases+1:N; +diseases = 1:Ndiseases; + +G = zeros(Ndiseases, Nfindings); +for i=1:Nfindings + v= rand(1,Ndiseases); + rents = find(v<0.8); + if (length(rents)==0) + rents=ceil(rand(1)*Ndiseases); + end + G(rents,i)=1; +end + +prior = pMax*rand(1,Ndiseases); +leak = 0.5*rand(1,Nfindings); % in real QMR, leak approx exp(-0.02) = 0.98 +%leak = ones(1,Nfindings); % turns off leaks, which makes inference much harder +inhibit = rand(Ndiseases, Nfindings); +inhibit(not(G)) = 1; + + +% first half of findings are +ve, second half -ve +% The very first and last findings are hidden +pos = 2:floor(Nfindings/2); +neg = (pos(end)+1):(Nfindings-1); + +% Make the bnet in the straightforward way +tabular_leaves = 0; +obs_nodes = myunion(pos, neg) + Ndiseases; +big_bnet = mk_qmr_bnet(G, inhibit, leak, prior, tabular_leaves, obs_nodes); +big_evidence = cell(1, N); +big_evidence(findings(pos)) = num2cell(repmat(2, 1, length(pos))); +big_evidence(findings(neg)) = num2cell(repmat(1, 1, length(neg))); + +%clf;draw_layout(big_bnet.dag); +%filename = '../public_html/Bayes/Figures/qmr.rnd.jpg'; +%% 3x3 inches +%set(gcf,'units','inches'); +%set(gcf,'PaperPosition',[0 0 3 3]) +%print(gcf,'-djpeg','-r100',filename); + + +% Marginalize out hidden leaves apriori +positive_leaves_only = 1; +[bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, leak, prior, pos, neg, positive_leaves_only); +obs_nodes = bnet.observed; +evidence = cell(1, Ndiseases + length(obs_nodes)); +evidence(obs_nodes) = num2cell(vals); + + +clear engine; +engine{1} = quickscore_inf_engine(inhibit, leak, prior); +engine{2} = jtree_inf_engine(big_bnet); +engine{3} = jtree_inf_engine(bnet); + +%fname = '/home/cs/murphyk/matlab/Misc/loopybel.txt'; +global BNT_HOME +fname = sprintf('%s/loopybel.txt', BNT_HOME); + + +max_iter = 6; +engine{4} = pearl_inf_engine(bnet, 'protocol', 'parallel', 'max_iter', max_iter); +%engine{5} = belprop_inf_engine(bnet, 'max_iter', max_iter, 'filename', fname); +engine{5} = belprop_inf_engine(bnet, 'max_iter', max_iter); + +E = length(engine); +exact = 1:3; +loopy = [4 5]; + +ll = zeros(1,E); +tic; engine{1} = enter_evidence(engine{1}, pos, neg); toc +tic; [engine{2}, ll(2)] = enter_evidence(engine{2}, big_evidence); toc +tic; [engine{3}, ll(3)] = enter_evidence(engine{3}, evidence); toc +tic; [engine{4}, ll(4), niter(4)] = enter_evidence(engine{4}, evidence); toc +tic; [engine{5}, niter(5)] = enter_evidence(engine{5}, evidence); toc + +ll + +post = zeros(E, Ndiseases); +for e=1:E + for i=diseases(:)' + m = marginal_nodes(engine{e}, i); + post(e, i) = m.T(2); + end +end + +for e=exact(:)' + for i=diseases(:)' + assert(approxeq(post(1, i), post(e, i))); + end +end + +a = zeros(Ndiseases, 2); +for ei=1:length(loopy) + for i=diseases(:)' + a(i,ei) = approxeq(post(1, i), post(loopy(ei), i)); + end +end +disp('is the loopy posterior correct?'); +disp(a)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/qmr2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,77 @@ +% Test jtree_compiled on a toy QMR network. + +rand('state', 0); +randn('state', 0); +pMax = 0.01; +Nfindings = 10; +Ndiseases = 5; + +N=Nfindings+Ndiseases; +findings = Ndiseases+1:N; +diseases = 1:Ndiseases; + +G = zeros(Ndiseases, Nfindings); +for i=1:Nfindings + v= rand(1,Ndiseases); + rents = find(v<0.8); + if (length(rents)==0) + rents=ceil(rand(1)*Ndiseases); + end + G(rents,i)=1; +end + +prior = pMax*rand(1,Ndiseases); +leak = 0.5*rand(1,Nfindings); % in real QMR, leak approx exp(-0.02) = 0.98 +%leak = ones(1,Nfindings); % turns off leaks, which makes inference much harder +inhibit = rand(Ndiseases, Nfindings); +inhibit(not(G)) = 1; + +% first half of findings are +ve, second half -ve +% The very first and last findings are hidden +pos = 2:floor(Nfindings/2); +neg = (pos(end)+1):(Nfindings-1); + +big = 1; + +if big + % Make the bnet in the straightforward way + tabular_leaves = 1; + obs_nodes = myunion(pos, neg) + Ndiseases; + bnet = mk_qmr_bnet(G, inhibit, leak, prior, tabular_leaves, obs_nodes); + evidence = cell(1, N); + evidence(findings(pos)) = num2cell(repmat(2, 1, length(pos))); + evidence(findings(neg)) = num2cell(repmat(1, 1, length(neg))); +else + % Marginalize out hidden leaves apriori + positive_leaves_only = 1; + [bnet, vals] = mk_minimal_qmr_bnet(G, inhibit, leak, prior, pos, neg, positive_leaves_only); + obs_nodes = bnet.observed; + evidence = cell(1, Ndiseases + length(obs_nodes)); + evidence(obs_nodes) = num2cell(vals); +end + +engine = {}; +engine{end+1} = jtree_inf_engine(bnet); + +E = length(engine); +exact = 1:E; +ll = zeros(1,E); +for e=1:E + tic; [engine{e}, ll(e)] = enter_evidence(engine{e}, evidence); toc +end + +assert(all(approxeq(ll(exact), ll(exact(1))))) + +post = zeros(E, Ndiseases); +for e=1:E + for i=diseases(:)' + m = marginal_nodes(engine{e}, i); + post(e, i) = m.T(2); + end +end +for e=exact(:)' + for i=diseases(:)' + assert(approxeq(post(1, i), post(e, i))); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/sample1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +% Check sampling on a mixture of experts model +% +% X \ +% | | +% Q | +% | / +% Y +% +% where all arcs point down. +% We condition everything on X, so X is a root node. Q is a softmax, and Y is a linear Gaussian. +% Q is hidden, X and Y are observed. + +X = 1; +Q = 2; +Y = 3; +dag = zeros(3,3); +dag(X,[Q Y]) = 1; +dag(Q,Y) = 1; +ns = [1 2 2]; +dnodes = [2]; +bnet = mk_bnet(dag, ns, dnodes); + +x = 0.5; +bnet.CPD{1} = root_CPD(bnet, 1, x); +bnet.CPD{2} = softmax_CPD(bnet, 2); +bnet.CPD{3} = gaussian_CPD(bnet, 3); + +data_case = sample_bnet(bnet, 'evidence', {0.8, [], []}) +ll = log_lik_complete(bnet, data_case) + +data_case = sample_bnet(bnet, 'evidence', {-11, [], []}) +ll = log_lik_complete(bnet, data_case) + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/softev1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +% Check that adding soft evidence to a hidden node is equivalent to evaluating its leaf CPD. + +% Make an HMM +T = 3; Q = 2; O = 2; cts_obs = 0; param_tying = 0; +bnet = mk_hmm_bnet(T, Q, O, cts_obs, param_tying); +N = 2*T; +onodes = bnet.observed; +hnodes = mysetdiff(1:N, onodes); +for i=1:N + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +ev = sample_bnet(bnet); +evidence = cell(1,N); +evidence(onodes) = ev(onodes); + +engine = jtree_inf_engine(bnet); + +[engine, ll] = enter_evidence(engine, evidence); +query = 1; +m = marginal_nodes(engine, query); + + +% Make a Markov chain with the same backbone +bnet2 = mk_markov_chain_bnet(T, Q); +for i=1:T + S = struct(bnet.CPD{hnodes(i)}); % violate object privacy + bnet2.CPD{i} = tabular_CPD(bnet2, i, S.CPT); +end + +% Evaluate the observed leaves of the HMM +soft_ev = cell(1,T); +for i=1:T + S = struct(bnet.CPD{onodes(i)}); % violate object privacy + dist = S.CPT(:, evidence{onodes(i)}); + soft_ev{i} = dist; +end + +% Use the leaf potentials as soft evidence +engine2 = jtree_inf_engine(bnet2); +[engine2, ll2] = enter_evidence(engine2, cell(1,T), 'soft', soft_ev); +m2 = marginal_nodes(engine2, query); + +assert(approxeq(m2.T, m.T)) +assert(approxeq(ll2, ll)) + + + +% marginal on node 1 without evidence +[engine2, ll2] = enter_evidence(engine2, cell(1,T)); +m2 = marginal_nodes(engine2, 1); + +% add soft evidence +soft_ev=cell(1,T); +soft_ev{1}=[0.7 0.3]; +[engine2, ll2] = enter_evidence(engine2, cell(1,T), 'soft', soft_ev); +m3 = marginal_nodes(engine2, 1); + +assert(approxeq(normalise(m2.T .* [0.7 0.3]'), m3.T)) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/softmax1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,109 @@ +% Check that softmax works with a simple classification demo. +% Based on netlab's demglm2 +% X -> Q where X is an input node, and Q is a softmax + +rand('state', 0); +randn('state', 0); + +% Check inference + +input_dim = 2; +num_classes = 3; +IRLS_iter = 3; + +net = glm(input_dim, num_classes, 'softmax'); + +dag = zeros(2); +dag(1,2) = 1; +discrete_nodes = [2]; +bnet = mk_bnet(dag, [input_dim num_classes], 'discrete', discrete_nodes, 'observed', 1); +bnet.CPD{1} = root_CPD(bnet, 1); +clamped = 0; +bnet.CPD{2} = softmax_CPD(bnet, 2, net.w1, net.b1, clamped, IRLS_iter); + +engine = jtree_inf_engine(bnet); + +x = rand(1, input_dim); +q = glmfwd(net, x); + +[engine, ll] = enter_evidence(engine, {x, []}); +m = marginal_nodes(engine, 2); +assert(approxeq(m.T(:), q(:))); + + +% Check learning +% We use EM, but in fact there is no hidden data. +% The M step will call IRLS on the softmax node. + +% Generate data from three classes in 2d +input_dim = 2; +num_classes = 3; + +% Fix seeds for reproducible results +randn('state', 42); +rand('state', 42); + +ndata = 10; +% Generate mixture of three Gaussians in two dimensional space +data = randn(ndata, input_dim); +targets = zeros(ndata, 3); + +% Priors for the clusters +prior(1) = 0.4; +prior(2) = 0.3; +prior(3) = 0.3; + +% Cluster centres +c = [2.0, 2.0; 0.0, 0.0; 1, -1]; + +ndata1 = prior(1)*ndata; +ndata2 = (prior(1) + prior(2))*ndata; +% Put first cluster at (2, 2) +data(1:ndata1, 1) = data(1:ndata1, 1) * 0.5 + c(1,1); +data(1:ndata1, 2) = data(1:ndata1, 2) * 0.5 + c(1,2); +targets(1:ndata1, 1) = 1; + +% Leave second cluster at (0,0) +data((ndata1 + 1):ndata2, :) = ... + data((ndata1 + 1):ndata2, :); +targets((ndata1+1):ndata2, 2) = 1; + +data((ndata2+1):ndata, 1) = data((ndata2+1):ndata,1) *0.6 + c(3, 1); +data((ndata2+1):ndata, 2) = data((ndata2+1):ndata,2) *0.6 + c(3, 2); +targets((ndata2+1):ndata, 3) = 1; + + +if 0 + ndata = 1; + data = x; + targets = [1 0 0]; +end + +options = foptions; +options(1) = -1; % verbose +options(14) = IRLS_iter; +[net2, options2] = glmtrain(net, options, data, targets); +net2.ll = options2(8); % type 'help foptions' for details + +cases = cell(2, ndata); +for l=1:ndata + q = find(targets(l,:)==1); + x = data(l,:); + cases{1,l} = x(:); + cases{2,l} = q; +end + +max_iter = 2; % we have complete observability, so 1 iter is enough +[bnet2, ll2] = learn_params_em(engine, cases, max_iter); + +w = get_field(bnet2.CPD{2},'weights'); +b = get_field(bnet2.CPD{2},'offset')'; + +w +net2.w1 + +b +net2.b1 + +% assert(approxeq(net2.ll, ll2)); % glmtrain returns ll after final M step, learn_params before +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/sprinkler1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,112 @@ +% Lawn sprinker example from Russell and Norvig p454 +% For a picture, see http://www.cs.berkeley.edu/~murphyk/Bayes/usage.html#basics + +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; + +false = 1; true = 2; +ns = 2*ones(1,N); % binary nodes + +%bnet = mk_bnet(dag, ns); +bnet = mk_bnet(dag, ns, 'names', {'cloudy','S','R','W'}, 'discrete', 1:4); +names = bnet.names; +%C = names{'cloudy'}; +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); + + +CPD{C} = reshape([0.5 0.5], 2, 1); +CPD{R} = reshape([0.8 0.2 0.2 0.8], 2, 2); +CPD{S} = reshape([0.5 0.9 0.5 0.1], 2, 2); +CPD{W} = reshape([1 0.1 0.1 0.01 0 0.9 0.9 0.99], 2, 2, 2); +joint = zeros(2,2,2,2); +for c=1:2 + for r=1:2 + for s=1:2 + for w=1:2 + joint(c,s,r,w) = CPD{C}(c) * CPD{S}(c,s) * CPD{R}(c,r) * ... + CPD{W}(s,r,w); + end + end + end +end + +joint2 = repmat(reshape(CPD{C}, [2 1 1 1]), [1 2 2 2]) .* ... + repmat(reshape(CPD{S}, [2 2 1 1]), [1 1 2 2]) .* ... + repmat(reshape(CPD{R}, [2 1 2 1]), [1 2 1 2]) .* ... + repmat(reshape(CPD{W}, [1 2 2 2]), [2 1 1 1]); + +assert(approxeq(joint, joint2)); + + +engine = jtree_inf_engine(bnet); + +evidence = cell(1,N); +evidence{W} = true; + +[engine, ll] = enter_evidence(engine, evidence); + +m = marginal_nodes(engine, S); +p1 = m.T(true) % P(S=true|W=true) = 0.4298 +lik1 = exp(ll); % P(W=true) = 0.6471 +assert(approxeq(p1, 0.4298)); +assert(approxeq(lik1, 0.6471)); + +pSandW = sumv(joint(:,true,:,true), [C R]); % P(S,W) = sum_cr P(CSRW) +pW = sumv(joint(:,:,:,true), [C S R]); +pSgivenW = pSandW / pW; % P(S=t|W=t) = P(S=t,W=t)/P(W=t) +assert(approxeq(pW, lik1)) +assert(approxeq(pSgivenW, p1)) + + +m = marginal_nodes(engine, R); +p2 = m.T(true) % P(R=true|W=true) = 0.7079 + +pRandW = sumv(joint(:,:,true,true), [C S]); % P(R,W) = sum_cr P(CSRW) +pRgivenW = pRandW / pW; % P(R=t|W=t) = P(R=t,W=t)/P(W=t) +assert(approxeq(pRgivenW, p2)) + + +% Add extra evidence that R=true +evidence{R} = true; + +[engine, ll] = enter_evidence(engine, evidence); + +m = marginal_nodes(engine, S); +p3 = m.T(true) % P(S=true|W=true,R=true) = 0.1945 +assert(approxeq(p3, 0.1945)) + + +pSandRandW = sumv(joint(:,true,true,true), [C]); % P(S,R,W) = sum_c P(cSRW) +pRandW = sumv(joint(:,:,true,true), [C S]); % P(R,W) = sum_cs P(cSRW) +pSgivenWR = pSandRandW / pRandW; % P(S=t|W=t,R=t) = P(S=t,R=t,W=t)/P(W=t,R=t) +assert(approxeq(pSgivenWR, p3)) + +% So the sprinkler is less likely to be on if we know that +% it is raining, since the rain can "explain away" the fact +% that the grass is wet. + +lik3 = exp(ll); % P(W=true, R=true) = 0.4581 +% So the combined evidence is less likely (of course) + + + + +% Joint distributions + +evidence = cell(1,N); +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]); + +evidence{R} = 2; +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +/add_ev_to_dmarginal.m/1.1.1.1/Thu Jun 27 20:34:32 2002// +/add_evidence_to_gmarginal.m/1.1.1.1/Wed May 29 15:59:56 2002// +/bnet_to_fgraph.m/1.1.1.1/Wed May 29 15:59:54 2002// +/compute_fwd_interface.m/1.1.1.1/Wed May 29 15:59:54 2002// +/compute_interface_nodes.m/1.1.1.1/Wed May 29 15:59:54 2002// +/compute_joint_pot.m/1.1.1.1/Mon Jun 7 15:50:34 2004// +/compute_minimal_interface.m/1.1.1.1/Wed May 29 15:59:54 2002// +/convert_dbn_CPDs_to_pots.m/1.1.1.1/Fri Nov 22 22:35:00 2002// +/convert_dbn_CPDs_to_tables.m/1.1.1.1/Thu Jan 23 18:44:50 2003// +/convert_dbn_CPDs_to_tables1.m/1.1.1.1/Thu Jan 23 18:49:48 2003// +/convert_dbn_CPDs_to_tables_slow.m/1.1.1.1/Wed May 29 15:59:58 2002// +/dbn_to_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dbn_to_hmm.m/1.1.1.1/Sun Feb 2 00:23:38 2003// +/determine_elim_constraints.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dispcpt.m/1.1.1.1/Wed May 29 15:59:58 2002// +/do_intervention.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dsep.m/1.1.1.1/Wed May 29 15:59:54 2002// +/dsep_test.m/1.1.1.1/Sat Jan 18 23:10:16 2003// +/enumerate_scenarios.m/1.1.1.1/Wed May 29 15:59:54 2002// +/fgraph_to_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/hodbn_to_bnet.m/1.1.1.1/Wed Jul 24 14:48:06 2002// +/is_mnet.m/1.1.1.1/Sun Jun 16 20:01:22 2002// +/linear_gaussian_to_cpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/log_lik_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/log_marg_lik_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_bnet.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_dbn.m/1.1.1.1/Sat Feb 1 19:42:14 2003// +/mk_fgraph.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_fgraph_given_ev.m/1.1.1.1/Mon Jun 24 18:56:26 2002// +/mk_higher_order_dbn.m/1.1.1.1/Tue Jul 23 13:17:04 2002// +/mk_limid.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_mnet.m/1.1.1.1/Sun Jun 16 19:52:12 2002// +/mk_mrf2.m/1.1.1.1/Tue Dec 31 22:06:48 2002// +/mk_mutilated_samples.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_named_CPT.m/1.1.1.1/Tue Mar 30 17:18:54 2004// +/mk_slice_and_half_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/noisyORtoTable.m/1.1.1.1/Mon Aug 2 22:05:58 2004// +/partition_dbn_nodes.m/1.1.1.1/Wed May 29 15:59:54 2002// +/partition_matrix_vec_3.m/1.1.1.1/Wed May 29 15:59:58 2002// +/sample_bnet.m/1.1.1.1/Thu Jun 10 01:17:46 2004// +/sample_bnet_nocell.m/1.1.1.1/Wed May 29 15:59:54 2002// +/sample_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/score_bnet_complete.m/1.1.1.1/Wed May 29 15:59:54 2002// +/shrink_obs_dims_in_gaussian.m/1.1.1.1/Wed May 29 15:59:58 2002// +/shrink_obs_dims_in_table.m/1.1.1.1/Wed May 29 15:59:58 2002// +/solve_limid.m/1.1.1.1/Mon Jun 7 15:48:02 2004// +/unroll_dbn_topology.m/1.1.1.1/Wed May 29 15:59:54 2002// +/unroll_higher_order_topology.m/1.1.1.1/Fri May 31 10:25:58 2002// +/unroll_set.m/1.1.1.1/Mon Dec 16 17:57:14 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/general
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +/bnet_to_gdl_graph.m/1.1.1.1/Wed May 29 15:59:54 2002// +/calc_mpe.m/1.1.1.1/Mon Jun 17 21:58:38 2002// +/calc_mpe_bucket.m/1.1.1.1/Wed May 29 15:59:54 2002// +/calc_mpe_dbn.m/1.1.1.1/Wed May 29 15:59:54 2002// +/calc_mpe_given_inf_engine.m/1.1.1.1/Wed May 29 15:59:54 2002// +/calc_mpe_global.m/1.1.1.1/Wed May 29 15:59:54 2002// +/compute_interface_nodes.m/1.1.1.1/Wed May 29 15:59:54 2002// +/mk_gdl_graph.m/1.1.1.1/Wed May 29 15:59:54 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/general/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/bnet_to_gdl_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function gdl = bnet_to_gdl_graph(bnet) +% BNET_TO_GDL_GRAPH Convert a Bayesian network to a GDL graph +% gdl = bnet_to_gdl_graph(bnet) +% +% Each node in the BN gets converted to a single node in the GDL graph, +% representing its family; its kernel function is the corresponding CPD. + +N = length(bnet.dag); +doms = cell(1,N); +for i=1:N + doms{i} = family(bnet.dag, i); +end + +U = mk_undirected(bnet.dag); +gdl = mk_gdl_graph(U, doms, bnet.node_sizes, bnet.CPD, 'equiv_class', bnet.equiv_class, ... + 'discrete', bnet.dnodes, 'chance', bnet.chance_nodes, ... + 'decision', bnet.decision_nodes, 'utility', bnet.utility_nodes); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/calc_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function [mpe, ll] = calc_mpe(engine, evidence, break_ties) +% CALC_MPE Computes the most probable explanation of the evidence +% [mpe, ll] = calc_mpe_given_inf_engine(engine, evidence, break_ties) +% +% INPUT +% engine must support max-propagation +% evidence{i} is the observed value of node i, or [] if hidden +% break_ties is optional. If 1, we will force ties to be broken consistently +% by calling enter_evidence N times. +% +% OUTPUT +% mpe{i} is the most likely value of node i (cell array!) +% ll is the log-likelihood of the globally best assignment +% +% This currently only works when all hidden nodes are discrete + +if nargin < 3, break_ties = 0; end + + +[engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + +observed = ~isemptycell(evidence); + +if 0 % fgraphs don't support bnet_from_engine +onodes = find(observed); +bnet = bnet_from_engine(engine); +pot_type = determine_pot_type(bnet, onodes); +assert(pot_type == 'd'); +end + +scalar = 1; +evidence = evidence(:); % hack to handle unrolled DBNs +N = length(evidence); +mpe = cell(1,N); +for i=1:N + m = marginal_nodes(engine, i); + % observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i) + mpe{i} = evidence{i}; + else + mpe{i} = argmax(m.T); + % Bug fix by Ron Zohar, 8/15/01 + % If there are ties, we must break them as follows (see Jensen96, p106) + if break_ties + evidence{i} = mpe{i}; + [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + end + end + if length(mpe{i}) > 1, scalar = 0; end +end + +if nargout >= 2 + bnet = bnet_from_engine(engine); + ll = log_lik_complete(bnet, mpe(:)); +end +if 0 % scalar + mpe = cell2num(mpe); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/calc_mpe_bucket.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,160 @@ +function [mpe, ll] = calc_mpe_bucket(bnet, new_evidence, max_over) +% +% PURPOSE: +% CALC_MPE Computes the most probable explanation to the network nodes +% given the evidence. +% +% [mpe, ll] = calc_mpe(engine, new_evidence, max_over) +% +% INPUT: +% bnet - the bayesian network +% new_evidence - optional, if specified - evidence to be incorporated [cell(1,n)] +% max_over - optional, if specified determines the variable elimination order [1:n] +% +% OUTPUT: +% mpe - the MPE assignmet for the net variables (or [] if no satisfying assignment) +% ll - log assignment probability. +% +% Notes: +% 1. Adapted from '@var_elim_inf_engine\marginal_nodes' for MPE by Ron Zohar, 8/7/01 +% 2. Only discrete potentials are supported at this time. +% 3. Complexity: O(nw*) where n is the number of nodes and w* is the induced tree width. +% 4. Implementation based on: +% - R. Dechter, "Bucket Elimination: A Unifying Framework for Probabilistic Inference", +% UA1 96, pp. 211-219. + + +ns = bnet.node_sizes; +n = length(bnet.dag); +evidence = cell(1,n); +if (nargin<2) + new_evidence = evidence; +end + +onodes = find(~isemptycell(new_evidence)); % observed nodes +hnodes = find(isemptycell(new_evidence)); % hidden nodes +pot_type = determine_pot_type(bnet, onodes); + +if pot_type ~= 'd' + error('only disrete potentials supported at this time') +end + +for i=1:n + fam = family(bnet.dag, i); + CPT{i} = convert_to_pot(bnet.CPD{bnet.equiv_class(i)}, pot_type, fam(:), evidence); +end + +% handle observed nodes: set impossible cases' probability to zero +% rather than prun matrix (this makes backtracking easier) + +for ii=onodes + lIdx = 1:ns(ii); + lIdx = setdiff(lIdx, new_evidence{ii}); + + sCPT=struct(CPT{ii}); % violate object privacy + + sargs = ''; + for jj=1:(length(sCPT.domain)-1) + sargs = [sargs, ':,']; + end + for jj=lIdx + eval(['sCPT.T(', sargs, num2str(jj), ')=0;']); + end + CPT{ii}=dpot(sCPT.domain, sCPT.sizes, sCPT.T); +end + +B = cell(1,n); +for b=1:n + B{b} = mk_initial_pot(pot_type, [], [], [], []); +end + +if (nargin<3) + max_over = (1:n); +end +order = max_over; % no attempt to optimize this + + +% Initialize the buckets with the CPDs assigned to them +for i=1:n + b = bucket_num(domain_pot(CPT{i}), order); + B{b} = multiply_pots(B{b}, CPT{i}); +end + +% Do backward phase +max_over = max_over(length(max_over):-1:1); % reverse +for i=max_over(1:end-1) + % max-ing over variable i which occurs in bucket j + j = bucket_num(i, order); + rest = mysetdiff(domain_pot(B{j}), i); + %temp = marginalize_pot_max(B{j}, rest); + temp = marginalize_pot(B{j}, rest, 1); + b = bucket_num(domain_pot(temp), order); + % fprintf('maxing over bucket %d (var %d), putting result into bucket %d\n', j, i, b); + sB=struct(B{b}); % violate object privacy + if ~isempty(sB.domain) + B{b} = multiply_pots(B{b}, temp); + else + B{b} = temp; + end +end +result = B{1}; +marginal = pot_to_marginal(result); +[prob, mpe] = max(marginal.T); + +% handle impossible cases +if ~(prob>0) + mpe = []; + ll = -inf; + %warning('evidence has zero probability') + return +end + +ll = log(prob); + +% Do forward phase +for ii=2:n + marginal = pot_to_marginal(B{ii}); + mpeidx = []; + for jj=order(1:length(mpe)) + assert(ismember(jj, marginal.domain)) %%% bug + temp = find_equiv_posns(jj, marginal.domain); + mpeidx = [mpeidx, temp] ; + if isempty(temp) + mpeidx = [mpeidx, Inf] ; + end + end + [mpeidxsorted sortedtompe] = sort(mpeidx) ; + + % maximize the matrix obtained from assigning values from previous buckets. + % this is done by building a string and using eval. + + kk=1; + sargs = '('; + for jj=1:length(marginal.domain) + if (jj~=1) + sargs = [sargs, ',']; + end + if (mpeidxsorted(kk)==jj) + sargs = [sargs, num2str(mpe(sortedtompe(kk)))]; + if (kk<length(mpe)) + kk = kk+1 ; + end + else + sargs = [sargs, ':']; + end + end + sargs = [sargs, ')'] ; + eval(['[val, loc] = max(marginal.T', sargs, ');']) + mpe = [mpe loc]; +end +[I,J] = sort(order); +mpe = mpe(J); + + + +%%%%%%%%% + +function b = bucket_num(domain, order) + +b = max(find_equiv_posns(domain, order)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/calc_mpe_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function [mpe, ll] = calc_mpe_dbn(engine, evidence, break_ties) +% CALC_MPE Computes the most probable explanation of the evidence +% [mpe, ll] = calc_mpe_dbn(engine, evidence, break_ties) +% +% INPUT +% engine must support max-propagation +% evidence{i,t} is the observed value of node i in slice t, or [] if hidden +% +% OUTPUT +% mpe{i,t} is the most likely value of node i (cell array!) +% ll is the log-likelihood of the globally best assignment +% +% This currently only works when all hidden nodes are discrete + +if nargin < 3, break_ties = 0; end + +if break_ties + disp('warning: break ties is ignored') +end + +[engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + +observed = ~isemptycell(evidence); +[ss T] = size(evidence); +scalar = 1; +N = length(evidence); +mpe = cell(ss,T); +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +for t=1:T + for i=1:ss + m = marginal_nodes(engine, i, t); + % observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i,t) + mpe{i,t} = evidence{i,t}; + else + assert(length(m.T) == ns(i)); + mpe{i,t} = argmax(m.T); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/calc_mpe_given_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [mpe, prob] = calc_mpe_given_inf_engine(engine, evidence) +% CALC_MPE_GIVEN_ENGINE Computes the most probable explanation of the evidence +% [mpe, prob] = calc_mpe_given_inf_engine(engine, evidence) +% +% INPUT +% engine must support max-propagation +% evidence{i} is the obsevred value of node i, or [] if hidden +% +% OUTPUT +% mpe(i) is the most likely value of node i +% prob is the likelihood of the globally best assignment +% +% This currently only works when all nodes are discrete + +[engine, ll] = enter_evidence(engine, evidence); + +observed = ~isemptycell(evidence); +N = length(evidence); +mpe = zeros(1,N); +for i=1:N + m = marginal_nodes(engine, i); + % discrete observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i) + mpe(i) = evidence{i}; + else + mpe(i) = argmax(m.T); + end +end + +bnet = bnet_from_engine(engine); +ll = log_lik_complete(bnet, num2cell(mpe(:))); +prob = exp(ll);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/calc_mpe_global.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function [mpe, ll] = calc_mpe_global(bnet, evidence) +% CALC_MPE_GLOBAL Compute the most probable explanation(s) from the global joint +% [mpe, ll] = calc_mpe_global(bnet, evidence) +% +% mpe(k,i) is the most probable value of node i in the k'th global mode +% ll is the log likelihood +% +% We assume all nodes are discrete + +engine = global_joint_inf_engine(bnet); +engine = enter_evidence(engine, evidence); +S1 = struct(engine); % violate object privacy +S2 = struct(S1.jpot); % joint potential +prob = max(S2.T(:)); +modes = find(S2.T(:) == prob); + +ens = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); +ens(onodes) = 1; +mpe = ind2subv(ens, modes); +for k=1:length(modes) + for i=onodes(:)' + mpe(k,i) = evidence{i}; + end +end +ll = log(prob); + +mpe = num2cell(mpe);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/compute_interface_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function [int, persist, transient] = compute_interface_nodes(intra, inter) +% COMPUTE_INTERFACE_NODES Find the nodes in a DBN that represent a sufficient statistic +% [int, persist, transient] = compute_interface_nodes(intra, inter) +% +% The interface nodes are all those that has an incoming temporal arc, +% or which have a child which has an incoming temporal arc, +% where a temporal arc means one coming from the previous slice. +% (The parents of nodes with incoming temporal arcs are needed +% because moralization will bring them into the clique.) +% +% The persisent nodes are all those that have one or more incoming temporal arc. +% The transient nodes are all the non-persistent. +% +% See U. Kjaerulff, "dHugin: A computational system for dynamic +% time-sliced Bayesian networks", Intl. J. Forecasting (11) 89-111, 1995 + +n = length(intra); +int = []; +persist = []; +for u=1:n + if any(inter(:,u)) + int = [int u]; + persist = [persist u]; + end + if any(inter(:, children(intra, u))) + int = [int u]; + end +end +int = unique(int); +persist = unique(persist); +transient = mysetdiff(1:n, persist);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/Old/mk_gdl_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function gdl = mk_gdl_graph(G, domains, node_sizes, kernels, varargin) +% MK_GDL_GRAPH Make a GDL (generalized distributed law) graph +% gdl = mk_gdl_graph(G, domains, node_sizes, kernels, ...) +% +% A GDL graph is like a moralized, but untriangulated, Bayes net: +% each "node" represents a domain with a corresponding kernel function. +% For details, see "The Generalized Distributive Law", Aji and McEliece, +% IEEE Trans. Info. Theory, 46(2): 325--343, 2000 +% +% G(i,j) = 1 if there is an (undirected) edge between domains i,j +% +% domains{i} is the domain of node i +% +% node_sizes(i) is the number of values node i can take on, +% or the length of node i if i is a continuous-valued vector. +% node_sizes(i) = 1 if i is a utility node. +% +% kernels is the list of kernel functions +% +% The list below gives optional arguments [default value in brackets]. +% +% equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F] +% discrete - the list of nodes which are discrete random variables [1:N] +% chance - the list of nodes which are random variables [1:N] +% decision - the list of nodes which are decision nodes [ [] ] +% utility - the list of nodes which are utility nodes [ [] ] + + +ns = node_sizes; +N = length(domains); +vars = []; +for i=1:N + vars = myunion(vars, domains{i}); +end +Nvars = length(vars); + +gdl.equiv_class = 1:length(kernels); +gdl.chance_nodes = 1:Nvars; +gdl.utility_nodes = []; +gdl.decision_nodes = []; +gdl.dnodes = 1:Nvars; + +if nargin >= 5 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'equiv_class', bnet.equiv_class = args{i+1}; + case 'chance', bnet.chance_nodes = args{i+1}; + case 'utility', bnet.utility_nodes = args{i+1}; + case 'decision', bnet.decision_nodes = args{i+1}; + case 'discrete', bnet.dnodes = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + + +gdl.G = G; +gdl.vars = vars; +gdl.doms = domains; +gdl.node_sizes = node_sizes; +gdl.cnodes = mysetdiff(vars, gdl.dnodes); +gdl.kernels = kernels; +gdl.type = 'gdl'; + +% Compute a bit vector representation of the set of domains +% dom_bitv(i,j) = 1 iff variable j occurs in domain i +gdl.dom_bitv = zeros(N, length(vars)); +for i=1:N + gdl.dom_bitv(i, domains{i}) = 1; +end + +% compute the interesection of the domains on either side of each edge (separating set) +gdl.sepset = cell(N, N); +gdl.nbrs = cell(1,N); +for i=1:N + nbrs = neighbors(G, i); + gdl.nbrs{i} = nbrs; + for j = nbrs(:)' + gdl.sepset{i,j} = myintersect(domains{i}, domains{j}); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/add_ev_to_dmarginal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function fmarginal = add_ev_to_dmarginal(fmarginal, evidence, ns) +% ADD_EV_TO_DMARGINAL 'pump up' observed nodes back to their original size. +% fmarginal = add_ev_to_dmarginal(fmarginal, evidence, ns) +% +% We introduce 0s into the array in positions which are incompatible with the evidence. + +dom = fmarginal.domain; +odom = dom(~isemptycell(evidence(dom))); +vals = cat(1, evidence{odom}); +index = mk_multi_index(length(dom), find_equiv_posns(odom, dom), vals); +T = 0*myones(ns(dom)); +ens = ns(:)'; +ens(odom) = 1; +T(index{:}) = myreshape(fmarginal.T, ens(dom)); +fmarginal.T = T;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/add_evidence_to_gmarginal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,78 @@ +function fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes) +% ADD_EVIDENCE_TO_GMARGINAL 'pump up' observed nodes back to their original size. +% function fullm = add_evidence_to_gmarginal(fmarginal, evidence, ns, cnodes) +% +% We introduce 0s into the array in positions which are incompatible with the evidence. +% for both discrete and continuous nodes. +% +% See also add_ev_to_dmarginal + +dom = fmarginal.domain; +fullm.domain = fmarginal.domain; + +% Find out which values of the discrete parents (if any) are compatible with +% the discrete evidence (if any). +dnodes = mysetdiff(1:length(ns), cnodes); +ddom = myintersect(dom, dnodes); +cdom = myintersect(dom, cnodes); +odom = dom(~isemptycell(evidence(dom))); +hdom = dom(isemptycell(evidence(dom))); + +% Find the entries in the big table that are compatible with the discrete evidence. +% (We will put the probabilities from the small inferred table into these positions.) +% We could use add_ev_to_dmarginal to do this. +dobs = myintersect(ddom, odom); +dvals = cat(1, evidence{dobs}); +ens = ns; % effective node sizes +ens(dobs) = 1; +S = prod(ens(ddom)); +subs = ind2subv(ens(ddom), 1:S); +mask = find_equiv_posns(dobs, ddom); +%subs(mask) = dvals; % bug fix by P. Brutti +for i=1:length(mask), + subs(:,mask(i)) = dvals(i); +end +supportedQs = subv2ind(ns(ddom), subs); + +if isempty(ddom) + Qarity = 1; +else + Qarity = prod(ns(ddom)); +end +fullm.T = zeros(Qarity, 1); +fullm.T(supportedQs) = fmarginal.T(:); +fullm.T = myreshape(fullm.T, ns(ddom)); + + +if isempty(cdom) + fullm.mu = []; + fullm.sigma = []; + return; +end + +% Now put the hidden cts parts into their right blocks, +% leaving the observed cts parts as 0. +cobs = myintersect(cdom, odom); +chid = myintersect(cdom, hdom); +cvals = cat(1, evidence{cobs}); +n = sum(ns(cdom)); +fullm.mu = zeros(n,Qarity); +fullm.Sigma = zeros(n,n,Qarity); + +if ~isempty(chid) + chid_blocks = block(find_equiv_posns(chid, cdom), ns(cdom)); +end +if ~isempty(cobs) + cobs_blocks = block(find_equiv_posns(cobs, cdom), ns(cdom)); +end + +for i=1:length(supportedQs) + Q = supportedQs(i); + if ~isempty(chid) + fullm.mu(chid_blocks, Q) = fmarginal.mu(:, i); + fullm.Sigma(chid_blocks, chid_blocks, Q) = fmarginal.Sigma(:,:,i); + end + if ~isempty(cobs) + fullm.mu(cobs_blocks, Q) = cvals(:); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/bnet_to_fgraph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function fg = bnet_to_fgraph(bnet) +% BNET_TO_FGRAPH Convert a Bayes net to a factor graph +% fg = bnet_to_fgraph(bnet) +% +% We create one factor per family, whose kernel is the CPD + +nnodes = length(bnet.dag); +G = zeros(nnodes, nnodes); +for i=1:nnodes + G(family(bnet.dag, i), i) = 1; +end + +fg = mk_fgraph(G, bnet.node_sizes, bnet.CPD, 'equiv_class', bnet.equiv_class, 'discrete', bnet.dnodes); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/compute_fwd_interface.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function int = compute_fwd_interface(intra, inter) +% COMPUTE_FWD_INTERFACE Compute nodes with children in the next slice +% function int = compute_fwd_interface(intra, inter) + +int = []; +ss = length(intra); +for u=1:ss + if any(inter(u,:)) + int = [int u]; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/compute_interface_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +function [interface, persist, transient] = compute_interface_nodes(intra, inter) +% COMPUTE_INTERFACE_NODES Find the nodes in a DBN that represent a sufficient statistic +% [interface, persist, transient] = compute_interface_nodes(intra, inter) +% +% The interface nodes are all those that has an incoming temporal arc, +% or which are parents of such nodes. +% If the parents are in the previous slice, this just means they have an +% outgoing temporal arc. +% (The parents of nodes with incoming temporal arcs are needed +% because moralization will bring them into the clique.) +% +% The persisent nodes are all those that have one or more incoming temporal arc. +% The transient nodes are all the non-persistent. +% +% See U. Kjaerulff, "dHugin: A computational system for dynamic +% time-sliced Bayesian networks", Intl. J. Forecasting (11) 89-111, 1995 + +n = length(intra); +interface = []; +persist = []; +% any nodes with incoming arcs +for u=1:n + if any(inter(:,u)) + interface = [interface u]; + persist = [persist u]; + end +end +% Any nodes which are parents of nodes with incoming arcs +for u=1:n + cs = children(intra, u); + if any(inter(:, cs)) + interface = [interface u]; + end + %cs = children(inter, u); + % if ~isempty(myintersect(cs, persist)) + % interface = [interface u]; + %end +end +interface = unique(interface); +persist = unique(persist); +transient = mysetdiff(1:n, persist); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/compute_joint_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function [jpot, loglik] = compute_joint_pot(bnet, nodes, evidence, domain) +% COMPUTE_JOINT_POT Compute the global joint potential of a Bayes net +% function jpot = compute_joint_pot(bnet, nodes, evidence, domain) + +if nargin < 4, domain = nodes; end + +onodes = find(~isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes, domain); + +jpot = mk_initial_pot(pot_type, domain, bnet.node_sizes, bnet.cnodes, onodes); +for i=nodes(:)' + e = bnet.equiv_class(i); + fam = family(bnet.dag, i); + pot = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + jpot = multiply_by_pot(jpot, pot); +end +%[jpot, loglik] = normalize_pot(jpot); % causes errors in asia_dt1 etc
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/compute_minimal_interface.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function clqs = compute_minimal_interface(intra, inter) + +int = compute_fwd_interface(intra, inter); +ss = length(intra); +Z = zeros(ss); +dag = [intra inter; + Z intra]; +G = moralize(dag); +intra2 = G(1:ss,1:ss); +inter2 = G(1:ss,(1:ss)+ss); +G = unroll_dbn_topology(intra2, inter2, ss); +T = ss; +last_slice = (1:ss) + (T-1)*ss; +G = (G + G')/2; % mk symmetric +G2 = (expm(full(G)) > 0); % closure of graph +G3 = G2(last_slice, last_slice); +[c,v] = scc(G3); % connected components +ncomp = size(v,1); +clqs = cell(1,ncomp); +for i=1:ncomp + ndx = find(v(i,:)>0); + clqs{i} = v(i,ndx); + clqs{i} = myintersect(clqs{i}, int); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/convert_dbn_CPDs_to_pots.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type, softCPDpot) +% CONVERT_DBN_CPDS_TO_POTS Convert CPDs of (possibly instantiated) DBN nodes to potentials +% CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type, softCPDpot) +% +% CPDpot{n,t} is a potential containing P(n,t|pa(n,t), ev) +% softCPDpot{n,t} is a potential containing P(n,t|pa(n,t), ev) insted of using n's CPD + +[ss T] = size(evidence); + +if nargin < 4, softCPDpot = cell(ss,T); end +CPDpot = softCPDpot; + +% Convert CPDs of instantiated nodes to potential form +t = 1; +for n=1:ss + fam = family(bnet.dag, n); + e = bnet.equiv_class(n, 1); + if isempty(softCPDpot{n,t}) + CPDpot{n,t} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence(:,1)); + end +end +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + for t=2:T + if isempty(softCPDpot{n,t}) + CPDpot{n,t} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence(:,t-1:t)); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/convert_dbn_CPDs_to_tables.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,201 @@ +function CPDpot = convert_dbn_CPDs_to_tables(bnet, evidence) +% CONVERT_DBN_CPDS_TO_TABLES Convert CPDs of (possibly instantiated) DBN nodes to tables +% CPDpot = convert_dbn_CPDs_to_tables(bnet, evidence) +% +% CPDpot{n,t} is a table containing P(n,t|pa(n,t), ev) +% All hidden nodes are assumed to be discrete. +% We assume the observed nodes are the same in every slice. +% +% Evaluating the conditional likelihood of long evidence sequences can be very slow, +% so we take pains to vectorize where possible. + +[ss T] = size(evidence); +%obs_bitv = ~isemptycell(evidence(:)); +obs_bitv = zeros(1, 2*ss); +obs_bitv(bnet.observed) = 1; +obs_bitv(bnet.observed+ss) = 1; + +ns = bnet.node_sizes(:); +CPDpot = cell(ss,T); + +for n=1:ss + % slice 1 + t = 1; + ps = parents(bnet.dag, n); + e = bnet.equiv_class(n, 1); + if ~any(obs_bitv(ps)) + CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t}); + else + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps n], evidence(:,1)); + end + +% special cases: c=child, p=parents, d=discrete, h=hidden, 1sl=1slice +% if c=h=1 then c=d=1, since hidden nodes must be discrete +% c=h c=d p=h p=d 1sl method +% --------------------------- +% 1 1 1 1 - replicate CPT +% - 1 - 1 - evaluate CPT on evidence * +% 0 1 1 1 1 dhmm +% 0 0 1 1 1 ghmm +% other loop +% +% * = any subset of the domain may be observed + +% Example where all of the special cases occur - a hierarchical HMM +% where the top layer (G) and leaves (Y) are observed and +% all nodes are discrete except Y. +% (O turns on if Y is an outlier) + +% G ---------> G +% | | +% v v +% S --------> S +% | | +% v v +% Y Y +% ^ ^ +% | | +% O O + +% Evaluating P(yt|St,Ot) is the ghmm case +% Evaluating P(St|S(t-1),gt) is the eval CPT case +% Evaluating P(gt|g(t-1) is the eval CPT case (hdom = []) +% Evaluating P(Ot) is the replicated CPT case + +% Cts parents (e.g., inputs) would require an additional special case for speed + + + % slices 2..T + [ss T] = size(evidence); + self = n+ss; + ps = parents(bnet.dag, self); + e = bnet.equiv_class(n, 2); + + if 1 + debug = 0; + hidden_child = ~obs_bitv(n); + discrete_child = myismember(n, bnet.dnodes); + hidden_ps = all(~obs_bitv(ps)); + discrete_ps = mysubset(ps, bnet.dnodes); + parents_in_same_slice = all(ps > ss); + + if hidden_child & discrete_child & hidden_ps & discrete_ps + CPDpot = helper_repl(bnet, evidence, n, CPDpot, obs_bitv, debug); + elseif discrete_child & discrete_ps + CPDpot = helper_eval(bnet, evidence, n, CPDpot, obs_bitv, debug); + elseif discrete_child & hidden_ps & discrete_ps & parents_in_same_slice + CPDpot = helper_dhmm(bnet, evidence, n, CPDpot, obs_bitv, debug); + elseif ~discrete_child & hidden_ps & discrete_ps & parents_in_same_slice + CPDpot = helper_ghmm(bnet, evidence, n, CPDpot, obs_bitv, debug); + else + if debug, fprintf('node %d, slow\n', n); end + for t=2:T + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end + end + + if 0 + for t=2:T + CPDpot2{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + if ~approxeq(CPDpot{n,t}, CPDpot2{n,t}) + fprintf('CPDpot n=%d, t=%d\n',n,t); + keyboard + end + end + end + + +end + + + + +%%%%%%% +function CPDpot = helper_repl(bnet, evidence, n, CPDpot, obs_bitv, debug) + +[ss T] = size(evidence); +if debug, fprintf('node %d, repl\n', n); end +e = bnet.equiv_class(n, 2); +CPT = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, []); +CPDpot(n,2:T) = num2cell(repmat(CPT, [1 1 T-1]), [1 2]); + + + +%%%%%%% +function CPDpot = helper_eval(bnet, evidence, n, CPDpot, obs_bitv, debug) + +[ss T] = size(evidence); +self = n+ss; +ps = parents(bnet.dag, self); +e = bnet.equiv_class(n, 2); +ns = bnet.node_sizes(:); +% Example: given CPT(p1, p2, p3, p4, c), where p1,p3 are observed +% we create CPT([p2 p4 c], [p1 p3]). +% We then convert all observed p1,p3 into indices ndx +% and return CPT(:, ndx) +CPT = CPD_to_CPT(bnet.CPD{e}); +domain = [ps self]; +% if dom is [3 7 8] and 3,8 are observed, odom_rel = [1 3], hdom_rel = 2, +% odom = [3 8], hdom = 7 +odom_rel = find(obs_bitv(domain)); +hdom_rel = find(~obs_bitv(domain)); +odom = domain(odom_rel); +hdom = domain(hdom_rel); +if isempty(hdom) + CPT = CPT(:); +else + CPT = permute(CPT, [hdom_rel odom_rel]); + CPT = reshape(CPT, prod(ns(hdom)), prod(ns(odom))); +end +parents_in_same_slice = all(ps > ss); +if parents_in_same_slice + if debug, fprintf('node %d eval 1 slice\n', n); end + data = cell2num(evidence(odom-ss,2:T)); %data(i,t) = val of i'th obs parent at t+1 +else + if debug, fprintf('node %d eval 2 slice\n', n); end + % there's probably a way of vectorizing this... + data = zeros(length(odom), T-1); + for t=2:T + ev = evidence(:,t-1:t); + ev = ev(:); + ev2 = ev(odom); + data(:,t-1) = cat(1, ev2{:}); + %data(:,t-1) = cell2num(ev2); + end +end +ndx = subv2ind(ns(odom), data'); % ndx(t) encodes data(:,t) +if isempty(hdom) + CPDpot(n,2:T) = num2cell(CPT(ndx)); % a cell array of floats +else + CPDpot(n,2:T) = num2cell(CPT(:, ndx), 1); % a cell array of column vectors +end + +%%%%%%% +function CPDpot = helper_dhmm(bnet, evidence, n, CPDpot, obs_bitv, debug) + +if debug, fprintf('node %d, dhmm\n', n); end +[ss T] = size(evidence); +self = n+ss; +ps = parents(bnet.dag, self); +e = bnet.equiv_class(n, 2); +ns = bnet.node_sizes(:); +CPT = CPD_to_CPT(bnet.CPD{e}); +CPT = reshape(CPT, [prod(ns(ps)) ns(self)]); % what if no parents? +%obslik = mk_dhmm_obs_lik(cell2num(evidence(n,2:T)), CPT); +obslik = eval_pdf_cond_multinomial(cell2num(evidence(n,2:T)), CPT); +CPDpot(n,2:T) = num2cell(obslik, 1); + + +%%%%%%% +function CPDpot = helper_ghmm(bnet, evidence, n, CPDpot, obs_bitv, debug) + +if debug, fprintf('node %d, ghmm\n', n); end +[ss T] = size(evidence); +e = bnet.equiv_class(n, 2); +S = struct(bnet.CPD{e}); +ev2 = cell2num(evidence(n,2:T)); +%obslik = mk_ghmm_obs_lik(ev2, S.mean, S.cov); +obslik = eval_pdf_cond_gauss(ev2, S.mean, S.cov); +CPDpot(n,2:T) = num2cell(obslik, 1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/convert_dbn_CPDs_to_tables1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,162 @@ +function CPDpot = convert_dbn_CPDs_to_tables1(bnet, evidence) +% CONVERT_DBN_CPDS_TO_TABLES Convert CPDs of (possibly instantiated) DBN nodes to tables +% CPDpot = convert_dbn_CPDs_to_tables(bnet, evidence) +% +% CPDpot{n,t} is a table containing P(n,t|pa(n,t), ev) +% All hidden nodes are assumed to be discrete +% We assume the observed nodes are the same in every slice +% +% Evaluating the conditional likelihood of the evidence can be very slow, +% so we take pains to vectorize where possible, i.e., we try to avoid +% calling convert_to_table + +[ss T] = size(evidence); +%obs_bitv = ~isemptycell(evidence(:)); +obs_bitv = zeros(1, 2*ss); +obs_bitv(bnet.observed) = 1; +obs_bitv(bnet.observed+ss) = 1; + +ns = bnet.node_sizes(:); +CPDpot = cell(ss,T); + +for n=1:ss + % slice 1 + t = 1; + ps = parents(bnet.dag, n); + e = bnet.equiv_class(n, 1); + if ~any(obs_bitv(ps)) + CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t}); + else + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps n], evidence(:,1)); + end + + % slices 2..T + debug = 1; + if ~obs_bitv(n) + CPDpot = helper_hidden_child(bnet, evidence, n, CPDpot, obs_bitv, debug); + else + CPDpot = helper_obs_child(bnet, evidence, n, CPDpot, obs_bitv, debug); + end +end + +if 0 +CPDpot2 = convert_dbn_CPDs_to_tables_slow(bnet, evidence); +for t=1:T + for n=1:ss + if ~approxeq(CPDpot{n,t}, CPDpot2{n,t}) + fprintf('CPDpot n=%d, t=%d\n',n,t); + keyboard + end + end +end +end + + +% special cases: c=child, p=parents, d=discrete, h=hidden, 1=1slice +% if c=h=1 then c=d=1, since hidden nodes must be discrete +% c=h c=d p=h p=d p=1 method +% --------------------------- +% 1 1 1 1 - replicate CPT +% 0 1 1 1 1 dhmm +% 0 0 1 1 1 ghmm +% - 1 - 1 - evaluate CPT on evidence +% other loop + +%%%%%%% +function CPDpot = helper_hidden_child(bnet, evidence, n, CPDpot, obs_bitv, debug) + +[ss T] = size(evidence); +self = n+ss; +ps = parents(bnet.dag, self); +e = bnet.equiv_class(n, 2); +ns = bnet.node_sizes(:); +if ~any(obs_bitv(ps)) % all parents are hidden (hence discrete) + if debug, fprintf('node %d is hidden, all ps are hidden\n', n); end + if myismember(n, bnet.dnodes) + %CPT = CPD_to_CPT(bnet.CPD{e}); + %CPT = reshape(CPT, [prod(ns(ps)) ns(self)]); + CPT = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, []); + CPDpot(n,2:T) = num2cell(repmat(CPT, [1 1 T-1]), [1 2]); + else + error(['hidden cts node disallowed']) + end +else % some parents are observed - slow + if mysubset(ps, bnet.dnodes) % all parents are discrete + % given CPT(p1, p2, p3, p4, c), where p1,p3 are observed + % we create CPT([p2 p4 c], [p1 p3]). + % We then convert all observed p1,p3 into indices ndx + % and return CPT(:, ndx) + CPT = CPD_to_CPT(bnet.CPD{e}); + domain = [ps self]; + % if dom is [3 7 8] and 3,8 are observed, odom_rel = [1 3], hdom_rel = 2, + % odom = [3 8], hdom = 7 + odom_rel = find(obs_bitv(domain)); + hdom_rel = find(~obs_bitv(domain)); + odom = domain(odom_rel); + hdom = domain(hdom_rel); + CPT = permute(CPT, [hdom_rel odom_rel]); + CPT = reshape(CPT, prod(ns(hdom)), prod(ns(odom))); + parents_in_same_slice = all(ps > ss); + if parents_in_same_slice + if debug, fprintf('node %d is hidden, some ps are obs, all ps discrete, 1 slice\n', n); end + data = cell2num(evidence(odom-ss,2:T)); %data(i,t) = val of i'th obs parent at t+1 + else + if debug, fprintf('node %d is hidden, some ps are obs, all ps discrete, 2 slice\n', n); end + data = zeros(length(odom), T-1); + for t=2:T + ev = evidence(:,t-1:t); + data(:,t-1) = cell2num(ev(odom)); + end + end + ndx = subv2ind(ns(odom), data'); % ndx(t) encodes data(:,t) + CPDpot(n,2:T) = num2cell(CPT(:, ndx), [1 2]); + else % some parents are cts - v slow + if debug, fprintf('node %d is hidden, some ps are obs, some ps cts\n', n); end + for t=2:T + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end +end + +%%%%%%% +function CPDpot = helper_obs_child(bnet, evidence, n, CPDpot, obs_bitv, debug) + +[ss T] = size(evidence); +self = n+ss; +ps = parents(bnet.dag, self); +e = bnet.equiv_class(n, 2); +ns = bnet.node_sizes(:); +if ~any(obs_bitv(ps)) % all parents are hidden + parents_in_same_slice = all(ps > ss); + if parents_in_same_slice + if debug, fprintf('node %d is obs, all ps are hidden, 1 slice\n', n); end + ps1 = ps - ss; + if myismember(n, bnet.dnodes) + CPT = CPD_to_CPT(bnet.CPD{e}); + CPT = reshape(CPT, [prod(ns(ps)) ns(self)]); % what if no parents? + obslik = eval_pdf_cond_multinomial(cell2num(evidence(n,2:T)), CPT); + CPDpot(n,2:T) = num2cell(obslik, 1); + else + S = struct(bnet.CPD{e}); + obslik = eval_pdf_cond_gauss(cell2num(evidence(n,2:T)), S.mean, S.cov); + CPDpot(n,2:T) = num2cell(obslik, 1); + end + else % parents span 2 slices - slow + if debug, fprintf('node %d is obs, all ps are hidden , 2 slice\n', n); end + for t=2:T + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end +else + if isempty(ps) % observed root + if debug, fprintf('node %d is obs, no ps\n', n); end + CPT = CPD_to_CPT(bnet.CPD{e}); + data = cell2num(evidence(n,2:T)); + CPDpot(n,2:T) = CPT(data); + else % some parents are observed - slow + if debug, fprintf('node %d is obs, some ps are obs\n', n); end + for t=2:T + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/convert_dbn_CPDs_to_tables_slow.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function CPDpot = convert_dbn_CPDs_to_tables_slow(bnet, evidence) +% CONVERT_DBN_CPDS_TO_TABLES_SLOW Convert CPDs of (possibly instantiated) DBN nodes to tables +% CPDpot = convert_dbn_CPDs_to_tables_slow(bnet, evidence) +% +% CPDpot{n,t} is a table containing P(n,t|pa(n,t), ev) +% All hidden nodes are assumed to be discrete +% +% Non-vectorized method; this is less efficient for long sequences of observed Gaussian +% nodes, because of the (unnecessary) repeated matrix inversion. + +obs_bitv = ~isemptycell(evidence(:)); +[ss T] = size(evidence); +ns = bnet.node_sizes(:); + +CPDpot = cell(ss,T); + +t = 1; +for n=1:ss + %ps = engine.bnet_parents{n}; + ps = parents(bnet.dag, n); + e = bnet.equiv_class(n, 1); + if ~any(obs_bitv(ps)) + CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t}); + else + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps n], evidence(:,1)); + end +end +for t=2:T + for n=1:ss + self = n+ss; + ps = parents(bnet.dag, self); + e = bnet.equiv_class(n, 2); + if ~any(obs_bitv(ps)) + CPDpot{n,t} = convert_CPD_to_table_hidden_ps(bnet.CPD{e}, evidence{n,t}); + else + CPDpot{n,t} = convert_to_table(bnet.CPD{e}, [ps self], evidence(:,t-1:t)); + end + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/dbn_to_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function bnet = dbn_to_bnet(dbn, T) +% DBN_TO_BNET Convert a DBN to a static network by unroll for T slices +% bnet = dbn_to_bnet(dbn, T) + +ss = length(dbn.intra); +eclass = [dbn.equiv_class(:,1) repmat(dbn.equiv_class(:,2), 1, T-1)]; +dnodes = unroll_set(dbn.dnodes_slice, ss, T); +ns = repmat(dbn.node_sizes_slice(:), 1, T); +dag = unroll_dbn_topology(dbn.intra, dbn.inter, T, dbn.intra1); +onodes = unroll_set(dbn.observed(:), ss, T); +bnet = mk_bnet(dag, ns(:), 'discrete', dnodes(:), 'equiv_class', eclass(:), 'observed', onodes(:)); +bnet.CPD = dbn.CPD; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/dbn_to_hmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +function [startprob, transprob, obsprob] = dbn_to_hmm(bnet) +% DBN_TO_HMM % Convert DBN params to HMM params +% [startprob, transprob, obsprob] = dbn_to_hmm(bnet, onodes) +% startprob(i) +% transprob(i,j) +% obsprob{k}.big_CPT(i,o) if k'th observed node is discrete +% obsprob{k}.big_mu(:,i), .big_Sigma(:,:,i) if k'th observed node is Gaussian +% Big means the domain contains all the hidden discrete nodes, not just the parents. + +% Called by constructor and by update_engine + +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +evidence = cell(ss, 2); +ns = bnet.node_sizes(:); +Qh = prod(ns(hnodes)); +tmp = dpot_to_table(compute_joint_pot(bnet, hnodes, evidence)); +startprob = reshape(tmp, Qh, 1); + +tmp = dpot_to_table(compute_joint_pot(bnet, hnodes+ss, evidence, [hnodes hnodes+ss])); +transprob = mk_stochastic(reshape(tmp, Qh, Qh)); + +% P(o|ps) is used by mk_hmm_obs_lik_vec for a single time slice +% P(o|h) (the big version), where h = all hidden nodes, is used by enter_evidence + +obsprob = cell(1, length(onodes)); +for i=1:length(onodes) + o = onodes(i); + if bnet.auto_regressive(o) + % We assume the parents of this node are all the hidden nodes in the slice, + % so the params already are "big". Also, we assume we regress only on our old selves. + % slice 1 + e = bnet.equiv_class(o); + CPD = struct(bnet.CPD{e}); + O = ns(o); + ps = bnet.parents{o}; + Qps = prod(ns(ps)); + obsprob{i}.big_mu0 = reshape(CPD.mean, [O Qps]); + obsprob{i}.big_Sigma0 = reshape(CPD.cov, [O O Qps]); + + % slice t>1 + e = bnet.equiv_class(o+ss); + CPD = struct(bnet.CPD{e}); + O = ns(o); + dps = mysetdiff(bnet.parents{o+ss}, o); + Qdps = prod(ns(dps)); + obsprob{i}.big_mu = reshape(CPD.mean, [O Qdps]); + obsprob{i}.big_Sigma = reshape(CPD.cov, [O O Qdps]); + obsprob{i}.big_W = reshape(CPD.weights, [O O Qdps]); + else + e = bnet.equiv_class(o+ss); + CPD = struct(bnet.CPD{e}); + O = ns(o); + ps = bnet.parents{o}; + Qps = prod(ns(ps)); + % We make a big potential, replicating the params if necessary + % e.g., for a 2 chain coupled HMM, mu(:,Q1) becomes mu(:,Q1,Q2) + bigpot = pot_to_marginal(compute_joint_pot(bnet, onodes(i), evidence, [hnodes onodes(i)])); + + if myismember(o, bnet.dnodes) + obsprob{i}.CPT = reshape(CPD.CPT, [Qps O]); + obsprob{i}.big_CPT = reshape(bigpot.T, Qh, O); + else + obsprob{i}.big_mu = bigpot.mu; + obsprob{i}.big_Sigma = bigpot.Sigma; + + if 1 + obsprob{i}.mu = reshape(CPD.mean, [O Qps]); + C = reshape(CPD.cov, [O O Qps]); + obsprob{i}.Sigma = C; + d = size(obsprob{i}.mu, 1); + for j=1:Qps + obsprob{i}.inv_Sigma(:,:,j) = inv(C(:,:,j)); + obsprob{i}.denom(j) = (2*pi)^(d/2)*sqrt(abs(det(C(:,:,j)))); + end + end + + end % if discrete + end % if ar +end % for
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/determine_elim_constraints.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function partial_order = determine_elim_constraints(bnet, onodes) +% DETERMINE_ELIM_CONSTRAINTS Determine what the constraints are (if any) on the elimination ordering. +% partial_order = determine_elim_constraints(bnet, onodes) +% +% A graph with different kinds of nodes (e.g., discrete and cts, or decision and rnd) is called marked. +% A strong root is guaranteed to exist if the marked graph is triangulated and does not have any paths of +% the form discrete -> cts -> discrete. In general we need to add extra edges to +% the moral graph to ensure this (see example in Lauritzen (1992) fig 3b). +% However, a simpler sufficient condition is to eliminate all the cts nodes before the discrete ones, +% because then, as we move from the leaves to the root, the cts nodes get marginalized away +% and we are left with purely discrete cliques. +% +% partial_order(i,j)=1 if we must marginalize j *before* i +% (so i will be nearer the strong root). +% If the hidden nodes are either all discrete or all cts, we set partial_order = []. +% +% For details, see +% - Jensen, Jensen and Dittmer, "From influence diagrams to junction trees", UAI 94. +% - Lauritzen, "Propgation of probabilities, means, and variances in mixed graphical +% association models", JASA 87(420):1098--1108, 1992. +% - K. Olesen, "Causal probabilistic networks with both discrete and continuous variables", +% IEEE Pami 15(3), 1993 + + +n = length(bnet.dag); +pot_type = determine_pot_type(bnet, onodes); +if (pot_type == 'd') | (pot_type == 'g') + partial_order = []; + return; +end + + +partial_order = sparse(n,n); +partial_order(bnet.dnodes, bnet.cnodes) = 1; + +% Integrate out cts nodes before their discrete parents - see Olesen (1993) p9 +% This method gives the wrong results on cg1.m! +if 0 +for i=bnet.cnodes(:)' + dps = myintersect(parents(bnet.dag, i), bnet.dnodes); + partial_order(dps, i)=1; +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/dispcpt.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function display_CPT(CPT) + +n = ndims(CPT); +parents_size = size(CPT); +parents_size = parents_size(1:end-1); +child_size = size(CPT,n); +c = 1; +for i=1:prod(parents_size) + parent_inst = ind2subv(parents_size, i); + fprintf(1, '%d ', parent_inst); + fprintf(1, ': '); + index = num2cell([parent_inst 1]); + index{n} = ':'; + fprintf(1, '%6.4f ', CPT(index{:})); + fprintf(1, '\n'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/do_intervention.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function bnet = mutilate_bnet(bnet, nodes, vals) +% MUTILATE_BNET Clamp nodes to specific values (perform a surgical intervention) +% bnet = mutilate_bnet(bnet, nodes, vals) +% +% We make all the clamped nodes roots. + +ns = bnet.node_sizes; +for i=1:length(nodes) + X = nodes(i); + x = vals(i); + bnet.dag(:,X) = 0; + bnet.CPD{X} = root_CPD(bnet, X, x); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/dsep.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function sep = dsep(X, Y, S, G) +% DSEP Is X indep Y given S wrt DAG G? +% sep = dsep(X, Y, S, G) +% +% Instead of using the Bayes-Ball criterion, we see if S separates X and Y +% in the moralized ancestral graph. + +conn = reachability_graph(G); +M = myunion(myunion(X, Y), S); +[A,junk] = find(conn(:, M)); +A = unique(A); +A = myunion(A, M); +GM = moralize(G(A,A)); +%sep = graph_separated(GM, X, Y, S); +sep = graph_separated(GM, find_equiv_posns(X,A), find_equiv_posns(Y,A), find_equiv_posns(S,A));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/dsep_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ + +% Cowell et al p72 +G = zeros(10); +G(1,2)=1; +G(2,3)=1; +G(3,7)=1; +G(4,[5 8])=1; +G(5,6)=1; +G(6,7)=1; +G(7,[9 10])=1; +G(8,9)=1; + +dsep(1, 4, [5 7], G) +dsep(1, 4, [7], G) +dsep(1, 4, [10 5], G)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/enumerate_scenarios.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function [scenarios, log_probs] = enumerate_scenarios(bnet, evidence) +% ENUMERATE_SCENARIOS Enumerate all assignments, and return the prob. of the non-zeros ones +% function [scenarios, log_probs] = enumerate_scenarios(bnet, evidence) + +assert(isempty(bnet.cnodes)); +n = length(bnet.dag); +observed = ~isemptycell(evidence); +vals = cat(1,evidence{observed}); +vals = vals(:)'; +ns = bnet.node_sizes; + +log_probs = []; +scenarios = []; +for i=1:prod(ns) + inst = ind2subv(ns, i); % i'th instantiation + if isempty(vals) | inst(observed) == vals % agrees with evidence + ll = log_lik_complete(bnet, num2cell(inst(:))); + log_probs = [log_probs ll]; + scenarios = [scenarios(:)' inst]; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/fgraph_to_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function bnet = fgraph_to_bnet(fg) +% FGRAPH_TO_BNET Convert a factor graph to a Bayes net +% bnet = fgraph_to_bnet(fg) +% +% We assume all factors are tabular_CPD. +% We create 1 dummy observed node for every factor. + +N = fg.nvars + fg.nfactors; +vnodes = 1:fg.nvars; +fnodes = fg.nvars+1:N; +dag = zeros(N); +for x=1:fg.nvars + dag(x, fnodes(fg.dep{x})) = 1; +end +ns = [fg.node_sizes ones(1, fg.nfactors)]; +discrete = [fg.dnodes fnodes]; +bnet = mk_bnet(dag, ns, 'discrete', discrete); +for x=1:fg.nvars + bnet.CPD{x} = tabular_CPD(bnet, x, 'CPT', 'unif'); +end +ev = cell(1, fg.nvars); % no evidence +for i=1:fg.nfactors + f = fnodes(i); + e = fg.equiv_class(i); + pot = convert_to_pot(fg.factors{e}, 'd', fg.dom{i}, ev); + m = pot_to_marginal(pot); + bnet.CPD{f} = tabular_CPD(bnet, f, 'CPT', m.T); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/hodbn_to_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function bnet = hodbn_to_bnet(dbn, T) +% DBN_TO_BNET Convert a DBN to a static network by unroll for T slices +% bnet = dbn_to_bnet(dbn, T) +ss = length(dbn.intra); +[row,order] = size(dbn.equiv_class); +eclass = []; +for i = 1:min(order,T) + eclass = [eclass ; dbn.equiv_class(:,i)]; +end +if T > order + eclass = [eclass ; repmat(dbn.equiv_class(:,order),T-order,1)]; +end + +dnodes = unroll_set(dbn.dnodes_slice, ss, T); +ns = repmat(dbn.node_sizes_slice(:), 1, T); +dag = unroll_higher_order_topology(dbn.intra, dbn.inter, T, dbn.intra1); +onodes = unroll_set(dbn.observed(:), ss, T); +bnet = mk_bnet(dag, ns(:), 'discrete', dnodes(:), 'equiv_class', eclass(:), 'observed', onodes(:)); +bnet.CPD = dbn.CPD; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/is_mnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function m = is_mnet(model) + +m = isfield(model, 'markov_net');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/linear_gaussian_to_cpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence) +% LINEAR_GAUSSIAN_TO_CPOT Convert a linear Gaussian CPD to a canonical potential. +% pot = linear_gaussian_to_cpot(mu, Sigma, W, domain, ns, cnodes, evidence) +% +% We include any cts evidence, but ignore any discrete evidence. +% (Use gaussian_CPD_params_given_dps to use discrete evidence to select mu, Sigma, W.) + +odom = domain(~isemptycell(evidence(domain))); +hdom = domain(isemptycell(evidence(domain))); +cobs = myintersect(cnodes, odom); +chid = myintersect(cnodes, hdom); +cvals = cat(1, evidence{cobs}); + +%[g,h,K] = gaussian_to_canonical(mu, Sigma, W); +Sinv = inv(Sigma); +g = -0.5*mu'*Sinv*mu + log(normal_coef(Sigma)); +if isempty(W) | (size(W,2)==0) % no cts parents + h = Sinv*mu; + K = Sinv; +else + h = [-W'*Sinv*mu; Sinv*mu]; + K = [W'*Sinv*W -W'*Sinv'; + -Sinv*W Sinv]; +end + +if ~isempty(cvals) + %[g, h, K] = enter_evidence_canonical(g, h, K, chid, cobs, cvals(:), ns); + [hx, hy, KXX, KXY, KYX, KYY] = partition_matrix_vec(h, K, chid, cobs, ns); + y = cvals(:); + g = g + hy'*y - 0.5*y'*KYY*y; + if length(hx)==0 % isempty(X) % i.e., we have instantiated everything away + h = []; + K = []; + else + h = hx - KXY*y; + K = KXX; + end +end + +ns(odom) = 0; +pot = cpot(domain, ns(domain), g, h, K); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/log_lik_complete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function L = log_lik_complete(bnet, cases, clamped) +% LOG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m), theta_i) for a completely observed data set +% L = log_lik_complete(bnet, cases, clamped) +% +% If there is a missing data, you must use an inference engine. +% cases(i,m) is the value assigned to node i in case m. +% (If there are vector-valued nodes, cases should be a cell array.) +% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros) +% Clamped nodes contribute a factor of 1.0 to the likelihood. + +if iscell(cases), usecell = 1; else usecell = 0; end + +n = length(bnet.dag); +ncases = size(cases, 2); +if n ~= size(cases, 1) + error('data should be of size nnodes * ncases'); +end + +if nargin < 3, clamped = zeros(n,ncases); end + +L = 0; +for i=1:n + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + u = find(clamped(i,:)==0); + ll = log_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u)); + if approxeq(exp(ll), 0), fprintf('node %d has very low likelihood\n'); end + L = L + ll; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/log_marg_lik_complete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function L = log_marg_lik_complete(bnet, cases, clamped) +% LOG_MARG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m)) for a completely observed data set +% L = log_marg_lik_complete(bnet, cases, clamped) +% +% This differs from log_lik_complete because we integrate out the parameters. +% If there is a missing data, you must use an inference engine. +% cases(i,m) is the value assigned to node i in case m. +% (If there are vector-valued nodes, cases should be a cell array.) +% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros) +% Clamped nodes contribute a factor of 1.0 to the likelihood. +% +% If there is a single case, clamped is a list of the clamped nodes, not a bit vector. + +if iscell(cases), usecell = 1; else usecell = 0; end + +n = length(bnet.dag); +ncases = size(cases, 2); +if n ~= size(cases, 1) + error('data should be of size nnodes * ncases'); +end + +if ncases == 1 + if nargin < 3, clamped = []; end + clamp_set = clamped; + clamped = zeros(n,1); + clamped(clamp_set) = 1; +else + if nargin < 3, clamped = zeros(n,ncases); end +end + +L = 0; +for i=1:n + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + u = find(clamped(i,:)==0); + L = L + log_marg_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u)); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +function bnet = mk_bnet(dag, node_sizes, varargin) +% MK_BNET Make a Bayesian network. +% +% BNET = MK_BNET(DAG, NODE_SIZES, ...) makes a graphical model with an arc from i to j iff DAG(i,j) = 1. +% Thus DAG is the adjacency matrix for a directed acyclic graph. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% +% node_sizes(i) is the number of values node i can take on, +% or the length of node i if i is a continuous-valued vector. +% node_sizes(i) = 1 if i is a utility node. +% +% Below are the names of optional arguments [and their default value in brackets]. +% Pass as 'PropertyName1', PropertyValue1, 'PropertyName2', PropertyValue2, ... +% +% discrete - the list of nodes which are discrete random variables [1:N] +% equiv_class - equiv_class(i)=j means node i gets its params from CPD{j} [1:N] +% observed - the list of nodes which will definitely be observed in every case [ [] ] +% 'names' - a cell array of strings to be associated with nodes 1:n [{}] +% This creates an associative array, so you write e.g. +% 'evidence(bnet.names{'bar'}) = 42' instead of 'evidence(2} = 42' +% assuming names = { 'foo', 'bar', ...}. +% +% e.g., bnet = mk_bnet(dag, ns, 'discrete', [1 3]) +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% bnet = mk_bnet(dag, node_sizes, discrete_nodes, equiv_class) + +n = length(dag); + +% default values for parameters +bnet.equiv_class = 1:n; +bnet.dnodes = 1:n; % discrete +bnet.observed = []; +bnet.names = {}; + +if nargin >= 3 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + if nargs >= 1, bnet.dnodes = args{1}; end + if nargs >= 2, bnet.equiv_class = args{2}; end + else + for i=1:2:nargs + switch args{i}, + case 'equiv_class', bnet.equiv_class = args{i+1}; + case 'discrete', bnet.dnodes = args{i+1}; + case 'observed', bnet.observed = args{i+1}; + case 'names', bnet.names = assocarray(args{i+1}, num2cell(1:n)); + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +bnet.observed = sort(bnet.observed); % for comparing sets +bnet.hidden = mysetdiff(1:n, bnet.observed(:)'); +bnet.hidden_bitv = zeros(1,n); +bnet.hidden_bitv(bnet.hidden) = 1; +bnet.dag = dag; +bnet.node_sizes = node_sizes(:)'; + +bnet.cnodes = mysetdiff(1:n, bnet.dnodes); +% too many functions refer to cnodes to rename it to cts_nodes - +% We hope it won't be confused with chance nodes! + +bnet.parents = cell(1,n); +for i=1:n + bnet.parents{i} = parents(dag, i); +end + +E = max(bnet.equiv_class); +mem = cell(1,E); +for i=1:n + e = bnet.equiv_class(i); + mem{e} = [mem{e} i]; +end +bnet.members_of_equiv_class = mem; + +bnet.CPD = cell(1, E); + +bnet.rep_of_eclass = zeros(1,E); +for e=1:E + mems = bnet.members_of_equiv_class{e}; + bnet.rep_of_eclass(e) = mems(1); +end + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + +bnet.order = topological_sort(bnet.dag);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,133 @@ +function bnet = mk_dbn(intra, inter, node_sizes, varargin) +% MK_DBN Make a Dynamic Bayesian Network. +% +% BNET = MK_DBN(INTRA, INTER, NODE_SIZES, ...) makes a DBN with arcs +% from i in slice t to j in slice t iff intra(i,j) = 1, and +% from i in slice t to j in slice t+1 iff inter(i,j) = 1, +% for i,j in {1, 2, ..., n}, where n = num. nodes per slice, and t >= 1. +% node_sizes(i) is the number of values node i can take on. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% See also mk_bnet. +% +% Optional arguments [default in brackets] +% 'discrete' - list of discrete nodes [1:n] +% 'observed' - the list of nodes which will definitely be observed in every slice of every case [ [] ] +% 'eclass1' - equiv class for slice 1 [1:n] +% 'eclass2' - equiv class for slice 2 [tie nodes with equivalent parents to slice 1] +% equiv_class1(i) = j means node i in slice 1 gets its parameters from bnet.CPD{j}, +% i.e., nodes i and j have tied parameters. +% 'intra1' - topology of first slice, if different from others +% 'names' - a cell array of strings to be associated with nodes 1:n [{}] +% This creates an associative array, so you write e.g. +% 'evidence(bnet.names{'bar'}) = 42' instead of 'evidence(2} = 42' +% assuming names = { 'foo', 'bar', ...}. +% +% For backwards compatibility with BNT2, arguments can also be specified as follows +% bnet = mk_dbn(intra, inter, node_sizes, dnodes, eclass1, eclass2, intra1) +% +% After calling this function, you must specify the parameters (conditional probability +% distributions) using bnet.CPD{i} = gaussian_CPD(...) or tabular_CPD(...) etc. + + +n = length(intra); +ss = n; +bnet.nnodes_per_slice = ss; +bnet.intra = intra; +bnet.inter = inter; +bnet.intra1 = intra; +dag = zeros(2*n); +dag(1:n,1:n) = bnet.intra1; +dag(1:n,(1:n)+n) = bnet.inter; +dag((1:n)+n,(1:n)+n) = bnet.intra; +bnet.dag = dag; +bnet.names = {}; + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + + +bnet.eclass1 = 1:n; +%bnet.eclass2 = (1:n)+n; +bnet.eclass2 = bnet.eclass1; +for i=1:ss + if isequal(parents(dag, i+ss), parents(dag, i)+ss) + %fprintf('%d has isomorphic parents, eclass %d\n', i, bnet.eclass2(i)) + else + bnet.eclass2(i) = max(bnet.eclass2) + 1; + %fprintf('%d has non isomorphic parents, eclass %d\n', i, bnet.eclass2(i)) + end +end + +dnodes = 1:n; +bnet.observed = []; + +if nargin >= 4 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + if nargs >= 1, dnodes = args{1}; end + if nargs >= 2, bnet.eclass1 = args{2}; end + if nargs >= 3, bnet.eclass2 = args{3}; end + if nargs >= 4, bnet.intra1 = args{4}; end + else + for i=1:2:nargs + switch args{i}, + case 'discrete', dnodes = args{i+1}; + case 'observed', bnet.observed = args{i+1}; + case 'eclass1', bnet.eclass1 = args{i+1}; + case 'eclass2', bnet.eclass2 = args{i+1}; + case 'intra1', bnet.intra1 = args{i+1}; + %case 'ar_hmm', bnet.ar_hmm = args{i+1}; % should check topology + case 'names', bnet.names = assocarray(args{i+1}, num2cell(1:n)); + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + + +bnet.observed = sort(bnet.observed); % for comparing sets +ns = node_sizes; +bnet.node_sizes_slice = ns(:)'; +bnet.node_sizes = [ns(:) ns(:)]; + +cnodes = mysetdiff(1:n, dnodes); +bnet.dnodes_slice = dnodes; +bnet.cnodes_slice = cnodes; +bnet.dnodes = [dnodes dnodes+n]; +bnet.cnodes = [cnodes cnodes+n]; + +bnet.equiv_class = [bnet.eclass1(:) bnet.eclass2(:)]; +bnet.CPD = cell(1,max(bnet.equiv_class(:))); +eclass = bnet.equiv_class(:); +E = max(eclass); +bnet.rep_of_eclass = zeros(1,E); +for e=1:E + mems = find(eclass==e); + bnet.rep_of_eclass(e) = mems(1); +end + +ss = n; +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +bnet.hidden_bitv = zeros(1,2*ss); +bnet.hidden_bitv(hnodes) = 1; +bnet.hidden_bitv(hnodes+ss) = 1; + +bnet.parents = cell(1, 2*ss); +for i=1:ss + bnet.parents{i} = parents(bnet.dag, i); + bnet.parents{i+ss} = parents(bnet.dag, i+ss); +end + +bnet.auto_regressive = zeros(1,ss); +% ar(i)=1 means (observed) node i depends on i in the previous slice +for o=bnet.observed(:)' + if any(bnet.parents{o+ss} <= ss) + bnet.auto_regressive(o) = 1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_fgraph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function fg = mk_fgraph(G, node_sizes, factors, varargin) +% MK_FGRAPH Make a factor graph +% fg = mk_fgraph(G, node_sizes, factors, ...) +% +% A factor graph is a bipartite graph, with one side containing variables, +% and the other containing functions of (subsets of) these variables. +% For details, see "Factor Graphs and the Sum-Product Algorithm", +% F. Kschischang and B. Frey and H-A. Loeliger, +% IEEE Trans. Info. Theory, 2001 +% +% G(i,j) = 1 if there is an arc from variable i to factor j +% +% node_sizes(i) is the number of values node i can take on, +% or the length of node i if i is a continuous-valued vector. +% +% 'factors' is the list of factors (kernel functions) +% +% The list below gives optional arguments [default value in brackets]. +% +% equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F] +% discrete - the list of nodes which are discrete random variables [1:N] +% +% e.g., fg = mk_fgraph(G, [2 2], {bnet.CPD{1},bnet.CPD{2}}, 'discrete', [1 2]) + +fg.G = G; +fg.node_sizes = node_sizes; +fg.factors = factors; +[fg.nvars fg.nfactors] = size(G); + +% default values for parameters +fg.equiv_class = 1:fg.nfactors; +fg.dnodes = 1:fg.nvars; + +if nargin >= 4 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'equiv_class', fg.equiv_class = args{i+1}; + case 'discrete', fg.dnodes = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +% so that determine_pot_type will work... +fg.utility_nodes = []; +%fg.decision_nodes = []; +%fg.chance_nodes = fg.nvars; + +fg.dom = cell(1, fg.nfactors); +for f=1:fg.nfactors + fg.dom{f} = find(G(:,f)); +end +fg.dep = cell(1, fg.nvars); +for x=1:fg.nvars + fg.dep{x} = find(G(x,:)); +end +fg.cnodes = mysetdiff(1:fg.nvars, fg.dnodes);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_fgraph_given_ev.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function fg = mk_fgraph_given_ev(G, node_sizes, factors, ev_CPD, evidence, varargin) +% MK_FGRAPH_GIVEN_EV Make a factor graph where each node has its own private evidence term +% fg = mk_fgraph(G, node_sizes, factors, ev_CPD, evidence, ...) +% +% G, node_sizes and factors are as in mk_fgraph, but they refer to the hidden nodes. +% ev_CPD{i} is a CPD for the i'th hidden node; this will be converted into a factor +% for node i using evidence{i}. +% We currently assume all hidden nodes are discrete, for simplicity. +% +% The list below gives optional arguments [default value in brackets]. +% +% equiv_class - equiv_class(i)=j means factor node i gets its params from factors{j} [1:F] +% ev_equiv_class - ev_equiv_class(i)=j means evidence node i gets its params from ev_CPD{j} [1:N] + + +N = length(node_sizes); +nfactors = length(factors); + +% default values for parameters +eclass = 1:nfactors; +ev_eclass = 1:N; + +if nargin >= 6 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'equiv_class', eclass = args{i+1}; + case 'ev_equiv_class', ev_eclass = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +pot_type = 'd'; +for x=1:N + ev = cell(1,2); % cell 1 is the hidden parent, cell 2 is the observed child + ev(2) = evidence(x); + dom = 1:2; + F = convert_to_pot(ev_CPD{ev_eclass(x)}, pot_type, dom(:), ev); + M = pot_to_marginal(F); + %factors{end+1} = tabular_CPD('self', 1, 'ps', [], 'sz', node_sizes(x), 'CPT', M.T); + factors{end+1} = mk_isolated_tabular_CPD(node_sizes(x), {'CPT', M.T}); +end + +E = max(eclass); +fg = mk_fgraph([G eye(N)], node_sizes, factors, 'equiv_class', [eclass E+1:E+N]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_higher_order_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +function bnet = mk_higher_order_dbn(intra, inter, node_sizes, varargin) +% MK_DBN Make a Dynamic Bayesian Network. +% +% BNET = MK_DBN(INTRA, INTER, NODE_SIZES, ...) makes a DBN with arcs +% from i in slice t to j in slice t iff intra(i,j) = 1, and +% from i in slice t to j in slice t+1 iff inter(i,j) = 1, +% for i,j in {1, 2, ..., n}, where n = num. nodes per slice, and t >= 1. +% node_sizes(i) is the number of values node i can take on. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% See also mk_bnet. +% +% Optional arguments [default in brackets] +% 'discrete' - list of discrete nodes [1:n] +% 'observed' - the list of nodes which will definitely be observed in every slice of every case [ [] ] +% 'eclass1' - equiv class for slice 1 [1:n] +% 'eclass2' - equiv class for slice 2 [tie nodes with equivalent parents to slice 1] +% equiv_class1(i) = j means node i in slice 1 gets its parameters from bnet.CPD{j}, +% i.e., nodes i and j have tied parameters. +% 'intra1' - topology of first slice, if different from others +% 'names' - a cell array of strings to be associated with nodes 1:n [{}] +% This creates an associative array, so you write e.g. +% 'evidence(bnet.names{'bar'}) = 42' instead of 'evidence(2} = 42' +% assuming names = { 'foo', 'bar', ...}. +% +% For backwards compatibility with BNT2, arguments can also be specified as follows +% bnet = mk_dbn(intra, inter, node_sizes, dnodes, eclass1, eclass2, intra1) +% +% After calling this function, you must specify the parameters (conditional probability +% distributions) using bnet.CPD{i} = gaussian_CPD(...) or tabular_CPD(...) etc. + + +n = length(intra); +ss = n; +bnet.nnodes_per_slice = ss; +bnet.intra = intra; +bnet.inter = inter; +bnet.intra1 = intra; + +% As this method is used to generate a higher order Markov Model +% also connect from time slice t - i -> t with i > 1 has to be +% taken into account. + +%inter should be a three dimensional array where inter(:,:,i) +%describes the connections from time-slice t - i to t. +[rows,columns,order] = size(inter); +assert(rows == n); +assert(columns == n); +dag = zeros((order + 1)*n); + +i = 0; +while i <= order + j = i; + while j <= order + if j == i + dag(1 + i*n:(i+1)*n,1+i*n:(i+1)*n) = intra; + else + dag(1+i*n:(i+1)*n,1+j*n:(j+1)*n) = inter(:,:,j - i); + end + j = j + 1; + end; + i = i + 1; +end; + +bnet.dag = dag; +bnet.names = {}; + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + +% Calculation of the equivalence classes +bnet.eclass1 = 1:n; +bnet.eclass = zeros(order + 1,ss); +bnet.eclass(1,:) = 1:n; +for i = 1:order + bnet.eclass(i+1,:) = bnet.eclass(i,:); + for j = 1:ss + if(isequal(parents(dag,(i-1)*n+j)+ss,parents(dag,(i*n + j)))) + %fprintf('%d has isomorphic parents, eclass %d \n',j,bnet.eclass(i,j)) + else + bnet.eclass(i + 1,j) = max(bnet.eclass(i+1,:))+1; + %fprintf('%d has non isomorphic parents, eclass %d \n',j,bnet.eclass(i,j)) + end; + end; +end; +bnet.eclass1 = 1:n; + +% To be compatible with whe rest of the code +bnet.eclass2 = bnet.eclass(2,:); + +dnodes = 1:n; +bnet.observed = []; + +if nargin >= 4 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + if nargs >= 1 dnodes = args{1}; end + if nargs >= 2 bnet.eclass1 = args{2}; bnet.eclass(1,:) = args{2}; end + if nargs >= 3 bnet.eclass2 = args{3}; bnet.eclass(2,:) = args{2}; end + if nargs >= 4 bnet.intra1 = args{4}; end + else + for i=1:2:nargs + switch args{i}, + case 'discrete', dnodes = args{i+1}; + case 'observed', bnet.observed = args{i+1}; + case 'eclass1', bnet.eclass1 = args{i+1}; bnet.eclass(1,:) = args{i+1}; + case 'eclass2', bnet.eclass2 = args{i+1}; bnet.eclass(2,:) = args{i+1}; + case 'eclass', bnet.eclass = args{i+1}; + case 'intra1', bnet.intra1 = args{i+1}; + %case 'ar_hmm', bnet.ar_hmm = args{i+1}; % should check topology + case 'names', bnet.names = assocarray(args{i+1}, num2cell(1:n)); + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +bnet.observed = sort(bnet.observed); % for comparing sets +ns = node_sizes; +bnet.node_sizes_slice = ns(:)'; +bnet.node_sizes = repmat(ns(:),1,order + 1); + +cnodes = mysetdiff(1:n, dnodes); +bnet.dnodes_slice = dnodes; +bnet.cnodes_slice = cnodes; +bnet.dnodes = dnodes; +bnet.cnodes = cnodes; +% To adapt the function to higher order Markov models include dnodes for more +% time slices +for i = 1:order + bnet.dnodes = [bnet.dnodes dnodes+i*n]; + bnet.cnodes = [bnet.cnodes cnodes+i*n]; +end + +% Generieren einer Matrix, deren i-te Spalte die Aequivalenzklassen +% der i-ten Zeitscheibe enthaelt. +bnet.equiv_class = [bnet.eclass(1,:)]'; +for i = 2:(order + 1) + bnet.equiv_class = [bnet.equiv_class bnet.eclass(i,:)']; +end + +bnet.CPD = cell(1,max(bnet.equiv_class(:))); + +ss = n; +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +bnet.hidden_bitv = zeros(1,(order + 1)*ss); +for i = 0:order + bnet.hidden_bitv(hnodes +i*ss) = 1; +end; + +bnet.parents = cell(1, (order + 1)*ss); +for i=1:(order + 1)*ss + bnet.parents{i} = parents(bnet.dag, i); +end + +bnet.auto_regressive = zeros(1,ss); +% ar(i)=1 means (observed) node i depends on i in the previous slice +for o=bnet.observed(:)' + if any(bnet.parents{o+ss} <= ss) + bnet.auto_regressive(o) = 1; + end +end + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_limid.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +function bnet = mk_limid(dag, node_sizes, varargin) +% MK_LIMID Make a limited information influence diagram +% +% BNET = MK_LIMID(DAG, NODE_SIZES, ...) +% DAG is the adjacency matrix for a directed acyclic graph. +% The nodes are assumed to be in topological order. Use TOPOLOGICAL_SORT if necessary. +% For decision nodes, the parents must explicitely include all nodes +% on which it can depends, in contrast to the implicit no-forgetting assumption of influence diagrams. +% (For details, see "Representing and solving decision problems with limited information", +% Lauritzen and Nilsson, Management Science, 2001.) +% +% node_sizes(i) is the number of values node i can take on, +% or the length of node i if i is a continuous-valued vector. +% node_sizes(i) = 1 if i is a utility node. +% +% The list below gives optional arguments [default value in brackets]. +% +% chance - the list of nodes which are random variables [1:N] +% decision - the list of nodes which are decision nodes [ [] ] +% utility - the list of nodes which are utility nodes [ [] ] +% equiv_class - equiv_class(i)=j means node i gets its params from CPD{j} [1:N] +% +% e.g., limid = mk_limid(dag, ns, 'chance', [1 3], 'utility', [2]) + +n = length(dag); + +% default values for parameters +bnet.chance_nodes = 1:n; +bnet.equiv_class = 1:n; +bnet.utility_nodes = []; +bnet.decision_nodes = []; +bnet.dnodes = 1:n; % discrete + +if nargin >= 3 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + if nargs >= 1, bnet.dnodes = args{1}; end + if nargs >= 2, bnet.equiv_class = args{2}; end + else + for i=1:2:nargs + switch args{i}, + case 'equiv_class', bnet.equiv_class = args{i+1}; + case 'chance', bnet.chance_nodes = args{i+1}; + case 'utility', bnet.utility_nodes = args{i+1}; + case 'decision', bnet.decision_nodes = args{i+1}; + case 'discrete', bnet.dnodes = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +bnet.limid = 1; + +bnet.dag = dag; +bnet.node_sizes = node_sizes(:)'; + +bnet.cnodes = mysetdiff(1:n, bnet.dnodes); +% too many functions refer to cnodes to rename it to cts_nodes - +% We hope it won't be confused with chance nodes! + +bnet.parents = cell(1,n); +for i=1:n + bnet.parents{i} = parents(dag, i); +end + +E = max(bnet.equiv_class); +mem = cell(1,E); +for i=1:n + e = bnet.equiv_class(i); + mem{e} = [mem{e} i]; +end +bnet.members_of_equiv_class = mem; + +bnet.CPD = cell(1, E); + +% for e=1:E +% i = bnet.members_of_equiv_class{e}(1); % pick arbitrary member +% switch type{e} +% case 'tabular', bnet.CPD{e} = tabular_CPD(bnet, i); +% case 'gaussian', bnet.CPD{e} = gaussian_CPD(bnet, i); +% otherwise, error(['unrecognized CPD type ' type{e}]); +% end +% end + +directed = 1; +if ~acyclic(dag,directed) + error('graph must be acyclic') +end + +bnet.order = topological_sort(bnet.dag);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_mnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function mnet = mk_mnet(graph, node_sizes, cliques, potentials) +% MK_MNET Make a Markov network (Markov Random Field) +% +% mnet = mk_mnet(adj_mat, node_sizes, cliques, potentials) +% +% cliques{i} is a list of the nodes in clq i +% potentials{i} is a dpot object corresponding to the potential for clique i +% + +mnet.markov_net = 1; +mnet.graph = graph; +mnet.node_sizes = node_sizes; +mnet.user_cliques = cliques; +mnet.user_potentials = potentials;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_mrf2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function mrf2 = mk_mrf2(adj_mat, pot) +% MK_MRF2 Make a Markov random field with pairwise potentials +% function mrf2 = mk_mrf2(adj_mat, pot) +% +% pot{i,j}(k1,k2) + +mrf2.adj_mat = adj_mat; +mrf2.pot = pot;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_mutilated_samples.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function [data, clamped] = mk_mutilated_samples(bnet, ncases, max_clamp, usecell) +% GEN_MUTILATED_SAMPLES Do random interventions and then draw random samples +% [data, clamped] = gen_mutilated_samples(bnet, ncases, max_clamp, usecell) +% +% At each step, we pick a random subset of size 0 .. max_clamp, and +% clamp these nodes to random values. +% +% data(i,m) is the value of node i in case m. +% clamped(i,m) = 1 if node i in case m was set by intervention. + +if nargin < 4, usecell = 1; end + +ns = bnet.node_sizes; +n = length(bnet.dag); +if usecell + data = cell(n, ncases); +else + data = zeros(n, ncases); +end +clamped = zeros(n, ncases); + +csubsets = subsets(1:n, max_clamp, 0); % includes the empty set +distrib_cset = normalise(ones(1, length(csubsets))); + +for m=1:ncases + cset = csubsets{sample_discrete(distrib_cset)}; + nvals = prod(ns(cset)); + distrib_cvals = normalise(ones(1, nvals)); + cvals = ind2subv(ns(cset), sample_discrete(distrib_cvals)); + mutilated_bnet = do_intervention(bnet, cset, cvals); + ev = sample_bnet(mutilated_bnet); + if usecell + data(:,m) = ev; + else + data(:,m) = cell2num(ev); + end + clamped(cset,m) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_named_CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function CPT2 = mk_named_CPT(family_names, names, dag, CPT1) +% MK_NAMED_CPT Permute the dimensions of a CPT so they agree with the internal numbering convention +% CPT2 = mk_named_CPT(family_names, names, dag, CPT1) +% +% This is best explained by example. +% Consider the following directed acyclic graph +% +% C +% / \ +% R S +% \ / +% W +% +% where all arcs point down. +% When we create the CPT for node W, we consider S as its first parent, and R as its +% second, and hence write +% +% S R W +% CPT1(1,1,:) = [1.0 0.0]; +% CPT1(2,1,:) = [0.2 0.8]; % P(W=1 | R=1, S=2) = 0.2 +% CPT1(1,2,:) = [0.1 0.9]; +% CPT1(2,2,:) = [0.01 0.99]; +% +% However, when we create the dag using mk_adj_mat, the nodes get topologically sorted, +% and by chance, node R preceeds node S in this ordering. +% Hence we should have written +% +% R S W +% CPT2(1,1,:) = [1.0 0.0]; +% CPT2(2,1,:) = [0.1 0.9]; +% CPT2(1,2,:) = [0.2 0.8]; % P(W=1 | R=1, S=2) = 0.2 +% CPT2(2,2,:) = [0.01 0.99]; +% +% Since we do not know the order of the nodes in advance, we can write +% CPT2 = mk_named_CPT({'S', 'R', 'W'}, names, dag, CPT1) +% where 'S', 'R', 'W' are the order of the dimensions we assumed (the child node must be last in this list), +% and names{i} is the name of the i'th node. + +n = length(family_names); +family_nums = zeros(1,n); +for i=1:n + family_nums(i) = stringmatch(family_names{i}, names); % was strmatch +end + +fam = family(dag, family_nums(end)); +perm = zeros(1,n); +for i=1:n + % perm(i) = find(family_nums(i) == fam); + perm(i) = find(fam(i) == family_nums); +end + +CPT2 = permute(CPT1, perm);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/mk_slice_and_half_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function bnet15 = mk_slice_and_half_dbn(bnet, int) +% function bnet = mk_slice_and_half_dbn(bnet, int) +% function bnet = mk_slice_and_half_dbn(bnet, int) +% +% Create a "1.5 slice" jtree, containing the interface nodes of slice 1 +% and all the nodes of slice 2 +% To keep the node numbering the same, we simply disconnect the non-interface nodes +% from slice 1, and set their size to 1. +% We do this to speed things up, and so that the likelihood is computed correctly. +% We do not need to do +% this if we just want to compute marginals (i.e., we can include nodes whose potentials will +% be left as all 1s). + +intra15 = bnet.intra; +ss = length(bnet.intra); +nonint = mysetdiff(1:ss, int); +for i=nonint(:)' + intra15(:,i) = 0; + intra15(i,:) = 0; + %assert(~any(bnet.inter(i,:))) +end +dag15 = [intra15 bnet.inter; + zeros(ss) bnet.intra]; +ns = bnet.node_sizes(:); +ns(nonint) = 1; % disconnected nodes get size 1 +obs_nodes = [bnet.observed(:) bnet.observed(:)+ss]; +bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ... + 'observed', obs_nodes(:));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/noisyORtoTable.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function CPT = noisyORtoTable(inhibit, leak_inhibit) +% NOISYORTOTABLE Convert noisyOR distribution to CPT +% function CPT = noisyORtoTable(inhibit, leak_inhibit) +% +% inhibit(i) = prob i'th parent will be inhibited (flipped from 1 to 0) +% leak_inhibit - optional suppression of leak +% CPT(U1,...,Un, X) = Pr(X|U1,...,Un) where the Us are the parents (excluding leak). +% State 1 = off, 2 = on + +if nargin < 2, leak_inhibit = 1; end + +q = [leak_inhibit inhibit(:)']; + +if length(q)==1 + CPT = [q 1-q]; + return; +end + +n = length(q); +Bn = ind2subv(2*ones(1,n), 1:(2^n))-1; % all n bit vectors, with the left most column toggling fastest (LSB) +CPT = zeros(2^n, 2); +% Pr(X=0 | U_1 .. U_n) = prod_{i: U_i = on} q_i = prod_i q_i ^ U_i = exp(u' * log(q_i)) +% This method is problematic when q contains zeros + +Q = repmat(q(:)', 2^n, 1); +Q(logical(~Bn)) = 1; +CPT(:,1) = prod(Q,2); +CPT(:,2) = 1-CPT(:,1); + +CPT = reshape(CPT(2:2:end), 2*ones(1,n)); % skip cases in which the leak is off + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/partition_dbn_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function [pnodes, tnodes] = partition_dbn_nodes(intra, inter) +% PARTITION_DBN_NODES Divide the nodes into a DBN into persistent and transient. +% [pnodes, tnodes] = partition_dbn_nodes(intra, inter) +% Persistent nodes have children in the next time slice, transient nodes do not. + +ss = length(intra); +pnodes = []; +tnodes = []; +for i=1:ss + cs = children(inter, i); + if isempty(cs) + tnodes = [tnodes i]; + else + pnodes = [pnodes i]; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/partition_matrix_vec_3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [A1, A2, B1, B2, C11, C12, C21, C22] = partition_matrix_vec_3(A, B, C, n1, n2, bs) + +dom = myunion(n1, n2); +n1i = block(find_equiv_posns(n1, dom), bs(dom)); +n2i = block(find_equiv_posns(n2, dom), bs(dom)); + + + A1 = A(n1i); + A2 = A(n2i); + if isempty(B) + B1 = zeros(size(n1i, 2),size(B, 2)); + B2 = zeros(size(n2i, 2),size(B, 2)); + else + B1 = B(n1i, :); + B2 = B(n2i, :); + end + + + C11 = C(n1i, n1i); + C12 = C(n1i, n2i); + C21 = C(n2i, n1i); + C22 = C(n2i, n2i);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/sample_bnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function sample = sample_bnet(bnet, varargin) +% SAMPLE_BNET Generate a random sample from a Bayes net. +% SAMPLE = SAMPLE_BNET(BNET, ...) +% +% sample{i} contains the value of the i'th node. +% i.e., the result is an Nx1 cell array. +% Nodes are sampled in the order given by bnet.order. +% +% Optional arguments: +% +% evidence - initial evidence; if evidence{i} is non-empty, node i won't be sampled. + +% set defauly params +n = length(bnet.dag); +sample = cell(n,1); + +% get optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'evidence', sample = args{i+1}(:); + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +for j=bnet.order(:)' + if isempty(sample{j}) + %ps = parents(bnet.dag, j); + ps = bnet.parents{j}; + e = bnet.equiv_class(j); + sample{j} = sample_node(bnet.CPD{e}, sample(ps)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/sample_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +function seq = sample_dbn(bnet, varargin) +% SAMPLE_DBN Generate a random sequence from a DBN. +% seq = sample_dbn(bnet, ...) +% +% seq{i,t} contains the values of the i'th node in the t'th slice. +% +% Optional arguments: +% +% length - length of sequence to be generated (can also just use sample_dbn(bnet,T)) +% stop_test - name of a function which is used to decide when to stop; +% This will be called as feval(stop_test, seq(:,t)) +% i.e., stop_test is passed a cell array containing all the nodes in the current slice. +% evidence - initial evidence; if evidence{i,t} is non-empty, this node won't be sampled. + +args = varargin; +nargs = length(args); + +if (nargs == 1) & ~isstr(args{1}) + % Old syntax: sample_dbn(bnet, T) + T = args{1}; +else + % get length + T = 1; + for i=1:2:nargs + switch args{i}, + case 'length', T = args{i+1}; + case 'evidence', T = size(args{i+1}, 2); + end + end +end + +ss = length(bnet.intra); +% set default arguments +seq = cell(ss, T); +stop_test = []; +for i=1:2:nargs + switch args{i}, + case 'evidence', seq = args{i+1}; % initialise observed nodes + case 'stop_test', stop_test = args{i+1}; + end +end + +t = 1; +for i=1:ss + if ~isempty(stop_test) | isempty(seq{i,t}) + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i,1); + pvals = seq(ps); + seq{i,t} = sample_node(bnet.CPD{e}, pvals); + %fprintf('sample i=%d,t=%d,val=%d,ps\n', i, t, seq(i,t)); pvals(:)' + end +end +t = 2; +done = 0; +while ~done + for i=1:ss + if ~isempty(stop_test) | isempty(seq{i,t}) + ps = parents(bnet.dag, i+ss) + (t-2)*ss; + e = bnet.equiv_class(i,2); + pvals = seq(ps); + seq{i,t} = sample_node(bnet.CPD{e}, pvals); + %fprintf('sample i=%d,t=%d,val=%d,ps\n', i, t, seq(i,t)); pvals(:)' + end + end + if ~isempty(stop_test) + done = feval(stop_test, seq(:,t)); + else + if t==T + done = 1; + end + end + t = t + 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/score_bnet_complete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function L = log_lik_complete(bnet, cases, clamped) +% LOG_LIK_COMPLETE Compute sum_m sum_i log P(x(i,m)| x(pi_i,m), theta_i) for a completely observed data set +% L = log_lik_complete(bnet, cases, clamped) +% +% If there is a missing data, you must use an inference engine. +% cases(i,m) is the value assigned to node i in case m. +% (If there are vector-valued nodes, cases should be a cell array.) +% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros) +% Clamped nodes contribute a factor of 1.0 to the likelihood. + +if iscell(cases), usecell = 1; else usecell = 0; end + +n = length(bnet.dag); +ncases = size(cases, 2); +if n ~= size(cases, 1) + error('data should be of size nnodes * ncases'); +end + +if nargin < 3, clamped = zeros(n,ncases); end + +L = 0; +for i=1:n + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + u = find(clamped(i,:)==0); + L = L + log_prob_node(bnet.CPD{e}, cases(i,u), cases(ps,u)); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/shrink_obs_dims_in_gaussian.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function marg2 = shrink_obs_dims_in_gaussian(marg1, dom, evidence, ns) +% SHRINK_OBS_DIMS_IN_GAUSSIAN Remove observed dimensions from mu/Sigma +% function marg2 = shrink_obs_dims_in_gaussian(marg1, dom, evidence, ns) + +% This is used by loopy + +hdom = dom(isemptycell(evidence(dom))); +ndx = find_equiv_posns(hdom, dom); +b = block(ndx, ns(dom)); +marg2.mu = marg1.mu(b); +marg2.Sigma = marg1.Sigma(b,b); +marg2.domain = marg1.domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/shrink_obs_dims_in_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function T2 = shrink_obs_dims_in_table(T1, dom, evidence) +% SHRINK_OBS_DIMS_IN_TABLE Set observed dimensions to size 1 +% T2 = shrink_obs_dims_in_table(T1, dom, evidence) +% +% If 'T1' contains observed nodes, it will have 0s in the positions that are +% inconsistent with the evidence. We now remove these 0s and set the corresponding dimensions to +% size 1, to be consistent with the way most inference engines handle evidence, which is to +% shrink observed nodes before doing inference. + +% This is used by pearl and enumerative inf. engines. + +odom = dom(~isemptycell(evidence(dom))); +vals = cat(1,evidence{odom}); +ndx = mk_multi_index(length(dom), find_equiv_posns(odom, dom), vals(:)); +T2 = T1(ndx{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/solve_limid.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function [strategy, MEU, niter] = solve_limid(engine, varargin) +% SOLVE_LIMID Find the (locally) optimal strategy for a LIMID +% [strategy, MEU, niter] = solve_limid(inf_engine, ...) +% +% strategy{d} = stochastic policy for node d (a decision node) +% MEU = maximum expected utility +% niter = num iterations used +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default in brackets] +% +% max_iter - max. num. iterations [ 1 ] +% tol - tolerance required of consecutive MEU values, used to assess convergence [1e-3] +% order - order in which decision nodes are optimized [ reverse numerical order ] +% +% e.g., solve_limid(engine, 'tol', 1e-2, 'max_iter', 10) + +bnet = bnet_from_engine(engine); + +% default values +max_iter = 1; +tol = 1e-3; +D = bnet.decision_nodes; +order = D(end:-1:1); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'max_iter', max_iter = args{i+1}; + case 'tol', tol = args{i+1}; + case 'order', order = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +CPDs = bnet.CPD; +ns = bnet.node_sizes; +N = length(ns); +evidence = cell(1,N); +strategy = cell(1, N); + +iter = 1; +converged = 0; +oldMEU = 0; +while ~converged & (iter <= max_iter) + for d=order(:)' + engine = enter_evidence(engine, evidence, 'exclude', d); + [m, pot] = marginal_family(engine, d); + %pot = marginal_family_pot(engine, d); + [policy, score] = upot_to_opt_policy(pot); + e = bnet.equiv_class(d); + CPDs{e} = set_fields(CPDs{e}, 'policy', policy); + engine = update_engine(engine, CPDs); + strategy{d} = policy; + end + engine = enter_evidence(engine, evidence); + [m, pot] = marginal_nodes(engine, []); + %pot = marginal_family_pot(engine, []); + [dummy, MEU] = upot_to_opt_policy(pot); + if approxeq(MEU, oldMEU, tol) + converged = 1; + end + oldMEU = MEU; + iter = iter + 1; +end +niter = iter - 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/unroll_dbn_topology.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function M = unroll_dbn_topology(intra, inter, T, intra1) +% UNROLL_DBN_TOPOLOGY Make the block diagonal adjacency matrix for a DBN consisting of T slices +% M = unroll_dbn_topology(intra, inter, T, intra1) +% +% intra is the connectivity within a slice, inter between two slices. +% M will have intra along the diagonal, and inter one above the diagonal. +% intra1 is an optional argumnet, in case the intra is different for the first slice. + +if nargin < 4, intra1 = intra; end + +ss = length(intra); % slice size +M = sparse(ss*T, ss*T); + +b = 1:ss; +M(b,b) = intra1; +M(b,b+ss) = inter; + +for t=2:T-1 + b = (1:ss) + (t-1)*ss; + M(b,b) = intra; + M(b,b+ss) = inter; +end + +t = T; +b = (1:ss) + (t-1)*ss; +M(b,b) = intra; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/unroll_higher_order_topology.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function M = unroll_higher_order_topology(intra, inter, T, intra1) +% UNROLL_DBN_TOPOLOGY Make the block diagonal adjacency matrix for a DBN consisting of T slices +% M = unroll_dbn_topology(intra, inter, T, intra1) +% +% intra is the connectivity within a slice, inter between two slices. +% M will have intra along the diagonal, and inter one above the diagonal. +% intra1 is an optional argumnet, in case the intra is different for the first slice. + +if nargin < 4 + intra1 = intra; +end; + + +ss = length(intra); % slice size +M = sparse(ss*T, ss*T); +[rows,columns,order] = size(inter); +for t1 = 1:T + b = 1 + (t1 - 1)*ss : t1*ss; + if t1 == 1 + M(b,b) = intra1; + else + M(b,b) = intra; + end + for t2 = 1:order + if t1 + t2 <= T + M(b,b+t2*ss) = inter(:,:,t2); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/general/unroll_set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function U = unroll_set(S, ss, T) +% UNROLL_SET Make T shifted copies of the set of nodes S in a slice of size ss. +% U = unroll_set(S, ss, T) + +offset = repmat(0:ss:(T-1)*ss, [length(S) 1]); +U = repmat(S(:), [1 T]) + offset; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/bnet_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/get_field.m/1.1.1.1/Wed May 29 15:59:56 2002// +/inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/@inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/marginal_family_pot.m/1.1.1.1/Wed May 29 15:59:56 2002// +/observed_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/@inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/Old/marginal_family_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function pot = marginal_family_pot(engine, i) +% MARGINAL_FAMILY_POT Compute the marginal on i's family and return as a potentila (inf_engine) +% function pot = marginal_family_pot(engine,i) + +% This function is only called by solve_limid. +% It requires that engine's marginal_family function return a potential. +% This is true for jtree_inf_engine, but not for, say, jtree_ndx_inf_engine. +% All limids must be solved using potentials, +% but this is not true for bnets. + +%[m, pot] = marginal_family(engine, i); + +bnet = bnet_from_engine(engine); +[m, pot] = marginal_nodes(engine, family(bnet.dag, i));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/Old/observed_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function onodes = observed_nodes(engine) +% OBSERVED_NODES Return nodes that are guaranteed to be observed, indep of evidence (generic inf_engine) +% onodes = observed_nodes(engine) + +onodes = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/bnet_from_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function bnet = bnet_from_engine(engine) +% BNET_FROM_ENGINE Return the bnet structure stored inside the engine (inf_engine) +% bnet = bnet_from_engine(engine) + +bnet = engine.bnet; + +% We cannot write 'engine.bnet' without writing a 'subsref' function, +% since engine is an object with private parts. +% The bnet field should be the only thing external users of the engine should need access to. +% We do not pass bnet as a separate argument, since it could get out of synch with the one +% encoded inside the engine. +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/get_field.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function val = get_field(engine, name) +% GET_FIELD Get the value of a named field from a generic engine +% val = get_field(engine, name) +% +% The following fields can be accessed +% +% bnet +% +% e.g., bnet = get_field(engine, 'bnet') + +switch name + case 'bnet', val = engine.bnet; + otherwise, + error(['invalid argument name ' name]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function engine = inf_engine(bnet) + +engine.bnet = bnet; +engine = class(engine, 'inf_engine'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function m = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on i's family (inf_engine) +% m = marginal_family(engine, i, t) +% +% t defaults to 1. + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, family(bnet.dag, i)); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + if any(fam<=ss) + % i has a parent in the preceeding slice + % Hence the lowest numbered slice containing the family is t-1 + m = marginal_nodes(engine, fam, t-1); + else + % The family all fits inside slice t + % Hence shift the indexes back to slice 1 + m = marginal_nodes(engine, fam-ss, t); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/@inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (inf_engine). +% engine = update_engine(engine, newCPDs) +% +% This generic method is suitable for engines that do not process the parameters until 'enter_evidence'. + +engine.bnet.CPD = newCPDs;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:22 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +A D/@inf_engine//// +A D/dynamic//// +A D/online//// +A D/static////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +/bk_ff_hmm_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_init_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_marginal_from_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_predict_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_update_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_update_bel1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/bk_ff_hmm_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function engine = bk_ff_hmm_inf_engine(bnet) +% BK_FF_HMM_INF_ENGINE Naive (HMM-based) implementation of fully factored form of Boyen-Koller +% engine = bk_ff_hmm_inf_engine(bnet) +% +% This is implemented on top of the forwards-backwards algo for HMMs, +% so it is *less* efficient than exact inference! However, it is good for educational purposes, +% because it illustrates the BK algorithm very clearly. + +[persistent_nodes, transient_nodes] = partition_dbn_nodes(bnet.intra, bnet.inter); +assert(isequal(sort(bnet.observed), transient_nodes)); +[engine.prior, engine.transmat] = dbn_to_hmm(bnet); + +ss = length(bnet.intra); + +engine.bel = []; +engine.bel_marginals = []; +engine.marginals = []; + + +engine = class(engine, 'bk_ff_hmm_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_init_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function engine = dbn_init_bel(engine) +% DBN_INIT_BEL Compute the initial belief state (bk_ff_hmm) +% engine = dbn_init_bel(engine) + +engine.bel = engine.prior(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_marginal_from_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function marginal = dbn_marginal_from_bel(engine, i) +% DBN_MARGINAL_FROM_BEL Compute the marginal on a node given the current belief state (bk_ff_hmm) +% marginal = dbn_marginal_from_bel(engine, i) + +marginal = pot_to_marginal(engine.bel_marginals{i});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_predict_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function engine = dbn_predict_bel(engine, lag) +% DBN_PREDICT_BEL Predict the belief state 'lag' steps into the future (bk_ff_hmm) +% engine = dbn_predict_bel(engine, lag) +% 'lag' defaults to 1 + +if nargin < 2, lag = 1; end + +for d=1:lag + %newbel = engine.transmat' * engine.bel; + newbel = normalise(engine.transmat' * engine.bel); + + hnodes = engine.hnodes; + bnet = bnet_from_engine(engine); + ns = bnet.node_sizes; + [marginals, marginalsT] = project_joint_onto_marginals(newbel, hnodes, ns); + newbel = combine_marginals_into_joint(marginalsT, hnodes, ns); + engine.bel_marginals = marginalsT; + engine.bel = newbel; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_update_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function [engine, loglik] = dbn_update_bel(engine, evidence) +% DBN_UPDATE_BEL Update the belief state (bk_ff_hmm) +% [engine, loglik] = dbn_update_bel(engine, evidence) +% +% evidence{i,1} contains the evidence on node i in slice t-1 +% evidence{i,2} contains the evidence on node i in slice t + +oldbel = engine.bel; +bnet = bnet_from_engine(engine); +obslik = mk_hmm_obs_lik_vec(bnet, evidence); +[newbel, lik] = normalise((engine.transmat' * oldbel) .* obslik); +loglik = log(lik); + +hnodes = engine.hnodes; +ns = bnet.node_sizes; +[marginals, marginalsT] = project_joint_onto_marginals(newbel, hnodes, ns); +newbel = combine_marginals_into_joint(marginalsT, hnodes, ns); +engine.bel_marginals = marginalsT; +engine.bel = newbel;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_update_bel1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function [engine, loglik] = dbn_update_bel1(engine, evidence) +% DBN_UPDATE_BEL Update the initial belief state (bk_ff_hmm) +% [engine, loglik] = dbn_update_bel(engine, evidence) +% +% evidence{i} contains the evidence on node i in slice 1 + +oldbel = engine.bel; +bnet = bnet_from_engine(engine); +obslik = mk_hmm_obs_lik_vec1(bnet, evidence); +[newbel, lik] = normalise(oldbel .* obslik); +loglik = log(lik); + +hnodes = engine.hnodes; +ns = bnet.node_sizes; +[marginals, marginalsT] = project_joint_onto_marginals(newbel, hnodes, ns); +newbel = combine_marginals_into_joint(marginalsT, hnodes, ns); +engine.bel_marginals = marginalsT; +engine.bel = newbel;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (bk_ff_hmm) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +T = size(evidence, 2); +assertBNT(~any(isemptycell(evidence(onodes,:)))); + +obslik = mk_hmm_obs_lik_mat(bnet, onodes, evidence); + +ns = bnet.node_sizes_slice; +ns(onodes) = 1; + +[gamma, loglik, marginals, marginalsT] = bk_ff_fb(engine.prior, engine.transmat, obslik, filter, hnodes, ns); + +for t=1:T + for i=hnodes(:)' + engine.marginals{i,t} = pot_to_marginal(marginalsT{i,t}); + end + for i=onodes(:)' + m.domain = i + (t-1)*ss; + m.T = 1; + engine.marginals{i,t} = m; + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function m = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (bk_ff_hmm) +% marginal = marginal_family(engine, i, t) + +error('bk_ff_hmm doesn''t support marginal_family');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk_ff_hmm) +% marginal = marginal_nodes(engine, i, t) + +assert(length(nodes)==1); +i = nodes(end); +%assert(myismember(i, engine.hnodes)); +marginal = engine.marginals{i,t}; +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +marginal.domain = i + (t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +/bk_ff_fb.m/1.1.1.1/Wed May 29 15:59:56 2002// +/combine_marginals_into_joint.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_to_hmm.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_hmm_obs_lik_mat.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_hmm_obs_lik_vec.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_hmm_obs_lik_vec1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/project_joint_onto_marginals.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/bk_ff_fb.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function [gamma, loglik, marginals, marginalsT] = bk_ff_fb(prior, transmat, obslik, filter_only, hnodes, ns) +% BK_FF_FB Fully factored Boyen-Koller version of forwards-backwards +% [gamma, loglik, marginals, marginalsT] = bk_ff_hmm(prior, transmat, obslik, filter_only, hnodes, ns) + +ss = length(ns); +S = length(prior); +T = size(obslik, 2); +marginals = cell(ss,T); +marginalsT = cell(ss,T); +scale = zeros(1,T); +alpha = zeros(S, T); + +transmat2 = transmat'; +for t=1:T + if t==1 + [alpha(:,t), scale(t)] = normalise(prior(:) .* obslik(:,t)); + else + [alpha(:,t), scale(t)] = normalise((transmat2 * alpha(:,t-1)) .* obslik(:,t)); + end + [marginals(:,t), marginalsT(:,t)] = project_joint_onto_marginals(alpha(:,t), hnodes, ns); + alpha(:,t) = combine_marginals_into_joint(marginalsT(:,t), hnodes, ns); + %fprintf('alpha t=%d\n', t); + %celldisp(marginals(1:8,t)) +end +loglik = sum(log(scale)); + +if filter_only + gamma = alpha; + return; +end + +beta = zeros(S,T); +gamma = zeros(S,T); +t = T; +beta(:,t) = ones(S,1); +gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); +[marginals(:,t), marginalsT(:,t)] = project_joint_onto_marginals(gamma(:,t), hnodes, ns); + +for t=T-1:-1:1 + b = beta(:,t+1) .* obslik(:,t+1); + beta(:,t) = normalise((transmat * b)); + [junk, tempT] = project_joint_onto_marginals(beta(:,t), hnodes, ns); + beta(:,t) = combine_marginals_into_joint(tempT, hnodes, ns); + %gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); + %[marginals(:,t), marginalsT(:,t)] = project_joint_onto_marginals(gamma(:,t), hnodes, ns); +end + +gamma2 = zeros(S,T); +for t=T-1:-1:1 + b = beta(:,t+1) .* obslik(:,t+1); + xi(:,:,t) = normalise((transmat .* (alpha(:,t) * b'))); + if t==T-1 + gamma2(:,T) = sum(xi(:,:,T-1), 1)'; + end + gamma2(:,t) = sum(xi(:,:,t), 2); + [marginals(:,t), marginalsT(:,t)] = project_joint_onto_marginals(gamma2(:,t), hnodes, ns); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/combine_marginals_into_joint.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function joint = combine_marginals_into_joint(marginalsT, hnodes, ns) + +jointT = dpot(hnodes, ns(hnodes)); +for i=hnodes(:)' + jointT = multiply_by_pot(jointT, marginalsT{i}); +end +m = pot_to_marginal(jointT); +joint = m.T(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/dbn_to_hmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function [prior, transmat] = dbn_to_hmm(bnet) +% DBN_TO_HMM Compute the discrete HMM matrices from a simple DBN +% [prior, transmat] = dbn_to_hmm(bnet) + +onodes = bnet.observed; +ss = length(bnet.intra); +evidence = cell(1,2*ss); +hnodes = mysetdiff(1:ss, onodes); +prior = multiply_CPTs(bnet, [], hnodes, evidence); +transmat = multiply_CPTs(bnet, hnodes, hnodes+ss, evidence); +%obsmat1 = multiply_CPTs(bnet, hnodes, onodes, evidence); +%obsmat = multiply_CPTs(bnet, hnodes+ss, onodes+ss, evidence); +%obsmat1 = obsmat if the observation matrices are tied across slices + + + +%%%%%%%%%%%% + +function mat = multiply_CPTs(bnet, pdom, cdom, evidence) + +% MULTIPLY_CPTS Make a matrix Pr(Y|X), where X represents all the parents, and Y all the children +% We assume the children have no intra-connections. +% +% e.g., Consider the DBN with interconnectivity i->i', j->j',k', k->i',k' +% Then transition matrix = Pr(i,j,k -> i',j',k') = Pr(i,k->i') Pr(j->j') Pr(j,k->k') + +dom = [pdom cdom]; +ns = bnet.node_sizes; +bigpot = dpot(dom, ns(dom)); +for j=cdom(:)' + e = bnet.equiv_class(j); + fam = family(bnet.dag, j); + pot = convert_to_pot(bnet.CPD{e}, 'd', fam(:), evidence); + bigpot = multiply_by_pot(bigpot, pot); +end +psize = prod(ns(pdom)); +csize = prod(ns(cdom)); +T = pot_to_marginal(bigpot); +mat = reshape(T.T, [psize csize]); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_mat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function obslik = mk_hmm_obs_lik_mat(bnet, onodes, evidence) +% MK_HMM_OBS_LIK_MAT Make the observation likelihood matrix for all slices +% obslik = mk_hmm_obs_lik_mat(bnet, onodes, evidence) +% +% obslik(i,t) = Pr(Y(t) | X(t)=i) + +[ss T] = size(evidence); + +hnodes = mysetdiff(1:ss, onodes); +ns = bnet.node_sizes_slice; +ns(onodes) = 1; +Q = prod(ns(hnodes)); +obslik = zeros(Q,T); + +dom = 1:ss; +for t=1:T + bigpot = dpot(dom, ns(dom)); + for i=onodes(:)' + if t==1 + e = bnet.equiv_class(i,1); + fam = family(bnet.dag, i); + else + e = bnet.equiv_class(i,2); + fam = family(bnet.dag, i, 2) + ss*(t-2); + end + pot = convert_to_pot(bnet.CPD{e}, 'd', fam(:), evidence); + pot = set_domain_pot(pot, family(bnet.dag, i)); + bigpot = multiply_by_pot(bigpot, pot); + end + m = pot_to_marginal(bigpot); + obslik(:,t) = m.T(:); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_vec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function obslik = mk_hmm_obs_lik_vec(bnet, evidence) +% MK_HMM_OBS_LIK_VEC Make the observation likelihood vector for one slice +% obslik = mk_obs_lik(bnet, evidence) +% +% obslik(i) = Pr(y(t) | X(t)=i) +% evidence{i,1} contains the evidence on node i in slice t-1 +% evidence{i,2} contains the evidence on node i in slice t + +ns = bnet.node_sizes; +ss = length(bnet.intra); +onodes = find(~isemptycell(evidence(:))); +hnodes = find(isemptycell(evidence(:))); +ens = ns; +ens(onodes) = 1; +Q = prod(ens(hnodes)); +obslik = zeros(1,Q); +dom = (1:ss)+ss; +bigpot = dpot(dom, ens(dom)); +onodes1 = find(~isemptycell(evidence(:,1))); +for i=onodes1(:)' + e = bnet.equiv_class(i,2); + fam = family(bnet.dag, i, 2); + pot = convert_to_pot(bnet.CPD{e}, 'd', fam, evidence); + bigpot = multiply_by_pot(bigpot, pot); +end +m = pot_to_marginal(bigpot); +obslik = m.T(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_vec1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function obslik = mk_hmm_obs_lik_vec1(bnet, evidence) +% MK_HMM_OBS_LIK_VEC1 Make the observation likelihood vector for the first slice +% obslik = mk_hmm_obs_lik_vec1(engine, evidence) +% +% obslik(i) = Pr(y(1) | X(1)=i) +% evidence{i} contains the evidence on node i in slice 1 + +ns = bnet.node_sizes; +ss = length(ns); +onodes = find(~isemptycell(evidence(:))); +hnodes = find(isemptycell(evidence(:))); +ens = ns; +ens(onodes) = 1; +Q = prod(ens(hnodes)); +obslik = zeros(1,Q); +dom = (1:ss); +bigpot = dpot(dom, ens(dom)); +for i=onodes(:)' + e = bnet.equiv_class(i,1); + fam = family(bnet.dag, i); + pot = convert_to_pot(bnet.CPD{e}, 'd', fam(:), evidence); + bigpot = multiply_by_pot(bigpot, pot); +end +m = pot_to_marginal(bigpot); +obslik = m.T(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_ff_hmm_inf_engine/private/project_joint_onto_marginals.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function [marginals, marginalsT] = project_joint_onto_marginals(joint, hnodes, ns) + +ss = length(ns); +jointT = dpot(hnodes, ns(hnodes), joint); +marginalsT = cell(1, ss); +marginals = cell(1,ss); +for i=hnodes(:)' + marginalsT{i} = marginalize_pot(jointT, i); + m = pot_to_marginal(marginalsT{i}); + marginals{i} = m.T(:); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +/bk_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_init_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_marginal_from_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_update_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dbn_update_bel1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Sat Jan 11 18:13:50 2003// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@bk_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/bk_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +function engine = bk_inf_engine(bnet, varargin) +% BK_INF_ENGINE Boyen-Koller approximate inference algorithm for DBNs. +% +% In the BK algorithm, the belief state is represented as a product of marginals, +% even though the factors may not be independent. +% +% engine = bk_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - if a cell array, clusters{i} specifies the terms in the i'th factor. +% - 'exact' means create one cluster that contains all the nodes in a slice [exact] +% - 'ff' means create one cluster per node (ff = fully factorised). +% +% +% For details, see +% - "Tractable Inference for Complex Stochastic Processes", X. Boyen and D. Koller, UAI 98. +% - "Approximate learning of dynamic models", X. Boyen and D. Koller, NIPS 98. +% (The UAI98 paper discusses filtering and theory, and the NIPS98 paper discusses smoothing.) + +ss = length(bnet.intra); +% set default params +clusters = 'exact'; + + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end + end +end + +if strcmp(clusters, 'exact') + %clusters = { compute_interface_nodes(bnet.intra, bnet.inter) }; + clusters = { 1:ss }; +elseif strcmp(clusters, 'ff') + clusters = num2cell(1:ss); +end + + +% We need to insert the prior on the clusters in slice 1, +% and extract the posterior on the clusters in slice 2. +C = length(clusters); +clusters2 = cell(1,2*C); +clusters2(1:C) = clusters; +for c=1:C + clusters2{c+C} = clusters{c} + ss; +end + +onodes = bnet.observed; +obs_nodes = [onodes(:) onodes(:)+ss]; +engine.sub_engine = jtree_inf_engine(bnet, 'clusters', clusters2); + +engine.clq_ass_to_cluster = zeros(C, 2); +for c=1:C + engine.clq_ass_to_cluster(c,1) = clq_containing_nodes(engine.sub_engine, clusters{c}); + engine.clq_ass_to_cluster(c,2) = clq_containing_nodes(engine.sub_engine, clusters{c}+ss); +end +engine.clusters = clusters; + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.sub_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.sub_engine, i+ss); +end + + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes, 1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.sub_engine1 = jtree_inf_engine(bnet1, 'clusters', clusters); + +engine.clq_ass_to_cluster1 = zeros(1,C); +for c=1:C + engine.clq_ass_to_cluster1(c) = clq_containing_nodes(engine.sub_engine1, clusters{c}); +end + +engine.clq_ass_to_node1 = zeros(1, ss); +for i=1:ss + engine.clq_ass_to_node1(i) = clq_containing_nodes(engine.sub_engine1, i); +end + +engine.clpot = []; % this is where we store the results between enter_evidence and marginal_nodes +engine.filter = []; +engine.maximize = []; +engine.T = []; + +engine.bel = []; +engine.bel_clpot = []; +engine.slice1 = []; +%engine.pot_type = 'cg'; +% hack for online inference so we can cope with hidden Gaussians and discrete +% it will not affect the pot type used in enter_evidence +engine.pot_type = determine_pot_type(bnet, onodes); + +engine = class(engine, 'bk_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/dbn_init_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function engine = dbn_init_bel(engine) +% DBN_INIT_BEL Compute the initial belief state (bk) +% engine = dbn_init_bel(engine)) + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +evidence = cell(1,ss); +engine = dbn_update_bel1(engine, evidence);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/dbn_marginal_from_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function marginal = dbn_marginal_from_bel(engine, i) +% DBN_MARGINAL_FROM_BEL Compute the marginal on a node given the current belief state (bk) +% marginal = dbn_marginal_from_bel(engine, i) + +if engine.slice1 + j = i; + c = clq_containing_nodes(engine.sub_engine1, j); +else + bnet = bnet_from_engine(engine); + ss = length(bnet.intra); + j = i+ss; + c = clq_containing_nodes(engine.sub_engine, j); +end +assert(c >= 1); +bigpot = engine.bel_clpot{c}; + +pot = marginalize_pot(bigpot, j); +marginal = pot_to_marginal(pot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/dbn_update_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function [engine, loglik] = dbn_update_bel(engine, evidence) +% DBN_UPDATE_BEL Update the belief state (bk) +% [engine, loglik] = dbn_update_bel(engine, evidence) +% +% evidence{i,1} contains the evidence on node i in slice t-1 +% evidence{i,2} contains the evidence on node i in slice t + +oldbel = engine.bel; + +ss = size(evidence, 1); +bnet = bnet_from_engine(engine); +CPDpot = cell(1, ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), evidence); +end + +observed = ~isemptycell(evidence); +onodes2 = find(observed(:)); +clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)]; +pots = [oldbel(:); CPDpot(:)]; + +[clpot, loglik] = enter_soft_evidence(engine.sub_engine, clqs, pots, onodes2(:), engine.pot_type); + +C = length(engine.clusters); +newbel = cell(1,C); +for c=1:C + k = engine.clq_ass_to_cluster(c,2); + cl = engine.clusters{c}; + newbel{c} = marginalize_pot(clpot{k}, cl+ss); % extract slice 2 posterior + newbel{c} = set_domain_pot(newbel{c}, cl); % shift back to slice 1 for re-use as prior +end + +engine.bel = newbel; +engine.bel_clpot = clpot; +engine.slice1 = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/dbn_update_bel1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [engine, loglik] = dbn_update_bel1(engine, evidence) +% DBN_UPDATE_BEL1 Update the initial belief state (bk) +% engine = dbn_update_bel1(engine, evidence) +% +% evidence{i} has the evidence on node i for slice 1 + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +CPDpot = cell(1,ss); +t = 1; +for n=1:ss + fam = family(bnet.dag, n); + e = bnet.equiv_class(n, 1); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), evidence); +end + +onodes = find(~isemptycell(evidence)); + +[clpot, loglik] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1, CPDpot, onodes, engine.pot_type); + +C = length(engine.clusters); +newbel = cell(1,C); +for c=1:C + k = engine.clq_ass_to_cluster1(c); + newbel{c} = marginalize_pot(clpot{k}, engine.clusters{c}); +end + +engine.bel = newbel; +engine.bel_clpot = clpot; +engine.slice1 = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (bk) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +[ss T] = size(evidence); +engine.filter = filter; +engine.maximize = maximize; +engine.T = T; + +if maximize + error('BK does not yet support max propagation') + % because it calls enter_soft_evidence, not enter_evidence +end + +observed_bitv = ~isemptycell(evidence); +onodes = find(observed_bitv); +bnet = bnet_from_engine(engine); +pot_type = determine_pot_type(bnet, onodes); +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type); +[engine.clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed_bitv, pot_type, filter); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (bk) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +C = length(engine.clusters); +Q = length(cliques_from_engine(engine.sub_engine)); +Q1 = length(cliques_from_engine(engine.sub_engine1)); +clpot = cell(Q,T); +alpha = cell(C,T); + +% Forwards +% The method is a generalization of the following HMM equation: +% alpha(j,t) = normalise( (sum_i alpha(i,t-1) * transmat(i,j)) * obsmat(j,t) ) +% where alpha(j,t) = Pr(Q(t)=j | y(1:t)) +t = 1; +[clpot(1:Q1,t), logscale(t)] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1(:), ... + CPDpot(:,1), find(observed(:,1)), pot_type); +for c=1:C + k = engine.clq_ass_to_cluster1(c); + alpha{c,t} = marginalize_pot(clpot{k,t}, engine.clusters{c}); +end +% For filtering, clpot{1} contains evidence on slice 1 only + +%fprintf('alphas t=%d\n', t); +%for c=1:8 +% temp = pot_to_marginal(alpha{c,t}); +% temp.T +%end + +% clpot{t} contains evidence from slices t-1, t for t > 1 +clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)]; +for t=2:T + pots = [alpha(:,t-1); CPDpot(:,t)]; + [clpot(:,t), logscale(t)] = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t-1:t)), pot_type); + for c=1:C + k = engine.clq_ass_to_cluster(c,2); + cl = engine.clusters{c}; + alpha{c,t} = marginalize_pot(clpot{k,t}, cl+ss); % extract slice 2 posterior + alpha{c,t} = set_domain_pot(alpha{c,t}, cl); % shift back to slice 1 for re-use as prior + end + +end + +loglik = sum(logscale); + +if filter + return; +end + +% Backwards +% The method is a generalization of the following HMM equation: +% beta(i,t) = (sum_j transmat(i,j) * obsmat(j,t+1) * beta(j,t+1)) +% where beta(i,t) = Pr(y(t+1:T) | Q(t)=i) +t = T; +bnet = bnet_from_engine(engine); +beta = cell(C,T); +for c=1:C + beta{c,t} = mk_initial_pot(pot_type, engine.clusters{c} + ss, bnet.node_sizes(:), bnet.cnodes(:), ... + find(observed(:,t-1:t))); +end +for t=T-1:-1:1 + clqs = [engine.clq_ass_to_cluster(:,2); engine.clq_ass_to_node(:,2)]; + pots = [beta(:,t+1); CPDpot(:,t+1)]; + temp = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type); + for c=1:C + k = engine.clq_ass_to_cluster(c,1); + cl = engine.clusters{c}; + beta{c,t} = marginalize_pot(temp{k}, cl); % extract slice 1 + beta{c,t} = set_domain_pot(beta{c,t}, cl + ss); % shift fwd to slice 2 + end +end + +% Combine +% The method is a generalization of the following HMM equation: +% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) +% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) +for t=1:T-1 + clqs = [engine.clq_ass_to_cluster(:); engine.clq_ass_to_node(:,2)]; + pots = [alpha(:,t); beta(:,t+1); CPDpot(:,t+1)]; + clpot(:,t+1) = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type); +end +% for smoothing, clpot{1} is undefined +for k=1:Q1 + clpot{k,1} = []; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function m = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (bk) +% marginal = marginal_family(engine, i, t) + +% This is just like inf_engine/marginal_family, except when we call +% marginal_nodes, we provide a 4th argument, to tell it's a family. + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, family(bnet.dag, i), t, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + if any(fam<=ss) + % i has a parent in the preceeding slice + % Hence the lowest numbered slice containing the family is t-1 + m = marginal_nodes(engine, fam, t-1, 1); + else + % The family all fits inside slice t + % Hence shift the indexes back to slice 1 + m = marginal_nodes(engine, fam-ss, t, 1); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function marginal = marginal_nodes(engine, nodes, t, fam) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk) +% +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% If enter_evidence used filtering instead of smoothing, this will return Pr(X(i,t) | Y(1:t)). +% +% marginal = marginal_nodes(engine, query, t) +% returns Pr(X(query(1),t), ... X(query(end),t) | Y(1:T)), +% where X(q,t) is the q'th node in the t'th slice. If q > ss (slice size), this is equal +% to X(q mod ss, t+1). That is, 't' specifies the time slice of the earliest node. +% 'query' cannot span more than 2 time slices. +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3. + +if nargin < 3, t = 1; end +if nargin < 4, fam = 0; else fam = 1; end + + +% clpot{t} contains slice t-1 and t +% Example +% clpot #: 1 2 3 +% slices: 1 1,2 2,3 +% For filtering, we must take care not to take future evidence into account. +% For smoothing, clpot{1} does not exist. + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +nodes2 = nodes; +if ~engine.filter + if t < engine.T + slice = t+1; + else % earliest t is T, so all nodes fit in one slice + slice = engine.T; + nodes2 = nodes + ss; + end +else + if t == 1 + slice = 1; + else + if all(nodes<=ss) + slice = t; + nodes2 = nodes + ss; + elseif t == engine.T + slice = t; + else + slice = t + 1; + end + end +end + +if engine.filter & t==1 + c = clq_containing_nodes(engine.sub_engine1, nodes2, fam); +else + c = clq_containing_nodes(engine.sub_engine, nodes2, fam); +end +assert(c >= 1); +bigpot = engine.clpot{c, slice}; + +pot = marginalize_pot(bigpot, nodes2); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@bk_inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (bk) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.sub_engine = update_engine(engine.sub_engine, newCPDs); + +bnet = bnet_from_engine(engine); +eclass1 = bnet.equiv_class(:,1); +engine.sub_engine1 = update_engine(engine.sub_engine1, newCPDs(1:max(eclass1))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +/cbk_inf_engine.m/1.1.1.1/Mon Nov 22 22:15:34 2004// +/dbn_init_bel.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/dbn_marginal_from_bel.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/dbn_update_bel.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/dbn_update_bel1.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/enter_evidence.m/1.1.1.1/Mon Jan 12 20:53:54 2004// +/enter_soft_evidence.m/1.1.1.1/Wed Feb 4 07:42:38 2004// +/junk/1.1.1.1/Wed Nov 24 20:12:38 2004// +/marginal_family.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +/marginal_nodes.m/1.1.1.1/Tue Dec 16 06:17:18 2003// +/update_engine.m/1.1.1.1/Tue Jul 29 02:44:58 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@cbk_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/cbk_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,175 @@ +function engine = cbk_inf_engine(bnet, varargin) +% Just the same as bk_inf_engine, but you can specify overlapping clusters. + +ss = length(bnet.intra); +% set default params +clusters = 'exact'; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end + end +end + +if strcmp(clusters, 'exact') + %clusters = { compute_interface_nodes(bnet.intra, bnet.inter) }; + clusters = { 1:ss }; +elseif strcmp(clusters, 'ff') + clusters = num2cell(1:ss); +end + + +% We need to insert the prior on the clusters in slice 1, +% and extract the posterior on the clusters in slice 2. +% We don't need to care about the separators, b/c they're subsets of the clusters. +C = length(clusters); +clusters2 = cell(1,2*C); +clusters2(1:C) = clusters; +for c=1:C + clusters2{c+C} = clusters{c} + ss; +end + +onodes = bnet.observed; +obs_nodes = [onodes(:) onodes(:)+ss]; +engine.sub_engine = jtree_inf_engine(bnet, 'clusters', clusters2); + +%FH >>> +%Compute separators. +ns = bnet.node_sizes(:,1); +ns(onodes) = 1; +[clusters, separators] = build_jt(clusters, 1:length(ns), ns); +S = length(separators); +engine.separators = separators; + +%Compute size of clusters. +cl_sizes = zeros(1,C); +for c=1:C + cl_sizes(c) = prod(ns(clusters{c})); +end + +%Assign separators to the smallest cluster subsuming them. +engine.cluster_ass_to_separator = zeros(S, 1); +for s=1:S + subsuming_clusters = []; + %find smallest cluster containing s + for c=1:C + if mysubset(separators{s}, clusters{c}) + subsuming_clusters(end+1) = c; + end + end + c = argmin(cl_sizes(subsuming_clusters)); + engine.cluster_ass_to_separator(s) = subsuming_clusters(c); +end + +%<<< FH + +engine.clq_ass_to_cluster = zeros(C, 2); +for c=1:C + engine.clq_ass_to_cluster(c,1) = clq_containing_nodes(engine.sub_engine, clusters{c}); + engine.clq_ass_to_cluster(c,2) = clq_containing_nodes(engine.sub_engine, clusters{c}+ss); +end +engine.clusters = clusters; + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.sub_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.sub_engine, i+ss); +end + + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes, 1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.sub_engine1 = jtree_inf_engine(bnet1, 'clusters', clusters); + +engine.clq_ass_to_cluster1 = zeros(1,C); +for c=1:C + engine.clq_ass_to_cluster1(c) = clq_containing_nodes(engine.sub_engine1, clusters{c}); +end + +engine.clq_ass_to_node1 = zeros(1, ss); +for i=1:ss + engine.clq_ass_to_node1(i) = clq_containing_nodes(engine.sub_engine1, i); +end + +engine.clpot = []; % this is where we store the results between enter_evidence and marginal_nodes +engine.filter = []; +engine.maximize = []; +engine.T = []; + +engine.bel = []; +engine.bel_clpot = []; +engine.slice1 = []; +%engine.pot_type = 'cg'; +% hack for online inference so we can cope with hidden Gaussians and discrete +% it will not affect the pot type used in enter_evidence +engine.pot_type = determine_pot_type(bnet, onodes); + +engine = class(engine, 'cbk_inf_engine', inf_engine(bnet)); + + + + +function [cliques, seps, jt_size] = build_jt(cliques, vars, ns) +% BUILD_JT connects the cliques into a jtree, computes the respective +% separators and the size of the resulting jtree. +% +% [cliques, seps, jt_size] = build_jt(cliques, vars, ns) +% ns(i) has to hold the size of vars(i) +% vars has to be a superset of the union of cliques. + +%======== Compute the jtree with tool from BNT. This wants the vars to be 1:N. +%==== Map from nodes to their indices. +%disp('Computing jtree for cliques with vars and ns:'); +%cliques +%vars +%ns' + +inv_nodes = sparse(1,max(vars)); +N = length(vars); +for i=1:N + inv_nodes(vars(i)) = i; +end + +tmp_cliques = cell(1,length(cliques)); +%==== Temporarily map clique vars to their indices. +for i=1:length(cliques) + tmp_cliques{i} = inv_nodes(cliques{i}); +end + +%=== Compute the jtree, using BNT. +[jtree, root, B, w] = cliques_to_jtree(tmp_cliques, ns); + + +%======== Now, compute the separators between connected cliques and their weights. +seps = {}; +s_w = []; +[is,js] = find(jtree > 0); +for k=1:length(is) + i = is(k); j = js(k); + sep = vars(find(B(i,:) & B(j,:))); % intersect(cliques{i}, cliques{j}); + if i>j | length(sep) == 0, continue; end; + seps{end+1} = sep; + s_w(end+1) = prod(ns(inv_nodes(seps{end}))); +end + +cl_w = sum(w); +sep_w = sum(s_w); +assert(cl_w > sep_w, 'Weight of cliques must be bigger than weight of separators'); + +jt_size = cl_w + sep_w; +% jt.cliques = cliques; +% jt.seps = seps; +% jt.size = jt_size; +% jt.ns = ns'; +% jt; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/dbn_init_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function engine = dbn_init_bel(engine) +% DBN_INIT_BEL Compute the initial belief state (bk) +% engine = dbn_init_bel(engine)) + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +evidence = cell(1,ss); +engine = dbn_update_bel1(engine, evidence);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/dbn_marginal_from_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function marginal = dbn_marginal_from_bel(engine, i) +% DBN_MARGINAL_FROM_BEL Compute the marginal on a node given the current belief state (bk) +% marginal = dbn_marginal_from_bel(engine, i) + +if engine.slice1 + j = i; + c = clq_containing_nodes(engine.sub_engine1, j); +else + bnet = bnet_from_engine(engine); + ss = length(bnet.intra); + j = i+ss; + c = clq_containing_nodes(engine.sub_engine, j); +end +assert(c >= 1); +bigpot = engine.bel_clpot{c}; + +pot = marginalize_pot(bigpot, j); +marginal = pot_to_marginal(pot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/dbn_update_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function [engine, loglik] = dbn_update_bel(engine, evidence) +% DBN_UPDATE_BEL Update the belief state (bk) +% [engine, loglik] = dbn_update_bel(engine, evidence) +% +% evidence{i,1} contains the evidence on node i in slice t-1 +% evidence{i,2} contains the evidence on node i in slice t + +oldbel = engine.bel; + +ss = size(evidence, 1); +bnet = bnet_from_engine(engine); +CPDpot = cell(1, ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), evidence); +end + +observed = ~isemptycell(evidence); +onodes2 = find(observed(:)); +clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)]; +pots = [oldbel(:); CPDpot(:)]; + +[clpot, loglik] = enter_soft_evidence(engine.sub_engine, clqs, pots, onodes2(:), engine.pot_type); + +C = length(engine.clusters); +newbel = cell(1,C); +for c=1:C + k = engine.clq_ass_to_cluster(c,2); + cl = engine.clusters{c}; + newbel{c} = marginalize_pot(clpot{k}, cl+ss); % extract slice 2 posterior + newbel{c} = set_domain_pot(newbel{c}, cl); % shift back to slice 1 for re-use as prior +end + +engine.bel = newbel; +engine.bel_clpot = clpot; +engine.slice1 = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/dbn_update_bel1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [engine, loglik] = dbn_update_bel1(engine, evidence) +% DBN_UPDATE_BEL1 Update the initial belief state (bk) +% engine = dbn_update_bel1(engine, evidence) +% +% evidence{i} has the evidence on node i for slice 1 + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +CPDpot = cell(1,ss); +t = 1; +for n=1:ss + fam = family(bnet.dag, n); + e = bnet.equiv_class(n, 1); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), evidence); +end + +onodes = find(~isemptycell(evidence)); + +[clpot, loglik] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1, CPDpot, onodes, engine.pot_type); + +C = length(engine.clusters); +newbel = cell(1,C); +for c=1:C + k = engine.clq_ass_to_cluster1(c); + newbel{c} = marginalize_pot(clpot{k}, engine.clusters{c}); +end + +engine.bel = newbel; +engine.bel_clpot = clpot; +engine.slice1 = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% this is unchanged from bk_inf_engine. +% ENTER_EVIDENCE Add the specified evidence to the network (bk) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +[ss T] = size(evidence); +engine.filter = filter; +engine.maximize = maximize; +engine.T = T; + +if maximize + error('BK does not yet support max propagation') + % because it calls enter_soft_evidence, not enter_evidence +end + +observed_bitv = ~isemptycell(evidence); +onodes = find(observed_bitv); +bnet = bnet_from_engine(engine); +pot_type = determine_pot_type(bnet, onodes); +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type); +[engine.clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed_bitv, pot_type, filter); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,115 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (bk) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +C = length(engine.clusters); +S = length(engine.separators); +Q = length(cliques_from_engine(engine.sub_engine)); +Q1 = length(cliques_from_engine(engine.sub_engine1)); +clpot = cell(Q,T); +alpha = cell(C,T); + +% Forwards +% The method is a generalization of the following HMM equation: +% alpha(j,t) = normalise( (sum_i alpha(i,t-1) * transmat(i,j)) * obsmat(j,t) ) +% where alpha(j,t) = Pr(Q(t)=j | y(1:t)) +t = 1; +[clpot(1:Q1,t), logscale(t)] = enter_soft_evidence(engine.sub_engine1, engine.clq_ass_to_node1(:), ... + CPDpot(:,1), find(observed(:,1)), pot_type); +for c=1:C + k = engine.clq_ass_to_cluster1(c); + alpha{c,t} = marginalize_pot(clpot{k,t}, engine.clusters{c}); +end + +%=== FH: For each separator s, divide some cluster potential by s's potential +alpha_orig = alpha(:,t); +for s=1:S + c = engine.cluster_ass_to_separator(s); + alpha{c,t} = divide_by_pot(alpha{c,t}, marginalize_pot(alpha_orig{c}, engine.separators{s})); +end + +% For filtering, clpot{1} contains evidence on slice 1 only + +%fprintf('alphas t=%d\n', t); +%for c=1:8 +% temp = pot_to_marginal(alpha{c,t}); +% temp.T +%end + +% clpot{t} contains evidence from slices t-1, t for t > 1 +clqs = [engine.clq_ass_to_cluster(:,1); engine.clq_ass_to_node(:,2)]; +for t=2:T + pots = [alpha(:,t-1); CPDpot(:,t)]; + [clpot(:,t), logscale(t)] = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t-1:t)), pot_type); + for c=1:C + k = engine.clq_ass_to_cluster(c,2); + cl = engine.clusters{c}; + alpha{c,t} = marginalize_pot(clpot{k,t}, cl+ss); % extract slice 2 posterior + alpha{c,t} = set_domain_pot(alpha{c,t}, cl); % shift back to slice 1 for re-use as prior + end + %=== FH: For each separator s, divide some cluster potential by s's potential + alpha_orig = alpha(:,t); + for s=1:S + c = engine.cluster_ass_to_separator(s); + alpha{c,t} = divide_by_pot(alpha{c,t}, marginalize_pot(alpha_orig{c}, engine.separators{s})); + end +end + +loglik = sum(logscale); + +if filter + return; +end + +% Backwards +% The method is a generalization of the following HMM equation: +% beta(i,t) = (sum_j transmat(i,j) * obsmat(j,t+1) * beta(j,t+1)) +% where beta(i,t) = Pr(y(t+1:T) | Q(t)=i) +t = T; +bnet = bnet_from_engine(engine); +beta = cell(C,T); +for c=1:C + beta{c,t} = mk_initial_pot(pot_type, engine.clusters{c} + ss, bnet.node_sizes(:), bnet.cnodes(:), ... + find(observed(:,t-1:t))); +end +%=== FH: For each separator s, divide some cluster potential by s's potential +beta_orig = beta(:,t); +for s=1:S + c = engine.cluster_ass_to_separator(s); + beta{c,t} = divide_by_pot(beta{c,t}, marginalize_pot(beta_orig{c}, engine.separators{s}+ss)); +end + +for t=T-1:-1:1 + clqs = [engine.clq_ass_to_cluster(:,2); engine.clq_ass_to_node(:,2)]; + pots = [beta(:,t+1); CPDpot(:,t+1)]; + temp = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type); + for c=1:C + k = engine.clq_ass_to_cluster(c,1); + cl = engine.clusters{c}; + beta{c,t} = marginalize_pot(temp{k}, cl); % extract slice 1 + beta{c,t} = set_domain_pot(beta{c,t}, cl + ss); % shift fwd to slice 2 + end + %=== FH: For each separator s, divide some cluster potential by s's potential + beta_orig = beta(:,t); + for s=1:S + c = engine.cluster_ass_to_separator(s); + beta{c,t} = divide_by_pot(beta{c,t}, marginalize_pot(beta_orig{c}, engine.separators{s}+ss)); + end +end + +% Combine +% The method is a generalization of the following HMM equation: +% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) +% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) +for t=1:T-1 + clqs = [engine.clq_ass_to_cluster(:); engine.clq_ass_to_node(:,2)]; + pots = [alpha(:,t); beta(:,t+1); CPDpot(:,t+1)]; + clpot(:,t+1) = enter_soft_evidence(engine.sub_engine, clqs, pots, find(observed(:,t:t+1)), pot_type); +end +% for smoothing, clpot{1} is undefined +for k=1:Q1 + clpot{k,1} = []; +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/junk Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,176 @@ +function engine = cbk_inf_engine(bnet, varargin) +% Just the same as bk_inf_engine, but you can specify overlapping clusters. + +ss = length(bnet.intra); +% set default params +clusters = 'exact'; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end + end +end + +if strcmp(clusters, 'exact') + %clusters = { compute_interface_nodes(bnet.intra, bnet.inter) }; + clusters = { 1:ss }; +elseif strcmp(clusters, 'ff') + clusters = num2cell(1:ss); +end + + +% We need to insert the prior on the clusters in slice 1, +% and extract the posterior on the clusters in slice 2. +% We don't need to care about the separators, b/c they're subsets of the clusters. +C = length(clusters); +clusters2 = cell(1,2*C); +clusters2(1:C) = clusters; +for c=1:C + clusters2{c+C} = clusters{c} + ss; +end + +onodes = bnet.observed; +obs_nodes = [onodes(:) onodes(:)+ss]; +engine.sub_engine = jtree_inf_engine(bnet, 'clusters', clusters2); + +%FH >>> +%Compute separators. +ns = bnet.node_sizes(:,1); +ns(onodes) = 1; +[clusters, separators] = build_jt(clusters, 1:length(ns), ns); +S = length(separators); +engine.separators = separators; + +%Compute size of clusters. +cl_sizes = zeros(1,C); +for c=1:C + cl_sizes(c) = prod(ns(clusters{c})); +end + +%Assign separators to the smallest cluster subsuming them. +engine.cluster_ass_to_separator = zeros(S, 1); +for s=1:S + subsuming_clusters = []; + %find smaunk + + for c=1:C + if mysubset(separators{s}, clusters{c}) + subsuming_clusters(end+1) = c; + end + end + c = argmin(cl_sizes(subsuming_clusters)); + engine.cluster_ass_to_separator(s) = subsuming_clusters(c); +end + +%<<< FH + +engine.clq_ass_to_cluster = zeros(C, 2); +for c=1:C + engine.clq_ass_to_cluster(c,1) = clq_containing_nodes(engine.sub_engine, clusters{c}); + engine.clq_ass_to_cluster(c,2) = clq_containing_nodes(engine.sub_engine, clusters{c}+ss); +end +engine.clusters = clusters; + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.sub_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.sub_engine, i+ss); +end + + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes, 1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.sub_engine1 = jtree_inf_engine(bnet1, 'clusters', clusters); + +engine.clq_ass_to_cluster1 = zeros(1,C); +for c=1:C + engine.clq_ass_to_cluster1(c) = clq_containing_nodes(engine.sub_engine1, clusters{c}); +end + +engine.clq_ass_to_node1 = zeros(1, ss); +for i=1:ss + engine.clq_ass_to_node1(i) = clq_containing_nodes(engine.sub_engine1, i); +end + +engine.clpot = []; % this is where we store the results between enter_evidence and marginal_nodes +engine.filter = []; +engine.maximize = []; +engine.T = []; + +engine.bel = []; +engine.bel_clpot = []; +engine.slice1 = []; +%engine.pot_type = 'cg'; +% hack for online inference so we can cope with hidden Gaussians and discrete +% it will not affect the pot type used in enter_evidence +engine.pot_type = determine_pot_type(bnet, onodes); + +engine = class(engine, 'cbk_inf_engine', inf_engine(bnet)); + + + + +function [cliques, seps, jt_size] = build_jt(cliques, vars, ns) +% BUILD_JT connects the cliques into a jtree, computes the respective +% separators and the size of the resulting jtree. +% +% [cliques, seps, jt_size] = build_jt(cliques, vars, ns) +% ns(i) has to hold the size of vars(i) +% vars has to be a superset of the union of cliques. + +%======== Compute the jtree with tool from BNT. This wants the vars to be 1:N. +%==== Map from nodes to their indices. +%disp('Computing jtree for cliques with vars and ns:'); +%cliques +%vars +%ns' + +inv_nodes = sparse(1,max(vars)); +N = length(vars); +for i=1:N + inv_nodes(vars(i)) = i; +end + +tmp_cliques = cell(1,length(cliques)); +%==== Temporarily map clique vars to their indices. +for i=1:length(cliques) + tmp_cliques{i} = inv_nodes(cliques{i}); +end + +%=== Compute the jtree, using BNT. +[jtree, root, B, w] = cliques_to_jtree(tmp_cliques, ns); + + +%======== Now, compute the separators between connected cliques and their weights. +seps = {}; +s_w = []; +[is,js] = find(jtree > 0); +for k=1:length(is) + i = is(k); j = js(k); + sep = vars(find(B(i,:) & B(j,:))); % intersect(cliques{i}, cliques{j}); + if i>j | length(sep) == 0, continue; end; + seps{end+1} = sep; + s_w(end+1) = prod(ns(inv_nodes(seps{end}))); +end + +cl_w = sum(w); +sep_w = sum(s_w); +assert(cl_w > sep_w, 'Weight of cliques must be bigger than weight of separators'); + +jt_size = cl_w + sep_w; +% jt.cliques = cliques; +% jt.seps = seps; +% jt.size = jt_size; +% jt.ns = ns'; +% jt;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function m = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (bk) +% marginal = marginal_family(engine, i, t) + +% This is just like inf_engine/marginal_family, except when we call +% marginal_nodes, we provide a 4th argument, to tell it's a family. + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, family(bnet.dag, i), t, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + if any(fam<=ss) + % i has a parent in the preceeding slice + % Hence the lowest numbered slice containing the family is t-1 + m = marginal_nodes(engine, fam, t-1, 1); + else + % The family all fits inside slice t + % Hence shift the indexes back to slice 1 + m = marginal_nodes(engine, fam-ss, t, 1); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function marginal = marginal_nodes(engine, nodes, t, fam) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk) +% +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% If enter_evidence used filtering instead of smoothing, this will return Pr(X(i,t) | Y(1:t)). +% +% marginal = marginal_nodes(engine, query, t) +% returns Pr(X(query(1),t), ... X(query(end),t) | Y(1:T)), +% where X(q,t) is the q'th node in the t'th slice. If q > ss (slice size), this is equal +% to X(q mod ss, t+1). That is, 't' specifies the time slice of the earliest node. +% 'query' cannot span more than 2 time slices. +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3. + +if nargin < 3, t = 1; end +if nargin < 4, fam = 0; else fam = 1; end + + +% clpot{t} contains slice t-1 and t +% Example +% clpot #: 1 2 3 +% slices: 1 1,2 2,3 +% For filtering, we must take care not to take future evidence into account. +% For smoothing, clpot{1} does not exist. + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +nodes2 = nodes; +if ~engine.filter + if t < engine.T + slice = t+1; + else % earliest t is T, so all nodes fit in one slice + slice = engine.T; + nodes2 = nodes + ss; + end +else + if t == 1 + slice = 1; + else + if all(nodes<=ss) + slice = t; + nodes2 = nodes + ss; + elseif t == engine.T + slice = t; + else + slice = t + 1; + end + end +end + +if engine.filter & t==1 + c = clq_containing_nodes(engine.sub_engine1, nodes2, fam); +else + c = clq_containing_nodes(engine.sub_engine, nodes2, fam); +end +assert(c >= 1); +bigpot = engine.clpot{c, slice}; + +pot = marginalize_pot(bigpot, nodes2); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@cbk_inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (bk) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.sub_engine = update_engine(engine.sub_engine, newCPDs); + +bnet = bnet_from_engine(engine); +eclass1 = bnet.equiv_class(:,1); +engine.sub_engine1 = update_engine(engine.sub_engine1, newCPDs(1:max(eclass1))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/ff_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/filter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/smooth_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@ff_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@ff_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/Old/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (bk_ff) +% [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +assert(pot_type == 'd'); +[ss T] = size(CPDpot); +fwd = cell(ss,T); +hnodes = engine.hnodes(:)'; +onodes = engine.onodes(:)'; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes2 = [onodes onodes+ss]; +ns(onodes2) = 1; + +logscale = zeros(1,T); +local_logscale = zeros(1,length(hnodes)); + +t = 1; +for i=hnodes + fwd{i,t} = CPDpot{i,t}; +end +for i=onodes + p = parents(bnet.dag, i); + assert(length(p)==1); + ev = marginalize_pot(CPDpot{i,t}, p); + fwd{p,t} = multiply_by_pot(fwd{p,t}, ev); +end +for i=hnodes + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); +end +logscale(t) = sum(local_logscale); + +for t=2:T + for i=hnodes + ps = parents(bnet.dag, i+ss); + assert(all(ps<=ss)); % in previous slice + prior = CPDpot{i,t}; + for p=ps(:)' + prior = multiply_by_pot(prior, fwd{p,t-1}); + end + fwd{i,t} = marginalize_pot(prior, i+ss); + fwd{i,t} = set_domain_pot(fwd{i,t}, i); + end + for i=onodes + p = parents(bnet.dag, i); + assert(length(p)==1); + temp = pot_to_marginal(CPDpot{i,t}); + ev = dpot(p, ns(p), temp.T); + fwd{p,t} = multiply_by_pot(fwd{p,t}, ev); + end + + for i=hnodes + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); + end + logscale(t) = sum(local_logscale); +end + +marginals = fwd; +loglik = sum(logscale);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/Old/enter_soft_evidence1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ +function [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (ff) +% [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +assert(pot_type == 'd'); +[ss T] = size(CPDpot); +fwd = cell(ss,T); +hnodes = engine.hnodes(:)'; +onodes = engine.onodes(:)'; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes2 = [onodes onodes+ss]; +ns(onodes2) = 1; + +logscale = zeros(1,T); +H = length(hnodes); +local_logscale = zeros(1,ss); + +obschild = zeros(1,ss); +for i=hnodes + ocs = myintersect(children(bnet.dag, i), onodes); + assert(length(ocs)==1); + obschild(i) = ocs(1); +end + +t = 1; +for i=hnodes + fwd{i,t} = CPDpot{i,t}; + c = obschild(i); + temp = pot_to_marginal(CPDpot{c,t}); + ev = dpot(i, ns(i), temp.T); + fwd{i,t} = multiply_by_pot(fwd{i,t}, ev); + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); +end +logscale(t) = sum(local_logscale); + +for t=2:T + for i=hnodes + ps = parents(bnet.dag, i+ss); + assert(all(ps<=ss)); % in previous slice + prior = CPDpot{i,t}; + for p=ps(:)' + prior = multiply_by_pot(prior, fwd{p,t-1}); + end + fwd{i,t} = marginalize_pot(prior, i+ss); + fwd{i,t} = set_domain_pot(fwd{i,t}, i); + c = obschild(i); + temp = pot_to_marginal(CPDpot{c,t}); + ev = dpot(i, ns(i), temp.T); + fwd{i,t} = multiply_by_pot(fwd{i,t}, ev); + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); + end + logscale(t) = sum(local_logscale); +end + +loglik = sum(logscale); + + +if filter + marginals = fwd; + return; +end + +back = cell(ss,T); +t = T; +for i=hnodes + back{i,t} = dpot(i, ns(i)); + back{i,t} = set_domain_pot(back{i,t}, i+ss); +end +for t=T-1:-1:1 + for i=hnodes + pot = CPDpot{i,t+1}; + pot = multiply_by_pot(pot, back{i,t+1}); + c = obschild(i); + temp = pot_to_marginal(CPDpot{c,t+1}); + ev = dpot(i, ns(i), temp.T); + pot = multiply_by_pot(pot, ev); + back{i,t} = marginalize_pot(pot, i); + back{i,t} = normalize_pot(back{i,t}); + back{i,t} = set_domain_pot(back{i,t}, i+ss); + end +end + + + +% COMBINE +for t=1:T + for i=hnodes + back{i,t} = set_domain_pot(back{i,t}, i); + fwd{i,t} = multiply_by_pot(fwd{i,t}, back{i,t}); + marginals{i,t} = normalize_pot(fwd{i,t}); + %fwdback{i,t} = normalize_pot(multiply_pots(fwd{i,t}, back{i,t})); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/Old/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function marginal = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (ff) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end + +% The method is similar to the following HMM equation: +% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) +% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) + +bnet = bnet_from_engine(engine); + +if myismember(i, engine.onodes) + ps = parents(bnet.dag, i); + p = ps(1); + marginal = pot_to_marginal(engine.marginals{p,t}); + marginal.domain = [p i]; + return; +end + +if t==1 + marginal = pot_to_marginal(engine.marginals{i,t}); + return; +end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +pot = engine.CPDpot{i,t}; +c = engine.obschild(i); +pot = multiply_by_pot(pot, engine.CPDpot{c,t}); +pot = multiply_by_pot(pot, engine.back{i,t}); +ps = parents(bnet.dag, i+ss); +for p=ps(:)' + pot = multiply_by_pot(pot, engine.fwd{p,t-1}); +end +marginal = pot_to_marginal(normalize_pot(pot)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (ff) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or +% column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); + + +[ss T] = size(evidence); +observed = ~isemptycell(evidence); +bnet = bnet_from_engine(engine); +%pot_type = determine_pot_type(find(observed(:,1)), bnet.cnodes_slice, bnet.intra); +pot_type = determine_pot_type(bnet, observed); +% we assume we can use the same pot_type in all slices + +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type); + +% Now convert CPDs on observed nodes to be potentials just on their parents +assert(pot_type == 'd'); +onodes = bnet.observed(:); +ns = bnet.node_sizes_slice; +ns(onodes) = 1; +for t=1:T + for i=onodes + p = parents(bnet.dag, i); + %CPDpot{i,t} = set_domain_pot(CPDpot{i,t}, p); % leaves size too long + temp = pot_to_marginal(CPDpot{i,t}); + CPDpot{i,t} = dpot(p, ns(p), temp.T); % assumes pot_type = d + end +end + +[engine.marginals, engine.fwd, engine.back, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter); + +engine.CPDpot = CPDpot; +engine.filter = filter;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function [marginals, fwd, back, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (ff) +% [marginals, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +if filter + [fwd, loglik] = filter_evidence(engine, CPDpot, observed, pot_type); + marginals = fwd; + back = []; +else + [marginals, fwd, back, loglik] = smooth_evidence(engine, CPDpot, observed, pot_type); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/ff_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function engine = ff_inf_engine(bnet) +% FF_INF_ENGINE Factored frontier inference engine for DBNs +% engine = ff_inf_engine(bnet) +% +% The model must be topologically isomorphic to an HMM. +% In addition, each hidden node is assumed to have at most one observed child, +% and each observed child is assumed to have exactly one hidden parent. +% +% For details of this algorithm, see +% "The Factored Frontier Algorithm for Approximate Inference in DBNs", +% Kevin Murphy and Yair Weiss, UAI 2001. +% +% THIS IS HIGHLY EXPERIMENTAL CODE! + +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); + +[persistent_nodes, transient_nodes] = partition_dbn_nodes(bnet.intra, bnet.inter); +assert(isequal(onodes, transient_nodes)); +assert(isequal(hnodes, persistent_nodes)); + +engine.onodes = onodes; +engine.hnodes = hnodes; +engine.marginals = []; +engine.fwd = []; +engine.back = []; +engine.CPDpot = []; +engine.filter = []; + +obschild = zeros(1,ss); +for i=engine.hnodes(:)' + %ocs = myintersect(children(bnet.dag, i), onodes); + ocs = children(bnet.intra, i); + assert(length(ocs) <= 1); + if length(ocs)==1 + obschild(i) = ocs(1); + end +end +engine.obschild = obschild; + + +engine = class(engine, 'ff_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/filter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function [fwd, loglik] = filter_evidence(engine, CPDpot, observed, pot_type) +% [fwd, loglik] = filter_evidence(engine, CPDpot, observed, pot_type) (ff) + +[ss T] = size(CPDpot); +fwd = cell(ss,T); +hnodes = engine.hnodes(:)'; +onodes = engine.onodes(:)'; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes2 = [onodes onodes+ss]; +ns(onodes2) = 1; + +logscale = zeros(1,T); +H = length(hnodes); +local_logscale = zeros(1,ss); + +t = 1; +for i=hnodes + fwd{i,t} = CPDpot{i,t}; + c = engine.obschild(i); + if c > 0 + fwd{i,t} = multiply_by_pot(fwd{i,t}, CPDpot{c, t}); + end + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); +end +logscale(t) = sum(local_logscale); + +for t=2:T + for i=hnodes + ps = parents(bnet.dag, i+ss); + assert(all(ps<=ss)); % in previous slice + prior = CPDpot{i,t}; + for p=ps(:)' + prior = multiply_by_pot(prior, fwd{p,t-1}); + end + fwd{i,t} = marginalize_pot(prior, i+ss); + fwd{i,t} = set_domain_pot(fwd{i,t}, i); + c = engine.obschild(i); + if c > 0 + fwd{i,t} = multiply_by_pot(fwd{i,t}, CPDpot{c,t}); + end + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); + end + logscale(t) = sum(local_logscale); +end + +loglik = sum(logscale); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function marginal = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (ff) +% marginal = marginal_family(engine, i, t) + + +if engine.filter + error('can''t currently use marginal_family when filtering with ff'); +end + +if nargin < 3, t = 1; end + +% The method is similar to the following HMM equation: +% xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) +% where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +if myismember(i, engine.onodes) + ps = parents(bnet.dag, i); + p = ps(1); + marginal = pot_to_marginal(engine.marginals{ps(1),t}); + fam = ([ps i]) + (t-1)*ss; +elseif t==1 + marginal = pot_to_marginal(engine.marginals{i,t}); + fam = i + (t-1)*ss; +else + pot = engine.CPDpot{i,t}; + c = engine.obschild(i); + if c>0 + pot = multiply_by_pot(pot, engine.CPDpot{c,t}); + end + pot = multiply_by_pot(pot, engine.back{i,t}); + ps = parents(bnet.dag, i+ss); + for p=ps(:)' + pot = multiply_by_pot(pot, engine.fwd{p,t-1}); + end + marginal = pot_to_marginal(normalize_pot(pot)); + fam = ([ps i+ss]) + (t-2)*ss; +end + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = fam;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (ff) +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% If enter_evidence used filtering instead of smoothing, this will return Pr(X(i,t) | Y(1:t)). + +if nargin < 3, t = 1; end +assert(length(nodes)==1); +i = nodes(end); +if myismember(i, engine.hnodes) + marginal = pot_to_marginal(engine.marginals{i,t}); +else + marginal = pot_to_marginal(dpot(i, 1, 1)); % observed +end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@ff_inf_engine/smooth_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,89 @@ +function [marginals, fwd, back, loglik] = smooth_evidence(engine, CPDpot, observed, pot_type) +% [marginals, fwd, back, loglik] = smooth_evidence(engine, CPDpot, observed, pot_type) (ff) + +error('ff smoothing is broken'); + +[ss T] = size(CPDpot); +fwd = cell(ss,T); +hnodes = engine.hnodes(:)'; +onodes = engine.onodes(:)'; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes2 = [onodes onodes+ss]; +ns(onodes2) = 1; + +logscale = zeros(1,T); +H = length(hnodes); +local_logscale = zeros(1,ss); + +t = 1; +for i=hnodes + fwd{i,t} = CPDpot{i,t}; + c = engine.obschild(i); + if 0 % c > 0 + fwd{i,t} = multiply_by_pot(fwd{i,t}, CPDpot{c, t}); + end + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); +end +logscale(t) = sum(local_logscale); + +for t=2:T + for i=hnodes + ps = parents(bnet.dag, i+ss); + assert(all(ps<=ss)); % in previous slice + prior = CPDpot{i,t}; + for p=ps(:)' + prior = multiply_by_pot(prior, fwd{p,t-1}); + end + fwd{i,t} = marginalize_pot(prior, i+ss); + fwd{i,t} = set_domain_pot(fwd{i,t}, i); + c = engine.obschild(i); + if 0 % c > 0 + fwd{i,t} = multiply_by_pot(fwd{i,t}, CPDpot{c,t}); + end + [fwd{i,t}, local_logscale(i)] = normalize_pot(fwd{i,t}); + end + logscale(t) = sum(local_logscale); +end + +loglik = sum(logscale); + +back = cell(ss,T); +t = T; +for i=hnodes + pot = dpot(i, ns(i)); + cs = children(bnet.intra, i); + for c=cs(:)' + pot = multiply_pots(pot, CPDpot{c,t}); + end + back{i,t} = marginalize_pot(pot, i); + back{i,t} = normalize_pot(back{i,t}); + back{i,t} = set_domain_pot(back{i,t}, i+ss); +end +for t=T-1:-1:1 + for i=hnodes + pot = dpot(i, ns(i)); + cs = children(bnet.inter, i); + for c=cs(:)' + pot = multiply_pots(pot, back{c,t+1}); + pot = multiply_pots(pot, CPDpot{c,t+1}); + end + cs = children(bnet.intra, i); + for c=cs(:)' + pot = multiply_pots(pot, CPDpot{c,t}); + end + back{i,t} = marginalize_pot(pot, i); + back{i,t} = normalize_pot(back{i,t}); + back{i,t} = set_domain_pot(back{i,t}, i+ss); + end +end + + +% COMBINE +for t=1:T + for i=hnodes + back{i,t} = set_domain_pot(back{i,t}, i); + fwd{i,t} = multiply_by_pot(fwd{i,t}, back{i,t}); + marginals{i,t} = normalize_pot(fwd{i,t}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/frontier_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fwdback.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@frontier_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (frontier) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +onodes = find(~isemptycell(evidence)); +cnodes = unroll_set(bnet.cnodes(:), ss, T); +pot_type = determine_pot_type(bnet, onodes); + +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type); + +[engine.fwdback, loglik, engine.fwd_frontier, engine.back_frontier] = ... + enter_soft_evidence(engine, CPDpot, onodes, pot_type, filter); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,142 @@ +function [fwdback, loglik, fwd_frontier, back_frontier] = enter_soft_evidence(engine, CPD, onodes, pot_type, filter) +% ENTER_SOFT_EVIDENCE Add soft evidence to network (frontier) +% [fwdback, loglik] = enter_soft_evidence(engine, CPDpot, onodes, filter) + +if nargin < 3, filter = 0; end + +[ss T] = size(CPD); +bnet = bnet_from_engine(engine); +ns = repmat(bnet.node_sizes_slice(:), 1, T); +cnodes = unroll_set(bnet.cnodes(:), ss, T); + +% FORWARDS +fwd = cell(ss,T); +ll = zeros(1,T); +S = 2*ss; % num. intermediate frontiers to get from t to t+1 +frontier = cell(S,T); + +% Start with empty frontier, and add each node in slice 1 +init = mk_initial_pot(pot_type, [], ns, cnodes, onodes); +t = 1; +s = 1; +j = 1; +frontier{s,t} = update(init, j, 1, CPD{j}, engine.fdom1{s}, pot_type, ns, cnodes, onodes); +fwd{j} = frontier{s,t}; +for s=2:ss + j = s; % add node j at step s + frontier{s,t} = update(frontier{s-1,t}, j, 1, CPD{j}, engine.fdom1{s}, pot_type, ns, cnodes, onodes); + fwd{j} = frontier{s,t}; +end +frontier{S,t} = frontier{ss,t}; +[frontier{S,t}, ll(1)] = normalize_pot(frontier{S,t}); + +% Now move frontier from slice to slice +OPS = engine.ops; +add = OPS>0; +nodes = [zeros(S,1) unroll_set(abs(OPS(:)), ss, T-1)]; +for t=2:T + offset = (t-2)*ss; + for s=1:S + if s==1 + prev_ndx = (t-2)*S + S; % S,t-1 + else + prev_ndx = (t-1)*S + s-1; % s-1,t + end + j = nodes(s,t); + frontier{s,t} = update(frontier{prev_ndx}, j, add(s), CPD{j}, engine.fdom{s}+offset, pot_type, ns, cnodes, onodes); + if add(s) + fwd{j} = frontier{s,t}; + end + end + [frontier{S,t}, ll(t)] = normalize_pot(frontier{S,t}); +end +loglik = sum(ll); + + +fwd_frontier = frontier; + +if filter + fwdback = fwd; + return; +end + + +% BACKWARDS +back = cell(ss,T); +add = ~add; % forwards add = backwards remove +frontier = cell(S,T+1); +t = T; +dom = (1:ss) + (t-1)*ss; +frontier{1,T+1} = mk_initial_pot(pot_type, dom, ns, cnodes, onodes); % all 1s for last slice +for t=T:-1:2 + offset = (t-2)*ss; + for s=S:-1:1 % reverse order + if s==S + prev_ndx = t*S + 1; % 1,t+1 + else + prev_ndx = (t-1)*S + (s+1); % s+1,t + end + j = nodes(s,t); + if ~add(s) + back{j} = frontier{prev_ndx}; % save frontier before removing + end + frontier{s,t} = rev_update(frontier{prev_ndx}, t, s, j, add(s), CPD{j}, engine.fdom{s}+offset, pot_type, ns, cnodes, onodes); + end + frontier{1,t} = normalize_pot(frontier{1,t}); +end +% Remove each node in first slice until left with empty set +t = 1; +frontier{ss+1,t} = frontier{1,2}; +add = 0; +for s=ss:-1:1 + j = s; % remove node j at step s + back{j} = frontier{s+1,t}; + frontier{s,t} = rev_update(frontier{s+1,t}, t, s, j, add, CPD{j}, 1:s, pot_type, ns, cnodes, onodes); +end + +% COMBINE +for t=1:T + for i=1:ss + %fwd{i,t} = multiply_by_pot(fwd{i,t}, back{i,t}); + %fwdback{i,t} = normalize_pot(fwd{i,t}); + fwdback{i,t} = normalize_pot(multiply_pots(fwd{i,t}, back{i,t})); + end +end + +back_frontier = frontier; + +%%%%%%%%%% +function new_frontier = update(old_frontier, j, add, CPD, newdom, pot_type, ns, cnodes, onodes) + +if add + new_frontier = mk_initial_pot(pot_type, newdom, ns, cnodes, onodes); + new_frontier = multiply_by_pot(new_frontier, old_frontier); + new_frontier = multiply_by_pot(new_frontier, CPD); +else + new_frontier = marginalize_pot(old_frontier, mysetdiff(domain_pot(old_frontier), j)); +end + + +%%%%%% +function new_frontier = rev_update(old_frontier, t, s, j, add, CPD, junk, pot_type, ns, cnodes, onodes) + +olddom = domain_pot(old_frontier); +assert(isequal(junk, olddom)); + +if add + % add: extend domain to include j by multiplying by 1 + newdom = myunion(olddom, j); + new_frontier = mk_initial_pot(pot_type, newdom, ns, cnodes, onodes); + new_frontier = multiply_by_pot(new_frontier, old_frontier); + %fprintf('t=%d, s=%d, add %d to %s to make %s\n', t, s, j, num2str(olddom), num2str(newdom)); +else + % remove: multiply in CPT and then marginalize out j + % parents of j are guaranteed to be in old_frontier, else couldn't have added j on fwds pass + old_frontier = multiply_by_pot(old_frontier, CPD); + newdom = mysetdiff(olddom, j); + new_frontier = marginalize_pot(old_frontier, newdom); + %newdom2 = domain_pot(new_frontier); + %fprintf('t=%d, s=%d, rem %d from %s to make %s\n', t, s, j, num2str(olddom), num2str(newdom2)); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/frontier_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,121 @@ +function engine = frontier_inf_engine(bnet) +% FRONTIER_INF_ENGINE Inference engine for DBNs which which uses the frontier algorithm. +% engine = frontier_inf_engine(bnet) +% +% The frontier algorithm extends the forwards-backwards algorithm to DBNs in the obvious way, +% maintaining a joint distribution (frontier) over all the nodes in a time slice. +% When all the hidden nodes in the DBN are persistent (have children in the next time slice), +% its theoretical running time is often similar to that of the junction tree algorithm, +% although in practice, this algorithm seems to very slow (at least in matlab). +% However, it is extremely simple to describe and implement. +% +% Suppose there are n binary nodes per slice, so the frontier takes O(2^n) space. +% Each time step takes between O(n 2^{n+1}) and O(n 2^{2n}) operations, depending on the graph structure. +% The lower bound is achieved by a set of n independent chains, as in a factorial HMM. +% The upper bound is achieved by a set of n fully interconnected chains, as in an HMM. +% +% The factor of n arises because we need to multiply in each CPD from slice t+1. +% The second factor depends on the size of the frontier to which we add the new node. +% In an FHMM, once we have added X(i,t+1), we can marginalize out X(i,t) from the frontier, since +% no other nodes depend on it; hence the frontier never contains more than n+1 nodes. +% In a fully coupled HMM, we must leave X(i,t) in the frontier until all X(j,t+1) have been +% added; hence the frontier will contain 2*n nodes at its peak. +% +% For details, see +% "The Factored Frontier Algorithm for Approximate Inference in DBNs", +% Kevin Murphy and Yair Weiss, UAI 01. + +ns = bnet.node_sizes_slice; +onodes = bnet.observed; +ns(onodes) = 1; +ss = length(bnet.intra); + +[engine.ops, engine.fdom] = best_first_frontier_seq(ns, bnet.dag); +engine.ops1 = 1:ss; + +engine.fwdback = []; +engine.fwd_frontier = []; +engine.back_frontier = []; + +engine.fdom1 = cell(1,ss); +for s=1:ss + engine.fdom1{s} = 1:s; +end + +engine = class(engine, 'frontier_inf_engine', inf_engine(bnet)); + + +%%%%%%%%% + +function [ops, frontier_set] = best_first_frontier_seq(ns, dag) +% BEST_FIRST_FRONTIER_SEQ Do a greedy search for the sequence of additions/removals to the frontier. +% [ops, frontier_set] = best_first_frontier_seq(ns, dag) +% +% We maintain 3 sets: the frontier (F), the right set (R), and the left set (L). +% The invariant is that the nodes in R are d-separated from L given F. +% We start with slice 1 in F and slice 2 in R. +% The goal is to move slice 1 from F to L, and slice 2 from R to F, so as to minimize the size +% of the frontier at each step, where the size(F) = product of the node-sizes of nodes in F. +% A node may be removed (from F to L) if it has no children in R. +% A node may be added (from R to F) if its parents are in F. +% +% ns(i) = num. discrete values node i can take on (i=1..ss, where ss = slice size) +% dag is the (2*ss) x (2*ss) adjacency matrix for the 2-slice DBN. + +% Example: +% +% 4 9 +% ^ ^ +% | | +% 2 -> 7 +% ^ ^ +% | | +% 1 -> 6 +% | | +% v v +% 3 -> 8 +% | | +% v V +% 5 10 +% +% ops = -4, -5, 6, -1, 7, -2, 8, -3, 9, 10 + +ss = length(ns); +ns = [ns(:)' ns(:)']; +ops = zeros(1,ss); +L = []; F = 1:ss; R = (1:ss)+ss; +frontier_set = cell(1,2*ss); +for s=1:2*ss + remcost = inf*ones(1,2*ss); + %disp(['L: ' num2str(L) ', F: ' num2str(F) ', R: ' num2str(R)]); + maybe_removable = myintersect(F, 1:ss); + for n=maybe_removable(:)' + cs = children(dag, n); + if isempty(myintersect(cs, R)) + remcost(n) = prod(ns(mysetdiff(F, n))); + end + end + %remcost + if any(remcost < inf) + n = argmin(remcost); + ops(s) = -n; + L = myunion(L, n); + F = mysetdiff(F, n); + else + addcost = inf*ones(1,2*ss); + for n=R(:)' + ps = parents(dag, n); + if mysubset(ps, F) + addcost(n) = prod(ns(myunion(F, [ps n]))); + end + end + %addcost + assert(any(addcost < inf)); + n = argmin(addcost); + ops(s) = n; + R = mysetdiff(R, n); + F = myunion(F, n); + end + %fprintf('op at step %d = %d\n\n', s, ops(s)); + frontier_set{s} = F; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function marginal = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on node i in slice t and its parents (frontier) +% marginal = marginal_family(engine, i, t) + +bnet = bnet_from_engine(engine); +fam = family(bnet.dag, i, t); +marginal = pot_to_marginal(normalize_pot(marginalize_pot(engine.fwdback{i,t}, fam)));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (frontier) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +assert(length(nodes)==1); +i = nodes(1); +bigpot = engine.fwdback{i,t}; +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +nodes = nodes + (t-1)*ss; +%if t > 1, nodes = nodes + ss; end +marginal = pot_to_marginal(marginalize_pot(bigpot, nodes));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@frontier_inf_engine/set_fwdback.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function engine = set_fwdback(engine, fb) +% SET_FWDBACK Set the field 'fwdback', which contains the frontiers after propagation +% engine = set_fwdback(engine, fb) +% +% This is used by frontier_fast_inf_engine/enter_evidence +% as a workaround for Matlab's annoying privacy control + +engine.fwdback = fb;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +/enter_evidence.m/1.2/Sat Sep 17 17:00:30 2005// +/find_mpe.m/1.1.1.1/Thu Jun 20 00:18:24 2002// +/fwdback_twoslice.m/1.1/Sat Nov 26 01:24:09 2005// +/hmm_inf_engine.m/1.1.1.1/Thu Nov 14 20:05:36 2002// +/marginal_family.m/1.1.1.1/Thu Nov 14 20:05:36 2002// +/marginal_nodes.m/1.1.1.1/Thu Nov 14 20:03:28 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old//// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@hmm_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/dhmm_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@hmm_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/Old/dhmm_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function engine = dhmm_inf_engine(bnet, onodes) +% DHMM_INF_ENGINE Inference engine for discrete DBNs which uses the forwards-backwards algorithm. +% engine = dhmm_inf_engine(bnet, onodes) +% +% 'onodes' specifies which nodes are observed; these must be leaves, and can be discrete or continuous. +% The remaining nodes are all hidden, and must be discrete. +% The DBN is converted to an HMM, with a single meganode, but which may have factored obs. + +ss = length(bnet.intra); +hnodes = mysetdiff(1:ss, onodes); +evidence = cell(ss, 2); +ns = bnet.node_sizes; +Q = prod(ns(hnodes)); +tmp = dpot_to_table(compute_joint_pot(bnet, hnodes, evidence)); +engine.startprob = reshape(tmp, Q, 1); +tmp = dpot_to_table(compute_joint_pot(bnet, [hnodes hnodes+ss], evidence)); +engine.transprob = mk_stochastic(reshape(tmp, Q, Q)); +engine.obsprob = cell(1, length(onodes)); +for i=1:length(onodes) + tmp = dpot_to_table(compute_joint_pot(bnet, [hnodes onodes(i)], evidence)); + O = ns(onodes(i)); + engine.obsprob{i} = mk_stochastic(reshape(tmp, Q, O)); +end + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.gamma = []; +engine.xi = []; + +engine.onodes = onodes; +engine.hnodes = hnodes; +engine.maximize = []; + +engine = class(engine, 'dhmm_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/Old/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (hmm) +% marginal = marginal_nodes(engine, i, t, add_ev) +% + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +if t==1 + fam = family(bnet.dag, i); + bigpot = engine.one_slice_marginal{t}; + nodes = fam; +else + fam = family(bnet.dag, i+ss); + if any(fam <= ss) % family spans 2 slices + bigpot = engine.two_slice_marginal{t-1}; % t-1 and t + nodes = fam + (t-2)*ss; + else + bigpot = engine.one_slice_marginal{t}; + nodes = fam-ss + (t-1)*ss; + end +end + +marginal = pot_to_marginal(marginalize_pot(bigpot, nodes, engine.maximize)); + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/Old/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (hmm) +% marginal = marginal_nodes(engine, nodes, t, add_ev) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +if all(nodes <= ss) + bigpot = engine.one_slice_marginal{t}; +else + bigpot = engine.two_slice_marginal{t}; +end + +nodes = nodes + (t-1)*ss; +marginal = pot_to_marginal(marginalize_pot(bigpot, nodes, engine.maximize)); + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (hmm) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filter - if 1, does filtering, else smoothing [0] +% oneslice - 1 means only compute marginals on nodes within a single slice [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; +oneslice = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + case 'oneslice', oneslice = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +[ss T] = size(evidence); +engine.maximize = maximize; +engine.evidence = evidence; +bnet = bnet_from_engine(engine); +engine.node_sizes = repmat(bnet.node_sizes_slice(:), [1 T]); + +obs_bitv = ~isemptycell(evidence(:)); +bitv = reshape(obs_bitv, ss, T); +for t=1:T + onodes = find(bitv(:,t)); + if ~isequal(onodes, bnet.observed(:)) + error(['dbn was created assuming observed nodes per slice were '... + num2str(bnet.observed(:)') ' but the evidence in slice ' num2str(t) ... + ' has observed nodes ' num2str(onodes(:)')]); + end +end + +obslik = mk_hmm_obs_lik_matrix(engine, evidence); + +%[alpha, beta, gamma, loglik, xi] = fwdback(engine.startprob, engine.transprob, obslik, ... +[alpha, beta, gamma, loglik, xi] = fwdback_twoslice(engine, engine.startprob,... + engine.transprob, obslik, ... + 'maximize', maximize, 'fwd_only', filter, ... + 'compute_xi', ~oneslice); + +engine.one_slice_marginal = gamma; % gamma(:,t) for t=1:T +if ~oneslice + Q = size(gamma,1); + engine.two_slice_marginal = reshape(xi, [Q*Q T-1]); % xi(:,t) for t=1:T-1 +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function mpe = find_mpe(engine, evidence) +% FIND_MPE Find the most probable explanation (Viterbi) +% mpe = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% + +obslik = mk_hmm_obs_lik_matrix(engine, evidence); +path = viterbi_path(engine.startprob, engine.transprob, obslik); +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes_slice; +ns(bnet.observed) = 1; +ass = ind2subv(ns, path); +mpe = num2cell(ass'); +mpe(bnet.observed,:) = evidence(bnet.observed,:); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/fwdback_twoslice.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,198 @@ +function [alpha, beta, gamma, loglik, xi, gamma2] = fwdback_twoslice(engine, init_state_distrib, transmat, obslik, varargin) +% FWDBACK Compute the posterior probs. in an HMM using the forwards backwards algo. +% +% [alpha, beta, gamma, loglik, xi, gamma2] = fwdback(init_state_distrib, transmat, obslik, ...) +% +% Notation: +% Y(t) = observation, Q(t) = hidden state, M(t) = mixture variable (for MOG outputs) +% A(t) = discrete input (action) (for POMDP models) +% +% INPUT: +% init_state_distrib(i) = Pr(Q(1) = i) +% transmat(i,j) = Pr(Q(t) = j | Q(t-1)=i) +% or transmat{a}(i,j) = Pr(Q(t) = j | Q(t-1)=i, A(t-1)=a) if there are discrete inputs +% obslik(i,t) = Pr(Y(t)| Q(t)=i) +% (Compute obslik using eval_pdf_xxx on your data sequence first.) +% +% Optional parameters may be passed as 'param_name', param_value pairs. +% Parameter names are shown below; default values in [] - if none, argument is mandatory. +% +% For HMMs with MOG outputs: if you want to compute gamma2, you must specify +% 'obslik2' - obslik(i,j,t) = Pr(Y(t)| Q(t)=i,M(t)=j) [] +% 'mixmat' - mixmat(i,j) = Pr(M(t) = j | Q(t)=i) [] +% +% For HMMs with discrete inputs: +% 'act' - act(t) = action performed at step t +% +% Optional arguments: +% 'fwd_only' - if 1, only do a forwards pass and set beta=[], gamma2=[] [0] +% 'scaled' - if 1, normalize alphas and betas to prevent underflow [1] +% 'maximize' - if 1, use max-product instead of sum-product [0] +% +% OUTPUTS: +% alpha(i,t) = p(Q(t)=i | y(1:t)) (or p(Q(t)=i, y(1:t)) if scaled=0) +% beta(i,t) = p(y(t+1:T) | Q(t)=i)*p(y(t+1:T)|y(1:t)) (or p(y(t+1:T) | Q(t)=i) if scaled=0) +% gamma(i,t) = p(Q(t)=i | y(1:T)) +% loglik = log p(y(1:T)) +% xi(i,j,t-1) = p(Q(t-1)=i, Q(t)=j | y(1:T)) +% gamma2(j,k,t) = p(Q(t)=j, M(t)=k | y(1:T)) (only for MOG outputs) +% +% If fwd_only = 1, these become +% alpha(i,t) = p(Q(t)=i | y(1:t)) +% beta = [] +% gamma(i,t) = p(Q(t)=i | y(1:t)) +% xi(i,j,t-1) = p(Q(t-1)=i, Q(t)=j | y(1:t)) +% gamma2 = [] +% +% Note: we only compute xi if it is requested as a return argument, since it can be very large. +% Similarly, we only compute gamma2 on request (and if using MOG outputs). +% +% Examples: +% +% [alpha, beta, gamma, loglik] = fwdback(pi, A, multinomial_prob(sequence, B)); +% +% [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat); +% [alpha, beta, gamma, loglik, xi, gamma2] = fwdback(pi, A, B, 'obslik2', B2, 'mixmat', mixmat); + + +if nargout >= 5, compute_xi = 1; else compute_xi = 0; end +if nargout >= 6, compute_gamma2 = 1; else compute_gamma2 = 0; end + +[obslik2, mixmat, fwd_only, scaled, act, maximize, compute_xi, compute_gamma2] = process_options(varargin, 'obslik2', [], 'mixmat', [], 'fwd_only', 0, 'scaled', 1, 'act', [], 'maximize', 0, 'compute_xi', compute_xi, 'compute_gamma2', compute_gamma2); + + +[Q T] = size(obslik); + +if isempty(obslik2) + compute_gamma2 = 0; +end + +if isempty(act) + act = ones(1,T); + transmat = { transmat } ; +end + +scale = ones(1,T); + +% scale(t) = Pr(O(t) | O(1:t-1)) = 1/c(t) as defined by Rabiner (1989). +% Hence prod_t scale(t) = Pr(O(1)) Pr(O(2)|O(1)) Pr(O(3) | O(1:2)) = Pr(O(1), ... ,O(T)) +% or log P = sum_t log scale(t). +% Rabiner suggests multiplying beta(t) by scale(t), but we can instead +% normalise beta(t) - the constants will cancel when we compute gamma. + +loglik = 0; + +alpha = zeros(Q,T); +gamma = zeros(Q,T); +if compute_xi + xi = zeros(Q,Q,T-1); +else + xi = []; +end + + +%%%%%%%%% Forwards %%%%%%%%%% + +t = 1; +alpha(:,1) = init_state_distrib(:) .* obslik(:,t); +if scaled + %[alpha(:,t), scale(t)] = normaliseC(alpha(:,t)); + [alpha(:,t), scale(t)] = normalise(alpha(:,t)); +end +if scaled, assert(approxeq(sum(alpha(:,t)),1)), end +for t=2:T + %trans = transmat(:,:,act(t-1))'; + trans = transmat{act(t-1)}; + if maximize + m = max_mult(trans', alpha(:,t-1)); + %A = repmat(alpha(:,t-1), [1 Q]); + %m = max(trans .* A, [], 1); + else + m = trans' * alpha(:,t-1); + end + alpha(:,t) = m(:) .* obslik(:,t); + if scaled + %[alpha(:,t), scale(t)] = normaliseC(alpha(:,t)); + [alpha(:,t), scale(t)] = normalise(alpha(:,t)); + end + if compute_xi & fwd_only % useful for online EM + %xi(:,:,t-1) = normaliseC((alpha(:,t-1) * obslik(:,t)') .* trans); + xi(:,:,t-1) = normalise((alpha(:,t-1) * obslik(:,t)') .* trans); + end + if scaled, assert(approxeq(sum(alpha(:,t)),1)), end +end +if scaled + if any(scale==0) + loglik = -inf; + else + loglik = sum(log(scale)); + end +else + loglik = log(sum(alpha(:,T))); +end + +if fwd_only + gamma = alpha; + beta = []; + gamma2 = []; + return; +end + + +%%%%%%%%% Backwards %%%%%%%%%% + +beta = zeros(Q,T); +if compute_gamma2 + M = size(mixmat, 2); + gamma2 = zeros(Q,M,T); +else + gamma2 = []; +end + +beta(:,T) = ones(Q,1); +%gamma(:,T) = normaliseC(alpha(:,T) .* beta(:,T)); +gamma(:,T) = normalise(alpha(:,T) .* beta(:,T)); +t=T; +if compute_gamma2 + denom = obslik(:,t) + (obslik(:,t)==0); % replace 0s with 1s before dividing + gamma2(:,:,t) = obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]) ./ repmat(denom, [1 M]); + %gamma2(:,:,t) = normaliseC(obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M])); % wrong! +end +for t=T-1:-1:1 + b = beta(:,t+1) .* obslik(:,t+1); + %trans = transmat(:,:,act(t)); + trans = transmat{act(t)}; + if maximize + B = repmat(b(:)', Q, 1); + beta(:,t) = max(trans .* B, [], 2); + else + beta(:,t) = trans * b; + end + if scaled + %beta(:,t) = normaliseC(beta(:,t)); + beta(:,t) = normalise(beta(:,t)); + end + %gamma(:,t) = normaliseC(alpha(:,t) .* beta(:,t)); + gamma(:,t) = normalise(alpha(:,t) .* beta(:,t)); + if compute_xi + %xi(:,:,t) = normaliseC((trans .* (alpha(:,t) * b'))); + xi(:,:,t) = normalise((trans .* (alpha(:,t) * b'))); + %xi(:,:,t) = (trans .* (alpha(:,t) * b')); + end + if compute_gamma2 + denom = obslik(:,t) + (obslik(:,t)==0); % replace 0s with 1s before dividing + gamma2(:,:,t) = obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M]) ./ repmat(denom, [1 M]); + %gamma2(:,:,t) = normaliseC(obslik2(:,:,t) .* mixmat .* repmat(gamma(:,t), [1 M])); + end +end + + +% We now explain the equation for gamma2 +% Let zt=y(1:t-1,t+1:T) be all observations except y(t) +% gamma2(Q,M,t) = P(Qt,Mt|yt,zt) = P(yt|Qt,Mt,zt) P(Qt,Mt|zt) / P(yt|zt) +% = P(yt|Qt,Mt) P(Mt|Qt) P(Qt|zt) / P(yt|zt) +% Now gamma(Q,t) = P(Qt|yt,zt) = P(yt|Qt) P(Qt|zt) / P(yt|zt) +% hence +% P(Qt,Mt|yt,zt) = P(yt|Qt,Mt) P(Mt|Qt) [P(Qt|yt,zt) P(yt|zt) / P(yt|Qt)] / P(yt|zt) +% = P(yt|Qt,Mt) P(Mt|Qt) P(Qt|yt,zt) / P(yt|Qt) +% \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/hmm_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +function engine = hmm_inf_engine(bnet, varargin) +% HMM_INF_ENGINE Inference engine for DBNs which uses the forwards-backwards algorithm. +% engine = hmm_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - 1 means max-product, 0 means sum-product [0] +% +% The DBN is converted to an HMM with a single meganode, but the observed nodes remain factored. +% This can be faster than jtree if the num. hidden nodes is low, because of lower constant factors. +% +% All hidden nodes must be discrete. +% All observed nodes are assumed to be leaves, i.e., they cannot be parents of anything. +% The parents of each observed leaf are assumed to be a subset of the hidden nodes within the same slice. +% The only exception is if bnet is an AR-HMM, where the parents are assumed to be self in the +% previous slice (continuous), plus all the discrete nodes in the current slice. + +ss = bnet.nnodes_per_slice; + +engine.maximize = 0; +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +% Stuff to do with speeding up marginal_family +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +engine.persist_bitv = zeros(1, ss); +engine.persist_bitv(engine.persist) = 1; + + +ns = bnet.node_sizes(:); +ns(bnet.observed) = 1; +ns(bnet.observed+ss) = 1; +engine.eff_node_sizes = ns; + +for o=bnet.observed(:)' + %if bnet.equiv_class(o,1) ~= bnet.equiv_class(o,2) + % error(['observed node ' num2str(o) ' is not tied']) + %end + cs = children(bnet.dag, o); + if ~isempty(cs) + error(['observed node ' num2str(o) ' is not allowed children']) + end +end + +[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.one_slice_marginal = []; +engine.two_slice_marginal = []; + +ss = length(bnet.intra); +engine.evidence = []; +engine.node_sizes = []; + +% avoid the need to do bnet_from_engine, which is slow +engine.slice_size = ss; +engine.parents = bnet.parents; + +engine = class(engine, 'hmm_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (hmm) +% marginal = marginal_family(engine, i, t, add_ev) + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +ns = engine.eff_node_sizes(:); +ss = engine.slice_size; + +if t==1 | ~engine.persist_bitv(i) + bigT = engine.one_slice_marginal(:,t); + ps = engine.parents{i}; + dom = [ps i] + (t-1)*ss; + bigdom = 1:ss; + bigsz = ns(bigdom); + bigdom = bigdom + (t-1)*ss; +else % some parents are in previous slice + bigT = engine.two_slice_marginal(:,t-1); % t-1 and t + ps = engine.parents{i+ss}; + dom = [ps i+ss] + (t-2)*ss; + bigdom = 1:(2*ss); % domain of xi(:,:,t) + bigsz = ns(bigdom); + bigdom = bigdom + (t-2)*ss; +end +marginal.domain = dom; + +marginal.T = marg_table(bigT, bigdom, bigsz, dom, engine.maximize); +marginal.mu = []; +marginal.Sigma = []; + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (hmm) +% marginal = marginal_nodes(engine, nodes, t, add_ev) +% +% 'nodes' must be a single node. +% t is the time slice. + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +assert(length(nodes)==1) +ss = engine.slice_size; + +i = nodes(1); +bigT = engine.one_slice_marginal(:,t); +dom = i + (t-1)*ss; + +ns = engine.eff_node_sizes(:); +bigdom = 1:ss; +marginal.T = marg_table(bigT, bigdom + (t-1)*ss, ns(bigdom), dom, engine.maximize); + +marginal.domain = dom; +marginal.mu = []; +marginal.Sigma = []; + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/mk_hmm_obs_lik_matrix.m/1.1.1.1/Sun May 4 21:42:26 2003// +/mk_hmm_obs_lik_vec.m/1.1.1.1/Thu Jan 23 18:50:10 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@hmm_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_matrix.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function obslik = mk_hmm_obs_lik_matrix(engine, evidence) + +T = size(evidence,2); +Q = length(engine.startprob); +obslik = ones(Q, T); +bnet = bnet_from_engine(engine); +% P(o1,o2| Q1,Q2) = P(o1|Q1,Q2) * P(o2|Q1,Q2) +onodes = bnet.observed; +for i=1:length(onodes) + data = cell2num(evidence(onodes(i),:)); + if bnet.auto_regressive(onodes(i)) + params = engine.obsprob{i}; + mu = params.big_mu; + Sigma = params.big_Sigma, + W = params.big_W; + mu0 = params.big_mu0; + Sigma0 = params.big_Sigma0; + %obslik_i = mk_arhmm_obs_lik(data, mu, Sigma, W, mu0, Sigma0 + obslik_i = clg_prob(data(:,1:T-1), data(:,2:T), mu, Sigma, W); + obslik_i = [mixgauss_prob(data(:,1), mu0, Sigma0) obslik_i]; + elseif myismember(onodes(i), bnet.dnodes) + %obslik_i = eval_pdf_cond_multinomial(data, engine.obsprob{i}.big_CPT); + obslik_i = multinomial_prob(data, engine.obsprob{i}.big_CPT); + else + %obslik_i = eval_pdf_cond_gauss(data, engine.obsprob{i}.big_mu, engine.obsprob{i}.big_Sigma); + obslik_i = mixgauss_prob(data, engine.obsprob{i}.big_mu, engine.obsprob{i}.big_Sigma); + end + obslik = obslik .* obslik_i; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_vec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function obslik = mk_hmm_obs_lik_vec(engine, evidence) + +% P(o1,o2| h) = P(o1|h) * P(o2|h) where h = Q1,Q2,... + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +ns = bnet.node_sizes(:); +ns(onodes) = 1; + +Q = length(engine.startprob); +obslik = ones(Q, 1); + +for i=1:length(onodes) + o = onodes(i); + %data = cell2num(evidence(o,1)); + data = evidence{o,1}; + if myismember(o, bnet.dnodes) + obslik_i = eval_pdf_cond_multinomial(data, engine.obsprob{i}.CPT); + else + if bnet.auto_regressive(o) + error('can''t handle AR nodes') + end + %% calling mk_ghmm_obs_lik, which calls gaussian_prob, is slow, so we inline it + %% and use the pre-computed inverse matrix + %obslik_i = mk_ghmm_obs_lik(data, engine.obsprob{i}.mu, engine.obsprob{i}.Sigma); + x = data(:); + m = engine.obsprob{i}.mu; + Qi = size(m, 2); + obslik_i = size(Qi, 1); + invC = engine.obsprob{i}.inv_Sigma; + denom = engine.obsprob{i}.denom; + for j=1:Qi + numer = exp(-0.5 * (x-m(:,j))' * invC(:,:,j) * (x-m(:,j))); + obslik_i(j) = numer / denom(j); + end + end + % convert P(o|ps) into P(o|h) by multiplying onto a (h,o) potential of all 1s + ps = bnet.parents{o}; + dom = [ps o]; + obspot_i = dpot(dom, ns(dom), obslik_i); + dom = [hnodes o]; + obspot = dpot(dom, ns(dom)); + obspot = multiply_by_pot(obspot, obspot_i); + % compute p(oi|h) * p(oj|h) + S = struct(obspot); + obslik = obslik .* S.T(:); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@hmm_inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (hmm) +% engine = update_engine(engine, newCPDs) + +%engine.inf_engine.bnet.CPD = newCPDs; +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet_from_engine(engine)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Broken/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/enter_soft_evidence1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence2.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence3.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence4.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Broken/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Broken/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,119 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +assert(C==engine.jtree_struct.root_clq); +D = engine.in_clq; +slice1 = 1:ss; +slice2 = slice1 + ss; +for t=2:T + if t==2 + clqs = engine.jtree_struct.clq_ass_to_node([slice1 slice2]); + pots = CPDpot(:,t-1:t); + else + %clqs = [D; engine.clq_ass_to_node(:,2)]; + clqs = [D engine.jtree_struct.clq_ass_to_node(slice2)]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + pots = [ {phiC}; CPDpot(:,t)]; % CPDpot domains are always slice 2 + end + [clpot(:,t), seppot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + find(observed(:,t-1:t)), bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); +end + + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:2 + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + if t >= 3 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,143 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = ones(1,T); % log(logscale(1)) = 0 +bnet = bnet_from_engine(engine); + +slice1 = 1:ss; +slice2 = slice1+ss; + +% calibrate each 2-slice jtree in isolation +for t=2:T + if t==2 + clqs = engine.jtree_struct.clq_ass_to_node([slice1 slice2]); + pots = CPDpot(:,t-1:t); + else + clqs = engine.jtree_struct.clq_ass_to_node(slice2); + pots = CPDpot(:,t); % CPDpot domains are always slice 2 + end + [clpot(:,t), sepot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + find(observed(:,t-1:t)), bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); +end + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +D = engine.in_clq; +for t=2:T-1 + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + phiD = marginalize_pot(clpot{D,t+1}, engine.interface, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t+1} = multiply_by_pot(clpot{D,t+1}, ratio); + + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(1); +end + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:2 + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(1); + + if t >= 3 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + +%%%%%%%%%% + +function [clpot, seppot] = calibrate(engine, clpot, seppot) + + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,125 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +assert(C==engine.jtree_struct.root_clq); +D = engine.in_clq; +slice1 = 1:ss; +slice2 = slice1 + ss; +Ntransient = length(engine.transient); +trans = cell(Ntransient,1); +for t=2:T + if t==2 + clqs = engine.jtree_struct.clq_ass_to_node([slice1 slice2]); + pots = CPDpot(:,t-1:t); + else + %clqs = [D; engine.clq_ass_to_node(:,2)]; + clqs = [D engine.jtree_struct.clq_ass_to_node([engine.transient slice2])]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{engine.transient(i), t-1}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; trans; CPDpot(:,t)]; + end + [clpot(:,t), seppot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + find(observed(:,t-1:t)), bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); +end + + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:2 + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + if t >= 3 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence4.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,149 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = ones(1,T); % log(logscale(1)) = 0 +bnet = bnet_from_engine(engine); + +slice1 = 1:ss; +slice2 = slice1+ss; +Ntransient = length(engine.transient); +trans = cell(Ntransient,1); + +% calibrate each 2-slice jtree in isolation +for t=2:T + if t==2 + clqs = engine.jtree_struct.clq_ass_to_node([slice1 slice2]); + pots = CPDpot(:,t-1:t); + else + clqs = engine.jtree_struct.clq_ass_to_node([engine.transient slice2]); + for i=1:Ntransient + trans{i} = CPDpot{engine.transient(i), t-1}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ trans; CPDpot(:,t)]; + end + [clpot(:,t), sepot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + find(observed(:,t-1:t)), bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); +end + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +D = engine.in_clq; +for t=2:T-1 + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + phiD = marginalize_pot(clpot{D,t+1}, engine.interface, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t+1} = multiply_by_pot(clpot{D,t+1}, ratio); + + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(1); +end + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:2 + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(1); + + if t >= 3 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + +%%%%%%%%%% + +function [clpot, seppot] = calibrate(engine, clpot, seppot) + + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Broken/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function marginal = marginal_nodes(engine, nodes, t, fam) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk) +% +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% +% marginal = marginal_nodes(engine, query, t) +% returns Pr(X(query(1),t), ... X(query(end),t) | Y(1:T)), +% where 't' specifies the time slice of the earliest node in the query. +% 'query' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3. + +if nargin < 3, t = 1; end +if nargin < 4, fam = 0; else fam = 1; end + + +% clpot{t} contains slice t-1 and t +% Example +% clpot #: 1 2 3 +% slices: 1 1,2 2,3 +% For filtering, we must take care not to take future evidence into account. +% For smoothing, clpot{1} does not exist. + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +if t < engine.T + slice = t+1; + nodes2 = nodes; +else % earliest t is T, so all nodes fit in one slice + slice = engine.T; + nodes2 = nodes + ss; +end + +c = clq_containing_nodes(engine.jtree_engine, nodes2, fam); +assert(c >= 1); + +%disp(['computing marginal on ' num2str(nodes) ' t = ' num2str(t)]); +%disp(['using ' num2str(nodes2) ' slice = ' num2str(slice) 'clq = ' num2str(c)]); + +bigpot = engine.clpot{c, slice}; + +pot = marginalize_pot(bigpot, nodes2, engine.maximize); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Sat Jan 11 18:41:30 2003// +/enter_soft_evidence.m/1.1.1.1/Thu Feb 19 01:12:08 2004// +/jtree_dbn_inf_engine.m/1.1.1.1/Thu Nov 14 16:32:00 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Fri Nov 22 23:51:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +A D/Broken//// +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/enter_soft_evidence_nonint.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence_trans.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_dbn_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_dbn_inf_engine1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_dbn_inf_engine2.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_dbn_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Old/enter_soft_evidence_nonint.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +assert(C==engine.jtree_struct.root_clq); +D = engine.in_clq; +slice1 = 1:ss; +slice2 = slice1 + ss; +Nnonint = length(engine.nonint); +nonint = cell(Nnonint, 1); +for t=1:T + if t==1 + pots = [CPDpot(:,1); CPDpot(engine.interface, 2)]; + clqs = engine.jtree_struct.clq_ass_to_node([slice1 engine.interface+ss]); + obs = find(observed(:,1:2)); + elseif t==T + clqs = [D engine.jtree_struct.clq_ass_to_node(engine.nonint)]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Nnonint + nonint{i} = CPDpot{engine.nonint(i), t}; + nonint{i} = set_domain_pot(nonint{i}, domain_pot(nonint{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; nonint]; + obs = find(observed(:,T)); + else + clqs = [D engine.jtree_struct.clq_ass_to_node([engine.nonint engine.interface+ss])]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Nnonint + nonint{i} = CPDpot{engine.nonint(i), t}; + nonint{i} = set_domain_pot(nonint{i}, domain_pot(nonint{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; nonint; CPDpot(engine.interface, t+1)]; + obs = find(observed(:,t:t+1)); + end + [clpot(:,t), seppot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + obs, bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); +end + + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:1 + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + %logscale(t) = ll(C); + + if t >= 2 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Old/enter_soft_evidence_trans.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type, filter) + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). +% Then propagate from D to later slices. + +C = engine.out_clq; +assert(C==engine.jtree_struct.root_clq); +D = engine.in_clq; +slice1 = 1:ss; +slice2 = slice1 + ss; +Ntransient = length(engine.transient); +trans = cell(Ntransient,1); +for t=1:T + if t==1 + pots = [CPDpot(:,1); CPDpot(engine.persist, 2)]; + clqs = engine.jtree_struct.clq_ass_to_node([slice1 engine.persist+ss]); + obs = find(observed(:,1:2)); + elseif t==T + clqs = [D engine.jtree_struct.clq_ass_to_node(engine.transient)]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{engine.transient(i), t}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; trans]; + obs = find(observed(:,T)); + else + clqs = [D engine.jtree_struct.clq_ass_to_node([engine.transient engine.persist+ss])]; + phiC = set_domain_pot(phiC, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{engine.transient(i), t}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phiC}; trans; CPDpot(engine.persist, t+1)]; + obs = find(observed(:,t:t+1)); + end + [clpot(:,t), seppot(:,:,t)] = init_pot(engine.jtree_struct.cliques, clqs, pots, pot_type, ... + obs, bnet.node_sizes(:), bnet.cnodes); + [clpot(:,t), seppot(:,:,t)] = collect_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.postorder, ... + engine.jtree_struct.postorder_parents,... + engine.jtree_struct.separator); + + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(C); + + phiC = marginalize_pot(clpot{C,t}, engine.interface+ss, engine.maximize); +end + + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +C = engine.in_clq; +D = engine.out_clq; +for t=T:-1:1 + [clpot(:,t), seppot(:,:,t)] = distribute_evidence(clpot(:,t), seppot(:,:,t), engine.maximize, ... + engine.jtree_struct.preorder, ... + engine.jtree_struct.preorder_children, ... + engine.jtree_struct.separator); + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + %logscale(t) = ll(C); + + if t >= 2 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end + +loglik = sum(logscale); + + +%%%%%%% +function [clpot, seppot] = init_pot(cliques, clqs, pots, pot_type, onodes, ns, cnodes); + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, ns, cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1 + + +%%%% +function [clpot, seppot] = collect_evidence(clpot, seppot, maximize, postorder, postorder_parents,... + separator) +for n=postorder %postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}, maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + + +%%%% +function [clpot, seppot] = distribute_evidence(clpot, seppot, maximize, preorder, preorder_children,... + separator) +for n=preorder + for c=preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function engine = jtree_dbn_inf_engine(bnet, varargin) +% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. + +ss = length(bnet.intra); + +onodes = []; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'observed', onodes = args{i+1}; + end + end +end + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +%engine.interface = engine.persist; % WRONG! +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +if 0 + % Create a 2 slice jtree + % We force there to be cliques containing the in and out interfaces for slices t and t+1. + obs_nodes = [onodes(:) onodes(:)+ss]; + engine.jtree_engine = jtree_inf_engine(bnet, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +else + % Create a "1.5 slice" jtree, containing slice 1 and the interface nodes of slice 2 + nodes15 = [1:ss int+ss]; + N = length(nodes15); + dag15 = bnet.dag(nodes15, nodes15); + ns15 = bnet.node_sizes(nodes15); + eclass15 = bnet.equiv_class(nodes15); + discrete_bitv = zeros(1,2*ss); + discrete_bitv(bnet.dnodes) = 1; + discrete15 = find(discrete_bitv(nodes15)); + bnet15 = mk_bnet(dag15, ns15, 'equiv_class', eclass15, 'discrete', discrete15); + bnet15.CPD = bnet.CPD; % CPDs for non-interface nodes in slice 2 will not be used + obs_bitv = zeros(1, 2*ss); + obs_bitv([onodes onodes+ss]) = 1; + obs_nodes15 = find(obs_bitv(nodes15)); + int_bitv = zeros(1,ss); + int_bitv(int) = 1; + engine.jtree_engine = jtree_inf_engine(bnet15, 'observed', obs_nodes15(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +end + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.jtree_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.jtree_engine, i+ss); +end + +engine.jtree_struct = struct(engine.jtree_engine); % violate object privacy + +% stuff needed by marginal_nodes +engine.clpot = []; +engine.maximize = []; +engine.T = []; + +engine = class(engine, 'jtree_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function engine = jtree_dbn_inf_engine(bnet, varargin) +% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. + +ss = length(bnet.intra); + +onodes = []; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'observed', onodes = args{i+1}; + end + end +end + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +%engine.interface = engine.persist; % WRONG! +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +if 1 + % Create a 2 slice jtree + % We force there to be cliques containing the in and out interfaces for slices t and t+1. + obs_nodes = [onodes(:) onodes(:)+ss]; + engine.jtree_engine = jtree_inf_engine(bnet, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +else + % Create a "1.5 slice" jtree, containing slice 1 and the interface nodes of slice 2 + % To keep the node numbering the same, we simply disconnect the non-interface nodes + % from slice 2. + intra15 = bnet.intra; + for i=engine.nonint(:)' + intra15(i,:) = 0; + intra15(:,i) = 0; + end + bnet15 = mk_dbn(intra15, bnet.inter, bnet.node_sizes_slice, bnet.dnodes_slice, ... + bnet.equiv_class(:,1), bnet.equiv_class(:,2), bnet.intra); + obs_nodes = [onodes(:) onodes(:)+ss]; + engine.jtree_engine = jtree_inf_engine(bnet15, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +end + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); + +engine.clq_ass_to_node = zeros(ss, 2); +for i=1:ss + engine.clq_ass_to_node(i, 1) = clq_containing_nodes(engine.jtree_engine, i); + engine.clq_ass_to_node(i, 2) = clq_containing_nodes(engine.jtree_engine, i+ss); +end + +engine.jtree_struct = struct(engine.jtree_engine); % violate object privacy + +% stuff needed by marginal_nodes +engine.clpot = []; +engine.maximize = []; +engine.T = []; + +engine = class(engine, 'jtree_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function engine = jtree_dbn_inf_engine(bnet, varargin) +% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. + +ss = length(bnet.intra); + +onodes = []; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'observed', onodes = args{i+1}; + end + end +end + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +%engine.interface = engine.persist; % WRONG! +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + + +% Create a 2 slice jtree +% We force there to be cliques containing the in and out interfaces for slices t and t+1. +obs_nodes = [onodes(:) onodes(:)+ss]; +engine.jtree_engine = jtree_inf_engine(bnet, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.jtree_struct = struct(engine.jtree_engine); % violate object privacy + + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, bnet.dnodes, bnet.equiv_class(:,1)); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.jtree_engine1 = jtree_inf_engine(bnet1, 'observed', onodes, 'clusters', {int}, ... + 'root', int); + +engine.in_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.jtree_struct1 = struct(engine.jtree_engine1); % violate object privacy + + + + +% stuff needed by marginal_nodes +engine.clpot = []; +engine.T = []; +engine.maximize = []; + +engine = class(engine, 'jtree_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [engine.maximize] +% softCPDpot{n,t} - use soft potential for node n instead of its CPD; set to [] to use CPD +% soft_evidence_nodes(i,1:2) = [n t] means the i'th piece of soft evidence is on node n in slice t +% soft_evidence{i} - prob distribution over values for soft_evidence_nodes(i,:) +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + + +% for add_ev in marginal_nodes +T = size(evidence, 2); +engine.evidence = evidence; +bnet = bnet_from_engine(engine); +ss = length(bnet.node_sizes_slice); +ns = bnet.node_sizes_slice(:); +engine.node_sizes = repmat(ns, [1 T]); +softCPDpot = cell(ss,T); +soft_evidence = {}; +soft_evidence_nodes = []; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + case 'softCPDpot', softCPDpot = args{i+1}; + case 'soft_evidence', soft_evidence = args{i+1}; + case 'soft_evidence_nodes', soft_evidence_nodes = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine.jtree_engine = set_fields(engine.jtree_engine, 'maximize', engine.maximize); +engine.jtree_engine1 = set_fields(engine.jtree_engine1, 'maximize', engine.maximize); + +[ss T] = size(evidence); +engine.T = T; +observed_bitv = ~isemptycell(evidence); +onodes = find(observed_bitv); +pot_type = determine_pot_type(bnet, onodes); +CPDpot = convert_dbn_CPDs_to_pots(bnet, evidence, pot_type, softCPDpot); + +if ~isempty(soft_evidence_nodes) + nsoft = size(soft_evidence_nodes,1); + for i=1:nsoft + n = soft_evidence_nodes(i,1); + t = soft_evidence_nodes(i,2); + if t==1 + dom = n; + else + dom = n+ss; + end + pot = dpot(dom, ns(n), soft_evidence{i}); + CPDpot{n,t} = multiply_by_pot(CPDpot{n,t}, pot); + end +end + +[engine.clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed_bitv, pot_type);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +function [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified soft evidence to the network (jtree_dbn) +% [clpot, loglik] = enter_soft_evidence(engine, CPDpot, observed, pot_type) + +scale = 1; +verbose = 0; + +[ss T] = size(CPDpot); +Q = length(engine.jtree_struct.cliques); +clpot = cell(Q,T); % clpot{t} contains evidence from slices (t-1, t) +seppot = cell(Q,Q,T); +ll = zeros(1,Q); +logscale = zeros(1,T); +bnet = bnet_from_engine(engine); +root = engine.jtree_struct.root_clq; + +% Forwards pass. +% Compute distribution on clq C, +% where C is the out interface to (t-1,t). +% Then pass this to clq D, where D is the in inferface to (t+1,t). + +% Then propagate from D to later slices. + +slice1 = 1:ss; +slice2 = slice1 + ss; +transient = engine.transient; +persist = engine.persist; +Ntransient = length(transient); +trans = cell(Ntransient,1); +if verbose, fprintf('forward pass\n'); end +for t=1:T + if verbose, fprintf('%d ', t); end + if t==1 + pots = [CPDpot(:,1); CPDpot(persist, 2)]; + clqs = engine.jtree_struct.clq_ass_to_node([slice1 persist+ss]); + obs = find(observed(:,1:2)); + elseif t==T + clqs = [engine.in_clq1 engine.jtree_struct1.clq_ass_to_node(transient)]; + phi = set_domain_pot(phi, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{transient(i), t}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phi}; trans]; + obs = find(observed(:,T)); + else + clqs = [engine.in_clq engine.jtree_struct.clq_ass_to_node([transient persist+ss])]; + phi = set_domain_pot(phi, engine.interface); % shift back to slice 1 + for i=1:Ntransient + trans{i} = CPDpot{transient(i), t}; + trans{i} = set_domain_pot(trans{i}, domain_pot(trans{i})-ss); % shift back to slice 1 + end + pots = [ {phi}; trans; CPDpot(persist, t+1)]; + obs = find(observed(:,t:t+1)); + end + + if t < T + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = init_pot(engine.jtree_engine, clqs, pots, pot_type, obs); + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = collect_evidence(engine.jtree_engine, clpot(1:Q,t), seppot(1:Q,1:Q,t)); + else + Q = length(engine.jtree_struct1.cliques); + root = engine.jtree_struct1.root_clq; + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = init_pot(engine.jtree_engine1, clqs, pots, pot_type, obs); + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = collect_evidence(engine.jtree_engine1, clpot(1:Q,t), seppot(1:Q,1:Q,t)); + end + + + if scale + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + logscale(t) = ll(root); + end + + if t < T + % bug fix by Bob Welch 30 Jan 04 + phi = marginalize_pot(clpot{engine.out_clq,t}, engine.interface+ss,engine.maximize); + %phi = marginalize_pot(clpot{root,t}, engine.interface+ss, engine.maximize); + end +end + +if scale +loglik = sum(logscale); +else +loglik = []; +end + + +% Backwards pass. +% Pass evidence from clq C to clq D, +% where C is the in interface to (t,t+1) and D is the out inferface to (t-1,t) +% Then propagate evidence from D to earlier slices. +% (C and D are reversed names from the tech report!) +D = engine.out_clq; +if verbose, fprintf('\nbackwards pass\n'); end +for t=T:-1:1 + if verbose, fprintf('%d ', t); end + + if t == T + Q = length(engine.jtree_struct1.cliques); + C = engine.in_clq1; + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = distribute_evidence(engine.jtree_engine1, clpot(1:Q,t), seppot(1:Q,1:Q,t)); + else + Q = length(engine.jtree_struct.cliques); + C = engine.in_clq; + [clpot(1:Q,t), seppot(1:Q,1:Q,t)] = distribute_evidence(engine.jtree_engine, clpot(1:Q,t), seppot(1:Q,1:Q,t)); + end + + if scale + for c=1:Q + [clpot{c,t}, ll(c)] = normalize_pot(clpot{c,t}); + end + end + + if t >= 2 + phiC = marginalize_pot(clpot{C,t}, engine.interface, engine.maximize); + phiC = set_domain_pot(phiC, engine.interface+ss); % shift forward to slice 2 + phiD = marginalize_pot(clpot{D,t-1}, engine.interface+ss, engine.maximize); + ratio = divide_by_pot(phiC, phiD); + clpot{D,t-1} = multiply_by_pot(clpot{D,t-1}, ratio); + end +end +if verbose, fprintf('\n'); end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/jtree_dbn_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,109 @@ +function engine = jtree_dbn_inf_engine(bnet, varargin) +% JTREE_DBN_INF_ENGINE Junction tree inference algorithm for DBNs. +% engine = jtree_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - specifies variables that must be grouped in the 1.5 slice DBN +% maximize - 1 means max-product, 0 means sum-product [0] +% +% e.g., engine = jtree_dbn_inf_engine(dbn, 'clusters', {[1 2]}); +% +% This uses all of slice t-1 plus the backwards interface of slice t. +% By contrast, jtree_2TBN_inf_engine in the online directory uses +% the forwards interface of slice t-1 plus all of slice t. +% See my thesis for details. + +ss = length(bnet.intra); + +engine.maximize = 0; +clusters = {}; + +args = varargin; +for i=1:2:length(args) + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + + +engine.evidence = []; +engine.node_sizes = []; + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +onodes = bnet.observed; + +if 0 + % Create a 2 slice jtree + % We force there to be cliques containing the in and out interfaces for slices t and t+1. + obs_nodes = [onodes(:) onodes(:)+ss]; + engine.jtree_engine = jtree_inf_engine(bnet, 'observed', obs_nodes(:), ... + 'clusters', {int, int+ss}, 'root', int+ss); +else + % Create a "1.5 slice" jtree, containing slice 1 and the interface nodes of slice 2 + % To keep the node numbering the same, we simply disconnect the non-interface nodes + % from slice 2, and set their size to 1. + % We do this to speed things up, and so that the likelihood is computed correctly - we do not need to do + % this if we just want to compute marginals. + intra15 = bnet.intra; + for i=engine.nonint(:)' + intra15(i,:) = 0; + intra15(:,i) = 0; + end + dag15 = [bnet.intra bnet.inter; + zeros(ss) intra15]; + ns = bnet.node_sizes(:); + ns(engine.nonint+ss) = 1; % disconnected nodes get size 1 + obs_nodes = [onodes(:) onodes(:)+ss]; + bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ... + 'observed', obs_nodes(:)); + + %bnet15 = mk_dbn(intra15, bnet.inter, bnet.node_sizes_slice, bnet.dnodes_slice, ... + % bnet.equiv_class(:,1), bnet.equiv_class(:,2), bnet.intra); + % with the dbn, we can't independently control the sizes of slice 2 nodes + + if 1 + % use unconstrained elimination, + % but force there to be a clique containing both interfaces + clusters(end+1:end+2) = {int, int+ss}; + engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); + else + % Use constrained elimination - this induces a clique that contain the 2nd interface, + % but not the first. + % Hence we throw in the first interface as an extra. + stages = {1:ss, [1:ss]+ss}; + clusters(end+1:end+2) = {int, int+ss}; + engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, ... + 'stages', stages, 'root', int+ss); + end +end + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.jtree_struct = struct(engine.jtree_engine); % violate object privacy + + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end + +engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); + +engine.in_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.jtree_struct1 = struct(engine.jtree_engine1); % violate object privacy + +% stuff needed by marginal_nodes +engine.clpot = []; +engine.T = []; + +engine = class(engine, 'jtree_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function m = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_dbn) +% marginal = marginal_family(engine, i, t) + +% This is just like inf_engine/marginal_family, except when we call +% marginal_nodes, we provide a 4th argument, to tell it's a family. + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, family(bnet.dag, i), t, add_ev, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + if any(fam<=ss) + % i has a parent in the preceeding slice + % Hence the lowest numbered slice containing the family is t-1 + m = marginal_nodes(engine, fam, t-1, add_ev, 1); + else + % The family all fits inside slice t + % Hence shift the indexes back to slice 1 + m = marginal_nodes(engine, fam-ss, t, add_ev, 1); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_dbn_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev, fam) +% MARGINAL_NODES Compute the marginal on the specified query nodes (bk) +% +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% +% marginal = marginal_nodes(engine, query, t) +% returns Pr(X(query(1),t), ... X(query(end),t) | Y(1:T)), +% where 't' specifies the time slice of the earliest node in the query. +% 'query' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3. +% +% marginal = marginal_nodes(engine, nodes, t, add_ev, fam) +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end +if nargin < 5, fam = 0; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); + +if t==1 | t==engine.T + slice = t; + nodes2 = nodes; +elseif mysubset(nodes, engine.persist) + slice = t-1; + nodes2 = nodes+ss; +else + slice = t; + nodes2 = nodes; +end + +%disp(['computing marginal on ' num2str(nodes) ' t = ' num2str(t) ' fam = ' num2str(fam)]); + +if t==engine.T + c = clq_containing_nodes(engine.jtree_engine1, nodes2, fam); +else + c = clq_containing_nodes(engine.jtree_engine, nodes2, fam); +end +if c == -1 + error(['no clique contains ' nodes2]) +end + + +%disp(['using ' num2str(nodes2) ' slice = ' num2str(slice) ' clq = ' num2str(c)]); + +bigpot = engine.clpot{c, slice}; + +pot = marginalize_pot(bigpot, nodes2, engine.maximize); +%pot = normalize_pot(pot); +marginal = pot_to_marginal(pot); + + +% we convert the domain to the unrolled numbering system +% so that add_ev_to_dmarginal (maybe called in update_ess) extracts the right evidence. +marginal.domain = nodes+(t-1)*ss; + +if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_unrolled_dbn_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function marginal = marginal_family(engine, i, t) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_unrolled_dbn) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +marginal = marginal_family(engine.sub_engine, i + (t-1)*ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/Old/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (jtree_unrolled_dbn) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' must occur in some clique. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +query = nodes + (t-1)*ss; +marginal = marginal_nodes(engine.sub_engine, query);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_unrolled_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% filter - if 1, does filtering (not supported), else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +if filter + error('jtree_unrolled_dbn does not support filtering') +end + +if size(evidence,2) ~= engine.nslices + error(['engine was created assuming there are ' num2str(engine.nslices) ... + ' slices, but evidence has ' num2str(size(evidence,2))]) +end + +[engine.unrolled_engine, loglik] = enter_evidence(engine.unrolled_engine, evidence, 'maximize', maximize); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/jtree_unrolled_dbn_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function engine = jtree_unrolled_dbn_inf_engine(bnet, T, varargin) +% JTREE_UNROLLED_DBN_INF_ENGINE Unroll the DBN for T time-slices and apply jtree to the resulting static net +% engine = jtree_unrolled_dbn_inf_engine(bnet, T, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% useC - 1 means use jtree_C_inf_engine instead of jtree_inf_engine [0] +% constrained - 1 means we constrain ourselves to eliminate slice t before t+1 [1] +% +% e.g., engine = jtree_unrolled_inf_engine(bnet, 'useC', 1); + +% set default params +N = length(bnet.intra); +useC = 0; +constrained = 1; + +if nargin >= 3 + args = varargin; + nargs = length(args); + if isstr(args{1}) + for i=1:2:nargs + switch args{i}, + case 'useC', useC = args{i+1}; + case 'constrained', constrained = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + else + error(['invalid argument name ' args{1}]); + end +end + +bnet2 = dbn_to_bnet(bnet, T); +ss = length(bnet.intra); +engine.ss = ss; + +% If constrained_order = 1 we constrain ourselves to eliminate slice t before t+1. +% This prevents cliques containing nodes from far-apart time-slices. +if constrained + stages = num2cell(unroll_set(1:ss, ss, T), 1); +else + stages = { 1:length(bnet2.dag) }; +end +if useC + jengine = jtree_C_inf_engine(bnet2, 'stages', stages); +else + jengine = jtree_inf_engine(bnet2, 'stages', stages); +end + +engine.unrolled_engine = jengine; +% we don't inherit from jtree_inf_engine, because that would only store bnet2, +% and we would lose access to the DBN-specific fields like intra/inter + +engine.nslices = T; +engine = class(engine, 'jtree_unrolled_dbn_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_unrolled_dbn) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end +assert(~add_ev); + +%marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss, add_ev); +marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (loopy_unrolled_dbn) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' must occur in some clique. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +marginal = marginal_nodes(engine.unrolled_engine, nodes + (t-1)*engine.ss, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@jtree_unrolled_dbn_inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (jtree_unrolled_dbn) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.unrolled_engine = update_engine(engine.unrolled_engine, newCPDs); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/kalman_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@kalman_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,83 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (kalman) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (same as sum-product for Gaussians!), else sum-product [0] +% filter - if 1, do filtering, else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); + +bnet = bnet_from_engine(engine); +n = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:n, onodes); +T = size(evidence, 2); +ns = bnet.node_sizes; +O = sum(ns(onodes)); +data = reshape(cat(1, evidence{onodes,:}), [O T]); + +A = engine.trans_mat; +C = engine.obs_mat; +Q = engine.trans_cov; +R = engine.obs_cov; +init_x = engine.init_state; +init_V = engine.init_cov; + +if filter + [x, V, VV, loglik] = kalman_filter(data, A, C, Q, R, init_x, init_V); +else + [x, V, VV, loglik] = kalman_smoother(data, A, C, Q, R, init_x, init_V); +end + + +% Wrap the posterior inside a potential, so it can be marginalized easily +engine.one_slice_marginal = cell(1,T); +engine.two_slice_marginal = cell(1,T); +ns(onodes) = 0; +ns(onodes+n) = 0; +ss = length(bnet.intra); +for t=1:T + dom = (1:n); + engine.one_slice_marginal{t} = mpot(dom+(t-1)*ss, ns(dom), 1, x(:,t), V(:,:,t)); +end +% for t=1:T-1 +% dom = (1:(2*n)); +% mu = [x(:,t); x(:,t)]; +% Sigma = [V(:,:,t) VV(:,:,t+1)'; +% VV(:,:,t+1) V(:,:,t+1)]; +% engine.two_slice_marginal{t} = mpot(dom+(t-1)*ss, ns(dom), 1, mu, Sigma); +% end +for t=2:T + %dom = (1:(2*n)); + current_slice = hnodes; + next_slice = hnodes + ss; + dom = [current_slice next_slice]; + mu = [x(:,t-1); x(:,t)]; + Sigma = [V(:,:,t-1) VV(:,:,t)'; + VV(:,:,t) V(:,:,t)]; + engine.two_slice_marginal{t-1} = mpot(dom+(t-2)*ss, ns(dom), 1, mu, Sigma); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/kalman_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function engine = kalman_inf_engine(bnet) +% KALMAN_INF_ENGINE Inference engine for Linear-Gaussian state-space models. +% engine = kalman_inf_engine(bnet) +% +% 'onodes' specifies which nodes are observed; these must be leaves. +% The remaining nodes are all hidden. All nodes must have linear-Gaussian CPDs. +% The hidden nodes must be persistent, i.e., they must have children in +% the next time slice. In addition, they may not have any children within the current slice, +% except to the observed leaves. In other words, the topology must be isomorphic to a standard LDS. +% +% There are many derivations of the filtering and smoothing equations for Linear Dynamical +% Systems in the literature. I particularly like the following +% - "From HMMs to LDSs", T. Minka, MIT Tech Report, (no date), available from +% ftp://vismod.www.media.mit.edu/pub/tpminka/papers/minka-lds-tut.ps.gz + +[engine.trans_mat, engine.trans_cov, engine.obs_mat, engine.obs_cov, engine.init_state, engine.init_cov] = ... + dbn_to_lds(bnet); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.one_slice_marginal = []; +engine.two_slice_marginal = []; + +engine = class(engine, 'kalman_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (kalman) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' cannot span more than 2 time slices. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +if all(nodes <= ss) + bigpot = engine.one_slice_marginal{t}; +else + bigpot = engine.two_slice_marginal{t}; +end + +nodes = nodes + (t-1)*ss; +pot = marginalize_pot(bigpot, nodes); +marginal = pot_to_marginal(pot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/dbn_to_lds.m/1.1.1.1/Wed May 29 15:59:56 2002// +/extract_params_from_gbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@kalman_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/private/dbn_to_lds.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function [trans_mat, trans_cov, obs_mat, obs_cov, init_state, init_cov] = dbn_to_lds(bnet) +% DBN_TO_LDS Compute the Linear Dynamical System parameters from the Gaussian DBN. +% [trans_mat, trans_cov, obs_mat, obs_cov, init_state, init_cov] = dbn_to_lds(bnet) + +onodes = bnet.observed; +ss = length(bnet.intra); +num_nodes = ss*2; +assert(isequal(bnet.cnodes_slice, 1:ss)); +[W,D,mu] = extract_params_from_gbn(bnet); + +hnodes = mysetdiff(1:ss, onodes); +bs = bnet.node_sizes(:); % block sizes + +obs_mat = W(block(hnodes,bs), block(onodes,bs))'; +u = block(onodes,bs); +obs_cov = D(u,u); + +trans_mat = W(block(hnodes,bs), block(hnodes + ss, bs))'; +u = block(hnodes + ss, bs); +trans_cov = D(u,u); + +u = block(hnodes,bs); +init_cov = D(u,u); +init_state = mu(u); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/private/extract_params_from_gbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function [B,D,mu] = extract_params_from_gbn(bnet) +% Extract all the local parameters of each Gaussian node, and collect them into global matrices. +% [B,D,mu] = extract_params_from_gbn(bnet) +% +% B(i,j) is a block matrix that contains the transposed weight matrix from node i to node j. +% D(i,i) is a block matrix that contains the noise covariance matrix for node i. +% mu(i) is a block vector that contains the shifted noise mean for node i. + +% In Shachter's model, the mean of each node in the global gaussian is +% the same as the node's local unconditional mean. +% In Alag's model (which we use), the global mean gets shifted. + + +num_nodes = length(bnet.dag); +bs = bnet.node_sizes(:); % bs = block sizes +N = sum(bs); % num scalar nodes + +B = zeros(N,N); +D = zeros(N,N); +mu = zeros(N,1); + +for i=1:num_nodes % in topological order + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + %[m, Sigma, weights] = extract_params_from_CPD(bnet.CPD{e}); + s = struct(bnet.CPD{e}); % violate privacy of object + m = s.mean; Sigma = s.cov; weights = s.weights; + if length(ps) == 0 + mu(block(i,bs)) = m; + else + mu(block(i,bs)) = m + weights * mu(block(ps,bs)); + end + B(block(ps,bs), block(i,bs)) = weights'; + D(block(i,bs), block(i,bs)) = Sigma; +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@kalman_inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (kalman) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +[engine.trans_mat, engine.trans_cov, engine.obs_mat, engine.obs_cov, engine.init_state, engine.init_cov] = ... + dbn_to_lds(bnet_from_engine(engine)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_ev.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/pearl_dbn_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old//// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +/correct_smooth.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/filter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/filter_evidence_obj_oriented.m/1.1.1.1/Wed May 29 15:59:56 2002// +/smooth_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/smooth_evidence_fast.m/1.1.1.1/Wed May 29 15:59:56 2002// +/wrong_smooth.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/correct_smooth.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,244 @@ +function [marginal, msg, loglik] = smooth_evidence(engine, evidence) +% [marginal, msg, loglik] = smooth_evidence(engine, evidence) (pearl_dbn) + +disp('warning: broken'); + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +onodes2 = unroll_set(engine.onodes(:), ss, T); +onodes2 = onodes2(:)'; + +hnodes2 = unroll_set(hnodes(:), ss, T); +hnodes2 = hnodes2(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +msg = init_msgs(bnet2.dag, ns, evidence, bnet2.equiv_class, bnet2.CPD); + +verbose = 0; + +niter = 1; +for iter=1:niter + % FORWARD + for t=1:T + if verbose, fprintf('t=%d\n', t); end + % observed leaves send lambda to parents + for i=engine.onodes(:)' + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + lam_msg = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); disp(lam_msg); end + end + end + + % update pi + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + if verbose, fprintf('%d computes pi\n', n); disp(msg{n}.pi); end + end + + % send pi msg to children + for i=hnodes + n = i + (t-1)*ss; + %cs = myintersect(children(bnet2.dag, n), hnodes2); + cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); disp(pi_msg); end + end + end + end + + % BACKWARD + for t=T:-1:1 + if verbose, fprintf('t = %d\n', t); end + % update lambda + for i=hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + msg{n}.lambda = compute_lambda(n, cs, msg, ns); + if verbose, fprintf('%d computes lambda\n', n); disp(msg{n}.lambda); end + end + % send lambda msgs to parents + for i=hnodes + n = i + (t-1)*ss; + %ps = myintersect(parents(bnet2.dag, n), hnodes2); + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + lam_msg = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); disp(lam_msg); end + end + end + end + +end + + +marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=1:ss + n = i + (t-1)*ss; + [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + marginal{i,t} = bel; + end +end + +loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +lam = msg{n}.lambda_from_self(:); +lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end + + +%%%%%%%%% + +function msg = init_msgs(dag, ns, evidence, eclass, CPD) +% INIT_MSGS Initialize the lambda/pi message and state vectors (pearl_dbn) +% msg = init_msgs(dag, ns, evidence) + +N = length(dag); +msg = cell(1,N); +observed = ~isemptycell(evidence(:)); + +for n=1:N + ps = parents(dag, n); + msg{n}.pi_from_parent = cell(1, length(ps)); + for i=1:length(ps) + p = ps(i); + msg{n}.pi_from_parent{i} = ones(ns(p), 1); + end + + cs = children(dag, n); + msg{n}.lambda_from_child = cell(1, length(cs)); + for i=1:length(cs) + c = cs(i); + msg{n}.lambda_from_child{i} = ones(ns(n), 1); + end + + msg{n}.lambda = ones(ns(n), 1); + msg{n}.lambda_from_self = ones(ns(n), 1); + msg{n}.pi = ones(ns(n), 1); + + % Initialize the lambdas with any evidence + if observed(n) + v = evidence{n}; + %msg{n}.lambda_from_self = zeros(ns(n), 1); + %msg{n}.lambda_from_self(v) = 1; % delta function + msg{n}.lambda = zeros(ns(n), 1); + msg{n}.lambda(v) = 1; % delta function + end + +end + + +%%%%%%%% + +function msg = init_ev_msgs(engine, evidence, msg) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +pot_type = 'd'; +t = 1; +hnodes = mysetdiff(1:ss, engine.onodes); +for i=engine.onodes(:)' + fam = family(bnet.dag, i); + e = bnet.equiv_class(i, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + temp = pot_to_marginal(CPDpot); + msg{i}.lambda_from_self = temp.T; +end +for t=2:T + for i=engine.onodes(:)' + fam = family(bnet.dag, i, 2); % extract from slice t + e = bnet.equiv_class(i, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + msg{n}.lambda_from_self = temp.T; + end +end + + +%%%%%%%%%%% + +function msg = init_ev_msgs2(engine, evidence, msg) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +pot_type = 'd'; +t = 1; +hnodes = mysetdiff(1:ss, engine.onodes); +for i=engine.onodes(:)' + fam = family(bnet.dag, i); + e = bnet.equiv_class(i, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + temp = pot_to_marginal(CPDpot); + msg{i}.lambda_from_self = temp.T; +end +for t=2:T + for i=engine.onodes(:)' + fam = family(bnet.dag, i, 2); % extract from slice t + e = bnet.equiv_class(i, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + msg{n}.lambda_from_self = temp.T; + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,123 @@ +function [engine, loglik] = enter_evidence(engine, evidence, filter) +% ENTER_EVIDENCE Add the specified evidence to the network (pearl_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, filter) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% If filter = 1, we do filtering, otherwise smoothing (default). + +if nargin < 3, filter = 0; end + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +msg = init_msgs(bnet2.dag, ns, evidence); +msg = init_ev_msgs(engine, evidence, msg); + +niter = 1; +for iter=1:niter + % FORWARD + for t=1:T + % update pi + for i=1:ss %hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + %msg{n}.pi = normalise(msg{n}.pi(:) .* msg{n}.lambda_from_self(:)); + end + % send pi msg to children + for i=1:ss % hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + msg{c}.pi_from_parent{j} = normalise(compute_pi_msg(n, cs, msg, c, ns)); + end + end + end + + if filter + disp('skipping smoothing'); + break; + end + + % BACKWARD + for t=T:-1:1 + % update lambda + for i=1:ss % hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + msg{n}.lambda = compute_lambda(n, cs, msg, ns); + end + % send lambda msgs to parents + for i=1:ss % hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + msg{p}.lambda_from_child{j} = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + end + end + end + +end + + +engine.marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=1:ss + n = i + (t-1)*ss; + [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + engine.marginal{i,t} = bel; + end +end + +engine.evidence = evidence; % needed by marginal_nodes and marginal_family +engine.msg = msg; % needed by marginal_family +loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +lam = msg{n}.lambda_from_self(:); +%lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/filter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,146 @@ +function [marginal, msg, loglik] = filter_evidence(engine, evidence) + +error('broken'); + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +onodes = engine.onodes; +hnodes = mysetdiff(1:ss, onodes); +hnodes = hnodes(:)'; + +ns = bnet.node_sizes(:); +onodes2 = [onodes(:); onodes(:)+ss]; +ns(onodes2) = 1; + +verbose = 1; +if verbose, fprintf('\nnew filtering\n'); end + +pot_type = 'd'; +niter = engine.max_iter; + +% msg(i1,t1,i2,j2) (i1,t1) -> (i2,t2) +%lambda_msg = cell(ss,T,ss,T); +%pi_msg = cell(ss,T,ss,T); + +% intra_lambda_msg(i,j,t) (i,t) -> (j,t), i is child +% inter_lambda_msg(i,j,t) (i,t+1) -> (j,t), i is child +% inter_pi_msg(i,j,t) (i,t-1) -> (j,t), i is parent +intra_lambda_msg = cell(ss,ss,T); +inter_lambda_msg = cell(ss,ss,T); +inter_pi_msg = cell(ss,ss,T); + +lambda = cell(ss,T); +pi = cell(ss,T); + +for t=1:T + for i=1:ss + lambda{i,t} = ones(ns(i), 1); + pi{i,t} = ones(ns(i), 1); + + cs = children(bnet.intra, i); + for c=cs(:)' + intra_lambda_msg{c,i,t} = ones(ns(i),1); + end + + cs = children(bnet.inter, i); + for c=cs(:)' + inter_lambda_msg{c,i,t} = ones(ns(i),1); + end + + ps = parents(bnet.inter, i); + for p=ps(:)' + inter_pi_msg{p,i,t} = ones(ns(i), 1); % not used for t==1 + end + end +end + +% each hidden node absorbs lambda from its observed child (if any) +for t=1:T + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, c, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + lam_msg = normalise(temp.T); + %if verbose, fprintf('(%d,%d) sends lambda to (%d,%d)\n', c,t, i,t); disp(lam_msg); end + intra_lambda_msg{c,i,t} = lam_msg; + end + end +end + +% FORWARD +for t=1:T + % update pi + for i=hnodes + if t==1 + e = bnet.equiv_class(i,1); + temp = struct(bnet.CPD{e}); + pi{i,t} = temp.CPT; + else + e = bnet.equiv_class(i,2); + temp = struct(bnet.CPD{e}); + ps = parents(bnet.inter, i); + dom = [ps i+ss]; + pot = dpot(dom, ns(dom), temp.CPT); + for p=ps(:)' + temp = dpot(p, ns(p), inter_pi_msg{p,i,t}); + pot = multiply_by_pot(pot, temp); + end + pot = marginalize_pot(pot, i+ss); + temp = pot_to_marginal(pot); + pi{i,t} = temp.T; + %if verbose, fprintf('(%d,%d) computes pi\n', i,t); disp(pi{i,t}); end + end + + c = engine.obschild(i); + if c > 0 + pi{i,t} = normalise(pi{i,t} .* intra_lambda_msg{c,i,t}); + end + %if verbose, fprintf('(%d,%d) recomputes pi\n', i,t); disp(pi{i,t}); end + if verbose, fprintf('%d recomputes pi\n', i+(t-1)*ss); disp(pi{i,t}); end + end + + % send pi msg to children + for i=hnodes + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pi{i,t}; + for k=cs(:)' + if k ~= c + pot = pot .* inter_lambda_msg{k,i,t}; + end + end + cs2 = children(bnet.intra, i); + for k=cs2(:)' + pot = pot .* intra_lambda_msg{k,i,t}; + end + pot = normalise(pot); + %if verbose, fprintf('(%d,%d) sends pi to (%d,%d)\n', i,t, c,t+1); disp(pot); end + if verbose, fprintf('%d sends pi to %d\n', i+(t-1)*ss, c+t*ss); disp(pot); end + inter_pi_msg{i,c,t+1} = pot; + end + end +end + + +marginal = cell(ss,T); +for t=1:T + for i=hnodes + %marginal{i,t} = normalise(pi{i,t} .* lambda{i,t}); + marginal{i,t} = normalise(pi{i,t}); + end +end + +loglik = 0; + +msg.inter_pi_msg = inter_pi_msg; +msg.inter_lambda_msg = inter_lambda_msg; +msg.intra_lambda_msg = intra_lambda_msg;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/filter_evidence_obj_oriented.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,158 @@ +function [marginal, msg, loglik] = filter_evidence_old(engine, evidence) +% [marginal, msg, loglik] = filter_evidence(engine, evidence) (pearl_dbn) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +msg = init_msgs(bnet2.dag, ns, evidence); +msg = init_ev_msgs(engine, evidence, msg); + +verbose = 1; +if verbose, fprintf('\nold filtering\n'); end + +for t=1:T + % update pi + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + %if verbose, fprintf('%d computes pi\n', n); disp(msg{n}.pi); end + msg{n}.pi = normalise(msg{n}.pi(:) .* msg{n}.lambda_from_self(:)); + if verbose, fprintf('%d recomputes pi\n', n); disp(msg{n}.pi); end + end + % send pi msg to children + for i=hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n,c); disp(pi_msg); end + end + end +end + + +marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=1:ss + n = i + (t-1)*ss; + %[bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + [bel, lik(n)] = normalise(msg{n}.pi); + marginal{i,t} = bel; + end +end + +loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +%lam = msg{n}.lambda_from_self(:); +lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end + + +%%%%%%%%%%% + +function msg = init_msgs(dag, ns, evidence) +% INIT_MSGS Initialize the lambda/pi message and state vectors (pearl_dbn) +% msg = init_msgs(dag, ns, evidence) +% +% We assume all the hidden nodes are discrete. + +N = length(dag); +msg = cell(1,N); +observed = ~isemptycell(evidence(:)); + +for n=1:N + ps = parents(dag, n); + msg{n}.pi_from_parent = cell(1, length(ps)); + for i=1:length(ps) + p = ps(i); + msg{n}.pi_from_parent{i} = ones(ns(p), 1); + end + + cs = children(dag, n); + msg{n}.lambda_from_child = cell(1, length(cs)); + for i=1:length(cs) + c = cs(i); + msg{n}.lambda_from_child{i} = ones(ns(n), 1); + end + + msg{n}.lambda = ones(ns(n), 1); + msg{n}.pi = ones(ns(n), 1); + + msg{n}.lambda_from_self = ones(ns(n), 1); +end + + +%%%%%%%%% + +function msg = init_ev_msgs(engine, evidence, msg) +% Initialize the lambdas with any evidence + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +pot_type = 'd'; +t = 1; +hnodes = mysetdiff(1:ss, engine.onodes); +for i=hnodes(:)' + c = engine.obschild(i); + if c > 0 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + temp = pot_to_marginal(CPDpot); + n = i; + msg{n}.lambda_from_self = temp.T; + end +end +for t=2:T + for i=hnodes(:)' + c = engine.obschild(i); + if c > 0 + fam = family(bnet.dag, c, 2); + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + msg{n}.lambda_from_self = temp.T; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/smooth_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +function [marginal, msg, loglik] = smooth_evidence(engine, evidence) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +onodes = engine.onodes; +hnodes = mysetdiff(1:ss, onodes); +hnodes = hnodes(:)'; + +ns = bnet.node_sizes(:); +onodes2 = [onodes(:); onodes(:)+ss]; +ns(onodes2) = 1; + +verbose = 0; +pot_type = 'd'; +niter = engine.max_iter; + +if verbose, fprintf('new smooth\n'); end + +% msg(i1,t1,i2,j2) (i1,t1) -> (i2,t2) +%lambda_msg = cell(ss,T,ss,T); +%pi_msg = cell(ss,T,ss,T); + +% intra_lambda_msg(i,j,t) (i,t) -> (j,t), i is child +% inter_lambda_msg(i,j,t) (i,t+1) -> (j,t), i is child +% inter_pi_msg(i,j,t) (i,t-1) -> (j,t), i is parent +intra_lambda_msg = cell(ss,ss,T); +inter_lambda_msg = cell(ss,ss,T); +inter_pi_msg = cell(ss,ss,T); + +lambda = cell(ss,T); +pi = cell(ss,T); + +for t=1:T + for i=1:ss + lambda{i,t} = ones(ns(i), 1); + pi{i,t} = ones(ns(i), 1); + + cs = children(bnet.intra, i); + for c=cs(:)' + intra_lambda_msg{c,i,t} = ones(ns(i),1); + end + + cs = children(bnet.inter, i); + for c=cs(:)' + inter_lambda_msg{c,i,t} = ones(ns(i),1); + end + + ps = parents(bnet.inter, i); + for p=ps(:)' + inter_pi_msg{p,i,t} = ones(ns(i), 1); % not used for t==1 + end + end +end + + +% each hidden node absorbs lambda from its observed child (if any) +for t=1:T + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, c, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + lam_msg = normalise(temp.T); + intra_lambda_msg{c,i,t} = lam_msg; + end + end +end + +for iter=1:engine.max_iter + % FORWARD + for t=1:T + % update pi + for i=hnodes + if t==1 + e = bnet.equiv_class(i,1); + CPD = struct(bnet.CPD{e}); + pi{i,t} = CPD.CPT; + else + e = bnet.equiv_class(i,2); + CPD = struct(bnet.CPD{e}); + ps = parents(bnet.inter, i); + dom = [ps i+ss]; + pot = dpot(dom, ns(dom), CPD.CPT); + for p=ps(:)' + temp = dpot(p, ns(p), inter_pi_msg{p,i,t}); + pot = multiply_by_pot(pot, temp); + end + pot = marginalize_pot(pot, i+ss); + temp = pot_to_marginal(pot); + pi{i,t} = temp.T; + end + if verbose, fprintf('%d updates pi\n', i+(t-1)*ss); disp(pi{i,t}); end + end + + % send pi msg to children + for i=hnodes + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pi{i,t}; + for k=cs(:)' + if k ~= c + pot = pot .* inter_lambda_msg{k,i,t}; + end + end + cs2 = children(bnet.intra, i); + for k=cs2(:)' + pot = pot .* intra_lambda_msg{k,i,t}; + end + inter_pi_msg{i,c,t+1} = normalise(pot); + if verbose, fprintf('%d sends pi to %d\n', i+(t-1)*ss, c+t*ss); disp(inter_pi_msg{i,c,t+1}); end + end + end + end + + if verbose, fprintf('backwards\n'); end + % BACKWARD + for t=T:-1:1 + % update lambda + for i=hnodes + pot = ones(ns(i), 1); + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pot .* inter_lambda_msg{c,i,t}; + end + cs = children(bnet.intra, i); + for c=cs(:)' + pot = pot .* intra_lambda_msg{c,i,t}; + end + lambda{i,t} = normalise(pot); + if verbose, fprintf('%d computes lambda\n', i+(t-1)*ss); disp(lambda{i,t}); end + end + + % send lambda msgs to hidden parents in prev slcie + for i=hnodes + ps = parents(bnet.inter, i); + if t > 1 + e = bnet.equiv_class(i, 2); + CPD = struct(bnet.CPD{e}); + fam = [ps i+ss]; + for p=ps(:)' + pot = dpot(fam, ns(fam), CPD.CPT); + temp = dpot(i+ss, ns(i), lambda{i,t}); + pot = multiply_by_pot(pot, temp); + for k=ps(:)' + if k ~= p + temp = dpot(k, ns(k), inter_pi_msg{k,i,t}); + pot = multiply_by_pot(pot, temp); + end + end + pot = marginalize_pot(pot, p); + temp = pot_to_marginal(pot); + inter_lambda_msg{i,p,t-1} = normalise(temp.T); + if verbose, fprintf('%d sends lambda to %d\n', i+(t-1)*ss, p+(t-2)*ss); disp(inter_lambda_msg{i,p,t-1}); end + end + end + end + end +end + + + +marginal = cell(ss,T); +for t=1:T + for i=hnodes + marginal{i,t} = normalise(pi{i,t} .* lambda{i,t}); + end +end + +loglik = 0; + +msg.inter_pi_msg = inter_pi_msg; +msg.inter_lambda_msg = inter_lambda_msg; +msg.intra_lambda_msg = intra_lambda_msg;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/smooth_evidence_fast.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,179 @@ +function [marginal, msg, loglik] = smooth_evidence_fast(engine, evidence) + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +onodes = engine.onodes; +hnodes = mysetdiff(1:ss, onodes); +hnodes = hnodes(:)'; + +ns = bnet.node_sizes(:); +onodes2 = [onodes(:); onodes(:)+ss]; +ns(onodes2) = 1; + +verbose = 0; +pot_type = 'd'; +niter = engine.max_iter; + +if verbose, fprintf('new smooth\n'); end + +% msg(i1,t1,i2,j2) (i1,t1) -> (i2,t2) +%lambda_msg = cell(ss,T,ss,T); +%pi_msg = cell(ss,T,ss,T); + +% intra_lambda_msg(i,j,t) (i,t) -> (j,t), i is child +% inter_lambda_msg(i,j,t) (i,t+1) -> (j,t), i is child +% inter_pi_msg(i,j,t) (i,t-1) -> (j,t), i is parent +intra_lambda_msg = cell(ss,ss,T); +inter_lambda_msg = cell(ss,ss,T); +inter_pi_msg = cell(ss,ss,T); + +lambda = cell(ss,T); +pi = cell(ss,T); + +for t=1:T + for i=1:ss + lambda{i,t} = ones(ns(i), 1); + pi{i,t} = ones(ns(i), 1); + + cs = children(bnet.intra, i); + for c=cs(:)' + intra_lambda_msg{c,i,t} = ones(ns(i),1); + end + + cs = children(bnet.inter, i); + for c=cs(:)' + inter_lambda_msg{c,i,t} = ones(ns(i),1); + end + + ps = parents(bnet.inter, i); + for p=ps(:)' + inter_pi_msg{p,i,t} = ones(ns(i), 1); % not used for t==1 + end + end +end + + +% each hidden node absorbs lambda from its observed child (if any) +for t=1:T + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, c, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + lam_msg = normalise(temp.T); + intra_lambda_msg{c,i,t} = lam_msg; + end + end +end + +for iter=1:engine.max_iter + % FORWARD + for t=1:T + % update pi + for i=hnodes + if t==1 + e = bnet.equiv_class(i,1); + temp = struct(bnet.CPD{e}); + pi{i,t} = temp.CPT; + else + e = bnet.equiv_class(i,2); + CPD = struct(bnet.CPD{e}); + ps = parents(bnet.inter, i); + temp = CPD.CPT; + for p=ps(:)' + temp(:) = temp(:) .* inter_pi_msg{p,i,t}(engine.mult_parent_ndx{i,p}); + end + dom = [ps i+ss]; + pot = dpot(dom, ns(dom), temp); + pot = marginalize_pot(pot, i+ss); + temp = pot_to_marginal(pot); + pi{i,t} = temp.T; + end + if verbose, fprintf('%d updates pi\n', i+(t-1)*ss); disp(pi{i,t}); end + end + + % send pi msg to children + for i=hnodes + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pi{i,t}; + for k=cs(:)' + if k ~= c + pot = pot .* inter_lambda_msg{k,i,t}; + end + end + cs2 = children(bnet.intra, i); + for k=cs2(:)' + pot = pot .* intra_lambda_msg{k,i,t}; + end + inter_pi_msg{i,c,t+1} = normalise(pot); + if verbose, fprintf('%d sends pi to %d\n', i+(t-1)*ss, c+t*ss); disp(inter_pi_msg{i,c,t+1}); end + end + end + end + + if verbose, fprintf('backwards\n'); end + % BACKWARD + for t=T:-1:1 + % update lambda + for i=hnodes + pot = ones(ns(i), 1); + cs = children(bnet.inter, i); + for c=cs(:)' + pot = pot .* inter_lambda_msg{c,i,t}; + end + cs = children(bnet.intra, i); + for c=cs(:)' + pot = pot .* intra_lambda_msg{c,i,t}; + end + lambda{i,t} = normalise(pot); + if verbose, fprintf('%d computes lambda\n', i+(t-1)*ss); disp(lambda{i,t}); end + end + + % send lambda msgs to hidden parents in prev slcie + for i=hnodes + ps = parents(bnet.inter, i); + if t > 1 + e = bnet.equiv_class(i, 2); + CPD = struct(bnet.CPD{e}); + for p=ps(:)' + temp = CPD.CPT(:) .* lambda{i,t}(engine.mult_self_ndx{i}); + for k=ps(:)' + if k ~= p + temp(:) = temp(:) .* inter_pi_msg{k,i,t}(engine.mult_parent_ndx{i,k}); + end + end + fam = [ps i+ss]; + pot = dpot(fam, ns(fam), temp); + pot = marginalize_pot(pot, p); + temp = pot_to_marginal(pot); + inter_lambda_msg{i,p,t-1} = normalise(temp.T); + if verbose, fprintf('%d sends lambda to %d\n', i+(t-1)*ss, p+(t-2)*ss); disp(inter_lambda_msg{i,p,t-1}); end + end + end + end + end +end + + + +marginal = cell(ss,T); +for t=1:T + for i=hnodes + marginal{i,t} = normalise(pi{i,t} .* lambda{i,t}); + end +end + +loglik = 0; + +msg.inter_pi_msg = inter_pi_msg; +msg.inter_lambda_msg = inter_lambda_msg; +msg.intra_lambda_msg = intra_lambda_msg;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/Old/wrong_smooth.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,210 @@ +function [marginal, msg, loglik] = smooth_evidence(engine, evidence) +% [marginal, msg, loglik] = smooth_evidence(engine, evidence) (pearl_dbn) + +disp('warning: pearl_dbn smoothing is broken'); + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +onodes2 = unroll_set(engine.onodes(:), ss, T); +onodes2 = onodes2(:)'; + +hnodes2 = unroll_set(hnodes(:), ss, T); +hnodes2 = hnodes2(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +msg = init_msgs(bnet2.dag, ns, evidence, bnet2.equiv_class, bnet2.CPD); + +verbose = 0; +pot_type = 'd'; +niter = 1; +for iter=1:niter + % FORWARD + for t=1:T + if verbose, fprintf('t=%d\n', t); end + + % each hidden node absorbs lambda from its observed child (if any) + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + lam_msg = normalise(temp.T); + j = engine.child_index{n}(c+(t-1)*ss); + assert(j==1); + msg{n}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', c + (t-1)*ss, n); disp(lam_msg); end + end + end + + % update pi + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + if verbose, fprintf('%d computes pi\n', n); disp(msg{n}.pi); end + end + + % send pi msg to children in next slice + for i=hnodes + n = i + (t-1)*ss; + %cs = myintersect(children(bnet2.dag, n), hnodes2); + cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); disp(pi_msg); end + end + end + end + + % BACKWARD + for t=T:-1:1 + if verbose, fprintf('t = %d\n', t); end + + % update lambda + for i=hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + msg{n}.lambda = compute_lambda(n, cs, msg, ns); + if verbose, fprintf('%d computes lambda\n', n); disp(msg{n}.lambda); end + end + + % send lambda msgs to hidden parents in prev slcie + for i=hnodes + n = i + (t-1)*ss; + %ps = myintersect(parents(bnet2.dag, n), hnodes2); + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + lam_msg = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); disp(lam_msg); end + end + end + + % send pi msg to observed children + if 0 + for i=hnodes + n = i + (t-1)*ss; + cs = myintersect(children(bnet2.dag, n), onodes2); + %cs = children(bnet2.dag, n); + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); disp(pi_msg); end + end + end + end + + end +end + + +marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=hnodes + n = i + (t-1)*ss; + [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + marginal{i,t} = bel; + end +end + +loglik = 0; +%loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +%lam = msg{n}.lambda_from_self(:); +lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end + + +%%%%%%%%% + +function msg = init_msgs(dag, ns, evidence, eclass, CPD) +% INIT_MSGS Initialize the lambda/pi message and state vectors (pearl_dbn) +% msg = init_msgs(dag, ns, evidence) + +N = length(dag); +msg = cell(1,N); +observed = ~isemptycell(evidence(:)); + +for n=1:N + ps = parents(dag, n); + msg{n}.pi_from_parent = cell(1, length(ps)); + for i=1:length(ps) + p = ps(i); + msg{n}.pi_from_parent{i} = ones(ns(p), 1); + end + + cs = children(dag, n); + msg{n}.lambda_from_child = cell(1, length(cs)); + for i=1:length(cs) + c = cs(i); + msg{n}.lambda_from_child{i} = ones(ns(n), 1); + end + + msg{n}.lambda = ones(ns(n), 1); + msg{n}.pi = ones(ns(n), 1); + + % Initialize the lambdas with any evidence + if observed(n) + v = evidence{n}; + msg{n}.lambda = zeros(ns(n), 1); + msg{n}.lambda(v) = 1; % delta function + msg{n}.lambda = []; + end + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (loopy_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, ....) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize); +assert(~filter); + +[engine.marginal, engine.msg, loglik] = enter_soft_ev(engine, evidence); +engine.evidence = evidence; % needed by marginal_nodes and marginal_family
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/enter_soft_ev.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,137 @@ +function [marginal, msg, loglik] = enter_soft_ev(engine, evidence) +% [marginal, msg, loglik] = smooth_evidence(engine, evidence) (pearl_dbn) + + +[ss T] = size(evidence); +bnet = bnet_from_engine(engine); +bnet2 = dbn_to_bnet(bnet, T); +ns = bnet2.node_sizes; +hnodes = mysetdiff(1:ss, engine.onodes); +hnodes = hnodes(:)'; + +onodes2 = unroll_set(engine.onodes(:), ss, T); +onodes2 = onodes2(:)'; + +hnodes2 = unroll_set(hnodes(:), ss, T); +hnodes2 = hnodes2(:)'; + +[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet2); + +rand_init = 0; +use_ev = 0; +msg = init_pearl_msgs(bnet2.dag, ns, evidence, rand_init, use_ev); +msg = init_pearl_dbn_ev_msgs(bnet, evidence, engine); + +verbose = 0; +pot_type = 'd'; +niter = engine.max_iter; + +if verbose, fprintf('old smooth\n'); end + +for iter=1:niter + % FORWARD + for t=1:T + if verbose, fprintf('t=%d\n', t); end + + % update pi + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + if t==1 + e = bnet.equiv_class(i,1); + else + e = bnet.equiv_class(i,2); + end + msg{n}.pi = compute_pi(bnet.CPD{e}, n, ps, msg); + if verbose, fprintf('%d computes pi\n', n); disp(msg{n}.pi); end + end + + % send pi msg to children + for i=hnodes + n = i + (t-1)*ss; + %cs = myintersect(children(bnet2.dag, n), hnodes2); + cs = children(bnet2.dag, n); % must use all children to get index right + for c=cs(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + pi_msg = normalise(compute_pi_msg(n, cs, msg, c, ns)); + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); disp(pi_msg); end + end + end + end + + % BACKWARD + for t=T:-1:1 + if verbose, fprintf('t = %d\n', t); end + + % update lambda + for i=hnodes + n = i + (t-1)*ss; + cs = children(bnet2.dag, n); + msg{n}.lambda = compute_lambda(n, cs, msg, ns); + if verbose, fprintf('%d computes lambda\n', n); disp(msg{n}.lambda); end + end + + % send lambda msgs to hidden parents in prev slcie + for i=hnodes + n = i + (t-1)*ss; + ps = parents(bnet2.dag, n); + for p=ps(:)' + j = engine.child_index{p}(n); % n is p's j'th child + if t > 1 + e = bnet.equiv_class(i, 2); + else + e = bnet.equiv_class(i, 1); + end + lam_msg = normalise(compute_lambda_msg(bnet.CPD{e}, n, ps, msg, p)); + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); disp(lam_msg); end + end + end + + end +end + + +marginal = cell(ss,T); +lik = zeros(1,ss*T); +for t=1:T + for i=hnodes + n = i + (t-1)*ss; + [bel, lik(n)] = normalise(msg{n}.pi .* msg{n}.lambda); + marginal{i,t} = bel; + end +end + +loglik = 0; +%loglik = sum(log(lik)); + + + +%%%%%%% + +function lambda = compute_lambda(n, cs, msg, ns) +% Pearl p183 eq 4.50 +lambda = prod_lambda_msgs(n, cs, msg, ns); + +%%%%%%% + +function pi_msg = compute_pi_msg(n, cs, msg, c, ns) +% Pearl p183 eq 4.53 and 4.51 +pi_msg = msg{n}.pi .* prod_lambda_msgs(n, cs, msg, ns, c); + +%%%%%%%%% + +function lam = prod_lambda_msgs(n, cs, msg, ns, except) + +if nargin < 5, except = -1; end + +%lam = msg{n}.lambda_from_self(:); +lam = ones(ns(n), 1); +for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function marginal = marginal_nodes(engine, nodes, t) +% MARGINAL_NODES Compute the marginal on the specified query nodes (pearl_dbn) +% marginal = marginal_nodes(engine, i, t) +% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice. +% If enter_evidence used filtering instead of smoothing, this will return Pr(X(i,t) | Y(1:t)). + +if nargin < 3, t = 1; end +assert(length(nodes)==1); +i = nodes(end); +if ~myismember(i, engine.onodes) + marginal.T = engine.marginal{i,t}; +else + marginal.T = 1; % observed +end + +% we convert the domain to the unrolled numbering system +% so that update_ess extracts the right evidence. +marginal.domain = nodes+(t-1)*engine.ss;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/pearl_dbn_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function engine = pearl_dbn_inf_engine(bnet, varargin) +% LOOPY_DBN_INF_ENGINE Loopy Pearl version of forwards-backwards +% engine = loopy_dbn_inf_engine(bnet, ...) +% +% Optional arguments +% 'max_iter' - specifies the max num. forward-backward passes to perform [1] +% 'tol' - as in loopy_pearl [1e-3] +% 'momentum' - as in loopy_pearl [0] + +error('pearl_dbn does not work yet') + +max_iter = 1; +tol = 1e-3; +momentum = 0; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'max_iter', max_iter = args{i+1}; + case 'tol', tol = args{i+1}; + case 'momentum', momentum = args{i+1}; + end + end +end + + +engine.max_iter = max_iter; +engine.tol = tol; +engine.momentum = momentum; +engine.pearl_engine = []; +engine.T = []; +engine.ss = length(bnet.intra); + +engine.marginal = []; +engine.evidence = []; +engine.msg = []; +engine.parent_index = []; +engine.child_index = []; +%[engine.parent_index, engine.child_index] = mk_pearl_msg_indices(bnet); % need to unroll first + +ss = length(bnet.intra); +engines.ss = ss; +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +obschild = zeros(1,ss); +for i=hnodes(:)' + %ocs = myintersect(children(bnet.dag, i), onodes); + ocs = children(bnet.intra, i); + assert(length(ocs) <= 1); + if length(ocs)==1 + obschild(i) = ocs(1); + end +end +engine.obschild = obschild; + +engine.mult_self_ndx = []; +engine.mult_parent_ndx = []; +engine.marg_self_ndx = []; +engine.marg_parent_ndx = []; + + +engine = class(engine, 'loopy_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/init_pearl_dbn_ev_msgs.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@pearl_dbn_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_dbn_inf_engine/private/init_pearl_dbn_ev_msgs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function msg = init_pearl_dbn_ev_msgs(bnet, evidence, engine) + +[ss T] = size(evidence); +pot_type = 'd'; + +% each hidden node absorbs lambda from its observed child (if any) +for t=1:T + for i=hnodes + c = engine.obschild(i); + if c > 0 + if t==1 + fam = family(bnet.dag, c); + e = bnet.equiv_class(c, 1); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,1)); + else + fam = family(bnet.dag, c, 2); % within 2 slice network + e = bnet.equiv_class(c, 2); + CPDpot = CPD_to_pot(pot_type, bnet.CPD{e}, fam, bnet.node_sizes(:), bnet.cnodes(:), evidence(:,t-1:t)); + end + temp = pot_to_marginal(CPDpot); + n = i + (t-1)*ss; + lam_msg = normalise(temp.T); + j = engine.child_index{n}(c+(t-1)*ss); + assert(j==1); + msg{n}.lambda_from_child{j} = lam_msg; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_unrolled_dbn_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/pearl_unrolled_dbn_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_unrolled_dbn_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_unrolled_dbn_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_unrolled_dbn_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function [engine, loglik, niter] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (loopy_unrolled_dbn) +% [engine, loglik, niter] = enter_evidence(engine, evidence, ....) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product (not yet supported), else sum-product [0] +% filename - as in loopy_pearl +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filename = engine.filename; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filename', filename = args{i+1}; + end + end +end + + +[ss T] = size(evidence); +if T ~= engine.T + bnetT = dbn_to_bnet(bnet_from_engine(engine), T); + engine.unrolled_engine = pearl_inf_engine(bnetT, 'protocol', engine.protocol, ... + 'max_iter', engine.max_iter_per_slice * T, ... + 'tol', engine.tol, 'momentum', engine.momentum); + engine.T = T; +end +[engine.unrolled_engine, loglik, niter] = enter_evidence(engine.unrolled_engine, evidence(:), ... + 'maximize', maximize, 'filename', filename); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_unrolled_dbn_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_unrolled_dbn) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end +assert(~add_ev); + +%marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss, add_ev); +marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_unrolled_dbn_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (loopy_unrolled_dbn) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' must occur in some clique. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +marginal = marginal_nodes(engine.unrolled_engine, nodes + (t-1)*engine.ss, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_unrolled_dbn_inf_engine/pearl_unrolled_dbn_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function engine = pearl_unrolled_dbn_inf_engine(bnet, varargin) +% LOOPY_DBN_INF_ENGINE Loopy Pearl version of forwards-backwards +% engine = loopy_unrolld_dbn_inf_engine(bnet, ...) +% +% Optional arguments +% 'max_iter' - specifies the max num. forward-backward passes to perform PER SLICE [2] +% 'tol' - as in loopy_pearl [1e-3] +% 'momentum' - as in loopy_pearl [0] +% protocol - tree or parallel [parallel] +% filename - as in pearl [ '' ] + +max_iter_per_slice = 2; +tol = 1e-3; +momentum = 0; +protocol = 'parallel'; +filename = ''; + +args = varargin; +for i=1:2:length(args) + switch args{i}, + case 'max_iter', max_iter_per_slice = args{i+1}; + case 'tol', tol = args{i+1}; + case 'momentum', momentum = args{i+1}; + case 'protocol', protocol = args{i+1}; + case 'filename', filename = args{i+1}; + end +end + +engine.filename = filename; +engine.max_iter_per_slice = max_iter_per_slice; +engine.tol = tol; +engine.momentum = momentum; +engine.unrolled_engine = []; +engine.T = -1; +engine.ss = length(bnet.intra); +engine.protocol = protocol; + +engine = class(engine, 'pearl_unrolled_dbn_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@pearl_unrolled_dbn_inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (pearl_unrolled_dbn) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.unrolled_engine = update_engine(engine.unrolled_engine, newCPDs);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/enter_evidence.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +/marginal_family.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +/marginal_nodes.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +/stable_ho_inf_engine.m/1.1.1.1/Fri Mar 14 09:45:34 2003// +/test_ho_inf_enginge.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +/update_engine.m/1.1.1.1/Wed Feb 19 09:52:12 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic/@stable_ho_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_unrolled_dbn) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% filter - if 1, does filtering (not supported), else smoothing [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +maximize = 0; +filter = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filter', filter = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +if filter + error('jtree_unrolled_dbn does not support filtering') +end + +if size(evidence,2) ~= engine.nslices + error(['engine was created assuming there are ' num2str(engine.nslices) ... + ' slices, but evidence has ' num2str(size(evidence,2))]) +end + +[engine.unrolled_engine, loglik] = enter_evidence(engine.unrolled_engine, evidence, 'maximize', maximize); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_unrolled_dbn) +% marginal = marginal_family(engine, i, t) + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end +assert(~add_ev); + +%marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss, add_ev); +marginal = marginal_family(engine.unrolled_engine, i + (t-1)*engine.ss); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (loopy_unrolled_dbn) +% marginal = marginal_nodes(engine, nodes, t) +% +% 't' specifies the time slice of the earliest node in 'nodes'. +% 'nodes' must occur in some clique. +% +% Example: +% Consider a DBN with 2 nodes per slice. +% Then t=2, nodes=[1 3] refers to node 1 in slice 2 and node 1 in slice 3, +% i.e., nodes 3 and 5 in the unrolled network, + +if nargin < 3, t = 1; end +if nargin < 4, add_ev = 0; end + +marginal = marginal_nodes(engine.unrolled_engine, nodes + (t-1)*engine.ss, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/stable_ho_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function engine = dv_unrolled_dbn_inf_engine(bnet, T, varargin) +% JTREE_UNROLLED_DBN_INF_ENGINE Unroll the DBN for T time-slices and apply jtree to the resulting static net +% engine = jtree_unrolled_dbn_inf_engine(bnet, T, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% useC - 1 means use jtree_C_inf_engine instead of jtree_inf_engine [0] +% constrained - 1 means we constrain ourselves to eliminate slice t before t+1 [1] +% +% e.g., engine = jtree_unrolled_inf_engine(bnet, 'useC', 1); + +% set default params +N = length(bnet.intra); +useC = 0; +constrained = 1; + +if nargin >= 3 + args = varargin; + nargs = length(args); + if isstr(args{1}) + for i=1:2:nargs + switch args{i}, + case 'useC', useC = args{i+1}; + case 'constrained', constrained = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + else + error(['invalid argument name ' args{1}]); + end +end + +bnet2 = hodbn_to_bnet(bnet, T); +ss = length(bnet.intra); +engine.ss = ss; + +% If constrained_order = 1 we constrain ourselves to eliminate slice t before t+1. +% This prevents cliques containing nodes from far-apart time-slices. +if constrained + stages = num2cell(unroll_set(1:ss, ss, T), 1); +else + stages = { 1:length(bnet2.dag) }; +end +if useC + %jengine = jtree_C_inf_engine(bnet2, 'stages', stages); + %function is not implemented + assert(0) +else + jengine = stab_cond_gauss_inf_engine(bnet2); +end + +engine.unrolled_engine = jengine; +% we don't inherit from jtree_inf_engine, because that would only store bnet2, +% and we would lose access to the DBN-specific fields like intra/inter + +engine.nslices = T; +engine = class(engine, 'stable_ho_inf_engine', inf_engine(bnet)); + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/test_ho_inf_enginge.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,87 @@ +function [engine,engine2] = test_ho_inf_enginge(order,T) + +assert(order >= 1) +% Model a SISO system, i. e. all node are one-dimensional +% The nodes are numbered as follows +% u(t) = 1 input +% y(t) = 2 model output +% z(t) = 3 noise +% q(t) = 4 observed output = noise + model output + +ns = [1 1 1 1]; + +% Model a linear system, i.e. there are no discrete nodes +dn = []; + +% Modeling of connections within a time slice +intra = zeros(4); +intra(2,4) = 1; % Connection y(t) -> q(t) +intra(3,4) = 1; % Connection z(t) -> q(t) + +% Connections to the next time slice +inter = zeros(4,4,order); +inter(1,2,1) = 1; % u(t) -> y(t+1); +inter(2,2,1) = 1; %y(t) -> y(t+1); +inter(3,3,1) = 1; %z(t) -> z(t+1); + +if order >= 2 + inter(1,2,2) = 1; % u(t) -> y(t+2); + inter(2,2,2) = 1; % y(t) -> y(t+2); +end + +for i = 3: order + inter(:,:,i) = inter(:,:,i-1); %u(t) -> y(t+i) y(t) -> y(t) +i +end; + + +% Compution of a higer order Markov Model +bnet = mk_higher_order_dbn(intra,inter,ns,'discrete',dn); +bnet2 = mk_dbn(intra,inter(:,:,1),ns,'discrete',dn) + + +%Calculation of the number of nodes with different parameters +%There is one input and one output nodes 2 +%There are two different disturbance node 2 +%There are order +1 nodes for y 1 + order +numOfNodes = 5 + order; + +% First input node +bnet.CPD{1} = gaussian_CPD(bnet,1,'mean',0); +bnet2.CPD{1} = gaussian_CPD(bnet,1,'mean',0); +% Modeled output +bnet.CPD{2} = gaussian_CPD(bnet,2,'mean',0); +bnet2.CPD{2} = gaussian_CPD(bnet,2,'mean',0); +%Disturbance +bnet.CPD{3} = gaussian_CPD(bnet,3,'mean',0); +bnet2.CPD{3} = gaussian_CPD(bnet,3,'mean',0); + +%Qutput +bnet.CPD{4} = gaussian_CPD(bnet,4,'mean',0); +bnet2.CPD{4} = gaussian_CPD(bnet,4,'mean',0); + + +%Output node in the second time-slice +%Remember that node number 6 is an example for +%the fifth equivalence class +bnet.CPD{5} = gaussian_CPD(bnet,6,'mean',0); +bnet2.CPD{5} = gaussian_CPD(bnet,6,'mean',0); + +%Disturbance node in the second time slice +bnet.CPD{6} = gaussian_CPD(bnet,7,'mean',0); +bnet2.CPD{6} = gaussian_CPD(bnet,7,'mean',0); + +% Modeling of the remaining nodes for y +for i = 7:numOfNodes + bnet.CPD{i} = gaussian_CPD(bnet,(i - 6)*4 + 7,'mean',0); +end + +% Generation of the inference engine +engine = dv_unrolled_dbn_inf_engine(bnet,T); +engine2 = jtree_unrolled_dbn_inf_engine(bnet,T); + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/@stable_ho_inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (jtree_unrolled_dbn) +% engine = update_engine(engine, newCPDs) + +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +engine.unrolled_engine = update_engine(engine.unrolled_engine, newCPDs); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:28 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +A D/@bk_ff_hmm_inf_engine//// +A D/@bk_inf_engine//// +A D/@cbk_inf_engine//// +A D/@ff_inf_engine//// +A D/@frontier_inf_engine//// +A D/@hmm_inf_engine//// +A D/@jtree_dbn_inf_engine//// +A D/@jtree_unrolled_dbn_inf_engine//// +A D/@kalman_inf_engine//// +A D/@pearl_dbn_inf_engine//// +A D/@pearl_unrolled_dbn_inf_engine//// +A D/@stable_ho_inf_engine////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/dynamic
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/dynamic/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@filter_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/bnet_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/filter_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@filter_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@filter_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@filter_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@filter_engine/bnet_from_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function bnet = bnet_from_engine(engine) +% BNET_FROM_ENGINE Return the bnet structure stored inside the engine (smoother_engine) +% bnet = bnet_from_engine(engine) + +bnet = bnet_from_engine(engine.tbn_engine);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@filter_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function [engine, LL] = enter_evidence(engine, ev, t) +% ENTER_EVIDENCE Call the online filter +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +engine.old_f = engine.f; +if t==1 + [engine.f, LL] = fwd1(engine.tbn_engine, ev, 1); +else + [engine.f, LL] = fwd(engine.tbn_engine, engine.old_f, ev, t); +end +engine.b = backT(engine.tbn_engine, engine.f, t); +engine.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@filter_engine/filter_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function engine = filter_engine(tbn_engine) +% FILTER_ENGINE Create an engine which does online filtering +% function engine = filter_engine(tbn_engine) + +engine.tbn_engine = tbn_engine; +engine.f = []; % space to store filtered message +engine.old_f = []; +engine.b = []; % space to store smoothed message +engine.t = []; +engine = class(engine, 'filter_engine');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@filter_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the joint distribution on a set of family (filter_engine) +% function marginal = marginal_family(engine, i, t, add_ev) + +if nargin < 4, add_ev = 0; end + +if t ~= engine.t + error('mixed up time stamps') +end + +marginal = marginal_family(engine.tbn_engine, engine.b, i, t, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@filter_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the joint distribution on a set of nodes (filter_engine) +% function marginal = marginal_nodes(engine, nodes, t, add_ev) + +if nargin < 4, add_ev = 0; end + +if t ~= engine.t + error('mixed up time stamps') +end +marginal = marginal_nodes(engine.tbn_engine, engine.b, nodes, t, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +/back.m/1.1.1.1/Wed May 29 15:59:56 2002// +/backT.m/1.1.1.1/Wed May 29 15:59:56 2002// +/fwd.m/1.1.1.1/Wed May 29 15:59:56 2002// +/fwd1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/hmm_2TBN_inf_engine.m/1.1.1.1/Thu Nov 14 20:03:50 2002// +/marginal_family.m/1.1.1.1/Thu Nov 14 20:05:36 2002// +/marginal_nodes.m/1.1.1.1/Thu Nov 14 20:02:46 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/back.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function b = back(engine, bfuture, f, t) + +if f.t ~= t + error('mixed up time stamps') +end + +b.t = t; +b.obslik = f.obslik; +bb_future = bfuture.beta .* bfuture.obslik; +if engine.maximize + B = repmat(bb_future(:)', length(bfuture.beta), 1); + b.beta = normalise(max(engine.transprob .* B, [], 2)); +else + b.beta = normalise((engine.transprob * bb_future)); +end +b.gamma = normalise(f.alpha .* b.beta); +if t > 1 + bb_t = b.beta .* b.obslik; + b.xi = normalise((engine.transprob .* (f.past_alpha * bb_t'))); % t-1,t +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/backT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function b = backT(engine, f, t) + +b.t = t; +b.obslik = f.obslik; +Q = length(f.alpha); +b.beta = ones(Q,1); +b.gamma = f.alpha; +if t > 1 + bb_t = b.obslik; + b.xi = normalise((engine.transprob .* (f.past_alpha * bb_t'))); % T-1,T +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/fwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function [f, logscale] = fwd(engine, fpast, ev, t) +% Forwards pass. + +f.obslik = mk_hmm_obs_lik_vec(engine, ev); +transmat = engine.transprob; +f.past_alpha = fpast.alpha; +if engine.maximize + Q = length(fpast.alpha); + A = repmat(fpast.alpha, [1 Q]); + m = max(transmat .* A, [], 1); + [f.alpha, scale] = normalise(m(:) .* f.obslik); +else + [f.alpha, scale] = normalise((transmat' * fpast.alpha) .* f.obslik); +end +logscale = log(scale); +%f.xi = normalise((fpast.alpha * obslik') .* transmat); % t-1,t +f.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/fwd1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function [f, logscale] = fwd1(engine, ev, t) +% Forwards pass for slice 1. + +if t ~= 1 + error('mixed up time stamps') +end +prior = engine.startprob(:); +f.obslik = mk_hmm_obs_lik_vec(engine, ev); +[f.alpha, lik] = normalise(prior .* f.obslik); +logscale = log(lik); +f.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/hmm_2TBN_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function engine = hmm_2TBN_inf_engine(bnet, varargin) +% HMM_2TBN_INF_ENGINE Inference engine for DBNs which uses the forwards-backwards algorithm. +% engine = hmm_2TBN_inf_engine(bnet, ...) +% +% The DBN is converted to an HMM with a single meganode, but the observed nodes remain factored. +% This can be faster than jtree if the num. hidden nodes is low, because of lower constant factors. +% +% All hidden nodes must be discrete. +% All observed nodes are assumed to be leaves. +% The parents of each observed leaf are assumed to be a subset of the hidden nodes within the same slice. +% The only exception is if bnet is an AR-HMM, where the parents are assumed to be self in the +% previous slice (continuous), plus all the discrete nodes in the current slice. + + +%% Optional arguments +%% ndx_type - 'B', 'D', or 'SD', used in marginal_family [ 'SD' ] + +ndx_type = 'SD'; +ss = bnet.nnodes_per_slice; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + %case 'ndx_type', ndx_type = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +% Stuff to do with speeding up marginal_family +%engine.ndx_type = ndx_type; + +[int, engine.persist, engine.transient] = compute_interface_nodes(bnet.intra, bnet.inter); +engine.persist_bitv = zeros(1, ss); +engine.persist_bitv(engine.persist) = 1; + + +ns = bnet.node_sizes(:); +ns(bnet.observed) = 1; +ns(bnet.observed+ss) = 1; +engine.eff_node_sizes = ns; + +% for n=1:ss +% dom = 1:(2*ss); % domain of xi(:,:,1) +% fam = family(bnet.dag, n+ss); +% engine.marg_fam2_ndx_id(n) = add_ndx(dom, fam, ns, ndx_type); + +% dom = 1:ss; % domain of gamma(:,:,1) +% fam = family(bnet.dag, n); +% engine.marg_fam1_ndx_id(n) = add_ndx(dom, fam, ns, ndx_type); + +% engine.marg_singleton_ndx_id(n) = add_ndx(dom, n, ns, ndx_type); +% end + +for o=bnet.observed(:)' + %if bnet.equiv_class(o,1) ~= bnet.equiv_class(o,2) + % error(['observed node ' num2str(o) ' is not tied']) + %end + cs = children(bnet.dag, o); + if ~isempty(cs) + error(['observed node ' num2str(o) ' is not allowed children']) + end +end + +[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.one_slice_marginal = []; +engine.two_slice_marginal = []; + +ss = length(bnet.intra); +engine.maximize = []; +engine.evidence = []; +engine.node_sizes = []; + +% avoid the need to do bnet_from_engine, which is slow +engine.slice_size = ss; +engine.parents = bnet.parents; + +engine.bel = []; +engine = class(engine, 'hmm_2TBN_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function marginal = marginal_family(engine, b, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (hmm_2TBN) +% marginal = marginal_family(engine, b, i, t, add_ev) + +ns = engine.eff_node_sizes(:); +ss = engine.slice_size; + +if t==1 % | ~engine.persist_bitv(i) + bigT = b.gamma; + ps = engine.parents{i}; + dom = [ps i]; + %id = engine.marg_fam1_ndx_id(i); + bigdom = 1:ss; + bigsz = ns(bigdom); + bigdom = bigdom + (t-1)*ss; +else % some parents are in previous slice + bigT = b.xi; % (t-1,t) + ps = engine.parents{i+ss}; + dom = [ps i+ss] + (t-2)*ss; + %id = engine.marg_fam2_ndx_id(i); + bigdom = 1:(2*ss); % domain of xi(:,:,t) + bigsz = ns(bigdom); + bigdom = bigdom + (t-2)*ss; +end +marginal.domain = dom; + +%ndx = get_ndx(id, engine.ndx_type); +%marginal.T = marg_table_ndx(bigT, engine.maximize, ndx, engine.ndx_type); +%global SD_NDX +%ndx = SD_NDX{id}; +%marginal.T = marg_table_ndxSD(bigT, engine.maximize, ndx); +marginal.T = marg_table(bigT, bigdom, bigsz, dom, engine.maximize); + +assert(~add_ev); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function marginal = marginal_nodes(engine, b, nodes, t, add_ev) +% MARGINAL_NODES Compute the marginal on the specified nodes (hmm_2TBN) +% marginal = marginal_nodes(engine, b, nodes, t, add_ev) +% +% nodes must be a singleton set + +assert(length(nodes)==1) +ss = engine.slice_size; + +i = nodes(1); +bigT = b.gamma; +dom = i + (t-1)*ss; + +%id = engine.marg_singleton_ndx_id(i); +%global SD_NDX +%ndx = SD_NDX{id}; +%marginal.T = marg_table_ndxSD(bigT, engine.maximize, ndx); + +ns = engine.eff_node_sizes(:); +bigdom = 1:ss; +marginal.T = marg_table(bigT, bigdom + (t-1)*ss, ns(bigdom), dom, engine.maximize); + +marginal.domain = dom; +assert(~add_ev); +%if add_ev +% marginal = add_ev_to_dmarginal(marginal, engine.evidence, engine.node_sizes); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/mk_hmm_obs_lik_vec.m/1.1.1.1/Sun May 4 21:47:44 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@hmm_2TBN_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/private/mk_hmm_obs_lik_vec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function obslik = mk_hmm_obs_lik_vec(engine, evidence) + +% P(o1,o2| h) = P(o1|h) * P(o2|h) where h = Q1,Q2,... + +bnet = bnet_from_engine(engine); +ss = length(bnet.intra); +onodes = bnet.observed; +hnodes = mysetdiff(1:ss, onodes); +ns = bnet.node_sizes(:); +ns(onodes) = 1; + +Q = length(engine.startprob); +obslik = ones(Q, 1); + +for i=1:length(onodes) + o = onodes(i); + %data = cell2num(evidence(o,1)); + data = evidence{o,1}; + if myismember(o, bnet.dnodes) + %obslik_i = eval_pdf_cond_multinomial(data, engine.obsprob{i}.CPT); + obslik_i = multinomial_prob(data, engine.obsprob{i}.CPT); + else + if bnet.auto_regressive(o) + error('can''t handle AR nodes') + end + %% calling mk_ghmm_obs_lik, which calls gaussian_prob, is slow, so we inline it + %% and use the pre-computed inverse matrix + %obslik_i = mk_ghmm_obs_lik(data, engine.obsprob{i}.mu, engine.obsprob{i}.Sigma); + x = data(:); + m = engine.obsprob{i}.mu; + Qi = size(m, 2); + obslik_i = size(Qi, 1); + invC = engine.obsprob{i}.inv_Sigma; + denom = engine.obsprob{i}.denom; + for j=1:Qi + numer = exp(-0.5 * (x-m(:,j))' * invC(:,:,j) * (x-m(:,j))); + obslik_i(j) = numer / denom(j); + end + end + % convert P(o|ps) into P(o|h) by multiplying onto a (h,o) potential of all 1s + ps = bnet.parents{o}; + dom = [ps o]; + obspot_i = dpot(dom, ns(dom), obslik_i); + dom = [hnodes o]; + obspot = dpot(dom, ns(dom)); + obspot = multiply_by_pot(obspot, obspot_i); + % compute p(oi|h) * p(oj|h) + S = struct(obspot); + obslik = obslik .* S.T(:); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@hmm_2TBN_inf_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (hmm) +% engine = update_engine(engine, newCPDs) + +%engine.inf_engine.bnet.CPD = newCPDs; +engine.inf_engine = update_engine(engine.inf_engine, newCPDs); +[engine.startprob, engine.transprob, engine.obsprob] = dbn_to_hmm(bnet_from_engine(engine)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +/back.m/1.1.1.1/Mon Jun 17 23:34:12 2002// +/back1.m/1.1.1.1/Mon Jun 17 23:34:26 2002// +/back1_mpe.m/1.1.1.1/Mon Jun 17 23:49:40 2002// +/backT.m/1.1.1.1/Mon Jun 17 23:34:20 2002// +/backT_mpe.m/1.1.1.1/Mon Jun 17 23:38:56 2002// +/back_mpe.m/1.1.1.1/Sun Jul 21 00:32:52 2002// +/fwd.m/1.1.1.1/Mon Jun 17 23:46:06 2002// +/fwd1.m/1.1.1.1/Mon Jun 17 23:46:20 2002// +/jtree_2TBN_inf_engine.m/1.1.1.1/Thu Nov 14 16:31:58 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fields.m/1.1.1.1/Sun Jul 21 01:25:30 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/jtree_2TBN_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@jtree_2TBN_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/Old/jtree_2TBN_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,116 @@ +function engine = jtree_2TBN_inf_engine(bnet, varargin) +% JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. +% engine = jtree_online_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - specifies variables that must be grouped in the 1.5 slice DBN +% maximize - 1 means do max-product, 0 means sum-product [0] +% +% The same nodes must be observed in every slice. + +ss = length(bnet.intra); +clusters = {}; +engine.maximize = 0; + +args = varargin; +nargs = length(args); +for i=1:2:length(args) + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +engine.evidence = []; +engine.node_sizes = []; + +%int = compute_interface_nodes(bnet.intra, bnet.inter); +int = []; + +if 1 +% include nodes with any outgoing arcs +for u=1:ss + if any(bnet.inter(u,:)) + int = [int u]; + end +end +end + +if 0 +% include nodes with any incoming arcs +incoming = []; +for u=1:ss + if any(bnet.inter(:,u)) + int = [int u]; + incoming = [incoming u]; + end +end +% include nodes which are parents of nodes with incoming +for u=1:ss + cs = children(bnet.intra, u); + if ~isempty(cs) & mysubset(cs, incoming) + int = [int u]; + end +end +int = unique(int); +end % if + +int +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +onodes = bnet.observed; + +% Create a "1.5 slice" jtree, containing the interface nodes of slice 1 +% and all the nodes of slice 2 +% To keep the node numbering the same, we simply disconnect the non-interface nodes +% from slice 1, and set their size to 1. +% We do this to speed things up, and so that the likelihood is computed correctly - we do not need to do +% this if we just want to compute marginals (i.e., we can include nodes whose potentials will +% be left as all 1s). +intra15 = bnet.intra; +for i=engine.nonint(:)' + intra15(:,i) = 0; + intra15(i,:) = 0; +end +dag15 = [intra15 bnet.inter; + zeros(ss) bnet.intra]; +ns = bnet.node_sizes(:); +%ns(engine.nonint) = 1; % disconnected nodes get size 1 +obs_nodes = [onodes(:) onodes(:)+ss]; +bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ... + 'observed', obs_nodes(:)); + +% use unconstrained elimination, +% but force there to be a clique containing both interfaces +clusters(end+1:end+2) = {int, int+ss}; +engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +jtree_engine = struct(engine.jtree_engine); % violate object privacy + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.clq_ass_to_node = jtree_engine.clq_ass_to_node; +engine.root = jtree_engine.root_clq; + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end +engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); +jtree_engine1 = struct(engine.jtree_engine1); % violate object privacy +engine.int_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.clq_ass_to_node1 = jtree_engine1.clq_ass_to_node; +engine.root1 = jtree_engine1.root_clq; + +engine.observed = [onodes onodes+ss]; +engine.observed1 = onodes; +engine.pot_type = determine_pot_type(bnet, onodes); +engine.slice_size = bnet.nnodes_per_slice; + +engine = class(engine, 'jtree_2TBN_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/back.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function b = back(engine, bfuture, f, t) + +if f.t ~= t + error('mixed up time stamps') +end +if t==1 + b = back1(engine, bfuture, f, t); + return; +end + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; +C = engine.out_clq; +phiD = marginalize_pot(bfuture.clpot{D}, int, engine.maximize); +phiD = set_domain_pot(phiD, int+ss); % shift to slice 2 +phiC = marginalize_pot(f.clpot{C}, int+ss, engine.maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[b.clpot, seppot] = distribute_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/back1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function b = back1(engine, bfuture, f, t) + +if t ~= 1 + error('mixed up time stamps') +end +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; % from J2 +C = engine.int_clq1; % from J1 +phiD = marginalize_pot(bfuture.clpot{D}, int, engine.maximize); +phiC = marginalize_pot(f.clpot{C}, int, engine.maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[b.clpot, seppot] = distribute_evidence(engine.jtree_engine1, f.clpot, f.seppot); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/back1_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [b, mpe] = back1_mpe(engine, bfuture, f, ev1, t) + +if t ~= 1 + error('mixed up time stamps') +end +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; +maximize = 1; + +int = engine.interface; +D = engine.in_clq; % from J2 +C = engine.int_clq1; % from J1 +phiD = marginalize_pot(bfuture.clpot{D}, int, maximize); +phiC = marginalize_pot(f.clpot{C}, int, maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[mpe, b.clpot] = find_max_config(engine.jtree_engine1, f.clpot, f.seppot, ev1); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/backT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function b = backT(engine, f, t) + +if t==1 + [b.clpot, seppot] = distribute_evidence(engine.jtree_engine1, f.clpot, f.seppot); +else + [b.clpot, seppot] = distribute_evidence(engine.jtree_engine, f.clpot, f.seppot); +end +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/backT_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function [b, mpe] = backT_mpe(engine, f, ev2, t) + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +if t==1 + % ev2 is just the evidence on slice 1 + [mpe, b.clpot] = find_max_config(engine.jtree_engine1, f.clpot, f.seppot, ev2); +else + [mpe, b.clpot] = find_max_config(engine.jtree_engine, f.clpot, f.seppot, ev2); + mpe = mpe((1:ss)+ss); % extract values for slice 2 +end +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/back_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function [b, mpe] = back_mpe(engine, bfuture, f, ev2, t) + +if f.t ~= t + error('mixed up time stamps') +end +if t==1 + error('should call back1_mpe') +end + +maximize = 1; +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; +C = engine.out_clq; +phiD = marginalize_pot(bfuture.clpot{D}, int, maximize); +phiD = set_domain_pot(phiD, int+ss); % shift to slice 2 +phiC = marginalize_pot(f.clpot{C}, int+ss, maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[mpe, b.clpot] = find_max_config(engine.jtree_engine, f.clpot, f.seppot, ev2); +mpe = mpe((1:ss)+ss); % extract values for slice 2 +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/fwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function [f, logscale] = fwd(engine, fpast, ev, t) +% Forwards pass. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +ev2 = cell(ss, 2); +ev2(:,1) = fpast.evidence; +ev2(:,2) = ev; + +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev2); +end +f.evidence = ev; +f.t = t; + +% get prior +int = engine.interface; +if fpast.t==1 + prior = marginalize_pot(fpast.clpot{engine.int_clq1}, int, engine.maximize); +else + prior = marginalize_pot(fpast.clpot{engine.out_clq}, int+ss, engine.maximize); + prior = set_domain_pot(prior, int); % shift back to slice 1 +end + +pots = [ {prior} CPDpot ]; +slice1 = 1:ss; +slice2 = slice1 + ss; +CPDclqs = engine.clq_ass_to_node(slice2); +D = engine.in_clq; +clqs = [D CPDclqs]; + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine, clqs, pots, engine.pot_type, engine.observed); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(f.clpot) + [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); +end +logscale = ll(engine.root); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/fwd1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function [f, logscale] = fwd1(engine, ev, t) +% Forwards pass for slice 1. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 1); + e = bnet.equiv_class(n, 1); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev); +end +f.t = t; +f.evidence = ev; + +pots = CPDpot; +slice1 = 1:ss; +CPDclqs = engine.clq_ass_to_node1(slice1); + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine1, CPDclqs, CPDpot, engine.pot_type, engine.observed1); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine1, f.clpot, f.seppot); +for c=1:length(f.clpot) + [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); +end +logscale = ll(engine.root1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/jtree_2TBN_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function engine = jtree_2TBN_inf_engine(bnet, varargin) +% JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. +% engine = jtree_online_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - specifies variables that must be grouped in the 1.5 slice DBN +% +% The same nodes must be observed in every slice. +% +% This uses the forwards interface of slice t-1 plus all of slice t. +% By contrast, jtree_dbn uses all of slice t-1 plus the backwards interface of slice t. +% See my thesis for details. + + +clusters = {}; + +args = varargin; +nargs = length(args); +for i=1:2:length(args) + switch args{i}, + case 'clusters', clusters = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +engine.maximize = 0; +engine.evidence = []; +engine.node_sizes = []; + +int = compute_fwd_interface(bnet.intra, bnet.inter); +engine.interface = int; +ss = length(bnet.intra); +engine.nonint = mysetdiff(1:ss, int); +onodes = bnet.observed; + +bnet15 = mk_slice_and_half_dbn(bnet, int); + +% use unconstrained elimination, +% but force there to be a clique containing both interfaces +clusters(end+1:end+2) = {int, int+ss}; +engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +jtree_engine = struct(engine.jtree_engine); % violate object privacy + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.clq_ass_to_node = jtree_engine.clq_ass_to_node; +engine.root = jtree_engine.root_clq; + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end +engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); +jtree_engine1 = struct(engine.jtree_engine1); % violate object privacy +engine.int_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.clq_ass_to_node1 = jtree_engine1.clq_ass_to_node; +engine.root1 = jtree_engine1.root_clq; + +engine.observed = [onodes onodes+ss]; +engine.observed1 = onodes; +engine.pot_type = determine_pot_type(bnet, onodes); +engine.slice_size = bnet.nnodes_per_slice; + +engine = class(engine, 'jtree_2TBN_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function m = marginal_family(engine, b, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_2TBN) +% marginal = marginal_family(engine, b, i, t, add_ev) + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, b, family(bnet.dag, i), t, add_ev, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + m = marginal_nodes(engine, b, fam, t, add_ev, 1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function marginal = marginal_nodes(engine, b, nodes, t, add_ev, is_fam) +% function marginal = marginal_nodes(engine, b, nodes, t, add_ev, is_fam) (jtree_2TBN) + +if nargin < 6, is_fam = 0; end +ss = engine.slice_size; + +if ~is_fam & (t > 1) & all(nodes<=ss) + nodes = nodes + ss; +end + +if t==1 + c = clq_containing_nodes(engine.jtree_engine1, nodes, is_fam); +else + c = clq_containing_nodes(engine.jtree_engine, nodes, is_fam); +end +if c == -1 + error(['no clique contains ' nodes]) +end +bigpot = b.clpot{c}; +pot = marginalize_pot(bigpot, nodes, engine.maximize); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that add_ev_to_dmarginal (maybe called in update_ess) extracts the right evidence. +if t > 1 + marginal.domain = nodes+(t-2)*engine.slice_size; +end +assert(~add_ev); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_2TBN_inf_engine/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', + engine.maximize = args{i+1}; + engine.jtree_engine = set_fields(engine.jtree_engine, 'maximize', args{i+1}); + engine.jtree_engine1 = set_fields(engine.jtree_engine1, 'maximize', args{i+1}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +/back.m/1.1.1.1/Wed May 29 15:59:56 2002// +/back1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/backT.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/fwd.m/1.1.1.1/Wed May 29 15:59:56 2002// +/fwd1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_sparse_2TBN_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@jtree_sparse_2TBN_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/back.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function b = back(engine, bfuture, f, t) + +if f.t ~= t + error('mixed up time stamps') +end +if t==1 + b = back1(engine, bfuture, f, t); + return; +end + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; +C = engine.out_clq; +phiD = marginalize_pot(bfuture.clpot{D}, int, engine.maximize); +phiD = set_domain_pot(phiD, int+ss); % shift to slice 2 +phiC = marginalize_pot(f.clpot{C}, int+ss, engine.maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[b.clpot, seppot] = distribute_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/back1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function b = back1(engine, bfuture, f, t) + +if t ~= 1 + error('mixed up time stamps') +end +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +int = engine.interface; +D = engine.in_clq; % from J2 +C = engine.int_clq1; % from J1 +phiD = marginalize_pot(bfuture.clpot{D}, int, engine.maximize); +phiC = marginalize_pot(f.clpot{C}, int, engine.maximize); +ratio = divide_by_pot(phiD, phiC); +f.clpot{C} = multiply_by_pot(f.clpot{C}, ratio); + +[b.clpot, seppot] = distribute_evidence(engine.jtree_engine1, f.clpot, f.seppot); +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/backT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function b = backT(engine, f, t) + +if t==1 + [b.clpot, seppot] = distribute_evidence(engine.jtree_engine1, f.clpot, f.seppot); +else + [b.clpot, seppot] = distribute_evidence(engine.jtree_engine, f.clpot, f.seppot); +end +for c=1:length(b.clpot) + [b.clpot{c}, ll(c)] = normalize_pot(b.clpot{c}); +end +b.t = t;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_online) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% + +engine.maximize = 0; +args = varargin; +for i=1:2:length(args) + switch args{i} + case 'maximize', engine.maximize = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +[engine, loglik] = offline_smoother(engine, evidence);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/fwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function [f, logscale] = fwd(engine, fpast, ev, t) +% Forwards pass. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +ev2 = cell(ss, 2); +ev2(:,1) = fpast.evidence; +ev2(:,2) = ev; +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 2); + e = bnet.equiv_class(n, 2); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev2); +end +f.evidence = ev; +f.t = t; + +% get prior +int = engine.interface; +if fpast.t==1 + prior = marginalize_pot(fpast.clpot{engine.int_clq1}, int, engine.maximize); +else + prior = marginalize_pot(fpast.clpot{engine.out_clq}, int+ss, engine.maximize); + prior = set_domain_pot(prior, int); % shift back to slice 1 +end + +pots = [ {prior} CPDpot ]; +slice1 = 1:ss; +slice2 = slice1 + ss; +CPDclqs = engine.clq_ass_to_node(slice2); +D = engine.in_clq; +clqs = [D CPDclqs]; + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine, clqs, pots, engine.pot_type, engine.observed); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine, f.clpot, f.seppot); +for c=1:length(f.clpot) + if isa(f.clpot{c}, 'struct') + domain = f.clpot{c}.domain; + sizes = f.clpot{c}.sizes; + T = f.clpot{c}.T; + f.clpot{c} = dpot(domain, sizes, T); + end + [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); +end +logscale = ll(engine.root); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/fwd1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [f, logscale] = fwd1(engine, ev, t) +% Forwards pass for slice 1. + +bnet = bnet_from_engine(engine); +ss = bnet.nnodes_per_slice; + +CPDpot = cell(1,ss); +for n=1:ss + fam = family(bnet.dag, n, 1); + e = bnet.equiv_class(n, 1); + CPDpot{n} = convert_to_pot(bnet.CPD{e}, engine.pot_type, fam(:), ev); +end +f.evidence = ev; +f.t = t; + +pots = CPDpot; +slice1 = 1:ss; +CPDclqs = engine.clq_ass_to_node1(slice1); + +[f.clpot, f.seppot] = init_pot(engine.jtree_engine1, CPDclqs, CPDpot, engine.pot_type, engine.observed1); +[f.clpot, f.seppot] = collect_evidence(engine.jtree_engine1, f.clpot, f.seppot); +for c=1:length(f.clpot) + if isa(f.clpot{c}, 'struct') + domain = f.clpot{c}.domain; + sizes = f.clpot{c}.sizes; + T = f.clpot{c}.T; + f.clpot{c} = dpot(domain, sizes, T); + end + [f.clpot{c}, ll(c)] = normalize_pot(f.clpot{c}); +end +logscale = ll(engine.root1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/jtree_sparse_2TBN_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +function engine = jtree_sparse_2TBN_inf_engine(bnet, varargin) +% JTREE_ONLINE_INF_ENGINE Online Junction tree inference algorithm for DBNs. +% engine = jtree_online_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - specifies variables that must be grouped in the 1.5 slice DBN +% maximize - 1 means do max-product, 0 means sum-product [0] +% +% The same nodes must be observed in every slice. + +ss = length(bnet.intra); +clusters = {}; +engine.maximize = 0; + +args = varargin; +nargs = length(args); +for i=1:2:length(args) + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, error(['unrecognized argument ' args{i}]) + end +end + +engine.evidence = []; +engine.node_sizes = []; + +int = []; +% include nodes with any outgoing arcs +for u=1:ss + if any(bnet.inter(u,:)) + int = [int u]; + end +end + +engine.interface = int; +engine.nonint = mysetdiff(1:ss, int); + +onodes = bnet.observed; + +% Create a "1.5 slice" jtree, containing the interface nodes of slice 1 +% and all the nodes of slice 2 +% To keep the node numbering the same, we simply disconnect the non-interface nodes +% from slice 1, and set their size to 1. +% We do this to speed things up, and so that the likelihood is computed correctly - we do not need to do +% this if we just want to compute marginals (i.e., we can include nodes whose potentials will +% be left as all 1s). +intra15 = bnet.intra; +for i=engine.nonint(:)' + intra15(:,i) = 0; + intra15(i,:) = 0; + assert(~any(bnet.inter(i,:))) +end +dag15 = [intra15 bnet.inter; + zeros(ss) bnet.intra]; +ns = bnet.node_sizes(:); +ns(engine.nonint) = 1; % disconnected nodes get size 1 +obs_nodes = [onodes(:) onodes(:)+ss]; +bnet15 = mk_bnet(dag15, ns, 'discrete', bnet.dnodes, 'equiv_class', bnet.equiv_class(:), ... + 'observed', obs_nodes(:)); + +% use unconstrained elimination, +% but force there to be a clique containing both interfaces +clusters(end+1:end+2) = {int, int+ss}; +%engine.jtree_engine = jtree_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +engine.jtree_engine = jtree_sparse_inf_engine(bnet15, 'clusters', clusters, 'root', int+ss); +jtree_engine = struct(engine.jtree_engine); % violate object privacy + +engine.in_clq = clq_containing_nodes(engine.jtree_engine, int); +engine.out_clq = clq_containing_nodes(engine.jtree_engine, int+ss); +engine.clq_ass_to_node = jtree_engine.clq_ass_to_node; +engine.root = jtree_engine.root_clq; + +% Also create an engine just for slice 1 +bnet1 = mk_bnet(bnet.intra1, bnet.node_sizes_slice, 'discrete', myintersect(bnet.dnodes,1:ss), ... + 'equiv_class', bnet.equiv_class(:,1), 'observed', onodes); +for i=1:max(bnet1.equiv_class) + bnet1.CPD{i} = bnet.CPD{i}; +end +%engine.jtree_engine1 = jtree_inf_engine(bnet1, 'clusters', {int}, 'root', int); +engine.jtree_engine1 = jtree_sparse_inf_engine(bnet1, 'clusters', {int}, 'root', int); +jtree_engine1 = struct(engine.jtree_engine1); % violate object privacy +engine.int_clq1 = clq_containing_nodes(engine.jtree_engine1, int); +engine.clq_ass_to_node1 = jtree_engine1.clq_ass_to_node; +engine.root1 = jtree_engine1.root_clq; + +engine.observed = [onodes onodes+ss]; +engine.observed1 = onodes; +engine.pot_type = determine_pot_type(bnet, onodes); +engine.slice_size = bnet.nnodes_per_slice; + +engine = class(engine, 'jtree_sparse_2TBN_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function m = marginal_family(engine, b, i, t, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree_2TBN) +% marginal = marginal_family(engine, b, i, t, add_ev) + +bnet = bnet_from_engine(engine); +if t==1 + m = marginal_nodes(engine, b, family(bnet.dag, i), t, add_ev, 1); +else + ss = length(bnet.intra); + fam = family(bnet.dag, i+ss); + m = marginal_nodes(engine, b, fam, t, add_ev, 1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@jtree_sparse_2TBN_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function marginal = marginal_nodes(engine, b, nodes, t, add_ev, is_fam) +% function marginal = marginal_nodes(engine, b, nodes, t, add_ev, is_fam) (jtree_2TBN) + +if nargin < 6, is_fam = 0; end +ss = engine.slice_size; + +if ~is_fam & (t > 1) & all(nodes<=ss) + nodes = nodes + ss; +end + +if t==1 + c = clq_containing_nodes(engine.jtree_engine1, nodes, is_fam); +else + c = clq_containing_nodes(engine.jtree_engine, nodes, is_fam); +end +if c == -1 + error(['no clique contains ' nodes]) +end +bigpot = b.clpot{c}; +pot = marginalize_pot(bigpot, nodes, engine.maximize); +marginal = pot_to_marginal(pot); + +% we convert the domain to the unrolled numbering system +% so that add_ev_to_dmarginal (maybe called in update_ess) extracts the right evidence. +if t > 1 + marginal.domain = nodes+(t-2)*engine.slice_size; +end +assert(~add_ev); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +/bnet_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Mon Jun 17 23:46:46 2002// +/find_mpe.m/1.1.1.1/Mon Jun 17 23:50:16 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/smoother_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/update_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online/@smoother_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/bnet_from_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function bnet = bnet_from_engine(engine) +% BNET_FROM_ENGINE Return the bnet structure stored inside the engine (smoother_engine) +% bnet = bnet_from_engine(engine) + +bnet = bnet_from_engine(engine.tbn_engine);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function [engine, LL] = enter_evidence(engine, ev) +% ENTER_EVIDENCE Call the offline smoother +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% + +T = size(ev, 2); +f = cell(1,T); +b = cell(1,T); % b{t}.clpot{c} +ll = zeros(1,T); +[f{1}, ll(1)] = fwd1(engine.tbn_engine, ev(:,1), 1); +for t=2:T + [f{t}, ll(t)] = fwd(engine.tbn_engine, f{t-1}, ev(:,t), t); +end +LL = sum(ll); +b{T} = backT(engine.tbn_engine, f{T}, T); +for t=T-1:-1:1 + b{t} = back(engine.tbn_engine, b{t+1}, f{t}, t); +end +engine.b = b;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +function mpe = find_mpe(engine, ev) +% FIND_MPE Find the most probable explanation (Viterbi) +% mpe = enter_evidence(engine, evidence, ...) +% +% evidence{i,t} = [] if if X(i,t) is hidden, and otherwise contains its observed value (scalar or column vector) +% + +mpe = cell(size(ev)); +engine.tbn_engine = set_fields(engine.tbn_engine, 'maximize', 1); + +T = size(ev, 2); +f = cell(1,T); +b = cell(1,T); % b{t}.clpot{c} +ll = zeros(1,T); +[f{1}, ll(1)] = fwd1(engine.tbn_engine, ev(:,1), 1); +for t=2:T + [f{t}, ll(t)] = fwd(engine.tbn_engine, f{t-1}, ev(:,t), t); +end + +if T==1 + [b{1}, mpe(:,1)] = backT_mpe(engine.tbn_engine, f{1}, ev(:,1), 1); +else + [b{T}, mpe(:,T)] = backT_mpe(engine.tbn_engine, f{T}, ev(:,T-1:T), T); + for t=T-1:-1:2 + [b{t}, mpe(:,t)] = back_mpe(engine.tbn_engine, b{t+1}, f{t}, ev(:,t-1:t), t); + end + t = 1; + [b{t}, mpe(:,t)] = back1_mpe(engine.tbn_engine, b{t+1}, f{t}, ev(:,1), t); +end +engine.b = b; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function marginal = marginal_family(engine, i, t, add_ev) +% MARGINAL_FAMILY Compute the joint distribution on a set of family (smoother_engine) +% function marginal = marginal_family(engine, i, t, add_ev) + +if nargin < 4, add_ev = 0; end +marginal = marginal_family(engine.tbn_engine, engine.b{t}, i, t, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function marginal = marginal_nodes(engine, nodes, t, add_ev) +% MARGINAL_NODES Compute the joint distribution on a set of nodes (smoother_engine) +% function marginal = marginal_nodes(engine, nodes, t, add_ev) + +if nargin < 4, add_ev = 0; end + +marginal = marginal_nodes(engine.tbn_engine, engine.b{t}, nodes, t, add_ev);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/smoother_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function engine = smoother_engine(tbn_engine) +% SMOOTHER_ENGINE Create an engine which does offline (fixed-interval) smoothing in O(T) space/time +% function engine = smoother_engine(tbn_engine) +% +% tbn_engine is any 2TBN inference engine which supports the following methods: +% fwd, fwd1, back, backT, back, marginal_nodes and marginal_family. + +engine.tbn_engine = tbn_engine; +engine.b = []; % space to store smoothed messages +engine = class(engine, 'smoother_engine'); +%engine = class(engine, 'smoother_engine', inf_engine(bnet_from_engine(tbn_engine))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/@smoother_engine/update_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function engine = update_engine(engine, newCPDs) +% UPDATE_ENGINE Update the engine to take into account the new parameters (smoother_engine). +% engine = update_engine(engine, newCPDs) + +engine.tbn_engine = update_engine(engine.tbn_engine, newCPDs);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:38 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +A D/@filter_engine//// +A D/@hmm_2TBN_inf_engine//// +A D/@jtree_2TBN_inf_engine//// +A D/@jtree_sparse_2TBN_inf_engine//// +A D/@smoother_engine////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/online
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/online/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/belprop_fg_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/find_mpe.m/1.1.1.1/Thu Jun 20 00:02:12 2002// +/loopy_converged.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_params.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_fg_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/belprop_fg_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function engine = belprop_fg_inf_engine(fg, varargin) +% BELPROP_FG_INF_ENGINE Make a belief propagation inference engine for factor graphs +% engine = belprop_fg_inf_engine(factor_graph, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default in brackets] +% e.g., engine = belprop_inf_engine(fg, 'tol', 1e-2, 'max_iter', 10) +% +% max_iter - max. num. iterations [ 2*num_nodes ] +% momentum - weight assigned to old message in convex combination (useful for damping oscillations) [0] +% tol - tolerance used to assess convergence [1e-3] +% maximize - 1 means use max-product, 0 means use sum-product [0] +% +% This uses potential objects, like belprop_inf_engine, and hence is quite slow. + +engine = init_fields; +engine = class(engine, 'belprop_fg_inf_engine'); + +% set params to default values +N = length(fg.G); +engine.max_iter = 2*N; +engine.momentum = 0; +engine.tol = 1e-3; +engine.maximize = 0; + +% parse optional arguments +engine = set_params(engine, varargin); + +engine.fgraph = fg; + +% store results computed by enter_evidence here +engine.marginal_nodes = cell(1, fg.nvars); +engine.evidence = []; + + +%%%%%%%%%%%% + +function engine = init_fields() + +engine.fgraph = []; +engine.max_iter = []; +engine.momentum = []; +engine.tol = []; +engine.maximize = []; +engine.marginal_nodes = []; +engine.evidence = []; +engine.niter = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +function [engine, ll, niter] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Propagate evidence using belief propagation +% [engine, ll, niter] = enter_evidence(engine, evidence, ...) +% +% The log-likelihood is not computed; ll = 0. +% niter contains the number of iterations used +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - 1 means use max-product, 0 means use sum-product [0] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +ll = 0; +maximize = 0; + +if nargin >= 3 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +verbose = 0; + +ns = engine.fgraph.node_sizes; +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +cnodes = engine.fgraph.cnodes; +pot_type = determine_pot_type(engine.fgraph, onodes); + +% prime each local kernel with evidence (if any) +nfactors = engine.fgraph.nfactors; +nvars = engine.fgraph.nvars; +factors = cell(1,nfactors); +for f=1:nfactors + K = engine.fgraph.factors{engine.fgraph.equiv_class(f)}; + factors{f} = convert_to_pot(K, pot_type, engine.fgraph.dom{f}(:), evidence); +end + +% initialise msgs +msg_var_to_fac = cell(nvars, nfactors); +for x=1:nvars + for f=engine.fgraph.dep{x} + msg_var_to_fac{x,f} = mk_initial_pot(pot_type, x, ns, cnodes, onodes); + end +end +msg_fac_to_var = cell(nfactors, nvars); +dom = cell(1, nfactors); +for f=1:nfactors + %hdom{f} = myintersect(engine.fgraph.dom{f}, hnodes); + dom{f} = engine.fgraph.dom{f}(:)'; + for x=dom{f} + msg_fac_to_var{f,x} = mk_initial_pot(pot_type, x, ns, cnodes, onodes); + %msg_fac_to_var{f,x} = marginalize_pot(factors{f}, x); + end +end + + + +converged = 0; +iter = 1; +var_prod = cell(1, nvars); +fac_prod = cell(1, nfactors); + +while ~converged & (iter <= engine.max_iter) + if verbose, fprintf('iter %d\n', iter); end + + % absorb + old_var_prod = var_prod; + for x=1:nvars + var_prod{x} = mk_initial_pot(pot_type, x, ns, cnodes, onodes); + for f=engine.fgraph.dep{x} + var_prod{x} = multiply_by_pot(var_prod{x}, msg_fac_to_var{f,x}); + end + end + for f=1:nfactors + fac_prod{f} = mk_initial_pot(pot_type, dom{f}, ns, cnodes, onodes); + for x=dom{f} + fac_prod{f} = multiply_by_pot(fac_prod{f}, msg_var_to_fac{x,f}); + end + end + + % send msgs to neighbors + old_msg_var_to_fac = msg_var_to_fac; + old_msg_fac_to_var = msg_fac_to_var; + converged = 1; + for x=1:nvars + %if verbose, disp(['var ' num2str(x) ' sending to fac ' num2str(engine.fgraph.dep{x})]); end + for f=engine.fgraph.dep{x} + temp = divide_by_pot(var_prod{x}, old_msg_fac_to_var{f,x}); + msg_var_to_fac{x,f} = normalize_pot(temp); + if ~approxeq_pot(msg_var_to_fac{x,f}, old_msg_var_to_fac{x,f}, engine.tol), converged = 0; end + end + end + for f=1:nfactors + %if verbose, disp(['fac ' num2str(f) ' sending to var ' num2str(dom{f})]); end + for x=dom{f} + temp = divide_by_pot(fac_prod{f}, old_msg_var_to_fac{x,f}); + temp2 = multiply_by_pot(factors{f}, temp); + temp3 = marginalize_pot(temp2, x, maximize); + msg_fac_to_var{f,x} = normalize_pot(temp3); + if ~approxeq_pot(msg_fac_to_var{f,x}, old_msg_fac_to_var{f,x}, engine.tol), converged = 0; end + end + end + + if iter==1 + converged = 0; + end + iter = iter + 1; +end + +niter = iter - 1; +engine.niter = niter; + +for x=1:nvars + engine.marginal_nodes{x} = normalize_pot(var_prod{x}); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (belprop_fg) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% This finds the marginally most likely value for each hidden node, +% and may give the wrong results even if the graph is acyclic, +% unless you set break_ties = 1. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% break_ties is optional. If 1, we will force ties to be broken consistently +% by calling enter_evidence N times. (see Jensen96, p106) Default = 1. + +break_ties = 1; + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'break_ties', break_ties = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +engine = enter_evidence(engine, evidence, 'maximize', 1); + +observed = ~isemptycell(evidence); +evidence = evidence(:); % hack to handle unrolled DBNs +N = length(evidence); +mpe = cell(1,N); +for i=1:N + m = marginal_nodes(engine, i); + % observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i) + mpe{i} = evidence{i}; + else + mpe{i} = argmax(m.T); + if break_ties + evidence{i} = mpe{i}; + [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/loopy_converged.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function niter = loopy_converged(engine) +% LOOPY_CONVERGED Did loopy belief propagation converge? 0 means no, eles we return the num. iterations. +% function niter = loopy_converged(engine) +% +% We use a simple heuristic: we say convergence occurred if the number of iterations +% used was less than the maximum allowed. + +if engine.niter == engine.max_iter + niter = 0; +else + niter = engine.niter; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function marginal = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (belprop) +% marginal = marginal_nodes(engine, query) + +assert(length(query)==1); +marginal = pot_to_marginal(engine.marginal_nodes{query});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_fg_inf_engine/set_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function engine = set_params(engine, varargin) +% SET_PARAMS Set the parameters (fields) for a belprop_inf_engine object +% engine = set_params(engine, name/value pairs) +% +% The following optional arguments can be specified in the form of name/value pairs: +% e.g., engine = set_params(engine, 'tol', 1e-2, 'max_iter', 10) +% +% max_iter - max. num. loopy iterations +% momentum - weight assigned to old message in convex combination +% tol - tolerance used to assess convergence +% maximize - 1 means use max-product, 0 means use sum-product + +args = varargin{1}; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'max_iter', engine.max_iter = args{i+1}; + case 'momentum', engine.momentum = args{i+1}; + case 'tol', engine.tol = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/belprop_inf_engine.m/1.1.1.1/Tue Dec 31 19:00:06 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/find_mpe.m/1.1.1.1/Wed Jun 19 22:08:40 2002// +/loopy_converged.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +A D/Old//// +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/belprop_gdl_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/belprop_inf_engine_nostr.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence1.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_domain.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/belprop_gdl_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function engine = belprop_gdl_inf_engine(gdl, varargin) +% BELPROP_GDL_INF_ENGINE Make a belief propagation inference engine for a GDL graph +% engine = belprop_gdl_inf_engine(gdl_graph, ...) +% +% If the GDL graph is a tree, this will give exact results. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default in brackets] +% e.g., engine = belprop_inf_engine(gdl, 'tol', 1e-2, 'max_iter', 10) +% +% protocol - 'tree' means send messages up then down the tree, +% 'parallel' means use synchronous updates ['parallel'] +% max_iter - max. num. iterations [ 2*num_nodes ] +% momentum - weight assigned to old message in convex combination (useful for damping oscillations) [0] +% tol - tolerance used to assess convergence [1e-3] +% maximize - 1 means use max-product, 0 means use sum-product [0] + + +engine = init_fields; +engine = class(engine, 'belprop_gdl_inf_engine'); + +% set default params +N = length(gdl.G); +engine.protocol = 'parallel'; +engine.max_iter = 2*N; +engine.momentum = 0; +engine.tol = 1e-3; +engine.maximize = 0; + +engine = set_params(engine, varargin); + +engine.gdl = gdl; + +if strcmp(engine.protocol, 'tree') + % Make a rooted tree, so there is a fixed message passing order. + root = N; + [engine.tree, engine.preorder, engine.postorder, height, cyclic] = mk_rooted_tree(gdl.G, root); + assert(~cyclic); +end + +% store results computed by enter_evidence here +ndoms = length(gdl.doms); +nvars = length(gdl.vars); +engine.marginal_domains = cell(1, ndoms); + +% to compute the marginal on each variable, we need to know which domain to marginalize +% and we want to choose the lightest. We compute the weight once we have seen the evidence. +engine.dom_weight = []; +engine.evidence = []; + + +%%%%%%%%% + +function engine = init_fields() + +engine.protocol = []; +engine.gdl = []; +engine.max_iter = []; +engine.momentum = []; +engine.tol = []; +engine.maximize = []; +engine.marginal_domains = []; +engine.evidence = []; +engine.tree = []; +engine.preorder = []; +engine.postorder = []; +engine.dom_weight = [];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/belprop_inf_engine_nostr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function engine = belprop_inf_engine(fg, max_iter, momentum, tol, maximize) + +if nargin < 2, max_iter = length(fg.G); end +if nargin < 3, momentum = 0; end +if nargin < 4, tol = 1e-3; end +if nargin < 5, maximize = 0; end + +engine.fgraph = fg; +engine.max_iter = max_iter; +engine.momentum = momentum; +engine.tol = tol; +engine.maximize = maximize; + +% store results computed by enter_evidence here +ndoms = length(fg.doms); +nvars = length(fg.vars); +engine.marginal_domains = cell(1, ndoms); + +% to compute the marginal on each variable, we need to know which domain to marginalize +% so we represent each domain as a bit vector, and compute its (pre-evidence) weight +engine.dom_weight = []; + +% engine.dom_bitv = sparse(ndoms, nvars); +% ns = fg.node_sizes; +% for i=1:ndoms +% engine.dom_bitv(i, fg.doms{i}) = 1; +% engine.dom_weight(i) = prod(ns(fg.doms{i})); +% end + + +engine = class(engine, 'belprop_inf_engine');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function engine = enter_evidence(engine, evidence) + +doms = engine.fg.doms; +ndoms = length(doms); +ns = engine.fg.node_sizes; +obs = find(~isemptycell(evidence)); +cobs = myintersect(obs, engine.fg.cnodes); +dobs = myintersect(obs, engine.fg.dnodes); +ns(cobs) = 0; +ns(dobs) = 1; + +% prime each local kernel with evidence (if any) +local_kernel = cell(1, ndoms); +for i=1:length(engine.fg.kernels_of_type) + u = engine.fg.kernels_of_type{i}; + local_kernel(u) = kernel_to_dpots(engine.fg.kernels{i}, evidence, engine.fg.domains_of_type{i}); +end + +% initialise all msgs to 1s +nedges = engine.fg.nedges; +msg = cell(1, nedges); +for i=1:nedges + msg{i} = dpot(engine.fg.sepset{i}, ns(engine.fg.sepset{i})); +end + +prod_of_msg = cell(1, ndoms); +bel = cell(1, ndoms); +old_bel = cell(1, ndoms); + +converged = 0; +iter = 1; +while ~converged & (iter <= engine.max_iter) + + % each node multiplies all its incoming msgs + for i=1:ndoms + prod_of_msg{i} = dpot(doms{i}, ns(doms{i})); + nbrs = engine.fg.nbrs{i}; + for j=1:length(nbrs) + ndx = engine.fg.edge_ndx(j,i); + prod_of_msg{i} = multiply_by_pot(prod_of_msg{i}, msg{ndx}); + end + end + old_msg = msg; + + % each node computes its local belief + for i=1:ndoms + bel{i} = normalize_pot(multiply_pots(prod_of_msg{i}, local_kernel{i})); + end + + % converged? + converged = 1; + for i=1:ndoms + if ~approxeq(bel{i}, old_bel{i}, engine.tol) + converged = 0; + break; + end + end + + if ~converged + % each node sends a msg to each of its neighbors + for i=1:ndoms + nbrs = engine.fg.nbrs{i}; + for j=1:length(nbrs) + % multiply all incoming msgs except from j + temp = prod_of_msg{i}; + ndx = engine.fg.edge_ndx(j,i); + temp = divide_by_pot(temp, old_msg{ndx}); + % send msg from i to j + temp = multiply_by_pot(temp, local_kernel{i}); + ndx = engine.fg.edge_ndx(i,j); + msg{ndx} = normalize_pot(marginalize_pot(temp, engine.fg.sepset{ndx})); + end + end + end + + iter = iter + 1; +end + + +engine.marginal = bel;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/enter_evidence1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ +function engine = enter_evidence(engine, evidence) + +doms = engine.fgraph.doms; +ndoms = length(doms); +ns = engine.fgraph.node_sizes; +obs = find(~isemptycell(evidence)); +cobs = myintersect(obs, engine.fgraph.cnodes); +dobs = myintersect(obs, engine.fgraph.dnodes); +ns(cobs) = 0; +ns(dobs) = 1; + +% recompute the weight of each domain now that we know what nodes are observed +for i=1:ndoms + engine.dom_weight(i) = prod(ns(engine.fgraph.doms{i})); +end + +% prime each local kernel with evidence (if any) +local_kernel = cell(1, ndoms); +for i=1:length(engine.fgraph.kernels_of_type) + u = engine.fgraph.kernels_of_type{i}; + local_kernel(u) = kernel_to_dpots(engine.fgraph.kernels{i}, evidence, engine.fgraph.domains_of_type{i}); +end + +% initialise all msgs to 1s +msg = cell(ndoms, ndoms); +for i=1:ndoms + nbrs = engine.fgraph.nbrs{i}; + for j=nbrs(:)' + dom = engine.fgraph.sepset{i,j}; + msg{i,j} = dpot(dom, ns(dom)); + end +end + +prod_of_msg = cell(1, ndoms); +bel = cell(1, ndoms); +old_bel = cell(1, ndoms); + +converged = 0; +iter = 1; +while ~converged & (iter <= engine.max_iter) + + % each node multiplies all its incoming msgs + for i=1:ndoms + prod_of_msg{i} = dpot(doms{i}, ns(doms{i})); + nbrs = engine.fgraph.nbrs{i}; + for j=nbrs(:)' + prod_of_msg{i} = multiply_by_pot(prod_of_msg{i}, msg{j,i}); + end + end + + % each node computes its local belief + old_bel = bel; + for i=1:ndoms + bel{i} = normalize_pot(multiply_pots(prod_of_msg{i}, local_kernel{i})); + end + + % converged? + if iter==1 + converged = 0; + else + converged = 1; + for i=1:ndoms + belT = get_params(bel{i}, 'table'); + old_belT = get_params(old_bel{i}, 'table'); + if ~approxeq(belT, old_belT, engine.tol) + converged = 0; + break; + end + end + end + + if ~converged + old_msg = msg; + % each node sends a msg to each of its neighbors + for i=1:ndoms + nbrs = engine.fgraph.nbrs{i}; + for j=nbrs(:)' + % multiply all incoming msgs except from j + temp = prod_of_msg{i}; + temp = divide_by_pot(temp, old_msg{j,i}); + % send msg from i to j + temp = multiply_by_pot(temp, local_kernel{i}); + msg{i,j} = normalize_pot(marginalize_pot(temp, engine.fgraph.sepset{i,j})); + end + end + end + + iter = iter + 1 +end + +engine.marginal_domains = bel; +%for i=1:ndoms + %engine.marginal_domains{i} = get_params(bel{i}, 'table'); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/Old/marginal_domain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function marginal = marginal_domain(engine, i) +% MARGINAL_DOMAIN Return the marginal on the specified domain (belprop) +% marginal = marginal_domain(engine, i) + +marginal = pot_to_marginal(engine.marginal_domains{i});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/belprop_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +function engine = belprop_inf_engine(bnet, varargin) +% BELPROP_INF_ENGINE Make a loopy belief propagation inference engine +% engine = belprop_inf_engine(bnet, ...) +% +% This is like pearl_inf_engine, except it uses potential objects, +% instead of lambda/pi structs. Hence it is slower. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default in brackets] +% +% protocol - 'tree' means send messages up then down the tree, +% 'parallel' means use synchronous updates ['parallel'] +% max_iter - max. num. iterations [ 2*num_nodes ] +% momentum - weight assigned to old message in convex combination (useful for damping oscillations) [0] +% tol - tolerance used to assess convergence [1e-3] +% maximize - 1 means use max-product, 0 means use sum-product [0] +% filename - name of file to write beliefs to after each iteration within enter_evidence [ [] ] +% +% e.g., engine = belprop_inf_engine(bnet, 'maximize', 1, 'max_iter', 10) + +% gdl = general distributive law +engine.gdl = bnet_to_gdl(bnet); + +% set default params +N = length(engine.gdl.G); +engine.protocol = 'parallel'; +engine.max_iter = 2*N; +engine.momentum = 0; +engine.tol = 1e-3; +engine.maximize = 0; +engine.filename = []; +engine.fid = []; + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'max_iter', engine.max_iter = args{i+1}; + case 'momentum', engine.momentum = args{i+1}; + case 'tol', engine.tol = args{i+1}; + case 'protocol', engine.protocol = args{i+1}; + case 'filename', engine.filename = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + + +if strcmp(engine.protocol, 'tree') + % Make a rooted tree, so there is a fixed message passing order. + root = N; + [engine.tree, engine.preorder, engine.postorder, height, cyclic] = mk_rooted_tree(engine.gdl.G, root); + assert(~cyclic); +end + +% store results computed by enter_evidence here +engine.marginal_domains = cell(1, N); + +engine.niter = []; + +engine = class(engine, 'belprop_inf_engine', inf_engine(bnet)); + +%%%%%%%%% + +function gdl = bnet_to_gdl(bnet) + +gdl.G = mk_undirected(bnet.dag); +N = length(bnet.dag); +gdl.doms = cell(1,N); +for i=1:N + gdl.doms{i} = family(bnet.dag, i); +end + +% Compute a bit vector representation of the set of domains +% dom_bitv(i,j) = 1 iff variable j occurs in domain i +gdl.dom_bitv = zeros(N, N); +for i=1:N + gdl.dom_bitv(i, gdl.doms{i}) = 1; +end + +% compute the interesection of the domains on either side of each edge (separating set) +gdl.sepset = cell(N, N); +gdl.nbrs = cell(1,N); +for i=1:N + nbrs = neighbors(gdl.G, i); + gdl.nbrs{i} = nbrs; + for j = nbrs(:)' + gdl.sepset{i,j} = myintersect(gdl.doms{i}, gdl.doms{j}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function [engine, ll, niter] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Propagate evidence using belief propagation +% [engine, ll, niter] = enter_evidence(engine, evidence, ...) +% +% The log-likelihood is not computed; ll = 0. +% niter contains the number of iterations used (if engine.protocol = 'parallel') +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - 1 means use max-product, 0 means use sum-product [0] +% exclude - list of nodes whose potential will not be included in the joint [ [] ] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) + +ll = 0; +exclude = []; +maximize = 0; + +if nargin >= 3 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'exclude', exclude = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine.maximize = maximize; + +if ~isempty(engine.filename) + engine.fid = fopen(engine.filename, 'w'); + if engine.fid == 0 + error(['can''t open ' engine.filename]); + end +else + engine.fid = []; +end + +gdl = engine.gdl; +bnet = bnet_from_engine(engine); + +ndoms = length(gdl.doms); +ns = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + +% prime each local kernel with evidence (if any) +local_kernel = cell(1, ndoms); +for i=1:ndoms + if myismember(i, exclude) + local_kernel{i} = mk_initial_pot(pot_type, gdl.doms{i}, ns, bnet.cnodes, onodes); + else + e = bnet.equiv_class(i); + local_kernel{i} = convert_to_pot(bnet.CPD{e}, pot_type, gdl.doms{i}(:), evidence); + end +end + +% initialise all msgs to 1s +msg = cell(ndoms, ndoms); +for i=1:ndoms + nbrs = gdl.nbrs{i}; + for j=nbrs(:)' + dom = gdl.sepset{i,j}; + msg{i,j} = mk_initial_pot(pot_type, dom, ns, bnet.cnodes, onodes); + end +end + +switch engine.protocol + case 'parallel', + [engine.marginal_domains, niter] = parallel_protocol(engine, evidence, pot_type, local_kernel, msg); + case 'tree', + engine.marginal_domains = serial_protocol(engine, evidence, pot_type, local_kernel, msg); + niter = 1; +end +engine.niter = niter; + +%fprintf('just finished %d iterations of belprop\n', niter); + +if ~isempty(engine.filename) + fclose(engine.fid); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (belprop) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% This finds the marginally most likely value for each hidden node, +% and may give the wrong results even if the graph is acyclic, +% unless you set break_ties = 1. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% break_ties is optional. If 1, we will force ties to be broken consistently +% by calling enter_evidence N times. (see Jensen96, p106) Default = 1. + +break_ties = 1; + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'break_ties', break_ties = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +engine = enter_evidence(engine, evidence, 'maximize', 1); + +observed = ~isemptycell(evidence); +evidence = evidence(:); % hack to handle unrolled DBNs +N = length(evidence); +mpe = cell(1,N); +for i=1:N + m = marginal_nodes(engine, i); + % observed nodes are all set to 1 inside the inference engine, so we must undo this + if observed(i) + mpe{i} = evidence{i}; + else + mpe{i} = argmax(m.T); + if break_ties + evidence{i} = mpe{i}; + [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/loopy_converged.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function niter = loopy_converged(engine) +% LOOPY_CONVERGED Did loopy belief propagation converge? 0 means no, eles we return the num. iterations. +% function niter = loopy_converged(engine) +% +% We use a simple heuristic: we say convergence occurred if the number of iterations +% used was less than the maximum allowed. + +if engine.niter == engine.max_iter + niter = 0; +else + niter = engine.niter; +end +%conv = (strcmp(engine.protocol, 'tree') | (engine.niter < engine.max_iter));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function [marginal, pot] = marginal_family(engine, query) +% MARGINAL_NODES Compute the marginal on the family of the specified query node (belprop) +% [marginal, pot] = marginal_family(engine, query) + +pot = engine.marginal_domains{query}; +marginal = pot_to_marginal(pot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function [marginal, pot] = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (belprop) +% [marginal, pot] = marginal_nodes(engine, query) +% +% query must be a subset of a family + +if isempty(query) + big_pot = engine.marginal_domains{1}; % pick an arbitrary domain +else + big_pot = engine.marginal_domains{query(end)}; +end +pot = marginalize_pot(big_pot, query); +marginal = pot_to_marginal(pot); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/junk/1.1.1.1/Wed May 29 15:59:56 2002// +/parallel_protocol.m/1.1.1.1/Wed May 29 15:59:56 2002// +/tree_protocol.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/private/junk Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +fgraph +fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph%fgraph +fgraph +fgraphffgraphufgraphnfgraphcfgraphtfgraphifgraphofgraphnfgraph fgraph[fgraphbfgraphefgraphlfgraph,fgraph fgraphifgraphtfgraphefgraphrfgraph]fgraph fgraph=fgraph fgraphpfgraphafgraphrfgraphafgraphlfgraphlfgraphefgraphlfgraph_fgraphpfgraphrfgraphofgraphtfgraphofgraphcfgraphofgraphlfgraph(fgraphefgraphnfgraphgfgraphifgraphnfgraphefgraph,fgraph fgraphefgraphvfgraphifgraphdfgraphefgraphnfgraphcfgraphefgraph,fgraph fgraphpfgraphofgraphtfgraph_fgraphtfgraphyfgraphpfgraphefgraph,fgraph fgraphlfgraphofgraphcfgraphafgraphlfgraph_fgraphkfgraphefgraphrfgraphnfgraphefgraphlfgraph,fgraph fgraphmfgraphsfgraphgfgraph)fgraph +fgraph +fgraphdfgraphofgraphmfgraphsfgraph fgraph=fgraph 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/private/parallel_protocol.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function [bel, niter] = parallel_protocol(engine, evidence, pot_type, local_kernel, msg) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); + +ndoms = length(engine.gdl.doms); +prod_of_msg = cell(1, ndoms); +bel = cell(1, ndoms); +old_bel = cell(1, ndoms); + +converged = 0; +iter = 1; +while ~converged & (iter <= engine.max_iter) + + % each node multiplies all its incoming msgs and computes its local belief + old_bel = bel; + for i=1:ndoms + prod_of_msg{i} = mk_initial_pot(pot_type, engine.gdl.doms{i}, ns, bnet.cnodes, onodes); + nbrs = engine.gdl.nbrs{i}; + for j=nbrs(:)' + prod_of_msg{i} = multiply_by_pot(prod_of_msg{i}, msg{j,i}); + end + bel{i} = normalize_pot(multiply_by_pot(local_kernel{i}, prod_of_msg{i})); + end + + if ~isempty(engine.fid) + for i=1:ndoms + tmp = pot_to_marginal(bel{i}); + %fprintf(engine.fid, '%9.7f ', tmp.T(1)); + fprintf(engine.fid, '%9.7f ', tmp.U(1)); + end + %fprintf(engine.fid, ' U '); + %for i=1:ndoms + % tmp = pot_to_marginal(bel{i}); + % fprintf(engine.fid, '%9.7f ', tmp.U(1)); + %end + fprintf(engine.fid, '\n'); + end + + % converged? + if iter==1 + converged = 0; + else + converged = 1; + for i=1:ndoms + if ~approxeq_pot(bel{i}, old_bel{i}, engine.tol) + converged = 0; + break; + end + end + end + + if ~converged + old_msg = msg; + % each node sends a msg to each of its neighbors + for i=1:ndoms + nbrs = engine.gdl.nbrs{i}; + for j=nbrs(:)' + % multiply all incoming msgs except from j + temp = prod_of_msg{i}; + temp = divide_by_pot(temp, old_msg{j,i}); + % send msg from i to j + temp = multiply_by_pot(temp, local_kernel{i}); + temp2 = marginalize_pot(temp, engine.gdl.sepset{i,j}, engine.maximize); + msg{i,j} = normalize_pot(temp2); + end + end + end + + iter = iter + 1; +end + + +niter = iter-1; + +if 0 +for i=1:ndoms + prod_of_msg{i} = mk_initial_pot(pot_type, engine.gdl.doms{i}, ns, bnet.cnodes, onodes); + nbrs = engine.gdl.nbrs{i}; + for j=nbrs(:)' + prod_of_msg{i} = multiply_by_pot(prod_of_msg{i}, msg{j,i}); + end + bel{i} = normalize_pot(multiply_by_pot(local_kernel{i}, prod_of_msg{i})); +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_inf_engine/private/tree_protocol.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function bel = tree_protocol(engine, evidence, pot_type, local_kernel, msg) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); + +ndoms = length(engine.gdl.doms); +prod_of_msg = cell(1, ndoms); +bel = cell(1, ndoms); + +% collect to root (node to parents) +for n=engine.postorder + % absorb msgs from children + prod_of_msg{n} = mk_initial_pot(pot_type, engine.gdl.doms{n}, ns, bnet.cnodes, onodes); + for c=children(engine.tree, n) + prod_of_msg{n} = multiply_by_pot(prod_of_msg{n}, msg{c,n}); + end + % send msg to parents + for p=parents(engine.tree, n) + if iter==1 + temp = prod_of_msg{n}; + else + temp = divide_by_pot(prod_of_msg{n}, old_msg{p,n}); + end + temp = multiply_by_pot(temp, local_kernel{n}); + temp2 = marginalize_pot(temp, engine.gdl.sepset{n,p}, engine.maximize); + %fprintf('%d sends %d\n', n, p); + msg{n,p} = normalize_pot(temp2); + end +end + +% distribute from root (node to children) +for n=engine.preorder + % absorb from parents + %prod_of_msg{n} = mk_initial_pot(pot_type, doms{n}, ns, cnodes, onodes); + for p=parents(engine.tree, n) + prod_of_msg{n} = multiply_by_pot(prod_of_msg{n}, msg{p,n}); + end + bel{n} = normalize_pot(multiply_pots(prod_of_msg{n}, local_kernel{n})); + % send msg to children + for c=children(engine.tree, n) + temp = divide_by_pot(prod_of_msg{n}, msg{c,n}); + temp = multiply_by_pot(temp, local_kernel{n}); + temp2 = marginalize_pot(temp, engine.gdl.sepset{n,c}, engine.maximize); + %fprintf('%d sends %d\n', n, c); + msg{n,c} = normalize_pot(temp2); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/belprop_mrf2_inf_engine.m/1.1.1.1/Fri Jan 3 22:01:56 2003// +/bp_mrf2.m/1.1.1.1/Mon Jan 5 01:23:34 2004// +/enter_soft_evidence.m/1.1.1.1/Thu Jan 2 17:29:54 2003// +/find_mpe.m/1.1.1.1/Thu Jan 2 17:49:18 2003// +/marginal_nodes.m/1.1.1.1/Tue Dec 31 21:24:30 2002// +/set_params.m/1.1.1.1/Thu Jan 2 17:28:56 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@belprop_mrf2_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/belprop_mrf2_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +function engine = belprop_mrf2_inf_engine(mrf2, varargin) +% BELPROP_MRF2_INF_ENGINE Belief propagation for MRFs with discrete pairwise potentials +% engine = belprop_mrf2_inf_engine(mrf2, ...) +% +% This is like belprop_inf_engine, except it is designed for mrf2, so is much faster. +% +% [ ... ] = belprop_mrf2_inf_engine(..., 'param1',val1, 'param2',val2, ...) +% allows you to specify optional parameters as name/value pairs. +% Parameters modifying behavior of enter_evidence are below [default value in brackets] +% +% max_iter - max. num. iterations [ 5*nnodes] +% momentum - weight assigned to old message in convex combination +% (useful for damping oscillations) [0] +% tol - tolerance used to assess convergence [1e-3] +% verbose - 1 means print error at every iteration [0] +% +% Parameters can be changed later using set_params + + +% The advantages of pairwise potentials are +% (1) we can compute messages using vector-matrix multiplication +% (2) we can easily specify the parameters: one potential per edge +% In contrast, potentials on larger cliques are more complicated to deal with. + + +nnodes = length(mrf2.adj_mat); + +[engine.max_iter, engine.momentum, engine.tol, engine.verbose] = ... + process_options(varargin, 'max_iter', [], 'momentum', 0, 'tol', 1e-3, ... + 'verbose', 0); + +if isempty(engine.max_iter) % no user supplied value, so compute default + engine.max_iter = 5*nnodes; + %if acyclic(mrf2.adj_mat, 0) --- can be very slow! + % engine.max_iter = nnodes; + %else + % engine.max_iter = 5*nnodes; + %end +end + +engine.bel = cell(1, nnodes); % store results of enter_evidence here +engine.mrf2 = mrf2; + +engine = class(engine, 'belprop_mrf2_inf_engine'); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/bp_mrf2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,209 @@ +function [new_bel, niter, new_msg, edge_id, nstates] = bp_mrf2_general(adj_mat, pot, local_evidence, varargin) +% BP_MRF2_GENERAL Belief propagation on an MRF with pairwise potentials +% function [bel, niter] = bp_mrf2_general(adj_mat, pot, local_evidence, varargin) +% +% Input: +% adj_mat(i,j) = 1 iff there is an edge between nodes i and j +% pot(ki,kj,i,j) or pot{i,j}(ki,kj) = potential on edge between nodes i,j +% If the potentials on all edges are the same, +% you can just pass in 1 array, pot(ki,kj) +% local_evidence(state, node) or local_evidence{i}(k) = Pr(observation at node i | Xi=k) +% +% Use cell arrays if the hidden nodes do not all have the same number of values. +% +% Output: +% bel(k,i) or bel{i}(k) = P(Xi=k|evidence) +% niter contains the number of iterations used +% +% [ ... ] = bp_mrf2(..., 'param1',val1, 'param2',val2, ...) +% allows you to specify optional parameters as name/value pairs. +% Parameters names are below [default value in brackets] +% +% max_iter - max. num. iterations [ 5*nnodes] +% momentum - weight assigned to old message in convex combination +% (useful for damping oscillations) - currently ignored i[0] +% tol - tolerance used to assess convergence [1e-3] +% maximize - 1 means use max-product, 0 means use sum-product [0] +% verbose - 1 means print error at every iteration [0] +% +% fn - name of function to call at end of every iteration [ [] ] +% fnargs - we call feval(fn, bel, iter, fnargs{:}) [ [] ] + +nnodes = length(adj_mat); + +[max_iter, momentum, tol, maximize, verbose, fn, fnargs] = ... + process_options(varargin, 'max_iter', 5*nnodes, 'momentum', 0, ... + 'tol', 1e-3, 'maximize', 0, 'verbose', 0, ... + 'fn', [], 'fnargs', []); + +if iscell(local_evidence) + use_cell = 1; +else + use_cell = 0; + [nstates nnodes] = size(local_evidence); +end + +if iscell(pot) + tied_pot = 0; +else + tied_pot = (ndims(pot)==2); +end + + +% give each edge a unique number +ndx = find(adj_mat); +nedges = length(ndx); +edge_id = zeros(1, nnodes*nnodes); +edge_id(ndx) = 1:nedges; +edge_id = reshape(edge_id, nnodes, nnodes); + +% initialise messages +if use_cell + prod_of_msgs = cell(1, nnodes); + old_bel = cell(1, nnodes); + nstates = zeros(1, nnodes); + old_msg = cell(1, nedges); + for i=1:nnodes + nstates(i) = length(local_evidence{i}); + prod_of_msgs{i} = local_evidence{i}; + old_bel{i} = local_evidence{i}; + end + for i=1:nnodes + nbrs = find(adj_mat(:,i)); + for j=nbrs(:)' + old_msg{edge_id(i,j)} = normalise(ones(nstates(j),1)); + end + end +else + prod_of_msgs = local_evidence; + old_bel = local_evidence; + %old_msg = zeros(nstates, nnodes, nnodes); + old_msg = zeros(nstates, nedges); + m = normalise(ones(nstates,1)); + for i=1:nnodes + nbrs = find(adj_mat(:,i)); + for j=nbrs(:)' + old_msg(:, edge_id(i,j)) = m; + %old_msg(:,i,j) = m; + end + end +end + + +converged = 0; +iter = 1; + +while ~converged & (iter <= max_iter) + + % each node sends a msg to each of its neighbors + for i=1:nnodes + nbrs = find(adj_mat(i,:)); + for j=nbrs(:)' + if tied_pot + pot_ij = pot; + else + if iscell(pot) + pot_ij = pot{i,j}; + else + pot_ij = pot(:,:,i,j); + end + end + pot_ij = pot_ij'; % now pot_ij(xj, xi) + % so pot_ij * msg(xi) = sum_xi pot(xj,xi) msg(xi) = f(xj) + + if 1 + % Compute temp = product of all incoming msgs except from j + % by dividing out old msg from j from the product of all msgs sent to i + if use_cell + temp = prod_of_msgs{i}; + m = old_msg{edge_id(j,i)}; + else + temp = prod_of_msgs(:,i); + m = old_msg(:, edge_id(j,i)); + end + if any(m==0) + fprintf('iter=%d, send from i=%d to j=%d\n', iter, i, j); + keyboard + end + m = m + (m==0); % valid since m(k)=0 => temp(k)=0, so can replace 0's with anything + temp = temp ./ m; + temp_div = temp; + end + + if 1 + % Compute temp = product of all incoming msgs except from j in obvious way + if use_cell + %temp = ones(nstates(i),1); + temp = local_evidence{i}; + for k=nbrs(:)' + if k==j, continue, end; + temp = temp .* old_msg{edge_id(k,i)}; + end + else + %temp = ones(nstates,1); + temp = local_evidence(:,i); + for k=nbrs(:)' + if k==j, continue, end; + temp = temp .* old_msg(:, edge_id(k,i)); + end + end + end + %assert(approxeq(temp, temp_div)) + assert(approxeq(normalise(pot_ij * temp), normalise(pot_ij * temp_div))) + + if maximize + newm = max_mult(pot_ij, temp); % bottleneck + else + newm = pot_ij * temp; + end + newm = normalise(newm); + if use_cell + new_msg{edge_id(i,j)} = newm; + else + new_msg(:, edge_id(i,j)) = newm; + end + end % for j + end % for i + old_prod_of_msgs = prod_of_msgs; + + % each node multiplies all its incoming msgs and computes its local belief + if use_cell + for i=1:nnodes + nbrs = find(adj_mat(:,i)); + prod_of_msgs{i} = local_evidence{i}; + for j=nbrs(:)' + prod_of_msgs{i} = prod_of_msgs{i} .* new_msg{edge_id(j,i)}; + end + new_bel{i} = normalise(prod_of_msgs{i}); + end + err = abs(cat(1,new_bel{:}) - cat(1, old_bel{:})); + else + for i=1:nnodes + nbrs = find(adj_mat(:,i)); + prod_of_msgs(:,i) = local_evidence(:,i); + for j=nbrs(:)' + prod_of_msgs(:,i) = prod_of_msgs(:,i) .* new_msg(:,edge_id(j,i)); + end + new_bel(:,i) = normalise(prod_of_msgs(:,i)); + end + err = abs(new_bel(:) - old_bel(:)); + end + converged = all(err < tol); + if verbose, fprintf('error at iter %d = %f\n', iter, sum(err)); end + if ~isempty(fn) + if isempty(fnargs) + feval(fn, new_bel); + else + feval(fn, new_bel, iter, fnargs{:}); + end + end + + iter = iter + 1; + old_msg = new_msg; + old_bel = new_bel; +end % while + +niter = iter-1; + +fprintf('converged in %d iterations\n', niter); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function [engine, ll, niter] = enter_soft_evidence(engine, local_evidence) +% ENTER_SOFT_EVIDENCE Propagate evidence using belief propagation +% [engine, ll, niter] = enter_soft_evidence(engine, local_evidence) +% +% local_evidence{i}(j) = Pr(observation at node i | S(i)=j) +% +% The log-likelihood is not computed; ll = 0. +% niter contains the number of iterations used + +ll = 0; +mrf2 = engine.mrf2; +[bel, niter] = bp_mrf2(mrf2.adj_mat, mrf2.pot, local_evidence, ... + 'max_iter', engine.max_iter, 'momentum', engine.momentum, ... + 'tol', engine.tol, 'maximize', 0, 'verbose', engine.verbose); +engine.bel = bel;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function mpe = find_mpe(engine, local_evidence) +% FIND_MPE Find the most probable explanation of the data +% function mpe = find_mpe(engine, local_evidence +% +% local_evidence{i}(j) = Pr(observation at node i | S(i)=j) +% +% This finds the marginally most likely value for each hidden node. +% It may give inconsistent results if there are ties. + +[mpe, niter] = bp_mpe_mrf2(engine.mrf2.adj_mat, engine.mrf2.pot, local_evidence, ... + 'max_iter', engine.max_iter, 'momentum', engine.momentum, ... + 'tol', engine.tol);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function marginal = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (belprop) +% marginal = marginal_nodes(engine, query) +% +% query must be a single node + +if length(query)>1 + error('can only handle single node marginals') +end +marginal = engine.bel{query};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@belprop_mrf2_inf_engine/set_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function engine = set_params(engine, varargin) +% SET_PARAMS Modify parameters of the inference engine +% engine = set_params(engine, 'param1',val1, 'param2',val2, ...) +% +% Parameter names are listed below. +% +% max_iter - max. num. iterations +% momentum - weight assigned to old message in convex combination +% (useful for damping oscillations) +% tol - tolerance used to assess convergence +% verbose - 1 means print error at every iteration [0] + +[engine.max_iter, engine.momentum, engine.tol, engine.verbose] = ... + process_options('max_iter', engine.max_iter, 'momentum', engine.momentum, ... + 'tol', engine.tol, 'verbose', engine.verbose);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@cond_gauss_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/cond_gauss_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@cond_gauss_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@cond_gauss_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@cond_gauss_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@cond_gauss_inf_engine/cond_gauss_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function engine = cond_gauss_inf_engine(bnet) +% COND_GAUSS_INF_ENGINE Conditional Gaussian inference engine +% engine = cond_gauss_inf_engine(bnet) +% +% Enumerates all the discrete roots, and runs jtree on the remaining Gaussian nodes. + +dnodes = mysetdiff(1:length(bnet.dag), bnet.cnodes); + +%onodes = dnodes; % all the discrete ndoes will be observed +%engine.sub_engine = jtree_inf_engine(bnet, onodes); +bnet2 = bnet; +bnet2.observed = dnodes; +engine.sub_engine = jtree_inf_engine(bnet2); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.T = []; +engine.mu = []; +engine.Sigma = []; +engine.joint_dmarginal = []; +engine.onodes = []; % needed for marginal_nodes +engine.evidence = []; % needed for marginal_nodes add_ev + +engine = class(engine, 'cond_gauss_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@cond_gauss_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (cond_gauss) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +observed = ~isemptycell(evidence); +onodes = find(observed); +hnodes = find(isemptycell(evidence)); +engine.evidence = evidence; + +% check there are no C->D links where C is hidden +pot_type = determine_pot_type(bnet, onodes); + +dhid = myintersect(hnodes, bnet.dnodes); +S = prod(ns(dhid)); +T = zeros(S,1); + +N = length(bnet.dag); +mu = cell(1,N); +Sigma = cell(1,N); +cobs = myintersect(bnet.cnodes, onodes); +chid = myintersect(bnet.cnodes, hnodes); +ens = ns; +ens(cobs) = 0; +for j=chid(:)' + mu{j} = zeros(ens(j), S); + Sigma{j} = zeros(ens(j), ens(j), S); +end + +for i=1:S + dvals = ind2subv(ns(dhid), i); + evidence(dhid) = num2cell(dvals); + [sub_engine, loglik] = enter_evidence(engine.sub_engine, evidence); + for j=chid(:)' + m = marginal_nodes(sub_engine, j); + mu{j}(:,i) = m.mu; + Sigma{j}(:,:,i) = m.Sigma; + end + T(i) = exp(loglik); +end + +[T, lik] = normalise(T); +loglik = log(lik); + +engine.T = T; +engine.mu = mu; +engine.Sigma = Sigma; + +dnodes = bnet.dnodes; +dobs = myintersect(dnodes, onodes); +ens(dobs) = 1; +engine.joint_dmarginal = dpot(dnodes, ens(dnodes), myreshape(engine.T, ens(dnodes))); + +engine.onodes = onodes;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@cond_gauss_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (cond_gauss) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a singleton set +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end + +if length(query) ~= 1 + error('cond_gauss_inf_engine can only handle marginal queries on single nodes') +end +j = query; +bnet = bnet_from_engine(engine); + +if myismember(j, bnet.cnodes) + if ~myismember(j, engine.onodes) + [m, C] = collapse_mog(engine.mu{j}, engine.Sigma{j}, engine.T); + marginal.mu = m; + marginal.Sigma = C; + marginal.T = 1.0; % single mixture component + else + marginal.mu = engine.evidence{j}; + k = bnet.node_sizes(j); + marginal.Sigma = zeros(k,k); + marginal.T = 1.0; % since P(E|E)=1 + end +else + marginal = pot_to_marginal(marginalize_pot(engine.joint_dmarginal, j)); + if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + end +end + +marginal.domain = query;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@enumerative_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enumerative_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@enumerative_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@enumerative_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@enumerative_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@enumerative_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function [engine, loglik] = enter_evidence(engine, evidence) +% ENTER_EVIDENCE Add the specified evidence to the network (enumerative_inf) +% [engine, loglik] = enter_evidence(engine, evidence) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +engine.evidence = evidence; +if nargout == 2 + [m, loglik] = marginal_nodes(engine, []); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@enumerative_inf_engine/enumerative_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function engine = enumerative_inf_engine(bnet) +% ENUMERATIVE_INF_ENGINE Inference engine for fully discrete BNs that uses exhaustive enumeration. +% engine = enumerative_inf_engine(bnet) + + +assert(isempty(bnet.cnodes)); + +% This is where we store stuff between enter_evidence and marginal_nodes +engine.evidence = []; + +engine = class(engine, 'enumerative_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@enumerative_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function [marginal, loglik] = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (enumerative_inf) +% [marginal, loglik] = marginal_nodes(engine, query) + + +if isempty(query) & nargout < 2 + marginal.T = 1; + marginal.domain = []; + return; +end + +evidence = engine.evidence; +bnet = bnet_from_engine(engine); +assert(isempty(bnet.cnodes)); +n = length(bnet.dag); +observed = ~isemptycell(evidence); +vals = cat(1,evidence{observed}); +vals = vals(:)'; +ns = bnet.node_sizes; + +sz = ns(query); +T = 0*myones(sz); +p = 0; +for i=1:prod(ns) + inst = ind2subv(ns, i); % i'th instantiation + if isempty(vals) | inst(observed) == vals % agrees with evidence + prob = exp(log_lik_complete(bnet, num2cell(inst(:)))); + p = p + prob; + v = inst(query); + j = subv2ind(sz, v); + T(j) = T(j) + prob; + end +end + +[T, lik] = normalise(T); +lik = p; +loglik = log(lik); + +Tsmall = shrink_obs_dims_in_table(T, query, evidence); +marginal.domain = query; +marginal.T = Tsmall;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/gaussian_inf_engine.m/1.1.1.1/Fri May 14 01:13:26 2004// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@gaussian_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (gaussian_inf_engine) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +O = find(~isemptycell(evidence)); +H = find(isemptycell(evidence)); +vals = cat(1, evidence{O}); + +% Compute Pr(H|o) +[Hmu, HSigma, loglik] = condition_gaussian(engine.mu, engine.Sigma, H, O, vals(:), ns); + +engine.Hmu = Hmu; +engine.HSigma = HSigma; +engine.hnodes = H; + +%%%%%%%% + +function [mu2, Sigma2, loglik] = condition_gaussian(mu, Sigma, X, Y, y, ns) +% CONDITION_GAUSSIAN Compute Pr(X|Y=y) where X and Y are jointly Gaussian. +% [mu2, Sigma2, ll] = condition_gaussian(mu, Sigma, X, Y, y, ns) + +if isempty(y) + mu2 = mu; + Sigma2 = Sigma; + loglik = 0; + return; +end + +use_log = 1; + +if length(Y)==length(mu) % instantiating every variable + mu2 = y; + Sigma2 = zeros(length(y)); + loglik = gaussian_prob(y, mu, Sigma, use_log); + return; +end + +[muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns); +K = SXY*inv(SYY); +mu2 = muX + K*(y-muY); +Sigma2 = SXX - K*SYX; +loglik = gaussian_prob(y, muY, SYY, use_log);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/gaussian_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function engine = gaussian_inf_engine(bnet) +% GAUSSIAN_INF_ENGINE Computes the joint multivariate Gaussian corresponding to the bnet +% engine = gaussian_inf_engine(bnet) +% +% For details on how to compute the joint Gaussian from the bnet, see +% - "Gaussian Influence Diagrams", R. Shachter and C. R. Kenley, Management Science, 35(5):527--550, 1989. +% Once we have the Gaussian, we can apply the standard formulas for conditioning and marginalization. + +assert(isequal(bnet.cnodes, 1:length(bnet.dag))); + +[W, D, mu] = extract_params_from_gbn(bnet); +U = inv(eye(size(W)) - W')'; +Sigma = U' * D * U; + +engine.mu = mu; +engine.Sigma = Sigma; +%engine.logp = log(normal_coef(Sigma)); + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.Hmu = []; +engine.HSigma = []; +engine.hnodes = []; + +engine = class(engine, 'gaussian_inf_engine', inf_engine(bnet)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function marginal = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (gaussian) +% marginal = marginal_nodes(engine, query) + +% Compute sum_{Hsum} Pr(Hkeep, Hsum | o) +H = engine.hnodes; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +Hkeep = myintersect(H, query); +Hsum = mysetdiff(H, Hkeep); + +[marginal.mu, marginal.Sigma] = marginalize_gaussian(engine.Hmu, engine.HSigma, Hkeep, Hsum, ns); +marginal.domain = query; +marginal.T = 1; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/extract_params_from_gbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@gaussian_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gaussian_inf_engine/private/extract_params_from_gbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function [B,D,mu] = extract_params_from_gbn(bnet) +% Extract all the local parameters of each Gaussian node, and collect them into global matrices. +% [B,D,mu] = extract_params_from_gbn(bnet) +% +% B(i,j) is a block matrix that contains the transposed weight matrix from node i to node j. +% D(i,i) is a block matrix that contains the noise covariance matrix for node i. +% mu(i) is a block vector that contains the shifted noise mean for node i. + +% In Shachter's model, the mean of each node in the global gaussian is +% the same as the node's local unconditional mean. +% In Alag's model (which we use), the global mean gets shifted. + + +num_nodes = length(bnet.dag); +bs = bnet.node_sizes(:); % bs = block sizes +N = sum(bs); % num scalar nodes + +B = zeros(N,N); +D = zeros(N,N); +mu = zeros(N,1); + +for i=1:num_nodes % in topological order + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + %[m, Sigma, weights] = extract_params_from_CPD(bnet.CPD{e}); + s = struct(bnet.CPD{e}); % violate privacy of object + m = s.mean; Sigma = s.cov; weights = s.weights; + if length(ps) == 0 + mu(block(i,bs)) = m; + else + mu(block(i,bs)) = m + weights * mu(block(ps,bs)); + end + B(block(ps,bs), block(i,bs)) = weights'; + D(block(i,bs), block(i,bs)) = Sigma; +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/gibbs_sampling_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function [engine, loglik] = enter_evidence(engine, evidence) +% ENTER_EVIDENCE Add the specified evidence to the network (gibbs_sampling_inf_engine) +% [engine, loglik] = enter_evidence(engine, evidence) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value +% +% loglik is not computed... we just return a 0 value + +bnet = bnet_from_engine(engine); + +engine.hnodes = find(isemptycell(evidence)); +engine.onodes = mysetdiff(1:length(evidence), engine.hnodes); + +engine.evidence = zeros(engine.slice_size, 1); + +% Reset all counts since they are no longer valid +engine.marginal_counts = {}; +%engine.state = sample_bnet (bnet, 1, 0); +engine.state = cell2num(sample_bnet(bnet)); + +% For speed, we use a normal (not cell) array. We're making use of +% the current restriction to discrete nodes. +for i = engine.onodes + engine.evidence(i) = evidence{i}; +end + +loglik = 0; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/gibbs_sampling_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +function engine = gibbs_sampling_inf_engine(bnet, varargin) +% GIBBS_SAMPLING_INF_ENGINE +% +% engine = gibbs_sampling_inf_engine(bnet, ...) +% +% Optional parameters [default in brackets] +% 'burnin' - How long before you start using the samples [100]. +% 'gap' - how often you use the samples in the estimate [1]. +% 'T' - number of samples [1000] +% i.e, number of node flips (so, for +% example if there are 10 nodes in the bnet, and T is 1000, each +% node will get flipped 100 times (assuming a deterministic schedule)) +% The total running time is proportional to burnin + T*gap. +% +% 'order' - if the sampling schedule is deterministic, use this +% parameter to specify the order in which nodes are sampled. +% Order is allowed to include multiple copies of nodes, which is +% useful if you want to, say, focus sampling on particular nodes. +% Default is to use a deterministic schedule that goes through the +% nodes in order. +% +% 'sampling_dist' - when using a stochastic sampling method, at +% each step the node to sample is chosen according to this +% distribution (may be unnormalized) +% +% The sampling_dist and order parameters shouldn't both be used, +% and this will cause an assert. +% +% +% Written by "Bhaskara Marthi" <bhaskara@cs.berkeley.edu> Feb 02. + + +engine.burnin = 100; +engine.gap = 1; +engine.T = 1000; +use_default_order = 1; +engine.deterministic = 1; +engine.order = {}; +engine.sampling_dist = {}; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i = 1:2:nargs + switch args{i} + case 'burnin' + engine.burnin = args{i+1}; + case 'gap' + engine.gap = args{i+1}; + case 'T' + engine.T = args{i+1}; + case 'order' + assert (use_default_order); + use_default_order = 0; + engine.order = args{i+1}; + case 'sampling_dist' + assert (use_default_order); + use_default_order = 0; + engine.deterministic = 0; + engine.sampling_dist = args{i+1}; + otherwise + error(['unrecognized parameter to gibbs_sampling_inf_engine']); + end + end +end + +engine.slice_size = size(bnet.dag, 2); +if (use_default_order) + engine.order = 1:engine.slice_size; +end +engine.hnodes = []; +engine.onodes = []; +engine.evidence = []; +engine.state = []; +engine.marginal_counts = {}; + +% Precompute the strides for each CPT +engine.strides = compute_strides(bnet); + +% Precompute graphical information +engine.families = compute_families(bnet); +engine.children = compute_children(bnet); + +% For convenience, store the CPTs as tables rather than objects +engine.CPT = get_cpts(bnet); + +engine = class(engine, 'gibbs_sampling_inf_engine', inf_engine(bnet)); + + + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ +function [marginal, engine] = marginal_nodes(engine, nodes, varargin); +% MARGINAL_NODES Compute the marginal on the specified query nodes +% (gibbs_sampling_engine) +% [marginal, engine] = marginal_nodes(engine, nodes, ...) +% +% returns Pr(X(nodes) | X(observedNodes)) +% +% The engine is also modified, and so it is returned as well, since +% Matlab doesn't support passing by reference(!) So +% if you want to, for example, incrementally run gibbs for a few 100 +% steps at a time, you should use the returned value. +% +% Optional arguments : +% +% 'reset_counts' is 1 if you want to reset the counts made in the +% past, and 0 otherwise (if the current query nodes are different +% from the previous query nodes, or if marginal_nodes has not been +% called before, reset_counts should be set to 1). +% By default it is 1. + + +reset_counts = 1; + +if (nargin > 3) + args = varargin; + nargs = length(args); + for i = 1:2:nargs + switch args{i} + case 'reset_counts' + reset_counts = args{i+1}; + otherwise + error(['Incorrect argument to gibbs_sampling_engine/' ... + ' marginal_nodes']); + end + end +end + +% initialization stuff +bnet = bnet_from_engine(engine); +slice_size = engine.slice_size; +hnodes = engine.hnodes; +onodes = engine.onodes; +nonqnodes = mysetdiff(1:slice_size, nodes); +gap = engine.gap; +burnin = engine.burnin; +T_max = engine.T; +ns = bnet.node_sizes(nodes); + + +% Cache the strides for the marginal table +marg_strides = [1 cumprod(ns(1:end-1))]; + +% Reset counts if necessary +if (reset_counts == 1) + %state = sample_bnet(bnet, 1, 0); + %state = cell2num(sample_bnet(bnet, 'evidence', num2cell(engine.evidence))); + state = cell2num(sample_bnet(bnet)); + state(onodes) = engine.evidence(onodes); + if (length(ns) == 1) + marginal_counts = zeros(ns(1),1); + else + marginal_counts = zeros(ns); + end + +% Otherwise, use the counts that have been stored in the engine +else + state = engine.state; + state(onodes, :) = engine.evidence(onodes, :); + marginal_counts = engine.marginal_counts; +end + +if (engine.deterministic == 1) + pos = 1; + order = engine.order; + orderSize = length(engine.order); +else + sampling_dist = normalise(engine.sampling_dist); +end + + +for t = 1:(T_max*gap+burnin) + + % First, select node m to sample + if (engine.deterministic == 1) + m = engine.order(pos); + pos = pos+1; + if (pos > orderSize) + pos = 1; + end + else + m = my_sample_discrete(sampling_dist); + end + + + % If the node is observed, then don't bother resampling + if (myismember(m, onodes)) + continue; + end + + % Next, compute the posterior + post = compute_posterior (bnet, state, m, engine.strides, engine.families, ... + engine.children, engine.CPT); + state(m) = my_sample_discrete(post); + + % Now update our monte carlo estimate of the posterior + % distribution on the query node + if ((mod(t-burnin, gap) == 0) & (t > burnin)) + + vals = state(nodes); + index = 1+marg_strides*(vals-1); + marginal_counts(index) = marginal_counts(index)+1; + end +end + +% Store results for future computation. Note that we store +% unnormalized counts +engine.state = state; +engine.marginal_counts = marginal_counts; + +marginal.T = normalise(marginal_counts); + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/CPT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function c = CPT(bnet, i) +% CPT Helper function avoid having to type in +% CPD_to_CPT(bnet.CPD{i}) every time + +c = CPD_to_CPT(bnet.CPD{i}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +/CPT.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_children.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_families.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_families_dbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_posterior.c/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_posterior_dbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_strides.m/1.1.1.1/Wed May 29 15:59:56 2002// +/get_cpts.m/1.1.1.1/Wed May 29 15:59:56 2002// +/get_slice_dbn.c/1.1.1.1/Wed May 29 15:59:56 2002// +/get_slice_dbn.m/1.1.1.1/Wed May 29 15:59:56 2002// +/my_sample_discrete.m/1.1.1.1/Wed May 29 15:59:56 2002// +/sample_single_discrete.c/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@gibbs_sampling_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/compute_children.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function c = compute_children(bnet) +% COMPUTE_CHILDREN +% precomputes the children of nodes in a bnet +% +% The return value is a cell array for now + +ss = size(bnet.dag, 1); +c = cell(ss, 1); +for i = 1:ss + c{i} = children(bnet.dag, i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/compute_families.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function families = compute_families(bnet) +% COMPUTE_FAMILIES +% precomputes the families of nodes in a bnet +% +% The return value is a cell array for now + +ss = size(bnet.dag, 1); +families = cell(ss, 1); +for i = 1:ss + families{i} = family(bnet.dag, i); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/compute_families_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function families = compute_families_dbn(bnet) +% COMPUTE_FAMILIES +% precomputes the families of nodes in a dbn +% +% The return value is a cell array for now + +ss = size(bnet.intra, 1); +families = cell(ss, 2); +for i = 1:ss + families{i, 1} = family(bnet.dag, i, 1); + families{i, 2} = family(bnet.dag, i, 2); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +#include "mex.h" + +/* Helper function that extracts a one-dimensional slice from a cpt */ +/* +void multiplySlice(mxArray *bnet, mxArray *state, int i, int nsi, int j, + mxArray *strides, mxArray *fam, mxArray *cpts, + double *y) +*/ +void multiplySlice(const mxArray *bnet, const mxArray *state, int i, int nsi, int j, + const mxArray *strides, const mxArray *fam, const mxArray *cpts, + double *y) +{ + mxArray *ec, *cpt, *family; + double *ecElts, *cptElts, *famElts, *strideElts, *ev; + int c1, k, famSize, startInd, strideStride, pos, stride; + + strideStride = mxGetM(strides); + strideElts = mxGetPr(strides); + + ev = mxGetPr(state); + + /* Get the CPT */ + ec = mxGetField (bnet, 0, "equiv_class"); + ecElts = mxGetPr(ec); + k = (int) ecElts[j-1]; + cpt = mxGetCell (cpts, k-1); + cptElts = mxGetPr (cpt); + + /* Get the family vector for this cpt */ + family = mxGetCell (fam, j-1); + famSize = mxGetNumberOfElements (family); + famElts = mxGetPr (family); + + /* Figure out starting position and stride */ + startInd = 0; + for (c1 = 0, pos = k-1; c1 < famSize; c1++, pos +=strideStride) { + if (famElts[c1] != i) { + startInd += strideElts[pos]*(ev[(int)famElts[c1]-1]-1); + } + else { + stride = strideElts[pos]; + } + } + + for (c1 = 0, pos = startInd; c1 < nsi; c1++, pos+=stride) { + y[c1] *= cptElts[pos]; + } +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray + *prhs[]) +{ + double *pi, *nsElts, *y, *childrenElts; + mxArray *ns, *children; + double sum; + int i, nsi, c1, numChildren; + + pi = mxGetPr(prhs[2]); + i = (int) pi[0]; + + ns = mxGetField(prhs[0], 0, "node_sizes"); + nsElts = mxGetPr(ns); + nsi = (int) nsElts[i-1]; + + /* Initialize the posterior */ + plhs[0] = mxCreateDoubleMatrix (1, nsi, mxREAL); + y = mxGetPr(plhs[0]); + for (c1 = 0; c1 < nsi; c1++) { + y[c1] = 1; + } + + /* Multiply in the cpt of the node i */ + multiplySlice(prhs[0], prhs[1], i, nsi, i, prhs[3], prhs[4], + prhs[6], y); + + + /* Multiply in cpts of children of i */ + children = mxGetCell (prhs[5], i-1); + numChildren = mxGetNumberOfElements (children); + childrenElts = mxGetPr (children); + + for (c1 = 0; c1 < numChildren; c1++) { + int j; + j = (int) childrenElts[c1]; + multiplySlice (prhs[0], prhs[1], i, nsi, j, prhs[3], prhs[4], + prhs[6], y); + } + + sum = 0; + /* normalize! */ + for (c1 = 0; c1 < nsi; c1++) { + sum += y[c1]; + } + + for (c1 = 0; c1 < nsi; c1++) { + y[c1] /= sum; + } +} + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function post = compute_posterior_dbn(bnet, state, i, n, strides, families, ... + CPT) +% COMPUTE_POSTERIOR +% +% post = compute_posterior(bnet, state, i, n, strides, families, +% cpts) +% +% Compute the posterior distribution on node X_i^n of a DBN, +% conditional on evidence in the cell array state +% +% strides is the cached result of compute_strides(bnet) +% families is the cached result of compute_families(bnet) +% cpt is the cached result of get_cpts(bnet) +% +% post is a one-dimensional table + + + +% First multiply in the cpt of the node itself +post = get_slice_dbn(bnet, state, i, n, i, n, strides, families, CPT); +post = post(:); + +% Then multiply in CPTs of children that are in this slice +for j = children(bnet.intra, i) + slice = get_slice_dbn(bnet, state, j, n, i, n, strides, families, CPT); + post = post.*slice(:); +end + +% Finally, if necessary, multiply in CPTs of children in the next +% slice +if (n < size(state,2)) + for j = children(bnet.inter, i) + slice = get_slice_dbn(bnet, state, j, n+1, i, n, strides, families, ... + CPT); + post = post.*slice(:); + end +end + +post = normalise(post); + + + + + + + + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/compute_strides.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function strides = compute_strides(bnet) +% COMPUTE_STRIDES For each CPT and each variable in that CPT, +% returns the stride of that variable. So in future, we can +% quickly extract a slice of the CPT. +% +% The return value is a 2d array, where strides(i,j) contains the +% stride of the jth variable in the ith CPT. Cell arrays would +% have saved space but they are slower. +% + +num_cpts = size(bnet.CPD, 2); +max_cpt_dim = 1 + max(sum(bnet.dag)); +strides = zeros(num_cpts, max_cpt_dim); + +for i = 1:num_cpts + c = CPT(bnet, i); + siz = size(CPT(bnet, i)); + + % Deal with the special case of a 1-d array separately + if siz(2) == 1 + dim = 1; + else + dim = size(siz, 2); + end + + strides(i, 1:dim ) = [1 cumprod(siz(1:dim-1))]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/get_cpts.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function c = get_cpts(bnet) +% Get all the cpts in tabular form + +cpds = bnet.CPD; +c = cell(size(cpds)); +for i = 1:length(c) + c{i} = CPT(bnet, i); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,116 @@ +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray + *prhs[]) +{ + double *pn, *pi, *pj, *pm, *y, *ecElts, *pcpt, *famElts, *strideElts, + *ev, *nsElts; + int i, k, j, m, n; + mxArray *ec, *cpt, *fam, *ns; + int c1, famSize, nsj; + int strideStride, startInd, stride, pos, numNodes; + + const int BNET = 0; + const int STATE = 1; + const int STRIDES = 6; + const int FAMILIES = 7; + const int CPT = 8; + + pn = mxGetPr(prhs[3]); + n = (int) pn[0]; + pi = mxGetPr(prhs[2]); + i = (int) pi[0]; + pj = mxGetPr(prhs[4]); + j = (int) pj[0]; + pm = mxGetPr(prhs[5]); + m = (int) pm[0]; + ev = mxGetPr(prhs[STATE]); + ns = mxGetField (prhs[BNET], 0, "node_sizes"); + nsElts = mxGetPr (ns); + numNodes = mxGetM(ns); + + strideStride = mxGetM(prhs[STRIDES]); + strideElts = mxGetPr(prhs[STRIDES]); + + + + /* Treat the case n = 1 separately */ + if (pn[0] == 1) { + + /* Get the appropriate CPT */ + ec = mxGetField (prhs[BNET], 0, "eclass1"); + ecElts = mxGetPr(ec); + k = (int) ecElts[i-1]; + cpt = mxGetCell (prhs[8], k-1); + pcpt = mxGetPr(cpt); + + nsj = (int) nsElts[j-1]; + + /* Get the correct family vector */ + /* (Note : MEX is painful) */ + fam = mxGetCell (prhs[FAMILIES], i - 1); + famSize = mxGetNumberOfElements(fam); + famElts = mxGetPr(fam); + + + /* Figure out starting position and stride */ + startInd = 0; + for (c1 = 0, pos = k-1; c1 < famSize; c1++, pos+=strideStride) { + if (famElts[c1] != j) { + startInd += strideElts[pos]*(ev[(int)famElts[c1]-1]-1); + } + else { + stride = strideElts[pos]; + } + } + + plhs[0] = mxCreateDoubleMatrix (1, nsj, mxREAL); + y = mxGetPr(plhs[0]); + for (c1 = 0, pos = startInd; c1 < nsj; c1++, pos+=stride) { + y[c1] = pcpt[pos]; + } + } + + /* Handle the case n > 1 */ + else { + + /* Get the appropriate CPT */ + ec = mxGetField (prhs[BNET], 0, "eclass2"); + ecElts = mxGetPr(ec); + k = (int) ecElts[i-1]; + cpt = mxGetCell (prhs[8], k-1); + pcpt = mxGetPr(cpt); + + /* Figure out size of slice */ + if (m == 1) { + nsj = (int) nsElts[j-1]; + } + else { + nsj = (int) nsElts[j-1+numNodes]; + } + + /* Figure out family */ + fam = mxGetCell (prhs[FAMILIES], i - 1 + numNodes); + famSize = mxGetNumberOfElements(fam); + famElts = mxGetPr(fam); + + startInd = 0; + for (c1 = 0, pos = k-1; c1 < famSize; c1++, pos+=strideStride) { + int f = (int) famElts[c1]; + + if (((f == j+numNodes) && (m == n)) || ((f == j) && (m == + n-1))) { + stride = strideElts[pos]; + } + else { + startInd += strideElts[pos] * (ev[f-1+((n-2)*numNodes)]-1); + } + } + + plhs[0] = mxCreateDoubleMatrix(1,nsj, mxREAL); + y = mxGetPr(plhs[0]); + for (c1 = 0, pos = startInd; c1 < nsj; c1++, pos+=stride) { + y[c1] = pcpt[pos]; + } + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,87 @@ +function slice = get_slice_dbn(bnet, state, i, n, j, m, strides, families, ... + CPT) +% slice = get_slice(bnet, state, i, n, j, m, strides, families, cpt) +% +% GET_SLICE get one-dimensional slice of the CPT for node X_i^n +% that corresponds to the different values of X_j^m, where all +% other nodes have values given by state. +% strides is the result of +% calling compute_strides(bnet) +% families is the result of calling compute_families(bnet) +% cpts is the result of calling get_cpts(bnet) +% +% slice is a 1-d array + + +if (n == 1) + + k = bnet.eclass1(i); + c = CPT{k}; + + % Figure out evidence on family + fam = families{i, 1}; + ev = state(fam, 1); + + % Remove evidence on node j + pos = find(fam == j); + ev(pos) = 1; + dim = size(ev, 1); + + % Compute initial index and stride + start_ind = 1+strides(k, 1:dim)*(ev-1); + stride = strides(k, pos); + + % Compute the slice + slice = c(start_ind:stride:start_ind+(bnet.node_sizes(j, 1)-1)*stride); + +else + + k = bnet.eclass2(i); + c = CPT{k}; + + fam = families{i, 2}; + ss = length(bnet.intra); + + % Divide the family into nodes in this time step and nodes in the + % previous time step + this_time_step = fam(find(fam > ss)); + prev_time_step = fam(find(fam <= ss)); + + % Normalize the node numbers + this_time_step = this_time_step - ss; + + % Get the evidence + this_step_ev = state(this_time_step, n); + prev_step_ev = state(prev_time_step, n-1); + + % Remove the evidence for X_j^m + if (m == n) + pos = find(this_time_step == j); + this_step_ev(pos) = 1; + pos = pos + size(prev_time_step, 2); + else + assert (m == n-1); + pos = find(prev_time_step == j); + prev_step_ev(pos) = 1; + end + + % Combine the two time steps + ev = [prev_step_ev; this_step_ev]; + dim = size(ev, 1); + + + % Compute starting index and stride + start_ind = 1 + strides(k, 1:dim)*(ev-1); + stride = strides(k, pos); + + % Compute slice + if (m == 1) + q = 1; + else + q = 2; + end + slice = c(start_ind:stride:start_ind+(bnet.node_sizes(j, q)-1)*stride); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/my_sample_discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function M = my_sample_discrete(prob) +% A faster version that calls a c subfunction. Will update one +% day to have r and c parameters as well + +R = rand (1,1); +M = sample_single_discrete(R, prob); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@gibbs_sampling_inf_engine/private/sample_single_discrete.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray + *prhs[]) +{ + double *y, *pr, *dist; + int k, distSize; + double r, cumSum; + + plhs[0] = mxCreateDoubleMatrix(1,1, mxREAL); + y = mxGetPr (plhs[0]); + + pr = mxGetPr (prhs[0]); + r = pr[0]; + + dist = mxGetPr (prhs[1]); + distSize = mxGetNumberOfElements (prhs[1]); + + for (k = 0, cumSum = 0; (k < distSize) && (r >= cumSum); cumSum += dist[k], k++); + + y[0] = k; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@global_joint_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/enter_evidence.m/1.1.1.1/Mon Jun 7 19:05:42 2004// +/find_mpe.m/1.1.1.1/Wed Jun 19 21:56:32 2002// +/global_joint_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Mon Jun 7 19:04:48 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@global_joint_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@global_joint_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@global_joint_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@global_joint_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (global_joint) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value. +% +% Warning: Computing the log likelihood requires marginalizing all the nodes and can be slow. +% +% The list below gives optional arguments [default value in brackets]. +% +% exclude - list of nodes whose potential will not be included in the joint [ [] ] +% +% e.g., engine = enter_evidence(engine, ev, 'exclude', 3) + +exclude = []; +maximize = 0; + +if nargin >= 3 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'exclude', exclude = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +assert(~maximize) +bnet = bnet_from_engine(engine); +N = length(bnet.node_sizes); +%[engine.jpot, loglik] = compute_joint_pot(bnet, mysetdiff(1:N, exclude), evidence, 1:N); +[engine.jpot] = compute_joint_pot(bnet, mysetdiff(1:N, exclude), evidence, 1:N); +% jpot should not be normalized, otherwise it gives wrong resutls for limids like asia_dt1 +if nargout == 2 + [m] = marginal_nodes(engine, []); + [T, lik] = normalize(m.T); + loglik = log(lik); +end +%[engine.jpot loglik] = normalize_pot(engine.jpot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@global_joint_inf_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function [mpe, ll] = find_mpe(engine, evidence) +% FIND_MPE_GLOBAL Compute the most probable explanation(s) from the global joint +% [mpe, ll] = find_mpe(engine, evidence) +% +% mpe(k,i) is the most probable value of node i in the k'th global mode (cell array) +% +% We assume all nodes are discrete + +%engine = global_joint_inf_engine(bnet); +bnet = bnet_from_engine(engine); +engine = enter_evidence(engine, evidence); +S1 = struct(engine); % violate object privacy +S2 = struct(S1.jpot); % joint potential +prob = max(S2.T(:)); +modes = find(S2.T(:) == prob); + +ens = bnet.node_sizes; +onodes = find(~isemptycell(evidence)); +ens(onodes) = 1; +mpe = ind2subv(ens, modes); +for k=1:length(modes) + for i=onodes(:)' + mpe(k,i) = evidence{i}; + end +end +ll = log(prob); + +mpe = num2cell(mpe);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@global_joint_inf_engine/global_joint_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function engine = global_joint_inf_engine(bnet) +% GLOBAL_JOINT_INF_ENGINE Construct the global joint distribution as a potential +% engine = global_joint_inf_engine(bnet) +% +% Warning: this has size exponential in the number of discrete hidden variables + +engine.jpot = []; +engine = class(engine, 'global_joint_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@global_joint_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function [m, pot] = marginal_family(engine, i) +% MARGINAL_FAMILY Compute the marginal on i's family (global_inf_engine) +% [m, pot] = marginal_family(engine, i) +% + +bnet = bnet_from_engine(engine); +[m, pot] = marginal_nodes(engine, family(bnet.dag, i));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@global_joint_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function [m, pot] = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified set of nodes (global_joint) +% [m, pot] = marginal_nodes(engine, query) + +pot = marginalize_pot(engine.jpot, query); +m = pot_to_marginal(pot); +%[m.T, lik] = normalize(m.T); +%loglik = log(lik);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +/cliques_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/clq_containing_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/collect_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/distribute_evidence.m/1.1.1.1/Mon Jun 17 21:00:08 2002// +/enter_evidence.m/1.1.1.1/Mon Jun 17 20:59:30 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/find_max_config.m/1.1.1.1/Mon Jun 17 23:14:52 2002// +/find_mpe.m/1.1.1.1/Mon Jun 17 23:14:08 2002// +/init_pot.m/1.1.1.1/Sun Jun 16 19:34:56 2002// +/jtree_inf_engine.m/1.1.1.1/Fri Oct 31 22:37:48 2003// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/collect_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/distribute_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/Old/collect_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function engine = collect_evidence(engine, root) + +if isempty(engine.postorder{root}) + % this is the first time we have collected to this root + % memoize the order + [jtree, preorder, postorder] = mk_rooted_tree(engine.jtree, root); + postorder_parents = cell(1,length(postorder)); + for n=postorder(1:end-1) + postorder_parents{n} = parents(jtree, n); + end + engine.postorder{root} = postorder; + engine.postorder_parents{root} = postorder_parents; +else + postorder = engine.postorder{root}; + postorder_parents = engine.postorder_parents{root}; +end + +C = length(engine.clpot); +seppot = cell(C, C); +% separators are implicitely initialized to 1s + +% collect to root (node to parents) +for n=postorder(1:end-1) + for p=postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + engine.seppot{p,n} = marginalize_pot(engine.clpot{n}, engine.separator{p,n}, engine.maximize); + engine.clpot{p} = multiply_by_pot(engine.clpot{p}, engine.seppot{p,n}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/Old/distribute_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function engine = distribute_evidence(engine, root) + +if isempty(engine.preorder{root}) + % this is the first time we have distributed from this root + % memoize the order + [jtree, preorder, postorder] = mk_rooted_tree(engine.jtree, root); + preorder_children = cell(1,length(preorder)); + for n=preorder + preorder_children{n} = children(jtree, n); + end + engine.preorder{root} = preorder; + engine.preorder_children{root} = preorder_children; +else + preorder = engine.preorder{root}; + preorder_children = engine.preorder_children{root}; +end + + +% distribute from root (node to children) +for n=preorder(:)' + for c=preorder_children{n}(:)' + engine.clpot{c} = divide_by_pot(engine.clpot{c}, engine.seppot{n,c}); + engine.seppot{n,c} = marginalize_pot(engine.clpot{n}, engine.separator{n,c}, engine.maximize); + engine.clpot{c} = multiply_by_pot(engine.clpot{c}, engine.seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/Old/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% engine = enter_evidence(engine, ev, soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +% set default params +exclude = []; +soft_evidence = cell(1,N); +maximize = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + if iscell(args{1}) + soft_evidence = args{1}; + else + for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +engine.maximize = maximize; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end + +%clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); +%[clpot, loglik] = enter_soft_evidence(engine, clqs, pot, onodes, pot_type); +%engine.clpot = clpot; % save the results for marginal_nodes + + +clique = engine.clq_ass_to_node([hard_nodes soft_nodes]); +potential = pot; + + +% Set the clique potentials to all 1s +C = length(engine.cliques); +for i=1:C + engine.clpot{i} = mk_initial_pot(pot_type, engine.cliques{i}, ns, bnet.cnodes, onodes); +end + +% Multiply on specified potentials +for i=1:length(clique) + c = clique(i); + engine.clpot{c} = multiply_by_pot(engine.clpot{c}, potential{i}); +end + +root = 1; % arbitrary +engine = collect_evidence(engine, root); +engine = distribute_evidence(engine, root); + +ll = zeros(1, C); +for i=1:C + [engine.clpot{i}, ll(i)] = normalize_pot(engine.clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/Old/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified potentials to the network (jtree) +% [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type, maximize) +% +% We multiply potential{i} onto clique(i) before propagating. +% We return all the modified clique potentials. + +[clpot, seppot] = init_pot(engine, clique, potential, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/cliques_from_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function cliques = cliques_from_engine(engine) +% CLIQUES_FROM_ENGINE Return the cliques stored inside the inf. engine (jtree) +% cliques = cliques_from_engine(engine) + +cliques = engine.cliques;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/clq_containing_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function c = clq_containing_nodes(engine, nodes, fam) +% CLQ_CONTAINING_NODES Find the lightest clique (if any) that contains the set of nodes +% c = clq_containing_nodes(engine, nodes, family) +% +% If the optional 'family' argument is specified, it means nodes = family(nodes(end)). +% (This is useful since clq_ass_to_node is not accessible to outsiders.) +% Returns c=-1 if there is no such clique. + +if nargin < 3, fam = 0; else fam = 1; end + +if length(nodes)==1 + c = engine.clq_ass_to_node(nodes(1)); +%elseif fam +% c = engine.clq_ass_to_node(nodes(end)); +else + B = engine.cliques_bitv; + w = engine.clique_weight; + clqs = find(all(B(:,nodes), 2)); % all selected columns must be 1 + if isempty(clqs) + c = -1; + else + c = clqs(argmin(w(clqs))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/collect_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function [clpot, seppot] = collect_evidence(engine, clpot, seppot) +% COLLECT_EVIDENCE Do message passing from leaves to root (children then parents) +% [clpot, seppot] = collect_evidence(engine, clpot, seppot) + +for n=engine.postorder %postorder(1:end-1) + for p=engine.postorder_parents{n} + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, engine.separator{p,n}, engine.maximize); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/distribute_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function [clpot, seppot] = distribute_evidence(engine, clpot, seppot) +% DISTRIBUTE_EVIDENCE Do message passing from root to leaves (parents then children) +% [clpot, seppot] = distribute_evidence(engine, clpot, seppot) + +for n=engine.preorder + for c=engine.preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, engine.separator{n,c}, engine.maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; % store this for marginal_nodes with add_ev option +engine.maximize = 0; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + case 'maximize', engine.maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +if is_mnet(bnet) + pot = engine.user_pot; + clqs = engine.nums_ass_to_user_clqs; +else + % Evaluate CPDs with evidence, and convert to potentials + pot = cell(1, N); + for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end + end + clqs = engine.clq_ass_to_node(1:N); +end + +% soft evidence +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end +for i=1:S + n = soft_nodes(i); + pot{end+1} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; + + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +engine.clpot = clpot;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified potentials to the network (jtree) +% [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type, maximize) +% +% We multiply potential{i} onto clique(i) before propagating. +% We return all the modified clique potentials. + +% only used by BK! + +[clpot, seppot] = init_pot(engine, clique, potential, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/find_max_config.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function [mpe, clpot, seppot] = find_max_config(engine, clpot, seppot, evidence) +% FIND_MAX_CONFIG Backwards pass of Viterbi fro jtree +% function [mpe, clpot, seppot] = find_max_config(engine, clpot, seppot, evidence) +% See Cowell99 p98 + +bnet = bnet_from_engine(engine); +nnodes = length(bnet.dag); +mpe = cell(1, nnodes); +maximize = 1; + +c = engine.root_clq; +pot = struct(clpot{c}); % violate object privacy +dom = pot.domain; +[indices, clpot{c}] = find_most_prob_entry(clpot{c}); +mpe(dom) = num2cell(indices); + +for n=engine.preorder + for c=engine.preorder_children{n} + clpot{c} = divide_by_pot(clpot{c}, seppot{n,c}); + seppot{n,c} = marginalize_pot(clpot{n}, engine.separator{n,c}, maximize); + clpot{c} = multiply_by_pot(clpot{c}, seppot{n,c}); + + pot = struct(clpot{c}); % violate object privacy + dom = pot.domain; + [indices, clpot{c}] = find_most_prob_entry(clpot{c}); + mpe(dom) = num2cell(indices); + end +end + +obs_nodes = find(~isemptycell(evidence)); +% indices for observed nodes will be 1 - need to overwrite these +mpe(obs_nodes) = evidence(obs_nodes); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end +engine.maximize = 1; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +mpe = find_max_config(engine, clpot, seppot, evidence); % instead of distribute evidence
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/init_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes, ndx) +% INIT_POT Initialise potentials with evidence (jtree_inf) +% function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes) + +cliques = engine.cliques; +bnet = bnet_from_engine(engine); +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, bnet.node_sizes(:), bnet.cnodes(:), onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/jtree_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,141 @@ +function engine = jtree_inf_engine(bnet, varargin) +% JTREE_INF_ENGINE Junction tree inference engine +% engine = jtree_inf_engine(bnet, ...) +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - a cell array of sets of nodes we want to ensure are in the same clique (in addition to families) [ {} ] +% root - the root of the junction tree will be a clique that contains this set of nodes [N] +% stages - stages{t} is a set of nodes we want to eliminate before stages{t+1}, ... [ {1:N} ] +% +% e.g., engine = jtree_inf_engine(bnet, 'maximize', 1); +% +% For more details on the junction tree algorithm, see +% - "Probabilistic networks and expert systems", Cowell, Dawid, Lauritzen and Spiegelhalter, Springer, 1999 +% - "Inference in Belief Networks: A procedural guide", C. Huang and A. Darwiche, +% Intl. J. Approximate Reasoning, 15(3):225-263, 1996. + + +% set default params +N = length(bnet.dag); +clusters = {}; +root = N; +stages = { 1:N }; +maximize = 0; + +if nargin >= 2 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + error('the interface to jtree has changed; now, onodes is not allowed and all optional params must be passed by name') + end + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'root', root = args{i+1}; + case 'stages', stages = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine = init_fields; +engine = class(engine, 'jtree_inf_engine', inf_engine(bnet)); + +engine.maximize = maximize; + +onodes = bnet.observed; + +%[engine.jtree, dummy, engine.cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(bnet, onodes, stages, clusters); + +porder = determine_elim_constraints(bnet, onodes); +strong = ~isempty(porder); +ns = bnet.node_sizes(:); +ns(onodes) = 1; % observed nodes have only 1 possible value +[engine.jtree, root2, engine.cliques, B, w] = ... + graph_to_jtree(moralize(bnet.dag), ns, porder, stages, clusters); + + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length(engine.cliques); +engine.clpot = cell(1,C); + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +engine.separator = cell(C,C); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + %c = clq_containing_nodes(engine, family(bnet.dag, i)); + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + +% Make the jtree rooted, so there is a fixed message passing order. +if strong + % the last clique is guaranteed to be a strong root + % engine.root_clq = length(engine.cliques); + + % --- 4/17/2010, by Wei Sun (George Mason University): + % It has been proved that the last clique is not necessary to be the + % strong root, instead, a clique called interface clique, that contains + % all discrete parents and at least one continuous node from a connected + % continuous component in a CLG, is guaranteed to be a strong root. + engine.root_clq = findroot(bnet, engine.cliques) ; +else + % jtree_dbn_inf_engine requires the root to contain the interface. + % This may conflict with the strong root requirement! *********** BUG ************* + engine.root_clq = clq_containing_nodes(engine, root); + if engine.root_clq <= 0 + error(['no clique contains ' num2str(root)]); + end +end + +[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root_clq); + +% collect +engine.postorder_parents = cell(1,length(engine.postorder)); +for n=engine.postorder(:)' + engine.postorder_parents{n} = parents(engine.jtree, n); +end +% distribute +engine.preorder_children = cell(1,length(engine.preorder)); +for n=engine.preorder(:)' + engine.preorder_children{n} = children(engine.jtree, n); +end + + + +%%%%%%%% + +function engine = init_fields() + +engine.jtree = []; +engine.cliques = []; +engine.separator = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.clpot = []; +engine.clq_ass_to_node = []; +engine.root_clq = []; +engine.preorder = []; +engine.postorder = []; +engine.preorder_children = []; +engine.postorder_parents = []; +engine.maximize = []; +engine.evidence = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree) +% marginal = marginal_family(engine, i) + +if nargin < 3, add_ev = 0; end +assert(~add_ev); + +bnet = bnet_from_engine(engine); +fam = family(bnet.dag, i); +c = engine.clq_ass_to_node(i); +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, fam));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (jtree) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a subset of some clique; an error will be raised if not. +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end + +c = clq_containing_nodes(engine, query); +if c == -1 + error(['no clique contains ' num2str(query)]); +end +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, query, engine.maximize)); + +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_inf_engine/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_limid_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_limid_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes_SS.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_limid_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/Old/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function [m, pot] = marginal_family(engine, query) +% MARGINAL_NODES Compute the marginal on the family of the specified node (jtree_limid) +% [m, pot] = marginal_family(engine, query) +% +% query should be a single decision node, or [] (to compute global max expected utility) + +bnet = bnet_from_engine(engine); +if isempty(query) + compute_meu = 1; + d = bnet.decision_nodes(1); % pick an arbitrary root to collect to + fam = []; % marginalize root pot down to a point +else + compute_meu = 0; + d = query; + assert(myismember(d, bnet.decision_nodes)); + fam = family(bnet.dag, d); +end + +clpot = init_clpot(bnet, engine.cliques, engine.clq_ass_to_node, engine.evidence, engine.exclude); + +% collect to root (clique containing d) +C = length(engine.cliques); +seppot = cell(C, C); % separators are implicitely initialized to 1s +for n=engine.postorder{d}(1:end-1) + for p=parents(engine.rooted_jtree{d}, n) + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, engine.separator{p,n}); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + +root = engine.clq_ass_to_node(d); +assert(root == engine.postorder{d}(end)); +pot = marginalize_pot(clpot{root}, fam); +m = pot_to_marginal(pot); + +%%%%%%%%%%% + + +function clpot = init_clpot(bnet, cliques, clq_ass_to_node, evidence, exclude) + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1, C); +ns = bnet.node_sizes; +for i=1:C + clpot{i} = upot(cliques{i}, ns(cliques{i})); +end + +N = length(bnet.dag); +nodes = mysetdiff(1:N, exclude); + +for n=nodes(:)' + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + c = clq_ass_to_node(n); + pot = convert_to_pot(bnet.CPD{e}, 'u', ns, fam, evidence); + clpot{c} = multiply_by_pot(clpot{c}, pot); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/Old/marginal_nodes_SS.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function [pot, MEU] = marginal_nodes(engine, d) + +C = length(cliques); +%clpot = init_clpot(limid, cliques, d, clq_ass_to_node); +clpot = init_clpot(limid, cliques, [], clq_ass_to_node); + +% collect to root +if 1 + % HUGIN + seppot = cell(C, C); % separators are implicitely initialized to 1s + for n=postorder{di}(1:end-1) + for p=parents(rooted_jtree{di}, n) + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, separator{p,n}); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end + end +else + % Shafer-Shenoy + msg = cell(C,C); + for n=postorder{di}(1:end-1) + for c=children(rooted_jtree{di}, n) + clpot{n} = multiply_by_pot(clpot{n}, msg{c,n}); + end + p = parents(rooted_jtree{di}, n); + %msg{n,p} = marginalize_pot(clpot{n}, cliques{p}); + msg{n,p} = marginalize_pot(clpot{n}, separator{n,p}); + end + root = clq_ass_to_node(d); + n=postorder{di}(end); + assert(n == root); + for c=children(rooted_jtree{di}, n) + clpot{n} = multiply_by_pot(clpot{n}, msg{c,n}); + end +end + +fam = family(limid.dag, d); +pot = marginalize_pot(clpot{root}, fam); + +%%%%%%% +jpot = compute_joint_pot_limid(limid); +pot2 = marginalize_pot(jpot, fam); +assert(approxeq_pot(pot, pot2)) +%%%%%% + +[policy, score] = extract_policy(pot); + +e = limid.equiv_class(d); +limid.CPD{e} = set_params(limid.CPD{e}, 'policy', policy); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function engine = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree_limid) +% engine = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value. +% +% The list below gives optional arguments [default value in brackets]. +% +% exclude - list of nodes whose potential will not be included in the joint [ [] ] +% +% e.g., engine = enter_evidence(engine, ev, 'exclude', 3) + +exclude = []; + +if nargin >= 3 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'exclude', exclude = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine.exclude = exclude; +engine.evidence = evidence;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/jtree_limid_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function engine = jtree_limid_inf_engine(bnet) +% JTREE_LIMID_INF_ENGINE Make a junction tree engine for use by solve_limid +% engine = jtree_limid_inf_engine(bnet) +% +% This engine is designed to compute marginals on decision nodes + + +MG = moralize(bnet.dag); +% We do not remove the utility nodes, because that complicates the book-keeping. +% Leaving them in will not introduce any un-necessary triangulation arcs, because they are always leaves. +% Also, since utility nodes have size 1, they do not increase the size of the potentials. + +ns = bnet.node_sizes; +elim_order = best_first_elim_order(MG, ns); +[MTG, engine.cliques] = triangulate(MG, elim_order); +[engine.jtree, root, B, w] = cliques_to_jtree(engine.cliques, ns); + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. +N = length(bnet.dag); +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +num_cliques = length(engine.cliques); +engine.separator = cell(num_cliques, num_cliques); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end + + +% create |D| different rooted jtree's +engine.rooted_jtree = cell(1, N); +engine.preorder = cell(1, N); +engine.postorder = cell(1, N); +for d=bnet.decision_nodes(:)' + root = engine.clq_ass_to_node(d); + [engine.rooted_jtree{d}, engine.preorder{d}, engine.postorder{d}] = mk_rooted_tree(engine.jtree, root); +end + +engine.exclude = []; +engine.evidence = []; + +engine = class(engine, 'jtree_limid_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function [m, pot] = marginal_family(engine, query) +% MARGINAL_NODES Compute the marginal on the family of the specified node (jtree_limid) +% [m, pot] = marginal_family(engine, query) +% +% query should be a single decision node + +bnet = bnet_from_engine(engine); +d = query; +assert(myismember(d, bnet.decision_nodes)); +fam = family(bnet.dag, d); + +clpot = init_clpot(bnet, engine.cliques, engine.clq_ass_to_node, engine.evidence, engine.exclude); + +% collect to root (clique containing d) +C = length(engine.cliques); +seppot = cell(C, C); % separators are implicitely initialized to 1s +for n=engine.postorder{d}(1:end-1) + for p=parents(engine.rooted_jtree{d}, n) + %clpot{p} = divide_by_pot(clpot{n}, seppot{p,n}); % dividing by 1 is redundant + seppot{p,n} = marginalize_pot(clpot{n}, engine.separator{p,n}); + clpot{p} = multiply_by_pot(clpot{p}, seppot{p,n}); + end +end + +root = engine.clq_ass_to_node(d); +assert(root == engine.postorder{d}(end)); +pot = marginalize_pot(clpot{root}, fam); +m = pot_to_marginal(pot); + +%%%%%%%%%%% + + +function clpot = init_clpot(bnet, cliques, clq_ass_to_node, evidence, exclude) + +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1, C); +ns = bnet.node_sizes; +for i=1:C + clpot{i} = upot(cliques{i}, ns(cliques{i})); +end + +N = length(bnet.dag); +nodes = mysetdiff(1:N, exclude); + +for n=nodes(:)' + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + c = clq_ass_to_node(n); + pot = convert_to_pot(bnet.CPD{e}, 'u', fam(:), evidence); + clpot{c} = multiply_by_pot(clpot{c}, pot); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_limid_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function [m, pot] = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified nodes (jtree_limid) +% [m, pot] = marginal_nodes(engine, query) +% +% query should be a subset of a family of a decision node + +if isempty(query) + bnet = bnet_from_engine(engine); + d = bnet.decision_nodes(1); % pick an arbitrary decision node + [dummy, big_pot] = marginal_family(engine, d); +else + [dummy, big_pot] = marginal_family(engine, query); +end +pot = marginalize_pot(big_pot, query); +m = pot_to_marginal(pot); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_mnet_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Mon Jun 17 20:30:02 2002// +/find_mpe.m/1.1.1.1/Mon Jun 17 20:29:40 2002// +/jtree_mnet_inf_engine.m/1.1.1.1/Sat Jan 18 22:13:32 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_mnet_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_mnet_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_mnet_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_mnet_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,82 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; % store this for marginal_nodes with add_ev option +engine.maximize = 0; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end +clqs = engine.clq_ass_to_node(1:N); + +% soft evidence +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end +for i=1:S + n = soft_nodes(i); + pot{end+1} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = [clqs engine.clq_ass_to_node(soft_nodes)]; + + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +engine.clpot = clpot;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_mnet_inf_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +function mpe = find_mpe(engine, evidence, varargin) +% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes) +% function mpe = find_mpe(engine, evidence,...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; + +% set default params +exclude = []; +soft_evidence = cell(1,N); + +% parse optional params +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end +end +engine.maximize = 1; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +mpe = find_max_config(engine, clpot, seppot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_mnet_inf_engine/jtree_mnet_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +function engine = jtree_mnet_inf_engine(model, varargin) +% JTREE_MNET_INF_ENGINE Junction tree inference engine for Markov nets +% engine = jtree_inf_engine(mnet, ...) +% + +% set default params +N = length(mnet.graph); +root = N; + +engine = init_fields; +engine = class(engine, 'jtree_mnet_inf_engine', inf_engine(bnet)); + +onodes = bnet.observed; +if is_mnet(bnet) + MG = bnet.graph; +else + error('should be a mnet') +end + +%[engine.jtree, dummy, engine.cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(bnet, onodes, stages, clusters); + +porder = determine_elim_constraints(bnet, onodes); +strong = ~isempty(porder); +ns = bnet.node_sizes(:); +ns(onodes) = 1; % observed nodes have only 1 possible value +[engine.jtree, root2, engine.cliques, B, w] = ... + graph_to_jtree(MG, ns, porder, stages, clusters); + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length(engine.cliques); +engine.clpot = cell(1,C); + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +engine.separator = cell(C,C); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + %c = clq_containing_nodes(engine, family(bnet.dag, i)); + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + +% Make the jtree rooted, so there is a fixed message passing order. +if strong + % the last clique is guaranteed to be a strong root + engine.root_clq = length(engine.cliques); +else + % jtree_dbn_inf_engine requires the root to contain the interface. + % This may conflict with the strong root requirement! *********** BUG ************* + engine.root_clq = clq_containing_nodes(engine, root); + if engine.root_clq <= 0 + error(['no clique contains ' num2str(root)]); + end +end + +[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root_clq); + +% collect +engine.postorder_parents = cell(1,length(engine.postorder)); +for n=engine.postorder(:)' + engine.postorder_parents{n} = parents(engine.jtree, n); +end +% distribute +engine.preorder_children = cell(1,length(engine.preorder)); +for n=engine.preorder(:)' + engine.preorder_children{n} = children(engine.jtree, n); +end + + + +%%%%%%%% + +function engine = init_fields() + +engine.jtree = []; +engine.cliques = []; +engine.separator = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.clpot = []; +engine.clq_ass_to_node = []; +engine.root_clq = []; +engine.preorder = []; +engine.postorder = []; +engine.preorder_children = []; +engine.postorder_parents = []; +engine.maximize = []; +engine.evidence = []; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +/cliques_from_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/clq_containing_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/collect_evidence.c/1.1.1.1/Wed May 29 15:59:56 2002// +/distribute_evidence.c/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/enter_soft_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/init_pot.c/1.1.1.1/Wed May 29 15:59:56 2002// +/jtree_sparse_inf_engine.m/1.1.1.1/Sat Jan 18 22:11:32 2003// +/marginal_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/set_fields.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_sparse_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/cliques_from_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function cliques = cliques_from_engine(engine) +% CLIQUES_FROM_ENGINE Return the cliques stored inside the inf. engine (jtree) +% cliques = cliques_from_engine(engine) + +cliques = engine.cliques;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/clq_containing_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function c = clq_containing_nodes(engine, nodes, fam) +% CLQ_CONTAINING_NODES Find the lightest clique (if any) that contains the set of nodes +% c = clq_containing_nodes(engine, nodes, family) +% +% If the optional 'family' argument is specified, it means nodes = family(nodes(end)). +% (This is useful since clq_ass_to_node is not accessible to outsiders.) +% Returns c=-1 if there is no such clique. + +if nargin < 3, fam = 0; else fam = 1; end + +if length(nodes)==1 + c = engine.clq_ass_to_node(nodes(1)); +%elseif fam +% c = engine.clq_ass_to_node(nodes(end)); +else + B = engine.cliques_bitv; + w = engine.clique_weight; + clqs = find(all(B(:,nodes), 2)); % all selected columns must be 1 + if isempty(clqs) + c = -1; + else + c = clqs(argmin(w(clqs))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/collect_evidence.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,634 @@ +/* C mex for collect_evidence.c in @jtree_sparse_inf_engine directory */ +/* File enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/******************************************/ +/* collect_evidence has 3 input & 2 output*/ +/* engine */ +/* clpot */ +/* seppot */ +/* */ +/* clpot */ +/* seppot */ +/******************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +mxArray* convert_ill_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr, *bpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + if(sdim == 0){ + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + mxSetField(bigPot, 0, "T", pTemp); + bpr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + sjc[0] = 0; + sjc[1] = NB; + for(i=0; i<NB; i++){ + bpr[i] = *spr; + sir[i] = i; + } + return; + } + + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *result, *bir, *sir, *rir, *bjc, *sjc, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + bpr[i] *= *spr; + } + return; + } + + pTemp1 = mxCreateSparse(NB, 1, NZB, mxREAL); + rpr = mxGetPr(pTemp1); + rir = mxGetIr(pTemp1); + rjc = mxGetJc(pTemp1); + rjc[0] = 0; + rjc[1] = NZB; + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + rpr[nzCounts] = bpr[i] * spr[position]; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NZB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +mxArray* marginal_null_to_spPot(const mxArray *bigPot, const mxArray *sDomain, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, ND; + int *mask, *sir, *sjc; + double *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *smallPot; + const char *field_names[] = {"domain", "T", "sizes"}; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + psDomain = mxGetPr(sDomain); + sdim = mxGetNumberOfElements(sDomain); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + smallPot = mxCreateStructMatrix(1, 1, 3, field_names); + pTemp = mxDuplicateArray(sDomain); + mxSetField(smallPot, 0, "domain", pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + if(sdim == 0){ + pTemp = mxCreateSparse(1, 1, 1, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + *spr = 0; + *sir = 0; + sjc[0] = 0; + sjc[1] = 1; + if(maximize) *spr = 1; + else *spr = NB; + + pTemp = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(pTemp) = 1; + mxSetField(smallPot, 0, "sizes", pTemp); + return smallPot; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + pTemp = mxCreateDoubleMatrix(1, count, mxREAL); + psSize = mxGetPr(pTemp); + NS = 1; + for(i=0; i<count; i++){ + psSize[i] = pbSize[mask[i]]; + NS *= (int)psSize[i]; + } + mxSetField(smallPot, 0, "sizes", pTemp); + + ND = NB / NS; + + pTemp = mxCreateSparse(NS, 1, NS, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + if(maximize){ + for(i=0; i<NS; i++){ + spr[i] = 1; + sir[i] = i; + } + } + else{ + for(i=0; i<NS; i++){ + spr[i] = ND; + sir[i] = i; + } + } + sjc[0] = 0; + sjc[1] = NS; + + free(mask); + return smallPot; +} + +mxArray* marginal_spPot_to_spPot(const mxArray *bigPot, const mxArray *sDomain, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, NZB, position, bindex, sindex, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr; + mxArray *pTemp, *smallPot; + const char *field_names[] = {"domain", "T", "sizes"}; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + psDomain = mxGetPr(sDomain); + sdim = mxGetNumberOfElements(sDomain); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + smallPot = mxCreateStructMatrix(1, 1, 3, field_names); + pTemp = mxDuplicateArray(sDomain); + mxSetField(smallPot, 0, "domain", pTemp); + + if(sdim == 0){ + pTemp = mxCreateSparse(1, 1, 1, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + *spr = 0; + *bir = 0; + bjc[0] = 0; + bjc[1] = 1; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + + pTemp = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(pTemp) = 1; + mxSetField(smallPot, 0, "sizes", pTemp); + return smallPot; + } + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + pTemp = mxCreateDoubleMatrix(1, count, mxREAL); + psSize = mxGetPr(pTemp); + NS = 1; + for(i=0; i<count; i++){ + psSize[i] = pbSize[mask[i]]; + NS *= (int)psSize[i]; + } + mxSetField(smallPot, 0, "sizes", pTemp); + + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++)sTable[i] = 0; + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(sTable, sequence, nzCounts, NS); + mxSetField(smallPot, 0, "T", pTemp); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); + + return smallPot; +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, n, p, np, pn, loop, loops, nCliques, temp, maximize; + int *collect_order; + double *pr, *pr1; + mxArray *pTemp, *pTemp1, *pPostP, *pClpot, *pSeppot, *pSeparator; + + pTemp = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pTemp); + loops = nCliques - 1; + pTemp = mxGetField(prhs[0], 0, "maximize"); + maximize = (int)mxGetScalar(pTemp); + pSeparator = mxGetField(prhs[0], 0, "separator"); + + collect_order = malloc(2 * loops * sizeof(int)); + + pTemp = mxGetField(prhs[0], 0, "postorder"); + pr = mxGetPr(pTemp); + pPostP = mxGetField(prhs[0], 0, "postorder_parents"); + for(i=0; i<loops; i++){ + temp = (int)pr[i] - 1; + pTemp = mxGetCell(pPostP, temp); + pr1 = mxGetPr(pTemp); + collect_order[i] = (int)pr1[0] - 1; + collect_order[i+loops] = temp; + } + + plhs[0] = mxDuplicateArray(prhs[1]); + plhs[1] = mxDuplicateArray(prhs[2]); + + for(loop=0; loop<loops; loop++){ + p = collect_order[loop]; + n = collect_order[loop+loops]; + np = p * nCliques + n; + pn = n * nCliques + p; + pClpot = mxGetCell(plhs[0], n); + pTemp1 = mxGetField(pClpot, 0, "T"); + pTemp = mxGetCell(pSeparator, pn); + if(pTemp1) + pSeppot = marginal_spPot_to_spPot(pClpot, pTemp, maximize); + else pSeppot = marginal_null_to_spPot(pClpot, pTemp, maximize); + mxSetCell(plhs[1], pn, pSeppot); + + pClpot = mxGetCell(plhs[0], p); + pTemp1 = mxGetField(pClpot, 0, "T"); + if(pTemp1) + multiply_spPot_by_spPot(pClpot, pSeppot); + else multiply_null_by_spPot(pClpot, pSeppot); + } + free(collect_order); +} + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/distribute_evidence.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,618 @@ +/* C mex for distribute_evidence.c in @jtree_sparse_inf_engine directory*/ +/* File enter_evidence.m in directory @jtree_sparse_inf_engine call it */ + +/*********************************************/ +/* distribute_evidence has 3 input & 2 output*/ +/* engine */ +/* clpot */ +/* seppot */ +/* */ +/* clpot */ +/* seppot */ +/*********************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +mxArray* convert_ill_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *result, *bir, *sir, *rir, *bjc, *sjc, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + bpr[i] *= *spr; + } + return; + } + + pTemp1 = mxCreateSparse(NB, 1, NZB, mxREAL); + rpr = mxGetPr(pTemp1); + rir = mxGetIr(pTemp1); + rjc = mxGetJc(pTemp1); + rjc[0] = 0; + rjc[1] = NZB; + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + rpr[nzCounts] = bpr[i] * spr[position]; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NZB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void marginal_spPot_to_spPot(const mxArray *bigPot, mxArray *smallPot, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, NZB, position, bindex, sindex, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + if(sdim == 0){ + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + *spr = 0; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + return; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + sTable[i] = 0; + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(smallPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(sTable, sequence, nzCounts, NS); + mxSetField(smallPot, 0, "T", pTemp); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void divide_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex; + int *samemask, *diffmask, *rir, *rjc, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *weight; + double *pbDomain, *psDomain, *pbSize, *psSize, *rpr, *spr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + pTemp1 = mxGetField(bigPot, 0, "T"); + if(pTemp1)mxDestroyArray(pTemp1); + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + mxSetField(bigPot, 0, "T", pTemp); + rpr = mxGetPr(pTemp); + rir = mxGetIr(pTemp); + rjc = mxGetJc(pTemp); + rjc[0] = 0; + rjc[1] = NB; + value = *spr; + if(value == 0) value = 1; + for(i=0; i<NB; i++){ + rpr[i] = 1 / value; + rir[i] = i; + } + return; + } + + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + rpr = mxGetPr(pTemp); + rir = mxGetIr(pTemp); + rjc = mxGetJc(pTemp); + rjc[0] = 0; + rjc[1] = NB; + for(i=0; i<NB; i++){ + rpr[i] = 1; + rir[i] = i; + } + + NZB = ND * NZS; + + diffdim = bdim - sdim; + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + rpr[bindex] = 1 / (spr[i]); + } + } + + pTemp1 = mxGetField(bigPot, 0, "T"); + if(pTemp1)mxDestroyArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void divide_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex; + int *mask, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp1 = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp1); + bir = mxGetIr(pTemp1); + bjc = mxGetJc(pTemp1); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + value = *spr; + if(value == 0)value = 1; + for(i=0; i<NZB; i++){ + bpr[i] /= value; + } + return; + } + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + bpr[i] /= spr[position]; + } + } + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, loop, loops, nCliques, temp, count, parent, child, maximize, *distribute_order; + double *pr, *pr1; + mxArray *pTemp, *pPreCh, *pClpot, *pSeppot; + + pTemp = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pTemp); + loops = nCliques - 1; + pTemp = mxGetField(prhs[0], 0, "maximize"); + maximize = (int)mxGetScalar(pTemp); + + distribute_order = malloc(2 * loops * sizeof(int)); + pTemp = mxGetField(prhs[0], 0, "preorder"); + pr = mxGetPr(pTemp); + pPreCh = mxGetField(prhs[0], 0, "preorder_children"); + count = 0; + for(i=0; i<nCliques; i++){ + temp = (int)pr[i] - 1; + pTemp = mxGetCell(pPreCh, temp); + pr1 = mxGetPr(pTemp); + loop = mxGetNumberOfElements(pTemp); + for(j=0; j<loop; j++){ + distribute_order[count] = temp; + distribute_order[count + loops] = (int)pr1[j] - 1; + count++; + } + } + + plhs[0] = mxDuplicateArray(prhs[1]); + plhs[1] = mxDuplicateArray(prhs[2]); + + for(loop=0; loop<loops; loop++){ + parent = distribute_order[loop]; + child = distribute_order[loop+loops]; + i = nCliques * child + parent; + pClpot = mxGetCell(plhs[0], child); + pTemp = mxGetField(pClpot, 0, "T"); + pSeppot = mxGetCell(plhs[1], i); + if(pTemp){ + if(mxIsEmpty(pTemp)) + divide_null_by_spPot(pClpot, pSeppot); + else + divide_spPot_by_spPot(pClpot, pSeppot); + } + else divide_null_by_spPot(pClpot, pSeppot); + + pClpot = mxGetCell(plhs[0], parent); + marginal_spPot_to_spPot(pClpot, pSeppot, maximize); + mxSetCell(plhs[1], i, pSeppot); + + pClpot = mxGetCell(plhs[0], child); + multiply_spPot_by_spPot(pClpot, pSeppot); + } + free(distribute_order); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,100 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (jtree) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector). +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% soft - a cell array of soft/virtual evidence; +% soft{i} is a prob. distrib. over i's values, or [] [ cell(1,N) ] +% +% e.g., engine = enter_evidence(engine, ev, 'soft', soft_ev) +% +% For backwards compatibility with BNT2, you can also specify the parameters in the following order +% engine = enter_evidence(engine, ev, soft_ev) + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +engine.evidence = evidence; % store this for marginal_nodes with add_ev option + +% set default params +exclude = []; +soft_evidence = cell(1,N); +maximize = 0; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + if iscell(args{1}) + soft_evidence = args{1}; + else + for i=1:2:nargs + switch args{i}, + case 'soft', soft_evidence = args{i+1}; + case 'maximize', maximize = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end + end +end + +engine.maximize = maximize; + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + if strcmp(pot_type, 'cg') + check_for_cd_arcs(onodes, bnet.cnodes, bnet.dag); +end + +hard_nodes = 1:N; +soft_nodes = find(~isemptycell(soft_evidence)); +S = length(soft_nodes); +if S > 0 + assert(pot_type == 'd'); + assert(mysubset(soft_nodes, bnet.dnodes)); +end + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N+S); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + if isempty(bnet.CPD{e}) + error(['must define CPD ' num2str(e)]) + else + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + end +end + +for i=1:S + n = soft_nodes(i); + pot{N+i} = dpot(n, ns(n), soft_evidence{n}); +end +clqs = engine.clq_ass_to_node([hard_nodes soft_nodes]); + +[clpot, seppot] = init_pot(engine, clqs, pot, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); +C = length(clpot); +ll = zeros(1, C); +for i=1:C + domain = clpot{i}.domain; + sizes = clpot{i}.sizes; + T = clpot{i}.T; + clpot{i} = dpot(domain, sizes, T); +end + +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +engine.clpot = clpot;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/enter_soft_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type) +% ENTER_SOFT_EVIDENCE Add the specified potentials to the network (jtree) +% [clpot, loglik] = enter_soft_evidence(engine, clique, potential, onodes, pot_type, maximize) +% +% We multiply potential{i} onto clique(i) before propagating. +% We return all the modified clique potentials. + +[clpot, seppot] = init_pot(engine, clique, potential, pot_type, onodes); +[clpot, seppot] = collect_evidence(engine, clpot, seppot); +[clpot, seppot] = distribute_evidence(engine, clpot, seppot); + +C = length(clpot); +ll = zeros(1, C); +for i=1:C + [clpot{i}, ll(i)] = normalize_pot(clpot{i}); +end +loglik = ll(1); % we can extract the likelihood from any clique + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/init_pot.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,624 @@ +/* C mex init_pot for in @jtree_sparse_inf_engine directory */ +/* The file enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/**************************************/ +/* init_pot.c has 5 input & 2 output */ +/* engine */ +/* clqs */ +/* pots */ +/* pot_type */ +/* onodes */ +/* */ +/* clpot */ +/* seppot */ +/**************************************/ +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +mxArray* convert_ill_table_to_sparse(const double *bigTable, const int *sequence, const int nzCounts, const int NB){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = bigTable[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, nzCounts=0; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *bir, *bjc; + double *pbDomain, *psDomain, *pbSize, *psSize, *spr, *bpr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + siz_b = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + siz_s = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<siz_b; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<siz_s; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + pTemp1 = mxCreateSparse(NB, 1, NB, mxREAL); + bpr = mxGetPr(pTemp1); + bir = mxGetIr(pTemp1); + bjc = mxGetJc(pTemp1); + bjc[0] = 0; + bjc[1] = NB; + + if(NS == 1){ + value = *spr; + for(i=0; i<NB; i++){ + bpr[i] = value; + bir[i] = i; + } + nzCounts = NB; + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + if(spr[i] != 0){ + bpr[nzCounts] = spr[i]; + bir[nzCounts] = i; + nzCounts++; + } + } + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbSize[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + sy[mask[i]] = sx[mask[i]]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + if(*spr != 0){ + bpr[nzCounts] = *spr; + bir[nzCounts] = j; + nzCounts++; + } + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + spr -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + spr += cpsy[i]; + break; + } + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, bindex, sindex, nzCounts=0; + int *mask, *bir, *bjc, *rir, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + pTemp1 = mxCreateSparse(NB, 1, NZB, mxREAL); + rpr = mxGetPr(pTemp1); + rir = mxGetIr(pTemp1); + rjc = mxGetJc(pTemp1); + rjc[0] = 0; + rjc[1] = NZB; + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + value = spr[sindex]; + if(value != 0){ + rpr[nzCounts] = bpr[i] * value; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NZB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *result, *bir, *sir, *rir, *bjc, *sjc, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + pTemp1 = mxCreateSparse(NB, 1, NZB, mxREAL); + rpr = mxGetPr(pTemp1); + rir = mxGetIr(pTemp1); + rjc = mxGetJc(pTemp1); + rjc[0] = 0; + rjc[1] = NZB; + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + rpr[nzCounts] = bpr[i] * spr[position]; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NZB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, c, loop, nNodes, nCliques, ndomain, ns_num, nOnodes, dims[2]; + double *pClqs, *pr, *pt, *pSize, *eff_ns; + mxArray *pTemp, *pTemp1, *pStruct, *pCliques, *pBigpot, *pSmallpot; + const char *field_names[] = {"domain", "T", "sizes"}; + + nNodes = mxGetNumberOfElements(prhs[1]); + pCliques = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pCliques); + pTemp = mxGetField(prhs[0], 0, "actual_node_sizes"); + ns_num = mxGetNumberOfElements(pTemp); + pSize = mxGetPr(pTemp); + + eff_ns = (double *)malloc(ns_num * sizeof(double)); + for(i=0; i<ns_num; i++) eff_ns[i] = pSize[i]; + nOnodes = mxGetNumberOfElements(prhs[4]); + pr = mxGetPr(prhs[4]); + for(i=0; i<nOnodes; i++) eff_ns[(int)pr[i] - 1] = 1; + + plhs[0] = mxCreateCellArray(1, &nCliques); + for(i=0; i<nCliques; i++){ + pStruct = mxCreateStructMatrix(1, 1, 3, field_names); + mxSetCell(plhs[0], i, pStruct); + pTemp = mxGetCell(pCliques, i); + ndomain = mxGetNumberOfElements(pTemp); + pt = mxGetPr(pTemp); + pTemp1 = mxDuplicateArray(pTemp); + mxSetField(pStruct, 0, "domain", pTemp1); + + pTemp = mxCreateDoubleMatrix(1, ndomain, mxREAL); + mxSetField(pStruct, 0, "sizes", pTemp); + pr = mxGetPr(pTemp); + for(j=0; j<ndomain; j++){ + pr[j] = eff_ns[(int)pt[j]-1]; + } + } + + pClqs = mxGetPr(prhs[1]); + for(loop=0; loop<nNodes; loop++){ + c = (int)pClqs[loop] - 1; + pSmallpot = mxGetCell(prhs[2], loop); + pTemp = mxGetField(pSmallpot, 0, "T"); + pBigpot = mxGetCell(plhs[0], c); + pTemp1 = mxGetField(pBigpot, 0, "T"); + if(pTemp1){ + if(mxIsSparse(pTemp)) + multiply_spPot_by_spPot(pBigpot, pSmallpot); + else multiply_spPot_by_fuPot(pBigpot, pSmallpot); + } + else{ + if(mxIsSparse(pTemp)) + multiply_null_by_spPot(pBigpot, pSmallpot); + else multiply_null_by_fuPot(pBigpot, pSmallpot); + } + } + + free(eff_ns); + dims[0] = nCliques; + dims[1] = nCliques; + plhs[1] = mxCreateCellArray(2, dims); +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/jtree_sparse_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +function engine = jtree_sparse_inf_engine(bnet, varargin) +% JTREE_SPARSE_INF_ENGINE Junction tree inference engine when CPTs and Potentials are sparse +% engine = jtree_sparse_inf_engine(bnet, ...) +% It differs from jtree_inf_engine with all CPTs and potentials are 1D sparse arrays. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% clusters - a cell array of sets of nodes we want to ensure are in the same clique (in addition to families) [ {} ] +% root - the root of the junction tree will be a clique that contains this set of nodes [N] +% stages - stages{t} is a set of nodes we want to eliminate before stages{t+1}, ... [ {1:N} ] +% +% e.g., engine = jtree_inf_engine(bnet, 'maximize', 1); +% +% For more details on the junction tree algorithm, see +% - "Probabilistic networks and expert systems", Cowell, Dawid, Lauritzen and Spiegelhalter, Springer, 1999 +% - "Inference in Belief Networks: A procedural guide", C. Huang and A. Darwiche, +% Intl. J. Approximate Reasoning, 15(3):225-263, 1996. + + +% set default params +N = length(bnet.dag); +clusters = {}; +root = N; +stages = { 1:N }; + +if nargin >= 2 + args = varargin; + nargs = length(args); + if ~isstr(args{1}) + error('the interface to jtree has changed; now, onodes is not allowed and all optional params must be passed by name') + end + for i=1:2:nargs + switch args{i}, + case 'clusters', clusters = args{i+1}; + case 'root', root = args{i+1}; + case 'stages', stages = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine = init_fields; +engine = class(engine, 'jtree_sparse_inf_engine', inf_engine(bnet)); + +onodes = bnet.observed; +%[engine.jtree, dummy, engine.cliques, B, w] = dag_to_jtree(bnet, onodes, stages, clusters); + +porder = determine_elim_constraints(bnet, onodes); +strong = ~isempty(porder); +ns = bnet.node_sizes(:); +ns(onodes) = 1; % observed nodes have only 1 possible value +[engine.jtree, root2, engine.cliques, B, w] = ... + graph_to_jtree(moralize(bnet.dag), ns, porder, stages, clusters); + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length(engine.cliques); +engine.clpot = cell(1,C); + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +engine.separator = cell(C,C); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +for i=1:N + %c = clq_containing_nodes(engine, family(bnet.dag, i)); + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + +% Make the jtree rooted, so there is a fixed message passing order. +engine.root_clq = clq_containing_nodes(engine, root); +if engine.root_clq <= 0 + error(['no clique contains ' num2str(root)]); +end + +[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root_clq); + +% collect +engine.postorder_parents = cell(1,length(engine.postorder)); +for n=engine.postorder(:)' + engine.postorder_parents{n} = parents(engine.jtree, n); +end +% distribute +engine.preorder_children = cell(1,length(engine.preorder)); +for n=engine.preorder(:)' + engine.preorder_children{n} = children(engine.jtree, n); +end + +ns = bnet.node_sizes; +engine.actual_node_sizes = ns; + + +%%%%%%%% + +function engine = init_fields() + +engine.jtree = []; +engine.cliques = []; +engine.separator = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.clpot = []; +engine.clq_ass_to_node = []; +engine.root_clq = []; +engine.preorder = []; +engine.postorder = []; +engine.preorder_children = []; +engine.postorder_parents = []; +engine.maximize = []; +engine.evidence = []; +engine.actual_node_sizes = []; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function marginal = marginal_family(engine, i, add_ev) +% MARGINAL_FAMILY Compute the marginal on the specified family (jtree) +% marginal = marginal_family(engine, i) + +if nargin < 3, add_ev = 0; end +assert(~add_ev); + +bnet = bnet_from_engine(engine); +fam = family(bnet.dag, i); +c = engine.clq_ass_to_node(i); +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, fam));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (jtree) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a subset of some clique; an error will be raised if not. +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end + +c = clq_containing_nodes(engine, query); +if c == -1 + error(['no clique contains ' num2str(query)]); +end +marginal = pot_to_marginal(marginalize_pot(engine.clpot{c}, query, engine.maximize)); + +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/collect_evidence.c/1.1.1.1/Wed May 29 15:59:56 2002// +/distribute_evidence.c/1.1.1.1/Wed May 29 15:59:56 2002// +/init_pot.c/1.1.1.1/Wed May 29 15:59:56 2002// +/init_pot1.c/1.1.1.1/Wed May 29 15:59:56 2002// +/init_pot1.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@jtree_sparse_inf_engine/old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/old/collect_evidence.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,635 @@ +/* C mex for collect_evidence.c in @jtree_sparse_inf_engine directory */ +/* File enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/******************************************/ +/* collect_evidence has 3 input & 2 output*/ +/* engine */ +/* clpot */ +/* seppot */ +/* */ +/* clpot */ +/* seppot */ +/******************************************/ + +#include <math.h> +#include <search.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +mxArray* convert_table_to_sparse(const double *bT, const int *index, const int nzCounts, const int N){ + mxArray *spTable; + int i, *irs, *jcs; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + sr[i] = bT[i]; + irs[i] = index[i]; + } + return spTable; +} + +mxArray* convert_ill_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr, *bpr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + if(sdim == 0){ + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + mxSetField(bigPot, 0, "T", pTemp); + bpr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + sjc[0] = 0; + sjc[1] = NB; + for(i=0; i<NB; i++){ + bpr[i] = *spr; + sir[i] = i; + } + return; + } + + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *index, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + bpr[i] *= *spr; + } + return; + } + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + bigTable[i] = 0; + } + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + value = bpr[i]; + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + value *= spr[position]; + bigTable[nzCounts] = value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +mxArray* marginal_null_to_spPot(const mxArray *bigPot, const mxArray *sDomain, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, ND; + int *mask, *sir, *sjc; + double *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *smallPot; + const char *field_names[] = {"domain", "T", "sizes"}; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + psDomain = mxGetPr(sDomain); + sdim = mxGetNumberOfElements(sDomain); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + smallPot = mxCreateStructMatrix(1, 1, 3, field_names); + pTemp = mxDuplicateArray(sDomain); + mxSetField(smallPot, 0, "domain", pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + if(sdim == 0){ + pTemp = mxCreateSparse(1, 1, 1, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + *spr = 0; + *sir = 0; + sjc[0] = 0; + sjc[1] = 1; + if(maximize) *spr = 1; + else *spr = NB; + + pTemp = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(pTemp) = 1; + mxSetField(smallPot, 0, "sizes", pTemp); + return smallPot; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + pTemp = mxCreateDoubleMatrix(1, count, mxREAL); + psSize = mxGetPr(pTemp); + NS = 1; + for(i=0; i<count; i++){ + psSize[i] = pbSize[mask[i]]; + NS *= (int)psSize[i]; + } + mxSetField(smallPot, 0, "sizes", pTemp); + + ND = NB / NS; + + pTemp = mxCreateSparse(NS, 1, NS, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + if(maximize){ + for(i=0; i<NS; i++){ + spr[i] = 1; + sir[i] = i; + } + } + else{ + for(i=0; i<NS; i++){ + spr[i] = ND; + sir[i] = i; + } + } + sjc[0] = 0; + sjc[1] = NS; + + free(mask); + return smallPot; +} + +mxArray* marginal_spPot_to_spPot(const mxArray *bigPot, const mxArray *sDomain, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, NZB, position, bindex, sindex, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr; + mxArray *pTemp, *smallPot; + const char *field_names[] = {"domain", "T", "sizes"}; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + psDomain = mxGetPr(sDomain); + sdim = mxGetNumberOfElements(sDomain); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + smallPot = mxCreateStructMatrix(1, 1, 3, field_names); + pTemp = mxDuplicateArray(sDomain); + mxSetField(smallPot, 0, "domain", pTemp); + + if(sdim == 0){ + pTemp = mxCreateSparse(1, 1, 1, mxREAL); + mxSetField(smallPot, 0, "T", pTemp); + spr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + *spr = 0; + *bir = 0; + bjc[0] = 0; + bjc[1] = 1; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + + pTemp = mxCreateDoubleMatrix(1, 1, mxREAL); + *mxGetPr(pTemp) = 1; + mxSetField(smallPot, 0, "sizes", pTemp); + return smallPot; + } + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + pTemp = mxCreateDoubleMatrix(1, count, mxREAL); + psSize = mxGetPr(pTemp); + NS = 1; + for(i=0; i<count; i++){ + psSize[i] = pbSize[mask[i]]; + NS *= (int)psSize[i]; + } + mxSetField(smallPot, 0, "sizes", pTemp); + + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++)sTable[i] = 0; + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(sTable, sequence, nzCounts, NS); + mxSetField(smallPot, 0, "T", pTemp); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); + + return smallPot; +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, n, p, np, pn, loop, loops, nCliques, temp, maximize; + int *collect_order; + double *pr, *pr1; + mxArray *pTemp, *pTemp1, *pPostP, *pClpot, *pSeppot, *pSeparator; + + pTemp = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pTemp); + loops = nCliques - 1; + pTemp = mxGetField(prhs[0], 0, "maximize"); + maximize = (int)mxGetScalar(pTemp); + pSeparator = mxGetField(prhs[0], 0, "separator"); + + collect_order = malloc(2 * loops * sizeof(int)); + + pTemp = mxGetField(prhs[0], 0, "postorder"); + pr = mxGetPr(pTemp); + pPostP = mxGetField(prhs[0], 0, "postorder_parents"); + for(i=0; i<loops; i++){ + temp = (int)pr[i] - 1; + pTemp = mxGetCell(pPostP, temp); + pr1 = mxGetPr(pTemp); + collect_order[i] = (int)pr1[0] - 1; + collect_order[i+loops] = temp; + } + + plhs[0] = mxDuplicateArray(prhs[1]); + plhs[1] = mxDuplicateArray(prhs[2]); + + for(loop=0; loop<loops; loop++){ + p = collect_order[loop]; + n = collect_order[loop+loops]; + np = p * nCliques + n; + pn = n * nCliques + p; + pClpot = mxGetCell(plhs[0], n); + pTemp1 = mxGetField(pClpot, 0, "T"); + pTemp = mxGetCell(pSeparator, pn); + if(pTemp1) + pSeppot = marginal_spPot_to_spPot(pClpot, pTemp, maximize); + else pSeppot = marginal_null_to_spPot(pClpot, pTemp, maximize); + mxSetCell(plhs[1], pn, pSeppot); + + pClpot = mxGetCell(plhs[0], p); + pTemp1 = mxGetField(pClpot, 0, "T"); + if(pTemp1) + multiply_spPot_by_spPot(pClpot, pSeppot); + else multiply_null_by_spPot(pClpot, pSeppot); + } + free(collect_order); +} + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/old/distribute_evidence.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,613 @@ +/* C mex for distribute_evidence.c in @jtree_sparse_inf_engine directory*/ +/* File enter_evidence.m in directory @jtree_sparse_inf_engine call it */ + +/*********************************************/ +/* distribute_evidence has 3 input & 2 output*/ +/* engine */ +/* clpot */ +/* seppot */ +/* */ +/* clpot */ +/* seppot */ +/*********************************************/ + +#include "mex.h" + +#include <math.h> +#include <search.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +mxArray* convert_table_to_sparse(const double *bT, const int *index, const int nzCounts, const int N){ + mxArray *spTable; + int i, *irs, *jcs; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + sr[i] = bT[i]; + irs[i] = index[i]; + } + return spTable; +} + +mxArray* convert_ill_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *index, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + bpr[i] *= *spr; + } + return; + } + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + value = bpr[i]; + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + value *= spr[position]; + bigTable[nzCounts] = value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void marginal_spPot_to_spPot(const mxArray *bigPot, mxArray *smallPot, const int maximize){ + int i, j, count, bdim, sdim, NB, NS, NZB, position, bindex, sindex, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + if(sdim == 0){ + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + *spr = 0; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + return; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + sTable[i] = 0; + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(smallPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(sTable, sequence, nzCounts, NS); + mxSetField(smallPot, 0, "T", pTemp); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void divide_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex; + int *samemask, *diffmask, *rir, *rjc, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *weight; + double *pbDomain, *psDomain, *pbSize, *psSize, *rpr, *spr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + mxSetField(bigPot, 0, "T", pTemp); + rpr = mxGetPr(pTemp); + rir = mxGetIr(pTemp); + rjc = mxGetJc(pTemp); + rjc[0] = 0; + rjc[1] = NB; + value = *spr; + if(value == 0) value = 1; + for(i=0; i<NB; i++){ + rpr[i] = 1 / value; + rir[i] = i; + } + return; + } + + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + + pTemp = mxCreateSparse(NB, 1, NB, mxREAL); + rpr = mxGetPr(pTemp); + rir = mxGetIr(pTemp); + rjc = mxGetJc(pTemp); + rjc[0] = 0; + rjc[1] = NB; + for(i=0; i<NB; i++){ + rpr[i] = 1; + rir[i] = i; + } + + NZB = ND * NZS; + + diffdim = bdim - sdim; + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + rpr[bindex] = 1 / (spr[i]); + } + } + + pTemp1 = mxGetField(bigPot, 0, "T"); + if(pTemp1)mxDestroyArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void divide_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex; + int *mask, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp1 = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp1); + bir = mxGetIr(pTemp1); + bjc = mxGetJc(pTemp1); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + if(sdim == 0){ + value = *spr; + if(value == 0)value = 1; + for(i=0; i<NZB; i++){ + bpr[i] /= value; + } + return; + } + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + bpr[i] /= spr[position]; + } + } + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, loop, loops, nCliques, temp, count, parent, child, maximize, *distribute_order; + double *pr, *pr1; + mxArray *pTemp, *pPreCh, *pClpot, *pSeppot; + + pTemp = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pTemp); + loops = nCliques - 1; + pTemp = mxGetField(prhs[0], 0, "maximize"); + maximize = (int)mxGetScalar(pTemp); + + distribute_order = malloc(2 * loops * sizeof(int)); + pTemp = mxGetField(prhs[0], 0, "preorder"); + pr = mxGetPr(pTemp); + pPreCh = mxGetField(prhs[0], 0, "preorder_children"); + count = 0; + for(i=0; i<nCliques; i++){ + temp = (int)pr[i] - 1; + pTemp = mxGetCell(pPreCh, temp); + pr1 = mxGetPr(pTemp); + loop = mxGetNumberOfElements(pTemp); + for(j=0; j<loop; j++){ + distribute_order[count] = temp; + distribute_order[count + loops] = (int)pr1[j] - 1; + count++; + } + } + + plhs[0] = mxDuplicateArray(prhs[1]); + plhs[1] = mxDuplicateArray(prhs[2]); + + for(loop=0; loop<loops; loop++){ + parent = distribute_order[loop]; + child = distribute_order[loop+loops]; + i = nCliques * child + parent; + pClpot = mxGetCell(plhs[0], child); + pTemp = mxGetField(pClpot, 0, "T"); + pSeppot = mxGetCell(plhs[1], i); + if(pTemp) + divide_spPot_by_spPot(pClpot, pSeppot); + else divide_null_by_spPot(pClpot, pSeppot); + + pClpot = mxGetCell(plhs[0], parent); + marginal_spPot_to_spPot(pClpot, pSeppot, maximize); + mxSetCell(plhs[1], i, pSeppot); + + pClpot = mxGetCell(plhs[0], child); + multiply_spPot_by_spPot(pClpot, pSeppot); + } + free(distribute_order); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/old/init_pot.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,637 @@ +/* C mex init_pot for in @jtree_sparse_inf_engine directory */ +/* The file enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/**************************************/ +/* init_pot.c has 6 input & 2 output */ +/* engine */ +/* clqs */ +/* pots */ +/* pot_type */ +/* onodes */ +/* ndx */ +/* */ +/* clpot */ +/* seppot */ +/**************************************/ +#include <math.h> +#include <search.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +mxArray* convert_to_sparse(const double *table, const int NB, const int counts){ + mxArray *spTable; + int i, k, *ir, *jc; + double *sr; + + spTable = mxCreateSparse(NB, 1, counts, mxREAL); + sr = mxGetPr(spTable); + ir = mxGetIr(spTable); + jc = mxGetJc(spTable); + + k = 0; + jc[0] = 0; + jc[1] = counts; + for(i=0; i<NB; i++){ + if(table[i] != 0.0){ + sr[k] = table[i]; + ir[k] = i; + k++; + } + } + + return spTable; +} + +mxArray* convert_table_to_sparse(const double *bT, const int *index, const int nzCounts, const int NB){ + mxArray *spTable; + int i, *irs, *jcs; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + sr[i] = bT[i]; + irs[i] = index[i]; + } + return spTable; +} + +mxArray* convert_ill_table_to_sparse(const double *bigTable, const int *sequence, const int nzCounts, const int NB){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = bigTable[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, nzCounts=0; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *jc; + double *pbDomain, *psDomain, *pbSize, *psSize, *bTable, *sTable, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + siz_b = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + siz_s = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<siz_b; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<siz_s; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + sTable = mxGetPr(pTemp); + bTable = malloc(NB * sizeof(double)); + for(i=0; i<NB; i++){ + bTable[i] = 0; + } + + if(NS == 1){ + value = *sTable; + for(i=0; i<NB; i++){ + bTable[i] = value; + } + nzCounts = NB; + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_to_sparse(bTable, NB, NB); + mxSetField(bigPot, 0, "T", pTemp); + free(bTable); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + bTable[i] = sTable[i]; + if(sTable[i] != 0) nzCounts++; + } + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_to_sparse(bTable, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp); + free(bTable); + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbSize[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + sy[mask[i]] = sx[mask[i]]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + bTable[j] = *sTable; + if(*sTable != 0.0) nzCounts++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sTable -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sTable += cpsy[i]; + break; + } + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_to_sparse(bTable, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp); + pTemp1 = mxGetField(bigPot, 0, "T"); + jc = mxGetJc(pTemp1); + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); + free(bTable); +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, bindex, sindex, nzCounts=0; + int *mask, *index, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + bigTable[i] = 0; + } + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + value = spr[sindex]; + if(value != 0){ + bigTable[nzCounts] = bpr[i] * value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *index, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + bigTable[i] = 0; + } + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + value = bpr[i]; + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + value *= spr[position]; + bigTable[nzCounts] = value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, c, loop, nNodes, nCliques, ndomain, dims[2]; + double *pClqs, *pr, *pt, *pSize; + mxArray *pTemp, *pTemp1, *pStruct, *pCliques, *pBigpot, *pSmallpot; + const char *field_names[] = {"domain", "T", "sizes"}; + + nNodes = mxGetNumberOfElements(prhs[1]); + pCliques = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pCliques); + pTemp = mxGetField(prhs[0], 0, "eff_node_sizes"); + pSize = mxGetPr(pTemp); + + plhs[0] = mxCreateCellArray(1, &nCliques); + for(i=0; i<nCliques; i++){ + pStruct = mxCreateStructMatrix(1, 1, 3, field_names); + mxSetCell(plhs[0], i, pStruct); + pTemp = mxGetCell(pCliques, i); + ndomain = mxGetNumberOfElements(pTemp); + pt = mxGetPr(pTemp); + pTemp1 = mxDuplicateArray(pTemp); + mxSetField(pStruct, 0, "domain", pTemp1); + + pTemp = mxCreateDoubleMatrix(1, ndomain, mxREAL); + mxSetField(pStruct, 0, "sizes", pTemp); + pr = mxGetPr(pTemp); + for(j=0; j<ndomain; j++){ + pr[j] = pSize[(int)pt[j]-1]; + } + } + + pClqs = mxGetPr(prhs[1]); + for(loop=0; loop<nNodes; loop++){ + c = (int)pClqs[loop] - 1; + pSmallpot = mxGetCell(prhs[2], loop); + pTemp = mxGetField(pSmallpot, 0, "T"); + pBigpot = mxGetCell(plhs[0], c); + pTemp1 = mxGetField(pBigpot, 0, "T"); + if(pTemp1){ + if(mxIsSparse(pTemp)) + multiply_spPot_by_spPot(pBigpot, pSmallpot); + else multiply_spPot_by_fuPot(pBigpot, pSmallpot); + } + else{ + if(mxIsSparse(pTemp)) + multiply_null_by_spPot(pBigpot, pSmallpot); + else multiply_null_by_fuPot(pBigpot, pSmallpot); + } + } + + dims[0] = nCliques; + dims[1] = nCliques; + plhs[1] = mxCreateCellArray(2, dims); +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/old/init_pot1.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,636 @@ +/* C mex init_pot for in @jtree_sparse_inf_engine directory */ +/* The file enter_evidence.m in directory @jtree_sparse_inf_engine call it*/ + +/**************************************/ +/* init_pot.c has 6 input & 2 output */ +/* engine */ +/* clqs */ +/* pots */ +/* pot_type */ +/* onodes */ +/* ndx */ +/* */ +/* clpot */ +/* seppot */ +/**************************************/ +#include <math.h> +#include <search.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void compute_fixed_weight(int *weight, const double *pbSize, const int *dmask, const int *bCumprod, const int ND, const int diffdim){ + int i, j; + int *eff_cumprod, *subv, *diffsize, *diff_cumprod; + + subv = malloc(diffdim * sizeof(int)); + eff_cumprod = malloc(diffdim * sizeof(int)); + diffsize = malloc(diffdim * sizeof(int)); + diff_cumprod = malloc(diffdim * sizeof(int)); + for(i=0; i<diffdim; i++){ + eff_cumprod[i] = bCumprod[dmask[i]]; + diffsize[i] = (int)pbSize[dmask[i]]; + } + diff_cumprod[0] = 1; + for(i=0; i<diffdim-1; i++){ + diff_cumprod[i+1] = diff_cumprod[i] * diffsize[i]; + } + for(i=0; i<ND; i++){ + ind_subv(i, diff_cumprod, diffdim, subv); + weight[i] = 0; + for(j=0; j<diffdim; j++){ + weight[i] += eff_cumprod[j] * subv[j]; + } + } + free(eff_cumprod); + free(subv); + free(diffsize); + free(diff_cumprod); +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +mxArray* convert_table_to_sparse(const double *bT, const int *index, const int nzCounts, const int NB){ + mxArray *spTable; + int i, *irs, *jcs; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + sr[i] = bT[i]; + irs[i] = index[i]; + } + return spTable; +} + +mxArray* convert_ill_table_to_sparse(const double *bigTable, const int *sequence, const int nzCounts, const int NB){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(NB, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = bigTable[temp]; + count++; + } + return spTable; +} + +void multiply_null_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, nzCounts=0; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *bir, *bjc; + double *pbDomain, *psDomain, *pbSize, *psSize, *spr, *bpr, value; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + siz_b = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + siz_s = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<siz_b; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<siz_s; i++){ + NS *= (int)psSize[i]; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + pTemp1 = mxCreateSparse(NB, 1, NB, mxREAL); + bpr = mxGetPr(pTemp1); + bir = mxGetIr(pTemp1); + bjc = mxGetJc(pTemp1); + bjc[0] = 0; + bjc[1] = NB; + + if(NS == 1){ + value = *spr; + for(i=0; i<NB; i++){ + bpr[i] = value; + bir[i] = i; + } + nzCounts = NB; + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + if(spr[i] != 0){ + bpr[nzCounts] = spr[i]; + bir[nzCounts] = i; + nzCounts++; + } + } + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbSize[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + sy[mask[i]] = sx[mask[i]]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + if(*spr != 0){ + bpr[nzCounts] = *spr; + bir[nzCounts] = j; + nzCounts++; + } + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + spr -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + spr += cpsy[i]; + break; + } + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + reset_nzmax(pTemp1, NB, nzCounts); + mxSetField(bigPot, 0, "T", pTemp1); + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} + +void multiply_null_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, count1, match, temp, bdim, sdim, diffdim, NB, NS, ND, NZB, NZS, bindex, sindex, nzCounts=0; + int *samemask, *diffmask, *sir, *sjc, *bCumprod, *sCumprod, *ssubv, *sequence, *weight; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *spr; + mxArray *pTemp, *pTemp1; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + NS = 1; + for(i=0; i<sdim; i++){ + NS *= (int)psSize[i]; + } + ND = NB / NS; + + if(ND == 1){ + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp1 = mxGetField(smallPot, 0, "T"); + pTemp = mxDuplicateArray(pTemp1); + mxSetField(bigPot, 0, "T", pTemp); + return; + } + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + NZB = ND * NZS; + + diffdim = bdim - sdim; + sequence = malloc(NZB * 2 * sizeof(int)); + bigTable = malloc(NZB * sizeof(double)); + samemask = malloc(sdim * sizeof(int)); + diffmask = malloc(diffdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + weight = malloc(ND * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + count1 = 0; + for(i=0; i<bdim; i++){ + match = 0; + for(j=0; j<sdim; j++){ + if(pbDomain[i] == psDomain[j]){ + samemask[count] = i; + match = 1; + count++; + break; + } + } + if(match == 0){ + diffmask[count1] = i; + count1++; + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + count = 0; + compute_fixed_weight(weight, pbSize, diffmask, bCumprod, ND, diffdim); + for(i=0; i<NZS; i++){ + sindex = sir[i]; + ind_subv(sindex, sCumprod, sdim, ssubv); + temp = 0; + for(j=0; j<sdim; j++){ + temp += ssubv[j] * bCumprod[samemask[j]]; + } + for(j=0; j<ND; j++){ + bindex = weight[j] + temp; + bigTable[nzCounts] = spr[i]; + sequence[count] = bindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + pTemp = convert_ill_table_to_sparse(bigTable, sequence, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(sequence); + free(bigTable); + free(samemask); + free(diffmask); + free(bCumprod); + free(sCumprod); + free(weight); + free(ssubv); +} + +void multiply_spPot_by_fuPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, bindex, sindex, nzCounts=0; + int *mask, *index, *bir, *bjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + value = spr[sindex]; + if(value != 0){ + bigTable[nzCounts] = bpr[i] * value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + +void multiply_spPot_by_spPot(mxArray *bigPot, const mxArray *smallPot){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *index, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *bigTable, *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + mxArray *pTemp; + + pTemp = mxGetField(bigPot, 0, "domain"); + pbDomain = mxGetPr(pTemp); + bdim = mxGetNumberOfElements(pTemp); + pTemp = mxGetField(smallPot, 0, "domain"); + psDomain = mxGetPr(pTemp); + sdim = mxGetNumberOfElements(pTemp); + + pTemp = mxGetField(bigPot, 0, "sizes"); + pbSize = mxGetPr(pTemp); + pTemp = mxGetField(smallPot, 0, "sizes"); + psSize = mxGetPr(pTemp); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + pTemp = mxGetField(bigPot, 0, "T"); + bpr = mxGetPr(pTemp); + bir = mxGetIr(pTemp); + bjc = mxGetJc(pTemp); + NZB = bjc[1]; + + pTemp = mxGetField(smallPot, 0, "T"); + spr = mxGetPr(pTemp); + sir = mxGetIr(pTemp); + sjc = mxGetJc(pTemp); + NZS = sjc[1]; + + bigTable = malloc(NZB * sizeof(double)); + index = malloc(NZB * sizeof(double)); + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + for(i=0; i<NZB; i++){ + bigTable[i] = 0; + } + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + value = bpr[i]; + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + value *= spr[position]; + bigTable[nzCounts] = value; + index[nzCounts] = bindex; + nzCounts++; + } + } + + pTemp = mxGetField(bigPot, 0, "T"); + if(pTemp)mxDestroyArray(pTemp); + pTemp = convert_table_to_sparse(bigTable, index, nzCounts, NB); + mxSetField(bigPot, 0, "T", pTemp); + + free(bigTable); + free(index); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +} + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, c, loop, nNodes, nCliques, ndomain, dims[2]; + double *pClqs, *pr, *pt, *pSize; + mxArray *pTemp, *pTemp1, *pStruct, *pCliques, *pBigpot, *pSmallpot; + const char *field_names[] = {"domain", "T", "sizes"}; + + nNodes = mxGetNumberOfElements(prhs[1]); + pCliques = mxGetField(prhs[0], 0, "cliques"); + nCliques = mxGetNumberOfElements(pCliques); + pTemp = mxGetField(prhs[0], 0, "eff_node_sizes"); + pSize = mxGetPr(pTemp); + + plhs[0] = mxCreateCellArray(1, &nCliques); + for(i=0; i<nCliques; i++){ + pStruct = mxCreateStructMatrix(1, 1, 3, field_names); + mxSetCell(plhs[0], i, pStruct); + pTemp = mxGetCell(pCliques, i); + ndomain = mxGetNumberOfElements(pTemp); + pt = mxGetPr(pTemp); + pTemp1 = mxDuplicateArray(pTemp); + mxSetField(pStruct, 0, "domain", pTemp1); + + pTemp = mxCreateDoubleMatrix(1, ndomain, mxREAL); + mxSetField(pStruct, 0, "sizes", pTemp); + pr = mxGetPr(pTemp); + for(j=0; j<ndomain; j++){ + pr[j] = pSize[(int)pt[j]-1]; + } + } + + pClqs = mxGetPr(prhs[1]); + for(loop=0; loop<nNodes; loop++){ + c = (int)pClqs[loop] - 1; + pSmallpot = mxGetCell(prhs[2], loop); + pTemp = mxGetField(pSmallpot, 0, "T"); + pBigpot = mxGetCell(plhs[0], c); + pTemp1 = mxGetField(pBigpot, 0, "T"); + if(pTemp1){ + if(mxIsSparse(pTemp)) + multiply_spPot_by_spPot(pBigpot, pSmallpot); + else multiply_spPot_by_fuPot(pBigpot, pSmallpot); + } + else{ + if(mxIsSparse(pTemp)) + multiply_null_by_spPot(pBigpot, pSmallpot); + else multiply_null_by_fuPot(pBigpot, pSmallpot); + } + } + + dims[0] = nCliques; + dims[1] = nCliques; + plhs[1] = mxCreateCellArray(2, dims); +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/old/init_pot1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes, ndx) +% INIT_POT Initialise potentials with evidence (jtree_inf) +% function [clpot, seppot] = init_pot(engine, clqs, pots, pot_type, onodes) + +cliques = engine.cliques; +bnet = bnet_from_engine(engine); +% Set the clique potentials to all 1s +C = length(cliques); +clpot = cell(1,C); +for i=1:C + clpot{i} = mk_initial_pot(pot_type, cliques{i}, bnet.node_sizes(:), bnet.cnodes(:), onodes); +end + +% Multiply on specified potentials +for i=1:length(clqs) + c = clqs(i); + clpot{c} = multiply_by_pot(clpot{c}, pots{i}); +end + +seppot = cell(C,C); % implicitely initialized to 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@jtree_sparse_inf_engine/set_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function engine = set_fields(engine, varargin) +% SET_FIELDS Set the fields for a generic engine +% engine = set_fields(engine, name/value pairs) +% +% e.g., engine = set_fields(engine, 'maximize', 1) + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'maximize', engine.maximize = args{i+1}; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@likelihood_weighting_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/likelihood_weighting_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@likelihood_weighting_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@likelihood_weighting_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@likelihood_weighting_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@likelihood_weighting_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function [engine, ll] = enter_evidence(engine, evidence, nsamples) +% ENTER_EVIDENCE Add the specified evidence to the network (likelihood_weighting) +% [engine, ll] = enter_evidence(engine, evidence, nsamples) +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% If nsamples is not specified, the value specified when the engine was created will be used. +% ll (log-likelihood) is set to []. + +ll = []; +if nargin < 3, nsamples = engine.nsamples; end + +bnet = bnet_from_engine(engine); +N = length(bnet.dag); +samples = cell(nsamples, N); +weights = zeros(1, nsamples); + +ns = bnet.node_sizes; +original_evidence = evidence; +observed = ~isemptycell(original_evidence); +for s=1:nsamples + evidence = original_evidence(:); % must be a column vector + w = 1; + for i=1:N + ps = parents(bnet.dag, i); + e = bnet.equiv_class(i); + if observed(i) + p = exp(log_prob_node(bnet.CPD{e}, evidence(i), evidence(ps))); + w = w * p; + else + x = sample_node(bnet.CPD{e}, evidence(ps)); + evidence{i} = x; + end + end + samples(s,:) = evidence; + weights(s) = w; +end + +engine.samples = samples; +engine.weights = weights;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@likelihood_weighting_inf_engine/likelihood_weighting_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function engine = likelihood_weighting_inf_engine(bnet, varargin) +% LIKELIHOOD_WEIGHTING_INF_ENGINE +% engine = likelihood_weighting_inf_engine(bnet, ...) +% +% Optional arguments [defaults] +% nsamples - [500] + +nsamples = 500; + +if nargin >= 2 + args = varargin; + nargs = length(args); + for i=1:2:nargs + switch args{i}, + case 'nsamples', nsamples= args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + +engine.nsamples = nsamples; +engine.samples = []; +engine.weights = []; +engine = class(engine, 'likelihood_weighting_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@likelihood_weighting_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function marginal = marginal_nodes(engine, nodes) +% MARGINAL_NODES Compute the marginal on the specified query nodes (likelihood_weighting) +% marginal = marginal_nodes(engine, nodes) + +bnet = bnet_from_engine(engine); +ddom = myintersect(nodes, bnet.dnodes); +cdom = myintersect(nodes, bnet.cnodes); +nsamples = size(engine.samples, 1); +ns = bnet.node_sizes; + +%w = normalise(engine.weights); +w = engine.weights; +if mysubset(nodes, ddom) + T = 0*myones(ns(nodes)); + P = prod(ns(nodes)); + indices = ind2subv(ns(nodes), 1:P); + samples = reshape(cat(1, engine.samples{:,nodes}), nsamples, length(nodes)); + for j = 1:P + rows = find_rows(samples, indices(j,:)); + T(j) = sum(w(rows)); + end + T = normalise(T); + marginal.T = T; +elseif subset(nodes, cdom) + samples = reshape(cat(1, engine.samples{:,nodes}), nsamples*sum(ns(nodes)), length(nodes)); + [marginal.mu, marginal.Sigma] = wstats(samples', normalise(w)); +else + error('can''t handle mixed marginals yet'); +end + +marginal.domain = nodes; + +%%%%%%%%% + +function rows = find_rows(M, v) +% FINDROWS Find rows which are equal to a specified vector +% rows = findrows(M, v) +% Each row of M is a sample + +temp = abs(M - repmat(v, size(M, 1), 1)); +rows = find(sum(temp,2) == 0); + +%%%%%%%% + +function [mu, Sigma] = wstats(X, w) + +% Computes the weighted mean and weighted covariance matrix for a given +% set of observations X(:,i), and a set of normalised weights w(i). +% Each column of X is a sample. + +d = X - repmat(X * w', 1, size(X, 2)); +mu = sum(X .* repmat(w, size(X, 1), 1), 2); +Sigma = d * diag(w) * d';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/bethe_free_energy.m/1.1.1.1/Sun Jul 6 20:57:18 2003// +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/loopy_converged.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_family.m/1.1.1.1/Fri Oct 18 20:05:16 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/pearl_inf_engine.m/1.1.1.1/Sat Jan 11 18:53:28 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@pearl_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/bethe_free_energy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +function loglik = bethe_free_energy(engine, evidence) +% BETHE_FREE_ENERGY Compute Bethe free energy approximation to the log likelihood +% loglik = bethe_free_energy(engine, evidence) +% +% The Bethe free energy is given by an exact energy term and an approximate entropy term. +% Energy +% E = -sum_f sum_i b(f,i) ln theta(f,i) +% where b(f,i) = approximate Pr(family f = i) +% and theta(f,i) = Pr(f = i) +% Entropy +% S = H1 - H2 +% H1 = sum_f sum_p H(b(f)) +% where b(f) = belief on family f, H(.) = entropy +% H2 = sum_n (q(n)-1) H(b(n)) +% where q(n) = num. neighbors of n +% +% This function was written by Yair Weiss, 8/22/01. + +hidden = find(isemptycell(evidence)); +bnet = bnet_from_engine(engine); +N = length(bnet.dag); + +add_ev = 1; +E=0;H1=0;H2=0; +loglik=0; +for n=1:N + ps=parents(bnet.dag,n); + if (length(ps)==0) % root node + qi=length(children(bnet.dag,n))-1; + else + qi=length(children(bnet.dag,n)); + end + bf = marginal_family(engine, n, add_ev); + bf = bf.T(:); + e = bnet.equiv_class(n); + T = CPD_to_CPT(bnet.CPD{e}); + T = T(:); + E = E-sum(log(T+(T==0)).*bf); + + if length(ps) > 0 + % root nodes don't count as fmailies + H1 = H1+sum(log(bf+(bf==0)).*bf); + end + + bi = marginal_nodes(engine, n, add_ev); + bi = bi.T(:); + H2 = H2+qi*sum(log(bi+(bi==0)).*bi); +end +loglik=E+H1-H2; +loglik=-loglik;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +function [engine, loglik, niter] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (pearl) +% [engine, loglik, num_iter] = enter_evidence(engine, evidence, ...) +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) +% +% The following optional arguments can be specified in the form of name/value pa irs: +% [default value in brackets] +% +% maximize - if 1, does max-product instead of sum-product [0] +% 'filename' - msgs will be printed to this file, so you can assess convergence while it runs [engine.filename] +% +% e.g., engine = enter_evidence(engine, ev, 'maximize', 1) +% +% For discrete nodes, loglik is the negative Bethe free energy evaluated at the final beliefs. +% For Gaussian nodes, loglik is currently always 0. +% +% 'num_iter' returns the number of iterations used. + +maximize = 0; +filename = engine.filename; + +% parse optional params +args = varargin; +nargs = length(args); +if nargs > 0 + for i=1:2:nargs + switch args{i}, + case 'maximize', maximize = args{i+1}; + case 'filename', filename = args{i+1}; + otherwise, + error(['invalid argument name ' args{i}]); + end + end +end + + +if maximize + error('can''t handle max-prop yet') +end + +engine.maximize = maximize; +engine.filename = filename; +engine.bel = []; % reset if necessary + +bnet = bnet_from_engine(engine); +N = length(bnet.dag); +ns = bnet.node_sizes(:); + +observed_bitv = ~isemptycell(evidence); +disconnected = find(engine.disconnected_nodes_bitv); +if ~all(observed_bitv(disconnected)) + error(['The following discrete nodes must be observed: ' num2str(disconnected)]) +end +msg = init_pearl_msgs(engine.msg_type, engine.msg_dag, ns, evidence); + +niter = 1; +switch engine.protocol + case 'parallel', [msg, niter] = parallel_protocol(engine, evidence, msg); + case 'tree', msg = tree_protocol(engine, evidence, msg); + otherwise, + error(['unrecognized protocol ' engine.protocol]) +end +engine.niter = niter; + +engine.marginal = cell(1,N); +nodes = find(~engine.disconnected_nodes_bitv); +for n=nodes(:)' + engine.marginal{n} = compute_bel(engine.msg_type, msg{n}.pi, msg{n}.lambda); +end + +engine.evidence = evidence; % needed by marginal_nodes and marginal_family +engine.msg = msg; % needed by marginal_family + +if (nargout >= 2) + if (engine.msg_type == 'd') + loglik = bethe_free_energy(engine, evidence); + else + loglik = 0; + end +end + + + +%%%%%%%%%%% + +function msg = init_pearl_msgs(msg_type, dag, ns, evidence) +% INIT_MSGS Initialize the lambda/pi message and state vectors +% msg = init_msgs(dag, ns, evidence) +% + +N = length(dag); +msg = cell(1,N); +observed = ~isemptycell(evidence); +lam_msg = 1; + +for n=1:N + ps = parents(dag, n); + msg{n}.pi_from_parent = cell(1, length(ps)); + for i=1:length(ps) + p = ps(i); + msg{n}.pi_from_parent{i} = mk_msg(msg_type, ns(p)); + end + + cs = children(dag, n); + msg{n}.lambda_from_child = cell(1, length(cs)); + for i=1:length(cs) + c = cs(i); + msg{n}.lambda_from_child{i} = mk_msg(msg_type, ns(n), lam_msg); + end + + msg{n}.lambda = mk_msg(msg_type, ns(n), lam_msg); + msg{n}.pi = mk_msg(msg_type, ns(n)); + + if observed(n) + msg{n}.lambda_from_self = mk_msg_with_evidence(msg_type, ns(n), evidence{n}); + else + msg{n}.lambda_from_self = mk_msg(msg_type, ns(n), lam_msg); + end +end + + + +%%%%%%%%% + +function msg = mk_msg(msg_type, sz, is_lambda_msg) + +if nargin < 3, is_lambda_msg = 0; end + +switch msg_type + case 'd', msg = ones(sz, 1); + case 'g', + if is_lambda_msg + msg.precision = zeros(sz, sz); + msg.info_state = zeros(sz, 1); + else + msg.Sigma = zeros(sz, sz); + msg.mu = zeros(sz,1); + end +end + +%%%%%%%%%%%% + +function msg = mk_msg_with_evidence(msg_type, sz, val) + +switch msg_type + case 'd', + msg = zeros(sz, 1); + msg(val) = 1; + case 'g', + %msg.observed_val = val(:); + msg.precision = inf; + msg.mu = val(:); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/loopy_converged.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function niter = loopy_converged(engine) +% LOOPY_CONVERGED Did loopy belief propagation converge? 0 means no, eles we return the num. iterations. +% function niter = loopy_converged(engine) +% +% We use a simple heuristic: we say convergence occurred if the number of iterations +% used was less than the maximum allowed. + +if engine.niter == engine.max_iter + niter = 0; +else + niter = engine.niter; +end +%conv = (strcmp(engine.protocol, 'tree') | (engine.niter < engine.max_iter));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/marginal_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function m = marginal_family(engine, n, add_ev) +% MARGINAL_FAMILY Compute the marginal on i's family (loopy) +% m = marginal_family(engine, n, add_ev) + +if nargin < 3, add_ev = 0; end + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +ps = parents(bnet.dag, n); +dom = [ps n]; +CPD = bnet.CPD{bnet.equiv_class(n)}; + +switch engine.msg_type + case 'd', + % The method is similar to the following HMM equation: + % xi(i,j,t) = normalise( alpha(i,t) * transmat(i,j) * obsmat(j,t+1) * beta(j,t+1) ) + % where xi(i,j,t) = Pr(Q(t)=i, Q(t+1)=j | y(1:T)) + % beta == lambda, alpha == pi, alpha from each parent = pi msg + % In general, if A,B are parents of C, + % P(A,B,C) = P(C|A,B) pi_msg(A->C) pi_msg(B->C) lambda(C) + % where lambda(C) = P(ev below and including C|C) = prod incoming lamba_msg(children->C) + % and pi_msg(X->C) = P(X|ev above) etc + + T = dpot(dom, ns(dom), CPD_to_CPT(CPD)); + for j=1:length(ps) + p = ps(j); + pi_msg = dpot(p, ns(p), engine.msg{n}.pi_from_parent{j}); + T = multiply_by_pot(T, pi_msg); + end + lambda = dpot(n, ns(n), engine.msg{n}.lambda); + T = multiply_by_pot(T, lambda); + T = normalize_pot(T); + m = pot_to_marginal(T); + if ~add_ev + m.T = shrink_obs_dims_in_table(m.T, dom, engine.evidence); + end + case 'g', + if engine.disconnected_nodes_bitv(n) + m.T = 1; + m.domain = dom; + if add_ev + m = add_ev_to_dmarginal(m, engine.evidence, ns) + end + return; + end + + [m, C, W] = gaussian_CPD_params_given_dps(CPD, dom, engine.evidence); + cdom = myintersect(dom, bnet.cnodes); + pot = linear_gaussian_to_cpot(m, C, W, dom, ns, cdom, engine.evidence); + % linear_gaussian_to_cpot will set the effective size of observed nodes to 0, + % so we need to do this explicitely for the messages, too, + % so they are all the same size. + obs_bitv = ~isemptycell(engine.evidence); + ps = parents(engine.msg_dag, n); + for j=1:length(ps) + p = ps(j); + msg = engine.msg{n}.pi_from_parent{j}; + if obs_bitv(p) + pi_msg = mpot(p, 0); + else + pi_msg = mpot(p, ns(p), 0, msg.mu, msg.Sigma); + end + pot = multiply_by_pot(pot, mpot_to_cpot(pi_msg)); + end + msg = engine.msg{n}.lambda; + if obs_bitv(n) + lambda = cpot(n, 0); + else + lambda = cpot(n, ns(n), 0, msg.info_state, msg.precision); + end + pot = multiply_by_pot(pot, lambda); + m = pot_to_marginal(pot); + if add_ev + m = add_evidence_to_gmarginal(m, engine.evidence, bnet.node_sizes, bnet.cnodes); + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (loopy) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a single node. +% add_ev is an optional argument; if 1, observed nodes will be set to their original size, +% otherwise they will be treated like points. + +if nargin < 3, add_ev = 0; end + +if length(query) > 1 + error('can only compute marginal on single nodes or families') +end +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); + +switch engine.msg_type + case 'd', + T = engine.marginal{query}; + if ~add_ev + marginal.T = shrink_obs_dims_in_table(T, query, engine.evidence); + else + marginal.T = T; + end + marginal.domain = query; + + case 'g', + if engine.disconnected_nodes_bitv(query) + marginal.T = 1; + marginal.domain = query; + if add_ev + marginal = add_ev_to_dmarginal(marginal, engine.evidence, ns) + end + return; + end + + marginal = engine.marginal{query}; + marginal.domain = query; + if ~add_ev + marginal = shrink_obs_dims_in_gaussian(marginal, query, engine.evidence, ns); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/pearl_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,158 @@ +function engine = pearl_inf_engine(bnet, varargin) +% PEARL_INF_ENGINE Pearl's algorithm (belief propagation) +% engine = pearl_inf_engine(bnet, ...) +% +% If the graph has no loops (undirected cycles), you should use the tree protocol, +% and the results will be exact. +% Otherwise, you should use the parallel protocol, and the results may be approximate. +% +% Optional arguments [default in brackets] +% 'protocol' - tree or parallel ['parallel'] +% +% Optional arguments for the loopy case +% 'max_iter' - specifies the max num. iterations to perform [2*num nodes] +% 'tol' - convergence criterion on messages [1e-3] +% 'momentum' - msg = (m*old + (1-m)*new). [m=0] +% 'filename' - msgs will be printed to this file, so you can assess convergence while it runs [[]] +% 'storebel' - 1 means save engine.bel{n,t} for every iteration t and hidden node n [0] +% +% If there are discrete and cts nodes, we assume all the discretes are observed. In this +% case, you must use the parallel protocol, and the evidence pattern must be fixed. + + +N = length(bnet.dag); +protocol = 'parallel'; +max_iter = 2*N; +% We use N+2 for the following reason: +% In N iterations, we get the exact answer for a tree. +% In the N+1st iteration, we notice that the results are the same as before, and terminate. +% In loopy_converged, we see that N+1 < max = N+2, and declare convergence. +tol = 1e-3; +momentum = 0; +filename = []; +storebel = 0; + +args = varargin; +for i=1:2:length(args) + switch args{i}, + case 'protocol', protocol = args{i+1}; + case 'max_iter', max_iter = args{i+1}; + case 'tol', tol = args{i+1}; + case 'momentum', momentum = args{i+1}; + case 'filename', filename = args{i+1}; + case 'storebel', storebel = args{i+1}; + end +end + +engine.filename = filename; +engine.storebel = storebel; +engine.bel = []; + +if strcmp(protocol, 'tree') + % We first send messages up to the root (pivot node), and then back towards the leaves. + % If the bnet is a singly connected graph (no loops), choosing a root induces a directed tree. + % Peot and Shachter discuss ways to pick the root so as to minimize the work, + % taking into account which nodes have changed. + % For simplicity, we always pick the root to be the last node in the graph. + % This means the first pass is equivalent to going forward in time in a DBN. + + engine.root = N; + [engine.adj_mat, engine.preorder, engine.postorder, loopy] = ... + mk_rooted_tree(bnet.dag, engine.root); + % engine.adj_mat might have different edge orientations from bnet.dag + if loopy + error('can only apply tree protocol to loop-less graphs') + end +else + engine.root = []; + engine.adj_mat = []; + engine.preorder = []; + engine.postorder = []; +end + +engine.niter = []; +engine.protocol = protocol; +engine.max_iter = max_iter; +engine.tol = tol; +engine.momentum = momentum; +engine.maximize = []; + +%onodes = find(~isemptycell(evidence)); +onodes = bnet.observed; +engine.msg_type = determine_pot_type(bnet, onodes, 1:N); % needed also by marginal_nodes +if strcmp(engine.msg_type, 'cg') + error('messages must be discrete or Gaussian') +end +[engine.msg_dag, disconnected_nodes] = mk_msg_dag(bnet, engine.msg_type, onodes); +engine.disconnected_nodes_bitv = zeros(1,N); +engine.disconnected_nodes_bitv(disconnected_nodes) = 1; + + +% this is where we store stuff between enter_evidence and marginal_nodes +engine.marginal = cell(1,N); +engine.evidence = []; +engine.msg = []; + +[engine.parent_index, engine.child_index] = mk_loopy_msg_indices(engine.msg_dag); + +engine = class(engine, 'pearl_inf_engine', inf_engine(bnet)); + + +%%%%%%%%% + +function [dag, disconnected_nodes] = mk_msg_dag(bnet, msg_type, onodes) + +% If we are using Gaussian msgs, all discrete nodes must be observed; +% they are then disconnected from the graph, so we don't try to send +% msgs to/from them: their observed value simply serves to index into +% the right set of parameters for the Gaussian nodes (which use CPD.ps +% instead of parents(dag), and hence are unaffected by this "surgery"). + +disconnected_nodes = []; +switch msg_type + case 'd', dag = bnet.dag; + case 'g', + disconnected_nodes = bnet.dnodes; + dag = bnet.dag; + for i=disconnected_nodes(:)' + ps = parents(bnet.dag, i); + cs = children(bnet.dag, i); + if ~isempty(ps), dag(ps, i) = 0; end + if ~isempty(cs), dag(i, cs) = 0; end + end +end + + +%%%%%%%%%% +function [parent_index, child_index] = mk_loopy_msg_indices(dag) +% MK_LOOPY_MSG_INDICES Compute "port numbers" for message passing +% [parent_index, child_index] = mk_loopy_msg_indices(bnet) +% +% child_index{n}(c) = i means c is n's i'th child, i.e., i = find_equiv_posns(c, children(n)) +% child_index{n}(c) = 0 means c is not a child of n. +% parent_index{n}{p} is defined similarly. +% We need to use these indices since the pi_from_parent/ lambda_from_child cell arrays +% cannot be sparse, and hence cannot be indexed by the actual number of the node. +% Instead, we use the number of the "port" on which the message arrived. + +N = length(dag); +child_index = cell(1,N); +parent_index = cell(1,N); +for n=1:N + cs = children(dag, n); + child_index{n} = sparse(1,N); + for i=1:length(cs) + c = cs(i); + child_index{n}(c) = i; + end + ps = parents(dag, n); + parent_index{n} = sparse(1,N); + for i=1:length(ps) + p = ps(i); + parent_index{n}(p) = i; + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/compute_bel.m/1.1.1.1/Wed May 29 15:59:56 2002// +/parallel_protocol.m/1.1.1.1/Sun Aug 21 20:00:12 2005// +/prod_lambda_msgs.m/1.1.1.1/Wed May 29 15:59:56 2002// +/tree_protocol.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@pearl_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/private/compute_bel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function bel = compute_bel(msg_type, pi, lambda) + +switch msg_type, + case 'd', bel = normalise(pi .* lambda); + case 'g', + if isinf(lambda.precision) % ignore pi because lambda is completely certain (observed) + bel.mu = lambda.mu; + bel.Sigma = zeros(length(bel.mu)); % infinite precision => 0 variance + elseif all(pi.Sigma==0) % ignore lambda because pi is completely certain (delta fn prior) + bel.Sigma = pi.Sigma; + bel.mu = pi.mu; + elseif all(isinf(pi.Sigma)) % ignore pi because pi is completely uncertain + bel.Sigma = inv(lambda.precision); + bel.mu = bel.Sigma * lambda.info_state; + elseif all(lambda.precision == 0) % ignore lambda because lambda is completely uncertain + bel.Sigma = pi.Sigma; + bel.mu = pi.mu; + else % combine both pi and lambda + pi_precision = inv(pi.Sigma); + bel.Sigma = inv(pi_precision + lambda.precision); + bel.mu = bel.Sigma*(pi_precision * pi.mu + lambda.info_state); + end + otherwise, error(['unrecognized msg type ' msg_type]) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/private/parallel_protocol.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +function [msg, niter] = parallel_protocol(engine, evidence, msg) + +bnet = bnet_from_engine(engine); +N = length(bnet.dag); +ns = bnet.node_sizes(:); + +if ~isempty(engine.filename) + fid = fopen(engine.filename, 'w'); + if fid == 0 + error(['could not open ' engine.filename ' for writing']) + end +else + fid = []; +end + +converged = 0; +iter = 1; +hidden = find(isemptycell(evidence)); +bel = cell(1,N); +old_bel = cell(1,N); +%nodes = mysetdiff(1:N, engine.disconnected_nodes); +nodes = find(~engine.disconnected_nodes_bitv); +while ~converged & (iter <= engine.max_iter) + % Everybody updates their state in parallel + for n=nodes(:)' + cs_msg = children(engine.msg_dag, n); + %msg{n}.lambda = compute_lambda(n, cs, msg); + msg{n}.lambda = prod_lambda_msgs(n, cs_msg, msg, engine.msg_type); + ps_orig = parents(bnet.dag, n); + msg{n}.pi = CPD_to_pi(bnet.CPD{bnet.equiv_class(n)}, engine.msg_type, n, ps_orig, msg, evidence); + end + + changed = 0; + if ~isempty(fid) + fprintf(fid, 'ITERATION %d\n', iter); + end + for n=hidden(:)' % this will not contain any disconnected nodes + old_bel{n} = bel{n}; + bel{n} = compute_bel(engine.msg_type, msg{n}.pi, msg{n}.lambda); + if ~isempty(fid) + fprintf(fid, 'node %d: %s\n', n, bel_to_str(bel{n}, engine.msg_type)); + end + if engine.storebel + engine.bel{n,iter} = bel{n}; + end + if (iter == 1) | ~approxeq_bel(bel{n}, old_bel{n}, engine.tol, engine.msg_type) + changed = 1; + end + end + %converged = ~changed; + converged = ~changed & (iter > 1); % Sonia Leach changed this + + if ~converged + % Everybody sends to all their neighbors in parallel + for n=nodes(:)' + % lambda msgs to parents + ps_msg = parents(engine.msg_dag, n); + ps_orig = parents(bnet.dag, n); + for p=ps_msg(:)' + j = engine.child_index{p}(n); % n is p's j'th child + old_msg = msg{p}.lambda_from_child{j}(:); + new_msg = CPD_to_lambda_msg(bnet.CPD{bnet.equiv_class(n)}, engine.msg_type, n, ps_orig, ... + msg, p, evidence); + lam_msg = convex_combination_msg(old_msg, new_msg, engine.momentum, engine.msg_type); + msg{p}.lambda_from_child{j} = lam_msg; + end + + % pi msgs to children + cs_msg = children(engine.msg_dag, n); + for c=cs_msg(:)' + j = engine.parent_index{c}(n); % n is c's j'th parent + old_msg = msg{c}.pi_from_parent{j}(:); + %new_msg = compute_pi_msg(n, cs, msg, c)); + new_msg = compute_bel(engine.msg_type, msg{n}.pi, prod_lambda_msgs(n, cs_msg, msg, engine.msg_type, c)); + pi_msg = convex_combination_msg(old_msg, new_msg, engine.momentum, engine.msg_type); + msg{c}.pi_from_parent{j} = pi_msg; + end + end + iter = iter + 1; + end +end + +if fid > 0, fclose(fid); end +%niter = iter - 1; +niter = iter; + +%%%%%%%%%% + +function str = bel_to_str(bel, type) + +switch type + case 'd', str = sprintf('%9.4f ', bel(:)'); + case 'g', str = sprintf('%9.4f ', bel.mu(:)'); +end + + +%%%%%%% + +function a = approxeq_bel(bel1, bel2, tol, type) + +switch type + case 'd', a = approxeq(bel1, bel2, tol); + case 'g', a = approxeq(bel1.mu, bel2.mu, tol) & approxeq(bel1.Sigma, bel2.Sigma, tol); +end + + +%%%%%%% + +function msg = convex_combination_msg(old_msg, new_msg, old_weight, type) + +switch type + case 'd', msg = old_weight * old_msg + (1-old_weight)*new_msg; + case 'g', msg = new_msg; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/private/prod_lambda_msgs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function lam = prod_lambda_msgs(n, cs, msg, msg_type, except) + +if nargin < 5, except = -1; end + +lam = msg{n}.lambda_from_self; +switch msg_type + case 'd', + for i=1:length(cs) + c = cs(i); + if c ~= except + lam = lam .* msg{n}.lambda_from_child{i}; + end + end + case 'g', + if isinf(lam.precision) % isfield(lam, 'observed_val') + return; % pass on the observed msg + end + for i=1:length(cs) + c = cs(i); + if c ~= except + m = msg{n}.lambda_from_child{i}; + lam.precision = lam.precision + m.precision; + lam.info_state = lam.info_state + m.info_state; + end + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@pearl_inf_engine/private/tree_protocol.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +function msg = tree_protocol(engine, evidence, msg) + +bnet = bnet_from_engine(engine); +N = length(bnet.dag); + +% Send messages from leaves to root +for i=1:N-1 + n = engine.postorder(i); + above = parents(engine.adj_mat, n); + msg = send_msgs_to_some_neighbors(n, msg, above, bnet, engine.child_index, engine.parent_index, ... + engine.msg_type, evidence); +end + +% Process root +n = engine.root; +cs = children(bnet.dag, n); +%msg{n}.lambda = compute_lambda(n, cs, msg, engine.msg_type); +msg{n}.lambda = prod_lambda_msgs(n, cs, msg, engine.msg_type); +ps = parents(bnet.dag, n); +msg{n}.pi = CPD_to_pi(bnet.CPD{bnet.equiv_class(n)}, engine.msg_type, n, ps, msg, evidence); + +% Send messages from root to leaves +for i=1:N + n = engine.preorder(i); + below = children(engine.adj_mat, n); + msg = send_msgs_to_some_neighbors(n, msg, below, bnet, engine.child_index, engine.parent_index, ... + engine.msg_type, evidence); +end + + +%%%%%%%%%% + +function msg = send_msgs_to_some_neighbors(n, msg, valid_nbrs, bnet, child_index, parent_index, ... + msg_type, evidence) + +verbose = 0; + +ns = bnet.node_sizes; +dag = bnet.dag; +e = bnet.equiv_class(n); +CPD = bnet.CPD{e}; + + +cs = children(dag, n); +%msg{n}.lambda = compute_lambda(n, cs, msg); +msg{n}.lambda = prod_lambda_msgs(n, cs, msg, msg_type); +if verbose, fprintf('%d computes lambda\n', n); display(msg{n}.lambda); end + +ps = parents(dag, n); +msg{n}.pi = CPD_to_pi(CPD, msg_type, n, ps, msg, evidence); +if verbose, fprintf('%d computes pi\n', n); display(msg{n}.pi); end + +ps2 = myintersect(parents(dag, n), valid_nbrs); +for p=ps2(:)' + lam_msg = CPD_to_lambda_msg(CPD, msg_type, n, ps, msg, p, evidence); + j = child_index{p}(n); % n is p's j'th child + msg{p}.lambda_from_child{j} = lam_msg; + if verbose, fprintf('%d sends lambda to %d\n', n, p); display(lam_msg); end +end + +cs2 = myintersect(cs, valid_nbrs); +for c=cs2(:)' + %pi_msg = compute_pi_msg(n, cs, msg, c); + pi_msg = compute_bel(msg_type, msg{n}.pi, prod_lambda_msgs(n, cs, msg, msg_type, c)); + j = parent_index{c}(n); % n is c's j'th parent + msg{c}.pi_from_parent{j} = pi_msg; + if verbose, fprintf('%d sends pi to %d\n', n, c); display(pi_msg); end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/enter_evidence.m/1.1.1.1/Wed May 29 15:59:56 2002// +/marginal_nodes.m/1.1.1.1/Wed May 29 15:59:56 2002// +/quickscore_inf_engine.m/1.1.1.1/Wed May 29 15:59:56 2002// +D/private////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@quickscore_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function engine = enter_evidence(engine, pos, neg) +% ENTER_EVIDENCE Add evidence to the QMR network +% engine = enter_evidence(engine, pos, neg) +% +% pos = list of leaves that have positive observations +% neg = list of leaves that have negative observations + +% Extract params for the observed findings +obs = myunion(pos, neg); +%inhibit_obs = engine.inhibit(obs, :); +inhibit_obs = engine.inhibit(:,obs)'; +leak_obs = engine.leak(obs); + +% Find what nodes correspond to the original observed leaves +pos2 = find_equiv_posns(pos, obs); +neg2 = find_equiv_posns(neg, obs); +engine.post = quickscore(pos2, neg2, inhibit_obs, engine.prior, leak_obs); +%engine.post = C_quickscore(pos2, neg2, inhibit_obs, engine.prior, leak_obs); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function m = marginal_nodes(engine, query) +% MARGINAL_NODES Compute the marginal on the specified query nodes (quickscore) +% marginal = marginal_nodes(engine, query) +% +% 'query' must be a single disease (root) node. + +assert(length(query)==1); +p = engine.post(query); +m.T = [1-p p]'; +m.domain = query; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/C_quickscore.c/1.1.1.1/Wed May 29 15:59:56 2002// +/nr.h/1.1.1.1/Wed May 29 15:59:56 2002// +/nrutil.c/1.1.1.1/Wed May 29 15:59:56 2002// +/nrutil.h/1.1.1.1/Wed May 29 15:59:56 2002// +/quickscore.m/1.1.1.1/Wed May 29 15:59:56 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@quickscore_inf_engine/private
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/C_quickscore.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,164 @@ +/* To compile, type "mex C_quickscore.c" */ + +#include <stdio.h> +#include "nrutil.h" +#include "nrutil.c" +#include <math.h> +#include "mex.h" + +#define MAX(X,Y) (X)>(Y)?(X):(Y) + +int two_to_the(int n) +{ + return 1 << n; +} + +void int2bin(int num, int nbits, int bits[]) +{ + int i, mask; + mask = 1 << (nbits-1); /* mask = 0010...0 , where the 1 is in col nbits (rightmost = col 1) */ + for (i = 0; i < nbits; i++) { + bits[i] = ((num & mask) == 0) ? 0 : 1; + num <<= 1; + } +} + + +void quickscore(int ndiseases, int nfindings, const double *fpos, int npos, const double *fneg, int nneg, + const double *inhibit, const double *prior, const double *leak, double *prob) +{ + double *Pon, *Poff, **Uon, **Uoff, **post, *pterm, *ptermOff, *ptermOn, temp, p, myp; + int *bits, nsubsets, *fmask; + int f, d, i, j, si, size_subset, sign; + + Pon = dvector(0, ndiseases); + Poff = dvector(0, ndiseases); + Pon[0] = 1; + Poff[0] = 0; + for (i=1; i <= ndiseases; i++) { + Pon[i] = prior[i-1]; + Poff[i] = 1-Pon[i]; + } + + Uon = dmatrix(0, nfindings-1, 0, ndiseases); + Uoff = dmatrix(0, nfindings-1, 0, ndiseases); + d = 0; + for (f=0; f < nfindings; f++) { + Uon[f][d] = leak[f]; + Uoff[f][d] = leak[f]; + } + for (f=0; f < nfindings; f++) { + for (d=1; d <= ndiseases; d++) { + Uon[f][d] = inhibit[f + nfindings*(d-1)]; + Uoff[f][d] = 1; + } + } + + post = dmatrix(0, ndiseases, 0, 1); + for (d = 0; d <= ndiseases; d++) { + post[d][0] = 0; + post[d][1] = 0; + } + + bits = ivector(0, npos-1); + fmask = ivector(0, nfindings-1); + pterm = dvector(0, ndiseases); + ptermOff = dvector(0, ndiseases); + ptermOn = dvector(0, ndiseases); + + nsubsets = two_to_the(npos); + + for (si = 0; si < nsubsets; si++) { + int2bin(si, npos, bits); + for (i=0; i < nfindings; i++) fmask[i] = 0; + for (i=0; i < nneg; i++) fmask[(int)fneg[i]-1] = 1; + size_subset = 0; + for (i=0; i < npos; i++) { + if (bits[i]) { + size_subset++; + fmask[(int)fpos[i]-1] = 1; + } + } + p = 1; + for (d=0; d <= ndiseases; d++) { + temp = 1; + for (j = 0; j < nfindings; j++) { + if (fmask[j]) temp *= Uoff[j][d]; + } + ptermOff[d] = temp; + + temp = 1; + for (j = 0; j < nfindings; j++) { + if (fmask[j]) temp *= Uon[j][d]; + } + ptermOn[d] = temp; + + pterm[d] = Poff[d]*ptermOff[d] + Pon[d]*ptermOn[d]; + p *= pterm[d]; + } + sign = (int) pow(-1, size_subset); + for (d=0; d <= ndiseases; d++) { + myp = p / pterm[d]; + post[d][0] += sign*(myp * ptermOff[d]); + post[d][1] += sign*(myp * ptermOn[d]); + } + } /* next si */ + + + for (d=0; d <= ndiseases; d++) { + post[d][0] *= Poff[d]; + post[d][1] *= Pon[d]; + } + for (d=0; d <= ndiseases; d++) { + temp = post[d][0] + post[d][1]; + post[d][0] /= temp; + post[d][1] /= temp; + if (d>0) { prob[d-1] = post[d][1]; } + } + + + free_dvector(Pon, 0, ndiseases); + free_dvector(Poff, 0, ndiseases); + free_dmatrix(Uon, 0, nfindings-1, 0, ndiseases); + free_dmatrix(Uoff, 0, nfindings-1, 0, ndiseases); + free_dmatrix(post, 0, ndiseases, 0, 1); + free_ivector(bits, 0, npos-1); + free_ivector(fmask, 0, nfindings-1); + free_dvector(pterm, 0, ndiseases); + free_dvector(ptermOff, 0, ndiseases); + free_dvector(ptermOn, 0, ndiseases); +} + + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[] + ) +{ + double *fpos, *fneg, *inhibit, *prior, *leak, *prob; + int npos, nneg, ndiseases, nfindings; + double *p; + + /* read the input args */ + fpos = mxGetPr(prhs[0]); + npos = MAX(mxGetM(prhs[0]), mxGetN(prhs[0])); + + fneg = mxGetPr(prhs[1]); + nneg = MAX(mxGetM(prhs[1]), mxGetN(prhs[1])); + + inhibit = mxGetPr(prhs[2]); /* inhibit(finding, disease) */ + nfindings = mxGetM(prhs[2]); + ndiseases = mxGetN(prhs[2]); + + prior = mxGetPr(prhs[3]); + + leak = mxGetPr(prhs[4]); + + + /* set the output pointers */ + plhs[0] = mxCreateDoubleMatrix(1, ndiseases, mxREAL); + prob = mxGetPr(plhs[0]); + + quickscore(ndiseases, nfindings, fpos, npos, fneg, nneg, inhibit, prior, leak, prob); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/nr.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,536 @@ +/* CAUTION: This is the ANSI C (only) version of the Numerical Recipes + utility file nr.h. Do not confuse this file with the same-named + file nr.h that is supplied in the 'misc' subdirectory. + *That* file is the one from the book, and contains both ANSI and + traditional K&R versions, along with #ifdef macros to select the + correct version. *This* file contains only ANSI C. */ + +#ifndef _NR_H_ +#define _NR_H_ + +#ifndef _FCOMPLEX_DECLARE_T_ +typedef struct FCOMPLEX {float r,i;} fcomplex; +#define _FCOMPLEX_DECLARE_T_ +#endif /* _FCOMPLEX_DECLARE_T_ */ + +#ifndef _ARITHCODE_DECLARE_T_ +typedef struct { + unsigned long *ilob,*iupb,*ncumfq,jdif,nc,minint,nch,ncum,nrad; +} arithcode; +#define _ARITHCODE_DECLARE_T_ +#endif /* _ARITHCODE_DECLARE_T_ */ + +#ifndef _HUFFCODE_DECLARE_T_ +typedef struct { + unsigned long *icod,*ncod,*left,*right,nch,nodemax; +} huffcode; +#define _HUFFCODE_DECLARE_T_ +#endif /* _HUFFCODE_DECLARE_T_ */ + +#include <stdio.h> + +void addint(double **uf, double **uc, double **res, int nf); +void airy(float x, float *ai, float *bi, float *aip, float *bip); +void amebsa(float **p, float y[], int ndim, float pb[], float *yb, + float ftol, float (*funk)(float []), int *iter, float temptr); +void amoeba(float **p, float y[], int ndim, float ftol, + float (*funk)(float []), int *iter); +float amotry(float **p, float y[], float psum[], int ndim, + float (*funk)(float []), int ihi, float fac); +float amotsa(float **p, float y[], float psum[], int ndim, float pb[], + float *yb, float (*funk)(float []), int ihi, float *yhi, float fac); +void anneal(float x[], float y[], int iorder[], int ncity); +double anorm2(double **a, int n); +void arcmak(unsigned long nfreq[], unsigned long nchh, unsigned long nradd, + arithcode *acode); +void arcode(unsigned long *ich, unsigned char **codep, unsigned long *lcode, + unsigned long *lcd, int isign, arithcode *acode); +void arcsum(unsigned long iin[], unsigned long iout[], unsigned long ja, + int nwk, unsigned long nrad, unsigned long nc); +void asolve(unsigned long n, double b[], double x[], int itrnsp); +void atimes(unsigned long n, double x[], double r[], int itrnsp); +void avevar(float data[], unsigned long n, float *ave, float *var); +void balanc(float **a, int n); +void banbks(float **a, unsigned long n, int m1, int m2, float **al, + unsigned long indx[], float b[]); +void bandec(float **a, unsigned long n, int m1, int m2, float **al, + unsigned long indx[], float *d); +void banmul(float **a, unsigned long n, int m1, int m2, float x[], float b[]); +void bcucof(float y[], float y1[], float y2[], float y12[], float d1, + float d2, float **c); +void bcuint(float y[], float y1[], float y2[], float y12[], + float x1l, float x1u, float x2l, float x2u, float x1, + float x2, float *ansy, float *ansy1, float *ansy2); +void beschb(double x, double *gam1, double *gam2, double *gampl, + double *gammi); +float bessi(int n, float x); +float bessi0(float x); +float bessi1(float x); +void bessik(float x, float xnu, float *ri, float *rk, float *rip, + float *rkp); +float bessj(int n, float x); +float bessj0(float x); +float bessj1(float x); +void bessjy(float x, float xnu, float *rj, float *ry, float *rjp, + float *ryp); +float bessk(int n, float x); +float bessk0(float x); +float bessk1(float x); +float bessy(int n, float x); +float bessy0(float x); +float bessy1(float x); +float beta(float z, float w); +float betacf(float a, float b, float x); +float betai(float a, float b, float x); +float bico(int n, int k); +void bksub(int ne, int nb, int jf, int k1, int k2, float ***c); +float bnldev(float pp, int n, long *idum); +float brent(float ax, float bx, float cx, + float (*f)(float), float tol, float *xmin); +float brent_arg(float ax, float bx, float cx, + float (*f)(float, void*), float tol, float *xmin, void *arg); +void broydn(float x[], int n, int *check, + void (*vecfunc)(int, float [], float [])); +void bsstep(float y[], float dydx[], int nv, float *xx, float htry, + float eps, float yscal[], float *hdid, float *hnext, + void (*derivs)(float, float [], float [])); +void caldat(long julian, int *mm, int *id, int *iyyy); +void chder(float a, float b, float c[], float cder[], int n); +float chebev(float a, float b, float c[], int m, float x); +void chebft(float a, float b, float c[], int n, float (*func)(float)); +void chebpc(float c[], float d[], int n); +void chint(float a, float b, float c[], float cint[], int n); +float chixy(float bang); +void choldc(float **a, int n, float p[]); +void cholsl(float **a, int n, float p[], float b[], float x[]); +void chsone(float bins[], float ebins[], int nbins, int knstrn, + float *df, float *chsq, float *prob); +void chstwo(float bins1[], float bins2[], int nbins, int knstrn, + float *df, float *chsq, float *prob); +void cisi(float x, float *ci, float *si); +void cntab1(int **nn, int ni, int nj, float *chisq, + float *df, float *prob, float *cramrv, float *ccc); +void cntab2(int **nn, int ni, int nj, float *h, float *hx, float *hy, + float *hygx, float *hxgy, float *uygx, float *uxgy, float *uxy); +void convlv(float data[], unsigned long n, float respns[], unsigned long m, + int isign, float ans[]); +void copy(double **aout, double **ain, int n); +void correl(float data1[], float data2[], unsigned long n, float ans[]); +void cosft(float y[], int n, int isign); +void cosft1(float y[], int n); +void cosft2(float y[], int n, int isign); +void covsrt(float **covar, int ma, int ia[], int mfit); +void crank(unsigned long n, float w[], float *s); +void cyclic(float a[], float b[], float c[], float alpha, float beta, + float r[], float x[], unsigned long n); +void daub4(float a[], unsigned long n, int isign); +float dawson(float x); +float dbrent(float ax, float bx, float cx, + float (*f)(float), float (*df)(float), float tol, float *xmin); +void ddpoly(float c[], int nc, float x, float pd[], int nd); +int decchk(char string[], int n, char *ch); +void derivs(float x, float y[], float dydx[]); +float df1dim(float x); +void dfour1(double data[], unsigned long nn, int isign); +void dfpmin(float p[], int n, float gtol, int *iter, float *fret, + float (*func)(float []), void (*dfunc)(float [], float [])); +float dfridr(float (*func)(float), float x, float h, float *err); +void dftcor(float w, float delta, float a, float b, float endpts[], + float *corre, float *corim, float *corfac); +void dftint(float (*func)(float), float a, float b, float w, + float *cosint, float *sinint); +void difeq(int k, int k1, int k2, int jsf, int is1, int isf, + int indexv[], int ne, float **s, float **y); +void dlinmin(float p[], float xi[], int n, float *fret, + float (*func)(float []), void (*dfunc)(float [], float[])); +double dpythag(double a, double b); +void drealft(double data[], unsigned long n, int isign); +void dsprsax(double sa[], unsigned long ija[], double x[], double b[], + unsigned long n); +void dsprstx(double sa[], unsigned long ija[], double x[], double b[], + unsigned long n); +void dsvbksb(double **u, double w[], double **v, int m, int n, double b[], + double x[]); +void dsvdcmp(double **a, int m, int n, double w[], double **v); +void eclass(int nf[], int n, int lista[], int listb[], int m); +void eclazz(int nf[], int n, int (*equiv)(int, int)); +float ei(float x); +void eigsrt(float d[], float **v, int n); +float elle(float phi, float ak); +float ellf(float phi, float ak); +float ellpi(float phi, float en, float ak); +void elmhes(float **a, int n); +float erfcc(float x); +float erff(float x); +float erffc(float x); +void eulsum(float *sum, float term, int jterm, float wksp[]); +float evlmem(float fdt, float d[], int m, float xms); +float expdev(long *idum); +float expint(int n, float x); +float f1(float x); +float f1dim(float x); +float f1dim_arg(float x, void *arg); +float f2(float y); +float f3(float z); +float factln(int n); +float factrl(int n); +void fasper(float x[], float y[], unsigned long n, float ofac, float hifac, + float wk1[], float wk2[], unsigned long nwk, unsigned long *nout, + unsigned long *jmax, float *prob); +void fdjac(int n, float x[], float fvec[], float **df, + void (*vecfunc)(int, float [], float [])); +void fgauss(float x, float a[], float *y, float dyda[], int na); +void fill0(double **u, int n); +void fit(float x[], float y[], int ndata, float sig[], int mwt, + float *a, float *b, float *siga, float *sigb, float *chi2, float *q); +void fitexy(float x[], float y[], int ndat, float sigx[], float sigy[], + float *a, float *b, float *siga, float *sigb, float *chi2, float *q); +void fixrts(float d[], int m); +void fleg(float x, float pl[], int nl); +void flmoon(int n, int nph, long *jd, float *frac); +float fmin(float x[]); +void four1(float data[], unsigned long nn, int isign); +void fourew(FILE *file[5], int *na, int *nb, int *nc, int *nd); +void fourfs(FILE *file[5], unsigned long nn[], int ndim, int isign); +void fourn(float data[], unsigned long nn[], int ndim, int isign); +void fpoly(float x, float p[], int np); +void fred2(int n, float a, float b, float t[], float f[], float w[], + float (*g)(float), float (*ak)(float, float)); +float fredin(float x, int n, float a, float b, float t[], float f[], float w[], + float (*g)(float), float (*ak)(float, float)); +void frenel(float x, float *s, float *c); +void frprmn(float p[], int n, float ftol, int *iter, float *fret, + float (*func)(float []), void (*dfunc)(float [], float [])); +void frprmn_arg(float p[], int n, float ftol, int *iter, float *fret, + float (*func)(float [], void*), void (*dfunc)(float [], float [], void*), void* arg); +void ftest(float data1[], unsigned long n1, float data2[], unsigned long n2, + float *f, float *prob); +float gamdev(int ia, long *idum); +float gammln(float xx); +float gammp(float a, float x); +float gammq(float a, float x); +float gasdev(long *idum); +void gaucof(int n, float a[], float b[], float amu0, float x[], float w[]); +void gauher(float x[], float w[], int n); +void gaujac(float x[], float w[], int n, float alf, float bet); +void gaulag(float x[], float w[], int n, float alf); +void gauleg(float x1, float x2, float x[], float w[], int n); +void gaussj(float **a, int n, float **b, int m); +void gcf(float *gammcf, float a, float x, float *gln); +float golden(float ax, float bx, float cx, float (*f)(float), float tol, + float *xmin); +void gser(float *gamser, float a, float x, float *gln); +void hpsel(unsigned long m, unsigned long n, float arr[], float heap[]); +void hpsort(unsigned long n, float ra[]); +void hqr(float **a, int n, float wr[], float wi[]); +void hufapp(unsigned long index[], unsigned long nprob[], unsigned long n, + unsigned long i); +void hufdec(unsigned long *ich, unsigned char *code, unsigned long lcode, + unsigned long *nb, huffcode *hcode); +void hufenc(unsigned long ich, unsigned char **codep, unsigned long *lcode, + unsigned long *nb, huffcode *hcode); +void hufmak(unsigned long nfreq[], unsigned long nchin, unsigned long *ilong, + unsigned long *nlong, huffcode *hcode); +void hunt(float xx[], unsigned long n, float x, unsigned long *jlo); +void hypdrv(float s, float yy[], float dyyds[]); +fcomplex hypgeo(fcomplex a, fcomplex b, fcomplex c, fcomplex z); +void hypser(fcomplex a, fcomplex b, fcomplex c, fcomplex z, + fcomplex *series, fcomplex *deriv); +unsigned short icrc(unsigned short crc, unsigned char *bufptr, + unsigned long len, short jinit, int jrev); +unsigned short icrc1(unsigned short crc, unsigned char onech); +unsigned long igray(unsigned long n, int is); +void iindexx(unsigned long n, long arr[], unsigned long indx[]); +void indexx(unsigned long n, float arr[], unsigned long indx[]); +void interp(double **uf, double **uc, int nf); +int irbit1(unsigned long *iseed); +int irbit2(unsigned long *iseed); +void jacobi(float **a, int n, float d[], float **v, int *nrot); +void jacobn(float x, float y[], float dfdx[], float **dfdy, int n); +long julday(int mm, int id, int iyyy); +void kendl1(float data1[], float data2[], unsigned long n, float *tau, float *z, + float *prob); +void kendl2(float **tab, int i, int j, float *tau, float *z, float *prob); +void kermom(double w[], double y, int m); +void ks2d1s(float x1[], float y1[], unsigned long n1, + void (*quadvl)(float, float, float *, float *, float *, float *), + float *d1, float *prob); +void ks2d2s(float x1[], float y1[], unsigned long n1, float x2[], float y2[], + unsigned long n2, float *d, float *prob); +void ksone(float data[], unsigned long n, float (*func)(float), float *d, + float *prob); +void kstwo(float data1[], unsigned long n1, float data2[], unsigned long n2, + float *d, float *prob); +void laguer(fcomplex a[], int m, fcomplex *x, int *its); +void lfit(float x[], float y[], float sig[], int ndat, float a[], int ia[], + int ma, float **covar, float *chisq, void (*funcs)(float, float [], int)); +void linbcg(unsigned long n, double b[], double x[], int itol, double tol, + int itmax, int *iter, double *err); +void linmin(float p[], float xi[], int n, float *fret, + float (*func)(float [])); +void linmin_arg(float p[], float xi[], int n, float *fret, + float (*func)(float [], void*), void *arg); +void lnsrch(int n, float xold[], float fold, float g[], float p[], float x[], + float *f, float stpmax, int *check, float (*func)(float [])); +void load(float x1, float v[], float y[]); +void load1(float x1, float v1[], float y[]); +void load2(float x2, float v2[], float y[]); +void locate(float xx[], unsigned long n, float x, unsigned long *j); +void lop(double **out, double **u, int n); +void lubksb(float **a, int n, int *indx, float b[]); +void ludcmp(float **a, int n, int *indx, float *d); +void machar(int *ibeta, int *it, int *irnd, int *ngrd, + int *machep, int *negep, int *iexp, int *minexp, int *maxexp, + float *eps, float *epsneg, float *xmin, float *xmax); +void matadd(double **a, double **b, double **c, int n); +void matsub(double **a, double **b, double **c, int n); +void medfit(float x[], float y[], int ndata, float *a, float *b, float *abdev); +void memcof(float data[], int n, int m, float *xms, float d[]); +int metrop(float de, float t); +void mgfas(double **u, int n, int maxcyc); +void mglin(double **u, int n, int ncycle); +float midexp(float (*funk)(float), float aa, float bb, int n); +float midinf(float (*funk)(float), float aa, float bb, int n); +float midpnt(float (*func)(float), float a, float b, int n); +float midsql(float (*funk)(float), float aa, float bb, int n); +float midsqu(float (*funk)(float), float aa, float bb, int n); +void miser(float (*func)(float []), float regn[], int ndim, unsigned long npts, + float dith, float *ave, float *var); +void mmid(float y[], float dydx[], int nvar, float xs, float htot, + int nstep, float yout[], void (*derivs)(float, float[], float[])); +void mnbrak(float *ax, float *bx, float *cx, float *fa, float *fb, + float *fc, float (*func)(float)); +void mnbrak_arg(float *ax, float *bx, float *cx, float *fa, float *fb, + float *fc, float (*func)(float, void*), void *arg); +void mnewt(int ntrial, float x[], int n, float tolx, float tolf); +void moment(float data[], int n, float *ave, float *adev, float *sdev, + float *var, float *skew, float *curt); +void mp2dfr(unsigned char a[], unsigned char s[], int n, int *m); +void mpadd(unsigned char w[], unsigned char u[], unsigned char v[], int n); +void mpdiv(unsigned char q[], unsigned char r[], unsigned char u[], + unsigned char v[], int n, int m); +void mpinv(unsigned char u[], unsigned char v[], int n, int m); +void mplsh(unsigned char u[], int n); +void mpmov(unsigned char u[], unsigned char v[], int n); +void mpmul(unsigned char w[], unsigned char u[], unsigned char v[], int n, + int m); +void mpneg(unsigned char u[], int n); +void mppi(int n); +void mprove(float **a, float **alud, int n, int indx[], float b[], + float x[]); +void mpsad(unsigned char w[], unsigned char u[], int n, int iv); +void mpsdv(unsigned char w[], unsigned char u[], int n, int iv, int *ir); +void mpsmu(unsigned char w[], unsigned char u[], int n, int iv); +void mpsqrt(unsigned char w[], unsigned char u[], unsigned char v[], int n, + int m); +void mpsub(int *is, unsigned char w[], unsigned char u[], unsigned char v[], + int n); +void mrqcof(float x[], float y[], float sig[], int ndata, float a[], + int ia[], int ma, float **alpha, float beta[], float *chisq, + void (*funcs)(float, float [], float *, float [], int)); +void mrqmin(float x[], float y[], float sig[], int ndata, float a[], + int ia[], int ma, float **covar, float **alpha, float *chisq, + void (*funcs)(float, float [], float *, float [], int), float *alamda); +void newt(float x[], int n, int *check, + void (*vecfunc)(int, float [], float [])); +void odeint(float ystart[], int nvar, float x1, float x2, + float eps, float h1, float hmin, int *nok, int *nbad, + void (*derivs)(float, float [], float []), + void (*rkqs)(float [], float [], int, float *, float, float, + float [], float *, float *, void (*)(float, float [], float []))); +void orthog(int n, float anu[], float alpha[], float beta[], float a[], + float b[]); +void pade(double cof[], int n, float *resid); +void pccheb(float d[], float c[], int n); +void pcshft(float a, float b, float d[], int n); +void pearsn(float x[], float y[], unsigned long n, float *r, float *prob, + float *z); +void period(float x[], float y[], int n, float ofac, float hifac, + float px[], float py[], int np, int *nout, int *jmax, float *prob); +void piksr2(int n, float arr[], float brr[]); +void piksrt(int n, float arr[]); +void pinvs(int ie1, int ie2, int je1, int jsf, int jc1, int k, + float ***c, float **s); +float plgndr(int l, int m, float x); +float poidev(float xm, long *idum); +void polcoe(float x[], float y[], int n, float cof[]); +void polcof(float xa[], float ya[], int n, float cof[]); +void poldiv(float u[], int n, float v[], int nv, float q[], float r[]); +void polin2(float x1a[], float x2a[], float **ya, int m, int n, + float x1, float x2, float *y, float *dy); +void polint(float xa[], float ya[], int n, float x, float *y, float *dy); +void powell(float p[], float **xi, int n, float ftol, int *iter, float *fret, + float (*func)(float [])); +void predic(float data[], int ndata, float d[], int m, float future[], int nfut); +float probks(float alam); +void psdes(unsigned long *lword, unsigned long *irword); +void pwt(float a[], unsigned long n, int isign); +void pwtset(int n); +float pythag(float a, float b); +void pzextr(int iest, float xest, float yest[], float yz[], float dy[], + int nv); +float qgaus(float (*func)(float), float a, float b); +void qrdcmp(float **a, int n, float *c, float *d, int *sing); +float qromb(float (*func)(float), float a, float b); +float qromo(float (*func)(float), float a, float b, + float (*choose)(float (*)(float), float, float, int)); +void qroot(float p[], int n, float *b, float *c, float eps); +void qrsolv(float **a, int n, float c[], float d[], float b[]); +void qrupdt(float **r, float **qt, int n, float u[], float v[]); +float qsimp(float (*func)(float), float a, float b); +float qtrap(float (*func)(float), float a, float b); +float quad3d(float (*func)(float, float, float), float x1, float x2); +void quadct(float x, float y, float xx[], float yy[], unsigned long nn, + float *fa, float *fb, float *fc, float *fd); +void quadmx(float **a, int n); +void quadvl(float x, float y, float *fa, float *fb, float *fc, float *fd); +float ran0(long *idum); +float ran1(long *idum); +float ran2(long *idum); +float ran3(long *idum); +float ran4(long *idum); +void rank(unsigned long n, unsigned long indx[], unsigned long irank[]); +void ranpt(float pt[], float regn[], int n); +void ratint(float xa[], float ya[], int n, float x, float *y, float *dy); +void ratlsq(double (*fn)(double), double a, double b, int mm, int kk, + double cof[], double *dev); +double ratval(double x, double cof[], int mm, int kk); +float rc(float x, float y); +float rd(float x, float y, float z); +void realft(float data[], unsigned long n, int isign); +void rebin(float rc, int nd, float r[], float xin[], float xi[]); +void red(int iz1, int iz2, int jz1, int jz2, int jm1, int jm2, int jmf, + int ic1, int jc1, int jcf, int kc, float ***c, float **s); +void relax(double **u, double **rhs, int n); +void relax2(double **u, double **rhs, int n); +void resid(double **res, double **u, double **rhs, int n); +float revcst(float x[], float y[], int iorder[], int ncity, int n[]); +void reverse(int iorder[], int ncity, int n[]); +float rf(float x, float y, float z); +float rj(float x, float y, float z, float p); +void rk4(float y[], float dydx[], int n, float x, float h, float yout[], + void (*derivs)(float, float [], float [])); +void rkck(float y[], float dydx[], int n, float x, float h, + float yout[], float yerr[], void (*derivs)(float, float [], float [])); +void rkdumb(float vstart[], int nvar, float x1, float x2, int nstep, + void (*derivs)(float, float [], float [])); +void rkqs(float y[], float dydx[], int n, float *x, + float htry, float eps, float yscal[], float *hdid, float *hnext, + void (*derivs)(float, float [], float [])); +void rlft3(float ***data, float **speq, unsigned long nn1, + unsigned long nn2, unsigned long nn3, int isign); +float rofunc(float b); +void rotate(float **r, float **qt, int n, int i, float a, float b); +void rsolv(float **a, int n, float d[], float b[]); +void rstrct(double **uc, double **uf, int nc); +float rtbis(float (*func)(float), float x1, float x2, float xacc); +float rtflsp(float (*func)(float), float x1, float x2, float xacc); +float rtnewt(void (*funcd)(float, float *, float *), float x1, float x2, + float xacc); +float rtsafe(void (*funcd)(float, float *, float *), float x1, float x2, + float xacc); +float rtsec(float (*func)(float), float x1, float x2, float xacc); +void rzextr(int iest, float xest, float yest[], float yz[], float dy[], int nv); +void savgol(float c[], int np, int nl, int nr, int ld, int m); +void score(float xf, float y[], float f[]); +void scrsho(float (*fx)(float)); +float select(unsigned long k, unsigned long n, float arr[]); +float selip(unsigned long k, unsigned long n, float arr[]); +void shell(unsigned long n, float a[]); +void shoot(int n, float v[], float f[]); +void shootf(int n, float v[], float f[]); +void simp1(float **a, int mm, int ll[], int nll, int iabf, int *kp, + float *bmax); +void simp2(float **a, int n, int l2[], int nl2, int *ip, int kp, float *q1); +void simp3(float **a, int i1, int k1, int ip, int kp); +void simplx(float **a, int m, int n, int m1, int m2, int m3, int *icase, + int izrov[], int iposv[]); +void simpr(float y[], float dydx[], float dfdx[], float **dfdy, + int n, float xs, float htot, int nstep, float yout[], + void (*derivs)(float, float [], float [])); +void sinft(float y[], int n); +void slvsm2(double **u, double **rhs); +void slvsml(double **u, double **rhs); +void sncndn(float uu, float emmc, float *sn, float *cn, float *dn); +double snrm(unsigned long n, double sx[], int itol); +void sobseq(int *n, float x[]); +void solvde(int itmax, float conv, float slowc, float scalv[], + int indexv[], int ne, int nb, int m, float **y, float ***c, float **s); +void sor(double **a, double **b, double **c, double **d, double **e, + double **f, double **u, int jmax, double rjac); +void sort(unsigned long n, float arr[]); +void sort2(unsigned long n, float arr[], float brr[]); +void sort3(unsigned long n, float ra[], float rb[], float rc[]); +void spctrm(FILE *fp, float p[], int m, int k, int ovrlap); +void spear(float data1[], float data2[], unsigned long n, float *d, float *zd, + float *probd, float *rs, float *probrs); +void sphbes(int n, float x, float *sj, float *sy, float *sjp, float *syp); +void splie2(float x1a[], float x2a[], float **ya, int m, int n, float **y2a); +void splin2(float x1a[], float x2a[], float **ya, float **y2a, int m, int n, + float x1, float x2, float *y); +void spline(float x[], float y[], int n, float yp1, float ypn, float y2[]); +void splint(float xa[], float ya[], float y2a[], int n, float x, float *y); +void spread(float y, float yy[], unsigned long n, float x, int m); +void sprsax(float sa[], unsigned long ija[], float x[], float b[], + unsigned long n); +void sprsin(float **a, int n, float thresh, unsigned long nmax, float sa[], + unsigned long ija[]); +void sprspm(float sa[], unsigned long ija[], float sb[], unsigned long ijb[], + float sc[], unsigned long ijc[]); +void sprstm(float sa[], unsigned long ija[], float sb[], unsigned long ijb[], + float thresh, unsigned long nmax, float sc[], unsigned long ijc[]); +void sprstp(float sa[], unsigned long ija[], float sb[], unsigned long ijb[]); +void sprstx(float sa[], unsigned long ija[], float x[], float b[], + unsigned long n); +void stifbs(float y[], float dydx[], int nv, float *xx, + float htry, float eps, float yscal[], float *hdid, float *hnext, + void (*derivs)(float, float [], float [])); +void stiff(float y[], float dydx[], int n, float *x, + float htry, float eps, float yscal[], float *hdid, float *hnext, + void (*derivs)(float, float [], float [])); +void stoerm(float y[], float d2y[], int nv, float xs, + float htot, int nstep, float yout[], + void (*derivs)(float, float [], float [])); +void svbksb(float **u, float w[], float **v, int m, int n, float b[], + float x[]); +void svdcmp(float **a, int m, int n, float w[], float **v); +void svdfit(float x[], float y[], float sig[], int ndata, float a[], + int ma, float **u, float **v, float w[], float *chisq, + void (*funcs)(float, float [], int)); +void svdvar(float **v, int ma, float w[], float **cvm); +void toeplz(float r[], float x[], float y[], int n); +void tptest(float data1[], float data2[], unsigned long n, float *t, float *prob); +void tqli(float d[], float e[], int n, float **z); +float trapzd(float (*func)(float), float a, float b, int n); +void tred2(float **a, int n, float d[], float e[]); +void tridag(float a[], float b[], float c[], float r[], float u[], + unsigned long n); +float trncst(float x[], float y[], int iorder[], int ncity, int n[]); +void trnspt(int iorder[], int ncity, int n[]); +void ttest(float data1[], unsigned long n1, float data2[], unsigned long n2, + float *t, float *prob); +void tutest(float data1[], unsigned long n1, float data2[], unsigned long n2, + float *t, float *prob); +void twofft(float data1[], float data2[], float fft1[], float fft2[], + unsigned long n); +void vander(double x[], double w[], double q[], int n); +void vegas(float regn[], int ndim, float (*fxn)(float [], float), int init, + unsigned long ncall, int itmx, int nprn, float *tgral, float *sd, + float *chi2a); +void voltra(int n, int m, float t0, float h, float *t, float **f, + float (*g)(int, float), float (*ak)(int, int, float, float)); +void wt1(float a[], unsigned long n, int isign, + void (*wtstep)(float [], unsigned long, int)); +void wtn(float a[], unsigned long nn[], int ndim, int isign, + void (*wtstep)(float [], unsigned long, int)); +void wwghts(float wghts[], int n, float h, + void (*kermom)(double [], double ,int)); +int zbrac(float (*func)(float), float *x1, float *x2); +void zbrak(float (*fx)(float), float x1, float x2, int n, float xb1[], + float xb2[], int *nb); +float zbrent(float (*func)(float), float x1, float x2, float tol); +void zrhqr(float a[], int m, float rtr[], float rti[]); +float zriddr(float (*func)(float), float x1, float x2, float xacc); +void zroots(fcomplex a[], int m, fcomplex roots[], int polish); + +#endif /* _NR_H_ */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/nrutil.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,321 @@ +/* CAUTION: This is the ANSI C (only) version of the Numerical Recipes + utility file nrutil.c. Do not confuse this file with the same-named + file nrutil.c that is supplied in the 'misc' subdirectory. + *That* file is the one from the book, and contains both ANSI and + traditional K&R versions, along with #ifdef macros to select the + correct version. *This* file contains only ANSI C. */ + +#include <stdio.h> +#include <stddef.h> +#include <stdlib.h> +#define NR_END 1 +#define FREE_ARG char* + +void nrerror(char error_text[]) +/* Numerical Recipes standard error handler */ +{ + fprintf(stderr,"Numerical Recipes run-time error...\n"); + fprintf(stderr,"%s\n",error_text); + fprintf(stderr,"...now exiting to system...\n"); + exit(1); +} + +float *vector(long nl, long nh) +/* allocate a float vector with subscript range v[nl..nh] */ +{ + float *v; + + v=(float *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(float))); + if (!v) nrerror("allocation failure in vector()"); + return v-nl+NR_END; +} + +int *ivector(long nl, long nh) +/* allocate an int vector with subscript range v[nl..nh] */ +{ + int *v; + + v=(int *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(int))); + if (!v) nrerror("allocation failure in ivector()"); + return v-nl+NR_END; +} + +unsigned char *cvector(long nl, long nh) +/* allocate an unsigned char vector with subscript range v[nl..nh] */ +{ + unsigned char *v; + + v=(unsigned char *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(unsigned char))); + if (!v) nrerror("allocation failure in cvector()"); + return v-nl+NR_END; +} + +unsigned long *lvector(long nl, long nh) +/* allocate an unsigned long vector with subscript range v[nl..nh] */ +{ + unsigned long *v; + + v=(unsigned long *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(long))); + if (!v) nrerror("allocation failure in lvector()"); + return v-nl+NR_END; +} + +double *dvector(long nl, long nh) +/* allocate a double vector with subscript range v[nl..nh] */ +{ + double *v; + + v=(double *)malloc((size_t) ((nh-nl+1+NR_END)*sizeof(double))); + if (!v) nrerror("allocation failure in dvector()"); + return v-nl+NR_END; +} + +float **matrix(long nrl, long nrh, long ncl, long nch) +/* allocate a float matrix with subscript range m[nrl..nrh][ncl..nch] */ +{ + long i, nrow=nrh-nrl+1,ncol=nch-ncl+1; + float **m; + + /* allocate pointers to rows */ + m=(float **) malloc((size_t)((nrow+NR_END)*sizeof(float*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + /* allocate rows and set pointers to them */ + m[nrl]=(float *) malloc((size_t)((nrow*ncol+NR_END)*sizeof(float))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for(i=nrl+1;i<=nrh;i++) m[i]=m[i-1]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +double **dmatrix(long nrl, long nrh, long ncl, long nch) +/* allocate a double matrix with subscript range m[nrl..nrh][ncl..nch] */ +{ + long i, nrow=nrh-nrl+1,ncol=nch-ncl+1; + double **m; + + /* allocate pointers to rows */ + m=(double **) malloc((size_t)((nrow+NR_END)*sizeof(double*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + /* allocate rows and set pointers to them */ + m[nrl]=(double *) malloc((size_t)((nrow*ncol+NR_END)*sizeof(double))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for(i=nrl+1;i<=nrh;i++) m[i]=m[i-1]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +int **imatrix(long nrl, long nrh, long ncl, long nch) +/* allocate a int matrix with subscript range m[nrl..nrh][ncl..nch] */ +{ + long i, nrow=nrh-nrl+1,ncol=nch-ncl+1; + int **m; + + /* allocate pointers to rows */ + m=(int **) malloc((size_t)((nrow+NR_END)*sizeof(int*))); + if (!m) nrerror("allocation failure 1 in matrix()"); + m += NR_END; + m -= nrl; + + + /* allocate rows and set pointers to them */ + m[nrl]=(int *) malloc((size_t)((nrow*ncol+NR_END)*sizeof(int))); + if (!m[nrl]) nrerror("allocation failure 2 in matrix()"); + m[nrl] += NR_END; + m[nrl] -= ncl; + + for(i=nrl+1;i<=nrh;i++) m[i]=m[i-1]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +float **submatrix(float **a, long oldrl, long oldrh, long oldcl, long oldch, + long newrl, long newcl) +/* point a submatrix [newrl..][newcl..] to a[oldrl..oldrh][oldcl..oldch] */ +{ + long i,j,nrow=oldrh-oldrl+1,ncol=oldcl-newcl; + float **m; + + /* allocate array of pointers to rows */ + m=(float **) malloc((size_t) ((nrow+NR_END)*sizeof(float*))); + if (!m) nrerror("allocation failure in submatrix()"); + m += NR_END; + m -= newrl; + + /* set pointers to rows */ + for(i=oldrl,j=newrl;i<=oldrh;i++,j++) m[j]=a[i]+ncol; + + /* return pointer to array of pointers to rows */ + return m; +} + +float **convert_matrix(float *a, long nrl, long nrh, long ncl, long nch) +/* allocate a float matrix m[nrl..nrh][ncl..nch] that points to the matrix +declared in the standard C manner as a[nrow][ncol], where nrow=nrh-nrl+1 +and ncol=nch-ncl+1. The routine should be called with the address +&a[0][0] as the first argument. */ +{ + long i,j,nrow=nrh-nrl+1,ncol=nch-ncl+1; + float **m; + + /* allocate pointers to rows */ + m=(float **) malloc((size_t) ((nrow+NR_END)*sizeof(float*))); + if (!m) nrerror("allocation failure in convert_matrix()"); + m += NR_END; + m -= nrl; + + /* set pointers to rows */ + m[nrl]=a-ncl; + for(i=1,j=nrl+1;i<nrow;i++,j++) m[j]=m[j-1]+ncol; + /* return pointer to array of pointers to rows */ + return m; +} + +double **convert_dmatrix(double *a, long nrl, long nrh, long ncl, long nch) +/* allocate a float matrix m[nrl..nrh][ncl..nch] that points to the matrix +declared in the standard C manner as a[nrow][ncol], where nrow=nrh-nrl+1 +and ncol=nch-ncl+1. The routine should be called with the address +&a[0][0] as the first argument. */ +{ + long i,j,nrow=nrh-nrl+1,ncol=nch-ncl+1; + double **m; + + /* allocate pointers to rows */ + m=(double **) malloc((size_t) ((nrow+NR_END)*sizeof(double*))); + if (!m) nrerror("allocation failure in convert_dmatrix()"); + m += NR_END; + m -= nrl; + + /* set pointers to rows */ + m[nrl]=a-ncl; + for(i=1,j=nrl+1;i<nrow;i++,j++) m[j]=m[j-1]+ncol; + /* return pointer to array of pointers to rows */ + return m; +} + +float ***f3tensor(long nrl, long nrh, long ncl, long nch, long ndl, long ndh) +/* allocate a float 3tensor with range t[nrl..nrh][ncl..nch][ndl..ndh] */ +{ + long i,j,nrow=nrh-nrl+1,ncol=nch-ncl+1,ndep=ndh-ndl+1; + float ***t; + + /* allocate pointers to pointers to rows */ + t=(float ***) malloc((size_t)((nrow+NR_END)*sizeof(float**))); + if (!t) nrerror("allocation failure 1 in f3tensor()"); + t += NR_END; + t -= nrl; + + /* allocate pointers to rows and set pointers to them */ + t[nrl]=(float **) malloc((size_t)((nrow*ncol+NR_END)*sizeof(float*))); + if (!t[nrl]) nrerror("allocation failure 2 in f3tensor()"); + t[nrl] += NR_END; + t[nrl] -= ncl; + + /* allocate rows and set pointers to them */ + t[nrl][ncl]=(float *) malloc((size_t)((nrow*ncol*ndep+NR_END)*sizeof(float))); + if (!t[nrl][ncl]) nrerror("allocation failure 3 in f3tensor()"); + t[nrl][ncl] += NR_END; + t[nrl][ncl] -= ndl; + + for(j=ncl+1;j<=nch;j++) t[nrl][j]=t[nrl][j-1]+ndep; + for(i=nrl+1;i<=nrh;i++) { + t[i]=t[i-1]+ncol; + t[i][ncl]=t[i-1][ncl]+ncol*ndep; + for(j=ncl+1;j<=nch;j++) t[i][j]=t[i][j-1]+ndep; + } + + /* return pointer to array of pointers to rows */ + return t; +} + +void free_vector(float *v, long nl, long nh) +/* free a float vector allocated with vector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_ivector(int *v, long nl, long nh) +/* free an int vector allocated with ivector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_cvector(unsigned char *v, long nl, long nh) +/* free an unsigned char vector allocated with cvector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_lvector(unsigned long *v, long nl, long nh) +/* free an unsigned long vector allocated with lvector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_dvector(double *v, long nl, long nh) +/* free a double vector allocated with dvector() */ +{ + free((FREE_ARG) (v+nl-NR_END)); +} + +void free_matrix(float **m, long nrl, long nrh, long ncl, long nch) +/* free a float matrix allocated by matrix() */ +{ + free((FREE_ARG) (m[nrl]+ncl-NR_END)); + free((FREE_ARG) (m+nrl-NR_END)); +} + +void free_dmatrix(double **m, long nrl, long nrh, long ncl, long nch) +/* free a double matrix allocated by dmatrix() */ +{ + free((FREE_ARG) (m[nrl]+ncl-NR_END)); + free((FREE_ARG) (m+nrl-NR_END)); +} + +void free_imatrix(int **m, long nrl, long nrh, long ncl, long nch) +/* free an int matrix allocated by imatrix() */ +{ + free((FREE_ARG) (m[nrl]+ncl-NR_END)); + free((FREE_ARG) (m+nrl-NR_END)); +} + +void free_submatrix(float **b, long nrl, long nrh, long ncl, long nch) +/* free a submatrix allocated by submatrix() */ +{ + free((FREE_ARG) (b+nrl-NR_END)); +} + +void free_convert_matrix(float **b, long nrl, long nrh, long ncl, long nch) +/* free a matrix allocated by convert_matrix() */ +{ + free((FREE_ARG) (b+nrl-NR_END)); +} + +void free_convert_dmatrix(double **b, long nrl, long nrh, long ncl, long nch) +/* free a matrix allocated by convert_matrix() */ +{ + free((FREE_ARG) (b+nrl-NR_END)); +} + +void free_f3tensor(float ***t, long nrl, long nrh, long ncl, long nch, + long ndl, long ndh) +/* free a float f3tensor allocated by f3tensor() */ +{ + free((FREE_ARG) (t[nrl][ncl]+ndl-NR_END)); + free((FREE_ARG) (t[nrl]+ncl-NR_END)); + free((FREE_ARG) (t+nrl-NR_END)); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/nrutil.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +/* CAUTION: This is the ANSI C (only) version of the Numerical Recipes + utility file nrutil.h. Do not confuse this file with the same-named + file nrutil.h that is supplied in the 'misc' subdirectory. + *That* file is the one from the book, and contains both ANSI and + traditional K&R versions, along with #ifdef macros to select the + correct version. *This* file contains only ANSI C. */ + +#ifndef _NR_UTILS_H_ +#define _NR_UTILS_H_ + +static float sqrarg; +#define SQR(a) ((sqrarg=(a)) == 0.0 ? 0.0 : sqrarg*sqrarg) + +static double dsqrarg; +#define DSQR(a) ((dsqrarg=(a)) == 0.0 ? 0.0 : dsqrarg*dsqrarg) + +static double dmaxarg1,dmaxarg2; +#define DMAX(a,b) (dmaxarg1=(a),dmaxarg2=(b),(dmaxarg1) > (dmaxarg2) ?\ + (dmaxarg1) : (dmaxarg2)) + +static double dminarg1,dminarg2; +#define DMIN(a,b) (dminarg1=(a),dminarg2=(b),(dminarg1) < (dminarg2) ?\ + (dminarg1) : (dminarg2)) + +static float maxarg1,maxarg2; +#define FMAX(a,b) (maxarg1=(a),maxarg2=(b),(maxarg1) > (maxarg2) ?\ + (maxarg1) : (maxarg2)) + +static float minarg1,minarg2; +#define FMIN(a,b) (minarg1=(a),minarg2=(b),(minarg1) < (minarg2) ?\ + (minarg1) : (minarg2)) + +static long lmaxarg1,lmaxarg2; +#define LMAX(a,b) (lmaxarg1=(a),lmaxarg2=(b),(lmaxarg1) > (lmaxarg2) ?\ + (lmaxarg1) : (lmaxarg2)) + +static long lminarg1,lminarg2; +#define LMIN(a,b) (lminarg1=(a),lminarg2=(b),(lminarg1) < (lminarg2) ?\ + (lminarg1) : (lminarg2)) + +static int imaxarg1,imaxarg2; +#define IMAX(a,b) (imaxarg1=(a),imaxarg2=(b),(imaxarg1) > (imaxarg2) ?\ + (imaxarg1) : (imaxarg2)) + +static int iminarg1,iminarg2; +#define IMIN(a,b) (iminarg1=(a),iminarg2=(b),(iminarg1) < (iminarg2) ?\ + (iminarg1) : (iminarg2)) + +#define SIGN(a,b) ((b) >= 0.0 ? fabs(a) : -fabs(a)) + +void nrerror(char error_text[]); +float *vector(long nl, long nh); +int *ivector(long nl, long nh); +unsigned char *cvector(long nl, long nh); +unsigned long *lvector(long nl, long nh); +double *dvector(long nl, long nh); +float **matrix(long nrl, long nrh, long ncl, long nch); +double **dmatrix(long nrl, long nrh, long ncl, long nch); +int **imatrix(long nrl, long nrh, long ncl, long nch); +float **submatrix(float **a, long oldrl, long oldrh, long oldcl, long oldch, + long newrl, long newcl); +float **convert_matrix(float *a, long nrl, long nrh, long ncl, long nch); +double **convert_dmatrix(double *a, long nrl, long nrh, long ncl, long nch); +float ***f3tensor(long nrl, long nrh, long ncl, long nch, long ndl, long ndh); +void free_vector(float *v, long nl, long nh); +void free_ivector(int *v, long nl, long nh); +void free_cvector(unsigned char *v, long nl, long nh); +void free_lvector(unsigned long *v, long nl, long nh); +void free_dvector(double *v, long nl, long nh); +void free_matrix(float **m, long nrl, long nrh, long ncl, long nch); +void free_dmatrix(double **m, long nrl, long nrh, long ncl, long nch); +void free_imatrix(int **m, long nrl, long nrh, long ncl, long nch); +void free_submatrix(float **b, long nrl, long nrh, long ncl, long nch); +void free_convert_matrix(float **b, long nrl, long nrh, long ncl, long nch); +void free_convert_dmatrix(double **b, long nrl, long nrh, long ncl, long nch); +void free_f3tensor(float ***t, long nrl, long nrh, long ncl, long nch, + long ndl, long ndh); + +#endif /* _NR_UTILS_H_ */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/quickscore.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function prob = quickscore(fpos, fneg, inhibit, prior, leak) +% QUICKSCORE Heckerman's algorithm for BN2O networks. +% prob = quickscore(fpos, fneg, inhibit, prior, leak) +% +% Consider a BN2O (Binary Node 2-layer Noisy-or) network such as QMR with +% dieases on the top and findings on the bottom. (We assume all findings are observed, +% since hidden leaves can be marginalized away.) +% This algorithm takes O(2^|fpos|) time to compute the marginal on all the diseases. +% +% Inputs: +% fpos = the positive findings (a vector of numbers in {1, ..., Nfindings}) +% fneg = the negative findings (a vector of numbers in {1, ..., Nfindings}) +% inhibit(i,j) = inhibition prob. for finding i, disease j, or 1.0 if j is not a parent. +% prior(j) = prior prob. disease j is ON. We assume prior(off) = 1-prior(on). +% leak(i) = inhibition prob. for the leak node for finding i +% +% Output: +% prob(d) = Pr(disease d = on | ev) +% +% For details, see +% - Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI89. +% - Rish and Dechter, "On the impact of causal independence", UCI tech report, 1998. +% +% Note that this algorithm is numerically unstable, since it adds a large number of positive and +% negative terms and hopes that some of them exactly cancel. +% +% For matlab experts, use 'mex' to compile C_quickscore, which has identical behavior to this function. + +[nfindings ndiseases] = size(inhibit); + +% make the first disease be always on, for the leak term +Pon = [1 prior(:)']; +Poff = 1-Pon; +Uon = [leak(:) inhibit]; % U(f,d) = Pr(f=0|d=1) +Uoff = [leak(:) ones(nfindings, ndiseases)]; % Uoff(f,d) = Pr(f=0|d=0) +ndiseases = ndiseases + 1; + +npos = length(fpos); +post = zeros(ndiseases, 2); +% post(d,1) = alpha Pr(d=off), post(d,2) = alpha Pr(d=m) + +FP = length(fpos); +%allbits = logical(dec2bitv(0:(2^FP - 1), FP)); +allbits = logical(ind2subv(2*ones(1,FP), 1:(2^FP))-1); + +for si=1:2^FP + bits = allbits(si,:); + fprime = fpos(bits); + fmask = zeros(1, nfindings); + fmask(fneg)=1; + fmask(fprime)=1; + fmask = logical(fmask); + p = 1; + pterm = zeros(1, ndiseases); + ptermOff = zeros(1, ndiseases); + ptermOn = zeros(1, ndiseases); + for d=1:ndiseases + ptermOff(d) = prod(Uoff(fmask,d)); + ptermOn(d) = prod(Uon(fmask,d)); + pterm(d) = Poff(d)*ptermOff(d) + Pon(d)*ptermOn(d); + end + p = prod(pterm); + sign = (-1)^(length(fprime)); + for d=1:ndiseases + myp = p / pterm(d); + post(d,1) = post(d,1) + sign*(myp * ptermOff(d)); + post(d,2) = post(d,2) + sign*(myp * ptermOn(d)); + end +end + +post(:,1) = post(:,1) .* Poff(:); +post(:,2) = post(:,2) .* Pon(:); +post = mk_stochastic(post); +prob = post(2:end,2)'; % skip the leak term + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/quickscore_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function engine = quickscore_inf_engine(inhibit, leak, prior) +% QUICKSCORE_INF_ENGINE Exact inference for the QMR network +% engine = quickscore_inf_engine(inhibit, leak, prior) +% +% We create an inference engine for QMR-like networks. +% QMR is a bipartite graph, where the top layer contains hidden disease nodes, +% and the bottom later contains observed finding nodes. +% The diseases have Bernoulli CPDs, the findings noisy-or CPDs. +% The original QMR (Quick Medical Reference) network has specific parameter values which we are not +% allowed to release, for commercial reasons. +% +% inhibit(f,d) = inhibition probability on f->d arc for disease d, finding f +% If inhibit(f,d) = 1, there is effectively no arc from d->f +% leak(j) = inhibition prob. on leak node -> finding j arc +% prior(i) = prob. disease i is on +% +% We use exact inference, which takes O(2^P) time, where P is the number of positive findings. +% For details, see +% - Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI 89. +% - Rish and Dechter, "On the impact of causal independence", UCI tech report, 1998. +% Note that this algorithm is numerically unstable, since it adds a large number of positive and +% negative terms and hopes that some of them exactly cancel. +% +% For an interesting variational approximation, see +% - Jaakkola and Jordan, "Variational probabilistic inference and the QMR-DT network", JAIR 10, 1999. +% +% See also +% - "Loopy belief propagation for approximate inference: an empirical study", +% K. Murphy, Y. Weiss and M. Jordan, UAI 99. + +engine.inhibit = inhibit; +engine.leak = leak; +engine.prior = prior; + +% store results here between enter_evidence and marginal_nodes +engine.post = []; + +engine = class(engine, 'quickscore_inf_engine'); % not a child of the inf_engine class!
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +/README/1.1.1.1/Sun May 11 15:39:50 2003// +/clq_containing_nodes.m/1.1.1.1/Wed May 29 11:59:46 2002// +/enter_evidence.m/1.1.1.1/Wed Mar 12 10:38:00 2003// +/marginal_difclq_nodes.m/1.1.1.1/Fri Feb 21 11:20:32 2003// +/marginal_nodes.m/1.1.1.1/Fri Feb 21 11:13:10 2003// +/marginal_singleclq_nodes.m/1.1.1.1/Wed Jan 29 11:23:58 2003// +/problems.txt/1.1.1.1/Wed May 29 11:59:46 2002// +/push.m/1.1.1.1/Mon Feb 10 15:38:04 2003// +/push_pot_toclique.m/1.1.1.1/Wed May 29 11:59:46 2002// +/stab_cond_gauss_inf_engine.m/1.1.1.1/Fri Mar 28 17:12:42 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/initialize_engine.m/1.1.1.1/Wed May 29 11:59:46 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@stab_cond_gauss_inf_engine/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/Old/initialize_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [engine, loglik] = initialize_engine(engine) +%initialize +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +N = length(bnet.dag); + +pot_type = 'scg' +check_for_cd_arcs([], bnet.cnodes, bnet.dag); + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N); +C = length(engine.cliques); +inited = zeros(1, C); +clpot = cell(1, C); +evidence = cell(1, N); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + pot{n} = CPD_to_scgpot(bnet.CPD{e}, fam, ns, bnet.cnodes, evidence); + cindex = engine.clq_ass_to_node(n); + if inited(cindex) + %clpot{cindex} = direct_combine_pots(clpot{cindex}, pot{n}); + clpot{cindex} = direct_combine_pots(pot{n}, clpot{cindex}); + else + clpot{cindex} = pot{n}; + inited(cindex) = 1; + end +end + +for i=1:C + if inited(i) == 0 + clpot{i} = scgpot([], [], [], []); + end +end + +seppot = cell(C, C); +% separators are is not need to initialize + +% collect to root (node to parents) +for n=engine.postorder(1:end-1) + for p=parents(engine.jtree, n) + [margpot, comppot] = complement_pot(clpot{n}, engine.separator{p,n}); + margpot = marginalize_pot(clpot{n}, engine.separator{p,n}); + clpot{n} = comppot; + %seppot{p, n} = margpot; + clpot{p} = combine_pots(clpot{p}, margpot); + %clpot{p} = combine_pots(margpot, clpot{p}); + end +end + +temppot = clpot; +%temppot = clpot{engine.root}; +for n=engine.preorder + for c=children(engine.jtree, n) + seppot{n,c} = marginalize_pot(temppot{n}, engine.separator{n,c}); + %seppot{n,c} = marginalize_pot(clpot{n}, engine.separator{n,c}); + %clpot{c} = direct_combine_pots(clpot{c}, seppot{n,c}); + temppot{c} = direct_combine_pots(temppot{c}, seppot{n,c}); + end +end + +engine.clpot = clpot; +engine.seppot = seppot; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +% Stable conditional Gaussian inference +% Originally written by Huang, Shan <shan.huang@intel.com> 2001 +% Fixed by Rainer Deventer 2003 + + +@techreport{Lauritzen99, + author = "S. Lauritzen and F. Jensen", + title = "Stable Local Computation with Conditional {G}aussian Distributions", + year = 1999, + number = "R-99-2014", + institution = "Dept. Math. Sciences, Aalborg Univ." +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/clq_containing_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function c = clq_containing_nodes(engine, nodes, fam) +% CLQ_CONTAINING_NODES Find the lightest clique (if any) that contains the set of nodes +% c = clq_containing_nodes(engine, nodes, family) +% +% If the optional 'family' argument is specified, it means nodes = family(nodes(end)). +% (This is useful since clq_ass_to_node is not accessible to outsiders.) +% Returns c=-1 if there is no such clique. + +if nargin < 3, fam = 0; else fam = 1; end + +if length(nodes)==1 + c = engine.clq_ass_to_node(nodes(1)); +elseif fam + c = engine.clq_ass_to_node(nodes(end)); +else + B = engine.cliques_bitv; + w = engine.clique_weight; + clqs = find(all(B(:,nodes), 2)); % all selected columns must be 1 + if isempty(clqs) + c = -1; + else + c = clqs(argmin(w(clqs))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,260 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE enter evidence to engine including discrete and continuous evidence +% [engine, ll] = enter_evidence(engine, evidence) +% +% ll is always 0, which is wrong. + +if ~isempty(engine.evidence) + bnet = bnet_from_engine(engine); + engine = stab_cond_gauss_inf_engine(bnet); + engine.evidence = evidence; +else + engine.evidence = evidence; + bnet = bnet_from_engine(engine); +end + +engine.evidence = evidence; +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes(:); +observed = ~isemptycell(evidence); +onodes = find(observed); +hnodes = find(isemptycell(evidence)); +cobs = myintersect(bnet.cnodes, onodes); +dobs = myintersect(bnet.dnodes, onodes); + +engine = incorporate_dis_evidence(engine, dobs, evidence); +l = length(cobs); +for i = 1:l + node = cobs(i); + engine = incorporate_singleconts_evidence(engine, node, evidence); +end +clpot = engine.clpot; + +clq_num = length(engine.cliques); +for n=engine.postorder(1:end-1) + for p=parents(engine.jtree, n) + [margpot, comppot] = complement_pot(clpot{n}, engine.separator{p,n}); + clpot{n} = comppot; + clpot{p} = combine_pots(clpot{p}, margpot); + end +end + +temppot = clpot; +for n=engine.preorder + for c=children(engine.jtree, n) + seppot{n,c} = marginalize_pot(temppot{n}, engine.separator{n,c}); + temppot{c} = direct_combine_pots(temppot{c}, seppot{n,c}); + end +end +engine.clpot = clpot; +engine.seppot = seppot; + +[pot,loglik]=normalize_pot(clpot{engine.root}); + +%%%%%%%%%%%%%%%%%% +function engine = incorporate_dis_evidence(engine, donodes, evidence) +l = length(donodes); +for i=donodes(:)' + node = i; + clqid = engine.clq_ass_to_node(node); + pot = struct(engine.clpot{clqid}); + ns = zeros(1, max(pot.domain)); + ns(pot.ddom) = pot.dsizes; + ns(pot.cheaddom) = pot.cheadsizes; + ns(pot.ctaildom) = pot.ctailsizes; + ddom = pot.ddom; + + potcarray = cell(1, pot.dsize); + for j =1:pot.dsize + tpotc = struct(pot.scgpotc{j}); + potcarray{j} = scgcpot(tpotc.cheadsize, tpotc.ctailsize, 0, tpotc.A, tpotc.B, tpotc.C); + end + + if length(ns(ddom)) == 1 + matrix = pot.scgpotc; + else + matrix = reshape(pot.scgpotc,ns(ddom)); + potcarray = reshape(potcarray, ns(ddom)); + end + + map = find_equiv_posns(node, ddom); + vals = cat(1, evidence{node}); + index = mk_multi_index(length(ddom), map, vals); + potcarray(index{:}) = matrix(index{:}); + potcarray = potcarray(:); + %keyboard; + engine.clpot{clqid} = scgpot(pot.ddom, pot.cheaddom, pot.ctaildom, ns, potcarray); +end + +%%%%%%%%%%%%%%%%%% +function engine = incorporate_singleconts_evidence(engine, node, evidence) +%incorporate_singleconts_evidence incorporate evidence of 1 continuous node +B = engine.cliques_bitv; +clqs_containnode = find(all(B(:,node), 2)); % all selected columns must be 1 +% Every continuous node necessarily apears as head in exactly one clique, +% which is the clique where it appears closest to the strong root. In all other +% clique potentials where it appears, it must be a tail node. +clq_ev_as_head = []; +for i = clqs_containnode(:)' + pot = struct(engine.clpot{i}); + if myismember(node, pot.cheaddom) + clq_ev_as_head = [clq_ev_as_head i]; + break; + end +end + +% If we will incorporate the evidence node which is head of a potential we must rearrange +% the juntion tree by push operation until the tail of the include potential is empty +if ~isempty(clq_ev_as_head) + assert(1 == length(clq_ev_as_head)); + i = clq_ev_as_head; + pot = struct(engine.clpot{i}); + while ~isempty(pot.ctaildom) + [engine, clqtoroot] = push(engine, i, node); + i = clqtoroot; + pot = struct(engine.clpot{i}); + end + B = engine.cliques_bitv; + clqs_containnode = find(all(B(:,node), 2)); +end + +for i = clqs_containnode(:)' + pot = struct(engine.clpot{i}); + if myismember(node, pot.cheaddom) + engine.clpot{i} = incoporate_evidence_headnode(engine.clpot{i}, node, evidence); + else + %assert(myismember(node, pot.ctaildom)); + engine.clpot{i} = incoporate_evidence_tailnode(engine.clpot{i}, node, evidence); + end +end + +%%%%%%%%%%%%%%%%%% +function newscgpot = incoporate_evidence_tailnode(pot, node, evidence) +%ENTER_EVIDENCE_TAILNODE enter the evidence of 1 tailnode of the scgpot +newscgpot = pot; +pot = struct(pot); +%if isempty(pot.ctaildom) +if ~myismember(node, pot.ctaildom) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % In this case there is no real dependency of the head nodes % + % on the tail. The potential should be returned unchanged % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + return; +end +%newscgpot = scgpot([], [], [], []); +assert(myismember(node, pot.ctaildom)); +ni = block(find_equiv_posns(node, pot.ctaildom), pot.ctailsizes); + +ctaildom = mysetdiff(pot.ctaildom, node); +cheaddom = pot.cheaddom; +ddom = pot.ddom; +domain = mysetdiff(pot.domain, node); +dsize = pot.dsize; +ns = zeros(1, max(pot.domain)); +ns(pot.ddom) = pot.dsizes; +ns(pot.cheaddom) = pot.cheadsizes; +ns(pot.ctaildom) = pot.ctailsizes; +cheadsizes = pot.cheadsizes; +cheadsize = pot.cheadsize; +ctailsizes = ns(ctaildom); +ctailsize = sum(ns(ctaildom)); + +potarray = cell(1, dsize); +for i=1:dsize + potc = struct(pot.scgpotc{i}); + B = potc.B; + A = potc.A + B(:, ni)*evidence{node}; + B(:, ni) = []; + potarray{i} = scgcpot(cheadsize, ctailsize, potc.p, A, B, potc.C); +end + +newscgpot = scgpot(ddom, cheaddom, ctaildom, ns, potarray); + +%%%%%%%%%%%%%%%% +function newscgpot = incoporate_evidence_headnode(pot, node, evidence) +%ENTER_EVIDENCE_HEADNODE +pot = struct(pot); +y2 = evidence{node}; +assert(myismember(node, pot.cheaddom)); +assert(isempty(pot.ctaildom)); +ddom = pot.ddom; +cheaddom = mysetdiff(pot.cheaddom, node); +ctaildom = pot.ctaildom; +dsize = pot.dsize; +domain = mysetdiff(pot.domain, node); + +ns = zeros(1, max(pot.domain)); +ns(pot.ddom) = pot.dsizes; +ns(pot.cheaddom) = pot.cheadsizes; +ns(pot.ctaildom) = pot.ctailsizes; +ctailsizes = ns(ctaildom); +ctailsize = sum(ctailsizes); +cheadsizes = ns(cheaddom); +cheadsize = sum(cheadsizes); +onodesize = ns(node); + +p = zeros(1,dsize); +A1 = zeros(cheadsize, dsize); +A2 = zeros(onodesize, dsize); +C11 = zeros(cheadsize, cheadsize, dsize); +C12 = zeros(cheadsize, onodesize, dsize); +C21 = zeros(onodesize, cheadsize, dsize); +C22 = zeros(onodesize, onodesize, dsize); +ZM = zeros(onodesize, onodesize); + +n1i = block(find_equiv_posns(cheaddom, pot.cheaddom), pot.cheadsizes); +n2i = block(find_equiv_posns(node, pot.cheaddom), pot.cheadsizes); + +indic = 0; +for i=1:dsize + potc = struct(pot.scgpotc{i}); + p(i) = potc.p; + if ~isempty(n1i) + A1(:,i) = potc.A(n1i); + end + if ~isempty(n2i) + A2(:,i) = potc.A(n2i); + end + C11(:,:,i) = potc.C(n1i, n1i); + C12(:,:,i) = potc.C(n1i, n2i); + C21(:,:,i) = potc.C(n2i, n1i); + C22(:,:,i) = potc.C(n2i, n2i); + if isequal(0, C22(:,:,i)) & isequal(evidence{node}, A2(:, i)) + indic = i; + end +end + +np = zeros(1,dsize); +nA = zeros(cheadsize, dsize); +nC = zeros(cheadsize, cheadsize, dsize); + +if indic + np(:) = 0; + np(indic) = p(indic); + nA = A1; + nC = C11; +else + for i=1:dsize + if isequal(0, C22(:,:,i)) + p(i) = 0; + nA(:, i) = A1(:, i); + nC(:,:,i) = C11(:,:,i); + else + sq = (y2 - A2(:,i))' * inv(C22(:,:,i)) * (y2 - A2(:,i)); + ex = exp(-0.5*sq); + %np(i) = p(i) * ex / ( (2 * pi)^(-onodesize/2) * sqrt(det(C22(:,:,i))) ); + np(i) = p(i) * ex / ( (2 * pi)^(onodesize/2) * sqrt(det(C22(:,:,i))) ); + nA(:,i) = A1(:,i) + C12(:,:,i) * inv(C22(:,:,i)) * (y2 - A2(:,i)); + tmp1 = C12(:,:,i) * inv(C22(:,:,i)) * C21(:,:,i); + nC(:,:,i) = C11(:,:,i) - tmp1; + end + end +end + +scpot = cell(1, dsize); +W = zeros(cheadsize,ctailsize); +for i=1:dsize + scpot{i} = scgcpot(cheadsize, ctailsize, np(i), nA(:,i), W, nC(:,:,i)); +end +ns(node) = 0; +newscgpot = scgpot(ddom, cheaddom, ctaildom, ns, scpot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/marginal_difclq_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function marginal = marginal_difclq_nodes(engine, query_nodes) +% MARGINAL_DIFCLQ_NODES get the marginal distribution of nodes which is not in a single clique +% marginal = marginal_difclq_nodes(engine, query_nodes) + +keyboard +num_clique = length(engine.cliques); +B = engine.cliques_bitv; +clqs_containnodes = []; +for i=1:length(query_nodes) + node = query_nodes(i); + tnodes = find(all(B(:, node), 2)); + clqs_containnodes = myunion(clqs_containnodes, tnodes); +end +% get all cliques contains query nodes + +% get the minimal sub tree in junction which contains these cliques and the node closest to the root of jtree +[subtree, nroot_node] = min_subtree_conti_nodes(engine.jtree, engine.root, clqs_containnodes); +if ~mysubset(query_nodes, engine.cliques{nroot_node}); + % if query nodes is not all memers of the clique closest to the root clique performe push operation + engine = push_tree(engine, subtree, query_nodes, nroot_node); +end + +if ~(nroot_node == engine.root) + % if the clique closest to the root clique is not the root clique we must direct combine the + % potential with the potential stored in separator toward to root + p = parents(engine.jtree, nroot_node); + tpot = direct_combine_pots(engine.clpot{nroot_node}, engine.seppot{p, nroot_node}); +else + tpot = engine.clpot{nroot_node}; +end + +pot = marginalize_pot(tpot, query_nodes); +marginal = pot_to_marginal(pot); +marginal.T = normalise(marginal.T); + + + +function engine = push_tree(engine, tree, query_nodes, inode) +% PUSH_TREE recursive perform push opeartion on tree +% engine = push_tree(engine, tree, query_nodes, inode) + +cs = children(tree, inode); +for i = 1:length(cs) + node = cs(i); + push_tree(engine, tree, query_nodes, node); + push_dom = myintersect(engine.cliques{node}, query_nodes); + [engine, clqtoroot] = push(engine, node, push_dom); +end + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,77 @@ +function marginal = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (stab_cond_gauss) +% marginal = marginal_nodes(engine, query, add_ev) +% +% 'query' must be a singleton set. +% add_ev is an optional argument; if 1, we will "inflate" the marginal of observed nodes +% to their original size, adding 0s to the positions which contradict the evidence + +if nargin < 3, add_ev = 0; end +if isempty(engine.evidence) + hquery = query; +else + hquery = []; + for i = query + if isempty(engine.evidence{i}) + hquery = [hquery i]; + end + end +end + +bnet = bnet_from_engine(engine); + +nclq = length(engine.cliques); +clique = 0; +for i = 1:nclq + if mysubset(hquery, engine.cliques{i}) + pot = struct(engine.clpot{i}); + %if mysubset(hquery, pot.cheaddom) | mysubset(hquery, pot.ddom) + if mysubset(hquery, pot.domain) + clique = i; + break; + end + end +end + +if isempty(hquery) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % If all requested variables are observed, no query is necessary % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + marginal.mu = []; + marginal.Sigma = []; + marginal.T = 1.0; + marginal.domain = query; +else + if clique == 0 + marginal = marginal_difclq_nodes(engine, hquery); + else + marginal = marginal_singleclq_nodes(engine, clique, hquery); + end + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Change the format of output, so that it is identical to the % + % format obtained by the same request for the junction-tree % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + marginal.domain = query; + bnet = bnet_from_engine(engine); + dquery = myintersect(bnet.dnodes,hquery); + ns = bnet.node_sizes(dquery); + if length(ns) == 0 + marginal.T = 1; + else + if length(ns) == 1 + ns = [1 ns]; + end + marginal.T = reshape(marginal.T,ns); + end +end +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/marginal_singleclq_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function marginal = marginal_singleclq_nodes(engine, i, query) +% MARGINAL_SINGLECLQ_NODES get the marginal distribution of nodes which is in a single clique +% marginal = marginal_singleclq_nodes(engine, i, query) + +pot = struct(engine.clpot{i}); +if isempty(pot.ctaildom) + if i ~= engine.root + p = parents(engine.jtree, i); + tpot = direct_combine_pots(engine.clpot{i}, engine.seppot{p, i}); + else + tpot = engine.clpot{i}; + end + pot = marginalize_pot(tpot, query); + + marginal = pot_to_marginal(pot); + marginal.T = normalise(marginal.T); +else + [engine, clqtoroot] = push(engine, i, query); + if clqtoroot == engine.root + tpot = engine.clpot{clqtoroot}; + else + p = parents(engine.jtree, clqtoroot); + tpot = direct_combine_pots(engine.clpot{clqtoroot}, engine.seppot{p, clqtoroot}); + end + pot = marginalize_pot(tpot, query); + + marginal = pot_to_marginal(pot); + marginal.T = normalise(marginal.T); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/problems.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +PROBLEMS WITH STAB_COND_GAUSS_INF_ENGINE + + +- enter_evidence always returns ll=0 + (I set ll=0 since it is not computed) + +- fails on scg_3node, probably because the engine needs to be +re-initialized every time before enter_evidence is called, not just +when the engine is constructed. + +??? Error using ==> assert +assertion violated: + +K>> dbstack +dbstack +> In /home/eecs/murphyk/matlab/BNT/HMM/assert.m at line 9 + In /home/eecs/murphyk/matlab/BNT/examples/static/SCG/scg_3node.m at line 45 + + + +- crashes on scg3 + +Error in ==> /home/eecs/murphyk/matlab/BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m +On line 77 ==> clpot{cindex} = direct_combine_pots(pot{n}, clpot{cindex}); + +K>> dbstack +dbstack +> In /home/eecs/murphyk/matlab/BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m at line 77 + In /home/eecs/murphyk/matlab/BNT/examples/static/SCG/scg3.m at line 41 +K>> + + + + + +- fails on scg1 and scg2 + +Warning: One or more output arguments not assigned during call to 'min_subtree_conti_nodes (nearsest_node2)'. +Warning in ==> /home/eecs/murphyk/matlab/BNT/graph/min_subtree_conti_nodes.m (nearsest_node2) +On line 60 ==> nea_node = nearsest_node2(tree, nodes, n); + +K>> dbstack +dbstack +> In /home/eecs/murphyk/matlab/BNT/graph/min_subtree_conti_nodes.m (nearsest_node2) at line 60 + In /home/eecs/murphyk/matlab/BNT/graph/min_subtree_conti_nodes.m (nearest_node) at line 50 + In /home/eecs/murphyk/matlab/BNT/graph/min_subtree_conti_nodes.m at line 11 + In /home/eecs/murphyk/matlab/BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_difclq_nodes.m at line 17 + In /home/eecs/murphyk/matlab/BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_nodes.m at line 23 + In /home/eecs/murphyk/matlab/BNT/examples/static/SCG/scg1.m at line 42 + + + + + +- This code fragment, from BNT/graph/min_subtree_conti_nodes, is clearly redundant + +function nea_node = nearest_node(tree, root, nodes) +%get the nearest node to the root in the tree +nea_node = nearsest_node2(tree, nodes, root); + +function nea_node = nearsest_node2(tree, nodes, inode) +if myismember(inode, nodes) + nea_node = inode; + return; +end +cs = children(tree, inode); +for i = 1:length(cs) + n = cs(i); + nea_node = nearsest_node2(tree, nodes, n); +end + + +- Some names are badly chosen. 'nearsest' is a mis-spelling. 'min_subtree_conti_nodes' should be +'min_subtree_containing_nodes' or 'min_subtree_con_nodes'. + +- In general, the code needs some heavy polishing.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/push.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function [engine, clqtoroot] = push(engine, clq, pushdom) +%PUSH_POT push the variables in putshdom which is subset of clq to the clique toword the root and get new engine +%pushdom is pushed variables set +%clq is the index of the clique that pushdom belongs to + +clqdom = engine.cliques{clq}; +assert( mysubset(pushdom, clqdom)); +clqtoroot = parents(engine.jtree, clq); +%sepdom = engine.separator{clq, clqtoroot}; +sepdom = engine.separator{clqtoroot, clq}; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculate the strong marginal of the union of pushdom and and the separatordomain and % +% the corresponding complement % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%[margpot, comppot] = complement_pot(engine.clpot{clq}, pushdom); +newsepdom = myunion(pushdom,sepdom); +[margpot,comppot] = complement_pot(engine.clpot{clq}, newsepdom); +engine.clpot{clqtoroot} = direct_combine_pots(engine.clpot{clqtoroot}, margpot); +engine.clpot{clq} = comppot; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculation of the new separator and separatorpotential of the junction tree % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +engine.seppot{clqtoroot, clq} = direct_combine_pots(engine.seppot{clqtoroot, clq}, margpot); +engine.separator{clqtoroot, clq} = myunion(engine.separator{clqtoroot, clq}, pushdom); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Add pushdomain to the clique towards the root % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +engine.cliques{clqtoroot} = myunion(engine.cliques{clqtoroot}, pushdom); + +num_cliques = length(engine.cliques); +B = sparse(num_cliques, 1); +for i=1:num_cliques + B(i, engine.cliques{i}) = 1; +end +engine.cliques_bitv = B;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/push_pot_toclique.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function engine = push_pot_toclique(engine, clqtarget, clq, nodes) +% PUSH_POT push the variables in putshdom which is subset of clq to the target clique toword the root and get new engine +% engine = push_pot_toclique(engine, clqtarget, clq, nodes) +[engine, clqtoroot] = push_pot(engine, clq, nodes) +while clqtoroot ~= clqtarget + [engine, clqtoroot] = push_pot(engine, clqtoroot, nodes) +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,178 @@ +function engine = stab_cond_gauss_inf_engine(bnet) +% STAB_COND_GAUSS_INF_ENGINE Junction tree using stable CG potentials +% engine = cond_gauss_inf_engine(bnet) +% +% This class was written by Shan Huang (shan.huang@intel.com) 2001 +% and fixed by Rainer Deventer deventer@informatik.uni-erlangen.de March 2003 +N = length(bnet.dag); +clusters = {}; +root = N; +stages = { 1:N }; +onodes = []; +engine = init_fields; +engine.evidence = []; +engine = class(engine, 'stab_cond_gauss_inf_engine', inf_engine(bnet)); + +ns = bnet.node_sizes(:); +ns(onodes) = 1; % observed nodes have only 1 possible value + +%[engine.jtree, dummy, engine.cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(bnet, onodes, stages, clusters); + + +partial_order = determine_elim_constraints(bnet, onodes); +strong = ~isempty(partial_order); +stages = {}; +clusters = {}; +[engine.jtree, dummy_root, engine.cliques, B, w, elim_order] = + graph_to_jtree(moralize(bnet.dag), ns, partial_order, stages, clusters); + + +engine.cliques_bitv = B; +engine.clique_weight = w; +C = length(engine.cliques); +engine.clpot = cell(1,C); + +% A node can be a member of many cliques, but is assigned to exactly one, to avoid +% double-counting its CPD. We assign node i to clique c if c is the "lightest" clique that +% contains i's family, so it can accomodate its CPD. + +engine.clq_ass_to_node = zeros(1, N); +num_cliques = length(engine.cliques); +for i=1:N + clqs_containing_family = find(all(B(:,family(bnet.dag, i)), 2)); % all selected columns must be 1 + c = clqs_containing_family(argmin(w(clqs_containing_family))); + engine.clq_ass_to_node(i) = c; +end + +% Compute the separators between connected cliques. +[is,js] = find(engine.jtree > 0); +engine.separator = cell(num_cliques, num_cliques); +for k=1:length(is) + i = is(k); j = js(k); + engine.separator{i,j} = find(B(i,:) & B(j,:)); % intersect(cliques{i}, cliques{j}); +end +%keyboard; +engine.seppot = cell(C,C); + +pot_type = 'scg'; +check_for_cd_arcs([], bnet.cnodes, bnet.dag); + +% Make the jtree rooted, so there is a fixed message passing order. +if strong + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Start the search for the strong root at the clique with the % + % highest number. % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + root = length(engine.cliques); + root_found = 0; + + while ((~root_found) & (root >= 1)) + root_found = test_strong_root(engine.jtree,engine.cliques,bnet.dnodes,root); + if ~root_found + root = root - 1; + end + end + assert(root > 0) + engine.root = root; + % the last clique is guaranteed to be a strong root + %engine.root = length(engine.cliques); +else + % jtree_dbn_inf_engine requires the root to contain the interface. + % This may conflict with the strong root requirement! *********** BUG ************* + engine.root = clq_containing_nodes(engine, root); + if engine.root <= 0 + error(['no clique contains ' num2str(root)]); + end +end + +[engine.jtree, engine.preorder, engine.postorder] = mk_rooted_tree(engine.jtree, engine.root); + +% Evaluate CPDs with evidence, and convert to potentials +pot = cell(1, N); +inited = zeros(1, C); +clpot = cell(1, C); +evidence = cell(1, N); +for n=1:N + fam = family(bnet.dag, n); + e = bnet.equiv_class(n); + %pot{n} = CPD_to_scgpot(bnet.CPD{e}, fam, ns, bnet.cnodes, evidence); + pot{n} = convert_to_pot(bnet.CPD{e}, pot_type, fam(:), evidence); + cindex = engine.clq_ass_to_node(n); + if inited(cindex) + clpot{cindex} = direct_combine_pots(pot{n}, clpot{cindex}); + else + clpot{cindex} = pot{n}; + inited(cindex) = 1; + end +end + +for i=1:C + if inited(i) == 0 + clpot{i} = scgpot([], [], [], []); + end +end + +seppot = cell(C, C); +% separators are is not need to initialize + +% collect to root (node to parents) +% Unlike the HUGIN architecture the complements are stored in the cliques during COLLECT +% and the separators are not playing a specific role during this process +for n=engine.postorder(1:end-1) + for p=parents(engine.jtree, n) + if ~isempty(engine.separator{p,n}) + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % The empty case might happen for unlinked nodes, i.e. the DAG is not % + % a single tree, but a forest % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + [margpot, comppot] = complement_pot(clpot{n}, engine.separator{p,n}); + clpot{n} = comppot; + clpot{p} = combine_pots(clpot{p}, margpot); + end + end +end + +% distribute message from root +% We have not to store the weak clique marginals and keep the original complement potentials. +% This is a minor variation of HUGIN architecture. +temppot = clpot; +for n=engine.preorder + for c=children(engine.jtree, n) + seppot{n,c} = marginalize_pot(temppot{n}, engine.separator{n,c}); + temppot{c} = direct_combine_pots(temppot{c}, seppot{n,c}); + end +end + +engine.clpot = clpot; +engine.seppot = seppot; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% init_fields() % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function engine = init_fields() + +engine.evidence = []; +engine.jtree = []; +engine.cliques = []; +engine.cliques_bitv = []; +engine.clique_weight = []; +engine.preorder = []; +engine.postorder = []; +engine.root = []; +engine.clq_ass_to_node = []; +engine.separator = []; +engine.clpot =[]; +engine.seppot = []; + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@var_elim_inf_engine/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +/enter_evidence.m/1.1.1.1/Wed Jun 19 22:05:04 2002// +/find_mpe.m/1.1.1.1/Wed Jun 19 22:11:42 2002// +/marginal_nodes.m/1.1.1.1/Thu Sep 30 03:09:00 2004// +/var_elim_inf_engine.m/1.1.1.1/Wed Jun 19 22:04:50 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@var_elim_inf_engine/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static/@var_elim_inf_engine
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@var_elim_inf_engine/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@var_elim_inf_engine/enter_evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function [engine, loglik] = enter_evidence(engine, evidence, varargin) +% ENTER_EVIDENCE Add the specified evidence to the network (var_elim) +% [engine, loglik] = enter_evidence(engine, evidence, ...) +% +% evidence{i} = [] if if X(i) is hidden, and otherwise contains its observed value (scalar or column vector) + +% we could pre-process the evidence here, to prevent repeated work, but we don't. +engine.evidence = evidence; + +if nargout == 2 + [m, loglik] = marginal_nodes(engine, [1]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@var_elim_inf_engine/find_mpe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,163 @@ +function mpe = find_mpe(engine, new_evidence, max_over) +% FIND_MPE Find the most probable explanation of the data (assignment to the hidden nodes) +% function mpe = find_mpe(engine, evidence, order) +% +% PURPOSE: +% CALC_MPE Computes the most probable explanation to the network nodes +% given the evidence. +% +% [mpe, ll] = calc_mpe(engine, new_evidence, max_over) +% +% INPUT: +% bnet - the bayesian network +% new_evidence - optional, if specified - evidence to be incorporated [cell(1,n)] +% max_over - optional, if specified determines the variable elimination order [1:n] +% +% OUTPUT: +% mpe - the MPE assignmet for the net variables (or [] if no satisfying assignment) +% ll - log assignment probability. +% +% Notes: +% 1. Adapted from '@var_elim_inf_engine\marginal_nodes' for MPE by Ron Zohar, 8/7/01 +% 2. Only discrete potentials are supported at this time. +% 3. Complexity: O(nw*) where n is the number of nodes and w* is the induced tree width. +% 4. Implementation based on: +% - R. Dechter, "Bucket Elimination: A Unifying Framework for Probabilistic Inference", +% UA1 96, pp. 211-219. + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +n = length(bnet.dag); +evidence = cell(1,n); +if (nargin<2) + new_evidence = evidence; +end + +onodes = find(~isemptycell(new_evidence)); % observed nodes +hnodes = find(isemptycell(new_evidence)); % hidden nodes +pot_type = determine_pot_type(bnet, onodes); + +if pot_type ~= 'd' + error('only disrete potentials supported at this time') +end + +for i=1:n + fam = family(bnet.dag, i); + CPT{i} = convert_to_pot(bnet.CPD{bnet.equiv_class(i)}, pot_type, fam(:), evidence); +end + +% handle observed nodes: set impossible cases' probability to zero +% rather than prun matrix (this makes backtracking easier) + +for ii=onodes + lIdx = 1:ns(ii); + lIdx = setdiff(lIdx, new_evidence{ii}); + + sCPT=struct(CPT{ii}); % violate object privacy + + sargs = ''; + for jj=1:(length(sCPT.domain)-1) + sargs = [sargs, ':,']; + end + for jj=lIdx + eval(['sCPT.T(', sargs, num2str(jj), ')=0;']); + end + CPT{ii}=dpot(sCPT.domain, sCPT.sizes, sCPT.T); +end + +B = cell(1,n); +for b=1:n + B{b} = mk_initial_pot(pot_type, [], [], [], []); +end + +if (nargin<3) + max_over = (1:n); +end +order = max_over; % no attempt to optimize this + + +% Initialize the buckets with the CPDs assigned to them +for i=1:n + b = bucket_num(domain_pot(CPT{i}), order); + B{b} = multiply_pots(B{b}, CPT{i}); +end + +% Do backward phase +max_over = max_over(length(max_over):-1:1); % reverse +maximize = 1; +for i=max_over(1:end-1) + % max-ing over variable i which occurs in bucket j + j = bucket_num(i, order); + rest = mysetdiff(domain_pot(B{j}), i); + %temp = marginalize_pot_max(B{j}, rest); + temp = marginalize_pot(B{j}, rest, maximize); + b = bucket_num(domain_pot(temp), order); + % fprintf('maxing over bucket %d (var %d), putting result into bucket %d\n', j, i, b); + sB=struct(B{b}); % violate object privacy + if ~isempty(sB.domain) + B{b} = multiply_pots(B{b}, temp); + else + B{b} = temp; + end +end +result = B{1}; +marginal = pot_to_marginal(result); +[prob, mpe] = max(marginal.T); + +% handle impossible cases +if ~(prob>0) + mpe = []; + ll = -inf; + %warning('evidence has zero probability') + return +end + +ll = log(prob); + +% Do forward phase +for ii=2:n + marginal = pot_to_marginal(B{ii}); + mpeidx = []; + for jj=order(1:length(mpe)) + %assert(ismember(jj, marginal.domain)) %%% bug + temp = find_equiv_posns(jj, marginal.domain); + mpeidx = [mpeidx, temp] ; + if isempty(temp) + mpeidx = [mpeidx, Inf] ; + end + end + [mpeidxsorted sortedtompe] = sort(mpeidx) ; + + % maximize the matrix obtained from assigning values from previous buckets. + % this is done by building a string and using eval. + + kk=1; + sargs = '('; + for jj=1:length(marginal.domain) + if (jj~=1) + sargs = [sargs, ',']; + end + if (mpeidxsorted(kk)==jj) + sargs = [sargs, num2str(mpe(sortedtompe(kk)))]; + if (kk<length(mpe)) + kk = kk+1 ; + end + else + sargs = [sargs, ':']; + end + end + sargs = [sargs, ')'] ; + eval(['[val, loc] = max(marginal.T', sargs, ');']) + mpe = [mpe loc]; +end +[I,J] = sort(order); +mpe = mpe(J); + +mpe = num2cell(mpe); + +%%%%%%%%% + +function b = bucket_num(domain, order) + +b = max(find_equiv_posns(domain, order)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@var_elim_inf_engine/marginal_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +function [marginal, loglik] = marginal_nodes(engine, query, add_ev) +% MARGINAL_NODES Compute the marginal on the specified query nodes (var_elim) +% [marginal, loglik] = marginal_nodes(engine, query) + +if nargin < 3, add_ev = 0; end + +assert(length(query)>=1); + +evidence = engine.evidence; + +bnet = bnet_from_engine(engine); +ns = bnet.node_sizes; +n = length(bnet.dag); + +onodes = find(~isemptycell(evidence)); +hnodes = find(isemptycell(evidence)); +pot_type = determine_pot_type(bnet, onodes); + +% Fold the evidence into the CPTs - this could be done in 'enter_evidence' +CPT = cell(1,n); +for i=1:n + fam = family(bnet.dag, i); + CPT{i} = convert_to_pot(bnet.CPD{bnet.equiv_class(i)}, pot_type, fam(:), evidence); +end + + + +sum_over = mysetdiff(1:n, query); +order = [query sum_over]; % no attempt to optimize this + +% Initialize the buckets with the product of the CPTs assigned to them +B = cell(1,n+1); +for b=1:n+1 + B{b} = mk_initial_pot(pot_type, [], [], [], []); +end +for i=1:n + b = bucket_num(domain_pot(CPT{i}), order); + B{b} = multiply_pots(B{b}, CPT{i}); +end + +% Do the marginalization +sum_over = sum_over(length(sum_over):-1:1); % reverse +for i=sum_over(:)' + % summing over variable i which occurs in bucket j + j = bucket_num(i, order); + rest = mysetdiff(domain_pot(B{j}), i); + % minka + if ~isempty(rest) + temp = marginalize_pot(B{j}, rest); + b = bucket_num(domain_pot(temp), order); + %fprintf('summing over bucket %d (var %d), putting result into bucket %d\n', j, i, b); + B{b} = multiply_pots(B{b}, temp); + end +end + +% Combine all the remaining buckets into one +result = B{1}; +for i=2:length(query) + if ~isempty(domain_pot(B{i})) + result = multiply_pots(result, B{i}); + end +end +[result, loglik] = normalize_pot(result); + + +marginal = pot_to_marginal(result); +% minka: from jtree_inf_engine +if add_ev + bnet = bnet_from_engine(engine); + %marginal = add_ev_to_dmarginal(marginal, engine.evidence, bnet.node_sizes); + marginal = add_evidence_to_gmarginal(marginal, engine.evidence, bnet.node_sizes, bnet.cnodes); +end + +%%%%%%%%% + +function b = bucket_num(domain, order) + +b = max(find_equiv_posns(domain, order)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@var_elim_inf_engine/var_elim_inf_engine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function engine = var_elim_inf_engine(bnet, varargin) +% VAR_ELIM_INF_ENGINE Variable elimination inference engine +% engine = var_elim_inf_engine(bnet) +% +% For details on variable elimination, see +% - R. Dechter, "Bucket Elimination: A Unifying Framework for Probabilistic Inference", UA1 96, pp. 211-219. +% - Z. Li and B. D'Ambrosio, "Efficient inference in Bayes networks as a combinatorial +% optimization problem", Intl. J. Approximate Reasoning, 11(1):55-81, 1994 +% - R. McEliece and S. M. Aji, "The Generalized Distributive Law", IEEE Trans. Inform. Theory, 46(2), 2000 + + +% This is where we will store the results between enter_evidence and marginal_nodes +engine.evidence = []; + +engine = class(engine, 'var_elim_inf_engine', inf_engine(bnet));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/dummy/1.1.1.1/Sat Jan 18 22:22:46 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +A D/@belprop_fg_inf_engine//// +A D/@belprop_inf_engine//// +A D/@belprop_mrf2_inf_engine//// +A D/@cond_gauss_inf_engine//// +A D/@enumerative_inf_engine//// +A D/@gaussian_inf_engine//// +A D/@gibbs_sampling_inf_engine//// +A D/@global_joint_inf_engine//// +A D/@jtree_inf_engine//// +A D/@jtree_limid_inf_engine//// +A D/@jtree_mnet_inf_engine//// +A D/@jtree_sparse_inf_engine//// +A D/@likelihood_weighting_inf_engine//// +A D/@pearl_inf_engine//// +A D/@quickscore_inf_engine//// +A D/@stab_cond_gauss_inf_engine//// +A D/@var_elim_inf_engine////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/inference/static
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/installC_BNT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +BNT_HOME = '/home/ai2/murphyk/matlab/FullBNT'; % edit this + +d = fullfile(BNT_HOME, 'BNT'); +%PC = (strncmp(computer,'PC',2)); + +cd(sprintf('%s/potentials/Tables', d)) +mex marg_table.c % used by @dpot/marginalize_pot.m +mex marg_sparse_table.c %used by sparse jtree +mex mult_by_table.c +mex mult_by_sparse_table.c +mex divide_by_table.c +mex divide_by_sparse_table.c +mex rep_mult.c + +% Written by Wei Hu +cd(sprintf('%s/CPDs/@discrete_CPD', d)) +mex convert_to_sparse_table.c + +% Written by Wei Hu +cd(sprintf('%s/inference/static/@jtree_sparse_inf_engine', d)) +mex init_pot.c +mex collect_evidence.c +mex distribute_evidence.c + +% written by Bhaskara Marthi +cd(sprintf('%s/inference/static/@gibbs_sampling_inf_engine/private', d)) +mex compute_posterior.c +mex get_slice_dbn.c +mex sample_single_discrete.c + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/installC_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +% These C functions were written by Ilya Shpitser. + +if isunix + mex -c elim.c; + mex -c cell.c; + mex -c map.c; + mex -DUNIX best_first_elim_order.c elim.o cell.o map.o; + mex -DUNIX triangulate.c elim.o cell.o map.o; +else + mex -c elim.c; + mex -c cell.c; + mex -c map.c; + mex best_first_elim_order.c elim.obj cell.obj map.obj; + mex triangulate.c elim.obj cell.obj map.obj; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +/CovMat.m/1.1.1.1/Sun Jul 28 23:09:42 2002// +/bayes_update_params.m/1.1.1.1/Wed May 29 15:59:56 2002// +/bic_score_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/compute_cooling_schedule.m/1.1.1.1/Wed May 29 15:59:56 2002// +/dirichlet_score_family.m/1.1.1.1/Wed May 29 15:59:56 2002// +/kpm_learn_struct_mcmc.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_params.m/1.1.1.1/Thu Jun 10 01:28:08 2004// +/learn_params_dbn.m/1.1.1.1/Sun Feb 2 00:23:38 2003// +/learn_params_dbn_em.m/1.1.1.1/Mon Aug 18 21:50:34 2003// +/learn_params_em.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_struct_K2.m/1.1.1.1/Thu Sep 26 08:39:16 2002// +/learn_struct_dbn_reveal.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_struct_mcmc.m/1.1.1.1/Fri Sep 5 14:06:40 2003// +/learn_struct_pdag_ic_star.m/1.1.1.1/Wed May 29 15:59:56 2002// +/learn_struct_pdag_pc.m/1.2/Mon Feb 12 01:15:13 2007// +/learn_struct_pdag_pc_constrain.m/1.1.1.1/Sun Jul 28 23:09:38 2002// +/mcmc_sample_to_hist.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_schedule.m/1.1.1.1/Wed May 29 15:59:56 2002// +/mk_tetrad_data_file.m/1.1.1.1/Wed May 29 15:59:56 2002// +/score_dags.m/1.1.1.1/Mon Apr 4 01:39:46 2005// +/score_dags_old.m/1.1.1.1/Wed May 29 15:59:56 2002// +/score_family.m/1.1.1.1/Thu Jun 10 01:33:14 2004// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/learning
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/CovMat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function [CovMatrix, obs, varfields] = CovMat(filename,row_cols) +%[CovMatrix, obs, varfields] = CovMat(filename,row_cols) +%% generates a Covariance Matrix from a file of data consisting of N columns of M data rows +%% filename string name (with path and extension) of file to open +%% row_cols Number_of_converstions_per_row (turns into [3 inf]) +%% Return +%% CovMatrix Covariance matrix +%% obs Number of observations read in +%% varfields Labels of the variables see filename structure below +%% +%% Filename structure: +%% Comma separated, starting with the variable labels, then the data in rows. +%% filename test.txt consists of: +%% +%% Earthquake,Burglar,Radio,Alarm,Call +%% 1,2,3,4,5 +%% 11,22,33,44,55 +%% . . . +%% +%% Example call: +%% [cvmat numdat lables] = CovMat('test.txt',5); +%% +%% Returns Covariance matrix, number of date rows and variable field names +%% Gary R. Bradski 7/2002 + +fmtstr = '%f'; +for i = 2:row_cols + fmtstr = strcat(fmtstr,',%f'); +end + +%% load data +fidCov = fopen(filename,'r'); + +varfields = fgetl(fidCov); +Corx = fscanf(fidCov,fmtstr,[row_cols inf]); +Corx= Corx'; +[obs bla] = size(Corx); +CovMatrix = cov(Corx); +fclose(fidCov); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/bayes_update_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function bnet = bayes_update_params(bnet, cases, clamped) +% BAYES_UPDATE_PARAMS Bayesian parameter updating given completely observed data +% bnet = bayes_update_params(bnet, cases, clamped) +% +% If there is a missing data, you must use EM. +% cases(i,m) is the value assigned to node i in case m (this can also be a cell array). +% clamped(i,m) = 1 if node i was set by intervention in case m (default: clamped = zeros). +% Clamped nodes are not updated. +% If there is a single case, clamped is a list of the clamped nodes, not a bit vector. + + +%if iscell(cases), usecell = 1; else usecell = 0; end + +n = length(bnet.dag); +ncases = size(cases, 2); +if n ~= size(cases, 1) + error('data must be of size nnodes * ncases'); +end + +if ncases == 1 % clamped is a list of nodes + if nargin < 3, clamped = []; end + clamp_set = clamped; + clamped = zeros(n,1); + clamped(clamp_set) = 1; +else % each row of clamped is a bit vector + if nargin < 3, clamped = zeros(n,ncases); end +end + +for i=1:n + e = bnet.equiv_class(i); + if adjustable_CPD(bnet.CPD{e}) + u = find(clamped(i,:)==0); + ps = parents(bnet.dag, i); + bnet.CPD{e} = bayes_update_params(bnet.CPD{e}, cases(i,u), cases(ps,u)); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/bic_score_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function [S, LL] = bic_score(counts, CPT, ncases) +% BIC_SCORE Bayesian Information Criterion score for a single family +% [S, LL] = bic_score(counts, CPT, ncases) +% +% S is a large sample approximation to the log marginal likelihood, +% which can be computed using dirichlet_score. +% +% S = \log [ prod_j _prod_k theta_ijk ^ N_ijk ] - 0.5*d*log(ncases) +% where counts encode N_ijk, theta_ijk is the MLE comptued from counts, +% and d is the num of free parameters. + +%CPT = mk_stochastic(counts); +tiny = exp(-700); +LL = sum(log(CPT(:) + tiny) .* counts(:)); +% CPT(i) = 0 iff counts(i) = 0 so it is okay to add tiny + +ns = mysize(counts); +ns_ps = ns(1:end-1); +ns_self = ns(end); +nparams = prod([ns_ps (ns_self-1)]); +% sum-to-1 constraint reduces the effective num. vals of the node by 1 + +S = LL - 0.5*nparams*log(ncases);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/compute_cooling_schedule.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function temp_schedule = compute_cooling_schedule(init_temp, final_temp, anneal_rate) + +temp_schedule = []; +i = 1; +temp_schedule(i)=init_temp; +while temp_schedule(i) > final_temp + i = i + 1; + temp_schedule(i)=temp_schedule(i-1)*anneal_rate; +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/dirichlet_score_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +function LL = dirichlet_score_family(counts, prior) +% DIRICHLET_SCORE Compute the log marginal likelihood of a single family +% LL = dirichlet_score(counts, prior) +% +% counts(a, b, ..., z) is the number of times parent 1 = a, parent 2 = b, ..., child = z +% prior is an optional multidimensional array of the same shape as counts. +% It defaults to a uniform prior. +% +% We marginalize out the parameters: +% LL = log \int \prod_m P(x(i,m) | x(Pa_i,m), theta_i) P(theta_i) d(theta_i) + + +% LL = log[ prod_j gamma(alpha_ij)/gamma(alpha_ij + N_ij) * +% prod_k gamma(alpha_ijk + N_ijk)/gamma(alpha_ijk) ] +% Call the prod_k term U and the prod_j term V. +% We reshape all quantities into (j,k) matrices +% This formula was first derived by Cooper and Herskovits, 1992. +% See also "Learning Bayesian Networks", Heckerman, Geiger and Chickering, MLJ 95. + +ns = mysize(counts); +ns_ps = ns(1:end-1); +ns_self = ns(end); + +if nargin < 2, prior = normalise(myones(ns)); end + + +if 1 + prior = reshape(prior(:), [prod(ns_ps) ns_self]); + counts = reshape(counts, [prod(ns_ps) ns_self]); + %U = prod(gamma(prior + counts) ./ gamma(prior), 2); % mult over k + LU = sum(gammaln(prior + counts) - gammaln(prior), 2); + alpha_ij = sum(prior, 2); % sum over k + N_ij = sum(counts, 2); + %V = gamma(alpha_ij) ./ gamma(alpha_ij + N_ij); + LV = gammaln(alpha_ij) - gammaln(alpha_ij + N_ij); + %L = prod(U .* V); + LL = sum(LU + LV); +else + CPT = mk_stochastic(prior + counts); + LL = sum(log(CPT(:) .* counts(:))); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/kpm_learn_struct_mcmc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,265 @@ +function [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, varargin) +% LEARN_STRUCT_MCMC Monte Carla Markov Chain search over DAGs assuming fully observed data +% [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, ...) +% +% data(i,m) is the value of node i in case m. +% ns(i) is the number of discrete values node i can take on. +% +% sampled_graphs{m} is the m'th sampled graph. +% accept_ratio(t) = acceptance ratio at iteration t +% num_edges(t) = number of edges in model at iteration t +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% nsamples - number of samples to draw from the chain after burn-in [ 100*N ] +% burnin - number of steps to take before drawing samples [ 5*N ] +% init_dag - starting point for the search [ zeros(N,N) ] +% +% e.g., samples = learn_struct_mcmc(data, ns, 'nsamples', 1000); +% +% This interface is not backwards compatible with BNT2, +% but is designed to be compatible with the other learn_struct_xxx routines. +% +% Note: We currently assume a uniform structural prior. + +[n ncases] = size(data); + + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + %params{i} = { 'prior', 1 }; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); +nsamples = 100*n; +burnin = 5*n; +dag = zeros(n); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'nsamples', nsamples = args{i+1}; + case 'burnin', burnin = args{i+1}; + case 'init_dag', dag = args{i+1}; + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +% We implement the fast acyclicity check described by P. Giudici and R. Castelo, +% "Improving MCMC model search for data mining", submitted to J. Machine Learning, 2001. +use_giudici = 1; +if use_giudici + [nbrs, ops, nodes] = mk_nbrs_of_digraph(dag); + A = init_ancestor_matrix(dag); +else + [nbrs, ops, nodes] = mk_nbrs_of_dag(dag); + A = []; +end + +num_accepts = 1; +num_rejects = 1; +T = burnin + nsamples; +accept_ratio = zeros(1, T); +num_edges = zeros(1, T); +sampled_graphs = cell(1, nsamples); +%sampled_bitv = zeros(nsamples, n^2); + +for t=1:T + [dag, nbrs, ops, nodes, A, accept] = take_step(dag, nbrs, ops, nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params); + num_edges(t) = sum(dag(:)); + num_accepts = num_accepts + accept; + num_rejects = num_rejects + (1-accept); + accept_ratio(t) = num_accepts/num_rejects; + if t > burnin + sampled_graphs{t-burnin} = dag; + %sampled_bitv(t-burnin, :) = dag(:)'; + end +end + + +%%%%%%%%% + + +function [new_dag, new_nbrs, new_ops, new_nodes, A, accept] = ... + take_step(dag, nbrs, ops, nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params) + + +use_giudici = ~isempty(A); +if use_giudici + [new_dag, op, i, j] = pick_digraph_nbr(dag, nbrs, ops, nodes, A); + %assert(acyclic(new_dag)); + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_digraph(new_dag); +else + d = sample_discrete(normalise(ones(1, length(nbrs)))); + new_dag = nbrs{d}; + op = ops{d}; + i = nodes(d, 1); j = nodes(d, 2); + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_dag(new_dag); +end + +bf = bayes_factor(dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params); + +%R = bf * (new_prior / prior) * (length(nbrs) / length(new_nbrs)); +R = bf * (length(nbrs) / length(new_nbrs)); +u = rand(1,1); +if u > min(1,R) % reject the move + accept = 0; + new_dag = dag; + new_nbrs = nbrs; + new_ops = ops; + new_nodes = nodes; +else + accept = 1; + if use_giudici + A = update_ancestor_matrix(A, op, i, j, new_dag); + end +end + + +%%%%%%%%% + +function bfactor = bayes_factor(old_dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params) + +u = find(clamped(j,:)==0); +LLnew = score_family(j, parents(new_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +LLold = score_family(j, parents(old_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +bf1 = exp(LLnew - LLold); + +if strcmp(op, 'rev') % must also multiply in the changes to i's family + u = find(clamped(i,:)==0); + LLnew = score_family(i, parents(new_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + LLold = score_family(i, parents(old_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + bf2 = exp(LLnew - LLold); +else + bf2 = 1; +end +bfactor = bf1 * bf2; + + +%%%%%%%% Giudici stuff follows %%%%%%%%%% + + +function [new_dag, op, i, j] = pick_digraph_nbr(dag, digraph_nbrs, ops, nodes, A) + +legal = 0; +while ~legal + d = sample_discrete(normalise(ones(1, length(digraph_nbrs)))); + i = nodes(d, 1); j = nodes(d, 2); + switch ops{d} + case 'add', + if A(i,j)==0 + legal = 1; + end + case 'del', + legal = 1; + case 'rev', + ps = mysetdiff(parents(dag, j), i); + % if any(A(ps,i)) then there is a path i -> parent of j -> j + % so reversing i->j would create a cycle + legal = ~any(A(ps, i)); + end +end +%new_dag = digraph_nbrs{d}; +new_dag = digraph_nbrs(:,:,d); +op = ops{d}; +i = nodes(d, 1); j = nodes(d, 2); + + +%%%%%%%%%%%%%% + + +function A = update_ancestor_matrix(A, op, i, j, dag) + +switch op + case 'add', + A = do_addition(A, op, i, j, dag); + case 'del', + A = do_removal(A, op, i, j, dag); + case 'rev', + A = do_removal(A, op, i, j, dag); + A = do_addition(A, op, j, i, dag); +end + + +%%%%%%%%%%%% + +function A = do_addition(A, op, i, j, dag) + +A(j,i) = 1; % i is an ancestor of j +anci = find(A(i,:)); +if ~isempty(anci) + A(j,anci) = 1; % all of i's ancestors are added to Anc(j) +end +ancj = find(A(j,:)); +descj = find(A(:,j)); +if ~isempty(ancj) + for k=descj(:)' + A(k,ancj) = 1; % all of j's ancestors are added to each descendant of j + end +end + +%%%%%%%%%%% + +function A = do_removal(A, op, i, j, dag) + +% find all the descendants of j, and put them in topological order +%descj = find(A(:,j)); +R = reachability_graph(dag); +descj = find(R(j,:)); +order = topological_sort(dag); +descj_topnum = order(descj); +[junk, perm] = sort(descj_topnum); +descj = descj(perm); +% Update j and all its descendants +A = update_row(A, j, dag); +for k = descj(:)' + A = update_row(A, k, dag); +end + +%%%%%%%%% + +function A = update_row(A, j, dag) + +% We compute row j of A +A(j, :) = 0; +ps = parents(dag, j); +if ~isempty(ps) + A(j, ps) = 1; +end +for k=ps(:)' + anck = find(A(k,:)); + if ~isempty(anck) + A(j, anck) = 1; + end +end + +%%%%%%%% + +function A = init_ancestor_matrix(dag) + +order = topological_sort(dag); +A = zeros(length(dag)); +for j=order(:)' + A = update_row(A, j, dag); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_params.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function bnet = learn_params(bnet, data) +% LEARN_PARAMS Find the maximum likelihood params for a fully observed model +% bnet = learn_params(bnet, data) +% +% data(i,m) is the value of node i in case m (can be a cell array) +% +% We set bnet.CPD{i} to its ML/MAP estimate. +% +% Currently we assume no param tying + +% AND THAT EACH DATA POINT IS A SCALAR - no longer assumed + +%if iscell(data) +% data=cell2num(data); +%end +[n ncases] = size(data); +for j=1:n + e = bnet.equiv_class(j); + assert(e==j); + if adjustable_CPD(bnet.CPD{e}) + fam = family(bnet.dag,j); + %bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam,:)); + bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, bnet.node_sizes, bnet.cnodes); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_params_dbn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function bnet = learn_params_dbn(bnet, data) +% LEARN_PARAM_DBN Estimate params of a DBN for a fully observed model +% bnet = learn_params_dbn(bnet, data) +% +% data(i,t) is the value of node i in slice t (can be a cell array) +% We currently assume there is a single time series +% +% We set bnet.CPD{i} to its ML/MAP estimate. +% +% Currently we assume each node in the first 2 slices has its own CPD (no param tying); +% all nodes in slices >2 share their params with slice 2 as usual. + +[ss T] = size(data); + +% slice 1 +for j=1:ss + if adjustable_CPD(bnet.CPD{j}) + fam = family(bnet.dag,j); + bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam,1)); + end +end + + +% slices 2:T +% data2(:,t) contains [data(:,t-1); data(:,t)]. +% Then we extract out the rows corresponding to the parents in the current and previous slice. +data2 = [data(:,1:T-1); + data(:,2:T)]; +for j=1:ss + j2 = j+ss; + if adjustable_CPD(bnet.CPD{j2}) + fam = family(bnet.dag,j2); + bnet.CPD{j2} = learn_params(bnet.CPD{j2}, data2(fam,:)); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_params_dbn_em.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,179 @@ +function [bnet, LL, engine] = learn_params_dbn_em(engine, evidence, varargin) +% LEARN_PARAMS_DBN Set the parameters in a DBN to their ML/MAP values using batch EM. +% [bnet, LLtrace, engine] = learn_params_dbn_em(engine, data, ...) +% +% data{l}{i,t} = value of node i in slice t of time-series l, or [] if hidden. +% Suppose you have L time series, each of length T, in an O*T*L array D, +% where O is the num of observed scalar nodes, and N is the total num nodes per slice. +% Then you can create data as follows, where onodes is the index of the observable nodes: +% data = cell(1,L); +% for l=1:L +% data{l} = cell(N, T); +% data{l}(onodes,:) = num2cell(D(:,:,l)); +% end +% Of course it is possible for different sets of nodes to be observed in +% each slice/ sequence, and for each sequence to be a different length. +% +% LLtrace is the learning curve: the vector of log-likelihood scores at each iteration. +% +% Optional arguments [default] +% +% max_iter - specifies the maximum number of iterations [100] +% thresh - specifies the thresold for stopping EM [1e-3] +% We stop when |f(t) - f(t-1)| / avg < threshold, +% where avg = (|f(t)| + |f(t-1)|)/2 and f is log lik. +% verbose - display loglik at each iteration [1] +% anneal - 1 means do deterministic annealing (only for entropic priors) [0] +% anneal_rate - geometric cooling rate [0.8] +% init_temp - initial annealing temperature [10] +% final_temp - final annealing temperature [1e-3] +% + +max_iter = 100; +thresh = 1e-3; +anneal = 0; +anneal_rate = 0.8; +init_temp = 10; +final_temp = 1e-3; +verbose = 1; + +for i=1:2:length(varargin) + switch varargin{i} + case 'max_iter', max_iter = varargin{i+1}; + case 'thresh', thresh = varargin{i+1}; + case 'anneal', anneal = varargin{i+1}; + case 'anneal_rate', anneal_rate = varargin{i+1}; + case 'init_temp', init_temp = varargin{i+1}; + case 'final_temp', final_temp = varargin{i+1}; + otherwise, error(['unrecognized argument' varargin{i}]) + end +end + +% take 1 EM step at each temperature value, then when temp=0, run to convergence +% When using an entropic prior, Z = 1-T, so +% T=2 => Z=-1 (max entropy) +% T=1 => Z=0 (max likelihood) +% T=0 => Z=1 (min entropy / max structure) +num_iter = 1; +LL = []; +if anneal + temperature = init_temp; + while temperature > final_temp + [engine, loglik, logpost] = EM_step(engine, evidence, temperature); + if verbose + fprintf('EM iteration %d, loglik = %8.4f, logpost = %8.4f, temp=%8.4f\n', ... + num_iter, loglik, logpost, temperature); + end + num_iter = num_iter + 1; + LL = [LL loglik]; + temperature = temperature * anneal_rate; + end + temperature = 0; + previous_loglik = loglik; + previous_logpost = logpost; +else + temperature = 0; + previous_loglik = -inf; + previous_logpost = -inf; +end + +converged = 0; +while ~converged & (num_iter <= max_iter) + [engine, loglik, logpost] = EM_step(engine, evidence, temperature); + if verbose + %fprintf('EM iteration %d, loglik = %8.4f, logpost = %8.4f\n', ... + % num_iter, loglik, logpost); + fprintf('EM iteration %d, loglik = %8.4f\n', num_iter, loglik); + end + num_iter = num_iter + 1; + [converged, decreased] = em_converged(loglik, previous_loglik, thresh); + %[converged, decreased] = em_converged(logpost, previous_logpost, thresh); + previous_loglik = loglik; + previous_logpost = logpost; + LL = [LL loglik]; +end + +bnet = bnet_from_engine(engine); + +%%%%%%%%% + +function [engine, loglik, logpost] = EM_step(engine, cases, temp) + +bnet = bnet_from_engine(engine); % engine contains the old params that are used for the E step +ss = length(bnet.intra); +CPDs = bnet.CPD; % these are the new params that get maximized +num_CPDs = length(CPDs); + +% log P(theta|D) = (log P(D|theta) + log P(theta)) - log(P(D)) +% where log P(D|theta) = sum_cases log P(case|theta) +% and log P(theta) = sum_CPDs log P(CPD) - only count once even if tied! +% logpost = log P(theta,D) (un-normalized) +% This should be negative, and increase at every step. + +adjustable = zeros(1,num_CPDs); +logprior = zeros(1, num_CPDs); +for e=1:num_CPDs + adjustable(e) = adjustable_CPD(CPDs{e}); +end +adj = find(adjustable); + +for e=adj(:)' + logprior(e) = log_prior(CPDs{e}); + CPDs{e} = reset_ess(CPDs{e}); +end + +loglik = 0; +for l=1:length(cases) + evidence = cases{l}; + if ~iscell(evidence) + error('training data must be a cell array of cell arrays') + end + [engine, ll] = enter_evidence(engine, evidence); + assert(~isnan(ll)) + loglik = loglik + ll; + T = size(evidence, 2); + + % We unroll ns etc because in update_ess, we refer to nodes by their unrolled number + % so that they extract evidence from the right place. + % (The CPD should really store its own version of ns and cnodes...) + ns = repmat(bnet.node_sizes_slice(:), [1 T]); + cnodes = unroll_set(bnet.cnodes_slice, ss, T); + + %hidden_bitv = repmat(bnet.hidden_bitv(1:ss), [1 T]); + hidden_bitv = zeros(ss, T); + hidden_bitv(isemptycell(evidence))=1; + % hidden_bitv(i) = 1 means node i is hidden. + % We pass this in, rather than using isemptycell(evidence(dom)), because + % isemptycell is very slow. + + t = 1; + for i=1:ss + e = bnet.equiv_class(i,1); + if adjustable(e) + fmarg = marginal_family(engine, i, t); + CPDs{e} = update_ess(CPDs{e}, fmarg, evidence, ns(:), cnodes(:), hidden_bitv(:)); + end + end + + for i=1:ss + e = bnet.equiv_class(i,2); + if adjustable(e) + for t=2:T + fmarg = marginal_family(engine, i, t); + CPDs{e} = update_ess(CPDs{e}, fmarg, evidence, ns(:), cnodes(:), hidden_bitv(:)); + end + end + end +end + +logpost = loglik + sum(logprior(:)); + +for e=adj(:)' + CPDs{e} = maximize_params(CPDs{e}, temp); +end + +engine = update_engine(engine, CPDs); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_params_em.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +function [bnet, LL, engine] = learn_params_em(engine, evidence, max_iter, thresh) +% LEARN_PARAMS_EM Set the parameters of each adjustable node to their ML/MAP values using batch EM. +% [bnet, LLtrace, engine] = learn_params_em(engine, data, max_iter, thresh) +% +% data{i,l} is the value of node i in case l, or [] if hidden. +% Suppose you have L training cases in an O*L array, D, where O is the num observed +% scalar nodes, and N is the total num nodes. +% Then you can create 'data' as follows, where onodes is the index of the observable nodes: +% data = cell(N, L); +% data(onodes,:) = num2cell(D); +% Of course it is possible for different sets of nodes to be observed in each case. +% +% We return the modified bnet and engine. +% To see the learned parameters for node i, use the construct +% s = struct(bnet.CPD{i}); % violate object privacy +% LLtrace is the learning curve: the vector of log-likelihood scores at each iteration. +% +% max_iter specifies the maximum number of iterations. Default: 10. +% +% thresh specifies the thresold for stopping EM. Default: 1e-3. +% We stop when |f(t) - f(t-1)| / avg < threshold, +% where avg = (|f(t)| + |f(t-1)|)/2 and f is log lik. + +if nargin < 3, max_iter = 10; end +if nargin < 4, thresh = 1e-3; end + +verbose = 1; + +loglik = 0; +previous_loglik = -inf; +converged = 0; +num_iter = 1; +LL = []; + +while ~converged & (num_iter <= max_iter) + [engine, loglik] = EM_step(engine, evidence); + if verbose, fprintf('EM iteration %d, ll = %8.4f\n', num_iter, loglik); end + num_iter = num_iter + 1; + converged = em_converged(loglik, previous_loglik, thresh); + previous_loglik = loglik; + LL = [LL loglik]; +end +if verbose, fprintf('\n'); end + +bnet = bnet_from_engine(engine); + +%%%%%%%%% + +function [engine, loglik] = EM_step(engine, cases) + +bnet = bnet_from_engine(engine); % engine contains the old params that are used for the E step +CPDs = bnet.CPD; % these are the new params that get maximized +num_CPDs = length(CPDs); +adjustable = zeros(1,num_CPDs); +for e=1:num_CPDs + adjustable(e) = adjustable_CPD(CPDs{e}); +end +adj = find(adjustable); +n = length(bnet.dag); + +for e=adj(:)' + CPDs{e} = reset_ess(CPDs{e}); +end + +loglik = 0; +ncases = size(cases, 2); +for l=1:ncases + evidence = cases(:,l); + [engine, ll] = enter_evidence(engine, evidence); + loglik = loglik + ll; + hidden_bitv = zeros(1,n); + hidden_bitv(isemptycell(evidence))=1; + for i=1:n + e = bnet.equiv_class(i); + if adjustable(e) + fmarg = marginal_family(engine, i); + CPDs{e} = update_ess(CPDs{e}, fmarg, evidence, bnet.node_sizes, bnet.cnodes, hidden_bitv); + end + end +end + +for e=adj(:)' + CPDs{e} = maximize_params(CPDs{e}); +end + +engine = update_engine(engine, CPDs); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_struct_K2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +function dag = learn_struct_K2(data, ns, order, varargin) +% LEARN_STRUCT_K2 Greedily learn the best structure compatible with a fixed node ordering +% best_dag = learn_struct_K2(data, node_sizes, order, ...) +% +% data(i,m) = value of node i in case m (can be a cell array). +% node_sizes(i) is the size of node i. +% order(i) is the i'th node in the topological ordering. +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% max_fan_in - this the largest number of parents we allow per node [N] +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% verbose - 'yes' means display output while running [ 'no' ] +% +% e.g., dag = learn_struct_K2(data, ns, order, 'scoring_fn', 'bic', 'params', []) +% +% To be backwards compatible with BNT2, you can also specify arguments as follows +% dag = learn_struct_K2(data, node_sizes, order, max_fan_in) +% +% This algorithm is described in +% - Cooper and Herskovits, "A Bayesian method for the induction of probabilistic +% networks from data", Machine Learning Journal 9:308--347, 1992 + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + %params{i} = { 'prior', 1 }; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); + +max_fan_in = n; +verbose = 0; + +args = varargin; +nargs = length(args); +if length(args) > 0 + if isstr(args{1}) + for i=1:2:nargs + switch args{i}, + case 'verbose', verbose = strcmp(args{i+1}, 'yes'); + case 'max_fan_in', max_fan_in = args{i+1}; + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end + end + else + max_fan_in = args{1}; + end +end + +dag = zeros(n,n); + +for i=1:n + ps = []; + j = order(i); + u = find(clamped(j,:)==0); + score = score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + if verbose, fprintf('\nnode %d, empty score %6.4f\n', j, score); end + done = 0; + while ~done & (length(ps) <= max_fan_in) + pps = mysetdiff(order(1:i-1), ps); % potential parents + nps = length(pps); + pscore = zeros(1, nps); + for pi=1:nps + p = pps(pi); + pscore(pi) = score_family(j, [ps p], type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + if verbose, fprintf('considering adding %d to %d, score %6.4f\n', p, j, pscore(pi)); end + end + [best_pscore, best_p] = max(pscore); + best_p = pps(best_p); + if best_pscore > score + score = best_pscore; + ps = [ps best_p]; + if verbose, fprintf('* adding %d to %d, score %6.4f\n', best_p, j, best_pscore); end + else + done = 1; + end + end + if ~isempty(ps) % need this check for matlab 5.2 + dag(ps, j) = 1; + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_struct_dbn_reveal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +function inter = learn_struct_dbn_reveal(seqs, ns, max_fan_in, penalty) +% LEARN_STRUCT_DBN_REVEAL Learn inter-slice adjacency matrix given fully observable discrete time series +% inter = learn_struct_dbn_reveal(seqs, node_sizes, max_fan_in, penalty) +% +% seqs{l}{i,t} = value of node i in slice t of time-series l. +% If you have a single time series in an N*T array D, use +% seqs = { num2cell(D) }. +% If you have L time series, each of length T, in an N*T*L array D, use +% seqs= cell(1,L); for l=1:L, seqs{l} = num2cell(D(:,:,l)); end +% or, in vectorized form, +% seqs = squeeze(num2cell(num2cell(D),[1 2])); +% Currently the data is assumed to be discrete (1,2,...) +% +% node_sizes(i) is the number of possible values for node i +% max_fan_in is the largest number of parents we allow per node (default: N) +% penalty is weight given to the complexity penalty (default: 0.5) +% A penalty of 0.5 gives the BIC score. +% A penalty of 0 gives the ML score. +% Maximizing likelihood is equivalent to maximizing mutual information between parents and child. +% +% inter(i,j) = 1 iff node in slice t connects to node j in slice t+1 +% +% The parent set for each node in slice 2 is computed by evaluating all subsets of nodes in slice 1, +% and picking the largest scoring one. This takes O(n^k) time per node, where n is the num. nodes +% per slice, and k <= n is the max fan in. +% Since all the nodes are observed, we do not need to use an inference engine. +% And since we are only learning the inter-slice matrix, we do not need to check for cycles. +% +% This algorithm is described in +% - "REVEAL: A general reverse engineering algorithm for inference of genetic network +% architectures", Liang et al. PSB 1998 +% - "Extended dependency analysis of large systems", +% Roger Conant, Intl. J. General Systems, 1988, vol 14, pp 97-141 +% - "Learning the structure of DBNs", Friedman, Murphy and Russell, UAI 1998. + +n = length(ns); + +if nargin < 3, max_fan_in = n; end +if nargin < 4, penalty = 0.5; end + +inter = zeros(n,n); + +if ~iscell(seqs) + data{1} = seqs; +end + +nseq = length(seqs); +nslices = 0; +data = cell(1, nseq); +for l=1:nseq + nslices = nslices + size(seqs{l}, 2); + data{l} = cell2num(seqs{l})'; % each row is a case +end +ndata = nslices - nseq; % subtract off the initial slice of each sequence + +% We concatenate the sequences as in the following example. +% Let there be 2 sequences of lengths 4 and 5, with n nodes per slice, +% and let i be the target node. +% Then we construct following matrix D +% +% s{1}{1,1} ... s{1}{1,3} s{2}{1,1} ... s{2}{1,4} +% .... +% s{1}{n,1} ... s{1}{n,3} s{2}{n,1} ... s{2}{n,4} +% s{1}{i,2} ... s{1}{i,4} s{2}{i,2} ... s{2}{i,5} +% +% D(1:n, i) is the i'th input and D(n+1, i) is the i'th output. +% +% We concatenate each sequence separately to avoid treating the transition +% from the end of one sequence to the beginning of another as a "normal" transition. + + +for i=1:n + D = []; + for l=1:nseq + T = size(seqs{l}, 2); + A = cell2num(seqs{l}(:, 1:T-1)); + B = cell2num(seqs{l}(i, 2:T)); + C = [A;B]; + D = [D C]; + end + SS = subsets(1:n, max_fan_in, 1); % skip the empty set + nSS = length(SS); + bic_score = zeros(1, nSS); + ll_score = zeros(1, nSS); + target = n+1; + ns2 = [ns ns(i)]; + for h=1:nSS + ps = SS{h}; + dom = [ps target]; + counts = compute_counts(D(dom, :), ns2(dom)); + CPT = mk_stochastic(counts); + [bic_score(h), ll_score(h)] = bic_score_family(counts, CPT, ndata); + end + if penalty == 0 + h = argmax(ll_score); + else + h = argmax(bic_score); + end + ps = SS{h}; + inter(ps, i) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_struct_mcmc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,284 @@ +function [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, varargin) +% MY_LEARN_STRUCT_MCMC Monte Carlo Markov Chain search over DAGs assuming fully observed data +% [sampled_graphs, accept_ratio, num_edges] = learn_struct_mcmc(data, ns, ...) +% +% data(i,m) is the value of node i in case m. +% ns(i) is the number of discrete values node i can take on. +% +% sampled_graphs{m} is the m'th sampled graph. +% accept_ratio(t) = acceptance ratio at iteration t +% num_edges(t) = number of edges in model at iteration t +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% nsamples - number of samples to draw from the chain after burn-in [ 100*N ] +% burnin - number of steps to take before drawing samples [ 5*N ] +% init_dag - starting point for the search [ zeros(N,N) ] +% +% e.g., samples = my_learn_struct_mcmc(data, ns, 'nsamples', 1000); +% +% Modified by Sonia Leach (SML) 2/4/02, 9/5/03 + + + +[n ncases] = size(data); + + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + %params{i} = { 'prior', 1}; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); +nsamples = 100*n; +burnin = 5*n; +dag = zeros(n); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'nsamples', nsamples = args{i+1}; + case 'burnin', burnin = args{i+1}; + case 'init_dag', dag = args{i+1}; + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +% We implement the fast acyclicity check described by P. Giudici and R. Castelo, +% "Improving MCMC model search for data mining", submitted to J. Machine Learning, 2001. + +% SML: also keep descendant matrix C +use_giudici = 1; +if use_giudici + [nbrs, ops, nodes, A] = mk_nbrs_of_digraph(dag); +else + [nbrs, ops, nodes] = mk_nbrs_of_dag(dag); + A = []; +end + +num_accepts = 1; +num_rejects = 1; +T = burnin + nsamples; +accept_ratio = zeros(1, T); +num_edges = zeros(1, T); +sampled_graphs = cell(1, nsamples); +%sampled_bitv = zeros(nsamples, n^2); + +for t=1:T + [dag, nbrs, ops, nodes, A, accept] = take_step(dag, nbrs, ops, ... + nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params); + num_edges(t) = sum(dag(:)); + num_accepts = num_accepts + accept; + num_rejects = num_rejects + (1-accept); + accept_ratio(t) = num_accepts/num_rejects; + if t > burnin + sampled_graphs{t-burnin} = dag; + %sampled_bitv(t-burnin, :) = dag(:)'; + end +end + + +%%%%%%%%% + + +function [new_dag, new_nbrs, new_ops, new_nodes, A, accept] = ... + take_step(dag, nbrs, ops, nodes, ns, data, clamped, A, ... + scoring_fn, discrete, type, params, prior_w) + + +use_giudici = ~isempty(A); +if use_giudici + [new_dag, op, i, j, new_A] = pick_digraph_nbr(dag, nbrs, ops, nodes,A); % updates A + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_digraph(new_dag, new_A); +else + d = sample_discrete(normalise(ones(1, length(nbrs)))); + new_dag = nbrs{d}; + op = ops{d}; + i = nodes(d, 1); j = nodes(d, 2); + [new_nbrs, new_ops, new_nodes] = mk_nbrs_of_dag(new_dag); +end + +bf = bayes_factor(dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params); + +%R = bf * (new_prior / prior) * (length(nbrs) / length(new_nbrs)); +R = bf * (length(nbrs) / length(new_nbrs)); +u = rand(1,1); +if u > min(1,R) % reject the move + accept = 0; + new_dag = dag; + new_nbrs = nbrs; + new_ops = ops; + new_nodes = nodes; +else + accept = 1; + if use_giudici +A = new_A; % new_A already updated in pick_digraph_nbr + end +end + + +%%%%%%%%% + +function bfactor = bayes_factor(old_dag, new_dag, op, i, j, ns, data, clamped, scoring_fn, discrete, type, params) + +u = find(clamped(j,:)==0); +LLnew = score_family(j, parents(new_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +LLold = score_family(j, parents(old_dag, j), type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); +bf1 = exp(LLnew - LLold); + +if strcmp(op, 'rev') % must also multiply in the changes to i's family + u = find(clamped(i,:)==0); + LLnew = score_family(i, parents(new_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + LLold = score_family(i, parents(old_dag, i), type{i}, scoring_fn, ns, discrete, data(:,u), params{i}); + bf2 = exp(LLnew - LLold); +else + bf2 = 1; +end +bfactor = bf1 * bf2; + + +%%%%%%%% Giudici stuff follows %%%%%%%%%% + + +% SML: This now updates A as it goes from digraph it choses +function [new_dag, op, i, j, new_A] = pick_digraph_nbr(dag, digraph_nbrs, ops, nodes, A) + +d = sample_discrete(normalise(ones(1, length(digraph_nbrs)))); +%d = myunidrnd(length(digraph_nbrs),1,1); +i = nodes(d, 1); j = nodes(d, 2); +new_dag = digraph_nbrs(:,:,d); +op = ops{d}; +new_A = update_ancestor_matrix(A, op, i, j, new_dag); + + +%%%%%%%%%%%%%% + + +function A = update_ancestor_matrix(A, op, i, j, dag) + +switch op +case 'add', + A = do_addition(A, op, i, j, dag); +case 'del', + A = do_removal(A, op, i, j, dag); +case 'rev', + A = do_removal(A, op, i, j, dag); + A = do_addition(A, op, j, i, dag); +end + + +%%%%%%%%%%%% + +function A = do_addition(A, op, i, j, dag) + +A(j,i) = 1; % i is an ancestor of j +anci = find(A(i,:)); +if ~isempty(anci) + A(j,anci) = 1; % all of i's ancestors are added to Anc(j) +end +ancj = find(A(j,:)); +descj = find(A(:,j)); +if ~isempty(ancj) + for k=descj(:)' + A(k,ancj) = 1; % all of j's ancestors are added to each descendant of j + end +end + +%%%%%%%%%%% +function A = do_removal(A, op, i, j, dag) + +% find all the descendants of j, and put them in topological order + +% SML: originally Kevin had the next line commented and the %* lines +% being used but I think this is equivalent and much less expensive +% I assume he put it there for debugging and never changed it back...? +descj = find(A(:,j)); +%* R = reachability_graph(dag); +%* descj = find(R(j,:)); + +order = topological_sort(dag); + +% SML: originally Kevin used the %* line but this was extracting the +% wrong things to sort +%* descj_topnum = order(descj); +[junk, perm] = sort(order); %SML:node i is perm(i)-TH in order +descj_topnum = perm(descj); %SML:descj(i) is descj_topnum(i)-th in order + +% SML: now re-sort descj by rank in descj_topnum +[junk, perm] = sort(descj_topnum); +descj = descj(perm); + +% Update j and all its descendants +A = update_row(A, j, dag); +for k = descj(:)' + A = update_row(A, k, dag); +end + +%%%%%%%%%%% + +function A = old_do_removal(A, op, i, j, dag) + +% find all the descendants of j, and put them in topological order +% SML: originally Kevin had the next line commented and the %* lines +% being used but I think this is equivalent and much less expensive +% I assume he put it there for debugging and never changed it back...? +descj = find(A(:,j)); +%* R = reachability_graph(dag); +%* descj = find(R(j,:)); + +order = topological_sort(dag); +descj_topnum = order(descj); +[junk, perm] = sort(descj_topnum); +descj = descj(perm); +% Update j and all its descendants +A = update_row(A, j, dag); +for k = descj(:)' + A = update_row(A, k, dag); +end + +%%%%%%%%% + +function A = update_row(A, j, dag) + +% We compute row j of A +A(j, :) = 0; +ps = parents(dag, j); +if ~isempty(ps) + A(j, ps) = 1; +end +for k=ps(:)' + anck = find(A(k,:)); + if ~isempty(anck) + A(j, anck) = 1; + end +end + +%%%%%%%% + +function A = init_ancestor_matrix(dag) + +order = topological_sort(dag); +A = zeros(length(dag)); +for j=order(:)' + A = update_row(A, j, dag); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_struct_pdag_ic_star.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,155 @@ +function [pdag, G] = learn_struct_pdag_ic_star(cond_indep, n, k, varargin) +% LEARN_STRUCT_PDAG_IC_STAR Learn a partially oriented DAG (pattern) with latent +% variables using the IC* algorithm +% P = learn_struct_pdag_ic_star(cond_indep, n, k, ...) +% +% n is the number of nodes. +% k is an optional upper bound on the fan-in (default: n) +% cond_indep is a boolean function that will be called as follows: +% feval(cond_indep, x, y, S, ...) +% where x and y are nodes, and S is a set of nodes (positive integers), +% and ... are any optional parameters passed to this function. +% +% The output P is an adjacency matrix, in which +% P(i,j) = -1 if there is either a latent variable L such that i <-L-> j +% OR there is a directed edge from i->j. +% P(i,j) = -2 if there is a marked directed i-*>j edge. +% P(i,j) = P(j,i) = 1 if there is and undirected edge i--j +% P(i,j) = P(j,i) = 2 if there is a latent variable L such that i<-L->j. +% +% The IC* algorithm learns a latent structure associated with a set of observed +% variables. +% The latent structure revealed is the projection in which every latent variable is +% 1) a root node +% 2) linked to exactly two observed variables. +% Latent variables in the projection are represented using a bidirectional graph, +% and thus remain implicit. +% +% See Pearl, "Causality: Models, Reasoning, and Inference", 2000, p52 for more details. +% Written by Tamar Kushnir, 2000 + +sep = cell(n,n); +ord = 0; +done = 0; +G = ones(n,n); +G = setdiag(G,0); +while ~done + done = 1; + [X,Y] = find(G); + for i=1:length(X) + x = X(i); y = Y(i); + nbrs = mysetdiff(myunion(neighbors(G, x), neighbors(G,y)), [x y]); + if length(nbrs) >= ord & G(x,y) ~= 0 + done = 0; + SS = subsets(nbrs, ord, ord); % all subsets of size ord + for si=1:length(SS) + S = SS{si}; + if feval(cond_indep, x, y, S, varargin{:}) + G(x,y) = 0; + G(y,x) = 0; + sep{x,y} = myunion(sep{x,y}, S); + sep{y,x} = myunion(sep{y,x}, S); + break; % no need to check any more subsets + end + end + end + end + ord = ord + 1; +end + +% Create the minimal pattern, +% i.e., the only directed edges are V structures. +pdag = G; +[X, Y] = find(G); +% We want to generate all unique triples x,y,z +% where y is a common neighbor to x and z +for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(G(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ~ismember(y, sep{x,z}) & ~ismember(y, sep{z,x}) + pdag(x,y) = -1; pdag(y,x) = 0; + pdag(z,y) = -1; pdag(y,z) = 0; + end + end +end + +% Convert the minimal pattern to a complete one using the following rules: +% Rule 1: +% if a and b are non-adjacent nodes with a common neighbor c, +% if a->c and not b->c then c-*>b (marked arrow). +% Rule 2: +% if a and b are adjacent and there is a directed path (marked links) from a to b +% then a->b (add arrowhead). +%Pearl (2000) + +arrowin = [-1 -2 2]; +old_pdag = zeros(n); +iter = 0; +while ~isequal(pdag, old_pdag) + iter = iter + 1; + old_pdag = pdag; + % rule 1 + [X, Y] = find(pdag); + for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(pdag(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ismember(pdag(x,y),arrowin) & ~ismember(pdag(z,y),arrowin) + pdag(y,z) = -2; pdag(z,y) = 0; + end + end + end + % rule 2 + [X, Y] = find(G); + %check all adjacent nodes because if pdag(x,y) = -1 + %and pdag(y,x) = 0 there could still be an bidirected edge between x & y. + for i=1:length(X) + x = X(i); + y = Y(i); + if ~ismember(pdag(x,y), arrowin) %x->y doesn't exist yet + %find marked path from x to y + add_arrow = marked_path(x,y,pdag); + if add_arrow + if pdag(y,x)==-1 %bidirected edge + pdag(x,y) = 2; pdag(y,x) = 2; + else + pdag(x,y) = -1;pdag(y,x) = 0; + end + end + end + end +end + + +%%%%%%%%%%%%% + +function t = marked_path(x,y,L) +% MARKED_PATH is a boolean function which returns 1 if a marked path +% between nodes x and y exists in the partially directed latent structure L. +% +% t = marked_path(x,y,L) +% +% x and y are the starting and ending nodes in the path, respectively. +% L is a latent structure (partially directed graph with possible latent variables). +% +% Rule 2 of IC* algorithm (see Pearl, 2000) + +t=0; + +%find set of marked links from x +marked = find(L(x,:)==-2); +if ismember(y,marked) + t=1; %marked path found +else + for m=marked(:)' + t = marked_path(m,y,L); + if t==1 + break; %stop when marked path found + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_struct_pdag_pc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,129 @@ +function [pdag, G] = learn_struct_pdag_pc(cond_indep, n, k, varargin) +% LEARN_STRUCT_PDAG_PC Learn a partially oriented DAG (pattern) using the PC algorithm +% P = learn_struct_pdag_pc(cond_indep, n, k, ...) +% +% n is the number of nodes. +% k is an optional upper bound on the fan-in (default: n) +% cond_indep is a boolean function that will be called as follows: +% feval(cond_indep, x, y, S, ...) +% where x and y are nodes, and S is a set of nodes (positive integers), +% and ... are any optional parameters passed to this function. +% +% The output P is an adjacency matrix, in which +% P(i,j) = -1 if there is an i->j edge. +% P(i,j) = P(j,i) = 1 if there is an undirected edge i <-> j +% +% The PC algorithm does structure learning assuming all variables are observed. +% See Spirtes, Glymour and Scheines, "Causation, Prediction and Search", 1993, p117. +% This algorithm may take O(n^k) time if there are n variables and k is the max fan-in, +% but this is quicker than the Verma-Pearl IC algorithm, which is always O(n^n). + + +sep = cell(n,n); +ord = 0; +done = 0; +G = ones(n,n); +G=setdiag(G,0); +while ~done + done = 1; + [X,Y] = find(G); + for i=1:length(X) + x = X(i); y = Y(i); + %nbrs = mysetdiff(myunion(neighbors(G, x), neighbors(G,y)), [x y]); + nbrs = mysetdiff(neighbors(G, y), x); % bug fix by Raanan Yehezkel <raanany@ee.bgu.ac.il> 6/27/04 + if length(nbrs) >= ord & G(x,y) ~= 0 + done = 0; + %SS = subsets(nbrs, ord, ord); % all subsets of size ord + SS = subsets1(nbrs, ord); + for si=1:length(SS) + S = SS{si}; + if feval(cond_indep, x, y, S, varargin{:}) + %if isempty(S) + % fprintf('%d indep of %d ', x, y); + %else + % fprintf('%d indep of %d given ', x, y); fprintf('%d ', S); + %end + %fprintf('\n'); + + % diagnostic + %[CI, r] = cond_indep_fisher_z(x, y, S, varargin{:}); + %fprintf(': r = %6.4f\n', r); + + G(x,y) = 0; + G(y,x) = 0; + sep{x,y} = myunion(sep{x,y}, S); + sep{y,x} = myunion(sep{y,x}, S); + break; % no need to check any more subsets + end + end + end + end + ord = ord + 1; +end + + +% Create the minimal pattern, +% i.e., the only directed edges are V structures. +pdag = G; +[X, Y] = find(G); +% We want to generate all unique triples x,y,z +% This code generates x,y,z and z,y,x. +for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(G(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ~ismember(y, sep{x,z}) & ~ismember(y, sep{z,x}) + %fprintf('%d -> %d <- %d\n', x, y, z); + pdag(x,y) = -1; pdag(y,x) = 0; + pdag(z,y) = -1; pdag(y,z) = 0; + end + end +end + +% Convert the minimal pattern to a complete one, +% i.e., every directed edge in P is compelled +% (must be directed in all Markov equivalent models), +% and every undirected edge in P is reversible. +% We use the rules of Pearl (2000) p51 (derived in Meek (1995)) + +old_pdag = zeros(n); +iter = 0; +while ~isequal(pdag, old_pdag) + iter = iter + 1; + old_pdag = pdag; + % rule 1 + [A,B] = find(pdag==-1); % a -> b + for i=1:length(A) + a = A(i); b = B(i); + C = find(pdag(b,:)==1 & G(a,:)==0); % all nodes adj to b but not a + if ~isempty(C) + pdag(b,C) = -1; pdag(C,b) = 0; + %fprintf('rule 1: a=%d->b=%d and b=%d-c=%d implies %d->%d\n', a, b, b, C, b, C); + end + end + % rule 2 + [A,B] = find(pdag==1); % unoriented a-b edge + for i=1:length(A) + a = A(i); b = B(i); + if any( (pdag(a,:)==-1) & (pdag(:,b)==-1)' ); + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 2: %d -> %d\n', a, b); + end + end + % rule 3 + [A,B] = find(pdag==1); % a-b + for i=1:length(A) + a = A(i); b = B(i); + C = find( (pdag(a,:)==1) & (pdag(:,b)==-1)' ); + % C contains nodes c s.t. a-c->ba + G2 = setdiag(G(C, C), 1); + if any(G2(:)==0) % there are 2 different non adjacent elements of C + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 3: %d -> %d\n', a, b); + end + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_struct_pdag_pc_constrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,177 @@ +function [pdag, G] = dn_learn_struct_pdag_pc_constrain(adj, cond_indep, n, k, varargin) +% LEARN_STRUCT_PDAG_PC Learn a partially oriented DAG (pattern) using the PC algorithm +% Pdag = learn_struct_pdag_pc_constrain(adj, cond_indep, n, k, ...) +% +% adj = adjacency matrix learned from dependency network P(i,j) = 1 => i--j; 0 => i j +% n is the number of nodes. +% k is an optional upper bound on the fan-in (default: n) +% cond_indep is a boolean function that will be called as follows: +% feval(cond_indep, x, y, S, ...) +% where x and y are nodes, and S is a set of nodes (positive integers), +% and ... are any optional parameters passed to this function. +% +%Output +% pdag Partially directed graph +% G Resulting adjacency graph prior to setting direction arrows +% +% The output P is an adjacency matrix, in which +% P(i,j) = -1 if there is an i->j edge. +% P(i,j) = P(j,i) = 1 if there is an undirected edge i <-> j +% +% The PC algorithm does structure learning assuming all variables are observed. +% See Spirtes, Glymour and Scheines, "Causation, Prediction and Search", 1993, p117. +% This algorithm may take O(n^k) time if there are n variables and k is the max fan-in, +% but this is quicker than the Verma-Pearl IC algorithm, which is always O(n^n). +% +%% Example +%% Given data in a comma separated, filename starting with the variable labels, then the data in rows. +%% filename test.txt consists of: +%% +%% Earthquake,Burglar,Radio,Alarm,Call +%% 1,2,2,2,1 +%% 1,1,2,1,2 +%% . . . +%[CovMatrix, obs, varfields] = CovMat('test.txt',5); +% +%dn = zeros(5,5); +%dn(1,2) = 1; % This was the known Markov blanket of the system that generated test.txt +%dn(2,1) = 1; +%dn(2,4) = 1; +%dn(4,2) = 1; +%dn(1,3) = 1; +%dn(3,1) = 1; +%dn(1,4) = 1; +%dn(4,1) = 1; +%dn(4,5) = 1; +%dn(5,4) = 1; +%dn(3,5) = 1; %loop r->c +%dn(5,3) = 1; %loop c-r +%dn(3,4) = 1; +%dn(4,3) = 1; +% +%max_fan_in = 4; +%alpha = 0.05; +% +%[pdag G] = learn_struct_pdag_pc_constrain(dn,'cond_indep_fisher_z', 5, max_fan_in, CovMatrix, obs, alpha); +%% +%% +%% Gary Bradski, 7/2002 Modified this to take an adjacency matrix from a dependency network. + + +sep = cell(n,n); +ord = 0; +done = 0; +G = ones(n,n); +G=setdiag(G,0); + +while ~done + done = 1; + [X,Y] = find(G); + for i=1:length(X) + x = X(i); y = Y(i); +% nbrs = mysetdiff(myunion(neighbors(G, x), neighbors(G,y)), [x y]);%parents, children, but not self + nbrs = mysetdiff(myunion(neighbors(adj, x), neighbors(adj,y)), [x y]);%parents, children, but not self + + if length(nbrs) >= ord & G(x,y) ~= 0 + done = 0; + SS = subsets(nbrs, ord, ord); % all subsets of size ord + for si=1:length(SS) + S = SS{si}; + %if (feval(dsep,x,y,S,adj)) | (feval(cond_indep, x, y, S, varargin{:})) + if feval(cond_indep, x, y, S, varargin{:}) + %if isempty(S) + % fprintf('%d indep of %d ', x, y); + %else + % fprintf('%d indep of %d given ', x, y); fprintf('%d ', S); + %end + %fprintf('\n'); + + % diagnostic + %[CI, r] = cond_indep_fisher_z(x, y, S, varargin{:}); + %fprintf(': r = %6.4f\n', r); + + G(x,y) = 0; + G(y,x) = 0; + adj(x,y) = 0; %make sure found cond. independencies are marked out + adj(y,x) = 0; + sep{x,y} = myunion(sep{x,y}, S); + sep{y,x} = myunion(sep{y,x}, S); + break; % no need to check any more subsets + end + end + end + end + ord = ord + 1; +end + + + + +% Create the minimal pattern, +% i.e., the only directed edges are V structures. + +pdag = G; +[X, Y] = find(G); +% We want to generate all unique triples x,y,z +% This code generates x,y,z and z,y,x. +for i=1:length(X) + x = X(i); + y = Y(i); + Z = find(G(y,:)); + Z = mysetdiff(Z, x); + for z=Z(:)' + if G(x,z)==0 & ~ismember(y, sep{x,z}) & ~ismember(y, sep{z,x}) + %fprintf('%d -> %d <- %d\n', x, y, z); + pdag(x,y) = -1; pdag(y,x) = 0; + pdag(z,y) = -1; pdag(y,z) = 0; + end + end +end + +% Convert the minimal pattern to a complete one, +% i.e., every directed edge in P is compelled +% (must be directed in all Markov equivalent models), +% and every undirected edge in P is reversible. +% We use the rules of Pearl (2000) p51 (derived in Meek (1995)) + +old_pdag = zeros(n); +iter = 0; +while ~isequal(pdag, old_pdag) + iter = iter + 1; + old_pdag = pdag; + % rule 1 + [A,B] = find(pdag==-1); % a -> b + for i=1:length(A) + a = A(i); b = B(i); + C = find(pdag(b,:)==1 & G(a,:)==0); % all nodes adj to b but not a + if ~isempty(C) + pdag(b,C) = -1; pdag(C,b) = 0; + %fprintf('rule 1: a=%d->b=%d and b=%d-c=%d implies %d->%d\n', a, b, b, C, b, C); + end + end + % rule 2 + [A,B] = find(pdag==1); % unoriented a-b edge + for i=1:length(A) + a = A(i); b = B(i); + if any( (pdag(a,:)==-1) & (pdag(:,b)==-1)' ); + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 2: %d -> %d\n', a, b); + end + end + % rule 3 + [A,B] = find(pdag==1); % a-b + for i=1:length(A) + a = A(i); b = B(i); + C = find( (G(a,:)==1) & (pdag(:,b)==-1)' ); + % C contains nodes c s.t. a-c->ba + G2 = setdiag(G(C, C), 1); + if any(G2(:)==0) % there are 2 different non adjacent elements of C + pdag(a,b) = -1; pdag(b,a) = 0; + %fprintf('rule 3: %d -> %d\n', a, b); + end + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/mcmc_sample_to_hist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function mcmc_post = mcmc_sample_to_hist(sampled_graphs, dags) +% MCMC_SAMPLE_TO_HIST Convert a set of sampled dags into a histogram over dags +% hist = mcmc_sample_to_hist(sampled_graphs, dags) +% +% sampled_graphs{m} is the m'th sampled dag +% dags{i} is the i'th dag in the hypothesis space +% hist(i) = Pr(model i | data) + +ndags = length(dags); +nsamples = length(sampled_graphs); +nnodes = length(dags{1}); +% sampled_bitv(m, :) is the m'th sampled graph represented as a vector of n^2 bits, computed +% by stacking the columns of the adjacency matrix vertically. +sampled_bitvs = zeros(nsamples, nnodes*nnodes); +for m=1:nsamples + sampled_bitvs(m, :) = sampled_graphs{m}(:)'; +end + +[ugraphs, I, J] = unique(sampled_bitvs, 'rows'); % each row of ugraphs is a unique bit vector +sampled_indices = subv2ind(2*ones(1,nnodes*nnodes), ugraphs+1); +counts = hist(J, 1:size(ugraphs,1)); % counts(i) = number of times graphs(i,:) occurs in the sample + +mcmc_post = zeros(1, ndags); +for i=1:ndags + bitv = dags{i}(:)'; + % Find the samples that corresponds to this graph by converting the graphs to bitvectors and + % then to integers. + ndx = subv2ind(2*ones(1,nnodes*nnodes), bitv+1); + locn = find(ndx == sampled_indices); + if ~isempty(locn) + mcmc_post(i) = counts(locn); + end +end +mcmc_post = normalise(mcmc_post); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/mk_schedule.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function schedule = mk_schedule(init_temp, final_temp, anneal_rate) + +init_temp = 10; final_temp = 1e-2; anneal_rate = 0.8; +schedule = []; +temp=init_temp; +schedule = [schedule temp]; +while temp > final_temp + temp = temp * anneal_rate; + schedule = [schedule temp]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/mk_tetrad_data_file.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function mk_tetrad_data_file(filename, samples, sig) +% MK_TETRAD_DATA_FILE Make a file containing raw discrete data for input to TETRAD +% mk_tetrad_data_file(filename, samples, sig) +% +% samples(i,j) is the value for case i, variable j +% The resulting file can be used for the 'build' part of Tetrad. +% For details on tetrad, see hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html + +[nsamples N] = size(samples); + +fid = fopen(filename, 'w'); +fprintf(fid, '/Raw\n'); +fprintf(fid, '%d\n', nsamples); +for i=1:N + fprintf(fid, 'x%d ', i); +end +fprintf(fid, '\n'); +for i=1:nsamples + fprintf(fid, '%d ', samples(i,:)-1); % tetrad counts from 0 + fprintf(fid, '\n'); +end +%fprintf(fid, '/Knowledge\n'); +%fprintf(fid, 'Significance %4.2f\n', sig); +fclose(fid); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/score_dags.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function score = score_dags(data, ns, dags, varargin) +% SCORE_DAGS Compute the score of one or more DAGs +% score = score_dags(data, ns, dags, varargin) +% +% data{i,m} = value of node i in case m (can be a cell array). +% node_sizes(i) is the number of size of node i. +% dags{g} is the g'th dag +% score(g) is the score of the i'th dag +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% +% e.g., score = score_dags(data, ns, mk_all_dags(n), 'scoring_fn', 'bic', 'params', []); +% +% If the DAGs have a lot of families in common, we can cache the sufficient statistics, +% making this potentially more efficient than scoring the DAGs one at a time. +% (Caching is not currently implemented, however.) + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; + +u = [1:ncases]'; % DWH +isclamped = 0; %DWH +clamped = zeros(n, ncases); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}, isclamped = 1; %DWH + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +NG = length(dags); +score = zeros(1, NG); +for g=1:NG + dag = dags{g}; + for j=1:n + if isclamped %DWH + u = find(clamped(j,:)==0); + end + ps = parents(dag, j); + score(g) = score(g) + score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/score_dags_old.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function score = score_dags(data, ns, dags, varargin) +% SCORE_DAGS Compute the score of one or more DAGs +% score = score_dags(data, ns, dags, varargin) +% +% data{i,m} = value of node i in case m (can be a cell array). +% node_sizes(i) is the number of size of node i. +% dags{g} is the g'th dag +% score(g) is the score of the i'th dag +% +% The following optional arguments can be specified in the form of name/value pairs: +% [default value in brackets] +% +% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ] +% Currently, only networks with all tabular nodes support Bayesian scoring. +% type - type{i} is the type of CPD to use for node i, where the type is a string +% of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ] +% params - params{i} contains optional arguments passed to the CPD constructor for node i, +% or [] if none. [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ] +% discrete - the list of discrete nodes [ 1:N ] +% clamped - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ] +% +% e.g., score = score_dags(data, ns, mk_all_dags(n), 'scoring_fn', 'bic', 'params', []); +% +% If the DAGs have a lot of families in common, we can cache the sufficient statistics, +% making this potentially more efficient than scoring the DAGs one at a time. +% (Caching is not currently implemented, however.) + +[n ncases] = size(data); + +% set default params +type = cell(1,n); +params = cell(1,n); +for i=1:n + type{i} = 'tabular'; + params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 }; +end +scoring_fn = 'bayesian'; +discrete = 1:n; +clamped = zeros(n, ncases); + +args = varargin; +nargs = length(args); +for i=1:2:nargs + switch args{i}, + case 'scoring_fn', scoring_fn = args{i+1}; + case 'type', type = args{i+1}; + case 'discrete', discrete = args{i+1}; + case 'clamped', clamped = args{i+1}; + case 'params', if isempty(args{i+1}), params = cell(1,n); else params = args{i+1}; end + end +end + +NG = length(dags); +score = zeros(1, NG); +for g=1:NG + dag = dags{g}; + for j=1:n + u = find(clamped(j,:)==0); + ps = parents(dag, j); + score(g) = score(g) + score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j}); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/learning/score_family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args) +% SCORE_FAMILY_COMPLETE Compute the score of a node and its parents given completely observed data +% score = score_family(j, ps, node_type, scoring_fn, ns, discrete, data, args) +% +% data(i,m) is the value of node i in case m (can be a cell array) +% args is a cell array containing optional arguments passed to the constructor, +% or is [] if none +% +% We create a whole Bayes net which only connects parents to node, +% where node has a CPD of the specified type (with default parameters). +% We then evaluate its score ('bic' or 'bayesian') + +% We should use a cache to avoid unnecessary computation. +% In particular, log_marginal_prob_node for tabular CPDs calls gammaln +% and compute_counts, both of which are slow. + +[n ncases] = size(data); +dag = zeros(n,n); +% SML added to sort ps b/c mk_bnet, learn_params use sorted ps to make +% CPTs +% Kevin had: if ~isempty(ps), dag(ps, j) = 1; end +if ~isempty(ps), dag(ps, j) = 1;, ps = sort(ps);, end + +bnet = mk_bnet(dag, ns, 'discrete', discrete); +%bnet.CPD{j} = xxx_CPD(bnet, j); +%eval(sprintf('bnet.CPD{j} = %s_CPD(bnet, j);', node_type)); +fname = sprintf('%s_CPD', node_type); +%fprintf('score CPD %d\n', j); +if isempty(args) + bnet.CPD{j} = feval(fname, bnet, j); +else + bnet.CPD{j} = feval(fname, bnet, j, args{:}); +end +switch scoring_fn + case 'bic', + fam = [ps j]; + %score = BIC_score_CPD(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + %bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + + % SML 03/16/04 had to special case gaussian b/c generic_CPD/learn_params + % no longer supported because of simple interface to learn_params + % introduced by KPM for tabular nodes below: + % KPM 9 June 04 - tabular nodes have changed back! + if 1 % (isempty(find(j==discrete))) + bnet.CPD{j} = learn_params(bnet.CPD{j}, fam, data, ns, bnet.cnodes); + else + bnet.CPD{j} = learn_params(bnet.CPD{j}, data(fam, :)); + end + L = log_prob_node(bnet.CPD{j}, data(j,:), data(ps,:)); + S = struct(bnet.CPD{j}); % violate object privacy + score = L - 0.5*S.nparams*log(ncases); + case 'bayesian', + %score = bayesian_score_CPD(bnet.CPD{j}, data(fam, :)); + score = log_marg_prob_node(bnet.CPD{j}, data(j,:), data(ps,:)); + otherwise, + error(['unrecognized scoring fn ' scoring_fn]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/license.gpl.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,450 @@ +This library is free software; you can redistribute it and/or +modify it under the terms of the GNU Library General Public +License version 2 as published by the Free Software Foundation. + +This library is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. + +GNU Library General Public License + +---------------------------------------------------------------------------- + +Table of Contents + + * GNU LIBRARY GENERAL PUBLIC LICENSE + o Preamble + o TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION + +---------------------------------------------------------------------------- + +GNU LIBRARY GENERAL PUBLIC LICENSE + +Version 2, June 1991 + +Copyright (C) 1991 Free Software Foundation, Inc. +675 Mass Ave, Cambridge, MA 02139, USA +Everyone is permitted to copy and distribute verbatim copies +of this license document, but changing it is not allowed. + +[This is the first released version of the library GPL. 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +/cg_can_to_mom.m/1.1.1.1/Wed May 29 15:59:58 2002// +/cg_mom_to_can.m/1.1.1.1/Wed May 29 15:59:58 2002// +/cgpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/enter_cts_evidence_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/enter_discrete_evidence_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_pots.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed Jul 30 13:38:24 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@cgpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed Jul 30 13:38:08 2003// +/simple_marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@cgpot/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/Old/normalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the CG potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +% Marginalize down to [], so that the normalizing constant becomes Pr(E) +temp = marginalize_pot(cg_can_to_mom(pot), []); +%loglik = temp.mom{1}.logp; +[temp2, loglik] = normalize_pot(temp.mom{1}); + +% Adjust scale factor to reflect the fact that the pot now represents Pr(X | E) instead of Pr(X,E). + +scale = -loglik; +if 1 +switch pot.subtype + case 'm' + for i=1:pot.dsize + pot.mom{i} = rescale_pot(pot.mom{i}, scale); + end + case 'c' + for i=1:pot.dsize + pot.can{i} = rescale_pot(pot.can{i}, scale); + end +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/Old/set_domain_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (cgpot) +% pot = set_domain_pot(pot, domain) + +delta = domain(1) - pot.domain(1); +assert(all(domain == pot.domain + delta)); +pot.domain = pot.domain + delta; +pot.ddom = pot.ddom + delta; +pot.cdom = pot.cdom + delta;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/Old/simple_marginalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function smallpot = marginalize_pot(bigpot, keep) +% MARGINALIZE_POT Marginalize a cgpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep) + +sumover = mysetdiff(bigpot.domain, keep); +csumover = myintersect(sumover, bigpot.cdom); +dsumover = myintersect(sumover, bigpot.ddom); +dkeep = myintersect(keep, bigpot.ddom); +ckeep = myintersect(keep, bigpot.cdom); +%ns = sparse(1, max(bigpot.domain)); % must be full, so I is an integer +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.ddom) = bigpot.dsizes; +ns(bigpot.cdom) = bigpot.csizes; + +% sum(ns(csumover))==0 is like isempty(csumover) but handles observed nodes. +% Similarly, prod(ns(dsumover))==1 is like isempty(dsumover) + +% Marginalize the cts parts. +% If we are in canonical form, we stay that way, since moment form might not exist. +% Besides, we would like to minimize the number of conversions. +if sum(ns(csumover)) > 0 + if bigpot.subtype == 'm' + for i=1:bigpot.dsize + bigpot.mom{i} = marginalize_pot(bigpot.mom{i}, ckeep); + end + else + for i=1:bigpot.dsize + bigpot.can{i} = marginalize_pot(bigpot.can{i}, ckeep); + end + end +end + +% If we are not marginalizing over any discrete nodes, we are done. +if prod(ns(dsumover))==1 + smallpot = cgpot(dkeep, ckeep, ns, bigpot.can, bigpot.mom, bigpot.subtype); + return; +end + +% To marginalize the discrete parts, we must be in moment form. +bigpot = cg_can_to_mom(bigpot); + +I = prod(ns(dkeep)); +J = prod(ns(dsumover)); +C = sum(ns(ckeep)); + +% Reshape bigpot into the form mu1(:,j,i), where i is in dkeep, j is in dsumover +T1 = zeros(I,J); +mu1 = zeros(C,J,I); +Sigma1 = zeros(C,C,J,I); +sum_map = find_equiv_posns(dsumover, bigpot.ddom); +keep_map = find_equiv_posns(dkeep, bigpot.ddom); +iv = zeros(1, length(bigpot.ddom)); % index vector +for i=1:I + keep_iv = ind2subv(ns(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(ns(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(ns(bigpot.ddom), iv); + mom = struct(bigpot.mom{k}); % violate object privacy + T1(i,j) = exp(mom.logp); + if C > 0 % so mu1 and Sigma1 are non-empty + mu1(:,j,i) = mom.mu; + Sigma1(:,:,j,i) = mom.Sigma; + end + end +end + +% Collapse the mixture of Gaussians +coef = mk_stochastic(T1); % coef must be convex combination +T2 = sum(T1,2); +T2 = T2 + (T2==0)*eps; +%if C > 0, disp('collapsing onto '); disp(leep); end +mu = []; +Sigma = []; +mom = cell(1,I); +for i=1:I + if C > 0 + [mu, Sigma] = collapse_mog(mu1(:,:,i), Sigma1(:,:,:,i), coef(i,:)); + end + logp = log(T2(i)); + mom{i} = mpot(ckeep, ns(ckeep), logp, mu, Sigma); +end + +smallpot = cgpot(dkeep, ckeep, ns, [], mom, 'm'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/cg_can_to_mom.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function pot = cg_can_to_mom(pot) +% CG_CAN_TO_MOM Convert a CG potential from canonical to moment form, if necessary. +% pot = cg_can_to_mom(pot) + +if pot.subtype ~= 'm' + for i=1:pot.dsize + pot.mom{i} = cpot_to_mpot(pot.can{i}); + end + pot.subtype = 'm'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/cg_mom_to_can.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function pot = cg_mom_to_can(pot) +% CG_MOM_TO_CAN Convert a CG potential from moment to canonical form, if necessary. +% pot = cg_mom_to_can(pot) + +if pot.subtype ~= 'c' + for i=1:pot.dsize + pot.can{i} = mpot_to_cpot(pot.mom{i}); + end + pot.subtype = 'c'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/cgpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function pot = cgpot(ddom, cdom, node_sizes, can, mom, subtype) +% CPOT Make a canonical CG potential. +% function pot = cgpot(ddom, cdom, node_sizes, can, mom, subtype) +% +% node_sizes(i) is the size of the i'th node. +% can and mom default to 0s. +% subtype defaults to 'c'. + +if nargin < 6, subtype = 'c'; end + +pot.ddom = ddom; +pot.cdom = cdom; +node_sizes = node_sizes(:)'; % row vectors print better +pot.domain = myunion(ddom, cdom); +pot.dsizes = node_sizes(pot.ddom); +pot.dsize = prod(node_sizes(pot.ddom)); +pot.csizes = node_sizes(pot.cdom); +pot.csize = sum(node_sizes(pot.cdom)); +pot.subtype = subtype; + +if nargin < 4 + can = cell(1, pot.dsize); + for i=1:pot.dsize + can{i} = cpot(cdom, node_sizes(cdom)); + end +end +pot.can = can; + +if nargin < 5 + mom = cell(1, pot.dsize); + for i=1:pot.dsize + mom{i} = mpot(cdom, node_sizes(cdom)); + end +end +pot.mom = mom; + +pot = class(pot, 'cgpot'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(pot) + +disp('conditional Gaussian potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/divide_by_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function bigpot = divide_by_pot(bigpot, smallpot) +% DIVIDE_BY_POT bigpot /= smallpot for cgpot +% bigpot = divide_by_pot(bigpot, smallpot) +% +% smallpot's domain must be a subset of bigpot's domain. + +bigpot = cg_mom_to_can(bigpot); +smallpot = cg_mom_to_can(smallpot); + +mask = find_equiv_posns(smallpot.ddom, bigpot.ddom); +for i=1:bigpot.dsize + if isempty(smallpot.ddom) + src = 1; + else + sub = ind2subv(bigpot.dsizes, i); + src = subv2ind(smallpot.dsizes, sub(mask)); + end + bigpot.can{i} = divide_by_pot(bigpot.can{i}, smallpot.can{src}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/domain_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function dom = domain_pot(pot) +% DOMAIN_POT Return the domain of this cgpot. +% dom = domain_pot(pot) + +dom = pot.domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/enter_cts_evidence_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function pot = enter_cts_evidence_pot(pot, Y, y) +% function pot = enter_cts_evidence_pot(pot, Y, y) cgpot + + +pot = cg_mom_to_can(pot); +for i=1:pot.dsize + pot.can{i} = enter_cts_evidence_pot(pot.can{i}, Y, y); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/enter_discrete_evidence_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function pot = enter_discrete_evidence_pot(pot, Y, y) + +%ns = sparse(1, max(pot.domain)); +ns = zeros(1, max(pot.domain)); +ns(pot.ddom) = pot.dsizes; +ns(pot.cdom) = pot.csizes; + +ddom = pot.ddom; +S = prod(ns(ddom)); +sub = ind2subv(ns(ddom), 1:S); +mask = find_equiv_posns(Y, ddom); +sub(mask) = y; +ndx = subv2ind(ns(ddom), sub); + +pot.can = pot.can(ndx); +pot.mom = pot.mom(ndx);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/marginalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +function smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% MARGINALIZE_POT Marginalize a cgpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% +% If maximize = 1, we raise an error. +% useC is ignored. + +if nargin < 3, maximize = 0; end +assert(~maximize); + + +sumover = mysetdiff(bigpot.domain, keep); +csumover = myintersect(sumover, bigpot.cdom); +dsumover = myintersect(sumover, bigpot.ddom); +dkeep = myintersect(keep, bigpot.ddom); +ckeep = myintersect(keep, bigpot.cdom); +%ns = sparse(1, max(bigpot.domain)); % must be full, so I is an integer +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.ddom) = bigpot.dsizes; +ns(bigpot.cdom) = bigpot.csizes; + +% sum(ns(csumover))==0 is like isempty(csumover) but handles observed nodes. +% Similarly, prod(ns(dsumover))==1 is like isempty(dsumover) + +% Marginalize the cts parts. +% If we are in canonical form, we stay that way, since moment form might not exist. +% Besides, we would like to minimize the number of conversions. +if sum(ns(csumover)) > 0 + if bigpot.subtype == 'm' + for i=1:bigpot.dsize + bigpot.mom{i} = marginalize_pot(bigpot.mom{i}, ckeep); + end + else + for i=1:bigpot.dsize + bigpot.can{i} = marginalize_pot(bigpot.can{i}, ckeep); + end + end +end + +% If we are not marginalizing over any discrete nodes, we are done. +if prod(ns(dsumover))==1 + smallpot = cgpot(dkeep, ckeep, ns, bigpot.can, bigpot.mom, bigpot.subtype); + return; +end + +% To marginalize the discrete parts, we partition the cts parts into those that depend +% on dkeep (i) and those that depend on on dsumover (j). + +I = prod(ns(dkeep)); +J = prod(ns(dsumover)); +C = sum(ns(ckeep)); +sum_map = find_equiv_posns(dsumover, bigpot.ddom); +keep_map = find_equiv_posns(dkeep, bigpot.ddom); +iv = zeros(1, length(bigpot.ddom)); % index vector + +% If in canonical form, marginalize if possible, else convert to moment form. +if 0 & bigpot.subtype == 'c' + p1 = zeros(I,J); + h1 = zeros(C,J,I); + K1 = zeros(C,C,J,I); + for i=1:I + keep_iv = ind2subv(ns(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(ns(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(ns(bigpot.ddom), iv); + can = struct(bigpot.can{k}); % violate object privacy + p1(i,j) = exp(can.g); + if C > 0 % so mu1 and Sigma1 are non-empty + h1(:,j,i) = can.h; + K1(:,:,j,i) = can.K; + end + end + end + + % If the cts parts do not depend on j, we can just marginalize the weighting coefficient g. + jdepends = 0; + for i=1:I + for j=2:J + if ~approxeq(h1(:,j,i), h1(:,1,i)) | ~approxeq(K1(:,:,j,i), K1(:,:,1,i)) + jdepends = 1; + break + end + end + end + + if ~jdepends + %g2 = log(sum(p1, 2)); + g2 = zeros(I,1); + for i=1:I + s = sum(p1(i,:)); + if s > 0 + g2(i) = log(s); + end + end + h2 = h1; + K2 = K1; + can = cell(1,I); + j = 1; % arbitrary + for i=1:I + can{i} = cpot(ckeep, ns(ckeep), g2(i), h2(:,j,i), K2(:,:,j,i)); + end + smallpot = cgpot(dkeep, ckeep, ns, can, [], 'c'); + return; + else + % Since the cts parts depend on j, we must convert to moment form + bigpot = cg_can_to_mom(bigpot); + end +end + + +% Marginalize in moment form +bigpot = cg_can_to_mom(bigpot); + +% Now partition the moment components. +T1 = zeros(I,J); +mu1 = zeros(C,J,I); +Sigma1 = zeros(C,C,J,I); +for i=1:I + keep_iv = ind2subv(ns(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(ns(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(ns(bigpot.ddom), iv); + mom = struct(bigpot.mom{k}); % violate object privacy + T1(i,j) = exp(mom.logp); + if C > 0 % so mu1 and Sigma1 are non-empty + mu1(:,j,i) = mom.mu; + Sigma1(:,:,j,i) = mom.Sigma; + end + end +end + +% Collapse the mixture of Gaussians +coef = mk_stochastic(T1); % coef must be convex combination +T2 = sum(T1,2); +T2 = T2 + (T2==0)*eps; +%if C > 0, disp('collapsing onto '); disp(leep); end +mu = []; +Sigma = []; +mom = cell(1,I); +for i=1:I + if C > 0 + [mu, Sigma] = collapse_mog(mu1(:,:,i), Sigma1(:,:,:,i), coef(i,:)); + end + logp = log(T2(i)); + mom{i} = mpot(ckeep, ns(ckeep), logp, mu, Sigma); +end + +smallpot = cgpot(dkeep, ckeep, ns, [], mom, 'm'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/multiply_by_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function bigpot = multiply_by_pot(bigpot, smallpot, varargin) +% MULTIPLY_BY_POT bigpot *= smallpot for cgpot +% bigpot = multiply_by_pot(bigpot, smallpot) +% +% smallpot's domain must be a subset of bigpot's domain. + +bigpot = cg_mom_to_can(bigpot); +smallpot = cg_mom_to_can(smallpot); + +mask = find_equiv_posns(smallpot.ddom, bigpot.ddom); +for i=1:bigpot.dsize + if isempty(smallpot.ddom) + src = 1; + else + sub = ind2subv(bigpot.dsizes, i); + src = subv2ind(smallpot.dsizes, sub(mask)); + end + bigpot.can{i} = multiply_by_pot(bigpot.can{i}, smallpot.can{src}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/multiply_pots.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function T = multiply_pots(T1, T2) +% MULTIPLY_POTS Multiply a pair of dpots together pointwise (cgpot) +% T = multiply_pots(pots) + +ddom = myunion(T1.ddom, T2.ddom); +cdom = myunion(T1.cdom, T2.cdom); +dom = myunion(ddom, cdom); +ns = zeros(1, max(dom)); +ns(T1.ddom) = T1.dsizes; +ns(T2.ddom) = T2.dsizes; +ns(T1.cdom) = T1.csizes; +ns(T2.cdom) = T2.csizes; + +T = cgpot(ddom, cdom, ns); +T = multiply_by_pot(T, T1); +T = multiply_by_pot(T, T2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/normalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the CG potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +% Marginalize down to [], so that the normalizing constant becomes Pr(E) +temp = cg_can_to_mom(marginalize_pot(pot, [])); +%loglik = temp.mom{1}.logp; +[temp2, loglik] = normalize_pot(temp.mom{1}); + +% Adjust scale factor to reflect the fact that the pot now represents Pr(X | E) instead of Pr(X,E). + +scale = -loglik; +if 1 +switch pot.subtype + case 'm' + for i=1:pot.dsize + pot.mom{i} = rescale_pot(pot.mom{i}, scale); + end + case 'c' + for i=1:pot.dsize + pot.can{i} = rescale_pot(pot.can{i}, scale); + end +end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/pot_to_marginal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a cgpot to a marginal structure. +% m = pot_to_marginal(pot) + +pot = cg_can_to_mom(pot); +m.domain = pot.domain; +n = pot.csize; +d = length(pot.mom); +if n==0 + m.mu = []; + m.Sigma = []; +else + m.mu = zeros(n, d); + m.Sigma = zeros(n, n, d); +end +m.T = 0*myones(pot.dsizes); +for i=1:pot.dsize + s = struct(pot.mom{i}); % violate privacy of object + if n > 0 + m.mu(:,i) = s.mu; + m.Sigma(:,:,i) = s.Sigma; + end + m.T(i) = exp(s.logp); +end +if isvectorBNT(m.T) + m.T = m.T(:)'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cgpot/set_domain_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (cgpot) +% pot = set_domain_pot(pot, domain) + +delta = domain(1) - pot.domain(1); +assert(all(domain == pot.domain + delta)); +pot.domain = pot.domain + delta; +pot.ddom = pot.ddom + delta; +pot.cdom = pot.cdom + delta; +cdomain = pot.cdom; +n = prod(pot.dsizes); +if(pot.subtype == 'm') + for i = 1: n + pot.mom{i} = set_domain_pot(pot.mom{i}, cdomain); + end +end +if(pot.subtype == 'c') + for i = 1: n + pot.can{i} = set_domain_pot(pot.can{i}, cdomain); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +/cpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/cpot_to_mpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/enter_cts_evidence_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_pots.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/rescale_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@cpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +/cpot_to_mpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.convert.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@cpot/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/Old/cpot_to_mpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function mom = cpot_to_mpot(can) +% CPOT_TO_MPOT Convert a canonical potential to moment form. +% mom = cpot_to_mpot(can) + +[logp, mu, Sigma] = canonical_to_moment(can.g, can.h, can.K); +mom = mpot(can.domain, can.sizes, logp, mu, Sigma); + +%%%%%%% + +function [logp, mu, Sigma] = canonical_to_moment(g, h, K) +% CANONICAL_TO_MOMENT Convert canonical characteristics to moment form. +% [logp, mu, Sigma] = canonical_to_moment(g, h, K) + +if det(K)==0 + Sigma = inf*size(K); +else + Sigma = inv(K); +end +mu = Sigma*h; +n = length(mu); +if isempty(mu) + logp = g - 0.5*(log(det(K)) - n*log(2*pi)); +else + logp = g - 0.5*(log(det(K)) - n*log(2*pi) - mu'*K*mu); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/Old/normalize_pot.convert.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function [mom2, loglik] = normalize_pot(can) +% NORMALIZE_POT Convert the canonical potential Pr(X,E) into moment potential Pr(X|E) and return log Pr(E). +% [mom, loglik] = normalize_pot(can) + +mom = cpot_to_mpot(can); +mom = struct(mom); % violate privacy of object +loglik = mom.logp; +%mom.logp = 0; % now represents Pr(X | E) instead of Pr(X, E). +mom2 = mpot(mom.domain, mom.sizes, 0, mom.mu, mom.Sigma);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/cpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function pot = cpot(members, sizes, g, h, K) +% CPOT Make a canonical Gaussian potential. +% pot = cpot(members, sizes, g, h, K) +% +% All params default to 0 if omitted. + +n = sum(sizes); +if nargin < 3, g = 0; end +if nargin < 4, h = zeros(n,1); end +if nargin < 5, K = zeros(n,n); end + +pot.domain = members; +pot.sizes = sizes(:)'; +pot.g = g; +pot.h = h; +pot.K = K; +pot = class(pot, 'cpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/cpot_to_mpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function mom = cpot_to_mpot(can) +% CPOT_TO_MPOT Convert a canonical potential to moment form. +% mom = cpot_to_mpot(can) + +[logp, mu, Sigma] = canonical_to_moment(can.g, can.h, can.K); +mom = mpot(can.domain, can.sizes, logp, mu, Sigma); + +%%%%%%% + +function [logp, mu, Sigma] = canonical_to_moment(g, h, K) +% CANONICAL_TO_MOMENT Convert canonical characteristics to moment form. +% [logp, mu, Sigma] = canonical_to_moment(g, h, K) + +n = length(K); +if isempty(K) + logp = g - 0.5*(log(det(K)) - n*log(2*pi)); + Sigma = []; + mu = []; +else + if det(K)==0 + Sigma = inf*ones(n,n); + mu = zeros(n,1); % if the precision is zero, the mean is arbitrary + logp = g; % the scaling factor for the uniform distribution is 1 + else + Sigma = inv(K); + mu = Sigma*h; + logp = g - 0.5*(log(det(K)) - n*log(2*pi) - mu'*K*mu); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(pot) + +disp('canonical potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/divide_by_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function bigpot = divide_by_pot(bigpot, smallpot) +% DIVIDE_BY_POT bigpot /= smallpot for cpot +% bigpot = divide_by_pot(bigpot, smallpot) +% +% smallpot's domain must be a subset of bigpot's domain. + +bigpot.g = bigpot.g - smallpot.g; +if sum(smallpot.sizes) > 0 + mask = find_equiv_posns(smallpot.domain, bigpot.domain); + u = block(mask, bigpot.sizes); + bigpot.h(u) = bigpot.h(u) - smallpot.h; + bigpot.K(u, u) = bigpot.K(u, u) - smallpot.K; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/domain_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function dom = domain_pot(pot) +% DOMAIN_POT Return the domain of this cpot. +% dom = domain_pot(pot) + +dom = pot.domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/enter_cts_evidence_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function pot = enter_cts_evidence_pot(pot, Y, y) +% function pot = enter_cts_evidence_pot(pot, Y, y) (cpot) + +ns = sparse(1, max(pot.domain)); +ns(pot.domain) = pot.sizes; + +X = mysetdiff(pot.domain, Y); +[hx, hy, KXX, KXY, KYX, KYY] = partition_matrix_vec(pot.h, pot.K, X, Y, ns); +pot.g = pot.g + hy'*y - 0.5*y'*KYY*y; +if ~isempty(X) + pot.h = hx - KXY*y; + pot.K = KXX; +end + +pot.sizes(find_equiv_posns(Y,pot.domain)) = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/marginalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% MARGINALIZE_POT Marginalize a cpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% +% The maximize argument is ignored - maxing out a Gaussian is the same as summing it out, +% since the mode and mean are equal. +% The useC argument is ignored. + +node_sizes = sparse(1, max(bigpot.domain)); +node_sizes(bigpot.domain) = bigpot.sizes; +sum_over = mysetdiff(bigpot.domain, keep); + +if sum(node_sizes(sum_over))==0 % isempty(sum_over) + %smallpot = bigpot; + smallpot = cpot(keep, node_sizes(keep), bigpot.g, bigpot.h, bigpot.K); +else + [h1, h2, K11, K12, K21, K22] = partition_matrix_vec(bigpot.h, bigpot.K, sum_over, keep, node_sizes); + n = length(h1); + K11inv = inv(K11); + g = bigpot.g + 0.5*(n*log(2*pi) - log(det(K11)) + h1'*K11inv*h1); + if length(h2) > 0 % ~isempty(keep) % we are are actually keeping something + A = K21*K11inv; + h = h2 - A*h1; + K = K22 - A*K12; + else + h = []; + K = []; + end + smallpot = cpot(keep, node_sizes(keep), g, h, K); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/multiply_by_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function bigpot = multiply_by_pot(bigpot, smallpot, varargin) +% MULTIPLY_BY_POT bigpot *= smallpot for cpot +% bigpot = multiply_by_pot(bigpot, smallpot) +% +% smallpot's domain must be a subset of bigpot's domain. + +bigpot.g = bigpot.g + smallpot.g; +if sum(smallpot.sizes) > 0 + mask = find_equiv_posns(smallpot.domain, bigpot.domain); + u = block(mask, bigpot.sizes); + bigpot.h(u) = bigpot.h(u) + smallpot.h; + bigpot.K(u, u) = bigpot.K(u, u) + smallpot.K; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/multiply_pots.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function T = multiply_pots(T1, T2) +% MULTIPLY_POTS Multiply a pair of dpots together pointwise (cpot) +% T = multiply_pots(pots) + +dom = myunion(T1.domain, T2.domain); +ns = sparse(1, max(dom)); +ns(T1.domain) = T1.sizes; +ns(T2.domain) = T2.sizes; +T = cpot(dom, ns(dom)); +T = multiply_by_pot(T, T1); +T = multiply_by_pot(T, T2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/normalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the canonical potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +mom = cpot_to_mpot(pot); % move the normalizing constant out of g, to reveal the coefficient +%loglik = scaling_factor_pot(mom); +%loglik = mom.logp; +[temp, loglik] = normalize_pot(mom); +pot.g = pot.g - loglik; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/pot_to_marginal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a cpot to a marginal structure. +% m = pot_to_marginal(pot) + +mom = cpot_to_mpot(pot); +m = pot_to_marginal(mom);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/rescale_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function pot = rescale_pot(pot, s) +% RESCALE_POT Add a constant to the cpot scale factor. +% pot = rescale_pot(pot, s) + +pot.g = pot.g + s;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@cpot/set_domain_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (dpot) +% pot = set_domain_pot(pot, domain) + +pot.domain = domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +/approxeq_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_pot.m/1.1.1.1/Thu Aug 5 15:25:08 2004// +/domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/dpot.m/1.1.1.1/Tue Oct 1 19:04:44 2002// +/dpot_to_table.m/1.1.1.1/Wed May 29 15:59:58 2002// +/find_most_prob_entry.m/1.1.1.1/Sun Jun 16 19:06:20 2002// +/get_fields.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed Aug 4 19:59:14 2004// +/multiply_by_pot.m/1.1.1.1/Wed Aug 4 19:59:14 2004// +/multiply_pots.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed Aug 4 15:54:48 2004// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/subsasgn.m/1.1.1.1/Wed Apr 27 18:34:48 2005// +/subsref.m/1.1.1.1/Wed Apr 27 18:34:48 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@dpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/approxeq_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function p = approxeq_pot(A, B, tol) + +if nargin < 3, tol = 1e-3; end + +p = approxeq(A.T, B.T, tol);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(pot) + +disp('discrete potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/divide_by_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function Tbig = divide_by_pot(Tbig, Tsmall) +% DIVIDE_BY_POT Tbig /= Tsmall +% Tbig = divide_by_pot(Tbig, Tsmall) +% +% Tsmall's domain must be a subset of Tbig's domain. + +%process sparse dpot, we do not concern only one of the two pots is sparse +if issparse(Tbig.T) & issparse(Tsmall.T) + Tbig.T = divide_by_sparse_table(Tbig.T, Tbig.domain, Tbig.sizes, Tsmall.T, Tsmall.domain, Tsmall.sizes); +else + Tbig.T = divide_by_table(Tbig.T, Tbig.domain, Tbig.sizes, Tsmall.T, Tsmall.domain, Tsmall.sizes); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/domain_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function dom = domain_pot(pot) +% DOMAIN_POT Return the domain of this dpot. +% dom = domain_pot(pot) + +dom = pot.domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/dpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function pot = dpot(domain, sizes, T) +% DPOT Make a discrete (sparse) potential. +% pot = dpot(domain, sizes, T, spar) +% +% sizes(i) is the size of the i'th domain element. +% T defaults to all 1s. + +%assert(length(sizes) == length(domain)); + +pot.domain = domain(:)'; % so we can see it when we display +if nargin < 3 + pot.T = myones(sizes); + %pot.T = ones(1,prod(sizes)); % 1D vector +else + if isempty(T) + pot.T = []; + else + if issparse(T) + pot.T = T; + else + pot.T = myreshape(T, sizes); + end + end +end +pot.sizes = sizes(:)'; +pot = class(pot, 'dpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/dpot_to_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function T = dpot_to_table(pot) + +T = pot.T;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/find_most_prob_entry.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function [indices, pot] = find_most_prob_entry(pot) +% function [indices, pot] = find_most_prob_entry(pot) +% function [indices, pot] = find_most_prob_entry(pot) +% Find the indices of the argmax, and set all other enties to 0. + +%indices = argmax(pot.T); +[m i] = max(pot.T(:)); +indices = ind2subv(pot.sizes, i); +pot.T = 0*myones(pot.sizes); +pot.T(i) = m;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/get_fields.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function val = get_params(pot, name) +% GET_PARAMS Accessor function for a field (dpot) +% val = get_params(pot, name) +% +% e.g., get_params(pot, 'table') or 'domain' + +switch name + case 'table', val = pot.T; + case 'domain', val = pot.domain; + otherwise, + error(['invalid field name ' name]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/marginalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function smallpot = marginalize_pot(bigpot, onto, maximize) +% MARGINALIZE_POT Marginalize a dpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, onto, maximize) +% +% 'onto' must be in ascending order. + +if nargin < 3, maximize = 0; end + +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.domain) = bigpot.sizes; +%assert(isequal(bigpot.sizes, mysize(bigpot.T))); % may fail if there are trailing dimensions of size 1 +if issparse(bigpot.T) + smallT = marg_sparse_table(bigpot.T, bigpot.domain, bigpot.sizes, onto, maximize); +else + smallT = marg_table(bigpot.T, bigpot.domain, bigpot.sizes, onto, maximize); +end +smallpot = dpot(onto, ns(onto), smallT);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/multiply_by_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function Tbig = multiply_by_pot(Tbig, Tsmall) +% MULTIPLY_BY_POT Tbig *= Tsmall +% Tbig = multiply_by_pot(Tbig, Tsmall) +% +% Tsmall's domain must be a subset of Tbig's domain. + +%process sparse dpot, we do not consider only one of the two pots is sparse +if issparse(Tbig.T) & issparse(Tsmall.T) + Tbig.T = mult_by_sparse_table(Tbig.T, Tbig.domain, Tbig.sizes, Tsmall.T, Tsmall.domain, Tsmall.sizes); +else + Tbig.T = mult_by_table(Tbig.T, Tbig.domain, Tbig.sizes, Tsmall.T, Tsmall.domain, Tsmall.sizes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/multiply_pots.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function T = multiply_pots(T1, T2) +% MULTIPLY_POTS Multiply a pair of dpots together pointwise. +% T = multiply_pots(pots) + +dom = myunion(T1.domain, T2.domain); +%ns = sparse(1, max(dom)); % causes problems in myreshape on NT +ns = zeros(1, max(dom)); +ns(T1.domain) = T1.sizes; +ns(T2.domain) = T2.sizes; +T = dpot(dom, ns(dom)); +T = multiply_by_pot(T, T1); +T = multiply_by_pot(T, T2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/normalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the discrete potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +if isempty(pot.T) %add to process sparse + loglik = 0; + return; +end +[pot.T, lik] = normalise(pot.T); +loglik = log(lik + (lik==0)*eps); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/pot_to_marginal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a dpot to a marginal structure. +% m = pot_to_marginal(pot) + +m.domain = pot.domain; +m.T = pot.T; +m.mu = []; +m.Sigma = []; + +%if isvector(m.T) +% m.T = m.T(:); +%end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/set_domain_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (dpot) +% pot = set_domain_pot(pot, domain) + +pot.domain = domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/subsasgn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function B = subsasgn(A, S, B) + +B = builtin('subsasgn', A, S, B);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@dpot/subsref.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function B = subsref(A, S) + +B = builtin('subsref', A, S);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/mpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/mpot_to_cpot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/rescale_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/set_domain_pot.m/1.1.1.1/Wed Jul 30 13:37:52 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@mpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(pot) + +disp('moment Gaussian potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/marginalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% MARGINALIZE_POT Marginalize a mpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep, maximize, useC) +% +% The maximize argument is ignored - maxing out a Gaussian is the same as summing it out, +% since the mode and mean are equal. +% The useC argument is ignored. + + +node_sizes = sparse(1, max(bigpot.domain)); +node_sizes(bigpot.domain) = bigpot.sizes; +sum_over = mysetdiff(bigpot.domain, keep); + +[logp, mu, Sigma] = marginalize_gaussian(bigpot.logp, bigpot.mu, bigpot.Sigma, ... + keep, sum_over, node_sizes); +smallpot = mpot(keep, node_sizes(keep), logp, mu, Sigma); + +%%%%%% + +function [logpX, muX, SXX] = marginalize_gaussian(logp, mu, Sigma, X, Y, ns) +% MARGINALIZE_GAUSSIAN Compute Pr(X) from Pr(X,Y) where X and Y are jointly Gaussian. +% [logpX, muX, SXX] = marginalize_gaussian(logp, mu, Sigma, X, Y, ns) +% +% sizes(i) is the size of the i'th block in domain. + +[muX, muY, SXX, SXY, SYX, SYY] = partition_matrix_vec(mu, Sigma, X, Y, ns); +logpX = logp; % Lauritzen (1996) p161
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/mpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function pot = mpot(members, sizes, logp, mu, Sigma) +% MPOT Make a moment Gaussian potential. +% pot = mpot(members, sizes, logp, mu, Sigma) +% +% All params default to 0 if omitted. + +n = sum(sizes); +if nargin < 3, logp = 0; end +if nargin < 4, mu = zeros(n,1); end +if nargin < 5, Sigma = zeros(n,n); end + +pot.domain = members; +pot.sizes = sizes; +pot.logp = logp; +pot.mu = mu; +pot.Sigma = Sigma;zeros(n,n); +pot = class(pot, 'mpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/mpot_to_cpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function can = mpot_to_cpot(mom) +% MPOT_TO_CPOT Convert a moment potential to canonical form. +% mom = mpot_to_cpot(can) + +[g, h, K] = moment_to_canonical(mom.logp, mom.mu, mom.Sigma); +can = cpot(mom.domain, mom.sizes, g, h, K); + +%%%%%%%%%%% + +function [g, h, K] = moment_to_canonical(logp, mu, Sigma) +% MOMENT_TO_CANONICAL Convert moment characteristics to canonical form. +% [g, h, K] = moment_to_canonical(logp, mu, Sigma) + +K = inv(Sigma); +h = K*mu; +n = length(K); +if isempty(mu) + g = logp + 0.5*(log(det(K)) - n*log(2*pi)); +else + g = logp + 0.5*(log(det(K)) - n*log(2*pi) - mu'*K*mu); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/normalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the moment potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +loglik = pot.logp; +pot.logp = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/pot_to_marginal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a mpot to a marginal structure. +% m = pot_to_marginal(pot) + +m.domain = pot.domain; +m.T = exp(pot.logp); +m.mu = pot.mu; +m.Sigma = pot.Sigma; + +if isvectorBNT(m.T) + m.T = m.T(:)'; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/rescale_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function pot = rescale_pot(pot, s) +% RESCALE_POT Add a constant to the mpot scale factor. +% pot = rescale_pot(pot, s) + +pot.logp = pot.logp + s;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@mpot/set_domain_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function pot = set_domain_pot(pot, domain) +% SET_DOMAIN_POT Change the domain of a potential (mpot) +% pot = set_domain_pot(pot, domain) + +pot.domain = domain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgcpot/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/marginalize_pot.m/1.1.1.1/Sun May 19 22:11:08 2002// +/normalize_pot.m/1.1.1.1/Sun May 19 22:11:08 2002// +/reduce_pot.m/1.1.1.1/Tue Mar 11 17:37:02 2003// +/rescale_pot.m/1.1.1.1/Sun May 19 22:11:08 2002// +/scgcpot.m/1.1.1.1/Sun May 19 22:11:08 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgcpot/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@scgcpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgcpot/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgcpot/marginalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function smallpot = marginalize_pot(bigpot, keepdom, sumoverdom, nodesizes) +% MARGINALIZE_POT Marginalize a mpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep) + +keepsize = sum(nodesizes(keepdom)); +[A1, A2, B1, B2, C11, C12, C21, C22] = partition_matrix_vec_3(bigpot.A, bigpot.B, bigpot.C, keepdom, sumoverdom, nodesizes); +smallpot = scgcpot(keepsize, bigpot.ctailsize, bigpot.p, A1, B1, C11); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgcpot/normalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the element of stable conditional gaussian potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +loglik = log(pot.p); +pot.p = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgcpot/reduce_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [reduced_pot,successful] = reduce(pot,tailnodes) +% Executes the reduce operation defined in +% Stable Local Computation with Conditional Gaussian Distributions +% Steffen L. Lauritzen +% Frank Jensen +% September 1999 +% The potential pot is reduced if B contains any zero columns +% The test are restricted to the positions in tailnodes. +% Any columns successfully deleted are entered in the array successful + +if nargin < 2 + tailnodes = 1:pot.ctailsize; +end + +successful = []; + +% Look for all columns beeing equal to zero +for i = tailnodes + if ~any(pot.B(:,i)) + successful = [successful i]; + end +end + +remain = mysetdiff(1:pot.ctailsize,successful); + +% Erase the zero-columns and decrease the tailsize +pot.B = pot.B(:,remain); +pot.ctailsize = pot.ctailsize - length(successful); + +% Return the reduced potential +reduced_pot = pot; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgcpot/rescale_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function pot = rescale_pot(pot, s) +% RESCALE_POT Add a constant to the mpot scale factor. +% pot = rescale_pot(pot, s) + +pot.p = pot.p*s;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgcpot/scgcpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function pot = scgcpot(cheadsize, ctailsize, p, A, B, C) +% SCGCPOT Make a base object of stable conditional gaussian potential. +% pot = scgcpot(cheadsize, ctailsize, p, A, B, C) +% +% cheadsize is the demension of head nodes. +% ctailsize is the demension of tail nodes. +% r = cheadsize, s = ctailsize +% p is discrete probability. +% A is table of r*1 vectors; +% B is r*s matrices +% C is r*r positive semidefinite symmetric matrices + +if nargin < 3 + p = 1; +end +if nargin < 4 + A = zeros(cheadsize,1); +end +if nargin < 5 + B = zeros(cheadsize,ctailsize); +end +if nargin < 6 + C = zeros(cheadsize,cheadsize); +end + +if isempty(A) + A = zeros(cheadsize,1); +end +if isempty(B) + B = zeros(cheadsize,ctailsize); +end +if isempty(C) + C = zeros(cheadsize,cheadsize); +end + +pot.cheadsize = cheadsize; +pot.ctailsize = ctailsize; + +pot.p = p; +pot.A = A; +pot.B = B; +pot.C = C; +%if cheadsize == 0 +% pot.A = []; +%end +%if ctailsize == 0 +% pot.B = []; +%end +pot = class(pot, 'scgcpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +/README/1.1.1.1/Thu Mar 20 15:07:16 2003// +/combine_pots.m/1.1.1.1/Tue Mar 11 17:49:28 2003// +/complement_pot.m/1.1.1.1/Wed May 21 13:49:34 2003// +/direct_combine_pots.m/1.1.1.1/Sun May 19 22:11:08 2002// +/extension_pot.m/1.1.1.1/Fri Jan 24 12:52:34 2003// +/marginalize_pot.m/1.1.1.1/Tue Mar 11 17:06:08 2003// +/normalize_pot.m/1.1.1.1/Wed May 21 13:49:44 2003// +/pot_to_marginal.m/1.1.1.1/Sun May 19 22:11:08 2002// +/recursive_combine_pots.m/1.1.1.1/Wed May 21 13:49:48 2003// +/reduce_pot.m/1.1.1.1/Tue Mar 11 18:07:12 2003// +/scgpot.m/1.1.1.1/Tue Mar 11 14:04:48 2003// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@scgpot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +% Stable conditional Gaussian inference +% Written by Rainer Deventer + + +@techreport{Lauritzen99, + author = "S. Lauritzen and F. Jensen", + title = "Stable Local Computation with Conditional {G}aussian Distributions", + year = 1999, + number = "R-99-2014", + institution = "Dept. Math. Sciences, Aalborg Univ." +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/combine_pots.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function pot = combine_pots(pot1, pot2) +% COMBINE_POTS combine two potentials +% pot = combine_pots(pot1, pot2) + +% Reduce both potentials before trying to combine them. +% Cf. "Stable Local computation with Conditional Gaussian Distributions", page 9 +% Consider again two potentials with minimal tail + +% Guarantee minimal tails. If pot1 or pot2 are minimal, they are not changed +pot1 = reduce_pot(pot1); +pot2 = reduce_pot(pot2); + +%if the intersect set of these two potentials' head conts. combination is undifined +if ~isempty( myintersect(pot1.cheaddom, pot2.cheaddom) ) + return; +end + +if isempty( myintersect(pot1.domain, pot2.cheaddom) ) | isempty( myintersect(pot2.domain, pot1.cheaddom)) + % if satisfy the condition of directed combine + pot = direct_combine_pots(pot1, pot2); +else + % perform recursive combine + pot = recursive_combine_pots(pot1, pot2); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/complement_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,221 @@ +function [margpot, comppot] = complement_pot(pot, keep) +% COMPLEMENT_POT complement means decompose of a potential into its strong marginal and +% its complement corresponds exactly to the decomposition of a probability distribution +% into its marginal and conditional +% [margpot, comppot] = complement_pot(pot, keep) + +% keep can only include continuous head nodes and discrete nodes +% margpot is the stable CG potential of keep nodes +% comppot is the stable CG potential of others in corresponds exactly to +% the discomposition of a probability distribution of its marginal and conditional + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Calculation of the marginal requires integration over % +% all variables in csumover. Thus cheadkeep contains all % +% continuous variables in the marginal potential % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%keyboard; +csumover = mysetdiff(pot.cheaddom, keep); +cheadkeep = mysetdiff(pot.cheaddom, csumover); + +nodesizes = zeros(1, max(pot.domain)); +nodesizes(pot.ddom) = pot.dsizes; +nodesizes(pot.cheaddom) = pot.cheadsizes; +nodesizes(pot.ctaildom) = pot.ctailsizes; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Description of the variables in the marginal domain % +% For the calculation of a strong marginal first integration % +% over all continuous variables in the head takes place. % +% The calculation of the marginal over the head variables % +% might result in a smaller or empty tail % +% If there are no head variables, and therefore no tail % +% variables, left marginalisation over discrete variables % +% may take place % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +margdom = mysetdiff(pot.domain,keep); +% margddom = pot.ddom; +margcheaddom = cheadkeep; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Marginalisation over discrete variables is only allowed when % +% the tail is empty % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +margddom = myintersect(pot.ddom,keep); % Discrete domain of marginal +margctaildom = myintersect(pot.ctaildom,keep); % Tail domain +assert(isempty(mysetdiff(pot.ddom,margddom)) | isempty(margctaildom)) + + +%margctaildom = pot.ctaildom; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Even if marginalisation over continuous variables is only defined % +% for head variables, the marginalisation over haed-variables might % +% result in a smaller tail % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +margctaildom = myintersect(pot.ctaildom,keep); + +margcheadsizes = nodesizes(margcheaddom); +margcheadsize = sum(margcheadsizes); +margctailsizes = nodesizes(margctaildom); +margctailsize = sum(margctailsizes); + +compdom = pot.domain; +compddom = pot.ddom; +compcheaddom = csumover; +compctaildom = myunion(pot.ctaildom, cheadkeep); +compcheadsizes = nodesizes(compcheaddom); +compcheadsize = sum(compcheadsizes); +compctailsizes = nodesizes(compctaildom); +compctailsize = sum(compctailsizes); + +dkeep = myintersect(pot.ddom, keep); +%if dom is only contain discrete node +if isempty(pot.cheaddom) + dsumover = mysetdiff(pot.ddom, dkeep); + + if isempty(dsumover) + margpot = pot; + comppot = scgpot([], [], [], []); + return; + end + + + I = prod(nodesizes(dkeep)); + J = prod(nodesizes(dsumover)); + sum_map = find_equiv_posns(dsumover, pot.ddom); + keep_map = find_equiv_posns(dkeep, pot.ddom); + iv = zeros(1, length(pot.ddom)); % index vector + p1 = zeros(I,J); + for i=1:I + keep_iv = ind2subv(nodesizes(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(nodesizes(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(nodesizes(pot.ddom), iv); + potc = struct(pot.scgpotc{k}); % violate object privacy + p1(i,j) = potc.p; + end + end + p2 = sum(p1,2); + p2 = p2 + (p2==0)*eps; + + margscpot = cell(1, I); + compscpot = cell(1, I*J); + iv = zeros(1, length(pot.ddom)); % index vector + for i=1:I + margscpot{i} = scgcpot(0, 0, p2(i)); + keep_iv = ind2subv(nodesizes(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(nodesizes(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(nodesizes(pot.ddom), iv); + q = p1(i,j)/p2(i); + compscpot{k} = scgcpot(0, 0, q); + end + end + + margpot = scgpot(dkeep, [], [], nodesizes, margscpot); + comppot = scgpot(pot.ddom, [], [], nodesizes,compscpot); + return; +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% head of the potential is not empty % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +dsize = pot.dsize; +compscpot = cell(1, dsize); + +fmaskh = find_equiv_posns(margcheaddom, compctaildom); +fmaskt = find_equiv_posns(margctaildom, compctaildom); + +fh = block(fmaskh, compctailsizes); +ft = block(fmaskt, compctailsizes); + + +if ~isempty(margcheaddom) + for i=1:dsize + potc = struct(pot.scgpotc{i}); + q = 1; + p = potc.p; + [A1, A2, B1, B2, C11, C12, C21, C22] = partition_matrix_vec_3(potc.A, potc.B, potc.C, margcheaddom, compcheaddom, nodesizes); + + if ~isempty(margcheaddom) + margscpot{i} = scgcpot(margcheadsize, margctailsize, p, A1, B1, C11); + else + margscpot{i} = scgcpot(margcheadsize, margctailsize, p); + end + + if ~isempty(compcheaddom) + if ~isempty(margcheaddom) + E = A2 - C21*pinv(C11)*A1; + tmp1 = C21*pinv(C11); + tmp2 = B2 - C21*pinv(C11)*B1; + F = zeros(compcheadsize, compctailsize); + F(:, fh) = tmp1; + F(:, ft) = tmp2; + G = C22 - C21*pinv(C11)*C12; + else + E = A2; + F = B2; + G = C22; + end + compscpot{i} = scgcpot(compcheadsize, compctailsize, q, E, F, G); + else + compscpot{i} = scgcpot(compcheadsize, 0, q); + end + if isempty(margcheaddom) + margpot = scgpot(margddom, [], [], nodesizes, margscpot); + else + margpot = scgpot(margddom, margcheaddom, margctaildom, nodesizes, margscpot); + end + end +else + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Marginalisation took place over all head variables. % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Calculate the strong marginal % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + margpot = marginalize_pot(pot,keep); + mPot = struct(margpot); + for i =1:dsize + potc = struct(pot.scgpotc{i}); + % Get the probability of the original potential % + q = potc.p; + + % Get the configuration defined by the index i% + config = ind2subv(pot.dsizes,i); + + % Calculate the corresponding configuration in the marginal potential + if isempty(margpot.dsizes) + % keep == [] + indMargPot = 1; + else + equivPos = find_equiv_posns(dkeep,pot.ddom); + indMargPot = subv2ind(margpot.dsizes,config(equivPos)); + end + % Figure out the corresponding marginal potential + mPotC = struct(mPot.scgpotc{indMargPot}); + p = mPotC.p; + if p == 0 + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % The following assignment is correct as p is only zero if q is also zero % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + compscpot{i} = scgcpot(compcheadsize,compctailsize,0,potc.A,potc.B,potc.C); + else + compscpot{i} = scgcpot(compcheadsize,compctailsize,q/p,potc.A,potc.B,potc.C); + end + end +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Put all components in one potential % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if isempty(compcheaddom) + comppot = scgpot(compddom, [], [], nodesizes,compscpot); +else + comppot = scgpot(compddom, compcheaddom, compctaildom, nodesizes,compscpot); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/direct_combine_pots.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,161 @@ +function pot = direct_combine_pots(pot1, pot2) +% DIRECTED_COMBINE_POTS The combination operation corresponds to ordinary composition of conditional distributions. +% In some sense is similar to that of forming disjoint union of set. +% pot = direct_combine_pots(pot1, pot2) + +% directed combine can be performed under the conditon that the head node set of pot1 is disjoint from the domain of +% pot2 or vice versa. if the last conditon was satisfied we exchange the pot1 and pot2 firstly then perform the operation. +% If neither of them was satified the directed combine is undifined. + + +if isempty( myintersect(pot1.domain, pot2.cheaddom) ) + pot1 = pot1; + pot2 = pot2; +elseif isempty( myintersect(pot2.domain, pot1.cheaddom)) + temppot = pot1; + pot1 = pot2; + pot2 = temppot; +else + assert(0); + return; +end + +domain = myunion(pot1.domain, pot2.domain); +nodesizes = zeros(1,max(domain)); +nodesizes(pot2.ctaildom) = pot2.ctailsizes; +nodesizes(pot2.cheaddom) = pot2.cheadsizes; +nodesizes(pot2.ddom) = pot2.dsizes; +nodesizes(pot1.ctaildom) = pot1.ctailsizes; +nodesizes(pot1.cheaddom) = pot1.cheadsizes; +nodesizes(pot1.ddom) = pot1.dsizes; + +dom_u = mysetdiff(pot2.ctaildom, pot1.cheaddom); +if ~isempty(dom_u) & ~mysubset(dom_u, pot1.ctaildom) + pot1 = extension_pot(pot1, [], [], dom_u, nodesizes(dom_u)); +end + +dom_u = myunion(pot1.cheaddom, pot1.ctaildom); +if ~isempty(dom_u) & ~mysubset(dom_u, pot2.ctaildom) + pot2 = extension_pot(pot2, [], [], dom_u, nodesizes(dom_u)); +end + + +cheaddom = myunion(pot1.cheaddom, pot2.cheaddom); +ctaildom = mysetdiff(myunion(pot1.ctaildom, pot2.ctaildom), cheaddom); +cdom = myunion(cheaddom, ctaildom); +ddom = mysetdiff(domain, cdom); +dsizes = nodesizes(ddom); +dsize = prod(nodesizes(ddom)); +cheadsizes = nodesizes(cheaddom); +cheadsize = sum(nodesizes(cheaddom)); +ctailsizes = nodesizes(ctaildom); +ctailsize = sum(nodesizes(ctaildom)); + +r1 = pot1.cheadsize; +s1 = pot1.ctailsize; +scpot = cell(1, dsize); +mask1 = []; +mask2 = []; +if ~isempty(pot1.ddom) + mask1 = find_equiv_posns(pot1.ddom, ddom); +end +if ~isempty(pot2.ddom) + mask2 = find_equiv_posns(pot2.ddom, ddom); +end +cmask1 = []; +cmask2 = []; +if ~isempty(pot1.cheaddom) + cmask1 = find_equiv_posns(pot1.cheaddom, cheaddom); +end +if ~isempty(pot2.cheaddom) + cmask2 = find_equiv_posns(pot2.cheaddom, cheaddom); +end + +u1 = block(cmask1, cheadsizes); +u2 = block(cmask2, cheadsizes); + +fmaskh = find_equiv_posns(pot1.cheaddom, pot2.ctaildom); +fmaskt = find_equiv_posns(pot1.ctaildom, pot2.ctaildom); + +fh = block(fmaskh, pot2.ctailsizes); +ft = block(fmaskt, pot2.ctailsizes); + +for i=1:dsize + sub = ind2subv(dsizes, i); + sub1 = sub(mask1); + sub2 = sub(mask2); + ind1 = subv2ind(pot1.dsizes, sub1); + ind2 = subv2ind(pot2.dsizes, sub2); + + if isempty(ind1) + ind1 = 1; + end + if isempty(ind2) + ind2 = 1; + end + potc1 = struct(pot1.scgpotc{ind1}); + potc2 = struct(pot2.scgpotc{ind2}); + p = potc1.p; + q = potc2.p; + ro = p*q; + + A = potc1.A; + B = potc1.B; + C = potc1.C; + + E = potc2.A; + F = potc2.B; + G = potc2.C; + + F1 = F(:, fh); + F2 = F(:, ft); + + if ~isempty(F1) + K1 = F1*A; + K2 = F1*B; + FCF = F1*C*F1'; + FC = F1*C; + CFT = C*F1'; + else + K1 = zeros(size(E)); + K2 = zeros(size(F2)); + FCF = zeros(size(G)); + FC = zeros(size(C, 1), size(G, 2)); + CFT = zeros(size(G, 2), size(C, 1)); + end + + + U = zeros(cheadsize,1); + W = zeros(cheadsize,cheadsize); + V = zeros(cheadsize,ctailsize); + + if cheadsize > 0 + U(u1) = A; + U(u2) = E + K1; + W(u1, u1) = C; + W(u2, u2) = G + FCF; + W(u1, u2) = CFT; + W(u2, u1) = FC; + else + U = zeros(cheadsize,1); + W = zeros(cheadsize,cheadsize); + end + if cheadsize > 0 | ctailsize > 0 + if ~isempty(u1) + V(u1, :) = B; + else + V(u1, :) = zeros(potc1.cheadsize, ctailsize); + end + if ~isempty(u2) + V(u2, :) = F2 + K2; + else + V(u2, :) = zeros(potc2.cheadsize, ctailsize); + end + else + V = zeros(cheadsize,ctailsize); + end + + scpot{i} = scgcpot(cheadsize, ctailsize, ro, U, V, W); +end + +pot = scgpot(ddom, cheaddom, ctaildom, nodesizes, scpot);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/extension_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function pot = extension_pot(oldpot, ddom_u, dsizes, ctaildom_u, csizes) +% EXTENSION_POT Extense a stable CG potential. +% pot = extension_pot(oldpot, ddom_u, ctaildom_u, dsizes, csizes) +% ddom_u Added discrete nodes +% ctaildom_u Added continuous tail nodes +% csizes is the size of the tail nodes. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% A CG potential can be extended by adding discrete variables to its % +% domain of continuous variables to its tail % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ddom = myunion(oldpot.ddom, ddom_u); +ctaildom = myunion(oldpot.ctaildom, ctaildom_u); +cheaddom = oldpot.cheaddom; +udom = myunion(ddom_u, ctaildom_u); +domain = myunion(oldpot.domain, udom); + +ns = zeros(1,max(domain)); +ns(ddom_u) = dsizes; +ns(ctaildom_u) = csizes; +ns(oldpot.ddom) = oldpot.dsizes; +ns(oldpot.cheaddom) = oldpot.cheadsizes; +ns(oldpot.ctaildom) = oldpot.ctailsizes; + +dsizes = ns(ddom); +dsize = prod(ns(ddom)); +cheadsizes = ns(cheaddom); +cheadsize = sum(ns(cheaddom)); +ctailsizes = ns(ctaildom); +ctailsize = sum(ns(ctaildom)); + +BZ = zeros(cheadsize, ctailsize); +potarray = cell(1, dsize); +mask = find_equiv_posns(oldpot.ddom, ddom); + +tmask = find_equiv_posns(oldpot.ctaildom, ctaildom); +tu = block(tmask, ctailsizes); + +for i=1:dsize + sub1 = ind2subv(dsizes, i); + sub2 = sub1(mask); + ind = subv2ind(oldpot.dsizes, sub2); + if isempty(ind) + ind = 1; + end + potc = struct(oldpot.scgpotc{ind}); + p = potc.p; + B = BZ; + if ~isempty(B) + B(:, tu) = potc.B; + end + potarray{i} = scgcpot(cheadsize, ctailsize, p, potc.A, B, potc.C); +end + +pot = scgpot(ddom, cheaddom, ctaildom, ns,potarray);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/marginalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +function smallpot = marginalize_pot(bigpot, keep) +% MARGINALIZE_POT Marginalize a cgpot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, keep) + +sumover = mysetdiff(bigpot.domain, keep); +cdom = myunion(bigpot.cheaddom, bigpot.ctaildom); +csumover = myintersect(sumover, bigpot.cheaddom); +dsumover = myintersect(sumover, bigpot.ddom); + +dkeep = myintersect(keep, bigpot.ddom); +ckeep = myintersect(keep, bigpot.cheaddom); +cheaddom = myintersect(keep, bigpot.cheaddom); + +assert(isempty(myintersect(csumover,bigpot.ctaildom))); +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.ddom) = bigpot.dsizes; +ns(bigpot.cheaddom) = bigpot.cheadsizes; +ns(bigpot.ctaildom) = bigpot.ctailsizes; + + +if sum(ns(csumover)) > 0 + for i=1:bigpot.dsize + bigpot.scgpotc{i} = marginalize_pot(bigpot.scgpotc{i}, ckeep, csumover, ns); + end +end + +if (isequal(csumover, cheaddom)) + bigpot.ctaildom = []; +end +% If we are not marginalizing over any discrete nodes, we are done. +if prod(ns(dsumover))==1 + smallpot = scgpot(dkeep, cheaddom, bigpot.ctaildom, ns, bigpot.scgpotc); + return; +end + +if (~isempty(bigpot.ctaildom)) + assert(0); + return; +end + +I = prod(ns(dkeep)); +J = prod(ns(dsumover)); +C = sum(ns(ckeep)); +sum_map = find_equiv_posns(dsumover, bigpot.ddom); +keep_map = find_equiv_posns(dkeep, bigpot.ddom); +iv = zeros(1, length(bigpot.ddom)); % index vector + +p1 = zeros(I,J); +A1 = zeros(C,J,I); +C1 = zeros(C,C,J,I); +for i=1:I + keep_iv = ind2subv(ns(dkeep), i); + iv(keep_map) = keep_iv; + for j=1:J + sum_iv = ind2subv(ns(dsumover), j); + iv(sum_map) = sum_iv; + k = subv2ind(ns(bigpot.ddom), iv); + pot = struct(bigpot.scgpotc{k}); % violate object privacy + p1(i,j) = pot.p; + if C > 0 % so mu1 and Sigma1 are non-empty + A1(:,j,i) = pot.A; + C1(:,:,j,i) = pot.C; + end + end +end + +% Collapse the mixture of Gaussians +coef = mk_stochastic(p1); % coef must be convex combination +%keyboard +p2 = sum(p1,2); +if (all(p2 == 0)) + p2 = p2 + (p2==0)*eps; +end +A = []; +S = []; + +pot = cell(1,I); +ctailsize = sum(ns(bigpot.ctaildom)); +tB = zeros(C, ctailsize); +for i=1:I + if C > 0 + [A, S] = collapse_mog(A1(:,:,i), C1(:,:,:,i), coef(i,:)); + end + p = p2(i); + pot{i} = scgcpot(C, ctailsize, p, A, tB, S); +end + +smallpot = scgpot(dkeep, ckeep, bigpot.ctaildom, ns, pot); + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/normalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the SCG potential Pr(X,E) into Pr(X|E) and return log Pr(E). +% [pot, loglik] = normalize_pot(pot) + +% Marginalize down to [], so that the normalizing constant becomes Pr(E) +temp = marginalize_pot(pot, []); +[temp2, loglik] = normalize_pot(temp.scgpotc{1}); + +% Adjust scale factor to reflect the fact that the pot now represents Pr(X | E) instead of Pr(X,E). + +scale = -loglik; +if 1 + for i=1:pot.dsize + pot.scgpotc{i} = rescale_pot( pot.scgpotc{i}, scale); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/pot_to_marginal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a scgpot to a marginal structure. +% m = pot_to_marginal(pot) + +assert(isempty(pot.ctaildom)) +m.domain = pot.domain; +n = pot.cheadsize; +d = pot.dsize; + +if n==0 + m.mu = []; + m.Sigma = []; +else + m.mu = zeros(n, d); + m.Sigma = zeros(n, n, d); +end +%m.T = 0*myones(pot.dsizes); +m.T = 0*myones(pot.dsize); +for i=1:pot.dsize + potc = struct(pot.scgpotc{i}); % violate privacy of object + if n > 0 + m.mu(:,i) = potc.A; + m.Sigma(:,:,i) = potc.C; + end + m.T(i) = potc.p; +end +if isvectorBNT(m.T) + m.T = m.T(:)'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/recursive_combine_pots.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function pot = recursive_combine_pots(pot1, pot2) +% RECURSIVE_COMBINE_POTS recursive combine two potentials +% pot = recursive_combine_pots(pot1, pot2) + +pot1 = reduce_pot(pot1); +pot2 = reduce_pot(pot2); +% Recursion is stopped, if recusive-combination is defined by direct combination, +% i.e. if the domain of one potential is disjoint from the head of the other. +if (isempty(myintersect(pot1.domain,pot2.cheaddom))|... + isempty(myintersect(pot1.cheaddom,pot2.domain))) + pot = direct_combine_pots(pot1,pot2); +else + % Test wether one of the set-differences is not empty + % as defined in Lauritzen99 "Stable Local Computation with Conditional Gaussian Distributions" + % on page 9 + D12 = mysetdiff(pot1.cheaddom, pot2.domain); + D21 = mysetdiff(pot2.cheaddom, pot1.domain); + if (isempty(D12) & isempty(D21)) + assert(0,'Recursive combination is not defined'); + end + + if ~isempty(D12) + % Calculate the complementary potential for the set + % D1\D12 as defined in Lauritzen 99, page 9 + keep = mysetdiff(pot1.domain,D12); + [margpot, comppot] = complement_pot(pot1,keep); + margpot = reduce_pot(margpot); + comppot = reduce_pot(comppot); + pot = direct_combine_pots( recursive_combine_pots(margpot, pot2), comppot); + elseif ~isempty(D21) + keep = mysetdiff(pot2.domain,D21); + [margpot, comppot] = complement_pot(pot2,D21); + margpot = reduce_pot(margpot); + comppot = reduce_pot(comppot); + pot = direct_combine_pots( recursive_combine_pots(pot1, margpot), comppot); + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/reduce_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function [reduced_pot,successful] = reduce_pot(pot,tailnodes) +% Executes the reduce operation defined in +% Stable Local Computation with Conditional Gaussian Distributions +% Steffen L. Lauritzen +% Frank Jensen +% September 1999 +% The potential pot is reduced if B contains any zero columns +% The test are restricted to the positions in tailnodes. +% Any columns successfully deleted are entered in the array successful +if nargin < 2 + tailnodes = pot.ctaildom; +end + +successful = []; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Keep track of remaining tailnodes % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +rem_tailnodes = pot.ctaildom; +for i = tailnodes + pos = find(i==rem_tailnodes); + successful_red = [pos]; + red_scgcpot = cell(1,pot.dsize); + j = 1; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % Test whether all components of pot.scgpotc can be reduced % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + while ((j <= pot.dsize) & ~isempty(successful_red)) + [cpot,successful_red] = reduce_pot(pot.scgpotc{j},pos); + red_scgcpot{j} = cpot; + j = j + 1; + end + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % If i is a reducible tailnode, then reduce the potential % + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + if ~isempty(successful_red) + successful = [successful i]; + pot.scgpotc = red_scgcpot; + rem_tailnodes = mysetdiff(rem_tailnodes,i); + end; +end + +pot.ctaildom = rem_tailnodes; +positions = find_equiv_posns(rem_tailnodes,pot.ctaildom); +pot.ctailsizes = pot.ctailsizes(positions); +pot.ctailsize = sum(pot.ctailsizes); +pot.domain = mysetdiff(pot.domain,successful); +reduced_pot = pot; + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@scgpot/scgpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function pot = scgpot(ddom, cheaddom, ctaildom, node_sizes, scgpotc) +% SCGPOT Make a stable CG potential. +% pot = scgpot(ddom, cheaddom, ctaildom, node_sizes, scgpotc) +% +% ddom is discrete nodes contains in the potential +% cheaddom is head nodes constains in the potential +% ctaildom is tail nodes contains in the potential +% node_sizes(i) is the size of the i'th node. +% scgpotc is list of scgcpot objects. + +pot.ddom = ddom; +pot.cheaddom = cheaddom; +pot.ctaildom = ctaildom; +pot.domain = myunion(ddom, myunion(cheaddom, ctaildom)); +pot.dsizes = node_sizes(pot.ddom); +pot.dsize = prod(node_sizes(pot.ddom)); +pot.cheadsizes = node_sizes(pot.cheaddom); +pot.cheadsize = sum(node_sizes(pot.cheaddom)); +pot.ctailsizes = node_sizes(pot.ctaildom); +pot.ctailsize = sum(node_sizes(pot.ctaildom)); + +if nargin < 5 + scgpotc = cell(1, pot.dsize); + for i=1:pot.dsize + scgpotc{i} = scgcpot(pot.cheadsize, pot.ctailsize); + end +end +pot.scgpotc = scgpotc; + +pot = class(pot, 'scgpot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +/approxeq_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/display.m/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/marginalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/multiply_by_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/normalize_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/pot_to_marginal.m/1.1.1.1/Wed May 29 15:59:58 2002// +/upot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/upot_to_opt_policy.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/@upot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/approxeq_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function p = approxeq_pot(A, B, tol) + +if nargin < 3, tol = 1e-3; end + +p = approxeq(A.p, B.p, tol) & approxeq(A.u, B.u, tol);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(pot) + +disp('utility potential object'); +disp(struct(pot));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/divide_by_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function Tbig = divide_by_pot(Tbig, Tsmall) +% DIVIDE_BY_POT Tbig /= Tsmall +% Tbig = divide_by_pot(Tbig, Tsmall) +% +% Tsmall's domain must be a subset of Tbig's domain. + +smallp = extend_domain_table(Tsmall.p, Tsmall.domain, Tsmall.sizes, Tbig.domain, Tbig.sizes); +smallp = smallp + (smallp==0); +Tbig.p = Tbig.p ./ smallp; + +smallu = extend_domain_table(Tsmall.u, Tsmall.domain, Tsmall.sizes, Tbig.domain, Tbig.sizes); +Tbig.u = Tbig.u - smallu; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/marginalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function smallpot = marginalize_pot(bigpot, onto, maximize) +% MARGINALIZE_POT Marginalize a upot onto a smaller domain. +% smallpot = marginalize_pot(bigpot, onto, maximize) +% +% The maximize argument is ignored + +numer = marg_table(bigpot.p .* bigpot.u, bigpot.domain, bigpot.sizes, onto); +denom = marg_table(bigpot.p, bigpot.domain, bigpot.sizes, onto); + +p = denom; +% replace 0s by 1s before dividing. This is valid since demon(i) = 0 => numer(i) = 0 +denom = denom + (denom == 0); +u = numer ./ denom; + +ns = zeros(1, max(bigpot.domain)); +ns(bigpot.domain) = bigpot.sizes; + +smallpot = upot(onto, ns(onto), p, u);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/multiply_by_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function Tbig = multiply_by_pot(Tbig, Tsmall) +% MULTIPLY_BY_POT Tbig *= Tsmall +% Tbig = multiply_by_pot(Tbig, Tsmall) +% +% Tsmall's domain must be a subset of Tbig's domain. + +smallp = extend_domain_table(Tsmall.p, Tsmall.domain, Tsmall.sizes, Tbig.domain, Tbig.sizes); +Tbig.p = Tbig.p .* smallp; + +smallu = extend_domain_table(Tsmall.u, Tsmall.domain, Tsmall.sizes, Tbig.domain, Tbig.sizes); +Tbig.u = Tbig.u + smallu; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/normalize_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function [pot, loglik] = normalize_pot(pot) +% NORMALIZE_POT Convert the probability part of a utility potential +% [pot, loglik] = normalize_pot(pot) + +[pot.p, lik] = normalise(pot.p); +%pot.u = pot.u - sum(pot.u(:)); +%pot.u = pot.u ./ sum(pot.u(:)); % same as normalise(pot.u) +%pot.u = normalise(pot.u); +%pot.u = pot.u / 726.8121; +pot.u = pot.u / 10; +loglik = log(lik + (lik==0)*eps); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/pot_to_marginal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function m = pot_to_marginal(pot) +% POT_TO_MARGINAL Convert a upot to a structure. +% m = pot_to_marginal(pot) + +m.domain = pot.domain; +m.T = pot.p; +m.U = pot.u; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/upot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function pot = upot(domain, sizes, p, u) +% UPOT Make a discrete utility potential. +% pot = upot(domain, sizes, p, u) +% +% sizes(i) is the size of the i'th domain element. +% p defaults to all 1s, u defaults to all 0s. + +if nargin < 3, p = myones(sizes); end +if nargin < 4, u = 0*myones(sizes); end + +pot.domain = domain; +pot.p = myreshape(p, sizes); +pot.u = myreshape(u, sizes); +pot.sizes = sizes(:)'; +pot = class(pot, 'upot');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/@upot/upot_to_opt_policy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function [policy, EU] = upot_to_opt_policy(pot) +% UPOT_TO_OPT_POLICY Compute an optimal deterministic policy given a utility potential +% [policy, EU] = upot_to_opt_policy(pot) +% +% policy(a,b, ..., z) = P(do z | a, b, ..), which will be a delta function +% EU is the contraction of this potential, i.e., P .* U + +sz = pot.sizes; % mysize(pot.p); +if isempty(sz) + EU = pot.u; + policy = []; + return; +end + +parent_size = prod(sz(1:end-1)); +self_size = sz(end); +C = pot.p .* pot.u; % contraction +C = reshape(C, parent_size, self_size); +policy = zeros(parent_size, self_size); +for i=1:parent_size + act = argmax(C(i,:)); + policy(i, act) = 1; +end +policy = myreshape(policy, sz); +EU = sum(C(:));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/CPD_to_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function pot = CPD_to_pot(pot_type, CPD, domain, ns, cnodes, evidence) +% CPD_TO_POT Convert a CPD to a potential of the specified form, incorporating any evidence +% pot = CPD_to_pot(pot_type, CPD, domain, node_sizes, cnodes, evidence) +% +% pot_type is one of 'd', 'g', or 'cg'. +% domain is the domain of CPD. +% node_sizes(i) is the size of node i. +% cnodes = the cts nodes +% evidence{i} is the evidence on the i'th node. + +switch pot_type + case 'd', + pot = CPD_to_dpot(CPD, domain, ns, cnodes, evidence); + case 'g', + pot = CPD_to_cpot(CPD, domain, ns, cnodes, evidence); + case 'cg', + pot = CPD_to_cgpot(CPD, domain, ns, cnodes, evidence); + otherwise, + error(['can''t handle pot_type ' pot_type]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +/CPD_to_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +/README/1.1.1.1/Wed May 29 15:59:58 2002// +/check_for_cd_arcs.m/1.1.1.1/Wed May 29 15:59:58 2002// +/determine_pot_type.m/1.1.1.1/Wed May 29 15:59:58 2002// +/genops.c/1.1.1.1/Sat Jul 28 15:43:40 2001// +/mk_initial_pot.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +A D/@cgpot//// +A D/@cpot//// +A D/@dpot//// +A D/@mpot//// +A D/@scgcpot//// +A D/@scgpot//// +A D/@upot//// +A D/Old//// +A D/Tables////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +/comp_eff_node_sizes.m/1.1.1.1/Wed May 29 15:59:58 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Old/comp_eff_node_sizes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function ens = comp_eff_node_sizes(ns, cnodes, ev, domain) + +dnodes = mysetdiff(1:length(ns), cnodes); +odom = domain(~isemptycell(evidence(domain))); +cdom = myintersect(cnodes, domain); +ddom = myintersect(dnodes, domain); +cobs = myintersect(cdom, odom); +dobs = myintersect(ddom, odom); +ens = ns; +ens(cobs) = 0; +ens(dobs) = 1;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,100 @@ +The following kinds of potentials are supported +- dpot: discrete +- upot: utility +- mpot: Gaussian in moment form +- cpot: Gaussian in canonical form +- cgpot: conditional (mixture) Gaussian, a list of mpots/cpot +- scgpot: stable conditional Gaussian, a list of scgcpots +- scgcpot: just used by scgpot + +Many of these are described in the following book + +@book{Cowell99, + author = "R. G. Cowell and A. P. Dawid and S. L. Lauritzen and D. J. Spiegelhalter", + title = "Probabilistic Networks and Expert Systems", + year = 1999, + publisher = "Springer" +} + +CPD_to_pot converts P(Z|A,B,...) to phi(A,B,...,Z). + +A table is like a dpot, except it is a structure, not an object. +Code that uses tables is faster but less flexible. + + ----------- + +A potential is a joint probability distribution on a set of nodes, +which we call the potential's domain (which is always sorted). +A potential supports the operations of multiplication and +marginalization. + +If the nodes are discrete, the potential can be represented as a table +(multi-dimensional array). If the nodes are Gaussian, the potential +can be represented as a quadratic form. If there are both discrete and +Gaussian nodes, we use a table of quadratic forms. For details on the +Gaussian case, see below. + +For discrete potentials, the 'sizes' field specifies the number of +values each node in the domain can take on. For continuous potentials, +the 'sizes' field specifies the block-size of each node. + +If some of the nodes are observed, extra complications arise. We +handle the discrete and continuous cases differently. Suppose the +domain is [X Y], with sizes [6 2], where X is observed to have value x. +In the discrete case, the potential will have many zeros in it +(T(X,:) will be 0 for all X ~= x), which can be inefficient. Instead, +we set sizes to [1 2], to indicate that X has only one possible value +(namely x). For continuous nodes, we set sizes = [0 2], to indicate that X no +longer appears in the mean vector or covariance matrix (we must avoid +0s in Sigma, lest it be uninvertible). When a potential is created, we +assume the sizes of the nodes have been adjusted to include the +evidence. This is so that the evidence can be incorporated at the +outset, and thereafter the inference algorithms can ignore it. + + ------------ + +A Gaussian potential can be represented in terms of its +moment characteristics (mu, Sigma, logp), or in terms of its canonical +characteristics (g, h, K). Although the moment characteristics are +more familiar, it turns out that canonical characteristics are +more convenient for the junction tree algorithm, for the same kinds of +reasons why backwards inference in an LDS uses the information form of +the Kalman filter (see Murphy (1998a) for a discussion). + +When working with *conditional* Gaussian potentials, the method proposed +by Lauritzen (1992), and implemented here, requires converting from +canonical to moment form before marginalizing the discrete variables, +and converting back from moment to canonical form before +multiplying/dividing. A new algorithm, due to Lauritzen and Jensen +(1999), works exclusively in moment form, and +hence is more numerically stable. It can also handle 0s in the +covariance matrix, i.e., deterministic relationships between cts +variables. However, it has not yet been implemented, +since it requires major changes to the jtree algorithm. + +In Murphy (1998b) we extend Lauritzen (1992) to handle +vector-valued nodes. This means the vectors and matrices become block +vectors and matrices. This manifests itself in the code as in the +following example. +Suppose we have a potential on nodes dom=[3,4,7] with block sizes=[2,1,3]. +Then nodes 3 and 7 correspond to blocks 1,3 which correspond to indices 1,2,4,5,6. +>> find_equiv_posns([3 7], dom)=[1,3] +>> block([1,3],blocks)=[1,2,4,5,6]. + +For more details, see + +- "Filtering and Smoothing in Linear Dynamical Systems using the Junction Tree Algorithm", + K. Murphy, 1998a. UCB Tech Report. + +- "Inference and learning in hybrid Bayesian networks", + K. Murphy. UCB Technical Report CSD-98-990, 1998b. + +- "Propagation of probabilities, means and variances in mixed + graphical association models", S. L. Lauritzen, 1992, JASA 87(420):1098--1108. + +- "Causal probabilistic networks with both discrete and continuous variables", + K. G. Olesen, 1993. PAMI 3(15). This discusses implementation details. + +- "Stable local computation with Conditional Gaussian distributions", + S. Lauritzen and F. Jensen, 1999. Univ. Aalborg Tech Report R-99-2014. + www.math.auc.dk/research/Reports.html.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +/divide_by_sparse_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/divide_by_table.m/1.1.1.1/Thu Aug 5 15:25:54 2004// +/extend_domain_table.m/1.1.1.1/Wed Aug 4 15:53:26 2004// +/marg_sparse_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/marg_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/marg_table.m/1.1.1.1/Wed Aug 4 15:51:48 2004// +/marg_tableC.c/1.1.1.1/Wed Oct 2 15:39:02 2002// +/marg_tableM.m/1.1.1.1/Tue Oct 1 17:39:08 2002// +/mult_by_sparse_table.c/1.1.1.1/Wed May 29 15:59:58 2002// +/mult_by_table.c/1.1.1.1/Tue Oct 1 21:23:22 2002// +/mult_by_table.m/1.1.1.1/Wed Aug 4 15:52:58 2004// +/mult_by_table2.m/1.1.1.1/Wed Oct 2 15:30:32 2002// +/mult_by_tableC.c/1.1.1.1/Tue Oct 1 21:33:50 2002// +/mult_by_tableM.m/1.1.1.1/Wed Oct 2 15:28:48 2002// +/mult_by_table_global.m/1.1.1.1/Sun Sep 29 10:21:30 2002// +/rep_mult.c/1.1.1.1/Wed May 29 15:59:58 2002// +/repmat_and_mult.c/1.1.1.1/Tue Oct 1 21:20:00 2002// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/BNT/potentials/Tables
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/divide_by_sparse_table.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +/* divide_by_sparse_table.c ../potential/tables*/ + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex; + int *mask, *result, *bir, *sir, *bjc, *sjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, value; + + plhs[0] = mxDuplicateArray(prhs[0]); + pbDomain = mxGetPr(prhs[1]); + bdim = mxGetNumberOfElements(prhs[1]); + psDomain = mxGetPr(prhs[4]); + sdim = mxGetNumberOfElements(prhs[4]); + + pbSize = mxGetPr(prhs[2]); + psSize = mxGetPr(prhs[5]); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + bpr = mxGetPr(plhs[0]); + bir = mxGetIr(plhs[0]); + bjc = mxGetJc(plhs[0]); + NZB = bjc[1]; + + spr = mxGetPr(prhs[3]); + sir = mxGetIr(prhs[3]); + sjc = mxGetJc(prhs[3]); + NZS = sjc[1]; + + if(sdim == 0){ + value = *spr; + if(value == 0)value = 1; + for(i=0; i<NZB; i++){ + bpr[i] /= value; + } + return; + } + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + bpr[i] /= spr[position]; + } + } + + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/divide_by_table.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,120 @@ +/* divide_by_table.c ../potential/tables */ + + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2; + double *pbDomain, *psDomain, *sp, *zp, *bs, value; + + plhs[0] = mxDuplicateArray(prhs[0]); + zp = mxGetPr(plhs[0]); + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[4]); + pbDomain = mxGetPr(prhs[1]); + psDomain = mxGetPr(prhs[4]); + + NB = mxGetNumberOfElements(prhs[0]); + NS = mxGetNumberOfElements(prhs[3]); + sp = mxGetPr(prhs[3]); + + bs = mxGetPr(prhs[2]); + + if(NS == 1){ + value = *sp; + if(value == 0) value = 1; + for(i=0; i<NB; i++){ + zp[i] /= value; + } + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + value = sp[i]; + if(value == 0) value = 1; + zp[i] /= value; + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)bs[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + value = *sp; + if(value == 0) value = 1; + *zp++ /= value; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/divide_by_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function bigT = divide_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% DIVIDE_BY_TABLE +% bigT = divide_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +% Replace 0s by 1s before dividing. This is valid, Ts(i)=0 iff Tbig(i)=0. +Ts = Ts + (Ts==0); +%Tbig.T(:) = Tbig.T(:) ./ Ts(:); +bigT(:) = bigT(:) ./ Ts(:); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/extend_domain_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function B = extend_domain_table(A, smalldom, smallsz, bigdom, bigsz) +% EXTEND_DOMAIN_TABLE Expand an array so it has the desired size. +% B = extend_domain_table(A, smalldom, smallsz, bigdom, bigsz) +% +% A is the array with domain smalldom and sizes smallsz. +% bigdom is the desired domain, with sizes bigsz. +% +% Example: +% smalldom = [1 3], smallsz = [2 4], bigdom = [1 2 3 4], bigsz = [2 1 4 5], +% so B(i,j,k,l) = A(i,k) for i in 1:2, j in 1:1, k in 1:4, l in 1:5 + +if isequal(size(A), [1 1]) % a scalar + B = A; % * myones(bigsz); + return; +end + +map = find_equiv_posns(smalldom, bigdom); +sz = ones(1, length(bigdom)); +sz(map) = smallsz; +B = myreshape(A, sz); % add dimensions for the stuff not in A +sz = bigsz; +sz(map) = 1; % don't replicate along A's dimensions +B = myrepmat(B, sz(:)'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/marg_sparse_table.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +/* marg_sparse_table.c ../potential/tables*/ + +/******************************************/ +/* 5 input & 1 output */ +/* Big sparse table */ +/* Big domain */ +/* Big sizes */ +/* onto */ +/* maximize, if missed, maximize=0 */ +/* */ +/* small sparse table */ +/******************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +mxArray* convert_table_to_sparse(const double *Table, const int *sequence, const int nzCounts, const int N){ + mxArray *spTable; + int i, temp, *irs, *jcs, count=0; + double *sr; + + spTable = mxCreateSparse(N, 1, nzCounts, mxREAL); + sr = mxGetPr(spTable); + irs = mxGetIr(spTable); + jcs = mxGetJc(spTable); + + jcs[0] = 0; + jcs[1] = nzCounts; + + for(i=0; i<nzCounts; i++){ + irs[i] = sequence[count]; + count++; + temp = sequence[count]; + sr[i] = Table[temp]; + count++; + } + return spTable; +} + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, bdim, sdim, NS, NZB, position, bindex, sindex, maximize, nzCounts=0; + int *mask, *sequence, *result, *bir, *bjc, *ssize, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *sTable, *pbDomain, *psDomain, *pbSize, *bpr, *spr; + const char *field_names[] = {"domain", "T", "sizes"}; + + if(nrhs < 5) maximize = 0; + else maximize = (int)mxGetScalar(prhs[4]); + + bdim = mxGetNumberOfElements(prhs[1]); + sdim = mxGetNumberOfElements(prhs[3]); + pbSize = mxGetPr(prhs[2]); + pbDomain = mxGetPr(prhs[1]); + psDomain = mxGetPr(prhs[3]); + bpr = mxGetPr(prhs[0]); + bir = mxGetIr(prhs[0]); + bjc = mxGetJc(prhs[0]); + NZB = bjc[1]; + + if(sdim == 0){ + plhs[0] = mxCreateSparse(1, 1, 1, mxREAL); + spr = mxGetPr(plhs[0]); + bir = mxGetIr(plhs[0]); + bjc = mxGetJc(plhs[0]); + *spr = 0; + *bir = 0; + bjc[0] = 0; + bjc[1] = 1; + if(maximize){ + for(i=0; i<NZB; i++){ + *spr = (*spr < bpr[i])? bpr[i] : *spr; + } + } + else{ + for(i=0; i<NZB; i++){ + *spr += bpr[i]; + } + } + return; + } + + mask = malloc(sdim * sizeof(int)); + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + sTable = malloc(NZB * sizeof(double)); + sequence = malloc(NZB * 2 * sizeof(double)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + ssize = malloc(sdim * sizeof(int)); + + NS = 1; + for(i=0; i<count; i++){ + ssize[i] = (int)pbSize[mask[i]]; + NS *= ssize[i]; + } + + for(i=0; i<NZB; i++)sTable[i] = 0; + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * ssize[i]; + } + + count = 0; + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sequence, nzCounts, sizeof(int)*2, compare); + if(result){ + position = (result - sequence) / 2; + if(maximize) + sTable[position] = (sTable[position] < bpr[i]) ? bpr[i] : sTable[position]; + else sTable[position] += bpr[i]; + } + else { + if(maximize) + sTable[nzCounts] = (sTable[nzCounts] < bpr[i]) ? bpr[i] : sTable[nzCounts]; + else sTable[nzCounts] += bpr[i]; + sequence[count] = sindex; + count++; + sequence[count] = nzCounts; + nzCounts++; + count++; + } + } + + qsort(sequence, nzCounts, sizeof(int) * 2, compare); + plhs[0] = convert_table_to_sparse(sTable, sequence, nzCounts, NS); + + free(sTable); + free(sequence); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); + free(ssize); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/marg_table.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,175 @@ +/* marg_table.c ../potential/tables */ + + +/******************************************/ +/* 5 input & 1 output */ +/* Big table */ +/* Big domain */ +/* Big sizes */ +/* onto */ +/* maximize, if missed, maximize=0 */ +/* */ +/* small table */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp, maximize; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *ssize; + double *pb, *ps, *bp, *sp, *pbd; + + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[3]); + pb = mxGetPr(prhs[1]); + ps = mxGetPr(prhs[3]); + + NB = mxGetNumberOfElements(prhs[0]); + bp = mxGetPr(prhs[0]); + + pbd = mxGetPr(prhs[2]); + + if(nrhs < 5) maximize = 0; + else maximize = (int)mxGetScalar(prhs[4]); + + if(siz_s == 0){ + plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL); + sp = mxGetPr(plhs[0]); + if(maximize){ + for(i=0; i<NB; i++){ + *sp = (*sp < bp[i])? bp[i] : *sp; + } + } + else{ + for(i=0; i<NB; i++){ + *sp += bp[i]; + } + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + ssize = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(ps[i] == pb[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbd[i]; + sy[i] = 1; + } + for(i=0; i<siz_s; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + ssize[i] = sx[temp]; + } + + NS = 1; + for(i=0; i<ndim; i++){ + NS *= sy[i]; + } + + plhs[0] = mxCreateNumericArray(siz_s, ssize, mxDOUBLE_CLASS, mxREAL); + sp = mxGetPr(plhs[0]); + + if(NS == 1){ + if(maximize){ + for(i=0; i<NB; i++){ + *sp = (*sp < bp[i])? bp[i] : *sp; + } + } + else{ + for(i=0; i<NB; i++){ + *sp += bp[i]; + } + } + free(mask); + free(sx); + free(sy); + free(ssize); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++) *sp++ = *bp++; + free(mask); + free(sx); + free(sy); + free(ssize); + return; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + if(maximize){ + for(j=0; j<NB; j++){ + *sp = (*sp < *bp)? *bp : *sp; + bp++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + } + else{ + for(j=0; j<NB; j++){ + *sp += *bp++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + } + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); + free(ssize); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/marg_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) +% MARG_TABLE Marginalize a table +% smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) + +if nargin < 5, maximize = 0; end + + +smallT = myreshape(bigT, bigsz); % make sure it is a multi-dim array +sum_over = mysetdiff(bigdom, onto); +ndx = find_equiv_posns(sum_over, bigdom); +if maximize + for i=1:length(ndx) + smallT = max(smallT, [], ndx(i)); + end +else + for i=1:length(ndx) + smallT = sum(smallT, ndx(i)); + end +end + + +ns = zeros(1, max(bigdom)); +%ns(bigdom) = mysize(bigT); % ignores trailing dimensions of size 1 +ns(bigdom) = bigsz; + +smallT = squeeze(smallT); % remove all dimensions of size 1 +smallT = myreshape(smallT, ns(onto)); % put back relevant dims of size 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/marg_tableC.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,175 @@ +/* marg_table.c ../potential/tables */ + + +/******************************************/ +/* 5 input & 1 output */ +/* Big table */ +/* Big domain */ +/* Big sizes */ +/* onto */ +/* maximize, if missed, maximize=0 */ +/* */ +/* small table */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp, maximize; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2, *ssize; + double *pb, *ps, *bp, *sp, *pbd; + + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[3]); + pb = mxGetPr(prhs[1]); + ps = mxGetPr(prhs[3]); + + NB = mxGetNumberOfElements(prhs[0]); + bp = mxGetPr(prhs[0]); + + pbd = mxGetPr(prhs[2]); + + if(nrhs < 5) maximize = 0; + else maximize = (int)mxGetScalar(prhs[4]); + + if(siz_s == 0){ + plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL); + sp = mxGetPr(plhs[0]); + if(maximize){ + for(i=0; i<NB; i++){ + *sp = (*sp < bp[i])? bp[i] : *sp; + } + } + else{ + for(i=0; i<NB; i++){ + *sp += bp[i]; + } + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + ssize = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(ps[i] == pb[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)pbd[i]; + sy[i] = 1; + } + for(i=0; i<siz_s; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + ssize[i] = sx[temp]; + } + + NS = 1; + for(i=0; i<ndim; i++){ + NS *= sy[i]; + } + + plhs[0] = mxCreateNumericArray(siz_s, ssize, mxDOUBLE_CLASS, mxREAL); + sp = mxGetPr(plhs[0]); + + if(NS == 1){ + if(maximize){ + for(i=0; i<NB; i++){ + *sp = (*sp < bp[i])? bp[i] : *sp; + } + } + else{ + for(i=0; i<NB; i++){ + *sp += bp[i]; + } + } + free(mask); + free(sx); + free(sy); + free(ssize); + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++) *sp++ = *bp++; + free(mask); + free(sx); + free(sy); + free(ssize); + return; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + if(maximize){ + for(j=0; j<NB; j++){ + *sp = (*sp < *bp)? *bp : *sp; + bp++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + } + else{ + for(j=0; j<NB; j++){ + *sp += *bp++; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + } + + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); + free(ssize); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/marg_tableM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function smallT = marg_tableM(bigT, bigdom, bigsz, onto, maximize) +% MARG_TABLE Marginalize a table +% smallT = marg_table(bigT, bigdom, bigsz, onto, maximize) + + % marg_tableM is the same as marg_table.m, but we are sure + % it is not marg_table.c + +if nargin < 5, maximize = 0; end + + +smallT = myreshape(bigT, bigsz); % make sure it is a multi-dim array +sum_over = mysetdiff(bigdom, onto); +ndx = find_equiv_posns(sum_over, bigdom); +if maximize + for i=1:length(ndx) + smallT = max(smallT, [], ndx(i)); + end +else + for i=1:length(ndx) + smallT = sum(smallT, ndx(i)); + end +end + + +ns = zeros(1, max(bigdom)); +%ns(bigdom) = mysize(bigT); % ignores trailing dimensions of size 1 +ns(bigdom) = bigsz; + +smallT = squeeze(smallT); % remove all dimensions of size 1 +smallT = myreshape(smallT, ns(onto)); % put back relevant dims of size 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/mult_by_sparse_table.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,155 @@ +/* mult_by_sparse_table.c ../potential/tables*/ + + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include <math.h> +#include <stdlib.h> +#include "mex.h" + +int compare(const void* src1, const void* src2){ + int i1 = *(int*)src1 ; + int i2 = *(int*)src2 ; + return i1-i2 ; +} + +void ind_subv(int index, const int *cumprod, int n, int *bsubv){ + int i; + + for (i = n-1; i >= 0; i--) { + bsubv[i] = ((int)floor(index / cumprod[i])); + index = index % cumprod[i]; + } +} + +int subv_ind(const int n, const int *cumprod, const int *subv){ + int i, index=0; + + for(i=0; i<n; i++){ + index += subv[i] * cumprod[i]; + } + return index; +} + +void reset_nzmax(mxArray *spArray, const int old_nzmax, const int new_nzmax){ + double *ptr; + void *newptr; + int *ir, *jc; + int nbytes; + + if(new_nzmax == old_nzmax) return; + nbytes = new_nzmax * sizeof(*ptr); + ptr = mxGetPr(spArray); + newptr = mxRealloc(ptr, nbytes); + mxSetPr(spArray, newptr); + nbytes = new_nzmax * sizeof(*ir); + ir = mxGetIr(spArray); + newptr = mxRealloc(ir, nbytes); + mxSetIr(spArray, newptr); + jc = mxGetJc(spArray); + jc[0] = 0; + jc[1] = new_nzmax; + mxSetNzmax(spArray, new_nzmax); +} + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, bdim, sdim, NB, NZB, NZS, position, bindex, sindex, nzCounts=0; + int *mask, *result, *bir, *sir, *rir, *bjc, *sjc, *rjc, *bCumprod, *sCumprod, *bsubv, *ssubv; + double *pbDomain, *psDomain, *pbSize, *psSize, *bpr, *spr, *rpr; + + pbDomain = mxGetPr(prhs[1]); + bdim = mxGetNumberOfElements(prhs[1]); + psDomain = mxGetPr(prhs[4]); + sdim = mxGetNumberOfElements(prhs[4]); + + pbSize = mxGetPr(prhs[2]); + psSize = mxGetPr(prhs[5]); + + NB = 1; + for(i=0; i<bdim; i++){ + NB *= (int)pbSize[i]; + } + + bpr = mxGetPr(prhs[0]); + bir = mxGetIr(prhs[0]); + bjc = mxGetJc(prhs[0]); + NZB = bjc[1]; + + spr = mxGetPr(prhs[3]); + sir = mxGetIr(prhs[3]); + sjc = mxGetJc(prhs[3]); + NZS = sjc[1]; + + plhs[0] = mxDuplicateArray(prhs[0]); + rpr = mxGetPr(plhs[0]); + rir = mxGetIr(plhs[0]); + rjc = mxGetJc(plhs[0]); + rjc[0] = 0; + rjc[1] = NZB; + + if(sdim == 0){ + for(i=0; i<NZB; i++){ + rpr[i] *= *spr; + } + return; + } + + mask = malloc(sdim * sizeof(int)); + bCumprod = malloc(bdim * sizeof(int)); + sCumprod = malloc(sdim * sizeof(int)); + bsubv = malloc(bdim * sizeof(int)); + ssubv = malloc(sdim * sizeof(int)); + + count = 0; + for(i=0; i<sdim; i++){ + for(j=0; j<bdim; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + bCumprod[0] = 1; + for(i=0; i<bdim-1; i++){ + bCumprod[i+1] = bCumprod[i] * (int)pbSize[i]; + } + sCumprod[0] = 1; + for(i=0; i<sdim-1; i++){ + sCumprod[i+1] = sCumprod[i] * (int)psSize[i]; + } + + for(i=0; i<NZB; i++){ + bindex = bir[i]; + ind_subv(bindex, bCumprod, bdim, bsubv); + for(j=0; j<sdim; j++){ + ssubv[j] = bsubv[mask[j]]; + } + sindex = subv_ind(sdim, sCumprod, ssubv); + result = (int *) bsearch(&sindex, sir, NZS, sizeof(int), compare); + if(result){ + position = result - sir; + rpr[nzCounts] = bpr[i] * spr[position]; + rir[nzCounts] = bindex; + nzCounts++; + } + } + + reset_nzmax(plhs[0], NZB, nzCounts); + free(mask); + free(bCumprod); + free(sCumprod); + free(bsubv); + free(ssubv); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/mult_by_table.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +/* mult_by_table.c ../potential/tables */ + + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2; + double *pbDomain, *psDomain, *sp, *zp, *bs; + + plhs[0] = mxDuplicateArray(prhs[0]); + zp = mxGetPr(plhs[0]); + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[4]); + pbDomain = mxGetPr(prhs[1]); + psDomain = mxGetPr(prhs[4]); + + NB = mxGetNumberOfElements(prhs[0]); + NS = mxGetNumberOfElements(prhs[3]); + sp = mxGetPr(prhs[3]); + + bs = mxGetPr(prhs[2]); + + if(NS == 1){ + for(i=0; i<NB; i++){ + zp[i] *= *sp; + } + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + zp[i] *= sp[i]; + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)bs[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + *zp++ *= *sp; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/mult_by_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% MULT_BY_TABLE +% bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +bigT(:) = bigT(:) .* Ts(:); % must have bigT(:) on LHS to preserve shape
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/mult_by_table2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function bigT = mult_by_table2(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% MULT_BY_TABLE +% bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + +%Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +%bigT(:) = bigT(:) .* Ts(:); % must have bigT(:) on LHS to preserve shape + +% extend_domain_table has a lot of overhead for small tables, +% since it calls myreshape and myrepmat, which check for 1 dimensional case. +% Here, we check up front. + +if length(bigdom)==1 % vector + bigT = bigT .* smallT; % smallT can be scalar or vector +else + if (length(bigsz) == length(smallsz)) & all(bigsz == smallsz) + bigT = bigT .* smallT; + else + map = find_equiv_posns(smalldom, bigdom); + sz = ones(1, length(bigdom)); + sz(map) = smallsz; + smallT = reshape(smallT, sz); % add dimensions of size 1 for missing domain + % we can use reshape instead of myreshape, because we know length(sz)>1 + sz = bigsz; + sz(map) = 1; % don't replicate along small domain, which is shared + % we can use repmat instead of myrepmat, because we know length(sz)>1 + smallT = repmat(smallT, sz(:)'); + bigT(:) = bigT(:) .* smallT(:); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/mult_by_tableC.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +/* mult_by_table.c ../potential/tables */ + + +/******************************************/ +/* 6 input & 1 output */ +/* Big table [0] */ +/* Big domain [1] */ +/* big sizes [2] */ +/* Small table [3] */ +/* small domain [4] */ +/* small sizes [5] */ +/* */ +/* New big table[0] */ +/******************************************/ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + int i, j, count, NB, NS, siz_b, siz_s, ndim, temp; + int *mask, *sx, *sy, *cpsy, *subs, *s, *cpsy2; + double *pbDomain, *psDomain, *sp, *zp, *bs; + + plhs[0] = mxDuplicateArray(prhs[0]); + zp = mxGetPr(plhs[0]); + + siz_b = mxGetNumberOfElements(prhs[1]); + siz_s = mxGetNumberOfElements(prhs[4]); + pbDomain = mxGetPr(prhs[1]); + psDomain = mxGetPr(prhs[4]); + + NB = mxGetNumberOfElements(prhs[0]); + NS = mxGetNumberOfElements(prhs[3]); + sp = mxGetPr(prhs[3]); + + bs = mxGetPr(prhs[2]); + + if(NS == 1){ + for(i=0; i<NB; i++){ + zp[i] *= *sp; + } + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + zp[i] *= sp[i]; + } + return; + } + + mask = malloc(siz_s * sizeof(int)); + count = 0; + for(i=0; i<siz_s; i++){ + for(j=0; j<siz_b; j++){ + if(psDomain[i] == pbDomain[j]){ + mask[count] = j; + count++; + break; + } + } + } + + ndim = siz_b; + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + sx[i] = (int)bs[i]; + sy[i] = 1; + } + for(i=0; i<count; i++){ + temp = mask[i]; + sy[temp] = sx[temp]; + } + + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i = 0; i < ndim; i++){ + subs[i] = 0; + s[i] = sx[i] - 1; + } + + for(i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + *zp++ *= *sp; + for(i = 0; i < ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) + sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) + sp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); + free(mask); +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/mult_by_tableM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% MULT_BY_TABLE +% bigT = mult_by_table(bigT, bigdom, bigsz, smallT, smalldom, smallsz) +% + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +bigT(:) = bigT(:) .* Ts(:); % must have bigT(:) on LHS to preserve shape
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/mult_by_table_global.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function mult_by_table_global(bigT, bigdom, bigsz, smallT, smalldom, smallsz) + +% all arguments are read only +global NEWBIGT_GLOBAL + +Ts = extend_domain_table(smallT, smalldom, smallsz, bigdom, bigsz); +NEWBIGT_GLOBAL = bigT(:) .* Ts(:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/rep_mult.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +/* rep_mult.c repmat first two operands to the size provided by */ +/* the third operand, then perform point multiply */ +/* 3 input, 1 output */ +/* C = rep_mult(A, B, sizes) */ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + double *xp, *yp, *zp, *pSizes; + int xnd, ynd, numElements = 1; + const int *xdim, *ydim; + int i, j, ndim; + int *s, *sx, *sy, *cpsx, *cpsy; + int *subs, *s1, *cpsx2, *cpsy2; + + if (nrhs != 3) + mexErrMsgTxt("Incorrect number of inputs."); + + if (nlhs > 1) + mexErrMsgTxt("Too many output arguments."); + + xnd = mxGetNumberOfDimensions(prhs[0]); + ynd = mxGetNumberOfDimensions(prhs[1]); + xdim = mxGetDimensions(prhs[0]); + ydim = mxGetDimensions(prhs[1]); + ndim = mxGetNumberOfElements(prhs[2]); + + pSizes = mxGetPr(prhs[2]); + + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + s = (int *)malloc(sizeof(int)*ndim); + s1 = (int *)malloc(sizeof(int)*ndim); + *(cpsx = (int *)malloc(sizeof(int)*ndim)) = 1; + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsx2 = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + subs[i] = 0; + sx[i] = (i < xnd) ? xdim[i] : 1; + sy[i] = (i < ynd) ? ydim[i] : 1; + s[i] = (int)pSizes[i]; + s1[i] = s[i] - 1; + numElements *= s[i]; + } + + for(i=0; i<ndim-1; i++){ + cpsx[i+1] = cpsx[i]*sx[i]--; + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsx2[i] = cpsx[i]*sx[i]; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsx2[ndim-1] = cpsx[ndim-1]*(--sx[ndim-1]); + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + plhs[0] = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + zp = mxGetPr(plhs[0]); + xp = mxGetPr(prhs[0]); + yp = mxGetPr(prhs[1]); + + for(j=0; j<numElements; j++){ + *zp++ = *xp * *yp; + for(i=0; i<ndim; i++){ + if(subs[i] == s1[i]){ + subs[i] = 0; + if(sx[i]) + xp -= cpsx2[i]; + if(sy[i]) + yp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sx[i]) + xp += cpsx[i]; + if(sy[i]) + yp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(s1); + free(cpsx); + free(cpsy); + free(subs); + free(cpsx2); + free(cpsy2); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/Tables/repmat_and_mult.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +/**************************************************** +A = mult_by_array(big, small) +implicitely copies small |big|/|small| times +and then does element-wise multiplication. + +i.e., +C = repmat(small(:), 1, length(big(:))/length(small(:))) +A = reshape(big(:) .* C(:), size(big)) + +However, this C version avoids the expense of the repmat. + +Written by wei.hu@intel.com, 28 Jan 2002. +/****************************************************/ + + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + double *sp, *zp; + int i, j, NB, NS, xnd, ynd, ndim; + const int *xdim, *ydim; + int *s, *sx, *sy, *cpsy, *subs, *cpsy2; + + if (nrhs != 2) + mexErrMsgTxt("Incorrect number of inputs."); + + if (nlhs > 1) + mexErrMsgTxt("Too many output arguments."); + + plhs[0] = mxDuplicateArray(prhs[0]); + zp = mxGetPr(plhs[0]); + sp = mxGetPr(prhs[1]); + + xnd = mxGetNumberOfDimensions(prhs[0]); + ynd = mxGetNumberOfDimensions(prhs[1]); + xdim = mxGetDimensions(prhs[0]); + ydim = mxGetDimensions(prhs[1]); + ndim = xnd; + + NB = mxGetNumberOfElements(prhs[0]); + NS = mxGetNumberOfElements(prhs[1]); + + if(NS == 1){ + for(i=0; i<NB; i++){ + *zp++ *= *sp; + } + return; + } + + if(NS == NB){ + for(i=0; i<NB; i++){ + *zp++ *= *sp++; + } + return; + } + + sx = (int *)malloc(sizeof(int)*ndim); + sy = (int *)malloc(sizeof(int)*ndim); + s = (int *)malloc(sizeof(int)*ndim); + *(cpsy = (int *)malloc(sizeof(int)*ndim)) = 1; + subs = (int *)malloc(sizeof(int)*ndim); + cpsy2 = (int *)malloc(sizeof(int)*ndim); + for(i=0; i<ndim; i++){ + subs[i] = 0; + sx[i] = xdim[i]; + sy[i] = (i < ynd) ? ydim[i] : 1; + s[i] = sx[i] - 1; + } + + for (i = 0; i < ndim-1; i++){ + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + for(j=0; j<NB; j++){ + *zp++ *= *sp; + for(i=0; i<ndim; i++){ + if(subs[i] == s[i]){ + subs[i] = 0; + if(sy[i]) sp -= cpsy2[i]; + } + else{ + subs[i]++; + if(sy[i]) sp += cpsy[i]; + break; + } + } + } + free(sx); + free(sy); + free(s); + free(cpsy); + free(subs); + free(cpsy2); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/check_for_cd_arcs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function check_for_cd_arcs(onodes, cnodes, dag) +% CHECK_FOR_CD_ARCS Raise an error if there are any C->D links where the C node is hidden. +% check_for_cd_arcs(onodes, cnodes, dag) +% +% We cannot convert the logistic/softmax function (C->D CPD) to a Gaussian potential +% unless we use the variational approximation discussed in +% "A variational approximation for Bayesian networks with discrete and continuous latent +% variables", K. Murphy, UAI 1999. + +n = length(dag); +hnodes = mysetdiff(1:n, onodes); +chid = myintersect(cnodes, hnodes); +dnodes = mysetdiff(1:n, cnodes); +for i=chid(:)' + dcs = myintersect(children(dag, i), dnodes); + if ~isempty(dcs) + error(['hidden cts node ' num2str(i) ' has a discrete child']); + end +end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/determine_pot_type.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function pot_type = determine_pot_type(model, onodes, nodes) +% DETERMINE_POT_TYPE Determine the type of potential based on the evidence pattern. +% pot_type = determine_pot_type(model, onodes, nodes) +% +% If there are any utility nodes, pot_type = 'u' +% else +% If all hidden nodes are discrete, pot_type = 'd'. +% If all hidden nodes are continuous, pot_type = 'g' (Gaussian). +% If some hidden nodes are discrete, and some cts, pot_type = 'cg' (conditional Gaussian). +% +% nodes defaults to all nodes in graph + +nnodes = length(model.node_sizes); +if nargin < 3, nodes = 1:nnodes; end + +hnodes = mysetdiff(nodes, onodes); +if isfield(model, 'limid') %~isempty(model.utility_nodes) + pot_type = 'u'; +elseif isempty(myintersect(model.cnodes, hnodes)) + pot_type = 'd'; +elseif mysubset(hnodes, model.cnodes) + pot_type = 'g'; +else + pot_type = 'cg'; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/genops.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,707 @@ +/* + + GENOPS.C + Generalized arithmetic operators overloading built-in functions. + + written by Douglas M. Schwarz + schwarz@servtech.com + 26 December 1998 + Last modified: 2 April 1999 + + Copyright 1998-1999 by Douglas M. Schwarz. All rights reserved. + +*/ + + +/* + +Build MEX file by entering the appropriate command at the MATLAB prompt +(-D<name> option is equivalent to #define <name> in source file): + +mex genops.c -DPLUS_MEX -output plus +mex genops.c -DMINUS_MEX -output minus +mex genops.c -DTIMES_MEX -output times +mex genops.c -DRDIVIDE_MEX -output rdivide +mex genops.c -DLDIVIDE_MEX -output ldivide +mex genops.c -DPOWER_MEX -output power +mex genops.c -DEQ_MEX -output eq +mex genops.c -DNE_MEX -output ne +mex genops.c -DLT_MEX -output lt +mex genops.c -DGT_MEX -output gt +mex genops.c -DLE_MEX -output le +mex genops.c -DGE_MEX -output ge + +*/ + +/* This file has been formatted for a tab equal to 4 spaces. */ + +#if defined(EQ_MEX) || defined(NE_MEX) || defined(LT_MEX) || defined(GT_MEX) \ + || defined(LE_MEX) || defined(GE_MEX) +#define RELOP_MEX +#endif + +#include "mex.h" +#include "matrix.h" +#ifdef POWER_MEX +#include <math.h> +#define PI 3.141592653589793 +#endif + +bool allequal(int, const int *, const int *); +void removeZeroImag(double *, double *, int, const int *, int, mxArray **); + +#define xMat prhs[0] +#define yMat prhs[1] +#define zMat plhs[0] + +#define min(A,B) ((A) < (B) ? (A) : (B)) +#define max(A,B) ((A) > (B) ? (A) : (B)) + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + double *xrp, *xip, *yrp, *yip; +#ifndef RELOP_MEX + double *zr, *zi, *zip; +#endif + double *zrp, *zrend; + int xnd, ynd, numElements = 1; + const int *xdim, *ydim; + bool xcmplx, ycmplx; + mxClassID yclass; + int *s, ndim, *sx, *sy, i, *cpsx, *cpsy; + int *subs, *s1, *cpsx2, *cpsy2; + int ix = 0, iy = 0; + mxArray *args[3], *result[1]; +#if defined(RDIVIDE_MEX) || defined(LDIVIDE_MEX) + double denom; +#endif +#ifdef POWER_MEX + double mag, theta, phi, magx; + int flops = 0; +#endif + + + if (nrhs != 2) + mexErrMsgTxt("Incorrect number of inputs."); + + if (nlhs > 1) + mexErrMsgTxt("Too many output arguments."); + + xnd = mxGetNumberOfDimensions(xMat); + ynd = mxGetNumberOfDimensions(yMat); + xdim = mxGetDimensions(xMat); + ydim = mxGetDimensions(yMat); + + yclass = mxGetClassID(yMat); + +/* If the built-in function in MATLAB can handle the arguments + then use that. */ + if (yclass != mxDOUBLE_CLASS || + (xnd == 2 && xdim[0] == 1 && xdim[1] == 1) || + (ynd == 2 && ydim[0] == 1 && ydim[1] == 1) || + (xnd == ynd && allequal(xnd,xdim,ydim))) + { +#ifdef PLUS_MEX + args[0] = mxCreateString("plus"); +#elif defined(MINUS_MEX) + args[0] = mxCreateString("minus"); +#elif defined(TIMES_MEX) + args[0] = mxCreateString("times"); +#elif defined(RDIVIDE_MEX) + args[0] = mxCreateString("rdivide"); +#elif defined(LDIVIDE_MEX) + args[0] = mxCreateString("ldivide"); +#elif defined(POWER_MEX) + args[0] = mxCreateString("power"); +#elif defined(EQ_MEX) + args[0] = mxCreateString("eq"); +#elif defined(NE_MEX) + args[0] = mxCreateString("ne"); +#elif defined(LT_MEX) + args[0] = mxCreateString("lt"); +#elif defined(GT_MEX) + args[0] = mxCreateString("gt"); +#elif defined(LE_MEX) + args[0] = mxCreateString("le"); +#elif defined(GE_MEX) + args[0] = mxCreateString("ge"); +#endif + args[1] = (mxArray *)xMat; + args[2] = (mxArray *)yMat; + mexCallMATLAB(1, result, 3, args, "builtin"); + mxDestroyArray(args[0]); + zMat = result[0]; + } + else /* X and Y are both N-D and different dimensionality. */ + { + ndim = max(xnd,ynd); + sx = (int *)mxMalloc(sizeof(int)*ndim); + sy = (int *)mxMalloc(sizeof(int)*ndim); + s = (int *)mxMalloc(sizeof(int)*ndim); + s1 = (int *)mxMalloc(sizeof(int)*ndim); + *(cpsx = (int *)mxMalloc(sizeof(int)*ndim)) = 1; + *(cpsy = (int *)mxMalloc(sizeof(int)*ndim)) = 1; + subs = (int *)mxMalloc(sizeof(int)*ndim); + cpsx2 = (int *)mxMalloc(sizeof(int)*ndim); + cpsy2 = (int *)mxMalloc(sizeof(int)*ndim); + for (i = 0; i < ndim; i++) + { + subs[i] = 0; + sx[i] = (i < xnd) ? xdim[i] : 1; + sy[i] = (i < ynd) ? ydim[i] : 1; + if (sx[i] == sy[i]) + s[i] = sx[i]; + else if (sx[i] == 1) + s[i] = sy[i]; + else if (sy[i] == 1) + s[i] = sx[i]; + else + { + mxFree(sx); + mxFree(sy); + mxFree(s); + mxFree(s1); + mxFree(cpsx); + mxFree(cpsy); + mxFree(subs); + mxFree(cpsx2); + mxFree(cpsy2); + mexErrMsgTxt("Array dimensions are not appropriate."); + } + s1[i] = s[i] - 1; + numElements *= s[i]; + } + + for (i = 0; i < ndim-1; i++) + { + cpsx[i+1] = cpsx[i]*sx[i]--; + cpsy[i+1] = cpsy[i]*sy[i]--; + cpsx2[i] = cpsx[i]*sx[i]; + cpsy2[i] = cpsy[i]*sy[i]; + } + cpsx2[ndim-1] = cpsx[ndim-1]*(--sx[ndim-1]); + cpsy2[ndim-1] = cpsy[ndim-1]*(--sy[ndim-1]); + + xcmplx = mxIsComplex(xMat); + ycmplx = mxIsComplex(yMat); + + if (!xcmplx && !ycmplx) /* X and Y both N-D, both real. */ + { +#ifdef POWER_MEX + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = zr = mxGetPr(zMat); + zip = zi = mxGetPi(zMat); +#elif defined(RELOP_MEX) + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + mxSetLogical(zMat); + zrp = mxGetPr(zMat); +#else + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + zrp = mxGetPr(zMat); +#endif + xrp = mxGetPr(xMat); + yrp = mxGetPr(yMat); + zrend = zrp + numElements; + while (zrp < zrend) + { +#ifdef PLUS_MEX + *zrp++ = *xrp + *yrp; +#elif defined(MINUS_MEX) + *zrp++ = *xrp - *yrp; +#elif defined(TIMES_MEX) + *zrp++ = *xrp * *yrp; +#elif defined(RDIVIDE_MEX) + *zrp++ = *xrp / *yrp; +#elif defined(LDIVIDE_MEX) + *zrp++ = *yrp / *xrp; +#elif defined(POWER_MEX) + if (*xrp < 0.0 && *yrp != floor(*yrp)) + { + mag = pow(-*xrp,*yrp); + theta = PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + *zrp++ = pow(*xrp,*yrp); + *zip++ = 0.0; + flops++; + } +#elif defined(EQ_MEX) + *zrp++ = (*xrp == *yrp); +#elif defined(NE_MEX) + *zrp++ = (*xrp != *yrp); +#elif defined(LT_MEX) + *zrp++ = (*xrp < *yrp); +#elif defined(GT_MEX) + *zrp++ = (*xrp > *yrp); +#elif defined(LE_MEX) + *zrp++ = (*xrp <= *yrp); +#elif defined(GE_MEX) + *zrp++ = (*xrp >= *yrp); +#endif + for (i = 0; i < ndim; i++) + { + if (subs[i] == s1[i]) + { + subs[i] = 0; + if (sx[i]) + xrp -= cpsx2[i]; + if (sy[i]) + yrp -= cpsy2[i]; + } + else + { + subs[i]++; + if (sx[i]) + xrp += cpsx[i]; + if (sy[i]) + yrp += cpsy[i]; + break; + } + } + } +#ifdef POWER_MEX + mexAddFlops(flops); + removeZeroImag(zr, zi, ndim, (const int *)s, numElements, &zMat); +#elif !defined(RELOP_MEX) + mexAddFlops(numElements); +#endif + } + else if (!xcmplx && ycmplx) /* X and Y both N-D, X real, Y complex. */ + { +#ifdef POWER_MEX + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = zr = mxGetPr(zMat); + zip = zi = mxGetPi(zMat); +#elif defined(RELOP_MEX) + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + mxSetLogical(zMat); + zrp = mxGetPr(zMat); +#else + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = mxGetPr(zMat); + zip = mxGetPi(zMat); +#endif + xrp = mxGetPr(xMat); + yrp = mxGetPr(yMat); + yip = mxGetPi(yMat); + zrend = zrp + numElements; + while (zrp < zrend) + { +#ifdef PLUS_MEX + *zrp++ = *xrp + *yrp; + *zip++ = *yip; +#elif defined(MINUS_MEX) + *zrp++ = *xrp - *yrp; + *zip++ = -*yip; +#elif defined(TIMES_MEX) + *zrp++ = *xrp * *yrp; + *zip++ = *xrp * *yip; +#elif defined(RDIVIDE_MEX) + denom = *yrp * *yrp + *yip * *yip; + *zrp++ = (*xrp * *yrp)/denom; + *zip++ = (-*xrp * *yip)/denom; +#elif defined(LDIVIDE_MEX) + *zrp++ = *yrp / *xrp; + *zip++ = *yip / *xrp; +#elif defined(POWER_MEX) + if (*yip == 0.0) + { + if (*xrp < 0.0 && *yrp != floor(*yrp)) + { + mag = pow(-*xrp,*yrp); + theta = PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + *zrp++ = pow(*xrp,*yrp); + *zip++ = 0.0; + flops++; + } + } + else + { + if (*xrp < 0.0) + { + mag = pow(-*xrp,*yrp)*exp(-PI * *yip); + theta = *yip * log(-*xrp) + PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + mag = pow(*xrp,*yrp); + theta = *yip * log(*xrp); + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 13; + } + } +#elif defined(EQ_MEX) + *zrp++ = (*xrp == *yrp) && (*yip == 0.0); +#elif defined(NE_MEX) + *zrp++ = (*xrp != *yrp) || (*yip != 0.0); +#elif defined(LT_MEX) + *zrp++ = (*xrp < *yrp); +#elif defined(GT_MEX) + *zrp++ = (*xrp > *yrp); +#elif defined(LE_MEX) + *zrp++ = (*xrp <= *yrp); +#elif defined(GE_MEX) + *zrp++ = (*xrp >= *yrp); +#endif + for (i = 0; i < ndim; i++) + { + if (subs[i] == s1[i]) + { + subs[i] = 0; + if (sx[i]) + xrp -= cpsx2[i]; + if (sy[i]) + { + yrp -= cpsy2[i]; + yip -= cpsy2[i]; + } + } + else + { + subs[i]++; + if (sx[i]) + xrp += cpsx[i]; + if (sy[i]) + { + yrp += cpsy[i]; + yip += cpsy[i]; + } + break; + } + } + } +#if defined(PLUS_MEX) || defined(MINUS_MEX) + mexAddFlops(2*numElements); +#elif defined(TIMES_MEX) || defined(RDIVIDE_MEX) || defined(LDIVIDE_MEX) + mexAddFlops(6*numElements); +#elif defined(POWER_MEX) + mexAddFlops(flops); + removeZeroImag(zr, zi, ndim, (const int *)s, numElements, &zMat); +#endif + } + else if (xcmplx && !ycmplx) /* X and Y both N-D, X complex, Y real. */ + { +#ifdef POWER_MEX + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = zr = mxGetPr(zMat); + zip = zi = mxGetPi(zMat); +#elif defined(RELOP_MEX) + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + mxSetLogical(zMat); + zrp = mxGetPr(zMat); +#else + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = mxGetPr(zMat); + zip = mxGetPi(zMat); +#endif + xrp = mxGetPr(xMat); + xip = mxGetPi(xMat); + yrp = mxGetPr(yMat); + zrend = zrp + numElements; + while (zrp < zrend) + { +#ifdef PLUS_MEX + *zrp++ = *xrp + *yrp; + *zip++ = *xip; +#elif defined(MINUS_MEX) + *zrp++ = *xrp - *yrp; + *zip++ = *xip; +#elif defined(TIMES_MEX) + *zrp++ = *xrp * *yrp; + *zip++ = *xip * *yrp; +#elif defined(RDIVIDE_MEX) + *zrp++ = *xrp / *yrp; + *zip++ = *xip / *yrp; +#elif defined(LDIVIDE_MEX) + denom = *xrp * *xrp + *xip * *xip; + *zrp++ = (*xrp * *yrp)/denom; + *zip++ = (-*xip * *yrp)/denom; +#elif defined(POWER_MEX) + if (*xip == 0.0) + { + if (*xrp < 0.0 && *yrp != floor(*yrp)) + { + mag = pow(-*xrp,*yrp); + theta = PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + *zrp++ = pow(*xrp,*yrp); + *zip++ = 0.0; + flops++; + } + } + else + { + mag = pow(*xrp * *xrp + *xip * *xip,0.5 * *yrp); + theta = *yrp*atan2(*xip,*xrp); + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } +#elif defined(EQ_MEX) + *zrp++ = (*xrp == *yrp) && (*xip == 0.0); +#elif defined(NE_MEX) + *zrp++ = (*xrp != *yrp) || (*xip != 0.0); +#elif defined(LT_MEX) + *zrp++ = (*xrp < *yrp); +#elif defined(GT_MEX) + *zrp++ = (*xrp > *yrp); +#elif defined(LE_MEX) + *zrp++ = (*xrp <= *yrp); +#elif defined(GE_MEX) + *zrp++ = (*xrp >= *yrp); +#endif + for (i = 0; i < ndim; i++) + { + if (subs[i] == s1[i]) + { + subs[i] = 0; + if (sx[i]) + { + xrp -= cpsx2[i]; + xip -= cpsx2[i]; + } + if (sy[i]) + yrp -= cpsy2[i]; + } + else + { + subs[i]++; + if (sx[i]) + { + xrp += cpsx[i]; + xip += cpsx[i]; + } + if (sy[i]) + yrp += cpsy[i]; + break; + } + } + } +#if defined(PLUS_MEX) || defined(MINUS_MEX) + mexAddFlops(2*numElements); +#elif defined(TIMES_MEX) || defined(RDIVIDE_MEX) || defined(LDIVIDE_MEX) + mexAddFlops(6*numElements); +#elif defined(POWER_MEX) + mexAddFlops(flops); + removeZeroImag(zr, zi, ndim, (const int *)s, numElements, &zMat); +#endif + } + else if (xcmplx && ycmplx) /* X and Y both N-D, both complex. */ + { +#if defined(RELOP_MEX) + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + mxSetLogical(zMat); + zrp = mxGetPr(zMat); +#else + zMat = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxCOMPLEX); + zrp = zr = mxGetPr(zMat); + zip = zi = mxGetPi(zMat); +#endif + xrp = mxGetPr(xMat); + xip = mxGetPi(xMat); + yrp = mxGetPr(yMat); + yip = mxGetPi(yMat); + zrend = zrp + numElements; + while (zrp < zrend) + { +#ifdef PLUS_MEX + *zrp++ = *xrp + *yrp; + *zip++ = *xip + *yip; +#elif defined(MINUS_MEX) + *zrp++ = *xrp - *yrp; + *zip++ = *xip - *yip; +#elif defined(TIMES_MEX) + *zrp++ = *xrp * *yrp - *xip * *yip; + *zip++ = *xip * *yrp + *xrp * *yip; +#elif defined(RDIVIDE_MEX) + denom = *yrp * *yrp + *yip * *yip; + *zrp++ = (*xrp * *yrp + *xip * *yip)/denom; + *zip++ = (*xip * *yrp - *xrp * *yip)/denom; +#elif defined(LDIVIDE_MEX) + denom = *xrp * *xrp + *xip * *xip; + *zrp++ = (*xrp * *yrp + *xip * *yip)/denom; + *zip++ = (*xrp * *yip - *xip * *yrp)/denom; +#elif defined(POWER_MEX) + if (*xip == 0.0 && *yip == 0.0) + { + if (*xrp < 0.0 && *yrp != floor(*yrp)) + { + mag = pow(-*xrp,*yrp); + theta = PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + *zrp++ = pow(*xrp,*yrp); + *zip++ = 0.0; + flops++; + } + } + else if (*xip == 0.0) + { + if (*xrp < 0.0) + { + mag = pow(-*xrp,*yrp)*exp(-PI * *yip); + theta = *yip * log(-*xrp) + PI * *yrp; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + mag = pow(*xrp,*yrp); + theta = *yip * log(*xrp); + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 13; + } + } + else if (*yip == 0.0) + { + mag = pow(*xrp * *xrp + *xip * *xip,0.5 * *yrp); + theta = *yrp * atan2(*xip,*xrp); + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } + else + { + magx = sqrt(*xrp * *xrp + *xip * *xip); + phi = atan2(*xip,*xrp); + mag = pow(magx,*yrp)*exp(-*yip * phi); + theta = *yip * log(magx) + *yrp * phi; + *zrp++ = mag*cos(theta); + *zip++ = mag*sin(theta); + flops += 18; + } +#elif defined(EQ_MEX) + *zrp++ = (*xrp == *yrp) && (*xip == *yip); +#elif defined(NE_MEX) + *zrp++ = (*xrp != *yrp) || (*xip != *yip); +#elif defined(LT_MEX) + *zrp++ = (*xrp < *yrp); +#elif defined(GT_MEX) + *zrp++ = (*xrp > *yrp); +#elif defined(LE_MEX) + *zrp++ = (*xrp <= *yrp); +#elif defined(GE_MEX) + *zrp++ = (*xrp >= *yrp); +#endif + for (i = 0; i < ndim; i++) + { + if (subs[i] == s1[i]) + { + subs[i] = 0; + if (sx[i]) + { + xrp -= cpsx2[i]; + xip -= cpsx2[i]; + } + if (sy[i]) + { + yrp -= cpsy2[i]; + yip -= cpsy2[i]; + } + } + else + { + subs[i]++; + if (sx[i]) + { + xrp += cpsx[i]; + xip += cpsx[i]; + } + if (sy[i]) + { + yrp += cpsy[i]; + yip += cpsy[i]; + } + break; + } + } + } +#if defined(PLUS_MEX) || defined(MINUS_MEX) + mexAddFlops(2*numElements); +#elif defined(TIMES_MEX) || defined(RDIVIDE_MEX) || defined(LDIVIDE_MEX) + mexAddFlops(6*numElements); +#elif defined(POWER_MEX) + mexAddFlops(flops); +#endif +#ifndef RELOP_MEX + removeZeroImag(zr, zi, ndim, (const int *)s, numElements, &zMat); +#endif + } + } +} + + +/*********************************************************** +* * +* Tests to see if the vectors xdim and ydim are equal. * +* * +***********************************************************/ +bool allequal(int ndim, const int *xdim, const int *ydim) +{ + int i; + bool result = true; + + for (i = 0; i < ndim; i++) + result = result && (xdim[i] == ydim[i]); + + return(result); +} + + +/****************************************************************************** +* * +* Tests to see if every imaginary element is identically zero and, if so, * +* creates a new array which is real and copies the real elements to it. * +* * +******************************************************************************/ +void removeZeroImag(double *zr, double *zi, int ndim, const int *s, + int numElements, mxArray *plhs[]) +{ + double *zrend, *ziend, *zip, *z1p, *z2p; + bool allImZero = true; + mxArray *temp; + + zip = zi; + ziend = zi + numElements; + while (zip < ziend) + { + allImZero = allImZero && (*zip++ == 0.0); + if (!allImZero) + return; + } + + temp = mxCreateNumericArray(ndim, s, mxDOUBLE_CLASS, mxREAL); + z1p = zr; + z2p = mxGetPr(temp); + zrend = z1p + numElements; + while (z1p < zrend) + *z2p++ = *z1p++; + mxDestroyArray(plhs[0]); + plhs[0] = temp; + return; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/potentials/mk_initial_pot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function pot = mk_initial_pot(pot_type, dom, ns, cnodes, onodes) +% MK_INITIAL_POT A "initial" potential is one which has not had any evidence entered into it. +% pot = mk_initial_pot(pot_type, domain, node_sizes, cnodes, onodes) +% +% pot_type is one of 'd', 'g', 'cg' or 'u' +% domain is the set of nodes to be included in the potential. +% node_sizes(i) is the size of node i. + +switch pot_type + case 'd', + ns(onodes) = 1; + pot = dpot(dom, ns(dom)); + case 'u', + ns(onodes) = 1; + pot = upot(dom, ns(dom)); + case 'g', + ns(onodes) = 0; + pot = cpot(dom, ns(dom)); + case 'cg', + dnodes = mysetdiff(1:length(ns), cnodes); + ddom = myintersect(dnodes, dom); + cdom = myintersect(cnodes, dom); + dobs = myintersect(dnodes, onodes); + cobs = myintersect(cnodes, onodes); + ns(dobs) = 1; + ns(cobs) = 0; + pot = cgpot(ddom, cdom, ns); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/test_BNT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,108 @@ +% If all is well, all of these scripts should run without errors. + + +% bnets +cg1 +cg2 +discrete1 +fa1 +gaussian1 +gaussian2 +if exist('@gibbs_sampling_inf_engine/private/compute_posterior','file') + % only exists if installC has been run + gibbs_test1 +end +learn1 +lw1 +mfa1 +mixexp1 +mixexp2 +mixexp3 +mog1 +mpe1 +mpe2 +qmr1 +qmr2 +sample1 +softev1 +softmax1 +sprinkler1 + + +% belief propagation +belprop_polytree_discrete +belprop_polytree_gauss % alag +belprop_polytree_cg +belprop_loop1_discrete +belprop_loop1_gauss +belprop_loopy_discrete +belprop_loopy_gauss +belprop_loopy_cg % like cg1 + + +% factor graphs +%fg1 failed since marginals were not exact + +fg2 +fg3 +fg_mrf1 +fg_mrf2 + + +% Structure learning +bic1 +cooper_yoo +k2demo1 +mcmc1 +model_select1 +pc1 +%pc2 failed due to numerical problems in KPMstats/cond_indep_fisher_z + + + + +% limids +asia_dt1 +id1 +oil1 +pigs1 + + +% dbns +arhmm1 +bat1 +bkff1 +chmm1 +dhmm1 +filter_test1 +ghmm1 +kalman1 +kjaerulff1 +loopy_dbn1 +mhmm1 +mildew1 +reveal1 +viterbi1 +water1 + + +% HHMMs +abcd_hhmm +sample_square_hhmm_discrete +%learn_square_hhmm_cts +sample_motif_hhmm + +%sparse jtree engine & ndx 2TBN engine +if exist('@jtree_sparse_inf_engine/init_pot','file') + % only exists if installC has been run + discrete2 + discrete3 + filter_test1 + water2 +end + +%find . -path '*.m' -exec wc -l {} \; | ~/count.pl + +% we cannot use tic;toc to time test_BNT, since functions within this script +% reset the tic;toc timer. Hence we use the following: +%clock0=clock; cpu0 = cputime; test_BNT; cpu=cputime-cpu0; elapsed=etime(clock, clock0)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bnt/uninstallC_BNT.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +dirs = {'potentials/Tables', ... + 'CPDs/@discrete_CPD', ... + 'inference/static/@jtree_sparse_inf_engine', ... + 'inference/static/@gibbs_sampling_inf_engine/private'}; + +BNT_HOME = '/home/ai2/murphyk/matlab/FullBNT'; % edit this +%global BNT_HOME + +for d=1:length(dirs) + f = fullfile(BNT_HOME, 'BNT', dirs{d}); + fprintf('removing Cmex files from %s\n', f); + cd(f) + delete *.mex* + delete *.dll + delete *.obj + delete *.o +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/bntRoot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function r = bntRoot() +% Return directory name where bnt is stored + r = fileparts(which(mfilename)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/ChangeLog.Sourceforge.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4436 @@ + + +2007-02-11 17:12 nsaunier + + * BNT/learning/learn_struct_pdag_pc.m: Bug submitted by Imme Ebert-Uphoff (ebert@tree.com) (see Thu Feb 8, 2007 email on the BNT mailing list). + +2005-11-26 12:12 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/fwdback_twoslice.m: merged + fwdback_twoslice.m to release branch + +2005-11-25 17:24 nsaunier + + * BNT/inference/dynamic/@hmm_inf_engine/fwdback_twoslice.m: adding + old missing fwdback_twoslice.m + +2005-11-25 17:24 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/fwdback_twoslice.m: file + fwdback_twoslice.m was added on branch release-1_0 on 2005-11-26 + 20:12:05 +0000 + +2005-09-25 15:54 yozhik + + * BNT/add_BNT_to_path.m: fix paths + +2005-09-25 15:30 yozhik + + * BNT/add_BNT_to_path.m: Restored directories to path. + +2005-09-25 15:29 yozhik + + * HMM/fwdback_twoslice.m: added missing fwdback_twoslice + +2005-09-17 11:14 yozhik + + * ChangeLog, BNT/add_BNT_to_path.m, BNT/test_BNT.m, + BNT/examples/static/cmp_inference_static.m, + BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m: Merged + bug fixes from HEAD. + +2005-09-17 11:11 yozhik + + * ChangeLog: added change log + +2005-09-17 11:11 yozhik + + * ChangeLog: file ChangeLog was added on branch release-1_0 on + 2005-09-17 18:14:47 +0000 + +2005-09-17 10:00 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m: Temporary + rollback to fix error, per Kevin. + +2005-09-17 09:59 yozhik + + * BNT/examples/static/cmp_inference_static.m: Commented out + erroneous line, per Kevin. + +2005-09-17 09:58 yozhik + + * BNT/add_BNT_to_path.m: Changed to require BNT_HOME to be + predefined. + +2005-09-17 09:56 yozhik + + * BNT/test_BNT.m: Commented out problematic tests. + +2005-09-17 09:38 yozhik + + * BNT/test_BNT.m: renable tests + +2005-09-12 22:18 yozhik + + * KPMtools/pca_kpm.m: Initial import of code base from Kevin + Murphy. + +2005-09-12 22:18 yozhik + + * KPMtools/pca_kpm.m: Initial revision + +2005-08-29 10:44 yozhik + + * graph/: README.txt, Old/best_first_elim_order.m, + Old/dag_to_jtree.m, Old/dfs.m, Old/dsep_test.m, + Old/mk_2D_lattice_slow.m, acyclic.m, assignEdgeNums.m, + best_first_elim_order.m, check_jtree_property.m, + check_triangulated.m, children.m, cliques_to_jtree.m, + cliques_to_strong_jtree.m, connected_graph.m, + dag_to_essential_graph.m, dfs.m, dfs_test.m, dijkstra.m, + family.m, graph_separated.m, graph_to_jtree.m, + min_subtree_con_nodes.m, minimum_spanning_tree.m, minspan.m, + mk_2D_lattice.m, mk_2D_lattice_slow.m, mk_adj_mat.m, + mk_adjmat_chain.m, mk_all_dags.m, mk_nbrs_of_dag.m, + mk_nbrs_of_digraph.m, mk_nbrs_of_digraph_broken.m, + mk_nbrs_of_digraph_not_vectorized.m, mk_rnd_dag.m, + mk_rnd_dag_given_edge_prob.m, mk_rooted_tree.m, mk_undirected.m, + moralize.m, neighbors.m, parents.m, pred2path.m, + reachability_graph.m, scc.m, strong_elim_order.m, test.m, + test_strong_root.m, topological_sort.m, trees.txt, triangulate.c, + triangulate.m, triangulate_2Dlattice_demo.m, triangulate_test.m: + Initial import of code base from Kevin Murphy. + +2005-08-29 10:44 yozhik + + * graph/: README.txt, Old/best_first_elim_order.m, + Old/dag_to_jtree.m, Old/dfs.m, Old/dsep_test.m, + Old/mk_2D_lattice_slow.m, acyclic.m, assignEdgeNums.m, + best_first_elim_order.m, check_jtree_property.m, + check_triangulated.m, children.m, cliques_to_jtree.m, + cliques_to_strong_jtree.m, connected_graph.m, + dag_to_essential_graph.m, dfs.m, dfs_test.m, dijkstra.m, + family.m, graph_separated.m, graph_to_jtree.m, + min_subtree_con_nodes.m, minimum_spanning_tree.m, minspan.m, + mk_2D_lattice.m, mk_2D_lattice_slow.m, mk_adj_mat.m, + mk_adjmat_chain.m, mk_all_dags.m, mk_nbrs_of_dag.m, + mk_nbrs_of_digraph.m, mk_nbrs_of_digraph_broken.m, + mk_nbrs_of_digraph_not_vectorized.m, mk_rnd_dag.m, + mk_rnd_dag_given_edge_prob.m, mk_rooted_tree.m, mk_undirected.m, + moralize.m, neighbors.m, parents.m, pred2path.m, + reachability_graph.m, scc.m, strong_elim_order.m, test.m, + test_strong_root.m, topological_sort.m, trees.txt, triangulate.c, + triangulate.m, triangulate_2Dlattice_demo.m, triangulate_test.m: + Initial revision + +2005-08-26 18:08 yozhik + + * KPMtools/fullfileKPM.m: Initial import of code base from Kevin + Murphy. + +2005-08-26 18:08 yozhik + + * KPMtools/fullfileKPM.m: Initial revision + +2005-08-21 13:00 yozhik + + * + BNT/inference/static/@pearl_inf_engine/private/parallel_protocol.m: + Initial import of code base from Kevin Murphy. + +2005-08-21 13:00 yozhik + + * + BNT/inference/static/@pearl_inf_engine/private/parallel_protocol.m: + Initial revision + +2005-07-11 12:07 yozhik + + * KPMtools/plotcov2New.m: Initial import of code base from Kevin + Murphy. + +2005-07-11 12:07 yozhik + + * KPMtools/plotcov2New.m: Initial revision + +2005-07-06 12:32 yozhik + + * KPMtools/montageKPM2.m: Initial import of code base from Kevin + Murphy. + +2005-07-06 12:32 yozhik + + * KPMtools/montageKPM2.m: Initial revision + +2005-06-27 18:35 yozhik + + * KPMtools/montageKPM3.m: Initial import of code base from Kevin + Murphy. + +2005-06-27 18:35 yozhik + + * KPMtools/montageKPM3.m: Initial revision + +2005-06-27 18:30 yozhik + + * KPMtools/cell2matPad.m: Initial import of code base from Kevin + Murphy. + +2005-06-27 18:30 yozhik + + * KPMtools/cell2matPad.m: Initial revision + +2005-06-15 14:13 yozhik + + * BNT/CPDs/@gaussian_CPD/gaussian_CPD.m: Initial import of code + base from Kevin Murphy. + +2005-06-15 14:13 yozhik + + * BNT/CPDs/@gaussian_CPD/gaussian_CPD.m: Initial revision + +2005-06-08 18:56 yozhik + + * Kalman/testKalman.m: Initial import of code base from Kevin + Murphy. + +2005-06-08 18:56 yozhik + + * Kalman/testKalman.m: Initial revision + +2005-06-08 18:25 yozhik + + * HMM/: testHMM.m, fixed_lag_smoother_demo.m: Initial import of + code base from Kevin Murphy. + +2005-06-08 18:25 yozhik + + * HMM/: testHMM.m, fixed_lag_smoother_demo.m: Initial revision + +2005-06-08 18:22 yozhik + + * HMM/: README.txt, dhmm_em.m: Initial import of code base from + Kevin Murphy. + +2005-06-08 18:22 yozhik + + * HMM/: README.txt, dhmm_em.m: Initial revision + +2005-06-08 18:17 yozhik + + * HMM/fwdback.m: Initial import of code base from Kevin Murphy. + +2005-06-08 18:17 yozhik + + * HMM/fwdback.m: Initial revision + +2005-06-05 11:46 yozhik + + * KPMtools/: rectintLoopC.c, rectintLoopC.dll: Initial import of + code base from Kevin Murphy. + +2005-06-05 11:46 yozhik + + * KPMtools/: rectintLoopC.c, rectintLoopC.dll: Initial revision + +2005-06-01 12:39 yozhik + + * KPMtools/montageKPM.m: Initial import of code base from Kevin + Murphy. + +2005-06-01 12:39 yozhik + + * KPMtools/montageKPM.m: Initial revision + +2005-05-31 21:49 yozhik + + * KPMtools/initFigures.m: Initial import of code base from Kevin + Murphy. + +2005-05-31 21:49 yozhik + + * KPMtools/initFigures.m: Initial revision + +2005-05-31 11:19 yozhik + + * KPMstats/unidrndKPM.m: Initial import of code base from Kevin + Murphy. + +2005-05-31 11:19 yozhik + + * KPMstats/unidrndKPM.m: Initial revision + +2005-05-30 15:08 yozhik + + * KPMtools/filepartsLast.m: Initial import of code base from Kevin + Murphy. + +2005-05-30 15:08 yozhik + + * KPMtools/filepartsLast.m: Initial revision + +2005-05-29 23:01 yozhik + + * KPMtools/plotBox.m: Initial import of code base from Kevin + Murphy. + +2005-05-29 23:01 yozhik + + * KPMtools/plotBox.m: Initial revision + +2005-05-25 18:31 yozhik + + * KPMtools/plotColors.m: Initial import of code base from Kevin + Murphy. + +2005-05-25 18:31 yozhik + + * KPMtools/plotColors.m: Initial revision + +2005-05-25 12:11 yozhik + + * KPMtools/genpathKPM.m: Initial import of code base from Kevin + Murphy. + +2005-05-25 12:11 yozhik + + * KPMtools/genpathKPM.m: Initial revision + +2005-05-23 17:03 yozhik + + * netlab3.3/demhmc1.m: Initial import of code base from Kevin + Murphy. + +2005-05-23 17:03 yozhik + + * netlab3.3/demhmc1.m: Initial revision + +2005-05-23 16:44 yozhik + + * netlab3.3/gmminit.m: Initial import of code base from Kevin + Murphy. + +2005-05-23 16:44 yozhik + + * netlab3.3/gmminit.m: Initial revision + +2005-05-23 16:07 yozhik + + * netlab3.3/metrop.m: Initial import of code base from Kevin + Murphy. + +2005-05-23 16:07 yozhik + + * netlab3.3/metrop.m: Initial revision + +2005-05-22 23:23 yozhik + + * netlab3.3/demmet1.m: Initial import of code base from Kevin + Murphy. + +2005-05-22 23:23 yozhik + + * netlab3.3/demmet1.m: Initial revision + +2005-05-22 16:32 yozhik + + * KPMstats/: dirichletrnd.m, dirichletpdf.m, test_dir.m, + multirnd.m, multipdf.m: Initial import of code base from Kevin + Murphy. + +2005-05-22 16:32 yozhik + + * KPMstats/: dirichletrnd.m, dirichletpdf.m, test_dir.m, + multirnd.m, multipdf.m: Initial revision + +2005-05-13 13:52 yozhik + + * KPMtools/: asort.m, dirKPM.m: Initial import of code base from + Kevin Murphy. + +2005-05-13 13:52 yozhik + + * KPMtools/: asort.m, dirKPM.m: Initial revision + +2005-05-09 18:32 yozhik + + * netlab3.3/dem2ddat.m: Initial import of code base from Kevin + Murphy. + +2005-05-09 18:32 yozhik + + * netlab3.3/dem2ddat.m: Initial revision + +2005-05-09 15:20 yozhik + + * KPMtools/: mkdirKPM.m, optimalMatching.m, optimalMatchingTest.m, + subsets1.m: Initial import of code base from Kevin Murphy. + +2005-05-09 15:20 yozhik + + * KPMtools/: mkdirKPM.m, optimalMatching.m, optimalMatchingTest.m, + subsets1.m: Initial revision + +2005-05-09 09:47 yozhik + + * KPMtools/bipartiteMatchingDemo.m: Initial import of code base + from Kevin Murphy. + +2005-05-09 09:47 yozhik + + * KPMtools/bipartiteMatchingDemo.m: Initial revision + +2005-05-08 22:25 yozhik + + * KPMtools/bipartiteMatchingIntProg.m: Initial import of code base + from Kevin Murphy. + +2005-05-08 22:25 yozhik + + * KPMtools/bipartiteMatchingIntProg.m: Initial revision + +2005-05-08 21:45 yozhik + + * KPMtools/bipartiteMatchingDemoPlot.m: Initial import of code base + from Kevin Murphy. + +2005-05-08 21:45 yozhik + + * KPMtools/bipartiteMatchingDemoPlot.m: Initial revision + +2005-05-08 19:55 yozhik + + * KPMtools/subsetsFixedSize.m: Initial import of code base from + Kevin Murphy. + +2005-05-08 19:55 yozhik + + * KPMtools/subsetsFixedSize.m: Initial revision + +2005-05-08 15:48 yozhik + + * KPMtools/centeringMatrix.m: Initial import of code base from + Kevin Murphy. + +2005-05-08 15:48 yozhik + + * KPMtools/centeringMatrix.m: Initial revision + +2005-05-08 10:51 yozhik + + * netlab3.3/demgmm1.m: Initial import of code base from Kevin + Murphy. + +2005-05-08 10:51 yozhik + + * netlab3.3/demgmm1.m: Initial revision + +2005-05-06 18:09 yozhik + + * BNT/add_BNT_to_path.m: Initial import of code base from Kevin + Murphy. + +2005-05-06 18:09 yozhik + + * BNT/add_BNT_to_path.m: Initial revision + +2005-05-03 21:35 yozhik + + * KPMstats/standardize.m: Initial import of code base from Kevin + Murphy. + +2005-05-03 21:35 yozhik + + * KPMstats/standardize.m: Initial revision + +2005-05-03 13:18 yozhik + + * KPMstats/histCmpChi2.m: Initial import of code base from Kevin + Murphy. + +2005-05-03 13:18 yozhik + + * KPMstats/histCmpChi2.m: Initial revision + +2005-05-03 12:01 yozhik + + * KPMtools/strsplit.m: Initial import of code base from Kevin + Murphy. + +2005-05-03 12:01 yozhik + + * KPMtools/strsplit.m: Initial revision + +2005-05-02 13:19 yozhik + + * KPMtools/hsvKPM.m: Initial import of code base from Kevin Murphy. + +2005-05-02 13:19 yozhik + + * KPMtools/hsvKPM.m: Initial revision + +2005-04-27 11:34 yozhik + + * BNT/potentials/@dpot/: subsasgn.m, subsref.m: Initial import of + code base from Kevin Murphy. + +2005-04-27 11:34 yozhik + + * BNT/potentials/@dpot/: subsasgn.m, subsref.m: Initial revision + +2005-04-27 10:58 yozhik + + * KPMtools/mahal2conf.m, nethelp3.3/conffig.htm, + nethelp3.3/confmat.htm, nethelp3.3/conjgrad.htm, + nethelp3.3/consist.htm, nethelp3.3/convertoldnet.htm, + nethelp3.3/datread.htm, nethelp3.3/datwrite.htm, + nethelp3.3/dem2ddat.htm, nethelp3.3/demard.htm, + nethelp3.3/demev1.htm, nethelp3.3/demev2.htm, + nethelp3.3/demev3.htm, nethelp3.3/demgauss.htm, + nethelp3.3/demglm1.htm, nethelp3.3/demglm2.htm, + nethelp3.3/demgmm1.htm, nethelp3.3/demgmm2.htm, + nethelp3.3/demgmm3.htm, nethelp3.3/demgmm4.htm, + nethelp3.3/demgmm5.htm, nethelp3.3/demgp.htm, + nethelp3.3/demgpard.htm, nethelp3.3/demgpot.htm, + nethelp3.3/demgtm1.htm, nethelp3.3/demgtm2.htm, + nethelp3.3/demhint.htm, nethelp3.3/demhmc1.htm, + nethelp3.3/demhmc2.htm, nethelp3.3/demhmc3.htm, + nethelp3.3/demkmn1.htm, nethelp3.3/demknn1.htm, + nethelp3.3/demmdn1.htm, nethelp3.3/demmet1.htm, + nethelp3.3/demmlp1.htm, nethelp3.3/demmlp2.htm, + nethelp3.3/demnlab.htm, nethelp3.3/demns1.htm, + nethelp3.3/demolgd1.htm, nethelp3.3/demopt1.htm, + nethelp3.3/dempot.htm, nethelp3.3/demprgp.htm, + nethelp3.3/demprior.htm, nethelp3.3/demrbf1.htm, + nethelp3.3/demsom1.htm, nethelp3.3/demtrain.htm, + nethelp3.3/dist2.htm, nethelp3.3/eigdec.htm, + nethelp3.3/errbayes.htm, nethelp3.3/evidence.htm, + nethelp3.3/fevbayes.htm, nethelp3.3/gauss.htm, + nethelp3.3/gbayes.htm, nethelp3.3/glm.htm, + nethelp3.3/glmderiv.htm, nethelp3.3/glmerr.htm, + nethelp3.3/glmevfwd.htm, nethelp3.3/glmfwd.htm, + nethelp3.3/glmgrad.htm, nethelp3.3/glmhess.htm, + nethelp3.3/glminit.htm, nethelp3.3/glmpak.htm, + nethelp3.3/glmtrain.htm, nethelp3.3/glmunpak.htm, + nethelp3.3/gmm.htm, nethelp3.3/gmmactiv.htm, + nethelp3.3/gmmem.htm, nethelp3.3/gmminit.htm, + nethelp3.3/gmmpak.htm, nethelp3.3/gmmpost.htm, + nethelp3.3/gmmprob.htm, nethelp3.3/gmmsamp.htm, + nethelp3.3/gmmunpak.htm, nethelp3.3/gp.htm, + nethelp3.3/gpcovar.htm, nethelp3.3/gpcovarf.htm, + nethelp3.3/gpcovarp.htm, nethelp3.3/gperr.htm, + nethelp3.3/gpfwd.htm, nethelp3.3/gpgrad.htm, + nethelp3.3/gpinit.htm, nethelp3.3/gppak.htm, + nethelp3.3/gpunpak.htm, nethelp3.3/gradchek.htm, + nethelp3.3/graddesc.htm, nethelp3.3/gsamp.htm, + nethelp3.3/gtm.htm, nethelp3.3/gtmem.htm, nethelp3.3/gtmfwd.htm, + nethelp3.3/gtminit.htm, nethelp3.3/gtmlmean.htm, + nethelp3.3/gtmlmode.htm, nethelp3.3/gtmmag.htm, + nethelp3.3/gtmpost.htm, nethelp3.3/gtmprob.htm, + nethelp3.3/hbayes.htm, nethelp3.3/hesschek.htm, + nethelp3.3/hintmat.htm, nethelp3.3/hinton.htm, + nethelp3.3/histp.htm, nethelp3.3/hmc.htm, nethelp3.3/index.htm, + nethelp3.3/kmeans.htm, nethelp3.3/knn.htm, nethelp3.3/knnfwd.htm, + nethelp3.3/linef.htm, nethelp3.3/linemin.htm, + nethelp3.3/maxitmess.htm, nethelp3.3/mdn.htm, + nethelp3.3/mdn2gmm.htm, nethelp3.3/mdndist2.htm, + nethelp3.3/mdnerr.htm, nethelp3.3/mdnfwd.htm, + nethelp3.3/mdngrad.htm, nethelp3.3/mdninit.htm, + nethelp3.3/mdnpak.htm, nethelp3.3/mdnpost.htm, + nethelp3.3/mdnprob.htm, nethelp3.3/mdnunpak.htm, + nethelp3.3/metrop.htm, nethelp3.3/minbrack.htm, + nethelp3.3/mlp.htm, nethelp3.3/mlpbkp.htm, + nethelp3.3/mlpderiv.htm, nethelp3.3/mlperr.htm, + nethelp3.3/mlpevfwd.htm, nethelp3.3/mlpfwd.htm, + nethelp3.3/mlpgrad.htm, nethelp3.3/mlphdotv.htm, + nethelp3.3/mlphess.htm, nethelp3.3/mlphint.htm, + nethelp3.3/mlpinit.htm, nethelp3.3/mlppak.htm, + nethelp3.3/mlpprior.htm, nethelp3.3/mlptrain.htm, + nethelp3.3/mlpunpak.htm, nethelp3.3/netderiv.htm, + nethelp3.3/neterr.htm, nethelp3.3/netevfwd.htm, + nethelp3.3/netgrad.htm, nethelp3.3/nethelp3.3.zip, + nethelp3.3/nethess.htm, nethelp3.3/netinit.htm, + nethelp3.3/netopt.htm, nethelp3.3/netpak.htm, + nethelp3.3/netunpak.htm, nethelp3.3/olgd.htm, nethelp3.3/pca.htm, + nethelp3.3/plotmat.htm, nethelp3.3/ppca.htm, + nethelp3.3/quasinew.htm, nethelp3.3/rbf.htm, + nethelp3.3/rbfbkp.htm, nethelp3.3/rbfderiv.htm, + nethelp3.3/rbferr.htm, nethelp3.3/rbfevfwd.htm, + nethelp3.3/rbffwd.htm, nethelp3.3/rbfgrad.htm, + nethelp3.3/rbfhess.htm, nethelp3.3/rbfjacob.htm, + nethelp3.3/rbfpak.htm, nethelp3.3/rbfprior.htm, + nethelp3.3/rbfsetbf.htm, nethelp3.3/rbfsetfw.htm, + nethelp3.3/rbftrain.htm, nethelp3.3/rbfunpak.htm, + nethelp3.3/rosegrad.htm, nethelp3.3/rosen.htm, + nethelp3.3/scg.htm, nethelp3.3/som.htm, nethelp3.3/somfwd.htm, + nethelp3.3/sompak.htm, nethelp3.3/somtrain.htm, + nethelp3.3/somunpak.htm, netlab3.3/Contents.m, netlab3.3/LICENSE, + netlab3.3/conffig.m, netlab3.3/confmat.m, netlab3.3/conjgrad.m, + netlab3.3/consist.m, netlab3.3/convertoldnet.m, + netlab3.3/datread.m, netlab3.3/datwrite.m, netlab3.3/demard.m, + netlab3.3/demev1.m, netlab3.3/demev2.m, netlab3.3/demev3.m, + netlab3.3/demgauss.m, netlab3.3/demglm1.m, netlab3.3/demglm2.m, + netlab3.3/demgmm2.m, netlab3.3/demgmm3.m, netlab3.3/demgmm4.m, + netlab3.3/demgmm5.m, netlab3.3/demgp.m, netlab3.3/demgpard.m, + netlab3.3/demgpot.m, netlab3.3/demgtm1.m, netlab3.3/demgtm2.m, + netlab3.3/demhint.m, netlab3.3/demhmc2.m, netlab3.3/demhmc3.m, + netlab3.3/demkmn1.m, netlab3.3/demknn1.m, netlab3.3/demmdn1.m, + netlab3.3/demmlp1.m, netlab3.3/demmlp2.m, netlab3.3/demnlab.m, + netlab3.3/demns1.m, netlab3.3/demolgd1.m, netlab3.3/demopt1.m, + netlab3.3/dempot.m, netlab3.3/demprgp.m, netlab3.3/demprior.m, + netlab3.3/demrbf1.m, netlab3.3/demsom1.m, netlab3.3/demtrain.m, + netlab3.3/dist2.m, netlab3.3/eigdec.m, netlab3.3/errbayes.m, + netlab3.3/evidence.m, netlab3.3/fevbayes.m, netlab3.3/gauss.m, + netlab3.3/gbayes.m, netlab3.3/glm.m, netlab3.3/glmderiv.m, + netlab3.3/glmerr.m, netlab3.3/glmevfwd.m, netlab3.3/glmfwd.m, + netlab3.3/glmgrad.m, netlab3.3/glmhess.m, netlab3.3/glminit.m, + netlab3.3/glmpak.m, netlab3.3/glmtrain.m, netlab3.3/glmunpak.m, + netlab3.3/gmm.m, netlab3.3/gmmactiv.m, netlab3.3/gmmem.m, + netlab3.3/gmmpak.m, netlab3.3/gmmpost.m, netlab3.3/gmmprob.m, + netlab3.3/gmmsamp.m, netlab3.3/gmmunpak.m, netlab3.3/gp.m, + netlab3.3/gpcovar.m, netlab3.3/gpcovarf.m, netlab3.3/gpcovarp.m, + netlab3.3/gperr.m, netlab3.3/gpfwd.m, netlab3.3/gpgrad.m, + netlab3.3/gpinit.m, netlab3.3/gppak.m, netlab3.3/gpunpak.m, + netlab3.3/gradchek.m, netlab3.3/graddesc.m, netlab3.3/gsamp.m, + netlab3.3/gtm.m, netlab3.3/gtmem.m, netlab3.3/gtmfwd.m, + netlab3.3/gtminit.m, netlab3.3/gtmlmean.m, netlab3.3/gtmlmode.m, + netlab3.3/gtmmag.m, netlab3.3/gtmpost.m, netlab3.3/gtmprob.m, + netlab3.3/hbayes.m, netlab3.3/hesschek.m, netlab3.3/hintmat.m, + netlab3.3/hinton.m, netlab3.3/histp.m, netlab3.3/hmc.m, + netlab3.3/kmeansNetlab.m, netlab3.3/knn.m, netlab3.3/knnfwd.m, + netlab3.3/linef.m, netlab3.3/linemin.m, netlab3.3/maxitmess.m, + netlab3.3/mdn.m, netlab3.3/mdn2gmm.m, netlab3.3/mdndist2.m, + netlab3.3/mdnerr.m, netlab3.3/mdnfwd.m, netlab3.3/mdngrad.m, + netlab3.3/mdninit.m, netlab3.3/mdnnet.mat, netlab3.3/mdnpak.m, + netlab3.3/mdnpost.m, netlab3.3/mdnprob.m, netlab3.3/mdnunpak.m, + netlab3.3/minbrack.m, netlab3.3/mlp.m, netlab3.3/mlpbkp.m, + netlab3.3/mlpderiv.m, netlab3.3/mlperr.m, netlab3.3/mlpevfwd.m, + netlab3.3/mlpfwd.m, netlab3.3/mlpgrad.m, netlab3.3/mlphdotv.m, + netlab3.3/mlphess.m, netlab3.3/mlphint.m, netlab3.3/mlpinit.m, + netlab3.3/mlppak.m, netlab3.3/mlpprior.m, netlab3.3/mlptrain.m, + netlab3.3/mlpunpak.m, netlab3.3/netderiv.m, netlab3.3/neterr.m, + netlab3.3/netevfwd.m, netlab3.3/netgrad.m, netlab3.3/nethess.m, + netlab3.3/netinit.m, netlab3.3/netlab3.3.zip, + netlab3.3/netlogo.mat, netlab3.3/netopt.m, netlab3.3/netpak.m, + netlab3.3/netunpak.m, netlab3.3/oilTrn.dat, netlab3.3/oilTst.dat, + netlab3.3/olgd.m, netlab3.3/pca.m, netlab3.3/plotmat.m, + netlab3.3/ppca.m, netlab3.3/quasinew.m, netlab3.3/rbf.m, + netlab3.3/rbfbkp.m, netlab3.3/rbfderiv.m, netlab3.3/rbferr.m, + netlab3.3/rbfevfwd.m, netlab3.3/rbffwd.m, netlab3.3/rbfgrad.m, + netlab3.3/rbfhess.m, netlab3.3/rbfjacob.m, netlab3.3/rbfpak.m, + netlab3.3/rbfprior.m, netlab3.3/rbfsetbf.m, netlab3.3/rbfsetfw.m, + netlab3.3/rbftrain.m, netlab3.3/rbfunpak.m, netlab3.3/rosegrad.m, + netlab3.3/rosen.m, netlab3.3/scg.m, netlab3.3/som.m, + netlab3.3/somfwd.m, netlab3.3/sompak.m, netlab3.3/somtrain.m, + netlab3.3/somunpak.m, netlab3.3/xor.dat, netlabKPM/README.txt, + netlabKPM/demgmm1_movie.m, netlabKPM/evidence_weighted.m, + netlabKPM/glmerr_weighted.m, netlabKPM/glmgrad_weighted.m, + netlabKPM/glmhess_weighted.m, netlabKPM/glmtrain_weighted.m, + netlabKPM/gmm1.avi, netlabKPM/gmmem2.m, + netlabKPM/gmmem_multi_restart.m, netlabKPM/kmeans_demo.m, + netlabKPM/mlperr_weighted.m, netlabKPM/mlpgrad_weighted.m, + netlabKPM/mlphdotv_weighted.m, netlabKPM/mlphess_weighted.m, + netlabKPM/neterr_weighted.m, netlabKPM/netgrad_weighted.m, + netlabKPM/nethess_weighted.m, netlabKPM/netopt_weighted.m, + netlabKPM/process_options.m: Initial import of code base from + Kevin Murphy. + +2005-04-27 10:58 yozhik + + * KPMtools/mahal2conf.m, nethelp3.3/conffig.htm, + nethelp3.3/confmat.htm, nethelp3.3/conjgrad.htm, + nethelp3.3/consist.htm, nethelp3.3/convertoldnet.htm, + nethelp3.3/datread.htm, nethelp3.3/datwrite.htm, + nethelp3.3/dem2ddat.htm, nethelp3.3/demard.htm, + nethelp3.3/demev1.htm, nethelp3.3/demev2.htm, + nethelp3.3/demev3.htm, nethelp3.3/demgauss.htm, + nethelp3.3/demglm1.htm, nethelp3.3/demglm2.htm, + nethelp3.3/demgmm1.htm, nethelp3.3/demgmm2.htm, + nethelp3.3/demgmm3.htm, nethelp3.3/demgmm4.htm, + nethelp3.3/demgmm5.htm, nethelp3.3/demgp.htm, + nethelp3.3/demgpard.htm, nethelp3.3/demgpot.htm, + nethelp3.3/demgtm1.htm, nethelp3.3/demgtm2.htm, + nethelp3.3/demhint.htm, nethelp3.3/demhmc1.htm, + nethelp3.3/demhmc2.htm, nethelp3.3/demhmc3.htm, + nethelp3.3/demkmn1.htm, nethelp3.3/demknn1.htm, + nethelp3.3/demmdn1.htm, nethelp3.3/demmet1.htm, + nethelp3.3/demmlp1.htm, nethelp3.3/demmlp2.htm, + nethelp3.3/demnlab.htm, nethelp3.3/demns1.htm, + nethelp3.3/demolgd1.htm, nethelp3.3/demopt1.htm, + nethelp3.3/dempot.htm, nethelp3.3/demprgp.htm, + nethelp3.3/demprior.htm, nethelp3.3/demrbf1.htm, + nethelp3.3/demsom1.htm, nethelp3.3/demtrain.htm, + nethelp3.3/dist2.htm, nethelp3.3/eigdec.htm, + nethelp3.3/errbayes.htm, nethelp3.3/evidence.htm, + nethelp3.3/fevbayes.htm, nethelp3.3/gauss.htm, + nethelp3.3/gbayes.htm, nethelp3.3/glm.htm, + nethelp3.3/glmderiv.htm, nethelp3.3/glmerr.htm, + nethelp3.3/glmevfwd.htm, nethelp3.3/glmfwd.htm, + nethelp3.3/glmgrad.htm, nethelp3.3/glmhess.htm, + nethelp3.3/glminit.htm, nethelp3.3/glmpak.htm, + nethelp3.3/glmtrain.htm, nethelp3.3/glmunpak.htm, + nethelp3.3/gmm.htm, nethelp3.3/gmmactiv.htm, + nethelp3.3/gmmem.htm, nethelp3.3/gmminit.htm, + nethelp3.3/gmmpak.htm, nethelp3.3/gmmpost.htm, + nethelp3.3/gmmprob.htm, nethelp3.3/gmmsamp.htm, + nethelp3.3/gmmunpak.htm, nethelp3.3/gp.htm, + nethelp3.3/gpcovar.htm, nethelp3.3/gpcovarf.htm, + nethelp3.3/gpcovarp.htm, nethelp3.3/gperr.htm, + nethelp3.3/gpfwd.htm, nethelp3.3/gpgrad.htm, + nethelp3.3/gpinit.htm, nethelp3.3/gppak.htm, + nethelp3.3/gpunpak.htm, nethelp3.3/gradchek.htm, + nethelp3.3/graddesc.htm, nethelp3.3/gsamp.htm, + nethelp3.3/gtm.htm, nethelp3.3/gtmem.htm, nethelp3.3/gtmfwd.htm, + nethelp3.3/gtminit.htm, nethelp3.3/gtmlmean.htm, + nethelp3.3/gtmlmode.htm, nethelp3.3/gtmmag.htm, + nethelp3.3/gtmpost.htm, nethelp3.3/gtmprob.htm, + nethelp3.3/hbayes.htm, nethelp3.3/hesschek.htm, + nethelp3.3/hintmat.htm, nethelp3.3/hinton.htm, + nethelp3.3/histp.htm, nethelp3.3/hmc.htm, nethelp3.3/index.htm, + nethelp3.3/kmeans.htm, nethelp3.3/knn.htm, nethelp3.3/knnfwd.htm, + nethelp3.3/linef.htm, nethelp3.3/linemin.htm, + nethelp3.3/maxitmess.htm, nethelp3.3/mdn.htm, + nethelp3.3/mdn2gmm.htm, nethelp3.3/mdndist2.htm, + nethelp3.3/mdnerr.htm, nethelp3.3/mdnfwd.htm, + nethelp3.3/mdngrad.htm, nethelp3.3/mdninit.htm, + nethelp3.3/mdnpak.htm, nethelp3.3/mdnpost.htm, + nethelp3.3/mdnprob.htm, nethelp3.3/mdnunpak.htm, + nethelp3.3/metrop.htm, nethelp3.3/minbrack.htm, + nethelp3.3/mlp.htm, nethelp3.3/mlpbkp.htm, + nethelp3.3/mlpderiv.htm, nethelp3.3/mlperr.htm, + nethelp3.3/mlpevfwd.htm, nethelp3.3/mlpfwd.htm, + nethelp3.3/mlpgrad.htm, nethelp3.3/mlphdotv.htm, + nethelp3.3/mlphess.htm, nethelp3.3/mlphint.htm, + nethelp3.3/mlpinit.htm, nethelp3.3/mlppak.htm, + nethelp3.3/mlpprior.htm, nethelp3.3/mlptrain.htm, + nethelp3.3/mlpunpak.htm, nethelp3.3/netderiv.htm, + nethelp3.3/neterr.htm, nethelp3.3/netevfwd.htm, + nethelp3.3/netgrad.htm, nethelp3.3/nethelp3.3.zip, + nethelp3.3/nethess.htm, nethelp3.3/netinit.htm, + nethelp3.3/netopt.htm, nethelp3.3/netpak.htm, + nethelp3.3/netunpak.htm, nethelp3.3/olgd.htm, nethelp3.3/pca.htm, + nethelp3.3/plotmat.htm, nethelp3.3/ppca.htm, + nethelp3.3/quasinew.htm, nethelp3.3/rbf.htm, + nethelp3.3/rbfbkp.htm, nethelp3.3/rbfderiv.htm, + nethelp3.3/rbferr.htm, nethelp3.3/rbfevfwd.htm, + nethelp3.3/rbffwd.htm, nethelp3.3/rbfgrad.htm, + nethelp3.3/rbfhess.htm, nethelp3.3/rbfjacob.htm, + nethelp3.3/rbfpak.htm, nethelp3.3/rbfprior.htm, + nethelp3.3/rbfsetbf.htm, nethelp3.3/rbfsetfw.htm, + nethelp3.3/rbftrain.htm, nethelp3.3/rbfunpak.htm, + nethelp3.3/rosegrad.htm, nethelp3.3/rosen.htm, + nethelp3.3/scg.htm, nethelp3.3/som.htm, nethelp3.3/somfwd.htm, + nethelp3.3/sompak.htm, nethelp3.3/somtrain.htm, + nethelp3.3/somunpak.htm, netlab3.3/Contents.m, netlab3.3/LICENSE, + netlab3.3/conffig.m, netlab3.3/confmat.m, netlab3.3/conjgrad.m, + netlab3.3/consist.m, netlab3.3/convertoldnet.m, + netlab3.3/datread.m, netlab3.3/datwrite.m, netlab3.3/demard.m, + netlab3.3/demev1.m, netlab3.3/demev2.m, netlab3.3/demev3.m, + netlab3.3/demgauss.m, netlab3.3/demglm1.m, netlab3.3/demglm2.m, + netlab3.3/demgmm2.m, netlab3.3/demgmm3.m, netlab3.3/demgmm4.m, + netlab3.3/demgmm5.m, netlab3.3/demgp.m, netlab3.3/demgpard.m, + netlab3.3/demgpot.m, netlab3.3/demgtm1.m, netlab3.3/demgtm2.m, + netlab3.3/demhint.m, netlab3.3/demhmc2.m, netlab3.3/demhmc3.m, + netlab3.3/demkmn1.m, netlab3.3/demknn1.m, netlab3.3/demmdn1.m, + netlab3.3/demmlp1.m, netlab3.3/demmlp2.m, netlab3.3/demnlab.m, + netlab3.3/demns1.m, netlab3.3/demolgd1.m, netlab3.3/demopt1.m, + netlab3.3/dempot.m, netlab3.3/demprgp.m, netlab3.3/demprior.m, + netlab3.3/demrbf1.m, netlab3.3/demsom1.m, netlab3.3/demtrain.m, + netlab3.3/dist2.m, netlab3.3/eigdec.m, netlab3.3/errbayes.m, + netlab3.3/evidence.m, netlab3.3/fevbayes.m, netlab3.3/gauss.m, + netlab3.3/gbayes.m, netlab3.3/glm.m, netlab3.3/glmderiv.m, + netlab3.3/glmerr.m, netlab3.3/glmevfwd.m, netlab3.3/glmfwd.m, + netlab3.3/glmgrad.m, netlab3.3/glmhess.m, netlab3.3/glminit.m, + netlab3.3/glmpak.m, netlab3.3/glmtrain.m, netlab3.3/glmunpak.m, + netlab3.3/gmm.m, netlab3.3/gmmactiv.m, netlab3.3/gmmem.m, + netlab3.3/gmmpak.m, netlab3.3/gmmpost.m, netlab3.3/gmmprob.m, + netlab3.3/gmmsamp.m, netlab3.3/gmmunpak.m, netlab3.3/gp.m, + netlab3.3/gpcovar.m, netlab3.3/gpcovarf.m, netlab3.3/gpcovarp.m, + netlab3.3/gperr.m, netlab3.3/gpfwd.m, netlab3.3/gpgrad.m, + netlab3.3/gpinit.m, netlab3.3/gppak.m, netlab3.3/gpunpak.m, + netlab3.3/gradchek.m, netlab3.3/graddesc.m, netlab3.3/gsamp.m, + netlab3.3/gtm.m, netlab3.3/gtmem.m, netlab3.3/gtmfwd.m, + netlab3.3/gtminit.m, netlab3.3/gtmlmean.m, netlab3.3/gtmlmode.m, + netlab3.3/gtmmag.m, netlab3.3/gtmpost.m, netlab3.3/gtmprob.m, + netlab3.3/hbayes.m, netlab3.3/hesschek.m, netlab3.3/hintmat.m, + netlab3.3/hinton.m, netlab3.3/histp.m, netlab3.3/hmc.m, + netlab3.3/kmeansNetlab.m, netlab3.3/knn.m, netlab3.3/knnfwd.m, + netlab3.3/linef.m, netlab3.3/linemin.m, netlab3.3/maxitmess.m, + netlab3.3/mdn.m, netlab3.3/mdn2gmm.m, netlab3.3/mdndist2.m, + netlab3.3/mdnerr.m, netlab3.3/mdnfwd.m, netlab3.3/mdngrad.m, + netlab3.3/mdninit.m, netlab3.3/mdnnet.mat, netlab3.3/mdnpak.m, + netlab3.3/mdnpost.m, netlab3.3/mdnprob.m, netlab3.3/mdnunpak.m, + netlab3.3/minbrack.m, netlab3.3/mlp.m, netlab3.3/mlpbkp.m, + netlab3.3/mlpderiv.m, netlab3.3/mlperr.m, netlab3.3/mlpevfwd.m, + netlab3.3/mlpfwd.m, netlab3.3/mlpgrad.m, netlab3.3/mlphdotv.m, + netlab3.3/mlphess.m, netlab3.3/mlphint.m, netlab3.3/mlpinit.m, + netlab3.3/mlppak.m, netlab3.3/mlpprior.m, netlab3.3/mlptrain.m, + netlab3.3/mlpunpak.m, netlab3.3/netderiv.m, netlab3.3/neterr.m, + netlab3.3/netevfwd.m, netlab3.3/netgrad.m, netlab3.3/nethess.m, + netlab3.3/netinit.m, netlab3.3/netlab3.3.zip, + netlab3.3/netlogo.mat, netlab3.3/netopt.m, netlab3.3/netpak.m, + netlab3.3/netunpak.m, netlab3.3/oilTrn.dat, netlab3.3/oilTst.dat, + netlab3.3/olgd.m, netlab3.3/pca.m, netlab3.3/plotmat.m, + netlab3.3/ppca.m, netlab3.3/quasinew.m, netlab3.3/rbf.m, + netlab3.3/rbfbkp.m, netlab3.3/rbfderiv.m, netlab3.3/rbferr.m, + netlab3.3/rbfevfwd.m, netlab3.3/rbffwd.m, netlab3.3/rbfgrad.m, + netlab3.3/rbfhess.m, netlab3.3/rbfjacob.m, netlab3.3/rbfpak.m, + netlab3.3/rbfprior.m, netlab3.3/rbfsetbf.m, netlab3.3/rbfsetfw.m, + netlab3.3/rbftrain.m, netlab3.3/rbfunpak.m, netlab3.3/rosegrad.m, + netlab3.3/rosen.m, netlab3.3/scg.m, netlab3.3/som.m, + netlab3.3/somfwd.m, netlab3.3/sompak.m, netlab3.3/somtrain.m, + netlab3.3/somunpak.m, netlab3.3/xor.dat, netlabKPM/README.txt, + netlabKPM/demgmm1_movie.m, netlabKPM/evidence_weighted.m, + netlabKPM/glmerr_weighted.m, netlabKPM/glmgrad_weighted.m, + netlabKPM/glmhess_weighted.m, netlabKPM/glmtrain_weighted.m, + netlabKPM/gmm1.avi, netlabKPM/gmmem2.m, + netlabKPM/gmmem_multi_restart.m, netlabKPM/kmeans_demo.m, + netlabKPM/mlperr_weighted.m, netlabKPM/mlpgrad_weighted.m, + netlabKPM/mlphdotv_weighted.m, netlabKPM/mlphess_weighted.m, + netlabKPM/neterr_weighted.m, netlabKPM/netgrad_weighted.m, + netlabKPM/nethess_weighted.m, netlabKPM/netopt_weighted.m, + netlabKPM/process_options.m: Initial revision + +2005-04-25 19:29 yozhik + + * KPMstats/KLgauss.m, KPMstats/README.txt, KPMstats/beta_sample.m, + KPMstats/chisquared_histo.m, KPMstats/chisquared_prob.m, + KPMstats/chisquared_readme.txt, KPMstats/chisquared_table.m, + KPMstats/clg_Mstep.m, KPMstats/clg_Mstep_simple.m, + KPMstats/clg_prob.m, KPMstats/condGaussToJoint.m, + KPMstats/cond_indep_fisher_z.m, + KPMstats/condgaussTrainObserved.m, KPMstats/condgauss_sample.m, + KPMstats/convertBinaryLabels.m, KPMstats/cwr_demo.m, + KPMstats/cwr_em.m, KPMstats/cwr_predict.m, KPMstats/cwr_prob.m, + KPMstats/cwr_readme.txt, KPMstats/cwr_test.m, + KPMstats/dirichlet_sample.m, KPMstats/distchck.m, + KPMstats/eigdec.m, KPMstats/est_transmat.m, + KPMstats/fit_paritioned_model_testfn.m, + KPMstats/fit_partitioned_model.m, KPMstats/gamma_sample.m, + KPMstats/gaussian_prob.m, KPMstats/gaussian_sample.m, + KPMstats/linear_regression.m, KPMstats/logist2.m, + KPMstats/logist2Apply.m, KPMstats/logist2ApplyRegularized.m, + KPMstats/logist2Fit.m, KPMstats/logist2FitRegularized.m, + KPMstats/logistK.m, KPMstats/logistK_eval.m, + KPMstats/marginalize_gaussian.m, KPMstats/matrix_T_pdf.m, + KPMstats/matrix_normal_pdf.m, KPMstats/mc_stat_distrib.m, + KPMstats/mixgauss_Mstep.m, KPMstats/mixgauss_classifier_apply.m, + KPMstats/mixgauss_classifier_train.m, KPMstats/mixgauss_em.m, + KPMstats/mixgauss_init.m, KPMstats/mixgauss_prob.m, + KPMstats/mixgauss_prob_test.m, KPMstats/mixgauss_sample.m, + KPMstats/mkPolyFvec.m, KPMstats/mk_unit_norm.m, + KPMstats/multinomial_prob.m, KPMstats/multinomial_sample.m, + KPMstats/normal_coef.m, KPMstats/partial_corr_coef.m, + KPMstats/parzen.m, KPMstats/parzenC.c, KPMstats/parzenC.dll, + KPMstats/parzenC.mexglx, KPMstats/parzenC_test.m, + KPMstats/parzen_fit_select_unif.m, KPMstats/pca.m, + KPMstats/rndcheck.m, KPMstats/sample.m, + KPMstats/sample_discrete.m, KPMstats/sample_gaussian.m, + KPMstats/student_t_logprob.m, KPMstats/student_t_prob.m, + KPMstats/unif_discrete_sample.m, KPMstats/weightedRegression.m, + KPMtools/README.txt, KPMtools/approx_unique.m, + KPMtools/approxeq.m, KPMtools/argmax.m, KPMtools/argmin.m, + KPMtools/assert.m, KPMtools/assignEdgeNums.m, + KPMtools/assign_cols.m, KPMtools/axis_pct.m, KPMtools/block.m, + KPMtools/cell2num.m, KPMtools/chi2inv.m, KPMtools/choose.m, + KPMtools/collapse_mog.m, KPMtools/colmult.c, + KPMtools/colmult.mexglx, KPMtools/computeROC.m, + KPMtools/compute_counts.m, KPMtools/conf2mahal.m, + KPMtools/cross_entropy.m, KPMtools/div.m, KPMtools/draw_circle.m, + KPMtools/draw_ellipse.m, KPMtools/draw_ellipse_axes.m, + KPMtools/em_converged.m, KPMtools/entropy.m, + KPMtools/exportfig.m, KPMtools/extend_domain_table.m, + KPMtools/factorial.m, KPMtools/find_equiv_posns.m, + KPMtools/hash_add.m, KPMtools/hash_del.m, KPMtools/hash_lookup.m, + KPMtools/hungarian.m, KPMtools/image_rgb.m, + KPMtools/imresizeAspect.m, KPMtools/ind2subv.c, + KPMtools/ind2subv.m, KPMtools/installC_KPMtools.m, + KPMtools/is_psd.m, KPMtools/is_stochastic.m, + KPMtools/isemptycell.m, KPMtools/isposdef.m, KPMtools/isscalar.m, + KPMtools/isvector.m, KPMtools/junk.c, KPMtools/loadcell.m, + KPMtools/logb.m, KPMtools/logdet.m, KPMtools/logsum.m, + KPMtools/logsum_simple.m, KPMtools/logsum_test.m, + KPMtools/logsumexp.m, KPMtools/logsumexpv.m, + KPMtools/marg_table.m, KPMtools/marginalize_table.m, + KPMtools/matprint.m, KPMtools/max_mult.c, KPMtools/max_mult.m, + KPMtools/mexutil.c, KPMtools/mexutil.h, + KPMtools/mk_multi_index.m, KPMtools/mk_stochastic.m, + KPMtools/mult_by_table.m, KPMtools/myintersect.m, + KPMtools/myismember.m, KPMtools/myones.m, KPMtools/myplot.m, + KPMtools/myrand.m, KPMtools/myrepmat.m, KPMtools/myreshape.m, + KPMtools/mysetdiff.m, KPMtools/mysize.m, KPMtools/mysubset.m, + KPMtools/mysymsetdiff.m, KPMtools/bipartiteMatchingHungarian.m, + KPMtools/myunion.m, KPMtools/nchoose2.m, KPMtools/ncols.m, + KPMtools/nonmaxsup.m, KPMtools/normalise.m, + KPMtools/normaliseC.c, KPMtools/normaliseC.dll, + KPMtools/normalize.m, KPMtools/nrows.m, KPMtools/num2strcell.m, + KPMtools/partitionData.m, KPMtools/partition_matrix_vec.m, + KPMtools/pca_netlab.m, KPMtools/pick.m, KPMtools/plotROC.m, + KPMtools/plotROCkpm.m, KPMtools/plot_axis_thru_origin.m, + KPMtools/plot_ellipse.m, KPMtools/plot_matrix.m, + KPMtools/plot_polygon.m, KPMtools/plotcov2.m, + KPMtools/plotcov3.m, KPMtools/plotgauss1d.m, + KPMtools/plotgauss2d.m, KPMtools/plotgauss2d_old.m, + KPMtools/polygon_area.m, KPMtools/polygon_centroid.m, + KPMtools/polygon_intersect.m, KPMtools/previewfig.m, + KPMtools/process_options.m, KPMtools/rand_psd.m, + KPMtools/rectintC.m, KPMtools/rectintLoopC.mexglx, + KPMtools/rectintSparse.m, KPMtools/rectintSparseC.m, + KPMtools/rectintSparseLoopC.c, KPMtools/rectintSparseLoopC.dll, + KPMtools/repmatC.c, KPMtools/repmatC.dll, + KPMtools/repmatC.mexglx, KPMtools/rgb2grayKPM.m, + KPMtools/rnd_partition.m, KPMtools/rotate_xlabel.m, + KPMtools/safeStr.m, KPMtools/sampleUniformInts.m, + KPMtools/sample_discrete.m, KPMtools/set_xtick_label.m, + KPMtools/set_xtick_label_demo.m, KPMtools/setdiag.m, + KPMtools/softeye.m, KPMtools/sort_evec.m, + KPMtools/splitLongSeqIntoManyShort.m, KPMtools/sprintf_intvec.m, + KPMtools/sqdist.m, KPMtools/strmatch_multi.m, + KPMtools/strmatch_substr.m, KPMtools/subplot2.m, + KPMtools/subplot3.m, KPMtools/subsets.m, KPMtools/subv2ind.c, + KPMtools/subv2ind.m, KPMtools/sumv.m, KPMtools/suptitle.m, + KPMtools/unaryEncoding.m, KPMtools/wrap.m, + KPMtools/xticklabel_rotate90.m, KPMtools/zipload.m, + KPMtools/zipsave.m: Initial import of code base from Kevin + Murphy. + +2005-04-25 19:29 yozhik + + * KPMstats/KLgauss.m, KPMstats/README.txt, KPMstats/beta_sample.m, + KPMstats/chisquared_histo.m, KPMstats/chisquared_prob.m, + KPMstats/chisquared_readme.txt, KPMstats/chisquared_table.m, + KPMstats/clg_Mstep.m, KPMstats/clg_Mstep_simple.m, + KPMstats/clg_prob.m, KPMstats/condGaussToJoint.m, + KPMstats/cond_indep_fisher_z.m, + KPMstats/condgaussTrainObserved.m, KPMstats/condgauss_sample.m, + KPMstats/convertBinaryLabels.m, KPMstats/cwr_demo.m, + KPMstats/cwr_em.m, KPMstats/cwr_predict.m, KPMstats/cwr_prob.m, + KPMstats/cwr_readme.txt, KPMstats/cwr_test.m, + KPMstats/dirichlet_sample.m, KPMstats/distchck.m, + KPMstats/eigdec.m, KPMstats/est_transmat.m, + KPMstats/fit_paritioned_model_testfn.m, + KPMstats/fit_partitioned_model.m, KPMstats/gamma_sample.m, + KPMstats/gaussian_prob.m, KPMstats/gaussian_sample.m, + KPMstats/linear_regression.m, KPMstats/logist2.m, + KPMstats/logist2Apply.m, KPMstats/logist2ApplyRegularized.m, + KPMstats/logist2Fit.m, KPMstats/logist2FitRegularized.m, + KPMstats/logistK.m, KPMstats/logistK_eval.m, + KPMstats/marginalize_gaussian.m, KPMstats/matrix_T_pdf.m, + KPMstats/matrix_normal_pdf.m, KPMstats/mc_stat_distrib.m, + KPMstats/mixgauss_Mstep.m, KPMstats/mixgauss_classifier_apply.m, + KPMstats/mixgauss_classifier_train.m, KPMstats/mixgauss_em.m, + KPMstats/mixgauss_init.m, KPMstats/mixgauss_prob.m, + KPMstats/mixgauss_prob_test.m, KPMstats/mixgauss_sample.m, + KPMstats/mkPolyFvec.m, KPMstats/mk_unit_norm.m, + KPMstats/multinomial_prob.m, KPMstats/multinomial_sample.m, + KPMstats/normal_coef.m, KPMstats/partial_corr_coef.m, + KPMstats/parzen.m, KPMstats/parzenC.c, KPMstats/parzenC.dll, + KPMstats/parzenC.mexglx, KPMstats/parzenC_test.m, + KPMstats/parzen_fit_select_unif.m, KPMstats/pca.m, + KPMstats/rndcheck.m, KPMstats/sample.m, + KPMstats/sample_discrete.m, KPMstats/sample_gaussian.m, + KPMstats/student_t_logprob.m, KPMstats/student_t_prob.m, + KPMstats/unif_discrete_sample.m, KPMstats/weightedRegression.m, + KPMtools/README.txt, KPMtools/approx_unique.m, + KPMtools/approxeq.m, KPMtools/argmax.m, KPMtools/argmin.m, + KPMtools/assert.m, KPMtools/assignEdgeNums.m, + KPMtools/assign_cols.m, KPMtools/axis_pct.m, KPMtools/block.m, + KPMtools/cell2num.m, KPMtools/chi2inv.m, KPMtools/choose.m, + KPMtools/collapse_mog.m, KPMtools/colmult.c, + KPMtools/colmult.mexglx, KPMtools/computeROC.m, + KPMtools/compute_counts.m, KPMtools/conf2mahal.m, + KPMtools/cross_entropy.m, KPMtools/div.m, KPMtools/draw_circle.m, + KPMtools/draw_ellipse.m, KPMtools/draw_ellipse_axes.m, + KPMtools/em_converged.m, KPMtools/entropy.m, + KPMtools/exportfig.m, KPMtools/extend_domain_table.m, + KPMtools/factorial.m, KPMtools/find_equiv_posns.m, + KPMtools/hash_add.m, KPMtools/hash_del.m, KPMtools/hash_lookup.m, + KPMtools/hungarian.m, KPMtools/image_rgb.m, + KPMtools/imresizeAspect.m, KPMtools/ind2subv.c, + KPMtools/ind2subv.m, KPMtools/installC_KPMtools.m, + KPMtools/is_psd.m, KPMtools/is_stochastic.m, + KPMtools/isemptycell.m, KPMtools/isposdef.m, KPMtools/isscalar.m, + KPMtools/isvector.m, KPMtools/junk.c, KPMtools/loadcell.m, + KPMtools/logb.m, KPMtools/logdet.m, KPMtools/logsum.m, + KPMtools/logsum_simple.m, KPMtools/logsum_test.m, + KPMtools/logsumexp.m, KPMtools/logsumexpv.m, + KPMtools/marg_table.m, KPMtools/marginalize_table.m, + KPMtools/matprint.m, KPMtools/max_mult.c, KPMtools/max_mult.m, + KPMtools/mexutil.c, KPMtools/mexutil.h, + KPMtools/mk_multi_index.m, KPMtools/mk_stochastic.m, + KPMtools/mult_by_table.m, KPMtools/myintersect.m, + KPMtools/myismember.m, KPMtools/myones.m, KPMtools/myplot.m, + KPMtools/myrand.m, KPMtools/myrepmat.m, KPMtools/myreshape.m, + KPMtools/mysetdiff.m, KPMtools/mysize.m, KPMtools/mysubset.m, + KPMtools/mysymsetdiff.m, KPMtools/bipartiteMatchingHungarian.m, + KPMtools/myunion.m, KPMtools/nchoose2.m, KPMtools/ncols.m, + KPMtools/nonmaxsup.m, KPMtools/normalise.m, + KPMtools/normaliseC.c, KPMtools/normaliseC.dll, + KPMtools/normalize.m, KPMtools/nrows.m, KPMtools/num2strcell.m, + KPMtools/partitionData.m, KPMtools/partition_matrix_vec.m, + KPMtools/pca_netlab.m, KPMtools/pick.m, KPMtools/plotROC.m, + KPMtools/plotROCkpm.m, KPMtools/plot_axis_thru_origin.m, + KPMtools/plot_ellipse.m, KPMtools/plot_matrix.m, + KPMtools/plot_polygon.m, KPMtools/plotcov2.m, + KPMtools/plotcov3.m, KPMtools/plotgauss1d.m, + KPMtools/plotgauss2d.m, KPMtools/plotgauss2d_old.m, + KPMtools/polygon_area.m, KPMtools/polygon_centroid.m, + KPMtools/polygon_intersect.m, KPMtools/previewfig.m, + KPMtools/process_options.m, KPMtools/rand_psd.m, + KPMtools/rectintC.m, KPMtools/rectintLoopC.mexglx, + KPMtools/rectintSparse.m, KPMtools/rectintSparseC.m, + KPMtools/rectintSparseLoopC.c, KPMtools/rectintSparseLoopC.dll, + KPMtools/repmatC.c, KPMtools/repmatC.dll, + KPMtools/repmatC.mexglx, KPMtools/rgb2grayKPM.m, + KPMtools/rnd_partition.m, KPMtools/rotate_xlabel.m, + KPMtools/safeStr.m, KPMtools/sampleUniformInts.m, + KPMtools/sample_discrete.m, KPMtools/set_xtick_label.m, + KPMtools/set_xtick_label_demo.m, KPMtools/setdiag.m, + KPMtools/softeye.m, KPMtools/sort_evec.m, + KPMtools/splitLongSeqIntoManyShort.m, KPMtools/sprintf_intvec.m, + KPMtools/sqdist.m, KPMtools/strmatch_multi.m, + KPMtools/strmatch_substr.m, KPMtools/subplot2.m, + KPMtools/subplot3.m, KPMtools/subsets.m, KPMtools/subv2ind.c, + KPMtools/subv2ind.m, KPMtools/sumv.m, KPMtools/suptitle.m, + KPMtools/unaryEncoding.m, KPMtools/wrap.m, + KPMtools/xticklabel_rotate90.m, KPMtools/zipload.m, + KPMtools/zipsave.m: Initial revision + +2005-04-03 18:39 yozhik + + * BNT/learning/score_dags.m: Initial import of code base from Kevin + Murphy. + +2005-04-03 18:39 yozhik + + * BNT/learning/score_dags.m: Initial revision + +2005-03-31 11:20 yozhik + + * BNT/installC_BNT.m: Initial import of code base from Kevin + Murphy. + +2005-03-31 11:20 yozhik + + * BNT/installC_BNT.m: Initial revision + +2005-03-30 11:59 yozhik + + * KPMtools/asdemo.html: Initial import of code base from Kevin + Murphy. + +2005-03-30 11:59 yozhik + + * KPMtools/asdemo.html: Initial revision + +2005-03-26 18:51 yozhik + + * KPMtools/asdemo.m: Initial import of code base from Kevin Murphy. + +2005-03-26 18:51 yozhik + + * KPMtools/asdemo.m: Initial revision + +2005-01-15 18:27 yozhik + + * BNT/CPDs/@tabular_CPD/: get_field.m, set_fields.m, tabular_CPD.m: + Initial import of code base from Kevin Murphy. + +2005-01-15 18:27 yozhik + + * BNT/CPDs/@tabular_CPD/: get_field.m, set_fields.m, tabular_CPD.m: + Initial revision + +2004-11-24 12:12 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/junk: Initial import of + code base from Kevin Murphy. + +2004-11-24 12:12 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/junk: Initial revision + +2004-11-22 14:41 yozhik + + * BNT/examples/dynamic/orig_water1.m: Initial import of code base + from Kevin Murphy. + +2004-11-22 14:41 yozhik + + * BNT/examples/dynamic/orig_water1.m: Initial revision + +2004-11-22 14:15 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/cbk_inf_engine.m: Initial + import of code base from Kevin Murphy. + +2004-11-22 14:15 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/cbk_inf_engine.m: Initial + revision + +2004-11-06 13:52 yozhik + + * BNT/examples/static/StructLearn/model_select2.m: Initial import + of code base from Kevin Murphy. + +2004-11-06 13:52 yozhik + + * BNT/examples/static/StructLearn/model_select2.m: Initial revision + +2004-11-06 12:55 yozhik + + * BNT/examples/static/StructLearn/model_select1.m: Initial import + of code base from Kevin Murphy. + +2004-11-06 12:55 yozhik + + * BNT/examples/static/StructLearn/model_select1.m: Initial revision + +2004-10-22 18:18 yozhik + + * HMM/viterbi_path.m: Initial import of code base from Kevin + Murphy. + +2004-10-22 18:18 yozhik + + * HMM/viterbi_path.m: Initial revision + +2004-09-29 20:09 yozhik + + * BNT/inference/static/@var_elim_inf_engine/marginal_nodes.m: + Initial import of code base from Kevin Murphy. + +2004-09-29 20:09 yozhik + + * BNT/inference/static/@var_elim_inf_engine/marginal_nodes.m: + Initial revision + +2004-09-12 20:21 yozhik + + * BNT/examples/limids/amnio.m: Initial import of code base from + Kevin Murphy. + +2004-09-12 20:21 yozhik + + * BNT/examples/limids/amnio.m: Initial revision + +2004-09-12 19:27 yozhik + + * BNT/examples/limids/oil1.m: Initial import of code base from + Kevin Murphy. + +2004-09-12 19:27 yozhik + + * BNT/examples/limids/oil1.m: Initial revision + +2004-09-12 14:01 yozhik + + * BNT/examples/static/sprinkler1.m: Initial import of code base + from Kevin Murphy. + +2004-09-12 14:01 yozhik + + * BNT/examples/static/sprinkler1.m: Initial revision + +2004-08-29 05:41 yozhik + + * HMM/transmat_train_observed.m: Initial import of code base from + Kevin Murphy. + +2004-08-29 05:41 yozhik + + * HMM/transmat_train_observed.m: Initial revision + +2004-08-05 08:25 yozhik + + * BNT/potentials/: @dpot/divide_by_pot.m, Tables/divide_by_table.m: + Initial import of code base from Kevin Murphy. + +2004-08-05 08:25 yozhik + + * BNT/potentials/: @dpot/divide_by_pot.m, Tables/divide_by_table.m: + Initial revision + +2004-08-04 12:59 yozhik + + * BNT/potentials/@dpot/: marginalize_pot.m, multiply_by_pot.m: + Initial import of code base from Kevin Murphy. + +2004-08-04 12:59 yozhik + + * BNT/potentials/@dpot/: marginalize_pot.m, multiply_by_pot.m: + Initial revision + +2004-08-04 12:36 yozhik + + * BNT/@assocarray/subsref.m: Initial import of code base from Kevin + Murphy. + +2004-08-04 12:36 yozhik + + * BNT/@assocarray/subsref.m: Initial revision + +2004-08-04 08:54 yozhik + + * BNT/potentials/@dpot/normalize_pot.m: Initial import of code base + from Kevin Murphy. + +2004-08-04 08:54 yozhik + + * BNT/potentials/@dpot/normalize_pot.m: Initial revision + +2004-08-04 08:51 yozhik + + * BNT/potentials/Tables/: marg_table.m, mult_by_table.m, + extend_domain_table.m: Initial import of code base from Kevin + Murphy. + +2004-08-04 08:51 yozhik + + * BNT/potentials/Tables/: marg_table.m, mult_by_table.m, + extend_domain_table.m: Initial revision + +2004-08-02 15:23 yozhik + + * BNT/CPDs/@noisyor_CPD/CPD_to_CPT.m: Initial import of code base + from Kevin Murphy. + +2004-08-02 15:23 yozhik + + * BNT/CPDs/@noisyor_CPD/CPD_to_CPT.m: Initial revision + +2004-08-02 15:05 yozhik + + * BNT/general/noisyORtoTable.m: Initial import of code base from + Kevin Murphy. + +2004-08-02 15:05 yozhik + + * BNT/general/noisyORtoTable.m: Initial revision + +2004-06-29 10:46 yozhik + + * BNT/learning/learn_struct_pdag_pc.m: Initial import of code base + from Kevin Murphy. + +2004-06-29 10:46 yozhik + + * BNT/learning/learn_struct_pdag_pc.m: Initial revision + +2004-06-15 10:50 yozhik + + * GraphViz/graph_to_dot.m: Initial import of code base from Kevin + Murphy. + +2004-06-15 10:50 yozhik + + * GraphViz/graph_to_dot.m: Initial revision + +2004-06-11 14:16 yozhik + + * BNT/CPDs/@tabular_CPD/log_marg_prob_node.m: Initial import of + code base from Kevin Murphy. + +2004-06-11 14:16 yozhik + + * BNT/CPDs/@tabular_CPD/log_marg_prob_node.m: Initial revision + +2004-06-09 18:56 yozhik + + * BNT/README.txt: Initial import of code base from Kevin Murphy. + +2004-06-09 18:56 yozhik + + * BNT/README.txt: Initial revision + +2004-06-09 18:53 yozhik + + * BNT/CPDs/@generic_CPD/learn_params.m: Initial import of code base + from Kevin Murphy. + +2004-06-09 18:53 yozhik + + * BNT/CPDs/@generic_CPD/learn_params.m: Initial revision + +2004-06-09 18:42 yozhik + + * BNT/examples/static/nodeorderExample.m: Initial import of code + base from Kevin Murphy. + +2004-06-09 18:42 yozhik + + * BNT/examples/static/nodeorderExample.m: Initial revision + +2004-06-09 18:33 yozhik + + * BNT/: learning/score_family.m, test_BNT.m: Initial import of code + base from Kevin Murphy. + +2004-06-09 18:33 yozhik + + * BNT/: learning/score_family.m, test_BNT.m: Initial revision + +2004-06-09 18:28 yozhik + + * BNT/: learning/learn_params.m, CPDs/@gaussian_CPD/learn_params.m, + examples/static/gaussian2.m: Initial import of code base from + Kevin Murphy. + +2004-06-09 18:28 yozhik + + * BNT/: learning/learn_params.m, CPDs/@gaussian_CPD/learn_params.m, + examples/static/gaussian2.m: Initial revision + +2004-06-09 18:25 yozhik + + * BNT/CPDs/@tabular_CPD/learn_params.m: Initial import of code base + from Kevin Murphy. + +2004-06-09 18:25 yozhik + + * BNT/CPDs/@tabular_CPD/learn_params.m: Initial revision + +2004-06-09 18:17 yozhik + + * BNT/general/sample_bnet.m: Initial import of code base from Kevin + Murphy. + +2004-06-09 18:17 yozhik + + * BNT/general/sample_bnet.m: Initial revision + +2004-06-07 12:45 yozhik + + * BNT/examples/static/discrete1.m: Initial import of code base from + Kevin Murphy. + +2004-06-07 12:45 yozhik + + * BNT/examples/static/discrete1.m: Initial revision + +2004-06-07 12:04 yozhik + + * BNT/: inference/static/@global_joint_inf_engine/marginal_nodes.m, + inference/static/@global_joint_inf_engine/enter_evidence.m, + examples/dynamic/mk_bat_dbn.m: Initial import of code base from + Kevin Murphy. + +2004-06-07 12:04 yozhik + + * BNT/: inference/static/@global_joint_inf_engine/marginal_nodes.m, + inference/static/@global_joint_inf_engine/enter_evidence.m, + examples/dynamic/mk_bat_dbn.m: Initial revision + +2004-06-07 08:53 yozhik + + * BNT/examples/limids/asia_dt1.m: Initial import of code base from + Kevin Murphy. + +2004-06-07 08:53 yozhik + + * BNT/examples/limids/asia_dt1.m: Initial revision + +2004-06-07 08:48 yozhik + + * BNT/general/: solve_limid.m, compute_joint_pot.m: Initial import + of code base from Kevin Murphy. + +2004-06-07 08:48 yozhik + + * BNT/general/: solve_limid.m, compute_joint_pot.m: Initial + revision + +2004-06-07 07:39 yozhik + + * Kalman/README.txt: Initial import of code base from Kevin Murphy. + +2004-06-07 07:39 yozhik + + * Kalman/README.txt: Initial revision + +2004-06-07 07:33 yozhik + + * GraphViz/README.txt: Initial import of code base from Kevin + Murphy. + +2004-06-07 07:33 yozhik + + * GraphViz/README.txt: Initial revision + +2004-05-31 15:19 yozhik + + * HMM/dhmm_sample.m: Initial import of code base from Kevin Murphy. + +2004-05-31 15:19 yozhik + + * HMM/dhmm_sample.m: Initial revision + +2004-05-25 17:32 yozhik + + * HMM/mhmm_sample.m: Initial import of code base from Kevin Murphy. + +2004-05-25 17:32 yozhik + + * HMM/mhmm_sample.m: Initial revision + +2004-05-24 15:26 yozhik + + * HMM/mc_sample.m: Initial import of code base from Kevin Murphy. + +2004-05-24 15:26 yozhik + + * HMM/mc_sample.m: Initial revision + +2004-05-18 07:50 yozhik + + * BNT/installC_graph.m: Initial import of code base from Kevin + Murphy. + +2004-05-18 07:50 yozhik + + * BNT/installC_graph.m: Initial revision + +2004-05-13 18:13 yozhik + + * BNT/inference/static/@gaussian_inf_engine/gaussian_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2004-05-13 18:13 yozhik + + * BNT/inference/static/@gaussian_inf_engine/gaussian_inf_engine.m: + Initial revision + +2004-05-11 12:23 yozhik + + * BNT/examples/dynamic/mk_chmm.m: Initial import of code base from + Kevin Murphy. + +2004-05-11 12:23 yozhik + + * BNT/examples/dynamic/mk_chmm.m: Initial revision + +2004-05-11 11:45 yozhik + + * BNT/examples/dynamic/mk_water_dbn.m: Initial import of code base + from Kevin Murphy. + +2004-05-11 11:45 yozhik + + * BNT/examples/dynamic/mk_water_dbn.m: Initial revision + +2004-05-05 06:32 yozhik + + * GraphViz/draw_dot.m: Initial import of code base from Kevin + Murphy. + +2004-05-05 06:32 yozhik + + * GraphViz/draw_dot.m: Initial revision + +2004-03-30 09:18 yozhik + + * BNT/: general/mk_named_CPT.m, + CPDs/@softmax_CPD/convert_to_table.m: Initial import of code base + from Kevin Murphy. + +2004-03-30 09:18 yozhik + + * BNT/: general/mk_named_CPT.m, + CPDs/@softmax_CPD/convert_to_table.m: Initial revision + +2004-03-22 14:32 yozhik + + * GraphViz/draw_graph.m: Initial import of code base from Kevin + Murphy. + +2004-03-22 14:32 yozhik + + * GraphViz/draw_graph.m: Initial revision + +2004-03-12 15:21 yozhik + + * GraphViz/dot_to_graph.m: Initial import of code base from Kevin + Murphy. + +2004-03-12 15:21 yozhik + + * GraphViz/dot_to_graph.m: Initial revision + +2004-03-04 14:34 yozhik + + * BNT/examples/static/burglary.m: Initial import of code base from + Kevin Murphy. + +2004-03-04 14:34 yozhik + + * BNT/examples/static/burglary.m: Initial revision + +2004-03-04 14:27 yozhik + + * BNT/examples/static/burglar-alarm-net.lisp.txt: Initial import of + code base from Kevin Murphy. + +2004-03-04 14:27 yozhik + + * BNT/examples/static/burglar-alarm-net.lisp.txt: Initial revision + +2004-02-28 09:25 yozhik + + * BNT/examples/static/learn1.m: Initial import of code base from + Kevin Murphy. + +2004-02-28 09:25 yozhik + + * BNT/examples/static/learn1.m: Initial revision + +2004-02-22 11:43 yozhik + + * BNT/examples/static/brainy.m: Initial import of code base from + Kevin Murphy. + +2004-02-22 11:43 yozhik + + * BNT/examples/static/brainy.m: Initial revision + +2004-02-20 14:00 yozhik + + * BNT/CPDs/@discrete_CPD/convert_to_pot.m: Initial import of code + base from Kevin Murphy. + +2004-02-20 14:00 yozhik + + * BNT/CPDs/@discrete_CPD/convert_to_pot.m: Initial revision + +2004-02-18 17:12 yozhik + + * + BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_soft_evidence.m: + Initial import of code base from Kevin Murphy. + +2004-02-18 17:12 yozhik + + * + BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_soft_evidence.m: + Initial revision + +2004-02-13 18:06 yozhik + + * HMM/mhmmParzen_train_observed.m: Initial import of code base from + Kevin Murphy. + +2004-02-13 18:06 yozhik + + * HMM/mhmmParzen_train_observed.m: Initial revision + +2004-02-12 15:08 yozhik + + * HMM/gausshmm_train_observed.m: Initial import of code base from + Kevin Murphy. + +2004-02-12 15:08 yozhik + + * HMM/gausshmm_train_observed.m: Initial revision + +2004-02-12 04:57 yozhik + + * BNT/examples/static/HME/hmemenu.m: Initial import of code base + from Kevin Murphy. + +2004-02-12 04:57 yozhik + + * BNT/examples/static/HME/hmemenu.m: Initial revision + +2004-02-07 20:52 yozhik + + * HMM/mhmm_em.m: Initial import of code base from Kevin Murphy. + +2004-02-07 20:52 yozhik + + * HMM/mhmm_em.m: Initial revision + +2004-02-04 15:53 yozhik + + * BNT/examples/dynamic/mk_orig_bat_dbn.m: Initial import of code + base from Kevin Murphy. + +2004-02-04 15:53 yozhik + + * BNT/examples/dynamic/mk_orig_bat_dbn.m: Initial revision + +2004-02-03 23:42 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/enter_soft_evidence.m: + Initial import of code base from Kevin Murphy. + +2004-02-03 23:42 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/enter_soft_evidence.m: + Initial revision + +2004-02-03 09:15 yozhik + + * GraphViz/Old/graphToDot.m: Initial import of code base from Kevin + Murphy. + +2004-02-03 09:15 yozhik + + * GraphViz/Old/graphToDot.m: Initial revision + +2004-01-30 18:57 yozhik + + * BNT/examples/dynamic/mk_orig_water_dbn.m: Initial import of code + base from Kevin Murphy. + +2004-01-30 18:57 yozhik + + * BNT/examples/dynamic/mk_orig_water_dbn.m: Initial revision + +2004-01-27 13:08 yozhik + + * GraphViz/: my_call.m, editGraphGUI.m: Initial import of code base + from Kevin Murphy. + +2004-01-27 13:08 yozhik + + * GraphViz/: my_call.m, editGraphGUI.m: Initial revision + +2004-01-27 13:01 yozhik + + * GraphViz/Old/: dot_to_graph.m, draw_graph.m: Initial import of + code base from Kevin Murphy. + +2004-01-27 13:01 yozhik + + * GraphViz/Old/: dot_to_graph.m, draw_graph.m: Initial revision + +2004-01-27 12:47 yozhik + + * GraphViz/Old/pre_pesha_graph_to_dot.m: Initial import of code + base from Kevin Murphy. + +2004-01-27 12:47 yozhik + + * GraphViz/Old/pre_pesha_graph_to_dot.m: Initial revision + +2004-01-27 12:42 yozhik + + * GraphViz/Old/draw_dot.m: Initial import of code base from Kevin + Murphy. + +2004-01-27 12:42 yozhik + + * GraphViz/Old/draw_dot.m: Initial revision + +2004-01-14 17:06 yozhik + + * BNT/examples/static/Models/mk_hmm_bnet.m: Initial import of code + base from Kevin Murphy. + +2004-01-14 17:06 yozhik + + * BNT/examples/static/Models/mk_hmm_bnet.m: Initial revision + +2004-01-12 12:53 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/enter_evidence.m: Initial + import of code base from Kevin Murphy. + +2004-01-12 12:53 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/enter_evidence.m: Initial + revision + +2004-01-04 17:23 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/bp_mrf2.m: Initial + import of code base from Kevin Murphy. + +2004-01-04 17:23 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/bp_mrf2.m: Initial + revision + +2003-12-15 22:17 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/marginal_nodes.m: Initial + import of code base from Kevin Murphy. + +2003-12-15 22:17 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/marginal_nodes.m: Initial + revision + +2003-10-31 14:37 yozhik + + * BNT/inference/static/@jtree_inf_engine/jtree_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-10-31 14:37 yozhik + + * BNT/inference/static/@jtree_inf_engine/jtree_inf_engine.m: + Initial revision + +2003-09-05 07:06 yozhik + + * BNT/learning/learn_struct_mcmc.m: Initial import of code base + from Kevin Murphy. + +2003-09-05 07:06 yozhik + + * BNT/learning/learn_struct_mcmc.m: Initial revision + +2003-08-18 14:50 yozhik + + * BNT/learning/learn_params_dbn_em.m: Initial import of code base + from Kevin Murphy. + +2003-08-18 14:50 yozhik + + * BNT/learning/learn_params_dbn_em.m: Initial revision + +2003-07-30 06:37 yozhik + + * BNT/potentials/: @mpot/set_domain_pot.m, + @cgpot/Old/set_domain_pot.m, @cgpot/set_domain_pot.m: Initial + import of code base from Kevin Murphy. + +2003-07-30 06:37 yozhik + + * BNT/potentials/: @mpot/set_domain_pot.m, + @cgpot/Old/set_domain_pot.m, @cgpot/set_domain_pot.m: Initial + revision + +2003-07-28 19:44 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/: dbn_init_bel.m, + dbn_marginal_from_bel.m, dbn_update_bel.m, dbn_update_bel1.m, + marginal_family.m, update_engine.m: Initial import of code base + from Kevin Murphy. + +2003-07-28 19:44 yozhik + + * BNT/inference/dynamic/@cbk_inf_engine/: dbn_init_bel.m, + dbn_marginal_from_bel.m, dbn_update_bel.m, dbn_update_bel1.m, + marginal_family.m, update_engine.m: Initial revision + +2003-07-28 15:44 yozhik + + * GraphViz/: approxeq.m, process_options.m: Initial import of code + base from Kevin Murphy. + +2003-07-28 15:44 yozhik + + * GraphViz/: approxeq.m, process_options.m: Initial revision + +2003-07-24 06:41 yozhik + + * BNT/CPDs/@hhmmQ_CPD/update_ess.m: Initial import of code base + from Kevin Murphy. + +2003-07-24 06:41 yozhik + + * BNT/CPDs/@hhmmQ_CPD/update_ess.m: Initial revision + +2003-07-22 15:55 yozhik + + * BNT/CPDs/@gaussian_CPD/update_ess.m: Initial import of code base + from Kevin Murphy. + +2003-07-22 15:55 yozhik + + * BNT/CPDs/@gaussian_CPD/update_ess.m: Initial revision + +2003-07-06 13:57 yozhik + + * BNT/inference/static/@pearl_inf_engine/bethe_free_energy.m: + Initial import of code base from Kevin Murphy. + +2003-07-06 13:57 yozhik + + * BNT/inference/static/@pearl_inf_engine/bethe_free_energy.m: + Initial revision + +2003-05-21 06:49 yozhik + + * BNT/potentials/@scgpot/: complement_pot.m, normalize_pot.m, + recursive_combine_pots.m: Initial import of code base from Kevin + Murphy. + +2003-05-21 06:49 yozhik + + * BNT/potentials/@scgpot/: complement_pot.m, normalize_pot.m, + recursive_combine_pots.m: Initial revision + +2003-05-20 07:10 yozhik + + * BNT/CPDs/@gaussian_CPD/maximize_params.m: Initial import of code + base from Kevin Murphy. + +2003-05-20 07:10 yozhik + + * BNT/CPDs/@gaussian_CPD/maximize_params.m: Initial revision + +2003-05-13 09:11 yozhik + + * HMM/mhmm_em_demo.m: Initial import of code base from Kevin + Murphy. + +2003-05-13 09:11 yozhik + + * HMM/mhmm_em_demo.m: Initial revision + +2003-05-13 07:35 yozhik + + * BNT/examples/dynamic/viterbi1.m: Initial import of code base from + Kevin Murphy. + +2003-05-13 07:35 yozhik + + * BNT/examples/dynamic/viterbi1.m: Initial revision + +2003-05-11 16:31 yozhik + + * BNT/CPDs/@gaussian_CPD/convert_to_table.m: Initial import of code + base from Kevin Murphy. + +2003-05-11 16:31 yozhik + + * BNT/CPDs/@gaussian_CPD/convert_to_table.m: Initial revision + +2003-05-11 16:13 yozhik + + * BNT/CPDs/@gaussian_CPD/gaussian_CPD_params_given_dps.m: Initial + import of code base from Kevin Murphy. + +2003-05-11 16:13 yozhik + + * BNT/CPDs/@gaussian_CPD/gaussian_CPD_params_given_dps.m: Initial + revision + +2003-05-11 08:39 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/README: Initial + import of code base from Kevin Murphy. + +2003-05-11 08:39 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/README: Initial + revision + +2003-05-04 15:31 yozhik + + * BNT/uninstallC_BNT.m: Initial import of code base from Kevin + Murphy. + +2003-05-04 15:31 yozhik + + * BNT/uninstallC_BNT.m: Initial revision + +2003-05-04 15:23 yozhik + + * BNT/examples/dynamic/: dhmm1.m, ghmm1.m, mhmm1.m: Initial import + of code base from Kevin Murphy. + +2003-05-04 15:23 yozhik + + * BNT/examples/dynamic/: dhmm1.m, ghmm1.m, mhmm1.m: Initial + revision + +2003-05-04 15:11 yozhik + + * HMM/mhmm_logprob.m: Initial import of code base from Kevin + Murphy. + +2003-05-04 15:11 yozhik + + * HMM/mhmm_logprob.m: Initial revision + +2003-05-04 15:01 yozhik + + * HMM/: dhmm_logprob.m, dhmm_em_online.m, dhmm_em_online_demo.m: + Initial import of code base from Kevin Murphy. + +2003-05-04 15:01 yozhik + + * HMM/: dhmm_logprob.m, dhmm_em_online.m, dhmm_em_online_demo.m: + Initial revision + +2003-05-04 14:58 yozhik + + * HMM/: pomdp_sample.m, dhmm_sample_endstate.m, dhmm_em_demo.m: + Initial import of code base from Kevin Murphy. + +2003-05-04 14:58 yozhik + + * HMM/: pomdp_sample.m, dhmm_sample_endstate.m, dhmm_em_demo.m: + Initial revision + +2003-05-04 14:47 yozhik + + * + BNT/inference/online/@hmm_2TBN_inf_engine/private/mk_hmm_obs_lik_vec.m: + Initial import of code base from Kevin Murphy. + +2003-05-04 14:47 yozhik + + * + BNT/inference/online/@hmm_2TBN_inf_engine/private/mk_hmm_obs_lik_vec.m: + Initial revision + +2003-05-04 14:42 yozhik + + * + BNT/inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_matrix.m: + Initial import of code base from Kevin Murphy. + +2003-05-04 14:42 yozhik + + * + BNT/inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_matrix.m: + Initial revision + +2003-04-22 14:00 yozhik + + * BNT/CPDs/@tabular_CPD/display.m: Initial import of code base from + Kevin Murphy. + +2003-04-22 14:00 yozhik + + * BNT/CPDs/@tabular_CPD/display.m: Initial revision + +2003-03-28 09:22 yozhik + + * BNT/examples/dynamic/ho1.m: Initial import of code base from + Kevin Murphy. + +2003-03-28 09:22 yozhik + + * BNT/examples/dynamic/ho1.m: Initial revision + +2003-03-28 09:12 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-03-28 09:12 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/stab_cond_gauss_inf_engine.m: + Initial revision + +2003-03-28 08:35 yozhik + + * GraphViz/arrow.m: Initial import of code base from Kevin Murphy. + +2003-03-28 08:35 yozhik + + * GraphViz/arrow.m: Initial revision + +2003-03-25 16:06 yozhik + + * BNT/examples/static/Models/mk_asia_bnet.m: Initial import of code + base from Kevin Murphy. + +2003-03-25 16:06 yozhik + + * BNT/examples/static/Models/mk_asia_bnet.m: Initial revision + +2003-03-20 07:07 yozhik + + * BNT/potentials/@scgpot/README: Initial import of code base from + Kevin Murphy. + +2003-03-20 07:07 yozhik + + * BNT/potentials/@scgpot/README: Initial revision + +2003-03-14 01:45 yozhik + + * + BNT/inference/dynamic/@stable_ho_inf_engine/stable_ho_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-03-14 01:45 yozhik + + * + BNT/inference/dynamic/@stable_ho_inf_engine/stable_ho_inf_engine.m: + Initial revision + +2003-03-12 02:38 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/enter_evidence.m: + Initial import of code base from Kevin Murphy. + +2003-03-12 02:38 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/enter_evidence.m: + Initial revision + +2003-03-11 10:07 yozhik + + * BNT/potentials/@scgpot/reduce_pot.m: Initial import of code base + from Kevin Murphy. + +2003-03-11 10:07 yozhik + + * BNT/potentials/@scgpot/reduce_pot.m: Initial revision + +2003-03-11 09:49 yozhik + + * BNT/potentials/@scgpot/combine_pots.m: Initial import of code + base from Kevin Murphy. + +2003-03-11 09:49 yozhik + + * BNT/potentials/@scgpot/combine_pots.m: Initial revision + +2003-03-11 09:37 yozhik + + * BNT/potentials/@scgcpot/reduce_pot.m: Initial import of code base + from Kevin Murphy. + +2003-03-11 09:37 yozhik + + * BNT/potentials/@scgcpot/reduce_pot.m: Initial revision + +2003-03-11 09:06 yozhik + + * BNT/potentials/@scgpot/marginalize_pot.m: Initial import of code + base from Kevin Murphy. + +2003-03-11 09:06 yozhik + + * BNT/potentials/@scgpot/marginalize_pot.m: Initial revision + +2003-03-11 06:04 yozhik + + * BNT/potentials/@scgpot/scgpot.m: Initial import of code base from + Kevin Murphy. + +2003-03-11 06:04 yozhik + + * BNT/potentials/@scgpot/scgpot.m: Initial revision + +2003-03-09 15:03 yozhik + + * BNT/CPDs/@gaussian_CPD/convert_to_pot.m: Initial import of code + base from Kevin Murphy. + +2003-03-09 15:03 yozhik + + * BNT/CPDs/@gaussian_CPD/convert_to_pot.m: Initial revision + +2003-03-09 14:44 yozhik + + * BNT/CPDs/@tabular_CPD/maximize_params.m: Initial import of code + base from Kevin Murphy. + +2003-03-09 14:44 yozhik + + * BNT/CPDs/@tabular_CPD/maximize_params.m: Initial revision + +2003-02-21 03:20 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_difclq_nodes.m: + Initial import of code base from Kevin Murphy. + +2003-02-21 03:20 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_difclq_nodes.m: + Initial revision + +2003-02-21 03:13 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_nodes.m: + Initial import of code base from Kevin Murphy. + +2003-02-21 03:13 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_nodes.m: + Initial revision + +2003-02-19 01:52 yozhik + + * BNT/inference/dynamic/@stable_ho_inf_engine/: enter_evidence.m, + marginal_family.m, marginal_nodes.m, test_ho_inf_enginge.m, + update_engine.m: Initial import of code base from Kevin Murphy. + +2003-02-19 01:52 yozhik + + * BNT/inference/dynamic/@stable_ho_inf_engine/: enter_evidence.m, + marginal_family.m, marginal_nodes.m, test_ho_inf_enginge.m, + update_engine.m: Initial revision + +2003-02-10 07:38 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/push.m: Initial + import of code base from Kevin Murphy. + +2003-02-10 07:38 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/push.m: Initial + revision + +2003-02-06 18:25 yozhik + + * KPMtools/checkpsd.m: Initial import of code base from Kevin + Murphy. + +2003-02-06 18:25 yozhik + + * KPMtools/checkpsd.m: Initial revision + +2003-02-05 19:16 yozhik + + * GraphViz/draw_hmm.m: Initial import of code base from Kevin + Murphy. + +2003-02-05 19:16 yozhik + + * GraphViz/draw_hmm.m: Initial revision + +2003-02-01 16:23 yozhik + + * BNT/: general/dbn_to_hmm.m, learning/learn_params_dbn.m: Initial + import of code base from Kevin Murphy. + +2003-02-01 16:23 yozhik + + * BNT/: general/dbn_to_hmm.m, learning/learn_params_dbn.m: Initial + revision + +2003-02-01 11:42 yozhik + + * BNT/general/mk_dbn.m: Initial import of code base from Kevin + Murphy. + +2003-02-01 11:42 yozhik + + * BNT/general/mk_dbn.m: Initial revision + +2003-01-30 16:13 yozhik + + * BNT/CPDs/@gaussian_CPD/maximize_params_debug.m: Initial import of + code base from Kevin Murphy. + +2003-01-30 16:13 yozhik + + * BNT/CPDs/@gaussian_CPD/maximize_params_debug.m: Initial revision + +2003-01-30 14:38 yozhik + + * BNT/CPDs/@gaussian_CPD/Old/maximize_params.m: Initial import of + code base from Kevin Murphy. + +2003-01-30 14:38 yozhik + + * BNT/CPDs/@gaussian_CPD/Old/maximize_params.m: Initial revision + +2003-01-29 03:23 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_singleclq_nodes.m: + Initial import of code base from Kevin Murphy. + +2003-01-29 03:23 yozhik + + * + BNT/inference/static/@stab_cond_gauss_inf_engine/marginal_singleclq_nodes.m: + Initial revision + +2003-01-24 11:36 yozhik + + * Kalman/sample_lds.m: Initial import of code base from Kevin + Murphy. + +2003-01-24 11:36 yozhik + + * Kalman/sample_lds.m: Initial revision + +2003-01-24 04:52 yozhik + + * BNT/potentials/@scgpot/extension_pot.m: Initial import of code + base from Kevin Murphy. + +2003-01-24 04:52 yozhik + + * BNT/potentials/@scgpot/extension_pot.m: Initial revision + +2003-01-23 10:49 yozhik + + * BNT/: general/convert_dbn_CPDs_to_tables1.m, + inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_vec.m: + Initial import of code base from Kevin Murphy. + +2003-01-23 10:49 yozhik + + * BNT/: general/convert_dbn_CPDs_to_tables1.m, + inference/dynamic/@hmm_inf_engine/private/mk_hmm_obs_lik_vec.m: + Initial revision + +2003-01-23 10:44 yozhik + + * BNT/general/convert_dbn_CPDs_to_tables.m: Initial import of code + base from Kevin Murphy. + +2003-01-23 10:44 yozhik + + * BNT/general/convert_dbn_CPDs_to_tables.m: Initial revision + +2003-01-22 13:38 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m: Initial + import of code base from Kevin Murphy. + +2003-01-22 13:38 yozhik + + * BNT/inference/dynamic/@hmm_inf_engine/enter_evidence.m: Initial + revision + +2003-01-22 12:32 yozhik + + * HMM/mc_sample_endstate.m: Initial import of code base from Kevin + Murphy. + +2003-01-22 12:32 yozhik + + * HMM/mc_sample_endstate.m: Initial revision + +2003-01-22 09:56 yozhik + + * HMM/fixed_lag_smoother.m: Initial import of code base from Kevin + Murphy. + +2003-01-22 09:56 yozhik + + * HMM/fixed_lag_smoother.m: Initial revision + +2003-01-20 08:56 yozhik + + * GraphViz/draw_graph_test.m: Initial import of code base from + Kevin Murphy. + +2003-01-20 08:56 yozhik + + * GraphViz/draw_graph_test.m: Initial revision + +2003-01-18 15:10 yozhik + + * BNT/general/dsep_test.m: Initial import of code base from Kevin + Murphy. + +2003-01-18 15:10 yozhik + + * BNT/general/dsep_test.m: Initial revision + +2003-01-18 15:00 yozhik + + * BNT/copyright.txt: Initial import of code base from Kevin Murphy. + +2003-01-18 15:00 yozhik + + * BNT/copyright.txt: Initial revision + +2003-01-18 14:49 yozhik + + * Kalman/tracking_demo.m: Initial import of code base from Kevin + Murphy. + +2003-01-18 14:49 yozhik + + * Kalman/tracking_demo.m: Initial revision + +2003-01-18 14:22 yozhik + + * BNT/: examples/dummy, inference/dummy, inference/dynamic/dummy, + inference/online/dummy, inference/static/dummy: Initial import of + code base from Kevin Murphy. + +2003-01-18 14:22 yozhik + + * BNT/: examples/dummy, inference/dummy, inference/dynamic/dummy, + inference/online/dummy, inference/static/dummy: Initial revision + +2003-01-18 14:16 yozhik + + * BNT/examples/dynamic/: ehmm1.m, jtree_clq_test.m: Initial import + of code base from Kevin Murphy. + +2003-01-18 14:16 yozhik + + * BNT/examples/dynamic/: ehmm1.m, jtree_clq_test.m: Initial + revision + +2003-01-18 14:11 yozhik + + * BNT/inference/static/: + @jtree_sparse_inf_engine/jtree_sparse_inf_engine.m, + @jtree_mnet_inf_engine/jtree_mnet_inf_engine.m: Initial import of + code base from Kevin Murphy. + +2003-01-18 14:11 yozhik + + * BNT/inference/static/: + @jtree_sparse_inf_engine/jtree_sparse_inf_engine.m, + @jtree_mnet_inf_engine/jtree_mnet_inf_engine.m: Initial revision + +2003-01-18 13:17 yozhik + + * GraphViz/draw_dbn_test.m: Initial import of code base from Kevin + Murphy. + +2003-01-18 13:17 yozhik + + * GraphViz/draw_dbn_test.m: Initial revision + +2003-01-11 10:53 yozhik + + * BNT/inference/static/@pearl_inf_engine/pearl_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-01-11 10:53 yozhik + + * BNT/inference/static/@pearl_inf_engine/pearl_inf_engine.m: + Initial revision + +2003-01-11 10:48 yozhik + + * BNT/examples/dynamic/HHMM/Map/learn_map.m: Initial import of code + base from Kevin Murphy. + +2003-01-11 10:48 yozhik + + * BNT/examples/dynamic/HHMM/Map/learn_map.m: Initial revision + +2003-01-11 10:41 yozhik + + * BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_evidence.m: + Initial import of code base from Kevin Murphy. + +2003-01-11 10:41 yozhik + + * BNT/inference/dynamic/@jtree_dbn_inf_engine/enter_evidence.m: + Initial revision + +2003-01-11 10:13 yozhik + + * BNT/inference/dynamic/@bk_inf_engine/enter_soft_evidence.m: + Initial import of code base from Kevin Murphy. + +2003-01-11 10:13 yozhik + + * BNT/inference/dynamic/@bk_inf_engine/enter_soft_evidence.m: + Initial revision + +2003-01-07 08:25 yozhik + + * BNT/CPDs/@softmax_CPD/softmax_CPD.m: Initial import of code base + from Kevin Murphy. + +2003-01-07 08:25 yozhik + + * BNT/CPDs/@softmax_CPD/softmax_CPD.m: Initial revision + +2003-01-03 14:01 yozhik + + * + BNT/inference/static/@belprop_mrf2_inf_engine/belprop_mrf2_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2003-01-03 14:01 yozhik + + * + BNT/inference/static/@belprop_mrf2_inf_engine/belprop_mrf2_inf_engine.m: + Initial revision + +2003-01-02 09:49 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/find_mpe.m: Initial + import of code base from Kevin Murphy. + +2003-01-02 09:49 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/find_mpe.m: Initial + revision + +2003-01-02 09:28 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/: set_params.m, + enter_soft_evidence.m: Initial import of code base from Kevin + Murphy. + +2003-01-02 09:28 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/: set_params.m, + enter_soft_evidence.m: Initial revision + +2002-12-31 14:06 yozhik + + * BNT/general/mk_mrf2.m: Initial import of code base from Kevin + Murphy. + +2002-12-31 14:06 yozhik + + * BNT/general/mk_mrf2.m: Initial revision + +2002-12-31 13:24 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/marginal_nodes.m: + Initial import of code base from Kevin Murphy. + +2002-12-31 13:24 yozhik + + * BNT/inference/static/@belprop_mrf2_inf_engine/marginal_nodes.m: + Initial revision + +2002-12-31 11:00 yozhik + + * BNT/inference/static/@belprop_inf_engine/belprop_inf_engine.m: + Initial import of code base from Kevin Murphy. + +2002-12-31 11:00 yozhik + + * BNT/inference/static/@belprop_inf_engine/belprop_inf_engine.m: + Initial revision + +2002-12-16 11:16 yozhik + + * BNT/examples/dynamic/HHMM/remove_hhmm_end_state.m: Initial import + of code base from Kevin Murphy. + +2002-12-16 11:16 yozhik + + * BNT/examples/dynamic/HHMM/remove_hhmm_end_state.m: Initial + revision + +2002-12-16 09:57 yozhik + + * BNT/general/unroll_set.m: Initial import of code base from Kevin + Murphy. + +2002-12-16 09:57 yozhik + + * BNT/general/unroll_set.m: Initial revision + +2002-11-26 14:14 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram3.m: Initial import of code + base from Kevin Murphy. + +2002-11-26 14:14 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram3.m: Initial revision + +2002-11-26 14:04 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram2.m: Initial import of code + base from Kevin Murphy. + +2002-11-26 14:04 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram2.m: Initial revision + +2002-11-22 16:44 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/Old/mgram2.m: Initial import of + code base from Kevin Murphy. + +2002-11-22 16:44 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/Old/mgram2.m: Initial revision + +2002-11-22 15:59 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram1.m: Initial import of code + base from Kevin Murphy. + +2002-11-22 15:59 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/mgram1.m: Initial revision + +2002-11-22 15:51 yozhik + + * BNT/inference/dynamic/@jtree_dbn_inf_engine/marginal_nodes.m: + Initial import of code base from Kevin Murphy. + +2002-11-22 15:51 yozhik + + * BNT/inference/dynamic/@jtree_dbn_inf_engine/marginal_nodes.m: + Initial revision + +2002-11-22 15:07 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/: num2letter.m, letter2num.m: + Initial import of code base from Kevin Murphy. + +2002-11-22 15:07 yozhik + + * BNT/examples/dynamic/HHMM/Mgram/: num2letter.m, letter2num.m: + Initial revision + +2002-11-22 14:35 yozhik + + * BNT/general/convert_dbn_CPDs_to_pots.m: Initial import of code + base from Kevin Murphy. + +2002-11-22 14:35 yozhik + + * BNT/general/convert_dbn_CPDs_to_pots.m: Initial revision + +2002-11-22 13:45 yozhik + + * HMM/mk_rightleft_transmat.m: Initial import of code base from + Kevin Murphy. + +2002-11-22 13:45 yozhik + + * HMM/mk_rightleft_transmat.m: Initial revision + +2002-11-14 12:33 yozhik + + * BNT/examples/dynamic/water2.m: Initial import of code base from + Kevin Murphy. + +2002-11-14 12:33 yozhik + + * BNT/examples/dynamic/water2.m: Initial revision + +2002-11-14 12:07 yozhik + + * BNT/examples/dynamic/water1.m: Initial import of code base from + Kevin Murphy. + +2002-11-14 12:07 yozhik + + * BNT/examples/dynamic/water1.m: Initial revision + +2002-11-14 12:02 yozhik + + * BNT/inference/: online/@hmm_2TBN_inf_engine/marginal_nodes.m, + dynamic/@hmm_inf_engine/marginal_nodes.m, + online/@hmm_2TBN_inf_engine/hmm_2TBN_inf_engine.m, + dynamic/@hmm_inf_engine/hmm_inf_engine.m, + dynamic/@hmm_inf_engine/marginal_family.m, + online/@hmm_2TBN_inf_engine/marginal_family.m: Initial import of + code base from Kevin Murphy. + +2002-11-14 12:02 yozhik + + * BNT/inference/: online/@hmm_2TBN_inf_engine/marginal_nodes.m, + dynamic/@hmm_inf_engine/marginal_nodes.m, + online/@hmm_2TBN_inf_engine/hmm_2TBN_inf_engine.m, + dynamic/@hmm_inf_engine/hmm_inf_engine.m, + dynamic/@hmm_inf_engine/marginal_family.m, + online/@hmm_2TBN_inf_engine/marginal_family.m: Initial revision + +2002-11-14 08:31 yozhik + + * BNT/inference/: + online/@jtree_2TBN_inf_engine/jtree_2TBN_inf_engine.m, + dynamic/@jtree_dbn_inf_engine/jtree_dbn_inf_engine.m: Initial + import of code base from Kevin Murphy. + +2002-11-14 08:31 yozhik + + * BNT/inference/: + online/@jtree_2TBN_inf_engine/jtree_2TBN_inf_engine.m, + dynamic/@jtree_dbn_inf_engine/jtree_dbn_inf_engine.m: Initial + revision + +2002-11-13 17:01 yozhik + + * BNT/examples/: static/qmr2.m, dynamic/arhmm1.m: Initial import of + code base from Kevin Murphy. + +2002-11-13 17:01 yozhik + + * BNT/examples/: static/qmr2.m, dynamic/arhmm1.m: Initial revision + +2002-11-03 08:44 yozhik + + * BNT/examples/static/Models/mk_alarm_bnet.m: Initial import of + code base from Kevin Murphy. + +2002-11-03 08:44 yozhik + + * BNT/examples/static/Models/mk_alarm_bnet.m: Initial revision + +2002-11-01 16:32 yozhik + + * Kalman/kalman_forward_backward.m: Initial import of code base + from Kevin Murphy. + +2002-11-01 16:32 yozhik + + * Kalman/kalman_forward_backward.m: Initial revision + +2002-10-23 08:17 yozhik + + * Kalman/learning_demo.m: Initial import of code base from Kevin + Murphy. + +2002-10-23 08:17 yozhik + + * Kalman/learning_demo.m: Initial revision + +2002-10-18 13:05 yozhik + + * BNT/inference/static/@pearl_inf_engine/marginal_family.m: Initial + import of code base from Kevin Murphy. + +2002-10-18 13:05 yozhik + + * BNT/inference/static/@pearl_inf_engine/marginal_family.m: Initial + revision + +2002-10-10 16:45 yozhik + + * BNT/examples/dynamic/jtree_clq_test2.m: Initial import of code + base from Kevin Murphy. + +2002-10-10 16:45 yozhik + + * BNT/examples/dynamic/jtree_clq_test2.m: Initial revision + +2002-10-10 16:14 yozhik + + * BNT/examples/dynamic/: mk_mildew_dbn.m, mk_uffe_dbn.m: Initial + import of code base from Kevin Murphy. + +2002-10-10 16:14 yozhik + + * BNT/examples/dynamic/: mk_mildew_dbn.m, mk_uffe_dbn.m: Initial + revision + +2002-10-09 13:36 yozhik + + * BNT/examples/dynamic/mk_ps_from_clqs.m: Initial import of code + base from Kevin Murphy. + +2002-10-09 13:36 yozhik + + * BNT/examples/dynamic/mk_ps_from_clqs.m: Initial revision + +2002-10-07 06:26 yozhik + + * BNT/CPDs/@deterministic_CPD/deterministic_CPD.m: Initial import + of code base from Kevin Murphy. + +2002-10-07 06:26 yozhik + + * BNT/CPDs/@deterministic_CPD/deterministic_CPD.m: Initial revision + +2002-10-02 08:39 yozhik + + * BNT/potentials/Tables/marg_tableC.c: Initial import of code base + from Kevin Murphy. + +2002-10-02 08:39 yozhik + + * BNT/potentials/Tables/marg_tableC.c: Initial revision + +2002-10-02 08:28 yozhik + + * BNT/potentials/Tables/: mult_by_tableM.m, mult_by_table2.m: + Initial import of code base from Kevin Murphy. + +2002-10-02 08:28 yozhik + + * BNT/potentials/Tables/: mult_by_tableM.m, mult_by_table2.m: + Initial revision + +2002-10-01 14:33 yozhik + + * BNT/potentials/Tables/mult_by_tableC.c: Initial import of code + base from Kevin Murphy. + +2002-10-01 14:33 yozhik + + * BNT/potentials/Tables/mult_by_tableC.c: Initial revision + +2002-10-01 14:23 yozhik + + * BNT/potentials/Tables/mult_by_table.c: Initial import of code + base from Kevin Murphy. + +2002-10-01 14:23 yozhik + + * BNT/potentials/Tables/mult_by_table.c: Initial revision + +2002-10-01 14:20 yozhik + + * BNT/potentials/Tables/repmat_and_mult.c: Initial import of code + base from Kevin Murphy. + +2002-10-01 14:20 yozhik + + * BNT/potentials/Tables/repmat_and_mult.c: Initial revision + +2002-10-01 12:04 yozhik + + * BNT/potentials/@dpot/dpot.m: Initial import of code base from + Kevin Murphy. + +2002-10-01 12:04 yozhik + + * BNT/potentials/@dpot/dpot.m: Initial revision + +2002-10-01 11:21 yozhik + + * BNT/examples/static/Belprop/belprop_polytree_discrete.m: Initial + import of code base from Kevin Murphy. + +2002-10-01 11:21 yozhik + + * BNT/examples/static/Belprop/belprop_polytree_discrete.m: Initial + revision + +2002-10-01 11:16 yozhik + + * BNT/examples/static/cmp_inference_static.m: Initial import of + code base from Kevin Murphy. + +2002-10-01 11:16 yozhik + + * BNT/examples/static/cmp_inference_static.m: Initial revision + +2002-10-01 10:39 yozhik + + * BNT/potentials/Tables/marg_tableM.m: Initial import of code base + from Kevin Murphy. + +2002-10-01 10:39 yozhik + + * BNT/potentials/Tables/marg_tableM.m: Initial revision + +2002-09-29 03:21 yozhik + + * BNT/potentials/Tables/mult_by_table_global.m: Initial import of + code base from Kevin Murphy. + +2002-09-29 03:21 yozhik + + * BNT/potentials/Tables/mult_by_table_global.m: Initial revision + +2002-09-26 01:39 yozhik + + * BNT/learning/learn_struct_K2.m: Initial import of code base from + Kevin Murphy. + +2002-09-26 01:39 yozhik + + * BNT/learning/learn_struct_K2.m: Initial revision + +2002-09-24 15:43 yozhik + + * BNT/: CPDs/@hhmm2Q_CPD/update_ess.m, + CPDs/@hhmm2Q_CPD/maximize_params.m, + examples/dynamic/HHMM/Map/disp_map_hhmm.m: Initial import of code + base from Kevin Murphy. + +2002-09-24 15:43 yozhik + + * BNT/: CPDs/@hhmm2Q_CPD/update_ess.m, + CPDs/@hhmm2Q_CPD/maximize_params.m, + examples/dynamic/HHMM/Map/disp_map_hhmm.m: Initial revision + +2002-09-24 15:34 yozhik + + * BNT/CPDs/@hhmm2Q_CPD/: hhmm2Q_CPD.m, reset_ess.m: Initial import + of code base from Kevin Murphy. + +2002-09-24 15:34 yozhik + + * BNT/CPDs/@hhmm2Q_CPD/: hhmm2Q_CPD.m, reset_ess.m: Initial + revision + +2002-09-24 15:13 yozhik + + * BNT/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m: Initial import + of code base from Kevin Murphy. + +2002-09-24 15:13 yozhik + + * BNT/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m: Initial revision + +2002-09-24 06:10 yozhik + + * BNT/CPDs/@hhmmQ_CPD/maximize_params.m: Initial import of code + base from Kevin Murphy. + +2002-09-24 06:10 yozhik + + * BNT/CPDs/@hhmmQ_CPD/maximize_params.m: Initial revision + +2002-09-24 06:02 yozhik + + * BNT/examples/dynamic/HHMM/Map/sample_from_map.m: Initial import + of code base from Kevin Murphy. + +2002-09-24 06:02 yozhik + + * BNT/examples/dynamic/HHMM/Map/sample_from_map.m: Initial revision + +2002-09-24 05:46 yozhik + + * BNT/CPDs/@hhmm2Q_CPD/CPD_to_CPT.m: Initial import of code base + from Kevin Murphy. + +2002-09-24 05:46 yozhik + + * BNT/CPDs/@hhmm2Q_CPD/CPD_to_CPT.m: Initial revision + +2002-09-24 03:49 yozhik + + * BNT/examples/dynamic/HHMM/Map/mk_map_hhmm.m: Initial import of + code base from Kevin Murphy. + +2002-09-24 03:49 yozhik + + * BNT/examples/dynamic/HHMM/Map/mk_map_hhmm.m: Initial revision + +2002-09-24 00:02 yozhik + + * BNT/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m: Initial import + of code base from Kevin Murphy. + +2002-09-24 00:02 yozhik + + * BNT/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m: Initial revision + +2002-09-23 21:19 yozhik + + * BNT/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m: Initial import of code base from + Kevin Murphy. + +2002-09-23 21:19 yozhik + + * BNT/CPDs/@hhmmQ_CPD/hhmmQ_CPD.m: Initial revision + +2002-09-23 19:58 yozhik + + * BNT/CPDs/@hhmmQ_CPD/update_CPT.m: Initial import of code base + from Kevin Murphy. + +2002-09-23 19:58 yozhik + + * BNT/CPDs/@hhmmQ_CPD/update_CPT.m: Initial revision + +2002-09-23 19:30 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_CPT.m: Initial import of code base + from Kevin Murphy. + +2002-09-23 19:30 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_CPT.m: Initial revision + +2002-09-21 14:37 yozhik + + * BNT/examples/dynamic/HHMM/abcd_hhmm.m: Initial import of code + base from Kevin Murphy. + +2002-09-21 14:37 yozhik + + * BNT/examples/dynamic/HHMM/abcd_hhmm.m: Initial revision + +2002-09-21 13:58 yozhik + + * BNT/examples/dynamic/HHMM/mk_hhmm.m: Initial import of code base + from Kevin Murphy. + +2002-09-21 13:58 yozhik + + * BNT/examples/dynamic/HHMM/mk_hhmm.m: Initial revision + +2002-09-10 10:44 yozhik + + * BNT/CPDs/@gaussian_CPD/log_prob_node.m: Initial import of code + base from Kevin Murphy. + +2002-09-10 10:44 yozhik + + * BNT/CPDs/@gaussian_CPD/log_prob_node.m: Initial revision + +2002-07-28 16:09 yozhik + + * BNT/learning/: learn_struct_pdag_pc_constrain.m, CovMat.m: + Initial import of code base from Kevin Murphy. + +2002-07-28 16:09 yozhik + + * BNT/learning/: learn_struct_pdag_pc_constrain.m, CovMat.m: + Initial revision + +2002-07-24 07:48 yozhik + + * BNT/general/hodbn_to_bnet.m: Initial import of code base from + Kevin Murphy. + +2002-07-24 07:48 yozhik + + * BNT/general/hodbn_to_bnet.m: Initial revision + +2002-07-23 06:17 yozhik + + * BNT/general/mk_higher_order_dbn.m: Initial import of code base + from Kevin Murphy. + +2002-07-23 06:17 yozhik + + * BNT/general/mk_higher_order_dbn.m: Initial revision + +2002-07-20 18:25 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/set_fields.m: Initial + import of code base from Kevin Murphy. + +2002-07-20 18:25 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/set_fields.m: Initial + revision + +2002-07-20 17:32 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/back_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-07-20 17:32 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/back_mpe.m: Initial + revision + +2002-07-02 15:56 yozhik + + * BNT/examples/dynamic/HHMM/Motif/learn_motif_hhmm.m: Initial + import of code base from Kevin Murphy. + +2002-07-02 15:56 yozhik + + * BNT/examples/dynamic/HHMM/Motif/learn_motif_hhmm.m: Initial + revision + +2002-06-27 13:34 yozhik + + * BNT/general/add_ev_to_dmarginal.m: Initial import of code base + from Kevin Murphy. + +2002-06-27 13:34 yozhik + + * BNT/general/add_ev_to_dmarginal.m: Initial revision + +2002-06-24 16:54 yozhik + + * BNT/CPDs/@hhmmF_CPD/update_ess.m: Initial import of code base + from Kevin Murphy. + +2002-06-24 16:54 yozhik + + * BNT/CPDs/@hhmmF_CPD/update_ess.m: Initial revision + +2002-06-24 16:38 yozhik + + * BNT/CPDs/@hhmmF_CPD/hhmmF_CPD.m: Initial import of code base from + Kevin Murphy. + +2002-06-24 16:38 yozhik + + * BNT/CPDs/@hhmmF_CPD/hhmmF_CPD.m: Initial revision + +2002-06-24 15:45 yozhik + + * BNT/CPDs/@hhmmF_CPD/update_CPT.m: Initial import of code base + from Kevin Murphy. + +2002-06-24 15:45 yozhik + + * BNT/CPDs/@hhmmF_CPD/update_CPT.m: Initial revision + +2002-06-24 15:35 yozhik + + * BNT/CPDs/@hhmmF_CPD/Old/: hhmmF_CPD.m, log_prior.m, + maximize_params.m, reset_ess.m, update_CPT.m, update_ess.m: + Initial import of code base from Kevin Murphy. + +2002-06-24 15:35 yozhik + + * BNT/CPDs/@hhmmF_CPD/Old/: hhmmF_CPD.m, log_prior.m, + maximize_params.m, reset_ess.m, update_CPT.m, update_ess.m: + Initial revision + +2002-06-24 15:23 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess4.m: Initial import of code + base from Kevin Murphy. + +2002-06-24 15:23 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess4.m: Initial revision + +2002-06-24 15:08 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess3.m: Initial import of code + base from Kevin Murphy. + +2002-06-24 15:08 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess3.m: Initial revision + +2002-06-24 14:20 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess2.m: Initial import of code + base from Kevin Murphy. + +2002-06-24 14:20 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/update_ess2.m: Initial revision + +2002-06-24 11:56 yozhik + + * BNT/: general/mk_fgraph_given_ev.m, + CPDs/mk_isolated_tabular_CPD.m: Initial import of code base from + Kevin Murphy. + +2002-06-24 11:56 yozhik + + * BNT/: general/mk_fgraph_given_ev.m, + CPDs/mk_isolated_tabular_CPD.m: Initial revision + +2002-06-24 11:19 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/: hhmmQ_CPD.m, log_prior.m, + maximize_params.m, reset_ess.m, update_ess.m: Initial import of + code base from Kevin Murphy. + +2002-06-24 11:19 yozhik + + * BNT/CPDs/@hhmmQ_CPD/Old/: hhmmQ_CPD.m, log_prior.m, + maximize_params.m, reset_ess.m, update_ess.m: Initial revision + +2002-06-20 13:30 yozhik + + * BNT/examples/dynamic/mildew1.m: Initial import of code base from + Kevin Murphy. + +2002-06-20 13:30 yozhik + + * BNT/examples/dynamic/mildew1.m: Initial revision + +2002-06-19 17:18 yozhik + + * BNT/: inference/dynamic/@hmm_inf_engine/find_mpe.m, + examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m: Initial + import of code base from Kevin Murphy. + +2002-06-19 17:18 yozhik + + * BNT/: inference/dynamic/@hmm_inf_engine/find_mpe.m, + examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m: Initial + revision + +2002-06-19 17:03 yozhik + + * BNT/examples/static/fgraph/fg1.m: Initial import of code base + from Kevin Murphy. + +2002-06-19 17:03 yozhik + + * BNT/examples/static/fgraph/fg1.m: Initial revision + +2002-06-19 16:59 yozhik + + * BNT/: examples/static/softev1.m, + inference/static/@belprop_fg_inf_engine/find_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-06-19 16:59 yozhik + + * BNT/: examples/static/softev1.m, + inference/static/@belprop_fg_inf_engine/find_mpe.m: Initial + revision + +2002-06-19 15:11 yozhik + + * BNT/inference/static/@var_elim_inf_engine/find_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-06-19 15:11 yozhik + + * BNT/inference/static/@var_elim_inf_engine/find_mpe.m: Initial + revision + +2002-06-19 15:08 yozhik + + * BNT/: inference/static/@belprop_inf_engine/find_mpe.m, + examples/static/mpe1.m, examples/static/mpe2.m: Initial import of + code base from Kevin Murphy. + +2002-06-19 15:08 yozhik + + * BNT/: inference/static/@belprop_inf_engine/find_mpe.m, + examples/static/mpe1.m, examples/static/mpe2.m: Initial revision + +2002-06-19 15:04 yozhik + + * BNT/inference/static/@var_elim_inf_engine/: + var_elim_inf_engine.m, enter_evidence.m: Initial import of code + base from Kevin Murphy. + +2002-06-19 15:04 yozhik + + * BNT/inference/static/@var_elim_inf_engine/: + var_elim_inf_engine.m, enter_evidence.m: Initial revision + +2002-06-19 14:56 yozhik + + * BNT/inference/static/@global_joint_inf_engine/find_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-06-19 14:56 yozhik + + * BNT/inference/static/@global_joint_inf_engine/find_mpe.m: Initial + revision + +2002-06-17 16:49 yozhik + + * BNT/inference/online/: @jtree_2TBN_inf_engine/back1_mpe.m, + @smoother_engine/find_mpe.m: Initial import of code base from + Kevin Murphy. + +2002-06-17 16:49 yozhik + + * BNT/inference/online/: @jtree_2TBN_inf_engine/back1_mpe.m, + @smoother_engine/find_mpe.m: Initial revision + +2002-06-17 16:46 yozhik + + * BNT/inference/online/: @jtree_2TBN_inf_engine/fwd.m, + @jtree_2TBN_inf_engine/fwd1.m, @smoother_engine/enter_evidence.m: + Initial import of code base from Kevin Murphy. + +2002-06-17 16:46 yozhik + + * BNT/inference/online/: @jtree_2TBN_inf_engine/fwd.m, + @jtree_2TBN_inf_engine/fwd1.m, @smoother_engine/enter_evidence.m: + Initial revision + +2002-06-17 16:38 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/backT_mpe.m: Initial + import of code base from Kevin Murphy. + +2002-06-17 16:38 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/backT_mpe.m: Initial + revision + +2002-06-17 16:34 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/: back.m, backT.m, + back1.m: Initial import of code base from Kevin Murphy. + +2002-06-17 16:34 yozhik + + * BNT/inference/online/@jtree_2TBN_inf_engine/: back.m, backT.m, + back1.m: Initial revision + +2002-06-17 16:14 yozhik + + * BNT/inference/static/@jtree_inf_engine/: find_mpe.m, + find_max_config.m: Initial import of code base from Kevin Murphy. + +2002-06-17 16:14 yozhik + + * BNT/inference/static/@jtree_inf_engine/: find_mpe.m, + find_max_config.m: Initial revision + +2002-06-17 14:58 yozhik + + * BNT/general/Old/calc_mpe.m: Initial import of code base from + Kevin Murphy. + +2002-06-17 14:58 yozhik + + * BNT/general/Old/calc_mpe.m: Initial revision + +2002-06-17 13:59 yozhik + + * BNT/inference/static/@jtree_inf_engine/: enter_evidence.m, + distribute_evidence.m: Initial import of code base from Kevin + Murphy. + +2002-06-17 13:59 yozhik + + * BNT/inference/static/@jtree_inf_engine/: enter_evidence.m, + distribute_evidence.m: Initial revision + +2002-06-17 13:29 yozhik + + * BNT/inference/static/@jtree_mnet_inf_engine/: find_mpe.m, + enter_evidence.m: Initial import of code base from Kevin Murphy. + +2002-06-17 13:29 yozhik + + * BNT/inference/static/@jtree_mnet_inf_engine/: find_mpe.m, + enter_evidence.m: Initial revision + +2002-06-16 13:01 yozhik + + * BNT/general/is_mnet.m: Initial import of code base from Kevin + Murphy. + +2002-06-16 13:01 yozhik + + * BNT/general/is_mnet.m: Initial revision + +2002-06-16 12:52 yozhik + + * BNT/general/mk_mnet.m: Initial import of code base from Kevin + Murphy. + +2002-06-16 12:52 yozhik + + * BNT/general/mk_mnet.m: Initial revision + +2002-06-16 12:34 yozhik + + * BNT/inference/static/@jtree_inf_engine/init_pot.m: Initial import + of code base from Kevin Murphy. + +2002-06-16 12:34 yozhik + + * BNT/inference/static/@jtree_inf_engine/init_pot.m: Initial + revision + +2002-06-16 12:06 yozhik + + * BNT/potentials/@dpot/find_most_prob_entry.m: Initial import of + code base from Kevin Murphy. + +2002-06-16 12:06 yozhik + + * BNT/potentials/@dpot/find_most_prob_entry.m: Initial revision + +2002-05-31 03:25 yozhik + + * BNT/general/unroll_higher_order_topology.m: Initial import of + code base from Kevin Murphy. + +2002-05-31 03:25 yozhik + + * BNT/general/unroll_higher_order_topology.m: Initial revision + +2002-05-29 08:59 yozhik + + * BNT/@assocarray/assocarray.m, + BNT/CPDs/@boolean_CPD/boolean_CPD.m, + BNT/CPDs/@discrete_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@discrete_CPD/CPD_to_pi.m, + BNT/CPDs/@discrete_CPD/CPD_to_scgpot.m, + BNT/CPDs/@discrete_CPD/README, + BNT/CPDs/@discrete_CPD/convert_CPD_to_table_hidden_ps.m, + BNT/CPDs/@discrete_CPD/convert_obs_CPD_to_table.m, + BNT/CPDs/@discrete_CPD/convert_to_sparse_table.c, + BNT/CPDs/@discrete_CPD/convert_to_table.m, + BNT/CPDs/@discrete_CPD/discrete_CPD.m, + BNT/CPDs/@discrete_CPD/dom_sizes.m, + BNT/CPDs/@discrete_CPD/log_prob_node.m, + BNT/CPDs/@discrete_CPD/prob_node.m, + BNT/CPDs/@discrete_CPD/sample_node.m, + BNT/CPDs/@discrete_CPD/Old/convert_to_pot.m, + BNT/CPDs/@discrete_CPD/Old/convert_to_table.m, + BNT/CPDs/@discrete_CPD/Old/prob_CPD.m, + BNT/CPDs/@discrete_CPD/Old/prob_node.m, + BNT/CPDs/@discrete_CPD/private/prod_CPT_and_pi_msgs.m, + BNT/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@gaussian_CPD/CPD_to_pi.m, + BNT/CPDs/@gaussian_CPD/CPD_to_scgpot.m, + BNT/CPDs/@gaussian_CPD/adjustable_CPD.m, + BNT/CPDs/@gaussian_CPD/convert_CPD_to_table_hidden_ps.m, + BNT/CPDs/@gaussian_CPD/display.m, + BNT/CPDs/@gaussian_CPD/get_field.m, + BNT/CPDs/@gaussian_CPD/reset_ess.m, + BNT/CPDs/@gaussian_CPD/sample_node.m, + BNT/CPDs/@gaussian_CPD/set_fields.m, + BNT/CPDs/@gaussian_CPD/Old/CPD_to_lambda_msg.m, + BNT/CPDs/@gaussian_CPD/Old/gaussian_CPD.m, + BNT/CPDs/@gaussian_CPD/Old/log_prob_node.m, + BNT/CPDs/@gaussian_CPD/Old/update_ess.m, + BNT/CPDs/@gaussian_CPD/Old/update_tied_ess.m, + BNT/CPDs/@gaussian_CPD/private/CPD_to_linear_gaussian.m, + BNT/CPDs/@generic_CPD/README, + BNT/CPDs/@generic_CPD/adjustable_CPD.m, + BNT/CPDs/@generic_CPD/display.m, + BNT/CPDs/@generic_CPD/generic_CPD.m, + BNT/CPDs/@generic_CPD/log_prior.m, + BNT/CPDs/@generic_CPD/set_clamped.m, + BNT/CPDs/@generic_CPD/Old/BIC_score_CPD.m, + BNT/CPDs/@generic_CPD/Old/CPD_to_dpots.m, + BNT/CPDs/@gmux_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@gmux_CPD/convert_to_pot.m, + BNT/CPDs/@gmux_CPD/CPD_to_pi.m, BNT/CPDs/@gmux_CPD/display.m, + BNT/CPDs/@gmux_CPD/gmux_CPD.m, BNT/CPDs/@gmux_CPD/sample_node.m, + BNT/CPDs/@gmux_CPD/Old/gmux_CPD.m, + BNT/CPDs/@hhmmF_CPD/log_prior.m, + BNT/CPDs/@hhmmF_CPD/maximize_params.m, + BNT/CPDs/@hhmmF_CPD/reset_ess.m, BNT/CPDs/@hhmmQ_CPD/log_prior.m, + BNT/CPDs/@hhmmQ_CPD/reset_ess.m, + BNT/CPDs/@mlp_CPD/convert_to_table.m, + BNT/CPDs/@mlp_CPD/maximize_params.m, BNT/CPDs/@mlp_CPD/mlp_CPD.m, + BNT/CPDs/@mlp_CPD/reset_ess.m, BNT/CPDs/@mlp_CPD/update_ess.m, + BNT/CPDs/@noisyor_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@noisyor_CPD/CPD_to_pi.m, + BNT/CPDs/@noisyor_CPD/noisyor_CPD.m, + BNT/CPDs/@noisyor_CPD/private/sum_prod_CPD_and_pi_msgs.m, + BNT/CPDs/@root_CPD/CPD_to_pi.m, + BNT/CPDs/@root_CPD/convert_to_pot.m, + BNT/CPDs/@root_CPD/log_marg_prob_node.m, + BNT/CPDs/@root_CPD/log_prob_node.m, + BNT/CPDs/@root_CPD/root_CPD.m, BNT/CPDs/@root_CPD/sample_node.m, + BNT/CPDs/@root_CPD/Old/CPD_to_CPT.m, + BNT/CPDs/@softmax_CPD/convert_to_pot.m, + BNT/CPDs/@softmax_CPD/display.m, + BNT/CPDs/@softmax_CPD/get_field.m, + BNT/CPDs/@softmax_CPD/maximize_params.m, + BNT/CPDs/@softmax_CPD/reset_ess.m, + BNT/CPDs/@softmax_CPD/sample_node.m, + BNT/CPDs/@softmax_CPD/set_fields.m, + BNT/CPDs/@softmax_CPD/update_ess.m, + BNT/CPDs/@softmax_CPD/private/extract_params.m, + BNT/CPDs/@tabular_CPD/CPD_to_CPT.m, + BNT/CPDs/@tabular_CPD/bayes_update_params.m, + BNT/CPDs/@tabular_CPD/log_nextcase_prob_node.m, + BNT/CPDs/@tabular_CPD/log_prior.m, + BNT/CPDs/@tabular_CPD/reset_ess.m, + BNT/CPDs/@tabular_CPD/update_ess.m, + BNT/CPDs/@tabular_CPD/update_ess_simple.m, + BNT/CPDs/@tabular_CPD/Old/BIC_score_CPD.m, + BNT/CPDs/@tabular_CPD/Old/bayesian_score_CPD.m, + BNT/CPDs/@tabular_CPD/Old/log_marg_prob_node_case.m, + BNT/CPDs/@tabular_CPD/Old/mult_CPD_and_pi_msgs.m, + BNT/CPDs/@tabular_CPD/Old/prob_CPT.m, + BNT/CPDs/@tabular_CPD/Old/prob_node.m, + BNT/CPDs/@tabular_CPD/Old/sample_node.m, + BNT/CPDs/@tabular_CPD/Old/sample_node_single_case.m, + BNT/CPDs/@tabular_CPD/Old/tabular_CPD.m, + BNT/CPDs/@tabular_CPD/Old/update_params.m, + BNT/CPDs/@tabular_decision_node/CPD_to_CPT.m, + BNT/CPDs/@tabular_decision_node/display.m, + BNT/CPDs/@tabular_decision_node/get_field.m, + BNT/CPDs/@tabular_decision_node/set_fields.m, + BNT/CPDs/@tabular_decision_node/tabular_decision_node.m, + BNT/CPDs/@tabular_decision_node/Old/tabular_decision_node.m, + BNT/CPDs/@tabular_kernel/convert_to_pot.m, + 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BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/pearl_unrolled_dbn_inf_engine.m, + BNT/inference/dynamic/@pearl_unrolled_dbn_inf_engine/update_engine.m, + BNT/inference/online/@filter_engine/bnet_from_engine.m, + BNT/inference/online/@filter_engine/enter_evidence.m, + BNT/inference/online/@filter_engine/filter_engine.m, + BNT/inference/online/@filter_engine/marginal_family.m, + BNT/inference/online/@filter_engine/marginal_nodes.m, + BNT/inference/online/@hmm_2TBN_inf_engine/back.m, + BNT/inference/online/@hmm_2TBN_inf_engine/backT.m, + BNT/inference/online/@hmm_2TBN_inf_engine/fwd.m, + BNT/inference/online/@hmm_2TBN_inf_engine/fwd1.m, + BNT/inference/online/@hmm_2TBN_inf_engine/update_engine.m, + BNT/inference/online/@jtree_2TBN_inf_engine/marginal_family.m, + BNT/inference/online/@jtree_2TBN_inf_engine/marginal_nodes.m, + BNT/inference/online/@jtree_2TBN_inf_engine/Old/jtree_2TBN_inf_engine.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/back.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/back1.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/backT.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/enter_evidence.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/fwd.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/fwd1.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/jtree_sparse_2TBN_inf_engine.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/marginal_family.m, + BNT/inference/online/@jtree_sparse_2TBN_inf_engine/marginal_nodes.m, + BNT/inference/online/@smoother_engine/bnet_from_engine.m, + BNT/inference/online/@smoother_engine/marginal_family.m, + BNT/inference/online/@smoother_engine/marginal_nodes.m, + BNT/inference/online/@smoother_engine/smoother_engine.m, + BNT/inference/online/@smoother_engine/update_engine.m, + BNT/inference/static/@belprop_fg_inf_engine/belprop_fg_inf_engine.m, + BNT/inference/static/@belprop_fg_inf_engine/enter_evidence.m, + BNT/inference/static/@belprop_fg_inf_engine/loopy_converged.m, + BNT/inference/static/@belprop_fg_inf_engine/marginal_nodes.m, + BNT/inference/static/@belprop_fg_inf_engine/set_params.m, + BNT/inference/static/@belprop_inf_engine/enter_evidence.m, + BNT/inference/static/@belprop_inf_engine/loopy_converged.m, + BNT/inference/static/@belprop_inf_engine/marginal_family.m, + BNT/inference/static/@belprop_inf_engine/marginal_nodes.m, + BNT/inference/static/@belprop_inf_engine/Old/belprop_gdl_inf_engine.m, + BNT/inference/static/@belprop_inf_engine/Old/belprop_inf_engine_nostr.m, + BNT/inference/static/@belprop_inf_engine/Old/enter_evidence.m, + BNT/inference/static/@belprop_inf_engine/Old/enter_evidence1.m, + BNT/inference/static/@belprop_inf_engine/Old/marginal_domain.m, + BNT/inference/static/@belprop_inf_engine/private/junk, + BNT/inference/static/@belprop_inf_engine/private/parallel_protocol.m, + BNT/inference/static/@belprop_inf_engine/private/tree_protocol.m, + BNT/inference/static/@cond_gauss_inf_engine/cond_gauss_inf_engine.m, + BNT/inference/static/@cond_gauss_inf_engine/enter_evidence.m, + BNT/inference/static/@cond_gauss_inf_engine/marginal_nodes.m, + BNT/inference/static/@enumerative_inf_engine/enter_evidence.m, + BNT/inference/static/@enumerative_inf_engine/enumerative_inf_engine.m, + BNT/inference/static/@enumerative_inf_engine/marginal_nodes.m, + BNT/inference/static/@gaussian_inf_engine/enter_evidence.m, + BNT/inference/static/@gaussian_inf_engine/marginal_nodes.m, + BNT/inference/static/@gaussian_inf_engine/private/extract_params_from_gbn.m, + BNT/inference/static/@gibbs_sampling_inf_engine/enter_evidence.m, + BNT/inference/static/@gibbs_sampling_inf_engine/gibbs_sampling_inf_engine.m, + BNT/inference/static/@gibbs_sampling_inf_engine/marginal_nodes.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/CPT.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_children.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_families.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_families_dbn.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior.c, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior_dbn.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_strides.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/get_cpts.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.c, + BNT/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/my_sample_discrete.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/sample_single_discrete.c, + BNT/inference/static/@global_joint_inf_engine/global_joint_inf_engine.m, + BNT/inference/static/@global_joint_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_inf_engine/cliques_from_engine.m, + BNT/inference/static/@jtree_inf_engine/clq_containing_nodes.m, + BNT/inference/static/@jtree_inf_engine/collect_evidence.m, + BNT/inference/static/@jtree_inf_engine/enter_soft_evidence.m, + BNT/inference/static/@jtree_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_inf_engine/marginal_nodes.m, + BNT/inference/static/@jtree_inf_engine/set_fields.m, + BNT/inference/static/@jtree_inf_engine/Old/collect_evidence.m, + BNT/inference/static/@jtree_inf_engine/Old/distribute_evidence.m, + BNT/inference/static/@jtree_inf_engine/Old/enter_evidence.m, + BNT/inference/static/@jtree_inf_engine/Old/enter_soft_evidence.m, + BNT/inference/static/@jtree_limid_inf_engine/enter_evidence.m, + BNT/inference/static/@jtree_limid_inf_engine/jtree_limid_inf_engine.m, + BNT/inference/static/@jtree_limid_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_limid_inf_engine/marginal_nodes.m, + BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_family.m, + BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_nodes_SS.m, + BNT/inference/static/@jtree_sparse_inf_engine/cliques_from_engine.m, + BNT/inference/static/@jtree_sparse_inf_engine/clq_containing_nodes.m, + BNT/inference/static/@jtree_sparse_inf_engine/collect_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/distribute_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/enter_evidence.m, + BNT/inference/static/@jtree_sparse_inf_engine/enter_soft_evidence.m, + BNT/inference/static/@jtree_sparse_inf_engine/init_pot.c, + BNT/inference/static/@jtree_sparse_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_sparse_inf_engine/marginal_nodes.m, + BNT/inference/static/@jtree_sparse_inf_engine/set_fields.m, + BNT/inference/static/@jtree_sparse_inf_engine/old/collect_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/distribute_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot1.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot1.m, + BNT/inference/static/@likelihood_weighting_inf_engine/enter_evidence.m, + BNT/inference/static/@likelihood_weighting_inf_engine/likelihood_weighting_inf_engine.m, + BNT/inference/static/@likelihood_weighting_inf_engine/marginal_nodes.m, + BNT/inference/static/@pearl_inf_engine/enter_evidence.m, + BNT/inference/static/@pearl_inf_engine/loopy_converged.m, + BNT/inference/static/@pearl_inf_engine/marginal_nodes.m, + BNT/inference/static/@pearl_inf_engine/private/compute_bel.m, + BNT/inference/static/@pearl_inf_engine/private/prod_lambda_msgs.m, + BNT/inference/static/@pearl_inf_engine/private/tree_protocol.m, + BNT/inference/static/@quickscore_inf_engine/enter_evidence.m, + BNT/inference/static/@quickscore_inf_engine/marginal_nodes.m, + BNT/inference/static/@quickscore_inf_engine/quickscore_inf_engine.m, + BNT/inference/static/@quickscore_inf_engine/private/C_quickscore.c, + BNT/inference/static/@quickscore_inf_engine/private/nr.h, + BNT/inference/static/@quickscore_inf_engine/private/nrutil.c, + BNT/inference/static/@quickscore_inf_engine/private/nrutil.h, + BNT/inference/static/@quickscore_inf_engine/private/quickscore.m, + BNT/learning/bayes_update_params.m, + BNT/learning/bic_score_family.m, + BNT/learning/compute_cooling_schedule.m, + BNT/learning/dirichlet_score_family.m, + BNT/learning/kpm_learn_struct_mcmc.m, + BNT/learning/learn_params_em.m, + BNT/learning/learn_struct_dbn_reveal.m, + BNT/learning/learn_struct_pdag_ic_star.m, + BNT/learning/mcmc_sample_to_hist.m, BNT/learning/mk_schedule.m, + BNT/learning/mk_tetrad_data_file.m, + BNT/learning/score_dags_old.m, HMM/dhmm_logprob_brute_force.m, + HMM/dhmm_logprob_path.m, HMM/mdp_sample.m, Kalman/AR_to_SS.m, + Kalman/SS_to_AR.m, Kalman/convert_to_lagged_form.m, + Kalman/ensure_AR.m, Kalman/eval_AR_perf.m, + Kalman/kalman_filter.m, Kalman/kalman_smoother.m, + Kalman/kalman_update.m, Kalman/learn_AR.m, + Kalman/learn_AR_diagonal.m, Kalman/learn_kalman.m, + Kalman/smooth_update.m, + BNT/general/convert_dbn_CPDs_to_tables_slow.m, + BNT/general/dispcpt.m, BNT/general/linear_gaussian_to_cpot.m, + BNT/general/partition_matrix_vec_3.m, + BNT/general/shrink_obs_dims_in_gaussian.m, + BNT/general/shrink_obs_dims_in_table.m, + BNT/potentials/CPD_to_pot.m, BNT/potentials/README, + BNT/potentials/check_for_cd_arcs.m, + BNT/potentials/determine_pot_type.m, + BNT/potentials/mk_initial_pot.m, + BNT/potentials/@cgpot/cg_can_to_mom.m, + BNT/potentials/@cgpot/cg_mom_to_can.m, + BNT/potentials/@cgpot/cgpot.m, BNT/potentials/@cgpot/display.m, + BNT/potentials/@cgpot/divide_by_pot.m, + BNT/potentials/@cgpot/domain_pot.m, + BNT/potentials/@cgpot/enter_cts_evidence_pot.m, + BNT/potentials/@cgpot/enter_discrete_evidence_pot.m, + BNT/potentials/@cgpot/marginalize_pot.m, + BNT/potentials/@cgpot/multiply_by_pot.m, + BNT/potentials/@cgpot/multiply_pots.m, + BNT/potentials/@cgpot/normalize_pot.m, + BNT/potentials/@cgpot/pot_to_marginal.m, + BNT/potentials/@cgpot/Old/normalize_pot.m, + BNT/potentials/@cgpot/Old/simple_marginalize_pot.m, + BNT/potentials/@cpot/cpot.m, BNT/potentials/@cpot/cpot_to_mpot.m, + BNT/potentials/@cpot/display.m, + BNT/potentials/@cpot/divide_by_pot.m, + BNT/potentials/@cpot/domain_pot.m, + BNT/potentials/@cpot/enter_cts_evidence_pot.m, + BNT/potentials/@cpot/marginalize_pot.m, + BNT/potentials/@cpot/multiply_by_pot.m, + BNT/potentials/@cpot/multiply_pots.m, + BNT/potentials/@cpot/normalize_pot.m, + BNT/potentials/@cpot/pot_to_marginal.m, + BNT/potentials/@cpot/rescale_pot.m, + BNT/potentials/@cpot/set_domain_pot.m, + BNT/potentials/@cpot/Old/cpot_to_mpot.m, + BNT/potentials/@cpot/Old/normalize_pot.convert.m, + BNT/potentials/@dpot/approxeq_pot.m, + BNT/potentials/@dpot/display.m, + BNT/potentials/@dpot/domain_pot.m, + BNT/potentials/@dpot/dpot_to_table.m, + BNT/potentials/@dpot/get_fields.m, + BNT/potentials/@dpot/multiply_pots.m, + BNT/potentials/@dpot/pot_to_marginal.m, + BNT/potentials/@dpot/set_domain_pot.m, + BNT/potentials/@mpot/display.m, + BNT/potentials/@mpot/marginalize_pot.m, + BNT/potentials/@mpot/mpot.m, BNT/potentials/@mpot/mpot_to_cpot.m, + BNT/potentials/@mpot/normalize_pot.m, + BNT/potentials/@mpot/pot_to_marginal.m, + BNT/potentials/@mpot/rescale_pot.m, + BNT/potentials/@upot/approxeq_pot.m, + BNT/potentials/@upot/display.m, + BNT/potentials/@upot/divide_by_pot.m, + BNT/potentials/@upot/marginalize_pot.m, + BNT/potentials/@upot/multiply_by_pot.m, + BNT/potentials/@upot/normalize_pot.m, + BNT/potentials/@upot/pot_to_marginal.m, + BNT/potentials/@upot/upot.m, + BNT/potentials/@upot/upot_to_opt_policy.m, + BNT/potentials/Old/comp_eff_node_sizes.m, + BNT/potentials/Tables/divide_by_sparse_table.c, + BNT/potentials/Tables/divide_by_table.c, + BNT/potentials/Tables/marg_sparse_table.c, + BNT/potentials/Tables/marg_table.c, + BNT/potentials/Tables/mult_by_sparse_table.c, + BNT/potentials/Tables/rep_mult.c, HMM/mk_leftright_transmat.m: + Initial import of code base from Kevin Murphy. + +2002-05-29 08:59 yozhik + + * BNT/@assocarray/assocarray.m, + BNT/CPDs/@boolean_CPD/boolean_CPD.m, + BNT/CPDs/@discrete_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@discrete_CPD/CPD_to_pi.m, + BNT/CPDs/@discrete_CPD/CPD_to_scgpot.m, + BNT/CPDs/@discrete_CPD/README, + BNT/CPDs/@discrete_CPD/convert_CPD_to_table_hidden_ps.m, + BNT/CPDs/@discrete_CPD/convert_obs_CPD_to_table.m, + BNT/CPDs/@discrete_CPD/convert_to_sparse_table.c, + BNT/CPDs/@discrete_CPD/convert_to_table.m, + BNT/CPDs/@discrete_CPD/discrete_CPD.m, + BNT/CPDs/@discrete_CPD/dom_sizes.m, + BNT/CPDs/@discrete_CPD/log_prob_node.m, + BNT/CPDs/@discrete_CPD/prob_node.m, + BNT/CPDs/@discrete_CPD/sample_node.m, + BNT/CPDs/@discrete_CPD/Old/convert_to_pot.m, + BNT/CPDs/@discrete_CPD/Old/convert_to_table.m, + BNT/CPDs/@discrete_CPD/Old/prob_CPD.m, + BNT/CPDs/@discrete_CPD/Old/prob_node.m, + BNT/CPDs/@discrete_CPD/private/prod_CPT_and_pi_msgs.m, + BNT/CPDs/@gaussian_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@gaussian_CPD/CPD_to_pi.m, + BNT/CPDs/@gaussian_CPD/CPD_to_scgpot.m, + BNT/CPDs/@gaussian_CPD/adjustable_CPD.m, + BNT/CPDs/@gaussian_CPD/convert_CPD_to_table_hidden_ps.m, + BNT/CPDs/@gaussian_CPD/display.m, + BNT/CPDs/@gaussian_CPD/get_field.m, + BNT/CPDs/@gaussian_CPD/reset_ess.m, + BNT/CPDs/@gaussian_CPD/sample_node.m, + BNT/CPDs/@gaussian_CPD/set_fields.m, + BNT/CPDs/@gaussian_CPD/Old/CPD_to_lambda_msg.m, + BNT/CPDs/@gaussian_CPD/Old/gaussian_CPD.m, + BNT/CPDs/@gaussian_CPD/Old/log_prob_node.m, + BNT/CPDs/@gaussian_CPD/Old/update_ess.m, + BNT/CPDs/@gaussian_CPD/Old/update_tied_ess.m, + BNT/CPDs/@gaussian_CPD/private/CPD_to_linear_gaussian.m, + BNT/CPDs/@generic_CPD/README, + BNT/CPDs/@generic_CPD/adjustable_CPD.m, + BNT/CPDs/@generic_CPD/display.m, + BNT/CPDs/@generic_CPD/generic_CPD.m, + BNT/CPDs/@generic_CPD/log_prior.m, + BNT/CPDs/@generic_CPD/set_clamped.m, + BNT/CPDs/@generic_CPD/Old/BIC_score_CPD.m, + BNT/CPDs/@generic_CPD/Old/CPD_to_dpots.m, + BNT/CPDs/@gmux_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@gmux_CPD/convert_to_pot.m, + BNT/CPDs/@gmux_CPD/CPD_to_pi.m, BNT/CPDs/@gmux_CPD/display.m, + BNT/CPDs/@gmux_CPD/gmux_CPD.m, BNT/CPDs/@gmux_CPD/sample_node.m, + BNT/CPDs/@gmux_CPD/Old/gmux_CPD.m, + BNT/CPDs/@hhmmF_CPD/log_prior.m, + BNT/CPDs/@hhmmF_CPD/maximize_params.m, + BNT/CPDs/@hhmmF_CPD/reset_ess.m, BNT/CPDs/@hhmmQ_CPD/log_prior.m, + BNT/CPDs/@hhmmQ_CPD/reset_ess.m, + BNT/CPDs/@mlp_CPD/convert_to_table.m, + BNT/CPDs/@mlp_CPD/maximize_params.m, BNT/CPDs/@mlp_CPD/mlp_CPD.m, + BNT/CPDs/@mlp_CPD/reset_ess.m, BNT/CPDs/@mlp_CPD/update_ess.m, + BNT/CPDs/@noisyor_CPD/CPD_to_lambda_msg.m, + BNT/CPDs/@noisyor_CPD/CPD_to_pi.m, + BNT/CPDs/@noisyor_CPD/noisyor_CPD.m, + BNT/CPDs/@noisyor_CPD/private/sum_prod_CPD_and_pi_msgs.m, + BNT/CPDs/@root_CPD/CPD_to_pi.m, + BNT/CPDs/@root_CPD/convert_to_pot.m, + BNT/CPDs/@root_CPD/log_marg_prob_node.m, + BNT/CPDs/@root_CPD/log_prob_node.m, + BNT/CPDs/@root_CPD/root_CPD.m, BNT/CPDs/@root_CPD/sample_node.m, + BNT/CPDs/@root_CPD/Old/CPD_to_CPT.m, + BNT/CPDs/@softmax_CPD/convert_to_pot.m, + BNT/CPDs/@softmax_CPD/display.m, + BNT/CPDs/@softmax_CPD/get_field.m, + BNT/CPDs/@softmax_CPD/maximize_params.m, + BNT/CPDs/@softmax_CPD/reset_ess.m, + BNT/CPDs/@softmax_CPD/sample_node.m, + BNT/CPDs/@softmax_CPD/set_fields.m, + BNT/CPDs/@softmax_CPD/update_ess.m, + BNT/CPDs/@softmax_CPD/private/extract_params.m, + BNT/CPDs/@tabular_CPD/CPD_to_CPT.m, + BNT/CPDs/@tabular_CPD/bayes_update_params.m, + BNT/CPDs/@tabular_CPD/log_nextcase_prob_node.m, + BNT/CPDs/@tabular_CPD/log_prior.m, + BNT/CPDs/@tabular_CPD/reset_ess.m, + BNT/CPDs/@tabular_CPD/update_ess.m, + BNT/CPDs/@tabular_CPD/update_ess_simple.m, + BNT/CPDs/@tabular_CPD/Old/BIC_score_CPD.m, + BNT/CPDs/@tabular_CPD/Old/bayesian_score_CPD.m, + BNT/CPDs/@tabular_CPD/Old/log_marg_prob_node_case.m, + BNT/CPDs/@tabular_CPD/Old/mult_CPD_and_pi_msgs.m, + BNT/CPDs/@tabular_CPD/Old/prob_CPT.m, + BNT/CPDs/@tabular_CPD/Old/prob_node.m, + BNT/CPDs/@tabular_CPD/Old/sample_node.m, + BNT/CPDs/@tabular_CPD/Old/sample_node_single_case.m, + BNT/CPDs/@tabular_CPD/Old/tabular_CPD.m, + BNT/CPDs/@tabular_CPD/Old/update_params.m, + BNT/CPDs/@tabular_decision_node/CPD_to_CPT.m, + BNT/CPDs/@tabular_decision_node/display.m, + BNT/CPDs/@tabular_decision_node/get_field.m, + BNT/CPDs/@tabular_decision_node/set_fields.m, + BNT/CPDs/@tabular_decision_node/tabular_decision_node.m, + BNT/CPDs/@tabular_decision_node/Old/tabular_decision_node.m, + BNT/CPDs/@tabular_kernel/convert_to_pot.m, + BNT/CPDs/@tabular_kernel/convert_to_table.m, + BNT/CPDs/@tabular_kernel/get_field.m, + BNT/CPDs/@tabular_kernel/set_fields.m, + BNT/CPDs/@tabular_kernel/tabular_kernel.m, + BNT/CPDs/@tabular_kernel/Old/tabular_kernel.m, + BNT/CPDs/@tabular_utility_node/convert_to_pot.m, + BNT/CPDs/@tabular_utility_node/display.m, + BNT/CPDs/@tabular_utility_node/tabular_utility_node.m, + BNT/CPDs/@tree_CPD/display.m, + BNT/CPDs/@tree_CPD/evaluate_tree_performance.m, + BNT/CPDs/@tree_CPD/get_field.m, + BNT/CPDs/@tree_CPD/learn_params.m, BNT/CPDs/@tree_CPD/readme.txt, + BNT/CPDs/@tree_CPD/set_fields.m, BNT/CPDs/@tree_CPD/tree_CPD.m, + BNT/CPDs/Old/@linear_gaussian_CPD/linear_gaussian_CPD.m, + BNT/CPDs/Old/@linear_gaussian_CPD/log_marg_prob_node.m, + BNT/CPDs/Old/@linear_gaussian_CPD/update_params_complete.m, + BNT/CPDs/Old/@root_gaussian_CPD/log_marg_prob_node.m, + BNT/CPDs/Old/@root_gaussian_CPD/root_gaussian_CPD.m, + BNT/CPDs/Old/@root_gaussian_CPD/update_params_complete.m, + BNT/CPDs/Old/@tabular_chance_node/CPD_to_upot.m, + BNT/CPDs/Old/@tabular_chance_node/tabular_chance_node.m, + BNT/examples/dynamic/bat1.m, BNT/examples/dynamic/bkff1.m, + BNT/examples/dynamic/chmm1.m, + BNT/examples/dynamic/cmp_inference_dbn.m, + BNT/examples/dynamic/cmp_learning_dbn.m, + BNT/examples/dynamic/cmp_online_inference.m, + BNT/examples/dynamic/fhmm_infer.m, + BNT/examples/dynamic/filter_test1.m, + BNT/examples/dynamic/kalman1.m, + BNT/examples/dynamic/kjaerulff1.m, + BNT/examples/dynamic/loopy_dbn1.m, + BNT/examples/dynamic/mk_collage_from_clqs.m, + BNT/examples/dynamic/mk_fhmm.m, BNT/examples/dynamic/reveal1.m, + BNT/examples/dynamic/scg_dbn.m, + BNT/examples/dynamic/skf_data_assoc_gmux.m, + BNT/examples/dynamic/HHMM/add_hhmm_end_state.m, + BNT/examples/dynamic/HHMM/hhmm_jtree_clqs.m, + BNT/examples/dynamic/HHMM/mk_hhmm_topo.m, + BNT/examples/dynamic/HHMM/mk_hhmm_topo_F1.m, + BNT/examples/dynamic/HHMM/pretty_print_hhmm_parse.m, + BNT/examples/dynamic/HHMM/Motif/fixed_args_mk_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Motif/mk_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Motif/sample_motif_hhmm.m, + BNT/examples/dynamic/HHMM/Old/mk_abcd_hhmm.m, + BNT/examples/dynamic/HHMM/Old/mk_arrow_alpha_hhmm3.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm2.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm3.m, + BNT/examples/dynamic/HHMM/Old/mk_hhmm3_args.m, + BNT/examples/dynamic/HHMM/Old/motif_hhmm.m, + BNT/examples/dynamic/HHMM/Old/remove_hhmm_end_state.m, + BNT/examples/dynamic/HHMM/Square/get_square_data.m, + BNT/examples/dynamic/HHMM/Square/hhmm_inference.m, + BNT/examples/dynamic/HHMM/Square/is_F2_true_D3.m, + BNT/examples/dynamic/HHMM/Square/learn_square_hhmm_discrete.m, + BNT/examples/dynamic/HHMM/Square/mk_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/plot_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_cts.m, + BNT/examples/dynamic/HHMM/Square/sample_square_hhmm_discrete.m, + BNT/examples/dynamic/HHMM/Square/square4.mat, + BNT/examples/dynamic/HHMM/Square/square4_cases.mat, + BNT/examples/dynamic/HHMM/Square/test_square_fig.m, + BNT/examples/dynamic/HHMM/Square/test_square_fig.mat, + BNT/examples/dynamic/HHMM/Square/Old/learn_square_hhmm.m, + BNT/examples/dynamic/HHMM/Square/Old/mk_square_hhmm.m, + 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BNT/examples/static/discrete2.m, BNT/examples/static/discrete3.m, + BNT/examples/static/fa1.m, BNT/examples/static/gaussian1.m, + BNT/examples/static/gibbs_test1.m, BNT/examples/static/lw1.m, + BNT/examples/static/mfa1.m, BNT/examples/static/mixexp1.m, + BNT/examples/static/mixexp2.m, BNT/examples/static/mixexp3.m, + BNT/examples/static/mog1.m, BNT/examples/static/qmr1.m, + BNT/examples/static/sample1.m, BNT/examples/static/softmax1.m, + BNT/examples/static/Belprop/belprop_loop1_discrete.m, + BNT/examples/static/Belprop/belprop_loop1_gauss.m, + BNT/examples/static/Belprop/belprop_loopy_cg.m, + BNT/examples/static/Belprop/belprop_loopy_discrete.m, + BNT/examples/static/Belprop/belprop_loopy_gauss.m, + BNT/examples/static/Belprop/belprop_polytree_cg.m, + BNT/examples/static/Belprop/belprop_polytree_gauss.m, + BNT/examples/static/Belprop/bp1.m, + BNT/examples/static/Belprop/gmux1.m, + BNT/examples/static/Brutti/Belief_IOhmm.m, + BNT/examples/static/Brutti/Belief_hmdt.m, + BNT/examples/static/Brutti/Belief_hme.m, + BNT/examples/static/Brutti/Sigmoid_Belief.m, + BNT/examples/static/HME/HMEforMatlab.jpg, + BNT/examples/static/HME/README, BNT/examples/static/HME/fhme.m, + BNT/examples/static/HME/gen_data.m, + BNT/examples/static/HME/hme_class_plot.m, + BNT/examples/static/HME/hme_reg_plot.m, + BNT/examples/static/HME/hme_topobuilder.m, + BNT/examples/static/HME/test_data_class.mat, + BNT/examples/static/HME/test_data_class2.mat, + BNT/examples/static/HME/test_data_reg.mat, + BNT/examples/static/HME/train_data_class.mat, + BNT/examples/static/HME/train_data_reg.mat, + BNT/examples/static/Misc/mixexp_data.txt, + BNT/examples/static/Misc/mixexp_graddesc.m, + BNT/examples/static/Misc/mixexp_plot.m, + BNT/examples/static/Misc/sprinkler.bif, + BNT/examples/static/Models/mk_cancer_bnet.m, + BNT/examples/static/Models/mk_car_bnet.m, + BNT/examples/static/Models/mk_ideker_bnet.m, + BNT/examples/static/Models/mk_incinerator_bnet.m, + BNT/examples/static/Models/mk_markov_chain_bnet.m, + BNT/examples/static/Models/mk_minimal_qmr_bnet.m, + BNT/examples/static/Models/mk_qmr_bnet.m, + BNT/examples/static/Models/mk_vstruct_bnet.m, + BNT/examples/static/Models/Old/mk_hmm_bnet.m, + BNT/examples/static/SCG/scg1.m, BNT/examples/static/SCG/scg2.m, + BNT/examples/static/SCG/scg3.m, + BNT/examples/static/SCG/scg_3node.m, + BNT/examples/static/SCG/scg_unstable.m, + BNT/examples/static/StructLearn/bic1.m, + BNT/examples/static/StructLearn/cooper_yoo.m, + BNT/examples/static/StructLearn/k2demo1.m, + BNT/examples/static/StructLearn/mcmc1.m, + BNT/examples/static/StructLearn/pc1.m, + BNT/examples/static/StructLearn/pc2.m, + BNT/examples/static/Zoubin/README, + BNT/examples/static/Zoubin/csum.m, + BNT/examples/static/Zoubin/ffa.m, + BNT/examples/static/Zoubin/mfa.m, + BNT/examples/static/Zoubin/mfa_cl.m, + BNT/examples/static/Zoubin/mfademo.m, + BNT/examples/static/Zoubin/rdiv.m, + BNT/examples/static/Zoubin/rprod.m, + BNT/examples/static/Zoubin/rsum.m, + BNT/examples/static/dtree/test_housing.m, + BNT/examples/static/dtree/test_restaurants.m, + BNT/examples/static/dtree/test_zoo1.m, + BNT/examples/static/dtree/tmp.dot, + BNT/examples/static/dtree/transform_data_into_bnt_format.m, + BNT/examples/static/fgraph/fg2.m, + BNT/examples/static/fgraph/fg3.m, + BNT/examples/static/fgraph/fg_mrf1.m, + BNT/examples/static/fgraph/fg_mrf2.m, + BNT/general/bnet_to_fgraph.m, + BNT/general/compute_fwd_interface.m, + BNT/general/compute_interface_nodes.m, + BNT/general/compute_minimal_interface.m, + BNT/general/dbn_to_bnet.m, + BNT/general/determine_elim_constraints.m, + BNT/general/do_intervention.m, BNT/general/dsep.m, + BNT/general/enumerate_scenarios.m, BNT/general/fgraph_to_bnet.m, + BNT/general/log_lik_complete.m, + BNT/general/log_marg_lik_complete.m, BNT/general/mk_bnet.m, + BNT/general/mk_fgraph.m, BNT/general/mk_limid.m, + BNT/general/mk_mutilated_samples.m, + BNT/general/mk_slice_and_half_dbn.m, + BNT/general/partition_dbn_nodes.m, + BNT/general/sample_bnet_nocell.m, BNT/general/sample_dbn.m, + BNT/general/score_bnet_complete.m, + BNT/general/unroll_dbn_topology.m, + BNT/general/Old/bnet_to_gdl_graph.m, + BNT/general/Old/calc_mpe_bucket.m, + BNT/general/Old/calc_mpe_dbn.m, + BNT/general/Old/calc_mpe_given_inf_engine.m, + BNT/general/Old/calc_mpe_global.m, + BNT/general/Old/compute_interface_nodes.m, + BNT/general/Old/mk_gdl_graph.m, GraphViz/draw_dbn.m, + GraphViz/make_layout.m, BNT/license.gpl.txt, + BNT/general/add_evidence_to_gmarginal.m, + BNT/inference/@inf_engine/bnet_from_engine.m, + BNT/inference/@inf_engine/get_field.m, + BNT/inference/@inf_engine/inf_engine.m, + BNT/inference/@inf_engine/marginal_family.m, + BNT/inference/@inf_engine/set_fields.m, + BNT/inference/@inf_engine/update_engine.m, + BNT/inference/@inf_engine/Old/marginal_family_pot.m, + BNT/inference/@inf_engine/Old/observed_nodes.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/bk_ff_hmm_inf_engine.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_init_bel.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_marginal_from_bel.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_predict_bel.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_update_bel.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/dbn_update_bel1.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/marginal_family.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/bk_ff_fb.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/combine_marginals_into_joint.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/dbn_to_hmm.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_mat.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_vec.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/mk_hmm_obs_lik_vec1.m, + BNT/inference/dynamic/@bk_ff_hmm_inf_engine/private/project_joint_onto_marginals.m, + BNT/inference/dynamic/@bk_inf_engine/bk_inf_engine.m, + BNT/inference/dynamic/@bk_inf_engine/dbn_init_bel.m, + BNT/inference/dynamic/@bk_inf_engine/dbn_marginal_from_bel.m, + BNT/inference/dynamic/@bk_inf_engine/dbn_update_bel.m, + BNT/inference/dynamic/@bk_inf_engine/dbn_update_bel1.m, + BNT/inference/dynamic/@bk_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@bk_inf_engine/marginal_family.m, + BNT/inference/dynamic/@bk_inf_engine/marginal_nodes.m, + BNT/inference/dynamic/@bk_inf_engine/update_engine.m, + BNT/inference/dynamic/@ff_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@ff_inf_engine/enter_soft_evidence.m, + BNT/inference/dynamic/@ff_inf_engine/ff_inf_engine.m, + BNT/inference/dynamic/@ff_inf_engine/filter_evidence.m, + BNT/inference/dynamic/@ff_inf_engine/marginal_family.m, + BNT/inference/dynamic/@ff_inf_engine/marginal_nodes.m, + 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BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence1.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence2.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence3.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/enter_soft_evidence4.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Broken/marginal_nodes.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/enter_soft_evidence_nonint.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/enter_soft_evidence_trans.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine1.m, + BNT/inference/dynamic/@jtree_dbn_inf_engine/Old/jtree_dbn_inf_engine2.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/enter_evidence.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/jtree_unrolled_dbn_inf_engine.m, + BNT/inference/dynamic/@jtree_unrolled_dbn_inf_engine/marginal_family.m, + 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BNT/inference/static/@cond_gauss_inf_engine/marginal_nodes.m, + BNT/inference/static/@enumerative_inf_engine/enter_evidence.m, + BNT/inference/static/@enumerative_inf_engine/enumerative_inf_engine.m, + BNT/inference/static/@enumerative_inf_engine/marginal_nodes.m, + BNT/inference/static/@gaussian_inf_engine/enter_evidence.m, + BNT/inference/static/@gaussian_inf_engine/marginal_nodes.m, + BNT/inference/static/@gaussian_inf_engine/private/extract_params_from_gbn.m, + BNT/inference/static/@gibbs_sampling_inf_engine/enter_evidence.m, + BNT/inference/static/@gibbs_sampling_inf_engine/gibbs_sampling_inf_engine.m, + BNT/inference/static/@gibbs_sampling_inf_engine/marginal_nodes.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/CPT.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_children.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_families.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_families_dbn.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior.c, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_posterior_dbn.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/compute_strides.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/get_cpts.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.c, + BNT/inference/static/@gibbs_sampling_inf_engine/private/get_slice_dbn.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/my_sample_discrete.m, + BNT/inference/static/@gibbs_sampling_inf_engine/private/sample_single_discrete.c, + BNT/inference/static/@global_joint_inf_engine/global_joint_inf_engine.m, + BNT/inference/static/@global_joint_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_inf_engine/cliques_from_engine.m, + BNT/inference/static/@jtree_inf_engine/clq_containing_nodes.m, + BNT/inference/static/@jtree_inf_engine/collect_evidence.m, + BNT/inference/static/@jtree_inf_engine/enter_soft_evidence.m, + BNT/inference/static/@jtree_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_inf_engine/marginal_nodes.m, + BNT/inference/static/@jtree_inf_engine/set_fields.m, + BNT/inference/static/@jtree_inf_engine/Old/collect_evidence.m, + BNT/inference/static/@jtree_inf_engine/Old/distribute_evidence.m, + BNT/inference/static/@jtree_inf_engine/Old/enter_evidence.m, + BNT/inference/static/@jtree_inf_engine/Old/enter_soft_evidence.m, + BNT/inference/static/@jtree_limid_inf_engine/enter_evidence.m, + BNT/inference/static/@jtree_limid_inf_engine/jtree_limid_inf_engine.m, + BNT/inference/static/@jtree_limid_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_limid_inf_engine/marginal_nodes.m, + BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_family.m, + BNT/inference/static/@jtree_limid_inf_engine/Old/marginal_nodes_SS.m, + BNT/inference/static/@jtree_sparse_inf_engine/cliques_from_engine.m, + BNT/inference/static/@jtree_sparse_inf_engine/clq_containing_nodes.m, + BNT/inference/static/@jtree_sparse_inf_engine/collect_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/distribute_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/enter_evidence.m, + BNT/inference/static/@jtree_sparse_inf_engine/enter_soft_evidence.m, + BNT/inference/static/@jtree_sparse_inf_engine/init_pot.c, + BNT/inference/static/@jtree_sparse_inf_engine/marginal_family.m, + BNT/inference/static/@jtree_sparse_inf_engine/marginal_nodes.m, + BNT/inference/static/@jtree_sparse_inf_engine/set_fields.m, + BNT/inference/static/@jtree_sparse_inf_engine/old/collect_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/distribute_evidence.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot1.c, + BNT/inference/static/@jtree_sparse_inf_engine/old/init_pot1.m, + BNT/inference/static/@likelihood_weighting_inf_engine/enter_evidence.m, + BNT/inference/static/@likelihood_weighting_inf_engine/likelihood_weighting_inf_engine.m, + BNT/inference/static/@likelihood_weighting_inf_engine/marginal_nodes.m, + BNT/inference/static/@pearl_inf_engine/enter_evidence.m, + BNT/inference/static/@pearl_inf_engine/loopy_converged.m, + BNT/inference/static/@pearl_inf_engine/marginal_nodes.m, + BNT/inference/static/@pearl_inf_engine/private/compute_bel.m, + BNT/inference/static/@pearl_inf_engine/private/prod_lambda_msgs.m, + BNT/inference/static/@pearl_inf_engine/private/tree_protocol.m, + BNT/inference/static/@quickscore_inf_engine/enter_evidence.m, + BNT/inference/static/@quickscore_inf_engine/marginal_nodes.m, + BNT/inference/static/@quickscore_inf_engine/quickscore_inf_engine.m, + BNT/inference/static/@quickscore_inf_engine/private/C_quickscore.c, + BNT/inference/static/@quickscore_inf_engine/private/nr.h, + BNT/inference/static/@quickscore_inf_engine/private/nrutil.c, + BNT/inference/static/@quickscore_inf_engine/private/nrutil.h, + BNT/inference/static/@quickscore_inf_engine/private/quickscore.m, + BNT/learning/bayes_update_params.m, + BNT/learning/bic_score_family.m, + BNT/learning/compute_cooling_schedule.m, + BNT/learning/dirichlet_score_family.m, + BNT/learning/kpm_learn_struct_mcmc.m, + BNT/learning/learn_params_em.m, + BNT/learning/learn_struct_dbn_reveal.m, + BNT/learning/learn_struct_pdag_ic_star.m, + BNT/learning/mcmc_sample_to_hist.m, BNT/learning/mk_schedule.m, + BNT/learning/mk_tetrad_data_file.m, + BNT/learning/score_dags_old.m, HMM/dhmm_logprob_brute_force.m, + HMM/dhmm_logprob_path.m, HMM/mdp_sample.m, Kalman/AR_to_SS.m, + Kalman/SS_to_AR.m, Kalman/convert_to_lagged_form.m, + Kalman/ensure_AR.m, Kalman/eval_AR_perf.m, + Kalman/kalman_filter.m, Kalman/kalman_smoother.m, + Kalman/kalman_update.m, Kalman/learn_AR.m, + Kalman/learn_AR_diagonal.m, Kalman/learn_kalman.m, + Kalman/smooth_update.m, + BNT/general/convert_dbn_CPDs_to_tables_slow.m, + BNT/general/dispcpt.m, BNT/general/linear_gaussian_to_cpot.m, + BNT/general/partition_matrix_vec_3.m, + BNT/general/shrink_obs_dims_in_gaussian.m, + BNT/general/shrink_obs_dims_in_table.m, + BNT/potentials/CPD_to_pot.m, BNT/potentials/README, + BNT/potentials/check_for_cd_arcs.m, + BNT/potentials/determine_pot_type.m, + BNT/potentials/mk_initial_pot.m, + BNT/potentials/@cgpot/cg_can_to_mom.m, + BNT/potentials/@cgpot/cg_mom_to_can.m, + BNT/potentials/@cgpot/cgpot.m, BNT/potentials/@cgpot/display.m, + BNT/potentials/@cgpot/divide_by_pot.m, + BNT/potentials/@cgpot/domain_pot.m, + BNT/potentials/@cgpot/enter_cts_evidence_pot.m, + BNT/potentials/@cgpot/enter_discrete_evidence_pot.m, + BNT/potentials/@cgpot/marginalize_pot.m, + BNT/potentials/@cgpot/multiply_by_pot.m, + BNT/potentials/@cgpot/multiply_pots.m, + BNT/potentials/@cgpot/normalize_pot.m, + BNT/potentials/@cgpot/pot_to_marginal.m, + BNT/potentials/@cgpot/Old/normalize_pot.m, + BNT/potentials/@cgpot/Old/simple_marginalize_pot.m, + BNT/potentials/@cpot/cpot.m, BNT/potentials/@cpot/cpot_to_mpot.m, + BNT/potentials/@cpot/display.m, + BNT/potentials/@cpot/divide_by_pot.m, + BNT/potentials/@cpot/domain_pot.m, + BNT/potentials/@cpot/enter_cts_evidence_pot.m, + BNT/potentials/@cpot/marginalize_pot.m, + BNT/potentials/@cpot/multiply_by_pot.m, + BNT/potentials/@cpot/multiply_pots.m, + BNT/potentials/@cpot/normalize_pot.m, + BNT/potentials/@cpot/pot_to_marginal.m, + BNT/potentials/@cpot/rescale_pot.m, + BNT/potentials/@cpot/set_domain_pot.m, + BNT/potentials/@cpot/Old/cpot_to_mpot.m, + BNT/potentials/@cpot/Old/normalize_pot.convert.m, + BNT/potentials/@dpot/approxeq_pot.m, + BNT/potentials/@dpot/display.m, + BNT/potentials/@dpot/domain_pot.m, + BNT/potentials/@dpot/dpot_to_table.m, + BNT/potentials/@dpot/get_fields.m, + BNT/potentials/@dpot/multiply_pots.m, + BNT/potentials/@dpot/pot_to_marginal.m, + BNT/potentials/@dpot/set_domain_pot.m, + BNT/potentials/@mpot/display.m, + BNT/potentials/@mpot/marginalize_pot.m, + BNT/potentials/@mpot/mpot.m, BNT/potentials/@mpot/mpot_to_cpot.m, + BNT/potentials/@mpot/normalize_pot.m, + BNT/potentials/@mpot/pot_to_marginal.m, + BNT/potentials/@mpot/rescale_pot.m, + BNT/potentials/@upot/approxeq_pot.m, + BNT/potentials/@upot/display.m, + BNT/potentials/@upot/divide_by_pot.m, + BNT/potentials/@upot/marginalize_pot.m, + BNT/potentials/@upot/multiply_by_pot.m, + BNT/potentials/@upot/normalize_pot.m, + BNT/potentials/@upot/pot_to_marginal.m, + BNT/potentials/@upot/upot.m, + BNT/potentials/@upot/upot_to_opt_policy.m, + BNT/potentials/Old/comp_eff_node_sizes.m, + BNT/potentials/Tables/divide_by_sparse_table.c, + BNT/potentials/Tables/divide_by_table.c, + BNT/potentials/Tables/marg_sparse_table.c, + BNT/potentials/Tables/marg_table.c, + BNT/potentials/Tables/mult_by_sparse_table.c, + BNT/potentials/Tables/rep_mult.c, HMM/mk_leftright_transmat.m: + Initial revision + +2002-05-29 04:59 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/: + clq_containing_nodes.m, problems.txt, push_pot_toclique.m, + Old/initialize_engine.m: Initial import of code base from Kevin + Murphy. + +2002-05-29 04:59 yozhik + + * BNT/inference/static/@stab_cond_gauss_inf_engine/: + clq_containing_nodes.m, problems.txt, push_pot_toclique.m, + Old/initialize_engine.m: Initial revision + +2002-05-19 15:11 yozhik + + * BNT/potentials/: @scgcpot/marginalize_pot.m, + @scgcpot/normalize_pot.m, @scgcpot/rescale_pot.m, + @scgcpot/scgcpot.m, @scgpot/direct_combine_pots.m, + @scgpot/pot_to_marginal.m: Initial import of code base from Kevin + Murphy. + +2002-05-19 15:11 yozhik + + * BNT/potentials/: @scgcpot/marginalize_pot.m, + @scgcpot/normalize_pot.m, @scgcpot/rescale_pot.m, + @scgcpot/scgcpot.m, @scgpot/direct_combine_pots.m, + @scgpot/pot_to_marginal.m: Initial revision + +2001-07-28 08:43 yozhik + + * BNT/potentials/genops.c: Initial import of code base from Kevin + Murphy. + +2001-07-28 08:43 yozhik + + * BNT/potentials/genops.c: Initial revision +
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/chmm5_nobold.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,187 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +6 225 1125 975 9975 +6 225 1125 975 9975 +2 2 0 4 0 0 100 0 -1 0.000 0 0 7 0 0 5 + 300 1200 900 1200 900 1800 300 1800 300 1200 +2 2 0 4 0 0 100 0 -1 0.000 0 0 7 0 0 5 + 300 3300 900 3300 900 3900 300 3900 300 3300 +2 2 0 4 0 0 100 0 -1 0.000 0 0 7 0 0 5 + 300 5400 900 5400 900 6000 300 6000 300 5400 +2 2 0 4 0 0 100 0 -1 0.000 0 0 7 0 0 5 + 300 7500 900 7500 900 8100 300 8100 300 7500 +2 2 0 4 0 0 100 0 -1 0.000 0 0 7 0 0 5 + 300 9300 900 9300 900 9900 300 9900 300 9300 +-6 +-6 +6 2925 1125 3675 9975 +2 2 0 4 0 0 100 0 -1 0.000 0 0 7 0 0 5 + 3000 1200 3600 1200 3600 1800 3000 1800 3000 1200 +2 2 0 4 0 0 100 0 -1 0.000 0 0 7 0 0 5 + 3000 3300 3600 3300 3600 3900 3000 3900 3000 3300 +2 2 0 4 0 0 100 0 -1 0.000 0 0 7 0 0 5 + 3000 5400 3600 5400 3600 6000 3000 6000 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4200 7500 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 4200 5100 300 300 4200 5100 4200 5400 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 4200 3000 300 300 4200 3000 4200 3300 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 4200 900 300 300 4200 900 4200 1200 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 1500 900 300 300 1500 900 1500 1200 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 1500 3000 300 300 1500 3000 1500 3300 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 1500 5100 300 300 1500 5100 1500 5400 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 1500 7200 300 300 1500 7200 1500 7500 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 1500 9000 300 300 1500 9000 1500 9300 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 6900 5100 300 300 6900 5100 6900 5400 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 6900 3000 300 300 6900 3000 6900 3300 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 6900 900 300 300 6900 900 6900 1200 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 7500 1200 7200 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 5400 1200 5100 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 3300 1200 3000 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 1200 1200 1050 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 1200 3900 1050 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 1500 3000 1500 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 3600 3000 1500 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 1500 3000 3600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 3600 3000 3600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 3600 3000 5700 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 5700 3000 3600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 3300 3900 3000 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 3600 5700 1500 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 3600 5700 5700 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 5700 3000 5700 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 7800 3000 5700 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 5700 3000 7800 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 7800 3000 7800 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 9600 3000 7800 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 9300 1200 9000 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 7800 3000 9600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 900 9600 3000 9600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 9600 5700 9600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 9300 3900 9000 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 9600 5700 7800 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 7800 5700 9600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 7800 5700 7800 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 7800 5700 5700 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 7500 3900 7200 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 5700 5700 7800 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 5700 5700 5700 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 5700 5700 3600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 3600 5700 3600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 1500 5700 3600 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 1500 5700 1500 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 6300 1200 6600 1050 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 6300 3300 6600 3000 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 6300 5400 6600 5100 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 6300 9300 6600 9000 +2 1 0 3 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 6300 7500 6600 7200 +2 1 0 3 0 0 100 0 5 0.000 0 0 7 1 0 2 + 0 0 1.50 90.00 180.00 + 3600 5400 3900 5100
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa.eps Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,356 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: (ImageMagick) +%%Title: (fa.eps) +%%CreationDate: (Tue Nov 16 19:51:50 2004) +%%BoundingBox: 0 0 131 161 +%%DocumentData: Clean7Bit +%%LanguageLevel: 1 +%%Pages: 1 +%%EndComments + +%%BeginDefaults +%%EndDefaults + +%%BeginProlog +% +% Display a color image. The image is displayed in color on +% Postscript viewers or printers that support color, otherwise +% it is displayed as grayscale. +% +/DirectClassPacket +{ + % + % Get a DirectClass packet. + % + % Parameters: + % red. + % green. + % blue. + % length: number of pixels minus one of this color (optional). + % + currentfile color_packet readhexstring pop pop + compression 0 eq + { + /number_pixels 3 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add 3 mul def + } ifelse + 0 3 number_pixels 1 sub + { + pixels exch color_packet putinterval + } for + pixels 0 number_pixels getinterval +} bind def + +/DirectClassImage +{ + % + % Display a DirectClass image. + % + systemdict /colorimage known + { + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { DirectClassPacket } false 3 colorimage + } + { + % + % No colorimage operator; convert to grayscale. + % + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { GrayDirectClassPacket } image + } ifelse +} bind def + +/GrayDirectClassPacket +{ + % + % Get a DirectClass packet; convert to grayscale. + % + % Parameters: + % red + % green + % blue + % length: number of pixels minus one of this color (optional). + % + currentfile color_packet readhexstring pop pop + color_packet 0 get 0.299 mul + color_packet 1 get 0.587 mul add + color_packet 2 get 0.114 mul add + cvi + /gray_packet exch def + compression 0 eq + { + /number_pixels 1 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add def + } ifelse + 0 1 number_pixels 1 sub + { + pixels exch gray_packet put + } for + pixels 0 number_pixels getinterval +} bind def + +/GrayPseudoClassPacket +{ + % + % Get a PseudoClass packet; convert to grayscale. + % + % Parameters: + % index: index into the colormap. + % length: number of pixels minus one of this color (optional). + % + currentfile byte readhexstring pop 0 get + /offset exch 3 mul def + /color_packet colormap offset 3 getinterval def + color_packet 0 get 0.299 mul + color_packet 1 get 0.587 mul add + color_packet 2 get 0.114 mul add + cvi + /gray_packet exch def + compression 0 eq + { + /number_pixels 1 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add def + } ifelse + 0 1 number_pixels 1 sub + { + pixels exch gray_packet put + } for + pixels 0 number_pixels getinterval +} bind def + +/PseudoClassPacket +{ + % + % Get a PseudoClass packet. + % + % Parameters: + % index: index into the colormap. + % length: number of pixels minus one of this color (optional). + % + currentfile byte readhexstring pop 0 get + /offset exch 3 mul def + /color_packet colormap offset 3 getinterval def + compression 0 eq + { + /number_pixels 3 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add 3 mul def + } ifelse + 0 3 number_pixels 1 sub + { + pixels exch color_packet putinterval + } for + pixels 0 number_pixels getinterval +} bind def + +/PseudoClassImage +{ + % + % Display a PseudoClass image. + % + % Parameters: + % class: 0-PseudoClass or 1-Grayscale. + % + currentfile buffer readline pop + token pop /class exch def pop + class 0 gt + { + currentfile buffer readline pop + token pop /depth exch def pop + /grays columns 8 add depth sub depth mul 8 idiv string def + columns rows depth + [ + columns 0 0 + rows neg 0 rows + ] + { currentfile grays readhexstring pop } image + } + { + % + % Parameters: + % colors: number of colors in the colormap. + % colormap: red, green, blue color packets. + % + currentfile buffer readline pop + token pop /colors exch def pop + /colors colors 3 mul def + /colormap colors string def + currentfile colormap readhexstring pop pop + systemdict /colorimage known + { + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { PseudoClassPacket } false 3 colorimage + } + { + % + % No colorimage operator; convert to grayscale. + % + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { GrayPseudoClassPacket } image + } ifelse + } ifelse +} bind def + +/DisplayImage +{ + % + % Display a DirectClass or PseudoClass image. + % + % Parameters: + % x & y translation. + % x & y scale. + % label pointsize. + % image label. + % image columns & rows. + % class: 0-DirectClass or 1-PseudoClass. + % compression: 0-none or 1-RunlengthEncoded. + % hex color packets. + % + gsave + /buffer 512 string def + /byte 1 string def + /color_packet 3 string def + /pixels 768 string def + + currentfile buffer readline pop + token pop /x exch def + token pop /y exch def pop + x y translate + currentfile buffer readline pop + token pop /x exch def + token pop /y exch def pop + currentfile buffer readline pop + token pop /pointsize exch def pop + /Times-Roman findfont pointsize scalefont setfont + x y scale + currentfile buffer readline pop + token pop /columns exch def + token pop /rows exch def pop + currentfile buffer readline pop + token pop /class exch def pop + currentfile buffer readline pop + token pop /compression exch def pop + class 0 gt 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 900 1950 300 225 900 1950 1200 2175 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 900 825 300 225 900 825 1200 1050 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 1125 900 1650 +4 0 -1 0 0 0 12 0.0000 4 135 135 825 900 X\001 +4 0 -1 0 0 0 12 0.0000 4 135 135 825 2025 Y\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa_caption.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 900 1950 300 225 900 1950 1200 2175 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 900 825 300 225 900 825 1200 1050 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 1125 900 1650 +4 0 -1 0 0 0 12 0.0000 4 135 135 750 2025 Y\001 +4 0 -1 0 0 0 12 0.0000 4 135 120 750 900 X\001 +4 0 -1 0 0 0 12 0.0000 4 180 1620 300 2625 Factor Analysis/PCA\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa_discrete.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +#FIG 3.2 +Portrait +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 975 600 300 225 975 600 1275 825 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 2100 600 300 225 2100 600 2400 825 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 825 525 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 825 1425 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2025 825 600 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2025 825 1425 1425 +2 2 0 1 -1 0 0 0 2 0.000 0 0 7 0 0 5 + 300 1425 750 1425 750 1875 300 1875 300 1425 +2 2 0 1 -1 0 0 0 2 0.000 0 0 7 0 0 5 + 1275 1425 1725 1425 1725 1875 1275 1875 1275 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 825 3000 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2025 825 3000 1425 +2 2 0 1 0 0 100 0 2 3.000 0 0 7 0 0 5 + 2775 1425 3225 1425 3225 1875 2775 1875 2775 1425 +4 0 -1 0 0 0 24 0.0000 4 30 270 1350 750 ...\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 1350 1725 R2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 825 675 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1950 675 Xn\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 375 1725 R1\001 +4 0 -1 0 0 0 12 0.0000 4 180 1920 600 2325 Discrete Factor Analysis\001 +4 0 -1 0 0 0 24 0.0000 4 30 270 2100 1650 ...\001 +4 0 0 100 0 -1 12 0.0000 4 135 210 2850 1725 Rn\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa_discrete_single.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +#FIG 3.2 +Portrait +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +1 1 0 1 0 7 100 0 -1 0.000 1 0.0000 825 375 300 225 825 375 1125 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 600 825 1275 +2 2 0 1 0 0 100 0 2 0.000 0 0 7 0 0 5 + 600 1275 975 1275 975 1725 600 1725 600 1275 +4 0 -1 0 0 0 12 0.0000 4 135 120 750 1575 R\001 +4 0 -1 0 0 0 12 0.0000 4 135 135 750 450 X\001 +4 0 0 100 0 0 12 0.0000 4 180 1785 300 2025 discrete factor analysis\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa_regular.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 225 300 3450 3150 +1 3 0 1 -1 7 0 0 -1 0.000 1 0.0000 999 1082 335 335 999 1082 1149 1382 +1 3 0 1 -1 7 0 0 -1 0.000 1 0.0000 2124 1082 335 335 2124 1082 2274 1382 +1 3 0 1 -1 0 0 0 2 0.000 1 0.0000 624 2807 335 335 624 2807 774 3107 +1 3 0 1 -1 0 0 0 2 0.000 1 0.0000 1674 2807 335 335 1674 2807 1824 3107 +1 3 0 1 -1 0 0 0 2 0.000 1 0.0000 2874 2807 335 335 2874 2807 3024 3107 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 1425 675 2475 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 975 1425 1575 2400 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 975 1425 2775 2475 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 1425 750 2475 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 1425 1650 2325 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 1425 2775 2400 +4 0 -1 0 0 0 24 0.0000 4 330 3225 225 600 regular factor analysis\001 +-6 +4 0 -1 0 0 0 12 0.0000 4 135 210 825 1200 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 1950 1200 X2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 525 2925 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1500 2850 Y2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 2700 2850 Y3\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa_scalar.eps Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,414 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: (ImageMagick) +%%Title: (fa_scalar.eps) +%%CreationDate: (Tue Nov 16 19:52:00 2004) +%%BoundingBox: 0 0 246 156 +%%DocumentData: Clean7Bit +%%LanguageLevel: 1 +%%Pages: 1 +%%EndComments + +%%BeginDefaults +%%EndDefaults + +%%BeginProlog +% +% Display a color image. 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa_scalar.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +6 300 375 3675 1950 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 975 600 300 225 975 600 1275 825 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 2475 600 300 225 2475 600 2775 825 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 600 1650 300 225 600 1650 900 1875 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 3315 1654 300 225 3315 1654 3615 1879 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1575 1650 300 225 1575 1650 1875 1875 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 825 600 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1200 750 3075 1500 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2400 825 750 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2625 825 3225 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 825 1425 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2400 825 1650 1350 +4 0 -1 0 0 0 12 0.0000 4 135 225 900 675 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 2325 675 Xn\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 450 1725 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 270 3150 1725 Ym\001 +4 0 -1 0 0 0 24 0.0000 4 30 270 1575 750 ...\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1425 1725 Y2\001 +4 0 -1 0 0 0 24 0.0000 4 30 270 2175 1650 ...\001 +-6
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/fa_scalar_caption.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 300 375 3675 1950 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 975 600 300 225 975 600 1275 825 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 2475 600 300 225 2475 600 2775 825 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 600 1650 300 225 600 1650 900 1875 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 3315 1654 300 225 3315 1654 3615 1879 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1575 1650 300 225 1575 1650 1875 1875 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 825 600 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1200 750 3075 1500 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2400 825 750 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2625 825 3225 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 825 1425 1425 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2400 825 1650 1350 +4 0 -1 0 0 0 12 0.0000 4 135 210 900 675 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 2325 675 Xn\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 450 1725 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 270 3150 1725 Ym\001 +4 0 -1 0 0 0 24 0.0000 4 30 270 1575 750 ...\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1425 1725 Y2\001 +4 0 -1 0 0 0 24 0.0000 4 30 270 2175 1650 ...\001 +-6 +4 0 -1 0 0 0 12 0.0000 4 180 1170 1200 2325 Factor analysis\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/factorial_hmm3.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 1 0 0 100 0 -1 0.000 0 1 1 0 2587.500 3637.500 1500 2775 1200 3675 1500 4500 + 0 0 1.00 60.00 120.00 +5 1 0 1 0 0 100 0 -1 0.000 0 1 1 0 2772.051 3261.376 1500 1875 900 3450 1425 4575 + 0 0 1.00 60.00 120.00 +5 1 0 1 0 0 100 0 -1 0.000 0 1 1 0 5962.500 3637.500 4875 2775 4575 3675 4875 4500 + 0 0 1.00 60.00 120.00 +5 1 0 1 0 0 100 0 -1 0.000 0 1 1 0 4162.500 3637.500 3075 2775 2775 3675 3075 4500 + 0 0 1.00 60.00 120.00 +5 1 0 1 0 0 100 0 -1 0.000 0 1 1 0 4347.051 3261.376 3075 1875 2475 3450 3000 4575 + 0 0 1.00 60.00 120.00 +5 1 0 1 0 0 100 0 -1 0.000 0 1 1 0 6147.051 3261.376 4875 1875 4275 3450 4800 4575 + 0 0 1.00 60.00 120.00 +6 1425 4350 2100 4875 +1 1 0 1 0 0 100 0 5 0.000 1 0.0000 1749 4596 300 225 1749 4596 2049 4821 +4 0 0 100 0 0 20 0.0000 4 195 345 1575 4725 Y1\001 +-6 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1770 3645 300 225 1770 3645 2070 3870 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 3420 3645 300 225 3420 3645 3720 3870 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 5220 3645 300 225 5220 3645 5520 3870 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1770 2745 300 225 1770 2745 2070 2970 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 3420 2745 300 225 3420 2745 3720 2970 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 5220 2745 300 225 5220 2745 5520 2970 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 5220 1845 300 225 5220 1845 5520 2070 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 3420 1845 300 225 3420 1845 3720 2070 +1 1 0 1 0 7 100 0 -1 0.000 1 0.0000 1770 1845 300 225 1770 1845 2070 2070 +1 1 0 1 0 0 100 0 5 0.000 1 0.0000 3399 4596 300 225 3399 4596 3699 4821 +1 1 0 1 0 0 100 0 5 0.000 1 0.0000 5199 4521 300 225 5199 4521 5499 4746 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 0 0 1 + 4200 4200 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2100 3600 3150 3600 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3750 3600 4950 3600 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 0 0 1 + 4200 3300 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2100 2700 3150 2700 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3750 2700 4950 2700 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 0 0 1 + 4200 2400 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2100 1800 3150 1800 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3750 1800 4950 1800 +2 1 0 1 0 0 100 0 5 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1725 3900 1725 4350 +2 1 0 1 0 0 100 0 5 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 5175 3825 5175 4275 +2 1 0 1 0 0 100 0 5 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3450 3900 3450 4350 +4 0 0 100 0 0 20 0.0000 4 195 345 1575 1950 A1\001 +4 0 0 100 0 0 20 0.0000 4 195 330 1575 2850 B1\001 +4 0 0 100 0 0 20 0.0000 4 195 330 1575 3750 C1\001 +4 0 0 100 0 0 20 0.0000 4 195 345 3225 1950 A2\001 +4 0 0 100 0 0 20 0.0000 4 195 330 3225 2850 B2\001 +4 0 0 100 0 0 20 0.0000 4 195 330 3225 3750 C2\001 +4 0 0 100 0 0 20 0.0000 4 195 345 5025 1950 A3\001 +4 0 0 100 0 0 20 0.0000 4 195 330 5025 2850 B3\001 +4 0 0 100 0 0 20 0.0000 4 195 330 5025 3750 C3\001 +4 0 0 100 0 0 30 0.0000 4 30 525 6375 3000 . . .\001 +4 0 0 100 0 0 20 0.0000 4 195 345 3225 4725 Y2\001 +4 0 0 100 0 0 20 0.0000 4 195 345 5025 4650 Y3\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/filter.eps Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9861 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: (ImageMagick) +%%Title: (filter.eps) +%%CreationDate: (Tue Nov 16 20:04:33 2004) +%%BoundingBox: 0 0 657 525 +%%DocumentData: Clean7Bit +%%LanguageLevel: 1 +%%Pages: 1 +%%EndComments + +%%BeginDefaults +%%EndDefaults + +%%BeginProlog +% +% Display a color image. The image is displayed in color on +% Postscript viewers or printers that support color, otherwise +% it is displayed as grayscale. +% +/DirectClassPacket +{ + % + % Get a DirectClass packet. + % + % Parameters: + % red. + % green. + % blue. + % length: number of pixels minus one of this color (optional). + % + currentfile color_packet readhexstring pop pop + compression 0 eq + { + /number_pixels 3 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add 3 mul def + } ifelse + 0 3 number_pixels 1 sub + { + pixels exch color_packet putinterval + } for + pixels 0 number_pixels getinterval +} bind def + +/DirectClassImage +{ + % + % Display a DirectClass image. + % + systemdict /colorimage known + { + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { DirectClassPacket } false 3 colorimage + } + { + % + % No colorimage operator; convert to grayscale. + % + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { GrayDirectClassPacket } image + } ifelse +} bind def + +/GrayDirectClassPacket +{ + % + % Get a DirectClass packet; convert to grayscale. + % + % Parameters: + % red + % green + % blue + % length: number of pixels minus one of this color (optional). + % + currentfile color_packet readhexstring pop pop + color_packet 0 get 0.299 mul + color_packet 1 get 0.587 mul add + color_packet 2 get 0.114 mul add + cvi + /gray_packet exch def + compression 0 eq + { + /number_pixels 1 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add def + } ifelse + 0 1 number_pixels 1 sub + { + pixels exch gray_packet put + } for + pixels 0 number_pixels getinterval +} bind def + +/GrayPseudoClassPacket +{ + % + % Get a PseudoClass packet; convert to grayscale. + % + % Parameters: + % index: index into the colormap. + % length: number of pixels minus one of this color (optional). + % + currentfile byte readhexstring pop 0 get + /offset exch 3 mul def + /color_packet colormap offset 3 getinterval def + color_packet 0 get 0.299 mul + color_packet 1 get 0.587 mul add + color_packet 2 get 0.114 mul add + cvi + /gray_packet exch def + compression 0 eq + { + /number_pixels 1 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add def + } ifelse + 0 1 number_pixels 1 sub + { + pixels exch gray_packet put + } for + pixels 0 number_pixels getinterval +} bind def + +/PseudoClassPacket +{ + % + % Get a PseudoClass packet. + % + % Parameters: + % index: index into the colormap. + % length: number of pixels minus one of this color (optional). + % + currentfile byte readhexstring pop 0 get + /offset exch 3 mul def + /color_packet colormap offset 3 getinterval def + compression 0 eq + { + /number_pixels 3 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add 3 mul def + } ifelse + 0 3 number_pixels 1 sub + { + pixels exch color_packet putinterval + } for + pixels 0 number_pixels getinterval +} bind def + +/PseudoClassImage +{ + % + % Display a PseudoClass image. + % + % Parameters: + % class: 0-PseudoClass or 1-Grayscale. + % + currentfile buffer readline pop + token pop /class exch def pop + class 0 gt + { + currentfile buffer readline pop + token pop /depth exch def pop + /grays columns 8 add depth sub depth mul 8 idiv string def + columns rows depth + [ + columns 0 0 + rows neg 0 rows + ] + { currentfile grays readhexstring pop } image + } + { + % + % Parameters: + % colors: number of colors in the colormap. + % colormap: red, green, blue color packets. + % + currentfile buffer readline pop + token pop /colors exch def pop + /colors colors 3 mul def + /colormap colors string def + currentfile colormap readhexstring pop pop + systemdict /colorimage known + { + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { PseudoClassPacket } false 3 colorimage + } + { + % + % No colorimage operator; convert to grayscale. + % + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { GrayPseudoClassPacket } image + } ifelse + } ifelse +} bind def + +/DisplayImage +{ + % + % Display a DirectClass or PseudoClass image. + % + % Parameters: + % x & y translation. + % x & y scale. + % label pointsize. + % image label. + % image columns & rows. + % class: 0-DirectClass or 1-PseudoClass. + % compression: 0-none or 1-RunlengthEncoded. + % hex color packets. + % + gsave + /buffer 512 string def + /byte 1 string def + /color_packet 3 string def + /pixels 768 string def + + currentfile buffer readline pop + token pop /x exch def + token pop /y exch def pop + x y translate + currentfile buffer readline pop + token pop /x exch def + token pop /y exch def pop + currentfile buffer readline pop + token pop /pointsize exch def pop + /Times-Roman findfont pointsize scalefont setfont + x y scale + currentfile buffer readline pop + token pop /columns exch def + token pop /rows exch def pop + currentfile buffer readline pop + token pop /class exch def pop + currentfile buffer readline pop + token pop /compression exch def pop + class 0 gt { PseudoClassImage } { DirectClassImage } ifelse + grestore +} bind def +%%EndProlog +%%Page: 1 1 +%%PageBoundingBox: 0 0 657 525 +userdict begin +DisplayImage +0 0 +657 525 +12.000000 +657 525 +1 +1 +1 +8 +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff +ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/filter.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +6 2550 5100 6300 6375 +2 2 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 5 + 2550 5475 4050 5475 4050 5775 2550 5775 2550 5475 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 2 + 4050 5475 6300 5475 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3300 6375 3300 5925 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 1 1 2 + 0 0 1.00 60.00 120.00 + 0 0 1.00 60.00 120.00 + 3450 6225 4050 6225 +4 0 0 100 0 0 18 0.0000 4 165 90 4050 5325 t\001 +4 0 0 100 0 0 18 0.0000 4 165 345 3600 6075 tau\001 +-6 +2 2 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 5 + 2550 2025 4050 2025 4050 2325 2550 2325 2550 2025 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 2 + 4050 2025 6300 2025 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 4050 2925 4050 2475 +2 2 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 5 + 2550 7200 6375 7200 6375 7500 2550 7500 2550 7200 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3975 8025 3975 7575 +2 2 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 5 + 2475 3525 3975 3525 3975 3825 2475 3825 2475 3525 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 2 + 3975 3525 6225 3525 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 1 1 2 + 0 0 1.00 60.00 120.00 + 0 0 1.00 60.00 120.00 + 3975 4275 4575 4275 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 4650 4425 4650 3975 +2 2 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 5 + 2550 600 4050 600 4050 900 2550 900 2550 600 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 0 0 2 + 4050 600 6300 600 +2 1 0 1 0 0 100 0 10 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 4050 1500 4050 1050 +4 0 0 100 0 0 18 0.0000 4 165 90 4050 1875 t\001 +4 0 0 50 0 0 24 0.0000 4 330 3540 6675 2175 argmax P(x(1:t) | y(1:t))\001 +4 0 0 50 0 0 24 0.0000 4 330 810 6975 2550 x(1:t)\001 +4 0 0 100 0 0 18 0.0000 4 165 90 3975 7125 t\001 +4 0 0 100 0 0 18 0.0000 4 195 180 6225 7125 T\001 +4 0 0 100 0 0 18 0.0000 4 165 90 3975 3375 t\001 +4 0 0 100 0 0 18 0.0000 4 195 540 3975 4125 delta\001 +4 0 0 100 0 0 18 0.0000 4 165 90 4050 450 t\001 +4 0 0 50 0 0 24 0.0000 4 330 1950 6675 750 P(X(t)|y(1:t))\001 +4 0 0 50 0 0 24 0.0000 4 330 2865 6450 3600 P(X(t+delta)|y(1:t))\001 +4 0 0 50 0 0 24 0.0000 4 330 2550 6600 5550 P(X(t-tau)|y(1:t))\001 +4 0 0 50 0 0 24 0.0000 4 330 2085 6750 7425 P(X(t)|y(1:T))\001 +4 0 0 50 0 0 24 0.0000 4 330 1140 600 900 filtering\001 +4 0 0 50 0 0 24 0.0000 4 330 1470 450 3750 prediction\001 +4 0 0 50 0 0 24 0.0000 4 255 1005 600 2175 Viterbi\001 +4 0 0 50 0 0 24 0.0000 4 330 1305 600 5775 fixed-lag\001 +4 0 0 50 0 0 24 0.0000 4 330 1575 525 7740 smoothing\001 +4 0 0 50 0 0 24 0.0000 4 330 1170 525 8055 (offline)\001 +4 0 0 50 0 0 24 0.0000 4 330 1575 600 6090 smoothing\001 +4 0 0 50 0 0 24 0.0000 4 255 1920 525 7425 fixed interval\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/filter.tex Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +%Latex +\documentstyle[fleqn,psfig,12pt,pstricks,pst-node,pst-tree]{article} + +%\documentclass[fleqn,12pt]{article} +%\usepackage{pstricks,pst-node,pst-tree} + +%latex2e +%\documentclass{article} +%\usepackage{epsfig,alltt,fancybox} + +\setlength{\textwidth}{6.5in} +\setlength{\oddsidemargin}{0in} +\setlength{\textheight}{8.5in} +\setlength{\headheight}{0in} +\setlength{\headsep}{-0.5in} +\setlength{\parindent}{0in} % block style +\setlength{\parskip}{0.3cm} + +\newcommand{\mytitle}[1]{\newpage \huge \begin{center}#1\vspace{0.8cm}\end{center} \LARGE} + + +\begin{document} + +\mytitle{Hello world} + +Hello world + +\centering +$ +\pstree[treemode=R]{\Tcircle{b}}{% + \pstree{\TC*^{a_1}}{% + \Tr{b_{11}}^{x_1} + \Tr{b_{12}}_{x_2} + } + \pstree{\TC*_{a_2}}{% + \Tr{b_{21}}^{x_1} + \Tr{b_{22}}_{x_2} + } +} +$ + + + +\mytitle{Hello world 2} + +\psline[linewidth=0.5cm](0,0)(2,0) +\psline[linewidth=0.05cm](2,-1)(6,-1) + + + +\end{document}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hme.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 225 150 2625 3825 +6 300 150 1800 3450 +5 1 0 1 -1 -1 0 0 -1 0.000 0 1 1 0 2212.500 2250.000 825 1425 600 2325 825 3075 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 -1 0 0 -1 0.000 0 1 1 0 3083.468 1905.242 750 525 375 2025 675 3150 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 -1 0 0 -1 0.000 0 0 1 0 679.747 1409.810 1200 450 1725 1725 1275 2325 + 0 0 1.00 60.00 120.00 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 975 375 300 225 975 375 1275 600 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1050 3225 300 225 1050 3225 1350 3450 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 825 1050 1200 1050 1200 1500 825 1500 825 1050 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 825 2100 1200 2100 1200 2550 825 2550 825 2100 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 600 1050 1050 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 1575 1050 2100 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 2625 1050 3000 +-6 +4 0 -1 0 0 0 12 0.0000 4 180 2370 225 3750 Hierarchical Mixture of Experts\001 +4 0 -1 0 0 0 12 0.0000 4 135 120 825 450 X\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 900 1350 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 900 2400 Q2\001 +4 0 -1 0 0 0 12 0.0000 4 135 135 900 3300 Y\001 +-6
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm3.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 525 1650 270 270 525 1650 675 1875 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 1575 1650 270 270 1575 1650 1725 1875 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 2625 1650 270 270 2625 1650 2775 1875 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 375 600 750 600 750 1050 375 1050 375 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 525 1050 525 1425 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 825 1350 825 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1425 600 1800 600 1800 1050 1425 1050 1425 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1575 1050 1575 1425 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1800 825 2325 825 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2400 600 2775 600 2775 1050 2400 1050 2400 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2550 1050 2550 1425 +4 0 0 100 0 2 12 0.0000 4 135 90 525 975 1\001 +4 0 0 100 0 2 12 0.0000 4 135 90 1575 975 3\001 +4 0 0 100 0 2 12 0.0000 4 135 90 2475 900 5\001 +4 0 0 100 0 2 12 0.0000 4 135 90 450 1725 2\001 +4 0 0 100 0 2 12 0.0000 4 135 90 1425 1725 4\001 +4 0 0 100 0 2 12 0.0000 4 135 90 2550 1725 6\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm3letter.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 525 1650 270 270 525 1650 675 1875 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 1575 1650 270 270 1575 1650 1725 1875 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 2625 1650 270 270 2625 1650 2775 1875 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 375 600 750 600 750 1050 375 1050 375 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 525 1050 525 1425 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 825 1350 825 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1425 600 1800 600 1800 1050 1425 1050 1425 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1575 1050 1575 1425 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1800 825 2325 825 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2400 600 2775 600 2775 1050 2400 1050 2400 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2550 1050 2550 1425 +4 0 0 100 0 0 12 0.0000 4 135 225 450 1725 Y1\001 +4 0 0 100 0 0 12 0.0000 4 135 225 1425 1725 Y2\001 +4 0 0 100 0 0 12 0.0000 4 135 225 2475 1725 Y3\001 +4 0 0 100 0 0 12 0.0000 4 135 225 450 900 X1\001 +4 0 0 100 0 0 12 0.0000 4 135 225 2475 900 X3\001 +4 0 0 100 0 0 12 0.0000 4 135 225 1500 900 X2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm4.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +1 3 0 2 0 7 50 0 -1 0.000 1 0.0000 600 1200 300 300 600 1200 900 1200 +1 3 0 2 0 7 50 0 -1 0.000 1 0.0000 1800 1200 300 300 1800 1200 2100 1200 +1 3 0 2 0 7 50 0 -1 0.000 1 0.0000 3000 1200 300 300 3000 1200 3300 1200 +1 3 0 2 0 7 50 0 -1 0.000 1 0.0000 4200 1200 300 300 4200 1200 4500 1200 +1 3 0 2 0 0 50 0 7 0.000 1 0.0000 600 2400 300 300 600 2400 900 2400 +1 3 0 2 0 0 50 0 7 0.000 1 0.0000 3000 2400 300 300 3000 2400 3300 2400 +1 3 0 2 0 0 50 0 7 0.000 1 0.0000 4200 2400 300 300 4200 2400 4500 2400 +1 3 0 2 0 0 50 0 7 0.000 1 0.0000 1800 2400 300 300 1800 2400 2100 2400 +2 1 0 2 0 0 50 0 7 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 600 1500 600 2100 +2 1 0 2 0 0 50 0 7 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 900 1200 1500 1200 +2 1 0 2 0 0 50 0 7 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 2100 1200 2700 1200 +2 1 0 2 0 0 50 0 7 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 3375 1200 3900 1200 +2 1 0 2 0 0 50 0 7 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 1800 1500 1800 2100 +2 1 0 2 0 0 50 0 7 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 3000 1500 3000 2100 +2 1 0 2 0 0 50 0 7 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 4200 1500 4200 2100 +4 0 0 100 0 0 30 0.0000 4 60 600 5025 1200 . . .\001 +4 0 0 50 0 0 24 0.0000 4 255 270 450 1275 X\001 +4 0 0 50 0 0 24 0.0000 4 255 270 1650 1275 X\001 +4 0 0 50 0 0 24 0.0000 4 255 270 2850 1275 X\001 +4 0 0 50 0 0 24 0.0000 4 255 240 450 2475 Y\001 +4 0 0 50 0 0 24 0.0000 4 255 240 2850 2475 Y\001 +4 0 0 50 0 0 24 0.0000 4 255 240 4050 2475 Y\001 +4 0 0 50 0 0 24 0.0000 4 255 180 675 1425 1\001 +4 0 0 50 0 0 24 0.0000 4 255 180 1875 1350 2\001 +4 0 0 50 0 0 24 0.0000 4 255 180 3075 1350 3\001 +4 0 0 50 0 0 24 0.0000 4 255 180 4275 1425 4\001 +4 0 0 50 0 0 24 0.0000 4 255 180 600 2550 1\001 +4 0 0 50 0 0 24 0.0000 4 255 180 3000 2550 3\001 +4 0 0 50 0 0 24 0.0000 4 255 180 4200 2550 4\001 +4 0 0 50 0 0 24 0.0000 4 255 270 4050 1275 X\001 +4 0 0 50 0 0 24 0.0000 4 255 180 1800 2625 2\001 +4 0 0 50 0 0 24 0.0000 4 255 240 1650 2475 Y\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm4_params.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 3150 2550 270 270 3150 2550 3300 2775 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 3975 2550 270 270 3975 2550 4125 2775 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 1350 2550 270 270 1350 2550 1500 2775 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 2250 2550 270 270 2250 2550 2400 2775 +1 3 1 1 0 7 100 0 -1 4.000 1 0.0000 2625 3825 270 270 2625 3825 2775 4050 +1 3 1 1 0 7 100 0 -1 4.000 1 0.0000 1425 600 270 270 1425 600 1575 825 +1 3 1 1 0 7 100 0 -1 4.000 1 0.0000 3000 600 270 270 3000 600 3150 825 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2925 1500 3300 1500 3300 1950 2925 1950 2925 1500 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3075 1950 3075 2325 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 3750 1500 4125 1500 4125 1950 3750 1950 3750 1500 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3300 1725 3750 1725 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3975 1950 3975 2250 +2 1 0 1 0 0 100 0 20 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1575 1725 2100 1725 +2 1 0 1 0 0 100 0 20 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2475 1725 2925 1725 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1200 1500 1575 1500 1575 1950 1200 1950 1200 1500 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1350 1950 1350 2325 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2100 1500 2475 1500 2475 1950 2100 1950 2100 1500 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2250 1950 2250 2325 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2475 3600 1500 2775 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2550 3600 2400 2850 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2700 3525 3075 2775 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2850 3675 3825 2775 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1425 900 1425 1500 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2925 900 2400 1500 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3075 900 3075 1425 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3225 750 3900 1500 +4 0 0 100 0 0 12 0.0000 4 135 225 3000 2625 Y3\001 +4 0 0 100 0 0 12 0.0000 4 165 225 3825 1800 Q4\001 +4 0 0 100 0 0 12 0.0000 4 165 225 3000 1800 Q3\001 +4 0 0 100 0 0 12 0.0000 4 135 225 3825 2625 Y4\001 +4 0 0 100 0 0 12 0.0000 4 135 225 1275 2625 Y1\001 +4 0 0 100 0 0 12 0.0000 4 165 225 1275 1800 Q1\001 +4 0 0 100 0 0 12 0.0000 4 135 225 2100 2625 Y2\001 +4 0 0 100 0 0 12 0.0000 4 165 225 2175 1800 Q2\001 +4 0 0 100 0 0 12 0.0000 4 135 195 1275 675 P1\001 +4 0 0 100 0 0 12 0.0000 4 135 195 2850 675 P2\001 +4 0 0 100 0 0 12 0.0000 4 135 195 2550 3900 P3\001 +4 0 0 100 0 0 30 0.0000 4 30 525 4350 1800 . . .\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm4_paramsX.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 3150 2550 270 270 3150 2550 3300 2775 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 3975 2550 270 270 3975 2550 4125 2775 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 1350 2550 270 270 1350 2550 1500 2775 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 2250 2550 270 270 2250 2550 2400 2775 +1 3 1 1 0 7 100 0 -1 4.000 1 0.0000 1425 600 270 270 1425 600 1575 825 +1 3 1 1 0 7 100 0 -1 4.000 1 0.0000 3000 600 270 270 3000 600 3150 825 +1 3 1 1 0 7 100 0 -1 4.000 1 0.0000 2615 3863 270 270 2615 3863 2765 4088 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2925 1500 3300 1500 3300 1950 2925 1950 2925 1500 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3075 1950 3075 2325 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 3750 1500 4125 1500 4125 1950 3750 1950 3750 1500 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3300 1725 3750 1725 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3975 1950 3975 2250 +2 1 0 1 0 0 100 0 20 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1575 1725 2100 1725 +2 1 0 1 0 0 100 0 20 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2475 1725 2925 1725 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1200 1500 1575 1500 1575 1950 1200 1950 1200 1500 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1350 1950 1350 2325 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2100 1500 2475 1500 2475 1950 2100 1950 2100 1500 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2250 1950 2250 2325 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2475 3600 1500 2775 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2550 3600 2400 2850 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2700 3525 3075 2775 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2850 3675 3825 2775 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1425 900 1425 1500 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2925 900 2400 1500 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3075 900 3075 1425 +2 1 1 1 0 7 100 0 -1 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3225 750 3900 1500 +4 0 0 100 0 0 12 0.0000 4 135 225 3000 2625 Y3\001 +4 0 0 100 0 0 12 0.0000 4 135 225 3825 2625 Y4\001 +4 0 0 100 0 0 12 0.0000 4 135 225 1275 2625 Y1\001 +4 0 0 100 0 0 12 0.0000 4 135 225 2100 2625 Y2\001 +4 0 0 100 0 0 30 0.0000 4 60 600 4350 1800 . . .\001 +4 0 0 50 0 0 12 0.0000 4 135 225 1275 1800 X1\001 +4 0 0 50 0 0 12 0.0000 4 135 225 2175 1800 X2\001 +4 0 0 50 0 0 12 0.0000 4 135 225 3000 1800 X3\001 +4 0 0 50 0 0 12 0.0000 4 135 225 3825 1800 X4\001 +4 0 0 50 0 0 18 0.0000 4 195 180 2550 3975 B\001 +4 0 0 50 0 0 18 0.0000 4 255 210 1275 750 pi\001 +4 0 0 50 0 0 18 0.0000 4 195 225 2850 750 A\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm4_square.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +6 1425 600 2025 1950 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 1725 1650 270 270 1725 1650 1875 1875 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1575 600 1950 600 1950 1050 1575 1050 1575 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1725 1050 1725 1425 +4 0 0 100 0 0 12 0.0000 4 135 225 1575 1725 Y2\001 +4 0 0 100 0 0 12 0.0000 4 165 225 1650 900 Q2\001 +-6 +6 525 600 1125 1950 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 825 1650 270 270 825 1650 975 1875 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 675 600 1050 600 1050 1050 675 1050 675 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 1050 825 1425 +4 0 0 100 0 0 12 0.0000 4 135 225 750 1725 Y1\001 +4 0 0 100 0 0 12 0.0000 4 165 225 750 900 Q1\001 +-6 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 2625 1650 270 270 2625 1650 2775 1875 +1 3 0 1 0 0 100 0 2 0.000 1 0.0000 3450 1650 270 270 3450 1650 3600 1875 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2400 600 2775 600 2775 1050 2400 1050 2400 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2550 1050 2550 1425 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 3225 600 3600 600 3600 1050 3225 1050 3225 600 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2775 825 3225 825 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3450 1050 3450 1350 +2 1 0 1 0 0 100 0 20 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 825 1575 825 +2 1 0 1 0 0 100 0 20 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 825 2400 825 +4 0 0 100 0 0 12 0.0000 4 135 225 2475 1725 Y3\001 +4 0 0 100 0 0 12 0.0000 4 165 225 3300 900 Q4\001 +4 0 0 100 0 0 12 0.0000 4 165 225 2475 900 Q3\001 +4 0 0 100 0 0 12 0.0000 4 135 225 3300 1725 Y4\001 +4 0 0 100 0 0 30 0.0000 4 30 525 3825 975 . . .\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_ar.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 225 225 2025 1875 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 525 1275 300 225 525 1275 825 1500 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1575 1275 300 225 1575 1275 1875 1500 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 450 1350 450 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 375 225 750 225 750 675 375 675 375 225 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 525 675 525 1050 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1425 225 1800 225 1800 675 1425 675 1425 225 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1575 675 1575 1050 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 1275 1275 1275 +4 0 -1 0 0 0 12 0.0000 4 165 225 450 525 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 1500 525 Q2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 375 1350 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1425 1350 Y2\001 +4 0 -1 0 0 0 12 0.0000 4 180 1785 225 1800 Auto Regressive HMM\001 +-6
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_coupled.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +6 300 225 1950 675 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 600 450 300 225 600 450 900 675 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1650 450 300 225 1650 450 1950 675 +-6 +6 450 1875 1875 2325 +6 450 1875 1875 2325 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 2100 1425 2100 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 450 1875 825 1875 825 2325 450 2325 450 1875 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1500 1875 1875 1875 1875 2325 1500 2325 1500 1875 +-6 +-6 +6 450 1050 1875 1500 +6 450 1050 1875 1500 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 1275 1425 1275 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 450 1050 825 1050 825 1500 450 1500 450 1050 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1500 1050 1875 1050 1875 1500 1500 1500 1500 1050 +-6 +-6 +6 300 2700 1950 3150 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 600 2925 300 225 600 2925 900 3150 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1650 2925 300 225 1650 2925 1950 3150 +-6 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 600 2325 600 2700 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1650 2325 1650 2700 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 600 1050 600 675 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1650 1050 1650 675 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 2100 1425 1350 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 900 1275 1350 2025 +4 0 -1 0 0 0 12 0.0000 4 135 210 450 525 C1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 525 1350 A1\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 525 2175 B1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 450 3000 D1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1500 3075 D2\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 1500 525 C2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1575 1350 A2\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 1575 2175 B2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_factorial.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 1501.355 1323.343 675 450 300 1275 525 2025 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 2263.600 1307.555 1650 525 1275 1200 1575 2025 + 0 0 1.00 60.00 120.00 +6 675 1050 2100 1500 +6 675 1050 2100 1500 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 1275 1650 1275 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 675 1050 1050 1050 1050 1500 675 1500 675 1050 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1725 1050 2100 1050 2100 1500 1725 1500 1725 1050 +-6 +-6 +6 675 225 2100 675 +6 675 225 2100 675 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 450 1650 450 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 675 225 1050 225 1050 675 675 675 675 225 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1725 225 2100 225 2100 675 1725 675 1725 225 +-6 +-6 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 825 2100 300 225 825 2100 1125 2325 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1875 2100 300 225 1875 2100 2175 2325 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 1500 825 1875 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1875 1500 1875 1875 +4 0 -1 0 0 0 12 0.0000 4 135 225 675 2175 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1800 2250 Y2\001 +4 0 -1 0 0 0 12 0.0000 4 135 1185 675 2850 Factorial HMM\001 +4 0 0 100 0 0 12 0.0000 4 135 225 750 525 A1\001 +4 0 0 100 0 0 12 0.0000 4 135 225 1800 525 A2\001 +4 0 0 100 0 0 12 0.0000 4 135 210 750 1350 B1\001 +4 0 0 100 0 0 12 0.0000 4 135 210 1800 1350 B2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_gauss.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 225 600 1875 1875 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 525 1650 300 225 525 1650 825 1875 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1575 1650 300 225 1575 1650 1875 1875 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 825 1350 825 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 375 600 750 600 750 1050 375 1050 375 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 525 1050 525 1425 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1425 600 1800 600 1800 1050 1425 1050 1425 600 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1575 1050 1575 1425 +-6 +4 0 -1 0 0 0 12 0.0000 4 165 225 450 900 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 375 1725 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1500 1725 Y2\001 +4 0 -1 0 0 0 12 0.0000 4 180 2130 150 2250 HMM with Gaussian output\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 1500 900 Q2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_io.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 1725.000 1200.000 600 450 375 1275 600 1950 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 2859.375 1303.125 1650 525 1425 1200 1575 1950 + 0 0 1.00 60.00 120.00 +6 525 150 2175 2325 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1875 2100 300 225 1875 2100 2175 2325 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 825 2100 300 225 825 2100 1125 2325 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 900 375 300 225 900 375 1200 600 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1875 375 300 225 1875 375 2175 600 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 1275 1650 1275 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 675 1050 1050 1050 1050 1500 675 1500 675 1050 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 1500 825 1875 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1725 1050 2100 1050 2100 1500 1725 1500 1725 1050 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1875 1500 1875 1875 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 675 825 1050 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1875 675 1875 1050 +-6 +4 0 -1 0 0 0 12 0.0000 4 165 225 750 1350 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 1800 1350 Q2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 750 450 U1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 750 2175 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1725 450 U2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1725 2175 Y2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_mixgauss.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +6 675 1650 1275 2100 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 975 1875 300 225 975 1875 1275 2100 +4 0 -1 0 0 0 12 0.0000 4 135 225 825 1950 Y1\001 +-6 +6 1725 1725 2325 2175 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 2025 1950 300 225 2025 1950 2325 2175 +4 0 -1 0 0 0 12 0.0000 4 135 225 1950 2025 Y2\001 +-6 +6 1500 900 1875 1350 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1500 900 1875 900 1875 1350 1500 1350 1500 900 +4 0 -1 0 0 0 12 0.0000 4 135 255 1575 1200 M2\001 +-6 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1275 450 1800 450 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 825 225 1200 225 1200 675 825 675 825 225 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1875 225 2250 225 2250 675 1875 675 1875 225 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 225 900 600 900 600 1350 225 1350 225 900 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 675 450 900 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 525 1350 750 1650 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 675 1050 1650 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1875 675 1725 900 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1800 1350 1950 1725 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2100 675 2100 1725 +4 0 -1 0 0 0 12 0.0000 4 165 225 900 525 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 135 255 300 1200 M1\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 1950 525 Q2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_zoo.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,162 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +5 1 0 2 0 0 50 0 -1 0.000 0 1 1 0 2784.375 4153.125 1575 3375 1350 4050 1500 4800 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 50 0 -1 0.000 0 1 1 0 1650.000 4050.000 525 3300 300 4125 525 4800 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 50 0 -1 0.000 0 1 1 0 6376.355 4323.343 5550 3450 5175 4275 5400 5025 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 50 0 -1 0.000 0 1 1 0 7138.600 4307.555 6525 3525 6150 4200 6450 5025 + 1 1 1.00 60.00 120.00 +1 1 0 2 7 0 50 0 20 0.000 1 0.0000 750 4950 300 225 750 4950 1050 5175 +1 1 0 2 7 0 50 0 20 0.000 1 0.0000 1800 4950 300 225 1800 4950 2100 5175 +1 1 0 2 7 0 50 0 20 0.000 1 0.0000 825 3225 300 225 825 3225 1125 3450 +1 1 0 2 7 0 50 0 20 0.000 1 0.0000 1800 3225 300 225 1800 3225 2100 3450 +1 1 0 2 7 0 50 0 20 0.000 1 0.0000 3150 3150 300 225 3150 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_zoo_names.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,169 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 1650.000 4050.000 525 3300 300 4125 525 4800 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 2784.375 4153.125 1575 3375 1350 4050 1500 4800 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 6376.355 4323.343 5550 3450 5175 4275 5400 5025 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 7138.600 4307.555 6525 3525 6150 4200 6450 5025 + 0 0 1.00 60.00 120.00 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 5550 1425 300 225 5550 1425 5850 1650 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 6600 1425 300 225 6600 1425 6900 1650 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 525 1650 300 225 525 1650 825 1875 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1800 4950 300 225 1800 4950 2100 5175 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 825 3225 300 225 825 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/hmm_zoo_small.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +75.00 +Single +-2 +1200 2 +5 1 0 2 0 7 50 0 -1 0.000 0 1 1 0 10209.375 1303.125 9000 525 8775 1200 8925 1950 + 0 0 2.00 120.00 240.00 +5 1 0 2 0 7 50 0 -1 0.000 0 1 1 0 9075.000 1200.000 7950 450 7725 1275 7950 1950 + 0 0 2.00 120.00 240.00 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 525 1650 300 225 525 1650 825 1875 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 1590 1654 300 225 1590 1654 1890 1879 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 3375 1800 300 225 3375 1800 3675 2025 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 4427 1882 300 225 4427 1882 4727 2107 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 5925 1800 300 225 5925 1800 6225 2025 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 6975 1800 300 225 6975 1800 7275 2025 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 8173 2107 300 225 8173 2107 8473 2332 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 9225 2100 300 225 9225 2100 9525 2325 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 8250 375 300 225 8250 375 8550 600 +1 1 0 2 0 0 50 0 20 0.000 1 0.0000 9225 375 300 225 9225 375 9525 600 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 1575 1050 1575 1425 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 825 825 1350 825 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 525 1050 525 1425 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 375 600 750 600 750 1050 375 1050 375 600 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 1425 600 1800 600 1800 1050 1425 1050 1425 600 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 3450 600 3450 1575 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 3225 600 2850 825 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 2625 825 3000 825 3000 1275 2625 1275 2625 825 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 3225 150 3600 150 3600 600 3225 600 3225 150 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 3675 375 4200 375 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 3900 825 4275 825 4275 1275 3900 1275 3900 825 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 4200 1275 4350 1650 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 4500 600 4500 1650 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 4275 150 4650 150 4650 600 4275 600 4275 150 +2 1 0 2 0 7 50 0 -1 0.000 0 0 -1 1 0 2 + 0 0 2.00 120.00 240.00 + 4275 600 3975 825 +2 1 0 2 0 7 50 0 -1 0.000 0 0 -1 1 0 2 + 0 0 2.00 120.00 240.00 + 2925 1275 3225 1575 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 8175 675 8175 1050 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 9225 675 9225 1050 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 9225 1500 9225 1875 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 8475 1275 9000 1275 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 8175 1500 8175 1875 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 8025 1050 8400 1050 8400 1500 8025 1500 8025 1050 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 9075 1050 9450 1050 9450 1500 9075 1500 9075 1050 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 5925 1200 5925 1575 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 6225 975 6750 975 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 6225 1800 6675 1800 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 6975 1200 6975 1575 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 6825 750 7200 750 7200 1200 6825 1200 6825 750 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 5775 750 6150 750 6150 1200 5775 1200 5775 750 +4 0 0 50 0 0 24 0.0000 4 255 945 375 2400 HMM\001 +4 0 0 50 0 0 24 0.0000 4 255 2580 2625 2475 MixGauss HMM\001 +4 0 0 50 0 0 24 0.0000 4 255 1470 8025 2700 IO-HMM\001 +4 0 0 50 0 0 24 0.0000 4 255 1575 5625 2475 AR-HMM\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/ifa.eps Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,470 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: (ImageMagick) +%%Title: (ifa.eps) +%%CreationDate: (Tue Nov 16 19:52:10 2004) +%%BoundingBox: 0 0 246 221 +%%DocumentData: Clean7Bit +%%LanguageLevel: 1 +%%Pages: 1 +%%EndComments + +%%BeginDefaults +%%EndDefaults + +%%BeginProlog +% +% Display a color image. The image is displayed in color on +% Postscript viewers or printers that support color, otherwise +% it is displayed as grayscale. +% +/DirectClassPacket +{ + % + % Get a DirectClass packet. + % + % Parameters: + % red. + % green. + % blue. + % length: number of pixels minus one of this color (optional). + % + currentfile color_packet readhexstring pop pop + compression 0 eq + { + /number_pixels 3 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add 3 mul def + } ifelse + 0 3 number_pixels 1 sub + { + pixels exch color_packet putinterval + } for + pixels 0 number_pixels getinterval +} bind def + +/DirectClassImage +{ + % + % Display a DirectClass image. + % + systemdict /colorimage known + { + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { DirectClassPacket } false 3 colorimage + } + { + % + % No colorimage operator; convert to grayscale. + % + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { GrayDirectClassPacket } image + } ifelse +} bind def + +/GrayDirectClassPacket +{ + % + % Get a DirectClass packet; convert to grayscale. + % + % Parameters: + % red + % green + % blue + % length: number of pixels minus one of this color (optional). + % + currentfile color_packet readhexstring pop pop + color_packet 0 get 0.299 mul + color_packet 1 get 0.587 mul add + color_packet 2 get 0.114 mul add + cvi + /gray_packet exch def + compression 0 eq + { + /number_pixels 1 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add def + } ifelse + 0 1 number_pixels 1 sub + { + pixels exch gray_packet put + } for + pixels 0 number_pixels getinterval +} bind def + +/GrayPseudoClassPacket +{ + % + % Get a PseudoClass packet; convert to grayscale. + % + % Parameters: + % index: index into the colormap. + % length: number of pixels minus one of this color (optional). + % + currentfile byte readhexstring pop 0 get + /offset exch 3 mul def + /color_packet colormap offset 3 getinterval def + color_packet 0 get 0.299 mul + color_packet 1 get 0.587 mul add + color_packet 2 get 0.114 mul add + cvi + /gray_packet exch def + compression 0 eq + { + /number_pixels 1 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add def + } ifelse + 0 1 number_pixels 1 sub + { + pixels exch gray_packet put + } for + pixels 0 number_pixels getinterval +} bind def + +/PseudoClassPacket +{ + % + % Get a PseudoClass packet. + % + % Parameters: + % index: index into the colormap. + % length: number of pixels minus one of this color (optional). + % + currentfile byte readhexstring pop 0 get + /offset exch 3 mul def + /color_packet colormap offset 3 getinterval def + compression 0 eq + { + /number_pixels 3 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add 3 mul def + } ifelse + 0 3 number_pixels 1 sub + { + pixels exch color_packet putinterval + } for + pixels 0 number_pixels getinterval +} bind def + +/PseudoClassImage +{ + % + % Display a PseudoClass image. + % + % Parameters: + % class: 0-PseudoClass or 1-Grayscale. + % + currentfile buffer readline pop + token pop /class exch def pop + class 0 gt + { + currentfile buffer readline pop + token pop /depth exch def pop + /grays columns 8 add depth sub depth mul 8 idiv string def + columns rows depth + [ + columns 0 0 + rows neg 0 rows + ] + { currentfile grays readhexstring pop } image + } + { + % + % Parameters: + % colors: number of colors in the colormap. + % colormap: red, green, blue color packets. + % + currentfile buffer readline pop + token pop /colors exch def pop + /colors colors 3 mul def + /colormap colors string def + currentfile colormap readhexstring pop pop + systemdict /colorimage known + { + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { PseudoClassPacket } false 3 colorimage + } + { + % + % No colorimage operator; convert to grayscale. + % + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { GrayPseudoClassPacket } image + } ifelse + } ifelse +} 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/ifa.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 0 0 2362.500 1537.500 1725 2325 2400 2550 3000 2325 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 0 0 1950.000 818.750 675 2400 1950 2850 3225 2400 +6 225 900 3600 2475 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 900 1125 300 225 900 1125 1200 1350 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 2400 1125 300 225 2400 1125 2700 1350 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 525 2175 300 225 525 2175 825 2400 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 3240 2179 300 225 3240 2179 3540 2404 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1500 2175 300 225 1500 2175 1800 2400 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 750 1350 525 1950 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 1275 3000 2025 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2325 1350 675 1950 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2550 1350 3150 1950 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 1350 1350 1950 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2325 1350 1575 1875 +4 0 -1 0 0 0 12 0.0000 4 135 225 825 1200 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 2250 1200 Xn\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 375 2250 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 270 3075 2250 Ym\001 +4 0 -1 0 0 0 24 0.0000 4 30 270 1500 1275 ...\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1350 2250 Y2\001 +4 0 -1 0 0 0 24 0.0000 4 30 270 2100 2175 ...\001 +-6 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 600 225 1050 225 1050 675 600 675 600 225 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2250 225 2700 225 2700 675 2250 675 2250 225 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 675 825 900 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2475 675 2475 900 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 825 2175 1200 2175 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 2625 2175 2925 2175 +4 0 -1 0 0 0 12 0.0000 4 165 225 675 525 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 2325 525 Qn\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/kf.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 225 225 1950 2250 +6 225 225 1950 675 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 525 450 300 225 525 450 825 675 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1650 450 300 225 1650 450 1950 675 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 450 1350 450 +-6 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 525 1575 300 225 525 1575 825 1800 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1650 1575 300 225 1650 1575 1950 1800 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 525 750 525 1275 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1650 750 1650 1275 +4 0 -1 0 0 0 12 0.0000 4 135 1455 225 2250 Kalman filter model\001 +-6 +4 0 -1 0 0 0 12 0.0000 4 135 210 375 525 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 1500 525 X2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 375 1650 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1500 1650 Y2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/kf_input.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 1 0 7 100 0 -1 0.000 0 1 1 0 3131.250 1462.500 1575 450 1275 1425 1575 2475 + 0 0 1.00 60.00 120.00 +5 1 0 1 0 7 100 0 -1 0.000 0 1 1 0 4272.606 1559.043 2775 525 2475 1275 2700 2475 + 0 0 1.00 60.00 120.00 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1875 2550 300 225 1875 2550 2175 2775 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 3000 2550 300 225 3000 2550 3300 2775 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1875 1425 300 225 1875 1425 2175 1650 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 3000 1425 300 225 3000 1425 3300 1650 +1 1 0 1 0 0 100 0 2 0.000 1 0.0000 1875 375 300 225 1875 375 2175 600 +1 1 0 1 0 0 100 0 2 0.000 1 0.0000 3000 375 300 225 3000 375 3300 600 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1875 1725 1875 2250 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3000 1725 3000 2250 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2175 1425 2700 1425 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1875 600 1875 1200 +2 1 0 1 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3000 600 3000 1200 +4 0 -1 0 0 0 12 0.0000 4 135 225 1725 1500 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 2850 1500 X2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1725 2625 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 2850 2625 Y2\001 +4 0 0 100 0 0 12 0.0000 4 135 225 1725 450 U1\001 +4 0 0 100 0 0 12 0.0000 4 135 225 2850 450 U2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/kf_notime.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +1 1 0 2 0 0 100 0 2 0.000 1 0.0000 525 1575 300 225 525 1575 825 1800 +1 1 0 2 0 0 100 0 2 0.000 1 0.0000 1650 1575 300 225 1650 1575 1950 1800 +1 1 0 2 0 0 100 0 -1 0.000 1 0.0000 525 450 300 225 525 450 825 675 +1 1 0 2 0 0 100 0 -1 0.000 1 0.0000 1650 450 300 225 1650 450 1950 675 +2 1 0 2 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 525 750 525 1275 +2 1 0 2 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 1650 750 1650 1275 +2 1 0 2 0 0 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 2.00 120.00 240.00 + 825 450 1350 450 +4 0 0 100 0 0 20 0.0000 4 195 210 375 525 X\001 +4 0 0 100 0 0 20 0.0000 4 195 210 1500 525 X\001 +4 0 0 100 0 0 20 0.0000 4 195 210 1500 1650 Y\001 +4 0 0 100 0 0 20 0.0000 4 195 210 375 1650 Y\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/kf_obs_track.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +#FIG 3.2 +Portrait +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 1350 1500 335 335 1200 1200 1500 1800 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 1350 2700 335 335 1200 2400 1500 3000 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 1350 3900 335 335 1200 3600 1500 4200 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 1350 5100 335 335 1200 4800 1500 5400 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 2850 5100 335 335 2700 4800 3000 5400 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 2850 3900 335 335 2700 3600 3000 4200 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 2850 2700 335 335 2700 2400 3000 3000 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 2850 1500 335 335 2700 1200 3000 1800 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 1500 2550 1500 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 2700 2550 1650 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 3900 2475 3900 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 5100 2625 4125 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 5100 2475 5100 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 2700 2550 2700 +4 0 0 100 0 0 20 0.0000 4 195 405 1125 2850 dx1\001 +4 0 0 100 0 0 20 0.0000 4 195 270 1125 4050 x2\001 +4 0 0 100 0 0 20 0.0000 4 195 405 1200 5175 dx2\001 +4 0 0 100 0 0 20 0.0000 4 195 270 1200 1650 x1\001 +4 0 0 100 0 0 20 0.0000 4 195 270 2700 1650 x1\001 +4 0 0 100 0 0 20 0.0000 4 195 405 2700 2850 dx1\001 +4 0 0 100 0 0 20 0.0000 4 195 270 2700 4050 x2\001 +4 0 0 100 0 0 20 0.0000 4 195 405 2700 5250 dx2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/kf_scalar_track.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +#FIG 3.2 +Portrait +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 2 0 7 100 0 -1 0.000 0 1 1 0 2726.786 5448.214 1125 4125 675 5775 1050 6675 + 0 0 4.00 120.00 240.00 +5 1 0 2 0 7 100 0 -1 0.000 0 1 1 0 6809.923 4980.705 1050 1725 225 5625 975 8100 + 0 0 4.00 120.00 240.00 +5 1 0 2 0 7 100 0 -1 0.000 0 0 1 0 1778.571 5400.000 3225 3900 3750 6075 3225 6900 + 0 0 4.00 120.00 240.00 +5 1 0 2 0 7 100 0 -1 0.000 0 0 1 0 -75.000 4837.500 3225 1500 4575 5475 3225 8175 + 0 0 4.00 120.00 240.00 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 1350 1500 335 335 1200 1200 1500 1800 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 1350 2700 335 335 1200 2400 1500 3000 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 1350 3900 335 335 1200 3600 1500 4200 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 1350 5100 335 335 1200 4800 1500 5400 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 2850 5100 335 335 2700 4800 3000 5400 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 2850 3900 335 335 2700 3600 3000 4200 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 2850 2700 335 335 2700 2400 3000 3000 +1 4 0 2 0 7 100 0 -1 0.000 1 0.0000 2850 1500 335 335 2700 1200 3000 1800 +1 4 0 2 0 0 100 0 2 0.000 1 0.0000 1350 6900 335 335 1200 6600 1500 7200 +1 4 0 2 0 0 100 0 2 0.000 1 0.0000 2850 6900 335 335 2700 6600 3000 7200 +1 4 0 2 0 0 100 0 2 0.000 1 0.0000 2850 8100 335 335 2700 7800 3000 8400 +1 4 0 2 0 0 100 0 2 0.000 1 0.0000 1350 8100 335 335 1200 7800 1500 8400 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 1500 2550 1500 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 2700 2550 1650 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 3900 2475 3900 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 5100 2625 4125 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 5100 2475 5100 +2 1 0 2 0 0 100 0 2 0.000 0 0 -1 1 0 2 + 0 0 4.00 120.00 240.00 + 1725 2700 2550 2700 +4 0 0 100 0 0 20 0.0000 4 195 405 1125 2850 dx1\001 +4 0 0 100 0 0 20 0.0000 4 195 270 1125 4050 x2\001 +4 0 0 100 0 0 20 0.0000 4 195 405 1200 5175 dx2\001 +4 0 0 100 0 0 20 0.0000 4 195 270 1200 1650 x1\001 +4 0 0 100 0 0 20 0.0000 4 195 270 2700 1650 x1\001 +4 0 0 100 0 0 20 0.0000 4 195 405 2700 2850 dx1\001 +4 0 0 100 0 0 20 0.0000 4 195 270 2700 4050 x2\001 +4 0 0 100 0 0 20 0.0000 4 195 405 2700 5250 dx2\001 +4 0 0 100 0 0 20 0.0000 4 255 270 1200 6975 y2\001 +4 0 0 100 0 0 20 0.0000 4 255 270 1200 8175 y1\001 +4 0 0 100 0 0 20 0.0000 4 255 270 2700 7050 y2\001 +4 0 0 100 0 0 20 0.0000 4 255 270 2700 8175 y1\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/mfa.eps Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,347 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: (ImageMagick) +%%Title: (mfa.eps) +%%CreationDate: (Tue Nov 16 19:52:06 2004) +%%BoundingBox: 0 0 126 151 +%%DocumentData: Clean7Bit +%%LanguageLevel: 1 +%%Pages: 1 +%%EndComments + +%%BeginDefaults +%%EndDefaults + +%%BeginProlog +% +% Display a color image. The image is displayed in color on +% Postscript viewers or printers that support color, otherwise +% it is displayed as grayscale. +% +/DirectClassPacket +{ + % + % Get a DirectClass packet. + % + % Parameters: + % red. + % green. + % blue. + % length: number of pixels minus one of this color (optional). + % + currentfile color_packet readhexstring pop pop + compression 0 eq + { + /number_pixels 3 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add 3 mul def + } ifelse + 0 3 number_pixels 1 sub + { + pixels exch color_packet putinterval + } for + pixels 0 number_pixels getinterval +} bind def + +/DirectClassImage +{ + % + % Display a DirectClass image. + % + systemdict /colorimage known + { + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { DirectClassPacket } false 3 colorimage + } + { + % + % No colorimage operator; convert to grayscale. + % + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { GrayDirectClassPacket } image + } ifelse +} bind def + +/GrayDirectClassPacket +{ + % + % Get a DirectClass packet; convert to grayscale. + % + % Parameters: + % red + % green + % blue + % length: number of pixels minus one of this color (optional). + % + currentfile color_packet readhexstring pop pop + color_packet 0 get 0.299 mul + color_packet 1 get 0.587 mul add + color_packet 2 get 0.114 mul add + cvi + /gray_packet exch def + compression 0 eq + { + /number_pixels 1 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add def + } ifelse + 0 1 number_pixels 1 sub + { + pixels exch gray_packet put + } for + pixels 0 number_pixels getinterval +} bind def + +/GrayPseudoClassPacket +{ + % + % Get a PseudoClass packet; convert to grayscale. + % + % Parameters: + % index: index into the colormap. + % length: number of pixels minus one of this color (optional). + % + currentfile byte readhexstring pop 0 get + /offset exch 3 mul def + /color_packet colormap offset 3 getinterval def + color_packet 0 get 0.299 mul + color_packet 1 get 0.587 mul add + color_packet 2 get 0.114 mul add + cvi + /gray_packet exch def + compression 0 eq + { + /number_pixels 1 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add def + } ifelse + 0 1 number_pixels 1 sub + { + pixels exch gray_packet put + } for + pixels 0 number_pixels getinterval +} bind def + +/PseudoClassPacket +{ + % + % Get a PseudoClass packet. + % + % Parameters: + % index: index into the colormap. + % length: number of pixels minus one of this color (optional). + % + currentfile byte readhexstring pop 0 get + /offset exch 3 mul def + /color_packet colormap offset 3 getinterval def + compression 0 eq + { + /number_pixels 3 def + } + { + currentfile byte readhexstring pop 0 get + /number_pixels exch 1 add 3 mul def + } ifelse + 0 3 number_pixels 1 sub + { + pixels exch color_packet putinterval + } for + pixels 0 number_pixels getinterval +} bind def + +/PseudoClassImage +{ + % + % Display a PseudoClass image. + % + % Parameters: + % class: 0-PseudoClass or 1-Grayscale. + % + currentfile buffer readline pop + token pop /class exch def pop + class 0 gt + { + currentfile buffer readline pop + token pop /depth exch def pop + /grays columns 8 add depth sub depth mul 8 idiv string def + columns rows depth + [ + columns 0 0 + rows neg 0 rows + ] + { currentfile grays readhexstring pop } image + } + { + % + % Parameters: + % colors: number of colors in the colormap. + % colormap: red, green, blue color packets. + % + currentfile buffer readline pop + token pop /colors exch def pop + /colors colors 3 mul def + /colormap colors string def + currentfile colormap readhexstring pop pop + systemdict /colorimage known + { + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { PseudoClassPacket } false 3 colorimage + } + { + % + % No colorimage operator; convert to grayscale. + % + columns rows 8 + [ + columns 0 0 + rows neg 0 rows + ] + { GrayPseudoClassPacket } image + } ifelse + } ifelse +} bind def + +/DisplayImage +{ + % + % Display a DirectClass or PseudoClass image. + % + % Parameters: + % x & y translation. + % x & y scale. + % label pointsize. + % image label. + % image columns & rows. + % class: 0-DirectClass or 1-PseudoClass. + % compression: 0-none or 1-RunlengthEncoded. + % hex color packets. + % + gsave + /buffer 512 string def + /byte 1 string def + /color_packet 3 string def + /pixels 768 string def + + currentfile buffer readline pop + token pop /x exch def + token pop /y exch def pop + x y translate + currentfile buffer readline pop + token pop /x exch def + token pop /y exch def pop + currentfile buffer readline pop + token pop /pointsize exch def pop + /Times-Roman findfont pointsize scalefont setfont + x y scale + currentfile buffer readline pop + token pop /columns exch def + token pop /rows exch def pop + currentfile buffer readline pop + token pop /class exch def pop + currentfile buffer readline pop + token pop /compression exch def pop + class 0 gt 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/mfa.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +6 225 225 1800 1650 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1500 450 300 225 1500 450 1800 675 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 975 1425 300 225 975 1425 1275 1650 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 225 225 600 225 600 675 225 675 225 225 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 525 675 900 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1425 675 1050 1200 +-6 +4 0 -1 0 0 0 12 0.0000 4 135 135 825 1500 Y\001 +4 0 -1 0 0 0 12 0.0000 4 165 135 300 525 Q\001 +4 0 -1 0 0 0 12 0.0000 4 135 135 1350 525 X\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/mixexp.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 525 300 1575 2925 +5 1 0 1 -1 7 0 0 -1 0.000 0 1 1 0 2055.000 1650.000 975 675 600 1650 975 2625 + 0 0 1.00 60.00 120.00 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1275 525 300 225 1275 525 1575 750 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1275 2700 300 225 1275 2700 1575 2925 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1050 1350 1425 1350 1425 1800 1050 1800 1050 1350 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1275 825 1275 1350 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1275 1800 1275 2475 +-6 +4 0 -1 0 0 0 12 0.0000 4 180 1425 450 3300 Mixture of Experts\001 +4 0 -1 0 0 0 12 0.0000 4 135 120 1200 600 X\001 +4 0 -1 0 0 0 12 0.0000 4 165 135 1125 1650 Q\001 +4 0 -1 0 0 0 12 0.0000 4 135 135 1200 2775 Y\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/qmr.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 750 2700 309 309 750 2700 1050 2775 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 3225 2700 309 309 3225 2700 3525 2775 +1 3 0 3 0 0 100 0 5 0.000 1 0.0000 4500 2700 309 309 4500 2700 4800 2775 +1 3 0 3 0 0 100 0 -1 0.000 1 0.0000 1950 2700 309 309 1950 2700 2025 3000 +1 3 0 3 0 0 100 0 -1 0.000 1 0.0000 5775 2700 309 309 5775 2700 5850 3000 +1 3 0 3 0 0 100 0 -1 0.000 1 0.0000 4125 1200 309 309 4125 1200 4200 1500 +1 3 0 3 0 0 100 0 -1 0.000 1 0.0000 3000 1200 309 309 3000 1200 3075 1500 +1 3 0 3 0 0 100 0 -1 0.000 1 0.0000 1875 1200 309 309 1875 1200 1950 1500 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 1 2 + 0 0 3.00 180.00 360.00 + 0 0 3.00 180.00 360.00 + 450 3750 6150 3750 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 1 2 + 0 0 3.00 180.00 360.00 + 0 0 3.00 180.00 360.00 + 1350 525 4650 525 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 3.00 180.00 360.00 + 1725 1500 900 2475 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 3.00 180.00 360.00 + 2850 1500 1050 2475 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 3.00 180.00 360.00 + 2925 1500 2100 2400 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 3.00 180.00 360.00 + 3975 1500 2100 2400 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 3.00 180.00 360.00 + 3075 1575 3150 2325 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 3.00 180.00 360.00 + 3150 1575 4200 2400 +2 1 0 3 0 7 100 0 -1 0.000 0 0 7 1 0 2 + 0 0 3.00 180.00 360.00 + 4200 1500 5475 2475 +4 0 0 100 0 0 24 0.0000 4 255 720 1950 4200 4000\001 +4 0 0 100 0 0 24 0.0000 4 330 1575 2850 4200 Symptoms\001 +4 0 0 100 0 0 24 0.0000 4 255 540 2100 375 600\001 +4 0 0 100 0 0 24 0.0000 4 255 1290 2775 375 Diseases\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/rainer_tied.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +#FIG 3.2 +Portrait +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 2 0 7 100 0 -1 0.000 0 1 1 0 3196.875 3000.000 1275 1875 975 3150 1275 4125 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 7 100 0 -1 0.000 0 1 1 0 5596.875 3000.000 3675 1875 3375 3150 3675 4125 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 7 100 0 -1 0.000 0 0 1 0 3849.948 3549.006 2475 3375 3075 2400 4500 2325 + 1 1 1.00 60.00 120.00 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 1500 1875 237 237 1500 1875 1575 2100 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 1500 2775 237 237 1500 2775 1575 3000 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 2400 2325 237 237 2400 2325 2475 2550 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 2325 3600 237 237 2325 3600 2400 3825 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 1500 4275 237 237 1500 4275 1575 4500 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 3900 1875 237 237 3900 1875 3975 2100 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 3900 2775 237 237 3900 2775 3975 3000 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 4800 2325 237 237 4800 2325 4875 2550 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 4725 3600 237 237 4725 3600 4800 3825 +1 3 0 2 0 7 100 0 -1 0.000 1 0.0000 3900 4275 237 237 3900 4275 3975 4500 +1 1 1 2 0 7 100 0 -1 6.000 1 0.0000 2625 1875 1725 300 2625 1875 4350 2175 +1 1 1 2 0 7 100 0 -1 6.000 1 0.0000 2625 4275 1800 300 2625 4275 4425 4575 +1 1 1 2 0 7 100 0 -1 6.000 1 0.0000 3375 3600 1725 300 3375 3600 5100 3900 +1 1 1 2 0 7 100 0 -1 6.000 1 0.0000 1500 2775 375 300 1500 2775 1875 3075 +1 1 1 2 0 7 100 0 -1 6.000 1 0.0000 3900 2775 375 300 3900 2775 4275 3075 +1 1 1 2 0 7 100 0 -1 6.000 1 0.0000 4800 2325 375 300 4800 2325 5175 2625 +1 1 1 2 0 7 100 0 -1 6.000 1 0.0000 2400 2325 375 225 2400 2325 2775 2550 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 1725 1950 2175 2250 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 2175 2400 1725 2700 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 2400 2550 2400 3375 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 1575 3000 2100 3450 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 3 + 1 1 1.00 60.00 120.00 + 1725 1950 2175 3375 2175 3375 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 2175 3750 1650 4125 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 4125 1950 4575 2250 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 4575 2400 4125 2700 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 4800 2550 4800 3375 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 3975 3000 4500 3450 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 3 + 1 1 1.00 60.00 120.00 + 4125 1950 4575 3375 4575 3375 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 4575 3750 4050 4125 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 2700 2325 3675 2700 +4 0 0 100 0 2 12 0.0000 4 135 90 1425 1950 1\001 +4 0 0 100 0 2 12 0.0000 4 135 90 2325 2400 2\001 +4 0 0 100 0 2 12 0.0000 4 135 90 1425 2850 3\001 +4 0 0 100 0 2 12 0.0000 4 135 90 2250 3675 4\001 +4 0 0 100 0 2 12 0.0000 4 135 90 1425 4350 5\001 +4 0 0 100 0 2 12 0.0000 4 135 90 3750 1950 6\001 +4 0 0 100 0 2 12 0.0000 4 135 90 4650 2400 7\001 +4 0 0 100 0 2 12 0.0000 4 135 90 3750 2850 8\001 +4 0 0 100 0 2 12 0.0000 4 135 90 4650 3675 9\001 +4 0 0 100 0 2 12 0.0000 4 135 180 3750 4350 10\001 +4 0 0 100 0 2 12 0.0000 4 135 210 2475 2700 E2\001 +4 0 0 100 0 2 12 0.0000 4 135 210 1350 4725 E5\001 +4 0 0 100 0 2 12 0.0000 4 135 210 1725 3900 E4\001 +4 0 0 100 0 2 12 0.0000 4 135 210 1350 3300 E3\001 +4 0 0 100 0 2 12 0.0000 4 135 210 1350 2325 E1\001 +4 0 0 100 0 2 12 0.0000 4 135 210 3750 3225 E7\001 +4 0 0 100 0 2 12 0.0000 4 135 210 4800 2850 E6\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/sar.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 375 225 2175 2325 +6 375 1350 2100 1800 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 675 1575 300 225 675 1575 975 1800 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1800 1575 300 225 1800 1575 2100 1800 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 975 1575 1500 1575 +-6 +6 450 225 1950 675 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 450 225 825 225 825 675 450 675 450 225 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1575 225 1950 225 1950 675 1575 675 1575 225 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 450 1575 450 +-6 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 675 675 675 1350 +2 1 0 1 -1 0 0 0 2 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1800 675 1800 1350 +2 1 1 1 -1 0 0 0 2 4.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 975 1425 1500 525 +4 0 -1 0 0 0 12 0.0000 4 180 1530 600 2250 Switching AR model\001 +-6 +4 0 -1 0 0 0 12 0.0000 4 165 225 525 525 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 1650 525 Q2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 525 1650 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1650 1650 Y2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/skf.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +#FIG 3.1 +Landscape +Center +Inches +1200 2 +6 300 300 2550 3375 +5 1 0 1 -1 -1 0 0 -1 0.000 0 1 1 0 3718.581 1734.122 1725 600 1425 1725 1575 2550 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 -1 0 0 -1 0.000 0 1 1 0 2652.330 1587.076 600 600 375 1575 525 2400 + 0 0 1.00 60.00 120.00 +6 1650 1200 2250 2775 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1950 2550 300 225 1950 2550 2250 2775 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1950 1425 300 225 1950 1425 2250 1650 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 1725 1950 2250 +-6 +6 675 300 2175 750 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 675 300 1050 300 1050 750 675 750 675 300 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1800 300 2175 300 2175 750 1800 750 1800 300 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 525 1800 525 +-6 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 825 2550 300 225 825 2550 1125 2775 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 825 1425 300 225 825 1425 1125 1650 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 750 825 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 750 1950 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 1425 1650 1425 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 1725 825 2250 +4 0 -1 0 0 0 12 0.0000 4 180 1740 750 3300 Switching Kalman filter\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 750 600 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 1875 600 Q2\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 675 1500 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 210 1800 1500 X2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 675 2625 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1800 2625 Y2\001 +-6
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/skf3.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +6 300 300 3750 3450 +5 1 0 2 0 0 50 0 -1 0.000 0 1 1 0 2100.000 1950.000 675 975 375 1875 675 2925 + 1 1 2.00 120.00 240.00 +5 1 0 2 0 0 50 0 -1 0.000 0 1 1 0 3300.000 1950.000 1875 975 1575 1875 1875 2925 + 1 1 2.00 120.00 240.00 +5 1 0 2 0 0 50 0 -1 0.000 0 1 1 0 4500.000 1950.000 3075 975 2775 1875 3075 2925 + 1 1 2.00 120.00 240.00 +1 3 0 2 0 0 50 0 -1 0.000 1 0.0000 975 1875 335 335 975 1875 1125 2175 +1 3 0 2 0 0 50 0 -1 0.000 1 0.0000 3375 1875 335 335 3375 1875 3525 2175 +1 3 0 2 0 0 50 0 20 0.000 1 0.0000 975 3075 335 335 975 3075 1125 3375 +1 3 0 2 0 0 50 0 20 0.000 1 0.0000 2175 3075 335 335 2175 3075 2325 3375 +1 3 0 2 0 0 50 0 20 0.000 1 0.0000 3375 3075 335 335 3375 3075 3525 3375 +1 3 0 2 0 0 50 0 -1 0.000 1 0.0000 2175 1875 335 335 2175 1875 2325 2175 +2 2 0 2 0 0 50 0 -1 0.000 0 0 7 0 0 5 + 675 375 1275 375 1275 975 675 975 675 375 +2 2 0 2 0 0 50 0 -1 0.000 0 0 7 0 0 5 + 1875 375 2475 375 2475 975 1875 975 1875 375 +2 2 0 2 0 0 50 0 -1 0.000 0 0 7 0 0 5 + 3075 375 3675 375 3675 975 3075 975 3075 375 +2 1 0 2 0 0 50 0 -1 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 975 975 975 1575 +2 1 0 2 0 0 50 0 -1 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 975 2175 975 2775 +2 1 0 2 0 0 50 0 -1 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 2175 975 2175 1575 +2 1 0 2 0 0 50 0 -1 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 2175 2175 2175 2775 +2 1 0 2 0 0 50 0 -1 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 3375 975 3375 1575 +2 1 0 2 0 0 50 0 -1 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 3375 2175 3375 2775 +2 1 0 2 0 0 50 0 -1 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 1275 1875 1875 1875 +2 1 0 2 0 0 50 0 -1 0.000 0 0 -1 1 0 2 + 1 1 2.00 120.00 240.00 + 2475 1875 3075 1875 +4 0 0 50 0 0 24 0.0000 4 255 225 750 750 Z\001 +4 0 0 50 0 0 24 0.0000 4 255 225 1950 750 Z\001 +4 0 0 50 0 0 24 0.0000 4 255 180 2175 900 2\001 +4 0 0 50 0 0 24 0.0000 4 255 225 3150 750 Z\001 +4 0 0 50 0 0 24 0.0000 4 255 180 3375 900 3\001 +4 0 0 50 0 0 24 0.0000 4 255 270 825 2025 X\001 +4 0 0 50 0 0 24 0.0000 4 255 180 1050 2175 1\001 +4 0 0 50 0 0 24 0.0000 4 255 180 2250 2175 2\001 +4 0 0 50 0 0 24 0.0000 4 255 180 3450 2175 3\001 +4 0 0 50 0 0 24 0.0000 4 255 270 3225 2025 X\001 +4 0 0 50 0 0 24 0.0000 4 255 270 2025 2025 X\001 +4 0 7 50 0 0 24 0.0000 4 255 240 825 3150 Y\001 +4 0 7 50 0 0 24 0.0000 4 255 240 2025 3150 Y\001 +4 0 7 50 0 0 24 0.0000 4 255 240 3225 3150 Y\001 +4 0 7 50 0 0 24 0.0000 4 255 180 1050 3300 1\001 +4 0 7 50 0 0 24 0.0000 4 255 180 2175 3375 2\001 +4 0 7 50 0 0 24 0.0000 4 255 180 3375 3300 3\001 +4 0 0 50 0 0 24 0.0000 4 255 180 975 900 1\001 +-6
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/skf3_nobold.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 1 -1 -1 0 0 -1 0.000 0 1 1 0 2652.330 1587.076 600 600 375 1575 525 2400 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 -1 0 0 -1 0.000 0 1 1 0 3718.581 1734.122 1725 600 1425 1725 1575 2550 + 0 0 1.00 60.00 120.00 +5 1 0 1 -1 -1 0 0 -1 0.000 0 1 1 0 4843.581 1734.122 2850 600 2550 1725 2700 2550 + 0 0 1.00 60.00 120.00 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 825 2550 300 225 825 2550 1125 2775 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 825 1425 300 225 825 1425 1125 1650 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1950 2550 300 225 1950 2550 2250 2775 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1950 1425 300 225 1950 1425 2250 1650 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 3075 1425 300 225 3075 1425 3375 1650 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 3075 2550 300 225 3075 2550 3375 2775 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 750 825 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 1725 825 2250 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 750 1950 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 1425 1650 1425 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3075 750 3075 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2250 1425 2775 1425 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 675 300 1050 300 1050 750 675 750 675 300 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1800 300 2175 300 2175 750 1800 750 1800 300 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 525 1800 525 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 1725 1950 2250 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3075 1725 3075 2250 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2850 300 3225 300 3225 750 2850 750 2850 300 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2100 525 2850 525 +4 0 -1 0 0 0 12 0.0000 4 135 225 675 1500 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 675 2625 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1800 1500 X2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1800 2625 Y2\001 +4 0 0 50 0 0 12 0.0000 4 135 225 2850 1500 X3\001 +4 0 0 0 0 0 12 0.0000 4 135 225 2925 2625 Y3\001 +4 0 0 50 0 0 12 0.0000 4 135 210 750 600 Z1\001 +4 0 0 50 0 0 12 0.0000 4 135 210 1875 600 Z2\001 +4 0 0 50 0 0 12 0.0000 4 135 210 2925 600 Z3\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/skf3_nosolid.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 2 0 7 50 0 -1 0.000 0 1 1 0 2652.330 1587.076 600 600 375 1575 525 2400 + 1 1 2.00 120.00 240.00 +5 1 0 2 0 7 50 0 -1 0.000 0 1 1 0 3718.581 1734.122 1725 600 1425 1725 1575 2550 + 1 1 2.00 120.00 240.00 +5 1 0 2 0 7 50 0 -1 0.000 0 1 1 0 4843.581 1734.122 2850 600 2550 1725 2700 2550 + 1 1 2.00 120.00 240.00 +1 1 0 2 0 7 50 0 -1 0.000 1 0.0000 1950 1425 300 225 1950 1425 2250 1650 +1 1 0 2 0 7 50 0 -1 0.000 1 0.0000 825 1425 300 225 825 1425 1125 1650 +1 1 0 2 0 0 50 0 7 0.000 1 0.0000 825 2550 300 225 825 2550 1125 2775 +1 1 0 2 0 0 50 0 7 0.000 1 0.0000 3075 2550 300 225 3075 2550 3375 2775 +1 1 0 2 0 0 50 0 7 0.000 1 0.0000 1950 2550 300 225 1950 2550 2250 2775 +1 1 0 2 0 7 50 0 -1 0.000 1 0.0000 3075 1425 300 225 3075 1425 3375 1650 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 825 1725 825 2250 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 1125 1425 1650 1425 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 1800 300 2175 300 2175 750 1800 750 1800 300 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 2100 525 2850 525 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 2850 300 3225 300 3225 750 2850 750 2850 300 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 1950 1725 1950 2250 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 2250 1425 2775 1425 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 1950 750 1950 1200 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 3075 750 3075 1200 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 3075 1725 3075 2250 +2 1 0 2 0 7 50 0 -1 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 825 750 825 1200 +2 2 0 2 0 7 50 0 -1 0.000 0 0 7 0 0 5 + 675 300 1050 300 1050 750 675 750 675 300 +2 1 0 2 0 0 50 0 7 0.000 0 0 7 1 0 2 + 1 1 2.00 120.00 240.00 + 1050 525 1800 525 +4 0 0 50 0 2 12 0.0000 4 135 225 675 2625 Y1\001 +4 0 0 50 0 2 12 0.0000 4 135 225 2925 2625 Y3\001 +4 0 0 50 0 2 12 0.0000 4 135 225 675 1500 X1\001 +4 0 0 50 0 2 12 0.0000 4 135 225 1800 1500 X2\001 +4 0 0 50 0 2 12 0.0000 4 135 225 2850 1500 X3\001 +4 0 0 50 0 2 12 0.0000 4 135 210 750 600 Z1\001 +4 0 0 50 0 2 12 0.0000 4 135 210 1875 600 Z2\001 +4 0 0 50 0 2 12 0.0000 4 135 210 2925 600 Z3\001 +4 0 0 50 0 2 12 0.0000 4 135 225 1875 2625 Y2\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/skf3_polytree.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 825 2550 300 225 825 2550 1125 2775 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 825 1425 300 225 825 1425 1125 1650 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 1950 2550 300 225 1950 2550 2250 2775 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 1950 1425 300 225 1950 1425 2250 1650 +1 1 0 1 -1 -1 0 0 -1 0.000 1 0.0000 3075 1425 300 225 3075 1425 3375 1650 +1 1 0 1 -1 0 0 0 2 0.000 1 0.0000 3075 2550 300 225 3075 2550 3375 2775 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 750 825 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 825 1725 825 2250 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 750 1950 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1125 1425 1650 1425 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3075 750 3075 1200 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2250 1425 2775 1425 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 675 300 1050 300 1050 750 675 750 675 300 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 1800 300 2175 300 2175 750 1800 750 1800 300 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1050 525 1800 525 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 1725 1950 2250 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 3075 1725 3075 2250 +2 2 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 5 + 2850 300 3225 300 3225 750 2850 750 2850 300 +2 1 0 1 -1 -1 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2100 525 2850 525 +4 0 -1 0 0 0 12 0.0000 4 165 225 750 600 Q1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 675 1500 X1\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 675 2625 Y1\001 +4 0 -1 0 0 0 12 0.0000 4 165 225 1875 600 Q2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1800 1500 X2\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 1800 2625 Y2\001 +4 0 0 50 0 0 12 0.0000 4 165 225 2925 600 Q3\001 +4 0 0 50 0 0 12 0.0000 4 135 225 2850 1500 X3\001 +4 0 0 0 0 0 12 0.0000 4 135 225 2925 2625 Y3\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/sprinkler.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +#FIG 3.2 +Portrait +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +6 3675 1275 6000 3450 +1 1 0 1 -1 7 0 0 -1 0.000 1 0.0000 4125 2400 450 225 4125 2400 4575 2625 +1 1 0 1 -1 7 0 0 -1 0.000 1 0.0000 4800 1500 450 225 4800 1500 5250 1725 +1 1 0 1 -1 7 0 0 -1 0.000 1 0.0000 5550 2400 450 225 5550 2400 6000 2625 +1 1 0 1 -1 7 0 0 -1 0.000 1 0.0000 4875 3225 450 225 4875 3225 5325 3450 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 4650 1725 4200 2175 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 5025 1725 5400 2175 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 4350 2625 4800 3000 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 5475 2700 5100 3000 +4 0 -1 0 0 0 12 0.0000 4 180 525 4575 1575 Cloudy\001 +4 0 -1 0 0 0 12 0.0000 4 180 675 3825 2475 Sprinkler\001 +4 0 -1 0 0 0 12 0.0000 4 135 345 5400 2475 Rain\001 +4 0 -1 0 0 0 12 0.0000 4 135 795 4500 3300 WetGrass\001 +-6 +6 3975 3900 6000 5775 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 3975 4275 6000 4275 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 1 + 4425 3975 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 4485 3960 4485 5730 +4 0 -1 0 0 0 12 0.0000 4 135 300 4050 4500 F F\001 +4 0 -1 0 0 0 12 0.0000 4 135 810 4650 4500 1.0 0.0\001 +4 0 -1 0 0 0 12 0.0000 4 135 300 4050 4875 T F\001 +4 0 -1 0 0 0 12 0.0000 4 135 300 4050 5250 F T\001 +4 0 -1 0 0 0 12 0.0000 4 135 345 4050 5625 T T\001 +4 0 -1 0 0 0 12 0.0000 4 135 855 4650 4875 0.1 0.9\001 +4 0 -1 0 0 0 12 0.0000 4 135 855 4650 5250 0.1 0.9\001 +4 0 -1 0 0 0 12 0.0000 4 135 900 4650 5625 0.01 0.99\001 +4 0 -1 0 0 0 12 0.0000 4 180 1785 4125 4230 S R P(W=F) P(W=T)\001 +-6 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 4125 525 5700 525 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 1650 2625 3375 2625 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 2025 2325 2025 3300 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 6525 2700 8250 2700 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 0 0 2 + 6900 2400 6900 3450 +4 0 -1 0 0 0 12 0.0000 4 180 1260 4290 450 P(C=F) P(C=T)\001 +4 0 -1 0 0 0 12 0.0000 4 135 855 4425 825 0.5 0.5\001 +4 0 -1 0 0 0 12 0.0000 4 135 105 1725 2925 F\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 2175 2925 0.5\001 +4 0 -1 0 0 0 12 0.0000 4 135 225 2775 2925 0.5\001 +4 0 -1 0 0 0 12 0.0000 4 135 105 1725 3225 T\001 +4 0 -1 0 0 0 12 0.0000 4 135 900 2175 3225 0.9 0.1\001 +4 0 -1 0 0 0 12 0.0000 4 135 120 6600 2625 C\001 +4 0 -1 0 0 0 12 0.0000 4 135 105 6600 3000 F\001 +4 0 -1 0 0 0 12 0.0000 4 135 105 6525 3375 T\001 +4 0 -1 0 0 0 12 0.0000 4 135 945 7050 3375 0.2 0.8\001 +4 0 -1 0 0 0 12 0.0000 4 135 945 7050 3000 0.8 0.2\001 +4 0 0 100 0 0 12 0.0000 4 135 120 1650 2550 C\001 +4 0 0 100 0 0 12 0.0000 4 180 1185 2100 2550 P(S=F) P(S=T)\001 +4 0 0 100 0 0 12 0.0000 4 180 1215 6975 2625 P(R=F) P(R=T)\001
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/sprinkler.noparams.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +#FIG 3.2 +Portrait +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +6 600 300 2925 2475 +1 1 0 1 -1 7 0 0 -1 0.000 1 0.0000 1050 1425 450 225 1050 1425 1500 1650 +1 1 0 1 -1 7 0 0 -1 0.000 1 0.0000 1725 525 450 225 1725 525 2175 750 +1 1 0 1 -1 7 0 0 -1 0.000 1 0.0000 2475 1425 450 225 2475 1425 2925 1650 +1 1 0 1 -1 7 0 0 -1 0.000 1 0.0000 1800 2250 450 225 1800 2250 2250 2475 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1575 750 1125 1200 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1950 750 2325 1200 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 1275 1650 1725 2025 +2 1 0 1 -1 7 0 0 -1 0.000 0 0 -1 1 0 2 + 0 0 1.00 60.00 120.00 + 2400 1725 2025 2025 +4 0 -1 0 0 0 12 0.0000 4 180 525 1500 600 Cloudy\001 +4 0 -1 0 0 0 12 0.0000 4 180 675 750 1500 Sprinkler\001 +4 0 -1 0 0 0 12 0.0000 4 135 345 2325 1500 Rain\001 +4 0 -1 0 0 0 12 0.0000 4 135 795 1425 2325 WetGrass\001 +-6
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/water3.cts.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,266 @@ +#FIG 3.2 +Landscape +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 2 0 0 100 0 -1 0.000 0 0 1 0 3229.069 1880.389 1950 3450 1275 1350 1875 375 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 100 0 -1 0.000 0 1 1 0 4200.000 6900.000 1950 5700 1650 6900 1950 8100 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 100 0 -1 0.000 0 1 1 0 3787.500 6900.000 1950 4950 1125 7200 1950 8850 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 100 0 -1 0.000 0 0 1 0 7232.812 1950.000 5775 3450 5175 1575 5775 450 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 100 0 -1 0.000 0 1 1 0 7387.500 6975.000 5775 5700 5400 7500 5775 8250 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 100 0 -1 0.000 0 1 1 0 7188.461 6911.539 5775 4950 5175 8250 5850 8925 + 1 1 1.00 60.00 120.00 +6 2100 75 4425 9225 +5 1 0 2 0 0 100 0 -1 0.000 0 0 1 0 5282.812 1950.000 3825 3450 3225 1575 3825 450 + 1 1 1.00 60.00 120.00 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/Figures/water3.fig Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,279 @@ +#FIG 3.2 +Portrait +Center +Inches +Letter +100.00 +Single +-2 +1200 2 +5 1 0 2 0 0 100 0 -1 0.000 0 0 1 0 7232.812 1950.000 5775 3450 5175 1575 5775 450 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 100 0 -1 0.000 0 0 1 0 5282.812 1950.000 3825 3450 3225 1575 3825 450 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 7 100 0 -1 0.000 0 1 1 0 5445.833 6562.500 1950 4200 1275 7200 1950 8925 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 7 100 0 -1 0.000 0 1 1 0 7595.833 6637.500 3825 4275 3150 6825 3825 9000 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 7 100 0 -1 0.000 0 1 1 0 8419.351 6558.096 5700 4200 5100 7950 5775 9000 + 1 1 1.00 60.00 120.00 +5 1 0 2 0 0 100 0 -1 0.000 0 0 1 0 3332.812 2025.000 1875 3525 1275 1650 1875 525 + 1 1 1.00 60.00 120.00 +2 2 0 3 -1 -1 0 0 -1 0.000 0 0 7 0 0 5 + 2400 7425 2400 6975 1950 6975 1950 7425 2400 7425 +2 2 0 3 -1 -1 0 0 -1 0.000 0 0 7 0 0 5 + 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5 + 2400 9150 2400 8700 1950 8700 1950 9150 2400 9150 +2 2 0 2 0 0 100 0 3 0.000 0 0 7 0 0 5 + 4275 9150 4275 8700 3825 8700 3825 9150 4275 9150 +2 2 0 2 0 0 100 0 3 0.000 0 0 7 0 0 5 + 6225 9150 6225 8700 5775 8700 5775 9150 6225 9150 +2 2 0 2 0 0 100 0 3 0.000 0 0 7 0 0 5 + 2400 1200 2400 750 1950 750 1950 1200 2400 1200 +2 2 0 2 0 0 100 0 3 0.000 0 0 7 0 0 5 + 2400 600 2400 150 1950 150 1950 600 2400 600 +2 1 0 2 0 7 100 0 -1 0.000 0 0 -1 1 0 2 + 1 1 1.00 60.00 120.00 + 2175 1650 2175 1200
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/adj2pajek2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +% ADJ2PAJEK2 Converts an adjacency matrix representation to a Pajek .net read format +% adj2pajek2(adj, filename-stem, 'argname1', argval1, ...) +% +% Set A(i,j)=-1 to get a dotted line +% +% Optional arguments +% +% nodeNames - cell array, defaults to {'v1','v2,...} +% shapes - cell array, defaults to {'ellipse','ellipse',...} +% Choices are 'ellipse', 'box', 'diamond', 'triangle', 'cross', 'empty' +% partition - vector of integers, defaults to [1 1 ... 1] +% This will automatically color-code the vertices by their partition +% +% Run pajek (available from http://vlado.fmf.uni-lj.si/pub/networks/pajek/) +% Choose File->Network->Read from the menu +% Then press ctrl-G (Draw->Draw) +% Optional: additionally load the partition file then press ctrl-P (Draw->partition) +% +% Examples +% A=zeros(5,5);A(1,2)=-1;A(2,1)=-1;A(1,[3 4])=1;A(2,5)=1; +% adj2pajek2(A,'foo') % makes foo.net +% +% adj2pajek2(A,'foo','partition',[1 1 2 2 2]) % makes foo.net and foo.clu +% +% adj2pajek2(A,'foo',... +% 'nodeNames',{'TF1','TF2','G1','G2','G3'},... +% 'shapes',{'box','box','ellipse','ellipse','ellipse'}); +% +% +% The file format is documented on p68 of the pajek manual +% and good examples are on p58, p72 +% +% Written by Kevin Murphy, 30 May 2007 +% Based on adj2pajek by Gergana Bounova +% http://stuff.mit.edu/people/gerganaa/www/matlab/routines.html +% Fixes a small bug (opens files as 'wt' instead of 'w' so it works in windows) +% Also, simplified her code and added some features. + +function []=adj2pajek2(adj,filename, varargin) + +N = length(adj); +for i=1:N + nodeNames{i} = strcat('"v',num2str(i),'"'); + shapes{i} = 'ellipse'; +end + +[nodeNames, shapes, partition] = process_options(varargin, ... + 'nodeNames', nodeNames, 'shapes', shapes, 'partition', []); + +if ~isempty(partition) + fid = fopen(sprintf('%s.clu', filename),'wt','native'); + fprintf(fid,'*Vertices %6i\n',N); + for i=1:N + fprintf(fid, '%d\n', partition(i)); + end + fclose(fid); +end + +fid = fopen(sprintf('%s.net', filename),'wt','native'); + +fprintf(fid,'*Vertices %6i\n',N); +for i=1:N + fprintf(fid,'%3i %s %s\n', i, nodeNames{i}, shapes{i}); +end + +%fprintf(fid,'*Edges\n'); +fprintf(fid,'*Arcs\n'); % directed +for i=1:N + for j=1:N + if adj(i,j) ~= 0 + fprintf(fid,' %4i %4i %2i\n',i,j,adj(i,j)); + end + end +end +fclose(fid) + + +if 0 +adj2pajek2(A,'foo',... + 'nodeNames',{'TF1','TF2','G1','G2','G3'},... + 'shapes',{'box','box','ellipse','ellipse','ellipse'}); + +N = 100; part = ones(1,N); part(intersect(reg.tfidxTest,1:N))=2; +G = reg.Atest(1:N, 1:N)'; +adj2pajek2(G, 'Ecoli100', 'partition', part) +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/bnsoftOld.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1078 @@ +<head> +<TITLE>Software Packages for Graphical Models / Bayesian Networks</TITLE> +</head> + +<body> + +<h1> +Software Packages for Graphical Models / Bayesian Networks +</h1> +<p> +Written by Kevin Murphy. +<br> +Last updated 31 October 2005. + +<h3>Remarks</h3> +<ul> + +<li> +A much more detailed comparison of some of these software packages is +available from Appendix B of +<a href="http://www.csse.monash.edu.au/bai/">Bayesian AI</a>, by +Ann Nicholson and Kevin Korb. +This appendix is +available +<a +href="http://www.csse.monash.edu.au/bai/book/appendix_b.pdf">here</a>, +and is based on the online comparison below. + +<li> +An online <a +href="http://servasi.insa-rouen.fr/enseignement/siteUV/rna/BNT/software.html">French +version</a> of this page is also available (not necessarily up-to-date). + +<!-- +<li> +Matt Turk has made a +<a href="http://ilab.cs.ucsb.edu/BayesNets/Bayesia_Software.pdf">pdf +version of this page</a> (not necessarily up-to-date) with some extra +comments on the packages. +--> +<!-- +<br> +(He also has a +<a href="http://ilab.cs.ucsb.edu/BayesNets">cached copy</a> of several of +the packages listed below, which you can look at if links are down.) +--> + +</ul> + +<h3>What do the headers in the table mean?</h3> + +<ul> +<li> Src = source code included? (N=no) If so, what language? + +<li> API = application program interface included? +(N means the program cannot be integrated into your code, i.e., it +must be run as a standalone executable.) + +<li> Exec = Executable runs on W = Windows (95/98/NT), U = Unix, M = +Mac, or - = any machine with a compiler. + +<li> Cts = are continuous (latent) nodes supported? +G = (conditionally) Gaussians nodes supported analytically, +Cs = continuous nodes supported by sampling, +Cd = continuous nodes supported by discretization, +Cx = continuous nodes supported by some unspecified method, +D = only discrete nodes supported. + + +<li> GUI = Graphical User Interface included? + +<li> Learns parameters? + +<li> Learns structure? CI = means uses conditional independency tests + +<!-- +<li> Sample = sampling methods (e.g., likelihood weighting, MCMC) supported? +--> + +<li> Utility = utility and decision nodes (i.e., influence diagrams) +supported? + +<li> Free? +0 = free (although possibly only for academic use). +$ = commercial software (although most have free versions +which are restricted in +various ways, e.g., the model size is limited, or models cannot be +saved, or there is no API.) + + +<li> Undir? +What kind of graphs are supported? +U = only undirected graphs, +D = only directed graphs, +UD = both undirected and directed, +CG = chain graphs (mixed directed/undirected). + + +<li> Inference = which inference algorithm is used? +jtree = junction tree, +varelim = variable (bucket) elimination, +MH = Metropols Hastings, +G = Gibbs sampling, +IS = importance sampling, +sampling = some other Monte Carlo method, +polytree = Pearl's algorithm restricted to a graph with no cycles, +none = no inference supported (hence the program is only designed for +structure learning from completely observed data) + +<li> Comments. +If in "quotes", I am quoting the authors at their request. + +</ul> + + + +<!-- +The following table is automatically generated as follows +bnsoft_to_html.pl < bnsoft.txt > ! soft.html +Then soft.html is inserted and the last line is tidied up. +--> + +<table border> +<tr> +<td> Name +<td> Authors +<td> Src +<td> API +<td> Exec +<td> Cts +<td> GUI +<td> Params +<td> Struct +<td> Utility +<td> Free +<td> Undir +<td> Inference +<td> Comments + + +<tr> +<td> <!--Name--> <a href=http://www.agenarisk.com > AgenaRisk</a> +<td> <!--Authors-->Agena +<td> <!--Src-->N +<td> <!--API-->Y +<td> <!--Exec-->W,U +<td> <!--Cts-->Cx +<td> <!--GUI-->Y +<td> <!--Params-->Y +<td> <!--Struct-->N +<td> <!--Utility-->N +<td> <!--Free-->$ +<td> <!--Undir-->D +<td> <!--Inference-->JTree +<td> <!--Comments-->Simulation by Dynamic discretisation + +<tr> +<td> <!--Name--> <a href=http://www.lumina.com > Analytica</a> +<td> <!--Authors-->Lumina +<td> <!--Src-->N +<td> <!--API-->Y +<td> <!--Exec-->W,M +<td> <!--Cts-->G +<td> <!--GUI-->Y +<td> <!--Params-->N +<td> <!--Struct-->N +<td> <!--Utility-->Y +<td> <!--Free-->$ +<td> <!--Undir-->D +<td> <!--Inference-->sampling +<td> <!--Comments-->spread sheet compatible + +<tr> +<td> <!--Name--> <a href=http://www.cs.duke.edu/~amink/software/banjo/> Banjo</a> +<td> <!--Authors-->Hartemink +<td> <!--Src-->Java +<td> <!--API-->Y +<td> <!--Exec-->W,U,M +<td> <!--Cts-->Cd +<td> <!--GUI-->N +<td> <!--Params-->N +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->none +<td> <!--Comments--> structure learning of +static or dynamic networks of discrete variables + + +<tr> +<td><a href=http://www.cs.Helsinki.FI/research/fdk/bassist/> Bassist</a> +<td> U. Helsinki +<td> C++ +<td> Y +<td> U +<td> G +<td> N +<td> Y +<td> N +<td> N +<td> 0 +<td> D +<td> MH +<td> Generates C++ for MCMC. + +<tr> +<td><a href=http://www.cs.Helsinki.FI/research/cosco/Projects/NONE/SW/ > Bayda</a> +<td> U. Helsinki +<td> Java +<td> Y +<td> WUM +<td> G +<td> Y +<td> Y +<td> N +<td> N +<td> 0 +<td> D +<td> ? +<td> Bayesian Naive Bayes classifier. + +<tr> +<td><a href=http://www.mbfys.kun.nl/snn/Research/bayesbuilder/ > BayesBuilder</a> +<td> Nijman (U. Nijmegen) +<td> N +<td> N +<td> W +<td> D +<td> Y +<td> N +<td> N +<td> N +<td> 0 +<td> D +<td> ? +<td> - + +<tr> +<td> <!--Name--> <a +href="http://www.bayesia.com">BayesiaLab</a> +<td> <!--Authors-->Bayesia Ltd +<td> <!--Src-->N +<td> <!--API-->N +<td> <!--Exec-->- +<td> <!--Cts-->Cd +<td> <!--GUI-->Y +<td> <!--Params-->Y +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->$ +<td> <!--Undir-->CG +<td> <!--Inference-->jtree,G +<td> <!--Comments--> +<!-- +Structural learning (association discovery, +classification), Bayesian clustering with analysis report generation, +missing values +processing, essential graphs, analysis toolbox, adaptive +questionnaires, dynamic models +--> +Structural learning, adaptive +questionnaires, dynamic models + + + + +<tr> + +<td><a href=http://www.bayesware.com>Bayesware Discoverer</a> +<td> Bayesware +<td> N +<td> N +<td> WUM +<td> Cd +<td> Y +<td> Y +<td> Y +<td> N +<td> $ +<td> D +<td> ? +<td> Uses bound and collapse for learning with missing data. + +<tr> +<td><a href=http://B-Course.hiit.fi/> B-course</a> +<td> U. Helsinki +<td> N +<td> N +<td> WUM +<td> Cd +<td> Y +<td> Y +<td> Y +<td> N +<td> 0 +<td> D +<td> ? +<td> Runs on their server: view results using a web browser. + +<!-- +<tr> +<td><a href="http://www.aist.go.jp/ETL/etl/suri/motomura/BN/bn-java.html">Bayonnet</a> +<td> Motomura (ETL) +<td> Java +<td> Y +<td> WUM +<td> NN +<td> Cx +<td> Y +<td> N +<td> N +<td> 0 +<td> D +<td> ? +<td> For learning, represents BN as a neural net. +--> + +<tr> +<td><a href=http://www.cs.ualberta.ca/~jcheng/bnpc.htm > Belief net power constructor</a> +<td> Cheng (U.Alberta) +<td> N +<td> W +<td> W +<td> D +<td> Y +<td> Y +<td> CI +<td> N +<td> 0 +<td> D +<td> ? +<td> - + +<tr> +<td><a href=http://www.cs.berkeley.edu/~murphyk/Bayes/bnt.html > BNT</a> +<td> Murphy (U.C.Berkeley) +<td> Matlab/C +<td> Y +<td> WUM +<td> G +<td> N +<td> Y +<td> Y +<td> Y +<td> 0 +<td> D,U +<td> Many +<td> Also handles dynamic models, like HMMs and Kalman filters. + + +<tr> +<td> <!--Name--> <a href="http://bndev.sourceforge.net/">BNJ</a> +<td> <!--Authors-->Hsu (Kansas) +<td> <!--Src-->Java +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->D +<td> <!--GUI-->Y +<td> <!--Params-->N +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->jtree, IS +<td> <!--Comments-->- + +<!-- +<tr> +<td><a href=http://94.parkview-court.net/project/ > BN Toolkit</a> +<td> Gowans (Imperial) +<td> Visual Basic +<td> Y +<td> W +<td> D +<td> Y +<td> N +<td> Y +<td> N +<td> 0 +<td> D +<td> Polytree +<td> Parser and GUI for the XML-BIF format. +--> + +<tr> +<td><a href=http://www.ics.uci.edu/~irinar > BucketElim</a> +<td> Rish (U.C.Irvine) +<td> C++ +<td> Y +<td> WU +<td> D +<td> N +<td> N +<td> N +<td> N +<td> 0 +<td> D +<td> Varelim +<td> - + +<tr> +<td><a href=http://www.mrc-bsu.cam.ac.uk/bugs > BUGS</a> +<td> MRC/Imperial College +<td> N +<td> N +<td> WU +<td> Cs +<td> W +<td> Y +<td> N +<td> N +<td> 0 +<td> D +<td> Gibbs +<td> - + +<tr> +<td><a href=http://www.data-digest.com > Business Navigator 5</a> +<td> Data Digest Corp +<td> N +<td> N +<td> W +<td> Cd +<td> Y +<td> Y +<td> Y +<td> N +<td> $ +<td> D +<td> Jtree +<td> - + +<tr> +<td><a href=http://www-pcd.stanford.edu/cousins/caben-1.1.tar.gz > CABeN</a> +<td> Cousins et al. (Wash. U.) +<td> C +<td> Y +<td> WU +<td> D +<td> N +<td> N +<td> N +<td> N +<td> 0 +<td> D +<td> 5 Sampling methods +<td> - + +<tr> +<td> <!--Name--> <a href= +http://discover1.mc.vanderbilt.edu/discover/public/>Causal discoverer</a> +<td> <!--Authors-->Vanderbilt +<td> <!--Src-->N +<td> <!--API-->N +<td> <!--Exec-->W +<td> <!--Cts-->- +<td> <!--GUI-->- +<td> <!--Params-->N +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->- +<td> <!--Comments-->structure learning only + + +<tr> +<td><a href=http://www.math.auc.dk/~jhb/CoCo/information.html > CoCo+Xlisp</a> +<td> Badsberg (U. Aalborg) +<td> C/lisp +<td> Y +<td> U +<td> D +<td> Y +<td> Y +<td> CI +<td> N +<td> 0 +<td> U +<td> Jtree +<td> Designed for contingency tables. + +<tr> +<td><a href=http://www.cs.ubc.ca/labs/lci/CIspace/ > CIspace</a> +<td> Poole et al. (UBC) +<td> Java +<td> N +<td> WU +<td> D +<td> Y +<td> N +<td> N +<td> N +<td> 0 +<td> D +<td> Varelim +<td> - + +<tr> +<td> <!--Name--> <a href="http://www.robots.ox.ac.uk/~parg/software.html">DBNbox</a> +<td> <!--Authors-->Roberts et al +<td> <!--Src-->Matlab +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->Y +<td> <!--GUI-->N +<td> <!--Params-->Y +<td> <!--Struct-->N +<td> <!--Utility-->N + +<td> <!--Free-->Y +<td> <!--Undir-->D +<td> <!--Inference-->Various +<td> <!--Comments-->DBNs + +<tr> +<td> <!--Name--><a href=http://www.math.auc.dk/novo/deal>Deal</a> +<td> <!--Authors-->Bottcher et al +<td> <!--Src-->R +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->G +<td> <!--GUI-->Y +<td> <!--Params-->Y +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->None +<td> <!--Comments--> +Structure learning. + +<tr> +<td><a href=http://www.deriveit.com >DeriveIt</a> +<td> <!--Authors-->DeriveIt LLC +<td> <!--Src-->N +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->? +<td> <!--GUI-->? +<td> <!--Params-->Y +<td> <!--Struct-->Y +<td> <!--Utility-->? +<td> <!--Free-->$ +<td> <!--Undir-->D +<td> <!--Inference-->Jtree +<td> <!--Comments--> +Exploits local structure in CPDs. + + +<tr> +<td><a href=http://www.noeticsystems.com>Ergo</> +<td> <!--Authors-->Noetic systems +<td> <!--Src-->N +<td> <!--API-->Y +<td> <!--Exec-->W,M +<td> <!--Cts-->D +<td> <!--GUI-->Y +<td> <!--Params-->N +<td> <!--Struct-->N +<td> <!--Utility-->N +<td> <!--Free-->$ +<td> <!--Undir-->D +<td> <!--Inference-->jtree +<td> <!--Comments-->- + +<!-- +<tr> +<td><a href=http://www.ens-lyon.fr/~jnarboux/Floue/index.html>FLoUE/BIFtoN</a> +<td> ENS Lyon +<td> Java +<td> Y +<td> WUM +<td> D +<td> N +<td> N +<td> N +<td> N +<td> 0 +<td> D +<td> Jtree +<td> - +--> + +<tr> +<td><a href=http://www.staff.ncl.ac.uk/d.j.wilkinson/software/gdagsim/ > GDAGsim</a> +<td> Wilkinson (U. Newcastle) +<td> C +<td> Y +<td> WUM +<td> G +<td> N +<td> N +<td> N +<td> N +<td> 0 +<td> D +<td> Exact +<td> Bayesian analysis of large linear Gaussian directed models. + + +<tr> +<td><a href=http://www2.sis.pitt.edu/~genie > Genie</a> +<td> U. Pittsburgh +<td> N +<td> WU +<td> WU +<td> D +<td> W +<td> N +<td> N +<td> Y +<td> 0 +<td> D +<td> Jtree +<td> - + +<tr> +<td><a href=http://www.math.ntnu.no/~hrue/GMRFsim/ > GMRFsim</a> +<td> Rue (U. Trondheim) +<td> C +<td> Y +<td> WUM +<td> G +<td> N +<td> N +<td> N +<td> N +<td> 0 +<td> U +<td> MCMC +<td> Bayesian analysis of large linear Gaussian undirected models. + + +<tr> +<td> <!--Name--> <a href=http://ssli.ee.washington.edu/~bilmes/gmtk/>GMTk</a> +<td> <!--Authors-->Bilmes (UW), Zweig (IBM) +<td> <!--Src-->N +<td> <!--API-->Y +<td> <!--Exec-->U +<td> <!--Cts-->D +<td> <!--GUI-->N +<td> <!--Params-->Y +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->Jtree +<td> <!--Comments--> +Designed for speech recognition. + +<tr> +<td> <!--Name--> <a href=http://www.r-project.org/gR>gR</a> +<td> <!--Authors-->Lauritzen et al. +<td> <!--Src-->R +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->- +<td> <!--GUI-->- +<td> <!--Params-->- +<td> <!--Struct-->- +<td> <!--Utility-->- +<td> <!--Free-->0 +<td> <!--Undir-->- +<td> <!--Inference-->- +<td> <!--Comments-->Currently vaporware + + +<tr> +<td> <!--Name--> <a href=http://www.stats.bris.ac.uk/~peter/Grappa/>Grappa</a> +<td> <!--Authors-->Green (Bristol) +<td> <!--Src-->R +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->D +<td> <!--GUI-->N +<td> <!--Params-->N) +<td> <!--Struct-->N +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->Jtree +<td> <!--Comments-->- + + +<tr> +<td><a href=http://www.hugin.com > Hugin Expert</a> +<td> Hugin +<td> N +<td> Y +<td> W +<td> G +<td> W +<td> Y +<td> CI +<td> Y +<td> $ +<td> CG +<td> Jtree +<td> - + + +<tr> +<td> <!--Name--> <a href=http://www.warnes.net/GregsSoftwareLinks/index_html?Tab=MCMC>Hydra</a> +<td> <!--Authors-->Warnes (U.Wash.) +<td> <!--Src-->Java +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->Cs +<td> <!--GUI-->Y +<td> <!--Params-->Y +<td> <!--Struct-->N +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->U,D +<td> <!--Inference-->MCMC +<td> <!--Comments-->- + + +<tr> +<!-- http://www.rpal.rockwell.com/ideal.html --> +<td><a href=http://yoda.cis.temple.edu:8080/ideal>Ideal</a> +<td> Rockwell +<td> Lisp +<td> Y +<td> WUM +<td> D +<td> Y +<td> N +<td> N +<td> Y +<td> 0 +<td> D +<td> Jtree +<td> GUI requires Allegro Lisp. + +<tr> +<td><a href=http://www.cs.cmu.edu/~javabayes/Home/ > Java Bayes</a> +<td> Cozman (CMU) +<td> Java +<td> Y +<td> WUM +<td> D +<td> Y +<td> N +<td> N +<td> Y +<td> 0 +<td> D +<td> Varelim, jtree +<td> - + + +<tr> +<td> <!--Name--><a href="http://www.codeas.com/kbaseai.php" >KBaseAI</a> +<td> <!--Authors-->Codeas +<td> <!--Src-->N +<td> <!--API-->Y +<td> <!--Exec-->W,U +<td> <!--Cts-->D +<td> <!--GUI-->N +<td> <!--Params-->N +<td> <!--Struct-->N +<td> <!--Utility-->N +<td> <!--Free-->$ +<td> <!--Undir-->D +<td> <!--Inference-->varelim +<td> <!--Comments-->client/server architecture, multiple users, access +control, query language + + +<tr> +<td> <!--Name--> <a href=http://www.cs.huji.ac.il/labs/compbio/LibB>LibB</a> +<td> <!--Authors-->Friedman (Hebrew U) +<td> <!--Src-->N +<td> <!--API-->Y +<td> <!--Exec-->W +<td> <!--Cts-->D +<td> <!--GUI-->N +<td> <!--Params-->Y +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->none +<td> <!--Comments--> +Structure learning + +<tr> +<td><a href=http://www.hypergraph.dk/ > MIM</a> +<td> HyperGraph Software +<td> N +<td> N +<td> W +<td> G +<td> Y +<td> Y +<td> Y +<td> N +<td> $ +<td> CG +<td> Jtree +<td> Up to 52 variables. + +<tr> +<td><a href=http://research.microsoft.com/adapt/MSBNx/ > MSBNx</a> +<td> Microsoft +<td> N +<td> Y +<td> W +<td> D +<td> W +<td> N +<td> N +<td> Y +<td> 0 +<td> D +<td> Jtree +<td> - + +<tr> +<td><a href=http://www.norsys.com > Netica</a> +<td> Norsys +<td> N +<td> WUM +<td> W +<td> G +<td> W +<td> Y +<td> N +<td> Y +<td> $ +<td> D +<td> jtree +<td> - + +<tr> +<td> <!--Name--> <a href="http://www.autonlab.org/autonweb/showSoftware/149/">Optimal +Reinsertion</a> +<td> <!--Authors-->Moore, Wong (CMU) +<td> <!--Src-->N +<td> <!--API-->N +<td> <!--Exec-->W,U +<td> <!--Cts-->D +<td> <!--GUI-->N +<td> <!--Params-->Y +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->none +<td> <!--Comments-->structure learning + + +<tr> +<td> <!--Name--> <a href="http://people.bu.edu/vladimir/pmt/index.html">PMT</a> +<td> <!--Authors-->Pavlovic (BU) +<td> <!--Src-->Matlab/C +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->D +<td> <!--GUI-->N +<td> <!--Params-->Y +<td> <!--Struct-->N +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->special purpose +<td> <!--Comments-->- + + + +<tr> +<td> <!--Name--> <a href=http://www.intel.com/research/mrl/pnl/>PNL</a> +<td> <!--Authors-->Eruhimov (Intel) +<td> <!--Src-->C++ +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->D +<td> <!--GUI-->N +<td> <!--Params-->Y +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->U,D +<td> <!--Inference-->Jtree +<td> <!--Comments--> +A C++ version of BNT; will be released 12/03. + + +<tr> +<td><a href=http://iridia.ulb.ac.be/pulcinella/Welcome.html > Pulcinella</a> +<td> IRIDIA +<td> Lisp +<td> Y +<td> WUM +<td> D +<td> Y +<td> N +<td> N +<td> N +<td> 0 +<td> D +<td> ? +<td> Uses valuation systems for non-probabilistic calculi. + +<tr> +<!-- http://civil.colorado.edu/~dodier/sonero-mirror/index.html --> +<td><a href=http://sourceforge.net/projects/riso>RISO</a> +<td> Dodier (U.Colorado) +<td> Java +<td> Y +<td> WUM +<td> G +<td> Y +<td> N +<td> N +<td> N +<td> 0 +<td> D +<td> Polytree +<td> Distributed implementation. + + + +<tr> +<td> <!--Name--> <a href=http://reasoning.cs.ucla.edu/samiam/> Sam Iam</a> +<td> <!--Authors-->Darwiche (UCLA) +<td> <!--Src-->N +<td> <!--API-->N ? +<td> <!--Exec-->WU ? (Java executable) +<td> <!--Cts-->G ? +<td> <!--GUI-->Y +<td> <!--Params-->Y +<td> <!--Struct-->N ? +<td> <!--Utility-->Y +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->Recursive conditioning +<td> <!--Comments-->Also does sensitivity Analysis + + +<tr> +<td><a href=http://www.phil.cmu.edu/tetrad/>Tetrad</a> +<td> CMU +<td> N +<td> N +<td> WU +<td> G +<td> N +<td> Y +<td> CI +<td> N +<td> 0 +<td> U,D +<td> None +<td> - + + +<tr> +<td> <!--Name--> <a href="https://sourceforge.net/projects/unbbayes/">UnBBayes</a> +<td> <!--Authors-->? +<td> <!--Src-->Java +<td> <!--API-->- +<td> <!--Exec-->- +<td> <!--Cts-->D +<td> <!--GUI-->Y +<td> <!--Params-->N +<td> <!--Struct-->Y +<td> <!--Utility-->N +<td> <!--Free-->0 +<td> <!--Undir-->D +<td> <!--Inference-->jtree +<td> <!--Comments-->K2 for struct learning + + +<tr> +<td><a href=http://www.inference.phy.cam.ac.uk/jmw39/>Vibes</a> +<td> Winn & Bishop (U. Cambridge) +<td> Java +<td> Y +<td> WU +<td> Cx +<td> Y +<td> Y +<td> N +<td> N +<td> 0 +<td> D +<td> Variational +<td> +Not yet available. + + +<tr> +<td><a href=http://snowhite.cis.uoguelph.ca/faculty_info/yxiang/ww3/> Web Weaver</a> +<td> Xiang (U.Regina) +<td> Java +<td> Y +<td> WUM +<td> D +<td> Y +<td> N +<td> N +<td> Y +<td> 0 +<td> D +<td> ? +<td> - + +<tr> +<td><a href=http://research.microsoft.com/~dmax/WinMine/tooldoc.htm > WinMine</a> +<td> Microsoft +<td> N +<td> N +<td> W +<td> Cx +<td> Y +<td> Y +<td> Y +<td> N +<td> 0 +<td> U,D +<td> None +<td> Learns BN or dependency net structure. + +<tr> +<td><a href=http://www.staff.city.ac.uk/~rgc/webpages/xbpage.html > XBAIES 2.0</a> +<td> Cowell (City U.) +<td> N +<td> N +<td> W +<td> G +<td> Y +<td> Y +<td> N +<td> Y +<td> 0 +<td> CG +<td> Jtree +<td> - + +<tr> + +</table> + + +<p> +<h2>Other sites related to (software for) graphical models</h2> +<ul> + + +<li> +<a +href="http://directory.google.com/Top/Computers/Artificial_Intelligence/Belief_Networks/Software/"> +Google's list</a> of Bayes net software. + +<!-- +<li> <a href="http://www.sis.pitt.edu/~dsl/da-software.html"> +Decision analysis programs</a>, list maintained by +Marek Druzdzel. +--> + +<li> <a href="http://www.cs.cmu.edu/~lorens/papers/mscthesis.html"> +Comparison of decision analysis software packages</a> by +Håkan L. Younes. The emphasis is on real-time decision making. + +<li> <a href="http://bayes.stat.washington.edu/almond/belief.html"> +Older belief net programs</a> (c 1996), a list created (but no longer maintained) +by Russ Almond. + +<li> +<a +href="http://www.cs.huji.ac.il/labs/compbio/Repository"> +Repository of Bayes nets</a>. + +</document>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/bnt.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,359 @@ +<html> <head> +<title>Bayes Net Toolbox for Matlab</title> +</head> + +<body> +<!--<body bgcolor="#FFFFFF"> --> + +<h1>Bayes Net Toolbox for Matlab</h1> +Written by Kevin Murphy, 1997--2002. +Last updated: 19 October 2007. + +<P><P> +<table> +<tr> +<td> +<img align=left src="Figures/mathbymatlab.gif" alt="Matlab logo"> +<!-- <img align=left src="toolbox.gif" alt="Toolbox logo">--> +<td> +<!--<center>--> +<a href="http://groups.yahoo.com/group/BayesNetToolbox/join"> +<img src="http://groups.yahoo.com/img/ui/join.gif" border=0><br> +Click to subscribe to the BNT email list</a> +<br> +(<a href="http://groups.yahoo.com/group/BayesNetToolbox"> +http://groups.yahoo.com/group/BayesNetToolbox</a>) +<!--</center>--> +</table> + + +<p> +<ul> +<li> <a href="changelog.html">Changelog</a> + +<li> <a +href="http://www.cs.ubc.ca/~murphyk/Software/BNT/FullBNT-1.0.4.zip">Download +zip file</a>. + +<li> <a href="install.html">Installation</a> + +<li> <a href="license.gpl">Terms and conditions of use (GNU Library GPL)</a> + + +<li> <a href="usage.html">How to use the toolbox</a> + +<li> <a href="whyNotSourceforge.html">Why I closed the sourceforge +site</a>. + +<!-- +<li> <a href="Talks/BNT_mathworks.ppt">Powerpoint slides on graphical models +and BNT</a>, presented to the Mathworks, June 2003 + + +<li> <a href="Talks/gR03.ppt">Powerpoint slides on BNT and object +recognition</a>, presented at the <a +href="http://www.math.auc.dk/gr/gr2003.html">gR</a> workshop, +September 2003. +--> + +<!-- +<li> <a href="gR03.pdf">Proposed design for gR, a graphical models +toolkit in R</a>, September 2003. +(For more information on the gR project, +click <a href="http://www.r-project.org/gR/">here</a>.) +--> + +<li> +<!-- +<img src = "../new.gif" alt="new"> +--> + +<a href="../../Papers/bnt.pdf">Invited paper on BNT</a>, +published in +Computing Science and Statistics, 2001. + +<li> <a href="../bnsoft.html">Other Bayes net software</a> + +<!--<li> <a href="software.html">Other Matlab software</a>--> + +<li> <a href="../../Bayes/bnintro.html">A brief introduction to +Bayesian Networks</a> + + +<li> <a href="#features">Major features</a> +<li> <a href="#models">Supported models</a> +<!--<li> <a href="#future">Future work</a>--> +<li> <a href="#give_away">Why do I give the code away?</a> +<li> <a href="#why_matlab">Why Matlab?</a> +<li> <a href="#ack">Acknowledgments</a> +</ul> +<p> + + + +<h2><a name="features">Major features</h2> +<ul> + +<li> BNT supports many types of +<b>conditional probability distributions</b> (nodes), +and it is easy to add more. +<ul> +<li>Tabular (multinomial) +<li>Gaussian +<li>Softmax (logistic/ sigmoid) +<li>Multi-layer perceptron (neural network) +<li>Noisy-or +<li>Deterministic +</ul> +<p> + +<li> BNT supports <b>decision and utility nodes</b>, as well as chance +nodes, +i.e., influence diagrams as well as Bayes nets. +<p> + +<li> BNT supports static and dynamic BNs (useful for modelling dynamical systems +and sequence data). +<p> + +<li> BNT supports many different <b>inference algorithms</b>, +and it is easy to add more. + +<ul> +<li> Exact inference for static BNs: +<ul> +<li>junction tree +<li>variable elimination +<li>brute force enumeration (for discrete nets) +<li>linear algebra (for Gaussian nets) +<li>Pearl's algorithm (for polytrees) +<li>quickscore (for QMR) +</ul> + +<p> +<li> Approximate inference for static BNs: +<ul> +<li>likelihood weighting +<li> Gibbs sampling +<li>loopy belief propagation +</ul> + +<p> +<li> Exact inference for DBNs: +<ul> +<li>junction tree +<li>frontier algorithm +<li>forwards-backwards (for HMMs) +<li>Kalman-RTS (for LDSs) +</ul> + +<p> +<li> Approximate inference for DBNs: +<ul> +<li>Boyen-Koller +<li>factored-frontier/loopy belief propagation +</ul> + +</ul> +<p> + +<li> +BNT supports several methods for <b>parameter learning</b>, +and it is easy to add more. +<ul> + +<li> Batch MLE/MAP parameter learning using EM. +(Each node type has its own M method, e.g. softmax nodes use IRLS,<br> +and each inference engine has its own E method, so the code is fully modular.) + +<li> Sequential/batch Bayesian parameter learning (for fully observed tabular nodes only). +</ul> + + +<p> +<li> +BNT supports several methods for <b>regularization</b>, +and it is easy to add more. +<ul> +<li> Any node can have its parameters clamped (made non-adjustable). +<li> Any set of compatible nodes can have their parameters tied (c.f., +weight sharing in a neural net). +<li> Some node types (e.g., tabular) supports priors for MAP estimation. +<li> Gaussian covariance matrices can be declared full or diagonal, and can +be tied across states of their discrete parents (if any). +</ul> + +<p> +<li> +BNT supports several methods for <b>structure learning</b>, +and it is easy to add more. +<ul> + +<li> Bayesian structure learning, +using MCMC or local search (for fully observed tabular nodes only). + +<li> Constraint-based structure learning (IC/PC and IC*/FCI). +</ul> + + +<p> +<li> The source code is extensively documented, object-oriented, and free, making it +an excellent tool for teaching, research and rapid prototyping. + +</ul> + + + +<h2><a name="models">Supported probabilistic models</h2> +<p> +It is trivial to implement all of +the following probabilistic models using the toolbox. +<ul> +<li>Static +<ul> +<li> Linear regression, logistic regression, hierarchical mixtures of experts + +<li> Naive Bayes classifiers, mixtures of Gaussians, +sigmoid belief nets + +<li> Factor analysis, probabilistic +PCA, probabilistic ICA, mixtures of these models + +</ul> + +<li>Dynamic +<ul> + +<li> HMMs, Factorial HMMs, coupled HMMs, input-output HMMs, DBNs + +<li> Kalman filters, ARMAX models, switching Kalman filters, +tree-structured Kalman filters, multiscale AR models + +</ul> + +<li> Many other combinations, for which there are (as yet) no names! + +</ul> + + +<!-- +<h2><a name="future">Future work</h2> + +I have a long <a href="wish.txt">wish list</a> +of features I would like to add to BNT +at some point in the future. +Please email me (<a +href="mailto:murphyk@cs.berkeley.edu">murphyk@cs.berkeley.edu</a>) +if you are interested in contributing! +--> + + + +<h2><a name="give_away">Why do I give the code away?</h2> + +<ul> + +<li> +I was hoping for a Linux-style effect, whereby people would contribute +their own Matlab code so that the package would grow. With a few +exceptions, this has not happened, +although several people have provided bug-fixes (see the <a +href="#ack">acknowledgements</a>). +Perhaps the <a +href="http://www.cs.berkeley.edu/~murphyk/OpenBayes/index.html">Open +Bayes Project</a> will be more +succesful in this regard, although the evidence to date is not promising. + +<p> +<li> +Knowing that someone else might read your code forces one to +document it properly, a good practice in any case, as anyone knows who +has revisited old code. +In addition, by having many "eye balls", it is easier to spot bugs. + + +<p> +<li> +I believe in the concept of +<a href="http://www-stat.stanford.edu/~donoho/Reports/1995/wavelab.pdf"> +reproducible research</a>. +Good science requires that other people be able +to replicate your experiments. +Often a paper does not give enough details about how exactly an +algorithm was implemented (e.g., how were the parameters chosen? what +initial conditions were used?), and these can make a big difference in +practice. +Hence one should release the code that +was actually used to generate the results in one's paper. +This also prevents re-inventing the wheel. + +<p> +<li> +I was fed up with reading papers where all people do is figure out how +to do exact inference and/or learning +in a model which is just a trivial special case of a general Bayes net, e.g., +input-output HMMs, coupled-HMMs, auto-regressive HMMs. +My hope is that, by releasing general purpose software, the field can +move on to more interesting questions. +As Alfred North Whitehead said in 1911, +"Civilization advances by extending the number of important operations +that we can do without thinking about them." + +</ul> + + + + + +<h2><a name="why_matlab">Why Matlab?</h2> + +Matlab is an interactive, matrix-oriented programming language that +enables one to express one's (mathematical) ideas very concisely and directly, +without having to worry about annoying details like memory allocation +or type checking. This considerably reduces development time and +keeps code short, readable and fully portable. +Matlab has excellent built-in support for many data analysis and +visualization routines. In addition, there are many useful toolboxes, e.g., for +neural networks, signal and image processing. +The main disadvantages of Matlab are that it can be slow (which is why +we are currently rewriting parts of BNT in C), and that the commercial +license is expensive (although the student version is only $100 in the US). +<p> +Many people ask me why I did not use +<a href="http://www.octave.org/">Octave</a>, +an open-source Matlab clone. +The reason is that +Octave does not support multi-dimensional arrays, +cell arrays, objects, etc. +<p> +Click <a href="../which_language.html">here</a> for a more detailed +comparison of matlab and other languages. + + + +<h2><a name="ack">Acknowledgments</h2> + +I would like to thank numerous people for bug fixes, including: +Rainer Deventer, Michael Robert James, Philippe Leray, Pedrito Maynard-Reid II, Andrew Ng, +Ron Parr, Ilya Shpitser, Xuejing Sun, Ursula Sondhauss. +<p> +I would like to thank the following people for contributing code: +Pierpaolo Brutti, Ali Taylan Cemgil, Tamar Kushnir, +Tom Murray, +Nicholas Saunier, +Ken Shan, +Yair Weiss, +Bob Welch, +Ron Zohar. +<p> +The following Intel employees have also contributed code: +Qian Diao, Shan Huang, Yimin Zhang and especially Wei Hu. + +<p> +I would like to thank Stuart Russell for funding me over the years as +I developed BNT, and Gary Bradksi for hiring me as an intern at Intel, +which has supported much of the recent developments of BNT. + + +</body> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/bnt_download.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +<title>Download BNT</title> + +Download BNT from <a href="http://bnt.sourceforge.net/">BNT sourceforge site</a> + +<!-- +Number of hits since 13 November 2002: +<!-- Begin of WebCounter.com code. Do not modify --> +<applet codebase="http://vc.webcounter.com/" code="vc5.class" width=88 height=31> +<param name=a value=37322><a href="http://www.webcounter.com/"> +<img src="http://vc.webcounter.com/0/t?a=37322&s=&g=" width=88 height=31></a></applet> +<!-- End of WebCounter.com code. Do not modify --> + +<p> +<a href="../FullBNT.zip">Download BNT</a> +<p> +-->
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/bnt_pre_sf.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,350 @@ +<html> <head> +<title>Bayes Net Toolbox for Matlab</title> +</head> + +<body> +<!--<body bgcolor="#FFFFFF"> --> + +<h1>Bayes Net Toolbox for Matlab</h1> +Written by Kevin Murphy. +<br> +<b>BNT is now available from <a href="http://bnt.sourceforge.net/">sourceforge</a>!</b> +<!-- +Last updated on 9 June 2004 (<a href="changelog.html">Detailed +changelog</a>) +--> +<p> + +<P><P> +<table> +<tr> +<td> +<img align=left src="Figures/mathbymatlab.gif" alt="Matlab logo"> +<!-- <img align=left src="toolbox.gif" alt="Toolbox logo">--> +<td> +<!--<center>--> +<a href="http://groups.yahoo.com/group/BayesNetToolbox/join"> +<img src="http://groups.yahoo.com/img/ui/join.gif" border=0><br> +Click to subscribe to the BNT email list</a> +<br> +(<a href="http://groups.yahoo.com/group/BayesNetToolbox"> +http://groups.yahoo.com/group/BayesNetToolbox</a>) +<!--</center>--> +</table> + + +<p> +<ul> +<!--<li> <a href="bnt_download.html">Download toolbox</a>--> +<li> Download BNT from <a href="http://bnt.sourceforge.net/">BNT sourceforge site</a> + +<li> <a href="license.gpl">Terms and conditions of use (GNU Library GPL)</a> + +<li> <a href="usage.html">How to use the toolbox</a> + + +<li> <a href="Talks/BNT_mathworks.ppt">Powerpoint slides on graphical models +and BNT</a>, presented to the Mathworks, June 2003 + +<!-- +<li> <a href="Talks/gR03.ppt">Powerpoint slides on BNT and object +recognition</a>, presented at the <a +href="http://www.math.auc.dk/gr/gr2003.html">gR</a> workshop, +September 2003. +--> + +<li> <a href="gR03.pdf">Proposed design for gR, a graphical models +toolkit in R</a>, September 2003. +<!-- +(For more information on the gR project, +click <a href="http://www.r-project.org/gR/">here</a>.) +--> + +<li> +<!-- +<img src = "../new.gif" alt="new"> +--> +<a href="../../Papers/bnt.pdf">Invited paper on BNT</a>, +published in +Computing Science and Statistics, 2001. + +<li> <a href="bnsoft.html">Other Bayes net software</a> + +<!--<li> <a href="software.html">Other Matlab software</a>--> + +<li> <a href="../../Bayes/bnintro.html">A brief introduction to +Bayesian Networks</a> + +<li> <a href="#features">Major features</a> +<li> <a href="#models">Supported models</a> +<!--<li> <a href="#future">Future work</a>--> +<li> <a href="#give_away">Why do I give the code away?</a> +<li> <a href="#why_matlab">Why Matlab?</a> +<li> <a href="#ack">Acknowledgments</a> +</ul> +<p> + + + +<h2><a name="features">Major features</h2> +<ul> + +<li> BNT supports many types of +<b>conditional probability distributions</b> (nodes), +and it is easy to add more. +<ul> +<li>Tabular (multinomial) +<li>Gaussian +<li>Softmax (logistic/ sigmoid) +<li>Multi-layer perceptron (neural network) +<li>Noisy-or +<li>Deterministic +</ul> +<p> + +<li> BNT supports <b>decision and utility nodes</b>, as well as chance +nodes, +i.e., influence diagrams as well as Bayes nets. +<p> + +<li> BNT supports static and dynamic BNs (useful for modelling dynamical systems +and sequence data). +<p> + +<li> BNT supports many different <b>inference algorithms</b>, +and it is easy to add more. + +<ul> +<li> Exact inference for static BNs: +<ul> +<li>junction tree +<li>variable elimination +<li>brute force enumeration (for discrete nets) +<li>linear algebra (for Gaussian nets) +<li>Pearl's algorithm (for polytrees) +<li>quickscore (for QMR) +</ul> + +<p> +<li> Approximate inference for static BNs: +<ul> +<li>likelihood weighting +<li> Gibbs sampling +<li>loopy belief propagation +</ul> + +<p> +<li> Exact inference for DBNs: +<ul> +<li>junction tree +<li>frontier algorithm +<li>forwards-backwards (for HMMs) +<li>Kalman-RTS (for LDSs) +</ul> + +<p> +<li> Approximate inference for DBNs: +<ul> +<li>Boyen-Koller +<li>factored-frontier/loopy belief propagation +</ul> + +</ul> +<p> + +<li> +BNT supports several methods for <b>parameter learning</b>, +and it is easy to add more. +<ul> + +<li> Batch MLE/MAP parameter learning using EM. +(Each node type has its own M method, e.g. softmax nodes use IRLS,<br> +and each inference engine has its own E method, so the code is fully modular.) + +<li> Sequential/batch Bayesian parameter learning (for fully observed tabular nodes only). +</ul> + + +<p> +<li> +BNT supports several methods for <b>regularization</b>, +and it is easy to add more. +<ul> +<li> Any node can have its parameters clamped (made non-adjustable). +<li> Any set of compatible nodes can have their parameters tied (c.f., +weight sharing in a neural net). +<li> Some node types (e.g., tabular) supports priors for MAP estimation. +<li> Gaussian covariance matrices can be declared full or diagonal, and can +be tied across states of their discrete parents (if any). +</ul> + +<p> +<li> +BNT supports several methods for <b>structure learning</b>, +and it is easy to add more. +<ul> + +<li> Bayesian structure learning, +using MCMC or local search (for fully observed tabular nodes only). + +<li> Constraint-based structure learning (IC/PC and IC*/FCI). +</ul> + + +<p> +<li> The source code is extensively documented, object-oriented, and free, making it +an excellent tool for teaching, research and rapid prototyping. + +</ul> + + + +<h2><a name="models">Supported probabilistic models</h2> +<p> +It is trivial to implement all of +the following probabilistic models using the toolbox. +<ul> +<li>Static +<ul> +<li> Linear regression, logistic regression, hierarchical mixtures of experts + +<li> Naive Bayes classifiers, mixtures of Gaussians, +sigmoid belief nets + +<li> Factor analysis, probabilistic +PCA, probabilistic ICA, mixtures of these models + +</ul> + +<li>Dynamic +<ul> + +<li> HMMs, Factorial HMMs, coupled HMMs, input-output HMMs, DBNs + +<li> Kalman filters, ARMAX models, switching Kalman filters, +tree-structured Kalman filters, multiscale AR models + +</ul> + +<li> Many other combinations, for which there are (as yet) no names! + +</ul> + + +<!-- +<h2><a name="future">Future work</h2> + +I have a long <a href="wish.txt">wish list</a> +of features I would like to add to BNT +at some point in the future. +Please email me (<a +href="mailto:murphyk@cs.berkeley.edu">murphyk@cs.berkeley.edu</a>) +if you are interested in contributing! +--> + + + +<h2><a name="give_away">Why do I give the code away?</h2> + +<ul> + +<li> +I was hoping for a Linux-style effect, whereby people would contribute +their own Matlab code so that the package would grow. With a few +exceptions, this has not happened, +although several people have provided bug-fixes (see the <a +href="#ack">acknowledgements</a>). +Perhaps the <a +href="http://www.cs.berkeley.edu/~murphyk/OpenBayes/index.html">Open +Bayes Project</a> will be more +succesful in this regard, although the evidence to date is not promising. + +<p> +<li> +Knowing that someone else might read your code forces one to +document it properly, a good practice in any case, as anyone knows who +has revisited old code. +In addition, by having many "eye balls", it is easier to spot bugs. + + +<p> +<li> +I believe in the concept of +<a href="http://www-stat.stanford.edu/~donoho/Reports/1995/wavelab.pdf"> +reproducible research</a>. +Good science requires that other people be able +to replicate your experiments. +Often a paper does not give enough details about how exactly an +algorithm was implemented (e.g., how were the parameters chosen? what +initial conditions were used?), and these can make a big difference in +practice. +Hence one should release the code that +was actually used to generate the results in one's paper. +This also prevents re-inventing the wheel. + +<p> +<li> +I was fed up with reading papers where all people do is figure out how +to do exact inference and/or learning +in a model which is just a trivial special case of a general Bayes net, e.g., +input-output HMMs, coupled-HMMs, auto-regressive HMMs. +My hope is that, by releasing general purpose software, the field can +move on to more interesting questions. +As Alfred North Whitehead said in 1911, +"Civilization advances by extending the number of important operations +that we can do without thinking about them." + +</ul> + + + + + +<h2><a name="why_matlab">Why Matlab?</h2> + +Matlab is an interactive, matrix-oriented programming language that +enables one to express one's (mathematical) ideas very concisely and directly, +without having to worry about annoying details like memory allocation +or type checking. This considerably reduces development time and +keeps code short, readable and fully portable. +Matlab has excellent built-in support for many data analysis and +visualization routines. In addition, there are many useful toolboxes, e.g., for +neural networks, signal and image processing. +The main disadvantages of Matlab are that it can be slow (which is why +we are currently rewriting parts of BNT in C), and that the commercial +license is expensive (although the student version is only $100 in the US). +<p> +Many people ask me why I did not use +<a href="http://www.octave.org/">Octave</a>, +an open-source Matlab clone. +The reason is that +Octave does not support multi-dimensional arrays, +cell arrays, objects, etc. +<p> +Click <a href="../which_language.html">here</a> for a more detailed +comparison of matlab and other languages. + + + +<h2><a name="ack">Acknowledgments</h2> + +I would like to thank numerous people for bug fixes, including: +Rainer Deventer, Michael Robert James, Philippe Leray, Pedrito Maynard-Reid II, Andrew Ng, +Ron Parr, Ilya Shpitser, Xuejing Sun, Ursula Sondhauss. +<p> +I would like to thank the following people for contributing code: +Pierpaolo Brutti, Ali Taylan Cemgil, Tamar Kushnir, Ken Shan, +<a href="http://www.cs.berkeley.edu/~yweiss">Yair Weiss</a>, +Ron Zohar. +<p> +The following Intel employees have also contributed code: +Qian Diao, Shan Huang, Yimin Zhang and especially Wei Hu. + +<p> +I would like to thank Stuart Russell for funding me over the years as +I developed BNT, and Gary Bradksi for hiring me as an intern at Intel, +which has supported much of the recent developments of BNT. + + +</body> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/cellarray.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +Cell arrays are a little tricky in Matlab. +Consider this example. + +C=num2cell(rand(2,3)) +C = + [0.4565] [0.8214] [0.6154] + [0.0185] [0.4447] [0.7919] + +C{1,2} % this is the contents of this cell (could be a vector or a string) +ans = + 0.8214 + +C{1:2,2} % this is the contents of these cells - returns multiple +answers! +ans = + 0.8214 +ans = + 0.4447 + +A = C(1:2,2) % this is a slice of the cell array +ans = + [0.8214] + [0.4447] + +A{1} % A is itself a cell array +ans = + 0.8214 + + + +>> C(1:2,2)=0 % can't assign a scalar to a cell array +C(1:2,2)=0 +??? Conversion to cell from double is not possible. + + +>> C(1:2,2)={0;0} % can assign a cell array to a cell array +C(1:2,2)={0;0} +C = + [0.4565] [0] [0.6154] + [0.0185] [0] [0.7919] + + +BTW, I use cell arrays for evidence for 2 reasons: + +1. [] indicates missing values +2. it can easily represent vector-valued nodes + +The following example makes this clear + +C{1,1} = [] +C{2,1} = rand(3,1) + +C = + [] [0] [0.6154] + [3x1 double] [0] [0.7919] + + +Hope this helps, +Kevin
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/changelog.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1617 @@ +<title>History of changes to BNT</title> +<h1>History of changes to BNT</h1> + + +<h2>Changes since 4 Oct 2007</h2> + +<pre> +- 19 Oct 07 murphyk + +* BNT\CPDs\@noisyor_CPD\CPD_to_CPT.m: 2nd half of the file is a repeat +of the first half and was deleted (thanks to Karl Kuschner) + +* KPMtools\myismember.m should return logical for use in "assert" so add line at end + p=logical(p); this prevents "assert" from failing on an integer input. +(thanks to Karl Kuschner) + + + +- 17 Oct 07 murphyk + +* Updated subv2ind and ind2subv in KPMtools to Tom Minka's implementation. +His ind2subv is faster (vectorized), but I had to modify it so it +matched the behavior of my version when called with siz=[]. +His subv2inv is slightly simpler than mine because he does not treat +the siz=[2 2 ... 2] case separately. +Note: there is now no need to ever use the C versions of these +functions (or any others, for that matter). + +* removed BNT/add_BNT_to_path since no longer needed. + + + +- 4 Oct 07 murphyk + +* moved code from sourceforge to UBC website, made version 1.0.4 + +* @pearl_inf_engine/pearl_inf_engine line 24, default +argument for protocol changed from [] to 'parallel'. +Also, changed private/parallel_protocol so it doesn't write to an +empty file id (Matlab 7 issue) + +* added foptions (Matlab 7 issue) + +* changed genpathKPM to exclude svn. Put it in toplevel directory to +massively simplify the installation process. + +</pre> + + +<h2>Sourceforge changelog</h2> + +BNT was first ported to sourceforge on 28 July 2001 by yozhik. +BNT was removed from sourceforge on 4 October 2007 by Kevin Murphy; +that version is cached as <a +href="FullBNT-1.0.3.zip">FullBNT-1.0.3.zip</a>. +See <a href="ChangeLog.Sourceforge.txt">Changelog from +sourceforge</a> for a history of that version of the code, +which formed the basis of the branch currently on Murphy's web page. + + +<h2> Changes from August 1998 -- July 2004</h2> + +Kevin Murphy made the following changes to his own private copy. +(Other small changes were made between July 2004 and October 2007, but were +not documented.) +These may or may not be reflected in the sourceforge version of the +code (which was independently maintained). + + +<ul> +<li> 9 June 2004 +<ul> +<li> Changed tabular_CPD/learn_params back to old syntax, to make it +compatible with gaussian_CPD/learn_params (and re-enabled +generic_CPD/learn_params). +Modified learning/learn_params.m and learning/score_family +appropriately. +(In particular, I undid the change Sonia Leach had to make to +score_family to handle this asymmetry.) +Added examples/static/gaussian2 to test this new functionality. + +<li> Added bp_mrf2 (for generic pairwise MRFs) to +inference/static/@bp_belprop_mrf2_inf_engine. [MRFs are not +"officially" supported in BNT, so this code is just for expert +hackers.] + +<li> Added examples/static/nodeorderExample.m to illustrate importance +of using topological ordering. + +<li> Ran dos2unix on all *.c files within BNT to eliminate compiler +warnings. + +</ul> + +<li> 7 June 2004 +<ul> +<li> Replaced normaliseC with normalise in HMM/fwdback, for maximum +portability (and negligible loss in speed). +<li> Ensured FullBNT versions of HMM, KPMstats etc were as up-to-date +as stand-alone versions. +<li> Changed add_BNT_to_path so it no longer uses addpath(genpath()), +which caused Old versions of files to mask new ones. +</ul> + +<li> 18 February 2004 +<ul> +<li> A few small bug fixes to BNT, as posted to the Yahoo group. +<li> Several new functions added to KPMtools, KPMstats and Graphviz +(none needed by BNT). +<li> Added CVS to some of my toolboxes. +</ul> + +<li> 30 July 2003 +<ul> +<li> qian.diao fixed @mpot/set_domain_pot and @cgpot/set_domain_pot +<li> Marco Grzegorczyk found, and Sonia Leach fixed, a bug in +do_removal inside learn_struct_mcmc +</ul> + + +<li> 28 July 2003 +<ul> +<li> Sebastian Luehr provided 2 minor bug fixes, to HMM/fwdback (if any(scale==0)) +and FullBNT\HMM\CPDs\@hhmmQ_CPD\update_ess.m (wrong transpose). +</ul> + +<li> 8 July 2003 +<ul> +<li> Removed buggy BNT/examples/static/MRF2/Old/mk_2D_lattice.m which was +masking correct graph/mk_2D_lattice. +<li> Fixed bug in graph/mk_2D_lattice_slow in the non-wrap-around case +(line 78) +</ul> + + +<li> 2 July 2003 +<ul> +<li> Sped up normalize(., 1) in KPMtools by avoiding general repmat +<li> Added assign_cols and marginalize_table to KPMtools +</ul> + + +<li> 29 May 2003 +<ul> +<li> Modified KPMstats/mixgauss_Mstep so it repmats Sigma in the tied +covariance case (bug found by galt@media.mit.edu). + +<li> Bob Welch found bug in gaussian_CPDs/maximize_params in the way +cpsz was computed. + +<li> Added KPMstats/mixgauss_em, because my code is easier to +understand/modify than netlab's (at least for me!). + +<li> Modified BNT/examples/dynamic/viterbi1 to call multinomial_prob +instead of mk_dhmm_obs_lik. + +<li> Moved parzen window and partitioned models code to KPMstats. + +<li> Rainer Deventer fixed some bugs in his scgpot code, as follows: +1. complement_pot.m +Problems occured for probabilities equal to zero. The result is an +division by zero error. +<br> +2. normalize_pot.m +This function is used during the calculation of the log-likelihood. +For a probability of zero a warning "log of zero" occurs. I have not +realy fixed the bug. As a workaround I suggest to calculate the +likelihhod based on realmin (the smallest real number) instead of +zero. +<br> +3. recursive_combine_pots +At the beginning of the function there was no test for the trivial case, +which defines the combination of two potentials as equal to the direct +combination. The result might be an infinite recursion which leads to +a stack overflow in matlab. +</ul> + + + +<li> 11 May 2003 +<ul> +<li> Fixed bug in gaussian_CPD/maximize_params so it is compatible +with the new clg_Mstep routine +<li> Modified KPMstats/cwr_em to handle single cluster case +separately. +<li> Fixed bug in netlab/gmminit. +<li> Added hash tables to KPMtools. +</ul> + + +<li> 4 May 2003 +<ul> +<li> +Renamed many functions in KPMstats so the name of the +distribution/model type comes first, +Mstep_clg -> clg_Mstep, +Mstep_cond_gauss -> mixgauss_Mstep. +Also, renamed eval_pdf_xxx functions to xxx_prob, e.g. +eval_pdf_cond_mixgauss -> mixgauss_prob. +This is simpler and shorter. + +<li> +Renamed many functions in HMM toolbox so the name of the +distribution/model type comes first, +log_lik_mhmm -> mhmm_logprob, etc. +mk_arhmm_obs_lik has finally been re-implemented in terms of clg_prob +and mixgauss_prob (for slice 1). +Removed the Demos directory, and put them in the main directory. +This code is not backwards compatible. + +<li> Removed some of the my_xxx functions from KPMstats (these were +mostly copies of functions from the Mathworks stats toolbox). + + +<li> Modified BNT to take into account changes to KPMstats and +HMM toolboxes. + +<li> Fixed KPMstats/Mstep_clg (now called clg_Mstep) for spherical Gaussian case. +(Trace was wrongly parenthesised, and I used YY instead of YTY. +The spherical case now gives the same result as the full case +for cwr_demo.) +Also, mixgauss_Mstep now adds 0.01 to the ML estimate of Sigma, +to act as a regularizer (it used to add 0.01 to E[YY'], but this was +ignored in the spherical case). + +<li> Added cluster weighted regression to KPMstats. + +<li> Added KPMtools/strmatch_substr. +</ul> + + + +<li> 28 Mar 03 +<ul> +<li> Added mc_stat_distrib and eval_pdf_cond_prod_parzen to KPMstats +<li> Fixed GraphViz/arrow.m incompatibility with matlab 6.5 +(replace all NaN's with 0). +Modified GraphViz/graph_to_dot so it also works on windows. +<li> I removed dag_to_jtree and added graph_to_jtree to the graph +toolbox; the latter expects an undirected graph as input. +<li> I added triangulate_2Dlattice_demo.m to graph. +<li> Rainer Deventer fixed the stable conditional Gaussian potential +classes (scgpot and scgcpot) and inference engine +(stab_cond_gauss_inf_engine). +<li> Rainer Deventer added (stable) higher-order Markov models (see +inference/dynamic/@stable_ho_inf_engine). +</ul> + + +<li> 14 Feb 03 +<ul> +<li> Simplified learning/learn_params so it no longer returns BIC +score. Also, simplified @tabular_CPD/learn_params so it only takes +local evidence. +Added learn_params_dbn, which does ML estimation of fully observed +DBNs. +<li> Vectorized KPMstats/eval_pdf_cond_mixgauss for tied Sigma +case (much faster!). +Also, now works in log-domain to prevent underflow. +eval_pdf_mixgauss now calls eval_pdf_cond_mixgauss and inherits these benefits. +<li> add_BNT_to_path now calls genpath with 2 arguments if using +matlab version 5. +</ul> + + +<li> 30 Jan 03 +<ul> +<li> Vectorized KPMstats/eval_pdf_cond_mixgauss for scalar Sigma +case (much faster!) +<li> Renamed mk_dotfile_from_hmm to draw_hmm and moved it to the +GraphViz library. +<li> Rewrote @gaussian_CPD/maximize_params.m so it calls +KPMstats/Mstep_clg. +This fixes bug when using clamped means (found by Rainer Deventer +and Victor Eruhimov) +and a bug when using a Wishart prior (no gamma term in the denominator). +It is also easier to read. +I rewrote the technical report re-deriving all the equations in a +clearer notation, making the solution to the bugs more obvious. +(See www.ai.mit.edu/~murphyk/Papers/learncg.pdf) +Modified Mstep_cond_gauss to handle priors. +<li> Fixed bug reported by Ramgopal Mettu in which add_BNT_to_path +calls genpath with only 1 argument, whereas version 5 requires 2. +<li> Fixed installC and uninstallC to search in FullBNT/BNT. +</ul> + + +<li> 24 Jan 03 +<ul> +<li> Major simplification of HMM code. +The API is not backwards compatible. +No new functionality has been added, however. +There is now only one fwdback function, instead of 7; +different behaviors are controlled through optional arguments. +I renamed 'evaluate observation likelihood' (local evidence) +to 'evaluate conditional pdf', since this is more general. +i.e., renamed +mk_dhmm_obs_lik to eval_pdf_cond_multinomial, +mk_ghmm_obs_lik to eval_pdf_cond_gauss, +mk_mhmm_obs_lik to eval_pdf_cond_mog. +These functions have been moved to KPMstats, +so they can be used by other toolboxes. +ghmm's have been eliminated, since they are just a special case of +mhmm's with M=1 mixture component. +mixgauss HMMs can now handle a different number of +mixture components per state. +init_mhmm has been eliminated, and replaced with init_cond_mixgauss +(in KPMstats) and mk_leftright/rightleft_transmat. +learn_dhmm can no longer handle inputs (although this is easy to add back). +</ul> + + + + + +<li> 20 Jan 03 +<ul> +<li> Added arrow.m to GraphViz directory, and commented out line 922, +in response to a bug report. +</ul> + +<li> 18 Jan 03 +<ul> +<li> Major restructuring of BNT file structure: +all code that is not specific to Bayes nets has been removed; +these packages must be downloaded separately. (Or just download FullBNT.) +This makes it easier to ensure different toolboxes are consistent. +misc has been slimmed down and renamed KPMtools, so it can be shared by other toolboxes, +such as HMM and Kalman; some of the code has been moved to BNT/general. +The Graphics directory has been slimmed down and renamed GraphViz. +The graph directory now has no dependence on BNT (dag_to_jtree has +been renamed graph_to_jtree and has a new API). +netlab2 no longer contains any netlab files, only netlab extensions. +None of the functionality has changed. +</ul> + + + +<li> 11 Jan 03 +<ul> +<li> jtree_dbn_inf_engine can now support soft evidence. + +<li> Rewrote graph/dfs to make it clearer. +Return arguments have changed, as has mk_rooted_tree. +The acyclicity check for large undirected graphs can cause a stack overflow. +It turns out that this was not a bug, but is because Matlab's stack depth +bound is very low by default. + +<li> Renamed examples/dynamic/filter2 to filter_test1, so it does not +conflict with the filter2 function in the image processing toolbox. + +<li> Ran test_BNT on various versions of matlab to check compatibility. +On matlab 6.5 (r13), elapsed time = 211s, cpu time = 204s. +On matlab 6.1 (r12), elapsed time = 173s, cpu time = 164s. +On matlab 5.3 (r11), elapsed time = 116s, cpu time = 114s. +So matlab is apparently getting slower with time!! +(All results were with a linux PIII machine.) +</ul> + + +<li> 14 Nov 02 +<ul> +<li> Removed all ndx inference routines, since they are only +marginally faster on toy problems, +and are slower on large problems due to having to store and lookup +the indices (causes cache misses). +In particular, I removed jtree_ndx_inf_eng and jtree_ndx_dbn_inf_eng, all the *ndx* +routines from potentials/Tables, and all the UID stuff from +add_BNT_to_path, +thus simplifying the code. +This required fixing hmm_(2TBN)_inf_engine/marginal_nodes\family, +and updating installC. + + +<li> Removed jtree_C_inf_engine and jtree_C_dbn_inf_engine. +The former is basically the same as using jtree_inf_engine with +mutliply_by_table.c and marginalize_table.c. +The latter benefited slightly by assuming potentials were tables +(arrays not objects), but these negligible savings don't justify the +complexity and code duplication. + +<li> Removed stab_cond_gauss_inf_engine and +scg_unrolled_dbn_inf_engine, +written by shan.huang@intel.com, since the code was buggy. + +<li> Removed potential_engine, which was only experimental anyway. + +</ul> + + + +<li> 13 Nov 02 +<ul> +<li> <b>Released version 5</b>. +The previous version, released on 7/28/02, is available +<a href="BNT4.zip">here</a>. + +<li> Moved code and documentation to MIT. + +<li> Added repmat.c from Thomas Minka's lightspeed library. +Modified it so it can return an empty matrix. + +<li> Tomas Kocka fixed bug in the BDeu option for tabular_CPD, +and contributed graph/dag_to_eg, to convert to essential graphs. + +<!--<li> Wrote a <a href="../Papers/fastmult.pdf">paper</a> which explains +the ndx methods and the ndx cache BNT uses for fast +multiplication/ marginalization of multi-dimensional arrays. +--> + +<li> Modified definition of hhmmQ_CPD, so that Qps can now accept +parents in either the current or previous slice. + +<li> Added hhmm2Q_CPD class, which is simpler than hhmmQ (no embedded +sub CPDs, etc), and which allows the conditioning parents, Qps, to +be before (in the topological ordering) the F or Q(t-1) nodes. +See BNT/examples/dynamic/HHMM/Map/mk_map_hhmm for an example. +</ul> + + +<li> 7/28/02 +<ul> +<li> Changed graph/best_first_elim_order from min-fill to min-weight. +<li> Ernest Chan fixed bug in Kalman/sample_lds (G{i} becomes G{m} in +line 61). +<li> Tal Blum <bloom@cs.huji.ac.il> fixed bug in HMM/init_ghmm (Q +becomes K, the number of states). +<li> Fixed jtree_2tbn_inf_engine/set_fields so it correctly sets the +maximize flag to 1 even in subengines. +<li> Gary Bradksi did a simple mod to the PC struct learn alg so you can pass it an +adjacency matrix as a constraint. Also, CovMat.m reads a file and +produces a covariance matrix. +<li> KNOWN BUG in CPDs/@hhmmQ_CPD/update_ess.m at line 72 caused by +examples/dynamic/HHMM/Square/learn_square_hhmm_cts.m at line 57. +<li> +The old version is available from www.cs.berkeley.edu/~murphyk/BNT.24june02.zip +</ul> + + +<li> 6/24/02 +<ul> +<li> Renamed dag_to_dot as graph_to_dot and added support for +undirected graphs. +<li> Changed syntax for HHMM CPD constructors: no need to specify d/D +anymore,so they can be used for more complex models. +<li> Removed redundant first argument to mk_isolated_tabular_CPD. +</ul> + + +<li> 6/19/02 +<ul> +<li> +Fixed most probable explanation code. +Replaced calc_mpe with find_mpe, which is now a method of certain +inference engines, e.g., jtree, belprop. +calc_mpe_global has become the find_mpe method of global_joint. +calc_mpe_bucket has become the find_mpe method of var_elim. +calc_mpe_dbn has become the find_mpe method of smoother. +These routines now correctly find the jointly most probable +explanation, instead of the marginally most probable assignments. +See examples/static/mpe1\mpe2 and examples/dynamic/viterbi1 +for examples. +Removed maximize flag from constructor and enter_evidence +methods, since this no longer needs to be specified by the user. + +<li> Rainer Deventer fixed in a bug in +CPDs/@gaussian_CPD/udpate_ess.m: +now, hidden_cps = any(hidden_bitv(cps)), whereas it used to be +hidden_cps = all(hidden_bitv(cps)). + +</ul> + + +<li> 5/29/02 +<ul> +<li> CPDs/@gaussian_CPD/udpate_ess.m fixed WX,WXX,WXY (thanks to Rainer Deventer and +Yohsuke Minowa for spotting the bug). Does the C version work?? +<li> potentials/@cpot/mpot_to_cpot fixed K==0 case (thanks to Rainer Deventer). +<li> CPDs/@gaussian_CPD/log_prob_node now accepts non-cell array data +on self (thanks to rishi <rishi@capsl.udel.edu> for catching this). +</ul> + + +<li> 5/19/02 +<ul> + +<!-- +<li> Finally added <a href="../Papers/wei_ndx.ps.gz">paper</a> by Wei Hu (written +November 2001) +describing ndxB, ndxD, and ndxSD. +--> + +<li> Wei Hu made the following changes. +<ul> +<li> Memory leak repair: + a. distribute_evidence.c in static/@jtree_C directory + b. distribute_evidence.c in static/@jtree_ndx directory + c. marg_tablec. in Tables dir + +<li> Add "@jtree_ndx_2TBN_inf_engine" in inference/online dir + +<li> Add "@jtree_sparse_inf_engine" in inference/static dir + +<li> Add "@jtree_sparse_2TBN_inf_engine" in inference/online dir + +<li> Modify "tabular_CPD.m" in CPDs/@tabular_CPD dir , used for sparse + +<li> In "@discrete_CPD" dir: + a. modify "convert_to_pot.m", used for sparse + b. add "convert_to_sparse_table.c" + +<li> In "potentials/@dpot" dir: + a. remove "divide_by_pot.c" and "multiply_by_pot.c" + b. add "divide_by_pot.m" and "multiply_by_pot.m" + c. modify "dpot.m", "marginalize_pot.m" and "normalize_pot.m" + +<li> In "potentials/Tables" dir: + a. modify mk_ndxB.c;(for speedup) + b. add "mult_by_table.m", + "divide_by_table.m", + "divide_by_table.c", + "marg_sparse_table.c", + "mult_by_sparse_table.c", + "divide_by_sparse_table.c". + +<li> Modify "normalise.c" in misc dir, used for sparse. + +<li>And, add discrete2, discrete3, filter2 and filter3 as test applications in test_BNT.m +Modify installC.m +</ul> + +<li> Kevin made the following changes related to strong junction +trees: +<ul> +<li> jtree_inf_engin line 75: +engine.root_clq = length(engine.cliques); +the last clq is guaranteed to be a strong root + +<li> dag_to_jtree line 38: [jtree, root, B, w] = +cliques_to_jtree(cliques, ns); +never call cliques_to_strong_jtree + +<li> strong_elim_order: use Ilya's code instead of topological sorting. +</ul> + +<li> Kevin fixed CPDs/@generic_CPD/learn_params, so it always passes +in the correct hidden_bitv field to update_params. + +</ul>. + + +<li> 5/8/02 +<ul> + +<li> Jerod Weinman helped fix some bugs in HHMMQ_CPD/maximize_params. + +<li> Removed broken online inference from hmm_inf_engine. +It has been replaced filter_inf_engine, which can take hmm_inf_engine +as an argument. + +<li> Changed graph visualization function names. +'draw_layout' is now 'draw_graph', +'draw_layout_dbn' is now 'draw_dbn', +'plotgraph' is now 'dag_to_dot', +'plothmm' is now 'hmm_to_dot', +added 'dbn_to_dot', +'mkdot' no longer exists': its functioality has been subsumed by dag_to_dot. +The dot functions now all take optional args in string/value format. +</ul> + + +<li> 4/1/02 +<ul> +<li> Added online inference classes. +See BNT/inference/online and BNT/examples/dynamic/filter1. +This is work in progress. +<li> Renamed cmp_inference to cmp_inference_dbn, and made its + interface and behavior more similar to cmp_inference_static. +<li> Added field rep_of_eclass to bnet and dbn, to simplify +parameter tying (see ~murphyk/Bayes/param_tieing.html). +<li> Added gmux_CPD (Gaussian mulitplexers). +See BNT/examples/dynamic/SLAM/skf_data_assoc_gmux for an example. +<li> Modified the forwards sampling routines. +general/sample_dbn and sample_bnet now take optional arguments as +strings, and can sample with pre-specified evidence. +sample_bnet can only generate a single sample, and it is always a cell +array. +sample_node can only generate a single sample, and it is always a +scalar or vector. +This eliminates the false impression that the function was +ever vectorized (which was only true for tabular_CPDs). +(Calling sample_bnet inside a for-loop is unlikely to be a bottleneck.) +<li> Updated usage.html's description of CPDs (gmux) and inference +(added gibbs_sampling and modified the description of pearl). +<li> Modified BNT/Kalman/kalman_filter\smoother so they now optionally +take an observed input (control) sequence. +Also, optional arguments are now passed as strings. +<li> Removed BNT/examples/static/uci_data to save space. +</ul> + +<li> 3/14/02 +<ul> +<li> pearl_inf_engine now works for (vector) Gaussian nodes, as well +as discrete. compute_pi has been renamed CPD_to_pi. compute_lambda_msg + has been renamed CPD_to_lambda_msg. These are now implemented for + the discrete_CPD class instead of tabular_CPD. noisyor and + Gaussian have their own private implemenations. +Created examples/static/Belprop subdirectory. +<li> Added examples/dynamic/HHMM/Motif. +<li> Added Matt Brand's entropic prior code. +<li> cmp_inference_static has changed. It no longer returns err. It + can check for convergence. It can accept 'observed'. +</ul> + + +<li> 3/4/02 +<ul> +<li> Fixed HHMM code. Now BNT/examples/dynamic/HHMM/mk_abcd_hhmm +implements the example in the NIPS paper. See also +Square/sample_square_hhmm_discrete and other files. + +<li> Included Bhaskara Marthi's gibbs_sampling_inf_engine. Currently +this only works if all CPDs are tabular and if you call installC. + +<li> Modified Kalman/tracking_demo so it calls plotgauss2d instead of + gaussplot. + +<li> Included Sonia Leach's speedup of mk_rnd_dag. +My version created all NchooseK subsets, and then picked among them. Sonia +reorders the possible parents randomly and choose +the first k. This saves on having to enumerate the large number of +possible subsets before picking from one. + +<li> Eliminated BNT/inference/static/Old, which contained some old +.mexglx files which wasted space. +</ul> + + + +<li> 2/15/02 +<ul> +<li> Removed the netlab directory, since most of it was not being +used, and it took up too much space (the goal is to have BNT.zip be +less than 1.4MB, so if fits on a floppy). +The required files have been copied into netlab2. +</ul> + +<li> 2/14/02 +<ul> +<li> Shan Huang fixed most (all?) of the bugs in his stable CG code. +scg1-3 now work, but scg_3node and scg_unstable give different + behavior than that reported in the Cowell book. + +<li> I changed gaussplot so it plots an ellipse representing the + eigenvectors of the covariance matrix, rather than numerically + evaluating the density and using a contour plot; this + is much faster and gives better pictures. The new function is + called plotgauss2d in BNT/Graphics. + +<li> Joni Alon <jalon@cs.bu.edu> fixed some small bugs: + mk_dhmm_obs_lik called forwards with the wrong args, and + add_BNT_to_path should quote filenames with spaces. + +<li> I added BNT/stats2/myunidrnd which is called by learn_struct_mcmc. + +<li> I changed BNT/potentials/@dpot/multiply_by_dpot so it now says +Tbig.T(:) = Tbig.T(:) .* Ts(:); +</ul> + + +<li> 2/6/02 +<ul> +<li> Added hierarchical HMMs. See BNT/examples/dynamic/HHMM and +CPDs/@hhmmQ_CPD and @hhmmF_CPD. +<li> sample_dbn can now sample until a certain condition is true. +<li> Sonia Leach fixed learn_struct_mcmc and changed mk_nbrs_of_digraph +so it only returns DAGs. +Click <a href="sonia_mcmc.txt">here</a> for details of her changes. +</ul> + + +<li> 2/4/02 +<ul> +<li> Wei Hu fixed a bug in +jtree_ndx_inf_engine/collect\distribute_evidence.c which failed when +maximize=1. +<li> +I fixed various bugs to do with conditional Gaussians, +so mixexp3 now works (thansk to Gerry Fung <gerry.fung@utoronto.ca> + for spotting the error). Specifically: +Changed softmax_CPD/convert_to_pot so it now puts cts nodes in cdom, and no longer inherits + this function from discrete_CPD. + Changed root_CPD/convert_to_put so it puts self in cdom. +</ul> + + +<li> 1/31/02 +<ul> +<li> Fixed log_lik_mhmm (thanks to ling chen <real_lingchen@yahoo.com> +for spotting the typo) +<li> Now many scripts in examples/static call cmp_inference_static. +Also, SCG scripts have been simplified (but still don't work!). +<li> belprop and belprop_fg enter_evidence now returns [engine, ll, + niter], with ll=0, so the order of the arguments is compatible with other engines. +<li> Ensured that all enter_evidence methods support optional + arguments such as 'maximize', even if they ignore them. +<li> Added Wei Hu's potentials/Tables/rep_mult.c, which is used to + totally eliminate all repmats from gaussian_CPD/update_ess. +</ul> + + +<li> 1/30/02 +<ul> +<li> update_ess now takes hidden_bitv instead of hidden_self and +hidden_ps. This allows gaussian_CPD to distinguish hidden discrete and +cts parents. Now learn_params_em, as well as learn_params_dbn_em, + passes in this info, for speed. + +<li> gaussian_CPD update_ess is now vectorized for any case where all + the continuous nodes are observed (eg., Gaussian HMMs, AR-HMMs). + +<li> mk_dbn now automatically detects autoregressive nodes. + +<li> hmm_inf_engine now uses indexes in marginal_nodes/family for + speed. Marginal_ndoes can now only handle single nodes. + (SDndx is hard-coded, to avoid the overhead of using marg_ndx, + which is slow because of the case and global statements.) + +<li> add_ev_to_dmarginal now retains the domain field. + +<li> Wei Hu wrote potentials/Tables/repmat_and_mult.c, which is used to + avoid some of the repmat's in gaussian_CPD/update_ess. + +<li> installC now longer sets the global USEC, since USEC is set to 0 + by add_BNT_to_path, even if the C files have already been compiled + in a previous session. Instead, gaussian_CPD checks to + see if repmat_and_mult exists, and (bat1, chmm1, water1, water2) + check to see if jtree_C_inf_engine/collect_evidence exists. + Note that checking if a file exists is slow, so we do the check + inside the gaussian_CPD constructor, not inside update_ess. + +<li> uninstallC now deletes both .mex and .dll files, just in case I + accidently ship a .zip file with binaries. It also deletes mex + files from jtree_C_inf_engine. + +<li> Now marginal_family for both jtree_limid_inf_engine and + global_joint_inf_engine returns a marginal structure and + potential, as required by solve_limid. + Other engines (eg. jtree_ndx, hmm) are not required to return a potential. +</ul> + + + +<li> 1/22/02 +<ul> +<li> Added an optional argument to mk_bnet and mk_dbn which lets you +add names to nodes. This uses the new assoc_array class. + +<li> Added Yimin Zhang's (unfinished) classification/regression tree +code to CPDs/tree_CPD. + +</ul> + + + +<li> 1/14/02 +<ul> +<li> Incorporated some of Shan Huang's (still broken) stable CG code. +</ul> + + +<li> 1/9/02 +<ul> +<li> Yimin Zhang vectorized @discrete_CPD/prob_node, which speeds up +structure learning considerably. I fixed this to handle softmax CPDs. + +<li> Shan Huang changed the stable conditional Gaussian code to handle +vector-valued nodes, but it is buggy. + +<li> I vectorized @gaussian_CPD/update_ess for a special case. + +<li> Removed denom=min(1, ... Z) from gaussian_CPD/maximize_params +(added to cope with negative temperature for entropic prior), which +gives wrong results on mhmm1. +</ul> + + +<li> 1/7/02 + +<ul> +<li> Removed the 'xo' typo from mk_qmr_bnet. + +<li> convert_dbn_CPDs_to_tables has been vectorized; it is now +substantially faster to compute the conditional likelihood for long sequences. + +<li> Simplified constructors for tabular_CPD and gaussian_CPD, so they +now both only take the form CPD(bnet, i, ...) for named arguments - +the CPD('self', i, ...) format is gone. Modified mk_fgraph_given_ev +to use mk_isolated_tabular_CPD instead. + +<li> Added entropic prior to tabular and Gaussian nodes. +For tabular_CPD, changed name of arguments to the constructor to +distinguish Dirichlet and entropic priors. In particular, +tabular_CPD(bnet, i, 'prior', 2) is now +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_weight', 2). + +<li> Added deterministic annealing to learn_params_dbn_em for use with +entropic priors. The old format learn(engine, cases, max_iter) has +been replaced by learn(engine, cases, 'max_iter', max_iter). + +<li> Changed examples/dynamic/bat1 and kjaerulff1, since default +equivalence classes have changed from untied to tied. +</ul> + +<li> 12/30/01 +<ul> +<li> DBN default equivalence classes for slice 2 has changed, so that +now parameters are tied for nodes with 'equivalent' parents in slices +1 and 2 (e.g., observed leaf nodes). This essentially makes passing in +the eclass arguments redundant (hooray!). +</ul> + + +<li> 12/20/01 +<ul> +<li> <b>Released version 4</b>. +Version 4 is considered a major new release +since it is not completely backwards compatible with V3. +Observed nodes are now specified when the bnet/dbn is created, +not when the engine is created. This changes the interface to many of +the engines, making the code no longer backwards compatible. +Hence support for non-named optional arguments (BNT2 style) has also +been removed; hence mk_dbn etc. requires arguments to be passed by name. + +<li> Ilya Shpitser's C code for triangulation now compiles under +Windows as well as Unix, thanks to Wei Hu. + +<li> All the ndx engines have been combined, and now take an optional +argument specifying what kind of index to use. + +<li> learn_params_dbn_em is now more efficient: +@tabular_CPD/update_ess for nodes whose families +are hidden does not need need to call add_evidence_to_dmarginal, which +is slow. + +<li> Wei Hu fixed bug in jtree_ndxD, so now the matlab and C versions +both work. + +<li> dhmm_inf_engine replaces hmm_inf_engine, since the former can +handle any kind of topology and is slightly more efficient. dhmm is +extended to handle Gaussian, as well as discrete, +observed nodes. The new hmm_inf_engine no longer supports online +inference (which was broken anyway). + +<li> Added autoregressive HMM special case to hmm_inf_engine for +speed. + +<li> jtree_ndxSD_dbn_inf_engine now computes likelihood of the +evidence in a vectorized manner, where possible, just like +hmm_inf_engine. + +<li> Added mk_limid, and hence simplified mk_bnet and mk_dbn. + + +<li> Gaussian_CPD now uses 0.01*I prior on covariance matrix by +default. To do ML estimation, set 'cov_prior_weight' to 0. + +<li> Gaussian_CPD and tabular_CPD +optional binary arguments are now set using 0/1 rather no 'no'/'yes'. + +<li> Removed Shan Huang's PDAG and decomposable graph code, which will +be put in a separate structure learning library. +</ul> + + +<li> 12/11/01 +<ul> +<li> Wei Hu fixed jtree_ndx*_dbn_inf_engine and marg_table.c. + +<li> Shan Huang contributed his implementation of stable conditional +Gaussian code (Lauritzen 1999), and methods to search through the +space of PDAGs (Markov equivalent DAGs) and undirected decomposable +graphs. The latter is still under development. +</ul> + + +<li> 12/10/01 +<ul> +<li> Included Wei Hu's new versions of the ndx* routines, which use +integers instead of doubles. The new versions are about 5 times faster +in C. In general, ndxSD is the best choice. + +<li> Fixed misc/add_ev_to_dmarginal so it works with the ndx routines +in bat1. + +<li> Added calc_mpe_dbn to do Viterbi parsing. + +<li> Updated dhmm_inf_engine so it computes marginals. +</ul> + + + +<li> 11/23/01 +<ul> +<li> learn_params now does MAP estimation (i.e., uses Dirichlet prior, +if define). Thanks to Simon Keizer skeizer@cs.utwente.nl for spotting +this. +<li> Changed plotgraph so it calls ghostview with the output of dotty, +instead of converting from .ps to .tif. The resulting image is much +easier to read. +<li> Fixed cgpot/multiply_by_pots.m. +<li> Wei Hu fixed ind2subv.c. +<li> Changed arguments to compute_joint_pot. +</ul> + + +<li> 11/1/01 +<ul> +<li> Changed sparse to dense in @dpot/multiply_pots, because sparse +arrays apparently cause a bug in the NT version of Matlab. + +<li> Fixed the bug in gaussian_CPD/log_prob_node.m which +incorrectly called the vectorized gaussian_prob with different means +when there were continuous parents and more than one case. +(Thanks to Dave Andre for finding this.) + +<li> Fixed the bug in root_CPD/convert_to_pot which did not check for +pot_type='g'. +(Thanks to Dave Andre for finding this.) + +<li> Changed calc_mpe and calc_mpe_global so they now return a cell array. + +<li> Combine pearl and loopy_pearl into a single inference engine +called 'pearl_inf_engine', which now takes optional arguments passed +in using the name/value pair syntax. +marginal_nodes/family now takes the optional add_ev argument (same as +jtree), which is the opposite of the previous shrink argument. + +<li> Created pearl_unrolled_dbn_inf_engine and "resurrected" +pearl_dbn_inf_engine in a simplified (but still broken!) form. + +<li> Wei Hi fixed the bug in ind2subv.c, so now ndxSD works. +He also made C versions of ndxSD and ndxB, and added (the unfinished) ndxD. + +</ul> + + +<li> 10/20/01 + +<ul> +<li> Removed the use_ndx option from jtree_inf, +and created 2 new inference engines: jtree_ndxSD_inf_engine and +jtree_ndxB_inf_engine. +The former stores 2 sets of indices for the small and difference +domains; the latter stores 1 set of indices for the big domain. +In Matlab, the ndxB version is often significantly faster than ndxSD +and regular jree, except when the clique size is large. +When compiled to C, the difference between ndxB and ndxSD (in terms of +speed) vanishes; again, both are faster than compiled jtree, except +when the clique size is large. +Note: ndxSD currently has a bug in it, so it gives the wrong results! +(The DBN analogs are jtree_dbn_ndxSD_inf_engine and +jtree_dbn_ndxB_inf_engine.) + +<li> Removed duplicate files from the HMM and Kalman subdirectories. +e.g., normalise is now only in BNT/misc, so when compiled to C, it +masks the unique copy of the Matlab version. +</ul> + + + +<li> 10/17/01 +<ul> +<li> Fixed bugs introduced on 10/15: +Renamed extract_gaussian_CPD_params_given_ev_on_dps.m to +gaussian_CPD_params_given_dps.m since Matlab can't cope with such long +names (this caused cg1 to fail). Fixed bug in +gaussian_CPD/convert_to_pot, which now calls convert_to_table in the +discrete case. + +<li> Fixed bug in bk_inf_engine/marginal_nodes. +The test 'if nodes < ss' is now +'if nodes <= ss' (bug fix due to Stephen seg_ma@hotmail.com) + +<li> Simplified uninstallC. +</ul> + + +<li> 10/15/01 +<ul> + +<li> Added use_ndx option to jtree_inf and jtree_dbn_inf. +This pre-computes indices for multiplying, dividing and marginalizing +discrete potentials. +This is like the old jtree_fast_inf_engine, but we use an extra level +of indirection to reduce the number of indices needed (see +uid_generator object). +Sometimes this is faster than the original way... +This is work in progress. + +<li> The constructor for dpot no longer calls myreshape, which is very +slow. +But new dpots still must call myones. +Hence discrete potentials are only sometimes 1D vectors (but should +always be thought of as multi-D arrays). This is work in progress. +</ul> + + +<li> 10/6/01 +<ul> +<li> Fixed jtree_dbn_inf_engine, and added kjaerulff1 to test this. +<li> Added option to jtree_inf_engine/marginal_nodes to return "full +sized" marginals, even on observed nodes. +<li> Clustered BK in examples/dynamic/bat1 seems to be broken, +so it has been commented out. +BK will be re-implemented on top of jtree_dbn, which should much more +efficient. +</ul> + +<li> 9/25/01 +<ul> +<li> jtree_dbn_inf_engine is now more efficient than calling BK with +clusters = exact, since it only uses the interface nodes, instead of +all of them, to maintain the belief state. +<li> Uninstalled the broken C version of strong_elim_order. +<li> Changed order of arguments to unroll_dbn_topology, so that intra1 +is no longer required. +<li> Eliminated jtree_onepass, which can be simulated by calling +collect_evidence on jtree. +<li> online1 is no longer in the test_BNT suite, since there is some +problem with online prediction with mixtures of Gaussians using BK. +This functionality is no longer supported, since doing it properly is +too much work. +</ul> +</li> + +<li> 9/7/01 +<ul> +<li> Added Ilya Shpitser's C triangulation code (43x faster!). +Currently this only compiles under linux; windows support is being added. +</ul> + + +<li> 9/5/01 +<ul> +<li> Fixed typo in CPDs/@tabular_kernel/convert_to_table (thanks, +Philippe!) +<li> Fixed problems with clamping nodes in tabular_CPD, learn_params, +learn_params_tabular, and bayes_update_params. See +BNT/examples/static/learn1 for a demo. +</ul> + + +<li> 9/3/01 +<ul> +<li> Fixed typo on line 87 of gaussian_CPD which caused error in cg1.m +<li> Installed Wei Hu's latest version of jtree_C_inf_engine, which +can now compute marginals on any clique/cluster. +<li> Added Yair Weiss's code to compute the Bethe free energy +approximation to the log likelihood in loopy_pearl (still need to add +this to belprop). The return arguments are now: engine, loglik and +niter, which is different than before. +</ul> + + + +<li> 8/30/01 +<ul> +<li> Fixed bug in BNT/examples/static/id1 which passed hard-coded +directory name to belprop_inf_engine. + +<li> Changed tabular_CPD and gaussian_CPD so they can now be created +without having to pass in a bnet. + +<li> Finished mk_fgraph_given_ev. See the fg* files in examples/static +for demos of factor graphs (work in progress). +</ul> + + + +<li> 8/22/01 +<ul> + +<li> Removed jtree_compiled_inf_engine, +since the C code it generated was so big that it would barf on large +models. + +<li> Tidied up the potentials/Tables directory. +Removed mk_marg/mult_ndx.c, +which have been superceded by the much faster mk_marg/mult_index.c +(written by Wei Hu). +Renamed the Matlab versions mk_marginalise/multiply_table_ndx.m +to be mk_marg/mult_index.m to be compatible with the C versions. +Note: nobody calls these routines anymore! +(jtree_C_inf_engine/enter_softev.c has them built-in.) +Removed mk_ndx.c, which was only used by jtree_compiled. +Removed mk_cluster_clq_ndx.m, mk_CPD_clq_ndx, and marginalise_table.m +which were not used. +Moved shrink_obs_dims_in_table.m to misc. + +<li> In potentials/@dpot directory: removed multiply_by_pot_C_old.c. +Now marginalize_pot.c can handle maximization, +and divide_by_pot.c has been implmented. +marginalize/multiply/divide_by_pot.m no longer have useC or genops options. +(To get the C versions, use installC.m) + +<li> Removed useC and genops options from jtree_inf_engine.m +To use the C versions, install the C code. + +<li> Updated BNT/installC.m. + +<li> Added fclose to @loopy_pearl_inf/enter_evidence. + +<li> Changes to MPE routines in BNT/general. +The maximize parameter is now specified inside enter_evidence +instead of when the engine is created. +Renamed calc_mpe_given_inf_engine to just calc_mpe. +Added Ron Zohar's optional fix to handle the case of ties. +Now returns log-likelihood instead of likelihood. +Added calc_mpe_global. +Removed references to genops in calc_mpe_bucket.m +Test file is now called mpe1.m + +<li> For DBN inference, filter argument is now passed by name, +as is maximize. This is NOT BACKWARDS COMPATIBLE. + +<li> Removed @loopy_dbn_inf_engine, which will was too complicated. +In the future, a new version, which applies static loopy to the +unrolled DBN, will be provided. + +<li> discrete_CPD class now contains the family sizes and supports the +method dom_sizes. This is because it could not access the child field +CPD.sizes, and mysize(CPT) may give the wrong answer. + +<li> Removed all functions of the form CPD_to_xxx, where xxx = dpot, cpot, +cgpot, table, tables. These have been replaced by convert_to_pot, +which takes a pot_type argument. +@discrete_CPD calls convert_to_table to implement a default +convert_to_pot. +@discrete_CPD calls CPD_to_CPT to implement a default +convert_to_table. +The convert_to_xxx routines take fewer arguments (no need to pass in +the globals node_sizes and cnodes!). +Eventually, convert_to_xxx will be vectorized, so it will operate on +all nodes in the same equivalence class "simultaneously", which should +be significantly quicker, at least for Gaussians. + +<li> Changed discrete_CPD/sample_node and prob_node to use +convert_to_table, instead of CPD_to_CPT, so mlp/softmax nodes can +benefit. + +<li> Removed @tabular_CPD/compute_lambda_msg_fast and +private/prod_CPD_and_pi_msgs_fast, since no one called them. + +<li> Renamed compute_MLE to learn_params, +by analogy with bayes_update_params (also because it may compute an +MAP estimate). + +<li> Renamed set_params to set_fields +and get_params to get_field for CPD and dpot objects, to +avoid confusion with the parameters of the CPD. + +<li> Removed inference/doc, which has been superceded +by the web page. + +<li> Removed inference/static/@stab_cond_gauss_inf_engine, which is +broken, and all references to stable CG. + +</ul> + + + + + +<li> 8/12/01 +<ul> +<li> I removed potentials/@dpot/marginalize_pot_max. +Now marginalize_pot for all potential classes take an optional third +argument, specifying whether to sum out or max out. +The dpot class also takes in optional arguments specifying whether to +use C or genops (the global variable USE_GENOPS has been eliminated). + +<li> potentials/@dpot/marginalize_pot has been simplified by assuming +that 'onto' is always in ascending order (i.e., we remove +Maynard-Reid's patch). This is to keep the code identical to the C +version and the other class implementations. + +<li> Added Ron Zohar's general/calc_mpe_bucket function, +and my general/calc_mpe_given_inf_engine, for calculating the most +probable explanation. + + +<li> Added Wei Hu's jtree_C_inf_engine. +enter_softev.c is about 2 times faster than enter_soft_evidence.m. + +<li> Added the latest version of jtree_compiled_inf_engine by Wei Hu. +The 'C' ndx_method now calls potentials/Tables/mk_marg/mult_index, +and the 'oldC' ndx_method calls potentials/Tables/mk_marg/mult_ndx. + +<li> Added potentials/@dpot/marginalize_pot_C.c and +multiply_by_pot_C.c by Wei Hu. +These can be called by setting the 'useC' argument in +jtree_inf_engine. + +<li> Added BNT/installC.m to compile all the mex files. + +<li> Renamed prob_fully_instantiated_bnet to log_lik_complete. + +<li> Added Shan Huang's unfinished stable conditional Gaussian +inference routines. +</ul> + + + +<li> 7/13/01 +<ul> +<li> Added the latest version of jtree_compiled_inf_engine by Wei Hu. +<li> Added the genops class by Doug Schwarz (see +BNT/genopsfun/README). This provides a 1-2x speed-up of +potentials/@dpot/multiply_by_pot and divide_by_pot. +<li> The function BNT/examples/static/qmr_compiled compares the +performance gains of these new functions. +</ul> + +<li> 7/6/01 +<ul> +<li> Made bk_inf_engine use the name/value argument syntax. This can +now do max-product (Viterbi) as well as sum-product +(forward-backward). +<li> Changed examples/static/mfa1 to use the new name/value argument +syntax. +</ul> + + +<li> 6/28/01 + +<ul> + +<li> <b>Released version 3</b>. +Version 3 is considered a major new release +since it is not completely backwards compatible with V2. +V3 supports decision and utility nodes, loopy belief propagation on +general graphs (including undirected), structure learning for non-tabular nodes, +a simplified way of handling optional +arguments to functions, +and many other features which are described below. +In addition, the documentation has been substantially rewritten. + +<li> The following functions can now take optional arguments specified +as name/value pairs, instead of passing arguments in a fixed order: +mk_bnet, jtree_inf_engine, tabular_CPD, gaussian_CPD, softmax_CPD, mlp_CPD, +enter_evidence. +This is very helpful if you want to use default values for most parameters. +The functions remain backwards compatible with BNT2. + +<li> dsoftmax_CPD has been renamed softmax_CPD, replacing the older +version of softmax. The directory netlab2 has been updated, and +contains weighted versions of some of the learning routines in netlab. +(This code is still being developed by P. Brutti.) + +<li> The "fast" versions of the inference engines, which generated +matlab code, have been removed. +@jtree_compiled_inf_engine now generates C code. +(This feature is currently being developed by Wei Hu of Intel (China), +and is not yet ready for public use.) + +<li> CPD_to_dpot, CPD_to_cpot, CPD_to_cgpot and CPD_to_upot +are in the process of being replaced by convert_to_pot. + +<li> determine_pot_type now takes as arguments (bnet, onodes) +instead of (onodes, cnodes, dag), +so it can detect the presence of utility nodes as well as continuous +nodes. +Hence this function is not backwards compatible with BNT2. + +<li> The structure learning code (K2, mcmc) now works with any node +type, not just tabular. +mk_bnets_tabular has been eliminated. +bic_score_family and dirichlet_score_family will be replaced by score_family. +Note: learn_struct_mcmc has a new interface that is not backwards +compatible with BNT2. + +<li> update_params_complete has been renamed bayes_update_params. +Also, learn_params_tabular has been replaced by learn_params, which +works for any CPD type. + +<li> Added decision/utility nodes. +</ul> + + +<li> 6/6/01 +<ul> +<li> Added soft evidence to jtree_inf_engine. +<li> Changed the documentation slightly (added soft evidence and +parameter tying, and separated parameter and structure learning). +<li> Changed the parameters of determine_pot_type, so it no longer +needs to be passed a DAG argument. +<li> Fixed parameter tying in mk_bnet (num. CPDs now equals num. equiv +classes). +<li> Made learn_struct_mcmc work in matlab version 5.2 (thanks to +Nimrod Megiddo for finding this bug). +<li> Made 'acyclic.m' work for undirected graphs. +</ul> + + +<li> 5/23/01 +<ul> +<li> Added Tamar Kushnir's code for the IC* algorithm +(learn_struct_pdag_ic_star). This learns the +structure of a PDAG, and can identify the presence of latent +variables. + +<li> Added Yair Weiss's code for computing the MAP assignment using +junction tree (i.e., a new method called @dpot/marginalize_pot_max +instead of marginalize_pot.) + +<li> Added @discrete_CPD/prob_node in addition to log_prob_node to handle +deterministic CPDs. +</ul> + + +<li> 5/12/01 +<ul> +<li> Pierpaolo Brutti updated his mlp and dsoftmax CPD classes, +and improved the HME code. + +<li> HME example now added to web page. (The previous example was non-hierarchical.) + +<li> Philippe Leray (author of the French documentation for BNT) +pointed out that I was including netlab.tar unnecessarily. +</ul> + + +<li> 5/4/01 +<ul> +<li> Added mlp_CPD which defines a CPD as a (conditional) multi-layer perceptron. +This class was written by Pierpaolo Brutti. + +<li> Added hierarchical mixtures of experts demo (due to Pierpaolo Brutti). + +<li> Fixed some bugs in dsoftmax_CPD. + +<li> Now the BNT distribution includes the whole +<a href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a> library in a +subdirectory. +It also includes my HMM and Kalman filter toolboxes, instead of just +fragments of them. +</ul> + + +<li> 5/2/01 +<ul> +<li> gaussian_inf_engine/enter_evidence now correctly returns the +loglik, even if all nodes are instantiated (bug fix due to +Michael Robert James). + +<li> Added dsoftmax_CPD which allows softmax nodes to have discrete +and continuous parents; the discrete parents act as indices into the +parameters for the continuous node, by analogy with conditional +Gaussian nodes. This class was written by Pierpaolo Brutti. +</ul> + + +<li> 3/27/01 +<ul> +<li> learn_struct_mcmc no longer returns sampled_bitv. +<li> Added mcmc_sample_to_hist to post-process the set of samples. +</ul> + +<li> 3/21/01 +<ul> +<li> Changed license from UC to GNU Library GPL. + +<li> Made all CPD constructors accept 0 arguments, so now bnets can be +saved to and loaded from files. + +<li> Improved the implementation of sequential and batch Bayesian +parameter learning for tabular CPDs with completely observed data (see +log_marg_lik_complete and update_params_complete). This code also +handles interventional data. + +<li> Added MCMC structure learning for completely observed, discrete, +static BNs. + +<li> Started implementing Bayesian estimation of linear Gaussian +nodes. See root_gaussian_CPD and +linear_gaussian_CPD. The old gaussian_CPD class has not been changed. + +<li> Renamed evaluate_CPD to log_prob_node, and simplified its +arguments. + +<li> Renamed sample_CPD to sample_node, simplified its +arguments, and vectorized it. + +<li> Renamed "learn_params_tabular" to "update_params_complete". +This does Bayesian updating, but no longer computes the BIC score. + +<li> Made routines for completely observed networks (sampling, +complete data likelihood, etc.) handle cell arrays or regular arrays, +which are faster. +If some nodes are not scalars, or are hidden, you must use cell arrays. +You must convert to a cell array before passing to an inference routine. + +<li> Fixed bug in gaussian_CPD constructor. When creating CPD with +more than 1 discrete parent with random parameters, the matrices were +the wrong shape (Bug fix due to Xuejing Sun). +</ul> + + + +<li> 11/24/00 +<ul> +<li> Renamed learn_params and learn_params_dbn to learn_params_em/ +learn_params_dbn_em. The return arguments are now [bnet, LLtrace, +engine] instead of [engine, LLtrace]. +<li> Added structure learning code for static nets (K2, PC). +<li> Renamed learn_struct_inter_full_obs as learn_struct_dbn_reveal, +and reimplemented it to make it simpler and faster. +<li> Added sequential Bayesian parameter learning (learn_params_tabular). +<li> Major rewrite of the documentation. +</ul> + +<!-- +<li> 6/1/00 +<ul> +<li> Subtracted 1911 off the counter, so now it counts hits from +5/22/00. (The initial value of 1911 was a conservative lower bound on the number of +hits from the time the page was created.) +</ul> +--> + +<li> 5/22/00 +<ul> +<li> Added online filtering and prediction. +<li> Added the factored frontier and loopy_dbn algorithms. +<li> Separated the online user manual into two, for static and dynamic +networks. +<!-- +<li> Added a counter to the BNT web page, and initialized it to 1911, +which is the number of people who have downloaded my software (BNT and +other toolboxes) since 8/24/98. +--> +<li> Added a counter to the BNT web page. +<!-- +Up to this point, 1911 people had downloaded my software (BNT and +other toolboxes) since 8/24/98. +--> +</ul> + + +<li> 4/27/00 +<ul> +<li> Fixed the typo in bat1.m +<li> Added preliminary code for online inference in DBNs +<li> Added coupled HMM example +</ul> + +<li> 4/23/00 +<ul> +<li> Fixed the bug in the fast inference routines where the indices +are empty (arises in bat1.m). +<li> Sped up marginal_family for the fast engines by precomputing indices. +</ul> + +<li> 4/17/00 +<ul> +<li> Simplified implementation of BK_inf_engine by using soft evidence. +<li> Added jtree_onepass_inf_engine (which computes a single marginal) +and modified jtree_dbn_fast to use it. +</ul> + +<li> 4/14/00 +<ul> +<li> Added fast versions of jtree and BK, which are +designed for models where the division into hidden/observed is fixed, +and all hidden variables are discrete. These routines are 2-3 times +faster than their non-fast counterparts. + +<li> Added graph drawing code +contributed by Ali Taylan Cemgil from the University of Nijmegen. +</ul> + +<li> 4/10/00 +<ul> +<li> Distinguished cnodes and cnodes_slice in DBNs so that kalman1 +works with BK. +<li> Removed dependence on cellfun (which only exists in matlab 5.3) +by adding isemptycell. Now the code works in 5.2. +<li> Changed the UC copyright notice. +</ul> + + + +<li> 3/29/00 +<ul> +<li><b>Released BNT 2.0</b>, now with objects! +Here are the major changes. + +<li> There are now 3 classes of objects in BNT: +Conditional Probability Distributions, potentials (for junction tree), +and inference engines. +Making an inference algorithm (junction tree, sampling, loopy belief +propagation, etc.) an object might seem counter-intuitive, but in +fact turns out to be a good idea, since the code and documentation +can be made modular. +(In Java, each algorithm would be a class that implements the +inferenceEngine interface. Since Matlab doesn't support interfaces, +inferenceEngine is an abstract (virtual) base class.) + +<p> +<li> +In version 1, instead of Matlab's built-in objects, +I used structs and a + simulated dispatch mechanism based on the type-tag system in the + classic textbook by Abelson + and Sussman ("Structure and Interpretation of Computer Programs", + MIT Press, 1985). This required editing the dispatcher every time a + new object type was added. It also required unique (and hence long) + names for each method, and allowed the user unrestricted access to + the internal state of objects. + +<p> +<li> The Bayes net itself is now a lightweight struct, and can be +used to specify a model independently of the inference algorithm used +to process it. +In version 1, the inference engine was stored inside the Bayes net. + +<!-- +See the list of <a href="differences2.html">changes from version +1</a>. +--> +</ul> + + + +<li> 11/24/99 +<ul> +<li> Added fixed lag smoothing, online EM and the ability to learn +switching HMMs (POMDPs) to the HMM toolbox. +<li> Renamed the HMM toolbox function 'mk_dhmm_obs_mat' to +'mk_dhmm_obs_lik', and similarly for ghmm and mhmm. Updated references +to these functions in BNT. +<li> Changed the order of return params from kalman_filter to make it +more natural. Updated references to this function in BNT. +</ul> + + + +<li>10/27/99 +<ul> +<li>Fixed line 42 of potential/cg/marginalize_cgpot and lines 32-39 of bnet/add_evidence_to_marginal +(thanks to Rainer Deventer for spotting these bugs!) +</ul> + + +<li>10/21/99 +<ul> +<li>Completely changed the blockmatrix class to make its semantics +more sensible. The constructor is not backwards compatible! +</ul> + +<li>10/6/99 +<ul> +<li>Fixed all_vals = cat(1, vals{:}) in user/enter_evidence +<li>Vectorized ind2subv and sub2indv and removed the C versions. +<li>Made mk_CPT_from_mux_node much faster by having it call vectorized +ind2subv +<li>Added Sondhauss's bug fix to line 68 of bnet/add_evidence_to_marginal +<li>In dbn/update_belief_state, instead of adding eps to likelihood if 0, +we leave it at 0, and set the scale factor to 0 instead of dividing. +</ul> + +<li>8/19/99 +<ul> +<li>Added Ghahramani's mfa code to examples directory to compare with +fa1, which uses BNT +<li>Changed all references of assoc to stringmatch (e.g., in +examples/mk_bat_topology) +</ul> + +<li>June 1999 +<ul> +<li><b>Released BNT 1.0</b> on the web. +</ul> + + +<li>August 1998 +<ul> +<li><b>Released BNT 0.0</b> via email. +</ul> + + +<li>October 1997 +<ul> +<li>First started working on Matlab version of BNT. +</ul> + +<li>Summer 1997 +<ul> +<li> First started working on C++ version of BNT while working at DEC (now Compaq) CRL. +</ul> + +<!-- +<li>Fall 1996 +<ul> +<li>Made a C++ program that generates DBN-specific C++ code +for inference using the frontier algorithm. +</ul> + +<li>Fall 1995 +<ul> +<li>Arrive in Berkeley, and first learn about Bayes Nets. Start using +Geoff Zweig's C++ code. +</ul> +--> + +</ul>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/dbn_hmm_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ + Example due to Wang Hee Lin" <engp1622@nus.edu.sg + + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; + +Q = 2; % num hidden states +O = 2; % num observable symbols +ns = [Q O];%number of states +dnodes = 1:2; +%onodes = [1:2]; % only possible with jtree, not hmm +onodes = [2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'observed', onodes); +for i=1:4 + bnet.CPD{i} = tabular_CPD(bnet, i); +end + +prior0 = normalise(rand(Q,1)); +transmat0 = mk_stochastic(rand(Q,Q)); +obsmat0 = mk_stochastic(rand(Q,O)); + +%engine = smoother_engine(hmm_2TBN_inf_engine(bnet)); +engine = smoother_engine(jtree_2TBN_inf_engine(bnet)); + +ss = 2;%slice size(ss) +ncases = 10;%number of examples +T=10; +max_iter=2;%iterations for EM +cases = cell(1, ncases); +for i=1:ncases + ev = sample_dbn(bnet, T); + cases{i} = cell(ss,T); + cases{i}(onodes,:) = ev(onodes, :); +end +[bnet2, LLtrace] = learn_params_dbn_em(engine, cases, 'max_iter', 4);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/graph_to_dot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function graph_to_dot(adj, varargin) +%GRAPH_TO_DOT Makes a GraphViz (AT&T) file representing an adjacency matrix +% graph_to_dot(adj, ...) writes to the specified filename. +% +% Optional arguments can be passed as name/value pairs: [default] +% +% 'filename' - if omitted, writes to 'tmp.dot' +% 'arc_label' - arc_label{i,j} is a string attached to the i-j arc [""] +% 'node_label' - node_label{i} is a string attached to the node i ["i"] +% 'width' - width in inches [10] +% 'height' - height in inches [10] +% 'leftright' - 1 means layout left-to-right, 0 means top-to-bottom [0] +% 'directed' - 1 means use directed arcs, 0 means undirected [1] +% +% For details on graphviz, See http://www.research.att.com/sw/tools/graphviz +% +% See also dot_to_graph and draw_dot. + +% First version written by Kevin Murphy 2002. +% Modified by Leon Peshkin, Jan 2004. +% Bugfix by Tom Minka, Mar 2004. + +node_label = []; arc_label = []; % set default args +width = 10; height = 10; +leftright = 0; directed = 1; filename = 'tmp.dot'; + +for i = 1:2:nargin-1 % get optional args + switch varargin{i} + case 'filename', filename = varargin{i+1}; + case 'node_label', node_label = varargin{i+1}; + case 'arc_label', arc_label = varargin{i+1}; + case 'width', width = varargin{i+1}; + case 'height', height = varargin{i+1}; + case 'leftright', leftright = varargin{i+1}; + case 'directed', directed = varargin{i+1}; + end +end +% minka +if ~directed + adj = triu(adj | adj'); +end + +fid = fopen(filename, 'w'); +if directed + fprintf(fid, 'digraph G {\n'); + arctxt = '->'; + if isempty(arc_label) + labeltxt = ''; + else + labeltxt = '[label="%s"]'; + end +else + fprintf(fid, 'graph G {\n'); + arctxt = '--'; + if isempty(arc_label) + labeltxt = '[dir=none]'; + else + labeltext = '[label="%s",dir=none]'; + end +end +edgeformat = strcat(['%d ',arctxt,' %d ',labeltxt,';\n']); +fprintf(fid, 'center = 1;\n'); +fprintf(fid, 'size=\"%d,%d\";\n', width, height); +if leftright + fprintf(fid, 'rankdir=LR;\n'); +end +Nnds = length(adj); +for node = 1:Nnds % process nodes + if isempty(node_label) + fprintf(fid, '%d;\n', node); + else + fprintf(fid, '%d [ label = "%s" ];\n', node, node_label{node}); + end +end +for node1 = 1:Nnds % process edges + arcs = find(adj(node1,:)); % children(adj, node); + for node2 = arcs + if ~isempty(arc_label) + fprintf(fid, edgeformat,node1,node2,arc_label{node1,node2}); + else + fprintf(fid, edgeformat, node1, node2); + end + end +end +fprintf(fid, '}'); +fclose(fid);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/graphviz.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +<h1>Visualizing graph structures in matlab</h1> + +We discuss some methods for visualizing graphs/ networks, including automatic +layout of the nodes. +We assume the graph is represented as an adjacency matrix. +If using BNT, you can access the DAG using +<pre> +G = bnet.dag; +</pre> + +<h2>Matlab's biograph function</h2> + +The Mathworks computational biology toolbox +has many useful graph related functions, including visualization. +<br> +Click +<a href="http://www.mathworks.com/products/bioinfo/demos.html?file=/products/demos/shipping/bioinfo/graphtheorydemo.html#4"> +here</a> +for a demo. + + + +<h2>Cemgil's draw_graph</h2> + +You can visualize an arbitrary graph (such as one learned using the +structure learning routines) with Matlab code written by +<a href="http://www-sigproc.eng.cam.ac.uk/~atc27/matlab/layout.html"> +Ali Taylan Cemgil</a> +from the University of Cambridge. +A modified version of this code +is <a href="GraphViz.zip">here</a> +(this is already bundled with BNT). +Just type +<pre> +draw_graph(G); +</pre> +For example, this is the output produced on a +<a href="http://www.cs.ubc.ca/~murphyk/Software/BNT/usage.html#qmr">random QMR-like model</a>: +<p> +<img src="Figures/qmr.rnd.jpg"> +<p> + +<h2>Pajek</h2> + +<a href="http://vlado.fmf.uni-lj.si/pub/networks/pajek">Pajek</a> +is an excellent, free Windows program for graph layout. +Use <a href="adj2pajek2.m">adj2pajek2.m</a> to convert a graph to the +Pajek file format. +<br> +Then Choose File->Network->Read from the menu. + +<h2>AT&T Graphviz</h2> + +<a href="http://www.research.att.com/sw/tools/graphviz">graphhviz</a> +is an +open-source graph visualization package from AT&T. +Use +<a href="graph_to_dot.m">graph_to_dot</a> +to convert an adjacency matrix to +the AT&T file format (the "dot" format). +You then use dot to convert it to postscript: +<pre> +graph_to_dot(G, 'filename', 'foo.dot'); +dot -Tps foo.dot -o foo.ps +ghostview foo.ps & +</pre>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/install.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +To install, just unzip FullBNT.zip, start Matlab, and proceed as +follows +<pre> +>> cd C:\kmurphy\FullBNT\FullBNT-1.0.4 +>> addpath(genpathKPM(pwd)) + +Warning: Function C:\kmurphy\FullBNT\FullBNT-1.0.4\KPMtools\isvector.m has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict. +> In path at 110 + In addpath at 89 +Warning: Function C:\kmurphy\FullBNT\FullBNT-1.0.4\KPMtools\isscalar.m has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict. +> In path at 110 + In addpath at 89 +Warning: Function C:\kmurphy\FullBNT\FullBNT-1.0.4\KPMtools\assert.m has the same name as a MATLAB builtin. We suggest you rename the function to avoid a potential name conflict. +> In path at 110 + In addpath at 89 + +>> test_BNT +</pre> +The genpathKPM function is like the builtin genpath function, but it +does not add directories called 'Old' to the path, thus preventing old +versions of functions accidently shadowing new ones. +The warnings occur because Matlab 7 added functions with the same +names as my functions. The BNT versions will shadow the built-in ones, +but this should be harmless. +<p> +Note: the functions installC_BNT etc. are not needed anymore: all C +code has either been removed or is unnecessary. +<p> Note: as mentioned above, with new versions of matlab you +will get lots of warning messages, but everything should still work. +<p> Note: Cory Reith tells me that, as of +15 Sep 2009, octave 3.2.2 can run most of BNT. +Please send email to crieth@ucsd.edu if you have questions.
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/majorFeatures.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,113 @@ + +<h2><a name="features">Major features</h2> +<ul> + +<li> BNT supports many types of +<b>conditional probability distributions</b> (nodes), +and it is easy to add more. +<ul> +<li>Tabular (multinomial) +<li>Gaussian +<li>Softmax (logistic/ sigmoid) +<li>Multi-layer perceptron (neural network) +<li>Noisy-or +<li>Deterministic +</ul> +<p> + +<li> BNT supports <b>decision and utility nodes</b>, as well as chance +nodes, +i.e., influence diagrams as well as Bayes nets. +<p> + +<li> BNT supports static and dynamic BNs (useful for modelling dynamical systems +and sequence data). +<p> + +<li> BNT supports many different <b>inference algorithms</b>, +and it is easy to add more. + +<ul> +<li> Exact inference for static BNs: +<ul> +<li>junction tree +<li>variable elimination +<li>brute force enumeration (for discrete nets) +<li>linear algebra (for Gaussian nets) +<li>Pearl's algorithm (for polytrees) +<li>quickscore (for QMR) +</ul> + +<p> +<li> Approximate inference for static BNs: +<ul> +<li>likelihood weighting +<li> Gibbs sampling +<li>loopy belief propagation +</ul> + +<p> +<li> Exact inference for DBNs: +<ul> +<li>junction tree +<li>frontier algorithm +<li>forwards-backwards (for HMMs) +<li>Kalman-RTS (for LDSs) +</ul> + +<p> +<li> Approximate inference for DBNs: +<ul> +<li>Boyen-Koller +<li>factored-frontier/loopy belief propagation +</ul> + +</ul> +<p> + +<li> +BNT supports several methods for <b>parameter learning</b>, +and it is easy to add more. +<ul> + +<li> Batch MLE/MAP parameter learning using EM. +(Each node type has its own M method, e.g. softmax nodes use IRLS,<br> +and each inference engine has its own E method, so the code is fully modular.) + +<li> Sequential/batch Bayesian parameter learning (for fully observed tabular nodes only). +</ul> + + +<p> +<li> +BNT supports several methods for <b>regularization</b>, +and it is easy to add more. +<ul> +<li> Any node can have its parameters clamped (made non-adjustable). +<li> Any set of compatible nodes can have their parameters tied (c.f., +weight sharing in a neural net). +<li> Some node types (e.g., tabular) supports priors for MAP estimation. +<li> Gaussian covariance matrices can be declared full or diagonal, and can +be tied across states of their discrete parents (if any). +</ul> + +<p> +<li> +BNT supports several methods for <b>structure learning</b>, +and it is easy to add more. +<ul> + +<li> Bayesian structure learning, +using MCMC or local search (for fully observed tabular nodes only). + +<li> Constraint-based structure learning (IC/PC and IC*/FCI). +</ul> + + +<p> +<li> The source code is extensively documented, object-oriented, and free, making it +an excellent tool for teaching, research and rapid prototyping. + +</ul> + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/matlab_comparison.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +<html> <head> +<title>Comparison of Matlab, R/S/Splus, Gauss, etc.</title> +</head> + +<body> +<!--<body bgcolor="#FFFFFF"> --> + +<h1>Comparison of Matlab, R/S/Splus, Gauss, etc.</h1> + +<ul> +<li> <a href="http://www.scientificweb.com/ncrunch/">Comparison of +mathematical programs for data analysis</a>, +Stefan Steinhaus, tech report, 2000. +<br> +This is a very detailed comparison of features and speed of several +interactive scientific programming environments, e.g. Matlab, +Mathematica, Splus. + + +<!-- +<p> +<li> <a href="Papers/gauss.econ.review.ps">Econometric +programming environments: Gauss, Ox and S-PLUS</a>, +Francisco Cribari-Neto. +J. of Applied Econometrics, 12(1):77-89, 1997 +<br> +Ox can not be used interactively, and has a C-style syntax (it even +requires users to pre-declare variables!). Its only advantage is speed. +S-Plus has tons of features and good documentation, but is slow. +Gauss is somewhere in between. + + +<p> +<li> <a href="Papers/matlab.econ.review.ps">MATLAB as an econometric +programming environment</a>, +Francisco Cribari-Neto and Mark J. Jensen. +J. of Applied Econometrics, 12(6):735-432, 1997. +<br> +The basic conclusion is that Matlab has excellent graphics and +sparse-matrix facilities, but is slower than Gauss/Ox (especially on +code +with loops), and has few statistical routines built-in (one must buy the +stats toolbox). + + + +<p> +<li> <a href="Papers/R.econ.review.ps">R: Yet another econometric +programming environment</a>, +Francisco Cribari-Neto and S. Zarkos. +J. of Applied Econometrics, 14(3):319-329, 1999. +<br> +The basic conclusion is that R is much faster than Splus on +code with loops, but a little bit slower on vectorized code. (Gauss/ +Ox is much faster than both; in my experience, R and Matlab have about +the same speed.) +However, R has much better memory management than Splus, and R is free. Otherwise, R/S/Splus +are essentially the same. +--> + + +</ul>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/param_tieing.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ + +<h2>Example of more complex parameter tieing</h2> + +We now give a more complex pattern of parameter tieing. +(This example is due to Rainer Deventer.) +The structure is as follows: +<p> +<img src="Figures/rainer_tied.gif" height="400"> +<!--<img src="rainer_dbn.jpg" height="600">--> +<p> +Since nodes 2 and 3 in slice 2 (N7 and N8) +have different parents than their counterparts in slice 1 (N2 and N3), +they must be put into different equivalence classes. +Hence we define +<pre> +eclass1 = [1 2 3 4 5]; +eclass2 = [1 6 7 4 5]; +</pre> +The dotted bubbles represent the equivalence classes. +Node 7 is the representative node for equivalence class +6, and node 8 is the rep. for class 7, so we need to write +<pre> +bnet.CPD{6} = xxx_CPD(bnet, 7, xxx); +bnet.CPD{7} = xxx_CPD(bnet, 8, xxx); +</pre> +In general, you can use the following code fragment: +<pre> +eclass = bnet.equiv_class(:); +for e=1:max(eclass) + i = bnet.rep_of_eclass(e); + bnet.CPD{e} = xxx_CPD(bnet,i); +end +</pre> +<!-- +which is equivalent to +<pre> +E = max(eclass); +rep = zeros(1,E); +for e=1:E + mems = find(eclass==e); + rep(e) = mems(1); +end +for e=1:E + bnet.CPD{e} = xxx_CPD(bnet, rep(e)); +end +</pre> +-->
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/supportedModels.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ + <h2><a name="models">Supported probabilistic models</h2> +<p> +It is trivial to implement all of +the following probabilistic models using the toolbox. +<ul> +<li>Static +<ul> +<li> Linear regression, logistic regression, hierarchical mixtures of experts + +<li> Naive Bayes classifiers, mixtures of Gaussians, +sigmoid belief nets + +<li> Factor analysis, probabilistic +PCA, probabilistic ICA, mixtures of these models + +</ul> + +<li>Dynamic +<ul> + +<li> HMMs, Factorial HMMs, coupled HMMs, input-output HMMs, DBNs + +<li> Kalman filters, ARMAX models, switching Kalman filters, +tree-structured Kalman filters, multiscale AR models + +</ul> + +<li> Many other combinations, for which there are (as yet) no names! + +</ul> + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/usage.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3072 @@ +<HEAD> +<TITLE>How to use the Bayes Net Toolbox</TITLE> +</HEAD> + +<BODY BGCOLOR="#FFFFFF"> +<!-- white background is better for the pictures and equations --> + +<h1>How to use the Bayes Net Toolbox</h1> + +This documentation was last updated on 29 October 2007. +<br> +Click +<a href="http://bnt.insa-rouen.fr/">here</a> +for a French version of this documentation (last updated in 2005). +<p> + +<ul> +<li> <a href="install.html">Installation</a> + +<li> <a href="#basics">Creating your first Bayes net</a> + <ul> + <li> <a href="#basics">Creating a model by hand</a> + <li> <a href="#file">Loading a model from a file</a> + <li> <a href="#GUI">Creating a model using a GUI</a> + <li> <a href="graphviz.html">Graph visualization</a> + </ul> + +<li> <a href="#inference">Inference</a> + <ul> + <li> <a href="#marginal">Computing marginal distributions</a> + <li> <a href="#joint">Computing joint distributions</a> + <li> <a href="#soft">Soft/virtual evidence</a> + <li> <a href="#mpe">Most probable explanation</a> + </ul> + +<li> <a href="#cpd">Conditional Probability Distributions</a> + <ul> + <li> <a href="#tabular">Tabular (multinomial) nodes</a> + <li> <a href="#noisyor">Noisy-or nodes</a> + <li> <a href="#deterministic">Other (noisy) deterministic nodes</a> + <li> <a href="#softmax">Softmax (multinomial logit) nodes</a> + <li> <a href="#mlp">Neural network nodes</a> + <li> <a href="#root">Root nodes</a> + <li> <a href="#gaussian">Gaussian nodes</a> + <li> <a href="#glm">Generalized linear model nodes</a> + <li> <a href="#dtree">Classification/regression tree nodes</a> + <li> <a href="#nongauss">Other continuous distributions</a> + <li> <a href="#cpd_summary">Summary of CPD types</a> + </ul> + +<li> <a href="#examples">Example models</a> + <ul> + <li> <a + href="http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt"> +Gaussian mixture models</a> + <li> <a href="#pca">PCA, ICA, and all that</a> + <li> <a href="#mixep">Mixtures of experts</a> + <li> <a href="#hme">Hierarchical mixtures of experts</a> + <li> <a href="#qmr">QMR</a> + <li> <a href="#cg_model">Conditional Gaussian models</a> + <li> <a href="#hybrid">Other hybrid models</a> + </ul> + +<li> <a href="#param_learning">Parameter learning</a> + <ul> + <li> <a href="#load_data">Loading data from a file</a> + <li> <a href="#mle_complete">Maximum likelihood parameter estimation from complete data</a> + <li> <a href="#prior">Parameter priors</a> + <li> <a href="#bayes_learn">(Sequential) Bayesian parameter updating from complete data</a> + <li> <a href="#em">Maximum likelihood parameter estimation with missing values (EM)</a> + <li> <a href="#tying">Parameter tying</a> + </ul> + +<li> <a href="#structure_learning">Structure learning</a> + <ul> + <li> <a href="#enumerate">Exhaustive search</a> + <li> <a href="#K2">K2</a> + <li> <a href="#hill_climb">Hill-climbing</a> + <li> <a href="#mcmc">MCMC</a> + <li> <a href="#active">Active learning</a> + <li> <a href="#struct_em">Structural EM</a> + <li> <a href="#graphdraw">Visualizing the learned graph structure</a> + <li> <a href="#constraint">Constraint-based methods</a> + </ul> + + +<li> <a href="#engines">Inference engines</a> + <ul> + <li> <a href="#jtree">Junction tree</a> + <li> <a href="#varelim">Variable elimination</a> + <li> <a href="#global">Global inference methods</a> + <li> <a href="#quickscore">Quickscore</a> + <li> <a href="#belprop">Belief propagation</a> + <li> <a href="#sampling">Sampling (Monte Carlo)</a> + <li> <a href="#engine_summary">Summary of inference engines</a> + </ul> + + +<li> <a href="#influence">Influence diagrams/ decision making</a> + + +<li> <a href="usage_dbn.html">DBNs, HMMs, Kalman filters and all that</a> +</ul> + +</ul> + + + +<h1><a name="basics">Creating your first Bayes net</h1> + +To define a Bayes net, you must specify the graph structure and then +the parameters. We look at each in turn, using a simple example +(adapted from Russell and +Norvig, "Artificial Intelligence: a Modern Approach", Prentice Hall, +1995, p454). + + +<h2>Graph structure</h2> + + +Consider the following network. + +<p> +<center> +<IMG SRC="Figures/sprinkler.gif"> +</center> +<p> + +<P> +To specify this directed acyclic graph (dag), we create an adjacency matrix: +<PRE> +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; +</PRE> +<P> +We have numbered the nodes as follows: +Cloudy = 1, Sprinkler = 2, Rain = 3, WetGrass = 4. +<b>The nodes must always be numbered in topological order, i.e., +ancestors before descendants.</b> +For a more complicated graph, this is a little inconvenient: we will +see how to get around this <a href="usage_dbn.html#bat">below</a>. +<p> +In Matlab 6, you can use logical arrays instead of double arrays, +which are 4 times smaller: +<pre> +dag = false(N,N); +dag(C,[R S]) = true; +... +</pre> +However, <b>some graph functions (eg acyclic) do not work on +logical arrays</b>! +<p> +You can visualize the resulting graph structure using +the methods discussed <a href="#graphdraw">below</a>. +For details on GUIs, +click <a href="#GUI">here</a>. + +<h2>Creating the Bayes net shell</h2> + +In addition to specifying the graph structure, +we must specify the size and type of each node. +If a node is discrete, its size is the +number of possible values +each node can take on; if a node is continuous, +it can be a vector, and its size is the length of this vector. +In this case, we will assume all nodes are discrete and binary. +<PRE> +discrete_nodes = 1:N; +node_sizes = 2*ones(1,N); +</pre> +If the nodes were not binary, you could type e.g., +<pre> +node_sizes = [4 2 3 5]; +</pre> +meaning that Cloudy has 4 possible values, +Sprinkler has 2 possible values, etc. +Note that these are cardinal values, not ordinal, i.e., +they are not ordered in any way, like 'low', 'medium', 'high'. +<p> +We are now ready to make the Bayes net: +<pre> +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes); +</PRE> +By default, all nodes are assumed to be discrete, so we can also just +write +<pre> +bnet = mk_bnet(dag, node_sizes); +</PRE> +You may also specify which nodes will be observed. +If you don't know, or if this not fixed in advance, +just use the empty list (the default). +<pre> +onodes = []; +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes, 'observed', onodes); +</PRE> +Note that optional arguments are specified using a name/value syntax. +This is common for many BNT functions. +In general, to find out more about a function (e.g., which optional +arguments it takes), please see its +documentation string by typing +<pre> +help mk_bnet +</pre> +See also other <a href="matlab_tips.html">useful Matlab tips</a>. +<p> +It is possible to associate names with nodes, as follows: +<pre> +bnet = mk_bnet(dag, node_sizes, 'names', {'cloudy','S','R','W'}, 'discrete', 1:4); +</pre> +You can then refer to a node by its name: +<pre> +C = bnet.names('cloudy'); % bnet.names is an associative array +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +</pre> +This feature uses my own associative array class. + + +<h2><a name="cpt">Parameters</h2> + +A model consists of the graph structure and the parameters. +The parameters are represented by CPD objects (CPD = Conditional +Probability Distribution), which define the probability distribution +of a node given its parents. +(We will use the terms "node" and "random variable" interchangeably.) +The simplest kind of CPD is a table (multi-dimensional array), which +is suitable when all the nodes are discrete-valued. Note that the discrete +values are not assumed to be ordered in any way; that is, they +represent categorical quantities, like male and female, rather than +ordinal quantities, like low, medium and high. +(We will discuss CPDs in more detail <a href="#cpd">below</a>.) +<p> +Tabular CPDs, also called CPTs (conditional probability tables), +are stored as multidimensional arrays, where the dimensions +are arranged in the same order as the nodes, e.g., the CPT for node 4 +(WetGrass) is indexed by Sprinkler (2), Rain (3) and then WetGrass (4) itself. +Hence the child is always the last dimension. +If a node has no parents, its CPT is a column vector representing its +prior. +Note that in Matlab (unlike C), arrays are indexed +from 1, and are layed out in memory such that the first index toggles +fastest, e.g., the CPT for node 4 (WetGrass) is as follows +<P> +<P><IMG ALIGN=BOTTOM SRC="Figures/CPTgrass.gif"><P> +<P> +where we have used the convention that false==1, true==2. +We can create this CPT in Matlab as follows +<PRE> +CPT = zeros(2,2,2); +CPT(1,1,1) = 1.0; +CPT(2,1,1) = 0.1; +... +</PRE> +Here is an easier way: +<PRE> +CPT = reshape([1 0.1 0.1 0.01 0 0.9 0.9 0.99], [2 2 2]); +</PRE> +In fact, we don't need to reshape the array, since the CPD constructor +will do that for us. So we can just write +<pre> +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +</pre> +The other nodes are created similarly (using the old syntax for +optional parameters) +<PRE> +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +</PRE> + + +<h2><a name="rnd_cpt">Random Parameters</h2> + +If we do not specify the CPT, random parameters will be +created, i.e., each "row" of the CPT will be drawn from the uniform distribution. +To ensure repeatable results, use +<pre> +rand('state', seed); +randn('state', seed); +</pre> +To control the degree of randomness (entropy), +you can sample each row of the CPT from a Dirichlet(p,p,...) distribution. +If p << 1, this encourages "deterministic" CPTs (one entry near 1, the rest near 0). +If p = 1, each entry is drawn from U[0,1]. +If p >> 1, the entries will all be near 1/k, where k is the arity of +this node, i.e., each row will be nearly uniform. +You can do this as follows, assuming this node +is number i, and ns is the node_sizes. +<pre> +k = ns(i); +ps = parents(dag, i); +psz = prod(ns(ps)); +CPT = sample_dirichlet(p*ones(1,k), psz); +bnet.CPD{i} = tabular_CPD(bnet, i, 'CPT', CPT); +</pre> + + +<h2><a name="file">Loading a network from a file</h2> + +If you already have a Bayes net represented in the XML-based +<a href="http://www.cs.cmu.edu/afs/cs/user/fgcozman/www/Research/InterchangeFormat/"> +Bayes Net Interchange Format (BNIF)</a> (e.g., downloaded from the +<a +href="http://www.cs.huji.ac.il/labs/compbio/Repository"> +Bayes Net repository</a>), +you can convert it to BNT format using +the +<a href="http://www.digitas.harvard.edu/~ken/bif2bnt/">BIF-BNT Java +program</a> written by Ken Shan. +(This is not necessarily up-to-date.) +<p> +<b>It is currently not possible to save/load a BNT matlab object to +file</b>, but this is easily fixed if you modify all the constructors +for all the classes (see matlab documentation). + +<h2><a name="GUI">Creating a model using a GUI</h2> + +<ul> +<li>Senthil Nachimuthu +has started (Oct 07) an open source +GUI for BNT called +<a href="http://projeny.sourceforge.net">projeny</a> +using Java. This is a successor to BNJ. + +<li> +Philippe LeRay has written (Sep 05) +a +<a href="http://banquiseasi.insa-rouen.fr/projects/bnt-editor/"> +BNT GUI</a> in matlab. + +<li> +<a +href="http://www.dataonstage.com/BNT/PACKAGES/LinkStrength/index.html"> +LinkStrength</a>, + a package by Imme Ebert-Uphoff for visualizing the strength of +dependencies between nodes. + +</ul> + +<h2>Graph visualization</h2> + +Click <a href="graphviz.html">here</a> for more information +on graph visualization. + + +<h1><a name="inference">Inference</h1> + +Having created the BN, we can now use it for inference. +There are many different algorithms for doing inference in Bayes nets, +that make different tradeoffs between speed, +complexity, generality, and accuracy. +BNT therefore offers a variety of different inference +"engines". We will discuss these +in more detail <a href="#engines">below</a>. +For now, we will use the junction tree +engine, which is the mother of all exact inference algorithms. +This can be created as follows. +<pre> +engine = jtree_inf_engine(bnet); +</pre> +The other engines have similar constructors, but might take +additional, algorithm-specific parameters. +All engines are used in the same way, once they have been created. +We illustrate this in the following sections. + + +<h2><a name="marginal">Computing marginal distributions</h2> + +Suppose we want to compute the probability that the sprinker was on +given that the grass is wet. +The evidence consists of the fact that W=2. All the other nodes +are hidden (unobserved). We can specify this as follows. +<pre> +evidence = cell(1,N); +evidence{W} = 2; +</pre> +We use a 1D cell array instead of a vector to +cope with the fact that nodes can be vectors of different lengths. +In addition, the value [] can be used +to denote 'no evidence', instead of having to specify the observation +pattern as a separate argument. +(Click <a href="cellarray.html">here</a> for a quick tutorial on cell +arrays in matlab.) +<p> +We are now ready to add the evidence to the engine. +<pre> +[engine, loglik] = enter_evidence(engine, evidence); +</pre> +The behavior of this function is algorithm-specific, and is discussed +in more detail <a href="#engines">below</a>. +In the case of the jtree engine, +enter_evidence implements a two-pass message-passing scheme. +The first return argument contains the modified engine, which +incorporates the evidence. The second return argument contains the +log-likelihood of the evidence. (Not all engines are capable of +computing the log-likelihood.) +<p> +Finally, we can compute p=P(S=2|W=2) as follows. +<PRE> +marg = marginal_nodes(engine, S); +marg.T +ans = + 0.57024 + 0.42976 +p = marg.T(2); +</PRE> +We see that p = 0.4298. +<p> +Now let us add the evidence that it was raining, and see what +difference it makes. +<PRE> +evidence{R} = 2; +[engine, loglik] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, S); +p = marg.T(2); +</PRE> +We find that p = P(S=2|W=2,R=2) = 0.1945, +which is lower than +before, because the rain can ``explain away'' the +fact that the grass is wet. +<p> +You can plot a marginal distribution over a discrete variable +as a barchart using the built 'bar' function: +<pre> +bar(marg.T) +</pre> +This is what it looks like + +<p> +<center> +<IMG SRC="Figures/sprinkler_bar.gif"> +</center> +<p> + +<h2><a name="observed">Observed nodes</h2> + +What happens if we ask for the marginal on an observed node, e.g. P(W|W=2)? +An observed discrete node effectively only has 1 value (the observed + one) --- all other values would result in 0 probability. +For efficiency, BNT treats observed (discrete) nodes as if they were + set to 1, as we see below: +<pre> +evidence = cell(1,N); +evidence{W} = 2; +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, W); +m.T +ans = + 1 +</pre> +This can get a little confusing, since we assigned W=2. +So we can ask BNT to add the evidence back in by passing in an optional argument: +<pre> +m = marginal_nodes(engine, W, 1); +m.T +ans = + 0 + 1 +</pre> +This shows that P(W=1|W=2) = 0 and P(W=2|W=2) = 1. + + + +<h2><a name="joint">Computing joint distributions</h2> + +We can compute the joint probability on a set of nodes as in the +following example. +<pre> +evidence = cell(1,N); +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]); +</pre> +m is a structure. The 'T' field is a multi-dimensional array (in +this case, 3-dimensional) that contains the joint probability +distribution on the specified nodes. +<pre> +>> m.T +ans(:,:,1) = + 0.2900 0.0410 + 0.0210 0.0009 +ans(:,:,2) = + 0 0.3690 + 0.1890 0.0891 +</pre> +We see that P(S=1,R=1,W=2) = 0, since it is impossible for the grass +to be wet if both the rain and sprinkler are off. +<p> +Let us now add some evidence to R. +<pre> +evidence{R} = 2; +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]) +m = + domain: [2 3 4] + T: [2x1x2 double] +>> m.T +m.T +ans(:,:,1) = + 0.0820 + 0.0018 +ans(:,:,2) = + 0.7380 + 0.1782 +</pre> +The joint T(i,j,k) = P(S=i,R=j,W=k|evidence) +should have T(i,1,k) = 0 for all i,k, since R=1 is incompatible +with the evidence that R=2. +Instead of creating large tables with many 0s, BNT sets the effective +size of observed (discrete) nodes to 1, as explained above. +This is why m.T has size 2x1x2. +To get a 2x2x2 table, type +<pre> +m = marginal_nodes(engine, [S R W], 1) +m = + domain: [2 3 4] + T: [2x2x2 double] +>> m.T +m.T +ans(:,:,1) = + 0 0.082 + 0 0.0018 +ans(:,:,2) = + 0 0.738 + 0 0.1782 +</pre> + +<p> +Note: It is not always possible to compute the joint on arbitrary +sets of nodes: it depends on which inference engine you use, as discussed +in more detail <a href="#engines">below</a>. + + +<h2><a name="soft">Soft/virtual evidence</h2> + +Sometimes a node is not observed, but we have some distribution over +its possible values; this is often called "soft" or "virtual" +evidence. +One can use this as follows +<pre> +[engine, loglik] = enter_evidence(engine, evidence, 'soft', soft_evidence); +</pre> +where soft_evidence{i} is either [] (if node i has no soft evidence) +or is a vector representing the probability distribution over i's +possible values. +For example, if we don't know i's exact value, but we know its +likelihood ratio is 60/40, we can write evidence{i} = [] and +soft_evidence{i} = [0.6 0.4]. +<p> +Currently only jtree_inf_engine supports this option. +It assumes that all hidden nodes, and all nodes for +which we have soft evidence, are discrete. +For a longer example, see BNT/examples/static/softev1.m. + + +<h2><a name="mpe">Most probable explanation</h2> + +To compute the most probable explanation (MPE) of the evidence (i.e., +the most probable assignment, or a mode of the joint), use +<pre> +[mpe, ll] = calc_mpe(engine, evidence); +</pre> +mpe{i} is the most likely value of node i. +This calls enter_evidence with the 'maximize' flag set to 1, which +causes the engine to do max-product instead of sum-product. +The resulting max-marginals are then thresholded. +If there is more than one maximum probability assignment, we must take + care to break ties in a consistent manner (thresholding the + max-marginals may give the wrong result). To force this behavior, + type +<pre> +[mpe, ll] = calc_mpe(engine, evidence, 1); +</pre> +Note that computing the MPE is someties called abductive reasoning. + +<p> +You can also use <tt>calc_mpe_bucket</tt> written by Ron Zohar, +that does a forwards max-product pass, and then a backwards traceback +pass, which is how Viterbi is traditionally implemented. + + + +<h1><a name="cpd">Conditional Probability Distributions</h1> + +A Conditional Probability Distributions (CPD) +defines P(X(i) | X(Pa(i))), where X(i) is the i'th node, and X(Pa(i)) +are the parents of node i. There are many ways to represent this +distribution, which depend in part on whether X(i) and X(Pa(i)) are +discrete, continuous, or a combination. +We will discuss various representations below. + + +<h2><a name="tabular">Tabular nodes</h2> + +If the CPD is represented as a table (i.e., if it is a multinomial +distribution), it has a number of parameters that is exponential in +the number of parents. See the example <a href="#cpt">above</a>. + + +<h2><a name="noisyor">Noisy-or nodes</h2> + +A noisy-OR node is like a regular logical OR gate except that +sometimes the effects of parents that are on get inhibited. +Let the prob. that parent i gets inhibited be q(i). +Then a node, C, with 2 parents, A and B, has the following CPD, where +we use F and T to represent off and on (1 and 2 in BNT). +<pre> +A B P(C=off) P(C=on) +--------------------------- +F F 1.0 0.0 +T F q(A) 1-q(A) +F T q(B) 1-q(B) +T T q(A)q(B) 1-q(A)q(B) +</pre> +Thus we see that the causes get inhibited independently. +It is common to associate a "leak" node with a noisy-or CPD, which is +like a parent that is always on. This can account for all other unmodelled +causes which might turn the node on. +<p> +The noisy-or distribution is similar to the logistic distribution. +To see this, let the nodes, S(i), have values in {0,1}, and let q(i,j) +be the prob. that j inhibits i. Then +<pre> +Pr(S(i)=1 | parents(S(i))) = 1 - prod_{j} q(i,j)^S(j) +</pre> +Now define w(i,j) = -ln q(i,j) and rho(x) = 1-exp(-x). Then +<pre> +Pr(S(i)=1 | parents(S(i))) = rho(sum_j w(i,j) S(j)) +</pre> +For a sigmoid node, we have +<pre> +Pr(S(i)=1 | parents(S(i))) = sigma(-sum_j w(i,j) S(j)) +</pre> +where sigma(x) = 1/(1+exp(-x)). Hence they differ in the choice of +the activation function (although both are monotonically increasing). +In addition, in the case of a noisy-or, the weights are constrained to be +positive, since they derive from probabilities q(i,j). +In both cases, the number of parameters is <em>linear</em> in the +number of parents, unlike the case of a multinomial distribution, +where the number of parameters is exponential in the number of parents. +We will see an example of noisy-OR nodes <a href="#qmr">below</a>. + + +<h2><a name="deterministic">Other (noisy) deterministic nodes</h2> + +Deterministic CPDs for discrete random variables can be created using +the deterministic_CPD class. It is also possible to 'flip' the output +of the function with some probability, to simulate noise. +The boolean_CPD class is just a special case of a +deterministic CPD, where the parents and child are all binary. +<p> +Both of these classes are just "syntactic sugar" for the tabular_CPD +class. + + + +<h2><a name="softmax">Softmax nodes</h2> + +If we have a discrete node with a continuous parent, +we can define its CPD using a softmax function +(also known as the multinomial logit function). +This acts like a soft thresholding operator, and is defined as follows: +<pre> + exp(w(:,i)'*x + b(i)) +Pr(Q=i | X=x) = ----------------------------- + sum_j exp(w(:,j)'*x + b(j)) + +</pre> +The parameters of a softmax node, w(:,i) and b(i), i=1..|Q|, have the +following interpretation: w(:,i)-w(:,j) is the normal vector to the +decision boundary between classes i and j, +and b(i)-b(j) is its offset (bias). For example, suppose +X is a 2-vector, and Q is binary. Then +<pre> +w = [1 -1; + 0 0]; + +b = [0 0]; +</pre> +means class 1 are points in the 2D plane with positive x coordinate, +and class 2 are points in the 2D plane with negative x coordinate. +If w has large magnitude, the decision boundary is sharp, otherwise it +is soft. +In the special case that Q is binary (0/1), the softmax function reduces to the logistic +(sigmoid) function. +<p> +Fitting a softmax function can be done using the iteratively reweighted +least squares (IRLS) algorithm. +We use the implementation from +<a href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a>. +Note that since +the softmax distribution is not in the exponential family, it does not +have finite sufficient statistics, and hence we must store all the +training data in uncompressed form. +If this takes too much space, one should use online (stochastic) gradient +descent (not implemented in BNT). +<p> +If a softmax node also has discrete parents, +we use a different set of w/b parameters for each combination of +parent values, as in the <a href="#gaussian">conditional linear +Gaussian CPD</a>. +This feature was implemented by Pierpaolo Brutti. +He is currently extending it so that discrete parents can be treated +as if they were continuous, by adding indicator variables to the X +vector. +<p> +We will see an example of softmax nodes <a href="#mixexp">below</a>. + + +<h2><a name="mlp">Neural network nodes</h2> + +Pierpaolo Brutti has implemented the mlp_CPD class, which uses a multi layer perceptron +to implement a mapping from continuous parents to discrete children, +similar to the softmax function. +(If there are also discrete parents, it creates a mixture of MLPs.) +It uses code from <a +href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a>. +This is work in progress. + +<h2><a name="root">Root nodes</h2> + +A root node has no parents and no parameters; it can be used to model +an observed, exogeneous input variable, i.e., one which is "outside" +the model. +This is useful for conditional density models. +We will see an example of root nodes <a href="#mixexp">below</a>. + + +<h2><a name="gaussian">Gaussian nodes</h2> + +We now consider a distribution suitable for the continuous-valued nodes. +Suppose the node is called Y, its continuous parents (if any) are +called X, and its discrete parents (if any) are called Q. +The distribution on Y is defined as follows: +<pre> +- no parents: Y ~ N(mu, Sigma) +- cts parents : Y|X=x ~ N(mu + W x, Sigma) +- discrete parents: Y|Q=i ~ N(mu(:,i), Sigma(:,:,i)) +- cts and discrete parents: Y|X=x,Q=i ~ N(mu(:,i) + W(:,:,i) * x, Sigma(:,:,i)) +</pre> +where N(mu, Sigma) denotes a Normal distribution with mean mu and +covariance Sigma. Let |X|, |Y| and |Q| denote the sizes of X, Y and Q +respectively. +If there are no discrete parents, |Q|=1; if there is +more than one, then |Q| = a vector of the sizes of each discrete parent. +If there are no continuous parents, |X|=0; if there is more than one, +then |X| = the sum of their sizes. +Then mu is a |Y|*|Q| vector, Sigma is a |Y|*|Y|*|Q| positive +semi-definite matrix, and W is a |Y|*|X|*|Q| regression (weight) +matrix. +<p> +We can create a Gaussian node with random parameters as follows. +<pre> +bnet.CPD{i} = gaussian_CPD(bnet, i); +</pre> +We can specify the value of one or more of the parameters as in the +following example, in which |Y|=2, and |Q|=1. +<pre> +bnet.CPD{i} = gaussian_CPD(bnet, i, 'mean', [0; 0], 'weights', randn(Y,X), 'cov', eye(Y)); +</pre> +<p> +We will see an example of conditional linear Gaussian nodes <a +href="#cg_model">below</a>. +<p> +<b>When learning Gaussians from data</b>, it is helpful to ensure the +data has a small magnitde +(see e.g., KPMstats/standardize) to prevent numerical problems. +Unless you have a lot of data, it is also a very good idea to use +diagonal instead of full covariance matrices. +(BNT does not currently support spherical covariances, although it +would be easy to add, since KPMstats/clg_Mstep supports this option; +you would just need to modify gaussian_CPD/update_ess to accumulate +weighted inner products.) + + + +<h2><a name="nongauss">Other continuous distributions</h2> + +Currently BNT does not support any CPDs for continuous nodes other +than the Gaussian. +However, you can use a mixture of Gaussians to +approximate other continuous distributions. We will see some an example +of this with the IFA model <a href="#pca">below</a>. + + +<h2><a name="glm">Generalized linear model nodes</h2> + +In the future, we may incorporate some of the functionality of +<a href = +"http://www.sci.usq.edu.au/staff/dunn/glmlab/glmlab.html">glmlab</a> +into BNT. + + +<h2><a name="dtree">Classification/regression tree nodes</h2> + +We plan to add classification and regression trees to define CPDs for +discrete and continuous nodes, respectively. +Trees have many advantages: they are easy to interpret, they can do +feature selection, they can +handle discrete and continuous inputs, they do not make strong +assumptions about the form of the distribution, the number of +parameters can grow in a data-dependent way (i.e., they are +semi-parametric), they can handle missing data, etc. +However, they are not yet implemented. +<!-- +Yimin Zhang is currently (Feb '02) implementing this. +--> + + +<h2><a name="cpd_summary">Summary of CPD types</h2> + +We list all the different types of CPDs supported by BNT. +For each CPD, we specify if the child and parents can be discrete (D) or +continuous (C) (Binary (B) nodes are a special case). +We also specify which methods each class supports. +If a method is inherited, the name of the parent class is mentioned. +If a parent class calls a child method, this is mentioned. +<p> +The <tt>CPD_to_CPT</tt> method converts a CPD to a table; this +requires that the child and all parents are discrete. +The CPT might be exponentially big... +<tt>convert_to_table</tt> evaluates a CPD with evidence, and +represents the the resulting potential as an array. +This requires that the child is discrete, and any continuous parents +are observed. +<tt>convert_to_pot</tt> evaluates a CPD with evidence, and +represents the resulting potential as a dpot, gpot, cgpot or upot, as +requested. (d=discrete, g=Gaussian, cg = conditional Gaussian, u = +utility). + +<p> +When we sample a node, all the parents are observed. +When we compute the (log) probability of a node, all the parents and +the child are observed. +<p> +We also specify if the parameters are learnable. +For learning with EM, we require +the methods <tt>reset_ess</tt>, <tt>update_ess</tt> and +<tt>maximize_params</tt>. +For learning from fully observed data, we require +the method <tt>learn_params</tt>. +By default, all classes inherit this from generic_CPD, which simply +calls <tt>update_ess</tt> N times, once for each data case, followed +by <tt>maximize_params</tt>, i.e., it is like EM, without the E step. +Some classes implement a batch formula, which is quicker. +<p> +Bayesian learning means computing a posterior over the parameters +given fully observed data. +<p> +Pearl means we implement the methods <tt>compute_pi</tt> and +<tt>compute_lambda_msg</tt>, used by +<tt>pearl_inf_engine</tt>, which runs on directed graphs. +<tt>belprop_inf_engine</tt> only needs <tt>convert_to_pot</tt>.H +The pearl methods can exploit special properties of the CPDs for +computing the messages efficiently, whereas belprop does not. +<p> +The only method implemented by generic_CPD is <tt>adjustable_CPD</tt>, +which is not shown, since it is not very interesting. + + +<p> + + +<table> +<table border units = pixels><tr> +<td align=center>Name +<td align=center>Child +<td align=center>Parents +<td align=center>Comments +<td align=center>CPD_to_CPT +<td align=center>conv_to_table +<td align=center>conv_to_pot +<td align=center>sample +<td align=center>prob +<td align=center>learn +<td align=center>Bayes +<td align=center>Pearl + + +<tr> +<!-- Name--><td> +<!-- Child--><td> +<!-- Parents--><td> +<!-- Comments--><td> +<!-- CPD_to_CPT--><td> +<!-- conv_to_table--><td> +<!-- conv_to_pot--><td> +<!-- sample--><td> +<!-- prob--><td> +<!-- learn--><td> +<!-- Bayes--><td> +<!-- Pearl--><td> + +<tr> +<!-- Name--><td>boolean +<!-- Child--><td>B +<!-- Parents--><td>B +<!-- Comments--><td>Syntactic sugar for tabular +<!-- CPD_to_CPT--><td>- +<!-- conv_to_table--><td>- +<!-- conv_to_pot--><td>- +<!-- sample--><td>- +<!-- prob--><td>- +<!-- learn--><td>- +<!-- Bayes--><td>- +<!-- Pearl--><td>- + +<tr> +<!-- Name--><td>deterministic +<!-- Child--><td>D +<!-- Parents--><td>D +<!-- Comments--><td>Syntactic sugar for tabular +<!-- CPD_to_CPT--><td>- +<!-- conv_to_table--><td>- +<!-- conv_to_pot--><td>- +<!-- sample--><td>- +<!-- prob--><td>- +<!-- learn--><td>- +<!-- Bayes--><td>- +<!-- Pearl--><td>- + +<tr> +<!-- Name--><td>Discrete +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>Virtual class +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Calls CPD_to_CPT +<!-- conv_to_pot--><td>Calls conv_to_table +<!-- sample--><td>Calls conv_to_table +<!-- prob--><td>Calls conv_to_table +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + +<tr> +<!-- Name--><td>Gaussian +<!-- Child--><td>C +<!-- Parents--><td>C/D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>Y +<!-- prob--><td>Y +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + +<tr> +<!-- Name--><td>gmux +<!-- Child--><td>C +<!-- Parents--><td>C/D +<!-- Comments--><td>multiplexer +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>N +<!-- prob--><td>N +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>Y + + +<tr> +<!-- Name--><td>MLP +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>multi layer perceptron +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Y +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>noisy-or +<!-- Child--><td>B +<!-- Parents--><td>B +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>Y +<!-- conv_to_table--><td>Inherits from discrete +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>Y + + +<tr> +<!-- Name--><td>root +<!-- Child--><td>C/D +<!-- Parents--><td>none +<!-- Comments--><td>no params +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>Y +<!-- prob--><td>Y +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>softmax +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Y +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>generic +<!-- Child--><td>C/D +<!-- Parents--><td>C/D +<!-- Comments--><td>Virtual class +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>N +<!-- sample--><td>N +<!-- prob--><td>N +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>Tabular +<!-- Child--><td>D +<!-- Parents--><td>D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>Y +<!-- conv_to_table--><td>Inherits from discrete +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>Y +<!-- Pearl--><td>Y + +</table> + + + +<h1><a name="examples">Example models</h1> + + +<h2>Gaussian mixture models</h2> + +Richard W. DeVaul has made a detailed tutorial on how to fit mixtures +of Gaussians using BNT. Available +<a href="http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt">here</a>. + + +<h2><a name="pca">PCA, ICA, and all that </h2> + +In Figure (a) below, we show how Factor Analysis can be thought of as a +graphical model. Here, X has an N(0,I) prior, and +Y|X=x ~ N(mu + Wx, Psi), +where Psi is diagonal and W is called the "factor loading matrix". +Since the noise on both X and Y is diagonal, the components of these +vectors are uncorrelated, and hence can be represented as individual +scalar nodes, as we show in (b). +(This is useful if parts of the observations on the Y vector are occasionally missing.) +We usually take k=|X| << |Y|=D, so the model tries to explain +many observations using a low-dimensional subspace. + + +<center> +<table> +<tr> +<td><img src="Figures/fa.gif"> +<td><img src="Figures/fa_scalar.gif"> +<td><img src="Figures/mfa.gif"> +<td><img src="Figures/ifa.gif"> +<tr> +<td align=center> (a) +<td align=center> (b) +<td align=center> (c) +<td align=center> (d) +</table> +</center> + +<p> +We can create this model in BNT as follows. +<pre> +ns = [k D]; +dag = zeros(2,2); +dag(1,2) = 1; +bnet = mk_bnet(dag, ns, 'discrete', []); +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', zeros(k,1), 'cov', eye(k), ... + 'cov_type', 'diag', 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(D,1), 'cov', diag(Psi0), 'weights', W0, ... + 'cov_type', 'diag', 'clamp_mean', 1); +</pre> + +The root node is clamped to the N(0,I) distribution, so that we will +not update these parameters during learning. +The mean of the leaf node is clamped to 0, +since we assume the data has been centered (had its mean subtracted +off); this is just for simplicity. +Finally, the covariance of the leaf node is constrained to be +diagonal. W0 and Psi0 are the initial parameter guesses. + +<p> +We can fit this model (i.e., estimate its parameters in a maximum +likelihood (ML) sense) using EM, as we +explain <a href="#em">below</a>. +Not surprisingly, the ML estimates for mu and Psi turn out to be +identical to the +sample mean and variance, which can be computed directly as +<pre> +mu_ML = mean(data); +Psi_ML = diag(cov(data)); +</pre> +Note that W can only be identified up to a rotation matrix, because of +the spherical symmetry of the source. + +<p> +If we restrict Psi to be spherical, i.e., Psi = sigma*I, +there is a closed-form solution for W as well, +i.e., we do not need to use EM. +In particular, W contains the first |X| eigenvectors of the sample covariance +matrix, with scalings determined by the eigenvalues and sigma. +Classical PCA can be obtained by taking the sigma->0 limit. +For details, see + +<ul> +<li> <a href="ftp://hope.caltech.edu/pub/roweis/Empca/empca.ps"> +"EM algorithms for PCA and SPCA"</a>, Sam Roweis, NIPS 97. +(<a href="ftp://hope.caltech.edu/pub/roweis/Code/empca.tar.gz"> +Matlab software</a>) + +<p> +<li> +<a +href=http://neural-server.aston.ac.uk/cgi-bin/tr_avail.pl?trnumber=NCRG/97/003> +"Mixtures of probabilistic principal component analyzers"</a>, +Tipping and Bishop, Neural Computation 11(2):443--482, 1999. +</ul> + +<p> +By adding a hidden discrete variable, we can create mixtures of FA +models, as shown in (c). +Now we can explain the data using a set of subspaces. +We can create this model in BNT as follows. +<pre> +ns = [M k D]; +dag = zeros(3); +dag(1,3) = 1; +dag(2,3) = 1; +bnet = mk_bnet(dag, ns, 'discrete', 1); +bnet.CPD{1} = tabular_CPD(bnet, 1, Pi0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(k, 1), 'cov', eye(k), 'cov_type', 'diag', ... + 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', Mu0', 'cov', repmat(diag(Psi0), [1 1 M]), ... + 'weights', W0, 'cov_type', 'diag', 'tied_cov', 1); +</pre> +Notice how the covariance matrix for Y is the same for all values of +Q; that is, the noise level in each sub-space is assumed the same. +However, we allow the offset, mu, to vary. +For details, see +<ul> + +<LI> +<a HREF="ftp://ftp.cs.toronto.edu/pub/zoubin/tr-96-1.ps.gz"> The EM +Algorithm for Mixtures of Factor Analyzers </A>, +Ghahramani, Z. and Hinton, G.E. (1996), +University of Toronto +Technical Report CRG-TR-96-1. +(<A HREF="ftp://ftp.cs.toronto.edu/pub/zoubin/mfa.tar.gz">Matlab software</A>) + +<p> +<li> +<a +href=http://neural-server.aston.ac.uk/cgi-bin/tr_avail.pl?trnumber=NCRG/97/003> +"Mixtures of probabilistic principal component analyzers"</a>, +Tipping and Bishop, Neural Computation 11(2):443--482, 1999. +</ul> + +<p> +I have included Zoubin's specialized MFA code (with his permission) +with the toolbox, so you can check that BNT gives the same results: +see 'BNT/examples/static/mfa1.m'. + +<p> +Independent Factor Analysis (IFA) generalizes FA by allowing a +non-Gaussian prior on each component of X. +(Note that we can approximate a non-Gaussian prior using a mixture of +Gaussians.) +This means that the likelihood function is no longer rotationally +invariant, so we can uniquely identify W and the hidden +sources X. +IFA also allows a non-diagonal Psi (i.e. correlations between the components of Y). +We recover classical Independent Components Analysis (ICA) +in the Psi -> 0 limit, and by assuming that |X|=|Y|, so that the +weight matrix W is square and invertible. +For details, see +<ul> +<li> +<a href="http://www.gatsby.ucl.ac.uk/~hagai/ifa.ps">Independent Factor +Analysis</a>, H. Attias, Neural Computation 11: 803--851, 1998. +</ul> + + + +<h2><a name="mixexp">Mixtures of experts</h2> + +As an example of the use of the softmax function, +we introduce the Mixture of Experts model. +<!-- +We also show +the Hierarchical Mixture of Experts model, where the hierarchy has two +levels. +(This is essentially a probabilistic decision tree of height two.) +--> +As before, +circles denote continuous-valued nodes, +squares denote discrete nodes, clear +means hidden, and shaded means observed. +<p> +<center> +<table> +<tr> +<td><img src="Figures/mixexp.gif"> +<!-- +<td><img src="Figures/hme.gif"> +--> +</table> +</center> +<p> +X is the observed +input, Y is the output, and +the Q nodes are hidden "gating" nodes, which select the appropriate +set of parameters for Y. During training, Y is assumed observed, +but for testing, the goal is to predict Y given X. +Note that this is a <em>conditional</em> density model, so we don't +associate any parameters with X. +Hence X's CPD will be a root CPD, which is a way of modelling +exogenous nodes. +If the output is a continuous-valued quantity, +we assume the "experts" are linear-regression units, +and set Y's CPD to linear-Gaussian. +If the output is discrete, we set Y's CPD to a softmax function. +The Q CPDs will always be softmax functions. + +<p> +As a concrete example, consider the mixture of experts model where X and Y are +scalars, and Q is binary. +This is just piecewise linear regression, where +we have two line segments, i.e., +<P> +<IMG ALIGN=BOTTOM SRC="Eqns/lin_reg_eqn.gif"> +<P> +We can create this model with random parameters as follows. +(This code is bundled in BNT/examples/static/mixexp2.m.) +<PRE> +X = 1; +Q = 2; +Y = 3; +dag = zeros(3,3); +dag(X,[Q Y]) = 1 +dag(Q,Y) = 1; +ns = [1 2 1]; % make X and Y scalars, and have 2 experts +onodes = [1 3]; +bnet = mk_bnet(dag, ns, 'discrete', 2, 'observed', onodes); + +rand('state', 0); +randn('state', 0); +bnet.CPD{1} = root_CPD(bnet, 1); +bnet.CPD{2} = softmax_CPD(bnet, 2); +bnet.CPD{3} = gaussian_CPD(bnet, 3); +</PRE> +Now let us fit this model using <a href="#em">EM</a>. +First we <a href="#load_data">load the data</a> (1000 training cases) and plot them. +<P> +<PRE> +data = load('/examples/static/Misc/mixexp_data.txt', '-ascii'); +plot(data(:,1), data(:,2), '.'); +</PRE> +<p> +<center> +<IMG SRC="Figures/mixexp_data.gif"> +</center> +<p> +This is what the model looks like before training. +(Thanks to Thomas Hofman for writing this plotting routine.) +<p> +<center> +<IMG SRC="Figures/mixexp_before.gif"> +</center> +<p> +Now let's train the model, and plot the final performance. +(We will discuss how to train models in more detail <a href="#param_learning">below</a>.) +<P> +<PRE> +ncases = size(data, 1); % each row of data is a training case +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); % each column of cases is a training case +engine = jtree_inf_engine(bnet); +max_iter = 20; +[bnet2, LLtrace] = learn_params_em(engine, cases, max_iter); +</PRE> +(We specify which nodes will be observed when we create the engine. +Hence BNT knows that the hidden nodes are all discrete. +For complex models, this can lead to a significant speedup.) +Below we show what the model looks like after 16 iterations of EM +(with 100 IRLS iterations per M step), when it converged +using the default convergence tolerance (that the +fractional change in the log-likelihood be less than 1e-3). +Before learning, the log-likelihood was +-322.927442; afterwards, it was -13.728778. +<p> +<center> +<IMG SRC="Figures/mixexp_after.gif"> +</center> +(See BNT/examples/static/mixexp2.m for details of the code.) + + + +<h2><a name="hme">Hierarchical mixtures of experts</h2> + +A hierarchical mixture of experts (HME) extends the mixture of experts +model by having more than one hidden node. A two-level example is shown below, along +with its more traditional representation as a neural network. +This is like a (balanced) probabilistic decision tree of height 2. +<p> +<center> +<IMG SRC="Figures/HMEforMatlab.jpg"> +</center> +<p> +<a href="mailto:pbrutti@stat.cmu.edu">Pierpaolo Brutti</a> +has written an extensive set of routines for HMEs, +which are bundled with BNT: see the examples/static/HME directory. +These routines allow you to choose the number of hidden (gating) +layers, and the form of the experts (softmax or MLP). +See the file hmemenu, which provides a demo. +For example, the figure below shows the decision boundaries learned +for a ternary classification problem, using a 2 level HME with softmax +gates and softmax experts; the training set is on the left, the +testing set on the right. +<p> +<center> +<!--<IMG SRC="Figures/hme_dec_boundary.gif">--> +<IMG SRC="Figures/hme_dec_boundary.png"> +</center> +<p> + + +<p> +For more details, see the following: +<ul> + +<li> <a href="http://www.cs.berkeley.edu/~jordan/papers/hierarchies.ps.Z"> +Hierarchical mixtures of experts and the EM algorithm</a> +M. I. Jordan and R. A. Jacobs. Neural Computation, 6, 181-214, 1994. + +<li> <a href = +"http://www.cs.berkeley.edu/~dmartin/software">David Martin's +matlab code for HME</a> + +<li> <a +href="http://www.cs.berkeley.edu/~jordan/papers/uai.ps.Z">Why the +logistic function? A tutorial discussion on +probabilities and neural networks.</a> M. I. Jordan. MIT Computational +Cognitive Science Report 9503, August 1995. + +<li> "Generalized Linear Models", McCullagh and Nelder, Chapman and +Halll, 1983. + +<li> +"Improved learning algorithms for mixtures of experts in multiclass +classification". +K. Chen, L. Xu, H. Chi. +Neural Networks (1999) 12: 1229-1252. + +<li> <a href="http://www.oigeeza.com/steve/"> +Classification Using Hierarchical Mixtures of Experts</a> +S.R. Waterhouse and A.J. Robinson. +In Proc. IEEE Workshop on Neural Network for Signal Processing IV (1994), pp. 177-186 + +<li> <a href="http://www.idiap.ch/~perry/"> +Localized mixtures of experts</a>, +P. Moerland, 1998. + +<li> "Nonlinear gated experts for time series", +A.S. Weigend and M. Mangeas, 1995. + +</ul> + + +<h2><a name="qmr">QMR</h2> + +Bayes nets originally arose out of an attempt to add probabilities to +expert systems, and this is still the most common use for BNs. +A famous example is +QMR-DT, a decision-theoretic reformulation of the Quick Medical +Reference (QMR) model. +<p> +<center> +<IMG ALIGN=BOTTOM SRC="Figures/qmr.gif"> +</center> +Here, the top layer represents hidden disease nodes, and the bottom +layer represents observed symptom nodes. +The goal is to infer the posterior probability of each disease given +all the symptoms (which can be present, absent or unknown). +Each node in the top layer has a Bernoulli prior (with a low prior +probability that the disease is present). +Since each node in the bottom layer has a high fan-in, we use a +noisy-OR parameterization; each disease has an independent chance of +causing each symptom. +The real QMR-DT model is copyright, but +we can create a random QMR-like model as follows. +<pre> +function bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% MK_QMR_BNET Make a QMR model +% bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% +% G(i,j) = 1 iff there is an arc from disease i to finding j +% inhibit(i,j) = inhibition probability on i->j arc +% leak(j) = inhibition prob. on leak->j arc +% prior(i) = prob. disease i is on + +[Ndiseases Nfindings] = size(inhibit); +N = Ndiseases + Nfindings; +finding_node = Ndiseases+1:N; +ns = 2*ones(1,N); +dag = zeros(N,N); +dag(1:Ndiseases, finding_node) = G; +bnet = mk_bnet(dag, ns, 'observed', finding_node); + +for d=1:Ndiseases + CPT = [1-prior(d) prior(d)]; + bnet.CPD{d} = tabular_CPD(bnet, d, CPT'); +end + +for i=1:Nfindings + fnode = finding_node(i); + ps = parents(G, i); + bnet.CPD{fnode} = noisyor_CPD(bnet, fnode, leak(i), inhibit(ps, i)); +end +</pre> +In the file BNT/examples/static/qmr1, we create a random bipartite +graph G, with 5 diseases and 10 findings, and random parameters. +(In general, to create a random dag, use 'mk_random_dag'.) +We can visualize the resulting graph structure using +the methods discussed <a href="#graphdraw">below</a>, with the +following results: +<p> +<img src="Figures/qmr.rnd.jpg"> + +<p> +Now let us put some random evidence on all the leaves except the very +first and very last, and compute the disease posteriors. +<pre> +pos = 2:floor(Nfindings/2); +neg = (pos(end)+1):(Nfindings-1); +onodes = myunion(pos, neg); +evidence = cell(1, N); +evidence(findings(pos)) = num2cell(repmat(2, 1, length(pos))); +evidence(findings(neg)) = num2cell(repmat(1, 1, length(neg))); + +engine = jtree_inf_engine(bnet); +[engine, ll] = enter_evidence(engine, evidence); +post = zeros(1, Ndiseases); +for i=diseases(:)' + m = marginal_nodes(engine, i); + post(i) = m.T(2); +end +</pre> +Junction tree can be quite slow on large QMR models. +Fortunately, it is possible to exploit properties of the noisy-OR +function to speed up exact inference using an algorithm called +<a href="#quickscore">quickscore</a>, discussed below. + + + + + +<h2><a name="cg_model">Conditional Gaussian models</h2> + +A conditional Gaussian model is one in which, conditioned on all the discrete +nodes, the distribution over the remaining (continuous) nodes is +multivariate Gaussian. This means we can have arcs from discrete (D) +to continuous (C) nodes, but not vice versa. +(We <em>are</em> allowed C->D arcs if the continuous nodes are observed, +as in the <a href="#mixexp">mixture of experts</a> model, +since this distribution can be represented with a discrete potential.) +<p> +We now give an example of a CG model, from +the paper "Propagation of Probabilities, Means amd +Variances in Mixed Graphical Association Models", Steffen Lauritzen, +JASA 87(420):1098--1108, 1992 (reprinted in the book "Probabilistic Networks and Expert +Systems", R. G. Cowell, A. P. Dawid, S. L. Lauritzen and +D. J. Spiegelhalter, Springer, 1999.) + +<h3>Specifying the graph</h3> + +Consider the model of waste emissions from an incinerator plant shown below. +We follow the standard convention that shaded nodes are observed, +clear nodes are hidden. +We also use the non-standard convention that +square nodes are discrete (tabular) and round nodes are +Gaussian. + +<p> +<center> +<IMG SRC="Figures/cg1.gif"> +</center> +<p> + +We can create this model as follows. +<pre> +F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; +n = 9; + +dag = zeros(n); +dag(F,E)=1; +dag(W,[E Min D]) = 1; +dag(E,D)=1; +dag(B,[C D])=1; +dag(D,[L Mout])=1; +dag(Min,Mout)=1; + +% node sizes - all cts nodes are scalar, all discrete nodes are binary +ns = ones(1, n); +dnodes = [F W B]; +cnodes = mysetdiff(1:n, dnodes); +ns(dnodes) = 2; + +bnet = mk_bnet(dag, ns, 'discrete', dnodes); +</pre> +'dnodes' is a list of the discrete nodes; 'cnodes' is the continuous +nodes. 'mysetdiff' is a faster version of the built-in 'setdiff'. +<p> + + +<h3>Specifying the parameters</h3> + +The parameters of the discrete nodes can be specified as follows. +<pre> +bnet.CPD{B} = tabular_CPD(bnet, B, 'CPT', [0.85 0.15]); % 1=stable, 2=unstable +bnet.CPD{F} = tabular_CPD(bnet, F, 'CPT', [0.95 0.05]); % 1=intact, 2=defect +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [2/7 5/7]); % 1=industrial, 2=household +</pre> + +<p> +The parameters of the continuous nodes can be specified as follows. +<pre> +bnet.CPD{E} = gaussian_CPD(bnet, E, 'mean', [-3.9 -0.4 -3.2 -0.5], ... + 'cov', [0.00002 0.0001 0.00002 0.0001]); +bnet.CPD{D} = gaussian_CPD(bnet, D, 'mean', [6.5 6.0 7.5 7.0], ... + 'cov', [0.03 0.04 0.1 0.1], 'weights', [1 1 1 1]); +bnet.CPD{C} = gaussian_CPD(bnet, C, 'mean', [-2 -1], 'cov', [0.1 0.3]); +bnet.CPD{L} = gaussian_CPD(bnet, L, 'mean', 3, 'cov', 0.25, 'weights', -0.5); +bnet.CPD{Min} = gaussian_CPD(bnet, Min, 'mean', [0.5 -0.5], 'cov', [0.01 0.005]); +bnet.CPD{Mout} = gaussian_CPD(bnet, Mout, 'mean', 0, 'cov', 0.002, 'weights', [1 1]); +</pre> + + +<h3><a name="cg_infer">Inference</h3> + +<!--Let us perform inference in the <a href="#cg_model">waste incinerator example</a>.--> +First we compute the unconditional marginals. +<pre> +engine = jtree_inf_engine(bnet); +evidence = cell(1,n); +[engine, ll] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, E); +</pre> +<!--(Of course, we could use <tt>cond_gauss_inf_engine</tt> instead of jtree.)--> +'marg' is a structure that contains the fields 'mu' and 'Sigma', which +contain the mean and (co)variance of the marginal on E. +In this case, they are both scalars. +Let us check they match the published figures (to 2 decimal places). +<!--(We can't expect +more precision than this in general because I have implemented the algorithm of +Lauritzen (1992), which can be numerically unstable.)--> +<pre> +tol = 1e-2; +assert(approxeq(marg.mu, -3.25, tol)); +assert(approxeq(sqrt(marg.Sigma), 0.709, tol)); +</pre> +We can compute the other posteriors similarly. +Now let us add some evidence. +<pre> +evidence = cell(1,n); +evidence{W} = 1; % industrial +evidence{L} = 1.1; +evidence{C} = -0.9; +[engine, ll] = enter_evidence(engine, evidence); +</pre> +Now we find +<pre> +marg = marginal_nodes(engine, E); +assert(approxeq(marg.mu, -3.8983, tol)); +assert(approxeq(sqrt(marg.Sigma), 0.0763, tol)); +</pre> + + +We can also compute the joint probability on a set of nodes. +For example, P(D, Mout | evidence) is a 2D Gaussian: +<pre> +marg = marginal_nodes(engine, [D Mout]) +marg = + domain: [6 8] + mu: [2x1 double] + Sigma: [2x2 double] + T: 1.0000 +</pre> +The mean is +<pre> +marg.mu +ans = + 3.6077 + 4.1077 +</pre> +and the covariance matrix is +<pre> +marg.Sigma +ans = + 0.1062 0.1062 + 0.1062 0.1182 +</pre> +It is easy to visualize this posterior using standard Matlab plotting +functions, e.g., +<pre> +gaussplot2d(marg.mu, marg.Sigma); +</pre> +produces the following picture. + +<p> +<center> +<IMG SRC="Figures/gaussplot.png"> +</center> +<p> + + +The T field indicates that the mixing weight of this Gaussian +component is 1.0. +If the joint contains discrete and continuous variables, the result +will be a mixture of Gaussians, e.g., +<pre> +marg = marginal_nodes(engine, [F E]) + domain: [1 3] + mu: [-3.9000 -0.4003] + Sigma: [1x1x2 double] + T: [0.9995 4.7373e-04] +</pre> +The interpretation is +Sigma(i,j,k) = Cov[ E(i) E(j) | F=k ]. +In this case, E is a scalar, so i=j=1; k specifies the mixture component. +<p> +We saw in the sprinkler network that BNT sets the effective size of +observed discrete nodes to 1, since they only have one legal value. +For continuous nodes, BNT sets their length to 0, +since they have been reduced to a point. +For example, +<pre> +marg = marginal_nodes(engine, [B C]) + domain: [4 5] + mu: [] + Sigma: [] + T: [0.0123 0.9877] +</pre> +It is simple to post-process the output of marginal_nodes. +For example, the file BNT/examples/static/cg1 sets the mu term of +observed nodes to their observed value, and the Sigma term to 0 (since +observed nodes have no variance). + +<p> +Note that the implemented version of the junction tree is numerically +unstable when using CG potentials +(which is why, in the example above, we only required our answers to agree with +the published ones to 2dp.) +This is why you might want to use <tt>stab_cond_gauss_inf_engine</tt>, +implemented by Shan Huang. This is described in + +<ul> +<li> "Stable Local Computation with Conditional Gaussian Distributions", +S. Lauritzen and F. Jensen, Tech Report R-99-2014, +Dept. Math. Sciences, Allborg Univ., 1999. +</ul> + +However, even the numerically stable version +can be computationally intractable if there are many hidden discrete +nodes, because the number of mixture components grows exponentially e.g., in a +<a href="usage_dbn.html#lds">switching linear dynamical system</a>. +In general, one must resort to approximate inference techniques: see +the discussion on <a href="#engines">inference engines</a> below. + + +<h2><a name="hybrid">Other hybrid models</h2> + +When we have C->D arcs, where C is hidden, we need to use +approximate inference. +One approach (not implemented in BNT) is described in +<ul> +<li> <a +href="http://www.cs.berkeley.edu/~murphyk/Papers/hybrid_uai99.ps.gz">A +Variational Approximation for Bayesian Networks with +Discrete and Continuous Latent Variables</a>, +K. Murphy, UAI 99. +</ul> +Of course, one can always use <a href="#sampling">sampling</a> methods +for approximate inference in such models. + + + +<h1><a name="param_learning">Parameter Learning</h1> + +The parameter estimation routines in BNT can be classified into 4 +types, depending on whether the goal is to compute +a full (Bayesian) posterior over the parameters or just a point +estimate (e.g., Maximum Likelihood or Maximum A Posteriori), +and whether all the variables are fully observed or there is missing +data/ hidden variables (partial observability). +<p> + +<TABLE BORDER> +<tr> + <TH></TH> + <th>Full obs</th> + <th>Partial obs</th> +</tr> +<tr> + <th>Point</th> + <td><tt>learn_params</tt></td> + <td><tt>learn_params_em</tt></td> +</tr> +<tr> + <th>Bayes</th> + <td><tt>bayes_update_params</tt></td> + <td>not yet supported</td> +</tr> +</table> + + +<h2><a name="load_data">Loading data from a file</h2> + +To load numeric data from an ASCII text file called 'dat.txt', where each row is a +case and columns are separated by white-space, such as +<pre> +011979 1626.5 0.0 +021979 1367.0 0.0 +... +</pre> +you can use +<pre> +data = load('dat.txt'); +</pre> +or +<pre> +load dat.txt -ascii +</pre> +In the latter case, the data is stored in a variable called 'dat' (the +filename minus the extension). +Alternatively, suppose the data is stored in a .csv file (has commas +separating the columns, and contains a header line), such as +<pre> +header info goes here +ORD,011979,1626.5,0.0 +DSM,021979,1367.0,0.0 +... +</pre> +You can load this using +<pre> +[a,b,c,d] = textread('dat.txt', '%s %d %f %f', 'delimiter', ',', 'headerlines', 1); +</pre> +If your file is not in either of these formats, you can either use Perl to convert +it to this format, or use the Matlab scanf command. +Type +<tt> +help iofun +</tt> +for more information on Matlab's file functions. +<!-- +<p> +To load data directly from Excel, +you should buy the +<a href="http://www.mathworks.com/products/excellink/">Excel Link</a>. +To load data directly from a relational database, +you should buy the +<a href="http://www.mathworks.com/products/database">Database +toolbox</a>. +--> +<p> +BNT learning routines require data to be stored in a cell array. +data{i,m} is the value of node i in case (example) m, i.e., each +<em>column</em> is a case. +If node i is not observed in case m (missing value), set +data{i,m} = []. +(Not all the learning routines can cope with such missing values, however.) +In the special case that all the nodes are observed and are +scalar-valued (as opposed to vector-valued), the data can be +stored in a matrix (as opposed to a cell-array). +<p> +Suppose, as in the <a href="#mixexp">mixture of experts example</a>, +that we have 3 nodes in the graph: X(1) is the observed input, X(3) is +the observed output, and X(2) is a hidden (gating) node. We can +create the dataset as follows. +<pre> +data = load('dat.txt'); +ncases = size(data, 1); +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); +</pre> +Notice how we transposed the data, to convert rows into columns. +Also, cases{2,m} = [] for all m, since X(2) is always hidden. + + +<h2><a name="mle_complete">Maximum likelihood parameter estimation from complete data</h2> + +As an example, let's generate some data from the sprinkler network, randomize the parameters, +and then try to recover the original model. +First we create some training data using forwards sampling. +<pre> +samples = cell(N, nsamples); +for i=1:nsamples + samples(:,i) = sample_bnet(bnet); +end +</pre> +samples{j,i} contains the value of the j'th node in case i. +sample_bnet returns a cell array because, in general, each node might +be a vector of different length. +In this case, all nodes are discrete (and hence scalars), so we +could have used a regular array instead (which can be quicker): +<pre> +data = cell2num(samples); +</pre +So now data(j,i) = samples{j,i}. +<p> +Now we create a network with random parameters. +(The initial values of bnet2 don't matter in this case, since we can find the +globally optimal MLE independent of where we start.) +<pre> +% Make a tabula rasa +bnet2 = mk_bnet(dag, node_sizes); +seed = 0; +rand('state', seed); +bnet2.CPD{C} = tabular_CPD(bnet2, C); +bnet2.CPD{R} = tabular_CPD(bnet2, R); +bnet2.CPD{S} = tabular_CPD(bnet2, S); +bnet2.CPD{W} = tabular_CPD(bnet2, W); +</pre> +Finally, we find the maximum likelihood estimates of the parameters. +<pre> +bnet3 = learn_params(bnet2, samples); +</pre> +To view the learned parameters, we use a little Matlab hackery. +<pre> +CPT3 = cell(1,N); +for i=1:N + s=struct(bnet3.CPD{i}); % violate object privacy + CPT3{i}=s.CPT; +end +</pre> +Here are the parameters learned for node 4. +<pre> +dispcpt(CPT3{4}) +1 1 : 1.0000 0.0000 +2 1 : 0.2000 0.8000 +1 2 : 0.2273 0.7727 +2 2 : 0.0000 1.0000 +</pre> +So we see that the learned parameters are fairly close to the "true" +ones, which we display below. +<pre> +dispcpt(CPT{4}) +1 1 : 1.0000 0.0000 +2 1 : 0.1000 0.9000 +1 2 : 0.1000 0.9000 +2 2 : 0.0100 0.9900 +</pre> +We can get better results by using a larger training set, or using +informative priors (see <a href="#prior">below</a>). + + + +<h2><a name="prior">Parameter priors</h2> + +Currently, only tabular CPDs can have priors on their parameters. +The conjugate prior for a multinomial is the Dirichlet. +(For binary random variables, the multinomial is the same as the +Bernoulli, and the Dirichlet is the same as the Beta.) +<p> +The Dirichlet has a simple interpretation in terms of pseudo counts. +If we let N_ijk = the num. times X_i=k and Pa_i=j occurs in the +training set, where Pa_i are the parents of X_i, +then the maximum likelihood (ML) estimate is +T_ijk = N_ijk / N_ij (where N_ij = sum_k' N_ijk'), which will be 0 if N_ijk=0. +To prevent us from declaring that (X_i=k, Pa_i=j) is impossible just because this +event was not seen in the training set, +we can pretend we saw value k of X_i, for each value j of Pa_i some number (alpha_ijk) +of times in the past. +The MAP (maximum a posterior) estimate is then +<pre> +T_ijk = (N_ijk + alpha_ijk) / (N_ij + alpha_ij) +</pre> +and is never 0 if all alpha_ijk > 0. +For example, consider the network A->B, where A is binary and B has 3 +values. +A uniform prior for B has the form +<pre> + B=1 B=2 B=3 +A=1 1 1 1 +A=2 1 1 1 +</pre> +which can be created using +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); +</pre> +This prior does not satisfy the likelihood equivalence principle, +which says that <a href="#markov_equiv">Markov equivalent</a> models +should have the same marginal likelihood. +A prior that does satisfy this principle is shown below. +Heckerman (1995) calls this the +BDeu prior (likelihood equivalent uniform Bayesian Dirichlet). +<pre> + B=1 B=2 B=3 +A=1 1/6 1/6 1/6 +A=2 1/6 1/6 1/6 +</pre> +where we put N/(q*r) in each bin; N is the equivalent sample size, +r=|A|, q = |B|. +This can be created as follows +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'BDeu'); +</pre> +Here, 1 is the equivalent sample size, and is the strength of the +prior. +You can change this using +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', ... + 'BDeu', 'dirichlet_weight', 10); +</pre> +<!--where counts is an array of pseudo-counts of the same size as the +CPT.--> +<!-- +<p> +When you specify a prior, you should set row i of the CPT to the +normalized version of row i of the pseudo-count matrix, i.e., to the +expected values of the parameters. This will ensure that computing the +marginal likelihood sequentially (see <a +href="#bayes_learn">below</a>) and in batch form gives the same +results. +To do this, proceed as follows. +<pre> +tabular_CPD(bnet, i, 'prior', counts, 'CPT', mk_stochastic(counts)); +</pre> +For a non-informative prior, you can just write +<pre> +tabular_CPD(bnet, i, 'prior', 'unif', 'CPT', 'unif'); +</pre> +--> + + +<h2><a name="bayes_learn">(Sequential) Bayesian parameter updating from complete data</h2> + +If we use conjugate priors and have fully observed data, we can +compute the posterior over the parameters in batch form as follows. +<pre> +cases = sample_bnet(bnet, nsamples); +bnet = bayes_update_params(bnet, cases); +LL = log_marg_lik_complete(bnet, cases); +</pre> +bnet.CPD{i}.prior contains the new Dirichlet pseudocounts, +and bnet.CPD{i}.CPT is set to the mean of the posterior (the +normalized counts). +(Hence if the initial pseudo counts are 0, +<tt>bayes_update_params</tt> and <tt>learn_params</tt> will give the +same result.) + + + + +<p> +We can compute the same result sequentially (on-line) as follows. +<pre> +LL = 0; +for m=1:nsamples + LL = LL + log_marg_lik_complete(bnet, cases(:,m)); + bnet = bayes_update_params(bnet, cases(:,m)); +end +</pre> + +The file <tt>BNT/examples/static/StructLearn/model_select1</tt> has an example of +sequential model selection which uses the same idea. +We generate data from the model A->B +and compute the posterior prob of all 3 dags on 2 nodes: + (1) A B, (2) A <- B , (3) A -> B +Models 2 and 3 are <a href="#markov_equiv">Markov equivalent</a>, and therefore indistinguishable from +observational data alone, so we expect their posteriors to be the same +(assuming a prior which satisfies likelihood equivalence). +If we use random parameters, the "true" model only gets a higher posterior after 2000 trials! +However, if we make B a noisy NOT gate, the true model "wins" after 12 +trials, as shown below (red = model 1, blue/green (superimposed) +represents models 2/3). +<p> +<img src="Figures/model_select.png"> +<p> +The use of marginal likelihood for model selection is discussed in +greater detail in the +section on <a href="structure_learning">structure learning</a>. + + + + +<h2><a name="em">Maximum likelihood parameter estimation with missing values</h2> + +Now we consider learning when some values are not observed. +Let us randomly hide half the values generated from the water +sprinkler example. +<pre> +samples2 = samples; +hide = rand(N, nsamples) > 0.5; +[I,J]=find(hide); +for k=1:length(I) + samples2{I(k), J(k)} = []; +end +</pre> +samples2{i,l} is the value of node i in training case l, or [] if unobserved. +<p> +Now we will compute the MLEs using the EM algorithm. +We need to use an inference algorithm to compute the expected +sufficient statistics in the E step; the M (maximization) step is as +above. +<pre> +engine2 = jtree_inf_engine(bnet2); +max_iter = 10; +[bnet4, LLtrace] = learn_params_em(engine2, samples2, max_iter); +</pre> +LLtrace(i) is the log-likelihood at iteration i. We can plot this as +follows: +<pre> +plot(LLtrace, 'x-') +</pre> +Let's display the results after 10 iterations of EM. +<pre> +celldisp(CPT4) +CPT4{1} = + 0.6616 + 0.3384 +CPT4{2} = + 0.6510 0.3490 + 0.8751 0.1249 +CPT4{3} = + 0.8366 0.1634 + 0.0197 0.9803 +CPT4{4} = +(:,:,1) = + 0.8276 0.0546 + 0.5452 0.1658 +(:,:,2) = + 0.1724 0.9454 + 0.4548 0.8342 +</pre> +We can get improved performance by using one or more of the following +methods: +<ul> +<li> Increasing the size of the training set. +<li> Decreasing the amount of hidden data. +<li> Running EM for longer. +<li> Using informative priors. +<li> Initialising EM from multiple starting points. +</ul> + +Click <a href="#gaussian">here</a> for a discussion of learning +Gaussians, which can cause numerical problems. +<p> +For a more complete example of learning with EM, +see the script BNT/examples/static/learn1.m. + +<h2><a name="tying">Parameter tying</h2> + +In networks with repeated structure (e.g., chains and grids), it is +common to assume that the parameters are the same at every node. This +is called parameter tying, and reduces the amount of data needed for +learning. +<p> +When we have tied parameters, there is no longer a one-to-one +correspondence between nodes and CPDs. +Rather, each CPD species the parameters for a whole equivalence class +of nodes. +It is easiest to see this by example. +Consider the following <a href="usage_dbn.html#hmm">hidden Markov +model (HMM)</a> +<p> +<img src="Figures/hmm3.gif"> +<p> +<!-- +We can create this graph structure, assuming we have T time-slices, +as follows. +(We number the nodes as shown in the figure, but we could equally well +number the hidden nodes 1:T, and the observed nodes T+1:2T.) +<pre> +N = 2*T; +dag = zeros(N); +hnodes = 1:2:2*T; +for i=1:T-1 + dag(hnodes(i), hnodes(i+1))=1; +end +onodes = 2:2:2*T; +for i=1:T + dag(hnodes(i), onodes(i)) = 1; +end +</pre> +<p> +The hidden nodes are always discrete, and have Q possible values each, +but the observed nodes can be discrete or continuous, and have O possible values/length. +<pre> +if cts_obs + dnodes = hnodes; +else + dnodes = 1:N; +end +ns = ones(1,N); +ns(hnodes) = Q; +ns(onodes) = O; +</pre> +--> +When HMMs are used for semi-infinite processes like speech recognition, +we assume the transition matrix +P(H(t+1)|H(t)) is the same for all t; this is called a time-invariant +or homogenous Markov chain. +Hence hidden nodes 2, 3, ..., T +are all in the same equivalence class, say class Hclass. +Similarly, the observation matrix P(O(t)|H(t)) is assumed to be the +same for all t, so the observed nodes are all in the same equivalence +class, say class Oclass. +Finally, the prior term P(H(1)) is in a class all by itself, say class +H1class. +This is illustrated below, where we explicitly represent the +parameters as random variables (dotted nodes). +<p> +<img src="Figures/hmm4_params.gif"> +<p> +In BNT, we cannot represent parameters as random variables (nodes). +Instead, we "hide" the +parameters inside one CPD for each equivalence class, +and then specify that the other CPDs should share these parameters, as +follows. +<pre> +hnodes = 1:2:2*T; +onodes = 2:2:2*T; +H1class = 1; Hclass = 2; Oclass = 3; +eclass = ones(1,N); +eclass(hnodes(2:end)) = Hclass; +eclass(hnodes(1)) = H1class; +eclass(onodes) = Oclass; +% create dag and ns in the usual way +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'equiv_class', eclass); +</pre> +Finally, we define the parameters for each equivalence class: +<pre> +bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior +bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix +if cts_obs + bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1)); +else + bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1)); +end +</pre> +In general, if bnet.CPD{e} = xxx_CPD(bnet, j), then j should be a +member of e's equivalence class; that is, it is not always the case +that e == j. You can use bnet.rep_of_eclass(e) to return the +representative of equivalence class e. +BNT will look up the parents of j to determine the size +of the CPT to use. It assumes that this is the same for all members of +the equivalence class. +Click <a href="param_tieing.html">here</a> for +a more complex example of parameter tying. +<p> +Note: +Normally one would define an HMM as a +<a href = "usage_dbn.html">Dynamic Bayes Net</a> +(see the function BNT/examples/dynamic/mk_chmm.m). +However, one can define an HMM as a static BN using the function +BNT/examples/static/Models/mk_hmm_bnet.m. + + + +<h1><a name="structure_learning">Structure learning</h1> + +Update (9/29/03): +Phillipe LeRay is developing some additional structure learning code +on top of BNT. Click +<a href="http://banquiseasi.insa-rouen.fr/projects/bnt-slp/"> +here</a> +for details. + +<p> + +There are two very different approaches to structure learning: +constraint-based and search-and-score. +In the <a href="#constraint">constraint-based approach</a>, +we start with a fully connected graph, and remove edges if certain +conditional independencies are measured in the data. +This has the disadvantage that repeated independence tests lose +statistical power. +<p> +In the more popular search-and-score approach, +we perform a search through the space of possible DAGs, and either +return the best one found (a point estimate), or return a sample of the +models found (an approximation to the Bayesian posterior). +<p> +The number of DAGs as a function of the number of +nodes, G(n), is super-exponential in n, +and is given by the following recurrence +<!--(where R(i)=G(n)):--> +<p> +<center> +<IMG SRC="numDAGsEqn2.png"> +</center> +<p> +The first few values +are shown below. + +<table> +<tr> <th>n</th> <th align=left>G(n)</th> </tr> +<tr> <td>1</td> <td>1</td> </tr> +<tr> <td>2</td> <td>3</td> </tr> +<tr> <td>3</td> <td>25</td> </tr> +<tr> <td>4</td> <td>543</td> </tr> +<tr> <td>5</td> <td>29,281</td> </tr> +<tr> <td>6</td> <td>3,781,503</td> </tr> +<tr> <td>7</td> <td>1.1 x 10^9</td> </tr> +<tr> <td>8</td> <td>7.8 x 10^11</td> </tr> +<tr> <td>9</td> <td>1.2 x 10^15</td> </tr> +<tr> <td>10</td> <td>4.2 x 10^18</td> </tr> +</table> + +Since the number of DAGs is super-exponential in the number of nodes, +we cannot exhaustively search the space, so we either use a local +search algorithm (e.g., greedy hill climbining, perhaps with multiple +restarts) or a global search algorithm (e.g., Markov Chain Monte +Carlo). +<p> +If we know a total ordering on the nodes, +finding the best structure amounts to picking the best set of parents +for each node independently. +This is what the K2 algorithm does. +If the ordering is unknown, we can search over orderings, +which is more efficient than searching over DAGs (Koller and Friedman, 2000). +<p> +In addition to the search procedure, we must specify the scoring +function. There are two popular choices. The Bayesian score integrates +out the parameters, i.e., it is the marginal likelihood of the model. +The BIC (Bayesian Information Criterion) is defined as +log P(D|theta_hat) - 0.5*d*log(N), where D is the data, theta_hat is +the ML estimate of the parameters, d is the number of parameters, and +N is the number of data cases. +The BIC method has the advantage of not requiring a prior. +<p> +BIC can be derived as a large sample +approximation to the marginal likelihood. +(It is also equal to the Minimum Description Length of a model.) +However, in practice, the sample size does not need to be very large +for the approximation to be good. +For example, in the figure below, we plot the ratio between the log marginal likelihood +and the BIC score against data-set size; we see that the ratio rapidly +approaches 1, especially for non-informative priors. +(This plot was generated by the file BNT/examples/static/bic1.m. It +uses the water sprinkler BN with BDeu Dirichlet priors with different +equivalent sample sizes.) + +<p> +<center> +<IMG SRC="Figures/bic.png"> +</center> +<p> + +<p> +As with parameter learning, handling missing data/ hidden variables is +much harder than the fully observed case. +The structure learning routines in BNT can therefore be classified into 4 +types, analogously to the parameter learning case. +<p> + +<TABLE BORDER> +<tr> + <TH></TH> + <th>Full obs</th> + <th>Partial obs</th> +</tr> +<tr> + <th>Point</th> + <td><tt>learn_struct_K2</tt> <br> +<!-- <tt>learn_struct_hill_climb</tt></td> --> + <td><tt>not yet supported</tt></td> +</tr> +<tr> + <th>Bayes</th> + <td><tt>learn_struct_mcmc</tt></td> + <td>not yet supported</td> +</tr> +</table> + + +<h2><a name="markov_equiv">Markov equivalence</h2> + +If two DAGs encode the same conditional independencies, they are +called Markov equivalent. The set of all DAGs can be paritioned into +Markov equivalence classes. Graphs within the same class can +have +the direction of some of their arcs reversed without changing any of +the CI relationships. +Each class can be represented by a PDAG +(partially directed acyclic graph) called an essential graph or +pattern. This specifies which edges must be oriented in a certain +direction, and which may be reversed. + +<p> +When learning graph structure from observational data, +the best one can hope to do is to identify the model up to Markov +equivalence. To distinguish amongst graphs within the same equivalence +class, one needs interventional data: see the discussion on <a +href="#active">active learning</a> below. + + + +<h2><a name="enumerate">Exhaustive search</h2> + +The brute-force approach to structure learning is to enumerate all +possible DAGs, and score each one. This provides a "gold standard" +with which to compare other algorithms. We can do this as follows. +<pre> +dags = mk_all_dags(N); +score = score_dags(data, ns, dags); +</pre> +where data(i,m) is the value of node i in case m, +and ns(i) is the size of node i. +If the DAGs have a lot of families in common, we can cache the sufficient statistics, +making this potentially more efficient than scoring the DAGs one at a time. +(Caching is not currently implemented, however.) +<p> +By default, we use the Bayesian scoring metric, and assume CPDs are +represented by tables with BDeu(1) priors. +We can override these defaults as follows. +If we want to use uniform priors, we can say +<pre> +params = cell(1,N); +for i=1:N + params{i} = {'prior', 'unif'}; +end +score = score_dags(data, ns, dags, 'params', params); +</pre> +params{i} is a cell-array, containing optional arguments that are +passed to the constructor for CPD i. +<p> +Now suppose we want to use different node types, e.g., +Suppose nodes 1 and 2 are Gaussian, and nodes 3 and 4 softmax (both +these CPDs can support discrete and continuous parents, which is +necessary since all other nodes will be considered as parents). +The Bayesian scoring metric currently only works for tabular CPDs, so +we will use BIC: +<pre> +score = score_dags(data, ns, dags, 'discrete', [3 4], 'params', [], + 'type', {'gaussian', 'gaussian', 'softmax', softmax'}, 'scoring_fn', 'bic') +</pre> +In practice, one can't enumerate all possible DAGs for N > 5, +but one can evaluate any reasonably-sized set of hypotheses in this +way (e.g., nearest neighbors of your current best guess). +Think of this as "computer assisted model refinement" as opposed to de +novo learning. + + +<h2><a name="K2">K2</h2> + +The K2 algorithm (Cooper and Herskovits, 1992) is a greedy search algorithm that works as follows. +Initially each node has no parents. It then adds incrementally that parent whose addition most +increases the score of the resulting structure. When the addition of no single +parent can increase the score, it stops adding parents to the node. +Since we are using a fixed ordering, we do not need to check for +cycles, and can choose the parents for each node independently. +<p> +The original paper used the Bayesian scoring +metric with tabular CPDs and Dirichlet priors. +BNT generalizes this to allow any kind of CPD, and either the Bayesian +scoring metric or BIC, as in the example <a href="#enumerate">above</a>. +In addition, you can specify +an optional upper bound on the number of parents for each node. +The file BNT/examples/static/k2demo1.m gives an example of how to use K2. +We use the water sprinkler network and sample 100 cases from it as before. +Then we see how much data it takes to recover the generating structure: +<pre> +order = [C S R W]; +max_fan_in = 2; +sz = 5:5:100; +for i=1:length(sz) + dag2 = learn_struct_K2(data(:,1:sz(i)), node_sizes, order, 'max_fan_in', max_fan_in); + correct(i) = isequal(dag, dag2); +end +</pre> +Here are the results. +<pre> +correct = + Columns 1 through 12 + 0 0 0 0 0 0 0 1 0 1 1 1 + Columns 13 through 20 + 1 1 1 1 1 1 1 1 +</pre> +So we see it takes about sz(10)=50 cases. (BIC behaves similarly, +showing that the prior doesn't matter too much.) +In general, we cannot hope to recover the "true" generating structure, +only one that is in its <a href="#markov_equiv">Markov equivalence +class</a>. + + +<h2><a name="hill_climb">Hill-climbing</h2> + +Hill-climbing starts at a specific point in space, +considers all nearest neighbors, and moves to the neighbor +that has the highest score; if no neighbors have higher +score than the current point (i.e., we have reached a local maximum), +the algorithm stops. One can then restart in another part of the space. +<p> +A common definition of "neighbor" is all graphs that can be +generated from the current graph by adding, deleting or reversing a +single arc, subject to the acyclicity constraint. +Other neighborhoods are possible: see +<a href="http://research.microsoft.com/~dmax/publications/jmlr02.pdf"> +Optimal Structure Identification with Greedy Search</a>, Max +Chickering, JMLR 2002. + +<!-- +Note: This algorithm is currently (Feb '02) being implemented by Qian +Diao. +--> + + +<h2><a name="mcmc">MCMC</h2> + +We can use a Markov Chain Monte Carlo (MCMC) algorithm called +Metropolis-Hastings (MH) to search the space of all +DAGs. +The standard proposal distribution is to consider moving to all +nearest neighbors in the sense defined <a href="#hill_climb">above</a>. +<p> +The function can be called +as in the following example. +<pre> +[sampled_graphs, accept_ratio] = learn_struct_mcmc(data, ns, 'nsamples', 100, 'burnin', 10); +</pre> +We can convert our set of sampled graphs to a histogram +(empirical posterior over all the DAGs) thus +<pre> +all_dags = mk_all_dags(N); +mcmc_post = mcmc_sample_to_hist(sampled_graphs, all_dags); +</pre> +To see how well this performs, let us compute the exact posterior exhaustively. +<p> +<pre> +score = score_dags(data, ns, all_dags); +post = normalise(exp(score)); % assuming uniform structural prior +</pre> +We plot the results below. +(The data set was 100 samples drawn from a random 4 node bnet; see the +file BNT/examples/static/mcmc1.) +<pre> +subplot(2,1,1) +bar(post) +subplot(2,1,2) +bar(mcmc_post) +</pre> +<img src="Figures/mcmc_post.jpg" width="800" height="500"> +<p> +We can also plot the acceptance ratio versus number of MCMC steps, +as a crude convergence diagnostic. +<pre> +clf +plot(accept_ratio) +</pre> +<img src="Figures/mcmc_accept.jpg" width="800" height="300"> +<p> +Even though the number of samples needed by MCMC is theoretically +polynomial (not exponential) in the dimensionality of the search space, in practice it has been +found that MCMC does not converge in reasonable time for graphs with +more than about 10 nodes. + + + + +<h2><a name="active">Active structure learning</h2> + +As was mentioned <a href="#markov_equiv">above</a>, +one can only learn a DAG up to Markov equivalence, even given infinite data. +If one is interested in learning the structure of a causal network, +one needs interventional data. +(By "intervention" we mean forcing a node to take on a specific value, +thereby effectively severing its incoming arcs.) +<p> +Most of the scoring functions accept an optional argument +that specifies whether a node was observed to have a certain value, or +was forced to have that value: we set clamped(i,m)=1 if node i was +forced in training case m. e.g., see the file +BNT/examples/static/cooper_yoo. +<p> +An interesting question is to decide which interventions to perform +(c.f., design of experiments). For details, see the following tech +report +<ul> +<li> <a href = "../../Papers/alearn.ps.gz"> +Active learning of causal Bayes net structure</a>, Kevin Murphy, March +2001. +</ul> + + +<h2><a name="struct_em">Structural EM</h2> + +Computing the Bayesian score when there is partial observability is +computationally challenging, because the parameter posterior becomes +multimodal (the hidden nodes induce a mixture distribution). +One therefore needs to use approximations such as BIC. +Unfortunately, search algorithms are still expensive, because we need +to run EM at each step to compute the MLE, which is needed to compute +the score of each model. An alternative approach is +to do the local search steps inside of the M step of EM, which is more +efficient since the data has been "filled in" - this is +called the structural EM algorithm (Friedman 1997), and provably +converges to a local maximum of the BIC score. +<p> +Wei Hu has implemented SEM for discrete nodes. +You can download his package from +<a href="../SEM.zip">here</a>. +Please address all questions about this code to +wei.hu@intel.com. +See also <a href="#phl">Phl's implementation of SEM</a>. + +<!-- +<h2><a name="reveal">REVEAL algorithm</h2> + +A simple way to learn the structure of a fully observed, discrete, +factored DBN from a time series is described <a +href="usage_dbn.html#struct_learn">here</a>. +--> + + +<h2><a name="graphdraw">Visualizing the graph</h2> + +Click <a href="graphviz.html">here</a> for more information +on graph visualization. + +<h2><a name = "constraint">Constraint-based methods</h2> + +The IC algorithm (Pearl and Verma, 1991), +and the faster, but otherwise equivalent, PC algorithm (Spirtes, Glymour, and Scheines 1993), +computes many conditional independence tests, +and combines these constraints into a +PDAG to represent the whole +<a href="#markov_equiv">Markov equivalence class</a>. +<p> +IC*/FCI extend IC/PC to handle latent variables: see <a href="#ic_star">below</a>. +(IC stands for inductive causation; PC stands for Peter and Clark, +the first names of Spirtes and Glymour; FCI stands for fast causal +inference. +What we, following Pearl (2000), call IC* was called +IC in the original Pearl and Verma paper.) +For details, see +<ul> +<li> +<a href="http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html">Causation, +Prediction, and Search</a>, Spirtes, Glymour and +Scheines (SGS), 2001 (2nd edition), MIT Press. +<li> +<a href="http://bayes.cs.ucla.edu/BOOK-2K/index.html">Causality: Models, Reasoning and Inference</a>, J. Pearl, +2000, Cambridge University Press. +</ul> + +<p> + +The PC algorithm takes as arguments a function f, the number of nodes N, +the maximum fan in K, and additional arguments A which are passed to f. +The function f(X,Y,S,A) returns 1 if X is conditionally independent of Y given S, and 0 +otherwise. +For example, suppose we cheat by +passing in a CI "oracle" which has access to the true DAG; the oracle +tests for d-separation in this DAG, i.e., +f(X,Y,S) calls dsep(X,Y,S,dag). We can to this as follows. +<pre> +pdag = learn_struct_pdag_pc('dsep', N, max_fan_in, dag); +</pre> +pdag(i,j) = -1 if there is definitely an i->j arc, +and pdag(i,j) = 1 if there is either an i->j or and i<-j arc. +<p> +Applied to the sprinkler network, this returns +<pre> +pdag = + 0 1 1 0 + 1 0 0 -1 + 1 0 0 -1 + 0 0 0 0 +</pre> +So as expected, we see that the V-structure at the W node is uniquely identified, +but the other arcs have ambiguous orientation. +<p> +We now give an example from p141 (1st edn) / p103 (2nd end) of the SGS +book. +This example concerns the female orgasm. +We are given a correlation matrix C between 7 measured factors (such +as subjective experiences of coital and masturbatory experiences), +derived from 281 samples, and want to learn a causal model of the +data. We will not discuss the merits of this type of work here, but +merely show how to reproduce the results in the SGS book. +Their program, +<a href="http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html">Tetrad</a>, +makes use of the Fisher Z-test for conditional +independence, so we do the same: +<pre> +max_fan_in = 4; +nsamples = 281; +alpha = 0.05; +pdag = learn_struct_pdag_pc('cond_indep_fisher_z', n, max_fan_in, C, nsamples, alpha); +</pre> +In this case, the CI test is +<pre> +f(X,Y,S) = cond_indep_fisher_z(X,Y,S, C,nsamples,alpha) +</pre> +The results match those of Fig 12a of SGS apart from two edge +differences; presumably this is due to rounding error (although it +could be a bug, either in BNT or in Tetrad). +This example can be found in the file BNT/examples/static/pc2.m. + +<p> + +The IC* algorithm (Pearl and Verma, 1991), +and the faster FCI algorithm (Spirtes, Glymour, and Scheines 1993), +are like the IC/PC algorithm, except that they can detect the presence +of latent variables. +See the file <tt>learn_struct_pdag_ic_star</tt> written by Tamar +Kushnir. The output is a matrix P, defined as follows +(see Pearl (2000), p52 for details): +<pre> +% P(i,j) = -1 if there is either a latent variable L such that i <-L->j OR there is a directed edge from i->j. +% P(i,j) = -2 if there is a marked directed i-*>j edge. +% P(i,j) = P(j,i) = 1 if there is and undirected edge i--j +% P(i,j) = P(j,i) = 2 if there is a latent variable L such that i<-L->j. +</pre> + + +<h2><a name="phl">Philippe Leray's structure learning package</h2> + +Philippe Leray has written a +<a href="http://bnt.insa-rouen.fr/ajouts.html"> +structure learning package</a> that uses BNT. + +It currently (Juen 2003) has the following features: +<ul> +<li>PC with Chi2 statistical test +<li> MWST : Maximum weighted Spanning Tree +<li> Hill Climbing +<li> Greedy Search +<li> Structural EM +<li> hist_ic : optimal Histogram based on IC information criterion +<li> cpdag_to_dag +<li> dag_to_cpdag +<li> ... +</ul> + + +</a> + + +<!-- +<h2><a name="read_learning">Further reading on learning</h2> + +I recommend the following tutorials for more details on learning. +<ul> +<li> <a +href="http://www.cs.berkeley.edu/~murphyk/Papers/intel.ps.gz">My short +tutorial</a> on graphical models, which contains an overview of learning. + +<li> +<A HREF="ftp://ftp.research.microsoft.com/pub/tr/TR-95-06.PS"> +A tutorial on learning with Bayesian networks</a>, D. Heckerman, +Microsoft Research Tech Report, 1995. + +<li> <A HREF="http://www-cad.eecs.berkeley.edu/~wray/Mirror/lwgmja"> +Operations for Learning with Graphical Models</a>, +W. L. Buntine, JAIR'94, 159--225. +</ul> +<p> +--> + + + + + +<h1><a name="engines">Inference engines</h1> + +Up until now, we have used the junction tree algorithm for inference. +However, sometimes this is too slow, or not even applicable. +In general, there are many inference algorithms each of which make +different tradeoffs between speed, accuracy, complexity and +generality. Furthermore, there might be many implementations of the +same algorithm; for instance, a general purpose, readable version, +and a highly-optimized, specialized one. +To cope with this variety, we treat each inference algorithm as an +object, which we call an inference engine. + +<p> +An inference engine is an object that contains a bnet and supports the +'enter_evidence' and 'marginal_nodes' methods. The engine constructor +takes the bnet as argument and may do some model-specific processing. +When 'enter_evidence' is called, the engine may do some +evidence-specific processing. Finally, when 'marginal_nodes' is +called, the engine may do some query-specific processing. + +<p> +The amount of work done when each stage is specified -- structure, +parameters, evidence, and query -- depends on the engine. The cost of +work done early in this sequence can be amortized. On the other hand, +one can make better optimizations if one waits until later in the +sequence. +For example, the parameters might imply +conditional indpendencies that are not evident in the graph structure, +but can nevertheless be exploited; the evidence indicates which nodes +are observed and hence can effectively be disconnected from the +graph; and the query might indicate that large parts of the network +are d-separated from the query nodes. (Since it is not the actual +<em>values</em> of the evidence that matters, just which nodes are observed, +many engines allow you to specify which nodes will be observed when they are constructed, +i.e., before calling 'enter_evidence'. Some engines can still cope if +the actual pattern of evidence is different, e.g., if there is missing +data.) +<p> + +Although being maximally lazy (i.e., only doing work when a query is +issued) may seem desirable, +this is not always the most efficient. +For example, +when learning using EM, we need to call marginal_nodes N times, where N is the +number of nodes. <a href="varelim">Variable elimination</a> would end +up repeating a lot of work +each time marginal_nodes is called, making it inefficient for +learning. The junction tree algorithm, by contrast, uses dynamic +programming to avoid this redundant computation --- it calculates all +marginals in two passes during 'enter_evidence', so calling +'marginal_nodes' takes constant time. +<p> +We will discuss some of the inference algorithms implemented in BNT +below, and finish with a <a href="#engine_summary">summary</a> of all +of them. + + + + + + + +<h2><a name="varelim">Variable elimination</h2> + +The variable elimination algorithm, also known as bucket elimination +or peeling, is one of the simplest inference algorithms. +The basic idea is to "push sums inside of products"; this is explained +in more detail +<a +href="http://HTTP.CS.Berkeley.EDU/~murphyk/Bayes/bayes.html#infer">here</a>. +<p> +The principle of distributing sums over products can be generalized +greatly to apply to any commutative semiring. +This forms the basis of many common algorithms, such as Viterbi +decoding and the Fast Fourier Transform. For details, see + +<ul> +<li> R. McEliece and S. M. Aji, 2000. +<!--<a href="http://www.systems.caltech.edu/EE/Faculty/rjm/papers/GDL.ps">--> +<a href="GDL.pdf"> +The Generalized Distributive Law</a>, +IEEE Trans. Inform. Theory, vol. 46, no. 2 (March 2000), +pp. 325--343. + + +<li> +F. R. Kschischang, B. J. Frey and H.-A. Loeliger, 2001. +<a href="http://www.cs.toronto.edu/~frey/papers/fgspa.abs.html"> +Factor graphs and the sum-product algorithm</a> +IEEE Transactions on Information Theory, February, 2001. + +</ul> + +<p> +Choosing an order in which to sum out the variables so as to minimize +computational cost is known to be NP-hard. +The implementation of this algorithm in +<tt>var_elim_inf_engine</tt> makes no attempt to optimize this +ordering (in contrast, say, to <tt>jtree_inf_engine</tt>, which uses a +greedy search procedure to find a good ordering). +<p> +Note: unlike most algorithms, var_elim does all its computational work +inside of <tt>marginal_nodes</tt>, not inside of +<tt>enter_evidence</tt>. + + + + +<h2><a name="global">Global inference methods</h2> + +The simplest inference algorithm of all is to explicitely construct +the joint distribution over all the nodes, and then to marginalize it. +This is implemented in <tt>global_joint_inf_engine</tt>. +Since the size of the joint is exponential in the +number of discrete (hidden) nodes, this is not a very practical algorithm. +It is included merely for pedagogical and debugging purposes. +<p> +Three specialized versions of this algorithm have also been implemented, +corresponding to the cases where all the nodes are discrete (D), all +are Gaussian (G), and some are discrete and some Gaussian (CG). +They are called <tt>enumerative_inf_engine</tt>, +<tt>gaussian_inf_engine</tt>, +and <tt>cond_gauss_inf_engine</tt> respectively. +<p> +Note: unlike most algorithms, these global inference algorithms do all their computational work +inside of <tt>marginal_nodes</tt>, not inside of +<tt>enter_evidence</tt>. + + +<h2><a name="quickscore">Quickscore</h2> + +The junction tree algorithm is quite slow on the <a href="#qmr">QMR</a> network, +since the cliques are so big. +One simple trick we can use is to notice that hidden leaves do not +affect the posteriors on the roots, and hence do not need to be +included in the network. +A second trick is to notice that the negative findings can be +"absorbed" into the prior: +see the file +BNT/examples/static/mk_minimal_qmr_bnet for details. +<p> + +A much more significant speedup is obtained by exploiting special +properties of the noisy-or node, as done by the quickscore +algorithm. For details, see +<ul> +<li> Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI 89. +<li> Rish and Dechter, "On the impact of causal independence", UCI +tech report, 1998. +</ul> + +This has been implemented in BNT as a special-purpose inference +engine, which can be created and used as follows: +<pre> +engine = quickscore_inf_engine(inhibit, leak, prior); +engine = enter_evidence(engine, pos, neg); +m = marginal_nodes(engine, i); +</pre> + + +<h2><a name="belprop">Belief propagation</h2> + +Even using quickscore, exact inference takes time that is exponential +in the number of positive findings. +Hence for large networks we need to resort to approximate inference techniques. +See for example +<ul> +<li> T. Jaakkola and M. Jordan, "Variational probabilistic inference and the +QMR-DT network", JAIR 10, 1999. + +<li> K. Murphy, Y. Weiss and M. Jordan, "Loopy belief propagation for approximate inference: an empirical study", + UAI 99. +</ul> +The latter approximation +entails applying Pearl's belief propagation algorithm to a model even +if it has loops (hence the name loopy belief propagation). +Pearl's algorithm, implemented as <tt>pearl_inf_engine</tt>, gives +exact results when applied to singly-connected graphs +(a.k.a. polytrees, since +the underlying undirected topology is a tree, but a node may have +multiple parents). +To apply this algorithm to a graph with loops, +use <tt>pearl_inf_engine</tt>. +This can use a centralized or distributed message passing protocol. +You can use it as in the following example. +<pre> +engine = pearl_inf_engine(bnet, 'max_iter', 30); +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, i); +</pre> +We found that this algorithm often converges, and when it does, often +is very accurate, but it depends on the precise setting of the +parameter values of the network. +(See the file BNT/examples/static/qmr1 to repeat the experiment for yourself.) +Understanding when and why belief propagation converges/ works +is a topic of ongoing research. +<p> +<tt>pearl_inf_engine</tt> can exploit special structure in noisy-or +and gmux nodes to compute messages efficiently. +<p> +<tt>belprop_inf_engine</tt> is like pearl, but uses potentials to +represent messages. Hence this is slower. +<p> +<tt>belprop_fg_inf_engine</tt> is like belprop, +but is designed for factor graphs. + + + +<h2><a name="sampling">Sampling</h2> + +BNT now (Mar '02) has two sampling (Monte Carlo) inference algorithms: +<ul> +<li> <tt>likelihood_weighting_inf_engine</tt> which does importance +sampling and can handle any node type. +<li> <tt>gibbs_sampling_inf_engine</tt>, written by Bhaskara Marthi. +Currently this can only handle tabular CPDs. +For a much faster and more powerful Gibbs sampling program, see +<a href="http://www.mrc-bsu.cam.ac.uk/bugs">BUGS</a>. +</ul> +Note: To generate samples from a network (which is not the same as inference!), +use <tt>sample_bnet</tt>. + + + +<h2><a name="engine_summary">Summary of inference engines</h2> + + +The inference engines differ in many ways. Here are +some of the major "axes": +<ul> +<li> Works for all topologies or makes restrictions? +<li> Works for all node types or makes restrictions? +<li> Exact or approximate inference? +</ul> + +<p> +In terms of topology, most engines handle any kind of DAG. +<tt>belprop_fg</tt> does approximate inference on factor graphs (FG), which +can be used to represent directed, undirected, and mixed (chain) +graphs. +(In the future, we plan to support exact inference on chain graphs.) +<tt>quickscore</tt> only works on QMR-like models. +<p> +In terms of node types: algorithms that use potentials can handle +discrete (D), Gaussian (G) or conditional Gaussian (CG) models. +Sampling algorithms can essentially handle any kind of node (distribution). +Other algorithms make more restrictive assumptions in exchange for +speed. +<p> +Finally, most algorithms are designed to give the exact answer. +The belief propagation algorithms are exact if applied to trees, and +in some other cases. +Sampling is considered approximate, even though, in the limit of an +infinite number of samples, it gives the exact answer. + +<p> + +Here is a summary of the properties +of all the engines in BNT which work on static networks. +<p> +<table> +<table border units = pixels><tr> +<td align=left width=0>Name +<td align=left width=0>Exact? +<td align=left width=0>Node type? +<td align=left width=0>topology +<tr> +<tr> +<td align=left> belprop +<td align=left> approx +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> belprop_fg +<td align=left> approx +<td align=left> D +<td align=left> factor graph +<tr> +<td align=left> cond_gauss +<td align=left> exact +<td align=left> CG +<td align=left> DAG +<tr> +<td align=left> enumerative +<td align=left> exact +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> gaussian +<td align=left> exact +<td align=left> G +<td align=left> DAG +<tr> +<td align=left> gibbs +<td align=left> approx +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> global_joint +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +<tr> +<td align=left> jtree +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +b<tr> +<td align=left> likelihood_weighting +<td align=left> approx +<td align=left> any +<td align=left> DAG +<tr> +<td align=left> pearl +<td align=left> approx +<td align=left> D,G +<td align=left> DAG +<tr> +<td align=left> pearl +<td align=left> exact +<td align=left> D,G +<td align=left> polytree +<tr> +<td align=left> quickscore +<td align=left> exact +<td align=left> noisy-or +<td align=left> QMR +<tr> +<td align=left> stab_cond_gauss +<td align=left> exact +<td align=left> CG +<td align=left> DAG +<tr> +<td align=left> var_elim +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +</table> + + + +<h1><a name="influence">Influence diagrams/ decision making</h1> + +BNT implements an exact algorithm for solving LIMIDs (limited memory +influence diagrams), described in +<ul> +<li> S. L. Lauritzen and D. Nilsson. +<a href="http://www.math.auc.dk/~steffen/papers/limids.pdf"> +Representing and solving decision problems with limited +information</a> +Management Science, 47, 1238 - 1251. September 2001. +</ul> +LIMIDs explicitely show all information arcs, rather than implicitely +assuming no forgetting. This allows them to model forgetful +controllers. +<p> +See the examples in <tt>BNT/examples/limids</tt> for details. + + + + +<h1>DBNs, HMMs, Kalman filters and all that</h1> + +Click <a href="usage_dbn.html">here</a> for documentation about how to +use BNT for dynamical systems and sequence data. + + +</BODY>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/usage_02nov13.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3215 @@ +<HEAD> +<TITLE>How to use the Bayes Net Toolbox</TITLE> +</HEAD> + +<BODY BGCOLOR="#FFFFFF"> +<!-- white background is better for the pictures and equations --> + +<h1>How to use the Bayes Net Toolbox</h1> + +This documentation was last updated on 13 November 2002. +<br> +Click <a href="changelog.html">here</a> for a list of changes made to +BNT. +<br> +Click +<a href="http://bnt.insa-rouen.fr/">here</a> +for a French version of this documentation (which might not +be up-to-date). + + +<p> + +<ul> +<li> <a href="#install">Installation</a> +<ul> +<li> <a href="#installM">Installing the Matlab code</a> +<li> <a href="#installC">Installing the C code</a> +<li> <a href="../matlab_tips.html">Useful Matlab tips</a>. +</ul> + +<li> <a href="#basics">Creating your first Bayes net</a> + <ul> + <li> <a href="#basics">Creating a model by hand</a> + <li> <a href="#file">Loading a model from a file</a> + <li> <a href="http://bnt.insa-rouen.fr/ajouts.html">Creating a model using a GUI</a> + </ul> + +<li> <a href="#inference">Inference</a> + <ul> + <li> <a href="#marginal">Computing marginal distributions</a> + <li> <a href="#joint">Computing joint distributions</a> + <li> <a href="#soft">Soft/virtual evidence</a> + <li> <a href="#mpe">Most probable explanation</a> + </ul> + +<li> <a href="#cpd">Conditional Probability Distributions</a> + <ul> + <li> <a href="#tabular">Tabular (multinomial) nodes</a> + <li> <a href="#noisyor">Noisy-or nodes</a> + <li> <a href="#deterministic">Other (noisy) deterministic nodes</a> + <li> <a href="#softmax">Softmax (multinomial logit) nodes</a> + <li> <a href="#mlp">Neural network nodes</a> + <li> <a href="#root">Root nodes</a> + <li> <a href="#gaussian">Gaussian nodes</a> + <li> <a href="#glm">Generalized linear model nodes</a> + <li> <a href="#dtree">Classification/regression tree nodes</a> + <li> <a href="#nongauss">Other continuous distributions</a> + <li> <a href="#cpd_summary">Summary of CPD types</a> + </ul> + +<li> <a href="#examples">Example models</a> + <ul> + <li> <a + href="http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt"> +Gaussian mixture models</a> + <li> <a href="#pca">PCA, ICA, and all that</a> + <li> <a href="#mixep">Mixtures of experts</a> + <li> <a href="#hme">Hierarchical mixtures of experts</a> + <li> <a href="#qmr">QMR</a> + <li> <a href="#cg_model">Conditional Gaussian models</a> + <li> <a href="#hybrid">Other hybrid models</a> + </ul> + +<li> <a href="#param_learning">Parameter learning</a> + <ul> + <li> <a href="#load_data">Loading data from a file</a> + <li> <a href="#mle_complete">Maximum likelihood parameter estimation from complete data</a> + <li> <a href="#prior">Parameter priors</a> + <li> <a href="#bayes_learn">(Sequential) Bayesian parameter updating from complete data</a> + <li> <a href="#em">Maximum likelihood parameter estimation with missing values (EM)</a> + <li> <a href="#tying">Parameter tying</a> + </ul> + +<li> <a href="#structure_learning">Structure learning</a> + <ul> + <li> <a href="#enumerate">Exhaustive search</a> + <li> <a href="#K2">K2</a> + <li> <a href="#hill_climb">Hill-climbing</a> + <li> <a href="#mcmc">MCMC</a> + <li> <a href="#active">Active learning</a> + <li> <a href="#struct_em">Structural EM</a> + <li> <a href="#graphdraw">Visualizing the learned graph structure</a> + <li> <a href="#constraint">Constraint-based methods</a> + </ul> + + +<li> <a href="#engines">Inference engines</a> + <ul> + <li> <a href="#jtree">Junction tree</a> + <li> <a href="#varelim">Variable elimination</a> + <li> <a href="#global">Global inference methods</a> + <li> <a href="#quickscore">Quickscore</a> + <li> <a href="#belprop">Belief propagation</a> + <li> <a href="#sampling">Sampling (Monte Carlo)</a> + <li> <a href="#engine_summary">Summary of inference engines</a> + </ul> + + +<li> <a href="#influence">Influence diagrams/ decision making</a> + + +<li> <a href="usage_dbn.html">DBNs, HMMs, Kalman filters and all that</a> +</ul> + +</ul> + + + + +<h1><a name="install">Installation</h1> + +<h2><a name="installM">Installing the Matlab code</h2> + +<ul> +<li> <a href="bnt_download.html">Download</a> the BNT.zip file. + +<p> +<li> Unpack the file. In Unix, type +<!--"tar xvf BNT.tar".--> +"unzip FullBNT.zip". +In Windows, use +a program like <a href="http://www.winzip.com">Winzip</a>. This will +create a directory called FullBNT, which contains BNT and other libraries. + +<p> +<li> Read the file <tt>BNT/README</tt> to make sure the date +matches the one on the top of <a href=bnt.html>the BNT home page</a>. +If not, you may need to press 'refresh' on your browser, and download +again, to get the most recent version. + +<p> +<li> <b>Edit the file "BNT/add_BNT_to_path.m"</b> so it contains the correct +pathname. +For example, in Windows, +I download FullBNT.zip into C:\kpmurphy\matlab, and +then comment out the second line (with the % character), and uncomment +the third line, which reads +<pre> +BNT_HOME = 'C:\kpmurphy\matlab\FullBNT'; +</pre> + +<p> +<li> Start up Matlab. + +<p> +<li> Type "ver" at the Matlab prompt (">>"). +<b>You need Matlab version 5.2 or newer to run BNT</b>. +(Versions 5.0 and 5.1 have a memory leak which seems to sometimes +crash BNT.) + +<p> +<li> Move to the BNT directory. +For example, in Windows, I type +<pre> +>> cd C:\kpmurphy\matlab\FullBNT\BNT +</pre> + +<p> +<li> Type "add_BNT_to_path". +This executes the command +<tt>addpath(genpath(BNT_HOME))</tt>, +which adds all directories below FullBNT to the matlab path. + +<p> +<li> Type "test_BNT". + +If all goes well, this will produce a bunch of numbers and maybe some +warning messages (which you can ignore), but no error messages. +(The warnings should only be of the form +"Warning: Maximum number of iterations has been exceeded", and are +produced by Netlab.) + +<p> +<li> <a href="http://groups.yahoo.com/group/BayesNetToolbox/join"> +Join the BNT email list</a> + +</ul> + + +If you are new to Matlab, you might like to check out +<a href="matlab_tips.html">some useful Matlab tips</a>. +For instance, this explains how to create a startup file, which can be +used to set your path variable automatically, so you can avoid having +to type the above commands every time. + + + + +<h2><a name="installC">Installing the C code</h2> + +Some BNT functions also have C implementations. +<b>It is not necessary to install the C code</b>, but it can result in a speedup +of a factor of 5-10. +To install all the C code, +edit installC_BNT.m so it contains the right path, +then type <tt>installC_BNT</tt>. +To uninstall all the C code, +edit uninstallC_BNT.m so it contains the right path, +then type <tt>uninstallC_BNT</tt>. +For an up-to-date list of the files which have C implementations, see +BNT/installC_BNT.m. + +<p> +mex is a script that lets you call C code from Matlab - it does not compile matlab to +C (see mcc below). +If your C/C++ compiler is set up correctly, mex should work out of +the box. +If not, you might need to type +<p> +<tt> mex -setup</tt> +<p> +before calling installC. +<p> +To make mex call gcc on Windows, +you must install <a +href="http://www.mrc-cbu.cam.ac.uk/Imaging/gnumex20.html">gnumex</a>. +You can use the <a href="http://www.mingw.org/">minimalist GNU for +Windows</a> version of gcc, or +the <a href="http://sources.redhat.com/cygwin/">cygwin</a> version. +<p> +In general, typing +'mex foo.c' from inside Matlab creates a file called +'foo.mexglx' or 'foo.dll' (the exact file +extension is system dependent - on Linux it is 'mexglx', on Windows it is '.dll'). +The resulting file will hide the original 'foo.m' (if it existed), i.e., +typing 'foo' at the prompt will call the compiled C version. +To reveal the original matlab version, just delete foo.mexglx (this is +what uninstallC does). +<p> +Sometimes it takes time for Matlab to realize that the file has +changed from matlab to C or vice versa; try typing 'clear all' or +restarting Matlab to refresh it. +To find out which version of a file you are running, type +'which foo'. +<p> +<a href="http://www.mathworks.com/products/compiler">mcc</a>, the +Matlab to C compiler, is a separate product, +and is quite different from mex. It does not yet support +objects/classes, which is why we can't compile all of BNT to C automatically. +Also, hand-written C code is usually much +better than the C code generated by mcc. + + +<p> +Acknowledgements: +Although I wrote some of the C code, most of +the C code (e.g., for jtree and dpot) was written by Wei Hu; +the triangulation C code was written by Ilya Shpitser. + + +<h1><a name="basics">Creating your first Bayes net</h1> + +To define a Bayes net, you must specify the graph structure and then +the parameters. We look at each in turn, using a simple example +(adapted from Russell and +Norvig, "Artificial Intelligence: a Modern Approach", Prentice Hall, +1995, p454). + + +<h2>Graph structure</h2> + + +Consider the following network. + +<p> +<center> +<IMG SRC="Figures/sprinkler.gif"> +</center> +<p> + +<P> +To specify this directed acyclic graph (dag), we create an adjacency matrix: +<PRE> +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; +</PRE> +<P> +We have numbered the nodes as follows: +Cloudy = 1, Sprinkler = 2, Rain = 3, WetGrass = 4. +<b>The nodes must always be numbered in topological order, i.e., +ancestors before descendants.</b> +For a more complicated graph, this is a little inconvenient: we will +see how to get around this <a href="usage_dbn.html#bat">below</a>. +<p> +In Matlab 6, you can use logical arrays instead of double arrays, +which are 4 times smaller: +<pre> +dag = false(N,N); +dag(C,[R S]) = true; +... +</pre> +<p> +A preliminary attempt to make a <b>GUI</b> +has been writte by Philippe LeRay and can be downloaded +from <a href="http://bnt.insa-rouen.fr/ajouts.html">here</a>. +<p> +You can visualize the resulting graph structure using +the methods discussed <a href="#graphdraw">below</a>. + +<h2>Creating the Bayes net shell</h2> + +In addition to specifying the graph structure, +we must specify the size and type of each node. +If a node is discrete, its size is the +number of possible values +each node can take on; if a node is continuous, +it can be a vector, and its size is the length of this vector. +In this case, we will assume all nodes are discrete and binary. +<PRE> +discrete_nodes = 1:N; +node_sizes = 2*ones(1,N); +</pre> +If the nodes were not binary, you could type e.g., +<pre> +node_sizes = [4 2 3 5]; +</pre> +meaning that Cloudy has 4 possible values, +Sprinkler has 2 possible values, etc. +Note that these are cardinal values, not ordinal, i.e., +they are not ordered in any way, like 'low', 'medium', 'high'. +<p> +We are now ready to make the Bayes net: +<pre> +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes); +</PRE> +By default, all nodes are assumed to be discrete, so we can also just +write +<pre> +bnet = mk_bnet(dag, node_sizes); +</PRE> +You may also specify which nodes will be observed. +If you don't know, or if this not fixed in advance, +just use the empty list (the default). +<pre> +onodes = []; +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes, 'observed', onodes); +</PRE> +Note that optional arguments are specified using a name/value syntax. +This is common for many BNT functions. +In general, to find out more about a function (e.g., which optional +arguments it takes), please see its +documentation string by typing +<pre> +help mk_bnet +</pre> +See also other <a href="matlab_tips.html">useful Matlab tips</a>. +<p> +It is possible to associate names with nodes, as follows: +<pre> +bnet = mk_bnet(dag, node_sizes, 'names', {'cloudy','S','R','W'}, 'discrete', 1:4); +</pre> +You can then refer to a node by its name: +<pre> +C = bnet.names{'cloudy'}; % bnet.names is an associative array +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +</pre> + + +<h2><a name="cpt">Parameters</h2> + +A model consists of the graph structure and the parameters. +The parameters are represented by CPD objects (CPD = Conditional +Probability Distribution), which define the probability distribution +of a node given its parents. +(We will use the terms "node" and "random variable" interchangeably.) +The simplest kind of CPD is a table (multi-dimensional array), which +is suitable when all the nodes are discrete-valued. Note that the discrete +values are not assumed to be ordered in any way; that is, they +represent categorical quantities, like male and female, rather than +ordinal quantities, like low, medium and high. +(We will discuss CPDs in more detail <a href="#cpd">below</a>.) +<p> +Tabular CPDs, also called CPTs (conditional probability tables), +are stored as multidimensional arrays, where the dimensions +are arranged in the same order as the nodes, e.g., the CPT for node 4 +(WetGrass) is indexed by Sprinkler (2), Rain (3) and then WetGrass (4) itself. +Hence the child is always the last dimension. +If a node has no parents, its CPT is a column vector representing its +prior. +Note that in Matlab (unlike C), arrays are indexed +from 1, and are layed out in memory such that the first index toggles +fastest, e.g., the CPT for node 4 (WetGrass) is as follows +<P> +<P><IMG ALIGN=BOTTOM SRC="Figures/CPTgrass.gif"><P> +<P> +where we have used the convention that false==1, true==2. +We can create this CPT in Matlab as follows +<PRE> +CPT = zeros(2,2,2); +CPT(1,1,1) = 1.0; +CPT(2,1,1) = 0.1; +... +</PRE> +Here is an easier way: +<PRE> +CPT = reshape([1 0.1 0.1 0.01 0 0.9 0.9 0.99], [2 2 2]); +</PRE> +In fact, we don't need to reshape the array, since the CPD constructor +will do that for us. So we can just write +<pre> +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +</pre> +The other nodes are created similarly (using the old syntax for +optional parameters) +<PRE> +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +</PRE> + + +<h2><a name="rnd_cpt">Random Parameters</h2> + +If we do not specify the CPT, random parameters will be +created, i.e., each "row" of the CPT will be drawn from the uniform distribution. +To ensure repeatable results, use +<pre> +rand('state', seed); +randn('state', seed); +</pre> +To control the degree of randomness (entropy), +you can sample each row of the CPT from a Dirichlet(p,p,...) distribution. +If p << 1, this encourages "deterministic" CPTs (one entry near 1, the rest near 0). +If p = 1, each entry is drawn from U[0,1]. +If p >> 1, the entries will all be near 1/k, where k is the arity of +this node, i.e., each row will be nearly uniform. +You can do this as follows, assuming this node +is number i, and ns is the node_sizes. +<pre> +k = ns(i); +ps = parents(dag, i); +psz = prod(ns(ps)); +CPT = sample_dirichlet(p*ones(1,k), psz); +bnet.CPD{i} = tabular_CPD(bnet, i, 'CPT', CPT); +</pre> + + +<h2><a name="file">Loading a network from a file</h2> + +If you already have a Bayes net represented in the XML-based +<a href="http://www.cs.cmu.edu/afs/cs/user/fgcozman/www/Research/InterchangeFormat/"> +Bayes Net Interchange Format (BNIF)</a> (e.g., downloaded from the +<a +href="http://www.cs.huji.ac.il/labs/compbio/Repository"> +Bayes Net repository</a>), +you can convert it to BNT format using +the +<a href="http://www.digitas.harvard.edu/~ken/bif2bnt/">BIF-BNT Java +program</a> written by Ken Shan. +(This is not necessarily up-to-date.) + + +<h2>Creating a model using a GUI</h2> + +Click <a href="http://bnt.insa-rouen.fr/ajouts.html">here</a>. + + + +<h1><a name="inference">Inference</h1> + +Having created the BN, we can now use it for inference. +There are many different algorithms for doing inference in Bayes nets, +that make different tradeoffs between speed, +complexity, generality, and accuracy. +BNT therefore offers a variety of different inference +"engines". We will discuss these +in more detail <a href="#engines">below</a>. +For now, we will use the junction tree +engine, which is the mother of all exact inference algorithms. +This can be created as follows. +<pre> +engine = jtree_inf_engine(bnet); +</pre> +The other engines have similar constructors, but might take +additional, algorithm-specific parameters. +All engines are used in the same way, once they have been created. +We illustrate this in the following sections. + + +<h2><a name="marginal">Computing marginal distributions</h2> + +Suppose we want to compute the probability that the sprinker was on +given that the grass is wet. +The evidence consists of the fact that W=2. All the other nodes +are hidden (unobserved). We can specify this as follows. +<pre> +evidence = cell(1,N); +evidence{W} = 2; +</pre> +We use a 1D cell array instead of a vector to +cope with the fact that nodes can be vectors of different lengths. +In addition, the value [] can be used +to denote 'no evidence', instead of having to specify the observation +pattern as a separate argument. +(Click <a href="cellarray.html">here</a> for a quick tutorial on cell +arrays in matlab.) +<p> +We are now ready to add the evidence to the engine. +<pre> +[engine, loglik] = enter_evidence(engine, evidence); +</pre> +The behavior of this function is algorithm-specific, and is discussed +in more detail <a href="#engines">below</a>. +In the case of the jtree engine, +enter_evidence implements a two-pass message-passing scheme. +The first return argument contains the modified engine, which +incorporates the evidence. The second return argument contains the +log-likelihood of the evidence. (Not all engines are capable of +computing the log-likelihood.) +<p> +Finally, we can compute p=P(S=2|W=2) as follows. +<PRE> +marg = marginal_nodes(engine, S); +marg.T +ans = + 0.57024 + 0.42976 +p = marg.T(2); +</PRE> +We see that p = 0.4298. +<p> +Now let us add the evidence that it was raining, and see what +difference it makes. +<PRE> +evidence{R} = 2; +[engine, loglik] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, S); +p = marg.T(2); +</PRE> +We find that p = P(S=2|W=2,R=2) = 0.1945, +which is lower than +before, because the rain can ``explain away'' the +fact that the grass is wet. +<p> +You can plot a marginal distribution over a discrete variable +as a barchart using the built 'bar' function: +<pre> +bar(marg.T) +</pre> +This is what it looks like + +<p> +<center> +<IMG SRC="Figures/sprinkler_bar.gif"> +</center> +<p> + +<h2><a name="observed">Observed nodes</h2> + +What happens if we ask for the marginal on an observed node, e.g. P(W|W=2)? +An observed discrete node effectively only has 1 value (the observed + one) --- all other values would result in 0 probability. +For efficiency, BNT treats observed (discrete) nodes as if they were + set to 1, as we see below: +<pre> +evidence = cell(1,N); +evidence{W} = 2; +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, W); +m.T +ans = + 1 +</pre> +This can get a little confusing, since we assigned W=2. +So we can ask BNT to add the evidence back in by passing in an optional argument: +<pre> +m = marginal_nodes(engine, W, 1); +m.T +ans = + 0 + 1 +</pre> +This shows that P(W=1|W=2) = 0 and P(W=2|W=2) = 1. + + + +<h2><a name="joint">Computing joint distributions</h2> + +We can compute the joint probability on a set of nodes as in the +following example. +<pre> +evidence = cell(1,N); +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]); +</pre> +m is a structure. The 'T' field is a multi-dimensional array (in +this case, 3-dimensional) that contains the joint probability +distribution on the specified nodes. +<pre> +>> m.T +ans(:,:,1) = + 0.2900 0.0410 + 0.0210 0.0009 +ans(:,:,2) = + 0 0.3690 + 0.1890 0.0891 +</pre> +We see that P(S=1,R=1,W=2) = 0, since it is impossible for the grass +to be wet if both the rain and sprinkler are off. +<p> +Let us now add some evidence to R. +<pre> +evidence{R} = 2; +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]) +m = + domain: [2 3 4] + T: [2x1x2 double] +>> m.T +m.T +ans(:,:,1) = + 0.0820 + 0.0018 +ans(:,:,2) = + 0.7380 + 0.1782 +</pre> +The joint T(i,j,k) = P(S=i,R=j,W=k|evidence) +should have T(i,1,k) = 0 for all i,k, since R=1 is incompatible +with the evidence that R=2. +Instead of creating large tables with many 0s, BNT sets the effective +size of observed (discrete) nodes to 1, as explained above. +This is why m.T has size 2x1x2. +To get a 2x2x2 table, type +<pre> +m = marginal_nodes(engine, [S R W], 1) +m = + domain: [2 3 4] + T: [2x2x2 double] +>> m.T +m.T +ans(:,:,1) = + 0 0.082 + 0 0.0018 +ans(:,:,2) = + 0 0.738 + 0 0.1782 +</pre> + +<p> +Note: It is not always possible to compute the joint on arbitrary +sets of nodes: it depends on which inference engine you use, as discussed +in more detail <a href="#engines">below</a>. + + +<h2><a name="soft">Soft/virtual evidence</h2> + +Sometimes a node is not observed, but we have some distribution over +its possible values; this is often called "soft" or "virtual" +evidence. +One can use this as follows +<pre> +[engine, loglik] = enter_evidence(engine, evidence, 'soft', soft_evidence); +</pre> +where soft_evidence{i} is either [] (if node i has no soft evidence) +or is a vector representing the probability distribution over i's +possible values. +For example, if we don't know i's exact value, but we know its +likelihood ratio is 60/40, we can write evidence{i} = [] and +soft_evidence{i} = [0.6 0.4]. +<p> +Currently only jtree_inf_engine supports this option. +It assumes that all hidden nodes, and all nodes for +which we have soft evidence, are discrete. +For a longer example, see BNT/examples/static/softev1.m. + + +<h2><a name="mpe">Most probable explanation</h2> + +To compute the most probable explanation (MPE) of the evidence (i.e., +the most probable assignment, or a mode of the joint), use +<pre> +[mpe, ll] = calc_mpe(engine, evidence); +</pre> +mpe{i} is the most likely value of node i. +This calls enter_evidence with the 'maximize' flag set to 1, which +causes the engine to do max-product instead of sum-product. +The resulting max-marginals are then thresholded. +If there is more than one maximum probability assignment, we must take + care to break ties in a consistent manner (thresholding the + max-marginals may give the wrong result). To force this behavior, + type +<pre> +[mpe, ll] = calc_mpe(engine, evidence, 1); +</pre> +Note that computing the MPE is someties called abductive reasoning. + +<p> +You can also use <tt>calc_mpe_bucket</tt> written by Ron Zohar, +that does a forwards max-product pass, and then a backwards traceback +pass, which is how Viterbi is traditionally implemented. + + + +<h1><a name="cpd">Conditional Probability Distributions</h1> + +A Conditional Probability Distributions (CPD) +defines P(X(i) | X(Pa(i))), where X(i) is the i'th node, and X(Pa(i)) +are the parents of node i. There are many ways to represent this +distribution, which depend in part on whether X(i) and X(Pa(i)) are +discrete, continuous, or a combination. +We will discuss various representations below. + + +<h2><a name="tabular">Tabular nodes</h2> + +If the CPD is represented as a table (i.e., if it is a multinomial +distribution), it has a number of parameters that is exponential in +the number of parents. See the example <a href="#cpt">above</a>. + + +<h2><a name="noisyor">Noisy-or nodes</h2> + +A noisy-OR node is like a regular logical OR gate except that +sometimes the effects of parents that are on get inhibited. +Let the prob. that parent i gets inhibited be q(i). +Then a node, C, with 2 parents, A and B, has the following CPD, where +we use F and T to represent off and on (1 and 2 in BNT). +<pre> +A B P(C=off) P(C=on) +--------------------------- +F F 1.0 0.0 +T F q(A) 1-q(A) +F T q(B) 1-q(B) +T T q(A)q(B) q-q(A)q(B) +</pre> +Thus we see that the causes get inhibited independently. +It is common to associate a "leak" node with a noisy-or CPD, which is +like a parent that is always on. This can account for all other unmodelled +causes which might turn the node on. +<p> +The noisy-or distribution is similar to the logistic distribution. +To see this, let the nodes, S(i), have values in {0,1}, and let q(i,j) +be the prob. that j inhibits i. Then +<pre> +Pr(S(i)=1 | parents(S(i))) = 1 - prod_{j} q(i,j)^S(j) +</pre> +Now define w(i,j) = -ln q(i,j) and rho(x) = 1-exp(-x). Then +<pre> +Pr(S(i)=1 | parents(S(i))) = rho(sum_j w(i,j) S(j)) +</pre> +For a sigmoid node, we have +<pre> +Pr(S(i)=1 | parents(S(i))) = sigma(-sum_j w(i,j) S(j)) +</pre> +where sigma(x) = 1/(1+exp(-x)). Hence they differ in the choice of +the activation function (although both are monotonically increasing). +In addition, in the case of a noisy-or, the weights are constrained to be +positive, since they derive from probabilities q(i,j). +In both cases, the number of parameters is <em>linear</em> in the +number of parents, unlike the case of a multinomial distribution, +where the number of parameters is exponential in the number of parents. +We will see an example of noisy-OR nodes <a href="#qmr">below</a>. + + +<h2><a name="deterministic">Other (noisy) deterministic nodes</h2> + +Deterministic CPDs for discrete random variables can be created using +the deterministic_CPD class. It is also possible to 'flip' the output +of the function with some probability, to simulate noise. +The boolean_CPD class is just a special case of a +deterministic CPD, where the parents and child are all binary. +<p> +Both of these classes are just "syntactic sugar" for the tabular_CPD +class. + + + +<h2><a name="softmax">Softmax nodes</h2> + +If we have a discrete node with a continuous parent, +we can define its CPD using a softmax function +(also known as the multinomial logit function). +This acts like a soft thresholding operator, and is defined as follows: +<pre> + exp(w(:,i)'*x + b(i)) +Pr(Q=i | X=x) = ----------------------------- + sum_j exp(w(:,j)'*x + b(j)) + +</pre> +The parameters of a softmax node, w(:,i) and b(i), i=1..|Q|, have the +following interpretation: w(:,i)-w(:,j) is the normal vector to the +decision boundary between classes i and j, +and b(i)-b(j) is its offset (bias). For example, suppose +X is a 2-vector, and Q is binary. Then +<pre> +w = [1 -1; + 0 0]; + +b = [0 0]; +</pre> +means class 1 are points in the 2D plane with positive x coordinate, +and class 2 are points in the 2D plane with negative x coordinate. +If w has large magnitude, the decision boundary is sharp, otherwise it +is soft. +In the special case that Q is binary (0/1), the softmax function reduces to the logistic +(sigmoid) function. +<p> +Fitting a softmax function can be done using the iteratively reweighted +least squares (IRLS) algorithm. +We use the implementation from +<a href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a>. +Note that since +the softmax distribution is not in the exponential family, it does not +have finite sufficient statistics, and hence we must store all the +training data in uncompressed form. +If this takes too much space, one should use online (stochastic) gradient +descent (not implemented in BNT). +<p> +If a softmax node also has discrete parents, +we use a different set of w/b parameters for each combination of +parent values, as in the <a href="#gaussian">conditional linear +Gaussian CPD</a>. +This feature was implemented by Pierpaolo Brutti. +He is currently extending it so that discrete parents can be treated +as if they were continuous, by adding indicator variables to the X +vector. +<p> +We will see an example of softmax nodes <a href="#mixexp">below</a>. + + +<h2><a name="mlp">Neural network nodes</h2> + +Pierpaolo Brutti has implemented the mlp_CPD class, which uses a multi layer perceptron +to implement a mapping from continuous parents to discrete children, +similar to the softmax function. +(If there are also discrete parents, it creates a mixture of MLPs.) +It uses code from <a +href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a>. +This is work in progress. + +<h2><a name="root">Root nodes</h2> + +A root node has no parents and no parameters; it can be used to model +an observed, exogeneous input variable, i.e., one which is "outside" +the model. +This is useful for conditional density models. +We will see an example of root nodes <a href="#mixexp">below</a>. + + +<h2><a name="gaussian">Gaussian nodes</h2> + +We now consider a distribution suitable for the continuous-valued nodes. +Suppose the node is called Y, its continuous parents (if any) are +called X, and its discrete parents (if any) are called Q. +The distribution on Y is defined as follows: +<pre> +- no parents: Y ~ N(mu, Sigma) +- cts parents : Y|X=x ~ N(mu + W x, Sigma) +- discrete parents: Y|Q=i ~ N(mu(:,i), Sigma(:,:,i)) +- cts and discrete parents: Y|X=x,Q=i ~ N(mu(:,i) + W(:,:,i) * x, Sigma(:,:,i)) +</pre> +where N(mu, Sigma) denotes a Normal distribution with mean mu and +covariance Sigma. Let |X|, |Y| and |Q| denote the sizes of X, Y and Q +respectively. +If there are no discrete parents, |Q|=1; if there is +more than one, then |Q| = a vector of the sizes of each discrete parent. +If there are no continuous parents, |X|=0; if there is more than one, +then |X| = the sum of their sizes. +Then mu is a |Y|*|Q| vector, Sigma is a |Y|*|Y|*|Q| positive +semi-definite matrix, and W is a |Y|*|X|*|Q| regression (weight) +matrix. +<p> +We can create a Gaussian node with random parameters as follows. +<pre> +bnet.CPD{i} = gaussian_CPD(bnet, i); +</pre> +We can specify the value of one or more of the parameters as in the +following example, in which |Y|=2, and |Q|=1. +<pre> +bnet.CPD{i} = gaussian_CPD(bnet, i, 'mean', [0; 0], 'weights', randn(Y,X), 'cov', eye(Y)); +</pre> +<p> +We will see an example of conditional linear Gaussian nodes <a +href="#cg_model">below</a>. +<p> +<b>When learning Gaussians from data</b>, it is helpful to ensure the +data has a small magnitde +(see e.g., KPMstats/standardize) to prevent numerical problems. +Unless you have a lot of data, it is also a very good idea to use +diagonal instead of full covariance matrices. +(BNT does not currently support spherical covariances, although it +would be easy to add, since KPMstats/clg_Mstep supports this option; +you would just need to modify gaussian_CPD/update_ess to accumulate +weighted inner products.) + + + +<h2><a name="nongauss">Other continuous distributions</h2> + +Currently BNT does not support any CPDs for continuous nodes other +than the Gaussian. +However, you can use a mixture of Gaussians to +approximate other continuous distributions. We will see some an example +of this with the IFA model <a href="#pca">below</a>. + + +<h2><a name="glm">Generalized linear model nodes</h2> + +In the future, we may incorporate some of the functionality of +<a href = +"http://www.sci.usq.edu.au/staff/dunn/glmlab/glmlab.html">glmlab</a> +into BNT. + + +<h2><a name="dtree">Classification/regression tree nodes</h2> + +We plan to add classification and regression trees to define CPDs for +discrete and continuous nodes, respectively. +Trees have many advantages: they are easy to interpret, they can do +feature selection, they can +handle discrete and continuous inputs, they do not make strong +assumptions about the form of the distribution, the number of +parameters can grow in a data-dependent way (i.e., they are +semi-parametric), they can handle missing data, etc. +However, they are not yet implemented. +<!-- +Yimin Zhang is currently (Feb '02) implementing this. +--> + + +<h2><a name="cpd_summary">Summary of CPD types</h2> + +We list all the different types of CPDs supported by BNT. +For each CPD, we specify if the child and parents can be discrete (D) or +continuous (C) (Binary (B) nodes are a special case). +We also specify which methods each class supports. +If a method is inherited, the name of the parent class is mentioned. +If a parent class calls a child method, this is mentioned. +<p> +The <tt>CPD_to_CPT</tt> method converts a CPD to a table; this +requires that the child and all parents are discrete. +The CPT might be exponentially big... +<tt>convert_to_table</tt> evaluates a CPD with evidence, and +represents the the resulting potential as an array. +This requires that the child is discrete, and any continuous parents +are observed. +<tt>convert_to_pot</tt> evaluates a CPD with evidence, and +represents the resulting potential as a dpot, gpot, cgpot or upot, as +requested. (d=discrete, g=Gaussian, cg = conditional Gaussian, u = +utility). + +<p> +When we sample a node, all the parents are observed. +When we compute the (log) probability of a node, all the parents and +the child are observed. +<p> +We also specify if the parameters are learnable. +For learning with EM, we require +the methods <tt>reset_ess</tt>, <tt>update_ess</tt> and +<tt>maximize_params</tt>. +For learning from fully observed data, we require +the method <tt>learn_params</tt>. +By default, all classes inherit this from generic_CPD, which simply +calls <tt>update_ess</tt> N times, once for each data case, followed +by <tt>maximize_params</tt>, i.e., it is like EM, without the E step. +Some classes implement a batch formula, which is quicker. +<p> +Bayesian learning means computing a posterior over the parameters +given fully observed data. +<p> +Pearl means we implement the methods <tt>compute_pi</tt> and +<tt>compute_lambda_msg</tt>, used by +<tt>pearl_inf_engine</tt>, which runs on directed graphs. +<tt>belprop_inf_engine</tt> only needs <tt>convert_to_pot</tt>.H +The pearl methods can exploit special properties of the CPDs for +computing the messages efficiently, whereas belprop does not. +<p> +The only method implemented by generic_CPD is <tt>adjustable_CPD</tt>, +which is not shown, since it is not very interesting. + + +<p> + + +<table> +<table border units = pixels><tr> +<td align=center>Name +<td align=center>Child +<td align=center>Parents +<td align=center>Comments +<td align=center>CPD_to_CPT +<td align=center>conv_to_table +<td align=center>conv_to_pot +<td align=center>sample +<td align=center>prob +<td align=center>learn +<td align=center>Bayes +<td align=center>Pearl + + +<tr> +<!-- Name--><td> +<!-- Child--><td> +<!-- Parents--><td> +<!-- Comments--><td> +<!-- CPD_to_CPT--><td> +<!-- conv_to_table--><td> +<!-- conv_to_pot--><td> +<!-- sample--><td> +<!-- prob--><td> +<!-- learn--><td> +<!-- Bayes--><td> +<!-- Pearl--><td> + +<tr> +<!-- Name--><td>boolean +<!-- Child--><td>B +<!-- Parents--><td>B +<!-- Comments--><td>Syntactic sugar for tabular +<!-- CPD_to_CPT--><td>- +<!-- conv_to_table--><td>- +<!-- conv_to_pot--><td>- +<!-- sample--><td>- +<!-- prob--><td>- +<!-- learn--><td>- +<!-- Bayes--><td>- +<!-- Pearl--><td>- + +<tr> +<!-- Name--><td>deterministic +<!-- Child--><td>D +<!-- Parents--><td>D +<!-- Comments--><td>Syntactic sugar for tabular +<!-- CPD_to_CPT--><td>- +<!-- conv_to_table--><td>- +<!-- conv_to_pot--><td>- +<!-- sample--><td>- +<!-- prob--><td>- +<!-- learn--><td>- +<!-- Bayes--><td>- +<!-- Pearl--><td>- + +<tr> +<!-- Name--><td>Discrete +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>Virtual class +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Calls CPD_to_CPT +<!-- conv_to_pot--><td>Calls conv_to_table +<!-- sample--><td>Calls conv_to_table +<!-- prob--><td>Calls conv_to_table +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + +<tr> +<!-- Name--><td>Gaussian +<!-- Child--><td>C +<!-- Parents--><td>C/D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>Y +<!-- prob--><td>Y +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + +<tr> +<!-- Name--><td>gmux +<!-- Child--><td>C +<!-- Parents--><td>C/D +<!-- Comments--><td>multiplexer +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>N +<!-- prob--><td>N +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>Y + + +<tr> +<!-- Name--><td>MLP +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>multi layer perceptron +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Y +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>noisy-or +<!-- Child--><td>B +<!-- Parents--><td>B +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>Y +<!-- conv_to_table--><td>Inherits from discrete +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>Y + + +<tr> +<!-- Name--><td>root +<!-- Child--><td>C/D +<!-- Parents--><td>none +<!-- Comments--><td>no params +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>Y +<!-- prob--><td>Y +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>softmax +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Y +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>generic +<!-- Child--><td>C/D +<!-- Parents--><td>C/D +<!-- Comments--><td>Virtual class +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>N +<!-- sample--><td>N +<!-- prob--><td>N +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>Tabular +<!-- Child--><td>D +<!-- Parents--><td>D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>Y +<!-- conv_to_table--><td>Inherits from discrete +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>Y +<!-- Pearl--><td>Y + +</table> + + + +<h1><a name="examples">Example models</h1> + + +<h2>Gaussian mixture models</h2> + +Richard W. DeVaul has made a detailed tutorial on how to fit mixtures +of Gaussians using BNT. Available +<a href="http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt">here</a>. + + +<h2><a name="pca">PCA, ICA, and all that </h2> + +In Figure (a) below, we show how Factor Analysis can be thought of as a +graphical model. Here, X has an N(0,I) prior, and +Y|X=x ~ N(mu + Wx, Psi), +where Psi is diagonal and W is called the "factor loading matrix". +Since the noise on both X and Y is diagonal, the components of these +vectors are uncorrelated, and hence can be represented as individual +scalar nodes, as we show in (b). +(This is useful if parts of the observations on the Y vector are occasionally missing.) +We usually take k=|X| << |Y|=D, so the model tries to explain +many observations using a low-dimensional subspace. + + +<center> +<table> +<tr> +<td><img src="Figures/fa.gif"> +<td><img src="Figures/fa_scalar.gif"> +<td><img src="Figures/mfa.gif"> +<td><img src="Figures/ifa.gif"> +<tr> +<td align=center> (a) +<td align=center> (b) +<td align=center> (c) +<td align=center> (d) +</table> +</center> + +<p> +We can create this model in BNT as follows. +<pre> +ns = [k D]; +dag = zeros(2,2); +dag(1,2) = 1; +bnet = mk_bnet(dag, ns, 'discrete', []); +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', zeros(k,1), 'cov', eye(k), ... + 'cov_type', 'diag', 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(D,1), 'cov', diag(Psi0), 'weights', W0, ... + 'cov_type', 'diag', 'clamp_mean', 1); +</pre> + +The root node is clamped to the N(0,I) distribution, so that we will +not update these parameters during learning. +The mean of the leaf node is clamped to 0, +since we assume the data has been centered (had its mean subtracted +off); this is just for simplicity. +Finally, the covariance of the leaf node is constrained to be +diagonal. W0 and Psi0 are the initial parameter guesses. + +<p> +We can fit this model (i.e., estimate its parameters in a maximum +likelihood (ML) sense) using EM, as we +explain <a href="#em">below</a>. +Not surprisingly, the ML estimates for mu and Psi turn out to be +identical to the +sample mean and variance, which can be computed directly as +<pre> +mu_ML = mean(data); +Psi_ML = diag(cov(data)); +</pre> +Note that W can only be identified up to a rotation matrix, because of +the spherical symmetry of the source. + +<p> +If we restrict Psi to be spherical, i.e., Psi = sigma*I, +there is a closed-form solution for W as well, +i.e., we do not need to use EM. +In particular, W contains the first |X| eigenvectors of the sample covariance +matrix, with scalings determined by the eigenvalues and sigma. +Classical PCA can be obtained by taking the sigma->0 limit. +For details, see + +<ul> +<li> <a href="ftp://hope.caltech.edu/pub/roweis/Empca/empca.ps"> +"EM algorithms for PCA and SPCA"</a>, Sam Roweis, NIPS 97. +(<a href="ftp://hope.caltech.edu/pub/roweis/Code/empca.tar.gz"> +Matlab software</a>) + +<p> +<li> +<a +href=http://neural-server.aston.ac.uk/cgi-bin/tr_avail.pl?trnumber=NCRG/97/003> +"Mixtures of probabilistic principal component analyzers"</a>, +Tipping and Bishop, Neural Computation 11(2):443--482, 1999. +</ul> + +<p> +By adding a hidden discrete variable, we can create mixtures of FA +models, as shown in (c). +Now we can explain the data using a set of subspaces. +We can create this model in BNT as follows. +<pre> +ns = [M k D]; +dag = zeros(3); +dag(1,3) = 1; +dag(2,3) = 1; +bnet = mk_bnet(dag, ns, 'discrete', 1); +bnet.CPD{1} = tabular_CPD(bnet, 1, Pi0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(k, 1), 'cov', eye(k), 'cov_type', 'diag', ... + 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', Mu0', 'cov', repmat(diag(Psi0), [1 1 M]), ... + 'weights', W0, 'cov_type', 'diag', 'tied_cov', 1); +</pre> +Notice how the covariance matrix for Y is the same for all values of +Q; that is, the noise level in each sub-space is assumed the same. +However, we allow the offset, mu, to vary. +For details, see +<ul> + +<LI> +<a HREF="ftp://ftp.cs.toronto.edu/pub/zoubin/tr-96-1.ps.gz"> The EM +Algorithm for Mixtures of Factor Analyzers </A>, +Ghahramani, Z. and Hinton, G.E. (1996), +University of Toronto +Technical Report CRG-TR-96-1. +(<A HREF="ftp://ftp.cs.toronto.edu/pub/zoubin/mfa.tar.gz">Matlab software</A>) + +<p> +<li> +<a +href=http://neural-server.aston.ac.uk/cgi-bin/tr_avail.pl?trnumber=NCRG/97/003> +"Mixtures of probabilistic principal component analyzers"</a>, +Tipping and Bishop, Neural Computation 11(2):443--482, 1999. +</ul> + +<p> +I have included Zoubin's specialized MFA code (with his permission) +with the toolbox, so you can check that BNT gives the same results: +see 'BNT/examples/static/mfa1.m'. + +<p> +Independent Factor Analysis (IFA) generalizes FA by allowing a +non-Gaussian prior on each component of X. +(Note that we can approximate a non-Gaussian prior using a mixture of +Gaussians.) +This means that the likelihood function is no longer rotationally +invariant, so we can uniquely identify W and the hidden +sources X. +IFA also allows a non-diagonal Psi (i.e. correlations between the components of Y). +We recover classical Independent Components Analysis (ICA) +in the Psi -> 0 limit, and by assuming that |X|=|Y|, so that the +weight matrix W is square and invertible. +For details, see +<ul> +<li> +<a href="http://www.gatsby.ucl.ac.uk/~hagai/ifa.ps">Independent Factor +Analysis</a>, H. Attias, Neural Computation 11: 803--851, 1998. +</ul> + + + +<h2><a name="mixexp">Mixtures of experts</h2> + +As an example of the use of the softmax function, +we introduce the Mixture of Experts model. +<!-- +We also show +the Hierarchical Mixture of Experts model, where the hierarchy has two +levels. +(This is essentially a probabilistic decision tree of height two.) +--> +As before, +circles denote continuous-valued nodes, +squares denote discrete nodes, clear +means hidden, and shaded means observed. +<p> +<center> +<table> +<tr> +<td><img src="Figures/mixexp.gif"> +<!-- +<td><img src="Figures/hme.gif"> +--> +</table> +</center> +<p> +X is the observed +input, Y is the output, and +the Q nodes are hidden "gating" nodes, which select the appropriate +set of parameters for Y. During training, Y is assumed observed, +but for testing, the goal is to predict Y given X. +Note that this is a <em>conditional</em> density model, so we don't +associate any parameters with X. +Hence X's CPD will be a root CPD, which is a way of modelling +exogenous nodes. +If the output is a continuous-valued quantity, +we assume the "experts" are linear-regression units, +and set Y's CPD to linear-Gaussian. +If the output is discrete, we set Y's CPD to a softmax function. +The Q CPDs will always be softmax functions. + +<p> +As a concrete example, consider the mixture of experts model where X and Y are +scalars, and Q is binary. +This is just piecewise linear regression, where +we have two line segments, i.e., +<P> +<IMG ALIGN=BOTTOM SRC="Eqns/lin_reg_eqn.gif"> +<P> +We can create this model with random parameters as follows. +(This code is bundled in BNT/examples/static/mixexp2.m.) +<PRE> +X = 1; +Q = 2; +Y = 3; +dag = zeros(3,3); +dag(X,[Q Y]) = 1 +dag(Q,Y) = 1; +ns = [1 2 1]; % make X and Y scalars, and have 2 experts +onodes = [1 3]; +bnet = mk_bnet(dag, ns, 'discrete', 2, 'observed', onodes); + +rand('state', 0); +randn('state', 0); +bnet.CPD{1} = root_CPD(bnet, 1); +bnet.CPD{2} = softmax_CPD(bnet, 2); +bnet.CPD{3} = gaussian_CPD(bnet, 3); +</PRE> +Now let us fit this model using <a href="#em">EM</a>. +First we <a href="#load_data">load the data</a> (1000 training cases) and plot them. +<P> +<PRE> +data = load('/examples/static/Misc/mixexp_data.txt', '-ascii'); +plot(data(:,1), data(:,2), '.'); +</PRE> +<p> +<center> +<IMG SRC="Figures/mixexp_data.gif"> +</center> +<p> +This is what the model looks like before training. +(Thanks to Thomas Hofman for writing this plotting routine.) +<p> +<center> +<IMG SRC="Figures/mixexp_before.gif"> +</center> +<p> +Now let's train the model, and plot the final performance. +(We will discuss how to train models in more detail <a href="#param_learning">below</a>.) +<P> +<PRE> +ncases = size(data, 1); % each row of data is a training case +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); % each column of cases is a training case +engine = jtree_inf_engine(bnet); +max_iter = 20; +[bnet2, LLtrace] = learn_params_em(engine, cases, max_iter); +</PRE> +(We specify which nodes will be observed when we create the engine. +Hence BNT knows that the hidden nodes are all discrete. +For complex models, this can lead to a significant speedup.) +Below we show what the model looks like after 16 iterations of EM +(with 100 IRLS iterations per M step), when it converged +using the default convergence tolerance (that the +fractional change in the log-likelihood be less than 1e-3). +Before learning, the log-likelihood was +-322.927442; afterwards, it was -13.728778. +<p> +<center> +<IMG SRC="Figures/mixexp_after.gif"> +</center> +(See BNT/examples/static/mixexp2.m for details of the code.) + + + +<h2><a name="hme">Hierarchical mixtures of experts</h2> + +A hierarchical mixture of experts (HME) extends the mixture of experts +model by having more than one hidden node. A two-level example is shown below, along +with its more traditional representation as a neural network. +This is like a (balanced) probabilistic decision tree of height 2. +<p> +<center> +<IMG SRC="Figures/HMEforMatlab.jpg"> +</center> +<p> +<a href="mailto:pbrutti@stat.cmu.edu">Pierpaolo Brutti</a> +has written an extensive set of routines for HMEs, +which are bundled with BNT: see the examples/static/HME directory. +These routines allow you to choose the number of hidden (gating) +layers, and the form of the experts (softmax or MLP). +See the file hmemenu, which provides a demo. +For example, the figure below shows the decision boundaries learned +for a ternary classification problem, using a 2 level HME with softmax +gates and softmax experts; the training set is on the left, the +testing set on the right. +<p> +<center> +<!--<IMG SRC="Figures/hme_dec_boundary.gif">--> +<IMG SRC="Figures/hme_dec_boundary.png"> +</center> +<p> + + +<p> +For more details, see the following: +<ul> + +<li> <a href="http://www.cs.berkeley.edu/~jordan/papers/hierarchies.ps.Z"> +Hierarchical mixtures of experts and the EM algorithm</a> +M. I. Jordan and R. A. Jacobs. Neural Computation, 6, 181-214, 1994. + +<li> <a href = +"http://www.cs.berkeley.edu/~dmartin/software">David Martin's +matlab code for HME</a> + +<li> <a +href="http://www.cs.berkeley.edu/~jordan/papers/uai.ps.Z">Why the +logistic function? A tutorial discussion on +probabilities and neural networks.</a> M. I. Jordan. MIT Computational +Cognitive Science Report 9503, August 1995. + +<li> "Generalized Linear Models", McCullagh and Nelder, Chapman and +Halll, 1983. + +<li> +"Improved learning algorithms for mixtures of experts in multiclass +classification". +K. Chen, L. Xu, H. Chi. +Neural Networks (1999) 12: 1229-1252. + +<li> <a href="http://www.oigeeza.com/steve/"> +Classification Using Hierarchical Mixtures of Experts</a> +S.R. Waterhouse and A.J. Robinson. +In Proc. IEEE Workshop on Neural Network for Signal Processing IV (1994), pp. 177-186 + +<li> <a href="http://www.idiap.ch/~perry/"> +Localized mixtures of experts</a>, +P. Moerland, 1998. + +<li> "Nonlinear gated experts for time series", +A.S. Weigend and M. Mangeas, 1995. + +</ul> + + +<h2><a name="qmr">QMR</h2> + +Bayes nets originally arose out of an attempt to add probabilities to +expert systems, and this is still the most common use for BNs. +A famous example is +QMR-DT, a decision-theoretic reformulation of the Quick Medical +Reference (QMR) model. +<p> +<center> +<IMG ALIGN=BOTTOM SRC="Figures/qmr.gif"> +</center> +Here, the top layer represents hidden disease nodes, and the bottom +layer represents observed symptom nodes. +The goal is to infer the posterior probability of each disease given +all the symptoms (which can be present, absent or unknown). +Each node in the top layer has a Bernoulli prior (with a low prior +probability that the disease is present). +Since each node in the bottom layer has a high fan-in, we use a +noisy-OR parameterization; each disease has an independent chance of +causing each symptom. +The real QMR-DT model is copyright, but +we can create a random QMR-like model as follows. +<pre> +function bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% MK_QMR_BNET Make a QMR model +% bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% +% G(i,j) = 1 iff there is an arc from disease i to finding j +% inhibit(i,j) = inhibition probability on i->j arc +% leak(j) = inhibition prob. on leak->j arc +% prior(i) = prob. disease i is on + +[Ndiseases Nfindings] = size(inhibit); +N = Ndiseases + Nfindings; +finding_node = Ndiseases+1:N; +ns = 2*ones(1,N); +dag = zeros(N,N); +dag(1:Ndiseases, finding_node) = G; +bnet = mk_bnet(dag, ns, 'observed', finding_node); + +for d=1:Ndiseases + CPT = [1-prior(d) prior(d)]; + bnet.CPD{d} = tabular_CPD(bnet, d, CPT'); +end + +for i=1:Nfindings + fnode = finding_node(i); + ps = parents(G, i); + bnet.CPD{fnode} = noisyor_CPD(bnet, fnode, leak(i), inhibit(ps, i)); +end +</pre> +In the file BNT/examples/static/qmr1, we create a random bipartite +graph G, with 5 diseases and 10 findings, and random parameters. +(In general, to create a random dag, use 'mk_random_dag'.) +We can visualize the resulting graph structure using +the methods discussed <a href="#graphdraw">below</a>, with the +following results: +<p> +<img src="Figures/qmr.rnd.jpg"> + +<p> +Now let us put some random evidence on all the leaves except the very +first and very last, and compute the disease posteriors. +<pre> +pos = 2:floor(Nfindings/2); +neg = (pos(end)+1):(Nfindings-1); +onodes = myunion(pos, neg); +evidence = cell(1, N); +evidence(findings(pos)) = num2cell(repmat(2, 1, length(pos))); +evidence(findings(neg)) = num2cell(repmat(1, 1, length(neg))); + +engine = jtree_inf_engine(bnet); +[engine, ll] = enter_evidence(engine, evidence); +post = zeros(1, Ndiseases); +for i=diseases(:)' + m = marginal_nodes(engine, i); + post(i) = m.T(2); +end +</pre> +Junction tree can be quite slow on large QMR models. +Fortunately, it is possible to exploit properties of the noisy-OR +function to speed up exact inference using an algorithm called +<a href="#quickscore">quickscore</a>, discussed below. + + + + + +<h2><a name="cg_model">Conditional Gaussian models</h2> + +A conditional Gaussian model is one in which, conditioned on all the discrete +nodes, the distribution over the remaining (continuous) nodes is +multivariate Gaussian. This means we can have arcs from discrete (D) +to continuous (C) nodes, but not vice versa. +(We <em>are</em> allowed C->D arcs if the continuous nodes are observed, +as in the <a href="#mixexp">mixture of experts</a> model, +since this distribution can be represented with a discrete potential.) +<p> +We now give an example of a CG model, from +the paper "Propagation of Probabilities, Means amd +Variances in Mixed Graphical Association Models", Steffen Lauritzen, +JASA 87(420):1098--1108, 1992 (reprinted in the book "Probabilistic Networks and Expert +Systems", R. G. Cowell, A. P. Dawid, S. L. Lauritzen and +D. J. Spiegelhalter, Springer, 1999.) + +<h3>Specifying the graph</h3> + +Consider the model of waste emissions from an incinerator plant shown below. +We follow the standard convention that shaded nodes are observed, +clear nodes are hidden. +We also use the non-standard convention that +square nodes are discrete (tabular) and round nodes are +Gaussian. + +<p> +<center> +<IMG SRC="Figures/cg1.gif"> +</center> +<p> + +We can create this model as follows. +<pre> +F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; +n = 9; + +dag = zeros(n); +dag(F,E)=1; +dag(W,[E Min D]) = 1; +dag(E,D)=1; +dag(B,[C D])=1; +dag(D,[L Mout])=1; +dag(Min,Mout)=1; + +% node sizes - all cts nodes are scalar, all discrete nodes are binary +ns = ones(1, n); +dnodes = [F W B]; +cnodes = mysetdiff(1:n, dnodes); +ns(dnodes) = 2; + +bnet = mk_bnet(dag, ns, 'discrete', dnodes); +</pre> +'dnodes' is a list of the discrete nodes; 'cnodes' is the continuous +nodes. 'mysetdiff' is a faster version of the built-in 'setdiff'. +<p> + + +<h3>Specifying the parameters</h3> + +The parameters of the discrete nodes can be specified as follows. +<pre> +bnet.CPD{B} = tabular_CPD(bnet, B, 'CPT', [0.85 0.15]); % 1=stable, 2=unstable +bnet.CPD{F} = tabular_CPD(bnet, F, 'CPT', [0.95 0.05]); % 1=intact, 2=defect +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [2/7 5/7]); % 1=industrial, 2=household +</pre> + +<p> +The parameters of the continuous nodes can be specified as follows. +<pre> +bnet.CPD{E} = gaussian_CPD(bnet, E, 'mean', [-3.9 -0.4 -3.2 -0.5], ... + 'cov', [0.00002 0.0001 0.00002 0.0001]); +bnet.CPD{D} = gaussian_CPD(bnet, D, 'mean', [6.5 6.0 7.5 7.0], ... + 'cov', [0.03 0.04 0.1 0.1], 'weights', [1 1 1 1]); +bnet.CPD{C} = gaussian_CPD(bnet, C, 'mean', [-2 -1], 'cov', [0.1 0.3]); +bnet.CPD{L} = gaussian_CPD(bnet, L, 'mean', 3, 'cov', 0.25, 'weights', -0.5); +bnet.CPD{Min} = gaussian_CPD(bnet, Min, 'mean', [0.5 -0.5], 'cov', [0.01 0.005]); +bnet.CPD{Mout} = gaussian_CPD(bnet, Mout, 'mean', 0, 'cov', 0.002, 'weights', [1 1]); +</pre> + + +<h3><a name="cg_infer">Inference</h3> + +<!--Let us perform inference in the <a href="#cg_model">waste incinerator example</a>.--> +First we compute the unconditional marginals. +<pre> +engine = jtree_inf_engine(bnet); +evidence = cell(1,n); +[engine, ll] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, E); +</pre> +<!--(Of course, we could use <tt>cond_gauss_inf_engine</tt> instead of jtree.)--> +'marg' is a structure that contains the fields 'mu' and 'Sigma', which +contain the mean and (co)variance of the marginal on E. +In this case, they are both scalars. +Let us check they match the published figures (to 2 decimal places). +<!--(We can't expect +more precision than this in general because I have implemented the algorithm of +Lauritzen (1992), which can be numerically unstable.)--> +<pre> +tol = 1e-2; +assert(approxeq(marg.mu, -3.25, tol)); +assert(approxeq(sqrt(marg.Sigma), 0.709, tol)); +</pre> +We can compute the other posteriors similarly. +Now let us add some evidence. +<pre> +evidence = cell(1,n); +evidence{W} = 1; % industrial +evidence{L} = 1.1; +evidence{C} = -0.9; +[engine, ll] = enter_evidence(engine, evidence); +</pre> +Now we find +<pre> +marg = marginal_nodes(engine, E); +assert(approxeq(marg.mu, -3.8983, tol)); +assert(approxeq(sqrt(marg.Sigma), 0.0763, tol)); +</pre> + + +We can also compute the joint probability on a set of nodes. +For example, P(D, Mout | evidence) is a 2D Gaussian: +<pre> +marg = marginal_nodes(engine, [D Mout]) +marg = + domain: [6 8] + mu: [2x1 double] + Sigma: [2x2 double] + T: 1.0000 +</pre> +The mean is +<pre> +marg.mu +ans = + 3.6077 + 4.1077 +</pre> +and the covariance matrix is +<pre> +marg.Sigma +ans = + 0.1062 0.1062 + 0.1062 0.1182 +</pre> +It is easy to visualize this posterior using standard Matlab plotting +functions, e.g., +<pre> +gaussplot2d(marg.mu, marg.Sigma); +</pre> +produces the following picture. + +<p> +<center> +<IMG SRC="Figures/gaussplot.png"> +</center> +<p> + + +The T field indicates that the mixing weight of this Gaussian +component is 1.0. +If the joint contains discrete and continuous variables, the result +will be a mixture of Gaussians, e.g., +<pre> +marg = marginal_nodes(engine, [F E]) + domain: [1 3] + mu: [-3.9000 -0.4003] + Sigma: [1x1x2 double] + T: [0.9995 4.7373e-04] +</pre> +The interpretation is +Sigma(i,j,k) = Cov[ E(i) E(j) | F=k ]. +In this case, E is a scalar, so i=j=1; k specifies the mixture component. +<p> +We saw in the sprinkler network that BNT sets the effective size of +observed discrete nodes to 1, since they only have one legal value. +For continuous nodes, BNT sets their length to 0, +since they have been reduced to a point. +For example, +<pre> +marg = marginal_nodes(engine, [B C]) + domain: [4 5] + mu: [] + Sigma: [] + T: [0.0123 0.9877] +</pre> +It is simple to post-process the output of marginal_nodes. +For example, the file BNT/examples/static/cg1 sets the mu term of +observed nodes to their observed value, and the Sigma term to 0 (since +observed nodes have no variance). + +<p> +Note that the implemented version of the junction tree is numerically +unstable when using CG potentials +(which is why, in the example above, we only required our answers to agree with +the published ones to 2dp.) +This is why you might want to use <tt>stab_cond_gauss_inf_engine</tt>, +implemented by Shan Huang. This is described in + +<ul> +<li> "Stable Local Computation with Conditional Gaussian Distributions", +S. Lauritzen and F. Jensen, Tech Report R-99-2014, +Dept. Math. Sciences, Allborg Univ., 1999. +</ul> + +However, even the numerically stable version +can be computationally intractable if there are many hidden discrete +nodes, because the number of mixture components grows exponentially e.g., in a +<a href="usage_dbn.html#lds">switching linear dynamical system</a>. +In general, one must resort to approximate inference techniques: see +the discussion on <a href="#engines">inference engines</a> below. + + +<h2><a name="hybrid">Other hybrid models</h2> + +When we have C->D arcs, where C is hidden, we need to use +approximate inference. +One approach (not implemented in BNT) is described in +<ul> +<li> <a +href="http://www.cs.berkeley.edu/~murphyk/Papers/hybrid_uai99.ps.gz">A +Variational Approximation for Bayesian Networks with +Discrete and Continuous Latent Variables</a>, +K. Murphy, UAI 99. +</ul> +Of course, one can always use <a href="#sampling">sampling</a> methods +for approximate inference in such models. + + + +<h1><a name="param_learning">Parameter Learning</h1> + +The parameter estimation routines in BNT can be classified into 4 +types, depending on whether the goal is to compute +a full (Bayesian) posterior over the parameters or just a point +estimate (e.g., Maximum Likelihood or Maximum A Posteriori), +and whether all the variables are fully observed or there is missing +data/ hidden variables (partial observability). +<p> + +<TABLE BORDER> +<tr> + <TH></TH> + <th>Full obs</th> + <th>Partial obs</th> +</tr> +<tr> + <th>Point</th> + <td><tt>learn_params</tt></td> + <td><tt>learn_params_em</tt></td> +</tr> +<tr> + <th>Bayes</th> + <td><tt>bayes_update_params</tt></td> + <td>not yet supported</td> +</tr> +</table> + + +<h2><a name="load_data">Loading data from a file</h2> + +To load numeric data from an ASCII text file called 'dat.txt', where each row is a +case and columns are separated by white-space, such as +<pre> +011979 1626.5 0.0 +021979 1367.0 0.0 +... +</pre> +you can use +<pre> +data = load('dat.txt'); +</pre> +or +<pre> +load dat.txt -ascii +</pre> +In the latter case, the data is stored in a variable called 'dat' (the +filename minus the extension). +Alternatively, suppose the data is stored in a .csv file (has commas +separating the columns, and contains a header line), such as +<pre> +header info goes here +ORD,011979,1626.5,0.0 +DSM,021979,1367.0,0.0 +... +</pre> +You can load this using +<pre> +[a,b,c,d] = textread('dat.txt', '%s %d %f %f', 'delimiter', ',', 'headerlines', 1); +</pre> +If your file is not in either of these formats, you can either use Perl to convert +it to this format, or use the Matlab scanf command. +Type +<tt> +help iofun +</tt> +for more information on Matlab's file functions. +<!-- +<p> +To load data directly from Excel, +you should buy the +<a href="http://www.mathworks.com/products/excellink/">Excel Link</a>. +To load data directly from a relational database, +you should buy the +<a href="http://www.mathworks.com/products/database">Database +toolbox</a>. +--> +<p> +BNT learning routines require data to be stored in a cell array. +data{i,m} is the value of node i in case (example) m, i.e., each +<em>column</em> is a case. +If node i is not observed in case m (missing value), set +data{i,m} = []. +(Not all the learning routines can cope with such missing values, however.) +In the special case that all the nodes are observed and are +scalar-valued (as opposed to vector-valued), the data can be +stored in a matrix (as opposed to a cell-array). +<p> +Suppose, as in the <a href="#mixexp">mixture of experts example</a>, +that we have 3 nodes in the graph: X(1) is the observed input, X(3) is +the observed output, and X(2) is a hidden (gating) node. We can +create the dataset as follows. +<pre> +data = load('dat.txt'); +ncases = size(data, 1); +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); +</pre> +Notice how we transposed the data, to convert rows into columns. +Also, cases{2,m} = [] for all m, since X(2) is always hidden. + + +<h2><a name="mle_complete">Maximum likelihood parameter estimation from complete data</h2> + +As an example, let's generate some data from the sprinkler network, randomize the parameters, +and then try to recover the original model. +First we create some training data using forwards sampling. +<pre> +samples = cell(N, nsamples); +for i=1:nsamples + samples(:,i) = sample_bnet(bnet); +end +</pre> +samples{j,i} contains the value of the j'th node in case i. +sample_bnet returns a cell array because, in general, each node might +be a vector of different length. +In this case, all nodes are discrete (and hence scalars), so we +could have used a regular array instead (which can be quicker): +<pre> +data = cell2num(samples); +</pre +So now data(j,i) = samples{j,i}. +<p> +Now we create a network with random parameters. +(The initial values of bnet2 don't matter in this case, since we can find the +globally optimal MLE independent of where we start.) +<pre> +% Make a tabula rasa +bnet2 = mk_bnet(dag, node_sizes); +seed = 0; +rand('state', seed); +bnet2.CPD{C} = tabular_CPD(bnet2, C); +bnet2.CPD{R} = tabular_CPD(bnet2, R); +bnet2.CPD{S} = tabular_CPD(bnet2, S); +bnet2.CPD{W} = tabular_CPD(bnet2, W); +</pre> +Finally, we find the maximum likelihood estimates of the parameters. +<pre> +bnet3 = learn_params(bnet2, samples); +</pre> +To view the learned parameters, we use a little Matlab hackery. +<pre> +CPT3 = cell(1,N); +for i=1:N + s=struct(bnet3.CPD{i}); % violate object privacy + CPT3{i}=s.CPT; +end +</pre> +Here are the parameters learned for node 4. +<pre> +dispcpt(CPT3{4}) +1 1 : 1.0000 0.0000 +2 1 : 0.2000 0.8000 +1 2 : 0.2273 0.7727 +2 2 : 0.0000 1.0000 +</pre> +So we see that the learned parameters are fairly close to the "true" +ones, which we display below. +<pre> +dispcpt(CPT{4}) +1 1 : 1.0000 0.0000 +2 1 : 0.1000 0.9000 +1 2 : 0.1000 0.9000 +2 2 : 0.0100 0.9900 +</pre> +We can get better results by using a larger training set, or using +informative priors (see <a href="#prior">below</a>). + + + +<h2><a name="prior">Parameter priors</h2> + +Currently, only tabular CPDs can have priors on their parameters. +The conjugate prior for a multinomial is the Dirichlet. +(For binary random variables, the multinomial is the same as the +Bernoulli, and the Dirichlet is the same as the Beta.) +<p> +The Dirichlet has a simple interpretation in terms of pseudo counts. +If we let N_ijk = the num. times X_i=k and Pa_i=j occurs in the +training set, where Pa_i are the parents of X_i, +then the maximum likelihood (ML) estimate is +T_ijk = N_ijk / N_ij (where N_ij = sum_k' N_ijk'), which will be 0 if N_ijk=0. +To prevent us from declaring that (X_i=k, Pa_i=j) is impossible just because this +event was not seen in the training set, +we can pretend we saw value k of X_i, for each value j of Pa_i some number (alpha_ijk) +of times in the past. +The MAP (maximum a posterior) estimate is then +<pre> +T_ijk = (N_ijk + alpha_ijk) / (N_ij + alpha_ij) +</pre> +and is never 0 if all alpha_ijk > 0. +For example, consider the network A->B, where A is binary and B has 3 +values. +A uniform prior for B has the form +<pre> + B=1 B=2 B=3 +A=1 1 1 1 +A=2 1 1 1 +</pre> +which can be created using +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); +</pre> +This prior does not satisfy the likelihood equivalence principle, +which says that <a href="#markov_equiv">Markov equivalent</a> models +should have the same marginal likelihood. +A prior that does satisfy this principle is shown below. +Heckerman (1995) calls this the +BDeu prior (likelihood equivalent uniform Bayesian Dirichlet). +<pre> + B=1 B=2 B=3 +A=1 1/6 1/6 1/6 +A=2 1/6 1/6 1/6 +</pre> +where we put N/(q*r) in each bin; N is the equivalent sample size, +r=|A|, q = |B|. +This can be created as follows +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'BDeu'); +</pre> +Here, 1 is the equivalent sample size, and is the strength of the +prior. +You can change this using +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', ... + 'BDeu', 'dirichlet_weight', 10); +</pre> +<!--where counts is an array of pseudo-counts of the same size as the +CPT.--> +<!-- +<p> +When you specify a prior, you should set row i of the CPT to the +normalized version of row i of the pseudo-count matrix, i.e., to the +expected values of the parameters. This will ensure that computing the +marginal likelihood sequentially (see <a +href="#bayes_learn">below</a>) and in batch form gives the same +results. +To do this, proceed as follows. +<pre> +tabular_CPD(bnet, i, 'prior', counts, 'CPT', mk_stochastic(counts)); +</pre> +For a non-informative prior, you can just write +<pre> +tabular_CPD(bnet, i, 'prior', 'unif', 'CPT', 'unif'); +</pre> +--> + + +<h2><a name="bayes_learn">(Sequential) Bayesian parameter updating from complete data</h2> + +If we use conjugate priors and have fully observed data, we can +compute the posterior over the parameters in batch form as follows. +<pre> +cases = sample_bnet(bnet, nsamples); +bnet = bayes_update_params(bnet, cases); +LL = log_marg_lik_complete(bnet, cases); +</pre> +bnet.CPD{i}.prior contains the new Dirichlet pseudocounts, +and bnet.CPD{i}.CPT is set to the mean of the posterior (the +normalized counts). +(Hence if the initial pseudo counts are 0, +<tt>bayes_update_params</tt> and <tt>learn_params</tt> will give the +same result.) + + + + +<p> +We can compute the same result sequentially (on-line) as follows. +<pre> +LL = 0; +for m=1:nsamples + LL = LL + log_marg_lik_complete(bnet, cases(:,m)); + bnet = bayes_update_params(bnet, cases(:,m)); +end +</pre> + +The file <tt>BNT/examples/static/StructLearn/model_select1</tt> has an example of +sequential model selection which uses the same idea. +We generate data from the model A->B +and compute the posterior prob of all 3 dags on 2 nodes: + (1) A B, (2) A <- B , (3) A -> B +Models 2 and 3 are <a href="#markov_equiv">Markov equivalent</a>, and therefore indistinguishable from +observational data alone, so we expect their posteriors to be the same +(assuming a prior which satisfies likelihood equivalence). +If we use random parameters, the "true" model only gets a higher posterior after 2000 trials! +However, if we make B a noisy NOT gate, the true model "wins" after 12 +trials, as shown below (red = model 1, blue/green (superimposed) +represents models 2/3). +<p> +<img src="Figures/model_select.png"> +<p> +The use of marginal likelihood for model selection is discussed in +greater detail in the +section on <a href="structure_learning">structure learning</a>. + + + + +<h2><a name="em">Maximum likelihood parameter estimation with missing values</h2> + +Now we consider learning when some values are not observed. +Let us randomly hide half the values generated from the water +sprinkler example. +<pre> +samples2 = samples; +hide = rand(N, nsamples) > 0.5; +[I,J]=find(hide); +for k=1:length(I) + samples2{I(k), J(k)} = []; +end +</pre> +samples2{i,l} is the value of node i in training case l, or [] if unobserved. +<p> +Now we will compute the MLEs using the EM algorithm. +We need to use an inference algorithm to compute the expected +sufficient statistics in the E step; the M (maximization) step is as +above. +<pre> +engine2 = jtree_inf_engine(bnet2); +max_iter = 10; +[bnet4, LLtrace] = learn_params_em(engine2, samples2, max_iter); +</pre> +LLtrace(i) is the log-likelihood at iteration i. We can plot this as +follows: +<pre> +plot(LLtrace, 'x-') +</pre> +Let's display the results after 10 iterations of EM. +<pre> +celldisp(CPT4) +CPT4{1} = + 0.6616 + 0.3384 +CPT4{2} = + 0.6510 0.3490 + 0.8751 0.1249 +CPT4{3} = + 0.8366 0.1634 + 0.0197 0.9803 +CPT4{4} = +(:,:,1) = + 0.8276 0.0546 + 0.5452 0.1658 +(:,:,2) = + 0.1724 0.9454 + 0.4548 0.8342 +</pre> +We can get improved performance by using one or more of the following +methods: +<ul> +<li> Increasing the size of the training set. +<li> Decreasing the amount of hidden data. +<li> Running EM for longer. +<li> Using informative priors. +<li> Initialising EM from multiple starting points. +</ul> + +Click <a href="#gaussian">here</a> for a discussion of learning +Gaussians, which can cause numerical problems. + +<h2><a name="tying">Parameter tying</h2> + +In networks with repeated structure (e.g., chains and grids), it is +common to assume that the parameters are the same at every node. This +is called parameter tying, and reduces the amount of data needed for +learning. +<p> +When we have tied parameters, there is no longer a one-to-one +correspondence between nodes and CPDs. +Rather, each CPD species the parameters for a whole equivalence class +of nodes. +It is easiest to see this by example. +Consider the following <a href="usage_dbn.html#hmm">hidden Markov +model (HMM)</a> +<p> +<img src="Figures/hmm3.gif"> +<p> +<!-- +We can create this graph structure, assuming we have T time-slices, +as follows. +(We number the nodes as shown in the figure, but we could equally well +number the hidden nodes 1:T, and the observed nodes T+1:2T.) +<pre> +N = 2*T; +dag = zeros(N); +hnodes = 1:2:2*T; +for i=1:T-1 + dag(hnodes(i), hnodes(i+1))=1; +end +onodes = 2:2:2*T; +for i=1:T + dag(hnodes(i), onodes(i)) = 1; +end +</pre> +<p> +The hidden nodes are always discrete, and have Q possible values each, +but the observed nodes can be discrete or continuous, and have O possible values/length. +<pre> +if cts_obs + dnodes = hnodes; +else + dnodes = 1:N; +end +ns = ones(1,N); +ns(hnodes) = Q; +ns(onodes) = O; +</pre> +--> +When HMMs are used for semi-infinite processes like speech recognition, +we assume the transition matrix +P(H(t+1)|H(t)) is the same for all t; this is called a time-invariant +or homogenous Markov chain. +Hence hidden nodes 2, 3, ..., T +are all in the same equivalence class, say class Hclass. +Similarly, the observation matrix P(O(t)|H(t)) is assumed to be the +same for all t, so the observed nodes are all in the same equivalence +class, say class Oclass. +Finally, the prior term P(H(1)) is in a class all by itself, say class +H1class. +This is illustrated below, where we explicitly represent the +parameters as random variables (dotted nodes). +<p> +<img src="Figures/hmm4_params.gif"> +<p> +In BNT, we cannot represent parameters as random variables (nodes). +Instead, we "hide" the +parameters inside one CPD for each equivalence class, +and then specify that the other CPDs should share these parameters, as +follows. +<pre> +hnodes = 1:2:2*T; +onodes = 2:2:2*T; +H1class = 1; Hclass = 2; Oclass = 3; +eclass = ones(1,N); +eclass(hnodes(2:end)) = Hclass; +eclass(hnodes(1)) = H1class; +eclass(onodes) = Oclass; +% create dag and ns in the usual way +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'equiv_class', eclass); +</pre> +Finally, we define the parameters for each equivalence class: +<pre> +bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior +bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix +if cts_obs + bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1)); +else + bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1)); +end +</pre> +In general, if bnet.CPD{e} = xxx_CPD(bnet, j), then j should be a +member of e's equivalence class; that is, it is not always the case +that e == j. You can use bnet.rep_of_eclass(e) to return the +representative of equivalence class e. +BNT will look up the parents of j to determine the size +of the CPT to use. It assumes that this is the same for all members of +the equivalence class. +Click <a href="http://www.cs.berkeley.edu/~murphyk/Bayes/param_tieing.html">here</a> for +a more complex example of parameter tying. +<p> +Note: +Normally one would define an HMM as a +<a href = "usage_dbn.html">Dynamic Bayes Net</a> +(see the function BNT/examples/dynamic/mk_chmm.m). +However, one can define an HMM as a static BN using the function +BNT/examples/static/Models/mk_hmm_bnet.m. + + + +<h1><a name="structure_learning">Structure learning</h1> + +Update (9/29/03): +Phillipe LeRay is developing some additional structure learning code +on top of BNT. Click <a +href="http://bnt.insa-rouen.fr/ajouts.html">here</a> +for details. + +<p> + +There are two very different approaches to structure learning: +constraint-based and search-and-score. +In the <a href="#constraint">constraint-based approach</a>, +we start with a fully connected graph, and remove edges if certain +conditional independencies are measured in the data. +This has the disadvantage that repeated independence tests lose +statistical power. +<p> +In the more popular search-and-score approach, +we perform a search through the space of possible DAGs, and either +return the best one found (a point estimate), or return a sample of the +models found (an approximation to the Bayesian posterior). +<p> +Unfortunately, the number of DAGs as a function of the number of +nodes, G(n), is super-exponential in n. +A closed form formula for G(n) is not known, but the first few values +are shown below (from Cooper, 1999). + +<table> +<tr> <th>n</th> <th align=left>G(n)</th> </tr> +<tr> <td>1</td> <td>1</td> </tr> +<tr> <td>2</td> <td>3</td> </tr> +<tr> <td>3</td> <td>25</td> </tr> +<tr> <td>4</td> <td>543</td> </tr> +<tr> <td>5</td> <td>29,281</td> </tr> +<tr> <td>6</td> <td>3,781,503</td> </tr> +<tr> <td>7</td> <td>1.1 x 10^9</td> </tr> +<tr> <td>8</td> <td>7.8 x 10^11</td> </tr> +<tr> <td>9</td> <td>1.2 x 10^15</td> </tr> +<tr> <td>10</td> <td>4.2 x 10^18</td> </tr> +</table> + +Since the number of DAGs is super-exponential in the number of nodes, +we cannot exhaustively search the space, so we either use a local +search algorithm (e.g., greedy hill climbining, perhaps with multiple +restarts) or a global search algorithm (e.g., Markov Chain Monte +Carlo). +<p> +If we know a total ordering on the nodes, +finding the best structure amounts to picking the best set of parents +for each node independently. +This is what the K2 algorithm does. +If the ordering is unknown, we can search over orderings, +which is more efficient than searching over DAGs (Koller and Friedman, 2000). +<p> +In addition to the search procedure, we must specify the scoring +function. There are two popular choices. The Bayesian score integrates +out the parameters, i.e., it is the marginal likelihood of the model. +The BIC (Bayesian Information Criterion) is defined as +log P(D|theta_hat) - 0.5*d*log(N), where D is the data, theta_hat is +the ML estimate of the parameters, d is the number of parameters, and +N is the number of data cases. +The BIC method has the advantage of not requiring a prior. +<p> +BIC can be derived as a large sample +approximation to the marginal likelihood. +(It is also equal to the Minimum Description Length of a model.) +However, in practice, the sample size does not need to be very large +for the approximation to be good. +For example, in the figure below, we plot the ratio between the log marginal likelihood +and the BIC score against data-set size; we see that the ratio rapidly +approaches 1, especially for non-informative priors. +(This plot was generated by the file BNT/examples/static/bic1.m. It +uses the water sprinkler BN with BDeu Dirichlet priors with different +equivalent sample sizes.) + +<p> +<center> +<IMG SRC="Figures/bic.png"> +</center> +<p> + +<p> +As with parameter learning, handling missing data/ hidden variables is +much harder than the fully observed case. +The structure learning routines in BNT can therefore be classified into 4 +types, analogously to the parameter learning case. +<p> + +<TABLE BORDER> +<tr> + <TH></TH> + <th>Full obs</th> + <th>Partial obs</th> +</tr> +<tr> + <th>Point</th> + <td><tt>learn_struct_K2</tt> <br> +<!-- <tt>learn_struct_hill_climb</tt></td> --> + <td><tt>not yet supported</tt></td> +</tr> +<tr> + <th>Bayes</th> + <td><tt>learn_struct_mcmc</tt></td> + <td>not yet supported</td> +</tr> +</table> + + +<h2><a name="markov_equiv">Markov equivalence</h2> + +If two DAGs encode the same conditional independencies, they are +called Markov equivalent. The set of all DAGs can be paritioned into +Markov equivalence classes. Graphs within the same class can +have +the direction of some of their arcs reversed without changing any of +the CI relationships. +Each class can be represented by a PDAG +(partially directed acyclic graph) called an essential graph or +pattern. This specifies which edges must be oriented in a certain +direction, and which may be reversed. + +<p> +When learning graph structure from observational data, +the best one can hope to do is to identify the model up to Markov +equivalence. To distinguish amongst graphs within the same equivalence +class, one needs interventional data: see the discussion on <a +href="#active">active learning</a> below. + + + +<h2><a name="enumerate">Exhaustive search</h2> + +The brute-force approach to structure learning is to enumerate all +possible DAGs, and score each one. This provides a "gold standard" +with which to compare other algorithms. We can do this as follows. +<pre> +dags = mk_all_dags(N); +score = score_dags(data, ns, dags); +</pre> +where data(i,m) is the value of node i in case m, +and ns(i) is the size of node i. +If the DAGs have a lot of families in common, we can cache the sufficient statistics, +making this potentially more efficient than scoring the DAGs one at a time. +(Caching is not currently implemented, however.) +<p> +By default, we use the Bayesian scoring metric, and assume CPDs are +represented by tables with BDeu(1) priors. +We can override these defaults as follows. +If we want to use uniform priors, we can say +<pre> +params = cell(1,N); +for i=1:N + params{i} = {'prior', 'unif'}; +end +score = score_dags(data, ns, dags, 'params', params); +</pre> +params{i} is a cell-array, containing optional arguments that are +passed to the constructor for CPD i. +<p> +Now suppose we want to use different node types, e.g., +Suppose nodes 1 and 2 are Gaussian, and nodes 3 and 4 softmax (both +these CPDs can support discrete and continuous parents, which is +necessary since all other nodes will be considered as parents). +The Bayesian scoring metric currently only works for tabular CPDs, so +we will use BIC: +<pre> +score = score_dags(data, ns, dags, 'discrete', [3 4], 'params', [], + 'type', {'gaussian', 'gaussian', 'softmax', softmax'}, 'scoring_fn', 'bic') +</pre> +In practice, one can't enumerate all possible DAGs for N > 5, +but one can evaluate any reasonably-sized set of hypotheses in this +way (e.g., nearest neighbors of your current best guess). +Think of this as "computer assisted model refinement" as opposed to de +novo learning. + + +<h2><a name="K2">K2</h2> + +The K2 algorithm (Cooper and Herskovits, 1992) is a greedy search algorithm that works as follows. +Initially each node has no parents. It then adds incrementally that parent whose addition most +increases the score of the resulting structure. When the addition of no single +parent can increase the score, it stops adding parents to the node. +Since we are using a fixed ordering, we do not need to check for +cycles, and can choose the parents for each node independently. +<p> +The original paper used the Bayesian scoring +metric with tabular CPDs and Dirichlet priors. +BNT generalizes this to allow any kind of CPD, and either the Bayesian +scoring metric or BIC, as in the example <a href="#enumerate">above</a>. +In addition, you can specify +an optional upper bound on the number of parents for each node. +The file BNT/examples/static/k2demo1.m gives an example of how to use K2. +We use the water sprinkler network and sample 100 cases from it as before. +Then we see how much data it takes to recover the generating structure: +<pre> +order = [C S R W]; +max_fan_in = 2; +sz = 5:5:100; +for i=1:length(sz) + dag2 = learn_struct_K2(data(:,1:sz(i)), node_sizes, order, 'max_fan_in', max_fan_in); + correct(i) = isequal(dag, dag2); +end +</pre> +Here are the results. +<pre> +correct = + Columns 1 through 12 + 0 0 0 0 0 0 0 1 0 1 1 1 + Columns 13 through 20 + 1 1 1 1 1 1 1 1 +</pre> +So we see it takes about sz(10)=50 cases. (BIC behaves similarly, +showing that the prior doesn't matter too much.) +In general, we cannot hope to recover the "true" generating structure, +only one that is in its <a href="#markov_equiv">Markov equivalence +class</a>. + + +<h2><a name="hill_climb">Hill-climbing</h2> + +Hill-climbing starts at a specific point in space, +considers all nearest neighbors, and moves to the neighbor +that has the highest score; if no neighbors have higher +score than the current point (i.e., we have reached a local maximum), +the algorithm stops. One can then restart in another part of the space. +<p> +A common definition of "neighbor" is all graphs that can be +generated from the current graph by adding, deleting or reversing a +single arc, subject to the acyclicity constraint. +Other neighborhoods are possible: see +<a href="http://research.microsoft.com/~dmax/publications/jmlr02.pdf"> +Optimal Structure Identification with Greedy Search</a>, Max +Chickering, JMLR 2002. + +<!-- +Note: This algorithm is currently (Feb '02) being implemented by Qian +Diao. +--> + + +<h2><a name="mcmc">MCMC</h2> + +We can use a Markov Chain Monte Carlo (MCMC) algorithm called +Metropolis-Hastings (MH) to search the space of all +DAGs. +The standard proposal distribution is to consider moving to all +nearest neighbors in the sense defined <a href="#hill_climb">above</a>. +<p> +The function can be called +as in the following example. +<pre> +[sampled_graphs, accept_ratio] = learn_struct_mcmc(data, ns, 'nsamples', 100, 'burnin', 10); +</pre> +We can convert our set of sampled graphs to a histogram +(empirical posterior over all the DAGs) thus +<pre> +all_dags = mk_all_dags(N); +mcmc_post = mcmc_sample_to_hist(sampled_graphs, all_dags); +</pre> +To see how well this performs, let us compute the exact posterior exhaustively. +<p> +<pre> +score = score_dags(data, ns, all_dags); +post = normalise(exp(score)); % assuming uniform structural prior +</pre> +We plot the results below. +(The data set was 100 samples drawn from a random 4 node bnet; see the +file BNT/examples/static/mcmc1.) +<pre> +subplot(2,1,1) +bar(post) +subplot(2,1,2) +bar(mcmc_post) +</pre> +<img src="Figures/mcmc_post.jpg" width="800" height="500"> +<p> +We can also plot the acceptance ratio versus number of MCMC steps, +as a crude convergence diagnostic. +<pre> +clf +plot(accept_ratio) +</pre> +<img src="Figures/mcmc_accept.jpg" width="800" height="300"> +<p> +Even though the number of samples needed by MCMC is theoretically +polynomial (not exponential) in the dimensionality of the search space, in practice it has been +found that MCMC does not converge in reasonable time for graphs with +more than about 10 nodes. + + + + +<h2><a name="active">Active structure learning</h2> + +As was mentioned <a href="#markov_equiv">above</a>, +one can only learn a DAG up to Markov equivalence, even given infinite data. +If one is interested in learning the structure of a causal network, +one needs interventional data. +(By "intervention" we mean forcing a node to take on a specific value, +thereby effectively severing its incoming arcs.) +<p> +Most of the scoring functions accept an optional argument +that specifies whether a node was observed to have a certain value, or +was forced to have that value: we set clamped(i,m)=1 if node i was +forced in training case m. e.g., see the file +BNT/examples/static/cooper_yoo. +<p> +An interesting question is to decide which interventions to perform +(c.f., design of experiments). For details, see the following tech +report +<ul> +<li> <a href = "../../Papers/alearn.ps.gz"> +Active learning of causal Bayes net structure</a>, Kevin Murphy, March +2001. +</ul> + + +<h2><a name="struct_em">Structural EM</h2> + +Computing the Bayesian score when there is partial observability is +computationally challenging, because the parameter posterior becomes +multimodal (the hidden nodes induce a mixture distribution). +One therefore needs to use approximations such as BIC. +Unfortunately, search algorithms are still expensive, because we need +to run EM at each step to compute the MLE, which is needed to compute +the score of each model. An alternative approach is +to do the local search steps inside of the M step of EM, which is more +efficient since the data has been "filled in" - this is +called the structural EM algorithm (Friedman 1997), and provably +converges to a local maximum of the BIC score. +<p> +Wei Hu has implemented SEM for discrete nodes. +You can download his package from +<a href="../SEM.zip">here</a>. +Please address all questions about this code to +wei.hu@intel.com. +See also <a href="#phl">Phl's implementation of SEM</a>. + +<!-- +<h2><a name="reveal">REVEAL algorithm</h2> + +A simple way to learn the structure of a fully observed, discrete, +factored DBN from a time series is described <a +href="usage_dbn.html#struct_learn">here</a>. +--> + + +<h2><a name="graphdraw">Visualizing the graph</h2> + +You can visualize an arbitrary graph (such as one learned using the +structure learning routines) with Matlab code contributed by +<a href="http://www.mbfys.kun.nl/~cemgil/matlab/layout.html">Ali +Taylan Cemgil</a> +from the University of Nijmegen. +For static BNs, call it as follows: +<pre> +draw_graph(bnet.dag); +</pre> +For example, this is the output produced on a +<a href="#qmr">random QMR-like model</a>: +<p> +<img src="Figures/qmr.rnd.jpg"> +<p> +If you install the excellent <a +href="http://www.research.att.com/sw/tools/graphviz">graphhviz</a>, an +open-source graph visualization package from AT&T, +you can create a much better visualization as follows +<pre> +graph_to_dot(bnet.dag) +</pre> +This works by converting the adjacency matrix to a file suitable +for input to graphviz (using the dot format), +then converting the output of graphviz to postscript, and displaying the results using +ghostview. +You can do each of these steps separately for more control, as shown +below. +<pre> +graph_to_dot(bnet.dag, 'filename', 'foo.dot'); +dot -Tps foo.dot -o foo.ps +ghostview foo.ps & +</pre> + +<h2><a name = "constraint">Constraint-based methods</h2> + +The IC algorithm (Pearl and Verma, 1991), +and the faster, but otherwise equivalent, PC algorithm (Spirtes, Glymour, and Scheines 1993), +computes many conditional independence tests, +and combines these constraints into a +PDAG to represent the whole +<a href="#markov_equiv">Markov equivalence class</a>. +<p> +IC*/FCI extend IC/PC to handle latent variables: see <a href="#ic_star">below</a>. +(IC stands for inductive causation; PC stands for Peter and Clark, +the first names of Spirtes and Glymour; FCI stands for fast causal +inference. +What we, following Pearl (2000), call IC* was called +IC in the original Pearl and Verma paper.) +For details, see +<ul> +<li> +<a href="http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html">Causation, +Prediction, and Search</a>, Spirtes, Glymour and +Scheines (SGS), 2001 (2nd edition), MIT Press. +<li> +<a href="http://bayes.cs.ucla.edu/BOOK-2K/index.html">Causality: Models, Reasoning and Inference</a>, J. Pearl, +2000, Cambridge University Press. +</ul> + +<p> + +The PC algorithm takes as arguments a function f, the number of nodes N, +the maximum fan in K, and additional arguments A which are passed to f. +The function f(X,Y,S,A) returns 1 if X is conditionally independent of Y given S, and 0 +otherwise. +For example, suppose we cheat by +passing in a CI "oracle" which has access to the true DAG; the oracle +tests for d-separation in this DAG, i.e., +f(X,Y,S) calls dsep(X,Y,S,dag). We can to this as follows. +<pre> +pdag = learn_struct_pdag_pc('dsep', N, max_fan_in, dag); +</pre> +pdag(i,j) = -1 if there is definitely an i->j arc, +and pdag(i,j) = 1 if there is either an i->j or and i<-j arc. +<p> +Applied to the sprinkler network, this returns +<pre> +pdag = + 0 1 1 0 + 1 0 0 -1 + 1 0 0 -1 + 0 0 0 0 +</pre> +So as expected, we see that the V-structure at the W node is uniquely identified, +but the other arcs have ambiguous orientation. +<p> +We now give an example from p141 (1st edn) / p103 (2nd end) of the SGS +book. +This example concerns the female orgasm. +We are given a correlation matrix C between 7 measured factors (such +as subjective experiences of coital and masturbatory experiences), +derived from 281 samples, and want to learn a causal model of the +data. We will not discuss the merits of this type of work here, but +merely show how to reproduce the results in the SGS book. +Their program, +<a href="http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html">Tetrad</a>, +makes use of the Fisher Z-test for conditional +independence, so we do the same: +<pre> +max_fan_in = 4; +nsamples = 281; +alpha = 0.05; +pdag = learn_struct_pdag_pc('cond_indep_fisher_z', n, max_fan_in, C, nsamples, alpha); +</pre> +In this case, the CI test is +<pre> +f(X,Y,S) = cond_indep_fisher_z(X,Y,S, C,nsamples,alpha) +</pre> +The results match those of Fig 12a of SGS apart from two edge +differences; presumably this is due to rounding error (although it +could be a bug, either in BNT or in Tetrad). +This example can be found in the file BNT/examples/static/pc2.m. + +<p> + +The IC* algorithm (Pearl and Verma, 1991), +and the faster FCI algorithm (Spirtes, Glymour, and Scheines 1993), +are like the IC/PC algorithm, except that they can detect the presence +of latent variables. +See the file <tt>learn_struct_pdag_ic_star</tt> written by Tamar +Kushnir. The output is a matrix P, defined as follows +(see Pearl (2000), p52 for details): +<pre> +% P(i,j) = -1 if there is either a latent variable L such that i <-L->j OR there is a directed edge from i->j. +% P(i,j) = -2 if there is a marked directed i-*>j edge. +% P(i,j) = P(j,i) = 1 if there is and undirected edge i--j +% P(i,j) = P(j,i) = 2 if there is a latent variable L such that i<-L->j. +</pre> + + +<h2><a name="phl">Philippe Leray's structure learning package</h2> + +Philippe Leray has written a +<a href="http://bnt.insa-rouen.fr/ajouts.html"> +structure learning package</a> that uses BNT. + +It currently (Juen 2003) has the following features: +<ul> +<li>PC with Chi2 statistical test +<li> MWST : Maximum weighted Spanning Tree +<li> Hill Climbing +<li> Greedy Search +<li> Structural EM +<li> hist_ic : optimal Histogram based on IC information criterion +<li> cpdag_to_dag +<li> dag_to_cpdag +<li> ... +</ul> + + +</a> + + +<!-- +<h2><a name="read_learning">Further reading on learning</h2> + +I recommend the following tutorials for more details on learning. +<ul> +<li> <a +href="http://www.cs.berkeley.edu/~murphyk/Papers/intel.ps.gz">My short +tutorial</a> on graphical models, which contains an overview of learning. + +<li> +<A HREF="ftp://ftp.research.microsoft.com/pub/tr/TR-95-06.PS"> +A tutorial on learning with Bayesian networks</a>, D. Heckerman, +Microsoft Research Tech Report, 1995. + +<li> <A HREF="http://www-cad.eecs.berkeley.edu/~wray/Mirror/lwgmja"> +Operations for Learning with Graphical Models</a>, +W. L. Buntine, JAIR'94, 159--225. +</ul> +<p> +--> + + + + + +<h1><a name="engines">Inference engines</h1> + +Up until now, we have used the junction tree algorithm for inference. +However, sometimes this is too slow, or not even applicable. +In general, there are many inference algorithms each of which make +different tradeoffs between speed, accuracy, complexity and +generality. Furthermore, there might be many implementations of the +same algorithm; for instance, a general purpose, readable version, +and a highly-optimized, specialized one. +To cope with this variety, we treat each inference algorithm as an +object, which we call an inference engine. + +<p> +An inference engine is an object that contains a bnet and supports the +'enter_evidence' and 'marginal_nodes' methods. The engine constructor +takes the bnet as argument and may do some model-specific processing. +When 'enter_evidence' is called, the engine may do some +evidence-specific processing. Finally, when 'marginal_nodes' is +called, the engine may do some query-specific processing. + +<p> +The amount of work done when each stage is specified -- structure, +parameters, evidence, and query -- depends on the engine. The cost of +work done early in this sequence can be amortized. On the other hand, +one can make better optimizations if one waits until later in the +sequence. +For example, the parameters might imply +conditional indpendencies that are not evident in the graph structure, +but can nevertheless be exploited; the evidence indicates which nodes +are observed and hence can effectively be disconnected from the +graph; and the query might indicate that large parts of the network +are d-separated from the query nodes. (Since it is not the actual +<em>values</em> of the evidence that matters, just which nodes are observed, +many engines allow you to specify which nodes will be observed when they are constructed, +i.e., before calling 'enter_evidence'. Some engines can still cope if +the actual pattern of evidence is different, e.g., if there is missing +data.) +<p> + +Although being maximally lazy (i.e., only doing work when a query is +issued) may seem desirable, +this is not always the most efficient. +For example, +when learning using EM, we need to call marginal_nodes N times, where N is the +number of nodes. <a href="varelim">Variable elimination</a> would end +up repeating a lot of work +each time marginal_nodes is called, making it inefficient for +learning. The junction tree algorithm, by contrast, uses dynamic +programming to avoid this redundant computation --- it calculates all +marginals in two passes during 'enter_evidence', so calling +'marginal_nodes' takes constant time. +<p> +We will discuss some of the inference algorithms implemented in BNT +below, and finish with a <a href="#engine_summary">summary</a> of all +of them. + + + + + + + +<h2><a name="varelim">Variable elimination</h2> + +The variable elimination algorithm, also known as bucket elimination +or peeling, is one of the simplest inference algorithms. +The basic idea is to "push sums inside of products"; this is explained +in more detail +<a +href="http://HTTP.CS.Berkeley.EDU/~murphyk/Bayes/bayes.html#infer">here</a>. +<p> +The principle of distributing sums over products can be generalized +greatly to apply to any commutative semiring. +This forms the basis of many common algorithms, such as Viterbi +decoding and the Fast Fourier Transform. For details, see + +<ul> +<li> R. McEliece and S. M. Aji, 2000. +<!--<a href="http://www.systems.caltech.edu/EE/Faculty/rjm/papers/GDL.ps">--> +<a href="GDL.pdf"> +The Generalized Distributive Law</a>, +IEEE Trans. Inform. Theory, vol. 46, no. 2 (March 2000), +pp. 325--343. + + +<li> +F. R. Kschischang, B. J. Frey and H.-A. Loeliger, 2001. +<a href="http://www.cs.toronto.edu/~frey/papers/fgspa.abs.html"> +Factor graphs and the sum-product algorithm</a> +IEEE Transactions on Information Theory, February, 2001. + +</ul> + +<p> +Choosing an order in which to sum out the variables so as to minimize +computational cost is known to be NP-hard. +The implementation of this algorithm in +<tt>var_elim_inf_engine</tt> makes no attempt to optimize this +ordering (in contrast, say, to <tt>jtree_inf_engine</tt>, which uses a +greedy search procedure to find a good ordering). +<p> +Note: unlike most algorithms, var_elim does all its computational work +inside of <tt>marginal_nodes</tt>, not inside of +<tt>enter_evidence</tt>. + + + + +<h2><a name="global">Global inference methods</h2> + +The simplest inference algorithm of all is to explicitely construct +the joint distribution over all the nodes, and then to marginalize it. +This is implemented in <tt>global_joint_inf_engine</tt>. +Since the size of the joint is exponential in the +number of discrete (hidden) nodes, this is not a very practical algorithm. +It is included merely for pedagogical and debugging purposes. +<p> +Three specialized versions of this algorithm have also been implemented, +corresponding to the cases where all the nodes are discrete (D), all +are Gaussian (G), and some are discrete and some Gaussian (CG). +They are called <tt>enumerative_inf_engine</tt>, +<tt>gaussian_inf_engine</tt>, +and <tt>cond_gauss_inf_engine</tt> respectively. +<p> +Note: unlike most algorithms, these global inference algorithms do all their computational work +inside of <tt>marginal_nodes</tt>, not inside of +<tt>enter_evidence</tt>. + + +<h2><a name="quickscore">Quickscore</h2> + +The junction tree algorithm is quite slow on the <a href="#qmr">QMR</a> network, +since the cliques are so big. +One simple trick we can use is to notice that hidden leaves do not +affect the posteriors on the roots, and hence do not need to be +included in the network. +A second trick is to notice that the negative findings can be +"absorbed" into the prior: +see the file +BNT/examples/static/mk_minimal_qmr_bnet for details. +<p> + +A much more significant speedup is obtained by exploiting special +properties of the noisy-or node, as done by the quickscore +algorithm. For details, see +<ul> +<li> Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI 89. +<li> Rish and Dechter, "On the impact of causal independence", UCI +tech report, 1998. +</ul> + +This has been implemented in BNT as a special-purpose inference +engine, which can be created and used as follows: +<pre> +engine = quickscore_inf_engine(inhibit, leak, prior); +engine = enter_evidence(engine, pos, neg); +m = marginal_nodes(engine, i); +</pre> + + +<h2><a name="belprop">Belief propagation</h2> + +Even using quickscore, exact inference takes time that is exponential +in the number of positive findings. +Hence for large networks we need to resort to approximate inference techniques. +See for example +<ul> +<li> T. Jaakkola and M. Jordan, "Variational probabilistic inference and the +QMR-DT network", JAIR 10, 1999. + +<li> K. Murphy, Y. Weiss and M. Jordan, "Loopy belief propagation for approximate inference: an empirical study", + UAI 99. +</ul> +The latter approximation +entails applying Pearl's belief propagation algorithm to a model even +if it has loops (hence the name loopy belief propagation). +Pearl's algorithm, implemented as <tt>pearl_inf_engine</tt>, gives +exact results when applied to singly-connected graphs +(a.k.a. polytrees, since +the underlying undirected topology is a tree, but a node may have +multiple parents). +To apply this algorithm to a graph with loops, +use <tt>pearl_inf_engine</tt>. +This can use a centralized or distributed message passing protocol. +You can use it as in the following example. +<pre> +engine = pearl_inf_engine(bnet, 'max_iter', 30); +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, i); +</pre> +We found that this algorithm often converges, and when it does, often +is very accurate, but it depends on the precise setting of the +parameter values of the network. +(See the file BNT/examples/static/qmr1 to repeat the experiment for yourself.) +Understanding when and why belief propagation converges/ works +is a topic of ongoing research. +<p> +<tt>pearl_inf_engine</tt> can exploit special structure in noisy-or +and gmux nodes to compute messages efficiently. +<p> +<tt>belprop_inf_engine</tt> is like pearl, but uses potentials to +represent messages. Hence this is slower. +<p> +<tt>belprop_fg_inf_engine</tt> is like belprop, +but is designed for factor graphs. + + + +<h2><a name="sampling">Sampling</h2> + +BNT now (Mar '02) has two sampling (Monte Carlo) inference algorithms: +<ul> +<li> <tt>likelihood_weighting_inf_engine</tt> which does importance +sampling and can handle any node type. +<li> <tt>gibbs_sampling_inf_engine</tt>, written by Bhaskara Marthi. +Currently this can only handle tabular CPDs. +For a much faster and more powerful Gibbs sampling program, see +<a href="http://www.mrc-bsu.cam.ac.uk/bugs">BUGS</a>. +</ul> +Note: To generate samples from a network (which is not the same as inference!), +use <tt>sample_bnet</tt>. + + + +<h2><a name="engine_summary">Summary of inference engines</h2> + + +The inference engines differ in many ways. Here are +some of the major "axes": +<ul> +<li> Works for all topologies or makes restrictions? +<li> Works for all node types or makes restrictions? +<li> Exact or approximate inference? +</ul> + +<p> +In terms of topology, most engines handle any kind of DAG. +<tt>belprop_fg</tt> does approximate inference on factor graphs (FG), which +can be used to represent directed, undirected, and mixed (chain) +graphs. +(In the future, we plan to support exact inference on chain graphs.) +<tt>quickscore</tt> only works on QMR-like models. +<p> +In terms of node types: algorithms that use potentials can handle +discrete (D), Gaussian (G) or conditional Gaussian (CG) models. +Sampling algorithms can essentially handle any kind of node (distribution). +Other algorithms make more restrictive assumptions in exchange for +speed. +<p> +Finally, most algorithms are designed to give the exact answer. +The belief propagation algorithms are exact if applied to trees, and +in some other cases. +Sampling is considered approximate, even though, in the limit of an +infinite number of samples, it gives the exact answer. + +<p> + +Here is a summary of the properties +of all the engines in BNT which work on static networks. +<p> +<table> +<table border units = pixels><tr> +<td align=left width=0>Name +<td align=left width=0>Exact? +<td align=left width=0>Node type? +<td align=left width=0>topology +<tr> +<tr> +<td align=left> belprop +<td align=left> approx +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> belprop_fg +<td align=left> approx +<td align=left> D +<td align=left> factor graph +<tr> +<td align=left> cond_gauss +<td align=left> exact +<td align=left> CG +<td align=left> DAG +<tr> +<td align=left> enumerative +<td align=left> exact +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> gaussian +<td align=left> exact +<td align=left> G +<td align=left> DAG +<tr> +<td align=left> gibbs +<td align=left> approx +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> global_joint +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +<tr> +<td align=left> jtree +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +b<tr> +<td align=left> likelihood_weighting +<td align=left> approx +<td align=left> any +<td align=left> DAG +<tr> +<td align=left> pearl +<td align=left> approx +<td align=left> D,G +<td align=left> DAG +<tr> +<td align=left> pearl +<td align=left> exact +<td align=left> D,G +<td align=left> polytree +<tr> +<td align=left> quickscore +<td align=left> exact +<td align=left> noisy-or +<td align=left> QMR +<tr> +<td align=left> stab_cond_gauss +<td align=left> exact +<td align=left> CG +<td align=left> DAG +<tr> +<td align=left> var_elim +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +</table> + + + +<h1><a name="influence">Influence diagrams/ decision making</h1> + +BNT implements an exact algorithm for solving LIMIDs (limited memory +influence diagrams), described in +<ul> +<li> S. L. Lauritzen and D. Nilsson. +<a href="http://www.math.auc.dk/~steffen/papers/limids.pdf"> +Representing and solving decision problems with limited +information</a> +Management Science, 47, 1238 - 1251. September 2001. +</ul> +LIMIDs explicitely show all information arcs, rather than implicitely +assuming no forgetting. This allows them to model forgetful +controllers. +<p> +See the examples in <tt>BNT/examples/limids</tt> for details. + + + + +<h1>DBNs, HMMs, Kalman filters and all that</h1> + +Click <a href="usage_dbn.html">here</a> for documentation about how to +use BNT for dynamical systems and sequence data. + + +</BODY>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/usage_cropped.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3232 @@ +<HEAD> +<TITLE>How to use the Bayes Net Toolbox</TITLE> +</HEAD> + +<BODY BGCOLOR="#FFFFFF"> +<!-- white background is better for the pictures and equations --> + +<h1>How to use the Bayes Net Toolbox</h1> + +This documentation was last updated on 13 November 2002. +<br> +Click <a href="changelog.html">here</a> for a list of changes made to +BNT. +<br> +Click +<a href="http://bnt.insa-rouen.fr/">here</a> +for a French version of this documentation (which might not +be up-to-date). + + +<p> + +<ul> +<li> <a href="#install">Installation</a> +<ul> +<li> <a href="#installM">Installing the Matlab code</a> +<li> <a href="#installC">Installing the C code</a> +<li> <a href="../matlab_tips.html">Useful Matlab tips</a>. +</ul> + +<li> <a href="#basics">Creating your first Bayes net</a> + <ul> + <li> <a href="#basics">Creating a model by hand</a> + <li> <a href="#file">Loading a model from a file</a> + <li> <a href="http://bnt.insa-rouen.fr/ajouts.html">Creating a model using a GUI</a> + </ul> + +<li> <a href="#inference">Inference</a> + <ul> + <li> <a href="#marginal">Computing marginal distributions</a> + <li> <a href="#joint">Computing joint distributions</a> + <li> <a href="#soft">Soft/virtual evidence</a> + <li> <a href="#mpe">Most probable explanation</a> + </ul> + +<li> <a href="#cpd">Conditional Probability Distributions</a> + <ul> + <li> <a href="#tabular">Tabular (multinomial) nodes</a> + <li> <a href="#noisyor">Noisy-or nodes</a> + <li> <a href="#deterministic">Other (noisy) deterministic nodes</a> + <li> <a href="#softmax">Softmax (multinomial logit) nodes</a> + <li> <a href="#mlp">Neural network nodes</a> + <li> <a href="#root">Root nodes</a> + <li> <a href="#gaussian">Gaussian nodes</a> + <li> <a href="#glm">Generalized linear model nodes</a> + <li> <a href="#dtree">Classification/regression tree nodes</a> + <li> <a href="#nongauss">Other continuous distributions</a> + <li> <a href="#cpd_summary">Summary of CPD types</a> + </ul> + +<li> <a href="#examples">Example models</a> + <ul> + <li> <a + href="http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt"> +Gaussian mixture models</a> + <li> <a href="#pca">PCA, ICA, and all that</a> + <li> <a href="#mixep">Mixtures of experts</a> + <li> <a href="#hme">Hierarchical mixtures of experts</a> + <li> <a href="#qmr">QMR</a> + <li> <a href="#cg_model">Conditional Gaussian models</a> + <li> <a href="#hybrid">Other hybrid models</a> + </ul> + +<li> <a href="#param_learning">Parameter learning</a> + <ul> + <li> <a href="#load_data">Loading data from a file</a> + <li> <a href="#mle_complete">Maximum likelihood parameter estimation from complete data</a> + <li> <a href="#prior">Parameter priors</a> + <li> <a href="#bayes_learn">(Sequential) Bayesian parameter updating from complete data</a> + <li> <a href="#em">Maximum likelihood parameter estimation with missing values (EM)</a> + <li> <a href="#tying">Parameter tying</a> + </ul> + +<li> <a href="#structure_learning">Structure learning</a> + <ul> + <li> <a href="#enumerate">Exhaustive search</a> + <li> <a href="#K2">K2</a> + <li> <a href="#hill_climb">Hill-climbing</a> + <li> <a href="#mcmc">MCMC</a> + <li> <a href="#active">Active learning</a> + <li> <a href="#struct_em">Structural EM</a> + <li> <a href="#graphdraw">Visualizing the learned graph structure</a> + <li> <a href="#constraint">Constraint-based methods</a> + </ul> + + +<li> <a href="#engines">Inference engines</a> + <ul> + <li> <a href="#jtree">Junction tree</a> + <li> <a href="#varelim">Variable elimination</a> + <li> <a href="#global">Global inference methods</a> + <li> <a href="#quickscore">Quickscore</a> + <li> <a href="#belprop">Belief propagation</a> + <li> <a href="#sampling">Sampling (Monte Carlo)</a> + <li> <a href="#engine_summary">Summary of inference engines</a> + </ul> + + +<li> <a href="#influence">Influence diagrams/ decision making</a> + + +<li> <a href="usage_dbn.html">DBNs, HMMs, Kalman filters and all that</a> +</ul> + +</ul> + + + + +<h1><a name="install">Installation</h1> + +<h2><a name="installM">Installing the Matlab code</h2> + +<ul> +<li> <a href="bnt_download.html">Download</a> the BNT.zip file. + +<p> +<li> Unpack the file. In Unix, type +<!--"tar xvf BNT.tar".--> +"unzip FullBNT.zip". +In Windows, use +a program like <a href="http://www.winzip.com">Winzip</a>. This will +create a directory called FullBNT, which contains BNT and other libraries. + +<p> +<li> Read the file <tt>BNT/README</tt> to make sure the date +matches the one on the top of <a href=bnt.html>the BNT home page</a>. +If not, you may need to press 'refresh' on your browser, and download +again, to get the most recent version. + +<p> +<li> <b>Edit the file "BNT/add_BNT_to_path.m"</b> so it contains the correct +pathname. +For example, in Windows, +I download FullBNT.zip into C:\kpmurphy\matlab, and +then comment out the second line (with the % character), and uncomment +the third line, which reads +<pre> +BNT_HOME = 'C:\kpmurphy\matlab\FullBNT'; +</pre> + +<p> +<li> Start up Matlab. + +<p> +<li> Type "ver" at the Matlab prompt (">>"). +<b>You need Matlab version 5.2 or newer to run BNT</b>. +(Versions 5.0 and 5.1 have a memory leak which seems to sometimes +crash BNT.) + +<p> +<li> Move to the BNT directory. +For example, in Windows, I type +<pre> +>> cd C:\kpmurphy\matlab\FullBNT\BNT +</pre> + +<p> +<li> Type "add_BNT_to_path". +This executes the command +<tt>addpath(genpath(BNT_HOME))</tt>, +which adds all directories below FullBNT to the matlab path. + +<p> +<li> Type "test_BNT". +<b>If all goes well, this will just produce a bunch of numbers and pictures.</b> +It may produce some +warning messages (which you can ignore), but should not produce any error messages. +(The warnings should only be of the form +"Warning: Maximum number of iterations has been exceeded", and are +produced by Netlab.) + +<p> +<li> <a href="http://groups.yahoo.com/group/BayesNetToolbox/join"> +Join the BNT email list</a> + +</ul> + + +If you are new to Matlab, you might like to check out +<a href="matlab_tips.html">some useful Matlab tips</a>. +For instance, this explains how to create a startup file, which can be +used to set your path variable automatically, so you can avoid having +to type the above commands every time. + + + + +<h2><a name="installC">Installing the C code</h2> + +Some BNT functions also have C implementations. +<b>It is not necessary to install the C code</b>, but it can result in a speedup +of a factor of 2-5 of certain simple functions. +To install all the C code, +edit installC_BNT.m so it contains the right path, +then type <tt>installC_BNT</tt>. +To uninstall all the C code, +edit uninstallC_BNT.m so it contains the right path, +then type <tt>uninstallC_BNT</tt>. +For an up-to-date list of the files which have C implementations, see +BNT/installC_BNT.m. + +<p> +mex is a script that lets you call C code from Matlab - it does not compile matlab to +C (see mcc below). +If your C/C++ compiler is set up correctly, mex should work out of +the box. (Matlab 6 now ships with its own minimal C compiler.) +If not, you might need to type +<p> +<tt> mex -setup</tt> +<p> +before calling installC. +<p> +To make mex call gcc on Windows, +you must install <a +href="http://www.mrc-cbu.cam.ac.uk/Imaging/gnumex20.html">gnumex</a>. +You can use the <a href="http://www.mingw.org/">minimalist GNU for +Windows</a> version of gcc, or +the <a href="http://sources.redhat.com/cygwin/">cygwin</a> version. +<p> +In general, typing +'mex foo.c' from inside Matlab creates a file called +'foo.mexglx' or 'foo.dll' (the exact file +extension is system dependent - on Linux it is 'mexglx', on Windows it is '.dll'). +The resulting file will hide the original 'foo.m' (if it existed), i.e., +typing 'foo' at the prompt will call the compiled C version. +To reveal the original matlab version, just delete foo.mexglx (this is +what uninstallC does). +<p> +Sometimes it takes time for Matlab to realize that the file has +changed from matlab to C or vice versa; try typing 'clear all' or +restarting Matlab to refresh it. +To find out which version of a file you are running, type +'which foo'. +<p> +<a href="http://www.mathworks.com/products/compiler">mcc</a>, the +Matlab to C compiler, is a separate product, +and is quite different from mex. It does not yet support +objects/classes, which is why we can't compile all of BNT to C automatically. +Also, hand-written C code is usually much +better than the C code generated by mcc. + + +<p> +Acknowledgements: +Although I wrote some of the C code, most of +the C code (e.g., for jtree and dpot) was written by Wei Hu; +the triangulation C code was written by Ilya Shpitser. + + +<h1><a name="basics">Creating your first Bayes net</h1> + +To define a Bayes net, you must specify the graph structure and then +the parameters. We look at each in turn, using a simple example +(adapted from Russell and +Norvig, "Artificial Intelligence: a Modern Approach", Prentice Hall, +1995, p454). + + +<h2>Graph structure</h2> + + +Consider the following network. + +<p> +<center> +<IMG SRC="Figures/sprinkler.gif"> +</center> +<p> + +<P> +To specify this directed acyclic graph (dag), we create an adjacency matrix: +<PRE> +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; +</PRE> +<P> +We have numbered the nodes as follows: +Cloudy = 1, Sprinkler = 2, Rain = 3, WetGrass = 4. +<b>The nodes must always be numbered in topological order, i.e., +ancestors before descendants.</b> +For a more complicated graph, this is a little inconvenient: we will +see how to get around this <a href="usage_dbn.html#bat">below</a>. +<p> +<!-- +In Matlab 6, you can use logical arrays instead of double arrays, +which are 4 times smaller: +<pre> +dag = false(N,N); +dag(C,[R S]) = true; +... +</pre> +-- +<p> +A preliminary attempt to make a <b>GUI</b> +has been writte by Philippe LeRay and can be downloaded +from <a href="http://bnt.insa-rouen.fr/ajouts.html">here</a>. +<p> +You can visualize the resulting graph structure using +the methods discussed <a href="#graphdraw">below</a>. + +<h2>Creating the Bayes net shell</h2> + +In addition to specifying the graph structure, +we must specify the size and type of each node. +If a node is discrete, its size is the +number of possible values +each node can take on; if a node is continuous, +it can be a vector, and its size is the length of this vector. +In this case, we will assume all nodes are discrete and binary. +<PRE> +discrete_nodes = 1:N; +node_sizes = 2*ones(1,N); +</pre> +If the nodes were not binary, you could type e.g., +<pre> +node_sizes = [4 2 3 5]; +</pre> +meaning that Cloudy has 4 possible values, +Sprinkler has 2 possible values, etc. +Note that these are cardinal values, not ordinal, i.e., +they are not ordered in any way, like 'low', 'medium', 'high'. +<p> +We are now ready to make the Bayes net: +<pre> +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes); +</PRE> +By default, all nodes are assumed to be discrete, so we can also just +write +<pre> +bnet = mk_bnet(dag, node_sizes); +</PRE> +You may also specify which nodes will be observed. +If you don't know, or if this not fixed in advance, +just use the empty list (the default). +<pre> +onodes = []; +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes, 'observed', onodes); +</PRE> +Note that optional arguments are specified using a name/value syntax. +This is common for many BNT functions. +In general, to find out more about a function (e.g., which optional +arguments it takes), please see its +documentation string by typing +<pre> +help mk_bnet +</pre> +See also other <a href="matlab_tips.html">useful Matlab tips</a>. +<p> +It is possible to associate names with nodes, as follows: +<pre> +bnet = mk_bnet(dag, node_sizes, 'names', {'cloudy','S','R','W'}, 'discrete', 1:4); +</pre> +You can then refer to a node by its name: +<pre> +C = bnet.names{'cloudy'}; % bnet.names is an associative array +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +</pre> + + +<h2><a name="cpt">Parameters</h2> + +A model consists of the graph structure and the parameters. +The parameters are represented by CPD objects (CPD = Conditional +Probability Distribution), which define the probability distribution +of a node given its parents. +(We will use the terms "node" and "random variable" interchangeably.) +The simplest kind of CPD is a table (multi-dimensional array), which +is suitable when all the nodes are discrete-valued. Note that the discrete +values are not assumed to be ordered in any way; that is, they +represent categorical quantities, like male and female, rather than +ordinal quantities, like low, medium and high. +(We will discuss CPDs in more detail <a href="#cpd">below</a>.) +<p> +Tabular CPDs, also called CPTs (conditional probability tables), +are stored as multidimensional arrays, where the dimensions +are arranged in the same order as the nodes, e.g., the CPT for node 4 +(WetGrass) is indexed by Sprinkler (2), Rain (3) and then WetGrass (4) itself. +Hence the child is always the last dimension. +If a node has no parents, its CPT is a column vector representing its +prior. +Note that in Matlab (unlike C), arrays are indexed +from 1, and are layed out in memory such that the first index toggles +fastest, e.g., the CPT for node 4 (WetGrass) is as follows +<P> +<P><IMG ALIGN=BOTTOM SRC="Figures/CPTgrass.gif"><P> +<P> +where we have used the convention that false==1, true==2. +We can create this CPT in Matlab as follows +<PRE> +CPT = zeros(2,2,2); +CPT(1,1,1) = 1.0; +CPT(2,1,1) = 0.1; +... +</PRE> +Here is an easier way: +<PRE> +CPT = reshape([1 0.1 0.1 0.01 0 0.9 0.9 0.99], [2 2 2]); +</PRE> +In fact, we don't need to reshape the array, since the CPD constructor +will do that for us. So we can just write +<pre> +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +</pre> +The other nodes are created similarly (using the old syntax for +optional parameters) +<PRE> +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +</PRE> + + +<h2><a name="rnd_cpt">Random Parameters</h2> + +If we do not specify the CPT, random parameters will be +created, i.e., each "row" of the CPT will be drawn from the uniform distribution. +To ensure repeatable results, use +<pre> +rand('state', seed); +randn('state', seed); +</pre> +To control the degree of randomness (entropy), +you can sample each row of the CPT from a Dirichlet(p,p,...) distribution. +If p << 1, this encourages "deterministic" CPTs (one entry near 1, the rest near 0). +If p = 1, each entry is drawn from U[0,1]. +If p >> 1, the entries will all be near 1/k, where k is the arity of +this node, i.e., each row will be nearly uniform. +You can do this as follows, assuming this node +is number i, and ns is the node_sizes. +<pre> +k = ns(i); +ps = parents(dag, i); +psz = prod(ns(ps)); +CPT = sample_dirichlet(p*ones(1,k), psz); +bnet.CPD{i} = tabular_CPD(bnet, i, 'CPT', CPT); +</pre> + + +<h2><a name="file">Loading a network from a file</h2> + +If you already have a Bayes net represented in the XML-based +<a href="http://www.cs.cmu.edu/afs/cs/user/fgcozman/www/Research/InterchangeFormat/"> +Bayes Net Interchange Format (BNIF)</a> (e.g., downloaded from the +<a +href="http://www.cs.huji.ac.il/labs/compbio/Repository"> +Bayes Net repository</a>), +you can convert it to BNT format using +the +<a href="http://www.digitas.harvard.edu/~ken/bif2bnt/">BIF-BNT Java +program</a> written by Ken Shan. +(This is not necessarily up-to-date.) + + +<h2>Creating a model using a GUI</h2> + +Click <a href="http://bnt.insa-rouen.fr/ajouts.html">here</a>. + + + +<h1><a name="inference">Inference</h1> + +Having created the BN, we can now use it for inference. +There are many different algorithms for doing inference in Bayes nets, +that make different tradeoffs between speed, +complexity, generality, and accuracy. +BNT therefore offers a variety of different inference +"engines". We will discuss these +in more detail <a href="#engines">below</a>. +For now, we will use the junction tree +engine, which is the mother of all exact inference algorithms. +This can be created as follows. +<pre> +engine = jtree_inf_engine(bnet); +</pre> +The other engines have similar constructors, but might take +additional, algorithm-specific parameters. +All engines are used in the same way, once they have been created. +We illustrate this in the following sections. + + +<h2><a name="marginal">Computing marginal distributions</h2> + +Suppose we want to compute the probability that the sprinker was on +given that the grass is wet. +The evidence consists of the fact that W=2. All the other nodes +are hidden (unobserved). We can specify this as follows. +<pre> +evidence = cell(1,N); +evidence{W} = 2; +</pre> +We use a 1D cell array instead of a vector to +cope with the fact that nodes can be vectors of different lengths. +In addition, the value [] can be used +to denote 'no evidence', instead of having to specify the observation +pattern as a separate argument. +(Click <a href="cellarray.html">here</a> for a quick tutorial on cell +arrays in matlab.) +<p> +We are now ready to add the evidence to the engine. +<pre> +[engine, loglik] = enter_evidence(engine, evidence); +</pre> +The behavior of this function is algorithm-specific, and is discussed +in more detail <a href="#engines">below</a>. +In the case of the jtree engine, +enter_evidence implements a two-pass message-passing scheme. +The first return argument contains the modified engine, which +incorporates the evidence. The second return argument contains the +log-likelihood of the evidence. (Not all engines are capable of +computing the log-likelihood.) +<p> +Finally, we can compute p=P(S=2|W=2) as follows. +<PRE> +marg = marginal_nodes(engine, S); +marg.T +ans = + 0.57024 + 0.42976 +p = marg.T(2); +</PRE> +We see that p = 0.4298. +<p> +Now let us add the evidence that it was raining, and see what +difference it makes. +<PRE> +evidence{R} = 2; +[engine, loglik] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, S); +p = marg.T(2); +</PRE> +We find that p = P(S=2|W=2,R=2) = 0.1945, +which is lower than +before, because the rain can ``explain away'' the +fact that the grass is wet. +<p> +You can plot a marginal distribution over a discrete variable +as a barchart using the built 'bar' function: +<pre> +bar(marg.T) +</pre> +This is what it looks like + +<p> +<center> +<IMG SRC="Figures/sprinkler_bar.gif"> +</center> +<p> + +<h2><a name="observed">Observed nodes</h2> + +What happens if we ask for the marginal on an observed node, e.g. P(W|W=2)? +An observed discrete node effectively only has 1 value (the observed + one) --- all other values would result in 0 probability. +For efficiency, BNT treats observed (discrete) nodes as if they were + set to 1, as we see below: +<pre> +evidence = cell(1,N); +evidence{W} = 2; +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, W); +m.T +ans = + 1 +</pre> +This can get a little confusing, since we assigned W=2. +So we can ask BNT to add the evidence back in by passing in an optional argument: +<pre> +m = marginal_nodes(engine, W, 1); +m.T +ans = + 0 + 1 +</pre> +This shows that P(W=1|W=2) = 0 and P(W=2|W=2) = 1. + + + +<h2><a name="joint">Computing joint distributions</h2> + +We can compute the joint probability on a set of nodes as in the +following example. +<pre> +evidence = cell(1,N); +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]); +</pre> +m is a structure. The 'T' field is a multi-dimensional array (in +this case, 3-dimensional) that contains the joint probability +distribution on the specified nodes. +<pre> +>> m.T +ans(:,:,1) = + 0.2900 0.0410 + 0.0210 0.0009 +ans(:,:,2) = + 0 0.3690 + 0.1890 0.0891 +</pre> +We see that P(S=1,R=1,W=2) = 0, since it is impossible for the grass +to be wet if both the rain and sprinkler are off. +<p> +Let us now add some evidence to R. +<pre> +evidence{R} = 2; +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]) +m = + domain: [2 3 4] + T: [2x1x2 double] +>> m.T +m.T +ans(:,:,1) = + 0.0820 + 0.0018 +ans(:,:,2) = + 0.7380 + 0.1782 +</pre> +The joint T(i,j,k) = P(S=i,R=j,W=k|evidence) +should have T(i,1,k) = 0 for all i,k, since R=1 is incompatible +with the evidence that R=2. +Instead of creating large tables with many 0s, BNT sets the effective +size of observed (discrete) nodes to 1, as explained above. +This is why m.T has size 2x1x2. +To get a 2x2x2 table, type +<pre> +m = marginal_nodes(engine, [S R W], 1) +m = + domain: [2 3 4] + T: [2x2x2 double] +>> m.T +m.T +ans(:,:,1) = + 0 0.082 + 0 0.0018 +ans(:,:,2) = + 0 0.738 + 0 0.1782 +</pre> + +<p> +Note: It is not always possible to compute the joint on arbitrary +sets of nodes: it depends on which inference engine you use, as discussed +in more detail <a href="#engines">below</a>. + + +<h2><a name="soft">Soft/virtual evidence</h2> + +Sometimes a node is not observed, but we have some distribution over +its possible values; this is often called "soft" or "virtual" +evidence. +One can use this as follows +<pre> +[engine, loglik] = enter_evidence(engine, evidence, 'soft', soft_evidence); +</pre> +where soft_evidence{i} is either [] (if node i has no soft evidence) +or is a vector representing the probability distribution over i's +possible values. +For example, if we don't know i's exact value, but we know its +likelihood ratio is 60/40, we can write evidence{i} = [] and +soft_evidence{i} = [0.6 0.4]. +<p> +Currently only jtree_inf_engine supports this option. +It assumes that all hidden nodes, and all nodes for +which we have soft evidence, are discrete. +For a longer example, see BNT/examples/static/softev1.m. + + +<h2><a name="mpe">Most probable explanation</h2> + +To compute the most probable explanation (MPE) of the evidence (i.e., +the most probable assignment, or a mode of the joint), use +<pre> +[mpe, ll] = calc_mpe(engine, evidence); +</pre> +mpe{i} is the most likely value of node i. +This calls enter_evidence with the 'maximize' flag set to 1, which +causes the engine to do max-product instead of sum-product. +The resulting max-marginals are then thresholded. +If there is more than one maximum probability assignment, we must take + care to break ties in a consistent manner (thresholding the + max-marginals may give the wrong result). To force this behavior, + type +<pre> +[mpe, ll] = calc_mpe(engine, evidence, 1); +</pre> +Note that computing the MPE is someties called abductive reasoning. + +<p> +You can also use <tt>calc_mpe_bucket</tt> written by Ron Zohar, +that does a forwards max-product pass, and then a backwards traceback +pass, which is how Viterbi is traditionally implemented. + + + +<h1><a name="cpd">Conditional Probability Distributions</h1> + +A Conditional Probability Distributions (CPD) +defines P(X(i) | X(Pa(i))), where X(i) is the i'th node, and X(Pa(i)) +are the parents of node i. There are many ways to represent this +distribution, which depend in part on whether X(i) and X(Pa(i)) are +discrete, continuous, or a combination. +We will discuss various representations below. + + +<h2><a name="tabular">Tabular nodes</h2> + +If the CPD is represented as a table (i.e., if it is a multinomial +distribution), it has a number of parameters that is exponential in +the number of parents. See the example <a href="#cpt">above</a>. + + +<h2><a name="noisyor">Noisy-or nodes</h2> + +A noisy-OR node is like a regular logical OR gate except that +sometimes the effects of parents that are on get inhibited. +Let the prob. that parent i gets inhibited be q(i). +Then a node, C, with 2 parents, A and B, has the following CPD, where +we use F and T to represent off and on (1 and 2 in BNT). +<pre> +A B P(C=off) P(C=on) +--------------------------- +F F 1.0 0.0 +T F q(A) 1-q(A) +F T q(B) 1-q(B) +T T q(A)q(B) q-q(A)q(B) +</pre> +Thus we see that the causes get inhibited independently. +It is common to associate a "leak" node with a noisy-or CPD, which is +like a parent that is always on. This can account for all other unmodelled +causes which might turn the node on. +<p> +The noisy-or distribution is similar to the logistic distribution. +To see this, let the nodes, S(i), have values in {0,1}, and let q(i,j) +be the prob. that j inhibits i. Then +<pre> +Pr(S(i)=1 | parents(S(i))) = 1 - prod_{j} q(i,j)^S(j) +</pre> +Now define w(i,j) = -ln q(i,j) and rho(x) = 1-exp(-x). Then +<pre> +Pr(S(i)=1 | parents(S(i))) = rho(sum_j w(i,j) S(j)) +</pre> +For a sigmoid node, we have +<pre> +Pr(S(i)=1 | parents(S(i))) = sigma(-sum_j w(i,j) S(j)) +</pre> +where sigma(x) = 1/(1+exp(-x)). Hence they differ in the choice of +the activation function (although both are monotonically increasing). +In addition, in the case of a noisy-or, the weights are constrained to be +positive, since they derive from probabilities q(i,j). +In both cases, the number of parameters is <em>linear</em> in the +number of parents, unlike the case of a multinomial distribution, +where the number of parameters is exponential in the number of parents. +We will see an example of noisy-OR nodes <a href="#qmr">below</a>. + + +<h2><a name="deterministic">Other (noisy) deterministic nodes</h2> + +Deterministic CPDs for discrete random variables can be created using +the deterministic_CPD class. It is also possible to 'flip' the output +of the function with some probability, to simulate noise. +The boolean_CPD class is just a special case of a +deterministic CPD, where the parents and child are all binary. +<p> +Both of these classes are just "syntactic sugar" for the tabular_CPD +class. + + + +<h2><a name="softmax">Softmax nodes</h2> + +If we have a discrete node with a continuous parent, +we can define its CPD using a softmax function +(also known as the multinomial logit function). +This acts like a soft thresholding operator, and is defined as follows: +<pre> + exp(w(:,i)'*x + b(i)) +Pr(Q=i | X=x) = ----------------------------- + sum_j exp(w(:,j)'*x + b(j)) + +</pre> +The parameters of a softmax node, w(:,i) and b(i), i=1..|Q|, have the +following interpretation: w(:,i)-w(:,j) is the normal vector to the +decision boundary between classes i and j, +and b(i)-b(j) is its offset (bias). For example, suppose +X is a 2-vector, and Q is binary. Then +<pre> +w = [1 -1; + 0 0]; + +b = [0 0]; +</pre> +means class 1 are points in the 2D plane with positive x coordinate, +and class 2 are points in the 2D plane with negative x coordinate. +If w has large magnitude, the decision boundary is sharp, otherwise it +is soft. +In the special case that Q is binary (0/1), the softmax function reduces to the logistic +(sigmoid) function. +<p> +Fitting a softmax function can be done using the iteratively reweighted +least squares (IRLS) algorithm. +We use the implementation from +<a href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a>. +Note that since +the softmax distribution is not in the exponential family, it does not +have finite sufficient statistics, and hence we must store all the +training data in uncompressed form. +If this takes too much space, one should use online (stochastic) gradient +descent (not implemented in BNT). +<p> +If a softmax node also has discrete parents, +we use a different set of w/b parameters for each combination of +parent values, as in the <a href="#gaussian">conditional linear +Gaussian CPD</a>. +This feature was implemented by Pierpaolo Brutti. +He is currently extending it so that discrete parents can be treated +as if they were continuous, by adding indicator variables to the X +vector. +<p> +We will see an example of softmax nodes <a href="#mixexp">below</a>. + + +<h2><a name="mlp">Neural network nodes</h2> + +Pierpaolo Brutti has implemented the mlp_CPD class, which uses a multi layer perceptron +to implement a mapping from continuous parents to discrete children, +similar to the softmax function. +(If there are also discrete parents, it creates a mixture of MLPs.) +It uses code from <a +href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a>. +This is work in progress. + +<h2><a name="root">Root nodes</h2> + +A root node has no parents and no parameters; it can be used to model +an observed, exogeneous input variable, i.e., one which is "outside" +the model. +This is useful for conditional density models. +We will see an example of root nodes <a href="#mixexp">below</a>. + + +<h2><a name="gaussian">Gaussian nodes</h2> + +We now consider a distribution suitable for the continuous-valued nodes. +Suppose the node is called Y, its continuous parents (if any) are +called X, and its discrete parents (if any) are called Q. +The distribution on Y is defined as follows: +<pre> +- no parents: Y ~ N(mu, Sigma) +- cts parents : Y|X=x ~ N(mu + W x, Sigma) +- discrete parents: Y|Q=i ~ N(mu(:,i), Sigma(:,:,i)) +- cts and discrete parents: Y|X=x,Q=i ~ N(mu(:,i) + W(:,:,i) * x, Sigma(:,:,i)) +</pre> +where N(mu, Sigma) denotes a Normal distribution with mean mu and +covariance Sigma. Let |X|, |Y| and |Q| denote the sizes of X, Y and Q +respectively. +If there are no discrete parents, |Q|=1; if there is +more than one, then |Q| = a vector of the sizes of each discrete parent. +If there are no continuous parents, |X|=0; if there is more than one, +then |X| = the sum of their sizes. +Then mu is a |Y|*|Q| vector, Sigma is a |Y|*|Y|*|Q| positive +semi-definite matrix, and W is a |Y|*|X|*|Q| regression (weight) +matrix. +<p> +We can create a Gaussian node with random parameters as follows. +<pre> +bnet.CPD{i} = gaussian_CPD(bnet, i); +</pre> +We can specify the value of one or more of the parameters as in the +following example, in which |Y|=2, and |Q|=1. +<pre> +bnet.CPD{i} = gaussian_CPD(bnet, i, 'mean', [0; 0], 'weights', randn(Y,X), 'cov', eye(Y)); +</pre> +<p> +We will see an example of conditional linear Gaussian nodes <a +href="#cg_model">below</a>. +<p> +When fitting a Gaussian using EM, you might encounter some numerical +problems. See <a +href="http://www.ai.mit.edu/~murphyk/Software/HMM/hmm.html#loglikpos">here</a> +for a discussion of this in the context of HMMs. +(Note: BNT does not currently support spherical covariances, although it +would be easy to add, since KPMstats/clg_Mstep supports this option; +you would just need to modify gaussian_CPD/update_ess to accumulate +weighted inner products.) + + +<h2><a name="nongauss">Other continuous distributions</h2> + +Currently BNT does not support any CPDs for continuous nodes other +than the Gaussian. +However, you can use a mixture of Gaussians to +approximate other continuous distributions. We will see some an example +of this with the IFA model <a href="#pca">below</a>. + + +<h2><a name="glm">Generalized linear model nodes</h2> + +In the future, we may incorporate some of the functionality of +<a href = +"http://www.sci.usq.edu.au/staff/dunn/glmlab/glmlab.html">glmlab</a> +into BNT. + + +<h2><a name="dtree">Classification/regression tree nodes</h2> + +We plan to add classification and regression trees to define CPDs for +discrete and continuous nodes, respectively. +Trees have many advantages: they are easy to interpret, they can do +feature selection, they can +handle discrete and continuous inputs, they do not make strong +assumptions about the form of the distribution, the number of +parameters can grow in a data-dependent way (i.e., they are +semi-parametric), they can handle missing data, etc. +However, they are not yet implemented. +<!-- +Yimin Zhang is currently (Feb '02) implementing this. +--> + + +<h2><a name="cpd_summary">Summary of CPD types</h2> + +We list all the different types of CPDs supported by BNT. +For each CPD, we specify if the child and parents can be discrete (D) or +continuous (C) (Binary (B) nodes are a special case). +We also specify which methods each class supports. +If a method is inherited, the name of the parent class is mentioned. +If a parent class calls a child method, this is mentioned. +<p> +The <tt>CPD_to_CPT</tt> method converts a CPD to a table; this +requires that the child and all parents are discrete. +The CPT might be exponentially big... +<tt>convert_to_table</tt> evaluates a CPD with evidence, and +represents the the resulting potential as an array. +This requires that the child is discrete, and any continuous parents +are observed. +<tt>convert_to_pot</tt> evaluates a CPD with evidence, and +represents the resulting potential as a dpot, gpot, cgpot or upot, as +requested. (d=discrete, g=Gaussian, cg = conditional Gaussian, u = +utility). + +<p> +When we sample a node, all the parents are observed. +When we compute the (log) probability of a node, all the parents and +the child are observed. +<p> +We also specify if the parameters are learnable. +For learning with EM, we require +the methods <tt>reset_ess</tt>, <tt>update_ess</tt> and +<tt>maximize_params</tt>. +For learning from fully observed data, we require +the method <tt>learn_params</tt>. +By default, all classes inherit this from generic_CPD, which simply +calls <tt>update_ess</tt> N times, once for each data case, followed +by <tt>maximize_params</tt>, i.e., it is like EM, without the E step. +Some classes implement a batch formula, which is quicker. +<p> +Bayesian learning means computing a posterior over the parameters +given fully observed data. +<p> +Pearl means we implement the methods <tt>compute_pi</tt> and +<tt>compute_lambda_msg</tt>, used by +<tt>pearl_inf_engine</tt>, which runs on directed graphs. +<tt>belprop_inf_engine</tt> only needs <tt>convert_to_pot</tt>.H +The pearl methods can exploit special properties of the CPDs for +computing the messages efficiently, whereas belprop does not. +<p> +The only method implemented by generic_CPD is <tt>adjustable_CPD</tt>, +which is not shown, since it is not very interesting. + + +<p> + + +<table> +<table border units = pixels><tr> +<td align=center>Name +<td align=center>Child +<td align=center>Parents +<td align=center>Comments +<td align=center>CPD_to_CPT +<td align=center>conv_to_table +<td align=center>conv_to_pot +<td align=center>sample +<td align=center>prob +<td align=center>learn +<td align=center>Bayes +<td align=center>Pearl + + +<tr> +<!-- Name--><td> +<!-- Child--><td> +<!-- Parents--><td> +<!-- Comments--><td> +<!-- CPD_to_CPT--><td> +<!-- conv_to_table--><td> +<!-- conv_to_pot--><td> +<!-- sample--><td> +<!-- prob--><td> +<!-- learn--><td> +<!-- Bayes--><td> +<!-- Pearl--><td> + +<tr> +<!-- Name--><td>boolean +<!-- Child--><td>B +<!-- Parents--><td>B +<!-- Comments--><td>Syntactic sugar for tabular +<!-- CPD_to_CPT--><td>- +<!-- conv_to_table--><td>- +<!-- conv_to_pot--><td>- +<!-- sample--><td>- +<!-- prob--><td>- +<!-- learn--><td>- +<!-- Bayes--><td>- +<!-- Pearl--><td>- + +<tr> +<!-- Name--><td>deterministic +<!-- Child--><td>D +<!-- Parents--><td>D +<!-- Comments--><td>Syntactic sugar for tabular +<!-- CPD_to_CPT--><td>- +<!-- conv_to_table--><td>- +<!-- conv_to_pot--><td>- +<!-- sample--><td>- +<!-- prob--><td>- +<!-- learn--><td>- +<!-- Bayes--><td>- +<!-- Pearl--><td>- + +<tr> +<!-- Name--><td>Discrete +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>Virtual class +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Calls CPD_to_CPT +<!-- conv_to_pot--><td>Calls conv_to_table +<!-- sample--><td>Calls conv_to_table +<!-- prob--><td>Calls conv_to_table +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + +<tr> +<!-- Name--><td>Gaussian +<!-- Child--><td>C +<!-- Parents--><td>C/D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>Y +<!-- prob--><td>Y +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + +<tr> +<!-- Name--><td>gmux +<!-- Child--><td>C +<!-- Parents--><td>C/D +<!-- Comments--><td>multiplexer +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>N +<!-- prob--><td>N +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>Y + + +<tr> +<!-- Name--><td>MLP +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>multi layer perceptron +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Y +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>noisy-or +<!-- Child--><td>B +<!-- Parents--><td>B +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>Y +<!-- conv_to_table--><td>Inherits from discrete +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>Y + + +<tr> +<!-- Name--><td>root +<!-- Child--><td>C/D +<!-- Parents--><td>none +<!-- Comments--><td>no params +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>Y +<!-- prob--><td>Y +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>softmax +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Y +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>generic +<!-- Child--><td>C/D +<!-- Parents--><td>C/D +<!-- Comments--><td>Virtual class +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>N +<!-- sample--><td>N +<!-- prob--><td>N +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>Tabular +<!-- Child--><td>D +<!-- Parents--><td>D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>Y +<!-- conv_to_table--><td>Inherits from discrete +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>Y +<!-- Pearl--><td>Y + +</table> + + + +<h1><a name="examples">Example models</h1> + + +<h2>Gaussian mixture models</h2> + +Richard W. DeVaul has made a detailed tutorial on how to fit mixtures +of Gaussians using BNT. Available +<a href="http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt">here</a>. + + +<h2><a name="pca">PCA, ICA, and all that </h2> + +In Figure (a) below, we show how Factor Analysis can be thought of as a +graphical model. Here, X has an N(0,I) prior, and +Y|X=x ~ N(mu + Wx, Psi), +where Psi is diagonal and W is called the "factor loading matrix". +Since the noise on both X and Y is diagonal, the components of these +vectors are uncorrelated, and hence can be represented as individual +scalar nodes, as we show in (b). +(This is useful if parts of the observations on the Y vector are occasionally missing.) +We usually take k=|X| << |Y|=D, so the model tries to explain +many observations using a low-dimensional subspace. + + +<center> +<table> +<tr> +<td><img src="Figures/fa.gif"> +<td><img src="Figures/fa_scalar.gif"> +<td><img src="Figures/mfa.gif"> +<td><img src="Figures/ifa.gif"> +<tr> +<td align=center> (a) +<td align=center> (b) +<td align=center> (c) +<td align=center> (d) +</table> +</center> + +<p> +We can create this model in BNT as follows. +<pre> +ns = [k D]; +dag = zeros(2,2); +dag(1,2) = 1; +bnet = mk_bnet(dag, ns, 'discrete', []); +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', zeros(k,1), 'cov', eye(k), ... + 'cov_type', 'diag', 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(D,1), 'cov', diag(Psi0), 'weights', W0, ... + 'cov_type', 'diag', 'clamp_mean', 1); +</pre> + +The root node is clamped to the N(0,I) distribution, so that we will +not update these parameters during learning. +The mean of the leaf node is clamped to 0, +since we assume the data has been centered (had its mean subtracted +off); this is just for simplicity. +Finally, the covariance of the leaf node is constrained to be +diagonal. W0 and Psi0 are the initial parameter guesses. + +<p> +We can fit this model (i.e., estimate its parameters in a maximum +likelihood (ML) sense) using EM, as we +explain <a href="#em">below</a>. +Not surprisingly, the ML estimates for mu and Psi turn out to be +identical to the +sample mean and variance, which can be computed directly as +<pre> +mu_ML = mean(data); +Psi_ML = diag(cov(data)); +</pre> +Note that W can only be identified up to a rotation matrix, because of +the spherical symmetry of the source. + +<p> +If we restrict Psi to be spherical, i.e., Psi = sigma*I, +there is a closed-form solution for W as well, +i.e., we do not need to use EM. +In particular, W contains the first |X| eigenvectors of the sample covariance +matrix, with scalings determined by the eigenvalues and sigma. +Classical PCA can be obtained by taking the sigma->0 limit. +For details, see + +<ul> +<li> <a href="ftp://hope.caltech.edu/pub/roweis/Empca/empca.ps"> +"EM algorithms for PCA and SPCA"</a>, Sam Roweis, NIPS 97. +(<a href="ftp://hope.caltech.edu/pub/roweis/Code/empca.tar.gz"> +Matlab software</a>) + +<p> +<li> +<a +href=http://neural-server.aston.ac.uk/cgi-bin/tr_avail.pl?trnumber=NCRG/97/003> +"Mixtures of probabilistic principal component analyzers"</a>, +Tipping and Bishop, Neural Computation 11(2):443--482, 1999. +</ul> + +<p> +By adding a hidden discrete variable, we can create mixtures of FA +models, as shown in (c). +Now we can explain the data using a set of subspaces. +We can create this model in BNT as follows. +<pre> +ns = [M k D]; +dag = zeros(3); +dag(1,3) = 1; +dag(2,3) = 1; +bnet = mk_bnet(dag, ns, 'discrete', 1); +bnet.CPD{1} = tabular_CPD(bnet, 1, Pi0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(k, 1), 'cov', eye(k), 'cov_type', 'diag', ... + 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', Mu0', 'cov', repmat(diag(Psi0), [1 1 M]), ... + 'weights', W0, 'cov_type', 'diag', 'tied_cov', 1); +</pre> +Notice how the covariance matrix for Y is the same for all values of +Q; that is, the noise level in each sub-space is assumed the same. +However, we allow the offset, mu, to vary. +For details, see +<ul> + +<LI> +<a HREF="ftp://ftp.cs.toronto.edu/pub/zoubin/tr-96-1.ps.gz"> The EM +Algorithm for Mixtures of Factor Analyzers </A>, +Ghahramani, Z. and Hinton, G.E. (1996), +University of Toronto +Technical Report CRG-TR-96-1. +(<A HREF="ftp://ftp.cs.toronto.edu/pub/zoubin/mfa.tar.gz">Matlab software</A>) + +<p> +<li> +<a +href=http://neural-server.aston.ac.uk/cgi-bin/tr_avail.pl?trnumber=NCRG/97/003> +"Mixtures of probabilistic principal component analyzers"</a>, +Tipping and Bishop, Neural Computation 11(2):443--482, 1999. +</ul> + +<p> +I have included Zoubin's specialized MFA code (with his permission) +with the toolbox, so you can check that BNT gives the same results: +see 'BNT/examples/static/mfa1.m'. + +<p> +Independent Factor Analysis (IFA) generalizes FA by allowing a +non-Gaussian prior on each component of X. +(Note that we can approximate a non-Gaussian prior using a mixture of +Gaussians.) +This means that the likelihood function is no longer rotationally +invariant, so we can uniquely identify W and the hidden +sources X. +IFA also allows a non-diagonal Psi (i.e. correlations between the components of Y). +We recover classical Independent Components Analysis (ICA) +in the Psi -> 0 limit, and by assuming that |X|=|Y|, so that the +weight matrix W is square and invertible. +For details, see +<ul> +<li> +<a href="http://www.gatsby.ucl.ac.uk/~hagai/ifa.ps">Independent Factor +Analysis</a>, H. Attias, Neural Computation 11: 803--851, 1998. +</ul> + + + +<h2><a name="mixexp">Mixtures of experts</h2> + +As an example of the use of the softmax function, +we introduce the Mixture of Experts model. +<!-- +We also show +the Hierarchical Mixture of Experts model, where the hierarchy has two +levels. +(This is essentially a probabilistic decision tree of height two.) +--> +As before, +circles denote continuous-valued nodes, +squares denote discrete nodes, clear +means hidden, and shaded means observed. +<p> +<center> +<table> +<tr> +<td><img src="Figures/mixexp.gif"> +<!-- +<td><img src="Figures/hme.gif"> +--> +</table> +</center> +<p> +X is the observed +input, Y is the output, and +the Q nodes are hidden "gating" nodes, which select the appropriate +set of parameters for Y. During training, Y is assumed observed, +but for testing, the goal is to predict Y given X. +Note that this is a <em>conditional</em> density model, so we don't +associate any parameters with X. +Hence X's CPD will be a root CPD, which is a way of modelling +exogenous nodes. +If the output is a continuous-valued quantity, +we assume the "experts" are linear-regression units, +and set Y's CPD to linear-Gaussian. +If the output is discrete, we set Y's CPD to a softmax function. +The Q CPDs will always be softmax functions. + +<p> +As a concrete example, consider the mixture of experts model where X and Y are +scalars, and Q is binary. +This is just piecewise linear regression, where +we have two line segments, i.e., +<P> +<IMG ALIGN=BOTTOM SRC="Eqns/lin_reg_eqn.gif"> +<P> +We can create this model with random parameters as follows. +(This code is bundled in BNT/examples/static/mixexp2.m.) +<PRE> +X = 1; +Q = 2; +Y = 3; +dag = zeros(3,3); +dag(X,[Q Y]) = 1 +dag(Q,Y) = 1; +ns = [1 2 1]; % make X and Y scalars, and have 2 experts +onodes = [1 3]; +bnet = mk_bnet(dag, ns, 'discrete', 2, 'observed', onodes); + +rand('state', 0); +randn('state', 0); +bnet.CPD{1} = root_CPD(bnet, 1); +bnet.CPD{2} = softmax_CPD(bnet, 2); +bnet.CPD{3} = gaussian_CPD(bnet, 3); +</PRE> +Now let us fit this model using <a href="#em">EM</a>. +First we <a href="#load_data">load the data</a> (1000 training cases) and plot them. +<P> +<PRE> +data = load('/examples/static/Misc/mixexp_data.txt', '-ascii'); +plot(data(:,1), data(:,2), '.'); +</PRE> +<p> +<center> +<IMG SRC="Figures/mixexp_data.gif"> +</center> +<p> +This is what the model looks like before training. +(Thanks to Thomas Hofman for writing this plotting routine.) +<p> +<center> +<IMG SRC="Figures/mixexp_before.gif"> +</center> +<p> +Now let's train the model, and plot the final performance. +(We will discuss how to train models in more detail <a href="#param_learning">below</a>.) +<P> +<PRE> +ncases = size(data, 1); % each row of data is a training case +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); % each column of cases is a training case +engine = jtree_inf_engine(bnet); +max_iter = 20; +[bnet2, LLtrace] = learn_params_em(engine, cases, max_iter); +</PRE> +(We specify which nodes will be observed when we create the engine. +Hence BNT knows that the hidden nodes are all discrete. +For complex models, this can lead to a significant speedup.) +Below we show what the model looks like after 16 iterations of EM +(with 100 IRLS iterations per M step), when it converged +using the default convergence tolerance (that the +fractional change in the log-likelihood be less than 1e-3). +Before learning, the log-likelihood was +-322.927442; afterwards, it was -13.728778. +<p> +<center> +<IMG SRC="Figures/mixexp_after.gif"> +</center> +(See BNT/examples/static/mixexp2.m for details of the code.) + + + +<h2><a name="hme">Hierarchical mixtures of experts</h2> + +A hierarchical mixture of experts (HME) extends the mixture of experts +model by having more than one hidden node. A two-level example is shown below, along +with its more traditional representation as a neural network. +This is like a (balanced) probabilistic decision tree of height 2. +<p> +<center> +<IMG SRC="Figures/HMEforMatlab.jpg"> +</center> +<p> +<a href="mailto:pbrutti@stat.cmu.edu">Pierpaolo Brutti</a> +has written an extensive set of routines for HMEs, +which are bundled with BNT: see the examples/static/HME directory. +These routines allow you to choose the number of hidden (gating) +layers, and the form of the experts (softmax or MLP). +See the file hmemenu, which provides a demo. +For example, the figure below shows the decision boundaries learned +for a ternary classification problem, using a 2 level HME with softmax +gates and softmax experts; the training set is on the left, the +testing set on the right. +<p> +<center> +<!--<IMG SRC="Figures/hme_dec_boundary.gif">--> +<IMG SRC="Figures/hme_dec_boundary.png"> +</center> +<p> + + +<p> +For more details, see the following: +<ul> + +<li> <a href="http://www.cs.berkeley.edu/~jordan/papers/hierarchies.ps.Z"> +Hierarchical mixtures of experts and the EM algorithm</a> +M. I. Jordan and R. A. Jacobs. Neural Computation, 6, 181-214, 1994. + +<li> <a href = +"http://www.cs.berkeley.edu/~dmartin/software">David Martin's +matlab code for HME</a> + +<li> <a +href="http://www.cs.berkeley.edu/~jordan/papers/uai.ps.Z">Why the +logistic function? A tutorial discussion on +probabilities and neural networks.</a> M. I. Jordan. MIT Computational +Cognitive Science Report 9503, August 1995. + +<li> "Generalized Linear Models", McCullagh and Nelder, Chapman and +Halll, 1983. + +<li> +"Improved learning algorithms for mixtures of experts in multiclass +classification". +K. Chen, L. Xu, H. Chi. +Neural Networks (1999) 12: 1229-1252. + +<li> <a href="http://www.oigeeza.com/steve/"> +Classification Using Hierarchical Mixtures of Experts</a> +S.R. Waterhouse and A.J. Robinson. +In Proc. IEEE Workshop on Neural Network for Signal Processing IV (1994), pp. 177-186 + +<li> <a href="http://www.idiap.ch/~perry/"> +Localized mixtures of experts</a>, +P. Moerland, 1998. + +<li> "Nonlinear gated experts for time series", +A.S. Weigend and M. Mangeas, 1995. + +</ul> + + +<h2><a name="qmr">QMR</h2> + +Bayes nets originally arose out of an attempt to add probabilities to +expert systems, and this is still the most common use for BNs. +A famous example is +QMR-DT, a decision-theoretic reformulation of the Quick Medical +Reference (QMR) model. +<p> +<center> +<IMG ALIGN=BOTTOM SRC="Figures/qmr.gif"> +</center> +Here, the top layer represents hidden disease nodes, and the bottom +layer represents observed symptom nodes. +The goal is to infer the posterior probability of each disease given +all the symptoms (which can be present, absent or unknown). +Each node in the top layer has a Bernoulli prior (with a low prior +probability that the disease is present). +Since each node in the bottom layer has a high fan-in, we use a +noisy-OR parameterization; each disease has an independent chance of +causing each symptom. +The real QMR-DT model is copyright, but +we can create a random QMR-like model as follows. +<pre> +function bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% MK_QMR_BNET Make a QMR model +% bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% +% G(i,j) = 1 iff there is an arc from disease i to finding j +% inhibit(i,j) = inhibition probability on i->j arc +% leak(j) = inhibition prob. on leak->j arc +% prior(i) = prob. disease i is on + +[Ndiseases Nfindings] = size(inhibit); +N = Ndiseases + Nfindings; +finding_node = Ndiseases+1:N; +ns = 2*ones(1,N); +dag = zeros(N,N); +dag(1:Ndiseases, finding_node) = G; +bnet = mk_bnet(dag, ns, 'observed', finding_node); + +for d=1:Ndiseases + CPT = [1-prior(d) prior(d)]; + bnet.CPD{d} = tabular_CPD(bnet, d, CPT'); +end + +for i=1:Nfindings + fnode = finding_node(i); + ps = parents(G, i); + bnet.CPD{fnode} = noisyor_CPD(bnet, fnode, leak(i), inhibit(ps, i)); +end +</pre> +In the file BNT/examples/static/qmr1, we create a random bipartite +graph G, with 5 diseases and 10 findings, and random parameters. +(In general, to create a random dag, use 'mk_random_dag'.) +We can visualize the resulting graph structure using +the methods discussed <a href="#graphdraw">below</a>, with the +following results: +<p> +<img src="Figures/qmr.rnd.jpg"> + +<p> +Now let us put some random evidence on all the leaves except the very +first and very last, and compute the disease posteriors. +<pre> +pos = 2:floor(Nfindings/2); +neg = (pos(end)+1):(Nfindings-1); +onodes = myunion(pos, neg); +evidence = cell(1, N); +evidence(findings(pos)) = num2cell(repmat(2, 1, length(pos))); +evidence(findings(neg)) = num2cell(repmat(1, 1, length(neg))); + +engine = jtree_inf_engine(bnet); +[engine, ll] = enter_evidence(engine, evidence); +post = zeros(1, Ndiseases); +for i=diseases(:)' + m = marginal_nodes(engine, i); + post(i) = m.T(2); +end +</pre> +Junction tree can be quite slow on large QMR models. +Fortunately, it is possible to exploit properties of the noisy-OR +function to speed up exact inference using an algorithm called +<a href="#quickscore">quickscore</a>, discussed below. + + + + + +<h2><a name="cg_model">Conditional Gaussian models</h2> + +A conditional Gaussian model is one in which, conditioned on all the discrete +nodes, the distribution over the remaining (continuous) nodes is +multivariate Gaussian. This means we can have arcs from discrete (D) +to continuous (C) nodes, but not vice versa. +(We <em>are</em> allowed C->D arcs if the continuous nodes are observed, +as in the <a href="#mixexp">mixture of experts</a> model, +since this distribution can be represented with a discrete potential.) +<p> +We now give an example of a CG model, from +the paper "Propagation of Probabilities, Means amd +Variances in Mixed Graphical Association Models", Steffen Lauritzen, +JASA 87(420):1098--1108, 1992 (reprinted in the book "Probabilistic Networks and Expert +Systems", R. G. Cowell, A. P. Dawid, S. L. Lauritzen and +D. J. Spiegelhalter, Springer, 1999.) + +<h3>Specifying the graph</h3> + +Consider the model of waste emissions from an incinerator plant shown below. +We follow the standard convention that shaded nodes are observed, +clear nodes are hidden. +We also use the non-standard convention that +square nodes are discrete (tabular) and round nodes are +Gaussian. + +<p> +<center> +<IMG SRC="Figures/cg1.gif"> +</center> +<p> + +We can create this model as follows. +<pre> +F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; +n = 9; + +dag = zeros(n); +dag(F,E)=1; +dag(W,[E Min D]) = 1; +dag(E,D)=1; +dag(B,[C D])=1; +dag(D,[L Mout])=1; +dag(Min,Mout)=1; + +% node sizes - all cts nodes are scalar, all discrete nodes are binary +ns = ones(1, n); +dnodes = [F W B]; +cnodes = mysetdiff(1:n, dnodes); +ns(dnodes) = 2; + +bnet = mk_bnet(dag, ns, 'discrete', dnodes); +</pre> +'dnodes' is a list of the discrete nodes; 'cnodes' is the continuous +nodes. 'mysetdiff' is a faster version of the built-in 'setdiff'. +<p> + + +<h3>Specifying the parameters</h3> + +The parameters of the discrete nodes can be specified as follows. +<pre> +bnet.CPD{B} = tabular_CPD(bnet, B, 'CPT', [0.85 0.15]); % 1=stable, 2=unstable +bnet.CPD{F} = tabular_CPD(bnet, F, 'CPT', [0.95 0.05]); % 1=intact, 2=defect +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [2/7 5/7]); % 1=industrial, 2=household +</pre> + +<p> +The parameters of the continuous nodes can be specified as follows. +<pre> +bnet.CPD{E} = gaussian_CPD(bnet, E, 'mean', [-3.9 -0.4 -3.2 -0.5], ... + 'cov', [0.00002 0.0001 0.00002 0.0001]); +bnet.CPD{D} = gaussian_CPD(bnet, D, 'mean', [6.5 6.0 7.5 7.0], ... + 'cov', [0.03 0.04 0.1 0.1], 'weights', [1 1 1 1]); +bnet.CPD{C} = gaussian_CPD(bnet, C, 'mean', [-2 -1], 'cov', [0.1 0.3]); +bnet.CPD{L} = gaussian_CPD(bnet, L, 'mean', 3, 'cov', 0.25, 'weights', -0.5); +bnet.CPD{Min} = gaussian_CPD(bnet, Min, 'mean', [0.5 -0.5], 'cov', [0.01 0.005]); +bnet.CPD{Mout} = gaussian_CPD(bnet, Mout, 'mean', 0, 'cov', 0.002, 'weights', [1 1]); +</pre> + + +<h3><a name="cg_infer">Inference</h3> + +<!--Let us perform inference in the <a href="#cg_model">waste incinerator example</a>.--> +First we compute the unconditional marginals. +<pre> +engine = jtree_inf_engine(bnet); +evidence = cell(1,n); +[engine, ll] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, E); +</pre> +<!--(Of course, we could use <tt>cond_gauss_inf_engine</tt> instead of jtree.)--> +'marg' is a structure that contains the fields 'mu' and 'Sigma', which +contain the mean and (co)variance of the marginal on E. +In this case, they are both scalars. +Let us check they match the published figures (to 2 decimal places). +<!--(We can't expect +more precision than this in general because I have implemented the algorithm of +Lauritzen (1992), which can be numerically unstable.)--> +<pre> +tol = 1e-2; +assert(approxeq(marg.mu, -3.25, tol)); +assert(approxeq(sqrt(marg.Sigma), 0.709, tol)); +</pre> +We can compute the other posteriors similarly. +Now let us add some evidence. +<pre> +evidence = cell(1,n); +evidence{W} = 1; % industrial +evidence{L} = 1.1; +evidence{C} = -0.9; +[engine, ll] = enter_evidence(engine, evidence); +</pre> +Now we find +<pre> +marg = marginal_nodes(engine, E); +assert(approxeq(marg.mu, -3.8983, tol)); +assert(approxeq(sqrt(marg.Sigma), 0.0763, tol)); +</pre> + + +We can also compute the joint probability on a set of nodes. +For example, P(D, Mout | evidence) is a 2D Gaussian: +<pre> +marg = marginal_nodes(engine, [D Mout]) +marg = + domain: [6 8] + mu: [2x1 double] + Sigma: [2x2 double] + T: 1.0000 +</pre> +The mean is +<pre> +marg.mu +ans = + 3.6077 + 4.1077 +</pre> +and the covariance matrix is +<pre> +marg.Sigma +ans = + 0.1062 0.1062 + 0.1062 0.1182 +</pre> +It is easy to visualize this posterior using standard Matlab plotting +functions, e.g., +<pre> +gaussplot2d(marg.mu, marg.Sigma); +</pre> +produces the following picture. + +<p> +<center> +<IMG SRC="Figures/gaussplot.png"> +</center> +<p> + + +The T field indicates that the mixing weight of this Gaussian +component is 1.0. +If the joint contains discrete and continuous variables, the result +will be a mixture of Gaussians, e.g., +<pre> +marg = marginal_nodes(engine, [F E]) + domain: [1 3] + mu: [-3.9000 -0.4003] + Sigma: [1x1x2 double] + T: [0.9995 4.7373e-04] +</pre> +The interpretation is +Sigma(i,j,k) = Cov[ E(i) E(j) | F=k ]. +In this case, E is a scalar, so i=j=1; k specifies the mixture component. +<p> +We saw in the sprinkler network that BNT sets the effective size of +observed discrete nodes to 1, since they only have one legal value. +For continuous nodes, BNT sets their length to 0, +since they have been reduced to a point. +For example, +<pre> +marg = marginal_nodes(engine, [B C]) + domain: [4 5] + mu: [] + Sigma: [] + T: [0.0123 0.9877] +</pre> +It is simple to post-process the output of marginal_nodes. +For example, the file BNT/examples/static/cg1 sets the mu term of +observed nodes to their observed value, and the Sigma term to 0 (since +observed nodes have no variance). + +<p> +Note that the implemented version of the junction tree is numerically +unstable when using CG potentials +(which is why, in the example above, we only required our answers to agree with +the published ones to 2dp.) +This is why you might want to use <tt>stab_cond_gauss_inf_engine</tt>, +implemented by Shan Huang. This is described in + +<ul> +<li> "Stable Local Computation with Conditional Gaussian Distributions", +S. Lauritzen and F. Jensen, Tech Report R-99-2014, +Dept. Math. Sciences, Allborg Univ., 1999. +</ul> + +However, even the numerically stable version +can be computationally intractable if there are many hidden discrete +nodes, because the number of mixture components grows exponentially e.g., in a +<a href="usage_dbn.html#lds">switching linear dynamical system</a>. +In general, one must resort to approximate inference techniques: see +the discussion on <a href="#engines">inference engines</a> below. + + +<h2><a name="hybrid">Other hybrid models</h2> + +When we have C->D arcs, where C is hidden, we need to use +approximate inference. +One approach (not implemented in BNT) is described in +<ul> +<li> <a +href="http://www.cs.berkeley.edu/~murphyk/Papers/hybrid_uai99.ps.gz">A +Variational Approximation for Bayesian Networks with +Discrete and Continuous Latent Variables</a>, +K. Murphy, UAI 99. +</ul> +Of course, one can always use <a href="#sampling">sampling</a> methods +for approximate inference in such models. + + + +<h1><a name="param_learning">Parameter Learning</h1> + +The parameter estimation routines in BNT can be classified into 4 +types, depending on whether the goal is to compute +a full (Bayesian) posterior over the parameters or just a point +estimate (e.g., Maximum Likelihood or Maximum A Posteriori), +and whether all the variables are fully observed or there is missing +data/ hidden variables (partial observability). +<p> + +<TABLE BORDER> +<tr> + <TH></TH> + <th>Full obs</th> + <th>Partial obs</th> +</tr> +<tr> + <th>Point</th> + <td><tt>learn_params</tt></td> + <td><tt>learn_params_em</tt></td> +</tr> +<tr> + <th>Bayes</th> + <td><tt>bayes_update_params</tt></td> + <td>not yet supported</td> +</tr> +</table> + + +<h2><a name="load_data">Loading data from a file</h2> + +To load numeric data from an ASCII text file called 'dat.txt', where each row is a +case and columns are separated by white-space, such as +<pre> +011979 1626.5 0.0 +021979 1367.0 0.0 +... +</pre> +you can use +<pre> +data = load('dat.txt'); +</pre> +or +<pre> +load dat.txt -ascii +</pre> +In the latter case, the data is stored in a variable called 'dat' (the +filename minus the extension). +Alternatively, suppose the data is stored in a .csv file (has commas +separating the columns, and contains a header line), such as +<pre> +header info goes here +ORD,011979,1626.5,0.0 +DSM,021979,1367.0,0.0 +... +</pre> +You can load this using +<pre> +[a,b,c,d] = textread('dat.txt', '%s %d %f %f', 'delimiter', ',', 'headerlines', 1); +</pre> +If your file is not in either of these formats, you can either use Perl to convert +it to this format, or use the Matlab scanf command. +Type +<tt> +help iofun +</tt> +for more information on Matlab's file functions. +<!-- +<p> +To load data directly from Excel, +you should buy the +<a href="http://www.mathworks.com/products/excellink/">Excel Link</a>. +To load data directly from a relational database, +you should buy the +<a href="http://www.mathworks.com/products/database">Database +toolbox</a>. +--> +<p> +BNT learning routines require data to be stored in a cell array. +data{i,m} is the value of node i in case (example) m, i.e., each +<em>column</em> is a case. +If node i is not observed in case m (missing value), set +data{i,m} = []. +(Not all the learning routines can cope with such missing values, however.) +In the special case that all the nodes are observed and are +scalar-valued (as opposed to vector-valued), the data can be +stored in a matrix (as opposed to a cell-array). +<p> +Suppose, as in the <a href="#mixexp">mixture of experts example</a>, +that we have 3 nodes in the graph: X(1) is the observed input, X(3) is +the observed output, and X(2) is a hidden (gating) node. We can +create the dataset as follows. +<pre> +data = load('dat.txt'); +ncases = size(data, 1); +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); +</pre> +Notice how we transposed the data, to convert rows into columns. +Also, cases{2,m} = [] for all m, since X(2) is always hidden. + + +<h2><a name="mle_complete">Maximum likelihood parameter estimation from complete data</h2> + +As an example, let's generate some data from the sprinkler network, randomize the parameters, +and then try to recover the original model. +First we create some training data using forwards sampling. +<pre> +samples = cell(N, nsamples); +for i=1:nsamples + samples(:,i) = sample_bnet(bnet); +end +</pre> +samples{j,i} contains the value of the j'th node in case i. +sample_bnet returns a cell array because, in general, each node might +be a vector of different length. +In this case, all nodes are discrete (and hence scalars), so we +could have used a regular array instead (which can be quicker): +<pre> +data = cell2num(samples); +</pre +So now data(j,i) = samples{j,i}. +<p> +Now we create a network with random parameters. +(The initial values of bnet2 don't matter in this case, since we can find the +globally optimal MLE independent of where we start.) +<pre> +% Make a tabula rasa +bnet2 = mk_bnet(dag, node_sizes); +seed = 0; +rand('state', seed); +bnet2.CPD{C} = tabular_CPD(bnet2, C); +bnet2.CPD{R} = tabular_CPD(bnet2, R); +bnet2.CPD{S} = tabular_CPD(bnet2, S); +bnet2.CPD{W} = tabular_CPD(bnet2, W); +</pre> +Finally, we find the maximum likelihood estimates of the parameters. +<pre> +bnet3 = learn_params(bnet2, samples); +</pre> +To view the learned parameters, we use a little Matlab hackery. +<pre> +CPT3 = cell(1,N); +for i=1:N + s=struct(bnet3.CPD{i}); % violate object privacy + CPT3{i}=s.CPT; +end +</pre> +Here are the parameters learned for node 4. +<pre> +dispcpt(CPT3{4}) +1 1 : 1.0000 0.0000 +2 1 : 0.2000 0.8000 +1 2 : 0.2273 0.7727 +2 2 : 0.0000 1.0000 +</pre> +So we see that the learned parameters are fairly close to the "true" +ones, which we display below. +<pre> +dispcpt(CPT{4}) +1 1 : 1.0000 0.0000 +2 1 : 0.1000 0.9000 +1 2 : 0.1000 0.9000 +2 2 : 0.0100 0.9900 +</pre> +We can get better results by using a larger training set, or using +informative priors (see <a href="#prior">below</a>). + + + +<h2><a name="prior">Parameter priors</h2> + +Currently, only tabular CPDs can have priors on their parameters. +The conjugate prior for a multinomial is the Dirichlet. +(For binary random variables, the multinomial is the same as the +Bernoulli, and the Dirichlet is the same as the Beta.) +<p> +The Dirichlet has a simple interpretation in terms of pseudo counts. +If we let N_ijk = the num. times X_i=k and Pa_i=j occurs in the +training set, where Pa_i are the parents of X_i, +then the maximum likelihood (ML) estimate is +T_ijk = N_ijk / N_ij (where N_ij = sum_k' N_ijk'), which will be 0 if N_ijk=0. +To prevent us from declaring that (X_i=k, Pa_i=j) is impossible just because this +event was not seen in the training set, +we can pretend we saw value k of X_i, for each value j of Pa_i some number (alpha_ijk) +of times in the past. +The MAP (maximum a posterior) estimate is then +<pre> +T_ijk = (N_ijk + alpha_ijk) / (N_ij + alpha_ij) +</pre> +and is never 0 if all alpha_ijk > 0. +For example, consider the network A->B, where A is binary and B has 3 +values. +A uniform prior for B has the form +<pre> + B=1 B=2 B=3 +A=1 1 1 1 +A=2 1 1 1 +</pre> +which can be created using +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); +</pre> +This prior does not satisfy the likelihood equivalence principle, +which says that <a href="#markov_equiv">Markov equivalent</a> models +should have the same marginal likelihood. +A prior that does satisfy this principle is shown below. +Heckerman (1995) calls this the +BDeu prior (likelihood equivalent uniform Bayesian Dirichlet). +<pre> + B=1 B=2 B=3 +A=1 1/6 1/6 1/6 +A=2 1/6 1/6 1/6 +</pre> +where we put N/(q*r) in each bin; N is the equivalent sample size, +r=|A|, q = |B|. +This can be created as follows +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'BDeu'); +</pre> +Here, 1 is the equivalent sample size, and is the strength of the +prior. +You can change this using +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', ... + 'BDeu', 'dirichlet_weight', 10); +</pre> +<!--where counts is an array of pseudo-counts of the same size as the +CPT.--> +<!-- +<p> +When you specify a prior, you should set row i of the CPT to the +normalized version of row i of the pseudo-count matrix, i.e., to the +expected values of the parameters. This will ensure that computing the +marginal likelihood sequentially (see <a +href="#bayes_learn">below</a>) and in batch form gives the same +results. +To do this, proceed as follows. +<pre> +tabular_CPD(bnet, i, 'prior', counts, 'CPT', mk_stochastic(counts)); +</pre> +For a non-informative prior, you can just write +<pre> +tabular_CPD(bnet, i, 'prior', 'unif', 'CPT', 'unif'); +</pre> +--> + + +<h2><a name="bayes_learn">(Sequential) Bayesian parameter updating from complete data</h2> + +If we use conjugate priors and have fully observed data, we can +compute the posterior over the parameters in batch form as follows. +<pre> +cases = sample_bnet(bnet, nsamples); +bnet = bayes_update_params(bnet, cases); +LL = log_marg_lik_complete(bnet, cases); +</pre> +bnet.CPD{i}.prior contains the new Dirichlet pseudocounts, +and bnet.CPD{i}.CPT is set to the mean of the posterior (the +normalized counts). +(Hence if the initial pseudo counts are 0, +<tt>bayes_update_params</tt> and <tt>learn_params</tt> will give the +same result.) + + + + +<p> +We can compute the same result sequentially (on-line) as follows. +<pre> +LL = 0; +for m=1:nsamples + LL = LL + log_marg_lik_complete(bnet, cases(:,m)); + bnet = bayes_update_params(bnet, cases(:,m)); +end +</pre> + +The file <tt>BNT/examples/static/StructLearn/model_select1</tt> has an example of +sequential model selection which uses the same idea. +We generate data from the model A->B +and compute the posterior prob of all 3 dags on 2 nodes: + (1) A B, (2) A <- B , (3) A -> B +Models 2 and 3 are <a href="#markov_equiv">Markov equivalent</a>, and therefore indistinguishable from +observational data alone, so we expect their posteriors to be the same +(assuming a prior which satisfies likelihood equivalence). +If we use random parameters, the "true" model only gets a higher posterior after 2000 trials! +However, if we make B a noisy NOT gate, the true model "wins" after 12 +trials, as shown below (red = model 1, blue/green (superimposed) +represents models 2/3). +<p> +<img src="Figures/model_select.png"> +<p> +The use of marginal likelihood for model selection is discussed in +greater detail in the +section on <a href="structure_learning">structure learning</a>. + + + + +<h2><a name="em">Maximum likelihood parameter estimation with missing values</h2> + +Now we consider learning when some values are not observed. +Let us randomly hide half the values generated from the water +sprinkler example. +<pre> +samples2 = samples; +hide = rand(N, nsamples) > 0.5; +[I,J]=find(hide); +for k=1:length(I) + samples2{I(k), J(k)} = []; +end +</pre> +samples2{i,l} is the value of node i in training case l, or [] if unobserved. +<p> +Now we will compute the MLEs using the EM algorithm. +We need to use an inference algorithm to compute the expected +sufficient statistics in the E step; the M (maximization) step is as +above. +<pre> +engine2 = jtree_inf_engine(bnet2); +max_iter = 10; +[bnet4, LLtrace] = learn_params_em(engine2, samples2, max_iter); +</pre> +LLtrace(i) is the log-likelihood at iteration i. We can plot this as +follows: +<pre> +plot(LLtrace, 'x-') +</pre> +Let's display the results after 10 iterations of EM. +<pre> +celldisp(CPT4) +CPT4{1} = + 0.6616 + 0.3384 +CPT4{2} = + 0.6510 0.3490 + 0.8751 0.1249 +CPT4{3} = + 0.8366 0.1634 + 0.0197 0.9803 +CPT4{4} = +(:,:,1) = + 0.8276 0.0546 + 0.5452 0.1658 +(:,:,2) = + 0.1724 0.9454 + 0.4548 0.8342 +</pre> +We can get improved performance by using one or more of the following +methods: +<ul> +<li> Increasing the size of the training set. +<li> Decreasing the amount of hidden data. +<li> Running EM for longer. +<li> Using informative priors. +<li> Initialising EM from multiple starting points. +</ul> + +<h2><a name="gaussianNumerical">Numerical problems when fitting +Gaussians using EM</h2> + +When fitting a <a href="#gaussian">Gaussian CPD</a> using EM, you +might encounter some numerical +problems. See <a +href="http://www.ai.mit.edu/~murphyk/Software/HMM/hmm.html#loglikpos">here</a> +for a discussion of this in the context of HMMs. +<!-- +(Note: BNT does not currently support spherical covariances, although it +would be easy to add, since KPMstats/clg_Mstep supports this option; +you would just need to modify gaussian_CPD/update_ess to accumulate +weighted inner products.) +--> + + +<h2><a name="tying">Parameter tying</h2> + +In networks with repeated structure (e.g., chains and grids), it is +common to assume that the parameters are the same at every node. This +is called parameter tying, and reduces the amount of data needed for +learning. +<p> +When we have tied parameters, there is no longer a one-to-one +correspondence between nodes and CPDs. +Rather, each CPD species the parameters for a whole equivalence class +of nodes. +It is easiest to see this by example. +Consider the following <a href="usage_dbn.html#hmm">hidden Markov +model (HMM)</a> +<p> +<img src="Figures/hmm3.gif"> +<p> +<!-- +We can create this graph structure, assuming we have T time-slices, +as follows. +(We number the nodes as shown in the figure, but we could equally well +number the hidden nodes 1:T, and the observed nodes T+1:2T.) +<pre> +N = 2*T; +dag = zeros(N); +hnodes = 1:2:2*T; +for i=1:T-1 + dag(hnodes(i), hnodes(i+1))=1; +end +onodes = 2:2:2*T; +for i=1:T + dag(hnodes(i), onodes(i)) = 1; +end +</pre> +<p> +The hidden nodes are always discrete, and have Q possible values each, +but the observed nodes can be discrete or continuous, and have O possible values/length. +<pre> +if cts_obs + dnodes = hnodes; +else + dnodes = 1:N; +end +ns = ones(1,N); +ns(hnodes) = Q; +ns(onodes) = O; +</pre> +--> +When HMMs are used for semi-infinite processes like speech recognition, +we assume the transition matrix +P(H(t+1)|H(t)) is the same for all t; this is called a time-invariant +or homogenous Markov chain. +Hence hidden nodes 2, 3, ..., T +are all in the same equivalence class, say class Hclass. +Similarly, the observation matrix P(O(t)|H(t)) is assumed to be the +same for all t, so the observed nodes are all in the same equivalence +class, say class Oclass. +Finally, the prior term P(H(1)) is in a class all by itself, say class +H1class. +This is illustrated below, where we explicitly represent the +parameters as random variables (dotted nodes). +<p> +<img src="Figures/hmm4_params.gif"> +<p> +In BNT, we cannot represent parameters as random variables (nodes). +Instead, we "hide" the +parameters inside one CPD for each equivalence class, +and then specify that the other CPDs should share these parameters, as +follows. +<pre> +hnodes = 1:2:2*T; +onodes = 2:2:2*T; +H1class = 1; Hclass = 2; Oclass = 3; +eclass = ones(1,N); +eclass(hnodes(2:end)) = Hclass; +eclass(hnodes(1)) = H1class; +eclass(onodes) = Oclass; +% create dag and ns in the usual way +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'equiv_class', eclass); +</pre> +Finally, we define the parameters for each equivalence class: +<pre> +bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior +bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix +if cts_obs + bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1)); +else + bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1)); +end +</pre> +In general, if bnet.CPD{e} = xxx_CPD(bnet, j), then j should be a +member of e's equivalence class; that is, it is not always the case +that e == j. You can use bnet.rep_of_eclass(e) to return the +representative of equivalence class e. +BNT will look up the parents of j to determine the size +of the CPT to use. It assumes that this is the same for all members of +the equivalence class. +Click <a href="http://www.cs.berkeley.edu/~murphyk/Bayes/param_tieing.html">here</a> for +a more complex example of parameter tying. +<p> +Note: +Normally one would define an HMM as a +<a href = "usage_dbn.html">Dynamic Bayes Net</a> +(see the function BNT/examples/dynamic/mk_chmm.m). +However, one can define an HMM as a static BN using the function +BNT/examples/static/Models/mk_hmm_bnet.m. + + + +<h1><a name="structure_learning">Structure learning</h1> + +Update (9/29/03): +Phillipe LeRay is developing some additional structure learning code +on top of BNT. Click <a +href="http://bnt.insa-rouen.fr/ajouts.html">here</a> +for details. + +<p> + +There are two very different approaches to structure learning: +constraint-based and search-and-score. +In the <a href="#constraint">constraint-based approach</a>, +we start with a fully connected graph, and remove edges if certain +conditional independencies are measured in the data. +This has the disadvantage that repeated independence tests lose +statistical power. +<p> +In the more popular search-and-score approach, +we perform a search through the space of possible DAGs, and either +return the best one found (a point estimate), or return a sample of the +models found (an approximation to the Bayesian posterior). +<p> +Unfortunately, the number of DAGs as a function of the number of +nodes, G(n), is super-exponential in n. +A closed form formula for G(n) is not known, but the first few values +are shown below (from Cooper, 1999). + +<table> +<tr> <th>n</th> <th align=left>G(n)</th> </tr> +<tr> <td>1</td> <td>1</td> </tr> +<tr> <td>2</td> <td>3</td> </tr> +<tr> <td>3</td> <td>25</td> </tr> +<tr> <td>4</td> <td>543</td> </tr> +<tr> <td>5</td> <td>29,281</td> </tr> +<tr> <td>6</td> <td>3,781,503</td> </tr> +<tr> <td>7</td> <td>1.1 x 10^9</td> </tr> +<tr> <td>8</td> <td>7.8 x 10^11</td> </tr> +<tr> <td>9</td> <td>1.2 x 10^15</td> </tr> +<tr> <td>10</td> <td>4.2 x 10^18</td> </tr> +</table> + +Since the number of DAGs is super-exponential in the number of nodes, +we cannot exhaustively search the space, so we either use a local +search algorithm (e.g., greedy hill climbining, perhaps with multiple +restarts) or a global search algorithm (e.g., Markov Chain Monte +Carlo). +<p> +If we know a total ordering on the nodes, +finding the best structure amounts to picking the best set of parents +for each node independently. +This is what the K2 algorithm does. +If the ordering is unknown, we can search over orderings, +which is more efficient than searching over DAGs (Koller and Friedman, 2000). +<p> +In addition to the search procedure, we must specify the scoring +function. There are two popular choices. The Bayesian score integrates +out the parameters, i.e., it is the marginal likelihood of the model. +The BIC (Bayesian Information Criterion) is defined as +log P(D|theta_hat) - 0.5*d*log(N), where D is the data, theta_hat is +the ML estimate of the parameters, d is the number of parameters, and +N is the number of data cases. +The BIC method has the advantage of not requiring a prior. +<p> +BIC can be derived as a large sample +approximation to the marginal likelihood. +(It is also equal to the Minimum Description Length of a model.) +However, in practice, the sample size does not need to be very large +for the approximation to be good. +For example, in the figure below, we plot the ratio between the log marginal likelihood +and the BIC score against data-set size; we see that the ratio rapidly +approaches 1, especially for non-informative priors. +(This plot was generated by the file BNT/examples/static/bic1.m. It +uses the water sprinkler BN with BDeu Dirichlet priors with different +equivalent sample sizes.) + +<p> +<center> +<IMG SRC="Figures/bic.png"> +</center> +<p> + +<p> +As with parameter learning, handling missing data/ hidden variables is +much harder than the fully observed case. +The structure learning routines in BNT can therefore be classified into 4 +types, analogously to the parameter learning case. +<p> + +<TABLE BORDER> +<tr> + <TH></TH> + <th>Full obs</th> + <th>Partial obs</th> +</tr> +<tr> + <th>Point</th> + <td><tt>learn_struct_K2</tt> <br> +<!-- <tt>learn_struct_hill_climb</tt></td> --> + <td><tt>not yet supported</tt></td> +</tr> +<tr> + <th>Bayes</th> + <td><tt>learn_struct_mcmc</tt></td> + <td>not yet supported</td> +</tr> +</table> + + +<h2><a name="markov_equiv">Markov equivalence</h2> + +If two DAGs encode the same conditional independencies, they are +called Markov equivalent. The set of all DAGs can be paritioned into +Markov equivalence classes. Graphs within the same class can +have +the direction of some of their arcs reversed without changing any of +the CI relationships. +Each class can be represented by a PDAG +(partially directed acyclic graph) called an essential graph or +pattern. This specifies which edges must be oriented in a certain +direction, and which may be reversed. + +<p> +When learning graph structure from observational data, +the best one can hope to do is to identify the model up to Markov +equivalence. To distinguish amongst graphs within the same equivalence +class, one needs interventional data: see the discussion on <a +href="#active">active learning</a> below. + + + +<h2><a name="enumerate">Exhaustive search</h2> + +The brute-force approach to structure learning is to enumerate all +possible DAGs, and score each one. This provides a "gold standard" +with which to compare other algorithms. We can do this as follows. +<pre> +dags = mk_all_dags(N); +score = score_dags(data, ns, dags); +</pre> +where data(i,m) is the value of node i in case m, +and ns(i) is the size of node i. +If the DAGs have a lot of families in common, we can cache the sufficient statistics, +making this potentially more efficient than scoring the DAGs one at a time. +(Caching is not currently implemented, however.) +<p> +By default, we use the Bayesian scoring metric, and assume CPDs are +represented by tables with BDeu(1) priors. +We can override these defaults as follows. +If we want to use uniform priors, we can say +<pre> +params = cell(1,N); +for i=1:N + params{i} = {'prior', 'unif'}; +end +score = score_dags(data, ns, dags, 'params', params); +</pre> +params{i} is a cell-array, containing optional arguments that are +passed to the constructor for CPD i. +<p> +Now suppose we want to use different node types, e.g., +Suppose nodes 1 and 2 are Gaussian, and nodes 3 and 4 softmax (both +these CPDs can support discrete and continuous parents, which is +necessary since all other nodes will be considered as parents). +The Bayesian scoring metric currently only works for tabular CPDs, so +we will use BIC: +<pre> +score = score_dags(data, ns, dags, 'discrete', [3 4], 'params', [], + 'type', {'gaussian', 'gaussian', 'softmax', softmax'}, 'scoring_fn', 'bic') +</pre> +In practice, one can't enumerate all possible DAGs for N > 5, +but one can evaluate any reasonably-sized set of hypotheses in this +way (e.g., nearest neighbors of your current best guess). +Think of this as "computer assisted model refinement" as opposed to de +novo learning. + + +<h2><a name="K2">K2</h2> + +The K2 algorithm (Cooper and Herskovits, 1992) is a greedy search algorithm that works as follows. +Initially each node has no parents. It then adds incrementally that parent whose addition most +increases the score of the resulting structure. When the addition of no single +parent can increase the score, it stops adding parents to the node. +Since we are using a fixed ordering, we do not need to check for +cycles, and can choose the parents for each node independently. +<p> +The original paper used the Bayesian scoring +metric with tabular CPDs and Dirichlet priors. +BNT generalizes this to allow any kind of CPD, and either the Bayesian +scoring metric or BIC, as in the example <a href="#enumerate">above</a>. +In addition, you can specify +an optional upper bound on the number of parents for each node. +The file BNT/examples/static/k2demo1.m gives an example of how to use K2. +We use the water sprinkler network and sample 100 cases from it as before. +Then we see how much data it takes to recover the generating structure: +<pre> +order = [C S R W]; +max_fan_in = 2; +sz = 5:5:100; +for i=1:length(sz) + dag2 = learn_struct_K2(data(:,1:sz(i)), node_sizes, order, 'max_fan_in', max_fan_in); + correct(i) = isequal(dag, dag2); +end +</pre> +Here are the results. +<pre> +correct = + Columns 1 through 12 + 0 0 0 0 0 0 0 1 0 1 1 1 + Columns 13 through 20 + 1 1 1 1 1 1 1 1 +</pre> +So we see it takes about sz(10)=50 cases. (BIC behaves similarly, +showing that the prior doesn't matter too much.) +In general, we cannot hope to recover the "true" generating structure, +only one that is in its <a href="#markov_equiv">Markov equivalence +class</a>. + + +<h2><a name="hill_climb">Hill-climbing</h2> + +Hill-climbing starts at a specific point in space, +considers all nearest neighbors, and moves to the neighbor +that has the highest score; if no neighbors have higher +score than the current point (i.e., we have reached a local maximum), +the algorithm stops. One can then restart in another part of the space. +<p> +A common definition of "neighbor" is all graphs that can be +generated from the current graph by adding, deleting or reversing a +single arc, subject to the acyclicity constraint. +Other neighborhoods are possible: see +<a href="http://research.microsoft.com/~dmax/publications/jmlr02.pdf"> +Optimal Structure Identification with Greedy Search</a>, Max +Chickering, JMLR 2002. + +<!-- +Note: This algorithm is currently (Feb '02) being implemented by Qian +Diao. +--> + + +<h2><a name="mcmc">MCMC</h2> + +We can use a Markov Chain Monte Carlo (MCMC) algorithm called +Metropolis-Hastings (MH) to search the space of all +DAGs. +The standard proposal distribution is to consider moving to all +nearest neighbors in the sense defined <a href="#hill_climb">above</a>. +<p> +The function can be called +as in the following example. +<pre> +[sampled_graphs, accept_ratio] = learn_struct_mcmc(data, ns, 'nsamples', 100, 'burnin', 10); +</pre> +We can convert our set of sampled graphs to a histogram +(empirical posterior over all the DAGs) thus +<pre> +all_dags = mk_all_dags(N); +mcmc_post = mcmc_sample_to_hist(sampled_graphs, all_dags); +</pre> +To see how well this performs, let us compute the exact posterior exhaustively. +<p> +<pre> +score = score_dags(data, ns, all_dags); +post = normalise(exp(score)); % assuming uniform structural prior +</pre> +We plot the results below. +(The data set was 100 samples drawn from a random 4 node bnet; see the +file BNT/examples/static/mcmc1.) +<pre> +subplot(2,1,1) +bar(post) +subplot(2,1,2) +bar(mcmc_post) +</pre> +<img src="Figures/mcmc_post.jpg" width="800" height="500"> +<p> +We can also plot the acceptance ratio versus number of MCMC steps, +as a crude convergence diagnostic. +<pre> +clf +plot(accept_ratio) +</pre> +<img src="Figures/mcmc_accept.jpg" width="800" height="300"> +<p> +Better convergence diagnostics can be found +<a href="<a href="http://www.lce.hut.fi/~ave/code/mcmcdiag/">here</a> +<p> +Even though the number of samples needed by MCMC is theoretically +polynomial (not exponential) in the dimensionality of the search space, in practice it has been +found that MCMC does not converge in reasonable time for graphs with +more than about 10 nodes. +<p> +<a href="http://www.bioss.sari.ac.uk/~dirk/software/DBmcmc/"> +Dirk Husmeier has extended MCMC model selection to DBNs</a>. + + +<h2><a name="active">Active structure learning</h2> + +As was mentioned <a href="#markov_equiv">above</a>, +one can only learn a DAG up to Markov equivalence, even given infinite data. +If one is interested in learning the structure of a causal network, +one needs interventional data. +(By "intervention" we mean forcing a node to take on a specific value, +thereby effectively severing its incoming arcs.) +<p> +Most of the scoring functions accept an optional argument +that specifies whether a node was observed to have a certain value, or +was forced to have that value: we set clamped(i,m)=1 if node i was +forced in training case m. e.g., see the file +BNT/examples/static/cooper_yoo. +<p> +An interesting question is to decide which interventions to perform +(c.f., design of experiments). For details, see the following tech +report +<ul> +<li> <a href = "../../Papers/alearn.ps.gz"> +Active learning of causal Bayes net structure</a>, Kevin Murphy, March +2001. +</ul> + + +<h2><a name="struct_em">Structural EM</h2> + +Computing the Bayesian score when there is partial observability is +computationally challenging, because the parameter posterior becomes +multimodal (the hidden nodes induce a mixture distribution). +One therefore needs to use approximations such as BIC. +Unfortunately, search algorithms are still expensive, because we need +to run EM at each step to compute the MLE, which is needed to compute +the score of each model. An alternative approach is +to do the local search steps inside of the M step of EM, which is more +efficient since the data has been "filled in" - this is +called the structural EM algorithm (Friedman 1997), and provably +converges to a local maximum of the BIC score. +<p> +Wei Hu has implemented SEM for discrete nodes. +You can download his package from +<a href="../SEM.zip">here</a>. +Please address all questions about this code to +wei.hu@intel.com. +See also <a href="#phl">Phl's implementation of SEM</a>. + +<!-- +<h2><a name="reveal">REVEAL algorithm</h2> + +A simple way to learn the structure of a fully observed, discrete, +factored DBN from a time series is described <a +href="usage_dbn.html#struct_learn">here</a>. +--> + + +<h2><a name="graphdraw">Visualizing the graph</h2> + +You can visualize an arbitrary graph (such as one learned using the +structure learning routines) with Matlab code contributed by +<a href="http://www.mbfys.kun.nl/~cemgil/matlab/layout.html">Ali +Taylan Cemgil</a> +from the University of Nijmegen. +For static BNs, call it as follows: +<pre> +draw_graph(bnet.dag); +</pre> +For example, this is the output produced on a +<a href="#qmr">random QMR-like model</a>: +<p> +<img src="Figures/qmr.rnd.jpg"> +<p> +If you install the excellent <a +href="http://www.research.att.com/sw/tools/graphviz">graphhviz</a>, an +open-source graph visualization package from AT&T, +you can create a much better visualization as follows +<pre> +graph_to_dot(bnet.dag) +</pre> +This works by converting the adjacency matrix to a file suitable +for input to graphviz (using the dot format), +then converting the output of graphviz to postscript, and displaying the results using +ghostview. +You can do each of these steps separately for more control, as shown +below. +<pre> +graph_to_dot(bnet.dag, 'filename', 'foo.dot'); +dot -Tps foo.dot -o foo.ps +ghostview foo.ps & +</pre> + +<h2><a name = "constraint">Constraint-based methods</h2> + +The IC algorithm (Pearl and Verma, 1991), +and the faster, but otherwise equivalent, PC algorithm (Spirtes, Glymour, and Scheines 1993), +computes many conditional independence tests, +and combines these constraints into a +PDAG to represent the whole +<a href="#markov_equiv">Markov equivalence class</a>. +<p> +IC*/FCI extend IC/PC to handle latent variables: see <a href="#ic_star">below</a>. +(IC stands for inductive causation; PC stands for Peter and Clark, +the first names of Spirtes and Glymour; FCI stands for fast causal +inference. +What we, following Pearl (2000), call IC* was called +IC in the original Pearl and Verma paper.) +For details, see +<ul> +<li> +<a href="http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html">Causation, +Prediction, and Search</a>, Spirtes, Glymour and +Scheines (SGS), 2001 (2nd edition), MIT Press. +<li> +<a href="http://bayes.cs.ucla.edu/BOOK-2K/index.html">Causality: Models, Reasoning and Inference</a>, J. Pearl, +2000, Cambridge University Press. +</ul> + +<p> + +The PC algorithm takes as arguments a function f, the number of nodes N, +the maximum fan in K, and additional arguments A which are passed to f. +The function f(X,Y,S,A) returns 1 if X is conditionally independent of Y given S, and 0 +otherwise. +For example, suppose we cheat by +passing in a CI "oracle" which has access to the true DAG; the oracle +tests for d-separation in this DAG, i.e., +f(X,Y,S) calls dsep(X,Y,S,dag). We can to this as follows. +<pre> +pdag = learn_struct_pdag_pc('dsep', N, max_fan_in, dag); +</pre> +pdag(i,j) = -1 if there is definitely an i->j arc, +and pdag(i,j) = 1 if there is either an i->j or and i<-j arc. +<p> +Applied to the sprinkler network, this returns +<pre> +pdag = + 0 1 1 0 + 1 0 0 -1 + 1 0 0 -1 + 0 0 0 0 +</pre> +So as expected, we see that the V-structure at the W node is uniquely identified, +but the other arcs have ambiguous orientation. +<p> +We now give an example from p141 (1st edn) / p103 (2nd end) of the SGS +book. +This example concerns the female orgasm. +We are given a correlation matrix C between 7 measured factors (such +as subjective experiences of coital and masturbatory experiences), +derived from 281 samples, and want to learn a causal model of the +data. We will not discuss the merits of this type of work here, but +merely show how to reproduce the results in the SGS book. +Their program, +<a href="http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html">Tetrad</a>, +makes use of the Fisher Z-test for conditional +independence, so we do the same: +<pre> +max_fan_in = 4; +nsamples = 281; +alpha = 0.05; +pdag = learn_struct_pdag_pc('cond_indep_fisher_z', n, max_fan_in, C, nsamples, alpha); +</pre> +In this case, the CI test is +<pre> +f(X,Y,S) = cond_indep_fisher_z(X,Y,S, C,nsamples,alpha) +</pre> +The results match those of Fig 12a of SGS apart from two edge +differences; presumably this is due to rounding error (although it +could be a bug, either in BNT or in Tetrad). +This example can be found in the file BNT/examples/static/pc2.m. + +<p> + +The IC* algorithm (Pearl and Verma, 1991), +and the faster FCI algorithm (Spirtes, Glymour, and Scheines 1993), +are like the IC/PC algorithm, except that they can detect the presence +of latent variables. +See the file <tt>learn_struct_pdag_ic_star</tt> written by Tamar +Kushnir. The output is a matrix P, defined as follows +(see Pearl (2000), p52 for details): +<pre> +% P(i,j) = -1 if there is either a latent variable L such that i <-L->j OR there is a directed edge from i->j. +% P(i,j) = -2 if there is a marked directed i-*>j edge. +% P(i,j) = P(j,i) = 1 if there is and undirected edge i--j +% P(i,j) = P(j,i) = 2 if there is a latent variable L such that i<-L->j. +</pre> + + +<h2><a name="phl">Philippe Leray's structure learning package</h2> + +Philippe Leray has written a +<a href="http://bnt.insa-rouen.fr/ajouts.html"> +structure learning package</a> that uses BNT. + +It currently (Juen 2003) has the following features: +<ul> +<li>PC with Chi2 statistical test +<li> MWST : Maximum weighted Spanning Tree +<li> Hill Climbing +<li> Greedy Search +<li> Structural EM +<li> hist_ic : optimal Histogram based on IC information criterion +<li> cpdag_to_dag +<li> dag_to_cpdag +<li> ... +</ul> + + +</a> + + +<!-- +<h2><a name="read_learning">Further reading on learning</h2> + +I recommend the following tutorials for more details on learning. +<ul> +<li> <a +href="http://www.cs.berkeley.edu/~murphyk/Papers/intel.ps.gz">My short +tutorial</a> on graphical models, which contains an overview of learning. + +<li> +<A HREF="ftp://ftp.research.microsoft.com/pub/tr/TR-95-06.PS"> +A tutorial on learning with Bayesian networks</a>, D. Heckerman, +Microsoft Research Tech Report, 1995. + +<li> <A HREF="http://www-cad.eecs.berkeley.edu/~wray/Mirror/lwgmja"> +Operations for Learning with Graphical Models</a>, +W. L. Buntine, JAIR'94, 159--225. +</ul> +<p> +--> + + + + + +<h1><a name="engines">Inference engines</h1> + +Up until now, we have used the junction tree algorithm for inference. +However, sometimes this is too slow, or not even applicable. +In general, there are many inference algorithms each of which make +different tradeoffs between speed, accuracy, complexity and +generality. Furthermore, there might be many implementations of the +same algorithm; for instance, a general purpose, readable version, +and a highly-optimized, specialized one. +To cope with this variety, we treat each inference algorithm as an +object, which we call an inference engine. + +<p> +An inference engine is an object that contains a bnet and supports the +'enter_evidence' and 'marginal_nodes' methods. The engine constructor +takes the bnet as argument and may do some model-specific processing. +When 'enter_evidence' is called, the engine may do some +evidence-specific processing. Finally, when 'marginal_nodes' is +called, the engine may do some query-specific processing. + +<p> +The amount of work done when each stage is specified -- structure, +parameters, evidence, and query -- depends on the engine. The cost of +work done early in this sequence can be amortized. On the other hand, +one can make better optimizations if one waits until later in the +sequence. +For example, the parameters might imply +conditional indpendencies that are not evident in the graph structure, +but can nevertheless be exploited; the evidence indicates which nodes +are observed and hence can effectively be disconnected from the +graph; and the query might indicate that large parts of the network +are d-separated from the query nodes. (Since it is not the actual +<em>values</em> of the evidence that matters, just which nodes are observed, +many engines allow you to specify which nodes will be observed when they are constructed, +i.e., before calling 'enter_evidence'. Some engines can still cope if +the actual pattern of evidence is different, e.g., if there is missing +data.) +<p> + +Although being maximally lazy (i.e., only doing work when a query is +issued) may seem desirable, +this is not always the most efficient. +For example, +when learning using EM, we need to call marginal_nodes N times, where N is the +number of nodes. <a href="varelim">Variable elimination</a> would end +up repeating a lot of work +each time marginal_nodes is called, making it inefficient for +learning. The junction tree algorithm, by contrast, uses dynamic +programming to avoid this redundant computation --- it calculates all +marginals in two passes during 'enter_evidence', so calling +'marginal_nodes' takes constant time. +<p> +We will discuss some of the inference algorithms implemented in BNT +below, and finish with a <a href="#engine_summary">summary</a> of all +of them. + + + + + + + +<h2><a name="varelim">Variable elimination</h2> + +The variable elimination algorithm, also known as bucket elimination +or peeling, is one of the simplest inference algorithms. +The basic idea is to "push sums inside of products"; this is explained +in more detail +<a +href="http://HTTP.CS.Berkeley.EDU/~murphyk/Bayes/bayes.html#infer">here</a>. +<p> +The principle of distributing sums over products can be generalized +greatly to apply to any commutative semiring. +This forms the basis of many common algorithms, such as Viterbi +decoding and the Fast Fourier Transform. For details, see + +<ul> +<li> R. McEliece and S. M. Aji, 2000. +<!--<a href="http://www.systems.caltech.edu/EE/Faculty/rjm/papers/GDL.ps">--> +<a href="GDL.pdf"> +The Generalized Distributive Law</a>, +IEEE Trans. Inform. Theory, vol. 46, no. 2 (March 2000), +pp. 325--343. + + +<li> +F. R. Kschischang, B. J. Frey and H.-A. Loeliger, 2001. +<a href="http://www.cs.toronto.edu/~frey/papers/fgspa.abs.html"> +Factor graphs and the sum-product algorithm</a> +IEEE Transactions on Information Theory, February, 2001. + +</ul> + +<p> +Choosing an order in which to sum out the variables so as to minimize +computational cost is known to be NP-hard. +The implementation of this algorithm in +<tt>var_elim_inf_engine</tt> makes no attempt to optimize this +ordering (in contrast, say, to <tt>jtree_inf_engine</tt>, which uses a +greedy search procedure to find a good ordering). +<p> +Note: unlike most algorithms, var_elim does all its computational work +inside of <tt>marginal_nodes</tt>, not inside of +<tt>enter_evidence</tt>. + + + + +<h2><a name="global">Global inference methods</h2> + +The simplest inference algorithm of all is to explicitely construct +the joint distribution over all the nodes, and then to marginalize it. +This is implemented in <tt>global_joint_inf_engine</tt>. +Since the size of the joint is exponential in the +number of discrete (hidden) nodes, this is not a very practical algorithm. +It is included merely for pedagogical and debugging purposes. +<p> +Three specialized versions of this algorithm have also been implemented, +corresponding to the cases where all the nodes are discrete (D), all +are Gaussian (G), and some are discrete and some Gaussian (CG). +They are called <tt>enumerative_inf_engine</tt>, +<tt>gaussian_inf_engine</tt>, +and <tt>cond_gauss_inf_engine</tt> respectively. +<p> +Note: unlike most algorithms, these global inference algorithms do all their computational work +inside of <tt>marginal_nodes</tt>, not inside of +<tt>enter_evidence</tt>. + + +<h2><a name="quickscore">Quickscore</h2> + +The junction tree algorithm is quite slow on the <a href="#qmr">QMR</a> network, +since the cliques are so big. +One simple trick we can use is to notice that hidden leaves do not +affect the posteriors on the roots, and hence do not need to be +included in the network. +A second trick is to notice that the negative findings can be +"absorbed" into the prior: +see the file +BNT/examples/static/mk_minimal_qmr_bnet for details. +<p> + +A much more significant speedup is obtained by exploiting special +properties of the noisy-or node, as done by the quickscore +algorithm. For details, see +<ul> +<li> Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI 89. +<li> Rish and Dechter, "On the impact of causal independence", UCI +tech report, 1998. +</ul> + +This has been implemented in BNT as a special-purpose inference +engine, which can be created and used as follows: +<pre> +engine = quickscore_inf_engine(inhibit, leak, prior); +engine = enter_evidence(engine, pos, neg); +m = marginal_nodes(engine, i); +</pre> + + +<h2><a name="belprop">Belief propagation</h2> + +Even using quickscore, exact inference takes time that is exponential +in the number of positive findings. +Hence for large networks we need to resort to approximate inference techniques. +See for example +<ul> +<li> T. Jaakkola and M. Jordan, "Variational probabilistic inference and the +QMR-DT network", JAIR 10, 1999. + +<li> K. Murphy, Y. Weiss and M. Jordan, "Loopy belief propagation for approximate inference: an empirical study", + UAI 99. +</ul> +The latter approximation +entails applying Pearl's belief propagation algorithm to a model even +if it has loops (hence the name loopy belief propagation). +Pearl's algorithm, implemented as <tt>pearl_inf_engine</tt>, gives +exact results when applied to singly-connected graphs +(a.k.a. polytrees, since +the underlying undirected topology is a tree, but a node may have +multiple parents). +To apply this algorithm to a graph with loops, +use <tt>pearl_inf_engine</tt>. +This can use a centralized or distributed message passing protocol. +You can use it as in the following example. +<pre> +engine = pearl_inf_engine(bnet, 'max_iter', 30); +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, i); +</pre> +We found that this algorithm often converges, and when it does, often +is very accurate, but it depends on the precise setting of the +parameter values of the network. +(See the file BNT/examples/static/qmr1 to repeat the experiment for yourself.) +Understanding when and why belief propagation converges/ works +is a topic of ongoing research. +<p> +<tt>pearl_inf_engine</tt> can exploit special structure in noisy-or +and gmux nodes to compute messages efficiently. +<p> +<tt>belprop_inf_engine</tt> is like pearl, but uses potentials to +represent messages. Hence this is slower. +<p> +<tt>belprop_fg_inf_engine</tt> is like belprop, +but is designed for factor graphs. + + + +<h2><a name="sampling">Sampling</h2> + +BNT now (Mar '02) has two sampling (Monte Carlo) inference algorithms: +<ul> +<li> <tt>likelihood_weighting_inf_engine</tt> which does importance +sampling and can handle any node type. +<li> <tt>gibbs_sampling_inf_engine</tt>, written by Bhaskara Marthi. +Currently this can only handle tabular CPDs. +For a much faster and more powerful Gibbs sampling program, see +<a href="http://www.mrc-bsu.cam.ac.uk/bugs">BUGS</a>. +</ul> +Note: To generate samples from a network (which is not the same as inference!), +use <tt>sample_bnet</tt>. + + + +<h2><a name="engine_summary">Summary of inference engines</h2> + + +The inference engines differ in many ways. Here are +some of the major "axes": +<ul> +<li> Works for all topologies or makes restrictions? +<li> Works for all node types or makes restrictions? +<li> Exact or approximate inference? +</ul> + +<p> +In terms of topology, most engines handle any kind of DAG. +<tt>belprop_fg</tt> does approximate inference on factor graphs (FG), which +can be used to represent directed, undirected, and mixed (chain) +graphs. +(In the future, we plan to support exact inference on chain graphs.) +<tt>quickscore</tt> only works on QMR-like models. +<p> +In terms of node types: algorithms that use potentials can handle +discrete (D), Gaussian (G) or conditional Gaussian (CG) models. +Sampling algorithms can essentially handle any kind of node (distribution). +Other algorithms make more restrictive assumptions in exchange for +speed. +<p> +Finally, most algorithms are designed to give the exact answer. +The belief propagation algorithms are exact if applied to trees, and +in some other cases. +Sampling is considered approximate, even though, in the limit of an +infinite number of samples, it gives the exact answer. + +<p> + +Here is a summary of the properties +of all the engines in BNT which work on static networks. +<p> +<table> +<table border units = pixels><tr> +<td align=left width=0>Name +<td align=left width=0>Exact? +<td align=left width=0>Node type? +<td align=left width=0>topology +<tr> +<tr> +<td align=left> belprop +<td align=left> approx +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> belprop_fg +<td align=left> approx +<td align=left> D +<td align=left> factor graph +<tr> +<td align=left> cond_gauss +<td align=left> exact +<td align=left> CG +<td align=left> DAG +<tr> +<td align=left> enumerative +<td align=left> exact +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> gaussian +<td align=left> exact +<td align=left> G +<td align=left> DAG +<tr> +<td align=left> gibbs +<td align=left> approx +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> global_joint +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +<tr> +<td align=left> jtree +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +b<tr> +<td align=left> likelihood_weighting +<td align=left> approx +<td align=left> any +<td align=left> DAG +<tr> +<td align=left> pearl +<td align=left> approx +<td align=left> D,G +<td align=left> DAG +<tr> +<td align=left> pearl +<td align=left> exact +<td align=left> D,G +<td align=left> polytree +<tr> +<td align=left> quickscore +<td align=left> exact +<td align=left> noisy-or +<td align=left> QMR +<tr> +<td align=left> stab_cond_gauss +<td align=left> exact +<td align=left> CG +<td align=left> DAG +<tr> +<td align=left> var_elim +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +</table> + + + +<h1><a name="influence">Influence diagrams/ decision making</h1> + +BNT implements an exact algorithm for solving LIMIDs (limited memory +influence diagrams), described in +<ul> +<li> S. L. Lauritzen and D. Nilsson. +<a href="http://www.math.auc.dk/~steffen/papers/limids.pdf"> +Representing and solving decision problems with limited +information</a> +Management Science, 47, 1238 - 1251. September 2001. +</ul> +LIMIDs explicitely show all information arcs, rather than implicitely +assuming no forgetting. This allows them to model forgetful +controllers. +<p> +See the examples in <tt>BNT/examples/limids</tt> for details. + + + + +<h1>DBNs, HMMs, Kalman filters and all that</h1> + +Click <a href="usage_dbn.html">here</a> for documentation about how to +use BNT for dynamical systems and sequence data. + + +</BODY>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/usage_dbn.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,719 @@ +<HEAD> +<TITLE>How to use BNT for DBNs</TITLE> +</HEAD> + +<BODY BGCOLOR="#FFFFFF"> +<!-- white background is better for the pictures and equations --> + +Documentation last updated on 7 June 2004 + +<h1>How to use BNT for DBNs</h1> + +<p> +<ul> +<li> <a href="#spec">Model specification</a> +<ul> +<li> <a href="#hmm">HMMs</a> +<li> <a href="#lds">Kalman filters</a> +<li> <a href="#chmm">Coupled HMMs</a> +<li> <a href="#water">Water network</a> +<li> <a href="#bat">BAT network</a> +</ul> + +<li> <a href="#inf">Inference</a> +<ul> +<li> <a href="#discrete">Discrete hidden nodes</a> +<li> <a href="#cts">Continuous hidden nodes</a> +</ul> + +<li> <a href="#learn">Learning</a> +<ul> +<li> <a href="#param_learn">Parameter learning</a> +<li> <a href="#struct_learn">Structure learning</a> +</ul> + +</ul> + +Note: +you are recommended to read an introduction +to DBNs first, such as +<a href="http://www.ai.mit.edu/~murphyk/Papers/dbnchapter.pdf"> +this book chapter</a>. +<br> +You may also want to consider using +<a href=http://ssli.ee.washington.edu/~bilmes/gmtk/>GMTk</a>, which is +an excellent C++ package for DBNs. + + +<h1><a name="spec">Model specification</h1> + + +<!--<h1><a name="dbn_intro">Dynamic Bayesian Networks (DBNs)</h1>--> + +Dynamic Bayesian Networks (DBNs) are directed graphical models of stochastic +processes. +They generalise <a href="#hmm">hidden Markov models (HMMs)</a> +and <a href="#lds">linear dynamical systems (LDSs)</a> +by representing the hidden (and observed) state in terms of state +variables, which can have complex interdependencies. +The graphical structure provides an easy way to specify these +conditional independencies, and hence to provide a compact +parameterization of the model. +<p> +Note that "temporal Bayesian network" would be a better name than +"dynamic Bayesian network", since +it is assumed that the model structure does not change, but +the term DBN has become entrenched. +We also normally assume that the parameters do not +change, i.e., the model is time-invariant. +However, we can always add extra +hidden nodes to represent the current "regime", thereby creating +mixtures of models to capture periodic non-stationarities. +<p> +There are some cases where the size of the state space can change over +time, e.g., tracking a variable, but unknown, number of objects. +In this case, we need to change the model structure over time. +BNT does not support this. +<!-- +, but see the following paper for a +discussion of some of the issues: +<ul> +<li> <a href="ftp://ftp.cs.monash.edu.au/pub/annn/smc.ps"> +Dynamic belief networks for discrete monitoring</a>, +A. E. Nicholson and J. M. Brady. +IEEE Systems, Man and Cybernetics, 24(11):1593-1610, 1994. +</ul> +--> + + +<h2><a name="hmm">Hidden Markov Models (HMMs)</h2> + +The simplest kind of DBN is a Hidden Markov Model (HMM), which has +one discrete hidden node and one discrete or continuous +observed node per slice. We illustrate this below. +As before, circles denote continuous nodes, squares denote +discrete nodes, clear means hidden, shaded means observed. +<!-- +(The observed nodes can be +discrete or continuous; the crucial thing about an HMM is that the +hidden nodes are discrete, so the system can model arbitrary dynamics +-- providing, of course, that the hidden state space is large enough.) +--> +<p> +<img src="Figures/hmm3.gif"> +<p> +We have "unrolled" the model for three "time slices" -- the structure and parameters are +assumed to repeat as the model is unrolled further. +Hence to specify a DBN, we need to +define the intra-slice topology (within a slice), +the inter-slice topology (between two slices), +as well as the parameters for the first two slices. +(Such a two-slice temporal Bayes net is often called a 2TBN.) +<p> +We can specify the topology as follows. +<PRE> +intra = zeros(2); +intra(1,2) = 1; % node 1 in slice t connects to node 2 in slice t + +inter = zeros(2); +inter(1,1) = 1; % node 1 in slice t-1 connects to node 1 in slice t +</pre> +We can specify the parameters as follows, +where for simplicity we assume the observed node is discrete. +<pre> +Q = 2; % num hidden states +O = 2; % num observable symbols + +ns = [Q O]; +dnodes = 1:2; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes); +for i=1:4 + bnet.CPD{i} = tabular_CPD(bnet, i); +end +</pre> +<p> +We assume the distributions P(X(t) | X(t-1)) and +P(Y(t) | X(t)) are independent of t for t > 1. +Hence the CPD for nodes 5, 7, ... is the same as for node 3, so we say they +are in the same equivalence class, with node 3 being the "representative" +for this class. In other words, we have tied the parameters for nodes +3, 5, 7, ... +Similarly, nodes 4, 6, 8, ... are tied. +Note, however, that (the parameters for) nodes 1 and 2 are not tied to +subsequent slices. +<p> +Above we assumed the observation model P(Y(t) | X(t)) is independent of t for t>1, but +it is conventional to assume this is true for all t. +So we would like to put nodes 2, 4, 6, ... all in the same class. +We can do this by explicitely defining the equivalence classes, as +follows (see <a href="usage.html#tying">here</a> for more details on +parameter tying). +<p> +We define eclass1(i) to be the equivalence class that node i in slice +1 belongs to. +Similarly, we define eclass2(i) to be the equivalence class that node i in slice +2, 3, ..., belongs to. +For an HMM, we have +<pre> +eclass1 = [1 2]; +eclass2 = [3 2]; +eclass = [eclass1 eclass2]; +</pre> +This ties the observation model across slices, +since e.g., eclass(4) = eclass(2) = 2. +<p> +By default, +eclass1 = 1:ss, and eclass2 = (1:ss)+ss, where ss = slice size = the +number of nodes per slice. +<!--This will tie nodes in slices 3, 4, ... to the the nodes in slice 2, +but none of the nodes in slice 2 to any in slice 1.--> +But by using the above tieing pattern, +we now only have 3 CPDs to specify, instead of 4: +<pre> +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2); +prior0 = normalise(rand(Q,1)); +transmat0 = mk_stochastic(rand(Q,Q)); +obsmat0 = mk_stochastic(rand(Q,O)); +bnet.CPD{1} = tabular_CPD(bnet, 1, prior0); +bnet.CPD{2} = tabular_CPD(bnet, 2, obsmat0); +bnet.CPD{3} = tabular_CPD(bnet, 3, transmat0); +</pre> +We discuss how to do <a href="#inf">inference</a> and <a href="#learn">learning</a> on this model +below. +(See also +my <a href="../HMM/hmm.html">HMM toolbox</a>, which is included with BNT.) + +<p> +Some common variants on HMMs are shown below. +BNT can handle all of these. +<p> +<center> +<table> +<tr> +<td><img src="Figures/hmm_gauss.gif"> +<td><img src="Figures/hmm_mixgauss.gif" +<td><img src="Figures/hmm_ar.gif"> +<tr> +<td><img src="Figures/hmm_factorial.gif"> +<td><img src="Figures/hmm_coupled.gif" +<td><img src="Figures/hmm_io.gif"> +<tr> +</table> +</center> + + + +<h2><a name="lds">Linear Dynamical Systems (LDSs) and Kalman filters</h2> + +A Linear Dynamical System (LDS) has the same topology as an HMM, but +all the nodes are assumed to have linear-Gaussian distributions, i.e., +<pre> + x(t+1) = A*x(t) + w(t), w ~ N(0, Q), x(0) ~ N(init_x, init_V) + y(t) = C*x(t) + v(t), v ~ N(0, R) +</pre> +Some simple variants are shown below. +<p> +<center> +<table> +<tr> +<td><img src="Figures/ar1.gif"> +<td><img src="Figures/sar.gif"> +<td><img src="Figures/kf.gif"> +<td><img src="Figures/skf.gif"> +</table> +</center> +<p> + +We can create a regular LDS in BNT as follows. +<pre> + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +X = 2; % size of hidden state +Y = 2; % size of observable state + +ns = [X Y]; +dnodes = []; +onodes = [2]; +eclass1 = [1 2]; +eclass2 = [3 2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2); + +x0 = rand(X,1); +V0 = eye(X); % must be positive semi definite! +C0 = rand(Y,X); +R0 = eye(Y); +A0 = rand(X,X); +Q0 = eye(X); + +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0, 'cov_prior_weight', 0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0, ... + 'clamp_mean', 1, 'cov_prior_weight', 0); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0, ... + 'clamp_mean', 1, 'cov_prior_weight', 0); +</pre> +We discuss how to do <a href="#inf">inference</a> and <a href="#learn">learning</a> on this model +below. +(See also +my <a href="../Kalman/kalman.html">Kalman filter toolbox</a>, which is included with BNT.) +<p> + + +<h2><a name="chmm">Coupled HMMs</h2> + +Here is an example of a coupled HMM with N=5 chains, unrolled for T=3 +slices. Each hidden discrete node has a private observed Gaussian +child. +<p> +<img src="Figures/chmm5.gif"> +<p> +We can make this using the function +<pre> +Q = 2; % binary hidden nodes +discrete_obs = 0; % cts observed nodes +Y = 1; % scalar observed nodes +bnet = mk_chmm(N, Q, Y, discrete_obs); +</pre> + +<!--We will use this model <a href="#pred">below</a> to illustrate online prediction.--> + + + +<h2><a name="water">Water network</h2> + +Consider the following model +of a water purification plant, developed +by Finn V. Jensen, Uffe Kjærulff, Kristian G. Olesen, and Jan +Pedersen. +<!-- +The clear nodes represent the hidden state of the system in +factored form, and the shaded nodes represent the observations in +factored form. +--> +<!-- +(Click <a +href="http://www-nt.cs.berkeley.edu/home/nir/public_html/Repository/water.htm">here</a> +for more details on this model. +Following Boyen and Koller, we have added discrete evidence nodes.) +--> +<!-- +We have "unrolled" the model for three "time slices" -- the structure and parameters are +assumed to repeat as the model is unrolled further. +Hence to specify a DBN, we need to +define the intra-slice topology (within a slice), +the inter-slice topology (between two slices), +as well as the parameters for the first two slices. +(Such a two-slice temporal Bayes net is often called a 2TBN.) +--> +<p> +<center> +<IMG SRC="Figures/water3_75.gif"> +</center> +We now show how to specify this model in BNT. +<pre> +ss = 12; % slice size +intra = zeros(ss); +intra(1,9) = 1; +intra(3,10) = 1; +intra(4,11) = 1; +intra(8,12) = 1; + +inter = zeros(ss); +inter(1, [1 3]) = 1; % node 1 in slice 1 connects to nodes 1 and 3 in slice 2 +inter(2, [2 3 7]) = 1; +inter(3, [3 4 5]) = 1; +inter(4, [3 4 6]) = 1; +inter(5, [3 5 6]) = 1; +inter(6, [4 5 6]) = 1; +inter(7, [7 8]) = 1; +inter(8, [6 7 8]) = 1; + +onodes = 9:12; % observed +dnodes = 1:ss; % discrete +ns = 2*ones(1,ss); % binary nodes +eclass1 = 1:12; +eclass2 = [13:20 9:12]; +eclass = [eclass1 eclass2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2); +for e=1:max(eclass) + bnet.CPD{e} = tabular_CPD(bnet, e); +end +</pre> +We have tied the observation parameters across all slices. +Click <a href="param_tieing.html">here</a> for a more complex example +of parameter tieing. + +<!-- +Let X(i,t) denote the i'th hidden node in slice t, +and Y(i,y) denote the i'th observed node in slice t. +We also use the notation Nj to refer to the j'th node in the +unrolled network, e.g., N25 = X(1,3), N33 = Y(1,3). +<p> +We assume the distributions P(X(i,t) | X(i,t-1)) and +P(Y(i,t) | X(i,t)) are independent of t for t > 1 and for all i. +Hence the CPD for N25, N37, ... is the same as for N13, so we say they +are in the same equivalence class, with N13 being the "representative" +for this class. In other words, we have tied the parameters for nodes +N13, N25, N37, ... +Note, however, that the parameters for the nodes in the first slice +are not tied, so each equivalence class for nodes 1..12 contains a +single node. +<p> +Above we assumed P(Y(i,t) | X(i,t)) is independent of t for t>1, but +it is conventional to assume this is true for all t. +So we would like to put N9, N21, N33, ... all in the same class, and +similarly for the other observed nodes. +We can do this by explicitely defining the equivalence classes, as +follows. +<p> +We define eclass1(i) to be the equivalence class that node i in slice +1 belongs to. +Similarly, we define eclass2(i) to be the equivalence class that node i in slice +2, 3, ..., belongs to. +For the water model, we have +<pre> +</pre> +This ties the observation model across slices, +since e.g., eclass(9) = eclass(21) = 9, so Y(1,1) and Y(1,2) belong to the +same class. +<p> +By default, +eclass1 = 1:ss, and eclass2 = (1:ss)+ss, where ss = slice size = the +number of nodes per slice. +This will tie nodes in slices 3, 4, ... to the the nodes in slice 2, +but none of the nodes in slice 2 to any in slice 1. +By using the above tieing pattern, +we now only have 20 CPDs to specify, instead of 24: +<pre> +bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2); +for e=1:max(eclass) + bnet.CPD{e} = tabular_CPD(bnet, e); +end +</pre> +--> + + + +<h2><a name="bat">BATnet</h2> + +As an example of a more complicated DBN, consider the following +example, +which is a model of a car's high level state, as might be used by +an automated car. +(The model is from Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a +Bayesian Automated Taxi", IJCAI 95. The figure is from +Boyen and Koller, "Tractable Inference for Complex Stochastic +Processes", UAI98. +For simplicity, we only show the observed nodes for slice 2.) +<p> +<center> +<IMG SRC="Figures/batnet.gif"> +</center> +<p> +Since this topology is so complicated, +it is useful to be able to refer to the nodes by name, instead of +number. +<pre> +names = {'LeftClr', 'RightClr', 'LatAct', ... 'Bclr', 'BYdotDiff'}; +ss = length(names); +</pre> +We can specify the intra-slice topology using a cell array as follows, +where each row specifies a connection between two named nodes: +<pre> +intrac = {... + 'LeftClr', 'LeftClrSens'; + 'RightClr', 'RightClrSens'; + ... + 'BYdotDiff', 'BcloseFast'}; +</pre> +Finally, we can convert this cell array to an adjacency matrix using +the following function: +<pre> +[intra, names] = mk_adj_mat(intrac, names, 1); +</pre> +This function also permutes the names so that they are in topological +order. +Given this ordering of the names, we can make the inter-slice +connectivity matrix as follows: +<pre> +interc = {... + 'LeftClr', 'LeftClr'; + 'LeftClr', 'LatAct'; + ... + 'FBStatus', 'LatAct'}; + +inter = mk_adj_mat(interc, names, 0); +</pre> + +To refer to a node, we must know its number, which can be computed as +in the following example: +<pre> +obs = {'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', 'FYdotDiffSens', ... + 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens'}; +for i=1:length(obs) + onodes(i) = strmatch(obs{i}, names); +end +onodes = sort(onodes); +</pre> +(We sort the onodes since most BNT routines assume that set-valued +arguments are in sorted order.) +We can now make the DBN: +<pre> +dnodes = 1:ss; +ns = 2*ones(1,ss); % binary nodes +bnet = mk_dbn(intra, inter, ns, 'iscrete', dnodes); +</pre> +To specify the parameters, we must know the order of the parents. +See the function BNT/general/mk_named_CPT for a way to do this in the +case of tabular nodes. For simplicity, we just generate random +parameters: +<pre> +for i=1:2*ss + bnet.CPD{i} = tabular_CPD(bnet, i); +end +</pre> +A complete version of this example is available in BNT/examples/dynamic/bat1.m. + + + + +<h1><a name="inf">Inference</h1> + + +The general inference problem for DBNs is to compute +P(X(i,t0) | Y(:, t1:t2)), where X(i,t) represents the i'th hidden +variable at time t and Y(:,t1:t2) represents all the evidence +between times t1 and t2. +There are several special cases of interest, illustrated below. +The arrow indicates t0: it is X(t0) that we are trying to estimate. +The shaded region denotes t1:t2, the available data. +<p> + +<img src="Figures/filter.gif"> + +<p> +BNT can currently only handle offline smoothing. +(The HMM engine handles filtering and, to a limited extent, prediction.) +The usage is similar to static +inference engines, except now the evidence is a 2D cell array of +size ss*T, where ss is the number of nodes per slice (ss = slice sizee) and T is the +number of slices. +Also, 'marginal_nodes' takes two arguments, the nodes and the time-slice. +For example, to compute P(X(i,t) | y(:,1:T)), we proceed as follows +(where onodes are the indices of the observedd nodes in each slice, +which correspond to y): +<pre> +ev = sample_dbn(bnet, T); +evidence = cell(ss,T); +evidence(onodes,:) = ev(onodes, :); % all cells besides onodes are empty +[engine, ll] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, i, t); +</pre> + + +<h2><a name="discrete">Discrete hidden nodes</h2> + +If all the hidden nodes are discrete, +we can use the junction tree algorithm to perform inference. +The simplest approach, +<tt>jtree_unrolled_dbn_inf_engine</tt>, +unrolls the DBN into a static network and applies jtree; however, for +long sequences, this +can be very slow and can result in numerical underflow. +A better approach is to apply the jtree algorithm to pairs of +neighboring slices at a time; this is implemented in +<tt>jtree_dbn_inf_engine</tt>. + +<p> +A DBN can be converted to an HMM if all the hidden nodes are discrete. +In this case, you can use +<tt>hmm_inf_engine</tt>. This is faster than jtree for small models +because the constant factors of the algorithm are lower, but can be +exponentially slower for models with many variables +(e.g., > 6 binary hidden nodes). + +<p> +The use of both +<tt>jtree_dbn_inf_engine</tt> +and +<tt>hmm_inf_engine</tt> +is deprecated. +A better approach is to construct a smoother engine out of lower-level +engines, which implement forward/backward operators. +You can create these engines as follows. +<pre> +engine = smoother_engine(hmm_2TBN_inf_engine(bnet)); +or +engine = smoother_engine(jtree_2TBN_inf_engine(bnet)); +</pre> +You then call them in the usual way: +<pre> +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, nodes, t); +</pre> +Note: you must declare the observed nodes in the bnet before using +hmm_2TBN_inf_engine. + + +<p> +Unfortunately, when all the hiddden nodes are discrete, +exact inference takes O(2^n) time, where n is the number of hidden +nodes per slice, +even if the model is sparse. +The basic reason for this is that two nodes become correlated, even if +there is no direct connection between them in the 2TBN, +by virtue of sharing common ancestors in the past. +Hence we need to use approximations. +<p> +A popular approximate inference algorithm for discrete DBNs, known as BK, is described in +<ul> +<li> +<A HREF="http://robotics.Stanford.EDU/~xb/uai98/index.html"> +Tractable inference for complex stochastic processes </A>, +Boyen and Koller, UAI 1998 +<li> +<A HREF="http://robotics.Stanford.EDU/~xb/nips98/index.html"> +Approximate learning of dynamic models</a>, Boyen and Koller, NIPS +1998. +</ul> +This approximates the belief state with a product of +marginals on a specified set of clusters. For example, +in the water network, we might use the following clusters: +<pre> +engine = bk_inf_engine(bnet, { [1 2], [3 4 5 6], [7 8] }); +</pre> +This engine can now be used just like the jtree engine. +Two special cases of the BK algorithm are supported: 'ff' (fully +factored) means each node has its own cluster, and 'exact' means there +is 1 cluster that contains the whole slice. These can be created as +follows: +<pre> +engine = bk_inf_engine(bnet, 'ff'); +engine = bk_inf_engine(bnet, 'exact'); +</pre> +For pedagogical purposes, an implementation of BK-FF that uses an HMM +instead of junction tree is available at +<tt>bk_ff_hmm_inf_engine</tt>. + + + +<h2><a name="cts">Continuous hidden nodes</h2> + +If all the hidden nodes are linear-Gaussian, <em>and</em> the observed nodes are +linear-Gaussian, +the model is a <a href="http://www.cs.berkeley.edu/~murphyk/Bayes/kalman.html"> +linear dynamical system</a> (LDS). +A DBN can be converted to an LDS if all the hidden nodes are linear-Gaussian +and if they are all persistent. In this case, you can use +<tt>kalman_inf_engine</tt>. +For more general linear-gaussian models, you can use +<tt>jtree_dbn_inf_engine</tt> or <tt>jtree_unrolled_dbn_inf_engine</tt>. + +<p> +For nonlinear systems with Gaussian noise, the unscented Kalman filter (UKF), +due to Julier and Uhlmann, is far superior to the well-known extended Kalman +filter (EKF), both in theory and practice. +<!-- +See +<A HREF="http://phoebe.robots.ox.ac.uk/default.html">"A General Method for +Approximating Nonlinear Transformations of +Probability Distributions"</A>. +(If the above link is down, +try <a href="http://www.ece.ogi.edu/~ericwan/pubs.html">Eric Wan's</a> +page, who has done a lot of work on the UKF.) +<p> +--> +The key idea of the UKF is that it is easier to estimate a Gaussian distribution +from a set of points than to approximate an arbitrary non-linear +function. +We start with points that are plus/minus sigma away from the mean along +each dimension, and then pipe them through the nonlinearity, and +then fit a Gaussian to the transformed points. +(No need to compute Jacobians, unlike the EKF!) + +<p> +For systems with non-Gaussian noise, I recommend +<a href="http://www.cs.berkeley.edu/~jfgf/smc/">Particle +filtering</a> (PF), which is a popular sequential Monte Carlo technique. + +<p> +The EKF can be used as a proposal distribution for a PF. +This method is better than either one alone. +See <a href="http://www.cs.berkeley.edu/~jfgf/upf.ps.gz">The Unscented Particle Filter</a>, +by R van der Merwe, A Doucet, JFG de Freitas and E Wan, May 2000. +<a href="http://www.cs.berkeley.edu/~jfgf/software.html">Matlab +software</a> for the UPF is also available. +<p> +Note: none of this software is part of BNT. + + + +<h1><a name="learn">Learning</h1> + +Learning in DBNs can be done online or offline. +Currently only offline learning is implemented in BNT. + + +<h2><a name="param_learn">Parameter learning</h2> + +Offline parameter learning is very similar to learning in static networks, +except now the training data is a cell-array of 2D cell-arrays. +For example, +cases{l}{i,t} is the value of node i in slice t in sequence l, or [] +if unobserved. +Each sequence can be a different length, and may have missing values +in arbitrary locations. +Here is a typical code fragment for using EM. +<pre> +ncases = 2; +cases = cell(1, ncases); +for i=1:ncases + ev = sample_dbn(bnet, T); + cases{i} = cell(ss,T); + cases{i}(onodes,:) = ev(onodes, :); +end +[bnet2, LLtrace] = learn_params_dbn_em(engine, cases, 'max_iter', 10); +</pre> +If the observed node is vector-valued and stored in an OxT array, you +need to assign each vector to a single cell, as in the following +example. +<pre> +data = [xpos(:)'; ypos(:)']; +ncases = 1; +cases = cell(1, ncases); +onodes = bnet.observed; +for i=1:ncases + cases{i} = cell(ss,T); + cases{i}(onodes,:) = num2cell(data(:,1:T), 1); +end +</pre> +<p> +For a complete code listing of how to do EM in a simple DBN, click +<a href="dbn_hmm_demo.m">here</a>. + +<h2><a name="struct_learn">Structure learning</h2> + +There is currently only one structure learning algorithm for DBNs. +This assumes all nodes are tabular and observed, and that there are +no intra-slice connections. Hence we can find the optimal set of +parents for each node separately, without worrying about directed +cycles or node orderings. +The function is called as follows +<pre> +inter = learn_struct_dbn_reveal(cases, ns, max_fan_in, penalty) +</pre> +A full example is given in BNT/examples/dynamic/reveal1.m. +Setting the penalty term to 0 gives the maximum likelihood model; this +is equivalent to maximizing the mutual information between parents and +child (in the bioinformatics community, this is known as the REVEAL +algorithm). A non-zero penalty invokes the BIC criterion, which +lessens the chance of overfitting. +<p> +<a href="http://www.bioss.sari.ac.uk/~dirk/software/DBmcmc/"> +Dirk Husmeier has extended MCMC model selection to DBNs</a>. + +</BODY>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/usage_dbn_02nov13.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,715 @@ +<HEAD> +<TITLE>How to use BNT for DBNs</TITLE> +</HEAD> + +<BODY BGCOLOR="#FFFFFF"> +<!-- white background is better for the pictures and equations --> + +Documentation last updated on 13 November 2002 + +<h1>How to use BNT for DBNs</h1> + +<p> +<ul> +<li> <a href="#spec">Model specification</a> +<ul> +<li> <a href="#hmm">HMMs</a> +<li> <a href="#lds">Kalman filters</a> +<li> <a href="#chmm">Coupled HMMs</a> +<li> <a href="#water">Water network</a> +<li> <a href="#bat">BAT network</a> +</ul> + +<li> <a href="#inf">Inference</a> +<ul> +<li> <a href="#discrete">Discrete hidden nodes</a> +<li> <a href="#cts">Continuous hidden nodes</a> +</ul> + +<li> <a href="#learn">Learning</a> +<ul> +<li> <a href="#param_learn">Parameter learning</a> +<li> <a href="#struct_learn">Structure learning</a> +</ul> + +</ul> + +Note: +you are recommended to read an introduction +to DBNs first, such as +<a href="http://www.ai.mit.edu/~murphyk/Papers/dbnchapter.pdf"> +this book chapter</a>. + + +<h1><a name="spec">Model specification</h1> + + +<!--<h1><a name="dbn_intro">Dynamic Bayesian Networks (DBNs)</h1>--> + +Dynamic Bayesian Networks (DBNs) are directed graphical models of stochastic +processes. +They generalise <a href="#hmm">hidden Markov models (HMMs)</a> +and <a href="#lds">linear dynamical systems (LDSs)</a> +by representing the hidden (and observed) state in terms of state +variables, which can have complex interdependencies. +The graphical structure provides an easy way to specify these +conditional independencies, and hence to provide a compact +parameterization of the model. +<p> +Note that "temporal Bayesian network" would be a better name than +"dynamic Bayesian network", since +it is assumed that the model structure does not change, but +the term DBN has become entrenched. +We also normally assume that the parameters do not +change, i.e., the model is time-invariant. +However, we can always add extra +hidden nodes to represent the current "regime", thereby creating +mixtures of models to capture periodic non-stationarities. +<p> +There are some cases where the size of the state space can change over +time, e.g., tracking a variable, but unknown, number of objects. +In this case, we need to change the model structure over time. +BNT does not support this. +<!-- +, but see the following paper for a +discussion of some of the issues: +<ul> +<li> <a href="ftp://ftp.cs.monash.edu.au/pub/annn/smc.ps"> +Dynamic belief networks for discrete monitoring</a>, +A. E. Nicholson and J. M. Brady. +IEEE Systems, Man and Cybernetics, 24(11):1593-1610, 1994. +</ul> +--> + + +<h2><a name="hmm">Hidden Markov Models (HMMs)</h2> + +The simplest kind of DBN is a Hidden Markov Model (HMM), which has +one discrete hidden node and one discrete or continuous +observed node per slice. We illustrate this below. +As before, circles denote continuous nodes, squares denote +discrete nodes, clear means hidden, shaded means observed. +<!-- +(The observed nodes can be +discrete or continuous; the crucial thing about an HMM is that the +hidden nodes are discrete, so the system can model arbitrary dynamics +-- providing, of course, that the hidden state space is large enough.) +--> +<p> +<img src="Figures/hmm3.gif"> +<p> +We have "unrolled" the model for three "time slices" -- the structure and parameters are +assumed to repeat as the model is unrolled further. +Hence to specify a DBN, we need to +define the intra-slice topology (within a slice), +the inter-slice topology (between two slices), +as well as the parameters for the first two slices. +(Such a two-slice temporal Bayes net is often called a 2TBN.) +<p> +We can specify the topology as follows. +<PRE> +intra = zeros(2); +intra(1,2) = 1; % node 1 in slice t connects to node 2 in slice t + +inter = zeros(2); +inter(1,1) = 1; % node 1 in slice t-1 connects to node 1 in slice t +</pre> +We can specify the parameters as follows, +where for simplicity we assume the observed node is discrete. +<pre> +Q = 2; % num hidden states +O = 2; % num observable symbols + +ns = [Q O]; +dnodes = 1:2; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes); +for i=1:4 + bnet.CPD{i} = tabular_CPD(bnet, i); +end +</pre> +<p> +We assume the distributions P(X(t) | X(t-1)) and +P(Y(t) | X(t)) are independent of t for t > 1. +Hence the CPD for nodes 5, 7, ... is the same as for node 3, so we say they +are in the same equivalence class, with node 3 being the "representative" +for this class. In other words, we have tied the parameters for nodes +3, 5, 7, ... +Similarly, nodes 4, 6, 8, ... are tied. +Note, however, that (the parameters for) nodes 1 and 2 are not tied to +subsequent slices. +<p> +Above we assumed the observation model P(Y(t) | X(t)) is independent of t for t>1, but +it is conventional to assume this is true for all t. +So we would like to put nodes 2, 4, 6, ... all in the same class. +We can do this by explicitely defining the equivalence classes, as +follows (see <a href="usage.html#tying">here</a> for more details on +parameter tying). +<p> +We define eclass1(i) to be the equivalence class that node i in slice +1 belongs to. +Similarly, we define eclass2(i) to be the equivalence class that node i in slice +2, 3, ..., belongs to. +For an HMM, we have +<pre> +eclass1 = [1 2]; +eclass2 = [3 2]; +eclass = [eclass1 eclass2]; +</pre> +This ties the observation model across slices, +since e.g., eclass(4) = eclass(2) = 2. +<p> +By default, +eclass1 = 1:ss, and eclass2 = (1:ss)+ss, where ss = slice size = the +number of nodes per slice. +<!--This will tie nodes in slices 3, 4, ... to the the nodes in slice 2, +but none of the nodes in slice 2 to any in slice 1.--> +But by using the above tieing pattern, +we now only have 3 CPDs to specify, instead of 4: +<pre> +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2); +prior0 = normalise(rand(Q,1)); +transmat0 = mk_stochastic(rand(Q,Q)); +obsmat0 = mk_stochastic(rand(Q,O)); +bnet.CPD{1} = tabular_CPD(bnet, 1, prior0); +bnet.CPD{2} = tabular_CPD(bnet, 2, obsmat0); +bnet.CPD{3} = tabular_CPD(bnet, 3, transmat0); +</pre> +We discuss how to do <a href="#inf">inference</a> and <a href="#learn">learning</a> on this model +below. +(See also +my <a href="../HMM/hmm.html">HMM toolbox</a>, which is included with BNT.) + +<p> +Some common variants on HMMs are shown below. +BNT can handle all of these. +<p> +<center> +<table> +<tr> +<td><img src="Figures/hmm_gauss.gif"> +<td><img src="Figures/hmm_mixgauss.gif" +<td><img src="Figures/hmm_ar.gif"> +<tr> +<td><img src="Figures/hmm_factorial.gif"> +<td><img src="Figures/hmm_coupled.gif" +<td><img src="Figures/hmm_io.gif"> +<tr> +</table> +</center> + + + +<h2><a name="lds">Linear Dynamical Systems (LDSs) and Kalman filters</h2> + +A Linear Dynamical System (LDS) has the same topology as an HMM, but +all the nodes are assumed to have linear-Gaussian distributions, i.e., +<pre> + x(t+1) = A*x(t) + w(t), w ~ N(0, Q), x(0) ~ N(init_x, init_V) + y(t) = C*x(t) + v(t), v ~ N(0, R) +</pre> +Some simple variants are shown below. +<p> +<center> +<table> +<tr> +<td><img src="Figures/ar1.gif"> +<td><img src="Figures/sar.gif"> +<td><img src="Figures/kf.gif"> +<td><img src="Figures/skf.gif"> +</table> +</center> +<p> + +We can create a regular LDS in BNT as follows. +<pre> + +intra = zeros(2); +intra(1,2) = 1; +inter = zeros(2); +inter(1,1) = 1; +n = 2; + +X = 2; % size of hidden state +Y = 2; % size of observable state + +ns = [X Y]; +dnodes = []; +onodes = [2]; +eclass1 = [1 2]; +eclass2 = [3 2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2); + +x0 = rand(X,1); +V0 = eye(X); % must be positive semi definite! +C0 = rand(Y,X); +R0 = eye(Y); +A0 = rand(X,X); +Q0 = eye(X); + +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0, 'cov_prior_weight', 0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0, ... + 'clamp_mean', 1, 'cov_prior_weight', 0); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0, ... + 'clamp_mean', 1, 'cov_prior_weight', 0); +</pre> +We discuss how to do <a href="#inf">inference</a> and <a href="#learn">learning</a> on this model +below. +(See also +my <a href="../Kalman/kalman.html">Kalman filter toolbox</a>, which is included with BNT.) +<p> + + +<h2><a name="chmm">Coupled HMMs</h2> + +Here is an example of a coupled HMM with N=5 chains, unrolled for T=3 +slices. Each hidden discrete node has a private observed Gaussian +child. +<p> +<img src="Figures/chmm5.gif"> +<p> +We can make this using the function +<pre> +Q = 2; % binary hidden nodes +discrete_obs = 0; % cts observed nodes +Y = 1; % scalar observed nodes +bnet = mk_chmm(N, Q, Y, discrete_obs); +</pre> + +<!--We will use this model <a href="#pred">below</a> to illustrate online prediction.--> + + + +<h2><a name="water">Water network</h2> + +Consider the following model +of a water purification plant, developed +by Finn V. Jensen, Uffe Kjærulff, Kristian G. Olesen, and Jan +Pedersen. +<!-- +The clear nodes represent the hidden state of the system in +factored form, and the shaded nodes represent the observations in +factored form. +--> +<!-- +(Click <a +href="http://www-nt.cs.berkeley.edu/home/nir/public_html/Repository/water.htm">here</a> +for more details on this model. +Following Boyen and Koller, we have added discrete evidence nodes.) +--> +<!-- +We have "unrolled" the model for three "time slices" -- the structure and parameters are +assumed to repeat as the model is unrolled further. +Hence to specify a DBN, we need to +define the intra-slice topology (within a slice), +the inter-slice topology (between two slices), +as well as the parameters for the first two slices. +(Such a two-slice temporal Bayes net is often called a 2TBN.) +--> +<p> +<center> +<IMG SRC="Figures/water3_75.gif"> +</center> +We now show how to specify this model in BNT. +<pre> +ss = 12; % slice size +intra = zeros(ss); +intra(1,9) = 1; +intra(3,10) = 1; +intra(4,11) = 1; +intra(8,12) = 1; + +inter = zeros(ss); +inter(1, [1 3]) = 1; % node 1 in slice 1 connects to nodes 1 and 3 in slice 2 +inter(2, [2 3 7]) = 1; +inter(3, [3 4 5]) = 1; +inter(4, [3 4 6]) = 1; +inter(5, [3 5 6]) = 1; +inter(6, [4 5 6]) = 1; +inter(7, [7 8]) = 1; +inter(8, [6 7 8]) = 1; + +onodes = 9:12; % observed +dnodes = 1:ss; % discrete +ns = 2*ones(1,ss); % binary nodes +eclass1 = 1:12; +eclass2 = [13:20 9:12]; +eclass = [eclass1 eclass2]; +bnet = mk_dbn(intra, inter, ns, 'discrete', dnodes, 'eclass1', eclass1, 'eclass2', eclass2); +for e=1:max(eclass) + bnet.CPD{e} = tabular_CPD(bnet, e); +end +</pre> +We have tied the observation parameters across all slices. +Click <a href="param_tieing.html">here</a> for a more complex example +of parameter tieing. + +<!-- +Let X(i,t) denote the i'th hidden node in slice t, +and Y(i,y) denote the i'th observed node in slice t. +We also use the notation Nj to refer to the j'th node in the +unrolled network, e.g., N25 = X(1,3), N33 = Y(1,3). +<p> +We assume the distributions P(X(i,t) | X(i,t-1)) and +P(Y(i,t) | X(i,t)) are independent of t for t > 1 and for all i. +Hence the CPD for N25, N37, ... is the same as for N13, so we say they +are in the same equivalence class, with N13 being the "representative" +for this class. In other words, we have tied the parameters for nodes +N13, N25, N37, ... +Note, however, that the parameters for the nodes in the first slice +are not tied, so each equivalence class for nodes 1..12 contains a +single node. +<p> +Above we assumed P(Y(i,t) | X(i,t)) is independent of t for t>1, but +it is conventional to assume this is true for all t. +So we would like to put N9, N21, N33, ... all in the same class, and +similarly for the other observed nodes. +We can do this by explicitely defining the equivalence classes, as +follows. +<p> +We define eclass1(i) to be the equivalence class that node i in slice +1 belongs to. +Similarly, we define eclass2(i) to be the equivalence class that node i in slice +2, 3, ..., belongs to. +For the water model, we have +<pre> +</pre> +This ties the observation model across slices, +since e.g., eclass(9) = eclass(21) = 9, so Y(1,1) and Y(1,2) belong to the +same class. +<p> +By default, +eclass1 = 1:ss, and eclass2 = (1:ss)+ss, where ss = slice size = the +number of nodes per slice. +This will tie nodes in slices 3, 4, ... to the the nodes in slice 2, +but none of the nodes in slice 2 to any in slice 1. +By using the above tieing pattern, +we now only have 20 CPDs to specify, instead of 24: +<pre> +bnet = mk_dbn(intra, inter, ns, dnodes, eclass1, eclass2); +for e=1:max(eclass) + bnet.CPD{e} = tabular_CPD(bnet, e); +end +</pre> +--> + + + +<h2><a name="bat">BATnet</h2> + +As an example of a more complicated DBN, consider the following +example, +which is a model of a car's high level state, as might be used by +an automated car. +(The model is from Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a +Bayesian Automated Taxi", IJCAI 95. The figure is from +Boyen and Koller, "Tractable Inference for Complex Stochastic +Processes", UAI98. +For simplicity, we only show the observed nodes for slice 2.) +<p> +<center> +<IMG SRC="Figures/batnet.gif"> +</center> +<p> +Since this topology is so complicated, +it is useful to be able to refer to the nodes by name, instead of +number. +<pre> +names = {'LeftClr', 'RightClr', 'LatAct', ... 'Bclr', 'BYdotDiff'}; +ss = length(names); +</pre> +We can specify the intra-slice topology using a cell array as follows, +where each row specifies a connection between two named nodes: +<pre> +intrac = {... + 'LeftClr', 'LeftClrSens'; + 'RightClr', 'RightClrSens'; + ... + 'BYdotDiff', 'BcloseFast'}; +</pre> +Finally, we can convert this cell array to an adjacency matrix using +the following function: +<pre> +[intra, names] = mk_adj_mat(intrac, names, 1); +</pre> +This function also permutes the names so that they are in topological +order. +Given this ordering of the names, we can make the inter-slice +connectivity matrix as follows: +<pre> +interc = {... + 'LeftClr', 'LeftClr'; + 'LeftClr', 'LatAct'; + ... + 'FBStatus', 'LatAct'}; + +inter = mk_adj_mat(interc, names, 0); +</pre> + +To refer to a node, we must know its number, which can be computed as +in the following example: +<pre> +obs = {'LeftClrSens', 'RightClrSens', 'TurnSignalSens', 'XdotSens', 'YdotSens', 'FYdotDiffSens', ... + 'FclrSens', 'BXdotSens', 'BclrSens', 'BYdotDiffSens'}; +for i=1:length(obs) + onodes(i) = stringmatch(obs{i}, names); +end +onodes = sort(onodes); +</pre> +(We sort the onodes since most BNT routines assume that set-valued +arguments are in sorted order.) +We can now make the DBN: +<pre> +dnodes = 1:ss; +ns = 2*ones(1,ss); % binary nodes +bnet = mk_dbn(intra, inter, ns, 'iscrete', dnodes); +</pre> +To specify the parameters, we must know the order of the parents. +See the function BNT/general/mk_named_CPT for a way to do this in the +case of tabular nodes. For simplicity, we just generate random +parameters: +<pre> +for i=1:2*ss + bnet.CPD{i} = tabular_CPD(bnet, i); +end +</pre> +A complete version of this example is available in BNT/examples/dynamic/bat1.m. + + + + +<h1><a name="inf">Inference</h1> + + +The general inference problem for DBNs is to compute +P(X(i,t0) | Y(:, t1:t2)), where X(i,t) represents the i'th hidden +variable at time t and Y(:,t1:t2) represents all the evidence +between times t1 and t2. +There are several special cases of interest, illustrated below. +The arrow indicates t0: it is X(t0) that we are trying to estimate. +The shaded region denotes t1:t2, the available data. +<p> + +<img src="Figures/filter.gif"> + +<p> +BNT can currently only handle offline smoothing. +(The HMM engine handles filtering and, to a limited extent, prediction.) +The usage is similar to static +inference engines, except now the evidence is a 2D cell array of +size ss*T, where ss is the number of nodes per slice (ss = slice sizee) and T is the +number of slices. +Also, 'marginal_nodes' takes two arguments, the nodes and the time-slice. +For example, to compute P(X(i,t) | y(:,1:T)), we proceed as follows +(where onodes are the indices of the observedd nodes in each slice, +which correspond to y): +<pre> +ev = sample_dbn(bnet, T); +evidence = cell(ss,T); +evidence(onodes,:) = ev(onodes, :); % all cells besides onodes are empty +[engine, ll] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, i, t); +</pre> + + +<h2><a name="discrete">Discrete hidden nodes</h2> + +If all the hidden nodes are discrete, +we can use the junction tree algorithm to perform inference. +The simplest approach, +<tt>jtree_unrolled_dbn_inf_engine</tt>, +unrolls the DBN into a static network and applies jtree; however, for +long sequences, this +can be very slow and can result in numerical underflow. +A better approach is to apply the jtree algorithm to pairs of +neighboring slices at a time; this is implemented in +<tt>jtree_dbn_inf_engine</tt>. + +<p> +A DBN can be converted to an HMM if all the hidden nodes are discrete. +In this case, you can use +<tt>hmm_inf_engine</tt>. This is faster than jtree for small models +because the constant factors of the algorithm are lower, but can be +exponentially slower for models with many variables +(e.g., > 6 binary hidden nodes). + +<p> +The use of both +<tt>jtree_dbn_inf_engine</tt> +and +<tt>hmm_inf_engine</tt> +is deprecated. +A better approach is to construct a smoother engine out of lower-level +engines, which implement forward/backward operators. +You can create these engines as follows. +<pre> +engine = smoother_engine(hmm_2TBN_inf_engine(bnet)); +or +engine = smoother_engine(jtree_2TBN_inf_engine(bnet)); +</pre> +You then call them in the usual way: +<pre> +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, nodes, t); +</pre> +Note: you must declare the observed nodes in the bnet before using +hmm_2TBN_inf_engine. + + +<p> +Unfortunately, when all the hiddden nodes are discrete, +exact inference takes O(2^n) time, where n is the number of hidden +nodes per slice, +even if the model is sparse. +The basic reason for this is that two nodes become correlated, even if +there is no direct connection between them in the 2TBN, +by virtue of sharing common ancestors in the past. +Hence we need to use approximations. +<p> +A popular approximate inference algorithm for discrete DBNs, known as BK, is described in +<ul> +<li> +<A HREF="http://robotics.Stanford.EDU/~xb/uai98/index.html"> +Tractable inference for complex stochastic processes </A>, +Boyen and Koller, UAI 1998 +<li> +<A HREF="http://robotics.Stanford.EDU/~xb/nips98/index.html"> +Approximate learning of dynamic models</a>, Boyen and Koller, NIPS +1998. +</ul> +This approximates the belief state with a product of +marginals on a specified set of clusters. For example, +in the water network, we might use the following clusters: +<pre> +engine = bk_inf_engine(bnet, { [1 2], [3 4 5 6], [7 8] }); +</pre> +This engine can now be used just like the jtree engine. +Two special cases of the BK algorithm are supported: 'ff' (fully +factored) means each node has its own cluster, and 'exact' means there +is 1 cluster that contains the whole slice. These can be created as +follows: +<pre> +engine = bk_inf_engine(bnet, 'ff'); +engine = bk_inf_engine(bnet, 'exact'); +</pre> +For pedagogical purposes, an implementation of BK-FF that uses an HMM +instead of junction tree is available at +<tt>bk_ff_hmm_inf_engine</tt>. + + + +<h2><a name="cts">Continuous hidden nodes</h2> + +If all the hidden nodes are linear-Gaussian, <em>and</em> the observed nodes are +linear-Gaussian, +the model is a <a href="http://www.cs.berkeley.edu/~murphyk/Bayes/kalman.html"> +linear dynamical system</a> (LDS). +A DBN can be converted to an LDS if all the hidden nodes are linear-Gaussian +and if they are all persistent. In this case, you can use +<tt>kalman_inf_engine</tt>. +For more general linear-gaussian models, you can use +<tt>jtree_dbn_inf_engine</tt> or <tt>jtree_unrolled_dbn_inf_engine</tt>. + +<p> +For nonlinear systems with Gaussian noise, the unscented Kalman filter (UKF), +due to Julier and Uhlmann, is far superior to the well-known extended Kalman +filter (EKF), both in theory and practice. +<!-- +See +<A HREF="http://phoebe.robots.ox.ac.uk/default.html">"A General Method for +Approximating Nonlinear Transformations of +Probability Distributions"</A>. +(If the above link is down, +try <a href="http://www.ece.ogi.edu/~ericwan/pubs.html">Eric Wan's</a> +page, who has done a lot of work on the UKF.) +<p> +--> +The key idea of the UKF is that it is easier to estimate a Gaussian distribution +from a set of points than to approximate an arbitrary non-linear +function. +We start with points that are plus/minus sigma away from the mean along +each dimension, and then pipe them through the nonlinearity, and +then fit a Gaussian to the transformed points. +(No need to compute Jacobians, unlike the EKF!) + +<p> +For systems with non-Gaussian noise, I recommend +<a href="http://www.cs.berkeley.edu/~jfgf/smc/">Particle +filtering</a> (PF), which is a popular sequential Monte Carlo technique. + +<p> +The EKF can be used as a proposal distribution for a PF. +This method is better than either one alone. +See <a href="http://www.cs.berkeley.edu/~jfgf/upf.ps.gz">The Unscented Particle Filter</a>, +by R van der Merwe, A Doucet, JFG de Freitas and E Wan, May 2000. +<a href="http://www.cs.berkeley.edu/~jfgf/software.html">Matlab +software</a> for the UPF is also available. +<p> +Note: none of this software is part of BNT. + + + +<h1><a name="learn">Learning</h1> + +Learning in DBNs can be done online or offline. +Currently only offline learning is implemented in BNT. + + +<h2><a name="param_learn">Parameter learning</h2> + +Offline parameter learning is very similar to learning in static networks, +except now the training data is a cell-array of 2D cell-arrays. +For example, +cases{l}{i,t} is the value of node i in slice t in sequence l, or [] +if unobserved. +Each sequence can be a different length, and may have missing values +in arbitrary locations. +Here is a typical code fragment for using EM. +<pre> +ncases = 2; +cases = cell(1, ncases); +for i=1:ncases + ev = sample_dbn(bnet, T); + cases{i} = cell(ss,T); + cases{i}(onodes,:) = ev(onodes, :); +end +[bnet2, LLtrace] = learn_params_dbn_em(engine, cases, 'max_iter', 10); +</pre> +If the observed node is vector-valued and stored in an OxT array, you +need to assign each vector to a single cell, as in the following +example. +<pre> +data = [xpos(:)'; ypos(:)']; +ncases = 1; +cases = cell(1, ncases); +onodes = bnet.observed; +for i=1:ncases + cases{i} = cell(ss,T); + cases{i}(onodes,:) = num2cell(data(:,1:T), 1); +end +</pre> +<p> +For a complete code listing of how to do EM in a simple DBN, click +<a href="dbn_hmm_demo.m">here</a>. + +<h2><a name="struct_learn">Structure learning</h2> + +There is currently only one structure learning algorithm for DBNs. +This assumes all nodes are tabular and observed, and that there are +no intra-slice connections. Hence we can find the optimal set of +parents for each node separately, without worrying about directed +cycles or node orderings. +The function is called as follows +<pre> +inter = learn_struct_dbn_reveal(cases, ns, max_fan_in, penalty) +</pre> +A full example is given in BNT/examples/dynamic/reveal1.m. +Setting the penalty term to 0 gives the maximum likelihood model; this +is equivalent to maximizing the mutual information between parents and +child (in the bioinformatics community, this is known as the REVEAL +algorithm). A non-zero penalty invokes the BIC criterion, which +lessens the chance of overfitting. +<p> +<a href="http://www.bioss.sari.ac.uk/~dirk/software/DBmcmc/"> +Dirk Husmeier has extended MCMC model selection to DBNs</a>. + +</BODY>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/usage_sf.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3242 @@ +<HEAD> +<TITLE>How to use the Bayes Net Toolbox</TITLE> +</HEAD> + +<BODY BGCOLOR="#FFFFFF"> +<!-- white background is better for the pictures and equations --> + +<h1>How to use the Bayes Net Toolbox</h1> + +This documentation was last updated on 7 June 2004. +<br> +Click <a href="changelog.html">here</a> for a list of changes made to +BNT. +<br> +Click +<a href="http://bnt.insa-rouen.fr/">here</a> +for a French version of this documentation (which might not +be up-to-date). +<br> +Update 23 May 2005: +Philippe LeRay has written +a +<a href="http://banquiseasi.insa-rouen.fr/projects/bnt-editor/"> +BNT GUI</a> +and +<a href="http://banquiseasi.insa-rouen.fr/projects/bnt-slp/"> +BNT Structure Learning Package</a>. + +<p> + +<ul> +<li> <a href="#install">Installation</a> +<ul> +<li> <a href="#install">Installing the Matlab code</a> +<li> <a href="#installC">Installing the C code</a> +<li> <a href="../matlab_tips.html">Useful Matlab tips</a>. +</ul> + +<li> <a href="#basics">Creating your first Bayes net</a> + <ul> + <li> <a href="#basics">Creating a model by hand</a> + <li> <a href="#file">Loading a model from a file</a> + <li> <a href="http://bnt.insa-rouen.fr/ajouts.html">Creating a model using a GUI</a> + </ul> + +<li> <a href="#inference">Inference</a> + <ul> + <li> <a href="#marginal">Computing marginal distributions</a> + <li> <a href="#joint">Computing joint distributions</a> + <li> <a href="#soft">Soft/virtual evidence</a> + <li> <a href="#mpe">Most probable explanation</a> + </ul> + +<li> <a href="#cpd">Conditional Probability Distributions</a> + <ul> + <li> <a href="#tabular">Tabular (multinomial) nodes</a> + <li> <a href="#noisyor">Noisy-or nodes</a> + <li> <a href="#deterministic">Other (noisy) deterministic nodes</a> + <li> <a href="#softmax">Softmax (multinomial logit) nodes</a> + <li> <a href="#mlp">Neural network nodes</a> + <li> <a href="#root">Root nodes</a> + <li> <a href="#gaussian">Gaussian nodes</a> + <li> <a href="#glm">Generalized linear model nodes</a> + <li> <a href="#dtree">Classification/regression tree nodes</a> + <li> <a href="#nongauss">Other continuous distributions</a> + <li> <a href="#cpd_summary">Summary of CPD types</a> + </ul> + +<li> <a href="#examples">Example models</a> + <ul> + <li> <a + href="http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt"> +Gaussian mixture models</a> + <li> <a href="#pca">PCA, ICA, and all that</a> + <li> <a href="#mixep">Mixtures of experts</a> + <li> <a href="#hme">Hierarchical mixtures of experts</a> + <li> <a href="#qmr">QMR</a> + <li> <a href="#cg_model">Conditional Gaussian models</a> + <li> <a href="#hybrid">Other hybrid models</a> + </ul> + +<li> <a href="#param_learning">Parameter learning</a> + <ul> + <li> <a href="#load_data">Loading data from a file</a> + <li> <a href="#mle_complete">Maximum likelihood parameter estimation from complete data</a> + <li> <a href="#prior">Parameter priors</a> + <li> <a href="#bayes_learn">(Sequential) Bayesian parameter updating from complete data</a> + <li> <a href="#em">Maximum likelihood parameter estimation with missing values (EM)</a> + <li> <a href="#tying">Parameter tying</a> + </ul> + +<li> <a href="#structure_learning">Structure learning</a> + <ul> + <li> <a href="#enumerate">Exhaustive search</a> + <li> <a href="#K2">K2</a> + <li> <a href="#hill_climb">Hill-climbing</a> + <li> <a href="#mcmc">MCMC</a> + <li> <a href="#active">Active learning</a> + <li> <a href="#struct_em">Structural EM</a> + <li> <a href="#graphdraw">Visualizing the learned graph structure</a> + <li> <a href="#constraint">Constraint-based methods</a> + </ul> + + +<li> <a href="#engines">Inference engines</a> + <ul> + <li> <a href="#jtree">Junction tree</a> + <li> <a href="#varelim">Variable elimination</a> + <li> <a href="#global">Global inference methods</a> + <li> <a href="#quickscore">Quickscore</a> + <li> <a href="#belprop">Belief propagation</a> + <li> <a href="#sampling">Sampling (Monte Carlo)</a> + <li> <a href="#engine_summary">Summary of inference engines</a> + </ul> + + +<li> <a href="#influence">Influence diagrams/ decision making</a> + + +<li> <a href="usage_dbn.html">DBNs, HMMs, Kalman filters and all that</a> +</ul> + +</ul> + + + + +<h1><a name="install">Installation</h1> + +<h2><a name="installM">Installing the Matlab code</h2> + +<ul> +<li> <a href="bnt_download.html">Download</a> the FullBNT.zip file. + +<p> +<li> Unpack the file. In Unix, type +<!--"tar xvf BNT.tar".--> +"unzip FullBNT.zip". +In Windows, use +a program like <a href="http://www.winzip.com">Winzip</a>. This will +create a directory called FullBNT, which contains BNT and other libraries. +(Files ending in ~ or # are emacs backup files, and can be ignored.) + +<p> +<li> Read the file <tt>BNT/README.txt</tt> to make sure the date +matches the one on the top of <a href=bnt.html>the BNT home page</a>. +If not, you may need to press 'refresh' on your browser, and download +again, to get the most recent version. + +<p> +<li> <b>Edit the file "FullBNT/BNT/add_BNT_to_path.m"</b> so it contains the correct +pathname. +For example, in Windows, +I download FullBNT.zip into C:\kmurphy\matlab, and +then ensure the second lines reads +<pre> +BNT_HOME = 'C:\kmurphy\matlab\FullBNT'; +</pre> + +<p> +<li> Start up Matlab. + +<p> +<li> Type "ver" at the Matlab prompt (">>"). +<b>You need Matlab version 5.2 or newer to run BNT</b>. +(Versions 5.0 and 5.1 have a memory leak which seems to sometimes +crash BNT.) +<b>BNT will not run on Octave</b>. + +<p> +<li> Move to the BNT directory. +For example, in Windows, I type +<pre> +>> cd C:\kpmurphy\matlab\FullBNT\BNT +</pre> + +<p> +<li> Type "add_BNT_to_path". +This executes the command +<tt>addpath(genpath(BNT_HOME))</tt>, +which adds all directories below FullBNT to the matlab path. + +<p> +<li> Type "test_BNT". + +If all goes well, this will produce a bunch of numbers and maybe some +warning messages (which you can ignore), but no error messages. +(The warnings should only be of the form +"Warning: Maximum number of iterations has been exceeded", and are +produced by Netlab.) + +<p> +<li> Problems? Did you remember to +<b>Edit the file "FullBNT/BNT/add_BNT_to_path.m"</b> so it contains +the right path?? + +<p> +<li> <a href="http://groups.yahoo.com/group/BayesNetToolbox/join"> +Join the BNT email list</a> + +<p> +<li>Read +<a href="../matlab_tips.html">some useful Matlab tips</a>. +<!-- +For instance, this explains how to create a startup file, which can be +used to set your path variable automatically, so you can avoid having +to type the above commands every time. +--> + +</ul> + + + +<h2><a name="installC">Installing the C code</h2> + +Some BNT functions also have C implementations. +<b>It is not necessary to install the C code</b>, but it can result in a speedup +of a factor of 2-5. +To install all the C code, +edit installC_BNT.m so it contains the right path, +then type <tt>installC_BNT</tt>. +(Ignore warnings of the form 'invalid white space character in directive'.) +To uninstall all the C code, +edit uninstallC_BNT.m so it contains the right path, +then type <tt>uninstallC_BNT</tt>. +For an up-to-date list of the files which have C implementations, see +BNT/installC_BNT.m. + +<p> +mex is a script that lets you call C code from Matlab - it does not compile matlab to +C (see mcc below). +If your C/C++ compiler is set up correctly, mex should work out of +the box. +If not, you might need to type +<p> +<tt> mex -setup</tt> +<p> +before calling installC. +<p> +To make mex call gcc on Windows, +you must install <a +href="http://www.mrc-cbu.cam.ac.uk/Imaging/gnumex20.html">gnumex</a>. +You can use the <a href="http://www.mingw.org/">minimalist GNU for +Windows</a> version of gcc, or +the <a href="http://sources.redhat.com/cygwin/">cygwin</a> version. +<p> +In general, typing +'mex foo.c' from inside Matlab creates a file called +'foo.mexglx' or 'foo.dll' (the exact file +extension is system dependent - on Linux it is 'mexglx', on Windows it is '.dll'). +The resulting file will hide the original 'foo.m' (if it existed), i.e., +typing 'foo' at the prompt will call the compiled C version. +To reveal the original matlab version, just delete foo.mexglx (this is +what uninstallC does). +<p> +Sometimes it takes time for Matlab to realize that the file has +changed from matlab to C or vice versa; try typing 'clear all' or +restarting Matlab to refresh it. +To find out which version of a file you are running, type +'which foo'. +<p> +<a href="http://www.mathworks.com/products/compiler">mcc</a>, the +Matlab to C compiler, is a separate product, +and is quite different from mex. It does not yet support +objects/classes, which is why we can't compile all of BNT to C automatically. +Also, hand-written C code is usually much +better than the C code generated by mcc. + + +<p> +Acknowledgements: +Most of the C code (e.g., for jtree and dpot) was written by Wei Hu; +the triangulation C code was written by Ilya Shpitser; +the Gibbs sampling C code (for discrete nodes) was written by Bhaskara +Marthi. + + + +<h1><a name="basics">Creating your first Bayes net</h1> + +To define a Bayes net, you must specify the graph structure and then +the parameters. We look at each in turn, using a simple example +(adapted from Russell and +Norvig, "Artificial Intelligence: a Modern Approach", Prentice Hall, +1995, p454). + + +<h2>Graph structure</h2> + + +Consider the following network. + +<p> +<center> +<IMG SRC="Figures/sprinkler.gif"> +</center> +<p> + +<P> +To specify this directed acyclic graph (dag), we create an adjacency matrix: +<PRE> +N = 4; +dag = zeros(N,N); +C = 1; S = 2; R = 3; W = 4; +dag(C,[R S]) = 1; +dag(R,W) = 1; +dag(S,W)=1; +</PRE> +<P> +We have numbered the nodes as follows: +Cloudy = 1, Sprinkler = 2, Rain = 3, WetGrass = 4. +<b>The nodes must always be numbered in topological order, i.e., +ancestors before descendants.</b> +For a more complicated graph, this is a little inconvenient: we will +see how to get around this <a href="usage_dbn.html#bat">below</a>. +<p> +In Matlab 6, you can use logical arrays instead of double arrays, +which are 4 times smaller: +<pre> +dag = false(N,N); +dag(C,[R S]) = true; +... +</pre> +However, <b>some graph functions (eg acyclic) do not work on +logical arrays</b>! +<p> +A preliminary attempt to make a <b>GUI</b> +has been writte by Philippe LeRay and can be downloaded +from <a href="http://bnt.insa-rouen.fr/ajouts.html">here</a>. +<p> +You can visualize the resulting graph structure using +the methods discussed <a href="#graphdraw">below</a>. + +<h2>Creating the Bayes net shell</h2> + +In addition to specifying the graph structure, +we must specify the size and type of each node. +If a node is discrete, its size is the +number of possible values +each node can take on; if a node is continuous, +it can be a vector, and its size is the length of this vector. +In this case, we will assume all nodes are discrete and binary. +<PRE> +discrete_nodes = 1:N; +node_sizes = 2*ones(1,N); +</pre> +If the nodes were not binary, you could type e.g., +<pre> +node_sizes = [4 2 3 5]; +</pre> +meaning that Cloudy has 4 possible values, +Sprinkler has 2 possible values, etc. +Note that these are cardinal values, not ordinal, i.e., +they are not ordered in any way, like 'low', 'medium', 'high'. +<p> +We are now ready to make the Bayes net: +<pre> +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes); +</PRE> +By default, all nodes are assumed to be discrete, so we can also just +write +<pre> +bnet = mk_bnet(dag, node_sizes); +</PRE> +You may also specify which nodes will be observed. +If you don't know, or if this not fixed in advance, +just use the empty list (the default). +<pre> +onodes = []; +bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes, 'observed', onodes); +</PRE> +Note that optional arguments are specified using a name/value syntax. +This is common for many BNT functions. +In general, to find out more about a function (e.g., which optional +arguments it takes), please see its +documentation string by typing +<pre> +help mk_bnet +</pre> +See also other <a href="matlab_tips.html">useful Matlab tips</a>. +<p> +It is possible to associate names with nodes, as follows: +<pre> +bnet = mk_bnet(dag, node_sizes, 'names', {'cloudy','S','R','W'}, 'discrete', 1:4); +</pre> +You can then refer to a node by its name: +<pre> +C = bnet.names('cloudy'); % bnet.names is an associative array +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +</pre> +This feature uses my own associative array class. + + +<h2><a name="cpt">Parameters</h2> + +A model consists of the graph structure and the parameters. +The parameters are represented by CPD objects (CPD = Conditional +Probability Distribution), which define the probability distribution +of a node given its parents. +(We will use the terms "node" and "random variable" interchangeably.) +The simplest kind of CPD is a table (multi-dimensional array), which +is suitable when all the nodes are discrete-valued. Note that the discrete +values are not assumed to be ordered in any way; that is, they +represent categorical quantities, like male and female, rather than +ordinal quantities, like low, medium and high. +(We will discuss CPDs in more detail <a href="#cpd">below</a>.) +<p> +Tabular CPDs, also called CPTs (conditional probability tables), +are stored as multidimensional arrays, where the dimensions +are arranged in the same order as the nodes, e.g., the CPT for node 4 +(WetGrass) is indexed by Sprinkler (2), Rain (3) and then WetGrass (4) itself. +Hence the child is always the last dimension. +If a node has no parents, its CPT is a column vector representing its +prior. +Note that in Matlab (unlike C), arrays are indexed +from 1, and are layed out in memory such that the first index toggles +fastest, e.g., the CPT for node 4 (WetGrass) is as follows +<P> +<P><IMG ALIGN=BOTTOM SRC="Figures/CPTgrass.gif"><P> +<P> +where we have used the convention that false==1, true==2. +We can create this CPT in Matlab as follows +<PRE> +CPT = zeros(2,2,2); +CPT(1,1,1) = 1.0; +CPT(2,1,1) = 0.1; +... +</PRE> +Here is an easier way: +<PRE> +CPT = reshape([1 0.1 0.1 0.01 0 0.9 0.9 0.99], [2 2 2]); +</PRE> +In fact, we don't need to reshape the array, since the CPD constructor +will do that for us. So we can just write +<pre> +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +</pre> +The other nodes are created similarly (using the old syntax for +optional parameters) +<PRE> +bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]); +bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]); +bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]); +bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]); +</PRE> + + +<h2><a name="rnd_cpt">Random Parameters</h2> + +If we do not specify the CPT, random parameters will be +created, i.e., each "row" of the CPT will be drawn from the uniform distribution. +To ensure repeatable results, use +<pre> +rand('state', seed); +randn('state', seed); +</pre> +To control the degree of randomness (entropy), +you can sample each row of the CPT from a Dirichlet(p,p,...) distribution. +If p << 1, this encourages "deterministic" CPTs (one entry near 1, the rest near 0). +If p = 1, each entry is drawn from U[0,1]. +If p >> 1, the entries will all be near 1/k, where k is the arity of +this node, i.e., each row will be nearly uniform. +You can do this as follows, assuming this node +is number i, and ns is the node_sizes. +<pre> +k = ns(i); +ps = parents(dag, i); +psz = prod(ns(ps)); +CPT = sample_dirichlet(p*ones(1,k), psz); +bnet.CPD{i} = tabular_CPD(bnet, i, 'CPT', CPT); +</pre> + + +<h2><a name="file">Loading a network from a file</h2> + +If you already have a Bayes net represented in the XML-based +<a href="http://www.cs.cmu.edu/afs/cs/user/fgcozman/www/Research/InterchangeFormat/"> +Bayes Net Interchange Format (BNIF)</a> (e.g., downloaded from the +<a +href="http://www.cs.huji.ac.il/labs/compbio/Repository"> +Bayes Net repository</a>), +you can convert it to BNT format using +the +<a href="http://www.digitas.harvard.edu/~ken/bif2bnt/">BIF-BNT Java +program</a> written by Ken Shan. +(This is not necessarily up-to-date.) +<p> +<b>It is currently not possible to save/load a BNT matlab object to +file</b>, but this is easily fixed if you modify all the constructors +for all the classes (see matlab documentation). + +<h2>Creating a model using a GUI</h2> + +Click <a href="http://bnt.insa-rouen.fr/ajouts.html">here</a>. + + + +<h1><a name="inference">Inference</h1> + +Having created the BN, we can now use it for inference. +There are many different algorithms for doing inference in Bayes nets, +that make different tradeoffs between speed, +complexity, generality, and accuracy. +BNT therefore offers a variety of different inference +"engines". We will discuss these +in more detail <a href="#engines">below</a>. +For now, we will use the junction tree +engine, which is the mother of all exact inference algorithms. +This can be created as follows. +<pre> +engine = jtree_inf_engine(bnet); +</pre> +The other engines have similar constructors, but might take +additional, algorithm-specific parameters. +All engines are used in the same way, once they have been created. +We illustrate this in the following sections. + + +<h2><a name="marginal">Computing marginal distributions</h2> + +Suppose we want to compute the probability that the sprinker was on +given that the grass is wet. +The evidence consists of the fact that W=2. All the other nodes +are hidden (unobserved). We can specify this as follows. +<pre> +evidence = cell(1,N); +evidence{W} = 2; +</pre> +We use a 1D cell array instead of a vector to +cope with the fact that nodes can be vectors of different lengths. +In addition, the value [] can be used +to denote 'no evidence', instead of having to specify the observation +pattern as a separate argument. +(Click <a href="cellarray.html">here</a> for a quick tutorial on cell +arrays in matlab.) +<p> +We are now ready to add the evidence to the engine. +<pre> +[engine, loglik] = enter_evidence(engine, evidence); +</pre> +The behavior of this function is algorithm-specific, and is discussed +in more detail <a href="#engines">below</a>. +In the case of the jtree engine, +enter_evidence implements a two-pass message-passing scheme. +The first return argument contains the modified engine, which +incorporates the evidence. The second return argument contains the +log-likelihood of the evidence. (Not all engines are capable of +computing the log-likelihood.) +<p> +Finally, we can compute p=P(S=2|W=2) as follows. +<PRE> +marg = marginal_nodes(engine, S); +marg.T +ans = + 0.57024 + 0.42976 +p = marg.T(2); +</PRE> +We see that p = 0.4298. +<p> +Now let us add the evidence that it was raining, and see what +difference it makes. +<PRE> +evidence{R} = 2; +[engine, loglik] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, S); +p = marg.T(2); +</PRE> +We find that p = P(S=2|W=2,R=2) = 0.1945, +which is lower than +before, because the rain can ``explain away'' the +fact that the grass is wet. +<p> +You can plot a marginal distribution over a discrete variable +as a barchart using the built 'bar' function: +<pre> +bar(marg.T) +</pre> +This is what it looks like + +<p> +<center> +<IMG SRC="Figures/sprinkler_bar.gif"> +</center> +<p> + +<h2><a name="observed">Observed nodes</h2> + +What happens if we ask for the marginal on an observed node, e.g. P(W|W=2)? +An observed discrete node effectively only has 1 value (the observed + one) --- all other values would result in 0 probability. +For efficiency, BNT treats observed (discrete) nodes as if they were + set to 1, as we see below: +<pre> +evidence = cell(1,N); +evidence{W} = 2; +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, W); +m.T +ans = + 1 +</pre> +This can get a little confusing, since we assigned W=2. +So we can ask BNT to add the evidence back in by passing in an optional argument: +<pre> +m = marginal_nodes(engine, W, 1); +m.T +ans = + 0 + 1 +</pre> +This shows that P(W=1|W=2) = 0 and P(W=2|W=2) = 1. + + + +<h2><a name="joint">Computing joint distributions</h2> + +We can compute the joint probability on a set of nodes as in the +following example. +<pre> +evidence = cell(1,N); +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]); +</pre> +m is a structure. The 'T' field is a multi-dimensional array (in +this case, 3-dimensional) that contains the joint probability +distribution on the specified nodes. +<pre> +>> m.T +ans(:,:,1) = + 0.2900 0.0410 + 0.0210 0.0009 +ans(:,:,2) = + 0 0.3690 + 0.1890 0.0891 +</pre> +We see that P(S=1,R=1,W=2) = 0, since it is impossible for the grass +to be wet if both the rain and sprinkler are off. +<p> +Let us now add some evidence to R. +<pre> +evidence{R} = 2; +[engine, ll] = enter_evidence(engine, evidence); +m = marginal_nodes(engine, [S R W]) +m = + domain: [2 3 4] + T: [2x1x2 double] +>> m.T +m.T +ans(:,:,1) = + 0.0820 + 0.0018 +ans(:,:,2) = + 0.7380 + 0.1782 +</pre> +The joint T(i,j,k) = P(S=i,R=j,W=k|evidence) +should have T(i,1,k) = 0 for all i,k, since R=1 is incompatible +with the evidence that R=2. +Instead of creating large tables with many 0s, BNT sets the effective +size of observed (discrete) nodes to 1, as explained above. +This is why m.T has size 2x1x2. +To get a 2x2x2 table, type +<pre> +m = marginal_nodes(engine, [S R W], 1) +m = + domain: [2 3 4] + T: [2x2x2 double] +>> m.T +m.T +ans(:,:,1) = + 0 0.082 + 0 0.0018 +ans(:,:,2) = + 0 0.738 + 0 0.1782 +</pre> + +<p> +Note: It is not always possible to compute the joint on arbitrary +sets of nodes: it depends on which inference engine you use, as discussed +in more detail <a href="#engines">below</a>. + + +<h2><a name="soft">Soft/virtual evidence</h2> + +Sometimes a node is not observed, but we have some distribution over +its possible values; this is often called "soft" or "virtual" +evidence. +One can use this as follows +<pre> +[engine, loglik] = enter_evidence(engine, evidence, 'soft', soft_evidence); +</pre> +where soft_evidence{i} is either [] (if node i has no soft evidence) +or is a vector representing the probability distribution over i's +possible values. +For example, if we don't know i's exact value, but we know its +likelihood ratio is 60/40, we can write evidence{i} = [] and +soft_evidence{i} = [0.6 0.4]. +<p> +Currently only jtree_inf_engine supports this option. +It assumes that all hidden nodes, and all nodes for +which we have soft evidence, are discrete. +For a longer example, see BNT/examples/static/softev1.m. + + +<h2><a name="mpe">Most probable explanation</h2> + +To compute the most probable explanation (MPE) of the evidence (i.e., +the most probable assignment, or a mode of the joint), use +<pre> +[mpe, ll] = calc_mpe(engine, evidence); +</pre> +mpe{i} is the most likely value of node i. +This calls enter_evidence with the 'maximize' flag set to 1, which +causes the engine to do max-product instead of sum-product. +The resulting max-marginals are then thresholded. +If there is more than one maximum probability assignment, we must take + care to break ties in a consistent manner (thresholding the + max-marginals may give the wrong result). To force this behavior, + type +<pre> +[mpe, ll] = calc_mpe(engine, evidence, 1); +</pre> +Note that computing the MPE is someties called abductive reasoning. + +<p> +You can also use <tt>calc_mpe_bucket</tt> written by Ron Zohar, +that does a forwards max-product pass, and then a backwards traceback +pass, which is how Viterbi is traditionally implemented. + + + +<h1><a name="cpd">Conditional Probability Distributions</h1> + +A Conditional Probability Distributions (CPD) +defines P(X(i) | X(Pa(i))), where X(i) is the i'th node, and X(Pa(i)) +are the parents of node i. There are many ways to represent this +distribution, which depend in part on whether X(i) and X(Pa(i)) are +discrete, continuous, or a combination. +We will discuss various representations below. + + +<h2><a name="tabular">Tabular nodes</h2> + +If the CPD is represented as a table (i.e., if it is a multinomial +distribution), it has a number of parameters that is exponential in +the number of parents. See the example <a href="#cpt">above</a>. + + +<h2><a name="noisyor">Noisy-or nodes</h2> + +A noisy-OR node is like a regular logical OR gate except that +sometimes the effects of parents that are on get inhibited. +Let the prob. that parent i gets inhibited be q(i). +Then a node, C, with 2 parents, A and B, has the following CPD, where +we use F and T to represent off and on (1 and 2 in BNT). +<pre> +A B P(C=off) P(C=on) +--------------------------- +F F 1.0 0.0 +T F q(A) 1-q(A) +F T q(B) 1-q(B) +T T q(A)q(B) q-q(A)q(B) +</pre> +Thus we see that the causes get inhibited independently. +It is common to associate a "leak" node with a noisy-or CPD, which is +like a parent that is always on. This can account for all other unmodelled +causes which might turn the node on. +<p> +The noisy-or distribution is similar to the logistic distribution. +To see this, let the nodes, S(i), have values in {0,1}, and let q(i,j) +be the prob. that j inhibits i. Then +<pre> +Pr(S(i)=1 | parents(S(i))) = 1 - prod_{j} q(i,j)^S(j) +</pre> +Now define w(i,j) = -ln q(i,j) and rho(x) = 1-exp(-x). Then +<pre> +Pr(S(i)=1 | parents(S(i))) = rho(sum_j w(i,j) S(j)) +</pre> +For a sigmoid node, we have +<pre> +Pr(S(i)=1 | parents(S(i))) = sigma(-sum_j w(i,j) S(j)) +</pre> +where sigma(x) = 1/(1+exp(-x)). Hence they differ in the choice of +the activation function (although both are monotonically increasing). +In addition, in the case of a noisy-or, the weights are constrained to be +positive, since they derive from probabilities q(i,j). +In both cases, the number of parameters is <em>linear</em> in the +number of parents, unlike the case of a multinomial distribution, +where the number of parameters is exponential in the number of parents. +We will see an example of noisy-OR nodes <a href="#qmr">below</a>. + + +<h2><a name="deterministic">Other (noisy) deterministic nodes</h2> + +Deterministic CPDs for discrete random variables can be created using +the deterministic_CPD class. It is also possible to 'flip' the output +of the function with some probability, to simulate noise. +The boolean_CPD class is just a special case of a +deterministic CPD, where the parents and child are all binary. +<p> +Both of these classes are just "syntactic sugar" for the tabular_CPD +class. + + + +<h2><a name="softmax">Softmax nodes</h2> + +If we have a discrete node with a continuous parent, +we can define its CPD using a softmax function +(also known as the multinomial logit function). +This acts like a soft thresholding operator, and is defined as follows: +<pre> + exp(w(:,i)'*x + b(i)) +Pr(Q=i | X=x) = ----------------------------- + sum_j exp(w(:,j)'*x + b(j)) + +</pre> +The parameters of a softmax node, w(:,i) and b(i), i=1..|Q|, have the +following interpretation: w(:,i)-w(:,j) is the normal vector to the +decision boundary between classes i and j, +and b(i)-b(j) is its offset (bias). For example, suppose +X is a 2-vector, and Q is binary. Then +<pre> +w = [1 -1; + 0 0]; + +b = [0 0]; +</pre> +means class 1 are points in the 2D plane with positive x coordinate, +and class 2 are points in the 2D plane with negative x coordinate. +If w has large magnitude, the decision boundary is sharp, otherwise it +is soft. +In the special case that Q is binary (0/1), the softmax function reduces to the logistic +(sigmoid) function. +<p> +Fitting a softmax function can be done using the iteratively reweighted +least squares (IRLS) algorithm. +We use the implementation from +<a href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a>. +Note that since +the softmax distribution is not in the exponential family, it does not +have finite sufficient statistics, and hence we must store all the +training data in uncompressed form. +If this takes too much space, one should use online (stochastic) gradient +descent (not implemented in BNT). +<p> +If a softmax node also has discrete parents, +we use a different set of w/b parameters for each combination of +parent values, as in the <a href="#gaussian">conditional linear +Gaussian CPD</a>. +This feature was implemented by Pierpaolo Brutti. +He is currently extending it so that discrete parents can be treated +as if they were continuous, by adding indicator variables to the X +vector. +<p> +We will see an example of softmax nodes <a href="#mixexp">below</a>. + + +<h2><a name="mlp">Neural network nodes</h2> + +Pierpaolo Brutti has implemented the mlp_CPD class, which uses a multi layer perceptron +to implement a mapping from continuous parents to discrete children, +similar to the softmax function. +(If there are also discrete parents, it creates a mixture of MLPs.) +It uses code from <a +href="http://www.ncrg.aston.ac.uk/netlab/">Netlab</a>. +This is work in progress. + +<h2><a name="root">Root nodes</h2> + +A root node has no parents and no parameters; it can be used to model +an observed, exogeneous input variable, i.e., one which is "outside" +the model. +This is useful for conditional density models. +We will see an example of root nodes <a href="#mixexp">below</a>. + + +<h2><a name="gaussian">Gaussian nodes</h2> + +We now consider a distribution suitable for the continuous-valued nodes. +Suppose the node is called Y, its continuous parents (if any) are +called X, and its discrete parents (if any) are called Q. +The distribution on Y is defined as follows: +<pre> +- no parents: Y ~ N(mu, Sigma) +- cts parents : Y|X=x ~ N(mu + W x, Sigma) +- discrete parents: Y|Q=i ~ N(mu(:,i), Sigma(:,:,i)) +- cts and discrete parents: Y|X=x,Q=i ~ N(mu(:,i) + W(:,:,i) * x, Sigma(:,:,i)) +</pre> +where N(mu, Sigma) denotes a Normal distribution with mean mu and +covariance Sigma. Let |X|, |Y| and |Q| denote the sizes of X, Y and Q +respectively. +If there are no discrete parents, |Q|=1; if there is +more than one, then |Q| = a vector of the sizes of each discrete parent. +If there are no continuous parents, |X|=0; if there is more than one, +then |X| = the sum of their sizes. +Then mu is a |Y|*|Q| vector, Sigma is a |Y|*|Y|*|Q| positive +semi-definite matrix, and W is a |Y|*|X|*|Q| regression (weight) +matrix. +<p> +We can create a Gaussian node with random parameters as follows. +<pre> +bnet.CPD{i} = gaussian_CPD(bnet, i); +</pre> +We can specify the value of one or more of the parameters as in the +following example, in which |Y|=2, and |Q|=1. +<pre> +bnet.CPD{i} = gaussian_CPD(bnet, i, 'mean', [0; 0], 'weights', randn(Y,X), 'cov', eye(Y)); +</pre> +<p> +We will see an example of conditional linear Gaussian nodes <a +href="#cg_model">below</a>. +<p> +<b>When learning Gaussians from data</b>, it is helpful to ensure the +data has a small magnitde +(see e.g., KPMstats/standardize) to prevent numerical problems. +Unless you have a lot of data, it is also a very good idea to use +diagonal instead of full covariance matrices. +(BNT does not currently support spherical covariances, although it +would be easy to add, since KPMstats/clg_Mstep supports this option; +you would just need to modify gaussian_CPD/update_ess to accumulate +weighted inner products.) + + + +<h2><a name="nongauss">Other continuous distributions</h2> + +Currently BNT does not support any CPDs for continuous nodes other +than the Gaussian. +However, you can use a mixture of Gaussians to +approximate other continuous distributions. We will see some an example +of this with the IFA model <a href="#pca">below</a>. + + +<h2><a name="glm">Generalized linear model nodes</h2> + +In the future, we may incorporate some of the functionality of +<a href = +"http://www.sci.usq.edu.au/staff/dunn/glmlab/glmlab.html">glmlab</a> +into BNT. + + +<h2><a name="dtree">Classification/regression tree nodes</h2> + +We plan to add classification and regression trees to define CPDs for +discrete and continuous nodes, respectively. +Trees have many advantages: they are easy to interpret, they can do +feature selection, they can +handle discrete and continuous inputs, they do not make strong +assumptions about the form of the distribution, the number of +parameters can grow in a data-dependent way (i.e., they are +semi-parametric), they can handle missing data, etc. +However, they are not yet implemented. +<!-- +Yimin Zhang is currently (Feb '02) implementing this. +--> + + +<h2><a name="cpd_summary">Summary of CPD types</h2> + +We list all the different types of CPDs supported by BNT. +For each CPD, we specify if the child and parents can be discrete (D) or +continuous (C) (Binary (B) nodes are a special case). +We also specify which methods each class supports. +If a method is inherited, the name of the parent class is mentioned. +If a parent class calls a child method, this is mentioned. +<p> +The <tt>CPD_to_CPT</tt> method converts a CPD to a table; this +requires that the child and all parents are discrete. +The CPT might be exponentially big... +<tt>convert_to_table</tt> evaluates a CPD with evidence, and +represents the the resulting potential as an array. +This requires that the child is discrete, and any continuous parents +are observed. +<tt>convert_to_pot</tt> evaluates a CPD with evidence, and +represents the resulting potential as a dpot, gpot, cgpot or upot, as +requested. (d=discrete, g=Gaussian, cg = conditional Gaussian, u = +utility). + +<p> +When we sample a node, all the parents are observed. +When we compute the (log) probability of a node, all the parents and +the child are observed. +<p> +We also specify if the parameters are learnable. +For learning with EM, we require +the methods <tt>reset_ess</tt>, <tt>update_ess</tt> and +<tt>maximize_params</tt>. +For learning from fully observed data, we require +the method <tt>learn_params</tt>. +By default, all classes inherit this from generic_CPD, which simply +calls <tt>update_ess</tt> N times, once for each data case, followed +by <tt>maximize_params</tt>, i.e., it is like EM, without the E step. +Some classes implement a batch formula, which is quicker. +<p> +Bayesian learning means computing a posterior over the parameters +given fully observed data. +<p> +Pearl means we implement the methods <tt>compute_pi</tt> and +<tt>compute_lambda_msg</tt>, used by +<tt>pearl_inf_engine</tt>, which runs on directed graphs. +<tt>belprop_inf_engine</tt> only needs <tt>convert_to_pot</tt>.H +The pearl methods can exploit special properties of the CPDs for +computing the messages efficiently, whereas belprop does not. +<p> +The only method implemented by generic_CPD is <tt>adjustable_CPD</tt>, +which is not shown, since it is not very interesting. + + +<p> + + +<table> +<table border units = pixels><tr> +<td align=center>Name +<td align=center>Child +<td align=center>Parents +<td align=center>Comments +<td align=center>CPD_to_CPT +<td align=center>conv_to_table +<td align=center>conv_to_pot +<td align=center>sample +<td align=center>prob +<td align=center>learn +<td align=center>Bayes +<td align=center>Pearl + + +<tr> +<!-- Name--><td> +<!-- Child--><td> +<!-- Parents--><td> +<!-- Comments--><td> +<!-- CPD_to_CPT--><td> +<!-- conv_to_table--><td> +<!-- conv_to_pot--><td> +<!-- sample--><td> +<!-- prob--><td> +<!-- learn--><td> +<!-- Bayes--><td> +<!-- Pearl--><td> + +<tr> +<!-- Name--><td>boolean +<!-- Child--><td>B +<!-- Parents--><td>B +<!-- Comments--><td>Syntactic sugar for tabular +<!-- CPD_to_CPT--><td>- +<!-- conv_to_table--><td>- +<!-- conv_to_pot--><td>- +<!-- sample--><td>- +<!-- prob--><td>- +<!-- learn--><td>- +<!-- Bayes--><td>- +<!-- Pearl--><td>- + +<tr> +<!-- Name--><td>deterministic +<!-- Child--><td>D +<!-- Parents--><td>D +<!-- Comments--><td>Syntactic sugar for tabular +<!-- CPD_to_CPT--><td>- +<!-- conv_to_table--><td>- +<!-- conv_to_pot--><td>- +<!-- sample--><td>- +<!-- prob--><td>- +<!-- learn--><td>- +<!-- Bayes--><td>- +<!-- Pearl--><td>- + +<tr> +<!-- Name--><td>Discrete +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>Virtual class +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Calls CPD_to_CPT +<!-- conv_to_pot--><td>Calls conv_to_table +<!-- sample--><td>Calls conv_to_table +<!-- prob--><td>Calls conv_to_table +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + +<tr> +<!-- Name--><td>Gaussian +<!-- Child--><td>C +<!-- Parents--><td>C/D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>Y +<!-- prob--><td>Y +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + +<tr> +<!-- Name--><td>gmux +<!-- Child--><td>C +<!-- Parents--><td>C/D +<!-- Comments--><td>multiplexer +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>N +<!-- prob--><td>N +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>Y + + +<tr> +<!-- Name--><td>MLP +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>multi layer perceptron +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Y +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>noisy-or +<!-- Child--><td>B +<!-- Parents--><td>B +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>Y +<!-- conv_to_table--><td>Inherits from discrete +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>Y + + +<tr> +<!-- Name--><td>root +<!-- Child--><td>C/D +<!-- Parents--><td>none +<!-- Comments--><td>no params +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>Y +<!-- sample--><td>Y +<!-- prob--><td>Y +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>softmax +<!-- Child--><td>D +<!-- Parents--><td>C/D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>Y +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>generic +<!-- Child--><td>C/D +<!-- Parents--><td>C/D +<!-- Comments--><td>Virtual class +<!-- CPD_to_CPT--><td>N +<!-- conv_to_table--><td>N +<!-- conv_to_pot--><td>N +<!-- sample--><td>N +<!-- prob--><td>N +<!-- learn--><td>N +<!-- Bayes--><td>N +<!-- Pearl--><td>N + + +<tr> +<!-- Name--><td>Tabular +<!-- Child--><td>D +<!-- Parents--><td>D +<!-- Comments--><td>- +<!-- CPD_to_CPT--><td>Y +<!-- conv_to_table--><td>Inherits from discrete +<!-- conv_to_pot--><td>Inherits from discrete +<!-- sample--><td>Inherits from discrete +<!-- prob--><td>Inherits from discrete +<!-- learn--><td>Y +<!-- Bayes--><td>Y +<!-- Pearl--><td>Y + +</table> + + + +<h1><a name="examples">Example models</h1> + + +<h2>Gaussian mixture models</h2> + +Richard W. DeVaul has made a detailed tutorial on how to fit mixtures +of Gaussians using BNT. Available +<a href="http://www.media.mit.edu/wearables/mithril/BNT/mixtureBNT.txt">here</a>. + + +<h2><a name="pca">PCA, ICA, and all that </h2> + +In Figure (a) below, we show how Factor Analysis can be thought of as a +graphical model. Here, X has an N(0,I) prior, and +Y|X=x ~ N(mu + Wx, Psi), +where Psi is diagonal and W is called the "factor loading matrix". +Since the noise on both X and Y is diagonal, the components of these +vectors are uncorrelated, and hence can be represented as individual +scalar nodes, as we show in (b). +(This is useful if parts of the observations on the Y vector are occasionally missing.) +We usually take k=|X| << |Y|=D, so the model tries to explain +many observations using a low-dimensional subspace. + + +<center> +<table> +<tr> +<td><img src="Figures/fa.gif"> +<td><img src="Figures/fa_scalar.gif"> +<td><img src="Figures/mfa.gif"> +<td><img src="Figures/ifa.gif"> +<tr> +<td align=center> (a) +<td align=center> (b) +<td align=center> (c) +<td align=center> (d) +</table> +</center> + +<p> +We can create this model in BNT as follows. +<pre> +ns = [k D]; +dag = zeros(2,2); +dag(1,2) = 1; +bnet = mk_bnet(dag, ns, 'discrete', []); +bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', zeros(k,1), 'cov', eye(k), ... + 'cov_type', 'diag', 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(D,1), 'cov', diag(Psi0), 'weights', W0, ... + 'cov_type', 'diag', 'clamp_mean', 1); +</pre> + +The root node is clamped to the N(0,I) distribution, so that we will +not update these parameters during learning. +The mean of the leaf node is clamped to 0, +since we assume the data has been centered (had its mean subtracted +off); this is just for simplicity. +Finally, the covariance of the leaf node is constrained to be +diagonal. W0 and Psi0 are the initial parameter guesses. + +<p> +We can fit this model (i.e., estimate its parameters in a maximum +likelihood (ML) sense) using EM, as we +explain <a href="#em">below</a>. +Not surprisingly, the ML estimates for mu and Psi turn out to be +identical to the +sample mean and variance, which can be computed directly as +<pre> +mu_ML = mean(data); +Psi_ML = diag(cov(data)); +</pre> +Note that W can only be identified up to a rotation matrix, because of +the spherical symmetry of the source. + +<p> +If we restrict Psi to be spherical, i.e., Psi = sigma*I, +there is a closed-form solution for W as well, +i.e., we do not need to use EM. +In particular, W contains the first |X| eigenvectors of the sample covariance +matrix, with scalings determined by the eigenvalues and sigma. +Classical PCA can be obtained by taking the sigma->0 limit. +For details, see + +<ul> +<li> <a href="ftp://hope.caltech.edu/pub/roweis/Empca/empca.ps"> +"EM algorithms for PCA and SPCA"</a>, Sam Roweis, NIPS 97. +(<a href="ftp://hope.caltech.edu/pub/roweis/Code/empca.tar.gz"> +Matlab software</a>) + +<p> +<li> +<a +href=http://neural-server.aston.ac.uk/cgi-bin/tr_avail.pl?trnumber=NCRG/97/003> +"Mixtures of probabilistic principal component analyzers"</a>, +Tipping and Bishop, Neural Computation 11(2):443--482, 1999. +</ul> + +<p> +By adding a hidden discrete variable, we can create mixtures of FA +models, as shown in (c). +Now we can explain the data using a set of subspaces. +We can create this model in BNT as follows. +<pre> +ns = [M k D]; +dag = zeros(3); +dag(1,3) = 1; +dag(2,3) = 1; +bnet = mk_bnet(dag, ns, 'discrete', 1); +bnet.CPD{1} = tabular_CPD(bnet, 1, Pi0); +bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(k, 1), 'cov', eye(k), 'cov_type', 'diag', ... + 'clamp_mean', 1, 'clamp_cov', 1); +bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', Mu0', 'cov', repmat(diag(Psi0), [1 1 M]), ... + 'weights', W0, 'cov_type', 'diag', 'tied_cov', 1); +</pre> +Notice how the covariance matrix for Y is the same for all values of +Q; that is, the noise level in each sub-space is assumed the same. +However, we allow the offset, mu, to vary. +For details, see +<ul> + +<LI> +<a HREF="ftp://ftp.cs.toronto.edu/pub/zoubin/tr-96-1.ps.gz"> The EM +Algorithm for Mixtures of Factor Analyzers </A>, +Ghahramani, Z. and Hinton, G.E. (1996), +University of Toronto +Technical Report CRG-TR-96-1. +(<A HREF="ftp://ftp.cs.toronto.edu/pub/zoubin/mfa.tar.gz">Matlab software</A>) + +<p> +<li> +<a +href=http://neural-server.aston.ac.uk/cgi-bin/tr_avail.pl?trnumber=NCRG/97/003> +"Mixtures of probabilistic principal component analyzers"</a>, +Tipping and Bishop, Neural Computation 11(2):443--482, 1999. +</ul> + +<p> +I have included Zoubin's specialized MFA code (with his permission) +with the toolbox, so you can check that BNT gives the same results: +see 'BNT/examples/static/mfa1.m'. + +<p> +Independent Factor Analysis (IFA) generalizes FA by allowing a +non-Gaussian prior on each component of X. +(Note that we can approximate a non-Gaussian prior using a mixture of +Gaussians.) +This means that the likelihood function is no longer rotationally +invariant, so we can uniquely identify W and the hidden +sources X. +IFA also allows a non-diagonal Psi (i.e. correlations between the components of Y). +We recover classical Independent Components Analysis (ICA) +in the Psi -> 0 limit, and by assuming that |X|=|Y|, so that the +weight matrix W is square and invertible. +For details, see +<ul> +<li> +<a href="http://www.gatsby.ucl.ac.uk/~hagai/ifa.ps">Independent Factor +Analysis</a>, H. Attias, Neural Computation 11: 803--851, 1998. +</ul> + + + +<h2><a name="mixexp">Mixtures of experts</h2> + +As an example of the use of the softmax function, +we introduce the Mixture of Experts model. +<!-- +We also show +the Hierarchical Mixture of Experts model, where the hierarchy has two +levels. +(This is essentially a probabilistic decision tree of height two.) +--> +As before, +circles denote continuous-valued nodes, +squares denote discrete nodes, clear +means hidden, and shaded means observed. +<p> +<center> +<table> +<tr> +<td><img src="Figures/mixexp.gif"> +<!-- +<td><img src="Figures/hme.gif"> +--> +</table> +</center> +<p> +X is the observed +input, Y is the output, and +the Q nodes are hidden "gating" nodes, which select the appropriate +set of parameters for Y. During training, Y is assumed observed, +but for testing, the goal is to predict Y given X. +Note that this is a <em>conditional</em> density model, so we don't +associate any parameters with X. +Hence X's CPD will be a root CPD, which is a way of modelling +exogenous nodes. +If the output is a continuous-valued quantity, +we assume the "experts" are linear-regression units, +and set Y's CPD to linear-Gaussian. +If the output is discrete, we set Y's CPD to a softmax function. +The Q CPDs will always be softmax functions. + +<p> +As a concrete example, consider the mixture of experts model where X and Y are +scalars, and Q is binary. +This is just piecewise linear regression, where +we have two line segments, i.e., +<P> +<IMG ALIGN=BOTTOM SRC="Eqns/lin_reg_eqn.gif"> +<P> +We can create this model with random parameters as follows. +(This code is bundled in BNT/examples/static/mixexp2.m.) +<PRE> +X = 1; +Q = 2; +Y = 3; +dag = zeros(3,3); +dag(X,[Q Y]) = 1 +dag(Q,Y) = 1; +ns = [1 2 1]; % make X and Y scalars, and have 2 experts +onodes = [1 3]; +bnet = mk_bnet(dag, ns, 'discrete', 2, 'observed', onodes); + +rand('state', 0); +randn('state', 0); +bnet.CPD{1} = root_CPD(bnet, 1); +bnet.CPD{2} = softmax_CPD(bnet, 2); +bnet.CPD{3} = gaussian_CPD(bnet, 3); +</PRE> +Now let us fit this model using <a href="#em">EM</a>. +First we <a href="#load_data">load the data</a> (1000 training cases) and plot them. +<P> +<PRE> +data = load('/examples/static/Misc/mixexp_data.txt', '-ascii'); +plot(data(:,1), data(:,2), '.'); +</PRE> +<p> +<center> +<IMG SRC="Figures/mixexp_data.gif"> +</center> +<p> +This is what the model looks like before training. +(Thanks to Thomas Hofman for writing this plotting routine.) +<p> +<center> +<IMG SRC="Figures/mixexp_before.gif"> +</center> +<p> +Now let's train the model, and plot the final performance. +(We will discuss how to train models in more detail <a href="#param_learning">below</a>.) +<P> +<PRE> +ncases = size(data, 1); % each row of data is a training case +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); % each column of cases is a training case +engine = jtree_inf_engine(bnet); +max_iter = 20; +[bnet2, LLtrace] = learn_params_em(engine, cases, max_iter); +</PRE> +(We specify which nodes will be observed when we create the engine. +Hence BNT knows that the hidden nodes are all discrete. +For complex models, this can lead to a significant speedup.) +Below we show what the model looks like after 16 iterations of EM +(with 100 IRLS iterations per M step), when it converged +using the default convergence tolerance (that the +fractional change in the log-likelihood be less than 1e-3). +Before learning, the log-likelihood was +-322.927442; afterwards, it was -13.728778. +<p> +<center> +<IMG SRC="Figures/mixexp_after.gif"> +</center> +(See BNT/examples/static/mixexp2.m for details of the code.) + + + +<h2><a name="hme">Hierarchical mixtures of experts</h2> + +A hierarchical mixture of experts (HME) extends the mixture of experts +model by having more than one hidden node. A two-level example is shown below, along +with its more traditional representation as a neural network. +This is like a (balanced) probabilistic decision tree of height 2. +<p> +<center> +<IMG SRC="Figures/HMEforMatlab.jpg"> +</center> +<p> +<a href="mailto:pbrutti@stat.cmu.edu">Pierpaolo Brutti</a> +has written an extensive set of routines for HMEs, +which are bundled with BNT: see the examples/static/HME directory. +These routines allow you to choose the number of hidden (gating) +layers, and the form of the experts (softmax or MLP). +See the file hmemenu, which provides a demo. +For example, the figure below shows the decision boundaries learned +for a ternary classification problem, using a 2 level HME with softmax +gates and softmax experts; the training set is on the left, the +testing set on the right. +<p> +<center> +<!--<IMG SRC="Figures/hme_dec_boundary.gif">--> +<IMG SRC="Figures/hme_dec_boundary.png"> +</center> +<p> + + +<p> +For more details, see the following: +<ul> + +<li> <a href="http://www.cs.berkeley.edu/~jordan/papers/hierarchies.ps.Z"> +Hierarchical mixtures of experts and the EM algorithm</a> +M. I. Jordan and R. A. Jacobs. Neural Computation, 6, 181-214, 1994. + +<li> <a href = +"http://www.cs.berkeley.edu/~dmartin/software">David Martin's +matlab code for HME</a> + +<li> <a +href="http://www.cs.berkeley.edu/~jordan/papers/uai.ps.Z">Why the +logistic function? A tutorial discussion on +probabilities and neural networks.</a> M. I. Jordan. MIT Computational +Cognitive Science Report 9503, August 1995. + +<li> "Generalized Linear Models", McCullagh and Nelder, Chapman and +Halll, 1983. + +<li> +"Improved learning algorithms for mixtures of experts in multiclass +classification". +K. Chen, L. Xu, H. Chi. +Neural Networks (1999) 12: 1229-1252. + +<li> <a href="http://www.oigeeza.com/steve/"> +Classification Using Hierarchical Mixtures of Experts</a> +S.R. Waterhouse and A.J. Robinson. +In Proc. IEEE Workshop on Neural Network for Signal Processing IV (1994), pp. 177-186 + +<li> <a href="http://www.idiap.ch/~perry/"> +Localized mixtures of experts</a>, +P. Moerland, 1998. + +<li> "Nonlinear gated experts for time series", +A.S. Weigend and M. Mangeas, 1995. + +</ul> + + +<h2><a name="qmr">QMR</h2> + +Bayes nets originally arose out of an attempt to add probabilities to +expert systems, and this is still the most common use for BNs. +A famous example is +QMR-DT, a decision-theoretic reformulation of the Quick Medical +Reference (QMR) model. +<p> +<center> +<IMG ALIGN=BOTTOM SRC="Figures/qmr.gif"> +</center> +Here, the top layer represents hidden disease nodes, and the bottom +layer represents observed symptom nodes. +The goal is to infer the posterior probability of each disease given +all the symptoms (which can be present, absent or unknown). +Each node in the top layer has a Bernoulli prior (with a low prior +probability that the disease is present). +Since each node in the bottom layer has a high fan-in, we use a +noisy-OR parameterization; each disease has an independent chance of +causing each symptom. +The real QMR-DT model is copyright, but +we can create a random QMR-like model as follows. +<pre> +function bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% MK_QMR_BNET Make a QMR model +% bnet = mk_qmr_bnet(G, inhibit, leak, prior) +% +% G(i,j) = 1 iff there is an arc from disease i to finding j +% inhibit(i,j) = inhibition probability on i->j arc +% leak(j) = inhibition prob. on leak->j arc +% prior(i) = prob. disease i is on + +[Ndiseases Nfindings] = size(inhibit); +N = Ndiseases + Nfindings; +finding_node = Ndiseases+1:N; +ns = 2*ones(1,N); +dag = zeros(N,N); +dag(1:Ndiseases, finding_node) = G; +bnet = mk_bnet(dag, ns, 'observed', finding_node); + +for d=1:Ndiseases + CPT = [1-prior(d) prior(d)]; + bnet.CPD{d} = tabular_CPD(bnet, d, CPT'); +end + +for i=1:Nfindings + fnode = finding_node(i); + ps = parents(G, i); + bnet.CPD{fnode} = noisyor_CPD(bnet, fnode, leak(i), inhibit(ps, i)); +end +</pre> +In the file BNT/examples/static/qmr1, we create a random bipartite +graph G, with 5 diseases and 10 findings, and random parameters. +(In general, to create a random dag, use 'mk_random_dag'.) +We can visualize the resulting graph structure using +the methods discussed <a href="#graphdraw">below</a>, with the +following results: +<p> +<img src="Figures/qmr.rnd.jpg"> + +<p> +Now let us put some random evidence on all the leaves except the very +first and very last, and compute the disease posteriors. +<pre> +pos = 2:floor(Nfindings/2); +neg = (pos(end)+1):(Nfindings-1); +onodes = myunion(pos, neg); +evidence = cell(1, N); +evidence(findings(pos)) = num2cell(repmat(2, 1, length(pos))); +evidence(findings(neg)) = num2cell(repmat(1, 1, length(neg))); + +engine = jtree_inf_engine(bnet); +[engine, ll] = enter_evidence(engine, evidence); +post = zeros(1, Ndiseases); +for i=diseases(:)' + m = marginal_nodes(engine, i); + post(i) = m.T(2); +end +</pre> +Junction tree can be quite slow on large QMR models. +Fortunately, it is possible to exploit properties of the noisy-OR +function to speed up exact inference using an algorithm called +<a href="#quickscore">quickscore</a>, discussed below. + + + + + +<h2><a name="cg_model">Conditional Gaussian models</h2> + +A conditional Gaussian model is one in which, conditioned on all the discrete +nodes, the distribution over the remaining (continuous) nodes is +multivariate Gaussian. This means we can have arcs from discrete (D) +to continuous (C) nodes, but not vice versa. +(We <em>are</em> allowed C->D arcs if the continuous nodes are observed, +as in the <a href="#mixexp">mixture of experts</a> model, +since this distribution can be represented with a discrete potential.) +<p> +We now give an example of a CG model, from +the paper "Propagation of Probabilities, Means amd +Variances in Mixed Graphical Association Models", Steffen Lauritzen, +JASA 87(420):1098--1108, 1992 (reprinted in the book "Probabilistic Networks and Expert +Systems", R. G. Cowell, A. P. Dawid, S. L. Lauritzen and +D. J. Spiegelhalter, Springer, 1999.) + +<h3>Specifying the graph</h3> + +Consider the model of waste emissions from an incinerator plant shown below. +We follow the standard convention that shaded nodes are observed, +clear nodes are hidden. +We also use the non-standard convention that +square nodes are discrete (tabular) and round nodes are +Gaussian. + +<p> +<center> +<IMG SRC="Figures/cg1.gif"> +</center> +<p> + +We can create this model as follows. +<pre> +F = 1; W = 2; E = 3; B = 4; C = 5; D = 6; Min = 7; Mout = 8; L = 9; +n = 9; + +dag = zeros(n); +dag(F,E)=1; +dag(W,[E Min D]) = 1; +dag(E,D)=1; +dag(B,[C D])=1; +dag(D,[L Mout])=1; +dag(Min,Mout)=1; + +% node sizes - all cts nodes are scalar, all discrete nodes are binary +ns = ones(1, n); +dnodes = [F W B]; +cnodes = mysetdiff(1:n, dnodes); +ns(dnodes) = 2; + +bnet = mk_bnet(dag, ns, 'discrete', dnodes); +</pre> +'dnodes' is a list of the discrete nodes; 'cnodes' is the continuous +nodes. 'mysetdiff' is a faster version of the built-in 'setdiff'. +<p> + + +<h3>Specifying the parameters</h3> + +The parameters of the discrete nodes can be specified as follows. +<pre> +bnet.CPD{B} = tabular_CPD(bnet, B, 'CPT', [0.85 0.15]); % 1=stable, 2=unstable +bnet.CPD{F} = tabular_CPD(bnet, F, 'CPT', [0.95 0.05]); % 1=intact, 2=defect +bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [2/7 5/7]); % 1=industrial, 2=household +</pre> + +<p> +The parameters of the continuous nodes can be specified as follows. +<pre> +bnet.CPD{E} = gaussian_CPD(bnet, E, 'mean', [-3.9 -0.4 -3.2 -0.5], ... + 'cov', [0.00002 0.0001 0.00002 0.0001]); +bnet.CPD{D} = gaussian_CPD(bnet, D, 'mean', [6.5 6.0 7.5 7.0], ... + 'cov', [0.03 0.04 0.1 0.1], 'weights', [1 1 1 1]); +bnet.CPD{C} = gaussian_CPD(bnet, C, 'mean', [-2 -1], 'cov', [0.1 0.3]); +bnet.CPD{L} = gaussian_CPD(bnet, L, 'mean', 3, 'cov', 0.25, 'weights', -0.5); +bnet.CPD{Min} = gaussian_CPD(bnet, Min, 'mean', [0.5 -0.5], 'cov', [0.01 0.005]); +bnet.CPD{Mout} = gaussian_CPD(bnet, Mout, 'mean', 0, 'cov', 0.002, 'weights', [1 1]); +</pre> + + +<h3><a name="cg_infer">Inference</h3> + +<!--Let us perform inference in the <a href="#cg_model">waste incinerator example</a>.--> +First we compute the unconditional marginals. +<pre> +engine = jtree_inf_engine(bnet); +evidence = cell(1,n); +[engine, ll] = enter_evidence(engine, evidence); +marg = marginal_nodes(engine, E); +</pre> +<!--(Of course, we could use <tt>cond_gauss_inf_engine</tt> instead of jtree.)--> +'marg' is a structure that contains the fields 'mu' and 'Sigma', which +contain the mean and (co)variance of the marginal on E. +In this case, they are both scalars. +Let us check they match the published figures (to 2 decimal places). +<!--(We can't expect +more precision than this in general because I have implemented the algorithm of +Lauritzen (1992), which can be numerically unstable.)--> +<pre> +tol = 1e-2; +assert(approxeq(marg.mu, -3.25, tol)); +assert(approxeq(sqrt(marg.Sigma), 0.709, tol)); +</pre> +We can compute the other posteriors similarly. +Now let us add some evidence. +<pre> +evidence = cell(1,n); +evidence{W} = 1; % industrial +evidence{L} = 1.1; +evidence{C} = -0.9; +[engine, ll] = enter_evidence(engine, evidence); +</pre> +Now we find +<pre> +marg = marginal_nodes(engine, E); +assert(approxeq(marg.mu, -3.8983, tol)); +assert(approxeq(sqrt(marg.Sigma), 0.0763, tol)); +</pre> + + +We can also compute the joint probability on a set of nodes. +For example, P(D, Mout | evidence) is a 2D Gaussian: +<pre> +marg = marginal_nodes(engine, [D Mout]) +marg = + domain: [6 8] + mu: [2x1 double] + Sigma: [2x2 double] + T: 1.0000 +</pre> +The mean is +<pre> +marg.mu +ans = + 3.6077 + 4.1077 +</pre> +and the covariance matrix is +<pre> +marg.Sigma +ans = + 0.1062 0.1062 + 0.1062 0.1182 +</pre> +It is easy to visualize this posterior using standard Matlab plotting +functions, e.g., +<pre> +gaussplot2d(marg.mu, marg.Sigma); +</pre> +produces the following picture. + +<p> +<center> +<IMG SRC="Figures/gaussplot.png"> +</center> +<p> + + +The T field indicates that the mixing weight of this Gaussian +component is 1.0. +If the joint contains discrete and continuous variables, the result +will be a mixture of Gaussians, e.g., +<pre> +marg = marginal_nodes(engine, [F E]) + domain: [1 3] + mu: [-3.9000 -0.4003] + Sigma: [1x1x2 double] + T: [0.9995 4.7373e-04] +</pre> +The interpretation is +Sigma(i,j,k) = Cov[ E(i) E(j) | F=k ]. +In this case, E is a scalar, so i=j=1; k specifies the mixture component. +<p> +We saw in the sprinkler network that BNT sets the effective size of +observed discrete nodes to 1, since they only have one legal value. +For continuous nodes, BNT sets their length to 0, +since they have been reduced to a point. +For example, +<pre> +marg = marginal_nodes(engine, [B C]) + domain: [4 5] + mu: [] + Sigma: [] + T: [0.0123 0.9877] +</pre> +It is simple to post-process the output of marginal_nodes. +For example, the file BNT/examples/static/cg1 sets the mu term of +observed nodes to their observed value, and the Sigma term to 0 (since +observed nodes have no variance). + +<p> +Note that the implemented version of the junction tree is numerically +unstable when using CG potentials +(which is why, in the example above, we only required our answers to agree with +the published ones to 2dp.) +This is why you might want to use <tt>stab_cond_gauss_inf_engine</tt>, +implemented by Shan Huang. This is described in + +<ul> +<li> "Stable Local Computation with Conditional Gaussian Distributions", +S. Lauritzen and F. Jensen, Tech Report R-99-2014, +Dept. Math. Sciences, Allborg Univ., 1999. +</ul> + +However, even the numerically stable version +can be computationally intractable if there are many hidden discrete +nodes, because the number of mixture components grows exponentially e.g., in a +<a href="usage_dbn.html#lds">switching linear dynamical system</a>. +In general, one must resort to approximate inference techniques: see +the discussion on <a href="#engines">inference engines</a> below. + + +<h2><a name="hybrid">Other hybrid models</h2> + +When we have C->D arcs, where C is hidden, we need to use +approximate inference. +One approach (not implemented in BNT) is described in +<ul> +<li> <a +href="http://www.cs.berkeley.edu/~murphyk/Papers/hybrid_uai99.ps.gz">A +Variational Approximation for Bayesian Networks with +Discrete and Continuous Latent Variables</a>, +K. Murphy, UAI 99. +</ul> +Of course, one can always use <a href="#sampling">sampling</a> methods +for approximate inference in such models. + + + +<h1><a name="param_learning">Parameter Learning</h1> + +The parameter estimation routines in BNT can be classified into 4 +types, depending on whether the goal is to compute +a full (Bayesian) posterior over the parameters or just a point +estimate (e.g., Maximum Likelihood or Maximum A Posteriori), +and whether all the variables are fully observed or there is missing +data/ hidden variables (partial observability). +<p> + +<TABLE BORDER> +<tr> + <TH></TH> + <th>Full obs</th> + <th>Partial obs</th> +</tr> +<tr> + <th>Point</th> + <td><tt>learn_params</tt></td> + <td><tt>learn_params_em</tt></td> +</tr> +<tr> + <th>Bayes</th> + <td><tt>bayes_update_params</tt></td> + <td>not yet supported</td> +</tr> +</table> + + +<h2><a name="load_data">Loading data from a file</h2> + +To load numeric data from an ASCII text file called 'dat.txt', where each row is a +case and columns are separated by white-space, such as +<pre> +011979 1626.5 0.0 +021979 1367.0 0.0 +... +</pre> +you can use +<pre> +data = load('dat.txt'); +</pre> +or +<pre> +load dat.txt -ascii +</pre> +In the latter case, the data is stored in a variable called 'dat' (the +filename minus the extension). +Alternatively, suppose the data is stored in a .csv file (has commas +separating the columns, and contains a header line), such as +<pre> +header info goes here +ORD,011979,1626.5,0.0 +DSM,021979,1367.0,0.0 +... +</pre> +You can load this using +<pre> +[a,b,c,d] = textread('dat.txt', '%s %d %f %f', 'delimiter', ',', 'headerlines', 1); +</pre> +If your file is not in either of these formats, you can either use Perl to convert +it to this format, or use the Matlab scanf command. +Type +<tt> +help iofun +</tt> +for more information on Matlab's file functions. +<!-- +<p> +To load data directly from Excel, +you should buy the +<a href="http://www.mathworks.com/products/excellink/">Excel Link</a>. +To load data directly from a relational database, +you should buy the +<a href="http://www.mathworks.com/products/database">Database +toolbox</a>. +--> +<p> +BNT learning routines require data to be stored in a cell array. +data{i,m} is the value of node i in case (example) m, i.e., each +<em>column</em> is a case. +If node i is not observed in case m (missing value), set +data{i,m} = []. +(Not all the learning routines can cope with such missing values, however.) +In the special case that all the nodes are observed and are +scalar-valued (as opposed to vector-valued), the data can be +stored in a matrix (as opposed to a cell-array). +<p> +Suppose, as in the <a href="#mixexp">mixture of experts example</a>, +that we have 3 nodes in the graph: X(1) is the observed input, X(3) is +the observed output, and X(2) is a hidden (gating) node. We can +create the dataset as follows. +<pre> +data = load('dat.txt'); +ncases = size(data, 1); +cases = cell(3, ncases); +cases([1 3], :) = num2cell(data'); +</pre> +Notice how we transposed the data, to convert rows into columns. +Also, cases{2,m} = [] for all m, since X(2) is always hidden. + + +<h2><a name="mle_complete">Maximum likelihood parameter estimation from complete data</h2> + +As an example, let's generate some data from the sprinkler network, randomize the parameters, +and then try to recover the original model. +First we create some training data using forwards sampling. +<pre> +samples = cell(N, nsamples); +for i=1:nsamples + samples(:,i) = sample_bnet(bnet); +end +</pre> +samples{j,i} contains the value of the j'th node in case i. +sample_bnet returns a cell array because, in general, each node might +be a vector of different length. +In this case, all nodes are discrete (and hence scalars), so we +could have used a regular array instead (which can be quicker): +<pre> +data = cell2num(samples); +</pre +So now data(j,i) = samples{j,i}. +<p> +Now we create a network with random parameters. +(The initial values of bnet2 don't matter in this case, since we can find the +globally optimal MLE independent of where we start.) +<pre> +% Make a tabula rasa +bnet2 = mk_bnet(dag, node_sizes); +seed = 0; +rand('state', seed); +bnet2.CPD{C} = tabular_CPD(bnet2, C); +bnet2.CPD{R} = tabular_CPD(bnet2, R); +bnet2.CPD{S} = tabular_CPD(bnet2, S); +bnet2.CPD{W} = tabular_CPD(bnet2, W); +</pre> +Finally, we find the maximum likelihood estimates of the parameters. +<pre> +bnet3 = learn_params(bnet2, samples); +</pre> +To view the learned parameters, we use a little Matlab hackery. +<pre> +CPT3 = cell(1,N); +for i=1:N + s=struct(bnet3.CPD{i}); % violate object privacy + CPT3{i}=s.CPT; +end +</pre> +Here are the parameters learned for node 4. +<pre> +dispcpt(CPT3{4}) +1 1 : 1.0000 0.0000 +2 1 : 0.2000 0.8000 +1 2 : 0.2273 0.7727 +2 2 : 0.0000 1.0000 +</pre> +So we see that the learned parameters are fairly close to the "true" +ones, which we display below. +<pre> +dispcpt(CPT{4}) +1 1 : 1.0000 0.0000 +2 1 : 0.1000 0.9000 +1 2 : 0.1000 0.9000 +2 2 : 0.0100 0.9900 +</pre> +We can get better results by using a larger training set, or using +informative priors (see <a href="#prior">below</a>). + + + +<h2><a name="prior">Parameter priors</h2> + +Currently, only tabular CPDs can have priors on their parameters. +The conjugate prior for a multinomial is the Dirichlet. +(For binary random variables, the multinomial is the same as the +Bernoulli, and the Dirichlet is the same as the Beta.) +<p> +The Dirichlet has a simple interpretation in terms of pseudo counts. +If we let N_ijk = the num. times X_i=k and Pa_i=j occurs in the +training set, where Pa_i are the parents of X_i, +then the maximum likelihood (ML) estimate is +T_ijk = N_ijk / N_ij (where N_ij = sum_k' N_ijk'), which will be 0 if N_ijk=0. +To prevent us from declaring that (X_i=k, Pa_i=j) is impossible just because this +event was not seen in the training set, +we can pretend we saw value k of X_i, for each value j of Pa_i some number (alpha_ijk) +of times in the past. +The MAP (maximum a posterior) estimate is then +<pre> +T_ijk = (N_ijk + alpha_ijk) / (N_ij + alpha_ij) +</pre> +and is never 0 if all alpha_ijk > 0. +For example, consider the network A->B, where A is binary and B has 3 +values. +A uniform prior for B has the form +<pre> + B=1 B=2 B=3 +A=1 1 1 1 +A=2 1 1 1 +</pre> +which can be created using +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); +</pre> +This prior does not satisfy the likelihood equivalence principle, +which says that <a href="#markov_equiv">Markov equivalent</a> models +should have the same marginal likelihood. +A prior that does satisfy this principle is shown below. +Heckerman (1995) calls this the +BDeu prior (likelihood equivalent uniform Bayesian Dirichlet). +<pre> + B=1 B=2 B=3 +A=1 1/6 1/6 1/6 +A=2 1/6 1/6 1/6 +</pre> +where we put N/(q*r) in each bin; N is the equivalent sample size, +r=|A|, q = |B|. +This can be created as follows +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'BDeu'); +</pre> +Here, 1 is the equivalent sample size, and is the strength of the +prior. +You can change this using +<pre> +tabular_CPD(bnet, i, 'prior_type', 'dirichlet', 'dirichlet_type', ... + 'BDeu', 'dirichlet_weight', 10); +</pre> +<!--where counts is an array of pseudo-counts of the same size as the +CPT.--> +<!-- +<p> +When you specify a prior, you should set row i of the CPT to the +normalized version of row i of the pseudo-count matrix, i.e., to the +expected values of the parameters. This will ensure that computing the +marginal likelihood sequentially (see <a +href="#bayes_learn">below</a>) and in batch form gives the same +results. +To do this, proceed as follows. +<pre> +tabular_CPD(bnet, i, 'prior', counts, 'CPT', mk_stochastic(counts)); +</pre> +For a non-informative prior, you can just write +<pre> +tabular_CPD(bnet, i, 'prior', 'unif', 'CPT', 'unif'); +</pre> +--> + + +<h2><a name="bayes_learn">(Sequential) Bayesian parameter updating from complete data</h2> + +If we use conjugate priors and have fully observed data, we can +compute the posterior over the parameters in batch form as follows. +<pre> +cases = sample_bnet(bnet, nsamples); +bnet = bayes_update_params(bnet, cases); +LL = log_marg_lik_complete(bnet, cases); +</pre> +bnet.CPD{i}.prior contains the new Dirichlet pseudocounts, +and bnet.CPD{i}.CPT is set to the mean of the posterior (the +normalized counts). +(Hence if the initial pseudo counts are 0, +<tt>bayes_update_params</tt> and <tt>learn_params</tt> will give the +same result.) + + + + +<p> +We can compute the same result sequentially (on-line) as follows. +<pre> +LL = 0; +for m=1:nsamples + LL = LL + log_marg_lik_complete(bnet, cases(:,m)); + bnet = bayes_update_params(bnet, cases(:,m)); +end +</pre> + +The file <tt>BNT/examples/static/StructLearn/model_select1</tt> has an example of +sequential model selection which uses the same idea. +We generate data from the model A->B +and compute the posterior prob of all 3 dags on 2 nodes: + (1) A B, (2) A <- B , (3) A -> B +Models 2 and 3 are <a href="#markov_equiv">Markov equivalent</a>, and therefore indistinguishable from +observational data alone, so we expect their posteriors to be the same +(assuming a prior which satisfies likelihood equivalence). +If we use random parameters, the "true" model only gets a higher posterior after 2000 trials! +However, if we make B a noisy NOT gate, the true model "wins" after 12 +trials, as shown below (red = model 1, blue/green (superimposed) +represents models 2/3). +<p> +<img src="Figures/model_select.png"> +<p> +The use of marginal likelihood for model selection is discussed in +greater detail in the +section on <a href="structure_learning">structure learning</a>. + + + + +<h2><a name="em">Maximum likelihood parameter estimation with missing values</h2> + +Now we consider learning when some values are not observed. +Let us randomly hide half the values generated from the water +sprinkler example. +<pre> +samples2 = samples; +hide = rand(N, nsamples) > 0.5; +[I,J]=find(hide); +for k=1:length(I) + samples2{I(k), J(k)} = []; +end +</pre> +samples2{i,l} is the value of node i in training case l, or [] if unobserved. +<p> +Now we will compute the MLEs using the EM algorithm. +We need to use an inference algorithm to compute the expected +sufficient statistics in the E step; the M (maximization) step is as +above. +<pre> +engine2 = jtree_inf_engine(bnet2); +max_iter = 10; +[bnet4, LLtrace] = learn_params_em(engine2, samples2, max_iter); +</pre> +LLtrace(i) is the log-likelihood at iteration i. We can plot this as +follows: +<pre> +plot(LLtrace, 'x-') +</pre> +Let's display the results after 10 iterations of EM. +<pre> +celldisp(CPT4) +CPT4{1} = + 0.6616 + 0.3384 +CPT4{2} = + 0.6510 0.3490 + 0.8751 0.1249 +CPT4{3} = + 0.8366 0.1634 + 0.0197 0.9803 +CPT4{4} = +(:,:,1) = + 0.8276 0.0546 + 0.5452 0.1658 +(:,:,2) = + 0.1724 0.9454 + 0.4548 0.8342 +</pre> +We can get improved performance by using one or more of the following +methods: +<ul> +<li> Increasing the size of the training set. +<li> Decreasing the amount of hidden data. +<li> Running EM for longer. +<li> Using informative priors. +<li> Initialising EM from multiple starting points. +</ul> + +Click <a href="#gaussian">here</a> for a discussion of learning +Gaussians, which can cause numerical problems. +<p> +For a more complete example of learning with EM, +see the script BNT/examples/static/learn1.m. + +<h2><a name="tying">Parameter tying</h2> + +In networks with repeated structure (e.g., chains and grids), it is +common to assume that the parameters are the same at every node. This +is called parameter tying, and reduces the amount of data needed for +learning. +<p> +When we have tied parameters, there is no longer a one-to-one +correspondence between nodes and CPDs. +Rather, each CPD species the parameters for a whole equivalence class +of nodes. +It is easiest to see this by example. +Consider the following <a href="usage_dbn.html#hmm">hidden Markov +model (HMM)</a> +<p> +<img src="Figures/hmm3.gif"> +<p> +<!-- +We can create this graph structure, assuming we have T time-slices, +as follows. +(We number the nodes as shown in the figure, but we could equally well +number the hidden nodes 1:T, and the observed nodes T+1:2T.) +<pre> +N = 2*T; +dag = zeros(N); +hnodes = 1:2:2*T; +for i=1:T-1 + dag(hnodes(i), hnodes(i+1))=1; +end +onodes = 2:2:2*T; +for i=1:T + dag(hnodes(i), onodes(i)) = 1; +end +</pre> +<p> +The hidden nodes are always discrete, and have Q possible values each, +but the observed nodes can be discrete or continuous, and have O possible values/length. +<pre> +if cts_obs + dnodes = hnodes; +else + dnodes = 1:N; +end +ns = ones(1,N); +ns(hnodes) = Q; +ns(onodes) = O; +</pre> +--> +When HMMs are used for semi-infinite processes like speech recognition, +we assume the transition matrix +P(H(t+1)|H(t)) is the same for all t; this is called a time-invariant +or homogenous Markov chain. +Hence hidden nodes 2, 3, ..., T +are all in the same equivalence class, say class Hclass. +Similarly, the observation matrix P(O(t)|H(t)) is assumed to be the +same for all t, so the observed nodes are all in the same equivalence +class, say class Oclass. +Finally, the prior term P(H(1)) is in a class all by itself, say class +H1class. +This is illustrated below, where we explicitly represent the +parameters as random variables (dotted nodes). +<p> +<img src="Figures/hmm4_params.gif"> +<p> +In BNT, we cannot represent parameters as random variables (nodes). +Instead, we "hide" the +parameters inside one CPD for each equivalence class, +and then specify that the other CPDs should share these parameters, as +follows. +<pre> +hnodes = 1:2:2*T; +onodes = 2:2:2*T; +H1class = 1; Hclass = 2; Oclass = 3; +eclass = ones(1,N); +eclass(hnodes(2:end)) = Hclass; +eclass(hnodes(1)) = H1class; +eclass(onodes) = Oclass; +% create dag and ns in the usual way +bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'equiv_class', eclass); +</pre> +Finally, we define the parameters for each equivalence class: +<pre> +bnet.CPD{H1class} = tabular_CPD(bnet, hnodes(1)); % prior +bnet.CPD{Hclass} = tabular_CPD(bnet, hnodes(2)); % transition matrix +if cts_obs + bnet.CPD{Oclass} = gaussian_CPD(bnet, onodes(1)); +else + bnet.CPD{Oclass} = tabular_CPD(bnet, onodes(1)); +end +</pre> +In general, if bnet.CPD{e} = xxx_CPD(bnet, j), then j should be a +member of e's equivalence class; that is, it is not always the case +that e == j. You can use bnet.rep_of_eclass(e) to return the +representative of equivalence class e. +BNT will look up the parents of j to determine the size +of the CPT to use. It assumes that this is the same for all members of +the equivalence class. +Click <a href="param_tieing.html">here</a> for +a more complex example of parameter tying. +<p> +Note: +Normally one would define an HMM as a +<a href = "usage_dbn.html">Dynamic Bayes Net</a> +(see the function BNT/examples/dynamic/mk_chmm.m). +However, one can define an HMM as a static BN using the function +BNT/examples/static/Models/mk_hmm_bnet.m. + + + +<h1><a name="structure_learning">Structure learning</h1> + +Update (9/29/03): +Phillipe LeRay is developing some additional structure learning code +on top of BNT. Click <a +href="http://bnt.insa-rouen.fr/ajouts.html">here</a> +for details. + +<p> + +There are two very different approaches to structure learning: +constraint-based and search-and-score. +In the <a href="#constraint">constraint-based approach</a>, +we start with a fully connected graph, and remove edges if certain +conditional independencies are measured in the data. +This has the disadvantage that repeated independence tests lose +statistical power. +<p> +In the more popular search-and-score approach, +we perform a search through the space of possible DAGs, and either +return the best one found (a point estimate), or return a sample of the +models found (an approximation to the Bayesian posterior). +<p> +Unfortunately, the number of DAGs as a function of the number of +nodes, G(n), is super-exponential in n. +A closed form formula for G(n) is not known, but the first few values +are shown below (from Cooper, 1999). + +<table> +<tr> <th>n</th> <th align=left>G(n)</th> </tr> +<tr> <td>1</td> <td>1</td> </tr> +<tr> <td>2</td> <td>3</td> </tr> +<tr> <td>3</td> <td>25</td> </tr> +<tr> <td>4</td> <td>543</td> </tr> +<tr> <td>5</td> <td>29,281</td> </tr> +<tr> <td>6</td> <td>3,781,503</td> </tr> +<tr> <td>7</td> <td>1.1 x 10^9</td> </tr> +<tr> <td>8</td> <td>7.8 x 10^11</td> </tr> +<tr> <td>9</td> <td>1.2 x 10^15</td> </tr> +<tr> <td>10</td> <td>4.2 x 10^18</td> </tr> +</table> + +Since the number of DAGs is super-exponential in the number of nodes, +we cannot exhaustively search the space, so we either use a local +search algorithm (e.g., greedy hill climbining, perhaps with multiple +restarts) or a global search algorithm (e.g., Markov Chain Monte +Carlo). +<p> +If we know a total ordering on the nodes, +finding the best structure amounts to picking the best set of parents +for each node independently. +This is what the K2 algorithm does. +If the ordering is unknown, we can search over orderings, +which is more efficient than searching over DAGs (Koller and Friedman, 2000). +<p> +In addition to the search procedure, we must specify the scoring +function. There are two popular choices. The Bayesian score integrates +out the parameters, i.e., it is the marginal likelihood of the model. +The BIC (Bayesian Information Criterion) is defined as +log P(D|theta_hat) - 0.5*d*log(N), where D is the data, theta_hat is +the ML estimate of the parameters, d is the number of parameters, and +N is the number of data cases. +The BIC method has the advantage of not requiring a prior. +<p> +BIC can be derived as a large sample +approximation to the marginal likelihood. +(It is also equal to the Minimum Description Length of a model.) +However, in practice, the sample size does not need to be very large +for the approximation to be good. +For example, in the figure below, we plot the ratio between the log marginal likelihood +and the BIC score against data-set size; we see that the ratio rapidly +approaches 1, especially for non-informative priors. +(This plot was generated by the file BNT/examples/static/bic1.m. It +uses the water sprinkler BN with BDeu Dirichlet priors with different +equivalent sample sizes.) + +<p> +<center> +<IMG SRC="Figures/bic.png"> +</center> +<p> + +<p> +As with parameter learning, handling missing data/ hidden variables is +much harder than the fully observed case. +The structure learning routines in BNT can therefore be classified into 4 +types, analogously to the parameter learning case. +<p> + +<TABLE BORDER> +<tr> + <TH></TH> + <th>Full obs</th> + <th>Partial obs</th> +</tr> +<tr> + <th>Point</th> + <td><tt>learn_struct_K2</tt> <br> +<!-- <tt>learn_struct_hill_climb</tt></td> --> + <td><tt>not yet supported</tt></td> +</tr> +<tr> + <th>Bayes</th> + <td><tt>learn_struct_mcmc</tt></td> + <td>not yet supported</td> +</tr> +</table> + + +<h2><a name="markov_equiv">Markov equivalence</h2> + +If two DAGs encode the same conditional independencies, they are +called Markov equivalent. The set of all DAGs can be paritioned into +Markov equivalence classes. Graphs within the same class can +have +the direction of some of their arcs reversed without changing any of +the CI relationships. +Each class can be represented by a PDAG +(partially directed acyclic graph) called an essential graph or +pattern. This specifies which edges must be oriented in a certain +direction, and which may be reversed. + +<p> +When learning graph structure from observational data, +the best one can hope to do is to identify the model up to Markov +equivalence. To distinguish amongst graphs within the same equivalence +class, one needs interventional data: see the discussion on <a +href="#active">active learning</a> below. + + + +<h2><a name="enumerate">Exhaustive search</h2> + +The brute-force approach to structure learning is to enumerate all +possible DAGs, and score each one. This provides a "gold standard" +with which to compare other algorithms. We can do this as follows. +<pre> +dags = mk_all_dags(N); +score = score_dags(data, ns, dags); +</pre> +where data(i,m) is the value of node i in case m, +and ns(i) is the size of node i. +If the DAGs have a lot of families in common, we can cache the sufficient statistics, +making this potentially more efficient than scoring the DAGs one at a time. +(Caching is not currently implemented, however.) +<p> +By default, we use the Bayesian scoring metric, and assume CPDs are +represented by tables with BDeu(1) priors. +We can override these defaults as follows. +If we want to use uniform priors, we can say +<pre> +params = cell(1,N); +for i=1:N + params{i} = {'prior', 'unif'}; +end +score = score_dags(data, ns, dags, 'params', params); +</pre> +params{i} is a cell-array, containing optional arguments that are +passed to the constructor for CPD i. +<p> +Now suppose we want to use different node types, e.g., +Suppose nodes 1 and 2 are Gaussian, and nodes 3 and 4 softmax (both +these CPDs can support discrete and continuous parents, which is +necessary since all other nodes will be considered as parents). +The Bayesian scoring metric currently only works for tabular CPDs, so +we will use BIC: +<pre> +score = score_dags(data, ns, dags, 'discrete', [3 4], 'params', [], + 'type', {'gaussian', 'gaussian', 'softmax', softmax'}, 'scoring_fn', 'bic') +</pre> +In practice, one can't enumerate all possible DAGs for N > 5, +but one can evaluate any reasonably-sized set of hypotheses in this +way (e.g., nearest neighbors of your current best guess). +Think of this as "computer assisted model refinement" as opposed to de +novo learning. + + +<h2><a name="K2">K2</h2> + +The K2 algorithm (Cooper and Herskovits, 1992) is a greedy search algorithm that works as follows. +Initially each node has no parents. It then adds incrementally that parent whose addition most +increases the score of the resulting structure. When the addition of no single +parent can increase the score, it stops adding parents to the node. +Since we are using a fixed ordering, we do not need to check for +cycles, and can choose the parents for each node independently. +<p> +The original paper used the Bayesian scoring +metric with tabular CPDs and Dirichlet priors. +BNT generalizes this to allow any kind of CPD, and either the Bayesian +scoring metric or BIC, as in the example <a href="#enumerate">above</a>. +In addition, you can specify +an optional upper bound on the number of parents for each node. +The file BNT/examples/static/k2demo1.m gives an example of how to use K2. +We use the water sprinkler network and sample 100 cases from it as before. +Then we see how much data it takes to recover the generating structure: +<pre> +order = [C S R W]; +max_fan_in = 2; +sz = 5:5:100; +for i=1:length(sz) + dag2 = learn_struct_K2(data(:,1:sz(i)), node_sizes, order, 'max_fan_in', max_fan_in); + correct(i) = isequal(dag, dag2); +end +</pre> +Here are the results. +<pre> +correct = + Columns 1 through 12 + 0 0 0 0 0 0 0 1 0 1 1 1 + Columns 13 through 20 + 1 1 1 1 1 1 1 1 +</pre> +So we see it takes about sz(10)=50 cases. (BIC behaves similarly, +showing that the prior doesn't matter too much.) +In general, we cannot hope to recover the "true" generating structure, +only one that is in its <a href="#markov_equiv">Markov equivalence +class</a>. + + +<h2><a name="hill_climb">Hill-climbing</h2> + +Hill-climbing starts at a specific point in space, +considers all nearest neighbors, and moves to the neighbor +that has the highest score; if no neighbors have higher +score than the current point (i.e., we have reached a local maximum), +the algorithm stops. One can then restart in another part of the space. +<p> +A common definition of "neighbor" is all graphs that can be +generated from the current graph by adding, deleting or reversing a +single arc, subject to the acyclicity constraint. +Other neighborhoods are possible: see +<a href="http://research.microsoft.com/~dmax/publications/jmlr02.pdf"> +Optimal Structure Identification with Greedy Search</a>, Max +Chickering, JMLR 2002. + +<!-- +Note: This algorithm is currently (Feb '02) being implemented by Qian +Diao. +--> + + +<h2><a name="mcmc">MCMC</h2> + +We can use a Markov Chain Monte Carlo (MCMC) algorithm called +Metropolis-Hastings (MH) to search the space of all +DAGs. +The standard proposal distribution is to consider moving to all +nearest neighbors in the sense defined <a href="#hill_climb">above</a>. +<p> +The function can be called +as in the following example. +<pre> +[sampled_graphs, accept_ratio] = learn_struct_mcmc(data, ns, 'nsamples', 100, 'burnin', 10); +</pre> +We can convert our set of sampled graphs to a histogram +(empirical posterior over all the DAGs) thus +<pre> +all_dags = mk_all_dags(N); +mcmc_post = mcmc_sample_to_hist(sampled_graphs, all_dags); +</pre> +To see how well this performs, let us compute the exact posterior exhaustively. +<p> +<pre> +score = score_dags(data, ns, all_dags); +post = normalise(exp(score)); % assuming uniform structural prior +</pre> +We plot the results below. +(The data set was 100 samples drawn from a random 4 node bnet; see the +file BNT/examples/static/mcmc1.) +<pre> +subplot(2,1,1) +bar(post) +subplot(2,1,2) +bar(mcmc_post) +</pre> +<img src="Figures/mcmc_post.jpg" width="800" height="500"> +<p> +We can also plot the acceptance ratio versus number of MCMC steps, +as a crude convergence diagnostic. +<pre> +clf +plot(accept_ratio) +</pre> +<img src="Figures/mcmc_accept.jpg" width="800" height="300"> +<p> +Even though the number of samples needed by MCMC is theoretically +polynomial (not exponential) in the dimensionality of the search space, in practice it has been +found that MCMC does not converge in reasonable time for graphs with +more than about 10 nodes. + + + + +<h2><a name="active">Active structure learning</h2> + +As was mentioned <a href="#markov_equiv">above</a>, +one can only learn a DAG up to Markov equivalence, even given infinite data. +If one is interested in learning the structure of a causal network, +one needs interventional data. +(By "intervention" we mean forcing a node to take on a specific value, +thereby effectively severing its incoming arcs.) +<p> +Most of the scoring functions accept an optional argument +that specifies whether a node was observed to have a certain value, or +was forced to have that value: we set clamped(i,m)=1 if node i was +forced in training case m. e.g., see the file +BNT/examples/static/cooper_yoo. +<p> +An interesting question is to decide which interventions to perform +(c.f., design of experiments). For details, see the following tech +report +<ul> +<li> <a href = "../../Papers/alearn.ps.gz"> +Active learning of causal Bayes net structure</a>, Kevin Murphy, March +2001. +</ul> + + +<h2><a name="struct_em">Structural EM</h2> + +Computing the Bayesian score when there is partial observability is +computationally challenging, because the parameter posterior becomes +multimodal (the hidden nodes induce a mixture distribution). +One therefore needs to use approximations such as BIC. +Unfortunately, search algorithms are still expensive, because we need +to run EM at each step to compute the MLE, which is needed to compute +the score of each model. An alternative approach is +to do the local search steps inside of the M step of EM, which is more +efficient since the data has been "filled in" - this is +called the structural EM algorithm (Friedman 1997), and provably +converges to a local maximum of the BIC score. +<p> +Wei Hu has implemented SEM for discrete nodes. +You can download his package from +<a href="../SEM.zip">here</a>. +Please address all questions about this code to +wei.hu@intel.com. +See also <a href="#phl">Phl's implementation of SEM</a>. + +<!-- +<h2><a name="reveal">REVEAL algorithm</h2> + +A simple way to learn the structure of a fully observed, discrete, +factored DBN from a time series is described <a +href="usage_dbn.html#struct_learn">here</a>. +--> + + +<h2><a name="graphdraw">Visualizing the graph</h2> + +You can visualize an arbitrary graph (such as one learned using the +structure learning routines) with Matlab code contributed by +<a href="http://www.mbfys.kun.nl/~cemgil/matlab/layout.html">Ali +Taylan Cemgil</a> +from the University of Nijmegen. +For static BNs, call it as follows: +<pre> +draw_graph(bnet.dag); +</pre> +For example, this is the output produced on a +<a href="#qmr">random QMR-like model</a>: +<p> +<img src="Figures/qmr.rnd.jpg"> +<p> +If you install the excellent <a +href="http://www.research.att.com/sw/tools/graphviz">graphhviz</a>, an +open-source graph visualization package from AT&T, +you can create a much better visualization as follows +<pre> +graph_to_dot(bnet.dag) +</pre> +This works by converting the adjacency matrix to a file suitable +for input to graphviz (using the dot format), +then converting the output of graphviz to postscript, and displaying the results using +ghostview. +You can do each of these steps separately for more control, as shown +below. +<pre> +graph_to_dot(bnet.dag, 'filename', 'foo.dot'); +dot -Tps foo.dot -o foo.ps +ghostview foo.ps & +</pre> + +<h2><a name = "constraint">Constraint-based methods</h2> + +The IC algorithm (Pearl and Verma, 1991), +and the faster, but otherwise equivalent, PC algorithm (Spirtes, Glymour, and Scheines 1993), +computes many conditional independence tests, +and combines these constraints into a +PDAG to represent the whole +<a href="#markov_equiv">Markov equivalence class</a>. +<p> +IC*/FCI extend IC/PC to handle latent variables: see <a href="#ic_star">below</a>. +(IC stands for inductive causation; PC stands for Peter and Clark, +the first names of Spirtes and Glymour; FCI stands for fast causal +inference. +What we, following Pearl (2000), call IC* was called +IC in the original Pearl and Verma paper.) +For details, see +<ul> +<li> +<a href="http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html">Causation, +Prediction, and Search</a>, Spirtes, Glymour and +Scheines (SGS), 2001 (2nd edition), MIT Press. +<li> +<a href="http://bayes.cs.ucla.edu/BOOK-2K/index.html">Causality: Models, Reasoning and Inference</a>, J. Pearl, +2000, Cambridge University Press. +</ul> + +<p> + +The PC algorithm takes as arguments a function f, the number of nodes N, +the maximum fan in K, and additional arguments A which are passed to f. +The function f(X,Y,S,A) returns 1 if X is conditionally independent of Y given S, and 0 +otherwise. +For example, suppose we cheat by +passing in a CI "oracle" which has access to the true DAG; the oracle +tests for d-separation in this DAG, i.e., +f(X,Y,S) calls dsep(X,Y,S,dag). We can to this as follows. +<pre> +pdag = learn_struct_pdag_pc('dsep', N, max_fan_in, dag); +</pre> +pdag(i,j) = -1 if there is definitely an i->j arc, +and pdag(i,j) = 1 if there is either an i->j or and i<-j arc. +<p> +Applied to the sprinkler network, this returns +<pre> +pdag = + 0 1 1 0 + 1 0 0 -1 + 1 0 0 -1 + 0 0 0 0 +</pre> +So as expected, we see that the V-structure at the W node is uniquely identified, +but the other arcs have ambiguous orientation. +<p> +We now give an example from p141 (1st edn) / p103 (2nd end) of the SGS +book. +This example concerns the female orgasm. +We are given a correlation matrix C between 7 measured factors (such +as subjective experiences of coital and masturbatory experiences), +derived from 281 samples, and want to learn a causal model of the +data. We will not discuss the merits of this type of work here, but +merely show how to reproduce the results in the SGS book. +Their program, +<a href="http://hss.cmu.edu/html/departments/philosophy/TETRAD/tetrad.html">Tetrad</a>, +makes use of the Fisher Z-test for conditional +independence, so we do the same: +<pre> +max_fan_in = 4; +nsamples = 281; +alpha = 0.05; +pdag = learn_struct_pdag_pc('cond_indep_fisher_z', n, max_fan_in, C, nsamples, alpha); +</pre> +In this case, the CI test is +<pre> +f(X,Y,S) = cond_indep_fisher_z(X,Y,S, C,nsamples,alpha) +</pre> +The results match those of Fig 12a of SGS apart from two edge +differences; presumably this is due to rounding error (although it +could be a bug, either in BNT or in Tetrad). +This example can be found in the file BNT/examples/static/pc2.m. + +<p> + +The IC* algorithm (Pearl and Verma, 1991), +and the faster FCI algorithm (Spirtes, Glymour, and Scheines 1993), +are like the IC/PC algorithm, except that they can detect the presence +of latent variables. +See the file <tt>learn_struct_pdag_ic_star</tt> written by Tamar +Kushnir. The output is a matrix P, defined as follows +(see Pearl (2000), p52 for details): +<pre> +% P(i,j) = -1 if there is either a latent variable L such that i <-L->j OR there is a directed edge from i->j. +% P(i,j) = -2 if there is a marked directed i-*>j edge. +% P(i,j) = P(j,i) = 1 if there is and undirected edge i--j +% P(i,j) = P(j,i) = 2 if there is a latent variable L such that i<-L->j. +</pre> + + +<h2><a name="phl">Philippe Leray's structure learning package</h2> + +Philippe Leray has written a +<a href="http://bnt.insa-rouen.fr/ajouts.html"> +structure learning package</a> that uses BNT. + +It currently (Juen 2003) has the following features: +<ul> +<li>PC with Chi2 statistical test +<li> MWST : Maximum weighted Spanning Tree +<li> Hill Climbing +<li> Greedy Search +<li> Structural EM +<li> hist_ic : optimal Histogram based on IC information criterion +<li> cpdag_to_dag +<li> dag_to_cpdag +<li> ... +</ul> + + +</a> + + +<!-- +<h2><a name="read_learning">Further reading on learning</h2> + +I recommend the following tutorials for more details on learning. +<ul> +<li> <a +href="http://www.cs.berkeley.edu/~murphyk/Papers/intel.ps.gz">My short +tutorial</a> on graphical models, which contains an overview of learning. + +<li> +<A HREF="ftp://ftp.research.microsoft.com/pub/tr/TR-95-06.PS"> +A tutorial on learning with Bayesian networks</a>, D. Heckerman, +Microsoft Research Tech Report, 1995. + +<li> <A HREF="http://www-cad.eecs.berkeley.edu/~wray/Mirror/lwgmja"> +Operations for Learning with Graphical Models</a>, +W. L. Buntine, JAIR'94, 159--225. +</ul> +<p> +--> + + + + + +<h1><a name="engines">Inference engines</h1> + +Up until now, we have used the junction tree algorithm for inference. +However, sometimes this is too slow, or not even applicable. +In general, there are many inference algorithms each of which make +different tradeoffs between speed, accuracy, complexity and +generality. Furthermore, there might be many implementations of the +same algorithm; for instance, a general purpose, readable version, +and a highly-optimized, specialized one. +To cope with this variety, we treat each inference algorithm as an +object, which we call an inference engine. + +<p> +An inference engine is an object that contains a bnet and supports the +'enter_evidence' and 'marginal_nodes' methods. The engine constructor +takes the bnet as argument and may do some model-specific processing. +When 'enter_evidence' is called, the engine may do some +evidence-specific processing. Finally, when 'marginal_nodes' is +called, the engine may do some query-specific processing. + +<p> +The amount of work done when each stage is specified -- structure, +parameters, evidence, and query -- depends on the engine. The cost of +work done early in this sequence can be amortized. On the other hand, +one can make better optimizations if one waits until later in the +sequence. +For example, the parameters might imply +conditional indpendencies that are not evident in the graph structure, +but can nevertheless be exploited; the evidence indicates which nodes +are observed and hence can effectively be disconnected from the +graph; and the query might indicate that large parts of the network +are d-separated from the query nodes. (Since it is not the actual +<em>values</em> of the evidence that matters, just which nodes are observed, +many engines allow you to specify which nodes will be observed when they are constructed, +i.e., before calling 'enter_evidence'. Some engines can still cope if +the actual pattern of evidence is different, e.g., if there is missing +data.) +<p> + +Although being maximally lazy (i.e., only doing work when a query is +issued) may seem desirable, +this is not always the most efficient. +For example, +when learning using EM, we need to call marginal_nodes N times, where N is the +number of nodes. <a href="varelim">Variable elimination</a> would end +up repeating a lot of work +each time marginal_nodes is called, making it inefficient for +learning. The junction tree algorithm, by contrast, uses dynamic +programming to avoid this redundant computation --- it calculates all +marginals in two passes during 'enter_evidence', so calling +'marginal_nodes' takes constant time. +<p> +We will discuss some of the inference algorithms implemented in BNT +below, and finish with a <a href="#engine_summary">summary</a> of all +of them. + + + + + + + +<h2><a name="varelim">Variable elimination</h2> + +The variable elimination algorithm, also known as bucket elimination +or peeling, is one of the simplest inference algorithms. +The basic idea is to "push sums inside of products"; this is explained +in more detail +<a +href="http://HTTP.CS.Berkeley.EDU/~murphyk/Bayes/bayes.html#infer">here</a>. +<p> +The principle of distributing sums over products can be generalized +greatly to apply to any commutative semiring. +This forms the basis of many common algorithms, such as Viterbi +decoding and the Fast Fourier Transform. For details, see + +<ul> +<li> R. McEliece and S. M. Aji, 2000. +<!--<a href="http://www.systems.caltech.edu/EE/Faculty/rjm/papers/GDL.ps">--> +<a href="GDL.pdf"> +The Generalized Distributive Law</a>, +IEEE Trans. Inform. Theory, vol. 46, no. 2 (March 2000), +pp. 325--343. + + +<li> +F. R. Kschischang, B. J. Frey and H.-A. Loeliger, 2001. +<a href="http://www.cs.toronto.edu/~frey/papers/fgspa.abs.html"> +Factor graphs and the sum-product algorithm</a> +IEEE Transactions on Information Theory, February, 2001. + +</ul> + +<p> +Choosing an order in which to sum out the variables so as to minimize +computational cost is known to be NP-hard. +The implementation of this algorithm in +<tt>var_elim_inf_engine</tt> makes no attempt to optimize this +ordering (in contrast, say, to <tt>jtree_inf_engine</tt>, which uses a +greedy search procedure to find a good ordering). +<p> +Note: unlike most algorithms, var_elim does all its computational work +inside of <tt>marginal_nodes</tt>, not inside of +<tt>enter_evidence</tt>. + + + + +<h2><a name="global">Global inference methods</h2> + +The simplest inference algorithm of all is to explicitely construct +the joint distribution over all the nodes, and then to marginalize it. +This is implemented in <tt>global_joint_inf_engine</tt>. +Since the size of the joint is exponential in the +number of discrete (hidden) nodes, this is not a very practical algorithm. +It is included merely for pedagogical and debugging purposes. +<p> +Three specialized versions of this algorithm have also been implemented, +corresponding to the cases where all the nodes are discrete (D), all +are Gaussian (G), and some are discrete and some Gaussian (CG). +They are called <tt>enumerative_inf_engine</tt>, +<tt>gaussian_inf_engine</tt>, +and <tt>cond_gauss_inf_engine</tt> respectively. +<p> +Note: unlike most algorithms, these global inference algorithms do all their computational work +inside of <tt>marginal_nodes</tt>, not inside of +<tt>enter_evidence</tt>. + + +<h2><a name="quickscore">Quickscore</h2> + +The junction tree algorithm is quite slow on the <a href="#qmr">QMR</a> network, +since the cliques are so big. +One simple trick we can use is to notice that hidden leaves do not +affect the posteriors on the roots, and hence do not need to be +included in the network. +A second trick is to notice that the negative findings can be +"absorbed" into the prior: +see the file +BNT/examples/static/mk_minimal_qmr_bnet for details. +<p> + +A much more significant speedup is obtained by exploiting special +properties of the noisy-or node, as done by the quickscore +algorithm. For details, see +<ul> +<li> Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI 89. +<li> Rish and Dechter, "On the impact of causal independence", UCI +tech report, 1998. +</ul> + +This has been implemented in BNT as a special-purpose inference +engine, which can be created and used as follows: +<pre> +engine = quickscore_inf_engine(inhibit, leak, prior); +engine = enter_evidence(engine, pos, neg); +m = marginal_nodes(engine, i); +</pre> + + +<h2><a name="belprop">Belief propagation</h2> + +Even using quickscore, exact inference takes time that is exponential +in the number of positive findings. +Hence for large networks we need to resort to approximate inference techniques. +See for example +<ul> +<li> T. Jaakkola and M. Jordan, "Variational probabilistic inference and the +QMR-DT network", JAIR 10, 1999. + +<li> K. Murphy, Y. Weiss and M. Jordan, "Loopy belief propagation for approximate inference: an empirical study", + UAI 99. +</ul> +The latter approximation +entails applying Pearl's belief propagation algorithm to a model even +if it has loops (hence the name loopy belief propagation). +Pearl's algorithm, implemented as <tt>pearl_inf_engine</tt>, gives +exact results when applied to singly-connected graphs +(a.k.a. polytrees, since +the underlying undirected topology is a tree, but a node may have +multiple parents). +To apply this algorithm to a graph with loops, +use <tt>pearl_inf_engine</tt>. +This can use a centralized or distributed message passing protocol. +You can use it as in the following example. +<pre> +engine = pearl_inf_engine(bnet, 'max_iter', 30); +engine = enter_evidence(engine, evidence); +m = marginal_nodes(engine, i); +</pre> +We found that this algorithm often converges, and when it does, often +is very accurate, but it depends on the precise setting of the +parameter values of the network. +(See the file BNT/examples/static/qmr1 to repeat the experiment for yourself.) +Understanding when and why belief propagation converges/ works +is a topic of ongoing research. +<p> +<tt>pearl_inf_engine</tt> can exploit special structure in noisy-or +and gmux nodes to compute messages efficiently. +<p> +<tt>belprop_inf_engine</tt> is like pearl, but uses potentials to +represent messages. Hence this is slower. +<p> +<tt>belprop_fg_inf_engine</tt> is like belprop, +but is designed for factor graphs. + + + +<h2><a name="sampling">Sampling</h2> + +BNT now (Mar '02) has two sampling (Monte Carlo) inference algorithms: +<ul> +<li> <tt>likelihood_weighting_inf_engine</tt> which does importance +sampling and can handle any node type. +<li> <tt>gibbs_sampling_inf_engine</tt>, written by Bhaskara Marthi. +Currently this can only handle tabular CPDs. +For a much faster and more powerful Gibbs sampling program, see +<a href="http://www.mrc-bsu.cam.ac.uk/bugs">BUGS</a>. +</ul> +Note: To generate samples from a network (which is not the same as inference!), +use <tt>sample_bnet</tt>. + + + +<h2><a name="engine_summary">Summary of inference engines</h2> + + +The inference engines differ in many ways. Here are +some of the major "axes": +<ul> +<li> Works for all topologies or makes restrictions? +<li> Works for all node types or makes restrictions? +<li> Exact or approximate inference? +</ul> + +<p> +In terms of topology, most engines handle any kind of DAG. +<tt>belprop_fg</tt> does approximate inference on factor graphs (FG), which +can be used to represent directed, undirected, and mixed (chain) +graphs. +(In the future, we plan to support exact inference on chain graphs.) +<tt>quickscore</tt> only works on QMR-like models. +<p> +In terms of node types: algorithms that use potentials can handle +discrete (D), Gaussian (G) or conditional Gaussian (CG) models. +Sampling algorithms can essentially handle any kind of node (distribution). +Other algorithms make more restrictive assumptions in exchange for +speed. +<p> +Finally, most algorithms are designed to give the exact answer. +The belief propagation algorithms are exact if applied to trees, and +in some other cases. +Sampling is considered approximate, even though, in the limit of an +infinite number of samples, it gives the exact answer. + +<p> + +Here is a summary of the properties +of all the engines in BNT which work on static networks. +<p> +<table> +<table border units = pixels><tr> +<td align=left width=0>Name +<td align=left width=0>Exact? +<td align=left width=0>Node type? +<td align=left width=0>topology +<tr> +<tr> +<td align=left> belprop +<td align=left> approx +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> belprop_fg +<td align=left> approx +<td align=left> D +<td align=left> factor graph +<tr> +<td align=left> cond_gauss +<td align=left> exact +<td align=left> CG +<td align=left> DAG +<tr> +<td align=left> enumerative +<td align=left> exact +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> gaussian +<td align=left> exact +<td align=left> G +<td align=left> DAG +<tr> +<td align=left> gibbs +<td align=left> approx +<td align=left> D +<td align=left> DAG +<tr> +<td align=left> global_joint +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +<tr> +<td align=left> jtree +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +b<tr> +<td align=left> likelihood_weighting +<td align=left> approx +<td align=left> any +<td align=left> DAG +<tr> +<td align=left> pearl +<td align=left> approx +<td align=left> D,G +<td align=left> DAG +<tr> +<td align=left> pearl +<td align=left> exact +<td align=left> D,G +<td align=left> polytree +<tr> +<td align=left> quickscore +<td align=left> exact +<td align=left> noisy-or +<td align=left> QMR +<tr> +<td align=left> stab_cond_gauss +<td align=left> exact +<td align=left> CG +<td align=left> DAG +<tr> +<td align=left> var_elim +<td align=left> exact +<td align=left> D,G,CG +<td align=left> DAG +</table> + + + +<h1><a name="influence">Influence diagrams/ decision making</h1> + +BNT implements an exact algorithm for solving LIMIDs (limited memory +influence diagrams), described in +<ul> +<li> S. L. Lauritzen and D. Nilsson. +<a href="http://www.math.auc.dk/~steffen/papers/limids.pdf"> +Representing and solving decision problems with limited +information</a> +Management Science, 47, 1238 - 1251. September 2001. +</ul> +LIMIDs explicitely show all information arcs, rather than implicitely +assuming no forgetting. This allows them to model forgetful +controllers. +<p> +See the examples in <tt>BNT/examples/limids</tt> for details. + + + + +<h1>DBNs, HMMs, Kalman filters and all that</h1> + +Click <a href="usage_dbn.html">here</a> for documentation about how to +use BNT for dynamical systems and sequence data. + + +</BODY>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/docs/whyNotSourceforge.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +On 4 October 2007, I decided to move BNT back from +<a href="http://bnt.sourceforge.net/">sourceforge</a> to my +own website, where I could modify it more easily. I have no plans to +add new functionality, but I at least want it to ensure it runs +correctly on new versions of matlab. Despite new users complaining on +the Yahoo group that BNT would +not work on the latest version of Matlab, nobody in the sourceforce +community did anything about it. As an opensource project, it was basically +a failure (although I would like +to thank Nicholas Saunier and Tom Murray (yozhik) for +making code updates, +as well as Hiroaki Ogawa and mayliszt, +and Bob Welch for answering many questions on the newsgroup.) +For historical purposes, +v1.0.3 (as of 4 Oct 07) has been cached +<a href="http://www.cs.ubc.ca/~murphyk/Software/BNT/FullBNT-1.0.3.zip">here</a>. +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/foptions.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function opt_vect = foptions() +% FOPTIONS Sets default parameters for optimisation routines +% For compatibility with MATLAB's foptions() +% +% Copyright (c) Dharmesh Maniyar, Ian T. Nabney (2004) + +opt_vect = zeros(1, 18); +opt_vect(2:3) = 1e-4; +opt_vect(4) = 1e-6; +opt_vect(16) = 1e-8; +opt_vect(17) = 0.1; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/genpathKPM.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +function p = genpathKPM(d) +% genpathKPM Like built-in genpath, but omits directories whose name is 'Old', 'old' or 'CVS' +% function p = genpathKPM(d) + +if nargin==0, + p = genpath(fullfile(matlabroot,'toolbox')); + if length(p) > 1, p(end) = []; end % Remove trailing pathsep + return +end + +% initialise variables +methodsep = '@'; % qualifier for overloaded method directories +p = ''; % path to be returned + +% Generate path based on given root directory +files = dir(d); +if isempty(files) + return +end + +% Add d to the path even if it is empty. +p = [p d pathsep]; + +% set logical vector for subdirectory entries in d +isdir = logical(cat(1,files.isdir)); +% +% Recursively descend through directories which are neither +% private nor "class" directories. +% +dirs = files(isdir); % select only directory entries from the current listing + +for i=1:length(dirs) + dirname = dirs(i).name; + if ~strcmp( dirname,'.') & ... + ~strcmp( dirname,'..') & ... + ~strcmp( dirname,'.svn') & ... + ~strncmp( dirname,methodsep,1)& ... + ~strcmp( dirname,'private') & ... + ~strcmp( dirname, 'CVS') & ... + isempty(strfind(dirname, 'Old')) & ... + isempty(strfind(dirname, 'old')) + p = [p genpathKPM(fullfile(d,dirname))]; % recursive calling of this function. + end +end + +%------------------------------------------------------------------------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +/README.txt/1.1.1.1/Mon Aug 29 17:44:02 2005// +/acyclic.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/assignEdgeNums.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/best_first_elim_order.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/check_jtree_property.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/check_triangulated.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/children.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/cliques_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/cliques_to_strong_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/connected_graph.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dag_to_essential_graph.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dfs.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dfs_test.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/dijkstra.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/family.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/graph_separated.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/graph_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/min_subtree_con_nodes.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/minimum_spanning_tree.m/1.1.1.1/Mon Aug 29 17:44:04 2005// +/minspan.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_2D_lattice.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_2D_lattice_slow.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_adj_mat.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_adjmat_chain.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_all_dags.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_dag.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph_broken.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_nbrs_of_digraph_not_vectorized.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rnd_dag.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rnd_dag_given_edge_prob.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_rooted_tree.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/mk_undirected.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/moralize.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/neighbors.m/1.1.1.1/Mon Aug 29 17:44:06 2005// +/parents.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/pred2path.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/reachability_graph.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/scc.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/strong_elim_order.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/test.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/test_strong_root.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/topological_sort.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/trees.txt/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate.c/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate_2Dlattice_demo.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +/triangulate_test.m/1.1.1.1/Mon Aug 29 17:44:08 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/CVS/Entries.Log Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +A D/Old////
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/graph
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/Old/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +/best_first_elim_order.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dag_to_jtree.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dfs.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/dsep_test.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +/mk_2D_lattice_slow.m/1.1.1.1/Mon Aug 29 17:44:02 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/Old/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/graph/Old
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/Old/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/Old/best_first_elim_order.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +function order = best_first_elim_order(G, node_sizes, stage) +% BEST_FIRST_ELIM_ORDER Greedily search for an optimal elimination order. +% order = best_first_elim_order(moral_graph, node_sizes) +% +% Find an order in which to eliminate nodes from the graph in such a way as to try and minimize the +% weight of the resulting triangulated graph. The weight of a graph is the sum of the weights of each +% of its cliques; the weight of a clique is the product of the weights of each of its members; the +% weight of a node is the number of values it can take on. +% +% Since this is an NP-hard problem, we use the following greedy heuristic: +% at each step, eliminate that node which will result in the addition of the least +% number of fill-in edges, breaking ties by choosing the node that induces the lighest clique. +% For details, see +% - Kjaerulff, "Triangulation of graphs -- algorithms giving small total state space", +% Univ. Aalborg tech report, 1990 (www.cs.auc.dk/~uk) +% - C. Huang and A. Darwiche, "Inference in Belief Networks: A procedural guide", +% Intl. J. Approx. Reasoning, 11, 1994 +% + +% Warning: This code is pretty old and could probably be made faster. + +n = length(G); +if nargin < 3, stage = { 1:n }; end % no constraints + +% For long DBNs, it may be useful to eliminate all the nodes in slice t before slice t+1. +% This will ensure that the jtree has a repeating structure (at least away from both edges). +% This is why we have stages. +% See the discussion of splicing jtrees on p68 of +% Geoff Zweig's PhD thesis, Dept. Comp. Sci., UC Berkeley, 1998. +% This constraint can increase the clique size significantly. + +MG = G; % copy the original graph +uneliminated = ones(1,n); +order = zeros(1,n); +t = 1; % Counts which time slice we are on +for i=1:n + U = find(uneliminated); + valid = myintersect(U, stage{t}); + % Choose the best node from the set of valid candidates + score1 = zeros(1,length(valid)); + score2 = zeros(1,length(valid)); + for j=1:length(valid) + k = valid(j); + ns = myintersect(neighbors(G, k), U); + l = length(ns); + M = MG(ns,ns); + score1(j) = l^2 - sum(M(:)); % num. added edges + score2(j) = prod(node_sizes([k ns])); % weight of clique + end + j1s = find(score1==min(score1)); + j = j1s(argmin(score2(j1s))); + k = valid(j); + uneliminated(k) = 0; + order(i) = k; + ns = myintersect(neighbors(G, k), U); + if ~isempty(ns) + G(ns,ns) = 1; + G = setdiag(G,0); + end + if ~any(logical(uneliminated(stage{t}))) % are we allowed to the next slice? + t = t + 1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/Old/dag_to_jtree.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function [jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... + dag_to_jtree(dag, node_sizes, partial_order, stages, clusters) +% DAG_TO_JTREE Moralize and triangulate a DAG, and make a junction tree from its cliques. +% [jtree, root, cliques, B, w, elim_order, moral_edges, fill_in_edges, strong] = ... +% dag_to_jtree(dag, node_sizes, partial_order, stages, clusters) +% +% Input: +% dag(i,j) +% jtree(i,j) = 1 iff there is an arc between clique i and clique j +% root = the root clique +% cliques{i} = the nodes in clique i +% B(i,j) = 1 iff node j occurs in clique i +% w(i) = weight of clique i + +N = length(bnet.dag); +if nargin < 2, obs_nodes = []; end +if nargin < 3, stages = { 1:N }; end +if nargin < 4, clusters = {}; end + +[MG, moral_edges] = moralize(bnet.dag); + +% Add extra arcs between nodes in each cluster to ensure they occur in the same clique +for i=1:length(clusters) + c = clusters{i}; + MG(c,c) = 1; +end +MG = setdiag(MG, 0); + +% Find an optimal elimination ordering (NP-hard problem!) +ns = bnet.node_sizes(:); +ns(obs_nodes) = 1; % observed nodes have only 1 possible value +partial_order = determine_elim_constraints(bnet, obs_nodes); + +if isempty(partial_order) + strong = 0; + elim_order = best_first_elim_order(MG, ns, stages); +else + strong = 1; + elim_order = strong_elim_order(MG, ns, partial_order); +end + +[MTG, cliques, fill_in_edges] = triangulate(MG, elim_order); + +% Connect the cliques up into a jtree, +[jtree, root, B, w] = cliques_to_jtree(cliques, ns); + +if 0 + disp('testing dag to jtree'); + % Find the cliques containing each node, and check they form a connected subtree + clqs_con_node = cell(1,N); + for i=1:N + clqs_con_node{i} = find(B(:,i))'; + end + check_jtree_property(clqs_con_node, jtree); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/Old/dfs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,84 @@ +function [d, pre, post, height, cycle, pred] = dfs(adj_mat, start, directed) +% DFS Perform a depth-first search of the graph starting from 'start'. +% [d, pre, post, height, cycle, pred] = dfs(adj_mat, start, directed) +% +% d(i) is the time at which node i is first discovered. +% pre is a listing of the nodes in the order in which they are first encountered (opened). +% post is a listing of the nodes in the order in which they are last encountered (closed). +% A node is last encountered once we have explored all of its neighbors. +% If the graph is directed, i's neighbors are its children. +% If the graph is a tree, preorder is parents before children, and +% postorder is children before parents. +% For a DAG, topological order = reverse(postorder). +% height(i) is the height (distance) of node i from the start. +% 'cycle' is true iff a (directed) cycle is found. +% pred(i) is the parent of i in the dfs tree rooted at start. +% See Cormen, Leiserson and Rivest, "An intro. to algorithms" 1994, p478. + +% We can detect undirected cycles by checking if we are about to visit a node n which we have +% already visited. To detect *directed* cycles, we need to know if n has been closed or is still open. +% For example (where arcs are directed down) +% 1 2 +% \ / +% 3 +% Assume we visit 1, 3 and then 2 in order. The fact that a child of 2 (namely, 3) has +% already been visited is okay, because 3 has been closed. +% The algorithms in Aho, Hopcroft and Ullman, and Sedgewick, do not detect directed cycles. + +n = length(adj_mat); + +global white gray black +white = 0; gray = 1; black = 2; + +color = white*ones(1,n); +d = zeros(1,n); +height = zeros(1,n); +pred = zeros(1,n); +pre = []; +post = []; +cycle = 0; +global count +count = 0; +h = 0; +[d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, start, directed, h, d, pre, post, height, cycle, color, pred); + + + +%%%%%%%%%% + +function [d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, i, directed, h, d, pre, post, height, cycle, color, pred) + +global count +global white gray black + +color(i) = gray; +count = count + 1; +d(i) = count; +pre = [pre i]; +height(i) = h; +if directed + ns = children(adj_mat, i); +else + ns = neighbors(adj_mat, i); +end +for j=1:length(ns) + n=ns(j); + if ~directed & n==pred(i) % don't go back up the edge you just came down + % continue + else + if color(n) == gray % going back to a non-closed vertex via a new edge + %fprintf(1, 'cycle from %d to %d\n', i, n); + cycle = 1; + end + if color(n) == white % not visited n before + pred(n)=i; + [d, pre, post, height, cycle, color, pred] = ... + dfs2(adj_mat, n, directed, h+1, d, pre, post, height, cycle, color, pred); + end + end +end +color(i) = black; +post = [post i]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/Old/dsep_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ + +% Cowell et al p72 +G = zeros(10); +G(1,2)=1; +G(2,3)=1; +G(3,7)=1; +G(4,[5 8])=1; +G(5,6)=1; +G(6,7)=1; +G(7,[9 10])=1; +G(8,9)=1; + +dsep(1, 4, [5 7], G) +dsep(1, 4, [7], G) +dsep(1, 4, [10 5], G)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/Old/mk_2D_lattice_slow.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,121 @@ +function G = mk_2D_lattice(nrows, ncols, wrap_around) +% MK_2D_LATTICE Return adjacency matrix for 4-nearest neighbor connected 2D lattice +% G = mk_2D_lattice(nrows, ncols, wrap_around) +% G(k1, k2) = 1 iff k1=(i1,j1) is connected to k2=(i2,j2) +% +% If wrap_around = 1, we use toroidal boundary conditions (default = 0) +% +% Nodes are assumed numbered as in the following 3x3 lattice +% 1 4 7 +% 2 5 8 +% 3 6 9 +% +% e.g., G = mk_2D_lattice(3, 3, 0) returns +% 0 1 0 1 0 0 0 0 0 +% 1 0 1 0 1 0 0 0 0 +% 0 1 0 0 0 1 0 0 0 +% 1 0 0 0 1 0 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 0 1 0 0 0 1 +% 0 0 0 1 0 0 0 1 0 +% 0 0 0 0 1 0 1 0 1 +% 0 0 0 0 0 1 0 1 0 +% so find(G(5,:)) = [2 4 6 8] +% but find(G(1,:)) = [2 4] +% +% Using wrap around, G = mk_2D_lattice(3, 3, 1), we get +% 0 1 1 1 0 0 1 0 0 +% 1 0 1 0 1 0 0 1 0 +% 1 1 0 0 0 1 0 0 1 +% 1 0 0 0 1 1 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 1 1 0 0 0 1 +% 1 0 0 1 0 0 0 1 1 +% 0 1 0 0 1 0 1 0 1 +% 0 0 1 0 0 1 1 1 0 +% so find(G(5,:)) = [2 4 6 8] +% and find(G(1,:)) = [2 3 4 7] + +if nargin < 3, wrap_around = 0; end + +% M contains the number of each cell e.g. +% 1 4 7 +% 2 5 8 +% 3 6 9 +% North neighbors (assuming wrap around) are +% 3 6 9 +% 1 4 7 +% 2 5 8 +% Without wrap around, they are +% 1 4 7 +% 1 4 7 +% 2 5 8 +% The first row is arbitrary, since pixels at the top have no north neighbor. + +npixels = nrows*ncols; + +N = 1; E = 2; S = 3; W = 4; +if wrap_around + rows{N} = [nrows 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [2:ncols 1]; + rows{S} = [2:nrows 1]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [ncols 1:ncols-1]; +else + rows{N} = [1 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [2:ncols 2]; + rows{S} = [2:nrows 2]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [1 1:ncols-1]; +end + +M = reshape(1:npixels, [nrows ncols]); +nbrs = cell(1, 4); +for i=1:4 + nbrs{i} = M(rows{i}, cols{i}); +end + + +G = zeros(npixels, npixels); +if wrap_around + for i=1:4 + if 0 + % naive + for p=1:npixels + G(p, nbrs{i}(p)) = 1; + end + else + % vectorized + ndx2 = sub2ind([npixels npixels], 1:npixels, nbrs{i}(:)'); + G(ndx2) = 1; + end + end +else + i = N; + mask = ones(nrows, ncols); + mask(1,:) = 0; % pixels in row 1 have no nbr to the north + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = E; + mask = ones(nrows, ncols); + mask(:,ncols) = 0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = S; + mask = ones(nrows, ncols); + mask(nrows,:)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = W; + mask = ones(nrows, ncols); + mask(:,1)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; +end + +G = setdiag(G, 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/README.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +Graph theory toolbox. +All functions written by Kevin Murphy (1998) unless otherwise noted. +The latest version is available from http://www.ai.mit.edu/~murphyk/Software/index.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/acyclic.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function b = acyclic(adj_mat, directed) +% ACYCLIC Returns true iff the graph has no (directed) cycles. +% b = acyclic(adj_mat, directed) + +adj_mat = double(adj_mat); +if nargin < 2, directed = 1; end + +% e.g., G = +% 1 -> 3 +% | +% v +% 2 <- 4 +% In this case, 1->2 in the transitive closure, but 1 cannot get to itself. +% If G was undirected, 1 could get to itself, but this graph is not cyclic. +% So we cannot use the closure test in the undirected case. + +if directed + R = reachability_graph(adj_mat); + b = ~any(diag(R)==1); +else + [d, pre, post, cycle] = dfs(adj_mat,[],directed); + b = ~cycle; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/assignEdgeNums.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function [edge_id, nedges] = assignEdgeNums(adj_mat) +% give each edge a unique number +% we number (i,j) for j>i first, in row, column order. +% Then we number the reverse links + +nnodes = length(adj_mat); +edge_id = zeros(nnodes); +e = 1; +for i=1:nnodes + for j=i+1:nnodes + if adj_mat(i,j) + edge_id(i,j) = e; + e = e+1; + end + end +end + +nedges = e-1; +tmp = edge_id; +ndx = find(tmp); +tmp(ndx) = tmp(ndx)+nedges; +edge_id = edge_id + triu(tmp)'; + + +if 0 +ndx = find(adj_mat); +nedges = length(ndx); +nnodes = length(adj_mat); +edge_id = zeros(1, nnodes*nnodes); +edge_id(ndx) = 1:nedges; +edge_id = reshape(edge_id, nnodes, nnodes); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/best_first_elim_order.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function order = best_first_elim_order(G, node_sizes, stage) +% BEST_FIRST_ELIM_ORDER Greedily search for an optimal elimination order. +% order = best_first_elim_order(moral_graph, node_sizes) +% +% Find an order in which to eliminate nodes from the graph in such a way as to try and minimize the +% weight of the resulting triangulated graph. The weight of a graph is the sum of the weights of each +% of its cliques; the weight of a clique is the product of the weights of each of its members; the +% weight of a node is the number of values it can take on. +% +% Since this is an NP-hard problem, we use the following greedy heuristic: +% at each step, eliminate that node which will result in the addition of the least +% number of fill-in edges, breaking ties by choosing the node that induces the lighest clique. +% For details, see +% - Kjaerulff, "Triangulation of graphs -- algorithms giving small total state space", +% Univ. Aalborg tech report, 1990 (www.cs.auc.dk/~uk) +% - C. Huang and A. Darwiche, "Inference in Belief Networks: A procedural guide", +% Intl. J. Approx. Reasoning, 11, 1994 +% + +% Warning: This code is pretty old and could probably be made faster. + +n = length(G); +if nargin < 3, stage = { 1:n }; end % no constraints + +% For long DBNs, it may be useful to eliminate all the nodes in slice t before slice t+1. +% This will ensure that the jtree has a repeating structure (at least away from both edges). +% This is why we have stages. +% See the discussion of splicing jtrees on p68 of +% Geoff Zweig's PhD thesis, Dept. Comp. Sci., UC Berkeley, 1998. +% This constraint can increase the clique size significantly. + +MG = G; % copy the original graph +uneliminated = ones(1,n); +order = zeros(1,n); +t = 1; % Counts which time slice we are on +for i=1:n + U = find(uneliminated); + valid = myintersect(U, stage{t}); + % Choose the best node from the set of valid candidates + min_fill = zeros(1,length(valid)); + min_weight = zeros(1,length(valid)); + for j=1:length(valid) + k = valid(j); + nbrs = myintersect(neighbors(G, k), U); + l = length(nbrs); + M = MG(nbrs,nbrs); + min_fill(j) = l^2 - sum(M(:)); % num. added edges + min_weight(j) = prod(node_sizes([k nbrs])); % weight of clique + end + lightest_nbrs = find(min_weight==min(min_weight)); + % break ties using min-fill heuristic + best_nbr_ndx = argmin(min_fill(lightest_nbrs)); + j = lightest_nbrs(best_nbr_ndx); % we will eliminate the j'th element of valid + %j1s = find(score1==min(score1)); + %j = j1s(argmin(score2(j1s))); + k = valid(j); + uneliminated(k) = 0; + order(i) = k; + ns = myintersect(neighbors(G, k), U); + if ~isempty(ns) + G(ns,ns) = 1; + G = setdiag(G,0); + end + if ~any(logical(uneliminated(stage{t}))) % are we allowed to the next slice? + t = t + 1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/check_jtree_property.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function check_jtree_property(cliques_containing_node, jtree) +% CHECK_JTREE_PROPERTY Raise an error if the graph does not satisfy the join tree property. +% check_jtree_property(cliques_containing_node, jtree_adj_mat) +% +% The join tree property says: +% For each node n in the dag, compute the node-induced subgraph G by looking at all the cliques +% that contain n, and make sure G forms a connected graph. +% This ensures that local propagation leads to global consistency. + +num_bn_nodes = length(cliques_containing_node); +directed = 0; +for i=1:num_bn_nodes + cs = cliques_containing_node{i}; + G = jtree(cs,cs); + if ~connected_graph(G, directed) + error(['node ' num2str(i) ' violates jtree property']); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/check_triangulated.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function [triangulated, order] = check_triangulated(G) +% CHECK_TRIANGULATED Return 1 if G is a triangulated (chordal) graph, 0 otherwise. +% [triangulated, order] = check_triangulated(G) +% +% A numbering alpha is perfect if Nbrs(alpha(i)) intersect {alpha(1)...alpha(i-1)} is complete. +% A graph is triangulated iff it has a perfect numbering. +% The Maximum Cardinality Search algorithm will create such a perfect numbering if possible. +% See Golumbic, "Algorithmic Graph Theory and Perfect Graphs", Cambridge Univ. Press, 1985, p85. +% or Castillo, Gutierrez and Hadi, "Expert systems and probabilistic network models", Springer 1997, p134. + + +G = setdiag(G, 1); +n = length(G); +order = zeros(1,n); +triangulated = 1; +numbered = [1]; +order(1) = 1; +for i=2:n + U = mysetdiff(1:n, numbered); % unnumbered nodes + score = zeros(1, length(U)); + for ui=1:length(U) + u = U(ui); + score(ui) = length(myintersect(neighbors(G, u), numbered)); + end + u = U(argmax(score)); + numbered = [numbered u]; + order(i) = u; + nns = myintersect(neighbors(G,u), order(1:i-1)); % numbered neighbors + if ~isequal(G(nns,nns), ones(length(nns))) % ~complete(G(nns,nns)) + triangulated = 0; + break; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/children.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function cs = children(adj_mat, i, t) +% CHILDREN Return the indices of a node's children in sorted order +% c = children(adj_mat, i, t) +% +% t is an optional argument: if present, dag is assumed to be a 2-slice DBN + +if nargin < 3 + cs = find(adj_mat(i,:)); +else + if t==1 + cs = find(adj_mat(i,:)); + else + ss = length(adj_mat)/2; + j = i+ss; + cs = find(adj_mat(j,:)) + (t-2)*ss; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/cliques_to_jtree.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function [jtree, root, B, w] = cliques_to_jtree(cliques, ns) +% MK_JTREE Make an optimal junction tree. +% [jtree, root, B, w] = mk_jtree(cliques, ns) +% +% A junction tree is a tree that satisfies the jtree property, which says: +% for each pair of cliques U,V with intersection S, all cliques on the path between U and V +% contain S. (This ensures that local propagation leads to global consistency.) +% +% We can create a junction tree by computing the maximal spanning tree of the junction graph. +% (The junction graph connects all cliques, and the weight of an edge (i,j) is +% |C(i) intersect C(j)|, where C(i) is the i'th clique.) +% +% The best jtree is the maximal spanning tree which minimizes the sum of the costs on each edge, +% where cost(i,j) = w(C(i)) + w(C(j)), and w(C) is the weight of clique C, +% which is the total number of values C can take on. +% +% For details, see +% - Jensen and Jensen, "Optimal Junction Trees", UAI 94. +% +% Input: +% cliques{i} = nodes in clique i +% ns(i) = number of values node i can take on +% Output: +% jtree(i,j) = 1 iff cliques i and j aer connected +% root = the clique that should be used as root +% B(i,j) = 1 iff node j occurs in clique i +% w(i) = weight of clique i + + + +num_cliques = length(cliques); +w = zeros(num_cliques, 1); +B = sparse(num_cliques, 1); +for i=1:num_cliques + B(i, cliques{i}) = 1; + w(i) = prod(ns(cliques{i})); +end + + +% C1(i,j) = length(intersect(cliques{i}, cliques{j})); +% The length of the intersection of two sets is the dot product of their bit vector representation. +C1 = B*B'; +C1 = setdiag(C1, 0); + +% C2(i,j) = w(i) + w(j) +num_cliques = length(w); +W = repmat(w, 1, num_cliques); +C2 = W + W'; +C2 = setdiag(C2, 0); + +jtree = sparse(minimum_spanning_tree(-C1, C2)); % Using -C1 gives *maximum* spanning tree + +% The root is arbitrary, but since the first pass is towards the root, +% we would like this to correspond to going forward in time in a DBN. +root = num_cliques; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/cliques_to_strong_jtree.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +function [jtree, root, cliques, B, w] = mk_strong_jtree(cliques, ns, elim_order, MTG) +% MK_SRONG_JTREE Make a strong junction tree. +% [jtree, root, cliques, B, w] = mk_strong_jtree(cliques, ns, elim_order, MTG) +% +% Here is a definition of a strong jtree from Jensen et al. 1994: +% "A junction tree is said to be strong if it has at least one distinguished clique R, +% called a strong root, s.t. for each pair (C1,C2) of adjacent cliques in the tree, +% with C1 closer to R than C2, there exists and ordering of [the nodes below] C2 +% that respects [the partial order] and with the vertices of the separator C1 intersect C2 +% preceeding the vertices [below C2] of C2 \ C1." +% +% For details, see +% - Jensen, Jensen and Dittmer, "From influence diagrams to junction trees", UAI 94. +% +% MTG is the moralized, triangulated graph. +% elim_order is the elimination ordering used to compute MTG. + + +% Warning: this is a very naive implementation of the algorithm in Jensen et al. + +n = length(elim_order); +alpha(elim_order) = n:-1:1; +% alpha(u) = i if we eliminate u at step n-i+1 +% i.e., vertices with higher alpha numbers are eliminated before vertices with lower numbers. +% e.g., from the Jensen et al paper +% node a=1 eliminated at step 6, so alpha(a)=16-6+1=11. +% alpha = [11 1 2 10 9 3 4 7 5 8 13 12 6 16 15 14] + + +% We sort the cliques in order of increasing index. The index of a clique C is defined as follows. +% Let lower = {u | alpha(u) < alpha(v)}, and +% let v in C be the highest-numbered vertex s.t. the vertices in W = lower intersect C +% have a common neighbor u in U, where U = lower \ C. +% If such a v exists, define index(C) = alpha(v), otherwise, index(C) = 1. +% Intuitively, index(C) is the step in the elimination process at which C disappears. + +num_cliques = length(cliques); +index = zeros(1, num_cliques); +for c = 1:num_cliques + C = cliques{c}; + highest_num = -inf; + for vi = 1:length(C) + v = C(vi); + lower = find(alpha < alpha(v)); + W = myintersect(lower, C); + U = mysetdiff(lower, C); + found = 0; + for ui=1:length(U) + u = U(ui); + if mysubset(W, neighbors(MTG, u)) + found = 1; + break; + end + end + if found + if alpha(v) > highest_num + highest_num = alpha(v); + end + end + end + if highest_num == -inf + index(c) = 1; + else + index(c) = highest_num; + end +end + + +% Permute the cliques so that they are ordered according to index +[dummy, clique_order] = sort(index); +cliques = cliques(clique_order); + +w = zeros(num_cliques, 1); +B = sparse(num_cliques, 1); +for i=1:num_cliques + B(i, cliques{i}) = 1; + w(i) = prod(ns(cliques{i})); +end + +% Pearl p113 suggests ordering the cliques by rank of the highest vertex in each clique. +% However, this will only work if we use maximum cardinality search. + + +% Join up the cliques so that they satisfy the Running Intersection Property. +% This states that, for all k > 1, S(k) subseteq C(j) for some j < k, where +% S(k) = C(k) intersect (union_{i=1}^{k-1} C(i)) +jtree = sparse(num_cliques, num_cliques); +for k=2:num_cliques + S = []; + for i=1:k-1 + S = myunion(S, cliques{i}); + end + S = myintersect(S, cliques{k}); + found = 0; + for j=1:k-1 + if mysubset(S, cliques{j}) + found = 1; + break; + end + end + if ~found + disp(['RIP is violated for clique ' num2str(k)]); + end + jtree(k,j)=1; + jtree(j,k)=1; +end + +% Pearl p113 suggests connecting Ci to a predecessor Cj (j < i) sharing +% the highest number of vertices with Ci (i.e., the heaviest i-j edge +% in the jgraph). However, this will only work if we use maximum cardinality search. + +root = 1; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/connected_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function b = connected(adj_mat, directed) +% +% b = connected(adj_mat). +% Returns true iff the graph is connected. + +n = length(adj_mat); +start = 1; +[d, pre] = dfs(adj_mat, start, directed); +b = (length(pre) == n);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/dag_to_essential_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,112 @@ + +function [eg] = dag_to_essential_graph(dag) +cpdag = dag_to_cpdag(dag); +eg = dag + dag .* (cpdag + cpdag'); + +return; + + + + +% Coverts a DAG into Essential Graph where edges are coded by 2 and 3, 2 is +% directed edge and 3 is bidirected edge and is at one (the same as the original DAG) of the two +% symetrical places. + +% Is implemented by the algorithm of Max Chickering in D.M.Chickering (1995). +% A transformational characterization of equivalent Bayesian network structures. +% In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, +% pages 87-98. Morgan Kaufmann +% http://research.microsoft.com/~dmax/publications/uai95.pdf + +% Implemented by Tomas Kocka, AAU. + +function [eg] = dag_to_essential_graph(dagx) + +%print_dag(dagx); % Just checking input + +order = topological_sort(dagx); % get the topological order of nodes and their number + +% fprintf('the topological order is: %d',order); +% fprintf('\n'); + +[nx,ny] = size(dagx); % gets the number of nodes, note that nx == ny +[I,J] = find(dagx); % finds all nonzero elements in the adjacency matrix, i.e. arcs in the DAG - however we will overwrite it in a special order +% we will sort the arcs from lowest possible y and highest possible x, arcs are x->y +e = 1; +for y = 1:ny + for x = nx:-1:1 + %fprintf('x %d ',order(x)); fprintf('y %d ',order(y)); + if dagx(order(x),order(y)) == 1 + I(e) = order(x); + J(e) = order(y); + e = e + 1; + %fprintf('x order %d',x); + %fprintf('y order %d',y); + %fprintf('\n'); + end + end +end + + +% fprintf('the arcs are: %d',I); +% fprintf('\n'); +% fprintf('the arcs are: %d',J); +% fprintf('\n'); + + +% Now we have to decide which arcs are part of the essential graph and +% which are undirected edges in the essential graph. +% Undecided arc in the DAG are 1, directed in EG are 2 and undirected in EG +% are 3. + + +for e = 1:length(I) + if dagx(I(e),J(e)) == 1 + cont = true; + for w = 1:nx + if dagx(w,I(e)) == 2 + if dagx(w,J(e)) ~= 0 + dagx(w,J(e)) = 2; + else + for ww = 1:nx + if dagx(ww,J(e)) ~= 0 + dagx(ww,J(e)) = 2; + end + end % and now skip the rest and start with another arc from the list + w = nx; + cont = false; + end + end + end + if cont + exists = false; + for z = 1:nx + %fprintf('test %d',dagx(z,J(e))); + if dagx(z,J(e)) ~= 0 & z ~= I(e) & dagx(z,I(e)) == 0 + exists = true; + for ww = 1:nx + if dagx(ww,J(e)) == 1 + dagx(ww,J(e)) = 2; + end + end + end + end + if ~ exists + for ww = 1:nx + if dagx(ww,J(e)) == 1 + dagx(ww,J(e)) = 3; + end + end + end + end + end +end + +%print_dag(dagx); % Just checking output + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/dfs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,91 @@ +function [d, pre, post, cycle, f, pred] = dfs(adj_mat, start, directed) +% DFS Perform a depth-first search of the graph starting from 'start'. +% [d, pre, post, cycle, f, pred] = dfs(adj_mat, start, directed) +% +% Input: +% adj_mat(i,j)=1 iff i is connected to j. +% start is the root vertex of the dfs tree; if [], all nodes are searched +% directed = 1 if the graph is directed +% +% Output: +% d(i) is the time at which node i is first discovered. +% pre is a list of the nodes in the order in which they are first encountered (opened). +% post is a list of the nodes in the order in which they are last encountered (closed). +% 'cycle' is true iff a (directed) cycle is found. +% f(i) is the time at which node i is finished. +% pred(i) is the predecessor of i in the dfs tree. +% +% If the graph is a tree, preorder is parents before children, +% and postorder is children before parents. +% For a DAG, topological order = reverse(postorder). +% +% See Cormen, Leiserson and Rivest, "An intro. to algorithms" 1994, p478. + +n = length(adj_mat); + +global white gray black color +white = 0; gray = 1; black = 2; +color = white*ones(1,n); + +global time_stamp +time_stamp = 0; + +global d f +d = zeros(1,n); +f = zeros(1,n); + +global pred +pred = zeros(1,n); + +global cycle +cycle = 0; + +global pre post +pre = []; +post = []; + +if ~isempty(start) + dfs_visit(start, adj_mat, directed); +end +for u=1:n + if color(u)==white + dfs_visit(u, adj_mat, directed); + end +end + + +%%%%%%%%%% + +function dfs_visit(u, adj_mat, directed) + +global white gray black color time_stamp d f pred cycle pre post + +pre = [pre u]; +color(u) = gray; +time_stamp = time_stamp + 1; +d(u) = time_stamp; +if directed + ns = children(adj_mat, u); +else + ns = neighbors(adj_mat, u); + ns = setdiff(ns, pred(u)); % don't go back to visit the guy who called you! +end +for v=ns(:)' + %fprintf('u=%d, v=%d, color(v)=%d\n', u, v, color(v)) + switch color(v) + case white, % not visited v before (tree edge) + pred(v)=u; + dfs_visit(v, adj_mat, directed); + case gray, % back edge - v has been visited, but is still open + cycle = 1; + %fprintf('cycle: back edge from v=%d to u=%d\n', v, u); + case black, % v has been visited, but is closed + % no-op + end +end +color(u) = black; +post = [post u]; +time_stamp = time_stamp + 1; +f(u) = time_stamp; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/dfs_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +% Do the example in fig 23.4 p479 of Cormen, Leiserson and Rivest (1994) + +u = 1; v = 2; w = 3; x = 4; y = 5; z = 6; +n = 6; +dag=zeros(n,n); +dag(u,[v x])=1; +dag(v,y)=1; +dag(w,[y z])=1; +dag(x,v)=1; +dag(y,x)=1; +dag(z,z)=1; + +[d, pre, post, cycle, f, pred] = dfs(dag, [], 1); +assert(isequal(d, [1 2 9 4 3 10])) +assert(isequal(f, [8 7 12 5 6 11]) +assert(cycle) + +% Now give it an undirected cyclic graph +G = mk_2D_lattice(2,2,0); +% 1 - 3 +% | | +% 2 - 4 +[d, pre, post, cycle, f, pred] = dfs(G, [], 0); +% d = [1 2 4 3] +assert(cycle) + +% Now break the cycle +G(1,2)=0; G(2,1)=0; +[d, pre, post, cycle, f, pred] = dfs(G, [], 0); +assert(~cycle)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/dijkstra.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,112 @@ +function [D,P] = dijk(A,s,t) +%DIJK Shortest paths from nodes 's' to nodes 't' using Dijkstra algorithm. +% [D,p] = dijk(A,s,t) +% A = n x n node-node weighted adjacency matrix of arc lengths +% (Note: A(i,j) = 0 => Arc (i,j) does not exist; +% A(i,j) = NaN => Arc (i,j) exists with 0 weight) +% s = FROM node indices +% = [] (default), paths from all nodes +% t = TO node indices +% = [] (default), paths to all nodes +% D = |s| x |t| matrix of shortest path distances from 's' to 't' +% = [D(i,j)], where D(i,j) = distance from node 'i' to node 'j' +% P = |s| x n matrix of predecessor indices, where P(i,j) is the +% index of the predecessor to node 'j' on the path from 's(i)' to 'j' +% (use PRED2PATH to convert P to paths) +% = path from 's' to 't', if |s| = |t| = 1 +% +% (If A is a triangular matrix, then computationally intensive node +% selection step not needed since graph is acyclic (triangularity is a +% sufficient, but not a necessary, condition for a graph to be acyclic) +% and A can have non-negative elements) +% +% (If |s| >> |t|, then DIJK is faster if DIJK(A',t,s) used, where D is now +% transposed and P now represents successor indices) +% +% (Based on Fig. 4.6 in Ahuja, Magnanti, and Orlin, Network Flows, +% Prentice-Hall, 1993, p. 109.) + +% Copyright (c) 1998-2001 by Michael G. Kay +% Matlog Version 5 22-Aug-2001 + +% Input Error Checking ****************************************************** +error(nargchk(1,3,nargin)); + +[n,cA] = size(A); + +if nargin < 2 | isempty(s), s = (1:n)'; else s = s(:); end +if nargin < 3 | isempty(t), t = (1:n)'; else t = t(:); end + +if ~any(any(tril(A) ~= 0)) % A is upper triangular + isAcyclic = 1; +elseif ~any(any(triu(A) ~= 0)) % A is lower triangular + isAcyclic = 2; +else % Graph may not be acyclic + isAcyclic = 0; +end + +if n ~= cA + error('A must be a square matrix'); +elseif ~isAcyclic & any(any(A < 0)) + error('A must be non-negative'); +elseif any(s < 1 | s > n) + error(['''s'' must be an integer between 1 and ',num2str(n)]); +elseif any(t < 1 | t > n) + error(['''t'' must be an integer between 1 and ',num2str(n)]); +end +% End (Input Error Checking) ************************************************ + +A = A'; % Use transpose to speed-up FIND for sparse A + +D = zeros(length(s),length(t)); +if nargout > 1, P = zeros(length(s),n); end + +for i = 1:length(s) + j = s(i); + + Di = Inf*ones(n,1); Di(j) = 0; + + isLab = logical(zeros(length(t),1)); + if isAcyclic == 1 + nLab = j - 1; + elseif isAcyclic == 2 + nLab = n - j; + else + nLab = 0; + UnLab = 1:n; + isUnLab = logical(ones(n,1)); + end + + while nLab < n & ~all(isLab) + if isAcyclic + Dj = Di(j); + else % Node selection + [Dj,jj] = min(Di(isUnLab)); + j = UnLab(jj); + UnLab(jj) = []; + isUnLab(j) = 0; + end + + nLab = nLab + 1; + if length(t) < n, isLab = isLab | (j == t); end + + [jA,kA,Aj] = find(A(:,j)); + Aj(isnan(Aj)) = 0; + + if isempty(Aj), Dk = Inf; else Dk = Dj + Aj; end + + if nargout > 1, P(i,jA(Dk < Di(jA))) = j; end + Di(jA) = min(Di(jA),Dk); + + if isAcyclic == 1 % Increment node index for upper triangular A + j = j + 1; + elseif isAcyclic == 2 % Decrement node index for lower triangular A + j = j - 1; + end + end + D(i,:) = Di(t)'; +end + +if nargout > 1 & length(s) == 1 & length(t) == 1 + P = pred2path(P,s,t); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/family.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function f = family(A,i,t) +% FAMILY Return the indices of parents and self in sorted order +% f = family(dag,i,t) +% +% t is an optional argument: if present, dag is assumed to be a 2-slice DBN + +if nargin < 3 + f = [parents(A,i) i]; +else + if t == 1 + f = [parents(A,i) i]; + else + ss = length(A)/2; + j = i+ss; + f = [parents(A,j) j] + (t-2)*ss; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/findroot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function root = findroot(bnet, cliques) + +%% findroot is to find the strong root in a clique tree assume it has one +%% in the tree. For a clique tree constructed from a strongly triangulated +%% graph, an interface clique that contains all discrete parents +%% and at least one continuous node from a connected continuous component +%% is for sure to be available as a guaranteed strong root. +%% -By Wei Sun, George Mason University, 4/17/2010. + +%% We choose the interface clique that contains the max number +%% of interface nodes to be the strong root. +n0 = 0 ; +for i=1:length(cliques) + % check hybrid cliques + hc = intersect(cliques{i}, bnet.cnodes) ; + hd = intersect(cliques{i}, bnet.dnodes) ; + if ~isempty(hd) & ~isempty(hc) + nd = length(hd) ; + if nd > n0 + root = i ; + n0 = nd ; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/graph_separated.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function sep = graph_separated(G, X, Y, S) + +G2 = G; +G2(S,:) = 0; +G2(:,S) = 0; +conn = reachability_graph(G2); +conn2 = conn(X,Y); +sep = all(conn2(:)==0); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/graph_to_jtree.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function [jtree, root, cliques, B, w, elim_order] = graph_to_jtree(MG, ns, partial_order, stages, clusters) +% GRAPH_TO_JTREE Triangulate a graph and make a junction tree from its cliques. +% [jtree, root, cliques, B, w, elim_order] = ... +% graph_to_jtree(graph, node_sizes, partial_order, stages, clusters) +% +% INPUT: +% graph(i,j) = 1 iff there is an edge between i,j +% node_weights(i) = num discrete values node i can take on [1 if observed] +% partial_order = {} if no constraints on elimination ordering +% stages{i} = nodes that must be eliminated at i'th stage (if porder is empty) +% clusters{i} = list of nodes that must get connected together in the moral graph +% +% OUTPUT: +% jtree(i,j) = 1 iff there is an arc between clique i and clique j +% root = the root clique +% cliques{i} = the nodes in clique i +% B(i,j) = 1 iff node j occurs in clique i +% w(i) = weight of clique i + +N = length(MG); + +if nargin >= 5 + % Add extra arcs between nodes in each cluster to ensure they occur in the same clique + for i=1:length(clusters) + c = clusters{i}; + MG(c,c) = 1; + end +end +MG = setdiag(MG, 0); + +% Find an optimal elimination ordering (NP-hard problem!) +if nargin < 4 + stages = {1:N}; +end +if nargin < 3 + partial_order = {}; +end +if isempty(partial_order) + strong = 0; + elim_order = best_first_elim_order(MG, ns, stages); +else + strong = 1; + elim_order = strong_elim_order(MG, ns, partial_order); +end + +[MTG, cliques, fill_in_edges] = triangulate(MG, elim_order); + +% Connect the cliques up into a jtree, +[jtree, root, B, w] = cliques_to_jtree(cliques, ns); + +if 0 + disp('testing dag to jtree'); + % Find the cliques containing each node, and check they form a connected subtree + clqs_con_node = cell(1,N); + for i=1:N + clqs_con_node{i} = find(B(:,i))'; + end + check_jtree_property(clqs_con_node, jtree); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/min_subtree_con_nodes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function [subtree, nroot_node] = min_subtree_con_nodes(jtree, root, nodes) +%min_subtree_con_nodes get the minimum subtree of tree which contains the nodes + +if isempty(jtree) | isempty(nodes) + subtree = []; + nroot_node = []; + return; +end + +rnodes = min_subtree_nodes(jtree, nodes); +nea_node = nearest_node(jtree, root, nodes); +node_num = length(jtree); +subtree = zeros(node_num); +subtree(rnodes, rnodes) = jtree(rnodes, rnodes); +nroot_node = nea_node; + + +function rnodes = min_subtree_nodes(tree, nodes) +rnodes = []; +if isempty(tree) | isempty(nodes) + return +end + +rnodes = nodes(1); +newnodes = neighbors(tree, nodes(1)); +while ~mysubset(nodes, rnodes) + swapnodes = newnodes; + newnodes = []; + added = 0; + for i=1:length(swapnodes) + inode = swapnodes(i); + tnodes = myunion(inode, rnodes); + if mysubset(nodes, tnodes) + added = 1; + break; + end + nns = neighbors(tree, inode); + add_nodes = mysetdiff(nns, tnodes); + newnodes = myunion(newnodes, add_nodes); + end + if added + rnodes = tnodes; + else + rnodes = myunion(rnodes, newnodes); + end +end + +function nea_node = nearest_node(tree, inode, nodes) +if myismember(inode, nodes) + nea_node = inode; + return; +end +cs = children(tree, inode); +for i = 1:length(cs) + n = cs(i); + nea_node = nearest_node(tree, n, nodes); +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/minimum_spanning_tree.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function A = minimum_spanning_tree(C1, C2) +% +% Find the minimum spanning tree using Prim's algorithm. +% C1(i,j) is the primary cost of connecting i to j. +% C2(i,j) is the (optional) secondary cost of connecting i to j, used to break ties. +% We assume that absent edges have 0 cost. +% To find the maximum spanning tree, used -1*C. +% See Aho, Hopcroft & Ullman 1983, "Data structures and algorithms", p 237. + +% Prim's is O(V^2). Kruskal's algorithm is O(E log E) and hence is more efficient +% for sparse graphs, but is implemented in terms of a priority queue. + +% We partition the nodes into those in U and those not in U. +% closest(i) is the vertex in U that is closest to i in V-U. +% lowcost(i) is the cost of the edge (i, closest(i)), or infinity is i has been used. +% In Aho, they say C(i,j) should be "some appropriate large value" if the edge is missing. +% We set it to infinity. +% However, since lowcost is initialized from C, we must distinguish absent edges from used nodes. + +n = length(C1); +if nargin==1, C2 = zeros(n); end +A = zeros(n); + +closest = ones(1,n); +used = zeros(1,n); % contains the members of U +used(1) = 1; % start with node 1 +C1(find(C1==0))=inf; +C2(find(C2==0))=inf; +lowcost1 = C1(1,:); +lowcost2 = C2(1,:); + +for i=2:n + ks = find(lowcost1==min(lowcost1)); + k = ks(argmin(lowcost2(ks))); + A(k, closest(k)) = 1; + A(closest(k), k) = 1; + lowcost1(k) = inf; + lowcost2(k) = inf; + used(k) = 1; + NU = find(used==0); + for ji=1:length(NU) + for j=NU(ji) + if C1(k,j) < lowcost1(j) + lowcost1(j) = C1(k,j); + lowcost2(j) = C2(k,j); + closest(j) = k; + end + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/minspan.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +function [t,nk] = minspan(IJC) +%MINSPAN Minimum weight spanning tree using Kruskal algorithm. +%[t,nk] = minspan(IJC) +% IJC = n x 3 matrix arc list [i j c] of arc heads, tails, and costs +% t = n-element logical vector, where +% t(i) = 1, if IJC(i,:) arc in spanning tree +% t(i) = k, if IJC(i,:) arc in component k of forest +% nk = number of components + +% Copyright (c) 1998-2001 by Michael G. Kay +% Matlog Version 5 22-Aug-2001 + +% Input Error Checking ****************************************************** +[n,cIJC] = size(IJC); +if cIJC ~= 3 + error('''IJC'' must be a 3-column matrix.') +elseif n < 1 + error('''IJC'' must have at least one row.') +elseif any(IJC(:,1) < 1) | any(any(~isint(IJC(:,[1 2])))) + error('Invalid arc index in IJC.') +end +% End (Input Error Checking) ************************************************ + +i = IJC(:,1); j = abs(IJC(:,2)); +m = max(max([i j])); + +sidxIJ = argsort(IJC(:,3)); +i = i(sidxIJ); j = j(sidxIJ); + +t = logical(zeros(n,1)); +k = 1; % Current arc +nt = 0; % Number of arcs in spanning tree +v = (1:m)'; % Arc labels + +while nt < m - 1 & k <= n + if (v(i(k)) ~= v(j(k))) + v(v==v(j(k))) = v(i(k)); + t(k) = 1; + nt = nt + 1; + end + k = k + 1; +end + +idxIJ = invperm(sidxIJ); +t = t(idxIJ); i = i(idxIJ); j = j(idxIJ); + +c = unique(v(unique([i; j]))); % Unique labels of arc vertices +nk = length(c); +if ~any(t), nk = 0; end % Self-loop not a component + +if nk > 1 + for k = 1:nk + t(t~=0 & v(i)==c(k)) = k; % Relabel to consecutive component numbers + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_2D_lattice.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function G = mk_2D_lattice(nrows, ncols, con) +% MK_2D_LATTICE Return adjacency matrix for nearest neighbor connected 2D lattice +% G = mk_2D_lattice(nrows, ncols, con) +% G(k1, k2) = 1 iff k1=(i1,j1) is a neighbor of k2=(i2,j2) +% (Two pixels are neighbors if their Euclidean distance is less than r.) +% Default connectivity = 4. +% +% WE ASSUME NO WRAP AROUND. +% +% This is the neighborhood as a function of con: +% +% con=4,r=1 con=8,r=sqrt(2) con=12,r=2 con=24,r=sqrt(8) +% nn 2nd order 4th order +% x x x x x x +% x x x x x x x x x x x x +% x o x x o x x x o x x x x o x x +% x x x x x x x x x x x x +% x x x x x x +% +% Examples: +% Consider a 3x4 grid +% 1 4 7 10 +% 2 5 8 11 +% 3 6 9 12 +% +% 4-connected: +% G=mk_2D_lattice(3,4,4); +% find(G(1,:)) = [2 4] +% find(G(5,:)) = [2 4 6 8] +% +% 8-connected: +% G=mk_2D_lattice(3,4,8); +% find(G(1,:)) = [2 4 5] +% find(G(5,:)) = [1 2 3 4 6 7 8 9] + +% meshgrid trick due to Temu Gautama (temu@neuro.kuleuven.ac.be) + +if nargin < 3, con = 4; end + +switch con, + case 4, r = 1; + case 8, r = sqrt(2); + case 12, r = 2; + case 24, r = sqrt(8); + otherwise, error(['unrecognized connectivity ' num2str(con)]) +end + + +npixels = nrows*ncols; + +[x y]=meshgrid(1:ncols, 1:nrows); +M = [x(:) y(:)]; +M1 = repmat(reshape(M',[1 2 npixels]),[npixels 1 1]); +M2 = repmat(M,[1 1 npixels]); +%D = squeeze(sum(abs(M1-M2),2)); % Manhattan distance +M3 = M1-M2; +D = sqrt(squeeze(M3(:,1,:)) .^2 + squeeze(M3(:,2,:)) .^2); % Euclidean distance +G = reshape(D <= r,npixels,npixels); +G = setdiag(G, 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_2D_lattice_slow.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ +function G = mk_2D_lattice_slow(nrows, ncols, wrap_around) +% MK_2D_LATTICE Return adjacency matrix for 4-nearest neighbor connected 2D lattice +% G = mk_2D_lattice(nrows, ncols, wrap_around) +% G(k1, k2) = 1 iff k1=(i1,j1) is connected to k2=(i2,j2) +% +% If wrap_around = 1, we use toroidal boundary conditions (default = 0) +% +% Nodes are assumed numbered as in the following 3x3 lattice +% 1 4 7 +% 2 5 8 +% 3 6 9 +% +% e.g., G = mk_2D_lattice(3, 3, 0) returns +% 0 1 0 1 0 0 0 0 0 +% 1 0 1 0 1 0 0 0 0 +% 0 1 0 0 0 1 0 0 0 +% 1 0 0 0 1 0 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 0 1 0 0 0 1 +% 0 0 0 1 0 0 0 1 0 +% 0 0 0 0 1 0 1 0 1 +% 0 0 0 0 0 1 0 1 0 +% so find(G(5,:)) = [2 4 6 8] +% but find(G(1,:)) = [2 4] +% +% Using wrap around, G = mk_2D_lattice(3, 3, 1), we get +% 0 1 1 1 0 0 1 0 0 +% 1 0 1 0 1 0 0 1 0 +% 1 1 0 0 0 1 0 0 1 +% 1 0 0 0 1 1 1 0 0 +% 0 1 0 1 0 1 0 1 0 +% 0 0 1 1 1 0 0 0 1 +% 1 0 0 1 0 0 0 1 1 +% 0 1 0 0 1 0 1 0 1 +% 0 0 1 0 0 1 1 1 0 +% so find(G(5,:)) = [2 4 6 8] +% and find(G(1,:)) = [2 3 4 7] + +if nargin < 3, wrap_around = 0; end + +% M contains the number of each cell e.g. +% 1 4 7 +% 2 5 8 +% 3 6 9 +% North neighbors (assuming wrap around) are +% 3 6 9 +% 1 4 7 +% 2 5 8 +% Without wrap around, they are +% 1 4 7 +% 1 4 7 +% 2 5 8 +% The first row is arbitrary, since pixels at the top have no north neighbor. + +if nrows==1 + G = zeros(1, ncols); + for i=1:ncols-1 + G(i,i+1) = 1; + G(i+1,i) = 1; + end + if wrap_around + G(1,ncols) = 1; + G(ncols,1) = 1; + end + return; +end + + +npixels = nrows*ncols; + +N = 1; E = 2; S = 3; W = 4; +if wrap_around + rows{N} = [nrows 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [2:ncols 1]; + rows{S} = [2:nrows 1]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [ncols 1:ncols-1]; +else + rows{N} = [1 1:nrows-1]; cols{N} = 1:ncols; + rows{E} = 1:nrows; cols{E} = [1 1:ncols-1]; + rows{S} = [2:nrows nrows]; cols{S} = 1:ncols; + rows{W} = 1:nrows; cols{W} = [2:ncols ncols]; +end + +M = reshape(1:npixels, [nrows ncols]); +nbrs = cell(1, 4); +for i=1:4 + nbrs{i} = M(rows{i}, cols{i}); +end + + +G = zeros(npixels, npixels); +if wrap_around + for i=1:4 + if 0 + % naive + for p=1:npixels + G(p, nbrs{i}(p)) = 1; + end + else + % vectorized + ndx2 = sub2ind([npixels npixels], 1:npixels, nbrs{i}(:)'); + G(ndx2) = 1; + end + end +else + i = N; + mask = ones(nrows, ncols); + mask(1,:) = 0; % pixels in row 1 have no nbr to the north + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = E; + mask = ones(nrows, ncols); + mask(:,ncols) = 0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = S; + mask = ones(nrows, ncols); + mask(nrows,:)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; + + i = W; + mask = ones(nrows, ncols); + mask(:,1)=0; + ndx = find(mask); + ndx2 = sub2ind([npixels npixels], ndx, nbrs{i}(ndx)); + G(ndx2) = 1; +end + +G = setdiag(G, 0);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_adj_mat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function [A, names] = mk_adj_mat(connections, names, topological) +% MK_ADJ_MAT Make a directed adjacency matrix from a list of connections between named nodes. +% +% A = mk_adj_mat(connections, name) +% This is best explaine by an example: +% names = {'WetGrass', 'Sprinkler', 'Cloudy', 'Rain'}; +% connections = {'Cloudy', 'Sprinkler'; 'Cloudy', 'Rain'; 'Sprinkler', 'WetGrass'; 'Rain', 'WetGrass'}; +% adds the arcs C -> S, C -> R, S -> W, R -> W. Node 1 is W, 2 is S, 3 is C, 4 is R. +% +% [A, names] = mk_adj_mat(connections, name, 1) +% The last argument of 1 indicates that we should topologically sort the nodes (parents before children). +% In the example, the numbering becomes: node 1 is C, 2 is R, 3 is S, 4 is W +% and the return value of names gets permuted to {'Cloudy', 'Rain', 'Sprinkler', 'WetGrass'}. +% Note that topological sorting the graph is only possible if it has no directed cycles. + +if nargin < 3, topological = 0; end + +n=length(names); +A=zeros(n); +[nr nc] = size(connections); +for r=1:nr + from = strmatch(connections{r,1}, names, 'exact'); + assert(~isempty(from)); + to = strmatch(connections{r,2}, names, 'exact'); + assert(~isempty(to)); + %fprintf(1, 'from %s %d to %s %d\n', connections{r,1}, from, connections{r,2}, to); + A(from,to) = 1; +end + +if topological + order = topological_sort(A); + A = A(order, order); + names = names(order); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_adjmat_chain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function G = mk_adjmat_chain(T) +% MK_DAG_CHAIN Make adjacency matrix for bi-directional Markov chain of T nodes +% function G = mk_dag_chain(T) +% +% G(t,t+1) = 1 for all t<T + +G = diag(ones(1,T-1),1) + diag(ones(1,T-1),-1); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_all_dags.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function Gs = mk_all_dags(N, order) +% MK_ALL_DAGS generate all DAGs on N variables +% G = mk_all_dags(N) +% +% G = mk_all_dags(N, order) only generates DAGs in which node i has parents from +% nodes in order(1:i-1). Default: order=[] (no constraints). +% +% G{i} is the i'th dag +% +% Note: the number of DAGs is super-exponential in N, so don't call this with N > 4. + +if nargin < 2, order = []; end + +use_file = 0; + +global BNT_HOME +fname = sprintf('%s/DAGS%d.mat', BNT_HOME, N); +if use_file & exist(fname, 'file') + S = load(fname, '-mat'); + fprintf('loading %s\n', fname); + Gs = S.Gs; + return; +end + +m = 2^(N*N); +ind = ind2subv(2*ones(1,N^2), 1:m); +Gs = {}; +j = 1; +directed = 1; +for i=1:m + dag = reshape(ind(i,:)-1, N, N); + if acyclic(dag, directed) + out_of_order = 0; + if ~isempty(order) + for k=1:N-1 + if any(dag(order(k+1:end), k)) + out_of_order = 1; + break; + end + end + end + if ~out_of_order + Gs{j} = dag; + j = j + 1; + end + end +end + +if use_file + disp(['mk_all_dags: saving to ' fname '!']); + save(fname, 'Gs'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_nbrs_of_dag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +function [Gs, op, nodes] = mk_nbrs_of_dag(G0) +% MK_NBRS_OF_DAG Make all DAGs that differ from G0 by a single edge deletion, addition or reversal +% [Gs, op, nodes] = mk_nbrs_of_dag(G0) +% +% Gs{i} is the i'th neighbor. +% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% nodes(i,1:2) are the head and tail of the operated-on arc. + +Gs = {}; +op = {}; +nodes = []; + +[I,J] = find(G0); +nnbrs = 1; +% all single edge deletions +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + G(i,j) = 0; + Gs{nnbrs} = G; + op{nnbrs} = 'del'; + nodes(nnbrs, :) = [i j]; + nnbrs = nnbrs + 1; +end + +% all single edge reversals +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + G(i,j) = 0; + G(j,i) = 1; + if acyclic(G) + Gs{nnbrs} = G; + op{nnbrs} = 'rev'; + nodes(nnbrs, :) = [i j]; + nnbrs = nnbrs + 1; + end +end + +[I,J] = find(~G0); +% all single edge additions +for e=1:length(I) + i = I(e); j = J(e); + if i ~= j % don't add self arcs + G = G0; + G(i,j) = 1; + if G(j,i)==0 % don't add i->j if j->i exists already + if acyclic(G) + Gs{nnbrs} = G; + op{nnbrs} = 'add'; + nodes(nnbrs, :) = [i j]; + nnbrs = nnbrs + 1; + end + end + end +end + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_nbrs_of_digraph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,132 @@ +function [Gs, op, nodes, A] = my_mk_nbs_of_digraph(G0,A) +% MY_MK_NBRS_OF_DIGRAPH Make all digraphs that differ from G0 by a single edge deletion, addition or reversal, subject to acyclicity +% [Gs, op, nodes, A] = my_mk_nbrs_of_digraph(G0,<A>) +% +% G0 is an adj matrix s.t. G0(i,j)=1 iff i->j in graph +% A is the ancestor matrix for G0 (opt, creates if necessary) +% +% Gs(:,:,i) is the i'th neighbor +% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% nodes(i,1:2) are the head and tail of the operated-on arc. +% Modified from mk_nbrs_of_digraph by Sonia Leach +% +% Modified by Sonia Leach Feb 02 + +if nargin ==1, A = reachability_graph(G0');, end + +n = length(G0); +[I,J] = find(G0); % I(k), J(k) is the k'th edge +E = length(I); % num edges present in G0 + + +% SINGLE EDGE DELETIONS +% all deletions are valid wrt acyclity + +Grep = repmat(G0(:), 1, E); % each column is a copy of G0 +% edge_ndx(k) is the scalar location of the k'th edge +edge_ndx = find(G0); + +% edge_ndx = subv2ind([n n], [I J]); % equivalent +% We set (ndx(k), k) to 0 for k=1:E in Grep +ndx = subv2ind(size(Grep), [edge_ndx(:) (1:E)']); +G1 = Grep; +G1(ndx) = 0; +Gdel = reshape(G1, [n n E]); + + +% SINGLE EDGE REVERSALS + +% SML: previously Kevin had that legal structure was if +% A(P,i)=1 for any P = { p | p in parents(j), p~=i} +% specifically he said +% "if any(A(ps,i)) then there is a path i -> parent of j -> j +% so reversing i->j would create a cycle" +% Thus put in another way: +% for each i,j if sum(G0(:,j)' * A(:,i)) > 0, reversing i->j +% is not legal. +% +% Ex. Suppose we want to check if 2->4 can be reversed in the +% following graph: +% G0 = A = +% 0 0 1 0 0 0 0 0 +% 0 0 1 1 0 0 0 0 +% 0 0 0 1 1 1 0 0 +% 0 0 0 0 1 1 1 0 +% +% Then parents(4) = G0(:,4) = [0 1 1 0]' +% and A(:,2) = [0 0 1 1]. Thus G0(:,4)'*A(:,2) = 1 b/c 3 is +% an ancestor of 4 and a child of 2. Note that this works b/c +% matrix multiplication has the effect of ANDing the two vectors +% and summing up the result (equiv. to the any(A(ps,i)) in kevin's code) +% +% So, we vectorize and check for all i,j pairs by looking for +% 1's in L = (G0'*A)' which has L(i,j)=1 if rev(i,j) not legal +% Note that this will give 1's where there are none in the G0 +% so we do a L=max(0, G0-L) to cancel out only the existing edges that +% aren't legal (subtracting where both are 1 and setting where +% G0=0 and A=1 back to 0). + +L = max(0, G0-(G0'*A)'); +[IL, JL] = find(L); % I(k), J(k) is the k'th legal edge to rev. +EL = length(IL); + + +% SML: First we have to DELETE THE EDGES WE ARE REVERSING +% We can't use G1 w/ reversed edges already deleted (as +% Kevin did) b/c the space of possible deletions are different +% now (some reverses aren't legal) + +Grep = repmat(G0(:), 1, EL); % each column is a copy of G0 +% edge_ndx(k) is the scalar location of the k'th edge +edge_ndx = subv2ind([n n], [IL JL]); +% We set (ndx(k), k) to 0 for k=1:E in Grep +ndx = subv2ind(size(Grep), [edge_ndx(:) (1:EL)']); +G1 = Grep; +G1(ndx) = 0; + +% SML: Now we add in our REVERSED EDGES +% rev_edge_ndx(k) is the scalar location of the k'th legal reversed edge +rev_edge_ndx = subv2ind([n n], [JL IL]); + +% We set (rev_edge_ndx(k), k) to 1 for k=1:EL in G1 +% We have already deleted i->j in the previous step +ndx = subv2ind(size(Grep), [rev_edge_ndx(:) (1:EL)']); +G1(ndx) = 1; +Grev = reshape(G1, [n n EL]); + +% SINGLE EDGE ADDITIONS + +% SML: previously Kevin had that any addition was legal if A(i,j)=0 +% however, you can not add i->j if j is a descendent of i. +% Thus, we create all possible additions in Gbar and then +% subtract the descendants of each edge as possible parents +% This means the potential parents of i (i.e. Gbar(:,i)) +% can not also be descendants if i i.e. (A(:,i)) which is accomplished +% by subtracting (Gbar-A == 1 iff Gbar=1 & A=0) + +Gbar = ~G0; % Gbar(i,j)=1 iff there is no i->j edge in G0 +Gbar = setdiag(Gbar, 0); % turn off self loops + +GbarL = Gbar-A; +[IbarL, JbarL] = find(GbarL); % I(k), J(k) is the k'th legal edge to add +EbarL = length(IbarL); + +bar_edge_ndx = find(GbarL); + +Grep = repmat(G0(:), 1, EbarL); % each column is a copy of G0 +ndx = subv2ind(size(Grep), [bar_edge_ndx(:) (1:EbarL)']); +Grep(ndx) = 1; +Gadd = reshape(Grep, [n n EbarL]); + + +Gs = cat(3, Gdel, Grev, Gadd); + +nodes = [I J; + IL JL; + IbarL JbarL]; + +op = cell(1, E+EL+EbarL); +op(1:E) = {'del'}; +op(E+(1:EL)) = {'rev'}; +op((E+EL+1):end) = {'add'}; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_nbrs_of_digraph_broken.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,111 @@ +function [Gs, op, nodes] = mk_nbrs_of_digraph(G0) +% MK_NBRS_OF_DIGRAPH Make all digraphs that differ from G0 by a single edge deletion, addition or reversal +% [Gs, op, nodes] = mk_nbrs_of_digraph(G0) +% +% Gs(:,:,i) is the i'th neighbor +% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% nodes(i,1:2) are the head and tail of the operated-on arc. + +debug = 0; % the vectorized version is about 3 to 10 times faster + +n = length(G0); +[I,J] = find(G0); % I(k), J(k) is the k'th edge +E = length(I); % num edges present in G0 + +% SINGLE EDGE DELETIONS + +Grep = repmat(G0(:), 1, E); % each column is a copy of G0 +% edge_ndx(k) is the scalar location of the k'th edge +edge_ndx = find(G0); +% edge_ndx = subv2ind([n n], [I J]); % equivalent +% We set (ndx(k), k) to 0 for k=1:E in Grep +ndx = subv2ind(size(Grep), [edge_ndx(:) (1:E)']); +G1 = Grep; +G1(ndx) = 0; +Gdel = reshape(G1, [n n E]); + + +% if debug +% % Non-vectorized version +% ctr = 1; +% for e=1:E +% i = I(e); j = J(e); +% Gdel2(:,:,ctr) = G0; +% Gdel2(i,j,ctr) = 0; +% ctr = ctr + 1; +% end +% assert(isequal(Gdel, Gdel2)); +% end + + +% SINGLE EDGE REVERSALS + +% rev_edge_ndx(k) is the scalar location of the k'th reversed edge +%rev_edge_ndx = find(G0'); % different order to edge_ndx, which is bad +rev_edge_ndx = subv2ind([n n], [J I]); +% We set (rev_edge_ndx(k), k) to 1 for k=1:E in G1 +% We have already deleted i->j in the previous step +ndx = subv2ind(size(Grep), [rev_edge_ndx(:) (1:E)']); +G1(ndx) = 1; +Grev = reshape(G1, [n n E]); + +% if debug +% % Non-vectorized version +% ctr = 1; +% for e=1:E +% i = I(e); j = J(e); +% Grev2(:,:,ctr) = G0; +% Grev2(i,j,ctr) = 0; +% Grev2(j,i,ctr) = 1; +% ctr = ctr + 1; +% end +% assert(isequal(Grev, Grev2)); +% end + + +% SINGLE EDGE ADDITIONS + +Gbar = ~G0; % Gbar(i,j)=1 iff there is no i->j edge in G0 +Gbar = setdiag(Gbar, 0); % turn off self loops +[Ibar,Jbar] = find(Gbar); + +bar_edge_ndx = find(Gbar); +Ebar = length(Ibar); % num edges present in Gbar +Grep = repmat(G0(:), 1, Ebar); % each column is a copy of G0 +ndx = subv2ind(size(Grep), [bar_edge_ndx(:) (1:Ebar)']); +Grep(ndx) = 1; +Gadd = reshape(Grep, [n n Ebar]); + +% if debug +% % Non-vectorized version +% ctr = 1; +% for e=1:length(Ibar) +% i = Ibar(e); j = Jbar(e); +% Gadd2(:,:,ctr) = G0; +% Gadd2(i,j,ctr) = 1; +% ctr = ctr + 1; +% end +% assert(isequal(Gadd, Gadd2)); +% end + + +Gs = cat(3, Gdel, Grev, Gadd); + +nodes = [I J; + I J; + Ibar Jbar]; + +op = cell(1, E+E+Ebar); +op(1:E) = {'del'}; +op(E+1:2*E) = {'rev'}; +op(2*E+1:end) = {'add'}; + + +% numeric output: +% op(i) = 1, 2, or 3, if the i'th neighbor was created by adding, deleting or reversing an arc. + +ADD = 1; +DEL = 2; +REV = 3; + +%op = [repmat(DEL, 1, E) repmat(REV, 1, E) repmat(ADD, 1, Ebar)];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_nbrs_of_digraph_not_vectorized.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function [Gs, op, nodes] = mk_nbrs_of_digraph2(G0) +% MK_NBRS_OF_DIGRAPH Make all digraphs that differ from G0 by a single edge deletion, addition or reversal +% [Gs, op, nodes] = mk_nbrs_of_digraph(G0) +% op{i} = 'add', 'del', or 'rev' is the operation used to create the i'th neighbor. +% nodes(i,1:2) are the head and tail of the operated-on arc. + +[I,J] = find(G0); +G0bar = setdiag(~G0, 0); % exclude self loops in graph complement +[Ibar,Jbar] = find(G0bar); +nnbrs = 2*length(I) + length(Ibar); +Gs = cell(1, nnbrs); +op = cell(1, nnbrs); +nodes = zeros(nnbrs, 2); + +nbr = 1; +% all single edge deletions +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + G(i,j) = 0; + Gs{nbr} = G; + op{nbr} = 'del'; + nodes(nbr, :) = [i j]; + nbr = nbr + 1; +end + +% all single edge reversals +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + G(i,j) = 0; + G(j,i) = 1; + Gs{nbr} = G; + op{nbr} = 'rev'; + nodes(nbr, :) = [i j]; + nbr = nbr + 1; +end + +[I,J] = find(~G0); +% all single edge additions +for e=1:length(I) + i = I(e); j = J(e); + G = G0; + if i ~= j % don't add self loops + G(i,j) = 1; + Gs{nbr} = G; + op{nbr} = 'add'; + nodes(nbr, :) = [i j]; + nbr = nbr + 1; + end +end + +assert(nnbrs == nbr-1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_rnd_dag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function [dag, order] = mk_rnd_dag(N, max_fan_in) +% MY_MK_RND_DAG Create a random directed acyclic graph +% +% [dag, order] = my_mk_rnd_dag(N, max_fan_in) +% max_fan_in defaults to N. +% order is the random topological order that was chosen + +% Modified by Sonia Leach 2/25/02 + +if nargin < 2, max_fan_in = N; end + +order = randperm(N); +dag = zeros(N,N); +for i=2:N + j = order(i); + %k = sample_discrete(normalise(ones(1, min(i-1, max_fan_in)))); + k = sample_discrete(normalise(ones(1, min(i-1, max_fan_in)+1))) - 1; % min = 0 (bug fix due to + % Pedrito, 7/28/04) + SS = order(1:i-1); % get Set of possible parentS + p = randperm(length(SS)); % permute order of set + dag(SS(p(1:k)),j) = 1; % take first k in permuted order + + % Kevin had: + %SS = subsets(order(1:i-1), k, k); + %p = sample_discrete(normalise(ones(1, length(SS)))); + %dag(SS{p}, j) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_rnd_dag_given_edge_prob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function dag = sample_dag(P) +% SAMPLE_DAG Create a random directed acyclic graph with edge probabilities P(i,j) +% dag = sample_dag(P) +% +% This uses rejection sampling to reject graphs with directed cycles. + +done = 0; +directed = 1; +iter = 1; +while ~done + dag = binornd(1, P); % each edge is an indep Bernoulli (0/1) random variable + dag = setdiag(dag, 0); + done = acyclic(dag, directed); + iter = iter + 1 +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_rooted_tree.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function [T, pre, post, cycle] = mk_rooted_tree(G, root) +% MK_ROOTED_TREE Make a directed tree pointing away from root +% [T, pre, post, cycle] = mk_rooted_tree(G, root) + +n = length(G); +T = sparse(n,n); % not the same as T = sparse(n) ! +directed = 0; +[d, pre, post, cycle, f, pred] = dfs(G, root, directed); +[junk, pre2] = sort(d); +assert(isequal(pre, pre2)) +[junk, post2] = sort(f); +assert(isequal(post, post2)); +%[d, pre, post, cycle, f, pred] = dfs(G, [], directed); +for i=1:length(pred) + if pred(i)>0 + T(pred(i),i)=1; + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/mk_undirected.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function U = mk_undirected(G) + +[nr nc] = size(G); +U = G; +for i=1:nr + for j=1:nc + if U(i,j)==1 + U(j,i) = 1; + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/moralize.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function [M, moral_edges] = moralize(G) +% MORALIZE Ensure that for every child, all its parents are married, and drop directionality of edges. +% [M, moral_edges] = moralize(G) + +M = G; +n = length(M); +for i=1:n + fam = family(G,i); + M(fam,fam)=1; +end +M = setdiag(M,0); +moral_edges = sparse(triu(max(0,M-G),1));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/neighbors.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function ns = neighbors(adj_mat, i) +% NEIGHBORS Find the parents and children of a node in a graph. +% ns = neighbors(adj_mat, i) + +%ns = myunion(children(adj_mat, i), parents(adj_mat, i)); +ns = [find(adj_mat(i,:)) find(adj_mat(:,i))']; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/parents.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function ps = parents(adj_mat, i) +% PARENTS Return the list of parents of node i +% ps = parents(adj_mat, i) + +ps = find(adj_mat(:,i))';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/pred2path.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,78 @@ +function rte = pred2path(P,s,t) +%PRED2PATH Convert predecessor indices to shortest paths from node 's' to 't'. +% rte = pred2path(P,s,t) +% P = |s| x n matrix of predecessor indices (from DIJK) +% s = FROM node indices +% = [] (default), paths from all nodes +% t = TO node indices +% = [] (default), paths to all nodes +% rte = |s| x |t| cell array of paths (or routes) from 's' to 't', where +% rte{i,j} = path from s(i) to t(j) +% = [], if no path exists from s(i) to t(j) +% +% (Used with output of DIJK) + +% Copyright (c) 1998-2001 by Michael G. Kay +% Matlog Version 5 22-Aug-2001 + +% Input Error Checking ****************************************************** +error(nargchk(1,3,nargin)); + +[rP,n] = size(P); + +if nargin < 2 | isempty(s), s = (1:n)'; else s = s(:); end +if nargin < 3 | isempty(t), t = (1:n)'; else t = t(:); end + +if any(P < 0 | P > n) + error(['Elements of P must be integers between 1 and ',num2str(n)]); +elseif any(s < 1 | s > n) + error(['''s'' must be an integer between 1 and ',num2str(n)]); +elseif any(t < 1 | t > n) + error(['''t'' must be an integer between 1 and ',num2str(n)]); +end +% End (Input Error Checking) ************************************************ + +rte = cell(length(s),length(t)); + +for i = 1:length(s) + if rP == 1 + si = 1; + else + si = s(i); + if si < 1 | si > rP + error('Invalid P matrix.') + end + end + for j = 1:length(t) + tj = t(j); + if tj == s(i) + r = tj; + elseif P(si,tj) == 0 + r = []; + else + r = tj; + while tj ~= s(i) + if tj < 1 | tj > n + error('Invalid element of P matrix found.') + end + r = [P(si,tj) r]; + tj = P(si,tj); + end + end + rte{i,j} = r; + end +end + +if length(s) == 1 & length(t) == 1 + rte = rte{:}; +end + +%rte = t; +while 0%t ~= s + if t < 1 | t > n | round(t) ~= t + error('Invalid ''pred'' element found prior to reaching ''s'''); + end + rte = [P(t) rte]; + t = P(t); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/reachability_graph.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function C = reachability_graph(G) +% REACHABILITY_GRAPH C(i,j) = 1 iff there is a path from i to j in DAG G +% C = reachability_graph(G) + +if 1 + % expm(G) = I + G + G^2 / 2! + G^3 / 3! + ... + M = expm(double(full(G))) - eye(length(G)); + C = (M>0); +else + % This computes C = G + G^2 + ... + G^{n-1} + n = length(G); + A = G; + C = zeros(n); + for i=1:n-1 + C = C + A; + A = A * G; + end + C = (C > 0); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/scc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +function [c,v] = scc(a,tol) + +% Finds the strongly connected sets of vertices +% in the DI-rected G-raph of A +% c = 0-1 matrix displaying accessibility +% v = displays the equivalent classes +% +% v(i,j) is the j'th member of the i'th equiv class (0 padded) +% +% http://www.math.wsu.edu/math/faculty/tsat/matlab.html + +[m,n] = size(a); +if m~=n 'Not a Square Matrix', return, end +b=abs(a); o=ones(size(a)); x=zeros(1,n); +msg='The Matrix is Irreducible !'; v='Connected Directed Graph !'; +if (nargin==1) tol=n*eps*norm(a,'inf'); end + +% Create a companion matrix +c = b>tol*o; +if (c==o) + % msg, return + v = 1:length(a); + return +end + + +% Compute accessibility in at most n-step paths +for k=1:n + for j=1:n + for i=1:n + % If index i accesses j, where can you go ? + if c(i,j) > 0 c(i,:) = c(i,:)+c(j,:); end + end + end +end +% Create a 0-1 matrix with the above information +c>zeros(size(a)); c=ans; if (c==o) msg, return, end + +% Identify equivalence classes +d=c.*c'+eye(size(a)); d>zeros(size(a)); d=ans; +v=zeros(size(a)); +for i=1:n find(d(i,:)); ans(n)=0; v(i,:)=ans; end + +% Eliminate displaying of identical rows +i=1; +while(i<n) + for k=i+1:n + if v(k,1) == v(i,1) + v(k,:)=x; + end + end + i=i+1; +end +j=1; +for i=1:n + if v(i,1)>0 + h(j,:)=v(i,:); + j=j+1; + end +end +v=h; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/strong_elim_order.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,75 @@ +function order = strong_elim_order(G, node_sizes, partial_order) +% STRONG_ELIM_ORDER Find an elimination order to produce a strongly triangulated graph. +% order = strong_elim_order(moral_graph, node_sizes, partial_order) +% +% partial_order(i,j)=1 if we must marginalize i *after* j +% (so i will be nearer the strong root). +% e.g., if j is a decision node and i is its information set: +% we cannot maximize j if we have marginalized out some of i +% e.g., if j is a continuous child and i is its discrete parent: +% we want to integrate out the cts nodes before the discrete ones, +% so that the marginal is strong. +% +% For details, see +% - Jensen, Jensen and Dittmer, "From influence diagrams to junction trees", UAI 94. +% - Lauritzen, "Propgation of probabilities, means, and variances in mixed graphical +% association models", JASA 87(420):1098--1108, 1992. +% +% On p369 of the Jensen paper, they state "the reverse of the elimination order must be some +% extension of [the partial order] to a total order". +% We make no attempt to find the best such total ordering, in the sense of minimizing the weight +% of the resulting cliques. + +% Example from the Jensen paper: +% Let us number the nodes in Fig 1 from top to bottom, left to right, +% so a=1,b=2,D1=3,c=4,...,l=14,j=15,k=16. +% The elimination ordering they propose on p370 is [14 15 16 11 12 1 4 5 10 8 13 9 7 6 3 2]; + +if 0 + total_order = topological_sort(partial_order); + order = total_order(end:-1:1); % no attempt to find an optimal constrained ordering! + return; +end + +% The following implementation is due to Ilya Shpitser and seems to give wrong +% results on cg1 + +n = length(G); +MG = G; % copy the original graph +uneliminated = ones(1,n); +order = zeros(1,n); + +for i=1:n + roots = []; + k = 1; + for j=1:n + if sum(partial_order(j,:)) == 0 + roots(k) = j; + k = k + 1; + end + end + U = find(uneliminated); + valid = myintersect(U, roots); + % Choose the best node from the set of valid candidates + score1 = zeros(1,length(valid)); + score2 = zeros(1,length(valid)); + for j=1:length(valid) + k = valid(j); + ns = myintersect(neighbors(G, k), U); + l = length(ns); + M = MG(ns,ns); + score1(j) = l^2 - sum(M(:)); % num. added edges + score2(j) = prod(node_sizes([k ns])); % weight of clique + end + j1s = find(score1==min(score1)); + j = j1s(argmin(score2(j1s))); + k = valid(j); + uneliminated(k) = 0; + order(i) = k; + ns = myintersect(neighbors(G, k), U); + if ~isempty(ns) + G(ns,ns) = 1; + G = setdiag(G,0); + end + partial_order(:,k) = 0; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +% A - B +% | +% D - C - E + +A=1;B=2;C=3;D=4;E=5; +dag = zeros(5,5); +dag(A,B)=1; +%dag(A,D)=1; +dag(B,C)=1; +dag(C,D)=1; +dag(E,C)=1; +[d, pre, post, cycle, f, pred] = dfs(dag, A, 0) + +[T, pre, post, cycle] = mk_rooted_tree(dag, A) + +%[T, pre, post, cycle] = mkRootedTree(dag, A)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/test_strong_root.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function strong = test_strong_root(jtree,cliques,dnodes,root) +% This function tests, whether root is a strong root of jtree. +% The following parameters are used +% Input: +% jtree An matrix with two colums. jtree(i,j) == jtree(j,i) is 1 if node +% i is connected with node j +% cliques Cells which contain the nodes in each clique +% dnodes An array with the discrete nodes of the juntion tree. +% root It is tested whether root is the strong root of the junction tree +% Output: +% strong The output is 1 if root is the strong root of the junction tree jtree. +% Please note, that the running intersection property is not tested. +if isempty(dnodes) + strong = 1; + return; +end + +children = find(jtree(root,:)==1); +i = 1; +strong = 1; +while (i <= length(children)) & (strong==1) + child = children(i); + jtree(child,root) = 0; + jtree(root,child) = 0; + sep = myintersect(cliques{child},cliques{root}); + diff = mysetdiff(cliques{child},cliques{root}); + if (mysubset(sep,dnodes) | isempty(myintersect(diff,dnodes))) + strong = test_strong_root(jtree,cliques,dnodes,child); + else + strong = 0; + end; + i = i+1; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/topological_sort.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function order = topological_sort(A) +% TOPOLOGICAL_SORT Return the nodes in topological order (parents before children). +% order = topological_sort(adj_mat) + +n = length(A); +indeg = zeros(1,n); +zero_indeg = []; % a stack of nodes with no parents +for i=1:n + indeg(i) = length(parents(A,i)); + if indeg(i)==0 + zero_indeg = [i zero_indeg]; + end +end + +t=1; +order = zeros(1,n); +while ~isempty(zero_indeg) + v = zero_indeg(1); % pop v + zero_indeg = zero_indeg(2:end); + order(t) = v; + t = t + 1; + cs = children(A, v); + for j=1:length(cs) + c = cs(j); + indeg(c) = indeg(c) - 1; + if indeg(c) == 0 + zero_indeg = [c zero_indeg]; % push c + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/trees.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,168 @@ + +% make undirected adjacency matrix of graph/tree +% e.g., +% 1 +% / \ +% 2 3 +T = zeros(3,3); +T(1,2) = 1; T(2,1)=1; +T(1,3)=1; T(3,1) = 1; + +root = 1; +[T, preorder, postorder] = mk_rooted_tree(T, root); + +% bottom up message passing leaves to root +for n=postorder(:)' + for p = parents(T, n) + % p is parent of n + end +end + +% top down, root to leaves +for n=preorder(:)' + for c= children(T,n) + % c is child of n + end +end + + +%%%%%%%%%%%%% + +function ps = parents(adj_mat, i) +% PARENTS Return the list of parents of node i +% ps = parents(adj_mat, i) + +ps = find(adj_mat(:,i))'; + + +%%%%%%%%%%%% + +function cs = children(adj_mat, i, t) +% CHILDREN Return the indices of a node's children in sorted order +% c = children(adj_mat, i, t) +% +% t is an optional argument: if present, dag is assumed to be a 2-slice DBN + +if nargin < 3 + cs = find(adj_mat(i,:)); +else + if t==1 + cs = find(adj_mat(i,:)); + else + ss = length(adj_mat)/2; + j = i+ss; + cs = find(adj_mat(j,:)) + (t-2)*ss; + end +end + +%%%%%%%%%%% + +function [T, pre, post, cycle] = mk_rooted_tree(G, root) +% MK_ROOTED_TREE Make a directed, rooted tree out of an undirected tree. +% [T, pre, post, cycle] = mk_rooted_tree(G, root) + +n = length(G); +T = sparse(n,n); % not the same as T = sparse(n) ! +directed = 0; +[d, pre, post, cycle, f, pred] = dfs(G, root, directed); +for i=1:length(pred) + if pred(i)>0 + T(pred(i),i)=1; + end +end + + +%%%%%%%%%%% + +function [d, pre, post, cycle, f, pred] = dfs(adj_mat, start, directed) +% DFS Perform a depth-first search of the graph starting from 'start'. +% [d, pre, post, cycle, f, pred] = dfs(adj_mat, start, directed) +% +% Input: +% adj_mat(i,j)=1 iff i is connected to j. +% start is the root vertex of the dfs tree; if [], all nodes are searched +% directed = 1 if the graph is directed +% +% Output: +% d(i) is the time at which node i is first discovered. +% pre is a list of the nodes in the order in which they are first encountered (opened). +% post is a list of the nodes in the order in which they are last encountered (closed). +% 'cycle' is true iff a (directed) cycle is found. +% f(i) is the time at which node i is finished. +% pred(i) is the predecessor of i in the dfs tree. +% +% If the graph is a tree, preorder is parents before children, +% and postorder is children before parents. +% For a DAG, topological order = reverse(postorder). +% +% See Cormen, Leiserson and Rivest, "An intro. to algorithms" 1994, p478. + +n = length(adj_mat); + +global white gray black color +white = 0; gray = 1; black = 2; +color = white*ones(1,n); + +global time_stamp +time_stamp = 0; + +global d f +d = zeros(1,n); +f = zeros(1,n); + +global pred +pred = zeros(1,n); + +global cycle +cycle = 0; + +global pre post +pre = []; +post = []; + +if ~isempty(start) + dfs_visit(start, adj_mat, directed); +else + for u=1:n + if color(u)==white + dfs_visit(u, adj_mat, directed); + end + end +end + + +%%%%%%%%%% + +function dfs_visit(u, adj_mat, directed) + +global white gray black color time_stamp d f pred cycle pre post + +pre = [pre u]; +color(u) = gray; +time_stamp = time_stamp + 1; +d(u) = time_stamp; +if directed + ns = children(adj_mat, u); +else + ns = neighbors(adj_mat, u); + ns = mysetdiff(ns, pred(u)); % don't go back to visit the guy who called you! +end +for v=ns(:)' + %fprintf('u=%d, v=%d, color(v)=%d\n', u, v, color(v)) + switch color(v) + case white, % not visited v before (tree edge) + pred(v)=u; + dfs_visit(v, adj_mat, directed); + case gray, % back edge - v has been visited, but is still open + cycle = 1; + %fprintf('cycle: back edge from v=%d to u=%d\n', v, u); + case black, % v has been visited, but is closed + % no-op + end +end +color(u) = black; +post = [post u]; +time_stamp = time_stamp + 1; +f(u) = time_stamp; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/triangulate.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,159 @@ +/* triangulate.c written by Ilya Shpitser */ + +#include <stdlib.h> + +#ifdef UNIX +#include "matlab.h" +#endif + +#include "matrix.h" +#include "mex.h" + +#include "elim.h" +#include "map.h" +#include "misc.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]){ + + int dims [2]; + int i, j, k, m, n; + long index; + double * G_pr; + double * stage_pr; + double * answer_G_pr, * fill_ins_pr; + double * matlab_clique_pr; + mxArray * matlab_clique; + Elimination e; + float ** adj_mat; + int ** order = (int **) NULL; + Iterator iter, iter2; + word w, w2; + int ** fill_ins; + Map cliques; + Map clique; + mxArray * fill_ins_mat; + int * nodes; + mxArray * full; + +// (original) full = mlfFull((mxArray *) prhs[0]); + full = (mxArray *) mlfFull((mxArray *) prhs[0]); // added typecasting + /* Obtain graph matrix information. */ + m = mxGetM(full); + n = mxGetN(full); + G_pr = mxGetPr(full); + + if(n < 1 || m < 1){ + return; + } + + /* Allocate and populate the log weight adjacency matrix corresponding + to the input graph. */ + adj_mat = (float **) malloc(sizeof(float *) * m); + adj_mat[0] = (float *) malloc(sizeof(float) * m * n); + for(i = 1; i < m; i++){ + adj_mat[i] = adj_mat[i - 1] + n; + } + /* We no longer have log weight info, but we have a (total) ordering on + the nodes already, so we do not need this information. */ + for(i = 0; i < m; i++){ + for(j = 0; j < n; j++){ + index = j * m + i; + if(G_pr[index] > 0){ + adj_mat[i][j] = 1; + } else { + adj_mat[i][j] = 0; + } + } + } + + /* Convert the total elimination ordering into a partial order argument + for the elimination routine. The elimination routine's purpose in this + mode of operation is to return cliques and fill-in edges. */ + if(nrhs > 1){ + order = (int **) malloc(sizeof(int *) * m); + order[0] = (int *) malloc(sizeof(int) * m * n); + for(i = 1; i < m; i++){ + order[i] = order[i - 1] + n; + } + for(i = 0; i < m; i++){ + for(j = 0; j < n; j++){ + order[i][j] = 0; + } + } + stage_pr = mxGetPr(prhs[1]); + for(i = 0; i < mxGetN(prhs[1]) - 1; i++){ + order[(int) stage_pr[i] - 1][(int) stage_pr[i + 1] - 1] = 1; + } + } + + /* Find the elimination ordering. */ + e = find_elim(n, adj_mat, order, -1); + + /* Allocate memory for the answer, and set the answer. */ + plhs[0] = mxCreateDoubleMatrix(m, n, mxREAL); + answer_G_pr = mxGetPr(plhs[0]); + cliques = get_cliques(e); +/* + dims[0] = 1; + dims[1] = get_size_Map(cliques); + plhs[1] = mxCreateCellArray(2, (const int *) dims);*/ + plhs[1] = mxCreateCellMatrix(get_size_Map(cliques), 1); + fill_ins = get_fill_ins(e); + fill_ins_mat = mxCreateDoubleMatrix(m, n, mxREAL); + fill_ins_pr = mxGetPr(fill_ins_mat); + + for(i = 0; i < n; i++){ + for(j = 0; j < m; j++){ + index = j * m + i; + answer_G_pr[index] = G_pr[index]; + if(fill_ins[i][j] > 0){ + answer_G_pr[index] = 1; + fill_ins_pr[index] = 1; + } + } + } + mxDestroyArray(full); +// (original) plhs[2] = mlfSparse(fill_ins_mat, NULL, NULL, NULL, NULL, NULL); + plhs[2] = (mxArray *) mlfSparse(fill_ins_mat, NULL, NULL, NULL, NULL, NULL); // added typecasting + mxDestroyArray(fill_ins_mat); + nodes = (int *) malloc(sizeof(int) * n); + k = 0; + iter = get_Iterator(cliques); + while(!is_empty(iter)){ + w = next_key(iter); + clique = (Map) w.v; + matlab_clique = mxCreateDoubleMatrix(1, get_size_Map(clique), mxREAL); + matlab_clique_pr = mxGetPr(matlab_clique); + for(i = 0; i < n; i++){ + nodes[i] = 0; + } + iter2 = get_Iterator(clique); + while(!is_empty(iter2)){ + w2 = next_key(iter2); + nodes[w2.i] = w2.i + 1; + } + j = 0; + for(i = 0; i < n; i++){ + if(nodes[i] > 0){ + matlab_clique_pr[j++] = nodes[i]; + } + } + mxSetCell(plhs[1], k++, matlab_clique); + } + free(nodes); + + /* Finally, free the allocated memory. */ + destroy_Elimination(e); + if(adj_mat){ + if(adj_mat[0]){ + free(adj_mat[0]); + } + free(adj_mat); + } + if(order){ + if(order[0]){ + free(order[0]); + } + free(order); + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/triangulate.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +function [G, cliques, fill_ins] = triangulate(G, order) +% TRIANGULATE Ensure G is triangulated (chordal), i.e., every cycle of length > 3 has a chord. +% [G, cliques, fill_ins, cliques_containing_node] = triangulate(G, order) +% +% cliques{i} is the i'th maximal complete subgraph of the triangulated graph. +% fill_ins(i,j) = 1 iff we add a fill-in arc between i and j. +% +% To find the maximal cliques, we save each induced cluster (created by adding connecting +% neighbors) that is not a subset of any previously saved cluster. (A cluster is a complete, +% but not necessarily maximal, set of nodes.) + +MG = G; +n = length(G); +eliminated = zeros(1,n); +cliques = {}; +for i=1:n + u = order(i); + U = find(~eliminated); % uneliminated + nodes = myintersect(neighbors(G,u), U); % look up neighbors in the partially filled-in graph + nodes = myunion(nodes, u); % the clique will always contain at least u + G(nodes,nodes) = 1; % make them all connected to each other + G = setdiag(G,0); + eliminated(u) = 1; + + exclude = 0; + for c=1:length(cliques) + if mysubset(nodes,cliques{c}) % not maximal + exclude = 1; + break; + end + end + if ~exclude + cnum = length(cliques)+1; + cliques{cnum} = nodes; + end +end + +fill_ins = sparse(triu(max(0, G - MG), 1)); + +%assert(check_triangulated(G)); % takes 72% of the time! + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/triangulate_2Dlattice_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,131 @@ +% Consider a 3x3 lattice with 4-nearest neighbor connectivity + +% 1 - 2 - 3 +% | | | +% 4 - 5 - 6 +% | | | +% 7 - 8 - 9 + +N = 3; +G = mk_2D_lattice(N,N,4); +G0 = G; + +% Now add in the diagonal edges + +if 0 +% 1 - 2 - 3 +% | x | x | +% 4 - 5 - 6 +% | x | x | +% 7 - 8 - 9 + +G(1,5)=1; G(5,1)=1; +G(2,6)=1; G(6,2)=1; +G(4,2)=1; G(2,4)=1; +G(5,3)=1; G(3,5)=1; + +G(4,8)=1; G(8,4)=1; +G(5,9)=1; G(9,5)=1; +G(7,5)=1; G(5,7)=1; +G(8,6)=1; G(6,8)=1; +end + +% 1 - 2 - 3 +% | / | \ | +% 4 - 5 - 6 +% | \ | / | +% 7 - 8 - 9 + +G(2,6)=1; G(6,2)=1; +G(4,2)=1; G(2,4)=1; +G(4,8)=1; G(8,4)=1; +G(8,6)=1; G(6,8)=1; + +% Is this a chordal (triangulated) graph? No! + +assert(~check_triangulated(G)) + +% The reason is that there is a chordless cycle around the outside nodes. +% To see this, imagine "picking up" node 5, leaving the rest on the plane +% (like a hoop skirt, or a tent), as shown below + +% 1 - 2 - 3 +% | / \ | +% 4 6 +% | \ / | +% 7 - 8 - 9 + + +% However, if we add in the 4-6 arc, it will be chordal. + +G2 = G; +G2(4,6)=1; G2(6,4)=1; +assert(check_triangulated(G2)) + +% Or we can add in the 2-8 arc +G2 = G; +G2(2,8)=1; G2(8,2)=1; +assert(check_triangulated(G2)) + + +if 0 +% 4x4 lattice with cross arcs +N=4;G0 = mk_2D_lattice(N,N,4); +vs = [1 6; 2 5; 2 7; 3 6; 3 8; 4 7; ... + 5 10; 6 9; 6 11; 7 10; 7 12; 8 11;... + 9 14; 10 13; 10 15; 11 14; 11 16; 12 15]; +for i=1:size(vs,1) + u = vs(i,1); v= vs(i,2); + G0(u,v) = 1; G0(v,u) = 1; +end +end + +% Here is how we can discover which edges to fill in automatically +% (although possibly sub-optimally) +weights = 2*ones(1,N*N); % all nodes are binar + +% fill-ins = 2-4, 2-6, 4-8, 6-8 and 4-6 +% cliques = 124, etc and 2456 4568 +greedy_order = best_first_elim_order(G0, weights); +[GT, cliques, fill_ins] = triangulate(G0, greedy_order) +assert(check_triangulated(GT)) + + + +greedy_order = best_first_elim_order(G, weights); +[GT, cliques, fill_ins] = triangulate(G, greedy_order) +assert(check_triangulated(GT)) + +% fill-ins = [4 6] + +% Cliques are the overlapping squares [1,2,4,5], [2 3 5 6], [4 5 7 8], [5 6 8 9] +% and the following caused by the fill-in: [2 4 5 6], [4 5 6 8] + +% Connect the maximal cliques of the triangulate graph into a junction tree +[jtree, root, B, clq_weights] = cliques_to_jtree(cliques, weights); + +% In this case, all cliques have weight 2^4 = 16 + + +% Now consider size of max clique as a function of grid size +% Note: this is not necessarily the optimal triangulation + +% N 5 10 15 16 17 18 +% m 6 15 23 25 28 28 +Ns = [5 10 15 16 17 18]; +for i=1:length(Ns) + N = Ns(i) + G = mk_2D_lattice(N,N,4); + weights = 2*ones(1,N*N); % all nodes are binary + greedy_order = best_first_elim_order(G, weights); % slow! + [GT, cliques, fill_ins] = triangulate(G, greedy_order); + %assert(check_triangulated(GT)) + [jtree, root, B, clq_weights] = cliques_to_jtree(cliques, weights); + m(i) = log2(max(clq_weights)); +end + +% plot distribution of clique sizes for fixed N +for c=1:length(cliques) + l(c) = length(cliques{c}); +end +hist(l)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/graph/triangulate_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +% Test the code using the dag in Fig 1 of Jensen, Jensen, Dittmer, +% "From influence diagrams to junction trees", UAI 94 + +% By reverse enginering Fig 2, we infer that the following arcs should +% be absent from the original dag: b->d1, e->d2, f->d2, g->d4 +a=1; b=2; d1=3; c=4; d=5; e=6; f=7; g=8; d2=9; d4=10; i=11; h=12; d3=13; l=14; j=15; k=16; +dag=zeros(16); +dag(a,c)=1; +%dag(b,[c d d1])=1; +dag(b,[c d])=1; +dag(d1,d)=1; +dag(c,e)=1; +dag(d,[e f])=1; +%dag(e,[g d2])=1; +dag(e,[g])=1; +%dag(f,[d2 h])=1; +dag(f,[h])=1; +%dag(g,[d4 i])=1; +dag(g,[i])=1; +dag(d2,i)=1; +dag(d4,l)=1; +dag(i,l)=1; +dag(h,[j k])=1; +dag(d3,k)=1; + + +[MG, moral_edges] = moralize(dag); +MG(j,k)=1; MG(k,j)=1; % simulate having a common utility child +% MG now equals fig 2 +order = [l j k i h a c d d4 g d3 d2 f e d1 b]; +[MTG, cliques, fill_ins] = triangulate(MG, order); +% MTG equals fig 3 +ns = 2*ones(1,16); +[jtree, root, cliques2] = mk_strong_jtree(cliques, ns, order, MTG); +jtree2 = mk_rooted_tree(jtree, root); +% jtree2 equals fig 4, with their arrows reversed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,174 @@ +/conffig.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/confmat.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/conjgrad.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/consist.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/convertoldnet.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/datread.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/datwrite.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/dem2ddat.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/demard.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/demev1.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/demev2.htm/1.1.1.1/Wed Apr 27 17:58:54 2005// +/demev3.htm/1.1.1.1/Wed Apr 27 17:58:56 2005// +/demgauss.htm/1.1.1.1/Wed Apr 27 17:58:56 2005// +/demglm1.htm/1.1.1.1/Wed Apr 27 17:58:56 2005// +/demglm2.htm/1.1.1.1/Wed Apr 27 17:58:56 2005// +/demgmm1.htm/1.1.1.1/Wed Apr 27 17:58:56 2005// +/demgmm2.htm/1.1.1.1/Wed Apr 27 17:58:56 2005// 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17:59:04 2005// +/rbfprior.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/rbfsetbf.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/rbfsetfw.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/rbftrain.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/rbfunpak.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/rosegrad.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/rosen.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/scg.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/som.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/somfwd.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/sompak.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/somtrain.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +/somunpak.htm/1.1.1.1/Wed Apr 27 17:59:04 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/nethelp3.3
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/conffig.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual conffig +</title> +</head> +<body> +<H1> conffig +</H1> +<h2> +Purpose +</h2> +Display a confusion matrix. + +<p><h2> +Synopsis +</h2> +<PRE> +conffig(y, t) +fh = conffig(y, t)</PRE> + + +<p><h2> +Description +</h2> +<CODE>conffig(y, t)</CODE> displays the confusion matrix +and classification performance for the predictions mat{y} +compared with the targets <CODE>t</CODE>. The data is assumed to be in a +1-of-N encoding, unless there is just one column, when it is assumed to +be a 2 class problem with a 0-1 encoding. Each row of <CODE>y</CODE> and <CODE>t</CODE> +corresponds to a single example. + +<p>In the confusion matrix, the rows represent the true classes and the +columns the predicted classes. + +<p><CODE>fh = conffig(y, t)</CODE> also returns the figure handle <CODE>fh</CODE> which +can be used, for instance, to delete the figure when it is no longer needed. + +<p><h2> +See Also +</h2> +<CODE><a href="confmat.htm">confmat</a></CODE>, <CODE><a href="demtrain.htm">demtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/confmat.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual confmat +</title> +</head> +<body> +<H1> confmat +</H1> +<h2> +Purpose +</h2> +Compute a confusion matrix. + +<p><h2> +Synopsis +</h2> +<PRE> +[C, rate] = confmat(y, t)</PRE> + + +<p><h2> +Description +</h2> +<CODE>[C, rate] = confmat(y, t)</CODE> computes the confusion matrix <CODE>C</CODE> +and classification performance <CODE>rate</CODE> for the predictions mat{y} +compared with the targets <CODE>t</CODE>. The data is assumed to be in a +1-of-N encoding, unless there is just one column, when it is assumed to +be a 2 class problem with a 0-1 encoding. Each row of <CODE>y</CODE> and <CODE>t</CODE> +corresponds to a single example. + +<p>In the confusion matrix, the rows represent the true classes and the +columns the predicted classes. The vector <CODE>rate</CODE> has two entries: +the percentage of correct classifications and the total number of +correct classifications. + +<p><h2> +See Also +</h2> +<CODE><a href="conffig.htm">conffig</a></CODE>, <CODE><a href="demtrain.htm">demtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/conjgrad.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +<html> +<head> +<title> +Netlab Reference Manual conjgrad +</title> +</head> +<body> +<H1> conjgrad +</H1> +<h2> +Purpose +</h2> +Conjugate gradients optimization. + +<p><h2> +Description +</h2> +<CODE>[x, options, flog, pointlog] = conjgrad(f, x, options, gradf)</CODE> uses a +conjugate gradients +algorithm to find the minimum of the function <CODE>f(x)</CODE> whose +gradient is given by <CODE>gradf(x)</CODE>. Here <CODE>x</CODE> is a row vector +and <CODE>f</CODE> returns a scalar value. +The point at which <CODE>f</CODE> has a local minimum +is returned as <CODE>x</CODE>. The function value at that point is returned +in <CODE>options(8)</CODE>. A log of the function values +after each cycle is (optionally) returned in <CODE>flog</CODE>, and a log +of the points visited is (optionally) returned in <CODE>pointlog</CODE>. + +<p><CODE>conjgrad(f, x, options, gradf, p1, p2, ...)</CODE> allows +additional arguments to be passed to <CODE>f()</CODE> and <CODE>gradf()</CODE>. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>, and the points visited +in the return argument <CODE>pointslog</CODE>. If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(2)</CODE> is a measure of the absolute precision required for the value +of <CODE>x</CODE> at the solution. If the absolute difference between +the values of <CODE>x</CODE> between two successive steps is less than +<CODE>options(2)</CODE>, then this condition is satisfied. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. If the absolute difference between the +objective function values between two successive steps is less than +<CODE>options(3)</CODE>, then this condition is satisfied. +Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(9)</CODE> is set to 1 to check the user defined gradient function. + +<p><CODE>options(10)</CODE> returns the total number of function evaluations (including +those in any line searches). + +<p><CODE>options(11)</CODE> returns the total number of gradient evaluations. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p><CODE>options(15)</CODE> is the precision in parameter space of the line search; +default <CODE>1e-4</CODE>. + +<p><h2> +Examples +</h2> +An example of +the use of the additional arguments is the minimization of an error +function for a neural network: +<PRE> + +w = quasinew('neterr', w, options, 'netgrad', net, x, t); +</PRE> + + +<p><h2> +Algorithm +</h2> + +The conjugate gradients algorithm constructs search +directions <CODE>di</CODE> that are conjugate: i.e. <CODE>di*H*d(i-1) = 0</CODE>, +where <CODE>H</CODE> is the Hessian matrix. This means that minimising along +<CODE>di</CODE> does not undo the effect of minimising along the previous +direction. The Polak-Ribiere formula is used to calculate new search +directions. The Hessian is not calculated, so there is only an +<CODE>O(W)</CODE> storage requirement (where <CODE>W</CODE> is the number of +parameters). However, relatively accurate line searches must be used +(default is <CODE>1e-04</CODE>). + +<p><h2> +See Also +</h2> +<CODE><a href="graddesc.htm">graddesc</a></CODE>, <CODE><a href="linemin.htm">linemin</a></CODE>, <CODE><a href="minbrack.htm">minbrack</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/consist.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,78 @@ +<html> +<head> +<title> +Netlab Reference Manual consist +</title> +</head> +<body> +<H1> consist +</H1> +<h2> +Purpose +</h2> +Check that arguments are consistent. + +<p><h2> +Synopsis +</h2> +<PRE> +errstring = consist(net, type, inputs, outputs) +errstring = consist(net, type, inputs) +errstring = consist(net, type) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>errstring = consist(net, type, inputs)</CODE> takes a network +data structure <CODE>net</CODE> together with a string <CODE>type</CODE> containing +the correct network type, a matrix <CODE>inputs</CODE> of input vectors and checks +that the data structure is consistent with the other arguments. An empty +string is returned if there is no error, otherwise the string contains the +relevant error message. If the <CODE>type</CODE> string is empty, then any +type of network is allowed. + +<p><CODE>errstring = consist(net, type)</CODE> takes a network data structure +<CODE>net</CODE> together with a string <CODE>type</CODE> containing the correct +network type, and checks that the two types match. + +<p><CODE>errstring = consist(net, type, inputs, outputs)</CODE> also checks that the +network has the correct number of outputs, and that the number of patterns +in the <CODE>inputs</CODE> and <CODE>outputs</CODE> is the same. The fields in <CODE>net</CODE> +that are used are +<PRE> + type + nin + nout +</PRE> + + +<p><h2> +Example +</h2> + +<p><CODE>mlpfwd</CODE>, the function that propagates values forward through an MLP +network, has the following check at the head of the file: +<PRE> + +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring) + error(errstring) +end +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="mlpfwd.htm">mlpfwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/convertoldnet.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual convertoldnet +</title> +</head> +<body> +<H1> convertoldnet +</H1> +<h2> +Purpose +</h2> +Convert pre-2.3 release MLP and MDN nets to new format + +<p><h2> +Synopsis +</h2> +<PRE> +net = convertoldnet(net) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = convertoldnet(net)</CODE> takes a network <CODE>net</CODE> and, if appropriate, +converts it from pre-2.3 to the current format. The difference is simply +that in MLPs and the MLP sub-net of MDNs the field <CODE>actfn</CODE> has been +renamed <CODE>outfn</CODE> to make it consistent with GLM and RBF networks. +If the network is not old-format or an MLP or MDN it is left unchanged. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mdn.htm">mdn</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/datread.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +<html> +<head> +<title> +Netlab Reference Manual datread +</title> +</head> +<body> +<H1> datread +</H1> +<h2> +Purpose +</h2> +Read data from an ascii file. + +<p><h2> +Synopsis +</h2> +<PRE> +[x, t, nin, nout, ndata] = datread(filename) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>[x, t, nin, nout, ndata] = datread(filename)</CODE> reads from +the file <CODE>filename</CODE> and returns a matrix <CODE>x</CODE> of input vectors, +a matrix <CODE>t</CODE> of target vectors, and integers <CODE>nin</CODE>, <CODE>nout</CODE> +and <CODE>ndata</CODE> specifying the number of inputs, the number of outputs +and the number of data points respectively. + +<p>The format of the data file is as follows: the first row contains the +string <CODE>nin</CODE> followed by the number of inputs, the second row +contains the string <CODE>nout</CODE> followed by the number of outputs, and +the third row contains the string <CODE>ndata</CODE> followed by the number +of data vectors. Subsequent lines each contain one input vector +followed by one output vector, with individual values separated by +spaces. + +<p><h2> +Example +</h2> +For the XOR data set we have + +<p><h2> +See Also +</h2> +<CODE><a href="nin2nout1ndata40.000000e+000.000000e+001.000000e+000.000000e+001.000000e+000.000000e+001.000000e+000.000000e+000.000000e+001.000000e+001.000000e+001.000000e+00SeeAlsodatwrite.htm">nin2nout1ndata40.000000e+000.000000e+001.000000e+000.000000e+001.000000e+000.000000e+001.000000e+000.000000e+000.000000e+001.000000e+001.000000e+001.000000e+00SeeAlsodatwrite</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/datwrite.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +<html> +<head> +<title> +Netlab Reference Manual datwrite +</title> +</head> +<body> +<H1> datwrite +</H1> +<h2> +Purpose +</h2> +Write data to ascii file. + +<p><h2> +Synopsis +</h2> +<PRE> +datwrite(filename, x, t) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>datwrite(filename, x, t)</CODE> takes a matrix <CODE>x</CODE> of input vectors +and a matrix <CODE>t</CODE> of target vectors and writes them to an ascii +file named <CODE>filename</CODE>. The file format is as follows: the first +row contains the string <CODE>nin</CODE> followed by the number of inputs, +the second row contains the string <CODE>nout</CODE> followed by the number +of outputs, and the third row contains the string <CODE>ndata</CODE> followed +by the number of data vectors. Subsequent lines each contain one input +vector followed by one output vector, with individual values separated +by spaces. + +<p><h2> +Example +</h2> +For the XOR data set we have + +<p><PRE> + + nin 2 + nout 1 + ndata 4 + 0.000000e+00 0.000000e+00 1.000000e+00 + 0.000000e+00 1.000000e+00 0.000000e+00 + 1.000000e+00 0.000000e+00 0.000000e+00 + 1.000000e+00 1.000000e+00 1.000000e+00 +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="datread.htm">datread</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/dem2ddat.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual dem2ddat +</title> +</head> +<body> +<H1> dem2ddat +</H1> +<h2> +Purpose +</h2> +Generates two dimensional data for demos. + +<p><h2> +Synopsis +</h2> +<PRE> +data = dem2ddat(ndata)</PRE> + +<PRE> +[data, c] = dem2ddat(ndata)</PRE> + + +<p><h2> +Description +</h2> +The data is +drawn from three spherical Gaussian distributions with priors 0.3, +0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard deviations +0.2, 0.5 and 1.0. <CODE>data = dem2ddat(ndata)</CODE> generates <CODE>ndata</CODE> +points. + +<p><CODE>[data, c] = dem2ddat(ndata)</CODE> also returns a matrix containing the +centres of the Gaussian distributions. + +<p><h2> +See Also +</h2> +<CODE><a href="demgmm1.htm">demgmm1</a></CODE>, <CODE><a href="demkmean.htm">demkmean</a></CODE>, <CODE><a href="demknn1.htm">demknn1</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demard.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +<html> +<head> +<title> +Netlab Reference Manual demard +</title> +</head> +<body> +<H1> demard +</H1> +<h2> +Purpose +</h2> +Automatic relevance determination using the MLP. + +<p><h2> +Synopsis +</h2> +<PRE> +demmlp1</PRE> + + +<p><h2> +Description +</h2> +This script demonstrates the technique of automatic relevance +determination (ARD) using a synthetic problem having three input +variables: <CODE>x1</CODE> is sampled uniformly from the range (0,1) and has +a low level of added Gaussian noise, <CODE>x2</CODE> is a copy of <CODE>x1</CODE> +with a higher level of added noise, and <CODE>x3</CODE> is sampled randomly +from a Gaussian distribution. The single target variable is determined +by <CODE>sin(2*pi*x1)</CODE> with additive Gaussian noise. Thus <CODE>x1</CODE> is +very relevant for determining the target value, <CODE>x2</CODE> is of some +relevance, while <CODE>x3</CODE> is irrelevant. The prior over weights is +given by the ARD Gaussian prior with a separate hyper-parameter for +the group of weights associated with each input. A multi-layer +perceptron is trained on this data, with re-estimation of the +hyper-parameters using <CODE>evidence</CODE>. The final values for the +hyper-parameters reflect the relative importance of the three inputs. + +<p><h2> +See Also +</h2> +<CODE><a href="demmlp1.htm">demmlp1</a></CODE>, <CODE><a href="demev1.htm">demev1</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demev1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +<html> +<head> +<title> +Netlab Reference Manual demev1 +</title> +</head> +<body> +<H1> demev1 +</H1> +<h2> +Purpose +</h2> +Demonstrate Bayesian regression for the MLP. + +<p><h2> +Synopsis +</h2> +<PRE> +demev1</PRE> + + +<p><h2> +Description +</h2> +The problem consists an input variable <CODE>x</CODE> which sampled from a +Gaussian distribution, and a target variable <CODE>t</CODE> generated by +computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian noise. A 2-layer +network with linear outputs is trained by minimizing a sum-of-squares +error function with isotropic Gaussian regularizer, using the scaled +conjugate gradient optimizer. The hyperparameters <CODE>alpha</CODE> and +<CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph +is plotted of the original function, the training data, the trained +network function, and the error bars. + +<p><h2> +See Also +</h2> +<CODE><a href="evidence.htm">evidence</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="demard.htm">demard</a></CODE>, <CODE><a href="demmlp1.htm">demmlp1</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demev2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual demev2 +</title> +</head> +<body> +<H1> demev2 +</H1> +<h2> +Purpose +</h2> +Demonstrate Bayesian classification for the MLP. + +<p><h2> +Synopsis +</h2> +<PRE> +demev2</PRE> + + +<p><h2> +Description +</h2> +A synthetic two class two-dimensional dataset <CODE>x</CODE> is sampled +from a mixture of four Gaussians. Each class is +associated with two of the Gaussians so that the optimal decision +boundary is non-linear. +A 2-layer +network with logistic outputs is trained by minimizing the cross-entropy +error function with isotroipc Gaussian regularizer (one hyperparameter for +each of the four standard weight groups), using the scaled +conjugate gradient optimizer. The hyperparameter vectors <CODE>alpha</CODE> and +<CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph +is plotted of the optimal, regularised, and unregularised decision +boundaries. A further plot of the moderated versus unmoderated contours +is generated. + +<p><h2> +See Also +</h2> +<CODE><a href="evidence.htm">evidence</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="demard.htm">demard</a></CODE>, <CODE><a href="demmlp2.htm">demmlp2</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demev3.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +<html> +<head> +<title> +Netlab Reference Manual demev3 +</title> +</head> +<body> +<H1> demev3 +</H1> +<h2> +Purpose +</h2> +Demonstrate Bayesian regression for the RBF. + +<p><h2> +Synopsis +</h2> +<PRE> +demev3</PRE> + + +<p><h2> +Description +</h2> +The problem consists an input variable <CODE>x</CODE> which sampled from a +Gaussian distribution, and a target variable <CODE>t</CODE> generated by +computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian noise. An RBF +network with linear outputs is trained by minimizing a sum-of-squares +error function with isotropic Gaussian regularizer, using the scaled +conjugate gradient optimizer. The hyperparameters <CODE>alpha</CODE> and +<CODE>beta</CODE> are re-estimated using the function <CODE>evidence</CODE>. A graph +is plotted of the original function, the training data, the trained +network function, and the error bars. + +<p><h2> +See Also +</h2> +<CODE><a href="demev1.htm">demev1</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="netevfwd.htm">netevfwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgauss.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual demgauss +</title> +</head> +<body> +<H1> demgauss +</H1> +<h2> +Purpose +</h2> +Demonstrate sampling from Gaussian distributions. + +<p><h2> +Synopsis +</h2> +<PRE> +demgauss +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>demgauss</CODE> provides a simple illustration of the generation of +data from Gaussian distributions. It first samples from a +one-dimensional distribution using <CODE>randn</CODE>, and then plots a +normalized histogram estimate of the distribution using <CODE>histp</CODE> +together with the true density calculated using <CODE>gauss</CODE>. + +<p><CODE>demgauss</CODE> then demonstrates sampling from a Gaussian distribution +in two dimensions. It creates a mean vector and a covariance matrix, +and then plots contours of constant density using the function +<CODE>gauss</CODE>. A sample of points drawn from this distribution, obtained +using the function <CODE>gsamp</CODE>, is then superimposed on the contours. + +<p><h2> +See Also +</h2> +<CODE><a href="gauss.htm">gauss</a></CODE>, <CODE><a href="gsamp.htm">gsamp</a></CODE>, <CODE><a href="histp.htm">histp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demglm1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual demglm1 +</title> +</head> +<body> +<H1> demglm1 +</H1> +<h2> +Purpose +</h2> +Demonstrate simple classification using a generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +demglm1</PRE> + + +<p><h2> +Description +</h2> + +The problem consists of a two dimensional input +matrix <CODE>data</CODE> and a vector of classifications <CODE>t</CODE>. The data is +generated from two Gaussian clusters, and a generalized linear model +with logistic output is trained using iterative reweighted least squares. +A plot of the data together with the 0.1, 0.5 and 0.9 contour lines +of the conditional probability is generated. + +<p><h2> +See Also +</h2> +<CODE><a href="demglm2.htm">demglm2</a></CODE>, <CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demglm2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual demglm2 +</title> +</head> +<body> +<H1> demglm2 +</H1> +<h2> +Purpose +</h2> +Demonstrate simple classification using a generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +demglm1</PRE> + + +<p><h2> +Description +</h2> + +The problem consists of a two dimensional input +matrix <CODE>data</CODE> and a vector of classifications <CODE>t</CODE>. The data is +generated from three Gaussian clusters, and a generalized linear model +with softmax output is trained using iterative reweighted least squares. +A plot of the data together with regions shaded by the classification +given by the network is generated. + +<p><h2> +See Also +</h2> +<CODE><a href="demglm1.htm">demglm1</a></CODE>, <CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgmm1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +<html> +<head> +<title> +Netlab Reference Manual demgmm1 +</title> +</head> +<body> +<H1> demgmm1 +</H1> +<h2> +Purpose +</h2> +Demonstrate EM for Gaussian mixtures. + +<p><h2> +Synopsis +</h2> +<PRE> +demgmm1</PRE> + + +<p><h2> +Description +</h2> +This script demonstrates the use of the EM algorithm to fit a mixture +of Gaussians to a set of data using maximum likelihood. A colour +coding scheme is used to illustrate the evaluation of the posterior +probabilities in the E-step of the EM algorithm. + +<p><h2> +See Also +</h2> +<CODE><a href="demgmm2.htm">demgmm2</a></CODE>, <CODE><a href="demgmm3.htm">demgmm3</a></CODE>, <CODE><a href="demgmm4.htm">demgmm4</a></CODE>, <CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="gmmpost.htm">gmmpost</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgmm2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +<html> +<head> +<title> +Netlab Reference Manual demgmm1 +</title> +</head> +<body> +<H1> demgmm1 +</H1> +<h2> +Purpose +</h2> +Demonstrate density modelling with a Gaussian mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> +demgmm1</PRE> + + +<p><h2> +Description +</h2> +The problem consists of modelling data generated by a mixture of three +Gaussians in 2 dimensions. The priors are 0.3, +0.5 and 0.2; the centres are (2, 3.5), (0, 0) and (0,2); the variances +are 0.2, 0.5 and 1.0. The first figure contains a + scatter plot of the data. + +<p>A Gaussian mixture model with three components is trained using EM. The +parameter vector is printed before training and after training. The user +should press any key to continue at these points. The parameter vector +consists of priors (the column), centres (given as (x, y) pairs as +the next two columns), and variances (the last column). + +<p>The second figure is a 3 dimensional view of the density function, while +the third shows the 1-standard deviation circles for the three components of +the mixture model. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE>, <CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgmm3.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +<html> +<head> +<title> +Netlab Reference Manual demgmm3 +</title> +</head> +<body> +<H1> demgmm3 +</H1> +<h2> +Purpose +</h2> +Demonstrate density modelling with a Gaussian mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> +demgmm3</PRE> + + +<p><h2> +Description +</h2> + +The problem consists of modelling data generated +by a mixture of three Gaussians in 2 dimensions with a mixture model +using diagonal covariance matrices. The priors are 0.3, 0.5 and 0.2; the +centres are (2, 3.5), (0, 0) and (0,2); the covariances are all axis aligned +(0.16, 0.64), (0.25, 1) and the identity +matrix. The first figure contains a scatter plot of the data. + +<p>A Gaussian mixture model with three components is trained using EM. The +parameter vector is printed before training and after training. The user +should press any key to continue at these points. The parameter vector +consists of priors (the column), and centres (given as (x, y) pairs as +the next two columns). The diagonal entries of the +covariance matrices are printed separately. + +<p>The second figure is a 3 dimensional view of the density function, +while the third shows the axes of the 1-standard deviation circles +for the three components of the mixture model. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE>, <CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgmm4.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +<html> +<head> +<title> +Netlab Reference Manual demgmm4 +</title> +</head> +<body> +<H1> demgmm4 +</H1> +<h2> +Purpose +</h2> +Demonstrate density modelling with a Gaussian mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> +demgmm4</PRE> + + +<p><h2> +Description +</h2> + +The problem consists of modelling data generated +by a mixture of three Gaussians in 2 dimensions with a mixture model +using full covariance matrices. The priors are 0.3, 0.5 and 0.2; the +centres are (2, 3.5), (0, 0) and (0,2); the variances are (0.16, 0.64) +axis aligned, (0.25, 1) rotated by 30 degrees and the identity +matrix. The first figure contains a scatter plot of the data. + +<p>A Gaussian mixture model with three components is trained using EM. The +parameter vector is printed before training and after training. The user +should press any key to continue at these points. The parameter vector +consists of priors (the column), and centres (given as (x, y) pairs as +the next two columns). The covariance matrices are printed separately. + +<p>The second figure is a 3 dimensional view of the density function, +while the third shows the axes of the 1-standard deviation ellipses +for the three components of the mixture model. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE>, <CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgmm5.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +<html> +<head> +<title> +Netlab Reference Manual demgmm5 +</title> +</head> +<body> +<H1> demgmm5 +</H1> +<h2> +Purpose +</h2> +Demonstrate density modelling with a PPCA mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> +demgmm5</PRE> + + +<p><h2> +Description +</h2> + +The problem consists of modelling data generated +by a mixture of three Gaussians in 2 dimensions with a mixture model +using full covariance matrices. The priors are 0.3, 0.5 and 0.2; the +centres are (2, 3.5), (0, 0) and (0,2); the variances are (0.16, 0.64) +axis aligned, (0.25, 1) rotated by 30 degrees and the identity +matrix. The first figure contains a scatter plot of the data. + +<p>A mixture model with three one-dimensional PPCA components is trained +using EM. The parameter vector is printed before training and after +training. The parameter vector consists of priors (the column), and +centres (given as (x, y) pairs as the next two columns). + +<p>The second figure is a 3 dimensional view of the density function, +while the third shows the axes of the 1-standard deviation ellipses +for the three components of the mixture model together with the one +standard deviation along the principal component of each mixture +model component. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE>, <CODE><a href="ppca.htm">ppca</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual demgp +</title> +</head> +<body> +<H1> demgp +</H1> +<h2> +Purpose +</h2> +Demonstrate simple regression using a Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +demgp</PRE> + + +<p><h2> +Description +</h2> +The problem consists of one input variable <CODE>x</CODE> and one target variable +<CODE>t</CODE>. The values in <CODE>x</CODE> are chosen in two separated clusters and the +target data is generated by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian +noise. Two Gaussian Processes, each with different covariance functions +are trained by optimising the hyperparameters +using the scaled conjugate gradient algorithm. The final predictions are +plotted together with 2 standard deviation error bars. + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE>, <CODE><a href="gpinit.htm">gpinit</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgpard.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual demgpard +</title> +</head> +<body> +<H1> demgpard +</H1> +<h2> +Purpose +</h2> +Demonstrate ARD using a Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +demgpare</PRE> + + +<p><h2> +Description +</h2> +The data consists of three input variables <CODE>x1</CODE>, <CODE>x2</CODE> and +<CODE>x3</CODE>, and one target variable +<CODE>t</CODE>. The +target data is generated by computing <CODE>sin(2*pi*x1)</CODE> and adding Gaussian +noise, x2 is a copy of x1 with a higher level of added +noise, and x3 is sampled randomly from a Gaussian distribution. +A Gaussian Process, is +trained by optimising the hyperparameters +using the scaled conjugate gradient algorithm. The final values of the +hyperparameters show that the model successfully identifies the importance +of each input. + +<p><h2> +See Also +</h2> +<CODE><a href="demgp.htm">demgp</a></CODE>, <CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE>, <CODE><a href="gpinit.htm">gpinit</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgpot.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual demgpot +</title> +</head> +<body> +<H1> demgpot +</H1> +<h2> +Purpose +</h2> +Computes the gradient of the negative log likelihood for a mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> +g = demgpot(x, mix)</PRE> + + +<p><h2> +Description +</h2> +This function computes the gradient of the negative log of the unconditional data +density <CODE>p(x)</CODE> with respect to the coefficients of the +data vector <CODE>x</CODE> for a Gaussian mixture model. The data structure +<CODE>mix</CODE> defines the mixture model, while the matrix <CODE>x</CODE> contains +the data vector as a row vector. Note the unusual order of the arguments: +this is so that the function can be used in <CODE>demhmc1</CODE> directly for +sampling from the distribution <CODE>p(x)</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="demhmc1.htm">demhmc1</a></CODE>, <CODE><a href="demmet1.htm">demmet1</a></CODE>, <CODE><a href="dempot.htm">dempot</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgtm1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual demgtm1 +</title> +</head> +<body> +<H1> demgtm1 +</H1> +<h2> +Purpose +</h2> +Demonstrate EM for GTM. + +<p><h2> +Synopsis +</h2> +<PRE> +demgtm1</PRE> + + +<p><h2> +Description +</h2> + +This script demonstrates the use of the EM +algorithm to fit a one-dimensional GTM to a two-dimensional set of data +using maximum likelihood. The location and spread of the Gaussian kernels +in the data space is shown during training. + +<p><h2> +See Also +</h2> +<CODE><a href="demgtm2.htm">demgtm2</a></CODE>, <CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmem.htm">gtmem</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demgtm2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual demgtm2 +</title> +</head> +<body> +<H1> demgtm2 +</H1> +<h2> +Purpose +</h2> +Demonstrate GTM for visualisation. + +<p><h2> +Synopsis +</h2> +<PRE> +demgtm2</PRE> + + +<p><h2> +Description +</h2> + +This script demonstrates the use of a +GTM with a two-dimensional latent space to visualise data in a higher +dimensional space. +This is done through the use of the mean responsibility and magnification +factors. + +<p><h2> +See Also +</h2> +<CODE><a href="demgtm1.htm">demgtm1</a></CODE>, <CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmem.htm">gtmem</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demhint.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +<html> +<head> +<title> +Netlab Reference Manual demhint +</title> +</head> +<body> +<H1> demhint +</H1> +<h2> +Purpose +</h2> +Demonstration of Hinton diagram for 2-layer feed-forward network. + +<p><h2> +Synopsis +</h2> +<PRE> +demhint +demhint(nin, nhidden, nout)</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>demhint</CODE> plots a Hinton diagram for a 2-layer feedforward network +with 5 inputs, 4 hidden units and 3 outputs. The weight vector is +chosen from a Gaussian distribution as described under <CODE>mlp</CODE>. + +<p><CODE>demhint(nin, nhidden, nout)</CODE> allows the user to specify the +number of inputs, hidden units and outputs. + +<p><h2> +See Also +</h2> +<CODE><a href="hinton.htm">hinton</a></CODE>, <CODE><a href="hintmat.htm">hintmat</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demhmc1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +<html> +<head> +<title> +Netlab Reference Manual demhmc1 +</title> +</head> +<body> +<H1> demhmc1 +</H1> +<h2> +Purpose +</h2> +Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. + +<p><h2> +Synopsis +</h2> +<PRE> +demhmc1</PRE> + + +<p><h2> +Description +</h2> +The problem consists of generating data from a mixture of two Gaussians +in two dimensions using a hybrid Monte Carlo algorithm with persistence. +A mixture model is then fitted to the sample to compare it with the +true underlying generator. + +<p><h2> +See Also +</h2> +<CODE><a href="demhmc3.htm">demhmc3</a></CODE>, <CODE><a href="hmc.htm">hmc</a></CODE>, <CODE><a href="dempot.htm">dempot</a></CODE>, <CODE><a href="demgpot.htm">demgpot</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demhmc2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +<html> +<head> +<title> +Netlab Reference Manual demhmc2 +</title> +</head> +<body> +<H1> demhmc2 +</H1> +<h2> +Purpose +</h2> +Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. + +<p><h2> +Synopsis +</h2> +<PRE> +demhmc2</PRE> + + +<p><h2> +Description +</h2> +The problem consists of one input variable <CODE>x</CODE> and one target variable +<CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then +generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian +noise. The model is a 2-layer network with linear outputs, and the hybrid Monte +Carlo algorithm (without persistence) is used to sample from the posterior +distribution of the weights. The graph shows the underlying function, +100 samples from the function given by the posterior distribution of the +weights, and the average prediction (weighted by the posterior probabilities). + +<p><h2> +See Also +</h2> +<CODE><a href="demhmc3.htm">demhmc3</a></CODE>, <CODE><a href="hmc.htm">hmc</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demhmc3.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +<html> +<head> +<title> +Netlab Reference Manual demhmc3 +</title> +</head> +<body> +<H1> demhmc3 +</H1> +<h2> +Purpose +</h2> +Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. + +<p><h2> +Synopsis +</h2> +<PRE> +demhmc3</PRE> + + +<p><h2> +Description +</h2> +The problem consists of one input variable <CODE>x</CODE> and one target variable +<CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then +generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian +noise. The model is a 2-layer network with linear outputs, and the hybrid Monte +Carlo algorithm (with persistence) is used to sample from the posterior +distribution of the weights. The graph shows the underlying function, +300 samples from the function given by the posterior distribution of the +weights, and the average prediction (weighted by the posterior probabilities). + +<p><h2> +See Also +</h2> +<CODE><a href="demhmc2.htm">demhmc2</a></CODE>, <CODE><a href="hmc.htm">hmc</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demkmn1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<html> +<head> +<title> +Netlab Reference Manual demkmean +</title> +</head> +<body> +<H1> demkmean +</H1> +<h2> +Purpose +</h2> +Demonstrate simple clustering model trained with K-means. + +<p><h2> +Synopsis +</h2> +<PRE> +demkmean</PRE> + + +<p><h2> +Description +</h2> +The problem consists of data in a two-dimensional space. +The data is +drawn from three spherical Gaussian distributions with priors 0.3, +0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard deviations +0.2, 0.5 and 1.0. The first figure contains a +scatter plot of the data. The data is the same as in <CODE>demgmm1</CODE>. + +<p>A cluster model with three components is trained using the batch +K-means algorithm. The matrix of centres is printed after training. +The second +figure shows the data labelled with a colour derived from the corresponding +cluster + +<p><h2> +See Also +</h2> +<CODE><a href="dem2ddat.htm">dem2ddat</a></CODE>, <CODE><a href="demgmm1.htm">demgmm1</a></CODE>, <CODE><a href="knn1.htm">knn1</a></CODE>, <CODE><a href="kmeans.htm">kmeans</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demknn1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual demknn1 +</title> +</head> +<body> +<H1> demknn1 +</H1> +<h2> +Purpose +</h2> +Demonstrate nearest neighbour classifier. + +<p><h2> +Synopsis +</h2> +<PRE> +demknn1</PRE> + + +<p><h2> +Description +</h2> +The problem consists of data in a two-dimensional space. +The data is +drawn from three spherical Gaussian distributions with priors 0.3, +0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard deviations +0.2, 0.5 and 1.0. The first figure contains a +scatter plot of the data. The data is the same as in <CODE>demgmm1</CODE>. + +<p>The second +figure shows the data labelled with the corresponding class given +by the classifier. + +<p><h2> +See Also +</h2> +<CODE><a href="dem2ddat.htm">dem2ddat</a></CODE>, <CODE><a href="demgmm1.htm">demgmm1</a></CODE>, <CODE><a href="knn.htm">knn</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demmdn1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual demmdn1 +</title> +</head> +<body> +<H1> demmdn1 +</H1> +<h2> +Purpose +</h2> +Demonstrate fitting a multi-valued function using a Mixture Density Network. + +<p><h2> +Synopsis +</h2> +<PRE> +demmdn1</PRE> + + +<p><h2> +Description +</h2> +The problem consists of one input variable +<CODE>x</CODE> and one target variable <CODE>t</CODE> with data generated by +sampling <CODE>t</CODE> at equal intervals and then generating target data by +computing <CODE>t + 0.3*sin(2*pi*t)</CODE> and adding Gaussian noise. A +Mixture Density Network with 3 centres in the mixture model is trained +by minimizing a negative log likelihood error function using the scaled +conjugate gradient optimizer. + +<p>The conditional means, mixing coefficients and variances are plotted +as a function of <CODE>x</CODE>, and a contour plot of the full conditional +density is also generated. + +<p><h2> +See Also +</h2> +<CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demmet1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual demmet1 +</title> +</head> +<body> +<H1> demmet1 +</H1> +<h2> +Purpose +</h2> +Demonstrate Markov Chain Monte Carlo sampling on a Gaussian. + +<p><h2> +Synopsis +</h2> +<PRE> +demmet1 +demmet1(plotwait)</PRE> + + +<p><h2> +Description +</h2> +The problem consists of generating data from a Gaussian +in two dimensions using a Markov Chain Monte Carlo algorithm. The points are +plotted one after another to show the path taken by the chain. + +<p><CODE>demmet1(plotwait)</CODE> allows the user to set the time (in a whole number +of seconds) between the plotting of points. This is passed to <CODE>pause</CODE> + +<p><h2> +See Also +</h2> +<CODE><a href="demhmc1.htm">demhmc1</a></CODE>, <CODE><a href="metrop.htm">metrop</a></CODE>, <CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="dempot.htm">dempot</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demmlp1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual demmlp1 +</title> +</head> +<body> +<H1> demmlp1 +</H1> +<h2> +Purpose +</h2> +Demonstrate simple regression using a multi-layer perceptron + +<p><h2> +Synopsis +</h2> +<PRE> +demmlp1</PRE> + + +<p><h2> +Description +</h2> +The problem consists of one input variable <CODE>x</CODE> and one target variable +<CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then +generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian +noise. A 2-layer network with linear outputs is trained by minimizing a +sum-of-squares error function using the scaled conjugate gradient optimizer. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demmlp2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +<html> +<head> +<title> +Netlab Reference Manual demmlp2 +</title> +</head> +<body> +<H1> demmlp2 +</H1> +<h2> +Purpose +</h2> +Demonstrate simple classification using a multi-layer perceptron + +<p><h2> +Synopsis +</h2> +<PRE> +demmlp2</PRE> + + +<p><h2> +Description +</h2> +The problem consists of input data in two dimensions drawn from a mixture +of three Gaussians: two of which are assigned to a single class. An MLP +with logistic outputs trained with a quasi-Newton optimisation algorithm is +compared with the optimal Bayesian decision rule. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="neterr.htm">neterr</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demnlab.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +<html> +<head> +<title> +Netlab Reference Manual demnlab +</title> +</head> +<body> +<H1> demnlab +</H1> +<h2> +Purpose +</h2> +A front-end Graphical User Interface to the demos + +<p><h2> +Synopsis +</h2> +<PRE> +demnlab</PRE> + + +<p><h2> +Description +</h2> +This function will start a user interface allowing the user to select +different demonstration functions to view. The demos are divided into 4 +groups, with the demo being executed by selecting the desired option +from a pop-up menu. + +<p><h2> +See Also +</h2> +<CODE><a href=".htm"></a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demns1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual demns1 +</title> +</head> +<body> +<H1> demns1 +</H1> +<h2> +Purpose +</h2> +Demonstrate Neuroscale for visualisation. + +<p><h2> +Synopsis +</h2> +<PRE> +demns1</PRE> + + +<p><h2> +Description +</h2> +This script demonstrates the use of the Neuroscale algorithm for +topographic projection and visualisation. A data sample is generated +from a mixture of two Gaussians in 4d space, and an RBF is trained +with the stress error function to project the data into 2d. The training +data and a test sample are both plotted in this projection. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfprior.htm">rbfprior</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demolgd1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual demolgd1 +</title> +</head> +<body> +<H1> demolgd1 +</H1> +<h2> +Purpose +</h2> +Demonstrate simple MLP optimisation with on-line gradient descent + +<p><h2> +Synopsis +</h2> +<PRE> +demolgd1</PRE> + + +<p><h2> +Description +</h2> +The problem consists of one input variable <CODE>x</CODE> and one target variable +<CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then +generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian +noise. A 2-layer network with linear outputs is trained by minimizing a +sum-of-squares error function using on-line gradient descent. + +<p><h2> +See Also +</h2> +<CODE><a href="demmlp1.htm">demmlp1</a></CODE>, <CODE><a href="olgd.htm">olgd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demopt1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +<html> +<head> +<title> +Netlab Reference Manual demopt1 +</title> +</head> +<body> +<H1> demopt1 +</H1> +<h2> +Purpose +</h2> +Demonstrate different optimisers on Rosenbrock's function. + +<p><h2> +Synopsis +</h2> +<PRE> +demopt1 +demopt1(xinit) +</PRE> + + +<p><h2> +Description +</h2> +The four general optimisers (quasi-Newton, conjugate gradients, +scaled conjugate gradients, and gradient descent) are applied to +the minimisation of Rosenbrock's well known `banana' function. +Each optimiser is run for at most 100 cycles, and a stopping +criterion of 1.0e-4 is used for both position and function value. +At the end, the trajectory of each algorithm is shown on a contour +plot of the function. + +<p><CODE>demopt1(xinit)</CODE> allows the user to specify a row vector with +two columns as the starting point. The default is the point [-1 1]. +Note that the contour plot has an x range of [-1.5, 1.5] and a y +range of [-0.5, 2.1], so it is best to choose a starting point in the +same region. + +<p><h2> +See Also +</h2> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="graddesc.htm">graddesc</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="rosen.htm">rosen</a></CODE>, <CODE><a href="rosegrad.htm">rosegrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/dempot.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +<html> +<head> +<title> +Netlab Reference Manual dempot +</title> +</head> +<body> +<H1> dempot +</H1> +<h2> +Purpose +</h2> +Computes the negative log likelihood for a mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> +e = dempot(x, mix)</PRE> + + +<p><h2> +Description +</h2> +This function computes the negative log of the unconditional data +density <CODE>p(x)</CODE> for a Gaussian mixture model. The data structure +<CODE>mix</CODE> defines the mixture model, while the matrix <CODE>x</CODE> contains +the data vectors. + +<p><h2> +See Also +</h2> +<CODE><a href="demgpot.htm">demgpot</a></CODE>, <CODE><a href="demhmc1.htm">demhmc1</a></CODE>, <CODE><a href="demmet1.htm">demmet1</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demprgp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual demprgp +</title> +</head> +<body> +<H1> demprgp +</H1> +<h2> +Purpose +</h2> +Demonstrate sampling from a Gaussian Process prior. + +<p><h2> +Synopsis +</h2> +<PRE> +demprgp</PRE> + + +<p><h2> +Description +</h2> +This function plots the functions represented by a Gaussian Process +model. The hyperparameter values can be adjusted +on a linear scale using the sliders (though the exponential +of the parameters is used in the covariance function), or +by typing values into the text boxes and pressing the return key. +Both types of covariance function are supported. An extra function +specific parameter is needed for the rational quadratic function. + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demprior.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual demprior +</title> +</head> +<body> +<H1> demprior +</H1> +<h2> +Purpose +</h2> +Demonstrate sampling from a multi-parameter Gaussian prior. + +<p><h2> +Synopsis +</h2> +<PRE> +demprior</PRE> + + +<p><h2> +Description +</h2> +This function plots the functions represented by a multi-layer perceptron +network when the weights are set to values drawn from a Gaussian prior +distribution. The parameters <CODE>aw1</CODE>, <CODE>ab1</CODE> <CODE>aw2</CODE> and <CODE>ab2</CODE> +control the inverse variances of the first-layer weights, the hidden unit +biases, the second-layer weights and the output unit biases respectively. +Their values can be adjusted on a logarithmic scale using the sliders, or +by typing values into the text boxes and pressing the return key. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demrbf1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual demrbf1 +</title> +</head> +<body> +<H1> demrbf1 +</H1> +<h2> +Purpose +</h2> +Demonstrate simple regression using a radial basis function network. + +<p><h2> +Synopsis +</h2> +<PRE> +demrbf1</PRE> + + +<p><h2> +Description +</h2> +The problem consists of one input variable <CODE>x</CODE> and one target variable +<CODE>t</CODE> with data generated by sampling <CODE>x</CODE> at equal intervals and then +generating target data by computing <CODE>sin(2*pi*x)</CODE> and adding Gaussian +noise. This data is the same as that used in demmlp1. + +<p>Three different RBF networks (with different activation functions) +are trained in two stages. First, a Gaussian mixture model is trained using +the EM algorithm, and the centres of this model are used to set the centres +of the RBF. Second, the output weights (and biases) are determined using the +pseudo-inverse of the design matrix. + +<p><h2> +See Also +</h2> +<CODE><a href="demmlp1.htm">demmlp1</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demsom1.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual demsom1 +</title> +</head> +<body> +<H1> demsom1 +</H1> +<h2> +Purpose +</h2> +Demonstrate SOM for visualisation. + +<p><h2> +Synopsis +</h2> +<PRE> +demsom1</PRE> + + +<p><h2> +Description +</h2> + +This script demonstrates the use of a +SOM with a two-dimensional grid to map onto data in +two-dimensional space. Both on-line and batch training algorithms +are shown. + +<p><h2> +See Also +</h2> +<CODE><a href="som.htm">som</a></CODE>, <CODE><a href="sompak.htm">sompak</a></CODE>, <CODE><a href="somtrain.htm">somtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/demtrain.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<html> +<head> +<title> +Netlab Reference Manual demtrain +</title> +</head> +<body> +<H1> demtrain +</H1> +<h2> +Purpose +</h2> +Demonstrate training of MLP network. + +<p><h2> +Synopsis +</h2> +<PRE> +demtrain</PRE> + + +<p><h2> +Description +</h2> +<CODE>demtrain</CODE> brings up a simple GUI to show the training of +an MLP network on classification and regression problems. The user +should load in a dataset (which should be in Netlab format: see +<CODE>datread</CODE>), select the output activation function, the + number of cycles and hidden units and then +train the network. The scaled conjugate gradient algorithm is used. +A graph shows the evolution of the error: the value is shown +<CODE>max(ceil(iterations / 50), 5)</CODE> cycles. + +<p>Once the network is trained, it is saved to the file <CODE>mlptrain.net</CODE>. +The results can then be viewed as a confusion matrix (for classification +problems) or a plot of output versus target (for regression problems). + +<p><h2> +See Also +</h2> +<CODE><a href="confmat.htm">confmat</a></CODE>, <CODE><a href="datread.htm">datread</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="netopt.htm">netopt</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/dist2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +<html> +<head> +<title> +Netlab Reference Manual dist2 +</title> +</head> +<body> +<H1> dist2 +</H1> +<h2> +Purpose +</h2> +Calculates squared distance between two sets of points. + +<p><h2> +Synopsis +</h2> +<PRE> +d = dist2(x, c) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>d = dist2(x, c)</CODE> takes two matrices of vectors and calculates the +squared Euclidean distance between them. Both matrices must be of the +same column dimension. If <CODE>x</CODE> has <CODE>m</CODE> rows and <CODE>n</CODE> columns, and +<CODE>c</CODE> has <CODE>l</CODE> rows and <CODE>n</CODE> columns, then the result has +<CODE>m</CODE> rows and <CODE>l</CODE> columns. The <CODE>i, j</CODE>th entry is the +squared distance from the <CODE>i</CODE>th row of <CODE>x</CODE> to the <CODE>j</CODE>th +row of <CODE>c</CODE>. + +<p><h2> +Example +</h2> +The following code is used in <CODE>rbffwd</CODE> to calculate the activation of +a thin plate spline function. +<PRE> + +n2 = dist2(x, c); +z = log(n2.^(n2.^2)); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gmmactiv.htm">gmmactiv</a></CODE>, <CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/eigdec.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual eigdec +</title> +</head> +<body> +<H1> eigdec +</H1> +<h2> +Purpose +</h2> +Sorted eigendecomposition + +<p><h2> +Synopsis +</h2> +<PRE> +evals = eigdec(x, N) +[evals, evec] = eigdec(x, N) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>evals = eigdec(x, N</CODE> computes the largest <CODE>N</CODE> eigenvalues of the +matrix <CODE>x</CODE> in descending order. <CODE>[evals, evec] = eigdec(x, N)</CODE> +also computes the corresponding eigenvectors. + +<p><h2> +See Also +</h2> +<CODE><a href="pca.htm">pca</a></CODE>, <CODE><a href="ppca.htm">ppca</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/errbayes.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual errbayes +</title> +</head> +<body> +<H1> errbayes +</H1> +<h2> +Purpose +</h2> +Evaluate Bayesian error function for network. + +<p><h2> +Synopsis +</h2> +<PRE> +e = errbayes(net, edata) +[e, edata, eprior] = errbayes(net, edata) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>e = errbayes(net, edata)</CODE> takes a network data structure +<CODE>net</CODE> together +the data contribution to the error for a set of inputs and targets. +It returns the regularised error using any zero mean Gaussian priors +on the weights defined in +<CODE>net</CODE>. + +<p><CODE>[e, edata, eprior] = errbayes(net, x, t)</CODE> additionally returns the +data and prior components of the error. + +<p><h2> +See Also +</h2> +<CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/evidence.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +<html> +<head> +<title> +Netlab Reference Manual evidence +</title> +</head> +<body> +<H1> evidence +</H1> +<h2> +Purpose +</h2> +Re-estimate hyperparameters using evidence approximation. + +<p><h2> +Synopsis +</h2> +<PRE> +[net] = evidence(net, x, t) +[net, gamma, logev] = evidence(net, x, t, num) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>[net] = evidence(net, x, t)</CODE> re-estimates the +hyperparameters <CODE>alpha</CODE> and <CODE>beta</CODE> by applying Bayesian +re-estimation formulae for <CODE>num</CODE> iterations. The hyperparameter +<CODE>alpha</CODE> can be a simple scalar associated with an isotropic prior +on the weights, or can be a vector in which each component is +associated with a group of weights as defined by the <CODE>index</CODE> +matrix in the <CODE>net</CODE> data structure. These more complex priors can +be set up for an MLP using <CODE>mlpprior</CODE>. Initial values for the iterative +re-estimation are taken from the network data structure <CODE>net</CODE> +passed as an input argument, while the return argument <CODE>net</CODE> +contains the re-estimated values. + +<p><CODE>[net, gamma, logev] = evidence(net, x, t, num)</CODE> allows the re-estimation +formula to be applied for <CODE>num</CODE> cycles in which the re-estimated +values for the hyperparameters from each cycle are used to re-evaluate +the Hessian matrix for the next cycle. The return value <CODE>gamma</CODE> is +the number of well-determined parameters and <CODE>logev</CODE> is the log +of the evidence. + +<p><h2> +See Also +</h2> +<CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="netgrad.htm">netgrad</a></CODE>, <CODE><a href="nethess.htm">nethess</a></CODE>, <CODE><a href="demev1.htm">demev1</a></CODE>, <CODE><a href="demard.htm">demard</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/fevbayes.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +<html> +<head> +<title> +Netlab Reference Manual fevbayes +</title> +</head> +<body> +<H1> fevbayes +</H1> +<h2> +Purpose +</h2> +Evaluate Bayesian regularisation for network forward propagation. + +<p><h2> +Synopsis +</h2> +<PRE> +extra = fevbayes(net, y, a, x, t, x_test) +[extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>extra = fevbayes(net, y, a, x, t, x_test)</CODE> takes a network data structure +<CODE>net</CODE> together with a set of hidden unit activations <CODE>a</CODE> from +test inputs <CODE>x_test</CODE>, training data inputs <CODE>x</CODE> and <CODE>t</CODE> and +outputs a matrix of extra information <CODE>extra</CODE> that consists of +error bars (variance) +for a regression problem or moderated outputs for a classification problem. +The optional argument (and return value) +<CODE>invhess</CODE> is the inverse of the network Hessian +computed on the training data inputs and targets. Passing it in avoids +recomputing it, which can be a significant saving for large training sets. + +<p>This is called by network-specific functions such as <CODE>mlpevfwd</CODE> which +are needed since the return values (predictions and hidden unit activations) +for different network types are in different orders (for good reasons). + +<p><h2> +See Also +</h2> +<CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE>, <CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE>, <CODE><a href="glmevfwd.htm">glmevfwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gauss.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual gauss +</title> +</head> +<body> +<H1> gauss +</H1> +<h2> +Purpose +</h2> +Evaluate a Gaussian distribution. + +<p><h2> +Synopsis +</h2> +<PRE> +y = gauss(mu, covar, x) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>y = gauss(mu, covar, x)</CODE> evaluates a multi-variate Gaussian +density in <CODE>d</CODE>-dimensions at a set of points given by the rows +of the matrix <CODE>x</CODE>. The Gaussian density has mean vector <CODE>mu</CODE> +and covariance matrix <CODE>covar</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gsamp.htm">gsamp</a></CODE>, <CODE><a href="demgauss.htm">demgauss</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gbayes.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual gbayes +</title> +</head> +<body> +<H1> gbayes +</H1> +<h2> +Purpose +</h2> +Evaluate gradient of Bayesian error function for network. + +<p><h2> +Synopsis +</h2> +<PRE> +g = gbayes(net, gdata) +[g, gdata, gprior] = gbayes(net, gdata) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = gbayes(net, gdata)</CODE> takes a network data structure <CODE>net</CODE> together +the data contribution to the error gradient +for a set of inputs and targets. +It returns the regularised error gradient using any zero mean Gaussian priors +on the weights defined in +<CODE>net</CODE>. In addition, if a <CODE>mask</CODE> is defined in <CODE>net</CODE>, then +the entries in <CODE>g</CODE> that correspond to weights with a 0 in the +mask are removed. + +<p><CODE>[g, gdata, gprior] = gbayes(net, gdata)</CODE> additionally returns the +data and prior components of the error. + +<p><h2> +See Also +</h2> +<CODE><a href="errbayes.htm">errbayes</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glm.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,85 @@ +<html> +<head> +<title> +Netlab Reference Manual glm +</title> +</head> +<body> +<H1> glm +</H1> +<h2> +Purpose +</h2> +Create a generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +net = glm(nin, nout, func) +net = glm(nin, nout, func, prior) +net = glm(nin, nout, func, prior, beta) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = glm(nin, nout, func)</CODE> takes the number of inputs +and outputs for a generalized linear model, together +with a string <CODE>func</CODE> which specifies the output unit activation function, +and returns a data structure <CODE>net</CODE>. The weights are drawn from a zero mean, +isotropic Gaussian, with variance scaled by the fan-in of the +output units. This makes use of the Matlab function +<CODE>randn</CODE> and so the seed for the random weight initialization can be +set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. The optional +argument <CODE>alpha</CODE> sets the inverse variance for the weight +initialization. + +<p>The fields in <CODE>net</CODE> are +<PRE> + type = 'glm' + nin = number of inputs + nout = number of outputs + nwts = total number of weights and biases + actfn = string describing the output unit activation function: + 'linear' + 'logistic' + 'softmax' + w1 = first-layer weight matrix + b1 = first-layer bias vector +</PRE> + + +<p><CODE>net = glm(nin, nout, func, prior)</CODE>, in which <CODE>prior</CODE> is +a scalar, allows the field +<CODE>net.alpha</CODE> in the data structure <CODE>net</CODE> to be set, corresponding +to a zero-mean isotropic Gaussian prior with inverse variance with +value <CODE>prior</CODE>. Alternatively, <CODE>prior</CODE> can consist of a data +structure with fields <CODE>alpha</CODE> and <CODE>index</CODE>, allowing individual +Gaussian priors to be set over groups of weights in the network. Here +<CODE>alpha</CODE> is a column vector in which each element corresponds to a +separate group of weights, which need not be mutually exclusive. The +membership of the groups is defined by the matrix <CODE>index</CODE> in which +the columns correspond to the elements of <CODE>alpha</CODE>. Each column has +one element for each weight in the matrix, in the order defined by the +function <CODE>glmpak</CODE>, and each element is 1 or 0 according to whether +the weight is a member of the corresponding group or not. + +<p><CODE>net = glm(nin, nout, func, prior, beta)</CODE> also sets the +additional field <CODE>net.beta</CODE> in the data structure <CODE>net</CODE>, where +beta corresponds to the inverse noise variance. + +<p><h2> +See Also +</h2> +<CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmderiv.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<html> +<head> +<title> +Netlab Reference Manual glmderiv +</title> +</head> +<body> +<H1> glmderiv +</H1> +<h2> +Purpose +</h2> +Evaluate derivatives of GLM outputs with respect to weights. + +<p><h2> +Synopsis +</h2> +<PRE> + +g = glmderiv(net, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = glmderiv(net, x)</CODE> takes a network data structure <CODE>net</CODE> and a matrix +of input vectors <CODE>x</CODE> and returns a three-index matrix mat{g} whose +<CODE>i</CODE>, <CODE>j</CODE>, <CODE>k</CODE> +element contains the derivative of network output <CODE>k</CODE> with respect to +weight or bias parameter <CODE>j</CODE> for input pattern <CODE>i</CODE>. The ordering of the +weight and bias parameters is defined by <CODE>glmunpak</CODE>. + +<p><h2> +See also +</h2> +<PRE> +glm, glmunpak, glmgrad</PRE> + + +<p><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmerr.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +<html> +<head> +<title> +Netlab Reference Manual glmerr +</title> +</head> +<body> +<H1> glmerr +</H1> +<h2> +Purpose +</h2> +Evaluate error function for generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +e = glmerr(net, x, t) +[e, edata, eprior] = glmerr(net, x, t) +[e, edata, eprior, y, a] = glmerr(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>e = glmerr(net, x, t)</CODE> takes a generalized +linear model data structure <CODE>net</CODE> together with a matrix <CODE>x</CODE> +of input vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates +the error function <CODE>e</CODE>. The choice of error function corresponds +to the output unit activation function. Each row of <CODE>x</CODE> +corresponds to one input vector and each row of <CODE>t</CODE> corresponds to +one target vector. + +<p><CODE>[e, edata, eprior, y, a] = glmerr(net, x, t)</CODE> also returns +the data and prior components of the total error. + +<p><CODE>[e, edata, eprior, y, a] = glmerr(net, x)</CODE> also returns a matrix <CODE>y</CODE> +giving the outputs of the models and a matrix <CODE>a</CODE> +giving the summed inputs to each output unit, where each row +corresponds to one pattern. + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmevfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +<html> +<head> +<title> +Netlab Reference Manual glmevfwd +</title> +</head> +<body> +<H1> glmevfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation with evidence for GLM + +<p><h2> +Synopsis +</h2> +<PRE> + +[y, extra] = glmevfwd(net, x, t, x_test) +[y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>y = glmevfwd(net, x, t, x_test)</CODE> takes a network data structure +<CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data +and input test data <CODE>x_test</CODE>. +It returns the normal forward propagation through the network <CODE>y</CODE> +together with a matrix <CODE>extra</CODE> which consists of error bars (variance) +for a regression problem or moderated outputs for a classification problem. + +<p>The optional argument (and return value) +<CODE>invhess</CODE> is the inverse of the network Hessian +computed on the training data inputs and targets. Passing it in avoids +recomputing it, which can be a significant saving for large training sets. + +<p><h2> +See Also +</h2> +<CODE><a href="fevbayes.htm">fevbayes</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<html> +<head> +<title> +Netlab Reference Manual glmfwd +</title> +</head> +<body> +<H1> glmfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +y = glmfwd(net, x) +[y, a] = glmfwd(net, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>y = glmfwd(net, x)</CODE> takes a generalized linear model +data structure <CODE>net</CODE> together with +a matrix <CODE>x</CODE> of input vectors, and forward propagates the inputs +through the network to generate a matrix <CODE>y</CODE> of output +vectors. Each row of <CODE>x</CODE> corresponds to one input vector and each +row of <CODE>y</CODE> corresponds to one output vector. + +<p><CODE>[y, a] = glmfwd(net, x)</CODE> also returns a matrix <CODE>a</CODE> +giving the summed inputs to each output unit, where each row +corresponds to one pattern. + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmgrad.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +<html> +<head> +<title> +Netlab Reference Manual glmgrad +</title> +</head> +<body> +<H1> glmgrad +</H1> +<h2> +Purpose +</h2> +Evaluate gradient of error function for generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> + +g = glmgrad(net, x, t) +[g, gdata, gprior] = glmgrad(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = glmgrad(net, x, t)</CODE> takes a generalized linear model +data structure <CODE>net</CODE> +together with a matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> +of target vectors, and evaluates the gradient <CODE>g</CODE> of the error +function with respect to the network weights. The error function +corresponds to the choice of output unit activation function. Each row +of <CODE>x</CODE> corresponds to one input vector and each row of <CODE>t</CODE> +corresponds to one target vector. + +<p><CODE>[g, gdata, gprior] = glmgrad(net, x, t)</CODE> also returns separately +the data and prior contributions to the gradient. + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmhess.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +<html> +<head> +<title> +Netlab Reference Manual glmhess +</title> +</head> +<body> +<H1> glmhess +</H1> +<h2> +Purpose +</h2> +Evaluate the Hessian matrix for a generalised linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +h = glmhess(net, x, t) +[h, hdata] = glmhess(net, x, t) +h = glmhess(net, x, t, hdata) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>h = glmhess(net, x, t)</CODE> takes a GLM network data structure <CODE>net</CODE>, +a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of target +values and returns the full Hessian matrix <CODE>h</CODE> corresponding to +the second derivatives of the negative log posterior distribution, +evaluated for the current weight and bias values as defined by +<CODE>net</CODE>. Note that the target data is not required in the calculation, +but is included to make the interface uniform with <CODE>nethess</CODE>. For +linear and logistic outputs, the computation is very simple and is +done (in effect) in one line in <CODE>glmtrain</CODE>. + +<p><CODE>[h, hdata] = glmhess(net, x, t)</CODE> returns both the Hessian matrix +<CODE>h</CODE> and the contribution <CODE>hdata</CODE> arising from the data dependent +term in the Hessian. + +<p><CODE>h = glmhess(net, x, t, hdata)</CODE> takes a network data structure +<CODE>net</CODE>, a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of +target values, together with the contribution <CODE>hdata</CODE> arising from +the data dependent term in the Hessian, and returns the full Hessian +matrix <CODE>h</CODE> corresponding to the second derivatives of the negative +log posterior distribution. This version saves computation time if +<CODE>hdata</CODE> has already been evaluated for the current weight and bias +values. + +<p><h2> +Example +</h2> +The Hessian matrix is used by <CODE>glmtrain</CODE> to take a Newton step for +softmax outputs. +<PRE> + +Hessian = glmhess(net, x, t); +deltaw = -gradient*pinv(Hessian); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE>, <CODE><a href="hesschek.htm">hesschek</a></CODE>, <CODE><a href="nethess.htm">nethess</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glminit.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<html> +<head> +<title> +Netlab Reference Manual glminit +</title> +</head> +<body> +<H1> glminit +</H1> +<h2> +Purpose +</h2> +Initialise the weights in a generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +net = glminit(net, prior) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = glminit(net, prior)</CODE> takes a generalized linear model +<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian +distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters +(weights and biases) are sampled from a single isotropic Gaussian with +inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data +structure similar to that in <CODE>mlpprior</CODE> but for a single layer of +weights, then the parameters +are sampled from multiple Gaussians according to their groupings +(defined by the <CODE>index</CODE> field) with corresponding variances +(defined by the <CODE>alpha</CODE> field). + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="mlpinit.htm">mlpinit</a></CODE>, <CODE><a href="mlpprior.htm">mlpprior</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +<html> +<head> +<title> +Netlab Reference Manual glmpak +</title> +</head> +<body> +<H1> glmpak +</H1> +<h2> +Purpose +</h2> +Combines weights and biases into one weights vector. + +<p><h2> +Synopsis +</h2> +<PRE> +w = glmpak(net) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>w = glmpak(net)</CODE> takes a network data structure <CODE>net</CODE> and +combines them into a single row vector <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmtrain.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +<html> +<head> +<title> +Netlab Reference Manual glmtrain +</title> +</head> +<body> +<H1> glmtrain +</H1> +<h2> +Purpose +</h2> +Specialised training of generalized linear model + +<p><h2> +Description +</h2> +<CODE>net = glmtrain(net, options, x, t)</CODE> uses +the iterative reweighted least squares (IRLS) +algorithm to set the weights in the generalized linear model structure +<CODE>net</CODE>. This is a more efficient alternative to using <CODE>glmerr</CODE> +and <CODE>glmgrad</CODE> and a non-linear optimisation routine through +<CODE>netopt</CODE>. +Note that for linear outputs, a single pass through the +algorithm is all that is required, since the error function is quadratic in +the weights. The algorithm also handles scalar <CODE>alpha</CODE> and <CODE>beta</CODE> +terms. If you want to use more complicated priors, you should use +general-purpose non-linear optimisation algorithms. + +<p>For logistic and softmax outputs, general priors can be handled, although +this requires the pseudo-inverse of the Hessian, giving up the better +conditioning and some of the speed advantage of the normal form equations. + +<p>The error function value at the final set of weights is returned +in <CODE>options(8)</CODE>. +Each row of <CODE>x</CODE> corresponds to one +input vector and each row of <CODE>t</CODE> corresponds to one target vector. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values during training. +If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(2)</CODE> is a measure of the precision required for the value +of the weights <CODE>w</CODE> at the solution. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(5)</CODE> is set to 1 if an approximation to the Hessian (which assumes +that all outputs are independent) is used for softmax outputs. With the default +value of 0 the exact Hessian (which is more expensive to compute) is used. + +<p><CODE>options(14)</CODE> is the maximum number of iterations for the IRLS algorithm; +default 100. + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glmunpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual glmunpak +</title> +</head> +<body> +<H1> glmunpak +</H1> +<h2> +Purpose +</h2> +Separates weights vector into weight and bias matrices. + +<p><h2> +Synopsis +</h2> +<PRE> +net = glmunpak(net, w) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = glmunpak(net, w)</CODE> takes a glm network data structure <CODE>net</CODE> and +a weight vector <CODE>w</CODE>, and returns a network data structure identical to +the input network, except that the first-layer weight matrix +<CODE>w1</CODE> and the first-layer bias vector <CODE>b1</CODE> have +been set to the corresponding elements of <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmm.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,96 @@ +<html> +<head> +<title> +Netlab Reference Manual gmm +</title> +</head> +<body> +<H1> gmm +</H1> +<h2> +Purpose +</h2> +Creates a Gaussian mixture model with specified architecture. + +<p><h2> +Synopsis +</h2> +<PRE> +mix = gmm(dim, ncentres, covartype) +mix = gmm(dim, ncentres, covartype, ppca_dim) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>mix = gmm(dim, ncentres, covartype)</CODE> takes +the dimension of the space <CODE>dim</CODE>, the number of centres in the +mixture model and the type of the mixture model, and returns a data +structure <CODE>mix</CODE>. +The mixture model type defines the covariance structure of each component +Gaussian: +<PRE> + + 'spherical' = single variance parameter for each component: stored as a vector + 'diag' = diagonal matrix for each component: stored as rows of a matrix + 'full' = full matrix for each component: stored as 3d array + 'ppca' = probabilistic PCA: stored as principal components (in a 3d array + and associated variances and off-subspace noise +</PRE> + +<CODE>mix = gmm(dim, ncentres, covartype, ppca_dim)</CODE> also sets the dimension of +the PPCA sub-spaces: the default value is one. + +<p>The priors are initialised to equal values summing to one, and the covariances +are all the identity matrix (or equivalent). The centres are +initialised randomly from a zero mean unit variance Gaussian. This makes use +of the MATLAB function <CODE>randn</CODE> and so the seed for the random weight +initialisation can be set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the +state value. + +<p>The fields in <CODE>mix</CODE> are +<PRE> + + type = 'gmm' + nin = the dimension of the space + ncentres = number of mixture components + covartype = string for type of variance model + priors = mixing coefficients + centres = means of Gaussians: stored as rows of a matrix + covars = covariances of Gaussians +</PRE> + +The additional fields for mixtures of PPCA are +<PRE> + + U = principal component subspaces + lambda = in-space covariances: stored as rows of a matrix +</PRE> + +The off-subspace noise is stored in <CODE>covars</CODE>. + +<p><h2> +Example +</h2> +<PRE> + +mix = gmm(2, 4, 'spherical'); +</PRE> + +This creates a Gaussian mixture model with 4 components in 2 dimensions. +The covariance structure is a spherical model. + +<p><h2> +See Also +</h2> +<CODE><a href="gmmpak.htm">gmmpak</a></CODE>, <CODE><a href="gmmunpak.htm">gmmunpak</a></CODE>, <CODE><a href="gmmsamp.htm">gmmsamp</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="gmmactiv.htm">gmmactiv</a></CODE>, <CODE><a href="gmmpost.htm">gmmpost</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmmactiv.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual gmmactiv +</title> +</head> +<body> +<H1> gmmactiv +</H1> +<h2> +Purpose +</h2> +Computes the activations of a Gaussian mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> + +a = gmmactiv(mix, x) +</PRE> + + +<p><h2> +Description +</h2> +This function computes the activations <CODE>a</CODE> (i.e. the +probability <CODE>p(x|j)</CODE> of the data conditioned on each component density) +for a Gaussian mixture model. For the PPCA model, each activation +is the conditional probability of <CODE>x</CODE> given that it is generated +by the component subspace. +The data structure <CODE>mix</CODE> defines the mixture model, while the matrix +<CODE>x</CODE> contains the data vectors. Each row of <CODE>x</CODE> represents a single +vector. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmpost.htm">gmmpost</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmmem.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,87 @@ +<html> +<head> +<title> +Netlab Reference Manual gmmem +</title> +</head> +<body> +<H1> gmmem +</H1> +<h2> +Purpose +</h2> +EM algorithm for Gaussian mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> + +[mix, options, errlog] = gmmem(mix, x, options) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>[mix, options, errlog] = gmmem(mix, x, options)</CODE> uses the Expectation +Maximization algorithm of Dempster et al. to estimate the parameters of +a Gaussian mixture model defined by a data structure <CODE>mix</CODE>. +The matrix <CODE>x</CODE> represents the data whose expectation +is maximized, with each row corresponding to a vector. + +The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>. +If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(3)</CODE> is a measure of the absolute precision required of the error +function at the solution. If the change in log likelihood between two steps of +the EM algorithm is less than this value, then the function terminates. + +<p><CODE>options(5)</CODE> is set to 1 if a covariance matrix is reset to its +original value when any of its singular values are too small (less +than MIN_COVAR which has the value eps). +With the default value of 0 no action is taken. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p>The optional return value <CODE>options</CODE> contains the final error value +(i.e. data log likelihood) in +<CODE>options(8)</CODE>. + +<p><h2> +Examples +</h2> +The following code fragment sets up a Gaussian mixture model, initialises +the parameters from the data, sets the options and trains the model. +<PRE> + +mix = gmm(inputdim, ncentres, 'full'); + +<p>options = foptions; +options(14) = 5; +mix = gmminit(mix, data, options); + +<p>options(1) = 1; % Prints out error values. +options(14) = 30; % Max. number of iterations. + +<p>mix = gmmem(mix, data, options); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmminit.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +<html> +<head> +<title> +Netlab Reference Manual gmminit +</title> +</head> +<body> +<H1> gmminit +</H1> +<h2> +Purpose +</h2> +Initialises Gaussian mixture model from data + +<p><h2> +Synopsis +</h2> +<PRE> + +mix = gmminit(mix, x, options) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>mix = gmminit(mix, x, options)</CODE> uses a dataset <CODE>x</CODE> +to initialise the parameters of a Gaussian mixture +model defined by the data structure <CODE>mix</CODE>. The k-means algorithm +is used to determine the centres. The priors are computed from the +proportion of examples belonging to each cluster. +The covariance matrices are calculated as the sample covariance of the +points associated with (i.e. closest to) the corresponding centres. +For a mixture of PPCA model, the PPCA decomposition is calculated +for the points closest to a given centre. +This initialisation can be used as the starting point for training the +model using the EM algorithm. + +<p><h2> +Example +</h2> +<PRE> + +mix = gmm(3, 2); +options = foptions; +options(14) = 5; +mix = gmminit(mix, data, options); +</PRE> + +This code sets up a Gaussian mixture model with 3 centres in 2 dimensions, and +then initialises the parameters from the data set <CODE>data</CODE> with 5 iterations +of the k means algorithm. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmmpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +<html> +<head> +<title> +Netlab Reference Manual gmmpak +</title> +</head> +<body> +<H1> gmmpak +</H1> +<h2> +Purpose +</h2> +Combines all the parameters in a Gaussian mixture model into one vector. + +<p><h2> +Synopsis +</h2> +<PRE> +p = gmmpak(mix) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>p = gmmpak(net)</CODE> takes a mixture data structure <CODE>mix</CODE> +and combines the component parameter matrices into a single row +vector <CODE>p</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmmpost.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual gmmpost +</title> +</head> +<body> +<H1> gmmpost +</H1> +<h2> +Purpose +</h2> +Computes the class posterior probabilities of a Gaussian mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> + +function post = gmmpost(mix, x) +</PRE> + + +<p><h2> +Description +</h2> +This function computes the posteriors <CODE>post</CODE> (i.e. the probability of each +component conditioned on the data <CODE>p(j|x)</CODE>) for a Gaussian mixture model. +The data structure <CODE>mix</CODE> defines the mixture model, while the matrix +<CODE>x</CODE> contains the data vectors. Each row of <CODE>x</CODE> represents a single +vector. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmactiv.htm">gmmactiv</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmmprob.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual gmmprob +</title> +</head> +<body> +<H1> gmmprob +</H1> +<h2> +Purpose +</h2> +Computes the data probability for a Gaussian mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> + +function prob = gmmprob(mix, x) +</PRE> + + +<p><h2> +Description +</h2> + +This function computes the unconditional data +density <CODE>p(x)</CODE> for a Gaussian mixture model. The data structure +<CODE>mix</CODE> defines the mixture model, while the matrix <CODE>x</CODE> contains +the data vectors. Each row of <CODE>x</CODE> represents a single vector. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmpost.htm">gmmpost</a></CODE>, <CODE><a href="gmmactiv.htm">gmmactiv</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmmsamp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual gmmsamp +</title> +</head> +<body> +<H1> gmmsamp +</H1> +<h2> +Purpose +</h2> +Sample from a Gaussian mixture distribution. + +<p><h2> +Synopsis +</h2> +<PRE> +data = gmmsamp(mix, n) +[data, label] = gmmsamp(mix, n) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>data = gsamp(mix, n)</CODE> generates a sample of size <CODE>n</CODE> from a +Gaussian mixture distribution defined by the <CODE>mix</CODE> data +structure. The matrix <CODE>x</CODE> has <CODE>n</CODE> +rows in which each row represents a <CODE>mix.nin</CODE>-dimensional sample vector. + +<p><CODE>[data, label] = gmmsamp(mix, n)</CODE> also returns a column vector of +classes (as an index 1..N) <CODE>label</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gsamp.htm">gsamp</a></CODE>, <CODE><a href="gmm.htm">gmm</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gmmunpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +<html> +<head> +<title> +Netlab Reference Manual gmmunpak +</title> +</head> +<body> +<H1> gmmunpak +</H1> +<h2> +Purpose +</h2> +Separates a vector of Gaussian mixture model parameters into its components. + +<p><h2> +Synopsis +</h2> +<PRE> +mix = gmmunpak(mix, p) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>mix = gmmunpak(mix, p)</CODE> +takes a GMM data structure <CODE>mix</CODE> and +a single row vector of parameters <CODE>p</CODE> and returns a mixture data structure +identical to the input <CODE>mix</CODE>, except that the mixing coefficients +<CODE>priors</CODE>, centres <CODE>centres</CODE> and covariances <CODE>covars</CODE> +(and, for PPCA, the lambdas and U (PCA sub-spaces)) are all set +to the corresponding elements of <CODE>p</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmpak.htm">gmmpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +<html> +<head> +<title> +Netlab Reference Manual gp +</title> +</head> +<body> +<H1> gp +</H1> +<h2> +Purpose +</h2> +Create a Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gp(nin, covarfn) +net = gp(nin, covarfn, prior) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = gp(nin, covarfn)</CODE> takes the number of inputs <CODE>nin</CODE> +for a Gaussian Process model with a single output, together +with a string <CODE>covarfn</CODE> which specifies the type of the covariance function, +and returns a data structure <CODE>net</CODE>. The parameters are set to zero. + +<p>The fields in <CODE>net</CODE> are +<PRE> + type = 'gp' + nin = number of inputs + nout = number of outputs: always 1 + nwts = total number of weights and covariance function parameters + bias = logarithm of constant offset in covariance function + noise = logarithm of output noise variance + inweights = logarithm of inverse length scale for each input + covarfn = string describing the covariance function: + 'sqexp' + 'ratquad' + fpar = covariance function specific parameters (1 for squared exponential, + 2 for rational quadratic) + trin = training input data (initially empty) + trtargets = training target data (initially empty) +</PRE> + + +<p><CODE>net = gp(nin, covarfn, prior)</CODE> sets a Gaussian prior on the +parameters of the model. <CODE>prior</CODE> must contain the fields +<CODE>pr_mean</CODE> and <CODE>pr_variance</CODE>. If <CODE>pr_mean</CODE> is a scalar, +then the Gaussian is assumed to be isotropic and the additional fields +<CODE>net.pr_mean</CODE> and <CODE>pr_variance</CODE> are set. Otherwise, +the Gaussian prior has a mean +defined by a column vector of parameters <CODE>prior.pr_mean</CODE> and +covariance defined by a column vector of parameters <CODE>prior.pr_variance</CODE>. +Each element of <CODE>prmean</CODE> corresponds to a separate group of parameters, which +need not be mutually exclusive. The membership of the groups is defined +by the matrix <CODE>prior.index</CODE> in which the columns correspond to the elements of +<CODE>prmean</CODE>. Each column has one element for each weight in the matrix, +in the order defined by the function <CODE>gppak</CODE>, and each element +is 1 or 0 according to whether the parameter is a member of the +corresponding group or not. The additional field <CODE>net.index</CODE> is set +in this case. + +<p><h2> +See Also +</h2> +<CODE><a href="gppak.htm">gppak</a></CODE>, <CODE><a href="gpunpak.htm">gpunpak</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpcovar.htm">gpcovar</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gpcovar.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +<html> +<head> +<title> +Netlab Reference Manual gpcovar +</title> +</head> +<body> +<H1> gpcovar +</H1> +<h2> +Purpose +</h2> +Calculate the covariance for a Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +cov = gpcovar(net, x) +[cov, covf] = gpcovar(net, x) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>cov = gpcovar(net, x)</CODE> takes +a Gaussian Process data structure <CODE>net</CODE> together with +a matrix <CODE>x</CODE> of input vectors, and computes the covariance +matrix <CODE>cov</CODE>. The inverse of this matrix is used when calculating +the mean and variance of the predictions made by <CODE>net</CODE>. + +<p><CODE>[cov, covf] = gpcovar(net, x)</CODE> also generates the covariance +matrix due to the covariance function specified by <CODE>net.covarfn</CODE> +as calculated by <CODE>gpcovarf</CODE>. + +<p><h2> +Example +</h2> +In the following example, the inverse covariance matrix is calculated +for a set of training inputs <CODE>x</CODE> and is then +passed to <CODE>gpfwd</CODE> so that predictions (with mean <CODE>ytest</CODE> and +variance <CODE>sigsq</CODE>) can be made for the test inputs +<CODE>xtest</CODE>. +<PRE> + +cninv = inv(gpcovar(net, x)); +[ytest, sigsq] = gpfwd(net, xtest, cninv); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gppak.htm">gppak</a></CODE>, <CODE><a href="gpunpak.htm">gpunpak</a></CODE>, <CODE><a href="gpcovarp.htm">gpcovarp</a></CODE>, <CODE><a href="gpcovarf.htm">gpcovarf</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gpcovarf.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual gpcovarf +</title> +</head> +<body> +<H1> gpcovarf +</H1> +<h2> +Purpose +</h2> +Calculate the covariance function for a Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +covf = gpcovarf(net, x1, x2) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>covf = gpcovarf(net, x1, x2)</CODE> takes +a Gaussian Process data structure <CODE>net</CODE> together with +two matrices <CODE>x1</CODE> and <CODE>x2</CODE> of input vectors, +and computes the matrix of the covariance function values +<CODE>covf</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpcovar.htm">gpcovar</a></CODE>, <CODE><a href="gpcovarp.htm">gpcovarp</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gpcovarp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<html> +<head> +<title> +Netlab Reference Manual gpcovarp +</title> +</head> +<body> +<H1> gpcovarp +</H1> +<h2> +Purpose +</h2> +Calculate the prior covariance for a Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +covp = gpcovarp(net, x1, x2) +[covp, covf] = gpcovarp(net, x1, x2) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>covp = gpcovarp(net, x1, x2)</CODE> takes +a Gaussian Process data structure <CODE>net</CODE> together with +two matrices <CODE>x1</CODE> and <CODE>x2</CODE> of input vectors, +and computes the matrix of the prior covariance. This is +the function component of the covariance plus the exponential of the bias +term. + +<p><CODE>[covp, covf] = gpcovarp(net, x1, x2)</CODE> also returns the function +component of the covariance. + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpcovar.htm">gpcovar</a></CODE>, <CODE><a href="gpcovarf.htm">gpcovarf</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gperr.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<html> +<head> +<title> +Netlab Reference Manual gperr +</title> +</head> +<body> +<H1> gperr +</H1> +<h2> +Purpose +</h2> +Evaluate error function for Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +edata = gperr(net, x, t) +[e, edata, eprior] = gperr(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>e = gperr(net, x, t)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> together +with a matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> of target +vectors, and evaluates the error function <CODE>e</CODE>. Each row +of <CODE>x</CODE> corresponds to one input vector and each row of <CODE>t</CODE> +corresponds to one target vector. + +<p><CODE>[e, edata, eprior] = gperr(net, x, t)</CODE> additionally returns the +data and hyperprior components of the error, assuming a Gaussian +prior on the weights with mean and variance parameters <CODE>prmean</CODE> and +<CODE>prvariance</CODE> taken from the network data structure <CODE>net</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpcovar.htm">gpcovar</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gpfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +<html> +<head> +<title> +Netlab Reference Manual gpfwd +</title> +</head> +<body> +<H1> gpfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +y = gpfwd(net, x) +[y, sigsq] = gpfwd(net, x) +[y, sigsq] = gpfwd(net, x, cninv) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>y = gpfwd(net, x)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> +together +with a matrix <CODE>x</CODE> of input vectors, and forward propagates the inputs +through the model to generate a matrix <CODE>y</CODE> of output +vectors. Each row of <CODE>x</CODE> corresponds to one input vector and each +row of <CODE>y</CODE> corresponds to one output vector. This assumes that the +training data (both inputs and targets) has been stored in <CODE>net</CODE> by +a call to <CODE>gpinit</CODE>; these are needed to compute the training +data covariance matrix. + +<p><CODE>[y, sigsq] = gpfwd(net, x)</CODE> also generates a column vector <CODE>sigsq</CODE> of +conditional variances (or squared error bars) where each value corresponds to a pattern. + +<p><CODE>[y, sigsq] = gpfwd(net, x, cninv)</CODE> uses the pre-computed inverse covariance +matrix <CODE>cninv</CODE> in the forward propagation. This increases efficiency if +several calls to <CODE>gpfwd</CODE> are made. + +<p><h2> +Example +</h2> +The following code creates a Gaussian Process, trains it, and then plots the +predictions on a test set with one standard deviation error bars: +<PRE> + +net = gp(1, 'sqexp'); +net = gpinit(net, x, t); +net = netopt(net, options, x, t, 'scg'); +[pred, sigsq] = gpfwd(net, xtest); +plot(xtest, pred, '-k'); +hold on +plot(xtest, pred+sqrt(sigsq), '-b', xtest, pred-sqrt(sigsq), '-b'); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="demgp.htm">demgp</a></CODE>, <CODE><a href="gpinit.htm">gpinit</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gpgrad.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual gpgrad +</title> +</head> +<body> +<H1> gpgrad +</H1> +<h2> +Purpose +</h2> +Evaluate error gradient for Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +g = gpgrad(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = gpgrad(net, x, t)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> together +with a matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> of target +vectors, and evaluates the error gradient <CODE>g</CODE>. Each row +of <CODE>x</CODE> corresponds to one input vector and each row of <CODE>t</CODE> +corresponds to one target vector. + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpcovar.htm">gpcovar</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gpinit.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +<html> +<head> +<title> +Netlab Reference Manual gpinit +</title> +</head> +<body> +<H1> gpinit +</H1> +<h2> +Purpose +</h2> +Initialise Gaussian Process model. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gpinit(net, trin, trtargets, prior) +net = gpinit(net, trin, trtargets, prior) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = gpinit(net, trin, trtargets)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> +together +with a matrix <CODE>trin</CODE> of training input vectors and a matrix <CODE>trtargets</CODE> of +training target +vectors, and stores them in <CODE>net</CODE>. These datasets are required if +the corresponding inverse covariance matrix is not supplied to <CODE>gpfwd</CODE>. +This is important if the data structure is saved and then reloaded before +calling <CODE>gpfwd</CODE>. +Each row +of <CODE>trin</CODE> corresponds to one input vector and each row of <CODE>trtargets</CODE> +corresponds to one target vector. + +<p><CODE>net = gpinit(net, trin, trtargets, prior)</CODE> additionally initialises the +parameters in <CODE>net</CODE> from the <CODE>prior</CODE> data structure which contains the +mean and variance of the Gaussian distribution which is sampled from. + +<p><h2> +Example +</h2> +Suppose that a Gaussian Process model is created and trained with input data <CODE>x</CODE> +and targets <CODE>t</CODE>: +<PRE> + +net = gp(2, 'sqexp'); +net = gpinit(net, x, t); +% Train the network +save 'gp.net' net; +</PRE> + +Another Matlab program can now read in the network and make predictions on a data set +<CODE>testin</CODE>: +<PRE> + +load 'gp.net'; +pred = gpfwd(net, testin); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gppak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +<html> +<head> +<title> +Netlab Reference Manual gppak +</title> +</head> +<body> +<H1> gppak +</H1> +<h2> +Purpose +</h2> +Combines GP hyperparameters into one vector. + +<p><h2> +Synopsis +</h2> +<PRE> +hp = gppak(net) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>hp = gppak(net)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> and +combines the hyperparameters into a single row vector <CODE>hp</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpunpak.htm">gpunpak</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gpunpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +<html> +<head> +<title> +Netlab Reference Manual gpunpak +</title> +</head> +<body> +<H1> gpunpak +</H1> +<h2> +Purpose +</h2> +Separates hyperparameter vector into components. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gpunpak(net, hp) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = gpunpak(net, hp)</CODE> takes an Gaussian Process data structure <CODE>net</CODE> and +a hyperparameter vector <CODE>hp</CODE>, and returns a Gaussian Process data structure +identical to +the input model, except that the covariance bias +<CODE>bias</CODE>, output noise <CODE>noise</CODE>, the input weight vector +<CODE>inweights</CODE> and the vector of covariance function specific parameters + <CODE>fpar</CODE> have all +been set to the corresponding elements of <CODE>hp</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gppak.htm">gppak</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gradchek.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +<html> +<head> +<title> +Netlab Reference Manual gradchek +</title> +</head> +<body> +<H1> gradchek +</H1> +<h2> +Purpose +</h2> +Checks a user-defined gradient function using finite differences. + +<p><h2> +Synopsis +</h2> +<PRE> + +gradchek(w, func, grad) +[gradient, delta] = gradchek(w, func, grad) +gradchek(w, func, grad, p1, p2, ...) +</PRE> + + +<p><h2> +Description +</h2> +This function is intended as a utility for other netlab functions +(particularly optimisation functions) to use. It enables the user +to check whether a gradient calculation has been correctly implmented +for a given function. +<CODE>gradchek(w, func, grad)</CODE> checks how accurate the gradient +<CODE>grad</CODE> of a function <CODE>func</CODE> is at a parameter vector <CODE>x</CODE>. +A central +difference formula with step size 1.0e-6 is used, and the results +for both gradient function and finite difference approximation are +printed. +The optional return value <CODE>gradient</CODE> is the gradient calculated +using the function <CODE>grad</CODE> and the return value <CODE>delta</CODE> is the +difference between the functional and finite difference methods of +calculating the graident. + +<p><CODE>gradchek(x, func, grad, p1, p2, ...)</CODE> allows additional arguments +to be passed to <CODE>func</CODE> and <CODE>grad</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="graddesc.htm">graddesc</a></CODE>, <CODE><a href="hmc.htm">hmc</a></CODE>, <CODE><a href="olgd.htm">olgd</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/graddesc.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +<html> +<head> +<title> +Netlab Reference Manual graddesc +</title> +</head> +<body> +<H1> graddesc +</H1> +<h2> +Purpose +</h2> +Gradient descent optimization. + +<p><h2> +Description +</h2> +<CODE>[x, options, flog, pointlog] = graddesc(f, x, options, gradf)</CODE> uses +batch gradient descent to find a local minimum of the function +<CODE>f(x)</CODE> whose gradient is given by <CODE>gradf(x)</CODE>. A log of the function values +after each cycle is (optionally) returned in <CODE>errlog</CODE>, and a log +of the points visited is (optionally) returned in <CODE>pointlog</CODE>. + +<p>Note that <CODE>x</CODE> is a row vector +and <CODE>f</CODE> returns a scalar value. +The point at which <CODE>f</CODE> has a local minimum +is returned as <CODE>x</CODE>. The function value at that point is returned +in <CODE>options(8)</CODE>. + +<p><CODE>graddesc(f, x, options, gradf, p1, p2, ...)</CODE> allows +additional arguments to be passed to <CODE>f()</CODE> and <CODE>gradf()</CODE>. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>, and the points visited +in the return argument <CODE>pointslog</CODE>. If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(2)</CODE> is the absolute precision required for the value +of <CODE>x</CODE> at the solution. If the absolute difference between +the values of <CODE>x</CODE> between two successive steps is less than +<CODE>options(2)</CODE>, then this condition is satisfied. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. If the absolute difference between the +objective function values between two successive steps is less than +<CODE>options(3)</CODE>, then this condition is satisfied. +Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(7)</CODE> determines the line minimisation method used. If it +is set to 1 then a line minimiser is used (in the direction of the negative +gradient). If it is 0 (the default), then each parameter update +is a fixed multiple (the learning rate) +of the negative gradient added to a fixed multiple (the momentum) of +the previous parameter update. + +<p><CODE>options(9)</CODE> should be set to 1 to check the user defined gradient +function <CODE>gradf</CODE> with <CODE>gradchek</CODE>. This is carried out at +the initial parameter vector <CODE>x</CODE>. + +<p><CODE>options(10)</CODE> returns the total number of function evaluations (including +those in any line searches). + +<p><CODE>options(11)</CODE> returns the total number of gradient evaluations. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p><CODE>options(15)</CODE> is the precision in parameter space of the line search; +default <CODE>foptions(2)</CODE>. + +<p><CODE>options(17)</CODE> is the momentum; default 0.5. It should be scaled by the +inverse of the number of data points. + +<p><CODE>options(18)</CODE> is the learning rate; default 0.01. It should be +scaled by the inverse of the number of data points. + +<p><h2> +Examples +</h2> +An example of how this function can be used to train a neural network is: +<PRE> + +options = zeros(1, 18); +options(17) = 0.1/size(x, 1); +net = netopt(net, options, x, t, 'graddesc'); +</PRE> + +Note how the learning rate is scaled by the number of data points. + +<p><h2> +See Also +</h2> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="linemin.htm">linemin</a></CODE>, <CODE><a href="olgd.htm">olgd</a></CODE>, <CODE><a href="minbrack.htm">minbrack</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gsamp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual gsamp +</title> +</head> +<body> +<H1> gsamp +</H1> +<h2> +Purpose +</h2> +Sample from a Gaussian distribution. + +<p><h2> +Synopsis +</h2> +<PRE> +x = gsamp(mu, covar, nsamp) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>x = gsamp(mu, covar, nsamp)</CODE> generates a sample of size <CODE>nsamp</CODE> +from a <CODE>d</CODE>-dimensional Gaussian distribution. The Gaussian density +has mean vector <CODE>mu</CODE> and covariance matrix <CODE>covar</CODE>, and the +matrix <CODE>x</CODE> has <CODE>nsamp</CODE> rows in which each row represents a +<CODE>d</CODE>-dimensional sample vector. + +<p><h2> +See Also +</h2> +<CODE><a href="gauss.htm">gauss</a></CODE>, <CODE><a href="demgauss.htm">demgauss</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtm.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +<html> +<head> +<title> +Netlab Reference Manual gtm +</title> +</head> +<body> +<H1> gtm +</H1> +<h2> +Purpose +</h2> +Create a Generative Topographic Map. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc) +net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)</CODE>, +takes the dimension of the latent space <CODE>dimlatent</CODE>, the +number of data points sampled in the latent space <CODE>nlatent</CODE>, the +dimension of the data space <CODE>dimdata</CODE>, the number of centres in the +RBF model <CODE>ncentres</CODE>, the activation function for the RBF +<CODE>rbfunc</CODE> +and returns a data structure <CODE>net</CODE>. The parameters in the +RBF and GMM sub-models are set by calls to the corresponding creation routines +<CODE>rbf</CODE> and <CODE>gmm</CODE>. + +<p>The fields in <CODE>net</CODE> are +<PRE> + type = 'gtm' + nin = dimension of data space + dimlatent = dimension of latent space + rbfnet = RBF network data structure + gmmnet = GMM data structure + X = sample of latent points +</PRE> + + +<p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)</CODE>, + sets a Gaussian zero mean prior on the +parameters of the RBF model. <CODE>prior</CODE> must be a scalar and represents +the inverse variance of the prior distribution. This gives rise to +a weight decay term in the error function. + +<p><h2> +See Also +</h2> +<CODE><a href="gtmfwd.htm">gtmfwd</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="gmm.htm">gmm</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtmem.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +<html> +<head> +<title> +Netlab Reference Manual gtmem +</title> +</head> +<body> +<H1> gtmem +</H1> +<h2> +Purpose +</h2> +EM algorithm for Generative Topographic Mapping. + +<p><h2> +Synopsis +</h2> +<PRE> + +[net, options, errlog] = gtmem(net, t, options) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>[net, options, errlog] = gtmem(net, t, options)</CODE> uses the Expectation +Maximization algorithm to estimate the parameters of +a GTM defined by a data structure <CODE>net</CODE>. +The matrix <CODE>t</CODE> represents the data whose expectation +is maximized, with each row corresponding to a vector. It is assumed +that the latent data <CODE>net.X</CODE> has been set following a call to +<CODE>gtminit</CODE>, for example. + +The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>. +If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(3)</CODE> is a measure of the absolute precision required of the error +function at the solution. If the change in log likelihood between two steps of +the EM algorithm is less than this value, then the function terminates. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p>The optional return value <CODE>options</CODE> contains the final error value +(i.e. data log likelihood) in +<CODE>options(8)</CODE>. + +<p><h2> +Examples +</h2> +The following code fragment sets up a GTM, initialises +the latent data sample and RBF +parameters from the data, sets the options and trains the model. +<PRE> + +% Create and initialise GTM model +net = gtm(latentdim, nlatent, datadim, numrbfcentres, ... + 'gaussian', 0.1); + +<p>options = foptions; +options(1) = -1; +options(7) = 1; % Set width factor of RBF +net = gtminit(net, options, data, 'regular', latentshape, [4 4]); + +<p>options = foptions; +options(14) = 30; +options(1) = 1; +[net, options] = gtmem(net, data, options); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtminit.htm">gtminit</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtmfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual gtmfwd +</title> +</head> +<body> +<H1> gtmfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through GTM. + +<p><h2> +Synopsis +</h2> +<PRE> +mix = gtmfwd(net) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>mix = gtmfwd(net)</CODE> takes a GTM +structure <CODE>net</CODE>, and forward +propagates the latent data sample <CODE>net.X</CODE> through the GTM to generate +the structure +<CODE>mix</CODE> which represents the Gaussian mixture model in data space. + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtminit.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +<html> +<head> +<title> +Netlab Reference Manual gtminit +</title> +</head> +<body> +<H1> gtminit +</H1> +<h2> +Purpose +</h2> +Initialise the weights and latent sample in a GTM. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gtminit(net, options, data, samptype) +net = gtminit(net, options, data, samptype, lsampsize, rbfsampsize) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = gtminit(net, options, data, samptype)</CODE> takes a GTM <CODE>net</CODE> +and generates a sample of latent data points and sets the centres (and +widths if appropriate) of +<CODE>net.rbfnet</CODE>. + +<p>If the <CODE>samptype</CODE> is <CODE>'regular'</CODE>, then regular grids of latent +data points and RBF centres are created. The dimension of the latent data +space must be +1 or 2. For one-dimensional latent space, the <CODE>lsampsize</CODE> parameter +gives the number of latent points and the <CODE>rbfsampsize</CODE> parameter +gives the number of RBF centres. For a two-dimensional latent space, +these parameters must be vectors of length 2 with the number of points +in each of the x and y directions to create a rectangular grid. The +widths of the RBF basis functions are set by a call to <CODE>rbfsetfw</CODE> +passing <CODE>options(7)</CODE> as the scaling parameter. + +<p>If the <CODE>samptype</CODE> is <CODE>'uniform'</CODE> or <CODE>'gaussian'</CODE> then the +latent data is found by sampling from a uniform or +Gaussian distribution correspondingly. The RBF basis function parameters +are set +by a call to <CODE>rbfsetbf</CODE> with the <CODE>data</CODE> parameter +as dataset and the <CODE>options</CODE> vector. + +<p>Finally, the output layer weights of the RBF are initialised by +mapping the mean of the latent variable to the mean of the target variable, +and the L-dimensional latent variale variance to the variance of the +targets along the first L principal components. + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmem.htm">gtmem</a></CODE>, <CODE><a href="pca.htm">pca</a></CODE>, <CODE><a href="rbfsetbf.htm">rbfsetbf</a></CODE>, <CODE><a href="rbfsetfw.htm">rbfsetfw</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtmlmean.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual gtmlmean +</title> +</head> +<body> +<H1> gtmlmean +</H1> +<h2> +Purpose +</h2> +Mean responsibility for data in a GTM. + +<p><h2> +Synopsis +</h2> +<PRE> +means = gtmlmean(net, data) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>means = gtmlmean(net, data)</CODE> takes a GTM +structure <CODE>net</CODE>, and computes the means of the responsibility +distributions for each data point in <CODE>data</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE>, <CODE><a href="gtmlmode.htm">gtmlmode</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtmlmode.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual gtmlmode +</title> +</head> +<body> +<H1> gtmlmode +</H1> +<h2> +Purpose +</h2> +Mode responsibility for data in a GTM. + +<p><h2> +Synopsis +</h2> +<PRE> +modes = gtmlmode(net, data) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>modes = gtmlmode(net, data)</CODE> takes a GTM +structure <CODE>net</CODE>, and computes the modes of the responsibility +distributions for each data point in <CODE>data</CODE>. These will always lie +at one of the latent space sample points <CODE>net.X</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE>, <CODE><a href="gtmlmean.htm">gtmlmean</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtmmag.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual gtmmag +</title> +</head> +<body> +<H1> gtmmag +</H1> +<h2> +Purpose +</h2> +Magnification factors for a GTM + +<p><h2> +Synopsis +</h2> +<PRE> +mags = gtmmag(net, latentdata) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>mags = gtmmag(net, latentdata)</CODE> takes a GTM +structure <CODE>net</CODE>, and computes the magnification factors +for each point the latent space contained in <CODE>latentdata</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE>, <CODE><a href="gtmlmean.htm">gtmlmean</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtmpost.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual gtmpost +</title> +</head> +<body> +<H1> gtmpost +</H1> +<h2> +Purpose +</h2> +Latent space responsibility for data in a GTM. + +<p><h2> +Synopsis +</h2> +<PRE> +post = gtmpost(net, data) +[post, a] = gtmpost(net, data) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>post = gtmpost(net, data)</CODE> takes a GTM +structure <CODE>net</CODE>, and computes the responsibility at each latent space +sample point <CODE>net.X</CODE> +for each data point in <CODE>data</CODE>. + +<p><CODE>[post, a] = gtmpost(net, data)</CODE> also returns the activations +<CODE>a</CODE> of the GMM <CODE>net.gmmnet</CODE> as computed by <CODE>gmmpost</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmem.htm">gtmem</a></CODE>, <CODE><a href="gtmlmean.htm">gtmlmean</a></CODE>, <CODE><a href="gmlmode.htm">gmlmode</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtmprob.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual gtmprob +</title> +</head> +<body> +<H1> gtmprob +</H1> +<h2> +Purpose +</h2> +Probability for data under a GTM. + +<p><h2> +Synopsis +</h2> +<PRE> +prob = gtmprob(net, data) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>prob = gtmprob(net, data)</CODE> takes a GTM +structure <CODE>net</CODE>, and computes the probability of each point in the +dataset <CODE>data</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmem.htm">gtmem</a></CODE>, <CODE><a href="gtmlmean.htm">gtmlmean</a></CODE>, <CODE><a href="gtmlmode.htm">gtmlmode</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/hbayes.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual hbayes +</title> +</head> +<body> +<H1> hbayes +</H1> +<h2> +Purpose +</h2> +Evaluate Hessian of Bayesian error function for network. + +<p><h2> +Synopsis +</h2> +<PRE> +h = hbayes(net, hdata) +[h, hdata] = hbayes(net, hdata) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>h = hbayes(net, hdata)</CODE> takes a network data structure <CODE>net</CODE> together +the data contribution to the Hessian +for a set of inputs and targets. +It returns the regularised Hessian using any zero mean Gaussian priors +on the weights defined in +<CODE>net</CODE>. In addition, if a <CODE>mask</CODE> is defined in <CODE>net</CODE>, then +the entries in <CODE>h</CODE> that correspond to weights with a 0 in the +mask are removed. + +<p><CODE>[h, hdata] = hbayes(net, hdata)</CODE> additionally returns the +data component of the Hessian. + +<p><h2> +See Also +</h2> +<CODE><a href="gbayes.htm">gbayes</a></CODE>, <CODE><a href="glmhess.htm">glmhess</a></CODE>, <CODE><a href="mlphess.htm">mlphess</a></CODE>, <CODE><a href="rbfhess.htm">rbfhess</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/hesschek.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +<html> +<head> +<title> +Netlab Reference Manual hesschek +</title> +</head> +<body> +<H1> hesschek +</H1> +<h2> +Purpose +</h2> +Use central differences to confirm correct evaluation of Hessian matrix. + +<p><h2> +Synopsis +</h2> +<PRE> +hesschek(net, x, t) +h = hesschek(net, x, t)</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>hesschek(net, x, t)</CODE> takes a network data structure <CODE>net</CODE>, together +with input and target data matrices <CODE>x</CODE> and <CODE>t</CODE>, and compares +the evaluation of the Hessian matrix using the function <CODE>nethess</CODE> +and using central differences with the function <CODE>neterr</CODE>. + +<p>The optional return value <CODE>h</CODE> is the Hessian computed using +<CODE>nethess</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="nethess.htm">nethess</a></CODE>, <CODE><a href="neterr.htm">neterr</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/hintmat.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +<html> +<head> +<title> +Netlab Reference Manual hintmat +</title> +</head> +<body> +<H1> hintmat +</H1> +<h2> +Purpose +</h2> +Evaluates the coordinates of the patches for a Hinton diagram. + +<p><h2> +Synopsis +</h2> +<PRE> +[xvals, yvals, color] = hintmat(w)</PRE> + + +<p><h2> +Description +</h2> +<PRE> +[xvals, yvals, color] = hintmat(w)</PRE> + +takes a matrix <CODE>w</CODE> and +returns coordinates <CODE>xvals, yvals</CODE> for the patches comrising the +Hinton diagram, together with a vector <CODE>color</CODE> labelling the color +(black or white) of the corresponding elements according to their +sign. + +<p><h2> +See Also +</h2> +<CODE><a href="hinton.htm">hinton</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/hinton.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual hinton +</title> +</head> +<body> +<H1> hinton +</H1> +<h2> +Purpose +</h2> +Plot Hinton diagram for a weight matrix. + +<p><h2> +Synopsis +</h2> +<PRE> +hinton(w) +h = hinton(w)</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>hinton(w)</CODE> takes a matrix <CODE>w</CODE> +and plots the Hinton diagram. + +<p><CODE>h = hinton(net)</CODE> also returns the figure handle <CODE>h</CODE> +which can be used, for instance, to delete the +figure when it is no longer needed. + +<p>To print the figure correctly in black and white, you should call +<CODE>set(h, 'InvertHardCopy', 'off')</CODE> before printing. + +<p><h2> +See Also +</h2> +<CODE><a href="demhint.htm">demhint</a></CODE>, <CODE><a href="hintmat.htm">hintmat</a></CODE>, <CODE><a href="mlphint.htm">mlphint</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/histp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +<html> +<head> +<title> +Netlab Reference Manual histp +</title> +</head> +<body> +<H1> histp +</H1> +<h2> +Purpose +</h2> +Histogram estimate of 1-dimensional probability distribution. + +<p><h2> +Synopsis +</h2> +<PRE> +h = histp(x, xmin, xmax, nbins) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>histp(x, xmin, xmax, nbins)</CODE> takes a column vector <CODE>x</CODE> +of data values and generates a normalized histogram plot of the +distribution. The histogram has <CODE>nbins</CODE> bins lying in the +range <CODE>xmin</CODE> to <CODE>xmax</CODE>. + +<p><CODE>h = histp(...)</CODE> returns a vector of patch handles. + +<p><h2> +See Also +</h2> +<CODE><a href="demgauss.htm">demgauss</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/hmc.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,129 @@ +<html> +<head> +<title> +Netlab Reference Manual hmc +</title> +</head> +<body> +<H1> hmc +</H1> +<h2> +Purpose +</h2> +Hybrid Monte Carlo sampling. + +<p><h2> +Synopsis +</h2> +<PRE> + +samples = hmc(f, x, options, gradf) +samples = hmc(f, x, options, gradf, P1, P2, ...) +[samples, energies, diagn] = hmc(f, x, options, gradf) +s = hmc('state') +hmc('state', s) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>samples = hmc(f, x, options, gradf)</CODE> uses a +hybrid Monte Carlo algorithm to sample from the distribution <CODE>p ~ exp(-f)</CODE>, +where <CODE>f</CODE> is the first argument to <CODE>hmc</CODE>. +The Markov chain starts at the point <CODE>x</CODE>, and the function <CODE>gradf</CODE> +is the gradient of the `energy' function <CODE>f</CODE>. + +<p><CODE>hmc(f, x, options, gradf, p1, p2, ...)</CODE> allows +additional arguments to be passed to <CODE>f()</CODE> and <CODE>gradf()</CODE>. + +<p><CODE>[samples, energies, diagn] = hmc(f, x, options, gradf)</CODE> also returns +a log of the energy values (i.e. negative log probabilities) for the +samples in <CODE>energies</CODE> and <CODE>diagn</CODE>, a structure containing +diagnostic information (position, momentum and +acceptance threshold) for each step of the chain in <CODE>diagn.pos</CODE>, +<CODE>diagn.mom</CODE> and +<CODE>diagn.acc</CODE> respectively. All candidate states (including rejected ones) +are stored in <CODE>diagn.pos</CODE>. + +<p><CODE>[samples, energies, diagn] = hmc(f, x, options, gradf)</CODE> also returns the +<CODE>energies</CODE> (i.e. negative log probabilities) corresponding to the samples. +The <CODE>diagn</CODE> structure contains three fields: + +<p><CODE>pos</CODE> the position vectors of the dynamic process. + +<p><CODE>mom</CODE> the momentum vectors of the dynamic process. + +<p><CODE>acc</CODE> the acceptance thresholds. + +<p><CODE>s = hmc('state')</CODE> returns a state structure that contains the state of the +two random number generators <CODE>rand</CODE> and <CODE>randn</CODE> and the momentum of +the dynamic process. These are contained in fields +<CODE>randstate</CODE>, <CODE>randnstate</CODE> +and <CODE>mom</CODE> respectively. The momentum state is +only used for a persistent momentum update. + +<p><CODE>hmc('state', s)</CODE> resets the state to <CODE>s</CODE>. If <CODE>s</CODE> is an integer, +then it is passed to <CODE>rand</CODE> and <CODE>randn</CODE> and the momentum variable +is randomised. If <CODE>s</CODE> is a structure returned by <CODE>hmc('state')</CODE> then +it resets the generator to exactly the same state. + +<p>The optional parameters in the <CODE>options</CODE> vector have the following +interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display the energy values and rejection +threshold at each step of the Markov chain. If the value is 2, then the +position vectors at each step are also displayed. + +<p><CODE>options(5)</CODE> is set to 1 if momentum persistence is used; default 0, for +complete replacement of momentum variables. + +<p><CODE>options(7)</CODE> defines the trajectory length (i.e. the number of leap-frog +steps at each iteration). Minimum value 1. + +<p><CODE>options(9)</CODE> is set to 1 to check the user defined gradient function. + +<p><CODE>options(14)</CODE> is the number of samples retained from the Markov chain; +default 100. + +<p><CODE>options(15)</CODE> is the number of samples omitted from the start of the +chain; default 0. + +<p><CODE>options(17)</CODE> defines the momentum used when a persistent update of +(leap-frog) momentum is used. This is bounded to the interval [0, 1). + +<p><CODE>options(18)</CODE> is the step size used in leap-frogs; default 1/trajectory +length. + +<p><h2> +Examples +</h2> +The following code fragment samples from the posterior distribution of +weights for a neural network. +<PRE> + +w = mlppak(net); +[samples, energies] = hmc('neterr', w, options, 'netgrad', net, x, t); +</PRE> + + +<p><h2> +Algorithm +</h2> + +The algroithm follows the procedure outlined in Radford Neal's technical +report CRG-TR-93-1 from the University of Toronto. The stochastic update of +momenta samples from a zero mean unit covariance gaussian. + +<p><h2> +See Also +</h2> +<CODE><a href="metrop.htm">metrop</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/index.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,537 @@ +<html> +<head> +<title> +NETLAB Reference Documentation +</title> +</head> +<body> +<H1> NETLAB Online Reference Documentation </H1> +Welcome to the NETLAB online reference documentation. +The NETLAB simulation software is designed to provide all the tools necessary +for principled and theoretically well founded application development. The +NETLAB library is based on the approach and techniques described in <I>Neural +Networks for Pattern Recognition </I>(Bishop, 1995). The library includes software +implementations of a wide range of data analysis techniques, many of which are +not widely available, and are rarely, if ever, included in standard neural +network simulation packages. +<p>The online reference documentation provides direct hypertext links to specific Netlab function descriptions. +<p>If you have any comments or problems to report, please contact Ian Nabney (<a href="mailto:i.t.nabney@aston.ac.uk"><tt>i.t.nabney@aston.ac.uk</tt></a>) or Christopher Bishop (<a href="mailto:c.m.bishop@aston.ac.uk"><tt>c.m.bishop@aston.ac.uk</tt></a>).<H1> Index +</H1> +An alphabetic list of functions in Netlab.<p> +<DL> +<DT> +<CODE><a href="conffig.htm">conffig</a></CODE><DD> + Display a confusion matrix. +<DT> +<CODE><a href="confmat.htm">confmat</a></CODE><DD> + Compute a confusion matrix. +<DT> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE><DD> + Conjugate gradients optimization. +<DT> +<CODE><a href="consist.htm">consist</a></CODE><DD> + Check that arguments are consistent. +<DT> +<CODE><a href="convertoldnet.htm">convertoldnet</a></CODE><DD> + Convert pre-2.3 release MLP and MDN nets to new format +<DT> +<CODE><a href="datread.htm">datread</a></CODE><DD> + Read data from an ascii file. +<DT> +<CODE><a href="datwrite.htm">datwrite</a></CODE><DD> + Write data to ascii file. +<DT> +<CODE><a href="dem2ddat.htm">dem2ddat</a></CODE><DD> + Generates two dimensional data for demos. +<DT> +<CODE><a href="demard.htm">demard</a></CODE><DD> + Automatic relevance determination using the MLP. +<DT> +<CODE><a href="demev1.htm">demev1</a></CODE><DD> + Demonstrate Bayesian regression for the MLP. +<DT> +<CODE><a href="demev2.htm">demev2</a></CODE><DD> + Demonstrate Bayesian classification for the MLP. +<DT> +<CODE><a href="demev3.htm">demev3</a></CODE><DD> + Demonstrate Bayesian regression for the RBF. +<DT> +<CODE><a href="demgauss.htm">demgauss</a></CODE><DD> + Demonstrate sampling from Gaussian distributions. +<DT> +<CODE><a href="demglm1.htm">demglm1</a></CODE><DD> + Demonstrate simple classification using a generalized linear model. +<DT> +<CODE><a href="demglm2.htm">demglm2</a></CODE><DD> + Demonstrate simple classification using a generalized linear model. +<DT> +<CODE><a href="demgmm1.htm">demgmm1</a></CODE><DD> + Demonstrate density modelling with a Gaussian mixture model. +<DT> +<CODE><a href="demgmm3.htm">demgmm3</a></CODE><DD> + Demonstrate density modelling with a Gaussian mixture model. +<DT> +<CODE><a href="demgmm4.htm">demgmm4</a></CODE><DD> + Demonstrate density modelling with a Gaussian mixture model. +<DT> +<CODE><a href="demgmm5.htm">demgmm5</a></CODE><DD> + Demonstrate density modelling with a PPCA mixture model. +<DT> +<CODE><a href="demgp.htm">demgp</a></CODE><DD> + Demonstrate simple regression using a Gaussian Process. +<DT> +<CODE><a href="demgpard.htm">demgpard</a></CODE><DD> + Demonstrate ARD using a Gaussian Process. +<DT> +<CODE><a href="demgpot.htm">demgpot</a></CODE><DD> + Computes the gradient of the negative log likelihood for a mixture model. +<DT> +<CODE><a href="demgtm1.htm">demgtm1</a></CODE><DD> + Demonstrate EM for GTM. +<DT> +<CODE><a href="demgtm2.htm">demgtm2</a></CODE><DD> + Demonstrate GTM for visualisation. +<DT> +<CODE><a href="demhint.htm">demhint</a></CODE><DD> + Demonstration of Hinton diagram for 2-layer feed-forward network. +<DT> +<CODE><a href="demhmc1.htm">demhmc1</a></CODE><DD> + Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. +<DT> +<CODE><a href="demhmc2.htm">demhmc2</a></CODE><DD> + Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. +<DT> +<CODE><a href="demhmc3.htm">demhmc3</a></CODE><DD> + Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. +<DT> +<CODE><a href="demkmean.htm">demkmean</a></CODE><DD> + Demonstrate simple clustering model trained with K-means. +<DT> +<CODE><a href="demknn1.htm">demknn1</a></CODE><DD> + Demonstrate nearest neighbour classifier. +<DT> +<CODE><a href="demmdn1.htm">demmdn1</a></CODE><DD> + Demonstrate fitting a multi-valued function using a Mixture Density Network. +<DT> +<CODE><a href="demmet1.htm">demmet1</a></CODE><DD> + Demonstrate Markov Chain Monte Carlo sampling on a Gaussian. +<DT> +<CODE><a href="demmlp1.htm">demmlp1</a></CODE><DD> + Demonstrate simple regression using a multi-layer perceptron +<DT> +<CODE><a href="demmlp2.htm">demmlp2</a></CODE><DD> + Demonstrate simple classification using a multi-layer perceptron +<DT> +<CODE><a href="demnlab.htm">demnlab</a></CODE><DD> + A front-end Graphical User Interface to the demos +<DT> +<CODE><a href="demns1.htm">demns1</a></CODE><DD> + Demonstrate Neuroscale for visualisation. +<DT> +<CODE><a href="demolgd1.htm">demolgd1</a></CODE><DD> + Demonstrate simple MLP optimisation with on-line gradient descent +<DT> +<CODE><a href="demopt1.htm">demopt1</a></CODE><DD> + Demonstrate different optimisers on Rosenbrock's function. +<DT> +<CODE><a href="dempot.htm">dempot</a></CODE><DD> + Computes the negative log likelihood for a mixture model. +<DT> +<CODE><a href="demprgp.htm">demprgp</a></CODE><DD> + Demonstrate sampling from a Gaussian Process prior. +<DT> +<CODE><a href="demprior.htm">demprior</a></CODE><DD> + Demonstrate sampling from a multi-parameter Gaussian prior. +<DT> +<CODE><a href="demrbf1.htm">demrbf1</a></CODE><DD> + Demonstrate simple regression using a radial basis function network. +<DT> +<CODE><a href="demsom1.htm">demsom1</a></CODE><DD> + Demonstrate SOM for visualisation. +<DT> +<CODE><a href="demtrain.htm">demtrain</a></CODE><DD> + Demonstrate training of MLP network. +<DT> +<CODE><a href="dist2.htm">dist2</a></CODE><DD> + Calculates squared distance between two sets of points. +<DT> +<CODE><a href="eigdec.htm">eigdec</a></CODE><DD> + Sorted eigendecomposition +<DT> +<CODE><a href="errbayes.htm">errbayes</a></CODE><DD> + Evaluate Bayesian error function for network. +<DT> +<CODE><a href="evidence.htm">evidence</a></CODE><DD> + Re-estimate hyperparameters using evidence approximation. +<DT> +<CODE><a href="fevbayes.htm">fevbayes</a></CODE><DD> + Evaluate Bayesian regularisation for network forward propagation. +<DT> +<CODE><a href="gauss.htm">gauss</a></CODE><DD> + Evaluate a Gaussian distribution. +<DT> +<CODE><a href="gbayes.htm">gbayes</a></CODE><DD> + Evaluate gradient of Bayesian error function for network. +<DT> +<CODE><a href="glm.htm">glm</a></CODE><DD> + Create a generalized linear model. +<DT> +<CODE><a href="glmderiv.htm">glmderiv</a></CODE><DD> + Evaluate derivatives of GLM outputs with respect to weights. +<DT> +<CODE><a href="glmerr.htm">glmerr</a></CODE><DD> + Evaluate error function for generalized linear model. +<DT> +<CODE><a href="glmevfwd.htm">glmevfwd</a></CODE><DD> + Forward propagation with evidence for GLM +<DT> +<CODE><a href="glmfwd.htm">glmfwd</a></CODE><DD> + Forward propagation through generalized linear model. +<DT> +<CODE><a href="glmgrad.htm">glmgrad</a></CODE><DD> + Evaluate gradient of error function for generalized linear model. +<DT> +<CODE><a href="glmhess.htm">glmhess</a></CODE><DD> + Evaluate the Hessian matrix for a generalised linear model. +<DT> +<CODE><a href="glminit.htm">glminit</a></CODE><DD> + Initialise the weights in a generalized linear model. +<DT> +<CODE><a href="glmpak.htm">glmpak</a></CODE><DD> + Combines weights and biases into one weights vector. +<DT> +<CODE><a href="glmtrain.htm">glmtrain</a></CODE><DD> + Specialised training of generalized linear model +<DT> +<CODE><a href="glmunpak.htm">glmunpak</a></CODE><DD> + Separates weights vector into weight and bias matrices. +<DT> +<CODE><a href="gmm.htm">gmm</a></CODE><DD> + Creates a Gaussian mixture model with specified architecture. +<DT> +<CODE><a href="gmmactiv.htm">gmmactiv</a></CODE><DD> + Computes the activations of a Gaussian mixture model. +<DT> +<CODE><a href="gmmem.htm">gmmem</a></CODE><DD> + EM algorithm for Gaussian mixture model. +<DT> +<CODE><a href="gmminit.htm">gmminit</a></CODE><DD> + Initialises Gaussian mixture model from data +<DT> +<CODE><a href="gmmpak.htm">gmmpak</a></CODE><DD> + Combines all the parameters in a Gaussian mixture model into one vector. +<DT> +<CODE><a href="gmmpost.htm">gmmpost</a></CODE><DD> + Computes the class posterior probabilities of a Gaussian mixture model. +<DT> +<CODE><a href="gmmprob.htm">gmmprob</a></CODE><DD> + Computes the data probability for a Gaussian mixture model. +<DT> +<CODE><a href="gmmsamp.htm">gmmsamp</a></CODE><DD> + Sample from a Gaussian mixture distribution. +<DT> +<CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><DD> + Separates a vector of Gaussian mixture model parameters into its components. +<DT> +<CODE><a href="gp.htm">gp</a></CODE><DD> + Create a Gaussian Process. +<DT> +<CODE><a href="gpcovar.htm">gpcovar</a></CODE><DD> + Calculate the covariance for a Gaussian Process. +<DT> +<CODE><a href="gpcovarf.htm">gpcovarf</a></CODE><DD> + Calculate the covariance function for a Gaussian Process. +<DT> +<CODE><a href="gpcovarp.htm">gpcovarp</a></CODE><DD> + Calculate the prior covariance for a Gaussian Process. +<DT> +<CODE><a href="gperr.htm">gperr</a></CODE><DD> + Evaluate error function for Gaussian Process. +<DT> +<CODE><a href="gpfwd.htm">gpfwd</a></CODE><DD> + Forward propagation through Gaussian Process. +<DT> +<CODE><a href="gpgrad.htm">gpgrad</a></CODE><DD> + Evaluate error gradient for Gaussian Process. +<DT> +<CODE><a href="gpinit.htm">gpinit</a></CODE><DD> + Initialise Gaussian Process model. +<DT> +<CODE><a href="gppak.htm">gppak</a></CODE><DD> + Combines GP hyperparameters into one vector. +<DT> +<CODE><a href="gpunpak.htm">gpunpak</a></CODE><DD> + Separates hyperparameter vector into components. +<DT> +<CODE><a href="gradchek.htm">gradchek</a></CODE><DD> + Checks a user-defined gradient function using finite differences. +<DT> +<CODE><a href="graddesc.htm">graddesc</a></CODE><DD> + Gradient descent optimization. +<DT> +<CODE><a href="gsamp.htm">gsamp</a></CODE><DD> + Sample from a Gaussian distribution. +<DT> +<CODE><a href="gtm.htm">gtm</a></CODE><DD> + Create a Generative Topographic Map. +<DT> +<CODE><a href="gtmem.htm">gtmem</a></CODE><DD> + EM algorithm for Generative Topographic Mapping. +<DT> +<CODE><a href="gtmfwd.htm">gtmfwd</a></CODE><DD> + Forward propagation through GTM. +<DT> +<CODE><a href="gtminit.htm">gtminit</a></CODE><DD> + Initialise the weights and latent sample in a GTM. +<DT> +<CODE><a href="gtmlmean.htm">gtmlmean</a></CODE><DD> + Mean responsibility for data in a GTM. +<DT> +<CODE><a href="gtmlmode.htm">gtmlmode</a></CODE><DD> + Mode responsibility for data in a GTM. +<DT> +<CODE><a href="gtmmag.htm">gtmmag</a></CODE><DD> + Magnification factors for a GTM +<DT> +<CODE><a href="gtmpost.htm">gtmpost</a></CODE><DD> + Latent space responsibility for data in a GTM. +<DT> +<CODE><a href="gtmprob.htm">gtmprob</a></CODE><DD> + Probability for data under a GTM. +<DT> +<CODE><a href="hbayes.htm">hbayes</a></CODE><DD> + Evaluate Hessian of Bayesian error function for network. +<DT> +<CODE><a href="hesschek.htm">hesschek</a></CODE><DD> + Use central differences to confirm correct evaluation of Hessian matrix. +<DT> +<CODE><a href="hintmat.htm">hintmat</a></CODE><DD> + Evaluates the coordinates of the patches for a Hinton diagram. +<DT> +<CODE><a href="hinton.htm">hinton</a></CODE><DD> + Plot Hinton diagram for a weight matrix. +<DT> +<CODE><a href="histp.htm">histp</a></CODE><DD> + Histogram estimate of 1-dimensional probability distribution. +<DT> +<CODE><a href="hmc.htm">hmc</a></CODE><DD> + Hybrid Monte Carlo sampling. +<DT> +<CODE><a href="kmeans.htm">kmeans</a></CODE><DD> + Trains a k means cluster model. +<DT> +<CODE><a href="knn.htm">knn</a></CODE><DD> + Creates a K-nearest-neighbour classifier. +<DT> +<CODE><a href="knnfwd.htm">knnfwd</a></CODE><DD> + Forward propagation through a K-nearest-neighbour classifier. +<DT> +<CODE><a href="linef.htm">linef</a></CODE><DD> + Calculate function value along a line. +<DT> +<CODE><a href="linemin.htm">linemin</a></CODE><DD> + One dimensional minimization. +<DT> +<CODE><a href="maxitmess.htm">maxitmess</a></CODE><DD> + Create a standard error message when training reaches max. iterations. +<DT> +<CODE><a href="mdn.htm">mdn</a></CODE><DD> + Creates a Mixture Density Network with specified architecture. +<DT> +<CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE><DD> + Converts an MDN mixture data structure to array of GMMs. +<DT> +<CODE><a href="mdndist2.htm">mdndist2</a></CODE><DD> + Calculates squared distance between centres of Gaussian kernels and data +<DT> +<CODE><a href="mdnerr.htm">mdnerr</a></CODE><DD> + Evaluate error function for Mixture Density Network. +<DT> +<CODE><a href="mdnfwd.htm">mdnfwd</a></CODE><DD> + Forward propagation through Mixture Density Network. +<DT> +<CODE><a href="mdngrad.htm">mdngrad</a></CODE><DD> + Evaluate gradient of error function for Mixture Density Network. +<DT> +<CODE><a href="mdninit.htm">mdninit</a></CODE><DD> + Initialise the weights in a Mixture Density Network. +<DT> +<CODE><a href="mdnpak.htm">mdnpak</a></CODE><DD> + Combines weights and biases into one weights vector. +<DT> +<CODE><a href="mdnpost.htm">mdnpost</a></CODE><DD> + Computes the posterior probability for each MDN mixture component. +<DT> +<CODE><a href="mdnprob.htm">mdnprob</a></CODE><DD> + Computes the data probability likelihood for an MDN mixture structure. +<DT> +<CODE><a href="mdnunpak.htm">mdnunpak</a></CODE><DD> + Separates weights vector into weight and bias matrices. +<DT> +<CODE><a href="metrop.htm">metrop</a></CODE><DD> + Markov Chain Monte Carlo sampling with Metropolis algorithm. +<DT> +<CODE><a href="minbrack.htm">minbrack</a></CODE><DD> + Bracket a minimum of a function of one variable. +<DT> +<CODE><a href="mlp.htm">mlp</a></CODE><DD> + Create a 2-layer feedforward network. +<DT> +<CODE><a href="mlpbkp.htm">mlpbkp</a></CODE><DD> + Backpropagate gradient of error function for 2-layer network. +<DT> +<CODE><a href="mlpderiv.htm">mlpderiv</a></CODE><DD> + Evaluate derivatives of network outputs with respect to weights. +<DT> +<CODE><a href="mlperr.htm">mlperr</a></CODE><DD> + Evaluate error function for 2-layer network. +<DT> +<CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE><DD> + Forward propagation with evidence for MLP +<DT> +<CODE><a href="mlpfwd.htm">mlpfwd</a></CODE><DD> + Forward propagation through 2-layer network. +<DT> +<CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><DD> + Evaluate gradient of error function for 2-layer network. +<DT> +<CODE><a href="mlphdotv.htm">mlphdotv</a></CODE><DD> + Evaluate the product of the data Hessian with a vector. +<DT> +<CODE><a href="mlphess.htm">mlphess</a></CODE><DD> + Evaluate the Hessian matrix for a multi-layer perceptron network. +<DT> +<CODE><a href="mlphint.htm">mlphint</a></CODE><DD> + Plot Hinton diagram for 2-layer feed-forward network. +<DT> +<CODE><a href="mlpinit.htm">mlpinit</a></CODE><DD> + Initialise the weights in a 2-layer feedforward network. +<DT> +<CODE><a href="mlppak.htm">mlppak</a></CODE><DD> + Combines weights and biases into one weights vector. +<DT> +<CODE><a href="mlpprior.htm">mlpprior</a></CODE><DD> + Create Gaussian prior for mlp. +<DT> +<CODE><a href="mlptrain.htm">mlptrain</a></CODE><DD> + Utility to train an MLP network for demtrain +<DT> +<CODE><a href="mlpunpak.htm">mlpunpak</a></CODE><DD> + Separates weights vector into weight and bias matrices. +<DT> +<CODE><a href="netderiv.htm">netderiv</a></CODE><DD> + Evaluate derivatives of network outputs by weights generically. +<DT> +<CODE><a href="neterr.htm">neterr</a></CODE><DD> + Evaluate network error function for generic optimizers +<DT> +<CODE><a href="netevfwd.htm">netevfwd</a></CODE><DD> + Generic forward propagation with evidence for network +<DT> +<CODE><a href="netgrad.htm">netgrad</a></CODE><DD> + Evaluate network error gradient for generic optimizers +<DT> +<CODE><a href="nethess.htm">nethess</a></CODE><DD> + Evaluate network Hessian +<DT> +<CODE><a href="netinit.htm">netinit</a></CODE><DD> + Initialise the weights in a network. +<DT> +<CODE><a href="netopt.htm">netopt</a></CODE><DD> + Optimize the weights in a network model. +<DT> +<CODE><a href="netpak.htm">netpak</a></CODE><DD> + Combines weights and biases into one weights vector. +<DT> +<CODE><a href="netunpak.htm">netunpak</a></CODE><DD> + Separates weights vector into weight and bias matrices. +<DT> +<CODE><a href="olgd.htm">olgd</a></CODE><DD> + On-line gradient descent optimization. +<DT> +<CODE><a href="pca.htm">pca</a></CODE><DD> + Principal Components Analysis +<DT> +<CODE><a href="plotmat.htm">plotmat</a></CODE><DD> + Display a matrix. +<DT> +<CODE><a href="ppca.htm">ppca</a></CODE><DD> + Probabilistic Principal Components Analysis +<DT> +<CODE><a href="quasinew.htm">quasinew</a></CODE><DD> + Quasi-Newton optimization. +<DT> +<CODE><a href="rbf.htm">rbf</a></CODE><DD> + Creates an RBF network with specified architecture +<DT> +<CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><DD> + Backpropagate gradient of error function for RBF network. +<DT> +<CODE><a href="rbfderiv.htm">rbfderiv</a></CODE><DD> + Evaluate derivatives of RBF network outputs with respect to weights. +<DT> +<CODE><a href="rbferr.htm">rbferr</a></CODE><DD> + Evaluate error function for RBF network. +<DT> +<CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE><DD> + Forward propagation with evidence for RBF +<DT> +<CODE><a href="rbffwd.htm">rbffwd</a></CODE><DD> + Forward propagation through RBF network with linear outputs. +<DT> +<CODE><a href="rbfgrad.htm">rbfgrad</a></CODE><DD> + Evaluate gradient of error function for RBF network. +<DT> +<CODE><a href="rbfhess.htm">rbfhess</a></CODE><DD> + Evaluate the Hessian matrix for RBF network. +<DT> +<CODE><a href="rbfjacob.htm">rbfjacob</a></CODE><DD> + Evaluate derivatives of RBF network outputs with respect to inputs. +<DT> +<CODE><a href="rbfpak.htm">rbfpak</a></CODE><DD> + Combines all the parameters in an RBF network into one weights vector. +<DT> +<CODE><a href="rbfprior.htm">rbfprior</a></CODE><DD> + Create Gaussian prior and output layer mask for RBF. +<DT> +<CODE><a href="rbfsetbf.htm">rbfsetbf</a></CODE><DD> + Set basis functions of RBF from data. +<DT> +<CODE><a href="rbfsetfw.htm">rbfsetfw</a></CODE><DD> + Set basis function widths of RBF. +<DT> +<CODE><a href="rbftrain.htm">rbftrain</a></CODE><DD> + Two stage training of RBF network. +<DT> +<CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><DD> + Separates a vector of RBF weights into its components. +<DT> +<CODE><a href="rosegrad.htm">rosegrad</a></CODE><DD> + Calculate gradient of Rosenbrock's function. +<DT> +<CODE><a href="rosen.htm">rosen</a></CODE><DD> + Calculate Rosenbrock's function. +<DT> +<CODE><a href="scg.htm">scg</a></CODE><DD> + Scaled conjugate gradient optimization. +<DT> +<CODE><a href="som.htm">som</a></CODE><DD> + Creates a Self-Organising Map. +<DT> +<CODE><a href="somfwd.htm">somfwd</a></CODE><DD> + Forward propagation through a Self-Organising Map. +<DT> +<CODE><a href="sompak.htm">sompak</a></CODE><DD> + Combines node weights into one weights matrix. +<DT> +<CODE><a href="somtrain.htm">somtrain</a></CODE><DD> + Kohonen training algorithm for SOM. +<DT> +<CODE><a href="somunpak.htm">somunpak</a></CODE><DD> + Replaces node weights in SOM. +</DL> + +<hr> +<p>Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997) +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/kmeans.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,89 @@ +<html> +<head> +<title> +Netlab Reference Manual kmeans +</title> +</head> +<body> +<H1> kmeans +</H1> +<h2> +Purpose +</h2> +Trains a k means cluster model. + +<p><h2> +Synopsis +</h2> +<PRE> +centres = kmeans(centres, data, options) +[centres, options] = kmeans(centres, data, options) +[centres, options, post, errlog] = kmeans(centres, data, options) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>centres = kmeans(centres, data, options)</CODE> +uses the batch K-means algorithm to set the centres of a cluster model. +The matrix <CODE>data</CODE> represents the data +which is being clustered, with each row corresponding to a vector. +The sum of squares error function is used. The point at which +a local minimum is achieved is returned as <CODE>centres</CODE>. The +error value at that point is returned in <CODE>options(8)</CODE>. + +<p><CODE>[centres, options, post, errlog] = kmeans(centres, data, options)</CODE> +also returns the cluster number (in a one-of-N encoding) for each data +point in <CODE>post</CODE> and a log of the error values after each cycle in +<CODE>errlog</CODE>. + +The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>. +If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(2)</CODE> is a measure of the absolute precision required for the value +of <CODE>centres</CODE> at the solution. If the absolute difference between +the values of <CODE>centres</CODE> between two successive steps is less than +<CODE>options(2)</CODE>, then this condition is satisfied. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the error +function at the solution. If the absolute difference between the +error functions between two successive steps is less than +<CODE>options(3)</CODE>, then this condition is satisfied. +Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p><h2> +Example +</h2> +<CODE>kmeans</CODE> can be used to initialise the centres of a Gaussian +mixture model that is then trained with the EM algorithm. +<PRE> + +[priors, centres, var] = gmmunpak(p, md); +centres = kmeans(centres, data, options); +p = gmmpak(priors, centres, var); +p = gmmem(p, md, data, options); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/knn.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +<html> +<head> +<title> +Netlab Reference Manual knn +</title> +</head> +<body> +<H1> knn +</H1> +<h2> +Purpose +</h2> +Creates a K-nearest-neighbour classifier. + +<p><h2> +Synopsis +</h2> +<PRE> + +net = knn(nin, nout, k, tr_in, tr_targets) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = knn(nin, nout, k, tr_in, tr_targets)</CODE> creates a KNN model <CODE>net</CODE> +with input dimension <CODE>nin</CODE>, output dimension <CODE>nout</CODE> and <CODE>k</CODE> +neighbours. The training data is also stored in the data structure and the +targets are assumed to be using a 1-of-N coding. + +<p>The fields in <CODE>net</CODE> are +<PRE> + + type = 'knn' + nin = number of inputs + nout = number of outputs + tr_in = training input data + tr_targets = training target data +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="knnfwd.htm">knnfwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/knnfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +<html> +<head> +<title> +Netlab Reference Manual knnfwd +</title> +</head> +<body> +<H1> knnfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through a K-nearest-neighbour classifier. + +<p><h2> +Synopsis +</h2> +<PRE> + +[y, l] = knnfwd(net, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>[y, l] = knnfwd(net, x)</CODE> takes a matrix <CODE>x</CODE> +of input vectors (one vector per row) + and uses the <CODE>k</CODE>-nearest-neighbour rule on the training data contained +in <CODE>net</CODE> to +produce +a matrix <CODE>y</CODE> of outputs and a matrix <CODE>l</CODE> of classification +labels. +The nearest neighbours are determined using Euclidean distance. +The <CODE>ij</CODE>th entry of <CODE>y</CODE> counts the number of occurrences that +an example from class <CODE>j</CODE> is among the <CODE>k</CODE> closest training +examples to example <CODE>i</CODE> from <CODE>x</CODE>. +The matrix <CODE>l</CODE> contains the predicted class labels +as an index 1..N, not as 1-of-N coding. + +<p><h2> +Example +</h2> +<PRE> + +net = knn(size(xtrain, 2), size(t_train, 2), 3, xtrain, t_train); +y = knnfwd(net, xtest); +conffig(y, t_test); +</PRE> + +Creates a 3 nearest neighbour model <CODE>net</CODE> and then applies it to +the data <CODE>xtest</CODE>. The results are plotted as a confusion matrix with +<CODE>conffig</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="knn.htm">knn</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/linef.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual linef +</title> +</head> +<body> +<H1> linef +</H1> +<h2> +Purpose +</h2> +Calculate function value along a line. + +<p><h2> +Description +</h2> +<CODE>linef(lambda, fn, x, d)</CODE> calculates the value of the function +<CODE>fn</CODE> at the point <CODE>x+lambda*d</CODE>. Here <CODE>x</CODE> is a row vector +and <CODE>lambda</CODE> is a scalar. + +<p><CODE>linef(lambda, fn, x, d, p1, p2, ...)</CODE> allows additional +arguments to be passed to <CODE>fn()</CODE>. +This function is used for convenience in some of the optimisation routines. + +<p><h2> +Examples +</h2> +An example of +the use of this function can be found in the function <CODE>linemin</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gradchek.htm">gradchek</a></CODE>, <CODE><a href="linemin.htm">linemin</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/linemin.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +<html> +<head> +<title> +Netlab Reference Manual linemin +</title> +</head> +<body> +<H1> linemin +</H1> +<h2> +Purpose +</h2> +One dimensional minimization. + +<p><h2> +Description +</h2> +<CODE>[x, options] = linemin(f, pt, dir, fpt, options)</CODE> uses Brent's +algorithm to find the minimum of the function <CODE>f(x)</CODE> along the +line <CODE>dir</CODE> through the point <CODE>pt</CODE>. The function value at the +starting point is <CODE>fpt</CODE>. The point at which <CODE>f</CODE> has a local minimum +is returned as <CODE>x</CODE>. The function value at that point is returned +in <CODE>options(8)</CODE>. + +<p><CODE>linemin(f, pt, dir, fpt, options, p1, p2, ...)</CODE> allows +additional arguments to be passed to <CODE>f()</CODE>. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values. + +<p><CODE>options(2)</CODE> is a measure of the absolute precision required for the value +of <CODE>x</CODE> at the solution. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p><h2> +Examples +</h2> +An example of the use of this function to find the minimum of a function +<CODE>f</CODE> in the direction <CODE>sd</CODE> can be found in <CODE>conjgrad</CODE> +<PRE> + +x = linemin(f, xold, sd, fold, lineoptions); +</PRE> + + +<p><h2> +Algorithm +</h2> + +Brent's algorithm uses a mixture of quadratic interpolation and golden +section search to find the minimum of a function of a single variable once +it has been bracketed (which is done with <CODE>minbrack</CODE>). This is adapted +to minimize a function along a line. +This implementation +is based on that in Numerical Recipes. + +<p><h2> +See Also +</h2> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="minbrack.htm">minbrack</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/maxitmess.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +<html> +<head> +<title> +Netlab Reference Manual maxitmess +</title> +</head> +<body> +<H1> maxitmess +</H1> +<h2> +Purpose +</h2> +Create a standard error message when training reaches max. iterations. + +<p><h2> +Synopsis +</h2> +<PRE> +s = maxitmess +</PRE> + + +<p><h2> +Description +</h2> +<CODE>s = maxitmess</CODE> returns a standard string that it used by training +algorithms when the maximum number of iterations (as specified in +<CODE>options(14)</CODE> is reached. + +<p><h2> +See Also +</h2> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="graddesc.htm">graddesc</a></CODE>, <CODE><a href="gtmem.htm">gtmem</a></CODE>, <CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="olgd.htm">olgd</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdn.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,84 @@ +<html> +<head> +<title> +Netlab Reference Manual mdn +</title> +</head> +<body> +<H1> mdn +</H1> +<h2> +Purpose +</h2> +Creates a Mixture Density Network with specified architecture. + +<p><h2> +Synopsis +</h2> +<PRE> +net = mdn(nin, nhidden, ncentres, dimtarget) +net = mdn(nin, nhidden, ncentres, dimtarget, mixtype, ... + prior, beta) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = mdn(nin, nhidden, ncentres, dimtarget)</CODE> takes the number of +inputs, +hidden units for a 2-layer feed-forward +network and the number of centres and target dimension for the +mixture model whose parameters are set from the outputs of the neural network. +The fifth argument <CODE>mixtype</CODE> is used to define the type of mixture +model. (Currently there is only one type supported: a mixture of Gaussians with +a single covariance parameter for each component.) For this model, +the mixture coefficients are computed from a group of softmax outputs, +the centres are equal to a group of linear outputs, and the variances are +obtained by applying the exponential function to a third group of outputs. + +<p>The network is initialised by a call to <CODE>mlp</CODE>, and the arguments +<CODE>prior</CODE>, and <CODE>beta</CODE> have the same role as for that function. +Weight initialisation uses the Matlab function <CODE>randn</CODE> + and so the seed for the random weight initialization can be +set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. +A specialised data structure (rather than <CODE>gmm</CODE>) +is used for the mixture model outputs to improve +the efficiency of error and gradient calculations in network training. +The fields are described in <CODE>mdnfwd</CODE> where they are set up. + +<p>The fields in <CODE>net</CODE> are +<PRE> + + type = 'mdn' + nin = number of input variables + nout = dimension of target space (not number of network outputs) + nwts = total number of weights and biases + mdnmixes = data structure for mixture model output + mlp = data structure for MLP network +</PRE> + + +<p><h2> +Example +</h2> +<PRE> + +net = mdn(2, 4, 3, 1, 'spherical'); +</PRE> + +This creates a Mixture Density Network with 2 inputs and 4 hidden units. +The mixture model has 3 components and the target space has dimension 1. + +<p><h2> +See Also +</h2> +<CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="mdnpak.htm">mdnpak</a></CODE>, <CODE><a href="mdnunpak.htm">mdnunpak</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdn2gmm.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +<html> +<head> +<title> +Netlab Reference Manual mdn2gmm +</title> +</head> +<body> +<H1> mdn2gmm +</H1> +<h2> +Purpose +</h2> +Converts an MDN mixture data structure to array of GMMs. + +<p><h2> +Synopsis +</h2> +<PRE> +gmmmixes = mdn2gmm(mdnmixes) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>gmmmixes = mdn2gmm(mdnmixes)</CODE> takes an MDN mixture data structure +<CODE>mdnmixes</CODE> +containing three matrices (for priors, centres and variances) where each +row represents the corresponding parameter values for a different mixture model +and creates an array of GMMs. These can then be used with the standard +Netlab Gaussian mixture model functions. + +<p><h2> +Example +</h2> +<PRE> + +mdnmixes = mdnfwd(net, x); +mixes = mdn2gmm(mdnmixes); +p = gmmprob(mixes(1), y); +</PRE> + +This creates an array GMM mixture models (one for each data point in +<CODE>x</CODE>). The vector <CODE>p</CODE> is then filled with the conditional +probabilities of the values <CODE>y</CODE> given <CODE>x(1,:)</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnfwd.htm">mdnfwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdndist2.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual mdndist2 +</title> +</head> +<body> +<H1> mdndist2 +</H1> +<h2> +Purpose +</h2> +Calculates squared distance between centres of Gaussian kernels and data + +<p><h2> +Synopsis +</h2> +<PRE> +n2 = mdndist2(mixparams, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>n2 = mdndist2(mixparams, t)</CODE> takes takes the centres of the Gaussian +contained in + <CODE>mixparams</CODE> and the target data matrix, <CODE>t</CODE>, and computes the squared +Euclidean distance between them. If <CODE>t</CODE> has <CODE>m</CODE> rows and <CODE>n</CODE> +columns, then the <CODE>centres</CODE> field in +the <CODE>mixparams</CODE> structure should have <CODE>m</CODE> rows and +<CODE>n*mixparams.ncentres</CODE> columns: the centres in each row relate to +the corresponding row in <CODE>t</CODE>. +The result has <CODE>m</CODE> rows and <CODE>mixparams.ncentres</CODE> columns. +The <CODE>i, j</CODE>th entry is the +squared distance from the <CODE>i</CODE>th row of <CODE>x</CODE> to the <CODE>j</CODE>th +centre in the <CODE>i</CODE>th row of <CODE>mixparams.centres</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdnprob.htm">mdnprob</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdnerr.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +<html> +<head> +<title> +Netlab Reference Manual mdnerr +</title> +</head> +<body> +<H1> mdnerr +</H1> +<h2> +Purpose +</h2> +Evaluate error function for Mixture Density Network. + +<p><h2> +Synopsis +</h2> +<PRE> +e = mdnerr(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>e = mdnerr(net, x, t)</CODE> takes a mixture density network data +structure <CODE>net</CODE>, a matrix <CODE>x</CODE> of input vectors and a matrix +<CODE>t</CODE> of target vectors, and evaluates the error function +<CODE>e</CODE>. The error function is the negative log likelihood of the +target data under the conditional density given by the mixture model +parameterised by the MLP. Each row of <CODE>x</CODE> corresponds to one +input vector and each row of <CODE>t</CODE> corresponds to one target vector. + +<p><h2> +See Also +</h2> +<CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdnfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +<html> +<head> +<title> +Netlab Reference Manual mdnfwd +</title> +</head> +<body> +<H1> mdnfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through Mixture Density Network. + +<p><h2> +Synopsis +</h2> +<PRE> +mixparams = mdnfwd(net, x) +[mixparams, y, z] = mdnfwd(net, x) +[mixparams, y, z, a] = mdnfwd(net, x) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>mixparams = mdnfwd(net, x)</CODE> takes a mixture density network data +structure <CODE>net</CODE> and a matrix <CODE>x</CODE> of input vectors, and forward +propagates the inputs through the network to generate a structure +<CODE>mixparams</CODE> which contains the parameters of several mixture models. +Each row of <CODE>x</CODE> represents +one input vector and the corresponding row of the matrices in <CODE>mixparams</CODE> +represents the parameters of a mixture model for the conditional probability +of target vectors given the input vector. This is not represented as an array +of <CODE>gmm</CODE> structures to improve the efficiency of MDN training. + +<p>The fields in <CODE>mixparams</CODE> are +<PRE> + + type = 'mdnmixes' + ncentres = number of mixture components + dimtarget = dimension of target space + mixcoeffs = mixing coefficients + centres = means of Gaussians: stored as one row per pattern + covars = covariances of Gaussians + nparams = number of parameters +</PRE> + + +<p><CODE>[mixparams, y, z] = mdnfwd(net, x)</CODE> also generates a matrix <CODE>y</CODE> of +the outputs of the MLP and a matrix <CODE>z</CODE> of the hidden +unit activations where each row corresponds to one pattern. + +<p><CODE>[mixparams, y, z, a] = mlpfwd(net, x)</CODE> also returns a matrix <CODE>a</CODE> +giving the summed inputs to each output unit, where each row +corresponds to one pattern. + +<p><h2> +See Also +</h2> +<CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdngrad.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual mdngrad +</title> +</head> +<body> +<H1> mdngrad +</H1> +<h2> +Purpose +</h2> +Evaluate gradient of error function for Mixture Density Network. + +<p><h2> +Synopsis +</h2> +<PRE> + +g = mdngrad(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>g = mdngrad(net, x, t)</CODE> takes a mixture density network data +structure <CODE>net</CODE>, a matrix <CODE>x</CODE> of input vectors and a matrix +<CODE>t</CODE> of target vectors, and evaluates the gradient <CODE>g</CODE> of the +error function with respect to the network weights. The error function +is negative log likelihood of the target data. Each row of <CODE>x</CODE> +corresponds to one input vector and each row of <CODE>t</CODE> corresponds to +one target vector. + +<p><h2> +See Also +</h2> +<CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdnprob.htm">mdnprob</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdninit.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual mdninit +</title> +</head> +<body> +<H1> mdninit +</H1> +<h2> +Purpose +</h2> +Initialise the weights in a Mixture Density Network. + +<p><h2> +Synopsis +</h2> +<PRE> +net = mdninit(net, prior) +net = mdninit(net, prior, t, options) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = mdninit(net, prior)</CODE> takes a Mixture Density Network +<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian +distribution. It calls <CODE>mlpinit</CODE> for the MLP component of <CODE>net</CODE>. + +<p><CODE>net = mdninit(net, prior, t, options)</CODE> uses the target data <CODE>t</CODE> to +initialise the biases for the output units after initialising the +other weights as above. It calls <CODE>gmminit</CODE>, with <CODE>t</CODE> and <CODE>options</CODE> +as arguments, to obtain a model of the unconditional density of <CODE>t</CODE>. The +biases are then set so that <CODE>net</CODE> will output the values in the Gaussian +mixture model. + +<p><h2> +See Also +</h2> +<CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpinit.htm">mlpinit</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdnpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +<html> +<head> +<title> +Netlab Reference Manual mdnpak +</title> +</head> +<body> +<H1> mdnpak +</H1> +<h2> +Purpose +</h2> +Combines weights and biases into one weights vector. + +<p><h2> +Synopsis +</h2> +<PRE> +w = mdnpak(net) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>w = mdnpak(net)</CODE> takes a mixture density +network data structure <CODE>net</CODE> and +combines the network weights into a single row vector <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnunpak.htm">mdnunpak</a></CODE>, <CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdnpost.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual mdnpost +</title> +</head> +<body> +<H1> mdnpost +</H1> +<h2> +Purpose +</h2> +Computes the posterior probability for each MDN mixture component. + +<p><h2> +Synopsis +</h2> +<PRE> +post = mdnpost(mixparams, t) +[post, a] = mdnpost(mixparams, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>post = mdnpost(mixparams, t)</CODE> computes the posterior +probability <CODE>p(j|t)</CODE> of each +data vector in <CODE>t</CODE> under the Gaussian mixture model represented by the +corresponding entries in <CODE>mixparams</CODE>. Each row of <CODE>t</CODE> represents a +single vector. + +<p><CODE>[post, a] = mdnpost(mixparams, t)</CODE> also computes the activations +<CODE>a</CODE> (i.e. the probability <CODE>p(t|j)</CODE> of the data conditioned on +each component density) for a Gaussian mixture model. + +<p><h2> +See Also +</h2> +<CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="mdnprob.htm">mdnprob</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdnprob.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual mdnprob +</title> +</head> +<body> +<H1> mdnprob +</H1> +<h2> +Purpose +</h2> +Computes the data probability likelihood for an MDN mixture structure. + +<p><h2> +Synopsis +</h2> +<PRE> +prob = mdnprob(mixparams, t) +[prob, a] = mdnprob(mixparams, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>prob = mdnprob(mixparams, t)</CODE> computes the probability <CODE>p(t)</CODE> of each +data vector in <CODE>t</CODE> under the Gaussian mixture model represented by the +corresponding entries in <CODE>mixparams</CODE>. Each row of <CODE>t</CODE> represents a +single vector. + +<p><CODE>[prob, a] = mdnprob(mixparams, t)</CODE> also computes the activations +<CODE>a</CODE> (i.e. the probability <CODE>p(t|j)</CODE> of the data conditioned on +each component density) for a Gaussian mixture model. + +<p><h2> +See Also +</h2> +<CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdnpost.htm">mdnpost</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mdnunpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual mdnunpak +</title> +</head> +<body> +<H1> mdnunpak +</H1> +<h2> +Purpose +</h2> +Separates weights vector into weight and bias matrices. + +<p><h2> +Synopsis +</h2> +<PRE> +net = mdnunpak(net, w) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = mdnunpak(net, w)</CODE> takes an mdn network data structure <CODE>net</CODE> and +a weight vector <CODE>w</CODE>, and returns a network data structure identical to +the input network, except that the weights in the MLP sub-structure are +set to the corresponding elements of <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnpak.htm">mdnpak</a></CODE>, <CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/metrop.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,108 @@ +<html> +<head> +<title> +Netlab Reference Manual metrop +</title> +</head> +<body> +<H1> metrop +</H1> +<h2> +Purpose +</h2> +Markov Chain Monte Carlo sampling with Metropolis algorithm. + +<p><h2> +Synopsis +</h2> +<PRE> + +samples = metrop(f, x, options) +samples = metrop(f, x, options, [], P1, P2, ...) +[samples, energies, diagn] = metrop(f, x, options) +s = metrop('state') +metrop('state', s) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>samples = metrop(f, x, options)</CODE> uses +the Metropolis algorithm to sample from the distribution +<CODE>p ~ exp(-f)</CODE>, where <CODE>f</CODE> is the first argument to <CODE>metrop</CODE>. +The Markov chain starts at the point <CODE>x</CODE> and each +candidate state is picked from a Gaussian proposal distribution and +accepted or rejected according to the Metropolis criterion. + +<p><CODE>samples = metrop(f, x, options, [], p1, p2, ...)</CODE> allows +additional arguments to be passed to <CODE>f()</CODE>. The fourth argument is +ignored, but is included for compatibility with <CODE>hmc</CODE> and the +optimisers. + +<p><CODE>[samples, energies, diagn] = metrop(f, x, options)</CODE> also returns +a log of the energy values (i.e. negative log probabilities) for the +samples in <CODE>energies</CODE> and <CODE>diagn</CODE>, a structure containing +diagnostic information (position and +acceptance threshold) for each step of the chain in <CODE>diagn.pos</CODE> and +<CODE>diagn.acc</CODE> respectively. All candidate states (including rejected +ones) are stored in <CODE>diagn.pos</CODE>. + +<p><CODE>s = metrop('state')</CODE> returns a state structure that contains the +state of the two random number generators <CODE>rand</CODE> and <CODE>randn</CODE>. +These are contained in fields +<CODE>randstate</CODE>, +<CODE>randnstate</CODE>. + +<p><CODE>metrop('state', s)</CODE> resets the state to <CODE>s</CODE>. If <CODE>s</CODE> is an integer, +then it is passed to <CODE>rand</CODE> and <CODE>randn</CODE>. +If <CODE>s</CODE> is a structure returned by <CODE>metrop('state')</CODE> then +it resets the generator to exactly the same state. + +<p>The optional parameters in the <CODE>options</CODE> vector have the following +interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display the energy values and rejection +threshold at each step of the Markov chain. If the value is 2, then the +position vectors at each step are also displayed. + +<p><CODE>options(14)</CODE> is the number of samples retained from the Markov chain; +default 100. + +<p><CODE>options(15)</CODE> is the number of samples omitted from the start of the +chain; default 0. + +<p><CODE>options(18)</CODE> is the variance of the proposal distribution; default 1. + +<p><h2> +Examples +</h2> +The following code fragment samples from the posterior distribution of +weights for a neural network. +<PRE> + +w = mlppak(net); +[samples, energies] = metrop('neterr', w, options, 'netgrad', net, x, t); +</PRE> + + +<p><h2> +Algorithm +</h2> + +The algorithm follows the procedure outlined in Radford Neal's technical +report CRG-TR-93-1 from the University of Toronto. + +<p><h2> +See Also +</h2> +<CODE><a href="hmc.htm">hmc</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/minbrack.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +<html> +<head> +<title> +Netlab Reference Manual minbrack +</title> +</head> +<body> +<H1> minbrack +</H1> +<h2> +Purpose +</h2> +Bracket a minimum of a function of one variable. + +<p><h2> +Description +</h2> +<CODE>brmin, brmid, brmax, numevals] = minbrack(f, a, b, fa)</CODE> +finds a bracket of three points around a local minimum of +<CODE>f</CODE>. The function <CODE>f</CODE> must have a one dimensional domain. +<CODE>a < b</CODE> is an initial guess at the minimum and maximum points +of a bracket, but <CODE>minbrack</CODE> will search outside this interval if +necessary. The bracket consists of three points (in increasing order) +such that <CODE>f(brmid) < f(brmin)</CODE> and <CODE>f(brmid) < f(brmax)</CODE>. +<CODE>fa</CODE> is the value of the function at <CODE>a</CODE>: it is included to +avoid unnecessary function evaluations in the optimization routines. +The return value <CODE>numevals</CODE> is the number of function evaluations +in <CODE>minbrack</CODE>. + +<p><CODE>minbrack(f, a, b, fa, p1, p2, ...)</CODE> allows additional +arguments to be passed to <CODE>f</CODE> + +<p><h2> +Examples +</h2> +An example of the use of this function to bracket the minimum of a function +<CODE>f</CODE> in the direction <CODE>sd</CODE> can be found in <CODE>linemin</CODE> +<PRE> + +[min, mid, max, nevals]] = minbrack('linef', 0.0, 1.0, fa, f, pt, dir); +</PRE> + +where the function <CODE>linef</CODE> is used to turn a general function <CODE>f</CODE> +into a one dimensional one. + +<p><h2> +Algorithm +</h2> + +Quadratic extrapolation with a limit to the maximum step size is +used to find the outside points of the bracket. This implementation +is based on that in Numerical Recipes. + +<p><h2> +See Also +</h2> +<CODE><a href="linemin.htm">linemin</a></CODE>, <CODE><a href="linef.htm">linef</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ +<html> +<head> +<title> +Netlab Reference Manual mlp +</title> +</head> +<body> +<H1> mlp +</H1> +<h2> +Purpose +</h2> +Create a 2-layer feedforward network. + +<p><h2> +Synopsis +</h2> +<PRE> +net = mlp(nin, nhidden, nout, func) +net = mlp(nin, nhidden, nout, func, prior) +net = mlp(nin, nhidden, nout, func, prior, beta) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = mlp(nin, nhidden, nout, func)</CODE> takes the number of inputs, +hidden units and output units for a 2-layer feed-forward network, +together with a string <CODE>func</CODE> which specifies the output unit +activation function, and returns a data structure <CODE>net</CODE>. The +weights are drawn from a zero mean, unit variance isotropic Gaussian, +with varianced scaled by the fan-in of the hidden or output units as +appropriate. This makes use of the Matlab function +<CODE>randn</CODE> and so the seed for the random weight initialization can be +set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. +The hidden units use the <CODE>tanh</CODE> activation function. + +<p>The fields in <CODE>net</CODE> are +<PRE> + + type = 'mlp' + nin = number of inputs + nhidden = number of hidden units + nout = number of outputs + nwts = total number of weights and biases + actfn = string describing the output unit activation function: + 'linear' + 'logistic + 'softmax' + w1 = first-layer weight matrix + b1 = first-layer bias vector + w2 = second-layer weight matrix + b2 = second-layer bias vector +</PRE> + +Here <CODE>w1</CODE> has dimensions <CODE>nin</CODE> times <CODE>nhidden</CODE>, <CODE>b1</CODE> has +dimensions <CODE>1</CODE> times <CODE>nhidden</CODE>, <CODE>w2</CODE> has +dimensions <CODE>nhidden</CODE> times <CODE>nout</CODE>, and <CODE>b2</CODE> has +dimensions <CODE>1</CODE> times <CODE>nout</CODE>. + +<p><CODE>net = mlp(nin, nhidden, nout, func, prior)</CODE>, in which <CODE>prior</CODE> is +a scalar, allows the field <CODE>net.alpha</CODE> in the data structure +<CODE>net</CODE> to be set, corresponding to a zero-mean isotropic Gaussian +prior with inverse variance with value <CODE>prior</CODE>. Alternatively, +<CODE>prior</CODE> can consist of a data structure with fields <CODE>alpha</CODE> +and <CODE>index</CODE>, allowing individual Gaussian priors to be set over +groups of weights in the network. Here <CODE>alpha</CODE> is a column vector +in which each element corresponds to a separate group of weights, +which need not be mutually exclusive. The membership of the groups is +defined by the matrix <CODE>indx</CODE> in which the columns correspond to +the elements of <CODE>alpha</CODE>. Each column has one element for each +weight in the matrix, in the order defined by the function +<CODE>mlppak</CODE>, and each element is 1 or 0 according to whether the +weight is a member of the corresponding group or not. A utility +function <CODE>mlpprior</CODE> is provided to help in setting up the +<CODE>prior</CODE> data structure. + +<p><CODE>net = mlp(nin, nhidden, nout, func, prior, beta)</CODE> also sets the +additional field <CODE>net.beta</CODE> in the data structure <CODE>net</CODE>, where +beta corresponds to the inverse noise variance. + +<p><h2> +See Also +</h2> +<CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpbkp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpbkp +</title> +</head> +<body> +<H1> mlpbkp +</H1> +<h2> +Purpose +</h2> +Backpropagate gradient of error function for 2-layer network. + +<p><h2> +Synopsis +</h2> +<PRE> +g = mlpbkp(net, x, z, deltas)</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = mlpbkp(net, x, z, deltas)</CODE> takes a network data structure +<CODE>net</CODE> together with a matrix <CODE>x</CODE> of input vectors, a matrix +<CODE>z</CODE> of hidden unit activations, and a matrix <CODE>deltas</CODE> of the +gradient of the error function with respect to the values of the +output units (i.e. the summed inputs to the output units, before the +activation function is applied). The return value is the gradient +<CODE>g</CODE> of the error function with respect to the network +weights. Each row of <CODE>x</CODE> corresponds to one input vector. + +<p>This function is provided so that the common backpropagation algorithm +can be used by multi-layer perceptron network models to compute +gradients for mixture density networks as well as standard error +functions. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="mlpderiv.htm">mlpderiv</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpderiv.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpderiv +</title> +</head> +<body> +<H1> mlpderiv +</H1> +<h2> +Purpose +</h2> +Evaluate derivatives of network outputs with respect to weights. + +<p><h2> +Synopsis +</h2> +<PRE> +g = mlpderiv(net, x)</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = mlpderiv(net, x)</CODE> takes a network data structure <CODE>net</CODE> +and a matrix of input vectors <CODE>x</CODE> and returns a three-index matrix +<CODE>g</CODE> whose <CODE>i</CODE>, <CODE>j</CODE>, <CODE>k</CODE> element contains the +derivative of network output <CODE>k</CODE> with respect to weight or bias +parameter <CODE>j</CODE> for input pattern <CODE>i</CODE>. The ordering of the +weight and bias parameters is defined by <CODE>mlpunpak</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlperr.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<html> +<head> +<title> +Netlab Reference Manual mlperr +</title> +</head> +<body> +<H1> mlperr +</H1> +<h2> +Purpose +</h2> +Evaluate error function for 2-layer network. + +<p><h2> +Synopsis +</h2> +<PRE> +e = mlperr(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>e = mlperr(net, x, t)</CODE> takes a network data structure <CODE>net</CODE> together +with a matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> of target +vectors, and evaluates the error function <CODE>e</CODE>. The choice of error +function corresponds to the output unit activation function. Each row +of <CODE>x</CODE> corresponds to one input vector and each row of <CODE>t</CODE> +corresponds to one target vector. + +<p><CODE>[e, edata, eprior] = mlperr(net, x, t)</CODE> additionally returns the +data and prior components of the error, assuming a zero mean Gaussian +prior on the weights with inverse variance parameters <CODE>alpha</CODE> and +<CODE>beta</CODE> taken from the network data structure <CODE>net</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpevfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpevfwd +</title> +</head> +<body> +<H1> mlpevfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation with evidence for MLP + +<p><h2> +Synopsis +</h2> +<PRE> + +[y, extra] = mlpevfwd(net, x, t, x_test) +[y, extra, invhess] = mlpevfwd(net, x, t, x_test, invhess) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>y = mlpevfwd(net, x, t, x_test)</CODE> takes a network data structure +<CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data +and input test data <CODE>x_test</CODE>. +It returns the normal forward propagation through the network <CODE>y</CODE> +together with a matrix <CODE>extra</CODE> which consists of error bars (variance) +for a regression problem or moderated outputs for a classification problem. +The optional argument (and return value) +<CODE>invhess</CODE> is the inverse of the network Hessian +computed on the training data inputs and targets. Passing it in avoids +recomputing it, which can be a significant saving for large training sets. + +<p><h2> +See Also +</h2> +<CODE><a href="fevbayes.htm">fevbayes</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpfwd +</title> +</head> +<body> +<H1> mlpfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through 2-layer network. + +<p><h2> +Synopsis +</h2> +<PRE> +y = mlpfwd(net, x) +[y, z] = mlpfwd(net, x) +[y, z, a] = mlpfwd(net, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>y = mlpfwd(net, x)</CODE> takes a network data structure <CODE>net</CODE> together with +a matrix <CODE>x</CODE> of input vectors, and forward propagates the inputs +through the network to generate a matrix <CODE>y</CODE> of output +vectors. Each row of <CODE>x</CODE> corresponds to one input vector and each +row of <CODE>y</CODE> corresponds to one output vector. + +<p><CODE>[y, z] = mlpfwd(net, x)</CODE> also generates a matrix <CODE>z</CODE> of the hidden +unit activations where each row corresponds to one pattern. + +<p><CODE>[y, z, a] = mlpfwd(net, x)</CODE> also returns a matrix <CODE>a</CODE> +giving the summed inputs to each output unit, where each row +corresponds to one pattern. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpgrad.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpgrad +</title> +</head> +<body> +<H1> mlpgrad +</H1> +<h2> +Purpose +</h2> +Evaluate gradient of error function for 2-layer network. + +<p><h2> +Synopsis +</h2> +<PRE> + +g = mlpgrad(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = mlpgrad(net, x, t)</CODE> takes a network data structure <CODE>net</CODE> +together with a matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> +of target vectors, and evaluates the gradient <CODE>g</CODE> of the error +function with respect to the network weights. The error funcion +corresponds to the choice of output unit activation function. Each row +of <CODE>x</CODE> corresponds to one input vector and each row of <CODE>t</CODE> +corresponds to one target vector. + +<p><CODE>[g, gdata, gprior] = mlpgrad(net, x, t)</CODE> also returns separately +the data and prior contributions to the gradient. In the case of +multiple groups in the prior, <CODE>gprior</CODE> is a matrix with a row +for each group and a column for each weight parameter. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlphdotv.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +<html> +<head> +<title> +Netlab Reference Manual mlphdotv +</title> +</head> +<body> +<H1> mlphdotv +</H1> +<h2> +Purpose +</h2> +Evaluate the product of the data Hessian with a vector. + +<p><h2> +Synopsis +</h2> +<PRE> +hdv = mlphdotv(net, x, t, v)</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>hdv = mlphdotv(net, x, t, v)</CODE> takes an MLP network data structure +<CODE>net</CODE>, together with the matrix <CODE>x</CODE> of input vectors, the +matrix <CODE>t</CODE> of target vectors and an arbitrary row vector <CODE>v</CODE> +whose length equals the number of parameters in the network, and +returns the product of the data-dependent contribution to the Hessian +matrix with <CODE>v</CODE>. The implementation is based on the R-propagation +algorithm of Pearlmutter. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlphess.htm">mlphess</a></CODE>, <CODE><a href="hesschek.htm">hesschek</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlphess.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +<html> +<head> +<title> +Netlab Reference Manual mlphess +</title> +</head> +<body> +<H1> mlphess +</H1> +<h2> +Purpose +</h2> +Evaluate the Hessian matrix for a multi-layer perceptron network. + +<p><h2> +Synopsis +</h2> +<PRE> +h = mlphess(net, x, t) +[h, hdata] = mlphess(net, x, t) +h = mlphess(net, x, t, hdata) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>h = mlphess(net, x, t)</CODE> takes an MLP network data structure <CODE>net</CODE>, +a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of target +values and returns the full Hessian matrix <CODE>h</CODE> corresponding to +the second derivatives of the negative log posterior distribution, +evaluated for the current weight and bias values as defined by +<CODE>net</CODE>. + +<p><CODE>[h, hdata] = mlphess(net, x, t)</CODE> returns both the Hessian matrix +<CODE>h</CODE> and the contribution <CODE>hdata</CODE> arising from the data dependent +term in the Hessian. + +<p><CODE>h = mlphess(net, x, t, hdata)</CODE> takes a network data structure +<CODE>net</CODE>, a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of +target values, together with the contribution <CODE>hdata</CODE> arising from +the data dependent term in the Hessian, and returns the full Hessian +matrix <CODE>h</CODE> corresponding to the second derivatives of the negative +log posterior distribution. This version saves computation time if +<CODE>hdata</CODE> has already been evaluated for the current weight and bias +values. + +<p><h2> +Example +</h2> +For the standard regression framework with a Gaussian conditional +distribution of target values given input values, and a simple +Gaussian prior over weights, the Hessian takes the form +<PRE> + + h = beta*hd + alpha*I +</PRE> + +where the contribution <CODE>hd</CODE> is evaluated by calls to <CODE>mlphdotv</CODE> and +<CODE>h</CODE> is the full Hessian. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="hesschek.htm">hesschek</a></CODE>, <CODE><a href="mlphdotv.htm">mlphdotv</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlphint.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual mlphint +</title> +</head> +<body> +<H1> mlphint +</H1> +<h2> +Purpose +</h2> +Plot Hinton diagram for 2-layer feed-forward network. + +<p><h2> +Synopsis +</h2> +<PRE> +mlphint(net) +[h1, h2] = mlphint(net)</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>mlphint(net)</CODE> takes a network structure <CODE>net</CODE> +and plots the Hinton diagram comprised of two +figure windows, one displaying the first-layer weights and biases, and +one displaying the second-layer weights and biases. + +<p><CODE>[h1, h2] = mlphint(net)</CODE> also returns handles <CODE>h1</CODE> and +<CODE>h2</CODE> to the figures which can be used, for instance, to delete the +figures when they are no longer needed. + +<p>To print the figure correctly, you should call +<CODE>set(h, 'InvertHardCopy', 'on')</CODE> before printing. + +<p><h2> +See Also +</h2> +<CODE><a href="demhint.htm">demhint</a></CODE>, <CODE><a href="hintmat.htm">hintmat</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpinit.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpinit +</title> +</head> +<body> +<H1> mlpinit +</H1> +<h2> +Purpose +</h2> +Initialise the weights in a 2-layer feedforward network. + +<p><h2> +Synopsis +</h2> +<PRE> +net = mlpinit(net, prior) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = mlpinit(net, prior)</CODE> takes a 2-layer feedforward network +<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian +distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters +(weights and biases) are sampled from a single isotropic Gaussian with +inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data +structure of the kind generated by <CODE>mlpprior</CODE>, then the parameters +are sampled from multiple Gaussians according to their groupings +(defined by the <CODE>index</CODE> field) with corresponding variances +(defined by the <CODE>alpha</CODE> field). + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlppak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +<html> +<head> +<title> +Netlab Reference Manual mlppak +</title> +</head> +<body> +<H1> mlppak +</H1> +<h2> +Purpose +</h2> +Combines weights and biases into one weights vector. + +<p><h2> +Synopsis +</h2> +<PRE> +w = mlppak(net) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>w = mlppak(net)</CODE> takes a network data structure <CODE>net</CODE> and +combines the component weight matrices bias vectors into a single row +vector <CODE>w</CODE>. The facility to switch between these two +representations for the network parameters is useful, for example, in +training a network by error function minimization, since a single +vector of parameters can be handled by general-purpose optimization +routines. + +<p>The ordering of the paramters in <CODE>w</CODE> is defined by +<PRE> + + w = [net.w1(:)', net.b1, net.w2(:)', net.b2]; +</PRE> + +where <CODE>w1</CODE> is the first-layer weight matrix, <CODE>b1</CODE> is the +first-layer bias vector, <CODE>w2</CODE> is the second-layer weight matrix, +and <CODE>b2</CODE> is the second-layer bias vector. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpprior.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpprior +</title> +</head> +<body> +<H1> mlpprior +</H1> +<h2> +Purpose +</h2> +Create Gaussian prior for mlp. + +<p><h2> +Synopsis +</h2> +<PRE> +prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)</PRE> + + +<p><h2> +Description +</h2> +<CODE>prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)</CODE> +generates a data structure +<CODE>prior</CODE>, with fields <CODE>prior.alpha</CODE> and <CODE>prior.index</CODE>, which +specifies a Gaussian prior distribution for the network weights in a +two-layer feedforward network. Two different cases are possible. In +the first case, <CODE>aw1</CODE>, <CODE>ab1</CODE>, <CODE>aw2</CODE> and <CODE>ab2</CODE> are all +scalars and represent the regularization coefficients for four groups +of parameters in the network corresponding to first-layer weights, +first-layer biases, second-layer weights, and second-layer biases +respectively. Then <CODE>prior.alpha</CODE> represents a column vector of +length 4 containing the parameters, and <CODE>prior.index</CODE> is a matrix +specifying which weights belong in each group. Each column has one +element for each weight in the matrix, using the standard ordering as +defined in <CODE>mlppak</CODE>, and each element is 1 or 0 according to +whether the weight is a member of the corresponding group or not. In +the second case the parameter <CODE>aw1</CODE> is a vector of length equal to +the number of inputs in the network, and the corresponding matrix +<CODE>prior.index</CODE> now partitions the first-layer weights into groups +corresponding to the weights fanning out of each input unit. This +prior is appropriate for the technique of automatic relevance +determination. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlptrain.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +<html> +<head> +<title> +Netlab Reference Manual mlptrain +</title> +</head> +<body> +<H1> mlptrain +</H1> +<h2> +Purpose +</h2> +Utility to train an MLP network for demtrain + +<p><h2> +Description +</h2> + +<p><CODE>[net, error] = mlptrain(net, x, t, its)</CODE> trains a network data +structure <CODE>net</CODE> using the scaled conjugate gradient algorithm +for <CODE>its</CODE> cycles with +input data <CODE>x</CODE>, target data <CODE>t</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="demtrain.htm">demtrain</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE>, <CODE><a href="netopt.htm">netopt</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlpunpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual mlpunpak +</title> +</head> +<body> +<H1> mlpunpak +</H1> +<h2> +Purpose +</h2> +Separates weights vector into weight and bias matrices. + +<p><h2> +Synopsis +</h2> +<PRE> +net = mlpunpak(net, w) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = mlpunpak(net, w)</CODE> takes an mlp network data structure <CODE>net</CODE> and +a weight vector <CODE>w</CODE>, and returns a network data structure identical to +the input network, except that the first-layer weight matrix +<CODE>w1</CODE>, the first-layer bias vector <CODE>b1</CODE>, the second-layer +weight matrix <CODE>w2</CODE> and the second-layer bias vector <CODE>b2</CODE> have all +been set to the corresponding elements of <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/netderiv.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual netderiv +</title> +</head> +<body> +<H1> netderiv +</H1> +<h2> +Purpose +</h2> +Evaluate derivatives of network outputs by weights generically. + +<p><h2> +Synopsis +</h2> +<PRE> +g = netderiv(w, net, x)</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>g = netderiv(w, net, x)</CODE> takes a weight vector <CODE>w</CODE> and a network +data structure <CODE>net</CODE>, together with the matrix <CODE>x</CODE> of input +vectors, and returns the +gradient of the outputs with respect to the weights evaluated at <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="netevfwd.htm">netevfwd</a></CODE>, <CODE><a href="netopt.htm">netopt</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/neterr.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual neterr +</title> +</head> +<body> +<H1> neterr +</H1> +<h2> +Purpose +</h2> +Evaluate network error function for generic optimizers + +<p><h2> +Synopsis +</h2> +<PRE> +e = neterr(w, net, x, t) +[e, varargout] = neterr(w, net, x, t) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>e = neterr(w, net, x, t)</CODE> takes a weight vector <CODE>w</CODE> and a network +data structure <CODE>net</CODE>, together with the matrix <CODE>x</CODE> of input +vectors and the matrix <CODE>t</CODE> of target vectors, and returns the +value of the error function evaluated at <CODE>w</CODE>. + +<p><CODE>[e, varargout] = neterr(w, net, x, t)</CODE> also returns any additional +return values from the error function. + +<p><h2> +See Also +</h2> +<CODE><a href="netgrad.htm">netgrad</a></CODE>, <CODE><a href="nethess.htm">nethess</a></CODE>, <CODE><a href="netopt.htm">netopt</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/netevfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +<html> +<head> +<title> +Netlab Reference Manual netevfwd +</title> +</head> +<body> +<H1> netevfwd +</H1> +<h2> +Purpose +</h2> +Generic forward propagation with evidence for network + +<p><h2> +Synopsis +</h2> +<PRE> + +[y, extra] = netevfwd(w, net, x, t, x_test) +[y, extra, invhess] = netevfwd(w, net, x, t, x_test, invhess) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>[y, extra] = netevfwd(w, net, x, t, x_test)</CODE> takes a network data +structure +<CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data +and input test data <CODE>x_test</CODE>. +It returns the normal forward propagation through the network <CODE>y</CODE> +together with a matrix <CODE>extra</CODE> which consists of error bars (variance) +for a regression problem or moderated outputs for a classification problem. + +<p>The optional argument (and return value) +<CODE>invhess</CODE> is the inverse of the network Hessian +computed on the training data inputs and targets. Passing it in avoids +recomputing it, which can be a significant saving for large training sets. + +<p><h2> +See Also +</h2> +<CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE>, <CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE>, <CODE><a href="glmevfwd.htm">glmevfwd</a></CODE>, <CODE><a href="fevbayes.htm">fevbayes</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/netgrad.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual netgrad +</title> +</head> +<body> +<H1> netgrad +</H1> +<h2> +Purpose +</h2> +Evaluate network error gradient for generic optimizers + +<p><h2> +Synopsis +</h2> +<PRE> +g = netgrad(w, net, x, t)</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>g = netgrad(w, net, x, t)</CODE> takes a weight vector <CODE>w</CODE> and a network +data structure <CODE>net</CODE>, together with the matrix <CODE>x</CODE> of input +vectors and the matrix <CODE>t</CODE> of target vectors, and returns the +gradient of the error function evaluated at <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="neterr.htm">neterr</a></CODE>, <CODE><a href="netopt.htm">netopt</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/nethess.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +<html> +<head> +<title> +Netlab Reference Manual nethess +</title> +</head> +<body> +<H1> nethess +</H1> +<h2> +Purpose +</h2> +Evaluate network Hessian + +<p><h2> +Synopsis +</h2> +<PRE> +h = neterr(w, net, x, t) +[h, varargout] = neterr(w, net, x, t, varargin) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>h = nethess(w, net, x, t)</CODE> takes a weight vector <CODE>w</CODE> and a network +data structure <CODE>net</CODE>, together with the matrix <CODE>x</CODE> of input +vectors and the matrix <CODE>t</CODE> of target vectors, and returns the +value of the Hessian evaluated at <CODE>w</CODE>. + +<p><CODE>[e, varargout] = nethess(w, net, x, t, varargin)</CODE> also returns any additional +return values from the network Hessian function, and passes additional arguments +to that function. + +<p><h2> +Example +</h2> + +<p>In <CODE>evidence</CODE>, this function is called once to compute the +data contribution to the Hessian +<PRE> + +[h, dh] = nethess(w, net, x, t, dh); +</PRE> + +and again to update the Hessian for new values of the hyper-parameters +<PRE> + +h = nethess(w, net, x, t, dh); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="neterr.htm">neterr</a></CODE>, <CODE><a href="netgrad.htm">netgrad</a></CODE>, <CODE><a href="netopt.htm">netopt</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/netinit.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<html> +<head> +<title> +Netlab Reference Manual netinit +</title> +</head> +<body> +<H1> netinit +</H1> +<h2> +Purpose +</h2> +Initialise the weights in a network. + +<p><h2> +Synopsis +</h2> +<PRE> +net = netinit(net, prior) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = netinit(net, prior)</CODE> takes a network data structure +<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian +distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters +(weights and biases) are sampled from a single isotropic Gaussian with +inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data +structure of the kind generated by <CODE>mlpprior</CODE>, then the parameters +are sampled from multiple Gaussians according to their groupings +(defined by the <CODE>index</CODE> field) with corresponding variances +(defined by the <CODE>alpha</CODE> field). + +<p><h2> +See Also +</h2> +<CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="netunpak.htm">netunpak</a></CODE>, <CODE><a href="rbfprior.htm">rbfprior</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/netopt.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,75 @@ +<html> +<head> +<title> +Netlab Reference Manual netopt +</title> +</head> +<body> +<H1> netopt +</H1> +<h2> +Purpose +</h2> +Optimize the weights in a network model. + +<p><h2> +Synopsis +</h2> +<PRE> +[net, options] = netopt(net, options, x, t, alg) +[net, options, varargout] = netopt(net, options, x, t, alg) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>netopt</CODE> is a helper function which facilitates the training of +networks using the general purpose optimizers as well as sampling from the +posterior distribution of parameters using general purpose Markov chain +Monte Carlo sampling algorithms. It can be used with any function that +searches in parameter space using error and gradient functions. + +<p><CODE>[net, options] = netopt(net, options, x, t, alg)</CODE> takes a network +data structure <CODE>net</CODE>, together with a vector <CODE>options</CODE> of +parameters governing the behaviour of the optimization algorithm, a +matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> of target +vectors, and returns the trained network as well as an updated +<CODE>options</CODE> vector. The string <CODE>alg</CODE> determines which optimization +algorithm (<CODE>conjgrad</CODE>, <CODE>quasinew</CODE>, <CODE>scg</CODE>, etc.) or Monte +Carlo algorithm (such as <CODE>hmc</CODE>) will be used. + +<p><CODE>[net, options, varargout] = netopt(net, options, x, t, alg)</CODE> +also returns any additional return values from the optimisation algorithm. + +<p><h2> +Examples +</h2> +Suppose we create a 4-input, 3 hidden unit, 2-output feed-forward +network using <CODE>net = mlp(4, 3, 2, 'linear')</CODE>. We can then train +the network with the scaled conjugate gradient algorithm by using +<CODE>net = netopt(net, options, x, t, 'scg')</CODE> where <CODE>x</CODE> and +<CODE>t</CODE> are the input and target data matrices respectively, and the +options vector is set appropriately for <CODE>scg</CODE>. + +<p>If we also wish to plot the learning curve, we can use the additional +return value <CODE>errlog</CODE> given by <CODE>scg</CODE>: +<PRE> + +[net, options, errlog] = netopt(net, options, x, t, 'scg'); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="netgrad.htm">netgrad</a></CODE>, <CODE><a href="bfgs.htm">bfgs</a></CODE>, <CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="graddesc.htm">graddesc</a></CODE>, <CODE><a href="hmc.htm">hmc</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/netpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +<html> +<head> +<title> +Netlab Reference Manual netpak +</title> +</head> +<body> +<H1> netpak +</H1> +<h2> +Purpose +</h2> +Combines weights and biases into one weights vector. + +<p><h2> +Synopsis +</h2> +<PRE> +w = netpak(net) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>w = netpak(net)</CODE> takes a network data structure <CODE>net</CODE> and +combines the component weight matrices into a single row +vector <CODE>w</CODE>. The facility to switch between these two +representations for the network parameters is useful, for example, in +training a network by error function minimization, since a single +vector of parameters can be handled by general-purpose optimization +routines. This function also takes into account a <CODE>mask</CODE> defined +as a field in <CODE>net</CODE> by removing any weights that correspond to +entries of 0 in the mask. + +<p><h2> +See Also +</h2> +<CODE><a href="net.htm">net</a></CODE>, <CODE><a href="netunpak.htm">netunpak</a></CODE>, <CODE><a href="netfwd.htm">netfwd</a></CODE>, <CODE><a href="neterr.htm">neterr</a></CODE>, <CODE><a href="netgrad.htm">netgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/netunpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +<html> +<head> +<title> +Netlab Reference Manual netunpak +</title> +</head> +<body> +<H1> netunpak +</H1> +<h2> +Purpose +</h2> +Separates weights vector into weight and bias matrices. + +<p><h2> +Synopsis +</h2> +<PRE> +net = netunpak(net, w) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = netunpak(net, w)</CODE> takes an net network data structure <CODE>net</CODE> and +a weight vector <CODE>w</CODE>, and returns a network data structure identical to +the input network, except that the componenet weight matrices have all +been set to the corresponding elements of <CODE>w</CODE>. If there is +a <CODE>mask</CODE> field in the <CODE>net</CODE> data structure, then the weights in +<CODE>w</CODE> are placed in locations corresponding to non-zero entries in the +mask (so <CODE>w</CODE> should have the same length as the number of non-zero +entries in the <CODE>mask</CODE>). + +<p><h2> +See Also +</h2> +<CODE><a href="netpak.htm">netpak</a></CODE>, <CODE><a href="netfwd.htm">netfwd</a></CODE>, <CODE><a href="neterr.htm">neterr</a></CODE>, <CODE><a href="netgrad.htm">netgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/olgd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +<html> +<head> +<title> +Netlab Reference Manual olgd +</title> +</head> +<body> +<H1> olgd +</H1> +<h2> +Purpose +</h2> +On-line gradient descent optimization. + +<p><h2> +Description +</h2> +<CODE>[net, options, errlog, pointlog] = olgd(net, options, x, t)</CODE> uses +on-line gradient descent to find a local minimum of the error function for the +network +<CODE>net</CODE> computed on the input data <CODE>x</CODE> and target values +<CODE>t</CODE>. A log of the error values +after each cycle is (optionally) returned in <CODE>errlog</CODE>, and a log +of the points visited is (optionally) returned in <CODE>pointlog</CODE>. +Because the gradient is computed on-line (i.e. after each pattern) +this can be quite inefficient in Matlab. + +<p>The error function value at final weight vector is returned +in <CODE>options(8)</CODE>. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>, and the points visited +in the return argument <CODE>pointslog</CODE>. If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(2)</CODE> is the precision required for the value +of <CODE>x</CODE> at the solution. If the absolute difference between +the values of <CODE>x</CODE> between two successive steps is less than +<CODE>options(2)</CODE>, then this condition is satisfied. + +<p><CODE>options(3)</CODE> is the precision required of the objective +function at the solution. If the absolute difference between the +error functions between two successive steps is less than +<CODE>options(3)</CODE>, then this condition is satisfied. +Both this and the previous condition must be +satisfied for termination. Note that testing the function value at each +iteration roughly halves the speed of the algorithm. + +<p><CODE>options(5)</CODE> determines whether the patterns are sampled randomly +with replacement. If it is 0 (the default), then patterns are sampled +in order. + +<p><CODE>options(6)</CODE> determines if the learning rate decays. If it is 1 +then the learning rate decays at a rate of <CODE>1/t</CODE>. If it is 0 +(the default) then the learning rate is constant. + +<p><CODE>options(9)</CODE> should be set to 1 to check the user defined gradient +function. + +<p><CODE>options(10)</CODE> returns the total number of function evaluations (including +those in any line searches). + +<p><CODE>options(11)</CODE> returns the total number of gradient evaluations. + +<p><CODE>options(14)</CODE> is the maximum number of iterations (passes through +the complete pattern set); default 100. + +<p><CODE>options(17)</CODE> is the momentum; default 0.5. + +<p><CODE>options(18)</CODE> is the learning rate; default 0.01. + +<p><h2> +Examples +</h2> +The following example performs on-line gradient descent on an MLP with +random sampling from the pattern set. +<PRE> + +net = mlp(5, 3, 1, 'linear'); +options = foptions; +options(18) = 0.01; +options(5) = 1; +net = olgd(net, options, x, t); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="graddesc.htm">graddesc</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/pca.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +<html> +<head> +<title> +Netlab Reference Manual pca +</title> +</head> +<body> +<H1> pca +</H1> +<h2> +Purpose +</h2> +Principal Components Analysis + +<p><h2> +Synopsis +</h2> +<PRE> +PCcoeff = pca(data) +PCcoeff = pca(data, N) +[PCcoeff, PCvec] = pca(data) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>PCcoeff = pca(data)</CODE> computes the eigenvalues of the covariance +matrix of the dataset <CODE>data</CODE> and returns them as <CODE>PCcoeff</CODE>. These +coefficients give the variance of <CODE>data</CODE> along the corresponding +principal components. + +<p><CODE>PCcoeff = pca(data, N)</CODE> returns the largest <CODE>N</CODE> eigenvalues. + +<p><CODE>[PCcoeff, PCvec] = pca(data)</CODE> returns the principal components as +well as the coefficients. This is considerably more computationally +demanding than just computing the eigenvalues. + +<p><h2> +See Also +</h2> +<CODE><a href="eigdec.htm">eigdec</a></CODE>, <CODE><a href="gtminit.htm">gtminit</a></CODE>, <CODE><a href="ppca.htm">ppca</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/plotmat.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual plotmat +</title> +</head> +<body> +<H1> plotmat +</H1> +<h2> +Purpose +</h2> +Display a matrix. + +<p><h2> +Synopsis +</h2> +<PRE> +plotmat(matrix, textcolour, gridcolour, fontsize)</PRE> + + +<p><h2> +Description +</h2> +<CODE>plotmat(matrix, textcolour, gridcolour, fontsize)</CODE> displays the matrix +<CODE>matrix</CODE> on the current figure. The <CODE>textcolour</CODE> and <CODE>gridcolour</CODE> +arguments control the colours of the numbers and grid labels respectively and +should follow the usual Matlab specification. +The parameter <CODE>fontsize</CODE> should be an integer. + +<p><h2> +See Also +</h2> +<CODE><a href="conffig.htm">conffig</a></CODE>, <CODE><a href="demmlp2.htm">demmlp2</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/ppca.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +<html> +<head> +<title> +Netlab Reference Manual ppca +</title> +</head> +<body> +<H1> ppca +</H1> +<h2> +Purpose +</h2> +Probabilistic Principal Components Analysis + +<p><h2> +Synopsis +</h2> +<PRE> +[var, U, lambda] = pca(x, ppca_dim) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>[var, U, lambda] = ppca(x, ppca_dim)</CODE> computes the principal component +subspace <CODE>U</CODE> of dimension <CODE>ppca_dim</CODE> using a centred +covariance matrix <CODE>x</CODE>. The variable <CODE>var</CODE> contains +the off-subspace variance (which is assumed to be spherical), while the +vector <CODE>lambda</CODE> contains the variances of each of the principal +components. This is computed using the eigenvalue and eigenvector +decomposition of <CODE>x</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="eigdec.htm">eigdec</a></CODE>, <CODE><a href="pca.htm">pca</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/quasinew.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,99 @@ +<html> +<head> +<title> +Netlab Reference Manual quasinew +</title> +</head> +<body> +<H1> quasinew +</H1> +<h2> +Purpose +</h2> +Quasi-Newton optimization. + +<p><h2> +Description +</h2> +<CODE>[x, options, flog, pointlog] = quasinew(f, x, options, gradf)</CODE> +uses a quasi-Newton +algorithm to find a local minimum of the function <CODE>f(x)</CODE> whose +gradient is given by <CODE>gradf(x)</CODE>. Here <CODE>x</CODE> is a row vector +and <CODE>f</CODE> returns a scalar value. +The point at which <CODE>f</CODE> has a local minimum +is returned as <CODE>x</CODE>. The function value at that point is returned +in <CODE>options(8)</CODE>. A log of the function values +after each cycle is (optionally) returned in <CODE>flog</CODE>, and a log +of the points visited is (optionally) returned in <CODE>pointlog</CODE>. + +<p><CODE>quasinew(f, x, options, gradf, p1, p2, ...)</CODE> allows +additional arguments to be passed to <CODE>f()</CODE> and <CODE>gradf()</CODE>. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>, and the points visited +in the return argument <CODE>pointslog</CODE>. If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(2)</CODE> is a measure of the absolute precision required for the value +of <CODE>x</CODE> at the solution. If the absolute difference between +the values of <CODE>x</CODE> between two successive steps is less than +<CODE>options(2)</CODE>, then this condition is satisfied. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. If the absolute difference between the +objective function values between two successive steps is less than +<CODE>options(3)</CODE>, then this condition is satisfied. +Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(9)</CODE> should be set to 1 to check the user defined gradient +function. + +<p><CODE>options(10)</CODE> returns the total number of function evaluations (including +those in any line searches). + +<p><CODE>options(11)</CODE> returns the total number of gradient evaluations. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p><CODE>options(15)</CODE> is the precision in parameter space of the line search; +default <CODE>1e-2</CODE>. + +<p><h2> +Examples +</h2> +An example of +the use of the additional arguments is the minimization of an error +function for a neural network: +<PRE> + +w = quasinew('neterr', w, options, 'netgrad', net, x, t); +</PRE> + + +<p><h2> +Algorithm +</h2> + +The quasi-Newton algorithm builds up an +approximation to the inverse Hessian over a number of steps. The +method requires order W squared storage, where W is the number of function +parameters. The Broyden-Fletcher-Goldfarb-Shanno formula for the +inverse Hessian updates is used. The line searches are carried out to +a relatively low precision (1.0e-2). + +<p><h2> +See Also +</h2> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="graddesc.htm">graddesc</a></CODE>, <CODE><a href="linemin.htm">linemin</a></CODE>, <CODE><a href="minbrack.htm">minbrack</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbf.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +<html> +<head> +<title> +Netlab Reference Manual rbf +</title> +</head> +<body> +<H1> rbf +</H1> +<h2> +Purpose +</h2> +Creates an RBF network with specified architecture + +<p><h2> +Synopsis +</h2> +<PRE> + +net = rbf(nin, nhidden, nout, rbfunc) +net = rbf(nin, nhidden, nout, rbfunc, outfunc) +net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = rbf(nin, nhidden, nout, rbfunc)</CODE> constructs and initialises +a radial basis function network returning a data structure <CODE>net</CODE>. +The weights are all initialised with a zero mean, unit variance normal +distribution, with the exception of the variances, which are set to one. +This makes use of the Matlab function +<CODE>randn</CODE> and so the seed for the random weight initialization can be +set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. The +activation functions are defined in terms of the distance between +the data point and the corresponding centre. Note that the functions are +computed to a convenient constant multiple: for example, the Gaussian +is not normalised. (Normalisation is not needed as the function outputs +are linearly combined in the next layer.) + +<p>The fields in <CODE>net</CODE> are +<PRE> + + type = 'rbf' + nin = number of inputs + nhidden = number of hidden units + nout = number of outputs + nwts = total number of weights and biases + actfn = string defining hidden unit activation function: + 'gaussian' for a radially symmetric Gaussian function. + 'tps' for r^2 log r, the thin plate spline function. + 'r4logr' for r^4 log r. + outfn = string defining output error function: + 'linear' for linear outputs (default) and SoS error. + 'neuroscale' for Sammon stress measure. + c = centres + wi = squared widths (null for rlogr and tps) + w2 = second layer weight matrix + b2 = second layer bias vector +</PRE> + + +<p><CODE>net = rbf(nin, nhidden, nout, rbfund, outfunc)</CODE> allows the user to +specify the type of error function to be used. The field <CODE>outfn</CODE> +is set to the value of this string. Linear outputs (for regression problems) +and Neuroscale outputs (for topographic mappings) are supported. + +<p><CODE>net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta)</CODE>, +in which <CODE>prior</CODE> is +a scalar, allows the field <CODE>net.alpha</CODE> in the data structure +<CODE>net</CODE> to be set, corresponding to a zero-mean isotropic Gaussian +prior with inverse variance with value <CODE>prior</CODE>. Alternatively, +<CODE>prior</CODE> can consist of a data structure with fields <CODE>alpha</CODE> +and <CODE>index</CODE>, allowing individual Gaussian priors to be set over +groups of weights in the network. Here <CODE>alpha</CODE> is a column vector +in which each element corresponds to a separate group of weights, +which need not be mutually exclusive. The membership of the groups is +defined by the matrix <CODE>indx</CODE> in which the columns correspond to +the elements of <CODE>alpha</CODE>. Each column has one element for each +weight in the matrix, in the order defined by the function +<CODE>rbfpak</CODE>, and each element is 1 or 0 according to whether the +weight is a member of the corresponding group or not. A utility +function <CODE>rbfprior</CODE> is provided to help in setting up the +<CODE>prior</CODE> data structure. + +<p><CODE>net = rbf(nin, nhidden, nout, func, prior, beta)</CODE> also sets the +additional field <CODE>net.beta</CODE> in the data structure <CODE>net</CODE>, where +beta corresponds to the inverse noise variance. + +<p><h2> +Example +</h2> +The following code constructs an RBF network with 1 input and output node +and 5 hidden nodes and then propagates some data <CODE>x</CODE> through it. +<PRE> + +net = rbf(1, 5, 1, 'tps'); +[y, act] = rbffwd(net, x); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfbkp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfbkp +</title> +</head> +<body> +<H1> rbfbkp +</H1> +<h2> +Purpose +</h2> +Backpropagate gradient of error function for RBF network. + +<p><h2> +Synopsis +</h2> +<PRE> +g = rbfbkp(net, x, z, n2, deltas)</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = rbfbkp(net, x, z, n2, deltas)</CODE> takes a network data structure +<CODE>net</CODE> together with a matrix <CODE>x</CODE> of input vectors, a matrix +<CODE>z</CODE> of hidden unit activations, a matrix <CODE>n2</CODE> of the squared +distances between centres and inputs, and a matrix <CODE>deltas</CODE> of the +gradient of the error function with respect to the values of the +output units (i.e. the summed inputs to the output units, before the +activation function is applied). The return value is the gradient +<CODE>g</CODE> of the error function with respect to the network +weights. Each row of <CODE>x</CODE> corresponds to one input vector. + +<p>This function is provided so that the common backpropagation algorithm +can be used by RBF network models to compute +gradients for the output values (in <CODE>rbfderiv</CODE>) as well as standard error +functions. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfderiv.htm">rbfderiv</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfderiv.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfderiv +</title> +</head> +<body> +<H1> rbfderiv +</H1> +<h2> +Purpose +</h2> +Evaluate derivatives of RBF network outputs with respect to weights. + +<p><h2> +Synopsis +</h2> +<PRE> +g = rbfderiv(net, x)</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = rbfderiv(net, x)</CODE> takes a network data structure <CODE>net</CODE> +and a matrix of input vectors <CODE>x</CODE> and returns a three-index matrix +<CODE>g</CODE> whose <CODE>i</CODE>, <CODE>j</CODE>, <CODE>k</CODE> element contains the +derivative of network output <CODE>k</CODE> with respect to weight or bias +parameter <CODE>j</CODE> for input pattern <CODE>i</CODE>. The ordering of the +weight and bias parameters is defined by <CODE>rbfunpak</CODE>. This +function also takes into account any mask in the network data structure. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbferr.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +<html> +<head> +<title> +Netlab Reference Manual rbferr +</title> +</head> +<body> +<H1> rbferr +</H1> +<h2> +Purpose +</h2> +Evaluate error function for RBF network. + +<p><h2> +Synopsis +</h2> +<PRE> +e = rbferr(net, x, t) +[e, edata, eprior] = rbferr(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>e = rbferr(net, x, t)</CODE> takes a network data structure <CODE>net</CODE> together +with a matrix <CODE>x</CODE> of input +vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates the +appropriate error function <CODE>e</CODE> depending on <CODE>net.outfn</CODE>. +Each row of <CODE>x</CODE> corresponds to one +input vector and each row of <CODE>t</CODE> contains the corresponding target vector. + +<p><CODE>[e, edata, eprior] = rbferr(net, x, t)</CODE> additionally returns the +data and prior components of the error, assuming a zero mean Gaussian +prior on the weights with inverse variance parameters <CODE>alpha</CODE> and +<CODE>beta</CODE> taken from the network data structure <CODE>net</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfevfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfevfwd +</title> +</head> +<body> +<H1> rbfevfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation with evidence for RBF + +<p><h2> +Synopsis +</h2> +<PRE> + +[y, extra] = rbfevfwd(net, x, t, x_test) +[y, extra, invhess] = rbfevfwd(net, x, t, x_test, invhess) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>y = rbfevfwd(net, x, t, x_test)</CODE> takes a network data structure +<CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data +and input test data <CODE>x_test</CODE>. +It returns the normal forward propagation through the network <CODE>y</CODE> +together with a matrix <CODE>extra</CODE> which consists of error bars (variance) +for a regression problem or moderated outputs for a classification problem. + +<p>The optional argument (and return value) +<CODE>invhess</CODE> is the inverse of the network Hessian +computed on the training data inputs and targets. Passing it in avoids +recomputing it, which can be a significant saving for large training sets. + +<p><h2> +See Also +</h2> +<CODE><a href="fevbayes.htm">fevbayes</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbffwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +<html> +<head> +<title> +Netlab Reference Manual rbffwd +</title> +</head> +<body> +<H1> rbffwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through RBF network with linear outputs. + +<p><h2> +Synopsis +</h2> +<PRE> +a = rbffwd(net, x) +function [a, z, n2] = rbffwd(net, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>a = rbffwd(net, x)</CODE> takes a network data structure +<CODE>net</CODE> and a matrix <CODE>x</CODE> of input +vectors and forward propagates the inputs through the network to generate +a matrix <CODE>a</CODE> of output vectors. Each row of <CODE>x</CODE> corresponds to one +input vector and each row of <CODE>a</CODE> contains the corresponding output vector. +The activation function that is used is determined by <CODE>net.actfn</CODE>. + +<p><CODE>[a, z, n2] = rbffwd(net, x)</CODE> also generates a matrix <CODE>z</CODE> of +the hidden unit activations where each row corresponds to one pattern. +These hidden unit activations represent the <CODE>design matrix</CODE> for +the RBF. The matrix <CODE>n2</CODE> is the squared distances between each +basis function centre and each pattern in which each row corresponds +to a data point. + +<p><h2> +Examples +</h2> +<PRE> + +[a, z] = rbffwd(net, x); + +<p>temp = pinv([z ones(size(x, 1), 1)]) * t; +net.w2 = temp(1: nd(2), :); +net.b2 = temp(size(x, nd(2)) + 1, :); +</PRE> + +Here <CODE>x</CODE> is the input data, <CODE>t</CODE> are the target values, and we use the +pseudo-inverse to find the output weights and biases. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfgrad.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfgrad +</title> +</head> +<body> +<H1> rbfgrad +</H1> +<h2> +Purpose +</h2> +Evaluate gradient of error function for RBF network. + +<p><h2> +Synopsis +</h2> +<PRE> + +g = rbfgrad(net, x, t) +[g, gdata, gprior] = rbfgrad(net, x, t) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = rbfgrad(net, x, t)</CODE> takes a network data structure <CODE>net</CODE> +together with a matrix <CODE>x</CODE> of input +vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates the gradient +<CODE>g</CODE> of the error function with respect to the network weights (i.e. +including the hidden unit parameters). The error +function is sum of squares. +Each row of <CODE>x</CODE> corresponds to one +input vector and each row of <CODE>t</CODE> contains the corresponding target vector. +If the output function is <CODE>'neuroscale'</CODE> then the gradient is only +computed for the output layer weights and biases. + +<p><CODE>[g, gdata, gprior] = rbfgrad(net, x, t)</CODE> also returns separately +the data and prior contributions to the gradient. In the case of +multiple groups in the prior, <CODE>gprior</CODE> is a matrix with a row +for each group and a column for each weight parameter. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE>, <CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfhess.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfhess +</title> +</head> +<body> +<H1> rbfhess +</H1> +<h2> +Purpose +</h2> +Evaluate the Hessian matrix for RBF network. + +<p><h2> +Synopsis +</h2> +<PRE> +h = rbfhess(net, x, t) +[h, hdata] = rbfhess(net, x, t) +h = rbfhess(net, x, t, hdata) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>h = rbfhess(net, x, t)</CODE> takes an RBF network data structure <CODE>net</CODE>, +a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of target +values and returns the full Hessian matrix <CODE>h</CODE> corresponding to +the second derivatives of the negative log posterior distribution, +evaluated for the current weight and bias values as defined by +<CODE>net</CODE>. Currently, the implementation only computes the +Hessian for the output layer weights. + +<p><CODE>[h, hdata] = rbfhess(net, x, t)</CODE> returns both the Hessian matrix +<CODE>h</CODE> and the contribution <CODE>hdata</CODE> arising from the data dependent +term in the Hessian. + +<p><CODE>h = rbfhess(net, x, t, hdata)</CODE> takes a network data structure +<CODE>net</CODE>, a matrix <CODE>x</CODE> of input values, and a matrix <CODE>t</CODE> of +target values, together with the contribution <CODE>hdata</CODE> arising from +the data dependent term in the Hessian, and returns the full Hessian +matrix <CODE>h</CODE> corresponding to the second derivatives of the negative +log posterior distribution. This version saves computation time if +<CODE>hdata</CODE> has already been evaluated for the current weight and bias +values. + +<p><h2> +Example +</h2> +For the standard regression framework with a Gaussian conditional +distribution of target values given input values, and a simple +Gaussian prior over weights, the Hessian takes the form +<PRE> + + h = beta*hdata + alpha*I +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="mlphess.htm">mlphess</a></CODE>, <CODE><a href="hesschek.htm">hesschek</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfjacob.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfjacob +</title> +</head> +<body> +<H1> rbfjacob +</H1> +<h2> +Purpose +</h2> +Evaluate derivatives of RBF network outputs with respect to inputs. + +<p><h2> +Synopsis +</h2> +<PRE> +g = rbfjacob(net, x)</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = rbfjacob(net, x)</CODE> takes a network data structure <CODE>net</CODE> +and a matrix of input vectors <CODE>x</CODE> and returns a three-index matrix +<CODE>g</CODE> whose <CODE>i</CODE>, <CODE>j</CODE>, <CODE>k</CODE> element contains the +derivative of network output <CODE>k</CODE> with respect to input +parameter <CODE>j</CODE> for input pattern <CODE>i</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfpak +</title> +</head> +<body> +<H1> rbfpak +</H1> +<h2> +Purpose +</h2> +Combines all the parameters in an RBF network into one weights vector. + +<p><h2> +Synopsis +</h2> +<PRE> +w = rbfpak(net) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>w = rbfpak(net)</CODE> takes a network data structure <CODE>net</CODE> and combines +the component parameter matrices into a single row vector <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="rbfunpak.htm">rbfunpak</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfprior.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfprior +</title> +</head> +<body> +<H1> rbfprior +</H1> +<h2> +Purpose +</h2> +Create Gaussian prior and output layer mask for RBF. + +<p><h2> +Synopsis +</h2> +<PRE> +[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)</PRE> + + +<p><h2> +Description +</h2> +<CODE>[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)</CODE> +generates a vector +<CODE>mask</CODE> that selects only the output +layer weights. This is because most uses of RBF networks in a Bayesian +context have fixed basis functions with the output layer as the only +adjustable parameters. In particular, the Neuroscale output error function +is designed to work only with this mask. + +<p>The return value +<CODE>prior</CODE> is a data structure, +with fields <CODE>prior.alpha</CODE> and <CODE>prior.index</CODE>, which +specifies a Gaussian prior distribution for the network weights in an +RBF network. The parameters <CODE>aw2</CODE> and <CODE>ab2</CODE> are all +scalars and represent the regularization coefficients for two groups +of parameters in the network corresponding to + second-layer weights, and second-layer biases +respectively. Then <CODE>prior.alpha</CODE> represents a column vector of +length 2 containing the parameters, and <CODE>prior.index</CODE> is a matrix +specifying which weights belong in each group. Each column has one +element for each weight in the matrix, using the standard ordering as +defined in <CODE>rbfpak</CODE>, and each element is 1 or 0 according to +whether the weight is a member of the corresponding group or not. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="evidence.htm">evidence</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfsetbf.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfsetbf +</title> +</head> +<body> +<H1> rbfsetbf +</H1> +<h2> +Purpose +</h2> +Set basis functions of RBF from data. + +<p><h2> +Synopsis +</h2> +<PRE> +net = rbfsetbf(net, options, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = rbfsetbf(net, options, x)</CODE> sets the basis functions of the +RBF network <CODE>net</CODE> so that they model the unconditional density of the +dataset <CODE>x</CODE>. This is done by training a GMM with spherical covariances +using <CODE>gmmem</CODE>. The <CODE>options</CODE> vector is passed to <CODE>gmmem</CODE>. +The widths of the functions are set by a call to <CODE>rbfsetfw</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfsetfw.htm">rbfsetfw</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfsetfw.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfsetfw +</title> +</head> +<body> +<H1> rbfsetfw +</H1> +<h2> +Purpose +</h2> +Set basis function widths of RBF. + +<p><h2> +Synopsis +</h2> +<PRE> +net = rbfsetfw(net, scale) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = rbfsetfw(net, scale)</CODE> sets the widths of +the basis functions of the +RBF network <CODE>net</CODE>. +If Gaussian basis functions are used, then the variances are set to +the largest squared distance between centres if <CODE>scale</CODE> is non-positive +and <CODE>scale</CODE> times the mean distance of each centre to its nearest +neighbour if <CODE>scale</CODE> is positive. Non-Gaussian basis functions do +not have a width. + +<p><h2> +See Also +</h2> +<CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfsetbf.htm">rbfsetbf</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbftrain.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +<html> +<head> +<title> +Netlab Reference Manual rbftrain +</title> +</head> +<body> +<H1> rbftrain +</H1> +<h2> +Purpose +</h2> +Two stage training of RBF network. + +<p><h2> +Description +</h2> +<CODE>net = rbftrain(net, options, x, t)</CODE> uses a +two stage training +algorithm to set the weights in the RBF model structure <CODE>net</CODE>. +Each row of <CODE>x</CODE> corresponds to one +input vector and each row of <CODE>t</CODE> contains the corresponding target vector. +The centres are determined by fitting a Gaussian mixture model +with circular covariances using the EM algorithm through a call to +<CODE>rbfsetbf</CODE>. (The mixture model is +initialised using a small number of iterations of the K-means algorithm.) +If the activation functions are Gaussians, then the basis function widths +are then set to the maximum inter-centre squared distance. + +<p>For linear outputs, +the hidden to output +weights that give rise to the least squares solution +can then be determined using the pseudo-inverse. For neuroscale outputs, +the hidden to output weights are determined using the iterative shadow +targets algorithm. + Although this two stage +procedure may not give solutions with as low an error as using general +purpose non-linear optimisers, it is much faster. + +<p>The options vector may have two rows: if this is the case, then the second row +is passed to <CODE>rbfsetbf</CODE>, which allows the user to specify a different +number iterations for RBF and GMM training. +The optional parameters to <CODE>rbftrain</CODE> have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values during EM training. + +<p><CODE>options(2)</CODE> is a measure of the precision required for the value +of the weights <CODE>w</CODE> at the solution. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(5)</CODE> is set to 1 if the basis functions parameters should remain +unchanged; default 0. + +<p><CODE>options(6)</CODE> is set to 1 if the output layer weights should be should +set using PCA. This is only relevant for Neuroscale outputs; default 0. + +<p><CODE>options(14)</CODE> is the maximum number of iterations for the shadow +targets algorithm; +default 100. + +<p><h2> +Example +</h2> +The following example creates an RBF network and then trains it: +<PRE> + +net = rbf(1, 4, 1, 'gaussian'); +options(1, :) = foptions; +options(2, :) = foptions; +options(2, 14) = 10; % 10 iterations of EM +options(2, 5) = 1; % Check for covariance collapse in EM +net = rbftrain(net, options, x, t); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE>, <CODE><a href="rbfsetbf.htm">rbfsetbf</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rbfunpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual rbfunpak +</title> +</head> +<body> +<H1> rbfunpak +</H1> +<h2> +Purpose +</h2> +Separates a vector of RBF weights into its components. + +<p><h2> +Synopsis +</h2> +<PRE> +net = rbfunpak(net, w) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = rbfunpak(net, w)</CODE> takes an RBF network data structure <CODE>net</CODE> and +a weight vector <CODE>w</CODE>, and returns a network data structure identical to +the input network, except that the centres +<CODE>c</CODE>, the widths <CODE>wi</CODE>, the second-layer +weight matrix <CODE>w2</CODE> and the second-layer bias vector <CODE>b2</CODE> have all +been set to the corresponding elements of <CODE>w</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rosegrad.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +<html> +<head> +<title> +Netlab Reference Manual rosegrad +</title> +</head> +<body> +<H1> rosegrad +</H1> +<h2> +Purpose +</h2> +Calculate gradient of Rosenbrock's function. + +<p><h2> +Synopsis +</h2> +<PRE> +g = rosegrad(x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>g = rosegrad(x)</CODE> computes the gradient of Rosenbrock's function +at each row of <CODE>x</CODE>, which should have two columns. + +<p><h2> +See Also +</h2> +<CODE><a href="demopt1.htm">demopt1</a></CODE>, <CODE><a href="rosen.htm">rosen</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/rosen.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +<html> +<head> +<title> +Netlab Reference Manual rosen +</title> +</head> +<body> +<H1> rosen +</H1> +<h2> +Purpose +</h2> +Calculate Rosenbrock's function. + +<p><h2> +Synopsis +</h2> +<PRE> +y = rosen(x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>y = rosen(x)</CODE> computes the value of Rosenbrock's function +at each row of <CODE>x</CODE>, which should have two columns. + +<p><h2> +See Also +</h2> +<CODE><a href="demopt1.htm">demopt1</a></CODE>, <CODE><a href="rosegrad.htm">rosegrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/scg.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +<html> +<head> +<title> +Netlab Reference Manual scg +</title> +</head> +<body> +<H1> scg +</H1> +<h2> +Purpose +</h2> +Scaled conjugate gradient optimization. + +<p><h2> +Description +</h2> +<CODE>[x, options] = scg(f, x, options, gradf)</CODE> uses a scaled conjugate +gradients +algorithm to find a local minimum of the function <CODE>f(x)</CODE> whose +gradient is given by <CODE>gradf(x)</CODE>. Here <CODE>x</CODE> is a row vector +and <CODE>f</CODE> returns a scalar value. +The point at which <CODE>f</CODE> has a local minimum +is returned as <CODE>x</CODE>. The function value at that point is returned +in <CODE>options(8)</CODE>. + +<p><CODE>[x, options, flog, pointlog, scalelog] = scg(f, x, options, gradf)</CODE> +also returns (optionally) a log of the function values +after each cycle in <CODE>flog</CODE>, a log +of the points visited in <CODE>pointlog</CODE>, and a log of the scale values +in the algorithm in <CODE>scalelog</CODE>. + +<p><CODE>scg(f, x, options, gradf, p1, p2, ...)</CODE> allows +additional arguments to be passed to <CODE>f()</CODE> and <CODE>gradf()</CODE>. + +The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>, and the points visited +in the return argument <CODE>pointslog</CODE>. If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(2)</CODE> is a measure of the absolute precision required for the value +of <CODE>x</CODE> at the solution. If the absolute difference between +the values of <CODE>x</CODE> between two successive steps is less than +<CODE>options(2)</CODE>, then this condition is satisfied. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. If the absolute difference between the +objective function values between two successive steps is less than +<CODE>options(3)</CODE>, then this condition is satisfied. +Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(9)</CODE> is set to 1 to check the user defined gradient function. + +<p><CODE>options(10)</CODE> returns the total number of function evaluations (including +those in any line searches). + +<p><CODE>options(11)</CODE> returns the total number of gradient evaluations. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p><h2> +Examples +</h2> +An example of +the use of the additional arguments is the minimization of an error +function for a neural network: +<PRE> + +w = scg('neterr', w, options, 'netgrad', net, x, t); +</PRE> + + +<p><h2> +Algorithm +</h2> +The search direction is re-started after every <CODE>nparams</CODE> +successful weight updates where <CODE>nparams</CODE> is the total number of +parameters in <CODE>x</CODE>. The algorithm is based on that given by Williams +(1991), with a simplified procedure for updating <CODE>lambda</CODE> when +<CODE>rho < 0.25</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="quasinew.htm">quasinew</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/som.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +<html> +<head> +<title> +Netlab Reference Manual som +</title> +</head> +<body> +<H1> som +</H1> +<h2> +Purpose +</h2> +Creates a Self-Organising Map. + +<p><h2> +Synopsis +</h2> +<PRE> + +net = som(nin, map_size) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = som(nin, map_size)</CODE> creates a SOM <CODE>net</CODE> +with input dimension (i.e. data dimension) <CODE>nin</CODE> and map dimensions +<CODE>map_size</CODE>. Only two-dimensional maps are currently implemented. + +<p>The fields in <CODE>net</CODE> are +<PRE> + + type = 'som' + nin = number of inputs + map_dim = dimension of map (constrained to be 2) + map_size = grid size: number of nodes in each dimension + num_nodes = number of nodes: the product of values in map_size + map = map_dim+1 dimensional array containing nodes + inode_dist = map of inter-node distances using Manhatten metric +</PRE> + + +<p>The map contains the node vectors arranged column-wise in the first +dimension of the array. + +<p><h2> +See Also +</h2> +<CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="somfwd.htm">somfwd</a></CODE>, <CODE><a href="somtrain.htm">somtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/somfwd.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +<html> +<head> +<title> +Netlab Reference Manual somfwd +</title> +</head> +<body> +<H1> somfwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through a Self-Organising Map. + +<p><h2> +Synopsis +</h2> +<PRE> + +d2 = somfwd(net, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>d2 = somfwd(net, x)</CODE> propagates the data matrix <CODE>x</CODE> through + a SOM <CODE>net</CODE>, returning the squared distance matrix <CODE>d2</CODE> with +dimension <CODE>nin</CODE> by <CODE>num_nodes</CODE>. The $i$th row represents the +squared Euclidean distance to each of the nodes of the SOM. + +<p><CODE>[d2, win_nodes] = somfwd(net, x)</CODE> also returns the indices of the +winning nodes for each pattern. + +<p><h2> +Example +</h2> + +<p>The following code fragment creates a SOM with a $5times 5$ map for an +8-dimensional data space. It then applies the test data to the map. +<PRE> + +net = som(8, [5, 5]); +[d2, wn] = somfwd(net, test_data); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="som.htm">som</a></CODE>, <CODE><a href="somtrain.htm">somtrain</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/sompak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual sompak +</title> +</head> +<body> +<H1> sompak +</H1> +<h2> +Purpose +</h2> +Combines node weights into one weights matrix. + +<p><h2> +Synopsis +</h2> +<PRE> +c = sompak(net) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>c = sompak(net)</CODE> takes a SOM data structure <CODE>net</CODE> and +combines the node weights into a matrix of centres +<CODE>c</CODE> where each row represents the node vector. + +<p>The ordering of the parameters in <CODE>w</CODE> is defined by the indexing of the +multi-dimensional array <CODE>net.map</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="som.htm">som</a></CODE>, <CODE><a href="somunpak.htm">somunpak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/somtrain.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +<html> +<head> +<title> +Netlab Reference Manual somtrain +</title> +</head> +<body> +<H1> somtrain +</H1> +<h2> +Purpose +</h2> +Kohonen training algorithm for SOM. + +<p><h2> +Synopsis +</h2> +<PRE> + +net = somtrain{net, options, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = somtrain{net, options, x)</CODE> uses Kohonen's algorithm to +train a SOM. Both on-line and batch algorithms are implemented. +The learning rate (for on-line) and neighbourhood size decay linearly. +There is no error function minimised during training (so there is +no termination criterion other than the number of epochs), but the +sum-of-squares is computed and returned in <CODE>options(8)</CODE>. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs learning +rate <CODE>alpha</CODE> and neighbourhood size <CODE>nsize</CODE>. +Otherwise nothing is displayed. + +<p><CODE>options(5)</CODE> determines whether the patterns are sampled randomly +with replacement. If it is 0 (the default), then patterns are sampled +in order. This is only relevant to the on-line algorithm. + +<p><CODE>options(6)</CODE> determines if the on-line or batch algorithm is +used. If it is 1 +then the batch algorithm is used. If it is 0 +(the default) then the on-line algorithm is used. + +<p><CODE>options(14)</CODE> is the maximum number of iterations (passes through +the complete pattern set); default 100. + +<p><CODE>options(15)</CODE> is the final neighbourhood size; default value is the +same as the initial neighbourhood size. + +<p><CODE>options(16)</CODE> is the final learning rate; default value is the same +as the initial learning rate. + +<p><CODE>options(17)</CODE> is the initial neighbourhood size; default 0.5*maximum +map size. + +<p><CODE>options(18)</CODE> is the initial learning rate; default 0.9. This parameter +must be positive. + +<p><h2> +Examples +</h2> +The following example performs on-line training on a SOM in two stages: +ordering and convergence. +<PRE> + +net = som(nin, [8, 7]); +options = foptions; + +<p>% Ordering phase +options(1) = 1; +options(14) = 50; +options(18) = 0.9; % Initial learning rate +options(16) = 0.05; % Final learning rate +options(17) = 8; % Initial neighbourhood size +options(15) = 1; % Final neighbourhood size +net2 = somtrain(net, options, x); + +<p>% Convergence phase +options(14) = 400; +options(18) = 0.05; +options(16) = 0.01; +options(17) = 0; +options(15) = 0; +net3 = somtrain(net2, options, x); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="som.htm">som</a></CODE>, <CODE><a href="somfwd.htm">somfwd</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/somunpak.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual somunpak +</title> +</head> +<body> +<H1> somunpak +</H1> +<h2> +Purpose +</h2> +Replaces node weights in SOM. + +<p><h2> +Synopsis +</h2> +<PRE> +net = somunpak(net, w) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = somunpak(net, w)</CODE> takes a SOM data structure <CODE>net</CODE> and +weight matrix <CODE>w</CODE> (each node represented by a row) and +puts the nodes back into the multi-dimensional array <CODE>net.map</CODE>. + +<p>The ordering of the parameters in <CODE>w</CODE> is defined by the indexing of the +multi-dimensional array <CODE>net.map</CODE>. + +<p><h2> +See Also +</h2> +<CODE><a href="som.htm">som</a></CODE>, <CODE><a href="sompak.htm">sompak</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,180 @@ +/Contents.m/1.1.1.1/Wed Apr 27 17:59:04 2005// +/LICENSE/1.1.1.1/Wed Apr 27 17:59:04 2005// +/conffig.m/1.1.1.1/Wed Apr 27 17:59:04 2005// +/confmat.m/1.1.1.1/Wed Apr 27 17:59:04 2005// +/conjgrad.m/1.1.1.1/Wed Apr 27 17:59:04 2005// +/consist.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/convertoldnet.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/datread.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/datwrite.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/dem2ddat.m/1.1.1.1/Tue May 10 01:32:18 2005// +/demard.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demev1.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demev2.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demev3.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgauss.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demglm1.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demglm2.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgmm1.m/1.1.1.1/Sun May 8 17:51:14 2005// +/demgmm2.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgmm3.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgmm4.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgmm5.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgp.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgpard.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgpot.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgtm1.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demgtm2.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demhint.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demhmc1.m/1.1.1.1/Tue May 24 00:03:34 2005// +/demhmc2.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demhmc3.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demkmn1.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demknn1.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demmdn1.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demmet1.m/1.1.1.1/Mon May 23 06:23:08 2005// +/demmlp1.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demmlp2.m/1.1.1.1/Wed Apr 27 17:59:06 2005// +/demnlab.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/demns1.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/demolgd1.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/demopt1.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/dempot.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/demprgp.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/demprior.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/demrbf1.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/demsom1.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/demtrain.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/dist2.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/eigdec.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/errbayes.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/evidence.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/fevbayes.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/gauss.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/gbayes.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/glm.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/glmderiv.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/glmerr.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/glmevfwd.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/glmfwd.m/1.1.1.1/Wed Apr 27 17:59:08 2005// +/glmgrad.m/1.1.1.1/Wed Apr 27 17:59:08 2005// 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Apr 27 17:59:10 2005// +/gppak.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gpunpak.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gradchek.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/graddesc.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gsamp.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtm.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtmem.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtmfwd.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtminit.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtmlmean.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtmlmode.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtmmag.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtmpost.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/gtmprob.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/hbayes.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/hesschek.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/hintmat.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/hinton.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/histp.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/hmc.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/kmeansNetlab.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/knn.m/1.1.1.1/Wed Apr 27 17:59:10 2005// +/knnfwd.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/linef.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/linemin.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/maxitmess.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdn.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdn2gmm.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdndist2.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdnerr.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdnfwd.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdngrad.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdninit.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdnnet.mat/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdnpak.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdnpost.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdnprob.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mdnunpak.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/metrop.m/1.1.1.1/Mon May 23 23:07:40 2005// +/minbrack.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlp.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlpbkp.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlpderiv.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlperr.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlpevfwd.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlpfwd.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlpgrad.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlphdotv.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlphess.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlphint.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlpinit.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlppak.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlpprior.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlptrain.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/mlpunpak.m/1.1.1.1/Wed Apr 27 17:59:12 2005// +/netderiv.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/neterr.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/netevfwd.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/netgrad.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/nethess.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/netinit.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/netlab3.3.zip/1.1.1.1/Wed Apr 27 17:59:14 2005// +/netlogo.mat/1.1.1.1/Wed Apr 27 17:59:14 2005// +/netopt.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/netpak.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/netunpak.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/oilTrn.dat/1.1.1.1/Wed Apr 27 17:59:14 2005// +/oilTst.dat/1.1.1.1/Wed Apr 27 17:59:14 2005// +/olgd.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/pca.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/plotmat.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/ppca.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/quasinew.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbf.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfbkp.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfderiv.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbferr.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfevfwd.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbffwd.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfgrad.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfhess.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfjacob.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfpak.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfprior.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfsetbf.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbfsetfw.m/1.1.1.1/Wed Apr 27 17:59:14 2005// +/rbftrain.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/rbfunpak.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/rosegrad.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/rosen.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/scg.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/som.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/somfwd.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/sompak.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/somtrain.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/somunpak.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/xor.dat/1.1.1.1/Wed Apr 27 17:59:16 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/netlab3.3
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/Contents.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,176 @@ +% Netlab Toolbox +% Version 3.3.1 18-Jun-2004 +% +% conffig - Display a confusion matrix. +% confmat - Compute a confusion matrix. +% conjgrad - Conjugate gradients optimization. +% consist - Check that arguments are consistent. +% convertoldnet- Convert pre-2.3 release MLP and MDN nets to new format +% datread - Read data from an ascii file. +% datwrite - Write data to ascii file. +% dem2ddat - Generates two dimensional data for demos. +% demard - Automatic relevance determination using the MLP. +% demev1 - Demonstrate Bayesian regression for the MLP. +% demev2 - Demonstrate Bayesian classification for the MLP. +% demev3 - Demonstrate Bayesian regression for the RBF. +% demgauss - Demonstrate sampling from Gaussian distributions. +% demglm1 - Demonstrate simple classification using a generalized linear model. +% demglm2 - Demonstrate simple classification using a generalized linear model. +% demgmm1 - Demonstrate density modelling with a Gaussian mixture model. +% demgmm3 - Demonstrate density modelling with a Gaussian mixture model. +% demgmm4 - Demonstrate density modelling with a Gaussian mixture model. +% demgmm5 - Demonstrate density modelling with a PPCA mixture model. +% demgp - Demonstrate simple regression using a Gaussian Process. +% demgpard - Demonstrate ARD using a Gaussian Process. +% demgpot - Computes the gradient of the negative log likelihood for a mixture model. +% demgtm1 - Demonstrate EM for GTM. +% demgtm2 - Demonstrate GTM for visualisation. +% demhint - Demonstration of Hinton diagram for 2-layer feed-forward network. +% demhmc1 - Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. +% demhmc2 - Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. +% demhmc3 - Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. +% demkmean - Demonstrate simple clustering model trained with K-means. +% demknn1 - Demonstrate nearest neighbour classifier. +% demmdn1 - Demonstrate fitting a multi-valued function using a Mixture Density Network. +% demmet1 - Demonstrate Markov Chain Monte Carlo sampling on a Gaussian. +% demmlp1 - Demonstrate simple regression using a multi-layer perceptron +% demmlp2 - Demonstrate simple classification using a multi-layer perceptron +% demnlab - A front-end Graphical User Interface to the demos +% demns1 - Demonstrate Neuroscale for visualisation. +% demolgd1 - Demonstrate simple MLP optimisation with on-line gradient descent +% demopt1 - Demonstrate different optimisers on Rosenbrock's function. +% dempot - Computes the negative log likelihood for a mixture model. +% demprgp - Demonstrate sampling from a Gaussian Process prior. +% demprior - Demonstrate sampling from a multi-parameter Gaussian prior. +% demrbf1 - Demonstrate simple regression using a radial basis function network. +% demsom1 - Demonstrate SOM for visualisation. +% demtrain - Demonstrate training of MLP network. +% dist2 - Calculates squared distance between two sets of points. +% eigdec - Sorted eigendecomposition +% errbayes - Evaluate Bayesian error function for network. +% evidence - Re-estimate hyperparameters using evidence approximation. +% fevbayes - Evaluate Bayesian regularisation for network forward propagation. +% gauss - Evaluate a Gaussian distribution. +% gbayes - Evaluate gradient of Bayesian error function for network. +% glm - Create a generalized linear model. +% glmderiv - Evaluate derivatives of GLM outputs with respect to weights. +% glmerr - Evaluate error function for generalized linear model. +% glmevfwd - Forward propagation with evidence for GLM +% glmfwd - Forward propagation through generalized linear model. +% glmgrad - Evaluate gradient of error function for generalized linear model. +% glmhess - Evaluate the Hessian matrix for a generalised linear model. +% glminit - Initialise the weights in a generalized linear model. +% glmpak - Combines weights and biases into one weights vector. +% glmtrain - Specialised training of generalized linear model +% glmunpak - Separates weights vector into weight and bias matrices. +% gmm - Creates a Gaussian mixture model with specified architecture. +% gmmactiv - Computes the activations of a Gaussian mixture model. +% gmmem - EM algorithm for Gaussian mixture model. +% gmminit - Initialises Gaussian mixture model from data +% gmmpak - Combines all the parameters in a Gaussian mixture model into one vector. +% gmmpost - Computes the class posterior probabilities of a Gaussian mixture model. +% gmmprob - Computes the data probability for a Gaussian mixture model. +% gmmsamp - Sample from a Gaussian mixture distribution. +% gmmunpak - Separates a vector of Gaussian mixture model parameters into its components. +% gp - Create a Gaussian Process. +% gpcovar - Calculate the covariance for a Gaussian Process. +% gpcovarf - Calculate the covariance function for a Gaussian Process. +% gpcovarp - Calculate the prior covariance for a Gaussian Process. +% gperr - Evaluate error function for Gaussian Process. +% gpfwd - Forward propagation through Gaussian Process. +% gpgrad - Evaluate error gradient for Gaussian Process. +% gpinit - Initialise Gaussian Process model. +% gppak - Combines GP hyperparameters into one vector. +% gpunpak - Separates hyperparameter vector into components. +% gradchek - Checks a user-defined gradient function using finite differences. +% graddesc - Gradient descent optimization. +% gsamp - Sample from a Gaussian distribution. +% gtm - Create a Generative Topographic Map. +% gtmem - EM algorithm for Generative Topographic Mapping. +% gtmfwd - Forward propagation through GTM. +% gtminit - Initialise the weights and latent sample in a GTM. +% gtmlmean - Mean responsibility for data in a GTM. +% gtmlmode - Mode responsibility for data in a GTM. +% gtmmag - Magnification factors for a GTM +% gtmpost - Latent space responsibility for data in a GTM. +% gtmprob - Probability for data under a GTM. +% hbayes - Evaluate Hessian of Bayesian error function for network. +% hesschek - Use central differences to confirm correct evaluation of Hessian matrix. +% hintmat - Evaluates the coordinates of the patches for a Hinton diagram. +% hinton - Plot Hinton diagram for a weight matrix. +% histp - Histogram estimate of 1-dimensional probability distribution. +% hmc - Hybrid Monte Carlo sampling. +% kmeans - Trains a k means cluster model. +% knn - Creates a K-nearest-neighbour classifier. +% knnfwd - Forward propagation through a K-nearest-neighbour classifier. +% linef - Calculate function value along a line. +% linemin - One dimensional minimization. +% maxitmess- Create a standard error message when training reaches max. iterations. +% mdn - Creates a Mixture Density Network with specified architecture. +% mdn2gmm - Converts an MDN mixture data structure to array of GMMs. +% mdndist2 - Calculates squared distance between centres of Gaussian kernels and data +% mdnerr - Evaluate error function for Mixture Density Network. +% mdnfwd - Forward propagation through Mixture Density Network. +% mdngrad - Evaluate gradient of error function for Mixture Density Network. +% mdninit - Initialise the weights in a Mixture Density Network. +% mdnpak - Combines weights and biases into one weights vector. +% mdnpost - Computes the posterior probability for each MDN mixture component. +% mdnprob - Computes the data probability likelihood for an MDN mixture structure. +% mdnunpak - Separates weights vector into weight and bias matrices. +% metrop - Markov Chain Monte Carlo sampling with Metropolis algorithm. +% minbrack - Bracket a minimum of a function of one variable. +% mlp - Create a 2-layer feedforward network. +% mlpbkp - Backpropagate gradient of error function for 2-layer network. +% mlpderiv - Evaluate derivatives of network outputs with respect to weights. +% mlperr - Evaluate error function for 2-layer network. +% mlpevfwd - Forward propagation with evidence for MLP +% mlpfwd - Forward propagation through 2-layer network. +% mlpgrad - Evaluate gradient of error function for 2-layer network. +% mlphdotv - Evaluate the product of the data Hessian with a vector. +% mlphess - Evaluate the Hessian matrix for a multi-layer perceptron network. +% mlphint - Plot Hinton diagram for 2-layer feed-forward network. +% mlpinit - Initialise the weights in a 2-layer feedforward network. +% mlppak - Combines weights and biases into one weights vector. +% mlpprior - Create Gaussian prior for mlp. +% mlptrain - Utility to train an MLP network for demtrain +% mlpunpak - Separates weights vector into weight and bias matrices. +% netderiv - Evaluate derivatives of network outputs by weights generically. +% neterr - Evaluate network error function for generic optimizers +% netevfwd - Generic forward propagation with evidence for network +% netgrad - Evaluate network error gradient for generic optimizers +% nethess - Evaluate network Hessian +% netinit - Initialise the weights in a network. +% netopt - Optimize the weights in a network model. +% netpak - Combines weights and biases into one weights vector. +% netunpak - Separates weights vector into weight and bias matrices. +% olgd - On-line gradient descent optimization. +% pca - Principal Components Analysis +% plotmat - Display a matrix. +% ppca - Probabilistic Principal Components Analysis +% quasinew - Quasi-Newton optimization. +% rbf - Creates an RBF network with specified architecture +% rbfbkp - Backpropagate gradient of error function for RBF network. +% rbfderiv - Evaluate derivatives of RBF network outputs with respect to weights. +% rbferr - Evaluate error function for RBF network. +% rbfevfwd - Forward propagation with evidence for RBF +% rbffwd - Forward propagation through RBF network with linear outputs. +% rbfgrad - Evaluate gradient of error function for RBF network. +% rbfhess - Evaluate the Hessian matrix for RBF network. +% rbfjacob - Evaluate derivatives of RBF network outputs with respect to inputs. +% rbfpak - Combines all the parameters in an RBF network into one weights vector. +% rbfprior - Create Gaussian prior and output layer mask for RBF. +% rbfsetbf - Set basis functions of RBF from data. +% rbfsetfw - Set basis function widths of RBF. +% rbftrain - Two stage training of RBF network. +% rbfunpak - Separates a vector of RBF weights into its components. +% rosegrad - Calculate gradient of Rosenbrock's function. +% rosen - Calculate Rosenbrock's function. +% scg - Scaled conjugate gradient optimization. +% som - Creates a Self-Organising Map. +% somfwd - Forward propagation through a Self-Organising Map. +% sompak - Combines node weights into one weights matrix. +% somtrain - Kohonen training algorithm for SOM. +% somunpak - Replaces node weights in SOM. +% +% Copyright (c) Ian T Nabney (1996-2001) +%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/LICENSE Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ + Copyright (c) 1996-2001, Ian T. Nabney + All rights reserved. + + Redistribution and use in source and binary + forms, with or without modification, are + permitted provided that the following + conditions are met: + + * Redistributions of source code must + retain the above copyright notice, this + list of conditions and the following + disclaimer. + * Redistributions in binary form must + reproduce the above copyright notice, + this list of conditions and the + following disclaimer in the + documentation and/or other materials + provided with the distribution. + * Neither the name of the Aston University, Birmingham, U.K. + nor the names of its contributors may be + used to endorse or promote products + derived from this software without + specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT + HOLDERS AND CONTRIBUTORS "AS IS" AND ANY + EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT + NOT LIMITED TO, THE IMPLIED WARRANTIES OF + MERCHANTABILITY AND FITNESS FOR A PARTICULAR + PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE + REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY + DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT + OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT + LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR + OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/conffig.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function fh=conffig(y, t) +%CONFFIG Display a confusion matrix. +% +% Description +% CONFFIG(Y, T) displays the confusion matrix and classification +% performance for the predictions mat{y} compared with the targets T. +% The data is assumed to be in a 1-of-N encoding, unless there is just +% one column, when it is assumed to be a 2 class problem with a 0-1 +% encoding. Each row of Y and T corresponds to a single example. +% +% In the confusion matrix, the rows represent the true classes and the +% columns the predicted classes. +% +% FH = CONFFIG(Y, T) also returns the figure handle FH which can be +% used, for instance, to delete the figure when it is no longer needed. +% +% See also +% CONFMAT, DEMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[C, rate] = confmat(y, t); + +fh = figure('Name', 'Confusion matrix', ... + 'NumberTitle', 'off'); + +plotmat(C, 'k', 'k', 14); +title(['Classification rate: ' num2str(rate(1)) '%'], 'FontSize', 14);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/confmat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +function [C,rate]=confmat(Y,T) +%CONFMAT Compute a confusion matrix. +% +% Description +% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and +% classification performance RATE for the predictions mat{y} compared +% with the targets T. The data is assumed to be in a 1-of-N encoding, +% unless there is just one column, when it is assumed to be a 2 class +% problem with a 0-1 encoding. Each row of Y and T corresponds to a +% single example. +% +% In the confusion matrix, the rows represent the true classes and the +% columns the predicted classes. The vector RATE has two entries: the +% percentage of correct classifications and the total number of correct +% classifications. +% +% See also +% CONFFIG, DEMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[n c]=size(Y); +[n2 c2]=size(T); + +if n~=n2 | c~=c2 + error('Outputs and targets are different sizes') +end + +if c > 1 + % Find the winning class assuming 1-of-N encoding + [maximum Yclass] = max(Y', [], 1); + + TL=[1:c]*T'; +else + % Assume two classes with 0-1 encoding + c = 2; + class2 = find(T > 0.5); + TL = ones(n, 1); + TL(class2) = 2; + class2 = find(Y > 0.5); + Yclass = ones(n, 1); + Yclass(class2) = 2; +end + +% Compute +correct = (Yclass==TL); +total=sum(sum(correct)); +rate=[total*100/n total]; + +C=zeros(c,c); +for i=1:c + for j=1:c + C(i,j) = sum((Yclass==j).*(TL==i)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/conjgrad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,168 @@ +function [x, options, flog, pointlog] = conjgrad(f, x, options, gradf, ... + varargin) +%CONJGRAD Conjugate gradients optimization. +% +% Description +% [X, OPTIONS, FLOG, POINTLOG] = CONJGRAD(F, X, OPTIONS, GRADF) uses a +% conjugate gradients algorithm to find the minimum of the function +% F(X) whose gradient is given by GRADF(X). Here X is a row vector and +% F returns a scalar value. The point at which F has a local minimum +% is returned as X. The function value at that point is returned in +% OPTIONS(8). A log of the function values after each cycle is +% (optionally) returned in FLOG, and a log of the points visited is +% (optionally) returned in POINTLOG. +% +% CONJGRAD(F, X, OPTIONS, GRADF, P1, P2, ...) allows additional +% arguments to be passed to F() and GRADF(). +% +% The optional parameters have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG, and the points visited in the +% return argument POINTSLOG. If OPTIONS(1) is set to 0, then only +% warning messages are displayed. If OPTIONS(1) is -1, then nothing is +% displayed. +% +% OPTIONS(2) is a measure of the absolute precision required for the +% value of X at the solution. If the absolute difference between the +% values of X between two successive steps is less than OPTIONS(2), +% then this condition is satisfied. +% +% OPTIONS(3) is a measure of the precision required of the objective +% function at the solution. If the absolute difference between the +% objective function values between two successive steps is less than +% OPTIONS(3), then this condition is satisfied. Both this and the +% previous condition must be satisfied for termination. +% +% OPTIONS(9) is set to 1 to check the user defined gradient function. +% +% OPTIONS(10) returns the total number of function evaluations +% (including those in any line searches). +% +% OPTIONS(11) returns the total number of gradient evaluations. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% OPTIONS(15) is the precision in parameter space of the line search; +% default 1E-4. +% +% See also +% GRADDESC, LINEMIN, MINBRACK, QUASINEW, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set up the options. +if length(options) < 18 + error('Options vector too short') +end + +if(options(14)) + niters = options(14); +else + niters = 100; +end + +% Set up options for line search +line_options = foptions; +% Need a precise line search for success +if options(15) > 0 + line_options(2) = options(15); +else + line_options(2) = 1e-4; +end + +display = options(1); + +% Next two lines allow conjgrad to work with expression strings +f = fcnchk(f, length(varargin)); +gradf = fcnchk(gradf, length(varargin)); + +% Check gradients +if (options(9)) + feval('gradchek', x, f, gradf, varargin{:}); +end + +options(10) = 0; +options(11) = 0; +nparams = length(x); +fnew = feval(f, x, varargin{:}); +options(10) = options(10) + 1; +gradnew = feval(gradf, x, varargin{:}); +options(11) = options(11) + 1; +d = -gradnew; % Initial search direction +br_min = 0; +br_max = 1.0; % Initial value for maximum distance to search along +tol = sqrt(eps); + +j = 1; +if nargout >= 3 + flog(j, :) = fnew; + if nargout == 4 + pointlog(j, :) = x; + end +end + +while (j <= niters) + + xold = x; + fold = fnew; + gradold = gradnew; + + gg = gradold*gradold'; + if (gg == 0.0) + % If the gradient is zero then we are done. + options(8) = fnew; + return; + end + + % This shouldn't occur, but rest of code depends on d being downhill + if (gradnew*d' > 0) + d = -d; + if options(1) >= 0 + warning('search direction uphill in conjgrad'); + end + end + + line_sd = d./norm(d); + [lmin, line_options] = feval('linemin', f, xold, line_sd, fold, ... + line_options, varargin{:}); + options(10) = options(10) + line_options(10); + options(11) = options(11) + line_options(11); + % Set x and fnew to be the actual search point we have found + x = xold + lmin * line_sd; + fnew = line_options(8); + + % Check for termination + if (max(abs(x - xold)) < options(2) & max(abs(fnew - fold)) < options(3)) + options(8) = fnew; + return; + end + + gradnew = feval(gradf, x, varargin{:}); + options(11) = options(11) + 1; + + % Use Polak-Ribiere formula to update search direction + gamma = ((gradnew - gradold)*(gradnew)')/gg; + d = (d .* gamma) - gradnew; + + if (display > 0) + fprintf(1, 'Cycle %4d Function %11.6f\n', j, line_options(8)); + end + + j = j + 1; + if nargout >= 3 + flog(j, :) = fnew; + if nargout == 4 + pointlog(j, :) = x; + end + end +end + +% If we get here, then we haven't terminated in the given number of +% iterations. + +options(8) = fold; +if (options(1) >= 0) + disp(maxitmess); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/consist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,87 @@ +function errstring = consist(model, type, inputs, outputs) +%CONSIST Check that arguments are consistent. +% +% Description +% +% ERRSTRING = CONSIST(NET, TYPE, INPUTS) takes a network data structure +% NET together with a string TYPE containing the correct network type, +% a matrix INPUTS of input vectors and checks that the data structure +% is consistent with the other arguments. An empty string is returned +% if there is no error, otherwise the string contains the relevant +% error message. If the TYPE string is empty, then any type of network +% is allowed. +% +% ERRSTRING = CONSIST(NET, TYPE) takes a network data structure NET +% together with a string TYPE containing the correct network type, and +% checks that the two types match. +% +% ERRSTRING = CONSIST(NET, TYPE, INPUTS, OUTPUTS) also checks that the +% network has the correct number of outputs, and that the number of +% patterns in the INPUTS and OUTPUTS is the same. The fields in NET +% that are used are +% type +% nin +% nout +% +% See also +% MLPFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Assume that all is OK as default +errstring = ''; + +% If type string is not empty +if ~isempty(type) + % First check that model has type field + if ~isfield(model, 'type') + errstring = 'Data structure does not contain type field'; + return + end + % Check that model has the correct type + s = model.type; + if ~strcmp(s, type) + errstring = ['Model type ''', s, ''' does not match expected type ''',... + type, '''']; + return + end +end + +% If inputs are present, check that they have correct dimension +if nargin > 2 + if ~isfield(model, 'nin') + errstring = 'Data structure does not contain nin field'; + return + end + + data_nin = size(inputs, 2); + if model.nin ~= data_nin + errstring = ['Dimension of inputs ', num2str(data_nin), ... + ' does not match number of model inputs ', num2str(model.nin)]; + return + end +end + +% If outputs are present, check that they have correct dimension +if nargin > 3 + if ~isfield(model, 'nout') + errstring = 'Data structure does not conatin nout field'; + return + end + data_nout = size(outputs, 2); + if model.nout ~= data_nout + errstring = ['Dimension of outputs ', num2str(data_nout), ... + ' does not match number of model outputs ', num2str(model.nout)]; + return + end + +% Also check that number of data points in inputs and outputs is the same + num_in = size(inputs, 1); + num_out = size(outputs, 1); + if num_in ~= num_out + errstring = ['Number of input patterns ', num2str(num_in), ... + ' does not match number of output patterns ', num2str(num_out)]; + return + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/convertoldnet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function net = convertoldnet(net) +%CONVERTOLDNET Convert pre-2.3 release MLP and MDN nets to new format +% +% Description +% NET = CONVERTOLDNET(NET) takes a network NET and, if appropriate, +% converts it from pre-2.3 to the current format. The difference is +% simply that in MLPs and the MLP sub-net of MDNs the field ACTFN has +% been renamed OUTFN to make it consistent with GLM and RBF networks. +% If the network is not old-format or an MLP or MDN it is left +% unchanged. +% +% See also +% MLP, MDN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +switch net.type + case 'mlp' + if (isfield(net, 'actfn')) + net.outfn = net.actfn; + net = rmfield(net, 'actfn'); + end + case 'mdn' + net.mlp = convertoldnet(net.mlp); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/datread.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,99 @@ +function [x, t, nin, nout, ndata] = datread(filename) +%DATREAD Read data from an ascii file. +% +% Description +% +% [X, T, NIN, NOUT, NDATA] = DATREAD(FILENAME) reads from the file +% FILENAME and returns a matrix X of input vectors, a matrix T of +% target vectors, and integers NIN, NOUT and NDATA specifying the +% number of inputs, the number of outputs and the number of data points +% respectively. +% +% The format of the data file is as follows: the first row contains the +% string NIN followed by the number of inputs, the second row contains +% the string NOUT followed by the number of outputs, and the third row +% contains the string NDATA followed by the number of data vectors. +% Subsequent lines each contain one input vector followed by one output +% vector, with individual values separated by spaces. +% +% See also +% nin 2 nout 1 ndata 4 0.000000e+00 0.000000e+00 +% 1.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 +% 1.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 +% 1.000000e+00 1.000000e+00 See Also +% DATWRITE +% + +% Copyright (c) Ian T Nabney (1996-2001) + +fid = fopen(filename, 'rt'); +if fid == -1 + error('Failed to open file.') +end + +% Read number of inputs +s1 = fscanf(fid, '%s', 1); +if ~strcmp(s1, 'nin') + fclose(fid); + error('String ''nin'' not found') +end +nin = fscanf(fid, '%d\n', 1); +if ~isnumeric(nin) + fclose(fid); + error('No number for nin') +end +if nin < 0 | round(nin) ~= nin + fclose(fid); + error('nin must be a non-negative integer') +end + +% Read number of outputs +s2 = fscanf(fid, '%s', 1); +if ~strcmp(s2, 'nout') + fclose(fid); + error('String ''nout'' not found') +end +nout = fscanf(fid, '%d\n', 1); +if ~isnumeric(nout) + fclose(fid); + error('No number for nout') +end +if nout < 0 | round(nout) ~= nout + fclose(fid); + error('nout must be a non-negative integer') +end + +% Read number of data values +s3 = fscanf(fid, '%s', 1); +if ~strcmp(s3, 'ndata') + fclose(fid); + error('String ''ndata'' not found') +end +ndata = fscanf(fid, '%d\n', 1); +if ~isnumeric(ndata) + fclose(fid); + error('No number for ndata') +end +if ndata < 0 | round(ndata) ~= ndata + fclose(fid); + error('ndata must be a non-negative integer') +end + +% The following line reads all of the remaining data to the end of file. +temp = fscanf(fid, '%f', inf); + +% Check that size of temp is correct +if size(temp, 1) * size(temp,2) ~= (nin+nout) * ndata + fclose(fid); + error('Incorrect number of elements in file') +end + +temp = reshape(temp, nin + nout, ndata)'; +x = temp(:, 1:nin); +t = temp(:, nin + 1 : nin + nout); + +flag = fclose(fid); +if flag == -1 + error('Failed to close file.') +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/datwrite.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function datwrite(filename, x, t) +%DATWRITE Write data to ascii file. +% +% Description +% +% DATWRITE(FILENAME, X, T) takes a matrix X of input vectors and a +% matrix T of target vectors and writes them to an ascii file named +% FILENAME. The file format is as follows: the first row contains the +% string NIN followed by the number of inputs, the second row contains +% the string NOUT followed by the number of outputs, and the third row +% contains the string NDATA followed by the number of data vectors. +% Subsequent lines each contain one input vector followed by one output +% vector, with individual values separated by spaces. +% +% See also +% DATREAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +nin = size(x, 2); +nout = size(t, 2); +ndata = size(x, 1); + +fid = fopen(filename, 'wt'); +if fid == -1 + error('Failed to open file.') +end + +if size(t, 1) ~= ndata + error('x and t must have same number of rows.'); +end + +fprintf(fid, ' nin %d\n nout %d\n ndata %d\n', nin , nout, ndata); +for i = 1 : ndata + fprintf(fid, '%13e ', x(i,:), t(i,:)); + fprintf(fid, '\n'); +end + +flag = fclose(fid); +if flag == -1 + error('Failed to close file.') +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/dem2ddat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function [data, c, prior, sd] = dem2ddat(ndata) +%DEM2DDAT Generates two dimensional data for demos. +% +% Description +% The data is drawn from three spherical Gaussian distributions with +% priors 0.3, 0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and +% standard deviations 0.2, 0.5 and 1.0. DATA = DEM2DDAT(NDATA) +% generates NDATA points. +% +% [DATA, C] = DEM2DDAT(NDATA) also returns a matrix containing the +% centres of the Gaussian distributions. +% +% See also +% DEMGMM1, DEMKMEAN, DEMKNN1 +% + +% Copyright (c) Ian T Nabney (1996-2001) + +input_dim = 2; + +% Fix seed for reproducible results +randn('state', 42); + +% Generate mixture of three Gaussians in two dimensional space +data = randn(ndata, input_dim); + +% Priors for the three clusters +prior(1) = 0.3; +prior(2) = 0.5; +prior(3) = 0.2; + +% Cluster centres +c = [2.0, 3.5; 0.0, 0.0; 0.0, 2.0]; + +% Cluster standard deviations +sd = [0.2 0.5 1.0]; + +% Put first cluster at (2, 3.5) +data(1:prior(1)*ndata, 1) = data(1:prior(1)*ndata, 1) * 0.2 + c(1,1); +data(1:prior(1)*ndata, 2) = data(1:prior(1)*ndata, 2) * 0.2 + c(1,2); + +% Leave second cluster at (0,0) +data((prior(1)*ndata + 1):(prior(2)+prior(1))*ndata, :) = ... + data((prior(1)*ndata + 1):(prior(2)+prior(1))*ndata, :) * 0.5; + +% Put third cluster at (0,2) +data((prior(1)+prior(2))*ndata +1:ndata, 2) = ... + data((prior(1)+prior(2))*ndata+1:ndata, 2) + c(3, 2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demard.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,157 @@ +%DEMARD Automatic relevance determination using the MLP. +% +% Description +% This script demonstrates the technique of automatic relevance +% determination (ARD) using a synthetic problem having three input +% variables: X1 is sampled uniformly from the range (0,1) and has a low +% level of added Gaussian noise, X2 is a copy of X1 with a higher level +% of added noise, and X3 is sampled randomly from a Gaussian +% distribution. The single target variable is determined by +% SIN(2*PI*X1) with additive Gaussian noise. Thus X1 is very relevant +% for determining the target value, X2 is of some relevance, while X3 +% is irrelevant. The prior over weights is given by the ARD Gaussian +% prior with a separate hyper-parameter for the group of weights +% associated with each input. A multi-layer perceptron is trained on +% this data, with re-estimation of the hyper-parameters using EVIDENCE. +% The final values for the hyper-parameters reflect the relative +% importance of the three inputs. +% +% See also +% DEMMLP1, DEMEV1, MLP, EVIDENCE +% + +% Copyright (c) Ian T Nabney (1996-2001) + +clc; +disp('This demonstration illustrates the technique of automatic relevance') +disp('determination (ARD) using a multi-layer perceptron.') +disp(' '); +disp('First, we set up a synthetic data set involving three input variables:') +disp('x1 is sampled uniformly from the range (0,1) and has a low level of') +disp('added Gaussian noise, x2 is a copy of x1 with a higher level of added') +disp('noise, and x3 is sampled randomly from a Gaussian distribution. The') +disp('single target variable is given by t = sin(2*pi*x1) with additive') +disp('Gaussian noise. Thus x1 is very relevant for determining the target') +disp('value, x2 is of some relevance, while x3 should in principle be') +disp('irrelevant.') +disp(' '); +disp('Press any key to see a plot of t against x1.') +pause; + +% Generate the data set. +randn('state', 0); +rand('state', 0); +ndata = 100; +noise = 0.05; +x1 = rand(ndata, 1) + 0.002*randn(ndata, 1); +x2 = x1 + 0.02*randn(ndata, 1); +x3 = 0.5 + 0.2*randn(ndata, 1); +x = [x1, x2, x3]; +t = sin(2*pi*x1) + noise*randn(ndata, 1); + +% Plot the data and the original function. +h = figure; +plotvals = linspace(0, 1, 200)'; +plot(x1, t, 'ob') +hold on +axis([0 1 -1.5 1.5]) +[fx, fy] = fplot('sin(2*pi*x)', [0 1]); +plot(fx, fy, '-g', 'LineWidth', 2); +legend('data', 'function'); + +disp(' '); +disp('Press any key to continue') +pause; clc; + +disp('The prior over weights is given by the ARD Gaussian prior with a') +disp('separate hyper-parameter for the group of weights associated with each') +disp('input. This prior is set up using the utility MLPPRIOR. The network is') +disp('trained by error minimization using scaled conjugate gradient function') +disp('SCG. There are two cycles of training, and at the end of each cycle') +disp('the hyper-parameters are re-estimated using EVIDENCE.') +disp(' '); +disp('Press any key to create and train the network.') +disp(' '); +pause; + +% Set up network parameters. +nin = 3; % Number of inputs. +nhidden = 2; % Number of hidden units. +nout = 1; % Number of outputs. +aw1 = 0.01*ones(1, nin); % First-layer ARD hyperparameters. +ab1 = 0.01; % Hyperparameter for hidden unit biases. +aw2 = 0.01; % Hyperparameter for second-layer weights. +ab2 = 0.01; % Hyperparameter for output unit biases. +beta = 50.0; % Coefficient of data error. + +% Create and initialize network. +prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2); +net = mlp(nin, nhidden, nout, 'linear', prior, beta); + +% Set up vector of options for the optimiser. +nouter = 2; % Number of outer loops +ninner = 10; % Number of inner loops +options = zeros(1,18); % Default options vector. +options(1) = 1; % This provides display of error values. +options(2) = 1.0e-7; % This ensures that convergence must occur +options(3) = 1.0e-7; +options(14) = 300; % Number of training cycles in inner loop. + +% Train using scaled conjugate gradients, re-estimating alpha and beta. +for k = 1:nouter + net = netopt(net, options, x, t, 'scg'); + [net, gamma] = evidence(net, x, t, ninner); + fprintf(1, '\n\nRe-estimation cycle %d:\n', k); + disp('The first three alphas are the hyperparameters for the corresponding'); + disp('input to hidden unit weights. The remainder are the hyperparameters'); + disp('for the hidden unit biases, second layer weights and output unit') + disp('biases, respectively.') + fprintf(1, ' alpha = %8.5f\n', net.alpha); + fprintf(1, ' beta = %8.5f\n', net.beta); + fprintf(1, ' gamma = %8.5f\n\n', gamma); + disp(' ') + disp('Press any key to continue.') + pause +end + +% Plot the function corresponding to the trained network. +figure(h); hold on; +[y, z] = mlpfwd(net, plotvals*ones(1,3)); +plot(plotvals, y, '-r', 'LineWidth', 2) +legend('data', 'function', 'network'); + +disp('Press any key to continue.'); +pause; clc; + +disp('We can now read off the hyperparameter values corresponding to the') +disp('three inputs x1, x2 and x3:') +disp(' '); +fprintf(1, ' alpha1: %8.5f\n', net.alpha(1)); +fprintf(1, ' alpha2: %8.5f\n', net.alpha(2)); +fprintf(1, ' alpha3: %8.5f\n', net.alpha(3)); +disp(' '); +disp('Since each alpha corresponds to an inverse variance, we see that the') +disp('posterior variance for weights associated with input x1 is large, that') +disp('of x2 has an intermediate value and the variance of weights associated') +disp('with x3 is small.') +disp(' ') +disp('Press any key to continue.') +disp(' ') +pause +disp('This is confirmed by looking at the corresponding weight values:') +disp(' '); +fprintf(1, ' %8.5f %8.5f\n', net.w1'); +disp(' '); +disp('where the three rows correspond to weights asssociated with x1, x2 and') +disp('x3 respectively. We see that the network is giving greatest emphasis') +disp('to x1 and least emphasis to x3, with intermediate emphasis on') +disp('x2. Since the target t is statistically independent of x3 we might') +disp('expect the weights associated with this input would go to') +disp('zero. However, for any finite data set there may be some chance') +disp('correlation between x3 and t, and so the corresponding alpha remains') +disp('finite.') + +disp(' '); +disp('Press any key to end.') +pause; clc; close(h); clear all +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demev1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,146 @@ +%DEMEV1 Demonstrate Bayesian regression for the MLP. +% +% Description +% The problem consists an input variable X which sampled from a +% Gaussian distribution, and a target variable T generated by computing +% SIN(2*PI*X) and adding Gaussian noise. A 2-layer network with linear +% outputs is trained by minimizing a sum-of-squares error function with +% isotropic Gaussian regularizer, using the scaled conjugate gradient +% optimizer. The hyperparameters ALPHA and BETA are re-estimated using +% the function EVIDENCE. A graph is plotted of the original function, +% the training data, the trained network function, and the error bars. +% +% See also +% EVIDENCE, MLP, SCG, DEMARD, DEMMLP1 +% + +% Copyright (c) Ian T Nabney (1996-2001) + +clc; +disp('This demonstration illustrates the application of Bayesian') +disp('re-estimation to determine the hyperparameters in a simple regression') +disp('problem. It is based on a local quadratic approximation to a mode of') +disp('the posterior distribution and the evidence maximization framework of') +disp('MacKay.') +disp(' ') +disp('First, we generate a synthetic data set consisting of a single input') +disp('variable x sampled from a Gaussian distribution, and a target variable') +disp('t obtained by evaluating sin(2*pi*x) and adding Gaussian noise.') +disp(' ') +disp('Press any key to see a plot of the data together with the sine function.') +pause; + +% Generate the matrix of inputs x and targets t. + +ndata = 16; % Number of data points. +noise = 0.1; % Standard deviation of noise distribution. +randn('state', 0); +x = 0.25 + 0.07*randn(ndata, 1); +t = sin(2*pi*x) + noise*randn(size(x)); + +% Plot the data and the original sine function. +h = figure; +nplot = 200; +plotvals = linspace(0, 1, nplot)'; +plot(x, t, 'ok') +xlabel('Input') +ylabel('Target') +hold on +axis([0 1 -1.5 1.5]) +fplot('sin(2*pi*x)', [0 1], '-g') +legend('data', 'function'); + +disp(' ') +disp('Press any key to continue') +pause; clc; + +disp('Next we create a two-layer MLP network having 3 hidden units and one') +disp('linear output. The model assumes Gaussian target noise governed by an') +disp('inverse variance hyperparmeter beta, and uses a simple Gaussian prior') +disp('distribution governed by an inverse variance hyperparameter alpha.') +disp(' '); +disp('The network weights and the hyperparameters are initialised and then') +disp('the weights are optimized with the scaled conjugate gradient') +disp('algorithm using the SCG function, with the hyperparameters kept') +disp('fixed. After a maximum of 500 iterations, the hyperparameters are') +disp('re-estimated using the EVIDENCE function. The process of optimizing') +disp('the weights with fixed hyperparameters and then re-estimating the') +disp('hyperparameters is repeated for a total of 3 cycles.') +disp(' ') +disp('Press any key to train the network and determine the hyperparameters.') +pause; + +% Set up network parameters. +nin = 1; % Number of inputs. +nhidden = 3; % Number of hidden units. +nout = 1; % Number of outputs. +alpha = 0.01; % Initial prior hyperparameter. +beta_init = 50.0; % Initial noise hyperparameter. + +% Create and initialize network weight vector. +net = mlp(nin, nhidden, nout, 'linear', alpha, beta_init); + +% Set up vector of options for the optimiser. +nouter = 3; % Number of outer loops. +ninner = 1; % Number of innter loops. +options = zeros(1,18); % Default options vector. +options(1) = 1; % This provides display of error values. +options(2) = 1.0e-7; % Absolute precision for weights. +options(3) = 1.0e-7; % Precision for objective function. +options(14) = 500; % Number of training cycles in inner loop. + +% Train using scaled conjugate gradients, re-estimating alpha and beta. +for k = 1:nouter + net = netopt(net, options, x, t, 'scg'); + [net, gamma] = evidence(net, x, t, ninner); + fprintf(1, '\nRe-estimation cycle %d:\n', k); + fprintf(1, ' alpha = %8.5f\n', net.alpha); + fprintf(1, ' beta = %8.5f\n', net.beta); + fprintf(1, ' gamma = %8.5f\n\n', gamma); + disp(' ') + disp('Press any key to continue.') + pause; +end + +fprintf(1, 'true beta: %f\n', 1/(noise*noise)); + +disp(' ') +disp('Network training and hyperparameter re-estimation are now complete.') +disp('Compare the final value for the hyperparameter beta with the true') +disp('value.') +disp(' ') +disp('Notice that the final error value is close to the number of data') +disp(['points (', num2str(ndata),') divided by two.']) +disp(' ') +disp('Press any key to continue.') +pause; clc; +disp('We can now plot the function represented by the trained network. This') +disp('corresponds to the mean of the predictive distribution. We can also') +disp('plot ''error bars'' representing one standard deviation of the') +disp('predictive distribution around the mean.') +disp(' ') +disp('Press any key to add the network function and error bars to the plot.') +pause; + +% Evaluate error bars. +[y, sig2] = netevfwd(mlppak(net), net, x, t, plotvals); +sig = sqrt(sig2); + +% Plot the data, the original function, and the trained network function. +[y, z] = mlpfwd(net, plotvals); +figure(h); hold on; +plot(plotvals, y, '-r') +xlabel('Input') +ylabel('Target') +plot(plotvals, y + sig, '-b'); +plot(plotvals, y - sig, '-b'); +legend('data', 'function', 'network', 'error bars'); + +disp(' ') +disp('Notice how the confidence interval spanned by the ''error bars'' is') +disp('smaller in the region of input space where the data density is high,') +disp('and becomes larger in regions away from the data.') +disp(' ') +disp('Press any key to end.') +pause; clc; close(h); +%clear all
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demev2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,248 @@ +%DEMEV2 Demonstrate Bayesian classification for the MLP. +% +% Description +% A synthetic two class two-dimensional dataset X is sampled from a +% mixture of four Gaussians. Each class is associated with two of the +% Gaussians so that the optimal decision boundary is non-linear. A 2- +% layer network with logistic outputs is trained by minimizing the +% cross-entropy error function with isotroipc Gaussian regularizer (one +% hyperparameter for each of the four standard weight groups), using +% the scaled conjugate gradient optimizer. The hyperparameter vectors +% ALPHA and BETA are re-estimated using the function EVIDENCE. A graph +% is plotted of the optimal, regularised, and unregularised decision +% boundaries. A further plot of the moderated versus unmoderated +% contours is generated. +% +% See also +% EVIDENCE, MLP, SCG, DEMARD, DEMMLP2 +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +clc; + +disp('This program demonstrates the use of the evidence procedure on') +disp('a two-class problem. It also shows the improved generalisation') +disp('performance that can be achieved with moderated outputs; that is') +disp('predictions where an approximate integration over the true') +disp('posterior distribution is carried out.') +disp(' ') +disp('First we generate a synthetic dataset with two-dimensional input') +disp('sampled from a mixture of four Gaussians. Each class is') +disp('associated with two of the Gaussians so that the optimal decision') +disp('boundary is non-linear.') +disp(' ') +disp('Press any key to see a plot of the data.') +pause; + +% Generate the matrix of inputs x and targets t. + +rand('state', 423); +randn('state', 423); + +ClassSymbol1 = 'r.'; +ClassSymbol2 = 'y.'; +PointSize = 12; +titleSize = 10; + +fh1 = figure; +set(fh1, 'Name', 'True Data Distribution'); +whitebg(fh1, 'k'); + +% +% Generate the data +% +n=200; + +% Set up mixture model: 2d data with four centres +% Class 1 is first two centres, class 2 from the other two +mix = gmm(2, 4, 'full'); +mix.priors = [0.25 0.25 0.25 0.25]; +mix.centres = [0 -0.1; 1.5 0; 1 1; 1 -1]; +mix.covars(:,:,1) = [0.625 -0.2165; -0.2165 0.875]; +mix.covars(:,:,2) = [0.25 0; 0 0.25]; +mix.covars(:,:,3) = [0.2241 -0.1368; -0.1368 0.9759]; +mix.covars(:,:,4) = [0.2375 0.1516; 0.1516 0.4125]; + +[data, label] = gmmsamp(mix, n); + +% +% Calculate some useful axis limits +% +x0 = min(data(:,1)); +x1 = max(data(:,1)); +y0 = min(data(:,2)); +y1 = max(data(:,2)); +dx = x1-x0; +dy = y1-y0; +expand = 5/100; % Add on 5 percent each way +x0 = x0 - dx*expand; +x1 = x1 + dx*expand; +y0 = y0 - dy*expand; +y1 = y1 + dy*expand; +resolution = 100; +step = dx/resolution; +xrange = [x0:step:x1]; +yrange = [y0:step:y1]; +% +% Generate the grid +% +[X Y]=meshgrid([x0:step:x1],[y0:step:y1]); +% +% Calculate the class conditional densities, the unconditional densities and +% the posterior probabilities +% +px_j = gmmactiv(mix, [X(:) Y(:)]); +px = reshape(px_j*(mix.priors)',size(X)); +post = gmmpost(mix, [X(:) Y(:)]); +p1_x = reshape(post(:, 1) + post(:, 2), size(X)); +p2_x = reshape(post(:, 3) + post(:, 4), size(X)); + +plot(data((label<=2),1),data(label<=2,2),ClassSymbol1, 'MarkerSize', ... +PointSize) +hold on +axis([x0 x1 y0 y1]) +plot(data((label>2),1),data(label>2,2),ClassSymbol2, 'MarkerSize', ... + PointSize) + +% Convert targets to 0-1 encoding +target=[label<=2]; +disp(' ') +disp('Press any key to continue') +pause; clc; + +disp('Next we create a two-layer MLP network with 6 hidden units and') +disp('one logistic output. We use a separate inverse variance') +disp('hyperparameter for each group of weights (inputs, input bias,') +disp('outputs, output bias) and the weights are optimised with the') +disp('scaled conjugate gradient algorithm. After each 100 iterations') +disp('the hyperparameters are re-estimated twice. There are eight') +disp('cycles of the whole algorithm.') +disp(' ') +disp('Press any key to train the network and determine the hyperparameters.') +pause; + +% Set up network parameters. +nin = 2; % Number of inputs. +nhidden = 6; % Number of hidden units. +nout = 1; % Number of outputs. +alpha = 0.01; % Initial prior hyperparameter. +aw1 = 0.01; +ab1 = 0.01; +aw2 = 0.01; +ab2 = 0.01; + +% Create and initialize network weight vector. +prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2); +net = mlp(nin, nhidden, nout, 'logistic', prior); + +% Set up vector of options for the optimiser. +nouter = 8; % Number of outer loops. +ninner = 2; % Number of innter loops. +options = foptions; % Default options vector. +options(1) = 1; % This provides display of error values. +options(2) = 1.0e-5; % Absolute precision for weights. +options(3) = 1.0e-5; % Precision for objective function. +options(14) = 100; % Number of training cycles in inner loop. + +% Train using scaled conjugate gradients, re-estimating alpha and beta. +for k = 1:nouter + net = netopt(net, options, data, target, 'scg'); + [net, gamma] = evidence(net, data, target, ninner); + fprintf(1, '\nRe-estimation cycle %d:\n', k); + disp([' alpha = ', num2str(net.alpha')]); + fprintf(1, ' gamma = %8.5f\n\n', gamma); + disp(' ') + disp('Press any key to continue.') + pause; +end + +disp(' ') +disp('Network training and hyperparameter re-estimation are now complete.') +disp('Notice that the final error value is close to the number of data') +disp(['points (', num2str(n), ') divided by two.']) +disp('Also, the hyperparameter values differ, which suggests that a single') +disp('hyperparameter would not be so effective.') +disp(' ') +disp('First we train an MLP without Bayesian regularisation on the') +disp('same dataset using 400 iterations of scaled conjugate gradient') +disp(' ') +disp('Press any key to train the network by maximum likelihood.') +pause; +% Train standard network +net2 = mlp(nin, nhidden, nout, 'logistic'); +options(14) = 400; +net2 = netopt(net2, options, data, target, 'scg'); +y2g = mlpfwd(net2, [X(:), Y(:)]); +y2g = reshape(y2g(:, 1), size(X)); + +disp(' ') +disp('We can now plot the function represented by the trained networks.') +disp('We show the decision boundaries (output = 0.5) and the optimal') +disp('decision boundary given by applying Bayes'' theorem to the true') +disp('data model.') +disp(' ') +disp('Press any key to add the boundaries to the plot.') +pause; + +% Evaluate predictions. +[yg, ymodg] = mlpevfwd(net, data, target, [X(:) Y(:)]); +yg = reshape(yg(:,1),size(X)); +ymodg = reshape(ymodg(:,1),size(X)); + +% Bayesian decision boundary +[cB, hB] = contour(xrange,yrange,p1_x,[0.5 0.5],'b-'); +[cNb, hNb] = contour(xrange,yrange,yg,[0.5 0.5],'r-'); +[cN, hN] = contour(xrange,yrange,y2g,[0.5 0.5],'g-'); +set(hB, 'LineWidth', 2); +set(hNb, 'LineWidth', 2); +set(hN, 'LineWidth', 2); +Chandles = [hB(1) hNb(1) hN(1)]; +legend(Chandles, 'Bayes', ... + 'Reg. Network', 'Network', 3); + +disp(' ') +disp('Note how the regularised network predictions are closer to the') +disp('optimal decision boundary, while the unregularised network is') +disp('overtrained.') + +disp(' ') +disp('We will now compare moderated and unmoderated outputs for the'); +disp('regularised network by showing the contour plot of the posterior') +disp('probability estimates.') +disp(' ') +disp('The first plot shows the regularised (moderated) predictions') +disp('and the second shows the standard predictions from the same network.') +disp('These agree at the level 0.5.') +disp('Press any key to continue') +pause +levels = 0:0.1:1; +fh4 = figure; +set(fh4, 'Name', 'Moderated outputs'); +hold on +plot(data((label<=2),1),data(label<=2,2),'r.', 'MarkerSize', PointSize) +plot(data((label>2),1),data(label>2,2),'y.', 'MarkerSize', PointSize) + +[cNby, hNby] = contour(xrange, yrange, ymodg, levels, 'k-'); +set(hNby, 'LineWidth', 1); + +fh5 = figure; +set(fh5, 'Name', 'Unmoderated outputs'); +hold on +plot(data((label<=2),1),data(label<=2,2),'r.', 'MarkerSize', PointSize) +plot(data((label>2),1),data(label>2,2),'y.', 'MarkerSize', PointSize) + +[cNbm, hNbm] = contour(xrange, yrange, yg, levels, 'k-'); +set(hNbm, 'LineWidth', 1); + +disp(' ') +disp('Note how the moderated contours are more widely spaced. This shows') +disp('that there is a larger region where the outputs are close to 0.5') +disp('and a smaller region where the outputs are close to 0 or 1.') +disp(' ') +disp('Press any key to exit') +pause +close(fh1); +close(fh4); +close(fh5); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demev3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,154 @@ +%DEMEV3 Demonstrate Bayesian regression for the RBF. +% +% Description +% The problem consists an input variable X which sampled from a +% Gaussian distribution, and a target variable T generated by computing +% SIN(2*PI*X) and adding Gaussian noise. An RBF network with linear +% outputs is trained by minimizing a sum-of-squares error function with +% isotropic Gaussian regularizer, using the scaled conjugate gradient +% optimizer. The hyperparameters ALPHA and BETA are re-estimated using +% the function EVIDENCE. A graph is plotted of the original function, +% the training data, the trained network function, and the error bars. +% +% See also +% DEMEV1, EVIDENCE, RBF, SCG, NETEVFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +clc; +disp('This demonstration illustrates the application of Bayesian') +disp('re-estimation to determine the hyperparameters in a simple regression') +disp('problem using an RBF netowk. It is based on a the fact that the') +disp('posterior distribution for the output weights of an RBF is Gaussian') +disp('and uses the evidence maximization framework of MacKay.') +disp(' ') +disp('First, we generate a synthetic data set consisting of a single input') +disp('variable x sampled from a Gaussian distribution, and a target variable') +disp('t obtained by evaluating sin(2*pi*x) and adding Gaussian noise.') +disp(' ') +disp('Press any key to see a plot of the data together with the sine function.') +pause; + +% Generate the matrix of inputs x and targets t. + +ndata = 16; % Number of data points. +noise = 0.1; % Standard deviation of noise distribution. +randn('state', 0); +rand('state', 0); +x = 0.25 + 0.07*randn(ndata, 1); +t = sin(2*pi*x) + noise*randn(size(x)); + +% Plot the data and the original sine function. +h = figure; +nplot = 200; +plotvals = linspace(0, 1, nplot)'; +plot(x, t, 'ok') +xlabel('Input') +ylabel('Target') +hold on +axis([0 1 -1.5 1.5]) +fplot('sin(2*pi*x)', [0 1], '-g') +legend('data', 'function'); + +disp(' ') +disp('Press any key to continue') +pause; clc; + +disp('Next we create a two-layer MLP network having 3 hidden units and one') +disp('linear output. The model assumes Gaussian target noise governed by an') +disp('inverse variance hyperparmeter beta, and uses a simple Gaussian prior') +disp('distribution governed by an inverse variance hyperparameter alpha.') +disp(' '); +disp('The network weights and the hyperparameters are initialised and then') +disp('the output layer weights are optimized with the scaled conjugate gradient') +disp('algorithm using the SCG function, with the hyperparameters kept') +disp('fixed. After a maximum of 50 iterations, the hyperparameters are') +disp('re-estimated using the EVIDENCE function. The process of optimizing') +disp('the weights with fixed hyperparameters and then re-estimating the') +disp('hyperparameters is repeated for a total of 3 cycles.') +disp(' ') +disp('Press any key to train the network and determine the hyperparameters.') +pause; + +% Set up network parameters. +nin = 1; % Number of inputs. +nhidden = 3; % Number of hidden units. +nout = 1; % Number of outputs. +alpha = 0.01; % Initial prior hyperparameter. +beta_init = 50.0; % Initial noise hyperparameter. + +% Create and initialize network weight vector. +net = rbf(nin, nhidden, nout, 'tps', 'linear', alpha, beta_init); +[net.mask, prior] = rbfprior('tps', nin, nhidden, nout, alpha, alpha); +net = netinit(net, prior); + +options = foptions; +options(14) = 5; % At most 5 EM iterations for basis functions +options(1) = -1; % Turn off all messages +net = rbfsetbf(net, options, x); % Initialise the basis functions + +% Now train the network +nouter = 5; +ninner = 2; +options = foptions; +options(1) = 1; +options(2) = 1.0e-5; % Absolute precision for weights. +options(3) = 1.0e-5; % Precision for objective function. +options(14) = 50; % Number of training cycles in inner loop. + +% Train using scaled conjugate gradients, re-estimating alpha and beta. +for k = 1:nouter + net = netopt(net, options, x, t, 'scg'); + [net, gamma] = evidence(net, x, t, ninner); + fprintf(1, '\nRe-estimation cycle %d:\n', k); + fprintf(1, ' alpha = %8.5f\n', net.alpha); + fprintf(1, ' beta = %8.5f\n', net.beta); + fprintf(1, ' gamma = %8.5f\n\n', gamma); + disp(' ') + disp('Press any key to continue.') + pause; +end + +fprintf(1, 'true beta: %f\n', 1/(noise*noise)); + +disp(' ') +disp('Network training and hyperparameter re-estimation are now complete.') +disp('Compare the final value for the hyperparameter beta with the true') +disp('value.') +disp(' ') +disp('Notice that the final error value is close to the number of data') +disp(['points (', num2str(ndata),') divided by two.']) +disp(' ') +disp('Press any key to continue.') +pause; clc; +disp('We can now plot the function represented by the trained network. This') +disp('corresponds to the mean of the predictive distribution. We can also') +disp('plot ''error bars'' representing one standard deviation of the') +disp('predictive distribution around the mean.') +disp(' ') +disp('Press any key to add the network function and error bars to the plot.') +pause; + +% Evaluate error bars. +[y, sig2] = netevfwd(netpak(net), net, x, t, plotvals); +sig = sqrt(sig2); + +% Plot the data, the original function, and the trained network function. +[y, z] = rbffwd(net, plotvals); +figure(h); hold on; +plot(plotvals, y, '-r') +xlabel('Input') +ylabel('Target') +plot(plotvals, y + sig, '-b'); +plot(plotvals, y - sig, '-b'); +legend('data', 'function', 'network', 'error bars'); + +disp(' ') +disp('Notice how the confidence interval spanned by the ''error bars'' is') +disp('smaller in the region of input space where the data density is high,') +disp('and becomes larger in regions away from the data.') +disp(' ') +disp('Press any key to end.') +pause; clc; close(h); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgauss.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +%DEMGAUSS Demonstrate sampling from Gaussian distributions. +% +% Description +% +% DEMGAUSS provides a simple illustration of the generation of data +% from Gaussian distributions. It first samples from a one-dimensional +% distribution using RANDN, and then plots a normalized histogram +% estimate of the distribution using HISTP together with the true +% density calculated using GAUSS. +% +% DEMGAUSS then demonstrates sampling from a Gaussian distribution in +% two dimensions. It creates a mean vector and a covariance matrix, and +% then plots contours of constant density using the function GAUSS. A +% sample of points drawn from this distribution, obtained using the +% function GSAMP, is then superimposed on the contours. +% +% See also +% GAUSS, GSAMP, HISTP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +clc +mean = 2; var = 5; nsamp = 3000; +xmin = -10; xmax = 10; nbins = 30; +disp('Demonstration of sampling from a uni-variate Gaussian with mean') +dstring = [num2str(mean), ' and variance ', num2str(var), '. ', ... + num2str(nsamp), ' samples are taken.']; +disp(dstring); +x = mean + sqrt(var)*randn(nsamp, 1); +fh1 = figure; +histp(x, xmin, xmax, nbins); +hold on; +axis([xmin xmax 0 0.2]); +plotvals = linspace(xmin, xmax, 200)'; +probs = gauss(mean, var, plotvals); +plot(plotvals, probs, '-r'); +xlabel('X') +ylabel('Density') + +disp(' ') +disp('Press any key to continue') +pause; +mu = [3 2]; +lam1 = 0.5; +lam2 = 5.0; +Sigma = lam1*[1,1]'*[1,1] + lam2*[1,-1]'*[1,-1]; +disp(' ') +disp('Demonstration of sampling from a bi-variate Gaussian. The mean is') +dstring = ['[', num2str(mu(1)), ', ', num2str(mu(2)), ... + '] and the covariance matrix is']; +disp(dstring) +disp(Sigma); +ngrid = 40; +cmin = -5; cmax = 10; +cvals = linspace(cmin, cmax, ngrid); +[X1, X2] = meshgrid(cvals, cvals); +XX = [X1(:), X2(:)]; +probs = gauss(mu, Sigma, XX); +probs = reshape(probs, ngrid, ngrid); + +fh2 = figure; +contour(X1, X2, probs, 'b'); +hold on + +nsamp = 300; +dstring = [num2str(nsamp), ' samples are generated.']; +disp('The plot shows the sampled data points with a contour plot of their density.') +samples = gsamp(mu, Sigma, nsamp); +plot(samples(:,1), samples(:,2), 'or'); +xlabel('X1') +ylabel('X2') +grid off; + +disp(' ') +disp('Press any key to end') +pause; +close(fh1); +close(fh2); +clear all; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demglm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +%DEMGLM1 Demonstrate simple classification using a generalized linear model. +% +% Description +% The problem consists of a two dimensional input matrix DATA and a +% vector of classifications T. The data is generated from two +% Gaussian clusters, and a generalized linear model with logistic +% output is trained using iterative reweighted least squares. A plot of +% the data together with the 0.1, 0.5 and 0.9 contour lines of the +% conditional probability is generated. +% +% See also +% DEMGLM2, GLM, GLMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate data from two classes in 2d +input_dim = 2; + +% Fix seeds for reproducible results +randn('state', 42); +rand('state', 42); + +ndata = 100; +% Generate mixture of two Gaussians in two dimensional space +mix = gmm(2, 2, 'spherical'); +mix.priors = [0.4 0.6]; % Cluster priors +mix.centres = [2.0, 2.0; 0.0, 0.0]; % Cluster centres +mix.covars = [0.5, 1.0]; + +[data, label] = gmmsamp(mix, ndata); +targets = label - ones(ndata, 1); + +% Plot the result + +clc +disp('This demonstration illustrates the use of a generalized linear model') +disp('to classify data from two classes in a two-dimensional space. We') +disp('begin by generating and plotting the data.') +disp(' ') +disp('Press any key to continue.') +pause + +fh1 = figure; +plot(data(label==1,1), data(label==1,2), 'bo'); +hold on +axis([-4 5 -4 5]) +set(gca, 'box', 'on') +plot(data(label==2,1), data(label==2,2), 'rx') +title('Data') + +clc +disp('Now we fit a model consisting of a logistic sigmoid function of') +disp('a linear combination of the input variables.') +disp(' ') +disp('The model is trained using the IRLS algorithm for 5 iterations') +disp(' ') +disp('Press any key to continue.') +pause + +net = glm(input_dim, 1, 'logistic'); +options = foptions; +options(1) = 1; +options(14) = 5; +net = glmtrain(net, options, data, targets); + +disp(' ') +disp('We now plot some density contours given by this model.') +disp('The contour labelled 0.5 is the decision boundary.') +disp(' ') +disp('Press any key to continue.') +pause +x = -4.0:0.2:5.0; +y = -4.0:0.2:5.0; +[X, Y] = meshgrid(x,y); +X = X(:); +Y = Y(:); +grid = [X Y]; +Z = glmfwd(net, grid); +Z = reshape(Z, length(x), length(y)); +v = [0.1 0.5 0.9]; +[c, h] = contour(x, y, Z, v); +title('Generalized Linear Model') +set(h, 'linewidth', 3) +clabel(c, h); + +clc +disp('Note that the contours of constant density are straight lines.') +disp(' ') +disp('Press any key to end.') +pause +close(fh1); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demglm2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,103 @@ +%DEMGLM2 Demonstrate simple classification using a generalized linear model. +% +% Description +% The problem consists of a two dimensional input matrix DATA and a +% vector of classifications T. The data is generated from three +% Gaussian clusters, and a generalized linear model with softmax output +% is trained using iterative reweighted least squares. A plot of the +% data together with regions shaded by the classification given by the +% network is generated. +% +% See also +% DEMGLM1, GLM, GLMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate data from three classes in 2d +input_dim = 2; + +% Fix seeds for reproducible results +randn('state', 42); +rand('state', 42); + +ndata = 100; +% Generate mixture of three Gaussians in two dimensional space +mix = gmm(2, 3, 'spherical'); +mix.priors = [0.4 0.3 0.3]; % Cluster priors +mix.centres = [2, 2; 0.0, 0.0; 1, -1]; % Cluster centres +mix.covars = [0.5 1.0 0.6]; + +[data, label] = gmmsamp(mix, ndata); +id = eye(3); +targets = id(label,:); + +% Plot the result + +clc +disp('This demonstration illustrates the use of a generalized linear model') +disp('to classify data from three classes in a two-dimensional space. We') +disp('begin by generating and plotting the data.') +disp(' ') +disp('Press any key to continue.') +pause + +fh1 = figure; +plot(data(label==1,1), data(label==1,2), 'bo'); +hold on +axis([-4 5 -4 5]); +set(gca, 'Box', 'on') +plot(data(label==2,1), data(label==2,2), 'rx') +plot(data(label==3, 1), data(label==3, 2), 'go') +title('Data') + +clc +disp('Now we fit a model consisting of a softmax function of') +disp('a linear combination of the input variables.') +disp(' ') +disp('The model is trained using the IRLS algorithm for up to 10 iterations') +disp(' ') +disp('Press any key to continue.') +pause + +net = glm(input_dim, size(targets, 2), 'softmax'); +options = foptions; +options(1) = 1; +options(14) = 10; +net = glmtrain(net, options, data, targets); + +disp(' ') +disp('We now plot the decision regions given by this model.') +disp(' ') +disp('Press any key to continue.') +pause + +x = -4.0:0.2:5.0; +y = -4.0:0.2:5.0; +[X, Y] = meshgrid(x,y); +X = X(:); +Y = Y(:); +grid = [X Y]; +Z = glmfwd(net, grid); +[foo , class] = max(Z'); +class = class'; +colors = ['b.'; 'r.'; 'g.']; +for i = 1:3 + thisX = X(class == i); + thisY = Y(class == i); + h = plot(thisX, thisY, colors(i,:)); + set(h, 'MarkerSize', 8); +end +title('Plot of Decision regions') + +hold off + +clc +disp('Note that the boundaries of decision regions are straight lines.') +disp(' ') +disp('Press any key to end.') +pause +close(fh1); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgmm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +%DEMGMM1 Demonstrate EM for Gaussian mixtures. +% +% Description +% This script demonstrates the use of the EM algorithm to fit a mixture +% of Gaussians to a set of data using maximum likelihood. A colour +% coding scheme is used to illustrate the evaluation of the posterior +% probabilities in the E-step of the EM algorithm. +% +% See also +% DEMGMM2, DEMGMM3, DEMGMM4, GMM, GMMEM, GMMPOST +% + +% Copyright (c) Ian T Nabney (1996-2001) + +clc; +disp('This demonstration illustrates the use of the EM (expectation-') +disp('maximization) algorithm for fitting of a mixture of Gaussians to a') +disp('data set by maximum likelihood.') +disp(' ') +disp('The data set consists of 40 data points in a 2-dimensional') +disp('space, generated by sampling from a mixture of 2 Gaussian') +disp('distributions.') +disp(' ') +disp('Press any key to see a plot of the data.') +pause; + +% Generate the data +randn('state', 0); rand('state', 0); +gmix = gmm(2, 2, 'spherical'); +ndat1 = 20; ndat2 = 20; ndata = ndat1+ndat2; +gmix.centres = [0.3 0.3; 0.7 0.7]; +gmix.covars = [0.01 0.01]; +x = gmmsamp(gmix, ndata); + +h = figure; +hd = plot(x(:, 1), x(:, 2), '.g', 'markersize', 30); +hold on; axis([0 1 0 1]); axis square; set(gca, 'box', 'on'); +ht = text(0.5, 1.05, 'Data', 'horizontalalignment', 'center'); +disp(' '); +disp('Press any key to continue.') +pause; clc; + +disp('We next create and initialize a mixture model consisting of a mixture') +disp('of 2 Gaussians having ''spherical'' covariance matrices, using the') +disp('function GMM. The Gaussian components can be displayed on the same') +disp('plot as the data by drawing a contour of constant probability density') +disp('for each component having radius equal to the corresponding standard') +disp('deviation. Component 1 is coloured red and component 2 is coloured') +disp('blue.') +disp(' ') +disp('Note that a particulary poor choice of initial parameters has been') +disp('made in order to illustrate more effectively the operation of the') +disp('EM algorithm.') +disp(' ') +disp('Press any key to see the initial configuration of the mixture model.') +pause; + +% Set up mixture model +ncentres = 2; input_dim = 2; +mix = gmm(input_dim, ncentres, 'spherical'); + +% Initialise the mixture model +mix.centres = [0.2 0.8; 0.8, 0.2]; +mix.covars = [0.01 0.01]; + +% Plot the initial model +ncirc = 30; theta = linspace(0, 2*pi, ncirc); +xs = cos(theta); ys = sin(theta); +xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs; +yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys; +hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r'); +hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b'); +set(ht, 'string', 'Initial Configuration'); +figure(h); +disp(' ') +disp('Press any key to continue'); +pause; clc; + +disp('Now we adapt the parameters of the mixture model iteratively using the') +disp('EM algorithm. Each cycle of the EM algorithm consists of an E-step') +disp('followed by an M-step. We start with the E-step, which involves the') +disp('evaluation of the posterior probabilities (responsibilities) which the') +disp('two components have for each of the data points.') +disp(' ') +disp('Since we have labelled the two components using the colours red and') +disp('blue, a convenient way to indicate the value of a posterior') +disp('probability for a given data point is to colour the point using a') +disp('scale ranging from pure red (corresponding to a posterior probability') +disp('of 1.0 for the red component and 0.0 for the blue component) through') +disp('to pure blue.') +disp(' ') +disp('Press any key to see the result of applying the first E-step.') +pause; + +% Initial E-step. +set(ht, 'string', 'E-step'); +post = gmmpost(mix, x); +dcols = [post(:,1), zeros(ndata, 1), post(:,2)]; +delete(hd); +for i = 1 : ndata + hd(i) = plot(x(i, 1), x(i, 2), 'color', dcols(i,:), ... + 'marker', '.', 'markersize', 30); +end +figure(h); + +disp(' '); +disp('Press any key to continue') +pause; clc; + +disp('Next we perform the corresponding M-step. This involves replacing the') +disp('centres of the component Gaussians by the corresponding weighted means') +disp('of the data. Thus the centre of the red component is replaced by the') +disp('mean of the data set, in which each data point is weighted according to') +disp('the amount of red ink (corresponding to the responsibility of') +disp('component 1 for explaining that data point). The variances and mixing') +disp('proportions of the two components are similarly re-estimated.') +disp(' ') +disp('Press any key to see the result of applying the first M-step.') +pause; + +% M-step. +set(ht, 'string', 'M-step'); +options = foptions; +options(14) = 1; % A single iteration +options(1) = -1; % Switch off all messages, including warning +mix = gmmem(mix, x, options); +delete(hc); +xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs; +yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys; +hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r'); +hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b'); +figure(h); +disp(' ') +disp('Press any key to continue') +pause; clc; + +disp('We can continue making alternate E and M steps until the changes in') +disp('the log likelihood at each cycle become sufficiently small.') +disp(' ') +disp('Press any key to see an animation of a further 9 EM cycles.') +pause; +figure(h); + +% Loop over EM iterations. +numiters = 9; +for n = 1 : numiters + + set(ht, 'string', 'E-step'); + post = gmmpost(mix, x); + dcols = [post(:,1), zeros(ndata, 1), post(:,2)]; + delete(hd); + for i = 1 : ndata + hd(i) = plot(x(i, 1), x(i, 2), 'color', dcols(i,:), ... + 'marker', '.', 'markersize', 30); + end + pause(1) + + set(ht, 'string', 'M-step'); + [mix, options] = gmmem(mix, x, options); + fprintf(1, 'Cycle %4d Error %11.6f\n', n, options(8)); + delete(hc); + xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs; + yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys; + hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r'); + hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b'); + pause(1) + +end + +disp(' '); +disp('Press any key to end.') +pause; clc; close(h); clear all +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgmm2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,152 @@ +%DEMGMM1 Demonstrate density modelling with a Gaussian mixture model. +% +% Description +% The problem consists of modelling data generated by a mixture of +% three Gaussians in 2 dimensions. The priors are 0.3, 0.5 and 0.2; +% the centres are (2, 3.5), (0, 0) and (0,2); the variances are 0.2, +% 0.5 and 1.0. The first figure contains a scatter plot of the data. +% +% A Gaussian mixture model with three components is trained using EM. +% The parameter vector is printed before training and after training. +% The user should press any key to continue at these points. The +% parameter vector consists of priors (the column), centres (given as +% (x, y) pairs as the next two columns), and variances (the last +% column). +% +% The second figure is a 3 dimensional view of the density function, +% while the third shows the 1-standard deviation circles for the three +% components of the mixture model. +% +% See also +% GMM, GMMINIT, GMMEM, GMMPROB, GMMUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Generate the data +% Fix seeds for reproducible results +randn('state', 42); +rand('state', 42); + +ndata = 500; +[data, datac, datap, datasd] = dem2ddat(ndata); + +clc +disp('This demonstration illustrates the use of a Gaussian mixture model') +disp('to approximate the unconditional probability density of data in') +disp('a two-dimensional space. We begin by generating the data from') +disp('a mixture of three Gaussians and plotting it.') +disp(' ') +disp('Press any key to continue') +pause + +fh1 = figure; +plot(data(:, 1), data(:, 2), 'o') +set(gca, 'Box', 'on') +% Set up mixture model +ncentres = 3; +input_dim = 2; +mix = gmm(input_dim, ncentres, 'spherical'); + +options = foptions; +options(14) = 5; % Just use 5 iterations of k-means in initialisation +% Initialise the model parameters from the data +mix = gmminit(mix, data, options); + +clc +disp('The data is drawn from a mixture with parameters') +disp(' Priors Centres Variances') +disp([datap' datac (datasd.^2)']) +disp(' ') +disp('The mixture model has three components and spherical covariance') +disp('matrices. The model parameters after initialisation using the') +disp('k-means algorithm are as follows') +% Print out model +disp(' Priors Centres Variances') +disp([mix.priors' mix.centres mix.covars']) +disp('Press any key to continue') +pause + +% Set up vector of options for EM trainer +options = zeros(1, 18); +options(1) = 1; % Prints out error values. +options(14) = 10; % Max. Number of iterations. + +disp('We now train the model using the EM algorithm for 10 iterations') +disp(' ') +disp('Press any key to continue') +pause +[mix, options, errlog] = gmmem(mix, data, options); + +% Print out model +disp(' ') +disp('The trained model has parameters ') +disp(' Priors Centres Variances') +disp([mix.priors' mix.centres mix.covars']) +disp('Note the close correspondence between these parameters and those') +disp('of the distribution used to generate the data, which are repeated here.') +disp(' Priors Centres Variances') +disp([datap' datac (datasd.^2)']) +disp(' ') +disp('Press any key to continue') +pause + +clc +disp('We now plot the density given by the mixture model as a surface plot') +disp(' ') +disp('Press any key to continue') +pause +% Plot the result +x = -4.0:0.2:5.0; +y = -4.0:0.2:5.0; +[X, Y] = meshgrid(x,y); +X = X(:); +Y = Y(:); +grid = [X Y]; +Z = gmmprob(mix, grid); +Z = reshape(Z, length(x), length(y)); +c = mesh(x, y, Z); +hold on +title('Surface plot of probability density') +hold off + +clc +disp('The final plot shows the centres and widths, given by one standard') +disp('deviation, of the three components of the mixture model.') +disp(' ') +disp('Press any key to continue.') +pause +% Try to calculate a sensible position for the second figure, below the first +fig1_pos = get(fh1, 'Position'); +fig2_pos = fig1_pos; +fig2_pos(2) = fig2_pos(2) - fig1_pos(4); +fh2 = figure; +set(fh2, 'Position', fig2_pos) + +hp1 = plot(data(:, 1), data(:, 2), 'bo'); +axis('equal'); +hold on +hp2 = plot(mix.centres(:, 1), mix.centres(:,2), 'g+'); +set(hp2, 'MarkerSize', 10); +set(hp2, 'LineWidth', 3); + +title('Plot of data and mixture centres') +angles = 0:pi/30:2*pi; +for i = 1 : mix.ncentres + x_circle = mix.centres(i,1)*ones(1, length(angles)) + ... + sqrt(mix.covars(i))*cos(angles); + y_circle = mix.centres(i,2)*ones(1, length(angles)) + ... + sqrt(mix.covars(i))*sin(angles); + plot(x_circle, y_circle, 'r') +end +hold off +disp('Note how the data cluster positions and widths are captured by') +disp('the mixture model.') +disp(' ') +disp('Press any key to end.') +pause + +close(fh1); +close(fh2); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgmm3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,192 @@ +%DEMGMM3 Demonstrate density modelling with a Gaussian mixture model. +% +% Description +% The problem consists of modelling data generated by a mixture of +% three Gaussians in 2 dimensions with a mixture model using diagonal +% covariance matrices. The priors are 0.3, 0.5 and 0.2; the centres +% are (2, 3.5), (0, 0) and (0,2); the covariances are all axis aligned +% (0.16, 0.64), (0.25, 1) and the identity matrix. The first figure +% contains a scatter plot of the data. +% +% A Gaussian mixture model with three components is trained using EM. +% The parameter vector is printed before training and after training. +% The user should press any key to continue at these points. The +% parameter vector consists of priors (the column), and centres (given +% as (x, y) pairs as the next two columns). The diagonal entries of +% the covariance matrices are printed separately. +% +% The second figure is a 3 dimensional view of the density function, +% while the third shows the axes of the 1-standard deviation circles +% for the three components of the mixture model. +% +% See also +% GMM, GMMINIT, GMMEM, GMMPROB, GMMUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Generate the data +ndata = 500; + +% Fix the seeds for reproducible results +randn('state', 42); +rand('state', 42); +data = randn(ndata, 2); +prior = [0.3 0.5 0.2]; +% Mixture model swaps clusters 1 and 3 +datap = [0.2 0.5 0.3]; +datac = [0 2; 0 0; 2 3.5]; +datacov = [1 1;1 0.25; 0.4*0.4 0.8*0.8]; +data1 = data(1:prior(1)*ndata,:); +data2 = data(prior(1)*ndata+1:(prior(2)+prior(1))*ndata, :); +data3 = data((prior(1)+prior(2))*ndata +1:ndata, :); + +% First cluster has axis aligned variance and centre (2, 3.5) +data1(:, 1) = data1(:, 1)*0.4 + 2.0; +data1(:, 2) = data1(:, 2)*0.8 + 3.5; + +% Second cluster has axis aligned variance and centre (0, 0) +data2(:,2) = data2(:, 2)*0.5; + +% Third cluster is at (0,2) with identity matrix for covariance +data3 = data3 + repmat([0 2], prior(3)*ndata, 1); + +% Put the dataset together again +data = [data1; data2; data3]; + +clc +disp('This demonstration illustrates the use of a Gaussian mixture model') +disp('with diagonal covariance matrices to approximate the unconditional') +disp('probability density of data in a two-dimensional space.') +disp('We begin by generating the data from a mixture of three Gaussians') +disp('with axis aligned covariance structure and plotting it.') +disp(' ') +disp('The first cluster has centre (0, 2).') +disp('The second cluster has centre (0, 0).') +disp('The third cluster has centre (2, 3.5).') +disp(' ') +disp('Press any key to continue') +pause + +fh1 = figure; +plot(data(:, 1), data(:, 2), 'o') +set(gca, 'Box', 'on') + +% Set up mixture model +ncentres = 3; +input_dim = 2; +mix = gmm(input_dim, ncentres, 'diag'); + +options = foptions; +options(14) = 5; % Just use 5 iterations of k-means in initialisation +% Initialise the model parameters from the data +mix = gmminit(mix, data, options); + +% Print out model +disp('The mixture model has three components and diagonal covariance') +disp('matrices. The model parameters after initialisation using the') +disp('k-means algorithm are as follows') +disp(' Priors Centres') +disp([mix.priors' mix.centres]) +disp('Covariance diagonals are') +disp(mix.covars) +disp('Press any key to continue.') +pause + +% Set up vector of options for EM trainer +options = zeros(1, 18); +options(1) = 1; % Prints out error values. +options(14) = 20; % Number of iterations. + +disp('We now train the model using the EM algorithm for 20 iterations.') +disp(' ') +disp('Press any key to continue.') +pause + +[mix, options, errlog] = gmmem(mix, data, options); + +% Print out model +disp(' ') +disp('The trained model has priors and centres:') +disp(' Priors Centres') +disp([mix.priors' mix.centres]) +disp('The data generator has priors and centres') +disp(' Priors Centres') +disp([datap' datac]) +disp('Model covariance diagonals are') +disp(mix.covars) +disp('Data generator covariance diagonals are') +disp(datacov) +disp('Note the close correspondence between these parameters and those') +disp('of the distribution used to generate the data.') +disp(' ') +disp('Press any key to continue.') +pause + +clc +disp('We now plot the density given by the mixture model as a surface plot.') +disp(' ') +disp('Press any key to continue.') +pause + +% Plot the result +x = -4.0:0.2:5.0; +y = -4.0:0.2:5.0; +[X, Y] = meshgrid(x,y); +X = X(:); +Y = Y(:); +grid = [X Y]; +Z = gmmprob(mix, grid); +Z = reshape(Z, length(x), length(y)); +c = mesh(x, y, Z); +hold on +title('Surface plot of probability density') +hold off +drawnow + +clc +disp('The final plot shows the centres and widths, given by one standard') +disp('deviation, of the three components of the mixture model. The axes') +disp('of the ellipses of constant density are shown.') +disp(' ') +disp('Press any key to continue.') +pause + +% Try to calculate a sensible position for the second figure, below the first +fig1_pos = get(fh1, 'Position'); +fig2_pos = fig1_pos; +fig2_pos(2) = fig2_pos(2) - fig1_pos(4); +fh2 = figure('Position', fig2_pos); + +h = plot(data(:, 1), data(:, 2), 'bo'); +hold on +axis('equal'); +title('Plot of data and covariances') +for i = 1:ncentres + v = [1 0]; + for j = 1:2 + start=mix.centres(i,:)-sqrt(mix.covars(i,:).*v); + endpt=mix.centres(i,:)+sqrt(mix.covars(i,:).*v); + linex = [start(1) endpt(1)]; + liney = [start(2) endpt(2)]; + line(linex, liney, 'Color', 'k', 'LineWidth', 3) + v = [0 1]; + end + % Plot ellipses of one standard deviation + theta = 0:0.02:2*pi; + x = sqrt(mix.covars(i,1))*cos(theta) + mix.centres(i,1); + y = sqrt(mix.covars(i,2))*sin(theta) + mix.centres(i,2); + plot(x, y, 'r-'); +end +hold off + +disp('Note how the data cluster positions and widths are captured by') +disp('the mixture model.') +disp(' ') +disp('Press any key to end.') +pause + +close(fh1); +close(fh2); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgmm4.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,210 @@ +%DEMGMM4 Demonstrate density modelling with a Gaussian mixture model. +% +% Description +% The problem consists of modelling data generated by a mixture of +% three Gaussians in 2 dimensions with a mixture model using full +% covariance matrices. The priors are 0.3, 0.5 and 0.2; the centres +% are (2, 3.5), (0, 0) and (0,2); the variances are (0.16, 0.64) axis +% aligned, (0.25, 1) rotated by 30 degrees and the identity matrix. The +% first figure contains a scatter plot of the data. +% +% A Gaussian mixture model with three components is trained using EM. +% The parameter vector is printed before training and after training. +% The user should press any key to continue at these points. The +% parameter vector consists of priors (the column), and centres (given +% as (x, y) pairs as the next two columns). The covariance matrices +% are printed separately. +% +% The second figure is a 3 dimensional view of the density function, +% while the third shows the axes of the 1-standard deviation ellipses +% for the three components of the mixture model. +% +% See also +% GMM, GMMINIT, GMMEM, GMMPROB, GMMUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate the data + +ndata = 500; + +% Fix the seeds for reproducible results +randn('state', 42); +rand('state', 42); +data = randn(ndata, 2); +prior = [0.3 0.5 0.2]; +% Mixture model swaps clusters 1 and 3 +datap = [0.2 0.5 0.3]; +datac = [0 2; 0 0; 2 3.5]; +datacov = repmat(eye(2), [1 1 3]); +data1 = data(1:prior(1)*ndata,:); +data2 = data(prior(1)*ndata+1:(prior(2)+prior(1))*ndata, :); +data3 = data((prior(1)+prior(2))*ndata +1:ndata, :); + +% First cluster has axis aligned variance and centre (2, 3.5) +data1(:, 1) = data1(:, 1)*0.4 + 2.0; +data1(:, 2) = data1(:, 2)*0.8 + 3.5; +datacov(:, :, 3) = [0.4*0.4 0; 0 0.8*0.8]; + +% Second cluster has variance axes rotated by 30 degrees and centre (0, 0) +rotn = [cos(pi/6) -sin(pi/6); sin(pi/6) cos(pi/6)]; +data2(:,1) = data2(:, 1)*0.5; +data2 = data2*rotn; +datacov(:, :, 2) = rotn' * [0.25 0; 0 1] * rotn; + +% Third cluster is at (0,2) +data3 = data3 + repmat([0 2], prior(3)*ndata, 1); + +% Put the dataset together again +data = [data1; data2; data3]; + +clc +disp('This demonstration illustrates the use of a Gaussian mixture model') +disp('with full covariance matrices to approximate the unconditional ') +disp('probability density of data in a two-dimensional space.') +disp('We begin by generating the data from a mixture of three Gaussians and') +disp('plotting it.') +disp(' ') +disp('The first cluster has axis aligned variance and centre (0, 2).') +disp('The second cluster has variance axes rotated by 30 degrees') +disp('and centre (0, 0). The third cluster has unit variance and centre') +disp('(2, 3.5).') +disp(' ') +disp('Press any key to continue.') +pause + +fh1 = figure; +plot(data(:, 1), data(:, 2), 'o') +set(gca, 'Box', 'on') + +% Set up mixture model +ncentres = 3; +input_dim = 2; +mix = gmm(input_dim, ncentres, 'full'); + +% Initialise the model parameters from the data +options = foptions; +options(14) = 5; % Just use 5 iterations of k-means in initialisation +mix = gmminit(mix, data, options); + +% Print out model +clc +disp('The mixture model has three components and full covariance') +disp('matrices. The model parameters after initialisation using the') +disp('k-means algorithm are as follows') +disp(' Priors Centres') +disp([mix.priors' mix.centres]) +disp('Covariance matrices are') +disp(mix.covars) +disp('Press any key to continue.') +pause + +% Set up vector of options for EM trainer +options = zeros(1, 18); +options(1) = 1; % Prints out error values. +options(14) = 50; % Number of iterations. + +disp('We now train the model using the EM algorithm for 50 iterations.') +disp(' ') +disp('Press any key to continue.') +pause +[mix, options, errlog] = gmmem(mix, data, options); + +% Print out model +disp(' ') +disp('The trained model has priors and centres:') +disp(' Priors Centres') +disp([mix.priors' mix.centres]) +disp('The data generator has priors and centres') +disp(' Priors Centres') +disp([datap' datac]) +disp('Model covariance matrices are') +disp(mix.covars(:, :, 1)) +disp(mix.covars(:, :, 2)) +disp(mix.covars(:, :, 3)) +disp('Data generator covariance matrices are') +disp(datacov(:, :, 1)) +disp(datacov(:, :, 2)) +disp(datacov(:, :, 3)) +disp('Note the close correspondence between these parameters and those') +disp('of the distribution used to generate the data. The match for') +disp('covariance matrices is not that close, but would be improved with') +disp('more iterations of the training algorithm.') +disp(' ') +disp('Press any key to continue.') +pause + +clc +disp('We now plot the density given by the mixture model as a surface plot.') +disp(' ') +disp('Press any key to continue.') +pause + +% Plot the result +x = -4.0:0.2:5.0; +y = -4.0:0.2:5.0; +[X, Y] = meshgrid(x,y); +X = X(:); +Y = Y(:); +grid = [X Y]; +Z = gmmprob(mix, grid); +Z = reshape(Z, length(x), length(y)); +c = mesh(x, y, Z); +hold on +title('Surface plot of probability density') +hold off +drawnow + +clc +disp('The final plot shows the centres and widths, given by one standard') +disp('deviation, of the three components of the mixture model. The axes') +disp('of the ellipses of constant density are shown.') +disp(' ') +disp('Press any key to continue.') +pause + +% Try to calculate a sensible position for the second figure, below the first +fig1_pos = get(fh1, 'Position'); +fig2_pos = fig1_pos; +fig2_pos(2) = fig2_pos(2) - fig1_pos(4) - 30; +fh2 = figure('Position', fig2_pos); + +h3 = plot(data(:, 1), data(:, 2), 'bo'); +axis equal; +hold on +title('Plot of data and covariances') +for i = 1:ncentres + [v,d] = eig(mix.covars(:,:,i)); + for j = 1:2 + % Ensure that eigenvector has unit length + v(:,j) = v(:,j)/norm(v(:,j)); + start=mix.centres(i,:)-sqrt(d(j,j))*(v(:,j)'); + endpt=mix.centres(i,:)+sqrt(d(j,j))*(v(:,j)'); + linex = [start(1) endpt(1)]; + liney = [start(2) endpt(2)]; + line(linex, liney, 'Color', 'k', 'LineWidth', 3) + end + % Plot ellipses of one standard deviation + theta = 0:0.02:2*pi; + x = sqrt(d(1,1))*cos(theta); + y = sqrt(d(2,2))*sin(theta); + % Rotate ellipse axes + ellipse = (v*([x; y]))'; + % Adjust centre + ellipse = ellipse + ones(length(theta), 1)*mix.centres(i,:); + plot(ellipse(:,1), ellipse(:,2), 'r-'); +end +hold off + +disp('Note how the data cluster positions and widths are captured by') +disp('the mixture model.') +disp(' ') +disp('Press any key to end.') +pause + +close(fh1); +close(fh2); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgmm5.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,147 @@ +%DEMGMM5 Demonstrate density modelling with a PPCA mixture model. +% +% Description +% The problem consists of modelling data generated by a mixture of +% three Gaussians in 2 dimensions with a mixture model using full +% covariance matrices. The priors are 0.3, 0.5 and 0.2; the centres +% are (2, 3.5), (0, 0) and (0,2); the variances are (0.16, 0.64) axis +% aligned, (0.25, 1) rotated by 30 degrees and the identity matrix. The +% first figure contains a scatter plot of the data. +% +% A mixture model with three one-dimensional PPCA components is trained +% using EM. The parameter vector is printed before training and after +% training. The parameter vector consists of priors (the column), and +% centres (given as (x, y) pairs as the next two columns). +% +% The second figure is a 3 dimensional view of the density function, +% while the third shows the axes of the 1-standard deviation ellipses +% for the three components of the mixture model together with the one +% standard deviation along the principal component of each mixture +% model component. +% +% See also +% GMM, GMMINIT, GMMEM, GMMPROB, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +ndata = 500; +data = randn(ndata, 2); +prior = [0.3 0.5 0.2]; +% Mixture model swaps clusters 1 and 3 +datap = [0.2 0.5 0.3]; +datac = [0 2; 0 0; 2 3.5]; +datacov = repmat(eye(2), [1 1 3]); +data1 = data(1:prior(1)*ndata,:); +data2 = data(prior(1)*ndata+1:(prior(2)+prior(1))*ndata, :); +data3 = data((prior(1)+prior(2))*ndata +1:ndata, :); + +% First cluster has axis aligned variance and centre (2, 3.5) +data1(:, 1) = data1(:, 1)*0.1 + 2.0; +data1(:, 2) = data1(:, 2)*0.8 + 3.5; +datacov(:, :, 3) = [0.1*0.1 0; 0 0.8*0.8]; + +% Second cluster has variance axes rotated by 30 degrees and centre (0, 0) +rotn = [cos(pi/6) -sin(pi/6); sin(pi/6) cos(pi/6)]; +data2(:,1) = data2(:, 1)*0.2; +data2 = data2*rotn; +datacov(:, :, 2) = rotn' * [0.04 0; 0 1] * rotn; + +% Third cluster is at (0,2) +data3(:, 2) = data3(:, 2)*0.1; +data3 = data3 + repmat([0 2], prior(3)*ndata, 1); + +% Put the dataset together again +data = [data1; data2; data3]; + +ndata = 100; % Number of data points. +noise = 0.2; % Standard deviation of noise distribution. +x = [0:1/(2*(ndata - 1)):0.5]'; +randn('state', 1); +rand('state', 1); +t = sin(2*pi*x) + noise*randn(ndata, 1); + +% Fit three one-dimensional PPCA models +ncentres = 3; +ppca_dim = 1; + +clc +disp('This demonstration illustrates the use of a Gaussian mixture model') +disp('with a probabilistic PCA covariance structure to approximate the') +disp('unconditional probability density of data in a two-dimensional space.') +disp('We begin by generating the data from a mixture of three Gaussians and') +disp('plotting it.') +disp(' ') +disp('The first cluster has axis aligned variance and centre (0, 2).') +disp('The variance parallel to the x-axis is significantly greater') +disp('than that parallel to the y-axis.') +disp('The second cluster has variance axes rotated by 30 degrees') +disp('and centre (0, 0). The third cluster has significant variance') +disp('parallel to the y-axis and centre (2, 3.5).') +disp(' ') +disp('Press any key to continue.') +pause + +fh1 = figure; +plot(data(:, 1), data(:, 2), 'o') +set(gca, 'Box', 'on') +axis equal +hold on + +mix = gmm(2, ncentres, 'ppca', ppca_dim); +options = foptions; +options(14) = 10; +options(1) = -1; % Switch off all warnings + +% Just use 10 iterations of k-means in initialisation +% Initialise the model parameters from the data +mix = gmminit(mix, data, options); +disp('The mixture model has three components with 1-dimensional') +disp('PPCA subspaces. The model parameters after initialisation using') +disp('the k-means algorithm are as follows') +disp(' Priors Centres') +disp([mix.priors' mix.centres]) +disp(' ') +disp('Press any key to continue') +pause + +options(1) = 1; % Prints out error values. +options(14) = 30; % Number of iterations. + +disp('We now train the model using the EM algorithm for up to 30 iterations.') +disp(' ') +disp('Press any key to continue.') +pause + +[mix, options, errlog] = gmmem(mix, data, options); +disp('The trained model has priors and centres:') +disp(' Priors Centres') +disp([mix.priors' mix.centres]) + +% Now plot the result +for i = 1:ncentres + % Plot the PC vectors + v = mix.U(:,:,i); + start=mix.centres(i,:)-sqrt(mix.lambda(i))*(v'); + endpt=mix.centres(i,:)+sqrt(mix.lambda(i))*(v'); + linex = [start(1) endpt(1)]; + liney = [start(2) endpt(2)]; + line(linex, liney, 'Color', 'k', 'LineWidth', 3) + % Plot ellipses of one standard deviation + theta = 0:0.02:2*pi; + x = sqrt(mix.lambda(i))*cos(theta); + y = sqrt(mix.covars(i))*sin(theta); + % Rotate ellipse axes + rot_matrix = [v(1) -v(2); v(2) v(1)]; + ellipse = (rot_matrix*([x; y]))'; + % Adjust centre + ellipse = ellipse + ones(length(theta), 1)*mix.centres(i,:); + plot(ellipse(:,1), ellipse(:,2), 'r-') +end + +disp(' ') +disp('Press any key to exit') +pause +close (fh1); +clear all; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,160 @@ +%DEMGP Demonstrate simple regression using a Gaussian Process. +% +% Description +% The problem consists of one input variable X and one target variable +% T. The values in X are chosen in two separated clusters and the +% target data is generated by computing SIN(2*PI*X) and adding Gaussian +% noise. Two Gaussian Processes, each with different covariance +% functions are trained by optimising the hyperparameters using the +% scaled conjugate gradient algorithm. The final predictions are +% plotted together with 2 standard deviation error bars. +% +% See also +% GP, GPERR, GPFWD, GPGRAD, GPINIT, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Find out if flops is available (i.e. pre-version 6 Matlab) +v = version; +if (str2num(strtok(v, '.')) >= 6) + flops_works = logical(0); +else + flops_works = logical(1); +end + +randn('state', 42); +x = [0.1 0.15 0.2 0.25 0.65 0.7 0.75 0.8 0.85 0.9]'; +ndata = length(x); +t = sin(2*pi*x) + 0.05*randn(ndata, 1); + +xtest = linspace(0, 1, 50)'; + +clc +disp('This demonstration illustrates the use of a Gaussian Process') +disp('model for regression problems. The data is generated from a noisy') +disp('sine function.') +disp(' ') +disp('Press any key to continue.') +pause + +flops(0); +% Initialise the parameters. +net = gp(1, 'sqexp'); +prior.pr_mean = 0; +prior.pr_var = 1; +net = gpinit(net, x, t, prior); + +clc +disp('The first GP uses the squared exponential covariance function.') +disp('The hyperparameters are initialised by sampling from a Gaussian with a') +disp(['mean of ', num2str(prior.pr_mean), ' and variance ', ... + num2str(prior.pr_var), '.']) +disp('After initializing the network, we train it using the scaled conjugate') +disp('gradients algorithm for 20 cycles.') +disp(' ') +disp('Press any key to continue') +pause + +% Now train to find the hyperparameters. +options = foptions; +options(1) = 1; % Display training error values +options(14) = 20; +flops(0) +[net, options] = netopt(net, options, x, t, 'scg'); +if flops_works + sflops = flops; +end + +disp('The second GP uses the rational quadratic covariance function.') +disp('The hyperparameters are initialised by sampling from a Gaussian with a') +disp(['mean of ', num2str(prior.pr_mean), ' and variance ', num2str(prior.pr_var)]) +disp('After initializing the network, we train it using the scaled conjugate') +disp('gradients algorithm for 20 cycles.') +disp(' ') +disp('Press any key to continue') +pause +flops(0) +net2 = gp(1, 'ratquad'); +net2 = gpinit(net2, x, t, prior); +flops(0) +[net2, options] = netopt(net2, options, x, t, 'scg'); +if flops_works + rflops = flops; +end + +disp(' ') +disp('Press any key to continue') +disp(' ') +pause +clc + +fprintf(1, 'For squared exponential covariance function,'); +if flops_works + fprintf(1, 'flops = %d', sflops); +end +fprintf(1, '\nfinal hyperparameters:\n') +format_string = strcat(' bias:\t\t\t%10.6f\n noise:\t\t%10.6f\n', ... + ' inverse lengthscale:\t%10.6f\n vertical scale:\t%10.6f\n'); +fprintf(1, format_string, ... + exp(net.bias), exp(net.noise), exp(net.inweights(1)), exp(net.fpar(1))); +fprintf(1, '\n\nFor rational quadratic covariance function,'); +if flops_works + fprintf(1, 'flops = %d', rflops); +end +fprintf(1, '\nfinal hyperparameters:\n') +format_string = [format_string ' cov decay order:\t%10.6f\n']; +fprintf(1, format_string, ... + exp(net2.bias), exp(net2.noise), exp(net2.inweights(1)), ... + exp(net2.fpar(1)), exp(net2.fpar(2))); +disp(' ') +disp('Press any key to continue') +pause + +disp(' ') +disp('Now we plot the data, underlying function, model outputs and two') +disp('standard deviation error bars on a single graph to compare the results.') +disp(' ') +disp('Press any key to continue.') +pause +cn = gpcovar(net, x); +cninv = inv(cn); +[ytest, sigsq] = gpfwd(net, xtest, cninv); +sig = sqrt(sigsq); + +fh1 = figure; +hold on +plot(x, t, 'ok'); +xlabel('Input') +ylabel('Target') +fplot('sin(2*pi*x)', [0 1], '--m'); +plot(xtest, ytest, '-k'); +plot(xtest, ytest+(2*sig), '-b', xtest, ytest-(2*sig), '-b'); +axis([0 1 -1.5 1.5]); +title('Squared exponential covariance function') +legend('data', 'function', 'GP', 'error bars'); +hold off + +cninv2 = inv(gpcovar(net2, x)); +[ytest2, sigsq2] = gpfwd(net2, xtest, cninv2); +sig2 = sqrt(sigsq2); +fh2 = figure; +hold on +plot(x, t, 'ok'); +xlabel('Input') +ylabel('Target') +fplot('sin(2*pi*x)', [0 1], '--m'); +plot(xtest, ytest2, '-k'); +plot(xtest, ytest2+(2*sig2), '-b', xtest, ytest2-(2*sig2), '-b'); +axis([0 1 -1.5 1.5]); +title('Rational quadratic covariance function') +legend('data', 'function', 'GP', 'error bars'); +hold off + +disp(' ') +disp('Press any key to end.') +pause +close(fh1); +close(fh2); +clear all; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgpard.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +%DEMGPARD Demonstrate ARD using a Gaussian Process. +% +% Description +% The data consists of three input variables X1, X2 and X3, and one +% target variable T. The target data is generated by computing +% SIN(2*PI*X1) and adding Gaussian noise, x2 is a copy of x1 with a +% higher level of added noise, and x3 is sampled randomly from a +% Gaussian distribution. A Gaussian Process, is trained by optimising +% the hyperparameters using the scaled conjugate gradient algorithm. +% The final values of the hyperparameters show that the model +% successfully identifies the importance of each input. +% +% See also +% DEMGP, GP, GPERR, GPFWD, GPGRAD, GPINIT, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + +clc; +randn('state', 1729); +rand('state', 1729); +disp('This demonstration illustrates the technique of automatic relevance') +disp('determination (ARD) using a Gaussian Process.') +disp(' '); +disp('First, we set up a synthetic data set involving three input variables:') +disp('x1 is sampled uniformly from the range (0,1) and has a low level of') +disp('added Gaussian noise, x2 is a copy of x1 with a higher level of added') +disp('noise, and x3 is sampled randomly from a Gaussian distribution. The') +disp('single target variable is given by t = sin(2*pi*x1) with additive') +disp('Gaussian noise. Thus x1 is very relevant for determining the target') +disp('value, x2 is of some relevance, while x3 should in principle be') +disp('irrelevant.') +disp(' '); +disp('Press any key to see a plot of t against x1.') +pause; + +ndata = 100; +x1 = rand(ndata, 1); +x2 = x1 + 0.05*randn(ndata, 1); +x3 = 0.5 + 0.5*randn(ndata, 1); +x = [x1, x2, x3]; +t = sin(2*pi*x1) + 0.1*randn(ndata, 1); + +% Plot the data and the original function. +h = figure; +plotvals = linspace(0, 1, 200)'; +plot(x1, t, 'ob') +hold on +xlabel('Input x1') +ylabel('Target') +axis([0 1 -1.5 1.5]) +[fx, fy] = fplot('sin(2*pi*x)', [0 1]); +plot(fx, fy, '-g', 'LineWidth', 2); +legend('data', 'function'); + +disp(' '); +disp('Press any key to continue') +pause; clc; + +disp('The Gaussian Process has a separate hyperparameter for each input.') +disp('The hyperparameters are trained by error minimisation using the scaled.') +disp('conjugate gradient optimiser.') +disp(' '); +disp('Press any key to create and train the model.') +disp(' '); +pause; + +net = gp(3, 'sqexp'); +% Initialise the parameters. +prior.pr_mean = 0; +prior.pr_var = 0.1; +net = gpinit(net, x, t, prior); + +% Now train to find the hyperparameters. +options = foptions; +options(1) = 1; +options(14) = 30; + +[net, options] = netopt(net, options, x, t, 'scg'); + +rel = exp(net.inweights); + +fprintf(1, ... + '\nFinal hyperparameters:\n\n bias:\t\t%10.6f\n noise:\t%10.6f\n', ... + exp(net.bias), exp(net.noise)); +fprintf(1, ' Vertical scale: %8.6f\n', exp(net.fpar(1))); +fprintf(1, ' Input 1:\t%10.6f\n Input 2:\t%10.6f\n', ... + rel(1), rel(2)); +fprintf(1, ' Input 3:\t%10.6f\n\n', rel(3)); +disp(' '); +disp('We see that the inverse lengthscale associated with') +disp('input x1 is large, that of x2 has an intermediate value and the variance') +disp('of weights associated with x3 is small.') +disp(' '); +disp('This implies that the Gaussian Process is giving greatest emphasis') +disp('to x1 and least emphasis to x3, with intermediate emphasis on') +disp('x2 in the covariance function.') +disp(' ') +disp('Since the target t is statistically independent of x3 we might') +disp('expect the weights associated with this input would go to') +disp('zero. However, for any finite data set there may be some chance') +disp('correlation between x3 and t, and so the corresponding hyperparameter remains') +disp('finite.') +disp('Press any key to continue.') +pause + +disp('Finally, we plot the output of the Gaussian Process along the line') +disp('x1 = x2 = x3, together with the true underlying function.') +xt = linspace(0, 1, 50); +xtest = [xt', xt', xt']; + +cn = gpcovar(net, x); +cninv = inv(cn); +[ytest, sigsq] = gpfwd(net, xtest, cninv); +sig = sqrt(sigsq); + +figure(h); hold on; +plot(xt, ytest, '-k'); +plot(xt, ytest+(2*sig), '-b', xt, ytest-(2*sig), '-b'); +axis([0 1 -1.5 1.5]); +fplot('sin(2*pi*x)', [0 1], '--m'); + +disp(' '); +disp('Press any key to end.') +pause; clc; close(h); clear all +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgpot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function g = demgpot(x, mix) +%DEMGPOT Computes the gradient of the negative log likelihood for a mixture model. +% +% Description +% This function computes the gradient of the negative log of the +% unconditional data density P(X) with respect to the coefficients of +% the data vector X for a Gaussian mixture model. The data structure +% MIX defines the mixture model, while the matrix X contains the data +% vector as a row vector. Note the unusual order of the arguments: this +% is so that the function can be used in DEMHMC1 directly for sampling +% from the distribution P(X). +% +% See also +% DEMHMC1, DEMMET1, DEMPOT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Computes the potential gradient + +temp = (ones(mix.ncentres,1)*x)-mix.centres; +temp = temp.*(gmmactiv(mix,x)'*ones(1, mix.nin)); +% Assume spherical covariance structure +if ~strcmp(mix.covar_type, 'spherical') + error('Spherical covariance only.') +end +temp = temp./(mix.covars'*ones(1, mix.nin)); +temp = temp.*(mix.priors'*ones(1, mix.nin)); +g = sum(temp, 1)/gmmprob(mix, x); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgtm1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,147 @@ +%DEMGTM1 Demonstrate EM for GTM. +% +% Description +% This script demonstrates the use of the EM algorithm to fit a one- +% dimensional GTM to a two-dimensional set of data using maximum +% likelihood. The location and spread of the Gaussian kernels in the +% data space is shown during training. +% +% See also +% DEMGTM2, GTM, GTMEM, GTMPOST +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Demonstrates the GTM with a 2D target space and a 1D latent space. +% +% This script generates a simple data set in 2 dimensions, +% with an intrinsic dimensionality of 1, and trains a GTM +% with a 1-dimensional latent variable to model this data +% set, visually illustrating the training process +% +% Synopsis: gtm_demo + +% Generate and plot a 2D data set + +data_min = 0.15; +data_max = 3.05; +T = [data_min:0.05:data_max]'; +T = [T (T + 1.25*sin(2*T))]; +fh1 = figure; +plot(T(:,1), T(:,2), 'ro'); +axis([data_min-0.05 data_max+0.05 data_min-0.05 data_max+0.05]); +clc; +disp('This demonstration shows in detail how the EM algorithm works') +disp('for training a GTM with a one dimensional latent space.') +disp(' ') +fprintf([... +'The figure shows data generated by feeding a 1D uniform distribution\n', ... +'(on the X-axis) through a non-linear function (y = x + 1.25*sin(2*x))\n', ... +'\nPress any key to continue ...\n\n']); +pause; + +% Generate a unit circle figure, to be used for plotting +src = [0:(2*pi)/(20-1):2*pi]'; +unitC = [sin(src) cos(src)]; + +% Generate and plot (along with the data) an initial GTM model + +clc; +num_latent_points = 20; +num_rbf_centres = 5; + +net = gtm(1, num_latent_points, 2, num_rbf_centres, 'gaussian'); + +options = zeros(1, 18); +options(7) = 1; +net = gtminit(net, options, T, 'regular', num_latent_points, ... + num_rbf_centres); + +mix = gtmfwd(net); +% Replot the figure +hold off; +plot(mix.centres(:,1), mix.centres(:,2), 'g'); +hold on; +for i=1:num_latent_points + c = 2*unitC*sqrt(mix.covars(1)) + [ones(20,1)*mix.centres(i,1) ... + ones(num_latent_points,1)*mix.centres(i,2)]; + fill(c(:,1), c(:,2), [0.8 1 0.8]); +end +plot(T(:,1), T(:,2), 'ro'); +plot(mix.centres(:,1), mix.centres(:,2), 'g+'); +plot(mix.centres(:,1), mix.centres(:,2), 'g'); +axis([data_min-0.05 data_max+0.05 data_min-0.05 data_max+0.05]); +drawnow; +title('Initial configuration'); +disp(' ') +fprintf([... +'The figure shows the starting point for the GTM, before the training.\n', ... +'A discrete latent variable distribution of %d points in 1 dimension \n', ... +'is mapped to the 1st principal component of the target data by an RBF.\n', ... +'with %d basis functions. Each of the %d points defines the centre of\n', ... +'a Gaussian in a Gaussian mixture, marked by the green ''+''-signs. The\n', ... +'mixture components all have equal variance, illustrated by the filled\n', ... +'circle around each ''+''-sign, the radii corresponding to 2 standard\n', ... +'deviations. The ''+''-signs are connected with a line according to their\n', ... +'corresponding ordering in latent space.\n\n', ... +'Press any key to begin training ...\n\n'], num_latent_points, ... +num_rbf_centres, num_latent_points); +pause; + +figure(fh1); +%%%% Train the GTM and plot it (along with the data) as training proceeds %%%% +options = foptions; +options(1) = -1; % Turn off all warning messages +options(14) = 1; +for j = 1:15 + [net, options] = gtmem(net, T, options); + hold off; + mix = gtmfwd(net); + plot(mix.centres(:,1), mix.centres(:,2), 'g'); + hold on; + for i=1:20 + c = 2*unitC*sqrt(mix.covars(1)) + [ones(20,1)*mix.centres(i,1) ... + ones(20,1)*mix.centres(i,2)]; + fill(c(:,1), c(:,2), [0.8 1.0 0.8]); + end + plot(T(:,1), T(:,2), 'ro'); + plot(mix.centres(:,1), mix.centres(:,2), 'g+'); + plot(mix.centres(:,1), mix.centres(:,2), 'g'); + axis([0 3.5 0 3.5]); + title(['After ', int2str(j),' iterations of training.']); + drawnow; + if (j == 4) + fprintf([... +'The GTM initially adapts relatively quickly - already after \n', ... +'4 iterations of training, a rough fit is attained.\n\n', ... +'Press any key to continue training ...\n\n']); +pause; +figure(fh1); + elseif (j == 8) + fprintf([... +'After another 4 iterations of training: from now on further \n', ... +'training only makes small changes to the mapping, which combined with \n', ... +'decrements of the Gaussian mixture variance, optimize the fit in \n', ... +'terms of likelihood.\n\n', ... +'Press any key to continue training ...\n\n']); +pause; +figure(fh1); + else + pause(1); + end +end + +clc; +fprintf([... +'After 15 iterations of training the GTM can be regarded as converged. \n', ... +'Is has been adapted to fit the target data distribution as well \n', ... +'as possible, given prior smoothness constraints on the mapping. It \n', ... +'captures the fact that the probabilty density is higher at the two \n', ... +'bends of the curve, and lower towards its end points.\n\n']); +disp(' '); +disp('Press any key to exit.'); +pause; + +close(fh1); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demgtm2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,194 @@ +%DEMGTM2 Demonstrate GTM for visualisation. +% +% Description +% This script demonstrates the use of a GTM with a two-dimensional +% latent space to visualise data in a higher dimensional space. This is +% done through the use of the mean responsibility and magnification +% factors. +% +% See also +% DEMGTM1, GTM, GTMEM, GTMPOST +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Fix seeds for reproducible results +rand('state', 420); +randn('state', 420); + +ndata = 300 +clc; +disp('This demonstration shows how a Generative Topographic Mapping') +disp('can be used to model and visualise high dimensional data. The') +disp('data is generated from a mixture of two spherical Gaussians in') +dstring = ['four dimensional space. ', num2str(ndata), ... + ' data points are generated.']; +disp(dstring); +disp(' '); +disp('Press any key to continue.') +pause +% Create data +data_dim = 4; +latent_dim = 2; +mix = gmm(data_dim, 2, 'spherical'); +mix.centres = [1 1 1 1; 0 0 0 0]; +mix.priors = [0.5 0.5]; +mix.covars = [0.1 0.1]; + +[data, labels] = gmmsamp(mix, ndata); + +latent_shape = [15 15]; % Number of latent points in each dimension +nlatent = prod(latent_shape); % Number of latent points +num_rbf_centres = 16; + +clc; +dstring = ['Next we generate and initialise the GTM. There are ',... + num2str(nlatent), ' latent points']; +disp(dstring); +dstring = ['arranged in a square of ', num2str(latent_shape(1)), ... + ' points on a side. There are ', num2str(num_rbf_centres), ... + ' centres in the']; +disp(dstring); +disp('RBF model, which has Gaussian activation functions.') +disp(' ') +disp('Once the model is created, the latent data sample') +disp('and RBF centres are placed uniformly in the square [-1 1 -1 1].') +disp('The output weights of the RBF are computed to map the latent'); +disp('space to the two dimensional PCA subspace of the data.'); +disp(' ') +disp('Press any key to continue.'); +pause; + +% Create and initialise GTM model +net = gtm(latent_dim, nlatent, data_dim, num_rbf_centres, ... + 'gaussian', 0.1); + +options = foptions; +options(1) = -1; +options(7) = 1; % Set width factor of RBF +net = gtminit(net, options, data, 'regular', latent_shape, [4 4]); + +options = foptions; +options(14) = 30; +options(1) = 1; + +clc; +dstring = ['We now train the model with ', num2str(options(14)), ... + ' iterations of']; +disp(dstring) +disp('the EM algorithm for the GTM.') +disp(' ') +disp('Press any key to continue.') +pause; + +[net, options] = gtmem(net, data, options); + +disp(' ') +disp('Press any key to continue.') +pause; + +clc; +disp('We now visualise the data by plotting, for each data point,'); +disp('the posterior mean and mode (in latent space). These give'); +disp('a summary of the entire posterior distribution in latent space.') +disp('The corresponding values are joined by a line to aid the') +disp('interpretation.') +disp(' ') +disp('Press any key to continue.'); +pause; +% Plot posterior means +means = gtmlmean(net, data); +modes = gtmlmode(net, data); +PointSize = 12; +ClassSymbol1 = 'r.'; +ClassSymbol2 = 'b.'; +fh1 = figure; +hold on; +title('Visualisation in latent space') +plot(means((labels==1),1), means(labels==1,2), ... + ClassSymbol1, 'MarkerSize', PointSize) +plot(means((labels>1),1),means(labels>1,2),... + ClassSymbol2, 'MarkerSize', PointSize) + +ClassSymbol1 = 'ro'; +ClassSymbol2 = 'bo'; +plot(modes(labels==1,1), modes(labels==1,2), ... + ClassSymbol1) +plot(modes(labels>1,1),modes(labels>1,2),... + ClassSymbol2) + +% Join up means and modes +for n = 1:ndata + plot([means(n,1); modes(n,1)], [means(n,2); modes(n,2)], 'g-') +end +% Place legend outside data plot +legend('Mean (class 1)', 'Mean (class 2)', 'Mode (class 1)',... + 'Mode (class 2)', -1); + +% Display posterior for a data point +% Choose an interesting one with a large distance between mean and +% mode +[distance, point] = max(sum((means-modes).^2, 2)); +resp = gtmpost(net, data(point, :)); + +disp(' ') +disp('For more detailed information, the full posterior distribution') +disp('(or responsibility) can be plotted in latent space for a') +disp('single data point. This point has been chosen as the one') +disp('with the largest distance between mean and mode.') +disp(' ') +disp('Press any key to continue.'); +pause; + +R = reshape(resp, fliplr(latent_shape)); +XL = reshape(net.X(:,1), fliplr(latent_shape)); +YL = reshape(net.X(:,2), fliplr(latent_shape)); + +fh2 = figure; +imagesc(net.X(:, 1), net.X(:,2), R); +hold on; +tstr = ['Responsibility for point ', num2str(point)]; +title(tstr); +set(gca,'YDir','normal') +colormap(hot); +colorbar +disp(' '); +disp('Press any key to continue.') +pause + +clc +disp('Finally, we visualise the data with the posterior means in') +disp('latent space as before, but superimpose the magnification') +disp('factors to highlight the separation between clusters.') +disp(' ') +disp('Note the large magnitude factors down the centre of the') +disp('graph, showing that the manifold is stretched more in') +disp('this region than within each of the two clusters.') +ClassSymbol1 = 'g.'; +ClassSymbol2 = 'b.'; + +fh3 = figure; +mags = gtmmag(net, net.X); +% Reshape into grid form +Mags = reshape(mags, fliplr(latent_shape)); +imagesc(net.X(:, 1), net.X(:,2), Mags); +hold on +title('Dataset visualisation with magnification factors') +set(gca,'YDir','normal') +colormap(hot); +colorbar +hold on; % Else the magnification plot disappears +plot(means(labels==1,1), means(labels==1,2), ... + ClassSymbol1, 'MarkerSize', PointSize) +plot(means(labels>1,1), means(labels>1,2), ... + ClassSymbol2, 'MarkerSize', PointSize) + +disp(' ') +disp('Press any key to exit.') +pause + +close(fh1); +close(fh2); +close(fh3); +clear all; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demhint.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function demhint(nin, nhidden, nout) +%DEMHINT Demonstration of Hinton diagram for 2-layer feed-forward network. +% +% Description +% +% DEMHINT plots a Hinton diagram for a 2-layer feedforward network with +% 5 inputs, 4 hidden units and 3 outputs. The weight vector is chosen +% from a Gaussian distribution as described under MLP. +% +% DEMHINT(NIN, NHIDDEN, NOUT) allows the user to specify the number of +% inputs, hidden units and outputs. +% +% See also +% HINTON, HINTMAT, MLP, MLPPAK, MLPUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin < 1 nin = 5; end +if nargin < 2 nhidden = 7; end +if nargin < 3 nout = 3; end + +% Fix the seed for reproducible results +randn('state', 42); +clc +disp('This demonstration illustrates the plotting of Hinton diagrams') +disp('for Multi-Layer Perceptron networks.') +disp(' ') +disp('Press any key to continue.') +pause +net = mlp(nin, nhidden, nout, 'linear'); + +[h1, h2] = mlphint(net); +clc +disp('The MLP has been created with') +disp([' ' int2str(nin) ' inputs']) +disp([' ' int2str(nhidden) ' hidden units']) +disp([' ' int2str(nout) ' outputs']) +disp(' ') +disp('One figure is produced for each layer of weights.') +disp('For each layer the fan-in weights are arranged in rows for each unit.') +disp('The bias weight is separated from the rest by a red vertical line.') +disp('The area of each box is proportional to the weight value: positive') +disp('values are white, and negative are black.') +disp(' ') +disp('Press any key to exit.'); +pause; +delete(h1); +delete(h2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demhmc1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,145 @@ +%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. +% +% Description +% The problem consists of generating data from a mixture of two +% Gaussians in two dimensions using a hybrid Monte Carlo algorithm with +% persistence. A mixture model is then fitted to the sample to compare +% it with the true underlying generator. +% +% See also +% DEMHMC3, HMC, DEMPOT, DEMGPOT +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +dim = 2; % Data dimension +ncentres = 2; % Number of centres in mixture model + +seed = 42; % Seed for random weight initialization. +randn('state', seed); +rand('state', seed); + +clc +disp('This demonstration illustrates the use of the hybrid Monte Carlo') +disp('algorithm to sample from a mixture of two Gaussians.') +disp('The means of the two components are [0 0] and [2 2].') +disp(' ') +disp('First we set up the parameters of the mixture model we are sampling') +disp('from.') +disp(' ') +disp('Press any key to continue.') +pause + +% Set up mixture model to sample from +mix = gmm(dim, ncentres, 'spherical'); +mix.centres(1, :) = [0 0]; +mix.centres(2, :) = [2 2]; +x = [0 1]; % Start vector + +% Set up vector of options for hybrid Monte Carlo. + +nsamples = 160; % Number of retained samples. + +options = foptions; % Default options vector. +options(1) = 1; % Switch on diagnostics. +options(5) = 1; % Use persistence +options(7) = 50; % Number of steps in trajectory. +options(14) = nsamples; % Number of Monte Carlo samples returned. +options(15) = 30; % Number of samples omitted at start of chain. +options(18) = 0.02; + +clc +disp(['Next we take ', num2str(nsamples),' samples from the distribution.'... + , 'The first ', num2str(options(15))]) +disp('samples at the start of the chain are omitted. As persistence') +disp('is used, the momentum has a small random component added at each step.') +disp([num2str(options(7)), ... + ' iterations are used at each step and the step size is ',... + num2str(options(18))]) +disp('Sampling starts at the point [0 1].') +disp('The new state is accepted if the threshold value is greater than') +disp('a random number between 0 and 1.') +disp(' ') +disp('Negative step numbers indicate samples discarded from the start of the') +disp('chain.') +disp(' ') +disp('Press any key to continue.') +pause + +[samples, energies] = hmc('dempot', x, options, 'demgpot', mix); + +disp(' ') +disp('Press any key to continue.') +pause +clc +disp('The plot shows the samples generated by the HMC function.') +disp('The different colours are used to show how the samples move from') +disp('one component to the other over time.') +disp(' ') +disp('Press any key to continue.') +pause +probs = exp(-energies); +fh1 = figure; +% Plot data in 4 groups +ngroups = 4; +g1end = floor(nsamples/ngroups); +g2end = floor(2*nsamples/ngroups); +g3end = floor(3*nsamples/ngroups); +p1 = plot(samples(1:g1end,1), samples(1:g1end,2), 'k.', 'MarkerSize', 12); +hold on +lstrings = char(['Samples 1-' int2str(g1end)], ... + ['Samples ' int2str(g1end+1) '-' int2str(g2end)], ... + ['Samples ' int2str(g2end+1) '-' int2str(g3end)], ... + ['Samples ' int2str(g3end+1) '-' int2str(nsamples)]); +p2 = plot(samples(g1end+1:g2end,1), samples(g1end+1:g2end,2), ... + 'r.', 'MarkerSize', 12); +p3 = plot(samples(g2end+1:g3end,1), samples(g2end+1:g3end,2), ... + 'g.', 'MarkerSize', 12); +p4 = plot(samples(g3end+1:nsamples,1), samples(g3end+1:nsamples,2), ... + 'b.', 'MarkerSize', 12); +legend([p1 p2 p3 p4], lstrings, 2); + +clc +disp('We now fit a Gaussian mixture model to the sampled data.') +disp('The model has spherical covariance structure and the correct') +disp('number of components.') +disp(' ') +disp('Press any key to continue.') +pause +% Fit a mixture model to the sample +newmix = gmm(dim, ncentres, 'spherical'); +options = foptions; +options(1) = -1; % Switch off all diagnostics +options(14) = 5; % Just use 5 iterations of k-means in initialisation +% Initialise the model parameters from the samples +newmix = gmminit(newmix, samples, options); + +% Set up vector of options for EM trainer +options = zeros(1, 18); +options(1) = 1; % Prints out error values. +options(14) = 15; % Max. Number of iterations. + +disp('We now train the model using the EM algorithm for 15 iterations') +disp(' ') +disp('Press any key to continue') +pause +[newmix, options, errlog] = gmmem(newmix, samples, options); + +% Print out model +disp(' ') +disp('The trained model has parameters ') +disp(' Priors Centres Variances') +disp([newmix.priors' newmix.centres newmix.covars']) +disp('Note the close correspondence between these parameters and those') +disp('of the distribution used to generate the data') +disp(' ') +disp(' Priors Centres Variances') +disp([mix.priors' mix.centres mix.covars']) +disp(' ') +disp('Press any key to exit') +pause + +close(fh1); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demhmc2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,132 @@ +%DEMHMC2 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. +% +% Description +% The problem consists of one input variable X and one target variable +% T with data generated by sampling X at equal intervals and then +% generating target data by computing SIN(2*PI*X) and adding Gaussian +% noise. The model is a 2-layer network with linear outputs, and the +% hybrid Monte Carlo algorithm (without persistence) is used to sample +% from the posterior distribution of the weights. The graph shows the +% underlying function, 100 samples from the function given by the +% posterior distribution of the weights, and the average prediction +% (weighted by the posterior probabilities). +% +% See also +% DEMHMC3, HMC, MLP, MLPERR, MLPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate the matrix of inputs x and targets t. +ndata = 20; % Number of data points. +noise = 0.1; % Standard deviation of noise distribution. +nin = 1; % Number of inputs. +nout = 1; % Number of outputs. + +seed = 42; % Seed for random weight initialization. +randn('state', seed); +rand('state', seed); + +x = 0.25 + 0.1*randn(ndata, nin); +t = sin(2*pi*x) + noise*randn(size(x)); + +clc +disp('This demonstration illustrates the use of the hybrid Monte Carlo') +disp('algorithm to sample from the posterior weight distribution of a') +disp('multi-layer perceptron.') +disp(' ') +disp('A regression problem is used, with the one-dimensional data drawn') +disp('from a noisy sine function. The x values are sampled from a normal') +disp('distribution with mean 0.25 and variance 0.01.') +disp(' ') +disp('First we initialise the network.') +disp(' ') +disp('Press any key to continue.') +pause + +% Set up network parameters. +nhidden = 5; % Number of hidden units. +alpha = 0.001; % Coefficient of weight-decay prior. +beta = 100.0; % Coefficient of data error. + +% Create and initialize network model. +% Initialise weights reasonably close to 0 +net = mlp(nin, nhidden, nout, 'linear', alpha, beta); +net = mlpinit(net, 10); + +clc +disp('Next we take 100 samples from the posterior distribution. The first') +disp('200 samples at the start of the chain are omitted. As persistence') +disp('is not used, the momentum is randomised at each step. 100 iterations') +disp('are used at each step. The new state is accepted if the threshold') +disp('value is greater than a random number between 0 and 1.') +disp(' ') +disp('Negative step numbers indicate samples discarded from the start of the') +disp('chain.') +disp(' ') +disp('Press any key to continue.') +pause +% Set up vector of options for hybrid Monte Carlo. +nsamples = 100; % Number of retained samples. + +options = foptions; % Default options vector. +options(1) = 1; % Switch on diagnostics. +options(7) = 100; % Number of steps in trajectory. +options(14) = nsamples; % Number of Monte Carlo samples returned. +options(15) = 200; % Number of samples omitted at start of chain. +options(18) = 0.002; % Step size. + +w = mlppak(net); +% Initialise HMC +hmc('state', 42); +[samples, energies] = hmc('neterr', w, options, 'netgrad', net, x, t); + +clc +disp('The plot shows the underlying noise free function, the 100 samples') +disp('produced from the MLP, and their average as a Monte Carlo estimate') +disp('of the true posterior average.') +disp(' ') +disp('Press any key to continue.') +pause +nplot = 300; +plotvals = [0 : 1/(nplot - 1) : 1]'; +pred = zeros(size(plotvals)); +fh = figure; +for k = 1:nsamples + w2 = samples(k,:); + net2 = mlpunpak(net, w2); + y = mlpfwd(net2, plotvals); + % Average sample predictions as Monte Carlo estimate of true integral + pred = pred + y; + h4 = plot(plotvals, y, '-r', 'LineWidth', 1); + if k == 1 + hold on + end +end +pred = pred./nsamples; + +% Plot data +h1 = plot(x, t, 'ob', 'LineWidth', 2, 'MarkerFaceColor', 'blue'); +axis([0 1 -3 3]) + +% Plot function +[fx, fy] = fplot('sin(2*pi*x)', [0 1], '--g'); +h2 = plot(fx, fy, '--g', 'LineWidth', 2); +set(gca, 'box', 'on'); + +% Plot averaged prediction +h3 = plot(plotvals, pred, '-c', 'LineWidth', 2); +hold off + +lstrings = char('Data', 'Function', 'Prediction', 'Samples'); +legend([h1 h2 h3 h4], lstrings, 3); + +disp('Note how the predictions become much further from the true function') +disp('away from the region of high data density.') +disp(' ') +disp('Press any key to exit.') +pause +close(fh); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demhmc3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ +%DEMHMC3 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. +% +% Description +% The problem consists of one input variable X and one target variable +% T with data generated by sampling X at equal intervals and then +% generating target data by computing SIN(2*PI*X) and adding Gaussian +% noise. The model is a 2-layer network with linear outputs, and the +% hybrid Monte Carlo algorithm (with persistence) is used to sample +% from the posterior distribution of the weights. The graph shows the +% underlying function, 300 samples from the function given by the +% posterior distribution of the weights, and the average prediction +% (weighted by the posterior probabilities). +% +% See also +% DEMHMC2, HMC, MLP, MLPERR, MLPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate the matrix of inputs x and targets t. +ndata = 20; % Number of data points. +noise = 0.1; % Standard deviation of noise distribution. +nin = 1; % Number of inputs. +nout = 1; % Number of outputs. + +seed = 42; % Seed for random number generators. +randn('state', seed); +rand('state', seed); + +x = 0.25 + 0.1*randn(ndata, nin); +t = sin(2*pi*x) + noise*randn(size(x)); + +clc +disp('This demonstration illustrates the use of the hybrid Monte Carlo') +disp('algorithm to sample from the posterior weight distribution of a') +disp('multi-layer perceptron.') +disp(' ') +disp('A regression problem is used, with the one-dimensional data drawn') +disp('from a noisy sine function. The x values are sampled from a normal') +disp('distribution with mean 0.25 and variance 0.01.') +disp(' ') +disp('First we initialise the network.') +disp(' ') +disp('Press any key to continue.') +pause + +% Set up network parameters. +nhidden = 5; % Number of hidden units. +alpha = 0.001; % Coefficient of weight-decay prior. +beta = 100.0; % Coefficient of data error. + +% Create and initialize network model. + +% Initialise weights reasonably close to 0 +net = mlp(nin, nhidden, nout, 'linear', alpha, beta); +net = mlpinit(net, 10); + +clc +disp('Next we take 100 samples from the posterior distribution. The first') +disp('300 samples at the start of the chain are omitted. As persistence') +disp('is used, the momentum has a small random component added at each step.') +disp('10 iterations are used at each step (compared with 100 in demhmc2).') +disp('The step size is 0.005 (compared with 0.002).') +disp('The new state is accepted if the threshold') +disp('value is greater than a random number between 0 and 1.') +disp(' ') +disp('Negative step numbers indicate samples discarded from the start of the') +disp('chain.') +disp(' ') +disp('Press any key to continue.') +pause + +% Set up vector of options for hybrid Monte Carlo. +nsamples = 100; % Number of retained samples. + +options = foptions; % Default options vector. +options(1) = 1; % Switch on diagnostics. +options(5) = 1; % Use persistence +options(7) = 10; % Number of steps in trajectory. +options(14) = nsamples; % Number of Monte Carlo samples returned. +options(15) = 300; % Number of samples omitted at start of chain. +options(17) = 0.95; % Alpha value in persistence +options(18) = 0.005; % Step size. + +w = mlppak(net); +% Initialise HMC +hmc('state', 42); +[samples, energies] = hmc('neterr', w, options, 'netgrad', net, x, t); + +clc +disp('The plot shows the underlying noise free function, the 100 samples') +disp('produced from the MLP, and their average as a Monte Carlo estimate') +disp('of the true posterior average.') +disp(' ') +disp('Press any key to continue.') +pause + +nplot = 300; +plotvals = [0 : 1/(nplot - 1) : 1]'; +pred = zeros(size(plotvals)); +fh1 = figure; +hold on +for k = 1:nsamples + w2 = samples(k,:); + net2 = mlpunpak(net, w2); + y = mlpfwd(net2, plotvals); + % Sum predictions + pred = pred + y; + h4 = plot(plotvals, y, '-r', 'LineWidth', 1); +end +pred = pred./nsamples; +% Plot data +h1 = plot(x, t, 'ob', 'LineWidth', 2, 'MarkerFaceColor', 'blue'); +axis([0 1 -3 3]) + +% Plot function +[fx, fy] = fplot('sin(2*pi*x)', [0 1], '--g'); +h2 = plot(fx, fy, '--g', 'LineWidth', 2); +set(gca, 'box', 'on'); + +% Plot averaged prediction +h3 = plot(plotvals, pred, '-c', 'LineWidth', 2); + +lstrings = char('Data', 'Function', 'Prediction', 'Samples'); +legend([h1 h2 h3 h4], lstrings, 3); +hold off + +disp('Note how the predictions become much further from the true function') +disp('away from the region of high data density.') +disp(' ') +disp('Press any key to exit.') +pause +close(fh1); +clear all; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demkmn1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,111 @@ +%DEMKMEAN Demonstrate simple clustering model trained with K-means. +% +% Description +% The problem consists of data in a two-dimensional space. The data is +% drawn from three spherical Gaussian distributions with priors 0.3, +% 0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard +% deviations 0.2, 0.5 and 1.0. The first figure contains a scatter plot +% of the data. The data is the same as in DEMGMM1. +% +% A cluster model with three components is trained using the batch K- +% means algorithm. The matrix of centres is printed after training. The +% second figure shows the data labelled with a colour derived from the +% corresponding cluster +% +% See also +% DEM2DDAT, DEMGMM1, KNN1, KMEANS +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Generate the data, fixing seeds for reproducible results +ndata = 250; +randn('state', 42); +rand('state', 42); +data = dem2ddat(ndata); + +% Randomise data order +data = data(randperm(ndata),:); + +clc +disp('This demonstration illustrates the use of a cluster model to') +disp('find centres that reflect the distribution of data points.') +disp('We begin by generating the data from a mixture of three Gaussians') +disp('in two-dimensional space and plotting it.') +disp(' ') +disp('Press any key to continue.') +pause + +fh1 = figure; +plot(data(:, 1), data(:, 2), 'o') +set(gca, 'Box', 'on') +title('Data') + +% Set up cluster model +ncentres = 3; +centres = zeros(ncentres, 2); + +% Set up vector of options for kmeans trainer +options = foptions; +options(1) = 1; % Prints out error values. +options(5) = 1; +options(14) = 10; % Number of iterations. + +clc +disp('The model is chosen to have three centres, which are initialised') +disp('at randomly selected data points. We now train the model using') +disp('the batch K-means algorithm with a maximum of 10 iterations and') +disp('stopping tolerance of 1e-4.') +disp(' ') +disp('Press any key to continue.') +pause + +% Train the centres from the data +[centres, options, post] = kmeans(centres, data, options); + +% Print out model +disp(' ') +disp('Note that training has terminated before 10 iterations as there') +disp('has been no change in the centres or error function.') +disp(' ') +disp('The trained model has centres:') +disp(centres); +disp('Press any key to continue.') +pause + +clc +disp('We now plot each data point coloured according to its classification') +disp('given by the nearest cluster centre. The cluster centres are denoted') +disp('by black crosses.') + +% Plot the result +fh2 = figure; + +hold on +colours = ['b.'; 'r.'; 'g.']; + +[tempi, tempj] = find(post); +hold on +for i = 1:3 + % Select data points closest to ith centre + thisX = data(tempi(tempj == i), 1); + thisY = data(tempi(tempj == i), 2); + hp(i) = plot(thisX, thisY, colours(i,:)); + set(hp(i), 'MarkerSize', 12); +end +set(gca, 'Box', 'on') +legend('Class 1', 'Class 2', 'Class 3', 2) +hold on +plot(centres(:, 1), centres(:,2), 'k+', 'LineWidth', 2, ... + 'MarkerSize', 8) +title('Centres and data labels') +hold off + +disp(' ') +disp('Press any key to end.') +pause + +close(fh1); +close(fh2); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demknn1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,103 @@ +%DEMKNN1 Demonstrate nearest neighbour classifier. +% +% Description +% The problem consists of data in a two-dimensional space. The data is +% drawn from three spherical Gaussian distributions with priors 0.3, +% 0.5 and 0.2; centres (2, 3.5), (0, 0) and (0,2); and standard +% deviations 0.2, 0.5 and 1.0. The first figure contains a scatter plot +% of the data. The data is the same as in DEMGMM1. +% +% The second figure shows the data labelled with the corresponding +% class given by the classifier. +% +% See also +% DEM2DDAT, DEMGMM1, KNN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +clc +disp('This program demonstrates the use of the K nearest neighbour algorithm.') +disp(' ') +disp('Press any key to continue.') +pause +% Generate the test data +ndata = 250; +randn('state', 42); +rand('state', 42); + +[data, c] = dem2ddat(ndata); + +% Randomise data order +data = data(randperm(ndata),:); + +clc +disp('We generate the data in two-dimensional space from a mixture of') +disp('three spherical Gaussians. The centres are shown as black crosses') +disp('in the plot.') +disp(' ') +disp('Press any key to continue.') +pause +fh1 = figure; +plot(data(:, 1), data(:, 2), 'o') +set(gca, 'Box', 'on') +hold on +title('Data') +hp1 = plot(c(:, 1), c(:,2), 'k+') +% Increase size of crosses +set(hp1, 'MarkerSize', 8); +set(hp1, 'LineWidth', 2); +hold off + +clc +disp('We next use the centres as training examplars for the K nearest') +disp('neighbour algorithm.') +disp(' ') +disp('Press any key to continue.') +pause + +% Use centres as training data +train_labels = [1, 0, 0; 0, 1, 0; 0, 0, 1]; + +% Label the test data up to kmax neighbours +kmax = 1; +net = knn(2, 3, kmax, c, train_labels); +[y, l] = knnfwd(net, data); + +clc +disp('We now plot each data point coloured according to its classification.') +disp(' ') +disp('Press any key to continue.') +pause +% Plot the result +fh2 = figure; +colors = ['b.'; 'r.'; 'g.']; +for i = 1:3 + thisX = data(l == i,1); + thisY = data(l == i,2); + hp(i) = plot(thisX, thisY, colors(i,:)); + set(hp(i), 'MarkerSize', 12); + if i == 1 + hold on + end +end +set(gca, 'Box', 'on'); +legend('Class 1', 'Class 2', 'Class 3', 2) +hold on +labels = ['1', '2', '3']; +hp2 = plot(c(:, 1), c(:,2), 'k+'); +% Increase size of crosses +set(hp2, 'MarkerSize', 8); +set(hp2, 'LineWidth', 2); + +test_labels = labels(l(:,1)); + +title('Training data and data labels') +hold off + +disp('The demonstration is now complete: press any key to exit.') +pause +close(fh1); +close(fh2); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demmdn1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,211 @@ +%DEMMDN1 Demonstrate fitting a multi-valued function using a Mixture Density Network. +% +% Description +% The problem consists of one input variable X and one target variable +% T with data generated by sampling T at equal intervals and then +% generating target data by computing T + 0.3*SIN(2*PI*T) and adding +% Gaussian noise. A Mixture Density Network with 3 centres in the +% mixture model is trained by minimizing a negative log likelihood +% error function using the scaled conjugate gradient optimizer. +% +% The conditional means, mixing coefficients and variances are plotted +% as a function of X, and a contour plot of the full conditional +% density is also generated. +% +% See also +% MDN, MDNERR, MDNGRAD, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate the matrix of inputs x and targets t. +seedn = 42; +seed = 42; +randn('state', seedn); +rand('state', seed); +ndata = 300; % Number of data points. +noise = 0.2; % Range of noise distribution. +t = [0:1/(ndata - 1):1]'; +x = t + 0.3*sin(2*pi*t) + noise*rand(ndata, 1) - noise/2; +axis_limits = [-0.2 1.2 -0.2 1.2]; + +clc +disp('This demonstration illustrates the use of a Mixture Density Network') +disp('to model multi-valued functions. The data is generated from the') +disp('mapping x = t + 0.3 sin(2 pi t) + e, where e is a noise term.') +disp('We begin by plotting the data.') +disp(' ') +disp('Press any key to continue') +pause +% Plot the data +fh1 = figure; +p1 = plot(x, t, 'ob'); +axis(axis_limits); +hold on +disp('Note that for x in the range 0.35 to 0.65, there are three possible') +disp('branches of the function.') +disp(' ') +disp('Press any key to continue') +pause + +% Set up network parameters. +nin = 1; % Number of inputs. +nhidden = 5; % Number of hidden units. +ncentres = 3; % Number of mixture components. +dim_target = 1; % Dimension of target space +mdntype = '0'; % Currently unused: reserved for future use +alpha = 100; % Inverse variance for weight initialisation + % Make variance small for good starting point + +% Create and initialize network weight vector. +net = mdn(nin, nhidden, ncentres, dim_target, mdntype); +init_options = zeros(1, 18); +init_options(1) = -1; % Suppress all messages +init_options(14) = 10; % 10 iterations of K means in gmminit +net = mdninit(net, alpha, t, init_options); + +% Set up vector of options for the optimiser. +options = foptions; +options(1) = 1; % This provides display of error values. +options(14) = 200; % Number of training cycles. + +clc +disp('We initialise the neural network model, which is an MLP with a') +disp('Gaussian mixture model with three components and spherical variance') +disp('as the error function. This enables us to model the complete') +disp('conditional density function.') +disp(' ') +disp('Next we train the model for 200 epochs using a scaled conjugate gradient') +disp('optimizer. The error function is the negative log likelihood of the') +disp('training data.') +disp(' ') +disp('Press any key to continue.') +pause + +% Train using scaled conjugate gradients. +[net, options] = netopt(net, options, x, t, 'scg'); + +disp(' ') +disp('Press any key to continue.') +pause + +clc +disp('We can also train a conventional MLP with sum of squares error function.') +disp('This will approximate the conditional mean, which is not always a') +disp('good representation of the data. Note that the error function is the') +disp('sum of squares error on the training data, which accounts for the') +disp('different values from training the MDN.') +disp(' ') +disp('We train the network with the quasi-Newton optimizer for 80 epochs.') +disp(' ') +disp('Press any key to continue.') +pause +mlp_nhidden = 8; +net2 = mlp(nin, mlp_nhidden, dim_target, 'linear'); +options(14) = 80; +[net2, options] = netopt(net2, options, x, t, 'quasinew'); +disp(' ') +disp('Press any key to continue.') +pause + +clc +disp('Now we plot the underlying function, the MDN prediction,') +disp('represented by the mode of the conditional distribution, and the') +disp('prediction of the conventional MLP.') +disp(' ') +disp('Press any key to continue.') +pause + +% Plot the original function, and the trained network function. +plotvals = [0:0.01:1]'; +mixes = mdn2gmm(mdnfwd(net, plotvals)); +axis(axis_limits); +yplot = t+0.3*sin(2*pi*t); +p2 = plot(yplot, t, '--y'); + +% Use the mode to represent the function +y = zeros(1, length(plotvals)); +priors = zeros(length(plotvals), ncentres); +c = zeros(length(plotvals), 3); +widths = zeros(length(plotvals), ncentres); +for i = 1:length(plotvals) + [m, j] = max(mixes(i).priors); + y(i) = mixes(i).centres(j,:); + c(i,:) = mixes(i).centres'; +end +p3 = plot(plotvals, y, '*r'); +p4 = plot(plotvals, mlpfwd(net2, plotvals), 'g'); +set(p4, 'LineWidth', 2); +legend([p1 p2 p3 p4], 'data', 'function', 'MDN mode', 'MLP mean', 4); +hold off + +clc +disp('We can also plot how the mixture model parameters depend on x.') +disp('First we plot the mixture centres, then the priors and finally') +disp('the variances.') +disp(' ') +disp('Press any key to continue.') +pause +fh2 = figure; +subplot(3, 1, 1) +plot(plotvals, c) +hold on +title('Mixture centres') +legend('centre 1', 'centre 2', 'centre 3') +hold off + +priors = reshape([mixes.priors], mixes(1).ncentres, size(mixes, 2))'; +%%fh3 = figure; +subplot(3, 1, 2) +plot(plotvals, priors) +hold on +title('Mixture priors') +legend('centre 1', 'centre 2', 'centre 3') +hold off + +variances = reshape([mixes.covars], mixes(1).ncentres, size(mixes, 2))'; +%%fh4 = figure; +subplot(3, 1, 3) +plot(plotvals, variances) +hold on +title('Mixture variances') +legend('centre 1', 'centre 2', 'centre 3') +hold off + +disp('The last figure is a contour plot of the conditional probability') +disp('density generated by the Mixture Density Network. Note how it') +disp('is well matched to the regions of high data density.') +disp(' ') +disp('Press any key to continue.') +pause +% Contour plot for MDN. +i = 0:0.01:1.0; +j = 0:0.01:1.0; + +[I, J] = meshgrid(i,j); +I = I(:); +J = J(:); +li = length(i); +lj = length(j); +Z = zeros(li, lj); +for k = 1:li; + Z(:,k) = gmmprob(mixes(k), j'); +end +fh5 = figure; +% Set up levels by hand to make a good figure +v = [2 2.5 3 3.5 5:3:18]; +contour(i, j, Z, v) +hold on +title('Contour plot of conditional density') +hold off + +disp(' ') +disp('Press any key to exit.') +pause +close(fh1); +close(fh2); +%%close(fh3); +%%close(fh4); +close(fh5); +%%clear all;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demmet1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,103 @@ +function samples=demmet1(plot_wait) +%DEMMET1 Demonstrate Markov Chain Monte Carlo sampling on a Gaussian. +% +% Description +% The problem consists of generating data from a Gaussian in two +% dimensions using a Markov Chain Monte Carlo algorithm. The points are +% plotted one after another to show the path taken by the chain. +% +% DEMMET1(PLOTWAIT) allows the user to set the time (in a whole number +% of seconds) between the plotting of points. This is passed to PAUSE +% +% See also +% DEMHMC1, METROP, GMM, DEMPOT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin == 0 | plot_wait < 0 + plot_wait = 0; % No wait if not specified or incorrect +end +dim = 2; % Data dimension +ncentres = 1; % Number of centres in mixture model + +seed = 42; % Seed for random weight initialization. +randn('state', seed); +rand('state', seed); + +clc +disp('This demonstration illustrates the use of the Markov chain Monte Carlo') +disp('algorithm to sample from a Gaussian distribution.') +disp('The mean is at [0 0].') +disp(' ') +disp('First we set up the parameters of the mixture model we are sampling') +disp('from.') +disp(' ') +disp('Press any key to continue.') +pause + +% Set up mixture model to sample from +mix = gmm(dim, ncentres, 'spherical'); +mix.centres(1, :) = [0 0]; +x = [0 4]; % Start vector + +% Set up vector of options for hybrid Monte Carlo. + +nsamples = 150; % Number of retained samples. + +options = foptions; % Default options vector. +options(1) = 0; % Switch off diagnostics. +options(14) = nsamples; % Number of Monte Carlo samples returned. +options(18) = 0.1; + +clc +disp('Next we take 150 samples from the distribution.') +disp('Sampling starts at the point [0 4].') +disp('The new state is accepted if the threshold value is greater than') +disp('a random number between 0 and 1.') +disp(' ') +disp('Press any key to continue.') +pause + +[samples, energies] = metrop('dempot', x, options, '', mix); + +clc +disp('The plot shows the samples generated by the MCMC function in order') +disp('as an animation to show the path taken by the Markov chain.') +disp('The different colours are used to show that the first few samples') +disp('should be discarded as they lie too far from the mean.') +disp(' ') +disp('Press any key to continue.') +pause +probs = exp(-energies); +fh1 = figure; +g1end = floor(nsamples/4); + +for n = 1:nsamples + + if n < g1end + Marker = 'k.'; + p1 = plot(samples(n,1), samples(n,2), Marker, ... + 'EraseMode', 'none', 'MarkerSize', 12); + if n == 1 + axis([-3 5 -2 5]) + end + else + Marker = 'r.'; + p2 = plot(samples(n,1), samples(n,2), Marker, ... + 'EraseMode', 'none', 'MarkerSize', 12); + end + hold on + drawnow; % Force drawing immediately + pause(plot_wait); +end +lstrings = char(['Samples 1-' int2str(g1end)], ... + ['Samples ' int2str(g1end+1) '-' int2str(nsamples)]); +legend([p1 p2], lstrings, 1); + +disp(' ') +disp('Press any key to exit.') +pause +close(fh1); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demmlp1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +%DEMMLP1 Demonstrate simple regression using a multi-layer perceptron +% +% Description +% The problem consists of one input variable X and one target variable +% T with data generated by sampling X at equal intervals and then +% generating target data by computing SIN(2*PI*X) and adding Gaussian +% noise. A 2-layer network with linear outputs is trained by minimizing +% a sum-of-squares error function using the scaled conjugate gradient +% optimizer. +% +% See also +% MLP, MLPERR, MLPGRAD, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate the matrix of inputs x and targets t. + +ndata = 20; % Number of data points. +noise = 0.2; % Standard deviation of noise distribution. +x = [0:1/(ndata - 1):1]'; +randn('state', 1); +t = sin(2*pi*x) + noise*randn(ndata, 1); + +clc +disp('This demonstration illustrates the use of a Multi-Layer Perceptron') +disp('network for regression problems. The data is generated from a noisy') +disp('sine function.') +disp(' ') +disp('Press any key to continue.') +pause + +% Set up network parameters. +nin = 1; % Number of inputs. +nhidden = 3; % Number of hidden units. +nout = 1; % Number of outputs. +alpha = 0.01; % Coefficient of weight-decay prior. + +% Create and initialize network weight vector. + +net = mlp(nin, nhidden, nout, 'linear', alpha); + +% Set up vector of options for the optimiser. + +options = zeros(1,18); +options(1) = 1; % This provides display of error values. +options(14) = 100; % Number of training cycles. + +clc +disp(['The network has ', num2str(nhidden), ' hidden units and a weight decay']) +disp(['coefficient of ', num2str(alpha), '.']) +disp(' ') +disp('After initializing the network, we train it use the scaled conjugate') +disp('gradients algorithm for 100 cycles.') +disp(' ') +disp('Press any key to continue') +pause + +% Train using scaled conjugate gradients. +[net, options] = netopt(net, options, x, t, 'scg'); + +disp(' ') +disp('Now we plot the data, underlying function, and network outputs') +disp('on a single graph to compare the results.') +disp(' ') +disp('Press any key to continue.') +pause + +% Plot the data, the original function, and the trained network function. +plotvals = [0:0.01:1]'; +y = mlpfwd(net, plotvals); +fh1 = figure; +plot(x, t, 'ob') +hold on +xlabel('Input') +ylabel('Target') +axis([0 1 -1.5 1.5]) +[fx, fy] = fplot('sin(2*pi*x)', [0 1]); +plot(fx, fy, '-r', 'LineWidth', 2) +plot(plotvals, y, '-k', 'LineWidth', 2) +legend('data', 'function', 'network'); + +disp(' ') +disp('Press any key to end.') +pause +close(fh1); +clear all;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demmlp2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,337 @@ +%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron +% +% Description +% The problem consists of input data in two dimensions drawn from a +% mixture of three Gaussians: two of which are assigned to a single +% class. An MLP with logistic outputs trained with a quasi-Newton +% optimisation algorithm is compared with the optimal Bayesian decision +% rule. +% +% See also +% MLP, MLPFWD, NETERR, QUASINEW +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Set up some figure parameters +AxisShift = 0.05; +ClassSymbol1 = 'r.'; +ClassSymbol2 = 'y.'; +PointSize = 12; +titleSize = 10; + +% Fix the seeds +rand('state', 423); +randn('state', 423); + +clc +disp('This demonstration shows how an MLP with logistic outputs and') +disp('and cross entropy error function can be trained to model the') +disp('posterior class probabilities in a classification problem.') +disp('The results are compared with the optimal Bayes rule classifier,') +disp('which can be computed exactly as we know the form of the generating') +disp('distribution.') +disp(' ') +disp('Press any key to continue.') +pause + +fh1 = figure; +set(fh1, 'Name', 'True Data Distribution'); +whitebg(fh1, 'k'); + +% +% Generate the data +% +n=200; + +% Set up mixture model: 2d data with three centres +% Class 1 is first centre, class 2 from the other two +mix = gmm(2, 3, 'full'); +mix.priors = [0.5 0.25 0.25]; +mix.centres = [0 -0.1; 1 1; 1 -1]; +mix.covars(:,:,1) = [0.625 -0.2165; -0.2165 0.875]; +mix.covars(:,:,2) = [0.2241 -0.1368; -0.1368 0.9759]; +mix.covars(:,:,3) = [0.2375 0.1516; 0.1516 0.4125]; + +[data, label] = gmmsamp(mix, n); + +% +% Calculate some useful axis limits +% +x0 = min(data(:,1)); +x1 = max(data(:,1)); +y0 = min(data(:,2)); +y1 = max(data(:,2)); +dx = x1-x0; +dy = y1-y0; +expand = 5/100; % Add on 5 percent each way +x0 = x0 - dx*expand; +x1 = x1 + dx*expand; +y0 = y0 - dy*expand; +y1 = y1 + dy*expand; +resolution = 100; +step = dx/resolution; +xrange = [x0:step:x1]; +yrange = [y0:step:y1]; +% +% Generate the grid +% +[X Y]=meshgrid([x0:step:x1],[y0:step:y1]); +% +% Calculate the class conditional densities, the unconditional densities and +% the posterior probabilities +% +px_j = gmmactiv(mix, [X(:) Y(:)]); +px = reshape(px_j*(mix.priors)',size(X)); +post = gmmpost(mix, [X(:) Y(:)]); +p1_x = reshape(post(:, 1), size(X)); +p2_x = reshape(post(:, 2) + post(:, 3), size(X)); + +% +% Generate some pretty pictures !! +% +colormap(hot) +colorbar +subplot(1,2,1) +hold on +plot(data((label==1),1),data(label==1,2),ClassSymbol1, 'MarkerSize', PointSize) +plot(data((label>1),1),data(label>1,2),ClassSymbol2, 'MarkerSize', PointSize) +contour(xrange,yrange,p1_x,[0.5 0.5],'w-'); +axis([x0 x1 y0 y1]) +set(gca,'Box','On') +title('The Sampled Data'); +rect=get(gca,'Position'); +rect(1)=rect(1)-AxisShift; +rect(3)=rect(3)+AxisShift; +set(gca,'Position',rect) +hold off + +subplot(1,2,2) +imagesc(X(:),Y(:),px); +hold on +[cB, hB] = contour(xrange,yrange,p1_x,[0.5 0.5],'w:'); +set(hB,'LineWidth', 2); +axis([x0 x1 y0 y1]) +set(gca,'YDir','normal') +title('Probability Density p(x)') +hold off + +drawnow; +clc; +disp('The first figure shows the data sampled from a mixture of three') +disp('Gaussians, the first of which (whose centre is near the origin) is') +disp('labelled red and the other two are labelled yellow. The second plot') +disp('shows the unconditional density of the data with the optimal Bayesian') +disp('decision boundary superimposed.') +disp(' ') +disp('Press any key to continue.') +pause +fh2 = figure; +set(fh2, 'Name', 'Class-conditional Densities and Posterior Probabilities'); +whitebg(fh2, 'w'); + +subplot(2,2,1) +p1=reshape(px_j(:,1),size(X)); +imagesc(X(:),Y(:),p1); +colormap hot +colorbar +axis(axis) +set(gca,'YDir','normal') +hold on +plot(mix.centres(:,1),mix.centres(:,2),'b+','MarkerSize',8,'LineWidth',2) +title('Density p(x|red)') +hold off + +subplot(2,2,2) +p2=reshape((px_j(:,2)+px_j(:,3)),size(X)); +imagesc(X(:),Y(:),p2); +colorbar +set(gca,'YDir','normal') +hold on +plot(mix.centres(:,1),mix.centres(:,2),'b+','MarkerSize',8,'LineWidth',2) +title('Density p(x|yellow)') +hold off + +subplot(2,2,3) +imagesc(X(:),Y(:),p1_x); +set(gca,'YDir','normal') +colorbar +title('Posterior Probability p(red|x)') +hold on +plot(mix.centres(:,1),mix.centres(:,2),'b+','MarkerSize',8,'LineWidth',2) +hold off + +subplot(2,2,4) +imagesc(X(:),Y(:),p2_x); +set(gca,'YDir','normal') +colorbar +title('Posterior Probability p(yellow|x)') +hold on +plot(mix.centres(:,1),mix.centres(:,2),'b+','MarkerSize',8,'LineWidth',2) +hold off + +% Now set up and train the MLP +nhidden=6; +nout=1; +alpha = 0.2; % Weight decay +ncycles = 60; % Number of training cycles. +% Set up MLP network +net = mlp(2, nhidden, nout, 'logistic', alpha); +options = zeros(1,18); +options(1) = 1; % Print out error values +options(14) = ncycles; + +mlpstring = ['We now set up an MLP with ', num2str(nhidden), ... + ' hidden units, logistic output and cross']; +trainstring = ['entropy error function, and train it for ', ... + num2str(ncycles), ' cycles using the']; +wdstring = ['quasi-Newton optimisation algorithm with weight decay of ', ... + num2str(alpha), '.']; + +% Force out the figure before training the MLP +drawnow; +disp(' ') +disp('The second figure shows the class conditional densities and posterior') +disp('probabilities for each class. The blue crosses mark the centres of') +disp('the three Gaussians.') +disp(' ') +disp(mlpstring) +disp(trainstring) +disp(wdstring) +disp(' ') +disp('Press any key to continue.') +pause + +% Convert targets to 0-1 encoding +target=[label==1]; + +% Train using quasi-Newton. +[net] = netopt(net, options, data, target, 'quasinew'); +y = mlpfwd(net, data); +yg = mlpfwd(net, [X(:) Y(:)]); +yg = reshape(yg(:,1),size(X)); + +fh3 = figure; +set(fh3, 'Name', 'Network Output'); +whitebg(fh3, 'k') +subplot(1, 2, 1) +hold on +plot(data((label==1),1),data(label==1,2),'r.', 'MarkerSize', PointSize) +plot(data((label>1),1),data(label>1,2),'y.', 'MarkerSize', PointSize) +% Bayesian decision boundary +[cB, hB] = contour(xrange,yrange,p1_x,[0.5 0.5],'b-'); +[cN, hN] = contour(xrange,yrange,yg,[0.5 0.5],'r-'); +set(hB, 'LineWidth', 2); +set(hN, 'LineWidth', 2); +Chandles = [hB(1) hN(1)]; +legend(Chandles, 'Bayes', ... + 'Network', 3); + +axis([x0 x1 y0 y1]) +set(gca,'Box','on','XTick',[],'YTick',[]) + +title('Training Data','FontSize',titleSize); +hold off + +subplot(1, 2, 2) +imagesc(X(:),Y(:),yg); +colormap hot +colorbar +axis(axis) +set(gca,'YDir','normal','XTick',[],'YTick',[]) +title('Network Output','FontSize',titleSize) + +clc +disp('This figure shows the training data with the decision boundary') +disp('produced by the trained network and the network''s prediction of') +disp('the posterior probability of the red class.') +disp(' ') +disp('Press any key to continue.') +pause + +% +% Now generate and classify a test data set +% +[testdata testlabel] = gmmsamp(mix, n); +testlab=[testlabel==1 testlabel>1]; + +% This is the Bayesian classification +tpx_j = gmmpost(mix, testdata); +Bpost = [tpx_j(:,1), tpx_j(:,2)+tpx_j(:,3)]; +[Bcon Brate]=confmat(Bpost, [testlabel==1 testlabel>1]); + +% Compute network classification +yt = mlpfwd(net, testdata); +% Convert single output to posteriors for both classes +testpost = [yt 1-yt]; +[C trate]=confmat(testpost,[testlabel==1 testlabel>1]); + +fh4 = figure; +set(fh4, 'Name', 'Decision Boundaries'); +whitebg(fh4, 'k'); +hold on +plot(testdata((testlabel==1),1),testdata((testlabel==1),2),... + ClassSymbol1, 'MarkerSize', PointSize) +plot(testdata((testlabel>1),1),testdata((testlabel>1),2),... + ClassSymbol2, 'MarkerSize', PointSize) +% Bayesian decision boundary +[cB, hB] = contour(xrange,yrange,p1_x,[0.5 0.5],'b-'); +set(hB, 'LineWidth', 2); +% Network decision boundary +[cN, hN] = contour(xrange,yrange,yg,[0.5 0.5],'r-'); +set(hN, 'LineWidth', 2); +Chandles = [hB(1) hN(1)]; +legend(Chandles, 'Bayes decision boundary', ... + 'Network decision boundary', -1); +axis([x0 x1 y0 y1]) +title('Test Data') +set(gca,'Box','On','Xtick',[],'YTick',[]) + +clc +disp('This figure shows the test data with the decision boundary') +disp('produced by the trained network and the optimal Bayes rule.') +disp(' ') +disp('Press any key to continue.') +pause + +fh5 = figure; +set(fh5, 'Name', 'Test Set Performance'); +whitebg(fh5, 'w'); +% Bayes rule performance +subplot(1,2,1) +plotmat(Bcon,'b','k',12) +set(gca,'XTick',[0.5 1.5]) +set(gca,'YTick',[0.5 1.5]) +grid('off') +set(gca,'XTickLabel',['Red ' ; 'Yellow']) +set(gca,'YTickLabel',['Yellow' ; 'Red ']) +ylabel('True') +xlabel('Predicted') +title(['Bayes Confusion Matrix (' num2str(Brate(1)) '%)']) + +% Network performance +subplot(1,2, 2) +plotmat(C,'b','k',12) +set(gca,'XTick',[0.5 1.5]) +set(gca,'YTick',[0.5 1.5]) +grid('off') +set(gca,'XTickLabel',['Red ' ; 'Yellow']) +set(gca,'YTickLabel',['Yellow' ; 'Red ']) +ylabel('True') +xlabel('Predicted') +title(['Network Confusion Matrix (' num2str(trate(1)) '%)']) + +disp('The final figure shows the confusion matrices for the') +disp('two rules on the test set.') +disp(' ') +disp('Press any key to exit.') +pause +whitebg(fh1, 'w'); +whitebg(fh2, 'w'); +whitebg(fh3, 'w'); +whitebg(fh4, 'w'); +whitebg(fh5, 'w'); +close(fh1); close(fh2); close(fh3); +close(fh4); close(fh5); +clear all; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demnlab.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,380 @@ +function demnlab(action); +%DEMNLAB A front-end Graphical User Interface to the demos +% +% Description +% This function will start a user interface allowing the user to select +% different demonstration functions to view. The demos are divided into +% 4 groups, with the demo being executed by selecting the desired +% option from a pop-up menu. +% +% See also +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% If run without parameters, initialise gui. +if nargin<1, + action='initialise'; +end; + +if strcmp(action,'initialise'), + + % Create figure + fig = figure( ... + 'Name', 'Netlab Demos', ... + 'NumberTitle', 'off', ... + 'Color', [0.7529 0.7529 0.7529], ... + 'Visible', 'on'); + + % Create GROUPS + % Bottom of demo buttons + group1_bot = 0.20; + group1_top = 0.75; + uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'Position', [0.03 group1_bot 0.94 group1_top - group1_bot], ... + 'BackgroundColor', [0.5 0.5 0.5]); + + % Bottom of help and close buttons + group2_bot = 0.04; + uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'Position', [0.03 group2_bot 0.94 0.12], ... + 'BackgroundColor', [0.5 0.5 0.5]); + + % Draw title + hLogoAxis = axes( ... + 'Units', 'normalized', ... + 'Position', [0.05 0.82 0.90 0.14], ... + 'Box', 'off', ... + 'XColor', [0 0 0], ... + 'YColor', [0 0 0], ... + 'Visible', 'on'); + + load netlogo; % load image and colour map + colormap(netcmap(1:3,:)); % change colour map: don't need many entries + image(nlogo); % draw logo + axis('image'); % ensures pixels on axis are square + axis off; % turn axes off + + % Create static text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Position', [0.05 group1_top-0.1 0.90 0.08], ... + 'String', 'Select demo to run:'); + + % First row text offset + tRow1Offset = 0.14; + % Offset between text and button + TBoffset = 0.07; + % First row button offset + bRow1Offset = tRow1Offset+TBoffset; + % ONE text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Position', [0.08 group1_top-tRow1Offset 0.36 0.05], ... + 'String', 'Regression'); + + popup1str(1) = {'Select Option'}; + popup1str(2) = {'Multi-Layer Perceptron'}; + popup1str(3) = {'Radial Basis Function'}; + popup1str(4) = {'Mixture Density Network'}; + % ONE popup + hPop1 = uicontrol(fig, ... + 'Style','popup', ... + 'Units','normalized', ... + 'String', popup1str, ... + 'Position', [0.08 group1_top-bRow1Offset 0.36 0.08], ... + 'BackgroundColor', [0.7 0.7 0.7], ... + 'Callback', 'demnlab popup1'); + + % TWO text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Position', [0.56 group1_top-tRow1Offset 0.36 0.05], ... + 'String', 'Classification'); + + popup2str(1) = popup1str(1); + popup2str(2) = {'Generalised Linear Model (2 class)'}; + popup2str(3) = {'Generalised Linear Model (3 class)'}; + popup2str(4) = {'Multi-Layer Perceptron'}; + popup2str(5) = {'K nearest neighbour'}; + % TWO popup + hPop2 = uicontrol(fig, ... + 'Style','popup', ... + 'Units','normalized', ... + 'String', popup2str, ... + 'Position', [0.56 group1_top-bRow1Offset 0.36 0.08], ... + 'BackgroundColor', [0.7 0.7 0.7], ... + 'Callback', 'demnlab popup2'); + + tRow2Offset = 0.30; + bRow2Offset = tRow2Offset+TBoffset; + % THREE text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Position', [0.08 group1_top - tRow2Offset 0.36 0.05], ... + 'String', 'Density Modelling and Clustering'); + + popup3str(1) = popup1str(1); + popup3str(2) = {'Gaussian Mixture (EM training)'}; + popup3str(3) = {'Gaussian Mixture (spherical)'}; + popup3str(4) = {'Gaussian Mixture (diagonal)'}; + popup3str(5) = {'Gaussian Mixture (full)'}; + popup3str(6) = {'Neuroscale'}; + popup3str(7) = {'GTM (EM training)'}; + popup3str(8) = {'GTM (visualisation)'}; + popup3str(9) = {'K-means clustering'}; + popup3str(10) = {'Self-Organising Map'}; + % TWO popup + % THREE popup + hPop3 = uicontrol(fig, ... + 'Style','popup', ... + 'Units','normalized', ... + 'String', popup3str, ... + 'Position', [0.08 group1_top - bRow2Offset 0.36 0.08], ... + 'BackgroundColor', [0.7 0.7 0.7], ... + 'Callback', 'demnlab popup3'); + + % FOUR text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Position', [0.56 group1_top - tRow2Offset 0.36 0.05], ... + 'String', 'Bayesian Methods'); + + popup4str(1) = popup1str(1); + popup4str(2) = {'Sampling the MLP Prior'}; + popup4str(3) = {'Evidence Approximation for MLP'}; + popup4str(4) = {'Evidence Approximation for RBF'}; + popup4str(5) = {'Evidence Approximation in Classification'}; + popup4str(6) = {'ARD for MLP'}; + popup4str(7) = {'Sampling the GP Prior'}; + popup4str(8) = {'GPs for Regression'}; + popup4str(9) = {'ARD for GP'}; + % FOUR popup + hPop4 = uicontrol(fig, ... + 'Style','popup', ... + 'Units','normalized', ... + 'String', popup4str, ... + 'Position', [0.56 group1_top - bRow2Offset 0.36 0.08], ... + 'BackgroundColor', [0.7 0.7 0.7], ... + 'Callback', 'demnlab popup4'); + + + tRow3Offset = 0.45; + bRow3Offset = tRow3Offset+TBoffset; + % FIVE text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Position', [0.08 group1_top - tRow3Offset 0.36 0.05], ... + 'String', 'Optimisation and Visualisation'); + + popup5str(1) = popup1str(1); + popup5str(2) = {'Algorithm Comparison'}; + popup5str(3) = {'On-line Gradient Descent'}; + popup5str(4) = {'Hinton Diagrams'}; + % FIVE popup + hPop5 = uicontrol(fig, ... + 'Style','popup', ... + 'Units','normalized', ... + 'String',popup5str, ... + 'Position', [0.08 group1_top - bRow3Offset 0.36 0.08], ... + 'BackgroundColor', [0.7 0.7 0.7], ... + 'Callback', 'demnlab popup5'); + + % SIX text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Position', [0.56 group1_top - tRow3Offset 0.36 0.05], ... + 'String', 'Sampling'); + + popup6str(1) = popup1str(1); + popup6str(2) = {'Sampling a Gaussian'}; + popup6str(3) = {'MCMC sampling (Metropolis)'}; + popup6str(4) = {'Hybrid MC (Gaussian mixture)'}; + popup6str(5) = {'Hybrid MC for MLP I'}; + popup6str(6) = {'Hybrid MC for MLP II'}; + % SIX popup + hPop6 = uicontrol(fig, ... + 'Style','popup', ... + 'Units','normalized', ... + 'String', popup6str, ... + 'Position', [0.56 group1_top - bRow3Offset 0.36 0.08], ... + 'BackgroundColor', [0.7 0.7 0.7], ... + 'Callback', 'demnlab popup6'); + + + % Create HELP button + uicontrol(fig, ... + 'Units', 'normalized', ... + 'Position' , [0.05 group2_bot+0.02 0.40 0.08], ... + 'String', 'Help', ... + 'Callback', 'demnlab help'); + + % Create CLOSE button + uicontrol(fig, ... + 'Units', 'normalized', ... + 'Position' , [0.55 group2_bot+0.02 0.40 0.08], ... + 'String', 'Close', ... + 'Callback', 'close(gcf)'); + + hndlList=[fig hPop1 hPop2 hPop3 hPop4 hPop5 hPop6]; + set(fig, 'UserData', hndlList); + set(fig, 'HandleVisibility', 'callback'); + +elseif strcmp(action, 'popup1'), + + hndlList=get(gcf,'UserData'); + hPop = hndlList(2); + + selected = get(hPop, 'Val'); + set(hPop, 'Val', [1]); + + switch selected + case 2 + demmlp1; + case 3 + demrbf1; + case 4 + demmdn1; + end; + +elseif strcmp(action,'popup2'), + + hndlList=get(gcf,'UserData'); + hPop = hndlList(3); + + selected = get(hPop, 'Val'); + set(hPop, 'Val', [1]); + + switch selected + case 2 + demglm1; + case 3 + demglm2; + case 4 + demmlp2; + case 5 + demknn1; + end + +elseif strcmp(action,'popup3'), + + hndlList=get(gcf,'UserData'); + hPop = hndlList(4); + + selected = get(hPop, 'Val'); + set(hPop, 'Val', [1]); + + switch selected + case 2 + demgmm1; + case 3 + demgmm2; + case 4 + demgmm3; + case 5 + demgmm4; + case 6 + demns1; + case 7 + demgtm1; + case 8 + demgtm2; + case 9 + demkmn1; + case 10 + demsom1; + end + +elseif strcmp(action,'popup4'), + + hndlList=get(gcf,'UserData'); + hPop = hndlList(5); + + selected = get(hPop, 'Val'); + set(hPop, 'Val', [1]); + + switch selected + case 2 + demprior; + case 3 + demev1; + case 4 + demev3; + case 5 + demev2; + case 6 + demard; + case 7 + demprgp; + case 8 + demgp; + case 9 + demgpard; + end + +elseif strcmp(action,'popup5'), + + hndlList=get(gcf,'UserData'); + hPop = hndlList(6); + + selected = get(hPop, 'Val'); + set(hPop, 'Val', [1]); + + switch selected + case 2 + demopt1; + case 3 + demolgd1; + case 4 + demhint; + end + + +elseif strcmp(action,'popup6'), + + hndlList=get(gcf,'UserData'); + hPop = hndlList(7); + + selected = get(hPop, 'Val'); + set(hPop, 'Val', [1]); + + switch selected + case 2 + demgauss; + case 3 + demmet1; + case 4 + demhmc1; + case 5 + demhmc2; + case 6 + demhmc3; + end + +elseif strcmp(action, 'help'), + + helpStr = {'To run a demo, press the appropriate button.'; ... + 'Instructions and information will appear in the Matlab';... + 'command window.'}; + + hHelpDlg = helpdlg(helpStr, 'Netlab Demo Help'); + +end; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demns1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +%DEMNS1 Demonstrate Neuroscale for visualisation. +% +% Description +% This script demonstrates the use of the Neuroscale algorithm for +% topographic projection and visualisation. A data sample is generated +% from a mixture of two Gaussians in 4d space, and an RBF is trained +% with the stress error function to project the data into 2d. The +% training data and a test sample are both plotted in this projection. +% +% See also +% RBF, RBFTRAIN, RBFPRIOR +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Generate the data +% Fix seeds for reproducible results +rand('state', 420); +randn('state', 420); + +input_dim = 4; +output_dim = 2; +mix = gmm(input_dim, 2, 'spherical'); +mix.centres = [1 1 1 1; 0 0 0 0]; +mix.priors = [0.5 0.5]; +mix.covars = [0.1 0.1]; + +ndata = 60; +[data, labels] = gmmsamp(mix, ndata); + +clc +disp('This demonstration illustrates the use of the Neuroscale model') +disp('to perform a topographic projection of data. We begin by generating') +disp('60 data points from a mixture of two Gaussians in 4 dimensional space.') +disp(' ') +disp('Press any key to continue') +pause + +ncentres = 10; +net = rbf(input_dim, ncentres, output_dim, 'tps', 'neuroscale'); +dstring = ['the Sammon mapping. The model has ', num2str(ncentres), ... + ' centres, two outputs, and uses']; +clc +disp('The Neuroscale model is an RBF with a Stress error measure as used in') +disp(dstring) +disp('thin plate spline basis functions.') +disp(' ') +disp('It is trained using the shadow targets algorithm for at most 60 iterations.') +disp(' ') +disp('Press any key to continue') +pause + +% First row controls shadow targets, second row controls rbfsetbf +options(1, :) = foptions; +options(2, :) = foptions; +options(1, 1) = 1; +options(1, 2) = 1e-2; +options(1, 3) = 1e-2; +options(1, 6) = 1; % Switch on PCA initialisation +options(1, 14) = 60; +options(2, 1) = -1; % Switch off all warnings +options(2, 5) = 1; +options(2, 14) = 10; +net2 = rbftrain(net, options, data); + +disp(' ') +disp('After training the model, we project the training data by a normal') +disp('forward propagation through the RBF network. Because there are two') +disp('outputs, the results can be plotted and visualised.') +disp(' ') +disp('Press any key to continue') +pause + +% Plot the result +y = rbffwd(net2, data); +ClassSymbol1 = 'r.'; +ClassSymbol2 = 'b.'; +PointSize = 12; +fh1 = figure; +hold on; +plot(y((labels==1),1),y(labels==1,2),ClassSymbol1, 'MarkerSize', PointSize) +plot(y((labels>1),1),y(labels>1,2),ClassSymbol2, 'MarkerSize', PointSize) + +disp(' ') +disp('In this plot, the red dots denote the first class and the blue') +disp('dots the second class.') +disp(' ') +disp('Press any key to continue.') +disp(' ') +pause + +disp('We now generate a further 100 data points from the original distribution') +disp('and plot their projection using star symbols. Note that a Sammon') +disp('mapping cannot be used to generalise to new data in this fashion.') + +[test_data, test_labels] = gmmsamp(mix, 100); +ytest = rbffwd(net2, test_data); +ClassSymbol1 = 'ro'; +ClassSymbol2 = 'bo'; +% Circles are rather large symbols +PointSize = 6; +hold on +plot(ytest((test_labels==1),1),ytest(test_labels==1,2), ... + ClassSymbol1, 'MarkerSize', PointSize) +plot(ytest((test_labels>1),1),ytest(test_labels>1,2),... + ClassSymbol2, 'MarkerSize', PointSize) +hold on +legend('Class 1', 'Class 2', 'Test Class 1', 'Test Class 2') +disp('Press any key to exit.') +pause + +close(fh1); +clear all; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demolgd1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,115 @@ +%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent +% +% Description +% The problem consists of one input variable X and one target variable +% T with data generated by sampling X at equal intervals and then +% generating target data by computing SIN(2*PI*X) and adding Gaussian +% noise. A 2-layer network with linear outputs is trained by minimizing +% a sum-of-squares error function using on-line gradient descent. +% +% See also +% DEMMLP1, OLGD +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate the matrix of inputs x and targets t. + +ndata = 20; % Number of data points. +noise = 0.2; % Standard deviation of noise distribution. +x = [0:1/(ndata - 1):1]'; +randn('state', 42); +rand('state', 42); +t = sin(2*pi*x) + noise*randn(ndata, 1); + +clc +disp('This demonstration illustrates the use of the on-line gradient') +disp('descent algorithm to train a Multi-Layer Perceptron network for') +disp('regression problems. It is intended to illustrate the drawbacks') +disp('of this algorithm compared to more powerful non-linear optimisation') +disp('algorithms, such as conjugate gradients.') +disp(' ') +disp('First we generate the data from a noisy sine function and construct') +disp('the network.') +disp(' ') +disp('Press any key to continue.') +pause +% Set up network parameters. +nin = 1; % Number of inputs. +nhidden = 3; % Number of hidden units. +nout = 1; % Number of outputs. +alpha = 0.01; % Coefficient of weight-decay prior. + +% Create and initialize network weight vector. +net = mlp(nin, nhidden, nout, 'linear'); +% Initialise weights reasonably close to 0 +net = mlpinit(net, 10); + +% Set up vector of options for the optimiser. +options = foptions; +options(1) = 1; % This provides display of error values. +options(14) = 20; % Number of training cycles. +options(18) = 0.1; % Learning rate +%options(17) = 0.4; % Momentum +options(17) = 0.4; % Momentum +options(5) = 1; % Do randomise pattern order +clc +disp('Then we set the options for the training algorithm.') +disp(['In the first phase of training, which lasts for ',... + num2str(options(14)), ' cycles,']) +disp(['the learning rate is ', num2str(options(18)), ... + ' and the momentum is ', num2str(options(17)), '.']) +disp('The error values are displayed at the end of each pass through the') +disp('entire pattern set.') +disp(' ') +disp('Press any key to continue.') +pause + +% Train using online gradient descent +[net, options] = olgd(net, options, x, t); + +% Now allow learning rate to decay and remove momentum +options(2) = 0; +options(3) = 0; +options(17) = 0.4; % Turn off momentum +options(5) = 1; % Randomise pattern order +options(6) = 1; % Set learning rate decay on +options(14) = 200; +options(18) = 0.1; % Initial learning rate + +disp(['In the second phase of training, which lasts for up to ',... + num2str(options(14)), ' cycles,']) +disp(['the learning rate starts at ', num2str(options(18)), ... + ', decaying at 1/t and the momentum is ', num2str(options(17)), '.']) +disp(' ') +disp('Press any key to continue.') +pause +[net, options] = olgd(net, options, x, t); + +clc +disp('Now we plot the data, underlying function, and network outputs') +disp('on a single graph to compare the results.') +disp(' ') +disp('Press any key to continue.') +pause + +% Plot the data, the original function, and the trained network function. +plotvals = [0:0.01:1]'; +y = mlpfwd(net, plotvals); +fh1 = figure; +plot(x, t, 'ob') +hold on +axis([0 1 -1.5 1.5]) +fplot('sin(2*pi*x)', [0 1], '--g') +plot(plotvals, y, '-r') +legend('data', 'function', 'network'); +hold off + +disp('Note the very poor fit to the data: this should be compared with') +disp('the results obtained in demmlp1.') +disp(' ') +disp('Press any key to exit.') +pause +close(fh1); +clear all; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demopt1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,170 @@ +function demopt1(xinit) +%DEMOPT1 Demonstrate different optimisers on Rosenbrock's function. +% +% Description +% The four general optimisers (quasi-Newton, conjugate gradients, +% scaled conjugate gradients, and gradient descent) are applied to the +% minimisation of Rosenbrock's well known `banana' function. Each +% optimiser is run for at most 100 cycles, and a stopping criterion of +% 1.0e-4 is used for both position and function value. At the end, the +% trajectory of each algorithm is shown on a contour plot of the +% function. +% +% DEMOPT1(XINIT) allows the user to specify a row vector with two +% columns as the starting point. The default is the point [-1 1]. Note +% that the contour plot has an x range of [-1.5, 1.5] and a y range of +% [-0.5, 2.1], so it is best to choose a starting point in the same +% region. +% +% See also +% CONJGRAD, GRADDESC, QUASINEW, SCG, ROSEN, ROSEGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Initialise start point for search +if nargin < 1 | size(xinit) ~= [1 2] + xinit = [-1 1]; % Traditional start point +end + +% Find out if flops is available (i.e. pre-version 6 Matlab) +v = version; +if (str2num(strtok(v, '.')) >= 6) + flops_works = logical(0); +else + flops_works = logical(1); +end + +% Set up options +options = foptions; % Standard options +options(1) = -1; % Turn off printing completely +options(3) = 1e-8; % Tolerance in value of function +options(14) = 100; % Max. 100 iterations of algorithm + +clc +disp('This demonstration compares the performance of four generic') +disp('optimisation routines when finding the minimum of Rosenbrock''s') +disp('function y = 100*(x2-x1^2)^2 + (1-x1)^2.') +disp(' ') +disp('The global minimum of this function is at [1 1].') +disp(['Each algorithm starts at the point [' num2str(xinit(1))... + ' ' num2str(xinit(2)) '].']) +disp(' ') +disp('Press any key to continue.') +pause + +% Generate a contour plot of the function +a = -1.5:.02:1.5; +b = -0.5:.02:2.1; +[A, B] = meshgrid(a, b); +Z = rosen([A(:), B(:)]); +Z = reshape(Z, length(b), length(a)); +l = -1:6; +v = 2.^l; +fh1 = figure; +contour(a, b, Z, v) +title('Contour plot of Rosenbrock''s function') +hold on + +clc +disp('We now use quasi-Newton, conjugate gradient, scaled conjugate') +disp('gradient, and gradient descent with line search algorithms') +disp('to find a local minimum of this function. Each algorithm is stopped') +disp('when 100 cycles have elapsed, or if the change in function value') +disp('is less than 1.0e-8 or the change in the input vector is less than') +disp('1.0e-4 in magnitude.') +disp(' ') +disp('Press any key to continue.') +pause + +clc +x = xinit; +flops(0) +[x, options, errlog, pointlog] = quasinew('rosen', x, options, 'rosegrad'); +fprintf(1, 'For quasi-Newton method:\n') +fprintf(1, 'Final point is (%f, %f), value is %f\n', x(1), x(2), options(8)) +fprintf(1, 'Number of function evaluations is %d\n', options(10)) +fprintf(1, 'Number of gradient evaluations is %d\n', options(11)) +if flops_works + opt_flops = flops; + fprintf(1, 'Number of floating point operations is %d\n', opt_flops) +end +fprintf(1, 'Number of cycles is %d\n', size(pointlog, 1) - 1); +disp(' ') + +x = xinit; +flops(0) +[x, options, errlog2, pointlog2] = conjgrad('rosen', x, options, 'rosegrad'); +fprintf(1, 'For conjugate gradient method:\n') +fprintf(1, 'Final point is (%f, %f), value is %f\n', x(1), x(2), options(8)) +fprintf(1, 'Number of function evaluations is %d\n', options(10)) +fprintf(1, 'Number of gradient evaluations is %d\n', options(11)) +if flops_works + opt_flops = flops; + fprintf(1, 'Number of floating point operations is %d\n', ... + opt_flops) +end +fprintf(1, 'Number of cycles is %d\n', size(pointlog2, 1) - 1); +disp(' ') + +x = xinit; +flops(0) +[x, options, errlog3, pointlog3] = scg('rosen', x, options, 'rosegrad'); +fprintf(1, 'For scaled conjugate gradient method:\n') +fprintf(1, 'Final point is (%f, %f), value is %f\n', x(1), x(2), options(8)) +fprintf(1, 'Number of function evaluations is %d\n', options(10)) +fprintf(1, 'Number of gradient evaluations is %d\n', options(11)) +if flops_works + opt_flops = flops; + fprintf(1, 'Number of floating point operations is %d\n', opt_flops) +end +fprintf(1, 'Number of cycles is %d\n', size(pointlog3, 1) - 1); +disp(' ') + +x = xinit; +options(7) = 1; % Line minimisation used +flops(0) +[x, options, errlog4, pointlog4] = graddesc('rosen', x, options, 'rosegrad'); +fprintf(1, 'For gradient descent method:\n') +fprintf(1, 'Final point is (%f, %f), value is %f\n', x(1), x(2), options(8)) +fprintf(1, 'Number of function evaluations is %d\n', options(10)) +fprintf(1, 'Number of gradient evaluations is %d\n', options(11)) +if flops_works + opt_flops = flops; + fprintf(1, 'Number of floating point operations is %d\n', opt_flops) +end +fprintf(1, 'Number of cycles is %d\n', size(pointlog4, 1) - 1); +disp(' ') +disp('Note that gradient descent does not reach a local minimum in') +disp('100 cycles.') +disp(' ') +disp('On this problem, where the function is cheap to evaluate, the') +disp('computational effort is dominated by the algorithm overhead.') +disp('However on more complex optimisation problems (such as those') +disp('involving neural networks), computational effort is dominated by') +disp('the number of function and gradient evaluations. Counting these,') +disp('we can rank the algorithms: quasi-Newton (the best), conjugate') +disp('gradient, scaled conjugate gradient, gradient descent (the worst)') +disp(' ') +disp('Press any key to continue.') +pause +clc +disp('We now plot the trajectory of search points for each algorithm') +disp('superimposed on the contour plot.') +disp(' ') +disp('Press any key to continue.') +pause +plot(pointlog4(:,1), pointlog4(:,2), 'bd', 'MarkerSize', 6) +plot(pointlog3(:,1), pointlog3(:,2), 'mx', 'MarkerSize', 6, 'LineWidth', 2) +plot(pointlog(:,1), pointlog(:,2), 'k.', 'MarkerSize', 18) +plot(pointlog2(:,1), pointlog2(:,2), 'g+', 'MarkerSize', 6, 'LineWidth', 2) +lh = legend( 'Gradient Descent', 'Scaled Conjugate Gradients', ... + 'Quasi Newton', 'Conjugate Gradients'); + +hold off + +clc +disp('Press any key to end.') +pause +close(fh1); +clear all; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/dempot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function e = dempot(x, mix) +%DEMPOT Computes the negative log likelihood for a mixture model. +% +% Description +% This function computes the negative log of the unconditional data +% density P(X) for a Gaussian mixture model. The data structure MIX +% defines the mixture model, while the matrix X contains the data +% vectors. +% +% See also +% DEMGPOT, DEMHMC1, DEMMET1 +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Computes the potential (negative log likelihood) +e = -log(gmmprob(mix, x)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demprgp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,542 @@ +function demprgp(action); +%DEMPRGP Demonstrate sampling from a Gaussian Process prior. +% +% Description +% This function plots the functions represented by a Gaussian Process +% model. The hyperparameter values can be adjusted on a linear scale +% using the sliders (though the exponential of the parameters is used +% in the covariance function), or by typing values into the text boxes +% and pressing the return key. Both types of covariance function are +% supported. An extra function specific parameter is needed for the +% rational quadratic function. +% +% See also +% GP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin<1, + action='initialize'; +end; + +if strcmp(action,'initialize') + + % Bounds on hyperparameter values + biasminval = -3.0; biasmaxval = 3.0; + noiseminval = -20; noisemaxval = -2; + fparminval = 0.0; fparmaxval = 2.0; + inwminval = 0; inwmaxval = 8; + % Initial hyperparameter values + bias = (biasminval+biasmaxval)/2; + noise = (noiseminval+noisemaxval)/2; + inweights = (inwminval+inwmaxval)/2; + fpar = (fparminval+fparmaxval)/2; + fpar2 = (fparminval+fparmaxval)/2; + + gptype = 'sqexp'; + + % Create FIGURE + fig=figure( ... + 'Name','Sampling from a Gaussian Process prior', ... + 'Position', [50 50 480 380], ... + 'NumberTitle','off', ... + 'Color', [0.8 0.8 0.8], ... + 'Visible','on'); + + % List box for covariance function type + nettype_box = uicontrol(fig, ... + 'Style', 'listbox', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'center', ... + 'Position', [0.52 0.77 0.40 0.12], ... + 'String', 'Squared Exponential|Rational Quadratic', ... + 'Max', 1, 'Min', 0, ... % Only allow one selection + 'Value', 1, ... % Initial value is squared exponential + 'BackgroundColor',[0.60 0.60 0.60],... + 'CallBack', 'demprgp GPtype'); + + % Title for list box + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'Position', [0.52 0.89 0.40 0.05], ... + 'String', 'Covariance Function Type', ... + 'BackgroundColor', get(fig, 'Color'), ... + 'HorizontalAlignment', 'center'); + + % Frames to enclose sliders + bottom_row = 0.04; + slider_frame_height = 0.15; + biasframe = uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'String', 'bias', ... + 'HorizontalAlignment', 'left', ... + 'Position', [0.05 bottom_row 0.35 slider_frame_height]); + + bpos = get(biasframe, 'Position'); + noise_frame_bottom = bpos(2) + bpos(4) + 0.02; + noiseframe = uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.05 noise_frame_bottom 0.35 slider_frame_height]); + + npos = get(noiseframe, 'Position'); + inw_frame_bottom = npos(2) + npos(4) + 0.02; + inwframe = uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.05 inw_frame_bottom 0.35 slider_frame_height]); + + inwpos = get(inwframe, 'Position'); + fpar_frame_bottom = inwpos(2) + inwpos(4) + 0.02; + % This frame sometimes has multiple parameters + uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.05 fpar_frame_bottom 0.35 2*slider_frame_height]); + + % Frame text + slider_text_height = 0.05; + slider_text_voffset = 0.08; + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 bottom_row+slider_text_voffset ... + 0.06 slider_text_height], ... + 'String', 'bias'); + + % Frame text + noiseframe = uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 noise_frame_bottom+slider_text_voffset ... + 0.08 slider_text_height], ... + 'String', 'noise'); + + % Frame text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 inw_frame_bottom+slider_text_voffset ... + 0.14 slider_text_height], ... + 'String', 'inweights'); + + % Frame text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 fpar_frame_bottom+slider_frame_height+ ... + slider_text_voffset 0.06 slider_text_height], ... + 'String', 'fpar'); + + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 fpar_frame_bottom+slider_text_voffset ... + 0.06 slider_text_height], ... + 'String', 'fpar2', ... + 'Tag', 'fpar2text', ... + 'Enable', 'off'); + + % Slider + slider_left = 0.07; + slider_width = 0.31; + slider_frame_voffset = 0.02; + biasslide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', bias, ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [slider_left bottom_row+slider_frame_voffset ... + slider_width 0.05], ... + 'Min', biasminval, 'Max', biasmaxval, ... + 'Callback', 'demprgp update'); + + % Slider + noiseslide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', noise, ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [slider_left noise_frame_bottom+slider_frame_voffset ... + slider_width 0.05], ... + 'Min', noiseminval, 'Max', noisemaxval, ... + 'Callback', 'demprgp update'); + + % Slider + inweightsslide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', inweights, ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [slider_left inw_frame_bottom+slider_frame_voffset ... + slider_width 0.05], ... + 'Min', inwminval, 'Max', inwmaxval, ... + 'Callback', 'demprgp update'); + + % Slider + fparslide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', fpar, ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [slider_left fpar_frame_bottom+slider_frame_height+ ... + slider_frame_voffset slider_width 0.05], ... + 'Min', fparminval, 'Max', fparmaxval, ... + 'Callback', 'demprgp update'); + + fpar2slide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', fpar2, ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [slider_left fpar_frame_bottom+slider_frame_voffset ... + slider_width 0.05], ... + 'Min', fparminval, 'Max', fparmaxval, ... + 'Callback', 'demprgp update', ... + 'Tag', 'fpar2slider', ... + 'Enable', 'off'); + + % Text display of hyper-parameter values + + format = '%8f'; + + hp_left = 0.20; + hp_width = 0.17; + biasval = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [hp_left bottom_row+slider_text_voffset ... + hp_width slider_text_height], ... + 'String', sprintf(format, bias), ... + 'Callback', 'demprgp newval'); + + noiseval = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [hp_left noise_frame_bottom+slider_text_voffset ... + hp_width slider_text_height], ... + 'String', sprintf(format, noise), ... + 'Callback', 'demprgp newval'); + + inweightsval = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [hp_left inw_frame_bottom+slider_text_voffset ... + hp_width slider_text_height], ... + 'String', sprintf(format, inweights), ... + 'Callback', 'demprgp newval'); + + fparval = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [hp_left fpar_frame_bottom+slider_frame_height+ ... + slider_text_voffset hp_width slider_text_height], ... + 'String', sprintf(format, fpar), ... + 'Callback', 'demprgp newval'); + + fpar2val = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [hp_left fpar_frame_bottom+slider_text_voffset ... + hp_width slider_text_height], ... + 'String', sprintf(format, fpar), ... + 'Callback', 'demprgp newval', ... + 'Enable', 'off', ... + 'Tag', 'fpar2val'); + + + % The graph box + haxes = axes('Position', [0.5 0.28 0.45 0.45], ... + 'Units', 'normalized', ... + 'Visible', 'on'); + + % The SAMPLE button + uicontrol(fig, ... + 'Style','push', ... + 'Units','normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position',[0.5 bottom_row 0.13 0.1], ... + 'String','Sample', ... + 'Callback','demprgp replot'); + + % The CLOSE button + uicontrol(fig, ... + 'Style','push', ... + 'Units','normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position',[0.82 bottom_row 0.13 0.1], ... + 'String','Close', ... + 'Callback','close(gcf)'); + + % The HELP button + uicontrol(fig, ... + 'Style','push', ... + 'Units','normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position',[0.66 bottom_row 0.13 0.1], ... + 'String','Help', ... + 'Callback','demprgp help'); + + % Save handles to objects + + hndlList=[fig biasslide noiseslide inweightsslide fparslide ... + biasval noiseval inweightsval ... + fparval haxes nettype_box]; + set(fig, 'UserData', hndlList); + + demprgp('replot') + + +elseif strcmp(action, 'update'), + + % Update when a slider is moved. + + hndlList = get(gcf, 'UserData'); + biasslide = hndlList(2); + noiseslide = hndlList(3); + inweightsslide = hndlList(4); + fparslide = hndlList(5); + biasval = hndlList(6); + noiseval = hndlList(7); + inweightsval = hndlList(8); + fparval = hndlList(9); + haxes = hndlList(10); + nettype_box = hndlList(11); + + + bias = get(biasslide, 'Value'); + noise = get(noiseslide, 'Value'); + inweights = get(inweightsslide, 'Value'); + fpar = get(fparslide, 'Value'); + fpar2 = get(findobj('Tag', 'fpar2slider'), 'Value'); + + format = '%8f'; + set(biasval, 'String', sprintf(format, bias)); + set(noiseval, 'String', sprintf(format, noise)); + set(inweightsval, 'String', sprintf(format, inweights)); + set(fparval, 'String', sprintf(format, fpar)); + set(findobj('Tag', 'fpar2val'), 'String', ... + sprintf(format, fpar2)); + + demprgp('replot'); + +elseif strcmp(action, 'newval'), + + % Update when text is changed. + + hndlList = get(gcf, 'UserData'); + biasslide = hndlList(2); + noiseslide = hndlList(3); + inweightsslide = hndlList(4); + fparslide = hndlList(5); + biasval = hndlList(6); + noiseval = hndlList(7); + inweightsval = hndlList(8); + fparval = hndlList(9); + haxes = hndlList(10); + + bias = sscanf(get(biasval, 'String'), '%f'); + noise = sscanf(get(noiseval, 'String'), '%f'); + inweights = sscanf(get(inweightsval, 'String'), '%f'); + fpar = sscanf(get(fparval, 'String'), '%f'); + fpar2 = sscanf(get(findobj('Tag', 'fpar2val'), 'String'), '%f'); + + set(biasslide, 'Value', bias); + set(noiseslide, 'Value', noise); + set(inweightsslide, 'Value', inweights); + set(fparslide, 'Value', fpar); + set(findobj('Tag', 'fpar2slider'), 'Value', fpar2); + + demprgp('replot'); + +elseif strcmp(action, 'GPtype') + hndlList = get(gcf, 'UserData'); + nettype_box = hndlList(11); + gptval = get(nettype_box, 'Value'); + if gptval == 1 + % Squared exponential, so turn off fpar2 + set(findobj('Tag', 'fpar2text'), 'Enable', 'off'); + set(findobj('Tag', 'fpar2slider'), 'Enable', 'off'); + set(findobj('Tag', 'fpar2val'), 'Enable', 'off'); + else + % Rational quadratic, so turn on fpar2 + set(findobj('Tag', 'fpar2text'), 'Enable', 'on'); + set(findobj('Tag', 'fpar2slider'), 'Enable', 'on'); + set(findobj('Tag', 'fpar2val'), 'Enable', 'on'); + end + demprgp('replot'); + +elseif strcmp(action, 'replot'), + + % Re-sample from the prior and plot graphs. + + oldFigNumber=watchon; + + hndlList = get(gcf, 'UserData'); + biasslide = hndlList(2); + noiseslide = hndlList(3); + inweightsslide = hndlList(4); + fparslide = hndlList(5); + haxes = hndlList(10); + nettype_box = hndlList(11); + gptval = get(nettype_box, 'Value'); + if gptval == 1 + gptype = 'sqexp'; + else + gptype = 'ratquad'; + end + + bias = get(biasslide, 'Value'); + noise = get(noiseslide, 'Value'); + inweights = get(inweightsslide, 'Value'); + fpar = get(fparslide, 'Value'); + + + axes(haxes); + cla + set(gca, ... + 'Box', 'on', ... + 'Color', [0 0 0], ... + 'XColor', [0 0 0], ... + 'YColor', [0 0 0], ... + 'FontSize', 14); + ymin = -10; + ymax = 10; + axis([-1 1 ymin ymax]); + set(gca,'DefaultLineLineWidth', 2); + + xvals = (-1:0.01:1)'; + nsample = 10; % Number of samples from prior. + hold on + plot([-1 0; 1 0], [0 ymin; 0 ymax], 'b--'); + net = gp(1, gptype); + net.bias = bias; + net.noise = noise; + net.inweights = inweights; + if strcmp(gptype, 'sqexp') + net.fpar = fpar; + else + fpar2 = get(findobj('Tag', 'fpar2slider'), 'Value'); + net.fpar = [fpar fpar2]; + end + cn = gpcovar(net, xvals); + cninv = inv(cn); + cnchol = chol(cn); + set(gca, 'DefaultLineLineWidth', 1); + for n = 1:nsample + y = (cnchol') * randn(size(xvals)); + plot(xvals, y, 'y'); + end + + watchoff(oldFigNumber); + +elseif strcmp(action, 'help'), + + % Provide help to user. + + oldFigNumber=watchon; + + helpfig = figure('Position', [100 100 480 400], ... + 'Name', 'Help', ... + 'NumberTitle', 'off', ... + 'Color', [0.8 0.8 0.8], ... + 'Visible','on'); + + % The HELP TITLE BAR frame + uicontrol(helpfig, ... + 'Style','frame', ... + 'Units','normalized', ... + 'HorizontalAlignment', 'center', ... + 'Position', [0.05 0.82 0.9 0.1], ... + 'BackgroundColor',[0.60 0.60 0.60]); + + % The HELP TITLE BAR text + uicontrol(helpfig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.26 0.85 0.6 0.05], ... + 'HorizontalAlignment', 'left', ... + 'String', 'Help: Sampling from a Gaussian Process Prior'); + + helpstr1 = strcat(... + 'This demonstration shows the effects of sampling from a Gaussian', ... + ' process prior. The parameters bias, noise, inweights and fpar', ... + ' control the corresponding terms in the covariance function of the',... + ' Gaussian process. Their values can be adjusted on a linear scale',... + ' using the sliders, or by typing values into the text boxes and',... + ' pressing the return key. After setting these values, press the',... + ' ''Sample'' button to see a new sample from the prior.'); + + helpstr2 = strcat(... + 'Observe how inweights controls horizontal length-scale of the',... + ' variation in the functions, noise controls the roughness of the',... + ' functions, and the bias controls the size of the', ... + ' vertical offset of the signal.'); + helpstr3 = strcat(... + 'There are two types of covariance function supported by', ... + ' Netlab which can be selected using the ''Covariance Function', ... + ' Type'' menu.'); + helpstr4 = strcat(... + 'The squared exponential has a single fpar which', ... + ' controls the vertical scale of the process.'); + helpstr5 = strcat(... + 'The rational quadratic has two fpar values. The first is', ... + ' is a scale parameter inside the rational function like the',... + ' first fpar for the squared exponential covariance, while the', ... + ' second gives the exponent of the rational function (i.e. the',... + ' rate of decay of the covariance function.'); + % Set up cell array with help strings + hstr(1) = {helpstr1}; + hstr(2) = {''}; + hstr(3) = {helpstr2}; + hstr(4) = {''}; + hstr(5) = {helpstr3}; + hstr(6) = {''}; + hstr(7) = {helpstr4}; + hstr(8) = {''}; + hstr(9) = {helpstr5}; + + % The HELP text + helpui = uicontrol(helpfig, ... + 'Style', 'Text', ... + 'Units', 'normalized', ... + 'ForegroundColor', [0 0 0], ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [1 1 1], ... + 'Min', 0, ... + 'Max', 2, ... + 'Position', [0.05 0.2 0.9 0.57]); + [hstrw, newpos] = textwrap(helpui, hstr); + set(helpui, 'String', hstrw, 'Position', [0.05, 0.2, 0.9 newpos(4)]); + + % The CLOSE button + uicontrol(helpfig, ... + 'Style','push', ... + 'Units','normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position',[0.4 0.05 0.2 0.1], ... + 'String','Close', ... + 'Callback','close(gcf)'); + + watchoff(oldFigNumber); + +end; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demprior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,406 @@ +function demprior(action); +%DEMPRIOR Demonstrate sampling from a multi-parameter Gaussian prior. +% +% Description +% This function plots the functions represented by a multi-layer +% perceptron network when the weights are set to values drawn from a +% Gaussian prior distribution. The parameters AW1, AB1 AW2 and AB2 +% control the inverse variances of the first-layer weights, the hidden +% unit biases, the second-layer weights and the output unit biases +% respectively. Their values can be adjusted on a logarithmic scale +% using the sliders, or by typing values into the text boxes and +% pressing the return key. +% +% See also +% MLP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin<1, + action='initialize'; +end; + +if strcmp(action,'initialize') + + aw1 = 0.01; + ab1 = 0.1; + aw2 = 1.0; + ab2 = 1.0; + + % Create FIGURE + fig=figure( ... + 'Name','Sampling from a Gaussian prior', ... + 'Position', [50 50 480 380], ... + 'NumberTitle','off', ... + 'Color', [0.8 0.8 0.8], ... + 'Visible','on'); + + % The TITLE BAR frame + uicontrol(fig, ... + 'Style','frame', ... + 'Units','normalized', ... + 'HorizontalAlignment', 'center', ... + 'Position', [0.5 0.82 0.45 0.1], ... + 'BackgroundColor',[0.60 0.60 0.60]); + + % The TITLE BAR text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.54 0.85 0.40 0.05], ... + 'HorizontalAlignment', 'left', ... + 'String', 'Sampling from a Gaussian prior'); + + % Frames to enclose sliders + uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.05 0.08 0.35 0.18]); + + uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.05 0.3 0.35 0.18]); + + uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.05 0.52 0.35 0.18]); + + uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.05 0.74 0.35 0.18]); + + % Frame text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 0.17 0.06 0.07], ... + 'String', 'aw1'); + + % Frame text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 0.39 0.06 0.07], ... + 'String', 'ab1'); + + % Frame text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 0.61 0.06 0.07], ... + 'String', 'aw2'); + + % Frame text + uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.07 0.83 0.06 0.07], ... + 'String', 'ab2'); + + % Slider + minval = -5; maxval = 5; + aw1slide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', log10(aw1), ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [0.07 0.1 0.31 0.05], ... + 'Min', minval, 'Max', maxval, ... + 'Callback', 'demprior update'); + + % Slider + ab1slide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', log10(ab1), ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [0.07 0.32 0.31 0.05], ... + 'Min', minval, 'Max', maxval, ... + 'Callback', 'demprior update'); + + % Slider + aw2slide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', log10(aw2), ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [0.07 0.54 0.31 0.05], ... + 'Min', minval, 'Max', maxval, ... + 'Callback', 'demprior update'); + + % Slider + ab2slide = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Value', log10(ab2), ... + 'BackgroundColor', [0.8 0.8 0.8], ... + 'Position', [0.07 0.76 0.31 0.05], ... + 'Min', minval, 'Max', maxval, ... + 'Callback', 'demprior update'); + + % The graph box + haxes = axes('Position', [0.5 0.28 0.45 0.45], ... + 'Units', 'normalized', ... + 'Visible', 'on'); + + % Text display of hyper-parameter values + + format = '%8f'; + + aw1val = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [0.15 0.17 0.23 0.07], ... + 'String', sprintf(format, aw1), ... + 'Callback', 'demprior newval'); + + ab1val = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [0.15 0.39 0.23 0.07], ... + 'String', sprintf(format, ab1), ... + 'Callback', 'demprior newval'); + + aw2val = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [0.15 0.61 0.23 0.07], ... + 'String', sprintf(format, aw2), ... + 'Callback', 'demprior newval'); + + ab2val = uicontrol(fig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'Position', [0.15 0.83 0.23 0.07], ... + 'String', sprintf(format, ab2), ... + 'Callback', 'demprior newval'); + + % The SAMPLE button + uicontrol(fig, ... + 'Style','push', ... + 'Units','normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position',[0.5 0.08 0.13 0.1], ... + 'String','Sample', ... + 'Callback','demprior replot'); + + % The CLOSE button + uicontrol(fig, ... + 'Style','push', ... + 'Units','normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position',[0.82 0.08 0.13 0.1], ... + 'String','Close', ... + 'Callback','close(gcf)'); + + % The HELP button + uicontrol(fig, ... + 'Style','push', ... + 'Units','normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position',[0.66 0.08 0.13 0.1], ... + 'String','Help', ... + 'Callback','demprior help'); + + % Save handles to objects + + hndlList=[fig aw1slide ab1slide aw2slide ab2slide aw1val ab1val aw2val ... + ab2val haxes]; + set(fig, 'UserData', hndlList); + + demprior('replot') + + +elseif strcmp(action, 'update'), + + % Update when a slider is moved. + + hndlList = get(gcf, 'UserData'); + aw1slide = hndlList(2); + ab1slide = hndlList(3); + aw2slide = hndlList(4); + ab2slide = hndlList(5); + aw1val = hndlList(6); + ab1val = hndlList(7); + aw2val = hndlList(8); + ab2val = hndlList(9); + haxes = hndlList(10); + + aw1 = 10^get(aw1slide, 'Value'); + ab1 = 10^get(ab1slide, 'Value'); + aw2 = 10^get(aw2slide, 'Value'); + ab2 = 10^get(ab2slide, 'Value'); + + format = '%8f'; + set(aw1val, 'String', sprintf(format, aw1)); + set(ab1val, 'String', sprintf(format, ab1)); + set(aw2val, 'String', sprintf(format, aw2)); + set(ab2val, 'String', sprintf(format, ab2)); + + demprior('replot'); + +elseif strcmp(action, 'newval'), + + % Update when text is changed. + + hndlList = get(gcf, 'UserData'); + aw1slide = hndlList(2); + ab1slide = hndlList(3); + aw2slide = hndlList(4); + ab2slide = hndlList(5); + aw1val = hndlList(6); + ab1val = hndlList(7); + aw2val = hndlList(8); + ab2val = hndlList(9); + haxes = hndlList(10); + + aw1 = sscanf(get(aw1val, 'String'), '%f'); + ab1 = sscanf(get(ab1val, 'String'), '%f'); + aw2 = sscanf(get(aw2val, 'String'), '%f'); + ab2 = sscanf(get(ab2val, 'String'), '%f'); + + set(aw1slide, 'Value', log10(aw1)); + set(ab1slide, 'Value', log10(ab1)); + set(aw2slide, 'Value', log10(aw2)); + set(ab2slide, 'Value', log10(ab2)); + + demprior('replot'); + +elseif strcmp(action, 'replot'), + + % Re-sample from the prior and plot graphs. + + oldFigNumber=watchon; + + hndlList = get(gcf, 'UserData'); + aw1slide = hndlList(2); + ab1slide = hndlList(3); + aw2slide = hndlList(4); + ab2slide = hndlList(5); + haxes = hndlList(10); + + aw1 = 10^get(aw1slide, 'Value'); + ab1 = 10^get(ab1slide, 'Value'); + aw2 = 10^get(aw2slide, 'Value'); + ab2 = 10^get(ab2slide, 'Value'); + + axes(haxes); + cla + set(gca, ... + 'Box', 'on', ... + 'Color', [0 0 0], ... + 'XColor', [0 0 0], ... + 'YColor', [0 0 0], ... + 'FontSize', 14); + axis([-1 1 -10 10]); + set(gca,'DefaultLineLineWidth', 2); + + nhidden = 12; + prior = mlpprior(1, nhidden, 1, aw1, ab1, aw2, ab2); + xvals = -1:0.005:1; + nsample = 10; % Number of samples from prior. + hold on + plot([-1 0; 1 0], [0 -10; 0 10], 'b--'); + net = mlp(1, nhidden, 1, 'linear', prior); + for i = 1:nsample + net = mlpinit(net, prior); + yvals = mlpfwd(net, xvals'); + plot(xvals', yvals, 'y'); + end + + watchoff(oldFigNumber); + +elseif strcmp(action, 'help'), + + % Provide help to user. + + oldFigNumber=watchon; + + helpfig = figure('Position', [100 100 480 400], ... + 'Name', 'Help', ... + 'NumberTitle', 'off', ... + 'Color', [0.8 0.8 0.8], ... + 'Visible','on'); + + % The HELP TITLE BAR frame + uicontrol(helpfig, ... + 'Style','frame', ... + 'Units','normalized', ... + 'HorizontalAlignment', 'center', ... + 'Position', [0.05 0.82 0.9 0.1], ... + 'BackgroundColor',[0.60 0.60 0.60]); + + % The HELP TITLE BAR text + uicontrol(helpfig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position', [0.26 0.85 0.6 0.05], ... + 'HorizontalAlignment', 'left', ... + 'String', 'Help: Sampling from a Gaussian Prior'); + + helpstr1 = strcat( ... + 'This demonstration shows the effects of sampling from a Gaussian', ... + ' prior over weights for a two-layer feed-forward network. The', ... + ' parameters aw1, ab1, aw2 and ab2 control the inverse variances of', ... + ' the first-layer weights, the hidden unit biases, the second-layer', ... + ' weights and the output unit biases respectively. Their values can', ... + ' be adjusted on a logarithmic scale using the sliders, or by', ... + ' typing values into the text boxes and pressing the return key.', ... + ' After setting these values, press the ''Sample'' button to see a', ... + ' new sample from the prior. '); + helpstr2 = strcat( ... + 'Observe how aw1 controls the horizontal length-scale of the', ... + ' variation in the functions, ab1 controls the input range over', ... + ' such variations occur, aw2 sets the vertical scale of the output', ... + ' and ab2 sets the vertical off-set of the output. The network has', ... + ' 12 hidden units. '); + hstr(1) = {helpstr1}; + hstr(2) = {''}; + hstr(3) = {helpstr2}; + + % The HELP text + helpui = uicontrol(helpfig, ... + 'Style', 'edit', ... + 'Units', 'normalized', ... + 'ForegroundColor', [0 0 0], ... + 'HorizontalAlignment', 'left', ... + 'BackgroundColor', [1 1 1], ... + 'Min', 0, ... + 'Max', 2, ... + 'Position', [0.05 0.2 0.9 0.8]); + + [hstrw , newpos] = textwrap(helpui, hstr, 70); + set(helpui, 'String', hstrw, 'Position', [0.05, 0.2, 0.9, newpos(4)]); + + + % The CLOSE button + uicontrol(helpfig, ... + 'Style','push', ... + 'Units','normalized', ... + 'BackgroundColor', [0.6 0.6 0.6], ... + 'Position',[0.4 0.05 0.2 0.1], ... + 'String','Close', ... + 'Callback','close(gcf)'); + + watchoff(oldFigNumber); + +end; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demrbf1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,138 @@ +%DEMRBF1 Demonstrate simple regression using a radial basis function network. +% +% Description +% The problem consists of one input variable X and one target variable +% T with data generated by sampling X at equal intervals and then +% generating target data by computing SIN(2*PI*X) and adding Gaussian +% noise. This data is the same as that used in demmlp1. +% +% Three different RBF networks (with different activation functions) +% are trained in two stages. First, a Gaussian mixture model is trained +% using the EM algorithm, and the centres of this model are used to set +% the centres of the RBF. Second, the output weights (and biases) are +% determined using the pseudo-inverse of the design matrix. +% +% See also +% DEMMLP1, RBF, RBFFWD, GMM, GMMEM +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +% Generate the matrix of inputs x and targets t. +randn('state', 42); +rand('state', 42); +ndata = 20; % Number of data points. +noise = 0.2; % Standard deviation of noise distribution. +x = (linspace(0, 1, ndata))'; +t = sin(2*pi*x) + noise*randn(ndata, 1); +mu = mean(x); +sigma = std(x); +tr_in = (x - mu)./(sigma); + +clc +disp('This demonstration illustrates the use of a Radial Basis Function') +disp('network for regression problems. The data is generated from a noisy') +disp('sine function.') +disp(' ') +disp('Press any key to continue.') +pause +% Set up network parameters. +nin = 1; % Number of inputs. +nhidden = 7; % Number of hidden units. +nout = 1; % Number of outputs. + +clc +disp('We assess the effect of three different activation functions.') +disp('First we create a network with Gaussian activations.') +disp(' ') +disp('Press any key to continue.') +pause +% Create and initialize network weight and parameter vectors. +net = rbf(nin, nhidden, nout, 'gaussian'); + +disp('A two-stage training algorithm is used: it uses a small number of') +disp('iterations of EM to position the centres, and then the pseudo-inverse') +disp('of the design matrix to find the second layer weights.') +disp(' ') +disp('Press any key to continue.') +pause +disp('Error values from EM training.') +% Use fast training method +options = foptions; +options(1) = 1; % Display EM training +options(14) = 10; % number of iterations of EM +net = rbftrain(net, options, tr_in, t); + +disp(' ') +disp('Press any key to continue.') +pause +clc +disp('The second RBF network has thin plate spline activations.') +disp('The same centres are used again, so we just need to calculate') +disp('the second layer weights.') +disp(' ') +disp('Press any key to continue.') +pause +% Create a second RBF with thin plate spline functions +net2 = rbf(nin, nhidden, nout, 'tps'); + +% Re-use previous centres rather than calling rbftrain again +net2.c = net.c; +[y, act2] = rbffwd(net2, tr_in); + +% Solve for new output weights and biases from RBF activations +temp = pinv([act2 ones(ndata, 1)]) * t; +net2.w2 = temp(1:nhidden, :); +net2.b2 = temp(nhidden+1, :); + +disp('The third RBF network has r^4 log r activations.') +disp(' ') +disp('Press any key to continue.') +pause +% Create a third RBF with r^4 log r functions +net3 = rbf(nin, nhidden, nout, 'r4logr'); + +% Overwrite weight vector with parameters from first RBF +net3.c = net.c; +[y, act3] = rbffwd(net3, tr_in); +temp = pinv([act3 ones(ndata, 1)]) * t; +net3.w2 = temp(1:nhidden, :); +net3.b2 = temp(nhidden+1, :); + +disp('Now we plot the data, underlying function, and network outputs') +disp('on a single graph to compare the results.') +disp(' ') +disp('Press any key to continue.') +pause +% Plot the data, the original function, and the trained network functions. +plotvals = [x(1):0.01:x(end)]'; +inputvals = (plotvals-mu)./sigma; +y = rbffwd(net, inputvals); +y2 = rbffwd(net2, inputvals); +y3 = rbffwd(net3, inputvals); +fh1 = figure; + +plot(x, t, 'ob') +hold on +xlabel('Input') +ylabel('Target') +axis([x(1) x(end) -1.5 1.5]) +[fx, fy] = fplot('sin(2*pi*x)', [x(1) x(end)]); +plot(fx, fy, '-r', 'LineWidth', 2) +plot(plotvals, y, '--g', 'LineWidth', 2) +plot(plotvals, y2, 'k--', 'LineWidth', 2) +plot(plotvals, y3, '-.c', 'LineWidth', 2) +legend('data', 'function', 'Gaussian RBF', 'Thin plate spline RBF', ... + 'r^4 log r RBF'); +hold off + +disp('RBF training errors are'); +disp(['Gaussian ', num2str(rbferr(net, tr_in, t)), ' TPS ', ... +num2str(rbferr(net2, tr_in, t)), ' R4logr ', num2str(rbferr(net3, tr_in, t))]); + +disp(' ') +disp('Press any key to end.') +pause +close(fh1); +clear all;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demsom1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,113 @@ +%DEMSOM1 Demonstrate SOM for visualisation. +% +% Description +% This script demonstrates the use of a SOM with a two-dimensional +% grid to map onto data in two-dimensional space. Both on-line and +% batch training algorithms are shown. +% +% See also +% SOM, SOMPAK, SOMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +randn('state', 42); +rand('state', 42); +nin = 2; +ndata = 300; +% Give data an offset so that network has something to learn. +x = rand(ndata, nin) + ones(ndata, 1)*[1.5 1.5]; + +clc; +disp('This demonstration of the SOM, or Kohonen network, shows how the') +disp('network units after training lie in regions of high data density.') +disp('First we show the data, which is generated uniformly from a square.') +disp('Red crosses denote the data and black dots are the initial locations') +disp('of the SOM units.') +disp(' ') +disp('Press any key to continue.') +pause +net = som(nin, [8, 7]); +c1 = sompak(net); +h1 = figure; +plot(x(:, 1), x(:, 2), 'r+'); +hold on +plot(c1(:,1), c1(:, 2), 'k.'); +drawnow; % Force figure to be drawn before training starts +options = foptions; + +% Ordering phase +options(1) = 1; +options(14) = 50; +%options(14) = 5; % Just for testing +options(18) = 0.9; % Initial learning rate +options(16) = 0.05; % Final learning rate +options(17) = 8; % Initial neighbourhood size +options(15) = 1; % Final neighbourhood size + +disp('The SOM network is trained in two phases using an on-line algorithm.') +disp('Initially the neighbourhood is set to 8 and is then reduced') +disp('linearly to 1 over the first 50 iterations.') +disp('Each iteration consists of a pass through the complete') +disp('dataset, while the weights are adjusted after each pattern.') +disp('The learning rate is reduced linearly from 0.9 to 0.05.') +disp('This ordering phase puts the units in a rough grid shape.') +disp('Blue circles denote the units at the end of this phase.') +disp(' ') +disp('Press any key to continue.') +pause +net2 = somtrain(net, options, x); +c2 = sompak(net2); +plot(c2(:, 1), c2(:, 2), 'bo'); +drawnow; + +% Convergence phase +options(1) = 1; +options(14) = 400; +options(18) = 0.05; +options(16) = 0.01; +options(17) = 0; +options(15) = 0; + +disp('The second, convergence, phase of learning just updates the winning node.') +disp('The learning rate is reduced from 0.05 to 0.01 over 400 iterations.') +disp('Note how the error value does not decrease monotonically; it is') +disp('difficult to decide when training is complete in a principled way.') +disp('The units are plotted as green stars.') +disp(' ') +disp('Press any key to continue.') +pause +net3 = somtrain(net2, options, x); +c3 = sompak(net3); +plot(c3(:, 1), c3(:, 2), 'g*'); +drawnow; + +% Now try batch training +options(1) = 1; +options(6) = 1; +options(14) = 50; +options(17) = 3; +options(15) = 0; +disp('An alternative approach to the on-line algorithm is a batch update') +disp('rule. Each unit is updated to be the average weights') +disp('in a neighbourhood (which reduces from 3 to 0) over 50 iterations.'); +disp('Note how the error is even more unstable at first, though eventually') +disp('it does converge.') +disp('The final units are shown as black triangles.') +disp(' ') +disp('Press any key to continue.') +pause +net4 = somtrain(net, options, x); +c4 = sompak(net4); +plot(c4(:, 1), c4(:, 2), 'k^') +legend('Data', 'Initial weights', 'Weights after ordering', ... + 'Weights after convergence', 'Batch weights', 2); +drawnow; + +disp(' ') +disp('Press any key to end.') +disp(' ') +pause + +close(h1); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/demtrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,362 @@ +function demtrain(action); +%DEMTRAIN Demonstrate training of MLP network. +% +% Description +% DEMTRAIN brings up a simple GUI to show the training of an MLP +% network on classification and regression problems. The user should +% load in a dataset (which should be in Netlab format: see DATREAD), +% select the output activation function, the number of cycles and +% hidden units and then train the network. The scaled conjugate +% gradient algorithm is used. A graph shows the evolution of the error: +% the value is shown MAX(CEIL(ITERATIONS / 50), 5) cycles. +% +% Once the network is trained, it is saved to the file MLPTRAIN.NET. +% The results can then be viewed as a confusion matrix (for +% classification problems) or a plot of output versus target (for +% regression problems). +% +% See also +% CONFMAT, DATREAD, MLP, NETOPT, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% If run without parameters, initialise gui. +if nargin<1, + action='initialise'; +end; + +% Global variable to reference GUI figure +global DEMTRAIN_FIG +% Global array to reference sub-figures for results plots +global DEMTRAIN_RES_FIGS +global NUM_DEMTRAIN_RES_FIGS + +if strcmp(action,'initialise'), + + file = ''; + path = '.'; + + % Create FIGURE + fig = figure( ... + 'Name', 'Netlab Demo', ... + 'NumberTitle', 'off', ... + 'Menubar', 'none', ... + 'Color', [0.7529 0.7529 0.7529], ... + 'Visible', 'on'); + % Initialise the globals + DEMTRAIN_FIG = fig; + DEMTRAIN_RES_FIGS = 0; + NUM_DEMTRAIN_RES_FIGS = 0; + + % Create GROUP for buttons + uicontrol(fig, ... + 'Style', 'frame', ... + 'Units', 'normalized', ... + 'Position', [0.03 0.08 0.94 0.22], ... + 'BackgroundColor', [0.5 0.5 0.5]); + + % Create MAIN axis + hMain = axes( ... + 'Units', 'normalized', ... + 'Position', [0.10 0.5 0.80 0.40], ... + 'XColor', [0 0 0], ... + 'YColor', [0 0 0], ... + 'Visible', 'on'); + + % Create static text for FILENAME and PATH + hFilename = uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.7529 0.7529 0.7529], ... + 'Position', [0.05 0.32 0.90 0.05], ... + 'HorizontalAlignment', 'center', ... + 'String', 'Please load data file.', ... + 'Visible', 'on'); + hPath = uicontrol(fig, ... + 'Style', 'text', ... + 'Units', 'normalized', ... + 'BackgroundColor', [0.7529 0.7529 0.7529], ... + 'Position', [0.05 0.37 0.90 0.05], ... + 'HorizontalAlignment', 'center', ... + 'String', '', ... + 'Visible', 'on'); + + % Create NO OF HIDDEN UNITS slider and text + hSliderText = uicontrol(fig, ... + 'Style', 'text', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Units', 'normalized', ... + 'Position', [0.27 0.12 0.17 0.04], ... + 'HorizontalAlignment', 'right', ... + 'String', 'Hidden Units: 5'); + hSlider = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Position', [0.45 0.12 0.26 0.04], ... + 'String', 'Slider', ... + 'Min', 1, 'Max', 25, ... + 'Value', 5, ... + 'Callback', 'demtrain slider_moved'); + + % Create ITERATIONS slider and text + hIterationsText = uicontrol(fig, ... + 'Style', 'text', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Units', 'normalized', ... + 'Position', [0.27 0.21 0.17 0.04], ... + 'HorizontalAlignment', 'right', ... + 'String', 'Iterations: 50'); + hIterations = uicontrol(fig, ... + 'Style', 'slider', ... + 'Units', 'normalized', ... + 'Position', [0.45 0.21 0.26 0.04], ... + 'String', 'Slider', ... + 'Min', 10, 'Max', 500, ... + 'Value', 50, ... + 'Callback', 'demtrain iterations_moved'); + + % Create ACTIVATION FUNCTION popup and text + uicontrol(fig, ... + 'Style', 'text', ... + 'BackgroundColor', [0.5 0.5 0.5], ... + 'Units', 'normalized', ... + 'Position', [0.05 0.20 0.20 0.04], ... + 'HorizontalAlignment', 'center', ... + 'String', 'Activation Function:'); + hPopup = uicontrol(fig, ... + 'Style', 'popup', ... + 'Units', 'normalized', ... + 'Position' , [0.05 0.10 0.20 0.08], ... + 'String', 'Linear|Logistic|Softmax', ... + 'Callback', ''); + + % Create MENU + hMenu1 = uimenu('Label', 'Load Data file...', 'Callback', ''); + uimenu(hMenu1, 'Label', 'Select training data file', ... + 'Callback', 'demtrain get_ip_file'); + hMenu2 = uimenu('Label', 'Show Results...', 'Callback', ''); + uimenu(hMenu2, 'Label', 'Show classification results', ... + 'Callback', 'demtrain classify'); + uimenu(hMenu2, 'Label', 'Show regression results', ... + 'Callback', 'demtrain predict'); + + % Create START button + hStart = uicontrol(fig, ... + 'Units', 'normalized', ... + 'Position' , [0.75 0.2 0.20 0.08], ... + 'String', 'Start Training', ... + 'Enable', 'off',... + 'Callback', 'demtrain start'); + + % Create CLOSE button + uicontrol(fig, ... + 'Units', 'normalized', ... + 'Position' , [0.75 0.1 0.20 0.08], ... + 'String', 'Close', ... + 'Callback', 'demtrain close'); + + % Save handles of important UI objects + hndlList = [hSlider hSliderText hFilename hPath hPopup ... + hIterations hIterationsText hStart]; + set(fig, 'UserData', hndlList); + % Hide window from command line + set(fig, 'HandleVisibility', 'callback'); + + +elseif strcmp(action, 'slider_moved'), + + % Slider has been moved. + + hndlList = get(gcf, 'UserData'); + hSlider = hndlList(1); + hSliderText = hndlList(2); + + val = get(hSlider, 'Value'); + if rem(val, 1) < 0.5, % Force up and down arrows to work! + val = ceil(val); + else + val = floor(val); + end; + set(hSlider, 'Value', val); + set(hSliderText, 'String', ['Hidden Units: ' int2str(val)]); + + +elseif strcmp(action, 'iterations_moved'), + + % Slider has been moved. + + hndlList = get(gcf, 'UserData'); + hSlider = hndlList(6); + hSliderText = hndlList(7); + + val = get(hSlider, 'Value'); + set(hSliderText, 'String', ['Iterations: ' int2str(val)]); + +elseif strcmp(action, 'get_ip_file'), + + % Get data file button pressed. + + hndlList = get(gcf, 'UserData'); + + [file, path] = uigetfile('*.dat', 'Get Data File', 50, 50); + + if strcmp(file, '') | file == 0, + set(hndlList(3), 'String', 'No data file loaded.'); + set(hndlList(4), 'String', ''); + else + set(hndlList(3), 'String', file); + set(hndlList(4), 'String', path); + end; + + % Enable training button + set(hndlList(8), 'Enable', 'on'); + + set(gcf, 'UserData', hndlList); + +elseif strcmp(action, 'start'), + + % Start training + + % Get handles of and values from UI objects + hndlList = get(gcf, 'UserData'); + hSlider = hndlList(1); % No of hidden units + hIterations = hndlList(6); + iterations = get(hIterations, 'Value'); + + hFilename = hndlList(3); % Data file name + filename = get(hFilename, 'String'); + + hPath = hndlList(4); % Data file path + path = get(hPath, 'String'); + + hPopup = hndlList(5); % Activation function + if get(hPopup, 'Value') == 1, + act_fn = 'linear'; + elseif get(hPopup, 'Value') == 2, + act_fn = 'logistic'; + else + act_fn = 'softmax'; + end; + nhidden = get(hSlider, 'Value'); + + % Check data file exists + if fopen([path '/' filename]) == -1, + errordlg('Training data file has not been selected.', 'Error'); + else + % Load data file + [x,t,nin,nout,ndata] = datread([path filename]); + + % Call MLPTRAIN function repeatedly, while drawing training graph. + figure(DEMTRAIN_FIG); + hold on; + + title('Training - please wait.'); + + % Create net and find initial error + net = mlp(size(x, 2), nhidden, size(t, 2), act_fn); + % Initialise network with inverse variance of 10 + net = mlpinit(net, 10); + error = mlperr(net, x, t); + % Work out reporting step: should be sufficiently big to let training + % algorithm have a chance + step = max(ceil(iterations / 50), 5); + + % Refresh and rescale axis. + cla; + max = error; + min = max/10; + set(gca, 'YScale', 'log'); + ylabel('log Error'); + xlabel('No. iterations'); + axis([0 iterations min max+1]); + iold = 0; + errold = error; + % Plot circle to show error of last iteration + % Setting erase mode to none prevents screen flashing during + % training + plot(0, error, 'ro', 'EraseMode', 'none'); + hold on + drawnow; % Force redraw + for i = step-1:step:iterations, + [net, error] = mlptrain(net, x, t, step); + % Plot line from last point to new point. + line([iold i], [errold error], 'Color', 'r', 'EraseMode', 'none'); + iold = i; + errold = error; + + % If new point off scale, redraw axes. + if error > max, + max = error; + axis([0 iterations min max+1]); + end; + if error < min + min = error/10; + axis([0 iterations min max+1]); + end + % Plot circle to show error of last iteration + plot(i, error, 'ro', 'EraseMode', 'none'); + drawnow; % Force redraw + end; + save mlptrain.net net + zoom on; + + title(['Training complete. Final error=', num2str(error)]); + + end; + +elseif strcmp(action, 'close'), + + % Close all the figures we have created + close(DEMTRAIN_FIG); + for n = 1:NUM_DEMTRAIN_RES_FIGS + if ishandle(DEMTRAIN_RES_FIGS(n)) + close(DEMTRAIN_RES_FIGS(n)); + end + end + +elseif strcmp(action, 'classify'), + + if fopen('mlptrain.net') == -1, + errordlg('You have not yet trained the network.', 'Error'); + else + + hndlList = get(gcf, 'UserData'); + filename = get(hndlList(3), 'String'); + path = get(hndlList(4), 'String'); + [x,t,nin,nout,ndata] = datread([path filename]); + load mlptrain.net net -mat + y = mlpfwd(net, x); + + % Save results figure so that it can be closed later + NUM_DEMTRAIN_RES_FIGS = NUM_DEMTRAIN_RES_FIGS + 1; + DEMTRAIN_RES_FIGS(NUM_DEMTRAIN_RES_FIGS)=conffig(y,t); + + end; + +elseif strcmp(action, 'predict'), + + if fopen('mlptrain.net') == -1, + errordlg('You have not yet trained the network.', 'Error'); + else + + hndlList = get(gcf, 'UserData'); + filename = get(hndlList(3), 'String'); + path = get(hndlList(4), 'String'); + [x,t,nin,nout,ndata] = datread([path filename]); + load mlptrain.net net -mat + y = mlpfwd(net, x); + + for i = 1:size(y,2), + % Save results figure so that it can be closed later + NUM_DEMTRAIN_RES_FIGS = NUM_DEMTRAIN_RES_FIGS + 1; + DEMTRAIN_RES_FIGS(NUM_DEMTRAIN_RES_FIGS) = figure; + hold on; + title(['Output no ' num2str(i)]); + plot([0 1], [0 1], 'r:'); + plot(y(:,i),t(:,i), 'o'); + hold off; + end; + end; + +end; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/dist2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function n2 = dist2(x, c) +%DIST2 Calculates squared distance between two sets of points. +% +% Description +% D = DIST2(X, C) takes two matrices of vectors and calculates the +% squared Euclidean distance between them. Both matrices must be of +% the same column dimension. If X has M rows and N columns, and C has +% L rows and N columns, then the result has M rows and L columns. The +% I, Jth entry is the squared distance from the Ith row of X to the +% Jth row of C. +% +% See also +% GMMACTIV, KMEANS, RBFFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[ndata, dimx] = size(x); +[ncentres, dimc] = size(c); +if dimx ~= dimc + error('Data dimension does not match dimension of centres') +end + +n2 = (ones(ncentres, 1) * sum((x.^2)', 1))' + ... + ones(ndata, 1) * sum((c.^2)',1) - ... + 2.*(x*(c')); + +% Rounding errors occasionally cause negative entries in n2 +if any(any(n2<0)) + n2(n2<0) = 0; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/eigdec.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function [evals, evec] = eigdec(x, N) +%EIGDEC Sorted eigendecomposition +% +% Description +% EVALS = EIGDEC(X, N computes the largest N eigenvalues of the +% matrix X in descending order. [EVALS, EVEC] = EIGDEC(X, N) also +% computes the corresponding eigenvectors. +% +% See also +% PCA, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(x, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the eigenvalues of the data covariance matrix +if evals_only + % Use eig function as always more efficient than eigs here + temp_evals = eig(x); +else + % Use eig function unless fraction of eigenvalues required is tiny + if (N/size(x, 2)) > 0.04 + [temp_evec, temp_evals] = eig(x); + else + options.disp = 0; + [temp_evec, temp_evals] = eigs(x, N, 'LM', options); + end + temp_evals = diag(temp_evals); +end + +% Eigenvalues nearly always returned in descending order, but just +% to make sure..... +[evals perm] = sort(-temp_evals); +evals = -evals(1:N); +if ~evals_only + if evals == temp_evals(1:N) + % Originals were in order + evec = temp_evec(:, 1:N); + return + else + % Need to reorder the eigenvectors + for i=1:N + evec(:,i) = temp_evec(:,perm(i)); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/errbayes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function [e, edata, eprior] = errbayes(net, edata) +%ERRBAYES Evaluate Bayesian error function for network. +% +% Description +% E = ERRBAYES(NET, EDATA) takes a network data structure NET together +% the data contribution to the error for a set of inputs and targets. +% It returns the regularised error using any zero mean Gaussian priors +% on the weights defined in NET. +% +% [E, EDATA, EPRIOR] = ERRBAYES(NET, X, T) additionally returns the +% data and prior components of the error. +% +% See also +% GLMERR, MLPERR, RBFERR +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Evaluate the data contribution to the error. +if isfield(net, 'beta') + e1 = net.beta*edata; +else + e1 = edata; +end + +% Evaluate the prior contribution to the error. +if isfield(net, 'alpha') + w = netpak(net); + if size(net.alpha) == [1 1] + eprior = 0.5*(w*w'); + e2 = eprior*net.alpha; + else + if (isfield(net, 'mask')) + nindx_cols = size(net.index, 2); + nmask_rows = size(find(net.mask), 1); + index = reshape(net.index(logical(repmat(net.mask, ... + 1, nindx_cols))), nmask_rows, nindx_cols); + else + index = net.index; + end + eprior = 0.5*(w.^2)*index; + e2 = eprior*net.alpha; + end +else + eprior = 0; + e2 = 0; +end + +e = e1 + e2;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/evidence.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +function [net, gamma, logev] = evidence(net, x, t, num) +%EVIDENCE Re-estimate hyperparameters using evidence approximation. +% +% Description +% [NET] = EVIDENCE(NET, X, T) re-estimates the hyperparameters ALPHA +% and BETA by applying Bayesian re-estimation formulae for NUM +% iterations. The hyperparameter ALPHA can be a simple scalar +% associated with an isotropic prior on the weights, or can be a vector +% in which each component is associated with a group of weights as +% defined by the INDEX matrix in the NET data structure. These more +% complex priors can be set up for an MLP using MLPPRIOR. Initial +% values for the iterative re-estimation are taken from the network +% data structure NET passed as an input argument, while the return +% argument NET contains the re-estimated values. +% +% [NET, GAMMA, LOGEV] = EVIDENCE(NET, X, T, NUM) allows the re- +% estimation formula to be applied for NUM cycles in which the re- +% estimated values for the hyperparameters from each cycle are used to +% re-evaluate the Hessian matrix for the next cycle. The return value +% GAMMA is the number of well-determined parameters and LOGEV is the +% log of the evidence. +% +% See also +% MLPPRIOR, NETGRAD, NETHESS, DEMEV1, DEMARD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, '', x, t); +if ~isempty(errstring) + error(errstring); +end + +ndata = size(x, 1); +if nargin == 3 + num = 1; +end + +% Extract weights from network +w = netpak(net); + +% Evaluate data-dependent contribution to the Hessian matrix. +[h, dh] = nethess(w, net, x, t); +clear h; % To save memory when Hessian is large +if (~isfield(net, 'beta')) + local_beta = 1; +end + +[evec, evl] = eig(dh); +% Now set the negative eigenvalues to zero. +evl = evl.*(evl > 0); +% safe_evl is used to avoid taking log of zero +safe_evl = evl + eps.*(evl <= 0); + +[e, edata, eprior] = neterr(w, net, x, t); + +if size(net.alpha) == [1 1] + % Form vector of eigenvalues + evl = diag(evl); + safe_evl = diag(safe_evl); +else + ngroups = size(net.alpha, 1); + gams = zeros(1, ngroups); + logas = zeros(1, ngroups); + % Reconstruct data hessian with negative eigenvalues set to zero. + dh = evec*evl*evec'; +end + +% Do the re-estimation. +for k = 1 : num + % Re-estimate alpha. + if size(net.alpha) == [1 1] + % Evaluate number of well-determined parameters. + L = evl; + if isfield(net, 'beta') + L = net.beta*L; + end + gamma = sum(L./(L + net.alpha)); + net.alpha = 0.5*gamma/eprior; + % Partially evaluate log evidence: only include unmasked weights + logev = 0.5*length(w)*log(net.alpha); + else + hinv = inv(hbayes(net, dh)); + for m = 1 : ngroups + group_nweights = sum(net.index(:, m)); + gams(m) = group_nweights - ... + net.alpha(m)*sum(diag(hinv).*net.index(:,m)); + net.alpha(m) = real(gams(m)/(2*eprior(m))); + % Weight alphas by number of weights in group + logas(m) = 0.5*group_nweights*log(net.alpha(m)); + end + gamma = sum(gams, 2); + logev = sum(logas); + end + % Re-estimate beta. + if isfield(net, 'beta') + net.beta = 0.5*(net.nout*ndata - gamma)/edata; + logev = logev + 0.5*ndata*log(net.beta) - 0.5*ndata*log(2*pi); + local_beta = net.beta; + end + + % Evaluate new log evidence + e = errbayes(net, edata); + if size(net.alpha) == [1 1] + logev = logev - e - 0.5*sum(log(local_beta*safe_evl+net.alpha)); + else + for m = 1:ngroups + logev = logev - e - ... + 0.5*sum(log(local_beta*(safe_evl*net.index(:, m))+... + net.alpha(m))); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/fevbayes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess) +%FEVBAYES Evaluate Bayesian regularisation for network forward propagation. +% +% Description +% EXTRA = FEVBAYES(NET, Y, A, X, T, X_TEST) takes a network data +% structure NET together with a set of hidden unit activations A from +% test inputs X_TEST, training data inputs X and T and outputs a matrix +% of extra information EXTRA that consists of error bars (variance) for +% a regression problem or moderated outputs for a classification +% problem. The optional argument (and return value) INVHESS is the +% inverse of the network Hessian computed on the training data inputs +% and targets. Passing it in avoids recomputing it, which can be a +% significant saving for large training sets. +% +% This is called by network-specific functions such as MLPEVFWD which +% are needed since the return values (predictions and hidden unit +% activations) for different network types are in different orders (for +% good reasons). +% +% See also +% MLPEVFWD, RBFEVFWD, GLMEVFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +w = netpak(net); +g = netderiv(w, net, x_test); +if nargin < 7 + % Need to compute inverse hessian + hess = nethess(w, net, x, t); + invhess = inv(hess); +end + +ntest = size(x_test, 1); +var = zeros(ntest, 1); +for idx = 1:1:net.nout, + for n = 1:1:ntest, + grad = squeeze(g(n,:,idx)); + var(n,idx) = grad*invhess*grad'; + end +end + +switch net.outfn + case 'linear' + % extra is variance + extra = ones(size(var))./net.beta + var; + case 'logistic' + % extra is moderated output + kappa = 1./(sqrt(ones(size(var)) + (pi.*var)./8)); + extra = 1./(1 + exp(-kappa.*a)); + case 'softmax' + % Use extended Mackay formula; beware that this may not + % be very accurate + kappa = 1./(sqrt(ones(size(var)) + (pi.*var)./8)); + temp = exp(kappa.*a); + extra = temp./(sum(temp, 2)*ones(1, net.nout)); + otherwise + error(['Unknown activation function ', net.outfn]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gauss.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +function y = gauss(mu, covar, x) +%GAUSS Evaluate a Gaussian distribution. +% +% Description +% +% Y = GAUSS(MU, COVAR, X) evaluates a multi-variate Gaussian density +% in D-dimensions at a set of points given by the rows of the matrix X. +% The Gaussian density has mean vector MU and covariance matrix COVAR. +% +% See also +% GSAMP, DEMGAUSS +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[n, d] = size(x); + +[j, k] = size(covar); + +% Check that the covariance matrix is the correct dimension +if ((j ~= d) | (k ~=d)) + error('Dimension of the covariance matrix and data should match'); +end + +invcov = inv(covar); +mu = reshape(mu, 1, d); % Ensure that mu is a row vector + +x = x - ones(n, 1)*mu; +fact = sum(((x*invcov).*x), 2); + +y = exp(-0.5*fact); + +y = y./sqrt((2*pi)^d*det(covar));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gbayes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +function [g, gdata, gprior] = gbayes(net, gdata) +%GBAYES Evaluate gradient of Bayesian error function for network. +% +% Description +% G = GBAYES(NET, GDATA) takes a network data structure NET together +% the data contribution to the error gradient for a set of inputs and +% targets. It returns the regularised error gradient using any zero +% mean Gaussian priors on the weights defined in NET. In addition, if +% a MASK is defined in NET, then the entries in G that correspond to +% weights with a 0 in the mask are removed. +% +% [G, GDATA, GPRIOR] = GBAYES(NET, GDATA) additionally returns the data +% and prior components of the error. +% +% See also +% ERRBAYES, GLMGRAD, MLPGRAD, RBFGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Evaluate the data contribution to the gradient. +if (isfield(net, 'mask')) + gdata = gdata(logical(net.mask)); +end +if isfield(net, 'beta') + g1 = gdata*net.beta; +else + g1 = gdata; +end + +% Evaluate the prior contribution to the gradient. +if isfield(net, 'alpha') + w = netpak(net); + if size(net.alpha) == [1 1] + gprior = w; + g2 = net.alpha*gprior; + else + if (isfield(net, 'mask')) + nindx_cols = size(net.index, 2); + nmask_rows = size(find(net.mask), 1); + index = reshape(net.index(logical(repmat(net.mask, ... + 1, nindx_cols))), nmask_rows, nindx_cols); + else + index = net.index; + end + + ngroups = size(net.alpha, 1); + gprior = index'.*(ones(ngroups, 1)*w); + g2 = net.alpha'*gprior; + end +else + gprior = 0; + g2 = 0; +end + +g = g1 + g2;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,82 @@ +function net = glm(nin, nout, outfunc, prior, beta) +%GLM Create a generalized linear model. +% +% Description +% +% NET = GLM(NIN, NOUT, FUNC) takes the number of inputs and outputs for +% a generalized linear model, together with a string FUNC which +% specifies the output unit activation function, and returns a data +% structure NET. The weights are drawn from a zero mean, isotropic +% Gaussian, with variance scaled by the fan-in of the output units. +% This makes use of the Matlab function RANDN and so the seed for the +% random weight initialization can be set using RANDN('STATE', S) +% where S is the seed value. The optional argument ALPHA sets the +% inverse variance for the weight initialization. +% +% The fields in NET are +% type = 'glm' +% nin = number of inputs +% nout = number of outputs +% nwts = total number of weights and biases +% actfn = string describing the output unit activation function: +% 'linear' +% 'logistic' +% 'softmax' +% w1 = first-layer weight matrix +% b1 = first-layer bias vector +% +% NET = GLM(NIN, NOUT, FUNC, PRIOR), in which PRIOR is a scalar, allows +% the field NET.ALPHA in the data structure NET to be set, +% corresponding to a zero-mean isotropic Gaussian prior with inverse +% variance with value PRIOR. Alternatively, PRIOR can consist of a data +% structure with fields ALPHA and INDEX, allowing individual Gaussian +% priors to be set over groups of weights in the network. Here ALPHA is +% a column vector in which each element corresponds to a separate +% group of weights, which need not be mutually exclusive. The +% membership of the groups is defined by the matrix INDEX in which the +% columns correspond to the elements of ALPHA. Each column has one +% element for each weight in the matrix, in the order defined by the +% function GLMPAK, and each element is 1 or 0 according to whether the +% weight is a member of the corresponding group or not. +% +% NET = GLM(NIN, NOUT, FUNC, PRIOR, BETA) also sets the additional +% field NET.BETA in the data structure NET, where beta corresponds to +% the inverse noise variance. +% +% See also +% GLMPAK, GLMUNPAK, GLMFWD, GLMERR, GLMGRAD, GLMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +net.type = 'glm'; +net.nin = nin; +net.nout = nout; +net.nwts = (nin + 1)*nout; + +outtfns = {'linear', 'logistic', 'softmax'}; + +if sum(strcmp(outfunc, outtfns)) == 0 + error('Undefined activation function. Exiting.'); +else + net.outfn = outfunc; +end + +if nargin > 3 + if isstruct(prior) + net.alpha = prior.alpha; + net.index = prior.index; + elseif size(prior) == [1 1] + net.alpha = prior; + else + error('prior must be a scalar or structure'); + end +end + +net.w1 = randn(nin, nout)/sqrt(nin + 1); +net.b1 = randn(1, nout)/sqrt(nin + 1); + +if nargin == 5 + net.beta = beta; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmderiv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function g = glmderiv(net, x) +%GLMDERIV Evaluate derivatives of GLM outputs with respect to weights. +% +% Description +% G = GLMDERIV(NET, X) takes a network data structure NET and a matrix +% of input vectors X and returns a three-index matrix mat{g} whose I, +% J, K element contains the derivative of network output K with respect +% to weight or bias parameter J for input pattern I. The ordering of +% the weight and bias parameters is defined by GLMUNPAK. +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'glm', x); +if ~isempty(errstring) + error(errstring); +end + +ndata = size(x, 1); +if isfield(net, 'mask') + nwts = size(find(net.mask), 1); + mask_array = logical(net.mask)*ones(1, net.nout); +else + nwts = net.nwts; +end +g = zeros(ndata, nwts, net.nout); + +temp = zeros(net.nwts, net.nout); +for n = 1:ndata + % Weight matrix w1 + temp(1:(net.nin*net.nout), :) = kron(eye(net.nout), (x(n, :))'); + % Bias term b1 + temp(net.nin*net.nout+1:end, :) = eye(net.nout); + if isfield(net, 'mask') + g(n, :, :) = reshape(temp(find(mask_array)), nwts, net.nout); + else + g(n, :, :) = temp; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmerr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function [e, edata, eprior, y, a] = glmerr(net, x, t) +%GLMERR Evaluate error function for generalized linear model. +% +% Description +% E = GLMERR(NET, X, T) takes a generalized linear model data +% structure NET together with a matrix X of input vectors and a matrix +% T of target vectors, and evaluates the error function E. The choice +% of error function corresponds to the output unit activation function. +% Each row of X corresponds to one input vector and each row of T +% corresponds to one target vector. +% +% [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X, T) also returns the data +% and prior components of the total error. +% +% [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X) also returns a matrix Y +% giving the outputs of the models and a matrix A giving the summed +% inputs to each output unit, where each row corresponds to one +% pattern. +% +% See also +% GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMGRAD, GLMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'glm', x, t); +if ~isempty(errstring); + error(errstring); +end + +[y, a] = glmfwd(net, x); + +switch net.outfn + + case 'linear' % Linear outputs + edata = 0.5*sum(sum((y - t).^2)); + + case 'logistic' % Logistic outputs + edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y))); + + case 'softmax' % Softmax outputs + edata = - sum(sum(t.*log(y))); + + otherwise + error(['Unknown activation function ', net.outfn]); +end + +[e, edata, eprior] = errbayes(net, edata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmevfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function [y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess) +%GLMEVFWD Forward propagation with evidence for GLM +% +% Description +% Y = GLMEVFWD(NET, X, T, X_TEST) takes a network data structure NET +% together with the input X and target T training data and input test +% data X_TEST. It returns the normal forward propagation through the +% network Y together with a matrix EXTRA which consists of error bars +% (variance) for a regression problem or moderated outputs for a +% classification problem. +% +% The optional argument (and return value) INVHESS is the inverse of +% the network Hessian computed on the training data inputs and targets. +% Passing it in avoids recomputing it, which can be a significant +% saving for large training sets. +% +% See also +% FEVBAYES +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[y, a] = glmfwd(net, x_test); +if nargin == 4 + [extra, invhess] = fevbayes(net, y, a, x, t, x_test); +else + [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function [y, a] = glmfwd(net, x) +%GLMFWD Forward propagation through generalized linear model. +% +% Description +% Y = GLMFWD(NET, X) takes a generalized linear model data structure +% NET together with a matrix X of input vectors, and forward propagates +% the inputs through the network to generate a matrix Y of output +% vectors. Each row of X corresponds to one input vector and each row +% of Y corresponds to one output vector. +% +% [Y, A] = GLMFWD(NET, X) also returns a matrix A giving the summed +% inputs to each output unit, where each row corresponds to one +% pattern. +% +% See also +% GLM, GLMPAK, GLMUNPAK, GLMERR, GLMGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'glm', x); +if ~isempty(errstring); + error(errstring); +end + +ndata = size(x, 1); + +a = x*net.w1 + ones(ndata, 1)*net.b1; + +switch net.outfn + + case 'linear' % Linear outputs + y = a; + + case 'logistic' % Logistic outputs + % Prevent overflow and underflow: use same bounds as glmerr + % Ensure that log(1-y) is computable: need exp(a) > eps + maxcut = -log(eps); + % Ensure that log(y) is computable + mincut = -log(1/realmin - 1); + a = min(a, maxcut); + a = max(a, mincut); + y = 1./(1 + exp(-a)); + + case 'softmax' % Softmax outputs + nout = size(a,2); + % Prevent overflow and underflow: use same bounds as glmerr + % Ensure that sum(exp(a), 2) does not overflow + maxcut = log(realmax) - log(nout); + % Ensure that exp(a) > 0 + mincut = log(realmin); + a = min(a, maxcut); + a = max(a, mincut); + temp = exp(a); + y = temp./(sum(temp, 2)*ones(1,nout)); + % Ensure that log(y) is computable + y(y<realmin) = realmin; + + otherwise + error(['Unknown activation function ', net.outfn]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmgrad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function [g, gdata, gprior] = glmgrad(net, x, t) +%GLMGRAD Evaluate gradient of error function for generalized linear model. +% +% Description +% G = GLMGRAD(NET, X, T) takes a generalized linear model data +% structure NET together with a matrix X of input vectors and a matrix +% T of target vectors, and evaluates the gradient G of the error +% function with respect to the network weights. The error function +% corresponds to the choice of output unit activation function. Each +% row of X corresponds to one input vector and each row of T +% corresponds to one target vector. +% +% [G, GDATA, GPRIOR] = GLMGRAD(NET, X, T) also returns separately the +% data and prior contributions to the gradient. +% +% See also +% GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMERR, GLMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'glm', x, t); +if ~isempty(errstring); + error(errstring); +end + +y = glmfwd(net, x); +delout = y - t; + +gw1 = x'*delout; +gb1 = sum(delout, 1); + +gdata = [gw1(:)', gb1]; + +[g, gdata, gprior] = gbayes(net, gdata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmhess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,106 @@ +function [h, hdata] = glmhess(net, x, t, hdata) +%GLMHESS Evaluate the Hessian matrix for a generalised linear model. +% +% Description +% H = GLMHESS(NET, X, T) takes a GLM network data structure NET, a +% matrix X of input values, and a matrix T of target values and returns +% the full Hessian matrix H corresponding to the second derivatives of +% the negative log posterior distribution, evaluated for the current +% weight and bias values as defined by NET. Note that the target data +% is not required in the calculation, but is included to make the +% interface uniform with NETHESS. For linear and logistic outputs, the +% computation is very simple and is done (in effect) in one line in +% GLMTRAIN. +% +% [H, HDATA] = GLMHESS(NET, X, T) returns both the Hessian matrix H and +% the contribution HDATA arising from the data dependent term in the +% Hessian. +% +% H = GLMHESS(NET, X, T, HDATA) takes a network data structure NET, a +% matrix X of input values, and a matrix T of target values, together +% with the contribution HDATA arising from the data dependent term in +% the Hessian, and returns the full Hessian matrix H corresponding to +% the second derivatives of the negative log posterior distribution. +% This version saves computation time if HDATA has already been +% evaluated for the current weight and bias values. +% +% See also +% GLM, GLMTRAIN, HESSCHEK, NETHESS +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'glm', x, t); +if ~isempty(errstring); + error(errstring); +end + +ndata = size(x, 1); +nparams = net.nwts; +nout = net.nout; +p = glmfwd(net, x); +inputs = [x ones(ndata, 1)]; + +if nargin == 3 + hdata = zeros(nparams); % Full Hessian matrix + % Calculate data component of Hessian + switch net.outfn + + case 'linear' + % No weighting function here + out_hess = [x ones(ndata, 1)]'*[x ones(ndata, 1)]; + for j = 1:nout + hdata = rearrange_hess(net, j, out_hess, hdata); + end + case 'logistic' + % Each output is independent + e = ones(1, net.nin+1); + link_deriv = p.*(1-p); + out_hess = zeros(net.nin+1); + for j = 1:nout + inputs = [x ones(ndata, 1)].*(sqrt(link_deriv(:,j))*e); + out_hess = inputs'*inputs; % Hessian for this output + hdata = rearrange_hess(net, j, out_hess, hdata); + end + + case 'softmax' + bb_start = nparams - nout + 1; % Start of bias weights block + ex_hess = zeros(nparams); % Contribution to Hessian from single example + for m = 1:ndata + X = x(m,:)'*x(m,:); + a = diag(p(m,:))-((p(m,:)')*p(m,:)); + ex_hess(1:nparams-nout,1:nparams-nout) = kron(a, X); + ex_hess(bb_start:nparams, bb_start:nparams) = a.*ones(net.nout, net.nout); + temp = kron(a, x(m,:)); + ex_hess(bb_start:nparams, 1:nparams-nout) = temp; + ex_hess(1:nparams-nout, bb_start:nparams) = temp'; + hdata = hdata + ex_hess; + end + otherwise + error(['Unknown activation function ', net.outfn]); + end +end + +[h, hdata] = hbayes(net, hdata); + +function hdata = rearrange_hess(net, j, out_hess, hdata) + +% Because all the biases come after all the input weights, +% we have to rearrange the blocks that make up the network Hessian. +% This function assumes that we are on the jth output and that all outputs +% are independent. + +bb_start = net.nwts - net.nout + 1; % Start of bias weights block +ob_start = 1+(j-1)*net.nin; % Start of weight block for jth output +ob_end = j*net.nin; % End of weight block for jth output +b_index = bb_start+(j-1); % Index of bias weight +% Put input weight block in right place +hdata(ob_start:ob_end, ob_start:ob_end) = out_hess(1:net.nin, 1:net.nin); +% Put second derivative of bias weight in right place +hdata(b_index, b_index) = out_hess(net.nin+1, net.nin+1); +% Put cross terms (input weight v bias weight) in right place +hdata(b_index, ob_start:ob_end) = out_hess(net.nin+1,1:net.nin); +hdata(ob_start:ob_end, b_index) = out_hess(1:net.nin, net.nin+1); + +return \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glminit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function net = glminit(net, prior) +%GLMINIT Initialise the weights in a generalized linear model. +% +% Description +% +% NET = GLMINIT(NET, PRIOR) takes a generalized linear model NET and +% sets the weights and biases by sampling from a Gaussian distribution. +% If PRIOR is a scalar, then all of the parameters (weights and biases) +% are sampled from a single isotropic Gaussian with inverse variance +% equal to PRIOR. If PRIOR is a data structure similar to that in +% MLPPRIOR but for a single layer of weights, then the parameters are +% sampled from multiple Gaussians according to their groupings (defined +% by the INDEX field) with corresponding variances (defined by the +% ALPHA field). +% +% See also +% GLM, GLMPAK, GLMUNPAK, MLPINIT, MLPPRIOR +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'glm'); +if ~isempty(errstring); + error(errstring); +end +if isstruct(prior) + sig = 1./sqrt(prior.index*prior.alpha); + w = sig'.*randn(1, net.nwts); +elseif size(prior) == [1 1] + w = randn(1, net.nwts).*sqrt(1/prior); +else + error('prior must be a scalar or a structure'); +end + +net = glmunpak(net, w); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function w = glmpak(net) +%GLMPAK Combines weights and biases into one weights vector. +% +% Description +% W = GLMPAK(NET) takes a network data structure NET and combines them +% into a single row vector W. +% +% See also +% GLM, GLMUNPAK, GLMFWD, GLMERR, GLMGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'glm'); +if ~errstring + error(errstring); +end + +w = [net.w1(:)', net.b1]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmtrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,206 @@ +function [net, options] = glmtrain(net, options, x, t) +%GLMTRAIN Specialised training of generalized linear model +% +% Description +% NET = GLMTRAIN(NET, OPTIONS, X, T) uses the iterative reweighted +% least squares (IRLS) algorithm to set the weights in the generalized +% linear model structure NET. This is a more efficient alternative to +% using GLMERR and GLMGRAD and a non-linear optimisation routine +% through NETOPT. Note that for linear outputs, a single pass through +% the algorithm is all that is required, since the error function is +% quadratic in the weights. The algorithm also handles scalar ALPHA +% and BETA terms. If you want to use more complicated priors, you +% should use general-purpose non-linear optimisation algorithms. +% +% For logistic and softmax outputs, general priors can be handled, +% although this requires the pseudo-inverse of the Hessian, giving up +% the better conditioning and some of the speed advantage of the normal +% form equations. +% +% The error function value at the final set of weights is returned in +% OPTIONS(8). Each row of X corresponds to one input vector and each +% row of T corresponds to one target vector. +% +% The optional parameters have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values during training. If +% OPTIONS(1) is set to 0, then only warning messages are displayed. If +% OPTIONS(1) is -1, then nothing is displayed. +% +% OPTIONS(2) is a measure of the precision required for the value of +% the weights W at the solution. +% +% OPTIONS(3) is a measure of the precision required of the objective +% function at the solution. Both this and the previous condition must +% be satisfied for termination. +% +% OPTIONS(5) is set to 1 if an approximation to the Hessian (which +% assumes that all outputs are independent) is used for softmax +% outputs. With the default value of 0 the exact Hessian (which is more +% expensive to compute) is used. +% +% OPTIONS(14) is the maximum number of iterations for the IRLS +% algorithm; default 100. +% +% See also +% GLM, GLMERR, GLMGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'glm', x, t); +if ~errstring + error(errstring); +end + +if(~options(14)) + options(14) = 100; +end + +display = options(1); +% Do we need to test for termination? +test = (options(2) | options(3)); + +ndata = size(x, 1); +% Add a column of ones for the bias +inputs = [x ones(ndata, 1)]; + +% Linear outputs are a special case as they can be found in one step +if strcmp(net.outfn, 'linear') + if ~isfield(net, 'alpha') + % Solve for the weights and biases using left matrix divide + temp = inputs\t; + elseif size(net.alpha == [1 1]) + if isfield(net, 'beta') + beta = net.beta; + else + beta = 1.0; + end + % Use normal form equation + hessian = beta*(inputs'*inputs) + net.alpha*eye(net.nin+1); + temp = pinv(hessian)*(beta*(inputs'*t)); + else + error('Only scalar alpha allowed'); + end + net.w1 = temp(1:net.nin, :); + net.b1 = temp(net.nin+1, :); + % Store error value in options vector + options(8) = glmerr(net, x, t); + return; +end + +% Otherwise need to use iterative reweighted least squares +e = ones(1, net.nin+1); +for n = 1:options(14) + + switch net.outfn + case 'logistic' + if n == 1 + % Initialise model + p = (t+0.5)/2; + act = log(p./(1-p)); + wold = glmpak(net); + end + link_deriv = p.*(1-p); + weights = sqrt(link_deriv); % sqrt of weights + if (min(min(weights)) < eps) + warning('ill-conditioned weights in glmtrain') + return + end + z = act + (t-p)./link_deriv; + if ~isfield(net, 'alpha') + % Treat each output independently with relevant set of weights + for j = 1:net.nout + indep = inputs.*(weights(:,j)*e); + dep = z(:,j).*weights(:,j); + temp = indep\dep; + net.w1(:,j) = temp(1:net.nin); + net.b1(j) = temp(net.nin+1); + end + else + gradient = glmgrad(net, x, t); + Hessian = glmhess(net, x, t); + deltaw = -gradient*pinv(Hessian); + w = wold + deltaw; + net = glmunpak(net, w); + end + [err, edata, eprior, p, act] = glmerr(net, x, t); + if n == 1 + errold = err; + wold = netpak(net); + else + w = netpak(net); + end + case 'softmax' + if n == 1 + % Initialise model: ensure that row sum of p is one no matter + % how many classes there are + p = (t + (1/size(t, 2)))/2; + act = log(p./(1-p)); + end + if options(5) == 1 | n == 1 + link_deriv = p.*(1-p); + weights = sqrt(link_deriv); % sqrt of weights + if (min(min(weights)) < eps) + warning('ill-conditioned weights in glmtrain') + return + end + z = act + (t-p)./link_deriv; + % Treat each output independently with relevant set of weights + for j = 1:net.nout + indep = inputs.*(weights(:,j)*e); + dep = z(:,j).*weights(:,j); + temp = indep\dep; + net.w1(:,j) = temp(1:net.nin); + net.b1(j) = temp(net.nin+1); + end + [err, edata, eprior, p, act] = glmerr(net, x, t); + if n == 1 + errold = err; + wold = netpak(net); + else + w = netpak(net); + end + else + % Exact method of calculation after w first initialised + % Start by working out Hessian + Hessian = glmhess(net, x, t); + gradient = glmgrad(net, x, t); + % Now compute modification to weights + deltaw = -gradient*pinv(Hessian); + w = wold + deltaw; + net = glmunpak(net, w); + [err, edata, eprior, p] = glmerr(net, x, t); + end + + otherwise + error(['Unknown activation function ', net.outfn]); + end + if options(1) + fprintf(1, 'Cycle %4d Error %11.6f\n', n, err) + end + % Test for termination + % Terminate if error increases + if err > errold + errold = err; + w = wold; + options(8) = err; + fprintf(1, 'Error has increased: terminating\n') + return; + end + if test & n > 1 + if (max(abs(w - wold)) < options(2) & abs(err-errold) < options(3)) + options(8) = err; + return; + else + errold = err; + wold = w; + end + end +end + +options(8) = err; +if (options(1) >= 0) + disp(maxitmess); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glmunpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function net = glmunpak(net, w) +%GLMUNPAK Separates weights vector into weight and bias matrices. +% +% Description +% NET = GLMUNPAK(NET, W) takes a glm network data structure NET and a +% weight vector W, and returns a network data structure identical to +% the input network, except that the first-layer weight matrix W1 and +% the first-layer bias vector B1 have been set to the corresponding +% elements of W. +% +% See also +% GLM, GLMPAK, GLMFWD, GLMERR, GLMGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'glm'); +if ~errstring + error(errstring); +end + +if net.nwts ~= length(w) + error('Invalid weight vector length') +end + +nin = net.nin; +nout = net.nout; +net.w1 = reshape(w(1:nin*nout), nin, nout); +net.b1 = reshape(w(nin*nout + 1: (nin + 1)*nout), 1, nout);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,113 @@ +function mix = gmm(dim, ncentres, covar_type, ppca_dim) +%GMM Creates a Gaussian mixture model with specified architecture. +% +% Description +% MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the dimension of the space +% DIM, the number of centres in the mixture model and the type of the +% mixture model, and returns a data structure MIX. The mixture model +% type defines the covariance structure of each component Gaussian: +% 'spherical' = single variance parameter for each component: stored as a vector +% 'diag' = diagonal matrix for each component: stored as rows of a matrix +% 'full' = full matrix for each component: stored as 3d array +% 'ppca' = probabilistic PCA: stored as principal components (in a 3d array +% and associated variances and off-subspace noise +% MIX = GMM(DIM, NCENTRES, COVARTYPE, PPCA_DIM) also sets the +% dimension of the PPCA sub-spaces: the default value is one. +% +% The priors are initialised to equal values summing to one, and the +% covariances are all the identity matrix (or equivalent). The centres +% are initialised randomly from a zero mean unit variance Gaussian. +% This makes use of the MATLAB function RANDN and so the seed for the +% random weight initialisation can be set using RANDN('STATE', S) where +% S is the state value. +% +% The fields in MIX are +% +% type = 'gmm' +% nin = the dimension of the space +% ncentres = number of mixture components +% covartype = string for type of variance model +% priors = mixing coefficients +% centres = means of Gaussians: stored as rows of a matrix +% covars = covariances of Gaussians +% The additional fields for mixtures of PPCA are +% U = principal component subspaces +% lambda = in-space covariances: stored as rows of a matrix +% The off-subspace noise is stored in COVARS. +% +% See also +% GMMPAK, GMMUNPAK, GMMSAMP, GMMINIT, GMMEM, GMMACTIV, GMMPOST, +% GMMPROB +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if ncentres < 1 + error('Number of centres must be greater than zero') +end + +mix.type = 'gmm'; +mix.nin = dim; +mix.ncentres = ncentres; + +vartypes = {'spherical', 'diag', 'full', 'ppca'}; + +if sum(strcmp(covar_type, vartypes)) == 0 + error('Undefined covariance type') +else + mix.covar_type = covar_type; +end + +% Make default dimension of PPCA subspaces one. +if strcmp(covar_type, 'ppca') + if nargin < 4 + ppca_dim = 1; + end + if ppca_dim > dim + error('Dimension of PPCA subspaces must be less than data.') + end + mix.ppca_dim = ppca_dim; +end + +% Initialise priors to be equal and summing to one +mix.priors = ones(1,mix.ncentres) ./ mix.ncentres; + +% Initialise centres +mix.centres = randn(mix.ncentres, mix.nin); + +% Initialise all the variances to unity +switch mix.covar_type + +case 'spherical' + mix.covars = ones(1, mix.ncentres); + mix.nwts = mix.ncentres + mix.ncentres*mix.nin + mix.ncentres; +case 'diag' + % Store diagonals of covariance matrices as rows in a matrix + mix.covars = ones(mix.ncentres, mix.nin); + mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ... + mix.ncentres*mix.nin; +case 'full' + % Store covariance matrices in a row vector of matrices + mix.covars = repmat(eye(mix.nin), [1 1 mix.ncentres]); + mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ... + mix.ncentres*mix.nin*mix.nin; +case 'ppca' + % This is the off-subspace noise: make it smaller than + % lambdas + mix.covars = 0.1*ones(1, mix.ncentres); + % Also set aside storage for principal components and + % associated variances + init_space = eye(mix.nin); + init_space = init_space(:, 1:mix.ppca_dim); + init_space(mix.ppca_dim+1:mix.nin, :) = ... + ones(mix.nin - mix.ppca_dim, mix.ppca_dim); + mix.U = repmat(init_space , [1 1 mix.ncentres]); + mix.lambda = ones(mix.ncentres, mix.ppca_dim); + % Take account of additional parameters + mix.nwts = mix.ncentres + mix.ncentres*mix.nin + ... + mix.ncentres + mix.ncentres*mix.ppca_dim + ... + mix.ncentres*mix.nin*mix.ppca_dim; +otherwise + error(['Unknown covariance type ', mix.covar_type]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmmactiv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,77 @@ +function a = gmmactiv(mix, x) +%GMMACTIV Computes the activations of a Gaussian mixture model. +% +% Description +% This function computes the activations A (i.e. the probability +% P(X|J) of the data conditioned on each component density) for a +% Gaussian mixture model. For the PPCA model, each activation is the +% conditional probability of X given that it is generated by the +% component subspace. The data structure MIX defines the mixture model, +% while the matrix X contains the data vectors. Each row of X +% represents a single vector. +% +% See also +% GMM, GMMPOST, GMMPROB +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check that inputs are consistent +errstring = consist(mix, 'gmm', x); +if ~isempty(errstring) + error(errstring); +end + +ndata = size(x, 1); +a = zeros(ndata, mix.ncentres); % Preallocate matrix + +switch mix.covar_type + +case 'spherical' + % Calculate squared norm matrix, of dimension (ndata, ncentres) + n2 = dist2(x, mix.centres); + + % Calculate width factors + wi2 = ones(ndata, 1) * (2 .* mix.covars); + normal = (pi .* wi2) .^ (mix.nin/2); + + % Now compute the activations + a = exp(-(n2./wi2))./ normal; + +case 'diag' + normal = (2*pi)^(mix.nin/2); + s = prod(sqrt(mix.covars), 2); + for j = 1:mix.ncentres + diffs = x - (ones(ndata, 1) * mix.centres(j, :)); + a(:, j) = exp(-0.5*sum((diffs.*diffs)./(ones(ndata, 1) * ... + mix.covars(j, :)), 2)) ./ (normal*s(j)); + end + +case 'full' + normal = (2*pi)^(mix.nin/2); + for j = 1:mix.ncentres + diffs = x - (ones(ndata, 1) * mix.centres(j, :)); + % Use Cholesky decomposition of covariance matrix to speed computation + c = chol(mix.covars(:, :, j)); + temp = diffs/c; + a(:, j) = exp(-0.5*sum(temp.*temp, 2))./(normal*prod(diag(c))); + end +case 'ppca' + log_normal = mix.nin*log(2*pi); + d2 = zeros(ndata, mix.ncentres); + logZ = zeros(1, mix.ncentres); + for i = 1:mix.ncentres + k = 1 - mix.covars(i)./mix.lambda(i, :); + logZ(i) = log_normal + mix.nin*log(mix.covars(i)) - ... + sum(log(1 - k)); + diffs = x - ones(ndata, 1)*mix.centres(i, :); + proj = diffs*mix.U(:, :, i); + d2(:,i) = (sum(diffs.*diffs, 2) - ... + sum((proj.*(ones(ndata, 1)*k)).*proj, 2)) / ... + mix.covars(i); + end + a = exp(-0.5*(d2 + ones(ndata, 1)*logZ)); +otherwise + error(['Unknown covariance type ', mix.covar_type]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmmem.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +function [mix, options, errlog] = gmmem(mix, x, options) +%GMMEM EM algorithm for Gaussian mixture model. +% +% Description +% [MIX, OPTIONS, ERRLOG] = GMMEM(MIX, X, OPTIONS) uses the Expectation +% Maximization algorithm of Dempster et al. to estimate the parameters +% of a Gaussian mixture model defined by a data structure MIX. The +% matrix X represents the data whose expectation is maximized, with +% each row corresponding to a vector. The optional parameters have +% the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG. If OPTIONS(1) is set to 0, then +% only warning messages are displayed. If OPTIONS(1) is -1, then +% nothing is displayed. +% +% OPTIONS(3) is a measure of the absolute precision required of the +% error function at the solution. If the change in log likelihood +% between two steps of the EM algorithm is less than this value, then +% the function terminates. +% +% OPTIONS(5) is set to 1 if a covariance matrix is reset to its +% original value when any of its singular values are too small (less +% than MIN_COVAR which has the value eps). With the default value of +% 0 no action is taken. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% The optional return value OPTIONS contains the final error value +% (i.e. data log likelihood) in OPTIONS(8). +% +% See also +% GMM, GMMINIT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check that inputs are consistent +errstring = consist(mix, 'gmm', x); +if ~isempty(errstring) + error(errstring); +end + +[ndata, xdim] = size(x); + +% Sort out the options +if (options(14)) + niters = options(14); +else + niters = 100; +end + +display = options(1); +store = 0; +if (nargout > 2) + store = 1; % Store the error values to return them + errlog = zeros(1, niters); +end +test = 0; +if options(3) > 0.0 + test = 1; % Test log likelihood for termination +end + +check_covars = 0; +if options(5) >= 1 + if display >= 0 + disp('check_covars is on'); + end + check_covars = 1; % Ensure that covariances don't collapse + MIN_COVAR = eps; % Minimum singular value of covariance matrix + init_covars = mix.covars; +end + +% Main loop of algorithm +for n = 1:niters + + % Calculate posteriors based on old parameters + [post, act] = gmmpost(mix, x); + + % Calculate error value if needed + if (display | store | test) + prob = act*(mix.priors)'; + % Error value is negative log likelihood of data + e = - sum(log(prob)); + if store + errlog(n) = e; + end + if display > 0 + fprintf(1, 'Cycle %4d Error %11.6f\n', n, e); + end + if test + if (n > 1 & abs(e - eold) < options(3)) + options(8) = e; + return; + else + eold = e; + end + end + end + + % Adjust the new estimates for the parameters + new_pr = sum(post, 1); + new_c = post' * x; + + % Now move new estimates to old parameter vectors + mix.priors = new_pr ./ ndata; + + mix.centres = new_c ./ (new_pr' * ones(1, mix.nin)); + + switch mix.covar_type + case 'spherical' + n2 = dist2(x, mix.centres); + for j = 1:mix.ncentres + v(j) = (post(:,j)'*n2(:,j)); + end + mix.covars = ((v./new_pr))./mix.nin; + if check_covars + % Ensure that no covariance is too small + for j = 1:mix.ncentres + if mix.covars(j) < MIN_COVAR + mix.covars(j) = init_covars(j); + end + end + end + case 'diag' + for j = 1:mix.ncentres + diffs = x - (ones(ndata, 1) * mix.centres(j,:)); + mix.covars(j,:) = sum((diffs.*diffs).*(post(:,j)*ones(1, ... + mix.nin)), 1)./new_pr(j); + end + if check_covars + % Ensure that no covariance is too small + for j = 1:mix.ncentres + if min(mix.covars(j,:)) < MIN_COVAR + mix.covars(j,:) = init_covars(j,:); + end + end + end + case 'full' + for j = 1:mix.ncentres + diffs = x - (ones(ndata, 1) * mix.centres(j,:)); + diffs = diffs.*(sqrt(post(:,j))*ones(1, mix.nin)); + mix.covars(:,:,j) = (diffs'*diffs)/new_pr(j); + end + if check_covars + % Ensure that no covariance is too small + for j = 1:mix.ncentres + if min(svd(mix.covars(:,:,j))) < MIN_COVAR + mix.covars(:,:,j) = init_covars(:,:,j); + end + end + end + case 'ppca' + for j = 1:mix.ncentres + diffs = x - (ones(ndata, 1) * mix.centres(j,:)); + diffs = diffs.*(sqrt(post(:,j))*ones(1, mix.nin)); + [tempcovars, tempU, templambda] = ... + ppca((diffs'*diffs)/new_pr(j), mix.ppca_dim); + if length(templambda) ~= mix.ppca_dim + error('Unable to extract enough components'); + else + mix.covars(j) = tempcovars; + mix.U(:, :, j) = tempU; + mix.lambda(j, :) = templambda; + end + end + if check_covars + if mix.covars(j) < MIN_COVAR + mix.covars(j) = init_covars(j); + end + end + otherwise + error(['Unknown covariance type ', mix.covar_type]); + end +end + +options(8) = -sum(log(gmmprob(mix, x))); +if (display >= 0) + disp(maxitmess); +end + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmminit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ +function mix = gmminit(mix, x, options) +%GMMINIT Initialises Gaussian mixture model from data +% +% Description +% MIX = GMMINIT(MIX, X, OPTIONS) uses a dataset X to initialise the +% parameters of a Gaussian mixture model defined by the data structure +% MIX. The k-means algorithm is used to determine the centres. The +% priors are computed from the proportion of examples belonging to each +% cluster. The covariance matrices are calculated as the sample +% covariance of the points associated with (i.e. closest to) the +% corresponding centres. For a mixture of PPCA model, the PPCA +% decomposition is calculated for the points closest to a given centre. +% This initialisation can be used as the starting point for training +% the model using the EM algorithm. +% +% See also +% GMM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[ndata, xdim] = size(x); + +% Check that inputs are consistent +errstring = consist(mix, 'gmm', x); +if ~isempty(errstring) + error(errstring); +end + +% Arbitrary width used if variance collapses to zero: make it 'large' so +% that centre is responsible for a reasonable number of points. +GMM_WIDTH = 1.0; + +% Use kmeans algorithm to set centres +options(5) = 1; +[mix.centres, options, post] = kmeansNetlab(mix.centres, x, options); + +% Set priors depending on number of points in each cluster +cluster_sizes = max(sum(post, 1), 1); % Make sure that no prior is zero +mix.priors = cluster_sizes/sum(cluster_sizes); % Normalise priors + +switch mix.covar_type +case 'spherical' + if mix.ncentres > 1 + % Determine widths as distance to nearest centre + % (or a constant if this is zero) + cdist = dist2(mix.centres, mix.centres); + cdist = cdist + diag(ones(mix.ncentres, 1)*realmax); + mix.covars = min(cdist); + mix.covars = mix.covars + GMM_WIDTH*(mix.covars < eps); + else + % Just use variance of all data points averaged over all + % dimensions + mix.covars = mean(diag(cov(x))); + end + case 'diag' + for j = 1:mix.ncentres + % Pick out data points belonging to this centre + c = x(find(post(:, j)),:); + diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :)); + mix.covars(j, :) = sum((diffs.*diffs), 1)/size(c, 1); + % Replace small entries by GMM_WIDTH value + mix.covars(j, :) = mix.covars(j, :) + GMM_WIDTH.*(mix.covars(j, :)<eps); + end + case 'full' + for j = 1:mix.ncentres + % Pick out data points belonging to this centre + c = x(find(post(:, j)),:); + diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :)); + mix.covars(:,:,j) = (diffs'*diffs)/(size(c, 1)); + % Add GMM_WIDTH*Identity to rank-deficient covariance matrices + if rank(mix.covars(:,:,j)) < mix.nin + mix.covars(:,:,j) = mix.covars(:,:,j) + GMM_WIDTH.*eye(mix.nin); + end + end + case 'ppca' + for j = 1:mix.ncentres + % Pick out data points belonging to this centre + c = x(find(post(:,j)),:); + diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :)); + [tempcovars, tempU, templambda] = ... + ppca((diffs'*diffs)/size(c, 1), mix.ppca_dim); + if length(templambda) ~= mix.ppca_dim + error('Unable to extract enough components'); + else + mix.covars(j) = tempcovars; + mix.U(:, :, j) = tempU; + mix.lambda(j, :) = templambda; + end + end + otherwise + error(['Unknown covariance type ', mix.covar_type]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmmpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function p = gmmpak(mix) +%GMMPAK Combines all the parameters in a Gaussian mixture model into one vector. +% +% Description +% P = GMMPAK(NET) takes a mixture data structure MIX and combines the +% component parameter matrices into a single row vector P. +% +% See also +% GMM, GMMUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(mix, 'gmm'); +if ~errstring + error(errstring); +end + +p = [mix.priors, mix.centres(:)', mix.covars(:)']; +if strcmp(mix.covar_type, 'ppca') + p = [p, mix.lambda(:)', mix.U(:)']; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmmpost.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function [post, a] = gmmpost(mix, x) +%GMMPOST Computes the class posterior probabilities of a Gaussian mixture model. +% +% Description +% This function computes the posteriors POST (i.e. the probability of +% each component conditioned on the data P(J|X)) for a Gaussian mixture +% model. The data structure MIX defines the mixture model, while the +% matrix X contains the data vectors. Each row of X represents a +% single vector. +% +% See also +% GMM, GMMACTIV, GMMPROB +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check that inputs are consistent +errstring = consist(mix, 'gmm', x); +if ~isempty(errstring) + error(errstring); +end + +ndata = size(x, 1); + +a = gmmactiv(mix, x); + +post = (ones(ndata, 1)*mix.priors).*a; +s = sum(post, 2); +if any(s==0) + warning('Some zero posterior probabilities') + % Set any zeros to one before dividing + zero_rows = find(s==0); + s = s + (s==0); + post(zero_rows, :) = 1/mix.ncentres; +end +post = post./(s*ones(1, mix.ncentres));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmmprob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function prob = gmmprob(mix, x) +%GMMPROB Computes the data probability for a Gaussian mixture model. +% +% Description +% This function computes the unconditional data density P(X) for a +% Gaussian mixture model. The data structure MIX defines the mixture +% model, while the matrix X contains the data vectors. Each row of X +% represents a single vector. +% +% See also +% GMM, GMMPOST, GMMACTIV +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check that inputs are consistent +errstring = consist(mix, 'gmm', x); +if ~isempty(errstring) + error(errstring); +end + +% Compute activations +a = gmmactiv(mix, x); + +% Form dot product with priors +prob = a * (mix.priors)'; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmmsamp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +function [data, label] = gmmsamp(mix, n) +%GMMSAMP Sample from a Gaussian mixture distribution. +% +% Description +% +% DATA = GSAMP(MIX, N) generates a sample of size N from a Gaussian +% mixture distribution defined by the MIX data structure. The matrix X +% has N rows in which each row represents a MIX.NIN-dimensional sample +% vector. +% +% [DATA, LABEL] = GMMSAMP(MIX, N) also returns a column vector of +% classes (as an index 1..N) LABEL. +% +% See also +% GSAMP, GMM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check input arguments +errstring = consist(mix, 'gmm'); +if ~isempty(errstring) + error(errstring); +end +if n < 1 + error('Number of data points must be positive') +end + +% Determine number to sample from each component +priors = rand(1, n); + +% Pre-allocate data array +data = zeros(n, mix.nin); +if nargout > 1 + label = zeros(n, 1); +end +cum_prior = 0; % Cumulative sum of priors +total_samples = 0; % Cumulative sum of number of sampled points +for j = 1:mix.ncentres + num_samples = sum(priors >= cum_prior & ... + priors < cum_prior + mix.priors(j)); + % Form a full covariance matrix + switch mix.covar_type + case 'spherical' + covar = mix.covars(j) * eye(mix.nin); + case 'diag' + covar = diag(mix.covars(j, :)); + case 'full' + covar = mix.covars(:, :, j); + case 'ppca' + covar = mix.covars(j) * eye(mix.nin) + ... + mix.U(:, :, j)* ... + (diag(mix.lambda(j, :))-(mix.covars(j)*eye(mix.ppca_dim)))* ... + (mix.U(:, :, j)'); + otherwise + error(['Unknown covariance type ', mix.covar_type]); + end + data(total_samples+1:total_samples+num_samples, :) = ... + gsamp(mix.centres(j, :), covar, num_samples); + if nargout > 1 + label(total_samples+1:total_samples+num_samples) = j; + end + cum_prior = cum_prior + mix.priors(j); + total_samples = total_samples + num_samples; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gmmunpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +function mix = gmmunpak(mix, p) +%GMMUNPAK Separates a vector of Gaussian mixture model parameters into its components. +% +% Description +% MIX = GMMUNPAK(MIX, P) takes a GMM data structure MIX and a single +% row vector of parameters P and returns a mixture data structure +% identical to the input MIX, except that the mixing coefficients +% PRIORS, centres CENTRES and covariances COVARS (and, for PPCA, the +% lambdas and U (PCA sub-spaces)) are all set to the corresponding +% elements of P. +% +% See also +% GMM, GMMPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(mix, 'gmm'); +if ~errstring + error(errstring); +end +if mix.nwts ~= length(p) + error('Invalid weight vector length') +end + +mark1 = mix.ncentres; +mark2 = mark1 + mix.ncentres*mix.nin; + +mix.priors = reshape(p(1:mark1), 1, mix.ncentres); +mix.centres = reshape(p(mark1 + 1:mark2), mix.ncentres, mix.nin); +switch mix.covar_type + case 'spherical' + mark3 = mix.ncentres*(2 + mix.nin); + mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres); + case 'diag' + mark3 = mix.ncentres*(1 + mix.nin + mix.nin); + mix.covars = reshape(p(mark2 + 1:mark3), mix.ncentres, mix.nin); + case 'full' + mark3 = mix.ncentres*(1 + mix.nin + mix.nin*mix.nin); + mix.covars = reshape(p(mark2 + 1:mark3), mix.nin, mix.nin, ... + mix.ncentres); + case 'ppca' + mark3 = mix.ncentres*(2 + mix.nin); + mix.covars = reshape(p(mark2 + 1:mark3), 1, mix.ncentres); + % Now also extract k and eigenspaces + mark4 = mark3 + mix.ncentres*mix.ppca_dim; + mix.lambda = reshape(p(mark3 + 1:mark4), mix.ncentres, ... + mix.ppca_dim); + mix.U = reshape(p(mark4 + 1:end), mix.nin, mix.ppca_dim, ... + mix.ncentres); + otherwise + error(['Unknown covariance type ', mix.covar_type]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +function net = gp(nin, covar_fn, prior) +%GP Create a Gaussian Process. +% +% Description +% +% NET = GP(NIN, COVARFN) takes the number of inputs NIN for a Gaussian +% Process model with a single output, together with a string COVARFN +% which specifies the type of the covariance function, and returns a +% data structure NET. The parameters are set to zero. +% +% The fields in NET are +% type = 'gp' +% nin = number of inputs +% nout = number of outputs: always 1 +% nwts = total number of weights and covariance function parameters +% bias = logarithm of constant offset in covariance function +% noise = logarithm of output noise variance +% inweights = logarithm of inverse length scale for each input +% covarfn = string describing the covariance function: +% 'sqexp' +% 'ratquad' +% fpar = covariance function specific parameters (1 for squared exponential, +% 2 for rational quadratic) +% trin = training input data (initially empty) +% trtargets = training target data (initially empty) +% +% NET = GP(NIN, COVARFN, PRIOR) sets a Gaussian prior on the parameters +% of the model. PRIOR must contain the fields PR_MEAN and PR_VARIANCE. +% If PR_MEAN is a scalar, then the Gaussian is assumed to be isotropic +% and the additional fields NET.PR_MEAN and PR_VARIANCE are set. +% Otherwise, the Gaussian prior has a mean defined by a column vector +% of parameters PRIOR.PR_MEAN and covariance defined by a column vector +% of parameters PRIOR.PR_VARIANCE. Each element of PRMEAN corresponds +% to a separate group of parameters, which need not be mutually +% exclusive. The membership of the groups is defined by the matrix +% PRIOR.INDEX in which the columns correspond to the elements of +% PRMEAN. Each column has one element for each weight in the matrix, in +% the order defined by the function GPPAK, and each element is 1 or 0 +% according to whether the parameter is a member of the corresponding +% group or not. The additional field NET.INDEX is set in this case. +% +% See also +% GPPAK, GPUNPAK, GPFWD, GPERR, GPCOVAR, GPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +net.type = 'gp'; +net.nin = nin; +net.nout = 1; % Only do single output GP + +% Store log parameters +net.bias = 0; +net.min_noise = sqrt(eps); % Prevent output noise collapsing completely +net.noise = 0; +net.inweights = zeros(1,nin); % Weights on inputs in covariance function + +covarfns = {'sqexp', 'ratquad'}; + +if sum(strcmp(covar_fn, covarfns)) == 0 + error('Undefined activation function. Exiting.'); +else + net.covar_fn = covar_fn; +end + +switch covar_fn + + case 'sqexp' % Squared exponential + net.fpar = zeros(1,1); % One function specific parameter + + case 'ratquad' % Rational quadratic + net.fpar = zeros(1, 2); % Two function specific parameters + + otherwise + error(['Unknown covariance function ', covar_fn]); +end + +net.nwts = 2 + nin + length(net.fpar); + +if nargin >= 3 + if size(prior.pr_mean) == [1 1] + net.pr_mean = prior.pr_mean; + net.pr_var = prior.pr_var; + else + net.pr_mean = prior.pr_mean; + net.pr_var = prior.pr_var; + net.index = prior.index; + end +end + +% Store training data as needed for gpfwd +net.tr_in = []; +net.tr_targets = []; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gpcovar.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function [cov, covf] = gpcovar(net, x) +%GPCOVAR Calculate the covariance for a Gaussian Process. +% +% Description +% +% COV = GPCOVAR(NET, X) takes a Gaussian Process data structure NET +% together with a matrix X of input vectors, and computes the +% covariance matrix COV. The inverse of this matrix is used when +% calculating the mean and variance of the predictions made by NET. +% +% [COV, COVF] = GPCOVAR(NET, X) also generates the covariance matrix +% due to the covariance function specified by NET.COVARFN as calculated +% by GPCOVARF. +% +% See also +% GP, GPPAK, GPUNPAK, GPCOVARP, GPCOVARF, GPFWD, GPERR, GPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'gp', x); +if ~isempty(errstring); + error(errstring); +end + +ndata = size(x, 1); + +% Compute prior covariance +if nargout >= 2 + [covp, covf] = gpcovarp(net, x, x); +else + covp = gpcovarp(net, x, x); +end + +% Add output noise variance +cov = covp + (net.min_noise + exp(net.noise))*eye(ndata); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gpcovarf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function covf = gpcovarf(net, x1, x2) +%GPCOVARF Calculate the covariance function for a Gaussian Process. +% +% Description +% +% COVF = GPCOVARF(NET, X1, X2) takes a Gaussian Process data structure +% NET together with two matrices X1 and X2 of input vectors, and +% computes the matrix of the covariance function values COVF. +% +% See also +% GP, GPCOVAR, GPCOVARP, GPERR, GPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'gp', x1); +if ~isempty(errstring); + error(errstring); +end + +if size(x1, 2) ~= size(x2, 2) + error('Number of variables in x1 and x2 must be the same'); +end + +n1 = size(x1, 1); +n2 = size(x2, 1); +beta = diag(exp(net.inweights)); + +% Compute the weighted squared distances between x1 and x2 +z = (x1.*x1)*beta*ones(net.nin, n2) - 2*x1*beta*x2' ... + + ones(n1, net.nin)*beta*(x2.*x2)'; + +switch net.covar_fn + + case 'sqexp' % Squared exponential + covf = exp(net.fpar(1) - 0.5*z); + + case 'ratquad' % Rational quadratic + nu = exp(net.fpar(2)); + covf = exp(net.fpar(1))*((ones(size(z)) + z).^(-nu)); + + otherwise + error(['Unknown covariance function ', net.covar_fn]); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gpcovarp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [covp, covf] = gpcovarp(net, x1, x2) +%GPCOVARP Calculate the prior covariance for a Gaussian Process. +% +% Description +% +% COVP = GPCOVARP(NET, X1, X2) takes a Gaussian Process data structure +% NET together with two matrices X1 and X2 of input vectors, and +% computes the matrix of the prior covariance. This is the function +% component of the covariance plus the exponential of the bias term. +% +% [COVP, COVF] = GPCOVARP(NET, X1, X2) also returns the function +% component of the covariance. +% +% See also +% GP, GPCOVAR, GPCOVARF, GPERR, GPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'gp', x1); +if ~isempty(errstring); + error(errstring); +end + +if size(x1, 2) ~= size(x2, 2) + error('Number of variables in x1 and x2 must be the same'); +end + +covf = gpcovarf(net, x1, x2); +covp = covf + exp(net.bias); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gperr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function [e, edata, eprior] = gperr(net, x, t) +%GPERR Evaluate error function for Gaussian Process. +% +% Description +% E = GPERR(NET, X, T) takes a Gaussian Process data structure NET +% together with a matrix X of input vectors and a matrix T of target +% vectors, and evaluates the error function E. Each row of X +% corresponds to one input vector and each row of T corresponds to one +% target vector. +% +% [E, EDATA, EPRIOR] = GPERR(NET, X, T) additionally returns the data +% and hyperprior components of the error, assuming a Gaussian prior on +% the weights with mean and variance parameters PRMEAN and PRVARIANCE +% taken from the network data structure NET. +% +% See also +% GP, GPCOVAR, GPFWD, GPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'gp', x, t); +if ~isempty(errstring); + error(errstring); +end + +cn = gpcovar(net, x); + +edata = 0.5*(sum(log(eig(cn, 'nobalance'))) + t'*inv(cn)*t); + +% Evaluate the hyperprior contribution to the error. +% The hyperprior is Gaussian with mean pr_mean and variance +% pr_variance +if isfield(net, 'pr_mean') + w = gppak(net); + m = repmat(net.pr_mean, size(w)); + if size(net.pr_mean) == [1 1] + eprior = 0.5*((w-m)*(w-m)'); + e2 = eprior/net.pr_var; + else + wpr = repmat(w, size(net.pr_mean, 1), 1)'; + eprior = 0.5*(((wpr - m').^2).*net.index); + e2 = (sum(eprior, 1))*(1./net.pr_var); + end +else + e2 = 0; + eprior = 0; +end + +e = edata + e2; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gpfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function [y, sigsq] = gpfwd(net, x, cninv) +%GPFWD Forward propagation through Gaussian Process. +% +% Description +% Y = GPFWD(NET, X) takes a Gaussian Process data structure NET +% together with a matrix X of input vectors, and forward propagates +% the inputs through the model to generate a matrix Y of output +% vectors. Each row of X corresponds to one input vector and each row +% of Y corresponds to one output vector. This assumes that the +% training data (both inputs and targets) has been stored in NET by a +% call to GPINIT; these are needed to compute the training data +% covariance matrix. +% +% [Y, SIGSQ] = GPFWD(NET, X) also generates a column vector SIGSQ of +% conditional variances (or squared error bars) where each value +% corresponds to a pattern. +% +% [Y, SIGSQ] = GPFWD(NET, X, CNINV) uses the pre-computed inverse +% covariance matrix CNINV in the forward propagation. This increases +% efficiency if several calls to GPFWD are made. +% +% See also +% GP, DEMGP, GPINIT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'gp', x); +if ~isempty(errstring); + error(errstring); +end + +if ~(isfield(net, 'tr_in') & isfield(net, 'tr_targets')) + error('Require training inputs and targets'); +end + +if nargin == 2 + % Inverse covariance matrix not supplied. + cninv = inv(gpcovar(net, net.tr_in)); +end +ktest = gpcovarp(net, x, net.tr_in); + +% Predict mean +y = ktest*cninv*net.tr_targets; + +if nargout >= 2 + % Predict error bar + ndata = size(x, 1); + sigsq = (ones(ndata, 1) * gpcovarp(net, x(1,:), x(1,:))) ... + - sum((ktest*cninv).*ktest, 2); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gpgrad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,100 @@ +function g = gpgrad(net, x, t) +%GPGRAD Evaluate error gradient for Gaussian Process. +% +% Description +% G = GPGRAD(NET, X, T) takes a Gaussian Process data structure NET +% together with a matrix X of input vectors and a matrix T of target +% vectors, and evaluates the error gradient G. Each row of X +% corresponds to one input vector and each row of T corresponds to one +% target vector. +% +% See also +% GP, GPCOVAR, GPFWD, GPERR +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'gp', x, t); +if ~isempty(errstring); + error(errstring); +end + +% Evaluate derivatives with respect to each hyperparameter in turn. +ndata = size(x, 1); +[cov, covf] = gpcovar(net, x); +cninv = inv(cov); +trcninv = trace(cninv); +cninvt = cninv*t; + +% Function parameters +switch net.covar_fn + + case 'sqexp' % Squared exponential + gfpar = trace(cninv*covf) - cninvt'*covf*cninvt; + + case 'ratquad' % Rational quadratic + beta = diag(exp(net.inweights)); + gfpar(1) = trace(cninv*covf) - cninvt'*covf*cninvt; + D2 = (x.*x)*beta*ones(net.nin, ndata) - 2*x*beta*x' ... + + ones(ndata, net.nin)*beta*(x.*x)'; + E = ones(size(D2)); + L = - exp(net.fpar(2)) * covf .* log(E + D2); % d(cn)/d(nu) + gfpar(2) = trace(cninv*L) - cninvt'*L*cninvt; + + otherwise + error(['Unknown covariance function ', net.covar_fn]); +end + +% Bias derivative +ndata = size(x, 1); +fac = exp(net.bias)*ones(ndata); +gbias = trace(cninv*fac) - cninvt'*fac*cninvt; + +% Noise derivative +gnoise = exp(net.noise)*(trcninv - cninvt'*cninvt); + +% Input weight derivatives +if strcmp(net.covar_fn, 'ratquad') + F = (exp(net.fpar(2))*E)./(E + D2); +end + +nparams = length(net.inweights); +for l = 1 : nparams + vect = x(:, l); + matx = (vect.*vect)*ones(1, ndata) ... + - 2.0*vect*vect' ... + + ones(ndata, 1)*(vect.*vect)'; + switch net.covar_fn + case 'sqexp' % Squared exponential + dmat = -0.5*exp(net.inweights(l))*covf.*matx; + + case 'ratquad' % Rational quadratic + dmat = - exp(net.inweights(l))*covf.*matx.*F; + otherwise + error(['Unknown covariance function ', net.covar_fn]); + end + + gw1(l) = trace(cninv*dmat) - cninvt'*dmat*cninvt; +end + +g1 = [gbias, gnoise, gw1, gfpar]; +g1 = 0.5*g1; + +% Evaluate the prior contribution to the gradient. +if isfield(net, 'pr_mean') + w = gppak(net); + m = repmat(net.pr_mean, size(w)); + if size(net.pr_mean) == [1 1] + gprior = w - m; + g2 = gprior/net.pr_var; + else + ngroups = size(net.pr_mean, 1); + gprior = net.index'.*(ones(ngroups, 1)*w - m); + g2 = (1./net.pr_var)'*gprior; + end +else + gprior = 0; + g2 = 0; +end + +g = g1 + g2;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gpinit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function net = gpinit(net, tr_in, tr_targets, prior) +%GPINIT Initialise Gaussian Process model. +% +% Description +% NET = GPINIT(NET, TRIN, TRTARGETS) takes a Gaussian Process data +% structure NET together with a matrix TRIN of training input vectors +% and a matrix TRTARGETS of training target vectors, and stores them +% in NET. These datasets are required if the corresponding inverse +% covariance matrix is not supplied to GPFWD. This is important if the +% data structure is saved and then reloaded before calling GPFWD. Each +% row of TRIN corresponds to one input vector and each row of TRTARGETS +% corresponds to one target vector. +% +% NET = GPINIT(NET, TRIN, TRTARGETS, PRIOR) additionally initialises +% the parameters in NET from the PRIOR data structure which contains +% the mean and variance of the Gaussian distribution which is sampled +% from. +% +% See also +% GP, GPFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'gp', tr_in, tr_targets); +if ~isempty(errstring); + error(errstring); +end + +if nargin >= 4 + % Initialise weights at random + if size(prior.pr_mean) == [1 1] + w = randn(1, net.nwts).*sqrt(prior.pr_var) + ... + repmat(prior.pr_mean, 1, net.nwts); + else + sig = sqrt(prior.index*prior.pr_var); + w = sig'.*randn(1, net.nwts) + (prior.index*prior.pr_mean)'; + end + net = gpunpak(net, w); +end + +net.tr_in = tr_in; +net.tr_targets = tr_targets;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gppak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function hp = gppak(net) +%GPPAK Combines GP hyperparameters into one vector. +% +% Description +% HP = GPPAK(NET) takes a Gaussian Process data structure NET and +% combines the hyperparameters into a single row vector HP. +% +% See also +% GP, GPUNPAK, GPFWD, GPERR, GPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'gp'); +if ~isempty(errstring); + error(errstring); +end +hp = [net.bias, net.noise, net.inweights, net.fpar];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gpunpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function net = gpunpak(net, hp) +%GPUNPAK Separates hyperparameter vector into components. +% +% Description +% NET = GPUNPAK(NET, HP) takes an Gaussian Process data structure NET +% and a hyperparameter vector HP, and returns a Gaussian Process data +% structure identical to the input model, except that the covariance +% bias BIAS, output noise NOISE, the input weight vector INWEIGHTS and +% the vector of covariance function specific parameters FPAR have all +% been set to the corresponding elements of HP. +% +% See also +% GP, GPPAK, GPFWD, GPERR, GPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'gp'); +if ~isempty(errstring); + error(errstring); +end +if net.nwts ~= length(hp) + error('Invalid weight vector length'); +end + +net.bias = hp(1); +net.noise = hp(2); + +% Unpack input weights +mark1 = 2 + net.nin; +net.inweights = hp(3:mark1); + +% Unpack function specific parameters +net.fpar = hp(mark1 + 1:size(hp, 2)); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gradchek.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function [gradient, delta] = gradchek(w, func, grad, varargin) +%GRADCHEK Checks a user-defined gradient function using finite differences. +% +% Description +% This function is intended as a utility for other netlab functions +% (particularly optimisation functions) to use. It enables the user to +% check whether a gradient calculation has been correctly implmented +% for a given function. GRADCHEK(W, FUNC, GRAD) checks how accurate the +% gradient GRAD of a function FUNC is at a parameter vector X. A +% central difference formula with step size 1.0e-6 is used, and the +% results for both gradient function and finite difference +% approximation are printed. The optional return value GRADIENT is the +% gradient calculated using the function GRAD and the return value +% DELTA is the difference between the functional and finite difference +% methods of calculating the graident. +% +% GRADCHEK(X, FUNC, GRAD, P1, P2, ...) allows additional arguments to +% be passed to FUNC and GRAD. +% +% See also +% CONJGRAD, GRADDESC, HMC, OLGD, QUASINEW, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Reasonable value for step size +epsilon = 1.0e-6; + +func = fcnchk(func, length(varargin)); +grad = fcnchk(grad, length(varargin)); + +% Treat +nparams = length(w); +deltaf = zeros(1, nparams); +step = zeros(1, nparams); +for i = 1:nparams + % Move a small way in the ith coordinate of w + step(i) = 1.0; + fplus = feval('linef', epsilon, func, w, step, varargin{:}); + fminus = feval('linef', -epsilon, func, w, step, varargin{:}); + % Use central difference formula for approximation + deltaf(i) = 0.5*(fplus - fminus)/epsilon; + step(i) = 0.0; +end +gradient = feval(grad, w, varargin{:}); +fprintf(1, 'Checking gradient ...\n\n'); +delta = gradient - deltaf; +fprintf(1, ' analytic diffs delta\n\n'); +disp([gradient', deltaf', delta'])
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/graddesc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,180 @@ +function [x, options, flog, pointlog] = graddesc(f, x, options, gradf, ... + varargin) +%GRADDESC Gradient descent optimization. +% +% Description +% [X, OPTIONS, FLOG, POINTLOG] = GRADDESC(F, X, OPTIONS, GRADF) uses +% batch gradient descent to find a local minimum of the function F(X) +% whose gradient is given by GRADF(X). A log of the function values +% after each cycle is (optionally) returned in ERRLOG, and a log of the +% points visited is (optionally) returned in POINTLOG. +% +% Note that X is a row vector and F returns a scalar value. The point +% at which F has a local minimum is returned as X. The function value +% at that point is returned in OPTIONS(8). +% +% GRADDESC(F, X, OPTIONS, GRADF, P1, P2, ...) allows additional +% arguments to be passed to F() and GRADF(). +% +% The optional parameters have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG, and the points visited in the +% return argument POINTSLOG. If OPTIONS(1) is set to 0, then only +% warning messages are displayed. If OPTIONS(1) is -1, then nothing is +% displayed. +% +% OPTIONS(2) is the absolute precision required for the value of X at +% the solution. If the absolute difference between the values of X +% between two successive steps is less than OPTIONS(2), then this +% condition is satisfied. +% +% OPTIONS(3) is a measure of the precision required of the objective +% function at the solution. If the absolute difference between the +% objective function values between two successive steps is less than +% OPTIONS(3), then this condition is satisfied. Both this and the +% previous condition must be satisfied for termination. +% +% OPTIONS(7) determines the line minimisation method used. If it is +% set to 1 then a line minimiser is used (in the direction of the +% negative gradient). If it is 0 (the default), then each parameter +% update is a fixed multiple (the learning rate) of the negative +% gradient added to a fixed multiple (the momentum) of the previous +% parameter update. +% +% OPTIONS(9) should be set to 1 to check the user defined gradient +% function GRADF with GRADCHEK. This is carried out at the initial +% parameter vector X. +% +% OPTIONS(10) returns the total number of function evaluations +% (including those in any line searches). +% +% OPTIONS(11) returns the total number of gradient evaluations. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% OPTIONS(15) is the precision in parameter space of the line search; +% default FOPTIONS(2). +% +% OPTIONS(17) is the momentum; default 0.5. It should be scaled by the +% inverse of the number of data points. +% +% OPTIONS(18) is the learning rate; default 0.01. It should be scaled +% by the inverse of the number of data points. +% +% See also +% CONJGRAD, LINEMIN, OLGD, MINBRACK, QUASINEW, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set up the options. +if length(options) < 18 + error('Options vector too short') +end + +if (options(14)) + niters = options(14); +else + niters = 100; +end + +line_min_flag = 0; % Flag for line minimisation option +if (round(options(7)) == 1) + % Use line minimisation + line_min_flag = 1; + % Set options for line minimiser + line_options = foptions; + if options(15) > 0 + line_options(2) = options(15); + end +else + % Learning rate: must be positive + if (options(18) > 0) + eta = options(18); + else + eta = 0.01; + end + % Momentum term: allow zero momentum + if (options(17) >= 0) + mu = options(17); + else + mu = 0.5; + end +end + +% Check function string +f = fcnchk(f, length(varargin)); +gradf = fcnchk(gradf, length(varargin)); + +% Display information if options(1) > 0 +display = options(1) > 0; + +% Work out if we need to compute f at each iteration. +% Needed if using line search or if display results or if termination +% criterion requires it. +fcneval = (options(7) | display | options(3)); + +% Check gradients +if (options(9) > 0) + feval('gradchek', x, f, gradf, varargin{:}); +end + +dxold = zeros(1, size(x, 2)); +xold = x; +fold = 0; % Must be initialised so that termination test can be performed +if fcneval + fnew = feval(f, x, varargin{:}); + options(10) = options(10) + 1; + fold = fnew; +end + +% Main optimization loop. +for j = 1:niters + xold = x; + grad = feval(gradf, x, varargin{:}); + options(11) = options(11) + 1; % Increment gradient evaluation counter + if (line_min_flag ~= 1) + dx = mu*dxold - eta*grad; + x = x + dx; + dxold = dx; + if fcneval + fold = fnew; + fnew = feval(f, x, varargin{:}); + options(10) = options(10) + 1; + end + else + sd = - grad./norm(grad); % New search direction. + fold = fnew; + % Do a line search: normalise search direction to have length 1 + [lmin, line_options] = feval('linemin', f, x, sd, fold, ... + line_options, varargin{:}); + options(10) = options(10) + line_options(10); + x = xold + lmin*sd; + fnew = line_options(8); + end + if nargout >= 3 + flog(j) = fnew; + if nargout >= 4 + pointlog(j, :) = x; + end + end + if display + fprintf(1, 'Cycle %5d Function %11.8f\n', j, fnew); + end + if (max(abs(x - xold)) < options(2) & abs(fnew - fold) < options(3)) + % Termination criteria are met + options(8) = fnew; + return; + end +end + +if fcneval + options(8) = fnew; +else + options(8) = feval(f, x, varargin{:}); + options(10) = options(10) + 1; +end +if (options(1) >= 0) + disp(maxitmess); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gsamp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function x = gsamp(mu, covar, nsamp) +%GSAMP Sample from a Gaussian distribution. +% +% Description +% +% X = GSAMP(MU, COVAR, NSAMP) generates a sample of size NSAMP from a +% D-dimensional Gaussian distribution. The Gaussian density has mean +% vector MU and covariance matrix COVAR, and the matrix X has NSAMP +% rows in which each row represents a D-dimensional sample vector. +% +% See also +% GAUSS, DEMGAUSS +% + +% Copyright (c) Ian T Nabney (1996-2001) + +d = size(covar, 1); + +mu = reshape(mu, 1, d); % Ensure that mu is a row vector + +[evec, eval] = eig(covar); + +deig=diag(eval); + +if (~isreal(deig)) | any(deig<0), + warning('Covariance Matrix is not OK, redefined to be positive definite'); + eval=abs(eval); +end + +coeffs = randn(nsamp, d)*sqrt(eval); + +x = ones(nsamp, 1)*mu + coeffs*evec';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +function net = gtm(dim_latent, nlatent, dim_data, ncentres, rbfunc, ... + prior) +%GTM Create a Generative Topographic Map. +% +% Description +% +% NET = GTM(DIMLATENT, NLATENT, DIMDATA, NCENTRES, RBFUNC), takes the +% dimension of the latent space DIMLATENT, the number of data points +% sampled in the latent space NLATENT, the dimension of the data space +% DIMDATA, the number of centres in the RBF model NCENTRES, the +% activation function for the RBF RBFUNC and returns a data structure +% NET. The parameters in the RBF and GMM sub-models are set by calls to +% the corresponding creation routines RBF and GMM. +% +% The fields in NET are +% type = 'gtm' +% nin = dimension of data space +% dimlatent = dimension of latent space +% rbfnet = RBF network data structure +% gmmnet = GMM data structure +% X = sample of latent points +% +% NET = GTM(DIMLATENT, NLATENT, DIMDATA, NCENTRES, RBFUNC, PRIOR), +% sets a Gaussian zero mean prior on the parameters of the RBF model. +% PRIOR must be a scalar and represents the inverse variance of the +% prior distribution. This gives rise to a weight decay term in the +% error function. +% +% See also +% GTMFWD, GTMPOST, RBF, GMM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +net.type = 'gtm'; +% Input to functions is data +net.nin = dim_data; +net.dim_latent = dim_latent; + +% Default is no regularisation +if nargin == 5 + prior = 0.0; +end + +% Only allow scalar prior +if isstruct(prior) | size(prior) ~= [1 1] + error('Prior must be a scalar'); +end + +% Create RBF network +net.rbfnet = rbf(dim_latent, ncentres, dim_data, rbfunc, ... + 'linear', prior); + +% Mask all but output weights +net.rbfnet.mask = rbfprior(rbfunc, dim_latent, ncentres, dim_data); + +% Create field for GMM output model +net.gmmnet = gmm(dim_data, nlatent, 'spherical'); + +% Create empty latent data sample +net.X = []; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtmem.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,135 @@ +function [net, options, errlog] = gtmem(net, t, options) +%GTMEM EM algorithm for Generative Topographic Mapping. +% +% Description +% [NET, OPTIONS, ERRLOG] = GTMEM(NET, T, OPTIONS) uses the Expectation +% Maximization algorithm to estimate the parameters of a GTM defined by +% a data structure NET. The matrix T represents the data whose +% expectation is maximized, with each row corresponding to a vector. +% It is assumed that the latent data NET.X has been set following a +% call to GTMINIT, for example. The optional parameters have the +% following interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG. If OPTIONS(1) is set to 0, then +% only warning messages are displayed. If OPTIONS(1) is -1, then +% nothing is displayed. +% +% OPTIONS(3) is a measure of the absolute precision required of the +% error function at the solution. If the change in log likelihood +% between two steps of the EM algorithm is less than this value, then +% the function terminates. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% The optional return value OPTIONS contains the final error value +% (i.e. data log likelihood) in OPTIONS(8). +% +% See also +% GTM, GTMINIT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check that inputs are consistent +errstring = consist(net, 'gtm', t); +if ~isempty(errstring) + error(errstring); +end + +% Sort out the options +if (options(14)) + niters = options(14); +else + niters = 100; +end + +display = options(1); +store = 0; +if (nargout > 2) + store = 1; % Store the error values to return them + errlog = zeros(1, niters); +end +test = 0; +if options(3) > 0.0 + test = 1; % Test log likelihood for termination +end + +% Calculate various quantities that remain constant during training +[ndata, tdim] = size(t); +ND = ndata*tdim; +[net.gmmnet.centres, Phi] = rbffwd(net.rbfnet, net.X); +Phi = [Phi ones(size(net.X, 1), 1)]; +PhiT = Phi'; +[K, Mplus1] = size(Phi); + +A = zeros(Mplus1, Mplus1); +cholDcmp = zeros(Mplus1, Mplus1); +% Use a sparse representation for the weight regularizing matrix. +if (net.rbfnet.alpha > 0) + Alpha = net.rbfnet.alpha*speye(Mplus1); + Alpha(Mplus1, Mplus1) = 0; +end + +for n = 1:niters + % Calculate responsibilities + [R, act] = gtmpost(net, t); + % Calculate error value if needed + if (display | store | test) + prob = act*(net.gmmnet.priors)'; + % Error value is negative log likelihood of data + e = - sum(log(max(prob,eps))); + if store + errlog(n) = e; + end + if display > 0 + fprintf(1, 'Cycle %4d Error %11.6f\n', n, e); + end + if test + if (n > 1 & abs(e - eold) < options(3)) + options(8) = e; + return; + else + eold = e; + end + end + end + + % Calculate matrix be inverted (Phi'*G*Phi + alpha*I in the papers). + % Sparse representation of G normally executes faster and saves + % memory + if (net.rbfnet.alpha > 0) + A = full(PhiT*spdiags(sum(R)', 0, K, K)*Phi + ... + (Alpha.*net.gmmnet.covars(1))); + else + A = full(PhiT*spdiags(sum(R)', 0, K, K)*Phi); + end + % A is a symmetric matrix likely to be positive definite, so try + % fast Cholesky decomposition to calculate W, otherwise use SVD. + % (PhiT*(R*t)) is computed right-to-left, as R + % and t are normally (much) larger than PhiT. + [cholDcmp singular] = chol(A); + if (singular) + if (display) + fprintf(1, ... + 'gtmem: Warning -- M-Step matrix singular, using pinv.\n'); + end + W = pinv(A)*(PhiT*(R'*t)); + else + W = cholDcmp \ (cholDcmp' \ (PhiT*(R'*t))); + end + % Put new weights into network to calculate responsibilities + % net.rbfnet = netunpak(net.rbfnet, W); + net.rbfnet.w2 = W(1:net.rbfnet.nhidden, :); + net.rbfnet.b2 = W(net.rbfnet.nhidden+1, :); + % Calculate new distances + d = dist2(t, Phi*W); + + % Calculate new value for beta + net.gmmnet.covars = ones(1, net.gmmnet.ncentres)*(sum(sum(d.*R))/ND); +end + +options(8) = -sum(log(gtmprob(net, t))); +if (display >= 0) + disp(maxitmess); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtmfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function mix = gtmfwd(net) +%GTMFWD Forward propagation through GTM. +% +% Description +% MIX = GTMFWD(NET) takes a GTM structure NET, and forward propagates +% the latent data sample NET.X through the GTM to generate the +% structure MIX which represents the Gaussian mixture model in data +% space. +% +% See also +% GTM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +net.gmmnet.centres = rbffwd(net.rbfnet, net.X); +mix = net.gmmnet;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtminit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +function net = gtminit(net, options, data, samp_type, varargin) +%GTMINIT Initialise the weights and latent sample in a GTM. +% +% Description +% NET = GTMINIT(NET, OPTIONS, DATA, SAMPTYPE) takes a GTM NET and +% generates a sample of latent data points and sets the centres (and +% widths if appropriate) of NET.RBFNET. +% +% If the SAMPTYPE is 'REGULAR', then regular grids of latent data +% points and RBF centres are created. The dimension of the latent data +% space must be 1 or 2. For one-dimensional latent space, the +% LSAMPSIZE parameter gives the number of latent points and the +% RBFSAMPSIZE parameter gives the number of RBF centres. For a two- +% dimensional latent space, these parameters must be vectors of length +% 2 with the number of points in each of the x and y directions to +% create a rectangular grid. The widths of the RBF basis functions are +% set by a call to RBFSETFW passing OPTIONS(7) as the scaling +% parameter. +% +% If the SAMPTYPE is 'UNIFORM' or 'GAUSSIAN' then the latent data is +% found by sampling from a uniform or Gaussian distribution +% correspondingly. The RBF basis function parameters are set by a call +% to RBFSETBF with the DATA parameter as dataset and the OPTIONS +% vector. +% +% Finally, the output layer weights of the RBF are initialised by +% mapping the mean of the latent variable to the mean of the target +% variable, and the L-dimensional latent variale variance to the +% variance of the targets along the first L principal components. +% +% See also +% GTM, GTMEM, PCA, RBFSETBF, RBFSETFW +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check for consistency +errstring = consist(net, 'gtm', data); +if ~isempty(errstring) + error(errstring); +end + +% Check type of sample +stypes = {'regular', 'uniform', 'gaussian'}; +if (strcmp(samp_type, stypes)) == 0 + error('Undefined sample type.') +end + +if net.dim_latent > size(data, 2) + error('Latent space dimension must not be greater than data dimension') +end +nlatent = net.gmmnet.ncentres; +nhidden = net.rbfnet.nhidden; + +% Create latent data sample and set RBF centres + +switch samp_type +case 'regular' + if nargin ~= 6 + error('Regular type must specify latent and RBF shapes'); + end + l_samp_size = varargin{1}; + rbf_samp_size = varargin{2}; + if round(l_samp_size) ~= l_samp_size + error('Latent sample specification must contain integers') + end + % Check existence and size of rbf specification + if any(size(rbf_samp_size) ~= [1 net.dim_latent]) | ... + prod(rbf_samp_size) ~= nhidden + error('Incorrect specification of RBF centres') + end + % Check dimension and type of latent data specification + if any(size(l_samp_size) ~= [1 net.dim_latent]) | ... + prod(l_samp_size) ~= nlatent + error('Incorrect dimension of latent sample spec.') + end + if net.dim_latent == 1 + net.X = [-1:2/(l_samp_size-1):1]'; + net.rbfnet.c = [-1:2/(rbf_samp_size-1):1]'; + net.rbfnet = rbfsetfw(net.rbfnet, options(7)); + elseif net.dim_latent == 2 + net.X = gtm_rctg(l_samp_size); + net.rbfnet.c = gtm_rctg(rbf_samp_size); + net.rbfnet = rbfsetfw(net.rbfnet, options(7)); + else + error('For regular sample, input dimension must be 1 or 2.') + end + + +case {'uniform', 'gaussian'} + if strcmp(samp_type, 'uniform') + net.X = 2 * (rand(nlatent, net.dim_latent) - 0.5); + else + % Sample from N(0, 0.25) distribution to ensure most latent + % data is inside square + net.X = randn(nlatent, net.dim_latent)/2; + end + net.rbfnet = rbfsetbf(net.rbfnet, options, net.X); +otherwise + % Shouldn't get here + error('Invalid sample type'); + +end + +% Latent data sample and basis function parameters chosen. +% Now set output weights +[PCcoeff, PCvec] = pca(data); + +% Scale PCs by eigenvalues +A = PCvec(:, 1:net.dim_latent)*diag(sqrt(PCcoeff(1:net.dim_latent))); + +[temp, Phi] = rbffwd(net.rbfnet, net.X); +% Normalise X to ensure 1:1 mapping of variances and calculate weights +% as solution of Phi*W = normX*A' +normX = (net.X - ones(size(net.X))*diag(mean(net.X)))*diag(1./std(net.X)); +net.rbfnet.w2 = Phi \ (normX*A'); +% Bias is mean of target data +net.rbfnet.b2 = mean(data); + +% Must also set initial value of variance +% Find average distance between nearest centres +% Ensure that distance of centre to itself is excluded by setting diagonal +% entries to realmax +net.gmmnet.centres = rbffwd(net.rbfnet, net.X); +d = dist2(net.gmmnet.centres, net.gmmnet.centres) + ... + diag(ones(net.gmmnet.ncentres, 1)*realmax); +sigma = mean(min(d))/2; + +% Now set covariance to minimum of this and next largest eigenvalue +if net.dim_latent < size(data, 2) + sigma = min(sigma, PCcoeff(net.dim_latent+1)); +end +net.gmmnet.covars = sigma*ones(1, net.gmmnet.ncentres); + +% Sub-function to create the sample data in 2d +function sample = gtm_rctg(samp_size) + +xDim = samp_size(1); +yDim = samp_size(2); +% Produce a grid with the right number of rows and columns +[X, Y] = meshgrid([0:1:(xDim-1)], [(yDim-1):-1:0]); + +% Change grid representation +sample = [X(:), Y(:)]; + +% Shift grid to correct position and scale it +maxXY= max(sample); +sample(:,1) = 2*(sample(:,1) - maxXY(1)/2)./maxXY(1); +sample(:,2) = 2*(sample(:,2) - maxXY(2)/2)./maxXY(2); +return; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtmlmean.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function means = gtmlmean(net, data) +%GTMLMEAN Mean responsibility for data in a GTM. +% +% Description +% MEANS = GTMLMEAN(NET, DATA) takes a GTM structure NET, and computes +% the means of the responsibility distributions for each data point in +% DATA. +% +% See also +% GTM, GTMPOST, GTMLMODE +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check for consistency +errstring = consist(net, 'gtm', data); +if ~isempty(errstring) + error(errstring); +end + +R = gtmpost(net, data); +means = R*net.X;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtmlmode.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function modes = gtmlmode(net, data) +%GTMLMODE Mode responsibility for data in a GTM. +% +% Description +% MODES = GTMLMODE(NET, DATA) takes a GTM structure NET, and computes +% the modes of the responsibility distributions for each data point in +% DATA. These will always lie at one of the latent space sample points +% NET.X. +% +% See also +% GTM, GTMPOST, GTMLMEAN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check for consistency +errstring = consist(net, 'gtm', data); +if ~isempty(errstring) + error(errstring); +end + +R = gtmpost(net, data); +% Mode is maximum responsibility +[max_resp, max_index] = max(R, [], 2); +modes = net.X(max_index, :);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtmmag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function mags = gtmmag(net, latent_data) +%GTMMAG Magnification factors for a GTM +% +% Description +% MAGS = GTMMAG(NET, LATENTDATA) takes a GTM structure NET, and +% computes the magnification factors for each point the latent space +% contained in LATENTDATA. +% +% See also +% GTM, GTMPOST, GTMLMEAN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'gtm'); +if ~isempty(errstring) + error(errstring); +end + +Jacs = rbfjacob(net.rbfnet, latent_data); +nlatent = size(latent_data, 1); +mags = zeros(nlatent, 1); +temp = zeros(net.rbfnet.nin, net.rbfnet.nout); +for m = 1:nlatent + temp = squeeze(Jacs(m, :, :)); % Turn into a 2d matrix + mags(m) = sqrt(det(temp*temp')); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtmpost.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function [post, a] = gtmpost(net, data) +%GTMPOST Latent space responsibility for data in a GTM. +% +% Description +% POST = GTMPOST(NET, DATA) takes a GTM structure NET, and computes +% the responsibility at each latent space sample point NET.X for each +% data point in DATA. +% +% [POST, A] = GTMPOST(NET, DATA) also returns the activations A of the +% GMM NET.GMMNET as computed by GMMPOST. +% +% See also +% GTM, GTMEM, GTMLMEAN, GMLMODE, GMMPROB +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check for consistency +errstring = consist(net, 'gtm', data); +if ~isempty(errstring) + error(errstring); +end + +net.gmmnet.centres = rbffwd(net.rbfnet, net.X); + +[post, a] = gmmpost(net.gmmnet, data);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/gtmprob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function prob = gtmprob(net, data) +%GTMPROB Probability for data under a GTM. +% +% Description +% PROB = GTMPROB(NET, DATA) takes a GTM structure NET, and computes +% the probability of each point in the dataset DATA. +% +% See also +% GTM, GTMEM, GTMLMEAN, GTMLMODE, GTMPOST +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check for consistency +errstring = consist(net, 'gtm', data); +if ~isempty(errstring) + error(errstring); +end + +net.gmmnet.centres = rbffwd(net.rbfnet, net.X); + +prob = gmmprob(net.gmmnet, data);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/hbayes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function [h, hdata] = hbayes(net, hdata) +%HBAYES Evaluate Hessian of Bayesian error function for network. +% +% Description +% H = HBAYES(NET, HDATA) takes a network data structure NET together +% the data contribution to the Hessian for a set of inputs and targets. +% It returns the regularised Hessian using any zero mean Gaussian +% priors on the weights defined in NET. In addition, if a MASK is +% defined in NET, then the entries in H that correspond to weights with +% a 0 in the mask are removed. +% +% [H, HDATA] = HBAYES(NET, HDATA) additionally returns the data +% component of the Hessian. +% +% See also +% GBAYES, GLMHESS, MLPHESS, RBFHESS +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if (isfield(net, 'mask')) + % Extract relevant entries in Hessian + nmask_rows = size(find(net.mask), 1); + hdata = reshape(hdata(logical(net.mask*(net.mask'))), ... + nmask_rows, nmask_rows); + nwts = nmask_rows; +else + nwts = net.nwts; +end +if isfield(net, 'beta') + h = net.beta*hdata; +else + h = hdata; +end + +if isfield(net, 'alpha') + if size(net.alpha) == [1 1] + h = h + net.alpha*eye(nwts); + else + if isfield(net, 'mask') + nindx_cols = size(net.index, 2); + index = reshape(net.index(logical(repmat(net.mask, ... + 1, nindx_cols))), nmask_rows, nindx_cols); + else + index = net.index; + end + h = h + diag(index*net.alpha); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/hesschek.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function h = hesschek(net, x, t) +%HESSCHEK Use central differences to confirm correct evaluation of Hessian matrix. +% +% Description +% +% HESSCHEK(NET, X, T) takes a network data structure NET, together with +% input and target data matrices X and T, and compares the evaluation +% of the Hessian matrix using the function NETHESS and using central +% differences with the function NETERR. +% +% The optional return value H is the Hessian computed using NETHESS. +% +% See also +% NETHESS, NETERR +% + +% Copyright (c) Ian T Nabney (1996-2001) + +w0 = netpak(net); +nwts = length(w0); +h = nethess(w0, net, x, t); + +w = w0; +hcent = zeros(nwts, nwts); +h1 = 0.0; h2 = 0.0; h3 = 0.0; h4 = 0.0; +epsilon = 1.0e-4; +fprintf(1, 'Checking Hessian ...\n\n'); +for k = 1:nwts; + for l = 1:nwts; + if(l == k) + w(k) = w0(k) + 2.0*epsilon; + h1 = neterr(w, net, x, t); + w(k) = w0(k) - 2.0*epsilon; + h2 = neterr(w, net, x, t); + w(k) = w0(k); + h3 = neterr(w, net, x, t); + hcent(k, k) = (h1 + h2 - 2.0*h3)/(4.0*epsilon^2); + else + w(k) = w0(k) + epsilon; + w(l) = w0(l) + epsilon; + h1 = neterr(w, net, x, t); + w(k) = w0(k) - epsilon; + w(l) = w0(l) - epsilon; + h2 = neterr(w, net, x, t); + w(k) = w0(k) + epsilon; + w(l) = w0(l) - epsilon; + h3 = neterr(w, net, x, t); + w(k) = w0(k) - epsilon; + w(l) = w0(l) + epsilon; + h4 = neterr(w, net, x, t); + hcent(k, l) = (h1 + h2 - h3 - h4)/(4.0*epsilon^2); + w(k) = w0(k); + w(l) = w0(l); + end + end +end + +fprintf(1, ' analytical numerical delta\n\n'); +temp = [h(:), hcent(:), (h(:) - hcent(:))]; +fprintf(1, '%12.6f %12.6f %12.6f\n', temp'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/hintmat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function [xvals, yvals, color] = hintmat(w); +%HINTMAT Evaluates the coordinates of the patches for a Hinton diagram. +% +% Description +% [xvals, yvals, color] = hintmat(w) +% takes a matrix W and returns coordinates XVALS, YVALS for the +% patches comrising the Hinton diagram, together with a vector COLOR +% labelling the color (black or white) of the corresponding elements +% according to their sign. +% +% See also +% HINTON +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set scale to be up to 0.9 of maximum absolute weight value, where scale +% defined so that area of box proportional to weight value. + +w = flipud(w); +[nrows, ncols] = size(w); + +scale = 0.45*sqrt(abs(w)/max(max(abs(w)))); +scale = scale(:); +color = 0.5*(sign(w(:)) + 3); + +delx = 1; +dely = 1; +[X, Y] = meshgrid(0.5*delx:delx:(ncols-0.5*delx), 0.5*dely:dely:(nrows-0.5*dely)); + +% Now convert from matrix format to column vector format, and then duplicate +% columns with appropriate offsets determined by normalized weight magnitudes. + +xtemp = X(:); +ytemp = Y(:); + +xvals = [xtemp-delx*scale, xtemp+delx*scale, ... + xtemp+delx*scale, xtemp-delx*scale]; +yvals = [ytemp-dely*scale, ytemp-dely*scale, ... + ytemp+dely*scale, ytemp+dely*scale]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/hinton.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function h = hinton(w); +%HINTON Plot Hinton diagram for a weight matrix. +% +% Description +% +% HINTON(W) takes a matrix W and plots the Hinton diagram. +% +% H = HINTON(NET) also returns the figure handle H which can be used, +% for instance, to delete the figure when it is no longer needed. +% +% To print the figure correctly in black and white, you should call +% SET(H, 'INVERTHARDCOPY', 'OFF') before printing. +% +% See also +% DEMHINT, HINTMAT, MLPHINT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set scale to be up to 0.9 of maximum absolute weight value, where scale +% defined so that area of box proportional to weight value. + +% Use no more than 640x480 pixels +xmax = 640; ymax = 480; + +% Offset bottom left hand corner +x01 = 40; y01 = 40; +x02 = 80; y02 = 80; + +% Need to allow 5 pixels border for window frame: but 30 at top +border = 5; +top_border = 30; + +ymax = ymax - top_border; +xmax = xmax - border; + +% First layer + +[xvals, yvals, color] = hintmat(w); +% Try to preserve aspect ratio approximately +if (8*size(w, 1) < 6*size(w, 2)) + delx = xmax; dely = xmax*size(w, 1)/(size(w, 2)); +else + delx = ymax*size(w, 2)/size(w, 1); dely = ymax; +end + +h = figure('Color', [0.5 0.5 0.5], ... + 'Name', 'Hinton diagram', ... + 'NumberTitle', 'off', ... + 'Colormap', [0 0 0; 1 1 1], ... + 'Units', 'pixels', ... + 'Position', [x01 y01 delx dely]); +set(gca, 'Visible', 'off', 'Position', [0 0 1 1]); +hold on +patch(xvals', yvals', color', 'Edgecolor', 'none'); +axis equal; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/histp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function h = histp(x, xmin, xmax, nbins) +%HISTP Histogram estimate of 1-dimensional probability distribution. +% +% Description +% +% HISTP(X, XMIN, XMAX, NBINS) takes a column vector X of data values +% and generates a normalized histogram plot of the distribution. The +% histogram has NBINS bins lying in the range XMIN to XMAX. +% +% H = HISTP(...) returns a vector of patch handles. +% +% See also +% DEMGAUSS +% + +% Copyright (c) Ian T Nabney (1996-2001) + +ndata = length(x); + +bins = linspace(xmin, xmax, nbins); + +binwidth = (xmax - xmin)/nbins; + +num = hist(x, bins); + +num = num/(ndata*binwidth); + +h = bar(bins, num, 0.6); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/hmc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,281 @@ +function [samples, energies, diagn] = hmc(f, x, options, gradf, varargin) +%HMC Hybrid Monte Carlo sampling. +% +% Description +% SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo +% algorithm to sample from the distribution P ~ EXP(-F), where F is the +% first argument to HMC. The Markov chain starts at the point X, and +% the function GRADF is the gradient of the `energy' function F. +% +% HMC(F, X, OPTIONS, GRADF, P1, P2, ...) allows additional arguments to +% be passed to F() and GRADF(). +% +% [SAMPLES, ENERGIES, DIAGN] = HMC(F, X, OPTIONS, GRADF) also returns a +% log of the energy values (i.e. negative log probabilities) for the +% samples in ENERGIES and DIAGN, a structure containing diagnostic +% information (position, momentum and acceptance threshold) for each +% step of the chain in DIAGN.POS, DIAGN.MOM and DIAGN.ACC respectively. +% All candidate states (including rejected ones) are stored in +% DIAGN.POS. +% +% [SAMPLES, ENERGIES, DIAGN] = HMC(F, X, OPTIONS, GRADF) also returns +% the ENERGIES (i.e. negative log probabilities) corresponding to the +% samples. The DIAGN structure contains three fields: +% +% POS the position vectors of the dynamic process. +% +% MOM the momentum vectors of the dynamic process. +% +% ACC the acceptance thresholds. +% +% S = HMC('STATE') returns a state structure that contains the state of +% the two random number generators RAND and RANDN and the momentum of +% the dynamic process. These are contained in fields randstate, +% randnstate and mom respectively. The momentum state is only used for +% a persistent momentum update. +% +% HMC('STATE', S) resets the state to S. If S is an integer, then it +% is passed to RAND and RANDN and the momentum variable is randomised. +% If S is a structure returned by HMC('STATE') then it resets the +% generator to exactly the same state. +% +% The optional parameters in the OPTIONS vector have the following +% interpretations. +% +% OPTIONS(1) is set to 1 to display the energy values and rejection +% threshold at each step of the Markov chain. If the value is 2, then +% the position vectors at each step are also displayed. +% +% OPTIONS(5) is set to 1 if momentum persistence is used; default 0, +% for complete replacement of momentum variables. +% +% OPTIONS(7) defines the trajectory length (i.e. the number of leap- +% frog steps at each iteration). Minimum value 1. +% +% OPTIONS(9) is set to 1 to check the user defined gradient function. +% +% OPTIONS(14) is the number of samples retained from the Markov chain; +% default 100. +% +% OPTIONS(15) is the number of samples omitted from the start of the +% chain; default 0. +% +% OPTIONS(17) defines the momentum used when a persistent update of +% (leap-frog) momentum is used. This is bounded to the interval [0, +% 1). +% +% OPTIONS(18) is the step size used in leap-frogs; default 1/trajectory +% length. +% +% See also +% METROP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Global variable to store state of momentum variables: set by set_state +% Used to initialise variable if set +global HMC_MOM +if nargin <= 2 + if ~strcmp(f, 'state') + error('Unknown argument to hmc'); + end + switch nargin + case 1 + samples = get_state(f); + return; + case 2 + set_state(f, x); + return; + end +end + +display = options(1); +if (round(options(5) == 1)) + persistence = 1; + % Set alpha to lie in [0, 1) + alpha = max(0, options(17)); + alpha = min(1, alpha); + salpha = sqrt(1-alpha*alpha); +else + persistence = 0; +end +L = max(1, options(7)); % At least one step in leap-frogging +if options(14) > 0 + nsamples = options(14); +else + nsamples = 100; % Default +end +if options(15) >= 0 + nomit = options(15); +else + nomit = 0; +end +if options(18) > 0 + step_size = options(18); % Step size. +else + step_size = 1/L; % Default +end +x = x(:)'; % Force x to be a row vector +nparams = length(x); + +% Set up strings for evaluating potential function and its gradient. +f = fcnchk(f, length(varargin)); +gradf = fcnchk(gradf, length(varargin)); + +% Check the gradient evaluation. +if (options(9)) + % Check gradients + feval('gradchek', x, f, gradf, varargin{:}); +end + +samples = zeros(nsamples, nparams); % Matrix of returned samples. +if nargout >= 2 + en_save = 1; + energies = zeros(nsamples, 1); +else + en_save = 0; +end +if nargout >= 3 + diagnostics = 1; + diagn_pos = zeros(nsamples, nparams); + diagn_mom = zeros(nsamples, nparams); + diagn_acc = zeros(nsamples, 1); +else + diagnostics = 0; +end + +n = - nomit + 1; +Eold = feval(f, x, varargin{:}); % Evaluate starting energy. +nreject = 0; +if (~persistence | isempty(HMC_MOM)) + p = randn(1, nparams); % Initialise momenta at random +else + p = HMC_MOM; % Initialise momenta from stored state +end +lambda = 1; + +% Main loop. +while n <= nsamples + + xold = x; % Store starting position. + pold = p; % Store starting momenta + Hold = Eold + 0.5*(p*p'); % Recalculate Hamiltonian as momenta have changed + + if ~persistence + % Choose a direction at random + if (rand < 0.5) + lambda = -1; + else + lambda = 1; + end + end + % Perturb step length. + epsilon = lambda*step_size*(1.0 + 0.1*randn(1)); + + % First half-step of leapfrog. + p = p - 0.5*epsilon*feval(gradf, x, varargin{:}); + x = x + epsilon*p; + + % Full leapfrog steps. + for m = 1 : L - 1 + p = p - epsilon*feval(gradf, x, varargin{:}); + x = x + epsilon*p; + end + + % Final half-step of leapfrog. + p = p - 0.5*epsilon*feval(gradf, x, varargin{:}); + + % Now apply Metropolis algorithm. + Enew = feval(f, x, varargin{:}); % Evaluate new energy. + p = -p; % Negate momentum + Hnew = Enew + 0.5*p*p'; % Evaluate new Hamiltonian. + a = exp(Hold - Hnew); % Acceptance threshold. + if (diagnostics & n > 0) + diagn_pos(n,:) = x; + diagn_mom(n,:) = p; + diagn_acc(n,:) = a; + end + if (display > 1) + fprintf(1, 'New position is\n'); + disp(x); + end + + if a > rand(1) % Accept the new state. + Eold = Enew; % Update energy + if (display > 0) + fprintf(1, 'Finished step %4d Threshold: %g\n', n, a); + end + else % Reject the new state. + if n > 0 + nreject = nreject + 1; + end + x = xold; % Reset position + p = pold; % Reset momenta + if (display > 0) + fprintf(1, ' Sample rejected %4d. Threshold: %g\n', n, a); + end + end + if n > 0 + samples(n,:) = x; % Store sample. + if en_save + energies(n) = Eold; % Store energy. + end + end + + % Set momenta for next iteration + if persistence + p = -p; + % Adjust momenta by a small random amount. + p = alpha.*p + salpha.*randn(1, nparams); + else + p = randn(1, nparams); % Replace all momenta. + end + + n = n + 1; +end + +if (display > 0) + fprintf(1, '\nFraction of samples rejected: %g\n', ... + nreject/(nsamples)); +end +if diagnostics + diagn.pos = diagn_pos; + diagn.mom = diagn_mom; + diagn.acc = diagn_acc; +end +% Store final momentum value in global so that it can be retrieved later +HMC_MOM = p; +return + +% Return complete state of sampler (including momentum) +function state = get_state(f) + +global HMC_MOM +state.randstate = rand('state'); +state.randnstate = randn('state'); +state.mom = HMC_MOM; +return + +% Set complete state of sampler (including momentum) or just set randn +% and rand with integer argument. +function set_state(f, x) + +global HMC_MOM +if isnumeric(x) + rand('state', x); + randn('state', x); + HMC_MOM = []; +else + if ~isstruct(x) + error('Second argument to hmc must be number or state structure'); + end + if (~isfield(x, 'randstate') | ~isfield(x, 'randnstate') ... + | ~isfield(x, 'mom')) + error('Second argument to hmc must contain correct fields') + end + rand('state', x.randstate); + randn('state', x.randnstate); + HMC_MOM = x.mom; +end +return
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/kmeansNetlab.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,124 @@ +function [centres, options, post, errlog] = kmeans(centres, data, options) +%KMEANS Trains a k means cluster model. +% +% Description +% CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means +% algorithm to set the centres of a cluster model. The matrix DATA +% represents the data which is being clustered, with each row +% corresponding to a vector. The sum of squares error function is used. +% The point at which a local minimum is achieved is returned as +% CENTRES. The error value at that point is returned in OPTIONS(8). +% +% [CENTRES, OPTIONS, POST, ERRLOG] = KMEANS(CENTRES, DATA, OPTIONS) +% also returns the cluster number (in a one-of-N encoding) for each +% data point in POST and a log of the error values after each cycle in +% ERRLOG. The optional parameters have the following +% interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG. If OPTIONS(1) is set to 0, then +% only warning messages are displayed. If OPTIONS(1) is -1, then +% nothing is displayed. +% +% OPTIONS(2) is a measure of the absolute precision required for the +% value of CENTRES at the solution. If the absolute difference between +% the values of CENTRES between two successive steps is less than +% OPTIONS(2), then this condition is satisfied. +% +% OPTIONS(3) is a measure of the precision required of the error +% function at the solution. If the absolute difference between the +% error functions between two successive steps is less than OPTIONS(3), +% then this condition is satisfied. Both this and the previous +% condition must be satisfied for termination. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% See also +% GMMINIT, GMMEM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[ndata, data_dim] = size(data); +[ncentres, dim] = size(centres); + +if dim ~= data_dim + error('Data dimension does not match dimension of centres') +end + +if (ncentres > ndata) + error('More centres than data') +end + +% Sort out the options +if (options(14)) + niters = options(14); +else + niters = 100; +end + +store = 0; +if (nargout > 3) + store = 1; + errlog = zeros(1, niters); +end + +% Check if centres and posteriors need to be initialised from data +if (options(5) == 1) + % Do the initialisation + perm = randperm(ndata); + perm = perm(1:ncentres); + + % Assign first ncentres (permuted) data points as centres + centres = data(perm, :); +end +% Matrix to make unit vectors easy to construct +id = eye(ncentres); + +% Main loop of algorithm +for n = 1:niters + + % Save old centres to check for termination + old_centres = centres; + + % Calculate posteriors based on existing centres + d2 = dist2(data, centres); + % Assign each point to nearest centre + [minvals, index] = min(d2', [], 1); + post = id(index,:); + + num_points = sum(post, 1); + % Adjust the centres based on new posteriors + for j = 1:ncentres + if (num_points(j) > 0) + centres(j,:) = sum(data(find(post(:,j)),:), 1)/num_points(j); + end + end + + % Error value is total squared distance from cluster centres + e = sum(minvals); + if store + errlog(n) = e; + end + if options(1) > 0 + fprintf(1, 'Cycle %4d Error %11.6f\n', n, e); + end + + if n > 1 + % Test for termination + if max(max(abs(centres - old_centres))) < options(2) & ... + abs(old_e - e) < options(3) + options(8) = e; + return; + end + end + old_e = e; +end + +% If we get here, then we haven't terminated in the given number of +% iterations. +options(8) = e; +if (options(1) >= 0) + disp(maxitmess); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/knn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function net = knn(nin, nout, k, tr_in, tr_targets) +%KNN Creates a K-nearest-neighbour classifier. +% +% Description +% NET = KNN(NIN, NOUT, K, TR_IN, TR_TARGETS) creates a KNN model NET +% with input dimension NIN, output dimension NOUT and K neighbours. +% The training data is also stored in the data structure and the +% targets are assumed to be using a 1-of-N coding. +% +% The fields in NET are +% type = 'knn' +% nin = number of inputs +% nout = number of outputs +% tr_in = training input data +% tr_targets = training target data +% +% See also +% KMEANS, KNNFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +net.type = 'knn'; +net.nin = nin; +net.nout = nout; +net.k = k; +errstring = consist(net, 'knn', tr_in, tr_targets); +if ~isempty(errstring) + error(errstring); +end +net.tr_in = tr_in; +net.tr_targets = tr_targets; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/knnfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function [y, l] = knnfwd(net, x) +%KNNFWD Forward propagation through a K-nearest-neighbour classifier. +% +% Description +% [Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector +% per row) and uses the K-nearest-neighbour rule on the training data +% contained in NET to produce a matrix Y of outputs and a matrix L of +% classification labels. The nearest neighbours are determined using +% Euclidean distance. The IJth entry of Y counts the number of +% occurrences that an example from class J is among the K closest +% training examples to example I from X. The matrix L contains the +% predicted class labels as an index 1..N, not as 1-of-N coding. +% +% See also +% KMEANS, KNN +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +errstring = consist(net, 'knn', x); +if ~isempty(errstring) + error(errstring); +end + +ntest = size(x, 1); % Number of input vectors. +nclass = size(net.tr_targets, 2); % Number of classes. + +% Compute matrix of squared distances between input vectors from the training +% and test sets. The matrix distsq has dimensions (ntrain, ntest). + +distsq = dist2(net.tr_in, x); + +% Now sort the distances. This generates a matrix kind of the same +% dimensions as distsq, in which each column gives the indices of the +% elements in the corresponding column of distsq in ascending order. + +[vals, kind] = sort(distsq); +y = zeros(ntest, nclass); + +for k=1:net.k + % We now look at the predictions made by the Kth nearest neighbours alone, + % and represent this as a 1-of-N coded matrix, and then accumulate the + % predictions so far. + + y = y + net.tr_targets(kind(k,:),:); + +end + +if nargout == 2 + % Convert this set of outputs to labels, randomly breaking ties + [temp, l] = max((y + 0.1*rand(size(y))), [], 2); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/linef.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function y = linef(lambda, fn, x, d, varargin) +%LINEF Calculate function value along a line. +% +% Description +% LINEF(LAMBDA, FN, X, D) calculates the value of the function FN at +% the point X+LAMBDA*D. Here X is a row vector and LAMBDA is a scalar. +% +% LINEF(LAMBDA, FN, X, D, P1, P2, ...) allows additional arguments to +% be passed to FN(). This function is used for convenience in some of +% the optimisation routines. +% +% See also +% GRADCHEK, LINEMIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check function string +fn = fcnchk(fn, length(varargin)); + +y = feval(fn, x+lambda.*d, varargin{:});
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/linemin.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,152 @@ +function [x, options] = linemin(f, pt, dir, fpt, options, ... + varargin) +%LINEMIN One dimensional minimization. +% +% Description +% [X, OPTIONS] = LINEMIN(F, PT, DIR, FPT, OPTIONS) uses Brent's +% algorithm to find the minimum of the function F(X) along the line DIR +% through the point PT. The function value at the starting point is +% FPT. The point at which F has a local minimum is returned as X. The +% function value at that point is returned in OPTIONS(8). +% +% LINEMIN(F, PT, DIR, FPT, OPTIONS, P1, P2, ...) allows additional +% arguments to be passed to F(). +% +% The optional parameters have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values. +% +% OPTIONS(2) is a measure of the absolute precision required for the +% value of X at the solution. +% +% OPTIONS(3) is a measure of the precision required of the objective +% function at the solution. Both this and the previous condition must +% be satisfied for termination. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% See also +% CONJGRAD, MINBRACK, QUASINEW +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set up the options. +if(options(14)) + niters = options(14); +else + niters = 100; +end +options(10) = 0; % Initialise count of function evaluations + +display = options(1); + +% Check function string +f = fcnchk(f, length(varargin)); + +% Value of golden section (1 + sqrt(5))/2.0 +phi = 1.6180339887499; +cphi = 1 - 1/phi; +TOL = sqrt(eps); % Maximal fractional precision +TINY = 1.0e-10; % Can't use fractional precision when minimum is at 0 + +% Bracket the minimum +[br_min, br_mid, br_max, num_evals] = feval('minbrack', 'linef', ... + 0.0, 1.0, fpt, f, pt, dir, varargin{:}); +options(10) = options(10) + num_evals; % Increment number of fn. evals + % No gradient evals in minbrack + +% Use Brent's algorithm to find minimum +% Initialise the points and function values +w = br_mid; % Where second from minimum is +v = br_mid; % Previous value of w +x = v; % Where current minimum is +e = 0.0; % Distance moved on step before last +fx = feval('linef', x, f, pt, dir, varargin{:}); +options(10) = options(10) + 1; +fv = fx; fw = fx; + +for n = 1:niters + xm = 0.5.*(br_min+br_max); % Middle of bracket + % Make sure that tolerance is big enough + tol1 = TOL * (max(abs(x))) + TINY; + % Decide termination on absolute precision required by options(2) + if (max(abs(x - xm)) <= options(2) & br_max-br_min < 4*options(2)) + options(8) = fx; + return; + end + % Check if step before last was big enough to try a parabolic step. + % Note that this will fail on first iteration, which must be a golden + % section step. + if (max(abs(e)) > tol1) + % Construct a trial parabolic fit through x, v and w + r = (fx - fv) .* (x - w); + q = (fx - fw) .* (x - v); + p = (x - v).*q - (x - w).*r; + q = 2.0 .* (q - r); + if (q > 0.0) p = -p; end + q = abs(q); + % Test if the parabolic fit is OK + if (abs(p) >= abs(0.5*q*e) | p <= q*(br_min-x) | p >= q*(br_max-x)) + % No it isn't, so take a golden section step + if (x >= xm) + e = br_min-x; + else + e = br_max-x; + end + d = cphi*e; + else + % Yes it is, so take the parabolic step + e = d; + d = p/q; + u = x+d; + if (u-br_min < 2*tol1 | br_max-u < 2*tol1) + d = sign(xm-x)*tol1; + end + end + else + % Step before last not big enough, so take a golden section step + if (x >= xm) + e = br_min - x; + else + e = br_max - x; + end + d = cphi*e; + end + % Make sure that step is big enough + if (abs(d) >= tol1) + u = x+d; + else + u = x + sign(d)*tol1; + end + % Evaluate function at u + fu = feval('linef', u, f, pt, dir, varargin{:}); + options(10) = options(10) + 1; + % Reorganise bracket + if (fu <= fx) + if (u >= x) + br_min = x; + else + br_max = x; + end + v = w; w = x; x = u; + fv = fw; fw = fx; fx = fu; + else + if (u < x) + br_min = u; + else + br_max = u; + end + if (fu <= fw | w == x) + v = w; w = u; + fv = fw; fw = fu; + elseif (fu <= fv | v == x | v == w) + v = u; + fv = fu; + end + end + if (display == 1) + fprintf(1, 'Cycle %4d Error %11.6f\n', n, fx); + end +end +options(8) = fx;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/maxitmess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function s = maxitmess() +%MAXITMESS Create a standard error message when training reaches max. iterations. +% +% Description +% S = MAXITMESS returns a standard string that it used by training +% algorithms when the maximum number of iterations (as specified in +% OPTIONS(14) is reached. +% +% See also +% CONJGRAD, GLMTRAIN, GMMEM, GRADDESC, GTMEM, KMEANS, OLGD, QUASINEW, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + +s = 'Maximum number of iterations has been exceeded'; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,77 @@ +function net = mdn(nin, nhidden, ncentres, dim_target, mix_type, ... + prior, beta) +%MDN Creates a Mixture Density Network with specified architecture. +% +% Description +% NET = MDN(NIN, NHIDDEN, NCENTRES, DIMTARGET) takes the number of +% inputs, hidden units for a 2-layer feed-forward network and the +% number of centres and target dimension for the mixture model whose +% parameters are set from the outputs of the neural network. The fifth +% argument MIXTYPE is used to define the type of mixture model. +% (Currently there is only one type supported: a mixture of Gaussians +% with a single covariance parameter for each component.) For this +% model, the mixture coefficients are computed from a group of softmax +% outputs, the centres are equal to a group of linear outputs, and the +% variances are obtained by applying the exponential function to a +% third group of outputs. +% +% The network is initialised by a call to MLP, and the arguments PRIOR, +% and BETA have the same role as for that function. Weight +% initialisation uses the Matlab function RANDN and so the seed for +% the random weight initialization can be set using RANDN('STATE', S) +% where S is the seed value. A specialised data structure (rather than +% GMM) is used for the mixture model outputs to improve the efficiency +% of error and gradient calculations in network training. The fields +% are described in MDNFWD where they are set up. +% +% The fields in NET are +% +% type = 'mdn' +% nin = number of input variables +% nout = dimension of target space (not number of network outputs) +% nwts = total number of weights and biases +% mdnmixes = data structure for mixture model output +% mlp = data structure for MLP network +% +% See also +% MDNFWD, MDNERR, MDN2GMM, MDNGRAD, MDNPAK, MDNUNPAK, MLP +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +% Currently ignore type argument: reserved for future use +net.type = 'mdn'; + +% Set up the mixture model part of the structure +% For efficiency we use a specialised data structure in place of GMM +mdnmixes.type = 'mdnmixes'; +mdnmixes.ncentres = ncentres; +mdnmixes.dim_target = dim_target; + +% This calculation depends on spherical variances +mdnmixes.nparams = ncentres + ncentres*dim_target + ncentres; + +% Make the weights in the mdnmixes structure null +mdnmixes.mixcoeffs = []; +mdnmixes.centres = []; +mdnmixes.covars = []; + +% Number of output nodes = number of parameters in mixture model +nout = mdnmixes.nparams; + +% Set up the MLP part of the network +if (nargin == 5) + mlpnet = mlp(nin, nhidden, nout, 'linear'); +elseif (nargin == 6) + mlpnet = mlp(nin, nhidden, nout, 'linear', prior); +elseif (nargin == 7) + mlpnet = mlp(nin, nhidden, nout, 'linear', prior, beta); +end + +% Create descriptor +net.mdnmixes = mdnmixes; +net.mlp = mlpnet; +net.nin = nin; +net.nout = dim_target; +net.nwts = mlpnet.nwts;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdn2gmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function gmmmixes = mdn2gmm(mdnmixes) +%MDN2GMM Converts an MDN mixture data structure to array of GMMs. +% +% Description +% GMMMIXES = MDN2GMM(MDNMIXES) takes an MDN mixture data structure +% MDNMIXES containing three matrices (for priors, centres and +% variances) where each row represents the corresponding parameter +% values for a different mixture model and creates an array of GMMs. +% These can then be used with the standard Netlab Gaussian mixture +% model functions. +% +% See also +% GMM, MDN, MDNFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +% Check argument for consistency +errstring = consist(mdnmixes, 'mdnmixes'); +if ~isempty(errstring) + error(errstring); +end + +nmixes = size(mdnmixes.centres, 1); +% Construct ndata structures containing the mixture model information. +% First allocate the memory. +tempmix = gmm(mdnmixes.dim_target, mdnmixes.ncentres, 'spherical'); +f = fieldnames(tempmix); +gmmmixes = cell(size(f, 1), 1, nmixes); +gmmmixes = cell2struct(gmmmixes, f,1); + +% Then fill each structure in turn using gmmunpak. Assume that spherical +% covariance structure is used. +for i = 1:nmixes + centres = reshape(mdnmixes.centres(i, :), mdnmixes.dim_target, ... + mdnmixes.ncentres)'; + gmmmixes(i) = gmmunpak(tempmix, [mdnmixes.mixcoeffs(i,:), ... + centres(:)', mdnmixes.covars(i,:)]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdndist2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function n2 = mdndist2(mixparams, t) +%MDNDIST2 Calculates squared distance between centres of Gaussian kernels and data +% +% Description +% N2 = MDNDIST2(MIXPARAMS, T) takes takes the centres of the Gaussian +% contained in MIXPARAMS and the target data matrix, T, and computes +% the squared Euclidean distance between them. If T has M rows and N +% columns, then the CENTRES field in the MIXPARAMS structure should +% have M rows and N*MIXPARAMS.NCENTRES columns: the centres in each row +% relate to the corresponding row in T. The result has M rows and +% MIXPARAMS.NCENTRES columns. The I, Jth entry is the squared distance +% from the Ith row of X to the Jth centre in the Ith row of +% MIXPARAMS.CENTRES. +% +% See also +% MDNFWD, MDNPROB +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +% Check arguments for consistency +errstring = consist(mixparams, 'mdnmixes'); +if ~isempty(errstring) + error(errstring); +end + +ncentres = mixparams.ncentres; +dim_target = mixparams.dim_target; +ntarget = size(t, 1); +if ntarget ~= size(mixparams.centres, 1) + error('Number of targets does not match number of mixtures') +end +if size(t, 2) ~= mixparams.dim_target + error('Target dimension does not match mixture dimension') +end + +% Build t that suits parameters, that is repeat t for each centre +t = kron(ones(1, ncentres), t); + +% Do subtraction and square +diff2 = (t - mixparams.centres).^2; + +% Reshape and sum each component +diff2 = reshape(diff2', dim_target, (ntarget*ncentres))'; +n2 = sum(diff2, 2); + +% Calculate the sum of distance, and reshape +% so that we have a distance for each centre per target +n2 = reshape(n2, ncentres, ntarget)'; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdnerr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +function e = mdnerr(net, x, t) +%MDNERR Evaluate error function for Mixture Density Network. +% +% Description +% E = MDNERR(NET, X, T) takes a mixture density network data structure +% NET, a matrix X of input vectors and a matrix T of target vectors, +% and evaluates the error function E. The error function is the +% negative log likelihood of the target data under the conditional +% density given by the mixture model parameterised by the MLP. Each +% row of X corresponds to one input vector and each row of T +% corresponds to one target vector. +% +% See also +% MDN, MDNFWD, MDNGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +% Check arguments for consistency +errstring = consist(net, 'mdn', x, t); +if ~isempty(errstring) + error(errstring); +end + +% Get the output mixture models +mixparams = mdnfwd(net, x); + +% Compute the probabilities of mixtures +probs = mdnprob(mixparams, t); +% Compute the error +e = sum( -log(max(eps, sum(probs, 2)))); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdnfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +function [mixparams, y, z, a] = mdnfwd(net, x) +%MDNFWD Forward propagation through Mixture Density Network. +% +% Description +% MIXPARAMS = MDNFWD(NET, X) takes a mixture density network data +% structure NET and a matrix X of input vectors, and forward propagates +% the inputs through the network to generate a structure MIXPARAMS +% which contains the parameters of several mixture models. Each row +% of X represents one input vector and the corresponding row of the +% matrices in MIXPARAMS represents the parameters of a mixture model +% for the conditional probability of target vectors given the input +% vector. This is not represented as an array of GMM structures to +% improve the efficiency of MDN training. +% +% The fields in MIXPARAMS are +% type = 'mdnmixes' +% ncentres = number of mixture components +% dimtarget = dimension of target space +% mixcoeffs = mixing coefficients +% centres = means of Gaussians: stored as one row per pattern +% covars = covariances of Gaussians +% nparams = number of parameters +% +% [MIXPARAMS, Y, Z] = MDNFWD(NET, X) also generates a matrix Y of the +% outputs of the MLP and a matrix Z of the hidden unit activations +% where each row corresponds to one pattern. +% +% [MIXPARAMS, Y, Z, A] = MLPFWD(NET, X) also returns a matrix A giving +% the summed inputs to each output unit, where each row corresponds to +% one pattern. +% +% See also +% MDN, MDN2GMM, MDNERR, MDNGRAD, MLPFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +% Check arguments for consistency +errstring = consist(net, 'mdn', x); +if ~isempty(errstring) + error(errstring); +end + +% Extract mlp and mixture model descriptors +mlpnet = net.mlp; +mixes = net.mdnmixes; + +ncentres = mixes.ncentres; % Number of components in mixture model +dim_target = mixes.dim_target; % Dimension of targets +nparams = mixes.nparams; % Number of parameters in mixture model + +% Propagate forwards through MLP +[y, z, a] = mlpfwd(mlpnet, x); + +% Compute the postion for each parameter in the whole +% matrix. Used to define the mixparams structure +mixcoeff = [1:1:ncentres]; +centres = [ncentres+1:1:(ncentres*(1+dim_target))]; +variances = [(ncentres*(1+dim_target)+1):1:nparams]; + +% Convert output values into mixture model parameters + +% Use softmax to calculate priors +% Prevent overflow and underflow: use same bounds as glmfwd +% Ensure that sum(exp(y), 2) does not overflow +maxcut = log(realmax) - log(ncentres); +% Ensure that exp(y) > 0 +mincut = log(realmin); +temp = min(y(:,1:ncentres), maxcut); +temp = max(temp, mincut); +temp = exp(temp); +mixpriors = temp./(sum(temp, 2)*ones(1,ncentres)); + +% Centres are just copies of network outputs +mixcentres = y(:,(ncentres+1):ncentres*(1+dim_target)); + +% Variances are exp of network outputs +mixwidths = exp(y(:,(ncentres*(1+dim_target)+1):nparams)); + +% Now build up all the mixture model weight vectors +ndata = size(x, 1); + +% Return parameters +mixparams.type = mixes.type; +mixparams.ncentres = mixes.ncentres; +mixparams.dim_target = mixes.dim_target; +mixparams.nparams = mixes.nparams; + +mixparams.mixcoeffs = mixpriors; +mixparams.centres = mixcentres; +mixparams.covars = mixwidths; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdngrad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +function g = mdngrad(net, x, t) +%MDNGRAD Evaluate gradient of error function for Mixture Density Network. +% +% Description +% G = MDNGRAD(NET, X, T) takes a mixture density network data +% structure NET, a matrix X of input vectors and a matrix T of target +% vectors, and evaluates the gradient G of the error function with +% respect to the network weights. The error function is negative log +% likelihood of the target data. Each row of X corresponds to one +% input vector and each row of T corresponds to one target vector. +% +% See also +% MDN, MDNFWD, MDNERR, MDNPROB, MLPBKP +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +% Check arguments for consistency +errstring = consist(net, 'mdn', x, t); +if ~isempty(errstring) + error(errstring); +end + +[mixparams, y, z] = mdnfwd(net, x); + +% Compute gradients at MLP outputs: put the answer in deltas +ncentres = net.mdnmixes.ncentres; +dim_target = net.mdnmixes.dim_target; +nmixparams = net.mdnmixes.nparams; +ntarget = size(t, 1); +deltas = zeros(ntarget, net.mlp.nout); +e = ones(ncentres, 1); +f = ones(1, dim_target); + +post = mdnpost(mixparams, t); + +% Calculate prior derivatives +deltas(:,1:ncentres) = mixparams.mixcoeffs - post; + +% Calculate centre derivatives +long_t = kron(ones(1, ncentres), t); +centre_err = mixparams.centres - long_t; + +% Get the post to match each u_jk: +% this array will be (ntarget, (ncentres*dim_target)) +long_post = kron(ones(dim_target, 1), post); +long_post = reshape(long_post, ntarget, (ncentres*dim_target)); + +% Get the variance to match each u_jk: +var = mixparams.covars; +var = kron(ones(dim_target, 1), var); +var = reshape(var, ntarget, (ncentres*dim_target)); + +% Compute centre deltas +deltas(:, (ncentres+1):(ncentres*(1+dim_target))) = ... + (centre_err.*long_post)./var; + +% Compute variance deltas +dist2 = mdndist2(mixparams, t); +c = dim_target*ones(ntarget, ncentres); +deltas(:, (ncentres*(1+dim_target)+1):nmixparams) = ... + post.*((dist2./mixparams.covars)-c)./(-2); + +% Now back-propagate deltas through MLP +g = mlpbkp(net.mlp, x, z, deltas);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdninit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function net = mdninit(net, prior, t, options) +%MDNINIT Initialise the weights in a Mixture Density Network. +% +% Description +% +% NET = MDNINIT(NET, PRIOR) takes a Mixture Density Network NET and +% sets the weights and biases by sampling from a Gaussian distribution. +% It calls MLPINIT for the MLP component of NET. +% +% NET = MDNINIT(NET, PRIOR, T, OPTIONS) uses the target data T to +% initialise the biases for the output units after initialising the +% other weights as above. It calls GMMINIT, with T and OPTIONS as +% arguments, to obtain a model of the unconditional density of T. The +% biases are then set so that NET will output the values in the +% Gaussian mixture model. +% +% See also +% MDN, MLP, MLPINIT, GMMINIT +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +% Initialise network weights from prior: this gives noise around values +% determined later +net.mlp = mlpinit(net.mlp, prior); + +if nargin > 2 + % Initialise priors, centres and variances from target data + temp_mix = gmm(net.mdnmixes.dim_target, net.mdnmixes.ncentres, 'spherical'); + temp_mix = gmminit(temp_mix, t, options); + + ncentres = net.mdnmixes.ncentres; + dim_target = net.mdnmixes.dim_target; + + % Now set parameters in MLP to yield the right values. + % This involves setting the biases correctly. + + % Priors + net.mlp.b2(1:ncentres) = temp_mix.priors; + + % Centres are arranged in mlp such that we have + % u11, u12, u13, ..., u1c, ... , uj1, uj2, uj3, ..., ujc, ..., um1, uM2, + % ..., uMc + % This is achieved by transposing temp_mix.centres before reshaping + end_centres = ncentres*(dim_target+1); + net.mlp.b2(ncentres+1:end_centres) = ... + reshape(temp_mix.centres', 1, ncentres*dim_target); + + % Variances + net.mlp.b2((end_centres+1):net.mlp.nout) = ... + log(temp_mix.covars); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdnpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function w = mdnpak(net) +%MDNPAK Combines weights and biases into one weights vector. +% +% Description +% W = MDNPAK(NET) takes a mixture density network data structure NET +% and combines the network weights into a single row vector W. +% +% See also +% MDN, MDNUNPAK, MDNFWD, MDNERR, MDNGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +errstring = consist(net, 'mdn'); +if ~errstring + error(errstring); +end +w = mlppak(net.mlp);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdnpost.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function [post, a] = mdnpost(mixparams, t) +%MDNPOST Computes the posterior probability for each MDN mixture component. +% +% Description +% POST = MDNPOST(MIXPARAMS, T) computes the posterior probability +% P(J|T) of each data vector in T under the Gaussian mixture model +% represented by the corresponding entries in MIXPARAMS. Each row of T +% represents a single vector. +% +% [POST, A] = MDNPOST(MIXPARAMS, T) also computes the activations A +% (i.e. the probability P(T|J) of the data conditioned on each +% component density) for a Gaussian mixture model. +% +% See also +% MDNGRAD, MDNPROB +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +[prob a] = mdnprob(mixparams, t); + +s = sum(prob, 2); +% Set any zeros to one before dividing +s = s + (s==0); +post = prob./(s*ones(1, mixparams.ncentres));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdnprob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function [prob,a] = mdnprob(mixparams, t) +%MDNPROB Computes the data probability likelihood for an MDN mixture structure. +% +% Description +% PROB = MDNPROB(MIXPARAMS, T) computes the probability P(T) of each +% data vector in T under the Gaussian mixture model represented by the +% corresponding entries in MIXPARAMS. Each row of T represents a single +% vector. +% +% [PROB, A] = MDNPROB(MIXPARAMS, T) also computes the activations A +% (i.e. the probability P(T|J) of the data conditioned on each +% component density) for a Gaussian mixture model. +% +% See also +% MDNERR, MDNPOST +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +% Check arguments for consistency +errstring = consist(mixparams, 'mdnmixes'); +if ~isempty(errstring) + error(errstring); +end + +ntarget = size(t, 1); +if ntarget ~= size(mixparams.centres, 1) + error('Number of targets does not match number of mixtures') +end +if size(t, 2) ~= mixparams.dim_target + error('Target dimension does not match mixture dimension') +end + +dim_target = mixparams.dim_target; +ntarget = size(t, 1); + +% Calculate squared norm matrix, of dimension (ndata, ncentres) +% vector (ntarget * ncentres) +dist2 = mdndist2(mixparams, t); + +% Calculate variance factors +variance = 2.*mixparams.covars; + +% Compute the normalisation term +normal = ((2.*pi).*mixparams.covars).^(dim_target./2); + +% Now compute the activations +a = exp(-(dist2./variance))./normal; + +% Accumulate negative log likelihood of targets +prob = mixparams.mixcoeffs.*a;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mdnunpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function net = mdnunpak(net, w) +%MDNUNPAK Separates weights vector into weight and bias matrices. +% +% Description +% NET = MDNUNPAK(NET, W) takes an mdn network data structure NET and a +% weight vector W, and returns a network data structure identical to +% the input network, except that the weights in the MLP sub-structure +% are set to the corresponding elements of W. +% +% See also +% MDN, MDNPAK, MDNFWD, MDNERR, MDNGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) +% David J Evans (1998) + +errstring = consist(net, 'mdn'); +if ~errstring + error(errstring); +end +if net.nwts ~= length(w) + error('Invalid weight vector length') +end + +net.mlp = mlpunpak(net.mlp, w);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/metrop.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,193 @@ +function [samples, energies, diagn] = metrop(f, x, options, gradf, varargin) +%METROP Markov Chain Monte Carlo sampling with Metropolis algorithm. +% +% Description +% SAMPLES = METROP(F, X, OPTIONS) uses the Metropolis algorithm to +% sample from the distribution P ~ EXP(-F), where F is the first +% argument to METROP. The Markov chain starts at the point X and each +% candidate state is picked from a Gaussian proposal distribution and +% accepted or rejected according to the Metropolis criterion. +% +% SAMPLES = METROP(F, X, OPTIONS, [], P1, P2, ...) allows additional +% arguments to be passed to F(). The fourth argument is ignored, but +% is included for compatibility with HMC and the optimisers. +% +% [SAMPLES, ENERGIES, DIAGN] = METROP(F, X, OPTIONS) also returns a log +% of the energy values (i.e. negative log probabilities) for the +% samples in ENERGIES and DIAGN, a structure containing diagnostic +% information (position and acceptance threshold) for each step of the +% chain in DIAGN.POS and DIAGN.ACC respectively. All candidate states +% (including rejected ones) are stored in DIAGN.POS. +% +% S = METROP('STATE') returns a state structure that contains the state +% of the two random number generators RAND and RANDN. These are +% contained in fields randstate, randnstate. +% +% METROP('STATE', S) resets the state to S. If S is an integer, then +% it is passed to RAND and RANDN. If S is a structure returned by +% METROP('STATE') then it resets the generator to exactly the same +% state. +% +% The optional parameters in the OPTIONS vector have the following +% interpretations. +% +% OPTIONS(1) is set to 1 to display the energy values and rejection +% threshold at each step of the Markov chain. If the value is 2, then +% the position vectors at each step are also displayed. +% +% OPTIONS(14) is the number of samples retained from the Markov chain; +% default 100. +% +% OPTIONS(15) is the number of samples omitted from the start of the +% chain; default 0. +% +% OPTIONS(18) is the variance of the proposal distribution; default 1. +% +% See also +% HMC +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin <= 2 + if ~strcmp(f, 'state') + error('Unknown argument to metrop'); + end + switch nargin + case 1 + % Return state of sampler + samples = get_state(f); % Function defined in this module + return; + case 2 + % Set the state of the sampler + set_state(f, x); % Function defined in this module + return; + end +end + +if 0 +seed = 42; +randn('state', seed); +rand('state', seed) +end + +display = options(1); +if options(14) > 0 + nsamples = options(14); +else + nsamples = 100; +end +if options(15) >= 0 + nomit = options(15); +else + nomit = 0; +end +if options(18) > 0.0 + std_dev = sqrt(options(18)); +else + std_dev = 1.0; % default +end +nparams = length(x); + +% Set up string for evaluating potential function. +f = fcnchk(f, length(varargin)); + +samples = zeros(nsamples, nparams); % Matrix of returned samples. +if nargout >= 2 + en_save = 1; + energies = zeros(nsamples, 1); +else + en_save = 0; +end +if nargout >= 3 + diagnostics = 1; + diagn_pos = zeros(nsamples, nparams); + diagn_acc = zeros(nsamples, 1); +else + diagnostics = 0; +end + +% Main loop. +n = - nomit + 1; +Eold = feval(f, x, varargin{:}); % Evaluate starting energy. +nreject = 0; % Initialise count of rejected states. +while n <= nsamples + + xold = x; + % Sample a new point from the proposal distribution + x = xold + randn(1, nparams)*std_dev; + %fprintf('netlab propose: xold = %5.3f,%5.3f, xnew = %5.3f,%5.3f\n',... + % xold(1), xold(2), x(1), x(2)); + + % Now apply Metropolis algorithm. + Enew = feval(f, x, varargin{:}); % Evaluate new energy. + a = exp(Eold - Enew); % Acceptance threshold. + if (diagnostics & n > 0) + diagn_pos(n,:) = x; + diagn_acc(n,:) = a; + end + if (display > 1) + fprintf(1, 'New position is\n'); + disp(x); + end + + r = rand(1); + %fprintf('netlab: n=%d, a=%f/%f=%5.3f (%5.3f), r=%5.3f\n',... + % n, exp(-Enew), exp(-Eold), a, exp(-Enew)/exp(-Eold), r); + if a > r % Accept the new state. + Eold = Enew; + if (display > 0) + fprintf(1, 'Finished step %4d Threshold: %g\n', n, a); + end + else % Reject the new state + if n > 0 + nreject = nreject + 1; + end + x = xold; % Reset position + if (display > 0) + fprintf(1, ' Sample rejected %4d. Threshold: %g\n', n, a); + end + end + if n > 0 + samples(n,:) = x; % Store sample. + if en_save + energies(n) = Eold; % Store energy. + end + end + n = n + 1; +end + +if (display > 0) + fprintf(1, '\nFraction of samples rejected: %g\n', ... + nreject/(nsamples)); +end + +if diagnostics + diagn.pos = diagn_pos; + diagn.acc = diagn_acc; +end + +% Return complete state of the sampler. +function state = get_state(f) + +state.randstate = rand('state'); +state.randnstate = randn('state'); +return + +% Set state of sampler, either from full state, or with an integer +function set_state(f, x) + +if isnumeric(x) + rand('state', x); + randn('state', x); +else + if ~isstruct(x) + error('Second argument to metrop must be number or state structure'); + end + if (~isfield(x, 'randstate') | ~isfield(x, 'randnstate')) + error('Second argument to metrop must contain correct fields') + end + rand('state', x.randstate); + randn('state', x.randnstate); +end +return
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/minbrack.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,127 @@ +function [br_min, br_mid, br_max, num_evals] = minbrack(f, a, b, fa, ... + varargin) +%MINBRACK Bracket a minimum of a function of one variable. +% +% Description +% BRMIN, BRMID, BRMAX, NUMEVALS] = MINBRACK(F, A, B, FA) finds a +% bracket of three points around a local minimum of F. The function F +% must have a one dimensional domain. A < B is an initial guess at the +% minimum and maximum points of a bracket, but MINBRACK will search +% outside this interval if necessary. The bracket consists of three +% points (in increasing order) such that F(BRMID) < F(BRMIN) and +% F(BRMID) < F(BRMAX). FA is the value of the function at A: it is +% included to avoid unnecessary function evaluations in the +% optimization routines. The return value NUMEVALS is the number of +% function evaluations in MINBRACK. +% +% MINBRACK(F, A, B, FA, P1, P2, ...) allows additional arguments to be +% passed to F +% +% See also +% LINEMIN, LINEF +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check function string +f = fcnchk(f, length(varargin)); + +% Value of golden section (1 + sqrt(5))/2.0 +phi = 1.6180339887499; + +% Initialise count of number of function evaluations +num_evals = 0; + +% A small non-zero number to avoid dividing by zero in quadratic interpolation +TINY = 1.e-10; + +% Maximal proportional step to take: don't want to make this too big +% as then spend a lot of time finding the minimum inside the bracket +max_step = 10.0; + +fb = feval(f, b, varargin{:}); +num_evals = num_evals + 1; + +% Assume that we know going from a to b is downhill initially +% (usually because gradf(a) < 0). +if (fb > fa) + % Minimum must lie between a and b: do golden section until we find point + % low enough to be middle of bracket + c = b; + b = a + (c-a)/phi; + fb = feval(f, b, varargin{:}); + num_evals = num_evals + 1; + while (fb > fa) + c = b; + b = a + (c-a)/phi; + fb = feval(f, b, varargin{:}); + num_evals = num_evals + 1; + end +else + % There is a valid bracket upper bound greater than b + c = b + phi*(b-a); + fc = feval(f, c, varargin{:}); + num_evals = num_evals + 1; + bracket_found = 0; + + while (fb > fc) + % Do a quadratic interpolation (i.e. to minimum of quadratic) + r = (b-a).*(fb-fc); + q = (b-c).*(fb-fa); + u = b - ((b-c)*q - (b-a)*r)/(2.0*(sign(q-r)*max([abs(q-r), TINY]))); + ulimit = b + max_step*(c-b); + + if ((b-u)'*(u-c) > 0.0) + % Interpolant lies between b and c + fu = feval(f, u, varargin{:}); + num_evals = num_evals + 1; + if (fu < fc) + % Have a minimum between b and c + br_min = b; + br_mid = u; + br_max = c; + return; + elseif (fu > fb) + % Have a minimum between a and u + br_min = a; + br_mid = c; + br_max = u; + return; + end + % Quadratic interpolation didn't give a bracket, so take a golden step + u = c + phi*(c-b); + elseif ((c-u)'*(u-ulimit) > 0.0) + % Interpolant lies between c and limit + fu = feval(f, u, varargin{:}); + num_evals = num_evals + 1; + if (fu < fc) + % Move bracket along, and then take a golden section step + b = c; + c = u; + u = c + phi*(c-b); + else + bracket_found = 1; + end + elseif ((u-ulimit)'*(ulimit-c) >= 0.0) + % Limit parabolic u to maximum value + u = ulimit; + else + % Reject parabolic u and use golden section step + u = c + phi*(c-b); + end + if ~bracket_found + fu = feval(f, u, varargin{:}); + num_evals = num_evals + 1; + end + a = b; b = c; c = u; + fa = fb; fb = fc; fc = fu; + end % while loop +end % bracket found +br_mid = b; +if (a < c) + br_min = a; + br_max = c; +else + br_min = c; + br_max = a; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +function net = mlp(nin, nhidden, nout, outfunc, prior, beta) +%MLP Create a 2-layer feedforward network. +% +% Description +% NET = MLP(NIN, NHIDDEN, NOUT, FUNC) takes the number of inputs, +% hidden units and output units for a 2-layer feed-forward network, +% together with a string FUNC which specifies the output unit +% activation function, and returns a data structure NET. The weights +% are drawn from a zero mean, unit variance isotropic Gaussian, with +% varianced scaled by the fan-in of the hidden or output units as +% appropriate. This makes use of the Matlab function RANDN and so the +% seed for the random weight initialization can be set using +% RANDN('STATE', S) where S is the seed value. The hidden units use +% the TANH activation function. +% +% The fields in NET are +% type = 'mlp' +% nin = number of inputs +% nhidden = number of hidden units +% nout = number of outputs +% nwts = total number of weights and biases +% actfn = string describing the output unit activation function: +% 'linear' +% 'logistic +% 'softmax' +% w1 = first-layer weight matrix +% b1 = first-layer bias vector +% w2 = second-layer weight matrix +% b2 = second-layer bias vector +% Here W1 has dimensions NIN times NHIDDEN, B1 has dimensions 1 times +% NHIDDEN, W2 has dimensions NHIDDEN times NOUT, and B2 has dimensions +% 1 times NOUT. +% +% NET = MLP(NIN, NHIDDEN, NOUT, FUNC, PRIOR), in which PRIOR is a +% scalar, allows the field NET.ALPHA in the data structure NET to be +% set, corresponding to a zero-mean isotropic Gaussian prior with +% inverse variance with value PRIOR. Alternatively, PRIOR can consist +% of a data structure with fields ALPHA and INDEX, allowing individual +% Gaussian priors to be set over groups of weights in the network. Here +% ALPHA is a column vector in which each element corresponds to a +% separate group of weights, which need not be mutually exclusive. The +% membership of the groups is defined by the matrix INDX in which the +% columns correspond to the elements of ALPHA. Each column has one +% element for each weight in the matrix, in the order defined by the +% function MLPPAK, and each element is 1 or 0 according to whether the +% weight is a member of the corresponding group or not. A utility +% function MLPPRIOR is provided to help in setting up the PRIOR data +% structure. +% +% NET = MLP(NIN, NHIDDEN, NOUT, FUNC, PRIOR, BETA) also sets the +% additional field NET.BETA in the data structure NET, where beta +% corresponds to the inverse noise variance. +% +% See also +% MLPPRIOR, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +net.type = 'mlp'; +net.nin = nin; +net.nhidden = nhidden; +net.nout = nout; +net.nwts = (nin + 1)*nhidden + (nhidden + 1)*nout; + +outfns = {'linear', 'logistic', 'softmax'}; + +if sum(strcmp(outfunc, outfns)) == 0 + error('Undefined output function. Exiting.'); +else + net.outfn = outfunc; +end + +if nargin > 4 + if isstruct(prior) + net.alpha = prior.alpha; + net.index = prior.index; + elseif size(prior) == [1 1] + net.alpha = prior; + else + error('prior must be a scalar or a structure'); + end +end + +net.w1 = randn(nin, nhidden)/sqrt(nin + 1); +net.b1 = randn(1, nhidden)/sqrt(nin + 1); +net.w2 = randn(nhidden, nout)/sqrt(nhidden + 1); +net.b2 = randn(1, nout)/sqrt(nhidden + 1); + +if nargin == 6 + net.beta = beta; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlpbkp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function g = mlpbkp(net, x, z, deltas) +%MLPBKP Backpropagate gradient of error function for 2-layer network. +% +% Description +% G = MLPBKP(NET, X, Z, DELTAS) takes a network data structure NET +% together with a matrix X of input vectors, a matrix Z of hidden unit +% activations, and a matrix DELTAS of the gradient of the error +% function with respect to the values of the output units (i.e. the +% summed inputs to the output units, before the activation function is +% applied). The return value is the gradient G of the error function +% with respect to the network weights. Each row of X corresponds to one +% input vector. +% +% This function is provided so that the common backpropagation +% algorithm can be used by multi-layer perceptron network models to +% compute gradients for mixture density networks as well as standard +% error functions. +% +% See also +% MLP, MLPGRAD, MLPDERIV, MDNGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Evaluate second-layer gradients. +gw2 = z'*deltas; +gb2 = sum(deltas, 1); + +% Now do the backpropagation. +delhid = deltas*net.w2'; +delhid = delhid.*(1.0 - z.*z); + +% Finally, evaluate the first-layer gradients. +gw1 = x'*delhid; +gb1 = sum(delhid, 1); + +g = [gw1(:)', gb1, gw2(:)', gb2];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlpderiv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function g = mlpderiv(net, x) +%MLPDERIV Evaluate derivatives of network outputs with respect to weights. +% +% Description +% G = MLPDERIV(NET, X) takes a network data structure NET and a matrix +% of input vectors X and returns a three-index matrix G whose I, J, K +% element contains the derivative of network output K with respect to +% weight or bias parameter J for input pattern I. The ordering of the +% weight and bias parameters is defined by MLPUNPAK. +% +% See also +% MLP, MLPPAK, MLPGRAD, MLPBKP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x); +if ~isempty(errstring); + error(errstring); +end + +[y, z] = mlpfwd(net, x); + +ndata = size(x, 1); + +if isfield(net, 'mask') + nwts = size(find(net.mask), 1); + temp = zeros(1, net.nwts); +else + nwts = net.nwts; +end + +g = zeros(ndata, nwts, net.nout); +for k = 1 : net.nout + delta = zeros(1, net.nout); + delta(1, k) = 1; + for n = 1 : ndata + if isfield(net, 'mask') + temp = mlpbkp(net, x(n, :), z(n, :), delta); + g(n, :, k) = temp(logical(net.mask)); + else + g(n, :, k) = mlpbkp(net, x(n, :), z(n, :),... + delta); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlperr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function [e, edata, eprior] = mlperr(net, x, t) +%MLPERR Evaluate error function for 2-layer network. +% +% Description +% E = MLPERR(NET, X, T) takes a network data structure NET together +% with a matrix X of input vectors and a matrix T of target vectors, +% and evaluates the error function E. The choice of error function +% corresponds to the output unit activation function. Each row of X +% corresponds to one input vector and each row of T corresponds to one +% target vector. +% +% [E, EDATA, EPRIOR] = MLPERR(NET, X, T) additionally returns the data +% and prior components of the error, assuming a zero mean Gaussian +% prior on the weights with inverse variance parameters ALPHA and BETA +% taken from the network data structure NET. +% +% See also +% MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPBKP, MLPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring); + error(errstring); +end +[y, z, a] = mlpfwd(net, x); + +switch net.outfn + + case 'linear' % Linear outputs + edata = 0.5*sum(sum((y - t).^2)); + + case 'logistic' % Logistic outputs + % Ensure that log(1-y) is computable: need exp(a) > eps + maxcut = -log(eps); + % Ensure that log(y) is computable + mincut = -log(1/realmin - 1); + a = min(a, maxcut); + a = max(a, mincut); + y = 1./(1 + exp(-a)); + edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y))); + + case 'softmax' % Softmax outputs + nout = size(a,2); + % Ensure that sum(exp(a), 2) does not overflow + maxcut = log(realmax) - log(nout); + % Ensure that exp(a) > 0 + mincut = log(realmin); + a = min(a, maxcut); + a = max(a, mincut); + temp = exp(a); + y = temp./(sum(temp, 2)*ones(1,nout)); + % Ensure that log(y) is computable + y(y<realmin) = realmin; + edata = - sum(sum(t.*log(y))); + + otherwise + error(['Unknown activation function ', net.outfn]); +end +[e, edata, eprior] = errbayes(net, edata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlpevfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function [y, extra, invhess] = mlpevfwd(net, x, t, x_test, invhess) +%MLPEVFWD Forward propagation with evidence for MLP +% +% Description +% Y = MLPEVFWD(NET, X, T, X_TEST) takes a network data structure NET +% together with the input X and target T training data and input test +% data X_TEST. It returns the normal forward propagation through the +% network Y together with a matrix EXTRA which consists of error bars +% (variance) for a regression problem or moderated outputs for a +% classification problem. The optional argument (and return value) +% INVHESS is the inverse of the network Hessian computed on the +% training data inputs and targets. Passing it in avoids recomputing +% it, which can be a significant saving for large training sets. +% +% See also +% FEVBAYES +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[y, z, a] = mlpfwd(net, x_test); +if nargin == 4 + [extra, invhess] = fevbayes(net, y, a, x, t, x_test); +else + [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlpfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [y, z, a] = mlpfwd(net, x) +%MLPFWD Forward propagation through 2-layer network. +% +% Description +% Y = MLPFWD(NET, X) takes a network data structure NET together with a +% matrix X of input vectors, and forward propagates the inputs through +% the network to generate a matrix Y of output vectors. Each row of X +% corresponds to one input vector and each row of Y corresponds to one +% output vector. +% +% [Y, Z] = MLPFWD(NET, X) also generates a matrix Z of the hidden unit +% activations where each row corresponds to one pattern. +% +% [Y, Z, A] = MLPFWD(NET, X) also returns a matrix A giving the summed +% inputs to each output unit, where each row corresponds to one +% pattern. +% +% See also +% MLP, MLPPAK, MLPUNPAK, MLPERR, MLPBKP, MLPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x); +if ~isempty(errstring); + error(errstring); +end + +ndata = size(x, 1); + +z = tanh(x*net.w1 + ones(ndata, 1)*net.b1); +a = z*net.w2 + ones(ndata, 1)*net.b2; + +switch net.outfn + + case 'linear' % Linear outputs + + y = a; + + case 'logistic' % Logistic outputs + % Prevent overflow and underflow: use same bounds as mlperr + % Ensure that log(1-y) is computable: need exp(a) > eps + maxcut = -log(eps); + % Ensure that log(y) is computable + mincut = -log(1/realmin - 1); + a = min(a, maxcut); + a = max(a, mincut); + y = 1./(1 + exp(-a)); + + case 'softmax' % Softmax outputs + + % Prevent overflow and underflow: use same bounds as glmerr + % Ensure that sum(exp(a), 2) does not overflow + maxcut = log(realmax) - log(net.nout); + % Ensure that exp(a) > 0 + mincut = log(realmin); + a = min(a, maxcut); + a = max(a, mincut); + temp = exp(a); + y = temp./(sum(temp, 2)*ones(1, net.nout)); + + otherwise + error(['Unknown activation function ', net.outfn]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlpgrad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +function [g, gdata, gprior] = mlpgrad(net, x, t) +%MLPGRAD Evaluate gradient of error function for 2-layer network. +% +% Description +% G = MLPGRAD(NET, X, T) takes a network data structure NET together +% with a matrix X of input vectors and a matrix T of target vectors, +% and evaluates the gradient G of the error function with respect to +% the network weights. The error funcion corresponds to the choice of +% output unit activation function. Each row of X corresponds to one +% input vector and each row of T corresponds to one target vector. +% +% [G, GDATA, GPRIOR] = MLPGRAD(NET, X, T) also returns separately the +% data and prior contributions to the gradient. In the case of multiple +% groups in the prior, GPRIOR is a matrix with a row for each group and +% a column for each weight parameter. +% +% See also +% MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring); + error(errstring); +end +[y, z] = mlpfwd(net, x); +delout = y - t; + +gdata = mlpbkp(net, x, z, delout); + +[g, gdata, gprior] = gbayes(net, gdata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlphdotv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +function hdv = mlphdotv(net, x, t, v) +%MLPHDOTV Evaluate the product of the data Hessian with a vector. +% +% Description +% +% HDV = MLPHDOTV(NET, X, T, V) takes an MLP network data structure NET, +% together with the matrix X of input vectors, the matrix T of target +% vectors and an arbitrary row vector V whose length equals the number +% of parameters in the network, and returns the product of the data- +% dependent contribution to the Hessian matrix with V. The +% implementation is based on the R-propagation algorithm of +% Pearlmutter. +% +% See also +% MLP, MLPHESS, HESSCHEK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring); + error(errstring); +end + +ndata = size(x, 1); + +[y, z] = mlpfwd(net, x); % Standard forward propagation. +zprime = (1 - z.*z); % Hidden unit first derivatives. +zpprime = -2.0*z.*zprime; % Hidden unit second derivatives. + +vnet = mlpunpak(net, v); % Unpack the v vector. + +% Do the R-forward propagation. + +ra1 = x*vnet.w1 + ones(ndata, 1)*vnet.b1; +rz = zprime.*ra1; +ra2 = rz*net.w2 + z*vnet.w2 + ones(ndata, 1)*vnet.b2; + +switch net.outfn + + case 'linear' % Linear outputs + + ry = ra2; + + case 'logistic' % Logistic outputs + + ry = y.*(1 - y).*ra2; + + case 'softmax' % Softmax outputs + + nout = size(t, 2); + ry = y.*ra2 - y.*(sum(y.*ra2, 2)*ones(1, nout)); + + otherwise + error(['Unknown activation function ', net.outfn]); +end + +% Evaluate delta for the output units. + +delout = y - t; + +% Do the standard backpropagation. + +delhid = zprime.*(delout*net.w2'); + +% Now do the R-backpropagation. + +rdelhid = zpprime.*ra1.*(delout*net.w2') + zprime.*(delout*vnet.w2') + ... + zprime.*(ry*net.w2'); + +% Finally, evaluate the components of hdv and then merge into long vector. + +hw1 = x'*rdelhid; +hb1 = sum(rdelhid, 1); +hw2 = z'*ry + rz'*delout; +hb2 = sum(ry, 1); + +hdv = [hw1(:)', hb1, hw2(:)', hb2];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlphess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function [h, hdata] = mlphess(net, x, t, hdata) +%MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network. +% +% Description +% H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a +% matrix X of input values, and a matrix T of target values and returns +% the full Hessian matrix H corresponding to the second derivatives of +% the negative log posterior distribution, evaluated for the current +% weight and bias values as defined by NET. +% +% [H, HDATA] = MLPHESS(NET, X, T) returns both the Hessian matrix H and +% the contribution HDATA arising from the data dependent term in the +% Hessian. +% +% H = MLPHESS(NET, X, T, HDATA) takes a network data structure NET, a +% matrix X of input values, and a matrix T of target values, together +% with the contribution HDATA arising from the data dependent term in +% the Hessian, and returns the full Hessian matrix H corresponding to +% the second derivatives of the negative log posterior distribution. +% This version saves computation time if HDATA has already been +% evaluated for the current weight and bias values. +% +% See also +% MLP, HESSCHEK, MLPHDOTV, EVIDENCE +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring); + error(errstring); +end + +if nargin == 3 + % Data term in Hessian needs to be computed + hdata = datahess(net, x, t); +end + +[h, hdata] = hbayes(net, hdata); + +% Sub-function to compute data part of Hessian +function hdata = datahess(net, x, t) + +hdata = zeros(net.nwts, net.nwts); + +for v = eye(net.nwts); + hdata(find(v),:) = mlphdotv(net, x, t, v); +end + +return
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlphint.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +function [h1, h2] = mlphint(net); +%MLPHINT Plot Hinton diagram for 2-layer feed-forward network. +% +% Description +% +% MLPHINT(NET) takes a network structure NET and plots the Hinton +% diagram comprised of two figure windows, one displaying the first- +% layer weights and biases, and one displaying the second-layer weights +% and biases. +% +% [H1, H2] = MLPHINT(NET) also returns handles H1 and H2 to the +% figures which can be used, for instance, to delete the figures when +% they are no longer needed. +% +% To print the figure correctly, you should call SET(H, +% 'INVERTHARDCOPY', 'ON') before printing. +% +% See also +% DEMHINT, HINTMAT, MLP, MLPPAK, MLPUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set scale to be up to 0.9 of maximum absolute weight value, where scale +% defined so that area of box proportional to weight value. + +% Use no more than 640x480 pixels +xmax = 640; ymax = 480; + +% Offset bottom left hand corner +x01 = 40; y01 = 40; +x02 = 80; y02 = 80; + +% Need to allow 5 pixels border for window frame: but 30 at top +border = 5; +top_border = 30; + +ymax = ymax - top_border; +xmax = xmax - border; + +% First layer + +wb1 = [net.w1; net.b1]; +[xvals, yvals, color] = hintmat(wb1'); +% Try to preserve aspect ratio approximately +if (8*net.nhidden < 6*(net.nin + 1)) + delx = xmax; dely = xmax*net.nhidden/(net.nin + 1); +else + delx = ymax*(net.nin + 1)/net.nhidden; dely = ymax; +end + +h1 = figure('Color', [0.5 0.5 0.5], ... + 'Name', 'Hinton diagram: first-layer weights and biases', ... + 'NumberTitle', 'off', ... + 'Colormap', [0 0 0; 1 1 1], ... + 'Units', 'pixels', ... + 'Position', [x01 y01 delx dely]); +set(gca, 'Visible', 'off', 'Position', [0 0 1 1]); +hold on + +cmap = [0 0 0; 1 1 1]; +colors(1, :, :) = cmap(color, :); +patch(xvals', yvals', colors, 'Edgecolor', 'none'); +axis equal; +xpos = net.nin; +line([xpos xpos], [0 net.nhidden], 'color', 'red', 'linewidth', 3); + +% Second layer + +wb2 = [net.w2; net.b2]; +[xvals, yvals, color] = hintmat(wb2'); +if (8*net.nout < 6*(net.nhidden + 1)) + delx = xmax; dely = xmax*net.nout/(net.nhidden + 1); +else + delx = ymax*(net.nhidden + 1)/net.nout; dely = ymax; +end + +h2 = figure('Color', [0.5 0.5 0.5], ... + 'Name', 'Hinton diagram: second-layer weights and biases', ... + 'NumberTitle', 'off', ... + 'Colormap', [0 0 0; 1 1 1], ... + 'Units', 'pixels', ... + 'Position', [x02 y02 delx dely]); +set(gca, 'Visible', 'off', 'Position', [0 0 1 1]); + +hold on +colors2(1, :, :) = cmap(color, :); +patch(xvals', yvals', colors2, 'Edgecolor', 'none'); +axis equal; +xpos = net.nhidden; +line([xpos xpos], [0 net.nout], 'color', 'red', 'linewidth', 3); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlpinit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function net = mlpinit(net, prior) +%MLPINIT Initialise the weights in a 2-layer feedforward network. +% +% Description +% +% NET = MLPINIT(NET, PRIOR) takes a 2-layer feedforward network NET and +% sets the weights and biases by sampling from a Gaussian distribution. +% If PRIOR is a scalar, then all of the parameters (weights and biases) +% are sampled from a single isotropic Gaussian with inverse variance +% equal to PRIOR. If PRIOR is a data structure of the kind generated by +% MLPPRIOR, then the parameters are sampled from multiple Gaussians +% according to their groupings (defined by the INDEX field) with +% corresponding variances (defined by the ALPHA field). +% +% See also +% MLP, MLPPRIOR, MLPPAK, MLPUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if isstruct(prior) + sig = 1./sqrt(prior.index*prior.alpha); + w = sig'.*randn(1, net.nwts); +elseif size(prior) == [1 1] + w = randn(1, net.nwts).*sqrt(1/prior); +else + error('prior must be a scalar or a structure'); +end + +net = mlpunpak(net, w); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlppak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function w = mlppak(net) +%MLPPAK Combines weights and biases into one weights vector. +% +% Description +% W = MLPPAK(NET) takes a network data structure NET and combines the +% component weight matrices bias vectors into a single row vector W. +% The facility to switch between these two representations for the +% network parameters is useful, for example, in training a network by +% error function minimization, since a single vector of parameters can +% be handled by general-purpose optimization routines. +% +% The ordering of the paramters in W is defined by +% w = [net.w1(:)', net.b1, net.w2(:)', net.b2]; +% where W1 is the first-layer weight matrix, B1 is the first-layer +% bias vector, W2 is the second-layer weight matrix, and B2 is the +% second-layer bias vector. +% +% See also +% MLP, MLPUNPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'mlp'); +if ~isempty(errstring); + error(errstring); +end + +w = [net.w1(:)', net.b1, net.w2(:)', net.b2]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlpprior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2) +%MLPPRIOR Create Gaussian prior for mlp. +% +% Description +% PRIOR = MLPPRIOR(NIN, NHIDDEN, NOUT, AW1, AB1, AW2, AB2) generates a +% data structure PRIOR, with fields PRIOR.ALPHA and PRIOR.INDEX, which +% specifies a Gaussian prior distribution for the network weights in a +% two-layer feedforward network. Two different cases are possible. In +% the first case, AW1, AB1, AW2 and AB2 are all scalars and represent +% the regularization coefficients for four groups of parameters in the +% network corresponding to first-layer weights, first-layer biases, +% second-layer weights, and second-layer biases respectively. Then +% PRIOR.ALPHA represents a column vector of length 4 containing the +% parameters, and PRIOR.INDEX is a matrix specifying which weights +% belong in each group. Each column has one element for each weight in +% the matrix, using the standard ordering as defined in MLPPAK, and +% each element is 1 or 0 according to whether the weight is a member of +% the corresponding group or not. In the second case the parameter AW1 +% is a vector of length equal to the number of inputs in the network, +% and the corresponding matrix PRIOR.INDEX now partitions the first- +% layer weights into groups corresponding to the weights fanning out of +% each input unit. This prior is appropriate for the technique of +% automatic relevance determination. +% +% See also +% MLP, MLPERR, MLPGRAD, EVIDENCE +% + +% Copyright (c) Ian T Nabney (1996-2001) + +nextra = nhidden + (nhidden + 1)*nout; +nwts = nin*nhidden + nextra; + +if size(aw1) == [1,1] + + indx = [ones(1, nin*nhidden), zeros(1, nextra)]'; + +elseif size(aw1) == [1, nin] + + indx = kron(ones(nhidden, 1), eye(nin)); + indx = [indx; zeros(nextra, nin)]; + +else + + error('Parameter aw1 of invalid dimensions'); + +end + +extra = zeros(nwts, 3); + +mark1 = nin*nhidden; +mark2 = mark1 + nhidden; +extra(mark1 + 1:mark2, 1) = ones(nhidden,1); +mark3 = mark2 + nhidden*nout; +extra(mark2 + 1:mark3, 2) = ones(nhidden*nout,1); +mark4 = mark3 + nout; +extra(mark3 + 1:mark4, 3) = ones(nout,1); + +indx = [indx, extra]; + +prior.index = indx; +prior.alpha = [aw1, ab1, aw2, ab2]';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlptrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function [net, error] = mlptrain(net, x, t, its); +%MLPTRAIN Utility to train an MLP network for demtrain +% +% Description +% +% [NET, ERROR] = MLPTRAIN(NET, X, T, ITS) trains a network data +% structure NET using the scaled conjugate gradient algorithm for ITS +% cycles with input data X, target data T. +% +% See also +% DEMTRAIN, SCG, NETOPT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +options = zeros(1,18); +options(1) = -1; % To prevent any messages at all +options(9) = 0; +options(14) = its; + +[net, options] = netopt(net, options, x, t, 'scg'); + +error = options(8); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlpunpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function net = mlpunpak(net, w) +%MLPUNPAK Separates weights vector into weight and bias matrices. +% +% Description +% NET = MLPUNPAK(NET, W) takes an mlp network data structure NET and a +% weight vector W, and returns a network data structure identical to +% the input network, except that the first-layer weight matrix W1, the +% first-layer bias vector B1, the second-layer weight matrix W2 and the +% second-layer bias vector B2 have all been set to the corresponding +% elements of W. +% +% See also +% MLP, MLPPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'mlp'); +if ~isempty(errstring); + error(errstring); +end + +if net.nwts ~= length(w) + error('Invalid weight vector length') +end + +nin = net.nin; +nhidden = net.nhidden; +nout = net.nout; + +mark1 = nin*nhidden; +net.w1 = reshape(w(1:mark1), nin, nhidden); +mark2 = mark1 + nhidden; +net.b1 = reshape(w(mark1 + 1: mark2), 1, nhidden); +mark3 = mark2 + nhidden*nout; +net.w2 = reshape(w(mark2 + 1: mark3), nhidden, nout); +mark4 = mark3 + nout; +net.b2 = reshape(w(mark3 + 1: mark4), 1, nout);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/netderiv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function g = netderiv(w, net, x) +%NETDERIV Evaluate derivatives of network outputs by weights generically. +% +% Description +% +% G = NETDERIV(W, NET, X) takes a weight vector W and a network data +% structure NET, together with the matrix X of input vectors, and +% returns the gradient of the outputs with respect to the weights +% evaluated at W. +% +% See also +% NETEVFWD, NETOPT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +fstr = [net.type, 'deriv']; +net = netunpak(net, w); +g = feval(fstr, net, x);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/neterr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function [e, varargout] = neterr(w, net, x, t) +%NETERR Evaluate network error function for generic optimizers +% +% Description +% +% E = NETERR(W, NET, X, T) takes a weight vector W and a network data +% structure NET, together with the matrix X of input vectors and the +% matrix T of target vectors, and returns the value of the error +% function evaluated at W. +% +% [E, VARARGOUT] = NETERR(W, NET, X, T) also returns any additional +% return values from the error function. +% +% See also +% NETGRAD, NETHESS, NETOPT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstr = [net.type, 'err']; +net = netunpak(net, w); + +[s{1:nargout}] = feval(errstr, net, x, t); +e = s{1}; +if nargout > 1 + for i = 2:nargout + varargout{i-1} = s{i}; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/netevfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function [y, extra, invhess] = netevfwd(w, net, x, t, x_test, invhess) +%NETEVFWD Generic forward propagation with evidence for network +% +% Description +% [Y, EXTRA] = NETEVFWD(W, NET, X, T, X_TEST) takes a network data +% structure NET together with the input X and target T training data +% and input test data X_TEST. It returns the normal forward propagation +% through the network Y together with a matrix EXTRA which consists of +% error bars (variance) for a regression problem or moderated outputs +% for a classification problem. +% +% The optional argument (and return value) INVHESS is the inverse of +% the network Hessian computed on the training data inputs and targets. +% Passing it in avoids recomputing it, which can be a significant +% saving for large training sets. +% +% See also +% MLPEVFWD, RBFEVFWD, GLMEVFWD, FEVBAYES +% + +% Copyright (c) Ian T Nabney (1996-2001) + +func = [net.type, 'evfwd']; +net = netunpak(net, w); +if nargin == 5 + [y, extra, invhess] = feval(func, net, x, t, x_test); +else + [y, extra, invhess] = feval(func, net, x, t, x_test, invhess); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/netgrad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function g = netgrad(w, net, x, t) +%NETGRAD Evaluate network error gradient for generic optimizers +% +% Description +% +% G = NETGRAD(W, NET, X, T) takes a weight vector W and a network data +% structure NET, together with the matrix X of input vectors and the +% matrix T of target vectors, and returns the gradient of the error +% function evaluated at W. +% +% See also +% MLP, NETERR, NETOPT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +gradstr = [net.type, 'grad']; + +net = netunpak(net, w); + +g = feval(gradstr, net, x, t);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/nethess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function [h, varargout] = nethess(w, net, x, t, varargin) +%NETHESS Evaluate network Hessian +% +% Description +% +% H = NETHESS(W, NET, X, T) takes a weight vector W and a network data +% structure NET, together with the matrix X of input vectors and the +% matrix T of target vectors, and returns the value of the Hessian +% evaluated at W. +% +% [E, VARARGOUT] = NETHESS(W, NET, X, T, VARARGIN) also returns any +% additional return values from the network Hessian function, and +% passes additional arguments to that function. +% +% See also +% NETERR, NETGRAD, NETOPT +% + +% Copyright (c) Ian T Nabney (1996-2001) + +hess_str = [net.type, 'hess']; + +net = netunpak(net, w); + +[s{1:nargout}] = feval(hess_str, net, x, t, varargin{:}); +h = s{1}; +for i = 2:nargout + varargout{i-1} = s{i}; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/netinit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function net = netinit(net, prior) +%NETINIT Initialise the weights in a network. +% +% Description +% +% NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets +% the weights and biases by sampling from a Gaussian distribution. If +% PRIOR is a scalar, then all of the parameters (weights and biases) +% are sampled from a single isotropic Gaussian with inverse variance +% equal to PRIOR. If PRIOR is a data structure of the kind generated by +% MLPPRIOR, then the parameters are sampled from multiple Gaussians +% according to their groupings (defined by the INDEX field) with +% corresponding variances (defined by the ALPHA field). +% +% See also +% MLPPRIOR, NETUNPAK, RBFPRIOR +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if isstruct(prior) + if (isfield(net, 'mask')) + if find(sum(prior.index, 2)) ~= find(net.mask) + error('Index does not match mask'); + end + sig = sqrt(prior.index*prior.alpha); + % Weights corresponding to zeros in mask will not be used anyway + % Set their priors to one to avoid division by zero + sig = sig + (sig == 0); + sig = 1./sqrt(sig); + else + sig = 1./sqrt(prior.index*prior.alpha); + end + w = sig'.*randn(1, net.nwts); +elseif size(prior) == [1 1] + w = randn(1, net.nwts).*sqrt(1/prior); +else + error('prior must be a scalar or a structure'); +end + +if (isfield(net, 'mask')) + w = w(logical(net.mask)); +end +net = netunpak(net, w); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/netopt.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function [net, options, varargout] = netopt(net, options, x, t, alg); +%NETOPT Optimize the weights in a network model. +% +% Description +% +% NETOPT is a helper function which facilitates the training of +% networks using the general purpose optimizers as well as sampling +% from the posterior distribution of parameters using general purpose +% Markov chain Monte Carlo sampling algorithms. It can be used with any +% function that searches in parameter space using error and gradient +% functions. +% +% [NET, OPTIONS] = NETOPT(NET, OPTIONS, X, T, ALG) takes a network +% data structure NET, together with a vector OPTIONS of parameters +% governing the behaviour of the optimization algorithm, a matrix X of +% input vectors and a matrix T of target vectors, and returns the +% trained network as well as an updated OPTIONS vector. The string ALG +% determines which optimization algorithm (CONJGRAD, QUASINEW, SCG, +% etc.) or Monte Carlo algorithm (such as HMC) will be used. +% +% [NET, OPTIONS, VARARGOUT] = NETOPT(NET, OPTIONS, X, T, ALG) also +% returns any additional return values from the optimisation algorithm. +% +% See also +% NETGRAD, BFGS, CONJGRAD, GRADDESC, HMC, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + +optstring = [alg, '(''neterr'', w, options, ''netgrad'', net, x, t)']; + +% Extract weights from network as single vector +w = netpak(net); + +% Carry out optimisation +[s{1:nargout}] = eval(optstring); +w = s{1}; + +if nargout > 1 + options = s{2}; + + % If there are additional arguments, extract them + nextra = nargout - 2; + if nextra > 0 + for i = 1:nextra + varargout{i} = s{i+2}; + end + end +end + +% Pack the weights back into the network +net = netunpak(net, w);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/netpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function w = netpak(net) +%NETPAK Combines weights and biases into one weights vector. +% +% Description +% W = NETPAK(NET) takes a network data structure NET and combines the +% component weight matrices into a single row vector W. The facility +% to switch between these two representations for the network +% parameters is useful, for example, in training a network by error +% function minimization, since a single vector of parameters can be +% handled by general-purpose optimization routines. This function also +% takes into account a MASK defined as a field in NET by removing any +% weights that correspond to entries of 0 in the mask. +% +% See also +% NET, NETUNPAK, NETFWD, NETERR, NETGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +pakstr = [net.type, 'pak']; +w = feval(pakstr, net); +% Return masked subset of weights +if (isfield(net, 'mask')) + w = w(logical(net.mask)); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/netunpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +function net = netunpak(net, w) +%NETUNPAK Separates weights vector into weight and bias matrices. +% +% Description +% NET = NETUNPAK(NET, W) takes an net network data structure NET and a +% weight vector W, and returns a network data structure identical to +% the input network, except that the componenet weight matrices have +% all been set to the corresponding elements of W. If there is a MASK +% field in the NET data structure, then the weights in W are placed in +% locations corresponding to non-zero entries in the mask (so W should +% have the same length as the number of non-zero entries in the MASK). +% +% See also +% NETPAK, NETFWD, NETERR, NETGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +unpakstr = [net.type, 'unpak']; + +% Check if we are being passed a masked set of weights +if (isfield(net, 'mask')) + if length(w) ~= size(find(net.mask), 1) + error('Weight vector length does not match mask length') + end + % Do a full pack of all current network weights + pakstr = [net.type, 'pak']; + fullw = feval(pakstr, net); + % Replace current weights with new ones + fullw(logical(net.mask)) = w; + w = fullw; +end + +net = feval(unpakstr, net, w); \ No newline at end of file
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1.008600e+00 3.087000e-01 4.039000e-01 +-1.970000e-02 7.719000e-01 4.634000e-01 5.700000e-01 5.143000e-01 6.520000e-01 -2.580000e-02 1.762600e+00 -1.730000e-02 2.023200e+00 2.500000e-02 -2.650000e-02 1.832000e-01 4.379000e-01
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/olgd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +function [net, options, errlog, pointlog] = olgd(net, options, x, t) +%OLGD On-line gradient descent optimization. +% +% Description +% [NET, OPTIONS, ERRLOG, POINTLOG] = OLGD(NET, OPTIONS, X, T) uses on- +% line gradient descent to find a local minimum of the error function +% for the network NET computed on the input data X and target values T. +% A log of the error values after each cycle is (optionally) returned +% in ERRLOG, and a log of the points visited is (optionally) returned +% in POINTLOG. Because the gradient is computed on-line (i.e. after +% each pattern) this can be quite inefficient in Matlab. +% +% The error function value at final weight vector is returned in +% OPTIONS(8). +% +% The optional parameters have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG, and the points visited in the +% return argument POINTSLOG. If OPTIONS(1) is set to 0, then only +% warning messages are displayed. If OPTIONS(1) is -1, then nothing is +% displayed. +% +% OPTIONS(2) is the precision required for the value of X at the +% solution. If the absolute difference between the values of X between +% two successive steps is less than OPTIONS(2), then this condition is +% satisfied. +% +% OPTIONS(3) is the precision required of the objective function at the +% solution. If the absolute difference between the error functions +% between two successive steps is less than OPTIONS(3), then this +% condition is satisfied. Both this and the previous condition must be +% satisfied for termination. Note that testing the function value at +% each iteration roughly halves the speed of the algorithm. +% +% OPTIONS(5) determines whether the patterns are sampled randomly with +% replacement. If it is 0 (the default), then patterns are sampled in +% order. +% +% OPTIONS(6) determines if the learning rate decays. If it is 1 then +% the learning rate decays at a rate of 1/T. If it is 0 (the default) +% then the learning rate is constant. +% +% OPTIONS(9) should be set to 1 to check the user defined gradient +% function. +% +% OPTIONS(10) returns the total number of function evaluations +% (including those in any line searches). +% +% OPTIONS(11) returns the total number of gradient evaluations. +% +% OPTIONS(14) is the maximum number of iterations (passes through the +% complete pattern set); default 100. +% +% OPTIONS(17) is the momentum; default 0.5. +% +% OPTIONS(18) is the learning rate; default 0.01. +% +% See also +% GRADDESC +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set up the options. +if length(options) < 18 + error('Options vector too short') +end + +if (options(14)) + niters = options(14); +else + niters = 100; +end + +% Learning rate: must be positive +if (options(18) > 0) + eta = options(18); +else + eta = 0.01; +end +% Save initial learning rate for annealing +lr = eta; +% Momentum term: allow zero momentum +if (options(17) >= 0) + mu = options(17); +else + mu = 0.5; +end + +pakstr = [net.type, 'pak']; +unpakstr = [net.type, 'unpak']; + +% Extract initial weights from the network +w = feval(pakstr, net); + +display = options(1); + +% Work out if we need to compute f at each iteration. +% Needed if display results or if termination +% criterion requires it. +fcneval = (display | options(3)); + +% Check gradients +if (options(9)) + feval('gradchek', w, 'neterr', 'netgrad', net, x, t); +end + +dwold = zeros(1, length(w)); +fold = 0; % Must be initialised so that termination test can be performed +ndata = size(x, 1); + +if fcneval + fnew = neterr(w, net, x, t); + options(10) = options(10) + 1; + fold = fnew; +end + +j = 1; +if nargout >= 3 + errlog(j, :) = fnew; + if nargout == 4 + pointlog(j, :) = w; + end +end + +% Main optimization loop. +while j <= niters + wold = w; + if options(5) + % Randomise order of pattern presentation: with replacement + pnum = ceil(rand(ndata, 1).*ndata); + else + pnum = 1:ndata; + end + for k = 1:ndata + grad = netgrad(w, net, x(pnum(k),:), t(pnum(k),:)); + if options(6) + % Let learning rate decrease as 1/t + lr = eta/((j-1)*ndata + k); + end + dw = mu*dwold - lr*grad; + w = w + dw; + dwold = dw; + end + options(11) = options(11) + 1; % Increment gradient evaluation count + if fcneval + fold = fnew; + fnew = neterr(w, net, x, t); + options(10) = options(10) + 1; + end + if display + fprintf(1, 'Iteration %5d Error %11.8f\n', j, fnew); + end + j = j + 1; + if nargout >= 3 + errlog(j) = fnew; + if nargout == 4 + pointlog(j, :) = w; + end + end + if (max(abs(w - wold)) < options(2) & abs(fnew - fold) < options(3)) + % Termination criteria are met + options(8) = fnew; + net = feval(unpakstr, net, w); + return; + end +end + +if fcneval + options(8) = fnew; +else + % Return error on entire dataset + options(8) = neterr(w, net, x, t); + options(10) = options(10) + 1; +end +if (options(1) >= 0) + disp(maxitmess); +end + +net = feval(unpakstr, net, w); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/pca.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +function [PCcoeff, PCvec] = pca(data, N) +%PCA Principal Components Analysis +% +% Description +% PCCOEFF = PCA(DATA) computes the eigenvalues of the covariance +% matrix of the dataset DATA and returns them as PCCOEFF. These +% coefficients give the variance of DATA along the corresponding +% principal components. +% +% PCCOEFF = PCA(DATA, N) returns the largest N eigenvalues. +% +% [PCCOEFF, PCVEC] = PCA(DATA) returns the principal components as well +% as the coefficients. This is considerably more computationally +% demanding than just computing the eigenvalues. +% +% See also +% EIGDEC, GTMINIT, PPCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + +if nargin == 1 + N = size(data, 2); +end + +if nargout == 1 + evals_only = logical(1); +else + evals_only = logical(0); +end + +if N ~= round(N) | N < 1 | N > size(data, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +% Find the sorted eigenvalues of the data covariance matrix +if evals_only + PCcoeff = eigdec(cov(data), N); +else + [PCcoeff, PCvec] = eigdec(cov(data), N); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/plotmat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function plotmat(matrix, textcolour, gridcolour, fontsize) +%PLOTMAT Display a matrix. +% +% Description +% PLOTMAT(MATRIX, TEXTCOLOUR, GRIDCOLOUR, FONTSIZE) displays the matrix +% MATRIX on the current figure. The TEXTCOLOUR and GRIDCOLOUR +% arguments control the colours of the numbers and grid labels +% respectively and should follow the usual Matlab specification. The +% parameter FONTSIZE should be an integer. +% +% See also +% CONFFIG, DEMMLP2 +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[m,n]=size(matrix); +for rowCnt=1:m, + for colCnt=1:n, + numberString=num2str(matrix(rowCnt,colCnt)); + text(colCnt-.5,m-rowCnt+.5,numberString, ... + 'HorizontalAlignment','center', ... + 'Color', textcolour, ... + 'FontWeight','bold', ... + 'FontSize', fontsize); + end; +end; + +set(gca,'Box','on', ... + 'Visible','on', ... + 'xLim',[0 n], ... + 'xGrid','on', ... + 'xTickLabel',[], ... + 'xTick',0:n, ... + 'yGrid','on', ... + 'yLim',[0 m], ... + 'yTickLabel',[], ... + 'yTick',0:m, ... + 'DataAspectRatio',[1, 1, 1], ... + 'GridLineStyle',':', ... + 'LineWidth',3, ... + 'XColor',gridcolour, ... + 'YColor',gridcolour); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/ppca.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function [var, U, lambda] = ppca(x, ppca_dim) +%PPCA Probabilistic Principal Components Analysis +% +% Description +% [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal +% component subspace U of dimension PPCA_DIM using a centred covariance +% matrix X. The variable VAR contains the off-subspace variance (which +% is assumed to be spherical), while the vector LAMBDA contains the +% variances of each of the principal components. This is computed +% using the eigenvalue and eigenvector decomposition of X. +% +% See also +% EIGDEC, PCA +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +if ppca_dim ~= round(ppca_dim) | ppca_dim < 1 | ppca_dim > size(x, 2) + error('Number of PCs must be integer, >0, < dim'); +end + +[ndata, data_dim] = size(x); +% Assumes that x is centred and responsibility weighted +% covariance matrix +[l Utemp] = eigdec(x, data_dim); +% Zero any negative eigenvalues (caused by rounding) +l(l<0) = 0; +% Now compute the sigma squared values for all possible values +% of q +s2_temp = cumsum(l(end:-1:1))./[1:data_dim]'; +% If necessary, reduce the value of q so that var is at least +% eps * largest eigenvalue +q_temp = min([ppca_dim; data_dim-min(find(s2_temp/l(1) > eps))]); +if q_temp ~= ppca_dim + wstringpart = 'Covariance matrix ill-conditioned: extracted'; + wstring = sprintf('%s %d/%d PCs', ... + wstringpart, q_temp, ppca_dim); + warning(wstring); +end +if q_temp == 0 + % All the latent dimensions have disappeared, so we are + % just left with the noise model + var = l(1)/data_dim; + lambda = var*ones(1, ppca_dim); +else + var = mean(l(q_temp+1:end)); +end +U = Utemp(:, 1:q_temp); +lambda(1:q_temp) = l(1:q_temp); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/quasinew.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,176 @@ +function [x, options, flog, pointlog] = quasinew(f, x, options, gradf, ... + varargin) +%QUASINEW Quasi-Newton optimization. +% +% Description +% [X, OPTIONS, FLOG, POINTLOG] = QUASINEW(F, X, OPTIONS, GRADF) uses a +% quasi-Newton algorithm to find a local minimum of the function F(X) +% whose gradient is given by GRADF(X). Here X is a row vector and F +% returns a scalar value. The point at which F has a local minimum is +% returned as X. The function value at that point is returned in +% OPTIONS(8). A log of the function values after each cycle is +% (optionally) returned in FLOG, and a log of the points visited is +% (optionally) returned in POINTLOG. +% +% QUASINEW(F, X, OPTIONS, GRADF, P1, P2, ...) allows additional +% arguments to be passed to F() and GRADF(). +% +% The optional parameters have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG, and the points visited in the +% return argument POINTSLOG. If OPTIONS(1) is set to 0, then only +% warning messages are displayed. If OPTIONS(1) is -1, then nothing is +% displayed. +% +% OPTIONS(2) is a measure of the absolute precision required for the +% value of X at the solution. If the absolute difference between the +% values of X between two successive steps is less than OPTIONS(2), +% then this condition is satisfied. +% +% OPTIONS(3) is a measure of the precision required of the objective +% function at the solution. If the absolute difference between the +% objective function values between two successive steps is less than +% OPTIONS(3), then this condition is satisfied. Both this and the +% previous condition must be satisfied for termination. +% +% OPTIONS(9) should be set to 1 to check the user defined gradient +% function. +% +% OPTIONS(10) returns the total number of function evaluations +% (including those in any line searches). +% +% OPTIONS(11) returns the total number of gradient evaluations. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% OPTIONS(15) is the precision in parameter space of the line search; +% default 1E-2. +% +% See also +% CONJGRAD, GRADDESC, LINEMIN, MINBRACK, SCG +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set up the options. +if length(options) < 18 + error('Options vector too short') +end + +if(options(14)) + niters = options(14); +else + niters = 100; +end + +% Set up options for line search +line_options = foptions; +% Don't need a very precise line search +if options(15) > 0 + line_options(2) = options(15); +else + line_options(2) = 1e-2; % Default +end +% Minimal fractional change in f from Newton step: otherwise do a line search +min_frac_change = 1e-4; + +display = options(1); + +% Next two lines allow quasinew to work with expression strings +f = fcnchk(f, length(varargin)); +gradf = fcnchk(gradf, length(varargin)); + +% Check gradients +if (options(9)) + feval('gradchek', x, f, gradf, varargin{:}); +end + +nparams = length(x); +fnew = feval(f, x, varargin{:}); +options(10) = options(10) + 1; +gradnew = feval(gradf, x, varargin{:}); +options(11) = options(11) + 1; +p = -gradnew; % Search direction +hessinv = eye(nparams); % Initialise inverse Hessian to be identity matrix +j = 1; +if nargout >= 3 + flog(j, :) = fnew; + if nargout == 4 + pointlog(j, :) = x; + end +end + +while (j <= niters) + + xold = x; + fold = fnew; + gradold = gradnew; + + x = xold + p; + fnew = feval(f, x, varargin{:}); + options(10) = options(10) + 1; + + % This shouldn't occur, but rest of code depends on sd being downhill + if (gradnew*p' >= 0) + p = -p; + if options(1) >= 0 + warning('search direction uphill in quasinew'); + end + end + + % Does the Newton step reduce the function value sufficiently? + if (fnew >= fold + min_frac_change * (gradnew*p')) + % No it doesn't + % Minimize along current search direction: must be less than Newton step + [lmin, line_options] = feval('linemin', f, xold, p, fold, ... + line_options, varargin{:}); + options(10) = options(10) + line_options(10); + options(11) = options(11) + line_options(11); + % Correct x and fnew to be the actual search point we have found + x = xold + lmin * p; + p = x - xold; + fnew = line_options(8); + end + + % Check for termination + if (max(abs(x - xold)) < options(2) & max(abs(fnew - fold)) < options(3)) + options(8) = fnew; + return; + end + gradnew = feval(gradf, x, varargin{:}); + options(11) = options(11) + 1; + v = gradnew - gradold; + vdotp = v*p'; + + % Skip update to inverse Hessian if fac not sufficiently positive + if (vdotp*vdotp > eps*sum(v.^2)*sum(p.^2)) + Gv = (hessinv*v')'; + vGv = sum(v.*Gv); + u = p./vdotp - Gv./vGv; + % Use BFGS update rule + hessinv = hessinv + (p'*p)/vdotp - (Gv'*Gv)/vGv + vGv*(u'*u); + end + + p = -(hessinv * gradnew')'; + + if (display > 0) + fprintf(1, 'Cycle %4d Function %11.6f\n', j, fnew); + end + + j = j + 1; + if nargout >= 3 + flog(j, :) = fnew; + if nargout == 4 + pointlog(j, :) = x; + end + end +end + +% If we get here, then we haven't terminated in the given number of +% iterations. + +options(8) = fold; +if (options(1) >= 0) + disp(maxitmess); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,123 @@ +function net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta) +%RBF Creates an RBF network with specified architecture +% +% Description +% NET = RBF(NIN, NHIDDEN, NOUT, RBFUNC) constructs and initialises a +% radial basis function network returning a data structure NET. The +% weights are all initialised with a zero mean, unit variance normal +% distribution, with the exception of the variances, which are set to +% one. This makes use of the Matlab function RANDN and so the seed for +% the random weight initialization can be set using RANDN('STATE', S) +% where S is the seed value. The activation functions are defined in +% terms of the distance between the data point and the corresponding +% centre. Note that the functions are computed to a convenient +% constant multiple: for example, the Gaussian is not normalised. +% (Normalisation is not needed as the function outputs are linearly +% combined in the next layer.) +% +% The fields in NET are +% type = 'rbf' +% nin = number of inputs +% nhidden = number of hidden units +% nout = number of outputs +% nwts = total number of weights and biases +% actfn = string defining hidden unit activation function: +% 'gaussian' for a radially symmetric Gaussian function. +% 'tps' for r^2 log r, the thin plate spline function. +% 'r4logr' for r^4 log r. +% outfn = string defining output error function: +% 'linear' for linear outputs (default) and SoS error. +% 'neuroscale' for Sammon stress measure. +% c = centres +% wi = squared widths (null for rlogr and tps) +% w2 = second layer weight matrix +% b2 = second layer bias vector +% +% NET = RBF(NIN, NHIDDEN, NOUT, RBFUND, OUTFUNC) allows the user to +% specify the type of error function to be used. The field OUTFN is +% set to the value of this string. Linear outputs (for regression +% problems) and Neuroscale outputs (for topographic mappings) are +% supported. +% +% NET = RBF(NIN, NHIDDEN, NOUT, RBFUNC, OUTFUNC, PRIOR, BETA), in which +% PRIOR is a scalar, allows the field NET.ALPHA in the data structure +% NET to be set, corresponding to a zero-mean isotropic Gaussian prior +% with inverse variance with value PRIOR. Alternatively, PRIOR can +% consist of a data structure with fields ALPHA and INDEX, allowing +% individual Gaussian priors to be set over groups of weights in the +% network. Here ALPHA is a column vector in which each element +% corresponds to a separate group of weights, which need not be +% mutually exclusive. The membership of the groups is defined by the +% matrix INDX in which the columns correspond to the elements of ALPHA. +% Each column has one element for each weight in the matrix, in the +% order defined by the function RBFPAK, and each element is 1 or 0 +% according to whether the weight is a member of the corresponding +% group or not. A utility function RBFPRIOR is provided to help in +% setting up the PRIOR data structure. +% +% NET = RBF(NIN, NHIDDEN, NOUT, FUNC, PRIOR, BETA) also sets the +% additional field NET.BETA in the data structure NET, where beta +% corresponds to the inverse noise variance. +% +% See also +% RBFERR, RBFFWD, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +net.type = 'rbf'; +net.nin = nin; +net.nhidden = nhidden; +net.nout = nout; + +% Check that function is an allowed type +actfns = {'gaussian', 'tps', 'r4logr'}; +outfns = {'linear', 'neuroscale'}; +if (strcmp(rbfunc, actfns)) == 0 + error('Undefined activation function.') +else + net.actfn = rbfunc; +end +if nargin <= 4 + net.outfn = outfns{1}; +elseif (strcmp(outfunc, outfns) == 0) + error('Undefined output function.') +else + net.outfn = outfunc; + end + +% Assume each function has a centre and a single width parameter, and that +% hidden layer to output weights include a bias. Only the Gaussian function +% requires a width +net.nwts = nin*nhidden + (nhidden + 1)*nout; +if strcmp(rbfunc, 'gaussian') + % Extra weights for width parameters + net.nwts = net.nwts + nhidden; +end + +if nargin > 5 + if isstruct(prior) + net.alpha = prior.alpha; + net.index = prior.index; + elseif size(prior) == [1 1] + net.alpha = prior; + else + error('prior must be a scalar or a structure'); + end + if nargin > 6 + net.beta = beta; + end +end + +w = randn(1, net.nwts); +net = rbfunpak(net, w); + +% Make widths equal to one +if strcmp(rbfunc, 'gaussian') + net.wi = ones(1, nhidden); +end + +if strcmp(net.outfn, 'neuroscale') + net.mask = rbfprior(rbfunc, nin, nhidden, nout); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfbkp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +function g = rbfbkp(net, x, z, n2, deltas) +%RBFBKP Backpropagate gradient of error function for RBF network. +% +% Description +% G = RBFBKP(NET, X, Z, N2, DELTAS) takes a network data structure NET +% together with a matrix X of input vectors, a matrix Z of hidden unit +% activations, a matrix N2 of the squared distances between centres and +% inputs, and a matrix DELTAS of the gradient of the error function +% with respect to the values of the output units (i.e. the summed +% inputs to the output units, before the activation function is +% applied). The return value is the gradient G of the error function +% with respect to the network weights. Each row of X corresponds to one +% input vector. +% +% This function is provided so that the common backpropagation +% algorithm can be used by RBF network models to compute gradients for +% the output values (in RBFDERIV) as well as standard error functions. +% +% See also +% RBF, RBFGRAD, RBFDERIV +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Evaluate second-layer gradients. +gw2 = z'*deltas; +gb2 = sum(deltas); + +% Evaluate hidden unit gradients +delhid = deltas*net.w2'; + +gc = zeros(net.nhidden, net.nin); +ndata = size(x, 1); +t1 = ones(ndata, 1); +t2 = ones(1, net.nin); +% Switch on activation function type +switch net.actfn + +case 'gaussian' % Gaussian + delhid = (delhid.*z); + % A loop seems essential, so do it with the shortest index vector + if (net.nin < net.nhidden) + for i = 1:net.nin + gc(:,i) = (sum(((x(:,i)*ones(1, net.nhidden)) - ... + (ones(ndata, 1)*(net.c(:,i)'))).*delhid, 1)./net.wi)'; + end + else + for i = 1:net.nhidden + gc(i,:) = sum((x - (t1*(net.c(i,:)))./net.wi(i)).*(delhid(:,i)*t2), 1); + end + end + gwi = sum((n2.*delhid)./(2.*(ones(ndata, 1)*(net.wi.^2))), 1); + +case 'tps' % Thin plate spline activation function + delhid = delhid.*(1+log(n2+(n2==0))); + for i = 1:net.nhidden + gc(i,:) = sum(2.*((t1*(net.c(i,:)) - x)).*(delhid(:,i)*t2), 1); + end + % widths are not adjustable in this model + gwi = []; +case 'r4logr' % r^4 log r activation function + delhid = delhid.*(n2.*(1+2.*log(n2+(n2==0)))); + for i = 1:net.nhidden + gc(i,:) = sum(2.*((t1*(net.c(i,:)) - x)).*(delhid(:,i)*t2), 1); + end + % widths are not adjustable in this model + gwi = []; +otherwise + error('Unknown activation function in rbfgrad') +end + +g = [gc(:)', gwi, gw2(:)', gb2];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfderiv.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function g = rbfderiv(net, x) +%RBFDERIV Evaluate derivatives of RBF network outputs with respect to weights. +% +% Description +% G = RBFDERIV(NET, X) takes a network data structure NET and a matrix +% of input vectors X and returns a three-index matrix G whose I, J, K +% element contains the derivative of network output K with respect to +% weight or bias parameter J for input pattern I. The ordering of the +% weight and bias parameters is defined by RBFUNPAK. This function +% also takes into account any mask in the network data structure. +% +% See also +% RBF, RBFPAK, RBFGRAD, RBFBKP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'rbf', x); +if ~isempty(errstring); + error(errstring); +end + +if ~strcmp(net.outfn, 'linear') + error('Function only implemented for linear outputs') +end + +[y, z, n2] = rbffwd(net, x); +ndata = size(x, 1); + +if isfield(net, 'mask') + nwts = size(find(net.mask), 1); + temp = zeros(1, net.nwts); +else + nwts = net.nwts; +end + +g = zeros(ndata, nwts, net.nout); +for k = 1 : net.nout + delta = zeros(1, net.nout); + delta(1, k) = 1; + for n = 1 : ndata + if isfield(net, 'mask') + temp = rbfbkp(net, x(n, :), z(n, :), n2(n, :), delta); + g(n, :, k) = temp(logical(net.mask)); + else + g(n, :, k) = rbfbkp(net, x(n, :), z(n, :), n2(n, :),... + delta); + end + end +end + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbferr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +function [e, edata, eprior] = rbferr(net, x, t) +%RBFERR Evaluate error function for RBF network. +% +% Description +% E = RBFERR(NET, X, T) takes a network data structure NET together +% with a matrix X of input vectors and a matrix T of target vectors, +% and evaluates the appropriate error function E depending on +% NET.OUTFN. Each row of X corresponds to one input vector and each +% row of T contains the corresponding target vector. +% +% [E, EDATA, EPRIOR] = RBFERR(NET, X, T) additionally returns the data +% and prior components of the error, assuming a zero mean Gaussian +% prior on the weights with inverse variance parameters ALPHA and BETA +% taken from the network data structure NET. +% +% See also +% RBF, RBFFWD, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +switch net.outfn +case 'linear' + errstring = consist(net, 'rbf', x, t); +case 'neuroscale' + errstring = consist(net, 'rbf', x); +otherwise + error(['Unknown output function ', net.outfn]); +end +if ~isempty(errstring); + error(errstring); +end + +switch net.outfn +case 'linear' + y = rbffwd(net, x); + edata = 0.5*sum(sum((y - t).^2)); +case 'neuroscale' + y = rbffwd(net, x); + y_dist = sqrt(dist2(y, y)); + % Take t as target distance matrix + edata = 0.5.*(sum(sum((t-y_dist).^2))); +otherwise + error(['Unknown output function ', net.outfn]); +end + +% Compute Bayesian regularised error +[e, edata, eprior] = errbayes(net, edata); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfevfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [y, extra, invhess] = rbfevfwd(net, x, t, x_test, invhess) +%RBFEVFWD Forward propagation with evidence for RBF +% +% Description +% Y = RBFEVFWD(NET, X, T, X_TEST) takes a network data structure NET +% together with the input X and target T training data and input test +% data X_TEST. It returns the normal forward propagation through the +% network Y together with a matrix EXTRA which consists of error bars +% (variance) for a regression problem or moderated outputs for a +% classification problem. +% +% The optional argument (and return value) INVHESS is the inverse of +% the network Hessian computed on the training data inputs and targets. +% Passing it in avoids recomputing it, which can be a significant +% saving for large training sets. +% +% See also +% FEVBAYES +% + +% Copyright (c) Ian T Nabney (1996-2001) + +y = rbffwd(net, x_test); +% RBF outputs must be linear, so just pass them twice (second copy is +% not used +if nargin == 4 + [extra, invhess] = fevbayes(net, y, y, x, t, x_test); +else + [extra, invhess] = fevbayes(net, y, y, x, t, x_test, invhess); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbffwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function [a, z, n2] = rbffwd(net, x) +%RBFFWD Forward propagation through RBF network with linear outputs. +% +% Description +% A = RBFFWD(NET, X) takes a network data structure NET and a matrix X +% of input vectors and forward propagates the inputs through the +% network to generate a matrix A of output vectors. Each row of X +% corresponds to one input vector and each row of A contains the +% corresponding output vector. The activation function that is used is +% determined by NET.ACTFN. +% +% [A, Z, N2] = RBFFWD(NET, X) also generates a matrix Z of the hidden +% unit activations where each row corresponds to one pattern. These +% hidden unit activations represent the design matrix for the RBF. The +% matrix N2 is the squared distances between each basis function centre +% and each pattern in which each row corresponds to a data point. +% +% See also +% RBF, RBFERR, RBFGRAD, RBFPAK, RBFTRAIN, RBFUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'rbf', x); +if ~isempty(errstring); + error(errstring); +end + +[ndata, data_dim] = size(x); + +% Calculate squared norm matrix, of dimension (ndata, ncentres) +n2 = dist2(x, net.c); + +% Switch on activation function type +switch net.actfn + + case 'gaussian' % Gaussian + % Calculate width factors: net.wi contains squared widths + wi2 = ones(ndata, 1) * (2 .* net.wi); + + % Now compute the activations + z = exp(-(n2./wi2)); + + case 'tps' % Thin plate spline + z = n2.*log(n2+(n2==0)); + + case 'r4logr' % r^4 log r + z = n2.*n2.*log(n2+(n2==0)); + + otherwise + error('Unknown activation function in rbffwd') +end + +a = z*net.w2 + ones(ndata, 1)*net.b2; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfgrad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [g, gdata, gprior] = rbfgrad(net, x, t) +%RBFGRAD Evaluate gradient of error function for RBF network. +% +% Description +% G = RBFGRAD(NET, X, T) takes a network data structure NET together +% with a matrix X of input vectors and a matrix T of target vectors, +% and evaluates the gradient G of the error function with respect to +% the network weights (i.e. including the hidden unit parameters). The +% error function is sum of squares. Each row of X corresponds to one +% input vector and each row of T contains the corresponding target +% vector. If the output function is 'NEUROSCALE' then the gradient is +% only computed for the output layer weights and biases. +% +% [G, GDATA, GPRIOR] = RBFGRAD(NET, X, T) also returns separately the +% data and prior contributions to the gradient. In the case of multiple +% groups in the prior, GPRIOR is a matrix with a row for each group and +% a column for each weight parameter. +% +% See also +% RBF, RBFFWD, RBFERR, RBFPAK, RBFUNPAK, RBFBKP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +switch net.outfn +case 'linear' + errstring = consist(net, 'rbf', x, t); +case 'neuroscale' + errstring = consist(net, 'rbf', x); +otherwise + error(['Unknown output function ', net.outfn]); +end +if ~isempty(errstring); + error(errstring); +end + +ndata = size(x, 1); + +[y, z, n2] = rbffwd(net, x); + +switch net.outfn +case 'linear' + + % Sum squared error at output units + delout = y - t; + + gdata = rbfbkp(net, x, z, n2, delout); + [g, gdata, gprior] = gbayes(net, gdata); + +case 'neuroscale' + % Compute the error gradient with respect to outputs + y_dist = sqrt(dist2(y, y)); + D = (t - y_dist)./(y_dist+diag(ones(ndata, 1))); + temp = y'; + gradient = 2.*sum(kron(D, ones(1, net.nout)) .* ... + (repmat(y, 1, ndata) - repmat((temp(:))', ndata, 1)), 1); + gradient = (reshape(gradient, net.nout, ndata))'; + % Compute the error gradient + gdata = rbfbkp(net, x, z, n2, gradient); + [g, gdata, gprior] = gbayes(net, gdata); +otherwise + error(['Unknown output function ', net.outfn]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfhess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,91 @@ +function [h, hdata] = rbfhess(net, x, t, hdata) +%RBFHESS Evaluate the Hessian matrix for RBF network. +% +% Description +% H = RBFHESS(NET, X, T) takes an RBF network data structure NET, a +% matrix X of input values, and a matrix T of target values and returns +% the full Hessian matrix H corresponding to the second derivatives of +% the negative log posterior distribution, evaluated for the current +% weight and bias values as defined by NET. Currently, the +% implementation only computes the Hessian for the output layer +% weights. +% +% [H, HDATA] = RBFHESS(NET, X, T) returns both the Hessian matrix H and +% the contribution HDATA arising from the data dependent term in the +% Hessian. +% +% H = RBFHESS(NET, X, T, HDATA) takes a network data structure NET, a +% matrix X of input values, and a matrix T of target values, together +% with the contribution HDATA arising from the data dependent term in +% the Hessian, and returns the full Hessian matrix H corresponding to +% the second derivatives of the negative log posterior distribution. +% This version saves computation time if HDATA has already been +% evaluated for the current weight and bias values. +% +% See also +% MLPHESS, HESSCHEK, EVIDENCE +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'rbf', x, t); +if ~isempty(errstring); + error(errstring); +end + +if nargin == 3 + % Data term in Hessian needs to be computed + [a, z] = rbffwd(net, x); + hdata = datahess(net, z, t); +end + +% Add in effect of regularisation +[h, hdata] = hbayes(net, hdata); + +% Sub-function to compute data part of Hessian +function hdata = datahess(net, z, t) + +% Only works for output layer Hessian currently +if (isfield(net, 'mask') & ~any(net.mask(... + 1:(net.nwts - net.nout*(net.nhidden+1))))) + hdata = zeros(net.nwts); + ndata = size(z, 1); + out_hess = [z ones(ndata, 1)]'*[z ones(ndata, 1)]; + for j = 1:net.nout + hdata = rearrange_hess(net, j, out_hess, hdata); + end +else + error('Output layer Hessian only.'); +end +return + +% Sub-function to rearrange Hessian matrix +function hdata = rearrange_hess(net, j, out_hess, hdata) + +% Because all the biases come after all the input weights, +% we have to rearrange the blocks that make up the network Hessian. +% This function assumes that we are on the jth output and that all outputs +% are independent. + +% Start of bias weights block +bb_start = net.nwts - net.nout + 1; +% Start of weight block for jth output +ob_start = net.nwts - net.nout*(net.nhidden+1) + (j-1)*net.nhidden... + + 1; +% End of weight block for jth output +ob_end = ob_start + net.nhidden - 1; +% Index of bias weight +b_index = bb_start+(j-1); +% Put input weight block in right place +hdata(ob_start:ob_end, ob_start:ob_end) = out_hess(1:net.nhidden, ... + 1:net.nhidden); +% Put second derivative of bias weight in right place +hdata(b_index, b_index) = out_hess(net.nhidden+1, net.nhidden+1); +% Put cross terms (input weight v bias weight) in right place +hdata(b_index, ob_start:ob_end) = out_hess(net.nhidden+1, ... + 1:net.nhidden); +hdata(ob_start:ob_end, b_index) = out_hess(1:net.nhidden, ... + net.nhidden+1); + +return \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfjacob.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function jac = rbfjacob(net, x) +%RBFJACOB Evaluate derivatives of RBF network outputs with respect to inputs. +% +% Description +% G = RBFJACOB(NET, X) takes a network data structure NET and a matrix +% of input vectors X and returns a three-index matrix G whose I, J, K +% element contains the derivative of network output K with respect to +% input parameter J for input pattern I. +% +% See also +% RBF, RBFGRAD, RBFBKP +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'rbf', x); +if ~isempty(errstring); + error(errstring); +end + +if ~strcmp(net.outfn, 'linear') + error('Function only implemented for linear outputs') +end + +[y, z, n2] = rbffwd(net, x); + +ndata = size(x, 1); +jac = zeros(ndata, net.nin, net.nout); +Psi = zeros(net.nin, net.nhidden); +% Calculate derivative of activations wrt n2 +switch net.actfn +case 'gaussian' + dz = -z./(ones(ndata, 1)*net.wi); +case 'tps' + dz = 2*(1 + log(n2+(n2==0))); +case 'r4logr' + dz = 2*(n2.*(1+2.*log(n2+(n2==0)))); +otherwise + error(['Unknown activation function ', net.actfn]); +end + +% Ignore biases as they cannot affect Jacobian +for n = 1:ndata + Psi = (ones(net.nin, 1)*dz(n, :)).* ... + (x(n, :)'*ones(1, net.nhidden) - net.c'); + % Now compute the Jacobian + jac(n, :, :) = Psi * net.w2; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function w = rbfpak(net) +%RBFPAK Combines all the parameters in an RBF network into one weights vector. +% +% Description +% W = RBFPAK(NET) takes a network data structure NET and combines the +% component parameter matrices into a single row vector W. +% +% See also +% RBFUNPAK, RBF +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'rbf'); +if ~errstring + error(errstring); +end + +w = [net.c(:)', net.wi, net.w2(:)', net.b2];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfprior.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +function [mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2) +%RBFPRIOR Create Gaussian prior and output layer mask for RBF. +% +% Description +% [MASK, PRIOR] = RBFPRIOR(RBFUNC, NIN, NHIDDEN, NOUT, AW2, AB2) +% generates a vector MASK that selects only the output layer weights. +% This is because most uses of RBF networks in a Bayesian context have +% fixed basis functions with the output layer as the only adjustable +% parameters. In particular, the Neuroscale output error function is +% designed to work only with this mask. +% +% The return value PRIOR is a data structure, with fields PRIOR.ALPHA +% and PRIOR.INDEX, which specifies a Gaussian prior distribution for +% the network weights in an RBF network. The parameters AW2 and AB2 are +% all scalars and represent the regularization coefficients for two +% groups of parameters in the network corresponding to second-layer +% weights, and second-layer biases respectively. Then PRIOR.ALPHA +% represents a column vector of length 2 containing the parameters, and +% PRIOR.INDEX is a matrix specifying which weights belong in each +% group. Each column has one element for each weight in the matrix, +% using the standard ordering as defined in RBFPAK, and each element is +% 1 or 0 according to whether the weight is a member of the +% corresponding group or not. +% +% See also +% RBF, RBFERR, RBFGRAD, EVIDENCE +% + +% Copyright (c) Ian T Nabney (1996-2001) + +nwts_layer2 = nout + (nhidden *nout); +switch rbfunc +case 'gaussian' + nwts_layer1 = nin*nhidden + nhidden; +case {'tps', 'r4logr'} + nwts_layer1 = nin*nhidden; +otherwise + error('Undefined activation function'); +end +nwts = nwts_layer1 + nwts_layer2; + +% Make a mask only for output layer +mask = [zeros(nwts_layer1, 1); ones(nwts_layer2, 1)]; + +if nargout > 1 + % Construct prior + indx = zeros(nwts, 2); + mark2 = nwts_layer1 + (nhidden * nout); + indx(nwts_layer1 + 1:mark2, 1) = ones(nhidden * nout, 1); + indx(mark2 + 1:nwts, 2) = ones(nout, 1); + + prior.index = indx; + prior.alpha = [aw2, ab2]'; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfsetbf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function net = rbfsetbf(net, options, x) +%RBFSETBF Set basis functions of RBF from data. +% +% Description +% NET = RBFSETBF(NET, OPTIONS, X) sets the basis functions of the RBF +% network NET so that they model the unconditional density of the +% dataset X. This is done by training a GMM with spherical covariances +% using GMMEM. The OPTIONS vector is passed to GMMEM. The widths of +% the functions are set by a call to RBFSETFW. +% +% See also +% RBFTRAIN, RBFSETFW, GMMEM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'rbf', x); +if ~isempty(errstring) + error(errstring); +end + +% Create a spherical Gaussian mixture model +mix = gmm(net.nin, net.nhidden, 'spherical'); + +% Initialise the parameters from the input data +% Just use a small number of k means iterations +kmoptions = zeros(1, 18); +kmoptions(1) = -1; % Turn off warnings +kmoptions(14) = 5; % Just 5 iterations to get centres roughly right +mix = gmminit(mix, x, kmoptions); + +% Train mixture model using EM algorithm +[mix, options] = gmmem(mix, x, options); + +% Now set the centres of the RBF from the centres of the mixture model +net.c = mix.centres; + +% options(7) gives scale of function widths +net = rbfsetfw(net, options(7)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfsetfw.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function net = rbfsetfw(net, scale) +%RBFSETFW Set basis function widths of RBF. +% +% Description +% NET = RBFSETFW(NET, SCALE) sets the widths of the basis functions of +% the RBF network NET. If Gaussian basis functions are used, then the +% variances are set to the largest squared distance between centres if +% SCALE is non-positive and SCALE times the mean distance of each +% centre to its nearest neighbour if SCALE is positive. Non-Gaussian +% basis functions do not have a width. +% +% See also +% RBFTRAIN, RBFSETBF, GMMEM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set the variances to be the largest squared distance between centres +if strcmp(net.actfn, 'gaussian') + cdist = dist2(net.c, net.c); + if scale > 0.0 + % Set variance of basis to be scale times average + % distance to nearest neighbour + cdist = cdist + realmax*eye(net.nhidden); + widths = scale*mean(min(cdist)); + else + widths = max(max(cdist)); + end + net.wi = widths * ones(size(net.wi)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbftrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,203 @@ +function [net, options] = rbftrain(net, options, x, t) +%RBFTRAIN Two stage training of RBF network. +% +% Description +% NET = RBFTRAIN(NET, OPTIONS, X, T) uses a two stage training +% algorithm to set the weights in the RBF model structure NET. Each row +% of X corresponds to one input vector and each row of T contains the +% corresponding target vector. The centres are determined by fitting a +% Gaussian mixture model with circular covariances using the EM +% algorithm through a call to RBFSETBF. (The mixture model is +% initialised using a small number of iterations of the K-means +% algorithm.) If the activation functions are Gaussians, then the basis +% function widths are then set to the maximum inter-centre squared +% distance. +% +% For linear outputs, the hidden to output weights that give rise to +% the least squares solution can then be determined using the pseudo- +% inverse. For neuroscale outputs, the hidden to output weights are +% determined using the iterative shadow targets algorithm. Although +% this two stage procedure may not give solutions with as low an error +% as using general purpose non-linear optimisers, it is much faster. +% +% The options vector may have two rows: if this is the case, then the +% second row is passed to RBFSETBF, which allows the user to specify a +% different number iterations for RBF and GMM training. The optional +% parameters to RBFTRAIN have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values during EM training. +% +% OPTIONS(2) is a measure of the precision required for the value of +% the weights W at the solution. +% +% OPTIONS(3) is a measure of the precision required of the objective +% function at the solution. Both this and the previous condition must +% be satisfied for termination. +% +% OPTIONS(5) is set to 1 if the basis functions parameters should +% remain unchanged; default 0. +% +% OPTIONS(6) is set to 1 if the output layer weights should be should +% set using PCA. This is only relevant for Neuroscale outputs; default +% 0. +% +% OPTIONS(14) is the maximum number of iterations for the shadow +% targets algorithm; default 100. +% +% See also +% RBF, RBFERR, RBFFWD, RBFGRAD, RBFPAK, RBFUNPAK, RBFSETBF +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +switch net.outfn +case 'linear' + errstring = consist(net, 'rbf', x, t); +case 'neuroscale' + errstring = consist(net, 'rbf', x); +otherwise + error(['Unknown output function ', net.outfn]); +end +if ~isempty(errstring) + error(errstring); +end + +% Allow options to have two rows: if this is the case, then the second row +% is passed to rbfsetbf +if size(options, 1) == 2 + setbfoptions = options(2, :); + options = options(1, :); +else + setbfoptions = options; +end + +if(~options(14)) + options(14) = 100; +end +% Do we need to test for termination? +test = (options(2) | options(3)); + +% Set up the basis function parameters to model the input data density +% unless options(5) is set. +if ~(logical(options(5))) + net = rbfsetbf(net, setbfoptions, x); +end + +% Compute the design (or activations) matrix +[y, act] = rbffwd(net, x); +ndata = size(x, 1); + +if strcmp(net.outfn, 'neuroscale') & options(6) + % Initialise output layer weights by projecting data with PCA + mu = mean(x); + [pcvals, pcvecs] = pca(x, net.nout); + xproj = (x - ones(ndata, 1)*mu)*pcvecs; + % Now use projected data as targets to compute output layer weights + temp = pinv([act ones(ndata, 1)]) * xproj; + net.w2 = temp(1:net.nhidden, :); + net.b2 = temp(net.nhidden+1, :); + % Propagate again to compute revised outputs + [y, act] = rbffwd(net, x); +end + +switch net.outfn +case 'linear' + % Sum of squares error function in regression model + % Solve for the weights and biases using pseudo-inverse from activations + Phi = [act ones(ndata, 1)]; + if ~isfield(net, 'alpha') + % Solve for the weights and biases using left matrix divide + temp = pinv(Phi)*t; + elseif size(net.alpha == [1 1]) + % Use normal form equation + hessian = Phi'*Phi + net.alpha*eye(net.nhidden+1); + temp = pinv(hessian)*(Phi'*t); + else + error('Only scalar alpha allowed'); + end + net.w2 = temp(1:net.nhidden, :); + net.b2 = temp(net.nhidden+1, :); + +case 'neuroscale' + % Use the shadow targets training algorithm + if nargin < 4 + % If optional input distances not passed in, then use + % Euclidean distance + x_dist = sqrt(dist2(x, x)); + else + x_dist = t; + end + Phi = [act, ones(ndata, 1)]; + % Compute the pseudo-inverse of Phi + PhiDag = pinv(Phi); + % Compute y_dist, distances between image points + y_dist = sqrt(dist2(y, y)); + + % Save old weights so that we can check the termination criterion + wold = netpak(net); + % Compute initial error (stress) value + errold = 0.5*(sum(sum((x_dist - y_dist).^2))); + + % Initial value for eta + eta = 0.1; + k_up = 1.2; + k_down = 0.1; + success = 1; % Force initial gradient calculation + + for j = 1:options(14) + if success + % Compute the negative error gradient with respect to network outputs + D = (x_dist - y_dist)./(y_dist+(y_dist==0)); + temp = y'; + neg_gradient = -2.*sum(kron(D, ones(1, net.nout)) .* ... + (repmat(y, 1, ndata) - repmat((temp(:))', ndata, 1)), 1); + neg_gradient = (reshape(neg_gradient, net.nout, ndata))'; + end + % Compute the shadow targets + t = y + eta*neg_gradient; + % Solve for the weights and biases + temp = PhiDag * t; + net.w2 = temp(1:net.nhidden, :); + net.b2 = temp(net.nhidden+1, :); + + % Do housekeeping and test for convergence + ynew = rbffwd(net, x); + y_distnew = sqrt(dist2(ynew, ynew)); + err = 0.5.*(sum(sum((x_dist-y_distnew).^2))); + if err > errold + success = 0; + % Restore previous weights + net = netunpak(net, wold); + err = errold; + eta = eta * k_down; + else + success = 1; + eta = eta * k_up; + errold = err; + y = ynew; + y_dist = y_distnew; + if test & j > 1 + w = netpak(net); + if (max(abs(w - wold)) < options(2) & abs(err-errold) < options(3)) + options(8) = err; + return; + end + end + wold = netpak(net); + end + if options(1) + fprintf(1, 'Cycle %4d Error %11.6f\n', j, err) + end + if nargout >= 3 + errlog(j) = err; + end + end + options(8) = errold; + if (options(1) >= 0) + disp('Warning: Maximum number of iterations has been exceeded'); + end +otherwise + error(['Unknown output function ', net.outfn]); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rbfunpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function net = rbfunpak(net, w) +%RBFUNPAK Separates a vector of RBF weights into its components. +% +% Description +% NET = RBFUNPAK(NET, W) takes an RBF network data structure NET and a +% weight vector W, and returns a network data structure identical to +% the input network, except that the centres C, the widths WI, the +% second-layer weight matrix W2 and the second-layer bias vector B2 +% have all been set to the corresponding elements of W. +% +% See also +% RBFPAK, RBF +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'rbf'); +if ~errstring + error(errstring); +end + +if net.nwts ~= length(w) + error('Invalid length of weight vector') +end + +nin = net.nin; +nhidden = net.nhidden; +nout = net.nout; + +mark1 = nin*nhidden; +net.c = reshape(w(1:mark1), nhidden, nin); +if strcmp(net.actfn, 'gaussian') + mark2 = mark1 + nhidden; + net.wi = reshape(w(mark1+1:mark2), 1, nhidden); +else + mark2 = mark1; + net.wi = []; +end +mark3 = mark2 + nhidden*nout; +net.w2 = reshape(w(mark2+1:mark3), nhidden, nout); +mark4 = mark3 + nout; +net.b2 = reshape(w(mark3+1:mark4), 1, nout); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rosegrad.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function g = rosegrad(x) +%ROSEGRAD Calculate gradient of Rosenbrock's function. +% +% Description +% G = ROSEGRAD(X) computes the gradient of Rosenbrock's function at +% each row of X, which should have two columns. +% +% See also +% DEMOPT1, ROSEN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Return gradient of Rosenbrock's test function + +nrows = size(x, 1); +g = zeros(nrows,2); + +g(:,1) = -400 * (x(:,2) - x(:,1).^2) * x(:,1) - 2 * (1 - x(:,1)); +g(:,2) = 200 * (x(:,2) - x(:,1).^2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/rosen.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function y = rosen(x) +%ROSEN Calculate Rosenbrock's function. +% +% Description +% Y = ROSEN(X) computes the value of Rosenbrock's function at each row +% of X, which should have two columns. +% +% See also +% DEMOPT1, ROSEGRAD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Calculate value of Rosenbrock's function: x should be nrows by 2 columns + +y = 100 * ((x(:,2) - x(:,1).^2).^2) + (1.0 - x(:,1)).^2;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/scg.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,208 @@ +function [x, options, flog, pointlog, scalelog] = scg(f, x, options, gradf, varargin) +%SCG Scaled conjugate gradient optimization. +% +% Description +% [X, OPTIONS] = SCG(F, X, OPTIONS, GRADF) uses a scaled conjugate +% gradients algorithm to find a local minimum of the function F(X) +% whose gradient is given by GRADF(X). Here X is a row vector and F +% returns a scalar value. The point at which F has a local minimum is +% returned as X. The function value at that point is returned in +% OPTIONS(8). +% +% [X, OPTIONS, FLOG, POINTLOG, SCALELOG] = SCG(F, X, OPTIONS, GRADF) +% also returns (optionally) a log of the function values after each +% cycle in FLOG, a log of the points visited in POINTLOG, and a log of +% the scale values in the algorithm in SCALELOG. +% +% SCG(F, X, OPTIONS, GRADF, P1, P2, ...) allows additional arguments to +% be passed to F() and GRADF(). The optional parameters have the +% following interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG, and the points visited in the +% return argument POINTSLOG. If OPTIONS(1) is set to 0, then only +% warning messages are displayed. If OPTIONS(1) is -1, then nothing is +% displayed. +% +% OPTIONS(2) is a measure of the absolute precision required for the +% value of X at the solution. If the absolute difference between the +% values of X between two successive steps is less than OPTIONS(2), +% then this condition is satisfied. +% +% OPTIONS(3) is a measure of the precision required of the objective +% function at the solution. If the absolute difference between the +% objective function values between two successive steps is less than +% OPTIONS(3), then this condition is satisfied. Both this and the +% previous condition must be satisfied for termination. +% +% OPTIONS(9) is set to 1 to check the user defined gradient function. +% +% OPTIONS(10) returns the total number of function evaluations +% (including those in any line searches). +% +% OPTIONS(11) returns the total number of gradient evaluations. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% See also +% CONJGRAD, QUASINEW +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Set up the options. +if length(options) < 18 + error('Options vector too short') +end + +if(options(14)) + niters = options(14); +else + niters = 100; +end + +display = options(1); +gradcheck = options(9); + +% Set up strings for evaluating function and gradient +f = fcnchk(f, length(varargin)); +gradf = fcnchk(gradf, length(varargin)); + +nparams = length(x); + +% Check gradients +if (gradcheck) + feval('gradchek', x, f, gradf, varargin{:}); +end + +sigma0 = 1.0e-4; +fold = feval(f, x, varargin{:}); % Initial function value. +fnow = fold; +options(10) = options(10) + 1; % Increment function evaluation counter. +gradnew = feval(gradf, x, varargin{:}); % Initial gradient. +gradold = gradnew; +options(11) = options(11) + 1; % Increment gradient evaluation counter. +d = -gradnew; % Initial search direction. +success = 1; % Force calculation of directional derivs. +nsuccess = 0; % nsuccess counts number of successes. +beta = 1.0; % Initial scale parameter. +betamin = 1.0e-15; % Lower bound on scale. +betamax = 1.0e100; % Upper bound on scale. +j = 1; % j counts number of iterations. +if nargout >= 3 + flog(j, :) = fold; + if nargout == 4 + pointlog(j, :) = x; + end +end + +% Main optimization loop. +while (j <= niters) + + % Calculate first and second directional derivatives. + if (success == 1) + mu = d*gradnew'; + if (mu >= 0) + d = - gradnew; + mu = d*gradnew'; + end + kappa = d*d'; + if kappa < eps + options(8) = fnow; + return + end + sigma = sigma0/sqrt(kappa); + xplus = x + sigma*d; + gplus = feval(gradf, xplus, varargin{:}); + options(11) = options(11) + 1; + theta = (d*(gplus' - gradnew'))/sigma; + end + + % Increase effective curvature and evaluate step size alpha. + delta = theta + beta*kappa; + if (delta <= 0) + delta = beta*kappa; + beta = beta - theta/kappa; + end + alpha = - mu/delta; + + % Calculate the comparison ratio. + xnew = x + alpha*d; + fnew = feval(f, xnew, varargin{:}); + options(10) = options(10) + 1; + Delta = 2*(fnew - fold)/(alpha*mu); + if (Delta >= 0) + success = 1; + nsuccess = nsuccess + 1; + x = xnew; + fnow = fnew; + else + success = 0; + fnow = fold; + end + + if nargout >= 3 + % Store relevant variables + flog(j) = fnow; % Current function value + if nargout >= 4 + pointlog(j,:) = x; % Current position + if nargout >= 5 + scalelog(j) = beta; % Current scale parameter + end + end + end + if display > 0 + fprintf(1, 'Cycle %4d Error %11.6f Scale %e\n', j, fnow, beta); + end + + if (success == 1) + % Test for termination + + if (max(abs(alpha*d)) < options(2) & max(abs(fnew-fold)) < options(3)) + options(8) = fnew; + return; + + else + % Update variables for new position + fold = fnew; + gradold = gradnew; + gradnew = feval(gradf, x, varargin{:}); + options(11) = options(11) + 1; + % If the gradient is zero then we are done. + if (gradnew*gradnew' == 0) + options(8) = fnew; + return; + end + end + end + + % Adjust beta according to comparison ratio. + if (Delta < 0.25) + beta = min(4.0*beta, betamax); + end + if (Delta > 0.75) + beta = max(0.5*beta, betamin); + end + + % Update search direction using Polak-Ribiere formula, or re-start + % in direction of negative gradient after nparams steps. + if (nsuccess == nparams) + d = -gradnew; + nsuccess = 0; + else + if (success == 1) + gamma = (gradold - gradnew)*gradnew'/(mu); + d = gamma*d - gradnew; + end + end + j = j + 1; +end + +% If we get here, then we haven't terminated in the given number of +% iterations. + +options(8) = fold; +if (options(1) >= 0) + disp(maxitmess); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/som.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function net = som(nin, map_size) +%SOM Creates a Self-Organising Map. +% +% Description +% NET = SOM(NIN, MAP_SIZE) creates a SOM NET with input dimension (i.e. +% data dimension) NIN and map dimensions MAP_SIZE. Only two- +% dimensional maps are currently implemented. +% +% The fields in NET are +% type = 'som' +% nin = number of inputs +% map_dim = dimension of map (constrained to be 2) +% map_size = grid size: number of nodes in each dimension +% num_nodes = number of nodes: the product of values in map_size +% map = map_dim+1 dimensional array containing nodes +% inode_dist = map of inter-node distances using Manhatten metric +% +% The map contains the node vectors arranged column-wise in the first +% dimension of the array. +% +% See also +% KMEANS, SOMFWD, SOMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +net.type = 'som'; +net.nin = nin; + +% Create Map of nodes +if round(map_size) ~= map_size | (map_size < 1) + error('SOM specification must contain positive integers'); +end + +net.map_dim = length(map_size); +if net.map_dim ~= 2 + error('SOM is a 2 dimensional map'); +end +net.num_nodes = prod(map_size); +% Centres are stored by column as first index of multi-dimensional array. +% This makes extracting them later more easy. +% Initialise with rand to create square grid +net.map = rand([nin, map_size]); +net.map_size = map_size; + +% Crude function to compute inter-node distances +net.inode_dist = zeros([map_size, net.num_nodes]); +for m = 1:net.num_nodes + node_loc = [1+fix((m-1)/map_size(2)), 1+rem((m-1),map_size(2))]; + for k = 1:map_size(1) + for l = 1:map_size(2) + net.inode_dist(k, l, m) = round(max(abs([k l] - node_loc))); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/somfwd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function [d2, win_nodes] = somfwd(net, x) +%SOMFWD Forward propagation through a Self-Organising Map. +% +% Description +% D2 = SOMFWD(NET, X) propagates the data matrix X through a SOM NET, +% returning the squared distance matrix D2 with dimension NIN by +% NUM_NODES. The $i$th row represents the squared Euclidean distance +% to each of the nodes of the SOM. +% +% [D2, WIN_NODES] = SOMFWD(NET, X) also returns the indices of the +% winning nodes for each pattern. +% +% See also +% SOM, SOMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check for consistency +errstring = consist(net, 'som', x); +if ~isempty(errstring) + error(errstring); +end + +% Turn nodes into matrix of centres +nodes = (reshape(net.map, net.nin, net.num_nodes))'; +% Compute squared distance matrix +d2 = dist2(x, nodes); +% Find winning node for each pattern: minimum value in each row +[w, win_nodes] = min(d2, [], 2);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/sompak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function [c] = sompak(net) +%SOMPAK Combines node weights into one weights matrix. +% +% Description +% C = SOMPAK(NET) takes a SOM data structure NET and combines the node +% weights into a matrix of centres C where each row represents the node +% vector. +% +% The ordering of the parameters in W is defined by the indexing of the +% multi-dimensional array NET.MAP. +% +% See also +% SOM, SOMUNPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'som'); +if ~isempty(errstring) + error(errstring); +end +% Returns map as a sequence of row vectors +c = (reshape(net.map, net.nin, net.num_nodes))';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/somtrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,157 @@ +function net = somtrain(net, options, x) +%SOMTRAIN Kohonen training algorithm for SOM. +% +% Description +% NET = SOMTRAIN{NET, OPTIONS, X) uses Kohonen's algorithm to train a +% SOM. Both on-line and batch algorithms are implemented. The learning +% rate (for on-line) and neighbourhood size decay linearly. There is no +% error function minimised during training (so there is no termination +% criterion other than the number of epochs), but the sum-of-squares +% is computed and returned in OPTIONS(8). +% +% The optional parameters have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs learning +% rate ALPHA and neighbourhood size NSIZE. Otherwise nothing is +% displayed. +% +% OPTIONS(5) determines whether the patterns are sampled randomly with +% replacement. If it is 0 (the default), then patterns are sampled in +% order. This is only relevant to the on-line algorithm. +% +% OPTIONS(6) determines if the on-line or batch algorithm is used. If +% it is 1 then the batch algorithm is used. If it is 0 (the default) +% then the on-line algorithm is used. +% +% OPTIONS(14) is the maximum number of iterations (passes through the +% complete pattern set); default 100. +% +% OPTIONS(15) is the final neighbourhood size; default value is the +% same as the initial neighbourhood size. +% +% OPTIONS(16) is the final learning rate; default value is the same as +% the initial learning rate. +% +% OPTIONS(17) is the initial neighbourhood size; default 0.5*maximum +% map size. +% +% OPTIONS(18) is the initial learning rate; default 0.9. This +% parameter must be positive. +% +% See also +% KMEANS, SOM, SOMFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) + +% Check arguments for consistency +errstring = consist(net, 'som', x); +if ~isempty(errstring) + error(errstring); +end + +% Set number of iterations in convergence phase +if (~options(14)) + options(14) = 100; +end +niters = options(14); + +% Learning rate must be positive +if (options(18) > 0) + alpha_first = options(18); +else + alpha_first = 0.9; +end +% Final learning rate must be no greater than initial learning rate +if (options(16) > alpha_first | options(16) < 0) + alpha_last = alpha_first; +else + alpha_last = options(16); +end + +% Neighbourhood size +if (options(17) >= 0) + nsize_first = options(17); +else + nsize_first = max(net.map_dim)/2; +end +% Final neighbourhood size must be no greater than initial size +if (options(15) > nsize_first | options(15) < 0) + nsize_last = nsize_first; +else + nsize_last = options(15); +end + +ndata = size(x, 1); + +if options(6) + % Batch algorithm + H = zeros(ndata, net.num_nodes); +end +% Put weights into matrix form +tempw = sompak(net); + +% Then carry out training +j = 1; +while j <= niters + if options(6) + % Batch version of algorithm + alpha = 0.0; + frac_done = (niters - j)/niters; + % Compute neighbourhood + nsize = round((nsize_first - nsize_last)*frac_done + nsize_last); + + % Find winning node: put weights back into net so that we can + % call somunpak + net = somunpak(net, tempw); + [temp, bnode] = somfwd(net, x); + for k = 1:ndata + H(k, :) = reshape(net.inode_dist(:, :, bnode(k))<=nsize, ... + 1, net.num_nodes); + end + s = sum(H, 1); + for k = 1:net.num_nodes + if s(k) > 0 + tempw(k, :) = sum((H(:, k)*ones(1, net.nin)).*x, 1)/ ... + s(k); + end + end + else + % On-line version of algorithm + if options(5) + % Randomise order of pattern presentation: with replacement + pnum = ceil(rand(ndata, 1).*ndata); + else + pnum = 1:ndata; + end + % Cycle through dataset + for k = 1:ndata + % Fraction done + frac_done = (((niters+1)*ndata)-(j*ndata + k))/((niters+1)*ndata); + % Compute learning rate + alpha = (alpha_first - alpha_last)*frac_done + alpha_last; + % Compute neighbourhood + nsize = round((nsize_first - nsize_last)*frac_done + nsize_last); + % Find best node + pat_diff = ones(net.num_nodes, 1)*x(pnum(k), :) - tempw; + [temp, bnode] = min(sum(abs(pat_diff), 2)); + + % Now update neighbourhood + neighbourhood = (net.inode_dist(:, :, bnode) <= nsize); + tempw = tempw + ... + ((alpha*(neighbourhood(:)))*ones(1, net.nin)).*pat_diff; + end + end + if options(1) + % Print iteration information + fprintf(1, 'Iteration %d; alpha = %f, nsize = %f. ', j, alpha, ... + nsize); + % Print sum squared error to nearest node + d2 = dist2(tempw, x); + fprintf(1, 'Error = %f\n', sum(min(d2))); + end + j = j + 1; +end + +net = somunpak(net, tempw); +options(8) = sum(min(dist2(tempw, x))); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/somunpak.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function net = somunpak(net, w) +%SOMUNPAK Replaces node weights in SOM. +% +% Description +% NET = SOMUNPAK(NET, W) takes a SOM data structure NET and weight +% matrix W (each node represented by a row) and puts the nodes back +% into the multi-dimensional array NET.MAP. +% +% The ordering of the parameters in W is defined by the indexing of the +% multi-dimensional array NET.MAP. +% +% See also +% SOM, SOMPAK +% + +% Copyright (c) Ian T Nabney (1996-2001) + +errstring = consist(net, 'som'); +if ~isempty(errstring) + error(errstring); +end +% Put weights back into network data structure +net.map = reshape(w', [net.nin net.map_size]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlab3.3/xor.dat Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +nin 2 +nout 1 +ndata 12 +1 0 1 +0 1 1 +0 0 0 +1 1 0 +1 0 1 +0 1 1 +0 0 0 +1 1 0 +1 0 1 +0 1 1 +0 0 0 +1 1 0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/CVS/Entries Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +/README.txt/1.1.1.1/Wed Apr 27 17:59:16 2005// +/demgmm1_movie.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/evidence_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/glmerr_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/glmgrad_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/glmhess_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/glmtrain_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/gmm1.avi/1.1.1.1/Wed Apr 27 17:59:16 2005// +/gmmem2.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/gmmem_multi_restart.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/kmeans_demo.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/mlperr_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/mlpgrad_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/mlphdotv_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/mlphess_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/neterr_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/netgrad_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/nethess_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/netopt_weighted.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +/process_options.m/1.1.1.1/Wed Apr 27 17:59:16 2005// +D
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/CVS/Repository Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +FullBNT/netlabKPM
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/CVS/Root Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/README.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +This directory contains some small modifications/additions to +netlab, http://www.ncrg.aston.ac.uk/netlab/. + +- gmmem2 allows a prior on the covariance, and lets you visualize +intermediate results of the learning +- gmmem_multi_restart is self-explanatory + +All the _weighted functions were written by Pierpaolo Brutti. +They are needed by BNT (softmax_CPD and mlp_CPD +maximize_params method).
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/demgmm1_movie.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,103 @@ +%DEMGMM1 Demonstrate EM for Gaussian mixtures. +% +% Description +% This script demonstrates the use of the EM algorithm to fit a mixture +% of Gaussians to a set of data using maximum likelihood. A colour +% coding scheme is used to illustrate the evaluation of the posterior +% probabilities in the E-step of the EM algorithm. +% +% See also +% DEMGMM2, DEMGMM3, DEMGMM4, GMM, GMMEM, GMMPOST +% + +% Copyright (c) Ian T Nabney (1996-2001) + +mov = avifile('movies/gmm1.avi','fps',1 ); + +% Generate the data +randn('state', 0); rand('state', 0); +gmix = gmm(2, 2, 'spherical'); +ndat1 = 20; ndat2 = 20; ndata = ndat1+ndat2; +gmix.centres = [0.3 0.3; 0.7 0.7]; +gmix.covars = [0.01 0.01]; +x = gmmsamp(gmix, ndata); + +h = figure; +hd = plot(x(:, 1), x(:, 2), '.g', 'markersize', 30); +hold on; axis([0 1 0 1]); axis square; set(gca, 'box', 'on'); +ht = text(0.5, 1.05, 'Data', 'horizontalalignment', 'center'); + + +% Set up mixture model +ncentres = 2; input_dim = 2; +mix = gmm(input_dim, ncentres, 'spherical'); + +% Initialise the mixture model +mix.centres = [0.2 0.8; 0.8, 0.2]; +mix.covars = [0.01 0.01]; + +% Plot the initial model +ncirc = 30; theta = linspace(0, 2*pi, ncirc); +xs = cos(theta); ys = sin(theta); +xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs; +yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys; +hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r'); +hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b'); +set(ht, 'string', 'Initial Configuration'); +figure(h); +mov = addframe(mov, getframe(gcf)); +mov = addframe(mov, getframe(gcf)); + +% Initial E-step. +set(ht, 'string', 'E-step'); +post = gmmpost(mix, x); +dcols = [post(:,1), zeros(ndata, 1), post(:,2)]; +delete(hd); +for i = 1 : ndata + hd(i) = plot(x(i, 1), x(i, 2), 'color', dcols(i,:), ... + 'marker', '.', 'markersize', 30); +end + +% M-step. +set(ht, 'string', 'M-step'); +options = foptions; +options(14) = 1; % A single iteration +options(1) = -1; % Switch off all messages, including warning +mix = gmmem(mix, x, options); +delete(hc); +xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs; +yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys; +hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r'); +hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b'); +figure(h); +mov = addframe(mov, getframe(gcf)); +mov = addframe(mov, getframe(gcf)); + +% Loop over EM iterations. +numiters = 9; +for n = 1 : numiters + + set(ht, 'string', 'E-step'); + post = gmmpost(mix, x); + dcols = [post(:,1), zeros(ndata, 1), post(:,2)]; + delete(hd); + for i = 1 : ndata + hd(i) = plot(x(i, 1), x(i, 2), 'color', dcols(i,:), ... + 'marker', '.', 'markersize', 30); + end + %pause(1) + + set(ht, 'string', 'M-step'); + [mix, options] = gmmem(mix, x, options); + fprintf(1, 'Cycle %4d Error %11.6f\n', n, options(8)); + delete(hc); + xvals = mix.centres(:, 1)*ones(1,ncirc) + sqrt(mix.covars')*xs; + yvals = mix.centres(:, 2)*ones(1,ncirc) + sqrt(mix.covars')*ys; + hc(1)=line(xvals(1,:), yvals(1,:), 'color', 'r'); + hc(2)=line(xvals(2,:), yvals(2,:), 'color', 'b'); + pause(1) + + mov = addframe(mov, getframe(gcf)); +end + +mov = close(mov);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/evidence_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +function [net, gamma, logev] = evidence_weighted(net, x, t, eso_w, num) +%EVIDENCE Re-estimate hyperparameters using evidence approximation. +% +% Description +% [NET] = EVIDENCE(NET, X, T) re-estimates the hyperparameters ALPHA +% and BETA by applying Bayesian re-estimation formulae for NUM +% iterations. The hyperparameter ALPHA can be a simple scalar +% associated with an isotropic prior on the weights, or can be a vector +% in which each component is associated with a group of weights as +% defined by the INDEX matrix in the NET data structure. These more +% complex priors can be set up for an MLP using MLPPRIOR. Initial +% values for the iterative re-estimation are taken from the network +% data structure NET passed as an input argument, while the return +% argument NET contains the re-estimated values. +% +% [NET, GAMMA, LOGEV] = EVIDENCE(NET, X, T, NUM) allows the re- +% estimation formula to be applied for NUM cycles in which the re- +% estimated values for the hyperparameters from each cycle are used to +% re-evaluate the Hessian matrix for the next cycle. The return value +% GAMMA is the number of well-determined parameters and LOGEV is the +% log of the evidence. +% +% See also +% MLPPRIOR, NETGRAD, NETHESS, DEMEV1, DEMARD +% + +% Copyright (c) Ian T Nabney (1996-9) + +errstring = consist(net, '', x, t); +if ~isempty(errstring) + error(errstring); +end + +ndata = size(x, 1); +if nargin == 4 + num = 1; +end + +if isfield(net,'beta') + beta = net.beta; +else + beta = 1; +end; + +% Extract weights from network +pakstr = [net.type, 'pak']; +w = feval(pakstr, net); + +% Evaluate data-dependent contribution to the Hessian matrix. +[h, dh] = nethess_weighted(w, net, x, t, eso_w); + +% Now set the negative eigenvalues to zero. +[evec, evl] = eig(dh); +evl = evl.*(evl > 0); +% safe_evl is used to avoid taking log of zero +safe_evl = evl + eps.*(evl <= 0); + +% Do the re-estimation. +for k = 1 : num + [e, edata, eprior] = neterr_weighted(w, net, x, t, eso_w); + h = nethess_weighted(w, net, x, t, eso_w, dh); + % Re-estimate alpha. + if size(net.alpha) == [1 1] + % Evaluate number of well-determined parameters. + if k == 1 + % Form vector of eigenvalues + evl = diag(evl); + safe_evl = diag(safe_evl); + end + B = beta*evl; + gamma = sum(B./(B + net.alpha)); + net.alpha = 0.5*gamma/eprior; + + % Partially evaluate log evidence + logev = e - 0.5*sum(log(safe_evl)) + 0.5*net.nwts*log(net.alpha) - ... + 0.5*ndata*log(2*pi); + else + ngroups = size(net.alpha, 1); + gams = zeros(1, ngroups); + logas = zeros(1, ngroups); + traces = zeros(1, ngroups); + % Reconstruct data hessian with negative eigenvalues set to zero. + dh = evec*evl*evec'; + hinv = inv(nethess_weighted(w, net, x, t, eso_w, dh)); + for m = 1 : ngroups + group_nweights = sum(net.index(:, m)); + gams(m) = group_nweights - ... + net.alpha(m)*sum(diag(hinv).*net.index(:,m)); + net.alpha(m) = real(gams(m)/(2*eprior(m))); + % Weight alphas by number of weights in group + logas(m) = 0.5*group_nweights*log(net.alpha(m)); + % Compute sum of evalues corresponding to group + traces(m) = sum(log(safe_evl*net.index(:,m))); + end + gamma = sum(gams, 2); + logev = e - 0.5*sum(traces) + sum(logas) - 0.5*ndata*log(2*pi); + end + % Re-estimate beta. + if isfield(net, 'beta') + net.beta = 0.5*(net.nout*ndata - gamma)/edata; + end + logev = logev + 0.5*ndata*log(beta); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/glmerr_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function [e, edata, eprior, y, a] = glmerr_weighted(net, x, t, eso_w) +%GLMERR Evaluate error function for generalized linear model. +% +% Description +% E = GLMERR(NET, X, T) takes a generalized linear model data +% structure NET together with a matrix X of input vectors and a matrix +% T of target vectors, and evaluates the error function E. The choice +% of error function corresponds to the output unit activation function. +% Each row of X corresponds to one input vector and each row of T +% corresponds to one target vector. +% +% [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X, T) also returns the data +% and prior components of the total error. +% +% [E, EDATA, EPRIOR, Y, A] = GLMERR(NET, X) also returns a matrix Y +% giving the outputs of the models and a matrix A giving the summed +% inputs to each output unit, where each row corresponds to one +% pattern. +% +% See also +% GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMGRAD, GLMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-9) + +% Check arguments for consistency +errstring = consist(net, 'glm', x, t); +if ~isempty(errstring); + error(errstring); +end + +[y, a] = glmfwd(net, x); + +%switch net.actfn + switch net.outfn + + case 'softmax' % Softmax outputs + + nout = size(a,2); + % Ensure that sum(exp(a), 2) does not overflow + maxcut = log(realmax) - log(nout); + % Ensure that exp(a) > 0 + mincut = log(realmin); + a = min(a, maxcut); + a = max(a, mincut); + temp = exp(a); + y = temp./(sum(temp, 2)*ones(1,nout)); + % Ensure that log(y) is computable + y(y<realmin) = realmin; + e_app=sum(t.*log(y),2); + edata = - eso_w'*e_app; + + otherwise + error(['Unknown activation function ', net.actfn]); +end + +[e, edata, eprior] = errbayes(net, edata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/glmgrad_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function [g, gdata, gprior] = glmgrad(net, x, t, eso_w) +%GLMGRAD Evaluate gradient of error function for generalized linear model. +% +% Description +% G = GLMGRAD(NET, X, T) takes a generalized linear model data +% structure NET together with a matrix X of input vectors and a matrix +% T of target vectors, and evaluates the gradient G of the error +% function with respect to the network weights. The error function +% corresponds to the choice of output unit activation function. Each +% row of X corresponds to one input vector and each row of T +% corresponds to one target vector. +% +% [G, GDATA, GPRIOR] = GLMGRAD(NET, X, T) also returns separately the +% data and prior contributions to the gradient. +% +% See also +% GLM, GLMPAK, GLMUNPAK, GLMFWD, GLMERR, GLMTRAIN +% + +% Copyright (c) Ian T Nabney (1996-9) + +% Check arguments for consistency +errstring = consist(net, 'glm', x, t); +if ~isempty(errstring); + error(errstring); +end + +y = glmfwd(net, x); +temp = y - t; +ndata = size(x, 1); +for m=1:ndata, + delout(m,:)=eso_w(m,1)*temp(m,:); +end +gw1 = x'*delout; +gb1 = sum(delout, 1); + +gdata = [gw1(:)', gb1]; + +[g, gdata, gprior] = gbayes(net, gdata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/glmhess_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function [h, hdata] = glmhess_weighted(net, x, t, eso_w, hdata) +%GLMHESS Evaluate the Hessian matrix for a generalised linear model. +% +% Description +% H = GLMHESS(NET, X, T) takes a GLM network data structure NET, a +% matrix X of input values, and a matrix T of target values and returns +% the full Hessian matrix H corresponding to the second derivatives of +% the negative log posterior distribution, evaluated for the current +% weight and bias values as defined by NET. Note that the target data +% is not required in the calculation, but is included to make the +% interface uniform with NETHESS. For linear and logistic outputs, the +% computation is very simple and is done (in effect) in one line in +% GLMTRAIN. +% +% See also +% GLM, GLMTRAIN, HESSCHEK, NETHESS +% +% Copyright (c) Ian T Nabney (1996-9) + +% Check arguments for consistency +errstring = consist(net, 'glm', x, t); +if ~isempty(errstring); + error(errstring); +end + +ndata = size(x, 1); +nparams = net.nwts; +nout = net.nout; +p = glmfwd(net, x); +inputs = [x ones(ndata, 1)]; + +if nargin == 4 + hdata = zeros(nparams); % Full Hessian matrix + % Calculate data component of Hessian + switch net.outfn + + case 'softmax' + bb_start = nparams - nout + 1; % Start of bias weights block + ex_hess = zeros(nparams); % Contribution to Hessian from single example + for m = 1:ndata + X = x(m,:)'*x(m,:); + a = diag(p(m,:))-((p(m,:)')*p(m,:)); + a=eso_w(m,1)*a; + ex_hess(1:nparams-nout,1:nparams-nout) = kron(a, X); + ex_hess(bb_start:nparams, bb_start:nparams) = a.*ones(net.nout, net.nout); + temp = kron(a, x(m,:)); + ex_hess(bb_start:nparams, 1:nparams-nout) = temp; + ex_hess(1:nparams-nout, bb_start:nparams) = temp'; + hdata = hdata + ex_hess; + end + + otherwise + error(['Unknown activation function ', net.actfn]); + end +end + +[h, hdata] = hbayes(net, hdata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/glmtrain_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,141 @@ +function [net, options] = glmtrain_weighted(net, options, x, t, eso_w, alfa) +%GLMTRAIN Specialised training of generalized linear model +% +% Description +% NET = GLMTRAIN(NET, OPTIONS, X, T) uses the iterative reweighted +% least squares (IRLS) algorithm to set the weights in the generalized +% linear model structure NET. This is a more efficient alternative to +% using GLMERR and GLMGRAD and a non-linear optimisation routine +% through NETOPT. Note that for linear outputs, a single pass through +% the algorithm is all that is required, since the error function is +% quadratic in the weights. The error function value at the final set +% of weights is returned in OPTIONS(8). Each row of X corresponds to +% one input vector and each row of T corresponds to one target vector. +% +% The optional parameters have the following interpretations. +% +% OPTIONS(1) is set to 1 to display error values during training. If +% OPTIONS(1) is set to 0, then only warning messages are displayed. If +% OPTIONS(1) is -1, then nothing is displayed. +% +% OPTIONS(2) is a measure of the precision required for the value of +% the weights W at the solution. +% +% OPTIONS(3) is a measure of the precision required of the objective +% function at the solution. Both this and the previous condition must +% be satisfied for termination. +% +% OPTIONS(5) is set to 1 if an approximation to the Hessian (which +% assumes that all outputs are independent) is used for softmax +% outputs. With the default value of 0 the exact Hessian (which is more +% expensive to compute) is used. +% +% OPTIONS(14) is the maximum number of iterations for the IRLS +% algorithm; default 100. +% +% See also +% GLM, GLMERR, GLMGRAD +% + +% Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997) + +% Check arguments for consistency +errstring = consist(net, 'glm', x, t); +if ~errstring + error(errstring); +end + +if(~options(14)) + options(14) = 100; +end + +display = options(1); + +test = (options(2) | options(3)); % Do we need to test for termination? + +ndata = size(x, 1); + +inputs = [x ones(ndata, 1)]; % Add a column of ones for the bias + +% Use weighted iterative reweighted least squares (WIRLS) +e = ones(1, net.nin+1); +for n = 1:options(14) + + %switch net.actfn + switch net.outfn + case 'softmax' + if n == 1 + p = (t + (1/size(t, 2)))/2; % Initialise model: ensure that row sum of p is one no matter + act = log(p./(1-p)); % how many classes there are + end + if options(5) == 1 | n == 1 + link_deriv = p.*(1-p); + weights = sqrt(link_deriv); % sqrt of weights + if (min(min(weights)) < eps) + fprintf(1, 'Warning: ill-conditioned weights in glmtrain\n') + return + end + z = act + (t-p)./link_deriv; + % Treat each output independently with relevant set of weights + for j = 1:net.nout + indep = inputs.*(weights(:,j)*e); + dep = z(:,j).*weights(:,j); + temp = indep\dep; + net.w1(:,j) = temp(1:net.nin); + net.b1(j) = temp(net.nin+1); + end + [err, edata, eprior, p, act] = glmerr_weighted(net, x, t, eso_w); + if n == 1 + errold = err; + wold = netpak(net); + else + w = netpak(net); + end + else + % Exact method of calculation after w first initialised + % Start by working out Hessian + Hessian = glmhess_weighted(net, x, t, eso_w); + temp = p-t; + for m=1:ndata, + temp(m,:)=eso_w(m,1)*temp(m,:); + end + gw1 = x'*(temp); + gb1 = sum(temp, 1); + gradient = [gw1(:)', gb1]; + % Now compute modification to weights + deltaw = -gradient*pinv(Hessian); + w = wold + alfa*deltaw; + net = glmunpak(net, w); + [err, edata, eprior, p] = glmerr_weighted(net, x, t, eso_w); + end + otherwise + error(['Unknown activation function ', net.actfn]); + end % switch' end + + if options(1)==1 + fprintf(1, 'Cycle %4d Error %11.6f\n', n, err) + end + % Test for termination + % Terminate if error increases + if err > errold + errold = err; + w = wold; + options(8) = err; + fprintf(1, 'Error has increased: terminating\n') + return; + end + if test & n > 1 + if (max(abs(w - wold)) < options(2) & abs(err-errold) < options(3)) + options(8) = err; + return; + else + errold = err; + wold = w; + end + end +end + +options(8) = err; +if (options(1) > 0) + disp('Warning: Maximum number of iterations has been exceeded'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/gmmem2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,186 @@ +function [mix, num_iter, ll] = gmmem_kpm(mix, x, varargin) +%GMMEM_KPM Like GMMEM, but with additional optional arguments +% function [mix, num_iter, ll] = gmmem_kpm(mix, x, varargin) +% +% Input: +% mix - structure created by gmminit or gmmem_multi_restart +% data - each row is an example +% +% Output: +% mix - modified structure +% num_iter - number of iterations needed to reach convergence +% ll - final log likelihood +% +% [ ... ] = gmmem_kpm(..., 'param1',val1, 'param2',val2, ...) allows you to +% specify optional parameter name/value pairs. +% Parameters are below [default value in brackets] +% +% 'max_iter' - maximum number of EM iterations [10] +% 'll_thresh' - change in log-likelihood threshold for convergence [1e-2] +% 'verbose' - 1 means display output while running [0] +% 'prior_cov' - this will be added to each estimated covariance +% to prevent singularities [1e-3*eye(d)] +% 'fn' - this function, if non-empty, will be called at every iteration +% (e.g., to display the parameters as they evolve) [ [] ] +% The fn is called as fn(mix, x, iter_num, fnargs). +% It is also called before the iteration starts as +% fn(mix, x, -1, fnargs), which can be used to initialize things. +% 'fnargs' - additional arguments to be passed to fn [ {} ] +% +% Modified by Kevin P Murphy, 29 Dec 2002 + + +% Check that inputs are consistent +errstring = consist(mix, 'gmm', x); +if ~isempty(errstring) + error(errstring); +end + +[ndata, xdim] = size(x); + +[max_iter, ll_thresh, verbose, prior_cov, fn, fnargs] = ... + process_options(varargin, ... + 'max_iter', 10, 'll_thresh', 1e-2, 'verbose', 1, ... + 'prior_cov', 1e-3*eye(xdim), 'fn', [], 'fnargs', {}); + +options = foptions; +if verbose, options(1)=1; else options(1)=-1; end +options(14) = max_iter; +options(3) = ll_thresh; + + +% Sort out the options +if (options(14)) + niters = options(14); +else + niters = 100; +end + +display = options(1); +test = 0; +if options(3) > 0.0 + test = 1; % Test log likelihood for termination +end + +check_covars = 0; +if options(5) >= 1 + if display >= 0 + disp('check_covars is on'); + end + check_covars = 1; % Ensure that covariances don't collapse + MIN_COVAR = eps; % Minimum singular value of covariance matrix + init_covars = mix.covars; +end + +mix0 = mix; % save init values for debugging + +if ~isempty(fn) + feval(fn, mix, x, -1, fnargs{:}); +end + +% Main loop of algorithm +for n = 1:niters + + % Calculate posteriors based on old parameters + [post, act] = gmmpost(mix, x); + + % Calculate error value if needed + if (display | test) + prob = act*(mix.priors)'; + % Error value is negative log likelihood of data + e = - sum(log(prob + eps)); + if display > 0 + fprintf(1, 'Cycle %4d Error %11.6f\n', n, e); + end + if test + if (n > 1 & abs(e - eold) < options(3)) + options(8) = e; + ll = -e; + num_iter = n; + return; %%%%%%%%%%%%%%%% Exit here if converged + else + eold = e; + end + end + end + + if ~isempty(fn) + feval(fn, mix, x, n, fnargs{:}); + end + + % Adjust the new estimates for the parameters + new_pr = sum(post, 1); + new_c = post' * x; + + % Now move new estimates to old parameter vectors + mix.priors = new_pr ./ ndata; + + mix.centres = new_c ./ (new_pr' * ones(1, mix.nin)); + + switch mix.covar_type + case 'spherical' + n2 = dist2(x, mix.centres); + for j = 1:mix.ncentres + v(j) = (post(:,j)'*n2(:,j)); + end + mix.covars = ((v./new_pr) + sum(diag(prior_cov)))./mix.nin; + if check_covars + % Ensure that no covariance is too small + for j = 1:mix.ncentres + if mix.covars(j) < MIN_COVAR + mix.covars(j) = init_covars(j); + end + end + end + case 'diag' + for j = 1:mix.ncentres + diffs = x - (ones(ndata, 1) * mix.centres(j,:)); + wts = (post(:,j)*ones(1, mix.nin)); + mix.covars(j,:) = sum((diffs.*diffs).*wts + prior_cov, 1)./new_pr(j); + end + if check_covars + % Ensure that no covariance is too small + for j = 1:mix.ncentres + if min(mix.covars(j,:)) < MIN_COVAR + mix.covars(j,:) = init_covars(j,:); + end + end + end + case 'full' + for j = 1:mix.ncentres + diffs = x - (ones(ndata, 1) * mix.centres(j,:)); + diffs = diffs.*(sqrt(post(:,j))*ones(1, mix.nin)); + mix.covars(:,:,j) = (diffs'*diffs + prior_cov)/new_pr(j); + end + if check_covars + % Ensure that no covariance is too small + for j = 1:mix.ncentres + if min(svd(mix.covars(:,:,j))) < MIN_COVAR + mix.covars(:,:,j) = init_covars(:,:,j); + end + end + end + case 'ppca' + for j = 1:mix.ncentres + diffs = x - (ones(ndata, 1) * mix.centres(j,:)); + diffs = diffs.*(sqrt(post(:,j))*ones(1, mix.nin)); + [mix.covars(j), mix.U(:,:,j), mix.lambda(j,:)] = ... + ppca((diffs'*diffs)/new_pr(j), mix.ppca_dim); + end + if check_covars + if mix.covars(j) < MIN_COVAR + mix.covars(j) = init_covars(j); + end + end + otherwise + error(['Unknown covariance type ', mix.covar_type]); + end +end + +ll = sum(log(gmmprob(mix, x))); +num_iter = n; + +%if (display >= 0) +% disp('Warning: Maximum number of iterations has been exceeded'); +%end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/gmmem_multi_restart.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [means, covs, weights, ll] = gmmem_multi_restart(K, data, varargin) +% GMMEM_MULTI_RESTART Multiple restart wrapper for gmmem_kpm +% function [means, covs, weights, ll] = gmmem_multi_restart(K, data, varargin) +% +% Input: +% K = number of mixture components +% data(i,:) is the i'th example (feature vector) +% +% Output: +% The parameters for the k'th mixture component, k=1:K, are +% means(k,:), covs(:,:,k) and weights(k) +% +% [ ... ] = gmmem_multi_restart(..., 'param1',val1, 'param2',val2, ...) +% allows you to specify optional parameter name/value pairs. +% Parameters are below [default value in brackets] +% +% 'nrestarts' - number of EM restarts [2] +% 'cov_type' - 'full', 'diag' or 'spherical' ['full'] +% 'init_cov' - the initial covariance matrix [0.1*cov(data) for each k] +% 'init_means' - [] means sample from randn(); otherwise, use +% init_means(k,:,r) for the k'th comp. on the r'th restart [ [] ] +% 'restartfn' - this function, if non-empty, will be called before/after every restart +% (e.g., to display the parameters as they evolve) [ [] ] +% The fn is called as fn(mix{r}, data, restart_num, niter, outerfnargs) +% where niter is the number of iterations performed (0 initially) +% 'restartfnargs' - additional arguments to be passed to restartfn [ {} ] +% +% Optional arguments for gmmem_kpm are passed through. +% +% Written by Kevin P Murphy, 30 Dec 2002 + +[ndata nfeatures] = size(data); + +%Cinit = repmat(0.1*diag(diag(cov(data))), [1 1 K]); +Cinit = repmat(0.1*cov(data), [1 1 K]); + +[nrestarts, init_cov, init_means, cov_type, ... + restartfn, restartfnargs, unused_args] = ... + process_options(varargin, ... + 'nrestarts', 2, 'init_cov', Cinit, 'init_means', [], ... + 'cov_type', 'full', 'restartfn', [], 'restartfnargs', {}); + +mix = cell(1, nrestarts); +cost = inf*ones(1,nrestarts); + +for r=1:nrestarts + mix{r} = gmm(nfeatures, K, cov_type); % random centers + if ~isempty(init_means), mix{r}.centres = init_means(:,:,r); end + mix{r}.covars = init_cov; + if ~isempty(restartfn) + feval(restartfn, mix{r}, data, r, 0, restartfnargs{:}); + end + [mix{r}, niter, ll] = gmmem_kpm(mix{r}, data, unused_args{:}); + cost(r) = -ll; %-sum(log(gmmprob(mix{r}, data))); + if ~isempty(restartfn) + feval(restartfn, mix{r}, data, r, niter, restartfnargs{:}); + end +end + +[nll, bestr] = min(cost); +fprintf('best r = %d\n', bestr); +ll = -nll; +means = mix{bestr}.centres; +covs = mix{bestr}.covars; +weights = mix{bestr}.priors;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/kmeans_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function kmeans_demo() + +% Generate T points from K=5 1D clusters, and try to recover the cluster +% centers using k-means. +% Requires BNT, netlab and the matlab stats toolbox v4. + +K = 5; +ndim = 1; +true_centers = 1:K; +sigma = 1e-6; +T = 100; +% data(t,:) is the t'th data point +data = zeros(T, ndim); +% ndx(t) = i means the t'th data point is sample from cluster i +%ndx = sample_discrete(normalise(ones(1,K))); +ndx = [1*ones(1,20) 2*ones(1,20) 3*ones(1,20) 4*ones(1,20) 5*ones(1,20)]; +for t=1:T + data(t) = sample_gaussian(true_centers(ndx(t)), sigma, 1); +end +plot(1:T, data, 'x') + + + +% set the centers randomly from Gauss(0) +mix = gmm(ndim, K, 'spherical'); +h = plot_centers_as_lines(mix, [], T); + +if 0 +% Place initial centers at K data points chosen at random, but add some noise +choose_ndx = randperm(T); +choose_ndx = choose_ndx(1:K); +init_centers = data(choose_ndx) + sample_gaussian(0, 0.1, K); +mix.centres = init_centers; +h = plot_centers_as_lines(mix, h, T); +end + +if 0 +% update centers using netlab k-means +options = foptions; +niter = 10; +options(14) = niter; +mix = gmminit(mix, data, options); +h = plot_centers_as_lines(mix, h, T); +end + +% use matlab stats toolbox k-means with multiple restarts +nrestarts = 5; +[idx, centers] = kmeans(data, K, 'replicates', nrestarts, ... + 'emptyAction', 'singleton', 'display', 'iter'); +mix.centres = centers; +h = plot_centers_as_lines(mix, h, T); + +% fine tune with EM; compute covariances of each cluster +options = foptions; +niter = 20; +options(1) = 1; % display cost fn at each iter +options(14) = niter; +mix = gmmem(mix, data, options); +h = plot_centers_as_lines(mix, h, T); + +%%%%%%%%% +function h = plot_centers_as_lines(mix, h, T) + +K = mix.ncentres; +hold on +if isempty(h) + for k=1:K + h(k)=line([0 T], [mix.centres(k) mix.centres(k)]); + end +else + for k=1:K + set(h(k), 'xdata', [0 T], 'ydata', [mix.centres(k) mix.centres(k)]); + end +end +hold off +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/mlperr_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function [e, edata, eprior] = mlperr_weighted(net, x, t, eso_w) +%MLPERR Evaluate error function for 2-layer network. +% +% Description +% E = MLPERR(NET, X, T) takes a network data structure NET together +% with a matrix X of input vectors and a matrix T of target vectors, +% and evaluates the error function E. The choice of error function +% corresponds to the output unit activation function. Each row of X +% corresponds to one input vector and each row of T corresponds to one +% target vector. +% +% [E, EDATA, EPRIOR] = MLPERR(NET, X, T) additionally returns the data +% and prior components of the error, assuming a zero mean Gaussian +% prior on the weights with inverse variance parameters ALPHA and BETA +% taken from the network data structure NET. +% +% See also +% MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPBKP, MLPGRAD +% + +% Copyright (c) Ian T Nabney (1996-9) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring); + error(errstring); +end +[y, z, a] = mlpfwd(net, x); + +switch net.actfn + + case 'linear' %Linear outputs + + edata = 0.5*sum(sum((y - t).^2)); + + case 'logistic' % Logistic outputs + + % Ensure that log(1-y) is computable: need exp(a) > eps + maxcut = -log(eps); + % Ensure that log(y) is computable + mincut = -log(1/realmin - 1); + a = min(a, maxcut); + a = max(a, mincut); + y = 1./(1 + exp(-a)); + edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y))); + + case 'softmax' % Softmax outputs + + nout = size(a,2); + % Ensure that sum(exp(a), 2) does not overflow + maxcut = log(realmax) - log(nout); + % Ensure that exp(a) > 0 + mincut = log(realmin); + a = min(a, maxcut); + a = max(a, mincut); + temp = exp(a); + y = temp./(sum(temp, 2)*ones(1,nout)); + % Ensure that log(y) is computable + y(y<realmin) = realmin; + e_app=sum(t.*log(y),2); + edata = - eso_w'*e_app; + clear e_app; + + otherwise + error(['Unknown activation function ', net.actfn]); +end + +[e, edata, eprior] = errbayes(net, edata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/mlpgrad_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function [g, gdata, gprior] = mlpgrad_weighted(net, x, t, eso_w) +%MLPGRAD Evaluate gradient of error function for 2-layer network. +% +% Description +% G = MLPGRAD(NET, X, T) takes a network data structure NET together +% with a matrix X of input vectors and a matrix T of target vectors, +% and evaluates the gradient G of the error function with respect to +% the network weights. The error funcion corresponds to the choice of +% output unit activation function. Each row of X corresponds to one +% input vector and each row of T corresponds to one target vector. +% +% [G, GDATA, GPRIOR] = MLPGRAD(NET, X, T) also returns separately the +% data and prior contributions to the gradient. In the case of multiple +% groups in the prior, GPRIOR is a matrix with a row for each group and +% a column for each weight parameter. +% +% See also +% MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP +% + +% Copyright (c) Ian T Nabney (1996-9) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring); + error(errstring); +end +[y, z] = mlpfwd(net, x); +temp = y - t; +ndata = size(x, 1); +for m=1:ndata, + delout(m,:)=eso_w(m,1)*temp(m,:); +end +clear temp; +gdata = mlpbkp(net, x, z, delout); + +[g, gdata, gprior] = gbayes(net, gdata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/mlphdotv_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +function hdv = mlphdotv_weighted(net, x, t, eso_w, v) +%MLPHDOTV Evaluate the product of the data Hessian with a vector. +% +% Description +% +% HDV = MLPHDOTV(NET, X, T, V) takes an MLP network data structure NET, +% together with the matrix X of input vectors, the matrix T of target +% vectors and an arbitrary row vector V whose length equals the number +% of parameters in the network, and returns the product of the data- +% dependent contribution to the Hessian matrix with V. The +% implementation is based on the R-propagation algorithm of +% Pearlmutter. +% +% See also +% MLP, MLPHESS, HESSCHEK +% + +% Copyright (c) Ian T Nabney (1996-9) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring); + error(errstring); +end + +ndata = size(x, 1); + +[y, z] = mlpfwd(net, x); % Standard forward propagation. +zprime = (1 - z.*z); % Hidden unit first derivatives. +zpprime = -2.0*z.*zprime; % Hidden unit second derivatives. + +vnet = mlpunpak(net, v); % Unpack the v vector. + +% Do the R-forward propagation. + +ra1 = x*vnet.w1 + ones(ndata, 1)*vnet.b1; +rz = zprime.*ra1; +ra2 = rz*net.w2 + z*vnet.w2 + ones(ndata, 1)*vnet.b2; + +switch net.actfn + case 'softmax' % Softmax outputs + + nout = size(t, 2); + ry = y.*ra2 - y.*(sum(y.*ra2, 2)*ones(1, nout)); + + otherwise + error(['Unknown activation function ', net.actfn]); +end + +% Evaluate a weighted delta for the output units. +temp = y - t; +for m=1:ndata, + delout(m,:)=eso_w(m,1)*temp(m,:); +end +clear temp; + +% Do the standard backpropagation. + +delhid = zprime.*(delout*net.w2'); + +% Now do the R-backpropagation. + +rdelhid = zpprime.*ra1.*(delout*net.w2') + zprime.*(delout*vnet.w2') + ... + zprime.*(ry*net.w2'); + +% Finally, evaluate the components of hdv and then merge into long vector. + +hw1 = x'*rdelhid; +hb1 = sum(rdelhid, 1); +hw2 = z'*ry + rz'*delout; +hb2 = sum(ry, 1); + +hdv = [hw1(:)', hb1, hw2(:)', hb2];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/mlphess_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function [h, hdata] = mlphess_weighted(net, x, t, eso_w, hdata) +%MLPHESS Evaluate the Hessian matrix for a multi-layer perceptron network. +% +% Description +% H = MLPHESS(NET, X, T) takes an MLP network data structure NET, a +% matrix X of input values, and a matrix T of target values and returns +% the full Hessian matrix H corresponding to the second derivatives of +% the negative log posterior distribution, evaluated for the current +% weight and bias values as defined by NET. +% +% [H, HDATA] = MLPHESS(NET, X, T) returns both the Hessian matrix H and +% the contribution HDATA arising from the data dependent term in the +% Hessian. +% +% H = MLPHESS(NET, X, T, HDATA) takes a network data structure NET, a +% matrix X of input values, and a matrix T of target values, together +% with the contribution HDATA arising from the data dependent term in +% the Hessian, and returns the full Hessian matrix H corresponding to +% the second derivatives of the negative log posterior distribution. +% This version saves computation time if HDATA has already been +% evaluated for the current weight and bias values. +% +% See also +% MLP, HESSCHEK, MLPHDOTV, EVIDENCE +% + +% Copyright (c) Ian T Nabney (1996-9) + +% Check arguments for consistency +errstring = consist(net, 'mlp', x, t); +if ~isempty(errstring); + error(errstring); +end + +if nargin == 4 + % Data term in Hessian needs to be computed + hdata = datahess(net, x, t, eso_w); +end + +[h, hdata] = hbayes(net, hdata); + +% Sub-function to compute data part of Hessian +function hdata = datahess(net, x, t, eso_w) + +hdata = zeros(net.nwts, net.nwts); + +for v = eye(net.nwts); + hdata(find(v),:) = mlphdotv_weighted(net, x, t, eso_w, v); +end + +return
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/neterr_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function [e, varargout] = neterr_weighted(w, net, x, t, eso_w) +%NETERR Evaluate network error function for generic optimizers +% +% Description +% +% E = NETERR(W, NET, X, T) takes a weight vector W and a network data +% structure NET, together with the matrix X of input vectors and the +% matrix T of target vectors, and returns the value of the error +% function evaluated at W. +% +% [E, VARARGOUT] = NETERR(W, NET, X, T) also returns any additional +% return values from the error function. +% +% See also +% NETGRAD, NETHESS, NETOPT +% + +% Copyright (c) Ian T Nabney (1996-9) + +errstr = [net.type, 'err_weighted']; +net = netunpak(net, w); + +[s{1:nargout}] = feval(errstr, net, x, t, eso_w); +e = s{1}; +if nargout > 1 + for i = 2:nargout + varargout{i-1} = s{i}; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/netgrad_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function g = netgrad_weighted(w, net, x, t, eso_w) +%NETGRAD Evaluate network error gradient for generic optimizers +% +% Description +% +% G = NETGRAD(W, NET, X, T) takes a weight vector W and a network data +% structure NET, together with the matrix X of input vectors and the +% matrix T of target vectors, and returns the gradient of the error +% function evaluated at W. +% +% See also +% MLP, NETERR, NETOPT +% + +% Copyright (c) Ian T Nabney (1996-9) + +gradstr = [net.type, 'grad_weighted']; + +net = netunpak(net, w); + +g = feval(gradstr, net, x, t, eso_w);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/nethess_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function [h, varargout] = nethess_weighted(w, net, x, t, eso_w, varargin) +%NETHESS Evaluate network Hessian +% +% Description +% +% H = NETHESS(W, NET, X, T) takes a weight vector W and a network data +% structure NET, together with the matrix X of input vectors and the +% matrix T of target vectors, and returns the value of the Hessian +% evaluated at W. +% +% [E, VARARGOUT] = NETHESS(W, NET, X, T, VARARGIN) also returns any +% additional return values from the network Hessian function, and +% passes additional arguments to that function. +% +% See also +% NETERR, NETGRAD, NETOPT +% + +% Copyright (c) Ian T Nabney (1996-9) + +hess_str = [net.type, 'hess_weighted']; + +net = netunpak(net, w); + +[s{1:nargout}] = feval(hess_str, net, x, t, eso_w, varargin{:}); +h = s{1}; +for i = 2:nargout + varargout{i-1} = s{i}; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/netopt_weighted.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +function [net, options, varargout] = netopt_weighted(net, options, x, t, eso_w, alg); +%NETOPT Optimize the weights in a network model. +% +% Description +% +% NETOPT is a helper function which facilitates the training of +% networks using the general purpose optimizers as well as sampling +% from the posterior distribution of parameters using general purpose +% Markov chain Monte Carlo sampling algorithms. It can be used with any +% function that searches in parameter space using error and gradient +% functions. +% +% [NET, OPTIONS] = NETOPT(NET, OPTIONS, X, T, ALG) takes a network +% data structure NET, together with a vector OPTIONS of parameters +% governing the behaviour of the optimization algorithm, a matrix X of +% input vectors and a matrix T of target vectors, and returns the +% trained network as well as an updated OPTIONS vector. The string ALG +% determines which optimization algorithm (CONJGRAD, QUASINEW, SCG, +% etc.) or Monte Carlo algorithm (such as HMC) will be used. +% +% [NET, OPTIONS, VARARGOUT] = NETOPT(NET, OPTIONS, X, T, ALG) also +% returns any additional return values from the optimisation algorithm. +% +% See also +% NETGRAD, BFGS, CONJGRAD, GRADDESC, HMC, SCG +% + +% Copyright (c) Ian T Nabney (1996-9) + +optstring = [alg, '(''neterr_weighted'', w, options, ''netgrad_weighted'', net, x, t, eso_w)']; + +% Extract weights from network as single vector +w = netpak(net); + +% Carry out optimisation +[s{1:nargout}] = eval(optstring); +w = s{1}; + +if nargout > 1 + options = s{2}; + + % If there are additional arguments, extract them + nextra = nargout - 2; + if nextra > 0 + for i = 1:nextra + varargout{i} = s{i+2}; + end + end +end + +% Pack the weights back into the network +net = netunpak(net, w);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/netlabKPM/process_options.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,132 @@ +% PROCESS_OPTIONS - Processes options passed to a Matlab function. +% This function provides a simple means of +% parsing attribute-value options. Each option is +% named by a unique string and is given a default +% value. +% +% Usage: [var1, var2, ..., varn[, unused]] = ... +% process_options(args, ... +% str1, def1, str2, def2, ..., strn, defn) +% +% Arguments: +% args - a cell array of input arguments, such +% as that provided by VARARGIN. Its contents +% should alternate between strings and +% values. +% str1, ..., strn - Strings that are associated with a +% particular variable +% def1, ..., defn - Default values returned if no option +% is supplied +% +% Returns: +% var1, ..., varn - values to be assigned to variables +% unused - an optional cell array of those +% string-value pairs that were unused; +% if this is not supplied, then a +% warning will be issued for each +% option in args that lacked a match. +% +% Examples: +% +% Suppose we wish to define a Matlab function 'func' that has +% required parameters x and y, and optional arguments 'u' and 'v'. +% With the definition +% +% function y = func(x, y, varargin) +% +% [u, v] = process_options(varargin, 'u', 0, 'v', 1); +% +% calling func(0, 1, 'v', 2) will assign 0 to x, 1 to y, 0 to u, and 2 +% to v. The parameter names are insensitive to case; calling +% func(0, 1, 'V', 2) has the same effect. The function call +% +% func(0, 1, 'u', 5, 'z', 2); +% +% will result in u having the value 5 and v having value 1, but +% will issue a warning that the 'z' option has not been used. On +% the other hand, if func is defined as +% +% function y = func(x, y, varargin) +% +% [u, v, unused_args] = process_options(varargin, 'u', 0, 'v', 1); +% +% then the call func(0, 1, 'u', 5, 'z', 2) will yield no warning, +% and unused_args will have the value {'z', 2}. This behaviour is +% useful for functions with options that invoke other functions +% with options; all options can be passed to the outer function and +% its unprocessed arguments can be passed to the inner function. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [varargout] = process_options(args, varargin) + +% Check the number of input arguments +n = length(varargin); +if (mod(n, 2)) + error('Each option must be a string/value pair.'); +end + +% Check the number of supplied output arguments +if (nargout < (n / 2)) + error('Insufficient number of output arguments given'); +elseif (nargout == (n / 2)) + warn = 1; + nout = n / 2; +else + warn = 0; + nout = n / 2 + 1; +end + +% Set outputs to be defaults +varargout = cell(1, nout); +for i=2:2:n + varargout{i/2} = varargin{i}; +end + +% Now process all arguments +nunused = 0; +for i=1:2:length(args) + found = 0; + for j=1:2:n + if strcmpi(args{i}, varargin{j}) + varargout{(j + 1)/2} = args{i + 1}; + found = 1; + break; + end + end + if (~found) + if (warn) + warning(sprintf('Option ''%s'' not used.', args{i})); + args{i} + else + nunused = nunused + 1; + unused{2 * nunused - 1} = args{i}; + unused{2 * nunused} = args{i + 1}; + end + end +end + +% Assign the unused arguments +if (~warn) + if (nunused) + varargout{nout} = unused; + else + varargout{nout} = cell(0); + end +end
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/CorrelogramArray.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function movie = CorrelogramArray(data, sr, frameRate, width) +% function movie = CorrelogramArray(data, sr, frameRate, width) +% Compute an array of correlogram frames, from the sound file data with +% a sampling rate of sr Hz. Compute frameRate frames per second, using +% a window size of "width" samples. + +% (c) 1998 Interval Research Corporation + +if nargin < 2, sr = 16000; end +if nargin < 3, frameRate = 12; end +if nargin < 4, width = 256; end + +[channels, len] = size(data); +frameIncrement = fix(sr/frameRate); +frameCount = floor((len-width)/frameIncrement)+1; +fprintf('Correlogram spacing is %g samples per frame.\n', frameIncrement); + +movie = zeros(channels*width, frameCount); +for i=1:frameCount + start = (i-1)*frameIncrement + 1; + pic = CorrelogramFrame(data, width, start, frameIncrement*4); + minimum = min(min(pic)); + maximum = max(max(pic)); + image((pic-minimum)/(maximum-minimum)*length(colormap)); + drawnow; + movie(:,i) = reshape(pic, channels*width, 1); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/CorrelogramFrame.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +% pic = CorrelogramFrame(data, picWidth, start, winLen) +% Compute one frame of a correlogram. The input data is a +% two-dimensional array of cochlear data, each row representing +% firing probabilities from one cochlear channel. The output +% picture is a two dimensional array of width "picWidth". +% +% The correlogram is computed with autocorrelation using +% data from the input array. For each channel, the data from +% is extracted starting at column "start" and extending for +% "winLength" time steps. + +% (c) 1998 Interval Research Corporation + +function pic = CorrelogramFrame(data, picWidth, start, winLen) + +if nargin < 2 + disp('Syntax: pic=CorrelogramFrame(data, picWidth[, start, len])'); + return +end + +if nargin < 3 + start = 1; +end + +if nargin < 4 + [channels, winLen] = size(data); +end + +[channels, dataLen] = size(data); +start = max(1, start); +last = min(dataLen, start+winLen-1); + +pic = zeros(channels, picWidth); +fftSize = 2^(nextpow2(max(picWidth, winLen))+1); +% disp(['CorrelogramFrame fftSize is ' int2str(fftSize)]); + +a = .54; +b = -.46; +wr = sqrt(64/256); +phi = pi/winLen; +ws = 2*wr/sqrt(4*a*a+2*b*b)*(a + b*cos(2*pi*(0:winLen-1)/winLen + phi)); + +for i=1:channels + f = zeros(1, fftSize); +% d = zeros(1, winLen); +% d(1:(last-start+1)) = data(i,start:last) .* ws(1:(last-start+1)); +% f(1:(winLen/2)) = d(winLen/2+1:winLen); +% f(fftSize-(winLen/2)+1:fftSize) = d(1:min(length(d),winLen/2)); + f(1:(last-start+1)) = data(i,start:last) .* ws(1:(last-start+1)); + f = fft(f); + f = ifft(f.*conj(f)); + pic(i,:) = real(f(1:picWidth)); + if pic(i,1) > pic(i,2) & pic(i,1) > pic(i,3) + pic(i,:) = pic(i,:)/sqrt(pic(i,1)); + else + pic(i,:) = zeros(1,picWidth); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/CorrelogramMovie.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function movie = CorrelogramMovie(data, sr, frameRate, width) +% function movie = CorrelogramMovie(data, sr, frameRate, width) +% Compute a Matlab movie of a sound array called "data" which has +% a sampling rate of sr Hz. Compute frameRate frames per second, each +% time taking "width" samples for analysis. + +% (c) 1998 Interval Research Corporation + +if nargin < 2, sr = 16000; end +if nargin < 3, frameRate = 12; end +if nargin < 4, width = 256; end + +[channels, len] = size(data); +frameIncrement = fix(sr/frameRate); +frameCount = floor((len-width)/frameIncrement)+1; + +movie = moviein(frameCount); +for i=1:frameCount + start = (i-1)*frameIncrement + 1; + pic = CorrelogramFrame(data, width, start, frameIncrement*2); + minimum = min(min(pic)); + maximum = max(max(pic)); + image((pic-minimum)/(maximum-minimum)*length(colormap)); + drawnow; + movie(:,i) = getframe; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/CorrelogramPitch.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +function [pitch,salience]=CorrelogramPitch(correlogram, width, sr, low, high); +% pitch=CorrelogramPitch(correlogram, width [, sr, lowPitch, highPitch]) +% computes the pitch of a correlogram sequence by finding the time lag +% with the largest correlation energy. +% +% (c) 1998 Interval Research Corporation + +if nargin < 3; sr=22254.54; end; +if nargin < 4; low=0; end; +if nargin < 5; high=inf; end; + +dropLow = floor(sr/high); +if low > 0 + dropHigh = min(width,ceil(sr/low)); +else + dropHigh = width; +end + +[pixels frames] = size(correlogram); +channels = pixels/width; +if channels < 1 | floor(channels) ~= channels + error('Correlogram Size Error'); +end + +pitch = zeros(1,frames); +salience = zeros(1,frames); +for j=1:frames + % Get one frame from the correlogram, reshape it, and compute + % the sum (as a function of time lag) across all channels. + if channels == 1 + summary=reshape(correlogram(:,j),channels,width); + else + summary=sum(reshape(correlogram(:,j),channels,width)); + end + zeroLag = summary(1); + % Now we need to find the first pitch past the peak at zero + % lag. The following lines smooth the summary pitch a bit, then + % look for the first point where the summary goes back up. + % Everything up to this point is zeroed out. + windowLength=16; + sumfilt=filter(ones(1,windowLength),[1],summary); + sumdif=sumfilt(2:width)-sumfilt(1:width-1); + sumdif(1:windowLength) = zeros(1,windowLength); + valleys=find(sumdif>0); + summary(1:valleys(1)) = zeros(1,valleys(1)); + summary(1:dropLow) = zeros(1,dropLow); + summary(dropHigh:width) = zeros(1,width-dropHigh+1); + plot(summary); + drawnow; + % Now find the location of the biggest peak and call this the pitch + [m p] = max(summary); + pitch(j) = sr/(p-1); + salience(j) = m/zeroLag; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/DesignLyonFilters.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,102 @@ +% [filters, freqs] = DesignLyonFilters(fs,EarQ,StepFactor) +% Design the cascade of second order filters and the front filters +% (outer/middle and compensator) needed for Lyon's Passive Short Wave +% (Second Order Sections) cochlear model. The variables used here come +% from Apple ATG Technical Report #13 titled "Lyon's Cochlear Model". +% +% Most of the parameters are hardwired into this m-function. The user +% settable parameters are the digital sampling rate, the basic Q of the +% each stage (usually 8 or 4), and the spacing factor between channels +% (usually .25 and .125, respectively.) +% +% The result is returned as rows of second order filters; three coefficients +% for the numerator and two for the denomiator. The coefficients are +% [A0 A1 A2 B1 B2]..........................(Malcolm 1 Feb. 1993) +% + +% (c) 1998 Interval Research Corporation +function [filters, CenterFreqs, gains] = DesignLyonFilters(fs,EarQ,StepFactor) + +if nargin < 2 + EarQ = 8; +end + +if nargin < 3 + StepFactor=EarQ/32; +end + +Eb = 1000.0; +EarZeroOffset = 1.5; +EarSharpness = 5.0; +EarPremphCorner = 300; + +% Find top frequency, allowing space for first cascade filter. +topf = fs/2.0; +topf = topf - (sqrt(topf^2+Eb^2)/EarQ*StepFactor*EarZeroOffset)+ ... + sqrt(topf^2+Eb^2)/EarQ*StepFactor; + +% Find place where CascadePoleQ < .5 +lowf = Eb/sqrt(4*EarQ^2-1); +NumberOfChannels = floor((EarQ*(-log(lowf + sqrt(lowf^2 + Eb^2)) + ... + log(topf + sqrt(Eb^2 + topf^2))))/StepFactor); + +% Now make an array of CenterFreqs..... This expression was derived by +% Mathematica by integrating 1/EarBandwidth(cf) and solving for f as a +% function of channel number. +cn = 1:NumberOfChannels; +CenterFreqs = (-((exp((cn*StepFactor)/EarQ)*Eb^2)/ ... + (topf + sqrt(Eb^2 + topf^2))) + ... + (topf + sqrt(Eb^2 + topf^2))./exp((cn*StepFactor)/EarQ))/2; + +% OK, now we can figure out the parameters of each stage filter. +EarBandwidth = sqrt(CenterFreqs.^2+Eb^2)/EarQ; +CascadeZeroCF = CenterFreqs + EarBandwidth * StepFactor * EarZeroOffset; +CascadeZeroQ = EarSharpness*CascadeZeroCF./EarBandwidth; +CascadePoleCF = CenterFreqs; +CascadePoleQ = CenterFreqs./EarBandwidth; + +% Now lets find some filters.... first the zeros then the poles +zerofilts = SecondOrderFilter(CascadeZeroCF, CascadeZeroQ, fs); +polefilts = SecondOrderFilter(CascadePoleCF, CascadePoleQ, fs); +filters = [zerofilts polefilts(:,2:3)]; + +% Now we can set the DC gain of each stage. +dcgain(2:NumberOfChannels)=CenterFreqs(1:NumberOfChannels-1)./ ... + CenterFreqs(2:NumberOfChannels); +dcgain(1) = dcgain(2); +for i=1:NumberOfChannels + filters(i,:) = SetGain(filters(i,:), dcgain(i), 0, fs); +end + +% Finally, let's design the front filters. +front(1,:) = SetGain([0 1 -exp(-2*pi*EarPremphCorner/fs) 0 0], 1, fs/4, fs); +topPoles = SecondOrderFilter(topf,CascadePoleQ(1), fs); +front(2,:) = SetGain([1 0 -1 topPoles(2:3)], 1, fs/4, fs); + +% Now, put them all together. +filters = [front; filters]; + +if nargout > 2 + % Compute the gains needed so that everything is flat + % when we do the filter inversion. + [channels, width] = size(filters); + len = length(CenterFreqs); + clear cfs2; + cfs2(1) = fs/2; + cfs2(1:2:2*len) = CenterFreqs; + cfs2(2:2:2*len-1) = (CenterFreqs(1:len-1) + CenterFreqs(2:len))/2; +% cfs2 = CenterFreqs; + + resp = zeros(length(cfs2), channels); + for i=1:channels + resp(:,i) = FreqResp(filters(i,:), cfs2, fs)'; + end + % Each column vector of resp contains one channel's response + % across all frequencies (in dB). + cumresp = cumsum(resp')'; + % cumresp now contains the total gain at output of i'th channel at + % frequency f + a=10.^(cumresp(:,3:channels)/20); + a = a.* a; % Have to go through each filter twice. + gains = [0; 0; a \ ones(length(cfs2),1)]; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/ERBFilterBank.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +function output = ERBFilterBank(x, fcoefs) +% function output = ERBFilterBank(x, fcoefs) +% Process an input waveform with a gammatone filter bank. This function +% takes a single sound vector, and returns an array of filter outputs, one +% channel per row. +% +% The fcoefs parameter, which completely specifies the Gammatone filterbank, +% should be designed with the MakeERBFilters function. If it is omitted, +% the filter coefficients are computed for you assuming a 22050Hz sampling +% rate and 64 filters regularly spaced on an ERB scale from fs/2 down to 100Hz. +% + +% Malcolm Slaney @ Interval, June 11, 1998. +% (c) 1998 Interval Research Corporation +% Thanks to Alain de Cheveigne' for his suggestions and improvements. + +if nargin < 1 + error('Syntax: output_array = ERBFilterBank(input_vector[, fcoefs]);'); +end + +if nargin < 2 + fcoefs = MakeERBFilters(22050,64,100); +end + +if size(fcoefs,2) ~= 10 + error('fcoefs parameter passed to ERBFilterBank is the wrong size.'); +end + +A0 = fcoefs(:,1); +A11 = fcoefs(:,2); +A12 = fcoefs(:,3); +A13 = fcoefs(:,4); +A14 = fcoefs(:,5); +A2 = fcoefs(:,6); +B0 = fcoefs(:,7); +B1 = fcoefs(:,8); +B2 = fcoefs(:,9); +gain= fcoefs(:,10); + +output = zeros(size(gain,1), length(x)); +for chan = 1: size(gain,1) + y1=filter([A0(chan)/gain(chan) A11(chan)/gain(chan) ... + A2(chan)/gain(chan)], ... + [B0(chan) B1(chan) B2(chan)], x); + y2=filter([A0(chan) A12(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y1); + y3=filter([A0(chan) A13(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y2); + y4=filter([A0(chan) A14(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y3); + output(chan, :) = y4; +end + +if 0 + semilogx((0:(length(x)-1))*(fs/length(x)),20*log10(abs(fft(output)))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/ERBSpace.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function cfArray = ERBSpace(lowFreq, highFreq, N) +% function cfArray = ERBSpace(lowFreq, highFreq, N) +% This function computes an array of N frequencies uniformly spaced between +% highFreq and lowFreq on an ERB scale. N is set to 100 if not specified. +% +% See also linspace, logspace, MakeERBCoeffs, MakeERBFilters. +% +% For a definition of ERB, see Moore, B. C. J., and Glasberg, B. R. (1983). +% "Suggested formulae for calculating auditory-filter bandwidths and +% excitation patterns," J. Acoust. Soc. Am. 74, 750-753. + +if nargin < 1 + lowFreq = 100; +end + +if nargin < 2 + highFreq = 44100/4; +end + +if nargin < 3 + N = 100; +end + +% Change the following three parameters if you wish to use a different +% ERB scale. Must change in MakeERBCoeffs too. +EarQ = 9.26449; % Glasberg and Moore Parameters +minBW = 24.7; +order = 1; + +% All of the followFreqing expressions are derived in Apple TR #35, "An +% Efficient Implementation of the Patterson-Holdsworth Cochlear +% Filter Bank." See pages 33-34. +cfArray = -(EarQ*minBW) + exp((1:N)'*(-log(highFreq + EarQ*minBW) + ... + log(lowFreq + EarQ*minBW))/N) * (highFreq + EarQ*minBW); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/EpsilonFromTauFS.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function y=EpslionFromTauFS(tau, fs) +% EpsilonFromTauFS - Find first order filter coefficient +% as a function of time constant (tau) and sample rate (fs) + +% (c) 1998 Interval Research Corporation +y=1-exp(-1/tau/fs);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/FMPoints.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +function points=FMPoints(len, freq, fmFreq, fmAmp, fs) +% points=FMPoints(len, freq, fmFreq, fmAmp, fs) +% Generates (fractional) sample locations for frequency-modulated impulses +% len = number of samples +% freq = pitch frequency (Hz) +% fmFreq = vibrato frequency (Hz) (defaults to 6 Hz) +% fmAmp = max change in pitch (defaults to 5% of freq) +% fs = sample frequency (defaults to 22254.545454 samples/s) +% +% Basic formula: phase angle = 2*pi*freq*t + (fmAmp/fmFreq)*sin(2*pi*fmFreq*t) +% k-th zero crossing approximately at sample number +% (fs/freq)*(k - (fmAmp/(2*pi*fmFreq))*sin(2*pi*k*(fmFreq/freq))) + +% (c) 1998 Interval Research Corporation + +if nargin<2, + fprintf('Format: sig=fmPoints(len, freq [, fmAmp, fmFreq, sampleRate])\n'); + return; + end; +if nargin<5, + fs=22254.545454; + end; +if nargin<4, + fmAmp=0.05*freq; + end; +if nargin<3, + fmFreq=6; +end; + +kmax=fix(freq*(len/fs)); +points=0:kmax-1; +points=1+(fs/freq)*(points-(fmAmp/(2*pi*fmFreq))* ... +sin(2*pi*(fmFreq/freq)*points));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/FreqResp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +% mag = FreqResp(filter, f, fs) +% Find the frequency response (in dB) of a filter (1x5 vector) at +% frequency f and sampling rate fs. + +% (c) 1998 Interval Research Corporation + +function mag=FreqResp(filter,f,fs) +cf = exp(i*2*pi*f/fs); +mag = (filter(3) + filter(2)*cf + filter(1)*cf.^2) ./ ... + (filter(5) + filter(4)*cf + cf.^2); +mag = 20*log10(abs(mag));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/LyonPassiveEar.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function y=LyonPassiveEar(x,sr,df,earQ,stepfactor,differ,agcf,taufactor) +% y=LyonPassiveEar(input, sample_rate, decimation, earQ, stepfactor); +% +% Changes +% Zeroed out the preemphasis channels before doing the AGC. This is needed +% so that cochlear inversion is possible (since we don't have those channels, +% we can't invert their gain.)..... Malcolm 6/26/95 + +% (c) 1998 Interval Research Corporation + +if nargin < 3 + fprintf('Syntax: y=LyonPassiveEar(input, sample_rate, decimation, earQ, stepfactor)\n'); + fprintf(' The input, sample_rate, and decimation parameters are mandatory.\n'); + return; +end + +if df < 1; df = 1; end +if nargin < 4; earQ = 8; end +if nargin < 5; stepfactor = earQ/32; end +if nargin < 6; differ=1; end +if nargin < 7; agcf=1; end +if nargin < 8; taufactor=3; end + +earFilters = DesignLyonFilters(sr, earQ, stepfactor); + +nSamples = length(x); +nOutputSamples = floor(nSamples/df); +[nChannels filterWidth] = size(earFilters); + +sosOutput = zeros(nChannels, df); +sosState = zeros(nChannels, 2); +agcState = zeros(nChannels, 4); +y = zeros(nChannels, nOutputSamples); + +decEps = EpsilonFromTauFS(df/sr*taufactor,sr); +decState = zeros(nChannels, 2); +decFilt = SetGain([0 0 1 -2*(1-decEps) (1-decEps)^2], 1, 0, sr); + +eps1 = EpsilonFromTauFS(.64,sr); +eps2 = EpsilonFromTauFS(.16,sr); +eps3 = EpsilonFromTauFS(.04,sr); +eps4 = EpsilonFromTauFS(.01,sr); + +tar1 = .0032; +tar2 = .0016; +tar3 = .0008; +tar4 = .0004; + +if 0 + fprintf('df=%g, earq=%g, stepfactor=%g, differ=%g\n',df,earQ,stepfactor,differ); + fprintf('agcf=%g, taufactor=%g\n', agcf, taufactor); + [tar1 tar2 tar3 tar4; eps1 eps2 eps3 eps4] +end + +for i=0:nOutputSamples-1 + [sosOutput sosState]= soscascade(x(i*df+1:i*df+df), earFilters, ... + sosState); + output = max(0, sosOutput); %% Half Wave Rectify + output(1) = 0; %% Test Hack to make inversion easier. + output(2) = 0; + if agcf > 0 + [output agcState] = agc(output, [tar1 tar2 tar3 tar4; ... + eps1 eps2 eps3 eps4], ... + agcState); + end + + if differ > 0 + output = [output(1,:);output(1:nChannels-1,:) - ... + output(2:nChannels,:)]; + output = max(0, output); + end + + if df > 1 + [output decState] = sosfilters(output, decFilt, decState); + end + y(:,i+1) = output(:,df); +end + +%y = min(y,2*tar4); +y=y(3:nChannels,:);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/MakeERBFilters.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,119 @@ +function fcoefs=MakeERBFilters(fs,numChannels,lowFreq) +% function [fcoefs]=MakeERBFilters(fs,numChannels,lowFreq) +% This function computes the filter coefficients for a bank of +% Gammatone filters. These filters were defined by Patterson and +% Holdworth for simulating the cochlea. +% +% The result is returned as an array of filter coefficients. Each row +% of the filter arrays contains the coefficients for four second order +% filters. The transfer function for these four filters share the same +% denominator (poles) but have different numerators (zeros). All of these +% coefficients are assembled into one vector that the ERBFilterBank +% can take apart to implement the filter. +% +% The filter bank contains "numChannels" channels that extend from +% half the sampling rate (fs) to "lowFreq". Alternatively, if the numChannels +% input argument is a vector, then the values of this vector are taken to +% be the center frequency of each desired filter. (The lowFreq argument is +% ignored in this case.) + +% Note this implementation fixes a problem in the original code by +% computing four separate second order filters. This avoids a big +% problem with round off errors in cases of very small cfs (100Hz) and +% large sample rates (44kHz). The problem is caused by roundoff error +% when a number of poles are combined, all very close to the unit +% circle. Small errors in the eigth order coefficient, are multiplied +% when the eigth root is taken to give the pole location. These small +% errors lead to poles outside the unit circle and instability. Thanks +% to Julius Smith for leading me to the proper explanation. + +% Execute the following code to evaluate the frequency +% response of a 10 channel filterbank. +% fcoefs = MakeERBFilters(16000,10,100); +% y = ERBFilterBank([1 zeros(1,511)], fcoefs); +% resp = 20*log10(abs(fft(y'))); +% freqScale = (0:511)/512*16000; +% semilogx(freqScale(1:255),resp(1:255,:)); +% axis([100 16000 -60 0]) +% xlabel('Frequency (Hz)'); ylabel('Filter Response (dB)'); + +% Rewritten by Malcolm Slaney@Interval. June 11, 1998. +% (c) 1998 Interval Research Corporation + +T = 1/fs; +if length(numChannels) == 1 + cf = ERBSpace(lowFreq, fs/2, numChannels); +else + cf = numChannels(1:end); + if size(cf,2) > size(cf,1) + cf = cf'; + end +end + +% Change the followFreqing three parameters if you wish to use a different +% ERB scale. Must change in ERBSpace too. +EarQ = 9.26449; % Glasberg and Moore Parameters +minBW = 24.7; +order = 1; + +ERB = ((cf/EarQ).^order + minBW^order).^(1/order); +B=1.019*2*pi*ERB; + +A0 = T; +A2 = 0; +B0 = 1; +B1 = -2*cos(2*cf*pi*T)./exp(B*T); +B2 = exp(-2*B*T); + +A11 = -(2*T*cos(2*cf*pi*T)./exp(B*T) + 2*sqrt(3+2^1.5)*T*sin(2*cf*pi*T)./ ... + exp(B*T))/2; +A12 = -(2*T*cos(2*cf*pi*T)./exp(B*T) - 2*sqrt(3+2^1.5)*T*sin(2*cf*pi*T)./ ... + exp(B*T))/2; +A13 = -(2*T*cos(2*cf*pi*T)./exp(B*T) + 2*sqrt(3-2^1.5)*T*sin(2*cf*pi*T)./ ... + exp(B*T))/2; +A14 = -(2*T*cos(2*cf*pi*T)./exp(B*T) - 2*sqrt(3-2^1.5)*T*sin(2*cf*pi*T)./ ... + exp(B*T))/2; + +gain = abs((-2*exp(4*i*cf*pi*T)*T + ... + 2*exp(-(B*T) + 2*i*cf*pi*T).*T.* ... + (cos(2*cf*pi*T) - sqrt(3 - 2^(3/2))* ... + sin(2*cf*pi*T))) .* ... + (-2*exp(4*i*cf*pi*T)*T + ... + 2*exp(-(B*T) + 2*i*cf*pi*T).*T.* ... + (cos(2*cf*pi*T) + sqrt(3 - 2^(3/2)) * ... + sin(2*cf*pi*T))).* ... + (-2*exp(4*i*cf*pi*T)*T + ... + 2*exp(-(B*T) + 2*i*cf*pi*T).*T.* ... + (cos(2*cf*pi*T) - ... + sqrt(3 + 2^(3/2))*sin(2*cf*pi*T))) .* ... + (-2*exp(4*i*cf*pi*T)*T + 2*exp(-(B*T) + 2*i*cf*pi*T).*T.* ... + (cos(2*cf*pi*T) + sqrt(3 + 2^(3/2))*sin(2*cf*pi*T))) ./ ... + (-2 ./ exp(2*B*T) - 2*exp(4*i*cf*pi*T) + ... + 2*(1 + exp(4*i*cf*pi*T))./exp(B*T)).^4); + +allfilts = ones(length(cf),1); +fcoefs = [A0*allfilts A11 A12 A13 A14 A2*allfilts B0*allfilts B1 B2 gain]; + +if (0) % Test Code + A0 = fcoefs(:,1); + A11 = fcoefs(:,2); + A12 = fcoefs(:,3); + A13 = fcoefs(:,4); + A14 = fcoefs(:,5); + A2 = fcoefs(:,6); + B0 = fcoefs(:,7); + B1 = fcoefs(:,8); + B2 = fcoefs(:,9); + gain= fcoefs(:,10); + chan=1; + x = [1 zeros(1, 511)]; + y1=filter([A0(chan)/gain(chan) A11(chan)/gain(chan) ... + A2(chan)/gain(chan)],[B0(chan) B1(chan) B2(chan)], x); + y2=filter([A0(chan) A12(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y1); + y3=filter([A0(chan) A13(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y2); + y4=filter([A0(chan) A14(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y3); + semilogx((0:(length(x)-1))*(fs/length(x)),20*log10(abs(fft(y4)))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/MakeVowel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,130 @@ +function y=MakeVowel(len, pitch, sampleRate, f1, f2, f3) +% MakeVowel(len, pitch [, sampleRate, f1, f2, f3]) - Make a vowel with +% "len" samples and the given pitch. The sample rate defaults to +% be 22254.545454 Hz (the native Mactinosh Sampling Rate). The +% formant frequencies are f1, f2 & f3. Some common vowels are +% Vowel f1 f2 f3 +% /a/ 730 1090 2440 +% /i/ 270 2290 3010 +% /u/ 300 870 2240 +% +% The pitch variable can either be a scalar indicating the actual +% pitch frequency, or an array of impulse locations. Using an +% array of impulses allows this routine to compute vowels with +% varying pitch. +% +% Alternatively, f1 can be replaced with one of the following strings +% 'a', 'i', 'u' and the appropriate formant frequencies are +% automatically selected. +% Modified by R. Duda, 3/13/94 + +% (c) 1998 Interval Research Corporation + +if nargin < 2, + fprintf('Format: y = MakeVowel(len, pitch [, sampleRate, f1, f2, f3])\n'); + return; +end; + +if nargin < 6; f3 = 0; end; +if nargin < 5; f2 = 0; end; +if nargin < 4, + f1 = 0; +else + if isstr(f1) + if f1 == 'a' | f1 == '/a/' + f1=730; f2=1090; f3=2440; + elseif f1 == 'i' | f1 == '/i/' + f1=270; f2=2290; f3=3010; + elseif f1 == 'u' | f1 == '/u/' + f1=300; f2=870; f3=2240; + end + end; +end; + +if nargin < 3, + sampleRate = 22254.545454; +elseif sampleRate < 1000, % Apparently for test purposes + sampleRate = 22254.545454; +end; + +% GlottalPulses(pitch, fs, len) - Generate a stream of +% glottal pulses with the given pitch (in Hz) and sampling +% frequency (sampleRate). A vector of the requested length is returned. +y=zeros(1,len); +if length(pitch) > 1, % If true, use to determine points + points=pitch; % Check for valid sequence of points + if any(points~=sort(points)), + error('Values in pitch array must be in ascending order.') + end; + if points(1) < 1, + error('Values in pitch array cannot be less than 1.'); + end; + kmax=sum(points <= len); + if kmax == 0, + error('All values in pitch array exceed "len"; none should.'); + elseif kmax < length(points), + fprintf('Some values in pitch array exceed "len"; truncating.\n'); + points=points(1:kmax); + end; +else + points=1:sampleRate/pitch:len; +end; +indices=floor(points); + +% Use a triangular approximation to an impulse function. The important +% part is to keep the total amplitude the same. +y(indices) = (indices+1)-points; +y(indices+1) = points-indices; + +% GlottalFilter(x,fs) - Filter an impulse train and simulate the glottal +% transfer function. The sampling interval (sampleRate) is given in Hz. +% The filtering performed by this function is two first-order filters +% at 250Hz. +a = exp(-250*2*pi/sampleRate); +%y=filter([1,0,-1],[1,-2*a,a*a],y); % Not as good as one below.... +y=filter([1],[1,0,-a*a],y); + +% FormantFilter(input, f, fs) - Filter an input sequence to model one +% formant in a speech signal. The formant frequency (in Hz) is given +% by f and the bandwidth of the formant is a constant 50Hz. The +% sampling frequency in Hz is given by fs. +if f1 > 0 + cft = f1/sampleRate; + bw = 50; + q = f1/bw; + rho = exp(-pi * cft / q); + theta = 2 * pi * cft * sqrt(1-1/(4 * q*q)); + a2 = -2*rho*cos(theta); + a3 = rho*rho; + y=filter([1+a2+a3],[1,a2,a3],y); +end; + +% FormantFilter(input, f, fs) - Filter an input sequence to model one +% formant in a speech signal. The formant frequency (in Hz) is given +% by f and the bandwidth of the formant is a constant 50Hz. The +% sampling frequency in Hz is given by fs. +if f2 > 0 + cft = f2/sampleRate; + bw = 50; + q = f2/bw; + rho = exp(-pi * cft / q); + theta = 2 * pi * cft * sqrt(1-1/(4 * q*q)); + a2 = -2*rho*cos(theta); + a3 = rho*rho; + y=filter([1+a2+a3],[1,a2,a3],y); +end; + +% FormantFilter(input, f, fs) - Filter an input sequence to model one +% formant in a speech signal. The formant frequency (in Hz) is given +% by f and the bandwidth of the formant is a constant 50Hz. The +% sampling frequency in Hz is given by fs. +if f3 > 0 + cft = f3/sampleRate; + bw = 50; + q = f3/bw; + rho = exp(-pi * cft / q); + theta = 2 * pi * cft * sqrt(1-1/(4 * q*q)); + a2 = -2*rho*cos(theta); + a3 = rho*rho; + y=filter([1+a2+a3],[1,a2,a3],y); +end;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/MeddisHairCell.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +function y = MeddisHairCell(data,sampleRate,subtractSpont) +% y = MeddisHairCell(data,sampleRate) +% This function calculates Ray Meddis' hair cell model for a +% number of channels. Data is arrayed as one channel per row. +% All channels are done in parallel (but each time step is +% sequential) so it will be much more efficient to process lots +% of channels at once. + +% (c) 1998 Interval Research Corporation +% Changed h and added comment at suggestion of Alain de Cheveigne' 12/11/98 + +if (nargin<3), subtractSpont=0; end + +% Parameters from Meddis' April 1990 JASA paper. +M = 1; +A = 5; +B = 300; +g = 2000; +y = 5.05; +l = 2500; +r = 6580; +x = 66.31; +h = 50000; % This parameter scales the discharge rate. Adjust as necessary. + % In combination with the gammatone filterbank (ERBFilterBank), + % h=50000 will produce a steady-state average discharge + % probability of about 135 spikes/s within the 1kHz channel, + % for an input consisting of a 1 kHz sinewave at 60 dB SPL + % (0 dB SPL corresponds to an RMS level of 1.0 at the + % input of the gammatone filter). Scaling and constant + % courtesy of Alain de Cheveigne' + + +% Internal constants +dt = 1/sampleRate; +gdt = g*dt; +ydt = y*dt; +ldt = l*dt; +rdt = r*dt; +xdt = x*dt; +[numChannels dataLength] = size(data); + +% Initial values +kt = g*A/(A+B); +spont = M*y*kt/(l*kt+y*(l+r)); +c = spont * ones(numChannels,1); +q = c*(l+r)/kt; +w = c*r/x; +zeroVector = zeros(numChannels,1); + +% Now iterate through each time slice of the data. Use the +% max function to implement the "if (0>" test. +y = zeros(numChannels, dataLength); +for i = 1:dataLength + limitedSt = max(data(:,i)+A,0); + kt = gdt*limitedSt./(limitedSt+B); + replenish = max(ydt*(M-q),zeroVector); + eject = kt.*q; + loss = ldt.*c; + reuptake = rdt.*c; + reprocess = xdt.*w; + + q = q + replenish - eject + reprocess; + c = c + eject - loss - reuptake; + w = w + reuptake - reprocess; + y(:,i) = c; +end + +y = h .* y; + +if (subtractSpont > 0) + y=max(0,y-spont); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +This is the Matlab Auditory Toolbox. Version 2. + +First published by Apple Computer as Apple Technical Report #45. This +new version is published by Interval Research Corporation as TR #1998-010. + +I have put this collection of code together to support my own research. I +hope by adding documentation and testing everything that other researchers +can also benefit from this work. + +Needless to say, support is limited. I use this code, so I am interested +in hearing bug reports. I'll fix them if I can reproduce them and I have +the time. But no guarantees. Sending bug fixes is a good way to make sure +I pay attention. + +This archive contains ".m" files, some Matlab mex files, and the C sources +needed to create the mex files. I have tested this code on Macintosh, PC, +SGI, and Sun computers running Matlab 5.2. The code is reasonably +portable, so I don't expect any problems on any machine running Matlab. + +After installing this software on your machine, use the test_auditory +script to run through the examples in the documentation. + +Please let me know if you have comments or questions. I can be reached +at + Malcolm Slaney + Interval Research Corporation + 1801 Page Mill Road, Building C + Palo Alto, CA 94304 + + malcolm@interval.com
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/SecondOrderFilter.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +% filter = SecondOrderFilter(f, q, fs) +% Design a second order digital filter with a center frequency of +% f, filter quality of q, and digital sampling rate of fs (Hz). + +% (c) 1998 Interval Research Corporation + +function filts = SecondOrderFilter(f,q,fs) +cft = f'/fs; +rho = exp(- pi * cft ./ q'); +theta = 2 * pi * cft .* sqrt(1-1 ./(4*q'.^2)); +filts = [ ones(size(rho)) -2*rho.*cos(theta) rho.^2 ];
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/SeneffEar.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,118 @@ +function y = SeneffEar(x, fs, plotChannel) + +% Compute the response of Stephanie Seneff's Auditory Model. The +% input waveform x (with sampling rate fs) is converted into fourty +% channels of auditory firing probabilities. +% +% This m-function is based on data from the following paper: +% Benjamin D. Bryant and John D. Gowdy, "Simulation of Stages +% I and II of Seneff's Auditory Model (SAM) Using Matlab", and +% published in the Proceedings of the 1993 Matlab User's Group +% Conference. +% Thanks to Benjamin Bryant for supplying us with his filter +% coefficients and the initial organization of this implementation. + +% (c) 1998 Interval Research Corporation + +global SeneffFS SeneffPreemphasis SeneffFilterBank +global SeneffForward SeneffBackward + +if nargin < 2; fs = 16000; end +if nargin < 3; plotChannel = 0; end + +if exist('SeneffFS') ~= 1 | length(SeneffFS) == 0 | SeneffFS ~= fs + [SeneffPreemphasis, SeneffFilterBank, ... + SeneffForward, SeneffBackward] = SeneffEarSetup(fs); + SeneffFS = fs; +end + +y=soscascade(filter(SeneffPreemphasis, [1], x), ... + SeneffFilterBank); + +[width,channels] = size(SeneffForward); +for j=1:channels + y(j,:) = filter(SeneffForward(:,j),SeneffBackward(:,j),y(j,:)); +end + +if plotChannel > 0 + clf + subplot(4,2,1); + plot(y(plotChannel,:)) + subplot(4,2,2); + f=find(y(plotChannel,:)>max(y(plotChannel,:))/20); + plotStart = max(1,f(1)-10); + plotEnd = min(plotStart+84,length(y(plotChannel,:))); + plot(y(plotChannel,plotStart:plotEnd)); + ylabel('Filter Bank') +end + +% Implement Seneff's detector non-linearity. +hwrA = 10; +hwrB = 65; +hwrG = 2.35; + +y = hwrA*atan(hwrB*max(0,y))+exp(hwrA*hwrB*min(0,y)); +if plotChannel > 0 + subplot(4,2,3); + plot(y(plotChannel,:)) + subplot(4,2,4); + plot(y(plotChannel,plotStart:plotEnd)); + ylabel('HWR'); +end + +% Implement Seneff's short-term adaptation (a reservoir hair cell +% model.) +start_Cn = 442.96875; +Tua = 58.3333/fs; +Tub = 8.3333/fs; + +len = length(x); +Cn = start_Cn*ones(channels,1)*0; % Does this work non zero? +for j=1:len + Sn = y(:,j); + flow = max(0,Tua*(Sn-Cn)); + Cn = Cn + flow - Tub*Cn; + y(:,j) = flow; +end + +% Implement Seneff's Low Pass filter (to model the loss of +% Synchrony. + +lpAlpha = .209611; +lpPoly = poly([lpAlpha lpAlpha lpAlpha lpAlpha]); +Glp = sum(lpPoly); + +for j=1:channels + y(j,:) = filter([Glp],lpPoly,y(j,:)); +end + +if plotChannel > 0 + subplot(4,2,5); + plot(y(plotChannel,:)) + subplot(4,2,6); + plot(y(plotChannel,plotStart:plotEnd)); + ylabel('Adaptation'); +end + +% Finally implement Seneff's Adaptive Gain Control. This computes +% y(n) = y(n)/(1+k y'(n)) +% where y'(n) is a low-pass filtered version of the input. Note, +% this is a single channel AGC. + +initial_yn = 0.23071276; +alpha_agc = 0.979382181; +kagc = 0.002; + +for j=1:channels + averageY(j,:) = filter([0 1-alpha_agc],[alpha_agc],y(j,:),initial_yn); +end +y = y./(1+kagc*averageY); + +if plotChannel > 0 + subplot(4,2,7); + plot(y(plotChannel,:)) + subplot(4,2,8); + plot(y(plotChannel,plotStart:plotEnd)); + ylabel('AGC'); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/SeneffEarSetup.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,224 @@ +function [SeneffPreemphasis, SeneffFilterBank, SeneffForward, SeneffBackward] ... + = SeneffEarSetup(fs) + +% This m-function is based on data from the following paper: +% Benjamin D. Bryant and John D. Gowdy, "Simulation of Stages +% I and II of Seneff's Auditory Model (SAM) Using Matlab", and +% published in the Proceedings of the 1993 Matlab User's Group +% Conference. +% Thanks to Benjamin Bryant for supplying us with his filter +% coefficients and the initial organization of this implementation. + +% (c) 1998 Interval Research Corporation + +% Set the following variable to a non-zero value to see a summary +% of the filter bank's behaviour. +plotTests = 0; + +% The following values were taken from Figure 2 of Bryant's paper. +PreemphasisRTheta = [0.86 3.1148863;0.99 0; 0.5 0; 0.95 3.14159]; + +% The following values were taken from Table 1 of Bryant's paper. +% They represent the cascade zeros (R-z and Theta-z), and the +% second order poles (radius and theta) and zeros (radius and theta/2). +% +% R-z Theta-z Radius Theta R-z2 +FilterBankRTheta = [ + 0 3.14159 0.740055 2.633909 0.8 + 0.86 2.997077 0.753637 2.178169 0.8 + 0.86 2.879267 0.775569 1.856744 0.8 + 0.86 2.761458 0.798336 1.617919 0.8 + 0.86 2.643648 0.819169 1.433496 0.8 + 0.86 2.525839 0.837158 1.286795 0.8 + 0.8 2.964876 0.852598 1.167321 0.8 + 0.86 2.408029 0.865429 1.068141 0.8 + 0.86 2.29022 0.876208 0.984489 0.8 + 0.86 2.17241 0.885329 0.912985 0.8 + 0.86 2.054601 0.893116 0.851162 0.8 + 0.86 1.936791 0.899823 0.797179 0.8 + 0.8 2.788161 0.906118 0.749633 0.8 + 0.86 1.818981 0.911236 0.70744 0.8 + 0.86 1.701172 0.915747 0.669742 0.8 + 0.86 1.583362 0.919753 0.635858 0.8 + 0.86 1.465552 0.923335 0.605237 0.8 + 0.86 1.347743 0.926565 0.57743 0.8 + 0.8 2.611447 0.929914 0.552065 0.8 + 0.86 1.229933 0.932576 0.528834 0.8 + 0.86 1.112123 0.944589 0.487783 0.75 + 0.86 0.994314 0.957206 0.452645 0.660714 + 0.86 0.876504 0.956548 0.42223 0.672143 + 0.86 0.758694 0.956653 0.395644 0.682143 + 0.8 2.434732 0.956518 0.372208 0.690966 + 0.86 0.640885 0.956676 0.351393 0.69881 + 0.86 0.523075 0.956741 0.316044 0.712143 + 0.8 2.258018 0.956481 0.287157 0.723052 + 0.8 2.081304 0.956445 0.263108 0.732143 + 0.8 1.904589 0.956481 0.242776 0.739835 + 0.86 0.405265 0.958259 0.217558 0.749384 + 0.8 1.727875 0.963083 0.197086 0.757143 + 0.8 1.55116 0.969757 0.175115 0.769048 + 0.8 1.374446 0.97003 0.153697 0.780662 + 0.8 1.197732 0.970382 0.134026 0.791337 + 0.8 1.021017 0.970721 0.118819 0.799596 + 0.8 1.5 0.970985 0.106711 0.8 + 0.8 1.2 0.971222 0.096843 0.8 + 0.8 1 0.97144 0.088645 0.8 + 0.8 0.9 0.971645 0.081727 0.8]; + +% Let's plot the cascade zero locations and the locations of the +% pole and zeros in the resonator. +if plotTests + clf; + subplot(3,3,1); + plot(FilterBankRTheta(:,1).*exp(i*FilterBankRTheta(:,2))) + axis([-1 1 0 1]) + title('Cascade Zero Locations') + + subplot(3,3,2); + plot([FilterBankRTheta(:,3).*exp(i*FilterBankRTheta(:,4)) ... + FilterBankRTheta(:,5).*exp(i*FilterBankRTheta(:,4)/2)],'+') + title('Resonator Pole/Zero') + drawnow; +end + +% Convert r-theta form, first into a list of roots, then a polynomial +roots=exp(i*PreemphasisRTheta(:,2)).*PreemphasisRTheta(:,1); +SeneffPreemphasis=real(poly([roots;conj(roots)])); + +% Plot the preemphasis filter response, if desired +if plotTests + subplot(3,3,3); + freqScale=(0:255)/256*8000; + freqresp = FreqResp(SeneffPreemphasis,[1], freqScale, 16000); + semilogx(freqScale,freqresp) + title('Preemphasis Response'); + axis([100 10000 -60 20]) + drawnow; +end + +% Now figure out the second order sections that make up the main +% filter bank cascade. We put the zeros into the numerator (b's) +% and there are no poles. Just to keep things simpler, we adjust +% the gain of each filter to keep it unity gain at DC. +[channels,width] = size(FilterBankRTheta); +roots=exp(i*FilterBankRTheta(:,2)).*FilterBankRTheta(:,1); +SeneffFilterBank = zeros(channels,5); +for j=1:channels + SeneffFilterBank(j,1:3) = poly([roots(j) conj(roots(j))]); + SeneffFilterBank(j,1:3) = SeneffFilterBank(j,1:3)/sum(SeneffFilterBank(j,1:3)); +end + +% Plot the cascade zero responses, if desired. +if plotTests + subplot(3,3,4); + y=soscascade([1 zeros(1,511)],SeneffFilterBank); + freqresp=20*log10(abs(fft(y(1:5:40,:)'))); + freqScale=(0:511)/512*16000; + semilogx(freqScale(1:256),freqresp(1:256,:)) + axis([100 10000 -150 0]); + title('Cascade Response'); + drawnow; +end + +% Now figure out the resonating filters. Each of these resonators +% is a double pole-zero pair. +zlocs = FilterBankRTheta(:,5).*exp(i*FilterBankRTheta(:,4)/2); +plocs = FilterBankRTheta(:,3).*exp(i*FilterBankRTheta(:,4)); +SeneffForward = zeros(5,channels); +SeneffBackward = zeros(5,channels); + +for j=1:channels + SeneffForward(:,j) = real(poly([zlocs(j) conj(zlocs(j)) ... + zlocs(j) conj(zlocs(j))]))'; + SeneffBackward(:,j) = real(poly([plocs(j) conj(plocs(j)) ... + plocs(j) conj(plocs(j))]))'; +end + +% Now plot the frequency response of just the resonating filters. +% These are all bandpass filters. +if plotTests + subplot(3,3,5); + impulse = [1 zeros(1,255)]; + y=zeros(256,channels); + for j=1:40 + y(:,j) = filter(SeneffForward(:,j),SeneffBackward(:,j),impulse)'; + end + freqresp=20*log10(abs(fft(y(:,1:5:40)))); + freqScale=(0:255)/256*16000; + semilogx(freqScale(1:128),freqresp(1:128,:)) + axis([100 10000 -30 40]); + title('Resonators Response') + drawnow; +end + +% The plot below shows the overall response of the preemphasis filters +% along with the just-designed cascade of zeros. +if plotTests + subplot(3,3,6); + impulse = [1 zeros(1,511)]; + y=soscascade(filter(SeneffPreemphasis, [1], impulse), ... + SeneffFilterBank); + freqresp=20*log10(abs(fft(y(1:5:40,:)'))); + freqScale=(0:511)/512*16000; + semilogx(freqScale(1:256),freqresp(1:256,:)) + axis([100 10000 -100 25]); + title('Preemphasis+Cascade'); + drawnow; +end + +% Now we need to normalize the gain of each channel. We run an impulse +% through the preemphasis filter, and then through the cascade of zeros. +% Finally, we run it through each of the resonator filters. +impulse = [1 zeros(1,255)]; +y=soscascade(filter(SeneffPreemphasis, [1], impulse), ... + SeneffFilterBank); +for j=1:channels + y(j,:) = filter(SeneffForward(:,j),SeneffBackward(:,j),y(j,:)); +end + +% Now we have impulse responses from each filter. We can find the FFT +% and then find the gain peak. We divide each forward polynomial by the +% maximum gain (to normalize) and then multiply by the desired low +% frequency roll-off. The Bryant paper says that the last 24 channels +% should be cut at 6dB per octave and that this occurs at 1600 Hz, but +% it looks to me like the gain change happens at 3200 Hz. +freqresp=abs(fft(y')); +gain = ones(1,channels)./max(freqresp); +cfs = FilterBankRTheta(:,4)/pi*fs/2; +rolloff = min(cfs/1600,1); + +for j=1:channels + SeneffForward(:,j)=SeneffForward(:,j)*gain(j)*rolloff(j); +end + +% All Done. The figure below should match Figure 3 of Bryant's paper. +if plotTests + subplot(3,3,8); + impulse = [1 zeros(1,511)]; + y=soscascade(filter(SeneffPreemphasis, [1], impulse), ... + SeneffFilterBank); + for j=1:channels + y(j,:) = filter(SeneffForward(:,j),SeneffBackward(:,j),y(j,:)); + end + + freqresp=20*log10(abs(fft(y(1:5:40,:)'))); + freqScale=(0:511)/512*16000; + plot(freqScale(1:256),freqresp(1:256,:)) + axis([100 10000 -120 0]); + title('Magnitude Response vs. Linear Frequency'); + drawnow; +end + + +function mag=FreqResp(b,a,f,fs) +cf = exp(i*2*pi*f/fs); +num = 0; +for i=1:length(b) + num = num + b(end-i+1)*cf.^i; +end + +denom = 0; +for i=1:length(a) + denom = denom + a(end-i+1)*cf.^i; +end +mag = 20*log10(abs(num./denom));
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/SetGain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +% filter = SetGain(filter, desired, f, fs) +% Set the gain of a filter (1x5 vector) to any desired gain +% at any desired frequency (f). + +% (c) 1998 Interval Research Corporation + +function filter = SetGain(filter, desired, f, fs) +oldGain = 10^(FreqResp(filter, f, fs)/20); +filter(1:3) = filter(1:3)*desired/oldGain;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/WhiteVowel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function [output,aCoeff] = WhiteVowel(data,sr,L,pos) +% function [output,aCoeff] = WhiteVowel(data,sr,L,pos) +% +% Speech is often described as having spectral peaks or formants which +% identify the phonetic signal. An interesting experiment, first proposed by +% XXX, filters a speech signal to remove all the formant information at one +% time during the speech. If there are no formant peaks, how can the speech +% be understood? It turns out that processing, much like RASTA, means that +% relative changes in spectrum are the most important, thus the speech signal +% is understood because the formant transitions carry the information. This +% gives speech an important transparency due +% +% This function takes a speech signal (data) with a given sampling rate (sr). +% It then finds the L-order LPC filter that describes the speech at the given +% position (pos ms). The entire speech signal is then filtered with the +% inverse of the LPC filter, effectively turning the speech spectrum at the +% given time white (flat). + +% Chris Pal, Interval, May 1997 +% (c) 1998 Interval Research Corporation + +fr = 20; fs = 30; preemp = .9378; % LPC defaults + +[row col] = size(data); +if col==1 data=data'; end + +nframe = 0; +msfr = round(sr/1000*fr); +msfs = round(sr/1000*fs); +duration = length(data); +msoverlap = msfs - msfr; +frameNumber = floor(pos/1000*sr/msfr); + +frameStart = round(pos/1000*sr - msfs/2); +frameData = data(frameStart:(frameStart+msfs-1)); +aCoeff = proclpc(frameData, sr, L, fr, fs, preemp); + % Calculate the filter response + % by evaluating the z-transform +spec=lpc_spec(aCoeff); +subplot(2,3,1); +plot(spec); +title('LPC Spectral Slice'); +ylabel('Original') + + % Now do the actual whitening filter +output = filter(aCoeff,1,data)'; + +frameData = output(frameStart:(frameStart+msfs-1)); +bCoeff = proclpc(frameData, sr, L, fr, fs, preemp); +spec=lpc_spec(bCoeff); +subplot(2,3,4); +plot(spec); +ylabel('Whitened'); xlabel('FFT Bin'); + +% 256-DFT +origSpec = 20*log10(abs(specgram(data,512,sr,msfs,msoverlap))); +subplot(2,3,2),imagesc(origSpec); axis xy; colormap(1-gray); +title('Spectrogram'); + +synSpec = 20*log10(abs(specgram(output,512,sr,msfs,msoverlap))); +subplot(2,3,5),imagesc(synSpec); axis xy; colormap(1-gray); +xlabel('Frame #'); + +origloc = origSpec(:,frameNumber); origloc=origloc-max(origloc);origmin=min(origloc); +subplot(2,3,3),plot(origloc),title('Spectrogram'), +axis([1 length(origloc) origmin 0]); + +filloc = synSpec(:,frameNumber); filloc=filloc-max(filloc); +subplot(2,3,6),plot(filloc);ylabel('db'); +axis([1 length(origloc) origmin 0]); +xlabel('FFT Bin'); + +function spec=lpc_spec(aCoeff) +gain=0; +cft=0:(1/255):1; +for index=1:size(aCoeff,1) + gain = gain + aCoeff(index)*exp(-i*2*pi*cft).^index; +end +gain = abs(1./gain); +spec = 20*log10(gain(1:128))';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/contents.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +% Auditory Toolbox +% by Malcolm Slaney +% (c) 1998 Interval Research Corporation +% Technical Report #1998-010 +% +% Lyon's Cochlear Model. +% agc - Adaptation process for Lyon's Passive +% longwave cochlear model +% DesignLyonCascade - Designs the filters needed to implement +% Lyon's passive cochlear model +% EpsilonFromTauFs - Calculate the first order decay +% coefficient (tau) +% FreqResp - Evaluate frequency response of the filter +% LyonPassiveEar - Calculate auditory nerve responses using +% Lyon's passive cochlear model +% SecondOrderSection - Design a second order filter section +% SetGain - Set the gain of a second order system +% soscascade - Implement a cascade of second order filters +% sosfilters - Implement a bank of second order filters +% +% Patterson/Meddis Models +% ERBSpace - Calculate frequencies for ERB filters +% MakeERBFilters - Design for ERB cochlear model +% ERBFilterBank - Implement a bank of ERB Gammatone filters +% MeddisHairCell - Implement Meddis' Inner Hair Cell Model +% +% Seneff Auditory Model +% SeneffEar - Implement Stages I/II of Seneff's Auditory Model +% SeneffEarSetup - Design filters for Seneff's model +% +% Correlogram Processing +% CorrelogramArray - Compute an array of correlogram frames +% CorrelogramFrame - Compute a single correlogram frame +% CorrelogramMovie - Compute a Matlab movie of a correlogram +% CorrelogramPitch - Compute the pitch of a signal with a correlogram +% +% Signal Processing. +% mfcc - Mel-frequency cepstral coefficient transform of +% an audio signal +% spectrogram - Compute the spectrogram of a signal +% rasta - Implement RelAtive SpecTrAl filtering +% proclpc - Perform Linear Preditive Coding (LPC) analysis +% synlpc - Synthesize speech from LPC coefficients +% +% Demonstrations +% MakeVowel - Synthesize a vowel sound +% FMPoints - Create a changing pitch signal for vowel synthesis +% WhiteVowel - Filter a speech signal so formants disappear
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/mfcc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +% mfcc - Mel frequency cepstrum coefficient analysis. +% [ceps,freqresp,fb,fbrecon,freqrecon] = ... +% mfcc(input, samplingRate, [frameRate]) +% Find the cepstral coefficients (ceps) corresponding to the +% input. Four other quantities are optionally returned that +% represent: +% the detailed fft magnitude (freqresp) used in MFCC calculation, +% the mel-scale filter bank output (fb) +% the filter bank output by inverting the cepstrals with a cosine +% transform (fbrecon), +% the smooth frequency response by interpolating the fb reconstruction +% (freqrecon) +% -- Malcolm Slaney, August 1993 +% Modified a bit to make testing an algorithm easier... 4/15/94 +% Fixed Cosine Transform (indices of cos() were swapped) - 5/26/95 +% Added optional frameRate argument - 6/8/95 +% Added proper filterbank reconstruction using inverse DCT - 10/27/95 +% Added filterbank inversion to reconstruct spectrum - 11/1/95 + +% (c) 1998 Interval Research Corporation + +function [ceps,freqresp,fb,fbrecon,freqrecon] = ... + mfcc(input, samplingRate, frameRate) +global mfccDCTMatrix mfccFilterWeights + +[r c] = size(input); +if (r > c) + input=input'; +end + +% Filter bank parameters +lowestFrequency = 133.3333; +linearFilters = 13; +linearSpacing = 66.66666666; +logFilters = 27; +logSpacing = 1.0711703; +fftSize = 512; +cepstralCoefficients = 13; +windowSize = 400; +windowSize = 256; % Standard says 400, but 256 makes more sense + % Really should be a function of the sample + % rate (and the lowestFrequency) and the + % frame rate. +if (nargin < 2) samplingRate = 16000; end; +if (nargin < 3) frameRate = 100; end; + +% Keep this around for later.... +totalFilters = linearFilters + logFilters; + +% Now figure the band edges. Interesting frequencies are spaced +% by linearSpacing for a while, then go logarithmic. First figure +% all the interesting frequencies. Lower, center, and upper band +% edges are all consequtive interesting frequencies. + +freqs = lowestFrequency + (0:linearFilters-1)*linearSpacing; +freqs(linearFilters+1:totalFilters+2) = ... + freqs(linearFilters) * logSpacing.^(1:logFilters+2); + +lower = freqs(1:totalFilters); +center = freqs(2:totalFilters+1); +upper = freqs(3:totalFilters+2); + +% We now want to combine FFT bins so that each filter has unit +% weight, assuming a triangular weighting function. First figure +% out the height of the triangle, then we can figure out each +% frequencies contribution +mfccFilterWeights = zeros(totalFilters,fftSize); +triangleHeight = 2./(upper-lower); +fftFreqs = (0:fftSize-1)/fftSize*samplingRate; + +for chan=1:totalFilters + mfccFilterWeights(chan,:) = ... + (fftFreqs > lower(chan) & fftFreqs <= center(chan)).* ... + triangleHeight(chan).*(fftFreqs-lower(chan))/(center(chan)-lower(chan)) + ... + (fftFreqs > center(chan) & fftFreqs < upper(chan)).* ... + triangleHeight(chan).*(upper(chan)-fftFreqs)/(upper(chan)-center(chan)); +end +%semilogx(fftFreqs,mfccFilterWeights') +%axis([lower(1) upper(totalFilters) 0 max(max(mfccFilterWeights))]) + +hamWindow = 0.54 - 0.46*cos(2*pi*(0:windowSize-1)/windowSize); + +if 0 % Window it like ComplexSpectrum + windowStep = samplingRate/frameRate; + a = .54; + b = -.46; + wr = sqrt(windowStep/windowSize); + phi = pi/windowSize; + hamWindow = 2*wr/sqrt(4*a*a+2*b*b)* ... + (a + b*cos(2*pi*(0:windowSize-1)/windowSize + phi)); +end + +% Figure out Discrete Cosine Transform. We want a matrix +% dct(i,j) which is totalFilters x cepstralCoefficients in size. +% The i,j component is given by +% cos( i * (j+0.5)/totalFilters pi ) +% where we have assumed that i and j start at 0. +mfccDCTMatrix = 1/sqrt(totalFilters/2)*cos((0:(cepstralCoefficients-1))' * ... + (2*(0:(totalFilters-1))+1) * pi/2/totalFilters); +mfccDCTMatrix(1,:) = mfccDCTMatrix(1,:) * sqrt(2)/2; + +%imagesc(mfccDCTMatrix); + +% Filter the input with the preemphasis filter. Also figure how +% many columns of data we will end up with. +if 1 + preEmphasized = filter([1 -.97], 1, input); +else + preEmphasized = input; +end +windowStep = samplingRate/frameRate; +cols = fix((length(input)-windowSize)/windowStep); + +% Allocate all the space we need for the output arrays. +ceps = zeros(cepstralCoefficients, cols); +if (nargout > 1) freqresp = zeros(fftSize/2, cols); end; +if (nargout > 2) fb = zeros(totalFilters, cols); end; + +% Invert the filter bank center frequencies. For each FFT bin +% we want to know the exact position in the filter bank to find +% the original frequency response. The next block of code finds the +% integer and fractional sampling positions. +if (nargout > 4) + fr = (0:(fftSize/2-1))'/(fftSize/2)*samplingRate/2; + j = 1; + for i=1:(fftSize/2) + if fr(i) > center(j+1) + j = j + 1; + end + if j > totalFilters-1 + j = totalFilters-1; + end + fr(i) = min(totalFilters-.0001, ... + max(1,j + (fr(i)-center(j))/(center(j+1)-center(j)))); + end + fri = fix(fr); + frac = fr - fri; + + freqrecon = zeros(fftSize/2, cols); +end + +% Ok, now let's do the processing. For each chunk of data: +% * Window the data with a hamming window, +% * Shift it into FFT order, +% * Find the magnitude of the fft, +% * Convert the fft data into filter bank outputs, +% * Find the log base 10, +% * Find the cosine transform to reduce dimensionality. +for start=0:cols-1 + first = start*windowStep + 1; + last = first + windowSize-1; + fftData = zeros(1,fftSize); + fftData(1:windowSize) = preEmphasized(first:last).*hamWindow; + fftMag = abs(fft(fftData)); + earMag = log10(mfccFilterWeights * fftMag'); + + ceps(:,start+1) = mfccDCTMatrix * earMag; + if (nargout > 1) freqresp(:,start+1) = fftMag(1:fftSize/2)'; end; + if (nargout > 2) fb(:,start+1) = earMag; end + if (nargout > 3) + fbrecon(:,start+1) = ... + mfccDCTMatrix(1:cepstralCoefficients,:)' * ... + ceps(:,start+1); + end + if (nargout > 4) + f10 = 10.^fbrecon(:,start+1); + freqrecon(:,start+1) = samplingRate/fftSize * ... + (f10(fri).*(1-frac) + f10(fri+1).*frac); + end +end + +% OK, just to check things, let's also reconstruct the original FB +% output. We do this by multiplying the cepstral data by the transpose +% of the original DCT matrix. This all works because we were careful to +% scale the DCT matrix so it was orthonormal. +if 1 & (nargout > 3) + fbrecon = mfccDCTMatrix(1:cepstralCoefficients,:)' * ceps; +% imagesc(mt(:,1:cepstralCoefficients)*mfccDCTMatrix); +end; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/proclpc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,158 @@ +function [aCoeff,resid,pitch,G,parcor,stream] = proclpc(data,sr,L,fr,fs,preemp) +% USAGE: [aCoeff,resid,pitch,G,parcor,stream] = proclpc(data,sr,L,fr,fs,preemp) +% +% This function computes the LPC (linear-predictive coding) coefficients that +% describe a speech signal. The LPC coefficients are a short-time measure of +% the speech signal which describe the signal as the output of an all-pole +% filter. This all-pole filter provides a good description of the speech +% articulators; thus LPC analysis is often used in speech recognition and +% speech coding systems. The LPC parameters are recalculated, by default in +% this implementation, every 20ms. +% +% The results of LPC analysis are a new representation of the signal +% s(n) = G e(n) - sum from 1 to L a(i)s(n-i) +% where s(n) is the original data. a(i) and e(n) are the outputs of the LPC +% analysis with a(i) representing the LPC model. The e(n) term represents +% either the speech source's excitation, or the residual: the details of the +% signal that are not captured by the LPC coefficients. The G factor is a +% gain term. +% +% LPC analysis is performed on a monaural sound vector (data) which has been +% sampled at a sampling rate of "sr". The following optional parameters modify +% the behaviour of this algorithm. +% L - The order of the analysis. There are L+1 LPC coefficients in the output +% array aCoeff for each frame of data. L defaults to 13. +% fr - Frame time increment, in ms. The LPC analysis is done starting every +% fr ms in time. Defaults to 20ms (50 LPC vectors a second) +% fs - Frame size in ms. The LPC analysis is done by windowing the speech +% data with a rectangular window that is fs ms long. Defaults to 30ms +% preemp - This variable is the epsilon in a digital one-zero filter which +% serves to preemphasize the speech signal and compensate for the 6dB +% per octave rolloff in the radiation function. Defaults to .9378. +% +% The output variables from this function are +% aCoeff - The LPC analysis results, a(i). One column of L numbers for each +% frame of data +% resid - The LPC residual, e(n). One column of sr*fs samples representing +% the excitation or residual of the LPC filter. +% pitch - A frame-by-frame estimate of the pitch of the signal, calculated +% by finding the peak in the residual's autocorrelation for each frame. +% G - The LPC gain for each frame. +% parcor - The parcor coefficients. The parcor coefficients give the ratio +% between adjacent sections in a tubular model of the speech +% articulators. There are L parcor coefficients for each frame of +% speech. +% stream - The LPC analysis' residual or excitation signal as one long vector. +% Overlapping frames of the resid output combined into a new one- +% dimensional signal and post-filtered. +% +% The synlpc routine inverts this transform and returns the original speech +% signal. +% +% This code was graciously provided by: +% Delores Etter (University of Colorado, Boulder) and +% Professor Geoffrey Orsak (Southern Methodist University) +% It was first published in +% Orsak, G.C. et al. "Collaborative SP education using the Internet and +% MATLAB" IEEE SIGNAL PROCESSING MAGAZINE Nov. 1995. vol.12, no.6, pp. +% 23-32. +% Modified and debugging plots added by Kate Nguyen and Malcolm Slaney + +% A more complete set of routines for LPC analysis can be found at +% http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html + +% (c) 1998 Interval Research Corporation + + +if (nargin<3), L = 13; end +if (nargin<4), fr = 20; end +if (nargin<5), fs = 30; end +if (nargin<6), preemp = .9378; end + +[row col] = size(data); +if col==1 data=data'; end + +nframe = 0; +msfr = round(sr/1000*fr); % Convert ms to samples +msfs = round(sr/1000*fs); % Convert ms to samples +duration = length(data); +speech = filter([1 -preemp], 1, data)'; % Preemphasize speech +msoverlap = msfs - msfr; +ramp = [0:1/(msoverlap-1):1]'; % Compute part of window + + +for frameIndex=1:msfr:duration-msfs+1 % frame rate=20ms + frameData = speech(frameIndex:(frameIndex+msfs-1)); % frame size=30ms + nframe = nframe+1; + autoCor = xcorr(frameData); % Compute the cross correlation + autoCorVec = autoCor(msfs+[0:L]); + + % Levinson's method + err(1) = autoCorVec(1); + k(1) = 0; + A = []; + for index=1:L + numerator = [1 A.']*autoCorVec(index+1:-1:2); + denominator = -1*err(index); + k(index) = numerator/denominator; % PARCOR coeffs + A = [A+k(index)*flipud(A); k(index)]; + err(index+1) = (1-k(index)^2)*err(index); + end + + aCoeff(:,nframe) = [1; A]; + parcor(:,nframe) = k'; + + % Calculate the filter + % response + % by evaluating the + % z-transform + if 0 + gain=0; + cft=0:(1/255):1; + for index=1:L + gain = gain + aCoeff(index,nframe)*exp(-i*2*pi*cft).^index; + end + gain = abs(1./gain); + spec(:,nframe) = 20*log10(gain(1:128))'; + plot(20*log10(gain)); + title(nframe); + drawnow; + end + + % Calculate the filter response + % from the filter's impulse + % response (to check above). + if 0 + impulseResponse = filter(1, aCoeff(:,nframe), [1 zeros(1,255)]); + freqResp = 20*log10(abs(fft(impulseResponse))); + plot(freqResp); + end + + errSig = filter([1 A'],1,frameData); % find excitation noise + + G(nframe) = sqrt(err(L+1)); % gain + autoCorErr = xcorr(errSig); % calculate pitch & voicing + % information + [B,I] = sort(autoCorErr); + num = length(I); + if B(num-1) > .3*B(num) + pitch(nframe) = abs(I(num) - I(num-1)); + else + pitch(nframe) = 0; + end + + resid(:,nframe) = errSig/G(nframe); + if(frameIndex==1) % add residual frames using a + stream = resid(1:msfr,nframe); % trapezoidal window + else + stream = [stream; + overlap+resid(1:msoverlap,nframe).*ramp; + resid(msoverlap+1:msfr,nframe)]; + end + if(frameIndex+msfr+msfs-1 > duration) + stream = [stream; resid(msfr+1:msfs,nframe)]; + else + overlap = resid(msfr+1:msfs,nframe).*flipud(ramp); + end +end +stream = filter(1, [1 -preemp], stream)';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/rasta.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +function y=rasta(x,fs,low,high) +% function y=rasta(x,fs) where x is the input data (rows of time data), +% and fs is the frame rate (sampling rate) in Hz. This is a modified +% version of the original filter. Here the RASTA filter is approximated +% by a simple fourth order Butterworth bandpass filter. See pages 50-51 +% of my second IRC logbook for the derivation. +% +% Hermansky and Morgan, "RASTA Processing of Speech." IEEE Transactions +% on Speech and Audio Processing. vol. 2, no. 4, October 1994 +% + +% (c) 1998 Interval Research Corporation +% Malcolm Slaney, January 30, 1996, Interval Research Corporation + +if (nargin < 2); fs=100; end +if (nargin < 3); low=.9; end +if (nargin < 4); high=12.8; end + +if (low == 0 & high == 0) % Original Filter + num = .1*[2 1 0 -1 -2]; + denum = [1 -.94]; +else % New fourth order + % Butterworth BP filter + w1=low/fs*2*pi; + w2=high/fs*2*pi; + theta=1; + + a=cos((w1+w2)/2)/cos((w2-w1)/2); + k=cot((w2-w1)/2)*tan(theta/2); + + num = [1 0 -2 0 1]; + denum = [(1 + 2*2^(1/2)*k + 4*k^2) ... + (-4*2^(1/2)*a*k - 16*a*k^2) ... + (-2 + 8*k^2 + 16*a^2*k^2) ... + (4*2^(1/2)*a*k - 16*a*k^2) ... + (1 - 2*2^(1/2)*k + 4*k^2)]; + scale = denum(1); % Scale by a(1) component + num = num/scale; + denum = denum/scale; +end + +if (0) + len = 1024; + impulse = zeros(1,len); + impulse(1) = 1; + + y=filter(num,denum,impulse); + ym = abs(fft(y)); + ym=20*log10(ym); + f=(0:(len-1))/len*fs; + semilogx(f(1:len/2),ym(1:len/2)); + drawnow; +end + +if (length(x) == size(x,1)*size(x,2)) + y = filter(num,denum,x,x(1)*[-1 -1 1 1]); +else + y = zeros(size(x)); + for i=1:size(x,1) + y(i,:) = filter(num,denum,x(i,:),x(i,1)*[-1 -1 1 1]/scale); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/spectrogram.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function [array,raw] = spectrogram(wave,segsize,nlap,ntrans); +%function array = spectrogram(wave,segsize,nlap,ntrans); +% defaults spectrogram(wave,128,8,4) +% nlap is number of hamming windows overlapping a point; +% ntrans is factor by which transform is bigger than segment; +% returns a spectrogram 'array' with fourth root of power, +% filter smoothed and formatted for display. + +% Added option to return raw spectrogram.... Malcolm 5/26/95 +% Added code so that input could be any direction ... Malcolm 5/26/95 +% (c) 1998 Interval Research Corporation + +if nargin < 4; ntrans=4; end +if nargin < 3; nlap=8; end +if nargin < 2; segsize=128; end + +[r c] = size(wave); +if (r < c) + wave = filter([1 -0.95],[1],wave'); +else + wave = filter([1 -0.95],[1],wave); +end + +s = length(wave); +nsegs = floor(s/(segsize/nlap))-nlap+1; +array = zeros(ntrans/2*segsize,nsegs); +window = 0.54-0.46*cos(2*pi/(segsize+1)*(1:segsize)'); +for i = 1:nsegs + seg = zeros(ntrans*segsize,1); % leave half full of zeroes + seg(1:segsize) = ... + window.*wave(((i-1)*segsize/nlap+1):((i+nlap-1)*segsize/nlap)); + seg = abs(fft(seg)); + % reverse for image display + array(:,i) = seg(((ntrans/2*segsize)+1):(ntrans*segsize)); +end + +if nargout > 1 + raw = array; +end + +array = array .* array; % back into power domain for smoothing + +for i=1:nsegs % smooth the spectral slices + array(:,i) = filter([.2 1 .2],[1],array(:,i)); +end + +for i=1:ntrans/2*segsize % smooth the channels + array(i,:) = filter([.2 1 .2],[1],array(i,:)); +end + +% compress with square root of amplitude (fourth root of power) +off = 0.0001*max(max(array)); % low end stabilization offset, +array = (off+array).^0.25-off^0.25; % better than a threshold hack! +array = 255/max(max(array))*array; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/synlpc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,130 @@ +function synWave = synlpc(aCoeff,source,sr,G,fr,fs,preemp) +% USAGE: synWave = synlpc(aCoeff,source,sr,G,fr,fs,preemp); +% +% This function synthesizes a (speech) signal based on a LPC (linear- +% predictive coding) model of the signal. The LPC coefficients are a +% short-time measure of the speech signal which describe the signal as the +% output of an all-pole filter. This all-pole filter provides a good +% description of the speech articulators; thus LPC analysis is often used in +% speech recognition and speech coding systems. The LPC analysis is done +% using the proclpc routine. This routine can be used to verify that the +% LPC analysis produces the correct answer, or as a synthesis stage after +% first modifying the LPC model. +% +% The results of LPC analysis are a new representation of the signal +% s(n) = G e(n) - sum from 1 to L a(i)s(n-i) +% where s(n) is the original data. a(i) and e(n) are the outputs of the LPC +% analysis with a(i) representing the LPC model. The e(n) term represents +% either the speech source's excitation, or the residual: the details of the +% signal that are not captured by the LPC coefficients. The G factor is a +% gain term. +% +% LPC synthesis produces a monaural sound vector (synWave) which is +% sampled at a sampling rate of "sr". The following parameters are mandatory +% aCoeff - The LPC analysis results, a(i). One column of L+1 numbers for each +% frame of data. The number of rows of aCoeff determines L. +% source - The LPC residual, e(n). One column of sr*fs samples representing +% the excitation or residual of the LPC filter. +% G - The LPC gain for each frame. +% +% The following parameters are optional and default to the indicated values. +% fr - Frame time increment, in ms. The LPC analysis is done starting every +% fr ms in time. Defaults to 20ms (50 LPC vectors a second) +% fs - Frame size in ms. The LPC analysis is done by windowing the speech +% data with a rectangular window that is fs ms long. Defaults to 30ms +% preemp - This variable is the epsilon in a digital one-zero filter which +% serves to preemphasize the speech signal and compensate for the 6dB +% per octave rolloff in the radiation function. Defaults to .9378. +% +% This code was graciously provided by: +% Delores Etter (University of Colorado, Boulder) and +% Professor Geoffrey Orsak (Southern Methodist University) +% It was first published in +% Orsak, G.C. et al. "Collaborative SP education using the Internet and +% MATLAB" IEEE SIGNAL PROCESSING MAGAZINE Nov. 1995. vol.12, no.6, pp. +% 23-32. +% Modified and debugging plots added by Kate Nguyen and Malcolm Slaney + +% (c) 1998 Interval Research Corporation +% A more complete set of routines for LPC analysis can be found at +% http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html + + + +if (nargin < 5), fr = 20; end; +if (nargin < 6), fs = 30; end; +if (nargin < 7), preemp = .9378; end; + +msfs = round(sr*fs/1000); +msfr = round(sr*fr/1000); +msoverlap = msfs - msfr; +ramp = [0:1/(msoverlap-1):1]'; +[L1 nframe] = size(aCoeff); % L1 = 1+number of LPC coeffs + +[row col] = size(source); +if(row==1 | col==1) % continous stream; must be + % windowed + postFilter = 0; duration = length(source); frameIndex = 1; + for sampleIndex=1:msfr:duration-msfs+1 + resid(:,frameIndex) = source(sampleIndex:(sampleIndex+msfs-1))'; + frameIndex = frameIndex+1; + end +else + postFilter = 1; resid = source; +end + +[row col] = size(resid); +%if ~(col==nframe) +% error('synLPC: numbers of LPC frames and source frames do not match'); +if col<nframe + nframe=col; +end + +for frameIndex=1:nframe + % Calculate the filter response + % by evaluating the z-transform +% if 1 +% gain=0; +% cft=0:(1/255):1; +% for index=1:L1-1 +% gain = gain + aCoeff(index,frameIndex)*exp(-i*2*pi*cft).^index; +% end +% gain = abs(1./gain); +% spec(:,frameIndex) = 20*log10(gain(1:128))'; + % plot(20*log10(gain)); + % title(frameIndex); + % drawnow; +% end + + % Calculate the filter response + % from the filter's impulse + % response (to check above). +% if 0 +% impulseResponse = filter(1, aCoeff(:,frameIndex), [1 zeros(1,255)]); +% freqResp = 20*log10(abs(fft(impulseResponse))); +% plot(freqResp); +% end + + + A = aCoeff(:,frameIndex); + residFrame = resid(:,frameIndex)*G(frameIndex); + synFrame = filter(1, A', residFrame); % synthesize speech from LPC + % coeffs + if(frameIndex==1) % add synthesize frames using a + synWave = synFrame(1:msfr); % trapezoidal window + else + synWave = [synWave; overlap+synFrame(1:msoverlap).*ramp; ... + synFrame(msoverlap+1:msfr)]; + end + if(frameIndex==nframe) + synWave = [synWave; synFrame(msfr+1:msfs)]; + else + overlap = synFrame(msfr+1:msfs).*flipud(ramp); + end + %length(synWave) +end; + +if(postFilter) + synWave = filter(1, [1 -preemp], synWave); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/AuditoryToolbox/test_auditory.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,332 @@ +function test_auditory(test) +% function test_auditory(test) +% Test each of the functions in the Auditory Toolbox. The single argument +% is a string, containing the word 'all' or the name of one routine from the +% toolbox. This routine defaults to running all tests. There is a pause after +% each plot, so be sure to hit the return key so the testing can proceed. +% + +% (c) 1998 Interval Research Corporation + +if nargin < 1 + test = 'all'; +end + +if strcmp(test,'agc') | strcmp(test,'all') + disp('agc test'); + set_window_size(200, 150); + agc(ones(1,20), [.5;.5]) + plot(agc(ones(1,30), [.8; .5])); pause; + plot(agc(ones(1,30),[.4;.5])); pause; + plot(agc(ones(1,30),[.4;.1])); pause; +end + +if strcmp(test,'CorrelogramArray') | strcmp(test,'all') + disp('CorrelogramArray test'); + u=MakeVowel(4000,FMPoints(4000,120),16000,'u'); + soundsc(u,16000) + coch=LyonPassiveEar(u,16000,1,4,.25); + width = 256; + cor=CorrelogramArray(coch,16000,16,width); + [pixels frames] = size(cor); + colormap(1-gray); + for j=1:frames + imagesc(reshape(cor(:,j),pixels/width,width)); + drawnow; + end +end + +if strcmp(test,'CorrelogramFrame') | strcmp(test,'all') + disp('CorrelogramFrame test'); + for j=20:-1:1 + c(j,:) = max(0,sin((1:256)/256*(21-j)*3*2*pi)); + end + picture=CorrelogramFrame(c,128,1,256); + imagesc(picture) + colormap(1-gray) +end + +if strcmp(test,'CorrelogramMovie') | strcmp(test,'all') + disp('CorrelogramMovie test'); + u=MakeVowel(4000,FMPoints(4000,120),16000,'u'); + soundsc(u,16000) + coch=LyonPassiveEar(u,16000,1,4,.25); + mov=CorrelogramMovie(coch,16000,16,256); + movie(mov,-10,16) +end + +if strcmp(test,'CorrelogramPitch') | strcmp(test,'all') + disp('CorrelogramPitch test'); + u=MakeVowel(20000,FMPoints(20000,120),22254,'u'); + cor=CorrelogramArray(u,22254,50,256); + p=CorrelogramPitch(cor,256,22254); + plot(p) + axis([0 45 110 130]); pause; + + coch=LyonPassiveEar(u,22254,1,4,.5); + cor=CorrelogramArray(coch,22254,50,256); + p=CorrelogramPitch(cor,256,22254); + plot(p) + axis([0 45 110 130]); pause; + + u=MakeVowel(20000,FMPoints(20000,120),22254,'u'); + n=randn([1 20000]).*(1:20000)/20000; + un=u+n/4; + coch=LyonPassiveEar(un,22254,1,4,.5); + cor=CorrelogramArray(coch,22254,50,256); + [pitch sal]=CorrelogramPitch(cor,256,22254,100,200); + plot(pitch); pause + plot(sal); pause +end + +if strcmp(test,'DesignLyonFilters') | strcmp(test,'all') + disp('DesignLyonFilters test'); + filts=DesignLyonFilters(16000); + size(filts) + filts(1:5,:) + + resp=soscascade([1 zeros(1,255)],filts); + freqResp=20*log10(abs(fft(resp(1:5:88,:)'))); + freqScale=(0:255)/256*16000; + semilogx(freqScale(1:128),freqResp(1:128,:)) + axis([100 10000 -60 20]); pause; +end + +if strcmp(test,'EpsilonFromTauFS') | strcmp(test,'all') + disp('EpsilonFromTauFS test'); + eps=EpsilonFromTauFS(5,1) + filter(1, [1 eps-1],[1 zeros(1,9)]) + sosfilters([1 zeros(1,9)],[1 0 0 eps-1 0]) +end + +if strcmp(test,'ERBSpace') | strcmp(test,'all') + set_window_size(250, 150); + disp('ERBSpace test') + plot(ERBSpace) +end + +if strcmp(test,'FMPoints') | strcmp(test,'all') + disp('FMPoints test'); + u=MakeVowel(20000,FMPoints(20000,120), 22050,'u'); + soundsc(u/max(u), 22050) +end + +if strcmp(test,'FreqResp') | strcmp(test,'all') + disp('FreqResp test'); + filts=DesignLyonFilters(16000); + f=10:10:7990; + resp=FreqResp(filts(2, :), f, 16000); + semilogx(f,resp); + axis([100 10000 -50 20]); +end + +if strcmp(test,'LyonPassiveEar') | strcmp(test,'all') + disp('LyonPassiveEar test'); + is=LyonPassiveEar([1 zeros(1,255)],16000,1); + imagesc(min(is, 0.0004)); pause + + s=sin((0:2041)/20000*2*pi*1000); + ys=LyonPassiveEar(s,20000,20); + imagesc(ys/max(max(ys))); pause; + + tap = wavread('tapestry.wav'); + coch=LyonPassiveEar(tap,16000,100); + imagesc(coch/max(max(coch))); pause; +end + +if strcmp(test,'MakeERBFilters') | strcmp(test,'all') + disp('MakeERBFilters test'); + fcoefs = MakeERBFilters(16000,10,100); + y = ERBFilterBank([1 zeros(1,511)], fcoefs); + resp = 20*log10(abs(fft(y'))); + freqScale = (0:511)/512*16000; + semilogx(freqScale(1:255),resp(1:255,:)); + axis([100 16000 -60 0]) + xlabel('Frequency (Hz)'); + ylabel('Filter Response (dB)'); pause; + + tap = wavread('tapestry.wav'); + fcoefs=MakeERBFilters(16000,40,100); + coch=ERBFilterBank(tap, fcoefs); + for j=1:size(coch,1) + c=max(coch(j,:),0); + c=filter([1],[1 -.99],c); + coch(j,:)=c; + end + imagesc(coch); +end + +if strcmp(test,'MakeVowel') | strcmp(test,'all') + disp('MakeVowel test'); + vowels=[MakeVowel(10000,100,16000,'a') ... + MakeVowel(10000,100,16000,'i') ... + MakeVowel(10000,100,16000,'u')]; + soundsc(vowels,16000); + + vowels=[MakeVowel(1000,100,16000,'a')... + MakeVowel(1000,100,16000,'i') ... + MakeVowel(1000,100,16000,'u')]; + s=spectrogram(vowels,256,2,2); + imagesc(s); pause +end + +if strcmp(test,'MeddisHairCell') | strcmp(test,'all') + disp('MeddisHairCell test'); + tone=sin((0:4999)/20000*2*pi*1000); + s=[zeros(1,5000) ... + tone*10^(40/20-1.35) zeros(1,5000) ... + tone*10^(45/20-1.35) zeros(1,5000) ... + tone*10^(50/20-1.35) zeros(1,5000) ... + tone*10^(55/20-1.35) zeros(1,5000) ... + tone*10^(60/20-1.35) zeros(1,5000) ... + tone*10^(65/20-1.35) zeros(1,5000) ... + tone*10^(70/20-1.35) zeros(1,5000) ... + tone*10^(75/20-1.35) zeros(1,5000) ... + tone*10^(80/20-1.35)]; + y=MeddisHairCell(s,20000); + plot((1:90000)/20000,y(1:90000)); pause +end + +if strcmp(test,'mfcc') | strcmp(test,'all') + disp('mfcc test'); + set_window_size(348, 188); + tap = wavread('tapestry.wav'); + [ceps,freqresp,fb,fbrecon,freqrecon] = mfcc(tap,16000,100); + imagesc(ceps); + colormap(1-gray); pause + imagesc(flipud(freqresp)); pause + imagesc(flipud(fb)); pause + imagesc(flipud(fbrecon)); pause + imagesc(flipud(freqrecon)); pause +end + +if strcmp(test,'proclpc') | strcmp(test,'all') + disp('proclpc test'); + set_window_size(348, 188); + [tap sr]= wavread('tapestry.wav'); + lpc_spec(tap,sr,13); pause + lpc_spec(tap,sr,26); pause +end + +if strcmp(test,'rasta') | strcmp(test,'all') + disp('rasta test'); + tap = wavread('tapestry.wav'); + [ceps,freqresp,fb,fbrecon,freqrecon] = mfcc(tap,16000,100); + rastaout = rasta(ceps,100); + + mfccDCTMatrix = 1/sqrt(40/2)*cos((0:(13-1))' * ... + (2*(0:(40-1))+1) * pi/2/40); + mfccDCTMatrix(1,:) = mfccDCTMatrix(1,:)*sqrt(2)/2; + + rastarecon = 0*fbrecon; + for i=1:size(rastaout,2) + rastarecon(:,i) = mfccDCTMatrix' * rastaout(:,i); + end + imagesc(flipud(rastarecon)); +end + +if strcmp(test,'SecondOrderFilter') | strcmp(test,'all') + disp('SecondOrderFilter test'); + f=10:10:7990; + sos=SecondOrderFilter(3000,5,16000) + filt=[1 0 0 sos(2:3)] + semilogx(f,FreqResp(filt,f,16000)); pause + + filt=[sos 0 0] + semilogx(f,FreqResp(filt,f,16000)); pause + + sos=SecondOrderFilter(3000,2,16000) + filt=[sos 0 0] + semilogx(f,FreqResp(filt,f,16000)); pause +end + +if strcmp(test,'SeneffEar') | strcmp(test,'all') + disp('SeneffEar test'); + s=[zeros(1,160) sin(2000*2*pi/16000*(1:1120))]; + y=SeneffEar(s,16000,15); pause; + + tap = wavread('tapestry.wav'); + hc=SeneffEar(tap,16000); + for j=1:40 + c=hc(j,:); + c=filter([1],[1, -.99],c); + h(j,:)=c(1:100:50381); + end + clf; imagesc(h); pause; +end + +if strcmp(test,'SetGain') | strcmp(test,'all') + disp('SetGain test'); + filts=DesignLyonFilters(16000); + filt=filts(42,:) + f=10:10:7990; + semilogx(f,FreqResp(filt,f,16000)); pause; + + newFilt = SetGain(filt, 10, 1960, 16000); + semilogx(f, FreqResp(newFilt, f, 16000)); pause +end + +if strcmp(test,'soscascade') | strcmp(test,'all') + disp('soscascade test'); + soscascade([1 0 0 0 0],[1 0 0 -.9 0;1 1 0 0 0]) +end + +if strcmp(test,'sosfilters') | strcmp(test,'all') + disp('sosfilters test'); + sosfilters([1 0 0 0 0 0],[1 0 0 -.9 0;1 0 0 -.8 0]) + sosfilters([1 0 0 0 0 0;2 0 0 0 0 0], ... + [1 0 0 -.9 0;1 0 0 -.8 0]) + sosfilters([1 0 0 0 0 0;2 0 0 0 0 0],[1 0 0 -.9 0]) +end + +if strcmp(test,'spectrogram') | strcmp(test,'all') + disp('spectrogram test'); + tap = wavread('tapestry.wav'); + spec=spectrogram(tap,64,2,1); + imagesc(spec); pause; +end + +if strcmp(test,'synlpc') | strcmp(test,'all') + disp('synlpc test'); + [tap sr] = wavread('tapestry.wav'); + [aCoeff,resid,pitch,G,parcor,stream] = proclpc(tap,sr,13); + syntap = synlpc(aCoeff, stream, sr, G);; + soundsc(syntap,16000); + + [aCoeff,resid,pitch,G,parcor,stream] = proclpc(tap,sr,13); + stream=randn(size(stream)); + syntap = synlpc(aCoeff, stream, sr, G);; + soundsc(syntap,16000); +end + +if strcmp(test,'WhiteVowel') | strcmp(test,'all') + disp('WhiteVowel test'); + [tap sr] = wavread('tapestry.wav'); + whitetap = WhiteVowel(tap,16000,13,1000); + soundsc(whitetap, 16000); +end + +function lpc_spec(tap,sr,L) +[aCoeff,resid,pitch,G,parcor,stream] = proclpc(tap,sr,L); +cft=0:(1/255):1; +spec = zeros(128, size(aCoeff,2)); +for nframe=1:size(aCoeff,2) + gain=20*log10(G(nframe)) * 0; + for index=1:size(aCoeff,1) + gain = gain + aCoeff(index,nframe)*exp(-i*2*pi*cft).^index; + end + gain = abs(1./gain); + spec(:,nframe) = 20*log10(gain(1:128))'; +end +imagesc(flipud(spec)); + + + +function set_window_size(width, height) +cursize = get(gcf, 'position'); +cursize(3) = width+1; +cursize(4) = height+1; +set(gcf, 'position', cursize); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/License.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,262 @@ +The MIRtoolbox 1.3.2 Software Package, Copyright © 2010, University of Jyvaskyla + +Developed by Olivier Lartillot, Petri Toiviainen and Tuomas Eerola Department of Music +lartillo@campus.jyu.fi + + +This program is free software; you can redistribute it and/or modify it +under the terms of version 2 of GNU General Public License as published by +the Free Software Foundation. + +This program is distributed in the hope that it will be useful, but WITHOUT ANY +WARRANTY; without even the implied warranty of MERCHANTABILITY or +FITNESS FOR A PARTICULAR PURPOSE. This is free software, and you are +welcome to redistribute it under certain conditions. See the GNU +General Public License for more details. + +You should have received a copy of the GNU General Public License along with this +program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, +Boston, MA 02111-1307, USA. + +GNU GENERAL PUBLIC LICENSE + +Version 2, June 1991 + +Copyright (C) 1989, 1991 Free Software Foundation, Inc. +59 Temple Place - Suite 330, Boston, MA 02111-1307, USA + +Everyone is permitted to copy and distribute verbatim copies of this license document, +but changing it is not allowed. + +Preamble + +The licenses for most software are designed to take away your freedom to share and +change it. By contrast, the GNU General Public License is intended to guarantee your +freedom to share and change free software--to make sure the software is free for all its +users. This General Public License applies to most of the Free Software Foundation's +software and to any other program whose authors commit to using it. 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miraudio/miraudio.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,410 @@ +function varargout = miraudio(orig,varargin) +% a = miraudio('filename') loads the sound file 'filename' (in WAV or AU +% format) into a miraudio object. +% a = miraudio('Folder') loads all the sound files in the CURRENT folder +% into a miraudio object. +% a = miraudio(v,sr), where v is a column vector, translates the vector v +% into a miraudio object. The sampling frequency is set to sr Hertz. +% Default value for sr: 44100 Hz. +% a = miraudio(b, ...), where b is already a miraudio object, performs +% operations on b specified by the optional arguments (see below). +% +% Transformation options: +% miraudio(...,'Mono',0) does not perform the default summing of +% channels into one single mono track, but instead stores each +% channel of the initial soundfile separately. +% miraudio(...,'Center') centers the signals. +% miraudio(...,'Sampling',r) resamples at sampling rate r (in Hz). +% (Requires the Signal Processing Toolbox.) +% miraudio(...,'Normal') normalizes with respect to RMS energy. +% Extraction options: +% miraudio(...,'Extract',t1,t2,u,f) extracts the signal between dates +% t1 and t2, expressed in the unit u. +% Possible values for u: +% 's' (seconds, by default), +% 'sp' (sample index, starting from 1). +% The additional optional argument f indicates the referential +% origin of the temporal positions. Possible values for f: +% 'Start' (by default) +% 'Middle' (of the sequence) +% 'End' of the sequence +% When using 'Middle' or 'End', negative values for t1 or t2 +% indicate values before the middle or the end of the audio +% sequence. +% miraudio(...,'Trim') trims the pseudo-silence beginning and end off +% the audio file. Silent frames are frames with RMS below t times +% the medium RMS of the whole audio file. +% Default value: t = 0.06 +% instead of 'Trim': +% 'TrimStart' only trims the beginning of the audio file, +% 'TrimEnd' only trims the end. +% miraudio(...,'TrimThreshold',t) specifies the trimming threshold t. +% miraudio(...,'Channel',c) or miraudio(...,'Channels',c) selects the +% channels indicated by the (array of) integer(s) c. +% Labeling option: +% miraudio(...,'Label',l) labels the audio signal(s) following the +% label(s) l. +% If l is a (series of) number(s), the audio signal(s) are +% labelled using the substring of their respective file name of +% index l. If l=0, the audio signal(s) are labelled using the +% whole file name. + + +if isnumeric(orig) + if size(orig,2) > 1 || size(orig,3) > 1 + mirerror('MIRAUDIO','Only column vectors can be imported into mirtoolbox.'); + end + if nargin == 1 + f = 44100; + else + f = varargin{1}; + end + b = 32; + if size(orig,1) == 1 + orig = orig'; + end + tp = (0:size(orig,1)-1)'/f; + t = mirtemporal([],'Time',{{tp}},'Data',{{orig}},... + 'FramePos',{{tp([1 end])}},'Sampling',{f},... + 'Name',{inputname(1)},'Label',{{}},'Clusters',{{}},... + 'Channels',[],'Centered',0,'NBits',{b},... + 'Title','Audio signal',... + 'PeakPos',{{{}}},'PeakVal',{{{}}},'PeakMode',{{{}}}); + aa.fresh = 1; + varargout = {class(aa,'miraudio',t)}; + return +end + + + center.key = 'Center'; + center.type = 'Boolean'; + center.default = 0; + center.when = 'After'; + option.center = center; + + normal.key = 'Normal'; + normal.type = 'Boolean'; + normal.default = 0; + normal.when = 'After'; + option.normal = normal; + + extract.key = {'Extract','Excerpt'}; + extract.type = 'Integer'; + extract.number = 2; + extract.default = []; + extract.unit = {'s','sp'}; + extract.defaultunit = 's'; + extract.from = {'Start','Middle','End'}; + extract.defaultfrom = 'Start'; + option.extract = extract; + + trim.type = 'String'; + trim.choice = {'NoTrim','Trim','TrimBegin','TrimStart','TrimEnd'}; + trim.default = 'NoTrim'; + trim.when = 'After'; + option.trim = trim; + + trimthreshold.key = 'TrimThreshold'; + trimthreshold.type = 'Integer'; + trimthreshold.default = .06; + trimthreshold.when = 'After'; + option.trimthreshold = trimthreshold; + + label.key = 'Label'; + label.default = ''; + label.when = 'After'; + option.label = label; + + sampling.key = 'Sampling'; + sampling.type = 'Integer'; + sampling.default = 0; + sampling.when = 'Both'; + option.sampling = sampling; + + % segment.key = 'Segment'; + % segment.type = 'Integer'; + % segment.default = []; + % segment.when = 'After'; + % option.segment = segment; + + reverse.key = 'Reverse'; + reverse.type = 'Boolean'; + reverse.default = 0; + reverse.when = 'After'; + option.reverse = reverse; + + mono.key = 'Mono'; + mono.type = 'Boolean'; + mono.default = NaN; + mono.when = 'After'; + option.mono = mono; + + separate.key = 'SeparateChannels'; + separate.type = 'Boolean'; + separate.default = 0; + option.separate = separate; + + Ch.key = {'Channel','Channels'}; + Ch.type = 'Integer'; + Ch.default = []; + Ch.when = 'After'; + option.Ch = Ch; + +specif.option = option; + +specif.beforechunk = {@beforechunk,'normal'}; +specif.eachchunk = @eachchunk; +specif.combinechunk = @combinechunk; + +if nargin > 1 && ischar(varargin{1}) && strcmp(varargin{1},'Now') + if nargin > 2 + extract = varargin{2}; + else + extract = []; + end + para = []; + varargout = {main(orig,[],para,[],extract)}; +else + varargout = mirfunction(@miraudio,orig,varargin,nargout,specif,@init,@main); +end +if isempty(varargout) + varargout = {{}}; +end + + +function [x type] = init(x,option) +if isa(x,'mirdesign') + if option.sampling + x = setresampling(x,option.sampling); + end +end +type = 'miraudio'; + + +function a = main(orig,option,after,index,extract) +if iscell(orig) + orig = orig{1}; +end +if ischar(orig) + if nargin < 5 + extract = []; + end + [d{1},tp{1},fp{1},f{1},b{1},n{1},ch{1}] = mirread(extract,orig,1,0); + t = mirtemporal([],'Time',tp,'Data',d,'FramePos',fp,'Sampling',f,... + 'Name',n,'Label',cell(1,length(d)),... + 'Clusters',cell(1,length(d)),... + 'Channels',ch,'Centered',0,'NBits',b); + t = set(t,'Title','Audio waveform'); + a.fresh = 1; + a = class(a,'miraudio',t); +else + if not(isempty(option)) && not(isempty(option.extract)) + if not(isstruct(after)) + after = struct; + end + after.extract = option.extract; + end + if isa(orig,'miraudio') + a = orig; + else + a.fresh = 1; + a = class(a,'miraudio',orig); + end +end +if not(isempty(after)) + a = post(a,after); +end + + +function a = post(a,para) +if a.fresh && isfield(para,'mono') + a.fresh = 0; + if isnan(para.mono) + para.mono = 1; + end +end +if isfield(para,'mono') && para.mono == 1 + a = mirsum(a,'Mean'); +end +d = get(a,'Data'); +t = get(a,'Time'); +ac = get(a,'AcrossChunks'); +f = get(a,'Sampling'); +cl = get(a,'Clusters'); +for h = 1:length(d) + for k = 1:length(d{h}) + tk = t{h}{k}; + dk = d{h}{k}; + if isfield(para,'extract') && not(isempty(para.extract)) + t1 = para.extract(1); + t2 = para.extract(2); + if para.extract(4) + if para.extract(4) == 1 + shift = round(size(tk,1)/2); + elseif para.extract(4) == 2 + shift = size(tk,1); + end + if para.extract(3) + shift = tk(shift,1,1); + end + t1 = t1+shift; + t2 = t2+shift; + end + if para.extract(3) % in seconds + ft = find(tk>=t1 & tk<=t2); + else % in samples + if not(t1) + warning('WARNING IN MIRAUDIO: Extract sample positions should be real positive integers.') + display('Positions incremented by one.'); + t1 = t1+1; + t2 = t2+1; + end + ft = t1:t2; + end + tk = tk(ft,:,:); + dk = dk(ft,:,:); + end + if isfield(para,'Ch') && not(isempty(para.Ch)) + dk = dk(:,:,para.Ch); + end + if isfield(para,'center') && para.center + dk = center(dk); + a = set(a,'Centered',1); + end + if isfield(para,'normal') && para.normal + nl = size(dk,1); + nc = size(dk,3); + if isempty(ac) + ee = 0; + for j = 1:nc + ee = ee+sum(dk(:,:,j).^2); + end + ee = sqrt(ee/nl/nc); + else + ee = sqrt(sum(ac.sqrsum.^2)/ac.samples); + end + dk = dk./repmat(ee,[nl,1,nc]); + end + if isfield(para,'trim') && not(isequal(para.trim,0)) ... + && not(strcmpi(para.trim,'NoTrim')) + if not(para.trimthreshold) + para.trimthreshold = 0.06; + end + trimframe = 100; + trimhop = 10; + nframes = floor((length(tk)-trimframe)/trimhop)+1; + rms = zeros(1,nframes); + for j = 1:nframes + st = floor((j-1)*trimhop)+1; + for z = 1:size(dk,3) + rms(1,j,z) = norm(dk(st:st+trimframe-1,1,z))/sqrt(trimframe); + end + end + rms = (rms-repmat(min(rms),[1,size(rms,2),1]))... + ./repmat(max(rms)-min(rms),[1,size(rms,2),1]); + nosil = find(rms>para.trimthreshold); + if strcmpi(para.trim,'Trim') || strcmpi(para.trim,'TrimStart') ... + || strcmpi(para.trim,'TrimBegin') + nosil1 = min(nosil); + if nosil1 > 1 + nosil1 = nosil1-1; + end + n1 = floor((nosil1-1)*trimhop)+1; + else + n1 = 1; + end + if strcmpi(para.trim,'Trim') || strcmpi(para.trim,'TrimEnd') + nosil2 = max(nosil); + if nosil2 < length(rms) + nosil2 = nosil2+1; + end + n2 = floor((nosil2-1)*trimhop)+1; + else + n2 = length(tk); + end + wh = ones(n2-n1+1,1); + dt = round(.02*f{h}); + ha = hann(dt*2); + wh(1:dt) = ha(1:dt); + wh(end-dt+1:end) = ha(dt+1:end); + tk = tk(n1:n2); + dk = dk(n1:n2,1,:);%.*repmat(wh,[1 1 size(dk,3)]); + end + if isfield(para,'sampling') && para.sampling + if and(f{k}, not(f{k} == para.sampling)) + for j = 1:size(dk,3) + rk(:,:,j) = resample(dk(:,:,j),para.sampling,f{k}); + end + dk = rk; + tk = repmat((0:size(dk,1)-1)',[1 1 size(tk,3)])... + /para.sampling + tk(1,:,:); + end + f{k} = para.sampling; + end + d{h}{k} = dk; + t{h}{k} = tk; + %if isfield(para,'reverse') && para.reverse + % d{h}{k} = flipdim(d{h}{k},1); + %end + end +end +a = set(a,'Data',d,'Time',t,'Sampling',f,'Clusters',cl); +if isfield(para,'label') + if isnumeric(para.label) + n = get(a,'Name'); + l = cell(1,length(d)); + for k = 1:length(d) + if para.label + l{k} = n{k}(para.label); + else + l{k} = n{k}; + end + end + a = set(a,'Label',l); + elseif iscell(para.label) + idx = mod(get(a,'Index'),length(para.label)); + if not(idx) + idx = length(para.label); + end + a = set(a,'Label',para.label{idx}); + elseif ischar(para.label) + l = cell(1,length(d)); + for k = 1:length(d) + l{k} = para.label; + end + a = set(a,'Label',l); + end +end + + +function [new orig] = beforechunk(orig,option,missing) +option.normal = 0; +a = miraudio(orig,option); +d = get(a,'Data'); +old = get(orig,'AcrossChunks'); +if isempty(old) + old.sqrsum = 0; + old.samples = 0; +end +new = mircompute(@crossum,d); +new = new{1}{1}; +new.sqrsum = old.sqrsum + new.sqrsum; +new.samples = old.samples + new.samples; + + +function s = crossum(d) +s.sqrsum = sum(d.^2); +s.samples = length(d); + + +function [y orig] = eachchunk(orig,option,missing) +y = miraudio(orig,option); + + +function y = combinechunk(old,new) +do = get(old,'Data'); +to = get(old,'Time'); +dn = get(new,'Data'); +tn = get(new,'Time'); +y = set(old,'Data',{{[do{1}{1};dn{1}{1}]}},... + 'Time',{{[to{1}{1};tn{1}{1}]}}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miraudio/mirplay.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,127 @@ +function varargout = mirplay(a,varargin) + +% mirplay method for miraudio objects. Help displayed in ../mirplay.m + + ch.key = 'Channel'; + ch.type = 'Integer'; + ch.default = 0; + option.ch = ch; + + sg.key = 'Segment'; + sg.type = 'Integer'; + sg.default = 0; + option.sg = sg; + + se.key = 'Sequence'; + se.type = 'Integer'; + se.default = 0; + option.se = se; + + inc.key = 'Increasing'; + inc.type = 'MIRtb'; + option.inc = inc; + + dec.key = 'Decreasing'; + dec.type = 'MIRtb'; + option.dec = dec; + + every.key = 'Every'; + every.type = 'Integer'; + option.every = every; + + burst.key = 'Burst'; + burst.type = 'Boolean'; + burst.default = 1; + option.burst = burst; + +specif.option = option; + +specif.eachchunk = 'Normal'; + +varargout = mirfunction(@mirplay,a,varargin,nargout,specif,@init,@main); +if nargout == 0 + varargout = {}; +end + + +function [x type] = init(x,option) +type = ''; + + +function noargout = main(a,option,postoption) +if iscell(a) + a = a{1}; +end +d = get(a,'Data'); +f = get(a,'Sampling'); +n = get(a,'Name'); +c = get(a,'Channels'); +fp = get(a,'FramePos'); +if not(option.se) + if length(d)>1 + if isfield(option,'inc') + [unused order] = sort(mirgetdata(option.inc)); + elseif isfield(option,'dec') + [unused order] = sort(mirgetdata(option.dec),'descend'); + else + order = 1:length(d); + end + if isfield(option,'every') + order = order(1:option.every:end); + end + else + order = 1; + end +else + order = option.se; +end +if not(isempty(order)) + for k = order(:)' + display(['Playing file: ' n{k}]) + dk = d{k}; + if not(iscell(dk)) + dk = {dk}; + end + if option.ch + if isempty(c{k}) + chk = option.ch; + else + [unused unused chk] = intersect(option.ch,c{k}); + end + else + chk = 1:size(dk{1},3); + end + if isempty(chk) + display('No channel to play.'); + end + for l = chk + if chk(end)>1 + display([' Playing channel #' num2str(l)]); + end + if option.sg + sgk = option.sg(find(option.sg<=length(dk))); + else + sgk = 1:length(dk); + end + for i = sgk + if sgk(end)>1 + display([' Playing segment #' num2str(i)]) + end + di = dk{i}; + for j = 1:size(di,2) + tic + sound(di(:,j,l),f{k}); + idealtime = size(di,1)/f{k}; + practime = toc; + if practime < idealtime + pause(idealtime-practime) + end + end + if option.burst && sgk(end)>1 + sound(rand(1,10)) + end + end + end + end +end +noargout = {}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miraudio/mirsave.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,99 @@ +function mirsave(a,f,multichan) + +ext = 0; % Specified new extension +if nargin < 2 + f = '.mir'; +elseif length(f)>3 && strcmpi(f(end-3:end),'.wav') + ext = '.wav'; + if length(f)==4 + f = '.mir'; + end +elseif length(f)>2 && strcmpi(f(end-2:end),'.au') + ext = '.au'; + if length(f)==3 + f = '.mir'; + end +end + +if nargin < 3 + multichan = ''; +end + +d = get(a,'Data'); +nf = length(d); +fs = get(a,'Sampling'); +nb = get(a,'NBits'); +nm = get(a,'Name'); +ch = get(a,'Channels'); +maxd = 0; +for i = 1:nf + for j = 1:length(d{i}) + maxd = max(max(max(max(abs(d{i}{j}),[],1),[],2),[],3),maxd); + end +end +for i = 1:nf + nbi = nb{i}; + di = d{i}; + fsi = fs{i}; + nmi = nm{i}; + chi = ch{i}; + out = []; + for j = 1:length(di) + di{j} = di{j}./repmat(maxd,size(di{j}))*.9999; + out = [out;reshape(di{j},[],size(di{j},3),1)]; + if length(di)>1 + out = [out;rand(100,size(di{j},3))]*.9; + end + end + + %Let's remove the extension from the original files + if length(nmi)>3 && strcmpi(nmi(end-3:end),'.wav') + nmi(end-3:end) = []; + elseif length(nmi)>2 && strcmpi(nmi(end-2:end),'.au') + nmi(end-2:end) = []; + end + + nchan = size(out,2); + if isempty(multichan) || nchan < 2 + if nf>1 || strcmp(f(1),'.') + %Let's add the new suffix + n = [nmi f]; + else + n = f; + end + if not(ischar(ext)) || strcmp(ext,'.wav') + if length(n)<4 || not(strcmpi(n(end-3:end),'.wav')) + n = [n '.wav']; + end + wavwrite(out,fsi,nbi,n) + elseif strcmp(ext,'.au') + if length(n)<3 || not(strcmpi(n(end-2:end),'.au')) + n = [n '.au']; + end + auwrite(out,fsi,nbi,'linear',n) + end + disp([n,' saved.']); + else + for j = 1:nchan + nb = num2str(chi(j)); + if nf>1 || strcmp(f(1),'.') + %Let's add the new suffix + n = [nmi nb f]; + else + n = [f nb]; + end + if not(ischar(ext)) || strcmp(ext,'.wav') + if length(n)<4 || not(strcmpi(n(end-3:end),'.wav')) + n = [n '.wav']; + end + wavwrite(out(:,j),fsi,nbi,n) + elseif strcmp(ext,'.au') + if length(n)<3 || not(strcmpi(n(end-2:end),'.au')) + n = [n '.au']; + end + auwrite(out(:,j),fsi,nbi,'linear',n) + end + disp([n,' saved.']); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miraudio/plus.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function c = plus(a,b) + +sa = get(a,'Sampling'); +if iscell(sa) + sa = sa{1}; +end +sb = get(b,'Sampling'); +if iscell(sb) + sb = sb{1}; +end +if sa>sb + b = miraudio(b,'Sampling',sa); +elseif sb>sa + a = miraudio(a,'Sampling',sb); +end +c = plus(mirtemporal(a),mirtemporal(b)); +c = miraudio(c); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miraudio/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function aa = set(a,varargin) +% SET Set properties for the miraudio object +% and return the updated object + +t = mirtemporal(a); +t = set(t,'Title',get(a,'Title'),'Abs',get(a,'Abs'),'Ord',get(a,'Ord'),... + varargin{:}); +aa.fresh = a.fresh; +aa = class(aa,'miraudio',t); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirautocor/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function val = get(a, propName) +% GET Get properties from the MIRautocor object +% and return the value + +switch propName + case 'Coeff' + val = get(mirdata(a),'Data'); + case 'Delay' + val = get(mirdata(a),'Pos'); + case 'Lag' + val = get(mirdata(a),'Pos'); + case 'FreqDomain' + val = a.freq; + case 'OfSpectrum' + val = a.ofspectrum; + case 'Window' + val = a.window; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirautocor/mirautocor.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,604 @@ +function varargout = mirautocor(orig,varargin) +% a = mirautocor(x) computes the autocorrelation function related to x. +% Optional parameters: +% mirautocor(...,'Min',mi) indicates the lowest delay taken into +% consideration. The unit can be precised: +% mirautocor(...,'Min',mi,'s') (default unit) +% mirautocor(...,'Min',mi,'Hz') +% Default value: 0 s. +% mirautocor(...,'Max',ma) indicates the highest delay taken into +% consideration. The unit can be specified as for 'Min'. +% Default value: +% if x is a signal, the highest delay is 0.05 s +% (corresponding to a minimum frequency of 20 Hz). +% if x is an envelope, the highest delay is 2 s. +% mirautocor(...,'Resonance',r) multiplies the autocorrelation function +% with a resonance curve: +% Possible values: +% 'Toiviainen' from (Toiviainen & Snyder, 2003) +% 'vanNoorden' from (van Noorden & Moelants, 2001) +% mirautocor(...,'Center',c) assigns the center value of the +% resonance curve, in seconds. +% Works mainly with 'Toiviainen' option. +% Default value: c = 0.5 +% mirautocor(...,'Enhanced',a) reduces the effect of subharmonics. +% The original autocorrelation function is half-wave rectified, +% time-scaled by the factor a (which can be a factor list as +% well), and substracted from the original clipped function. +% (Tolonen & Karjalainen) +% If the 'Enhanced' option is not followed by any value, +% default value is a = 2:10 +% mirautocor(...,'Halfwave') performs a half-wave rectification on the +% result. +% mirautocor(...,'Freq') represents the autocorrelation function in the +% frequency domain. +% mirautocor(...,'NormalWindow',w): applies a window to the input +% signal and divides the autocorrelation by the autocorrelation of +% that window (Boersma 1993). +% Possible values: any windowing function proposed in the Signal +% Processing Toolbox (help window) plus 'rectangle' (no +% windowing) +% Default value: w = 'hanning' +% mirautocor(...,'NormalWindow',0): toggles off this normalization +% (which is on by default). +% All the parameters described previously can be applied to an +% autocorrelation function itself, in order to arrange the results +% after the actual computation of the autocorrelation computations. +% For instance: a = mirautocor(a,'Resonance','Enhanced') +% Other optional parameter: +% mirautocor(...,'Compres',k) computes the autocorrelation in the +% frequency domain and includes a magnitude compression of the +% spectral representation. A normal autocorrelation corresponds +% to the value k=2, but values lower than 2 are suggested by +% (Tolonen & Karjalainen, 2000). +% Default value: k = 0.67 +% mirautocor(...,'Normal',n) or simply mirautocor(...,n) specifies +% the normalization strategy. Accepted values are 'biased', +% 'unbiased', 'coeff' (default value) and 'none'. +% See help xcorr for an explanation. + + min.key = 'Min'; + min.type = 'Integer'; + min.unit = {'s','Hz'}; + if isamir(orig,'mirspectrum') + min.defaultunit = 'Hz'; + else + min.defaultunit = 's'; + end + min.default = 0; + min.opposite = 'max'; + option.min = min; + + max.key = 'Max'; + max.type = 'Integer'; + max.unit = {'s','Hz'}; + if isamir(orig,'mirspectrum') + max.defaultunit = 'Hz'; + else + max.defaultunit = 's'; + end + if isamir(orig,'mirenvelope') || isamir(orig,'mirdiffenvelope') + max.default = 2; % for envelopes, longest period: 2 seconds. + elseif isamir(orig,'miraudio') || ischar(orig) % for audio signal,lowest frequency: 20 Hz. + max.default = 1/20; + else + max.default = Inf; + end + max.opposite = 'min'; + option.max = max; + + scaleoptbw.key = 'Normal'; %'Normal' keyword optional + scaleoptbw.key = 'Boolean'; + option.scaleoptbw = scaleoptbw; + scaleopt.type = 'String'; + scaleopt.choice = {'biased','unbiased','coeff','none'}; + scaleopt.default = 'coeff'; + option.scaleopt = scaleopt; + + gener.key = {'Generalized','Compres'}; + gener.type = 'Integer'; + gener.default = 2; + gener.keydefault = .67; + option.gener = gener; + + ni.key = 'NormalInput'; %% Normalize before frame or chunk?? + ni.type = 'Boolean'; + ni.default = 0; + option.ni = ni; + + reso.key = 'Resonance'; + reso.type = 'String'; + reso.choice = {'ToiviainenSnyder','Toiviainen','vanNoorden','no','off',0}; + reso.keydefault = 'Toiviainen'; + reso.when = 'After'; + reso.default = 0; + option.reso = reso; + + resocenter.key = {'Center','Centre'}; + resocenter.type = 'Integer'; + resocenter.when = 'After'; + option.resocenter = resocenter; + + h.key = 'Halfwave'; + h.type = 'Boolean'; + h.when = 'After'; + h.default = 0; + option.h = h; + + e.key = 'Enhanced'; + e.type = 'Integers'; + e.default = []; + e.keydefault = 2:10; + e.when = 'After'; + option.e = e; + + fr.key = 'Freq'; + fr.type = 'Boolean'; + fr.default = 0; + fr.when = 'After'; + option.fr = fr; + + nw.key = 'NormalWindow'; + nw.when = 'Both'; + if isamir(orig,'mirspectrum') + nw.default = 0; + elseif isamir(orig,'mirenvelope') + nw.default = 'rectangular'; + else + nw.default = 'hanning'; + end + option.nw = nw; + + win.key = 'Window'; + win.type = 'String'; + win.default = NaN; + option.win = win; + +specif.option = option; + +specif.defaultframelength = 0.05; +specif.defaultframehop = 0.5; +specif.eachchunk = @eachchunk; +specif.combinechunk = @combinechunk; + +if isamir(orig,'mirscalar') || isamir(orig,'mirenvelope') + specif.nochunk = 1; +end + +varargout = mirfunction(@mirautocor,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = 'mirautocor'; + + +function a = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +if isa(orig,'mirautocor') + a = orig; + if not(isempty(option)) && ... + (option.min || iscell(option.max) || option.max < Inf) + coeff = get(a,'Coeff'); + delay = get(a,'Delay'); + for h = 1:length(coeff) + if a.freq + mi = 1/option.max; + ma = 1/option.min; + else + mi = option.min; + ma = option.max; + end + for k = 1:length(coeff{h}) + range = find(and(delay{h}{k}(:,1,1) >= mi,... + delay{h}{k}(:,1,1) <= ma)); + coeff{h}{k} = coeff{h}{k}(range,:,:); + delay{h}{k} = delay{h}{k}(range,:,:); + end + end + a = set(a,'Coeff',coeff,'Delay',delay); + end + if not(isempty(postoption)) && not(isequal(postoption,0)) + a = post(a,postoption); + end +elseif ischar(orig) + a = mirautocor(miraudio(orig),option,postoption); +else + if nargin == 0 + orig = []; + end + a.freq = 0; + a.ofspectrum = 0; + a.window = {}; + a.normalwindow = 0; + a = class(a,'mirautocor',mirdata(orig)); + a = purgedata(a); + a = set(a,'Ord','coefficients'); + sig = get(orig,'Data'); + if isa(orig,'mirspectrum') + a = set(a,'Title','Spectrum autocorrelation','OfSpectrum',1,... + 'Abs','frequency (Hz)'); + pos = get(orig,'Pos'); + else + if isa(orig,'mirscalar') + a = set(a,'Title',[get(orig,'Title') ' autocorrelation']); + pos = get(orig,'FramePos'); + for k = 1:length(sig) + for l = 1:length(sig{k}) + sig{k}{l} = sig{k}{l}'; + pos{k}{l} = pos{k}{l}(1,:,:)'; + end + end + else + if isa(orig,'mirenvelope') + a = set(a,'Title','Envelope autocorrelation'); + elseif not(isa(orig,'mirautocor')) + a = set(a,'Title','Waveform autocorrelation'); + end + pos = get(orig,'Pos'); + end + a = set(a,'Abs','lag (s)'); + end + f = get(orig,'Sampling'); + + if isnan(option.win) + if isequal(option.nw,0) || ... + strcmpi(option.nw,'Off') || strcmpi(option.nw,'No') + option.win = 0; + elseif isequal(option.nw,1) || strcmpi(option.nw,'On') || ... + strcmpi(option.nw,'Yes') + option.win = 'hanning'; + else + option.win = postoption.nw; + end + end + + coeff = cell(1,length(sig)); + lags = cell(1,length(sig)); + wind = cell(1,length(sig)); + for k = 1:length(sig) + s = sig{k}; + p = pos{k}; + fk = f{k}; + if iscell(option.max) + mi = option.min{k}; + ma = option.max{k}; + else + mi = option.min; + ma = option.max; + end + coeffk = cell(1,length(s)); + lagsk = cell(1,length(s)); + windk = cell(1,length(s)); + for l = 1:length(s) + sl = s{l}; + sl(isnan(sl)) = 0; + if option.ni + mxsl = repmat(max(sl),[size(sl,1),1,1]); + mnsl = repmat(min(sl),[size(sl,1),1,1]); + sl = (sl-mnsl)./(mxsl-mnsl); + end + pl = p{l}; + pl = pl-repmat(pl(1,:,:),[size(pl,1),1,1]); + ls = size(sl,1); + if mi + misp = find(pl(:,1,1)>=mi); + if isempty(misp) + warning('WARNING IN MIRAUTOCOR: The specified range of delays exceeds the temporal length of the signal.'); + disp('Minimum delay set to zero.') + misp = 1; % misp is the lowest index of the lag range + mi = 0; + else + misp = misp(1); + end + else + misp = 1; + end + if ma + masp = find(pl(:,1,1)>=ma); + if isempty(masp) + masp = Inf; + else + masp = masp(1); + end + else + masp = Inf; + end + masp = min(masp,ceil(ls/2)); + if masp <= misp + if size(sl,2) > 1 + warning('WARNING IN MIRAUTOCOR: Frame length is too small.'); + else + warning('WARNING IN MIRAUTOCOR: The audio sequence is too small.'); + end + display('The autocorrelation is not defined for this range of delays.'); + end + sl = center(sl); + if not(ischar(option.win)) || strcmpi(option.win,'Rectangular') + kw = ones(size(sl)); + else + N = size(sl,1); + winf = str2func(option.win); + try + w = window(winf,N); + catch + if strcmpi(option.win,'hamming') + disp('Signal Processing Toolbox does not seem to be installed. Recompute the hamming window manually.'); + w = 0.54 - 0.46 * cos(2*pi*(0:N-1)'/(N-1)); + else + warning(['WARNING in MIRAUTOCOR: Unknown windowing function ',option.win,' (maybe Signal Processing Toolbox is not installed).']); + disp('No windowing performed.') + w = ones(size(sl,1),1); + end + end + kw = repmat(w,[1,size(sl,2),size(sl,3)]); + sl = sl.* kw; + end + + if strcmpi(option.scaleopt,'coeff') + scaleopt = 'none'; + else + scaleopt = option.scaleopt; + end + c = zeros(masp,size(sl,2),size(sl,3)); + for i = 1:size(sl,2) + for j = 1:size(sl,3) + if option.gener == 2 + cc = xcorr(sl(:,i,j),masp-1,scaleopt); + c(:,i,j) = cc(masp:end); + else + ss = abs(fft(sl(:,i,j))); + ss = ss.^option.gener; + cc = ifft(ss); + ll = (0:masp-1); + c(:,i,j) = cc(ll+1); + end + end + if strcmpi(option.scaleopt,'coeff') && option.gener == 2 + % to be adapted to generalized autocor + c(:,i,:) = c(:,i,:)/xcorr(sum(sl(:,i,:),3),0); + % This is a kind of generalization of the 'coeff' + % normalization for multi-channels signals. In the + % original 'coeff' option, the autocorrelation at zero + % lag is identically 1.0. In this multi-channels + % version, the autocorrelation at zero lag is such that + % the sum over channels becomes identically 1.0. + end + end + coeffk{l} = c(misp:end,:,:); + pl = pl(find(pl(:,1,1) >=mi),:,:); + lagsk{l} = pl(1:min(size(coeffk{l},1),size(pl,1)),:,:); + windk{l} = kw; + end + coeff{k} = coeffk; + lags{k} = lagsk; + wind{k} = windk; + end + a = set(a,'Coeff',coeff,'Delay',lags,'Window',wind); + if not(isempty(postoption)) + a = post(a,postoption); + end +end + + +function a = post(a,option) +debug = 0; +coeff = get(a,'Coeff'); +lags = get(a,'Delay'); +wind = get(a,'Window'); +freq = option.fr && not(get(a,'FreqDomain')); +if isequal(option.e,1) + option.e = 2:10; +end +if max(option.e) > 1 + pa = mirpeaks(a,'NoBegin','NoEnd','Contrast',.01); + va = mirpeaks(a,'Valleys','Contrast',.01); + pv = get(pa,'PeakVal'); + vv = get(va,'PeakVal'); +end +for k = 1:length(coeff) + for l = 1:length(coeff{k}) + c = coeff{k}{l}; % Coefficients of autocorrelation + t = lags{k}{l}; % Delays of autocorrelation + if not(isempty(c)) + if not(isequal(option.nw,0) || strcmpi(option.nw,'No') || ... + strcmpi(option.nw,'Off') || a.normalwindow) % 'NormalWindow' option + xw = zeros(size(c)); + lc = size(c,1); + for j = 1:size(c,3) + for i = 1:size(c,2) + xwij = xcorr(wind{k}{l}(:,i,j),lc,'coeff'); + xw(:,i,j) = xwij(lc+2:end); + end + end + c = c./ xw; + a.normalwindow = 1; + end + if ischar(option.reso) && ... + (strcmpi(option.reso,'ToiviainenSnyder') || ... + strcmpi(option.reso,'Toiviainen') || ... + strcmpi(option.reso,'vanNoorden')) + if isa(a,'mirautocor') && get(a,'FreqDomain') + ll = 1./t; + else + ll = t; + end + if not(option.resocenter) + option.resocenter = .5; + end + if strcmpi(option.reso,'ToiviainenSnyder') || ... + strcmpi(option.reso,'Toiviainen') + w = max(1 - 0.25*(log2(max(ll,1e-12)/option.resocenter)).^2, 0); + elseif strcmpi(option.reso,'vanNoorden') + f0=2.193; b=option.resocenter; + f=1./ll; a1=(f0*f0-f.*f).^2+b*f.^2; a2=f0^4+f.^4; + w=(1./sqrt(a1))-(1./sqrt(a2)); + end + if max(w) == 0 + warning('The resonance curve, not defined for this range of delays, will not be applied.') + else + w = w/max(w); + c = c.* repmat(w,[1,size(c,2),size(c,3)]); + end + end + if option.h + c = hwr(c); + end + if max(option.e) > 1 + if a.freq + freq = 1; + for i = 1:size(c,3) + c(:,:,i) = flipud(c(:,:,i)); + end + t = flipud(1./t); + end + + for g = 1:size(c,2) + for h = 1:size(c,3) + cgh = c(:,g,h); + if length(cgh)>1 + pvk = pv{k}{l}{1,g,h}; + mv = []; + if not(isempty(pvk)) + mp = min(pv{k}{l}{1,g,h}); %Lowest peak + vvv = vv{k}{l}{1,g,h}; %Valleys + mv = vvv(find(vvv<mp,1,'last')); + %Highest valley below the lowest peak + + if not(isempty(mv)) + cgh = cgh-mv; + end + end + cgh2 = cgh; + tgh2 = t(:,g,1); + coef = cgh(2)-cgh(1); % initial slope of the autocor curve + tcoef = tgh2(2)-tgh2(1); + deter = 0; + inter = 0; + + repet = find(not(diff(tgh2))); % Avoid bug if repeated x-values + if repet + warning('WARNING in MIRAUTOCOR: Two successive samples have exactly same temporal position.'); + tgh2(repet+1) = tgh2(repet)+1e-12; + end + + if coef < 0 + % initial descending slope removed + deter = find(diff(cgh2)>0,1)-1; + % number of removed points + if isempty(deter) + deter = 0; + end + cgh2(1:deter) = []; + tgh2(1:deter) = []; + coef = cgh2(2)-cgh2(1); + end + + if coef > 0 + % initial ascending slope prolonged to the left + % until it reaches the x-axis + while cgh2(1) > 0 + coef = coef*1.1; + % the further to the left, ... + % the more ascending is the slope + % (not sure it always works, though...) + inter = inter+1; + % number of added points + cgh2 = [cgh2(1)-coef; cgh2]; + tgh2 = [tgh2(1)-tcoef; tgh2]; + end + cgh2(1) = 0; + end + + for i = option.e % Enhancing procedure + % option.e is the list of scaling factors + % i is the scaling factor + if i + be = find(tgh2 & tgh2/i >= tgh2(1),1); + % starting point of the substraction + % on the X-axis + + if not(isempty(be)) + ic = interp1(tgh2,cgh2,tgh2/i); + % The scaled autocorrelation + ic(1:be-1) = 0; + ic(find(isnan(ic))) = Inf; + % All the NaN values are changed + % into 0 in the resulting curve + ic = max(ic,0); + + if debug + hold off,plot(tgh2,cgh2) + end + + cgh2 = cgh2 - ic; + % The scaled autocorrelation + % is substracted to the initial one + + cgh2 = max(cgh2,0); + % Half-wave rectification + + if debug + hold on,plot(tgh2,ic,'r') + hold on,plot(tgh2,cgh2,'g') + drawnow + figure + end + end + end + end + + % The temporary modifications are + % removed from the final curve + if inter>=deter + c(:,g,h) = cgh2(inter-deter+1:end); + if not(isempty(mv)) + c(:,g,h) = c(:,g,h) + mv; + end + else + c(:,g,h) = [zeros(deter-inter,1);cgh2]; + end + end + end + end + end + if freq + if t(1,1) == 0 + c = c(2:end,:,:); + t = t(2:end,:,:); + end + for i = 1:size(c,3) + c(:,:,i) = flipud(c(:,:,i)); + end + t = flipud(1./t); + end + coeff{k}{l} = c; + lags{k}{l} = t; + end + end +end +a = set(a,'Coeff',coeff,'Delay',lags,'Freq'); +if freq + a = set(a,'FreqDomain',1,'Abs','frequency (Hz)'); +end + + +function [y orig] = eachchunk(orig,option,missing,postchunk) +option.scaleopt = 'none'; +y = mirautocor(orig,option); + + +function y = combinechunk(old,new) +do = get(old,'Data'); +do = do{1}{1}; +dn = get(new,'Data'); +dn = dn{1}{1}; +if abs(size(dn,1)-size(do,1)) <= 2 % Probleme of border fluctuation + mi = min(size(dn,1),size(do,1)); + dn = dn(1:mi,:,:); + do = do(1:mi,:,:); +elseif length(dn) < length(do) + dn(length(do),:,:) = 0; % Zero-padding +end +y = set(old,'ChunkData',do+dn);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirautocor/mtimes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function c = mtimes(a,b) + +if not(get(a,'OfSpectrum')) + a = mirautocor(a,'Freq'); +end +if isa(b,'mirautocor') + if not(get(b,'OfSpectrum')) + b = mirautocor(b,'Freq'); + end +elseif isa(b,'mircepstrum') + b = mircepstrum(b,'Freq'); +end +c = mirtimes(a,b); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirautocor/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function aa = set(a,varargin) +% SET Set properties for the MIRautocor object +% and return the updated object + +propertyArgIn = varargin; +f = a.freq; +s = a.ofspectrum; +w = a.window; +nw = a.normalwindow; +d = mirdata(a); +d = set(d,'Title',get(a,'Title'),'Abs',get(a,'Abs'),'Ord',get(a,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Coeff' + d = set(d,'Data',val); + case 'Delay' + d = set(d,'Pos',val); + case 'FreqDomain' + f = val; + case 'OfSpectrum' + s = val; + case 'Window' + w = val; + case 'LowRemoved' + lr = val; + otherwise + d = set(d,prop,val); + end +end +aa.freq = f; +aa.ofspectrum = s; +aa.window = w; +aa.normalwindow = nw; +aa = class(aa,'mirautocor',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mircepstrum/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function val = get(a, propName) +% GET Get properties from the MIRcepstrum object +% and return the value + +switch propName + case 'Magnitude' + val = get(mirdata(a),'Data'); + case 'Phase' + val = a.phase; + case 'Quefrency' + val = get(mirdata(a),'Pos'); + case 'FreqDomain' + val = a.freq; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mircepstrum/mircepstrum.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,149 @@ +function varargout = mircepstrum(orig,varargin) +% s = mircepstrum(x) computes the cepstrum, which indicates +% periodicities, and is used for instance for pitch detection. +% x can be either a spectrum, an audio signal, or the name of an audio file. +% Optional parameter: +% mircepstrum(...,'Min',min) specifies the lowest delay taken into +% consideration, in seconds. +% Default value: 0.0002 s (corresponding to a maximum frequency of +% 5 kHz). +% mircepstrum(...,'Max',max) specifies the highest delay taken into +% consideration, in seconds. +% Default value: 0.05 s (corresponding to a minimum frequency of +% 20 Hz). +% mircepstrum(...,'Freq') represents the cepstrum in the frequency +% domain. + + mi.key = 'Min'; + mi.type = 'Integer'; + mi.default = 0.0002; + mi.unit = {'s','Hz'}; + mi.defaultunit = 's'; + mi.opposite = 'ma'; + option.mi = mi; + + ma.key = 'Max'; + ma.type = 'Integer'; + ma.default = .05; + ma.unit = {'s','Hz'}; + ma.defaultunit = 's'; + ma.opposite = 'mi'; + option.ma = ma; + + fr.key = 'Freq'; + fr.type = 'Boolean'; + fr.default = 0; + option.fr = fr; + + complex.key = 'Complex'; + complex.type = 'Boolean'; + complex.default = 0; + option.complex = complex; + +specif.option = option; + +specif.defaultframelength = 0.05; +specif.defaultframehop = 0.5; + +varargout = mirfunction(@mircepstrum,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mircepstrum')) + x = mirspectrum(x); +end +type = 'mircepstrum'; + + +function c = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +c.phase = []; +if isa(orig,'mircepstrum') + c.freq = orig.freq; +else + c.freq = 0; +end +c = class(c,'mircepstrum',mirdata(orig)); +c = purgedata(c); +c = set(c,'Title','Cepstrum','Abs','quefrency (s)','Ord','magnitude'); + +if isa(orig,'mircepstrum') + if option.ma < Inf || option.mi > 0 || get(orig,'FreqDomain') + mag = get(orig,'Magnitude'); + pha = get(orig,'Phase'); + que = get(orig,'Quefrency'); + for h = 1:length(mag) + for k = 1:length(mag{h}) + if get(orig,'FreqDomain') + mag{h}{k} = flipud(mag{h}{k}); + que{h}{k} = flipud(1./que{h}{k}); + pha{h}{k} = flipud(pha{h}{k}); + end + range = find(que{h}{k}(:,1,1) <= option.ma & ... + que{h}{k}(:,1,1) >= option.mi); + mag{h}{k} = mag{h}{k}(range,:,:); + pha{h}{k} = pha{h}{k}(range,:,:); + que{h}{k} = que{h}{k}(range,:,:); + end + end + c = set(c,'Magnitude',mag,'Phase',pha,'Quefrency',que,'FreqDomain',0); + end + c = modif(c,option); +elseif isa(orig,'mirspectrum') + mag = get(orig,'Magnitude'); + pha = get(orig,'Phase'); + f = get(orig,'Sampling'); + q = cell(1,length(mag)); + for h = 1:length(mag) + len = ceil(option.ma*f{h}); + start = ceil(option.mi*f{h})+1; + q{h} = cell(1,length(mag{h})); + for k = 1:length(mag{h}) + m = mag{h}{k}.*exp(1i*pha{h}{k}); + m = [m(1:end-1,:) ; conj(flipud(m))]; % Reconstitution of the complete abs(FFT) + if not(option.complex) + m = abs(m); + end + m = log(m); + c0=fft(m); + q0=repmat((0:(size(c0,1)-1))'/f{k},[1,size(m,2),size(m,3)]); + len = min(len,floor(size(c0,1)/2)); + mag{h}{k} = abs(c0(start:len,:,:)); + if option.complex + pha{h}{k} = unwrap(angle(c0(start:len,:,:))); + else + pha{h}{k} = nan(size(c0(start:len,:,:))); + end + q{h}{k} = q0(start:len,:,:); + end + end + c = set(c,'Magnitude',mag,'Phase',pha,'Quefrency',q); + c = modif(c,option); +end + + +function c = modif(c,option) +mag = get(c,'Magnitude'); +que = get(c,'Quefrency'); +if option.fr && not(get(c,'FreqDomain')) + for k = 1:length(mag) + for l = 1:length(mag{k}) + m = mag{k}{l}; + q = que{k}{l}; + if not(isempty(m)) + if q(1,1) == 0 + m = m(2:end,:,:); + q = q(2:end,:,:); + end + m = flipud(m); + q = flipud(1./q); + end + mag{k}{l} = m; + que{k}{l} = q; + end + end + c = set(c,'FreqDomain',1,'Abs','frequency (Hz)'); +end +c = set(c,'Magnitude',mag,'Quefrency',que,'Freq'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mircepstrum/mtimes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function c = mtimes(a,b) + +a = mircepstrum(a,'Freq'); +if isa(b,'mirautocor') + if not(get(b,'OfSpectrum')) + b = mirautocor(b,'Freq'); + end +elseif isa(b,'mircepstrum') + b = mircepstrum(b,'Freq'); +end +c = mirtimes(a,b); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mircepstrum/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function ss = set(s,varargin) +% SET Set properties for the MIRcepstrum object +% and return the updated object + +propertyArgIn = varargin; +p = s.phase; +f = s.freq; +d = mirdata(s); +d = set(d,'Title',get(s,'Title'),'Abs',get(s,'Abs'),'Ord',get(s,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Magnitude' + d = set(d,'Data',val); + case 'Phase' + p = val; + case 'Quefrency' + d = set(d,'Pos',val); + case 'FreqDomain' + f = val; + otherwise + d = set(d,prop,val); + end +end +ss.phase = p; +ss.freq = f; +ss = class(ss,'mircepstrum',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirchromagram/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function val = get(a, propName) +% GET Get properties from the MIRchromagram object +% and return the value + +switch propName + case 'Magnitude' + val = get(mirdata(a),'Data'); + case 'Chroma' + val = get(mirdata(a),'Pos'); + case 'ChromaClass' + val = a.chromaclass; + case 'ChromaFreq' + val = a.chromafreq; + case 'Register' + val = a.register; + case 'PitchLabel' + val = a.plabel; + case 'Wrap' + val = a.wrap; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirchromagram/mirchromagram.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,322 @@ +function varargout = mirchromagram(orig,varargin) +% c = mirchromagram(x) computes the chromagram, or distribution of energy +% along pitches, of the audio signal x. +% (x can be the name of an audio file as well, or a spectrum, ...) +% Optional argument: +% c = mirchromagram(...,'Tuning',t): specifies the central frequency +% (in Hz.) associated to chroma C. +% Default value, t = 261.6256 Hz +% c = mirchromagram(...,'Wrap',w): specifies whether the chromagram is +% wrapped or not. +% w = 1: groups all the pitches belonging to same pitch classes +% (default value) +% w = 0: pitches are considered as absolute values. +% c = mirchromagram(...,'Frame',l,h) orders a frame decomposition of window +% length l (in seconds) and hop factor h, expressed relatively to +% the window length. For instance h = 1 indicates no overlap. +% Default values: l = .2 seconds and h = .05 +% c = mirchromagram(...,'Center'): centers the result. +% c = mirchromagram(...,'Normal',n): performs a n-norm of the +% resulting chromagram. Toggled off if n = 0 +% Default value: n = Inf (corresponding to a normalization by +% the maximum value). +% c = mirchromagram(...,'Pitch',p): specifies how to label chromas in +% the figures. +% p = 1: chromas are labeled using pitch names (default) +% alternative syntax: chromagram(...,'Pitch') +% p = 0: chromas are labeled using MIDI pitch numbers +% c = mirchromagram(...,'Triangle'): weight the contribution of each +% frequency with respect to the distance with the actual +% frequency of the corresponding chroma. +% c = mirchromagram(...,'Weight',o): specifies the relative radius of +% the weighting window, with respect to the distance between +% frequencies of successive chromas. +% o = 1: each window begins at the centers of the previous one. +% o = .5: each window begins at the end of the previous one. +% (default value) +% mirchromagram(...,'Min',mi) indicates the lowest frequency taken into +% consideration in the spectrum computation, expressed in Hz. +% Default value: 100 Hz. (Gomez, 2006) +% mirchromagram(...,'Max',ma) indicates the highest frequency taken into +% consideration in the spectrum computation, expressed in Hz. +% This upper limit is further shifted to a highest value until +% the frequency range covers an exact multiple of octaves. +% Default value: 5000 Hz. (Gomez, 2006) +% mirchromagram(...,'Res',r) indicates the resolution of the +% chromagram in number of bins per octave. +% Default value, r = 12. +% +% Gómez, E. (2006). Tonal description of music audio signal. Phd thesis, +% Universitat Pompeu Fabra, Barcelona . + + cen.key = 'Center'; + cen.type = 'Boolean'; + cen.default = 0; + option.cen = cen; + + nor.key = {'Normal','Norm'}; + nor.type = 'Integer'; + nor.default = Inf; + option.nor = nor; + + wth.key = 'Weight'; + wth.type = 'Integer'; + wth.default = .5; + option.wth = wth; + + tri.key = 'Triangle'; + tri.type = 'Boolean'; + tri.default = 0; + option.tri = tri; + + wrp.key = 'Wrap'; + wrp.type = 'Boolean'; + wrp.default = 1; + option.wrp = wrp; + + plabel.key = 'Pitch'; + plabel.type = 'Boolean'; + plabel.default = 1; + option.plabel = plabel; + + thr.key = {'Threshold','dB'}; + thr.type = 'Integer'; + thr.default = 20; + option.thr = thr; + + min.key = 'Min'; + min.type = 'Integer'; + min.default = 100; + option.min = min; + + max.key = 'Max'; + max.type = 'Integer'; + max.default = 5000; + option.max = max; + + res.key = 'Res'; + res.type = 'Integer'; + res.default = 12; + option.res = res; + + origin.key = 'Tuning'; + origin.type = 'Integer'; + origin.default = 261.6256; + option.origin = origin; + +specif.option = option; +specif.defaultframelength = .2; +specif.defaultframehop = .05; + +varargout = mirfunction(@mirchromagram,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if isamir(x,'mirtemporal') || isamir(x,'mirspectrum') + freqmin = option.min; + freqmax = freqmin*2; + while freqmax < option.max + freqmax = freqmax*2; + end + %freqres = freqmin*(2.^(1/option.res)-1); + % Minimal frequency resolution should correspond to frequency range + % between the first two bins of the chromagram + + x = mirspectrum(x,'dB',option.thr,'Min',freqmin,'Max',freqmax,... + 'NormalInput','MinRes',option.res,'OctaveRatio',.85); + %freqres*.5,... + % 'WarningRes',freqres); +end +type = 'mirchromagram'; + + +function c = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +if option.res == 12 + chromascale = {'C','C#','D','D#','E','F','F#','G','G#','A','A#','B'}; +else + chromascale = 1:option.res; + option.plabel = 0; +end +if isa(orig,'mirchromagram') + c = modif(orig,option,chromascale); +else + c.plabel = 1; + c.wrap = 0; + c.chromaclass = {}; + c.chromafreq = {}; + c.register = {}; + c = class(c,'mirchromagram',mirdata(orig)); + c = purgedata(c); + c = set(c,'Title','Chromagram','Ord','magnitude','Interpolable',0); + if option.wrp + c = set(c,'Abs','chroma class'); + else + c = set(c,'Abs','chroma'); + end + m = get(orig,'Magnitude'); + f = get(orig,'Frequency'); + %disp('Computing chromagram...') + fs = get(orig,'Sampling'); + n = cell(1,length(m)); % The final structured list of magnitudes. + cc = cell(1,length(m)); % The final structured list of chroma classes. + o = cell(1,length(m)); % The final structured list of octave registers. + p = cell(1,length(m)); % The final structured list of chromas. + cf = cell(1,length(m)); % The final structured list of central frequencies related to chromas. + for i = 1:length(m) + mi = m{i}; + fi = f{i}; + if not(iscell(mi)) + mi = {mi}; + fi = {fi}; + end + ni = cell(1,length(mi)); % The list of magnitudes. + ci = cell(1,length(mi)); % The list of chroma classes. + oi = cell(1,length(mi)); % The list of octave registers. + pi = cell(1,length(mi)); % The list of absolute chromas. + cfi = cell(1,length(mi)); % The central frequency of each chroma. + for j = 1:length(mi) + mj = mi{j}; + fj = fi{j}; + + % Let's remove the frequencies exceeding the last whole octave. + minfj = min(min(min(fj))); + maxfj = max(max(max(fj))); + maxfj = minfj*2^(floor(log2(maxfj/minfj))); + fz = find(fj(:,1,1,1) > maxfj); + mj(fz,:,:,:) = []; + fj(fz,:,:,:) = []; + + [s1 s2 s3] = size(mj); + + cj = freq2chro(fj,option.res,option.origin); + if not(ismember(min(cj)+1,cj)) + warning('WARNING IN MIRCHROMAGRAM: Frequency resolution of the spectrum is too low.'); + display('The conversion of low frequencies into chromas may be incorrect.'); + end + ccj = min(min(min(cj))):max(max(max(cj))); + sc = length(ccj); % The size of range of absolute chromas. + mat = zeros(s1,sc); + fc = chro2freq(ccj,option.res,option.origin); % The absolute chromas in Hz. + fl = chro2freq(ccj-1,option.res,option.origin); % Each previous chromas in Hz. + fr = chro2freq(ccj+1,option.res,option.origin); % Each related next chromas in Hz. + for k = 1:sc + rad = find(and(fj(:,1) > fc(k)-option.wth*(fc(k)-fl(k)),... + fj(:,1) < fc(k)-option.wth*(fc(k)-fr(k)))); + if option.tri + dist = fc(k) - fj(:,1,1,1); + rad1 = dist/(fc(k) - fl(k))/option.wth; + rad2 = dist/(fc(k) - fr(k))/option.wth; + ndist = max(rad1,rad2); + mat(:,k) = max(min(1-ndist,1),0)/length(rad); + else + mat(rad,k) = ones(length(rad),1)/length(rad); + end + if k ==1 || k == sc + mat(:,k) = mat(:,k)/2; + end + end + nj = zeros(sc,s2,s3); + for k = 1:s2 + for l = 1:s3 + nj(:,k,l) = (mj(:,k,l)'*mat)'; + end + end + cj = mod(ccj',option.res); + oi{j} = floor(ccj/option.res)+4; + if option.plabel + pj = strcat(chromascale(cj+1)',num2str(oi{j}')); + else + pj = ccj'+60; + end + ci{j} = repmat(cj,[1,s2,s3]); + pi{j} = repmat(pj,[1,s2,s3]); + ni{j} = nj; + cfi{j} = fc; + end + n{i} = ni; + cc{i} = ci; + o{i} = oi; + p{i} = pi; + cf{i} = cfi; + end + c = set(c,'Magnitude',n,'Chroma',p,'ChromaClass',cc,... + 'ChromaFreq',cf,'Register',o); + c = modif(c,option,chromascale); + c = {c orig}; +end + + +function c = modif(c,option,chromascale) +if option.plabel + c = set(c,'PitchLabel',1); +end +if option.cen || option.nor || option.wrp + n = get(c,'Magnitude'); + p = get(c,'Chroma'); + cl = get(c,'ChromaClass'); + fp = get(c,'FramePos'); + n2 = cell(1,length(n)); + p2 = cell(1,length(n)); + wrp = option.wrp && not(get(c,'Wrap')); + for i = 1:length(n) + ni = n{i}; + pi = p{i}; + cli = cl{i}; + if not(iscell(ni)) + ni = {ni}; + pi = {pi}; + cli = {cli}; + end + if wrp + c = set(c,'Wrap',option.wrp); + end + n2i = cell(1,length(ni)); + p2i = cell(1,length(ni)); + for j = 1:length(ni) + nj = ni{j}; + pj = pi{j}; + clj = cli{j}; + if wrp + n2j = zeros(option.res,size(nj,2),size(nj,3)); + for k = 1:size(pj,1) + n2j(clj(k)+1,:,:) = n2j(clj(k)+1,:,:) + nj(k,:,:); % squared sum (parameter) + end + p2i{j} = chromascale'; + else + n2j = nj; + p2i{j} = pi{j}; + end + if option.cen + n2j = n2j - repmat(mean(n2j),[size(n2j,1),1,1]); + end + if option.nor + n2j = n2j ./ repmat(vectnorm(n2j,option.nor) + ... + repmat(1e-6,[1,size(n2j,2),size(n2j,3)] )... + ,[size(n2j,1),1,1]); + end + n2i{j} = n2j; + end + n2{i} = n2i; + p2{i} = p2i; + end + c = set(c,'Magnitude',n2,'Chroma',p2,'FramePos',fp); +end + + +function c = freq2chro(f,res,origin) +c = round(res*log2(f/origin)); + + +function f = chro2freq(c,res,origin) +f = 2.^(c/res)*origin; + + +function y = vectnorm(x,p) +if isinf(p) + y = max(x); +else + y = sum(abs(x).^p).^(1/p); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirchromagram/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function cc = set(c,varargin) +% SET Set properties for the MIRchromagram object +% and return the updated object + +propertyArgIn = varargin; +pl = c.plabel; +wr = c.wrap; +cl = c.chromaclass; +cf = c.chromafreq; +or = c.register; +d = mirdata(c); +d = set(d,'Title',get(c,'Title'),'Abs',get(c,'Abs'),'Ord',get(c,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Magnitude' + d = set(d,'Data',val); + case 'Chroma' + d = set(d,'Pos',val); + case 'ChromaClass' + cl = val; + case 'ChromaFreq' + cf = val; + case 'Register' + or = val; + case 'PitchLabel' + pl = val; + case 'Wrap' + wr = val; + otherwise + d = set(d,prop,val); + end +end +cc.plabel = pl; +cc.wrap = wr; +cc.chromaclass = cl; +cc.chromafreq = cf; +cc.register = or; +cc = class(cc,'mirchromagram',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirclassify/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function display(c) + +% CLASSIFY/DISPLAY display of classification + +disp('Classification results:') +c.classes + +if isnan(c.correct) + disp('No label has been associated to the test set. Correct classification rate cannot be computed.'); +else + disp(['Correct classification rate: ',num2str(c.correct)]); +end + +%disp(['Number of observations: ',num2str(c.nbobs)]) +%disp(['Number of free parameters: ',num2str(c.nbparam)]) + +%disp('Posterior probability:') +%c.post{:} + +if 0 + figure + hold on + vt = c.training; + lt = c.labtraining; + va = c.test; + la = c.labtest; + for i = 1:size(vt,2) + scatter3(vt(1,i),vt(2,i),vt(3,i),'k+','SizeData',2); + text(vt(1,i),vt(2,i),vt(3,i),lt{i},'Color','k'); + end + for i = 1:size(va,2) + scatter3(va(1,:),va(2,:),va(3,:),'r+','SizeData',2); + text(va(1,i),va(2,i),va(3,i),la{i},'Color','r'); + end + xlabel('1') + ylabel('2') + zlabel('3') + rotate3d +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirclassify/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function val = get(a, propName) +% GET Get properties from the MIRclassify object +% and return the value + +switch propName + case 'Correct' + val = a.correct; + case 'Data' + val = a.correct; + case 'Classes' + val = a.classes; + otherwise + error([propName,' is not a valid MIRdata property']) +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirclassify/mirclassify.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,270 @@ +function c = mirclassify(a,da,t,dt,varargin) +% c = mirclassify(test,features_test,train,features_train) classifies the +% audio sequence(s) contained in the audio object test, along the +% analytic feature(s) features_test, following the supervised +% learning of a training set defined by the audio object train and +% the corresponding analytic feature(s) features_train. +% * The analytic feature(s) features_test should *not* be frame +% decomposed. Frame-decomposed data should first be +% summarized, using for instance mirmean or mirstd. +% * Multiple analytic features have to be grouped into one array +% of cells. +% You can also integrate your own arrays of numbers computed outside +% MIRtoolbox as part of the features. These arrays should be +% given as matrices where each successive column is the analysis +% of each successive file. +% Example: +% mirclassify(test, mfcc(test), train, mfcc(train)) +% mirclassify(test, {mfcc(test), centroid(test)}, ... +% train, {mfcc(train), centroid(train)}) +% Optional argument: +% mirclassify(...,'Nearest') uses the minimum distance strategy. +% (by default) +% mirclassify(...,'Nearest',k) uses the k-nearest-neighbour strategy. +% Default value: k = 1, corresponding to the minimum distance +% strategy. +% mirclassify(...,'GMM',ng) uses a gaussian mixture model. Each class is +% modeled by at most ng gaussians. +% Default value: ng = 1. +% Additionnally, the type of mixture model can be specified, +% using the set of value proposed in the gmm function: i.e., +% 'spherical','diag','full' (default value) and 'ppca'. +% (cf. help gmm) +% Requires the Netlab toolbox. + +lab = get(t,'Label'); +c.labtraining = lab; +rlab = get(a,'Label'); +c.labtest = rlab; +[k,ncentres,covartype,kmiter,emiter,d,norml,mahl] = scanargin(varargin); +disp('Classifying...') +if not(iscell(dt)) + dt = {dt}; +end +lvt = length(get(t,'Data')); +vt = []; +for i = 1:length(dt) + if isnumeric(dt{i}) + d = cell(1,size(dt{i},2)); + for j = 1:size(dt{i},2) + d{j} = dt{i}(:,j); + end + else + d = get(dt{i},'Data'); + end + vt = integrate(vt,d,lvt,norml); + if isa(dt{i},'scalar') + m = mode(dt{i}); + if not(isempty(m)) + vt = integrate(vt,m,lvt,norml); + end + end +end +c.training = vt; +dim = size(vt,1); +if not(iscell(da)) + da = {da}; +end +lva = length(get(a,'Data')); +va = []; +for i = 1:length(da) + if isnumeric(da{i}) + d = cell(1,size(da{i},2)); + for j = 1:size(da{i},2) + d{j} = da{i}(:,j); + end + else + d = get(da{i},'Data'); + end + va = integrate(va,d,lva,norml); + if isa(da{i},'scalar') + m = mode(da{i}); + if not(isempty(m)) + va = integrate(va,m,lva,norml); + end + end +end +c.test = va; +c.nbobs = lvt; +totva = [vt va]; +mahl = cov(totva'); +if k % k-Nearest Neighbour + c.nbparam = lvt; + for l = 1:lva + [sv,idx] = sort(distance(va(:,l),vt,d,mahl)); + labs = cell(0); % Class labels + founds = []; % Number of found elements in each class + for i = idx(1:k) + labi = lab{i}; + found = 0; + for j = 1:length(labs) + if isequal(labi,labs{j}) + found = j; + end + end + if found + founds(found) = founds(found)+1; + else + labs{end+1} = labi; + founds(end+1) = 1; + end + end + [b ib] = max(founds); + c.classes{l} = labs{ib}; + end +elseif ncentres % Gaussian Mixture Model + labs = cell(0); % Class labels + founds = cell(0); % Elements associated to each label. + for i = 1:lvt + labi = lab{i}; + found = 0; + for j = 1:length(labs) + if isequal(labi,labs{j}) + founds{j}(end+1) = i; + found = 1; + end + end + if not(found) + labs{end+1} = labi; + founds{end+1} = i; + end + end + options = zeros(1, 18); + options(2:3) = 1e-4; + options(4) = 1e-6; + options(16) = 1e-8; + options(17) = 0.1; + options(1) = 0; %Prints out error values, -1 else + c.nbparam = 0; + OK = 0; + while not(OK) + OK = 1; + for i = 1:length(labs) + options(14) = kmiter; + try + mix{i} = gmm(dim,ncentres,covartype); + catch + error('ERROR IN CLASSIFY: Netlab toolbox not installed.'); + end + mix{i} = netlabgmminit(mix{i},vt(:,founds{i})',options); + options(5) = 1; + options(14) = emiter; + try + mix{i} = gmmem(mix{i},vt(:,founds{i})',options); + c.nbparam = c.nbparam + ... + length(mix{i}.centres(:)) + length(mix{i}.covars(:)); + catch + err = lasterr; + warning('WARNING IN CLASSIFY: Problem when calling GMMEM:'); + disp(err); + disp('Let us try again...'); + OK = 0; + end + end + end + pr = zeros(lva,length(labs)); + for i = 1:length(labs) + prior = length(founds{i})/lvt; + pr(:,i) = prior * gmmprob(mix{i},va'); + %c.post{i} = gmmpost(mix{i},va'); + end + [mm ib] = max(pr'); + for i = 1:lva + c.classes{i} = labs{ib(i)}; + end +end +if isempty(rlab) + c.correct = NaN; +else + correct = 0; + for i = 1:lva + if isequal(c.classes{i},rlab{i}) + correct = correct + 1; + end + end + c.correct = correct / lva; +end +c = class(c,'mirclassify'); + + +function vt = integrate(vt,v,lvt,norml) +vtl = []; +for l = 1:lvt + vl = v{l}; + if iscell(vl) + vl = vl{1}; + end + if iscell(vl) + vl = vl{1}; + end + if size(vl,2) > 1 + mirerror('MIRCLASSIFY','The analytic features guiding the classification should not be frame-decomposed.'); + end + vtl(:,l) = vl; +end +if norml + dnom = repmat(std(vtl,0,2),[1 size(vtl,2)]); + dnom = dnom + (dnom == 0); % In order to avoid division by 0 + vtl = (vtl - repmat(mean(vtl,2),[1 size(vtl,2)])) ./ dnom; +end +vt(end+1:end+size(vtl,1),:) = vtl; + + +function [k,ncentres,covartype,kmiter,emiter,d,norml,mahl] = scanargin(v) +k = 1; +d = 0; +i = 1; +ncentres = 0; +covartype = 'full'; +kmiter = 10; +emiter = 100; +norml = 1; +mahl = 1; +while i <= length(v) + arg = v{i}; + if ischar(arg) && strcmpi(arg,'Nearest') + k = 1; + if length(v)>i && isnumeric(v{i+1}) + i = i+1; + k = v{i}; + end + elseif ischar(arg) && strcmpi(arg,'GMM') + k = 0; + ncentres = 1; + if length(v)>i + if isnumeric(v{i+1}) + i = i+1; + ncentres = v{i}; + if length(v)>i && ischar(v{i+1}) + i = i+1; + covartype = v{i}; + end + elseif ischar(v{i+1}) + i = i+1; + covartype = v{i}; + if length(v)>i && isnumeric(v{i+1}) + i = i+1; + ncentres = v{i}; + end + end + end + elseif isnumeric(arg) + k = v{i}; + else + error('ERROR IN MIRCLASSIFY: Syntax error. See help mirclassify.'); + end + i = i+1; +end + + +function y = distance(a,t,d,mahl) + +for i = 1:size(t,2) + if det(mahl) > 0 % more generally, uses cond + lham = inv(mahl); + else + lham = pinv(mahl); + end + y(i) = sqrt((a - t(:,i))'*lham*(a - t(:,i))); +end +%y = sqrt(sum(repmat(a,[1,size(t,2)])-t,1).^2); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/combine.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +function c = combine(varargin) + +c = varargin{1}; +l = length(varargin); +p = cell(1,l); +ch = cell(1,l); +d = cell(1,l); +fp = cell(1,l); +sr = cell(1,l); +n = cell(1,l); +la = cell(1,l); +cl = cell(1,l); +pp = cell(1,l); +pm = cell(1,l); +pv = cell(1,l); +ppp = cell(1,l); +ppv = cell(1,l); +tp = cell(1,l); +tv = cell(1,l); +tpp = cell(1,l); +tpv = cell(1,l); +ap = cell(1,l); +rp = cell(1,l); +if isa(c,'temporal') + nb = cell(1,l); +end +if isa(c,'mirscalar') + m = cell(1,l); +end +if isa(c,'miremotion') + dd = cell(1,l); + cd = cell(1,l); +end +for i = 1:l + argin = varargin{i}; + p{i} = getargin(argin,'Pos'); + ch{i} = getargin(argin,'Channels'); + d{i} = getargin(argin,'Data'); + fp{i} = getargin(argin,'FramePos'); + sr{i} = getargin(argin,'Sampling'); + nb{i} = getargin(argin,'NBits'); + n{i} = getargin(argin,'Name'); + la{i} = getargin(argin,'Label'); + cl{i} = getargin(argin,'Clusters'); + pp{i} = getargin(argin,'PeakPos'); + pm{i} = getargin(argin,'PeakMode'); + pv{i} = getargin(argin,'PeakVal'); + ppp{i} = getargin(argin,'PeakPrecisePos'); + ppv{i} = getargin(argin,'PeakPreciseVal'); + tp{i} = getargin(argin,'TrackPos'); + tv{i} = getargin(argin,'TrackVal'); + tpp{i} = getargin(argin,'TrackPrecisePos'); + tpv{i} = getargin(argin,'TrackPreciseVal'); + ap{i} = getargin(argin,'AttackPos'); + rp{i} = getargin(argin,'ReleasePos'); + if isa(c,'temporal') + ct = getargin(argin,'Centered'); + nb{i} = getargin(argin,'NBits'); + end + if isa(c,'mirscalar') + m{i} = getargin(argin,'Mode'); + end + if isa(c,'miremotion') + dd{i} = getargin(argin,'DimData'); + cd{i} = getargin(argin,'ClassData'); + end +end +c = set(c,'Pos',p,'Data',d,'FramePos',fp,'Channels',ch,... + 'Sampling',sr,'NBits',nb,'Name',n,'Label',la,... + 'Clusters',cl,'PeakPos',pp,'PeakMode',pm,'PeakVal',pv,... + 'PeakPrecisePos',ppp,'PeakPreciseVal',ppv,... + 'TrackPos',tp,'TrackVal',tv,... + 'TrackPrecisePos',tpp,'TrackPreciseVal',tpv,... + 'AttackPos',ap,'ReleasePos',rp); +if isa(c,'temporal') + c = set(c,'Centered',ct,'NBits',nb); +end +if isa(c,'mirscalar') + c = set(c,'Mode',m); +end +if isa(c,'miremotion') + c = set(c,'DimData',dd,'ClassData',cd); +end + + +function y = getargin(argin,field) +yi = get(argin,field); +if isempty(yi) || ischar(yi) + y = yi; +else + y = yi{1}; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function display(d) +% MIRDATA/DISPLAY display of a MIR data + +mirdisplay(d,inputname(1)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/exportation.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +function x = exportation(s) + +% EXPORTATION exports the peaks of a vectorial dimension + +v = get(s,'Data'); +n = get(s,'Name'); +pt = get(s,'PeakPosUnit'); +pp = get(s,'PeakPos'); +t = get(s,'Title'); +frames = 0; +for i = 1:length(v) + if iscell(v{i}) + v{i} = v{i}{1}; %% Segmented audio cannot be exported properly now. + end + if size(v{i},2) > 1 + frames = 1; + end +end +for i = 1:length(v) + if size(v{i},4) == 1 + vi = v{i}; + else + lvi = size(v{i},1); + vi = []; + for j = 1:size(v{i},4) + vi(lvi*(j-1)+1:lvi*j,:,:) = v{i}(:,:,:,1); + end + end + if i == 1 + x{1,1} = ['mean',t]; + x{1,2} = ['std',t]; + end + x{i+1,1} = mean(mean(vi)); + x{i+1,2} = mean(std(vi)); + pti = pt{i}; + ppi = pp{i}; + if not(isempty(ppi{1})) + if iscell(ppi{1}) + ppi = ppi{1}; %% Segmented audio cannot be exported properly now. + pti = pti{1}; + if iscell(pti{1}) + pti = ppi; + end + end + for j = 1:size(vi,2) + pep = ppi{1,j,1}; + if isempty(pep) + pet(j) = NaN; + pev(j) = NaN; + else + pet(j) = pti{1,j,1}(1); + if size(vi,3) > 1 && size(vi,1) == 1 + pev(j) = pep(1); + else + pev(j) = vi(pep(1),j,1); + end + end + end + if frames + if i == 1 + x{1,3} = ['meanpeakpos',t]; + x{1,4} = ['stdpeakpos',t]; + x{1,5} = ['meanpeakval',t]; + x{1,6} = ['stdpeakval',t]; + end + x{i+1,3} = mean(pet); + x{i+1,4} = std(pet); + x{i+1,5} = mean(pev); + x{i+1,6} = std(pev); + else + if i == 1 + x{1,3} = ['peakpos',t]; + x{1,4} = ['peakval',t]; + end + x{i+1,3} = pet; + x{i+1,4} = pev; + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,226 @@ +function val = get(a, propName) +% GET Get properties from the MIRdata object +% and return the value + +switch propName + case 'Pos' + val = a.pos; + case 'Data' + val = a.data; + case 'Unit' + val = a.unit; + case 'FramePos' + val = a.framepos; + case 'Framed' + val = a.framed; + case 'Sampling' + val = a.sr; + case 'NBits' + val = a.nbits; + case 'Name' + val = a.name; + case 'Name2' + val = a.name2; + case 'Label' + val = a.label; + case 'Channels' + val = a.channels; + case 'Clusters' + val = a.clusters; + case 'MultiData' + val = a.multidata; + case 'PeakPos' + val = a.peak.pos; + case {'PeakPosUnit','AttackPosUnit','ReleasePosUnit'} + switch propName + case 'PeakPosUnit' + pp = a.peak.pos; + case 'AttackPosUnit' + pp = a.attack.pos; + case 'ReleasePosUnit' + pp = a.release.pos; + end + po = a.pos; + d = a.data; + val = cell(1,length(pp)); + if isempty(d) + return + end + for k = 1:length(pp) + val{k} = cell(1,length(pp{k})); + if isempty(pp{k}) + nseg = 0; + elseif iscell(pp{k}{1}) + nseg = length(pp{k}); + else + nseg = 1; + end + for i = 1:nseg + ppi = pp{k}{i}; + if isempty(po) + poi = (1:size(d{k}{i},2))'; + elseif iscell(po{k}) + if isempty(po{k}) + poi = mean(a.framepos{k}{1})'; + elseif isempty(a.pos) + poi = po{k}{i}'; + elseif ischar(po{k}{1}) + poi = (1:length(po{k}))'; + else + poi = po{k}{i}; + end + else + for j = 1:size(po,3) + poi(:,:,j) = po{k}(:,:,j)'; + end + end + val{k}{i} = cell(size(ppi)); + for h = 1:size(ppi,3) + for j = 1:size(ppi,2) + if size(poi,3) > 1 && size(poi,1) == 1 + val{k}{i}{1,j,h} = ppi{1,j,h}; + else + val{k}{i}{1,j,h} = poi(ppi{1,j,h},1); + end + end + end + end + end + case 'PeakPrecisePos' + val = a.peak.precisepos; + case 'PeakVal' + val = a.peak.val; + case 'PeakPreciseVal' + val = a.peak.preciseval; + case 'PeakMaxVal' + pv = a.peak.val; + val = cell(1,length(pv)); + for h = 1:length(pv) + val{h} = cell(1,length(pv{h})); + for i = 1:length(pv{h}) + pvi = pv{h}{i}; + %if iscell(pvi) + % pvi = pvi{1}; % Segmented data not taken into consideration yet. + %end + val{h}{i} = zeros(1,length(pvi)); + for j = 1:length(pvi) + if isempty(pvi{j}) + val{h}{i}(1,j) = NaN; + else + val{h}{i}(1,j) = max(pvi{j}); + end + end + end + end + case 'PeakMode' + val = a.peak.mode; + case 'AttackPos' + if isempty(a.attack) + val = []; + else + val = a.attack.pos; + end + case 'ReleasePos' + if isempty(a.release) + val = []; + else + val = a.release.pos; + end + case 'TrackPos' + if isempty(a.track) + val = []; + else + val = a.track.pos; + end + case 'TrackPosUnit' + if isempty(a.track) + val = []; + else + pp = a.track.pos; + po = a.pos; + d = a.data; + val = cell(1,length(pp)); + for k = 1:length(pp) + val{k} = cell(1,length(pp{k})); + if isempty(pp{k}) + nseg = 0; + elseif iscell(pp{k}{1}) + nseg = length(pp{k}); + else + nseg = 1; + end + for i = 1:nseg + ppi = pp{k}{i}{1}; + if isempty(po) + poi = (1:size(d{k}{i},2))'; + elseif iscell(po{k}) + if isempty(po{k}) + poi = mean(a.framepos{k}{1})'; + elseif isempty(a.pos) + poi = po{k}{i}'; + elseif ischar(po{k}{1}) + poi = (1:length(po{k}))'; + else + poi = po{k}{i}; + end + else + for j = 1:size(po,3) + poi(:,:,j) = po{k}(:,:,j)'; + end + end + val{k}{i}{1} = zeros(size(ppi)); + if size(poi,3) > 1 && size(poi,1) == 1 + val{k}{i}{1} = ppi; + else + for h = 1:size(ppi,2) + for j = 1:size(ppi,1) + if ppi(j,h) + val{k}{i}{1}(j,h) = poi(ppi(j,h),1); + else + val{k}{i}{1}(j,h) = 0; + end + end + end + end + end + end + end + case 'TrackVal' + if isempty(a.track) + val = []; + else + val = a.track.val; + end + case 'TrackPrecisePos' + if isempty(a.track) + val = []; + else + val = a.track.precisepos; + end + case 'TrackPreciseVal' + if isempty(a.track) + val = []; + else + val = a.track.preciseval; + end + case 'Title' + val = a.title; + case 'Abs' + val = a.abs; + case 'Ord' + val = a.ord; + case 'InterChunk' + val = a.interchunk; + case 'TmpIdx' + val = a.tmpidx; + case 'AcrossChunks' + val = a.acrosschunks; + case 'Interpolable' + val = a.interpolable; + case 'TmpFile' + val = a.tmpfile; + case 'Index' + val = a.index; + otherwise + error([propName,' is not a valid MIRdata property']) +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/gettmp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function [tmp y] = gettmp(x,y) + +idx = get(x,'TmpIdx')+1; +tmps = get(x,'InterChunk'); +if idx > length(tmps) + tmp = []; +else + tmp = tmps{idx}; +end +if nargin<2 + y = x; +end +y = set(y,'InterChunk',tmps,'TmpIdx',idx); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/intersect.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +function i = intersect(a,b,thr) + +if nargin<3 + thr = .01; +end + +va = get(a,'PeakVal'); +pa = get(a,'PeakPos'); +ma = get(a,'PeakMode'); +if isa(a,'mirscalar') + xa = get(a,'FramePos'); +else + xa = get(a,'Pos'); +end +vb = get(b,'PeakVal'); +pb = get(b,'PeakPos'); +mb = get(b,'PeakMode'); +if isa(b,'mirscalar') + xb = get(b,'FramePos'); +else + xb = get(b,'Pos'); +end + +for j = 1:length(va) + for k = 1:length(va{j}) + [nl nc np] = size(va{j}{k}); + for c = 1:nc + for p = 1:np + [pacp,ix] = sort(pa{j}{k}{1,c,p}); + vacp = va{j}{k}{1,c,p}(ix); + macp = ma{j}{k}{1,c,p}(ix); + [pbcp,ix] = sort(pb{j}{k}{1,c,p}); + xajk = xa{j}{k}; + xbjk = xb{j}{k}; + if isa(a,'mirscalar') + xajk = mean(xajk); + end + if isa(b,'mirscalar') + xbjk = mean(xbjk); + end + ia = 1; + ib = 1; + ii = 1; + while ia <= length(pacp) && ib <= length(pbcp) + if abs(xajk(pacp(ia))-xbjk(pbcp(ib)))<thr + picp(ii) = pacp(ia); + vicp(ii) = vacp(ia); + micp(ii) = macp(ia); + ia = ia+1; + ib = ib+1; + ii = ii+1; + elseif xajk(pacp(ia))<xbjk(pbcp(ib)) + ia = ia+1; + else + ib = ib+1; + end + end + pa{j}{k}{1,c,p} = picp; + va{j}{k}{1,c,p} = vicp; + ma{j}{k}{1,c,p} = micp; + end + end + end +end + +i = set(a,'PeakPos',pa,'PeakVal',va,'PeakMode',ma); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/isframed.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function b = isframed(d) + +dat = get(d,'Data'); +b = 0; +i = 0; +while not(b) && i < length(dat) + i = i+1; + j = 0; + while not(b) && j < length(dat{i}) + j = j+1; + if size(dat{i}{j},2) > 1 + b = 1; + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/log.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function y = log(x) + +d = get(x,'Data'); +e = cell(1,length(d)); +l = get(x,'Name'); +m = cell(1,length(l)); + +for i = 1:length(d) + e{i} = cell(1,length(d{i})); + for j = 1:length(d{i}) + e{i}{j} = log(d{i}{j}); + end + m{i} = ['log_' l{i}]; +end +y = set(x,'Data',e,'Name',m); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/mirdata.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function d = mirdata(orig,varargin) +% d = mirdata(x) creates a MIR object. + +if nargin > 0 && isa(orig,'mirdata') + d.pos = orig.pos; + d.data = orig.data; + d.unit = orig.unit; + d.framepos = orig.framepos; + d.framed = orig.framed; + d.sr = orig.sr; + d.nbits = orig.nbits; + d.name = orig.name; + d.name2 = orig.name2; + d.label = orig.label; + d.channels = orig.channels; + d.clusters = orig.clusters; + d.multidata = orig.multidata; + d.peak = orig.peak; + d.attack = orig.attack; + d.release = orig.release; + d.track = orig.track; + d.title = orig.title; + d.abs = orig.abs; + d.ord = orig.ord; + d.interchunk = orig.interchunk; + d.tmpidx = orig.tmpidx; + d.acrosschunks = orig.acrosschunks; + d.interpolable = orig.interpolable; + d.tmpfile = orig.tmpfile; + d.index = orig.index; +else + d.pos = {}; + d.data = {}; + d.unit = ''; + d.framepos = {}; + d.framed = 0; + d.sr = {}; + d.nbits = {}; + d.name = {}; + d.name2 = {}; + d.label = {}; + d.channels = []; + d.clusters = {}; + d.multidata = []; + d.peak.pos = {}; + d.peak.val = {}; + d.peak.precisepos = {}; + d.peak.preciseval = {}; + d.peak.mode = {}; + d.attack = {}; + d.release = {}; + d.track = {}; + d.title = 'Unspecified data'; + d.abs = 'Unspecified abscissa'; + d.ord = 'Unspecified ordinate'; + d.interchunk = []; + d.tmpidx = 0; + d.acrosschunks = []; + d.interpolable = 1; % If the abscissae axis is non-numeric (0), + % then peak picking has to be done without interpolation. + d.tmpfile = []; + d.index = NaN; +end +d = class(d,'mirdata'); +d = set(d,varargin{:}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/mirdisplay.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +function mirdisplay(d,varargin) +% MIRDATA/DISPLAY display of a MIR data + +disp(' '); +v = d.data; +f = d.sr; +n = d.name; +l = d.label; +p = d.pos; +fp = d.framepos; +pp = d.peak.pos; +pm = d.peak.mode; +ld = length(v); +if isempty(d.attack) + ap = cell(ld); +else + ap = d.attack.pos; +end +if isempty(d.release) + rp = cell(ld); +else + rp = d.release.pos; +end +if isempty(d.track) + tp = cell(ld); + tv = cell(ld); +else + tp = d.track.pos; + tv = d.track.val; +end +if ld == 0 + disp('No data.'); +else + for i = 1:length(v) + if nargin < 2 + va = inputname(1); + else + va = varargin{1}; + end + if isempty(va) + va = 'ans'; + end + if length(v)>1 + va = [va,'(',num2str(i),')']; + end + if not(isempty(l)) && iscell(l) && not(isempty(l{i})) + lab = ' with label '; + if isnumeric(l{i}) + lab = [lab,num2str(l{i})]; + else + lab = [lab,l{i}]; + end + else + lab = ''; + end + disp([va,' is the ',d.title,' related to ',n{i},lab,... + ', of sampling rate ',num2str(f{i}),' Hz.']) + if size(v{i},2) == 0 + if isempty(d.init) + disp('It does not contain any data.'); + else + disp('It has not been loaded yet.'); + end + else + if iscell(d.channels) + cha = d.channels{i}; + else + cha = []; + end + flag = displot(p{i},v{i},d.abs,d.ord,d.title,fp{i},pp{i},tp{i},tv{i},... + cha,d.multidata,pm{i},ap{i},rp{i},d.clusters{i}); + if flag + fig = get(0,'CurrentFigure'); + disp(['Its content is displayed in Figure ',num2str(fig),'.']); + else + disp('It does not contain any data.'); + end + end + end +end +disp(' '); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/mpower.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function c = mpower(a,b) + +d = get(a,'Data'); +f = cell(1,length(d)); +n = get(a,'Name'); +t = get(a,'Title'); +if not(isa(a,'miraudio')) + if isa(b,'mirdata') + t = [t,' ^ ',get(b,'Title')]; + else + t = [t,' ^ ',num2str(b)]; + end +end + +if isa(b,'mirdata') + e = get(b,'Data'); + m = get(b,'Name'); +else + e = {{b}}; + m = {num2str(b)}; +end + +for i = 1:length(d) + f{i} = cell(1,length(d{i})); + for j = 1:length(d{i}) + ld = size(d{i}{j},1); + le = size(e{i}{j},1); + if ld > le + f{i}{j} = d{i}{j} .^ [e{i}{j};zeros(ld-le,size(e,2),size(e,3))]; + else + f{i}{j} = [d{i}{j};zeros(le-ld,size(d,2),size(d,3))] .^ e{i}{j}; + end + end + if isa(a,'miraudio') + n{i} = [n{i} '^' m{i}]; + end +end +c = set(a,'Data',f,'Name',n,'Title',t); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/mtimes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function c = mtimes(a,b) + +d = get(a,'Data'); +f = cell(1,length(d)); +n = get(a,'Name'); +t = get(a,'Title'); +if not(isa(a,'miraudio')) + if isa(b,'mirdata') + t = [t,' * ',get(b,'Title')]; + else + t = [t,' * ',num2str(b)]; + end +end + +if isa(b,'mirdata') + e = get(b,'Data'); + m = get(b,'Name'); +else + e = cell(1,length(d)); + for i = 1:length(d) + e{i} = {b}; + end + m = {num2str(b)}; +end + +for i = 1:length(d) + f{i} = cell(1,length(d{i})); + for j = 1:length(d{i}) + ld = size(d{i}{j},1); + le = size(e{i}{j},1); + if ld > le + f{i}{j} = d{i}{j} .* [e{i}{j};zeros(ld-le,size(e,2),size(e,3))]; + elseif ld < le + f{i}{j} = [d{i}{j};zeros(le-ld,size(d,2),size(d,3))] .* e{i}{j}; + else + f{i}{j} = d{i}{j} .* e{i}{j}; + end + end + if isa(a,'miraudio') + n{i} = [n{i} '*' m{i}]; + end +end +c = set(a,'Data',f,'Name',n,'Title',t); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/plus.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function c = plus(a,b) + +d = get(a,'Data'); +f = cell(1,length(d)); +n = get(a,'Name'); +t = get(a,'Title'); + +if isa(b,'mirdata') + e = get(b,'Data'); + m = get(b,'Name'); + u = get(b,'Title'); + if not(isa(a,'miraudio')) + t = [t,' + ',get(b,'Title')]; + end +else + e = {{b}}; + m = {num2str(b)}; + t = [t,' + ',num2str(b)]; +end + +for i = 1:length(d) + f{i} = cell(1,length(d{i})); + for j = 1:length(d{i}) + ld = size(d{i}{j},1); + le = size(e{i}{j},1); + if ld > le + f{i}{j} = d{i}{j} + [e{i}{j};zeros(ld-le,size(e,2),size(e,3))]; + elseif ld < le + f{i}{j} = [d{i}{j};zeros(le-ld,size(d,2),size(d,3))] + e{i}{j}; + else + f{i}{j} = d{i}{j} + e{i}{j}; + end + end + if isa(a,'miraudio') + n{i} = [n{i} '+' m{i}]; + end +end +c = set(a,'Data',f,'Name',n,'Title',t); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +function a = set(a,varargin) +% SET Set properties for the MIRdata object +% and return the updated object + +propertyArgIn = varargin; +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Pos' + a.pos = val; + case {'Data','ChunkData'} + if strcmp(prop,'ChunkData') + val = {{val}}; + end + a.data = val; + case 'Unit' + a.unit = val; + case 'FramePos' + a.framepos = val; + case 'Sampling' + a.sr = val; + case 'NBits' + a.nbits = val; + case 'Name' + a.name = val; + case 'Name2' + a.name2 = val; + case 'Label' + a.label = val; + case 'Channels' + a.channels = val; + case 'Clusters' + a.clusters = val; + case 'MultiData' + a.multidata = val; + case 'Title' + a.title = val; + case 'Abs' + a.abs = val; + case 'Ord' + a.ord = val; + case 'PeakPos' + a.peak.pos = val; + case 'PeakVal' + a.peak.val = val; + case 'PeakPrecisePos' + a.peak.precisepos = val; + case 'PeakPreciseVal' + a.peak.preciseval = val; + case 'PeakMode' + a.peak.mode = val; + case 'AttackPos' + a.attack.pos = val; + case 'ReleasePos' + a.release.pos = val; + case 'TrackPos' + a.track.pos = val; + case 'TrackVal' + a.track.val = val; + case 'TrackPrecisePos' + a.track.precisepos = val; + case 'TrackPreciseVal' + a.track.preciseval = val; + case 'InterChunk' + a.interchunk = val; + case 'TmpIdx' + a.tmpidx = val; + case 'AcrossChunks' + a.acrosschunks = val; + case 'Interpolable' + a.interpolable = val; + case 'TmpFile' + a.tmpfile = val; + case 'Index' + a.index = val; + otherwise + error(['Unknown MIRdata property: ' prop]) + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdata/settmp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function x = settmp(x,tmp) + +idx = get(x,'TmpIdx'); +tmps = get(x,'InterChunk'); +tmps{idx} = tmp; +x = set(x,'InterChunk',tmps); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function display(d) +% MIRDESIGN/DISPLAY display of a MIR design + +disp(' '); +va = inputname(1); +if isempty(va) + va = 'ans'; +end +disp([va,' is the following non-evaluated MIRtoolbox command:']); +method = d.method +option = d.option; +if not(isempty(option)) + option +end +postoption = d.postoption; +if not(isempty(postoption)) + postoption +end +argument = d.argin; +if not(ischar(d.argin)) + disp('***Argument:') + argument +end +disp(' '); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/evaleach.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,986 @@ +function [y d2] = evaleach(d,single,name) +% Top-down traversal of the design flowchart, at the beginning of the +% evaluation phase. +% Called by mirfunction, mireval, mirframe and mirsegment. +% This is during that traversal that we check whether a chunk decomposition +% needs to be performed or not, and carry out that chunk decomposition. + +if nargin<3 || isempty(name) + if not(ischar(d.method)) + name = func2str(d.method); + end +end +if nargin<2 + single = 0; +end + +CHUNKLIM = mirchunklim; +f = d.file; +fr = d.frame; +frnochunk = isfield(d.frame,'dontchunk'); +frchunkbefore = isfield(d.frame,'chunkbefore'); +sg = d.segment; +sr = d.sampling; +sr2 = d.resampling; +w = d.size; +lsz = w(2)-w(1)+1; +len = lsz/sr; +if ischar(sg) + error('ERROR in MIREVAL: mirsegment of design object accepts only array of numbers as second argument.'); +end +if not(isempty(sg)) + over = find(sg(2,:) > len); + if not(isempty(over)) + sg = sg(:,1:over-1); + end +end +a = d.argin; +ch = d.chunk; +chan = d.channel; +specif = d.specif; +if isaverage(specif) + specif.eachchunk = 'Normal'; +end + +if ischar(a) + % The top-down traversal of the design flowchart now reaches the lowest + % layer, i.e., audio file loading. + % Now the actual evaluation will be carried out bottom-up. + + if isempty(ch) + % No chunk decomposition + y = miraudio(f,'Now',[w(:)' 0 chan]); + else + % Chunk decomposition + y = miraudio(f,'Now',[ch(1),ch(2) 0 chan]); + end + if not(isempty(d.postoption)) && d.postoption.mono + y = miraudio(y,'Mono',1); + end + y = set(y,'AcrossChunks',get(d,'AcrossChunks')); + d2 = d; + +elseif d.chunkdecomposed && isempty(d.tmpfile) + % Already in a chunk decomposition process + + [y d2] = evalnow(d); + +elseif isempty(fr) || frnochunk || not(isempty(sg)) %% WHAT ABOUT CHANNELS? + % No frame or segment decomposition in the design to evaluate + % (Or particular frame decomposition, where chunks are not distributed to children (frnochunk).) + + if not(isfield(specif,'eachchunk')) ... + || d.nochunk ... + || (not(isempty(single)) && isnumeric(single) && single > 1 ... + && isfield(specif,'combinechunk') ... + && iscell(specif.combinechunk)) + chunks = []; + elseif not(isempty(sg)) + meth = 'Segment '; + if size(sg,1) == 1 + chunks = floor(sg(1:end-1)*sr)+1; + chunks(2,:) = min( floor(sg(2:end)*sr)-1,lsz-1)+1; + else + chunks = floor(sg*sr); + chunks(1,:) = chunks(1,:)+1; + end + else + meth = 'Chunk '; + if isempty(fr) + if lsz > CHUNKLIM + % The required memory exceed the max memory threshold. + nch = ceil(lsz/CHUNKLIM); + %%% TAKE INTO CONSIDERATION NUMBER OF CHANNELS; ETC... + chunks = max(0,lsz-CHUNKLIM*(nch:-1:1))+w(1); + chunks(2,:) = lsz-CHUNKLIM*(nch-1:-1:0)+w(1)-1; + else + chunks = []; + end + else + chunks = compute_frames(fr,sr,sr2,w,lsz,CHUNKLIM,d.overlap); + end + end + + if not(isempty(chunks)) + % The chunk decomposition is performed. + nch = size(chunks,2); + d = callbeforechunk(d,d,w,lsz); % Some optional initialisation + tmp = []; + if mirwaitbar + h = waitbar(0,['Computing ' name]); + else + h = 0; + end + if not(isempty(d.tmpfile)) && d.tmpfile.fid == 0 + % When applicable, a new temporary file is created. + d.tmpfile.fid = fopen('tmpfile.mirtoolbox','w'); + end + tmpfile = []; + if not(d.ascending) + chunks = fliplr(chunks); + end + + afterpostoption = d.postoption; % Used only when: + % - eachchunk is set to 'Normal', + % - combinechunk is not set to 'Average', and + % - no afterchunk field has been specified. + % afterpostoption will be used for the final call + % to the method after the chunk decomposition. + method = d.method; + if ischar(specif.eachchunk) && strcmpi(specif.eachchunk,'Normal') + if not(isempty(d.postoption)) + pof = fieldnames(d.postoption); + for o = 1:length(pof) + if isfield(specif.option.(pof{o}),'chunkcombine') + afterpostoption = rmfield(afterpostoption,pof{o}); + else + d.postoption = rmfield(d.postoption,pof{o}); + end + end + end + else + method = specif.eachchunk; + end + + d2 = d; + d2.method = method; + y = {}; + for i = 1:size(chunks,2) + disp([meth,num2str(i),'/',num2str(nch),'...']) + d2 = set(d2,'Chunk',[chunks(1,i) chunks(2,i) (i == size(chunks,2))]); + + if not(ischar(specif.eachchunk) && ... + strcmpi(specif.eachchunk,'Normal')) + if frnochunk + d2.postoption = 0; + else + diffchunks = diff(chunks); % Usual chunk size + d2.postoption = max(diffchunks) - diffchunks(i); + % Reduction of the current chunk size to be taken into + % consideration in mirspectrum, for instance, using + % zeropadding + end + end + + d2 = set(d2,'InterChunk',tmp); + d2.chunkdecomposed = 1; + + [ss d3] = evalnow(d2); + + if iscell(ss) && not(isempty(ss)) + tmp = get(ss{1},'InterChunk'); + elseif isstruct(ss) + tmp = []; + else + tmp = get(ss,'InterChunk'); + end + + % d2 is like d3 except that its argument is now evaluated. + d3.postoption = d.postoption; % Pas joli joli + d3.method = method; + d2 = d3; % This new argument is transfered to d + + y = combinechunk_noframe(y,ss,sg,i,d2,chunks,single); + + clear ss + if h + if not(d.ascending) + close(h) + h = waitbar((chunks(1,i)-chunks(1,end))/chunks(2,1),... + ['Computing ' func2str(d.method) ' (backward)']); + else + waitbar((chunks(2,i)-chunks(1))/chunks(end),h) + end + end + end + + y = afterchunk_noframe(y,lsz,d,afterpostoption,d2); + % Final operations to be executed after the chunk decomposition + + if isa(d,'mirstruct') && ... + (isempty(d.frame) || isfield(d.frame,'dontchunk')) + y = evalbranches(d,y); + end + if h + close(h) + end + drawnow + + else + % No chunk decomposition + [y d2] = evalnow(d); + if isa(d,'mirstruct') && isfield(d.frame,'dontchunk') + y = evalbranches(d,y); + end + end +elseif d.nochunk + [y d2] = evalnow(d); +else + % Frame decomposition in the design to be evaluated. + chunks = compute_frames(fr,sr,sr2,w,lsz,CHUNKLIM,d.overlap); + if size(chunks,2)>1 + % The chunk decomposition is performed. + if mirwaitbar + h = waitbar(0,['Computing ' name]); + else + h = 0; + end + inter = []; + d = set(d,'FrameDecomposition',1); + d2 = d; + nch = size(chunks,2); + y = {}; + + if frchunkbefore + d2after = d2; + d2.method = d2.argin.method; + d2.option = d2.argin.option; + d2.postoption = d2.argin.postoption; + d2.argin = d2.argin.argin; + end + + for fri = 1:nch % For each chunk... + disp(['Chunk ',num2str(fri),'/',num2str(nch),'...']) + d2 = set(d2,'Chunk',chunks(:,fri)'); + d2 = set(d2,'InterChunk',inter); + %d2.postoption = []; + [res d2] = evalnow(d2); + if not(isempty(res)) + if iscell(res) + inter = get(res{1},'InterChunk'); + elseif not(isstruct(res)) + inter = get(res,'InterChunk'); + res = {res}; + end + end + + y = combinechunk_frame(y,res,d2,fri); + if h + waitbar(chunks(2,fri)/chunks(end),h); + end + end + + if frchunkbefore + y = d2after.method(y,d2after.option,d2after.postoption); + end + + if isa(d,'mirstruct') && get(d,'Stat') + y = mirstat(y); + end + if h + close(h) + end + else + % No chunk decomposition + [y d2] = evalnow(d); + end +end + + +if iscell(y) + for i = 1:length(y) + if not(isempty(y{i}) || isstruct(y{i})) + if iscell(y{i}) + for j = 1:length(y{i}) + y{i}{j} = set(y{i}{j},'InterChunk',[]); + end + else + y{i} = set(y{i},'InterChunk',[]); + end + end + end +end + + +function chunks = compute_frames(fr,sr,sr2,w,lsz,CHUNKLIM,frov) +if strcmpi(fr.length.unit,'s') + fl = fr.length.val*sr; + fl2 = fr.length.val*sr2; +elseif strcmpi(fr.length.unit,'sp') + fl = fr.length.val; + fl2 = fl; +end +if strcmpi(fr.hop.unit,'/1') + h = fr.hop.val*fl; + h2 = fr.hop.val*fl2; +elseif strcmpi(fr.hop.unit,'%') + h = fr.hop.val*fl*.01; + h2 = fr.hop.val*fl2*.01; +elseif strcmpi(fr.hop.unit,'s') + h = fr.hop.val*sr; + h2 = fr.hop.val*sr2; +elseif strcmpi(fr.hop.unit,'sp') + h = fr.hop.val; + h2 = fr.hop.val; +elseif strcmpi(fr.hop.unit,'Hz') + h = sr/fr.hop.val; + h2 = sr2/fr.hop.val; +end +n = floor((lsz-fl)/h)+1; % Number of frames +if n < 1 + %warning('WARNING IN MIRFRAME: Frame length longer than total sequence size. No frame decomposition.'); + fp = w; + fp2 = (w-1)/sr*sr2+1; +else + st = floor(((1:n)-1)*h)+w(1); + st2 = floor(((1:n)-1)*h2)+w(1); + fp = [st; floor(st+fl-1)]; + fp(:,fp(2,:)>w(2)) = []; + fp2 = [st2; floor(st2+fl2-1)]; + fp2(:,fp2(2,:)>(w(2)-w(1))/sr*sr2+w(2)) = []; +end +fpe = (fp2-1)/sr2-(fp-1)/sr; %Rounding error if resampling +fpsz = (fp(2,1)-fp(1,1)) * n; % Total number of samples +fpsz2 = (fp2(2,1)-fp2(1,1)) * n; % Total number of samples +if max(fpsz,fpsz2) > CHUNKLIM + % The required memory exceed the max memory threshold. + nfr = size(fp,2); % Total number of frames + frch = max(ceil(CHUNKLIM/(fp(2,1)-fp(1,1))),2); % Number of frames per chunk + frch = max(frch,frov*2); + [unused cut] = min(abs(fpe(1,frch:-1:1))); + %frch = frch - cut; + % this correspond to the frame with less rounding error + nch = ceil((nfr-frch)/(frch-frov))+1; % Number of chunks + chbeg = (frch-frov)*(0:nch-1)+1; % First frame in the chunk + chend = (frch-frov)*(0:nch-1)+frch; % Last frame in the chunk + chend = min(chend,nfr); + if frov > 1 + chbeg = chend-frch+1; + end + chunks = [fp(1,chbeg) ; fp(2,chend)+1]; % After resampling, one sample may be missing, leading to a complete frame drop. + chunks(end) = min(chunks(end),fp(end)); % Last chunk should not extend beyond audio size. +else + chunks = []; +end + + +function res = combinechunk_frame(old,new,d2,fri) +if isempty(mirgetdata(new)) + res = old; + return +end +if isstruct(old) + f = fields(old); + for i = 1:length(f) + res.(f{i}) = combinechunk_frame(old.(f{i}),new.(f{i}),d2,fri); + end + return +end +if fri == 1 + res = new; +elseif isfield(d2,'specif') && isfield(d2.specif,'combineframes') + res = d2.specif.combineframes(old{1},new{1}); +else + res = combineframes(old,new); +end + + +function res = combinechunk_noframe(old,new,sg,i,d2,chunks,single) +if isempty(new) + res = {}; + return +end +if isempty(mirgetdata(new)) + res = old; + return +end +if not(iscell(new)) + new = {new}; +end +if not(iscell(old)) + old = {old}; +end +if not(isempty(old)) && isstruct(old{1}) + f = fields(old{1}); + for j = 1:length(f) + index.type = '.'; + index.subs = f{j}; + res.(f{j}) = combinechunk_noframe(old{1}.(f{j}),new{1}.(f{j}),... + sg,i,subsref(d2,index),chunks,single); + end + return +end +if ischar(single) && not(isempty(old)) + old = {old{1}}; +end +if isempty(sg) + if isaverage(d2.specif) + % Measure total size for later averaging + if iscell(new) + new1 = new{1}; + else + new1 = new; + end + dnew = get(new1,'Data'); + dnew = mircompute(@multweight,dnew,chunks(2,i)-chunks(1,i)+1); + if iscell(new) + new{1} = set(new1,'Data',dnew); + else + new = set(new1,'Data',dnew); + end + end + %tmp = get(new{1},'InterChunk'); + if not(isempty(d2.tmpfile)) && d2.tmpfile.fid > 0 + % If temporary file is used, chunk results are written + % in the file + if i < size(chunks,2) + ds = get(new{1},'Data'); + ps = get(new{1},'Pos'); + % ftell(d2.tmpfile.fid) + count = fwrite(d2.tmpfile.fid,ds{1}{1},'double'); + count = fwrite(d2.tmpfile.fid,ps{1}{1},'double'); + % ftell(d2.tmpfile.fid) + clear ds ps + end + res = new; + else + % Else, chunk results are directly combined in active + % memory + if i == 1 + res = new; + else + if isfield(d2.specif,'combinechunk') + if not(iscell(d2.specif.combinechunk)) + method = {d2.specif.combinechunk}; + else + method = d2.specif.combinechunk; + end + for z = 1:length(old) + if isframed(old{z}) + res(z) = combineframes(old{z},new{z}); + elseif ischar(method{z}) + if strcmpi(method{z},'Concat') + res{z} = concatchunk(old{z},new{z},d2.ascending); + elseif strcmpi(method{z},'Average') + res{z} = sumchunk(old{z},new{z}); + else + error(['SYNTAX ERROR: ',... + method{z},... + ' is not a known keyword for combinechunk.']); + end + else + res{z} = method{z}(old{z},new{z}); + end + end + else + for z = 1:length(old) + if isframed(old{z}) + res(z) = combineframes(old{z},new{z}); + else + mirerror('MIREVAL','Chunk recombination in non-framed mode is not available for all features yet. Please turn off the chunk decomposition.'); + end + end + end + end + end +else + if i == 1 + res = new; + else + for z = 1:length(old) + res{z} = combinesegment(old{z},new{z}); + end + end +end + + +function res = afterchunk_noframe(input,lsz,d,afterpostoption,d2) +if isstruct(input) + %mirerror('MIREVAL','Sorry, mirstruct only accepts frame-decomposed objects as ''tmp'' fields.'); + res = input; + %f = fields(input); + %for i = 1:length(f) + % index.type = '.'; + % index.subs = f{i}; + % res.(f{i}) = afterchunk_noframe(input.(f{i}),lsz,... + % subsref(d,index),afterpostoption,subsref(d2,index)); + %end + return +end +if isfield(d2.specif,'afterchunk') + res{1} = d2.specif.afterchunk(input{1},lsz,d.postoption); +elseif isaverage(d2.specif) + res{1} = divideweightchunk(input{1},lsz); +elseif not(isempty(afterpostoption)) && isempty(d2.tmpfile) + res{1} = d.method(input{1},[],afterpostoption); +else + res = input; +end +if not(isempty(d2.tmpfile)) + adr = ftell(d2.tmpfile.fid); + fclose(d2.tmpfile.fid); + ytmpfile.fid = fopen('tmpfile.mirtoolbox'); + fseek(ytmpfile.fid,adr,'bof'); + ytmpfile.data = input{1}; + ytmpfile.layer = 0; + res{1} = set(input{1},'TmpFile',ytmpfile); +end + + +function old = combineframes(old,new) +if not(iscell(old)) + old = {old}; +end +if not(iscell(new)) + new = {new}; +end +for var = 1:length(new) + ov = old{var}; + nv = new{var}; + if isa(ov,'mirscalar') + ov = combinedata(ov,nv,'Data'); + ov = combinedata(ov,nv,'Mode'); + if isa(ov,'mirpitch') + ov = combinedata(ov,nv,'Amplitude'); + end + else + if isa(ov,'mirtemporal') + [ov omatch nmatch] = combinedata(ov,nv,'Time',[],[],@modiftime); + else + [ov omatch nmatch] = combinedata(ov,nv,'Pos',[],[]); + if isa(ov,'mirspectrum') + [ov omatch nmatch] = combinedata(ov,nv,'Phase',[],[]); + end + end + ov = combinedata(ov,nv,'Data',omatch,nmatch); + end + ov = combinedata(ov,nv,'FramePos'); + ov = combinedata(ov,nv,'PeakPos'); + ov = combinedata(ov,nv,'PeakVal'); + ov = combinedata(ov,nv,'PeakMode'); + old{var} = ov; +end + + +function [ov omatch nmatch] = combinedata(ov,nv,key,omatch,nmatch,modifdata) +if isstruct(ov) + omatch = []; + nmatch = []; + f = fields(ov); + for i = 1:length(f) + ov.(f{i}) = combinedata(ov.(f{i}),nv.(f{i}),key); + end + return +end +odata = get(ov,key); +if isempty(odata) || isempty(odata{1}) + return +end +odata = odata{1}; +if iscell(odata) + if ischar(odata{1}) + return + else + odata = odata{1}; + end +end +ndata = get(nv,key); +ndata = ndata{1}; +if iscell(ndata) + ndata = ndata{1}; +end +if nargin>3 + if isempty(omatch) + ol = size(odata,1); + nl = size(ndata,1); + unmatch = ol-nl; + if unmatch>0 + [unused idx] = min(odata(1:1+unmatch,1,1)-ndata(1)); + omatch = idx:idx+nl-1; + nmatch = 1:nl; + elseif unmatch<0 + [unused idx] = min(ndata(1:1-unmatch,1,1)-odata(1)); + nmatch = idx:idx+ol-1; + omatch = 1:ol; + else + nmatch = 1:nl; + omatch = 1:ol; + end + end + odata(omatch,end+1:end+size(ndata,2),:,:) = ndata(nmatch,:,:,:); %4.D for keysom +else + odata(:,end+1:end+size(ndata,2),:,:) = ndata; +end +ov = set(ov,key,{{odata}}); %{odata} for warped chromagram for instance.... + + +function d = modiftime(d,p) +d = d + p; + + +function [y d] = evalnow(d) +% Go one step further in the top-down evaluation initialisation +argin = d.argin; +if not(iscell(argin)) + argin = {argin}; +end +for i = 1:length(argin) + a = argin{i}; + if not(d.ascending) + a.ascending = 0; + end + if isa(a,'mirdata') + % Input already computed + tmpfile = get(a,'TmpFile'); + if not(isempty(tmpfile)) && tmpfile.fid > 0 + % The input can be read from the temporary file + ch = get(d,'Chunk'); + a = tmpfile.data; + a = set(a,'InterChunk',get(d,'InterChunk'),'TmpFile',tmpfile); + channels = get(a,'Channels'); + channels = length(channels{1}); + if not(channels) + channels = 1; + end + size = (ch(2)-ch(1)+1); + current = ftell(tmpfile.fid); + fseek(tmpfile.fid,current-size*(channels+1)*8,'bof'); + %ftell(tmpfile.fid) + [data count] = fread(tmpfile.fid,[size,channels],'double'); + %count + data = reshape(data,[size,1,channels]); + [pos count] = fread(tmpfile.fid,size,'double'); + %count + % ftell(tmpfile.fid) + fseek(tmpfile.fid,current-size*(channels+1)*8,'bof'); + a = set(a,'Data',{{data}},'Pos',{{pos}}); + if ch(3) + fclose(tmpfile.fid); + delete('tmpfile.mirtoolbox'); + end + argin{i} = a; + end + elseif isa(a,'mirdesign') + if isempty(a.stored) + % The design parameters are transfered to the previous component + % in the design process + a.size = d.size; + a.chunk = d.chunk; + a.file = d.file; + a.channel = d.channel; + a.eval = 1; + a.interchunk = d.interchunk; + a.sampling = d.sampling; + if isstruct(d.frame) && isfield(d.frame,'decomposition') ... + && not(isempty(d.frame.decomposition)) + a.chunkdecomposed = 1; + else + a.chunkdecomposed = d.chunkdecomposed; + end + if not(isempty(d.frame)) && ... + not(strcmp(func2str(d.method),'mirframe')) + a.frame = d.frame; + end + a.ready = 1; + a.acrosschunks = d.acrosschunks; + a.index = d.index; + argin{i} = a; + else + % Variable already calculated + tmp = get(d,'Struct'); + if not(isempty(tmp)) + for j = 1:length(a.stored) % (if modified, modify also mirframe) + stored = a.stored{j}; + if iscell(stored) + if length(stored)>1 + tmp = tmp{stored{1},stored{2}}; + else + tmp = tmp{stored{1}}; + end + else + tmp = getfield(tmp,stored); + end + end + if iscell(tmp) + tmp = tmp{1}; + end + else + mirerror('evaleach','THERE is a problem..'); + end + argin{i} = tmp; + end + end +end +if not(iscell(d.argin)) + argin = argin{1}; +end +d.option.struct = get(d,'Struct'); +if iscell(d.postoption) + [y argin] = d.method(argin,d.option,d.postoption{:}); +else + [y argin] = d.method(argin,d.option,d.postoption); +end +d = set(d,'Argin',argin); +if isa(d,'mirstruct') && not(isfield(d.frame,'dontchunk')) && isempty(get(d,'Chunk')) + y = evalbranches(d,y); +end + + +function z = evalbranches(d,y) +% For complex flowcharts, now that the first temporary variable (y) has been +% computed, the dependent features (d) should be evaluated as well. +branch = get(d,'Data'); + +for i = 1:length(branch) + if isa(branch{i},'mirdesign') && get(branch{i},'NoChunk') == 1 + % if the value is 2, it is OK. + %mirerror('mireval','Flowchart badly designed: mirstruct should not be used if one or several final variables do not accept chunk decomposition.'); + end +end + +fields = get(d,'Fields'); +z = struct; +tmp = get(d,'Tmp'); +for i = 1:length(branch) + z.(fields{i}) = evalbranch(branch{i},tmp,y); +end +if get(d,'Stat') && isempty(get(d,'Chunk')) + z = mirstat(z,'FileNames',0); +end + + +function b = evalbranch(b,d,y) +% We need to evaluate the branch reaching the current node (b) from the parent +% corresponding to the temporary variable (d), + +if iscell(b) + mirerror('MIREVAL','Sorry, forked branching of temporary variable cannnot be evaluated in current version of MIRtoolbox.'); +end +if isstruct(b) + % Subtrees are evaluated branch after branch. + f = fields(b); + for i = 1:length(f) + b.(f{i}) = evalbranch(b.(f{i}),d,y); + end + return +end +if isequal(b,d) + %% Does it happen ever?? + b = y; + return +end +if not(isa(b,'mirdesign')) + mirerror('MIRSTRUCT','In the mirstruct object you defined, the final output should only depend on ''tmp'' variables, and should not therefore reuse the ''Design'' keyword.'); +end +v = get(b,'Stored'); +if length(v)>1 && ischar(v{2}) + % + f = fields(d); + for i = 1:length(f) + if strcmpi(v{2},f) + b = y; % OK, now the temporary variable has been found. + % End of recursion. + return + end + end +end + +argin = evalbranch(get(b,'Argin'),d,y); % Recursion one parent up + +% The operation corresponding to the branch from the parent to the node +% is finally evaluated. +if iscell(b.postoption) + b = b.method(argin,b.option,b.postoption{:}); +else + b = b.method(argin,b.option,b.postoption); +end + + +function res = isaverage(specif) +res = isfield(specif,'combinechunk') && ... + ((ischar(specif.combinechunk) && ... + strcmpi(specif.combinechunk,'Average')) || ... + (iscell(specif.combinechunk) && ... + ischar(specif.combinechunk{1}) && ... + strcmpi(specif.combinechunk{1},'Average'))); + + +function d0 = callbeforechunk(d0,d,w,lsz) +% If necessary, the chunk decomposition is performed a first time for +% initialisation purposes. +% Currently used only for miraudio(...,'Normal') +if not(ischar(d)) && not(iscell(d)) + specif = d.specif; + CHUNKLIM = mirchunklim; + nch = ceil(lsz/CHUNKLIM); + if isfield(specif,'beforechunk') ... + && ((isfield(d.option,specif.beforechunk{2}) ... + && d.option.(specif.beforechunk{2})) ... + || (isfield(d.postoption,specif.beforechunk{2}) ... + && d.postoption.(specif.beforechunk{2})) ) + if mirwaitbar + h = waitbar(0,['Preparing ' func2str(d.method)]); + else + h = 0; + end + for i = 1:nch + disp(['Chunk ',num2str(i),'/',num2str(nch),'...']) + chbeg = CHUNKLIM*(i-1); + chend = CHUNKLIM*i-1; + d2 = set(d,'Size',d0.size,'File',d0.file,... + 'Chunk',[chbeg+w(1) min(chend,lsz-1)+w(1)]); + d2.method = specif.beforechunk{1}; + d2.postoption = {chend-lsz}; + d2.chunkdecomposed = 1; + [tmp d] = evalnow(d2); + d0 = set(d0,'AcrossChunks',tmp); + if h + waitbar(chend/lsz,h) + end + end + if h + close(h); + end + drawnow + else + d0 = callbeforechunk(d0,d.argin,w,lsz); + end +end + + +function y = concatchunk(old,new,ascending) +do = get(old,'Data'); +dn = get(new,'Data'); +fpo = get(old,'FramePos'); +fpn = get(new,'FramePos'); +if isa(old,'mirscalar') + y = set(old,'Data',{{[do{1}{1},dn{1}{1}]}},... + 'FramePos',{{[fpo{1}{1},fpn{1}{1}]}}); +else + to = get(old,'Pos'); + tn = get(new,'Pos'); + if ascending + y = set(old,'Data',{{[do{1}{1};dn{1}{1}]}},... + 'Pos',{{[to{1}{1};tn{1}{1}]}},... + 'FramePos',{{[fpo{1}{1},fpn{1}{1}]}}); + else + y = set(old,'Data',{{[dn{1}{1};do{1}{1}]}},... + 'Pos',{{[tn{1}{1};to{1}{1}]}},... + 'FramePos',{{[fpo{1}{1},fpn{1}{1}]}}); + end +end + + +function y = combinesegment(old,new) +do = get(old,'Data'); +to = get(old,'Pos'); +fpo = get(old,'FramePos'); +ppo = get(old,'PeakPos'); +pppo = get(old,'PeakPrecisePos'); +pvo = get(old,'PeakVal'); +ppvo = get(old,'PeakPreciseVal'); +pmo = get(old,'PeakMode'); +apo = get(old,'AttackPos'); +rpo = get(old,'ReleasePos'); +tpo = get(old,'TrackPos'); +tvo = get(old,'TrackVal'); + +dn = get(new,'Data'); +tn = get(new,'Pos'); +fpn = get(new,'FramePos'); +ppn = get(new,'PeakPos'); +pppn = get(new,'PeakPrecisePos'); +pvn = get(new,'PeakVal'); +ppvn = get(new,'PeakPreciseVal'); +pmn = get(new,'PeakMode'); +apn = get(new,'AttackPos'); +rpn = get(new,'ReleasePos'); +tpn = get(new,'TrackPos'); +tvn = get(new,'TrackVal'); + +if not(isempty(do)) + y = set(old,'Data',{{do{1}{:},dn{1}{:}}}); +end + +y = set(old,'FramePos',{{fpo{1}{:},fpn{1}{:}}}); + +if not(isempty(to)) && size(do{1},2) == size(to{1},2) + y = set(y,'Pos',{{to{1}{:},tn{1}{:}}}); +end + +if not(isempty(ppo)) + y = set(y,'PeakPos',{{ppo{1}{:},ppn{1}{:}}},... + 'PeakVal',{{pvo{1}{:},pvn{1}{:}}},... + 'PeakMode',{{pmo{1}{:},pmn{1}{:}}}); +end + +if not(isempty(pppn)) + y = set(y,'PeakPrecisePos',{[pppo{1},pppn{1}{1}]},... + 'PeakPreciseVal',{[ppvo{1},ppvn{1}{1}]}); +end + +if not(isempty(apn)) + y = set(y,'AttackPos',{[apo{1},apn{1}{1}]}); +end + +if not(isempty(rpn)) + y = set(y,'ReleasePos',{[rpo{1},rpn{1}{1}]}); +end + +if not(isempty(tpn)) + y = set(y,'TrackPos',{[tpo{1},tpn{1}{1}]}); +end + +if not(isempty(tvn)) + y = set(y,'TrackVal',{[tvo{1},tvn{1}{1}]}); +end + +if isa(old,'miremotion') + deo = get(old,'DimData'); + ceo = get(old,'ClassData'); + den = get(new,'DimData'); + cen = get(new,'ClassData'); + afo = get(old,'ActivityFactors'); + vfo = get(old,'ValenceFactors'); + tfo = get(old,'TensionFactors'); + hfo = get(old,'HappyFactors'); + sfo = get(old,'SadFactors'); + tdo = get(old,'TenderFactors'); + ago = get(old,'AngerFactors'); + ffo = get(old,'FearFactors'); + afn = get(new,'ActivityFactors'); + vfn = get(new,'ValenceFactors'); + tfn = get(new,'TensionFactors'); + hfn = get(new,'HappyFactors'); + sfn = get(new,'SadFactors'); + tdn = get(new,'TenderFactors'); + agn = get(new,'AngerFactors'); + ffn = get(new,'FearFactors'); + y = set(y,'DimData',{[deo{1},den{1}{1}]},... + 'ClassData',{[ceo{1},cen{1}{1}]},... + 'ActivityFactors',{[afo{1},afn{1}{1}]},... + 'ValenceFactors',{[vfo{1},vfn{1}{1}]},... + 'TensionFactors',{[tfo{1},tfn{1}{1}]},... + 'HappyFactors',{[hfo{1},hfn{1}{1}]},... + 'SadFactors',{[sfo{1},sfn{1}{1}]},... + 'TenderFactors',{[tdo{1},tdn{1}{1}]},... + 'AngerFactors',{[ago{1},agn{1}{1}]},... + 'FearFactors',{[ffo{1},ffn{1}{1}]}... + ); +end + + +function y = sumchunk(old,new,order) +%do = mirgetdata(old); +%dn = mirgetdata(new); +do = get(old,'Data'); +do = do{1}{1}; +dn = get(new,'Data'); +dn = dn{1}{1}; +y = set(old,'ChunkData',do+dn); + + +function y = divideweightchunk(orig,length) +d = get(orig,'Data'); +if isempty(d) + y = orig; +else + v = mircompute(@divideweight,d,length); + y = set(orig,'Data',v); +end + +function e = multweight(d,length) +e = d*length; + +function e = divideweight(d,length) +e = d/length; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,130 @@ +function varargout = get(a,varargin) +% GET Get properties from the MIRdesign object and return the value + +if ischar(varargin{1}) + switch varargin{1} + case 'Method' + varargout = {a.method}; + return + case 'File' + varargout = {a.file}; + return + case 'FrameLength' + if isstruct(a.frame) + varargout = {a.frame.length.val}; + else + varargout = {[]}; + end + return + case 'FrameHop' + if isstruct(a.frame) + varargout = {a.frame.hop.val}; + else + varargout = {[]}; + end + return + case 'FrameLengthUnit' + if isstruct(a.frame) + varargout = {a.frame.length.unit}; + else + varargout = {[]}; + end + return + case 'FrameHopUnit' + if isstruct(a.frame) + varargout = {a.frame.hop.unit}; + else + varargout = {[]}; + end + return + case 'FrameDontChunk' + if isstruct(a.frame) + varargout = {a.frame.dontchunk}; + else + varargout = {[]}; + end + return + case 'Segment' + varargout = {a.segment}; + return + case 'ChunkDecomposed' + varargout = {a.chunkdecomposed}; + return + case 'Size' + varargout = {a.size}; + return + case 'Type' + varargout = {a.type}; + return + case 'Chunk' + varargout = {a.chunk}; + return + case 'Eval' + varargout = {a.eval}; + return + case 'Argin' + varargout = {a.argin}; + return + case 'VarArgin' + varargout = {a.varargin}; + return + case 'Specif' + varargout = {a.specif}; + return + case 'InterChunk' + varargout = {a.interchunk}; + return + case 'AcrossChunks' + varargout = {a.acrosschunks}; + return + case 'Option' + varargout = {a.option}; + return + case 'PostOption' + varargout = {a.postoption}; + return + case 'NoChunk' + varargout = {a.nochunk}; + return + case 'Ready' + varargout = {a.ready}; + return + case 'Struct' + varargout = {a.struct}; + return + case 'Stored' + varargout = {a.stored}; + return + case 'Index' + varargout = {a.index}; + return + case 'TmpFile' + varargout = {a.tmpfile}; + return + case 'TmpOf' + varargout = {a.tmpof}; + return + case 'Ascending' + varargout = {a.ascending}; + return + case 'SeparateChannels' + varargout = {a.separate}; + return + case 'Channel' + varargout = {a.channel}; + return + end +end + + prop.type = 'String'; + prop.position = 2; + prop.default = ''; + option.prop = prop; +specif.option = option; +varargout = mirfunction(@get,a,varargin,nargout,specif,@init,@main); + +function [x type] = init(x,option) +type = ''; + +function val = main(a,option,postoption) +val = get(a,option.prop); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/getsampling.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function s = getsampling(d) + +s = d.sampling; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/isframed.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function b = isframed(d) + +if isstruct(d.frame) && not(isfield(d.frame,'dontchunk')) + b = 1; +else + b = 0; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/mirdesign.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,129 @@ +function d = mirdesign(orig,argin,option,postoption,specif,type); %,nout) + +if isa(orig,'mirdesign') + d.method = orig.method; + d.argin = orig.argin; + d.option = orig.option; + d.postoption = orig.postoption; + d.specif = orig.specif; + d.type = orig.type; + d.frame = orig.frame; + d.segment = orig.segment; + d.chunkdecomposed = orig.chunkdecomposed; + d.size = orig.size; + d.file = orig.file; + d.channel = orig.channel; + d.sampling = orig.sampling; + d.resampling = orig.resampling; + d.nochunk = orig.nochunk; + d.ascending = orig.ascending; + d.overlap = orig.overlap; + d.separate = orig.separate; + d.chunk = orig.chunk; + d.eval = orig.eval; + d.interchunk = orig.interchunk; + d.acrosschunks = orig.acrosschunks; + d.ready = orig.ready; + d.struct = orig.struct; + d.stored = orig.stored; + d.index = orig.index; + d.tmpfile = orig.tmpfile; + d.tmpof = orig.tmpof; +else + d.method = orig; + d.argin = argin; + d.option = option; + d.postoption = postoption; + d.specif = specif; + d.type = type; + if ischar(argin) + d.frame = {}; + d.segment = {}; + d.chunkdecomposed = 0; + d.size = {}; + d.file = ''; + d.channel = []; + d.sampling = 0; + d.resampling = 0; + d.nochunk = 0; + if not(isempty(orig)) && ... + strcmp(func2str(orig),'mirenvelope') && d.option.zp == 2 + d.ascending = 0; + else + d.ascending = 1; + end + d.overlap = 0; + d.separate = 0; + else + if iscell(argin) + argin = argin{1}; + end + if (strcmp(func2str(orig),'mirspectrum') && d.option.alongbands) ... + || (isfield(specif,'nochunk') && specif.nochunk) + d.frame = []; + if isfield(d.specif,'eachchunk') + d.specif = rmfield(d.specif,'eachchunk'); + d.specif = rmfield(d.specif,'combinechunk'); + end + else + d.frame = argin.frame; + if not(isempty(d.frame)) + if isfield(d.specif,'extensive') + d.frame.dontchunk = 1; + % Some frame-decomposed extractor should not be evaluated + % chunk after chunk because the whole result is needed for + % subsequent computations. + elseif isfield(d.specif,'chunkframebefore') + d.frame.chunkbefore = 1; + end + end + end + d.segment = argin.segment; + d.chunkdecomposed = argin.chunkdecomposed; + d.size = argin.size; + d.file = argin.file; + d.channel = argin.channel; + d.sampling = argin.sampling; + d.resampling = argin.resampling; + if (isfield(specif,'nochunk') && specif.nochunk) + d.nochunk = 1; % was previously 2 + elseif not(isempty(argin.stored)) + % a temporary variable will be already computed. + d.nochunk = 2; % Flag to indicate that no chunk should be + % performed. Temporary variables cannot for the + % moment be dispatched to dependent variables + % chunk by chunk, but only once the whole + % variable has been computed. + else + d.nochunk = argin.nochunk; + end + if strcmp(func2str(orig),'mirenvelope') + if d.option.zp == 2 + d.ascending = not(isempty(d.segment)); + else + d.ascending = 1; + end + else + d.ascending = argin.ascending; + end + d.overlap = argin.overlap; + d.separate = argin.separate; + end + d.chunk = []; + d.eval = 0; + d.interchunk = []; % Data that can be passed between successive chunks during the main process. + d.acrosschunks = []; % Data that can be accumulated among chunks during the beforechunk process. + d.ready = 0; + d.struct = []; + d.stored = []; + d.index = NaN; + if not(isempty(orig)) && strcmp(func2str(orig),'mirenvelope') && ... + d.option.zp == 2 && isempty(d.segment) + % Triggers the use of temporary file for the mirenvelope computation + d.tmpfile.fid = 0; + else + d.tmpfile = []; + end + d.tmpof = []; +end +d = class(d,'mirdesign'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/mtimes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function varargout = mtimes(a,b) + +varargout = mirfunction(@mtimescell,{a,b},{},1,struct,@init,@mtimescell); + + +function [x type] = init(x,option) +type = get(x{1},'Type'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/plus.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function varargout = plus(a,b) + +varargout = mirfunction(@pluscell,{a,b},{},1,struct,@init,@plus); + + +function [x type] = init(x,option) +type = get(x{1},'Type'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +function a = set(a,varargin) +% SET Set properties for the MIRdesign object +% and return the updated object + +propertyArgIn = varargin; +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Eval' + a.eval = val; + case 'File' + a.file = val; + case 'Sampling' + a.sampling = val; + case 'Size' + a.size = val; + case 'Chunk' + a.chunk = val; + case 'SamplesIn' + a.samplesin = val; + a.samplesout = val; + case 'SamplesOut' + a.samplesout = val; + case 'Frame' + a.frame = val; + case 'Segment' + a.segment = val; + case 'FrameDecomposition' + a.frame.decomposition = val; + case 'FrameLength' + a.frame.length.val = val; + case 'FrameHop' + a.frame.hop.val = val; + case 'FrameLengthUnit' + a.frame.length.unit = val; + case 'FrameHopUnit' + a.frame.hop.unit = val; + case 'FrameEval' + a.frame.eval = val; + case 'FrameDontChunk' + a.frame.dontchunk = val; + case 'Path' + a.path = val; + case 'Specif' + a.specif = val; + case 'InterChunk' + a.interchunk = val; + case 'AcrossChunks' + a.acrosschunks = val; + case 'NoChunk' + a.nochunk = val; + case 'Struct' + a.struct = val; + case 'Stored' + a.stored = val; + case 'Index' + a.index = val; + case 'TmpFile' + a.tmpfile = val; + case 'TmpOf' + a.tmpof = val; + case 'ChunkDecomposed' + a.chunkdecomposed = val; + case 'Argin' + a.argin = val; + case 'Option' + a.option = val; + case 'Overlap' + a.overlap = val; + case 'SeparateChannels' + a.separate = val; + case 'Channel' + a.channel = val; + otherwise + error(['Unknown MIRdesign property: ' prop]) + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirdesign/setresampling.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function d = setresampling(d,s) + +d.resampling = s; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miremotion/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function display(e) +% MIREMOTION/DISPLAY display of emotion contents +d = get(e,'Dim'); +dd = get(e,'DimData'); +c = get(e,'Class'); +cd = get(e,'ClassData'); +fp = get(e,'FramePos'); +n = get(e,'Name'); +if not(isempty(d)) + s = mirscalar(e); + s = set(s,'Pos',repmat({{d'}},size(dd)),'Data',dd,... + 'Title','Dimensional Emotion','Abs','Emotion Dimensions') +end +if not(isempty(c)) + s = mirscalar(e); + s = set(s,'Pos',repmat({{c'}},size(cd)),'Data',cd,... + 'Title','Basic Emotion Set','Abs','Emotion Classes') +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miremotion/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function val = get(a, propName) +% GET Get properties from the MIRemotion object +% and return the value + +switch propName + case 'Dim' + val = a.dim; + case 'Class' + val = a.class; + case 'DimData' + val = a.dimdata; + case 'ClassData' + val = a.classdata; + case 'ActivityFactors' + val = a.activity_fact; + case 'ValenceFactors' + val = a.valence_fact; + case 'TensionFactors' + val = a.tension_fact; + case 'HappyFactors' + val = a.happy_fact; + case 'SadFactors' + val = a.sad_fact; + case 'TenderFactors' + val = a.tender_fact; + case 'AngerFactors' + val = a.anger_fact; + case 'FearFactors' + val = a.fear_fact; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miremotion/miremotion.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,470 @@ +function varargout = miremotion(orig,varargin) +% Predicts emotion along three dimensions and five basic concepts. +% Optional parameters: +% miremotion(...,'Dimensions',0) excludes all three dimensions. +% miremotion(...,'Dimensions',3) includes all three dimensions (default). +% miremotion(...,'Activity') includes the 'Activity' dimension. +% miremotion(...,'Valence') includes the 'Valence' dimension. +% miremotion(...,'Tension') includes the 'Tension' dimension. +% miremotion(...,'Dimensions',2) includes 'Activity' and 'Valence'. +% miremotion(...,'Arousal') includes 'Activity' and 'Tension'. +% miremotion(...,'Concepts',0) excludes all five concepts. +% miremotion(...,'Concepts') includes all five concepts (default). +% miremotion(...,'Happy') includes the 'Happy' concept. +% miremotion(...,'Sad') includes the 'Sad' concept. +% miremotion(...,'Tender') includes the 'Tender' concept. +% miremotion(...,'Anger') includes the 'Anger' concept. +% miremotion(...,'Fear') includes the 'Fear' concept. +% miremotion(...,'Frame',...) predict emotion frame by frame. +% +% Selection of features and coefficients are taken from a study: +% Eerola, T., Lartillot, O., and Toiviainen, P. +% (2009). Prediction of multidimensional emotional ratings in +% music from audio using multivariate regression models. +% In Proceedings of 10th International Conference on Music Information Retrieval +% (ISMIR 2009), pages 621-626. +% +% The implemented models are based on multiple linear regression with 5 best +% predictors (MLR option in the paper). The box-cox transformations have now been +% removed until the normalization values have been established with a large sample of music. +% +% TODO: Revision of coefficients to (a) force the output range between 0 - 1 and +% (b) to be based on alternative models and materials (training sets). +% +% Updated 03.05.2010 TE +% + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [0 0]; + frame.keydefault = [1 1]; + option.frame = frame; + + dim.key = 'Dimensions'; + dim.type = 'Integer'; + dim.default = NaN; + dim.keydefault = 3; + option.dim = dim; + + activity.key = 'Activity'; + activity.type = 'Boolean'; + activity.default = NaN; + option.activity = activity; + + valence.key = 'Valence'; + valence.type = 'Boolean'; + valence.default = NaN; + option.valence = valence; + + tension.key = 'Tension'; + tension.type = 'Boolean'; + tension.default = NaN; + option.tension = tension; + + arousal.key = 'Arousal'; + arousal.type = 'Boolean'; + arousal.default = NaN; + option.arousal = arousal; + + concepts.key = 'Concepts'; + concepts.type = 'Boolean'; + concepts.default = NaN; + option.concepts = concepts; + + happy.key = 'Happy'; + happy.type = 'Boolean'; + happy.default = NaN; + option.happy = happy; + + sad.key = 'Sad'; + sad.type = 'Boolean'; + sad.default = NaN; + option.sad = sad; + + tender.key = 'Tender'; + tender.type = 'Boolean'; + tender.default = NaN; + option.tender = tender; + + anger.key = 'Anger'; + anger.type = 'Boolean'; + anger.default = NaN; + option.anger = anger; + + fear.key = 'Fear'; + fear.type = 'Boolean'; + fear.default = NaN; + option.fear = fear; + +specif.option = option; +specif.defaultframelength = 1; +%specif.defaultframehop = .5; + +specif.combinechunk = {'Average',@nothing}; +specif.extensive = 1; + +varargout = mirfunction(@miremotion,orig,varargin,nargout,specif,@init,@main); + + +%% +function [x type] = init(x,option) + +option = process(option); + +if option.frame.length.val + hop = option.frame.hop.val; + if strcmpi(option.frame.hop.unit,'Hz') + hop = 1/hop; + option.frame.hop.unit = 's'; + end + if strcmpi(option.frame.hop.unit,'s') + hop = hop*get(x,'Sampling'); + end + if strcmpi(option.frame.hop.unit,'%') + hop = hop/100; + option.frame.hop.unit = '/1'; + end + if strcmpi(option.frame.hop.unit,'/1') + hop = hop*option.frame.length.val; + end + frames = 0:hop:1000000; + x = mirsegment(x,[frames;frames+option.frame.length.val]); +elseif isa(x,'mirdesign') + x = set(x,'NoChunk',1); +end +rm = mirrms(x,'Frame',.046,.5); + +le = 0; %mirlowenergy(rm,'ASR'); + +o = mironsets(x,'Filterbank',15,'Contrast',0.1); +at = mirattacktime(o); +as = 0; %mirattackslope(o); +ed = 0; %mireventdensity(o,'Option1'); + +fl = mirfluctuation(x,'Summary'); +fp = mirpeaks(fl,'Total',1); +fc = 0; %mircentroid(fl); + +tp = 0; %mirtempo(x,'Frame',2,.5,'Autocor','Spectrum'); +pc = mirpulseclarity(x,'Frame',2,.5); %%%%%%%%%%% Why 'Frame'?? + +s = mirspectrum(x,'Frame',.046,.5); +sc = mircentroid(s); +ss = mirspread(s); +sr = mirroughness(s); + +%ps = mirpitch(x,'Frame',.046,.5,'Tolonen'); + +c = mirchromagram(x,'Frame','Wrap',0,'Pitch',0); %%%%%%%%%%%%%%%%%%%% Previous frame size was too small. +cp = mirpeaks(c,'Total',1); +ps = 0;%cp; +ks = mirkeystrength(c); +[k kc] = mirkey(ks); +mo = mirmode(ks); +hc = mirhcdf(c); + +se = mirentropy(mirspectrum(x,'Collapsed','Min',40,'Smooth',70,'Frame',1.5,.5)); %%%%%%%%% Why 'Frame'?? + +ns = mirnovelty(mirspectrum(x,'Frame',.1,.5,'Max',5000),'Normal',0); +nt = mirnovelty(mirchromagram(x,'Frame',.2,.25),'Normal',0); %%%%%%%%%%%%%%%%%%%% Previous frame size was too small. +nr = mirnovelty(mirchromagram(x,'Frame',.2,.25,'Wrap',0),'Normal',0); %%%%%%%%%%%%%%%%%%%% Previous frame size was too small. + + + +x = {rm,le, at,as,ed, fp,fc, tp,pc, sc,ss,sr, ps, cp,kc,mo,hc, se, ns,nt,nr}; + +type = {'miremotion','mirscalar','mirscalar',... + 'mirscalar','mirscalar','mirscalar',... + 'mirspectrum','mirscalar',... + 'mirscalar','mirscalar',... + 'mirscalar','mirscalar','mirscalar',... + 'mirscalar',... + 'mirchromagram','mirscalar','mirscalar','mirscalar',... + 'mirscalar',... + 'mirscalar','mirscalar','mirscalar'}; + + +%% +function e = main(x,option,postoption) + +warning('WARNING IN MIRENOTION: The current model of miremotion is not correctly calibrated with this version of MIRtoolbox (but with version 1.3 only).'); + +option = process(option); +rm = get(x{1},'Data'); +%le = get(x{2},'Data'); +at = get(x{3},'Data'); +%as = get(x{4},'Data'); +%ed = get(x{5},'Data'); +%fpp = get(x{6},'PeakPosUnit'); +fpv = get(x{6},'PeakVal'); +%fc = get(x{7},'Data'); +%tp = get(x{8},'Data'); +pc = get(x{9},'Data'); +sc = get(x{10},'Data'); +ss = get(x{11},'Data'); +rg = get(x{12},'Data'); +%ps = get(x{13},'PeakPosUnit'); +cp = get(x{14},'PeakPosUnit'); +kc = get(x{15},'Data'); +mo = get(x{16},'Data'); +hc = get(x{17},'Data'); +se = get(x{18},'Data'); +ns = get(x{19},'Data'); +nt = get(x{20},'Data'); +nr = get(x{21},'Data'); + + +e.dim = {}; +e.dimdata = mircompute(@initialise,rm); +if option.activity == 1 + [e.dimdata e.activity_fact] = mircompute(@activity,e.dimdata,rm,fpv,sc,ss,se); + e.dim = [e.dim,'Activity']; +else + e.activity_fact = NaN; +end +if option.valence == 1 + [e.dimdata e.valence_fact] = mircompute(@valence,e.dimdata,rm,fpv,kc,mo,ns); + e.dim = [e.dim,'Valence']; +else + e.valence_fact = NaN; +end +if option.tension == 1 + [e.dimdata e.tension_fact] = mircompute(@tension,e.dimdata,rm,fpv,kc,hc,nr); + e.dim = [e.dim,'Tension']; +else + e.tension_fact = NaN; +end + +e.class = {}; +e.classdata = mircompute(@initialise,rm); +if option.happy == 1 + [e.classdata e.happy_fact] = mircompute(@happy,e.classdata,fpv,ss,cp,kc,mo); + e.class = [e.class,'Happy']; +else + e.happy_fact = NaN; +end +if option.sad == 1 + [e.classdata e.sad_fact] = mircompute(@sad,e.classdata,ss,cp,mo,hc,nt); + e.class = [e.class,'Sad']; +else + e.sad_fact = NaN; +end +if option.tender == 1 + [e.classdata e.tender_fact] = mircompute(@tender,e.classdata,sc,rg,kc,hc,ns); + e.class = [e.class,'Tender']; +else + e.tender_fact = NaN; +end +if option.anger == 1 + [e.classdata e.anger_fact] = mircompute(@anger,e.classdata,rg,kc,se,nr); + e.class = [e.class,'Anger']; +else + e.anger_fact = NaN; +end +if option.fear == 1 + [e.classdata e.fear_fact] = mircompute(@fear,e.classdata,rm,at,fpv,kc,mo); + e.class = [e.class,'Fear']; +else + e.fear_fact = NaN; +end + +e = class(e,'miremotion',mirdata(x{1})); +e = purgedata(e); +fp = mircompute(@noframe,get(x{1},'FramePos')); +e = set(e,'Title','Emotion','Abs','emotions','Ord','magnitude','FramePos',fp); + +%% +function option = process(option) +if option.arousal==1 + option.activity = 1; + option.tension = 1; + if isnan(option.dim) + option.dim = 0; + end +end +if option.activity==1 || option.valence==1 || option.tension==1 + if isnan(option.activity) + option.activity = 0; + end + if isnan(option.valence) + option.valence = 0; + end + if isnan(option.tension) + option.tension = 0; + end + if isnan(option.concepts) + option.concepts = 0; + end +end +if not(isnan(option.dim)) && option.dim + if isnan(option.concepts) + option.concepts = 0; + end +end +if not(isnan(option.concepts)) && option.concepts + if isnan(option.dim) + option.dim = 0; + end +end +if not(isnan(option.dim)) + switch option.dim + case 0 + if isnan(option.activity) + option.activity = 0; + end + if isnan(option.valence) + option.valence = 0; + end + if isnan(option.tension) + option.tension = 0; + end + case 2 + option.activity = 1; + option.valence = 1; + if isnan(option.tension) + option.tension = 0; + end + case 3 + option.activity = 1; + option.valence = 1; + option.tension = 1; + end +end +if isnan(option.activity) + option.activity = 1; +end +if isnan(option.valence) + option.valence = 1; +end +if isnan(option.tension) + option.tension = 1; +end +if isnan(option.concepts) + option.concepts = 1; +end +if option.concepts + option.happy = 1; + option.sad = 1; + option.tender = 1; + option.anger = 1; + option.fear = 1; +end +if option.happy==1 || option.sad==1 || option.tender==1 ... + || option.anger==1 || option.fear==1 + if isnan(option.happy) + option.happy = 0; + end + if isnan(option.sad) + option.sad = 0; + end + if isnan(option.tender) + option.tender = 0; + end + if isnan(option.anger) + option.anger = 0; + end + if isnan(option.fear) + option.fear = 0; + end +end + + +%% +function e = initialise(rm) +e = []; + + +function e = activity(e,rm,fpv,sc,ss,se) % without the box-cox transformation, revised coefficients +af = zeros(5,1); +af(1) = 0.6664* ((mean(rm) - 0.0559)/0.0337); % +af(2) = 0.6099 * ((mean(fpv{1}) - 13270.1836)/10790.655); +af(3) = 0.4486*((mean(cell2mat(sc)) - 1677.7)./570.34); +af(4) = -0.4639*((mean(cell2mat(ss)) - 250.5574)./205.3147); +af(5) = 0.7056*((mean(se) - 0.954)./0.0258); +af(isnan(af)) = []; +e(end+1,:) = sum(af)+5.4861; +e = {e af}; + +function e = valence(e,rm,fpv,kc,mo,ns) % without the box-cox transformation, revised coefficients +vf = zeros(5,1); +vf(1) = -0.3161 * ((std(rm) - 0.024254)./0.015667); +vf(2) = 0.6099 * ((mean(fpv{1}) - 13270.1836)/10790.655); +vf(3) = 0.8802 * ((mean(kc) - 0.5123)./0.091953); +vf(4) = 0.4565 * ((mean(mo) - -0.0019958)./0.048664); +ns(isnan(ns)) = []; +vf(5) = 0.4015 * ((mean(ns) - 131.9503)./47.6463); +vf(isnan(vf)) = []; +e(end+1,:) = sum(vf)+5.2749; +e = {e vf}; + +function e = tension(e,rm,fpv,kc,hc,nr) +tf = zeros(5,1); +tf(1) = 0.5382 * ((std(rm) - 0.024254)./0.015667); +tf(2) = -0.5406 * ((mean(fpv{1}) - 13270.1836)/10790.655); +tf(3) = -0.6808 * ((mean(kc) - 0.5124)./0.092); +tf(4) = 0.8629 * ((mean(hc) - 0.2962)./0.0459); +tf(5) = -0.5958 * ((mean(nr) - 71.8426)./46.9246); +tf(isnan(tf)) = []; +e(end+1,:) = sum(tf)+5.4679; +e = {e tf}; + + +% BASIC EMOTION PREDICTORS + +function e = happy(e,fpv,ss,cp,kc,mo) +ha_f = zeros(5,1); +ha_f(1) = 0.7438*((mean(cell2mat(fpv)) - 13270.1836)./10790.655); +ha_f(2) = -0.3965*((mean(cell2mat(ss)) - 250.5574)./205.3147); +ha_f(3) = 0.4047*((std(cell2mat(cp)) - 8.5321)./2.5899); +ha_f(4) = 0.7780*((mean(kc) - 0.5124)./0.092); +ha_f(5) = 0.6220*((mean(mo) - -0.002)./0.0487); +ha_f(isnan(ha_f)) = []; +e(end+1,:) = sum(ha_f)+2.6166; +e = {e ha_f}; + +function e = sad(e,ss,cp,mo,hc,nt) +sa_f = zeros(5,1); +sa_f(1) = 0.4324*((mean(cell2mat(ss)) - 250.5574)./205.3147); +sa_f(2) = -0.3137*((std(cell2mat(cp)) - 8.5321)./2.5899); +sa_f(3) = -0.5201*((mean(mo) - -0.0020)./0.0487); +sa_f(4) = -0.6017*((mean(hc) - 0.2962)./0.0459); +sa_f(5) = 0.4493*((mean(nt) - 42.2022)./36.7782); +sa_f(isnan(sa_f)) = []; +e(end+1,:) = sum(sa_f)+2.9756; +e = {e sa_f}; + +function e = tender(e,sc,rg,kc,hc,ns) +te_f = zeros(5,1); +te_f(1) = -0.2709*((mean(cell2mat(sc)) - 1677.7106)./570.3432); +te_f(2) = -0.4904*((std(rg) - 85.9387)./106.0767); +te_f(3) = 0.5192*((mean(kc) - 0.5124)./0.0920); +te_f(4) = -0.3995*((mean(hc) - 0.2962)./0.0459); +te_f(5) = 0.3391*((mean(ns) - 131.9503)./47.6463); +te_f(isnan(te_f)) = []; +e(end+1,:) = sum(te_f)+2.9756; +e = {e te_f}; + +function e = anger(e,rg,kc,se,nr) % +an_f = zeros(5,1); +%an_f(1) = -0.2353*((mean(pc) - 0.1462)./.1113); +an_f(2) = 0.5517*((mean(rg) - 85.9387)./106.0767); +an_f(3) = -.5802*((mean(kc) - 0.5124)./0.092); +an_f(4) = .2821*((mean(se) - 0.954)./0.0258); +an_f(5) = -.2971*((mean(nr) - 71.8426)./46.9246); +an_f(isnan(an_f)) = []; +e(end+1,:) = sum(an_f)+1.9767; +e = {e an_f}; + +function e = fear(e,rm,at,fpv,kc,mo) +fe_f = zeros(5,1); +fe_f(1) = 0.4069*((std(rm) - 0.0243)./0.0157); +fe_f(2) = -0.6388*((mean(at) - 0.0707)./0.015689218536423); +fe_f(3) = -0.2538*((mean(cell2mat(fpv)) - 13270.1836)./10790.655); +fe_f(4) = -0.9860*((mean(kc) - 0.5124)./0.0920); +fe_f(5) = -0.3144*((mean(mo) - -0.0019958)./0.048663550639094); +fe_f(isnan(fe_f)) = []; +e(end+1,:) = sum(fe_f)+2.7847; +e = {e fe_f}; + +function fp = noframe(fp) +fp = [fp(1);fp(end)]; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@miremotion/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +function ee = set(e,varargin) +% SET Set properties for the MIRemotion object +% and return the updated object + +propertyArgIn = varargin; +%dim = e.dim; +dimdata = e.dimdata; +activity_fact = e.activity_fact; +valence_fact = e.valence_fact; +tension_fact = e.tension_fact; +%classes = e.class; +classdata = e.classdata; +happy_fact = e.happy_fact; +sad_fact = e.sad_fact; +tender_fact = e.tender_fact; +anger_fact = e.anger_fact; +fear_fact = e.fear_fact; +d = mirdata(e); +d = set(d,'Title',get(e,'Title'),'Abs',get(e,'Abs'),'Ord',get(e,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'DimData' + dimdata = val; + case 'ClassData' + classdata = val; + case 'ActivityFactors' + activity_fact = val; + case 'ValenceFactors' + valence_fact = val; + case 'TensionFactors' + tension_fact = val; + case 'HappyFactors' + happy_fact = val; + case 'SadFactors' + sad_fact = val; + case 'TenderFactors' + tender_fact = val; + case 'AngerFactors' + anger_fact = val; + case 'FearFactors' + fear_fact = val; + otherwise + d = set(d,prop,val); + end +end +ee.dim = e.dim; +ee.dimdata = dimdata; +ee.activity_fact = activity_fact; +ee.valence_fact = valence_fact; +ee.tension_fact = tension_fact; +ee.class = e.class; +ee.classdata = classdata; +ee.happy_fact = happy_fact; +ee.sad_fact = sad_fact; +ee.tender_fact = tender_fact; +ee.anger_fact = anger_fact; +ee.fear_fact = fear_fact; +ee = class(ee,'miremotion',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirenvelope/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function display(d) +% MIRDATA/DISPLAY display of a MIRenvelope + +if d.hwr + d = set(d,'Title',[get(d,'Title'),' (half-wave rectified)']); +end +mirdisplay(mirtemporal(d),inputname(1)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirenvelope/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function val = get(e, propName) +% GET Get properties from the MIRenvelope object +% and return the value + +switch propName + case 'DownSampling' + val = e.downsampl; + case 'Halfwave' + val = e.hwr; + case 'Diff' + val = e.diff; + case 'Phase' + val = e.phase; + otherwise + val = get(mirtemporal(e),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirenvelope/mirenvelope.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,665 @@ +function varargout = mirenvelope(orig,varargin) +% e = mirenvelope(x) extracts the envelope of x, showing the global shape +% of the waveform. +% mirenvelope(...,m) specifies envelope extraction method. +% Possible values: +% m = 'Filter' uses a low-pass filtering. (Default strategy) +% m = 'Spectro' uses a spectrogram. +% +% Options related to the 'Filter' method: +% mirenvelope(...,'Hilbert'): performs a preliminary Hilbert +% transform. +% mirenvelope(...,'PreDecim',N) downsamples by a factor N>1, where +% N is an integer, before the low-pass filtering (Klapuri, 1999). +% Default value: N = 1. +% mirenvelope(...,'Filtertype',f) specifies the filter type. +% Possible values are: +% f = 'IIR': filter with one autoregressive coefficient +% (default) +% f = 'HalfHann': half-Hanning (raised cosine) filter +% (Scheirer, 1998) +% Option related to the 'IIR' option: +% mirenvelope(...,'Tau',t): time constant of low-pass filter in +% seconds. +% Default value: t = 0.02 s. +% mirenvelope(...,'PostDecim',N) downsamples by a factor N>1, where +% N is an integer, after the low-pass filtering. +% Default value: N = 16 if 'PreDecim' is not used, else N = 1. +% mirenvelope(...,'Trim'): trims the initial ascending phase of the +% curves related to the transitory state. +% +% Options related to the 'Spectro' method: +% mirenvelope(...,b) specifies whether the frequency range is further +% decomposed into bands. Possible values: +% b = 'Freq': no band decomposition (default value) +% b = 'Mel': Mel-band decomposition +% b = 'Bark': Bark-band decomposition +% b = 'Cents': decompositions into cents +% mirenvelope(...,'Frame',...) specifies the frame configuration. +% Default value: length: .1 s, hop factor: 10 %. +% mirenvelope(...,'UpSample',N) upsamples by a factor N>1, where +% N is an integer. +% Default value if 'UpSample' called: N = 2 +% mirenvelope(...,'Complex') toggles on the 'Complex' method for the +% spectral flux computation. +% +% Other available for all methods: +% mirenvelope(...,'Sampling',r): resamples to rate r (in Hz). +% 'Down' and 'Sampling' options cannot therefore be combined. +% mirenvelope(...,'Halfwave'): performs a half-wave rectification. +% mirenvelope(...,'Center'): centers the extracted envelope. +% mirenvelope(...,'HalfwaveCenter'): performs a half-wave +% rectification on the centered envelope. +% mirenvelope(...,'Log'): computes the common logarithm (base 10) of +% the envelope. +% mirenvelope(...,'Mu',mu): computes the logarithm of the +% envelope, before the eventual differentiation, using a mu-law +% compression (Klapuri, 2006). +% Default value for mu: 100 +% mirenvelope(...,'Log'): computes the logarithm of the envelope. +% mirenvelope(...,'Power'): computes the power (square) of the +% envelope. +% mirenvelope(...,'Diff'): computes the differentation of the +% envelope, i.e., the differences between successive samples. +% mirenvelope(...,'HalfwaveDiff'): performs a half-wave +% rectification on the differentiated envelope. +% mirenvelope(...,'Normal'): normalizes the values of the envelope by +% fixing the maximum value to 1. +% mirenvelope(...,'Lambda',l): sums the half-wave rectified envelope +% with the non-differentiated envelope, using the respective +% weight 0<l<1 and (1-l). (Klapuri et al., 2006) +% mirenvelope(...,'Smooth',o): smooths the envelope using a moving +% average of order o. +% Default value when the option is toggled on: o=30 +% mirenvelope(...,'Gauss',o): smooths the envelope using a gaussian +% of standard deviation o samples. +% Default value when the option is toggled on: o=30 +% mirenvelope(...,'Klapuri06'): follows the model proposed in +% (Klapuri et al., 2006). + + method.type = 'String'; + method.choice = {'Filter','Spectro'}; + method.default = 'Filter'; + option.method = method; + +%% options related to 'Filter': + + hilb.key = 'Hilbert'; + hilb.type = 'Boolean'; + hilb.default = 0; + option.hilb = hilb; + + decim.key = {'Decim','PreDecim'}; + decim.type = 'Integer'; + decim.default = 0; + option.decim = decim; + + filter.key = 'FilterType'; + filter.type = 'String'; + filter.choice = {'IIR','HalfHann',0}; + if isamir(orig,'mirenvelope') + filter.default = 0; % no more envelope extraction, already done + else + filter.default = 'IIR'; + end + option.filter = filter; + + %% options related to 'IIR': + tau.key = 'Tau'; + tau.type = 'Integer'; + tau.default = .02; + option.tau = tau; + + zp.key = 'ZeroPhase'; % internal use: for manual filtfilt + zp.type = 'Boolean'; + if isamir(orig,'mirenvelope') + zp.default = 0; + else + zp.default = NaN; + end + option.zp = zp; + + ds.key = {'Down','PostDecim'}; + ds.type = 'Integer'; + if isamir(orig,'mirenvelope') + ds.default = 1; + else + ds.default = NaN; % 0 if 'PreDecim' is used, else 16 + end + ds.when = 'After'; + ds.chunkcombine = 'During'; + option.ds = ds; + + trim.key = 'Trim'; + trim.type = 'Boolean'; + trim.default = 0; + trim.when = 'After'; + option.trim = trim; + +%% Options related to 'Spectro': + + band.type = 'String'; + band.choice = {'Freq','Mel','Bark','Cents'}; + band.default = 'Freq'; + option.band = band; + + up.key = {'UpSample'}; + up.type = 'Integer'; + up.default = 0; + up.keydefault = 2; + up.when = 'After'; + option.up = up; + + complex.key = 'Complex'; + complex.type = 'Boolean'; + complex.default = 0; + complex.when = 'After'; + option.complex = complex; + +%% Options related to all methods: + + sampling.key = 'Sampling'; + sampling.type = 'Integer'; + sampling.default = 0; + sampling.when = 'After'; + option.sampling = sampling; + + hwr.key = 'Halfwave'; + hwr.type = 'Boolean'; + hwr.default = 0; + hwr.when = 'After'; + option.hwr = hwr; + + c.key = 'Center'; + c.type = 'Boolean'; + c.default = 0; + c.when = 'After'; + option.c = c; + + chwr.key = 'HalfwaveCenter'; + chwr.type = 'Boolean'; + chwr.default = 0; + chwr.when = 'After'; + option.chwr = chwr; + + mu.key = 'Mu'; + mu.type = 'Integer'; + mu.default = 0; + mu.keydefault = 100; + mu.when = 'After'; + option.mu = mu; + + oplog.key = 'Log'; + oplog.type = 'Boolean'; + oplog.default = 0; + oplog.when = 'After'; + option.log = oplog; + + oppow.key = 'Power'; + oppow.type = 'Integer'; + oppow.default = 0; + oppow.when = 'After'; + option.power = oppow; + + diff.key = 'Diff'; + diff.type = 'Integer'; + diff.default = 0; + diff.keydefault = 1; + diff.when = 'After'; + option.diff = diff; + + diffhwr.key = 'HalfwaveDiff'; + diffhwr.type = 'Integer'; + diffhwr.default = 0; + diffhwr.keydefault = 1; + diffhwr.when = 'After'; + option.diffhwr = diffhwr; + + lambda.key = 'Lambda'; + lambda.type = 'Integer'; + lambda.default = 1; + lambda.when = 'After'; + option.lambda = lambda; + + aver.key = 'Smooth'; + aver.type = 'Integer'; + aver.default = 0; + aver.keydefault = 30; + aver.when = 'After'; + option.aver = aver; + + gauss.key = 'Gauss'; + gauss.type = 'Integer'; + gauss.default = 0; + gauss.keydefault = 30; + gauss.when = 'After'; + option.gauss = gauss; + + norm.key = 'Normal'; + norm.type = 'Boolean'; + norm.default = 0; + norm.when = 'After'; + option.norm = norm; + + presel.type = 'String'; + presel.choice = {'Klapuri06'}; + presel.default = 0; + option.presel = presel; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [.1 .1]; + option.frame = frame; + +specif.option = option; + +specif.eachchunk = 'Normal'; +specif.combinechunk = 'Concat'; +specif.extensive = 1; + +varargout = mirfunction(@mirenvelope,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = 'mirenvelope'; +if isamir(x,'mirscalar') + return +end +if ischar(option.presel) && strcmpi(option.presel,'Klapuri06') + option.method = 'Spectro'; +end +if not(isamir(x,'mirenvelope')) + if strcmpi(option.method,'Filter') + if isnan(option.zp) + if strcmpi(option.filter,'IIR') + option.zp = 1; + else + option.zp = 0; + end + end + if option.zp == 1 + x = mirenvelope(x,'ZeroPhase',2,'Down',1,... + 'Tau',option.tau,'PreDecim',option.decim); + end + elseif strcmpi(option.method,'Spectro') + x = mirspectrum(x,'Frame',option.frame.length.val,... + option.frame.length.unit,... + option.frame.hop.val,... + option.frame.hop.unit,... + 'Window','hanning',... + option.band,'Power'); + + end +end + + +function e = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +if isamir(orig,'mirscalar') + d = get(orig,'Data'); + fp = get(orig,'FramePos'); + for i = 1:length(d) + for j = 1:length(d{i}) + d{i}{j} = reshape(d{i}{j},size(d{i}{j},2),1,size(d{i}{j},3)); + p{i}{j} = mean(fp{i}{j})'; + end + end + e.downsampl = 0; + e.hwr = 0; + e.diff = 0; + e.method = 'Spectro'; + e.phase = {{}}; + e = class(e,'mirenvelope',mirtemporal(orig)); + e = set(e,'Title','Envelope','Data',d,'Pos',p,'FramePos',{{}}); + postoption.trim = 0; + postoption.ds = 0; + e = post(e,postoption); + return +end +if isfield(option,'presel') && ischar(option.presel) && ... + strcmpi(option.presel,'Klapuri06') + option.method = 'Spectro'; + postoption.up = 2; + postoption.mu = 100; + postoption.diffhwr = 1; + postoption.lambda = .8; +end +if isfield(postoption,'ds') && isnan(postoption.ds) + if option.decim + postoption.ds = 0; + else + postoption.ds = 16; + end +end +if not(isfield(option,'filter')) || not(ischar(option.filter)) + e = post(orig,postoption); +elseif strcmpi(option.method,'Spectro') + d = get(orig,'Data'); + fp = get(orig,'FramePos'); + sr = get(orig,'Sampling'); + ch = get(orig,'Channels'); + ph = get(orig,'Phase'); + for h = 1:length(d) + sr{h} = 0; + for i = 1:length(d{h}) + if size(d{h}{i},3)>1 % Already in bands (channels in 3d dim) + d{h}{i} = permute(sum(d{h}{i}),[2 1 3]); + ph{h}{i} = permute(ph{h}{i},[2 1 3]); + else % Simple spectrogram, frequency range sent to 3d dim + d{h}{i} = permute(d{h}{i},[2 3 1]); + ph{h}{i} = permute(ph{h}{i},[2 3 1]); + end + p{h}{i} = mean(fp{h}{i})'; + if not(sr{h}) && size(fp{h}{i},2)>1 + sr{h} = 1/(fp{h}{i}(1,2)-fp{h}{i}(1,1)); + end + end + if not(sr{h}) + warning('WARNING IN MIRENVELOPE: The frame decomposition did not succeed. Either the input is of too short duration, or the chunk size is too low.'); + end + ch{h} = (1:size(d{h}{1},3))'; + end + e.downsampl = 0; + e.hwr = 0; + e.diff = 0; + %e.todelete = {}; + e.method = 'Spectro'; + e.phase = ph; + e = class(e,'mirenvelope',mirtemporal(orig)); + e = set(e,'Title','Envelope','Data',d,'Pos',p,... + 'Sampling',sr,'Channels',ch); + postoption.trim = 0; + postoption.ds = 0; + e = post(e,postoption); +else + if isnan(option.zp) + if strcmpi(option.filter,'IIR') + option.zp = 1; + else + option.zp = 0; + end + end + if option.zp == 1 + option.decim = 0; + end + e.downsampl = 1; + e.hwr = 0; + e.diff = 0; + e.method = option.filter; + e.phase = {}; + e = class(e,'mirenvelope',mirtemporal(orig)); + e = purgedata(e); + e = set(e,'Title','Envelope'); + sig = get(e,'Data'); + x = get(e,'Pos'); + sr = get(e,'Sampling'); + disp('Extracting envelope...') + d = cell(1,length(sig)); + [state e] = gettmp(orig,e); + for k = 1:length(sig) + if option.decim + sr{k} = sr{k}/option.decim; + end + if strcmpi(option.filter,'IIR') + a2 = exp(-1/(option.tau*sr{k})); % filter coefficient + a = [1 -a2]; + b = 1-a2; + elseif strcmpi(option.filter,'HalfHann') + a = 1; + b = hann(sr{k}*.4); + b = b(ceil(length(b)/2):end); + end + d{k} = cell(1,length(sig{k})); + for i = 1:length(sig{k}) + sigi = sig{k}{i}; + if option.zp == 2 + sigi = flipdim(sigi,1); + end + if option.hilb + try + for h = 1:size(sigi,2) + for j = 1:size(sigi,3) + sigi(:,h,j) = hilbert(sigi(:,h,j)); + end + end + catch + disp('Signal Processing Toolbox does not seem to be installed. No Hilbert transform.'); + end + end + sigi = abs(sigi); + + if option.decim + dsigi = zeros(ceil(size(sigi,1)/option.decim),... + size(sigi,2),size(sigi,3)); + for f = 1:size(sigi,2) + for c = 1:size(sigi,3) + dsigi(:,f,c) = decimate(sigi(:,f,c),option.decim); + end + end + sigi = dsigi; + clear dsigi + x{k}{i} = x{k}{i}(1:option.decim:end,:,:); + end + + % tmp = filtfilt(1-a,[1 -a],sigi); % zero-phase IIR filter for smoothing the envelope + + % Manual filtfilt + emptystate = isempty(state); + tmp = zeros(size(sigi)); + for c = 1:size(sigi,3) + if emptystate + [tmp(:,:,c) state(:,c,1)] = filter(b,a,sigi(:,:,c)); + else + [tmp(:,:,c) state(:,c,1)] = filter(b,a,sigi(:,:,c),... + state(:,c,1)); + end + end + + tmp = max(tmp,0); % For security reason... + if option.zp == 2 + tmp = flipdim(tmp,1); + end + d{k}{i} = tmp; + %td{k} = round(option.tau*sr{k}*1.5); + end + end + e = set(e,'Data',d,'Pos',x,'Sampling',sr); %,'ToDelete',td + e = settmp(e,state); + if not(option.zp == 2) + e = post(e,postoption); + end +end +if isfield(option,'presel') && ischar(option.presel) && ... + strcmpi(option.presel,'Klapuri06') + e = mirsum(e,'Adjacent',10); +end + + +function e = post(e,postoption) +if isempty(postoption) + return +end +if isfield(postoption,'lambda') && not(postoption.lambda) + postoption.lambda = 1; +end +d = get(e,'Data'); +tp = get(e,'Time'); +sr = get(e,'Sampling'); +ds = get(e,'DownSampling'); +ph = get(e,'Phase'); +for k = 1:length(d) + if isfield(postoption,'sampling') + if postoption.sampling + newsr = postoption.sampling; + elseif isfield(postoption,'ds') && postoption.ds>1 + newsr = sr{k}/postoption.ds; + else + newsr = sr{k}; + end + end + if isfield(postoption,'up') && postoption.up + [z,p,gain] = butter(6,10/newsr/postoption.up*2,'low'); + [sos,g] = zp2sos(z,p,gain); + Hd = dfilt.df2tsos(sos,g); + end + for i = 1:length(d{k}) + if isfield(postoption,'sampling') && postoption.sampling + if and(sr{k}, not(sr{k} == postoption.sampling)) + dk = d{k}{i}; + for j = 1:size(dk,3) + if not(sr{k} == round(sr{k})) + mirerror('mirenvelope','The ''Sampling'' postoption cannot be used after using the ''Down'' postoption.'); + end + rk(:,:,j) = resample(dk(:,:,j),postoption.sampling,sr{k}); + end + d{k}{i} = rk; + tp{k}{i} = repmat((0:size(d{k}{i},1)-1)',... + [1 1 size(tp{k}{i},3)])... + /postoption.sampling + tp{k}{i}(1,:,:); + if not(iscell(ds)) + ds = cell(length(d)); + end + ds{k} = round(sr{k}/postoption.sampling); + end + elseif isfield(postoption,'ds') && postoption.ds>1 + if not(postoption.ds == round(postoption.ds)) + mirerror('mirenvelope','The ''Down'' sampling rate should be an integer.'); + end + ds = postoption.ds; + tp{k}{i} = tp{k}{i}(1:ds:end,:,:); % Downsampling... + d{k}{i} = d{k}{i}(1:ds:end,:,:); + end + if isfield(postoption,'sampling') + if not(strcmpi(e.method,'Spectro')) && postoption.trim + tdk = round(newsr*.1); + d{k}{i}(1:tdk,:,:) = repmat(d{k}{i}(tdk,:,:),[tdk,1,1]); + d{k}{i}(end-tdk+1:end,:,:) = repmat(d{k}{i}(end-tdk,:,:),[tdk,1,1]); + end + if postoption.log + d{k}{i} = log10(d{k}{i}); + end + if postoption.mu + dki = max(0,d{k}{i}); + mu = postoption.mu; + dki = log(1+mu*dki)/log(1+mu); + dki(~isfinite(d{k}{i})) = NaN; + d{k}{i} = dki; + end + if postoption.power + d{k}{i} = d{k}{i}.^2; + end + if postoption.up + dki = zeros(size(d{k}{i},1).*postoption.up,... + size(d{k}{i},2),size(d{k}{i},3)); + dki(1:postoption.up:end,:,:) = d{k}{i}; + dki = filter(Hd,[dki;... + zeros(6,size(d{k}{i},2),size(d{k}{i},3))]); + d{k}{i} = dki(1+ceil(6/2):end-floor(6/2),:,:); + tki = zeros(size(tp{k}{i},1).*postoption.up,... + size(tp{k}{i},2),... + size(tp{k}{i},3)); + dt = repmat((tp{k}{i}(2)-tp{k}{i}(1))... + /postoption.up,... + [size(tp{k}{i},1),1,1]); + for j = 1:postoption.up + tki(j:postoption.up:end,:,:) = tp{k}{i}+dt*(j-1); + end + tp{k}{i} = tki; + newsr = sr{k}*postoption.up; + end + if (postoption.diffhwr || postoption.diff) && ... + not(get(e,'Diff')) + tp{k}{i} = tp{k}{i}(1:end-1,:,:); + order = max(postoption.diffhwr,postoption.diff); + if postoption.complex + dph = diff(ph{k}{i},2); + dph = dph/(2*pi);% - round(dph/(2*pi)); + ddki = sqrt(d{k}{i}(3:end,:,:).^2 + d{k}{i}(2:end-1,:,:).^2 ... + - 2.*d{k}{i}(3:end,:,:)... + .*d{k}{i}(2:end-1,:,:)... + .*cos(dph)); + d{k}{i} = d{k}{i}(2:end,:,:); + tp{k}{i} = tp{k}{i}(2:end,:,:); + elseif order == 1 + ddki = diff(d{k}{i},1,1); + else + b = firls(order,[0 0.9],[0 0.9*pi],'differentiator'); + ddki = filter(b,1,... + [repmat(d{k}{i}(1,:,:),[order,1,1]);... + d{k}{i};... + repmat(d{k}{i}(end,:,:),[order,1,1])]); + ddki = ddki(order+1:end-order-1,:,:); + end + if postoption.diffhwr + ddki = hwr(ddki); + end + d{k}{i} = (1-postoption.lambda)*d{k}{i}(1:end-1,:,:)... + + postoption.lambda*sr{k}/10*ddki; + end + if postoption.aver + y = filter(ones(1,postoption.aver),1,... + [d{k}{i};zeros(postoption.aver,... + size(d{k}{i},2),... + size(d{k}{i},3))]); + d{k}{i} = y(1+ceil(postoption.aver/2):... + end-floor(postoption.aver/2),:,:); + end + if postoption.gauss + sigma = postoption.gauss; + gauss = 1/sigma/2/pi... + *exp(- (-4*sigma:4*sigma).^2 /2/sigma^2); + y = filter(gauss,1,[d{k}{i};zeros(4*sigma,1,size(d{k}{i},3))]); + y = y(4*sigma:end,:,:); + d{k}{i} = y(1:size(d{k}{i},1),:,:); + end + if postoption.chwr + d{k}{i} = center(d{k}{i}); + d{k}{i} = hwr(d{k}{i}); + end + if postoption.hwr + d{k}{i} = hwr(d{k}{i}); + end + if postoption.c + d{k}{i} = center(d{k}{i}); + end + if postoption.norm + d{k}{i} = d{k}{i}./repmat(max(abs(d{k}{i})),... + [size(d{k}{i},1),1,1]); + end + end + end + if isfield(postoption,'sampling') + sr{k} = newsr; + end +end +if isfield(postoption,'ds') && postoption.ds>1 + e = set(e,'DownSampling',postoption.ds,'Sampling',sr); +elseif isfield(postoption,'sampling') && postoption.sampling + e = set(e,'DownSampling',ds,'Sampling',sr); +elseif isfield(postoption,'up') && postoption.up + e = set(e,'Sampling',sr); +end +if isfield(postoption,'sampling') + if postoption.hwr + e = set(e,'Halfwave',1); + end + if postoption.diff + e = set(e,'Diff',1,'Halfwave',0,'Title','Differentiated envelope'); + end + if postoption.diffhwr + e = set(e,'Diff',1,'Halfwave',1,'Centered',0); + end + if postoption.c + e = set(e,'Centered',1); + end + if postoption.chwr + e = set(e,'Halfwave',1,'Centered',1); + end +end +e = set(e,'Data',d,'Time',tp); +%if isfield(postoption,'frame') && isstruct(postoption.frame) +% e = mirframenow(e,postoption); +%end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirenvelope/mirplay.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,129 @@ +function mirplay(e,varargin) + +% mirplay method for mirenvelope objects. Help displayed in ../mirplay.m + + ch.key = 'Channel'; + ch.type = 'Integer'; + ch.default = 0; + option.ch = ch; + + sg.key = 'Segment'; + sg.type = 'Integer'; + sg.default = 0; + option.sg = sg; + + se.key = 'Sequence'; + se.type = 'Integer'; + se.default = 0; + option.se = se; + + inc.key = 'Increasing'; + inc.type = 'MIRtb'; + option.inc = inc; + + dec.key = 'Decreasing'; + dec.type = 'MIRtb'; + option.dec = dec; + + every.key = 'Every'; + every.type = 'Integer'; + option.every = every; + + burst.key = 'Burst'; + burst.type = 'Boolean'; + burst.default = 1; + option.burst = burst; + +specif.option = option; + +specif.eachchunk = 'Normal'; + +varargout = mirfunction(@mirplay,e,varargin,nargout,specif,@init,@main); +if nargout == 0 + varargout = {}; +end + + +function [x type] = init(x,option) +type = ''; + + +function noargout = main(a,option,postoption) +if iscell(a) + a = a{1}; +end +d = get(a,'Data'); +f = get(a,'Sampling'); +n = get(a,'Name'); +c = get(a,'Channels'); +fp = get(a,'FramePos'); +if not(option.se) + if length(d)>1 + if isfield(option,'inc') + [unused order] = sort(mirgetdata(option.inc)); + elseif isfield(option,'dec') + [unused order] = sort(mirgetdata(option.dec),'descend'); + else + order = 1:length(d); + end + if isfield(option,'every') + order = order(1:option.every:end); + end + else + order = 1; + end +else + order = option.se; +end +if not(isempty(order)) + for k = order(:)' + display(['Playing envelope of file: ' n{k}]) + dk = d{k}; + if not(iscell(dk)) + dk = {dk}; + end + if option.ch + if isempty(c{k}) + chk = option.ch; + else + [unused unused chk] = intersect(option.ch,c{k}); + end + else + chk = 1:size(dk{1},3); + end + if isempty(chk) + display('No channel to play.'); + end + for l = chk + if chk(end)>1 + display([' Playing channel #' num2str(l)]); + end + if option.sg + sgk = option.sg(find(option.sg<=length(dk))); + else + sgk = 1:length(dk); + end + for i = sgk + if sgk(end)>1 + display([' Playing segment #' num2str(i)]) + end + di = dk{i}; + for j = 1:size(di,2) + djl = resample(di(:,j,l),11025,round(f{k})); + djl = djl/max(djl); + djl = rand(length(djl),1).*djl; + sound(djl,11025); + idealtime = length(djl)/11025; + practime = toc; + if practime < idealtime + pause(idealtime-practime) + end + end + if option.burst && sgk(end)>1 + sound(rand(1,10)) + end + end + end + end +end +noargout = {}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirenvelope/mirsave.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +function mirsave(e,f) + +ext = 0; % Specified new extension +if nargin == 1 + f = '.envelope.mir'; +elseif length(f)>3 && strcmpi(f(end-3:end),'.wav') + ext = '.wav'; + if length(f)==4 + f = '.mir'; + end +elseif length(f)>2 && strcmpi(f(end-2:end),'.au') + ext = '.au'; + if length(f)==3 + f = '.mir'; + end +end + +d = get(e,'Data'); +nf = length(d); +fs = get(e,'Sampling'); +nm = get(e,'Name'); +for i = 1:nf + di = d{i}{1}; + fsi = fs{i}; + nmi = nm{i}; + di = resample(di,11025,round(fs{i})); + di = rand(size(di)).*di; + di = di/max(max(max(abs(di))))*.9999; + di = reshape(di,[],1); + + %Let's remove the extension from the original files + if length(nmi)>3 && strcmpi(nmi(end-3:end),'.wav') + nmi(end-3:end) = []; + elseif length(nmi)>2 && strcmpi(nmi(end-2:end),'.au') + nmi(end-2:end) = []; + end + + if nf>1 || strcmp(f(1),'.') + %Let's add the new suffix + n = [nmi f]; + else + n = f; + end + + if not(ischar(ext)) || strcmp(ext,'.wav') + if length(n)<4 || not(strcmpi(n(end-3:end),'.wav')) + n = [n '.wav']; + end + wavwrite(di,11025,32,n) + elseif strcmp(ext,'.au') + if length(n)<3 || not(strcmpi(n(end-2:end),'.au')) + n = [n '.au']; + end + auwrite(di,11025,32,'linear',n) + end + disp([n,' saved.']); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirenvelope/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +function ee = set(e,varargin) +% SET Set properties for the MIRenvelope object +% and return the updated object + +propertyArgIn = varargin; +ds = e.downsampl; +hw = e.hwr; +df = e.diff; +mt = e.method; +ph = e.phase; +t = mirtemporal(e); +t = set(t,'Title',get(e,'Title'),'Abs',get(e,'Abs'),'Ord',get(e,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'DownSampling' + ds = val; + case 'Halfwave' + hw = val; + case 'Diff' + df = val; + otherwise + t = set(t,prop,val); + end +end +ee.downsampl = ds; +ee.hwr = hw; +ee.diff = df; +ee.method = mt; +ee.phase = ph; +ee = class(ee,'mirenvelope',t); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirhisto/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +function display(s) +% SCALAR/DISPLAY display the values of an histogram +disp(' '); +d = get(s,'Data'); +b = get(s,'Bins'); +t = ['Histogram of ',get(s,'Title')]; +n = get(s,'Name'); +l = get(s,'MultiData'); +u = get(s,'Unit'); +if not(isempty(u)) && not(strcmp(u,'.')) + u = [' (in ',u,')']; +end +pp = get(s,'PeakPos'); +for i = 1:length(d) + figure + set(gca,'NextPlot','replacechildren',... + 'LineStyleOrder',{'-',':','--',':','-.'}) + nl = size(b{i},1); + for j = 1:nl + bar((b{i}(j,:,1)+b{i}(j,:,2))/2,d{i}(j,:)); + hold all + end + if not(isempty(pp{i}{1})) + for j = 1:nl; + ppj = pp{i}{1}{1,j,1}; + bar((b{i}(j,ppj,1)+b{i}(j,ppj,2))/2,d{i}(j,ppj),'or') + end + end + xlabel(['values',u]); + ylabel('number of occurrences') + title(t) + nl = size(d{i},1); + if nl>1 + legend(l,'Location','Best') + end + fig = get(0,'CurrentFigure'); + va = inputname(1); + if isempty(va) + va = 'ans'; + end + disp([va,' is the ',t,' related to file ',n{i},... + ' is displayed in Figure ',num2str(fig),'.']); +end +disp(' '); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirhisto/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function val = get(a, propName) +% GET Get properties from the MIRchromagram object +% and return the value + +switch propName + case 'Bins' + val = get(mirdata(a),'Pos'); + case 'Weight' + val = get(mirdata(a),'Data'); + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirhisto/mirhisto.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,117 @@ +function varargout = mirhisto(x,varargin) +% h = mirhisto(x) constructs the histogram from x. The elements of x are +% binned into equally spaced containers. +% Optional argument: +% mirhisto(...,'Number',n): specifies the number of containers. +% Default value : n = 10. +% mirhisto(...,'Ampli'): adds the amplitude of the elements,instead of +% simply counting then. + + + n.key = 'Number'; + n.type = 'Integer'; + n.default = 10; + option.n = n; + + a.key = 'Ampli'; + a.type = 'Boolean'; + a.default = 0; + option.a = a; + +specif.option = option; + + +varargout = mirfunction(@mirhisto,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = 'mirhisto'; + + +function h = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +d = get(x,'Data'); +%disp('Computing histogram...') +ddd = cell(1,length(d)); +bbb = cell(1,length(d)); +for i = 1:length(d) + di = d{i}{1}; % To be generalized for segmented data + if iscell(di) + mx = -Inf; + mn = Inf; + nc = size(di,2); + for k = 1:nc + dk = di{k}; + if size(dk,4) == 2 + dk(end+1:end*2,:,:,1) = dk(:,:,:,2); + dk(:,:,:,2) = []; + end + mxk = max(dk); + mnk = min(dk); + if mxk > mx + mx = mxk; + end + if mnk < mn + mn = mnk; + end + end + if isinf(mx) || isinf(mx) + b = []; + dd = []; + else + dd = zeros(1,option.n); + if mn == mx + b(1,:) = mn-ceil(option.n/2) : mn+floor(option.n/2); + else + b(1,:) = mn : (mx-mn)/option.n : mx; + end + for k = 1:nc + dk = di{k}; + for j = 1:option.n + found = find(and(dk>=b(1,j),dk<=b(1,j+1))); + if option.a + dd(1,j) = dd(1,j) + sum(dk(found)); + else + dd(1,j) = dd(1,j) + length(found); + end + end + end + end + else + if isa(x,'mirscalar') + di = permute(di,[3 2 1]); + end + if size(di,4) == 2 + di(end+1:end*2,:,:,1) = di(:,:,:,2); + di(:,:,:,2) = []; + end + nl = size(di,1); + nc = size(di,2); + np = size(di,3); + dd = zeros(1,option.n,np); + for l = 1:np + mx = max(max(di(:,:,l),[],1),[],2); + mn = min(min(di(:,:,l),[],1),[],2); + b(l,:) = mn:(mx-mn)/option.n:mx; + for k = 1:nc + dk = di(:,k,l); + for j = 1:option.n + found = (find(and(dk>=b(l,j),dk<=b(l,j+1)))); + if option.a + dd(1,j,l) = dd(1,j,l) + sum(dk(found)); + else + dd(1,j,l) = dd(1,j,l) + length(found); + end + end + end + end + end + ddd{i} = ipermute(dd,[3 2 1]); + bbb{i}(:,:,1) = b(:,1:end-1); + bbb{i}(:,:,2) = b(:,2:end); +end +h = class(struct,'mirhisto',mirdata(x)); +h = purgedata(h); +h = set(h,'Bins',bbb,'Weight',ddd); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirhisto/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function cc = set(c,varargin) +% SET Set properties for the MIRchromagram object +% and return the updated object + +propertyArgIn = varargin; +d = mirdata(c); +d = set(d,'Title',get(c,'Title'),'Abs',get(c,'Abs'),'Ord',get(c,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Bins' + d = set(d,'Pos',val); + case 'Weight' + d = set(d,'Data',val); + otherwise + d = set(d,prop,val); + end +end +cc = class(struct,'mirhisto',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirkeysom/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +function mov = display(k,filename) +% KEYSOM/DISPLAY display of key som + +mov = []; +load keysomaudiodata; +w = get(k,'Weight'); +n = get(k,'Name'); +if nargin>1 + mov = avifile(filename); +%elseif and(nargout>0,size(w)>0) +% mov = moviein(size(w{1}{1},2)); %no longer needed as of MATLAB Release 11 (5.3). +end +for i = 1:length(w) + wi = w{i}; + fig = figure; + for j = 1:length(wi) + wj = wi{j}; + for k = 1:size(wj,2) + for l = 1:size(wj,3) + h = pcolor(squeeze(wj(:,k,l,:))); + shading interp + axis([1,36,1,24]), view(2) , caxis([-1 1]) + axis off; + hold on + for m=1:24 + text(0.99*keyx(m)-1, 0.98*keyy(m)+1, keyN(m,:),... + 'FontSize',16,'FontName','Arial'); + end + hold off + set(gca,'PlotBoxAspectRatio',[1.5 1 1]) + colormap('jet') + title('Self-organizing map projection of chromagram') + drawnow + if nargin>1 + mov = addframe(mov,gca); + elseif nargout>0 + colormap('jet') + if k == 1 + mov = getframe; + else + mov(k) = getframe; + end + end + end + end + end + disp(['The key som related to file ',n{i},... + ' is displayed in Figure ',num2str(fig),'.']); +end +if nargin>1 + mov = close(mov); + disp(['Data exported to file ',filename,'.']); +end +disp(' '); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirkeysom/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function val = get(a, propName) +% GET Get properties from the MIRchromagram object +% and return the value + +switch propName + case 'Weight' + val = get(mirdata(a),'Data'); + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirkeysom/mirkeysom.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +function varargout = mirkeysom(orig,varargin) +% ks = mirkeysom(x) projects a chromagram on a self-organizing map. +% Creates a pseudocolor map showing the projection of chromagram onto a +% self-organizing map trained with the Krumhansl-Kessler profiles (modified +% for chromagrams). Colors correspond to Pearson correlation values. +% References: +% Toiviainen, P. & Krumhansl, C. L. (2003). Measuring and modeling +% real-time responses to music: the dynamics of tonality induction. +% Perception, 32(6), 741-766. +% Krumhansl, C. L., & Toiviainen, P. (2001) Tonal cognition. +% In R. J. Zatorre & I. Peretz (Eds.), The Biological Foundations of Music. +% Annals of the New York Academy of Sciences. + + % filename.key = 'File'; + % filename.type = 'String'; + % filename.default = 0; + % option.filename = filename; + +specif.option = struct; +specif.defaultframelength = 1; +specif.defaultframehop = .5; + +varargout = mirfunction(@mirkeysom,orig,varargin,nargout,specif,@init,@main); + + +function [c type] = init(orig,option) +c = mirchromagram(orig,'Normal'); +type = 'mirkeysom'; + + +function s = main(c,option,postoption) +if iscell(c) + c = c{1}; +end +load keysomaudiodata; +m = get(c,'Magnitude'); +disp('Projecting the chromagram to a self-organizing map...') +z = cell(1,length(m)); +p = cell(1,length(m)); +for i = 1:length(m) + mi = m{i}; + if not(iscell(mi)) + mi = {mi}; + end + zi = cell(1,length(mi)); + pi = cell(1,length(mi)); + for j = 1:length(mi) + mj = mi{j}; + zj = zeros(24,size(mj,2),size(mj,3),36); + pi{j} = zeros(24,size(mj,2),size(mj,3),36); + for k = 1:size(mj,2) + for l = 1:size(mj,3) + for kk=1:36 + for ll=1:24 + tmp = corrcoef([mj(:,k,l) somw(:,kk,ll)]); + zj(ll,k,l,kk) = tmp(1,2); + end + end + end + end + zi{j} = zj; + end + z{i} = zi; + p{i} = pi; +end +s = class(struct,'mirkeysom',mirdata(c)); +s = purgedata(s); +s = set(s,'Title','Key SOM','Abs','','Ord','','Weight',z,'Pos',p); +%if option.filename +% mov = display(s,option.filename); +%else +% mov = display(s); +%end +%s = {s,mov}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirkeysom/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function cc = set(c,varargin) +% SET Set properties for the MIRchromagram object +% and return the updated object + +propertyArgIn = varargin; +d = mirdata(c); +d = set(d,'Title',get(c,'Title'),'Abs',get(c,'Abs'),'Ord',get(c,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Weight' + d = set(d,'Data',val); + otherwise + d = set(d,prop,val); + end +end +cc = class(struct,'mirkeysom',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirkeystrength/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function val = get(a, propName) +% GET Get properties from the MIRchromagram object +% and return the value + +switch propName + case 'Strength' + val = get(mirdata(a),'Data'); + case 'Tonic' + val = get(mirdata(a),'Pos'); + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirkeystrength/mirkeystrength.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function varargout = mirkeystrength(orig,varargin) +% ks = mirkeystrength(x) computes the key strength, i.e., the probability +% associated with each possible key candidate. +% Optional parameters: +% mirkeystrength(...,'Frame',l,h) orders a frame decomposition of window +% length l (in seconds) and hop factor h, expressed relatively to +% the window length. For instance h = 1 indicates no overlap. +% Default values: l = 1 seconds and h = .5 +% The mirchromagram options 'Weight' and 'Triangle' can be specified. +% [ks,c] = mirkeystrength(...) also displays the chromagram used for the key +% strength estimation. +% +% Krumhansl, Cognitive foundations of musical pitch. Oxford UP, 1990. +% Gomez, Tonal description of polyphonic audio for music content processing, +% INFORMS Journal on Computing, 18-3, pp. 294-304, 2006. + + wth.key = 'Weight'; + wth.type = 'Integer'; + wth.default = .5; + option.wth = wth; + + tri.key = 'Triangle'; + tri.type = 'Boolean'; + tri.default = 0; + option.tri = tri; + +specif.option = option; +specif.defaultframelength = .1; +specif.defaultframehop = .125; + +varargout = mirfunction(@mirkeystrength,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirchromagram')) + x = mirchromagram(x,'Weight',option.wth,'Triangle',option.tri,'Normal'); +else + x = mirchromagram(x,'Wrap','Normal'); +end +type = 'mirkeystrength'; + + +function k = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +if isa(orig,'mirkeystrength') + c = []; + k = orig; +else + c = orig; + load gomezprofs; + m = get(c,'Magnitude'); + st = cell(1,length(m)); + kk = cell(1,length(m)); + %disp('Computing key strengths...') + for i = 1:length(m) + mi = m{i}; + if not(iscell(mi)) + mi = {mi}; + end + si = cell(1,length(mi)); + ki = cell(1,length(mi)); + for j = 1:length(mi) + mj = mi{j}; + sj = zeros(12,size(mj,2),size(mj,3),2); + for k = 1:size(mj,2) + for l = 1:size(mj,3) + tmp = corrcoef([mj(:,k,l) gomezprofs']); + sj(:,k,l,1) = tmp(1,2:13); + sj(:,k,l,2) = tmp(1,14:25); + kj(:,k,l) = {'C','C#','D','D#','E','F','F#','G','G#','A','A#','B'}; + end + end + si{j} = sj; + ki{j} = kj; + end + st{i} = si; + kk{i} = ki; + end + k = class(struct,'mirkeystrength',mirdata(c)); + k = purgedata(k); + k = set(k,'Title','Key strength','Abs','tonal center','Ord','strength',... + 'Tonic',kk,'Strength',st,'MultiData',{'maj','min'},'Interpolable',0); +end +k = {k c}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirkeystrength/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function cc = set(c,varargin) +% SET Set properties for the MIRchromagram object +% and return the updated object + +propertyArgIn = varargin; +d = mirdata(c); +d = set(d,'Title',get(c,'Title'),'Abs',get(c,'Abs'),'Ord',get(c,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Strength' + d = set(d,'Data',val); + case 'Tonic' + d = set(d,'Pos',val); + otherwise + d = set(d,prop,val); + end +end +cc = class(struct,'mirkeystrength',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmatrix/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function display(m) +% MATRIX/DISPLAY display of a matrix +disp(' '); +d = get(m,'Data'); +n = get(m,'Name'); +t = get(m,'Title'); +for i = 1:length(d) + for j = 1:length(d{i}) + figure + h = imagesc(d{i}{j}); + set(gca,'YDir','normal') + title(t) + fig = get(0,'CurrentFigure'); + disp(['The ',t,' related to file ',n{i},' is displayed in Figure ',num2str(fig),'.']); + end +end +disp(' '); +drawnow \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmatrix/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function val = get(a, propName) +% GET Get properties from the MIRsimatrix object +% and return the value + +switch propName + case 'DiagWidth' + val = a.diagwidth; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmatrix/mirmatrix.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function m = mirmatrix(orig,varargin) +% Simple numerical matrix object used to display for instance correlation +% coefficients. + +m = class(struct,'mirmatrix',mirdata(orig)); +m = purgedata(m); +m = set(m,'FramePos',[],varargin{:}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmfcc/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function val = get(a, propName) +% GET Get properties from the MIRspectrum object +% and return the value + +switch propName + case 'Rank' + val = get(mirdata(a),'Pos'); + case 'Delta' + val = a.delta; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmfcc/mirmfcc.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,156 @@ +function varargout = mirmfcc(orig,varargin) +% c = mirmfcc(a) finds the Mel frequency cepstral coefficients (ceps), +% a numerical description of the spectrum envelope. +% +% Requires the Auditory Toolbox. +% +% Optional arguments: +% c = mirmfcc(...,'Rank',N) computes the coefficients of rank(s) N +% (default: N = 1:13). +% If a is a frame decomposition, the temporal evolution of the MFCC, +% along the successive frames, is returned. In this case, a second +% option is available: +% mirmfcc(...,'Delta',d) performs d temporal differentiations of +% the coefficients, also called delta-MFCC (for d = 1) or +% delta-delta-MFCC (for d = 2). +% mirmfcc(...,'Delta') corresponds to mirmfcc(...,'Delta',1) +% Optional arguments related to the delta computation: +% mirmfcc(...,'Radius',r) specifies, for each frame, the number of +% successive and previous neighbouring frames taken into +% consideration for the least-square approximation. +% Usually 1 or 2. +% Default value: 2. + + nbbands.key = 'Bands'; + nbbands.type = 'Integer'; + nbbands.default = 40; + option.nbbands = nbbands; + + rank.key = 'Rank'; + rank.type = 'Integer'; + rank.default = 1:13; + option.rank = rank; + + delta.key = 'Delta'; + delta.type = 'Integer'; + delta.default = 0; + delta.keydefault = 1; + option.delta = delta; + + radius.key = 'Radius'; + radius.type = 'Integer'; + radius.default = 2; + option.radius = radius; + +specif.option = option; + +varargout = mirfunction(@mirmfcc,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if isamir(x,'miraudio') || isamir(x,'mirspectrum') + x = mirspectrum(x,'Mel','log','Bands',option.nbbands); +end +type = 'mirmfcc'; + + +function c = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +if isa(orig,'mirmfcc') + c = orig; + if option.rank + magn = get(c,'Data'); + rank = get(c,'Rank'); + for h = 1:length(magn) + for k = 1:length(magn{h}) + m = magn{h}{k}; + r = rank{h}{k}; + r1 = r(:,1,1); + range = find(ismember(r1,option.rank)); + magn{h}{k} = m(range,:,:); + rank{h}{k} = r(range,:,:); + end + end + c = set(c,'Data',magn,'Rank',rank); + end + c = modif(c,option); +else + c.delta = 0; + %disp('Computing Mel frequency cepstral coefficients...'); + e = get(orig,'Magnitude'); + + % The following is largely based on the source code from Auditory Toolbox + % (A part that I could not call directly from MIRtoolbox) + + % (Malcolm Slaney, August 1993, (c) 1998 Interval Research Corporation) + + try + MakeERBFilters(1,1,1); % Just to be sure that the Auditory Toolbox is installed + catch + error(['ERROR IN MIRFILTERBANK: Auditory Toolbox needs to be installed.']); + end + + dc = cell(1,length(e)); + rk = cell(1,length(e)); + for h = 1:length(e) + dc{h} = cell(1,length(e{h})); + rk{h} = cell(1,length(e{h})); + for i = 1:length(e{h}) + ei = e{h}{i}; + totalFilters = size(ei,3); %Number of mel bands. + + % Figure out Discrete Cosine Transform. We want a matrix + % dct(i,j) which is totalFilters x cepstralCoefficients in size. + % The i,j component is given by + % cos( i * (j+0.5)/totalFilters pi ) + % where we have assumed that i and j start at 0. + mfccDCTMatrix = 1/sqrt(totalFilters/2)*... + cos(option.rank' * ... + (2*(0:(totalFilters-1))+1) * ... + pi/2/totalFilters); + rank0 = find(option.rank == 0); + mfccDCTMatrix(rank0,:) = mfccDCTMatrix(rank0,:) * sqrt(2)/2; + ceps = zeros(size(mfccDCTMatrix,1),size(ei,2)); + for j = 1:size(ei,2) + ceps(:,j) = mfccDCTMatrix * permute(ei(1,j,:),[3 1 2]); + end + dc{h}{i} = ceps; + rk{h}{i} = repmat(option.rank(:),[1 size(ceps,2) size(ceps,3)]); + end + end + c = class(c,'mirmfcc',mirdata(orig)); + c = purgedata(c); + c = set(c,'Title','MFCC','Abs','coefficient ranks','Ord','magnitude',... + 'Data',dc,'Rank',rk); + c = modif(c,option); +end +c = {c orig}; + + +function c = modif(c,option) +d = get(c,'Data'); +fp = get(c,'FramePos'); +t = get(c,'Title'); +if option.delta + M = option.radius; + for k = 1:option.delta + for h = 1:length(d) + for i = 1:length(d{h}) + nc = size(d{h}{i},2)-2*M; + di = zeros(size(d{h}{i},1),nc); + for j = 1:M + di = di + j * (d{h}{i}(:,M+j+(1:nc)) ... + - d{h}{i}(:,M-j+(1:nc))); + end + di = di / 2 / sum((1:M).^2); % MULTIPLY BY 2 INSTEAD OF SQUARE FOR NORMALIZATION ? + d{h}{i} = di; + fp{h}{i} = fp{h}{i}(:,M+1:end-M); + end + end + t = ['Delta-',t]; + end +end +c = set(c,'Data',d,'FramePos',fp,'Delta',get(c,'Delta')+option.delta,... + 'Title',t); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmfcc/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +function ss = set(s,varargin) +% SET Set properties for the MIRspectrum object +% and return the updated object + +propertyArgIn = varargin; +dl = s.delta; +d = mirdata(s); +d = set(d,'Title',get(s,'Title'),'Abs',get(s,'Abs'),'Ord',get(s,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Rank' + d = set(d,'Pos',val); + case 'Delta' + df = val; + otherwise + d = set(d,prop,val); + end +end +ss.delta = dl; +ss = class(ss,'mirmfcc',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmidi/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function display(m) +% MIRMIDI/DISPLAY display of MIDI representation + +figure +disp(' '); +d = get(m,'Data'); +n = get(m,'Name'); +for i = 1:length(d) + pianoroll(d{i}); + fig = get(0,'CurrentFigure'); + va = inputname(1); + if isempty(va) + va = 'ans'; + end + disp([va,' is the MIDI representation related to file ',n{i},... + ' is displayed in Figure ',num2str(fig),'.']); +end +disp(' '); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmidi/mirmidi.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +function varargout = mirmidi(orig,varargin) +% m = mirmidi(x) converts into a MIDI sequence. +% Option associated to mirpitch function can be specified: +% 'Contrast' with default value c = .3 + + thr.key = 'Contrast'; + thr.type = 'Integer'; + thr.default = .3; +option.thr = thr; + + mono.key = 'Mono'; + mono.type = 'Boolean'; + mono.default = 1; +option.mono = mono; + + release.key = {'Release','Releases'}; + release.type = 'String'; + release.choice = {'Olivier','Valeri',0,'no','off'}; + release.default = 'Valeri'; +option.release = release; + +specif.option = option; + +varargout = mirfunction(@mirmidi,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +try + hz2midi(440); +catch + mirerror('MIRMIDI','MIDItoolbox does not seem to be installed.'); +end +if not(isamir(x,'mirmidi')) && not(isamir(x,'mirpitch')) + if isa(x,'mirdesign') && not(option.mono) + x = set(x,'SeparateChannels',1); + end + o = mironsets(x,'Attacks','Releases',option.release); + x = {o x}; +end +type = 'mirmidi'; + + +function m = main(x,option,postoption) +if iscell(x) %not(isamir(x,'mirmidi')) + o = x{1}; + a = x{2}; + s = mirsegment(a,o); + x = mirpitch(s,'Contrast',option.thr,'Sum',0); + do = get(o,'PeakVal'); + da = get(o,'AttackPos'); + dr = get(o,'ReleasePos'); + df = get(o,'FramePos'); +else + do = NaN; +end +dp = get(x,'Data'); +%fp = get(x,'FramePos'); +nmat = cell(1,length(dp)); +for i = 1:length(dp) + nmat{i} = []; + for j = 2:length(dp{i}) + tij = mean(df{i}{1}(:,da{i}{1}{1}(j-1))); + dij = mean(df{i}{1}(:,dr{i}{1}{1}(j-1))) - tij; + if not(iscell(do)) + vij = 120; + else + vij = round(do{i}{1}{1}(j-1)/max(do{i}{1}{1})*120); + end + for k = 1:size(dp{i}{j},3) + for l = 1:size(dp{i}{j},2) + for n = 1:length(dp{i}{j}{1,l,k}) + f = dp{i}{j}{1,l,k}(n); + p = round(hz2midi(f)); + nmat{i} = [nmat{i}; tij dij 1 p vij tij dij]; + end + end + end + end +end +m = class(struct,'mirmidi',mirdata(x)); +m = set(m,'Data',nmat); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmidi/mirplay.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function varargout = mirplay(a,varargin) +% mirplay method for mirmidi objects. + +specif.option = struct; + +specif.eachchunk = 'Normal'; + +varargout = mirfunction(@mirplay,a,varargin,nargout,specif,@init,@main); +if nargout == 0 + varargout = {}; +end + + +function [x type] = init(x,option) +type = ''; + + +function noargout = main(a,option,postoption) +if iscell(a) + a = a{1}; +end +d = get(a,'Data'); +n = get(a,'Name'); +for k = 1:length(d) + display(['Playing analysis of file: ' n{k}]) + playmidi(d{k}); +end +noargout = {}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirmidi/mirsave.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function mirsave(m) + +nmat = get(m,'Data'); +n = get(m,'Name'); +for k = 1:length(nmat) + writemidi(nmat{k},[n{k},'.mid'],120,60); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirpattern/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function display(p) + +% MIRPATTERN/DISPLAY display of pattern + +figure +hold on + +k = 0; + +for i = 1:length(p.pattern) + display(['Pattern # ',num2str(i)]) + col = num2col(i); + for j = 1:length(p.pattern{i}.occurrence) + display(['Occurrence # ',num2str(j)]) + p.pattern{i}.occurrence{j} + fill([p.pattern{i}.occurrence{j}.start ... + p.pattern{i}.occurrence{j}.end ... + p.pattern{i}.occurrence{j}.end ... + p.pattern{i}.occurrence{j}.start], ... + [k k k+1 k+1],col); + k = k+1; + end + display('**************') +end + +xlabel('time (in s.)') +ylabel('pattern and their occurrences') +set(gca,'YTick',[]) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirpattern/mirpattern.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +function varargout = mirpattern(orig,varargin) +% p = mirpattern(a) + + period.key = 'Period'; + period.type = 'Boolean'; + period.when = 'After'; + period.default = 0; + option.period = period; + +specif.option = option; + +varargout = mirfunction(@mirpattern,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirpattern')) + x = mirsimatrix(x); +end +type = 'mirpattern'; + + +function p = main(orig,option,postoption) +if not(isamir(orig,'mirpattern')) + b = get(orig,'Branch'); + fp = get(orig,'FramePos'); + pp = get(orig,'PeakPos'); + for i = 1:length(b) + bi = b{i}{1}{1}; + pi1 = sort(pp{i}{1}{bi(1,1)}); + pi2 = sort(pp{i}{1}{bi(end,1)}); + p.pattern{1}.occurrence{1}.start = ... + fp{i}{1}(1,bi(1,1)) - mean(fp{i}{1}(1:2,pi1(bi(1,2)))); + p.pattern{end}.occurrence{2}.start = ... + fp{i}{1}(1,bi(1,1)); + p.pattern{end}.occurrence{1}.end = ... + fp{i}{1}(1,bi(end,1)) - mean(fp{i}{1}(1:2,pi2(bi(end,2)))); + p.pattern{end}.occurrence{2}.end = ... + fp{i}{1}(1,bi(end,1)); + end + p = class(p,'mirpattern',mirdata(orig)); +end +if postoption.period + for i = 1:length(p.pattern) + poi = p.pattern{i}.occurrence; + if poi{1}.end > poi{2}.start + poi{1}.end = poi{2}.start; + cycle = poi{1}.end - poi{1}.start; + ncycles = floor((poi{2}.end-poi{2}.start)/cycle)+2; + poi{ncycles}.end = poi{2}.end; + poi{2}.end = poi{2}.start + cycle; + for j = 2:ncycles-1 + poi{j}.end = poi{j}.start + cycle; + poi{j+1}.start = poi{j}.end; + end + end + p.pattern{i}.occurrence = poi; + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirpattern/mirplay.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +function varargout = mirplay(p,varargin) + + pat.key = 'Pattern'; + pat.type = 'Integer'; + pat.default = 0; + option.pat = pat; + +specif.option = option; + +specif.eachchunk = 'Normal'; + +varargout = mirfunction(@mirplay,p,varargin,nargout,specif,@init,@main); +if nargout == 0 + varargout = {}; +end + + +function [x type] = init(x,option) +type = ''; + + +function noargout = main(p,option,postoption) +if not(option.pat) + option.pat = 1:length(p.pattern); +end +n = get(p,'Name'); +for h = 1:length(n) + for i = option.pat + display(['Pattern # ',num2str(i)]) + for j = 1:length(p.pattern{i}.occurrence) + display(['Occurrence # ',num2str(j)]) + a = miraudio(n{h},'Extract',p.pattern{i}.occurrence{j}.start,... + p.pattern{i}.occurrence{j}.end); + mirplay(a) + end + end +end +noargout = {}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirpitch/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function val = get(p, propName) +% GET Get properties from the MIRpitch object +% and return the value + +switch propName + case 'Amplitude' + val = p.amplitude; + otherwise + val = get(mirscalar(p),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirpitch/mirpitch.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,378 @@ +function varargout = mirpitch(orig,varargin) +% p = mirpitch(x) evaluates the pitch frequencies (in Hz). +% Specification of the method(s) for pitch estimation (these methods can +% be combined): +% mirpitch(...,'Autocor') computes an autocorrelation function +% (Default method) +% mirpitch(...'Enhanced',a) computes enhanced autocorrelation +% (see help mirautocor) +% toggled on by default +% mirpitch(...,'Compress',k) performs magnitude compression +% (see help mirautocor) +% mirpitch(...,fb) specifies a type of filterbank. +% Possible values: +% fb = 'NoFilterBank': no filterbank decomposition +% fb = '2Channels' (default value) +% fb = 'Gammatone' +% mirpitch(...,'AutocorSpectrum') computes the autocorrelation of +% the FFT spectrum +% mirpitch(...,'Cepstrum') computes the cepstrum +% Alternatively, an autocorrelation or a cepstrum can be directly +% given as first argument of the mirpitch function. +% Peak picking options: +% mirpitch(...,'Total',m) selects the m best pitches. +% Default value: m = Inf, no limit is set concerning the number +% of pitches to be detected. +% mirpitch(...,'Mono') corresponds to morpitch(...,'Total',1) +% mirpitch(...,'Min',mi) indicates the lowest frequency taken into +% consideration. +% Default value: 75 Hz. (Praat) +% mirpitch(...,'Max',ma) indicates the highest frequency taken into +% consideration. +% Default value: 2400 Hz. Because there seems to be some problems +% with higher frequency, due probably to the absence of +% pre-whitening in our implementation of Tolonen and Karjalainen +% approach (used by default, cf. below). +% mirpitch(...,'Contrast',thr) specifies a threshold value. +% (see help peaks) +% Default value: thr = .1 +% mirpitch(...,'Order',o) specifies the ordering for the peak picking. +% Default value: o = 'Amplitude'. +% Alternatively, the result of a mirpeaks computation can be directly +% given as first argument of the mirpitch function. +% Post-processing options: +% mirpitch(...,'Sum','no') does not sum back the channels at the end +% of the computation. The resulting pitch information remains +% therefore decomposed into several channels. +% mirpitch(...,'Median') performs a median filtering of the pitch +% curve. When several pitches are extracted in each frame, the +% pitch curve contains the best peak of each successive frame. +% mirpitch(...,'Stable',th,n) remove pitch values when the difference +% (or more precisely absolute logarithmic quotient) with the +% n precedent frames exceeds the threshold th. +% if th is not specified, the default value .1 is used +% if n is not specified, the default value 3 is used +% mirpitch(...'Reso',r) removes peaks whose distance to one or +% several higher peaks is lower than a given threshold. +% Possible value for the threshold r: +% 'SemiTone': ratio between the two peak positions equal to +% 2^(1/12) +% mirpitch(...,'Frame',l,h) orders a frame decomposition of window +% length l (in seconds) and hop factor h, expressed relatively to +% the window length. For instance h = 1 indicates no overlap. +% Default values: l = 46.4 ms and h = 10 ms (Tolonen and +% Karjalainen, 2000) +% Preset model: +% mirpitch(...,'Tolonen') implements (part of) the model proposed in +% (Tolonen & Karjalainen, 2000). It is equivalent to +% mirpitch(...,'Enhanced',2:10,'Generalized',.67,'2Channels') +% [p,a] = mirpitch(...) also displays the result of the method chosen for +% pitch estimation, and shows in particular the peaks corresponding +% to the pitch values. +% p = mirpitch(f,a,<r>) creates a mirpitch object based on the frequencies +% specified in f and the related amplitudes specified in a, using a +% frame sampling rate of r Hz (set by default to 100 Hz). +% +% T. Tolonen, M. Karjalainen, "A Computationally Efficient Multipitch +% Analysis Model", IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, +% VOL. 8, NO. 6, NOVEMBER 2000 + + ac.key = 'Autocor'; + ac.type = 'Boolean'; + ac.default = 0; + option.ac = ac; + + enh.key = 'Enhanced'; + enh.type = 'Integer'; + enh.default = 2:10; + option.enh = enh; + + filtertype.type = 'String'; + filtertype.choice = {'NoFilterBank','2Channels','Gammatone'}; + filtertype.default = '2Channels'; + option.filtertype = filtertype; + + gener.key = {'Generalized','Compress'}; + gener.type = 'Integer'; + gener.default = .5; + option.gener = gener; + + as.key = 'AutocorSpectrum'; + as.type = 'Boolean'; + as.default = 0; + option.as = as; + + s.key = 'Spectrum'; + s.type = 'Boolean'; + s.default = 0; + option.s = s; + + ce.key = 'Cepstrum'; + ce.type = 'Boolean'; + ce.default = 0; + option.ce = ce; + +%% peak picking options + + m.key = 'Total'; + m.type = 'Integer'; + m.default = Inf; + option.m = m; + + multi.key = 'Multi'; + multi.type = 'Boolean'; + multi.default = 0; + option.multi = multi; + + mono.key = 'Mono'; + mono.type = 'Boolean'; + mono.default = 0; + option.mono = mono; + + mi.key = 'Min'; + mi.type = 'Integer'; + mi.default = 75; + option.mi = mi; + + ma.key = 'Max'; + ma.type = 'Integer'; + ma.default = 2400; + option.ma = ma; + + thr.key = 'Contrast'; + thr.type = 'Integer'; + thr.default = .1; + option.thr = thr; + + order.key = 'Order'; + order.type = 'String'; + order.choice = {'Amplitude','Abscissa'}; + order.default = 'Amplitude'; + option.order = order; + + reso.key = 'Reso'; + reso.type = 'String'; + reso.choice = {0,'SemiTone'}; + reso.default = 0; + option.reso = reso; + + track.key = 'Track'; % Not used yet + track.type = 'Boolean'; + track.default = 0; + option.track = track; + +%% post-processing options + + stable.key = 'Stable'; + stable.type = 'Integer'; + stable.number = 2; + stable.default = [Inf 0]; + stable.keydefault = [.1 3]; + option.stable = stable; + + median.key = 'Median'; + median.type = 'Integer'; + median.default = 0; + median.keydefault = .1; + option.median = median; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [0 0]; + frame.keydefault = [NaN NaN]; + option.frame = frame; + + sum.key = 'Sum'; + sum.type = 'Boolean'; + sum.default = 1; + option.sum = sum; + +%% preset model + + tolo.key = 'Tolonen'; + tolo.type = 'Boolean'; + tolo.default = 0; + option.tolo = tolo; + +specif.option = option; +specif.chunkframebefore = 1; + +if isnumeric(orig) + if nargin<3 + f = 100; + else + f = varargin{2}; + end + fp = (0:size(orig,1)-1)/f; + fp = [fp;fp+1/f]; + p.amplitude = {{varargin{1}'}}; + s = mirscalar([],'Data',{{orig'}},'Title','Pitch','Unit','Hz',... + 'FramePos',{{fp}},'Sampling',f,'Name',{inputname(1)}); + p = class(p,'mirpitch',s); + varargout = {p}; +else + varargout = mirfunction(@mirpitch,orig,varargin,nargout,specif,@init,@main); +end + + + +function [y type] = init(orig,option) +if option.tolo + option.enh = 2:10; + option.gener = .67; + option.filtertype = '2Channels'; +end +if not(option.ac) && not(option.as) && not(option.ce) && not(option.s) + option.ac = 1; +end +if isnan(option.frame.length.val) + option.frame.length.val = .0464; +end +if isnan(option.frame.hop.val) + option.frame.hop.val = .01; + option.frame.hop.unit = 's'; +end +if isamir(orig,'mirscalar') || haspeaks(orig) + y = orig; +else + if isamir(orig,'mirautocor') + y = mirautocor(orig,'Min',option.mi,'Hz','Max',option.ma,'Hz','Freq'); + elseif isamir(orig,'mircepstrum') + y = orig; + elseif isamir(orig,'mirspectrum') + if not(option.as) && not(option.ce) && not(option.s) + option.ce = 1; + end + if option.as + y = mirautocor(orig,... + 'Min',option.mi,'Hz','Max',option.ma,'Hz'); + end + if option.ce + ce = mircepstrum(orig,'freq',... + 'Min',option.mi,'Hz','Max',option.ma,'Hz'); + if option.as + y = y*ce; + else + y = ce; + end + end + else + if option.ac + x = orig; + if not(strcmpi(option.filtertype,'NoFilterBank')) + x = mirfilterbank(x,option.filtertype); + end + x = mirframenow(x,option); + y = mirautocor(x,'Generalized',option.gener,... + 'Min',option.mi,'Hz','Max',option.ma,'Hz'); + if option.sum + y = mirsummary(y); + end + y = mirautocor(y,'Enhanced',option.enh,'Freq'); + end + if option.as || option.ce || option.s + x = mirframenow(orig,option); + y = mirspectrum(x); + if option.as + as = mirautocor(y,... + 'Min',option.mi,'Hz','Max',option.ma,'Hz'); + if option.ac + y = y*as; + else + y = as; + end + end + if option.ce + ce = mircepstrum(y,'freq',... + 'Min',option.mi,'Hz','Max',option.ma,'Hz'); + if option.ac || option.as + y = y*ce; + else + y = ce; + end + end + end + end +end +type = {'mirpitch',mirtype(y)}; + + +function o = main(x,option,postoption) +if option.multi && option.m == 1 + option.m = Inf; +end +if option.mono && option.m == Inf + option.m = 1; +end +if iscell(x) + x = x{1}; +end +if not(isa(x,'mirpitch')) + x = mirpeaks(x,'Total',option.m,'Track',option.track,... + 'Contrast',option.thr,'Threshold',.4,... + 'Reso',option.reso,'NoBegin','NoEnd',... + 'Order',option.order); +end +if isa(x,'mirscalar') + pf = get(x,'Data'); +else + pf = get(x,'PeakPrecisePos'); + pa = get(x,'PeakPreciseVal'); +end +fp = get(x,'FramePos'); +if option.stable(1) < Inf + for i = 1:length(pf) + for j = 1:length(pf{i}) + for k = 1:size(pf{i}{j},3) + for l = size(pf{i}{j},2):-1:option.stable(2)+1 + for m = length(pf{i}{j}{1,l,k}):-1:1 + found = 0; + for h = 1:option.stable(2) + for n = 1:length(pf{i}{j}{1,l-h,k}) + if abs(log10(pf{i}{j}{1,l,k}(m) ... + /pf{i}{j}{1,l-h,k}(n))) ... + < option.stable(1) + found = 1; + end + end + end + if not(found) + pf{i}{j}{1,l,k}(m) = []; + end + end + pf{i}{j}{1,1,k} = zeros(1,0); + end + end + end + end +end +if option.median + sr = get(x,'Sampling'); + for i = 1:length(pf) + for j = 1:length(pf{i}) + if size(fp{i}{j},2) > 1 + npf = zeros(size(pf{i}{j})); + for k = 1:size(pf{i}{j},3) + for l = 1:size(pf{i}{j},2) + if isempty(pf{i}{j}{1,l,k}) + npf(1,l,k) = NaN; + else + npf(1,l,k) = pf{i}{j}{1,l,k}(1); + end + end + end + pf{i}{j} = medfilt1(npf,... + round(option.median/(fp{i}{j}(1,2)-fp{i}{j}(1,1)))); + end + end + end +end +if isa(x,'mirscalar') + p.amplitude = 0; +else + p.amplitude = pa; +end +s = mirscalar(x,'Data',pf,'Title','Pitch','Unit','Hz'); +p = class(p,'mirpitch',s); +o = {p,x}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirpitch/mirsave.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +function mirsave(a) + +d = get(a,'Data'); +if isa(a,'mirpitch') + amp = get(a,'Amplitude'); +end +n = get(a,'Name'); +t = get(a,'Title'); +c = get(a,'Channels'); +fp = get(a,'FramePos'); +out = cell(1,length(d)); + +for k = 1:length(d) + dk = d{k}; + if not(iscell(dk)) + dk = {dk}; + end + out = []; + for l = 1:size(dk{1},3) + for i = 1:length(dk) + di = dk{i}; + if isa(a,'mirpitch') + ampi = amp{k}{i}; + end + synth = zeros(1,ceil((fp{k}{i}(end)-fp{k}{i}(1))*44100)+1); + for j = 1:size(di,2) + if iscell(di) + dj = di{j}; + else + dj = di(:,j); + end + dj(isnan(dj)) = 0; + ampj = zeros(size(dj)); + if iscell(ampi) + ampj(1:size(ampi{j})) = ampi{j}; + else + ampj(1:size(ampi(:,j))) = ampi(:,j); + end + if not(isempty(dj)) + k1 = floor((fp{k}{i}(1,j)-fp{k}{i}(1))*44100)+1; + k2 = floor((fp{k}{i}(2,j)-fp{k}{i}(1))*44100)+1; + if isa(a,'mirpitch') + ampj = repmat(ampj,1,k2-k1+1); + else + ampj = ones(size(dj),k2-k1+1); + end + synth(k1:k2) = synth(k1:k2) ... + + sum(ampj.*sin(2*pi*dj*(0:k2-k1)/44100),1) ... + .*hann(k2-k1+1)'; + end + end + out = [out synth]; + if size(dk{1},3)>1 + out = [out rand(1,10)]; + end + end + end + fout = miraudio(out,44100); + mirsave(fout,[n{k},'.',t]); +end + + +function oldmirsave % not used anymore +d = get(a,'Data'); +nf = length(d); +fp = get(a,'FramePos'); +nm = get(a,'Name'); +t = get(a,'Title'); +for i = 1:nf + nmi = nm{i}; + di = d{i}{1}; + fpi = fp{i}{1}; + + %Let's remove the extension from the original files + if length(nmi)>3 && strcmpi(nmi(end-3:end),'.wav') + nmi(end-3:end) = []; + elseif length(nmi)>2 && strcmpi(nmi(end-2:end),'.au') + nmi(end-2:end) = []; + end + n = [nmi,'.',lower(t),'.txt']; + + fid = fopen(n, 'wt'); + fprintf(fid,'Frame_start Frame_end Data \n'); + + for j = 1:length(di) + fprintf(fid,'%g %g %g \n',fpi(1,j),fpi(2,j),di(j)); + end + fclose(fid); + disp([n,' saved.']); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirpitch/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function pp = set(p,varargin) +% SET Set properties for the MIRpitch object +% and return the updated object + +propertyArgIn = varargin; +a = p.amplitude; +s = mirscalar(p); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Amplitude' + a = val; + otherwise + s = set(s,prop,val); + end +end +pp.amplitude = a; +pp = class(pp,'mirpitch',s); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirquery/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function display(q) + +% MIRQUERY/DISPLAY display of mirquery + +disp(['Query: ',q.query.name{1}]); +disp('********') +disp('Retrieval:') +for i = 1:length(q.dist) + disp([num2str(i),'. ',q.name{i},' (dist = ',num2str(q.dist(i)),')']); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirquery/mirplay.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function mirplay(q,varargin) + +rank = 1:length(q.name); +if nargin>2 && strcmpi(varargin{1},'Sequence') + rank = intersect(rank,varargin{2}); +end +disp(['Query: ',q.query.name{1}]); +mirplay(q.query.name{1}); +disp('********') +disp('Retrieval:') +for i = rank + disp([num2str(i),'. ',q.name{i},' (dist = ',num2str(q.dist(i)),')']); + mirplay(q.name{i}); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirquery/mirquery.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +function res = mirquery(varargin) +% r = mirquery(q,b), where +% q is the analysis of one audio file and +% b is the analysis of a folder of audio files, +% according to the same mirtoolbox feature, +% returns the name of the audio files in the database b in an +% increasing distance to q with respect to the chosen feature. +% r = mirquery(d), where +% d is the distance between one audio file and a folder of audio +% file, according to a mirtoolbox feature, +% returns the name of the audio files in an increasing distance d. +% +% Optional argument: +% mirquery(...,'Best',n) returns the name of the n closest audio +% files. +% mirquery(..,'Distance',d) specifies the distance to use. +% Default value: d = 'Cosine' (cf. mirdist) + +[distfunc,nbout] = scanargin(varargin); + +if nargin<2 || not(isa(varargin{2},'mirdata')) + returnval=0; + dist = varargin{1}; + name = get(dist,'Name2'); + res.query.val = []; + res.query.name = get(dist,'Name'); +else + returnval=1; + query = varargin{1}; + base = varargin{2}; + name = get(base,'Name'); + res.query.val = mirgetdata(query); + res.query.name = get(query,'Name'); + database = mirgetdata(base); + dist = mirdist(query,base,distfunc); +end +datadist = mirgetdata(dist); + +[ordist order] = sort(datadist); +order(isnan(ordist)) = []; +nbout = min(nbout,length(order)); +res.dist = ordist(1:nbout); +if returnval + res.val = database(order); +else + res.val = []; +end +res.name = name(order); + +res = class(res,'mirquery'); + + + +function [distfunc,nbout] = scanargin(v) +distfunc = 'Cosine'; +nbout=Inf; +i = 1; +while i <= length(v) + arg = v{i}; + if ischar(arg) && strcmpi(arg,'Distance') + if length(v)>i && ischar(v{i+1}) + i = i+1; + distfunc = v{i}; + end + elseif ischar(arg) && strcmpi(arg,'Best') + if length(v)>i && isnumeric(v{i+1}) + i = i+1; + nbout = v{i}; + end + %else + % error('ERROR IN MIRQUERY: Syntax error. See help mirquery.'); + end + i = i+1; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirscalar/corrcoef.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function r = corrcoef(x,y) + +dx = get(x,'Data'); +dy = get(y,'Data'); +fx = get(x,'FramePos'); +fy = get(y,'FramePos'); +dr = cell(1,length(fx)); +for h = 1:length(fx) + dr{h} = cell(1,length(fx{h})); + for i = 1:length(fx{h}) + [f,ix,iy] = intersect(fx{h}{i}',fy{h}{i}','rows'); + dxi = dx{h}{i}(:,ix,:,:); + dyi = dy{h}{i}(:,iy,:,:); + dr{h}{i} = corrcoef(dxi,dyi); + end +end +r = mirmatrix(x,'Data',dr,'Title','Correlation coefficients','Unit',''); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirscalar/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,343 @@ +function display(s,varargin) +% SCALAR/DISPLAY display the values of a scalar object +disp(' '); +p = get(s,'Pos'); +v = get(s,'Data'); +m = get(s,'Mode'); +t = get(s,'Title'); +n = get(s,'Name'); +n2 = get(s,'Name2'); +u = get(s,'Unit'); +fp = get(s,'FramePos'); +pp = get(s,'PeakPos'); +leg = get(s,'Legend'); +legm = get(s,'MultiData'); +cha = get(s,'Channels'); +for i = 1:length(v) % For each audio file + vi = v{i}; + if isempty(m) + mi = []; + else + mi = m{i}; + end + fpi = fp{i}; + if isempty(pp) + ppi = []; + else + ppi = pp{i}; + end + + if isempty(vi) + disp(['The ',t,' related to file ',n{i},... + ' does not contain any data.']); + return + end + + %if iscell(ppi) + % ppi = ppi{i}; + %end + + if size(vi,1) == 1 && size(vi,2) == 1 && size(vi,3) == 1 && ... + (not(iscell(vi)) || (size(vi{1},1) == 1 && ... + size(vi{1},2) == 1 && size(vi{1},3) == 1)) && ... + (not(iscell(vi{1})) || (size(vi{1}{1},1) == 1 && ... + size(vi{1}{1},2) == 1 && size(vi{1},3) == 1)) + % Simple results, returned directly in the Command Window + + if iscell(vi) + vi = vi{1}; % There is only one segment, so let's look at it + if not(isempty(mi)) + mi = mi{1}; + end + end + if iscell(vi) + if size(vi,2) > 1 + error('BUG IN DISPLAY'); %test if this condition exists + end + vi = vi{1}; % There is only one frame, so let's look at it + if not(isempty(mi)) + mi = mi{1}; + end + end + if size(vi,1) == 1 && size(vi,3) == 1 + if isempty(leg) + r = num2str(vi); + else + r = leg{vi}; + end + if not(isempty(mi)) + u = legm{mi}; + end + if strcmp(u,'/1') + u = ''; + end + if strcmp(r,'NaN') + r = 'undefined'; + u = ''; + end + if not(isempty(n2)) + disp(['The ',t,' between files ',n{1},' and ',n2{i},... + ' is ',r,' ',u]); + else + disp(['The ',t,' related to file ',n{i},' is ',r,' ',u]); + end + else + vi = squeeze(vi); + disp(['The ',t,' related to file ',n{i},' are:']); + for j = 1:size(vi,1) + if isempty(leg) + r = num2str(vi(j)); + else + r = leg{vi(j)}; + end + if not(isempty(mi)) + u = legm{mi(j)}; + end + if strcmp(r,'NaN') + r = 'undefined'; + u = ''; + end + disp(['Value #',num2str(j),': ',r,' ',u]); + end + end + + else + %Graphical display + figure + + if not(iscell(vi)) + vi = {vi}; + fpi = {fpi}; + ppi = {ppi}; + end + + nl = size(vi{1},1); % Number of bins + nc = size(vi{1},2); % Number of frames + l = size(vi{1},3); % Number of channels + il = (1-0.15)/l; + + if nc==1 && l>1 % If one frame and several channels + xlab = 'Channels'; % channels will be represented in the x axis + l = 1; + else + xlab = 'Temporal location of events (in s.)'; + end + + varpeaks = 0; % Variable data size over frames? + if iscell(vi{1}) + for j = 1:length(vi) + for k = 1:l + for h = 1:size(vi{j},2) + if length(vi{j}{1,h,k}) > 1 + varpeaks = 1; + end + end + end + end + if not(varpeaks) + for j = 1:length(vi) + vj = zeros(size(vi{j})); + for k = 1:l + for h = 1:size(vi{j},2) + if isempty(vi{j}{1,h,k}) + vj(1,h,k) = NaN; + else + vj(1,h,k) = vi{j}{1,h,k}; + end + end + end + vi{j} = vj; + end + end + end + + if varpeaks % Peaks displayed with diamonds + diamond = 1; + set(gca,'NextPlot','replacechildren') + hold on + else + diamond = 0; + hold all + end + + for k = 1:l % For each channel + if l>1 + subplot('Position',[0.1 (k-1)*il+0.1 0.89 il-0.02]) + end + hold on + + vold = NaN; % Buffers used to link curves between segments + fold = NaN; + + for j = 1:length(vi) % for each segment + vj = vi{j}; + + fpj = fpi{j}; + if isempty(ppi) || length(ppi)<j + ppj = []; + else + ppj = ppi{j}; + end + + if strcmp(xlab,'Channels') + %vj = squeeze(vj); % does not work with lowenergy + %ppj = squeeze(ppj); + vj = shiftdim(vj,1); + ppj = shiftdim(ppj,1); + fpj = cha{i}; + end + + minvi = Inf; % The limits of the rectangle bordering segments + maxvi = -Inf; + + if diamond + % Variable data size for each frame + legobj = []; + legval = []; + + if not(strcmp(xlab,'Channels')) + if iscell(fpi(1,j)) + fpj = fpi{j}; + else + error('BUG IN DISPLAY'); %test if this condition exists + fpj = fpi(:,j); + end + end + + for h = 1:size(vj,2) % for each frame + if not(isempty(vj{1,h,k})) + if isempty(mi) + % No use of legends + if not(isempty(vj{1,h,k})) + plot(mean(fpj(:,h),1),vj{1,h,k},'d',... + 'LineWidth',2,'MarkerFaceColor','b'); + end + + else + % Use of legends + mj = mi{j}{1,h,k}; + lmj = length(mj); + for kk = 1:lmj + handle = plot(mean(fpj(:,h),1),vj{1,h,k}(kk),'d',... + 'Color',num2col(mj(kk)),... + 'MarkerSize',7+(lmj-kk)*2,... + 'MarkerFaceColor',num2col(mj(kk))); + if isempty(find(legval == mj(kk))) + legval = [legval mj(kk)]; + legobj = [legobj handle]; + end + end + end + + %if not(isempty(ppi)) ... % Peaks display + %&& not(isempty(find(ppi{1} == j))) + % plot(mean(fpj(:,h),1),vj{1,h,k},'+r') + %end + end + + if not(isempty(vj{1,h,k})) + minvi = min(minvi,min(vj{1,h,k})); + maxvi = max(maxvi,max(vj{1,h,k})); + end + end + + if (exist('legm') == 1) && not(isempty(legm)) + legend(legobj,legm{legval},'Location','Best') + end + + else + % Constant data size along frames + + for h = 1:nl % For each dimension represented + %if not(isempty(vj(h,:,k))) + if isnan(vold) + plot(mean(fpj,1),vj(h,:,k)',... + '+-','Color',num2col(h)) + else + plot([fold mean(fpj,1)],[vold(h) vj(h,:,k)]',... + '+-','Color',num2col(h)) + % Current curve linked with curve from + % previous segment + end + if h == nl + if isempty(vj) + vold = NaN; + fold = NaN; + else + vold = vj(:,end,k); + fold = mean(fpj(:,end),1); + end + end + % else + % vold = NaN; + % fold = NaN; + % end + end + + if not(isempty(ppj)) % Peaks display + for h = 1:nl; + ppj = ppj{1,h,k}; + plot(mean(fpj(:,ppj)),vj(h,ppj,k),'or') + end + end + + minvi = min(minvi,min(min(vj(h,:,k)))); + maxvi = max(maxvi,max(max(vj(h,:,k)))); + end + + if length(vi)>1 && size(vj,2)>1 && minvi<Inf && maxvi>-Inf + % Display of the segment rectangle + rectangle('Position',[fpj(1),minvi,... + fpj(end)-fpj(1),maxvi-minvi]+1e-16,... + 'EdgeColor',num2col(j),'Curvature',.1,'LineWidth',1) + end + end + if k == l + title([t,', ',n{i}]) + end + if k == 1 + xlabel(xlab) + end + if not(isempty(leg)) + set(gca,'ytick',(1:length(leg))) + set(gca,'yticklabel',leg); + end + if l > 1 + pos = get(gca,'Position'); + hfig = axes('Position',[pos(1)-.05 pos(2)+pos(4)/2 .01 .01],... + 'Visible','off'); + text(0,0,num2str(cha{i}(k)),'FontSize',12,'Color','r') + end + end + if i == 1 + if strcmp(u,'/1') + u = ' (between 0 and 1)'; + elseif isempty(u) + u = ''; + else + u = [' (in ',u,')']; + end + end + ylabel(['coefficient value', u]) + if nl>1 + legnd = cell(nl,1); + for j = 1:nl + if isempty(p) + legnd{j} = num2str(j); + else + legnd{j} = p{i}{1}{j}; + end + end + legend(legnd,'Location','Best') + end + fig = get(0,'CurrentFigure'); + disp(['The ',t,' related to file ',n{i},... + ' is displayed in Figure ',num2str(fig),'.']); + if nargin>1 + saveas(fig,[n{i},varargin{1}]); + disp(['and is saved in file ',n{i},varargin{1}]); + end + end +end +disp(' '); +drawnow \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirscalar/exportation.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +function x = exportation(s) + +% SCALAR/EXPORTATION exports the values of a scalar object +% Filterbank not taken into consideration yet. + +v = get(s,'Data'); +t = get(s,'Title'); +n = get(s,'Name'); +pt = get(s,'PeakPosUnit'); +pp = get(s,'PeakPos'); +frames = 0; +for i = 1:length(v) + if iscell(v{i}) + v{i} = v{i}{1}; %% Segmented audio cannot be exported properly now. + end + if iscell(v{i}) + v{i} = v{i}{1}; + end + if size(v{i},2) > 1 || size(v{i},3) > 1 + frames = 1; + end +end +for i = 1:length(v) + vi = v{i}; + pti = pt{i}; + ppi = pp{i}; + for j = 1:size(vi,1) + me = mean(vi(j,:,1),2); + if frames + st = std(vi(j,:,1),0,2); + if not(isempty(pti{1})) + if i == 1 + if size(vi,1) > 1 + x{1,j*4-3} = ['mean',t,num2str(j)]; + x{1,j*4-2} = ['std',t,num2str(j)]; + x{1,j*4-1} = ['peakpos',t,num2str(j)]; + x{1,j*4} = ['peakval',t,num2str(j)]; + else + x{1,j*4-3} = ['mean',t]; + x{1,j*4-2} = ['std',t]; + x{1,j*4-1} = ['peakpos',t]; + x{1,j*4} = ['peakval',t]; + end + end + pep = ppi{1,j,1}{1}; %% only first peak is taken + pet = pti{1,j,1}{1}; %% only first peak is taken + pev = vi(j,pep,1); + x{i+1,j*4-3} = me; + x{i+1,j*4-2} = st; + x{i+1,j*4-1} = pet; + x{i+1,j*4} = pev; + else + if i == 1 + if size(vi,1) > 1 + x{1,j*2-1} = ['mean',t,num2str(j)]; + x{1,j*2} = ['std',t,num2str(j)]; + else + x{1,j*2-1} = ['mean',t]; + x{1,j*2} = ['std',t]; + end + end + x{i+1,j*2-1} = me; + x{i+1,j*2} = st; + end + else + if i == 1 + x{1,j} = t; + end + x{i+1,j} = me; + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirscalar/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function val = get(a, propName) +% GET Get properties from the MIRdata object +% and return the value + +switch propName + case 'Mode' + val = a.mode; + case 'Legend' + val = a.legend; + case 'Parameter' + val = a.parameter; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirscalar/mirsave.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +function mirsave(a) + +d = get(a,'Data'); +n = get(a,'Name'); +t = get(a,'Title'); +c = get(a,'Channels'); +fp = get(a,'FramePos'); +out = cell(1,length(d)); + +for k = 1:length(d) + dk = d{k}; + if not(iscell(dk)) + dk = {dk}; + end + out = []; + for l = 1:size(dk{1},3) + for i = 1:length(dk) + di = dk{i}; + synth = zeros(1,ceil((fp{k}{i}(end)-fp{k}{i}(1))*44100)+1); + for j = 1:size(di,2) + if iscell(di) + dj = di{j}; + else + dj = di(j); + end + if not(isempty(dj)) + k1 = floor((fp{k}{i}(1,j)-fp{k}{i}(1))*44100)+1; + k2 = floor((fp{k}{i}(2,j)-fp{k}{i}(1))*44100)+1; + synth(k1:k2) = synth(k1:k2) ... + + sum(sin(2*pi*dj*(0:k2-k1)/44100),1).*hann(k2-k1+1)'; + end + end + out = [out synth]; + if size(dk{1},3)>1 + out = [out rand(1,10)]; + end + end + end + fout = miraudio(out,44100); + mirsave(fout,[n{k},'.',t]); +end + + +function oldmirsave % not used anymore +d = get(a,'Data'); +nf = length(d); +fp = get(a,'FramePos'); +nm = get(a,'Name'); +t = get(a,'Title'); +for i = 1:nf + nmi = nm{i}; + di = d{i}{1}; + fpi = fp{i}{1}; + + %Let's remove the extension from the original files + if length(nmi)>3 && strcmpi(nmi(end-3:end),'.wav') + nmi(end-3:end) = []; + elseif length(nmi)>2 && strcmpi(nmi(end-2:end),'.au') + nmi(end-2:end) = []; + end + n = [nmi,'.',lower(t),'.txt']; + + fid = fopen(n, 'wt'); + fprintf(fid,'Frame_start Frame_end Data \n'); + + for j = 1:length(di) + fprintf(fid,'%g %g %g \n',fpi(1,j),fpi(2,j),di(j)); + end + fclose(fid); + disp([n,' saved.']); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirscalar/mirscalar.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function s = mirscalar(orig,varargin) +% s = mirscalar(x,n) creates a scalar object + +if nargin == 0 + orig = []; +end +if iscell(orig) + orig = orig{1}; +end +if isa(orig,'mirscalar') + s.mode = orig.mode; + s.legend = orig.legend; + s.parameter = orig.parameter; +else + s.mode = []; + s.legend = ''; + s.parameter = struct; +end +s = class(s,'mirscalar',mirdata(orig)); +s = purgedata(s); +s = set(s,'Pos',{},'Abs','Temporal position of frames','Ord','Value',varargin{:}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirscalar/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function ss = set(s,varargin) +% SET Set properties for the MIRscalar object +% and return the updated object + +propertyArgIn = varargin; +m = s.mode; +l = s.legend; +p = s.parameter; +d = mirdata(s); +d = set(d,'Title',get(s,'Title'),'Abs',get(s,'Abs'),'Ord',get(s,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Mode' + m = val; + case 'Legend' + l = val; + case 'Parameter' + p = val; + otherwise + d = set(d,prop,val); + end +end +ss.mode = m; +ss.legend = l; +ss.parameter = p; +ss = class(ss,'mirscalar',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirsimatrix/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,136 @@ +function display(m) +% DISSIMATRIX/DISPLAY display of a dissimilarity matrix +disp(' '); +d = get(m,'Data'); +nam = get(m,'Name'); +fp = get(m,'FramePos'); +cha = get(m,'Channels'); +t = get(m,'Title'); +tp = get(m,'TrackPos'); +tv = get(m,'TrackVal'); +pp = get(m,'PeakPos'); +g = get(m,'Graph'); +b = get(m,'Branch'); +for i = 1:length(d) + if iscell(d{i}) + d{i} = d{i}{1}; + end + l = size(d{1},3); % Number of channels + il = (1-0.15)/l; + figure + for k = 1:l % For each channel + if l>1 + subplot('Position',[0.1 (k-1)*il+0.1 0.89 il-0.02]) + end + fpi = cell2mat(fp{i}); + if size(fpi,1) == 2 + fpi = (fpi(1,:,:,:)+fpi(2,:,:,:))/2; + end + if strcmp(m.view,'l') + h = imagesc(fpi,fpi(1:size(d{i},1))-fpi(1),d{i}(:,:,k)); + else + h = imagesc(fpi,fpi,d{i}(:,:,k)); + end + if not(isempty(g)) + hold on + pi = cell(1,size(g{i}{1},2)); + for j = 1:size(g{i}{1},2) + pi{j} = sort(pp{i}{1}{j}); + end + for j = 1:size(g{i}{1},2) + for h = 1:length(g{i}{1}{1,j,l}) + next = g{i}{1}{1,j,l}{h}; + if length(next)>1 + for n = 1:size(next,1) + plot([fpi(j) fpi(next(n,1))],... + [fpi(pi{j}(h)) - fpi(1), ... + fpi(pi{next(n,1)}(next(n,2))) - fpi(1)],... + 'k','LineWidth',1) + %plot([fpi(j) fpi(next(n,1))],... + % [fpi(pi{j}(h)) - fpi(1), ... + % fpi(pi{next(n,1)}(next(n,2))) - fpi(1)],... + % 'w+','MarkerSize',10) + plot([fpi(j) fpi(next(n,1))],... + [fpi(pi{j}(h)) - fpi(1), ... + fpi(pi{next(n,1)}(next(n,2))) - fpi(1)],... + 'kx','MarkerSize',10) + end + end + end + end + bi = b{i}{1}{1}; + for j = 1:size(bi,1)-1 + plot([fpi(bi(j,1)) fpi(bi(j+1,1))],... + [fpi(pi{bi(j,1)}(bi(j,2))) - fpi(1), ... + fpi(pi{bi(j+1,1)}(bi(j+1,2))) - fpi(1)],... + 'w','LineWidth',1.5) + plot([fpi(bi(j,1)) fpi(bi(j+1,1))],... + [fpi(pi{bi(j,1)}(bi(j,2))) - fpi(1), ... + fpi(pi{bi(j+1,1)}(bi(j+1,2))) - fpi(1)],... + 'w+','MarkerSize',15) + plot([fpi(bi(j,1)) fpi(bi(j+1,1))],... + [fpi(pi{bi(j,1)}(bi(j,2))) - fpi(1), ... + fpi(pi{bi(j+1,1)}(bi(j+1,2))) - fpi(1)],... + 'kx','MarkerSize',15) + end + elseif not(isempty(tp)) && not(isempty(tp{i})) + hold on + for k = 1:size(tp{i}{1}{1},1) + prej = 0; + for j = 1:size(tp{i}{1}{1},2) + if tv{i}{1}{1}(k,j) + if prej% && not(isempty(tp(k,j))) + plot([fpi(prej) fpi(j)],... + [fpi(tp{i}{1}{1}(k,prej)) - fpi(1) ... + fpi(tp{i}{1}{1}(k,j)) - fpi(1)],... + 'k','LineWidth',1) + plot([fpi(prej) fpi(j)],... + [fpi(tp{i}{1}{1}(k,prej)) - fpi(1) ... + fpi(tp{i}{1}{1}(k,j)) - fpi(1)],... + 'w+','MarkerSize',10) + plot([fpi(prej) fpi(j)],... + [fpi(tp{i}{1}{1}(k,prej)) - fpi(1) ... + fpi(tp{i}{1}{1}(k,j)) - fpi(1)],... + 'kx','MarkerSize',10) + end + prej = j; + end + end + end + elseif not(isempty(pp)) && not(isempty(pp{i})) + hold on + for k = 1:length(pp{i}{1}) + for j = 1:length(pp{i}{1}{k}) + plot(fpi(k),fpi(pp{i}{1}{k}(j)) - fpi(1), ... + 'w+','MarkerSize',10) + end + end + end + set(gca,'YDir','normal') + if k == l + title(t) + end + if k == 1 + xlabel('temporal location of frame centers (in s.)') + end + if k == ceil(l/2) + if strcmp(m.view,'h') + ylabel('relative distance between compared frames (in s.)') + elseif strcmp(m.view,'l') + ylabel('temporal lag (in s.)') + else + ylabel('temporal location of frame centers (in s.)') + end + end + if l > 1 + pos = get(gca,'Position'); + hfig = axes('Position',[pos(1)-.05 pos(2)+pos(4)/2 .01 .01],... + 'Visible','off'); + text(0,0,num2str(cha{i}(k)),'FontSize',12,'Color','r') + end + end + fig = get(0,'CurrentFigure'); + disp(['The ',t,' related to file ',nam{i},' is displayed in Figure ',num2str(fig),'.']); +end +disp(' '); +drawnow \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirsimatrix/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function val = get(a, propName) +% GET Get properties from the MIRsimatrix object +% and return the value + +switch propName + case 'DiagWidth' + val = a.diagwidth; + case 'Graph' + val = a.graph; + case 'Branch' + val = a.branch; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirsimatrix/mirsimatrix.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,414 @@ +function varargout = mirsimatrix(orig,varargin) +% m = mirsimatrix(x) computes the similarity matrix resulting from the +% mutual comparison between each possible frame analysis in x. +% By default, x is the spectrum of the frame decomposition. +% But it can be any other frame analysis. +% Optional argument: +% mirsimatrix(...,'Distance',f) specifies the name of a distance +% function, from those proposed in the Statistics Toolbox +% (help pdist). +% default value: f = 'cosine' +% mirsimatrix(...,'Dissimilarity') return the dissimilarity matrix, +% which is the intermediary result before the computation of the +% actual similarity matrix. It shows the distance between each +% possible frame analysis in x. +% mirsimatrix(...,'Similarity',f) indicates the function f specifying +% the way the distance values in the dissimilarity matrix are +% transformed into similarity values. +% Possible values: +% f = 'oneminus' (default value) +% corresponding to f(x) = 1-x +% f = 'exponential' +% corresponding to f(x)= exp(-x) +% mirsimatrix(...,'Width',w) or more simply dissimatrix(...,w) +% specifies the size of the diagonal bandwidth, in samples, +% outside which the dissimilarity will not be computed. +% if w = inf (default value), all the matrix will be computed. +% mirsimatrix(...,'Horizontal') rotates the matrix 45 degrees in order to +% make the first diagonal horizontal, and to restrict on the +% diagonal bandwidth only. +% mirsimatrix(...,'TimeLag') transforms the (non-rotated) matrix into +% a time-lag matrix, making the first diagonal horizontal as well +% (corresponding to the zero-lag line). +% +% mirsimatrix(M,r) creates a mirsimatrix similarity matrix based on +% the Matlab square matrix M, of frame rate r (in Hz.) +% By default r = 20 Hz. +% mirsimatrix(M,r,'Dissimilarity') creates instead a mirsimatrix +% dissimilarity matrix. +% +% Foote, J. & Cooper, M. (2003). Media Segmentation using Self-Similarity +% Decomposition,. In Proc. SPIE Storage and Retrieval for Multimedia +% Databases, Vol. 5021, pp. 167-75. + +% mirsimatrix(...,'Filter',10) filter along the diagonal of the matrix, +% using a uniform moving average filter of size f. The result is +% represented in a time-lag matrix. + + + distance.key = 'Distance'; + distance.type = 'String'; + distance.default = 'cosine'; + option.distance = distance; + + simf.key = 'Similarity'; + simf.type = 'String'; + simf.default = 'oneminus'; + simf.keydefault = 'Similarity'; + simf.when = 'After'; + option.simf = simf; + + dissim.key = 'Dissimilarity'; + dissim.type = 'Boolean'; + dissim.default = 0; + option.dissim = dissim; + + K.key = 'Width'; + K.type = 'Integer'; + K.default = Inf; + option.K = K; + + filt.key = 'Filter'; + filt.type = 'Integer'; + filt.default = 0; + filt.when = 'After'; + option.filt = filt; + + view.type = 'String'; + view.default = 'Standard'; + view.choice = {'Standard','Horizontal','TimeLag'}; + view.when = 'After'; + option.view = view; + + rate.type = 'Integer'; + rate.position = 2; + rate.default = 20; + option.rate = rate; + +specif.option = option; +specif.nochunk = 1; +varargout = mirfunction(@mirsimatrix,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if isnumeric(x) + m.diagwidth = Inf; + m.view = 's'; + m.similarity = NaN; + m.graph = {}; + m.branch = {}; + m = class(m,'mirsimatrix',mirdata); + m = set(m,'Title','Dissimilarity matrix'); + fp = repmat(((1:size(x,1))-.5)/option.rate,[2,1]); + x = set(m,'Data',{x},'Pos',[],... + 'FramePos',{{fp}},'Name',{inputname(1)}); +else + if not(isamir(x,'mirsimatrix')) + if (isamir(x,'miraudio')) + if isframed(x) + x = mirspectrum(x); + else + x = mirspectrum(x,'Frame',0.05,1); + end + end + end +end +type = 'mirsimatrix'; + + +function m = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +if isa(orig,'mirsimatrix') + d = get(orig,'Data'); + for k = 1:length(d) + if iscell(option) && isfield(option,'K') && option.K < orig.diagwidth + nl = size(d{k},1); + if strcmp(orig.view,'h') + dl = (nl - option.K)/2; + dk = d{k}(ceil(dl):nl-floor(dl),:); + else + [spA,spd] = spdiags(d{k},-ceil(option.K/2):ceil(option.K/2)); + dk = full(spdiags(spA,spd,nl,size(d{k},2))); + end + d{k} = dk; + orig.diagwidth = option.K; + end + end + m = set(orig,'Data',d); +else + v = get(orig,'Data'); + d = cell(1,length(v)); + for k = 1:length(v) + vk = v{k}; + if mirwaitbar + handle = waitbar(0,'Computing dissimilarity matrix...'); + else + handle = 0; + end + if 0 %iscell(vk) + try + vk = cell2mat(vk); + end + end + if 0 %iscell(vk) %&& length(vk) > 1 %%% ATTENTION KL!!<<<<<<<<<<<< + l = length(vk); + dk = NaN(l,l); + hK = floor(option.K/2); + for i = 1:l + if handle + waitbar(i/l,handle); + end + for j = max(1,i-hK):min(l,i+hK) + dk(i,j) = KL(vk{i},vk{j}); + end + end + else + if not(iscell(vk)) + vk = {vk}; + end + for z = 1:length(vk) + vz = vk{z}; + ll = size(vz,1); + l = size(vz,2); + nc = size(vz,3); + if ll==1 && nc>1 + vz = squeeze(vz)'; + ll = nc; + nc = 1; + end + nd = size(vz,4); + if not(isempty(postoption)) && ... + strcmpi(postoption.view,'TimeLag') + lK = ceil(option.K/2); + if isinf(lK) + lK = l; + end + dk{z} = NaN(lK,l,nc); + else + dk{z} = NaN(l,l,nc); + end + for g = 1:nc + if nd == 1 + vv = vz; + else + vv = zeros(ll*nd,l); + for h = 1:nd + if iscell(vz) + for i = 1:ll + for j = 1:l + vj = vz{i,j,g,h}; + if isempty(vj) + vv((h-1)*ll+i,j) = NaN; + else + vv((h-1)*ll+i,j) = vj; + end + end + end + else + tic + vv((h-1)*ll+1:h*ll,:) = vz(:,:,g,h); + toc + end + end + end + if isinf(option.K) && not(strcmpi(postoption.view,'TimeLag')) + try + manually = 0; + dk{z}(:,:,g) = squareform(pdist(vv',option.distance)); + if option.K < Inf + [spA,spd] = spdiags(dk{z},... + -ceil(option.K/2):ceil(option.K/2)); + dk{z}(:,:,g) = full(spdiags(spA,spd,size(dk,1),size(dk{z},2))); + end + catch + %err = lasterror; + %warning(err.message) + %disp('Statistics Toolbox does not seem to be + %installed. Recompute the distance matrix manually.'); + manually = 1; + end + else + manually = 1; + end + if manually + disf = str2func(option.distance); + if strcmpi(option.distance,'cosine') + for i = 1:l + vv(:,i) = vv(:,i)/norm(vv(:,i)); + end + end + if not(isempty(postoption)) && ... + strcmpi(postoption.view,'TimeLag') + lK = ceil(option.K/2); + for i = 1:l + if mirwaitbar && (mod(i,100) == 1 || i == l) + waitbar(i/l,handle); + end + ij = min(i+lK-1,l); + dkij = disf(vv(:,i),vv(:,i:ij)); + for j = 1:ij-i+1 + dk{z}(j,i+j-1,g) = dkij(j); + end + end + else + for i = 1:l + if mirwaitbar && (mod(i,100) == 1 || i == l) + waitbar(i/l,handle); + end + j = min(i+option.K-1,l); + dkij = disf(vv(:,i),vv(:,i:j)); + dk{z}(i,i:j,g) = dkij; + dk{z}(i:j,i,g) = dkij'; + end + end + end + end + end + end + d{k} = dk; + if handle + delete(handle) + end + end + m.diagwidth = option.K; + if not(isempty(postoption)) && strcmpi(postoption.view,'TimeLag') + m.view = 'l'; + else + m.view = 's'; + end + m.similarity = 0; + m.graph = {}; + m.branch = {}; + m = class(m,'mirsimatrix',mirdata(orig)); + m = purgedata(m); + m = set(m,'Title','Dissimilarity matrix'); + m = set(m,'Data',d,'Pos',[]); +end +if not(isempty(postoption)) + if strcmpi(m.view,'s') + if strcmpi(postoption.view,'Horizontal') + for k = 1:length(d) + for z = 1:length(d{k}) + d{k}{z} = rotatesim(d{k}{z},m.diagwidth); + if option.K < m.diagwidth + W = size(d{k}{z},1); + hW = ceil(W/2); + hK = ceil(option.K/2); + d{k}{z} = d{k}{z}(hW-hK:hW+hK,:); + m.diagwidth = option.K; + end + end + end + m = set(m,'Data',d); + m.view = 'h'; + elseif strcmpi(postoption.view,'TimeLag') || postoption.filt + W = ceil(m.diagwidth/2); + for k = 1:length(d) + for z = 1:length(d{k}) + if isinf(W) + dz = NaN(size(d{k}{z})); + else + dz = NaN(W,size(d{k}{z},2)); + end + for l = 1:size(dz,1) + dz(l,l:end) = diag(d{k}{z},l-1)'; + end + if option.K < m.diagwidth + dz = dz(1:ceil(option.K/2),:); + m.diagwidth = option.K; + end + d{k}{z}= dz; + end + end + m = set(m,'Data',d); + m.view = 'l'; + end + end + if ischar(postoption.simf) + if strcmpi(postoption.simf,'Similarity') + if not(isequal(m.similarity,NaN)) + option.dissim = 0; + end + postoption.simf = 'oneminus'; + end + if isequal(m.similarity,0) && isstruct(option) ... + && isfield(option,'dissim') && not(option.dissim) + simf = str2func(postoption.simf); + for k = 1:length(d) + for z = 1:length(d{k}) + d{k}{z} = simf(d{k}{z}); + end + end + m.similarity = postoption.simf; + m = set(m,'Title','Similarity matrix','Data',d); + elseif length(m.similarity) == 1 && isnan(m.similarity) ... + && option.dissim + m.similarity = 0; + end + end + if postoption.filt + fp = get(m,'FramePos'); + for k = 1:length(d) + for z = 1:length(d{k}) + dz = filter(ones(postoption.filt,1),1,d{k}{z}); + d{k}{z} = dz(postoption.filt:end,1:end-postoption.filt+1); + fp{k}{z} = [fp{k}{z}(1,1:end-postoption.filt+1);... + fp{k}{z}(1,postoption.filt:end)]; + end + end + m = set(m,'Data',d,'FramePos',fp); + end +end + + +function S = rotatesim(d,K) +if length(d) == 1; + S = d; +else + K = min(K,size(d,1)*2); + lK = floor(K/2); + S = NaN(K,size(d,2),size(d,3)); + for k = 1:size(d,3) + for j = -lK:lK + S(lK+1+j,:,k) = [NaN(1,floor(abs(j)/2)) diag(d(:,:,k),j)' ... + NaN(1,ceil(abs(j)/2))]; + end + end +end + +function d = cosine(r,s) +d = 1-r'*s; +%nr = sqrt(r'*r); +%ns = sqrt(s'*s); +%if or(nr == 0, ns == 0); +% d = 1; +%else +% d = 1 - r'*s/nr/ns; +%end + + +function d = KL(x,y) +% Kullback-Leibler distance +if size(x,4)>1 + x(end+1:2*end,:,:,1) = x(:,:,:,2); + x(:,:,:,2) = []; +end +if size(y,4)>1 + y(end+1:2*end,:,:,1) = y(:,:,:,2); + y(:,:,:,2) = []; +end +m1 = mean(x); +m2 = mean(y); +S1 = cov(x); +S2 = cov(y); +d = (trace(S1/S2)+trace(S2/S1)+(m1-m2)'*inv(S1+S2)*(m1-m2))/2 - size(S1,1); + + +function s = exponential(d) + s = exp(-d); + + +function s = oneminus(d) + s = 1-d; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirsimatrix/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function mm = set(m,varargin) +% SET Set properties for the MIRsimatrix object +% and return the updated object + +propertyArgIn = varargin; +graph = m.graph; +branch = m.branch; +d = mirdata(m); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Graph' + graph = val; + case 'Branch' + branch = val; + otherwise + d = set(d,prop,val); + end +end +mm.diagwidth = m.diagwidth; +mm.view = m.view; +mm.similarity = m.similarity; +mm.graph = graph; +mm.branch = branch; +mm = class(mm,'mirsimatrix',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirspectrum/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function val = get(a, propName) +% GET Get properties from the MIRspectrum object +% and return the value + +switch propName + case 'Magnitude' + val = get(mirdata(a),'Data'); + case 'Frequency' + val = get(mirdata(a),'Pos'); + case 'Phase' + val = a.phase; + case 'log' + val = a.log; + case 'XScale' + val = a.xscale; + case 'Power' + val = a.pow; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirspectrum/mirspectrum.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,942 @@ +function varargout = mirspectrum(orig,varargin) +% s = mirspectrum(x) computes the spectrum of the audio signal x, showing +% the distribution of the energy along the frequencies. +% (x can be the name of an audio file as well.) +% Optional argument: +% mirspectrum(...,'Frame',l,h) computes spectrogram, using frames of +% length l seconds and a hop rate h. +% Default values: l = .05 s, h = .5. +% mirspectrum(...,'Min',mi) indicates the lowest frequency taken into +% consideration, expressed in Hz. +% Default value: 0 Hz. +% mirspectrum(...,'Max',ma) indicates the highest frequency taken into +% consideration, expressed in Hz. +% Default value: the maximal possible frequency, corresponding to +% the sampling rate of x divided by 2. +% mirspectrum(...,'Window',w): windowing method: +% either w = 0 (no windowing) or any windowing function proposed +% in the Signal Processing Toolbox (help window). +% default value: w = 'hamming' +% +% mirspectrum(...,'Cents'): decomposes the energy in cents. +% mirspectrum(...,'Collapsed'): collapses the spectrum into one +% octave divided into 1200 cents. +% Redistribution of the frequencies into bands: +% mirspectrum(...,'Mel'): Mel bands. +% (Requires the Auditory Toolbox.) +% mirspectrum(...,'Bark'): Bark bands. +% (Code based on Pampalk's MA toolbox). +% If the audio signal was frame decomposed, the output s is a +% band-decomposed spectrogram. It is then possible to compute +% the spectrum of the temporal signal in each band, +% using the following syntax: +% mirspectrum(s,'AlongBands') +% This corresponds to fluctuation (cf. mirfluctuation). +% mirspectrum(...,'Mask'): Models masking phenomena in each band. +% (Code based on Pampalk's MA toolbox). +% mirspectrum(...,'Normal'): normalizes with respect to energy. +% mirspectrum(...,'NormalInput'): input signal is normalized from 0 to 1. +% mirspectrum(...,'Power'): squares the energy. +% mirspectrum(...,'dB'): represents the spectrum energy in decibel scale. +% mirspectrum(...,'dB',th): keeps only the highest energy over a +% range of th dB. +% mirspectrum(...,'Terhardt'): modulates the energy following +% (Terhardt, 1979) outer ear model. (Code based on Pampalk's MA +% toolbox). +% mirspectrum(...,'Resonance',r): multiplies the spectrum with a +% resonance curve. Possible value for r: +% 'ToiviainenSnyder': highlights best perceived meter +% (Toiviainen & Snyder 2003) +% (default value for spectrum of envelopes) +% 'Fluctuation': fluctuation strength (Fastl 1982) +% (default value for spectrum of band channels) +% mirspectrum(...,'Prod',m): Enhances components that have harmonics +% located at multiples of range(s) m of the signal's fundamental +% frequency. Computed by compressing the signal by the list of +% factors m, and by multiplying all the results with the original +% signa (Alonso et al, 2003). +% mirspectrum(...,'Sum',s): Similar option using summation instead of +% product. +% +% mirspectrum(...,'MinRes',mr): Indicates a minimal accepted +% frequency resolution, in Hz. The audio signal is zero-padded in +% order to reach the desired resolution. +% If the 'Mel' option is toggled on, 'MinRes' is set by default +% to 66 Hz. +% mirspectrum(...,'MinRes',mr,'OctaveRatio',tol): Indicates the +% minimal accepted resolution in terms of number of divisions of +% the octave. Low frequencies are ignored in order to reach the +% desired resolution. +% The corresponding required frequency resolution is equal to +% the difference between the first frequency bins, multiplied +% by the constraining multiplicative factor tol (set by +% default to .75). +% mirspectrum(...,'Res',r): Indicates the required precise frequency +% resolution, in Hz. The audio signal is zero-padded in order to +% reach the desired resolution. +% mirspectrum(...,'Length',l): Specifies the length of the FFT, +% overriding the FFT length initially planned. +% mirspectrum(...,'ZeroPad',s): Zero-padding of s samples. +% mirspectrum(...,'WarningRes',s): Indicates a required frequency +% resolution, in Hz, for the input signal. If the resolution does +% not reach that prerequisite, a warning is displayed. +% mirspectrum(...,'ConstantQ',nb): Carries out a Constant Q Transform +% instead of a FFT, with a number of bins per octave fixed to nb. +% Default value for nb: 12 bins per octave. +% +% mirspectrum(...,'Smooth',o): smooths the envelope using a movering +% average of order o. +% Default value when the option is toggled on: o=10 +% mirspectrum(...,'Gauss',o): smooths the envelope using a gaussian +% of standard deviation o samples. +% Default value when the option is toggled on: o=10 +% mirspectrum(...,'Phase',0): do not compute the FFT phase. + + win.key = 'Window'; + win.type = 'String'; + win.default = 'hamming'; + option.win = win; + + min.key = 'Min'; + min.type = 'Integer'; + min.default = 0; + option.min = min; + + max.key = 'Max'; + max.type = 'Integer'; + max.default = Inf; + option.max = max; + + mr.key = 'MinRes'; + mr.type = 'Integer'; + mr.default = 0; + option.mr = mr; + + res.key = 'Res'; + res.type = 'Integer'; + res.default = NaN; + option.res = res; + + length.key = 'Length'; + length.type = 'Integer'; + length.default = NaN; + option.length = length; + + zp.key = 'ZeroPad'; + zp.type = 'Integer'; + zp.default = 0; + zp.keydefault = Inf; + option.zp = zp; + + wr.key = 'WarningRes'; + wr.type = 'Integer'; + wr.default = 0; + option.wr = wr; + + octave.key = 'OctaveRatio'; + octave.type = 'Boolean'; + octave.default = 0; + option.octave = octave; + + constq.key = 'ConstantQ'; + constq.type = 'Integer'; + constq.default = 0; + constq.keydefault = 12; + option.constq = constq; + + alongbands.key = 'AlongBands'; + alongbands.type = 'Boolean'; + alongbands.default = 0; + option.alongbands = alongbands; + + ni.key = 'NormalInput'; + ni.type = 'Boolean'; + ni.default = 0; + option.ni = ni; + + norm.key = 'Normal'; + norm.type = 'Integer'; + norm.default = 0; + norm.keydefault = 1; + norm.when = 'After'; + option.norm = norm; + + band.type = 'String'; + band.choice = {'Freq','Mel','Bark','Cents'}; + band.default = 'Freq'; + band.when = 'Both'; + option.band = band; + + nbbands.key = 'Bands'; + nbbands.type = 'Integer'; + nbbands.default = 0; + nbbands.when = 'After'; + option.nbbands = nbbands; + + mprod.key = 'Prod'; + mprod.type = 'Integers'; + mprod.default = []; + mprod.keydefault = 2:6; + mprod.when = 'After'; + option.mprod = mprod; + + msum.key = 'Sum'; + msum.type = 'Integers'; + msum.default = []; + msum.keydefault = 2:6; + msum.when = 'After'; + option.msum = msum; + + reso.key = 'Resonance'; + reso.type = 'String'; + reso.choice = {'ToiviainenSnyder','Fluctuation','Meter',0,'no','off'}; + reso.default = 0; + reso.when = 'After'; + option.reso = reso; + + log.key = 'log'; + log.type = 'Boolean'; + log.default = 0; + log.when = 'After'; + option.log = log; + + db.key = 'dB'; + db.type = 'Integer'; + db.default = 0; + db.keydefault = Inf; + db.when = 'After'; + option.db = db; + + pow.key = 'Power'; + pow.type = 'Boolean'; + pow.default = 0; + pow.when = 'After'; + option.pow = pow; + + terhardt.key = 'Terhardt'; + terhardt.type = 'Boolean'; + terhardt.default = 0; + terhardt.when = 'After'; + option.terhardt = terhardt; + + mask.key = 'Mask'; + mask.type = 'Boolean'; + mask.default = 0; + mask.when = 'After'; + option.mask = mask; + + % e.key = 'Enhanced'; + % e.type = 'Integer'; + % e.default = []; + % e.keydefault = 2:10; + % e.when = 'After'; + %option.e = e; + + collapsed.key = 'Collapsed'; + collapsed.type = 'Boolean'; + collapsed.default = 0; + collapsed.when = 'Both'; + option.collapsed = collapsed; + + aver.key = 'Smooth'; + aver.type = 'Integer'; + aver.default = 0; + aver.keydefault = 10; + aver.when = 'After'; + option.aver = aver; + + gauss.key = 'Gauss'; + gauss.type = 'Integer'; + gauss.default = 0; + gauss.keydefault = 10; + gauss.when = 'After'; + option.gauss = gauss; + + rapid.key = 'Rapid'; + rapid.type = 'Boolean'; + rapid.default = 0; + option.rapid = rapid; + + phase.key = 'Phase'; + phase.type = 'Boolean'; + phase.default = 1; + option.phase = phase; + +specif.option = option; + +specif.defaultframelength = 0.05; +specif.defaultframehop = 0.5; + +specif.eachchunk = @eachchunk; +specif.combinechunk = @combinechunk; + +if isamir(orig,'mirscalar') || isamir(orig,'mirenvelope') + specif.nochunk = 1; +end + +varargout = mirfunction(@mirspectrum,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = 'mirspectrum'; + + +function s = main(orig,option,postoption) +if isstruct(option) + if option.collapsed + option.band = 'Cents'; + end + if isnan(option.res) && strcmpi(option.band,'Cents') && option.min + option.res = option.min *(2^(1/1200)-1)*.9; + end +end +if not(isempty(postoption)) + if not(strcmpi(postoption.band,'Freq') && isempty(postoption.msum) ... + || isempty(postoption.mprod)) || postoption.log || postoption.db ... + || postoption.pow || postoption.mask || postoption.collapsed ... + || postoption.aver || postoption.gauss + option.phase = 0; + end +end +if iscell(orig) + orig = orig{1}; +end +if isa(orig,'mirspectrum') && ... + not(isfield(option,'alongbands') && option.alongbands) + s = orig; + if isfield(option,'min') && ... + (option.min || iscell(option.max) || option.max < Inf) + magn = get(s,'Magnitude'); + freq = get(s,'Frequency'); + for k = 1:length(magn) + m = magn{k}; + f = freq{k}; + if iscell(option.max) + mi = option.min{k}; + ma = option.max{k}; + else + mi = option.min; + ma = option.max; + end + if not(iscell(m)) + m = {m}; + f = {f}; + end + for l = 1:length(m) + range = find(and(f{l}(:,1) >= mi,f{l}(:,1) <= ma)); + m{l} = m{l}(range,:,:); + f{l} = f{l}(range,:,:); + end + magn{k} = m; + freq{k} = f; + end + s = set(s,'Magnitude',magn,'Frequency',freq); + end + if not(isempty(postoption)) && not(isequal(postoption,0)) + s = post(s,postoption); + end +elseif ischar(orig) + s = mirspectrum(miraudio(orig),option,postoption); +else + if nargin == 0 + orig = []; + end + s.phase = []; + s.log = 0; + s.xscale = 'Freq'; + s.pow = 1; + s = class(s,'mirspectrum',mirdata(orig)); + s = purgedata(s); + s = set(s,'Title','Spectrum','Abs','frequency (Hz)','Ord','magnitude'); + %disp('Computing spectrum...') + sig = get(orig,'Data'); + t = get(orig,'Pos'); + if isempty(t) + t = get(orig,'FramePos'); + for k = 1:length(sig) + for l = 1:length(sig{k}) + sig{k}{l} = sig{k}{l}'; + t{k}{l} = t{k}{l}(1,:,:)'; + end + end + end + fs = get(orig,'Sampling'); + fp = get(orig,'FramePos'); + m = cell(1,length(sig)); + p = cell(1,length(sig)); + f = cell(1,length(sig)); + for i = 1:length(sig) + d = sig{i}; + fpi = fp{i}; + if not(iscell(d)) + d = {d}; + end + if option.alongbands + fsi = 1 / (fpi{1}(1,2) - fpi{1}(1,1)); + else + fsi = fs{i}; + end + mi = cell(1,length(d)); + phi = cell(1,length(d)); + fi = cell(1,length(d)); + for J = 1:length(d) + dj = d{J}; + if option.ni + mxdj = repmat(max(dj),[size(dj,1),1,1]); + mndj = repmat(min(dj),[size(dj,1),1,1]); + dj = (dj-mndj)./(mxdj-mndj); + end + if option.alongbands + if size(dj,1)>1 + error('ERROR IN MIRSPECTRUM: ''AlongBands'' is restricted to spectrum decomposed into bands, such as ''Mel'' and ''Bark''.') + end + dj = reshape(dj,[size(dj,2),1,size(dj,3)]); + fp{i}{J} = fp{i}{J}([1;end]); + end + + if option.constq + % Constant Q Transform + r = 2^(1/option.constq); + Q = 1 / (r - 1); + f_max = min(fsi/2,option.max); + f_min = option.min; + if not(f_min) + f_min = 16.3516; + end + B = floor(log(f_max/f_min) / log(r)); % number of bins + N0 = round(Q*fsi/f_min); % maximum Nkcq + j2piQn = -j*2*pi*Q*(0:N0-1)'; + + fj = f_min * r.^(0:B-1)'; + transf = NaN(B,size(dj,2),size(dj,3)); + for kcq = 1:B + Nkcq = round(Q*fsi/fj(kcq)); + win = mirwindow(dj,option.win,Nkcq); + exq = repmat(exp(j2piQn(1:Nkcq)/Nkcq),... + [1,size(win,2),size(win,3)]); + transf(kcq,:) = sum(win.* exq) / Nkcq; + end + else + % FFT + dj = mirwindow(dj,option.win); + if option.zp + if option.zp < Inf + dj = [dj;zeros(option.zp,size(dj,2),size(dj,3))]; + else + dj = [dj;zeros(size(dj))]; + end + end + if isstruct(postoption) + if strcmpi(postoption.band,'Mel') && ... + (not(option.mr) || option.mr > 66) + option.mr = 66; + end + else + %warning('WARNING in MIRSPECTRUM (for debugging purposes): By default, minimum resolution specified.') + if not(option.mr) + option.mr = 66; + end + end + if option.octave + N = size(dj,1); + res = (2.^(1/option.mr)-1)*option.octave; + % Minimal freq ratio between 2 first bins. + % freq resolution should be > option.min * res + Nrec = fsi/(option.min*res); + % Recommended minimal sample length. + if Nrec > N + % If actual sample length is too small. + option.min = fsi/N / res; + warning('WARNING IN MIRSPECTRUM: The input signal is too short to obtain the desired octave resolution. Lowest frequencies will be ignored.'); + display(['(The recommended minimal input signal length would be ' num2str(Nrec/fsi) ' s.)']); + display(['New low frequency range: ' num2str(option.min) ' Hz.']); + end + N = 2^nextpow2(N); + elseif isnan(option.length) + if isnan(option.res) + N = size(dj,1); + if option.mr && N < fsi/option.mr + if option.wr && N < fsi/option.wr + warning('WARNING IN MIRSPECTRUM: The input signal is too short to obtain the desired frequency resolution. Performed zero-padding will not guarantee the quality of the results.'); + end + N = max(N,fsi/option.mr); + end + N = 2^nextpow2(N); + else + N = ceil(fsi/option.res); + end + else + N = option.length; + end + + % Here is the spectrum computation itself + transf = fft(dj,N); + + len = ceil(size(transf,1)/2); + fj = fsi/2 * linspace(0,1,len)'; + if option.max + maxf = find(fj>=option.max,1); + if isempty(maxf) + maxf = len; + end + else + maxf = len; + end + if option.min + minf = find(fj>=option.min,1); + if isempty(minf) + maxf = len; + end + else + minf = 1; + end + + transf = transf(minf:maxf,:,:); + fj = fj(minf:maxf); + end + + mi{J} = abs(transf); + if option.phase + phi{J} = angle(transf); + end + fi{J} = repmat(fj,[1,size(transf,2),size(transf,3)]); + end + if iscell(sig{i}) + m{i} = mi; + p{i} = phi; + f{i} = fi; + else + m{i} = mi{1}; + p{i} = phi{1}; + f{i} = fi{1}; + end + end + s = set(s,'Frequency',f,'Magnitude',m,'Phase',p,'FramePos',fp); + if not(isempty(postoption)) && isstruct(postoption) + s = post(s,postoption); + end +end + + +function s = post(s,option) +if option.collapsed + option.band = 'Cents'; +end +m = get(s,'Magnitude'); +f = get(s,'Frequency'); +for k = 1:length(m) + if not(iscell(m{k})) + m{k} = {m{k}}; + f{k} = {f{k}}; + end +end +if get(s,'Power') == 1 && ... + (option.pow || any(option.mprod) || any(option.msum)) + for h = 1:length(m) + for l = 1:length(m{k}) + m{h}{l} = m{h}{l}.^2; + end + end + s = set(s,'Power',2,'Title',['Power ',get(s,'Title')]); +end +if any(option.mprod) + for h = 1:length(m) + for l = 1:length(m{k}) + z0 = m{h}{l}; + z1 = z0; + for k = 1:length(option.mprod) + mpr = option.mprod(k); + if mpr + zi = ones(size(z0)); + zi(1:floor(end/mpr),:,:) = z0(mpr:mpr:end,:,:); + z1 = z1.*zi; + end + end + m{h}{l} = z1; + end + end + s = set(s,'Title','Spectral product'); +end +if any(option.msum) + for h = 1:length(m) + for l = 1:length(m{k}) + z0 = m{h}{l}; + z1 = z0; + for k = 1:length(option.msum) + mpr = option.msum(k); + if mpr + zi = ones(size(z0)); + zi(1:floor(end/mpr),:,:) = z0(mpr:mpr:end,:,:); + z1 = z1+zi; + end + end + m{h}{l} = z1; + end + end + s = set(s,'Title','Spectral sum'); +end +%for i = option.e % Enhancing procedure %%%% TO CHECK, t IS NOT DEFINED! +% for h = 1:length(m) +% for l = 1:length(m{k}) +% c = m{h}{l}; +% % option.e is the list of scaling factors +% % i is the scaling factor +% if i +% ic = zeros(size(c)); +% for g = 1:size(c,2) +% for h2 = 1:size(c,3) +% beg = find(t(:,g,h2)/i >= t(1,g,h2)) +% if not(isempty(beg)) +% ic(:,g,h2) = interp1(t(:,g,h2),c(:,g,h2),t(:,g,h2)/i); %,'cubic'); + % The scaled autocorrelation +% ic(1:beg(1)-1,g,h2) = 0; +% end +% end +% end +% ic(find(isnan(ic))) = Inf; % All the NaN values are changed into 0 in the resulting curve +% ic = max(ic,0); +% c = c - ic; % The scaled autocorrelation is substracted to the initial one +% c = max(c,0); % Half-wave rectification + %figure + %plot(c) +% end +% end +% end +%end +if option.norm + for k = 1:length(m) + for l = 1:length(m{k}) + mkl = m{k}{l}; + nkl = zeros(1,size(mkl,2),size(mkl,3)); + for kk = 1:size(mkl,2) + for ll = 1:size(mkl,3) + nkl(1,kk,l) = norm(mkl(:,kk,ll)); + end + end + m{k}{l} = mkl./repmat(nkl,[size(m{k}{k},1),1,1]); + end + end +end +if option.terhardt && not(isempty(find(f{1}{1}))) % This excludes the case where spectrum already along bands + % Code taken from Pampalk's MA Toolbox + for h = 1:length(m) + for l = 1:length(m{k}) + W_Adb = zeros(size(f{k}{l})); + W_Adb(2:size(f{k}{l},1),:,:) = ... + + 10.^((-3.64*(f{k}{l}(2:end,:,:)/1000).^-0.8 ... + + 6.5 * exp(-0.6 * (f{k}{l}(2:end,:,:)/1000 - 3.3).^2) ... + - 0.001*(f{k}{l}(2:end,:,:)/1000).^4)/20); + W_Adb = W_Adb.^2; + m{h}{l} = m{h}{l}.*W_Adb; + end + end +end +if option.reso + if not(ischar(option.reso)) + if strcmp(get(orig,'XScale'),'Mel') + option.reso = 'Fluctuation'; + else + option.reso = 'ToiviainenSnyder'; + end + end + %if strcmpi(option.reso,'ToiviainenSnyder') || ... + % strcmpi(option.reso,'Meter') || ... + % strcmpi(option.reso,'Fluctuation') + for k = 1:length(m) + for l = 1:length(m{k}) + if strcmpi(option.reso,'ToiviainenSnyder') || strcmpi(option.reso,'Meter') + w = max(0,... + 1 - 0.25*(log2(max(1./max(f{k}{l},1e-12),1e-12)/0.5)).^2); + elseif strcmpi(option.reso,'Fluctuation') + w1 = f{k}{l} / 4; % ascending part of the fluctuation curve; + w2 = 1 - 0.3 * (f{k}{l} - 4)/6; % descending part; + w = min(w1,w2); + end + if max(w) == 0 + warning('The resonance curve, not defined for this range of delays, will not be applied.') + else + m{k}{l} = m{k}{l}.* w; + end + end + end +end +if strcmp(s.xscale,'Freq') + if strcmpi(option.band,'Mel') + % The following is largely based on the source code from Auditory Toolbox + % (A part that I could not call directly from MIRtoolbox) + + % (Malcolm Slaney, August 1993, (c) 1998 Interval Research Corporation) + + try + MakeERBFilters(1,1,1); % Just to be sure that the Auditory Toolbox is installed + catch + error('ERROR IN MIRFILTERBANK: Auditory Toolbox needs to be installed.'); + end + + %disp('Computing Mel-frequency spectral representation...') + lowestFrequency = 133.3333; + if not(option.nbbands) + option.nbbands = 40; + end + linearFilters = min(13,option.nbbands); + linearSpacing = 66.66666666; + logFilters = option.nbbands - linearFilters; + logSpacing = 1.0711703; + totalFilters = option.nbbands; + + % Figure the band edges. Interesting frequencies are spaced + % by linearSpacing for a while, then go logarithmic. First figure + % all the interesting frequencies. Lower, center, and upper band + % edges are all consequtive interesting frequencies. + freqs = lowestFrequency + (0:linearFilters-1)*linearSpacing; + freqs(linearFilters+1:totalFilters+2) = ... + freqs(linearFilters) * logSpacing.^(1:logFilters+2); + lower = freqs(1:totalFilters); + center = freqs(2:totalFilters+1); + upper = freqs(3:totalFilters+2); + + % Figure out the height of the triangle so that each filter has + % unit weight, assuming a triangular weighting function. + triangleHeight = 2./(upper-lower); + + e = cell(1,length(m)); + nch = cell(1,length(m)); + for h = 1:length(m) + e{h} = cell(1,length(m{h})); + for i = 1:length(m{h}) + mi = m{h}{i}; + fi = f{h}{i}(:,1,1); + fftSize = size(fi,1); + + % We now want to combine FFT bins and figure out + % each frequencies contribution + mfccFilterWeights = zeros(totalFilters,fftSize); + for chan=1:totalFilters + mfccFilterWeights(chan,:) = triangleHeight(chan).*... + ((fi > lower(chan) & fi <= center(chan)).* ... + (fi-lower(chan))/(center(chan)-lower(chan)) + ... + (fi > center(chan) & fi < upper(chan)).* ... + (upper(chan)-fi)/(upper(chan)-center(chan))); + end + if find(diff(not(sum(mfccFilterWeights,2)))==-1) + % If one channel has no weight whereas the higher one + % has a positive weight. + warning('WARNING in MIRSPECTRUM: The frequency resolution of the spectrum is too low for the Mel transformation. Some Mel components are undefined.') + display('Recommended frequency resolution: at least 66 Hz.') + end + e{h}{i} = zeros(1,size(mi,2),totalFilters); + for J = 1:size(mi,2) + if max(mi(:,J)) > 0 + fftData = zeros(fftSize,1); % Zero-padding ? + fftData(1:size(mi,1)) = mi(:,J); + p = mfccFilterWeights * fftData + 1e-16; + e{h}{i}(1,J,:) = reshape(p,[1,1,length(p)]); + end + end + f{h}{i} = zeros(1,size(mi,2),totalFilters); + end + nch{h} = (1:totalFilters)'; + end + m = e; + s = set(s,'XScale','Mel','Title','Mel-Spectrum','Abs','Mel bands','Channels',nch); + elseif strcmpi(option.band,'Bark') + sr = get(s,'Sampling'); + % Code taken from Pampalk's MA Toolbox. + %% zwicker & fastl: psychoacoustics 1999, page 159 + bark_upper = [10 20 30 40 51 63 77 92 108 127 148 172 200 232 270 315 370 440 530 640 770 950 1200 1550]*10; %% Hz + e = cell(1,length(m)); + nch = cell(1,length(m)); + for h = 1:length(m) + %% ignore critical bands outside of sampling rate range + cb = min(min([find(bark_upper>sr{h}/2),length(bark_upper)]),length(bark_upper)); + e{h} = cell(1,length(m{h})); + for i = 1:length(m{h}) + mi = sum(m{h}{i},3); + e{h}{i} = zeros(1,size(mi,2),cb); + k = 1; + for J=1:cb, %% group into bark bands + idx = find(f{h}{i}(k:end,1,1)<=bark_upper(J)); + idx = idx + k-1; + e{h}{i}(1,:,J) = sum(mi(idx,:,:),1); + k = max(idx)+1; + end + f{h}{i} = zeros(1,size(mi,2),cb); + end + nch{h} = (1:length(bark_upper))'; + end + m = e; + s = set(s,'XScale','Bark','Title','Bark-Spectrum','Abs','Bark bands','Channels',nch); + elseif strcmpi(option.band,'Cents') || option.collapsed + for h = 1:length(m) + for i = 1:length(m{h}) + mi = m{h}{i}; + fi = f{h}{i}; + isgood = fi(:,1,1)*(2^(1/1200)-1) >= fi(2,1,1)-fi(1,1,1); + good = find(isgood); + if isempty(good) + mirerror('mirspectrum',... + 'The frequency resolution of the spectrum is too low to be decomposed into cents.'); + display('Hint: if you specify a minimum value for the frequency range (''Min'' option)'); + display(' and if you do not specify any frequency resolution (''Res'' option), '); + display(' the correct frequency resolution will be automatically chosen.'); + end + if good>1 + warning(['MIRSPECTRUM: Frequency resolution in cent is achieved for frequencies over ',... + num2str(floor(fi(good(1)))),' Hz. Lower frequencies are ignored.']) + display('Hint: if you specify a minimum value for the frequency range (''Min'' option)'); + display(' and if you do not specify any frequency resolution (''Res'' option), '); + display(' the correct frequency resolution will be automatically chosen.'); + end + fi = fi(good,:,:); + mi = mi(good,:,:); + f2cents = 440*2.^(1/1200*(-1200*10:1200*10-1)'); + % The frequencies corresponding to the cent range + cents = repmat((0:1199)',[20,size(fi,2),size(fi,3)]); + % The cent range is for the moment centered to A440 + octaves = ones(1200,1)*(-10:10); + octaves = repmat(octaves(:),[1,size(fi,2),size(fi,3)]); + select = find(f2cents>fi(1) & f2cents<fi(end)); + % Cent range actually used in the current spectrum + f2cents = repmat(f2cents(select),[1,size(fi,2),size(fi,3)]); + cents = repmat(cents(select),[1,size(fi,2),size(fi,3)]); + octaves = repmat(octaves(select),[1,size(fi,2),size(fi,3)]); + m{h}{i} = interp1(fi(:,1,1),mi,f2cents(:,1,1)); + f{h}{i} = octaves*1200+cents + 6900; + % Now the cent range is expressed in midicent + end + end + s = set(s,'Abs','pitch (in midicents)','XScale','Cents'); + end +end +if option.mask + if strcmp(s.xscale,'Freq') + warning('WARNING IN MIRSPECTRUM: ''Mask'' option available only for Mel-spectrum and Bark-spectrum.'); + disp('''Mask'' option ignored'); + else + nch = get(s,'Channels'); + for h = 1:length(m) + % Code taken from Pampalk's MA Toolbox. + %% spreading function: schroeder et al., 1979, JASA, optimizing + %% digital speech coders by exploiting masking properties of the human ear + cb = length(nch{h}); % Number of bands. + for i = 1:cb, + spread(i,:) = 10.^((15.81+7.5*((i-(1:cb))+0.474)-17.5*(1+((i-(1:cb))+0.474).^2).^0.5)/10); + end + for i = 1:length(m{h}) + for J = 1:size(m{h}{i},2) + mj = m{h}{i}(1,J,:); + mj = spread(1:length(mj),1:length(mj))*mj(:); + m{h}{i}(1,J,:) = reshape(mj,1,1,length(mj)); + end + end + end + end +end +if option.collapsed + for h = 1:length(m) + for i = 1:length(m{h}) + mi = m{h}{i}; + fi = f{h}{i}; + centclass = rem(fi(:,1,1),1200); + %neg = find(centclass<0); + %centclass(neg) = 1200 + centclass(neg); + m{h}{i} = NaN(1200,size(mi,2),size(mi,3)); + for k = 0:1199 + m{h}{i}(k+1,:,:) = sum(mi(find(centclass==k),:,:),1); + end + f{h}{i} = repmat((0:1199)',[1,size(fi,2),size(fi,3)]); + end + end + s = set(s,'Abs','Cents','XScale','Cents(Collapsed)'); +end +if option.log || option.db + if not(isa(s,'mirspectrum') && s.log) + if option.db + s = set(s,'log',10); + else + s = set(s,'log',1); + end + for k = 1:length(m) + if not(iscell(m{k})) + m{k} = {m{k}}; + f{k} = {f{k}}; + end + for l = 1:length(m{k}) + m{k}{l} = log10(m{k}{l}+1e-16); + if option.db + m{k}{l} = 10*m{k}{l}; + end + end + end + elseif isa(s,'mirspectrum') && s.log<10 + for k = 1:length(m) + for l = 1:length(m{k}) + m{k}{l} = 10*m{k}{l}; + end + end + end + if option.db>0 && option.db < Inf + for k = 1:length(m) + for l = 1:length(m{k}) + m{k}{l} = m{k}{l}-repmat(max(m{k}{l}),[size(m{k}{l},1) 1 1]); + m{k}{l} = option.db + max(-option.db,m{k}{l}); + end + end + end +end +if option.aver + for k = 1:length(m) + for i = 1:length(m{k}) + m{k}{i} = filter(ones(1,option.aver),1,m{k}{i}); + end + end +end +if option.gauss + for k = 1:length(m) + for i = 1:length(m{k}) + sigma = option.gauss; + gauss = 1/sigma/2/pi... + *exp(- (-4*sigma:4*sigma).^2 /2/sigma^2); + y = filter(gauss,1,[m{k}{i};zeros(4*sigma,1)]); + y = y(4*sigma:end,:,:); + m{k}{i} = y(1:size(m{k}{i},1),:,:); + end + end +end +s = set(s,'Magnitude',m,'Frequency',f); + + +function dj = mirwindow(dj,win,N) +if nargin<3 + N = size(dj,1); +elseif size(dj,1)<N + dj(N,1,1) = 0; +end +if not(win == 0) + winf = str2func(win); + try + w = window(winf,N); + catch + if strcmpi(win,'hamming') + disp('Signal Processing Toolbox does not seem to be installed. Recompute the hamming window manually.'); + w = 0.54 - 0.46 * cos(2*pi*(0:N-1)'/(N-1)); + else + error(['ERROR in MIRSPECTRUM: Unknown windowing function ',win,' (maybe Signal Processing Toolbox is not installed).']); + end + end + kw = repmat(w,[1,size(dj,2),size(dj,3)]); + dj = dj(1:N,:,:).* kw; +end + + +function [y orig] = eachchunk(orig,option,missing,postchunk) +option.zp = option.zp+missing; +y = mirspectrum(orig,option); + + +function y = combinechunk(old,new) +do = get(old,'Data'); +do = do{1}{1}; +dn = get(new,'Data'); +dn = dn{1}{1}; +y = set(old,'ChunkData',do+dn); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirspectrum/mtimes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function c = mtimes(a,b) + +if isa(b,'mirautocor') + if not(get(b,'OfSpectrum')) + b = mirautocor(b,'Freq'); + end +elseif isa(b,'mircepstrum') + b = mircepstrum(b,'Freq'); +end +c = mirtimes(a,b); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirspectrum/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function ss = set(s,varargin) +% SET Set properties for the MIRspectrum object +% and return the updated object + +propertyArgIn = varargin; +ph = s.phase; +log = s.log; +pow = s.pow; +xs = s.xscale; +d = mirdata(s); +d = set(d,'Title',get(s,'Title'),'Abs',get(s,'Abs'),'Ord',get(s,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Magnitude' + d = set(d,'Data',val); + case 'Frequency' + d = set(d,'Pos',val); + case 'Phase' + ph = val; + case 'log' + log = val; + case 'XScale' + xs = val; + case 'Power' + pow = val; + otherwise + d = set(d,prop,val); + end +end +ss.phase = ph; +ss.log = log; +ss.xscale = xs; +ss.pow = pow; +ss = class(ss,'mirspectrum',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirstruct/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function display(d) +% MIRDESIGN/DISPLAY display of a MIR design + +va = inputname(1); +if isempty(va) + va = 'ans'; +end +disp(' '); +for f = 1:length(d.fields) + disp(['Field ',d.fields{f},' has value:']); + d.data{f} +end +disp(['Temporary field has value:']); +d.tmp
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirstruct/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function v = get(a,varargin) +% GET Get properties from the MIRstruct object and return the value + +switch varargin{1} + case 'Fields' + v = a.fields; + case 'Data' + v = a.data; + case 'Tmp' + v = a.tmp; + case 'Stat' + v = a.stat; + otherwise + v = get(mirdesign(a),varargin{:}); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirstruct/mirstruct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function s = mirstruct(varargin) + +d = mirdesign('','',{},{},struct,{}); %,0); +s.fields = {}; +s.data = {}; +s.tmp = struct; +s.stat = 0; +s = class(s,'mirstruct',d); +s = set(s,varargin{:}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirstruct/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +function ss = set(s,varargin) +% SET Set properties from the MIRstruct object and return the value + +propertyArgIn = varargin; +f = s.fields; +d = s.data; +t = s.tmp; +st = s.stat; +des = mirdesign(s); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Fields' + f = val; + case 'Data' + d = val; + case 'Tmp' + t = val; + case 'Stat' + st = val; + otherwise + des = set(des,prop,val); + end +end +ss.fields = f; +ss.data = d; +ss.tmp = t; +ss.stat = st; +ss = class(ss,'mirstruct',des); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirstruct/subsasgn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function a = subsasgn(a,index,val) +% SUBSASGN Define index assignment for mirstruct objects +switch index(1).type +case '.' + if strcmpi(index(1).subs,'tmp') + fields = a.fields; + data = a.data; + + if isa(val,'mirdata') || (iscell(val) && isa(val{1},'mirdata')) + % If the 'tmp' data turns to be an actual evaluated data, + % the mirstruct object is transformed into a simple struct. + a = struct; + for i = 1:length(fields) + a.(fields{i}) = data{i}; + end + a.tmp.(index(2).subs) = val; + return + end + + if isa(val,'mirdesign') + val = set(val,'Stored',{index.subs}); + end + if length(index)>2 + if strcmpi(index(3).type,'{}') + isubs = index(3).subs; + if length(isubs)>1 + a.tmp.(index(2).subs){isubs{1},isubs{2}} = val; + else + a.tmp.(index(2).subs){isubs{1}} = val; + end + end + else + a.tmp.(index(2).subs) = val; + end + aa = struct; + aa.fields = fields; + aa.data = data; + aa.tmp = a.tmp; + aa.stat = a.stat; + a = class(aa,'mirstruct',val); + return + end + [is,id] = ismember(index(1).subs,a.fields); + if not(is) + a.fields{end+1} = index(1).subs; + a.data{end+1} = []; + id = length(a.fields); + end + if length(index) == 1 + a.data{id} = val; + else + a.data{id} = subsasgn(a.data{id},index(2:end),val); + end + if get(val,'NoChunk') && isframed(a) + a = set(a,'FrameDontChunk',1); + % Frame-decomposed flowchart where one dependent variable requires + % a complete computation. Should not therefore be evaluated + % chunk after chunk. + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirstruct/subsref.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function b = subsref(a,index) +% Define field name indexing for mirstruct objects +switch index(1).type +case '.' + if strcmpi(index(1).subs,'tmp') + if length(index)== 1 + b = []; + else + if length(index)>2 + if strcmpi(index(3).type,'{}') + isubs = index(3).subs; + if length(isubs)>1 + b = a.tmp.(index(2).subs){isubs{1},isubs{2}}; + else + b = a.tmp.(index(2).subs){isubs{1}}; + end + end + else + b = a.tmp.(index(2).subs); + end + end + return + end + [is,id] = ismember(index(1).subs,a.fields); + if length(index) == 1 + b = a.data{id}; + else + b = subsref(a.data{id},index(2:end)); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirtemporal/display.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function display(d) +% MIRDATA/DISPLAY display of a MIRtemporal + +if d.centered + d = set(d,'Title',[get(d,'Title'),' (centered)']); +end +mirdisplay(mirdata(d),inputname(1)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirtemporal/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function val = get(a, propName) +% GET Get properties from the MIRtemporal object +% and return the value + +switch propName + case 'Time' + val = get(a,'Pos'); + case 'Centered' + val = a.centered; + case 'NBits' + val = a.nbits; + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirtemporal/mirtemporal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function t = mirtemporal(orig,varargin) +% t = mirtemporal(x) creates a temporal object from signal x. +% Optional arguments: +% mirtemporal(...,'Center') centers the signal x. + +if nargin > 0 && isa(orig,'mirtemporal') + t.centered = orig.centered; + t.nbits = orig.nbits; +else + t.centered = 0; + t.nbits = {}; +end +t = class(t,'mirtemporal',mirdata(orig)); +if nargin == 0 || not(isa(orig,'mirtemporal')) + t = set(t,'Title','Temporal signal','Abs','time (s)','Ord','amplitude'); +end +if nargin>1 + for i = 1:nargin-1 + if strcmp(varargin{i},'Center') + d = get(t,'Data'); + for h = 1:length(d) + for k = 1:length(d{h}) + d{h}{k} = center(d{k}); + end + end + t = set(t,'Data',d); + end + end + t = set(t,varargin{:}); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirtemporal/plus.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function c = plus(a,b) + +d = get(a,'Data'); +e = get(b,'Data'); +f = cell(1,length(d)); +p = get(a,'Time'); +q = get(b,'Time'); +r = cell(1,length(d)); +l = get(a,'Name'); +m = get(b,'Name'); +n = cell(1,length(l)); +for i = 1:length(d) + f{i} = cell(1,length(d{i})); + for j = 1:length(d{i}) + ld = size(d{i}{j},1); + le = size(e{i}{j},1); + if ld > le + r{i}{j} = p{i}{j}; + f{i}{j} = d{i}{j} + [e{i}{j};zeros(ld-le,size(e,2),size(e,3))]; + else + r{i}{j} = q{i}{j}; + f{i}{j} = [d{i}{j};zeros(le-ld,size(d,2),size(d,3))] + e{i}{j}; + end + end + n{i} = [l{i} '+' m{i}]; +end +c = set(a,'Pos',r,'Data',f,'Name',n); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirtemporal/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function tt = set(t,varargin) +% SET Set properties for the MIRtemporal object +% and return the updated object + +propertyArgIn = varargin; +c = t.centered; +b = t.nbits; +d = mirdata(t); +d = set(d,'Title',get(t,'Title'),'Abs',get(t,'Abs'),'Ord',get(t,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Time' + d = set(d,'Pos',val); + case 'Centered' + c = val; + case 'NBits' + b = val; + otherwise + d = set(d,prop,val); + end +end +tt.centered = c; +tt.nbits = b; +tt = class(tt,'mirtemporal',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirtonalcentroid/get.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function val = get(a, propName) +% GET Get properties from the MIRchromagram object +% and return the value + +switch propName + case 'Positions' + val = get(mirdata(a),'Data'); + case 'Dimensions' + val = get(mirdata(a),'Pos'); + otherwise + val = get(mirdata(a),propName); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirtonalcentroid/mirtonalcentroid.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +function varargout = mirtonalcentroid(orig,varargin) +% c = mirtonalcentroid(x) calculates the 6-dimensional tonal centroid +% vector from the chromagram. +% It corresponds to a projection of the chords along circles of fifths, +% of minor thirds, and of major thirds. +% [c ch] = mirtonalcentroid(x) also returns the intermediate chromagram. +% +% C. A. Harte and M. B. Sandler, Detecting harmonic change in musical +% audio, in Proceedings of Audio and Music Computing for Multimedia +% Workshop, Santa Barbara, CA, 2006. + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [0 0]; + frame.keydefault = [.743 .1]; + option.frame = frame; + +specif.option = option; + +varargout = mirfunction(@mirtonalcentroid,orig,varargin,nargout,specif,@init,@main); + + +function [c type] = init(orig,option) +if option.frame.length.val + c = mirchromagram(orig,'Frame',option.frame.length.val,... + option.frame.length.unit,... + option.frame.hop.val,... + option.frame.hop.unit); +else + c = mirchromagram(orig); +end +type = 'mirtonalcentroid'; + + +function tc = main(ch,option,postoption) +if iscell(ch) + ch = ch{1}; +end +if isa(ch,'mirtonalcentroid') + tc = orig; + ch = []; +else + x1 = sin(pi*7*(0:11)/6)'; + y1 = cos(pi*7*(0:11)/6)'; + % minor thirds circle + x2 = sin(pi*3*(0:11)/2)'; + y2 = cos(pi*3*(0:11)/2)'; + % major thirds circle + x3 = 0.5 * sin(pi*2*(0:11)/3)'; + y3 = 0.5 * cos(pi*2*(0:11)/3)'; + c = [x1 y1 x2 y2 x3 y3]; + c = c'; + tc = class(struct,'mirtonalcentroid',mirdata(ch)); + tc = purgedata(tc); + tc = set(tc,'Title','Tonal centroid','Abs','dimensions','Ord','position'); + m = get(ch,'Magnitude'); + %disp('Computing tonal centroid...') + n = cell(1,length(m)); % The final structured list of magnitudes. + d = cell(1,length(m)); % The final structured list of centroid dimensions. + for i = 1:length(m) + mi = m{i}; + if not(iscell(mi)) + mi = {mi}; + end + ni = cell(1,length(mi)); % The list of magnitudes. + di = cell(1,length(mi)); % The list of centroid dimensions. + for j = 1:length(mi) + mj = mi{j}; + ni{j} = zeros(6,size(mj,2),size(mi,3)); + for k = 1:size(mj,3) + ni{j}(:,:,k) = c * mj(:,:,k); + end + di{j} = repmat((1:6)',[1,size(mj,2),size(mi,3)]); + end + n{i} = ni; + d{i} = di; + end + tc = set(tc,'Positions',n,'Dimensions',d); +end +tc = {tc,ch}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/@mirtonalcentroid/set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +function cc = set(c,varargin) +% SET Set properties for the MIRtonalcentroid object +% and return the updated object + +propertyArgIn = varargin; +d = mirdata(c); +d = set(d,'Title',get(c,'Title'),'Abs',get(c,'Abs'),'Ord',get(c,'Ord')); +while length(propertyArgIn) >= 2, + prop = propertyArgIn{1}; + val = propertyArgIn{2}; + propertyArgIn = propertyArgIn(3:end); + switch prop + case 'Positions' + d = set(d,'Data',val); + case 'Dimensions' + d = set(d,'Pos',val); + otherwise + d = set(d,prop,val); + end +end +cc = class(struct,'mirtonalcentroid',d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/Contents.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +% MIRtoolbox +% Version 1.3.2 19-January-2011 +% +% A more detailed documentation of each function is available using the +% help command. For instance, type help miraudio. +% +% A complete documentation is available in the downloaded folder and online. +% http://www.jyu.fi/music/coe/materials/mirtoolbox +% +%BASIC AUDIO OPERATIONS +% miraudio - Loads and return waveform +% mirframe - Decomposes into successive frames +% mirsegment - Decomposes into successive segments +% mirlength - Temporal length +% +%DATA OUTPUT +% mirgetdata - Return result as a Matlab standard structure +% mirsave - Save audio and other temporal data into audio files +% mirexport - Export the analytical results to a text file +% mirplay - Plays audio and other temporal data +% +%ANALYTICAL OPERATORS +% mirspectrum - FFT spectrum with many post-processing operations +% mirautocor - Autocorrelation function +% mirfilterbank - Decomposes into channels via a bank of filters +% mirsum - Sums the channels of a filterbank +% +%DYNAMIC +% mirrms - Root mean square energy +% mirlowenergy - Number of frames with lower than average energy +% mirenvelope - Amplitude envelope (global shape of the waveform) +% mironsets - Note onset positions and characteristics +% mirattacktime - Duration of note attacks +% mirattackslope - Average slope of note attacks +% mireventdensity - Average frequency of events +% +%RHYTHM +% mirtempo - Tempo (in beats per minute) +% mirfluctuation - Fluctuation strength (periodicities in each channel) +% mirbeatspectrum - Beat spectrum, characterizing the rhythmic content +% mirpulseclarity - Rhythmic clarity, i.e., beat strength +% +%TIMBRE +% mirbrightness - Spectral brightness (high-frequency rate) +% mirrolloff - Spectral rolloff (frequency above which is located a +% certain amount of energy) +% mirmfcc - Mel-frequency cepstrum coefficients +% (numerical description of the spectrum envelope) +% mirinharmonicity - Inharmonicity (partials non-multiple of fundamental) +% mirroughness - Roughness (sensory dissonance) +% mirregularity - Spectrum irregularity (amplitude variability of +% successive peaks) +% +%PITCH +% mirpitch - Pitch frequencies +% mircepstrum - Cepstrum representation (showing periodicities) +% mirmidi - Attempts a conversion of audio into MIDI +% +%TONALITY +% mirchromagram - Chromagram (distribution of energy along pitches) +% mirkeystrength - Key strengths (probability of key candidates) +% mirkey - Best keys and modes (in the 12 tone system) +% mirkeysom - Visualizes key strengths with self-organizing map +% mirmode - General estimation of mode (major/minor) +% mirtonalcentroid - Tonal centroid (using circles of fifths and thirds) +% mirhcdf - Harmonic Change Detection Function +% +%PREDICTIONS +% miremotion - Emotion, represented both as classes and dimensions +% +%ANALYSIS +% mirmean - Returns the mean of any feature +% mirstd - Returns the standard deviation of any feature +% mirstat - Returns statistics of any feature +% mirpeaks - Peaks +% mirhisto - Histogram +% mirentropy - Entropy +% mirzerocross - Sign-changes ratio +% mircentroid - Centroid (center of gravity) +% mirspread - Spread (non-concentration) +% mirskewness - Skewness (lack of symmetry) +% mirkurtosis - Kurtosis (peakiness) +% mirflatness - Flatness +% +%SIMILARITY +% mirflux - Flux, i.e., distance between successive frames +% mirsimatrix - Similarity matrix +% mirnovelty - Novelty score +% mirdist - Distance between audio files +% mirquery - Query by example +% +%OTHER +% mirclassify - Classifies audio sequences +% mircluster - Clusters segments or frames +% mirfeatures - Compute a large range of features +% mirmap - Performs statistical mapping +% +%MATLAB FUNCTIONS generalized to the MIRtoolbox data +% + - Superposes audio files +% * - Combines autocor, cepstrum curves +% corrcoef - Computes correlation between curves +% +%PREFERENCES +% mirchunklim - Get or set the chunk size threshold +% mirwaitbar - Toggles on/off the display of progress bars +% mirverbose - Toggles on/off the display of ongoing operations +% mirparallel - Toggles on/off parallel processing
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/MakeERBFilters.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,120 @@ +function fcoefs=MakeERBFilters(fs,numChannels,lowFreq) +% function [fcoefs]=MakeERBFilters(fs,numChannels,lowFreq) +% This function computes the filter coefficients for a bank of +% Gammatone filters. These filters were defined by Patterson and +% Holdworth for simulating the cochlea. +% +% The result is returned as an array of filter coefficients. Each row +% of the filter arrays contains the coefficients for four second order +% filters. The transfer function for these four filters share the same +% denominator (poles) but have different numerators (zeros). All of these +% coefficients are assembled into one vector that the ERBFilterBank +% can take apart to implement the filter. +% +% The filter bank contains "numChannels" channels that extend from +% half the sampling rate (fs) to "lowFreq". Alternatively, if the numChannels +% input argument is a vector, then the values of this vector are taken to +% be the center frequency of each desired filter. (The lowFreq argument is +% ignored in this case.) + +% Note this implementation fixes a problem in the original code by +% computing four separate second order filters. This avoids a big +% problem with round off errors in cases of very small cfs (100Hz) and +% large sample rates (44kHz). The problem is caused by roundoff error +% when a number of poles are combined, all very close to the unit +% circle. Small errors in the eigth order coefficient, are multiplied +% when the eigth root is taken to give the pole location. These small +% errors lead to poles outside the unit circle and instability. Thanks +% to Julius Smith for leading me to the proper explanation. + +% Execute the following code to evaluate the frequency +% response of a 10 channel filterbank. +% fcoefs = MakeERBFilters(16000,10,100); +% y = ERBFilterBank([1 zeros(1,511)], fcoefs); +% resp = 20*log10(abs(fft(y'))); +% freqScale = (0:511)/512*16000; +% semilogx(freqScale(1:255),resp(1:255,:)); +% axis([100 16000 -60 0]) +% xlabel('Frequency (Hz)'); ylabel('Filter Response (dB)'); + +% Rewritten by Malcolm Slaney@Interval. June 11, 1998. +% (c) 1998 Interval Research Corporation + +T = 1/fs; +if length(numChannels) == 1 + cf = ERBSpace(lowFreq, fs/2, numChannels); +else + cf = numChannels(1:end); + if size(cf,2) > size(cf,1) + cf = cf'; + end +end + +% Change the followFreqing three parameters if you wish to use a different +% ERB scale. Must change in ERBSpace too. +EarQ = 9.26449; % Glasberg and Moore Parameters +minBW = 24.7; +order = 1; + +ERB = ((cf/EarQ).^order + minBW^order).^(1/order); +B=1.019*2*pi*ERB; + +A0 = T; +A2 = 0; +B0 = 1; +B1 = -2*cos(2*cf*pi*T)./exp(B*T); +B2 = exp(-2*B*T); + +A11 = -(2*T*cos(2*cf*pi*T)./exp(B*T) + 2*sqrt(3+2^1.5)*T*sin(2*cf*pi*T)./ ... + exp(B*T))/2; +A12 = -(2*T*cos(2*cf*pi*T)./exp(B*T) - 2*sqrt(3+2^1.5)*T*sin(2*cf*pi*T)./ ... + exp(B*T))/2; +A13 = -(2*T*cos(2*cf*pi*T)./exp(B*T) + 2*sqrt(3-2^1.5)*T*sin(2*cf*pi*T)./ ... + exp(B*T))/2; +A14 = -(2*T*cos(2*cf*pi*T)./exp(B*T) - 2*sqrt(3-2^1.5)*T*sin(2*cf*pi*T)./ ... + exp(B*T))/2; + +gain = abs((-2*exp(4*i*cf*pi*T)*T + ... + 2*exp(-(B*T) + 2*i*cf*pi*T).*T.* ... + (cos(2*cf*pi*T) - sqrt(3 - 2^(3/2))* ... + sin(2*cf*pi*T))) .* ... + (-2*exp(4*i*cf*pi*T)*T + ... + 2*exp(-(B*T) + 2*i*cf*pi*T).*T.* ... + (cos(2*cf*pi*T) + sqrt(3 - 2^(3/2)) * ... + sin(2*cf*pi*T))).* ... + (-2*exp(4*i*cf*pi*T)*T + ... + 2*exp(-(B*T) + 2*i*cf*pi*T).*T.* ... + (cos(2*cf*pi*T) - ... + sqrt(3 + 2^(3/2))*sin(2*cf*pi*T))) .* ... + (-2*exp(4*i*cf*pi*T)*T + 2*exp(-(B*T) + 2*i*cf*pi*T).*T.* ... + (cos(2*cf*pi*T) + sqrt(3 + 2^(3/2))*sin(2*cf*pi*T))) ./ ... + (-2 ./ exp(2*B*T) - 2*exp(4*i*cf*pi*T) + ... + 2*(1 + exp(4*i*cf*pi*T))./exp(B*T)).^4); + +allfilts = ones(length(cf),1); +fcoefs = [A0*allfilts A11 A12 A13 A14 A2*allfilts B0*allfilts B1 B2 gain]; + +if 0 % Test Code + figure + A0 = fcoefs(:,1); + A11 = fcoefs(:,2); + A12 = fcoefs(:,3); + A13 = fcoefs(:,4); + A14 = fcoefs(:,5); + A2 = fcoefs(:,6); + B0 = fcoefs(:,7); + B1 = fcoefs(:,8); + B2 = fcoefs(:,9); + gain= fcoefs(:,10); + chan=1; + x = [1 zeros(1, 511)]; + y1=filter([A0(chan)/gain(chan) A11(chan)/gain(chan) ... + A2(chan)/gain(chan)],[B0(chan) B1(chan) B2(chan)], x); + y2=filter([A0(chan) A12(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y1); + y3=filter([A0(chan) A13(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y2); + y4=filter([A0(chan) A14(chan) A2(chan)], ... + [B0(chan) B1(chan) B2(chan)], y3); + semilogx((0:(length(x)-1))*(fs/length(x)),20*log10(abs(fft(y4)))); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/aiffread.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,575 @@ +function [data,Fs,nBits,formChunk] = aiffread(filePath,indexRange) +%AIFFREAD Read AIFF (Audio Interchange File Format) sound file. +% Y = AIFFREAD(FILE) reads an AIFF file specified by the string FILE, +% returning the sampled data in Y. The ".aif" extension is appended if no +% extension is given. +% +% [Y,FS,NBITS,CHUNKDATA] = AIFFREAD(FILE) returns the sample rate (FS) in +% Hertz, the number of bits per sample (NBITS) used to encode the data in +% the file, and a complete structure of the chunk data (CHUNKDATA) +% contained in the AIFF file (minus the actual audio data returned in Y). +% See below for a description of CHUNKDATA. +% +% [...] = AIFFREAD(FILE,N) returns only the first N samples from each +% channel in the file. +% +% [...] = AIFFREAD(FILE,[N1 N2]) returns only samples N1 through N2 from +% each channel in the file. +% +% [SIZ,...] = AIFFREAD(FILE,'size') returns the size of the audio data +% contained in the file in place of the actual audio data, where +% SIZ = [nSampleFrames nChannels]. +% +%-NOTES-------------------------------------------------------------------- +% +% A note on compressed files: +% +% Both AIFF and AIFC/AIFF-C (compressed) file types can be read by +% AIFFREAD, but the data returned for AIFC/AIFF-C files will be the +% raw, compressed data (i.e. AIFFREAD loads the data from the file +% without modification). Currently, since there are many compression +% formats, it is the responsibility of the user to uncompress the +% sound data using parameters defined in the COMM chunk (contained in +% the returned CHUNKDATA structure). When loading AIFC/AIFF-C files, +% any optional numerical subranges are ignored and the entire set of +% compressed data is returned as a column vector of signed bytes +% (INT8 type). +% +% A note on the CHUNKDATA structure: +% +% The CHUNKDATA structure has the following fields: +% +% -'chunkID': The 4-character ID for the chunk. This will always be +% the string 'FORM'. +% -'chunkSize': The size (in bytes) of the remaining data in the +% file. +% -'formType': A 4-character string for the file type that should +% be either 'AIFF' or 'AIFC'. +% -'chunkArray': An array of structures, one entry for every chunk +% that is in the file (not counting this parent FORM +% chunk). The fields of this structure are: +% -'chunkID': The 4-character ID for the chunk. +% -'chunkSize': The size (in bytes) of the +% remaining data in the chunk. +% -'chunkData': A structure of data for the +% chunk. The form of this data for +% a given chunkID can be found at +% the links given below for the +% file format standards. +% +% The data portion of certain chunks may not have a clearly defined +% format, or that format may be dependent on the implementation or +% application that will be using the data. In such cases, the data +% returned for that chunk in the CHUNKDATA structure will be in a raw +% format (vectors of signed or unsigned 8-bit integers) and it will be +% up to the user/application to parse and format this data correctly. +% The following is a list of such AIFF/AIFF-C chunks: +% +% -Audio Recording Chunk (chunkID = 'AESD'): The chunkData +% structure has one field 'aesChannelStatusData' that stores a +% column vector of 24 8-bit unsigned integers. +% -Application Specific Chunk (chunkID = 'APPL'): The chunkData +% structure has two fields. 'applicationSignature' stores a +% 4-character string identifying the application. 'data' stores a +% column vector of 8-bit signed integers. +% -MIDI Data Chunk (chunkID = 'MIDI'): The chunkData structure has +% one field 'midiData' that stores a column vector of 8-bit +% unsigned integers. +% -Sound Accelerator (SAXEL) Chunk (chunkID = 'SAXL'): There is no +% finalized format for Saxel chunks, so the chunkData structure +% follows the draft format given in Appendix D of the AIFF-C +% standard. +% +% Description for the AIFF standard can be found here: +% +% http://muratnkonar.com/aiff/index.html +% +% Descriptions for the AIFC/AIFF-C standard can be found here: +% +% http://www.cnpbagwell.com/aiff-c.txt +% http://www-mmsp.ece.mcgill.ca/Documents/AudioFormats/AIFF/Docs/... +% AIFF-C.9.26.91.pdf + +% Author: Ken Eaton +% Last modified: 3/17/09 +%-------------------------------------------------------------------------- + + % Initializations: + + aiffChunkPrecedence = {'COMM' 'SSND' 'MARK' 'INST' 'COMT' 'NAME' ... + 'AUTH' '[c] ' 'ANNO' 'AESD' 'MIDI' 'APPL'}; + aiffChunkLimits = [1 1 1 1 1 1 1 1 inf 1 inf inf]; + aifcChunkPrecedence = {'FVER' 'COMM' 'INST' 'SAXL' 'COMT' 'MARK' ... + 'SSND' 'NAME' 'AUTH' '[c] ' 'ANNO' 'AESD' ... + 'MIDI' 'APPL'}; + aifcChunkLimits = [1 1 1 inf 1 1 1 1 1 1 inf 1 inf inf]; + fid = -1; + + % Check the number of input arguments: + + switch nargin, + case 0, + error(error_message('notEnoughInputs')); + case 1, + indexRange = [1 inf]; + end + + % Check the file name input argument: + + if ~ischar(filePath), + error(error_message('badArgumentType','File name','char')); + end + [filePath,fileName,fileExtension] = fileparts(filePath); + if isempty(fileExtension), + fileExtension = '.aif'; + end + if ~any(strcmpi(fileExtension,{'.aif' '.afc' '.aiff' '.aifc'})), + error(error_message('unknownExtension',fileExtension)); + end + + % Check the optional input argument: + + if isnumeric(indexRange), % Numeric range specification + + indexRange = double(indexRange); + nRange = numel(indexRange); + if (nRange > 2) || any(indexRange < 1) || any(isnan(indexRange)), + error(error_message('badIndexValue')); + end + indexRange = [ones(nRange ~= 2) round(indexRange) inf(nRange == 0)]; + + elseif ischar(indexRange), % Specification for returning just the size + + if ~strncmpi(indexRange,'size',numel(indexRange)), + error(error_message('invalidString')); + end + indexRange = []; + + else % Invalid input + + error(error_message('badArgumentType','Optional argument',... + 'numeric or char')); + + end + + % Check that the file exists and can be opened: + + fid = fopen(fullfile(filePath,[fileName fileExtension]),'r','b'); + if fid == -1, + error(error_message('invalidFile',[fileName fileExtension])); + end + + % Initialize formChunk structure: + + formChunk = struct('chunkID',[],'chunkSize',[],'formType',[],... + 'chunkArray',[]); + + % Read FORM chunk data: + + formChunk.chunkID = read_text(fid,4); + if ~strcmp(formChunk.chunkID,'FORM'), + error(error_message('invalidFileFormat',fileExtension)); + end + formChunk.chunkSize = fread(fid,1,'int32'); + formType = read_text(fid,4); + if ~any(strcmp(formType,{'AIFF' 'AIFC'})), + error(error_message('invalidFileFormat',fileExtension)); + end + formChunk.formType = formType; + + % Since the order of chunks is not guaranteed, first skip through the + % file and read just the chunkIDs and chunkSizes: + + iChunk = 0; + chunkIDArray = {}; + chunkSizeArray = {}; + chunkDataIndex = []; + nextChunkID = read_text(fid,4); + while ~feof(fid), + iChunk = iChunk+1; + chunkIDArray{iChunk} = nextChunkID; + chunkSize = fread(fid,1,'int32'); + chunkSizeArray{iChunk} = chunkSize; + chunkDataIndex(iChunk) = ftell(fid); + fseek(fid,chunkSize+rem(chunkSize,2),'cof'); + nextChunkID = read_text(fid,4); + end + + % Check for the presence of required chunks: + + if ~ismember('COMM',chunkIDArray), + error(error_message('missingChunk','COMM',formType)); + end + if strcmp(formType,'AIFC') && ~ismember('FVER',chunkIDArray), + error(error_message('missingChunk','FVER',formType)); + end + + % Check for unknown chunks and order chunks based on chunk precedence: + + if strcmp(formType,'AIFF'), + [isChunk,orderIndex] = ismember(chunkIDArray,aiffChunkPrecedence); + else + [isChunk,orderIndex] = ismember(chunkIDArray,aifcChunkPrecedence); + end + if ~all(isChunk), + unknownChunks = [chunkIDArray(~isChunk); ... + repmat({', '},1,sum(~isChunk)-1) {'.'}]; + orderIndex = orderIndex(isChunk); + chunkIDArray = chunkIDArray(isChunk); + chunkSizeArray = chunkSizeArray(isChunk); + chunkDataIndex = chunkDataIndex(isChunk); + warning('aiffread:unknownChunk',... + ['The following chunk IDs are unknown for an ' formType ... + ' file and will be ignored: ' unknownChunks{:}]); + end + [index,orderIndex] = sort(orderIndex); + chunkIDArray = chunkIDArray(orderIndex); + chunkSizeArray = chunkSizeArray(orderIndex); + chunkDataIndex = chunkDataIndex(orderIndex); + + % Check for chunks that should not appear more than once: + + index = unique(index(diff(index) < 1)); + if strcmp(formType,'AIFF'), + repeatChunks = aiffChunkPrecedence(aiffChunkLimits(index) == 1); + else + repeatChunks = aifcChunkPrecedence(aifcChunkLimits(index) == 1); + end + if ~isempty(repeatChunks), + repeatChunks = [repeatChunks; ... + repmat({', '},1,numel(repeatChunks)-1) {'.'}]; + error(error_message('repeatChunk',formType,[repeatChunks{:}])); + end + + % Initialize chunkArray data: + + formChunk.chunkArray = struct('chunkID',chunkIDArray,... + 'chunkSize',chunkSizeArray,... + 'chunkData',[]); + + % Read the data for each chunk: + + for iChunk = 1:numel(chunkIDArray), + chunkData = []; + fseek(fid,chunkDataIndex(iChunk),'bof'); + switch chunkIDArray{iChunk}, + + case '[c] ', % Copyright Chunk + + chunkData.text = read_text(fid,chunkSizeArray{iChunk}); + + case 'AESD', % Audio Recording Chunk + + chunkData.aesChannelStatusData = ... + fread(fid,[chunkSizeArray{iChunk} 1],'*uint8'); + + case 'ANNO', % Annotation Chunk + + chunkData.text = read_text(fid,chunkSizeArray{iChunk}); + + case 'APPL', % Application Specific Chunk + + chunkData.applicationSignature = read_text(fid,4); + chunkData.data = fread(fid,[chunkSizeArray{iChunk}-4 1],'*int8'); + + case 'AUTH', % Author Chunk + + chunkData.text = read_text(fid,chunkSizeArray{iChunk}); + + case 'COMM', % Common Chunk + + nChannels = fread(fid,1,'int16'); + chunkData.nChannels = nChannels; + nSampleFrames = fread(fid,1,'uint32'); + if (nSampleFrames > 0) && ~ismember('SSND',chunkIDArray), + error(error_message('missingChunk','SSND',formType)); + end + chunkData.nSampleFrames = nSampleFrames; + nBits = fread(fid,1,'int16'); + chunkData.sampleSize = nBits; + exponent = fread(fid,1,'uint16'); + highMantissa = fread(fid,1,'uint32'); + lowMantissa = fread(fid,1,'uint32'); + Fs = extended2double(exponent,highMantissa,lowMantissa); + chunkData.sampleRate = Fs; + if strcmp(formType,'AIFF'), + compressionType = 'NONE'; + else + compressionType = read_text(fid,4); + chunkData.compressionType = compressionType; + chunkData.compressionName = read_pstring(fid); + end + + case 'COMT', % Comments Chunk + + nComments = fread(fid,1,'uint16'); + chunkData.nComments = nComments; + chunkData.commentArray = struct('timeStamp',cell(nComments,1),... + 'marker',[],'count',[],'text',[]); + for iComment = 1:nComments, + chunkData.commentArray(iComment) = read_comment(fid); + end + + case 'FVER', % Format Version Chunk (AIFC/AIFF-C only) + + timeStamp = fread(fid,1,'uint32'); + if timeStamp ~= 2726318400, + warning('aiffread:unknownVersion',... + ['File contains an unrecognized version of the ' ... + 'AIFC/AIFF-C standard.']); + end + chunkData.timeStamp = timeStamp; + + case 'INST', % Instrument Chunk + + chunkData.baseNote = fread(fid,1,'int8'); + chunkData.detune = fread(fid,1,'int8'); + chunkData.lowNote = fread(fid,1,'int8'); + chunkData.highNote = fread(fid,1,'int8'); + chunkData.lowVelocity = fread(fid,1,'int8'); + chunkData.highVelocity = fread(fid,1,'int8'); + chunkData.gain = fread(fid,1,'int16'); + chunkData.sustainLoop = read_loop(fid); + chunkData.releaseLoop = read_loop(fid); + + case 'MARK', % Marker Chunk + + nMarkers = fread(fid,1,'uint16'); + chunkData.nMarkers = nMarkers; + chunkData.markerArray = struct('id',cell(nMarkers,1),... + 'position',[],'markerName',[]); + for iMarker = 1:nMarkers, + chunkData.markerArray(iMarker) = read_marker(fid); + end + markerIDs = [chunkData.markerArray.id]; + if any(markerIDs < 1) || (numel(unique(markerIDs)) < nMarkers), + warning('aiffread:invalidMarkers',... + 'Invalid or repeated marker IDs were detected.'); + end + + case 'MIDI', % MIDI Data Chunk + + chunkData.midiData = fread(fid,[chunkSizeArray{iChunk} 1],... + '*uint8'); + + case 'NAME', % Name Chunk + + chunkData.text = read_text(fid,chunkSizeArray{iChunk}); + + case 'SAXL', % Sound Accelerator (SAXEL) Chunk (AIFC/AIFF-C only) + + nSaxels = fread(fid,1,'uint16'); + chunkData.nSaxels = nSaxels; + chunkData.saxelArray = struct('id',cell(nSaxels,1),'size',[],... + 'saxelData',[]); + for iSaxel = 1:nSaxels, + chunkData.saxelArray(iSaxel) = read_saxel(fid); + end + + case 'SSND', % Sound Data Chunk + + nBytes = ceil(nBits/8); + chunkData.offset = fread(fid,1,'uint32'); + chunkData.blockSize = fread(fid,1,'uint32'); + if isempty(indexRange), + data = [nSampleFrames nChannels]; + elseif strcmp(compressionType,'NONE'), + if (chunkSizeArray{iChunk}-8 ~= nChannels*nSampleFrames*nBytes), + error(error_message('sizeMismatch')); + end + fseek(fid,nBytes*nChannels*(indexRange(1)-1),'cof'); + nRead = min(indexRange(2),nSampleFrames)-indexRange(1)+1; + data = fread(fid,[nChannels nRead],['*bit' int2str(nBytes*8)]).'; + if nBits < nBytes*8, + data = data./(2^(nBytes*8-nBits)); + end + else + data = fread(fid,[chunkSizeArray{iChunk}-8 1],'*int8'); + end + + end + formChunk.chunkArray(iChunk).chunkData = chunkData; + end + + % Close the file: + + fclose(fid); + +%~~~Begin nested functions~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + %------------------------------------------------------------------------ + function errorStruct = error_message(errorCode,varargin) + % + % Initialize an error message (and close open files, if necessary). + % + %------------------------------------------------------------------------ + + % Close open files, if necessary: + + if ~isempty(fopen(fid)), + fclose(fid); + end + + % Initialize error message text: + + switch errorCode, + case 'badArgumentType', + errorText = [varargin{1} ' should be of type ' varargin{2} '.']; + case 'badIndexValue', + errorText = ['Index range must be specified as a scalar or ' ... + '2-element vector of positive, non-zero, non-NaN ' ... + 'values.']; + case 'invalidFile', + errorText = ['Could not open file ''' varargin{1} '''.']; + case 'invalidFileFormat', + errorText = ['Not a valid ' varargin{1} ' file.']; + case 'invalidString', + errorText = '''size'' is the only valid string argument.'; + case 'missingChunk', + errorText = ['''' varargin{1} ''' chunk is required for a ' ... + varargin{2} ' file.']; + case 'notEnoughInputs', + errorText = 'Not enough input arguments.'; + case 'repeatChunk', + errorText = ['The following chunk IDs should not appear more ' ... + 'than once in an ' varargin{1} ' file: ' varargin{2}]; + case 'sizeMismatch', + errorText = 'Data size mismatch between COMM and SSND chunks.'; + case 'unknownExtension', + errorText = ['Unknown file extension ''' varargin{1} '''.']; + end + + % Create error structure: + + errorStruct = struct('message',errorText,... + 'identifier',['aiffread:' errorCode]); + + end + +%~~~End nested functions~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +end + +%~~~Begin subfunctions~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +%-------------------------------------------------------------------------- +function value = extended2double(exponent,highMantissa,lowMantissa) +% +% Converts an 80-bit extended floating-point type to a double. +% +%-------------------------------------------------------------------------- + + signBit = bitand(exponent,32768); + exponent = bitand(exponent,32767); + highMantissa = bitand(highMantissa,4294967295); + lowMantissa = bitand(lowMantissa,4294967295); + if (exponent == 0) && (highMantissa == 0) && (lowMantissa == 0), + value = 0; + elseif exponent == 32767, + if (highMantissa > 0) || (lowMantissa > 0), + value = nan; + else + value = inf; + end + else + value = highMantissa*2^(exponent-16414)+lowMantissa*2^(exponent-16446); + end + if signBit, + value = -value; + end + +end + +%-------------------------------------------------------------------------- +function commentStruct = read_comment(fid) +% +% Reads a structure of comment data from a file. +% +%-------------------------------------------------------------------------- + + commentStruct = struct('timeStamp',fread(fid,1,'uint32'),... + 'marker',fread(fid,1,'int16'),... + 'count',[],'text',[]); + charCount = fread(fid,1,'uint16'); + commentStruct.count = charCount; + commentStruct.text = read_text(fid,charCount); + +end + +%-------------------------------------------------------------------------- +function loopStruct = read_loop(fid) +% +% Reads a structure of loop data from a file. +% +%-------------------------------------------------------------------------- + + loopStruct = struct('playMode',fread(fid,1,'int16'),... + 'beginLoop',fread(fid,1,'int16'),... + 'endLoop',fread(fid,1,'int16')); + +end + +%-------------------------------------------------------------------------- +function markerStruct = read_marker(fid) +% +% Reads a structure of marker data from a file. +% +%-------------------------------------------------------------------------- + + markerStruct = struct('id',fread(fid,1,'int16'),... + 'position',fread(fid,1,'uint32'),... + 'markerName',read_pstring(fid)); + +end + +%-------------------------------------------------------------------------- +function pascalString = read_pstring(fid) +% +% Reads a Pascal-style string from a file, and afterwards shifts the file +% pointer ahead by one byte if necessary to make the total number of bytes +% read an even number. +% +%-------------------------------------------------------------------------- + + charCount = fread(fid,1,'uint8'); + pascalString = fread(fid,[1 charCount],'int8=>char'); + if rem(charCount+1,2), + fseek(fid,1,'cof'); + end + +end + +%-------------------------------------------------------------------------- +function saxelStruct = read_saxel(fid) +% +% Reads a structure of saxel data from a file. +% +%-------------------------------------------------------------------------- + + saxelStruct = struct('id',fread(fid,1,'int16'),'size',[],'saxelData',[]); + saxelBytes = fread(fid,1,'uint16'); + saxelStruct.size = saxelBytes; + saxelStruct.saxelData = fread(fid,[saxelBytes 1],'*int8'); + if rem(saxelBytes,2), + fseek(fid,1,'cof'); + end + +end + +%-------------------------------------------------------------------------- +function textString = read_text(fid,charCount) +% +% Reads ASCII text from a file, and afterwards shifts the file pointer +% ahead by one byte if necessary to make the total number of bytes read an +% even number. +% +%-------------------------------------------------------------------------- + + textString = fread(fid,[1 charCount],'int8=>char'); + if rem(charCount,2), + fseek(fid,1,'cof'); + end + +end + +%~~~End subfunctions~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/center.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +function c = center(x) +if isempty(x) + c = []; +else + c = x - repmat(mean(x),[size(x,1),1,1]); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/combinepeaks.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +function c = combinepeaks(p,v,thr) +% dedicated function for (Klapuri, 99) that creates a curve made of burst +% at position of peaks p and with amplitude related to peaks v. + +dp = get(p,'Data'); +dv = get(v,'Data'); +pp = get(p,'PeakPos'); +pv = get(v,'PeakPos'); +sr = get(v,'Sampling'); +l = length(dp); +empty = cell(1,l); +for i = 1:l + thr = round(thr*sr{i}); + empty{i} = cell(1,length(dp{i})); + for h = 1:length(dp{i}) + dih = zeros(size(dp{i}{h})); + for l = 1:size(pp{i}{h},3) + for k = 1:size(pp{i}{h},2) + j = 1; + ppkl = pp{i}{h}{1,k,l}; + pvkl = pv{i}{h}{1,k,l}; + while j < length(ppkl) + if ppkl(j+1)-ppkl(j) < thr + decreas = dv{i}{h}(pvkl(j+1),k,l) ... + < dv{i}{h}(pvkl(j),k,l); + ppkl(j+decreas) = []; + pvkl(j+decreas) = []; + else + j = j+1; + end + end + dih(ppkl,k,l) = dv{i}{h}(pvkl,k,l); + end + end + dv{i}{h} = dih; + end +end +c = set(p,'Data',dv,'PeakPos',empty,'PeakVal',empty,... + 'PeakPrecisePos',{},'PeakPreciseVal',{},'PeakMode',empty,... + 'InterChunk',[]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/convolve2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,223 @@ +function y = convolve2(x, m, shape, tol) +%CONVOLVE2 Two dimensional convolution. +% Y = CONVOLVE2(X, M) performs the 2-D convolution of matrices X and +% M. If [mx,nx] = size(X) and [mm,nm] = size(M), then size(Y) = +% [mx+mm-1,nx+nm-1]. Values near the boundaries of the output array are +% calculated as if X was surrounded by a border of zero values. +% +% Y = CONVOLVE2(X, M, SHAPE) where SHAPE is a string returns a +% subsection of the 2-D convolution with size specified by SHAPE: +% +% 'full' - (default) returns the full 2-D convolution, +% 'same' - returns the central part of the convolution +% that is the same size as A (using zero padding), +% 'valid' - returns only those parts of the convolution +% that are computed without the zero-padded +% edges, size(Y) = [mx-mm+1,nx-nm+1] when +% size(X) > size(M), +% 'wrap' - as for 'same' except that instead of using +% zero-padding the input A is taken to wrap round as +% on a toroid. +% 'reflect' - as for 'same' except that instead of using +% zero-padding the input A is taken to be reflected +% at its boundaries. +% +% CONVOLVE2 is fastest when mx > mm and nx > nm - i.e. the first +% argument is the input and the second is the mask. +% +% If the rank of the mask M is low, CONVOLVE2 will decompose it into a +% sum of outer product masks, each of which is applied efficiently as +% convolution with a row vector and a column vector, by calling CONV2. +% The function will often be faster than CONV2 or FILTER2 (in some +% cases much faster) and will produce the same results as CONV2 to +% within a small tolerance. +% +% Y = CONVOLVE2(... , TOL) where TOL is a number in the range 0.0 to +% 1.0 computes the convolution using a reduced-rank approximation to +% M, provided this will speed up the computation. TOL limits the +% relative sum-squared error in the effective mask; that is, if the +% effective mask is E, the error is controlled such that +% +% sum(sum( (M-E) .* (M-E) )) +% -------------------------- <= TOL +% sum(sum( M .* M )) +% +% See also CONV2, FILTER2. + +% David Young, Department of Informatics, University of Sussex, February 2002, +% revised January 2005. + +% Deal with optional arguments +error(nargchk(2,4,nargin)); +if nargin < 3 + shape = 'full'; % shape default as for CONV2 + tol = 0; +elseif nargin < 4 + if isnumeric(shape) + tol = shape; + shape = 'full'; + else + tol = 0; + end +end; + +% Set up to do the wrap & reflect operations, not handled by conv2 +if strcmp(shape, 'wrap') + x = wraparound(x, m); + shape = 'valid'; +elseif strcmp(shape, 'reflect') + x = reflectborders(x, m); + shape = 'valid'; +end + +% do the convolution itself +y = doconv(x, m, shape, tol); + +%----------------------------------------------------------------------- + +function y = doconv(x, m, shape, tol); +% Carry out convolution +[mx, nx] = size(x); +[mm, nm] = size(m); + +% If the mask is bigger than the input, or it is 1-D already, +% just let CONV2 handle it. +if mm > mx | nm > nx | mm == 1 | nm == 1 + y = conv2(x, m, shape); +else + % Get svd of mask + if mm < nm; m = m'; end % svd(..,0) wants m > n + [u,s,v] = svd(m, 0); + s = diag(s); + rank = trank(m, s, tol); + if rank*(mm+nm) < mm*nm % take advantage of low rank + if mm < nm; t = u; u = v; v = t; end % reverse earlier transpose + vp = v'; + % For some reason, CONV2(H,C,X) is very slow, so use the normal call + y = conv2(conv2(x, u(:,1)*s(1), shape), vp(1,:), shape); + for r = 2:rank + y = y + conv2(conv2(x, u(:,r)*s(r), shape), vp(r,:), shape); + end + else + if mm < nm; m = m'; end % reverse earlier transpose + y = conv2(x, m, shape); + end +end + +%----------------------------------------------------------------------- + +function r = trank(m, s, tol) +% Approximate rank function - returns rank of matrix that fits given +% matrix to within given relative rms error. Expects original matrix +% and vector of singular values. +if tol < 0 | tol > 1 + error('Tolerance must be in range 0 to 1'); +end +if tol == 0 % return estimate of actual rank + tol = length(m) * max(s) * eps; + r = sum(s > tol); +else + ss = s .* s; + t = (1 - tol) * sum(ss); + r = 0; + sm = 0; + while sm < t + r = r + 1; + sm = sm + ss(r); + end +end + +%----------------------------------------------------------------------- + +function y = wraparound(x, m) +% Extend x so as to wrap around on both axes, sufficient to allow a +% "valid" convolution with m to return the cyclical convolution. +% We assume mask origin near centre of mask for compatibility with +% "same" option. +[mx, nx] = size(x); +[mm, nm] = size(m); +if mm > mx | nm > nx + error('Mask does not fit inside array') +end + +mo = floor((1+mm)/2); no = floor((1+nm)/2); % reflected mask origin +ml = mo-1; nl = no-1; % mask left/above origin +mr = mm-mo; nr = nm-no; % mask right/below origin +me = mx-ml+1; ne = nx-nl+1; % reflected margin in input +mt = mx+ml; nt = nx+nl; % top of image in output +my = mx+mm-1; ny = nx+nm-1; % output size + +y = zeros(my, ny); +y(mo:mt, no:nt) = x; % central region +if ml > 0 + y(1:ml, no:nt) = x(me:mx, :); % top side + if nl > 0 + y(1:ml, 1:nl) = x(me:mx, ne:nx); % top left corner + end + if nr > 0 + y(1:ml, nt+1:ny) = x(me:mx, 1:nr); % top right corner + end +end +if mr > 0 + y(mt+1:my, no:nt) = x(1:mr, :); % bottom side + if nl > 0 + y(mt+1:my, 1:nl) = x(1:mr, ne:nx); % bottom left corner + end + if nr > 0 + y(mt+1:my, nt+1:ny) = x(1:mr, 1:nr); % bottom right corner + end +end +if nl > 0 + y(mo:mt, 1:nl) = x(:, ne:nx); % left side +end +if nr > 0 + y(mo:mt, nt+1:ny) = x(:, 1:nr); % right side +end + +%----------------------------------------------------------------------- + +function y = reflectborders(x, m) +% Extend x so as to reflect at each boundary, sufficient to allow a +% "valid" convolution with m to return a matrix the same size as +% the orginal. +% We assume mask origin near centre of mask for compatibility with +% "same" option. +[mx, nx] = size(x); +[mm, nm] = size(m); +if mm > mx | nm > nx + error('Mask does not fit inside array') +end + +mo = floor((1+mm)/2); no = floor((1+nm)/2); % reflected mask origin +ml = mo-1; nl = no-1; % mask left/above origin +mr = mm-mo; nr = nm-no; % mask right/below origin +me = mx-mr+1; ne = nx-nr+1; % translated margin in input +mt = mx+ml; nt = nx+nl; % top/right of image in output +my = mx+mm-1; ny = nx+nm-1; % output size + +y = zeros(my, ny); +y(mo:mt, no:nt) = x; % central region +if ml > 0 + y(1:ml, no:nt) = x(ml:-1:1, :); % top side + if nl > 0 + y(1:ml, 1:nl) = x(ml:-1:1, nl:-1:1); % top left corner + end + if nr > 0 + y(1:ml, nt+1:ny) = x(ml:-1:1, nx:-1:ne); % top right corner + end +end +if mr > 0 + y(mt+1:my, no:nt) = x(mx:-1:me, :); % bottom side + if nl > 0 + y(mt+1:my, 1:nl) = x(mx:-1:me, nl:-1:1); % bottom left corner + end + if nr > 0 + y(mt+1:my, nt+1:ny) = x(mx:-1:me, nx:-1:ne); % bottom right corner + end +end +if nl > 0 + y(mo:mt, 1:nl) = x(:, nl:-1:1); % left side +end +if nr > 0 + y(mo:mt, nt+1:ny) = x(:, nx:-1:ne); % right side +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/displot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,514 @@ +function res = displot(x,y,xlab,ylab,t,fp,pp,tp,tv,ch,multidata,pm,ap,rp,cl) +% graphical display of any data (except mirscalar data) computed by MIRToolbox + +%opengl('OpenGLWobbleTesselatorBug',1) +% Rendering complex patch object may cause segmentation violation and +% return a tesselator error message in the stack trace. This command +% enables a workaround of that bug that might work sometimes... + +if isempty(y) + res = 0; + return +end +manychannels = 0; +y1 = y{1}; +if length(y) == 1 + y = y{1}; + if length(x) == 1 + x = x{1}; + end +end +if isempty(y) + res = 0; + return +end + +if isstruct(cl) + y = cl.centr(:,cl.index); +end + +figure +fp2 = uncell(fp); +c = size(y,2); +lx = size(y,1); +l = size(y1,3); % Number of channels +il = (1-0.15)/l; + +if iscell(y) + if size(y{1},3) > 1 && size(y{1},1) == 1 + for j = 1:c + y{j} = reshape(y{j},size(y{j},2),size(y{j},3)); + y{j} = y{j}'; + end + lx = l; + l = 1; + end +else + if l > 1 && lx == 1 + y = reshape(y,size(y,2),size(y,3)); + y = y'; + x = repmat(ch,[1 size(y,2) size(y,3)]); + lx = l; + l = 1; + end +end +if not(iscell(y)) && l > 20 %&& size(y,3) == 1 + manychannels = 1; + if lx == 1 + y = reshape(y,[c l])'; + lx = l; + l = 1; + else + y = reshape(y,[lx l])'; + fp = reshape(x,[1 lx size(x,3)]); + fp = fp(:,:,1); + fp2 = fp; + c = l; + l = 1; + x = ch'; + end +end +curve = (not(iscell(y)) && not(isequal(fp2,0)) && size(fp2,2)==1) || ... + ...%(iscell(y) && size(y{1}) == 1) || ... + c == 1 || (strcmp(xlab,'time (s)') && not(manychannels)); +if curve + for i = 1:l + if l>1 + subplot(l,1,l-i+1,'align'); + end + if not(iscell(y)) + y = {y}; + x = {x}; + end + col = cell(length(y)); + for h = 1:length(y) + col{h} = zeros(size(y{h},2),3,size(y{h},4)); + for j = 1:size(y{h},2) + if ischar(x{h}) + xj = x{h}; + else + xj = x{h}(:,j); + end + yj = y{h}(:,j,i,:); + lk = size(yj,4); + for k = 1:lk + if c > 1 + col{h}(j,:,k) = rand(1,3); + else + col{h}(j,:,k) = num2col(k); + end + yk = yj(:,:,:,k); + if not(isempty(yk)) + if iscell(xj) + lj = length(xj); + abs = 0:lj-1; + elseif ischar(xj) + abs = 1:length(yk); + else + abs = xj; + end + if length(abs) < 20 && lk == 1 && c == 1 + bar(abs,yk); + else + plot(abs,yk,'Color',col{h}(j,:,k)); + end + if iscell(xj) + tick = 0:ceil(lj/14):lj-1; + set(gca,'xtick',tick); + set(gca,'xticklabel',xj(tick+1)); + elseif ischar(xj) + set(gca,'xticklabel',x); + end + hold on + end + end + end + if length(y) > 1 + if isempty(cl)% || isempty(cl{1}) ... this because of a bug.. + %|| isempty(cl{i}) + colr = h; + %elseif iscell(cl{i}) + % colr = cl{i}(h); + elseif length(cl) == 1 + colr = cl; + else + colr = cl(h); %cl{i}(h); + end + if not(isempty(x{h})) + rectangle('Position',[x{h}(1),... + min(min(y{h}(:,:,i))),... + x{h}(end)-x{h}(1),... + max(max(y{h}(:,:,i)))-... + min(min(y{h}(:,:,i)))]+1e-16,... + 'EdgeColor',num2col(colr),'Curvature',.1,'LineWidth',1) + end + end + end + if not(isempty(multidata)) + legend(multidata{:},'Location','Best') + end + for h = 1:length(y) + for j = 1:size(y{h},2) + xj = x{h}(:,j); + yj = y{h}(:,j,i,:); + lk = size(yj,4); + for k = 1:lk + yk = yj(:,:,:,k); + if not(isempty(yk)) + if size(y{h},2) > 1 + rectangle('Position',[xj(1),min(yk),... + xj(end)-xj(1),max(yk)-min(yk)]+1e-16,... + 'EdgeColor',col{h}(j,:,k)) + end + if not(isempty(pp)) && not(isempty(pp{1})) + [ppj order] = sort(pp{h}{1,j,i}); + if not(isempty(pm)) && not(isempty(pm{1})) + pmj = pm{h}{1,j,i}(order); + [R C] = find(pmj==k); + if iscell(xj) + plot(ppj(R,C)-1,yk(ppj(R,C)),'or') + else + plot(xj(ppj(R,C)),yk(ppj(R,C)),'or') + end + else + if lk > 1 + [R C] = find(ppj(:,:,2)==k); + plot(xj(ppj(R,C)),yk(ppj(R,C)),'or') + else + plot(xj(ppj),yk(ppj),'or') + end + end + if not(isempty(ap)) && not(isempty(ap{1})) + apj = ap{h}{1,j,i}; + plot(xj(apj),yk(apj),'dr') + for g = 1:length(apj) + line([xj(ppj(g)),xj(apj(g))],... + [yk(ppj(g)),yk(apj(g))],... + 'Color','r') + end + end + if not(isempty(rp)) && not(isempty(rp{1})) + rpj = rp{h}{1,j,i}; + plot(xj(rpj),yk(rpj),'dr') + for g = 1:length(rpj) + line([xj(ppj(g)),xj(rpj(g))],... + [yk(ppj(g)),yk(rpj(g))],... + 'Color','r') + end + end + end + end + end + end + end + if i == l + title(t) + end + if i == 1 + xlabel(xlab) + end + if l > 1 + %if iscell(x) + % num = x{i}(1); + %else + num = ch(i); + %end + pos = get(gca,'Position'); + axes('Position',[pos(1)-.05 pos(2)+pos(4)/2 .01 .01],'Visible','off'); + text(0,0,num2str(num),'FontSize',12,'Color','r') + end + end +else + % 2-dimensional image + displayseg = 0; + if iscell(x) && ischar(x{1}) + if size(y,4) > 1 || size(y,1) > size(x,1) + ticky = 0.5:23.5; + tickylab = {'CM','C#M','DM','D#M','EM','FM','F#M',... + 'GM','G#M','AM','A#M','BM','Cm','C#m','Dm','D#m','Em',... + 'Fm','F#m','Gm','G#m','Am','A#m','Bm'}; + else + ticky = (1:size(x,1))'; + tickylab = x; + displayseg = 1; + end + x = (1:size(y,1))'; + elseif iscell(x) && iscell(x{1}) && ischar(x{1}{1}) + if size(y{1},4) > 1 + ticky = 0.5:23.5; + tickylab = {'CM','C#M','DM','D#M','EM','FM','F#M',... + 'GM','G#M','AM','A#M','BM','Cm','C#m','Dm','D#m','Em',... + 'Fm','F#m','Gm','G#m','Am','A#m','Bm'}; + else + ticky = (1:size(x{1},1))'; + tickylab = x{1}; + displayseg = 1; + end + for i = 1:length(x) + x{i} = (1:size(x{i},1))'; + end + else + ticky = []; + end + if iscell(y) + displayseg = 1; + for i = 1:l + if l>1 + subplot(l,1,l-i+1,'align'); +% subplot('Position',[0.1 (i-1)*il+0.1 0.89 il-0.02]) + end + hold on + %surfplot(segt,x{1},repmat(x{1}/x{1}(end)*.1,[1,length(segt)])); + if length(x)==1 && length(y)>1 + for k = 2:length(y) + x{k} = x{1}; + end + end + for j = 1:length(x) + if length(x{j}) > 1 + if size(x{j},1) == 1 && size(x{j},3) > 1 + x{j} = reshape(x{j},size(x{j},2),size(x{j},3))'; + mel = 1; + else + mel = 0; + end + xx = zeros(size(x{j},1)*size(y{j},4),1); %,size(x{j},2)); + yy = zeros(size(y{j},1)*size(y{j},4),size(y{j},2)); + for k = 1:size(y{j},4) + xx((k-1)*size(x{j},1)+1:k*size(x{j},1),1) = x{j}(:,1); + yy((k-1)*size(y{j},1)+1:k*size(y{j},1),:) = y{j}(:,:,i,k); + end + if size(y{j},4) == 1 && not(mel) + xxx = [1.5*xx(1,1)-0.5*xx(2,1);... + (xx(1:end-1)+xx(2:end))/2;... + 1.5*xx(end,1)-0.5*xx(end-1,1)]; + else + xxx = (0:size(yy,1))'; + end + if size(fp{j},2) > 1 + segt = [fp{j}(1,1),... + mean([fp{j}(2,1:end-1);fp{j}(1,2:end)]),... + fp{j}(2,end)]; + else + segt = fp{j}'; + end + surfplot(segt,xxx,yy(:,:)); + if not(isempty(ticky)) + set(gca,'ytick',ticky); + set(gca,'yticklabel',tickylab); + end + set(gca,'YDir','normal') + if (exist('pp') == 1) && not(isempty(pp)) + if not(isempty(pp{j})) + for k = 1:length(pp{j}) + ppj = pp{j}{k}; + if size(ppj,3) == 2 + ppj(:,:,1) = ppj(:,:,1) + (ppj(:,:,2)-1)*size(x,1); + ppj(:,:,2) = []; + plot(mean(fp{j}(:,k)),ppj-.5,'+w') + elseif 0 %(exist('pm') == 1) && not(isempty(pm)) + pmj = pm{k}{1,1,i}; + ppj(:,:) = ppj(:,:) + (pmj(:,:)-1)*size(x,1); + plot(mean(fp{j}(:,k)),ppj-.5,'+w') % fp shows segmentation points + elseif not(isempty(ppj)) + plot(mean(fp{j}(:,k)),xx(ppj),'+k') + end + end + end + end + end + end + if i == l + title(t) + end + if i == 1 + if manychannels + xlabel(xlab); + elseif displayseg + xlabel('time axis (in s.)'); + else + xlabel('temporal location of beginning of frame (in s.)'); + end + end + if l > 1 + num = ch(i); + pos = get(gca,'Position'); + axes('Position',[pos(1)-.05 pos(2)+pos(4)/2 .01 .01],'Visible','off'); + text(0,0,num2str(num),'FontSize',12,'Color','r') + end + end + y = y{1}; + else + mel = 0; + if iscell(x) + x = x{1}; + end + if size(x,1) == 1 && size(y,1) > 1 + if size(x,3) == 1 + x = x'; + else + mel = 1; + end + end + if not(isempty(pp)) && iscell(pp{1}) + pp = pp{1}; + pm = pm{1}; + end + for i = 1:l + if l>1 + subplot('Position',[0.1 (i-1)*il+0.1 0.89 il-0.02]) + end + xx = zeros(size(x,1)*size(y,4),1); %,size(x,2)); + yy = zeros(size(y,1)*size(y,4),size(y,2)); + for k = 1:size(y,4) + xx((k-1)*size(x,1)+1:k*size(x,1),1) = x(:,1); + yy((k-1)*size(y,1)+1:k*size(y,1),:) = y(:,:,i,k); + end + if iscell(fp) + fp = uncell(fp); + end + lx = length(xx); + if lx < 6 + curve = 1; + ttt = (fp(1,:)+fp(2,:))/2; + plot(ttt,yy) + if i == 1 + legdata = cell(1,lx); + if strcmp(xlab(end),'s') + xlab(end) = []; + end + for j = 1:lx + legdata{j} = [xlab,' ',num2str(j)]; + end + legend(legdata) + end + else + ttt = [fp(1,:) 2*fp(1,end)-fp(1,end-1)]; + if size(y,4) == 1 && not(mel) + xxx = [1.5*xx(1)-0.5*xx(2);... + (xx(1:end-1)+xx(2:end))/2;... + 1.5*xx(end)-0.5*xx(end-1)]; + else + xxx = (0:size(yy,1))'; + end + surfplot(ttt,xxx,yy); + if not(isempty(ticky)) + set(gca,'ytick',ticky); + set(gca,'yticklabel',tickylab); + end + set(gca,'YDir','normal'); + end + hold on + %if iscell(pp) + % pp = uncell(pp); + % if iscell(pm) + % pm = uncell(pm); + % end + %end + if (exist('tp') == 1) && not(isempty(tp)) + tp = tp{i}{1}; + tv = tv{i}{1}; + for k = 1:size(tp,1) + prej = 0; + for j = 1:size(tp,2) + if tv(k,j) + if prej% && not(isempty(tp(k,j))) + plot([(ttt(prej)+ttt(prej+1))/2,... + (ttt(j)+ttt(j+1))/2],... + [(xxx(tp(k,prej))+xxx(tp(k,prej)+1))/2,... + (xxx(tp(k,j))+xxx(tp(k,j)+1))/2],... + 'k','LineWidth',1) + plot([(ttt(prej)+ttt(prej+1))/2,... + (ttt(j)+ttt(j+1))/2],... + [(xxx(tp(k,prej))+xxx(tp(k,prej)+1))/2,... + (xxx(tp(k,j))+xxx(tp(k,j)+1))/2],... + 'w+','MarkerSize',10) + plot([(ttt(prej)+ttt(prej+1))/2,... + (ttt(j)+ttt(j+1))/2],... + [(xxx(tp(k,prej))+xxx(tp(k,prej)+1))/2,... + (xxx(tp(k,j))+xxx(tp(k,j)+1))/2],... + 'kx','MarkerSize',10) + end + prej = j; + end + end + end + elseif (exist('pp') == 1) && not(isempty(pp)) + if size(pp,3) == l + for j = 1:size(pp,2) + ppj = pp{1,j,i}; + if (exist('pm') == 1) + pmj = pm{1,j,i}; + else + pmj = []; + end + if iscell(ppj) + ppj = uncell(ppj); + if iscell(pmj) + pmj = uncell(pmj); + end + end + if size(ppj,3) == 2 + ppj(:,:,1) = ppj(:,:,1) + (ppj(:,:,2)-1)*size(x,1); + ppj(:,:,2) = []; + %plot((fp(1,j)+fp(2,j))/2,ppj-.5,'+w') + plot((ttt(j)+ttt(j+1))/2,(xxx(ppj)+xxx(ppj+1))/2,'+w') + else + if not(isempty(pmj)) + ppj(:,:,:) = ppj(:,:,:) + (pmj(:,:,:)-1)*size(x,1); + end + if not(isempty(ppj)) + %plot((segt(j)+segt(j+1))/2,xx(ppj),'+k','MarkerSize',10) + plot((ttt(j)+ttt(j+1))/2,(xxx(ppj)+xxx(ppj+1))/2,'+w','MarkerSize',10) + end + end + end + else + for j = 1:size(pp,3) + ppj = pp{1,1,j}; + if (exist('pm') == 1) + pmj = pm{1,1,j}; + else + pmj = []; + end + if not(isempty(ppj)) + %plot((segt(j)+segt(j+1))/2,xx(ppj),'+k','MarkerSize',10) + plot((ttt(ppj)+ttt(ppj+1))/2,(xxx(j)+xxx(j+1))/2,'+w','MarkerSize',10) + end + end + end + end + if i == l + title(t) + end + if i == 1 + xlabel('time axis (in s.)'); + end + if l > 1 + if iscell(x) + num = x{i}(1); + else + num = ch(i); + end + pos = get(gca,'Position'); + hfig = axes('Position',[pos(1)-.05 pos(2)+pos(4)/2 .01 .01],'Visible','off'); + text(0,0,num2str(num),'FontSize',12,'Color','r') + end + end + end +end +if l == 1 + if curve + %if (exist('dbv') == 1) && dbv + % ylabel([ylab ' (logarithmic scale)']); + %else + ylabel(ylab); + %end + elseif manychannels + ylabel('Channels'); + else + ylabel(xlab) + end +end +res = 1; +drawnow \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/haspeaks.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function h = haspeaks(d) + +if isa(d,'mirdesign') + h = strcmp(func2str(get(d,'Method')),'mirpeaks'); +else + if iscell(d) + d = d{1}; + end + p = get(d,'PeakVal'); + h = not(isempty(p) || isempty(p{1}) || isempty(p{1}{1})); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/hwr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function y = hwr(x) +% Half-Wave Rectifier + +y = 0.5 * (x + abs(x)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/isamir.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +function b = isamir(x,class) + +if isempty(x) || isnumeric(x) + b = 0; + return +end + +if iscell(x) + x = x{1}; +end + +if isa(x,class) + b = 1; + return +elseif ischar(x) && strcmpi(class,'miraudio') + b = 1; + return +elseif not(isa(x,'mirdesign')) + b = 0; + return +end + +type = get(x,'Type'); +if iscell(type) + type = type{1}; +end +types = lineage(type); +b = 0; +i = 0; +while not(b) && i<length(types) + i = i+1; + if strcmpi(types(i),class) + b = 1; + end +end + + +function types = lineage(class) +switch class + case {'miraudio','mirenvelope'} + parent = 'mirtemporal'; + case {'mirautocor','mircepstrum','mirchromagram','mirhisto',... + 'mirkeysom','mirkeystrength','mirmatrix','mirmfcc',... + 'mirscalar','mirsimatrix','mirspectrum',... + 'mirtemporal','mirtonalcentroid'} + parent = 'mirdata'; + otherwise + parent = ''; +end + +if isempty(parent) + types = {class}; +else + parents = lineage(parent); + types = {class parents{:}}; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirattacks.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function varargout = mirattacks(orig,varargin) +% Obsolete function, replaced by mironsets(...,'Attacks',...) + + aver.key = 'Smooth'; + aver.type = 'Integer'; + aver.default = 20; + option.aver = aver; + +specif.option = option; + +varargout = mirfunction(@mirattacks,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +x = mironsets(x,'Attack',option.aver); +type = mirtype(x); + + +function a = main(a,option,postoption) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirattackslope.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,78 @@ +function varargout = mirattackslope(orig,varargin) +% a = mirattackslope(x) estimates the average slope of each note attack. +% Optional arguments: +% a = mirattackslope(x,m) specifies a method for slope computation. +% Possible values: +% m = 'Diff': ratio between the magnitude difference at the +% beginning and the ending of the attack period, and the +% corresponding time difference. +% m = 'Gauss': average of the slope, weighted by a gaussian +% curve that emphasizes values at the middle of the attack +% period. (similar to Peeters 2004). +% mirattackslope(...,'Contrast',c) specifies the 'Contrast' parameter +% used in mironsets for event detection through peak picking. +% Same default value as in mironsets. +% +% Peeters. G. (2004). A large set of audio features for sound description +% (similarity and classification) in the CUIDADO project. version 1.0 + + meth.type = 'String'; + meth.choice = {'Diff','Gauss'}; + meth.default = 'Diff'; + option.meth = meth; + + cthr.key = 'Contrast'; + cthr.type = 'Integer'; + cthr.default = NaN; + option.cthr = cthr; + +specif.option = option; + +varargout = mirfunction(@mirattackslope,orig,varargin,nargout,specif,@init,@main); + + +function [o type] = init(x,option) +o = mironsets(x,'Attack','Contrast',option.cthr); +type = mirtype(x); + + +function sl = main(o,option,postoption) +if iscell(o) + o = o{1}; +end +po = get(o,'PeakPos'); +pa = get(o,'AttackPos'); +pou = get(o,'PeakPosUnit'); +pau = get(o,'AttackPosUnit'); +sr = get(o,'Sampling'); +d = get(o,'Data'); +sl = mircompute(@algo,po,pa,pou,pau,d,option.meth,sr); +fp = mircompute(@frampose,pau,pou); +sl = mirscalar(o,'Data',sl,'FramePos',fp,'Title','Attack Slope'); +sl = {sl,o}; + + +function fp = frampose(pa,po) +pa = sort(pa{1}); +po = sort(po{1}); +fp = [pa';po']; + + +function sl = algo(po,pa,pou,pau,d,meth,sr) +pa = sort(pa{1}); +po = sort(po{1}); +pau = sort(pau{1}); +pou = sort(pou{1}); +sl = zeros(1,length(pa)); +for i = 1:length(pa) + switch meth + case 'Diff' + sl(i) = (d(po(i))-d(pa(i)))/(pou(i)-pau(i)); + case 'Gauss' + l = po(i)-pa(i); + h = ceil(l/2); + gauss = exp(-(1-h:l-h).^2/(l/4)^2); + dat = diff(d(pa(i):po(i))).*gauss'; + sl(i) = mean(dat)*sr; + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirattacktime.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function varargout = mirattacktime(orig,varargin) +% a = mirattacktime(x) returns the duration (in s.) of each note attack. +% Optional arguments: +% a = mirattacktime(x,l) specifies whether to consider the duration in s. +% (l='Lin') or the logarithm of that duration (l='Log') following the +% approach proposed in Krimphoff et al. (1994). +% Default value: l='Lin'. +% +% Krimphoff, J., McAdams, S. & Winsberg, S. (1994), Caractérisation du +% timbre des sons complexes. II : Analyses acoustiques et quantification +% psychophysique. Journal de Physique, 4(C5), 625-628. + + scale.type = 'String'; + scale.choice = {'Lin','Log'}; + scale.default = 'Lin'; + option.scale = scale; + +specif.option = option; + +varargout = mirfunction(@mirattacktime,orig,varargin,nargout,specif,@init,@main); + + +function [o type] = init(x,option) +o = mironsets(x,'Attack'); +type = mirtype(x); + + +function at = main(o,option,postoption) +if iscell(o) + o = o{1}; +end +po = get(o,'PeakPosUnit'); +pa = get(o,'AttackPosUnit'); +at = mircompute(@algo,po,pa,option.scale); +fp = mircompute(@frampose,pa,po); +at = mirscalar(o,'Data',at,'FramePos',fp,'Title','Attack Time'); +at = {at,o}; + + +function fp = frampose(pa,po) +pa = sort(pa{1}); +po = sort(po{1}); +fp = [pa';po']; + + +function at = algo(po,pa,sc) +po = sort(po{1}); +pa = sort(pa{1}); +at = po-pa; +if strcmpi(sc,'Log') + at = log10(at); +end +at = at'; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirauditory.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function varargout = mirauditory(x,varargin) +% Produces the output based on an auditory modelling, of the signal x, +% using a gammatone filterbank. +% Optional argument: +% mirtempo(...,'Filterbank',b) indicates the number of channels in +% the filterbank decomposition. +% Default value: b = 40. + + fb.key = 'Filterbank'; + fb.type = 'Integer'; + fb.default = 40; + option.fb = fb; + +specif.option = option; + +varargout = mirfunction(@mirauditory,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if isamir(x,'miraudio') + x = mirfilterbank(x,'NbChannels',option.fb); + x = mirenvelope(x,'Center','Diff','Halfwave','Center'); +end +type = 'mirenvelope'; + + +function x = main(x,option,postoption) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirbeatspectrum.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,75 @@ +function varargout = mirbeatspectrum(orig,varargin) +% n = mirbeatspectrum(m) evaluates the beat spectrum. +% [n,m] = mirbeatspectrum(m) also return the similarity matrix on which +% the estimation is made. +% Optional argument: +% mirbeatspectrum(...,s) specifies the estimation method. +% Possible values: +% s = 'Diag', summing simply along the diagonals of the matrix. +% s = 'Autocor', based on the autocorrelation of the matrix. +% mirbeatspectrum(...,'Distance',f) specifies the name of a dissimilarity +% distance function, from those proposed in the Statistics Toolbox +% (help pdist). +% default value: f = 'cosine' +% J. Foote, M. Cooper, U. Nam, "Audio Retrieval by Rhythmic Similarity", +% ISMIR 2002. + + + dist.key = 'Distance'; + dist.type = 'String'; + dist.default = 'cosine'; + option.dist = dist; + + meth.type = 'String'; + meth.choice = {'Diag','Autocor'}; + meth.default = 'Autocor'; + option.meth = meth; + +specif.option = option; +varargout = mirfunction(@mirbeatspectrum,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirscalar')) + if isamir(x,'miraudio') + x = mirmfcc(x,'frame',.025,'s',.01,'s','Rank',8:30); + end + x = mirsimatrix(x,'Distance',option.dist,'Similarity'); +end +type = 'mirscalar'; + + +function y = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +fp = get(orig,'FramePos'); +if not(isa(orig,'mirscalar')) + s = get(orig,'Data'); + total = cell(1,length(s)); + for k = 1:length(s) + for h = 1:length(s{k}) + maxfp = find(fp{k}{h}(2,:)>4,1); + if isempty(maxfp) + maxfp = Inf; + else + fp{k}{h}(:,maxfp+1:end) = []; + end + l = min(length(s{k}{h}),maxfp); + total{k}{h} = zeros(1,l); + if strcmpi(option.meth,'Diag') + for i = 1:l + total{k}{h}(i) = mean(diag(s{k}{h},i-1)); + end + else + for i = 1:l + total{k}{h}(i) = mean(mean(s{k}{h}(:,1:l-i+1).*s{k}{h}(:,i:l))); + end + end + end + end +else + total = get(orig,'Data'); +end +n = mirscalar(orig,'Data',total,'FramePos',fp,'Title','Beat Spectrum'); +y = {n orig}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirbrightness.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +function varargout = mirbrightness(x,varargin) +% b = mirbrightness(s) calculates the spectral brightness, i.e. the amount +% of spectral energy corresponding to frequencies higher than a given +% cut-off threshold. +% Optional arguments: +% b = mirbrightness(s,'CutOff',f) specifies the frequency cut-off +% threshold in Hz. +% Default value: f = 1500 Hz. +% +% Typical values for the frequency cut-off threshold: +% 3000 Hz in Juslin 2000, p. 1802. +% 1000 Hz and 500 Hz in Laukka, Juslin and Bresin 2005. +% +% Juslin, P. N. (2000). Cue utilization in communication of emotion in +% music performance: relating performance to perception. Journal of +% Experimental Psychology: Human Perception and Performance, 26(6), 1797?813. +% Laukka, P., Juslin, P. N., and Bresin, R. (2005). A dimensional approach +% to vocal expression of emotion. Cognition and Emotion, 19, 633?653. + + + cutoff.key = 'CutOff'; + cutoff.type = 'Integer'; + cutoff.default = 1500; + option.cutoff = cutoff; + +specif.option = option; +specif.defaultframelength = .05; +specif.defaultframehop = .5; + + +varargout = mirfunction(@mirbrightness,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirspectrum')) + x = mirspectrum(x); +end +type = 'mirscalar'; + + +function b = main(s,option,postoption) +if iscell(s) + s = s{1}; +end +m = get(s,'Magnitude'); +f = get(s,'Frequency'); +w = warning('query','MATLAB:divideByZero'); +warning('off','MATLAB:divideByZero'); +v = mircompute(@algo,m,f,option.cutoff); +warning(w.state,'MATLAB:divideByZero'); +b = mirscalar(s,'Data',v,'Title','Brightness'); + + +function v = algo(m,f,k) +if not(any(max(f)>k)) + warning('WARNING in MIRBRIGHTNESS: Frequency range of the spectrum too low for the estimation of brightness.'); +end +sm = sum(m); +v = sum(m(f(:,1,1) > k,:,:)) ./ sm; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mircentroid.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function varargout = mircentroid(x,varargin) +% c = mircentroid(x) calculates the centroid (or center of gravity) of x. +% x can be either: +% - a spectrum (spectral centroid), +% - an envelope (temporal centroid) +% - a histogram, +% - or any data. Only the positive ordinates of the data are taken +% into consideration. +% c = mircentroid(x,'Peaks') calculates the centroid of the peaks only. + +% Beauchamp 1982 version? + + peaks.key = 'Peaks'; + peaks.type = 'String'; + peaks.choice = {0,'NoInterpol','Interpol'}; + peaks.default = 0; + peaks.keydefault = 'NoInterpol'; + option.peaks = peaks; + +specif.option = option; + +varargout = mirfunction(@mircentroid,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirdata')) || isamir(x,'miraudio') + x = mirspectrum(x); +end +type = 'mirscalar'; + + +function c = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +if option.peaks + if strcmpi(option.peaks,'Interpol') + pt = get(x,'PeakPrecisePos'); + pv = get(x,'PeakPreciseVal'); + else + pt = get(x,'PeakPos'); + pv = get(x,'PeakVal'); + end + cx = cell(1,length(pt)); + for h = 1:length(pt) + cx{h} = cell(1,length(pt{h})); + for i = 1:length(pt{h}) + pti = pt{h}{i}; + pvi = pv{h}{i}; + %if isempty(pti) + + nfr = size(pti,2); + nbd = size(pti,3); + ci = zeros(1,nfr,nbd); + for j = 1:nfr + for k = 1:nbd + ptk = pti{1,j,k}; + pvk = pvi{1,j,k}; + sk = sum(pvk); + ci(1,j,k) = sum(ptk.*pvk) ./ sk; + end + end + cx{h}{i} = ci; + end + end +else + cx = peaksegments(@centroid,get(x,'Data'),get(x,'Pos')); +end +if isa(x,'mirspectrum') + t = 'Spectral centroid'; +elseif isa(x,'mirenvelope') + t = 'Temporal centroid'; +else + t = ['centroid of ',get(x,'Title')]; +end +c = mirscalar(x,'Data',cx,'Title',t); + + +function c = centroid(d,p) +c = (p'*d) ./ sum(d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirchunklim.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +function cl = mirchunklim(lim) +% c = mirchunklim returns the maximal chunk size. +% If the size of a long audio files exceeds that size, it will be +% decomposed into chunks of that size, before being analyzed in the +% different functions on the toolbox. +% mirchunklim(c) specifies a new maximal chunk size. +% If MIRtoolbox tends to use more memory than what is available in the +% RAM of your computer, you should decrease the maximal chunk size. +% mirchunklim(Inf) toggles off the automated chunk decomposition. + +persistent chunklim + +if nargin + if not(isnumeric(lim)) + error('ERROR IN MIRCHUNKLIM: The argument should be a number.') + end + chunklim = lim; +else + if isempty(chunklim) + chunklim = 5e5; + end +end + +cl = chunklim; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mircluster.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,238 @@ +function [a d] = mircluster(a,varargin) +% c = mircluster(a,f) clusters the segments in the audio sequence(s) +% contained in the audio object a, along the analytic feature(s) +% f, using the k-means strategy. Multiple analytic features have to +% be grouped into one array of cells. +% Example: +% sg = mirsegment(a); +% mircluster(sg, mirmfcc(sg)) +% mircluster(sg, {mirmfcc(sg), mircentroid(sg)}) +% c = mircluster(d) clusters the frame-decomposed data d into groups +% using K-means clustering. +% Example: +% cc = mirmfcc(a,'Frame'); +% mircluster(cc) +% Optional argument: +% mircluster(...,n) indicates the maximal number of clusters. +% Default value: n = 2. +% mircluster(...,'Runs',r) indicates the maximal number of runs. +% Default value: r = 5. +% +% Requires SOM Toolbox (included in the MIRtoolbox distribution). + + + nruns.key = 'Runs'; + nruns.type = 'Integer'; + nruns.default = 5; + option.nruns = nruns; + + nclust.position = 2; + nclust.type = 'Integer'; + nclust.default = 2; + option.nclust = nclust; + +specif.option = option; + +specif.nochunk = 1; + +d = a; +if isa(a,'mirdesign') + if not(get(a,'Eval')) + % During bottom-up construction of the general design + + [unused option] = miroptions(@mircluster,a,specif,varargin); + type = get(a,'Type'); + a = mirdesign(@mircluster,a,option,{},struct,type); + a = set(a,'NoChunk',1); + else + % During top-down evaluation initiation + + e = evaleach(a); + if iscell(e) + e = e{1}; + end + a = mircluster(e,varargin{:}); + end +else + if not(isa(a,'mirdata')) + mirerror('mircluster','The input should be either frame- or segment-decomposed.'); + end + + if isempty(varargin) || (not(isa(varargin{1},'mirdata') || ... + (iscell(varargin{1}) && ... + isa(varargin{1}{1},'mirdata')))) + % mircluster version for frame-decomposed data: + % frames are clustered into groups using K-means clustering. + [unused option] = miroptions(@mircluster,a,specif,varargin); + da = get(a,'Data'); + lva = length(da); % Number of audio files in the audio object. + c = cell(1,lva); + display('Clustering frames...'); + if mirwaitbar + handle = waitbar(0,'Clustering frames...'); + else + handle = 0; + end + for j = 1:lva % For each audio file,... + va = []; % Data transmitted to the kmeans_cluster function. + v = da{j}; + if iscell(v) + v = uncell(v,-Inf); %v{1}; + end + if size(v,4)>1 + v(end+1:2*end,:,:,1) = v(:,:,:,2); + v(:,:,:,2) = []; + end + % Standardization + %stv = std(v,0,2); + %stv(find(stv == 0)) = 1; + va(end+1:end+size(v,1),:,:) = v;%... + %(v - repmat(mean(v,2),[1 size(v,2) ])) ... + %./ repmat(stv,[1 size(v,2) ]); + if isa(a,'mirscalar') + m = get(a,'Mode'); + if not(isempty(m)) + m = m{j}; + val = []; + for l = 1:nseg + vl = m{l}; + if iscell(vl) + vl = vl{1}; + end + val(:,l) = vl; + end + stv = std(val,0,2); + stv(find(stv == 0)) = 1; + va(end+1:end+size(val,1),:) = ... + (val - repmat(mean(val,2),[1 size(val,2) ])) ... + ./ repmat(stv,[1 size(val,2) ]); + end + end + if size(va,3)>1 + mel = 1; + va = reshape(va,size(va,2),size(va,3))'; + else + mel = 0; + end + [cc, p, err, ind] = kmeans_clusters(va',option.nclust,option.nruns); + [minind select] = min(ind); + c{j}.centr = cc{select}'; + c{j}.index = p{select}; + c{j}.weight = zeros(1,size(cc{select},1)); + c{j}.covar = zeros(size(cc{select}')); + ii = 1; + for i = 1:size(c{j}.centr,2) + clus = va(:,c{j}.index == ii); + if isempty(clus) + higher = find(c{j}.index > ii); + c{j}.index(higher) = c{j}.index(higher)-1; + c{j}.centr(:,ii) = []; + c{j}.weight(ii) = []; + c{j}.covar(:,ii) = []; + else + c{j}.weight(ii) = size(clus,2)/size(va,2); + if c{j}.weight(ii) == 0 + pause + end + c{j}.covar(:,ii) = mean((clus'-ones(1,size(clus,1))*c{j}.centr(:,ii)).^2); + ii = ii+1; + end + end + if handle + waitbar(j/lva,handle); + end + end + if handle + delete(handle) + end + a = set(a,'Clusters',c); + else + % mircluster version for segmented audio: + % segments are clustered into groups using K-means clustering. + da = varargin{1}; + varargin(1) = []; + [unused option] = miroptions(@mircluster,a,specif,varargin); + display('Clustering segments...'); + if isa(da,'mirdata') || (iscell(da) && isa(da{1},'mirdata')) + if not(iscell(da)) + da = {da}; + end + vala = get(a,'Data'); % Data contained in the audio object a. + lva = length(vala); % Number of audio files in the audio object. + clus = cell(1,lva); + for j = 1:lva % For each audio file,... + va = []; % Data transmitted to the kmeans_cluster function. + nseg = length(vala{j}); % Number of segments in the audio file. + for i = 1:length(da) % For each analytic feature,... + v = get(da{i},'Data'); + v = v{j}; + if iscell(v) + v = uncell(v,-Inf); %v{1}; + end + val = []; + if size(v,4)>1 + v(end+1:2*end,:,:,1) = v(:,:,:,2); + v(:,:,:,2) = []; + end + + % Standardization + stv = std(v,0,2); + stv(find(stv == 0)) = 1; + va(end+1:end+size(v,1),:) = ... + (v - repmat(mean(v,2),[1 size(v,2) ])) ... + ./ repmat(stv,[1 size(v,2) ]); + if isa(da{i},'mirscalar') + m = get(da{i},'Mode'); + if not(isempty(m)) + m = m{j}; + val = []; + for l = 1:nseg + vl = m{l}; + if iscell(vl) + vl = vl{1}; + end + val(:,l) = vl; + end + stv = std(val,0,2); + stv(find(stv == 0)) = 1; + va(end+1:end+size(val,1),:) = ... + (val - repmat(mean(val,2),[1 size(val,2) ])) ... + ./ repmat(stv,[1 size(val,2) ]); + end + end + + end + [cc, p, err, ind] = kmeans_clusters(va',min(option.nclust,nseg),option.nruns); + clus{j} = p{end}; + end + a = set(a,'Clusters',clus); + t = get(a,'Time'); + fp = get(a,'FramePos'); + for j = 1:lva % For each audio file,... + aj = vala{j}; + tj = t{j}; + fpj = fp{j}; + clj = clus{j}; + k = 2; + while k <= length(aj) + if clj(k) == clj(k-1) + aj{k-1} = [aj{k-1};aj{k}]; + aj(k) = []; + tj{k-1} = [tj{k-1};tj{k}]; + tj(k) = []; + fpj{k-1} = [fpj{k-1}(1);fpj{k}(2)]; + fpj(k) = []; + clj(k) = []; + k = k-1; + end + k = k+1; + end + vala{j} = aj; + t{j} = tj; + fp{j} = fpj; + cl{j} = clj; + end + a = set(a,'Data',vala,'Time',t,'FramePos',fp,'Clusters',cl); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mircompute.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function varargout = mircompute(algo,varargin) + +l = length(varargin{1}); % number of audio files. +for i = 1:l + % for each audio file + v = varargin; + for j = 1:length(v) + % for each variable associated to that audio + if isa(v{j},'mirdata') + v{j} = get(v{j},'Data'); + end + if iscell(v{j}) + v{j} = v{j}{i}; + end + %if not(iscell(v{j})) + % v{j} = {v{j}}; + %end + end + % final result for that audio + for k = 1:length(v{1}) + % for each segment in that audio file + vk = v; + for j = 1:length(vk) + if iscell(vk{j}) + vk{j} = vk{j}{k}; + end + end + if 1 %nargout == 1 + res = algo(vk{:}); + else %% used for miremotion, provokes bug else... + [res res2 res3 res4] = algo(vk{:}); + end + if iscell(res) + lr = length(res); + for j = 1:lr + varargout{j}{i}{k} = res{j}; + end + else + varargout{1}{i}{k} = res; + if nargout == 4 + varargout{2}{i}{k} = res2; + varargout{3}{i}{k} = res3; + varargout{4}{i}{k} = res4; + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirdist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,122 @@ +function d = mirdist(x,y,dist) +% d = mirdist(x,y) evaluates the distance between x and y. +% x is the feature values corresponding to one audio file, and y is the +% values (for the same feature) corrdponding to one (or several) +% audio files. +% If x and y are not decomposed into frames, +% d = mirdist(x,y,f) specifies distance function. +% Default value: f = 'Cosine' +% If x and y are composed of clustered frames (using mircluster), the +% cluster signatures are compared using Earth Mover Distance. +% (Logan, Salomon, 2001) +% If x and y contains peaks, the vectors representing the peak +% distributions are compared using Euclidean distance. +% (used with mirnovelty in Jacobson, 2006) +% +% The Earth Mover Distance is based on the implementation by Yossi Rubner, +% wrapped for Matlab by Elias Pampalk. + +if not(isa(x,'mirdata')) + x = miraudio(x); +end +if not(isa(y,'mirdata')) + y = miraudio(y); +end + +clx = get(x,'Clusters'); +if isempty(clx{1}) + px = get(x,'PeakPos'); + if not(iscell(px)) || isempty(px{1}) || ... + not(iscell(px{1})) || isempty(px{1}{1}) || not(iscell(px{1}{1})) + if nargin < 3 + dist = 'Cosine'; + end + + d = get(x,'Data'); + dd = d{1}{1}; + if iscell(dd) + dd = dd{1}; + end + if size(dd,2)>1 + if size(dd,1)>1 + error('ERROR IN MIRDIST: If the input is decomposed into frames, they should first be clustered.'); + else + dd = dd'; + end + end + + e = get(y,'Data'); + dt = cell(1,length(e)); + for h = 1:length(e) + ee = e{h}{1}; + if iscell(ee) + ee = ee{1}; + end + if size(ee,2)>1 + if size(ee,1)>1 + error('ERROR IN MIRDIST: If the input is decomposed into frames, they should first be clustered.'); + else + ee = ee'; + end + end + if isempty(ee) + if isempty(dd) + dt{h}{1} = 0; + else + dt{h}{1} = Inf; + end + else + if length(dd)<length(ee) + dd(length(ee)) = 0; + %ee = ee(1:length(d)); + elseif length(ee)<length(dd) + ee(length(dd)) = 0; + %dd = dd(1:length(ee)); + end + if length(dd) == 1 + dt{h}{1} = abs(dd-ee); + elseif norm(dd) && norm(ee) + dt{h}{1} = pdist([dd(:)';ee(:)'],dist); + else + dt{h}{1} = NaN; + end + end + end + else + % Euclidean distance between vectors to compare data with peaks + % (used with mirnovelty in Jacobson, 2006). + sig = pi/4; + dx = get(x,'Data'); + nx = length(px{1}{1}{1}); + cx = mean(px{1}{1}{1}/length(dx{1}{1})); + dy = get(y,'Data'); + py = get(y,'PeakPos'); + dt = cell(1,length(py)); + for h = 1:length(py) + ny = length(py{h}{1}{1}); + cy = mean(py{h}{1}{1}/length(dy{h}{1})); + dt{h}{1} = sqrt((nx*cos(sig*cx)-ny*cos(sig*cy))^2 ... + +(nx*sin(sig*cx)-ny*sin(sig*cy))^2); + end + end +else + % Earth Mover's Distance to compare clustered data. + cly = get(y,'Clusters'); + dt = cell(1,length(cly)); + for h = 1:length(cly) + cost = zeros(length(clx{1}.weight),length(cly{h}.weight)); + for i = 1:length(clx{1}.weight) + for j = 1:length(cly{h}.weight) + covx = clx{1}.covar(:,i); + covy = cly{h}.covar(:,j); + mux = clx{1}.centr(:,i); + muy = cly{h}.centr(:,j); + cost(i,j) = sum(covx./covy + covy./covx + ... + (mux-muy).^2.*(1./covx + 1./covy) - 2); + end + end + dt{h}{1} = emd_wrapper(cost,clx{1}.weight,cly{h}.weight); + end +end +d = mirscalar(y,'Data',dt,'Title',[get(y,'Title'),' Distance'],... + 'Name',get(x,'Name'),'Name2',get(y,'Name')); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirentropy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function varargout = mirentropy(x,varargin) +% h = mirentropy(a) calculates the relative entropy of a. +% (Cf. User's Manual.) +% mirentropy(..., ?Center?) centers the input data before +% transforming it into a probability distribution. + + center.key = 'Center'; + center.type = 'Boolean'; + center.default = 0; + option.center = center; + +specif.option = option; + +varargout = mirfunction(@mirentropy,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if isamir(x,'miraudio') + x = mirspectrum(x); +end +type = 'mirscalar'; + + +function h = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +m = get(x,'Data'); +v = cell(1,length(m)); +for h = 1:length(m) + v{h} = cell(1,length(m{h})); + for k = 1:length(m{h}) + mk = m{h}{k}; + mn = mk; + if isa(x,'mirhisto') || isa(x,'mirscalar') + mn = mn'; + end + + if option.center + mn = center(mn); + end + + % Negative data is trimmed: + mn(mn<0) = 0; + + % Data is normalized such that the sum is equal to 1. + mn = mn./repmat(sum(mn)+repmat(1e-12,... + [1 size(mn,2) size(mn,3) size(mn,4)]),... + [size(mn,1) 1 1 1]); + + % Actual computation of entropy + v{h}{k} = -sum(mn.*log(mn + 1e-12))./log(size(mn,1)); + + if isa(x,'mirhisto') || isa(x,'mirscalar') + v{h}{k} = v{h}{k}'; + end + end +end +t = ['Entropy of ',get(x,'Title')]; +h = mirscalar(x,'Data',v,'Title',t); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirerror.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function mirerror(operator,message) + +errordlg([operator,': ',message],'MIRtoolbox error'); +error(['ERROR using ',operator,': ', message]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mireval.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,498 @@ +function v = mireval(d,file,single,export) +% mireval(d,filename) applies the mirdesign object d to the audio file +% named filename. +% mireval(d,'Folder') applied the mirdesign object to each audio files in +% the current directory. +% mireval(d,'Folders') applied the mirdesign object recursively to the +% subfolders. +% Optional argument: mireval(...,'Single') only keeps the first +% output when several output are returned for a given mirdesign +% object. + +% mireval performs the actual evaluation of the design flowchart. +% If 'Folder' is used, the evaluation is carried out for each audio file +% successively. +% If d is a structure or a cell array, evaluate each component +% separately. +% The evaluation starts with a top-down traversal of the design flowchart +% (evaleach). + +%if not(isa(d,'mirdesign')) +% error('ERROR IN MIREVAL: the first input should be a flowchart (using ''Design'')') +%end +if not(ischar(file)) + error('ERROR IN MIREVAL: the second input should be a file name or ''Folder''') +end + +if nargin<3 + single = []; +end +if nargin<4 + export = []; +end + +% First, let's look at the content of the file(s): size, sampling rate, +% etc. +w = []; % Array containing the index positions of the starting and ending dates. +s = getsize(d); +ch = 1; +if strcmpi(file,'Folder') || strcmpi(file,'Folders') + [l w sr a] = evalfolder('',s,0,[],[],{},strcmpi(file,'Folders')); + if l == 0 + disp('No sound file detected in this folder.') + end +elseif length(file)>3 && strcmpi(file(end-3:end),'.txt') + a = importdata(file); + l = length(a); + for i = 1:l + [di,tpi,fpi,fi] = mirread([],a{i},0,0,0); + if not(isempty(s)) + interval = s(1:2); + if s(3) + interval = round(interval*fi)+1; + end + if s(4) == 1 + interval = interval+round(di/2); + elseif s(4) == 2 + interval = interval+di; + end + w(:,i) = min(max(interval,1),di); + else + w(:,i) = [1;di]; + end + if getsampling(d) + sr(i) = getsampling(d); + else + sr(i) = fi; + end + end +else + l = 1; + [d1,tp1,fp1,f1,b,n,ch] = mirread([],file,0,0,0); + if length(s)>1 + interval = s(1:2)'; + if s(3) + interval = round(interval*f1)+1; + end + if s(4) == 1 + interval = interval+round(d1/2); + elseif s(4) == 2 + interval = interval+d1; + end + if d1 < interval(2) + warning('WARNING IN MIRAUDIO: The temporal region to be extracted exceeds the temporal extent of the whole audio file.'); + end + w = min(max(interval,1),d1); + else + w = [1;d1]; + end + if isa(d,'mirdesign') && getsampling(d) + sr = getsampling(d); + else + sr = f1; + end + a = {file}; +end + +if not(l) + v = []; + return +end + +if isempty(export) + y = cell(1,l); +end +order = 1:l; +if isa(d,'mirdesign') && isequal(get(d,'Method'),@mirplay) + op = get(d,'Option'); + if isfield(op,'inc') + if not(isnumeric(op.inc)) + op.inc = mirgetdata(op.inc); + end + [unused order] = sort(op.inc); + elseif isfield(op,'dec') + if not(isnumeric(op.inc)) + op.inc = mirgetdata(op.inc); + end + [unused order] = sort(op.dec,'descend'); + end + if isfield(op,'every') + order = order(1:op.every:end); + end + order = order(:)'; +end + +parallel = 0; +if mirparallel + try + matlabpool; + parallel = 1; + mirwaitbar(0) + mirverbose(0) + end +end + +if parallel + % The evaluation is carried out for each audio file successively + % (or in parallel). + parfor i = 1:l + if l > 1 + fprintf('\n') + display(['*** File # ',num2str(i),'/',num2str(l),': ',a{i}]); + end + tic + yi = evalaudiofile(d,a{i},sr(i),w(:,i),{},0,i,single,'',ch); + toc + y{i} = yi; + if not(isempty(export)) + if strncmpi(export,'Separately',10) + filename = a{i}; + filename(filename == '/') = '.'; + filename = [filename export(11:end)]; + if i == 1 + mkdir('Backup'); + end + mirexport(filename,yi); + elseif i==1 + mirexport(export,yi); + else + mirexport(export,yi,'#add'); + end + end + clear yi + end +else + % The evaluation is carried out for each audio file successively. + isstat = isfield(d,'Stat'); + for i = 1:length(order) + f = order(i); + if l > 1 + fprintf('\n') + display(['*** File # ',num2str(i),'/',num2str(l),': ',a{f}]); + end + tic + yf = evalaudiofile(d,a{f},sr(f),w(:,f),{},0,f,single,'',ch); + toc + y{f} = yf; + if not(isempty(export)) + if strncmpi(export,'Separately',10) + filename = a{f}; + filename(filename == '/') = '.'; + filename = ['Backup/' filename export(11:end)]; + if i == 1 + mkdir('Backup'); + end + mirexport(filename,yf); + elseif i==1 + mirexport(export,yf); + else + mirexport(export,yf,'#add'); + end + end + clear yf + end +end + +%if isempty(export) + v = combineaudiofile(a,isstat,y{:}); +%else +% v = []; +%end + + +function v = evalaudiofile(d,file,sampling,size,struc,istmp,index,single,name,ch) +% Now let's perform the analysis (or analyses) on the different files. +% If d is a structure or a cell array, evaluate each component +% separately. +if isstruct(d) + v = struct; + if istmp + struc.tmp = struct; + end + isstat = isfield(d,'Stat'); + if isstat + d = rmfield(d,'Stat'); + end + fields = fieldnames(d); + for fi = 1:length(fields) + fieldname = fields{fi}; + field = d.(fieldname); + display(['*******',fieldname,'******']); + if isstat + if isa(field,'mirstruct') + field = set(field,'Stat',1); + elseif isa(field,'mirdesign') + field = mirstat(field,'FileNames',0); + else + field.Stat = 1; + end + end + res = evalaudiofile(field,file,sampling,size,struc,istmp,index,... + single,fieldname,ch); + if not(isempty(single)) && not(isequal(single,0)) && ... + iscell(res) && isa(field,'mirdesign') + res = res{1}; + end + v.(fieldname) = res; + if istmp + struc.tmp.(fieldname) = res; + end + if fi == 1 + if isfield(res,'Class') + v.Class = res.Class; + v.(fieldname) = rmfield(res,'Class'); + end + end + end + if isfield(v,'tmp') + v = rmfield(v,'tmp'); + end +elseif iscell(d) + l = length(d); + v = cell(1,l); + for j = 1:l + v{j} = evalaudiofile(d{j},file,sampling,size,struc,istmp,index,... + single,[name,num2str(j)],ch); + end +elseif isa(d,'mirstruct') && isempty(get(d,'Argin')) + mirerror('MIRSTRUCT','You should always use tmp fields when using mirstruct. Else, just use struct.'); +else + if get(d,'SeparateChannels') + v = cell(1,ch); + for i = 1:ch + d = set(d,'File',file,'Sampling',sampling,'Size',size,... + 'Eval',1,'Index',index,'Struct',struc,'Channel',i); + % For that particular file or this particular feature, let's begin the + % actual evaluation process. + v{i} = evaleach(d,single,name); + % evaleach performs a top-down traversal of the design flowchart. + end + v = combinechannels(v); + else + d = set(d,'File',file,'Sampling',sampling,'Size',size,... + 'Eval',1,'Index',index,'Struct',struc); + % For that particular file or this particular feature, let's begin the + % actual evaluation process. + v = evaleach(d,single,name); + % evaleach performs a top-down traversal of the design flowchart. + end +end + + +function y = combinechannels(c) +y = c{1}; +v = get(y,'Data'); +for h = 2:length(c) + d = get(c{h},'Data'); + for i = 1:length(d) + if isa(y,'mirmidi') + d{i}(:,3) = h; + v{i} = sortrows([v{i};d{i}]); + else + for j = 1:length(d{i}) + v{i}{j}(:,:,h) = d{i}{j}; + end + end + end +end +y = set(y,'Data',v); + + +function c = combineaudiofile(filename,isstat,varargin) % Combine output from several audio files into one single +c = varargin{1}; % The (series of) input(s) related to the first audio file +if isempty(c) + return +end +if isstruct(c) + %fields = fieldnames(c); + for j = 1:length(varargin) + if j == 1 + fields = fieldnames(varargin{1}); + else + fields = union(fields,fieldnames(varargin{j})); + end + end + for i = 1:length(fields) + field = fields{i}; + v = {}; + for j = 1:length(varargin) + if isfield(varargin{j},field) + v{j} = varargin{j}.(field); + else + v{j} = NaN; + end + end + c.(field) = combineaudiofile('',isstat,v{:}); + if strcmp(field,'Class') + c.Class = c.Class{1}; + end + end + if not(isempty(filename)) && isstat + c.FileNames = filename; + end + return +end +if ischar(c) + c = varargin; + return +end +if (not(iscell(c)) && not(isa(c,'mirdata'))) + for j = 1:length(varargin) + if j == 1 + lv = size(varargin{j},1); + else + lv = max(lv,size(varargin{j},1)); + end + end + c = NaN(lv,length(varargin)); + for i = 1:length(varargin) + if not(isempty(varargin{i})) + c(1:length(varargin{i}),i) = varargin{i}; + end + end + return +end +if (iscell(c) && not(isa(c{1},'mirdata'))) + for i = 1:length(c) + v = cell(1,nargin-2); + for j = 1:nargin-2 + v{j} = varargin{j}{i}; + end + c{i} = combineaudiofile(filename,isstat,v{:}); + end + return +end +if not(iscell(c)) + c = {c}; +end +nv = length(c); % The number of input variables for each different audio file +for j = 1:nv % Combine files for each different input variable + v = varargin; + for i = 1:length(varargin) + if iscell(v{i}) + v{i} = v{i}{j}; + end + end + if not(isempty(v)) && not(isempty(v{1})) + c{j} = combine(v{:}); + end +end + + +function s = getsize(d) +if isa(d,'mirstruct') + d = get(d,'Data'); + if isempty(d) + error('ERROR in MIREVAL: Your mirstruct object does not have any field (besides tmp).'); + s = 0; + else + s = getsize(d{1}); + end +elseif isstruct(d) + fields = fieldnames(d); + s = getsize(d.(fields{1})); +elseif iscell(d) + s = getsize(d{1}); +else + s = get(d,'Size'); % Starting and ending dates in seconds. +end + + +function d2 = sortnames(d,d2,n) +if length(n) == 1 + d2(end+1) = d(1); + return +end +first = zeros(1,length(n)); +for i = 1:length(n) + if isempty(n{i}) + first(i) = -Inf; + else + ni = n{i}{1}; + if ischar(ni) + first(i) = ni-10058; + else + first(i) = ni; + end + end +end +[o i] = sort(first); +n = {n{i}}; +d = d(i); +i = 0; +while i<length(n) + i = i+1; + if isempty(n{i}) + d2(end+1) = d(i); + else + dmp = (d(i)); + tmp = {n{i}(2:end)}; + while i+1<=length(n) && n{i+1}{1} == n{i}{1}; + i = i+1; + dmp(end+1) = d(i); + tmp{end+1} = n{i}(2:end); + end + d2 = sortnames(dmp,d2,tmp); + end +end + + +function [l w sr a] = evalfolder(path,s,l,w,sr,a,folders) +if not(isempty(path)) + path = [path '/']; +end +dd = dir; +dn = {dd.name}; +nn = cell(1,length(dn)); % Modified file names +for i = 1:length(dn) % Each file name is considered + j = 0; + while j<length(dn{i}) % Each successive character is modified if necessary + j = j+1; + tmp = dn{i}(j) - '0'; + if tmp>=0 && tmp<=9 + while j+1<length(dn{i}) && dn{i}(j+1)>='0' && dn{i}(j+1)<='9' + j = j+1; + tmp = tmp*10 + (dn{i}(j)-'0'); + end + else + tmp = dn{i}(j); + end + nn{i}{end+1} = tmp; + end +end +dd = sortnames(dd,[],nn); +for i = 1:length(dd); + nf = dd(i).name; + if folders && dd(i).isdir + if not(strcmp(nf(1),'.')) + cd(dd(i).name) + [l w sr a] = evalfolder([path nf],s,l,w,sr,a,1); + %l = l + l2; + %w = [w w2]; + %sr = [sr sr2]; + %a = [a a2]; + cd .. + end + else + [di,tpi,fpi,fi,bi,ni] = mirread([],nf,0,1,0); + if not(isempty(ni)) + l = l+1; + if not(isempty(s)) + interval = s(1:2); + if s(3) + interval = round(interval*fi)+1; + end + if s(4) == 1 + interval = interval+round(di/2); + elseif s(4) == 2 + interval = interval+di; + end + w(:,l) = min(max(interval,1),di); + else + w(:,l) = [1;di]; + end + sr(l) = fi; + a{l} = [path ni]; + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mireventdensity.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function varargout = mireventdensity(x,varargin) +% e = mireventdensity(x) estimate the mean frequency of events (i.e., how +% many note onsets per second) in the temporal data x. + +% Optional arguments: Option1, Option2 +% Tuomas Eerola, 14.08.2008 +% + normal.type = 'String'; + normal.choice = {'Option1','Option2'}; + normal.default = 'Option1'; + option.normal = normal; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [0 0]; + frame.keydefault = [10 1]; + option.frame = frame; + +specif.option = option; + +specif.defaultframelength = 1.00; +specif.defaultframehop = 0.5; + +%specif.eachchunk = 'Normal'; +specif.combinechunk = {'Average','Concat'}; + +varargout = mirfunction(@mireventdensity,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirenvelope')) + if option.frame.length.val + x = mironsets(x,'Klapuri99', 'Frame',option.frame.length.val,... + option.frame.length.unit,... + option.frame.hop.val,... + option.frame.hop.unit); + else + x = mironsets(x,'Klapuri99'); + end +end +type = 'mirscalar'; + + +function e = main(o,option,postoption) +if iscell(o) + o = o{1}; +end +sr = get(o,'Sampling'); +p = mirpeaks(o); %%%%<<<<<<< MORE OPTIONS HERE +pv = get(p,'PeakVal'); +v = mircompute(@algo,pv,o,option,sr); +e = mirscalar(o,'Data',v,'Title','Event density','Unit','per second'); +e = {e o}; + + +function e = algo(pv,o,option,sr) +nc = size(o,2); +nch = size(o,3); +e = zeros(1,nc,nch); +% for i = 1:nch +% for j = 1:nc +% if option.root +% e(1,j,i) = norm(d(:,j,i)); +% else +% disp('do the calc...') +% % e(1,j,i) = d(:,j,i)'*d(:,j,i); +% %tmp = mironsets(d,'Filterbank',10,'Contrast',0.1); % Change by TE, was only FB=20, no other params +% e2 = mirpeaks(e) +% [o1,o2] = mirgetdata(e); +% e(1,j,i) = length(o2)/mirgetdata(mirlength(d)); +% end +% end +% end +for i = 1:nch + for j = 1:nc + e(1,j,i) = length(pv{1,j,i}); + if strcmpi(option.normal,'Option1') + e(1,j,i) = e(1,j,i) *sr/size(o,1); + elseif strcmpi(option.normal,'Option2') + pvs = pv{1}; + high_pvs = length(find(mean(pvs)>pvs)); + e(1,j,i) = high_pvs(1,j,i) *sr/size(o,1); % only those which are larger than mean + end + end +end + + + +%function [y orig] = eachchunk(orig,option,missing,postchunk) +%y = mireventdensity(orig,option); + + +%function y = combinechunk(old,new) +%do = mirgetdata(old); +%dn = mirgetdata(new); +%y = set(old,'ChunkData',do+dn);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirexport.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,375 @@ +function m = mirexport(f,varargin) +% mirexport(filename,...) exports statistical information related to +% diverse data into a text file called filename. +% mirexport('Workspace',...) instead directly output the statistical +% information in a structure array saved in the Matlab workspace. +% This structure contains three fields: +% filenames: the name of the original audio files, +% types: the name of the features, +% data: the data. +% The exported data should be related to the same initial audio file +% or the same ordered set of audio files. +% The data listed after the first arguments can be: +% - any feature computed in MIRtoolbox. +% What will be exported is the statistical description of the +% feature (using the mirstat function) +% - any structure array of such features. +% Such as the ouput of the mirstat function. +% - any cell array of such features. +% - the name of a text file. +% The text file is imported with the Matlab importdata command. +% Each line of the file should contains a fixed number of data +% delimited by tabulations. The first line, or 'header', +% indicates the name of each of these columns. +% The file format of the output can be either: +% - a text file. +% It follows the same text file representation as for the input +% text files. The first column of the matrix indicates the name +% of the audio files. The text file can be opened in Matlab, +% or in a spreadsheet program, such as Microsoft Excel, where the +% data matrix can be automatically reconstructed. +% - an attribute-relation file. +% It follows the ARFF standard, used in particular in the WEKA +% data mining environment. + +stored.data = {}; +stored.textdata = {}; +stored.name = {}; +narg = nargin; +if strcmpi(f,'Workspace') + format = 'Workspace'; +elseif length(f)>4 && strcmpi(f(end-4:end),'.arff') + format = 'ARFF'; +else + format = 'Matrix'; +end +v = ver('MIRtoolbox'); +title = ['MIRtoolbox' v.Version]; +class = {}; +if not(isempty(varargin)) && ischar(varargin{end}) && strcmp(varargin{end},'#add') + add = 1; + varargin(end) = []; + narg = narg-1; +else + add = 0; +end +for v = 2:narg + argv = varargin{v-1}; + if isa(argv,'mirdesign') + mirerror('MIREXPORT','You can only export features that have been already evaluated (using mireval).'); + end + if ischar(argv) + if strcmpi(argv,'Matrix') + format = 'Matrix'; + elseif strcmpi(argv,'ARFF') + format = 'ARFF'; + else + imported = importdata(argv,'\t',1); + imported.name = {}; + [stored class] = integrate(stored,imported); + end + elseif isstruct(argv) && isfield(argv,'data') + new.data = argv.data; + new.textdata = argv.fields; + new.name = {}; + [stored class] = integrate(stored,new); + else + new.data = argv; + new.textdata = ''; + new.name = {}; + [stored class] = integrate(stored,new); + end +end +switch format + case 'Matrix' + matrixformat(stored,f,title,add); + m = 1; + case 'ARFF' + classes = {}; + for i = 1:length(class) + if isempty(strcmp(class{i},classes)) || not(max(strcmp(class{i},classes))) + classes{end+1} = class{i}; + end + end + ARFFformat(stored,f,title,class,classes,add); + m = 1; + case 'Workspace' + m = variableformat(stored,f,title); +end + + + +function [stored class] = integrate(stored,new,class) + +if nargin<3 + class = {}; +end + +% Input information +data = new.data; +textdata = new.textdata; +if isfield(new,'name') + name = new.name; +else + name = {}; +end + +% Input information after processing +newdata = {}; +newtextdata = {}; +newname = {}; + +if isstruct(data) + if isfield(data,'Class') + class = data.Class; + data = rmfield(data,'Class'); + end + + if isfield(data,'FileNames') + name = data.FileNames; + data = rmfield(data,'FileNames'); + end + + fields = fieldnames(data); + nfields = length(fields); + + for w = 1:nfields + % Field information + field = fields{w}; + newfield.data = data.(field); + if 1 %not(isnumeric(newfield.data) && all(all(isnan(newfield.data)))) + if isempty(textdata) + newfield.textdata = field; + else + newfield.textdata = strcat(textdata,'_',field); + end + + % Processing of the field + [n class] = integrate({},newfield,class); + + % Concatenation of the results + newdata = {newdata{:} n.data{:}}; + newtextdata = {newtextdata{:} n.textdata{:}}; + newname = checkname(newname,name); + end + end +elseif isa(data,'mirdata') + newinput.data = mirstat(data); + if isfield(newinput.data,'FileNames') + newinput.data = rmfield(newinput.data,'FileNames'); + end + title = get(data,'Title'); + newinput.textdata = [textdata '_' title(find(not(isspace(title))))]; + [n class] = integrate({},newinput,class); + newdata = n.data; + newtextdata = n.textdata; + newname = get(data,'Name'); +elseif iscell(textdata) + % Input comes from importdata + nvar = size(data,2); + newdata = cell(1,nvar); + newtextdata = cell(1,nvar); + for i = 1:nvar + newdata{i} = data(:,i); + newtextdata{i} = textdata{i}; + end + newname = {}; +elseif iscell(data) + for i = 1:length(data) + if not(isempty(data{i})) + % Element information + newelement.data = data{i}; + newelement.textdata = [textdata num2str(i)]; + + % Processing of the element + [n class] = integrate({},newelement,class); + + % Concatenation of the results + newdata = {newdata{:} n.data{:}}; + newtextdata = {newtextdata{:} n.textdata{:}}; + newname = checkname(newname,n.name); + end + end +elseif size(data,4)>1 + % Input is vector + for w = 1:size(data,4) + % Bin information + bin.data = data(:,:,:,w); + if isempty(textdata) + bin.textdata = num2str(w); + else + bin.textdata = strcat(textdata,'_',num2str(w)); + end + + % Processing of the bin + [n class] = integrate({},bin,class); + + % Concatenation of the results + newdata = {newdata{:} n.data{:}}; + newtextdata = {newtextdata{:} n.textdata{:}}; + end +elseif size(data,3)>1 + % Input is vector + for w = 1:size(data,3) + % Bin information + bin.data = data(:,:,w,:); + if isempty(textdata) + bin.textdata = num2str(w); + else + bin.textdata = strcat(textdata,'_',num2str(w)); + end + + % Processing of the bin + [n class] = integrate({},bin,class); + + % Concatenation of the results + newdata = {newdata{:} n.data{:}}; + newtextdata = {newtextdata{:} n.textdata{:}}; + end +elseif size(data,1)>1 && size(data,1)<=50 + % Input is vector + for w = 1:size(data,1) + % Bin information + bin.data = data(w,:,:,:); + if isempty(textdata) + bin.textdata = num2str(w); + else + bin.textdata = strcat(textdata,'_',num2str(w)); + end + + % Processing of the bin + [n class] = integrate({},bin,class); + + % Concatenation of the results + newdata = {newdata{:} n.data{:}}; + newtextdata = {newtextdata{:} n.textdata{:}}; + end +else + if size(data,1)>1 + data = mean(data); + end + newdata = {data}; + newtextdata = {textdata}; + newname = {}; +end +if isempty(stored) + stored.data = newdata; + stored.textdata = newtextdata; + stored.name = newname; +else + stored.data = {stored.data{:} newdata{:}}; + stored.textdata = {stored.textdata{:} newtextdata{:}}; + if isempty(stored.name) + stored.name = newname; + else + stored.name = checkname(newname,stored.name); + end +end + + +function m = matrixformat(data,filename,title,add) +named = ~isempty(data.name); +if named + if not(add) + m(1,:) = {title,data.textdata{:}}; + end + for i = 1:length(data.name) + m{i+~add,1} = data.name{i}; + end +elseif not(add) + m(1,:) = {data.textdata{:}}; +end +for i = 1:length(data.data) + m((1:length(data.data{i}))+~add,i+named) = num2cell(data.data{i}); +end +if add + fid = fopen(filename,'at'); +else + fid = fopen(filename,'wt'); +end +for i = 1:size(m,1) + for j = 1:size(m,2) + if ischar(m{i,j}) + fprintf(fid,'%s\t', m{i,j}(find(not(m{i,j} == ' ')))); + else + if iscell(m{i,j}) % Problem with key strength pos to be solved + fprintf(fid,'%f\t', m{i,j}{1}); + else + fprintf(fid,'%f\t', m{i,j}); + end + end + end + %if i < size(m,1) + fprintf(fid,'\n'); + %end +end +fclose(fid); +disp(['Data exported to file ',filename,'.']); + + +function ARFFformat(data,filename,title,class,classes,add) +if add + fid = fopen(filename,'at'); +else + fid = fopen(filename,'wt'); + fprintf(fid,['%% Attribution-Relation File automatically generated using ',title,'\n\n']); + fprintf(fid,'@RELATION %s\n\n',title); + for i = 1:length(data.textdata) + fprintf(fid,'@ATTRIBUTE %s NUMERIC\n',data.textdata{i}); + end + if not(isempty(class)) + fprintf(fid,'@ATTRIBUTE class {'); + for i = 1:length(classes) + if i>1 + fprintf(fid,','); + end + fprintf(fid,'%s',classes{i}); + end + fprintf(fid,'}\n'); + end + fprintf(fid,'\n@DATA\n'); + fid2 = fopen([filename(1:end-5) '.filenames.txt'],'wt'); + for i = 1:length(data.name) + fprintf(fid2,'%s\n',data.name{i}); + end + fclose(fid2); +end + +try + data = cell2mat(data.data(:))'; +catch + error('ERROR IN MIREXPORT: Are you sure all the data to be exported relate to the same ordered list of audio files?'); +end +for i = 1:size(data,1) + fprintf(fid,'%d ',data(i,:)); + if not(isempty(class)) + fprintf(fid,'%s',class{i}); + end + fprintf(fid,'\n'); +end +fclose(fid); +disp(['Data exported to file ',filename,'.']); + + +function m = variableformat(data,filename,title) +m.types = data.textdata; +m.filenames = data.name; +for i = 1:length(data.data) + m.data{i} = data.data{i}; +end + + +function name = checkname(newname,name) +if not(isempty(newname)) && not(isempty(name)) + if length(newname) == length(name) + for i = 1:length(name) + if not(strcmp(name{i},newname{i})) + error('ERROR IN MIREXPORT: All the input are not associated to the same audio files (or the same ordering of these files.'); + end + end + else + error('ERROR IN MIREXPORT: All the input are not associated to the same audio files.'); + end +elseif isempty(name) + name = newname; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirfeatures.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +function r = mirfeatures(x,varargin) +% f = mirfeatures(x) computes a large set of features from one or several +% audio files. x can be either the name of an audio file, or the +% 'Folder' keyword. +% mirfeatures(...,'Stat') returns the statistics of the features instead +% of the complete features themselves. +% mirfeatures(...,'Segment',t) segments the audio sequence at the +% temporal positions indicated in the array t (in s.), and analyzes +% each segment separately. + +%(not available yet) +% mirfeatures(...,'Filterbank',nc) computes the analysis on each channel +% of a filterbank decomposition. +% Default value: nc = 5 +% mirfeatures(...,'Frame',...) +% mirfeatures(...,'Normal') +% mirfeatures(...,'Sampling',s) +% miraudio options (Extract, ...) + +[stat,nchan,segm,feat] = scanargin(varargin); + +if isa(x,'miraudio') || isa(x,'mirdesign') + a = miraudio(x,'Normal'); % normalize with respect to RMS energy + % in order to consider timbre independently of + % energy +else + a = miraudio('Design','Normal'); +end + +if not(isempty(segm)) + a = mirsegment(a,segm); +end + + + +% DYNAMICS +% -------- + +r.dynamics.rms = mirrms(a,'Frame'); +% Perceived dynamics: spectral slope? + +% RHYTHM +% ------ + +r.fluctuation = mirstruct; +r.fluctuation.tmp.f = mirfluctuation(a,'Summary'); +r.fluctuation.peak = mirpeaks(r.fluctuation.tmp.f,'Total',1);%only one? +r.fluctuation.centroid = mircentroid(r.fluctuation.tmp.f); + +r.rhythm = mirstruct; +r.rhythm.tmp.onsets = mironsets(a); + +%r.rhythm.eventdensity = ... + +r.rhythm.tempo = mirtempo(r.rhythm.tmp.onsets,'Frame'); +%r.rhythm.pulseclarity = mirpulseclarity(r.tmp.onsets,'Frame'); + % Should use the second output of mirtempo. + +attacks = mironsets(r.rhythm.tmp.onsets,'Attacks'); +r.rhythm.attack.time = mirattacktime(attacks); +r.rhythm.attack.slope = mirattackslope(attacks); + +% TIMBRE +% ------ + +f = mirframe(a,.05,.5); +r.spectral = mirstruct; +r.spectral.tmp.s = mirspectrum(f); +%pitch = mirpitch(a,'Frame',.05,.5); + +r.spectral.centroid = mircentroid(r.spectral.tmp.s); +r.spectral.brightness = mirbrightness(r.spectral.tmp.s); +r.spectral.spread = mirspread(r.spectral.tmp.s); +r.spectral.skewness = mirskewness(r.spectral.tmp.s); +r.spectral.kurtosis = mirkurtosis(r.spectral.tmp.s); +r.spectral.rolloff95 = mirrolloff(r.spectral.tmp.s,95); +r.spectral.rolloff85 = mirrolloff(r.spectral.tmp.s,85); +r.spectral.spectentropy = mirentropy(r.spectral.tmp.s); +r.spectral.flatness = mirflatness(r.spectral.tmp.s); + +r.spectral.roughness = mirroughness(r.spectral.tmp.s); +r.spectral.irregularity = mirregularity(r.spectral.tmp.s); +%r.spectral.inharmonicity = mirinharmonicity(r.spectral.tmp.s,'f0',pitch); + +r.spectral.mfcc = mirmfcc(r.spectral.tmp.s); +r.spectral.dmfcc = mirmfcc(r.spectral.mfcc,'Delta'); +r.spectral.ddmfcc = mirmfcc(r.spectral.dmfcc,'Delta'); + +r.timbre.zerocross = mirzerocross(f); +r.timbre.lowenergy = mirlowenergy(f); +r.timbre.spectralflux = mirflux(f); + +% PITCH +% ----- + +r.tonal = mirstruct; +r.tonal.tmp.chromagram = mirchromagram(a,'Frame','Wrap',0,'Pitch',0); +r.tonal.chromagram.peak=mirpeaks(r.tonal.tmp.chromagram,'Total',1); +r.tonal.chromagram.centroid=mircentroid(r.tonal.tmp.chromagram); + +% TONALITY/HARMONY +% ---------------- + +keystrengths = mirkeystrength(r.tonal.tmp.chromagram); +[k r.tonal.keyclarity] = mirkey(keystrengths,'Total',1); +%r.tonal.keyclarity = k{2}; +r.tonal.mode = mirmode(keystrengths); +r.tonal.hcdf = mirhcdf(r.tonal.tmp.chromagram); + +if stat + r = mirstat(r); + % SHOULD COMPUTE STAT OF CURVES FROM FRAMED_DECOMPOSED HIGH FEATURES +end + +if not(isa(x,'miraudio')) && not(isa(x,'mirdesign')) + r = mireval(r,x); +end + + +function [stat,nchan,segm,feat] = scanargin(v) +stat = 0; +nchan = 1; +segm = []; +feat = {}; +i = 1; +while i <= length(v) + arg = v{i}; + if ischar(arg) && strcmpi(arg,'Filterbank') + i = i+1; + if i <= length(v) + nchan = v{i}; + else + nchan = 10; + end + elseif ischar(arg) && strcmpi(arg,'Stat') + i = i+1; + if i <= length(v) + stat = v{i}; + else + stat = 1; + end + elseif ischar(arg) && strcmpi(arg,'Segment') + i = i+1; + if i <= length(v) + segm = v{i}; + else + segm = 1; + end + else + feat{end+1} = arg; + end + i = i+1; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirfilterbank.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,333 @@ +function varargout = mirfilterbank(orig,varargin) +% b = mirfilterbank(x) performs a filterbank decomposition of an audio +% waveform. +% Optional arguments: +% mirfilterbank(...,t) selects a type of filterbank. +% Possible values: +% t = 'Gammatone' (default for audio files). +% Requires the Auditory Toolbox. +% mirfilterbank(...,'Lowest',f): lowest frequency in Hz +% (default: 50) +% t = '2Channels' proposed in (Tolonen & Karjalainen, 2000) +% mirfilterbank(...,'NbChannels',N), or simply filterbank(x,N): +% specifies the number of channels in the bank. +% (default: N = 10) +% mirfilterbank(...,'Channel',c) only output the channels whose +% ranks are indicated in the array c. +% (default: c = (1:N)) +% mirfilterbank(...,'Manual',f) specifies a set of low-pass, band- +% pass and high-pass eliptic filters (Scheirer, 1998). +% The series of cut-off frequencies as to be specified +% as next parameter f. +% If this series of frequencies f begins with -Inf, +% the first filter is low-pass. +% If this series of frequencies f ends with Inf, +% the last filter is high-pass. +% mirfilterbank(...,'Order',o) specifies the order of the filter. +% (Default: o = 4) (Scheirer, 1998) +% mirfilterbank(...,'Hop',h) specifies the degree of spectral +% overlapping between successive channels. +% If h = 1 (default value), the filters are non-overlapping. +% If h = 2, the filters are half-overlapping. +% If h = 3, the spectral hop factor between successive +% filters is a third of the whole frequency region, etc. +% mirfilterbank(...,p) specifies predefined filterbanks, all +% implemented using elliptic filters, by default of order 4. +% Possible values: +% p = 'Mel' (mel-scale) +% p = 'Bark' (bark-scale) +% p = 'Scheirer' proposed in (Scheirer, 1998) corresponds to +% 'Manual',[-Inf 200 400 800 1600 3200 Inf] +% p = 'Klapuri' proposed in (Klapuri, 1999) corresponds to +% 'Manual',44*[2.^ ([ 0:2, ( 9+(0:17) )/3 ]) ] + + nCh.key = 'NbChannels'; + nCh.type = 'Integer'; + nCh.default = 10; + option.nCh = nCh; + + Ch.key = {'Channel','Channels'}; + Ch.type = 'Integer'; + Ch.default = 0; + option.Ch = Ch; + + lowF.key = 'Lowest'; + lowF.type = 'Integer'; + lowF.default = 50; + option.lowF = lowF; + + freq.key = 'Manual'; + freq.type = 'Integer'; + freq.default = NaN; + option.freq = freq; + + overlap.key = 'Hop'; + overlap.type = 'Boolean'; + overlap.default = 1; + option.overlap = overlap; + + filtertype.type = 'String'; + filtertype.choice = {'Gammatone','2Channels',0}; + filtertype.default = 'Gammatone'; + option.filtertype = filtertype; + + filterorder.key = 'Order'; + filterorder.type = 'Integer'; + filterorder.default = 4; + option.filterorder = filterorder; + + presel.type = 'String'; + presel.choice = {'Scheirer','Klapuri','Mel','Bark'}; + presel.default = ''; + option.presel = presel; + +specif.option = option; + +specif.eachchunk = @eachchunk; +specif.combinechunk = @combinechunk; + +varargout = mirfunction(@mirfilterbank,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = 'miraudio'; + + +function b = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +f = get(x,'Sampling'); + +if strcmpi(option.presel,'Scheirer') + option.freq = [-Inf 200 400 800 1600 3200 Inf]; +elseif strcmpi(option.presel,'Klapuri') + option.freq = 44*[2.^([0:2,(9+(0:17))/3])]; +elseif strcmpi(option.presel,'Mel') + lowestFrequency = 133.3333; + linearFilters = 13; + linearSpacing = 66.66666666; + logFilters = 27; + logSpacing = 1.0711703; + totalFilters = linearFilters + logFilters; + cepstralCoefficients = 13; + option.freq = lowestFrequency + (0:linearFilters-1)*linearSpacing; + option.freq(linearFilters+1:totalFilters+2) = ... + option.freq(linearFilters) * logSpacing.^(1:logFilters+2); + + option.overlap = 2; +elseif strcmpi(option.presel,'Bark') + option.freq = [10 20 30 40 51 63 77 92 108 127 148 172 200 232 ... + 270 315 370 440 530 640 770 950 1200 1550]*10; %% Hz +end +if not(isnan(option.freq)) + option.filtertype = 'Manual'; +end + +d = get(x,'Data'); +if size(d{1}{1},3) > 1 + warning('WARNING IN MIRFILTERBANK: The input data is already decomposed into channels. No more channel decomposition.'); + if option.Ch + cx = get(x,'Channels'); + db = cell(1,length(d)); + for k = 1:length(d) + db{k} = cell(1,length(d{k})); + for l = 1:length(d{k}) + for i = 1:length(option.Ch) + db{k}{l}(:,:,i) = d{k}{l}(:,:,find(cx{k} == option.Ch(i))); + end + end + end + b = set(x,'Data',db,'Channels',option.Ch); + else + b = x; + end +else + i = 1; + while i <= length(d) + if size(d{i}{1},2) > 1 + warning('WARNING IN MIRFILTERBANK: The frame decomposition should be performed after the filterbank decomposition.'); + disp(['Suggestion: Use the ' char(34) 'Frame' char(34) ' option available to some of the MIRtoolbox functions.']) + break + end + i = i+1; + end + nCh = option.nCh; + Ch = option.Ch; + if strcmpi(option.filtertype, 'Gammatone'); + [tmp x] = gettmp(x); %get(x,'Tmp'); + if not(Ch) + Ch = 1:nCh; + end + erb = cell(1,length(d)); + for i = 1:length(d) + erb{i} = cell(1,length(d{i})); + nch{i} = Ch; + for j = 1:length(d{i}) + try + coef = MakeERBFilters(f{i},nCh,option.lowF); + catch + error(['ERROR IN MIRFILTERBANK: Auditory Toolbox needs to be installed.']); + end + [erb{i}{j} tmp] = ERBfilterbank(d{i}{j},coef,Ch,tmp); + end + end + b = set(x,'Data',erb,'Channels',nch);%,'Tmp',tmp); + clear erb + b = settmp(b,tmp); + elseif strcmpi(option.filtertype, '2Channels'); + if not(Ch) + Ch = 1:2; + end + output = cell(1,length(d)); + try + for i = 1:length(d) + output{i} = cell(1,length(d{i})); + [bl,al] = butter(4,[70 1000]/f{i}*2); + k = 1; + if ismember(1,Ch) + Hdl = dfilt.df2t(bl,al); + %fvtool(Hdl) + Hdl.PersistentMemory = true; + for j = 1:length(d{i}) + low = filter(Hdl,d{i}{j}); + output{i}{j}(:,:,k) = low; + end + k = k+1; + end + if ismember(2,Ch) + if f{i} < 20000 + [bh,ah] = butter(2,1000/f{i}*2,'high'); + else + [bh,ah] = butter(2,[1000 10000]/f{i}*2); + end + Hdlh = dfilt.df2t(bl,al); + %fvtool(Hdlh) + Hdh = dfilt.df2t(bh,ah); + %fvtool(Hdh) + Hdlh.PersistentMemory = true; + Hdh.PersistentMemory = true; + for j = 1:length(d{i}) + high = filter(Hdh,d{i}{j}); + high = max(high,0); + high = filter(Hdlh,high); + output{i}{j}(:,:,k) = high; + end + end + nch{i} = Ch; + end + b = set(x,'Data',output,'Channels',nch); + catch + warning(['WARNING IN MIRFILTERBANK: Signal Processing Toolbox (version 6.2.1, or higher) not installed: no filterbank decomposition.']); + b = x; + end + elseif strcmpi(option.filtertype,'Manual'); + output = cell(1,length(d)); + for i = 1:length(d) + freqi = option.freq; + j = 1; + while j <= length(freqi) + if not(isinf(freqi(j))) && freqi(j)>f{i}/2 + if j == length(freqi) + freqi(j) = Inf; + else + freqi(j) = []; + j = j-1; + end + end + j = j+1; + end + output{i} = cell(1,length(d{i})); + step = option.overlap; + for j = 1:length(freqi)-step + if isinf(freqi(j)) + [z{j},p{j},k{j}] = ellip(option.filterorder,3,40,... + freqi(j+step)/f{i}*2); + elseif isinf(freqi(j+step)) + [z{j},p{j},k{j}] = ellip(option.filterorder,3,40,... + freqi(j)/f{i}*2,'high'); + else + [z{j},p{j},k{j}] = ellip(option.filterorder,3,40,... + freqi([j j+step])/f{i}*2); + end + end + for j = 1:length(z) + [sos,g] = zp2sos(z{j},p{j},k{j}); + Hd(j) = dfilt.df2tsos(sos,g); + Hd(j).PersistentMemory = true; + for h = 1:length(d{i}) + output{i}{h}(:,:,j) = filter(Hd(j),d{i}{h}); + end + end + %fvtool(Hd) + nch{i} = 1:length(freqi)-step; + end + b = set(x,'Data',output,'Channels',nch); + end + end + + +function [output tmp] = ERBfilterbank(x, fcoefs, chans, tmp) +% The following is based on the source code from Auditory Toolbox +% (A part that I could not call directly from MIRtoolbox, and that I +% generalize to chunk decomposition) + +% (Malcolm Slaney, August 1993, (c) 1998 Interval Research Corporation) + +% Process an input waveform with a gammatone filter bank. +% The fcoefs parameter, which completely specifies the Gammatone filterbank, +% should be designed with the MakeERBFilters function. + +A0 = fcoefs(:,1); +A11 = fcoefs(:,2); +A12 = fcoefs(:,3); +A13 = fcoefs(:,4); +A14 = fcoefs(:,5); +A2 = fcoefs(:,6); +B0 = fcoefs(:,7); +B1 = fcoefs(:,8); +B2 = fcoefs(:,9); +gain= fcoefs(:,10); +lc = length(chans); +output = zeros(size(x,1),size(x,2),lc); +if isempty(tmp) + emptytmp = 1; +else + emptytmp = 0; +end +for i = 1:lc + chan = length(gain)-chans(i)+1; % Revert the channels order + aa1 = [A0(chan)/gain(chan) A11(chan)/gain(chan) A2(chan)/gain(chan)]; + bb1 = [B0(chan) B1(chan) B2(chan)]; + aa2 = [A0(chan) A12(chan) A2(chan)]; + bb2 = [B0(chan) B1(chan) B2(chan)]; + aa3 = [A0(chan) A12(chan) A2(chan)]; + bb3 = [B0(chan) B1(chan) B2(chan)]; + aa4 = [A0(chan) A13(chan) A2(chan)]; + bb4 = [B0(chan) B1(chan) B2(chan)]; + if emptytmp + [y1 tmp(:,:,i,1)] = filter(aa1,bb1,x); + [y2 tmp(:,:,i,2)] = filter(aa2,bb2,y1); + [y3 tmp(:,:,i,3)] = filter(aa3,bb3,y2); + [y4 tmp(:,:,i,4)] = filter(aa4,bb4,y3); + else + [y1 tmp(:,:,i,1)] = filter(aa1,bb1,x,tmp(:,:,i,1)); + [y2 tmp(:,:,i,2)] = filter(aa2,bb2,y1,tmp(:,:,i,2)); + [y3 tmp(:,:,i,3)] = filter(aa3,bb3,y2,tmp(:,:,i,3)); + [y4 tmp(:,:,i,4)] = filter(aa4,bb4,y3,tmp(:,:,i,4)); + end + output(:,:,i) = y4; +end + + +function [y orig] = eachchunk(orig,option,missing) +y = mirfilterbank(orig,option); + + +function y = combinechunk(old,new) +do = get(old,'Data'); +to = get(old,'Time'); +dn = get(new,'Data'); +tn = get(new,'Time'); +y = set(old,'Data',{{[do{1}{1};dn{1}{1}]}},... + 'Time',{{[to{1}{1};tn{1}{1}]}}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirflatness.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function varargout = mirflatness(orig,varargin) +% f = mirflatness(x) calculates the flatness of x, which can be either: +% - a spectrum (spectral flatness), +% - an envelope (temporal flatness), or +% - any histogram. + + +varargout = mirfunction(@mirflatness,orig,varargin,nargout,struct,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirenvelope') || isamir(x,'mirhisto')) + x = mirspectrum(x); +end +type = 'mirscalar'; + + +function f = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +m = get(x,'Data'); +y = cell(1,length(m)); +for h = 1:length(m) + if not(iscell(m{h})) % for histograms + m{h} = {m{h}'}; + end + y{h} = cell(1,length(m{h})); + for i = 1:length(m{h}) + mm = m{h}{i}; + nl = size(mm,1); + ari = mean(mm); + geo = (prod(mm.^(1/nl))); + divideByZero = warning('query','MATLAB:divideByZero'); + logZero = warning('query','MATLAB:log:logOfZero'); + warning('off','MATLAB:divideByZero'); + warning('off','MATLAB:log:logOfZero'); + y{h}{i} = ...10*log10( + geo./ari;%); + warning(divideByZero.state,'MATLAB:divideByZero'); + warning(logZero.state,'MATLAB:log:logOfZero'); + nany = find(isinf(y{h}{i})); + y{h}{i}(nany) = zeros(size(nany)); + end +end +if isa(x,'mirenvelope') + t = 'Temporal flatness'; +elseif isa(x,'mirspectrum') + t = 'Spectral flatness'; +else + t = ['Flatness of ',get(x,'Title')]; +end +f = mirscalar(x,'Data',y,'Title',t,'Unit',''); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirfluctuation.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +function varargout = mirfluctuation(orig,varargin) +% f = mirfluctuation(x) calculates the fluctuation strength, indicating the +% rhythmic periodicities along the different channels. +% Optional arguments: +% mirfluctuation(...,'MinRes',mr) specifies the minimal frequency +% resolution of the resulting spectral decomposition, in Hz. +% Default: mr = .01 Hz +% mirfluctuation(...,'Summary') returns the summary of the fluctuation, +% i.e., the summation along the critical bands. +% +% E. Pampalk, A. Rauber, D. Merkl, "Content-based Organization and +% Visualization of Music Archives", + + sum.key = 'Summary'; + sum.type = 'Boolean'; + sum.default = 0; + option.sum = sum; + + mr.key = 'MinRes'; + mr.type = 'Integer'; + mr.default = .01; + option.mr = mr; + +specif.option = option; + +varargout = mirfunction(@mirfluctuation,orig,varargin,nargout,specif,@init,@main); + + +function [f type] = init(x,option) +if iscell(x) + x = x{1}; +end +if isamir(x,'miraudio') && not(isframed(x)) + x = mirframe(x,.023,.5); +end +m = mirspectrum(x,'Power','Terhardt','Bark','dB','Mask'); +f = mirspectrum(m,'AlongBands','Max',10,'Window',0,... + 'Resonance','Fluctuation','MinRes',option.mr); +if option.sum + f = mirsummary(f); +end +type = 'mirspectrum'; + + +function f = main(f,option,postoption) +f = set(f,'Title','Fluctuation'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirflux.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,254 @@ +function varargout = mirflux(orig,varargin) +% f = mirflux(x) measures distance between successive frames. +% First argument: +% If x is a spectrum, this corresponds to spectral flux. +% But the flux of any other data can be computed as well. +% If x is an audio file or audio signal, the spectral flux is +% computed by default. +% Optional arguments: +% f = mirflux(x,'Frame',...) specifies the frame parameters, if x is +% not already decomposed into frames. Default values are frame +% length of .2 seconds and hop factor of 1.3. +% f = mirflux(x,'Dist',d) specifies the distance between +% successive frames: (IF 'COMPLEX': DISTANCE = 'CITY' ALWAYS) +% d = 'Euclidian': Euclidian distance (Default) +% d = 'City': City-block distance +% d = 'Cosine': Cosine distance (or normalized correlation) +% f = mirflux(...,'Inc'): Only positive difference between frames are +% summed, in order to focus on increase of energy solely. +% f = mirflux(...,'Complex'), for spectral flux, combines use of +% energy and phase information (Bello et al, 2004). +% f = mirflux(...,'Halfwave'): performs a half-wave rectification on +% the result. +% f = mirflux(...,'Median',l,C): removes small spurious peaks by +% subtracting to the result its median filtering. The median +% filter computes the point-wise median inside a window of length +% l (in seconds), that includes a same number of previous and +% next samples. C is a scaling factor whose purpose is to +% artificially rise the curve slightly above the steady state of +% the signal. If no parameters are given, the default values are: +% l = 0.2 s. and C = 1.3 +% f = mirflux(...,'Median',l,C,'Halfwave'): The scaled median +% filtering is designed to be succeeded by the half-wave +% rectification process in order to select peaks above the +% dynamic threshold calculated with the help of the median +% filter. The resulting signal is called "detection function" +% (Alonso, David, Richard, 2004). To ensure accurate detection, +% the length l of the median filter must be longer than the +% average width of the peaks of the detection function. +% +% (Bello et al, 2004) Juan P. Bello, Chris Duxbury, Mike Davies, and Mark +% Sandler, On the Use of Phase and Energy for Musical Onset Detection in +% the Complex Domain, IEEE SIGNAL PROCESSING LETTERS, VOL. 11, NO. 6, +% JUNE 2004 + + dist.key = 'Dist'; + dist.type = 'String'; + dist.choice = {'Euclidian','City','Cosine'}; %PDIST???? (euclidean) + dist.default = 'Euclidian'; + option.dist = dist; + + inc.key = 'Inc'; + inc.type = 'Boolean'; + inc.default = 0; + option.inc = inc; + + complex.key = 'Complex'; + complex.type = 'Boolean'; + complex.default = 0; + option.complex = complex; + + h.key = 'Halfwave'; + h.type = 'Boolean'; + h.default = 0; + h.when = 'After'; + option.h = h; + + median.key = 'Median'; + median.type = 'Integer'; + median.number = 2; + median.default = [0 0]; + median.keydefault = [.2 1.3]; + median.when = 'After'; + option.median = median; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [.05 .5]; + option.frame = frame; + +specif.option = option; + +varargout = mirfunction(@mirflux,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if isamir(x,'miraudio') + if isframed(x) + x = mirspectrum(x); + else + x = mirspectrum(x,'Frame',option.frame.length.val,option.frame.length.unit,... + option.frame.hop.val,option.frame.hop.unit); + end +end +if isa(x,'mirdesign') + x = set(x,'Overlap',1); +end +type = 'mirscalar'; + + +function f = main(s,option,postoption) +if iscell(s) + s = s{1}; +end +t = get(s,'Title'); +if isa(s,'mirscalar') && ... + (strcmp(t,'Harmonic Change Detection Function') || ... + (length(t)>4 && strcmp(t(end-3:end),'flux')) || ... + (length(t)>5 && strcmp(t(end-4:end-1),'flux'))) + if not(isempty(postoption)) + f = modif(s,postoption); + end +else + if isa(s,'mirspectrum') + t = 'Spectral'; + end + m = get(s,'Data'); + if option.complex + if not(isa(s,'mirspectrum')) + error('ERROR IN MIRFLUX: ''Complex'' option only defined for spectral flux.'); + end + ph = get(s,'Phase'); + end + param.complex = option.complex; + param.inc = option.inc; + fp = get(s,'FramePos'); + if strcmp(t,'Tonal centroid') + t = 'Harmonic Change Detection Function'; + else + t = [t,' flux']; + end + disp(['Computing ' t '...']) + ff = cell(1,length(m)); + newsr = cell(1,length(m)); + dist = str2func(option.dist); + %[tmp s] = gettmp(s); %get(s,'Tmp'); + for h = 1:length(m) + ff{h} = cell(1,length(m{h})); + if not(iscell(m{h})) + m{h} = {m{h}}; + end + for i = 1:length(m{h}) + mi = m{h}{i}; + if size(mi,3) > 1 && size(mi,1) == 1 + mi = reshape(mi,size(mi,2),size(mi,3))'; + end + if option.complex + phi = ph{h}{i}; + end + fpi = fp{h}{i}; + %if 0 %not(isempty(tmp)) && isstruct(tmp) && isfield(tmp,'mi') + % mi = [tmp.mi mi]; + % fpi = [tmp.fpi fpi]; + %end + nc = size(mi,2); + np = size(mi,3); + if nc == 1 + warning('WARNING IN MIRFLUX: Flux can only be computed on signal decomposed into frames.'); + ff{h}{i} = NaN(1,1,np); + else + if option.complex + fl = zeros(1,nc-2,np); + for k = 1:np + d = diff(phi(:,:,k),2,2); + d = d/(2*pi) - round(d/(2*pi)); + g = sqrt(mi(:,3:end,k).^2 + mi(:,2:(end-1),k).^2 ... + - 2.*mi(:,3:end,k)... + .*mi(:,2:(end-1),k)... + .*cos(d)); + fl(1,:,k) = sum(g); + end + fp{h}{i} = fpi(:,3:end); + else + fl = zeros(1,nc-1,np); + for k = 1:np + for j = 1:nc-1 + fl(1,j,k) = dist(mi(:,j,k),mi(:,j+1,k),option.inc); + end + end + fp{h}{i} = fpi(:,2:end); + end + ff{h}{i} = fl; + %tmp.mi = mi(:,end,:); + %tmp.fpi = fpi(:,end,:); + end + end + %tmp = []; + if size(fpi,2)<2 + newsr{h} = 0; + else + newsr{h} = 1/(fpi(1,2)-fpi(1,1)); + end + end + f = mirscalar(s,'Data',ff,'FramePos',fp,'Sampling',newsr,... + 'Title',t,'Parameter',param); %,'Tmp',tmp); + %f = settmp(f,tmp); + if not(isempty(postoption)) + f = modif(f,postoption); + end +end + + +function f = modif(f,option) +fl = get(f,'Data'); +r = get(f,'Sampling'); +for h = 1:length(fl) + for i = 1:length(fl{h}) + fli = fl{h}{i}; + nc = size(fli,2); + np = size(fli,3); + if option.median(1) + ffi = zeros(1,nc,np); + lr = round(option.median(1)*r{i}); + for k = 1:np + for j = 1:nc + ffi(:,j,k) = fli(:,j,k) - ... + option.median(2) * median(fli(:,max(1,j-lr):min(nc-1,j+lr),k)); + end + end + fli = ffi; + end + if option.h + fli = hwr(fli); + end + fl{h}{i} = fli; + end +end +f = set(f,'Data',fl); + + +function y = Euclidian(mi,mj,inc) +if inc + y = sqrt(sum(max(0,(mj-mi)).^2)); +else + y = sqrt(sum((mj-mi).^2)); +end + + +function y = City(mi,mj,inc) +if inc + y = sum(max(0,(mj-mi))); +else + y = sum(abs(mj-mi)); +end + + +function d = Cosine(r,s,inc) +nr = sqrt(r'*r); +ns = sqrt(s'*s); +if or(nr == 0, ns == 0); + d = 1; +else + d = 1 - r'*s/nr/ns; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirframe.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,241 @@ +function [f x] = mirframe(x,varargin) +% f = mirframe(x) creates the frame decomposition of the audio signal x. +% (x can be a file name as well.) +% Optional arguments: +% mirframe(x,'Length',w,wu): +% w is the length of the window in seconds (default: .05 seconds) +% u is the unit, either +% 's' (seconds, default unit) +% or 'sp' (number of samples) +% mirframe(x,'Hop',h,hu): +% h is the hop factor, or distance between successive frames +% (default: half overlapping: each frame begins at the middle +% of the previous frame) +% u is the unit, either +% '/1' (ratio with respect to the frame length, default unit) +% '%' (ratio as percentage) +% 's' (seconds) +% 'sp' (number of samples) +% or 'Hz' (hertz), i.e., number of frames per second: the +% exactness of the frame rate is ensured and may cause a +% slight fluctuation of the elementary hop distances. +% These arguments can also be written as follows: +% mirframe(x,w,wu,h,hu) +% (where some of these parameters can be omitted). + +if isempty(x) + f = {}; + return +end +if iscell(x) + x = x{1}; +end +if nargin == 0 + f = miraudio; +elseif isa(x,'mirdesign') + if not(get(x,'Eval')) + % During bottom-up construction of the general design + + para = scanargin(varargin); + type = get(x,'Type'); + f = mirdesign(@mirframe,x,para,{},struct,type); + + fl = get(x,'FrameLength'); + fh = get(x,'FrameHop'); + flu = get(x,'FrameLengthUnit'); + fhu = get(x,'FrameHopUnit'); + if fl + f = set(f,'FrameLength',fl,'FrameLengthUnit',flu,... + 'FrameHop',fh,'FrameHopUnit',fhu); + else + f = set(f,'FrameLength',para.wlength.val,... + 'FrameLengthUnit',para.wlength.unit,... + 'FrameHop',para.hop.val,... + 'FrameHopUnit',para.hop.unit); + end + f = set(f,'FrameEval',1,... + 'SeparateChannels',get(x,'SeparateChannels')); + if not(isamir(x,'miraudio')) + f = set(f,'NoChunk',1); + end + else + % During top-down evaluation initiation + + if isstruct(varargin{1}) && isfield(varargin{1},'struct') + tmp = varargin{1}.struct; + x = set(x,'Struct',tmp); + varargin{1} = rmfield(varargin{1},'struct'); + end + e = evaleach(x); + if iscell(e) + e = e{1}; + end + if isempty(mirgetdata(e)) + f = e; + else + f = mirframe(e,varargin{:}); + end + end +elseif isa(x,'mirdata') + if isframed(x) + warning('WARNING IN MIRFRAME: The input data is already decomposed into frames. No more frame decomposition.'); + f = x; + else + x = purgedata(x); + dx = get(x,'Data'); + if isa(x,'mirtemporal') + dt = get(x,'Time'); + else + dt = get(x,'FramePos'); + end + sf = get(x,'Sampling'); + para = scanargin(varargin); + dx2 = cell(1,length(dx)); % magnitude in framed structure + dt2 = cell(1,length(dx)); % time positions in framed structure + fp = cell(1,length(dx)); % frame positions + for k = 1:length(dx) % For each audio file, ... + dxk = dx{k}; + dtk = dt{k}; + if strcmpi(para.wlength.unit,'s') + l = para.wlength.val*sf{k}; + elseif strcmpi(para.wlength.unit,'sp') + l = para.wlength.val; + end + if strcmpi(para.hop.unit,'/1') + h = para.hop.val*l; + elseif strcmpi(para.hop.unit,'%') + h = para.hop.val*l*.01; + elseif strcmpi(para.hop.unit,'s') + h = para.hop.val*sf{k}; + elseif strcmpi(para.hop.unit,'sp') + h = para.hop.val; + elseif strcmpi(para.hop.unit,'Hz') + h = sf{k}/para.hop.val; + end + l = floor(l); + dx2k = cell(1,length(dxk)); + dt2k = cell(1,length(dxk)); + fpk = cell(1,length(dxk)); + for j = 1:length(dxk) % For each segment, ... + dxj = dxk{j}; + dtj = dtk{j}; + if not(isa(x,'mirtemporal')) + dxj = dxj'; + dtj = dtj(1,:)'; + end + + n = floor((size(dxj,1)-l)/h)+1; % Number of frames + dx2j = zeros(l,n,size(dxj,3)); + dt2j = zeros(l,n); + fpj = zeros(2,n); + if n < 1 + disp('Frame length longer than total sequence size. No frame decomposition.'); + dx2j = dxj(:,1,:); + dt2j = dtj; + fpj = [dtj(1) ; dtj(end)]; + else + for i = 1:n % For each frame, ... + st = floor((i-1)*h+1); + stend = st+l-1; + dx2j(:,i,:) = dxj(st:stend,1,:); + dt2j(:,i) = dtj(st:stend); + fpj(:,i) = [dtj(st) dtj(stend)]; + end + end + dx2k{j} = dx2j; + dt2k{j} = dt2j; + fpk{j} = fpj; + end + dx2{k} = dx2k; + dt2{k} = dt2k; + fp{k} = fpk; + end + if isa(x,'mirtemporal') + f = set(x,'Time',dt2,'Data',dx2,'FramePos',fp); + else + f = mirtemporal([],'Time',dt2,'Data',dx2,'FramePos',fp,... + 'Sampling',get(x,'Sampling'),'Name',get(x,'Name'),... + 'Label',get(x,'Label'),'Channels',get(x,'Channels'),... + 'Centered',0,'Title',get(x,'Title')); + end + end +else + f = mirframe(miraudio(x),varargin{:}); +end + + +function para = scanargin(v) +if not(isempty(v)) && isstruct(v{1}) + if length(v) == 1 + para = v{1}; + else + para.wlength = v{1}; + para.hop = v{2}; + end + return +end +para.wlength.val = 0.05; +para.wlength.unit = 's'; +para.hop.val = 0.5; +para.hop.unit = '/1'; +nv = length(v); +i = 1; +j = 1; +while i <= nv + arg = v{i}; + if strcmpi(arg,'WinLength') || strcmpi(arg,'Length') + if i < nv && isnumeric(v{i+1}) + i = i+1; + j = 0; + para.wlength.val = v{i}; + else + error('ERROR IN MIRFRAME: Incorrect use of Length option. See help mirframe.'); + end + if i < nv && ischar(v{i+1}) && ... + (strcmpi(v{i+1},'s') || strcmpi(v{i+1},'sp')) + i = i+1; + para.wlength.unit = v{i}; + end + elseif strcmpi(arg,'Hop') + if i < nv && isnumeric(v{i+1}) + i = i+1; + j = 0; + para.hop.val = v{i}; + else + error('ERROR IN MIRFRAME: Incorrect use of Hop option. See help mirframe.'); + end + if i < nv && ischar(v{i+1}) && ... + (strcmpi(v{i+1},'%') || strcmpi(v{i+1},'/1') || ... + strcmpi(v{i+1},'s') || strcmpi(v{i+1},'sp') || ... + strcmpi(v{i+1},'Hz')) + i = i+1; + para.hop.unit = v{i}; + end + elseif isnumeric(arg) + switch j + case 1 + j = 2; + para.wlength.val = arg; + if i < nv && ischar(v{i+1}) && ... + (strcmpi(v{i+1},'s') || strcmpi(v{i+1},'sp')) + i = i+1; + para.wlength.unit = v{i}; + end + case 2 + j = 3; + para.hop.val = arg; + if i < nv && ischar(v{i+1}) && ... + (strcmpi(v{i+1},'%') || strcmpi(v{i+1},'/1') || ... + strcmpi(v{i+1},'s') || strcmpi(v{i+1},'sp') || ... + strcmpi(v{i+1},'Hz')) + i = i+1; + para.hop.unit = v{i}; + end + otherwise + error('ERROR IN MIRFRAME: Syntax error. See help mirframe.'); + end + elseif not(isempty(arg)) + error('ERROR IN MIRFRAME: Syntax error. See help mirframe.'); + end + i = i+1; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirframenow.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function x = mirframenow(x,option) + +if isfield(option,'frame') && ... + isstruct(option.frame) && option.frame.length.val + x = mirframe(x,... + option.frame.length.val,option.frame.length.unit,... + option.frame.hop.val,option.frame.hop.unit); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirfunction.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,207 @@ +function o = mirfunction(method,x,varg,nout,specif,init,main) +% Meta function called by all MIRtoolbox functions. +% Integrates the function into the general flowchart +% and eventually launches the "mireval" evaluation process. +% Here are the successive steps in the following code: +% - If the input is an audio filename, instantiates a new design flowchart. +% - Reads all the options specified by the user. +% - Performs the 'init' part of the MIRtoolbox function: +% - If the input is a design flowchart, +% add the 'init' part in the flowchart. +% - If the input is some MIRtoolbox data, +% execute the 'init' part on that data. +% - Performs the 'main' part of the MIRtoolbox function. + +if isempty(x) + o = {{},{},{}}; + return +end + +if ischar(x) % The input is a file name. + % Starting point of the design process + design_init = 1; + filename = x; + if strcmpi(func2str(method),'miraudio') + postoption = {}; + else + postoption.mono = 1; + end + orig = mirdesign(@miraudio,'Design',{varg},postoption,struct,'miraudio'); + % Implicitly, the audio file needs to be loaded first. +elseif isnumeric(x) + mirerror(func2str(method),'The input should be a file name or a MIRtoolbox object.'); +else + design_init = 0; + orig = x; +end + +% Reads all the options specified by the user. +[orig during after] = miroptions(method,orig,specif,varg); + +% Performs the 'init' part of the MIRtoolbox function. +if isa(orig,'mirdesign') + if not(get(orig,'Eval')) + % Top-down construction of the general design flowchart + + if isstruct(during) && isfield(during,'frame') && ... + isstruct(during.frame) && during.frame.auto + % 'Frame' option: + % Automatic insertion of the mirframe step in the design + orig = mirframe(orig,during.frame.length.val,... + during.frame.length.unit,... + during.frame.hop.val,... + during.frame.hop.unit); + end + + % The 'init' part of the function can be integrated into the design + % flowchart. This leads to a top-down construction of the + % flowchart. + % Automatic development of the implicit prerequisites, + % with management of the data types throughout the design process. + [orig type] = init(orig,during); + + o = mirdesign(method,orig,during,after,specif,type); + + if design_init && not(strcmpi(filename,'Design')) + % Now the design flowchart has been completed created. + % If the 'Design' keyword not used, + % the function is immediately evaluated + o = mireval(o,filename,nout); + else + o = returndesign(o,nout); + end + if not(iscell(o)) + o = {o}; + end + return + else + % During the top-down traversal of the flowchart (evaleach), at the + % beginning of the evaluation process. + + if not(isempty(get(orig,'TmpFile'))) && get(orig,'ChunkDecomposed') + orig = evaleach(orig); + if iscell(orig) + orig = orig{1}; + end + x = orig; + else + [orig x] = evaleach(orig); + end + + if not(isequal(method,@nthoutput)) + if iscell(orig) + orig = orig{1}; + end + if isempty(get(orig,'InterChunk')) + orig = set(orig,'InterChunk',get(x,'InterChunk')); + end + end + end +else + design = 0; + if iscell(orig) + i = 0; + while i<length(orig) && not(design) + i = i+1; + if isa(orig{i},'mirdesign') + design = i; + end + end + end + if design + % For function with multiple inputs + if design == 1 && not(get(orig{1},'Eval')) + % Progressive construction of the general design + [orig type] = init(orig,during); + o = mirdesign(method,orig,during,after,specif,type); + o = set(o,'Size',get(orig{1},'Size')); + o = returndesign(o,nout); + return + else + % Evaluation of the design. + % First top-down initiation (evaleach), then bottom-up process. + for io = 1:length(orig) + if isa(orig{io},'mirdesign') + o = evaleach(orig{io}); + if iscell(o) + o = o{:}; + end + orig{io} = o; + end + end + end + elseif not(isempty(init)) && not(isempty(during)) + if isstruct(during) && isfield(during,'frame') && ... + isstruct(during.frame) && during.frame.auto + orig = mirframe(orig,during.frame.length,during.frame.hop); + end + % The input of the function is not a design flowchart, which + % the 'init' part of the function could be integrated into. + % (cf. previous call of 'init' in this script). + % For that reason, the 'init' part of the function needs to be + % evaluated now. + orig = init(orig,during); + end +end + +% Performs the 'main' part of the MIRtoolbox function. +if not(iscell(orig) && not(ischar(orig{1}))) && ... + not(isa(orig,'mirdesign') || isa(orig,'mirdata')) + o = {orig}; + return +end +filenamearg = orig; +if iscell(filenamearg) && not(ischar(filenamearg{1})) + filenamearg = filenamearg{1}; +end +if iscell(filenamearg) && not(ischar(filenamearg{1})) + filenamearg = filenamearg{1}; +end +filename = get(filenamearg,'Name'); +if not(isempty(during)) && mirverbose +% if length(filename) == 1 +% disp(['Computing ',func2str(method),' related to ',filename{1},'...']) +% else +% disp(['Computing ',func2str(method),' for all audio files ...']) +% end +end +if iscell(x) + x1 = x{1}; +else + x1 = x; +end +if not(iscell(orig) || isnumeric(x)) + orig = set(orig,'Index',get(x1,'Index')); +end +if iscell(orig) + o = main(orig,during,after); +else + d = get(orig,'Data'); + if isamir(orig,'miraudio') && ... + length(d) == 1 && length(d{1}) == 1 && isempty(d{1}{1}) + % To solve a problem when MP3read returns empty chunk. + % Warning: it should not be a cell, because for instance nthoutput can have first input empty... + o = orig; + else + o = main(orig,during,after); + end +end +if not(iscell(o) && length(o)>1) || (isa(x,'mirdesign') && get(x,'Eval')) + o = {o x}; +elseif iscell(x) && isa(x{1},'mirdesign') && get(x{1},'Eval') + o = {o x{1}}; +elseif not(isempty(varg)) && isstruct(varg{1}) ... + && not(iscell(o) && iscell(o{1})) + % When the function was called by mireval, the output should be packed + % into one single cell array (in order to be send back to calling + % routines). + o = {o}; +end + + +function o = returndesign(i,nout) +o = cell(1,nout); +o{1} = i; +for k = 2:nout + o{k} = nthoutput(i,k); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirgetdata.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function [d,d2] = mirgetdata(x,varargin) +% d = mirgetdata(x) return the data contained in the object x in a +% structure that can be used for further computation outside MIRtoolbox. +% If x corresponds to one non-segmented audio sequence, the result is +% returned as a matrix. The columns of the matrix usually +% correspond to the successive frames of the audio signal. The +% third dimension of the matrix corresponds to the different +% channels of a filterbank. +% If x is a keystrength curve, the fourth dimension distinguishes +% between major and minor keys. i.e. d(:,:,:,1) is the +% keystrength for the major keys, and d(:,:,:,2) is the +% keystrength for the minor keys. +% If x is a key estimation, two output are returned: the first one +% gives the keys (from 1 to 12) and the second one indicates the +% modes (1 for major, 2 for minor). +% +% If x corresponds to a set of audio sequences, and if each sequence +% has same number of frames, the corresponding resulting matrices +% are concatenated columnwise one after the other. If the number +% of raws of the elementary matrices varies, the missing values +% are replaced by NaN in the final matrix. On the contrary, if +% the number of columns (i.e., frames) differs, then the result +% remains a cell array of matrices. +% Idem if x corresponds to one or several segmented audio +% sequence(s). +% +% If x is the result of a peak detection, +% [px,py] = getdata(x) return the position of the peaks (px) and the +% value corresponding to these peaks (py), in the units +% predefined for this data. + +if isempty(x) + d = {}; + d2 = {}; + return +end + +if isstruct(x) + fields = fieldnames(x); + for f = 1:length(fields) + d.(fields{f}) = mirgetdata(x.(fields{f})); + end + d2 = {}; + return +end + +if iscell(x) + x = x{1}; +end +v = get(x,'Data'); +if isa(x,'mirscalar') + m = get(x,'Mode'); +end +d2 = {}; + +if isa(x,'mirclassify') + d = get(x,'Data'); + return +end + +if isa(x,'mirsimatrix') + pt = []; +else + pt = get(x,'PeakPrecisePos'); +end +pv = get(x,'PeakPreciseVal'); +if not(isempty(pt)) && not(isempty(pt{1})) && not(isempty(pt{1}{1})) + d = uncell(pt); + d2 = uncell(pv); + if not(isempty(d)) + return + end +end + +if isa(x,'mirsimatrix') + pt = []; +else + pt = get(x,'PeakPosUnit'); +end +pv = get(x,'PeakVal'); +if not(isempty(pt)) && not(isempty(pt{1})) && not(isempty(pt{1}{1})) + d = uncell(pt); + d2 = uncell(pv); + if not(isempty(d)) + return + end +end + +d = uncell(v,isa(x,'mirscalar')); +if iscell(d) && not(isempty(d)) && nargin == 1 + disp('The result is an array of cell.') + disp(['If d is the name of the output variable, ',... + 'the successive cells can be accessed by typing d{1}, d{2}, etc.']); +end +if exist('m')==1 && not(isempty(m)) && not(isempty(m{1})) + d2 = uncell(m); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirgetrange.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +function r = mirgetrange(x) + +if isempty(x) + r = {}; + return +end + +if isstruct(x) + fields = fieldnames(x); + for f = 1:length(fields) + d.(fields{f}) = mirgetrange(x.(fields{f})); + end + return +end + +if iscell(x) + x = x{1}; +end +v = get(x,'Data'); +if isa(x,'mirscalar') + m = get(x,'Mode'); +end + +if isa(x,'mirclassify') + d = get(x,'Data'); + return +end + +if isa(x,'miremotion') + return +end + +if isa(x,'mirsimatrix') + return +end + +pv = get(x,'PeakVal'); +if not(isempty(pv)) && not(isempty(pv{1})) && not(isempty(pv{1}{1})) + d = uncell(pv); +else + d = uncell(v,isa(x,'mirscalar')); +end + +if not(iscell(d)) + d = {d}; +end +r = []; +for i = 1:length(d) + r = [r;d{i}(:)]; +end +r = sort(r); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirhcdf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function varargout = mirhcdf(orig,varargin) +% df = mirhcdf(x) calculates the Harmonic Change Detection Function +% related to x. +% +% C. A. Harte and M. B. Sandler, Detecting harmonic change in musical +% audio, in Proceedings of Audio and Music Computing for Multimedia +% Workshop, Santa Barbara, CA, 2006. + +specif.defaultframelength = .743; +specif.defaultframehop = .1; +varargout = mirfunction(@mirhcdf,orig,varargin,nargout,specif,@init,@main); + + +function [df type] = init(orig,option) +if isamir(orig,'mirscalar') + df = orig; +else + if isframed(orig) + tc = mirtonalcentroid(orig); + else + tc = mirtonalcentroid(orig,'Frame'); + end + df = mirflux(tc); +end +type = 'mirscalar'; + + +function df = main(df,option,postoption) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirinharmonicity.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,120 @@ +function varargout = mirinharmonicity(orig,varargin) +% ih = mirinharmonicity(x) estimates the inharmonicity of x, i.e., the +% amount of partials that are not multiples of the fundamental +% frequency. +% x can be either an audio file, a miraudio or a mirspectrum object. +% WARNING: This function presupposes that there is only one fundamental +% frequency. +% Optional argument: +% mirinharmonicity(...,'f0',f) bases the computation of the +% inharmonicity on the fundamental frequency indicated by f. +% Default value: f = mirpitch(...,'Mono') +% [ih,s] = mirinharmonicity(x) also displays the spectrum used for the +% computation of the inharmonicity. +% [ih,s,p] = mirinharmonicity(x) also displays the result of the +% estimation of the fundamental frequency. + + f0.key = 'f0'; + f0.default = []; + option.f0 = f0; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [0 0]; + frame.keydefault = [.1 .125]; + option.frame = frame; + +specif.option = option; + +varargout = mirfunction(@mirinharmonicity,orig,varargin,nargout,specif,@init,@main); + + +function [i type] = init(x,option) +if isamir(x,'miraudio') + if option.frame.length.val + s = mirspectrum(x,'Frame',option.frame.length.val,... + option.frame.length.unit,... + option.frame.hop.val,... + option.frame.hop.unit); + else + s = mirspectrum(x); + end +else + s = x; +end +if isempty(option.f0) + if option.frame.length.val + p = mirpitch(x,'Mono','Frame',option.frame.length.val,... + option.frame.length.unit,... + option.frame.hop.val,... + option.frame.hop.unit); + else + p = mirpitch(x,'Mono'); + end +else + p = option.f0; +end +i = {s,p}; +type = {'mirscalar','mirspectrum','mirscalar'}; + + +function ih = main(x,option,postoption) +if isa(x{2},'mirdesign') + x = x{1}; +end +s = x{1}; +p = x{2}; +if iscell(p) + p = p{1}; +end +m = get(s,'Magnitude'); +f = get(s,'Frequency'); +fp1 = get(s,'FramePos'); +if isnumeric(p) + pf = {{{p}}}; +else + pf = get(p,'Data'); + fp2 = get(p,'FramePos'); +end +v = cell(1,length(m)); +for h = 1:length(m) + v{h} = cell(1,length(m{h})); + for i = 1:length(m{h}) + mi = m{h}{i}; + fi = f{h}{i}; + pfi = pf{h}{i}; + v{h}{i} = zeros(1,size(mi,2),size(mi,3)); + if not(size(mi,2) == size(pfi,2)) + beg = find(fp2{h}{i}(1,:) == fp1{h}{i}(1,1)); + if isempty(beg) || (beg + size(mi,2)-1 > size(pfi,2)) + error('ERROR IN MIRINHARMONICITY: The ''f0'' argument should have the same frame decomposition than the main input.'); + end + pfi = pfi(:,beg:beg+size(mi,2)-1); + end + for j = 1:size(mi,3) + for k = 1:size(mi,2) + mk = mi(:,k,j); + fk = fi(:,k,j); + pfk = pfi(:,k); + if isempty(pfk{1}) + v{h}{i}(1,k,j) = NaN; + else + r = fk/pfk{1}(1); + rr = 2*abs(r-round(r)); + if isempty(rr) + v{h}{i}(1,k,j) = NaN; + else + v{h}{i}(1,k,j) = sum(rr.*mk) / sum(mk); + end + end + end + end + end +end +ih = mirscalar(s,'Data',v,'Title','Inharmonicity'); +if isa(p,'mirdata') + ih = {ih s p}; +else + ih = {ih s}; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirkey.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +function varargout = mirkey(orig,varargin) +% k = mirkey(x) estimates the key. +% Optional argument: +% mirkey(...,'Total',m) selects not only the most probable key, but +% the m most probable keys. +% The other parameter 'Contrast' related to mirpeaks can be specified +% here (see help mirchromagram). +% The optional parameters 'Weight' and 'Triangle' related to +% mirchromagram can be specified here (see help mirchromagram). +% [k,ks] = mirkey(...) also returns the key clarity, corresponding here +% to the key strength associated to the best candidate. +% [k,ks,ksc] = mirkey(...) also displays the key strength curve used for +% the key estimation and shows in particular the peaks corresponding +% to the selected key(s). + + tot.key = 'Total'; + tot.type = 'Integer'; + tot.default = 1; + option.tot = tot; + + thr.key = 'Contrast'; + thr.type = 'Integer'; + thr.default = .1; + option.thr = thr; + + wth.key = 'Weight'; + wth.type = 'Integer'; + wth.default = .5; + option.wth = wth; + + tri.key = 'Triangle'; + tri.type = 'Boolean'; + tri.default = 0; + option.tri = tri; + +specif.option = option; +specif.defaultframelength = 1; +specif.defaultframehop = .5; + +varargout = mirfunction(@mirkey,orig,varargin,nargout,specif,@init,@main); + + +function [p type] = init(x,option) +if not(isamir(x,'mirkeystrength')) + x = mirkeystrength(x,'Weight',option.wth,'Triangle',option.tri); +end +p = mirpeaks(x,'Total',option.tot,'Contrast',option.thr); +type = {'mirscalar','mirscalar','mirkeystrength'}; + + +function k = main(p,option,postoption) +if iscell(p) + p = p{1}; +end +pc = get(p,'PeakPos'); +pv = get(p,'PeakMaxVal'); +pm = get(p,'PeakMode'); +k = mirscalar(p,'Data',pc,'Mode',pm,'Title','Key',... + 'Legend',{'C','C#','D','D#','E','F','F#','G','G#','A','A#','B'}); +m = mirscalar(p,'Data',pv,'Title','Key clarity','MultiData',{}); +k = {k m p}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirkurtosis.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function varargout = mirkurtosis(orig,varargin) +% k = mirkurtosis(x) calculates the kurtosis of x, indicating whether +% the curve is peaked or flat relative to a normal distribution. +% x can be either: +% - a spectrum (spectral kurtosis), +% - an envelope (temporal kurtosis), or +% - any histogram. + + +varargout = mirfunction(@mirkurtosis,orig,varargin,nargout,struct,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirdata')) || isamir(x,'miraudio') + x = mirspectrum(x); +end +type = 'mirscalar'; + + +function k = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +y = peaksegments(@kurtosis,get(x,'Data'),... + get(x,'Pos'),... + get(mircentroid(x),'Data'),... + get(mirspread(x),'Data')); +if isa(x,'mirspectrum') + t = 'Spectral kurtosis'; +elseif isa(x,'mirenvelope') + t = 'Temporal kurtosis'; +else + t = ['Kurtosis of ',get(x,'Title')]; +end +k = mirscalar(x,'Data',y,'Title',t,'Unit',''); + + +function k = kurtosis(d,p,c,s) +k = sum((p-c).^4.*d) ./ sum(d) ./ s.^4; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirlength.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function varargout = mirlength(orig,varargin) +% mirlength(x) indicates the temporal length of x. +% Optional argument: +% mirlength(...,'Unit',u) indicates the length unit. +% Possible values: +% u = 'Second': duration in seconds (Default). +% u = 'Sample': length in number of samples. + + unit.key = 'Unit'; + unit.type = 'String'; + unit.choice = {'Second','Sample'}; + unit.default = 'Second'; + option.unit = unit; + +specif.option = option; + +varargout = mirfunction(@mirlength,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = 'mirscalar'; + + +function z = main(a,option,postoption) +if iscell(a) + a = a{1}; +end +d = get(a,'Data'); +f = get(a,'Sampling'); +v = cell(1,length(d)); +for h = 1:length(d) + v{h} = cell(1,length(d{h})); + for i = 1:length(d{h}) + di = d{h}{i}; + v{h}{i} = size(d{h}{i},1); + if strcmp(option.unit,'Second') + v{h}{i} = v{h}{i}/f{h}; + end + end +end +z = mirscalar(a,'Data',v,'Title','Temporal length','Unit','s.'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirlowenergy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +function varargout = mirlowenergy(x,varargin) +% p = mirlowenergy(f) computes the percentage of frames showing a RMS +% energy that is lower than a given threshold. +% For instance, for a musical excerpt with some very loud frames and +% lots of silent frames, we would get a high low-energy rate. +% Optional argument: +% mirlowenergy(...,'Threshold',t) expressed as a ratio to the average +% energy over the frames. +% Default value: t = 1 +% mirlowenergy(...,'Frame',l,h) specifies the use of frames of +% length l seconds and a hop rate h. +% Default values: l = .05 s, h = .5 +% mirlowenergy(...,'Root',0) uses mean square instead of root mean +% square +% mirlowenergy(...,'ASR') computes the Average Silence Ratio, which +% corresponds in fact to mirlowenergy(...,'Root',0,'Threshold',t) +% where t is fixed here by default to t = .5 +% [p,e] = mirlowenergy(...) also returns the RMS energy curve. + + asr.key = 'ASR'; + asr.type = 'Boolean'; + asr.default = 0; + option.asr = asr; + + root.key = 'Root'; + root.type = 'Boolean'; + root.default = 1; + option.root = root; + + thr.key = 'Threshold'; + thr.type = 'Integer'; + thr.default = NaN; + option.thr = thr; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [.05 .5]; + option.frame = frame; + +specif.option = option; + +specif.combinechunk = {'Average',@nothing}; +specif.extensive = 1; + +varargout = mirfunction(@mirlowenergy,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if option.asr + option.root = 0; +end +if isamir(x,'miraudio') + if isframed(x) + x = mirrms(x,'Root',option.root); + else + x = mirrms(x,'Frame',option.frame.length.val,option.frame.length.unit,... + option.frame.hop.val,option.frame.hop.unit,... + 'Root',option.root); + end +end +type = 'mirscalar'; + + +function e = main(r,option,postoption) +if iscell(r) + r = r{1}; +end +if isnan(option.thr) + if option.asr + option.thr = .5; + else + option.thr = 1; + end +end +v = mircompute(@algo,get(r,'Data'),option.thr); +fp = mircompute(@noframe,get(r,'FramePos')); +e = mirscalar(r,'Data',v,'Title','Low energy','Unit','/1','FramePos',fp); +e = {e,r}; + + +function v = algo(d,thr) +v = sum(d < repmat(thr*mean(d,2),[1 size(d,2) 1])); +v = v / size(d,2); + + +function fp = noframe(fp) +fp = [fp(1);fp(end)]; + + +function y = nothing(old,new) +y = old; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirmap.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,305 @@ +function res = mirmap(predics_ref,ratings_ref,dist,alpha,delta) +% mirmap(predictors_file, ratings_file) performs a statistical mapping +% between ratings associated to a set of audio recordings, and a set of +% variables computed for the same set of audio recordings. It might be +% useful in particular when the set of predictors is large and diverse. + +if nargin<3 + dist = 'lse'; +end +if nargin<4 + alpha = 1; +end +if nargin<5 + delta = .00001; +end + +crosscor_thres = .6; + +dist = str2func(dist); + +predics = import(predics_ref); +ratings = import(ratings_ref); + +predics = normalize(predics,dist,alpha,delta,'predictions',predics_ref); +if isempty(predics.data) + res = []; + return +end + +ratings = normalize(ratings,dist,alpha,delta,'ratings',ratings_ref); + +res = map(predics,ratings,crosscor_thres); + + +%% +function output = import(ref) +output.data = []; +output.fields = {}; +output.normal = []; +if not(iscell(ref)) + ref = {ref}; +end +for i = 1:length(ref) + if not(iscell(ref{i})) + ref{i} = {ref{i}}; + end + if ischar(ref{i}{1}) + for j = 1:length(ref{i}) + importj = importdata(ref{i}{j}); + if not(isstruct(importj)) + imp = importj; + importj = struct; + importj.data = imp; + for k = 1:size(imp,2) + importj.textdata{k} = [ref{i}{j},'_',num2str(k)]; + end + end + if j == 1 + import = importj; + else + [c ia ib] = intersect(import.textdata(1,2:end),... + importj.textdata(1,2:end)); + import.textdata = [import.textdata(:,[1 ia+1]);... + importj.textdata(2:end,[1 ib+1])]; + import.data = [import.data(:,ia);importj.data(:,[1 ib+1])]; + end + end + output.data = [output.data import.data]; + nbfields = size(import.data,2); + field = import.textdata(1,:); + field = field(end-nbfields+1 : end); + output.fields = [output.fields field]; + else + output.data = [output.data ref{i}{1}]; + for j = 1:size(ref{i},2) + output.fields = [output.fields num2str(j)]; + end + end +end +ndata = size(output.data,2); +output.normal = NaN(1,ndata); +warning('off','stats:lillietest:OutOfRangeP'); +for i = 1:ndata + data = output.data(:,i); + data = data(~isnan(data)); + if length(data) < 4 + output.normal(i) = 0; + else + output.normal(i) = ~lillietest(data); + end +end + + +%% +function res = map(predics,ratings,crosscor_thres) +nbrates = size(ratings.data,2); +nbpreds = size(predics.data,2); +res.normal.cor = NaN(nbpreds,nbrates); +res.normal.pval = NaN(nbpreds,nbrates); + +res.significant = cell(nbrates,1); +for j = 1:nbrates + figure + set(gca,'YDir','reverse') + hold on + select = []; % list of selected features + for i = 1:nbpreds + if ~predics.normal(i) + res.normal.cor(i,j) = NaN; + continue + end + [R P] = corrcoef(predics.data(:,i),ratings.data(:,j),... + 'rows','pairwise'); + res.normal.pval(i,j) = P(2); + if P(2)<.05 % statistically significant + select(end+1) = i; % included into the list of selected features + res.normal.cor(i,j) = R(2); + else + res.normal.cor(i,j) = NaN; + end + end + %res.inter(:,:,j) = corrcoef(predics.data,'rows','pairwise'); + + [unused index] = sort(abs(res.normal.cor(select,j)),'descend'); + index = select(index); + res.significant{j}.cor = res.normal.cor(index,j); + res.significant{j}.pval = res.normal.pval(index,j); + res.significant{j}.inter = corrcoef(predics.data(:,index),'rows','pairwise'); + %res.inter(index,index,j); + res.significant{j}.fields = {predics.fields{index}}; + res.significant{j}.alpha = predics.alpha(index); + res.significant{j}.lambda = predics.lambda(index); + k = 0; + corfields = {}; + corbest = []; + for i = 1:length(select) + inter = max(abs(res.significant{j}.inter(1:i-1,i))); + if i == 1 || inter < crosscor_thres + % Features sufficiently independent to the better scoring ones + % are selected + k = k+1; + if i == 1 + own = 1; + else + own = 1-inter; + end + col = res.significant{j}.pval(i); + x1 = min(0,res.significant{j}.cor(i)); + y1 = k-own/2; + x2 = x1+abs(res.significant{j}.cor(i)); + y2 = k+own/2; + pcolor([x1 x1;x2 x2],[y1 y2;y1 y2],[col col;col col]) + corbest = [corbest i]; + corfields = [corfields res.significant{j}.fields{i}]; + i + res.significant{j}.fields{i} + res.significant{j}.cor(i) + inter + end + end + + set(gca,'YTick',1:k,'YTickLabel',corfields)%,... +% 'Position',[.4 .11 .5 .815]) + colorbar + grid on + title(['Correlation with rating ',ratings.fields{j}]) + + predata = standardize(predics.data(:,index(corbest))); %% to be put out of the loop + res.best{j}.index = corbest; + res.best{j}.fields = {res.significant{j}.fields{corbest}}; + res.best{j}.alpha = res.significant{j}.alpha(corbest); + res.best{j}.lambda = res.significant{j}.lambda(corbest); + if isempty(select) + close + elseif length(corbest)>1 + display('****************************************') + display(['Regression for rating ',ratings.fields{j}]) + ratej = standardize(ratings.data(:,j)); + stepwisefit(predata,ratej,'maxiter',10); + [b,se,pval,inmodel,stats,nextstep,history] ... + = stepwisefit(predata,ratej,'maxiter',10,'scale','on'); + res.best{j}.fields{find(inmodel)} + %stepwise(predata,ratej,[]);%,.001,.001); + pause + end +end + +function x = standardize(x) +%mx = mean(x); +%x = x-repmat(mx,[size(x,1) 1]); +%sx = std(x); +%sx(sx==0) = 1; +%x = x./repmat(sx,[size(x,1) 1]); + + +%% +function output = normalize(input,dist,alpha,delta,txt,filename) +fields = input.fields; +nbfields = length(fields); +output = input; +h = waitbar(0,['Normalizing ',txt]); +for i = 1:nbfields + waitbar((i-1)/nbfields,h,['Normalizing ',txt,fields{i}]); + d = input.data(:,i); + if length(d(~isnan(d)))>3 && ~input.normal(i) + %figure(1) + %hist(d,round(7.5*log(size(d,1)))); + %figure(2) + %normplot(d) + + [output.data(:,i) output.alpha(i) output.lambda(i)] = ... + boxcox_search(d,dist,alpha,delta); + %figure(3) + %hist(output.data(:,i),round(7.5*log(size(d,1)))); + output.normal(i) = ~lillietest(output.data(:,i),.01); + %figure(4) + %normplot(data(:,i)) + end +end +display('Excluded:') +output.fields{~output.normal} +display('..') +output.data(:,~output.normal) = []; +output.fields(~output.normal) = []; +output.normal(~output.normal) = []; +if max(~output.normal) + output.alpha(~output.normal) = []; + output.lambda(~output.normal) = []; +end + %if strcmpi(filename(end-3:end),'.txt') + % filename = filename(1:end-4); + %end + %filename = [filename '.mir.txt']; + %mirexport(filename,output); +close(h); + + +function [y resalpha lambda] = boxcox_search(x,dist,alpha,delta) +if nargin<2 + dist = @lse; +end +if nargin<3 + alpha = 0; +end +lambda = -10:.01:10; +if min(x)<0 + x = x-min(x); + resalpha = -min(x); +else + resalpha = 0; +end +y = boxcox_transf(x,lambda); +[y lambda d] = optimize(y,lambda,dist,x); +%y = y(~isnan(y)); +while lillietest(y,.01) && alpha > delta + yp = boxcox_transf(x+alpha,lambda); + %plot(alpha,lse(yp,lambda,x)) + if dist(yp,lambda,x)<d + x = x+alpha; + resalpha = resalpha + alpha; + y = yp; + alpha = alpha*.999; + d = dist(y,lambda,x); + else + minalpha = min(alpha,min(x)); % to avoid negative values + yn = boxcox_transf(x - minalpha,lambda); + %plot(alpha,lse(yn,lambda,x)) + if dist(yn,lambda,x)<d + x = minalpha; + resalpha = resalpha - minalpha; + y = yn; + alpha = alpha*.999; + d = dist(y,lambda,x); + else + alpha = alpha/2; + end + end +end +%plot(lambda,dist(y,lambda,x),'.r') + + +function y = boxcox_transf(x,lambda) +lambda0 = find(~lambda); +if min(x)<0 + x = x-min(x); +end +x = repmat(x,[1 length(lambda)]); +lambda = repmat(lambda,[size(x,1) 1]); +y = (x.^lambda-1)./lambda; +y(:,lambda0) = log(x(:,lambda0)); + + +function [z,lambda,d] = optimize(y,lambda,dist,x) +[d index] = min(dist(y,lambda,x)); +lambda = lambda(index); +z = y(:,index); + + +function d = lse(y,lambda,x) +d = skewness(y).^2 + (kurtosis(y)-3).^2/4; % equation used in Jarque-Bera test +%plot(lambda,d,'.') + + +function d = mle(y,lambda,x) +d = -size(y,1)/2*log(var(y))+(lambda-1)*sum(log(x)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirmean.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,111 @@ +function varargout = mirmean(f,varargin) +% m = mirmean(f) returns the mean along frames of the feature f +% +% f can be a structure array composed of features. In this case, +% m will be structured the same way. + +if isa(f,'mirstruct') + data = get(f,'Data'); + for fi = 1:length(data) + data{fi} = mirmean(data{fi}); + end + varargout = {set(f,'Data',data)}; +elseif isstruct(f) + fields = fieldnames(f); + for i = 1:length(fields) + field = fields{i}; + stat.(field) = mirmean(f.(field)); + end + varargout = {stat}; +else + specif.nochunk = 1; + varargout = mirfunction(@mirmean,f,varargin,nargout,specif,@init,@main); +end + + +function [x type] = init(x,option) +type = ''; + + +function m = main(f,option,postoption) +if iscell(f) + f = f{1}; +end +if isa(f,'mirhisto') + warning('WARNING IN MIRMEAN: histograms are not taken into consideration yet.') + m = struct; + return +end +fp = get(f,'FramePos'); +ti = get(f,'Title'); +if 0 %get(f,'Peaks') + if not(isempty(get(f,'PeakPrecisePos'))) + stat = addstat(struct,get(f,'PeakPrecisePos'),fp,'PeakPos'); + stat = addstat(stat,get(f,'PeakPreciseVal'),fp,'PeakMag'); + else + stat = addstat(struct,get(f,'PeakPosUnit'),fp,'PeakPos'); + stat = addstat(stat,get(f,'PeakVal'),fp,'PeakMag'); + end +else + d = get(f,'Data'); +end +l = length(d); +for i = 1:l + if iscell(d{i}) + if length(d{i}) > 1 + error('ERROR IN MIRMEAN: segmented data not accepted yet.'); + else + dd = d{i}{1}; + end + else + dd = d{i}; + end + %dd = uncell(dd); + if 0 %iscell(dd) + j = 0; + singul = 1; + ddd = []; + while j<length(dd) && singul + j = j+1; + if length(dd{j}) > 1 + singul = 0; + elseif length(dd{j}) == 1 + ddd(end+1) = dd{j}; + end + end + if singul + dd = ddd; + end + end + if iscell(dd) + m{i} = {zeros(1,length(dd))}; + for j = 1:length(dd) + m{i}{1}(j) = mean(dd{j}); + end + elseif size(dd,2) < 2 + nonan = find(not(isnan(dd))); + dn = dd(nonan); + m{i}{1} = mean(dn,2); + else + %diffp = fp{i}{1}(1,2:end) - fp{i}{1}(1,1:end-1); + %if round((diffp(2:end)-diffp(1:end-1))*1000) + % Not regular sampling (in mirattacktime for instance) + % framesampling = NaN; + %else + % framesampling = fp{i}{1}(1,2)-fp{i}{1}(1,1); + %end + dd = mean(dd,4); + m{i} = {NaN(size(dd,1),1,size(dd,3))}; + for k = 1:size(dd,1) + for l = 1:size(dd,3) + dk = dd(k,:,l); + nonan = find(not(isnan(dk))); + if not(isempty(nonan)) + dn = dk(nonan); + m{i}{1}(k,1,l) = mean(dn,2); + end + end + end + end +end +m = mirscalar(f,'Data',m,'Title',['Average of ',ti]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirmode.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function varargout = mirmode(x,varargin) +% m = mirmode(a) estimates the mode. A value of 0 indicates a complete +% incertainty, whereas a positive value indicates a dominance of +% major mode and a negative value indicates a dominance of minor mode. +% Optional arguments: +% mirmode(a,s) specifies a strategy. +% Possible values for s: 'Sum', 'Best'(default) + + stra.type = 'String'; + stra.default = 'Best'; + stra.choice = {'Best','Sum'}; + option.stra = stra; + +specif.option = option; +specif.defaultframelength = 1; +specif.defaultframehop = .5; + +varargout = mirfunction(@mirmode,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirkeystrength')) + x = mirkeystrength(x); +end +type = 'mirscalar'; + + +function o = main(s,option,postoption) +if iscell(s) + s = s{1}; +end +m = get(s,'Data'); +if strcmpi(option.stra,'sum') + v = mircompute(@algosum,m); +elseif strcmpi(option.stra,'best') + v = mircompute(@algobest,m); +end +b = mirscalar(s,'Data',v,'Title','Mode'); +o = {b,s}; + + +function v = algosum(m) +v = sum(abs(m(:,:,:,1))) - sum(abs(m(:,:,:,2))); + + +function v = algobest(m) +v = max(m(:,:,:,1)) - max(m(:,:,:,2)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirnovelty.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,208 @@ +function varargout = mirnovelty(orig,varargin) +% n = mirnovelty(m) evaluates the novelty score from a similarity matrix. +% [n,m] = mirnovelty(m) also return the similarity matrix. +% Optional argument: +% mirnovelty(...,'Distance',f) specifies the name of a dissimilarity +% distance function, from those proposed in the Statistics Toolbox +% (help pdist). +% default value: f = 'cosine' +% mirnovelty(...,'Similarity',f) specifies the name of a similarity +% measure function. This function is applied to the result of the +% distance function. cf. mirsimatrix +% default value: f = 'exponential' +% corresponding to f(x) = exp(-x) +% mirnovelty(...,'KernelSize',s) or more simply mirnovelty(...,s) +% specifies the length of the gaussian kernel, in samples. +% default value: s = 64. +% mirnovelty(...,'Normal',0) does not normalize the novelty curve +% between the values 0 and 1. +% +% Foote, J. & Cooper, M. (2003). Media Segmentation using Self-Similarity +% Decomposition,. In Proc. SPIE Storage and Retrieval for Multimedia +% Databases, Vol. 5021, pp. 167-75. + + dist.key = 'Distance'; + dist.type = 'String'; + dist.default = 'cosine'; + option.dist = dist; + + sm.key = {'Measure','Similarity'}; + sm.type = 'String'; + sm.default = 'exponential'; + option.sm = sm; + + K.key = {'KernelSize','Width'}; + K.type = 'Integer'; + K.default = 64; + option.K = K; + + transf.type = 'String'; + transf.default = 'TimeLag'; + transf.choice = {'Horizontal','TimeLag'}; + option.transf = transf; + + normal.key = 'Normal'; + normal.type = 'Boolean'; + normal.default = 1; + normal.when = 'After'; + option.normal = normal; + +specif.option = option; +specif.combineframes = @combineframes; +specif.nochunk = 1; +varargout = mirfunction(@mirnovelty,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = 'mirscalar'; +if not(isamir(x,'mirscalar') && strcmp(get(x,'Title'),'Novelty')) + x = mirsimatrix(x,'Distance',option.dist,'Similarity',option.sm,... + 'Width',option.K,option.transf); +end +if isa(x,'mirdesign') + x = set(x,'Overlap',ceil(option.K)); +end + + +function y = main(orig,option,postoption) +if iscell(orig) + orig = orig{1}; +end +if not(isa(orig,'mirscalar')) + s = get(orig,'Data'); + dw = get(orig,'DiagWidth'); + for k = 1:length(s) + if isnumeric(dw) + dwk = dw; + else + dwk = dw{k}; + end + if option.K + cgs = min(option.K,dwk); + else + cgs = dwk; + end + cg = checkergauss(cgs,option.transf); + disp('Computing convolution, please wait...') + for z = 1:length(s{k}) + sz = s{k}{z}; + szm = max(max(sz)); + for i = find(isnan(sz)) + sz(i) = szm; + end + cv = convolve2(sz,cg,'same'); + nl = size(cv,1); + nc = size(cv,2); + if nl == 0 + warning('WARNING IN NOVELTY: No frame decomposition. The novelty score cannot be computed.'); + score{k}{z} = []; + else + sco = cv(floor(size(cv,1)/2),:); + incr = find(diff(sco)>=0); + if not(isempty(incr)) + decr = find(diff(sco)<=0); + sco(1:incr(1)-1) = NaN(1,incr(1)-1); + if not(isempty(decr)) + sco(decr(end)+1:end) = NaN(1,length(sco)-decr(end)); + end + incr = find(diff(sco)>=0); + sco2 = sco; + if not(isempty(incr)) + sco2 = sco2(1:incr(end)+1); + end + decr = find(diff(sco)<=0); + if not(isempty(decr)) && decr(1)>2 + sco2 = sco2(decr(1)-1:end); + end + mins = min(sco2); + rang = find(sco>= mins); + if not(isempty(rang)) + sco(1:rang(1)-1) = NaN(1,rang(1)-1); + sco(rang(end)+1:end) = NaN(1,length(sco)-rang(end)); + end + end + score{k}{z} = sco; + end + end + end +else + score = get(orig,'Data'); +end +if not(isempty(postoption)) && postoption.normal + for k = 1:length(score) + for l = 1:length(score{k}) + sco = score{k}{l}; + sco = (sco-min(sco))/(max(sco)-min(sco)); + score{k}{l} = sco; + end + end +end +n = mirscalar(orig,'Data',score,'Title','Novelty'); +y = {n orig}; + + +function old = combineframes(old,new) +if not(iscell(old)) + old = {old}; +end +if not(iscell(new)) + new = {new}; +end +for var = 1:length(new) + ov = old{var}; + nv = new{var}; + ofp = get(ov,'FramePos'); + ofp = ofp{1}{1}; + nfp = get(nv,'FramePos'); + nfp = nfp{1}{1}; + od = get(ov,'Data'); + od = od{1}{1}; + onan = find(isnan(od)); + od(onan) = []; + ofp(:,onan) = []; + nd = get(nv,'Data'); + nd = nd{1}{1}; + nnan = find(isnan(nd)); + nd(nnan) = []; + nfp(:,nnan) = []; + [unused omatch nmatch] = intersect(ofp(1,:),nfp(1,:)); + if isempty(omatch) + ov = set(ov,'FramePos',{{[ofp nfp]}},'Data',{{[od nd]}}); + else + lm = length(omatch); + ov = set(ov,'FramePos',{{[ofp(:,1:omatch(1)-1) nfp]}},... + 'Data',{{[od(1:omatch(1)-1),... + (od(omatch).*(lm:-1:1) + nd(nmatch).*(1:lm))/(lm+1),... + nd(nmatch(end)+1:end)]}}); + end + old{var} = ov; +end + + +function y = checkergauss(N,transf) +hN = ceil(N/2); +if strcmpi(transf,'TimeLag') + y = zeros(hN,N); + for j = 1:N + for i = 1:hN + g = exp(-((i/hN)^2 + (((j-hN)/hN)^2))*4); + if j>hN && j<hN+i + y(hN-i+1,j) = -g; + else + y(hN-i+1,j) = g; + end + end + end +else + y = zeros(N); + for i = 1:N + for j = 1:N + g = exp(-(((i-hN)/hN)^2 + (((j-hN)/hN)^2))*4); + if xor(j>i,j>N-i) + y(i,j) = -g; + else + y(i,j) = g; + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mironsets.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,643 @@ +function varargout = mironsets(x,varargin) +% o = mironsets(x) shows a temporal curve where peaks relate to the +% position of note onset times, and estimates those note onset +% positions. +% Optional arguments: +% mironsets(...,f) selects the strategy for the computation of the +% onset detection function. +% f = 'Envelope': Envelope of the audio signal. (Default choice). +% With two methods for envelope extraction: +% mironsets(...,'Spectro') (Default): +% mironsets(...,'SpectroFrame',fl,fh) species the frame +% length fl (in s.) and the hop factor fh (as a value +% between 0 and 1) +% Default values: fl = .1 s., fh = .1 +% the frequency reassigment method can be specified: +% 'Freq' (default), 'Mel', 'Bark' or 'Cents' (cf. mirspectrum). +% mironsets(...,'Filter'): +% mironsets(...,'Filterbank',nc) specifies a preliminary +% filterbank decomposition into nc channels. If nc = 0, +% no decomposition is performed. +% Default value: 40. +% mironsets(...,'FilterbankType',ft) specifies the type of +% filterbank (see mirfilterbank). +% Default value: 'Gammatone'; +% Options associated to the mirenvelope function can be +% passed here as well (see help mirenvelope): +% 'FilterType','Tau','PreDecim' +% mironsets(...,'Sum','no') does not sum back the channels at +% the end of the computation. The resulting onset curve +% remains therefore decomposed into several channels. +% Options associated to the mirenvelope function can be +% passed here as well (see help mirenvelope): +% 'HalfwaveCenter','Diff','HalfwaveDiff','Center', +% 'Smooth', 'Sampling','Log','Power','Lambda', +% ,'PostDecim','UpSample' +% f = 'SpectralFlux': Spectral flux of the audio signal. +% Options associated to the mirflux function can be +% passed here as well (see help mirflux): +% 'Inc' (toggled on by default here), +% 'Halfwave' (toggled on by default here), +% 'Complex' (toggled off by default), +% 'Median' (toggled on by default here) +% f = 'Pitch ':computes a frame-decomposed autocorrelation function , +% of same default characteristics than those returned +% by mirpitch, with however a range of frequencies set by +% the following options: +% 'Min' (set by default to 30 Hz), +% 'Max' (set by default to 1000 Hz), +% and subsequently computes the novelty curve of the +% resulting similatrix matrix. +% Option associated to the mirnovelty function can be +% passed here as well (see help mirnovelty): +% 'KernelSize' (set by default to 32 samples) +% mironsets(...,'Detect',d) toggles on or off the onset detection, +% which is based on the onset detection function. +% (By default toggled on.) +% Option associated to the mirpeaks function can be specified as +% well: +% 'Contrast' with default value c = .01 +% 'Threshold' with default value t = 0 +% mironsets(...,'Attack') (or 'Attacks') detects attack phases. +% mironsets(...,'Release') (or 'Releases') detects release phases. +% mironsets(...,'Gauss',o) estimate the attack and/or release +% points using a gaussian envelope smoothing of order o of the +% onset curve. +% mironsets(...,'Frame',...) decomposes into frames, with default frame +% length 3 seconds and hop factor .1 +% Preselected onset detection models: +% mironsets(...,'Scheirer') corresponds to (Scheirer, 1998): +% mironsets(...,'FilterBankType','Scheirer',... +% 'FilterType','HalfHann','Sampling',200,... +% 'HalfWaveDiff','Sum',0,'Detect',0) +% mironsets(...,'Klapuri99') corresponds to most of (Klapuri, 1999). + +%% options related to 'Envelope': + + env.key = 'Envelope'; + env.type = 'Boolean'; + env.default = NaN; + option.env = env; + + envmethod.key = 'Method'; % optional + envmethod.type = 'Boolean'; + option.envmethod = envmethod; + + envmeth.type = 'String'; + envmeth.choice = {'Filter','Spectro'}; + envmeth.default = 'Spectro'; + option.envmeth = envmeth; + +%% options related to 'Filter': + + filter.key = 'FilterType'; + filter.type = 'String'; + filter.choice = {'IIR','HalfHann'}; + filter.default = 'IIR'; + option.filter = filter; + + tau.key = 'Tau'; + tau.type = 'Integer'; + tau.default = .02; + option.tau = tau; + + fb.key = {'Filterbank','NbChannels'}; + fb.type = 'Integer'; + fb.default = 40; + option.fb = fb; + + filtertype.key = 'FilterbankType'; + filtertype.type = 'String'; + %filtertype.choice = {'Gammatone','2Channels','Scheirer','Klapuri'}; + filtertype.default = 'Gammatone'; + option.filtertype = filtertype; + + decim.key = {'Decim','PreDecim'}; + decim.type = 'Integer'; + decim.default = 0; + option.decim = decim; + +%% options related to 'Spectro': + + band.type = 'String'; + band.choice = {'Freq','Mel','Bark','Cents'}; + band.default = 'Freq'; + option.band = band; + + specframe.key = 'SpectroFrame'; + specframe.type = 'Integer'; + specframe.number = 2; + specframe.default = [.1 .1]; + option.specframe = specframe; + + sum.key = 'Sum'; + sum.type = 'Boolean'; + sum.default = 1; + option.sum = sum; + + chwr.key = 'HalfwaveCenter'; + chwr.type = 'Boolean'; + chwr.default = 0; + chwr.when = 'After'; + option.chwr = chwr; + + mu.key = 'Mu'; + mu.type = 'Boolean'; + mu.default = 0; + mu.when = 'After'; + option.mu = mu; + + oplog.key = 'Log'; + oplog.type = 'Boolean'; + oplog.default = 0; + oplog.when = 'After'; + option.log = oplog; + + oppow.key = 'Power'; + oppow.type = 'Boolean'; + oppow.default = 0; + oppow.when = 'After'; + option.power = oppow; + + diffenv.key = 'DiffEnvelope'; % obsolete, replaced by 'Diff' + diffenv.type = 'Boolean'; + diffenv.default = 0; + option.diffenv = diffenv; + + diff.key = 'Diff'; + diff.type = 'Integer'; + diff.default = 0; + diff.keydefault = 1; + diff.when = 'After'; + option.diff = diff; + + diffhwr.key = 'HalfwaveDiff'; + diffhwr.type = 'Integer'; + diffhwr.default = 0; + diffhwr.keydefault = 1; + diffhwr.when = 'After'; + option.diffhwr = diffhwr; + + lambda.key = 'Lambda'; + lambda.type = 'Integer'; + lambda.default = 1; + lambda.when = 'After'; + option.lambda = lambda; + + c.key = 'Center'; + c.type = 'Boolean'; + c.default = 0; + c.when = 'After'; + option.c = c; + + aver.key = 'Smooth'; + aver.type = 'Integer'; + aver.default = 0; + aver.keydefault = 30; + aver.when = 'After'; + option.aver = aver; + + ds.key = {'Down','PostDecim'}; + ds.type = 'Integer'; + if isamir(x,'mirenvelope') + ds.default = 1; + else + ds.default = NaN; + end + ds.when = 'After'; + ds.chunkcombine = 'During'; + option.ds = ds; + + sampling.key = 'Sampling'; + sampling.type = 'Integer'; + sampling.default = 0; + sampling.when = 'After'; + option.sampling = sampling; + + up.key = {'UpSample'}; + up.type = 'Integer'; + up.default = 0; + up.keydefault = 2; + option.up = up; + +%% options related to 'SpectralFlux' + flux.key = 'SpectralFlux'; + flux.type = 'Boolean'; + flux.default = 0; + option.flux = flux; + + complex.key = 'Complex'; + complex.type = 'Boolean'; + complex.when = 'Both'; + complex.default = 0; + option.complex = complex; + + inc.key = 'Inc'; + inc.type = 'Boolean'; + inc.default = 1; + option.inc = inc; + + median.key = 'Median'; + median.type = 'Integer'; + median.number = 2; + median.default = [.2 1.3]; + median.when = 'After'; + option.median = median; + + hw.key = 'Halfwave'; + hw.type = 'Boolean'; + hw.default = 1; + hw.when = 'After'; + option.hw = hw; + +%% options related to 'Pitch': + pitch.key = 'Pitch'; + pitch.type = 'Boolean'; + pitch.default = 0; + option.pitch = pitch; + + min.key = 'Min'; + min.type = 'Integer'; + min.default = 30; + option.min = min; + + max.key = 'Max'; + max.type = 'Integer'; + max.default = 1000; + option.max = max; + + kernelsize.key = 'KernelSize'; + kernelsize.type = 'Integer'; + kernelsize.default = 32; + option.kernelsize = kernelsize; + +%% options related to event detection + detect.key = 'Detect'; + detect.type = 'String'; + detect.choice = {'Peaks','Valleys',0,'no','off'}; + detect.default = 'Peaks'; + detect.keydefault = 'Peaks'; + detect.when = 'After'; + option.detect = detect; + + cthr.key = 'Contrast'; + cthr.type = 'Integer'; + cthr.default = NaN; + cthr.when = 'After'; + option.cthr = cthr; + + thr.key = 'Threshold'; + thr.type = 'Integer'; + thr.default = 0; + thr.when = 'After'; + option.thr = thr; + + attack.key = {'Attack','Attacks'}; + attack.type = 'Boolean'; + attack.default = 0; + attack.when = 'After'; + option.attack = attack; + + release.key = {'Release','Releases'}; + release.type = 'String'; + release.choice = {'Olivier','Valeri',0,'no','off'}; + release.default = 0; + release.keydefault = 'Olivier'; + release.when = 'After'; + option.release = release; + + gauss.key = 'Gauss'; + gauss.type = 'Integer'; + gauss.default = 0; + gauss.when = 'After'; + option.gauss = gauss; + +%% preselection + presel.choice = {'Scheirer','Klapuri99'}; + presel.type = 'String'; + presel.default = 0; + option.presel = presel; + + +%% 'Frame' option + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.when = 'Both'; + frame.number = 2; + frame.default = [0 0]; + frame.keydefault = [3 .1]; + option.frame = frame; + +specif.option = option; + +specif.eachchunk = 'Normal'; +specif.combinechunk = 'Concat'; +specif.extensive = 1; + +specif.title = 'Onset curve'; %used for miroptions + +varargout = mirfunction(@mironsets,x,varargin,nargout,specif,@init,@main); + + +%% INIT + +function [y type] = init(x,option) +if iscell(x) + x = x{1}; +end +if ischar(option.presel) + if strcmpi(option.presel,'Scheirer') + option.filtertype = 'Scheirer'; + option.filter = 'HalfHann'; + option.envmeth = 'Filter'; + elseif strcmpi(option.presel,'Klapuri99') + option.filtertype = 'Klapuri'; + option.filter = 'HalfHann'; + option.envmeth = 'Filter'; + option.decim = 180; + end +end +if option.diffenv + option.env = 1; +end +if isnan(option.env) + if option.flux || option.pitch + option.env = 0; + else + option.env = 1; + end +end +if isamir(x,'miraudio') + if option.env + if strcmpi(option.envmeth,'Filter') && option.fb>1 + fb = mirfilterbank(x,option.filtertype,'NbChannels',option.fb); + else + fb = x; + end + y = mirenvelope(fb,option.envmeth,option.band,... + 'Frame',option.specframe(1),option.specframe(2),... + 'FilterType',option.filter,... + 'Tau',option.tau,'UpSample',option.up,... + 'PreDecim',option.decim,'PostDecim',0); + type = 'mirenvelope'; + elseif option.flux + x = mirframenow(x,option); + y = mirflux(x,'Inc',option.inc,'Complex',option.complex); + type = 'mirscalar'; + elseif option.pitch + [unused ac] = mirpitch(x,'Frame','Min',option.min,'Max',option.max); + y = mirnovelty(ac,'KernelSize',option.kernelsize); + type = 'mirscalar'; + end +elseif (option.pitch && not(isamir(x,'mirscalar'))) ... + || isamir(x,'mirsimatrix') + y = mirnovelty(x,'KernelSize',option.kernelsize); + type = 'mirscalar'; +elseif isamir(x,'mirscalar') || isamir(x,'mirenvelope') + y = x; %mirframenow(x,option); + type = mirtype(x); +else + x = mirframenow(x,option); + y = mirflux(x,'Inc',option.inc,'Complex',option.complex); %Not used... + type = 'mirscalar'; +end + + +%% MAIN + +function o = main(o,option,postoption) +if not(isempty(option)) && ischar(option.presel) + if strcmpi(option.presel,'Scheirer') + postoption.sampling = 200; + postoption.diffhwr = 1; + option.sum = 0; + postoption.detect = 0; + elseif strcmpi(option.presel,'Klapuri99') + postoption.mu = 1; + postoption.diffhwr = 1; + option.sum = 0; + postoption.ds = 0; + o2 = o; + end +end +if iscell(o) + o = o{1}; +end +if not(isempty(option)) && option.diffenv + postoption.diff = 1; +end +if isa(o,'mirenvelope') + if isfield(postoption,'sampling') && postoption.sampling + o = mirenvelope(o,'Sampling',postoption.sampling); + elseif isfield(postoption,'ds') + if isnan(postoption.ds) + if option.decim || strcmpi(option.envmeth,'Spectro') + postoption.ds = 0; + else + postoption.ds = 16; + end + end + if postoption.ds + o = mirenvelope(o,'Down',postoption.ds); + end + end +end +if isfield(postoption,'cthr') + if isa(o,'mirenvelope') + if postoption.mu + o = mirenvelope(o,'Mu'); + end + if postoption.log + o = mirenvelope(o,'Log'); + end + if postoption.power + o = mirenvelope(o,'Power'); + end + if postoption.diff + o = mirenvelope(o,'Diff',postoption.diff,... + 'Lambda',postoption.lambda,... + 'Complex',postoption.complex); + end + if postoption.diffhwr + o = mirenvelope(o,'HalfwaveDiff',postoption.diffhwr,... + 'Lambda',postoption.lambda,... + 'Complex',postoption.complex); + end + if postoption.aver + o = mirenvelope(o,'Smooth',postoption.aver); + end + if postoption.chwr + o = mirenvelope(o,'HalfwaveCenter'); + end + if postoption.c + o = mirenvelope(o,'Center'); + end + elseif isa(o,'mirscalar') && strcmp(get(o,'Title'),'Spectral flux') + if postoption.median + o = mirflux(o,'Median',postoption.median(1),postoption.median(2),... + 'Halfwave',postoption.hw); + else + o = mirflux(o,'Halfwave',postoption.hw); + end + end +end +if isfield(option,'sum') && option.sum + o = mirsum(o,'Adjacent',option.sum); +end +if isfield(option,'presel') && ... + ischar(option.presel) && strcmpi(option.presel,'Klapuri99') + % o, already computed, corresponds to mirenvelope(o,'Mu','HalfwaveDiff'); + % o is the relative distance function W in (Klapuri, 99); + o2 = mirenvelope(o2,'HalfwaveDiff'); + % o2 is the absolute distance function D in (Klapuri, 99); + p = mirpeaks(o,'Contrast',.2,'Chrono'); + p2 = mirpeaks(o2,'ScanForward',p,'Chrono'); + o = combinepeaks(p,p2,.05); + clear o2 p p2 + filtfreq = 44*[2.^ ([ 0:2, ( 9+(0:17) )/3 ]) ];% Center frequencies of bands + o = mirsum(o,'Weights',(filtfreq(1:end-1)+filtfreq(2:end))/2); + o = mirenvelope(o,'Smooth',12); +end +if not(isa(o,'mirscalar')) + o = mirframenow(o,postoption); +end +if isfield(postoption,'detect') && ischar(postoption.detect) + if isnan(postoption.cthr) || not(postoption.cthr) + if ischar(postoption.detect) || postoption.detect + postoption.cthr = .01; + end + elseif postoption.cthr + if not(ischar(postoption.detect) || postoption.detect) + postoption.detect = 'Peaks'; + end + end + if strcmpi(postoption.detect,'Peaks') + o = mirpeaks(o,'Total',Inf,'SelectFirst',... + 'Threshold',postoption.thr,'Contrast',postoption.cthr,... + 'Order','Abscissa','NoBegin','NoEnd'); + elseif strcmpi(postoption.detect,'Valleys') + o = mirpeaks(o,'Total',Inf,'SelectFirst',... + 'Threshold',postoption.thr,'Contrast',postoption.cthr,... + 'Valleys','Order','Abscissa','NoBegin','NoEnd'); + end + nop = cell(size(get(o,'Data'))); + o = set(o,'AttackPos',nop,'ReleasePos',nop); +end +if (isfield(postoption,'attack') && postoption.attack) || ... + (isfield(postoption,'release') && postoption.release) + p = get(o,'PeakPos'); + pm = get(o,'PeakMode'); + d = get(o,'Data'); + if postoption.attack + [st p pm] = mircompute(@startattack,d,p,pm); + end + if ischar(postoption.release) && ~strcmpi(postoption.release,'No') ... + && ~strcmpi(postoption.release,'Off') + [rl p pm st] = mircompute(@endrelease,d,p,pm,st,postoption.release); + o = set(o,'ReleasePos',rl); + end + o = set(o,'AttackPos',st,'PeakPos',p,'PeakMode',pm); +end +title = get(o,'Title'); +if not(length(title)>11 && strcmp(title(1:11),'Onset curve')) + o = set(o,'Title',['Onset curve (',title,')']); +end + + +function st = startattack(d,z,pm) +z = sort(z{1}); +pm = pm{1}; +st = zeros(size(z)); +i = 1; +dd = diff(d,1,1); % d' +ddd = diff(dd,1,1); % d'' +dddd = diff(ddd,1,1); % d''' +while i<=length(z) + % Start attack is identified to previous peak in d''. + p = find(dddd((z(i)-1)-1:-1:1)<0,1); % previous decreasing d'' + if isempty(p) + st(i) = 1; + else + n = find(dddd((z(i)-1)-p-1:-1:1)>0,1); % previous increasing d'' + if isempty(n) + st(i) = 1; + else + st(i) = ((z(i)-1)-p-(n-1))+1; + end + if i>1 && st(i-1)==st(i) + if d(z(i))>d(z(i-1)) + del = i-1; + else + del = i; + end + st(del) = []; + z(del) = []; + pm(del) = []; + i = i-1; + end + end + i = i+1; +end +st = {{st} {z} {pm}}; + + +function rt = endrelease(d,z,pm,st,meth) +z = sort(z{1}); +pm = pm{1}; +if not(isempty(st)) + st = st{1}; +end +rt = zeros(size(z)); +i = 1; +dd = diff(d,1,1); % d' +ddd = diff(dd,1,1); % d'' +dddd = diff(ddd,1,1); % d''' +while i<=length(z) + if strcmpi(meth,'Olivier') + % Release attack is identified to next (sufficiently positive) peak + % in d''. + l = find(ddd((z(i)-1):end)<min(ddd)/100,1); + % next d'' sufficiently negative + if isempty(l) + rt(i) = length(d); + else + p = find(ddd((z(i)-1)+(l-1)+1:end)>max(ddd)/100,1); % next increasing d'' + if isempty(p) + rt(i) = length(d); + else + n = find(dddd((z(i)-1)+(l-1)+p+1:end)<0,1); % next decreasing d'' + if isempty(n) + rt(i) = length(d); + else + rt(i) = ((z(i)-1)+(l-1)+p+n)+1; + end + end + end + elseif strcmpi(meth,'Valeri') + p = find(dd((z(i)-1)+1:end)>min(dd)/100,1); % find point nearest to min(dd)/100 from current peak. + if isempty(p) + rt(i) = length(d); + elseif p<=3 %that means if p is less than 3 points away from the peak then it can not be considered as the end point of release. + %Assumption is that the whole DSR(decay sustain release) section can not be shorter than 30 ms (sampling rate is 100 Hz), also, no successive note can be nearer than 30ms. + rt(i) = z(i)+3; + else + rt(i) = (z(i)-1)+(p-1); + end + end + if i>1 && rt(i-1)==rt(i) + if d(z(i))>d(z(i-1)) + del = i-1; + else + del = i; + end + rt(del) = []; + z(del) = []; + pm(del) = []; + if not(isempty(st)) + st(del) = []; + end + i = i-1; + end + i = i+1; +end +rt = {{rt} {z} {pm} {st}}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/miroptions.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,416 @@ +function [orig during after] = miroptions(method,orig,specif,varg) + +DEFAULTFRAMELENGTH = .05; +DEFAULTFRAMEHOP = .5; + +% The options are determined during the bottom-up process design (see below). + +% During the following top-down evaluation initiation, the options being +% therefore already computed have simply been passed as fourth and fifth +% arguments of miroptions. +if not(isempty(varg)) && (isstruct(varg{1}) || isempty(varg{1})) + during = varg{1}; + if isstruct(varg{1}) + if isfield(during,'struct') + if isa(orig,'mirdesign') + orig = set(orig,'Struct',during.struct); + elseif iscell(orig) + for i = 1:length(orig) + if isa(orig{i},'mirdesign') + orig{i} = set(orig{i},'Struct',during.struct); + end + end + end + during = rmfield(during,'struct'); + end + end + if length(varg) > 1 + after = varg{2}; + else + after = []; + end + return +%elseif length(varg) > 1 && isstruct(varg{2}) +% during = varg{1}; +% after = varg{2}; +% return +%elseif length(varg) >= 1 && isempty(varg{1}) +% during = []; +% after = []; +% return +end + + +during = struct; +after = struct; +if isfield(specif,'option') + option = specif.option; +else + option = struct; +end +frame = []; +after = []; +fields = fieldnames(option); +persoframe = ''; +for i = 1:length(fields) + field = fields{i}; + if isfield(option.(field),'key') && ischar(option.(field).key) ... + && strcmpi(option.(field).key,'Frame') + persoframe = field; + during.(field).auto = 0; + end + if isfield(option.(field),'when') && ... + (strcmpi(option.(field).when,'After') || ... + strcmpi(option.(field).when,'Both')) + if isamir(orig,func2str(method)) ... + && not(strcmp(func2str(method),'miraudio')) + after.(field) = 0; + elseif strcmp(field,'detect') + %if haspeaks(orig) + % after.(field) = 0; + %else + after.(field) = 'Peaks'; + %end + elseif isfield(specif,'title') + if isa(orig,'mirdata') + title = get(orig,'Title'); + sameclass = (length(title) > length(specif.title) && ... + strcmp(title(1:length(specif.title)),specif.title)); + else + sameclass = strcmp(func2str(get(orig,'Method')),'mironsets'); + end + if sameclass + after.(field) = 0; + else + after.(field) = option.(field).default; + end + elseif isfield(option.(field),'default') + after.(field) = option.(field).default; + else + after.(field) = 0; + end + end + if not(isfield(option.(field),'when')) || strcmpi(option.(field).when,'Both') + if isfield(option.(field),'default') + if strcmpi(persoframe,field) + during.(field).length.val = option.(field).default(1); + during.(field).length.unit = 's'; + during.(field).hop.val = option.(field).default(2); + during.(field).hop.unit = '/1'; + else + during.(field) = option.(field).default; + end + end + end +end +i = 1; +while i <= length(varg) + arg = varg{i}; + if strcmpi(arg,'Frame') + frame.auto = isempty(persoframe); + frame.length.unit = 's'; + frame.hop.unit = '/1'; + if length(varg) > i && isnumeric(varg{i+1}) + i = i+1; + frame.length.val = varg{i}; + if length(varg) > i && ischar(varg{i+1}) && ... + (strcmpi(varg{i+1},'s') || strcmpi(varg{i+1},'sp')) + i = i+1; + frame.length.unit = varg{i}; + end + if length(varg) > i && isnumeric(varg{i+1}) + i = i+1; + frame.hop.val = varg{i}; + if length(varg) > i && ischar(varg{i+1}) && ... + (strcmpi(varg{i+1},'%') || strcmpi(varg{i+1},'/1') || ... + strcmpi(varg{i+1},'s') || strcmpi(varg{i+1},'sp')|| ... + strcmpi(varg{i+1},'Hz')) + i = i+1; + frame.hop.unit = varg{i}; + end + if not(frame.hop.val || strcmpi(frame.hop.unit,'Hz')) + mirerror(func2str(method),'The hop factor should be strictly positive.') + end + else + if not(isempty(persoframe)) + if isfield(option.(persoframe),'keydefault') + frame.hop.val = option.(persoframe).keydefault(2); + else + frame.hop.val = option.(persoframe).default(2); + end + elseif isfield(specif,'defaultframehop') + frame.hop.val = specif.defaultframehop; + else + frame.hop.val = DEFAULTFRAMEHOP; + end + end + else + if not(isempty(persoframe)) + if isfield(option.(persoframe),'keydefault') + frame.length.val = option.(persoframe).keydefault(1); + else + frame.length.val = option.(persoframe).default(1); + end + elseif isfield(specif,'defaultframelength') + frame.length.val = specif.defaultframelength; + else + frame.length.val = DEFAULTFRAMELENGTH; + end + if not(isempty(persoframe)) + if isfield(option.(persoframe),'keydefault') + frame.hop.val = option.(persoframe).keydefault(2); + else + frame.hop.val = option.(persoframe).default(2); + end + elseif isfield(specif,'defaultframehop') + frame.hop.val = specif.defaultframehop; + else + frame.hop.val = DEFAULTFRAMEHOP; + end + end + frame.eval = 0; + if not(isfield(option,'frame')) || ... + not(isfield(option.frame,'when')) || ... + strcmpi(option.frame.when,'Before') || ... + strcmpi(option.frame.when,'Both') + during.frame = frame; + end + if isfield(option,'frame') && ... + isfield(option.frame,'when') && ... + (strcmpi(option.frame.when,'After') || ... + strcmpi(option.frame.when,'Both')) + after.frame = frame; + end + else + match = 0; + k = 0; + while not(match) && k<length(fields) + k = k+1; + field = fields{k}; + if isfield(option.(field),'key') + key = option.(field).key; + if not(iscell(key)) + key = {key}; + end + for j = 1:length(key) + if strcmpi(arg,key{j}) + match = 1; + end + end + if match + if isfield(option.(field),'type') + type = option.(field).type; + else + type = []; + end + if isfield(option.(field),'unit') + unit = option.(field).unit; + defaultunit = option.(field).defaultunit; + else + unit = {}; + end + if isfield(option.(field),'from') + from = option.(field).from; + defaultfrom = option.(field).defaultfrom; + else + from = {}; + end + if strcmpi(type,'String') + if length(varg) > i && ... + (ischar(varg{i+1}) || varg{i+1} == 0) + if isfield(option.(field),'choice') + match2 = 0; + arg2 = varg{i+1}; + for j = option.(field).choice + if (ischar(j{1}) && strcmpi(arg2,j)) || ... + (not(ischar(j{1})) && isequal(arg2,j{1})) + match2 = 1; + i = i+1; + optionvalue = arg2; + end + end + if not(match2) + if isfield(option.(field),'keydefault') + optionvalue = option.(field).keydefault; + else + error(['SYNTAX ERROR IN ',func2str(method),... + ': Unexpected keyword after key ',arg'.']); + end + end + else + i = i+1; + optionvalue = varg{i}; + end + elseif isfield(option.(field),'keydefault') + optionvalue = option.(field).keydefault; + elseif isfield(option.(field),'default') + optionvalue = option.(field).default; + else + error(['SYNTAX ERROR IN ',func2str(method),... + ': A string should follow the key ',arg'.']); + end + elseif strcmpi(type,'Boolean') + if length(varg) > i && (isnumeric(varg{i+1}) || islogical(varg{i+1})) + i = i+1; + optionvalue = varg{i}; + elseif length(varg) > i && ischar(varg{i+1}) ... + && (strcmpi(varg{i+1},'on') || ... + strcmpi(varg{i+1},'yes')) + i = i+1; + optionvalue = 1; + elseif length(varg) > i && ischar(varg{i+1}) ... + && (strcmpi(varg{i+1},'off') || ... + strcmpi(varg{i+1},'no')) + i = i+1; + optionvalue = 0; + else + optionvalue = 1; + end + elseif strcmpi(type,'Integer') || strcmpi(type,'Integers') + if length(varg) > i && isnumeric(varg{i+1}) + i = i+1; + optionvalue = varg{i}; + elseif isfield(option.(field),'keydefault') + if strcmpi(type,'Integers') + optionvalue = option.(field).keydefault; + else + optionvalue = option.(field).keydefault(1); + end + elseif isfield(option.(field),'default') + if strcmpi(type,'Integers') + optionvalue = option.(field).default; + else + optionvalue = option.(field).default(1); + end + else + error(['SYNTAX ERROR IN ',func2str(method),... + ': An integer should follow the key ',arg'.']); + end + if isfield(option.(field),'number')... + && option.(field).number == 2 + if length(varg) > i && isnumeric(varg{i+1}) + i = i+1; + optionvalue = [optionvalue varg{i}]; + elseif isfield(option.(field),'keydefault') + optionvalue = [optionvalue option.(field).keydefault(2)]; + elseif isfield(option.(field),'default') + optionvalue = [optionvalue option.(field).default(2)]; + else + error(['SYNTAX ERROR IN ',func2str(method),... + ': Two integers should follow the key ',arg'.']); + end + end + if not(isempty(unit)) + if (strcmpi(unit{1},'s') || ... + strcmpi(unit{2},'s')) && ... + (strcmpi(unit{1},'Hz') || ... + strcmpi(unit{2},'Hz')) + if length(varg) > i && ... + ischar(varg{i+1}) && ... + (strcmpi(varg{i+1},'s') || ... + strcmpi(varg{i+1},'Hz')) + i = i+1; + if not(strcmpi(varg{i},defaultunit)) + if isfield(option.(field),'opposite') + field = option.(field).opposite; + end + optionvalue = 1/optionvalue; + end + end + end + if (strcmpi(unit{1},'s') || ... + strcmpi(unit{2},'s')) && ... + (strcmpi(unit{1},'sp') || ... + strcmpi(unit{2},'sp')) + if length(varg) > i && ... + ischar(varg{i+1}) && ... + (strcmpi(varg{i+1},'sp') || ... + strcmpi(varg{i+1},'s')) + i = i+1; + if strcmpi(varg{i},'sp') + optionvalue = [optionvalue 0]; + else + optionvalue = [optionvalue 1]; + end + else + optionvalue = [optionvalue 1]; + end + end + end + if not(isempty(from)) + if length(varg) > i && ... + ischar(varg{i+1}) && ... + (strcmpi(varg{i+1},'Start') || ... + strcmpi(varg{i+1},'Middle') || ... + strcmpi(varg{i+1},'End')) + i = i+1; + if strcmpi(varg{i},'Start') + optionvalue = [optionvalue 0]; + elseif strcmpi(varg{i},'Middle') + optionvalue = [optionvalue 1]; + elseif strcmpi(varg{i},'End') + optionvalue = [optionvalue 2]; + end + else + optionvalue = [optionvalue 0]; + end + if isa(orig,'mirdesign') + orig = set(orig,'Size',optionvalue); + end + end + else + if length(varg) > i + i = i+1; + optionvalue = varg{i}; + elseif isfield(option.(field),'keydefault') + optionvalue = option.(field).keydefault(1); + else + error(['SYNTAX ERROR IN ',func2str(method),... + ': Data should follow the key ',arg'.']); + end + end + end + else + if isfield(option.(field),'choice') + for j = option.(field).choice + if strcmpi(arg,j) + match = 1; + optionvalue = arg; + end + end + end + end + if not(match) + if isfield(option.(field),'position') + if i+1 == option.(field).position + match = 1; + optionvalue = arg; + end + %else + % error(['SYNTAX ERROR IN ',func2str(method),... + % ': Badly specified key ',arg'.']); + end + end + if match == 1 + match = 2; + if isfield(option.(field),'when') ... + && (strcmpi(option.(field).when,'After') || ... + strcmpi(option.(field).when,'Both')) + after.(field) = optionvalue; + end + if not(isfield(option.(field),'when')) ... + || strcmpi(option.(field).when,'Both') + during.(field) = optionvalue; + end + end + end + if not(match) + if isnumeric(arg) || islogical(arg) + arg = num2str(arg); + end + error(['SYNTAX ERROR IN ',func2str(method),... + ': Unknown parameter ',arg'.']); + end + end + i = i+1; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirparallel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function p = mirparallel(s) +% mirparallel(1) toggles on parallel processing: (BETA) +% When ?Folder? or ?Folders? is used, several audio files can be analysed +% in parallel using several parallel Matlab sessions running on the +% different processors and/or processor cores of your computer. +% (Requires MathWorks? Parallel Computing Toolbox.) +% mirparallel(0) toggles back off parallel processing. + +persistent mir_parallel + +if nargin + warning('MIRtoolbox Parallel computing is currently in Beta Version.'); + if s + try + matlabpool size; + catch + mirerror('mirparallel','Are you sure Parallel Processing Toolbox is installed?'); + end + end + mir_parallel = s; +else + if isempty(mir_parallel) + mir_parallel = 0; + end +end + +p = mir_parallel; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirpeaks.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1132 @@ +function varargout = mirpeaks(orig,varargin) +% p = mirpeaks(x) detect peaks in x. +% Optional argument: +% mirpeaks(...,'Total',m): only the m highest peaks are selected. +% If m=Inf, no limitation of number of peaks. +% Default value: m = Inf +% mirpeaks(...,'Order',o): specifies the ordering of the peaks. +% Possible values for o: +% 'Amplitude': orders the peaks from highest to lowest +% (Default choice.) +% 'Abscissa': orders the peaks along the abscissa axis. +% mirpeaks(...,'Contrast',cthr): a threshold value. A given local +% maximum will be considered as a peak if its distance with the +% previous and successive local minima (if any) is higher than +% this threshold. This distance is expressed with respect to the +% total amplitude of x: a distance of 1, for instance, is +% equivalent to the distance between the maximum and the minimum +% of x. +% Default value: cthr = 0.1 +% mirpeaks(...,'SelectFirst',fthr): If the 'Contrast' selection has +% been chosen, this additional option specifies that when one +% peak has to be chosen out of two candidates, and if the +% difference of their amplitude is below the threshold fthr, +% then the most ancien one is selected. +% Option toggled off by default: +% Default value if toggled on: fthr = cthr/2 +% mirpeaks(...,'Threshold',thr): a threshold value. +% A given local maximum will be considered as a peak if its +% normalized amplitude is higher than this threshold. +% A given local minimum will be considered as a valley if its +% normalized amplitude is lower than this threshold. +% The normalized amplitude can have value between 0 (the minimum +% of the signal in each frame) and 1 (the maximum in each +% frame). +% Default value: thr=0 for peaks thr = 1 for valleys +% mirpeaks(...,'Interpol',i): estimates more precisely the peak +% position and amplitude using interpolation. Performed only on +% data with numerical abscissae axis. +% Possible value for i: +% '', 'no', 'off', 0: no interpolation +% 'Quadratic': quadratic interpolation. (default value). +% mirpeaks(...,'Valleys'): detect valleys instead of peaks. +% mirpeaks(...,'Reso',r): removes peaks whose distance to one or +% several higher peaks is lower than a given threshold. +% Possible value for the threshold r: +% 'SemiTone': ratio between the two peak positions equal to +% 2^(1/12) +% a numerical value : difference between the two peak +% positions equal to that value +% When two peaks are distant by an interval lower than the +% resolution, the highest of them is selected by default. +% mirpeaks(...,'Reso',r,'First') specifies on the contrary that +% the first of them is selected by default. +% mirpeaks(...,'Nearest',t,s): takes the peak nearest a given abscisse +% values t. The distance is computed either on a linear scale +% (s = 'Lin') or logarithmic scale (s = 'Log'). In this case, +% only one peak is extracted. +% mirpeaks(...,'Pref',c,std): indicates a region of preference for +% the peak picking, centered on the abscisse value c, with a +% standard deviation of std. +% mirpeaks(...,'NoBegin'): does not consider the first sample as a +% possible peak candidate. +% mirpeaks(...,'NoEnd'): does not consider the last sample as a possible +% peak candidate. +% mirpeaks(...,'Normalize',n): specifies whether frames are +% normalized globally or individually. +% Possible value for n: +% 'Global': normalizes the whole frames altogether from 0 to +% 1 (default choice). +% 'Local': normalizes each frame from 0 to 1. +% mirpeaks(...,'Extract'): extracts from the initial curves all the +% positive continuous segments (or "curve portions") where peaks +% are located. +% mirpeaks(...,'Only'): keeps from the original curve only the data +% corresponding to the peaks, and zeroes the remaining data. +% mirpeaks(...,'Track',t): tracks temporal continuities of peaks. If +% a value t is specified, the variation between successive peaks +% is tolerated up to t samples. +% mirpeaks(...,'CollapseTrack',ct): collapses tracks into one single +% track, and remove small track transitions, of length shorter +% than ct samples. Default value: ct = 7 + + m.key = 'Total'; + m.type = 'Integer'; + m.default = Inf; + option.m = m; + + nobegin.key = 'NoBegin'; + nobegin.type = 'Boolean'; + nobegin.default = 0; + option.nobegin = nobegin; + + noend.key = 'NoEnd'; + noend.type = 'Boolean'; + noend.default = 0; + option.noend = noend; + + order.key = 'Order'; + order.type = 'String'; + order.choice = {'Amplitude','Abscissa'}; + order.default = 'Amplitude'; + option.order = order; + + chro.key = 'Chrono'; % obsolete, corresponds to 'Order','Abscissa' + chro.type = 'Boolean'; + chro.default = 0; + option.chro = chro; + + ranked.key = 'Ranked'; % obsolete, corresponds to 'Order','Amplitude' + ranked.type = 'Boolean'; + ranked.default = 0; + option.ranked = ranked; + + vall.key = 'Valleys'; + vall.type = 'Boolean'; + vall.default = 0; + option.vall = vall; + + cthr.key = 'Contrast'; + cthr.type = 'Integer'; + cthr.default = .1; + option.cthr = cthr; + + first.key = 'SelectFirst'; + first.type = 'Integer'; + first.default = 0; + first.keydefault = NaN; + option.first = first; + + thr.key = 'Threshold'; + thr.type = 'Integer'; + thr.default = NaN; + option.thr = thr; + + smthr.key = 'MatrixThreshold'; % to be documented in version 1.3 + smthr.type = 'Integer'; + smthr.default = NaN; + option.smthr = smthr; + + graph.key = 'Graph'; + graph.type = 'Integer'; + graph.default = 0; + graph.keydefault = .25; + option.graph = graph; + + interpol.key = 'Interpol'; + interpol.type = 'String'; + interpol.default = 'Quadratic'; + interpol.keydefault = 'Quadratic'; + option.interpol = interpol; + + reso.key = 'Reso'; + %reso.type = 'String'; + %reso.choice = {0,'SemiTone'}; + reso.default = 0; + option.reso = reso; + + resofirst.key = 'First'; + resofirst.type = 'Boolean'; + resofirst.default = 0; + option.resofirst = resofirst; + + c.key = 'Pref'; + c.type = 'Integer'; + c.number = 2; + c.default = [0 0]; + option.c = c; + + near.key = 'Nearest'; + near.type = 'Integer'; + near.default = NaN; + option.near = near; + + logsc.type = 'String'; + logsc.choice = {'Lin','Log',0}; + logsc.default = 'Lin'; + option.logsc = logsc; + + normal.key = 'Normalize'; + normal.type = 'String'; + normal.choice = {'Local','Global'}; + normal.default = 'Global'; + option.normal = normal; + + extract.key = 'Extract'; + extract.type = 'Boolean'; + extract.default = 0; + option.extract = extract; + + only.key = 'Only'; + only.type = 'Boolean'; + only.default = 0; + option.only = only; + + delta.key = 'Track'; + delta.type = 'Integer'; + delta.default = 0; + delta.keydefault = Inf; + option.delta = delta; + + mem.key = 'TrackMem'; + mem.type = 'Integer'; + mem.default = Inf; + option.mem = mem; + + shorttrackthresh.key = 'CollapseTracks'; + shorttrackthresh.type = 'Integer'; + shorttrackthresh.default = 0; + shorttrackthresh.keydefault = 7; + option.shorttrackthresh = shorttrackthresh; + + scan.key = 'ScanForward'; % specific to mironsets(..., 'Klapuri99') + scan.default = []; + option.scan = scan; + +specif.option = option; + +varargout = mirfunction(@mirpeaks,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = mirtype(x); + + +function p = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +if option.chro + option.order = 'Abscissa'; +elseif option.ranked + option.order = 'Amplitude'; +end +if not(isnan(option.near)) && option.m == 1 + option.m = Inf; +end +x = purgedata(x); +if option.m <= 0 + p = x; + return +end +d = get(x,'Data'); +sr = get(x,'Sampling'); +cha = 0; % Indicates when it is possible to represent as a curve along the + % Z-axis (channels) instead of the X-axis (initial abscissa). +if isnan(option.first) + option.first = option.cthr / 2; +end +if isa(x,'mirscalar') + t = get(x,'FramePos'); + for i = 1:length(d) + for j = 1:length(d{i}) + d{i}{j} = d{i}{j}'; + if size(t{i},1) == 1 + t{i}{j} = t{i}{j}(1,:,:)'; + else + t{i}{j} = (t{i}{j}(1,:,:)+t{i}{j}(2,:,:))'/2; + end + end + end +elseif isa(x,'mirsimatrix') + t = get(x,'FramePos'); + for i = 1:length(t) + for j = 1:length(t{i}) + t{i}{j} = repmat((t{i}{j}(1,:,:)+t{i}{j}(2,:,:))'/2,... + [1 size(d{i}{j},2) 1]); + end + end +elseif isa(x,'mirhisto') + error('ERROR IN MIRPEAKS: peaks of histogram not considered yet.'); +else + for i = 1:length(d) + for j = 1:length(d{i}) + if iscell(d{i}) + dij = d{i}{j}; + if ~cha && j == 1 && size(dij,3) > 1 && size(dij,1) == 1 + cha = 1; + end + if cha && j > 1 && size(dij,1) > 1 + cha = -1; + end + end + end + for j = 1:length(d{i}) + if iscell(d{i}) + dij = d{i}{j}; + if cha == 1 + if iscell(dij) + for k = 1:size(dij,2) + d{i}{j}{k} = reshape(dij{k},size(dij{k},2),size(dij{k},3)); + d{i}{j}{k} = d{i}{j}{k}'; + end + else + d{i}{j} = reshape(dij,size(dij,2),size(dij,3)); + d{i}{j} = d{i}{j}'; + end + end + end + end + end + if cha == 1 + t = get(x,'Channels'); + else + t = get(x,'Pos'); + end +end +pp = cell(1,length(d)); +pv = cell(1,length(d)); +pm = cell(1,length(d)); +ppp = cell(1,length(d)); +ppv = cell(1,length(d)); +tp = cell(1,length(d)); +tv = cell(1,length(d)); + +if isnan(option.thr) + option.thr = 0; +else + if option.vall + option.thr = 1-option.thr; + end +end +%if isnan(option.lthr) +% option.lthr = 1; +%else +% if option.vall +% option.lthr = 1-option.lthr; +% end +%end +if isnan(option.smthr) + option.smthr = option.thr - .2; +end + +if not(isempty(option.scan)) + pscan = get(option.scan,'PeakPos'); +end + +interpol = get(x,'Interpolable') && not(isempty(option.interpol)) && ... + ((isnumeric(option.interpol) && option.interpol) || ... + (ischar(option.interpol) && not(strcmpi(option.interpol,'No')) && not(strcmpi(option.interpol,'Off')))); + +for i = 1:length(d) % For each audio file,... + di = d{i}; + if cha == 1 + ti = t; %sure ? + else + ti = t{i}; + end + if not(iscell(di)) + di = {di}; + if isa(x,'mirchromagram') && not(cha) + ti = {ti}; + end + end + for h = 1:length(di) % For each segment,... + dh0 = di{h}; + if option.vall + dh0 = -dh0; + end + dh2 = dh0; + [nl0 nc np nd0] = size(dh0); + if cha == 1 + if iscell(ti) + %% problem here!!! + ti = ti{i}; %%%%%it seems that sometimes we need to use instead ti{i}(:); + end + th = repmat(ti,[1,nc,np,nd0]); + else + th = ti{h}; + if iscell(th) % Non-numerical abscissae are transformed into numerical ones. + th = repmat((1:size(th,1))',[1,nc,np]); + else + if size(th,3)<np + th = repmat(th,[1,1,np]); + end + end + end + if option.c % If a prefered region is specified, the data is amplified accordingly + dh0 = dh0.*exp(-(th-option.c(1)).^2/2/option.c(2)^2)... + /option.c(2)/sqrt(2*pi)/2; + end + + % Now the data is rescaled. the minimum is set to 0 + % and the maximum to 1. + state = warning('query','MATLAB:divideByZero'); + warning('off','MATLAB:divideByZero'); + + % Let's first normalize all frames globally: + dh0 = (dh0-repmat(min(min(min(dh0,[],1),[],2),[],4),[nl0 nc 1 nd0]))./... + repmat(max(max(max(dh0,[],1),[],2),[],4)... + -min(min(min(dh0,[],1),[],2),[],4),[nl0 nc 1 nd0]); + for l = 1:nd0 + [unused lowc] = find(max(dh0(:,:,:,l))<option.thr); + dh0(:,lowc,1,l) = 0; + end + + if strcmpi(option.normal,'Local') + % Normalizing each frame separately: + dh0 = (dh0-repmat(min(min(dh0,[],1),[],4),[nl0 1 1 nd0]))./... + repmat(max(max(dh0,[],1),[],4)... + -min(min(dh0,[],1),[],4),[nl0 1 1 nd0]); + end + warning(state.state,'MATLAB:divideByZero'); + + if option.nobegin + dh0 = [Inf(1,nc,np,nd0);dh0]; + % This infinite value at the beginning + % prevents the selection of the first sample of data + dh2 = [Inf(1,nc,np,nd0);dh2]; + th = [NaN(1,nc,np,1);th]; + else + dh0 = [-Inf(1,nc,np,nd0);dh0]; + % This infinite negative value at the beginning + % ensures the selection of the first sample of data + dh2 = [-Inf(1,nc,np,nd0);dh2]; + th = [NaN(1,nc,np,1);th]; + end + if option.noend + dh0 = [dh0;Inf(1,nc,np,nd0)]; + % idem for the last sample of data + dh2 = [dh2;Inf(1,nc,np,nd0)]; + th = [th;NaN(1,nc,np,1)]; + else + dh0 = [dh0;-Inf(1,nc,np,nd0)]; + dh2 = [dh2;-Inf(1,nc,np,nd0)]; + th = [th;NaN(1,nc,np,1)]; + end + nl0 = nl0+2; + + % Rearrange the 4th dimension (if used) into the 1st one. + nl = nl0*nd0; + dh = zeros(nl,nc,np); + dh3 = zeros(nl,nc,np); + th2 = zeros(nl,nc,np); + for l = 1:nd0 + dh((l-1)*nl0+(1:nl0)',:,:) = dh0(:,:,:,l); + dh3((l-1)*nl0+(1:nl0)',:,:) = dh2(:,:,:,l); + th2((l-1)*nl0+(1:nl0)',:,:) = th(:,:,:); + end + + th = th2; % The X-abscissa + + ddh = diff(dh); + % Let's find the local maxima + for l = 1:np + dl = dh(2:end-1,:,l); + for k = 1:nc + dk = dl(:,k); + mx{1,k,l} = find(and(and(dk >= option.cthr, ... + dk >= option.thr),... + ... dk <= option.lthr)), + and(ddh(1:(end-1),k,l) > 0, ... + ddh(2:end,k,l) <= 0)))+1; + end + end + if option.cthr + for l = 1:np + for k = 1:nc + finalmxk = []; + mxk = mx{1,k,l}; + if not(isempty(mxk)) + wait = 0; + if length(mxk)>5000 + wait = waitbar(0,['Selecting peaks... (0 out of 0)']); + end + %if option.m < Inf + % [unused,idx] = sort(dh(mxk,k,l),'descend'); % The peaks are sorted in decreasing order + % mxk = mxk(sort(idx(1:option.m))); + %end + j = 1; % Scans the peaks from begin to end. + mxkj = mxk(j); % The current peak + jj = j+1; + bufmin = Inf; + bufmax = dh(mxkj,k,l); + oldbufmin = min(dh(1:mxk(1)-1,k,l)); + while jj <= length(mxk) + if wait && not(mod(jj,5000)) + waitbar(jj/length(mxk),wait,['Selecting peaks... (',num2str(length(finalmxk)),' out of ',num2str(jj),')']); + end + bufmin = min(bufmin, ... + min(dh(mxk(jj-1)+1:mxk(jj)-1,k,l))); + if bufmax - bufmin < option.cthr + % There is no contrastive notch + if dh(mxk(jj),k,l) > bufmax && ... + (dh(mxk(jj),k,l) - bufmax > option.first ... + || (bufmax - oldbufmin < option.cthr)) + % If the new peak is significantly + % higher than the previous one, + % The peak is transfered to the new + % position + j = jj; + mxkj = mxk(j); % The current peak + bufmax = dh(mxkj,k,l); + oldbufmin = min(oldbufmin,bufmin); + bufmin = Inf; + elseif dh(mxk(jj),k,l) - bufmax <= option.first + bufmax = max(bufmax,dh(mxk(jj),k,l)); + oldbufmin = min(oldbufmin,bufmin); + end + else + % There is a contrastive notch + if bufmax - oldbufmin < option.cthr + % But the previous peak candidate + % is too weak and therefore + % discarded + oldbufmin = min(oldbufmin,bufmin); + else + % The previous peak candidate is OK + % and therefore stored + finalmxk(end+1) = mxkj; + oldbufmin = bufmin; + end + bufmax = dh(mxk(jj),k,l); + j = jj; + mxkj = mxk(j); % The current peak + bufmin = Inf; + end + jj = jj+1; + end + if bufmax - oldbufmin >= option.cthr && ... + bufmax - min(dh(mxk(j)+1:end,k,l)) >= option.cthr + % The last peak candidate is OK and stored + finalmxk(end+1) = mxk(j); + end + if wait + waitbar(1,wait); + close(wait); + drawnow + end + end + mx{1,k,l} = finalmxk; + end + end + end + if not(isempty(option.scan)) % 'ScanForward' option, used for 'Klapuri99' in mironsets + for l = 1:np + for k = 1:nc + pscankl = pscan{i}{h}{1,k,l}; % scan seed positions + mxkl = []; + lp = length(pscankl); % number of peaks + for jj = 1:lp % for each peak + fmx = find(mx{1,k,l}>pscankl(jj),1); + % position of the next max following the + % current seed + fmx = mx{1,k,l}(fmx); + if jj<lp && (isempty(fmx) || fmx>=pscankl(jj+1)) + [unused fmx] = max(dh(pscankl(jj):... + pscankl(jj+1)-1,k,l)); + fmx = fmx+pscankl(jj)-1; + elseif jj==lp && isempty(fmx) + [unused fmx] = max(dh(pscankl(jj):end,k,l)); + fmx = fmx+pscankl(jj)-1; + end + mxkl = [mxkl fmx]; + end + mx{1,k,l} = mxkl; + end + end + end + if not(isequal(option.reso,0)) % Removing peaks too close to higher peaks + if ischar(option.reso) && strcmpi(option.reso,'SemiTone') + compar = @semitone_compar; + elseif isnumeric(option.reso) + compar = @dist_compar; + end + for l = 1:np + for k = 1:nc + mxlk = sort(mx{1,k,l}); + j = 1; + while j < length(mxlk)-1 + if compar(th(mxlk(j+1),k,l),th(mxlk(j),k,l),option.reso) + decreas = option.resofirst || ... + dh(mxlk(j+1),k,l)<dh(mxlk(j),k,l); + mxlk(j + decreas) = []; + else + j = j+1; + end + end + if length(mxlk)>1 && compar(th(mxlk(end),k,l),... + th(mxlk(end-1),k,l),... + option.reso) + decreas = not(option.resofirst) &&... + dh(mxlk(end),k,l)>dh(mxlk(end-1),k,l); + mxlk(end-decreas) = []; + end + mx{1,k,l} = mxlk; + end + end + end + if not(isnan(option.near)) % Finding a peak nearest a given prefered location + for l = 1:np + for k = 1:nc + mxlk = mx{1,k,l}; + if strcmp(option.logsc,'Log') + [M I] = min(abs(log(th(mxlk,k,l)/option.near))); + else + [M I] = min(abs(th(mxlk,k,l)-option.near)); + end + mx{1,k,l} = mxlk(I); + end + end + end + if option.delta % Peak tracking + tp{i}{h} = cell(1,np); + if interpol + tpp{i}{h} = cell(1,np); + tpv{i}{h} = cell(1,np); + end + for l = 1:np + + % mxl will be the resulting track position matrix + % and myl the related track amplitude + % In the first frame, tracks can be identified to peaks. + mxl = mx{1,1,l}(:)-1; + myl = dh(mx{1,1,l}(:),k,l); + + % To each peak is associated the related track ID + tr2 = 1:length(mx{1,1,l}); + + grvy = []; % The graveyard... + + wait = 0; + if nc-1>500 + wait = waitbar(0,['Tracking peaks...']); + end + + for k = 1:nc-1 + % For each successive frame... + + if not(isempty(grvy)) + old = find(grvy(:,2) == k-option.mem-1); + grvy(old,:) = []; + end + + if wait && not(mod(k,100)) + waitbar(k/(nc-1),wait); + end + + mxk1 = mx{1,k,l}; % w^k + mxk2 = mx{1,k+1,l}; % w^{k+1} + thk1 = th(mxk1,k,l); + thk2 = th(mxk2,k,l); + myk2 = dh(mx{1,k+1,l},k,l); % amplitude + tr1 = tr2; + tr2 = NaN(1,length(mxk2)); + + mxl(:,k+1) = mxl(:,k); + + if isempty(thk1) || isempty(thk2) + %% IS THIS TEST NECESSARY?? + + myl(:,k+1) = 0; + else + for n = 1:length(mxk1) + % Let's check each track. + tr = tr1(n); % Current track. + + if not(isnan(tr)) + % still alive... + + % Step 1 in Mc Aulay & Quatieri + [int m] = min(abs(thk2-thk1(n))); + if isinf(int) || int > option.delta + % all w^{k+1} outside matching interval: + % partial becomes dead + mxl(tr,k+1) = mxl(tr,k); + myl(tr,k+1) = 0; + grvy = [grvy; tr k]; % added to the graveyard + else + % closest w^{k+1} is tentatively selected: + % candidate match + + % Step 2 in Mc Aulay & Quatieri + [best mm] = min(abs(thk2(m)-th(mx{1,k,l}))); + if mm == n + % no better match to remaining w^k: + % definite match + mxl(tr,k+1) = mxk2(m)-1; + myl(tr,k+1) = myk2(m); + tr2(m) = tr; + thk1(n) = -Inf; % selected w^k is eliminated from further consideration + thk2(m) = Inf; % selected w^{k+1} is eliminated as well + if not(isempty(grvy)) + zz = find ((mxl(grvy(:,1),k) >= mxl(tr,k) & ... + mxl(grvy(:,1),k) <= mxl(tr,k+1)) | ... + (mxl(grvy(:,1),k) <= mxl(tr,k) & ... + mxl(grvy(:,1),k) >= mxl(tr,k+1))); + grvy(zz,:) = []; + end + else + % let's look at adjacent lower w^{k+1}... + [int mmm] = min(abs(thk2(1:m)-thk1(n))); + if int > best || ... % New condition added (Lartillot 16.4.2010) + isinf(int) || ... % Conditions proposed in Mc Aulay & Quatieri (all w^{k+1} below matching interval) + int > option.delta + % partial becomes dead + mxl(tr,k+1) = mxl(tr,k); + myl(tr,k+1) = 0; + grvy = [grvy; tr k]; % added to the graveyard + else + % definite match + mxl(tr,k+1) = mxk2(mmm)-1; + myl(tr,k+1) = myk2(mmm); + tr2(mmm) = tr; + thk1(n) = -Inf; % selected w^k is eliminated from further consideration + thk2(mmm) = Inf; % selected w^{k+1} is eliminated as well + if not(isempty(grvy)) + zz = find ((mxl(grvy(:,1),k) >= mxl(tr,k) & ... + mxl(grvy(:,1),k) <= mxl(tr,k+1)) | ... + (mxl(grvy(:,1),k) <= mxl(tr,k) & ... + mxl(grvy(:,1),k) >= mxl(tr,k+1))); + grvy(zz,:) = []; + end + end + end + end + end + end + end + + % Step 3 in Mc Aulay & Quatieri + for m = 1:length(mxk2) + if not(isinf(thk2(m))) + % unmatched w^{k+1} + + if isempty(grvy) + int = []; + else + % Let's try to reuse a zombie from the + % graveyard (Lartillot). + [int z] = min(abs(th(mxl(grvy(:,1),k+1)+1,k,l)-thk2(m))); + end + if isempty(int) || int > option.delta ... + || int > min(abs(th(mxl(:,k+1)+1,k,l)-thk2(m))) + % No suitable zombie. + % birth of a new partial (Mc Aulay & + % Quatieri) + mxl = [mxl;zeros(1,k+1)]; + tr = size(mxl,1); + mxl(tr,k) = mxk2(m)-1; + else + % Suitable zombie found. (Lartillot) + tr = grvy(z,1); + grvy(z,:) = []; + end + mxl(tr,k+1) = mxk2(m)-1; + myl(tr,k+1) = myk2(m); + tr2(m) = tr; + end + end + end + + if wait + waitbar(1,wait); + close(wait); + drawnow + end + + if size(mxl,1) > option.m + tot = sum(myl,2); + [tot ix] = sort(tot,'descend'); + mxl(ix(option.m+1:end),:) = []; + myl(ix(option.m+1:end),:) = []; + end + + mxl(:,not(max(myl))) = 0; + + if option.shorttrackthresh + [myl bestrack] = max(myl,[],1); + mxl = mxl(bestrack + (0:size(mxl,2)-1)*size(mxl,1)); + changes = find(not(bestrack(1:end-1) == bestrack(2:end)))+1; + if not(isempty(changes)) + lengths = diff([1 changes nc+1]); + shorts = find(lengths < option.shorttrackthresh); + for k = 1:length(shorts) + if shorts(k) == 1 + k1 = 1; + else + k1 = changes(shorts(k)-1); + end + k2 = k1 + lengths(shorts(k)) -1; + myl(1,k1:k2) = 0; + mxl(1,k1:k2) = 0; + end + end + end + + tp{i}{h}{l} = mxl; + tv{i}{h}{l} = myl; + + if interpol + tpv{i}{h}{l} = zeros(size(mxl)); + tpp{i}{h}{l} = zeros(size(mxl)); + for k = 1:size(mxl,2) + for j = 1:size(mxl,1) + mj = mxl(j,k); + if mj>2 && mj<size(dh3,1)-1 + % More precise peak position + y0 = dh3(mj,k,l); + ym = dh3(mj-1,k,l); + yp = dh3(mj+1,k,l); + p = (yp-ym)/(2*(2*y0-yp-ym)); + tpv{i}{h}{l}(j,k) = y0 - 0.25*(ym-yp)*p; + if p >= 0 + tpp{i}{h}{l}(j,k) = (1-p)*th(mj,k,l)+p*th(mj+1,k,l); + elseif p < 0 + tpp{i}{h}{l}(j,k) = (1+p)*th(mj,k,l)-p*th(mj-1,k,l); + end + elseif mj + tpv{i}{h}{l}(j,k) = dh3(mj,k,l); + tpp{i}{h}{l}(j,k) = th(mj,k,l); + end + end + end + end + end + end + if isa(x,'mirsimatrix') && option.graph + % Finding the best branch inside a graph constructed out of a + % similarity matrix + g{i}{h} = cell(1,nc,np); + % Branch info related to each peak + br{i}{h} = {}; + % Info related to each branch + scog{i}{h} = cell(1,nc,np); + % Score related to each peak + scob{i}{h} = []; + % Score related to each branch + for l = 1:np + wait = waitbar(0,['Creating peaks graph...']); + for k = 1:nc + g{i}{h}{1,k,l} = cell(size(mx{1,k,l})); + scog{i}{h}{1,k,l} = zeros(size(mx{1,k,l})); + if wait && not(mod(k,50)) + waitbar(k/(nc-1),wait); + end + mxk = mx{1,k,l}; % Peaks in current frame + for j = k-1:-1:max(1,k-100) % Recent frames + mxj = mx{1,j,l}; % Peaks in one recent frame + for kk = 1:length(mxk) + mxkk = mxk(kk); % For each of current peaks + for jj = 1:length(mxj) + mxjj = mxj(jj); % For each of recent peaks + sco = k-j - abs(mxkk-mxjj); + % Crossprogression from recent to + % current peak + if sco >= 0 + % Negative crossprogression excluded + dist = 0; + % The distance between recent and + % current peak is the sum of all the + % simatrix values when joining the two + % peaks with a straight line. + for m = j:k + % Each point in that straight line. + mxm = mxjj + (mxkk-mxjj)*(m-j)/(k-j); + if mxm == floor(mxm) + dist = dist + 1-dh(mxm,m,l); + else + dhm0 = dh(floor(mxm),m,l); + dhm1 = dh(ceil(mxm),m,l); + dist = dist + 1-... + (dhm0 + ... + (dhm1-dhm0)*(mxm-floor(mxm))); + end + if dist > option.graph + break + end + end + if dist < option.graph + % If the distance between recent + % and current peak is not too high, + % a new edge is formed between the + % peaks, and added to the graph. + gj = g{i}{h}{1,j,l}{jj}; + % Branch information associated + % with recent peak + gk = g{i}{h}{1,k,l}{kk}; + % Branch information associated + % with current peak + if isempty(gk) || ... + sco > scog{i}{h}{1,k,l}(kk) + % Current peak branch to be updated + if isempty(gj) + % New branch starting + % from scratch + newsco = sco; + scob{i}{h}(end+1) = newsco; + bid = length(scob{i}{h}); + g{i}{h}{1,j,l}{jj} = ... + [k kk bid newsco]; + br{i}{h}{bid} = [j jj;k kk]; + else + newsco = scog{i}{h}{1,j,l}(jj)+sco; + if length(gj) == 1 + % Recent peak not + % associated with other + % branch + % -> Branch extension + bid = gj; + g{i}{h}{1,j,l}{jj} = ... + [k kk bid newsco]; + br{i}{h}{bid}(end+1,:) = [k kk]; + else + % Recent peak already + % associated with other + % branch + % -> Branch fusion + bid = length(scob{i}{h})+1; + g{i}{h}{1,j,l}{jj} = ... + [k kk bid newsco; gj]; + other = br{i}{h}{gj(1,3)}; + % Other branch + % info + % Let's copy its + % prefix to the new + % branch: + other(other(:,1)>j,:) = []; + br{i}{h}{bid} = [other;k kk]; + end + scob{i}{h}(bid) = newsco; + end + g{i}{h}{1,k,l}{kk} = bid; + % New peak associated with + % branch + scog{i}{h}{1,k,l}(kk) = newsco; + end + end + end + end + end + end + end + [scob{i}{h} IX] = sort(scob{i}{h},'descend'); + % Branch are ordered from best score to lowest + br{i}{h} = br{i}{h}(IX); + if wait + waitbar(1,wait); + close(wait); + drawnow + end + end + end + for l = 1:np % Orders the peaks and select the best ones + for k = 1:nc + mxk = mx{1,k,l}; + if length(mxk) > option.m + [unused,idx] = sort(dh(mxk,k,l),'descend'); + idx = idx(1:option.m); + elseif strcmpi(option.order,'Amplitude') + [unused,idx] = sort(dh(mxk,k,l),'descend'); + else + idx = 1:length(dh(mxk,k,l)); + end + if strcmpi(option.order,'Abscissa') + mx{1,k,l} = sort(mxk(idx)); + elseif strcmpi(option.order,'Amplitude') + mx{1,k,l} = mxk(idx); + end + end + end + if option.extract % Extracting the positive part of the curve containing the peaks + if isa(x,'mirtemporal') + filn = floor(sr{i}/25); + else + filn = 10; + end + if filn>1 && size(dh3,1)>5 + filn = min(filn,floor(size(dh3,1)/3)); + fild = filtfilt(ones(1,filn)/2,1,dh3(2:end-1,:,:))/filn/2; + else + fild = dh3(2:end-1,:,:); + end + fild = [zeros(1,size(fild,2),size(fild,3));diff(fild)]; + for l = 1:np + for k = 1:nc + idx = 1:size(dh,1); + mxlk = sort(mx{1,k,l}-1); + for j = 1:length(mxlk) + + if fild(mxlk(j),k,l) < 0 + bef0 = find(fild(1:mxlk(j)-1,k,l)>=0); + if isempty(bef0) + bef0 = []; + end + else + bef0 = mxlk(j)-1; + end + if isempty(bef0) + bef = 0; + else + bef = find(fild(1:bef0(end),k,l)<=0); + if isempty(bef) + bef = 0; + end + end + if j>1 && bef(end)<aft(1)+2 + idx(mxlk(j-1):mxlk(j)) = 0; + [unused btw] = min(dh3(mxlk(j-1)+1:mxlk(j)+1,k,l)); + btw = btw+mxlk(j-1); + idx(btw-2:btw+2) = btw-2:btw+2; + bef = btw+2; + end + + if fild(mxlk(j),k,l) > 0 + aft0 = find(fild(mxlk(j)+1:end,k,l)<=0)+mxlk(j); + if isempty(aft0) + aft0 = []; + end + else + aft0 = mxlk(j)+1; + end + if isempty(aft0) + aft = size(d{i}{h},1)+1; + else + aft = find(fild(aft0(1):end,k,l)>=0)+mxlk(j); + if isempty(aft) + aft = size(d{i}{h},1)+1; + end + end + + idx(bef(end)+3:aft(1)-3) = 0; + end + idx = idx(find(idx)); + dh3(idx,k,l) = NaN; + end + end + end + if option.vall + dh3 = -dh3; + end + mmx = cell(1,nc,np); + mmy = cell(1,nc,np); + mmv = cell(1,nc,np); + for l = 1:np + for k = 1:nc + mmx{1,k,l} = mod(mx{1,k,l}(:,:,1),nl0)-1; + mmy{1,k,l} = ceil(mx{1,k,l}/nl0); + mmv{1,k,l} = dh3(mx{1,k,l}(:,:,1),k,l); + end + end + pp{i}{h} = mmx; + pm{i}{h} = mmy; + pv{i}{h} = mmv; + if not(interpol) + ppp{i}{h} = {}; + ppv{i}{h} = {}; + else % Interpolate to find the more exact peak positions + pih = cell(1,nc,np); + vih = cell(1,nc,np); + for l = 1:np + for k = 1:nc + mxlk = mx{1,k,l}; + vih{1,k,l} = zeros(length(mxlk),1); + pih{1,k,l} = zeros(length(mxlk),1); + for j = 1:length(mxlk) + mj = mxlk(j); % Current values + if strcmpi(option.interpol,'quadratic') + if mj>2 && mj<length(dh3)-1 + % More precise peak position + y0 = dh3(mj,k,l); + ym = dh3(mj-1,k,l); + yp = dh3(mj+1,k,l); + p = (yp-ym)/(2*(2*y0-yp-ym)); + vih{1,k,l}(j) = y0 - 0.25*(ym-yp)*p; + if p >= 0 + pih{1,k,l}(j) = (1-p)*th(mj,k,l)+p*th(mj+1,k,l); + elseif p < 0 + pih{1,k,l}(j) = (1+p)*th(mj,k,l)-p*th(mj-1,k,l); + end + else + vih{1,k,l}(j) = dh3(mj,k,l); + pih{1,k,l}(j) = th(mj,k,l); + end + end + end + end + end + ppp{i}{h} = pih; + ppv{i}{h} = vih; + end + if not(iscell(d{i})) % for chromagram + d{i} = dh3(2:end-1,:,:,:); + else + if cha == 1 + d{i}{h} = zeros(1,size(dh3,2),size(dh3,1)-2); + for k = 1:size(dh3,2) + d{i}{h}(1,k,:) = dh3(2:end-1,k); + end + else + d{i}{h} = dh3(2:end-1,:,:,:); + end + end + if option.only + dih = zeros(size(d{i}{h})); + for l = 1:np + for k = 1:nc + dih(pp{i}{h}{1,k,l},k,l) = ... + d{i}{h}(pp{i}{h}{1,k,l},k,l); + end + end + d{i}{h} = dih; + end + end +end +p = set(x,'PeakPos',pp,'PeakVal',pv,'PeakMode',pm); +if interpol + p = set(p,'PeakPrecisePos',ppp,'PeakPreciseVal',ppv); +end +if option.extract + p = set(p,'Data',d); +end +empty = cell(1,length(d)); +if option.only + p = set(p,'Data',d,'PeakPos',empty,'PeakVal',empty,'PeakMode',empty); +end +if option.delta + p = set(p,'TrackPos',tp,'TrackVal',tv); + if interpol + p = set(p,'TrackPrecisePos',tpp,'TrackPreciseVal',tpv); + end +end +if isa(x,'mirsimatrix') && option.graph + p = set(p,'Graph',g,'Branch',br); +end + + +function y = semitone_compar(p1,p2,thres) +y = p1/p2 < 2^(1/12); + + +function y = dist_compar(p1,p2,thres) +y = p1-p2 < thres; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirplay.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,183 @@ +function varargout = mirplay(a,varargin) +% mirplay(a) plays audio signal, envelope, or pitches. +% If a is an envelope, what is actually played is a white noise of +% same envelope. +% If a is a mirpitch object, pitches are played using sinusoids. +% Optional arguments: +% mirplay(...,'Channel',i) plays the channel(s) of rank(s) indicated by +% the array i. +% mirplay(...,'Segment',k) plays the segment(s) of rank(s) indicated by +% the array k. +% mirplay(...,'Sequence',l) plays the sequence(s) of rank(s) indicated +% by the array l. +% mirplay(...,'Increasing',d) plays the sequences in increasing order +% of d, which could be either an array or a mirscalar data. +% mirplay(...,'Decreasing',d) plays the sequences in decreasing order +% of d, which could be either an array or a mirscalar data. +% mirplay(...,'Every',s) plays every s sequence, where s is a number +% indicating the step between sequences. +% mirplay(...,'Burst',0) toggles off the burst sound between +% segments. +% Example: mirplay(mirenvelope('Folder'),... +% 'increasing', mirrms('Folder'),... +% 'every',5) + +if ischar(a) + varargout = mirplay(miraudio(a),varargin{:}); +elseif isscalar(a) + ch.key = 'Channel'; + ch.type = 'Integer'; + ch.default = 0; + option.ch = ch; + + sg.key = 'Segment'; + sg.type = 'Integer'; + sg.default = 0; + option.sg = sg; + + se.key = 'Sequence'; + se.type = 'Integer'; + se.default = 0; + option.se = se; + + inc.key = 'Increasing'; + inc.type = 'MIRtb'; + option.inc = inc; + + dec.key = 'Decreasing'; + dec.type = 'MIRtb'; + option.dec = dec; + + every.key = 'Every'; + every.type = 'Integer'; + option.every = every; + + burst.key = 'Burst'; + burst.type = 'Boolean'; + burst.default = 1; + option.burst = burst; + + specif.option = option; + + specif.eachchunk = 'Normal'; + + varargout = mirfunction(@mirplay,a,varargin,nargout,specif,@init,@main); + if nargout == 0 + varargout = {}; + end +else + mirerror('mirplay','You cannot play this type of object.') +end + + +function [x type] = init(x,option) +type = ''; + + +function noargout = main(a,option,postoption) +if iscell(a) + a = a{1}; +end +d = get(a,'Data'); +if isa(a,'mirpitch') + amp = get(a,'Amplitude'); +end +f = get(a,'Sampling'); +n = get(a,'Name'); +c = get(a,'Channels'); +fp = get(a,'FramePos'); +if not(option.se) + if length(d)>1 + if isfield(option,'inc') + [unused order] = sort(mirgetdata(option.inc)); + elseif isfield(option,'dec') + [unused order] = sort(mirgetdata(option.dec),'descend'); + else + order = 1:length(d); + end + if isfield(option,'every') + order = order(1:option.every:end); + end + else + order = 1; + end +else + order = option.se; +end +if not(isempty(order)) + for k = order(:)' + display(['Playing analysis of file: ' n{k}]) + dk = d{k}; + if not(iscell(dk)) + dk = {dk}; + end + if option.ch + if isempty(c{k}) + chk = option.ch; + else + [unused unused chk] = intersect(option.ch,c{k}); + end + else + chk = 1:size(dk{1},3); + end + if isempty(chk) + display('No channel to play.'); + end + for l = chk + if chk(end)>1 + display([' Playing channel #' num2str(l)]); + end + if option.sg + sgk = option.sg(find(option.sg<=length(dk))); + else + sgk = 1:length(dk); + end + for i = sgk + if sgk(end)>1 + display([' Playing segment #' num2str(i)]) + end + di = dk{i}; + if isa(a,'mirpitch') + ampi = amp{k}{i}; + end + synth = zeros(1,ceil((fp{k}{i}(end)-fp{k}{i}(1))*44100)+1); + for j = 1:size(di,2) + if iscell(di) + dj = di{j}; + else + dj = di(:,j); + end + dj(isnan(dj)) = 0; + if isa(a,'mirpitch') + ampj = zeros(size(dj)); + if iscell(ampi) + ampj(1:size(ampi{j})) = ampi{j}; + else + ampj(1:size(ampi(:,j))) = ampi(:,j); + end + end + if not(isempty(dj)) + k1 = floor((fp{k}{i}(1,j)-fp{k}{i}(1))*44100)+1; + k2 = floor((fp{k}{i}(2,j)-fp{k}{i}(1))*44100)+1; + if isa(a,'mirpitch') + ampj = repmat(ampj,1,k2-k1+1); + else + ampj = ones(size(dj),k2-k1+1); + end + synth(k1:k2) = synth(k1:k2) ... + + sum(ampj.*sin(2*pi*dj*(0:k2-k1)/44100),1) ... + .*hann(k2-k1+1)'; + %plot((ampj.*sin(2*pi*dj*(0:k2-k1)/44100))') + %drawnow + end + end + soundsc(synth,44100); + if option.burst && sgk(end)>1 + sound(rand(1,10)) + end + %pause(0.5) + end + end + end +end +noargout = {}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirpulseclarity.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,450 @@ +function varargout = mirpulseclarity(orig,varargin) +% r = mirpulseclarity(x) estimates the rhythmic clarity, indicating the +% strength of the beats estimated by the mirtempo function. +% Optional arguments: +% mirpulseclarity(...,s): specifies a strategy for pulse clarity +% estimation. +% Possible values: 'MaxAutocor' (default), 'MinAutocor', +% 'KurtosisAutocor', MeanPeaksAutocor', 'EntropyAutocor', +% 'InterfAutocor', 'TempoAutocor', 'ExtremEnvelop', +% 'Attack', 'Articulation' +% mirpulseclarity(...,'Frame',l,h): orders a frame decomposition of +% the audio input of window length l (in seconds) and hop factor +% h, expressed relatively to the window length. +% Default values: l = 5 seconds and h = .1 +% Onset detection strategies: 'Envelope' (default), 'DiffEnvelope', +% 'SpectralFlux', 'Pitch'. +% Options related to the autocorrelation computation can be specified +% as well: 'Min', 'Max', 'Resonance', 'Enhanced' +% Options related to the tempo estimation can be specified here +% as well: 'Sum', 'Total', 'Contrast'. +% cf. User's Manual for more details. +% [r,a] = mirpulseclarity(x) also returns the beat autocorrelation. + + model.key = 'Model'; + model.type = 'Integer'; + model.default = 0; + option.model = model; + + stratg.type = 'String'; + stratg.choice = {'MaxAutocor','MinAutocor','MeanPeaksAutocor',... + 'KurtosisAutocor','EntropyAutocor',... + 'InterfAutocor','TempoAutocor','ExtremEnvelop',... + 'Attack','Articulation'}; ...,'AttackDiff' + stratg.default = 'MaxAutocor'; + option.stratg = stratg; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.keydefault = [5 .1]; + frame.default = [0 0]; + option.frame = frame; + +%% options related to mironsets: + + fea.type = 'String'; + fea.choice = {'Envelope','DiffEnvelope','SpectralFlux','Pitch'}; + fea.default = 'Envelope'; + option.fea = fea; + + + %% options related to 'Envelope': + + envmeth.key = 'Method'; + envmeth.type = 'String'; + envmeth.choice = {'Filter','Spectro'}; + envmeth.default = 'Spectro'; + option.envmeth = envmeth; + + %% options related to 'Filter': + + ftype.key = 'FilterType'; + ftype.type = 'String'; + ftype.choice = {'IIR','HalfHann'}; + ftype.default = 'IIR'; + option.ftype = ftype; + + fb.key = 'Filterbank'; + fb.type = 'Integer'; + fb.default = 20; + option.fb = fb; + + fbtype.key = 'FilterbankType'; + fbtype.type = 'String'; + fbtype.choice = {'Gammatone','Scheirer','Klapuri'}; + fbtype.default = 'Scheirer'; + option.fbtype = fbtype; + + %% options related to 'Spectro': + + band.type = 'String'; + band.choice = {'Freq','Mel','Bark','Cents'}; + band.default = 'Freq'; + option.band = band; + + + diffhwr.key = 'HalfwaveDiff'; + diffhwr.type = 'Integer'; + diffhwr.default = 0; + diffhwr.keydefault = 1; + option.diffhwr = diffhwr; + + lambda.key = 'Lambda'; + lambda.type = 'Integer'; + lambda.default = 1; + option.lambda = lambda; + + aver.key = 'Smooth'; + aver.type = 'Integer'; + aver.default = 0; + aver.keydefault = 30; + option.aver = aver; + + oplog.key = 'Log'; + oplog.type = 'Boolean'; + oplog.default = 0; + option.log = oplog; + + mu.key = 'Mu'; + mu.type = 'Boolean'; + mu.default = 1; + option.mu = mu; + + %% options related to 'SpectralFlux' + + inc.key = 'Inc'; + inc.type = 'Boolean'; + inc.default = 1; + option.inc = inc; + + median.key = 'Median'; + median.type = 'Integer'; + median.number = 2; + median.default = [0 0]; % Not same default as in mirtempo + option.median = median; + + hw.key = 'Halfwave'; + hw.type = 'Boolean'; + hw.default = 0; %NaN; %0; % Not same default as in mirtempo + option.hw = hw; + + +%% options related to mirattackslope + slope.type = 'String'; + slope.choice = {'Diff','Gauss'}; + slope.default = 'Diff'; + option.slope = slope; + +%% options related to mirautocor: + + enh.key = 'Enhanced'; + enh.type = 'Integers'; + enh.default = []; + enh.keydefault = 2:10; + option.enh = enh; + + r.key = 'Resonance'; + r.type = 'String'; + r.choice = {'ToiviainenSnyder','vonNoorden',0,'off','no'}; + r.default = 'ToiviainenSnyder'; + option.r = r; + + mi.key = 'Min'; + mi.type = 'Integer'; + mi.default = 40; + option.mi = mi; + + ma.key = 'Max'; + ma.type = 'Integer'; + ma.default = 200; + option.ma = ma; + +%% options related to mirtempo: + + sum.key = 'Sum'; + sum.type = 'String'; + sum.choice = {'Before','After','Adjacent'}; + sum.default = 'Before'; + option.sum = sum; + + m.key = 'Total'; + m.type = 'Integer'; + m.default = 1; + option.m = m; + + thr.key = 'Contrast'; + thr.type = 'Integer'; + thr.default = 0.01; % Not same default as in mirtempo + option.thr = thr; + +specif.option = option; + +varargout = mirfunction(@mirpulseclarity,orig,varargin,nargout,specif,@init,@main); + + + +%% Initialisation + +function [x type] = init(x,option) +%if isframed(x) +% warning('WARNING IN MIRPULSECLARITY: The input should not be already decomposed into frames.'); +% disp(['Suggestion: Use the ''Frame'' option instead.']) +%end +if iscell(x) + x = x{1}; +end +if isamir(x,'mirautocor') + type = {'mirscalar','mirautocor'}; +elseif length(option.model) > 1 + a = x; + type = {'mirscalar'}; + for m = 1:length(option.model) + if option.frame.length.val + y = mirpulseclarity(a,'Model',option.model(m),... + 'Frame',option.frame.length.val,... + option.frame.length.unit,... + option.frame.hop.val,... + option.frame.hop.unit); + else + y = mirpulseclarity(a,'Model',option.model(m)); + end + if m == 1 + x = y; + else + x = x + y; + end + end +else + if option.model + switch option.model + case 1 + case 2 + option.envmeth = 'Filter'; + option.fbtype = 'Gammatone'; + option.mu = 0; + option.r = 0; + option.lambda = .8; + option.sum = 'After'; + end + end + if length(option.stratg)>7 && strcmpi(option.stratg(end-6:end),'Autocor') + if (strcmpi(option.stratg,'MaxAutocor') || ... + strcmpi(option.stratg,'MinAutocor') || ... + strcmpi(option.stratg,'EntropyAutocor')) + option.m = 0; + end + if strcmpi(option.stratg,'MinAutocor') + option.enh = 0; + end + if option.frame.length.val + [t,x] = mirtempo(x,option.fea,'Method',option.envmeth,... + option.band,... + 'Sum',option.sum,'Enhanced',option.enh,... + 'Resonance',option.r,'Smooth',option.aver,... + 'HalfwaveDiff',option.diffhwr,... + 'Lambda',option.lambda,... + 'Frame',option.frame.length.val,... + option.frame.length.unit,... + option.frame.hop.val,... + option.frame.hop.unit,... + 'FilterbankType',option.fbtype,... + 'FilterType',option.ftype,... + 'Filterbank',option.fb,'Mu',option.mu,... + 'Log',option.log,... + 'Inc',option.inc,'Halfwave',option.hw,... + 'Median',option.median(1),option.median(2),... + 'Min',option.mi,'Max',option.ma,... + 'Total',option.m,'Contrast',option.thr); + else + [t,x] = mirtempo(x,option.fea,'Method',option.envmeth,... + option.band,... + 'Sum',option.sum,'Enhanced',option.enh,... + 'Resonance',option.r,'Smooth',option.aver,... + 'HalfwaveDiff',option.diffhwr,... + 'Lambda',option.lambda,... + 'FilterbankType',option.fbtype,... + 'FilterType',option.ftype,... + 'Filterbank',option.fb,'Mu',option.mu,... + 'Log',option.log,... + 'Inc',option.inc,'Halfwave',option.hw,... + 'Median',option.median(1),option.median(2),... + 'Min',option.mi,'Max',option.ma,... + 'Total',option.m,'Contrast',option.thr); + end + type = {'mirscalar','mirautocor'}; + elseif strcmpi(option.stratg,'ExtremEnvelop') + x = mironsets(x,'Filterbank',option.fb); + type = {'mirscalar','mirenvelope'}; + elseif strcmpi(option.stratg,'Attack') + x = mirattackslope(x,option.slope); + type = {'mirscalar','mirenvelope'}; +% elseif strcmpi(option.stratg,'AttackDiff') +% type = {'mirscalar','mirenvelope'}; + elseif strcmpi(option.stratg,'Articulation') + x = mirlowenergy(x,'ASR'); + type = {'mirscalar','mirscalar'}; + else + type = {'mirscalar','miraudio'}; + end +end + + + +%% Main function + +function o = main(a,option,postoption) +if option.model == 2 + option.stratg = 'InterfAutocor'; +end +if isa(a,'mirscalar') && not(strcmpi(option.stratg,'Attack')) % not very nice test... to improve. + o = {a}; + return +end +if option.m == 1 && ... + (strcmpi(option.stratg,'InterfAutocor') || ... + strcmpi(option.stratg,'MeanPeaksAutocor')) + option.m = Inf; +end +if iscell(a) + a = a{1}; +end +if strcmpi(option.stratg,'MaxAutocor') + d = get(a,'Data'); + rc = mircompute(@max,d); +elseif strcmpi(option.stratg,'MinAutocor') + d = get(a,'Data'); + rc = mircompute(@minusmin,d); +elseif strcmpi(option.stratg,'MeanPeaksAutocor') + m = get(a,'PeakVal'); + rc = mircompute(@meanpeaks,m); +elseif strcmpi(option.stratg,'KurtosisAutocor') + a = mirpeaks(a,'Extract','Total',option.m,'NoBegin','NoEnd'); + k = mirkurtosis(a); + %d = get(k,'Data'); + %rc = mircompute(@meanpeaks,d); + rc = mirmean(k); +elseif strcmpi(option.stratg,'EntropyAutocor') + rc = mirentropy(a); +elseif strcmpi(option.stratg,'InterfAutocor') + a = mirpeaks(a,'Total',option.m,'NoBegin','NoEnd'); + m = get(a,'PeakVal'); + p = get(a,'PeakPosUnit'); + rc = mircompute(@interf,m,p); +elseif strcmpi(option.stratg,'TempoAutocor') + a = mirpeaks(a,'Total',1,'NoBegin','NoEnd'); + p = get(a,'PeakPosUnit'); + rc = mircompute(@tempo,p); +elseif strcmpi(option.stratg,'ExtremEnvelop') + a = mirenvelope(a,'Normal'); + p = mirpeaks(a,'Order','Abscissa'); + p = get(p,'PeakPreciseVal'); + n = mirpeaks(a,'Valleys','Order','Abscissa'); + n = get(n,'PeakPreciseVal'); + rc = mircompute(@shape,p,n); +elseif strcmpi(option.stratg,'Attack') + rc = mirmean(a); +%elseif strcmpi(option.stratg,'AttackDiff') +% a = mirpeaks(a); +% m = get(a,'PeakVal'); +% rc = mircompute(@meanpeaks,m); +elseif strcmpi(option.stratg,'Articulation') + rc = a; +end + +if iscell(rc) + pc = mirscalar(a,'Data',rc,'Title','Pulse clarity'); +else + pc = set(rc,'Title',['Pulse clarity (',get(rc,'Title'),')']); +end + +if option.model + switch option.model + case 1 + alpha = 0; + beta = 2.2015; + lambda = .1; + case 2 + alpha = 0; + beta = 3.5982; + lambda = 1.87; + end + if not(lambda == 0) + pc = (pc+alpha)^lambda * beta; + else + pc = log(pc+alpha) * beta; + end + title = ['Pulse clarity (Model ',num2str(option.model),')']; + pc = set(pc,'Title',title); +end + +o = {pc a}; + + +%% Routines + +function r = shape(p,n) +p = p{1}; +n = n{1}; +if length(p)>length(n) + d = sum(p(1:end-1) - n) + sum(p(2:end) - n); + r = d/(2*length(n)); +elseif length(p)<length(n) + d = sum(p - n(1:end-1)) + sum(p - n(2:end)); + r = d/(2*length(p)); +else + d = sum(p(2:end) - n(1:end-1)) + sum(p(1:end-1) - n(2:end)); + r = d/(2*(length(p)-1)); +end + + +function rc = minusmin(ac) +rc = -min(ac); + + +function rc = meanpeaks(ac) +rc = zeros(1,length(ac)); +for j = 1:length(ac) + if isempty(ac{j}) + rc(j) = NaN; + else + rc(j) = mean(ac{j}); + end +end + + +function rc = interf(mk,pk) +rc = zeros(size(mk)); +for j = 1:size(mk,3) + for i = 1:size(mk,2) + pij = pk{1,i,j}; + mij = mk{1,i,j}; + if isempty(pij) + rc(1,i,j) = 0; + else + high = max(pij(2:end),pij(1)); + low = min(pij(2:end),pij(1)); + quo = rem(high,low)./low; + nomult = quo>.15 & quo<.85; + fij = mij(2:end)/mij(1) .*nomult; + fij(fij<0) = 0; + rc(1,i,j) = exp(-sum(fij)/4); % Pulsations that are not in integer ratio + % with dominant pulse decrease clarity + end + end +end + + +function rc = tempo(pk) +rc = zeros(size(pk)); +for j = 1:size(pk,3) + for i = 1:size(pk,2) + pij = pk{1,i,j}; + if isempty(pij) + rc(1,i,j) = 0; + else + rc(1,i,j) = exp(-pij(1)/4)/exp(-.33/4); % Fast dominant pulse + % increases clarity + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirread.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,138 @@ +function [d,tp,fp,f,b,n,ch] = mirread(extract,orig,load,folder,verbose) +% Read the audio file ORIG, at temporal position indicated by EXTRACT. If +% EXTRACT is empty, all the audio file is loaded. +% If LOAD is set to 0, just the meta-data is collected, and the actual +% audio data is not taken into consideration. If it is set to 1, the +% data are loaded from the current directory. If LOAD is a string, it +% is considered as the path of the directory. +% If FOLDER is set to 1, no error is returned if an audio file cannot be +% loaded. +% Output: +% D is the audio signal, +% TP are the temporal positions, +% FP are the two extreme temporal positions (used for frame positions), +% F is the sampling rate, +% B is the resolution in number of bits, +% N is the file name. +% CH are the channel index. + +if nargin < 5 + verbose = 0; +end +d = {}; +f = {}; +b = {}; +tp = {}; +fp = {}; +n = {}; +ch = {}; +try + [d,f,b,tp,fp,n,ch] = audioread(extract,@wavread,orig,load,verbose,folder); +catch + err.wav = lasterr; + try + [d,f,b,tp,fp,n,ch] = audioread(extract,@auread,orig,load,verbose,folder); + catch + err.au = lasterr; + try + [d,f,b,tp,fp,n,ch] = audioread(extract,@mp3read,orig,load,verbose,folder); + catch + err.mp3 = lasterr; + try + [d,f,b,tp,fp,n,ch] = audioread(extract,@aiffread,orig,load,verbose,folder); + catch + err.aiff = lasterr; + if length(orig)>4 && strcmpi(orig(end-3:end),'.bdf') + try + [d,f,b,tp,fp,n,ch] = audioread(extract,@bdfread,orig,load,verbose,folder); + catch + if not(strcmp(err.wav(1:16),'Error using ==> ') && folder) + misread(orig, err); + end + end + else + if not(strcmp(err.wav(1:16),'Error using ==> ') && folder) + misread(orig, err); + end + end + end + end + end +end + + +function [d,f,b,tp,fp,n,ch] = audioread(extract,reader,file,load,verbose,folder) +n = file; +if folder + file = ['./',file]; +end +if load + if isempty(extract) + [s,f,b] = reader(file); + else + [unused,f,b] = reader(file,1); + s = reader(file,extract(1:2)); + if length(extract) > 3 + s = s(:,extract(4)); + end + end + if verbose + disp([file,' loaded.']); + end + d{1} = reshape(s,size(s,1),1,size(s,2)); %channels along dim 3 + ch = 1:size(s,2); + if isempty(extract) || extract(3) + tp{1} = (0:size(s,1)-1)'/f; + else + tp{1} = (extract(1)-1+(0:size(s,1)-1))'/f; + end + if isempty(s) + fp{1} = 0; + else + fp{1} = tp{1}([1 end]); + end +else + [unused,f,b] = reader(file,1); + dsize = reader(file,'size'); + d = dsize(1); + tp = {}; + fp = {}; + ch = dsize(2); +end + + +function [y,fs,nbits] = bdfread(file,check) +DAT = openbdf(file); +NRec = DAT.Head.NRec; +if not(length(check)==2) + b = readbdf(DAT,1); + y = length(b.Record(43,:)) * NRec; +else + y = []; + if mirwaitbar + handle = waitbar(0,'Loading BDF channel...'); + else + handle = 0; + end + for i = 1:NRec + b = readbdf(DAT,i); + y = [y;b.Record(43,:)']; + if handle + waitbar(i/NRec,handle); + end + end + if handle + delete(handle) + end +end +fs = DAT.Head.SampleRate(43); +nbits = NaN; + + +function misread(file,err) +display('Here are the error message returned by each reader:'); +display(err.wav); +display(err.au); +display(err.mp3); +display(err.aiff); +mirerror('MIRREAD',['Cannot open file ',file]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirregularity.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,90 @@ +function varargout = mirregularity(orig,varargin) +% i = mirregularity(x) calculates the irregularity of a spectrum, i.e., +% the degree of variation of the successive peaks of the spectrum. +% Specification of the definition of irregularity: +% mirregularity(...,'Jensen') is based on (Jensen, 1999), +% where the irregularity is the sum of the square of the +% difference in amplitude between adjoining partials. +% (Default approach) +% mirregularity(...,'Krimphoff') is based on (Krimphoff et al., 1994), +% where the irregularity is the sum of the amplitude minus the +% mean of the preceding, same and next amplitude. +% If the input x is not already a spectrum with peak extracted, the peak +% picking is performed prior to the calculation of the irregularity. +% In this case the 'Contrast' parameter used in mirpeaks can be +% modified, and is set by default to .1. +% +% [Krimphoff et al. 1994] J. Krimphoff, S. McAdams, S. Winsberg, +% Caracterisation du timbre des sons complexes. II Analyses +% acoustiques et quantification psychophysique. Journal de Physique +% IV, Colloque C5, Vol. 4. 1994. +% [Jensen, 1999] K. Jensen, Timbre Models of Musical Sounds, Ph.D. +% dissertation, University of Copenhagen, Rapport Nr. 99/7. + + + meth.type = 'String'; + meth.default = 'Jensen'; + meth.choice = {'Jensen','Krimphoff'}; + option.meth = meth; + + cthr.key = 'Contrast'; + cthr.type = 'Integer'; + cthr.default = .01; + option.cthr = cthr; + +specif.option = option; + +varargout = mirfunction(@mirregularity,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirdata')) || isamir(x,'miraudio') + x = mirspectrum(x); +end +if not(haspeaks(x)) + x = mirpeaks(x,'Reso','SemiTone','Contrast',option.cthr); %% FIND BETTER +end +type = 'mirscalar'; + + +function o = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +m = get(x,'PeakVal'); +p = get(x,'PeakPos'); +y = cell(1,length(m)); +for h = 1:length(m) + y{h} = cell(1,length(m{h})); + for k = 1:length(m{h}) + y{h}{k} = zeros(size(m{h}{k})); + for j = 1:size(m{h}{k},3) + for l = 1:size(m{h}{k},2) + state = warning('query','MATLAB:divideByZero'); + warning('off','MATLAB:divideByZero'); + mm = m{h}{k}{1,l,j}; + pp = p{h}{k}{1,l,j}; + [pp oo] = sort(pp); % Sort peaks in ascending order of x abscissae. + mm = mm(oo); + if strcmpi(option.meth,'Jensen') + y{h}{k}(1,l,j) = sum((mm(2:end,:)-mm(1:end-1,:)).^2)... + ./sum(mm.^2); + elseif strcmpi(option.meth,'Krimphoff') + avrg = filter(ones(3,1),1,mm)/3; + y{h}{k}(1,l,j) = log10(sum(abs(mm(2:end-1,:)-avrg(3:end)))); + end + warning(state.state,'MATLAB:divideByZero'); + if isnan(y{h}{k}(1,l,j)) + y{h}{k}(1,l,j) = 0; + end + end + end + end +end +if isa(x,'mirspectrum') + t = 'Spectral irregularity'; +else + t = ['Irregularity of ',get(x,'Title')];; +end +i = mirscalar(x,'Data',y,'Title',t); +o = {i,x}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirrms.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,82 @@ +function varargout = mirrms(x,varargin) +% e = mirrms(x) calculates the root mean square energy. +% Optional arguments: +% mirrms(...,'Frame') computes the temporal evolution of the energy. +% mirrms(...,'Root',0) does not apply the root operation to the mean +% square energy. + + normal.key = 'Normal'; + normal.type = 'Boolean'; + normal.default = 1; + option.normal = normal; + + root.key = 'Root'; + root.type = 'Boolean'; + root.default = 1; + option.root = root; + +specif.option = option; + +specif.defaultframelength = 0.05; +specif.defaultframehop = 0.5; + +specif.eachchunk = @eachchunk; +specif.combinechunk = @combinechunk; +specif.afterchunk = @afterchunk; + +varargout = mirfunction(@mirrms,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = 'mirscalar'; + + +function e = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +d = get(x,'Data'); +v = mircompute(@algo,d,option); +e = mirscalar(x,'Data',v,'Title','RMS energy'); + + +function e = algo(d,option) +nl = size(d,1); +nc = size(d,2); +nch = size(d,3); +e = zeros(1,nc,nch); +for i = 1:nch + for j = 1:nc + if option.root + e(1,j,i) = norm(d(:,j,i)); + else + e(1,j,i) = d(:,j,i)'*d(:,j,i); + end + end +end +if option.normal + e = e/sqrt(nl); +end + + +function [y orig] = eachchunk(orig,option,missing,postchunk) +option.normal = 0; +y = mirrms(orig,option); + + +function y = combinechunk(old,new) +do = get(old,'Data'); +do = do{1}{1}; +dn = get(new,'Data'); +dn = dn{1}{1}; +y = set(old,'ChunkData',sqrt(do^2+dn^2)); + + +function y = afterchunk(orig,length,postoption) +d = get(orig,'Data'); +v = mircompute(@afternorm,d,length); +y = set(orig,'Data',v); + + +function e = afternorm(d,length) +e = d/sqrt(length); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirrolloff.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function varargout = mirrolloff(x,varargin) +% r = mirrolloff(s) calculates the spectral roll-off in Hz. +% Optional arguments: +% r = mirrolloff(s,'Threshold',p) specifies the energy threshold in +% percentage. (Default: .85) +% p can be either a value between 0 and 1. But if p exceeds 1, it +% is understood as a percentage, i.e. between 1 and 100. +% In other words, r is the frequency under which p percents +% of the spectral energy is distributed. +% +% Typical values for the energy threshold: +% 85% in G. Tzanetakis, P. Cook. Musical genre classification of audio +% signals. IEEE Tr. Speech and Audio Processing, 10(5),293-302, 2002. +% 95% in T. Pohle, E. Pampalk, G. Widmer. Evaluation of Frequently +% Used Audio Features for Classification of Music Into Perceptual +% Categories, ? + + p.key = 'Threshold'; + p.type = 'Integer'; + p.default = 85; + p.position = 2; + option.p = p; + +specif.option = option; + +varargout = mirfunction(@mirrolloff,x,varargin,nargout,specif,@init,@main); + + +function [s type] = init(x,option) +s = mirspectrum(x); +type = 'mirscalar'; + + +function r = main(s,option,postoption) +if iscell(s) + s = s{1}; +end +m = get(s,'Magnitude'); +f = get(s,'Frequency'); +if option.p>1 + option.p = option.p/100; +end +v = mircompute(@algo,m,f,option.p); +r = mirscalar(s,'Data',v,'Title','Rolloff','Unit','Hz.'); + + +function v = algo(m,f,p) +cs = cumsum(m); % accumulation of spectrum energy +thr = cs(end,:,:)*p; % threshold corresponding to the rolloff point +v = zeros(1,size(cs,2),size(cs,3)); +for l = 1:size(cs,3) + for k = 1:size(cs,2) + fthr = find(cs(:,k,l) >= thr(1,k,l)); % find the location of the threshold + if isempty(fthr) + v(1,k,l) = NaN; + else + v(1,k,l) = f(fthr(1)); + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirroughness.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,103 @@ +function varargout = mirroughness(x,varargin) +% r = mirroughness(x) calculates the roughness, or sensory dissonance, +% due to beating phenomenon between close frequency peaks. +% The frequency components are supposed to remain sufficiently +% constant throughout each frame of each audio file. +% r = mirroughness(...,'Contrast',c) specifies the contrast parameter +% used for peak picking (cf. mirpeaks). +% Default value: c = .01 +% [r,s] = mirroughness(x) also displays the spectrum and its peaks, used +% for the computation of roughness. +% Optional arguments: +% Method used: +% mirroughness(...,'Sethares') (default): based on the summation +% of roughness between all pairs of sines (obtained through +% spectral peak-picking). +% mirroughness(...,'Vassilakis'): variant of 'Sethares' model +% with a more complex weighting (Vassilakis, 2001, Eq. 6.23). + + meth.type = 'String'; + meth.choice = {'Sethares','Vassilakis'}; + meth.default = 'Sethares'; + option.meth = meth; + + cthr.key = 'Contrast'; + cthr.type = 'Integer'; + cthr.default = .01; + option.cthr = cthr; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [.05 .5]; + option.frame = frame; + +specif.option = option; +specif.defaultframelength = .05; +specif.defaultframehop = .5; + + +varargout = mirfunction(@mirroughness,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if isamir(x,'miraudio') && not(isframed(x)) + x = mirframenow(x,option); +end +x = mirspectrum(x); +if not(haspeaks(x)) + x = mirpeaks(x,'Contrast',option.cthr); +end +type = 'mirscalar'; + + +function r = main(p,option,postoption) +if iscell(p) + p = p{1}; +end +if strcmpi(option.meth,'Sethares') || strcmpi(option.meth,'Vassilakis') + pf = get(p,'PeakPosUnit'); + pv = get(p,'PeakVal'); + rg = cell(1,length(pf)); + for h = 1:length(pf) + rg{h} = cell(1,length(pf{h})); + for i = 1:length(pf{h}) + pfi = pf{h}{i}; + pvi = pv{h}{i}; + rg{h}{i} = zeros(1,length(pfi)); + for k = 1:size(pfi,3) + for j = 1:size(pfi,2) + pfj = pfi{1,j,k}; + pvj = pvi{1,j,k}; + f1 = repmat(pfj,[1 length(pfj)]); + f2 = repmat(pfj',[length(pfj) 1]); + v1 = repmat(pvj,[1 length(pvj)]); + v2 = repmat(pvj',[length(pvj) 1]); + rj = plomp(f1,f2); + if strcmpi(option.meth,'Sethares') + rj = v1.*v2.*rj; + elseif strcmpi(option.meth,'Vassilakis') + rj = (v1.*v2).^.1.*.5.*(2*v2./(v1+v2)).^3.11.*rj; + end + rg{h}{i}(1,j,k) = sum(sum(rj)); + end + end + end + end +else +end +r = mirscalar(p,'Data',rg,'Title','Roughness'); +r = {r,p}; + + +function pd = plomp(f1, f2) +% returns the dissonance of two pure tones at frequencies f1 & f2 Hz +% according to the Plomp-Levelt curve (see Sethares) + b1 = 3.51; + b2 = 5.75; + xstar = .24; + s1 = .0207; + s2 = 18.96; + s = tril(xstar ./ (s1 * min(f1,f2) + s2 )); + pd = exp(-b1*s.*abs(f2-f1)) - exp(-b2*s.*abs(f2-f1)); +return \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirsave.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function mirsave(d,varargin) +% mirsave(d) saves temporal data d in a file. +% If d is a miraudio object, the waveform is directly saved. +% mirenvelope data is sonified using modulated white noise. +% mirpitch data is sonified using sinusoids. +% (cf. User's Manual for more details). +% The file(s) name is based on the original file name(s), adding '.mir' +% before the standard extension of the file. +% mirsave(d,f) specifies the file names. +% If d contains one single audio sequence, d is saved in a file +% named f. +% If d contains multiple audio sequences, each sequence is saved +% in a file whose name is the concatenation of the original +% name and f. +% If f ends with '.wav', the file is saved in WAV format (by +% default). +% If f ends with '.au', the file is saved in AU format. +% mirsave(d,f,'SeparateChannels') save each separate channel in a +% different file. + + +mirsave(miraudio(d),varargin{:}) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirsegment.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,402 @@ +function [f,p,m,fe] = mirsegment(x,varargin) +% f = mirsegment(a) segments an audio signal. It can also be the name of an +% audio file or 'Folder', for the analysis of the audio files in the +% current folder. The segmentation of audio signal already decomposed +% into frames is not available for the moment. +% f = mirsegment(...,'Novelty') segments using a self-similarity matrix +% (Foote & Cooper, 2003) (by default) +% f = mirsegment(...,feature) bases the segmentation strategy on a +% specific feature. +% 'Spectrum': from FFT spectrum (by default) +% 'MFCC': from MFCCs +% 'Keystrength': from the key strength profile +% 'AutocorPitch': from the autocorrelation function computed as +% for pitch extraction. +% The option related to this feature extraction can be specified. +% Example: mirsegment(...,'Spectrum','Window','bartlett') +% mirsegment(...,'MFCC','Rank',1:10) +% mirsegment(...,'Keystrength','Weight',.5) +% These feature need to be frame-based, in order to appreciate their +% temporal evolution. Therefore, the audio signal x is first +% decomposed into frames. This decomposition can be controled +% using the 'Frame' keyword. +% The options available for the chosen strategies can be specified +% directly as options of the segment function. +% Example: mirsegment(a,'Novelty','KernelSize',10) +% f = mirsegment(...,'HCDF') segments using the Harmonic Change Detection +% Function (Harte & Sandler, 2006) +% f = mirsegment(...,'RMS') segments at positions of long silences. A +% frame decomposed RMS is computed using mirrms (with default +% options), and segments are selected from temporal positions +% where the RMS rises to a given 'On' threshold, until temporal +% positions where the RMS drops back to a given 'Off' threshold. +% f = mirsegment(...,'Off',t1) specifies the RMS 'Off' threshold. +% Default value: t1 = .01 +% f = mirsegment(...,'On',t2) specifies the RMS 'On' threshold. +% Default value: t2 = .02 +% +% f = mirsegment(a,s) segments a using the results of a segmentation +% analysis s. s can be the peaks detected on an analysis of the +% audio for instance. +% +% f = mirsegment(a,v) where v is an array of numbers, segments a using +% the temporal positions specified in v (in s.) +% +% Foote, J. & Cooper, M. (2003). Media Segmentation using Self-Similarity +% Decomposition,. In Proc. SPIE Storage and Retrieval for Multimedia +% Databases, Vol. 5021, pp. 167-75. +% Harte, C. A. & Sandler, M. B. (2006). Detecting harmonic change in +% musical audio, in Proceedings of Audio and Music Computing for +% Multimedia Workshop, Santa Barbara, CA. + + +% [f,p] = mirsegment(...) also displays the analysis produced by the chosen +% strategy. +% For 'Novelty', p is the novelty curve. +% For 'HCDF', p is the Harmonic Change Detection Function. +% [f,p,m] = mirsegment(...) also displays the preliminary analysis +% undertaken in the chosen strategy. +% For 'Novelty', m is the similarity matrix. +% For 'HCDF', m is the tonal centroid. +% [f,p,m,fe] = mirsegment(...) also displays the temporal evolution of the +% feature used for the analysis. + +% f = mirsegment(...,'Novelty') + + mfc.key = {'Rank','MFCC'}; + mfc.type = 'Integers'; + mfc.default = 0; + mfc.keydefault = 1:13; + option.mfc = mfc; + + K.key = 'KernelSize'; + K.type = 'Integer'; + K.default = 128; + option.K = K; + + distance.key = 'Distance'; + distance.type = 'String'; + distance.default = 'cosine'; + option.distance = distance; + + measure.key = {'Measure','Similarity'}; + measure.type = 'String'; + measure.default = 'exponential'; + option.measure = measure; + + tot.key = 'Total'; + tot.type = 'Integer'; + tot.default = Inf; + option.tot = tot; + + cthr.key = 'Contrast'; + cthr.type = 'Integer'; + cthr.default = .1; + option.cthr = cthr; + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [0 0]; + frame.keydefault = [3 .1]; + option.frame = frame; + + ana.type = 'String'; + ana.choice = {'Spectrum','Keystrength','AutocorPitch','Pitch'}; + ana.default = 0; + option.ana = ana; + +% f = mirsegment(...,'Spectrum') + + band.choice = {'Mel','Bark','Freq'}; + band.type = 'String'; + band.default = 'Freq'; + option.band = band; + + mi.key = 'Min'; + mi.type = 'Integer'; + mi.default = 0; + option.mi = mi; + + ma.key = 'Max'; + ma.type = 'Integer'; + ma.default = 0; + option.ma = ma; + + norm.key = 'Normal'; + norm.type = 'Boolean'; + norm.default = 0; + option.norm = norm; + + win.key = 'Window'; + win.type = 'String'; + win.default = 'hamming'; + option.win = win; + +% f = mirsegment(...,'Silence') + + throff.key = 'Off'; + throff.type = 'Integer'; + throff.default = .01; + option.throff = throff; + + thron.key = 'On'; + thron.type = 'Integer'; + thron.default = .02; + option.thron = thron; + + strat.choice = {'Novelty','HCDF','RMS'}; % should remain as last field + strat.default = 'Novelty'; + strat.position = 2; + option.strat = strat; + +specif.option = option; + + +p = {}; +m = {}; +fe = {}; + +if isa(x,'mirdesign') + if not(get(x,'Eval')) + % During bottom-up construction of the general design + + [unused option] = miroptions(@mirframe,x,specif,varargin); + type = get(x,'Type'); + f = mirdesign(@mirsegment,x,option,{},struct,type); + + sg = get(x,'Segment'); + if not(isempty(sg)) + f = set(f,'Segment',sg); + else + f = set(f,'Segment',option.strat); + end + + else + % During top-down evaluation initiation + + f = evaleach(x); + if iscell(f) + f = f{1}; + end + p = x; + end +elseif isa(x,'mirdata') + [unused option] = miroptions(@mirframe,x,specif,varargin); + if ischar(option.strat) + dx = get(x,'Data'); + if size(dx{1},2) > 1 + error('ERROR IN MIRSEGMENT: The segmentation of audio signal already decomposed into frames is not available for the moment.'); + end + if strcmpi(option.strat,'Novelty') + if not(option.frame.length.val) + if strcmpi(option.ana,'Keystrength') + option.frame.length.val = .5; + option.frame.hop.val = .2; + elseif strcmpi(option.ana,'AutocorPitch') ... + || strcmpi(option.ana,'Pitch') + option.frame.length.val = .05; + option.frame.hop.val = .01; + else + option.frame.length.val = .05; + option.frame.hop.val = 1; + end + end + fr = mirframenow(x,option); + if not(isequal(option.mfc,0)) + fe = mirmfcc(fr,'Rank',option.mfc); + elseif strcmpi(option.ana,'Spectrum') + fe = mirspectrum(fr,'Min',option.mi,'Max',option.ma,... + 'Normal',option.norm,option.band,... + 'Window',option.win); + elseif strcmpi(option.ana,'Keystrength') + fe = mirkeystrength(fr); + elseif strcmpi(option.ana,'AutocorPitch') ... + || strcmpi(option.ana,'Pitch') + [unused,fe] = mirpitch(x,'Frame'); + else + fe = fr; + end + [n m] = mirnovelty(fe,'Distance',option.distance,... + 'Measure',option.measure,... + 'KernelSize',option.K); + p = mirpeaks(n,'Total',option.tot,... + 'Contrast',option.cthr,... + 'Chrono','NoBegin','NoEnd'); + elseif strcmpi(option.strat,'HCDF') + if not(option.frame.length.val) + option.frame.length.val = .743; + option.frame.hop.val = 1/8; + end + fr = mirframenow(x,option); + %[df m fe] = mirhcdf(fr); + df = mirhcdf(fr); + p = mirpeaks(df); + elseif strcmpi(option.strat,'RMS') + if not(option.frame.length.val) + option.frame.length.val = .05; + option.frame.hop.val = .5; + end + fr = mirframenow(x,option); + %[df m fe] = mirhcdf(fr); + df = mirrms(fr); + fp = get(df,'FramePos'); + p = mircompute(@findsilence,df,fp,option.throff,option.thron); + end + f = mirsegment(x,p); + else + dx = get(x,'Data'); + dt = get(x,'Time'); + + if isa(option.strat,'mirscalar') + ds = get(option.strat,'PeakPos'); + fp = get(option.strat,'FramePos'); + elseif isa(option.strat,'mirdata') + ds = get(option.strat,'AttackPos'); + if isempty(ds) || isempty(ds{1}) + ds = get(option.strat,'PeakPos'); + end + xx = get(option.strat,'Pos'); + else + ds = option.strat; + fp = cell(1,length(dx)); + end + st = cell(1,length(dx)); + sx = cell(1,length(dx)); + cl = cell(1,length(dx)); + for k = 1:length(dx) + dxk = dx{k}{1}; % values in kth audio file + dtk = dt{k}{1}; % time positions in kth audio file + if isa(option.strat,'mirdata') + dsk = ds{k}{1}; % segmentation times in kth audio file + else + dsk = {ds}; + end + fsk = []; % the structured array of segmentation times + % needs to be flatten + for j = 1:length(dsk) + if isa(option.strat,'mirdata') + dsj = dsk{j}; % segmentation times in jth segment + else + dsj = ds; + end + if not(iscell(dsj)) + dsj = {dsj}; + end + for m = 1:length(dsj) + % segmentation times in mth bank channel + if isa(option.strat,'mirscalar') + dsm = fp{k}{m}(1,dsj{m}); + elseif isa(option.strat,'mirdata') + dsm = xx{k}{m}(dsj{m}); + else + dsm = dsj{m}; + end + if iscell(dsm) + dsm = dsm{1}; + end + dsm(:,find(dsm(1,:) < dtk(1))) = []; + dsm(:,find(dsm(end,:) > dtk(end))) = []; + % It is presupposed here that the segmentations times + % for a given channel are not decomposed per frames, + % because the segmentation of the frame decomposition + % is something that does not seem very clear. + % Practically, the peak picking for instance is based + % therefore on a frame analysis (such as novelty), and + % segmentation are inferred between these frames... + if size(dsm,2) == 1 + dsm = dsm'; + end + fsk = [fsk dsm]; + end + end + + fsk = sort(fsk); % Here is the chronological ordering + + if isempty(fsk) + ffsk = {[0;dtk(end)]}; + sxk = {dxk}; + stk = {dtk}; + n = 1; + elseif size(fsk,1) == 1 + ffsk = cell(1,length(fsk)+1); + ffsk{1} = [dtk(1);fsk(1)]; + for h = 1:length(fsk)-1 + ffsk{h+1} = [fsk(h);fsk(h+1)]; + end + ffsk{end} = [fsk(end);dtk(end)]; + + n = length(ffsk); + + crd = zeros(1,n+1); % the sample positions of the + % segmentations in the channel + crd0 = 0; + for i = 1:n + crd0 = crd0 + find(dtk(crd0+1:end)>=ffsk{i}(1),1); + crd(i) = crd0; + end + crd(n+1) = size(dxk,1)+1; + + sxk = cell(1,n); % each cell contains a segment + stk = cell(1,n); % each cell contains + % the corresponding time positions + + for i = 1:n + sxk{i} = dxk(crd(i):crd(i+1)-1,1,:); + stk{i} = dtk(crd(i):crd(i+1)-1); + end + + elseif size(fsk,1) == 2 + ffsk = cell(1,size(fsk,2)); + for h = 1:length(fsk) + ffsk{h} = [fsk(1,h);fsk(2,h)]; + end + n = length(ffsk); + crd = zeros(2,n); % the sample positions of the + % segmentations in the channel + crd0 = 0; + for i = 1:n + crd0 = crd0 + find(dtk(crd0+1:end)>=ffsk{i}(1),1); + crd(i,1) = crd0; + crd0 = crd0 + find(dtk(crd0+1:end)>=ffsk{i}(2),1); + crd(i,2) = crd0; + end + sxk = cell(1,n); % each cell contains a segment + stk = cell(1,n); % each cell contains + % the corresponding time positions + for i = 1:n + sxk{i} = dxk(crd(i,1):crd(i,2),1,:); + stk{i} = dtk(crd(i,1):crd(i,2)); + end + end + sx{k} = sxk; + st{k} = stk; + fp{k} = ffsk; + cl{k} = 1:n; + end + f = set(x,'Data',sx,'Time',st,'FramePos',fp,'Clusters',cl); + p = strat; + m = {}; + fe = {}; + end +else + [f p] = mirsegment(miraudio(x),varargin{:}); +end + + +function p = findsilence(d,fp,throff,thron) +d = [0 d 0]; +begseg = find(d(1:end-1)<thron & d(2:end)>=thron); +nseg = length(begseg); +endseg = zeros(1,nseg); +removed = []; +for i = 1:nseg + endseg(i) = begseg(i) + find(d(begseg(i)+1:end)<=throff, 1)-1; + if i>1 && endseg(i) == endseg(i-1) + removed = [removed i]; + end +end +begseg(removed) = []; +%endseg(removed) = []; +%endseg(end) = min(endseg(end),length(d)+1); +p = fp(1,begseg); %; fp(2,endseg-1)]; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirskewness.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function varargout = mirskewness(orig,varargin) +% s = skewness(x) calculates the skewness of x, showing the (lack of) +% symmetry of the curve. +% x can be either: +% - a spectrum (spectral skewness), +% - an envelope (temporal skewness), or +% - any histogram. + + +varargout = mirfunction(@mirskewness,orig,varargin,nargout,struct,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirdata')) || isamir(x,'miraudio') + x = mirspectrum(x); +end +type = 'mirscalar'; + + +function s = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +y = peaksegments(@skewness,get(x,'Data'),... + get(x,'Pos'),... + get(mircentroid(x),'Data'),... + get(mirspread(x),'Data')); +if isa(x,'mirspectrum') + t = 'Spectral skewness'; +elseif isa(x,'mirenvelope') + t = 'Temporal skewness'; +else + t = ['Skewness of ',get(x,'Title')]; +end +s = mirscalar(x,'Data',y,'Title',t,'Unit',''); + + +function s = skewness(d,p,c,sp) +s = sum((p-c).^3.*d) ./ sum(d) ./ sp.^3; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirspread.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +function varargout = mirspread(orig,varargin) +% S = mirspread(x) calculates the spread of x, which can be either: +% - a spectrum (spectral spread), +% - an envelope (temporal spread), or +% - any histogram. + + +varargout = mirfunction(@mirspread,orig,varargin,nargout,struct,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirdata')) || isamir(x,'miraudio') + x = mirspectrum(x); +end +type = 'mirscalar'; + + +function S = main(x,option,postoption) +if iscell(x) + x = x{1}; +end +y = peaksegments(@spread,get(x,'Data'),... + get(x,'Pos'),... + get(mircentroid(x),'Data')); +if isa(x,'mirspectrum') + t = 'Spectral spread'; +elseif isa(x,'mirenvelope') + t = 'Temporal spread'; +else + t = ['Spread of ',get(x,'Title')]; +end +S = mirscalar(x,'Data',y,'Title',t); + + +function s = spread(d,p,c) +s = sqrt( sum((p-c).^2.*d) ) ./ sum(d); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirstat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,292 @@ +function varargout = mirstat(f,varargin) +% stat = mirstat(f) returns basic statistics of the feature f as a +% structure array stat such that: +% stat.Mean is the mean of the feature; +% stat.Std is the standard deviation; +% stat.Slope is the linear slope of the curve; +% stat.PeriodFreq is the frequency (in Hz) of the main periodicity; +% stat.PeriodAmp is the normalized amplitude of the main periodicity; +% stat.PeriodEntropy is the entropy of the periodicity curve. +% The main periodicity and periodicity curve are evaluated through the +% computation of the autocorrelation sequence related to f. +% +% f can be itself a structure array composed of features. In this case, +% stat will be structured the same way. +% +% mirstat does not work for multi-channels objects. + +if isa(f,'mirstruct') + varargout = {set(f,'Stat',1)}; +elseif isstruct(f) + if isdesign(f) + f.Stat = 1; + varargout = {f}; + else + fields = fieldnames(f); + for i = 1:length(fields) + field = fields{i}; + ff = f.(field); + if iscell(ff) && isa(ff{1},'mirdata') + ff = ff{1}; + end + if isa(ff,'mirdata') + if i == 1 + la = get(ff,'Label'); + if not(isempty(la) || isempty(la{1})) + stat.Class = la; + end + end + ff = set(ff,'Label',''); + end + stat.(field) = mirstat(ff,'FileNames',0); + end + if isempty(varargin) + f0 = f; + while isstruct(f0) + fields = fieldnames(f0); + f0 = f0.(fields{1}); + end + if iscell(f0) + f0 = f0{1}; + end + stat.FileNames = get(f0,'Name'); + end + varargout = {stat}; + end +elseif iscell(f) + if 0 %ischar(f{1}) + varargout = {f}; + else + res = zeros(length(f),1); + for i = 1:length(f) + res(i) = mirstat(f{i}); + end + varargout = {res}; + end +elseif isnumeric(f) + f(isnan(f)) = []; + varargout = {mean(f)}; +else + alongfiles.key = 'AlongFiles'; + alongfiles.type = 'Boolean'; + alongfiles.default = 0; + option.alongfiles = alongfiles; + + filenames.key = 'FileNames'; + filenames.type = 'Boolean'; + filenames.default = 1; + option.filenames = filenames; + + specif.option = option; + + specif.nochunk = 1; + varargout = mirfunction(@mirstat,f,varargin,nargout,specif,@init,@main); +end + + +function [x type] = init(x,option) +type = ''; + + +function stat = main(f,option,postoption) +if iscell(f) + f = f{1}; +end +if isa(f,'mirhisto') + warning('WARNING IN MIRSTAT: histograms are not taken into consideration yet.') + stat = struct; + return +end +fp = get(f,'FramePos'); +if haspeaks(f) + ppp = get(f,'PeakPrecisePos'); + if not(isempty(ppp)) && not(isempty(ppp{1})) + stat = addstat(struct,ppp,fp,'PeakPos'); + stat = addstat(stat,get(f,'PeakPreciseVal'),fp,'PeakMag'); + else + if isa(f,'mirkeystrength') || (isa(f,'mirchromagram') && get(f,'Wrap')) + stat = struct; % This needs to be defined using some kind of circular statistics.. + else + stat = addstat(struct,get(f,'PeakPosUnit'),fp,'PeakPos'); + end + stat = addstat(stat,get(f,'PeakVal'),fp,'PeakMag'); + end +else + stat = addstat(struct,get(f,'Data'),fp,'',option.alongfiles,... + get(f,'Name'),get(f,'Label')); +end +if option.filenames + stat.FileNames = get(f,'Name'); +end + + +function stat = addstat(stat,d,fp,field,alongfiles,filename,labels) +l = length(d); +if nargin<5 + alongfiles = 0; +end +if nargin<7 + labels = {}; +end +if not(alongfiles) || l == 1 + anyframe = 0; + for i = 1:l + if not(iscell(d{i})) + d{i} = {d{i}}; + end + for k = 1:length(d{i}) + dd = d{i}{k}; + if iscell(dd) + if length(dd)>1 + anyframe = 1; + end + dn = cell2array(dd); + [m{i}{k},s{i}{k},sl{i}{k},pa{i}{k},pf{i}{k},pe{i}{k}] ... + = computestat(dn,fp{i}{k}); + elseif size(dd,2) < 2 + nonan = find(not(isnan(dd))); + dn = dd(nonan); + if isempty(dn) + m{i}{k} = NaN; + else + m{i}{k} = mean(dn,2); + end + s{i}{k} = NaN; + sl{i}{k} = NaN; + pa{i}{k} = NaN; + pf{i}{k} = NaN; + pe{i}{k} = NaN; + else + anyframe = 1; + s1 = size(dd,1); + s3 = size(dd,3); + dd = mean(dd,4); %mean(dd,3),4); + m{i}{k} = NaN(s1,1,s3); + s{i}{k} = NaN(s1,1,s3); + sl{i}{k} = NaN(s1,1,s3); + pa{i}{k} = NaN(s1,1,s3); + pf{i}{k} = NaN(s1,1,s3); + pe{i}{k} = NaN(s1,1,s3); + for h = 1:s3 + for j = 1:s1 + [m{i}{k}(j,1,h),s{i}{k}(j,1,h),sl{i}{k}(j,1,h),... + pa{i}{k}(j,1,h),pf{i}{k}(j,1,h),pe{i}{k}(j,1,h)] = ... + computestat(dd(j,:,h),fp{i}{k}); + end + end + end + end + end + if anyframe + fields = {'Mean','Std','Slope','PeriodFreq','PeriodAmp','PeriodEntropy'}; + stats = {m,s,sl,pf,pa,pe}; + else + fields = {'Mean'}; + stats = {m}; + end + for i = 1:length(stats) + data = stats{i}; + data = uncell(data,NaN); + stat.(strcat(field,fields{i})) = data; + end +else + if iscell(d{1}{1}) + slash = strfind(filename{1},'/'); + nbfolders = 1; + infolder = 0; + foldername{1} = filename{1}(1:slash(end)-1); + for i = 1:length(d) + slash = strfind(filename{i},'/'); + if not(strcmpi(filename{i}(1:slash(end)-1),foldername)) + nbfolders = nbfolders + 1; + infolder = 0; + foldername{nbfolders} = filename{i}(1:slash(end)-1); + end + infolder = infolder+1; + dd{nbfolders}(infolder,:) = cell2array(d{i}{1}); + end + for i = 1:length(dd) + figure + plot(dd{i}') + stat.Mean{i} = mean(dd{i}); + stat.Std{i} = std(dd{i}); + end + end +end +if not(isempty(labels) || isempty(labels{1})) + stat.Class = labels; +end + + +function dn = cell2array(dd) +dn = zeros(1,length(dd)); +for j = 1:length(dd) + if isempty(dd{j}) + dn(j) = NaN; + else + dn(j) = dd{j}(1); + end +end + + +function [m,s,sl,pa,pf,pe] = computestat(d,fp) +m = NaN; +s = NaN; +sl = NaN; +pa = NaN; +pf = NaN; +pe = NaN; +diffp = fp(1,2:end) - fp(1,1:end-1); +if isempty(diffp) || sum(round((diffp(2:end)-diffp(1:end-1))*1000)) + % Not regular sampling (in mirattacktime for instance) + framesampling = NaN; +else + framesampling = fp(1,2)-fp(1,1); +end +nonan = find(not(isnan(d))); +if not(isempty(nonan)) + dn = d(nonan); + m = mean(dn,2); + s = std(dn,0,2); + if not(isnan(s)) + if s + dk = (dn-m)/s; + tk = linspace(0,1,size(d,2)); + sl = dk(:)'/tk(nonan); + elseif size(s,2) == 1 + s = NaN; + end + end + if length(dn)>1 + cor = xcorr(dn',dn','coeff'); + cor = cor(ceil(length(cor)/2):end); + % let's zero the first descending slope of the + % autocorrelation function + firstmin = find(cor(2:end)>cor(1:end-1)); + if not(isempty(firstmin) || isnan(framesampling)) + cor2 = cor; + cor2(1:firstmin(1)) = 0; + [pa,pfk] = max(cor2); + if pfk > 1 + pf = 1/(pfk-1)/framesampling; + end + end + cor = abs(cor); + cor = cor/sum(cor); + pe = -sum(cor.*log(cor+1e-12))./log(length(cor)); + end +end + + +function b = isdesign(f) +if iscell(f) + f = f{1}; +end +if isa(f,'mirdesign') || isa(f,'mirstruct') + b = 1; +elseif isa(f,'mirdata') || not(isstruct(f)) + b = 0; +else + fields = fieldnames(f); + b = isdesign(f.(fields{1})); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirstd.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +function varargout = mirstd(f,varargin) +% m = mirstd(f) returns the standard deviation along frames of the feature f +% +% f can be a structure array composed of features. In this case, +% m will be structured the same way. + +if isa(f,'mirstruct') + data = get(f,'Data'); + for fi = 1:length(data) + data{fi} = mirstd(data{fi}); + end + varargout = {set(f,'Data',data)}; +elseif isstruct(f) + fields = fieldnames(f); + for i = 1:length(fields) + field = fields{i}; + stat.(field) = mirstd(f.(field)); + end + varargout = {stat}; +else + + normdiff.key = 'NormDiff'; + normdiff.type = 'Boolean'; + normdiff.default = 0; + specif.option.normdiff = normdiff; + + specif.nochunk = 1; + + varargout = mirfunction(@mirstd,f,varargin,nargout,specif,@init,@main); +end + + +function [x type] = init(x,option) +type = ''; + + +function m = main(f,option,postoption) +if iscell(f) + f = f{1}; +end +if isa(f,'mirhisto') + warning('WARNING IN MIRSTD: histograms are not taken into consideration yet.') + m = struct; + return +end +fp = get(f,'FramePos'); +ti = get(f,'Title'); +d = get(f,'Data'); +l = length(d); +for i = 1:l + if iscell(d{i}) + if length(d{i}) > 1 + error('ERROR IN MIRSTD: segmented data not accepted yet.'); + else + dd = d{i}{1}; + end + else + dd = d{i}; + end + if iscell(dd) + m{i} = {zeros(1,length(dd))}; + for j = 1:length(dd) + m{i}{1}(j) = std(dd{j}); + end + elseif size(dd,2) < 2 + nonan = find(not(isnan(dd))); + dn = dd(nonan); + if option.normdiff + m{i}{1} = norm(diff(dn,2)); + else + m{i}{1} = std(dn,0,2); + end + else + dd = mean(dd,4); + m{i} = {NaN(size(dd,1),1,size(dd,3))}; + for k = 1:size(dd,1) + for l = 1:size(dd,3) + dk = dd(k,:,l); + nonan = find(not(isnan(dk))); + if not(isempty(nonan)) + dn = dk(nonan); + if option.normdiff + m{i}{1}(k,1,l) = norm(diff(dn,2)); + else + m{i}{1}(k,1,l) = std(dn,0,2); + end + end + end + end + end +end +m = mirscalar(f,'Data',m,'Title',['Standard deviation of ',ti]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirsum.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,195 @@ +function varargout = mirsum(orig,varargin) +% s = mirsum(f) sums the envelope of the multi-channel object f. +% +% Optional arguments: +% mirsum(f,'Centered') centers the resulting envelope. +% mirsum(f,'Mean') divides the summation by the number of channels. + +% mirsum(f,'Weights')... + + c.key = 'Centered'; + c.type = 'Boolean'; + c.default = 0; + c.when = 'After'; + option.c = c; + + m.key = 'Mean'; + m.type = 'Boolean'; + m.default = 0; + m.when = 'After'; + option.m = m; + + adj.key = 'Adjacent'; + adj.type = 'Integer'; + adj.default = 1; + option.adj = adj; + + weights.key = 'Weights'; + weights.type = 'Integer'; + weights.default = []; + option.weights = weights; + +specif.option = option; + +if isamir(orig,'mirtemporal') + specif.eachchunk = @eachtemporalchunk; + specif.combinechunk = 'Concat'; %@combinetemporalchunk; +else + specif.combinechunk = @combinechunk; +end +varargout = mirfunction(@mirsum,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = mirtype(x); + + +function s = main(x,option,postoption) +x = purgedata(x); +if iscell(x) + x = x{1}; +end +d = get(x,'Data'); +pp = get(x,'PeakPos'); +pv = get(x,'PeakVal'); +pm = get(x,'PeakMode'); +p = get(x,'Pos'); +sc = cell(1,length(d)); +spp = cell(1,length(d)); +spv = cell(1,length(d)); +spm = cell(1,length(d)); +for h = 1:length(d) + dh = d{h}; + sch = cell(1,length(dh)); + spph = cell(1,length(dh)); + spvh = cell(1,length(dh)); + spmh = cell(1,length(dh)); + for i = 1:length(dh) + % Summation of signal + s3 = size(dh{i},3); + if not(isempty(option.weights)) + weights = reshape(option.weights,1,1,length(option.weights)); + if length(weights)~=s3 + %warning('WARNING in MIRSUM..'); + weights = weights(1,1,1:s3); + end + dh{i} = dh{i}.*repmat(weights,[size(dh{i},1),size(dh{i},2),1]); + end + if option.adj < 2 + sch{i} = sum(dh{i},3); + else + m0 = option.adj; + nc1 = size(dh{i},3); + nc2 = ceil(nc1/m0); + sch{i} = zeros(size(dh{i},1),size(dh{i},2),nc2); + for j = 1:nc2 + sch{i}(:,:,j) = sum(dh{i}(:,:,(j-1)*m0+1:min(j*m0,nc1))... + ,3); + end + end + % Summation of peaks + if not(isempty(pp)) && not(isempty(pp{1})) + ppi = pp{h}{i}; + pvi = pv{h}{i}; + pmi = pm{h}{i}; + nfr = size(ppi,2); + nbd = size(ppi,3); + sppi = cell(1,nfr,1); + spvi = cell(1,nfr,1); + spmi = cell(1,nfr,1); + for j = 1:nfr + sppj = []; + spvj = []; + spmj = []; + for k = 1:nbd + ppk = ppi{1,j,k}; + pvk = pvi{1,j,k}; + pmk = pmi{1,j,k}; + for l = 1:length(ppk) + fp = find(ppk(l) == sppj); + if fp + spvj(fp) = spvj(fp) + pvk(l); + else + sppj(end+1) = ppk(l); + spvj(end+1) = pvk(l); + spmj(end+1) = pmk(l); + end + end + end + sppi{1,j,1} = sppj; + spvi{1,j,1} = spvj; + spmi{1,j,1} = spmj; + end + spph{i} = sppi; + spvh{i} = spvi; + spmh{i} = spmi; + else + spph{i} = []; + spvh{i} = []; + spmh{i} = []; + end + if not(isempty(p)) && not(isempty(p{h})) + p{h}{i} = p{h}{i}(:,:,1); + end + end + sc{h} = sch; + spp{h} = spph; + spv{h} = spvh; + spm{h} = spmh; +end +s = set(x,'Data',sc,'Pos',p,'PeakPos',spp,'PeakVal',spv,'PeakMode',spm); +if not(isempty(postoption)) + s = post(s,postoption); +end + + +function s = post(s,option) +if option.c + d = get(s,'Data'); + for k = 1:length(d) + for i = 1:length(d{k}) + d{k}{i} = center(d{k}{i}); + end + end + s = set(s,'Data',d); +end +if option.m + d = get(s,'Data'); + ch = get(s,'Channels'); + if not(isempty(ch)) + for k = 1:length(d) + for i = 1:length(d{k}) + d{k}{i} = d{k}{i}/length(ch{k}); + end + ch{k} = [1]; + end + s = set(s,'Data',d,'Channels',ch); + end +end + + +function [y orig] = eachtemporalchunk(orig,option,missing) +[y orig] = mirsum(orig,option); + + +%function y = combinetemporalchunk(old,new) +%do = get(old,'Data'); +%to = get(old,'Time'); +%dn = get(new,'Data'); +%tn = get(new,'Time'); +%y = set(old,'Data',{{[do{1}{1};dn{1}{1}]}},... +% 'Time',{{[to{1}{1};tn{1}{1}]}}); +% + +function y = combinechunk(old,new) +if isa(old,'mirspectrum') + warning('WARNING IN MIRSUM: not yet fully generalized to mirspectrum') +end +do = get(old,'Data'); +do = do{1}{1}; +dn = get(new,'Data'); +dn = dn{1}{1}; +if length(dn) < length(do) + dn(length(do),:,:) = 0; % Zero-padding +end +y = set(old,'ChunkData',do+dn); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirsummary.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function varargout = mirsummary(varargin) +% mirsummary is the same function as mirsum. + +vout = ''; +for i = 1:max(1,nargout) + vout = [vout,'y',num2str(i),' ']; +end +eval(['[ ',vout,'] = mirsum(varargin{:});']) +eval(['varargout = { ',vout,'};']) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirtempo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,405 @@ +function varargout = mirtempo(x,varargin) +% t = mirtempo(x) evaluates the tempo in beats per minute (BPM). +% Optional arguments: +% mirtempo(...,'Total',m) selects not only the best tempo, but the m +% best tempos. +% mirtempo(...,'Frame',l,h) orders a frame decomposition of window +% length l (in seconds) and hop factor h, expressed relatively to +% the window length. For instance h = 1 indicates no overlap. +% Default values: l = 3 seconds and h = .1 +% mirtempo(...,'Min',mi) indicates the lowest tempo taken into +% consideration, expressed in bpm. +% Default value: 40 bpm. +% mirtempo(...,'Max',ma) indicates the highest tempo taken into +% consideration, expressed in bpm. +% Default value: 200 bpm. +% mirtempo(...,s) selects the tempo estimation strategy: +% s = 'Autocor': Approach based on the computation of the +% autocorrelation. (Default strategy) +% Option associated to the mirautocor function can be +% passed here as well (see help mirautocor): +% 'Enhanced' (toggled on by default here) +% s = 'Spectrum': Approach based on the computation of the +% spectrum . +% Option associated to the mirspectrum function can be +% passed here as well (see help mirspectrum): +% 'ZeroPad' (set by default to 10000 samples) +% 'Prod' (toggled off by default) +% These two strategies can be combined: the autocorrelation +% function is translated into the frequency domain in order +% to be compared to the spectrum curve. +% tempo(...,'Autocor','Spectrum') multiplies the two curves. +% Alternatively, an autocorrelation function ac or a spectrum sp +% can be directly passed to the function tempo: +% mirtempo(ac) or mirtempo(sp) +% The options related to the onset detection phase can be specified +% here as well (see help mironsets): +% onset detection strategies: 'Envelope', 'DiffEnvelope' +% (corresponding to 'Envelope', 'Diff'), 'SpectralFlux, +% 'Pitch', 'Log', 'Mu', 'Filterbank' +% mironsets(...,'Sum',w) specifies when to sum the channels. +% Possible values: +% w = 'Before': sum before the autocorrelation or +% spectrum computation. +% w = 'After': autocorrelation or spectrum computed +% for each band, and summed into a "summary". +% mirenvelope options: 'HalfwaveCenter','Diff' (toggled on by +% default here),'HalfwaveDiff','Center','Smooth', +% 'Sampling' +% mirflux options: 'Inc','Halfwave','Complex','Median' +% mirtempo(...,'Resonance',r) specifies the resonance curve, which +% emphasizes the periods that are more easily perceived. +% Possible values: 'ToiviainenSnyder' (default), 0 (toggled off) +% Optional arguments used for the peak picking (cf. help mirpeaks) +% mirtempo(...,'Contrast',thr): a threshold value. A given local +% maximum will be considered as a peak if its distance with the +% previous and successive local minima (if any) is higher than +% this threshold. This distance is expressed with respect to the +% total amplitude of the autocorrelation function. +% if no value for thr is given, the value thr=0.1 is chosen +% by default. +% +% [t,p] = mirtempo(...) also displays the result of the signal analysis +% leading to the tempo estimation, and shows in particular the +% peaks corresponding to the tempo values. + + + sum.key = 'Sum'; + sum.type = 'String'; + sum.choice = {'Before','After','Adjacent',0}; + sum.default = 'Before'; + option.sum = sum; + +%% options related to mironsets: + + frame.key = 'Frame'; + frame.type = 'Integer'; + frame.number = 2; + frame.default = [0 0]; + frame.keydefault = [3 .1]; + option.frame = frame; + + fea.type = 'String'; + fea.choice = {'Envelope','DiffEnvelope','SpectralFlux','Pitch'}; + fea.default = 'Envelope'; + option.fea = fea; + + %% options related to 'Envelope': + + envmeth.key = 'Method'; + envmeth.type = 'String'; + envmeth.choice = {'Filter','Spectro'}; + envmeth.default = 'Filter'; + option.envmeth = envmeth; + + %% options related to 'Filter': + + fb.key = 'Filterbank'; + fb.type = 'Integer'; + fb.default = 10; + option.fb = fb; + + fbtype.key = 'FilterbankType'; + fbtype.type = 'String'; + fbtype.choice = {'Gammatone','Scheirer','Klapuri'}; + fbtype.default = 'Gammatone'; + option.fbtype = fbtype; + + ftype.key = 'FilterType'; + ftype.type = 'String'; + ftype.choice = {'IIR','HalfHann'}; + ftype.default = 'IIR'; + option.ftype = ftype; + + %% options related to 'Spectro': + + band.type = 'String'; + band.choice = {'Freq','Mel','Bark','Cents'}; + band.default = 'Freq'; + option.band = band; + + + chwr.key = 'HalfwaveCenter'; + chwr.type = 'Boolean'; + chwr.default = 0; + option.chwr = chwr; + + diff.key = 'Diff'; + diff.type = 'Boolean'; + diff.default = 1; % Different default for mirtempo + option.diff = diff; + + diffhwr.key = 'HalfwaveDiff'; + diffhwr.type = 'Integer'; + diffhwr.default = 0; + diffhwr.keydefault = 1; + option.diffhwr = diffhwr; + + lambda.key = 'Lambda'; + lambda.type = 'Integer'; + lambda.default = 1; + option.lambda = lambda; + + mu.key = 'Mu'; + mu.type = 'Boolean'; + mu.default = 0; + option.mu = mu; + + log.key = 'Log'; + log.type = 'Boolean'; + log.default = 0; + option.log = log; + + c.key = 'Center'; + c.type = 'Boolean'; + c.default = 0; + option.c = c; + + aver.key = 'Smooth'; + aver.type = 'Integer'; + aver.default = 0; + aver.keydefault = 30; + option.aver = aver; + + sampling.key = 'Sampling'; + sampling.type = 'Integer'; + sampling.default = 0; + option.sampling = sampling; + + %% options related to 'SpectralFlux' + + complex.key = 'Complex'; + complex.type = 'Boolean'; + complex.default = 0; + option.complex = complex; + + inc.key = 'Inc'; + inc.type = 'Boolean'; + inc.default = 1; + option.inc = inc; + + median.key = 'Median'; + median.type = 'Integer'; + median.number = 2; + median.default = [.2 1.3]; + option.median = median; + + hw.key = 'Halfwave'; + hw.type = 'Boolean'; + hw.default = 1; + option.hw = hw; + + +%% options related to mirautocor: + + aut.key = 'Autocor'; + aut.type = 'Integer'; + aut.default = 0; + aut.keydefault = 1; + option.aut = aut; + + nw.key = 'NormalWindow'; + nw.default = 0; + option.nw = nw; + + enh.key = 'Enhanced'; + enh.type = 'Integers'; + enh.default = 2:10; + enh.keydefault = 2:10; + option.enh = enh; + + r.key = 'Resonance'; + r.type = 'String'; + r.choice = {'ToiviainenSnyder','vanNoorden',0,'off','no'}; + r.default = 'ToiviainenSnyder'; + option.r = r; + + +%% options related to mirspectrum: + + spe.key = 'Spectrum'; + spe.type = 'Integer'; + spe.default = 0; + spe.keydefault = 1; + option.spe = spe; + + zp.key = 'ZeroPad'; + zp.type = 'Integer'; + zp.default = 10000; + zp.keydefault = Inf; + option.zp = zp; + + prod.key = 'Prod'; + prod.type = 'Integers'; + prod.default = 0; + prod.keydefault = 2:6; + option.prod = prod; + + +%% options related to the peak detection + + m.key = 'Total'; + m.type = 'Integer'; + m.default = 1; + option.m = m; + + thr.key = 'Contrast'; + thr.type = 'Integer'; + thr.default = 0.1; + option.thr = thr; + + mi.key = 'Min'; + mi.type = 'Integer'; + mi.default = 40; + option.mi = mi; + + ma.key = 'Max'; + ma.type = 'Integer'; + ma.default = 200; + option.ma = ma; + + track.key = 'Track'; + track.type = 'Boolean'; + track.default = 0; + option.track = track; + + pref.key = 'Pref'; + pref.type = 'Integer'; + pref.number = 2; + pref.default = [0 .2]; + option.pref = pref; + + perio.key = 'Periodicity'; + perio.type = 'Boolean'; + perio.default = 0; + option.perio = perio; + +specif.option = option; + +varargout = mirfunction(@mirtempo,x,varargin,nargout,specif,@init,@main); + + +%% INIT + +function [y type] = init(x,option) +if iscell(x) + x = x{1}; +end +if option.perio + option.m = 3; + option.enh = 2:10; +end +if not(isamir(x,'mirautocor')) && not(isamir(x,'mirspectrum')) + if isframed(x) && strcmpi(option.fea,'Envelope') && not(isamir(x,'mirscalar')) + warning('WARNING IN MIRTEMPO: The input should not be already decomposed into frames.'); + disp(['Suggestion: Use the ''Frame'' option instead.']) + end + if strcmpi(option.sum,'Before') + optionsum = 1; + elseif strcmpi(option.sum,'Adjacent') + optionsum = 5; + else + optionsum = 0; + end + if option.frame.length.val + x = mironsets(x,option.fea,'Filterbank',option.fb,... + 'FilterbankType',option.fbtype,... + 'FilterType',option.ftype,... + 'Sum',optionsum,'Method',option.envmeth,... + option.band,'Center',option.c,... + 'HalfwaveCenter',option.chwr,'Diff',option.diff,... + 'HalfwaveDiff',option.diffhwr,'Lambda',option.lambda,... + 'Smooth',option.aver,'Sampling',option.sampling,... + 'Complex',option.complex,'Inc',option.inc,... + 'Median',option.median(1),option.median(2),... + 'Halfwave',option.hw,'Detect',0,... + 'Mu',option.mu,'Log',option.log,... + 'Frame',option.frame.length.val,... + option.frame.length.unit,... + option.frame.hop.val,... + option.frame.hop.unit); + else + x = mironsets(x,option.fea,'Filterbank',option.fb,... + 'FilterbankType',option.fbtype,... + 'FilterType',option.ftype,... + 'Sum',optionsum,'Method',option.envmeth,... + option.band,'Center',option.c,... + 'HalfwaveCenter',option.chwr,'Diff',option.diff,... + 'HalfwaveDiff',option.diffhwr,'Lambda',option.lambda,... + 'Smooth',option.aver,'Sampling',option.sampling,... + 'Complex',option.complex,'Inc',option.inc,... + 'Median',option.median(1),option.median(2),... + 'Halfwave',option.hw,'Detect',0,... + 'Mu',option.mu,'Log',option.log); + end +end +if option.aut == 0 && option.spe == 0 + option.aut = 1; +end +if isamir(x,'mirautocor') || (option.aut && not(option.spe)) + y = mirautocor(x,'Min',60/option.ma,'Max',60/option.mi,... + 'Enhanced',option.enh,...'NormalInput','coeff',... + 'Resonance',option.r,'NormalWindow',option.nw); +elseif isamir(x,'mirspectrum') || (option.spe && not(option.aut)) + y = mirspectrum(x,'Min',option.mi/60,'Max',option.ma/60,... + 'Prod',option.prod,...'NormalInput',... + 'ZeroPad',option.zp,'Resonance',option.r); +elseif option.spe && option.aut + ac = mirautocor(x,'Min',60/option.ma,'Max',60/option.mi,... + 'Enhanced',option.enh,...'NormalInput','coeff',... + 'Resonance',option.r); + sp = mirspectrum(x,'Min',option.mi/60,'Max',option.ma/60,... + 'Prod',option.prod,...'NormalInput',... + 'ZeroPad',option.zp,'Resonance',option.r); + y = ac*sp; +end +if ischar(option.sum) + y = mirsum(y); +end +y = mirpeaks(y,'Total',option.m,'Track',option.track,... + 'Pref',option.pref(1),option.pref(2),... + 'Contrast',option.thr,'NoBegin','NoEnd',... + 'Normalize','Local'); +type = {'mirscalar',mirtype(y)}; + + +%% MAIN + +function o = main(p,option,postoption) +if iscell(p) + p = p{1}; +end +pt = get(p,'TrackPrecisePos'); +track = 1; +if isempty(pt) || isempty(pt{1}) + pt = get(p,'PeakPrecisePos'); + track = 0; +end +bpm = cell(1,length(pt)); +for j = 1:length(pt) + bpm{j} = cell(1,length(pt{j})); + for k = 1:length(pt{j}) + ptk = pt{j}{k}; + bpmk = cell(1,size(ptk,2)); + for h = 1:size(ptk,3) + for l = 1:size(ptk,2) + ptl = ptk{1,l,h}; + if isempty(ptl) + bpmk{1,l,h} = NaN; + else + if isa(p,'mirautocor') && not(get(p,'FreqDomain')) + bpmk{1,l,h} = 60./ptl; + else + bpmk{1,l,h} = ptl*60; + end + end + end + end + if track + bpmk = bpmk{1}; + end + bpm{j}{k} = bpmk; + end +end +t = mirscalar(p,'Data',bpm,'Title','Tempo','Unit','bpm'); +o = {t,p}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirtimes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,41 @@ +function c = mirtimes(a,b) + +d = get(a,'Data'); +e = get(b,'Data'); +f = cell(1,length(d)); +p = get(a,'Pos'); +q = get(b,'Pos'); +r = cell(1,length(d)); +l = get(a,'Name'); +m = get(b,'Name'); +n = cell(1,length(l)); +for i = 1:length(d) + f{i} = cell(1,length(d{i})); + for j = 1:length(d{i}) + pij = p{i}{j}(:,1,1); + qij = q{i}{j}(:,1,1); + pq = pij; %union(pij,qij); + % The sampling of the product is the sampling of the first + % operand. + dij = d{i}{j}; %interp1(pij,d{i}{j},pq); + eij = interp1(qij,e{i}{j},pq); + dij = max(dij,0); + eij = max(eij,0); + eij = eij./repmat(max(eij)+1e-16,[size(eij,1),1,1,1]); + % The second operand of mirtimes is scaled from 0 to 1. + % In this way, the range of value of the first operand is kept. + f{i}{j} = dij.*eij; + [x y] = find(isnan(f{i}{j})); + x = unique(x); + pq(x) = []; + f{i}{j}(x,:) = []; + r{i}{j} = repmat(pq,[1 size(p{i}{j},2) size(p{i}{j},3)]); + end + if strcmpi(l{i},m{i}) + n{i} = l{i}; + else + n{i} = [l{i} '*' m{i}]; + end +end +c = set(a,'Pos',r,'Data',f,'Name',n,... + 'Title',[get(a,'Title') ' * ' get(b,'Title')]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirtype.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function type = mirtype(x) + +if iscell(x) + for i = 1:length(x) + type{i} = mirtype(x{i}); + end + return +end + +if isa(x,'mirdesign') + type = get(x,'Type'); +else + type = class(x); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirverbose.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function v = mirverbose(s) +% mirverbose(0) toggles off the display by MIRtoolbox of minor informations +% in the Matlab Command Window (such as "Computing mirfunction ..."). +% mirverbose(1) toggles back on the display of such information. + +persistent mir_verbose + +if nargin + mir_verbose = s; +else + if isempty(mir_verbose) + mir_verbose = 1; + end +end + +v = mir_verbose; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirwaitbar.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function pb = mirwaitbar(s) +% mirwaitbar(0) toggles off the display by MIRtoolbox of waitbar windows. +% mirverbose(1) toggles back on the display of these waitbar windows. + +persistent mir_wait_bar + +if nargin + mir_wait_bar = s; +else + if isempty(mir_wait_bar) + mir_wait_bar = 1; + end +end + +pb = mir_wait_bar; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirzerocross.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +function varargout = mirzerocross(orig,varargin) +% mirzeroscross(x) computes the sign-changes rate along the signal x, +% i.e., how many time the waveform crosses the X-axis. When applied on +% an audio waveform, gives a notion of noise. +% Optional argument: +% mirzerocross(...,'Per',p) precises the temporal reference for the +% rate computation. +% Possible values: +% p = 'Second': number of sign-changes per second (Default). +% p = 'Sample': number of sign-changes divided by the total +% number of samples. +% The 'Second' option returns a result equal to the one returned +% by the 'Sample' option multiplied by the sampling rate. +% mirzerocross(...,'Dir',d) precises the definition of sign change. +% Possible values: +% d = 'One': number of sign-changes from negative to positive +% only (or, equivalently, from positive to negative only). +% (Default) +% d = 'Both': number of sign-changes in both ways. +% The 'Both' option returns a result equal to twice the one +% returned by the 'One' option. + + + per.key = 'Per'; + per.type = 'String'; + per.choice = {'Second','Sample'}; + per.default = 'Second'; + option.per = per; + + dir.key = 'Dir'; + dir.type = 'String'; + dir.choice = {'One','Both'}; + dir.default = 'One'; + option.dir = dir; + +specif.option = option; + +varargout = mirfunction(@mirzerocross,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +if not(isamir(x,'mirdata')) + x = miraudio(x); +end +type = 'mirscalar'; + + +function z = main(a,option,postoption) +if iscell(a) + a = a{1}; +end +d = get(a,'Data'); +f = get(a,'Sampling'); +v = cell(1,length(d)); +for h = 1:length(d) + v{h} = cell(1,length(d{h})); + for i = 1:length(d{h}) + di = d{h}{i}; + nc = size(di,2); + nf = size(di,3); + nl = size(di,1); + zc = sum( di(2:end,:,:).*di(1:(end-1),:,:) < 0 ) /nl; + if strcmp(option.per,'Second') + zc = zc*f{h}; + end + if strcmp(option.dir,'One') + zc = zc/2; + end + v{h}{i} = zc; + end +end +z = mirscalar(a,'Data',v,'Title','Zero-crossing rate'); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mp3read.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,334 @@ +function [Y,FS,NBITS,OPTS] = mp3read(FILE,N,MONO,DOWNSAMP,DELAY) +% MP3READ Read MP3 audio file via use of external binaries. +% Y = MP3READ(FILE) reads an mp3-encoded audio file into the +% vector Y just like wavread reads a wav-encoded file (one channel +% per column). Extension ".mp3" is added if FILE has none. +% Also accepts other formats of wavread, such as +% Y = MP3READ(FILE,N) to read just the first N sample frames (N +% scalar), or the frames from N(1) to N(2) if N is a two-element vector. +% Y = MP3READ(FILE,FMT) or Y = mp3read(FILE,N,FMT) +% with FMT as 'native' returns int16 samples instead of doubles; +% FMT can be 'double' for default behavior (to exactly mirror the +% syntax of wavread). +% +% [Y,FS,NBITS,OPTS] = MP3READ(FILE...) returns extra information: +% FS is the sampling rate, NBITS is the bit depth (always 16), +% OPTS.fmt is a format info string; OPTS has multiple other +% fields, see WAVREAD. +% +% SIZ = MP3READ(FILE,'size') returns the size of the audio data contained +% in the file in place of the actual audio data, returning the +% 2-element vector SIZ=[samples channels]. +% +% [Y...] = MP3READ(FILE,N,MONO,DOWNSAMP,DELAY) extends the +% WAVREAD syntax to allow access to special features of the +% mpg123 engine: MONO = 1 forces output to be mono (by +% averaging stereo channels); DOWNSAMP = 2 or 4 downsamples by +% a factor of 2 or 4 (thus FS returns as 22050 or 11025 +% respectively for a 44 kHz mp3 file); DELAY controls how many +% "warm up" samples to drop at the start of the file; the +% default value of 2257 makes an mp3write/mp3read loop for a 44 +% kHz mp3 file be as close as possible to being temporally +% aligned; specify as 0 to prevent discard of initial samples. +% +% Example: +% To read an mp3 file as doubles at its original width and sampling rate: +% [Y,FS] = mp3read('piano.mp3'); +% To read the first 1 second of the same file, downsampled by a +% factor of 4, cast to mono, using the default filename +% extension: +% [Y,FS4] = mp3read('piano', FS/4, 1, 4); +% +% Note: Because the mp3 format encodes samples in blocks of 26 ms (at +% 44 kHz), and because of the "warm up" period of the encoder, +% the file length may not be exactly what you expect. +% +% Note: requires external binaries mpg123 and mp3info; you +% can find binaries for several platforms at: +% http://labrosa.ee.columbia.edu/matlab/mp3read.html +% +% See also mp3write, wavread. + +% 2003-07-20 dpwe@ee.columbia.edu This version calls mpg123. +% 2004-08-31 Fixed to read whole files correctly +% 2004-09-08 Uses mp3info to get info about mp3 files too +% 2004-09-18 Reports all mp3info fields in OPTS.fmt; handles MPG2LSF sizes +% + added MONO, DOWNSAMP flags, changed default behavior. +% 2005-09-28 Fixed bug reading full-rate stereo as 1ch (thx bjoerns@vjk.dk) +% 2006-09-17 Chop off initial 2257 sample delay (for 44.1 kHz mp3) +% so read-write loop doesn't get progressively delayed. +% You can suppress this with a 5th argument of 0. +% 2007-02-04 Added support for FMT argument to match wavread +% Added automatic selection of binary etc. to allow it +% to work cross-platform without editing prior to +% submitting to Matlab File Exchange +% 2007-07-23 Tweaks to 'size' mode so it exactly agrees with read data. + +% find our baseline directory +path = fileparts(which('mp3read')); + +% %%%%% Directory for temporary file (if needed) +% % Try to read from environment, or use /tmp if it exists, or use CWD +tmpdir = getenv('TMPDIR'); +if isempty(tmpdir) || exist(tmpdir,'file')==0 + tmpdir = '/tmp'; +end +if exist(tmpdir,'file')==0 + tmpdir = ''; +end +% ensure it exists +%if length(tmpdir) > 0 && exist(tmpdir,'file')==0 +% mkdir(tmpdir); +%end + +%%%%%% Command to delete temporary file (if needed) +rmcmd = 'rm'; + +%%%%%% Location of the binaries - attempt to choose automatically +%%%%%% (or edit to be hard-coded for your installation) +ext = lower(computer); +if ispc + ext = 'exe'; + rmcmd = 'del'; +end +mpg123 = fullfile(path,['mpg123.',ext]); +mp3info = fullfile(path,['mp3info.',ext]); + +%%%%% Process input arguments +if nargin < 2 + N = 0; +end + +% Check for FMT spec (per wavread) +FMT = 'double'; +if ischar(N) + FMT = lower(N); + N = 0; +end + +if length(N) == 1 + % Specified N was upper limit + N = [1 N]; +end +if nargin < 3 + forcemono = 0; +else + % Check for 3rd arg as FMT + if ischar(MONO) + FMT = lower(MONO); + MONO = 0; + end + forcemono = (MONO ~= 0); +end +if nargin < 4 + downsamp = 1; +else + downsamp = DOWNSAMP; +end +if downsamp ~= 1 && downsamp ~= 2 && downsamp ~= 4 + error('DOWNSAMP can only be 1, 2, or 4'); +end +if nargin < 5 + mpg123delay44kHz = 2257; % empirical delay of lame/mpg123 loop + delay = round(mpg123delay44kHz/downsamp); +else + delay = DELAY; +end + +if strcmp(FMT,'native') == 0 && strcmp(FMT,'double') == 0 && ... + strcmp(FMT,'size') == 0 + error(['FMT must be ''native'' or ''double'' (or ''size''), not ''',FMT,'''']); +end + + +%%%%%% Constants +NBITS=16; + +%%%%% add extension if none (like wavread) +[path,file,ext] = fileparts(FILE); +if isempty(ext) + FILE = [FILE, '.mp3']; +end + +%%%%%% Probe file to find format, size, etc. using "mp3info" utility +cmd = ['"',mp3info, '" -r m -p "%Q %u %b %r %v * %C %e %E %L %O %o %p" "', FILE,'"']; +% Q = samprate, u = #frames, b = #badframes (needed to get right answer from %u) +% r = bitrate, v = mpeg version (1/2/2.5) +% C = Copyright, e = emph, E = CRC, L = layer, O = orig, o = mono, p = pad +w = mysystem(cmd); +% Break into numerical and ascii parts by finding the delimiter we put in +starpos = findstr(w,'*'); +nums = str2num(w(1:(starpos - 2))); +strs = tokenize(w((starpos+2):end)); + +SR = nums(1); +nframes = nums(2); +nchans = 2 - strcmp(strs{6}, 'mono'); +layer = length(strs{4}); +bitrate = nums(4)*1000; +mpgv = nums(5); +% Figure samples per frame, after +% http://board.mp3-tech.org/view.php3?bn=agora_mp3techorg&key=1019510889 +if layer == 1 + smpspfrm = 384; +elseif SR < 32000 && layer ==3 + smpspfrm = 576; + if mpgv == 1 + error('SR < 32000 but mpeg version = 1'); + end +else + smpspfrm = 1152; +end + +OPTS.fmt.mpgBitrate = bitrate; +OPTS.fmt.mpgVersion = mpgv; +% fields from wavread's OPTS +OPTS.fmt.nAvgBytesPerSec = bitrate/8; +OPTS.fmt.nSamplesPerSec = SR; +OPTS.fmt.nChannels = nchans; +OPTS.fmt.nBlockAlign = smpspfrm/SR*bitrate/8; +OPTS.fmt.nBitsPerSample = NBITS; +OPTS.fmt.mpgNFrames = nframes; +OPTS.fmt.mpgCopyright = strs{1}; +OPTS.fmt.mpgEmphasis = strs{2}; +OPTS.fmt.mpgCRC = strs{3}; +OPTS.fmt.mpgLayer = strs{4}; +OPTS.fmt.mpgOriginal = strs{5}; +OPTS.fmt.mpgChanmode = strs{6}; +OPTS.fmt.mpgPad = strs{7}; +OPTS.fmt.mpgSampsPerFrame = smpspfrm; + +if SR == 16000 && downsamp == 4 + error('mpg123 will not downsample 16 kHz files by 4 (only 2)'); +end + +if downsamp == 1 + downsampstr = ''; +else + downsampstr = [' -',num2str(downsamp)]; +end +FS = SR/downsamp; + +if forcemono == 1 + nchans = 1; + chansstr = ' -m'; +else + chansstr = ''; +end + +% Size-reading version +if strcmp(FMT,'size') == 1 + Y = [floor(smpspfrm*nframes/downsamp)-delay, nchans]; +else + + % Temporary file to use + tmpfile = fullfile(tmpdir, ['tmp',num2str(round(1000*rand(1))),'.wav']); + + skipx = 0; + skipblks = 0; + skipstr = ''; + sttfrm = N(1)-1; + + % chop off transcoding delay? + %sttfrm = sttfrm + delay; % empirically measured + % no, we want to *decode* those samples, then drop them + % so delay gets added to skipx instead + + if sttfrm > 0 + skipblks = floor(sttfrm*downsamp/smpspfrm); + skipx = sttfrm - (skipblks*smpspfrm/downsamp); + skipstr = [' -k ', num2str(skipblks)]; + end + skipx = skipx + delay; + + lenstr = ''; + endfrm = -1; + decblk = 0; + if length(N) > 1 + endfrm = N(2); + if endfrm > sttfrm + decblk = ceil((endfrm+delay)*downsamp/smpspfrm) - skipblks + 10; + % we read 10 extra blks (+10) to cover the case where up to 10 bad + % blocks are included in the part we are trying to read (it happened) + lenstr = [' -n ', num2str(decblk)]; + % This generates a spurious "Warn: requested..." if reading right + % to the last sample by index (or bad blks), but no matter. + end + end + + % Run the decode + cmd=['"',mpg123,'"', downsampstr, chansstr, skipstr, lenstr, ... + ' -q -w "', tmpfile,'" "',FILE,'"']; + %w = + mysystem(cmd); + + % Load the data + Y = wavread(tmpfile); + +% % pad delay on to end, just in case +% Y = [Y; zeros(delay,size(Y,2))]; +% % no, the saved file is just longer + + if decblk > 0 && length(Y) < decblk*smpspfrm/downsamp + % This will happen if the selected block range includes >1 bad block + disp(['Warn: requested ', num2str(decblk*smpspfrm/downsamp),' frames, returned ',num2str(length(Y))]); + end + + % Delete tmp file + mysystem([rmcmd,' "', tmpfile,'"']); + + % debug +% disp(['sttfrm=',num2str(sttfrm),' endfrm=',num2str(endfrm),' skipx=',num2str(skipx),' delay=',num2str(delay),' len=',num2str(length(Y))]); + + % Select the desired part + if skipx+endfrm-sttfrm > length(Y) + endfrm = length(Y)+sttfrm-skipx; + end + + if endfrm > sttfrm + Y = Y(skipx+(1:(endfrm-sttfrm)),:); + elseif skipx > 0 + Y = Y((skipx+1):end,:); + end + + % Convert to int if format = 'native' + if strcmp(FMT,'native') + Y = int16((2^15)*Y); + end + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function w = mysystem(cmd) +% Run system command; report error; strip all but last line +[s,w] = system(cmd); +if s ~= 0 + error(['unable to execute ',cmd,' (',w,')']); +end +% Keep just final line +w = w((1+max([0,findstr(w,10)])):end); +% Debug +%disp([cmd,' -> ','*',w,'*']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function a = tokenize(s) +% Break space-separated string into cell array of strings +% 2004-09-18 dpwe@ee.columbia.edu +a = []; +p = 1; +n = 1; +l = length(s); +nss = findstr([s(p:end),' '],' '); +for ns = nss + % Skip initial spaces + if ns == p + p = p+1; + else + if p <= l + a{n} = s(p:(ns-1)); + n = n+1; + p = ns+1; + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mp3write.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +function mp3write(D,SR,NBITS,FILE,OPTIONS) +% MP3WRITE Write MP3 file by use of external binary +% MP3WRITE(Y,FS,NBITS,FILE) writes waveform data Y to mp3-encoded +% file FILE at sampling rate FS using bitdepth NBITS. +% The syntax exactly mirrors WAVWRITE. NBITS must be 16. +% MP3WRITE(Y,FS,FILE) assumes NBITS is 16 +% MP3WRITE(Y,FILE) further assumes FS = 8000. +% +% MP3WRITE(..., OPTIONS) specifies additional compression control +% options as a string passed directly to the lame encoder +% program; default is '--quiet -h' for high-quality model. +% +% Example: +% To convert a wav file to mp3 (assuming the sample rate is +% supported): +% [Y,FS] = wavread('piano.wav'); +% mp3write(Y,FS,'piano.mp3'); +% To force lame to use 160 kbps (instead of default 128 kbps) +% with the default filename extension (mp3): +% mp3write(Y,FS,'piano','--quiet -h -b 160'); +% +% Note: The actual mp3 encoding is done by an external binary, +% lame, which is available for multiple platforms. Usable +% binaries are available from: +% http://labrosa.ee.columbia.edu/matlab/mp3read.html +% +% Note: MP3WRITE will use the mex file popenw, if available, to +% open a pipe to the lame encoder. Otherwise, it will have to +% write a large temporary file, then execute lame on that file. +% popenw is available at: +% http://labrosa.ee.columbia.edu/matlab/popenrw.html +% This is a nice way to save large audio files as the +% incremental output of your code, but you'll have to adapt the +% central loop of this function (rather than using it directly). +% +% See also: mp3read, wavwrite, popenw. + +% 2005-11-10 Original version +% 2007-02-04 Modified to exactly match wavwrite syntax, and to +% automatically find architecture-dependent binaries. +% 2007-07-26 Writing of stereo files via tmp file fixed (thx Yu-ching Lin) +% +% $Header: /Users/dpwe/matlab/columbiafns/RCS/mp3write.m,v 1.2 2007/07/26 15:09:16 dpwe Exp $ + +% find our baseline directory +[path] = fileparts(which('mp3write')); + +% %%%%% Directory for temporary file (if needed) +% % Try to read from environment, or use /tmp if it exists, or use CWD +tmpdir = getenv('TMPDIR'); +if isempty(tmpdir) || exist(tmpdir,'file')==0 + tmpdir = '/tmp'; +end +if exist(tmpdir,'file')==0 + tmpdir = ''; +end +% ensure it exists +%if length(tmpdir) > 0 && exist(tmpdir,'file')==0 +% mkdir(tmpdir); +%end + +%%%%%% Command to delete temporary file (if needed) +rmcmd = 'rm'; + +%%%%%% Location of the binary - attempt to choose automatically +%%%%%% (or edit to be hard-coded for your installation) +ext = lower(computer); +if ispc + ext = 'exe'; + rmcmd = 'del'; +end +lame = fullfile(path,['lame.',ext]); + +%%%% Process input arguments +% Do we have NBITS? +mynargin = nargin; +if ischar(NBITS) + % NBITS is a string i.e. it's actually the filename + if mynargin > 3 + OPTIONS = FILE; + end + FILE = NBITS; + NBITS = 16; + % it's as if NBITS had been specified... + mynargin = mynargin + 1; +end + +if mynargin < 5 + OPTIONS = '--quiet -h'; % -h means high-quality psych model +end + +[nr, nc] = size(D); +if nc < nr + D = D'; + [nr, nc] = size(D); +end +% Now rows are channels, cols are time frames (so interleaving is right) + +%%%%% add extension if none (like wavread) +[path,file,ext] = fileparts(FILE); +if isempty(ext) + FILE = [FILE, '.mp3']; +end + +nchan = nr; +nfrm = nc; + +if nchan == 1 + monostring = ' -m m'; +else + monostring = ''; +end + +lameopts = [' ', OPTIONS, monostring, ' ']; + +%if exist('popenw') == 3 +if length(which('popenw')) > 0 + + % We have the writable stream process extensions + cmd = ['"',lame,'"', lameopts, '-r -s ',num2str(SR),' - "',FILE,'"']; + + p = popenw(cmd); + if p < 0 + error(['Error running popen(',cmd,')']); + end + + % We feed the audio to the encoder in blocks of <blksize> frames. + % By adapting this loop, you can create your own code to + % write a single, large, MP3 file one part at a time. + + blksiz = 10000; + + nrem = nfrm; + base = 0; + + while nrem > 0 + thistime = min(nrem, blksiz); + done = popenw(p,32767*D(:,base+(1:thistime)),'int16be'); + nrem = nrem - thistime; + base = base + thistime; + %disp(['done=',num2str(done)]); + end + + % Close pipe + popenw(p,[]); + +else + disp('Warning: popenw not available, writing temporary file'); + + tmpfile = fullfile(tmpdir,['tmp',num2str(round(1000*rand(1))),'.wav']); + + wavwrite(D',SR,tmpfile); + + cmd = ['"',lame,'"', lameopts, '"',tmpfile, '" "', FILE, '"']; + + mysystem(cmd); + + % Delete tmp file + mysystem([rmcmd, ' "', tmpfile,'"']); + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function w = mysystem(cmd) +% Run system command; report error; strip all but last line +[s,w] = system(cmd); +if s ~= 0 + error(['unable to execute ',cmd,' (',w,')']); +end +% Keep just final line +w = w((1+max([0,findstr(w,10)])):end); +% Debug +%disp([cmd,' -> ','*',w,'*']);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/mtimescell.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function varargout = mtimescell(x,varargin) + +specif.combinechunk = 'Average'; + +varargout = mirfunction(@mtimescell,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = mirtype(x{1}); + + +function y = main(x,option,postoption) +y = mtimes(x{1},x{2}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/netlabgmminit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +function mix = netlabgmminit(mix, x, options) +%GMMINIT Initialises Gaussian mixture model from data +%(Renamed NETLABGMMINIT in MIRtoolbox to avoid conflict with statistics +%toolbox) +% Description +% MIX = GMMINIT(MIX, X, OPTIONS) uses a dataset X to initialise the +% parameters of a Gaussian mixture model defined by the data structure +% MIX. The k-means algorithm is used to determine the centres. The +% priors are computed from the proportion of examples belonging to each +% cluster. The covariance matrices are calculated as the sample +% covariance of the points associated with (i.e. closest to) the +% corresponding centres. For a mixture of PPCA model, the PPCA +% decomposition is calculated for the points closest to a given centre. +% This initialisation can be used as the starting point for training +% the model using the EM algorithm. +% +% See also +% GMM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[ndata, xdim] = size(x); + +% Check that inputs are consistent +errstring = consist(mix, 'gmm', x); +if ~isempty(errstring) + error(errstring); +end + +% Arbitrary width used if variance collapses to zero: make it 'large' so +% that centre is responsible for a reasonable number of points. +GMM_WIDTH = 1.0; + +% Use kmeans algorithm to set centres +options(5) = 1; +[mix.centres, options, post] = netlabkmeans(mix.centres, x, options); + +% Set priors depending on number of points in each cluster +cluster_sizes = max(sum(post, 1), 1); % Make sure that no prior is zero +mix.priors = cluster_sizes/sum(cluster_sizes); % Normalise priors + +switch mix.covar_type +case 'spherical' + if mix.ncentres > 1 + % Determine widths as distance to nearest centre + % (or a constant if this is zero) + cdist = dist2(mix.centres, mix.centres); + cdist = cdist + diag(ones(mix.ncentres, 1)*realmax); + mix.covars = min(cdist); + mix.covars = mix.covars + GMM_WIDTH*(mix.covars < eps); + else + % Just use variance of all data points averaged over all + % dimensions + mix.covars = mean(diag(cov(x))); + end + case 'diag' + for j = 1:mix.ncentres + % Pick out data points belonging to this centre + c = x(find(post(:, j)),:); + diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :)); + mix.covars(j, :) = sum((diffs.*diffs), 1)/size(c, 1); + % Replace small entries by GMM_WIDTH value + mix.covars(j, :) = mix.covars(j, :) + GMM_WIDTH.*(mix.covars(j, :)<eps); + end + case 'full' + for j = 1:mix.ncentres + % Pick out data points belonging to this centre + c = x(find(post(:, j)),:); + diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :)); + mix.covars(:,:,j) = (diffs'*diffs)/(size(c, 1)); + % Add GMM_WIDTH*Identity to rank-deficient covariance matrices + if rank(mix.covars(:,:,j)) < mix.nin + mix.covars(:,:,j) = mix.covars(:,:,j) + GMM_WIDTH.*eye(mix.nin); + end + end + case 'ppca' + for j = 1:mix.ncentres + % Pick out data points belonging to this centre + c = x(find(post(:,j)),:); + diffs = c - (ones(size(c, 1), 1) * mix.centres(j, :)); + [tempcovars, tempU, templambda] = ... + ppca((diffs'*diffs)/size(c, 1), mix.ppca_dim); + if length(templambda) ~= mix.ppca_dim + error('Unable to extract enough components'); + else + mix.covars(j) = tempcovars; + mix.U(:, :, j) = tempU; + mix.lambda(j, :) = templambda; + end + end + otherwise + error(['Unknown covariance type ', mix.covar_type]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/netlabkmeans.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +function [centres, options, post, errlog] = netlabkmeans(centres, data, options) +%KMEANS Trains a k means cluster model. +%(Renamed NETLABKMEANS in MIRtoolbox in order to avoid conflict with +% statistics toolbox...) +% +% Description +% CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means +% algorithm to set the centres of a cluster model. The matrix DATA +% represents the data which is being clustered, with each row +% corresponding to a vector. The sum of squares error function is used. +% The point at which a local minimum is achieved is returned as +% CENTRES. The error value at that point is returned in OPTIONS(8). +% +% [CENTRES, OPTIONS, POST, ERRLOG] = KMEANS(CENTRES, DATA, OPTIONS) +% also returns the cluster number (in a one-of-N encoding) for each +% data point in POST and a log of the error values after each cycle in +% ERRLOG. The optional parameters have the following +% interpretations. +% +% OPTIONS(1) is set to 1 to display error values; also logs error +% values in the return argument ERRLOG. If OPTIONS(1) is set to 0, then +% only warning messages are displayed. If OPTIONS(1) is -1, then +% nothing is displayed. +% +% OPTIONS(2) is a measure of the absolute precision required for the +% value of CENTRES at the solution. If the absolute difference between +% the values of CENTRES between two successive steps is less than +% OPTIONS(2), then this condition is satisfied. +% +% OPTIONS(3) is a measure of the precision required of the error +% function at the solution. If the absolute difference between the +% error functions between two successive steps is less than OPTIONS(3), +% then this condition is satisfied. Both this and the previous +% condition must be satisfied for termination. +% +% OPTIONS(14) is the maximum number of iterations; default 100. +% +% See also +% GMMINIT, GMMEM +% + +% Copyright (c) Ian T Nabney (1996-2001) + +[ndata, data_dim] = size(data); +[ncentres, dim] = size(centres); + +if dim ~= data_dim + error('Data dimension does not match dimension of centres') +end + +if (ncentres > ndata) + error('More centres than data') +end + +% Sort out the options +if (options(14)) + niters = options(14); +else + niters = 100; +end + +store = 0; +if (nargout > 3) + store = 1; + errlog = zeros(1, niters); +end + +% Check if centres and posteriors need to be initialised from data +if (options(5) == 1) + % Do the initialisation + perm = randperm(ndata); + perm = perm(1:ncentres); + + % Assign first ncentres (permuted) data points as centres + centres = data(perm, :); +end +% Matrix to make unit vectors easy to construct +id = eye(ncentres); + +% Main loop of algorithm +for n = 1:niters + + % Save old centres to check for termination + old_centres = centres; + + % Calculate posteriors based on existing centres + d2 = dist2(data, centres); + % Assign each point to nearest centre + [minvals, index] = min(d2', [], 1); + post = id(index,:); + + num_points = sum(post, 1); + % Adjust the centres based on new posteriors + for j = 1:ncentres + if (num_points(j) > 0) + centres(j,:) = sum(data(find(post(:,j)),:), 1)/num_points(j); + end + end + + % Error value is total squared distance from cluster centres + e = sum(minvals); + if store + errlog(n) = e; + end + if options(1) > 0 + fprintf(1, 'Cycle %4d Error %11.6f\n', n, e); + end + + if n > 1 + % Test for termination + if max(max(abs(centres - old_centres))) < options(2) & ... + abs(old_e - e) < options(3) + options(8) = e; + return; + end + end + old_e = e; +end + +% If we get here, then we haven't terminated in the given number of +% iterations. +options(8) = e; +if (options(1) >= 0) + disp(maxitmess); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/nthoutput.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function varargout = nthoutput(orig,varargin) + + nth.type = 'Integer'; + nth.default = 1; + nth.position = 2; + option.nth = nth; +specif.option = option; + +varargout = mirfunction(@nthoutput,orig,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = mirtype(x); +type = type{option.nth}; + + +function y = main(x,option,postoption) +y = x{option.nth}; +%y = x{1}{option.nth}; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/num2col.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function col = num2col(k); + +switch k + case 1 + col = [0 0 1]; + case 2 + col = [1 0 0]; + case 3 + col = [0 1 0]; + case 4 + col = [0 0 0]; + case 5 + col = [1 0 1]; + case 6 + col = [1 1 0]; + case 7 + col = [0 1 1]; + case 8 + col = [1 0 .5]; + case 9 + col = [.5 1 0]; + case 10 + col = [0 .5 1]; + case 11 + col = [1 .5 0]; + case 12 + col = [.5 0 1]; + case 13 + col = [0 1 .5]; + otherwise + col = [rand rand rand]; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/openbdf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,192 @@ +function [DAT,H1]=openbdf(FILENAME) +% EDF=openedf(FILENAME) +% Opens an EDF File (European Data Format for Biosignals) in MATLAB (R) +% <A HREF="http://www.medfac.leidenuniv.nl/neurology/knf/kemp/edf.htm">About EDF</A> + +% Copyright (C) 1997-1998 by Alois Schloegl +% a.schloegl@ieee.org +% Ver 2.20 18.Aug.1998 +% Ver 2.21 10.Oct.1998 +% Ver 2.30 5.Nov.1998 +% +% For use under Octave define the following function +% function s=upper(s); s=toupper(s); end; + +% V2.12 Warning for missing Header information +% V2.20 EDF.AS.* changed +% V2.30 EDF.T0 made Y2K compatible until Year 2090 + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. +% Name changed Sept 6,2002 T.S. Lorig + +SLASH='/'; % defines Seperator for Subdirectories +BSLASH=setstr(92); + +cname=computer; +if cname(1:2)=='PC' SLASH=BSLASH; end; + +fid=fopen(FILENAME,'r','ieee-le'); +if fid<0 + % fprintf(2,['Error LOADEDF: File ' FILENAME ' not found\n']); + % Warning desactivated in MIRtoolbox + return; +end; + +EDF.FILE.FID=fid; +EDF.FILE.OPEN = 1; +EDF.FileName = FILENAME; + +PPos=min([max(find(FILENAME=='.')) length(FILENAME)+1]); +SPos=max([0 find((FILENAME=='/') | (FILENAME==BSLASH))]); +EDF.FILE.Ext = FILENAME(PPos+1:length(FILENAME)); +EDF.FILE.Name = FILENAME(SPos+1:PPos-1); +if SPos==0 + EDF.FILE.Path = pwd; +else + EDF.FILE.Path = FILENAME(1:SPos-1); +end; +EDF.FileName = [EDF.FILE.Path SLASH EDF.FILE.Name '.' EDF.FILE.Ext]; + +H1=setstr(fread(EDF.FILE.FID,256,'char')'); % +EDF.VERSION=H1(1:8); % 8 Byte Versionsnummer +%if 0 fprintf(2,'LOADEDF: WARNING Version EDF Format %i',ver); end; +EDF.PID = deblank(H1(9:88)); % 80 Byte local patient identification +EDF.RID = deblank(H1(89:168)); % 80 Byte local recording identification +%EDF.H.StartDate = H1(169:176); % 8 Byte +%EDF.H.StartTime = H1(177:184); % 8 Byte +EDF.T0=[str2num(H1(168+[7 8])) str2num(H1(168+[4 5])) str2num(H1(168+[1 2])) str2num(H1(168+[9 10])) str2num(H1(168+[12 13])) str2num(H1(168+[15 16])) ]; + +% Y2K compatibility until year 2090 +if EDF.VERSION(1)=='0' + if EDF.T0(1) < 91 + EDF.T0(1)=2000+EDF.T0(1); + else + EDF.T0(1)=1900+EDF.T0(1); + end; +else ; + % in a future version, this is hopefully not needed +end; + +EDF.HeadLen = str2num(H1(185:192)); % 8 Byte Length of Header +% reserved = H1(193:236); % 44 Byte +EDF.NRec = str2num(H1(237:244)); % 8 Byte # of data records +EDF.Dur = str2num(H1(245:252)); % 8 Byte # duration of data record in sec +EDF.NS = str2num(H1(253:256)); % 8 Byte # of signals + +EDF.Label = setstr(fread(EDF.FILE.FID,[16,EDF.NS],'char')'); +EDF.Transducer = setstr(fread(EDF.FILE.FID,[80,EDF.NS],'char')'); +EDF.PhysDim = setstr(fread(EDF.FILE.FID,[8,EDF.NS],'char')'); + +EDF.PhysMin= str2num(setstr(fread(EDF.FILE.FID,[8,EDF.NS],'char')')); +EDF.PhysMax= str2num(setstr(fread(EDF.FILE.FID,[8,EDF.NS],'char')')); +EDF.DigMin = str2num(setstr(fread(EDF.FILE.FID,[8,EDF.NS],'char')')); % +EDF.DigMax = str2num(setstr(fread(EDF.FILE.FID,[8,EDF.NS],'char')')); % + +% check validity of DigMin and DigMax +if (length(EDF.DigMin) ~= EDF.NS) + fprintf(2,'Warning OPENEDF: Failing Digital Minimum\n'); + EDF.DigMin = -(2^15)*ones(EDF.NS,1); +end +if (length(EDF.DigMax) ~= EDF.NS) + fprintf(2,'Warning OPENEDF: Failing Digital Maximum\n'); + EDF.DigMax = (2^15-1)*ones(EDF.NS,1); +end +if (any(EDF.DigMin >= EDF.DigMax)) + fprintf(2,'Warning OPENEDF: Digital Minimum larger than Maximum\n'); +end +% check validity of PhysMin and PhysMax +if (length(EDF.PhysMin) ~= EDF.NS) + fprintf(2,'Warning OPENEDF: Failing Physical Minimum\n'); + EDF.PhysMin = EDF.DigMin; +end +if (length(EDF.PhysMax) ~= EDF.NS) + fprintf(2,'Warning OPENEDF: Failing Physical Maximum\n'); + EDF.PhysMax = EDF.DigMax; +end +if (any(EDF.PhysMin >= EDF.PhysMax)) + fprintf(2,'Warning OPENEDF: Physical Minimum larger than Maximum\n'); + EDF.PhysMin = EDF.DigMin; + EDF.PhysMax = EDF.DigMax; +end +EDF.PreFilt= setstr(fread(EDF.FILE.FID,[80,EDF.NS],'char')'); % +tmp = fread(EDF.FILE.FID,[8,EDF.NS],'char')'; % samples per data record +EDF.SPR = str2num(setstr(tmp)); % samples per data record + +fseek(EDF.FILE.FID,32*EDF.NS,0); + +EDF.Cal = (EDF.PhysMax-EDF.PhysMin)./ ... + (EDF.DigMax-EDF.DigMin); +EDF.Off = EDF.PhysMin - EDF.Cal .* EDF.DigMin; +tmp = find(EDF.Cal < 0); +EDF.Cal(tmp) = ones(size(tmp)); +EDF.Off(tmp) = zeros(size(tmp)); + +EDF.Calib=[EDF.Off';(diag(EDF.Cal))]; +%EDF.Calib=sparse(diag([1; EDF.Cal])); +%EDF.Calib(1,2:EDF.NS+1)=EDF.Off'; + +EDF.SampleRate = EDF.SPR / EDF.Dur; + +EDF.FILE.POS = ftell(EDF.FILE.FID); +if EDF.NRec == -1 % unknown record size, determine correct NRec + fseek(EDF.FILE.FID, 0, 'eof'); + endpos = ftell(EDF.FILE.FID); + EDF.NRec = floor((endpos - EDF.FILE.POS) / (sum(EDF.SPR) * 2)); + fseek(EDF.FILE.FID, EDF.FILE.POS, 'bof'); + H1(237:244)=sprintf('%-8i',EDF.NRec); % write number of records +end; + +EDF.Chan_Select=(EDF.SPR==max(EDF.SPR)); +for k=1:EDF.NS + if EDF.Chan_Select(k) + EDF.ChanTyp(k)='N'; + else + EDF.ChanTyp(k)=' '; + end; + if findstr(upper(EDF.Label(k,:)),'ECG') + EDF.ChanTyp(k)='C'; + elseif findstr(upper(EDF.Label(k,:)),'EKG') + EDF.ChanTyp(k)='C'; + elseif findstr(upper(EDF.Label(k,:)),'EEG') + EDF.ChanTyp(k)='E'; + elseif findstr(upper(EDF.Label(k,:)),'EOG') + EDF.ChanTyp(k)='O'; + elseif findstr(upper(EDF.Label(k,:)),'EMG') + EDF.ChanTyp(k)='M'; + end; +end; + +EDF.AS.spb = sum(EDF.SPR); % Samples per Block +bi=[0;cumsum(EDF.SPR)]; + +idx=[];idx2=[]; +for k=1:EDF.NS, + idx2=[idx2, (k-1)*max(EDF.SPR)+(1:EDF.SPR(k))]; +end; +maxspr=max(EDF.SPR); +idx3=zeros(EDF.NS*maxspr,1); +for k=1:EDF.NS, idx3(maxspr*(k-1)+(1:maxspr))=bi(k)+ceil((1:maxspr)'/maxspr*EDF.SPR(k));end; + +%EDF.AS.bi=bi; +EDF.AS.IDX2=idx2; +%EDF.AS.IDX3=idx3; + + +DAT.Head=EDF; +DAT.MX.ReRef=1; + +%DAT.MX=feval('loadxcm',EDF); + +return;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/peaksegments.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function y = peaksegments(func,d,p,varargin) + +y = cell(1,length(d)); +for h = 1:length(d) + if not(iscell(d{h})) % for histograms + p{h} = {p{h}(:,:,1)'}; + d{h} = {d{h}'}; + end + for i = 1:length(d{h}) + %sum(var(p{h}{i},1,2)) + %p{h}{i} = repmat(p{h}{i}(:,1,1),[1,size(p{h}{i},2),size(p{h}{i},3)]); + d{h}{i} = d{h}{i} + repmat(min(d{h}{i}),[size(d{h}{i},1),1,1]); + for j = 1:size(d{h}{i},3) + for k = 1:size(d{h}{i},2) + yk = []; + nb = 0; + f = find(not(isnan(d{h}{i}(1:end,k,j)))); + while not(isempty(f)) + l = f(1); + f = find(isnan(d{h}{i}(l:end,k,j)))+l-1; + if isempty(f) + f = size(d{h}{i},1)+1; + end + r = l:f(1)-1; + if length(r)>1 + nb = nb+1; + if nargin > 3 + for g = 1:length(varargin) + if g == 1 + v{g} = repmat(varargin{g}{h}{i}{1,k,j}(nb),... + [length(r),1,1]); + else + v{g} = varargin{g}{h}{i}{1,k,j}(nb); + end + end + else + v = {}; + end + yk(nb,1) = func(abs(d{h}{i}(r,k,j)),... + p{h}{i}(r,k,1),... + v{:}); + end + f = find(not(isnan(d{h}{i}(f(1):end,k,j))))+f(1)-1; + end + y{h}{i}{1,k,j} = yk; + end + end + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/pluscell.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function varargout = pluscell(x,varargin) + +specif.combinechunk = 'Average'; + +varargout = mirfunction(@pluscell,x,varargin,nargout,specif,@init,@main); + + +function [x type] = init(x,option) +type = mirtype(a{1}); + + +function y = main(x,option,postoption) +y = plus(x{1},x{2}); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/purgedata.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function c = purgedata(c) + +d = get(c,'Data'); +l = length(d); +empty = cell(1,l); +for i = 1:l + empty{i} = cell(1,length(d{i})); +end +c = set(c,'PeakPos',empty,'PeakVal',empty,... + 'PeakPrecisePos',{},'PeakPreciseVal',{},'PeakMode',empty); + %,...'Tmp',[]); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/readbdf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,81 @@ +function [DAT,S]=readbdf(DAT,Records,Mode) +% [DAT,signal]=readedf(EDF_Struct,Records) +% Loads selected Records of an EDF File (European Data Format for Biosignals) into MATLAB +% <A HREF="http://www.medfac.leidenuniv.nl/neurology/knf/kemp/edf.htm">About EDF</A> +% +% Records1 List of Records for Loading +% Mode 0 default +% 1 No AutoCalib +% Mode+2 Concatanated (channels with lower sampling rate if more than 1 record is loaded) + +% Version 2.11 +% 03.02.1998 +% Copyright (c) 1997,98 by Alois Schloegl +% a.schloegl@ieee.org + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This program has been modified from the original version for .EDF files +% The modifications are to the number of bytes read on line 53 (from 2 to +% 3) and to the type of data read - line 54 (from int16 to bit24). Finally the name +% was changed from readedf to readbdf +% T.S. Lorig Sept 6, 2002 +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargin<3 Mode=0; end; + +EDF=DAT.Head; +RecLen=max(EDF.SPR); + +S=NaN*zeros(RecLen,EDF.NS); +DAT.Record=zeros(length(Records)*RecLen,EDF.NS); +DAT.Valid=uint8(zeros(1,length(Records)*RecLen)); +DAT.Idx=Records(:)'; + +for nrec=1:length(Records), + + NREC=(DAT.Idx(nrec)-1); + if NREC<0 fprintf(2,'Warning READEDF: invalid Record Number %i \n',NREC);end; + + fseek(EDF.FILE.FID,(EDF.HeadLen+NREC*EDF.AS.spb*3),'bof'); + [s, count]=fread(EDF.FILE.FID,EDF.AS.spb,'bit24'); + + S(EDF.AS.IDX2)=s; + + %%%%% Test on Over- (Under-) Flow +% V=sum([(S'==EDF.DigMax(:,ones(RecLen,1))) + (S'==EDF.DigMin(:,ones(RecLen,1)))])==0; + V=sum([(S(:,EDF.Chan_Select)'>=EDF.DigMax(EDF.Chan_Select,ones(RecLen,1))) + ... + (S(:,EDF.Chan_Select)'<=EDF.DigMin(EDF.Chan_Select,ones(RecLen,1)))])==0; + EDF.ERROR.DigMinMax_Warning(find(sum([(S'>EDF.DigMax(:,ones(RecLen,1))) + (S'<EDF.DigMin(:,ones(RecLen,1)))]')>0))=1; + % invalid=[invalid; find(V==0)+l*k]; + + if floor(Mode/2)==1 + for k=1:EDF.NS, + DAT.Record(nrec*EDF.SPR(k)+(1-EDF.SPR(k):0),k)=S(1:EDF.SPR(k),k); + end; + else + DAT.Record(nrec*RecLen+(1-RecLen:0),:)=S; + end; + + DAT.Valid(nrec*RecLen+(1-RecLen:0))=V; +end; +if rem(Mode,2)==0 % Autocalib + DAT.Record=[ones(RecLen*length(Records),1) DAT.Record]*EDF.Calib; +end; + +DAT.Record=DAT.Record'; +return; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/surfplot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +function h = surfplot(varargin) +%SURFPLOT Pseudocolor (checkerboard) plot. +% SURFPLOT(C) is a pseudocolor or "checkerboard" plot of matrix C. +% The values of the elements of C specify the color in each +% cell of the plot. In the default shading mode, 'faceted', +% each cell has a constant color and the last row and column of +% C are not used. With shading('interp'), each cell has color +% resulting from bilinear interpolation of the color at its +% four vertices and all elements of C are used. +% The smallest and largest elements of C are assigned the first and +% last colors given in the color table; colors for the remainder of the +% elements in C are determined by table-lookup within the remainder of +% the color table. +% +% SURFPLOT(X,Y,C), where X and Y are vectors or matrices, makes a +% pseudocolor plot on the grid defined by X and Y. X and Y could +% define the grid for a "disk", for example. +% +% SURFPLOT(AX,..) plots into AX instead of GCA. +% +% H = SURFPLOT(...) returns a handle to a SURFACE object. +% +% SURFPLOT is really a SURF with its view set to directly above. + +% SURFPLOT is equivalent to PCOLOR, but slighted corrected for MIRToolbox + +%------------------------------- +% Additional details: +% +% +% PCOLOR sets the View property of the SURFACE object to directly +% overhead. +% +% If the NextPlot axis property is REPLACE (HOLD is off), PCOLOR resets +% all axis properties, except Position, to their default values +% and deletes all axis children (line, patch, surf, image, and +% text objects). View is set to [0 90]. + +% Copyright 1984-2002 The MathWorks, Inc. +% $Revision: 5.9.4.1 $ $Date: 2002/10/24 02:14:11 $ + +% Slightly corrected for MIRToolbox + +% J.N. Little 1-5-92 + +% Parse possible Axes input +[cax,args,nargs] = axescheck(varargin{:}); +error(nargchk(1,4,nargs)) + +cax = newplot(cax); +hold_state = ishold(cax); + +if nargs == 1 + x = args{1}; + hh = surface(zeros(size(x)),x,'parent',cax); + [m,n] = size(x); + lims = [ 1 n 1 m]; +elseif nargs == 3 + [x,y,c] = deal(args{1:3}); + %cc = zeros(size(y,1),size(x,2)); + %cc(1:size(c,1),1:size(c,2)) = c; + hh = surface(x,y,zeros(size(y,1),size(x,2)),c,'parent',cax,'EdgeColor','none'); % Here are the modification + lims = [min(min(x)) max(max(x)) min(min(y)) max(max(y))]; +else + error('Must have one or three input data arguments.') +end +if ~hold_state + set(cax,'View',[0 90]); + set(cax,'Box','on'); + axis(cax,lims); +end +if nargout == 1 + h = hh; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolbox/uncell.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,140 @@ +function x = uncell(x,lvl) + % lvl >= 0: The recursion level, starting from 0, when observing the + % cell structures of cell structures of etc. of the input data x. + % lvl = NaN: + % lvl = -Inf: used in display of segmented mirscalar +if nargin < 2 + lvl = 0; +end +if iscell(x) + if isempty(x) + x = []; + elseif length(x) == 1 + x = uncell(x{1},lvl+1); + else + het = 0; % Flags different kinds of structures: + % - structures with fixed column size but variable nb of columns (het = 1) + % - structures with variable column size but fixed nb of columns (het = 2) + % - structures with variable column size and variable nb of columns (het = NaN) + l = length(x(:,:,1)); + nf = size(x,3); + z = cell(size(x)); + for i1 = 1:size(x,1) + for i2 = 1:size(x,2) + for i3 = 1:size(x,3) + z{i1,i2,i3} = uncell(x{i1,i2,i3},lvl+1); + if i1 == 1 && i2 == 1 && i3 == 1 + s = size(z{i1,i2,i3}); + elseif not(isequal(s,size(z{i1,i2,i3}))) + % A break in the homogeneity has been discovered. + if lvl>0 + if (het == 0 || het == 2) && (s(2) == size(z{i1,i2,i3},2)) + het = 2; + s = max(s,size(z{i1,i2,i3})); + elseif (het == 0 || het == 1) && (s(1) == size(z{i1,i2,i3},1)) + het = 1; + s = max(s,size(z{i1,i2,i3})); + else + het = NaN; + end + else + het = NaN; + end + end + end + end + end + if isnan(het) + x = z; + else + if size(s)<3 + s(3) = 1; + end + if (s(1)>1 || isnan(lvl)) || lvl == -Inf + x = NaN([s(1) l*s(2) nf*s(3:end)]); + for j = 1:nf + for i = 1:l + if size(z,1)>size(z,2) + zij = z{i,1,j}; + else + zij = z{1,i,j}; + end + if size(zij,3) == 1 + x(1:size(z{1,i,j},1),... + (i-1)*s(2)+1:(i-1)*s(2)+size(z{1,i,j},2),... + j,1:size(z{1,i,j},4)) = z{1,i,j}; + elseif nf == 1 + x(1:size(z{1,i,j},1),... + (i-1)*s(2)+1:(i-1)*s(2)+size(z{1,i,j},2),... + :,1:size(z{1,i,j},4)) = z{1,i,j}; + end + end + end + else + x = NaN([l*s(1) s(2) nf*s(3:end)]); + for j = 1:nf + for i = 1:l + if size(z,1)>size(z,2) + zij = z{i,1,j}; + else + zij = z{1,i,j}; + end + if ischar(zij) && length(zij) == 1 && zij>='A' && zij <= 'G' + %zi + zij = zij-'A'; + end + x((i-1)*s(1)*size(zij,1)+1:(i-1)*s(1)*size(zij,1)+size(zij,1),... + 1:size(zij,2),... + j,... + 1:size(zij,4)) = zij; + end + end + end + end + end +elseif ischar(x) + switch x + case 'C' + x = 0; + case 'C#' + x = 1; + case 'D' + x = 2; + case 'D#' + x = 3; + case 'E' + x = 4; + case 'F' + x = 5; + case 'F#' + x = 6; + case 'G' + x = 7; + case 'G#' + x = 8; + case 'A' + x = 9; + case 'A#' + x = 10; + case 'B' + x = 11; + end +elseif 0 %scal + if size(x,1)==1 && size(x,2)==1 && size(x,3)>1 + x = shiftdim(x,2); + else + %y = zeros(size(x,2),size(x,1),size(x,3)); + %y = zeros(size(x)); + %for i = 1:size(x,3) + % y(:,:,i) = x(:,:,i); + %end + %x = y; + end +elseif 0 %not(isnan(lvl)) && lvl>=0 + %x = squeeze(x); + if size(x,1) == 1 + x = x(:); + end +elseif 0 %size(x,3) > 1 && size(x,1) == 1 + x = reshape(x,size(x,2),size(x,3))'; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo0grandbuffet.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +help mirtoolbox +help miraudio +a = miraudio('ragtime','Center','Sampling',11025) +mirplay(a) +mirspectrum(a) +mirgetdata(a) +get(a,'Sampling') + +a = miraudio('laksin') +a = miraudio(a,'Trim') +a = miraudio(a,'Extract',0,1) +e = mirenvelope(a) +e = mirenvelope(a,'Diff') +e = mirenvelope(a,'HalfwaveDiff') + +pause, close all + +s = mirspectrum('Amin3') +s = mirspectrum(s,'Max',3000) +s = mirspectrum(s,'dB') +s = mirspectrum('Amin3','Mel') +mirpeaks(s) + +pause, close all + +ac = mirautocor('Amin3') +ac = mirautocor(ac,'Enhanced',2:10) +ac = mirautocor(ac,'Freq') + +pause, close all + +f = mirframe('ragtime',.1,.5) +s = mirspectrum(f) +s = mirspectrum('ragtime','frame',.1,.5) +mirpeaks(s) +mirflux(s) +mirflux(mirautocor(a,'Frame')) + +pause, close all + +fb = mirfilterbank('ragtime','NbChannels',5) +e = mirenvelope(fb) +ae = mirautocor(e) +sa = mirsum(ae) + +pause, close all + +r1 = mirrms('movie1','Frame') +r2 = mirrms('movie2','Frame') +mirlowenergy(r1) +mirlowenergy(r2) + +pause, close all + +[t,ac] = mirtempo('ragtime') +[t,ac] = mirtempo('czardas','frame') + +pause, close all + +at = mirattacks('ragtime') +mirattackslope(at) +mirbrightness('ragtime','frame') +mirroughness('ragtime','frame') + +pause, close all + +[p,a] = mirpitch('ragtime','frame') +mirchromagram('ragtime','wrap','no') +mirchromagram('ragtime') +mirkey('ragtime') +k = mirkey('ragtime','frame') +mirmode('ragtime') +m = mirmode('ragtime','frame') + +pause, close all + +mirstat(m) +mirhisto(m) +mirexport('result.txt',k,m) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo1pitch.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,147 @@ +function demo1pitch + +% Demo 1:Pitch extraction +% Aims: To study the diverse tools available in the toolbox for pitch +%extraction. +% In the following exercises, we will try to extract the pitch contents of various audio files using diverse techniques. All the audio files are included in the ?MIRtoolbox_Demos? folder. + +%% 1. Analyis of a single chord + +% Load the audio file 'Amin3.wav'. +a = miraudio('Amin3') +%and play it: +mirplay(a) + +% Observe the periodicities contained in the signal by computing the autocorrelation function: +ac = mirautocor(a) +% The autocorrelation function can also be displayed in the frequency domain (?Freq?), and the frequency range can be specified, for instance between 75 and 2400 Hz: +ac = mirautocor(ac, 'Freq','Min',75,'Hz','Max',2400,'Hz') + +% The peaks of the curve indicates the most important periodicities: +pac = mirpeaks(ac) +% But the two peaks at the start and end of the curves are meaningless, so should be removed: +pac = mirpeaks(ac, 'NoBegin','NoEnd') + +% The corresponding pitch height are given by: +p = mirpitch(pac) + +% The actual numerical data (here, the frequency of each pitch) is given by: +mirgetdata(p) + +% The resulting pitches can be finally played: +mirplay(p) + +% So far, the results do not seem very convincing, do they? + +pause, close all + +%% 2. Improving the analysis + +% Clearly, we need to refine the methods in order to retrieve the notes played in the chord. First of all, the autocorrelation function can be ?generalized?, i.e., ?compressed? by a factor of, for instance, 0.5 (cf. MIRtoolbox User?s Manual at the mirautocor section for an explanation of the 'Compres' option): +ac = mirautocor(a,'Compres',.5) + +% The rest of the computation can actually be performed using the shortcut: +[p pac] = mirpitch(ac) + +% Look and listen to the results. +mirgetdata(p) +mirplay(p) + +% Does it sound better? What is the problem this time? + +pause, close all + +%% 3. Improving the analysis, again + +% In fact, the autocorrelation function contains a lot of harmonics that need to be removed. For that purpose, the autocorrelation function can be ?enhanced? (cf. MIRtoolbox User?s Manual at the mirautocor section for an explanation of the 'Enhanced' option): +ac = mirautocor(ac,'Enhanced') + +% Carry out the rest of the computation as in section 2.2. +[p pac] = mirpitch(ac) +mirgetdata(p) +mirplay(p) + +% What do you think of the results? + +pause, close all + +%% 4. Improving the analysis, still + +% An additional improvement consists in first decomposing the audio into two channels using the command: +fb = mirfilterbank(a,'2Channels') + +% Compute the autocorrelation on each channel: +ac = mirautocor(fb,'Compres',.5) + +% The autocorrelation of each channel can then be summed together: +ac = mirsum(ac) + +% And the enhancement can be performed afterwards: +ac = mirautocor(ac,'Enhanced') + +% And the rest of the computation follows the same principle than before. +[p ac] = mirpitch(ac) + +% The result should be better this way. +mirgetdata(p) +mirplay(p) + +% Hopefully, the whole chain of operation can be performed automatically using the simple command: +[p ac] = mirpitch(a) +[p ac] = mirpitch(a,'Cepstrum') +[p ac] = mirpitch(a,'AutocorSpectrum') + +pause, close all + +%% 5. Frame-based analysis + +% Let?s analyze a longer musical sequence, such as ragtime.wav: +a = miraudio('ragtime'); +mirplay(a) + +% A direct analysis of the recording using the previous command +[p ac] = mirpitch(a) +% is not concluding, as it tries to find the pitches contained in the whole signal. +mirgetdata(p) +mirplay(p) + +% It is better to estimate the pitch content frame-by-frame: +[p ac] = mirpitch(a,'Frame') +mirgetdata(p) + +% Don?t forget to listen to the results. +mirplay(p) + +pause, close all + +%% 6. Segment-based analysis + +% But it might get more sense to first decompose the signal into notes, using the commands: +o = mironsets(a,'Attacks') +% (The 'Attacks' option enables to segment at the beginning of each note +% attack phase, i.e., where the energy is minimum, instead of segmenting where the energy is maximum.) +mirplay(o) + +sg = mirsegment(a,o) +mirplay(sg) + +% and to compute the pitch on each successive segment: +[p ac] = mirpitch(sg) +mirgetdata(p) + +% Again, don?t forget to listen to the results. +mirplay(p) + +pause, close all + +%% 7. Monody analysis + +% Conclude with the analysis of a Finnish folk song, Läksin minä kesäyönä, whose excerpt is recorded in the file ?laksin.wav?. As the melody is sung by one voice only, you can use the ?Mono? option in mirpitch. +mirplay('laksin') +o = mironsets('laksin','Attacks','Contrast',.1) +mirplay(o) +sg = mirsegment('laksin',o) +mirplay(sg) +p = mirpitch(sg,'mono') +mirgetdata(p) +mirplay(p) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo2timbre.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function demo2timbre + +try + cd('train_set') +catch +end + +a = miraudio('Folder'); + +z = mirzerocross(a) +disp('Ascending order of zero crossing...') +mirplay(a,'Increasing',z,'Every',5) + +l = mirlowenergy(a) +disp('Ascending order of low energy rate...') +mirplay(a,'Increasing',l,'Every',5) + +c = mircentroid(a) +disp('Ascending order of spectral centroid...') +mirplay(a,'Increasing',c,'Every',5) + +r = mirrolloff(a) +disp('Ascending order of spectral roll-off...') +mirplay(a,'Increasing',r,'Every',5) + +i = mirregularity(a) +disp('Ascending order of spectral irregularity...') +mirplay(a,'Increasing',i,'Every',5) + +e = mirentropy(a) +disp('Ascending order of spectral entropy...') +mirplay(a,'Increasing',e,'Every',5) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo3tempo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +function demo3tempo +% To get familiar with tempo estimation from audio using the MIR Toolbox. +% To assess the performance of the tempo estimation method. + +% 1. Let's investigate the different stages needed for tempo estimation. + +d = miraudio('ragtime') +mirenvelope(d) +e = mirenvelope(d,'Halfwavediff') + +% Decompose the audio file with a filter bank. +f = mirfilterbank(d) + +% Calculate also a half-wave rectified differentiated envelope. +ee = mirenvelope(f,'HalfwaveDiff') + +% Sum the frequency channels.ok +s = mirsum(ee,'Centered') + +d2 = miraudio('vivaldi') +f2 = mirfilterbank(d2) +ee2 = mirenvelope(f2,'HalfwaveDiff') +s2 = mirsum(ee2,'Centered') + +% Calculate the autocorrelation function. +ac = mirautocor(s) + +% Apply the resonance model to the autocorrelation function. +ac = mirautocor(s,'Resonance') + +% Find peaks in the autocorrelation function. +p = mirpeaks(ac) +mirgetdata(p) + +% Get the period of the peaks. +t = mirtempo(p,'Total',1) + +display('Strike any key to continue...'); +pause +close all + +% 2. All the functions we used are integrated into the function tempo. +help mirtempo + +% For instance, we can simply write: +[t,ac] = mirtempo('ragtime') + +% As you can see in the help, the resonance is integrated by default in the +% tempo function. To toggle off the use of the resonance function, type: +[t,ac] = mirtempo('ragtime','Resonance',0) + +[t,ac] = mirtempo('ragtime','total',5) + +display('Strike any key to continue...'); +pause +close all + +% 3. The excerpt 'laksin' and 'czardas' have variable tempi. Use frame- +% based tempo analysis to estimate the variation of the tempi. +% Apply to this end the tempo command with the 'frame' option. +[t1,p1] = mirtempo('laksin','Frame') +[t2,p2] = mirtempo('czardas','Frame') + +% What is the range of variation of tempi? +help mirhisto +h1 = mirhisto(t1) +h2 = mirhisto(t2) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo4segmentation.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,154 @@ +function demo4segmentation +% To get familiar with some approaches of segmentation of audio files +% using MIRtoolbox. + +% 1. Load an audio file (for instance, guitar.wav). +a = miraudio('guitar'); + +% 2. We will perform the segmentation strategy as proposed in (Foote & +% Cooper, 2003). First, decompose the file into successive frames of 50 ms +% without overlap. +help mirframe +fr = mirframe(a,0.05,1) + +% 3. Compute the spectrum representation (FFT) of the frames. +sp = mirspectrum(fr) +clear fr +% (Remove from the memory any data that will not be used any more.) + +% 4. Compute the similarity matrix that shows the similarity between the +% spectrum of different frames. +help mirsimatrix +sm = mirsimatrix(sp) +clear sp +% Look at the structures shown in the matrix and find the relation with the +% structure heard when listening to the extract. + +% 5. Estimate the novelty score related to the similarity matrix. It +% consists in a convolution of the diagonal of the matrix with a +% checker-board Gaussian kernel. Use the novelty function for that purpose. +help mirnovelty +nv = mirnovelty(sm) + +% 6. Detect the peaks in the novelty score. +help mirpeaks +p1 = mirpeaks(nv) + +% You can change the threshold value of the peak picker function in order to +% get better results. +p2 = mirpeaks(nv,'Contrast',0.01) + +clear nv + +% 7. Segment the original audio file using the peaks as position for +% segmentation. +help mirsegment +s1 = mirsegment(a,p1) +clear p1 + +% 8. Listen to the results. +mirplay(s1) + +%s2 = mirsegment(a,p2) +%clear p2 +%mirplay(s2) + +% 9. Compute the similarity matrix of this obtained segmentation, in order +% to view the relationships between the different segments and their +% possible clustering into higher-level groups. +mirsimatrix(s1,'Similarity') +clear s1 +%mirsimatrix(s2) +%clear s2 + +display('Strike any key to continue...'); +pause +close all + +% 10. Change the size of the kernel used in the novelty function, in order +% to obtain segmentations of different levels of detail, from detailed +% analysis of the local texture, to very simple segmentation of the whole +% piece. +n100 = mirnovelty(sm,'KernelSize',100) +n50 = mirnovelty(sm,'KernelSize',50) +n10 = mirnovelty(sm,'KernelSize',10) +clear sm +% As you can see, the smaller the gaussian kernel is, the more peaks can be +% found in the novelty score. Indeed, if the kernel is small, the cumulative +% multiplication of its elements with the superposed elements in the +% similarity matrix may vary more easily, throughout the progressive +% sliding of the kernel along the diagonal of the similarity matrix, and +% local change of texture may be more easily detected. On the contrary, +% when the kernel is large, only large-scale change of texture are +% detected. + +display('Strike any key to continue...'); +pause +close all + +p100 = mirpeaks(n100,'NoBegin','NoEnd') +clear n100 +p50 = mirpeaks(n50,'NoBegin','NoEnd') +clear n50 +p10 = mirpeaks(n10,'NoBegin','NoEnd') +clear n10 +s100 = mirsegment(a,p100) +clear p100 +mirplay(s100) +clear s100 +s50 = mirsegment(a,p50) +clear p50 +mirplay(s50) +clear s50 +s10 = mirsegment(a,p10) +clear p10 +mirplay(s10) +clear s10 + +display('Strike any key to continue...'); +pause +close all + +% One more compact way of writing these commands is as follows: +mirsegment(a,'Novelty') +mirsegment(a,'Novelty','Contrast',0.01) +mirsegment(a,'Novelty','KernelSize',100) + +display('Strike any key to continue...'); +pause +close all + +% Besides, if you want to see the novelty curve with the peaks, just add a +% second output: +[s50 p50] = mirsegment(a,'Novelty','KernelSize',50) +clear s50 p50 +[s10 p10] = mirsegment(a,'Novelty','KernelSize',10) +clear a s10 p10 + +display('Strike any key to continue...'); +pause +close all + + +% 11. Try the whole process with MFCC instead of spectrum analysis. Take the +% first ten MFCC for instance. +help mirsegment +% The segment function can simply be called as follows: +sc = mirsegment('czardas','Novelty','MFCC','Rank',1:10) +clear sc + +% Here are some other examples of use: +[ssp p m b] = mirsegment('valse_triste_happy','Spectrum',... + 'KernelSize',150,'Contrast',.1) +clear p m b +mirplay(ssp) +clear ssp + +display('Strike any key to continue...'); +pause +close all + +[smfcc2 p m a] = mirsegment('valse_triste_happy','MFCC',2:10,... + 'KernelSize',150,'Contrast',.1) +clear p m a +mirplay(smfcc2) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo5export.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function demo5export +% Example of use of the export function. +% For more examples, cf. mirfeatures. + +s = mirspectrum('Folder'); +if not(isempty(get(s,'Data'))) + ch = mirchromagram(s); + k = mirkeystrength(ch); + %h = mirhisto(k); + e = mirenvelope('Folder'); + d = mirenvelope(e,'Diff'); + mirexport('NoFrameNoPeaks.txt',ch,k); + + ch = mirpeaks(ch,'Total',1); + k = mirpeaks(k,'Total',1); + %h = mirpeaks(h); + e = mirpeaks(e,'Total',1); + d = mirpeaks(d,'Total',1); + mirexport('NoFrameButPeaks.txt',ch,k,e,d); + clear ch k e d + + ac = mirautocor('Folder','Frame',.5,.2); + s = mirspectrum('Folder','Frame',.5,.2); + sf = mirflux(s); + ms = mirspectrum(s,'Mel'); + m = mirmfcc(ms); + ch = mirchromagram(s); + k = mirkeystrength(ch); + %h = mirhisto(k); + e = mirenvelope('Folder','Frame',.5,.2); + d = mirenvelope(e,'Diff'); + mirexport('FrameNoPeaks.txt',ms,m,ch,k); + + ac = mirpeaks(ac,'Total',1,'NoBegin'); + s = mirpeaks(s,'Total',1); + sf = mirpeaks(sf,'Total',1); + ms = mirpeaks(ms,'Total',1); + m = mirpeaks(m,'Total',1,'Interpol',0); + ch = mirpeaks(ch,'Total',1); + k = mirpeaks(k,'Total',1); + %h = mirpeaks(h) + e = mirpeaks(e,'Total',1); + d = mirpeaks(d,'Total',1); + mirexport('FrameAndPeaks.txt',ac,s,sf,ms,m,ch,k,e,d); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo6curves.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function demo6curves(file) +% Example of use of curve analysis tools, such as moment estimations. + +if nargin == 0 + file = 'trumpet'; +end + +s = mirspectrum(file) +mircentroid(s) +mirspread(s) +mirskewness(s) +mirkurtosis(s) +mirflatness(s) +mirregularity(s) +mirentropy(s) +clear s + +e = mirenvelope(file) +mircentroid(e) +mirspread(e) +mirskewness(e) +mirkurtosis(e) +mirflatness(e) +mirentropy(e) + +display('Strike any key to continue...'); +pause +close all + +s = mirspectrum(file,'Frame',.1,1) +mircentroid(s) +mirspread(s) +mirskewness(s) +mirkurtosis(s) +mirflatness(s) +mirregularity(s) +mirentropy(s) +clear s + +display('Strike any key to continue...'); +pause +close all + +fe = mirframe(e,.1,1) +clear e +mircentroid(fe) +mirspread(fe) +mirskewness(fe) +mirkurtosis(fe) +mirflatness(fe) +mirentropy(fe) +clear fe + +display('Strike any key to continue...'); +pause +close all + +if nargin == 0 + file = 'czardas'; +end +t = mirtempo(file,'Periodicity','Frame') +h = mirhisto(t) +clear t +mircentroid(h) +mirspread(h) +mirskewness(h) +mirkurtosis(h) +mirflatness(h) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo7tonality.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,100 @@ +function demo7tonality +%To get familiar with some approaches of tonal analysis using MIRtoolbox, +% and to assess their performances. + +% Part 1. We will first investigate the performance of the chromagram +% analysis, using very simple musical samples. + +% 1.3. In the audio file ÔtrumpetÕ, the same pitch is played by a trumpet. +% Compute its chromagram. What are the chromas detected by the function? +% Can you explain the result? +mirchromagram('trumpet') + +% 1.4. A more detailed representation of the chromagram can be obtained by +% decomposing each pitch class into its different possible absolute values. +% For that purpose, just add the parameter: +c = mirchromagram('trumpet','Wrap',0) + +% 1.5. Compute also the key strength related to the chromagram. +mirkeystrength(c) + +pause, close all + +% 1.6. In the audio file ÔpianoF4Õ, the same pitch is played by a piano. +[ks c] = mirkeystrength('pianoF4') + +a = miraudio('pianoF4','excerpt',.7,2); +[ks c] = mirkeystrength(a) + +pause, close all + +% 1.7. Investigate the chromagram analysis of triad chords +[ks c] = mirkeystrength('Amin3') +p = mirpeaks(ks) +mirkey(p) + +pause, close all + +[k kc ks] = mirkey('Amaj3') +[k kc ks] = mirkey('Amin4') +[k kc ks] = mirkey('Amaj4') + +pause, close all + +[k kc ks] = mirkey('Amin5') +[k kc ks] = mirkey('Amaj5') +[k kc ks] = mirkey('Cmaj') + +pause, close all + +[k kc ks] = mirkey(miraudio('Amin3','Excerpt',.2,1)) +[k kc ks] = mirkey(miraudio('Amin4','Excerpt',.2,1)) +[k kc ks] = mirkey(miraudio('Amaj3','Excerpt',.2,1)) +[k kc ks] = mirkey(miraudio('Amaj4','Excerpt',.2,1)) + +pause, close all + +[k kc ks] = mirkey(miraudio('Amin5','Excerpt',.2,1)) +[k kc ks] = mirkey(miraudio('Amaj5','Excerpt',.2,1)) +[k kc ks] = mirkey(miraudio('Cmaj','Excerpt',.2,1)) + +pause, close all + +%Part 2. Let's analyze several extracts from +%real music. For each extract, try the +%following: + +%2.1. Listen to the piece: +mirplay('vivaldi') +soundsc(sin(2*pi*440*(0:1/8192:1))) + +%2.2. Compute the chromagram of the +%whole extract. What tonal center could be +%inferred from the curve? Does it +%correspond to your expectation? +c = mirchromagram('vivaldi') + +%2.3. Compute the key strength related to +%the chromagram. Is the result congruent +%with the tonality inferred in 2.1. and 2.2.? +ks = mirkeystrength(c) +[k kc ks] = mirkey(ks) + +%2.4. A more detailed representation of the +%key strengths can be obtained by +%computing the self-organizing map: +som = mirkeysom(c) + +pause, close all + +%2.5. Analyse in the same way other audio files +[k kc ks] = mirkey('czardas') +mirkeysom('czardas') + +pause, close all + +%Part 3. The temporal evolution of the +%tonal dimension can be assessed by +%decomposing first the audio into frames +[k kc ks] = mirkey('czardas','frame') +mirkeysom('czardas','frame') \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo8classification.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,162 @@ +function demo8classification +% To get familiar with different approaches of classification using +% MIRtoolbox, and to assess their performances. + +% Part 1. The aim of this experiment is to categorize a set of very short +% musical excerpts according to their genres, through a supervised learning. + +% 1.3. Select the training set for current directory. +try + cd train_set +catch + error('Please change current directory to ''MIRtoolboxDemos'' directory') +end + +% Load all the files of the folder into one audio structure (called for +% instance training), and associate for each folder a label defined by the +% first two letters of the respective file name. +train = miraudio('Folder','Label',1:2); +cd .. + +% In the same way, select the testing set for current directory, and load +% all the files including their labels: +cd test_set +test = miraudio('Folder','Label',1:2); +cd .. + +% 1.4. Compute the mel-frequency cepstrum coefficient for each different +% audio file of both sets: +mfcc_train = mirmfcc(train); +mfcc_test = mirmfcc(test); + +% 1.5. Estimate the label (i.e., genre) of each file from the testing set, +% based on a prior learning using the training set. Use for this purpose +% the classify function. +help mirclassify + +% Let's first try a classification based of mfcc, for instance, using the +% minimum distance strategy: +mirclassify(test,mfcc_test,train,mfcc_train) + +% The results indicates the outcomes and the total correct classification +% rate (CCR). + +% 1.6. Let's try a k-nearest-neighbour strategy. For instance, for k = 5: +mirclassify(test,mfcc_test,train,mfcc_train,5) + +% 1.7. Use a Gaussian mixture modelling with one gaussian per class: +mirclassify(test,mfcc_test,train,mfcc_train,'GMM',1) + +% try also with three Gaussians per class. +mirclassify(test,mfcc_test,train,mfcc_train,'GMM',3) + +% As this strategy is stochastic, the results vary for every trial. +mirclassify(test,mfcc_test,train,mfcc_train,'GMM',1) +mirclassify(test,mfcc_test,train,mfcc_train,'GMM',1) +mirclassify(test,mfcc_test,train,mfcc_train,'GMM',3) +mirclassify(test,mfcc_test,train,mfcc_train,'GMM',3) + +% 1.8. Carry out the classification using other features such as spectral +% centroid: +spectrum_train = mirspectrum(train); +spectrum_test = mirspectrum(test); +centroid_train = mircentroid(spectrum_train); +centroid_test = mircentroid(spectrum_test); +mirclassify(test,centroid_test,train,centroid_train,'GMM',1) +mirclassify(test,centroid_test,train,centroid_train,'GMM',1) +mirclassify(test,centroid_test,train,centroid_train,'GMM',3) +mirclassify(test,centroid_test,train,centroid_train,'GMM',3) + +% try also spectral entropy and spectral irregularity. +entropy_train = mirentropy(spectrum_train); +entropy_test = mirentropy(spectrum_test); +mirclassify(test,entropy_test,train,entropy_train,'GMM',1) +mirclassify(test,entropy_test,train,entropy_train,'GMM',1) +mirclassify(test,entropy_test,train,entropy_train,'GMM',3) +mirclassify(test,entropy_test,train,entropy_train,'GMM',3) + +irregularity_train = mirregularity(spectrum_train,'Contrast',.1); +irregularity_test = mirregularity(spectrum_test,'Contrast',.1); +mirclassify(test,irregularity_test,train,irregularity_train,'GMM',1) +mirclassify(test,irregularity_test,train,irregularity_train,'GMM',1) +mirclassify(test,irregularity_test,train,irregularity_train,'GMM',3) +mirclassify(test,irregularity_test,train,irregularity_train,'GMM',3) + +% Try classification based on a set of features such as: +mirclassify(test,{entropy_test,centroid_test},... + train,{entropy_train,centroid_train},'GMM',1) +mirclassify(test,{entropy_test,centroid_test},... + train,{entropy_train,centroid_train},'GMM',1) +mirclassify(test,{entropy_test,centroid_test},... + train,{entropy_train,centroid_train},'GMM',3) +mirclassify(test,{entropy_test,centroid_test},... + train,{entropy_train,centroid_train},'GMM',3) + +% 1.9. By varying the features used for classification, the strategies and +% their parameters, try to find an optimal strategy that give best correct +% classification rate. +bright_train = mirbrightness(spectrum_train); +bright_test = mirbrightness(spectrum_test); +rolloff_train = mirbrightness(spectrum_train); +rolloff_test = mirbrightness(spectrum_test); +spread_train = mirspread(spectrum_train); +spread_test = mirspread(spectrum_test); +mirclassify(test,{bright_test,rolloff_test,spread_test},... + train,{bright_train,rolloff_train,spread_train},'GMM',3) +skew_train = mirskewness(spectrum_train); +skew_test = mirskewness(spectrum_test); +kurtosis_train = mirkurtosis(spectrum_train); +kurtosis_test = mirkurtosis(spectrum_test); +flat_train = mirflatness(spectrum_train); +flat_test = mirflatness(spectrum_test); +mirclassify(test,{skew_test,kurtosis_test,flat_test},... + train,{skew_train,kurtosis_train,flat_train},'GMM',3) +for i = 1:3 + mirclassify(test,{mfcc_test,centroid_test,skew_test,kurtosis_test,... + flat_test,entropy_test,irregularity_test,... + bright_test,rolloff_test,spread_test},... + train,{mfcc_train,centroid_train,skew_train,kurtosis_train,... + flat_train,entropy_train,irregularity_train,... + bright_train,rolloff_train,spread_train},'GMM',3) +end + +% You can also try to change the size of the training and testing sets (by +% simply interverting them for instance). +for i = 1:3 + mirclassify(train,{mfcc_train,centroid_train,skew_train,kurtosis_train,... + flat_train,entropy_train,irregularity_train,... + bright_train,rolloff_train,spread_train},... + test,{mfcc_test,centroid_test,skew_test,kurtosis_test,... + flat_test,entropy_test,irregularity_test,... + bright_test,rolloff_test,spread_test},'GMM',3) +end + +%% +% Part 2. In this second experiment, we will try to cluster the segments of +% an audio file according to their mutual similarity. + +% 2.1. To simplify the computation, downsample +% the audio file to 11025 Hz. +a = miraudio('czardas','Sampling',11025); + +% 2.2. Decompose the file into successive frames of 2 seconds with half- +% overlapping. +f = mirframe(a,2,.1); + +% 2.3. Segment the file based on the novelty of the key strengths. +n = mirnovelty(mirkeystrength(f),'KernelSize',5) +p = mirpeaks(n) +s = mirsegment(a,p) + +% 2.4. Compute the key strengths of each segment. +ks = mirkeystrength(s) + +% 2.5. Cluster the segments according to their key strengths. +help mircluster +mircluster(s,ks) + +% The k means algorithm used in the clustering is stochastic, and its +% results may vary at each run. By default, the algorithm is run 5 times +% and the best result is selected. Try the analysis with a higher number of +% runs: +mircluster(s,ks,'Runs',10)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/demo9retrieval.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function demo9retrieval(query) + +if nargin<1 + query = 'vivaldi'; +end + +%% +% 1. Timbre + +cc0 = mirmfcc(query,'frame',.025,'s',.01,'s','Rank',8:30) +cl0 = mircluster(cc0,16) + +cc = mirmfcc('Folder','frame',.025,'s',.01,'s','Rank',8:30); +cl = mircluster(cc,16); + +d1 = mirdist(cl0,cl) +mirquery(cl0,cl) +mirplay(ans,'Sequence',1:6) + +pause + +%% +% 2. Rhythm + +[bs0 sm0] = mirbeatspectrum(query) +bs = mirbeatspectrum('Folder'); + +d2 = mirdist(bs0,bs) +mirquery(bs0,bs) +mirplay(ans,'Sequence',1:6) + + % Variant + + [tp0,ac0] = mirtempo(query) + [tp,ac] = mirtempo('Folder'); + ac0 = purgedata(ac0); + ac = purgedata(ac); + d2bis = mirdist(ac0,ac) + mirquery(ac0,ac) + mirplay(ans,'Sequence',1:6) + +pause + +%% +% 3. Structure + +pk0 = mirpeaks(mirnovelty(query)) +pk = mirpeaks(mirnovelty('Folder')); + +d3 = mirdist(pk0,pk) +mirquery(pk0,pk) +mirplay(ans,'Sequence',1:6) + +pause + +%% +% 4. Combination + +d = d1*.6 + d2*.3 + d3*.1 +mirquery(d) +mirplay(ans,'Sequence',1:6) + +dbis = d1*.6 + d2bis*.3+ d3*.1 +mirquery(dbis) +mirplay(ans,'Sequence',1:6) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/mirdemo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +%%%% SEGMENTATION + +m = mirmfcc('valse_triste_happy','Rank',2:10,'Frame',0.05,1) +sim = mirsimatrix(m) +n = mirnovelty(sim,'KernelSize',150) +p = mirpeaks(n,'Contrast',.1,'Total',Inf,'NoBegin','NoEnd') +seg = mirsegment('valse_triste_happy',p) +mirplay(seg) + +display('Strike any key to continue...'); +pause +close all + +[seg p m a] = mirsegment('valse_triste_happy','MFCC',2:10,... + 'KernelSize',150,'Contrast',.1) + +display('Strike any key to continue...'); +pause +close all + +%%%% TEMPO + +fb = mirfilterbank('czardas') +%mirplay(fb) +e = mirenvelope(fb) +de = mirenvelope(e,'Diff','Halfwave') +s = mirsum(de,'Centered') +f = mirframe(s,3,.2); +ac = mirautocor(s,'Resonance','Enhanced') +p = mirpeaks(ac,'Total',1) +t = mirtempo(p) + +display('Strike any key to continue...'); +pause +close all + +[t,p] = mirtempo('czardas','Periodicity','Frame') +h = mirhisto(t) + +display('Strike any key to continue...'); +pause +close all + +%%%% TONALITY + +c = mirchromagram('vivaldi','Frame',2) +k = mirkeystrength(c) +p = mirpeaks(k,'Total',1) + +[k,p] = mirkey('vivaldi','Frame',1) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/mirtest.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +function mirtest(audio) + +if not(nargin) + audio = 'ragtime'; +end + +%% Version 1.0 + +mirpeaks(mirspectrum(audio,'Mel')) +f = mirframe(audio,.5,.5) +mirpeaks(mirspectrum(f,'Mel'),'Total',1) +mirpeaks(mirautocor(f),'Total',1,'NoBegin') +mirpeaks(mirspectrum(f),'Total',1,'NoBegin') +mirpeaks(mirchromagram(f),'Total',1) +mirpeaks(mirkeystrength(f),'Total',1) +%mirpeaks(mirfluctuation(f),'Total',1,'NoBegin') %Not implemented yet.. +[a,b,c] = mirkey(audio) + +%% +%pause +clear a b c f +close all + +f = mirfeatures(audio); +sf = mirstat(f); + +f.dynamics.rms{1} +f.fluctuation.peak{1} +f.fluctuation.centroid{1} +f.rhythm.tempo{:} +f.rhythm.attack.time{:} +f.rhythm.attack.slope{1} +sf.dynamics.rms +sf.fluctuation.peak +sf.fluctuation.centroid +sf.rhythm.tempo +sf.rhythm.attack.time +sf.rhythm.attack.slope + +%pause +close all + +f.timbre.zerocross{:} +f.spectral.centroid{:} +f.spectral.brightness{:} +f.spectral.spread{:} +f.spectral.skewness{:} +f.spectral.kurtosis{:} +f.spectral.rolloff95{:} +f.spectral.rolloff85{:} +f.spectral.spectentropy{:} +f.spectral.flatness{:} +sf.timbre.zerocross +sf.spectral.centroid +sf.spectral.brightness +sf.spectral.spread +sf.spectral.skewness +sf.spectral.kurtosis +sf.spectral.rolloff95 +sf.spectral.rolloff85 +sf.spectral.spectentropy +sf.spectral.flatness + +%pause +close all + +f.spectral.roughness{:} +f.spectral.irregularity{:} +%f.timbre.inharmonicity{:} +f.spectral.mfcc{:} +f.spectral.dmfcc{:} +f.spectral.ddmfcc{:} +f.timbre.lowenergy{:} +sf.spectral.roughness +sf.spectral.irregularity +%sf.timbre.inharmonicity +sf.spectral.mfcc +sf.spectral.dmfcc +sf.spectral.ddmfcc +sf.timbre.lowenergy + +%pause +close all + +f.timbre.spectralflux{:} +%f.pitch.salient{:} +f.tonal.chromagram.peak{:} +f.tonal.chromagram.centroid{:} +f.tonal.keyclarity{:} +f.tonal.mode{:} +f.tonal.hcdf{:} +sf.timbre.spectralflux +%sf.pitch.salient +sf.tonal.chromagram.peak +sf.tonal.chromagram.centroid +sf.tonal.keyclarity +sf.tonal.mode +sf.tonal.hcdf + +mirexport('resultdemo.txt',sf) +mirexport('resultdemo.arff',f) + +%% Version 1.1 + +%pause +clear f +close all + +mirlength(audio) +s = mirspectrum(audio,'cents','Min',50) +s = mirspectrum(s,'Collapsed') +mirspectrum(s,'Gauss') +ss = mirspectrum(s,'Smooth') +p = mirpeaks(ss,'Extract') +mirkurtosis(p) +[le,f] = mirlowenergy(audio,'ASR') +p = mirpitch(audio,'frame') +mirpitch(p,'median') +mirauditory(audio) +mirroughness('ragtime') + +%% +%pause +clear s ss p le f +close all + +fb = mirfilterbank('Design','NbChannels',5) +f = mirfeatures(fb); +%sf = mirstat(f); +f = mireval(f,audio) + +f.dynamics.rms{1} +f.fluctuation.peak{1} +f.fluctuation.centroid{1} +f.rhythm.tempo{:} +f.rhythm.attack.time{:} +f.rhythm.attack.slope{1} + +%pause +close all + +f.timbre.zerocross{:} +f.spectral.centroid{:} +f.spectral.brightness{:} +f.spectral.spread{:} +f.spectral.skewness{:} +f.spectral.kurtosis{:} +f.spectral.rolloff95{:} +f.spectral.rolloff85{:} +f.spectral.spectentropy{:} +f.spectral.flatness{:} + +%pause +close all + +f.spectral.roughness{:} +f.spectral.irregularity{:} +%f.timbre.inharmonicity{:} +f.spectral.mfcc{:} +f.spectral.dmfcc{:} +f.spectral.ddmfcc{:} +f.timbre.lowenergy{:} + +%pause +close all + +f.timbre.spectralflux{:} +%f.pitch.salient{:} +f.tonal.chromagram.peak{:} +f.tonal.chromagram.centroid{:} +f.tonal.keyclarity{:} +f.tonal.mode{:} +f.tonal.hcdf{:} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/MIRToolboxDemos/tutorial.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +help mirtoolbox +help miraudio + + +a = miraudio('ragtime','Center','Sampling',11025,'Normal') +mirplay(a) +a = miraudio('ragtime','Extract',0,1) +mirplay(a) +miraudio('ragtime','Trim') +a1 = miraudio('pianoA4'); +a2 = miraudio('pianoF4'); +a3 = a1+a2; +mirplay(a3) +mirsave(a3) + +f = mirframe('ragtime',1,.5) +mirplay(f) + +mirenvelope('ragtime') +mirenvelope('ragtime','Tau',.05) +mirenvelope('ragtime','Diff') +mirenvelope('ragtime','HalfwaveDiff') + +s = mirspectrum('pianoF4') +mirspectrum(s,'Max',3000) +mirspectrum('pianoF4','dB') +mirspectrum('pianoF4','Mel') +mirspectrum('trumpet') +mirspectrum('trumpet','Prod',2:6) + +c = mircepstrum('pianoA4') +mircepstrum(c,'Freq') + +mirautocor('trumpet') +ac = mirautocor('Amin3','Freq') +mirautocor(ac,'Halfwave') +mirautocor(ac,'Enhanced') +mirautocor(ac,'Enhanced',2:10) + +as = mirautocor(mirspectrum('Amin3')) +ac = mirautocor('Amin3','Freq') +cp = mircepstrum('Amin3','Freq') +ac*as +ac*cp +as*cp + +mirspectrum('ragtime','frame') +mirflux(ans) +mircepstrum('ragtime','frame') +mirflux(ans) + +fb = mirfilterbank('ragtime','Gammatone') +mirsum(fb) +s = mirspectrum(fb) +mirsummary(s) +mirauditory('ragtime') +mirauditory('ragtime','Filterbank',20) + +mirpeaks(mirspectrum('ragtime','mel')) +mirpeaks(mirspectrum('ragtime','mel','frame'),'total',1) + + +r1 = mirrms('movie1','Frame') +r2 = mirrms('movie2','Frame') +mirlowenergy(r1) +mirlowenergy(r2) + +s = mirspectrum('ragtime','Frame',.023,.5,'Mel', 'dB') +s2 = mirspectrum(s,'AlongBands','Max',10,'Window', 0,'Resonance', 'Fluctuation') +mirsum(s2) + +mironsets('ragtime') +mironsets('ragtime','Detect',0) +mironsets('ragtime','Diffenvelope') +mironsets('ragtime','diffenvelope','Contrast',.1) +mironsets('ragtime','SpectralFlux') +mironsets('ragtime','SpectralFlux','Inc','off') +mironsets('ragtime','SpectralFlux','Complex') + +[t,a] = mirtempo('ragtime') +[t,a] = mirtempo('ragtime','spectrum') +[t,a] = mirtempo('ragtime','frame') + +%[p s] = mirpulseclarity('ragtime') + +mirattacks('ragtime') +mirattacktime('ragtime') +mirattackslope('ragtime') + +t = mirtempo('czardas','frame') +st = mirstat(t) +h = mirhisto(t) +mirexport('result.txt',t) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/readme.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,96 @@ +Congratulations! You have downloaded MIRtoolbox 1.3.2 +----------------------------------------------------- + +The list of new features and bug fixes offered by this new version is shown in the ReleaseNotes text file located in the toolbox folder, and also available from our website. New versions of the toolbox will also be released at the same address: +http://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox + +Please inform me if you find any bug when using the toolbox. + +** + +Conditions of Use +----------------- The Toolbox is free software; you can redistribute it and/or modify it under the terms of version 2 of GNU General Public License as published by the Free Software Foundation. + When MIRtoolbox is used for academic research, we would highly appreciate if scientific publications of works partly based on MIRtoolbox cite one of the following publications: + Olivier Lartillot, Petri Toiviainen, ÒA Matlab Toolbox for Musical Feature Extraction From AudioÓ, International Conference on Digital Audio Effects, Bordeaux, 2007. + Olivier Lartillot, Petri Toiviainen, Tuomas Eerola, ÒA Matlab Toolbox for Music Information RetrievalÓ, in C. Preisach, H. Burkhardt, L. Schmidt-Thieme, R. Decker (Eds.), Data Analysis, Machine Learning and Applications, Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, 2008. + For commercial use of MIRtoolbox, please contact the authors. + +** + +Please register to the MIRtoolbox announcement list.Ê +-------------------------------------------- +http://www.freelists.org/list/mirtoolboxnews +-------------------------------------------- +This will allow us to estimate the number of users, and this will allow you in return to get informed on the new major releases (including critical bug fixes). + +** + +MIRtoolbox requires Matlab version 7 and Mathworks' Signal Processing toolbox. + +** + +This distribution actually includes four different toolboxes: + +- the MIRtoolbox itself, version 1.3.2, + +- the Auditory toolbox, version 2, by Malcolm Slaney +(also available directly at http://cobweb.ecn.purdue.edu/~malcolm/interval/1998-010/), + +- the Netlab toolbox, version 3.3, by Ian Nabney +(also available directly at http://www.ncrg.aston.ac.uk/netlab/) + +- the SOM toolbox, version 2.0, by Esa Alhoniemi, Johan Himberg, Jukka Parviainen and Juha Vesanto +(also available directly at http://www.cis.hut.fi/projects/somtoolbox/) + +** + +MIRtoolbox license is based on GPL 2.0. As such, it can integrate codes from other GPL 2.0 projects, as long as their origins are explicitly stated. + - codes from the Music Analysis Toolbox by Elias Pampalk (2004), related to the computation of Terhardt outer ear modeling, Bark band decomposition and masking effects. + +- openbdf and readbdf script by T.S. Lorig to read BDF files, based on openedf and readedf by Alois Schloegl, + +- implementation of Earth Mover Distance written by Yossi Rubner and wrapped for Matlab by Elias Pampalk. + +Code integrated with BSD license: + - mp3read for Matlab by Dan Ellis, which calls the mpg123 decoder and the mp3info scanner, + - aiffread for Matlab by Kenneth Eaton. + +** + +To install MIRtoolbox in your Matlab environment, copy all the toolboxes folders (or only those that are not installed yet in your computer) into your Matlab "toolbox" folder. Then add each folder in your Matlab path. + +** + +NOTICE: If you replace an older version of MIRtoolbox with a new one, please update your Matlab path using the following command: +rehash toolboxcache +Update also the class structure of the toolbox, either by restarting Matlab, or by typing the following command: +clear classes + +** + +To get an overview of the functions available in the toolbox, type: +help mirtoolbox + +A complete documentation in PDF is provided in the main toolbox folder. + +A short documentation for each function is available directly in Matlab using the help command. For instance: help miraudio + +Examples of use of the toolbox are shown in the MIRToolboxDemos folder: +mirdemo +demo1basics +demo2timbre +demo3segmentation +demo4tempo +demo5export +demo6curves +demo7tonality +demo8classification +demo9retrieval + +** + +If you have any problem or request, please contact us. + +Olivier Lartillot, Petri Toiviainen and Tuomas Eerola + +Olivier.Lartillot@campus.jyu.fi \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/Contents.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,196 @@ +% SOM Toolbox +% Version 2.0beta, May 30 2002 +% +% Copyright 1997-2000 by +% Esa Alhoniemi, Johan Himberg, Juha Parhankangas and Juha Vesanto +% Contributed files may contain copyrights of their own. +% +% SOM Toolbox comes with ABSOLUTELY NO WARRANTY; for details +% see License.txt in the program package. This is free software, +% and you are welcome to redistribute it under certain conditions; +% see License.txt for details. +% +% +% Demos +% +% som_demo1 SOM Toolbox demo 1: basic properties +% som_demo2 SOM Toolbox demo 2: basic usage +% som_demo3 SOM Toolbox demo 3: visualization +% som_demo4 SOM Toolbox demo 4: data analysis +% +% Creation of structs +% +% som_set create & set (& check) values to structs +% som_info print out information on a given struct +% som_data_struct create & initialize a data struct +% som_map_struct create & initialize a map struct +% som_topol_struct create & initialize a topology struct +% som_train_struct create & initialize a train struct +% som_clstruct create a cluster struct +% som_clset set properties in a cluster struct +% som_clget get stuff from a cluster struct +% +% Struct conversion and file I/O +% +% som_vs1to2 converts a version 1.0 struct to version 2.0 struct +% som_vs2to1 converts a version 2.0 struct to version 1.0 struct +% som_read_data reads a (SOM_PAK format) ASCII data file +% som_write_data writes a SOM_PAK format codebook file +% som_write_cod writes a SOM_PAK format data file +% som_read_cod reads a SOM_PAK format codebook file +% +% Data preprocessing +% +% som_normalize normalize data set +% som_denormalize denormalize data set +% som_norm_variable (de)normalize one variable +% preprocess preprocessing GUI +% +% Initialization and training functions +% +% som_make create, initialize and train a SOM +% som_randinit random initialization algorithm +% som_lininit linear initialization algorithm +% som_seqtrain sequential training algorithm +% som_batchtrain batch training algorithm +% som_gui SOM initialization and training GUI +% som_prototrain a simple version of sequential training: easy to modify +% +% Clustering algorithms +% +% som_kmeans k-means algorithm (was earlier kmeans) +% kmeans_clusters try and evaluate several k-means clusterings +% neural_gas neural gas vector quantization algorithm +% som_linkage hierarchical clustering algorithms +% som_cllinkage hierarchical clustering of SOM +% som_dmatminima local minima from distance (or U-) matrix +% som_dmatclusters distance (or U-) matrix based clustering +% som_clspread spreads clusters to unassinged map units +% som_cldist calculate distances between clusters +% som_gapindex gap validity index of clustering +% db_index Davies-Bouldin validity index of clustering +% +% Supervised/classification algorithms +% +% som_supervised supervised SOM algorithm +% lvq1 LVQ1 algorithm +% lvq3 LVQ3 algorithm +% knn k-NN classification algorithm +% knn_old k-NN classification algorithm (old version) +% +% SOM error measures +% +% som_quality quantization and topographic error of SOM +% som_distortion SOM distortion measure +% som_distortion3 elements of the SOM distortion measure +% +% Auxiliary functions +% +% som_bmus calculates BMUs for given data vectors +% som_eucdist2 pairwise squared euclidian distances between vectors +% som_mdist calculates pairwise distances between vectors +% som_divide extract subsets of data based on map +% som_label give labels to map units +% som_label2num rcodes string data labels to interger class labels +% som_autolabel automatically labels the SOM based on given data +% som_unit_coords calculates coordinates in output space for map units +% som_unit_dists distances in output space between map units +% som_unit_neighs units in 1-neighborhood for each map unit +% som_neighborhood calculates neighborhood matrix for the given map +% som_neighbors calculates different kinds of neighborhoods +% som_neighf calculates neighborhood function values +% som_select GUI for manual selection of map units +% som_estimate_gmm create Gaussian mixture model on top of SOM +% som_probability_gmm evaluate Gaussian mixture model +% som_ind2sub from linear index to subscript index +% som_sub2ind from subscript index to linear index +% som_ind2cod from linear index to SOM_PAK linear index +% som_cod2ind from SOM_linear index to SOM_PAK linear index +% nanstats mean, std and median which ignore NaNs +% som_modify_dataset add, remove, or extract samples and components +% som_fillnans fill NaNs in a data set based on given SOM +% som_stats statistics of a data set +% som_drmake calculate descriptive rules for a cluster +% som_dreval evaluate descriptive rules for a cluster +% som_drsignif rule significance measures +% +% Using SOM_PAK from Matlab +% +% som_sompaktrain uses SOM_PAK to train a map +% sompak_gui GUI for using SOM_PAK from Matlab +% sompak_init call SOM_PAK's initialization programs from Matlab +% sompak_init_gui GUI for using SOM_PAK's initialization from Matlab +% sompak_rb_control an auxiliary function for sompak_*_gui functions. +% sompak_sammon call SOM_PAK's Sammon program from Matlab +% sompak_sammon_gui GUI for using SOM_PAK's Sammon program from Matlab +% sompak_train call SOM_PAK's training program from Matlab +% sompak_train_gui GUI for using SOM_PAK's training program from Matlab +% +% Visualization +% +% som_show basic visualization +% som_show_add add labels, hits and trajectories +% som_show_clear remove extra markers +% som_recolorbar refresh/reconfigure colorbars +% som_show_gui GUI for using som_show and associated functions +% som_grid visualization of SOM grid +% som_cplane component planes and U-matrices +% som_barplane bar chart visualization of map +% som_pieplane pie chart visualization of map +% som_plotplane plot chart visualization of map +% som_trajectory launches a GUI for presenting comet-trajectories +% som_dendrogram visualization of clustering tree +% som_plotmatrix pairwise scatter plots and histograms +% som_order_cplanes order and visualize the component planes +% som_clplot plots of clusters (based on cluster struct) +% som_projections_plot projections plots (see som_projections) +% som_stats_plot plots of statistics (see som_stats) +% +% Auxiliary functions for visualization +% +% hits calculates hits, or sum of values for each map unit +% som_hits calculates the response of data on the map +% som_umat calculates the U-matrix +% cca curvilinear component analysis projection algorithm +% pcaproj principal component projection algorithm +% sammon Sammon's mapping projection algorithm +% som_connection connection matrix for map +% som_vis_coords map unit coordinates used in visualizations +% som_colorcode create color coding for map/2D data +% som_bmucolor colors of the BMUs from a given map color code +% som_normcolor simulate indexed colormap +% som_clustercolor color coding which depends on clustering structure +% som_kmeanscolor color coding according to k-means clustering +% som_kmeanscolor2 a newer version of the som_kmeanscolor function +% som_fuzzycolor a fuzzy color coding +% som_coloring a SOM-based color coding +% som_projections calculates a default set of projections +% +% Report generation stuff +% +% som_table_struct creates a table struct +% som_table_modify modifies a table struct +% som_table_print print a table in various formats +% rep_utils various utilities for printing report elements +% som_stats_table a table of data set statistics +% som_stats_report report on data set statistics +% +% Low level routines used by visualization functions +% +% vis_patch defines hexagonal and rectangular patches +% vis_som_show_data returns UserData and subplot handles stored by som_show.m +% vis_valuetype used for type checks +% vis_footnote adds a movable text to the current figure +% vis_trajgui the actual GUI started by som_trajectory.m +% vis_PlaneAxisProperties set axis properties in visualization functions +% vis_footnoteButtonDownFcn callback function for vis_footnote.m +% vis_planeGetArgs converts topol struct to lattice, msize argument pair +% vis_show_gui_comp internal function used by som_show_gui.m +% vis_show_gui_tool internal function used by som_show_gui.m +% +% Other +% +% somtoolbox this file +% iris.data IRIS data set (used in demos) +% License.txt GNU General Public License +% Copyright.txt Copyright notice
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/Copyright.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +COPYRIGHT +--------- + +SOM Toolbox 2.0, a software library for Matlab 5 implementing the +Self-Organizing Map algorithm is Copyright (C) 1999 by Esa Alhoniemi, +Johan Himberg, Jukka Parviainen and Juha Vesanto. + +This package is free software; you can redistribute it and/or +modify it under the terms of the GNU General Public License +as published by the Free Software Foundation; either version 2 +of the License, or any later version. + +Note: only part of the files distributed in the package belong to the +SOM Toolbox. The package also contains contributed files, which may +have their own copyright notices. If not, the GNU General Public +License holds for them, too, but so that the author(s) of the file +have the Copyright. + +This package is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this package (file License.txt); if not, write to the Free +Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA +02111-1307, USA. + + +LICENSING THE LIBRARY FOR PROPRIETARY PROGRAMS +---------------------------------------------- + +As stated in the GNU General Public License (see License.txt) it is +not possible to include this software library in a commercial +proprietary program without written permission from the owners of the +copyright. If you wish to obtain such permission, you can reach us by +paper mail: + + SOM Toolbox team + Laboratory of Computer and Information Science + P.O.Box 5400 + FIN-02015 HUT + Finland + Europe + +and by email: + + somtlbx@mail.cis.hut.fi + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/License.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,386 @@ +GNU GENERAL PUBLIC LICENSE + +Version 2, June 1991 + +Copyright (C) 1989, 1991 Free Software Foundation, Inc. +59 Temple Place - Suite 330, Boston, MA 02111-1307, USA + +Everyone is permitted to copy and distribute verbatim copies +of this license document, but changing it is not allowed. + +------------------------------------------------------------------------ + +Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. By contrast, the GNU General Public +License is intended to guarantee your freedom to share and change free +software--to make sure the software is free for all its users. This +General Public License applies to most of the Free Software +Foundation's software and to any other program whose authors commit to +using it. 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/cca.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,282 @@ +function [P] = cca(D, P, epochs, Mdist, alpha0, lambda0) + +%CCA Projects data vectors using Curvilinear Component Analysis. +% +% P = cca(D, P, epochs, [Dist], [alpha0], [lambda0]) +% +% P = cca(D,2,10); % projects the given data to a plane +% P = cca(D,pcaproj(D,2),5); % same, but with PCA initialization +% P = cca(D, 2, 10, Dist); % same, but the given distance matrix is used +% +% Input and output arguments ([]'s are optional): +% D (matrix) the data matrix, size dlen x dim +% (struct) data or map struct +% P (scalar) output dimension +% (matrix) size dlen x odim, the initial projection +% epochs (scalar) training length +% [Dist] (matrix) pairwise distance matrix, size dlen x dlen. +% If the distances in the input space should +% be calculated otherwise than as euclidian +% distances, the distance from each vector +% to each other vector can be given here, +% size dlen x dlen. For example PDIST +% function can be used to calculate the +% distances: Dist = squareform(pdist(D,'mahal')); +% [alpha0] (scalar) initial step size, 0.5 by default +% [lambda0] (scalar) initial radius of influence, 3*max(std(D)) by default +% +% P (matrix) size dlen x odim, the projections +% +% Unknown values (NaN's) in the data: projections of vectors with +% unknown components tend to drift towards the center of the +% projection distribution. Projections of totally unknown vectors are +% set to unknown (NaN). +% +% See also SAMMON, PCAPROJ. + +% Reference: Demartines, P., Herault, J., "Curvilinear Component +% Analysis: a Self-Organizing Neural Network for Nonlinear +% Mapping of Data Sets", IEEE Transactions on Neural Networks, +% vol 8, no 1, 1997, pp. 148-154. + +% Contributed to SOM Toolbox 2.0, February 2nd, 2000 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% juuso 171297 040100 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +error(nargchk(3, 6, nargin)); % check the number of input arguments + +% input data +if isstruct(D), + if strcmp(D.type,'som_map'), D = D.codebook; else D = D.data; end +end +[noc dim] = size(D); +noc_x_1 = ones(noc, 1); % used frequently +me = zeros(1,dim); st = zeros(1,dim); +for i=1:dim, + me(i) = mean(D(find(isfinite(D(:,i))),i)); + st(i) = std(D(find(isfinite(D(:,i))),i)); +end + +% initial projection +if prod(size(P))==1, + P = (2*rand(noc,P)-1).*st(noc_x_1,1:P) + me(noc_x_1,1:P); +else + % replace unknown projections with known values + inds = find(isnan(P)); P(inds) = rand(size(inds)); +end +[dummy odim] = size(P); +odim_x_1 = ones(odim, 1); % this is used frequently + +% training length +train_len = epochs*noc; + +% random sample order +rand('state',sum(100*clock)); +sample_inds = ceil(noc*rand(train_len,1)); + +% mutual distances +if nargin<4 | isempty(Mdist) | all(isnan(Mdist(:))), + fprintf(2, 'computing mutual distances\r'); + dim_x_1 = ones(dim,1); + for i = 1:noc, + x = D(i,:); + Diff = D - x(noc_x_1,:); + N = isnan(Diff); + Diff(find(N)) = 0; + Mdist(:,i) = sqrt((Diff.^2)*dim_x_1); + N = find(sum(N')==dim); %mutual distance unknown + if ~isempty(N), Mdist(N,i) = NaN; end + end +else + % if the distance matrix is output from PDIST function + if size(Mdist,1)==1, Mdist = squareform(Mdist); end + if size(Mdist,1)~=noc, + error('Mutual distance matrix size and data set size do not match'); + end +end + +% alpha and lambda +if nargin<5 | isempty(alpha0) | isnan(alpha0), alpha0 = 0.5; end +alpha = potency_curve(alpha0,alpha0/100,train_len); + +if nargin<6 | isempty(lambda0) | isnan(lambda0), lambda0 = max(st)*3; end +lambda = potency_curve(lambda0,0.01,train_len); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +k=0; fprintf(2, 'iterating: %d / %d epochs\r',k,epochs); + +for i=1:train_len, + + ind = sample_inds(i); % sample index + dx = Mdist(:,ind); % mutual distances in input space + known = find(~isnan(dx)); % known distances + + if ~isempty(known), + % sample vector's projection + y = P(ind,:); + + % distances in output space + Dy = P(known,:) - y(noc_x_1(known),:); + dy = sqrt((Dy.^2)*odim_x_1); + + % relative effect + dy(find(dy==0)) = 1; % to get rid of div-by-zero's + fy = exp(-dy/lambda(i)) .* (dx(known) ./ dy - 1); + + % Note that the function F here is e^(-dy/lambda)) + % instead of the bubble function 1(lambda-dy) used in the + % paper. + + % Note that here a simplification has been made: the derivatives of the + % F function have been ignored in calculating the gradient of error + % function w.r.t. to changes in dy. + + % update + P(known,:) = P(known,:) + alpha(i)*fy(:,odim_x_1).*Dy; + end + + % track + if rem(i,noc)==0, + k=k+1; fprintf(2, 'iterating: %d / %d epochs\r',k,epochs); + end + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% clear up + +% calculate error +error = cca_error(P,Mdist,lambda(train_len)); +fprintf(2,'%d iterations, error %f \n', epochs, error); + +% set projections of totally unknown vectors as unknown +unknown = find(sum(isnan(D)')==dim); +P(unknown,:) = NaN; + +return; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% tips + +% to plot the results, use the code below + +%subplot(2,1,1), +%switch(odim), +% case 1, plot(P(:,1),ones(dlen,1),'x') +% case 2, plot(P(:,1),P(:,2),'x'); +% otherwise, plot3(P(:,1),P(:,2),P(:,3),'x'); rotate3d on +%end +%subplot(2,1,2), dydxplot(P,Mdist); + +% to a project a new point x in the input space to the output space +% do the following: + +% Diff = D - x(noc_x_1,:); Diff(find(isnan(Diff))) = 0; +% dx = sqrt((Diff.^2)*dim_x_1); +% p = project_point(P,x,dx); % this function can be found from below +% tlen = size(p,1); +% plot(P(:,1),P(:,2),'bx',p(tlen,1),p(tlen,2),'ro',p(:,1),p(:,2),'r-') + +% similar trick can be made to the other direction + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function vals = potency_curve(v0,vn,l) + + % curve that decreases from v0 to vn with a rate that is + % somewhere between linear and 1/t + vals = v0 * (vn/v0).^([0:(l-1)]/(l-1)); + + +function error = cca_error(P,Mdist,lambda) + + [noc odim] = size(P); + noc_x_1 = ones(noc,1); + odim_x_1 = ones(odim,1); + + error = 0; + for i=1:noc, + known = find(~isnan(Mdist(:,i))); + if ~isempty(known), + y = P(i,:); + Dy = P(known,:) - y(noc_x_1(known),:); + dy = sqrt((Dy.^2)*odim_x_1); + fy = exp(-dy/lambda); + error = error + sum(((Mdist(known,i) - dy).^2).*fy); + end + end + error = error/2; + + +function [] = dydxplot(P,Mdist) + + [noc odim] = size(P); + noc_x_1 = ones(noc,1); + odim_x_1 = ones(odim,1); + Pdist = zeros(noc,noc); + + for i=1:noc, + y = P(i,:); + Dy = P - y(noc_x_1,:); + Pdist(:,i) = sqrt((Dy.^2)*odim_x_1); + end + + Pdist = tril(Pdist,-1); + inds = find(Pdist > 0); + n = length(inds); + plot(Pdist(inds),Mdist(inds),'.'); + xlabel('dy'), ylabel('dx') + + +function p = project_point(P,x,dx) + + [noc odim] = size(P); + noc_x_1 = ones(noc,1); + odim_x_1 = ones(odim,1); + + % initial projection + [dummy,i] = min(dx); + y = P(i,:)+rand(1,odim)*norm(P(i,:))/20; + + % lambda + lambda = norm(std(P)); + + % termination + eps = 1e-3; i_max = noc*10; + + i=1; p(i,:) = y; + ready = 0; + while ~ready, + + % mutual distances + Dy = P - y(noc_x_1,:); % differences in output space + dy = sqrt((Dy.^2)*odim_x_1); % distances in output space + f = exp(-dy/lambda); + + fprintf(2,'iteration %d, error %g \r',i,sum(((dx - dy).^2).*f)); + + % all the other vectors push the projected one + fy = f .* (dx ./ dy - 1) / sum(f); + + % update + step = - sum(fy(:,odim_x_1).*Dy); + y = y + step; + + i=i+1; + p(i,:) = y; + ready = (norm(step)/norm(y) < eps | i > i_max); + + end + fprintf(2,'\n'); + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/db_index.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,83 @@ +function [t,r] = db_index(D, cl, C, p, q) + +% DB_INDEX Davies-Bouldin clustering evaluation index. +% +% [t,r] = db_index(D, cl, C, p, q) +% +% Input and output arguments ([]'s are optional): +% D (matrix) data (n x dim) +% (struct) map or data struct +% cl (vector) cluster numbers corresponding to data samples (n x 1) +% [C] (matrix) prototype vectors (c x dim) (default = cluster means) +% [p] (scalar) norm used in the computation (default == 2) +% [q] (scalar) moment used to calculate cluster dispersions (default = 2) +% +% t (scalar) Davies-Bouldin index for the clustering (=mean(r)) +% r (vector) maximum DB index for each cluster (size c x 1) +% +% See also KMEANS, KMEANS_CLUSTERS, SOM_GAPINDEX. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +if isstruct(D), + switch D.type, + case 'som_map', D = D.codebook; + case 'som_data', D = D.data; + end +end + +% cluster centroids +[l dim] = size(D); +u = unique(cl); +c = length(u); +if nargin <3, + C = zeros(c,dim); + for i=1:c, + me = nanstats(D(find(cl==u(i)),:)); + C(i,:) = me'; + end +end + +u2i = zeros(max(u),1); u2i(u) = 1:c; +D = som_fillnans(D,C,u2i(cl)); % replace NaN's with cluster centroid values + +if nargin <4, p = 2; end % euclidian distance between cluster centers +if nargin <5, q = 2; end % dispersion = standard deviation + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +% dispersion in each cluster +for i = 1:c + ind = find(cl==u(i)); % points in this cluster + l = length(ind); + if l > 0 + S(i) = (mean(sqrt(sum((D(ind,:) - ones(l,1) * C(i,:)).^2,2)).^q))^(1/q); + else + S(i) = NaN; + end +end + +% distances between clusters +%for i = 1:c +% for j = i+1:c +% M(i,j) = sum(abs(C(i,:) - C(j,:)).^p)^(1/p); +% end +%end +M = som_mdist(C,p); + +% Davies-Bouldin index +R = NaN * zeros(c); +r = NaN * zeros(c,1); +for i = 1:c + for j = i+1:c + R(i,j) = (S(i) + S(j))/M(i,j); + end + r(i) = max(R(i,:)); +end + +t = mean(r(isfinite(r))); + +return; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/hits.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +function [hits,ninvalid] = hits(bmus, mmax, values) + +%HITS Calculate number of occurances of each value. +% +% hits = hits(bmus,[mmax],[values]) +% +% h = hits(bmus); +% h = hits(bmus,length(sM.codebook)); +% +% Input and output arguments ([]'s are optional): +% bmus (vector) BMU indeces (or other similar) +% [mmax] (scalar) maximum index, default value max(bmus) +% (struct) map or topology struct from where the maximum +% index is acquired +% [values] (vector) values associated with the data (default = 1) +% +% hits (vector) the number of occurances of each index +% (or if values are given, their sum for each index) +% ninvalid (scalar) number of invalid indeces (NaN, Inf or +% <=0 or > mmax) +% +% See also SOM_HITS, SOM_BMUS. + +% Copyright (c) 2002 by the SOM toolbox programming team. +% Contributed to SOM Toolbox by Juha Vesanto, April 24th, 2002 +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 240402 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargin<2 | isempty(mmax), + mmax = max(bmus); +elseif isstruct(mmax), + switch mmax.type, + case 'som_map', mmax = prod(mmax.topol.msize); + case 'som_topol', mmax = prod(mmax.msize); + otherwise, + error('Illegal struct for 2nd argument.') + end +end + +if nargin<3, values = 1; end + +valid_bmus = find(isfinite(bmus) & bmus>0 & bmus<=mmax); +ninvalid = length(bmus)-length(valid_bmus); + +bmus = bmus(valid_bmus); +if length(values)>length(bmus), values = values(valid_bmus); end +hits = full(sum(sparse(bmus,1:length(bmus),values,mmax,length(bmus)),2)); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/iris.data Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +4 +#n SepalL SepalW PetalL PetalW +5.1 3.5 1.4 0.2 Setosa +4.9 3.0 1.4 0.2 Setosa +4.7 3.2 1.3 0.2 Setosa +4.6 3.1 1.5 0.2 Setosa +5.0 3.6 1.4 0.2 Setosa +5.4 3.9 1.7 0.4 Setosa +4.6 3.4 1.4 0.3 Setosa +5.0 3.4 1.5 0.2 Setosa +4.4 2.9 1.4 0.2 Setosa +4.9 3.1 1.5 0.1 Setosa +5.4 3.7 1.5 0.2 Setosa +4.8 3.4 1.6 0.2 Setosa +4.8 3.0 1.4 0.1 Setosa +4.3 3.0 1.1 0.1 Setosa +5.8 4.0 1.2 0.2 Setosa +5.7 4.4 1.5 0.4 Setosa +5.4 3.9 1.3 0.4 Setosa +5.1 3.5 1.4 0.3 Setosa +5.7 3.8 1.7 0.3 Setosa +5.1 3.8 1.5 0.3 Setosa +5.4 3.4 1.7 0.2 Setosa +5.1 3.7 1.5 0.4 Setosa +4.6 3.6 1.0 0.2 Setosa +5.1 3.3 1.7 0.5 Setosa +4.8 3.4 1.9 0.2 Setosa +5.0 3.0 1.6 0.2 Setosa +5.0 3.4 1.6 0.4 Setosa +5.2 3.5 1.5 0.2 Setosa +5.2 3.4 1.4 0.2 Setosa +4.7 3.2 1.6 0.2 Setosa +4.8 3.1 1.6 0.2 Setosa +5.4 3.4 1.5 0.4 Setosa +5.2 4.1 1.5 0.1 Setosa +5.5 4.2 1.4 0.2 Setosa +4.9 3.1 1.5 0.1 Setosa +5.0 3.2 1.2 0.2 Setosa +5.5 3.5 1.3 0.2 Setosa +4.9 3.1 1.5 0.1 Setosa +4.4 3.0 1.3 0.2 Setosa +5.1 3.4 1.5 0.2 Setosa +5.0 3.5 1.3 0.3 Setosa +4.5 2.3 1.3 0.3 Setosa +4.4 3.2 1.3 0.2 Setosa +5.0 3.5 1.6 0.6 Setosa +5.1 3.8 1.9 0.4 Setosa +4.8 3.0 1.4 0.3 Setosa +5.1 3.8 1.6 0.2 Setosa +4.6 3.2 1.4 0.2 Setosa +5.3 3.7 1.5 0.2 Setosa +5.0 3.3 1.4 0.2 Setosa +7.0 3.2 4.7 1.4 Versicolor +6.4 3.2 4.5 1.5 Versicolor +6.9 3.1 4.9 1.5 Versicolor +5.5 2.3 4.0 1.3 Versicolor +6.5 2.8 4.6 1.5 Versicolor +5.7 2.8 4.5 1.3 Versicolor +6.3 3.3 4.7 1.6 Versicolor +4.9 2.4 3.3 1.0 Versicolor +6.6 2.9 4.6 1.3 Versicolor +5.2 2.7 3.9 1.4 Versicolor +5.0 2.0 3.5 1.0 Versicolor +5.9 3.0 4.2 1.5 Versicolor +6.0 2.2 4.0 1.0 Versicolor +6.1 2.9 4.7 1.4 Versicolor +5.6 2.9 3.6 1.3 Versicolor +6.7 3.1 4.4 1.4 Versicolor +5.6 3.0 4.5 1.5 Versicolor +5.8 2.7 4.1 1.0 Versicolor +6.2 2.2 4.5 1.5 Versicolor +5.6 2.5 3.9 1.1 Versicolor +5.9 3.2 4.8 1.8 Versicolor +6.1 2.8 4.0 1.3 Versicolor +6.3 2.5 4.9 1.5 Versicolor +6.1 2.8 4.7 1.2 Versicolor +6.4 2.9 4.3 1.3 Versicolor +6.6 3.0 4.4 1.4 Versicolor +6.8 2.8 4.8 1.4 Versicolor +6.7 3.0 5.0 1.7 Versicolor +6.0 2.9 4.5 1.5 Versicolor +5.7 2.6 3.5 1.0 Versicolor +5.5 2.4 3.8 1.1 Versicolor +5.5 2.4 3.7 1.0 Versicolor +5.8 2.7 3.9 1.2 Versicolor +6.0 2.7 5.1 1.6 Versicolor +5.4 3.0 4.5 1.5 Versicolor +6.0 3.4 4.5 1.6 Versicolor +6.7 3.1 4.7 1.5 Versicolor +6.3 2.3 4.4 1.3 Versicolor +5.6 3.0 4.1 1.3 Versicolor +5.5 2.5 4.0 1.3 Versicolor +5.5 2.6 4.4 1.2 Versicolor +6.1 3.0 4.6 1.4 Versicolor +5.8 2.6 4.0 1.2 Versicolor +5.0 2.3 3.3 1.0 Versicolor +5.6 2.7 4.2 1.3 Versicolor +5.7 3.0 4.2 1.2 Versicolor +5.7 2.9 4.2 1.3 Versicolor +6.2 2.9 4.3 1.3 Versicolor +5.1 2.5 3.0 1.1 Versicolor +5.7 2.8 4.1 1.3 Versicolor +6.3 3.3 6.0 2.5 Virginica +5.8 2.7 5.1 1.9 Virginica +7.1 3.0 5.9 2.1 Virginica +6.3 2.9 5.6 1.8 Virginica +6.5 3.0 5.8 2.2 Virginica +7.6 3.0 6.6 2.1 Virginica +4.9 2.5 4.5 1.7 Virginica +7.3 2.9 6.3 1.8 Virginica +6.7 2.5 5.8 1.8 Virginica +7.2 3.6 6.1 2.5 Virginica +6.5 3.2 5.1 2.0 Virginica +6.4 2.7 5.3 1.9 Virginica +6.8 3.0 5.5 2.1 Virginica +5.7 2.5 5.0 2.0 Virginica +5.8 2.8 5.1 2.4 Virginica +6.4 3.2 5.3 2.3 Virginica +6.5 3.0 5.5 1.8 Virginica +7.7 3.8 6.7 2.2 Virginica +7.7 2.6 6.9 2.3 Virginica +6.0 2.2 5.0 1.5 Virginica +6.9 3.2 5.7 2.3 Virginica +5.6 2.8 4.9 2.0 Virginica +7.7 2.8 6.7 2.0 Virginica +6.3 2.7 4.9 1.8 Virginica +6.7 3.3 5.7 2.1 Virginica +7.2 3.2 6.0 1.8 Virginica +6.2 2.8 4.8 1.8 Virginica +6.1 3.0 4.9 1.8 Virginica +6.4 2.8 5.6 2.1 Virginica +7.2 3.0 5.8 1.6 Virginica +7.4 2.8 6.1 1.9 Virginica +7.9 3.8 6.4 2.0 Virginica +6.4 2.8 5.6 2.2 Virginica +6.3 2.8 5.1 1.5 Virginica +6.1 2.6 5.6 1.4 Virginica +7.7 3.0 6.1 2.3 Virginica +6.3 3.4 5.6 2.4 Virginica +6.4 3.1 5.5 1.8 Virginica +6.0 3.0 4.8 1.8 Virginica +6.9 3.1 5.4 2.1 Virginica +6.7 3.1 5.6 2.4 Virginica +6.9 3.1 5.1 2.3 Virginica +5.8 2.7 5.1 1.9 Virginica +6.8 3.2 5.9 2.3 Virginica +6.7 3.3 5.7 2.5 Virginica +6.7 3.0 5.2 2.3 Virginica +6.3 2.5 5.0 1.9 Virginica +6.5 3.0 5.2 2.0 Virginica +6.2 3.4 5.4 2.3 Virginica +5.9 3.0 5.1 1.8 Virginica +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/kmeans_clusters.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,120 @@ +function [centers,clusters,errors,ind] = kmeans_clusters(sD, n_max, c_max, verbose) + +% KMEANS_CLUSTERS Clustering with k-means with different values for k. +% +% [c, p, err, ind] = kmeans_clusters(sD, [n_max], [c_max], [verbose]) +% +% [c, p, err, ind] = kmeans_clusters(sD); +% +% Input and output arguments ([]'s are optional): +% D (struct) map or data struct +% (matrix) size dlen x dim, the data +% [n_max] (scalar) maximum number of clusters, default is sqrt(dlen) +% [c_max] (scalar) maximum number of k-means runs, default is 5 +% [verbose] (scalar) verbose level, 0 by default +% +% c (cell array) c{i} contains cluster centroids for k=i +% p (cell array) p{i} contains cluster indeces for k=i +% err (vector) squared sum of errors for each value of k +% ind (vector) Davies-Bouldin index value for each clustering +% +% Makes a k-means to the given data set with different values of +% k. The k-means is run multiple times for each k, and the best of +% these is selected based on sum of squared errors. Finally, the +% Davies-Bouldin index is calculated for each clustering. +% +% For example to cluster a SOM: +% [c, p, err, ind] = kmeans_clusters(sM); % find clusterings +% [dummy,i] = min(ind); % select the one with smallest index +% som_show(sM,'color',{p{i},sprintf('%d clusters',i)}); % visualize +% colormap(jet(i)), som_recolorbar % change colormap +% +% See also SOM_KMEANS. + +% References: +% Jain, A.K., Dubes, R.C., "Algorithms for Clustering Data", +% Prentice Hall, 1988, pp. 96-101. +% +% Davies, D.L., Bouldin, D.W., "A Cluster Separation Measure", +% IEEE Transactions on Pattern Analysis and Machine Intelligence, +% vol. PAMI-1, no. 2, 1979, pp. 224-227. +% +% Vesanto, J., Alhoniemi, E., "Clustering of the Self-Organizing +% Map", IEEE Transactions on Neural Networks, 2000. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi +% Copyright (c) by Esa Alhoniemi +% http://www.cis.hut.fi/projects/somtoolbox/ + +% ecco 301299 juuso 020200 211201 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments and initialization + +if isstruct(sD), + if isfield(sD,'data'), D = sD.data; + else D = sD.codebook; + end +else D = sD; +end +[dlen dim] = size(D); + +if nargin < 2 | isempty(n_max) | isnan(n_max), n_max = ceil(sqrt(dlen)); end +if nargin < 3 | isempty(c_max) | isnan(c_max), c_max = 5; end +if nargin < 4 | isempty(verbose) | isnan(verbose), verbose = 0; end + +centers = cell(n_max,1); +clusters = cell(n_max,1); +ind = zeros(1,n_max)+NaN; +errors = zeros(1,n_max)+NaN; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +% the case k=1 is trivial, but Davies-Boulding index cannot be evaluated +m = zeros(1,dim); +for i=1:dim, m(i)=mean(D(isfinite(D(:,i)),i)); end +centers{1} = m; +clusters{1} = ones(dlen,1); +[dummy qerr] = som_bmus(m,D); +errors(1) = sum(qerr.^2); +ind(1) = NaN; + +if verbose, fprintf(2,'Doing k-means for 2-%d clusters\n',n_max); end + +for i = 2:n_max, % number of clusters + + % make k-means with k=i for c_max times and select the best based + % on sum-of-squared errors (SSE) + best = realmax; + for j = 1:c_max % run number j for cluster i + if verbose, + fprintf('%d/%d clusters, k-means run %d/%d\r', i, n_max,j, c_max); + end + [c, k, err] = som_kmeans('batch', D, i, 100, 0); + if err < best, k_best = k'; c_best = c; best = err; end + % ' added in k_best = k'; by kr 1.10.02 + end + if verbose, fprintf(1, '\n'); end + + % store the results + centers{i} = c_best; + clusters{i} = k_best; + errors(i) = best; +% ind(i) = db_index(D, c_best, k_best, 2); wrong version in somtbx ?? + ind(i) = db_index(D, k_best, c_best, 2); % modified by kr 1.10.02 + + % if verbose mode, plot the index & SSE + if verbose + subplot(2,1,1), plot(ind), grid + title('Davies-Bouldin''s index') + subplot(2,1,2), plot(errors), grid + title('SSE') + drawnow + end +end + +return; + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/knn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,158 @@ +function [C,P]=knn(d, Cp, K) + +%KNN K-Nearest Neighbor classifier using an arbitrary distance matrix +% +% [C,P]=knn(d, Cp, [K]) +% +% Input and output arguments ([]'s are optional): +% d (matrix) of size NxP: This is a precalculated dissimilarity (distance matrix). +% P is the number of prototype vectors and N is the number of data vectors +% That is, d(i,j) is the distance between data item i and prototype j. +% Cp (vector) of size Px1 that contains integer class labels. Cp(j) is the class of +% jth prototype. +% [K] (scalar) the maximum K in K-NN classifier, default is 1 +% C (matrix) of size NxK: integers indicating the class +% decision for data items according to the K-NN rule for each K. +% C(i,K) is the classification for data item i using the K-NN rule +% P (matrix) of size NxkxK: the relative amount of prototypes of +% each class among the K closest prototypes for each classifiee. +% That is, P(i,j,K) is the relative amount of prototypes of class j +% among K nearest prototypes for data item i. +% +% If there is a tie between representatives of two or more classes +% among the K closest neighbors to the classifiee, the class i selected randomly +% among these candidates. +% +% IMPORTANT If K>1 this function uses 'sort' which is considerably slower than +% 'max' which is used for K=1. If K>1 the knn always calculates +% results for all K-NN models from 1-NN up to K-NN. +% +% EXAMPLE 1 +% +% sP; % a SOM Toolbox data struct containing labeled prototype vectors +% [Cp,label]=som_label2num(sP); % get integer class labels for prototype vectors +% sD; % a SOM Toolbox data struct containing vectors to be classified +% d=som_eucdist2(sD,sP); % calculate euclidean distance matrix +% class=knn(d,Cp,10); % classify using 1,2,...,10-rules +% class(:,5); % includes results for 5NN +% label(class(:,5)) % original class labels for 5NN +% +% EXAMPLE 2 (leave-one-out-crossvalidate KNN for selection of proper K) +% +% P; % a data matrix of prototype vectors (rows) +% Cp; % column vector of integer class labels for vectors in P +% d=som_eucdist2(P,P); % calculate euclidean distance matrix PxP +% d(eye(size(d))==1)=NaN; % set self-dissimilarity to NaN: +% % this drops the prototype itself away from its neighborhood +% % leave-one-out-crossvalidation (LOOCV) +% class=knn(d,Cp,size(P,1)); % classify using all possible K +% % calculate and plot LOOC-validated errors for all K +% failratep = ... +% 100*sum((class~=repmat(Cp,1,size(P,1))))./size(P,1); plot(1:size(P,1),failratep) + +% See also SOM_LABEL2NUM, SOM_EUCDIST2, PDIST. +% +% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 291000 + +%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Check K +if nargin<3 | isempty(K), + K=1; +end + +if ~vis_valuetype(K,{'1x1'}) + error('Value for K must be a scalar'); +end + +% Check that dist is a matrix +if ~vis_valuetype(d,{'nxm'}), + error('Distance matrix not valid.') +end + +[N_data N_proto]=size(d); + +% Check class label vector: must be numerical and of integers +if ~vis_valuetype(Cp,{[N_proto 1]}); + error(['Class vector is invalid: has to be a N-of-data_rows x 1' ... + ' vector of integers']); +elseif sum(fix(Cp)-Cp)~=0 + error('Class labels in vector ''Cp'' must be integers.'); +end + +if size(d,2) ~= length(Cp), + error('Distance matrix and prototype class vector dimensions do not match.'); +end + +% Check if the classes are given as labels (no class input arg.) +% if they are take them from prototype struct + +% Find all class labels +ClassIndex=unique(Cp); +N_class=length(ClassIndex); % number of different classes + + +%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if K==1, % sort distances only if K>1 + + % 1NN + % Select the closest prototype + [tmp,proto_index]=min(d,[],2); + C=Cp(proto_index); + +else + + % Sort the prototypes for each classifiee according to distance + [tmp, proto_index]=sort(d'); + + %% Select up to K closest prototypes + proto_index=proto_index(1:K,:); + knn_class=Cp(proto_index); + for i=1:N_class, + classcounter(:,:,i)=cumsum(knn_class==ClassIndex(i)); + end + + %% Vote between classes of K neighbors + [winner,vote_index]=max(classcounter,[],3); + + %%% Handle ties + + % Set index to classes that got as much votes as winner + + equal_to_winner=(repmat(winner,[1 1 N_class])==classcounter); + + % set index to ties + [tie_indexi,tie_indexj]=find(sum(equal_to_winner,3)>1); % drop the winner from counter + + % Go through tie cases and reset vote_index randomly to one + % of them + + for i=1:length(tie_indexi), + tie_class_index=find(squeeze(equal_to_winner(tie_indexi(i),tie_indexj(i),:))); + fortuna=randperm(length(tie_class_index)); + vote_index(tie_indexi(i),tie_indexj(i))=tie_class_index(fortuna(1)); + end + + C=ClassIndex(vote_index)'; +end + +%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Relative amount of classes in K neighbors for each classifiee + +if K==1, + P=zeros(N_data,N_class); + if nargout>1, + for i=1:N_data, + P(i,ClassIndex==C(i))=1; + end + end +else + P=shiftdim(classcounter,1)./repmat(shiftdim(1:K,-1), [N_data N_class 1]); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/knn_old.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,251 @@ +function [Class,P]=knn_old(Data, Proto, proto_class, K) + +%KNN_OLD A K-nearest neighbor classifier using Euclidean distance +% +% [Class,P]=knn_old(Data, Proto, proto_class, K) +% +% [sM_class,P]=knn_old(sM, sData, [], 3); +% [sD_class,P]=knn_old(sD, sM, class); +% [class,P]=knn_old(data, proto, class); +% [class,P]=knn_old(sData, sM, class,5); +% +% Input and output arguments ([]'s are optional): +% Data (matrix) size Nxd, vectors to be classified (=classifiees) +% (struct) map or data struct: map codebook vectors or +% data vectors are considered as classifiees. +% Proto (matrix) size Mxd, prototype vector matrix (=prototypes) +% (struct) map or data struct: map codebook vectors or +% data vectors are considered as prototypes. +% [proto_class] (vector) size Nx1, integers 1,2,...,k indicating the +% classes of corresponding protoptypes, default: see the +% explanation below. +% [K] (scalar) the K in KNN classifier, default is 1 +% +% Class (matrix) size Nx1, vector of 1,2, ..., k indicating the class +% desicion according to the KNN rule +% P (matrix) size Nxk, the relative amount of prototypes of +% each class among the K closest prototypes for +% each classifiee. +% +% If 'proto_class' is _not_ given, 'Proto' _must_ be a labeled SOM +% Toolbox struct. The label of the data vector or the first label of +% the map model vector is considered as class label for th prototype +% vector. In this case the output 'Class' is a copy of 'Data' (map or +% data struct) relabeled according to the classification. If input +% argument 'proto_class' _is_ given, the output argument 'Class' is +% _always_ a vector of integers 1,2,...,k indiacating the class. +% +% If there is a tie between representatives of two or more classes +% among the K closest neighbors to the classifiee, the class is +% selected randomly among these candidates. +% +% IMPORTANT +% +% ** Even if prototype vectors are given in a map struct the mask _is not +% taken into account_ when calculating Euclidean distance +% ** The function calculates the total distance matrix between all +% classifiees and prototype vectors. This results to an MxN matrix; +% if N is high it is recommended to divide the matrix 'Data' +% (the classifiees) into smaller sets in order to avoid memory +% overflow or swapping. Also, if K>1 this function uses 'sort' which is +% considerably slower than 'max' which is used for K==1. +% +% See also KNN, SOM_LABEL, SOM_AUTOLABEL + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 040200 + +%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% This must exist later +classnames=''; + +% Check K +if nargin<4 | isempty(K), + K=1; +end + +if ~vis_valuetype(K,{'1x1'}) + error('Value for K must be a scalar.'); +end + +% Take data from data or map struct + +if isstruct(Data); + if isfield(Data,'type') & ischar(Data.type), + ; + else + error('Invalid map/data struct?'); + end + switch Data.type + case 'som_map' + data=Data.codebook; + case 'som_data' + data=Data.data; + end +else + % is already a matrix + data=Data; +end + +% Take prototype vectors from prototype struct + +if isstruct(Proto), + + if isfield(Proto,'type') & ischar(Proto.type), + ; + else + error('Invalid map/data struct?'); + end + switch Proto.type + case 'som_map' + proto=Proto.codebook; + case 'som_data' + proto=Proto.data; + end +else + % is already a matrix + proto=Proto; +end + +% Check that inputs are matrices +if ~vis_valuetype(proto,{'nxm'}) | ~vis_valuetype(data,{'nxm'}), + error('Prototype or data input not valid.') +end + +% Record data&proto sizes and check their dims +[N_data dim_data]=size(data); +[N_proto dim_proto]=size(proto); +if dim_proto ~= dim_data, + error('Data and prototype vector dimension does not match.'); +end + +% Check if the classes are given as labels (no class input arg.) +% if they are take them from prototype struct + +if nargin<3 | isempty(proto_class) + if ~isstruct(Proto) + error(['If prototypes are not in labeled map or data struct' ... + 'class must be given.']); + % transform to interger (numerical) class labels + else + [proto_class,classnames]=class2num(Proto.labels); + end +end + +% Check class label vector: must be numerical and of integers +if ~vis_valuetype(proto_class,{[N_proto 1]}); + error(['Class vector is invalid: has to be a N-of-data_rows x 1' ... + ' vector of integers']); +elseif sum(fix(proto_class)-proto_class)~=0 + error('Class labels in vector ''Class'' must be integers.'); +end + +% Find all class labels +ClassIndex=unique(proto_class); +N_class=length(ClassIndex); % number of different classes + +% Calculate euclidean distances between classifiees and prototypes +d=distance(proto,data); + +%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if K==1, % sort distances only if K>1 + + % 1NN + % Select the closest prototype + [tmp,proto_index]=min(d); + class=proto_class(proto_index); + +else + + % Sort the prototypes for each classifiee according to distance + [tmp,proto_index]=sort(d); + + %% Select K closest prototypes + proto_index=proto_index(1:K,:); + knn_class=proto_class(proto_index); + for i=1:N_class, + classcounter(i,:)=sum(knn_class==ClassIndex(i)); + end + + %% Vote between classes of K neighbors + [winner,vote_index]=max(classcounter); + + %% Handle ties + + % set index to clases that got as amuch votes as winner + + equal_to_winner=(repmat(winner,N_class,1)==classcounter); + + % set index to ties + tie_index=find(sum(equal_to_winner)>1); % drop the winner from counter + + % Go through equal classes and reset vote_index randomly to one + % of them + + for i=1:length(tie_index), + tie_class_index=find(equal_to_winner(:,tie_index(i))); + fortuna=randperm(length(tie_class_index)); + vote_index(tie_index(i))=tie_class_index(fortuna(1)); + end + + class=ClassIndex(vote_index); +end + +%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Relative amount of classes in K neighbors for each classifiee + +if K==1, + P=zeros(N_data,N_class); + if nargout>1, + for i=1:N_data, + P(i,ClassIndex==class(i))=1; + end + end +else + P=classcounter'./K; +end + +% xMake class names to struct if they exist +if ~isempty(classnames), + Class=Data; + for i=1:N_data, + Class.labels{i,1}=classnames{class(i)}; + end +else + Class=class; +end + + +%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [nos,names] = class2num(class) + +% Change string labels in map/data struct to integer numbers + +names = {}; +nos = zeros(length(class),1); +for i=1:length(class) + if ~isempty(class{i}) & ~any(strcmp(class{i},names)) + names=cat(1,names,class(i)); + end +end + +tmp_nos = (1:length(names))'; +for i=1:length(class) + if ~isempty(class{i}) + nos(i,1) = find(strcmp(class{i},names)); + end +end + +function d=distance(X,Y); + +% Euclidean distance matrix between row vectors in X and Y + +U=~isnan(Y); Y(~U)=0; +V=~isnan(X); X(~V)=0; +d=X.^2*U'+V*Y'.^2-2*X*Y';
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/lvq1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,180 @@ +function codebook=lvq1(codebook, data, rlen, alpha); + +%LVQ1 Trains a codebook with the LVQ1 -algorithm. +% +% sM = lvq1(sM, D, rlen, alpha) +% +% sM = lvq1(sM,sD,30*length(sM.codebook),0.08); +% +% Input and output arguments: +% sM (struct) map struct, the class information must be +% present on the first column of .labels field +% D (struct) data struct, the class information must +% be present on the first column of .labels field +% rlen (scalar) running length +% alpha (scalar) learning parameter +% +% sM (struct) map struct, the trained codebook +% +% NOTE: does not take mask into account. +% +% For more help, try 'type lvq1', or check out online documentation. +% See also LVQ3, SOM_SUPERVISED, SOM_SEQTRAIN. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% lvq1 +% +% PURPOSE +% +% Trains codebook with the LVQ1 -algorithm (described below). +% +% SYNTAX +% +% sM = lvq1(sM, D, rlen, alpha) +% +% DESCRIPTION +% +% Trains codebook with the LVQ1 -algorithm. Codebook contains a number +% of vectors (mi, i=1,2,...,n) and so does data (vectors xj, +% j=1,2,...,k). Both vector sets are classified: vectors may have a +% class (classes are set to the first column of data or map -structs' +% .labels -field). For each xj there is defined the nearest codebook +% -vector index c by searching the minimum of the euclidean distances +% between the current xj and codebook -vectors: +% +% c = min{ ||xj - mi|| }, i=[1,..,n], for fixed xj +% i +% If xj and mc belong to the same class, mc is updated as follows: +% mc(t+1) = mc(t) + alpha * (xj(t) - mc(t)) +% If xj and mc belong to different classes, mc is updated as follows: +% mc(t+1) = mc(t) - alpha * (xj(t) - mc(t)) +% Otherwise updating is not performed. +% +% Argument 'rlen' tells how many times training sequence is performed. +% LVQ1 -algorithm may be stopped after a number of steps, that is +% 30-50 times the number of codebook vectors. +% +% Argument 'alpha' is the learning rate, recommended to be smaller +% than 0.1. +% +% NOTE: does not take mask into account. +% +% REFERENCES +% +% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, +% Berlin, 1995, pp. 176-179. +% +% See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% REQUIRED INPUT ARGUMENTS +% +% sM The data to be trained. +% (struct) A map struct. +% +% D The data to use in training. +% (struct) A data struct. +% +% rlen (integer) Running length of LVQ1 -algorithm. +% +% alpha (float) Learning rate used in training. +% +% OUTPUT ARGUMENTS +% +% codebook Trained data. +% (struct) A map struct. +% +% EXAMPLE +% +% lab = unique(sD.labels(:,1)); % different classes +% mu = length(lab)*5; % 5 prototypes for each +% sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes +% sM.labels = [lab;lab;lab;lab;lab]; % their classes +% sM = lvq1(sM,sD,50*mu,0.05); % use LVQ1 to adjust +% % the prototypes +% sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3); % then use LVQ3 +% +% SEE ALSO +% +% lvq3 Use LVQ3 algorithm for training. +% som_supervised Train SOM using supervised training. +% som_seqtrain Train SOM with sequential algorithm. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 310100 juuso 020200 + +cod = codebook.codebook; +c_class = class2num(codebook.labels(:,1)); + +dat = data.data; +d_class = class2num(data.labels(:,1)); + +x=size(dat,1); +y=size(cod,2); + +ONES=ones(size(cod,1),1); + +for t=1:rlen + + fprintf(1,'\rTraining round: %d',t); + tmp=NaN*ones(x,y); + + for j=1:x + no_NaN=find(~isnan(dat(j,:))); + di = sqrt(sum([cod(:,no_NaN) - ONES*dat(j,no_NaN)].^2,2)); + + [foo,ind] = min(di); + + if d_class(j) & d_class(j) == c_class(ind) % 0 is for unclassified vectors + tmp(ind,:) = cod(ind,:) + alpha * (dat(j,:) - cod(ind,:)); + elseif d_class(j) + tmp(ind,:) = cod(ind,:) - alpha*(dat(j,:) - cod(ind,:)); + end + end + + inds = find(~isnan(sum(tmp,2))); + cod(inds,:) = tmp(inds,:); +end + +codebook.codebook = cod; + +sTrain = som_set('som_train','algorithm','lvq1',... + 'data_name',data.name,... + 'neigh','',... + 'mask',ones(y,1),... + 'radius_ini',NaN,... + 'radius_fin',NaN,... + 'alpha_ini',alpha,... + 'alpha_type','constant',... + 'trainlen',rlen,... + 'time',datestr(now,0)); +codebook.trainhist(end+1) = sTrain; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function nos = class2num(class) + +names = {}; +nos = zeros(length(class),1); + +for i=1:length(class) + if ~isempty(class{i}) & ~any(strcmp(class{i},names)) + names=cat(1,names,class(i)); + end +end + +tmp_nos = (1:length(names))'; + +for i=1:length(class) + if ~isempty(class{i}) + nos(i,1) = find(strcmp(class{i},names)); + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/lvq3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,215 @@ +function codebook = lvq3(codebook,data,rlen,alpha,win,epsilon) + +%LVQ3 trains codebook with LVQ3 -algorithm +% +% sM = lvq3(sM,D,rlen,alpha,win,epsilon) +% +% sM = lvq3(sM,sD,50*length(sM.codebook),0.05,0.2,0.3); +% +% Input and output arguments: +% sM (struct) map struct, the class information must be +% present on the first column of .labels field +% D (struct) data struct, the class information must +% be present on the first column of .labels field +% rlen (scalar) running length +% alpha (scalar) learning parameter, e.g. 0.05 +% win (scalar) window width parameter, e.g. 0.25 +% epsilon (scalar) relative learning parameter, e.g. 0.3 +% +% sM (struct) map struct, the trained codebook +% +% NOTE: does not take mask into account. +% +% For more help, try 'type lvq3', or check out online documentation. +% See also LVQ1, SOM_SUPERVISED, SOM_SEQTRAIN. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% lvq3 +% +% PURPOSE +% +% Trains codebook with the LVQ3 -algorithm (described below). +% +% SYNTAX +% +% sM = lvq3(sM, data, rlen, alpha, win, epsilon) +% +% DESCRIPTION +% +% Trains codebook with the LVQ3 -algorithm. Codebook contains a number +% of vectors (mi, i=1,2,...,n) and so does data (vectors xj, j=1,2,...k). +% Both vector sets are classified: vectors may have a class (classes are +% set to data- or map -structure's 'labels' -field. For each xj the two +% closest codebookvectors mc1 and mc2 are searched (euclidean distances +% d1 and d2). xj must fall into the zone of window. That happens if: +% +% min(d1/d2, d2/d1) > s, where s = (1-win) / (1+win). +% +% If xj belongs to the same class of one of the mc1 and mc1, codebook +% is updated as follows (let mc1 belong to the same class as xj): +% mc1(t+1) = mc1(t) + alpha * (xj(t) - mc1(t)) +% mc2(t+1) = mc2(t) - alpha * (xj(t) - mc2(t)) +% If both mc1 and mc2 belong to the same class as xj, codebook is +% updated as follows: +% mc1(t+1) = mc1(t) + epsilon * alpha * (xj(t) - mc1(t)) +% mc2(t+1) = mc2(t) + epsilon * alpha * (xj(t) - mc2(t)) +% Otherwise updating is not performed. +% +% Argument 'rlen' tells how many times training -sequence is performed. +% +% Argument 'alpha' is recommended to be smaller than 0.1 and argument +% 'epsilon' should be between 0.1 and 0.5. +% +% NOTE: does not take mask into account. +% +% REFERENCES +% +% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, +% Berlin, 1995, pp. 181-182. +% +% See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% REQUIRED INPUT ARGUMENTS +% +% sM The data to be trained. +% (struct) A map struct. +% +% data The data to use in training. +% (struct) A data struct. +% +% rlen (integer) Running length of LVQ3 -algorithm. +% +% alpha (float) Learning rate used in training, e.g. 0.05 +% +% win (float) Window length, e.g. 0.25 +% +% epsilon (float) Relative learning parameter, e.g. 0.3 +% +% OUTPUT ARGUMENTS +% +% sM Trained data. +% (struct) A map struct. +% +% EXAMPLE +% +% lab = unique(sD.labels(:,1)); % different classes +% mu = length(lab)*5; % 5 prototypes for each +% sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes +% sM.labels = [lab;lab;lab;lab;lab]; % their classes +% sM = lvq1(sM,sD,50*mu,0.05); % use LVQ1 to adjust +% % the prototypes +% sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3); % then use LVQ3 +% +% SEE ALSO +% +% lvq1 Use LVQ1 algorithm for training. +% som_supervised Train SOM using supervised training. +% som_seqtrain Train SOM with sequential algorithm. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 310100 juuso 020200 + +NOTFOUND = 1; + +cod = codebook.codebook; +dat = data.data; + +c_class = codebook.labels(:,1); +d_class = data.labels(:,1); + +s = (1-win)/(1+win); + +x = size(dat,1); +y = size(cod,2); + +c_class=class2num(c_class); +d_class=class2num(d_class); + +ONES=ones(size(cod,1),1); + +for t=1:rlen + fprintf('\rTraining round: %d/%d',t,rlen); + tmp = NaN*ones(x,y); + + for j=1:x + flag = 0; + mj = 0; + mi = 0; + no_NaN=find(~isnan(dat(j,:))); + di=sqrt(sum([cod(:,no_NaN) - ONES*dat(j,no_NaN)].^2,2)); + [foo, ind1] = min(di); + di(ind1)=Inf; + [foo,ind2] = min(di); + + %ind2=ind2+1; + + if d_class(j) & d_class(j)==c_class(ind1) + mj = ind1; + mi = ind2; + if d_class(j)==c_class(ind2) + flag = 1; + end + elseif d_class(j) & d_class(j)==c_class(ind2) + mj = ind2; + mi = ind1; + if d_class(j)==c_class(ind1) + flag = 1; + end + end + + if mj & mi + if flag + tmp([mj mi],:) = cod([mj mi],:) + epsilon*alpha*... + (dat([j j],:) - cod([mj mi],:)); + else + tmp(mj,:) = cod(mj,:) + alpha * (dat(j,:)-cod(mj,:)); + tmp(mi,:) = cod(mi,:) - alpha * (dat(j,:)-cod(mj,:)); + end + end + end + inds = find(~isnan(sum(tmp,2))); + cod(inds,:) = tmp(inds,:); +end +fprintf(1,'\n'); + +sTrain = som_set('som_train','algorithm','lvq3',... + 'data_name',data.name,... + 'neigh','',... + 'mask',ones(y,1),... + 'radius_ini',NaN,... + 'radius_fin',NaN,... + 'alpha_ini',alpha,... + 'alpha_type','constant',... + 'trainlen',rlen,... + 'time',datestr(now,0)); +codebook.trainhist(end+1) = sTrain; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function nos = class2num(class) + +names = {}; +nos = zeros(length(class),1); + +for i=1:length(class) + if ~isempty(class{i}) & ~any(strcmp(class{i},names)) + names=cat(1,names,class(i)); + end +end + +tmp_nos = (1:length(names))'; + +for i=1:length(class) + if ~isempty(class{i}) + nos(i,1) = find(strcmp(class{i},names)); + end +end + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/nanstats.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [me, st, md, no] = nanstats(D) + +%NANSTATS Statistical operations that ignore NaNs and Infs. +% +% [mean, std, median, nans] = nanstats(D) +% +% Input and output arguments: +% D (struct) data or map struct +% (matrix) size dlen x dim +% +% me (double) columnwise mean +% st (double) columnwise standard deviation +% md (double) columnwise median +% no (vector) columnwise number of samples (finite, not-NaN) + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 300798 200900 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +(nargchk(1, 1, nargin)); % check no. of input args is correct + +if isstruct(D), + if strcmp(D.type,'som_map'), D = D.codebook; + else D = D.data; + end +end +[dlen dim] = size(D); +me = zeros(dim,1)+NaN; +md = me; +st = me; +no = me; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% computation + +for i = 1:dim, + ind = find(isfinite(D(:, i))); % indices of non-NaN/Inf elements + n = length(ind); % no of non-NaN/Inf elements + + me(i) = sum(D(ind, i)); % compute average + if n == 0, me(i) = NaN; else me(i) = me(i) / n; end + + if nargout>1, + md(i) = median(D(ind, i)); % compute median + + if nargout>2, + st(i) = sum((me(i) - D(ind, i)).^2); % compute standard deviation + if n == 0, st(i) = NaN; + elseif n == 1, st(i) = 0; + else st(i) = sqrt(st(i) / (n - 1)); + end + + if nargout>3, + no(i) = n; % number of samples (finite, not-NaN) + end + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/neural_gas.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,89 @@ +function [Neurons] = neural_gas(D,n,epochs,alpha0,lambda0) + +%NEURAL_GAS Quantizes the data space using the neural gas algorithm. +% +% Neurons = neural_gas(D, n, epochs, [alpha0], [lambda0]) +% +% C = neural_gas(D,50,10); +% sM = som_map_struct(sD); +% sM.codebook = neural_gas(sD,size(sM.codebook,1),10); +% +% Input and output arguments ([]'s are optional): +% D (matrix) the data matrix, size dlen x dim +% (struct) a data struct +% n (scalar) the number of neurons +% epochs (scalar) the number of training epochs (the number of +% training steps is dlen*epochs) +% [alpha0] (scalar) initial step size, 0.5 by default +% [lambda0] (scalar) initial decay constant, n/2 by default +% +% Neurons (matrix) the neuron matrix, size n x dim +% +% See also SOM_MAKE, KMEANS. + +% References: +% T.M.Martinetz, S.G.Berkovich, and K.J.Schulten. "Neural-gas" network +% for vector quantization and its application to time-series prediction. +% IEEE Transactions on Neural Networks, 4(4):558-569, 1993. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% juuso 101297 020200 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments and initialize + +error(nargchk(3, 5, nargin)); % check the number of input arguments + +if isstruct(D), D = D.data; end +[dlen,dim] = size(D); +Neurons = (rand(n,dim)-0.5)*10e-5; % small initial values +train_len = epochs*dlen; + +if nargin<4 | isempty(alpha0) | isnan(alpha0), alpha0 = 0.5; end +if nargin<5 | isempty(lambda0) | isnan(lambda0), lambda0 = n/2; end + +% random sample order +rand('state',sum(100*clock)); +sample_inds = ceil(dlen*rand(train_len,1)); + +% lambda +lambda = lambda0 * (0.01/lambda0).^([0:(train_len-1)]/train_len); + +% alpha +alpha = alpha0 * (0.005/alpha0).^([0:(train_len-1)]/train_len); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +for i=1:train_len, + + % sample vector + x = D(sample_inds(i),:); % sample vector + known = ~isnan(x); % its known components + X = x(ones(n,1),known); % we'll need this + + % neighborhood ranking + Dx = Neurons(:,known) - X; % difference between vector and all map units + [qerrs, inds] = sort((Dx.^2)*known'); % 1-BMU, 2-BMU, etc. + ranking(inds) = [0:(n-1)]; + h = exp(-ranking/lambda(i)); + H = h(ones(length(known),1),:)'; + + % update + Neurons = Neurons + alpha(i)*H.*(x(ones(n,1),known) - Neurons(:,known)); + + % track + fprintf(1,'%d / %d \r',i,train_len); + if 0 & mod(i,50) == 0, + hold off, plot3(D(:,1),D(:,2),D(:,3),'bo') + hold on, plot3(Neurons(:,1),Neurons(:,2),Neurons(:,3),'r+') + drawnow + end +end + +fprintf(1,'\n'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/pcaproj.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +function [P,V,me,l] = pcaproj(D,arg1,arg2) + +%PCAPROJ Projects data vectors using Principal Component Analysis. +% +% [P,V,me,l] = pcaproj(D, odim) +% P = pcaproj(D, V, me) +% +% Input and output arguments ([]'s are optional) +% D (matrix) size dlen x dim, the data matrix +% (struct) data or map struct +% odim (scalar) how many principal vectors are used +% +% P (matrix) size dlen x odim, the projections +% V (matrix) size dim x odim, principal eigenvectors (unit length) +% me (vector) size 1 x dim, center point of D +% l (vector) size 1 x odim, the corresponding eigenvalues, +% relative to total sum of eigenvalues +% +% See also SAMMON, CCA. + +% Contributed to SOM Toolbox 2.0, February 2nd, 2000 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% juuso 191297 070200 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(2, 3, nargin)); % check the number of input arguments + +% the data +if isstruct(D), + if strcmp(D.type,'som_map'), D=D.codebook; else D=D.data; end +end +[dlen dim] = size(D); + +if nargin==2, + + odim = arg1; + + % autocorrelation matrix + A = zeros(dim); + me = zeros(1,dim); + for i=1:dim, + me(i) = mean(D(isfinite(D(:,i)),i)); + D(:,i) = D(:,i) - me(i); + end + for i=1:dim, + for j=i:dim, + c = D(:,i).*D(:,j); c = c(isfinite(c)); + A(i,j) = sum(c)/length(c); A(j,i) = A(i,j); + end + end + + % eigenvectors, sort them according to eigenvalues, and normalize + [V,S] = eig(A); + eigval = diag(S); + [y,ind] = sort(abs(eigval)); + eigval = eigval(flipud(ind)); + V = V(:,flipud(ind)); + for i=1:odim, V(:,i) = (V(:,i) / norm(V(:,i))); end + + % take only odim first eigenvectors + V = V(:,1:odim); + l = abs(eigval)/sum(abs(eigval)); + l = l(1:odim); + +else % nargin==3, + + V = arg1; + me = arg2; + odim = size(V,2); + D = D-me(ones(dlen,1),:); + +end + +% project the data using odim first eigenvectors +P = D*V; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/preprocess.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6642 @@ +function preprocess(sData,arg2) + +%PREPROCESS A GUI for data preprocessing. +% +% preprocess(sData) +% +% preprocess(sData) +% +% Launches a preprocessing GUI. The optional input argument can be +% either a data struct or a struct array of such. However, primarily +% the processed data sets are loaded to the application using the +% tools in the GUI. Also, the only way to get the preprocessed data +% sets back into the workspace is to use the tools in the GUI (press +% the button DATA SET MANAGEMENT). +% +% For a more throughout description, see online documentation. +% See also SOM_GUI. + +%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% IN FILES: preprocess.html,preproc.jpg,sDman.jpg,clip.jpg,delay.jpg,window.jpg,selVect.jpg + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas and the SOM Toolbox team + +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +global no_of_sc % every Nth component in 'relative values' is drawn stronger. +no_of_sc=5; + +if nargin < 1 | nargin > 2 + error('Invalid number of input arguments'); + return; +end + +if nargin == 1, arg2=[]; end + +if ~isstr(sData) %%% Preprocess is started... +data.LOG{1}='% Starting the ''Preprocess'' -window...'; +data.LOG{2}=cat(2,'preprocess(',... + sprintf('%s);',inputname(1))); + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +if ~isempty(pre_h) + figure(pre_h); + msgbox('''Preprocess''-figure already exists.'); + return; +end + +h0 = figure('Color',[0.8 0.8 0.8], ... + 'PaperPosition',[18 180 576 432], ... + 'PaperUnits','points', ... + 'Position',[595 216 600 775], ... + 'Tag','Preprocess'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.015 0.06064516129032258 0.9550000000000001 0.1458064516129032], ... + 'Style','text', ... + 'Tag','StaticText1'); + +data.results_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess close', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.8067 0.0142 0.1667 0.0348],... + 'String','CLOSE', ... + 'Tag','Pushbutton1'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.01833333333333333 0.2141935483870968 0.07000000000000001 0.01806451612903226], ... + 'String','LOG', ... + 'Style','text', ... + 'Tag','StaticText2'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess sel_comp',... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.7983333333333333 0.2090322580645161 0.1666666666666667 0.03483870967741935], ... + 'String',' ', ... + 'Style','popupmenu', ... + 'Tag','sel_comp_h', ... + 'Value',1); + +data.sel_comp_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.0183 0.2568 0.2133 0.1290], ... + 'Style','text', ... + 'Tag','StaticText3'); + +data.sel_cdata_h=h1; + +h1 = axes('Parent',h0, ... + 'CameraUpVector',[0 1 0], ... + 'CameraUpVectorMode','manual', ... + 'Color',[1 1 1], ... + 'Position',[0.2583 0.2568 0.2133 0.1290], ... + 'Tag','Axes1', ... + 'XColor',[0 0 0], ... + 'XTickLabel',['0 ';'0.5';'1 '], ... + 'XTickLabelMode','manual', ... + 'XTickMode','manual', ... + 'YColor',[0 0 0], ... + 'YTickMode','manual', ... + 'ZColor',[0 0 0]); + +data.sel_chist_h=h1; + +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[0.4960629921259843 -0.08080808080808044 9.160254037844386], ... + 'Tag','Axes1Text4', ... + 'VerticalAlignment','cap'); +set(get(h2,'Parent'),'XLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[-0.0551181102362206 0.4848484848484853 9.160254037844386], ... + 'Rotation',90, ... + 'Tag','Axes1Text3', ... + 'VerticalAlignment','baseline'); +set(get(h2,'Parent'),'YLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','right', ... + 'Position',[-1.2283 5.7980 9.1603], ... + 'Tag','Axes1Text2', ... + 'Visible','off'); +set(get(h2,'Parent'),'ZLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[0.4960629921259843 1.070707070707071 9.160254037844386], ... + 'Tag','Axes1Text1', ... + 'VerticalAlignment','bottom'); +set(get(h2,'Parent'),'Title',h2); + +h1 = axes('Parent',h0, ... + 'CameraUpVector',[0 1 0], ... + 'CameraUpVectorMode','manual', ... + 'Color',[0.7529 0.7529 0.7529], ... + 'Position',[0.4950000000000001 0.2567741935483871 0.4766666666666667 0.1290322580645161], ... + 'Tag','Axes2', ... + 'XColor',[0 0 0], ... + 'XTickMode','manual', ... + 'YColor',[0 0 0], ... + 'YTick',[0 0.5 1], ... + 'YTickMode','manual', ... + 'ZColor',[0 0 0]); + +data.vector_h=h1; + +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[0.4982456140350879 -0.08080808080808044 9.160254037844386], ... + 'Tag','Axes2Text4', ... + 'VerticalAlignment','cap'); +set(get(h2,'Parent'),'XLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[-0.1018 0.4848 9.1603], ... + 'Rotation',90, ... + 'Tag','Axes2Text3', ... + 'VerticalAlignment','baseline'); +set(get(h2,'Parent'),'YLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','right', ... + 'Position',[-1.045614035087719 5.797979797979799 9.160254037844386], ... + 'Tag','Axes2Text2', ... + 'Visible','off'); +set(get(h2,'Parent'),'ZLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[0.4982456140350879 1.070707070707071 9.160254037844386], ... + 'Tag','Axes2Text1', ... + 'VerticalAlignment','bottom'); +set(get(h2,'Parent'),'Title',h2); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.01833333333333333 0.3922580645161291 0.17 0.01806451612903226], ... + 'String','STATISTICS', ... + 'Style','text', ... + 'Tag','StaticText4'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.2583333333333334 0.3922580645161291 0.1633333333333333 0.01806451612903226], ... + 'String','HISTOGRAM', ... + 'Style','text', ... + 'Tag','StaticText5'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi',... + 'FontSize',6,... + 'HorizontalAlignment','left',... + 'String',{'LEFT: NEW SELECTION';'RIGHT: ADD TO SELECTION'}, ... + 'ListboxTop',0, ... + 'Position',[0.5016666666666667 0.38 0.235 0.03741935483870968], ... + 'Style','text', ... + 'Tag','StaticText6', ... + 'UserData','[ ]'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess selall', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.8066666666666668 0.3922580645161291 0.1666666666666667 0.03483870967741935], ... + 'String','SELECT ALL', ... + 'Tag','Pushbutton2', ... + 'UserData','[ ]'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.7529 0.7529 0.7529], ... + 'Position',[0.01833333333333333 0.4503225806451613 0.23 0.3225806451612903], ... + 'String',' ', ... + 'Style','listbox', ... + 'Tag','Listbox1', ... + 'Value',1); + +data.comp_names_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Position',[0.4950000000000001 0.4503225806451613 0.2333333333333333 0.3225806451612903], ... + 'String',' ', ... + 'Style','listbox', ... + 'Tag','Listbox2', ... + 'Value',1); + +data.vect_mean_h = h1; + +h1 = axes('Parent',h0, ... + 'CameraUpVector',[0 1 0], ... + 'CameraUpVectorMode','manual', ... + 'Color',[1 1 1], ... + 'Position',[0.7383333333333334 0.4503225806451613 0.2333333333333333 0.3225806451612903], ... + 'Tag','Axes3', ... + 'XColor',[0 0 0], ... + 'XTickMode','manual', ... + 'YColor',[0 0 0], ... + 'YTickMode','manual', ... + 'ZColor',[0 0 0]); + +data.sel_cplot_h = h1; + +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[0.4964028776978418 -0.03212851405622486 9.160254037844386], ... + 'Tag','Axes3Text4', ... + 'VerticalAlignment','cap'); +set(get(h2,'Parent'),'XLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[-0.05035971223021596 0.493975903614458 9.160254037844386], ... + 'Rotation',90, ... + 'Tag','Axes3Text3', ... + 'VerticalAlignment','baseline'); +set(get(h2,'Parent'),'YLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','right', ... + 'Position',[-3.1942 1.7028 9.1603], ... + 'Tag','Axes3Text2', ... + 'Visible','off'); +set(get(h2,'Parent'),'ZLabel',h2); +h2 = text('Parent',h1, ... + 'Color',[0 0 0], ... + 'HandleVisibility','off', ... + 'HorizontalAlignment','center', ... + 'Position',[0.4964028776978418 1.028112449799197 9.160254037844386], ... + 'Tag','Axes3Text1', ... + 'VerticalAlignment','bottom'); +set(get(h2,'Parent'),'Title',h2); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess plxy', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.265 0.4683870967741936 0.125 0.03483870967741935], ... + 'String','XY-PLOT', ... + 'Tag','Pushbutton3'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess hist', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.265 0.5303225806451613 0.125 0.03483870967741935], ... + 'String','HISTOGRAM', ... + 'Tag','Pushbutton4'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess bplo', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.265 0.5922580645161291 0.125 0.03483870967741935], ... + 'String','BOX PLOT', ... + 'Tag','Pushbutton5'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess plot', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.265 0.654195483870968 0.125 0.03483870967741935], ... + 'String','PLOT', ... + 'Tag','Pushbutton6'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.4088888888888889 0.5333333333333333 0.06 0.03268817204301075], ... + 'String','30', ... + 'Style','edit', ... + 'Tag','EditText1'); + +data.no_of_bins_h = h1; + + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.01833333333333333 0.775483870967742 0.2016666666666667 0.01806451612903226], ... + 'String','COMPONENT LIST', ... + 'Style','text', ... + 'Tag','StaticText7'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4950000000000001 0.775483870967742 0.1966666666666667 0.01806451612903226], ... + 'String','AVERAGE', ... + 'Style','text', ... + 'Tag','StaticText8'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.7383333333333334 0.775483870967742 0.225 0.01806451612903226], ... + 'String','RELATIVE VALUES', ... + 'Style','text', ... + 'Tag','StaticText9'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',10, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.01833333333333333 0.8154838709677419 0.2033333333333333 0.0232258064516129], ... + 'String','COMPONENTS', ... + 'Style','text', ... + 'Tag','StaticText10'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',10, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4950000000000001 0.8154838709677419 0.2 0.0232258064516129], ... + 'String','VECTORS', ... + 'Style','text', ... + 'Tag','StaticText11'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess sD_management', ... + 'FontSize',5, ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.01833333333333333 0.8503225806451613 0.1666666666666667 0.03483870967741935], ... + 'String','DATA SET MANAGEMENT', ... + 'Tag','Pushbutton7'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'Callback','preprocess sel_sD', ... + 'ListboxTop',0, ... + 'Position',[0.01833333333333333 0.8890322580645161 0.1666666666666667 0.03483870967741935], ... + 'String',' ', ... + 'Style','popupmenu', ... + 'Tag','PopupMenu2', ... + 'Value',1); + +data.sD_set_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.2516666666666667 0.8503225806451613 0.7216666666666667 0.07354838709677419], ... + 'Style','text', ... + 'Tag','StaticText12'); + +data.sD_name_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',10, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.01833333333333333 0.9341935483870968 0.1616666666666667 0.02064516129032258], ... + 'String','DATA SETS', ... + 'Style','text', ... + 'Tag','StaticText13'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',10, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.2516666666666667 0.9341935483870968 0.2833333333333333 0.02064516129032258], ... + 'String','SELECTED DATA SET', ... + 'Style','text', ... + 'Tag','StaticText14'); + +if ~isstruct(sData), + sData=som_data_struct(sData); +end + +ui_h=uimenu('Label','&Normalization'); +uimenu(ui_h,'Label','Scale [0,1]','Callback','preprocess zscale'); +uimenu(ui_h,'Label','Scale var=1','Callback','preprocess vscale'); +uimenu(ui_h,'Label','HistD','Callback','preprocess histeq'); +uimenu(ui_h,'Label','HistC','Callback','preprocess histeq2'); +uimenu(ui_h,'Label','Log','Callback','preprocess log'); +uimenu(ui_h,'Label','Eval (1-comp)','Callback','preprocess eval1'); + +ui_h=uimenu('Label','&Components'); +uimenu(ui_h,'Label','Move component','Callback','preprocess move'); +uimenu(ui_h,'Label','Copy component','Callback','preprocess copy'); +uimenu(ui_h,'Label','Add: N binary types','Callback','preprocess oneo'); +uimenu(ui_h,'Label','Add: zeros','Callback','preprocess zero'); +uimenu(ui_h,'Label','Remove component','Callback','preprocess remove'); +uimenu(ui_h,'Label','Remove selected vectors',... + 'Callback','preprocess remove_vects'); +uimenu(ui_h,'Label','Select all components',... + 'Callback','preprocess sel_all_comps'); + +ui_h=uimenu('Label','&Misc'); +ui_h1=uimenu(ui_h,'Label','Calculate'); +ui_h2=uimenu(ui_h,'Label','Process'); + +uimenu(ui_h,'Label','Get LOG-file','Callback','preprocess LOG'); +uimenu(ui_h,'Label','Indices of the selected vectors',... + 'Callback','preprocess get_inds'); +uimenu(ui_h,'Label','Undo','Callback','preprocess undo'); +uimenu(ui_h1,'Label','Number of values','Callback','preprocess noof'); +uimenu(ui_h1,'Label','Number of selected vectors',... + 'Callback','preprocess no_of_sel'); +uimenu(ui_h1,'Label','Correlation','Callback','preprocess corr'); +uimenu(ui_h2,'Label','Unit length','Callback','preprocess unit'); +uimenu(ui_h2,'Label','Eval','Callback','preprocess eval2'); +uimenu(ui_h2,'Label','Clipping','Callback','preprocess clipping'); +uimenu(ui_h2,'Label','Delay','Callback','preprocess delay'); +uimenu(ui_h2,'Label','Windowed','Callback','preprocess window'); +uimenu(ui_h2,'Label','Select vectors','Callback','preprocess select'); + +len=getfield(size(sData(1).data),{1}); +data.selected_vects=find(ones(1,len)); +data.sD_set=sData; +set(data.vector_h,'ButtonDownFcn','preprocess(''vector_bdf'',''down'')'); +set(gcf,'UserData',data); +if ~set_sD_stats; + return; +end +sel_sD; +return; %%% Preprocess-window is ready. + +else + arg=sData; + if strcmp(arg,'rename') + rename(arg2); + elseif strcmp(arg,'sel_sD') + if isempty(arg2) + sel_sD; + else + sel_sD(arg2); + end + elseif strcmp(arg,'zscale') + if isempty(arg2) + zero2one_scale; + else + zero2one_scale(arg2); + end + elseif strcmp(arg,'vscale'); + if isempty(arg2) + var_scale; + else + var_scale(arg2); + end + elseif strcmp(arg,'histeq2') + if isempty(arg2) + hist_eq2; + else + hist_eq2(arg2); + end + elseif strcmp(arg,'log') + if isempty(arg2) + logarithm; + else + logarithm(arg2); + end + elseif strcmp(arg,'eval1') + if isempty(arg2) + eval1; + else + eval1(arg2); + end + elseif strcmp(arg,'eval2') + if isempty(arg2) + eval2; + else + eval2(arg2); + end + elseif strcmp(arg,'histeq'); + if isempty(arg2) + hist_eq; + else + hist_eq(arg2); + end + elseif strcmp(arg,'selall') + if isempty(arg2) + select_all; + else + select_all(arg2); + end + elseif strcmp(arg,'sel_button'); + if isempty(arg2) + sel_button; + else + sel_button(arg2); + end + elseif strcmp(arg,'clear_button') + if isempty(arg2) + clear_button; + else + clear_button(arg2) + end + elseif(strcmp(arg,'move')) + if isempty(arg2) + move_component; + else + move_component(arg2); + end + elseif(strcmp(arg,'copy')) + if isempty(arg2) + copy_component; + else + copy_component(arg2); + end + elseif strcmp(arg,'oneo') + if isempty(arg2) + one_of_n; + else + one_of_n(arg2); + end + elseif strcmp(arg,'zero') + if isempty(arg2) + add_zeros; + else + add_zeros(arg2); + end + elseif strcmp(arg,'remove') + if isempty(arg2) + remove_component; + else + remove_component(arg2); + end + elseif strcmp(arg,'remove_vects') + if isempty(arg2) + remove_vects; + else + remove_vects(arg2); + end + elseif strcmp(arg,'noof') + if isempty(arg2) + no_of_values; + else + no_of_values(arg2); + end + elseif strcmp(arg,'corr'); + if isempty(arg2) + correlation; + else + correlation(arg2); + end + elseif strcmp(arg,'unit') + if isempty(arg2) + unit_length; + else + unit_length(arg2); + end + elseif strcmp(arg,'clip_data') + clip_data(arg2); + elseif strcmp(arg,'copy_delete') + copy_delete(arg2); + elseif strcmp(arg,'and_or_cb') + and_or_cb(arg2); + elseif strcmp(arg,'all_sel_cb') + all_sel_cb(arg2); + elseif strcmp(arg,'clip_exp_cb') + clip_exp_cb(arg2); + elseif strcmp(arg,'window_cb') + window_cb(arg2); + elseif strcmp(arg,'set_state_vals') + set_state_vals(arg2); + elseif strcmp(arg,'vector_bdf') + vector_bdf(arg2); + elseif strcmp(arg,'sD_management'); + if isempty(arg2) + sD_management; + else + sD_management(arg2); + end + elseif strcmp(arg,'clipping') + if isempty(arg2) + clipping; + else + clipping(arg2); + end + elseif strcmp(arg,'delay') + if isempty(arg2) + delay; + else + delay(arg2); + end + elseif strcmp(arg,'window'); + if isempty(arg2) + window; + else + window(arg2); + end + elseif strcmp(arg,'select'); + if isempty(arg2) + select; + else + select(arg2); + end + elseif strcmp(arg,'import') + if isempty(arg2) + import; + else + import(arg2); + end + elseif strcmp(arg,'export') + if isempty(arg2) + export; + else + export(arg2); + end + elseif strcmp(arg,'undo'); + if isempty(arg2) + undo; + else + undo(arg2); + end + elseif strcmp(arg,'delay_data') + if isempty(arg2) + delay_data; + else + delay_data(arg2); + end + elseif strcmp(arg,'eval_windowed') + if isempty(arg2) + eval_windowed; + else + eval_windowed(arg2); + end + elseif strcmp(arg,'get_inds') + if isempty(arg2) + get_selected_inds; + else + get_selected_inds(arg2); + end + elseif strcmp(arg,'no_of_sel') + if isempty(arg2) + no_of_selected; + else + no_of_selected(arg2); + end + elseif strcmp(arg,'sel_comp'); + if isempty(arg2) + sel_comp; + else + sel_comp(arg2); + end + elseif strcmp(arg,'sel_all_comps') + if isempty(arg2) + select_all_comps; + else + select_all_comps(arg2); + end + elseif strcmp(arg,'refresh') + set_var_names; + elseif any(strcmp(arg,{'close_c','close_d','close_s','close_w','close_sD'})) + if isempty(arg2) + close_func(arg) + else + close_func(arg,arg2); + end + end + + + switch arg + case 'sD_stats' + sD_stats; + case 'LOG' + log_file; + otherwise + pro_tools(arg); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function set_compnames(sData,h) + +%SET_COMPNAMES +% +% set_compnames(sData,h) +% +% ARGUMENTS +% +% sData (struct) som_data_struct +% h (scalar) handle to a list box object +% +% +% This function sets the component names of sData to the list box +% indicated by 'h'. +% + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +if isempty(pre_h) + error('Figure ''Preprocess'' does not exist. Closing program...'); + close_preprocess; +end + +udata=get(pre_h,'UserData'); + +set(h,'Value',[]); +for i=1:length(sData.comp_names) + tmp=sprintf('#%d: ',i); + names{i,1}=cat(2,tmp, sData.comp_names{i}); +end + + +set(h,'String',names,'Max',2); +set(udata.sel_comp_h,'String',names); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function draw_vectors(vectors,h) + +%DRAW_VECTORS +% +% draw_vectors(vectors,h) +% +% ARGUMENTS +% +% vectors (vector) vector of 0's and 1's +% h (scalar) handle to an axis object +% +% +% This function draws an horizontal bar of 'vectors' in the axis +% indicated by 'h'. +% +% + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +subplot(h); +hold off; +cla; + +set(h,'YLim',[0 1]); +set(h,'YTick',[]); +set(h,'XLim',[0 length(vectors)+1]); +hold on; + +comp_no=get(getfield(get(pre_h,'UserData'),'sel_comp_h'),'Value'); +comp=getfield(get(pre_h,'UserData'),'sData'); +comp=comp.data(:,comp_no); +Max = max(comp); +Min = min(comp); +lims=get(gca,'YLim'); +lims(1)=Min; +h=abs(0.1*Max); +lims(2)=Max; +if Max - Min <= eps + tmp=Max; + lims(1)=tmp-1; + lims(2)=tmp+1; +end +lims(2)=lims(2)+h; +if ~all(isnan(lims)) + set(gca,'YLim',lims); +end +h=(lims(2)-lims(1))/4; +set(gca,'YTickMode','auto'); +t=1:length(vectors); +h=plot(t,comp); +set(h,'ButtonDownFcn','preprocess(''vector_bdf'',''down'')'); +indices =find(vectors); +vectors(indices)=0.1*(getfield(get(gca,'YLim'),... + {2})-getfield(get(gca,'YLim'),{1})); +plot(indices,vectors(indices)+getfield(get(gca,'YLim'),{1}),... + 'ored','MarkerSize',4); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function vect_means(sData,handle,indices) + +%VECT_MEANS +% +% vect_means(sData,handle,indices) +% +% ARGUMENTS +% +% sData (struct) som_data_struct +% handle (scalar) handle to the static text box object +% indices (vector) indices of selected vectors +% +% +% This function calculates means of selected vectors' components +% and writes them in the static text box indicated by 'handle'. +% +% + +sData= sData.data(indices,:); + +for i=1:length(sData(1,:)) + names{i}=sprintf('#%d: ',i); +end + + +for i=1:length(sData(1,:)) + tmp=sData(:,i); + tmp=cat(2,names{i},sprintf('%-10.3g',mean(tmp(find(~isnan(tmp)))))); + string{i}=tmp; +end + +set(handle,'String',string); +set(handle,'HorizontalAlignment','left'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function vector_bdf(arg) + +%VECTOR_BDF A button down function. +% +% vector_bdf(arg) +% +% ARGUMENTS +% +% arg (string) 'down' or 'up', tells the mouse button's state. +% +% +% This function selects vectors in the vector-window and plots maxima, +% minima and means of the selected vectors. It also writes means of the +% selected vectors' components in a static text box and takes care of +% changes of the chosen component's data. +% +% See also VECTOR_MEANS, SEL_COMP +% +% + + +arg2=arg(6:length(arg)); +if ~isempty(arg2) + LOG=1; +else + LOG=0; +end +arg=arg(1:4); + +%%% arg's first "word" is 4 letters long and it can be: +%%% +%%% 'key ' +%%% 'down' +%%% 'drag' +%%% 'up ' + +if strcmp(arg,'key ') %string is 'key' + 1 space!!! + if ~LOG + key=get(gcf,'CurrentCharacter'); + else + key=arg2 + end + if ~strcmp(key,'<') & ~strcmp(key,'>') + return; + end + data=get(gcf,'UserData'); + sel=data.selected_vects; + if length(sel) == 1 + if strcmp(key,'<') & sel ~= 1 + data.selected_vects=sel-1; + set(gcf,'UserData',data); + elseif strcmp(key,'>') & sel ~= length(data.sData.data(:,1)) + data.selected_vects = sel + 1; + set(gcf,'UserData',data); + end + else + if strcmp(key,'<') & sel(1) ~= 1 + data.selected_vects=cat(2,sel(1)-1,sel); + set(gcf,'UserData',data); + elseif strcmp(key,'>') & sel(length(sel)) ~= length(sel) + data.selected_vects=cat(2,sel,sel(length(sel))+1); + set(gcf,'UserData',data); + end + end + cplot_mimema; + pro_tools('plot_hist'); + pro_tools('c_stat'); + vects=zeros(1,length(data.sData.data(:,1))); + vects(data.selected_vects)=1; + draw_vectors(vects,data.vector_h); + + if ~LOG + data=get(gcf,'UserData'); + data.LOG{length(data.LOG)+1}=... + sprintf('preprocess(''vector_bdf'',''key %s'');',key); + %string is 'key'+2spaces+%s + set(gcf,'UserData',data); + end + return; +end + +switch arg + case 'down' + set(gcf,'WindowButtonUpFcn','preprocess(''vector_bdf'',''up '')'); + set(gcf,'WindowButtonMotionFcn','preprocess(''vector_bdf'',''drag'')'); + switch get(gcf,'SelectionType') + case 'normal' + data.lims1=round(getfield(get(gca,'CurrentPoint'),{1,1})); + data.lims2=[]; + case 'alt' + tmp=round(getfield(get(gca,'CurrentPoint'),{1,1})); + if isempty(get(gca,'UserData')) + data.lims1=tmp; + data.lims2=[]; + else + data.lims1=cat(2,getfield(get(gca,'UserData'),'lims1'),tmp); + data.lims2=getfield(get(gca,'UserData'),'lims2'); + end + end + coords=get(gca,'CurrentPoint'); + h=line([coords(1),coords(1)],get(gca,'YLim'),'EraseMode','xor'); + set(h,'Color','red'); + h2=line([coords(1),coords(1)],get(gca,'YLim'),'EraseMode','xor'); + set(h2,'Color','red'); + data.h=h; + data.h2=h2; + set(gca,'UserData',data); + + case 'drag' + coords=get(gca,'CurrentPoint'); + lim=get(gca,'XLim'); + h2=getfield(get(gca,'UserData'),'h2'); + if lim(1) >= coords(1) + set(h2,'XData',[lim(1) lim(1)]); + elseif lim(2) <= coords(2) + set(h2,'XData',[lim(2) lim(2)]); + else + set(h2,'XData',[coords(1) coords(1)]); + end + case 'up ' % string is 'up' + 2 spaces!!! + set(gcf,'WindowButtonUpFcn',''); + set(gcf,'WindowButtonMotionFcn',''); + if ~LOG + data=get(gca,'UserData'); + delete(data.h); + delete(data.h2); + tmp=round(getfield(get(gca,'CurrentPoint'),{1,1})); + data.lims2=cat(2,data.lims2,tmp); + tmp_data=sort(cat(1,data.lims1,data.lims2)); + high=getfield(get(gca,'XLim'),{2})-1; + vectors=zeros(1,high); + tmp_data(find(tmp_data<1))=1; + tmp_data(find(tmp_data>high))=high; + + for i=1:getfield(size(tmp_data),{2}) + vectors(tmp_data(1,i):tmp_data(2,i))=1; + end + selected_vects=find(vectors); + else + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + len=size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data')); + vectors=zeros(1,len(1)); + i=1; + while i <= length(arg2) & (isspace(arg2(i)) | ~isletter(arg2(i))) + i=i+1; + end + arg3=arg2(i:length(arg2)); + selected_vects=str2num(arg2(1:i-1)); + if ~isempty(arg3) & ~all(isspace(arg3)) + selected_vects=unique(cat(2,selected_vects,... + getfield(get(pre_h,'UserData'),'selected_vects'))); + end + vectors(selected_vects)=1; + set(pre_h,'CurrentAxes',getfield(get(pre_h,'UserData'),'vector_h')); + set(0,'CurrentFigure',pre_h); + end + draw_vectors(vectors,gca); + sData=getfield(get(gcf,'UserData'),'sData'); + h=getfield(get(gcf,'UserData'),'vect_mean_h'); + vect_means(sData,h,selected_vects); + if ~LOG + set(gca,'UserData',data); + end + data=get(gcf,'UserData'); + data.undo.sData=data.sData; + data.undo.selected=data.selected_vects; + data.selected_vects=selected_vects; + if ~LOG + data.LOG{length(data.LOG)+1}='% Vector selection by using the mouse...'; + tmp=sprintf('preprocess(''vector_bdf'',''up %s'');',... + num2str(data.selected_vects)); + if length(tmp) > 500 + tmp=textwrap({tmp},500); + data.LOG{length(data.LOG)+1}=cat(2,tmp{1},''');'); + for i=2:length(tmp)-1 + data.LOG{length(data.LOG)+1}=... + cat(2,sprintf('preprocess(''vector_bdf'',''up %s',... + tmp{i}),'add'');'); + end + data.LOG{length(data.LOG)+1}=... + cat(2,sprintf('preprocess(''vector_bdf'',''up %s',... + tmp{length(tmp)}(1:length(tmp{length(tmp)})-3)),' add'');'); + else + data.LOG{length(data.LOG)+1}=tmp; + end + end + set(gcf,'UserData',data); + cplot_mimema; + sel_comp; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function sel_button(varargin) + +%SEL_BUTTON A Callback function. It performs the operations needed +% when vector components are selected. +% +% See also SEL_COMP +% + +if nargin == 1 + LOG=1; + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + string=getfield(get(pre_h,'UserData'),'comp_names_h'); + string=getfield(get(string,'String'),{str2num(varargin{1})}); + set(0,'CurrentFigure',pre_h); +else + LOG=0; + val=get(getfield(get(gcf,'UserData'),'comp_names_h'),'Value'); +end + +sel_button_h=getfield(get(gcf,'UserData'),'sel_button_h'); +sel_comps_h=getfield(get(gcf,'UserData'),'sel_comps_h'); +comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h'); +if ~LOG + string=getfield(get(comp_names_h,'String'),{get(comp_names_h,'Value')}); +end +tmp_string=get(sel_comps_h,'String'); + +if iscell(tmp_string) + + for i=1:length(string) + if ~any(strcmp(string{i},tmp_string)) + tmp_string=cat(1,tmp_string,string(i)); + end + end + string=tmp_string; +end + +set(sel_comps_h,'String',string); +set(comp_names_h,'Value',[]); +sel_comp; +if ~LOG + data=get(gcf,'UserData'); + data.LOG{length(data.LOG)+1}='% Select components'; + data.LOG{length(data.LOG)+1}=sprintf('preprocess(''sel_button'',''%s'');',... + num2str(val)); + set(gcf,'UserData',data); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function clear_button(varargin) + +%CLEAR_BUTTON Function callback evaluated when a 'Clear'-button is +% pressed. It removes texts from the 'selected components' +% -window and the 'selected component data' -window and +% clears the 'histogram' -axis. +% +% + +if nargin==1 + LOG=1; + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + set(0,'CurrentFigure',pre_h); +else + LOG=0; +end + +sel_comp_h=getfield(get(gcf,'UserData'),'sel_comp_h'); +sel_cdata_h=getfield(get(gcf,'UserData'),'sel_cdata_h'); +sel_cplot_h=getfield(get(gcf,'UserData'),'sel_cplot_h'); +sel_chist_h=getfield(get(gcf,'UserData'),'sel_chist_h'); +vector_h=getfield(get(gcf,'UserData'),'vector_h'); + +set(sel_comp_h,'Value',1); +set(sel_cdata_h,'String',' '); +subplot(sel_chist_h); +hold off; +cla; + +selected=getfield(get(gcf,'UserData'),'selected_vects'); +dims=size(getfield(getfield(get(gcf,'UserData'),'sData'),'data')); +vectors=zeros(1,dims(1)); +vectors(selected)=1; +subplot(vector_h); +draw_vectors(vectors,vector_h); +if ~LOG + data=get(gcf,'UserData'); + data.LOG{length(data.LOG)+1}='% Remove components from the selected list.'; + data.LOG{length(data.LOG)+1}='preprocess(''clear_button'',''foo'');'; + set(gcf,'UserData',data); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function sel_comp(varargin) + +%SEL_COMP performs the operations needed when vector components are +% chosen. It writes maxima, minima, mean and standard deviation +% of the chosen component to a text box window and draws a +% histogram of the chosen component of selected vectors' +% +% + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +set(0,'CurrentFigure',pre_h); +sel_comp_h=getfield(get(pre_h,'UserData'),'sel_comp_h'); + +if nargin == 1 + set(sel_comp_h,'Value',str2num(varargin{1})); +elseif ~isempty(gcbo) + no=get(sel_comp_h,'Value'); + data=get(gcf,'UserData'); + data.LOG{length(data.LOG)+1}='% Select one component'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''sel_comp'',''',... + num2str(no),''');'); + set(gcf,'UserData',data); +end + +pro_tools('c_stat'); +pro_tools('plot_hist'); +data=get(gcf,'UserData'); +sData=data.sData; +vector_h=data.vector_h; +len=length(sData.data(:,1)); +vects=zeros(1,len); +vects(data.selected_vects)=1; +draw_vectors(vects,vector_h); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function cplot_mimema + +global no_of_sc + +sData=getfield(get(gcf,'UserData'),'sData'); +sel_cplot_h=getfield(get(gcf,'UserData'),'sel_cplot_h'); +selected=getfield(get(gcf,'UserData'),'selected_vects'); + +set(sel_cplot_h,'YLim',[0 length(sData.data(1,:))+1]); + +subplot(sel_cplot_h); +hold off; +cla; +hold on; + +for i=1:length(sData.data(1,:)) + Max=max(sData.data(:,i)); + Min=min(sData.data(:,i)); + tmp=sData.data(selected,i); + + selMax=max(tmp); + selMin=min(tmp); + Mean=abs(mean(tmp(find(~isnan(tmp))))); + Median=abs(median(tmp(find(~isnan(tmp))))); + + if Max ~= Min & ~all(isnan(sData.data(:,i))) + + if rem(i,no_of_sc) % no_of_sc is defined in the beginning of this file... + + line([abs(selMin-Min)/(Max-Min) (selMax-Min)/(Max-Min)],... + [i i],'Color','black'); + plot(abs(Mean-Min)/(Max-Min),i,'oblack'); + plot(abs(Median-Min)/(Max-Min),i,'xblack'); + else + line([abs(selMin-Min)/(Max-Min) (selMax-Min)/(Max-Min)],... + [i i],'Color','black','LineWidth',2); + plot(abs(Mean-Min)/(Max-Min),i,'oblack','LineWidth',2); + plot(abs(Median-Min)/(Max-Min),i,'xblack','LineWidth',2); + end + else + + if rem(i,no_of_sc) % N is defined in the beginning of this file. + + plot(mean(get(gca,'XLim')),i,'oblack'); + plot(mean(get(gca,'XLim')),i,'xblack'); + else + plot(mean(get(gca,'XLim')),i,'oblack','LineWidth',2); + plot(mean(get(gca,'XLim')),i,'xblack','LineWidth',2); + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +function bool=set_sD_stats + +%SET_SD_STATS Writes the data set names to popup menu. +% +% + +bool=1; +data=get(gcf,'UserData'); + +for i=1:length(data.sD_set) + % if ~isvalid_var_name({data.sD_set(i).name}) + % close_preprocess; + % bool=0; + % return; + % end + string{i}=cat(2,sprintf('#%d: ',i),data.sD_set(i).name); +end + +set(data.sD_set_h,'String',string); +data.sData=data.sD_set(get(data.sD_set_h,'Value')); +data.sData.MODIFIED=0; +data.sData.INDEX=1; +set(gcf,'UserData',data); +write_sD_stats; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function write_sD_stats + +%WRITE_SD_STATS writes data's name, length and dimension to text box. +% +% + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + + +sD_name_h=getfield(get(pre_h,'UserData'),'sD_name_h'); +sData=getfield(get(pre_h,'UserData'),'sData'); +dims=size(sData.data); +string{1}=cat(2,'Name: ',sData.name); +string{2}=cat(2,'Length: ',sprintf('%d',dims(1))); +string{3}=cat(2,'Dim: ',sprintf('%d',dims(2))); + +set(sD_name_h,'String',string); +set(sD_name_h,'HorizontalAlignment','left'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function sel_sD(varargin) + +%SEL_SD sets new data to UserData's 'sData'. +% +% + +if nargin==1 + LOG=1; + index=str2num(varargin{1}); + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + set(0,'CurrentFigure',pre_h); +else + LOG=0; +end + +sD_set_h=getfield(get(gcf,'UserData'),'sD_set_h'); +comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h'); +vector_h=getfield(get(gcf,'UserData'),'vector_h'); +vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h'); + +if ~LOG + index=get(sD_set_h,'Value'); +end +data=get(gcf,'UserData'); +data.undo = []; +INDEX=data.sData.INDEX; +data.sData=rmfield(data.sData,'MODIFIED'); +data.sData=rmfield(data.sData,'INDEX'); + +tmp=data.sD_set(index); +tmp.MODIFIED=0; +tmp.INDEX=index; +data.sD_set(INDEX)=data.sData; +data.sData=tmp; + +len=getfield(size(tmp.data),{1}); + +data.selected_vects=find(ones(1,len)); +if ~LOG + data.LOG{length(data.LOG)+1}='% Select a new data set.'; + data.LOG{length(data.LOG)+1}=sprintf('preprocess(''sel_sD'',''%s'');',... + num2str(index)); +end +set(gcf,'UserData',data); +write_sD_stats; +set_compnames(tmp,comp_names_h); +draw_vectors(ones(1,len),vector_h); +vect_means(tmp,vect_mean_h,data.selected_vects); +clear_button; +sel_comp; +cplot_mimema; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function indices=get_indices + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h'); +indices = get(comp_names_h,'Value'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function sD_management(varargin) + +if nargin ~= 1 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + preh_udata=get(pre_h,'UserData'); + preh_udata.LOG{length(preh_udata.LOG)+1}=... + '% Starting the ''Data Set Management'' -window...'; + preh_udata.LOG{length(preh_udata.LOG)+1}=... + 'preprocess(''sD_management'',''foo'');'; + set(pre_h,'UserData',preh_udata); +end + +man_h=findobj(get(0,'Children'),'Tag','Management'); +if ~isempty(man_h) + figure(man_h); + return; +end + +h0 = figure('BackingStore','off', ... + 'Color',[0.8 0.8 0.8], ... + 'Name','Data Set Management', ... + 'PaperPosition',[18 180 576 432], ... + 'PaperUnits','points', ... + 'Position',[753 523 324 470], ... + 'RendererMode','manual', ... + 'Tag','Management'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Max',2, ... + 'Position',[0.02777777777777778 0.0723404255319149 0.7716049382716049 0.1914893617021277], ... + 'String',' ', ... + 'Style','edit', ... + 'Tag','EditText1'); + +data.new_c_name_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Callback','preprocess rename comp',... + 'Units','normalized', ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.8240740740740741 0.2106382978723404 0.154320987654321 0.05319148936170213], ... + 'String','RENAME', ... + 'Tag','Pushbutton1'); + +h1 = uicontrol('Parent',h0, ... + 'Callback','preprocess close_sD',... + 'Units','normalized', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.8240740740740741 0.01914893617021277 0.154320987654321 0.05319148936170213], ... + 'String','CLOSE', ... + 'Tag','Pushbutton2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.02777777777777778 0.2680851063829787 0.345679012345679 0.02978723404255319], ... + 'String','COMPONENTS:', ... + 'Style','text', ... + 'Tag','StaticText1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'HorizontalAlignment','left', ... + 'Position',[0.02777777777777778 0.3170212765957447 0.3549382716049382 0.5319148936170213], ... + 'String',' ', ... + 'Style','listbox', ... + 'Tag','Listbox1', ... + 'Value',1); + +data.sets_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'HorizontalAlignment','left', ... + 'Position',[0.6234567901234568 0.3170212765957447 0.3549382716049382 0.5319148936170213], ... + 'String',' ', ... + 'Style','listbox', ... + 'Tag','Listbox2', ... + 'Value',1); + + +data.variables_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Callback','preprocess export',... + 'Units','normalized', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.4259259259259259 0.551063829787234 0.154320987654321 0.0425531914893617], ... + 'String','->', ... + 'Tag','Pushbutton4'); + +h1 = uicontrol('Parent',h0, ... + 'Callback','preprocess import',... + 'Units','normalized', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.4259259259259259 0.625531914893617 0.154320987654321 0.0425531914893617], ... + 'String','<-', ... + 'Tag','Pushbutton3'); + + + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.02777777777777778 0.8531914893617022 0.2993827160493827 0.02978723404255319], ... + 'String','DATA SETS', ... + 'Style','text', ... + 'Tag','StaticText2'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.6234567901234568 0.8531914893617022 0.2561728395061728 0.02978723404255319], ... + 'String','WORKSPACE', ... + 'Style','text', ... + 'Tag','StaticText3'); + +h1 = uicontrol('Parent',h0, ... + 'Callback','preprocess rename set',... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1820987654320987 0.9127659574468086 0.7808641975308641 0.0425531914893617], ... + 'Style','edit', ... + 'Tag','EditText2'); + +data.new_name_h = h1; + + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.02777777777777778 0.9127659574468086 0.1388888888888889 0.02978723404255319], ... + 'String','NAME:', ... + 'Style','text', ... + 'Tag','StaticText4'); + + +ui_h=uimenu('Label','&Tools'); +uimenu(ui_h,'Label','Copy','Callback','preprocess copy_delete copy'); +uimenu(ui_h,'Label','Delete','Callback','preprocess copy_delete delete'); +uimenu(ui_h,'Label','Refresh','Callback','preprocess refresh'); + +set(gcf,'UserData',data); +set_var_names; +sD_names; +sD_stats; + + + +%%% Subfunction: set_var_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function set_var_names + + +variables_h=getfield(get(gcf,'UserData'),'variables_h'); +value=get(variables_h,'Value'); +len=evalin('base','length(who)'); + +names=cell(len,1); + +for i=1:len + string=cat(2,'getfield(who,{',num2str(i),'})'); + names(i)=evalin('base',string); +end + +set(variables_h,'String',names); +if(value > length(names)) + set(variables_h,'Value',1); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: sD_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function sD_names + +sets_h=getfield(get(gcf,'UserData'),'sets_h'); +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +sD_set = getfield(get(pre_h,'UserData'),'sD_set'); + +for i=1:length(sD_set) + names{i,1}=cat(2,sprintf('#%d: ',i),sD_set(i).name); +end + +set(sets_h,'String',names); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: sD_stats %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function sD_stats + +man_h=findobj(get(0,'Children'),'Tag','Management'); +c_names_h=getfield(get(man_h,'UserData'),'new_c_name_h'); +sD_name_h=getfield(get(man_h,'UserData'),'new_name_h'); +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +INDEX=getfield(getfield(get(pre_h,'UserData'),'sData'),'INDEX'); +MODIFIED=getfield(getfield(get(pre_h,'UserData'),'sData'),'MODIFIED'); +value=get(getfield(get(man_h,'UserData'),'sets_h'),'Value'); + +if value==INDEX + data=get(pre_h,'UserData'); + sData=rmfield(data.sData,[{'INDEX'};{'MODIFIED'}]); + data.sD_set(INDEX)=sData; + data.sData.MODIFIED=0; + set(pre_h,'UserData',data); +end + +sData=getfield(getfield(get(pre_h,'UserData'),'sD_set'),{value}); +string1=[{sData.name}]; + + +set(sD_name_h,'String',string1); +set(c_names_h,'String',sData.comp_names); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: import %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function import(varargin) + +if nargin==1 + LOG=1; + man_h=findobj(get(0,'Children'),'Tag','Management'); + set(0,'CurrentFigure',man_h); + name=varargin; +else + LOG=0; +end + +variables_h=getfield(get(gcf,'UserData'),'variables_h'); +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +if ~LOG + name=getfield(get(variables_h,'String'),{get(variables_h,'Value')}); +end +errstr='Data to be imported must be real matrix or ''som_data_struct''.'; +new_sD=evalin('base',name{1}); + +if isempty(pre_h) + errordlg('''Preprocess'' -figure does not exist. Terminating program...'); + close_preprocess; + return; +end + +if isstr(new_sD) | (~isstruct(new_sD) & ~isreal(new_sD)) + errordlg(errstr); + return; +elseif isstruct(new_sD) & length(new_sD) > 1 + errordlg(errstr) + return; +elseif ~isstruct(new_sD) + new_sD=som_data_struct(new_sD); + new_sD.name=name{1}; +end + +new_sD_names=fieldnames(new_sD); +right_names=fieldnames(som_data_struct(1)); +for i=1:length(new_sD_names) + if ~any(strcmp(new_sD_names(i),right_names)); + errordlg(errstr); + return; + end +end + +data=get(pre_h,'UserData'); +data.sD_set(length(data.sD_set) + 1)=new_sD; +if ~LOG + data.LOG{length(data.LOG)+1}='% Import a data set from the workspace.'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''import'',''',... + name{1},''');'); +end +set(pre_h,'UserData',data); +sD_names; +sD_stats; +old =gcf; +set(0,'CurrentFigure',pre_h); +set_sD_stats; +set(0,'CurrentFigure',old); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: export %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function export(varargin) + +if nargin == 1 + LOG=1; + man_h=findobj(get(0,'Children'),'Tag','Management'); + set(0,'CurrentFigure',man_h); + index=str2num(varargin{1}); +else + LOG=0; +end + +sets_h=getfield(get(gcf,'UserData'),'sets_h'); +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +if ~LOG + index=get(sets_h,'Value'); +end + +if isempty(pre_h) + errordlg('''Preprocess''-figure does not exist. Terminating program...'); + close(findobj(get(0,'Children'),'Tag','Management')); + close(findobj(get(0,'Children'),'Tag','PlotWin')); + return; +end + +sData=getfield(getfield(get(pre_h,'UserData'),'sD_set'),{index}); + +if ~isvalid_var_name({sData.name}) + return; +end + +assignin('base',sData.name,sData); +disp(sprintf('Data set ''%s'' is set to the workspace.',sData.name)); +if ~LOG + data=get(pre_h,'UserData'); + data.LOG{length(data.LOG)+1}='% Export a data set to the workspace.'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''export'',''',... + num2str(index),''');'); + set(pre_h,'UserData',data); +end +set_var_names; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Subfunction: rename %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function rename(arg) + +i=1; +while i <= length(arg) & arg(i) ~= ' ' + i=i+1; +end + +arg2=arg(i+1:length(arg)); +arg=arg(1:i-1); +if ~isempty(arg2) + LOG=1; + i=1; + if arg2(1) ~= '{' + while i <= length(arg2) & arg2(i) ~= ' ' + i=i+1; + end + index=str2num(arg2(i+1:length(arg2))); + arg2=arg2(1:i-1); + else + while i <= length(arg2) & arg2(i) ~= '}' + i=i+1; + end + index=str2num(arg2(i+1:length(arg2))); + arg2=arg2(1:i); + end +else + LOG=0; +end + +new_name_h=getfield(get(gcf,'UserData'),'new_name_h'); +new_c_name_h=getfield(get(gcf,'UserData'),'new_c_name_h'); +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +if isempty(pre_h) + errordlg('''Preprocess'' -figure does not exist. Terminating program...'); + close_preprocess; + return; +end + +switch arg + case 'set' + if LOG + name={arg2}; + else + name=get(new_name_h,'String'); + end + if ~isempty(name{1}) & ~any(isspace(name{1})) + if ~isvalid_var_name(name) + sD_stats; + return; + end + if ~LOG + index=get(getfield(get(gcf,'UserData'),'sets_h'),'Value'); + end + data=get(pre_h,'UserData'); + tmp_set.name=name{1}; + data.sD_set(index).name=name{1}; + if data.sData.INDEX == index + data.sData.name=name{1}; + end + if ~LOG + data.LOG{length(data.LOG)+1}='% Rename a data set.'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''rename'',''set ',... + name{1},' ',... + num2str(index),... + ''');'); + end + + set(pre_h,'UserData',data); + sD_names; + string=get(data.sD_set_h,'String'); + string{index}=cat(2,sprintf('#%d: ',index),name{1}); + set(data.sD_set_h,'String',string); + string=get(data.sD_name_h,'String'); + string{1}=cat(2,'Name: ',name{1}); + if index==data.sData.INDEX + set(data.sD_name_h,'String',string); + end + else + sD_stats; + end + case 'comp' + if ~LOG + names=get(new_c_name_h,'String'); + index=get(getfield(get(gcf,'UserData'),'sets_h'),'Value'); + else + names=eval(arg2); + end + if check_cell_names(names) + data=get(pre_h,'UserData'); + sData=data.sD_set(index); + if length(sData.comp_names)==length(names) + data.sD_set(index).comp_names=names; + if index == data.sData.INDEX + for i=1:length(names) + names{i}=cat(2,sprintf('#%d: ',i),names{i}); + end + set(data.comp_names_h,'String',names); + set(data.sel_comp_h,'String',names); + end + if ~LOG + data.LOG{length(data.LOG)+1}='% Rename components.'; + str='preprocess(''rename'',''comp {'; + for i=1:length(names)-1 + str=cat(2,str,'''''',names{i},''''','); + end + str=cat(2,str,'''''',names{length(names)},'''''} ',... + num2str(index),''');'); + data.LOG{length(data.LOG)+1}=str; + else + set(new_c_name_h,'String',names); + end + set(pre_h,'UserData',data); + else + errordlg('There are less components in data.'); + sD_stats; + return; + end + else + sD_stats; + end +end + +%%% Subfunction: check_cell_names %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function bool=check_cell_names(names) + +bool = 1; + +if isempty(names) + bool= 0; + return; +end +for i=1:length(names) + if isempty(names{i}) | isspace(names{i}) + bool = 0; + return; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: isvalid_var_name %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function bool=isvalid_var_name(name) + +bool=1; + +tmp=name{1}; +if ~((tmp(1)>='a' & tmp(1)<='z') | (tmp(1)>='A' & tmp(1)<='Z')) + errordlg('Invalid name.'); + bool=0; + return; +end +for j=1:length(tmp) + if ~((tmp(j)>='a' & tmp(j)<='z') | ... + (tmp(j)>='A' & tmp(j)<='Z') | ... + (j>1 & tmp(j) == '_') | ... + (tmp(j)>='0' & tmp(j) <= '9')) | tmp(j) == '.' + errordlg('Invalid name.'); + bool=0; + return; + end + if j == length(tmp) & tmp(j) == '_' + errordlg('Invalid name.'); + bool=0; + return; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: copy_delete %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function copy_delete(arg) + +i=1; +while i <= length(arg) & arg(i) ~= ' ' + i=i+1; +end + +arg2=arg(i+1:length(arg)); +arg=arg(1:i-1); + +if ~isempty(arg2) + index=str2num(arg2); + LOG=1; +else + LOG=0; +end + +sets_h=getfield(get(gcf,'UserData'),'sets_h'); +if ~LOG + index=get(sets_h,'Value'); +end +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +if isempty(pre_h) + errordlg('''Preprocess'' -figure does not exist. Terminating program.'); + close_preprocess; + return; +end + +switch arg + case 'copy' + data=get(pre_h,'UserData'); + data.sD_set(length(data.sD_set)+1)=data.sD_set(index); + if ~LOG + data.LOG{length(data.LOG)+1}='% Copy a data set.'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''copy_delete'',''',... + 'copy ',num2str(index),''');'); + end + set(pre_h,'UserData',data); + sD_names; + old=gcf; + set(0,'CurrentFigure',pre_h); + set_sD_stats; + set(0,'CurrentFigure',old); + case 'delete' + if length(get(sets_h,'String')) == 1 + msgbox('No data left. Closing program...') + close_preprocess; + return; + end + data=get(pre_h,'UserData'); + if ~isempty(data.undo) & any(strcmp('index',fieldnames(data.undo))) + if data.undo.index > index + data.undo.index = data.undo.index-1; + elseif data.undo.index==index; + data.undo=[]; + end + end + set1=data.sD_set(1:index-1); + set2=data.sD_set(index+1:length(data.sD_set)); + + if ~isempty(set1) + data.sD_set=[set1 set2]; + else + data.sD_set=set2; + end + if ~LOG + data.LOG{length(data.LOG)+1}='% Delete a data set.'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''copy_delete'',''',... + 'delete ',num2str(index),''');'); + end + set(pre_h,'UserData',data); + + set(sets_h,'Value',1); + sD_names; + sD_stats; + old = gcf; + set(0,'CurrentFigure',pre_h); + + for i=1:length(data.sD_set) + string{i}=cat(2,sprintf('#%d: ',i),data.sD_set(i).name); + end + + set(data.sD_set_h,'String',string); + data.sData=data.sD_set(get(data.sD_set_h,'Value')); + data.sData.MODIFIED=0; + data.sData.INDEX=1; + set(gcf,'UserData',data); + write_sD_stats; + + sData=getfield(get(gcf,'UserData'),'sData'); + if sData.INDEX > index + value=get(getfield(get(gcf,'UserData'),'sD_set_h'),'Value'); + set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',value-1); + sData.INDEX = sData.INDEX -1; + elseif sData.INDEX == index + set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',1); + end + + sel_sD; + set(0,'CurrentFigure',old); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function clipping(varargin) + +if nargin ~= 1 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + preh_udata=get(pre_h,'UserData'); + preh_udata.LOG{length(preh_udata.LOG)+1}=... + '% Starting the ''Clipping'' -window...'; + preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''clipping'',''foo'');'; + set(pre_h,'UserData',preh_udata); +end + +clip_h=findobj(get(0,'Children'),'Tag','Clipping'); + +if ~isempty(clip_h) + figure(clip_h); + return; +end + +h0 = figure('Color',[0.8 0.8 0.8], ... + 'PaperPosition',[18 180 575 432], ... + 'PaperUnits','points', ... + 'Position',[718 389 300 249], ... + 'Tag','Clipping'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.03 0.03614457831325301 0.4666666666666667 0.9236947791164658], ... + 'Style','frame', ... + 'Tag','Frame1'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.05333333333333334 0.5983935742971887 0.42 0.3333333333333333], ... + 'Style','frame', ... + 'Tag','Frame2'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Style','frame', ... + 'Position',[0.05333333333333334 0.33 0.42 0.24], ... + 'Tag','Frame3'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Style','frame', ... + 'Position',[0.05333333333333334 0.06 0.42 0.24],... + 'Tag','Frame4'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.5133333333333334 0.6385542168674698 0.4666666666666667 0.321285140562249], ... + 'Style','frame', ... + 'Tag','Frame5'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.5366666666666667 0.6666666666666666 0.42 0.2650602409638554], ... + 'Style','frame', ... + 'Tag','Frame6'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.31 0.823293172690763 0.15 0.09638554216867469], ... + 'Style','edit', ... + 'Tag','EditText1'); + +data.big_val_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.31 0.7148594377510039 0.15 0.09638554216867469], ... + 'Style','edit', ... + 'Tag','EditText2'); + +data.small_val_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.31 0.606425702811245 0.15 0.09638554216867469], ... + 'Style','edit', ... + 'Tag','EditText3'); + +data.equal_val_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.06000000000000001 0.8473895582329316 0.22 0.05622489959839357], ... + 'String','Bigger than', ... + 'Style','text', ... + 'Tag','StaticText1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.06000000000000001 0.7389558232931727 0.24 0.04819277108433735], ... + 'String','Smaller than', ... + 'Style','text', ... + 'Tag','StaticText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.06000000000000001 0.610441767068273 0.22 0.07228915662650602], ... + 'String','Equal to', ... + 'Style','text', ... + 'Tag','StaticText3'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.07000000000000001 0.465863453815261 0.06333333333333334 0.07228915662650602], ... + 'Style','radiobutton', ... + 'Value',1,... + 'Tag','Radiobutton1'); + +data.and_button_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.07000000000000001 0.3734939759036144 0.06333333333333334 0.07228915662650602], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton2'); + +data.or_button_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.1466666666666667 0.45 0.2333333333333333 0.07228915662650602], ... + 'String','AND', ... + 'Style','text', ... + 'Tag','StaticText4'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'String','OR', ... + 'Position',[0.1466666666666667 0.35 0.2333333333333333 0.07228915662650602], ... + 'Style','text', ... + 'Tag','StaticText5'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.07000000000000001 0.1967871485943775 0.06333333333333334 0.07228915662650602], ... + 'Style','radiobutton', ... + 'Value',1,... + 'Tag','Radiobutton3'); + +data.all_button_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.07000000000000001 0.09236947791164658 0.06333333333333334 0.07228915662650602], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton4'); + +data.sel_vects_button_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1466666666666667 0.1927710843373494 0.2333333333333333 0.07228915662650602], ... + 'String','All vectors', ... + 'Style','text', ... + 'Tag','StaticText6'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1466666666666667 0.09638554216867469 0.3133333333333334 0.05622489959839357], ... + 'String','Among selected', ... + 'Style','text', ... + 'Tag','StaticText7'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.7866666666666667 0.823293172690763 0.1366666666666667 0.09236947791164658], ... + 'Style','edit', ... + 'Tag','EditText4'); + +data.replace_val_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',6, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.5633333333333334 0.8273092369477911 0.2066666666666667 0.07630522088353413], ... + 'String','Replace', ... + 'Style','text', ... + 'Tag','StaticText8'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.5700000000000001 0.6827309236947791 0.3566666666666667 0.08032128514056225], ... + 'String','Replace', ... + 'Tag','Pushbutton1'); + +data.OK_button_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess close_c',... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.6633333333333333 0.07228915662650602 0.2833333333333333 0.09638554216867469], ... + 'String','Close', ... + 'Tag','Pushbutton2'); + + +data.state.and=1; +data.state.all=1; +data.state.big=[]; +data.state.small=[]; +data.state.equal=[]; +data.state.replace=[]; + +set(data.or_button_h,'Callback','preprocess and_or_cb or'); +set(data.and_button_h,'Callback','preprocess and_or_cb and'); +set(data.and_button_h,'Value',1); +set(data.all_button_h,'Callback','preprocess all_sel_cb all'); +set(data.sel_vects_button_h,'Callback','preprocess all_sel_cb sel'); +set(data.big_val_h,'Callback','preprocess set_state_vals big'); +set(data.small_val_h,'Callback','preprocess set_state_vals small'); +set(data.equal_val_h,'Callback','preprocess set_state_vals equal'); +set(data.replace_val_h,'Callback','preprocess set_state_vals replace'); +set(data.OK_button_h,'Callback','preprocess clip_data clip'); +set(h0,'UserData',data); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function select(varargin) + +if nargin ~= 1 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + preh_udata=get(pre_h,'UserData'); + preh_udata.LOG{length(preh_udata.LOG)+1}=... + '% Starting the ''Select'' -window...'; + preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''select'',''foo'');'; + set(pre_h,'UserData',preh_udata); +end + +sel_h=findobj(get(0,'Children'),'Tag','Select'); + +if ~isempty(sel_h) + figure(sel_h); + return; +end + +h0 = figure('Color',[0.8 0.8 0.8], ... + 'PaperPosition',[18 180 576 432], ... + 'PaperUnits','points', ... + 'Position',[750 431 168 365], ... + 'Tag','Select'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.05357142857142857 0.2712328767123288 0.8333333333333333 0.6301369863013698], ... + 'Style','frame', ... + 'Tag','Frame1'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.05357142857142857 0.1041095890410959 0.8333333333333333 0.1397260273972603], ... + 'Style','frame', ... + 'Tag','Frame2'); + +h1 = uicontrol('Parent',h0,... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.6547945205479452 0.75 0.2273972602739726], ... + 'Style','frame', ... + 'Tag','Frame3'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.4794520547945206 0.75 0.1506849315068493], ... + 'Style','frame', ... + 'Tag','Frame4'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.2986301369863014 0.75 0.1506849315068493], ... + 'Style','frame', ... + 'Tag','Frame5'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.5535714285714285 0.8082191780821918 0.2678571428571429 0.06575342465753425], ... + 'Style','edit', ... + 'Tag','EditText1'); + +data.big_val_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.5535714285714285 0.7342465753424657 0.2678571428571429 0.06575342465753425], ... + 'Style','edit', ... + 'Tag','EditText2'); + +data.small_val_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.5535714285714285 0.6602739726027397 0.2678571428571429 0.06575342465753425], ... + 'Style','edit', ... + 'Tag','EditText3'); + +data.equal_val_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Units','normalized', ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1071 0.8247 0.3929 0.0384], ... + 'String','Bigger than', ... + 'Style','text', ... + 'Tag','StaticText1'); + +h1 = uicontrol('Parent',h0, ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Units','normalized', ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1071 0.7507 0.4286 0.0329], ... + 'String','Smaller than', ... + 'Style','text', ... + 'Tag','StaticText2'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1071 0.6630 0.3929 0.0493], ... + 'String','Equal to', ... + 'Style','text', ... + 'Tag','StaticText3'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.125 0.5643835616438356 0.1130952380952381 0.04931506849315068], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton1'); + +data.and_button_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.125 0.5013698630136987 0.1130952380952381 0.04931506849315068], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton2'); + +data.or_button_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.2619047619047619 0.5561643835616439 0.3809523809523809 0.05205479452054795], ... + 'String','AND', ... + 'Style','text', ... + 'Tag','StaticText4'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.2619047619047619 0.4986301369863014 0.3809523809523809 0.04657534246575343], ... + 'String','OR', ... + 'Style','text', ... + 'Tag','StaticText5'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.125 0.3808219178082192 0.1130952380952381 0.04931506849315068], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton3'); + +data.all_button_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.125 0.3095890410958904 0.1130952380952381 0.04931506849315068], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton4'); + +data.sel_vects_button_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.2619047619047619 0.3780821917808219 0.4166666666666666 0.04931506849315068], ... + 'String','All vectors', ... + 'Style','text', ... + 'Tag','StaticText6'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.2619047619047619 0.3123287671232877 0.5595238095238095 0.03835616438356165], ... + 'String','Among selected', ... + 'Style','text', ... + 'Tag','StaticText7'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.0952 0.1178 0.7500 0.1068], ... + 'Style','frame', ... + 'Tag','Frame6'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.5298 0.1342 0.2738 0.0712], ... + 'Style','edit', ... + 'Tag','EditText4'); + +data.replace_val_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',8,... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1369047619047619 0.136986301369863 0.3214285714285714 0.06027397260273973], ... + 'String','Vectors', ... + 'Style','text', ... + 'Tag','StaticText8'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.05357142857142857 0.01917808219178082 0.3869047619047619 0.0684931506849315], ... + 'String','OK', ... + 'Tag','Pushbutton1'); + +data.OK_button_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'Callback','preprocess close_s',... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.5 0.01917808219178082 0.3869047619047619 0.0684931506849315], ... + 'String','Close', ... + 'Tag','Pushbutton2'); + + + +data.state.and=1; +data.state.all=1; +data.state.big=[]; +data.state.small=[]; +data.state.equal=[]; +data.state.replace=[]; + +set(data.or_button_h,'Callback','preprocess and_or_cb or'); +set(data.and_button_h,'Callback','preprocess and_or_cb and'); +set(data.and_button_h,'Value',1); +set(data.all_button_h,'Callback','preprocess all_sel_cb all'); +set(data.sel_vects_button_h,'Callback','preprocess all_sel_cb sel'); +set(data.big_val_h,'Callback','preprocess set_state_vals big'); +set(data.small_val_h,'Callback','preprocess set_state_vals small'); +set(data.equal_val_h,'Callback','preprocess set_state_vals equal'); +set(data.replace_val_h,'Callback','preprocess set_state_vals replace'); +set(data.OK_button_h,'Callback','preprocess clip_data sel'); +set(h0,'UserData',data); + + + +%%% Subfunction: and_or_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function and_or_cb(arg) + +%AND_OR_CB A callback function. Checks that only one of the radiobox +% buttons 'AND' and 'OR' is pressed down. +% +% + +and_button_h=getfield(get(gcf,'UserData'),'and_button_h'); +or_button_h=getfield(get(gcf,'UserData'),'or_button_h'); +data=get(gcf,'UserData'); + +switch arg + case 'or' + set(and_button_h,'Value',0); + set(or_button_h,'Value',1); + data.state.and=0; + case 'and' + set(or_button_h,'Value',0); + set(and_button_h,'Value',1); + data.state.and=1; +end + +set(gcf,'UserData',data); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: all_sel_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function all_sel_cb(arg) + +all_button_h=getfield(get(gcf,'UserData'),'all_button_h'); +sel_vects_button_h=getfield(get(gcf,'UserData'),'sel_vects_button_h'); +data=get(gcf,'UserData'); + +switch arg + case 'all' + set(sel_vects_button_h,'Value',0); + set(all_button_h,'Value',1); + data.state.all=1; + case 'sel' + set(all_button_h,'Value',0); + set(sel_vects_button_h,'Value',1); + data.state.all=0; +end + +set(gcf,'UserData',data); + +%%% Subfunction: set_state_vals %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function set_state_vals(arg) + +%SET_STATE_VALS sets the values to the UserData's state-struct. +% +% + + +data=get(gcf,'UserData'); + +switch arg + case 'big' + big_val_h=getfield(get(gcf,'UserData'),'big_val_h'); + val =str2num(get(big_val_h,'String')); + dims=size(val); + if dims(1) ~= 1 | dims(2) ~= 1 + errordlg('Argument of the operation must be scalar.'); + set(big_val_h,'String',''); + return; + end + if isreal(val) + data.state.big=val; + else + errordlg('Limits of the operation must be real.'); + set(big_val_h,'String',''); + return; + end + case 'small' + small_val_h=getfield(get(gcf,'UserData'),'small_val_h'); + val=str2num(get(small_val_h,'String')); + dims=size(val); + if dims(1) ~= 1 | dims(2) ~= 1 + errordlg('Argument of the operation must be scalar.') + set(small_val_h,'String',''); + return; + end + if isreal(val) + data.state.small=val; + else + errordlg('Limits of the operation must be real.'); + set(small_val_h,'String',''); + return; + end + case 'equal' + equal_val_h=getfield(get(gcf,'UserData'),'equal_val_h'); + val = str2num(get(equal_val_h,'String')); + dims=size(val); + if dims(1) ~= 1 | dims(2) ~= 1 + errordlg('Argument of the operation must be scalar.'); + set(equal_val_h,'String',''); + return; + end + if isreal(val) + data.state.equal=val; + else + errordlg('Limits of the operation must be real.'); + set(equal_val_h,'String',''); + return; + end + case 'replace' + replace_val_h=getfield(get(gcf,'UserData'),'replace_val_h'); + val=str2num(get(replace_val_h,'String')); + dims=size(val); + if (dims(1) ~= 1 | dims(2) ~= 1) & ~strcmp(get(gcf,'Tag'),'Select') + errordlg('Argument of the operation must be scalar.'); + set(replace_val_h,'String',''); + return; + end + if isreal(val) + data.state.replace=val; + else + errordlg('Limits of the operation must be real.'); + set(replace_val_h,'String',''); + return; + end +end + +set(gcf,'UserData',data); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: clip_data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function clip_data(arg) + +%CLIP_DATA A callback function. Filters the data. +% +% + +i=1; +while i <= length(arg) & arg(i) ~= ' ' + i=i+1; +end + +arg2=arg(i+1:length(arg)); +arg=arg(1:i-1); + +if ~isempty(arg2) + LOG=1; + if strcmp(arg,'sel') + c_h=findobj(get(0,'Children'),'Tag','Select'); + else + c_h=findobj(get(0,'Children'),'Tag','Clipping'); + end + set(0,'CurrentFigure',c_h); + i=1; + while i <= length(arg2) & arg2(i) ~= ' ' + i=i+1; + end + BT=str2num(arg2(1:i-1)); + i=i+1; + j=i; + while i <= length(arg2) & arg2(i) ~= ' ' + i=i+1; + end + ST=str2num(arg2(j:i-1)); + i=i+1; + j=i; + while i <= length(arg2) & arg2(i) ~= ' ' + i=i+1; + end + EQ=str2num(arg2(j:i-1)); + i=i+1; + j=i; + while i <= length(arg2) & arg2(i) ~= ' ' + i=i+1; + end + AND_OR=str2num(arg2(j:i-1)); + i=i+1; + j=i; + while i <= length(arg2) & arg2(i) ~= ' ' + i=i+1; + end + ALL_AMONG=str2num(arg2(j:i-1)); + i=i+1; + j=i; + while i <= length(arg2) + i=i+1; + end + VECT_REPL=str2num(arg2(j:i-1)); +else + LOG=0; +end + +if ~LOG + big_val_h=getfield(get(gcf,'UserData'),'big_val_h'); + small_val_h=getfield(get(gcf,'UserData'),'small_val_h'); + equal_val_h=getfield(get(gcf,'UserData'),'equal_val_h'); + replace_val_h=getfield(get(gcf,'UserData'),'replace_val_h'); +end + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +if isempty(pre_h) + errordlg('''Preprocess'' -figure does not exist. Terminating program...'); + pro_tools('close'); + return; +end + +comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h'); +selected=getfield(get(pre_h,'UserData'),'selected_vects'); +sData=getfield(get(pre_h,'UserData'),'sData'); +undo = sData; +state=getfield(get(gcf,'UserData'),'state'); + +if LOG + state.big=BT; + state.small=ST; + state.equal=EQ; + state.replace=VECT_REPL; + state.and=AND_OR; + state.all=ALL_AMONG; +end + +if isempty(pre_h) + pro_tools('close'); +end + +if isempty(get(comp_names_h,'Value')) + clear_state_vals; + errordlg('There must be one component chosen for the operation.'); + return; +end + +n_th_comp=getfield(get_indices,{1}); + +if isempty(state.big) & isempty(state.small) & isempty(state.equal) & ... + strcmp(arg,'clip') + clear_state_vals; + errordlg('At least one limit must be chosen for the-operation.'); + return; +end + +if ~isempty(state.replace) & strcmp(arg,'sel') + if ~all(state.replace == round(state.replace)) | any(state.replace < 1) + errordlg('Indices of vectors must be positive integers.'); + return; + elseif any(state.replace > length(sData.data(:,1))) + errordlg('Indices of the vectors to be selected are too big.'); + return; + end +end + +if isempty(state.replace) & strcmp(arg,'clip') + clear_state_vals; + errordlg('Replace value must be determined for Clipping-operation.'); + return; +end + +if isempty(state.big) & isempty(state.small) & isempty(state.equal) & ... + isempty(state.replace) + clear_state_vals; + return; +end + +bt_indices=[]; +lt_indices=[]; +equal_indices=[]; + + +if ~isempty(state.big) + if state.all + bt_indices=find(sData.data(:,n_th_comp) > state.big); + else + bt_indices=selected(find(sData.data(selected,n_th_comp) > state.big)); + end +end + +if ~isempty(state.small) + if state.all + lt_indices=find(sData.data(:,n_th_comp) < state.small); + else + lt_indices=selected(find(sData.data(selected,n_th_comp) < state.small)); + end +end + +if ~isempty(state.equal) + if isnan(state.equal) + if state.all + equal_indices=find(isnan(sData.data(:,n_th_comp))); + else + equal_indices=selected(find(isnan(sData.data(selected,n_th_comp)))); + end + elseif state.all + equal_indices=find(sData.data(:,n_th_comp)==state.equal); + else + equal_indices=selected(find(sData.data(selected,n_th_comp)==state.equal)); + end +end + +if state.and + + if ~isempty(bt_indices) | ~isempty(lt_indices) | ~isempty(equal_indices)... + | strcmp(arg,'sel') + + if isempty(bt_indices) & isempty(lt_indices) & isempty(equal_indices) &... + isempty(state.replace) + clear_state_vals; + return; + end + if isempty(bt_indices) + if ~state.all + bt_indices=selected; + else + bt_indices=1:getfield(size(sData.data),{1}); + end + end + if isempty(lt_indices) + if ~state.all + lt_indices=selected; + else + lt_indices=1:getfield(size(sData.data),{1}); + end + end + if isempty(equal_indices) + if ~state.all + equal_indices=selected; + else + equal_indices=1:getfield(size(sData.data),{1}); + end + end + + indices=intersect(intersect(bt_indices,lt_indices),equal_indices); + if strcmp(arg,'sel') + if ~isempty(indices) | ~isempty(state.replace) + if isempty(state.replace) + NOTEMPTY=0; + if ~state.all + state.replace=selected; + else + state.replace=1:getfield(size(sData.data),{1}); + end + else + NOTEMPTY=1; + end + if isempty(indices) + indices=selected; + end + indices=intersect(indices,state.replace); + if isempty(indices) + indices=selected; + end + data=get(pre_h,'UserData'); + data.undo.sData=sData; + data.undo.selected=data.selected_vects; + data.selected_vects=indices; + if ~LOG + if ~NOTEMPTY + data.LOG{length(data.LOG)+1}='% Select vectors.'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',... + arg,... + ' ',num2str(state.big),... + ' ',num2str(state.small),... + ' ',num2str(state.equal),... + ' ',num2str(state.and),... + ' ',num2str(state.all),... + ''');'); + else + code=write_log_code(state.replace,... + arg,... + state.big,... + state.small,... + state.equal,... + state.and,... + state.all); + data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code; + end + end + set(pre_h,'UserData',data); + old=gcf; + set(0,'CurrentFigure',pre_h); + sel_comp; + cplot_mimema; + vect_means(data.sData,data.vect_mean_h,data.selected_vects); + set(0,'CurrentFigure',old); + end + clear_state_vals; + return; + end + sData.data(indices,n_th_comp) = state.replace; + sData.MODIFIED=1; + end +else + indices=union(union(bt_indices,lt_indices),equal_indices); + if ~isempty(indices) | strcmp(arg,'sel') + if strcmp(arg,'sel') + if ~isempty(indices) | ~isempty(state.replace') + data=get(pre_h,'UserData'); + data.undo.sData=sData; + data.undo.selected=data.selected_vects; + data.selected_vects=union(indices,state.replace); + if ~LOG + if isempty(state.replace); + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',... + arg,... + ' ',num2str(state.big),... + ' ',num2str(state.small),... + ' ',num2str(state.equal),... + ' ',num2str(state.and),... + ' ',num2str(state.all),... + ''');'); + else + code=write_log_code(state.replace,... + arg,... + state.big,... + state.small,... + state.equal,... + state.and,... + state.all); + data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code; + end + end + set(pre_h,'UserData',data); + old=gcf; + set(0,'CurrentFigure',pre_h); + sel_comp; + vect_means(data.sData,data.vect_mean_h,data.selected_vects); + cplot_mimema; + set(0,'CurrentFigure',old); + end + clear_state_vals; + return; + end + sData.data(indices,n_th_comp)=state.replace; + sData.MODIFIED=1; + end +end + +if sData.MODIFIED + data=get(pre_h,'UserData'); + data.sData=sData; + data.undo.sData=undo; + if ~LOG + if strcmp(arg,'sel') + data.LOG{length(data.LOG)+1}='% Select vectors'; + else + data.LOG{length(data.LOG)+1}='% Clip values.'; + end + if strcmp(arg,'clip') | isempty(state.replace) + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''clip_data'',''',arg,... + ' ',num2str(state.big),... + ' ',num2str(state.small),... + ' ',num2str(state.equal),... + ' ',num2str(state.and),... + ' ',num2str(state.all),... + ' ',num2str(state.replace),... + ''');'); + else + code=write_log_code(state.replace,... + arg,... + state.big,... + state.small,... + state.equal,... + state.and,... + state.all); + data.LOG(length(data.LOG)+1:length(data.LOG)+length(code))=code; + end + end + set(pre_h,'UserData',data); + old=gcf; + set(0,'CurrentFigure',pre_h) + + vector_h=getfield(get(gcf,'UserData'),'vector_h'); + vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h'); + set(gcf,'CurrentAxes',vector_h); + vect_means(sData,vect_mean_h,selected); + cplot_mimema; + sel_comp; + + set(0,'CurrentFigure',old); +end + +clear_state_vals; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: clear_state_vals %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function clear_state_vals + +%CLEAR_STATE_VALS Sets the fields of the UserData's state-struct empty. +% +% + + +data=get(gcf,'UserData'); +set(data.big_val_h,'String',''); +set(data.small_val_h,'String',''); +set(data.equal_val_h,'String',''); +set(data.replace_val_h,'String',''); +data.state.big=[]; +data.state.small=[]; +data.state.equal=[]; +data.state.replace=[]; +set(gcf,'UserData',data); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function delay(varargin) + +delay_h=findobj(get(0,'Children'),'Tag','Delay'); + +if nargin ~= 1 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + preh_udata=get(pre_h,'UserData'); + preh_udata.LOG{length(preh_udata.LOG)+1}=... + '% Starting the ''Delay'' -window...'; + preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''delay'',''foo'');'; + set(pre_h,'UserData',preh_udata); +end + +if ~isempty(delay_h) + figure(delay_h); + return; +end + +h0 = figure('Color',[0.8 0.8 0.8], ... + 'PaperPosition',[18 180 576 432], ... + 'PaperUnits','points', ... + 'Position',[759 664 162 215], ... + 'Tag','Delay'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.05555555555555555 0.2046511627906977 0.8950617283950617 0.7441860465116279], ... + 'Style','frame', ... + 'Tag','Frame1'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.08641975308641975 0.6976744186046512 0.8333333333333333 0.2232558139534884], ... + 'Style','frame', ... + 'Tag','Frame2'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.08641975308641975 0.227906976744186 0.8333333333333333 0.4418604651162791], ... + 'Style','frame', ... + 'Tag','Frame3'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'Callback','preprocess delay_data',... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.0556 0.0326 0.4012 0.1163], ... + 'String','OK', ... + 'Tag','Pushbutton1'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'Callback','preprocess close_d',... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.5494 0.0326 0.4012 0.1163], ... + 'String','Close', ... + 'Tag','Pushbutton2'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.4876543209876543 0.7534883720930232 0.3518518518518519 0.1255813953488372], ... + 'Style','edit', ... + 'Tag','EditText1'); + +data.delay_val_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1173 0.7860 0.3086 0.0651], ... + 'String','Delay', ... + 'Style','text', ... + 'Tag','StaticText1'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'Callback','preprocess clip_exp_cb c_this',... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.1173 0.5349 0.1173 0.0837], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton1'); + +data.c_this_button_h=h1; +data.mode='c_this'; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess clip_exp_cb c_all',... + 'ListboxTop',0, ... + 'Position',[0.1173 0.4047 0.1173 0.0837], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton2'); + +data.c_all_button_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess clip_exp_cb e_all',... + 'ListboxTop',0, ... + 'Position',[0.1173 0.2651 0.1173 0.0837], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton3'); + +data.e_all_button_h=h1; + + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.26 0.5534883720930233 0.4135802469135802 0.06511627906976744], ... + 'String','Clip this', ... + 'Style','text', ... + 'Tag','StaticText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.26 0.413953488372093 0.3765432098765432 0.06511627906976744], ... + 'String','Clip all', ... + 'Style','text', ... + 'Tag','StaticText3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.26 0.2744186046511628 0.4197530864197531 0.06511627906976744], ... + 'String','Expand all', ... + 'Style','text', ... + 'Tag','StaticText4'); + + +set(gcf,'UserData',data); + +%%% Subfunction clip_exp_cb %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function clip_exp_cb(arg) + +c_this_button_h=getfield(get(gcf,'UserData'),'c_this_button_h'); +c_all_button_h=getfield(get(gcf,'UserData'),'c_all_button_h'); +e_all_button_h=getfield(get(gcf,'UserData'),'e_all_button_h'); +data=get(gcf,'UserData'); + + +switch arg + case 'c_this' + set(c_all_button_h,'Value',0); + set(e_all_button_h,'Value',0); + set(c_this_button_h,'Value',1); + data.mode='c_this'; + case 'c_all' + set(c_this_button_h,'Value',0); + set(e_all_button_h,'Value',0); + set(c_all_button_h,'Value',1); + data.mode='c_all'; + case 'e_all' + set(c_this_button_h,'Value',0); + set(c_all_button_h,'Value',0); + set(e_all_button_h,'Value',1); + data.mode='e_all'; +end + +set(gcf,'UserData',data); + +%%% Subfunction: delay_data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function delay_data(varargin) + +if nargin == 1 + del_h=findobj(get(0,'Children'),'Tag','Delay'); + set(0,'CurrentFigure',del_h); + LOG=1; + arg=varargin{1}; + i=1; + while i <= length(arg) & arg(i) ~= ' ' + i=i+1; + end + delay=str2num(arg(1:i-1)); + no=str2num(arg(i+1:length(arg))); +else + LOG=0; +end + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +q='Delay operation is not evaluated.'; +t='Warning'; +if isempty(pre_h) + errordlg('''Preprocess'' -figure does not exist. Terminating program...'); + pro_tools('close'); + return; +end + +sData=getfield(get(pre_h,'UserData'),'sData'); +undo = sData; +data=get(gcf,'UserData'); +if ~LOG + delay=str2num(get(data.delay_val_h,'String')); + if isempty(delay) + errordlg('Value of ''Delay'' must be defined.'); + return + end + set(data.delay_val_h,'String',''); + if round(delay) ~= delay + errordlg('Value of ''Delay'' must be integer.'); + return; + end +end +comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h'); +if isempty(get(comp_names_h,'Value')) + errordlg('There are not components chosen.'); + return; +end +n_th_comp=getfield(get_indices,{1}); +len=length(sData.data(:,1)); + +if LOG + switch no + case 1 + data.mode='c_this'; + preprocess('clip_exp_cb','c_this'); + case 2 + data.mode='c_all'; + preprocess('clip_exp_cb','c_all'); + case 3 + data.mode='e_all'; + preprocess('clip_exp_cb','e_all'); + end +end + +switch data.mode + case 'c_this' + MODE='1'; + if delay > 0 + sData.data(delay+1:len,n_th_comp)=sData.data(1:len-delay); + if delay >= len + errordlg(q,t); + return; + else + sData.data(1:delay,n_th_comp)=NaN; + end + elseif delay < 0 + sData.data(1:len+delay,n_th_comp)=... + sData.data(abs(delay)+1:len,n_th_comp); + if abs(delay) >= len + errordlg(q,t); + return; + else + sData.data(len+delay+1:len,n_th_comp)=NaN; + end + end + if delay ~= 0 + data=get(pre_h,'UserData'); + sData.MODIFIED=1; + sData.comp_norm(n_th_comp)=[]; + data.sData=sData; + data.undo.sData=undo; + set(pre_h,'UserData',data); + old = gcf; + set(0,'CurrentFigure',pre_h); + sel_comp; + cplot_mimema; + set(0,'CurrentFigure',old); + end + case 'c_all' + MODE='2'; + if delay > 0 + sData.data(delay+1:len,n_th_comp)=sData.data(1:len-delay,n_th_comp); + if delay >= len + errordlg(q,t); + return; + else + sData.data=sData.data(delay+1:len,:); + end + elseif delay < 0 + sData.data(1:len+delay,n_th_comp)=sData.data(abs(delay)+1:len,n_th_comp); + if abs(delay) >= len + errordlg(q,t); + return; + else + sData.data=sData.data(1:len+delay,:); + end + end + if delay ~= 0 + data=get(pre_h,'UserData'); + sData.MODIFIED=1; + sData.comp_norm(:,:)={[]}; + data.sData=sData; + data.undo.sData=undo; + data.undo.selected=data.selected_vects; + if delay > 0 + data.selected_vects=... + data.selected_vects(find(data.selected_vects>delay)); + data.selected_vects=data.selected_vects-delay; + elseif nargin == 1 + data.selected_vects=... + data.selected_vects(find(data.selected_vects<=len-abs(delay))); + end + set(pre_h,'UserData',data); + old=gcf; + set(0,'CurrentFigure',pre_h); + vects=zeros(1,length(sData.data(:,1))); + vects(data.selected_vects)=1; + write_sD_stats; + draw_vectors(vects,data.vector_h); + sel_comp; + cplot_mimema; + set(0,'CurrentFigure',old); + end + case 'e_all' + MODE='3'; + if delay > 0 + sData.data(len+1:len+delay,:)=NaN; + sData.data(1+delay:delay+len,n_th_comp)=sData.data(1:len,n_th_comp); + sData.data(1:delay,n_th_comp)=NaN; + elseif delay < 0 + delay=abs(delay); + sData.data(delay+1:len+delay,:)=sData.data; + sData.data(1:delay,:)=NaN; + sData.data(1:len,n_th_comp)=sData.data(delay+1:len+delay,n_th_comp); + sData.data(len+1:len+delay,n_th_comp)=NaN; + end + if delay ~= 0 + data=get(pre_h,'UserData'); + sData.MODIFIED=1; + sData.comp_norm(:,:)={[]}; + data.sData=sData; + data.undo.sData=undo; + data.undo.selected=data.selected_vects; + set(pre_h,'UserData',data); + old=gcf; + set(0,'CurrentFigure',pre_h); + write_sD_stats; + pro_tools('selall'); + set(0,'CurrentFigure',old); + end +end + +if ~LOG + data=get(pre_h,'UserData'); + data.LOG{length(data.LOG)+1}='% Delay a component.'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''delay_data'',''',... + num2str(delay),' ',MODE,''');'); + set(pre_h,'UserData',data); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function window(varargin) + +if nargin ~= 1 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + preh_udata=get(pre_h,'UserData'); + preh_udata.LOG{length(preh_udata.LOG)+1}=... + '% Starting the ''Windowed'' -window...'; + preh_udata.LOG{length(preh_udata.LOG)+1}='preprocess(''window'',''foo'');'; + set(pre_h,'UserData',preh_udata); +end + +win_h=findobj(get(0,'Children'),'Tag','Window'); + +if ~isempty(win_h) + figure(win_h); + return; +end + +h0 = figure('Color',[0.8 0.8 0.8], ... + 'PaperPosition',[18 180 576 432], ... + 'PaperUnits','points', ... + 'Position',[513 703 288 219], ... + 'Tag','Window'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.03125 0.1552511415525114 0.9375 0.7990867579908676], ... + 'Style','frame', ... + 'Tag','Frame1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.04861111111111111 0.7214611872146118 0.9027777777777777 0.2009132420091324], ... + 'Style','frame', ... + 'Tag','Frame2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.04861111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ... + 'Style','frame', ... + 'Tag','Frame3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.3611111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ... + 'Style','frame', ... + 'Tag','Frame4'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.6736111111111111 0.1780821917808219 0.2777777777777778 0.5251141552511416], ... + 'Style','frame', ... + 'Tag','Frame5'); + +h1 = uicontrol('Parent',h0, ... + 'Callback','preprocess eval_windowed',... + 'Units','normalized', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.03125 0.0319634703196347 0.2256944444444444 0.091324200913242], ... + 'String','OK', ... + 'Tag','Pushbutton1'); + +h1 = uicontrol('Parent',h0, ... + 'Callback','preprocess close_w', ... + 'Units','normalized', ... + 'FontWeight','demi', ... + 'ListboxTop',0, ... + 'Position',[0.7430555555555555 0.0319634703196347 0.2256944444444444 0.091324200913242], ... + 'String','Close', ... + 'Tag','Pushbutton2'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'ListboxTop',0, ... + 'Position',[0.7083333333333333 0.7625570776255708 0.2083333333333333 0.1232876712328767], ... + 'Style','edit', ... + 'Tag','EditText1'); + +data.win_len_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.07638888888888888 0.8036529680365296 0.3784722222222222 0.0547945205479452], ... + 'String','Window length', ... + 'Style','text', ... + 'Tag','StaticText1'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess window_cb centered',... + 'ListboxTop',0, ... + 'Position',[0.06597222222222222 0.5616438356164384 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton1'); + +data.centered_h=h1; +data.position='center'; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess window_cb previous',... + 'ListboxTop',0, ... + 'Position',[0.06597222222222222 0.4018264840182648 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton2'); + +data.previous_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess window_cb next',... + 'ListboxTop',0, ... + 'Position',[0.06597222222222222 0.2465753424657534 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton3'); + +data.next_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess window_cb mean',... + 'ListboxTop',0, ... + 'Position',[0.3784722222222222 0.5799086757990868 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton4'); + +data.mean_h=h1; +data.mode='mean'; + + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess window_cb median',... + 'ListboxTop',0, ... + 'Position',[0.3784722222222222 0.4611872146118721 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton5'); + + +data.median_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess window_cb max',... + 'ListboxTop',0, ... + 'Position',[0.3784722222222222 0.3515981735159817 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton6'); + +data.max_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'Callback','preprocess window_cb min',... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'ListboxTop',0, ... + 'Position',[0.3784722222222222 0.2374429223744292 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton7'); + +data.min_h = h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess window_cb clip',... + 'ListboxTop',0, ... + 'Position',[0.6909722222222222 0.5525114155251141 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton8'); + +data.clip_h=h1; +data.eval_mode='clip'; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Callback','preprocess window_cb expand',... + 'ListboxTop',0, ... + 'Position',[0.6909722222222222 0.2922374429223744 0.06597222222222222 0.0821917808219178], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton9'); + +data.expand_h=h1; + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.132 0.5799 0.19 0.0548], ... + 'String','Centered', ... + 'Style','text', ... + 'Tag','StaticText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.132 0.4247 0.1667 0.0548], ... + 'String','Previous', ... + 'Style','text', ... + 'Tag','StaticText3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.132 0.2648 0.1632 0.0548], ... + 'String','Next', ... + 'Style','text', ... + 'Tag','StaticText4'); +h1 = uicontrol('Parent',h0, ..., + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.445 0.6027397260273972 0.19 0.0547945205479452], ... + 'String','Mean', ... + 'Style','text', ... + 'Tag','StaticText5'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.445 0.4795 0.1806 0.0548], ... + 'String','Median', ... + 'Style','text', ... + 'Tag','StaticText6'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.445 0.3699 0.1667 0.0548], ... + 'String','Max', ... + 'Style','text', ... + 'Tag','StaticText7'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.445 0.2557077625570776 0.1597222222222222 0.0547945205479452], ... + 'String','Min', ... + 'Style','text', ... + 'Tag','StaticText8'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.7535 0.5753 0.1354 0.054], ... + 'String','Clip', ... + 'Style','text', ... + 'Tag','StaticText9'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'FontSize',8,... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.7534722222222222 0.3150684931506849 0.1527777777777778 0.0547945205479452], ... + 'String','Expand', ... + 'Style','text', ... + 'Tag','StaticText10'); + + + +set(gcf,'UserData',data); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function window_cb(arg) + +data=get(gcf,'UserData'); + +if any(strcmp(arg,[{'centered'},{'previous'},{'next'}])) + switch arg + case 'centered' + data.position='center'; + set(data.previous_h,'Value',0); + set(data.next_h,'Value',0); + set(data.centered_h,'Value',1); + case 'previous' + data.position='previous'; + set(data.centered_h,'Value',0); + set(data.next_h,'Value',0); + set(data.previous_h,'Value',1); + case 'next' + data.position='next'; + set(data.centered_h,'Value',0); + set(data.previous_h,'Value',0); + set(data.next_h,'Value',1); + end +elseif any(strcmp(arg,[{'mean'},{'median'},{'min'},{'max'}])) + switch arg + case 'mean' + data.mode='mean'; + set(data.median_h,'Value',0); + set(data.min_h,'Value',0); + set(data.max_h,'Value',0); + set(data.mean_h,'Value',1); + case 'median' + data.mode='median'; + set(data.mean_h,'Value',0); + set(data.max_h,'Value',0); + set(data.min_h,'Value',0); + set(data.median_h,'Value',1); + case 'max' + data.mode='max'; + set(data.mean_h,'Value',0); + set(data.median_h,'Value',0); + set(data.min_h,'Value',0); + set(data.max_h,'Value',1); + case 'min' + data.mode='min'; + set(data.mean_h,'Value',0); + set(data.median_h,'Value',0); + set(data.max_h,'Value',0); + set(data.min_h,'Value',1); + end +elseif any(strcmp(arg,[{'clip','expand'}])) + switch arg + case 'clip' + data.eval_mode='clip'; + set(data.expand_h,'Value',0); + set(data.clip_h,'Value',1); + case 'expand' + data.eval_mode='expand'; + set(data.clip_h,'Value',0); + set(data.expand_h,'Value',1); + end +end + +set(gcf,'UserData',data); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function eval_windowed(varargin) + +if nargin == 1 + LOG=1; + i=1; + arg=varargin{1}; + while i <= length(arg) & arg(i) ~= ' ' + i=i+1; + end + value=str2num(arg(1:i-1)); + i=i+1; + j=i; + while i <= length(arg) & arg(i) ~= ' ' + i=i+1; + end + position=arg(j:i-1); + i=i+1; + j=i; + while i <= length(arg) & arg(i) ~= ' ' + i=i+1; + end + mode=arg(j:i-1); + i=i+1; + j=i; + while i <= length(arg) & arg(i) ~= ' ' + i=i+1; + end + eval_mode=arg(j:i-1); +else + LOG=0; +end + +data=get(gcf,'UserData'); +if LOG + data.position=position; + data.eval_mode=eval_mode; + data.mode=mode; +end +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +if isempty(pre_h) + errordlg('''Preprocess''-window does not exist. Terminating program...'); + pro_tools('close'); + return; +end + +comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h'); +sData=getfield(get(pre_h,'UserData'),'sData'); +undo=sData; + +if isempty(get(comp_names_h,'Value')) + errordlg('There are not components chosen.'); + return; +end + +if ~LOG + if isempty(get(data.win_len_h,'String')) + errordlg('Window length must be defined'); + return; + end + + value=str2num(get(data.win_len_h,'String')); +end + +set(data.win_len_h,'String',''); + +if ~LOG + if isempty(value) | value < 0 | value ~= round(value) + errordlg('Window length must be positive integer.'); + return; + end + + if value > length(sData.data(:,1)) + errordlg('Length of window is too big.'); + return; + end +end + +index=getfield(get_indices,{1}); + +sData=eval_operation(sData,value,data.mode,data.eval_mode,data.position,index); +sData.comp_norm(index)={[]}; +u_data=get(pre_h,'UserData'); +u_data.sData=sData; +u_data.undo.sData=undo; +u_data.undo.selected=u_data.selected_vects; + +if ~LOG + u_data.LOG{length(u_data.LOG)+1}=... + '% Evaluating the wanted ''windowed'' -operation.'; + u_data.LOG{length(u_data.LOG)+1}=cat(2,'preprocess(''eval_windowed'',',... + '''',num2str(value),... + ' ',data.position,' ',data.mode,... + ' ',data.eval_mode,''');'); +end + +set(pre_h,'UserData',u_data); +old=gcf; +set(0,'CurrentFigure',pre_h); + +if strcmp(data.eval_mode,'expand'); + write_sD_stats; + pro_tools('selall'); +else + sel_comp; + cplot_mimema; +end + +set(0,'CurrentFigure',old); + + + +%%% Subfunction: eval_operation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +function sData=eval_operation(sData,winlen,mode,evalmode,position,n) + + +len=length(sData.data(:,1)); +dim=length(sData.data(1,:)); + +switch(position) + case 'center' + prev=round(winlen/2)-1; + next=winlen-round(winlen/2); + case 'previous' + prev=winlen-1; + next=0; + case 'next' + prev=0; + next=winlen-1; +end + +switch(evalmode) + case 'clip' + for center=1:len + win=center-prev:center-prev+winlen-1; + win=win(find(win > 0 & win <= len)); + str=cat(2,mode,'(sData.data(win(find(~isnan(sData.data(win,n)))),n))'); + tmp(center)=eval(str); + end + sData.data(:,n)=tmp; + case 'expand' + for i=1:len+winlen-1 + win=i-(winlen-1):i; + win=win(find(win > 0 & win <= len)); + str=cat(2,mode,'(sData.data(win(find(~isnan(sData.data(win,n)))),n))'); + tmp(i)=eval(str); + end + sData.data=cat(1,repmat(NaN,next,dim),sData.data,repmat(NaN,prev,dim)); + sData.data(:,n)=tmp; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function pro_tools(arg) + +switch arg + case 'close' + close_preprocess; + case 'c_stat' + write_c_stats; + case 'plot_hist' + plot_hist; + case 'plot' + plot_button; + case 'plxy' + plxy_button; + case 'bplo' + bplo_button; + case 'hist' + hist_button; +end + + +%%% Subfunction close_preprocess %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function close_preprocess + + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +man_h=findobj(get(0,'Children'),'Tag','Management'); +clip_h=findobj(get(0,'Children'),'Tag','Clipping'); +plot_h=findobj(get(0,'Children'),'Tag','PlotWin'); +delay_h=findobj(get(0,'Children'),'Tag','Delay'); +window_h=findobj(get(0,'Children'),'Tag','Window'); +sel_h=findobj(get(0,'Children'),'Tag','Select'); + +if ~isempty(man_h) + close(man_h); +end +if ~isempty(clip_h) + close(clip_h); +end +if ~isempty(plot_h) + close(plot_h); +end +if ~isempty(delay_h) + close(delay_h); +end +if ~isempty(window_h) + close(window_h); +end +if ~isempty(sel_h) + close(sel_h); +end +if ~isempty(pre_h) + close(pre_h); +end + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: undo %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function undo(varargin) + +if nargin == 1 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + set(0,'CurrentFigure',pre_h); + LOG=1; +else + LOG=0; +end + +data=get(gcf,'UserData'); +if ~isempty(data.undo) + if any(strcmp('selected',fieldnames(data.undo))) + data.selected_vects=data.undo.selected; + end + if ~any(strcmp('index',fieldnames(data.undo))) + data.sData=data.undo.sData; + data.undo=[]; + if ~LOG + data.LOG{length(data.LOG)+1}='% Undo the most recent operation.'; + data.LOG{length(data.LOG)+1}='preprocess(''undo'',''foo'');'; + end + set(gcf,'UserData',data); + set_compnames(data.sData,data.comp_names_h); + write_sD_stats; + vect_means(data.sData,data.vect_mean_h,data.selected_vects); + sel_comp; + cplot_mimema; + return; + end + + % 'undo.sData' does not exist in sD_set - array + + index=data.undo.index; + data.undo.sData=rmfield(data.undo.sData,[{'INDEX'};{'MODIFIED'}]); + if index<=length(data.sD_set) + rest=data.sD_set(index:length(data.sD_set)); + else + rest=[]; + end + data.sD_set=cat(2,data.sD_set(1:index-1),data.undo.sData,rest); + data.undo=[]; + if ~LOG + data.LOG{length(data.LOG)+1}='% Undo the most recent operation.'; + data.LOG{length(data.LOG)+1}='preprocess(''undo'',''foo'');'; + end + set(gcf,'UserData',data); + set(getfield(get(gcf,'UserData'),'sD_set_h'),'Value',index); + set_sD_stats; + sel_sD; +else + msgbox('Can''t do...'); +end + +%%% Subfunction: write_c_stats %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function write_c_stats(varargin) + + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +comp_names_h=getfield(get(pre_h,'UserData'),'comp_names_h'); +sel_comp_h=getfield(get(pre_h,'UserData'),'sel_comp_h'); +sel_chist_h=getfield(get(pre_h,'UserData'),'sel_chist_h'); + +if nargin==1 + val1=varargin(1); +else + val1=get(sel_comp_h,'String'); +end + +if ~isempty(val1) & iscell(val1) + selected_vects=getfield(get(pre_h,'UserData'),'selected_vects'); + sData=getfield(get(pre_h,'UserData'),'sData'); + sel_cdata_h=getfield(get(pre_h,'UserData'),'sel_cdata_h'); + name=getfield(get(sel_comp_h,'String'),{get(sel_comp_h,'Value')}); + name=name{1}; + i=2; + + while ~isempty(str2num(name(i))) + value(i-1)=name(i); + i=i+1; + end + + value=str2num(value); + + + data=sData.data(selected_vects,value); + + string{1} = cat(2,'Min: ',sprintf('%-10.3g',min(data))); + string{2} = cat(2,'Mean: ',sprintf('%-10.3g',mean(data(find(~isnan(data)))))); + string{3} = cat(2,'Max: ',sprintf('%-10.3g',max(data))); + string{4} = cat(2,'Std: ',sprintf('%-10.3g',std(data(find(~isnan(data)))))); + string{5} = cat(2,'Number of NaNs: ',sprintf('%-10.3g',sum(isnan(data)))); + string{6} = cat(2,'NaN (%):',... + sprintf('%-10.3g',100*sum(isnan(data))/length(data))); + string{7} = cat(2,'Number of values: ',sprintf('%-10.3g',... + length(find(~isnan(unique(data)))))); + set(sel_cdata_h,'String',string); + set(sel_cdata_h,'HorizontalAlignment','left'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction plot_hist %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function plot_hist + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +sel_chist_h=getfield(get(pre_h,'UserData'),'sel_chist_h'); +sData=getfield(get(pre_h,'UserData'),'sData'); +selected=getfield(get(pre_h,'UserData'),'selected_vects'); + +value=get(getfield(get(pre_h,'UserData'),'sel_comp_h'),'Value'); +subplot(sel_chist_h); +hold off; +cla; +if all(isnan(sData.data(:,value))); + return; +end +hold on; +lim1=min(sData.data(:,value)); +lim2=max(sData.data(:,value)); +if lim2 - lim1 >= eps + x=lim1:(lim2-lim1)/(30-1):lim2; + set(sel_chist_h,'XLim',[lim1 lim2]); +elseif lim1 ~= 0 + x=(lim1)/2:lim1/(30-1):lim1+(lim1)/2; + set(sel_chist_h,'Xlim',[lim1-abs(lim1/2) lim1+abs(lim1/2)]); +else + x=-1:2/(30-1):1; + set(sel_chist_h,'XLim',[-1 1]); +end + +hist(sData.data(selected,value),x); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: select_all %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function select_all(varargin) + +if nargin == 1 + LOG=1; +else + LOG=0; +end + +data=get(gcf,'UserData'); +data.selected_vects=(1:length(data.sData.data(:,1))); +if ~LOG + data.LOG{length(data.LOG)+1}='% Select all vectors.'; + data.LOG{length(data.LOG)+1}='selall(''foo'');'; +end +set(gcf,'UserData',data); +tmp=zeros(1,length(data.sData.data(:,1))); +tmp(data.selected_vects)=1; +draw_vectors(tmp,data.vector_h); +cplot_mimema; +vect_means(data.sData,data.vect_mean_h,data.selected_vects); +sel_comp; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: plot_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function plot_button + +%PLOT_BUTTON A callback function. Plots all the components and marks +% the chosen components. +% +% + +sData=getfield(get(gcf,'UserData'),'sData'); +selected=getfield(get(gcf,'UserData'),'selected_vects'); + +indices=get_indices; +if isempty(indices) + return; +end +h=findobj(get(0,'Children'),'Tag','PlotWin'); +if isempty(h) + h= figure; + set(h,'Tag','PlotWin'); +end + +names=sData.comp_names(indices); +data=sData.data(:,indices); + +set(0,'CurrentFigure',h); +hold off; +clf; +t=0:1/(getfield(size(data),{1})-1):1; +tmp=setdiff(1:length(data(:,1)),selected); +for i=1:length(names) + subplot(length(names),1,i); + hold on; + if max(data(:,i))- min(data(:,i)) <= eps + set(gca,'YLim',[max(data(:,i))-1 max(data(:,i))+1]); + end + plot(t,data(:,i)); + if ~isempty(tmp); + data(tmp,i)=NaN; + end + plot(t,data(:,i),'red'); + ylabel(names{i}); + set(gca,'XTick',[]); +end +set(gcf,'Name','Plotted Data Components'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: plxy_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function plxy_button + +%PLXY_BUTTON A callback function. XY-plots the first and the second +% components chosen. +% +% + + +sData=getfield(get(gcf,'UserData'),'sData'); +selected=getfield(get(gcf,'UserData'),'selected_vects'); + +inds = get_indices; +if length(inds) < 2 + errordlg('There must be two components chosen for XY-plot.'); + return; +end + +inds=inds(1:2); +names=getfield(sData,'comp_names',{inds}); + +h=findobj(get(0,'Children'),'Tag','PlotWin'); + +if isempty(h) + h= figure; + set(h,'Tag','PlotWin'); +end + +set(0,'CurrentFigure',h); +clf; +axes; +if max(sData.data(:,inds(1))) - min(sData.data(:,inds(1))) <= eps + set(gca,'XLim',[max(sData.data(:,inds(1)))-1 max(sData.data(:,inds(1)))+1]); +end +if max(sData.data(:,inds(2))) - min(sData.data(:,inds(2))) <= eps + set(gca,'YLim',[max(sData.data(:,inds(2)))-1 max(sData.data(:,inds(2)))+1]); +end +hold on; +plot(sData.data(:,inds(1)),sData.data(:,inds(2)),'o'); +x=sData.data(selected,inds(1)); +y=sData.data(selected,inds(2)); + +plot(x,y,'ored','MarkerSize',4); +xlabel(names(1)); +ylabel(names(2)); +set(h,'Name','Plotted Data Components'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Sub_function: bplo_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function bplo_button + +%BPLO_BUTTON A callback function. Box-plots the first component chosen. + + +sData=getfield(get(gcf,'UserData'),'sData'); +selected=getfield(get(gcf,'UserData'),'selected_vects'); + +if length(selected) == 1 + errordlg('There are too few vectors chosen for box-plotting.'); +else + indices=get_indices; + if isempty(indices) + return; + end + for i=1:length(indices) + if length(unique(sData.data(selected,indices(i))))==1 + errordlg('All the values are the same. Operation can''t be evaluated.'); + return; + end + end + names=getfield(sData,'comp_names',{indices}); + h= findobj(get(0,'Children'),'Tag','PlotWin'); + if isempty(h) + h= figure; + set(h,'Tag','PlotWin'); + end + + data=sData.data(selected,indices); + + set(0,'CurrentFigure',h); + hold off; + clf; + hold on; + for i=1:getfield(size(data),{2}) + subplot(getfield(size(data),{2}),1,i); + if ~all(isnan(data(:,i))) + boxplot(data(:,i)); + end + name=names{i}; + tmp=get(get(gca,'YLabel'),'String'); + ylabel(cat(2,sprintf('[%s] ',name),tmp)); + end + set(h,'Name','Box-plot'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: hist_button %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function hist_button + +no_of_bins_h=getfield(get(gcf,'UserData'),'no_of_bins_h'); +selected=getfield(get(gcf,'UserData'),'selected_vects'); +sData=getfield(get(gcf,'UserData'),'sData'); +n=str2num(get(no_of_bins_h,'String')); +s1='Invalid number of bins.'; +s2=sprintf('\nSet new value to the box under the ''Histogram''-button.'); + +if isempty(n) + errordlg(cat(2,s1,s2)); +else + indices=get_indices; + if isempty(indices) + return; + end + n=round(n); + if n < 1 + errordlg('Number of bins must be positive integer.'); + else + h= findobj(get(0,'Children'),'Tag','PlotWin'); + if isempty(h) + h= figure; + set(h,'Tag','PlotWin'); + end + + set(0,'CurrentFigure',h); + hold off; + clf; + data=sData.data(selected,indices); + names=sData.comp_names(indices); + for i=1:length(names) + subplot(length(names),1,i); + hold on; + lim1=min(sData.data(:,indices(i))); + lim2=max(sData.data(:,indices(i))); + if n > 1 + if lim2 - lim1 >= eps + x=lim1:(lim2-lim1)/(n-1):lim2; + set(gca,'XLim',[lim1 lim2]); + elseif lim1 ~= 0 + x=lim1/2:lim1/(n-1):lim1/2+lim1; + if ~all(isnan([lim1 lim2])) + set(gca,'XLim',[lim1-abs(lim1/2) lim1+abs(lim1/2)]); + end + else + x=-1:2/(n-1):1; + set(gca,'XLim',[-1 1]); + end + else + x=1; + if lim2 ~= lim1 + set(gca,'XLim',[lim1 lim2]); + else + set(gca,'XLim',[lim1/2 lim1/2+lim1]); + end + end + if ~all(isnan(data(:,i))) + hist(data(:,i),x); + end + name=names{i}; + xlabel(name); + end + set(h,'Name','Histogram'); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: no_of_values %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function no_of_values(varargin); + +%NO_OF_VALUES A callback function. Calculates the number of different +% values of the chosen components. +% +% + +if nargin==1; + LOG=1; +else + LOG=0; +end + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +results_h=getfield(get(pre_h,'UserData'),'results_h'); +sData=getfield(get(pre_h,'UserData'),'sData'); +selected=getfield(get(pre_h,'UserData'),'selected_vects'); +str1='There must be one component chosen for ''Number of Values''-operation'; + + +if ~LOG & isempty(get_indices) + errordlg(str1); +else + indices=get_indices; + data=sData.data(selected,indices); + + string{1} = 'Number of different values:'; + + for i=1:getfield(size(data),{2}) + + tmp=data(:,i); + string{i+1}=cat(2,sprintf('#%d:',indices(i)),... + sprintf('%d',length(find(~isnan(unique(data(:,i))))))); + end + + set(results_h,'String',string); + set(results_h,'HorizontalAlignment','left'); + if ~LOG + data=get(pre_h,'UserData'); + data.LOG{length(data.LOG)+1}='% Number of values'; + data.LOG{length(data.LOG)+1}='preprocess(''noof'',''foo'');'; + set(pre_h,'UserData',data); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: correlation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function correlation(varargin) + +if nargin == 1 + LOG=1; +else + LOG=0; +end + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +results_h=getfield(get(pre_h,'UserData'),'results_h'); +selected=getfield(get(pre_h,'UserData'),'selected_vects'); +sData=getfield(get(pre_h,'UserData'),'sData'); + +if length(get_indices) < 2 + errordlg('There must be two components chosen for Correlation'); +else + indices=getfield(get_indices,{1:2}); + data=sData.data(selected,indices); + inds=find(~isnan(data(:,1)) & ~isnan(data(:,2))); + value=getfield(corrcoef(data(inds,1),data(inds,2)),{1,2}); + names=sData.comp_names(indices); + string{1}='Correlation between'; + string{2}=cat(2,names{1},' and ',names{2},':'); + string{3}=sprintf('%-10.3g',value); + + set(results_h,'String',string); + set(results_h,'HorizontalAlignment','left'); + if ~LOG + data=get(pre_h,'UserData'); + data.LOG{length(data.LOG)+1}='% Correlation'; + data.LOG{length(data.LOG)+1}='preprocess(''corr'',''foo'');'; + set(pre_h,'UserData',data); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: unit_length %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function unit_length(varargin) + +%UNIT_LENGTH A callback function Scales all the vectors to the unit +% length. +% +% + +if nargin==1 + LOG=1; +else + LOG=0; +end + +vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h'); +sData=getfield(get(gcf,'UserData'),'sData'); +sData.MODIFIED=1; +scaled=sData.data; +comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h'); + +if ~LOG & isempty(get(comp_names_h,'Value')) + errordlg('There must be components chosen for the ''unit length''- operation'); + return; +end +inds=get_indices; +for i=1:length(scaled(:,1)); + x=find(~isnan(scaled(i,inds))); + scaled(i,inds(x))=(1/sqrt(sum(scaled(i,inds(x)).^2)))*scaled(i,inds(x)); +end + +data=get(gcf,'UserData'); + + +data.undo.sData = sData; +data.sData.data=scaled; + +for i=1:length(inds) + data.sData.comp_norm{inds(i)}=[]; +end + +if ~LOG + data.LOG{length(data.LOG)+1}='% Unit length'; + data.LOG{length(data.LOG)+1}='preprocess(''unit'',''foo'');'; +end +set(gcf,'UserData',data); + +vects=zeros(1,length(sData.data(:,1))); +vects(data.selected_vects)=1; + +draw_vectors(vects,data.vector_h); +vect_means(sData,vect_mean_h,data.selected_vects); +cplot_mimema; +plot_hist; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: one_of_n %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function one_of_n(varargin) + +if nargin==1 + LOG=1; +else + LOG=0; +end + +data=get(gcf,'UserData'); +vector_h=getfield(get(gcf,'Userdata'),'vector_h'); +comp_names_h=getfield(get(gcf,'Userdata'),'comp_names_h'); +vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h'); +sData=data.sData; +undo=data.sData; +selected=getfield(get(gcf,'UserData'),'selected_vects'); +msg='Creating over 10 new components. Stop operation?'; + +if ~LOG + if isempty(get(data.comp_names_h,'Value')) + errordlg('There must be one component chosen for ''Add: N binary types'' -operation'); + return; + end +end + +index=getfield(get_indices,{1}); + +tmp=unique(sData.data(:,index)); +n=length(tmp); + +if ~LOG + if n>10 + answer=questdlg(msg,'Question','Yes','No','Yes'); + + if strcmp(answer,'Yes') + msgbox('Operation stopped.'); + return; + end + + end +end + +dim1=getfield(size(sData.data),{1}); +dim2=getfield(size(sData.data),{2}); +sData.data=cat(2,sData.data,zeros(dim1,n)); + +dim=dim2+n; +for i=1:n + sData.data(:,dim-(n-i))=(sData.data(:,index) == tmp(i)); +end + +INDEX=sData.INDEX; +for i=1:n + sData.comp_names{dim2+i}=sprintf('%dNewVar',dim2+i); +end +tmp_norm=cat(1,sData.comp_norm,cell(n,1)); +sData=som_data_struct(sData.data,... + 'name',sData.name,... + 'labels',sData.labels,... + 'comp_names',sData.comp_names); + +sData.MODIFIED=1; +sData.INDEX=INDEX; +sData.comp_norm=tmp_norm; +data.undo.sData=undo; +data.sData=sData; +data.selected_vects=1:length(sData.data(:,1)); +if ~LOG + data.LOG{length(data.LOG)+1}='% Add: N binary types'; + data.LOG{length(data.LOG)+1}='preprocess(''oneo'',''foo'');'; +end +set(gcf,'UserData',data); +clear_button; +write_sD_stats; +set_compnames(sData,comp_names_h); +tmp=ones(1,length(sData.data(:,1))); +draw_vectors(tmp,vector_h); +vect_means(sData,vect_mean_h,1:length(sData.data(:,1))); +cplot_mimema; +sel_comp; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: add_zeros %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function add_zeros(varargin) + +if nargin == 1 + LOG=1; +else + LOG=0; +end + +data=get(gcf,'UserData'); +vector_h=getfield(get(gcf,'Userdata'),'vector_h'); +comp_names_h=getfield(get(gcf,'Userdata'),'comp_names_h'); +vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h'); +sData=data.sData; +undo=sData; + +dim1=getfield(size(sData.data),{1}); +dim2=getfield(size(sData.data),{2}); +sData.data=cat(2,sData.data,zeros(dim1,1)); + +INDEX=sData.INDEX; + +sData.comp_names{dim2+1}=sprintf('%dNewVar',dim2+1); +tmp_norm=cat(1,sData.comp_norm,cell(1,1)); +sData=som_data_struct(sData.data,... + 'name',sData.name,... + 'labels',sData.labels,... + 'comp_names',sData.comp_names); + +sData.MODIFIED=1; +sData.INDEX=INDEX; +sData.comp_norm=tmp_norm; +data.sData=sData; +data.undo.sData=undo; +data.selected_vects=1:length(sData.data(:,1)); +if ~LOG + data.LOG{length(data.LOG)+1}='% Add: zeros'; + data.LOG{length(data.LOG)+1}='preprocess(''zero'',''foo'');'; +end +set(gcf,'UserData',data); +clear_button; +write_sD_stats; +set_compnames(sData,comp_names_h); +tmp=ones(1,length(sData.data(:,1))); +draw_vectors(tmp,vector_h); +vect_means(sData,vect_mean_h,1:length(sData.data(:,1))); +cplot_mimema; +sel_comp; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: move_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function move_component(varargin) + +%MOVE_COMPONENT A callback function. Moves one component of vectors to +% the position wanted. +% +% + +if nargin == 1 + LOG=1; + i=1; + while varargin{1}(i) ~= ' ' + value(i)=varargin{1}(i); + i=i+1; + end + value=str2num(value); % the new place + index=str2num(varargin{1}(i:length(varargin{1}))); % index of the chosen + % component +else + LOG=0; +end + +data=get(gcf,'UserData'); +sData=data.sData; +undo=sData; +prompt='Enter the number of the new component place:'; + + +if isempty(get(data.comp_names_h,'Value')) + errordlg('There must be one component chosen for ''Move Component''-operation'); + return; +end + +if ~LOG + index=getfield(get_indices,{1}); + answer=inputdlg(prompt); + + if isempty(answer) | (iscell(answer) & isempty(answer{1})) + msgbox('No components moved'); + return; + end + + value=str2num(answer{1}); + + + dims=size(value); + + if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value) + errordlg('The new component place must be positive integer.') + return; + end + + if value <= 0 | round(value) ~= value + errordlg('The new component place must be positive integer.'); + return; + end + + if value > getfield(size(sData.data),{2}) + errordlg('Too big value for the new component place.'); + return; + end +end + +sData.MODIFIED=1; +if index < value + indices1=setdiff(1:value,index); + indices2=setdiff(value+1:length(sData.data(1,:)),index); +elseif index > value + indices1=setdiff(1:value-1,index); + indices2=setdiff(value:length(sData.data(1,:)),index); +else + data.sData=sData; + data.undo.sData=undo; + set(gcf,'UserData',data); + return; +end + +tmp1=sData.data(:,indices1); +tmp2=sData.data(:,indices2); +sData.data=cat(2,tmp1,sData.data(:,index),tmp2); + +tmp1=sData.comp_names(indices1); +tmp2=sData.comp_names(indices2); +sData.comp_names=cat(1,tmp1,sData.comp_names(index),tmp2); + +tmp1=sData.comp_norm(indices1); +tmp2=sData.comp_norm(indices2); +sData.comp_norm=cat(1,tmp1,sData.comp_norm(index),tmp2); + +data.sData=sData; +data.undo.sData=undo; +if ~LOG + data.LOG{length(data.LOG)+1}='% Move component.'; + data.LOG{length(data.LOG)+1}=sprintf('preprocess(''move'',''%s %s'');',... + num2str(value),num2str(index)); +end +comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h'); +vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h'); +vector_h=getfield(get(gcf,'UserData'),'vector_h'); +data.selected_vects=1:length(sData.data(:,1)); +set(gcf,'UserData',data); +clear_button; +set_compnames(sData,comp_names_h); +draw_vectors(ones(1,length(sData.data(:,1))),vector_h); +vect_means(sData,vect_mean_h,data.selected_vects); +cplot_mimema; +sel_comp; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: copy_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function copy_component(varargin) + +%COPY_COMPONENT Copies one component of vectors to the position wanted. +% +% + +if nargin == 1 + LOG=1; + i=1; + while varargin{1}(i) ~= ' ' + value(i)=varargin{1}(i); + i=i+1; + end + value=str2num(value); % the new place + index=str2num(varargin{1}(i:length(varargin{1}))); % index of the chosen + % component +else + LOG=0; +end + + +data=get(gcf,'UserData'); +sData=data.sData; +undo=sData; +if ~LOG + prompt='Enter the number of the new component place:'; + + + if isempty(get(data.comp_names_h,'Value')) + errordlg('There must be one component chosen for ''Copy Component''-operation'); + return; + end + + index=getfield(get_indices,{1}); + answer=inputdlg(prompt); + + if isempty(answer) | (iscell(answer) & isempty(answer{1})) + msgbox('No components moved'); + return + end + + + value=str2num(answer{1}); + dims=size(value); + + if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value) + errordlg('The new component place must be positive integer.') + return; + end + + if value <= 0 | round(value) ~= value + errordlg('The new component place must be positive integer.'); + return; + end + + if value > getfield(size(sData.data),{2}) + 1 + errordlg('Too big value for the new component place.'); + return; + end +end + +sData.MODIFIED=1; + +indices1=1:value-1; +indices2=value:length(sData.data(1,:)); +tmp1=sData.data(:,indices1); +tmp2=sData.data(:,indices2); +sData.data=cat(2,tmp1,sData.data(:,index),tmp2); + +tmp1=sData.comp_names(indices1); +tmp2=sData.comp_names(indices2); +name=cell(1,1); +name{1}=cat(2,'Copied',sData.comp_names{index}); +sData.comp_names=cat(1,tmp1,name,tmp2); + +tmp1=sData.comp_norm(indices1); +tmp2=sData.comp_norm(indices2); +norm=cell(1,1); +norm{1}=sData.comp_norm{index}; +sData.comp_norm=cat(1,tmp1,norm,tmp2); + +data.sData=sData; +data.undo.sData=undo; +if ~LOG + data.LOG{length(data.LOG)+1}='% Copy component'; + data.LOG{length(data.LOG)+1}=sprintf('preprocess(''copy'',''%s %s'');',... + num2str(value),num2str(index)); +end + + +comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h'); +vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h'); +vector_h=getfield(get(gcf,'UserData'),'vector_h'); +data.selected_vects=1:length(sData.data(:,1)); +set(gcf,'UserData',data); +clear_button; +write_sD_stats; +set_compnames(sData,comp_names_h); +draw_vectors(ones(1,length(sData.data(:,1))),vector_h); +vect_means(sData,vect_mean_h,data.selected_vects); +cplot_mimema; +sel_comp; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: remove_component %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function remove_component(varargin) + +if nargin == 1 + LOG=1; + value=str2num(varargin{1}); +else + LOG=0; +end + +data=get(gcf,'UserData'); +vect_mean_h=getfield(get(gcf,'UserData'),'vect_mean_h'); +vector_h=getfield(get(gcf,'UserData'),'vector_h'); +comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h'); +sData=data.sData; +undo=sData; +prompt='Enter the number of component to be removed.'; +dim=length(sData.data(1,:)); + +if ~LOG + answer=inputdlg(prompt); + + if isempty(answer) | (iscell(answer) & isempty(answer{1})) + msgbox('Components not removed.'); + return; + end + + value=str2num(answer{1}); + dims=size(value); + + if dims(1) ~= 1 | dims(2) ~= 1 | ~isreal(value) + errordlg('Number of the component to be removed must be positive integer.') + return; + end + + if value <= 0 | round(value) ~= value + errordlg('Number of the component to be removed must be positive integer.'); + return; + end + + if value > getfield(size(sData.data),{2}) + errordlg('There are less components.'); + return; + end +end + +sD_set_h=getfield(get(gcf,'UserData'),'sD_set_h'); +index=get(sD_set_h,'Value'); +if value == 1 & getfield(size(sData.data),{2}) == 1 + if length(get(sD_set_h,'String')) == 1 + msgbox('No data left. Closing program...') + pro_tools('close'); + return; + end + + set1=data.sD_set(1:index-1); + set2=data.sD_set(index+1:length(data.sD_set)); + data.sD_set=[set1 set2]; + set(gcf,'UserData',data); + + set_sD_stats; + sel_sD; + data=get(gcf,'UserData'); + data.undo.sData=undo; + data.undo.index=index; + set(gcf,'UserData',data); + return; +end +dims=size(sData.data); +tmp_data=cat(2,sData.data(:,1:value-1),sData.data(:,value+1:dims(2))); +tmp_norm=cat(1,sData.comp_norm(1:value-1),sData.comp_norm(value+1:dims(2))); +names=cat(1,sData.comp_names(1:value-1),sData.comp_names(value+1:dims(2))); +INDEX=sData.INDEX; +comp_norm=sData.comp_norm; +sData=som_data_struct(tmp_data,... + 'name',sData.name,... + 'labels',sData.labels,... + 'comp_names',names); +sData.comp_norm=tmp_norm; +sData.MODIFIED=1; +sData.INDEX=INDEX; +data=get(gcf,'UserData'); +data.sData=sData; +data.undo.sData=undo; +data.selected_vects=1:length(sData.data(:,1)); +if ~LOG + data.LOG{length(data.LOG)+1}='% Remove component'; + data.LOG{length(data.LOG)+1}=sprintf('preprocess(''remove'',''%s'');',... + answer{1}); +end +set(gcf,'UserData',data); +clear_button; +write_sD_stats; +set_compnames(sData,comp_names_h); +tmp=ones(1,length(sData.data(:,1))); +draw_vectors(tmp,vector_h); +vect_means(sData,vect_mean_h,1:length(sData.data(:,1))); +cplot_mimema; +sel_comp; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: remove_vects %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function remove_vects(varargin) + +if nargin==1 + LOG=1; + tmp_str=varargin{1}; +else + LOG=0; + tmp_str='_foo'; +end +data=get(gcf,'UserData'); +vect_mean_h=data.vect_mean_h; +vector_h=data.vector_h; +sData=data.sData; +undo=sData; + +if length(data.selected_vects) == getfield(size(sData.data),{1}) + if LOG + answer='Yes'; + else + answer=questdlg('Do you want to delete this data set?'); + end + if strcmp(answer,'No') + return; + else + index=get(data.sD_set_h,'Value'); + if length(get(data.sD_set_h,'String')) == 1 + msgbox('No data left. Closing program...') + pro_tools('close'); + return; + end + + set1=data.sD_set(1:index-1); + set2=data.sD_set(index+1:length(data.sD_set)); + data.sD_set=[set1 set2]; + set(gcf,'UserData',data); + + set(data.sD_set_h,'Value',1); + set_sD_stats; + sel_sD; + data=get(gcf,'UserData'); + data.undo.sData=undo; + data.undo.index=index; + if ~LOG + data.LOG{length(data.LOG)+1}='% Remove selected vectors'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''remove_vects'',''',... + tmp_str,''');'); + end + set(gcf,'UserData',data); + return; + end +end + +tmp=sData.data(data.selected_vects,:); +if ~LOG + answer=questdlg('Do you want to save removed values to workspace?'); +else + if ~strcmp(tmp_str,'_foo') + answer='Yes'; + else + answer='No'; + end +end +old=gcf; +if strcmp(answer,'Yes') + if ~LOG + answer=inputdlg('Give the name of the output -variable.'); + else + answer={tmp_str}; + end + if isvalid_var_name(answer) + assignin('base',answer{1},tmp); + disp(sprintf('Removed values are set to workspace as''%s''.',answer{1})); + tmp_str=answer{1}; + end +end +set(0,'CurrentFigure',old); +sData.data(data.selected_vects,:)=[]; +sData.labels(data.selected_vects,:)=[]; +sData.MODIFIED=1; +data.sData=sData; +data.selected=1:length(sData.data(:,1)); +data.undo.sData=undo; +if ~LOG + data.LOG{length(data.LOG)}='% Remove selected vectors'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''remove_vects'',''',... + tmp_str,''');'); +end +set(gcf,'UserData',data); + + +draw_vectors(ones(1,length(data.selected)),data.vector_h); +write_sD_stats; +select_all('foo'); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + +%%% Subfunction: eval1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function eval1(varargin); + +if nargin == 1 + answer=varargin + LOG=1; +else + LOG=0; +end + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +if isempty(pre_h) + errordlg('''Preprocess''-figure does not exist. Terminating program...'); + pro_tools('close'); + return; +end + +undo=getfield(get(pre_h,'UserData'),'sData'); + +if ~LOG + prompt={'Enter the expression to be evaluated.',... + 'Enter the inverse normalization method (optional).'}; + title='Single component eval.'; + answer= inputdlg(prompt,title,1); +end + +if ~isempty(answer) + tmp=[]; + if ~isempty(answer{1}) + [tmp,method]=build_expr(answer{1},'single'); + if ~isstr(tmp) + sData=getfield(get(gcf,'UserData'),'sData'); + tmp='Done.'; + %if ~isempty(answer{2}) + % sN=som_norm_struct('eval',{method,answer{2}}); + %else + % sN=som_norm_struct('eval',{method}); + %end + %sN=som_set(sN,'status','done'); + params={answer{1};answer{2}}; + ind=getfield(get_indices,{1}); + x.type=''; + x.method='eval'; + x.params={answer{1};answer{2}}; + x.status=''; + sData.comp_norm{ind}=x; + data=get(gcf,'UserData'); + data.undo.sData=undo; + data.sData=sData; + if ~LOG + data.LOG{length(data.LOG)+1}='% Eval (1-comp)'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess eval1 ',... + sprintf('{''%s'' ''%s''};',answer{1},answer{2})); + end + set(pre_h,'UserData',data); + + end + end + set(getfield(get(pre_h,'UserData'),'results_h'),'String',tmp); +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: eval2 + +function eval2(varargin) + +if nargin == 1 + answer=varargin{1}; + LOG=1; +else + LOG=0; +end + +undo=getfield(get(gcf,'UserData'),'sData'); +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +if isempty(pre_h) + errordlg('''Preprocess''-figure does not exist. Terminating program.'); + pro_tools('close'); + return; +end + +if ~LOG + prompt='Enter the expression to be evaluated.'; + title ='Eval'; + answer=inputdlg(prompt,title,1); +end + +if ~isempty(answer) & ~isempty(answer{1}) + str=answer{1}; + [answer,foo]=build_expr(answer{1},'multiple'); + if ~isstr(answer) + + answer='Done.'; + data=get(gcf,'UserData'); + data.undo.sData=undo; + if ~LOG + data.LOG{length(data.LOG)+1}='% Eval'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''eval2'',',... + sprintf('{''%s''});',str)); + end + set(gcf,'UserData',data); + end +end + +set(getfield(get(pre_h,'UserData'),'results_h'),'String',answer); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: zero2one_scale %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function zero2one_scale(varargin) + +if nargin == 1 + LOG=1; +else + LOG=0; +end + + +data=get(gcf,'UserData'); +sData=data.sData; +undo=sData; +INDEX=sData.INDEX; +sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]); + +if isempty(get(data.comp_names_h,'Value')) + errordlg('There must be components chosen for scaling.'); + return; +end + +sData=som_normalize(sData,'range',get_indices); +sData.MODIFIED=1; +sData.INDEX=INDEX; + +data.sData=sData; +data.undo.sData=undo; +if ~LOG + data.LOG{length(data.LOG)+1}='% Scale [0,1]'; + data.LOG{length(data.LOG)+1}='preprocess(''zscale'', ''foo'');'; +end +set(gcf,'UserData',data); + +vects=zeros(1,length(sData.data(:,1))); +vects(data.selected_vects)=1; + +cplot_mimema; +plot_hist; +vect_means(sData,data.vect_mean_h,data.selected_vects); +draw_vectors(vects,data.vector_h); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: var_scale %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function var_scale(varargin) + +if nargin == 1 + LOG=1; +else + LOG=0; +end + +data=get(gcf,'UserData'); +sData=data.sData; +undo=sData; +INDEX=sData.INDEX; +sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]); + +if isempty(get(data.comp_names_h,'Value')) + errordlg('There must be components chosen for scaling.'); + return; +end + +sData=som_normalize(sData,'var',get_indices); + +sData.INDEX=INDEX; +sData.MODIFIED=1; + +data.sData=sData; +data.undo.sData=undo; +if ~LOG + data.LOG{length(data.LOG)+1}='% Scale var=1'; + data.LOG{length(data.LOG)+1}='preprocess(''vscale'', ''foo'');'; +end +set(gcf,'UserData',data); + +vects=zeros(1,length(sData.data(:,1))); +vects(data.selected_vects)=1; + +cplot_mimema; +plot_hist; +vect_means(sData,data.vect_mean_h,data.selected_vects); +draw_vectors(vects,data.vector_h); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: hist_eq %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function hist_eq(varargin) + +if nargin == 1 + LOG=1; +else + LOG=0; +end + +data=get(gcf,'UserData'); +sData=data.sData; +undo=sData; +INDEX=sData.INDEX; +sData=rmfield(sData,[{'INDEX'},{'MODIFIED'}]); + +if isempty(get(data.comp_names_h,'Value')) + errordlg('There must be components chosen for ''Histogram eq''.'); + return; +end + +sData=som_normalize(sData,'histD',get_indices); + +sData.INDEX=INDEX; +sData.MODIFIED=1; + +data.sData=sData; +data.undo.sData=undo; +if ~LOG + data.LOG{length(data.LOG)+1}='% Histogram eq'; + data.LOG{length(data.LOG)+1}='preprocess(''histeq'', ''foo'');'; +end +set(gcf,'UserData',data); + +vects=zeros(1,length(sData.data(:,1))); +vects(data.selected_vects)=1; + +cplot_mimema; +plot_hist; +vect_means(sData,data.vect_mean_h,data.selected_vects); +draw_vectors(vects,data.vector_h); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: hist_eq2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function hist_eq2(varargin) + +if nargin == 1 + LOG=1; +else + LOG=0; +end + + +data=get(gcf,'UserData'); +sData=data.sData; +undo=sData; + +INDEX=sData.INDEX; +sData=rmfield(sData,[{'INDEX'};{'MODIFIED'}]); + +if isempty(get(data.comp_names_h,'Value')) + errordlg('There must be components chosen for ''Histogram eq2''.'); + return; +end + +inds=get_indices; +%%%[sData,ok]=som_normalize(sData,inds,'histC'); +sData=som_normalize(sData,'histC',inds); +sData.INDEX=INDEX; +sData.MODIFIED=1; + +data.sData=sData; +data.undo.sData=undo; +if ~LOG + data.LOG{length(data.LOG)+1}='% Histogram eq2'; + data.LOG{length(data.LOG)+1}='preprocess(''histeq2'', ''foo'');'; +end +set(gcf,'UserData',data); + +vects=zeros(1,length(sData.data(:,1))); +vects(data.selected_vects)=1; + +cplot_mimema; +plot_hist; +vect_means(sData,data.vect_mean_h,data.selected_vects); +draw_vectors(vects,data.vector_h); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: logarithm %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function logarithm(varargin) + +if nargin == 1 + LOG=1; +else + LOG=0; +end + +data=get(gcf,'UserData'); +sData=data.sData; +undo=sData; + +INDEX=sData.INDEX; +sData=rmfield(sData,[{'INDEX'},{'MODIFIED'}]); + +if isempty(get(data.comp_names_h,'Value')) + errordlg('There must be components chosen for ''Log''.'); + return; +end + +Data=som_normalize(sData,'log',get_indices); + +sData.INDEX=INDEX; +sData.MODIFIED=1; + +data.sData=sData; +data.undo.sData=undo; +if ~LOG + data.LOG{length(data.LOG)+1}='% Log'; + data.LOG{length(data.LOG)+1}='preprocess(''log'', ''foo'');'; +end +set(gcf,'UserData',data); + +vects=zeros(1,length(sData.data(:,1))); +vects(data.selected_vects)=1; + +cplot_mimema; +plot_hist; +vect_means(sData,data.vect_mean_h,data.selected_vects); +draw_vectors(vects,data.vector_h); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [answer,method]=build_expr(string,evaltype) + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +method=[]; +if isempty(pre_h) + close_preprocess; + errordlg('''Preprocess'' -figure does not exist. Terminating program...'); + return; + +end + +if isempty(string) + str = '[]'; + return; +end + +tmp=[]; +[name,assign,skip]=check_assign(string,evaltype); + +if ~strcmp(assign,'NOTASSIGN') & ~strcmp(assign,'error') + string=string(skip:length(string)); +end + +if ~strcmp(assign,'error') + if isempty(string) + answer='Illegal expression.'; + return; + end + [str,skip]=check_token(string,evaltype); + method=string; + while ~strcmp(str,'error') & ~strcmp(tmp,'error') & skip < length(string) + if ~strcmp(tmp,')') + str=cat(2,str,tmp); + end + [tmp,skip2]=check_token(string(skip+1:length(string)),evaltype); + skip=skip+skip2; + + end + if ~strcmp(tmp,')') & ~strcmp(tmp,'error') + str=cat(2,str,tmp); + elseif strcmp(tmp,'error') + str='error'; + end +end + +if ~strcmp(assign,'error') & ~strcmp(str,'error'); + answer=evalin('caller',str,'lasterr'); +else + answer='??? Illegal expression.'; +end + + +data=get(pre_h,'UserData'); +sData=data.sData; +if strcmp(assign,'NOTASSIGN') & strcmp(evaltype,'single') & ~isstr(answer) + if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value')) + errordlg('There are not components chosen.'); + answer='??? Illegal expression.'; + return; + end + index=getfield(get_indices,{1}); + if strcmp(assign,'NOTASSIGN') + if length(sData.data(:,index)) ~=length(answer) & ~isscalar(answer) + answer='??? Illegal assignment.'; + else + sData.data(:,index)=answer; + sData.MODIFIED=1; + data.sData=sData; + set(pre_h,'UserData',data); + end + else + if length(sData.data(str2num(assign),index)) ~=length(answer) & ~isscalar(answer) + answer='??? Illegal assignment.'; + else + sData.data(str2num(assign),index)=answer; + sData.MODIFIED=1; + data.sData=sData; + set(pre_h,'UserData',data); + end + end +elseif ~strcmp(assign,'error') & ~isstr(answer) & ~strcmp(assign,'NOTASSIGN') + switch name + case 'x' + if isempty(get(data.comp_names_h,'Value')) + return; + end + index = getfield(get_indices,{1}); + if isempty(assign) + if length(sData.data(:,index)) ~= length(answer) & ~isscalar(answer) + answer='??? Illegal assignment.'; + else + sData.data(:,index)=answer; + sData.MODIFIED=1; + data.sData=sData; + if strcmp(evaltype,'multiple') + data.sData.comp_norm(index)={[]}; + end + set(pre_h,'UserData',data); + end + else + args=create_args(assign,'x'); + if length(args) == 1 + len=max(str2num(args{1})); + if ~isscalar(len) + answer='??? Illegal assignment.'; + return; + elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1 + answer='??? Illegal assignment.'; + return; + elseif ~all(size(sData.data(str2num(args{1}),index))) == size(answer) & ~isscalar(answer) + answer='??? Illegal assignment.'; + return; + else + sData.data(str2num(args{1}),index)=answer; + sData.MODIFIED=1; + data.sData=sData; + if strcmp(evaltype,'multiple') + data.sData.comp_norm(index)={[]}; + end + set(pre_h,'UserData',data); + end + else + len=max(str2num(args{1})); + dim=max(str2num(args{2})); + asize=size(answer); + msize=size(sData.data); + if ~isscalar(len) | ~isscalar(dim) + answer='??? Illegal assignment.'; + return; + elseif len > length(sData.data(:,1)) | len < 1 + answer='??? Illegal assignment.'; + return; + elseif dim > 1 | dim > msize(2) | min(str2num(args{2})) < 1 + answer='??? Illegal assignment.'; + return; + end + len=length(str2num(args{1})); + dim=length(str2num(args{1})); + if ~all([len dim] == asize) & ~isscalar(answer) + answer='??? Illegal assignment.'; + return; + else + tmp=sData.data(:,index); + tmp([str2num(args{1})],[str2num(args{2})])=answer; + sData.data(:,index)=tmp; + sData.MODIFIED=1; + data.sData=sData; + if strcmp(evaltype,'multiple') + data.sData.comp_norm(index)={[]}; + end + set(pre_h,'UserData',data); + end + end + end + + case 'xs' + if isempty(get(data.comp_names_h,'Value')) + return; + end + indices=get_indices; + if isempty(assign) + if ~all(size(answer) == size(sData.data(:,indices))) & ~isscalar(answer) + answer='??? Illegal assignment.'; + else + sData.data(:,indices) = answer; + sData.MODIFIED=1; + data.sData=sData; + data.sData.comp_norm(indices)={[]}; + set(pre_h,'UserData',data); + end + else + args=create_args(assign,'xs'); + if length(args) == 1 + len=max(str2num(args{1})); + if ~isscalar(len) + answer='??? Illegal assignment.'; + return; + elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1 + answer='??? Illegal assignment.'; + return; + end + if ~all(size(answer) == size(sData.data(str2num(args{1})))) &... + ~isscalar(answer) + answer='??? Illegal assignment.'; + return; + else + tmp=sData.data(:,indices); + tmp(str2num(args{1}))=answer; + sData.data(:,indices)=tmp; + sData.MODIFIED=1; + sData.comp_norm{indices}={[]}; + data.sData=sData; + set(pre_h,'UserData',data); + end + else + len=max(str2num(args{1})); + dim=max(str2num(args{2})); + asize=size(answer); + msize=size(sData.data(:,indices)); + if ~isscalar(len) | ~isscalar(dim) + answer='??? Illegal assignment.'; + return; + elseif len > msize(1) | min(str2num(args{1})) < 1 + answer='??? Illegal assignment.'; + return; + elseif dim > msize(2) | min(str2num(args{2})) < 1 + answer='??? Illegal assignment.'; + return; + end + len=length(str2num(args{1})); + dim=length(str2num(args{2})); + if ~all([len dim] == asize) & ~isscalar(answer) + answer='??? Illegal assignment'; + return; + else + tmp=sData.data(:,indices); + tmp([str2num(args{1})],[str2num(args{2})])=answer; + sData.MODIFIED=1; + sData.data(:,indices)=tmp; + data.sData=sData; + data.sData.comp_norm(indices)={[]}; + set(pre_h,'UserData',data); + end + + end + end + + case 'D' + if isempty(assign) + if ~all(size(answer) == size(sData.data)) & ~isscalar(answer) + answer='??? Illegal assignment.'; + else + if isscalar(answer) + sData.data(:,:)=answer; + else + sData.data=answer; + end + sData.MODIFIED=1; + data.sData=sData; + data.sData.comp_norm(1:length(sData.data(1,:)))={[]}; + set(pre_h,'UserData',data); + end + else + args=create_args(assign,'D'); + if length(args) == 1 + len=max(str2num(args{1})); + if ~isscalar(len) + answer='??? Illegal assignment.'; + return; + elseif len > length(sData.data(:,1)) | min(str2num(args{1})) < 1 + answer='??? Illegal assignment.'; + return; + end + if ~all(size(answer) == size(sData.data(str2num(args{1})))) &... + ~isscalar(answer) + answer='??? Illegal assignment.'; + else + sData.data(str2num(args{1}))=answer; + sData.MODIFIED=1; + data.sData=sData; + [i,j]=ind2sub(size(sData.data),str2num(args{1})); + data.sData.comp_norm(j)={[]}; + set(pre_h,'UserData',data); + end + else + len=max(str2num(args{1})); + dim=max(str2num(args{2})); + asize=size(answer); + msize=size(sData.data); + if ~isscalar(len) | ~isscalar(dim) + answer='??? Illegal assignment.'; + return; + elseif len > msize(1) | min(str2num(args{1})) < 1 + answer='??? Illegal assignment.'; + return; + elseif dim > msize(2) | min(str2num(args{2})) < 1 + answer= '??? Illegal assignment.'; + return; + end + len = length(str2num(args{1})); + dim = length(str2num(args{2})); + if ~all([len dim] == asize) & ~isscalar(answer) + answer='??? Illegal assignment.'; + return; + else + sData.data([str2num(args{1})],[str2num(args{2})])=answer; + sData.MODIFIED=1; + data.sData=sData; + data.sData.comp_norm(str2num(args{2}))={[]}; + set(pre_h,'UserData',data); + end + end + end + end +end +if sData.MODIFIED + selected=getfield(get(pre_h,'UserData'),'selected_vects'); + vector_h=getfield(get(pre_h,'UserData'),'vector_h'); + vect_mean_h=getfield(get(pre_h,'UserData'),'vect_mean_h'); + vects=zeros(length(sData.data(:,1))); + vects(selected)=1; + draw_vectors(vects,vector_h); + vect_means(sData,vect_mean_h,selected); + pro_tools('plot_hist'); + pro_tools('c_stat'); + cplot_mimema; +end + + +%%% Subfunction: check_assign %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [name,string,skip]=check_assign(string,evaltype) + + +reswords=[{'D'};{'x'};{'xs'}]; +flag=0; +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +if isempty(pre_h) + man_h=findobj(get(0,'Children'),'Tag','Management'); + clip_h=findobj(get(0,'Children'),'Tag','Clipping'); + errordlg('''Preprocess'' -window does not exist. Terminating program.'); + if ~isempty(man_h) + close man_h; + end + if ~isempty(clip_h) + close clip_h; + end + return; +end + +EMPTY=isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value')); + +[name,s]=give_token(string,evaltype); +skip=length(s); + +if strcmp(evaltype,'single') & ~strcmp(name,'x') + string='NOTASSIGN'; + return; +end + +if strcmp(name,'other') & ~strcmp(s,'x') + string = 'error'; + return; +end + +if strcmp(name,[{'x'};{'xs'}]) + comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h'); + if isempty(get(comp_names_h,'Value')) + errordlg('There are not components chosen.'); + string='error'; + return; + end +end + + +if skip == length(string) | ~strcmp(name,reswords) + string = 'NOTASSIGN'; + return; +end + +if (strcmp(name,'x') | strcmp(name,'xs')) & EMPTY + errordlg('There are not components chosen.'); + string = 'error'; + return; +end + +[t,s]=give_token(string(length(name)+1),evaltype); + +if strcmp(t,'(') + flag=1; +end + +[foo,skip]=check_token(string,evaltype); +if length(name) ~= skip-1 + skip=skip-1; + tmp=string(length(name)+1:skip); +else + tmp = []; +end + +if flag & tmp(length(tmp)) ~= ')' + tmp(length(tmp)+1)=')'; +end + +if skip==length(string) + return; +end + +skip=skip+1; +if length(string) ~= skip + [t,s]=give_token(string(skip+1:length(string)),evaltype); +else + string='NOTASSIGN'; + return; +end + +if ~strcmp(t,'=') + string = 'NOTASSIGN'; + return; +end +string=tmp; +skip = skip+2; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: isscalar %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function bool = isscalar(x) + + m= size(x); + + bool = m(1) == 1 & m(2) == 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: create_args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function args=create_args(string,type) + +arg2=''; +i=2; +j=1; +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +msize=size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data')); + + +if string(i) == ':' + arg1=num2str(cat(2,'1:',num2str(msize(1)))); + i=i+1; + j=j+length(arg1); +end + +while string(i) ~=',' & string(i) ~=')' + arg1(j)=string(i); + i=i+1; + j=j+1; +end + + + +if string(i) ==',' + j=1; + i=i+1; + if string(i)==':' + switch type + case 'x' + arg2='1'; + case 'cs' + arg2=num2str(get_indices); + case 'D' + arg2=num2str(cat(2,'1:',num2str(msize(2)))); + end + i=i+1; + j=j+length(arg2); + end + + while string(i) ~= ')' + arg2(j)=string(i); + j=j+1; + i=i+1; + end +end + + +args{1}=arg1; +if ~isempty(arg2) + args{2} = arg2; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [str,skip] = check_token(string,evaltype) + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +tmp_string=string; +[t,s]=give_token(tmp_string,evaltype); +skip=length(s); + +if strcmp(t,'c') + if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value')) + errordlg('There are no components chosen.'); + str='error'; + return; + end + index=getfield(get_indices,{1}); + str=cat(2,'[',num2str(index),']'); + if skip == length(tmp_string) + return; + end + tmp_string=tmp_string(skip+1:length(tmp_string)); + [t,s] = give_token(tmp_string,evaltype); + if ~strcmp(t,'(') + return; + end + [args,skip2] = get_args(tmp_string(length(s)+1:length(tmp_string)),'c',... + evaltype); + skip=skip+skip2+2; + if strcmp(args,'error') + str = 'error' + return; + elseif ~strcmp(args,'all') + str=cat(2,'getfield(',str,',',args,')'); + else + str=cat(2,'getfield(',str,',{[1]})'); + end +elseif strcmp(t,'cs') + if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value')) + errordlg('There are no components chosen.'); + str='error'; + return; + end + str =cat(2,'[',num2str(get_indices),']'); + if length(s) == length(string) + return; + end + tmp_string=tmp_string(1+length(s):length(string)); + [t,s]=give_token(tmp_string,evaltype); + if ~strcmp(t,'(') + return; + else + [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'cs',... + evaltype); + skip=2+skip+skip2; + if strcmp(args,'error') + str='error'; + return; + elseif ~strcmp(args,'all') + str = cat(2,'getfield(',str,',',args,')'); + else + tmp_str=str; + str=cat(2,'[getfield(',str,',','{1})'); + for i=2:length(get_indices) + str=cat(2,str,';getfield(',tmp_str,',',sprintf('{%d})',i)); + end + str=cat(2,str,']'); + end + end +elseif strcmp(t,'dim') + ind1=getfield(size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data')),{2}); + str=cat(2,'[',num2str(ind1),']'); + if length(s)==length(string) + return; + end + tmp_string=string(1+length(s):length(string)); + [t,s]=give_token(tmp_string,evaltype); + if ~strcmp(t,'(') + return; + end + skip=1+skip+length(s); + [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'dim',... + evaltype); + if strcmp(args,'error') + str = 'error'; + return; + else + skip=skip+skip2; + if ~strcmp(args,'all') + str=cat(2,'getfield(',str,',',args,')'); + end + end + +elseif strcmp(t,'dlen') + ind1=getfield(size(getfield(getfield(get(pre_h,'UserData'),'sData'),'data')),{1}); + str=cat(2,'[',num2str(ind1),']'); + if length(s)==length(string) + return; + end + tmp_string=string(1+length(s):length(string)); + [t,s]=give_token(tmp_string,evaltype); + if ~strcmp(t,'(') + return; + end + skip=skip+length(s); + [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'dlen',... + evaltype); + if strcmp(args,'error') + str='error'; + return; + else + skip=1+skip+skip2; + if ~strcmp(args,'all') + str=cat(2,'getfield(',str,',',args,')'); + end + end + +elseif strcmp(t,'x') + if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value')) + errordlg('There are not components chosen.'); + str='error'; + return; + end + len=getfield(size(getfield(getfield(get(pre_h,'UserData'),... + 'sData'),'data')),{1}); + index=num2str(getfield(get_indices,{1})); + h_str='findobj(get(0,''Children''),''Tag'',''Preprocess'')'; + get_str=cat(2,'getfield(get(',h_str,',''UserData''),''sData'')'); + get_str=cat(2,'getfield(',get_str,',''data'')'); + str=cat(2,'getfield(',get_str,',{[1:',num2str(len),'],',index,'})'); + if length(s) == length(string) + return; + end + tmp_string=string(1+length(s):length(string)); + [t,s]=give_token(tmp_string,evaltype); + if ~strcmp(t,'('); + return; + end + skip=skip+length(s); + [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'x',... + evaltype); + if strcmp(args,'error') + str = 'error'; + return; + else + skip=1+skip+skip2; + if ~strcmp(args,'all') + str=cat(2,'getfield(',str,',',args,')'); + end + end + +elseif strcmp(t,'xs') + if isempty(get(getfield(get(pre_h,'UserData'),'comp_names_h'),'Value')) + errordlg('There are not components chosen.'); + str='error'; + return; + end + len=getfield(size(getfield(getfield(get(pre_h,'UserData'),... + 'sData'),'data')),{1}); + index=get_indices; + index=cat(2,'[',num2str(index),']'); + h_str='findobj(get(0,''Children''),''Tag'',''Preprocess'')'; + get_str=cat(2,'getfield(get(',h_str,',''UserData''),''sData'')'); + get_str=cat(2,'getfield(',get_str,',''data'')'); + str=cat(2,'getfield(',get_str,',{[1:',num2str(len),'],',index,'})'); + if length(s) == length(string) + return; + end + tmp_string=string(1+length(s):length(string)); + [t,s]=give_token(tmp_string,evaltype); + if ~strcmp(t,'(') + return; + end + skip=1+skip+length(s); + + [args,skip2]=get_args(tmp_string(1+length(s):length(tmp_string)),'xs',... + evaltype); + if strcmp(args,'error') + str = 'error'; + return; + elseif ~strcmp(args,'all') + str=cat(2,'getfield(',str,',',args,')'); + skip=skip+skip2; + else + skip=skip+skip2; + [dlen,dim]=size(eval(str)); + tmp_str=str; + str=cat(2,'[','getfield(',tmp_str,sprintf(',{1:%d,1})',dlen)); + for i=2:dim + tmp=sprintf(',{1:%d,%d})',dlen,dim); + str=cat(2,str,';','getfield(',tmp_str,tmp); + end + str=cat(2,str,']'); + end +elseif strcmp(t,'D') + get_h='findobj(get(0,''Children''),''Tag'',''Preprocess'')'; + str=cat(2,'getfield(getfield(get(',get_h,',''UserData''),''sData''),''data'')'); + + if length(s) >= length(tmp_string) + return; + end + + tmp_string=tmp_string(1+length(s):length(tmp_string)); + [t,s]=give_token(tmp_string,evaltype); + if ~strcmp(t,'(') + return; + else + tmp_string=tmp_string(1+length(s):length(tmp_string)); + skip = skip+length(s); + [args, skip2]=get_args(tmp_string,'D',evaltype); + if strcmp(args,'error') + str='error'; + return; + elseif ~strcmp(args,'all') + str=cat(2,'getfield(',str,',',args,')'); + skip=1+skip+skip2; + else + skip=1+skip+skip2; + [dlen,dim]=size(eval(str)); + tmp_str=str; + str=cat(2,'[getfield(',str,sprintf(',{1:%d,1})',dlen)); + for i=2:dim + tmp=sprintf(',{1:%d,%d}',dlen,i); + str=cat(2,str,';getfield(',tmp_str,tmp,')'); + end + str=cat(2,str,']'); + end + end +else + if strcmp(t,'(') + str = t; + str2=''; + tmp_string=tmp_string(1+length(s):length(tmp_string)); + while ~strcmp(str2,')') & ~isempty(tmp_string) + [str2,skip2]=check_token(tmp_string,evaltype); + if strcmp(str2,'error') + str='error'; + return; + end + skip=skip+skip2; + tmp_string=tmp_string(skip2+1:length(tmp_string)); + str=cat(2,str,str2); + end + if ~strcmp(str2,')') + str = 'error'; + end + else + str = s; + end +end + +%%% Subfunction: get_args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [str,skip] = get_args(string,flag,evaltype) + +res_words=[{'D'};{'c'};{'cs'};{'dim'};{'dlen'};{'x'};{'xs'}]; +NOTALL=1; +if isempty(string) + str='error' + skip=[]; + return; +end +[t,s] = give_token(string,evaltype); + + +skip=length(s); +if any(strcmp(t,res_words)); + [str,skip2] = check_token(string,evaltype); + string=string(1+length(s):length(string)); + str=cat(2,'{[',str); + [t,s]=give_token(string,evaltype); +elseif t==')' | t==',' + str = 'error'; + return; +elseif strcmp(t,':'); + if length(s) == length(string) + str='error'; + return; + end + [t,s]=give_token(string(1+length(s):length(string)),evaltype); + if t == ')' + str = 'all'; + return; + end + switch flag + case {'c','cs','dim','dlen'} + str= '{[1'; + otherwise + str=cat(2,'{[',get_all('vect')); + end + NOTALL=0; + string=string(1+length(s):length(string)); + [t,s]=give_token(string,evaltype); + skip=skip+1; +else + str = cat(2,'{[',s); +end +str2 =[]; + + +if ~strcmp(t,',') & ~strcmp(t,')') + skip=skip-length(s); +end + + + +while ~strcmp(t,',') & ~strcmp(t,')') & NOTALL; + str=cat(2,str,str2); + [t,s] = give_token(string,evaltype); + if length(s) == length(string) + str = 'error'; + return; + end + string=string(1+length(s):length(string)); + skip=skip+length(s); + [t,s]=give_token(string,evaltype); + if length(s) == length(string) & ~strcmp(t,')') + str = 'error'; + return; + end + + [str2,foo]=check_token(string,evaltype); +end + +if NOTALL & ~strcmp(t,')') + skip=skip+1; +end + +if strcmp(t,')') + str=cat(2,str,']}'); + return +end + +str=cat(2,str,']',',','['); +str2 = []; + + +[t,s] = give_token(string,evaltype); +if strcmp(t,')') + str = 'error' + return; +end +NOTALL=1; +string=string(1+length(s):length(string)); +[t,s]=give_token(string,evaltype); +if strcmp(t,':'); + switch flag + case {'c','dim','dlen','x'} + str=cat(2,str,'1'); + case 'D' + str=cat(2,str,get_all('comp')); + case {'cs','xs'} + str=cat(2,str,'1:',num2str(length(get_indices))); + end + NOTALL=0; + if length(s) == length(string) + str='error'; + return; + end + string=string(1+length(s):length(string)); + [t,s]=give_token(string,evaltype); +end + +if ~strcmp(t,')') & NOTALL + skip=skip-1; +end + +while ~strcmp(t,')') & NOTALL + str=cat(2,str,str2); + skip=skip+length(s); + if length(s) == length(string) & ~strcmp(t,')') + str='error'; + return; + end + [str2,foo]=check_token(string,evaltype); + string=string(1+length(s):length(string)); + [t,s]=give_token(string,evaltype); +end +if ~strcmp(t,')') + str='error'; + return; +end + + +str=cat(2,str,str2,']}'); +skip=skip+length(s); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: get_all %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function str=get_all(vect_or_comp) + +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + +switch vect_or_comp + case 'vect' + dim=getfield(size(getfield(getfield(get(pre_h,'UserData'),... + 'sData'),'data')),{1}); + str=cat(2,'1:',num2str(dim)); + case 'comp' + dim=getfield(size(getfield(getfield(get(pre_h,'UserData'),... + 'sData'),'data')),{2}); + str=cat(2,'1:',num2str(dim)); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [token,str]=give_token(string,evaltype) + +n=length(string); +i=1; +char=string(i); + +switch analyze_char(string(i)); + case 'num' + token='num'; + while i <= n & strcmp('num',analyze_char(string(i))) + str(i)=string(i); + i=i+1; + end + case 'other' + switch string(i) + case ':' + token = ':'; + case ',' + token = ','; + case '(' + token = '('; + case ')' + token = ')'; + case '=' + token = '='; + otherwise + token='other'; + end + str=string(i); + case 'alpha' + while i <= n & strcmp('alpha',analyze_char(string(i))) + str(i)=string(i); + i=i+1; + end + token = find_res_word(str,evaltype); +end + +%%% Subfunction: analyze_char %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function type=analyze_char(char) + + +if ((char-0) >= ('0'-0) & (char-0) <= ('9'-0)) + type='num'; +elseif ((char-0) >= ('a'-0) & (char-0) <= ('z'-0)) ... + | ((char-0) >= ('A'-0) & (char-0) <= ('Z'-0)) + type='alpha'; +else + type='other'; +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%% Subfunction: find_res_word %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function token = find_res_word(string,evaltype) + +reswords=[{'D'};{'c'};{'cs'};{'dim'};{'dlen'};{'x'};{'xs'};{'other'}]; + +for i=1:length(reswords); + token=reswords{i}; + if strcmp(string,reswords{i}) + if strcmp(evaltype,'single') & ~strcmp(string,'x') + token = 'other'; + end + return; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function close_func(varargin) + +switch varargin{1} + case 'close_c' + str='% Closing the ''Clipping'' -window...'; + clip_h=findobj(get(0,'Children'),'Tag','Clipping'); + close(clip_h); + case 'close_sD' + str='% Closing the ''Data Set Management'' -window...'; + sD_h=findobj(get(0,'Children'),'Tag','Management'); + close(sD_h); + case 'close_w' + str='% Closing the ''Windowed'' -window...'; + win_h=findobj(get(0,'Children'),'Tag','Window'); + close(win_h); + case 'close_s' + str='% Closing the ''Select'' -window...'; + sel_h=findobj(get(0,'Children'),'Tag','Select'); + close(sel_h); + case 'close_d' + str='% Closing the ''Delay'' -window...'; + del_h=findobj(get(0,'Children'),'Tag','Delay'); + close(del_h); +end + +if nargin ~= 2 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + preh_udata=get(pre_h,'UserData'); + str2=cat(2,'preprocess(''',varargin{1},''',''foo'');'); + preh_udata.LOG{length(preh_udata.LOG)+1}=str; + preh_udata.LOG{length(preh_udata.LOG)+1}=str2; + set(pre_h,'UserData',preh_udata); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function log_file + +answer=inputdlg('Give the name of the outputfile:','LOG function',1,... + {'log_function'}); + +if isempty(answer) + return; +end + + +tmp=clock; +str =cat(2,'% Created: ',... + date,... + ' ',sprintf('%d:%d\n%\n\n',tmp(4),tmp(5))); +pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); +LOG=getfield(get(pre_h,'UserData'),'LOG'); +file=cat(2,pwd,'/',answer{1},'.m'); +fid =fopen(file,'w'); + +arg=LOG{2}(12:length(LOG{2})-2); +fprintf(fid,'%s\n \n',cat(2,'function ',answer{1},'(',arg,')')); +fprintf(fid,'%s\n',str); +for i=1:length(LOG) + fprintf(fid,'%s\n',LOG{i}); +end +fclose(fid); +disp(sprintf('LOG-file ''%s'' is done.',file)); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function get_selected_inds(varargin) + +if nargin == 1 + LOG=1; + answer = {varargin{1}}; +else + LOG=0; +end + +selected=getfield(get(gcf,'UserData'),'selected_vects'); +if ~LOG + answer=inputdlg('Give the name of the output variable:',... + '',1,{'indices'}); +end + +if isempty(answer) | isempty(answer{1}) + return; +else + assignin('base',answer{1},selected); + disp(cat(2,'Indices of the selected vectors are set to the workspace ',... + sprintf(' as ''%s''.',answer{1}))); + if ~LOG + data=get(gcf,'UserData'); + data.LOG{length(data.LOG)+1}=... + '% Saving indices of the selected vectors to the workspace.'; + data.LOG{length(data.LOG)+1}=cat(2,'preprocess(''get_inds'',',... + '''',answer{1},''');'); + set(gcf,'UserData',data); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function no_of_selected(varargin) + +if nargin == 1 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + set(0,'CurrentFigure',pre_h); + LOG = 1; +else + LOG = 0; +end + +results_h=getfield(get(gcf,'UserData'),'results_h'); +no=length(getfield(get(gcf,'UserData'),'selected_vects')); +str={sprintf('Number of selected vectors: %d\n', no)}; +set(results_h,'String',str,'HorizontalAlignment','left'); + +if ~LOG + data=get(gcf,'UserData'); + data.LOG{length(data.LOG)+1}='% Number of selected vectors'; + data.LOG{length(data.LOG)+1}='preprocess(''no_of_sel'',''foo'');'; + set(gcf,'UserData',data); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function select_all_comps(varargin) + +if nargin == 1 + pre_h=findobj(get(0,'Children'),'Tag','Preprocess'); + set(0,'CurrentFigure',pre_h); + LOG=1; +else + LOG=0; +end + +comp_names_h=getfield(get(gcf,'UserData'),'comp_names_h'); + +set(comp_names_h,'Value',[1:length(get(comp_names_h,'String'))]); +sel_comp; + +if ~LOG + data=get(gcf,'UserData'); + data.LOG{length(data.LOG)+1}='% Select all components'; + data.LOG{length(data.LOG)+1}='preprocess(''sel_all_comps'',''foo'');'; + set(gcf,'UserData',data); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function code=write_log_code(indices,arg1,arg2,arg3,arg4,arg5,arg6); + +str=textwrap({num2str(indices)},500); + +code{1}=sprintf('inds=[];'); +for i=1:length(str); + code{i+1}=sprintf(' inds=cat(2,inds,[%s]);',str{i}); +end +str=cat(2,'preprocess(''''clip_data'''',''''',arg1,' ',num2str(arg2),' ',... + num2str(arg3),' ',num2str(arg4),... + ' ',num2str(arg5),' ',num2str(arg6),' '); +code{length(code)+1}=cat(2,'eval(cat(2,',... + '''',str,'''',... + ',num2str(inds),'''''');''));'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/rep_utils.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,572 @@ +function aout = rep_utils(action,fmt,fid) + +%REP_UTILS Utilities for print reports and report elements. +% +% aout = rep_utils(action,fmt,[fid]) +% +% Input and output arguments ([]'s are optional): +% action (string) action identifier +% (cell array) {action,par1,par2,...} +% the action identifier, followed by action +% parameters +% [fmt] (string) format of output, 'txt' by default +% [fid] (scalar) output file id, by default NaN in which +% case output is not written, only returned +% in aout +% +% aout (varies) output of the action +% +% Here are the actions and their arguments: +% 'printlines' par1 (cellstr) print par1, each cell on a new line +% 'header' par1 (string) print document header using par1 as title +% 'footer' print document footer +% 'compile' par1 (string) compile the named document (only 'ps' and 'pdf') +% 'inserttable' par1 (struct) print given table +% par2 (scalar) print lines between rows if par2=1 +% par3 (scalar) use longtable format (only 'ps' and 'pdf') +% 'printfigure' par1 (string) print current figure to file, par1 = filename +% par2 (scalar) used resolution (150 dpi by default) +% par3 (scalar) if par3=1, insert figure in minipage +% 'insertfigure' par1 (string) insert figure to report, par1 = filename of figure +% par2 (vector) size 2 x 1, size of figure relative to page size +% NaN = automatic scaling +% par3 (scalar) if par3=1, insert figure in minipage (only 'ps' and 'pdf') +% 'insertbreak' insert paragraph break into report +% +% See also REP_STATS. + +% Contributed to SOM Toolbox 2.0, January 2nd, 2002 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 020102 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +pars = {''}; +if iscell(action), + if length(action)>1, pars = action(2:end); end + action = action{1}; +end + +if nargin<2 | isempty(fmt), fmt = 'txt'; end +global REPORT_OUTPUT_FMT +REPORT_OUTPUT_FMT = fmt; + +if nargin<3 | isempty(fid), fid = NaN; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +aout = []; +printable = 0; + +switch action, +case 'printlines', + aout = pars{1}; +case 'header', + switch fmt, + case {'ps','pdf'}, aout = tex_startdocument(pars{1}); + case 'html', aout = html_startpage(pars{1}); + case 'txt', aout = cell(0); + end + printable = 1; +case 'footer', + switch fmt, + case {'ps','pdf'}, aout = tex_enddocument; + case 'html', aout = html_endpage; + case 'txt', aout = cell(0); + end + printable = 1; +case 'compile', aout = compiledocument(pars{1}); +case 'inserttable', aout = inserttable(pars{:}); printable = 1; +case 'printfigure', printfigure(pars{:}); +case 'insertfigure', aout = insertfigure(pars{:}); printable = 1; +case 'insertbreak', aout = insertbreak; printable = 1; +case 'joinstr', aout = joinstr(pars{:}); printable = 1; +case 'rulestr', aout = rulestr(pars{:}); printable = 1; +case 'c_and_p_str', aout = c_and_p_str(pars{:}); printable = 1; +case 'p_str', aout = p_str(pars{:}); printable = 1; +end + +% if output file is given, print lines +if ~isnan(fid) & printable, + if ~iscell(aout), aout = {aout}; end + for i = 1:length(aout), fprintf(fid,'%s\n',fmtline(aout{i})); end +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +%% simple formatter strings + +function s = joinstr(cs, sep1, sep2) + if nargin==1, sep1 = ', '; sep2 = ' and '; end + if nargin<3, sep2 = sep1; end + if isempty(cs), + s = ''; + elseif strcmp(sep1,'\n'), + if size(cs,1)==1, cs = cs'; end + s = char(cs); + else + s = cs{1}; + for i=2:length(cs)-1, s = [s sep1 cs{i}]; end + if length(cs)>1, s = [s sep2 cs{end}]; end + end + return; + +function str = c_and_p_str(n,m) + + % return a string of form # (%), e.g. '23 (12%)' + if n==m, p = '100'; + elseif n==0, p = '0'; + else p = sprintf('%.2g',100*n/m); + end + str = sprintf('%d (%s%%)',round(n),p); + return; + +function str = p_str(p) + % return a string of form %, e.g. '12%' + if round(p*100)>=100, p = sprintf('%3g',100*p); + elseif abs(p)<eps, p = '0'; + else p = sprintf('%.2g',100*p); + end + str = sprintf('%s%%',p); + return; + +function cs = rulestr(sR,cnames) + global REPORT_OUTPUT_FMT + switch REPORT_OUTPUT_FMT + case {'ps','pdf'}, [leq,geq,infi,m,less,in] = deal('\leq','\geq','\inf','$','<','\in'); + case 'html', [leq,geq,infi,m,less,in] = deal('<=','>=','Inf',' ','<',' '); + case 'txt', [leq,geq,infi,m,less,in] = deal('<=','>=','inf',' ','<',''); + end + nr = length(sR); + cs = cell(nr,1); + fmt = '%.2g'; + if nargin<2, cnames = {sR.name}; end + if isempty(cnames), cnames = cell(nr,1); cnames(:) = {''}; end + for i=1:nr, + low = sR(i).low; + high = sR(i).high; + switch isfinite(low) + 2*isfinite(high), + case 0, cs{i} = [cnames{i} ' ' 'any']; + case 1, cs{i} = [cnames{i} ' ' m geq sprintf(fmt,low) m]; + case 2, cs{i} = [cnames{i} ' ' m less sprintf(fmt,high) m]; + case 3, cs{i} = [cnames{i} ' ' m in '[' sprintf(fmt,low) ',' sprintf(fmt,high) ']' m]; + end + end + return; + +%% print figure + +function imgfmt = fmt2imgfmt + global REPORT_OUTPUT_FMT + switch REPORT_OUTPUT_FMT, + case 'ps', imgfmt = 'ps'; + case 'pdf', imgfmt = 'pdf'; + case 'html', imgfmt = 'png'; + case 'txt', imgfmt = ''; + end + return; + +function printfigure(fname,resolution) + if nargin<2, resolution = 150; end + fnameps = [fname '.ps']; + switch fmt2imgfmt, + case 'ps', + print('-dpsc2',fnameps); + case 'pdf', + print('-dpsc2',fnameps); + eval(sprintf('!ps2pdf %s',fnameps)); + case 'gif', + print('-dpsc2',fnameps); + cmd = 'pstogif'; + opt = sprintf('-depth 1 -density %d',resolution); + unix(sprintf('%s %s -out %s %s',cmd,opt,[fname '.gif'],fnameps)); + case 'png', + opt = sprintf('-r%d',resolution); + print('-dpng',opt,[fname '.png']); + end + return; + +%% headers and footers, and compilation + +function cs = tex_startdocument(title) + % tex document headers + global REPORT_OUTPUT_FMT + cs = cell(0); + cs{end+1} = '\documentclass[10pt,a4paper]{article}'; + cs{end+1} = '\usepackage[dvips]{epsfig,graphicx,color}'; + cs{end+1} = '\usepackage{float,graphics,subfigure}'; + cs{end+1} = '\usepackage{multirow,rotating,portland,lscape,longtable,pifont}'; + cs{end+1} = '\usepackage[T1]{fontenc}'; + if strcmp(REPORT_OUTPUT_FMT,'pdf'), cs{end+1} = '\usepackage{pslatex}'; end + cs{end+1} = '\usepackage[english]{babel}'; + + cs{end+1} = '\oddsidemargin 0 mm'; + cs{end+1} = '\evensidemargin 0 mm'; + cs{end+1} = '\textwidth 17 cm'; + cs{end+1} = '\topmargin 0 mm'; + cs{end+1} = '\textheight 21 cm'; + cs{end+1} = '\voffset 0 mm'; + + cs{end+1} = '\begin{document}'; + cs{end+1} = ['\title{' title '}']; + cs{end+1} = '\maketitle'; + %cs{end+1} = '\tableofcontents'; + %cs{end+1} = '\clearpage'; + return; + +function cs = tex_enddocument + cs = cell(0); + cs{end+1} = '\end{document}'; + return; + +function cs = html_startpage(title) + % print HTML document headers + cs = cell(0); + cs{end+1} = '<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2 Final//EN">'; + cs{end+1} = '<HTML>'; + cs{end+1} = '<HEAD>'; + cs{end+1} = sprintf(' <TITLE>%s</TITLE>',title); + cs{end+1} = '</HEAD>'; + cs{end+1} = '<BODY bgcolor=white vlink="#000033" link="#0000ff" text="#000000">'; + if ~isempty(title), cs{end+1} = sprintf('<H1>%s</H1>',title); end + return; + +function cs = html_endpage + % print HTML document footers + cs = cell(0); + cs{end+1} = '<P><HR>'; + cs{end+1} = '</BODY>'; + cs{end+1} = '</HTML>'; + return; + +function files = compiledocument(filename) + global REPORT_OUTPUT_FMT + switch REPORT_OUTPUT_FMT, + case 'pdf', + eval(sprintf('!pdflatex --interaction batchmode %s.tex',filename)); + eval(sprintf('!pdflatex --interaction batchmode %s.tex',filename)); + %eval(sprintf('!acroread %s.pdf &',filename)); + files = {[filename '.aux'],[filename '.log'],[filename '.out'],[filename '.pdf']}; + case 'ps', + eval(sprintf('!latex --interaction batchmode %s.tex',filename)); + eval(sprintf('!latex --interaction batchmode %s.tex',filename)); + eval(sprintf('!dvips %s.dvi',filename)); + eval(sprintf('!ps2pdf %s.ps',filename)); + %eval(sprintf('!ghostview %s.ps &',filename)); + files = {[filename '.aux'],[filename '.log'],[filename '.out'],[filename '.dvi'],[filename '.pdf']}; + case 'html', + case 'txt', + end + return; + + +function vstr = defaultformat(val) + global REPORT_OUTPUT_FMT + if ischar(val), vstr = val; + elseif iscellstr(val), vstr = char(val); + elseif isempty(val), vstr = ''; + elseif isnumeric(val), + if val==round(val), fmt = '%d'; else fmt = '%.3g'; end + if abs(val)<=eps, vstr = '0'; else vstr = sprintf(fmt,val); end + elseif isstruct(val) & isfield(val,'values') & isfield(val,'headers'), + % a table + vstr = joinstr(inserttable(val,0),'\n'); + if any(strcmp(REPORT_OUTPUT_FMT,{'ps','pdf'})), + vstr= inserttominipage(vstr); + end + else + vstr = ''; fprintf(1,'defaultformat unable to handle input\n'); + whos val + end + return; + +%% report elements (list, table, image, link) + +function str = fmtline(str) + % replace some formatting elements depeding on output format + global REPORT_OUTPUT_FMT + if isempty(str), str = ''; return; end + switch REPORT_OUTPUT_FMT, + case {'ps','pdf'}, + str = strrep(str,'<B>', '{\bf '); + str = strrep(str,'<I>', '{\em '); + str = strrep(str,'<TT>', '{\tt '); + str = strrep(str,'</B>', '}'); + str = strrep(str,'</I>', '}'); + str = strrep(str,'</TT>','}'); + str = strrep(str,'#','\#'); + str = strrep(str,'%','\%'); + case 'html', % nil + case 'txt', + str = strrep(str,'<B>', '*'); + str = strrep(str,'<I>', '*'); + str = strrep(str,'<TT>', ''); + str = strrep(str,'</B>', '*'); + str = strrep(str,'</I>', '*'); + str = strrep(str,'</TT>',''); + end + return; + +function cs = insertbreak + global REPORT_OUTPUT_FMT + cs = cell(0); + switch REPORT_OUTPUT_FMT + case {'ps','pdf'}, cs{end+1} = ''; + case 'html', cs{end+1} = '<P>'; + case 'txt', cs{end+1} = ''; + end + return; + +function insertlist(list,enum) + % make list + global REPORT_OUTPUT_FMT + if nargin<2, enum = 0; end + cs = cell(0); + switch REPORT_OUTPUT_FMT + case {'ps','pdf'}, + if enum, tag = 'enumerate'; else tag = 'itemize'; end + starttag = ['\begin{' tag '}']; + listtag = '\item '; + endtag = ['\end{' tag '}']; + case 'html', + if enum, tag = 'OL'; else tag = 'UL'; end + starttag = ['<' tag '>']; + listtag = '<LI>'; + endtag = ['</' tag '>']; + case 'txt', + starttag = ''; + listtag = '- '; + endtag = ''; + end + cs{end+1} = starttag; + for i=1:length(list), cs{end+1} = sprintf('%s %s',listtag,list{i}); end + cs{end+1} = endtag; + return; + +function csout = tablerow(cs,emp,span) + % construct one table row + global REPORT_OUTPUT_FMT + if nargin<2 | isempty(emp), emp = 'none'; end + if nargin<3 | isempty(span), span = ones(length(cs),2); end + rowspan = span(:,1); colspan = span(:,2); + switch emp, + case 'bold', emp1 = '<B>'; emp2 = '</B>'; + case 'italic', emp1 = '<I>'; emp2 = '</I>'; + case 'fixed', emp1 = '<TT>'; emp2 = '</TT>'; + case 'none', emp1 = ''; emp2 = ''; + case 'header', emp1 = ''; emp2 = ''; tag = 'TH'; + end + csout = cell(0); + switch REPORT_OUTPUT_FMT, + case {'pdf','ps'}, + %switch emp, + % case 'bold', emp1 = '{\bf '; emp2 = '}'; + % case 'italic', emp1 = '{\em '; emp2 = '}'; + % case 'fixed', emp1 = '{\tt '; emp2 = '}'; + % case 'none', emp1 = ''; emp2 = ''; + %end + s0 = ''; + for i=1:length(cs), + if rowspan(i) & colspan(i), + sp1 = ''; sp2 = ''; + if colspan(i)>1, sp1 = [sp1 ' \multicolumn{' num2str(colspan(i)) '}{|c|}{']; sp2 = [sp2 '}']; end + if rowspan(i)>1, sp1 = [sp1 ' \multirow{' num2str(rowspan(i)) '}{2cm}{']; sp2 = [sp2 '}']; end + s = s0; + content = cellstr(defaultformat(cs{i})); + csout{end+1} = [s sp1 emp1 content{1}]; + for j=2:length(content), csout{end+1} = content{j}; end + csout{end} = [csout{end} emp2 sp2]; + s0 = ' & '; + end + end + csout{end} = [csout{end} ' \\']; + case 'html', + tag = 'TD'; + csout{end+1} = '<TR>'; + for i=1:length(cs), + if rowspan(i) & colspan(i), + sp = ''; + if rowspan(i)>1, sp = [sp ' ROWSPAN=' num2str(rowspan(i))]; end + if colspan(i)>1, sp = [sp ' COLSPAN=' num2str(colspan(i))]; end + s = sprintf('<%s%s>%s',tag,sp,emp1); + content = cellstr(defaultformat(cs{i})); + csout{end+1} = [s content{1}]; + for j=2:length(content), csout{end+1} = content{j}; end + csout{end} = [csout{end} emp2 '</' tag '>']; + end + end + csout{end+1} = '</TR>'; + case 'txt', + for i=1:length(cs), csout{end+1} = defaultformat(cs{i}); end + end + return; + +function cs = inserttable(sTable,rowlines,long) + % put table contents to cellstr + global REPORT_OUTPUT_FMT + if nargin<2, rowlines = 1; end + if nargin<3, long = 0; end + [rows cols] = size(sTable.values); + cs = cell(0); + if isempty(sTable.colfmt), cf = 'c'; sTable.colfmt = cf(ones(1,cols)); end + if isempty(sTable.span), sTable.span = ones([rows cols 2]); end + switch REPORT_OUTPUT_FMT + case {'ps','pdf','tex','latex'} + li1 = ' \hline'; + if rowlines>0, li2 = li1; li3 = li1; + elseif rowlines==0, li2 = ''; li3 = li1; + else li1 = ''; li2 = ''; li3 = ''; + end + if long, tbl = 'longtable'; else tbl = 'tabular'; end + cs{end+1} = ['\begin{' tbl '}{' sTable.colfmt '}' li1]; + if ~isempty(sTable.headers), + row = tablerow(sTable.headers,'bold'); + for i=1:length(row), cs{end+1} = row{i}; end + cs{end} = [cs{end} li1 li2]; + end + for i=1:rows, + row = tablerow(sTable.values(i,:),'',squeeze(sTable.span(i,:,:))); + for i=1:length(row), cs{end+1} = row{i}; end + cs{end} = [cs{end} li2]; + end + if ~rowlines, cs{end} = [cs{end} li3]; end + cs{end+1} = ['\end{' tbl '}']; + case 'html' + cs{end+1} = ['<TABLE BORDER=' num2str(rowlines>0) '>']; + if ~isempty(sTable.headers), + row = tablerow(sTable.headers,'header'); + for i=1:length(row), cs{end+1} = row{i}; end + end + for i=1:rows, + row = tablerow(sTable.values(i,:),'',squeeze(sTable.span(i,:,:))); + for i=1:length(row), cs{end+1} = row{i}; end + end + cs{end+1} = '</TABLE>'; + case 'txt' + cT = [sTable.headers(:)'; sTable.values]; + A = cell2char(cT); + for i=1:size(A,1), cs{end+1} = A(i,:); end + end + return; + +function A = cell2char(T) + + [nrow,ncol] = size(T); + rowsep = 0; + colsep = 1; + + % change to strings + for i=1:nrow, + for j=1:ncol, + t = T{i,j}; + if ischar(t), % ok + elseif isempty(t), T{i,j} = ''; + elseif isstruct(t), % ?? + elseif iscell(t), T{i,j} = cell2char(t); + elseif isnumeric(t), T{i,j} = num2str(t,3); + end + end + end + + % widths of columns and heights of rows + HW = ones(nrow,ncol,2); + for i=1:nrow, for j=1:ncol, HW(i,j,:) = size(T{i,j}); end, end + colw = max(HW(:,:,2),[],1); + rowh = max(HW(:,:,1),[],2); + + % the table itself + A = char(32*ones(sum(rowh)+rowsep*(nrow-1),sum(colw)+colsep*(ncol-1))); + for i=1:nrow, + for j=1:ncol, + i0 = (i-1)*rowsep+sum(rowh(1:i-1)); + j0 = (j-1)*colsep+sum(colw(1:j-1)); + S = char(32*ones(rowh(i),colw(j))); + si = size(T{i,j}); S(1:si(1),1:si(2)) = T{i,j}; + A(i0+[1:rowh(i)],j0+[1:colw(j)]) = S; + end + end + return; + + +function s = inserttominipage(s,width) + if nargin<2 | isempty(width) | isnan(width), width = 1; end + width = ['{' num2str(width) '\columnwidth}']; + mp1 = '\begin{minipage}[t]'; mp2 = '\end{minipage}'; + if size(s,1)==1, s = [mp1 width s mp2]; + else s = char({[mp1 width]; s; mp2}); + end + return; + +function cs = insertfigure(fname,boxsize,inminipage) + global REPORT_OUTPUT_FMT + if nargin<2, boxsize = [NaN 1]; end + if nargin<3, inminipage = 0; end + htmlpagewidth = 800; + si = cell(0); + switch REPORT_OUTPUT_FMT, + case {'ps','pdf'}, + if ~isnan(boxsize(1)), si{end+1} = ['height=' num2str(boxsize(1)) '\textheight']; end + if ~isnan(boxsize(2)), si{end+1} = ['width=' num2str(boxsize(2)) '\columnwidth']; end + if length(si), si = [', ' joinstr(si, ', ', ', ')]; end + case 'html', + if ~isnan(boxsize(1)), si{end+1} = ['HEIGHT=' num2str(htmlpagewidth*boxsize(1))]; end + if ~isnan(boxsize(2)), si{end+1} = ['WIDTH=' num2str(htmlpagewidth*boxsize(2))]; end + if length(si), si = [' ' joinstr(si, ' ', ' ')]; end + case 'txt', + % nil + end + switch REPORT_OUTPUT_FMT, + case 'ps', s = ['\epsfig{file=./' fname '.ps ' si '}']; + case 'pdf', s = ['\includegraphics[' si ']{./' fname '.pdf}']; + case 'html', + fn = [fname '.' fmt2imgfmt]; + s = ['<IMG SRC="' fn '" ALIGN="center" ALT="' fname '"' si '>']; + s = makelinkfrom(fn,s); + case 'txt', + s = ['[image:' fname ']']; + end + switch REPORT_OUTPUT_FMT, + case {'ps','pdf'}, + if inminipage, s = inserttominipage(s,boxsize(2)); end + case 'html', + s = ['<CENTER>' s '</CENTER>']; + case 'txt', + % nil + end + cs = {s}; + return; + +function str = makelinkfrom(linkto,anchor) + global REPORT_OUTPUT_FMT + if iscell(linkto), + if strcmp(REPORT_OUTPUT_FMT,'html'), linkto = joinstr(linkto,'','#'); + else linkto = joinstr(linkto,'',''); + end + end + switch REPORT_OUTPUT_FMT, + case 'pdf', str = ['\hyperlink{' linkto '}{' anchor '}']; + case 'ps', str = [anchor ' (p.\pageref{' linkto '})']; + case 'html', str = ['<a href="' linkto '">' anchor '</a>']; + case 'txt', str = ''; + end + return; + +function str = makelinkto(linkname) + global REPORT_OUTPUT_FMT + switch REPORT_OUTPUT_FMT, + case 'pdf', + fmt = '\pdfdest name {%s} fit \pdfoutline goto name {%s} {%s}'; + str = sprintf(fmt,linkname,linkname,linkname); + case 'ps', str = ['\label{' linkname '}']; + case 'html', str = ['<a name="' linkname '"> </a>']; + case 'txt', str = ''; + end + return; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sammon.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,249 @@ +function P = sammon(D, P, varargin) + +%SAMMON Computes Sammon's mapping of a data set. +% +% P = sammon(D, P, [value], [mode], [alpha], [Mdist]) +% +% P = sammon(D,2); % projection to 2-dim space +% P = sammon(sMap,3); % projects the codebook vectors +% P = sammon(sMap,3,[],[],[],Md) % uses distance matrix Md +% som_grid(sMap,'Coord',P) % visualization of map projection +% +% Input and output arguments ([]'s are optional): +% D (matrix) size dlen x dim, data to be projected +% (struct) data or map struct +% P (scalar) output dimension +% (matrix) size dlen x odim, initial projection matrix +% [value] (scalar) all different modes (the next argument) require +% a value, default = 100 +% [mode] (string) 'steps' or 'errlimit' or 'errchange' or 'seconds', +% see below, default is 'steps' +% [alpha] (scalar) iteration step size, default = 0.2 +% [Dist] (matrix) pairwise distance matrix, size dlen x dlen. +% If the distances in the input space should +% be calculated otherwise than as euclidian +% distances, the distance from each vector +% to each other vector can be given here, +% size dlen x dlen. For example PDIST +% function can be used to calculate the +% distances: Dist = squareform(pdist(D,'mahal')); +% +% P (matrix) size dlen x odim, the projections +% +% The output dimension must be 2 or higher but (naturally) lower +% than data set dimension. +% +% The mode argument determines the end condition for iteration. If +% the mode argument is used, also the value argument has to be +% specified. Different mode possibilities are: +% 'steps' the iteration is terminated when it is run <value> +% 'errlimit' steps, the iteration is terminated when projection error +% is lower than <value>, +% 'errchange' the iteration is terminated when change between +% projection error on two successive iteration rounds +% is less than <value> percent of total error, and +% 'seconds' the iteration is terminated after <value> seconds +% of iteration. +% +% See also CCA, PCAPROJ, SOM_GRID. + +% Reference: Sammon, J.W. Jr., "A nonlinear mapping for data +% structure analysis", IEEE Transactions on Computers, vol. C-18, +% no. 5, 1969, pp. 401-409. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% juuso 040100 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(2, 6, nargin)); % check no. of input arguments is correct + +% input data +if isstruct(D), + if isfield(D, 'data'), D = D.data; % data struct + elseif isfield(D, 'codebook'), D = D.codebook; % map struct + else error('Invalid structure'); + end +end +if any(isnan(D(:))), + error('Cannot make Sammon''s projection for data with unknown components') +end + +% compute data dimensions +orig_si = size(D); +dim = orig_si(end); +noc = prod(orig_si)/dim; +if length(orig_si)>2, D = reshape(D,[noc dim]); end + +% output dimension / initial projection matrix +if prod(size(P))==1, + odim = P; + P = rand(noc,odim)-0.5; +else + si = size(P); + odim = si(end); + if prod(si) ~= noc*odim, + error('Initial projection matrix size does not match data size'); + end + if length(si)>2, P = reshape(P,[noc odim]); end + inds = find(isnan(P)); + if length(inds), P(inds) = rand(size(inds)); end +end +if odim > dim | odim < 2, + error('Output dimension must be within [2, dimension of data]'); +end + +% determine operating mode +if nargin < 3 | isempty(varargin{1}) | isnan(varargin{1}), value=100; +else value = varargin{1}; +end + +if nargin < 4 | isempty(varargin{2}) | isnan(varargin{2}), mode='steps'; +else mode = varargin{2}; +end +switch mode, +case 'steps', runlen = value; +case 'errlimit', errlimit = value; +case 'errchange', errchange = value; e_prev = 0; +case 'seconds', endtime = value; +otherwise, error(['Illegal mode: ' mode]); +end + +% iteration step size +if nargin > 4, alpha = varargin{3}; else alpha = NaN; end +if isempty(alpha) | isnan(alpha), alpha = 0.2; end + +% mutual distances +if nargin > 5, Mdist = varargin{4}; else Mdist = []; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialization + +% these are used quite frequently +noc_x_1 = ones(noc, 1); +odim_x_1 = ones(odim,1); + +% compute mutual distances between vectors +if isempty(Mdist) | all(isnan(Mdist(:))), + fprintf(2, 'computing mutual distances\r'); + dim_x_1 = ones(dim,1); + for i = 1:noc, + x = D(i,:); + Diff = D - x(noc_x_1,:); + N = isnan(Diff); + Diff(find(N)) = 0; + Mdist(:,i) = sqrt((Diff.^2)*dim_x_1); + N = find(sum(N')==dim); %mutual distance unknown + if ~isempty(N), Mdist(N,i) = NaN; end + end +else + % if the distance matrix is output from PDIST function + if size(Mdist,1)==1, Mdist = squareform(Mdist); end + if size(Mdist,1)~=noc, + error('Mutual distance matrix size and data set size do not match'); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +if strcmp(mode, 'seconds'), tic; end; +fprintf(2, 'iterating \r'); + +% sammon iteration + +x = P ; +xu = zeros(noc, odim); +xd = zeros(noc, odim); +dq = zeros(noc, 1); +dr = zeros(noc, 1); + +i = 0; +ready = 0; +while ~ready + for j = 1:noc, + xd = -x + x(j*noc_x_1,:); + xd2 = xd.^2; + dpj = sqrt(sum(xd2'))'; + dq = Mdist(:,j) - dpj; + dr = Mdist(:,j) .* dpj; + ind = find(dr ~= 0); + term = dq(ind) ./ dr(ind); + e1 = sum(xd(ind,:) .* term(:,odim_x_1)); + term2 = ((1.0 + dq(ind) ./ dpj(ind)) ./ dpj(ind)) ./ dr(ind); + e2 = sum(term) - sum(xd2(ind,:) .* term2(:,odim_x_1)); + xu(j,:) = x(j,:) + alpha * e1 ./ abs(e2); + end + + % move the center of mass to the center + + c = sum(xu) / noc; + x = xu - c(noc_x_1, :); + + i = i + 1; + + % compute mapping error + % doing this adds about 25% to computing time + if 0, + e = 0; tot = 0; + for j = 2:noc, + d = Mdist(1:(j - 1), j); + tot = tot + sum(d); + ind = find(d ~= 0); + xd = -x(1:(j - 1), :) + x(j * ones(j - 1, 1), :); + ee = d - sqrt(sum(xd'.^2))'; + e = e + sum(ee(ind).^2 ./ d(ind)); + end + e = e/tot; + fprintf(2, '\r%d iterations, error %f', i, e); + else + fprintf(2, '\r%d iterations', i); + end + + % determine is the iteration ready + + switch mode + case 'steps', + if i == runlen, ready = 1; end; + case 'errlimit', + if e < errlimit, ready = 1; end; + case 'errchange', + if i > 1 + change = 100 * abs(e - e_prev) / e_prev; + if change < errchange, ready = 1; end; + fprintf(2, ', change of error %f %% ', change); + end + e_prev = e; + case 'seconds' + if toc > endtime, ready = 1; end; + fprintf(2, ', elapsed time %f seconds ', toc); + end + fprintf(2, ' '); + + % If you want to see the Sammon's projection plotted (in 2-D and 3-D case), + % execute the code below; it is not in use by default to speed up + % computation. + if 0, + clf + if odim == 1, plot(x(:,1), noc_x_1, 'o'); + elseif odim == 2, plot(x(:,1), x(:,2), 'o'); + else plot3(x(:,1), x(:,2), x(:,3), 'o') + end + drawnow + end +end + +fprintf(2, '\n'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% clean up + +% reshape +orig_si(end) = odim; +P = reshape(x, orig_si); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_autolabel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,237 @@ +function [sTo] = som_autolabel(sTo, sFrom, mode, inds) + +%SOM_AUTOLABEL Automatical labeling, or clearing of labels. +% +% sTo = som_autolabel(sTo, sFrom, [mode], [inds]) +% +% sM = som_autolabel(sM,sD); +% sD = som_autolabel(sD,sM); +% sM = som_autolabel(sM,sD,'vote',[5]); +% +% Input and output arguments ([]'s are optional): +% sTo (struct) data or map struct to which the labels are put, +% the modified struct is returned +% sFrom (struct) data or map struct from which the labels are taken +% [mode] (string) labeling algorithm: 'add' (the default), 'freq' +% or 'vote' +% [inds] (vector) the column-indexes of the labels that are to be +% used in the operation (e.g. [2] would mean to use +% only the second column of labels array in sFrom) +% +% The modes: +% 'add': all labels from sFrom are added to sTo (even multiple +% copies of same) +% 'add1': only one instance of each label is kept +% 'freq': only one instance of each label is kept and '(#)', where +% # is the frequency of the label, is added to the end of +% the label. Labels are ordered according to frequency. +% 'vote': only the label with most instances is kept +% +% NOTE: The operations are only performed for the new labels. +% The old labels in sTo are left as they are. +% NOTE: all empty labels ('') are ignored. +% +% For more help, try 'type som_autolabel' or check out online documentation. +% See also SOM_LABEL, SOM_BMUS, SOM_SHOW_ADD, SOM_SHOW. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_autolabel +% +% PURPOSE +% +% Automatically label to map/data structs based on given data/map. +% +% SYNTAX +% +% sTo = som_autolabel(sTo, sFrom) +% sTo = som_autolabel(sTo, sFrom, 'add') +% sTo = som_autolabel(sTo, sFrom, 'freq') +% sTo = som_autolabel(sTo, sFrom, 'vote') +% sTo = som_autolabel(..., inds) +% +% DESCRIPTION +% +% This function automatically labels given map/data struct based on an +% already labelled data/map struct. Basically, the BMU of each vector in the +% sFrom struct is found from among the vectors in sTo, and the vectors in +% sFrom are added to the corresponding vector in the sTo struct. The actual +% labels to add are selected based on the mode ('add', 'freq' or 'vote'). +% +% 'add' : all labels from sFrom are added to sTo - even if there would +% be multiple instances of the same label +% 'add1' : only one instance of each label is kept +% 'freq' : only one instance of each label is kept and '(#)', where +% # is the frequency of the label, is added to the end of +% the label. Labels are ordered according to frequency. +% 'vote' : only the label with most instances is added +% +% Note that these operations do not effect the old labels of sTo: they +% are left as they were. +% +% NOTE: empty labels ('') are ignored. +% +% REQUIRED INPUT ARGUMENTS +% +% sTo (struct) data or map struct to which the labels are put +% sFrom (struct) data or map struct from which the labels are taken +% +% OPTIONAL INPUT ARGUMENTS +% +% mode (string) The mode of operation: 'add' (default), +% 'add1', 'freq' or 'vote' +% inds (vector) The columns of the '.labels' field in sFrom to be +% used in operation +% +% OUTPUT ARGUMENTS +% +% sTo (struct) the given data/map struct with modified labels +% +% EXAMPLES +% +% To label a trained map based on (labelled) training data, just do +% +% sM = som_autolabel(sM,sD); +% +% This operation is sometimes called "calibration" in the literature. +% You can also do this the other way around: use a labelled map to +% label a data set: +% +% sD = som_autolabel(sD,sM); +% +% If you only want a single instance of each label, use the 'freq' mode: +% +% sM = som_autolabel(sM,sD,'freq'); +% +% If you already have labels in the struct, and want to perform 'freq' on +% them, do the following: +% +% sMtemp = som_label(sM,'clear','all'); % make a map struct with no labels +% sM = som_autolabel(sMtemp,sM,'freq'); % add labels to it +% +% The third mode 'vote' votes between the labels and only adds the one +% which is most frequent. If two labels are equally frequent, one or the +% other is chosen based on which appears first in the list. +% +% sM = som_autolabel(sM,sD,'vote'); +% +% The lat argument is useful if you have specific labels in each column +% of the '.labels' field. For example, the first column might be an +% identifier, the next a typecode and the last a year. In this case, you +% might want to label the map based only on the typecode: +% +% sM = som_autolabel(sM,sD,'vote',2); +% +% SEE ALSO +% +% som_label Give/remove labels from a map/data set. +% som_bmus Find BMUs from the map for the given set of data vectors. +% som_show Show map planes. +% som_show_add Add for example labels to the SOM_SHOW visualization. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 101297 +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(2, 4, nargin)); % check no. of input args is correct + +% sTo +todata = strcmp(sTo.type,'som_data'); + +% sFrom +[dummy m] = size(sFrom.labels); + +% mode +if nargin<3 | isempty(mode), mode = 'add'; end + +% inds +if nargin<4, inds = 1:m; end +inds = inds(find(inds>0 & inds<=m)); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% get a list of the labels to be added to each vector + +% calculate BMUs +if todata, bmus = som_bmus(sFrom,sTo,1); +else bmus = som_bmus(sTo,sFrom,1); end + +% for each vector in sTo, make a list of all new labels +Labels = cell(size(sTo.labels,1),1); +for d=1:length(bmus), + m = bmus(d); + if todata, t = d; f = m; else t = m; f = d; end + if ~isnan(m), + % add the labels + for j=1:length(inds), + if ~isempty(sFrom.labels{f,inds(j)}), + Labels{t}{length(Labels{t})+1} = sFrom.labels{f,inds(j)}; + end + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% insert the labels to sTo + + +if strcmp(mode,'add1') | strcmp(mode,'freq') | strcmp(mode,'vote'), + + % modify the Labels array apprpriately + + for i=1:length(Labels), + + % calculate frequency of each label in each node + new_labels = {}; + new_freq = []; + for j=1:length(Labels{i}), + if isempty(Labels{i}{j}), % ignore + elseif ~any(strcmp(Labels{i}{j},new_labels)), % a new one! + k = length(new_labels) + 1; + new_labels{k} = Labels{i}{j}; + new_freq(k) = sum(strcmp(new_labels{k},Labels{i})); + else, % an old one, ignore + end + end + + % based on frequency, select label(s) to be added + if length(new_labels) > 0, + if strcmp(mode,'add1'), + Labels{i} = new_labels; + else + + % sort labels according to frequency + [dummy order] = sort(1./(1+new_freq)); + new_labels = new_labels(order); + new_freq = new_freq(order); + + switch mode, + case 'freq', + % replace each label with 'label(#)' where # is the frequency + for j=1:length(new_labels), + labf = sprintf('%s(%d)',new_labels{j},new_freq(j)); + new_labels{j} = labf; + end + Labels{i} = new_labels; + case 'vote', + % place only the one with most votes + Labels{i} = {new_labels{1}}; + end + end + end + + end + +end + +sTo = som_label(sTo,'add',[1:length(Labels)]',Labels); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_barplane.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,444 @@ +function h = som_barplane(varargin) + +%SOM_BARPLANE Visualize the map prototype vectors as bar charts +% +% h = som_barplane(lattice, msize, data, [color], [scaling], [gap], [pos]) +% h = som_barplane(topol, data, [color], [scaling], [gap], [pos]) +% +% som_barplane('hexa',[5 5], rand(25,4), jet(4)) +% som_barplane(sM, sM.codebook,'none') +% +% Input and output argumetns ([]'s are optional): +% lattice (string) grid 'hexa' or 'rect' +% msize (vector) size 1x2, defines the map grid size msize, M=prod(msize) +% (matrix) size Mx2, gives explicit coordinates for each node: +% in this case the first argument does not matter. +% topol (struct) map or topology struct +% data (matrix) size Mxd, each row defines heights of the bars +% [color] (matrix) size dx3, of RGB triples. The rows define colors +% for each bar in a node. Default is hsv(d). A ColorSpec or +% (string) A ColorSpec or 'none' gives each bar the same color. +% [scaling] (string) 'none', 'unitwise' or 'varwise'. The scaling +% mode for the values. Default is 'varwise'. +% [gap] (scalar) Defines the gap between bars, limits: 0 <= gap <= 1 +% where 0=no gap, 1=bars are thin lines. Default is 0.25. +% [pos] (vector) 1x2 vector defines the position of origin. +% Default is [1 1]. +% +% h (scalar) the object handle to the PATCH object +% +% Axis are set as in SOM_CPLANE. +% +% For more help, try 'type som_barplane' or check out online documentation. +% See also SOM_CPLANE, SOM_PLOTPLANE, SOM_PIEPLANE. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_barplane +% +% PURPOSE +% +% Visualizes the map prototype vectors as bar charts. +% +% SYNTAX +% +% h = som_barplane(topol, data) +% h = som_barplane(lattice, msize, data) +% h = som_barplane(..., color) +% h = som_barplane(..., color, scaling) +% h = som_barplane(..., color, scaling, gap) +% h = som_barplane(..., color, scaling, gap, pos) +% +% DESCRIPTION +% +% Visualizes the map prototype vectors as bar charts. +% +% REQUIRED INPUT ARGUMENTS +% +% lattice The basic shape of the map units +% (string) 'hexa' or 'rect' positions the bar charts according to +% hexagonal or rectangular map lattice +% +% msize The size of the map grid +% (vector) [n1 n2] vector defines the map size (height: n1 units widht: n2 +% units, total: M=n1xn2 units). The units will be placed to their +% topological locations in order to form a uniform hexagonal or +% rectangular grid. +% (matrix) Mx2 matrix defines arbitary coordinates for the N units. In +% this case the argument 'lattice' has no effect +% +% topol Topology of the map grid +% +% (struct) map or topology struct from which the topology is taken +% +% data The data to use when constructing the bar charts. +% Typically, the map codebook or some of its components. +% (matrix) Mxd matrix. A row defines heights of the bars. +% +% OPTIONAL INPUT ARGUMENTS +% +% Note: if unspecified or given an empty value ('' or []), default +% values are used for optional input arguments. +% +% color The color of the bars in each pie +% (ColorSpec) or (string) 'none' gives the same color for each slice. +% (matrix) dx3 matrix assigns an RGB color determined by the dth row of +% the matrix to the dth bar (variable) in each bar plot. +% Default value is hsv(d). +% +% scaling How to scale the values +% (string) 'none', 'unitwise' or 'varwise'. This determines the +% scaling of codebook values when drawing the bars. +% +% 'none' don't scale at all. The bars are not limited +% to remain inside he units' area: That is, if value of +% some variable exceeds [-.625,.625] for 'rect' (and +% in "worst case" [-.5,-.5] for 'hexa') the bars may +% overlap other units. +% +% Base line (zero value line) +% - is in the middle of the unit if data (codebook) contains both +% negative and positive values (or is completely zero). +% - is in the top the unit if data (codebook) contains only +% non-positive values (everything <=0). +% - is in the bottom the unit if data (codebook) contains only +% non-negative values (everything >=0). +% +% 'varwise' scales values so that each variable is scaled separately +% so that when it gets its overall maximum value, the +% corresponding bar gets maximum range and for minimum value +% it gets the minimum range. Baseline: see scaling 'none' +% This is the default. +% +% 'unitwise' scales values in each unit individually so that the +% bars for variables having minimum and maximum values have minimum +% and maximum range inside each unit, respectively. +% In this case the zero value line may move depending on the values. +% + +% gap The gap between bars +% (scalar) 0: no gap: bars are glued together +% ... default value is 0.25 +% 1: maximum gap: bars are thin lines +% +% pos Position of origin +% (vector) size 1x2. This is meant for drawing the plane in arbitrary +% location in a figure. Note the operation: if this argument is +% given, the axis limits setting part in the routine is skipped and +% the limits setting will be left to be done by MATLAB's defaults. +% Default is [1 1]. +% +% OUTPUT ARGUMENTS +% +% h (scalar) handle to the created patch object +% +% OBJECT TAGS +% +% One object handle is returned: field Tag is set to 'planeBar' +% +% FEATURES +% +% - The colors are fixed: changing colormap in the figure (see help +% colormap) will not change the coloring of the bars. +% +% EXAMPLES +% +% %%% Create the data and make a map +% +% data=rand(100,5); map=som_make(data); +% +% %%% Create a 'jet' colormap that has as many rows as the data has variables +% +% colors=jet(5); +% +% %%% Draw bars +% +% som_barplane(map.topol.lattice, map.topol.msize, map.codebook, colors); +% or som_barplane(map.topol, map.codebook, colors); +% or som_barplane(map, map.codebook, colors); +% +% %%% Draw the bars so that the gap between the bars is bigger and all +% bars are black +% +% som_barplane(map, map.codebook, 'k', '', 0.6); +% +% SEE ALSO +% +% som_cplane Visualize a 2D component plane, u-matrix or color plane +% som_plotplane Visualize the map prototype vectors as line graphs +% som_pieplane Visualize the map prototype vectors as pie charts + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Juha P 110599, Johan 140799, juuso 151199 140300 070600 + +%%% Check & Init arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +[nargin, lattice, msize, data, color, scaling, gap, pos] = vis_planeGetArgs(varargin{:}); +error(nargchk(3, 7, nargin)) % check that no. of input args is correct + +% Check pos + +if nargin < 7 | isempty(pos) + pos=NaN; % default value for pos (no translation) +elseif ~vis_valuetype(pos,{'1x2'}) + error('Position of origin has to be given as an 1x2 vector'); +end + +% Check gap + +if nargin < 6 | isempty(gap), + gap=0.25; % default value for gap +elseif ~vis_valuetype(gap, {'1x1'}), + error('Gap value must be scalar.'); +elseif ~(gap >= 0 & gap<=1) + error('Gap value must be in interval [0,1].') +end + +% Check scaling + +if nargin < 5 | isempty(scaling), + scaling='varwise'; +elseif ~vis_valuetype(scaling,{'string'}) | ... + ~any(strcmp(scaling,{'none','unitwise','varwise'})), + error('scaling sholud be ''none'', ''unitwise'' or ''varwise''.'); +end + +% Check msize + +if ~vis_valuetype(msize,{'1x2','nx2'}), + error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.'); +end + +% Check data + +if ~isnumeric(data), + error('Data matrix has to be numeric.'); +elseif length(size((data)))>2 + error('Data matrix has too many dimensions!'); +else + d=size(data,2); + N=size(data,1); +end + +s=.8; % patch size scaling factor + +switch scaling, +case 'none' + % no scaling: don't scale + % Check data max and min values + positive=any(data(:)>0); negative=any(data(:)<0); + if (positive & negative) | (~positive & ~negative), + % Data contains both negative and positive values (or is + % completely zero) baseline to centre + zeroline='zero'; + elseif positive & ~negative + % Data contains only positive values: baseline to bottom + zeroline='bottom'; + elseif ~positive & negative + % Data contains only negative values: baseline to top + zeroline='top'; + end +case 'unitwise' + % scale the variables so that the bar for variable with the maximum + % value in the unit spans to the upper edge of the unit + % and the bar for the variable with minimum value spans to the lower edge, + % respectively. + zeroline='moving'; + case 'varwise' + % Check data max and min values + positive=any(data(:)>0); negative=any(data(:)<0); + if (positive & negative) | (~positive & ~negative), + % Data contains both negative and positive values (or is + % completely zero) baseline to + % centre, scale data so that it doesn't overflow + data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5; + zeroline='zero'; + elseif positive & ~negative + % Data contains only positive values: baseline to + % bottom, scale data so that it doesn't overflow + data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5; + zeroline='bottom'; + elseif ~positive & negative + % Data contains only negative values: baseline to + % top, scale data so that it doesn't overflow + zeroline='top'; + data=data./repmat(max(abs([max(data); min(data)])),N,1)*.5; + end +otherwise + error('Unknown scaling mode?'); +end + +for i=1:N, % calculate patch coordinates for + v=data(i,:); + [nx,ny]=vis_barpatch(v,gap,zeroline); % bars + barx(:,(1+(i-1)*d):(i*d))=s*nx; + bary(:,(1+(i-1)*d):(i*d))=s*ny; +end +l=size(barx,1); + +if size(msize,1) == 1, + xdim=msize(2); + ydim=msize(1); + if xdim*ydim~=N + error('Data matrix has wrong size.'); + else + y=reshape(repmat(1:ydim,d,1),1,d*ydim); y=repmat(repmat(y,l,1),1,xdim); + x=reshape(repmat(1:xdim,l*ydim*d,1),l,N*d); + end +else + x=reshape(repmat(msize(:,1),1,l*d)',l,d*N); + y=reshape(repmat(msize(:,2),1,l*d)',l,d*N); + if N ~= size(msize,1), + error('Data matrix has wrong size.'); + else + lattice='rect'; + if isnan(pos), + pos=[0 0]; + end + end +end + +% Check lattice + +if ~ischar(lattice) + error('Invalid lattice.'); +end + +switch lattice +case {'hexa','rect'} + pos=pos-1; +otherwise + error([ 'Lattice' lattice ' not implemented!']); +end + +% Check color +% C_FLAG is for color 'none' + +if nargin < 4 | isempty(color) + color=hsv(d); % default n hsv colors +end +if ~vis_valuetype(color, {[d 3],'nx3rgb'},'all') & ... + ~vis_valuetype(color,{'colorstyle','1x3rgb'}) +error('The color matrix has wrong size or has invalid values.'); +elseif ischar(color) & strcmp(color,'none') + C_FLAG=1; + color='w'; +else + C_FLAG=0; +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Making lattice. +% Command view([0 90]) shows the map in 2D properly oriented + +switch lattice + case 'hexa' + t=find(rem(y(1,:),2)); % move even rows by .5 + x(:,t)=x(:,t)-.5; + x=x+barx+.5; + y=y+bary; + case 'rect' + x=x+barx; + y=y+bary; +end + +% NB: The coordinates in hexa are not uniform in order to get even +% y-coordinates for the nodes. This is handled by setting _axis scaling_ +% so that the hexa-nodes look like uniform hexagonals. See +% vis_PlaneAxisProperties + +if ~isnan(pos) + x=x+pos(1);y=y+pos(2); % move upper left corner +end % to pos + +%% Set axes properties + +ax=newplot; % get current axis +vis_PlaneAxisProperties(ax,lattice, msize, pos); + +%% Rearrange dx3 color matrix + +if ~isstr(color) & size(color,1)~=1, + color=reshape(repmat(color,N,1),[1 N*d 3]); +end + +%% Draw the plane! + +if isnumeric(color), + % explicit color settings by RGB-triplets won't work with + % patch in 'painters' mode, unless there only a single triplet + si = size(color); + if length(si)~=2 | any(si==[1 3]), set(gcf,'renderer','zbuffer'); end +end + +h_=patch(x,y,color); + +if C_FLAG + set(h_,'FaceColor','none'); +end + +set(h_,'Tag','planeBar'); % tag the object + +%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargout>0, h=h_; end % Set h only if + % there really is output + +%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [xcoord,ycoord]=vis_barpatch(y,gap,zeroline) + +x = length(y); +d = gap/(2*(x-1)+2); +step= -.5:1/x:.5; + +miny=min(y); +maxy=max(y); + +switch(zeroline) + case 'moving' + if miny < 0 + if maxy > 0 + zl = .5 - (abs(miny)/(maxy-miny)); %reverse mode + y= .5 - ((y-miny*ones(1,x))./(maxy-miny)); + else + zl = -.5; + y=-.5+abs(y./miny); + end + else + zl = .5; %reverse mode + y=.5-y./maxy; + end + case 'moveNotScale' + if miny < 0 + if maxy > 0 + zl = 0.5+miny; + y = zl - y; + else + zl=-.5; + y=-.5+abs(y); + end + else + zl=.5; + y =.5-y; + end + case 'zero' + zl=0; y=zl-y; + case 'top' + zl=-.5; y=zl-2*y; + case 'bottom' + zl=.5; y=zl-2*y; +end + +for i=1:x + xcoord(:,i) = [d+step(i);d+step(i);step(i+1)-d;step(i+1)-d;d+step(i)]; + ycoord(:,i) = [zl;y(i);y(i);zl;zl]; +end + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_batchtrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,539 @@ +function [sMap,sTrain] = som_batchtrain(sMap, D, varargin) + +%SOM_BATCHTRAIN Use batch algorithm to train the Self-Organizing Map. +% +% [sM,sT] = som_batchtrain(sM, D, [argID, value, ...]) +% +% sM = som_batchtrain(sM,D); +% sM = som_batchtrain(sM,sD,'radius',[10 3 2 1 0.1],'tracking',3); +% [M,sT] = som_batchtrain(M,D,'ep','msize',[10 3],'hexa'); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map struct, the trained and updated map is returned +% (matrix) codebook matrix of a self-organizing map +% size munits x dim or msize(1) x ... x msize(k) x dim +% The trained map codebook is returned. +% D (struct) training data; data struct +% (matrix) training data, size dlen x dim +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% sT (struct) learning parameters used during the training +% +% Here are the valid argument IDs and corresponding values. The values which +% are unambiguous (marked with '*') can be given without the preceeding argID. +% 'mask' (vector) BMU search mask, size dim x 1 +% 'msize' (vector) map size +% 'radius' (vector) neighborhood radiuses, length 1, 2 or trainlen +% 'radius_ini' (scalar) initial training radius +% 'radius_fin' (scalar) final training radius +% 'tracking' (scalar) tracking level, 0-3 +% 'trainlen' (scalar) training length in epochs +% 'train' *(struct) train struct, parameters for training +% 'sTrain','som_train' = 'train' +% 'neigh' *(string) neighborhood function, 'gaussian', 'cutgauss', +% 'ep' or 'bubble' +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'lattice' *(string) map lattice, 'hexa' or 'rect' +% 'shape' *(string) map shape, 'sheet', 'cyl' or 'toroid' +% 'weights' (vector) sample weights: each sample is weighted +% +% For more help, try 'type som_batchtrain' or check out online documentation. +% See also SOM_MAKE, SOM_SEQTRAIN, SOM_TRAIN_STRUCT. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_batchtrain +% +% PURPOSE +% +% Trains a Self-Organizing Map using the batch algorithm. +% +% SYNTAX +% +% sM = som_batchtrain(sM,D); +% sM = som_batchtrain(sM,sD); +% sM = som_batchtrain(...,'argID',value,...); +% sM = som_batchtrain(...,value,...); +% [sM,sT] = som_batchtrain(M,D,...); +% +% DESCRIPTION +% +% Trains the given SOM (sM or M above) with the given training data +% (sD or D) using batch training algorithm. If no optional arguments +% (argID, value) are given, a default training is done. Using optional +% arguments the training parameters can be specified. Returns the +% trained and updated SOM and a train struct which contains +% information on the training. +% +% REFERENCES +% +% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, +% Berlin, 1995, pp. 127-128. +% Kohonen, T., "Things you haven't heard about the Self-Organizing +% Map", In proceedings of International Conference +% on Neural Networks (ICNN), San Francisco, 1993, pp. 1147-1156. +% +% KNOWN BUGS +% +% Batchtrain does not work correctly for a map with a single unit. +% This is because of the way 'min'-function works. +% +% REQUIRED INPUT ARGUMENTS +% +% sM The map to be trained. +% (struct) map struct +% (matrix) codebook matrix (field .data of map struct) +% Size is either [munits dim], in which case the map grid +% dimensions (msize) should be specified with optional arguments, +% or [msize(1) ... msize(k) dim] in which case the map +% grid dimensions are taken from the size of the matrix. +% Lattice, by default, is 'rect' and shape 'sheet'. +% D Training data. +% (struct) data struct +% (matrix) data matrix, size [dlen dim] +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is +% used. The valid IDs and corresponding values are listed below. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% +% Below is the list of valid arguments: +% 'mask' (vector) BMU search mask, size dim x 1. Default is +% the one in sM (field '.mask') or a vector of +% ones if only a codebook matrix was given. +% 'msize' (vector) map grid dimensions. Default is the one +% in sM (field sM.topol.msize) or +% 'si = size(sM); msize = si(1:end-1);' +% if only a codebook matrix was given. +% 'radius' (vector) neighborhood radius +% length = 1: radius_ini = radius +% length = 2: [radius_ini radius_fin] = radius +% length > 2: the vector given neighborhood +% radius for each step separately +% trainlen = length(radius) +% 'radius_ini' (scalar) initial training radius +% 'radius_fin' (scalar) final training radius +% 'tracking' (scalar) tracking level: 0, 1 (default), 2 or 3 +% 0 - estimate time +% 1 - track time and quantization error +% 2 - plot quantization error +% 3 - plot quantization error and two first +% components +% 'trainlen' (scalar) training length in epochs +% 'train' *(struct) train struct, parameters for training. +% Default parameters, unless specified, +% are acquired using SOM_TRAIN_STRUCT (this +% also applies for 'trainlen', 'radius_ini' +% and 'radius_fin'). +% 'sTrain', 'som_topol' (struct) = 'train' +% 'neigh' *(string) The used neighborhood function. Default is +% the one in sM (field '.neigh') or 'gaussian' +% if only a codebook matrix was given. Other +% possible values is 'cutgauss', 'ep' and 'bubble'. +% 'topol' *(struct) topology of the map. Default is the one +% in sM (field '.topol'). +% 'sTopol', 'som_topol' (struct) = 'topol' +% 'lattice' *(string) map lattice. Default is the one in sM +% (field sM.topol.lattice) or 'rect' +% if only a codebook matrix was given. +% 'shape' *(string) map shape. Default is the one in sM +% (field sM.topol.shape) or 'sheet' +% if only a codebook matrix was given. +% 'weights' (vector) weight for each data vector: during training, +% each data sample is weighted with the corresponding +% value, for example giving weights = [1 1 2 1] +% would have the same result as having third sample +% appear 2 times in the data +% +% OUTPUT ARGUMENTS +% +% sM the trained map +% (struct) if a map struct was given as input argument, a +% map struct is also returned. The current training +% is added to the training history (sM.trainhist). +% The 'neigh' and 'mask' fields of the map struct +% are updated to match those of the training. +% (matrix) if a matrix was given as input argument, a matrix +% is also returned with the same size as the input +% argument. +% sT (struct) train struct; information of the accomplished training +% +% EXAMPLES +% +% Simplest case: +% sM = som_batchtrain(sM,D); +% sM = som_batchtrain(sM,sD); +% +% To change the tracking level, 'tracking' argument is specified: +% sM = som_batchtrain(sM,D,'tracking',3); +% +% The change training parameters, the optional arguments 'train','neigh', +% 'mask','trainlen','radius','radius_ini' and 'radius_fin' are used. +% sM = som_batchtrain(sM,D,'neigh','cutgauss','trainlen',10,'radius_fin',0); +% +% Another way to specify training parameters is to create a train struct: +% sTrain = som_train_struct(sM,'dlen',size(D,1)); +% sTrain = som_set(sTrain,'neigh','cutgauss'); +% sM = som_batchtrain(sM,D,sTrain); +% +% By default the neighborhood radius goes linearly from radius_ini to +% radius_fin. If you want to change this, you can use the 'radius' argument +% to specify the neighborhood radius for each step separately: +% sM = som_batchtrain(sM,D,'radius',[5 3 1 1 1 1 0.5 0.5 0.5]); +% +% You don't necessarily have to use the map struct, but you can operate +% directly with codebook matrices. However, in this case you have to +% specify the topology of the map in the optional arguments. The +% following commads are identical (M is originally a 200 x dim sized matrix): +% M = som_batchtrain(M,D,'msize',[20 10],'lattice','hexa','shape','cyl'); +% or +% M = som_batchtrain(M,D,'msize',[20 10],'hexa','cyl'); +% or +% sT= som_set('som_topol','msize',[20 10],'lattice','hexa','shape','cyl'); +% M = som_batchtrain(M,D,sT); +% or +% M = reshape(M,[20 10 dim]); +% M = som_batchtrain(M,D,'hexa','cyl'); +% +% The som_batchtrain also returns a train struct with information on the +% accomplished training. This struct is also added to the end of the +% trainhist field of map struct, in case a map struct was given. +% [M,sTrain] = som_batchtrain(M,D,'msize',[20 10]); +% [sM,sTrain] = som_batchtrain(sM,D); % sM.trainhist{end}==sTrain +% +% SEE ALSO +% +% som_make Initialize and train a SOM using default parameters. +% som_seqtrain Train SOM with sequential algorithm. +% som_train_struct Determine default training parameters. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 071197 041297 +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +error(nargchk(2, Inf, nargin)); % check the number of input arguments + +% map +struct_mode = isstruct(sMap); +if struct_mode, + sTopol = sMap.topol; +else + orig_size = size(sMap); + if ndims(sMap) > 2, + si = size(sMap); dim = si(end); msize = si(1:end-1); + M = reshape(sMap,[prod(msize) dim]); + else + msize = [orig_size(1) 1]; + dim = orig_size(2); + end + sMap = som_map_struct(dim,'msize',msize); + sTopol = sMap.topol; +end +[munits dim] = size(sMap.codebook); + +% data +if isstruct(D), + data_name = D.name; + D = D.data; +else + data_name = inputname(2); +end +nonempty = find(sum(isnan(D),2) < dim); +D = D(nonempty,:); % remove empty vectors from the data +[dlen ddim] = size(D); % check input dimension +if dim ~= ddim, + error('Map and data input space dimensions disagree.'); +end + +% varargin +sTrain = som_set('som_train','algorithm','batch','neigh', ... + sMap.neigh,'mask',sMap.mask,'data_name',data_name); +radius = []; +tracking = 1; +weights = 1; + +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'msize', i=i+1; sTopol.msize = varargin{i}; + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case 'shape', i=i+1; sTopol.shape = varargin{i}; + case 'mask', i=i+1; sTrain.mask = varargin{i}; + case 'neigh', i=i+1; sTrain.neigh = varargin{i}; + case 'trainlen', i=i+1; sTrain.trainlen = varargin{i}; + case 'tracking', i=i+1; tracking = varargin{i}; + case 'weights', i=i+1; weights = varargin{i}; + case 'radius_ini', i=i+1; sTrain.radius_ini = varargin{i}; + case 'radius_fin', i=i+1; sTrain.radius_fin = varargin{i}; + case 'radius', + i=i+1; + l = length(varargin{i}); + if l==1, + sTrain.radius_ini = varargin{i}; + else + sTrain.radius_ini = varargin{i}(1); + sTrain.radius_fin = varargin{i}(end); + if l>2, radius = varargin{i}; end + end + case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i}; + case {'topol','sTopol','som_topol'}, + i=i+1; + sTopol = varargin{i}; + if prod(sTopol.msize) ~= munits, + error('Given map grid size does not match the codebook size.'); + end + % unambiguous values + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; + case {'gaussian','cutgauss','ep','bubble'}, sTrain.neigh = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', + sTopol = varargin{i}; + if prod(sTopol.msize) ~= munits, + error('Given map grid size does not match the codebook size.'); + end + case 'som_train', sTrain = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_batchtrain) Ignoring invalid argument #' num2str(i+2)]); + end + i = i+1; +end + +% take only weights of non-empty vectors +if length(weights)>dlen, weights = weights(nonempty); end + +% trainlen +if ~isempty(radius), sTrain.trainlen = length(radius); end + +% check topology +if struct_mode, + if ~strcmp(sTopol.lattice,sMap.topol.lattice) | ... + ~strcmp(sTopol.shape,sMap.topol.shape) | ... + any(sTopol.msize ~= sMap.topol.msize), + warning('Changing the original map topology.'); + end +end +sMap.topol = sTopol; + +% complement the training struct +sTrain = som_train_struct(sTrain,sMap,'dlen',dlen); +if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize + +M = sMap.codebook; +mask = sTrain.mask; +trainlen = sTrain.trainlen; + +% neighborhood radius +if trainlen==1, + radius = sTrain.radius_ini; +elseif length(radius)<=2, + r0 = sTrain.radius_ini; r1 = sTrain.radius_fin; + radius = r1 + fliplr((0:(trainlen-1))/(trainlen-1)) * (r0 - r1); +else + % nil +end + +% distance between map units in the output space +% Since in the case of gaussian and ep neighborhood functions, the +% equations utilize squares of the unit distances and in bubble case +% it doesn't matter which is used, the unitdistances and neighborhood +% radiuses are squared. +Ud = som_unit_dists(sTopol); +Ud = Ud.^2; +radius = radius.^2; +% zero neighborhood radius may cause div-by-zero error +radius(find(radius==0)) = eps; + +% The training algorithm involves calculating weighted Euclidian distances +% to all map units for each data vector. Basically this is done as +% for i=1:dlen, +% for j=1:munits, +% for k=1:dim +% Dist(j,i) = Dist(j,i) + mask(k) * (D(i,k) - M(j,k))^2; +% end +% end +% end +% where mask is the weighting vector for distance calculation. However, taking +% into account that distance between vectors m and v can be expressed as +% |m - v|^2 = sum_i ((m_i - v_i)^2) = sum_i (m_i^2 + v_i^2 - 2*m_i*v_i) +% this can be made much faster by transforming it to a matrix operation: +% Dist = (M.^2)*mask*ones(1,d) + ones(m,1)*mask'*(D'.^2) - 2*M*diag(mask)*D' +% Of the involved matrices, several are constant, as the mask and data do +% not change during training. Therefore they are calculated beforehand. + +% For the case where there are unknown components in the data, each data +% vector will have an individual mask vector so that for that unit, the +% unknown components are not taken into account in distance calculation. +% In addition all NaN's are changed to zeros so that they don't screw up +% the matrix multiplications and behave correctly in updating step. +Known = ~isnan(D); +W1 = (mask*ones(1,dlen)) .* Known'; +D(find(~Known)) = 0; + +% constant matrices +WD = 2*diag(mask)*D'; % constant matrix +dconst = ((D.^2)*mask)'; % constant in distance calculation for each data sample + % W2 = ones(munits,1)*mask'; D2 = (D'.^2); + +% initialize tracking +start = clock; +qe = zeros(trainlen,1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +% With the 'blen' parameter you can control the memory consumption +% of the algorithm, which is in practive directly proportional +% to munits*blen. If you're having problems with memory, try to +% set the value of blen lower. +blen = min(munits,dlen); + +% reserve some space +bmus = zeros(1,dlen); +ddists = zeros(1,dlen); + +for t = 1:trainlen, + + % batchy train - this is done a block of data (inds) at a time + % rather than in a single sweep to save memory consumption. + % The 'Dist' and 'Hw' matrices have size munits*blen + % which - if you have a lot of data - would be HUGE if you + % calculated it all at once. A single-sweep version would + % look like this: + % Dist = (M.^2)*W1 - M*WD; %+ W2*D2 + % [ddists, bmus] = min(Dist); + % (notice that the W2*D2 term can be ignored since it is constant) + % This "batchy" version is the same as single-sweep if blen=dlen. + i0 = 0; + while i0+1<=dlen, + inds = [(i0+1):min(dlen,i0+blen)]; i0 = i0+blen; + Dist = (M.^2)*W1(:,inds) - M*WD(:,inds); + [ddists(inds), bmus(inds)] = min(Dist); + end + + % tracking + if tracking > 0, + ddists = ddists+dconst; % add the constant term + ddists(ddists<0) = 0; % rounding errors... + qe(t) = mean(sqrt(ddists)); + trackplot(M,D,tracking,start,t,qe); + end + + % neighborhood + % notice that the elements Ud and radius have been squared! + % note: 'bubble' matches the original "Batch Map" algorithm + switch sTrain.neigh, + case 'bubble', H = (Ud<=radius(t)); + case 'gaussian', H = exp(-Ud/(2*radius(t))); + case 'cutgauss', H = exp(-Ud/(2*radius(t))) .* (Ud<=radius(t)); + case 'ep', H = (1-Ud/radius(t)) .* (Ud<=radius(t)); + end + + % update + + % In principle the updating step goes like this: replace each map unit + % by the average of the data vectors that were in its neighborhood. + % The contribution, or activation, of data vectors in the mean can + % be varied with the neighborhood function. This activation is given + % by matrix H. So, for each map unit the new weight vector is + % + % m = sum_i (h_i * d_i) / sum_i (h_i), + % + % where i denotes the index of data vector. Since the values of + % neighborhood function h_i are the same for all data vectors belonging to + % the Voronoi set of the same map unit, the calculation is actually done + % by first calculating a partition matrix P with elements p_ij=1 if the + % BMU of data vector j is i. + + P = sparse(bmus,[1:dlen],weights,munits,dlen); + + % Then the sum of vectors in each Voronoi set are calculated (P*D) and the + % neighborhood is taken into account by calculating a weighted sum of the + % Voronoi sum (H*). The "activation" matrix A is the denominator of the + % equation above. + + S = H*(P*D); + A = H*(P*Known); + + % If you'd rather make this without using the Voronoi sets try the following: + % Hi = H(:,bmus); + % S = Hi * D; % "sum_i (h_i * d_i)" + % A = Hi * Known; % "sum_i (h_i)" + % The bad news is that the matrix Hi has size [munits x dlen]... + + % only update units for which the "activation" is nonzero + nonzero = find(A > 0); + M(nonzero) = S(nonzero) ./ A(nonzero); + +end; % for t = 1:trainlen + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Build / clean up the return arguments + +% tracking +if tracking > 0, fprintf(1,'\n'); end + +% update structures +sTrain = som_set(sTrain,'time',datestr(now,0)); +if struct_mode, + sMap = som_set(sMap,'codebook',M,'mask',sTrain.mask,'neigh',sTrain.neigh); + tl = length(sMap.trainhist); + sMap.trainhist(tl+1) = sTrain; +else + sMap = reshape(M,orig_size); +end + +return; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +%%%%%%%% +function [] = trackplot(M,D,tracking,start,n,qe) + + l = length(qe); + elap_t = etime(clock,start); + tot_t = elap_t*l/n; + fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t) + switch tracking + case 1, + case 2, + plot(1:n,qe(1:n),(n+1):l,qe((n+1):l)) + title('Quantization error after each epoch'); + drawnow + otherwise, + subplot(2,1,1), plot(1:n,qe(1:n),(n+1):l,qe((n+1):l)) + title('Quantization error after each epoch'); + subplot(2,1,2), plot(M(:,1),M(:,2),'ro',D(:,1),D(:,2),'b+'); + title('First two components of map units (o) and data vectors (+)'); + drawnow + end + % end of trackplot
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_bmucolor.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,93 @@ +function bmu_colors=som_bmucolor(bmus, m, colors); + +% SOM_BMUCOLOR Returns the colors of the bmus according to a map colorcode +% +% bmu_colors=som_bmucolor(bmus, msize, colors); +% +% INPUT ARGUMENTS ([]'s are optional) +% +% bmus (matrix) Nx1 vector of BMU indexes +% msize (map struct, topol struct or 1x2 vector) +% gives the map grid size +% colors (matrix) colormap(s): munits x 3 x d matrix of RGB vectors +% +% OUTPUT ARGUMENTS +% +% bmu_colors (Nx3xd matrix) color of the data point according to its BMU's +% color(s). +% +% Idea is to get a color for each data point that links it to its BMU. +% +% EXAMPLE +% +% We want to show how an time series is projected to a map. Instead of +% a trajectory, we use 'color linking' +% +% map=som_make(multi_dim_signal); +% bmus=som_bmu(map,multi_dim_signal); +% Colors=som_bmucolor(bmus, map, som_colorcode(map,'rgb1')); +% colorsignal(Colors, multi_dim_signal); +% +% See also SOM_COLORCODE. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0alpha Johan 170699 + +%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(3, 3, nargin)) % check no. of input args is correct + +% Check map grid size + +if vis_valuetype(m,{'1x2'}), + msize=m; +else + [tmp,ok,tmp]=som_set(m); + if isstruct(m) & all(ok) % check m type + switch m.type + case 'som_topol' + msize=m.msize; + lattice=m.lattice; + case 'som_map' + msize=m.topol.msize; + lattice=m.topol.lattice; + otherwise + error('Invalid map or topol struct.'); + end + end +end + +if length(msize)>2 + error('Only 2D maps allowed!'); +end + +n=prod(msize) + +% Check colorcode size + +if ~vis_valuetype(colors,{'nx3xdimrgb','nx3rgb'}) + error('Colorcode matrix not valid!'); +end + +% Check bmu vector + +if ~vis_valuetype(bmus,{'nx1'}), + error('Need a column vector of BMU indexes!'); +else + bmus=round(bmus); + if max(bmus) > n | min(bmus) < 1 + error('BMU indexes exeed the map size!') + end +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +bmu_c=colors(bmus,:,:); + +%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +bmu_colors=squeeze(bmu_c); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_bmus.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,253 @@ +function [Bmus,Qerrors] = som_bmus(sMap, sData, which_bmus, mask) + +%SOM_BMUS Find the best-matching units from the map for the given vectors. +% +% [Bmus, Qerrors] = som_bmus(sMap, sData, [which], [mask]) +% +% bmus = som_bmus(sM,sD); +% [bmus,qerrs] = som_bmus(sM,D,[1 2 3]); +% bmus = som_bmus(sM,D,1,[1 1 0 0 1]); +% +% Input and output arguments ([]'s are optional): +% sMap (struct) map struct +% (matrix) codebook matrix, size munits x dim +% sData (struct) data struct +% (matrix) data matrix, size dlen x dim +% [which] (vector) which BMUs are returned, [1] by default +% (string) 'all', 'best' or 'worst' meaning [1:munits], +% [1] and [munits] respectively +% [mask] (vector) mask vector, length=dim, sMap.mask by default +% +% Bmus (matrix) the requested BMUs for each data vector, +% size dlen x length(which) +% Qerrors (matrix) the corresponding quantization errors, size as Bmus +% +% NOTE: for a vector with all components NaN's, bmu=NaN and qerror=NaN +% NOTE: the mask also effects the quantization errors +% +% For more help, try 'type som_bmus' or check out online documentation. +% See also SOM_QUALITY. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_bmus +% +% PURPOSE +% +% Finds Best-Matching Units (BMUs) for given data vector from a given map. +% +% SYNTAX +% +% Bmus = som_bmus(sMap, sData) +% Bmus = som_bmus(..., which) +% Bmus = som_bmus(..., which, mask) +% [Bmus, Qerrs] = som_bmus(...) +% +% DESCRIPTION +% +% Returns the indexes and corresponding quantization errors of the +% vectors in sMap that best matched the vectors in sData. +% +% By default only the index of the best matching unit (/vector) is +% returned, but the 'which' argument can be used to get others as +% well. For example it might be desirable to get also second- and +% third-best matching units as well (which = [1:3]). +% +% A mask can be used to weight the search process. The mask is used to +% weight the influence of components in the distance calculation, as +% follows: +% +% distance(x,y) = (x-y)' diag(mask) (x-y) +% +% where x and y are two vectors, and diag(mask) is a diagonal matrix with +% the elements of mask vector on the diagonal. +% +% The vectors in the data set (sData) can contain unknown components +% (NaNs), but the map (sMap) cannot. If there are completely empty +% vectors (all NaNs), the returned BMUs and quantization errors for those +% vectors are NaNs. +% +% REQUIRED INPUT ARGUMENTS +% +% sMap The vectors from among which the BMUs are searched +% for. These must not have any unknown components (NaNs). +% (struct) map struct +% (matrix) codebook matrix, size munits x dim +% +% sData The data vector(s) for which the BMUs are searched. +% (struct) data struct +% (matrix) data matrix, size dlen x dim +% +% OPTIONAL INPUT ARGUMENTS +% +% which (vector) which BMUs are returned, +% by default only the best (ie. which = [1]) +% (string) 'all', 'best' or 'worst' meaning [1:munits], +% [1] and [munits] respectively +% mask (vector) mask vector to be used in BMU search, +% by default sMap.mask, or ones(dim,1) in case +% a matrix was given +% +% OUTPUT ARGUMENTS +% +% Bmus (matrix) the requested BMUs for each data vector, +% size dlen x length(which) +% Qerrors (matrix) the corresponding quantization errors, +% size equal to that of Bmus +% +% EXAMPLES +% +% Simplest case: +% bmu = som_bmus(sM, [0.3 -0.4 1.0]); +% % 3-dimensional data, returns BMU for vector [0.3 -0.4 1] +% bmu = som_bmus(sM, [0.3 -0.4 1.0], [3 5]); +% % as above, except returns the 3rd and 5th BMUs +% bmu = som_bmus(sM, [0.3 -0.4 1.0], [], [1 0 1]); +% % as above, except ignores second component in searching +% [bmus qerrs] = som_bmus(sM, D); +% % returns BMUs and corresponding quantization errors +% % for each vector in D +% bmus = som_bmus(sM, sD); +% % returns BMUs for each vector in sD using the mask in sM +% +% SEE ALSO +% +% som_quality Measure the quantization and topographic error of a SOM. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 071197, 101297 +% Version 2.0alpha juuso 201198 080200 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments and initialize + +error(nargchk(1, 4, nargin)); % check no. of input args is correct + +% sMap +if isstruct(sMap), + switch sMap.type, + case 'som_map', M = sMap.codebook; + case 'som_data', M = sMap.data; + otherwise, error('Invalid 1st argument.'); + end +else + M = sMap; +end +[munits dim] = size(M); +if any(any(isnan(M))), + error ('Map codebook must not have missing components.'); +end + +% data +if isstruct(sData), + switch sData.type, + case 'som_map', D = sData.codebook; + case 'som_data', D = sData.data; + otherwise, error('Invalid 2nd argument.'); + end +else + D = sData; +end +[dlen ddim] = size(D); +if dim ~= ddim, + error('Data and map dimensions do not match.') +end + +% which_bmus +if nargin < 3 | isempty(which_bmus) | any(isnan(which_bmus)), + which_bmus = 1; +else + if ischar(which_bmus), + switch which_bmus, + case 'best', which_bmus = 1; + case 'worst', which_bmus = munits; + case 'all', which_bmus = [1:munits]; + end + end +end + +% mask +if nargin < 4 | isempty(mask) | any(isnan(mask)), + if isstruct(sMap) & strcmp(sMap.type,'som_map'), + mask = sMap.mask; + elseif isstruct(sData) & strcmp(sData.type,'som_map'), + mask = sData.mask; + else + mask = ones(dim,1); + end +end +if size(mask,1)==1, mask = mask'; end +if all(mask == 0), + error('All components masked off. BMU search cannot be done.'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +Bmus = zeros(dlen,length(which_bmus)); +Qerrors = Bmus; + +% The BMU search involves calculating weighted Euclidian distances +% to all map units for each data vector. Basically this is done as +% for i=1:dlen, +% for j=1:munits, +% for k=1:dim, +% Dist(j,i) = Dist(j,i) + mask(k) * (D(i,k) - M(j,k))^2; +% end +% end +% end +% where mask is the weighting vector for distance calculation. However, taking +% into account that distance between vectors m and v can be expressed as +% |m - v|^2 = sum_i ((m_i - v_i)^2) = sum_i (m_i^2 + v_i^2 - 2*m_i*v_i) +% this can be made much faster by transforming it to a matrix operation: +% Dist = (M.^2)*mask*ones(1,d) + ones(m,1)*mask'*(D'.^2) - 2*M*diag(mask)*D' +% +% In the case where there are unknown components in the data, each data +% vector will have an individual mask vector so that for that unit, the +% unknown components are not taken into account in distance calculation. +% In addition all NaN's are changed to zeros so that they don't screw up +% the matrix multiplications. + +% calculate distances & bmus + +% This is done a block of data at a time rather than in a +% single sweep to save memory consumption. The 'Dist' matrix has +% size munits*blen which would be HUGE if you did it in a single-sweep +% operation. If you _want_ to use the single-sweep version, just +% set blen = dlen. If you're having problems with memory, try to +% set the value of blen lower. +blen = min(munits,dlen); + +% handle unknown components +Known = ~isnan(D); +W1 = (mask*ones(1,dlen)) .* Known'; +D(find(~Known)) = 0; +unknown = find(sum(Known')==0); % completely unknown vectors + +% constant matrices +WD = 2*diag(mask)*D'; % constant matrix +dconst = ((D.^2)*mask); % constant term in the distances + +i0 = 0; +while i0+1<=dlen, + % calculate distances + inds = [(i0+1):min(dlen,i0+blen)]; i0 = i0+blen; + Dist = (M.^2)*W1(:,inds) - M*WD(:,inds); % plus dconst for each sample + + % find the bmus and the corresponding quantization errors + if all(which_bmus==1), [Q B] = min(Dist); else [Q B] = sort(Dist); end + if munits==1, Bmus(inds,:) = 1; else Bmus(inds,:) = B(which_bmus,:)'; end + Qerrors(inds,:) = Q(which_bmus,:)' + dconst(inds,ones(length(which_bmus),1)); +end + +% completely unknown vectors +if ~isempty(unknown), + Bmus(unknown,:) = NaN; + Qerrors(unknown,:) = NaN; +end + +Qerrors = sqrt(Qerrors); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_cldist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,227 @@ +function Cd = som_cldist(D,clinds1,clinds2,cldist,q,mask) + +% SOM_CLDIST Distances between two clusters. +% +% Cd = som_cldist(Md,c1,c2,'single') +% Cd = som_cldist(Md,c1,c2,'average') +% Cd = som_cldist(Md,c1,c2,'complete') +% Cd = som_cldist(Md,c1,c2,'neighf',H) +% Cd = som_cldist(Md,c1,[],...) +% Cd = som_cldist(D,c1,c2,'centroid',q,mask) +% Cd = som_cldist(D,c1,c2,'ward',q,mask) +% Cd = som_cldist(D,c1,[],...) +% +% Input and output arguments ([]'s are optional): +% D (matrix) size dlen x dim, the data set +% (struct) map or data struct +% Md (matrix) size dlen x dlen, mutual distance matrix, see SOM_MDIST +% c1 (cell array) size n1 x 1, indices of clusters from which +% the distances should be calculated, each cell +% contains indices of vectors that belong to that +% cluster (indices are between 1...dlen) +% c2 (cell array) size n2 x 1, same as c1 but have the clusters +% to which the distances should be calculated +% (empty) c1 is used in place of c2 +% [q] (scalar) distance norm, default = 2 +% [mask] (vector) size dim x 1, the weighting mask, a vector of ones +% by default +% H (matrix) size dlen x dlen, neighborhood function values +% +% Cd (matrix) size n1 x n2, distances between the clusters +% +% See also SOM_MDIST. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on XXX by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 250800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +[dlen dim] = size(D); +if nargin<5, q = 2; end +if nargin<6, mask = ones(dim,1); end +if ~iscell(clinds1), clinds1 = {clinds1}; end +if ~isempty(clinds2) & ~iscell(clinds2), clinds2 = {clinds2}; end + +n1 = length(clinds1); +n2 = length(clinds2); +if n2>0, Cd = zeros(n1,n2); else Cd = zeros(n1); end +if n1==0, return; end + +switch cldist, + + % centroid distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + case 'centroid', + + C1 = zeros(n1,dim); for i=1:n1, C1(i,:) = mean(D(clinds1{i},:),1); end + C2 = zeros(n2,dim); for i=1:n2, C2(i,:) = mean(D(clinds2{i},:),1); end + if n2==0, + for i=1:n1-1, + for j=i+1:n1, + diff = C1(i,:)-C1(j,:); + switch q, + case 1, Cd(i,j)=abs(diff)*mask; + case 2, Cd(i,j)=sqrt((diff.^2)*mask); + case Inf, Cd(i,j)=max(diag(mask)*abs(diff),[],2); + otherwise, Cd(i,j)=((abs(diff).^q)*mask).^(1/q); + end + end + Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])'; + end + else + for i=1:n1, + for j=1:n2, + diff = C1(i,:)-C2(j,:); + switch q, + case 1, Cd(i,j)=abs(diff)*mask; + case 2, Cd(i,j)=sqrt((diff.^2)*mask); + case Inf, Cd(i,j)=max(diag(mask)*abs(diff),[],2); + otherwise, Cd(i,j)=((abs(diff).^q)*mask).^(1/q); + end + end + end + end + + % ward distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + case 'ward', + + C1 = zeros(n1,dim); nn1 = zeros(n1,dim); + for i=1:n1, C1(i,:) = mean(D(clinds1{i},:),1); nn1(i) = length(clinds1{i}); end + C2 = zeros(n2,dim); nn2 = zeros(n2,dim); + for i=1:n2, C2(i,:) = mean(D(clinds2{i},:),1); nn2(i) = length(clinds2{i}); end + if n2==0, + for i=1:n1-1, + for j=i+1:n1, + diff = C1(i,:) - C1(j,:); + f = 2*nn1(i)*nn1(j) / (nn1(i)+nn1(j)); + switch q, + case 1, Cd(i,j)=f*abs(diff)*mask; + case 2, Cd(i,j)=f*sqrt((diff.^2)*mask); + case Inf, Cd(i,j)=f*max(diag(mask)*abs(diff),[],2); + otherwise, Cd(i,j)=f*((abs(diff).^q)*mask).^(1/q); + end + end + Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])'; + end + else + for i=1:n1, + for j=1:n2, + diff = C1(i,:) - C2(j,:); + f = 2*nn1(i)*nn2(j) / (nn1(i)+nn2(j)); + switch q, + case 1, Cd(i,j)=f*abs(diff)*mask; + case 2, Cd(i,j)=f*sqrt((diff.^2)*mask); + case Inf, Cd(i,j)=f*max(diag(mask)*abs(diff),[],2); + otherwise, Cd(i,j)=f*((abs(diff).^q)*mask).^(1/q); + end + end + end + end + + % single linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + case 'single', + + if n2==0, + for i=1:n1-1, + for j=i+1:n1, + vd = D(clinds1{i},clinds1{j}); + fi = isfinite(vd(:)); + if any(fi), Cd(i,j) = min(vd(fi)); else Cd(i,j) = Inf; end + end + Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])'; + end + else + for i=1:n1, + for j=1:n2, + vd = D(clinds1{i},clinds2{j}); + fi = isfinite(vd(:)); + if any(fi), Cd(i,j) = min(vd(fi)); else Cd(i,j) = Inf; end + end + end + end + + % average linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + case 'average', + + if n2==0, + for i=1:n1-1, + for j=i+1:n1, + vd = D(clinds1{i},clinds1{j}); + fi = isfinite(vd(:)); + if any(fi), Cd(i,j) = mean(vd(fi)); else Cd(i,j) = Inf; end + end + Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])'; + end + else + for i=1:n1, + for j=1:n2, + vd = D(clinds1{i},clinds2{j}); + fi = isfinite(vd(:)); + if any(fi), Cd(i,j) = mean(vd(fi)); else Cd(i,j) = Inf; end + end + end + end + + % complete linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + case 'complete', + + if n2==0, + for i=1:n1-1, + for j=i+1:n1, + vd = D(clinds1{i},clinds1{j}); + fi = isfinite(vd(:)); + if any(fi), Cd(i,j) = max(vd(fi)); else Cd(i,j) = Inf; end + end + Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])'; + end + else + for i=1:n1, + for j=1:n2, + vd = D(clinds1{i},clinds2{j}); + fi = isfinite(vd(:)); + if any(fi), Cd(i,j) = max(vd(fi)); else Cd(i,j) = Inf; end + end + end + end + + % neighborhood function linkage distance %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + case 'neighf', + + if n2==0, + for i=1:n1-1, + for j=i+1:n1, + vd = D(clinds1{i},clinds1{j}); + fi = isfinite(vd(:)); + if any(fi), + hd = q(clinds1{i},clinds1{j}); + hd = hd(fi); + Cd(i,j) = sum(hd.*vd(fi))/sum(hd); + else Cd(i,j) = Inf; + end + end + Cd([(i+1):n1],i) = Cd(i,[(i+1):n1])'; + end + else + for i=1:n1, + for j=1:n2, + vd = D(clinds1{i},clinds2{j}); + fi = isfinite(vd(:)); + if any(fi), + hd = q(clinds1{i},clinds2{j}); + hd = hd(fi); + Cd(i,j) = sum(hd.*vd(fi))/sum(hd); + else Cd(i,j) = Inf; + end + end + end + end + + otherwise, error(['Unknown cluster distance metric: ' cldist]); +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_clget.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,115 @@ +function a = som_clget(sC, mode, ind) + +%SOM_CLGET Get properties of specified clusters. +% +% a = som_clget(sC, mode, ind) +% +% inds = som_clget(sC,'dinds',20); +% col = som_clget(sC,'depth',[1 2 3 20 54]); +% +% Input and output arguments: +% sC (struct) clustering struct +% mode (string) what kind of property is requested +% 'binds' (a union over) indeces of base clusters +% belonging to the specified cluster(s) +% 'dinds' (a union over) indeces of the data vectors +% belonging to the specified cluster(s) +% 'dlen' number of data vectors belonging +% to each of the specified cluster(s) +% 'depth' depths of the specified clusters +% (depth of the root cluster is 0, +% depth of its children are 1, etc.) +% 'child' (a union over) children clusters +% of specified cluster(s), including +% the clusters themselves +% 'base' base partitioning based on given +% clusters +% ind (vector) indeces of the clusters +% +% a (vector) the answer +% +% See also SOM_CLSTRUCT, SOM_CLPLOT. + +% Copyright (c) 2000 by the SOM toolbox programming team. +% Contributed to SOM Toolbox on XXX by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 180800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +clen = size(sC.tree,1)+1; + +switch mode, + case 'binds', + a = []; + for i=1:length(ind), a = [a, getbaseinds(sC.tree,ind(i))]; end + a = unique(a); + case 'dinds', + b = []; + for i=1:length(ind), b = [b, getbaseinds(sC.tree,ind(i))]; end + b = unique(b); + a = zeros(length(sC.base),1); + for i=1:length(b), a(find(sC.base==b(i)))=1; end + a = find(a); + case 'dlen', + a = zeros(length(ind),1); + for i=1:length(ind), + b = getbaseinds(sC.tree,ind(i)); + for j=1:length(b), a(i) = a(i) + sum(sC.base==b(j)); end + end + case 'depth', + a = getdepth(sC.tree); + a = a(ind); + case 'child', + a = getchildren(sC.tree,ind); + case 'base', + a = sC.base*0; + ind = -sort(-ind); + for i=1:length(ind), a(som_clget(sC,'dinds',ind(i))) = ind(i); end +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function ch = getchildren(Z,ind) + + clen = size(Z,1)+1; + ch = ind; cho = ind; + while any(cho), + i = cho(1); cho = cho(2:end); + j = Z(i-clen,1); k = Z(i-clen,2); + if j>clen, cho(end+1) = j; end + if k>clen, cho(end+1) = k; end + ch(end+1) = j; ch(end+1) = k; + end + return; + +function binds = getbaseinds(Z,ind) + + clen = size(Z,1)+1; + binds = ind; + while binds(1)>clen, + i = binds(1); + binds = binds(2:end); + j = Z(i-clen,1); k = Z(i-clen,2); + if j>clen, binds = [j binds]; else binds(end+1) = j; end + if k>clen, binds = [k binds]; else binds(end+1) = k; end + end + return; + +function depth = getdepth(Z) + + clen = size(Z,1)+1; + depth = zeros(2*clen-1,1); + ch = 2*clen-1; % active nodes + while any(ch), + c = ch(1); ch = ch(2:end); + if c>clen & isfinite(Z(c-clen,3)), + chc = Z(c-clen,1:2); % children of c + depth(chc) = depth(c) + 1; % or +(ind==chc(1)) + ch = [ch, chc]; + end + end + return;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_cllinkage.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,272 @@ +function sC = som_cllinkage(sM,varargin) + +%SOM_CLLINKAGE Make a hierarchical linkage of the SOM map units. +% +% sC = som_cllinkage(sM, [[argID,] value, ...]) +% +% sC = som_cllinkage(sM); +% sC = som_cllinkage(D,'complete'); +% sC = som_cllinkage(sM,'single','ignore',find(~som_hits(sM,D))); +% sC = som_cllinkage(sM,pdist(sM.codebook,'mahal')); +% som_clplot(sC); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map or data struct to be clustered +% (matrix) size dlen x dim, a data set: the matrix must not +% contain any NaN's! +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% sC (struct) a clustering struct with e.g. the following fields +% (for more information see SOMCL_STRUCT) +% .base (vector) if base partitioning is given, this is a newly +% coded version of it so that the cluster indices +% go from 1 to the number of clusters. +% .tree (matrix) size clen-1 x 3, the linkage info +% Z(i,1) and Z(i,2) hold the indeces of clusters +% combined on level i (starting from bottom). The new +% cluster has index dlen+i. The initial cluster +% index of each unit is its linear index in the +% original data matrix. Z(i,3) is the distance +% between the combined clusters. See LINKAGE +% function in the Statistics Toolbox. +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'topol' *(struct) topology struct +% 'connect' *(string) 'neighbors' or 'any' (default), whether the +% connections should be allowed only between +% neighbors or between any vectors +% (matrix) size dlen x dlen indicating the connections +% between vectors +% 'linkage' *(string) the linkage criteria to use: 'single' (the +% default), 'average', 'complete', 'centroid', or 'ward' +% 'dist' (matrix) size dlen x dlen, pairwise distance matrix to +% be used instead of euclidian distances +% (vector) as the output of PDIST function +% (scalar) distance norm to use (default is euclidian = 2) +% 'mask' (vector) size dim x 1, the search mask used to +% weight distance calculation. By default +% sM.mask or a vector of ones is used. +% 'base' (vector) giving the base partitioning of the data: +% base(i) = j denotes that vector i belongs to +% base cluster j, and base(i) = NaN that vector +% i does not belong to any cluster, but should be +% ignored. At the beginning of the clustering, the +% vector of each cluster are averaged, and these +% averaged vectors are then clustered using +% hierarchical clustering. +% 'ignore' (vector) units to be ignored (in addition to those listed +% in base argument) +% 'tracking' (scalar) 1 or 0: whether to show tracking bar or not (default = 0) +% +% Note that if 'connect'='neighbors' and some vector are ignored (as denoted +% by NaNs in the base vector), there may be areas on the map which will +% never be connected: connections across the ignored map units simply do not +% exist. In such a case, the neighborhood is gradually increased until +% the areas can be connected. +% +% See also KMEANS_CLUSTERS, LINKAGE, PDIST, DENDROGRAM. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on XXX by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 160600 250800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +% the data +if isstruct(sM), + switch sM.type, + case 'som_map', M = sM.codebook; sT = sM.topol; mask = sM.mask; data_name = sM.name; sTr = sM.trainhist(end); + case 'som_data', M = sM.data; sT = []; mask = []; data_name = sM.name; sTr = []; + case 'som_topol', M = []; sT = sM; mask = []; data_name = inputname(1); + sTr = som_set('som_train','neigh','gaussian','radius_fin',1); + otherwise, error('Bad first argument'); + end +else M = sM; sT = []; mask = []; data_name = inputname(1); sTr = []; +end +[dlen dim] = size(M); +if isempty(mask), mask = ones(dim,1); end +if any(isnan(M(:))), error('Data matrix must not have any NaNs.'); end + +% varargin +q = 2; +Md = []; +linkage = 'single'; +ignore = []; +Ne = 'any'; +base = []; +tracking = 0; +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case {'topol','som_topol','sTopol'}, i=i+1; sT = varargin{i}; + case 'connect', i=i+1; Ne = varargin{i}; + case 'ignore', i=i+1; ignore = varargin{i}; + case 'dist', i=i+1; Md = varargin{i}; + case 'linkage', i=i+1; linkage = varargin{i}; + case 'mask', i=i+1; mask = varargin{i}; + case 'tracking',i=i+1; tracking = varargin{i}; + case 'base', i=i+1; base = varargin{i}; + % unambiguous values + case 'neighbors', Ne = varargin{i}; + case 'any', Ne = varargin{i}; + case {'single','average','complete','neighf','centroid','ward'}, linkage = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', sT = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, disp(['(som_cllinkage) Ignoring invalid argument #' num2str(i+1)]); end + i = i+1; +end + +% check distance metric +if prod(size(Md))==1, q = Md; Md = []; end +if ~isempty(Md) & prod(size(Md))<dlen^2, Md = squareform(Md); end +if prod(size(Md))>0 & any(strcmp(linkage,{'ward','centroid'})), + warning(['The linkage method ' linkage ' cannot be performed with precalculated distance matrix.']); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% distance matrix and connections between units + +% base partition +if isempty(base), base = 1:dlen; end +if ~isempty(ignore), base(ignore) = NaN; end +cid = unique(base(isfinite(base))); +nc = length(cid); +if max(cid)>nc | min(cid)<1, + b = base; for i=1:nc, base(find(b==cid(i))) = i; end +end + +% initial clusters +clinds = cell(nc,1); +for i=1:nc, clinds{i} = find(base==i); end + +% neighborhood constraint (calculate connection matrix Ne) +if ischar(Ne), + switch Ne, + case 'any', Ne = []; + case 'neighbors', if ischar(Ne), Ne = som_unit_neighs(sT); end + otherwise, error(['Unrecognized connection mode ' Ne]); + end +end +if ~isempty(Ne), l = size(Ne,1); Ne([0:l-1]*l+[1:l]) = 1; end % diagonal=1 +if all(Ne(:)>0), Ne = []; end + +% neighborhood function values +if strcmp(linkage,'neighf') + if isempty(sTr), error('Cannot use neighf linkage.'); end + q = som_unit_dists(sT).^2; + r = sTr.radius_fin^2; + if isnan(r) | isempty(r), r = 1; end + switch sTr.neigh, + case 'bubble', q = (q <= r); + case 'gaussian', q = exp(-q/(2*r)); + case 'cutgauss', q = exp(-q/(2*r)) .* (q <= r); + case 'ep', q = (1-q/r) .* (q <= r); + end +end + +% mutual distances and initial cluster distances +Cd = []; +if any(strcmp(linkage,{'single','average','complete','neighf'})), + M = som_mdist(M,2,mask,Ne); + if (nc == dlen & all(base==[1:dlen])), Cd = M; end +end +if isempty(Cd), Cd = som_cldist(M,clinds,[],linkage,q,mask); end +Cd([0:nc-1]*nc+[1:nc]) = NaN; % NaNs on the diagonal + +% check out from Ne which of the clusters are not connected +if ~isempty(Ne) & any(strcmp(linkage,{'centroid','ward'})), + Clconn = sparse(nc); + for i=1:nc-1, + for j=i+1:nc, Clconn(i,j) = any(any(Ne(clinds{i},clinds{j}))); end + Clconn(i+1:nc,i) = Clconn(i,i+1:nc)'; + end + Cd(Clconn==0) = Inf; +else + Clconn = []; +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% construct dendrogram + +clen = nc; +cid = 1:clen; +Z = zeros(nc-1,3)+NaN; % merged clusters and distance for each step +if tracking, h = waitbar(0,'Making hierarchical clustering'); end + +for i=1:clen-1, + if tracking, waitbar(i/clen,h); end + + % find two closest clusters and combine them + [d,c1] = min(min(Cd)); % cluster1 + [d,c2] = min(Cd(:,c1)); % cluster2 + i1 = clinds{c1}; % vectors belonging to c1 + i2 = clinds{c2}; % vectors belonging to c2 + clinds{c1} = [i1; i2]; % insert clusters to c1 + Z(i,:) = [cid(c1), cid(c2), d]; % update tree info + + % remove cluster c2 + notc2 = [1:c2-1,c2+1:nc]; + nc = nc-1; if nc<=1, break; end + if c1>c2, c1=c1-1; end + clinds = clinds(notc2); + Cd = Cd(notc2,notc2); + cid = cid(notc2); + if ~isempty(Clconn), Clconn = Clconn(notc2,notc2); end + + % update cluster distances + notc1 = [1:c1-1,c1+1:nc]; + Cd(c1,notc1) = som_cldist(M,clinds(c1),clinds(notc1),linkage,q,mask); + Cd(notc1,c1) = Cd(c1,notc1)'; + if ~isempty(Clconn), + for j=notc1, Clconn(c1,j) = any(any(Ne(clinds{c1},clinds{j}))); end + Clconn(notc1,c1) = Clconn(c1,notc1)'; + Cd(Clconn==0) = Inf; + end + +end + +if tracking, close(h); end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% return values + +% to maintain compatibility with Statistics Toolbox, the values in +% Z must be yet transformed so that they are similar to the output +% of LINKAGE function + +clen = size(Z,1)+1; +Zs = Z; +current_cluster = 1:clen; +for i=1:size(Z,1), + Zs(i,1) = current_cluster(Z(i,1)); + Zs(i,2) = current_cluster(Z(i,2)); + current_cluster(Z(i,[1 2])) = clen+i; +end +Z = Zs; + +% make a clustering struct +name = sprintf('Clustering of %s at %s',data_name,datestr(datenum(now),0)); +sC = som_clstruct(Z,'base',base,'name',name); + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_clplot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,238 @@ +function h = som_clplot(sC,varargin) + +%SOM_CLPLOT Visualize clustering. +% +% h = som_clplot(sC, [[argID,] value, ...]) +% som_clplot(sM, part) +% +% som_clplot(sC); +% som_clplot(som_clstruct(Z)) +% som_clplot(sC,sM); +% som_clplot(sC,'coord',P); +% som_clplot(sC,'dendrogram',[1 1 1 1 0 0 1 1 0 0 1]); +% som_clplot(sC,'linewidth',10); +% som_clplot(sC,'size',10); +% som_clplot(sM,part); +% +% Input and output arguments ([]'s are optional): +% sC (struct) clustering struct, as produced by SOM_CLSTRUCT +% [argID, (string) See below. Each pair is the fieldname and +% value] (varies) the value to be given to that field. +% sM (struct) map struct +% part (vector) length = munits, partitioning for the map +% +% h (vector) handles to the arcs between +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'linecolor' (string) color of the arc lines, 'k' by default +% (vector) size 1 x 3 +% 'linewidth' (scalar) width of the arc lines +% 'size' (vector) length 2*clen-1, sizes for each of the +% cluster markers +% (scalar) this size is used for all cluster markers +% 'dendrogram'(vector) size 2*clen-1, indicates which clusters +% are shown in the dendrogram +% *(string) 'on' or 'off' ('on' by default) +% 'coord' (matrix) size dlen x odim, the coordinates +% for the data. If odim<=2, these are used as is. +% Otherwise a 2-dimensional PCA-projection is +% first made (see function PCAPROJ). These +% coordinates are applied also to the clusters. +% *(struct) data struct: as above +% map or topology struct: the coordinates given +% by SOM_VIS_COORDS are used for the data +% 'color' (matrix) size dlen x 3, color for each data. By +% default the colors defined for base +% clusters are used (sC.color(sC.base,:)). +% For ignored data figure background color is used. +% (vector) size dlen x 1, indexed colors are used +% +% See also SOM_CLSTRUCT, SOM_LINKAGE, SOM_CLPRUNE, LINKAGE, DENDROGRAM. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on XXX by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 180600 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% read the arguments + +% sC +if strcmp(sC.type,'som_map'), + base = varargin{1}; + clen = length(unique(base(isfinite(base)))); + Z = ones(clen-1,3); + Z(:,1) = randperm(clen-1)'; + Z(:,2) = [clen:2*clen-2]'; + Z(:,3) = [1:clen-1]'; + sT = sC; + sC = som_clstruct(Z,'base',varargin{1}); + h = som_clplot(sC,'coord',sT,'dendrogram','off',varargin{2:end}); + return; +end +clen = size(sC.tree,1)+1; + +% varargin +show = 'on'; +markersize = 10; +linecolor = 'k'; +linewidth = 1; +datacoord = []; +datacolor = []; + +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + case 'dendrogram', i=i+1; show = varargin{i}; + case 'size', i=i+1; markersize = varargin{i}; + case 'linecolor', i=i+1; linecolor = varargin{i}; + case 'linewidth', i=i+1; linewidth = varargin{i}; + case 'color', i=i+1; datacolor = varargin{i}; + case 'coord', i=i+1; datacoord = varargin{i}; + case {'on','off'}, show = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}), datacoord = varargin{i}; + else argok = 0; + end + if ~argok, disp(['(som_clplot) Ignoring invalid argument #' num2str(i+1)]); end + i=i+1; +end + +% markersize +if length(markersize)==1, markersize = ones(2*clen-1,1)*markersize; end + +% datacoord +if ~isempty(datacoord), + if isstruct(datacoord), + switch datacoord.type, + case 'som_map', datacoord = datacoord.topol; + case 'som_topol', %nil + case 'som_data', datacoord = datacoord.data; + otherwise, datacoord = []; + end + end + if isstruct(datacoord), + sC = som_clstruct(sC,'coord',som_vis_coords(datacoord.lattice,datacoord.msize)); + else + [dlen dim] = size(datacoord); + if dim>2, datacoord = pcaproj(datacoord,2); end + sC = som_clstruct(sC,'coord',datacoord); + end +end + +% show +if ischar(show), show = strcmp(show,'on'); end +if prod(size(show)) == 1, show = ones(2*clen-1,1)*show; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize values + +% find the children to show for each cluster +sTree0 = struct('parent',0,'children',[]); +sTree = sTree0; +for i=2:(2*clen-1), sTree(i) = sTree0; end +for i=(clen+1):(2*clen-1), + if isfinite(sC.tree(i-clen,3)), + ch = sC.tree(i-clen,1:2); + sTree(i).children = ch; + for j=1:length(ch), sTree(ch(j)).parent = i; end + end +end +if any(show==0), % some clusters are not shown + for i=(clen+1):(2*clen-1), + if ~show(i), + p = sTree(i).parent; + ch = sTree(i).children; + if p, + j = find(sTree(p).children == i); + sTree(p).children = [sTree(p).children([1:(j-1),(j+1):end]), ch]; + for j=1:length(ch), sTree(ch(j)).parent = p; end + end + end + end +end + +% the arcs +lfrom = []; lto = []; ladd = []; +for i=(clen+1):(2*clen-1), + if show(i), + ch = sTree(i).children'; + %ch = ch(find(show(ch)==1)); + lfrom = [lfrom; i*ones(length(ch),1)]; + lto = [lto; ch]; + end +end + +% infinite height +%isinf = ~isfinite(sC.height); +%sC.height(isinf) = 2*max(sC.height(~isinf)); + +% the coordinates of the arcs +Co = [sC.coord, sC.height]; +if size(Co,2)==2, + Lx = [Co(lfrom,1), Co(lto,1), Co(lto,1)]; + Ly = [Co(lfrom,end), Co(lfrom,end), Co(lto,end)]; + Lz = []; +else + Lx = [Co(lfrom,1), Co(lto,1), Co(lto,1)]; + Ly = [Co(lfrom,2), Co(lto,2), Co(lto,2)]; + Lz = [Co(lfrom,end), Co(lfrom,end), Co(lto,end)]; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% plot + +washold = ishold; +if ~washold, cla; hold on; end + +% plot data +if ~isempty(datacoord), + if isempty(datacolor), + nancolor = get(gcf,'Color'); + Col = nancolor(ones(length(sC.base),1),:); + ind = find(isfinite(sC.base)); + Col(ind,:) = sC.color(sC.base(ind),:); + elseif size(datacolor,2)==1, Col = som_normcolor(datacolor,jet); + else Col = datacolor; + end + if isstruct(datacoord), som_cplane(datacoord,Col); + else som_grid('rect',[length(sC.base) 1],'line','none',... + 'Coord',datacoord,'Markercolor',Col); + end +end + +h = []; +if any(show), + + % plot the lines + if isempty(Lz), + h = line(Lx',Ly','color',linecolor,'linewidth',linewidth); + else + h = line(Lx',Ly',Lz','color',linecolor,'linewidth',linewidth); + if ~washold, view(3); end + rotate3d on + end + + % plot the nodes + inds = find(show); + som_grid('rect',[length(inds) 1],'line','none',... + 'Coord',Co(inds,:),... + 'Markercolor',sC.color(inds,:),... + 'Markersize',markersize(inds)); +end + +if ~washold, hold off, end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_clset.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,278 @@ +function [sC,old2new,newi] = som_clset(sC,action,par1,par2) + +% SOM_CLSET Create and/or set values in the som_clustering struct. +% +% first argument +% sC (struct) a som_clustering struct +% Z (matrix) size nb-1 x 3, as given by LINKAGE function +% base (vector) size dlen x 1, a partitioning of the data +% +% actions +% 'remove' removes the indicated clusters (par1: vector) +% 'add' add a cluster by making a combination of the indicated +% clusters (par1: vector) +% %'move' moves a child cluster (par1: scalar) from a parent to another +% % (par2: vector 1 x 2) +% 'merge' like 'add', followed by removing the indicated clusters (par1: vector) +% %'split' the indicated cluster (par1: scalar) is partitioned into indicated +% % parts (par2: vector), which are then added, while the indicated cluster +% % (par1) is removed +% 'coord' sets the coordinates of base clusters (par1: matrix nb x *), and +% recalculates coordinates of the derived clusters (by averaging base cluster +% coordinates) +% 'color' sets the colors of base clusters (par1: matrix nb x 3), and recalculates +% colors of the derived clusters (as averages of base cluster colors) +% +% sC +% .type (string) 'som_clustering' +% .name (string) Identifier for the clustering. +% .nb (scalar) Number of base clusters in the clustering. +% .base (vector) Size dlen x 1, the basic groups of data +% forming the base clusters, e.g. as a result +% of partitive clustering. Allowed values are +% 1:nb indicating the base cluster +% to which the data belongs to. +% NaN indicating that the data has +% been ignored in the clustering +% .nc (scalar) Number of clusters in the clustering (nb + derived clusters). +% .children (cellarray) size nc x 1, each cell gives the list of indeces +% of child clusters for the cluster +% .parent (vector) size nc x 1, the index of parent of each cluster +% (or zero if the cluster does not have a parent) +% .coord (matrix) size nc x *, visualization coordinates for each cluster +% By default the coordinates are set so that +% the base clusters are ordered on a line, and the +% position of each combined cluster is average of +% the base clusters that constitute it. +% .color (matrix) size nc x 3, color for each cluster. +% By default the colors are set so that the +% base clusters are ordered on a line, +% and then colors are assigned from the 'hsv' +% colormap to the base clusters. The color +% of each combined cluster is average as above. +% .cldist (string) Default cluster distance function. + +inew = []; +if isstruct(sC), + % it should be a som_clustering struct + old2new = [1:sC.nc]; +elseif size(sC,2)==3, + % assume it is a cluster hierarchy matrix Z + sC = Z2sC(sC); + old2new = [1:sC.nc]; +else + % assume it is a partitioning vector + base = sC; + u = unique(base(isfinite(base))); + old2new = sparse(u,1,1:length(u)); + base = old2new(base); + sC = part2sC(base); +end + +switch action, +case 'remove', + for i=1:length(par1), + [sC,o2n] = removecluster(sC,old2new(par1(i))); + old2new = o2n(old2new); + end +case 'add', + [sC,old2new,inew] = addmergedcluster(sC,par1); +case 'move', + % not implemented yet +case 'split', + % not implemented yet +case 'merge', + [sC,old2new,inew] = addmergedcluster(sC,par1); + for i=1:length(par1), + [sC,o2n] = removecluster(sC,old2new(par1(i))); + old2new = o2n(old2new); + end +case 'color', + sC.color = derivative_average(sC,par1); +case 'coord', + sC.coord = derivative_average(sC,par1); +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function sC = clstruct(nb,nc) + + sC = struct('type','som_clustering',... + 'name','','base',[],'nb',nb,'nc',nc,... + 'parent',[],'children',[],'coord',[],'color',[],'cldist','centroid'); + sC.base = [1:nb]; + sC.parent = zeros(nc,1); + sC.children = cell(nc,1); sC.children(:) = {[]}; + sC.coord = zeros(nc,2); + sC.color = zeros(nc,3); + return; + +function Z = sC2Z(sC,height) + + if nargin<2, height = 'level'; end + + root = find(sC.parent==0); + order = [root]; + ch = sC.children(root); + while any(ch), i = ch(1); order = [ch(1), order]; ch = [ch(2:end), sC.children{i}]; end + + he = zeros(sC.nc,1); + if strcmp(height,'level'), + ch = sC.children{root}; + while any(ch), + i = ch(1); he(i) = he(sC.parent(i))+1; + ch = [ch(2:end), sC.children{i}]; + end + he = max(he)-he; + elseif strcmp(height,'level2'), + for i=order, if any(sC.children{i}), he(i) = max(he(sC.children{i}))+1; end, end + else + %he = som_cldist ( between children ) + end + + Z = zeros(sC.nb-1,3); + i = sC.nb-1; + inds = root; + while i>0, + ch = sC.children{inds(1)}; h = he(inds(1)); inds = [inds(2:end), ch]; + if length(ch)>=2, + for k=1:length(ch)-2, Z(i,:) = [i-1, ch(k), h]; i = i - 1; end + Z(i,:) = [ch(end-1) ch(end) h]; i = i - 1; + end + end + return; + +function sC = Z2sC(Z) + + nb = size(Z,1)+1; + nc = 2*nb-1; + sC = clstruct(nb,nc); + sC.base = [1:nb]; + for i=1:nc, + j = find(Z(:,1)==i | Z(:,2)==i); + sC.parent(i) = nb+j; + sC.children{sC.parent(i)}(end+1) = i; + end + % coords and color + order = nc; + nonleaves = 1; + while any(nonleaves), + j = nonleaves(1); + ch = sC.children{order(j)}; + if j==1, oleft = []; else oleft = order(1:(j-1)); end + if j==length(order), oright = []; else oright = order((j+1):length(order)); end + order = [oleft, ch, oright]; + nonleaves = find(order>nb); + end + [dummy,co] = sort(order); + sC.coord = derivative_average(sC,co'); + H = hsv(nb+1); + sC.color = derivative_average(sC,H(co,:)); + return; + +function sC = part2sC(part) + + nb = max(part); + nc = nb+1; + sC = clstruct(nb,nc); + sC.base = part; + sC.parent(1:nb) = nc; + sC.children{nc} = [1:nb]; + co = [1:nb]'; + sC.coord = derivative_average(sC,co); + H = hsv(nb+1); + sC.color = derivative_average(sC,H(1:nb,:)); + return; + +function [sC,old2new] = removecluster(sC,ind) + + old2new = [1:sC.nc]; + parent_ind = sC.parent(ind); + ch = sC.children{ind}; + if ~parent_ind, + % trying to remove root cluster - no go + return; + elseif ~any(ch), + % trying to remove a base cluster - no go + return; + else + % ok, proceed + old2new = [1:ind-1 0 ind:sC.nc-1]; + % update parent and child fields + sC.parent(ch) = parent_ind; + sC.children{parent_ind} = setdiff([sC.children{parent_ind}, ch],ind); + % remove old cluster + j = [1:ind-1, ind+1:sC.nc]; + sC.parent = sC.parent(j); + sC.children = sC.children(j); + sC.color = sC.color(j,:); + sC.coord = sC.coord(j,:); + sC.nc = sC.nc-1; + % update old indeces to new indices + sC.parent = old2new(sC.parent); + for i=1:sC.nc, sC.children{i} = old2new(sC.children{i}); end + end + return; + +function [sC,old2new,inew] = addmergedcluster(sC,inds) + + old2new = [1:sC.nc]; + inew = 0; + p_inds = sC.parent(inds); + if ~all(p_inds(1)==p_inds), + % clusters are not siblings - no go + return; + end + parent_ind = p_inds(1); + if isempty(setdiff(sC.children{parent_ind},inds)), + % such a merged cluster exists already + return; + else + % ok, proceed + inew = parent_ind; + old2new = [1:inew-1,inew+1:sC.nc+1]; + % add the new cluster (=copy of parent_ind) + j = [1:inew,inew:sC.nc]; + sC.parent = sC.parent(j); + sC.children = sC.children(j); + sC.color = sC.color(j,:); + sC.coord = sC.coord(j,:); + sC.nc = sC.nc+1; + % update old indeces to new indices + sC.parent = old2new(sC.parent); + for i=1:sC.nc, sC.children{i} = old2new(sC.children{i}); end + inds = old2new(inds); + parent_ind = old2new(parent_ind); + % update parent, child, color and coord fields + sC.parent(inds) = inew; + sC.parent(inew) = parent_ind; + sC.children{inew} = inds; + sC.children{parent_ind} = [setdiff(sC.children{parent_ind}, inds), inew]; + b = baseind(sC,inew); + sC.color(inew,:) = mean(sC.color(b,:)); + sC.coord(inew,:) = mean(sC.coord(b,:)); + end + return; + +function C = derivative_average(sC,Cbase) + + [n dim] = size(Cbase); + if n ~= sC.nb, error('Color / Coord matrix should have nb rows'); end + C = zeros(sC.nc,dim); + for i=1:sC.nc, C(i,:) = mean(Cbase(baseind(sC,i),:)); end + return; + +function bi = baseind(sC,ind) + + bi = [ind]; + i = 1; + while i<=length(bi), bi = [bi, sC.children{bi(i)}]; end + bi = bi(bi<=sC.nb); + return; + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_clspread.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,161 @@ +function base = som_clspread(sM,base,cldist,Ne,verbosity) + +% SOM_CLSPREAD Partition the given data by flooding. +% +% part = som_clspread(sM,part,cldist,[Ne],[verbos]) +% +% Input and output arguments ([]'s are optional): +% sM (struct) map or data struct +% (matrix) size dlen x dim, the data set +% base (vector) initial partition, where if base(i) is +% 0 i should be assigned to some cluster +% NaN i should not be assigned to any cluster +% otherwise i belongs to cluster base(i) +% cldist (string) cluster distance measure: 'single', 'average', +% 'complete', 'neighf', 'ward', 'centroid', 'BMU' +% [Ne] (scalar) 0 = not constrined to neighborhood +% 1 = constrained +% (matrix) size dlen x dlen, indicating possible connections +% [verbos] (scalar) 1 (default) = show status bar +% 0 = don't +% +% See also SOM_CLDIST. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on XXX by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 220800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +q = 2; + +% map/data +if isstruct(sM), + switch sM.type, + case 'som_map', M = sM.codebook; mask = sM.mask; sT = sM.topol; + case 'som_data', M = sM.data; mask = []; sT = []; + end +else M = sM; mask = []; sT = []; +end +[dlen dim] = size(M); +if isempty(mask), mask = ones(dim,1); end + +% simple option +if any(strcmp(cldist,{'closest','BMU'})), + i0 = find(base==0); + i1 = find(base>0); + bmus = som_bmus(M(i1,:),M(i0,:)); + base(i0) = base(i1(bmus)); + return; +end + +% constrained clustering +if nargin<4, Ne = []; end +if prod(size(Ne))==1, + if Ne & isempty(sT), + warning('Cannot use constrained clustering.'); Ne = 0; + end + if Ne, Ne = som_unit_neighs(sT); else Ne = []; end +end +if ~isempty(Ne), + Ne([0:dlen-1]*dlen+[1:dlen]) = 1; % set diagonal elements = 1 + if all(Ne(:)>0), Ne = []; end +end + +if nargin<5, verbosity = 1; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize + +if size(base,1)==1, base = base'; end + +cid = unique(base(isfinite(base) & base~=0)); % cluster IDs +nc = length(cid); +uind = find(base==0); % unclustered points +nu = length(uind); +if nu==0, return; end + +% initial clusters +clinds = cell(nc,1); for i=1:nc, clinds{i} = find(base==i); end +clinds2 = cell(nu,1); for i=1:nu, clinds2{i} = uind(i); end + +% neighborhood function values +if strcmp(cldist,'neighf') + if isempty(sT), error('Cannot use neighf linkage.'); end + q = som_unit_dists(sT).^2; + r = sM.trainhist(end).radius_fin^2; + if isnan(r) | isempty(r), r = 1; end + switch sM.neigh, + case 'bubble', q = (q <= r); + case 'gaussian', q = exp(-q/(2*r)); + case 'cutgauss', q = exp(-q/(2*r)) .* (q <= r); + case 'ep', q = (1-q/r) .* (q <= r); + end +end + +% distance of each cluster to the unclustered points +if any(strcmp(cldist,{'single','average','complete','neighf'})), + M = som_mdist(M,2,mask,Ne); +end +Cd = som_cldist(M,clinds,clinds2,cldist,q,mask); + +% check out from Ne which of the clusters are not connected +if ~isempty(Ne) & any(strcmp(cldist,{'centroid','ward'})), + Clconn = sparse(nc,nu); + for i=1:nc, for j=1:nu, Clconn(i,j) = any(any(Ne(clinds{i},uind(j)))); end, end + Cd(Clconn==0) = Inf; +else + Clconn = []; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +if verbosity, + nu0 = nu; + h = waitbar(1-nu/nu0,'Assigning unclustered points'); % tracking +end + +while 1, + + % find closest unclustered point + [dk,k] = min(Cd,[],2); % min distance from each unclustered point + [d,c] = min(dk); % cluster to which it is assigned + k = k(c); + + if ~isfinite(d), + break; + end + + % add k to cluster c + base(uind(k)) = cid(c); + clinds{c} = [clinds{c}; uind(k)]; + + % remove point k + notk = [1:k-1,k+1:nu]; + nu = nu-1; if nu<=0, break; end + Cd = Cd(:,notk); + uind = uind(notk); + clinds2 = clinds2(notk); + if ~isempty(Clconn), Clconn = Clconn(:,notk); end + + % update cluster distances to c + Cd(c,:) = som_cldist(M,clinds(c),clinds2,cldist,q,mask); + if ~isempty(Clconn), + for j=1:nu, Clconn(c,j) = any(any(Ne(clinds{c},uind(j)))); end + Cd(c,find(Clconn(c,:)==0)) = Inf; + end + + if verbosity, waitbar(1-nu/nu0,h); end % tracking + +end +if verbosity, close(h); end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_clstruct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,206 @@ +function sC = som_clstruct(Z,varargin) + +%SOM_CLSTRUCT Create a clustering struct or set its field values. +% +% sC = som_clstruct(Z, [argID, value, ...]) +% +% Z = linkage(pdist(sM.codebook)); +% sC = som_clstruct(Z); +% sC = som_clstruct(sC,'coord',som_vis_coords(lattice,msize)); +% sC = som_clstruct(sC,'color',som_colorcode(sM)); +% sC = som_clstruct(sC,'base',sC.base(som_bmus(sM,sD))); +% +% Input and output arguments ([]'s are optional): +% Z (matrix) size clen-1 x 3, where clen is the number of +% base clusters. This is a clustering matrix +% similar to that produced by LINKAGE in +% Statistical Toolbox. See SOM_LINKAGE. +% (struct) clustering struct (as produced by this function) +% [argID, (string) See below. Each pair is the fieldname and +% value] (varies) the value to be given to that field. +% +% sC (struct) clustering struct +% +% The clustering struct is based on the assumption that there +% is a base partitioning of the SOM (or data) which is saved in +% the .base field of the struct. Then a hierarchical clustering +% is applied to this base partitioning. The results are saved to +% .tree field of the struct. Each cluster (base and combined) +% has also three properties: height, coordinate and color, which +% are used in the visualizations. The fields of the struct are: +% .type (string) 'som_clustering' +% .name (string) Identifier for the clustering. +% .tree (matrix) Size clen-1 x 3, as argument Z above. +% .base (vector) Size dlen x 1, the basic groups of data +% forming the base clusters, e.g. as a result +% of partitive clustering. Allowed values are +% 1:clen indicating the base cluster +% to which the data belongs to. +% NaN indicating that the data has +% been ignored in the clustering +% By default [1:clen]. +% .height (vector) Size 2*clen-1 x 1, (clustering) height for each +% cluster. By default 0 for each base cluster and +% .tree(:,3) for the others. +% .coord (matrix) Size 2*clen-1 x *, coordinate for each cluster, +% By default the coordinates are set so that +% the base clusters are ordered on a line, and the +% position of each combined cluster is average of +% the base clusters that constitute it. +% .color (matrix) Size 2*clen-1 x 3, color for each cluster. +% By default the colors are set so that the +% base clusters are ordered on a line, like above, +% and then colors are assigned from the 'hsv' +% colormap to the base clusters. The color +% of each combined cluster is average as above. +% +% Height, coord and color can also be specified in alternate forms: +% 'height' (vector) size 2*clen-1 x 1, if given explicitly +% size clen-1 x 1, specified heights of the +% combined clusters (the base cluster heights +% are all = 0) +% size 0 x 0, default value is used +% 'coord' (matrix) size 2*clen-1 x *, if given explicitly +% size clen x *, to give coordinates for base +% clusters; the coordinate of combined clusters +% are averaged from these +% size dlen x *, to give coordinates of the +% original data: the cluster coordinates are +% averaged from these based on base clusters +% size 0 x 0, default value is used +% 'color' (matrix) as 'coord' +% +% See also SOM_CLPLOT, SOM_CLVALIDITY, SOM_CLGET, SOM_CLLINKAGE. + +% Copyright (c) 2000 by the SOM toolbox programming team. +% Contributed to SOM Toolbox on XXX by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 180800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(Z), + base = Z.base; + color = Z.color; + coord = Z.coord; + height = Z.height; + name = Z.name; + Z = Z.tree; +else + base = []; + color = []; + coord = []; + height = []; + name = ''; +end +clen = size(Z,1)+1; + +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + case 'tree', i=i+1; Z = varargin{i}; clen = size(Z,1)+1; + case 'base', i=i+1; base = varargin{i}; + case 'color', i=i+1; color = varargin{i}; + case 'coord', i=i+1; coord = varargin{i}; + case 'height', i=i+1; height = varargin{i}; + case 'name', i=i+1; name = varargin{i}; + otherwise argok=0; + end + else argok = 0; + end + if ~argok, disp(['(som_clstruct) Ignoring invalid argument #' num2str(i+1)]); end + i = i+1; +end + +if isempty(base), + dlen = clen; + base = 1:dlen; +else + dlen = length(base); + if any(base)>clen | any(base)<1, error('Incorrect base partition vector.'); end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% analysis of hierarchy + +% order of base clusters +order = 2*clen-1; +nonleaves = 1; +while any(nonleaves), + j = nonleaves(1); + ch = Z(order(j)-clen,1:2); + if j==1, oleft = []; else oleft = order(1:(j-1)); end + if j==length(order), oright = []; else oright = order((j+1):length(order)); end + order = [oleft, ch, oright]; + nonleaves = find(order>clen); +end + +% base cluster indeces for each non-base cluster +basecl = cell(clen-1,1); +for i=1:clen-1, + c1 = Z(i,1); if c1>clen, c1 = basecl{c1-clen}; end + c2 = Z(i,2); if c2>clen, c2 = basecl{c2-clen}; end + basecl{i} = [c1 c2]; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% set coordinates, color and height and make the struct + +% coordinates +if size(coord,1)==2*clen-1, % this is ok already +else + if size(coord,1)==0, % the default + [dummy,coord] = sort(order); + coord = coord'; + elseif size(coord,1)==dlen & dlen>clen, % coordinates given for original data + codata = coord; + coord = zeros(clen,size(coord,2)); + for i=1:clen, coord(i,:) = mean(codata(find(base==i),:),1); end + end + if size(coord,1)==clen, % average from base clusters + coord = [coord; zeros(clen-1,size(coord,2))]; + for i=1:clen-1, coord(i+clen,:) = mean(coord(basecl{i},:),1); end + else + error('Incorrect coordinate matrix.'); + end +end + +% color +if size(color,1)==2*clen-1, % this is ok already +else + if size(color,1)==0, % the default + color(order,:) = hsv(length(order)); + elseif size(color,1)==dlen & dlen>clen, % colors given for original data + codata = color; + color = zeros(clen,3); + for i=1:clen, color(i,:) = mean(codata(find(base==i),:),1); end + end + if size(color,1)==clen, % average from base clusters + color = [color; zeros(clen-1,3)]; + for i=1:clen-1, color(i+clen,:) = mean(color(basecl{i},:),1); end + else + error('Incorrect color matrix.'); + end +end + +% height +if isempty(height), + height = [zeros(clen,1); Z(:,3)]; +elseif length(height)==clen-1, + if size(height,2)==clen-1, height = height'; end + height = [zeros(clen,1); height]; +elseif length(height)~=2*clen-1, + error('Incorrect height vector.'); +end + +% make the struct +sC = struct('type','som_clustering',... + 'name',name,'base',base,'tree',Z,... + 'color',color,'coord',coord,'height',height); +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_clustercolor.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,152 @@ +function color=som_clustercolor(m, class, colorcode) + +% SOM_CLUSTERCOLOR Sets map unit coloring according to classification +% +% syntax 1: color = som_clustercolor(m, class, [colorcode]) +% syntax 2: color = som_clustercolor(class, colormatrix) +% +% Input and output arguments ([]'s are optional): +% m (struct) map or topol struct +% (cell array) of form {str,[m1 m2]} where str = 'hexa' +% or 'rect' and [m1 m2] = msize. +% class (matrix) Mxn matrix of integers (class labels) +% where M is the number of map units and each +% column gives some classification for the units. +% colorcode (string) 'rgb1', 'rgb2' (default), 'rgb3', 'rgb4', 'hsv'. +% colormatrix (matrix) Mx3 matrix of RGB triplets giving the +% initial color code for each unit. +% color (matrix) size Mx3xn of RGB triplets giving the +% resulting color code for each unit +% +% The function gives a color coding by class and location for the +% map units. The color is determined by calculating the mean of the +% initial RGB values of units belonging to the same class. +% +% Function has two syntaxes: +% +% * If first argument gives the map topology, i.e. is map or topol struct +% or cell indicating the topology, the initial color coding of the +% units may be given by a string ('rgb1','rgb2','rgb3','rgb4', or 'hsv') +% which describe a predefined coloring scheme. (see SOM_COLORCODE). +% or an initial color matrix of size Mx3 with RGB triplets as rows. +% * Another possibility is to give just the classification vector +% of size Mx1 and an initial color matrix of size Mx3 with RGB +% triplets as rows. +% +% EXAMPLE (requires Matlab Statistics Toolbox) +% +% % Do a 10-cluster single linkage hierachical clustering for SOM units +% class=cluster(linkage(pdist(sM.codebook),'single'),10); +% % Color code the clusters +% C=som_clustercolor(sM, class, 'rgb2'); +% % Visualize +% som_show(sM,'color',C); +% +% See also SOM_COLORCODE, SOM_KMEANSCOLOR, SOM_CPLANE, SOM_SHOW + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 100200 + +%%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(2, 3, nargin)); % check no. of input args is correct + +% Check 1s argument + +% Class matrix? +if vis_valuetype(m, {'nxm'}); + colorcode=class; + class=m; + if ~vis_valuetype(colorcode,{'nx3rgb',[size(class,1) 3]},'all'), + error(['If map or topol is not specified the colorcode must be a' ... + ' [size(class,1) 3] sized RGB matrix.']); + end +else + [tmp,ok,tmp]=som_set(m); + if isstruct(m) & all(ok) + switch m.type + case 'som_topol' % topol? + msize=m.msize; + lattice=m.lattice; + case 'som_map' + msize=m.topol.msize; % map? + lattice=m.topol.lattice; + otherwise + error('Invalid map or topol struct.'); + end + % cell? + elseif iscell(m) & vis_valuetype(size(m),{[1 2]}), + if vis_valuetype(m{2},{[1 2]}) & vis_valuetype(m{1},{'string'}), + lattice=m{1}; + msize=m{2}; + else + error('Invalid map size information.'); + end + else + % not known type + error('Invalid first argument!'); + end + % Check map parameters + switch lattice % lattice + case 'hexa' + ; + case 'rect' + ; + otherwise + error('Unknown lattice type'); + end + if length(msize)>2 % dimension + error('Only 2D maps allowed!'); + end + % Check colorcode + if nargin<3 | isempty(colorcode) + colorcode='rgb2'; + end +end + +% Check class +if any(class~=round(class)) + error('Class labels must be integer numbers.'); +end + +if min(class)<=0 + error('Class numbers should be greater than 0'); +end + +if ischar(colorcode), + switch colorcode + case{'rgb1','rgb2','rgb3','rgb4','hsv'} + colorcode=som_colorcode(m, colorcode); + otherwise + error(['Color code not known: should be ''rgb1'',''rgb2'',' ... + ' ''rgb3'',''rgb4'' or ''hsv''.']); + end +elseif ~vis_valuetype(colorcode,{'nx3rgb',[size(class,1) 3]},'all'); + error(['Invalid colorcode matrix: should be a ' ... + '[length(class) 3] sized RGB matrix.']); +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Go through all i classifications (columns) +for i=1:size(class,2), + % Get unique class labels in ith classification + c=unique(class(:,i))'; % row vector for loop indexing + % Go through all class in ith classification + for j=c; + index=(class(:,i)==j); + N=sum(index); + colors=colorcode(index,:); + % Calculate the mean color + meancolor=repmat(mean(colors,1),N,1); + % Select the original color that is closest to this mean + dist=sum((meancolor-colors).^2,2); + [tmp,min_dist_index]=min(dist); + best_color=repmat(colors(min_dist_index,:),N,1); + % Set the color to output variable + color(index,:,i)=best_color; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_cod2ind.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function ind = som_cod2ind(msize,cind) + +%SOM_COD2IND Matlab linear index from SOM_PAK style linear indeces. +% +% ind = som_cod2ind(msize,cind) +% +% ind = som_cod2ind([10 15],44); +% ind = som_cod2ind(sMap,44); +% ind = som_cod2ind(sMap.msize,44); +% ind = som_cod2ind([10 15],[44 13 91]'); +% +% Input and output arguments: +% msize (struct) map or topology struct +% (vector) size 1 x m, specifies the map grid size +% cind (vector) size n x 1, SOM_PAK style linear indeces for n map units +% (row first, then column) +% +% ind (vector) size n x 1, Matlab linear indeces +% +% See also SOM_IND2COD. + +% Contributed to SOM Toolbox vs2, January 14th, 2002 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 140102 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(msize), + if strcmp(msize.type,'som_map'), msize = msize.topol.msize; + elseif strcmp(msize.type,'som_topol'), msize = msize.msize; + else error('Invalid first argument.'); end +end + +if nargin<2, cind = 1:prod(msize); end + +I2C = som_ind2cod(msize,[1:prod(msize)]); +[dummy,C2I] = sort(I2C); +ind = C2I(cind); + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_colorcode.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,333 @@ +function colors=som_colorcode(m, colorcode, scaling) + +%SOM_COLORCODE Calculates a heuristic color coding for the SOM grid +% +% colors = som_colorcode(m, colorcode, scaling) +% +% Input and output arguments ([]'s are optional): +% m (struct) map or topol struct +% (cell array) of form {str,[m1 m2]} where +% str = 'hexa' or 'rect' and [m1 m2] = msize +% (matrix) size N x 2, unit coordinates +% [colorcode] (string) 'rgb1' (default),'rgb2','rgb3','rgb4','hsv' +% [scaling] (scalar) 1=on (default), 0=off. Has effect only +% if m is a Nx2 matrix of coordinates: +% controls whether these are scaled to +% range [0,1] or not. +% +% colors (matrix) size N x 3, RGB colors for each unit (or point) +% +% The function gives a color coding by location for the map grid +% (or arbitrary set of points). Map grid coordinates are always linearly +% normalized to a unit square (x and y coordinates between [0,1]), except +% if m is a Nx2 matrix and scaling=0. In that case too, the coordinates +% must be in range [0,1]. +% +% Following heuristic color codings are available: +% +% 'rgb1' slice of RGB-cube so that green - yellow +% the corners have colors: | | +% blue - magenta +% +% 'rgb2' slice of RGB-cube so that red - yellow +% the corners have colors: | | +% blue - cyan +% +% 'rgb3' slice of RGB-cube so that mixed_green - orange +% the corners have colors: | | +% light_blue - pink +% +% 'rgb4' has 'rgb1' on the diagonal + additional colors in corners +% (more resolution but visually strongly discontinuous) +% +% 'hsv' angle and radius from map centre are coded by hue and +% intensity (more resoluton but visually discontinuous) +% +% See also SOM_CPLANE, SOM_SHOW, SOM_CLUSTERCOLOR, SOM_KMEANSCOLOR, +% SOM_BMUCOLOR. + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0 Johan 140799 + +%%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(1, 3, nargin)); % check no. of input args is correct + +%% Check m: map, topol, cell or data? + +if vis_valuetype(m,{'nx2'}), + p=m; % explicit coordinates + +else + + % map, topol, cell + + [tmp,ok,tmp]=som_set(m); + if isstruct(m) & all(ok) + switch m.type + case 'som_topol' % topol + msize=m.msize; + lattice=m.lattice; + case 'som_map' + msize=m.topol.msize; % map + lattice=m.topol.lattice; + otherwise + error('Invalid map or topol struct.'); + end + + % cell + + elseif iscell(m) & vis_valuetype(size(m),{[1 2]}), + if vis_valuetype(m{2},{[1 2]}) & vis_valuetype(m{1},{'string'}), + lattice=m{1}; + msize=m{2}; + else + error('Invalid map size information.'); + end + end + + %% Check map parameters + + switch lattice % lattice + case 'hexa' + ; + case 'rect' + ; + otherwise + error('Unknown lattice type'); + end + + if length(msize)>2 % dimension + error('Only 2D maps allowed!'); + end + + + % Calculate coordinates + p=som_unit_coords(msize,lattice,'sheet'); + + % Set scaling to 1 as it is done always in this case + scaling=1; +end + +% Check colorcode + +if nargin < 2 | isempty(colorcode), + colorcode='rgb1'; +end +if ~ischar(colorcode) + error('String value for colorcode mode expected.'); +else + switch colorcode + case { 'rgb1', 'rgb2', 'rgb3' , 'rgb4' ,'hsv'} + otherwise + error([ 'Colorcode mode ' colorcode ' not implemented.']); + end +end + +% Check scaling + +if nargin < 3 | isempty(scaling) + scaling=1; +end + +if ~vis_valuetype(scaling,{'1x1'}) + error('Scaling should be 0 (off) or 1 (on).'); +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% scale coordintes between [0,1] + +if scaling + n=size(p,1); + mn=min(p); + e=max(p)-mn; + p=(p-repmat(mn,n,1))./repmat(e,n,1); +elseif sum(p(:,1)>1+p(:,1)<0+p(:,2)>1+p(:,2)<0), + error('Coordinates out of range [0,1].'); +end + +switch colorcode +case 'rgb1' + h(:,1)=p(:,1); + h(:,2)=1-p(:,2); + h(:,3)=p(:,2); +case 'rgb2' + h(:,1)=p(:,1); + h(:,2)=1-p(:,2); + h(:,3)=1-p(:,1); +case 'rgb3' + h(:,1)=p(:,1); + h(:,2)=.5; + h(:,3)=p(:,2); +case 'rgb4' + p=rgb4(p); + h(:,1)=p(:,1); + h(:,2)=1-p(:,2); + h(:,3)=p(:,3); +case 'hsv' + munits = n; + Hsv = zeros(munits,3); + for i=1:n, + dx = .5-p(i,1); + dy = .5-p(i,2); + r = sqrt(dx^2+dy^2); + if r==0, + h=1; + elseif dx==0, + h=.5; %h=ay; + elseif dy==0, + h=.5; %h=ax; + else + h = min(abs(.5/(dx/r)),abs(.5/(dy/r))); + end + + if r==0, + angle = 0; + else + angle = acos(dx/r); + if dy<0, + angle = 2*pi-angle; + end + end + + Hsv(i,1) = 1-sin(angle/4); + Hsv(i,2) = 1; + Hsv(i,3) = r/h; + h = hsv2rgb(Hsv); + end +end + + +%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +colors=h; + +%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% juha %%%% + +function p=rgb4(coord) + +for i=1:size(coord,1); + p(i,:)=get_coords(coord(i,:))'; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function coords=get_coords(coords) + +%GET_COORDS +% +% get_coords(coords) +% +% ARGUMENTS +% +% coords (1x2 or 2x1 vector) coords(1) is an x-coordinate and coords(2) +% y-coordinate. +% +% +% RETURNS +% +% coords (3x1 vector) x,y and z-coordinates. +% + +if ~(all(size(coords) == [1 2]) | all(size(coords) == [2 1])) + error('Argument ''coords'' must be an 2x1 or 1x2 vector.'); +end + +if all(size(coords) == [1 2]) + coords=coords'; +end + +if any(coords > 1) any(coords < 0) + error('Coordinates must lay inside the interval [0,1].'); +end + +if coords(1) <= 1/(sqrt(2)+1), + if coords(2) <= line3(coords(1)) + coords=coords_in_base(4,coords); + elseif coords(2) <= line2(coords(1)) + coords=coords_in_base(1,coords); + else + coords=coords_in_base(2,coords); + end +elseif coords(1) <= sqrt(2)/(sqrt(2)+1) + if coords(2) <= line1(coords(1)) + coords=coords_in_base(3,coords); + elseif coords(2) <= line2(coords(1)) + coords=coords_in_base(1,coords); + else + coords=coords_in_base(2,coords); + end +else + if coords(2) <= line1(coords(1)), + coords=coords_in_base(3,coords); + elseif coords(2) <= line4(coords(1)) + coords=coords_in_base(1,coords); + else + coords=coords_in_base(5,coords); + end +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function coords=coords_in_base(base_no,coords) + +A=[0;1/(sqrt(2)+1)]; +E=[1;1]; +F=[0;0]; +G=[1;0]; +H=[0;1]; + +const=1+1/sqrt(2); + +switch base_no + case 1 + x=(coords-A)*const; + coords=[(1/sqrt(2))*(x(1)-x(2));0.5*(x(1)+x(2));0.5*(x(1)+x(2))]; + case 2 + x=(coords-H)*const; + coords=[0;x(1);1+x(2)]; + case 3 + x=(coords-G)*const; + coords=[1;1+x(1);x(2)]; + case 4 + x=(coords-F)*const; + coords=[0.5+(1/sqrt(2))*(x(1)-x(2));... + 0.5-(1/sqrt(2))*(x(1)+x(2));... + 0]; + case 5 + x=(coords-E)*const; + coords=[0.5+(1/sqrt(2))*(x(1)-x(2));... + 0.5-(1/sqrt(2))*(x(1)+x(2));... + 1]; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function y=line1(x) + +y = x-1/(sqrt(2)+1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function y=line2(x) + +y = x+1/(sqrt(2)+1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function y=line3(x) + +y = -x+1/(sqrt(2)+1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function y= line4(x) + +y = -x+(2*sqrt(2)+1)/(sqrt(2)+1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_coloring.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,98 @@ +function Col = som_coloring(sM,ncol,chaingap,dw) + +% SOM_COLORING Make a SOM-based coloring for given data/map. +% +% Col = som_coloring(sM,[ncol],[chaingap],[dw]) +% +% Col = som_coloring(sM,5); +% som_show(sM,'color',Col); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map or data struct +% (matrix) data matrix +% [ncol] (scalar) number of colors to use +% [chaingap] (scalar) size of gap in the color circle (see below), +% 0.1 by default +% [dw] (scalar) 1 = use input space distances to stretch +% the color circle (default) +% 0 = don't use +% +% Col (matrix) color for each data/codebook vector +% +% This function trains a 1-dimensional SOM using the input data +% (codebook of a SOM, or a set of data vectors). A color from the +% color circle (see HSV function) is associated with each map unit, +% and each data/codebook vector of the input data picks its color +% from its BMU on the 1-dimensional SOM. +% +% If the chaingap argument == 0, the 1-dimensional map has a cylinder +% (in effect, a ring) topology. Otherwise, the topology is rectangular +% (in effect, a chain). +% +% The colors are mapped to the 1-dimensional SOM simply by picking colors +% from the color circle. If chaingap>0, a slice of the color circle is +% removed before map units pick their colors from it. This creates a +% discontiuity in the coloring at the ends of the 1-dimensional SOM. +% +% If the dw argument == 0, the colors are picked from the color circle +% equidistantly. If not, the distances between the prototype vectors +% in the 1-dimensional SOM are taken into account. +% +% See also SOM_KMEANSCOLOR, SOM_KMEANSCOLOR2, SOM_FUZZYCOLOR. + +% Contributed to SOM Toolbox 2.0, December 21st, 2001 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 211201 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(sM), + if strcmp(sM.type,'som_map'), ismap = 1; D = sM.codebook; + else ismap = 0; D = sM.data; + end +else ismap = 0; D = sM; +end + +if nargin<2 | isempty(ncol) | isnan(ncol), ncol = min(64,size(D,1)); end +if nargin<3 | isempty(chaingap) | isnan(chaingap), chaingap = 0.1; end +if nargin<4 | isempty(dw) | isnan(dw), dw = 1; end + +if chaingap == 0, lattice = 'sheet'; else lattice = 'cyl'; end +sMring = som_make(D,'msize',[1,ncol],lattice,'tracking',0); +b = som_bmus(sMring,D); + +Colmap = hsv(ceil(ncol*(1+chaingap))); +Colmap = Colmap(1:ncol,:); + +if dw, % take distances in input space into account + dist = sqrt(sum((sMring.codebook-sMring.codebook([2:end 1],:)).^2,2)); + ind = round([0; cumsum(dist)/sum(dist)]*(ncol-1)) + 1; + Colmap = Colmap(ind,:); +end +Col = Colmap(b,:); + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% visualization + if ismap, + a = som_bmus(sM.codebook,sMring.codebook); + if chaingap==0, a(end+1) = a(1); end + som_show(sM,'color',Col); + som_show_add('traj',a) + else + i = find(sum(isnan(D),2)==0); + [P,V,me] = pcaproj(D(i,:),2); + Pr = pcaproj(sMring.codebook,V,me); + a = som_bmus(D(i,:),sMring.codebook); % Pr = P(a,:); + som_grid({'rect',[length(i) 1]},'line','none',... + 'coord',P,'markercolor',Col(i,:)); + hold on + if chaingap==0, Pr(end+1,:) = Pr(1,:); end + som_grid({'rect',[size(Pr,1) 1]},'linecolor','k',... + 'linewidth',2,'markercolor','k','coord',Pr); + end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_connection.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +function C=som_connection(S) + +%SOM_CONNECTION Connection matrix for 'hexa' and 'rect' lattices +% +% C=som_connection(S) +% +% C=som_connection(sMap); +% C=som_connection(sTopol); +% C=som_connection({'hexa', [6 5], 'sheet'}); +% +% Input and output arguments: +% S (struct) map or topol struct +% (cell array) a cell array of form {lattice, msize, shape}, where +% lattice: 'hexa' or 'rect' +% msize : 1x2 vector +% shape : 'sheet', 'cyl or 'toroid' +% +% C (sparse) An NxN connection matrix, N=prod(msize) +% +% The function returns a connection matrix, e.g., for drawing +% connections between map units in the function som_grid. Note that +% the connections are defined only in the upper triangular part to +% save some memory!! Function SOM_UNIT_NEIGHS does the same thing, +% but also has values in the lower triangular. It is also slower. +% +% For more help, try 'type som_connection' or check out online documentation. +% See also SOM_GRID, SOM_UNIT_NEIGHS. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_connection +% +% PURPOSE +% +% To create a connection matrix of SOM 'hexa' and 'rect' negihborhoods +% +% SYNTAX +% +% C = som_connection(S) +% +% DESCRIPTION +% +% Creates a connection matrix of SOM 'hexa' and 'rect' +% neighborhoods. The connections are defined only in the upper +% triangular part to save some memory. +% +% Function SOM_UNIT_NEIGHS does the same thing, but also has values +% in the lower triangular. It is also slower, except for +% 'toroid' shape because in that case this function calls +% SOM_UNIT_NEIGHS... +% +% REQUIRED INPUT ARGUMENTS +% +% S map topology +% (map struct) S.topol is used to build the matrix +% (topol struct) topology information is used to build the matrix +% (cell array) of form {lattice, msize, shape}, where +% lattice: 'hexa' or 'rect' +% msize : 1x2 vector +% shape : 'sheet', 'cyl or 'toroid' +% +% OUTPUT ARGUMENTS +% +% C (sparse) munits x munits sparse matrix which describes +% nearest neighbor connections between units +% +% EXAMPLE +% +% C = som_connection('hexa',[3 4],'sheet'); +% full(C) +% ans = +% +% 0 1 0 1 0 0 0 0 0 0 0 0 +% 0 0 1 1 1 1 0 0 0 0 0 0 +% 0 0 0 0 0 1 0 0 0 0 0 0 +% 0 0 0 0 1 0 1 0 0 0 0 0 +% 0 0 0 0 0 1 1 1 1 0 0 0 +% 0 0 0 0 0 0 0 0 1 0 0 0 +% 0 0 0 0 0 0 0 1 0 1 0 0 +% 0 0 0 0 0 0 0 0 1 1 1 1 +% 0 0 0 0 0 0 0 0 0 0 0 1 +% 0 0 0 0 0 0 0 0 0 0 1 0 +% 0 0 0 0 0 0 0 0 0 0 0 1 +% 0 0 0 0 0 0 0 0 0 0 0 0 +% +% SEE ALSO +% +% som_grid Visualization of a SOM grid +% som_unit_neighs Units in 1-neighborhood for all map units. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0alpha Johan 061099 juuso 151199 170400 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Check arguments + +error(nargchk(1, 1, nargin)); % check number of input arguments + +[tmp,ok,tmp]=som_set(S); +if isstruct(S) & all(ok), % check m type + switch S.type + case 'som_topol' + msize=S.msize; + lattice=S.lattice; + shape=S.shape; + case 'som_map' + msize=S.topol.msize; + lattice=S.topol.lattice; + shape=S.topol.shape; + otherwise + error('Invalid map or topol struct.'); + end +elseif iscell(S), + if vis_valuetype(S,{'topol_cell'}), + lattice=S{1}; + msize=S{2}; + shape=S{3}; + else + error('{lattice, msize, shape} expected for cell input.') + end +else + error('{lattice, msize, shape}, or map or topol struct expected.') +end + +if ~vis_valuetype(msize,{'1x2'}) + error('Invalid map size: only 2D maps allowed.') +end + +%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +N=msize(1)*msize(2); + +%% Action & Build output arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +switch lattice +case 'hexa' + l1=ones(N,1); l1((msize(1)+1):msize(1):end)=0; + l2=zeros(msize(1),1); l3=l2; + l2(1:2:end-1)=1; l3(3:2:end)=1; + l2=repmat(l2,msize(2),1); + l3=repmat(l3,msize(2),1); + C= ... + spdiags([l1 l2 ones(N,1) l3], [1 msize(1)-1:msize(1)+1],N,N); +case 'rect' + l1=ones(N,1);l1((msize(1)+1):msize(1):end)=0; + C=spdiags([l1 ones(N,1)],[1 msize(1)],N,N); +otherwise + error('Unknown lattice.') +end + +switch shape +case 'sheet' + ; +case 'cyl' + C=spdiags(ones(N,1),msize(1)*(msize(2)-1),C); +case 'toroid' + %warning('Toroid not yet implemented: using ''cyl''.'); + %C=spdiags(ones(N,1),msize(1)*(msize(2)-1),C); + %l=zeros(N,1); l(1:msize(2):end)=1; + %C=spdiags(l,msize(1),C); + + % use som_unit_neighs to calculate these + C = som_unit_neighs(msize,lattice,'toroid'); + % to be consistent, set the lower triangular values to zero + munits = prod(msize); + for i=1:(munits-1), C((i+1):munits,i) = 0; end +otherwise + error('Unknown shape.'); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_cplane.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,470 @@ +function h=som_cplane(varargin) + +%SOM_CPLANE Visualize one 2D component plane, U-matrix or color plane. +% +% h=som_cplane(lattice, msize, color, [s], [pos]) +% h=som_cplane(topol, color, [s], [pos]) +% +% som_cplane('hexa', [10 5], 'none'); +% som_cplane('rect', [10 5], 'r'); +% som_cplane(sM.topol, sM.codebook(:,1)); +% U = som_umat(sM); som_cplane('hexaU',sM.topol.msize,U(:)); +% +% Input and output arguments ([]'s are optional): +% lattice (string) 'hexa', 'rect' (component planes) +% 'hexaU', 'rectU' (corresponding U-matrices) +% (matrix) defines the patch (see function VIS_PATCH). +% msize (vector) 1x2 vector defines grid size (M=prod(msize)) +% (matrix) Mx2 matrix gives explicit coordinates for each node +% topol (struct) map or topology struct +% color color for the nodes +% (matrix) Mx1 matrix gives indexed colors for the units +% Mx3 matrix of RGB triples gives explicit +% color for each unit +% (Note: in case of U-matrix, the number of color +% values is 4*prod(msize)-2*sum(msize)+1, not prod(msize)) +% (string) ColorSpec gives the same color for each node +% 'none' draws black edges only. +% [s] (matrix) size Mx1, gives individual size scaling for each node +% (scalar) gives the same size for each node, default=1. +% Additional features: see 'type som_cplane' +% This argument is ignored if the lattice is 'rectU' or 'hexaU'. +% [pos] (vector) a 1x2 vector that determines position of origin, +% default is [1 1]. +% +% h (scalar) the object handle for the PATCH object +% +% Axis are set to the 'ij' mode with equal spacing and turned off if +% 'pos' is not given. If 'lattice' is 'rect', 'hexa', 'rectU' or +% 'hexaU' the node (a,b) has coordinates (a,b) (+pos), except on the +% even numbered rows on the 'hexa' and 'hexaU' grids where the +% coordinates are (a,b+0.5) (+pos). +% +% For more help, try 'type som_cplane' or check out online documentation. +% See also SOM_PIEPLANE, SOM_PLOTPLANE, SOM_BARPLANE, VIS_PATCH, +% SOM_VIS_COORDS + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_cplane +% +% PURPOSE +% +% Visualizes a 2D component plane or u-matrix +% +% SYNTAX +% +% h = som_cplane(topol, color) +% h = som_cplane(lattice, msize, color) +% h = som_cplane(lattice, msize, color) +% h = som_cplane(..., size) +% h = som_cplane(..., size, pos) +% +% DESCRIPTION +% +% Creates some basic visualizations of the SOM grid: the component plane and +% the unified distance matrix. The routine draws the SOM grid as a patch +% object according to the specifications given in the input arguments and +% returns its object handle. +% +% Each unit of the map is presented by a polygon whose color, size, shape +% and location can be specified in various ways. The usual procedure +% is to choose the lattice and map size used in the map training. Then +% the function creates the standard sheet shaped topological +% representation of the map grid with hexagonal or rectangular units. +% When the values from a map codebook component (or from SOM_UMAT) +% are given to the function it produces an indexed coloring for the +% units (as in SURF command). Another possibility is to give a fixed +% RGB color for each unit explicitly. +% +% Special effects (variable unit size, location or shape) can be produced +% giving different types of input variables. +% +% KNOWN BUGS +% +% Using 1x3 or 3x1 grids causes problem, as the MATLAB will treat the color +% information vector 1x3 or 3x1 as a single RGB triple. So, using indexed +% colors is not possible for this particular map size. +% +% It is not possible to specify explicit coordinates for map +% consistig of just one unit as then the msize is interpreted as +% map size. +% +% REQUIRED INPUT ARGUMENTS +% +% Note: M is the number of map units +% +% lattice The basic shape of the map units +% +% (string) 'hexa' or 'rect' creates standard component plane; +% 'hexaU' or 'rectU' creates standard u-matrix. +% (matrix) Lx2 matrix defines the cornes of an arbitary polygon to be used +% as the unit marker. (L is the number of patch vertex: L=6 for +% 'hexa' and L=4 for 'rect') +% +% msize The size of the map grid +% +% (vector) [n1 n2] vector defines the map size (height n1 units, width +% n2 units, total M=n1 x n2 units). The units will be placed to their +% topological locations to form a uniform hexagonal or rectangular grid. +% (matrix) Mx2 matrix defines arbitrary coordinates for the M units +% In this case the argument 'lattice' defines the unit form only. +% +% topol Topology of the map grid +% +% (struct) map or topology struct from which the topology is taken +% +% color Unit colors +% +% (string) (ColorSpec) gives the same color for each unit, 'none' +% draws black unit edges only. +% (vector) Mx1 column vector gives indexed color for each unit using the +% current colormap (see help colormap). +% (matrix) Mx3 matrix of RGB triples as rows gives each unit a fixed color. +% +% OPTIONAL INPUT ARGUMENTS +% +% Note: M is the number of map units. +% Note: if unspecified or given empty values ('' or []) default +% values are used for optional input arguments. +% +% s The size scaling factors for the units +% +% (scalar) scalar gives each unit the same size scaling: +% 0 unit disappears (edges can be seen as a dot). +% 1 by default unit has its normal size (ie. no scaling) +% >1 unit overlaps others +% (matrix) Mx1 double: each unit gets individual size scaling +% +% pos Position of origin +% +% (vector) This argument exists to be able drawing component planes +% in arbitrary locations in a figure. Note the operation: +% if this argument is given, the axis limits setting +% part in the routine is skipped and the limits setting +% will be left to be done by MATLAB's default +% operation. +% +% OUTPUT ARGUMENTS +% +% h (scalar) handle to the created patch object +% +% OBJECT TAGS +% +% One object handle is returned: field Tag is set to +% 'planeC' for component plane +% 'planeU' for U-matrix +% +% FEATURES +% +% There are some extra features in following arguments +% +% size +% - MxL matrix: radial scaling: the distance between +% the center of node m and its kth vertex is scaled by +% s(m,k). +% - Mx1x2 matrix: the uniform scaling is done separately for +% x- and y-directions +% - MxLx2 matrix: the scaling is done separately to x- and y- +% directions for each vertex. +% +% color +% Each vertex may be given individual color. +% The PATCH object interpolates the colors on the +% face if shading is turned to interp. +% - 1xMxL matrix: colormap index for each vertex +% - LxMx3 matrix: RGB color for each vertex +% +% Note: In both cases (size and color) the ordering of the patch +% vertices in the "built-in" patches is the following +% +% 'rect' 'hexa' +% 1 3 1 +% 2 4 5 2 +% 6 3 +% 4 +% +% The color interpolation result seem to depend on the order +% in which the patch vertices are defined. Anyway, it gives +% unfavourable results in our case especially with hexa grid: +% this is a MATLAB feature. +% +% EXAMPLES +% +% m=som_make(rand(100,4),'msize',[6 5]) % make a map +% +% % show the first variable plane using indexed color coding +% +% som_cplane(m.topol.lattice,m.topol.msize,m.codebook(:,1)); +% or som_cplane(m.topol,m.codebook(:,1)); +% or som_cplane(m,m.codebook(:,1)); +% +% % show the first variable using different sized black units +% +% som_cplane(m,'k',m.codebook(:,1)); +% +% % Show the u-matrix. First we have to calculate it. +% % Note: som_umat returns a matrix therefore we write u(:) to get +% % a vector which contains the values in the proper order. +% +% u=som_umat(m); +% som_cplane('hexaU', m.topol.msize, u(:)); +% +% % Show three first variables coded as RGB colors +% % and turn the unit edges off +% +% h=som_cplane(m, m.codebook(:,1:3),1) +% set(h,'edgecolor','none'); +% +% % Try this! (see section FEATURES) +% +% som_cplane('rect',[5 5],'none',rand(25,4)); +% som_cplane('rect',[5 5],rand(1,25,4)); +% +% SEE ALSO +% +% som_barplane Visualize the map prototype vectors as bar diagrams +% som_plotplane Visualize the map prototype vectors as line graphs +% som_pieplane Visualize the map prototype vectors as pie charts +% som_umat Compute unified distance matrix of self-organizing map +% vis_patch Define the basic patches used in som_cplane +% som_vis_coords The default 'hexa' and 'rect' coordinates in visualizations + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 061099 juuso 151199 juuso 070600 + +%%% Check & Init arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +[nargin, lattice, msize, color, s, pos]=vis_planeGetArgs(varargin{:}); +error(nargchk(3, 5, nargin)); % check no. of input args is correct + +%% Translation? + +if nargin < 5 | isempty(pos) + pos=NaN; % "no translation" flag +elseif ~vis_valuetype(pos,{'1x2'}), + error('Position of origin has to be given as an 1x2 vector.'); +end + +%% Patchform %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +switch class(lattice) +case 'char' % built-in patchforms + pos=pos-1; + switch lattice + case {'hexa', 'hexaU'} + patchform=vis_patch('hexa'); + case {'rect', 'rectU'} + patchform=vis_patch('rect'); + otherwise + error([ 'Lattice ' lattice ' not implemented!']); + end +case { 'double', 'sparse'} + if vis_valuetype(lattice,{'nx2'}), + patchform=lattice; % users patchform + lattice='rect'; + else + error('Patchform matrix has wrong size'); + end +otherwise + error('String or matrix expected for lattice.'); +end + +l=size(patchform,1); % number of vertices +planeType=lattice(end); % 'U' if umatrix otherwise something else + +if ~vis_valuetype(msize,{ '1x2', 'nx2'}), + error('msize has to be given as 1x2 or nx2 vectors.'); +end + +%% msize or coordinates %%%%%%%%%%%%%%%%%%%%%%% + +if size(msize,1)>1 + % msize is coordinate matrix Nx2? + + if planeType == 'U', % don't accept u-matrix + error('U-matrix visualization doesn''t work with free coordinates.'); + end + + % set number of map unit and unit coordinates + munits=size(msize,1); + unit_coords=msize; msize=[munits 1]; + + if isnan(pos), % no translation is done here + pos=[0 0]; % using [0 0] in order to prevent + end % axis tightening in + % vis_PlaneAxisProperties (arbitary coords!) +else + % msize is built-in lattice + + unit_coords=som_vis_coords(lattice,msize); + + % Calculate matrices x and y which 'moves' nodes + % to the correct positions: + % For U-matrix, the size has to be recalculated + if planeType == 'U', + xdim=2*msize(1)-1;ydim=2*msize(2)-1; + else + xdim=msize(1);ydim=msize(2); + end + munits=xdim*ydim; + + % Feature warning + if munits == 3 + warning('Problems with 1x3 and 3x1 maps. See documentation.'); + end +end + +%% Color matrix %%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if ~isnumeric(color) & ~ischar(color), + error('Color matrix is invalid.'); +else + d=size(color); + switch length(d) + case 2 %% Flat colors + if ischar(color) % Check for string 'none' + if strcmp(color,'none'), + color=NaN; + end + else + if ~(d(1)== 1 & d(2) == 3) & ... + ~(d(1) == munits & (d(2)==1 | d(2)==3)) + error('Color data matrix has wrong size.'); + elseif d(1)~=1 & d(2)==3 + if any(color>1 | color<0) + error('Color data matrix has invalid RGB values.'); + end + color=reshape(color,[1 munits 3]); % RGB colors + elseif d(2)==1 + color=color'; % indexed + end + end + case 3 %% Interpolated colors + if d(1) == 1 & d(2) == munits & d(3) == l, + color=reshape(color, l, munits); + elseif ~(d(1) == l & d(2) == munits & d(3) == 3) + error('Color data matrix has wrong size.'); + end + otherwise + error('Color data matrix has too many dimensions.'); + end +end + +%% Size matrix? %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargin < 4 | isempty(s), + s=1; % default value for s (no scaling) +elseif ~isnumeric(s) + error('Size matrix is not numeric.'); +end + +%%Determine the type of size matrix +d=size(s); +switch length(d) +case 2 + if (d(1)==1 & d(2)==1), + % Each node gets the same, uniform scaling. + s=s'; sx=s; sy=s; + elseif (d(1)==munits & d(2)==l), + % Each vertex is scaled radially respetc to the + % node center. + s=s'; sx=s; sy=s; + elseif d(1)==munits & d(2)==1 + % Each node gets an individual uniform scaling. + sx=repmat(s',l,1); sy=sx; + else + error('Size matrix has wrong size.'); + end +case 3 + if d(1)==munits & d(2)==1 & d(3)==2, + % Each node is individually and uniformly + % scaled separately to x- and y-directions. + sx=repmat(shiftdim(s(:,:,1))',l,1); + sy=repmat(shiftdim(s(:,:,2))',l,1); + elseif d(1)==munits & d(2)==l & d(3)==2, + % Each vertex is scaled separately to x- and y-directions + % with respect to the node center. + sx=shiftdim(s(:,:,1))'; + sy=shiftdim(s(:,:,2))'; + else + error('Size matrix has wrong size.'); + end +otherwise + error('Size matrix has too many dimensions.'); +end + +% Size zero would cause division by zero. eps is as good (node disappears) +% I tried first NaN, it works well otherwise, but the node is +% then not on the axis and some commands may the work oddly. +% The edge may be visible, though. + +sx(sx==0)=eps; +sy(sy==0)=eps; + +% Rescale sizes for u-matrix +if planeType=='U', + sx=sx/2;sy=sy/2; +end + +%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Making grid. %%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Translation for each patch + +x=repmat(unit_coords(:,1)',l,1); +y=repmat(unit_coords(:,2)',l,1); + +% patch vertex coordiantes + +nx=repmat(patchform(:,1),1,munits); +ny=repmat(patchform(:,2),1,munits); + +% NB: The hexagons are not uniform in order to get even +% y-coordinates for the nodes. This is handled by setting _axis scaling_ +% so that the hexa-nodes look like uniform hexagonals. See +% vis_PlaneAxisProperties + +%% Make and scale the final input for PATCH: + +% 1: combine translation and scaling of each patch +x=(x./sx+nx).*sx; y=(y./sy+ny).*sy; + +%% 2: translation of origin (pos) +if ~isnan(pos) + x=x+pos(1);y=y+pos(2); % move upper left corner +end % to pos + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%% Set axes properties +%% Command view([0 90]) shows the map in 2D properly oriented + +ax=newplot; % set new plot +vis_PlaneAxisProperties(ax,lattice,msize,pos); + +%% Draw the map! + +if ~isnan(color) + h_=patch(x,y,color); +else + h_=patch(x,y,'k'); % empty grid + set(h_,'FaceColor','none'); +end + +%% Set object tag + +if planeType=='U' + set(h_,'Tag','planeU'); +else + set(h_,'Tag','planeC'); +end + +%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargout>0, h=h_; end % Set h only, + % if there really is output +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_data_struct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +function sData = som_data_struct(D, varargin) + +%SOM_DATA_STRUCT Create a data struct. +% +% sData = som_data_struct(D, [argID, value, ...]) +% +% sData = som_data_struct(D); +% sData = som_data_struct(D,'name','my_data','labels',labs); +% +% Input and output arguments ([]'s are optional): +% D (matrix) data matrix, size dlen x dim +% [argID, (string) See below. These are given as argID, value pairs. +% value] (varies) +% +% sData (struct) created data struct +% +% Here are the argument IDs and corresponding values: +% 'labels' (string array / cellstr) labels for each data vector, +% length=dlen +% 'name' (string) data name +% 'comp_names' (string array / cellstr) component names, size dim x 1 +% 'comp_norm' (cell array) normalization operations for each +% component, size dim x 1. Each cell is either empty, +% or a cell array of normalization structs. +% +% For more help, try 'type som_data_struct' or check out online documentation. +% See also SOM_SET, SOM_INFO, SOM_MAP_STRUCT. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_data_struct +% +% PURPOSE +% +% Creates a data structure. +% +% SYNTAX +% +% sD = som_data_struct(D); +% sD = som_data_struct(...,'argID',value,...); +% +% DESCRIPTION +% +% Creates a data struct. The struct contains, in addition to the data +% matrix, component names, normalization operations for the components, +% labels for each vector, and a name for the whole data set. All of these +% can be given in the optional arguments of the function. If left +% unspecified, they are given default values. +% +% Field Type Size / default value +% ------------------------------------------------------------------------ +% .type (string) 'som_data' +% .data (matrix) size dlen x dim +% .name (string) 'unnamed' +% .labels (cellstr) size dlen x m, {''; ''; ... ''} +% .comp_names (cellstr) size dim x 1, {'Variable1', 'Variable2', ...} +% .comp_norm (cell array) size dim x 1, {[], [], ... []} +% .label_names (cellstr) size m x 1, [] +% +% '.type' field is the struct identifier. Do not change it. +% '.data' field is the data matrix, each row is one data vector +% '.name' field is the identifier for the whole data struct +% '.labels' field contains the labels for each of the vectors. The ith +% of '.labels' contains the labels for ith data vector. Note that +% if some vectors have more labels than others, the others are +% are given empty labels ('') to pad the '.labels' array up. +% '.comp_names' field contains the names of the vector components +% '.comp_norm' field contains normalization information for each +% component. Each cell of '.comp_norm' is itself a cell array of +% normalization structs. If no normalizations are performed for +% the particular component, the cell is empty ([]). +% '.label_names' is similar to .comp_names field holding the names for +% each data label column +% +% REQUIRED INPUT ARGUMENTS +% +% D (matrix) The data matrix, size dlen x dim. The data matrix may +% contain unknown values, indicated by NaNs. +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs as +% listed below. If an argument is given value multiple times, the +% last one is used. +% +% 'labels' (string array / cellstr) labels for each data vector, +% size dlen x m +% 'name' (string) data name +% 'comp_names' (string array / cellstr) component names, size dim x 1 +% 'comp_norm' (cell array) normalization operations for each +% component, size dim x 1. Each cell is either empty, +% or a cell array of normalization structs. +% 'label_names'(string array / cellstr) label names, size m x 1 +% +% OUTPUT ARGUMENTS +% +% sD (struct) the data struct +% +% EXAMPLES +% +% Simplest case: +% D = rand(8, 3); % 8 3-dimensional vectors +% sD = som_data_struct(D); +% +% With optional arguments, the other fields can be given values: +% labs = cell(8, 1); labs{1, 1} = 'first_label'; +% cnames = {'first'; 'second'; 'third'}; +% +% sD = som_data_struct(D,'labels',labs,'name','a data struct'); +% sD = som_data_struct(D,'comp_names',cnames); +% +% SEE ALSO +% +% som_set Set values and create SOM Toolbox structs. +% som_map_struct Create a map struct. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 071197 +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% data +[dlen dim] = size(D); + +% default values +if ~isempty(inputname(1)), name = inputname(1); +else name = 'unnamed'; end +labels = cell(dlen,1); +labels(1:dlen) = {''}; +%for i=1:dlen, labels{i} = ''; end +comp_names = cell(dim,1); +for i = 1:dim, comp_names{i} = sprintf('Variable%d', i); end +comp_norm = cell(dim,1); +label_names = []; + +% varargin +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'comp_names', i=i+1; comp_names = varargin{i}; + case 'labels', i=i+1; labels = varargin{i}; + case 'name', i=i+1; name = varargin{i}; + case 'comp_norm', i=i+1; comp_norm = varargin{i}; + case 'label_names',i=i+1; label_names = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_data_struct) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +% create struct +sData = som_set('som_data','data',D,'labels',labels,... + 'name',name,'comp_names',comp_names,... + 'comp_norm',comp_norm,'label_names',label_names); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_demo1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,294 @@ + +%SOM_DEMO1 Basic properties and behaviour of the Self-Organizing Map. + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 071197 +% Version 2.0beta juuso 030200 + +clf reset; +figure(gcf) +echo on + + + +clc +% ========================================================== +% SOM_DEMO1 - BEHAVIOUR AND PROPERTIES OF SOM +% ========================================================== + +% som_make - Create, initialize and train a SOM. +% som_randinit - Create and initialize a SOM. +% som_lininit - Create and initialize a SOM. +% som_seqtrain - Train a SOM. +% som_batchtrain - Train a SOM. +% som_bmus - Find best-matching units (BMUs). +% som_quality - Measure quality of SOM. + +% SELF-ORGANIZING MAP (SOM): + +% A self-organized map (SOM) is a "map" of the training data, +% dense where there is a lot of data and thin where the data +% density is low. + +% The map constitutes of neurons located on a regular map grid. +% The lattice of the grid can be either hexagonal or rectangular. + +subplot(1,2,1) +som_cplane('hexa',[10 15],'none') +title('Hexagonal SOM grid') + +subplot(1,2,2) +som_cplane('rect',[10 15],'none') +title('Rectangular SOM grid') + +% Each neuron (hexagon on the left, rectangle on the right) has an +% associated prototype vector. After training, neighboring neurons +% have similar prototype vectors. + +% The SOM can be used for data visualization, clustering (or +% classification), estimation and a variety of other purposes. + +pause % Strike any key to continue... + +clf +clc +% INITIALIZE AND TRAIN THE SELF-ORGANIZING MAP +% ============================================ + +% Here are 300 data points sampled from the unit square: + +D = rand(300,2); + +% The map will be a 2-dimensional grid of size 10 x 10. + +msize = [10 10]; + +% SOM_RANDINIT and SOM_LININIT can be used to initialize the +% prototype vectors in the map. The map size is actually an +% optional argument. If omitted, it is determined automatically +% based on the amount of data vectors and the principal +% eigenvectors of the data set. Below, the random initialization +% algorithm is used. + +sMap = som_randinit(D, 'msize', msize); + +% Actually, each map unit can be thought as having two sets +% of coordinates: +% (1) in the input space: the prototype vectors +% (2) in the output space: the position on the map +% In the two spaces, the map looks like this: + +subplot(1,3,1) +som_grid(sMap) +axis([0 11 0 11]), view(0,-90), title('Map in output space') + +subplot(1,3,2) +plot(D(:,1),D(:,2),'+r'), hold on +som_grid(sMap,'Coord',sMap.codebook) +title('Map in input space') + +% The black dots show positions of map units, and the gray lines +% show connections between neighboring map units. Since the map +% was initialized randomly, the positions in in the input space are +% completely disorganized. The red crosses are training data. + +pause % Strike any key to train the SOM... + +% During training, the map organizes and folds to the training +% data. Here, the sequential training algorithm is used: + +sMap = som_seqtrain(sMap,D,'radius',[5 1],'trainlen',10); + +subplot(1,3,3) +som_grid(sMap,'Coord',sMap.codebook) +hold on, plot(D(:,1),D(:,2),'+r') +title('Trained map') + +pause % Strike any key to view more closely the training process... + + +clf + +clc +% TRAINING THE SELF-ORGANIZING MAP +% ================================ + +% To get a better idea of what happens during training, let's look +% at how the map gradually unfolds and organizes itself. To make it +% even more clear, the map is now initialized so that it is away +% from the data. + +sMap = som_randinit(D,'msize',msize); +sMap.codebook = sMap.codebook + 1; + +subplot(1,2,1) +som_grid(sMap,'Coord',sMap.codebook) +hold on, plot(D(:,1),D(:,2),'+r'), hold off +title('Data and original map') + +% The training is based on two principles: +% +% Competitive learning: the prototype vector most similar to a +% data vector is modified so that it it is even more similar to +% it. This way the map learns the position of the data cloud. +% +% Cooperative learning: not only the most similar prototype +% vector, but also its neighbors on the map are moved towards the +% data vector. This way the map self-organizes. + +pause % Strike any key to train the map... + +echo off +subplot(1,2,2) +o = ones(5,1); +r = (1-[1:60]/60); +for i=1:60, + sMap = som_seqtrain(sMap,D,'tracking',0,... + 'trainlen',5,'samples',... + 'alpha',0.1*o,'radius',(4*r(i)+1)*o); + som_grid(sMap,'Coord',sMap.codebook) + hold on, plot(D(:,1),D(:,2),'+r'), hold off + title(sprintf('%d/300 training steps',5*i)) + drawnow +end +title('Sequential training after 300 steps') +echo on + +pause % Strike any key to continue with 3D data... + +clf + +clc +% TRAINING DATA: THE UNIT CUBE +% ============================ + +% Above, the map dimension was equal to input space dimension: both +% were 2-dimensional. Typically, the input space dimension is much +% higher than the 2-dimensional map. In this case the map cannot +% follow perfectly the data set any more but must find a balance +% between two goals: + +% - data representation accuracy +% - data set topology representation accuracy + +% Here are 500 data points sampled from the unit cube: + +D = rand(500,3); + +subplot(1,3,1), plot3(D(:,1),D(:,2),D(:,3),'+r') +view(3), axis on, rotate3d on +title('Data') + +% The ROTATE3D command enables you to rotate the picture by +% dragging the pointer above the picture with the leftmost mouse +% button pressed down. + +pause % Strike any key to train the SOM... + + + + +clc +% DEFAULT TRAINING PROCEDURE +% ========================== + +% Above, the initialization was done randomly and training was done +% with sequential training function (SOM_SEQTRAIN). By default, the +% initialization is linear, and batch training algorithm is +% used. In addition, the training is done in two phases: first with +% large neighborhood radius, and then finetuning with small radius. + +% The function SOM_MAKE can be used to both initialize and train +% the map using default parameters: + +pause % Strike any key to use SOM_MAKE... + +sMap = som_make(D); + +% Here, the linear initialization is done again, so that +% the results can be compared. + +sMap0 = som_lininit(D); + +subplot(1,3,2) +som_grid(sMap0,'Coord',sMap0.codebook,... + 'Markersize',2,'Linecolor','k','Surf',sMap0.codebook(:,3)) +axis([0 1 0 1 0 1]), view(-120,-25), title('After initialization') + +subplot(1,3,3) +som_grid(sMap,'Coord',sMap.codebook,... + 'Markersize',2,'Linecolor','k','Surf',sMap.codebook(:,3)) +axis([0 1 0 1 0 1]), view(3), title('After training'), hold on + +% Here you can see that the 2-dimensional map has folded into the +% 3-dimensional space in order to be able to capture the whole data +% space. + +pause % Strike any key to evaluate the quality of maps... + + + +clc +% BEST-MATCHING UNITS (BMU) +% ========================= + +% Before going to the quality, an important concept needs to be +% introduced: the Best-Matching Unit (BMU). The BMU of a data +% vector is the unit on the map whose model vector best resembles +% the data vector. In practise the similarity is measured as the +% minimum distance between data vector and each model vector on the +% map. The BMUs can be calculated using function SOM_BMUS. This +% function gives the index of the unit. + +% Here the BMU is searched for the origin point (from the +% trained map): + +bmu = som_bmus(sMap,[0 0 0]); + +% Here the corresponding unit is shown in the figure. You can +% rotate the figure to see better where the BMU is. + +co = sMap.codebook(bmu,:); +text(co(1),co(2),co(3),'BMU','Fontsize',20) +plot3([0 co(1)],[0 co(2)],[0 co(3)],'ro-') + +pause % Strike any key to analyze map quality... + + + + +clc +% SELF-ORGANIZING MAP QUALITY +% =========================== + +% The maps have two primary quality properties: +% - data representation accuracy +% - data set topology representation accuracy + +% The former is usually measured using average quantization error +% between data vectors and their BMUs on the map. For the latter +% several measures have been proposed, e.g. the topographic error +% measure: percentage of data vectors for which the first- and +% second-BMUs are not adjacent units. + +% Both measures have been implemented in the SOM_QUALITY function. +% Here are the quality measures for the trained map: + +[q,t] = som_quality(sMap,D) + +% And here for the initial map: + +[q0,t0] = som_quality(sMap0,D) + +% As can be seen, by folding the SOM has reduced the average +% quantization error, but on the other hand the topology +% representation capability has suffered. By using a larger final +% neighborhood radius in the training, the map becomes stiffer and +% preserves the topology of the data set better. + + +echo off + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_demo2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,406 @@ + +%SOM_DEMO2 Basic usage of the SOM Toolbox. + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 071197 +% Version 2.0beta juuso 070200 + +clf reset; +figure(gcf) +echo on + + + +clc +% ========================================================== +% SOM_DEMO2 - BASIC USAGE OF SOM TOOLBOX +% ========================================================== + +% som_data_struct - Create a data struct. +% som_read_data - Read data from file. +% +% som_normalize - Normalize data. +% som_denormalize - Denormalize data. +% +% som_make - Initialize and train the map. +% +% som_show - Visualize map. +% som_show_add - Add markers on som_show visualization. +% som_grid - Visualization with free coordinates. +% +% som_autolabel - Give labels to map. +% som_hits - Calculate hit histogram for the map. + +% BASIC USAGE OF THE SOM TOOLBOX + +% The basic usage of the SOM Toolbox proceeds like this: +% 1. construct data set +% 2. normalize it +% 3. train the map +% 4. visualize map +% 5. analyse results + +% The four first items are - if default options are used - very +% simple operations, each executable with a single command. For +% the last, several different kinds of functions are provided in +% the Toolbox, but as the needs of analysis vary, a general default +% function or procedure does not exist. + +pause % Strike any key to construct data... + + + +clc +% STEP 1: CONSTRUCT DATA +% ====================== + +% The SOM Toolbox has a special struct, called data struct, which +% is used to group information regarding the data set in one +% place. + +% Here, a data struct is created using function SOM_DATA_STRUCT. +% First argument is the data matrix itself, then is the name +% given to the data set, and the names of the components +% (variables) in the data matrix. + +D = rand(1000,3); % 1000 samples from unit cube +sData = som_data_struct(D,'name','unit cube','comp_names',{'x','y','z'}); + +% Another option is to read the data directly from an ASCII file. +% Here, the IRIS data set is loaded from a file (please make sure +% the file can be found from the current path): + +try, + sDiris = som_read_data('iris.data'); +catch + echo off + + warning('File ''iris.data'' not found. Using simulated data instead.') + + D = randn(50,4); + D(:,1) = D(:,1)+5; D(:,2) = D(:,2)+3.5; + D(:,3) = D(:,3)/2+1.5; D(:,4) = D(:,4)/2+0.3; + D(find(D(:)<=0)) = 0.01; + + D2 = randn(100,4); D2(:,2) = sort(D2(:,2)); + D2(:,1) = D2(:,1)+6.5; D2(:,2) = D2(:,2)+2.8; + D2(:,3) = D2(:,3)+5; D2(:,4) = D2(:,4)/2+1.5; + D2(find(D2(:)<=0)) = 0.01; + + sDiris = som_data_struct([D; D2],'name','iris (simulated)',... + 'comp_names',{'SepalL','SepalW','PetalL','PetalW'}); + sDiris = som_label(sDiris,'add',[1:50]','Setosa'); + sDiris = som_label(sDiris,'add',[51:100]','Versicolor'); + sDiris = som_label(sDiris,'add',[101:150]','Virginica'); + + echo on +end + +% Here are the histograms and scatter plots of the four variables. + +echo off +k=1; +for i=1:4, + for j=1:4, + if i==j, + subplot(4,4,k); + hist(sDiris.data(:,i)); title(sDiris.comp_names{i}) + elseif i<j, + subplot(4,4,k); + plot(sDiris.data(:,i),sDiris.data(:,j),'k.') + xlabel(sDiris.comp_names{i}) + ylabel(sDiris.comp_names{j}) + end + k=k+1; + end +end +echo on + +% Actually, as you saw in SOM_DEMO1, most SOM Toolbox functions +% can also handle plain data matrices, but then one is without the +% convenience offered by component names, labels and +% denormalization operations. + + +pause % Strike any key to normalize the data... + + + + + +clc +% STEP 2: DATA NORMALIZATION +% ========================== + +% Since SOM algorithm is based on Euclidian distances, the scale of +% the variables is very important in determining what the map will +% be like. If the range of values of some variable is much bigger +% than of the other variables, that variable will probably dominate +% the map organization completely. + +% For this reason, the components of the data set are usually +% normalized, for example so that each component has unit +% variance. This can be done with function SOM_NORMALIZE: + +sDiris = som_normalize(sDiris,'var'); + +% The function has also other normalization methods. + +% However, interpreting the values may be harder when they have +% been normalized. Therefore, the normalization operations can be +% reversed with function SOM_DENORMALIZE: + +x = sDiris.data(1,:) + +orig_x = som_denormalize(x,sDiris) + +pause % Strike any key to to train the map... + + + + + +clc +% STEP 3: MAP TRAINING +% ==================== + +% The function SOM_MAKE is used to train the SOM. By default, it +% first determines the map size, then initializes the map using +% linear initialization, and finally uses batch algorithm to train +% the map. Function SOM_DEMO1 has a more detailed description of +% the training process. + +sMap = som_make(sDiris); + + +pause % Strike any key to continues... + +% The IRIS data set also has labels associated with the data +% samples. Actually, the data set consists of 50 samples of three +% species of Iris-flowers (a total of 150 samples) such that the +% measurements are width and height of sepal and petal leaves. The +% label associated with each sample is the species information: +% 'Setosa', 'Versicolor' or 'Virginica'. + +% Now, the map can be labelled with these labels. The best +% matching unit of each sample is found from the map, and the +% species label is given to the map unit. Function SOM_AUTOLABEL +% can be used to do this: + +sMap = som_autolabel(sMap,sDiris,'vote'); + +pause % Strike any key to visualize the map... + + + + + +clc +% STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_SHOW +% ===================================================== + +% The basic visualization of the SOM is done with function SOM_SHOW. + +colormap(1-gray) +som_show(sMap,'norm','d') + +% Notice that the names of the components are included as the +% titles of the subplots. Notice also that the variable values +% have been denormalized to the original range and scale. + +% The component planes ('PetalL', 'PetalW', 'SepalL' and 'SepalW') +% show what kind of values the prototype vectors of the map units +% have. The value is indicated with color, and the colorbar on the +% right shows what the colors mean. + +% The 'U-matrix' shows distances between neighboring units and thus +% visualizes the cluster structure of the map. Note that the +% U-matrix visualization has much more hexagons that the +% component planes. This is because distances *between* map units +% are shown, and not only the distance values *at* the map units. + +% High values on the U-matrix mean large distance between +% neighboring map units, and thus indicate cluster +% borders. Clusters are typically uniform areas of low +% values. Refer to colorbar to see which colors mean high +% values. In the IRIS map, there appear to be two clusters. + +pause % Strike any key to continue... + +% The subplots are linked together through similar position. In +% each axis, a particular map unit is always in the same place. For +% example: + +h=zeros(sMap.topol.msize); h(1,2) = 1; +som_show_add('hit',h(:),'markercolor','r','markersize',0.5,'subplot','all') + +% the red marker is on top of the same unit on each axis. + +pause % Strike any key to continue... + + + +clf + +clc + +% STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_SHOW_ADD +% ========================================================= + +% The SOM_SHOW_ADD function can be used to add markers, labels and +% trajectories on top of SOM_SHOW created figures. The function +% SOM_SHOW_CLEAR can be used to clear them away. + +% Here, the U-matrix is shown on the left, and an empty grid +% named 'Labels' is shown on the right. + +som_show(sMap,'umat','all','empty','Labels') + +pause % Strike any key to add labels... + +% Here, the labels added to the map with SOM_AUTOLABEL function +% are shown on the empty grid. + +som_show_add('label',sMap,'Textsize',8,'TextColor','r','Subplot',2) + +pause % Strike any key to add hits... + +% An important tool in data analysis using SOM are so called hit +% histograms. They are formed by taking a data set, finding the BMU +% of each data sample from the map, and increasing a counter in a +% map unit each time it is the BMU. The hit histogram shows the +% distribution of the data set on the map. + +% Here, the hit histogram for the whole data set is calculated +% and visualized on the U-matrix. + +h = som_hits(sMap,sDiris); +som_show_add('hit',h,'MarkerColor','w','Subplot',1) + +pause % Strike any key to continue... + +% Multiple hit histograms can be shown simultaniously. Here, three +% hit histograms corresponding to the three species of Iris +% flowers is calculated and shown. + +% First, the old hit histogram is removed. + +som_show_clear('hit',1) + +% Then, the histograms are calculated. The first 50 samples in +% the data set are of the 'Setosa' species, the next 50 samples +% of the 'Versicolor' species and the last 50 samples of the +% 'Virginica' species. + +h1 = som_hits(sMap,sDiris.data(1:50,:)); +h2 = som_hits(sMap,sDiris.data(51:100,:)); +h3 = som_hits(sMap,sDiris.data(101:150,:)); + +som_show_add('hit',[h1, h2, h3],'MarkerColor',[1 0 0; 0 1 0; 0 0 1],'Subplot',1) + +% Red color is for 'Setosa', green for 'Versicolor' and blue for +% 'Virginica'. One can see that the three species are pretty well +% separated, although 'Versicolor' and 'Virginica' are slightly +% mixed up. + +pause % Strike any key to continue... + + + +clf +clc + +% STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_GRID +% ===================================================== + +% There's also another visualization function: SOM_GRID. This +% allows visualization of the SOM in freely specified coordinates, +% for example the input space (of course, only upto 3D space). This +% function has quite a lot of options, and is pretty flexible. + +% Basically, the SOM_GRID visualizes the SOM network: each unit is +% shown with a marker and connected to its neighbors with lines. +% The user has control over: +% - the coordinate of each unit (2D or 3D) +% - the marker type, color and size of each unit +% - the linetype, color and width of the connecting lines +% There are also some other options. + +pause % Strike any key to see some visualizations... + +% Here are four visualizations made with SOM_GRID: +% - The map grid in the output space. + +subplot(2,2,1) +som_grid(sMap,'Linecolor','k') +view(0,-90), title('Map grid') + +% - A surface plot of distance matrix: both color and +% z-coordinate indicate average distance to neighboring +% map units. This is closely related to the U-matrix. + +subplot(2,2,2) +Co=som_unit_coords(sMap); U=som_umat(sMap); U=U(1:2:size(U,1),1:2:size(U,2)); +som_grid(sMap,'Coord',[Co, U(:)],'Surf',U(:),'Marker','none'); +view(-80,45), axis tight, title('Distance matrix') + +% - The map grid in the output space. Three first components +% determine the 3D-coordinates of the map unit, and the size +% of the marker is determined by the fourth component. +% Note that the values have been denormalized. + +subplot(2,2,3) +M = som_denormalize(sMap.codebook,sMap); +som_grid(sMap,'Coord',M(:,1:3),'MarkerSize',M(:,4)*2) +view(-80,45), axis tight, title('Prototypes') + +% - Map grid as above, but the original data has been plotted +% also: coordinates show the values of three first components +% and color indicates the species of each sample. Fourth +% component is not shown. + +subplot(2,2,4) +som_grid(sMap,'Coord',M(:,1:3),'MarkerSize',M(:,4)*2) +hold on +D = som_denormalize(sDiris.data,sDiris); +plot3(D(1:50,1),D(1:50,2),D(1:50,3),'r.',... + D(51:100,1),D(51:100,2),D(51:100,3),'g.',... + D(101:150,1),D(101:150,2),D(101:150,3),'b.') +view(-72,64), axis tight, title('Prototypes and data') + +pause % Strike any key to continue... + +% STEP 5: ANALYSIS OF RESULTS +% =========================== + +% The purpose of this step highly depends on the purpose of the +% whole data analysis: is it segmentation, modeling, novelty +% detection, classification, or something else? For this reason, +% there is not a single general-purpose analysis function, but +% a number of individual functions which may, or may not, prove +% useful in any specific case. + +% Visualization is of course part of the analysis of +% results. Examination of labels and hit histograms is another +% part. Yet another is validation of the quality of the SOM (see +% the use of SOM_QUALITY in SOM_DEMO1). + +[qe,te] = som_quality(sMap,sDiris) + +% People have contributed a number of functions to the Toolbox +% which can be used for the analysis. These include functions for +% vector projection, clustering, pdf-estimation, modeling, +% classification, etc. However, ultimately the use of these +% tools is up to you. + +% More about visualization is presented in SOM_DEMO3. +% More about data analysis is presented in SOM_DEMO4. + +echo off +warning on + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_demo3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,706 @@ + +%SOM_DEMO3 Self-organizing map visualization. + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 071197 +% Version 2.0beta juuso 080200 070600 + +clf reset; +figure(gcf) +echo on + + + + +clc +% ========================================================== +% SOM_DEMO3 - VISUALIZATION +% ========================================================== + +% som_show - Visualize map. +% som_grid - Visualization with free coordinates. +% +% som_show_add - Add markers on som_show visualization. +% som_show_clear - Remove markers from som_show visualization. +% som_recolorbar - Refresh and rescale colorbars in som_show +% visualization. +% +% som_cplane - Visualize component/color/U-matrix plane. +% som_pieplane - Visualize prototype vectors as pie charts. +% som_barplane - Visualize prototype vectors as bar charts. +% som_plotplane - Visualize prototype vectors as line graphs. +% +% pcaproj - Projection to principal component space. +% cca - Projection with Curvilinear Component Analysis. +% sammon - Projection with Sammon's mapping. +% som_umat - Calculate U-matrix. +% som_colorcode - Color coding for the map. +% som_normcolor - RGB values of indexed colors. +% som_hits - Hit histograms for the map. + +% The basic functions for SOM visualization are SOM_SHOW and +% SOM_GRID. The SOM_SHOW has three auxiliary functions: +% SOM_SHOW_ADD, SOM_SHOW_CLEAR and SOM_RECOLORBAR which are used +% to add and remove markers and to control the colorbars. +% SOM_SHOW actually uses SOM_CPLANE to make the visualizations. +% Also SOM_{PIE,BAR,PLOT}PLANE can be used to visualize SOMs. + +% The other functions listed above do not themselves visualize +% anything, but their results are used in the visualizations. + +% There's an important limitation that visualization functions have: +% while the SOM Toolbox otherwise supports N-dimensional map grids, +% visualization only works for 1- and 2-dimensional map grids!!! + +pause % Strike any key to create demo data and map... + + + + + +clc +% DEMO DATA AND MAP +% ================= + +% The data set contructed for this demo consists of random vectors +% in three gaussian kernels the centers of which are at [0, 0, 0], +% [3 3 3] and [9 0 0]. The map is trained using default parameters. + +D1 = randn(100,3); +D2 = randn(100,3) + 3; +D3 = randn(100,3); D3(:,1) = D3(:,1) + 9; + +sD = som_data_struct([D1; D2; D3],'name','Demo3 data',... + 'comp_names',{'X-coord','Y-coord','Z-coord'}); +sM = som_make(sD); + +% Since the data (and thus the prototypes of the map) are +% 3-dimensional, they can be directly plotted using PLOT3. +% Below, the data is plotted using red 'o's and the map +% prototype vectors with black '+'s. + +plot3(sD.data(:,1),sD.data(:,2),sD.data(:,3),'ro',... + sM.codebook(:,1),sM.codebook(:,2),sM.codebook(:,3),'k+') +rotate3d on + +% From the visualization it is pretty easy to see what the data is +% like, and how the prototypes have been positioned. One can see +% that there are three clusters, and that there are some prototype +% vectors between the clusters, although there is actually no +% data there. The map units corresponding to these prototypes +% are called 'dead' or 'interpolative' map units. + +pause % Strike any key to continue... + + + +clc +% VISUALIZATION OF MULTIDIMENSIONAL DATA +% ====================================== + +% Usually visualization of data sets is not this straightforward, +% since the dimensionality is much higher than three. In principle, +% one can embed additional information to the visualization by +% using properties other than position, for example color, size or +% shape. + +% Here the data set and map prototypes are plotted again, but +% information of the cluster is shown using color: red for the +% first cluster, green for the second and blue for the last. + +plot3(sD.data(1:100,1),sD.data(1:100,2),sD.data(1:100,3),'ro',... + sD.data(101:200,1),sD.data(101:200,2),sD.data(101:200,3),'go',... + sD.data(201:300,1),sD.data(201:300,2),sD.data(201:300,3),'bo',... + sM.codebook(:,1),sM.codebook(:,2),sM.codebook(:,3),'k+') +rotate3d on + +% However, this works only for relatively small dimensionality, say +% less than 10. When the information is added this way, the +% visualization becomes harder and harder to understand. Also, not +% all properties are equal: the human visual system perceives +% colors differently from position, not to mention the complex +% rules governing perception of shape. + +pause % Strike any key to learn about linking... + + + + + +clc +% LINKING MULTIPLE VISUALIZATIONS +% =============================== + +% The other option is to use *multiple visualizations*, so called +% small multiples, instead of only one. The problem is then how to +% link these visualizations together: one should be able to idetify +% the same object from the different visualizations. + +% This could be done using, for example, color: each object has +% the same color in each visualization. Another option is to use +% similar position: each object has the same position in each +% small multiple. + +% For example, here are four subplots, one for each component and +% one for cluster information, where color denotes the value and +% position is used for linking. The 2D-position is derived by +% projecting the data into the space spanned by its two greatest +% eigenvectors. + +[Pd,V,me] = pcaproj(sD.data,2); % project the data +Pm = pcaproj(sM.codebook,V,me); % project the prototypes +colormap(hot); % colormap used for values + +echo off +for c=1:3, + subplot(2,2,c), cla, hold on + som_grid('rect',[300 1],'coord',Pd,'Line','none',... + 'MarkerColor',som_normcolor(sD.data(:,c))); + som_grid(sM,'Coord',Pm,'Line','none','marker','+'); + hold off, title(sD.comp_names{c}), xlabel('PC 1'), ylabel('PC 2'); +end + +subplot(2,2,4), cla +plot(Pd(1:100,1),Pd(1:100,2),'ro',... + Pd(101:200,1),Pd(101:200,2),'go',... + Pd(201:300,1),Pd(201:300,2),'bo',... + Pm(:,1),Pm(:,2),'k+') +title('Cluster') +echo on + +pause % Strike any key to use color for linking... + +% Here is another example, where color is used for linking. On the +% top right triangle are the scatter plots of each variable without +% color coding, and on the bottom left triangle with the color +% coding. In the colored figures, each data sample can be +% identified by a unique color. Well, almost identified: there are +% quite a lot of samples with almost the same color. Color is not as +% precise linking method as position. + +echo off +Col = som_normcolor([1:300]',jet(300)); +k=1; +for i=1:3, + for j=1:3, + if i<j, i1=i; i2=j; else i1=j; i2=i; end + if i<j, + subplot(3,3,k); cla + plot(sD.data(:,i1),sD.data(:,i2),'ko') + xlabel(sD.comp_names{i1}), ylabel(sD.comp_names{i2}) + elseif i>j, + subplot(3,3,k); cla + som_grid('rect',[300 1],'coord',sD.data(:,[i1 i2]),... + 'Line','none','MarkerColor',Col); + xlabel(sD.comp_names{i1}), ylabel(sD.comp_names{i2}) + end + k=k+1; + end +end +echo on + +pause % Strike any key to learn about data visualization using SOM... + + +clc +% DATA VISUALIZATION USING SOM +% ============================ + +% The basic visualization functions and their usage have already +% been introduced in SOM_DEMO2. In this demo, a more structured +% presentation is given. + +% Data visualization techniques using the SOM can be divided to +% three categories based on their goal: + +% 1. visualization of clusters and shape of the data: +% projections, U-matrices and other distance matrices +% +% 2. visualization of components / variables: +% component planes, scatter plots +% +% 3. visualization of data projections: +% hit histograms, response surfaces + +pause % Strike any key to visualize clusters with distance matrices... + + + +clf +clc +% 1. VISUALIZATION OF CLUSTERS: DISTANCE MATRICES +% =============================================== + +% Distance matrices are typically used to show the cluster +% structure of the SOM. They show distances between neighboring +% units, and are thus closely related to single linkage clustering +% techniques. The most widely used distance matrix technique is +% the U-matrix. + +% Here, the U-matrix of the map is shown (using all three +% components in the distance calculation): + +colormap(1-gray) +som_show(sM,'umat','all'); + +pause % Strike any key to see more examples of distance matrices... + +% The function SOM_UMAT can be used to calculate U-matrix. The +% resulting matrix holds distances between neighboring map units, +% as well as the median distance from each map unit to its +% neighbors. These median distances corresponding to each map unit +% can be easily extracted. The result is a distance matrix using +% median distance. + +U = som_umat(sM); +Um = U(1:2:size(U,1),1:2:size(U,2)); + +% A related technique is to assign colors to the map units such +% that similar map units get similar colors. + +% Here, four clustering figures are shown: +% - U-matrix +% - median distance matrix (with grayscale) +% - median distance matrix (with map unit size) +% - similarity coloring, made by spreading a colormap +% on top of the principal component projection of the +% prototype vectors + +subplot(2,2,1) +h=som_cplane([sM.topol.lattice,'U'],sM.topol.msize, U(:)); +set(h,'Edgecolor','none'); title('U-matrix') + +subplot(2,2,2) +h=som_cplane(sM, Um(:)); +set(h,'Edgecolor','none'); title('D-matrix (grayscale)') + +subplot(2,2,3) +som_cplane(sM,'none',1-Um(:)/max(Um(:))) +title('D-matrix (marker size)') + +subplot(2,2,4) +C = som_colorcode(Pm); % Pm is the PC-projection calculated earlier +som_cplane(sM,C) +title('Similarity coloring') + +pause % Strike any key to visualize shape and clusters with projections... + + + +clf +clc +% 1. VISUALIZATION OF CLUSTERS AND SHAPE: PROJECTIONS +% =================================================== + +% In vector projection, a set of high-dimensional data samples is +% projected to a lower dimensional such that the distances between +% data sample pairs are preserved as well as possible. Depending +% on the technique, the projection may be either linear or +% non-linear, and it may place special emphasis on preserving +% local distances. + +% For example SOM is a projection technique, since the prototypes +% have well-defined positions on the 2-dimensional map grid. SOM as +% a projection is however a very crude one. Other projection +% techniques include the principal component projection used +% earlier, Sammon's mapping and Curvilinear Component Analysis +% (to name a few). These have been implemented in functions +% PCAPROJ, SAMMON and CCA. + +% Projecting the map prototype vectors and joining neighboring map +% units with lines gives the SOM its characteristic net-like look. +% The projection figures can be linked to the map planes using +% color coding. + +% Here is the distance matrix, color coding, a projection without +% coloring and a projection with one. In the last projection, +% the size of interpolating map units has been set to zero. + +subplot(2,2,1) +som_cplane(sM,Um(:)); +title('Distance matrix') + +subplot(2,2,2) +C = som_colorcode(sM,'rgb4'); +som_cplane(sM,C); +title('Color code') + +subplot(2,2,3) +som_grid(sM,'Coord',Pm,'Linecolor','k'); +title('PC-projection') + +subplot(2,2,4) +h = som_hits(sM,sD); s=6*(h>0); +som_grid(sM,'Coord',Pm,'MarkerColor',C,'Linecolor','k','MarkerSize',s); +title('Colored PC-projection') + +pause % Strike any key to visualize component planes... + + +clf +clc +% 2. VISUALIZATION OF COMPONENTS: COMPONENT PLANES +% ================================================ + +% The component planes visualizations shows what kind of values the +% prototype vectors of the map units have for different vector +% components. + +% Here is the U-matrix and the three component planes of the map. + +som_show(sM) + +pause % Strike any key to continue... + +% Besides SOM_SHOW and SOM_CPLANE, there are three other +% functions specifically designed for showing the values of the +% component planes: SOM_PIEPLANE, SOM_BARPLANE, SOM_PLOTPLANE. + +% SOM_PIEPLANE shows a single pie chart for each map unit. Each +% pie shows the relative proportion of each component of the sum of +% all components in that map unit. The component values must be +% positive. + +% SOM_BARPLANE shows a barchart in each map unit. The scaling of +% bars can be made unit-wise or variable-wise. By default it is +% determined variable-wise. + +% SOM_PLOTPLANE shows a linegraph in each map unit. + +M = som_normalize(sM.codebook,'range'); + +subplot(1,3,1) +som_pieplane(sM, M); +title('som\_pieplane') + +subplot(1,3,2) +som_barplane(sM, M, '', 'unitwise'); +title('som\_barplane') + +subplot(1,3,3) +som_plotplane(sM, M, 'b'); +title('som\_plotplane') + +pause % Strike any key to visualize cluster properties... + + + +clf +clc +% 2. VISUALIZATION OF COMPONENTS: CLUSTERS +% ======================================== + +% An interesting question is of course how do the values of the +% variables relate to the clusters: what are the values of the +% components in the clusters, and which components are the ones +% which *make* the clusters. + +som_show(sM) + +% From the U-matrix and component planes, one can easily see +% what the typical values are in each cluster. + +pause % Strike any key to continue... + +% The significance of the components with respect to the clustering +% is harder to visualize. One indication of importance is that on +% the borders of the clusters, values of important variables change +% very rabidly. + +% Here, the distance matrix is calculated with respect to each +% variable. + +u1 = som_umat(sM,'mask',[1 0 0]'); u1=u1(1:2:size(u1,1),1:2:size(u1,2)); +u2 = som_umat(sM,'mask',[0 1 0]'); u2=u2(1:2:size(u2,1),1:2:size(u2,2)); +u3 = som_umat(sM,'mask',[0 0 1]'); u3=u3(1:2:size(u3,1),1:2:size(u3,2)); + +% Here, the distance matrices are shown, as well as a piechart +% indicating the relative importance of each variable in each +% map unit. The size of piecharts has been scaled by the +% distance matrix calculated from all components. + +subplot(2,2,1) +som_cplane(sM,u1(:)); +title(sM.comp_names{1}) + +subplot(2,2,2) +som_cplane(sM,u2(:)); +title(sM.comp_names{2}) + +subplot(2,2,3) +som_cplane(sM,u3(:)); +title(sM.comp_names{3}) + +subplot(2,2,4) +som_pieplane(sM, [u1(:), u2(:), u3(:)], hsv(3), Um(:)/max(Um(:))); +title('Relative importance') + +% From the last subplot, one can see that in the area where the +% bigger cluster border is, the 'X-coord' component (red color) +% has biggest effect, and thus is the main factor in separating +% that cluster from the rest. + +pause % Strike any key to learn about correlation hunting... + + +clf +clc +% 2. VISUALIZATION OF COMPONENTS: CORRELATION HUNTING +% =================================================== + +% Finally, the component planes are often used for correlation +% hunting. When the number of variables is high, the component +% plane visualization offers a convenient way to visualize all +% components at once and hunt for correlations (as opposed to +% N*(N-1)/2 scatterplots). + +% Hunting correlations this way is not very accurate. However, it +% is easy to select interesting combinations for further +% investigation. + +% Here, the first and third components are shown with scatter +% plot. As with projections, a color coding is used to link the +% visualization to the map plane. In the color coding, size shows +% the distance matrix information. + +C = som_colorcode(sM); +subplot(1,2,1) +som_cplane(sM,C,1-Um(:)/max(Um(:))); +title('Color coding + distance matrix') + +subplot(1,2,2) +som_grid(sM,'Coord',sM.codebook(:,[1 3]),'MarkerColor',C); +title('Scatter plot'); xlabel(sM.comp_names{1}); ylabel(sM.comp_names{3}) +axis equal + +pause % Strike any key to visualize data responses... + + +clf +clc +% 3. DATA ON MAP +% ============== + +% The SOM is a map of the data manifold. An interesting question +% then is where on the map a specific data sample is located, and +% how accurate is that localization? One is interested in the +% response of the map to the data sample. + +% The simplest answer is to find the BMU of the data sample. +% However, this gives no indication of the accuracy of the +% match. Is the data sample close to the BMU, or is it actually +% equally close to the neighboring map units (or even approximately +% as close to all map units)? Sometimes accuracy doesn't really +% matter, but if it does, it should be visualized somehow. + +% Here are different kinds of response visualizations for two +% vectors: [0 0 0] and [99 99 99]. +% - BMUs indicated with labels +% - BMUs indicated with markers, relative quantization errors +% (in this case, proportion between distances to BMU and +% Worst-MU) with vertical lines +% - quantization error between the samples and all map units +% - fuzzy response (a non-linear function of quantization +% error) of all map units + +echo off +[bm,qe] = som_bmus(sM,[0 0 0; 99 99 99],'all'); % distance to all map units +[dummy,ind] = sort(bm(1,:)); d0 = qe(1,ind)'; +[dummy,ind] = sort(bm(2,:)); d9 = qe(2,ind)'; +bmu0 = bm(1,1); bmu9 = bm(2,1); % bmus + +h0 = zeros(prod(sM.topol.msize),1); h0(bmu0) = 1; % crisp hits +h9 = zeros(prod(sM.topol.msize),1); h9(bmu9) = 1; + +lab = cell(prod(sM.topol.msize),1); +lab{bmu0} = '[0,0,0]'; lab{bmu9} = '[99,99,99]'; + +hf0 = som_hits(sM,[0 0 0],'fuzzy'); % fuzzy response +hf9 = som_hits(sM,[99 99 99],'fuzzy'); + +som_show(sM,'umat',{'all','BMU'},... + 'color',{d0,'Qerror 0'},'color',{hf0,'Fuzzy response 0'},... + 'empty','BMU+qerror',... + 'color',{d9,'Qerror 99'},'color',{hf9,'Fuzzy response 99'}); +som_show_add('label',lab,'Subplot',1,'Textcolor','r'); +som_show_add('hit',[h0, h9],'Subplot',4,'MarkerColor','r'); +hold on +Co = som_vis_coords(sM.topol.lattice,sM.topol.msize); +plot3(Co(bmu0,[1 1]),Co(bmu0,[2 2]),[0 10*qe(1,1)/qe(1,end)],'r-') +plot3(Co(bmu9,[1 1]),Co(bmu9,[2 2]),[0 10*qe(2,1)/qe(2,end)],'r-') +view(3), axis equal +echo on + +% Here are the distances to BMU, 2-BMU and WMU: + +qe(1,[1,2,end]) % [0 0 0] +qe(2,[1,2,end]) % [99 99 99] + +% One can see that for [0 0 0] the accuracy is pretty good as the +% quantization error of the BMU is much lower than that of the +% WMU. On the other hand [99 99 99] is very far from the map: +% distance to BMU is almost equal to distance to WMU. + +pause % Strike any key to visualize responses of multiple samples... + + + +clc +clf +% 3. DATA ON MAP: HIT HISTOGRAMS +% ============================== + +% One can also investigate whole data sets using the map. When the +% BMUs of multiple data samples are aggregated, a hit histogram +% results. Instead of BMUs, one can also aggregate for example +% fuzzy responses. + +% The hit histograms (or aggregated responses) can then be compared +% with each other. + +% Here are hit histograms of three data sets: one with 50 first +% vectors of the data set, one with 150 samples from the data +% set, and one with 50 randomly selected samples. In the last +% subplot, the fuzzy response of the first data set. + +dlen = size(sD.data,1); +Dsample1 = sD.data(1:50,:); h1 = som_hits(sM,Dsample1); +Dsample2 = sD.data(1:150,:); h2 = som_hits(sM,Dsample2); +Dsample3 = sD.data(ceil(rand(50,1)*dlen),:); h3 = som_hits(sM,Dsample3); +hf = som_hits(sM,Dsample1,'fuzzy'); + +som_show(sM,'umat','all','umat','all','umat','all','color',{hf,'Fuzzy'}) +som_show_add('hit',h1,'Subplot',1,'Markercolor','r') +som_show_add('hit',h2,'Subplot',2,'Markercolor','r') +som_show_add('hit',h3,'Subplot',3,'Markercolor','r') + +pause % Strike any key to visualize trajectories... + + + +clc +clf +% 3. DATA ON MAP: TRAJECTORIES +% ============================ + +% A special data mapping technique is trajectory. If the samples +% are ordered, forming a time-series for example, their response on +% the map can be tracked. The function SOM_SHOW_ADD can be used to +% show the trajectories in two different modes: 'traj' and 'comet'. + +% Here, a series of data points is formed which go from [8,0,0] +% to [2,2,2]. The trajectory is plotted using the two modes. + +Dtraj = [linspace(9,2,20); linspace(0,2,20); linspace(0,2,20)]'; +T = som_bmus(sM,Dtraj); + +som_show(sM,'comp',[1 1]); +som_show_add('traj',T,'Markercolor','r','TrajColor','r','subplot',1); +som_show_add('comet',T,'MarkerColor','r','subplot',2); + +% There's also a function SOM_TRAJECTORY which lauches a GUI +% specifically designed for displaying trajectories (in 'comet' +% mode). + +pause % Strike any key to learn about color handling... + + + + +clc +clf +% COLOR HANDLING +% ============== + +% Matlab offers flexibility in the colormaps. Using the COLORMAP +% function, the colormap may be changed. There are several useful +% colormaps readily available, for example 'hot' and 'jet'. The +% default number of colors in the colormaps is 64. However, it is +% often advantageous to use less colors in the colormap. This way +% the components planes visualization become easier to interpret. + +% Here the three component planes are visualized using the 'hot' +% colormap and only three colors. + +som_show(sM,'comp',[1 2 3]) +colormap(hot(3)); +som_recolorbar + +pause % Press any key to change the colorbar labels... + +% The function SOM_RECOLORBAR can be used to reconfigure +% the labels beside the colorbar. + +% Here the colorbar of the first subplot is labeled using labels +% 'small', 'medium' and 'big' at values 0, 1 and 2. For the +% colorbar of the second subplot, values are calculated for the +% borders between colors. + +som_recolorbar(1,{[0 4 9]},'',{{'small','medium','big'}}); +som_recolorbar(2,'border',''); + +pause % Press any key to learn about SOM_NORMCOLOR... + +% Some SOM Toolbox functions do not use indexed colors if the +% underlying Matlab function (e.g. PLOT) do not use indexed +% colors. SOM_NORMCOLOR is a convenient function to simulate +% indexed colors: it calculates fixed RGB colors that +% are similar to indexed colors with the specified colormap. + +% Here, two SOM_GRID visualizations are created. One uses the +% 'surf' mode to show the component colors in indexed color +% mode, and the other uses SOM_NORMALIZE to do the same. + +clf +colormap(jet(64)) +subplot(1,2,1) +som_grid(sM,'Surf',sM.codebook(:,3)); +title('Surf mode') + +subplot(1,2,2) +som_grid(sM,'Markercolor',som_normcolor(sM.codebook(:,3))); +title('som\_normcolor') + +pause % Press any key to visualize different map shapes... + + + +clc +clf +% DIFFERENT MAP SHAPES +% ==================== + +% There's no direct way to visualize cylinder or toroid maps. When +% visualized, they are treated exactly as if they were sheet +% shaped. However, if function SOM_UNIT_COORDS is used to provide +% unit coordinates, then SOM_GRID can be used to visualize these +% alternative map shapes. + +% Here the grids of the three possible map shapes (sheet, cylinder +% and toroid) are visualized. The last subplot shows a component +% plane visualization of the toroid map. + +Cor = som_unit_coords(sM.topol.msize,'hexa','sheet'); +Coc = som_unit_coords(sM.topol.msize,'hexa','cyl'); +Cot = som_unit_coords(sM.topol.msize,'hexa','toroid'); + +subplot(2,2,1) +som_grid(sM,'Coord',Cor,'Markersize',3,'Linecolor','k'); +title('sheet'), view(0,-90), axis tight, axis equal + +subplot(2,2,2) +som_grid(sM,'Coord',Coc,'Markersize',3,'Linecolor','k'); +title('cylinder'), view(5,1), axis tight, axis equal + +subplot(2,2,3) +som_grid(sM,'Coord',Cot,'Markersize',3,'Linecolor','k'); +title('toroid'), view(-100,0), axis tight, axis equal + +subplot(2,2,4) +som_grid(sM,'Coord',Cot,'Surf',sM.codebook(:,3)); +colormap(jet), colorbar +title('toroid'), view(-100,0), axis tight, axis equal + +echo off
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_demo4.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,307 @@ + +%SOM_DEMO4 Data analysis using the SOM. + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 071197 +% Version 2.0beta juuso 090200 070600 + +clf reset; +f0 = gcf; +echo on + + + + +clc +% ========================================================== +% SOM_DEMO4 - DATA ANALYSIS USING THE SOM +% ========================================================== + +% In this demo, the IRIS data set is analysed using SOM. First, the +% data is read from ascii file (please make sure they can be found +% from the current path), normalized, and a map is +% trained. Since the data also had labels, the map is labelled. + +try, + sD = som_read_data('iris.data'); +catch + echo off + + warning('File ''iris.data'' not found. Using simulated data instead.') + + D = randn(50,4); + D(:,1) = D(:,1)+5; D(:,2) = D(:,2)+3.5; + D(:,3) = D(:,3)/2+1.5; D(:,4) = D(:,4)/2+0.3; + D2 = randn(100,4); D2(:,2) = sort(D2(:,2)); + D2(:,1) = D2(:,1)+6.5; D2(:,2) = D2(:,2)+2.8; + D2(:,3) = D2(:,3)+5; D2(:,4) = D2(:,4)/2+1.5; + sD = som_data_struct([D; D2],'name','iris (simulated)',... + 'comp_names',{'SepalL','SepalW','PetalL','PetalW'}); + sD = som_label(sD,'add',[1:50]','Setosa'); + sD = som_label(sD,'add',[51:100]','Versicolor'); + sD = som_label(sD,'add',[101:150]','Virginica'); + + echo on +end + +sD = som_normalize(sD,'var'); +sM = som_make(sD); +sM = som_autolabel(sM,sD,'vote'); + +pause % Strike any key to visualize the map... + +clc +% VISUAL INSPECTION OF THE MAP +% ============================ + +% The first step in the analysis of the map is visual inspection. +% Here is the U-matrix, component planes and labels (you may +% need to enlarge the figure in order to make out the labels). + +som_show(sM,'umat','all','comp',[1:4],'empty','Labels','norm','d'); +som_show_add('label',sM.labels,'textsize',8,'textcolor','r','subplot',6); + +% From this first visualization, one can see that: +% - there are essentially two clusters +% - PetalL and PetalW are highly correlated +% - SepalL is somewhat correlated to PetalL and PetalW +% - one cluster corresponds to the Setosa species and exhibits +% small petals and short but wide sepals +% - the other cluster corresponds to Virginica and Versicolor +% such that Versicolor has smaller leaves (both sepal and +% petal) than Virginica +% - inside both clusters, SepalL and SepalW are highly correlated + +pause % Strike any key to continue... + +% Next, the projection of the data set is investigated. A +% principle component projection is made for the data, and applied +% to the map. The colormap is done by spreading a colormap on the +% projection. Distance matrix information is extracted from the +% U-matrix, and it is modified by knowledge of zero-hits +% (interpolative) units. Finally, three visualizations are shown: +% the color code, with clustering information and the number of +% hits in each unit, the projection and the labels. + +echo off + +f1=figure; +[Pd,V,me,l] = pcaproj(sD,2); Pm = pcaproj(sM,V,me); % PC-projection +Code = som_colorcode(Pm); % color coding +hits = som_hits(sM,sD); % hits +U = som_umat(sM); % U-matrix +Dm = U(1:2:size(U,1),1:2:size(U,2)); % distance matrix +Dm = 1-Dm(:)/max(Dm(:)); Dm(find(hits==0)) = 0; % clustering info + +subplot(1,3,1) +som_cplane(sM,Code,Dm); +hold on +som_grid(sM,'Label',cellstr(int2str(hits)),... + 'Line','none','Marker','none','Labelcolor','k'); +hold off +title('Color code') + +subplot(1,3,2) +som_grid(sM,'Coord',Pm,'MarkerColor',Code,'Linecolor','k'); +hold on, plot(Pd(:,1),Pd(:,2),'k+'), hold off, axis tight, axis equal +title('PC projection') + +subplot(1,3,3) +som_cplane(sM,'none') +hold on +som_grid(sM,'Label',sM.labels,'Labelsize',8,... + 'Line','none','Marker','none','Labelcolor','r'); +hold off +title('Labels') + +echo on + +% From these figures one can see that: +% - the projection confirms the existence of two different clusters +% - interpolative units seem to divide the Virginica +% flowers into two classes, the difference being in the size of +% sepal leaves + +pause % Strike any key to continue... + +% Finally, perhaps the most informative figure of all: simple +% scatter plots and histograms of all variables. The species +% information is coded as a fifth variable: 1 for Setosa, 2 for +% Versicolor and 3 for Virginica. Original data points are in the +% upper triangle, map prototype values on the lower triangle, and +% histograms on the diagonal: black for the data set and red for +% the map prototype values. The color coding of the data samples +% has been copied from the map (from the BMU of each sample). Note +% that the variable values have been denormalized. + +echo off + +% denormalize and add species information +names = sD.comp_names; names{end+1} = 'species'; +D = som_denormalize(sD.data,sD); dlen = size(D,1); +s = zeros(dlen,1)+NaN; s(strcmp(sD.labels,'Setosa'))=1; +s(strcmp(sD.labels,'Versicolor'))=2; s(strcmp(sD.labels,'Virginica'))=3; +D = [D, s]; +M = som_denormalize(sM.codebook,sM); munits = size(M,1); +s = zeros(munits,1)+NaN; s(strcmp(sM.labels,'Setosa'))=1; +s(strcmp(sM.labels,'Versicolor'))=2; s(strcmp(sM.labels,'Virginica'))=3; +M = [M, s]; + +f2=figure; + +% color coding copied from the map +bmus = som_bmus(sM,sD); Code_data = Code(bmus,:); + +k=1; +for i=1:5, for j=1:5, + if i<j, i1=i; i2=j; else i1=j; i2=i; end + subplot(5,5,k); cla + if i<j, + som_grid('rect',[dlen 1],'coord',D(:,[i1 i2]),... + 'Line','none','MarkerColor',Code_data,'Markersize',2); + title(sprintf('%s vs. %s',names{i1},names{i2})) + elseif i>j, + som_grid(sM,'coord',M(:,[i1 i2]),... + 'markersize',2,'MarkerColor',Code); + title(sprintf('%s vs. %s',names{i1},names{i2})) + else + if i1<5, b = 10; else b = 3; end + [nd,x] = hist(D(:,i1),b); nd=nd/sum(nd); + nm = hist(M(:,i1),x); nm = nm/sum(nm); + h=bar(x,nd,0.8); set(h,'EdgeColor','none','FaceColor','k'); + hold on + h=bar(x,nm,0.3); set(h,'EdgeColor','none','FaceColor','r'); + hold off + title(names{i1}) + end + k=k+1; + end +end + +echo on + +% This visualization shows quite a lot of information: +% distributions of single and pairs of variables both in the data +% and in the map. If the number of variables was even slightly +% more, it would require a really big display to be convenient to +% use. + +% From this visualization we can conform many of the earlier +% conclusions, for example: +% - there are two clusters: 'Setosa' (blue, dark green) and +% 'Virginica'/'Versicolor' (light green, yellow, reds) +% (see almost any of the subplots) +% - PetalL and PetalW have a high linear correlation (see +% subplots 4,3 and 3,4) +% - SepalL is correlated (at least in the bigger cluster) with +% PetalL and PetalW (in subplots 1,3 1,4 3,1 and 4,1) +% - SepalL and SepalW have a clear linear correlation, but it +% is slightly different for the two main clusters (in +% subplots 2,1 and 1,2) + +pause % Strike any key to cluster the map... + +close(f1), close(f2), figure(f0), clf + +clc +% CLUSTERING OF THE MAP +% ===================== + +% Visual inspection already hinted that there are at least two +% clusters in the data, and that the properties of the clusters are +% different from each other (esp. relation of SepalL and +% SepalW). For further investigation, the map needs to be +% partitioned. + +% Here, the KMEANS_CLUSTERS function is used to find an initial +% partitioning. The plot shows the Davies-Boulding clustering +% index, which is minimized with best clustering. + +subplot(1,3,1) +[c,p,err,ind] = kmeans_clusters(sM, 7); % find at most 7 clusters +plot(1:length(ind),ind,'x-') +[dummy,i] = min(ind) +cl = p{i}; + +% The Davies-Boulding index seems to indicate that there are +% two clusters on the map. Here is the clustering info +% calculated previously and the partitioning result: + +subplot(1,3,2) +som_cplane(sM,Code,Dm) +subplot(1,3,3) +som_cplane(sM,cl) + +% You could use also function SOM_SELECT to manually make or modify +% the partitioning. + +% After this, the analysis would proceed with summarization of the +% results, and analysis of each cluster one at a time. +% Unfortunately, you have to do that yourself. The SOM Toolbox does +% not, yet, have functions for those purposes. + +pause % Strike any key to continue... + + +clf +clc +% MODELING +% ======== + +% One can also build models on top of the SOM. Typically, these +% models are simple local or nearest-neighbor models. + +% Here, SOM is used for probability density estimation. Each map +% prototype is the center of a gaussian kernel, the parameters +% of which are estimated from the data. The gaussian mixture +% model is estimated with function SOM_ESTIMATE_GMM and the +% probabilities can be calculated with SOM_PROBABILITY_GMM. + +[K,P] = som_estimate_gmm(sM,sD); +[pd,Pdm,pmd] = som_probability_gmm(sD,sM,K,P); + +% Here is the probability density function value for the first data +% sample (x=sD.data(:,1)) in terms of each map unit (m): + +som_cplane(sM,Pdm(:,1)) +colorbar +title('p(x|m)') + +pause % Strike any key to continue... + +% Here, SOM is used for classification. Although the SOM can be +% used for classification as such, one has to remember that it does +% not utilize class information at all, and thus its results are +% inherently suboptimal. However, with small modifications, the +% network can take the class into account. The function +% SOM_SUPERVISED does this. + +% Learning vector quantization (LVQ) is an algorithm that is very +% similar to the SOM in many aspects. However, it is specifically +% designed for classification. In the SOM Toolbox, there are +% functions LVQ1 and LVQ3 that implement two versions of this +% algorithm. + +% Here, the function SOM_SUPERVISED is used to create a classifier +% for IRIS data set: + +sM = som_supervised(sD,'small'); + +som_show(sM,'umat','all'); +som_show_add('label',sM.labels,'TextSize',8,'TextColor','r') + +sD2 = som_label(sD,'clear','all'); +sD2 = som_autolabel(sD2,sM); % classification +ok = strcmp(sD2.labels,sD.labels); % errors +100*(1-sum(ok)/length(ok)) % error percentage (%) + +echo off + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_dendrogram.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,292 @@ +function [h,Coord,Color,height] = som_dendrogram(Z,varargin) + +%SOM_DENDROGRAM Visualize a dendrogram. +% +% [h,Coord,Color,height] = som_dendrogram(Z, [[argID,] value, ...]) +% +% Z = som_linkage(sM); +% som_dendrogram(Z); +% som_dendrogram(Z,sM); +% som_dendrogram(Z,'coord',co); +% +% Input and output arguments ([]'s are optional): +% h (vector) handle to the arc lines +% Z (matrix) size n-1 x 1, the hierarchical cluster matrix +% returned by functions like LINKAGE and SOM_LINKAGE +% n is the number of original data samples. +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% Coord (matrix) size 2*n-1 x {1,2}, the coordinates of the +% original data samples and cluster nodes used +% in the visualization +% Color (matrix) size 2*n-1 x 3, the colors of ... +% height (vector) size 2*n-1 x 1, the heights of ... +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'data' *(struct) map or data struct: many other optional +% arguments require this +% (matrix) data matrix +% 'coord' (matrix) size n x 1 or n x 2, the coordinates of +% the original data samples either in 1D or 2D +% (matrix) size 2*n-1 x {1,2}, the coordinates of both +% original data samples and each cluster +% *(string) 'SOM', 'pca#', 'sammon#', or 'cca#': the coordinates +% are calculated using the given data and the +% required projection algorithm. The '#' at the +% end of projection algorithms refers to the +% desired output dimension and can be either 1 or 2 +% (2 by default). In case of 'SOM', the unit +% coordinates (given by SOM_VIS_COORDS) are used. +% 'color' (matrix) size n x 3, the color of the original data samples +% (matrix) size 2*n-1 x 3, the colors of both original +% data samples and each cluster +% (string) color specification, e.g. 'r.', used for each node +% 'height' (vector) size n-1 x 1, the heights used for each cluster +% (vector) size 2*n-1 x 1, the heights used for both original +% data samples and each cluster +% *(string) 'order', the order of combination determines height +% 'depth', the depth at which the combination +% happens determines height +% 'linecolor' (string) color specification for the arc color, 'k' by default +% (vector) size 1 x 3 +% +% See also SOM_LINKAGE, DENDROGRAM. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 160600 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% read the arguments + +% Z +nd = size(Z,1)+1; +nc = size(Z,1); + +% varargin +Coordtype = 'natural'; Coord = []; codim = 1; +Colortype = 'none'; Color = []; +height = [zeros(nd,1); Z(:,3)]; +M = []; +linecol = 'k'; + +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + case 'data', i = i + 1; M = varargin{i}; + case 'coord', + i=i+1; + if isnumeric(varargin{i}), Coord = varargin{i}; Coordtype = 'given'; + else + if strcmp(varargin{i},'SOM'), Coordtype = 'SOM'; + else Coordtype = 'projection'; Coord = varargin{i}; + end + end + case 'color', + i=i+1; + if isempty(varargin{i}), Colortype = 'none'; + elseif ischar(varargin{i}), Colortype = 'colorspec'; Color = varargin{i}; + else Colortype = 'given'; Color = varargin{i}; + end + case 'height', i=i+1; height = varargin{i}; + case 'linecolor', i=i+1; linecol = varargin{i}; + case 'SOM', + Coordtype = 'SOM'; + case {'pca','pca1','pca2','sammon','sammon1','sammon2','cca','cca1','cca2'}, + Coordtype = 'projection'; Coord = varargin{i}; + case {'order','depth'}, height = varargin{i}; + end + elseif isstruct(varargin{i}), M = varargin{i}; + else + argok = 0; + end + if ~argok, + disp(['(som_dendrogram) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +switch Coordtype, + case 'SOM', + if isempty(M) | ~any(strcmp(M.type,{'som_map','som_topol'})) , + error('Cannot determine SOM coordinates without a SOM.'); + end + if strcmp(M.type,'som_map'), M = M.topol; end + case 'projection', + if isempty(M), error('Cannot do projection without the data.'); end + if isstruct(M), + if strcmp(M.type,'som_data'), M = M.data; + elseif strcmp(M.type,'som_map'), M = M.codebook; + end + end + if size(M,1) ~= nd, + error('Given data must be equal in length to the number of original data samples.') + end + case 'given', + if size(Coord,1) ~= nd & size(Coord,1) ~= nd+nc, + error('Size of given coordinate matrix does not match the cluster hierarchy.'); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialization + +% Coordinates +switch Coordtype, + case 'natural', o = leavesorder(Z)'; [dummy,Coord] = sort(o); codim = 1; + case 'SOM', Coord = som_vis_coords(M.lattice,M.msize); codim = 2; + case 'projection', + switch Coord, + case {'pca','pca2'}, Coord = pcaproj(M,2); codim = 2; + case 'pca1', Coord = pcaproj(M,1); codim = 1; + case {'cca','cca2'}, Coord = cca(M,2,20); codim = 2; + case 'cca1', Coord = cca(M,1,20); codim = 1; + case {'sammon','sammon2'}, Coord = sammon(M,2,50); codim = 2; + case 'sammon1', Coord = sammon(M,1,50); codim = 1; + end + case 'given', codim = min(size(Coord,2),2); % nill +end + +if size(Coord,1) == nd, + Coord = [Coord; zeros(nc,size(Coord,2))]; + for i=(nd+1):(nd+nc), + leaves = leafnodes(Z,i,nd); + if any(leaves), Coord(i,:) = mean(Coord(leaves,:),1); else Coord(i,:) = Inf; end + end +end + +% Colors +switch Colortype, + case 'colorspec', % nill + case 'none', Color = ''; + case 'given', + if size(Color,1) == nd, + Color = [Color; zeros(nc,3)]; + for i=(nd+1):(nd+nc), + leaves = leafnodes(Z,i,nd); + if any(leaves), Color(i,:) = mean(Color(leaves,:),1); + else Color(i,:) = 0.8; + end + end + end +end + +% height +if ischar(height), + switch height, + case 'order', height = [zeros(nd,1); [1:nc]']; + case 'depth', height = nodedepth(Z); height = max(height) - height; + end +else + if length(height)==nc, height = [zeros(nd,1); height]; end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% draw + +% the arcs +lfrom = []; lto = []; +for i=1:nd+nc, + if i<=nd, ch = []; + elseif ~isfinite(Z(i-nd,3)), ch = []; + else ch = Z(i-nd,1:2)'; + end + if any(ch), + lfrom = [lfrom; i*ones(length(ch),1)]; + lto = [lto; ch]; + end +end + +% the coordinates of the arcs +if codim == 1, + Lx = [Coord(lfrom), Coord(lto), Coord(lto)]; + Ly = [height(lfrom), height(lfrom), height(lto)]; + Lz = []; +else + Lx = [Coord(lfrom,1), Coord(lto,1), Coord(lto,1)]; + Ly = [Coord(lfrom,2), Coord(lto,2), Coord(lto,2)]; + Lz = [height(lfrom), height(lfrom), height(lto)]; +end + +washold = ishold; +if ~washold, cla; end + +% plot the lines +if isempty(Lz), + h = line(Lx',Ly','color',linecol); +else + h = line(Lx',Ly',Lz','color',linecol); + if ~washold, view(3); end + rotate3d on +end + +% plot the nodes +hold on +switch Colortype, + case 'none', % nill + case 'colorspec', + if codim == 1, plot(Coord,height,Color); + else plot3(Coord(:,1), Coord(:,2), height, Color); + end + case 'given', + som_grid('rect',[nd+nc 1],'line','none','Coord',[Coord, height],... + 'Markersize',10,'Markercolor',Color); +end +if ~washold, hold off, end + +return; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function depth = nodedepth(Z) + + nd = size(Z,1)+1; + nc = size(Z,1); + depth = zeros(nd+nc,1); + ch = nc+nd-1; + while any(ch), + c = ch(1); ch = ch(2:end); + if c>nd & isfinite(Z(c-nd,3)), + chc = Z(c-nd,1:2); + depth(chc) = depth(c) + 1; + ch = [ch, chc]; + end + end + return; + +function inds = leafnodes(Z,i,nd) + + inds = []; + ch = i; + while any(ch), + c = ch(1); ch = ch(2:end); + if c>nd & isfinite(Z(c-nd,3)), ch = [ch, Z(c-nd,1:2)]; end + if c<=nd, inds(end+1) = c; end + end + return; + +function order = leavesorder(Z) + + nd = size(Z,1)+1; + order = 2*nd-1; + nonleaves = 1; + while any(nonleaves), + j = nonleaves(1); + ch = Z(order(j)-nd,1:2); + if j==1, oleft = []; else oleft = order(1:(j-1)); end + if j==length(order), oright = []; else oright = order((j+1):length(order)); end + order = [oleft, ch, oright]; + nonleaves = find(order>nd); + end + return; + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_denormalize.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,293 @@ +function sD = som_denormalize(sD,varargin) + +%SOM_DENORMALIZE Denormalize data. +% +% sS = som_denormalize(sS, [argID, value, ...]) +% +% sS = som_denormalize(sS) +% sS = som_denormalize(sS,[1:3 10],'remove') +% D = som_denormalize(D,sM.comp_norm) +% D = som_denormalize(D,sM,[1:3 10]) +% +% Input and output arguments ([]'s are optional): +% sS The data to which the denormalization is applied. +% The modified and updated data is returned. +% (struct) data or map struct +% (matrix) data matrix (a matrix is also returned) +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% Here are the valid argument IDs and corresponding values. The values which +% are unambiguous (marked with '*') can be given without the preceeding argID. +% 'norm' *(struct) Normalization struct, or an array of such. +% Alternatively, a map/data struct can be given +% in which case its '.comp_norm' field is used +% (see below). +% *(cell array) Of normalization structs. Typically, the +% '.comp_norm' field of a map/data struct. The +% length of the array must be equal to data dimension. +% 'remove' *(string) If 'remove' tag is specified, the +% normalization operations are not only undone, +% they are also removed from the struct. +% 'comps' *(vector) the components to which the denormalization is +% applied, default is [1:dim] ie. all components +% +% For more help, try 'type som_denormalize' or check out online documentation. +% See also SOM_NORMALIZE, SOM_NORM_VARIABLE, SOM_INFO. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_denormalize +% +% PURPOSE +% +% Undo normalizations of data structs/sets. +% +% SYNTAX +% +% sS = som_denormalize(sS) +% sS = som_denormalize(...,'argID',value,...); +% sS = som_denormalize(...,value,...); +% D = som_denormalize(D,sNorm) +% +% DESCRIPTION +% +% This function is used to undo normalizations of data structs/sets. If a +% data/map struct is given, all normalizations in the '.comp_norm' field are +% undone and, thus, the values in the original data context are returned. If +% a matrix is given, the normalizations to undo must be given as the second +% argument. SOM_DENORMALIZE actually uses function SOM_NORM_VARIABLE to +% handle the normalization operations, and only handles the data struct/set +% specific stuff itself. +% +% Normalizations are always one-variable operations. In the data and map +% structs the normalization information for each component is saved in the +% '.comp_norm' field, which is a cell array of length dim. Each cell +% contains normalizations for one vector component in a +% struct array of normalization structs. Each component may have different +% amounts of different kinds of normalizations. Typically, all +% normalizations are either 'undone' or 'done', but in special situations +% this may not be the case. The easiest way to check out the status of the +% normalizations is to use function SOM_INFO, e.g. som_info(sS,3) +% +% REQUIRED INPUT ARGUMENTS +% +% sS The data to which the denormalization is applied. +% (struct) Data or map struct. The normalizations in the +% '.comp_norm' field are undone for the specified +% components. +% (matrix) Data matrix. The normalization to undo must be +% given in the second argument. +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is used. The +% valid IDs and corresponding values are listed below. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% +% sNorm *(struct) Normalization struct, or an array of structs, which +% is undone for all specified components. If the +% '.status' field of the struct(s) is 'uninit', +% the undoing operation is interrupted - it cannot be +% done. Alternatively, the struct may be map or +% data struct in which case its '.comp_norm' field +% is used (see the cell array option below). +% *(cell array) In practice, the '.comp_norm' field of +% a data/map struct. The length of the array +% must be equal to the dimension of the given +% data set (sS). Each cell contains the +% normalization(s) for one component. Only the +% normalizations listed in comps argument are +% undone though. +% +% 'remove' *(string) If 'remove' tag is specified, the +% normalization operations are not only undone, +% they are also removed from the struct. +% +% 'comps' *(vector) The components which are denormalized. +% Default is to undo all components. +% *(string) 'all' +% +% OUTPUT ARGUMENTS +% +% sS Modified and/or updated data. +% (struct) If a struct was given as input argument, the +% same struct is returned with denormalized data and +% updated '.comp_norm' fields. +% (matrix) If a matrix was given as input argument, the +% denormalized data matrix is returned. +% +% EXAMPLES +% +% To undo normalization of a data/map struct: +% +% sD = som_denormalize(sD); +% sM = som_denormalize(sM); +% +% To completely remove the normalizations, use the 'remove' tag: +% +% sD = som_denormalize(sD,'remove'); +% +% To undo only a few selected components, use the comps argument: +% +% sD = som_denormalize(sD,[1 3:5]); +% +% To denormalize a set of values from a data set D (which must be +% of equal dimension as the data in sD): +% +% D = som_denormalize(D,sD); +% or +% D = som_denormalize(D,sD.comp_norm); +% only denormalize a few components +% D = som_denormalize(D,sD,[1 3:5]); +% +% Assuming you have a few values of a certain vector component (i) +% in a vector (x) which you want to denormalize: +% +% xorig = som_denormalize(x,sD.comp_norm{i}); +% or using SOM_NORM_VARIABLE +% xorig = som_norm_variable(x,sD.comp_norm{i},'undo'); +% +% To check out the status of normalization in a struct use SOM_INFO: +% +% som_info(sM,3) +% som_info(sD,3) +% +% SEE ALSO +% +% som_normalize Add/apply/redo normalizations of a data struct/set. +% som_norm_variable Normalization operations for a set of scalar values. +% som_info User-friendly information of SOM Toolbox structs. + +% Copyright (c) 1998-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 151199 150300 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(1, 3, nargin)); % check no. of input arguments is correct + +% sD +struct_mode = isstruct(sD); +if struct_mode, + switch sD.type + case 'som_map', D = sD.codebook; + case 'som_data', D = sD.data; + otherwise, error('Illegal struct.') + end +else + D = sD; +end +[dlen dim] = size(D); + +% varargin +comps = [1:dim]; +remove_tag = 0; +if struct_mode, sNorm = sD.comp_norm; else sNorm = []; end +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'comps', i=i+1; comps = varargin{i}; + case {'norm','sNorm','som_norm'}, i=i+1; sNorm = varargin{i}; + % unambiguous values + case 'remove', remove_tag = 1; + otherwise argok=0; + end + elseif isnumeric(varargin{i}), + comps = varargin{i}; + elseif isstruct(varargin{i}), + sNorm = varargin{i}; + elseif iscell(varargin{i}), + sNorm = varargin{i}; + else + argok = 0; + end + if ~argok, + disp(['(som_denormalize) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +% check comps +if ischar(comps), comps = [1:dim]; end +if isempty(comps), return; end +if size(comps,1)>1, comps = comps'; end % make it a row vector + +% sNorm +% check out the given normalization +% (and if necessary, copy it for each specified component) +if isstruct(sNorm), + switch sNorm(1).type, + case {'som_map','som_data'}, csNorm = sNorm(1).comp_norm; + case {'som_norm'}, for i=comps, csNorm{i} = sNorm; end + otherwise, + error('Invalid struct for sNorm.') + end +elseif iscell(sNorm), + csNorm = sNorm; +else + error('Illegal value for sNorm.') +end + +% check that csNorm and comps possibly agree +if max(comps) > length(csNorm), + error('Given normalizations does not match the components.') +end +if length(csNorm) ~= dim, + error('Given normalizations does not match data dimension.') +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +% undo the normalizations +for i = comps, + len = length(csNorm{i}); + for j=len:-1:1, + sN = csNorm{i}(j); + if struct_mode, + if strcmp(sN.status,'done'), + [x,sN] = som_norm_variable(D(:,i), sN, 'undo'); + D(:,i) = x; + csNorm{i}(j) = sN; + end + else + D(:,i) = som_norm_variable(D(:,i), sN, 'undo'); + end + end +end + +% remove normalizations +if struct_mode & remove_tag, + for i = comps, csNorm{i} = []; end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% output + + +if struct_mode, + switch sD.type + case 'som_map', sD.codebook = D; + case 'som_data', sD.data = D; + otherwise, error('Illegal struct.') + end + sD.comp_norm = csNorm; +else + sD = D; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_distortion.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,138 @@ +function [adm,admu,tdmu] = som_distortion(sM, D, arg1, arg2) + +%SOM_DISTORTION Calculate distortion measure for the map. +% +% [adm,admu,tdmu] = som_distortion(sMap, D, [radius], ['prob']) +% +% adm = som_distortion(sMap,D); +% [adm,admu] = som_distortion(sMap,D); +% som_show(sMap,'color',admu); +% +% Input and output arguments: +% sMap (struct) a map struct +% D (struct) a data struct +% (matrix) size dlen x dim, a data matrix +% [radius] (scalar) neighborhood function radius to be used. +% Defaults to the last radius_fin in the +% trainhist field of the map struct, or 1 if +% that is missing. +% ['prob'] (string) If given, this argument forces the +% neigborhood function values for each map +% unit to be normalized so that they sum to 1. +% +% adm (scalar) average distortion measure (sum(dm)/dlen) +% admu (vector) size munits x 1, average distortion in each unit +% tdmu (vector) size munits x 1, total distortion for each unit +% +% The distortion measure is defined as: +% 2 +% E = sum sum h(bmu(i),j) ||m(j) - x(i)|| +% i j +% +% where m(i) is the ith prototype vector of SOM, x(j) is the jth data +% vector, and h(.,.) is the neighborhood function. In case of fixed +% neighborhood and discreet data, the distortion measure can be +% interpreted as the energy function of the SOM. Note, though, that +% the learning rule that follows from the distortion measure is +% different from the SOM training rule, so SOM only minimizes the +% distortion measure approximately. +% +% If the 'prob' argument is given, the distortion measure can be +% interpreted as an expected quantization error when the neighborhood +% function values give the likelyhoods of accidentally assigning +% vector j to unit i. The normal quantization error is a special case +% of this with zero incorrect assignement likelihood. +% +% NOTE: when calculating BMUs and distances, the mask of the given +% map is used. +% +% See also SOM_QUALITY, SOM_BMUS, SOM_HITS. + +% Reference: Kohonen, T., "Self-Organizing Map", 2nd ed., +% Springer-Verlag, Berlin, 1995, pp. 120-121. +% +% Graepel, T., Burger, M. and Obermayer, K., +% "Phase Transitions in Stochastic Self-Organizing Maps", +% Physical Review E, Vol 56, No 4, pp. 3876-3890 (1997). + +% Contributed to SOM Toolbox vs2, Feb 3rd, 2000 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 030200 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% input arguments +if nargin < 2, error('Not enough input arguments.'); end + +% map +M = sM.codebook; +munits = prod(sM.topol.msize); + +% data +if isstruct(D), D = D.data; end +[dlen dim] = size(D); + +% arg1, arg2 +rad = NaN; +normalize = 0; +if nargin>2, + if isnumeric(arg1), rad = arg1; + elseif ischar(arg1) & strcmp(arg1,'prob'), normalize = 0; + end +end +if nargin>3, + if isnumeric(arg2), rad = arg2; + elseif ischar(arg2) & strcmp(arg2,'prob'), normalize = 0; + end +end + +% neighborhood radius +if isempty(rad) | isnan(rad), + if ~isempty(sM.trainhist), rad = sM.trainhist(end).radius_fin; + else rad = 1; + end +end +if rad<eps, rad = eps; end + +% neighborhood +Ud = som_unit_dists(sM.topol); +switch sM.neigh, + case 'bubble', H = (Ud <= rad); + case 'gaussian', H = exp(-(Ud.^2)/(2*rad*rad)); + case 'cutgauss', H = exp(-(Ud.^2)/(2*rad*rad)) .* (Ud <= rad); + case 'ep', H = (1 - (Ud.^2)/rad) .* (Ud <= rad); +end +if normalize, + for i=1:munits, H(:,i) = H(:,i)/sum(H(:,i)); end +end + +% total distortion measure +mu_x_1 = ones(munits,1); +tdmu = zeros(munits,1); +hits = zeros(munits,1); +for i=1:dlen, + x = D(i,:); % data sample + known = ~isnan(x); % its known components + Dx = M(:,known) - x(mu_x_1,known); % each map unit minus the vector + dist2 = (Dx.^2)*sM.mask(known); % squared distances + [qerr bmu] = min(dist2); % find BMU + tdmu = tdmu + dist2.*H(:,bmu); % add to distortion measure + hits(bmu) = hits(bmu)+1; % add to hits +end + +% average distortion per unit +admu = tdmu; +ind = find(hits>0); +admu(ind) = admu(ind) ./ hits(ind); + +% average distortion measure +adm = sum(tdmu)/dlen; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_distortion3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,207 @@ +function [Err,sPropTotal,sPropMunits,sPropComps] = som_distortion3(sM,D,rad) + +%SOM_DISTORTION3 Map distortion measures. +% +% [sE,Err] = som_distortion3(sM,[D],[rad]); +% +% sE = som_distortion3(sM); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map struct +% [D] (matrix) a matrix, size dlen x dim +% (struct) data or map struct +% by default the map struct is used +% [rad] (scalar) neighborhood radius, looked from sM.trainhist +% by default, or = 1 if that has no valid values +% +% Err (matrix) size munits x dim x 3 +% distortion error elements (quantization error, +% neighborhood bias, and neighborhood variance) +% for each map unit and component +% sPropTotal (struct) .n = length of data +% .h = mean neighborhood function value +% .err = errors +% sPropMunits (struct) .Ni = hits per map unit +% .Hi = sum of neighborhood values for each map unit +% .Err = errors per map unit +% sPropComps (struct) .e1 = total squared distance to centroid +% .eq = total squared distance to BMU +% .Err = errors per component +% +% See also SOM_QUALITY. + +% Contributed to SOM Toolbox 2.0, January 3rd, 2002 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 030102 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% arguments + +% map +[munits dim] = size(sM.codebook); + +% neighborhood radius +if nargin<3, + if ~isempty(sM.trainhist), + rad = sM.trainhist(end).radius_fin; + else + rad = 1; + end +end +if rad<eps, rad = eps; end +if isempty(rad) | isnan(rad), rad = 1; end + +% neighborhood function +Ud = som_unit_dists(sM.topol); +switch sM.neigh, + case 'bubble', H = (Ud <= rad); + case 'gaussian', H = exp(-(Ud.^2)/(2*rad*rad)); + case 'cutgauss', H = exp(-(Ud.^2)/(2*rad*rad)) .* (Ud <= rad); + case 'ep', H = (1 - (Ud.^2)/rad) .* (Ud <= rad); +end +Hi = sum(H,2); + +% data +if nargin<2, D = sM.codebook; end +if isstruct(D), D = D.data; end +[dlen dim] = size(D); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% quality measures + +% find Voronoi sets, and calculate their properties + +[bmus,qerr] = som_bmus(sM,D); +M = sM.codebook; +Vn = M; +Vm = M; +Ni = zeros(munits,dim); +for i=1:munits, + inds = find(bmus==i); + Ni(i,:) = sum(isfinite(D(inds,:)),1); % size of Voronoi set + if any(Ni(i,:)), Vn(i,:) = centroid(D(inds,:),M(i,:)); end % centroid of Voronoi set + Vm(i,:) = centroid(M,M(i,:),H(i,:)'); % centroid of neighborhood +end + +HN = repmat(Hi,1,dim).*Ni; + +%% distortion + +% quantization error (in each Voronoi set and for each component) + +Eqx = zeros(munits,dim); +Dx = (Vn(bmus,:) - D).^2; +Dx(isnan(Dx)) = 0; +for i = 1:dim, + Eqx(:,i) = full(sum(sparse(bmus,1:dlen,Dx(:,i),munits,dlen),2)); +end +Eqx = repmat(Hi,1,dim).*Eqx; + +% bias in neighborhood (in each Voronoi set / component) + +Enb = (Vn-Vm).^2; +Enb = HN.*Enb; + +% variance in neighborhood (in each Voronoi set / component) + +Env = zeros(munits,dim); +for i=1:munits, Env(i,:) = H(i,:)*(M-Vm(i*ones(munits,1),:)).^2; end +Env = Ni.*Env; + +% total distortion (in each Voronoi set / component) + +Ed = Eqx + Enb + Env; + +%% other error measures + +% squared quantization error (to data centroid) + +me = centroid(D,mean(M)); +Dx = D - me(ones(dlen,1),:); +Dx(isnan(Dx)) = 0; +e1 = sum(Dx.^2,1); + +% squared quantization error (to map units) + +Dx = D - M(bmus,:); +Dx(isnan(Dx)) = 0; +eq = sum(Dx.^2,1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% output + +% distortion error matrix + +Err = zeros(munits,dim,5); +Err(:,:,1) = Eqx; +Err(:,:,2) = Enb; +Err(:,:,3) = Env; + +% total errors + +sPropTotal = struct('n',sum(Ni),'h',mean(Hi),'e1',sum(e1),'err',sum(sum(Err,2),1)); + +% properties of map units + +sPropMunits = struct('Ni',[],'Hi',[],'Err',[]); +sPropMunits.Ni = Ni; +sPropMunits.Hi = Hi; +sPropMunits.Err = squeeze(sum(Err,2)); + +% properties of components + +sPropComps = struct('Err',[],'e1',[],'eq',[]); +sPropComps.Err = squeeze(sum(Err,1)); +sPropComps.e1 = e1; +sPropComps.eq = eq; + + +return; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55 +%% subfunctions + +function v = centroid(D,default,weights) + + [n dim] = size(D); + I = sparse(isnan(D)); + D(I) = 0; + + if nargin==3, + W = weights(:,ones(1,dim)); + W(I) = 0; + D = D.*W; + nn = sum(W,1); + else + nn = n-sum(I,1); + end + + c = sum(D,1); + v = default; + i = find(nn>0); + v(i) = c(i)./nn(i); + + return; + + +function vis + + figure + som_show(sM,'color',{Hi,'Hi'},'color',{Ni,'hits'},... + 'color',{Ed,'distortion'},'color',{Eqx,'qxerror'},... + 'color',{Enb,'N-bias'},'color',{Env,'N-Var'}); + + ed = Eqx + Enb + Env; + i = find(ed>0); + eqx = 0*ed; eqx(i) = Eqx(i)./ed(i); + enb = 0*ed; enb(i) = Enb(i)./ed(i); + env = 0*ed; env(i) = Env(i)./ed(i); + + figure + som_show(sM,'color',Hi,'color',Ni,'color',Ed,... + 'color',eqx,'color',enb,'color',env); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_divide.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,96 @@ +function [V,I]=som_divide(sMap, D, inds, mode) + +%SOM_DIVIDE Divides a dataset according to a given map. +% +% [V,I]=som_divide(sMap, sData, [inds], [mode]) +% +% ARGUMENTS ([]'s are optional) +% +% sMap (struct or matrix) map struct or codebook (size munits x dim) +% sData (struct or matrix) data struct or matrix (size N x dim ) +% [inds] From which map units should the local data sets +% be constructed. Interpretation depends on mode +% argument. By default [1:munits]. +% 'class': (vector) munits x 1 matrix of class numbers +% 'index': (vector) K x 1 vector of map node indexes +% 'index': (matrix) K x k matrix of map node subscripts +% [mode] (string) 'index' or 'class', if inds is a vector of length +% munits, default is 'class', otherwise 'index'. +% RETURNS +% +% If mode == 'index' +% V (matrix) data vectors hitting the specified nodes (size K x dim) +% I (vector) corresponding data row indexes (size K x 1) +% +% If mode == 'class' (this can be used after using som_select) +% V (cell array) V{K} includes vectors whose BMU has class number +% K in the input matrix 'coord'. Note that +% values of K below 1 are ignored. +% I (cell array) corresponding data indexes in the cell array +% +% NOTE: if the same node is specified multiple times, only one +% set of hits is returned. +% +% See also SOM_BMU, SOM_HITS, SOM_SELECT. + +% Version 1.0beta 260997 Johan +% Version 2.0beta 230300 juuso + +% Contributed to SOM Toolbox vs2, Mar 23rd, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +%%%% Init & Check %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(0, 4, nargin)) % check if no. of input args is correct + +% map +if isstruct(sMap), + msize = sMap.topol.msize; + dim = size(sMap.codebook,2); +else + msize = [size(sMap,1) 1]; + dim = size(sMap,2); +end +munits = prod(msize); + +% data +if isstruct(D), D=D.data; end + +% inds +if nargin<3, inds = 1:munits; end +isvec = prod(size(inds))==length(inds); + +% mode +if nargin<4, + if isvec & length(inds)==munits, + mode = 'class'; + else + mode = 'index'; + end +end + +%%% Action & Build output according to the mode string output + +if ~isvec, inds = som_sub2ind(msize,inds); end + +bmus=som_bmus(sMap,D); + +switch mode + case 'index' + I=find(ismember(bmus,inds)); + V=D(I,:); + case 'class' + K=max(inds); % classes + V = cell(K,1); + I = cell(K,1); + for i=1:K, + N_ind=find(inds == i); % indexes of the units of class i + I{i}=find(ismember(bmus,N_ind)); % data indexes + V{i}=D(I{i},:); + end +end + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_dmat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function dmat = som_dmat(sM,Ne,mode) + +%SOM_DMAT Find distance to neighbors for each map unit. +% +% dmat = som_dmat(sM,[Ne],[mode]) +% +% Input and output arguments ([]'s are optional): +% sM (struct) map or data struct +% (matrix) data matrix, size n x dim +% [Ne] (matrix) neighborhood connections matrix +% (string) 'Nk' (on map) or 'kNN' (any vector set) +% where k = some number, e.g. 'N1' or '10NN' +% (empty) use default +% [mode] (string) 'min', 'median', 'mean', 'max', or +% some arbitrary scalar function of +% a set of vectors +% +% dmat (vector) size n x 1, distance associated with each vector +% +% See also KMEANS_CLUSTERS, SOM_CLLINKAGE, SOM_CLSTRUCT. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 220800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% map +if isstruct(sM), + switch sM.type, + case 'som_map', M = sM.codebook; mask = sM.mask; + case 'som_data', M = sM.data; mask = ones(size(M,2),1); + end +else + M = sM; mask = ones(size(M,2),1); +end +[n dim] = size(M); + +% neighborhoods +if nargin<2 | isempty(Ne), Ne = som_neighbors(sM); +elseif ischar(Ne), Ne = som_neighbors(sM,Ne); +end +l = size(Ne,1); Ne([0:l-1]*l+[1:l]) = 0; % set diagonal elements = 0 + +% mode +if nargin<3 | isempty(mode), mode = 'median'; end +calc = sprintf('%s(x)',mode); + +% distances +dmat = zeros(n,1); +for i=1:n, + ne = find(Ne(i,:)); + if any(ne), + [dummy,x] = som_bmus(M(ne,:),M(i,:),[1:length(ne)],mask); + dmat(i) = eval(calc); + else + dmat(i) = NaN; + end +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_dmatclusters.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +function [base,seed] = som_dmatclusters(sM,linkage,neigh,ignore) + +% SOM_DMATCLUSTERS Cluster map based on neighbor distance matrix. +% +% base = som_dmatclusters(sM,linkage,neigh,ignore) +% +% sM (struct) map or data struct +% (matrix) data matrix, size n x dim +% [linkage] (string) 'closest', 'single', 'average', 'complete', +% 'centroid', 'ward', and 'neighf' (last for SOM only) +% default is 'centroid' +% [neigh] (string) 'kNN' or 'Nk' (which is valid for a SOM only) +% for example '6NN' or 'N1' +% default is '10NN' for a data set and 'N1' for SOM +% (matrix) 0/1 matrix of size size n x n, 1=connection exists +% [ignore] (vector) indeces of vectors to be ignored in the spreading +% phase, empty vector by default +% +% base (vector) size n x 1, cluster indeces (1...c) +% seed (vector) size c x 1, indeces of seed units for the clusters +% +% See also SOM_NEIGHBORS, KMEANS_CLUSTERS, SOM_DMATMINIMA. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +if nargin<2 | isempty(linkage), linkage = 'centroid'; end + +if nargin<3 | isempty(neigh), + if isstruct(sM) & strcmp(sM.type,'som_map'), + neigh = 'N1'; + else + neigh = '10NN'; + end +end + +if nargin<4, ignore = []; end +n = size(sM.codebook,1); + +% neighborhoods +if ischar(neigh), + Ne = som_neighbors(sM,neigh); +else + Ne = neigh; +end + +% find seed points +seed = som_dmatminima(sM,[],Ne); + +% make partition +base = zeros(n,1); +base(seed) = 1:length(seed); +if any(ignore), base(ignore) = NaN; end +base = som_clspread(sM,base,linkage,Ne,0); + +% assign the ignored units, too +base(isnan(base)) = 0; +if any(base==0), base = som_clspread(sM,base,linkage,Ne,0); end + +return; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_dmatminima.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +function minima = som_dmatminima(sM,U,Ne) + +%SOM_DMATMINIMA Find clusters based on local minima of U-matrix. +% +% minima = som_dmatminima(sM,[U],[Ne]) +% +% Input and output arguments ([]'s are optional): +% sM (struct) map struct +% U (matrix) the distance matrix from which minima is +% searched from +% size msize(1) x ... x msize(end) or +% 2*msize(1)-1 x 2*msize(2)-1 or +% munits x 1 +% Ne (matrix) neighborhood connections matrix +% +% minima (vector) indeces of the map units where locla minima of +% of U-matrix (or other distance matrix occured) +% +% See also KMEANS_CLUSTERS, SOM_CLLINKAGE, SOM_CLSTRUCT. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 220800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% map +if isstruct(sM), + switch sM.type, + case 'som_map', M = sM.codebook; mask = sM.mask; + case 'som_data', M = sM.data; mask = ones(size(M,2),1); + end +else + M = sM; mask = ones(size(M,2),1); +end +[munits dim] = size(M); + +% distances between map units +if nargin<2, U = []; end + +% neighborhoods +if nargin<3, Ne = som_neighbors(sM); end + +% distance matrix +if nargin<2 | isempty(U), U = som_dmat(sM,Ne,'median'); end +if prod(size(U))>munits, U = U(1:2:size(U,1),1:2:size(U,2)); end +U = U(:); +if length(U) ~= munits, error('Distance matrix has incorrect size.'); end + +% find local minima +minima = []; +for i=1:munits, + ne = find(Ne(i,:)); + if all(U(i)<=U(ne)) & ~anycommon(ne,minima), minima(end+1)=i; end +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function t = anycommon(i1,i2) + if isempty(i1) | isempty(i2), t = 0; + else + m = max(max(i1),max(i2)); + t = any(sparse(i1,1,1,m,1) & sparse(i2,1,1,m,1)); + end + return; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_dreval.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +function [sig,cm,truex,truey] = som_dreval(sR,D,sigmea,inds1,inds2,andor) + +% SOM_DREVAL Evaluate the significance of the given descriptive rule. +% +% [sig,Cm,truex,truey] = som_dreval(cR,D,sigmea,[inds1],[inds2],[andor]) +% +% sR (struct) a rule struct, or an array of rule structs +% D (matrix) the data, of size [dlen x nr] +% sigmea (string) significance measure ('accuracy','accuracyI','mutuconf'), +% see definitions below +% [inds1] (vector) indeces belonging to the group +% (by default: the whole data set) +% [inds2] (vector) indeces belonging to the contrast group +% (by default: the rest of the data set) +% [andor] (string) 'and' or 'or': which conjunction operator to use +% to join the rules for each variable +% +% sig (scalar) significance of the rule +% cm (vector) length 4, vectorized confusion matrix ([a,c,b,d]: see below) +% truex (vector) binary vector indicating for each item in the +% group whether it was true or not +% truey (vector) binary vector indicating for each item in the +% contrast group whether it was true or not +% +% Descriptive rule significance is measured as the match between the +% given groups (inds1 = G1, inds2 = G2) and the rule being true or false. +% +% G1 G2 +% --------------- accuracy = (a+d) / (a+b+c+d) +% true | a | b | +% |-------------- mutuconf = a*a / ((a+b)(a+c)) +% false | c | d | +% --------------- accuracyI = a / (a+b+c) +% +% See also SOM_DRSIGNIF, SOM_DRMAKE. + +% Contributed to SOM Toolbox 2.0, March 4th, 2002 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 040302 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% input arguments +if isstruct(D), + switch D.type, + case 'som_data', D = D.data; + case 'som_map', D = D.codebook; + end +end +[dlen,dim] = size(D); +if nargin<4, inds1 = 1:dlen; end +if nargin<5, i = ones(dlen,1); i(inds1) = 0; inds2 = find(i); end +if nargin<6, andor = 'and'; end + +% initialize +nr = length(sR); +X = D(inds1,:); +Y = D(inds2,:); +nx = size(X,1); +ny = size(Y,1); +truex = ones(nx,1); +truey = ones(ny,1); + +% go through the individual rules +for i=1:nr, + tx = (X(:,i)>=sR(i).low & X(:,i)<sR(i).high); + tx(isnan(X(:,i))) = sR(i).nanis; + + ty = (Y(:,i)>=sR(i).low & Y(:,i)<sR(i).high); + ty(isnan(Y(:,i))) = sR(i).nanis; + + switch andor, + case 'and', truex = (truex & tx); truey = (truey & ty); + case 'or', truex = (truex | tx); truey = (truey | ty); + end +end + +% evaluate criteria +tix = sum(truex(isfinite(truex))); +tiy = sum(truey(isfinite(truey))); +cm = [tix,nx-tix,tiy,ny-tiy]; +sig = som_drsignif(sigmea,cm); + +return; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_drmake.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,167 @@ +function [sR,best,sig,Cm] = som_drmake(D,inds1,inds2,sigmea,nanis) + +% SOM_DRMAKE Make descriptive rules for given group within the given data. +% +% sR = som_drmake(D,[inds1],[inds2],[sigmea],[nanis]) +% +% D (struct) map or data struct +% (matrix) the data, of size [dlen x dim] +% [inds1] (vector) indeces belonging to the group +% (the whole data set by default) +% [inds2] (vector) indeces belonging to the contrast group +% (the rest of the data set by default) +% [sigmea] (string) significance measure: 'accuracy', +% 'mutuconf' (default), or 'accuracyI'. +% (See definitions below). +% [nanis] (scalar) value given for NaNs: 0 (=FALSE, default), +% 1 (=TRUE) or NaN (=ignored) +% +% sR (struct array) best rule for each component. Each +% struct has the following fields: +% .type (string) 'som_rule' +% .name (string) name of the component +% .low (scalar) the low end of the rule range +% .high (scalar) the high end of the rule range +% .nanis (scalar) how NaNs are handled: NaN, 0 or 1 +% +% best (vector) indeces of rules which make the best combined rule +% sig (vector) significance measure values for each rule, and for the combined rule +% Cm (matrix) A matrix of vectorized confusion matrices for each rule, +% and for the combined rule: [a, c, b, d] (see below). +% +% For each rule, such rules sR.low <= x < sR.high are found +% which optimize the given significance measure. The confusion +% matrix below between the given grouping (G: group - not G: contrast group) +% and rule (R: true or false) is used to determine the significance values: +% +% G not G +% --------------- accuracy = (a+d) / (a+b+c+d) +% true | a | b | +% |-------------- mutuconf = a*a / ((a+b)(a+c)) +% false | c | d | +% --------------- accuracyI = a / (a+b+c) +% +% See also SOM_DREVAL, SOM_DRTABLE. + +% Contributed to SOM Toolbox 2.0, January 7th, 2002 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 070102 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +if isstruct(D), + switch D.type, + case 'som_data', cn = D.comp_names; D = D.data; + case 'som_map', cn = D.comp_names; D = D.codebook; + end +else + cn = cell(size(D,2),1); + for i=1:size(D,2), cn{i} = sprintf('Variable%d',i); end +end + +[dlen,dim] = size(D); +if nargin<2 | isempty(inds1), inds1 = 1:dlen; end +if nargin<3 | isempty(inds2), i = ones(dlen,1); i(inds1) = 0; inds2 = find(i); end +if nargin<4, sigmea = 'mutuconf'; end +if nargin<5, nanis = 0; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +sig = zeros(dim+1,1); +Cm = zeros(dim+1,4); + +sR1tmp = struct('type','som_rule','name','','low',-Inf,'high',Inf,'nanis',nanis,'lowstr','','highstr',''); +sR = sR1tmp; + +% single variable rules +for i=1:dim, + + % bin edges + mi = min(D(:,i)); + ma = max(D(:,i)); + [histcount,bins] = hist([mi,ma],10); + if size(bins,1)>1, bins = bins'; end + edges = [-Inf, (bins(1:end-1)+bins(2:end))/2, Inf]; + + % find the rule for this variable + [low,high,s,cm] = onevar_descrule(D(inds1,i),D(inds2,i),sigmea,nanis,edges); + sR1 = sR1tmp; + sR1.name = cn{i}; + sR1.low = low; + sR1.high = high; + sR(i) = sR1; + sig(i) = s; + Cm(i,:) = cm; + +end + +% find combined rule +[dummy,order] = sort(-sig); +maxsig = sig(order(1)); bestcm = Cm(order(1),:); +best = order(1); +for i=2:dim, + com = [best, order(i)]; + [s,cm,truex,truey] = som_dreval(sR(com),D(:,com),sigmea,inds1,inds2,'and'); + if s>maxsig, best = com; maxsig = s; bestcm = cm; end +end +sig(end) = maxsig; +Cm(end,:) = cm; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55 +%% descriptive rules + +function [low,high,sig,cm] = onevar_descrule(x,y,sigmea,nanis,edges) + + % Given a set of bin edges, find the range of bins with best significance. + % + % x data values in cluster + % y data values not in cluster + % sigmea significance measure + % bins bin centers + % nanis how to handle NaNs + + % histogram counts + if isnan(nanis), x = x(~isnan(x)); y = y(~isnan(y)); end + [xcount,xbin] = histc(x,edges); + [ycount,ybin] = histc(y,edges); + xcount = xcount(1:end-1); + ycount = ycount(1:end-1); + xnan=sum(isnan(x)); + ynan=sum(isnan(y)); + + % find number of true items in both groups in all possible ranges + n = length(xcount); + V = zeros(n*(n+1)/2,4); + s1 = cumsum(xcount); + s2 = cumsum(xcount(end:-1:1)); s2 = s2(end:-1:1); + m = s1(end); + Tx = triu(s1(end)-m*log(exp(s1/m)*exp(s2/m)')+repmat(xcount',[n 1])+repmat(xcount,[1 n]),0); + s1 = cumsum(ycount); + s2 = cumsum(ycount(end:-1:1)); s2 = s2(end:-1:1); + Ty = triu(s1(end)-m*log(exp(s1/m)*exp(s2/m)')+repmat(ycount',[n 1])+repmat(ycount,[1 n]),0); + [i,j] = find(Tx+Ty); + k = sub2ind(size(Tx),i,j); + V = [i, j, Tx(k), Ty(k)]; + tix = V(:,3) + nanis*xnan; + tiy = V(:,4) + nanis*ynan; + + % select the best range + nix = length(x); + niy = length(y); + Cm = [tix,nix-tix,tiy,niy-tiy]; + [s,k] = max(som_drsignif(sigmea,Cm)); + + % output + low = edges(V(k,1)); + high = edges(V(k,2)+1); + sig = s; + cm = Cm(k,:); + + return; + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_drsignif.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,63 @@ +function sig = som_drsignif(sigmea,Cm) + +% SOM_DRSIGNIF Significance measure from confusion matrix between two clusters and a rule. +% +% sig = som_drsignif(sigmea,Cm) +% +% sigmea (string) significance measure: 'accuracy', +% 'mutuconf' (default), or 'accuracyI'. +% (See definitions below). +% Cn Vectorized confusion matrix, or a matrix of such vectors. +% (vector) [a, c, b, d] (see below) +% (matrix) [[a1,c1,b1,d1], ..., [an,cn,bn,dn]] +% +% sig (vector) length=n, significance values +% +% The confusion matrix Cm below between group (G) and contrast group (not G) +% and rule (true - false) is used to determine the significance values: +% +% G not G +% --------------- accuracy = (a+d) / (a+b+c+d) +% true | a | b | +% |-------------- mutuconf = a*a / ((a+b)(a+c)) +% false | c | d | +% --------------- accuracyI = a / (a+b+c) +% +% See also SOM_DREVAL, SOM_DRMAKE. + +% Contributed to SOM Toolbox 2.0, March 4th, 2002 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 040302 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +true_x = Cm(:,1); % x = in group +false_x = Cm(:,2); % false = rule is false +true_y = Cm(:,3); % true = rule is true +false_y = Cm(:,4); % y = not in group + +true_items = true_x + true_y; +x_items = true_x + false_x; +all_items = true_x + false_x + true_y + false_y; +true_or_x = x_items + true_items - true_x; + +switch sigmea, +case 'mutuconf', + % mutual confidence, or relevance (as defined in WSOM2001 paper) + sig = zeros(size(true_x)); + i = find(true_items>0 & x_items>0); + sig(i) = (true_x(i).^2) ./ (true_items(i).*x_items(i)); +case 'accuracy', + % accuracy + sig = (true_x + false_y) ./ all_items; +case 'accuracyI', + % accuracy such that false_y is left out of consideration + sig = true_x./true_or_x; +otherwise, + error(['Unrecognized significance measures: ' sigmea]); +end + +return;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_estimate_gmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +function [K,P] = som_estimate_gmm(sM, sD) + +%SOM_ESTIMATE_GMM Estimate a gaussian mixture model based on map. +% +% [K,P] = som_estimate_gmm(sM, sD) +% +% Input and output arguments: +% sM (struct) map struct +% sD (struct) data struct +% (matrix) size dlen x dim, the data to use when estimating +% the gaussian kernels +% +% K (matrix) size munits x dim, kernel width parametes for +% each map unit +% P (vector) size 1 x munits, a priori probability of each map unit +% +% See also SOM_PROBABILITY_GMM. + +% Reference: Alhoniemi, E., Himberg, J., Vesanto, J., +% "Probabilistic measures for responses of Self-Organizing Maps", +% Proceedings of Computational Intelligence Methods and +% Applications (CIMA), 1999, Rochester, N.Y., USA, pp. 286-289. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi +% Copyright (c) by Esa Alhoniemi +% http://www.cis.hut.fi/projects/somtoolbox/ + +% ecco 180298 juuso 050100 250400 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +[c, dim] = size(sM.codebook); +M = sM.codebook; + +if isstruct(sD), D = sD.data; else D = sD; end +dlen = length(D(:,1)); + +%%%%%%%%%%%%%%%%%%%%% +% compute hits & bmus + +[bmus, qerrs] = som_bmus(sM, D); +hits = zeros(1,c); +for i = 1:c, hits(i) = sum(bmus == i); end + +%%%%%%%%%%%%%%%%%%%% +% a priori + +% neighborhood kernel +r = sM.trainhist(end).radius_fin; % neighborhood radius +if isempty(r) | isnan(r), r=1; end +Ud = som_unit_dists(sM); +Ud = Ud.^2; +r = r^2; +if r==0, r=eps; end % to get rid of div-by-zero errors +switch sM.neigh, + case 'bubble', H = (Ud<=r); + case 'gaussian', H = exp(-Ud/(2*r)); + case 'cutgauss', H = exp(-Ud/(2*r)) .* (Ud<=r); + case 'ep', H = (1-Ud/r) .* (Ud<=r); +end + +% a priori prob. = hit histogram weighted by the neighborhood kernel +P = hits*H; +P = P/sum(P); + +%%%%%%%%%%%%%%%%%%%% +% kernel widths (& centers) + +K = ones(c, dim) * NaN; % kernel widths +for m = 1:c, + w = H(bmus,m); + w = w/sum(w); + for i = 1:dim, + d = (D(:,i) - M(m,i)).^2; % compute variance of ith + K(m,i) = w'*d; % variable of centroid m + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_eucdist2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ +function d=som_eucdist2(Data, Proto) + +%SOM_EUCDIST2 Calculates matrix of squared euclidean distances between set of vectors or map, data struct +% +% d=som_eucdist2(D, P) +% +% d=som_eucdist(sMap, sData); +% d=som_eucdist(sData, sMap); +% d=som_eucdist(sMap1, sMap2); +% d=som_eucdist(datamatrix1, datamatrix2); +% +% Input and output arguments ([]'s are optional): +% D (matrix) size Nxd +% (struct) map or data struct +% P (matrix) size Pxd +% (struct) map or data struct +% d (matrix) distance matrix of size NxP +% +% IMPORTANT +% +% * Calculates _squared_ euclidean distances +% * Observe that the mask in the map struct is not taken into account while +% calculating the euclidean distance +% +% See also KNN, PDIST. + +% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 291000 + +%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(Data); + if isfield(Data,'type') & ischar(Data.type), + ; + else + error('Invalid map/data struct?'); + end + switch Data.type + case 'som_map' + data=Data.codebook; + case 'som_data' + data=Data.data; + end +else + % is already a matrix + data=Data; +end + +% Take prototype vectors from prototype struct + +if isstruct(Proto), + + if isfield(Proto,'type') & ischar(Proto.type), + ; + else + error('Invalid map/data struct?'); + end + switch Proto.type + case 'som_map' + proto=Proto.codebook; + case 'som_data' + proto=Proto.data; + end +else + % is already a matrix + proto=Proto; +end + +% Check that inputs are matrices +if ~vis_valuetype(proto,{'nxm'}) | ~vis_valuetype(data,{'nxm'}), + error('Prototype or data input not valid.') +end + +% Record data&proto sizes and check their dims +[N_data dim_data]=size(data); +[N_proto dim_proto]=size(proto); +if dim_proto ~= dim_data, + error('Data and prototype vector dimension does not match.'); +end + +% Calculate euclidean distances between classifiees and prototypes +d=distance(data,proto); + +%%%% Classification %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function d=distance(X,Y); + +% Euclidean distance matrix between row vectors in X and Y + +U=~isnan(Y); Y(~U)=0; +V=~isnan(X); X(~V)=0; +d=abs(X.^2*U'+V*Y'.^2-2*X*Y');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_fillnans.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +function sD = som_fillnans(sD,sM,bmus) + +% SOM_FILLNANS Replaces NaNs in the data matrix with values from +% SOM prototypes. +% +% sD = som_fillnans(sD,sM, [bmus]) +% +% sD (struct) data struct +% (matrix) size dlen x dim +% sM (struct) data struct, with .data of size dlen x dim +% (matrix) size dlen x dim, a matrix from which +% the values are taken from directly +% (struct) map struct: replacement values are taken from +% sM.codebook(bmus,:) +% [bmus] (vector) BMU for each data vector (calculated if not specified) +% +% See also SOM_MAKE. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(sD), + [dlen dim] = size(sD.data); + nans = find(isnan(sD.data)); +else + [dlen dim] = size(sD); + nans = find(isnan(sD)); +end + +if nargin<3, + bmus = som_bmus(sM,sD); +end + +if isstruct(sM) & strcmp(sM.type,'som_map'), + sM = sM.codebook(bmus,:); +elseif isstruct(sM), + sM = sM.data(bmus,:); +else + sM = sM(bmus,:); +end +me = mean(sM); + +if any(size(sM) ~= [dlen dim]), + error('Invalid input arguments.') +end + +if isstruct(sD), + sD.data(nans) = sM(nans); +else + sD(nans) = sM(nans); +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_fuzzycolor.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,205 @@ +function [color,X]=som_fuzzycolor(sM,T,R,mode,initRGB,S) + +% SOM_FUZZYCOLOR Heuristic contraction projection/soft cluster color coding for SOM +% +% function [color,X]=som_fuzzycolor(map,[T],[R],[mode],[initRGB],[S]) +% +% sM (map struct) +% [T] (scalar) parameter that defines the speed of contraction +% T<1: slow contraction, T>1: fast contraction. Default: 1 +% [R] (scalar) number of rounds, default: 30 +% [mode] (string) 'lin' or 'exp', default: 'lin' +% [initRGB] (string) Strings accepted by SOM_COLORCODE, default: 'rgb2' +% [S] (matrix) MxM matrix a precalculated similarity matrix +% color (matrix) of size MxRx3 resulting color codes at each step +% X (matrix) of size MxRx2 coordiantes for projected unit weight vectors +% at each step of iteration. (Color code C is calculated using this +% projection.) +% +% The idea of the projection is to use a naive contraction model which +% pulls the units together. Units that are close to each other in the +% output space (clusters) contract faster into the same point in the +% projection. The original position for each unit is its location in +% the topological grid. +% +% This is an explorative tool to color code the map units so that +% similar units (in the sense of euclidean norm) have similar coloring +% (See also SOM_KMEANSCOLOR) The tool gives a series of color codings +% which start from an initial color coding (see SOM_COLORCODE) and +% show the how the fuzzy clustering process evolves. +% +% The speed of contraction is controlled by the input parameter T. If +% it is high the projection contracts more slowly and reveals more +% intermediate stages (hierarchy). A good value for T must be +% searched manually. It is probable that the default values do not +% yield good results. +% +% The conatrction process may be slow. In this case the mode can be +% set to 'exp' instead of 'lin', however, then the computing becomes +% heavier. +% +% EXAMPLE +% +% load iris; % or any other map struct sM +% [color]=som_fuzzycolor(sM,'lin',10); +% som_show(sM,'color',color); +% +% See also SOM_KMEANSCOLOR, SOM_COLORCODE, SOM_CLUSTERCOLOR +% +% REFERENCES +% +% Johan Himberg, "A SOM Based Cluster Visualization and Its +% Application for False Coloring", in Proceedings of International +% Joint Conference on Neural Networks (IJCNN2000)}, +% pp. 587--592,Vol. 3, 2000 +% +% Esa Alhoniemi, Johan Himberg, and Juha Vesanto, Probabilistic +% Measures for Responses of Self-Organizing Map Units, pp. 286--290, +% in Proceedings of the International ICSC Congress on Computational +% Intelligence Methods and Applications (CIMA '99)}, ICSC Academic +% Press}, 1999 +% +% Outline of the heuristic +% +% First a matrix D of squared pairwise euclidean distances +% D(i,j)=d(i,j)^2 between map weight vectors is calculated. This +% matrix is transformed into a similarity matrix S, +% s(i,j)=exp(-(D(i,j)/(T.^2*v)), where T is a free input parameter and +% v the variance of all elements of D v=var(D(:)). The matrix is +% further normalized so that all rows sum to one. The original +% topological coordinates X=som_unit_coords(sM) are successively +% averaged using this matrix. X(:,:,i)=S^i*X(:,:,1); As the process is +% actually a series of successive weighted averagings of the initial +% coordinates, all projected points eventually contract into one +% point. T is a user defined parameter that defines how fast the +% projection contracts into this center point. If T is too small, the +% process will end into the center point at once. +% +% In practise, we don't calculate powers of S, but compute +% +% X(:,:,i)=S.*X(:,:,i-1); % mode: 'lin' +% +% The contraction process may be slow if T is selected to be large, +% then for each step the similarity matrix is squared +% +% X(:,:,i)=S*X(:,:,1); S=S*S % mode: 'exp' +% +% The coloring is done using the function SOM_COLORCODE according to +% the projections in X, The coordinates are rescaled in order to +% achieve maximum color resolution. + +% Contributed to SOM Toolbox vs2, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Previously rownorm function normalized the rows of S erroneously +% into unit length, this major bug was corrected 14042003. Now the +% rownorm normalizes the rows to have unit sum as it should johan 14042003 + +%% Check input arguments + +if isstruct(sM), + if ~isfield(sM,'topol') + error('Topology field missing.'); + end + M=size(sM.codebook,1); +else + error('Requires a map struct.'); +end + +if nargin<2 | isempty(T), + T=1; +end +if ~vis_valuetype(T,{'1x1'}) + error('Input for T must be a scalar.'); +end + +if nargin<3 | isempty(R), + R=30; +end +if ~vis_valuetype(R,{'1x1'}) + error('Input for R must be a scalar.'); +end + +if nargin < 4 | isempty(mode), + mode='lin'; +end +if ~ischar(mode), + error('String input expected for mode.'); +else + mode=lower(mode); + switch mode + case {'lin','exp'} + ; + otherwise + error('Input for mode must be ''lin'' or ''exp''.'); + end +end + +if nargin < 5 | isempty(initRGB) + initRGB='rgb2'; +end + +if ischar(initRGB), + try + dummy=som_colorcode(sM,initRGB); + catch + error(['Color code ''' initRGB ''' not known, see SOM_COLORCODE.']); + end +else + error('Invalid color code string'); +end + +if nargin<6 | isempty(S), + S=fuzzysimilarity(sM,1./T); +end + +if ~vis_valuetype(S,{[M M]}), + error('Similarity matrix must be a MunitsxMunits matrix.') +end + +x = maxnorm(som_unit_coords(sM.topol.msize,sM.topol.lattice,'sheet')); + +x = x-repmat(mean(x),size(x,1),1); + +X(:,:,1)=x; +color(:,:,1)=som_colorcode(x,'rgb2',1); + +%%% Actions + +for i=1:R, + switch mode + case 'exp' + S=rownorm(S*S); + tmpX=S*X(:,:,1); + case 'lin' + tmpX=S*X(:,:,i); + end + X(:,:,i+1)=tmpX; + color(:,:,i+1)=som_colorcode(X(:,:,i+1),initRGB); +end + +color(isnan(color))=0; + +function r=fuzzysimilarity(sM,p) + % Calculate a "fuzzy response" similarity matrix + % sM: map + % p: sharpness factor + d=som_eucdist2(sM,sM); + v=std(sqrt(d(:))).^2; + r=rownorm(exp(-p^2*(d./v))); + r(~isfinite(r))=0; + return; + + +function X = rownorm(X) + + r = sum(X,2); + X = X ./ r(:,ones(size(X,2),1)); + return; + + +function X = maxnorm(X) + + for i=1:size(X,2), r = (max(X(:,i))-min(X(:,i))); if r, X(:,i) = X(:,i) / r; end, end + return;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_gapindex.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +function [t,r,Cd,S] = som_gapindex(sM, base, between) + +% SOM_GAPINDEX Gap clustering evaluation index. +% +% [t,r] = som_gapindex(sM, base, [between]) +% +% Input and output arguments ([]'s are optional): +% sM (struct) map struct +% base (vector) clusters indeces for each map unit, map units +% with index<=0 or NaN are not taken into account +% [between] (vector) indices of prototypes which are "between" clusters: +% the associated distances are doubled +% +% t (scalar) Gap index index for the clustering (=mean(r)) +% r (vector) maximum Gap index for each cluster (size max(base) x 1) +% +% See also KMEANS, KMEANS_CLUSTERS, SOM_GAPINDEX. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargin<3, between = find(isnan(base)); end + +nc = max(base); +cinds = cell(nc,1); +for i=1:nc, cinds{i} = find(base==i); end + +% distances between neighboring prototypes +Ne = som_neighbors(sM,'N1'); +Md = som_mdist(sM.codebook,2,[],Ne); +Md(Ne==0) = NaN; + +Md(between,:) = Md(between,:)*2; +Md(:,between) = Md(:,between)*2; +Md(between,between) = Md(between,between)/2; + +% dispersion in each cluster +S = zeros(nc,1); +for i=1:nc, + inds = setdiff(cinds{i},between); + if any(inds), + indist = Md(inds,inds); + for j=1:size(indist,1), indist(j,j) = NaN; end + indist = indist(isfinite(indist(:))); + if any(indist), S(i) = mean(indist); end + end +end + +% distances between clusters +Cd = zeros(nc,nc) + NaN; +for i=1:nc, + inds1 = cinds{i}; + for j=1:nc, + inds2 = cinds{j}; + od = Md(inds1,inds2); + od = od(isfinite(od(:))); + if any(od), Cd(i,j) = mean(od(:)); end + end +end + +% Gap index +R = NaN * zeros(nc); +for i = 1:nc + for j = i+1:nc + R(i,j) = (S(i) + S(j))/Cd(i,j); + R(j,i) = R(i,j); + end +end +r = max(R,[],2); + +t = mean(r(isfinite(r))); + +return;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_grid.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,771 @@ +function [S,m,l,t,s]=som_grid(varargin) + +%SOM_GRID Visualization of a SOM grid +% +% [sGrid,m,l,t,s]=som_grid(sGrid, ['argID', value, ...]) +% [sGrid,m,l,t,s]=som_grid(topol, ['argID', value, ...]) +% [sGrid,m,l,t,s]=som_grid(lattice, msize, ['argID', value, ...]) +% +% Input and output arguments ([]'s are optional) +% sGrid (struct) som_grid struct (see output arguments) +% topol (struct) map or topol struct for giving the topology +% (cell array) of form {'lattice', msize, ['shape']}. +% Default value for 'shape' is 'sheet'. +% lattice (string) 'hexa', 'rect' +% (matrix) size M x M, defines topological connections +% msize (vector) 1x2 vector defines the grid size, M=msize(1)*msize(2) +% ['argID',(string) Other arguments can be given as 'argID', value +% value] (varies) pairs. See list below for valid values. +% +% sGrid (struct) with fields S.msize, S.shape, S.lattice, S.coord, S.marker, +% S.markersize, S.markercolor, S.line, S.linewidth, S.linecolor, +% S.surf, S.label, S.labelsize, S.labelcolor +% m (matrix) handels to LINE objects (unit markers) +% l (matrix) handles to LINE objects (lines connecting the units) +% t (matrix) handles to TEXT objects (labels) +% s (scalar) handle to SURF object (surface between units) +% +% Here are the valid argument IDs (case insensitive) and +% associated values: +% 'Coord' Mx2 or Mx3 matrix of coordinates +% (default: according to lattice as in som_cplane) +% 'Marker' string 'o','+','x','*','v','^','<','>','h','s','d','p','.', +% 'none' or Mx1 cell or char array of these strings +% Default: 'o'. +% 'MarkerSize' scalar or Mx1 matrix of double. Default: 6. +% 'MarkerColor' ColorSpec or Mx3 matrix of RGB triples. Default: 'k'. +% 'Line' string '-',':','--' or '-.' or 'none'. Default: '-'. +% 'Surf' [], Mx1 or Mx3 matrix of RGB triples +% to define surface values. Default: [] = no surf. +% Note: shading is turned to 'interp'. +% 'LineWidth' scalar or MxM matrix, default: 0.5 +% 'LineColor' ColorSepc, MxMx3 matrix of RGB triples or a cell array +% of form {r g b} where r,g, and b are MxM +% (sparse) matrices of R,G, and B values +% 'Label' Mx1 char array, cell array of strings size MxL +% or [] to indicate no labels, default: [] = no labels. +% 'LabelSize' scalar +% 'LabelColor' ColorSpec or string 'none', default: 'g'. +% +% For more help, try 'type som_grid' or check out online documentation. +% See also SOM_CONNECTION, SOM_SHOW, SOM_CPLANE, SOM_SET, SCATTER, SCATTER3. + +%%%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_grid +% +% PURPOSE +% +% To visualize the SOM grid in various ways +% +% SYNTAX +% +% [sGrid,m,l,t,s]=som_grid(sGrid) +% [sGrid,m,l,t,s]=som_grid(sTopol) +% [sGrid,m,l,t,s]=som_grid(sMap) +% [sGrid,m,l,t,s]=som_grid({lattice, msize, [shape]}) +% [sGrid,m,l,t,s]=som_grid(lattice, msize) +% [sGrid,m,l,t,s]=som_grid(..., ['argID', value, ...]) +% +% DESCRIPTION +% +% The SOM can be defined as a set of units (neurons) and their +% topological relations. This function is used to visualize these in +% various ways. The units may be drawn using different markers and +% colors, in different sizes and in different locations in 2D or +% 3D. However the topological neighborhood is limited to be +% 2-dimensional. The connections between these units may be drawn using +% lines having different thicknesses and colors. Labeling text may be +% plotted on the units. It is possible also to draw a surface between +% the units. The surface coloring is either indexed (one value per +% unit) or fixed RGB (a 1x3 RGB triple per unit). +% +% REQUIRED INPUT ARGUMENTS +% +% Note: M is the number of map units. +% +% The first (or first two) argument may have various different types of values +% +% 1. sGrid (struct) som_grid struct (the output of this function) +% +% The struct initiates the visualization. The argID-value -pairs +% are used to alter the initiation. +% +% Following argument types may be used to give the topology for the grid +% +% 2. sTopol (struct) som_topol struct +% 3. sMap (struct) som_map struct (only topology matters) +% 4. {lattice, msize} or {lattice, msize, sheet} (cell array) +% - lattice must be 'hexa' or 'rect' +% - msize must be a 1x2 vector +% - shape (if specified) must be string 'sheet', 'cyl' or 'toroid' +% If shape is not given it is 'sheet' by default. +% 5. lattice (string or matrix) AND msize (1x2 vector) as two separate arguments +% - lattice may be string 'rect' or 'hexa' or a connection matrix +% (see SOM_CONNECTION) to define a free topology. This connection +% matrix is of size MxM and its element i,j (i<j) is set +% to 1 if there is a connection between units i and j, otherwise to +% zero. Shape is set to 'sheet' by default. Shape does not have any +% meaning if a free topology is specified, anyway. +% - msize must be a 1x2 vector +% +% In cases 2...5 the sGrid structure is initiated by default values +% which are set in SOM_SET. These include black markers 'o' (6pt), +% light gray conncection lines (graph edges), unit coordinates +% according to the lattice ('hexa','rect'), no labels, and no +% surface. +% +% OPTIONAL INPUT ARGUMENTS +% +% Note: M is the number of map units. +% +% Here is a list of the valid arguments IDs and the associated +% values (identifiers are case insensitive): +% +% 'Coord' Unit coordinates +% This defines the coordinates of the units. Default: the +% topological coordinates (calculated as in function +% SOM_VIS_COORDS and SOM_CPLANE). If the topology is free +% (lattice is a connection matrix) this argument is obligatory! +% (matrix) size Mx2 of 2D coordinates for each unit +% (matrix) size Mx3 of 3D coordinates for each unit +% +% 'Marker' Unit markers, default is 'o'. +% (string) 'o','+','x','*','v','^','<','>','h','s','d', 'p','.', or 'none' +% give the same marker for each unit. +% (cell array) of size Mx1 of previous strings gives individual +% markers for each unit. +% +% 'MarkerSize' Size (pt) of unit markers, default is 6 (pt). +% (scalar) gives the same size for every unit. +% (matrix) Mx1 gives an individual size for each unit marker. +% +% 'MarkerColor' Unit marker colors, default is 'k' +% (ColorSpec) gives the same color each unit. +% (matrix) Mx3 of RGB triples gives individual color for each unit +% Note that indexed coloring - like in SOM_CPLANE - is +% not possible. If indexed coloring is needed, you can +% use SOM_NORMCOLOR to calculate RGB colors that +% emulate indexed coloring. However, the colors for the +% units are fixed, so changing colormap will not +% change the colors. +% +% 'Line' Line type, default is '-'. +% (string) '-',':','--' or '-.' or 'none'. Only one linetype in +% grid is allowed. +% +% 'LineWidth' Width of the topological connection lines (edges) +% (scalar) gives the same width for each line. Default is 0.5. +% (matrix) MxM sparse (or full) matrix gives individual width for +% each connection. The element (i,j), i<j, gives the line width for +% connection between nodes i and j. (The sparse form is +% recommended for saving memory, a full matrix works as well, +% of course). Note that only the elements satisfying i<j +% matter - as the elememts for which j >= i are ignored in +% order to avoid ambiguous situations if the matrix would be +% non-symmetric. The "connections to oneself" is not drawn. +% +% Line width zero is valid and causes the line to disappear. +% +% 'LineColor' Color of connection lines, default is [0.9 0.9 0.9]. +% (ColorSpec) gives the same color for each line +% (matrix) MxMx3 matrix of RGB triples gives individual width for +% each connection. The element (i,j,:), i<j, gives the RGB triple for +% line between nodes i and j. +% (cell array) of three sparse (or full) matrices {r,g,b} where +% r(i,j), g(i,j) and b(i,j) gives the R,G, and B values in the RGB +% triple for the line between nodes i and j. (The motivation for this +% form is the fact that a 3D arrays can't use sparse format in +% Matlab version 5.1.) +% +% Note that only the elements satisfying i<j matter - the elememts +% for which j >= i are ignored in order to avoid ambiguous situations +% if the matrix was non-symmetric. The "connections to oneself" +% is not drawn. +% +% +% 'Label' Labels for units, default is []. +% (empty) [] means no labels. +% (char array) of size Mx1. Element (i,:) has the label for unit i. +% (cell array) of size MxL consisting of sets of labels. Element {i,:} +% contains the labeling for unit i. +% In case of multiple labels, the labels for one unit are shown +% in one column centered at that unit. +% +% 'LabelSize' Text size of labels (points), default is 10. +% (scalar) Default is 10. +% +% 'LabelColor' Color of labels, default is 'c' (cyan). +% (ColorSpec) gives the same color for each label string 'xor' +% sets the colors automatically so that they differ +% from the background (using Matlab's built-in xor-color feature.) +% +% 'Surf' Surface between nodes, default is []. +% (empty) [] gives no surface +% (vector) Mx1 gives an indexed interpolated color surface between +% units using the actual colormap. +% (matrix) Mx3 matrix of RGB triples gives a interpolated color surface +% between units using fixed RGB colors. +% +% Note that the interpolation is done using Matlab's built-in +% color interpolation for SURF objects. +% +% OUTPUT ARGUMENTS +% +% sGrid (struct) with fields S.msize, S.shape, S.lattice, S.coord, S.marker, +% S.markersize, S.markercolor, S.line, S.linewidth, S.linecolor, +% S.surf, S.label, S.labelsize, S.labelcolor +% +% m (matrix) handels to LINE objects (unit markers) +% +% l (matrix) handles to LINE objects (lines connecting the units) +% +% t (matrix) handles to TEXT objects (labels) +% +% s (scalar) handle to SURF object (surface between units) +% +% EXAMPLES +% +% % Make map of size 15x10 on random data: +% +% map=som_make(rand(1000,4),'msize',[15 10], 'lattice', 'hexa'); +% +% % Draw the grid using two frist varable values as coordinates +% % and store the sGrid struct in varable S: +% +% S=som_grid(map, 'coord', map.codebook(:,[1 2])) +% +% %Define some things: +% % +% % Create a cell array of size 150x1 that divides map in to two label classes +% % 'circles' and 'squares' +% +% L(1:75,1)='o'; L(76:150,1)='s'; L = cellstr(L); +% +% % Create a coloring according to the 3rd variable according to current +% % colormap: +% +% C = som_normcolor(map.codebook(:,3)); +% +% % Change the visualization: use black lines, unit markers in M and unit +% % color in C, and set unit size to 10: +% +% S=som_grid(S, 'linecolor', 'k', 'marker', L, 'MarkerColor',C, ... +% 'MarkerSize', 10); +% +% % Do a new visualization, use indexed color surface calcualted from the +% % first variable, coordinates according to the lattice (default) but +% % no markers nor lines: +% +% S=som_grid(map,'line','none','marker','none','surf',map.codebook(:,1)); +% +% % Set coordinates according to three last varables +% +% som_grid(S,'coord',map.codebook(:,2:4)); +% +% % Create a random connection matrix R1 and the usual hexagonal +% % neighborhood connection matrix R2: +% +% R1=sparse(rand(150,150)>0.9); +% R2=som_connection(map); +% +% % Show these connections. Note that coordinates _must_ now be given +% % explicitly: we form default topological coordinates using +% % som_unit_coords. +% +% som_grid(R1,map.topol.msize,'coord',som_unit_coords(map)); +% som_grid(R2,map.topol.msize,'coord',som_unit_coords(map)); +% +% % Show connections (R1 AND R2) +% som_grid(R2.*R2,map.topol.msize,'coord',som_unit_coords(map)); +% +% OBJECT TAGS +% +% No tags are set. +% +% SEE ALSO +% +% som_show The basic map visualization routine +% som_cplane The basic component plane visualization +% som_connection The basic topological connections +% scatter Scatter plots +% scatter3 3-dimensional scatter plots + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 061099 juuso 151199 310300 + +%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +True=1; False=0; % const. +m=[]; l=[]; t=[]; s=[]; % default values for outputs +Ref=som_set('som_grid'); % reference struct + +num_of_args=length(varargin); % numb. of varargins + +if num_of_args==0, + S=som_set('som_grid'); + return; +end + +switch class(varargin{1}) +case 'struct' + S=varargin{1}; + first_identifier=2; + if ~isfield(S,'type'), + error('Input struct is invalid: field ''type'' is missing.'); + end + switch S.type + case 'som_grid' + S=varargin{1}; + first_identifier=2; + case 'som_map' + Ref.lattice=S.topol.lattice; + Ref.msize=S.topol.msize; + Ref.shape=S.topol.shape; + S=Ref; + first_identifier=2; + case 'som_topol' + Ref.lattice=S.lattice; + Ref.msize=S.msize; + Ref.shape=S.shape; + S=Ref; + first_identifier=2; + otherwise + error('Input struct has to be of type som_grid, som_map or som_topol.'); + end +case 'cell' + S=varargin{1}; + first_identifier=2; + if vis_valuetype(S,{'topol_cell_no_shape'}), + Ref.lattice=S{1}; + Ref.msize=S{2}; + elseif vis_valuetype(S,{'topol_cell'}), + Ref.lattice=S{1}; + Ref.msize=S{2}; + Ref.shape=S{3}; + else + error(['The cell value for 1st argument has to be {lattice, msize}' ... + 'or {lattice, msize, shape}.']); + end + S=Ref; +case{'double','sparse','char'} + % Set defaults + S=Ref; + first_identifier=3; + if num_of_args<2, + error('Not enough input arguments.'); + end + S.lattice=varargin{1}; + S.msize=varargin{2}; +otherwise + error('Invalid input arguments!'); +end + +%% Check input args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +for i=first_identifier:2:num_of_args, + if ischar(varargin{i}) & isfield(Ref,lower(varargin{i})), + if i+1>num_of_args, + error('Invalid identifier-value pairs or wrong argument order.'); + else + S=setfield(S,lower(varargin{i}),varargin{i+1}); + end + elseif ischar(varargin{i}), + error(['Identifier ''' varargin{i} ''' is unknown.']); + else + error('Invalid identifier-value pairs or wrong argument order.'); + end +end + +% msize + +if ~vis_valuetype(S.msize,{'1x2'}), + error('msize has to be a 1x2 vector.'); +end +munits=prod(S.msize); + +% Default coordinates according to negihborhood + +if isempty(S.coord), + if ischar(S.lattice), + switch S.lattice, + case{'hexa','rect'} + S.coord=som_vis_coords(S.lattice,S.msize); + otherwise + error('String value for lattice must be ''hexa'' or ''rect''.'); + end + else + error('Lattice is not ''hexa'' or ''rect'': coordinates must be given.'); + end +end + +% connections + +type=class(S.lattice); +switch type +case {'sparse','double'} % free topology + fixedline=False; +case 'char' % default topologies (hexa,char) + switch S.lattice + case 'hexa' + hexa=True; + case 'rect' + hexa=False; + otherwise + error('Unknown lattice or neighborhood.'); + end + + % If topology is hexa/rect but linetype, color etc. is + % not constant, the topology is set to free + + if size(S.linewidth,1)>1 | size(S.linecolor,1)>1 | ... + iscell(S.linecolor) % matrix or cell = not constant + fixedline=False; + S.lattice=som_connection({S.lattice,S.msize,S.shape}); + else + fixedline=True; + end +end + +% Check coordinate matrix size and set dummy zeros to z-axis +% if 2D coordinates (always 3D plots!) + +if ~vis_valuetype(S.coord,{[munits 2],[munits 3]}), + error('Coordinate matrix has wrong size.'); +elseif size(S.coord,2)==2, + S.coord(:,3)=0; +end + +% Fixed marker size, color, type? + +if size(S.markersize,1)>1 | size(S.markercolor,1)>1 | size(S.marker,1)>1 + fixedmarker=False; +else + fixedmarker=True; +end + +% Check labels + +if ~vis_valuetype(S.label,{'chararray','2Dcellarray_of_char'}) ... + & ~isempty(S.label), + error('Labels should be in a char array or cell array of strings.'); +elseif ischar(S.label) + S.label=cellstr(S.label); +end + +if size(S.label,1) ~= munits & ~isempty(S.label), + error('Number of labels and map size do not match.'); +end + +% Check line width, marker size, marker color, +% label size label color and surf sizes&types: + +if ~vis_valuetype(S.linewidth,{[munits munits] [1 1]}), + error('LineWidth matrix value has wrong size or dimension.'); +elseif any(S.linewidth(:)<0), + error('All elements of LineWidth must be non-negative.'); +elseif ~vis_valuetype(S.markersize,{[munits 1] [1 1]}), + error('MarkerSize matrix value has wrong size or dimension.'); +elseif any(S.markersize(:)<0), + error('All elements of MarkerSize must be non-negative.'); +elseif ~vis_valuetype(S.markercolor,{'1x3rgb','colorstyle'}) & ... + ~vis_valuetype(S.markercolor,{[munits 3],'nx3rgb'},'all'), + error('MarkerColor should be a ColorSpec or Mx3 matrix of RGB triples.'); +elseif ~vis_valuetype(S.labelcolor,{'1x3rgb','colorstyle','xor'}), + error('LabelColor shoud be a ColorSpec or ''xor'' or ''none''.') +elseif ~vis_valuetype(S.labelsize,{'1x1'}) + error('LabelSize should be a scalar.'); +elseif ~isempty(S.surf) & ~vis_valuetype(S.surf,{[munits 1] [munits 3]}); + error('Surf matrix value has wrong size or dimension.'); +end + +% Check marker type & size + +if vis_valuetype(S.marker,{'cellcolumn_of_char'}) + % Don't bother to check the mareker strings in this case + % let the plot3 handle them; it returns quite understandable + % error messages, anyway + + if ~size(S.marker) == [munits 1], + error(['Marker should be one of Matlab''s valid marker type,' ... + ' string ''none'' or a Mx1 cell array of these.']); + end +elseif ~vis_valuetype(S.marker,{'markerstyle','none'}), + error(['Marker should be one of Matlab''s valid marker type,' ... + ' string ''none'' or a Mx1 cell array of these.']); +end + +% Check line type & size: only one line style allowed + +if ~vis_valuetype(S.line,{'linestyle','none'}) + error(['Line should be a valid Matlab''s line style string or' ... + ' string ''none''.']); +end + +% Check line color + +if iscell(S.linecolor), + if ndims(S.linecolor) ~= 2 | any(size(S.linecolor) ~= [1 3]), + error('Cell input for LineColor should be of form {r,g,b}.') + elseif ~vis_valuetype(S.linecolor{1},{[munits munits],'nxn[0,1]'},'all')| ... + ~vis_valuetype(S.linecolor{2},{[munits munits],'nxn[0,1]'},'all')| ... + ~vis_valuetype(S.linecolor{3},{[munits munits],'nxn[0,1]'},'all'), + error(['In cell input {r,g,b} some matrix r,g or b is invalid: ' ... + 'Size must be MxM and values in interval [0,1].']); + end +elseif ~vis_valuetype(S.linecolor,{'colorstyle','1x3rgb'}) & ... + ~vis_valuetype(S.linecolor,{'nxnx3rgb', [munits munits 3]},'all'), + error('Invalid LineColor: see help text for valid values.'), +elseif vis_valuetype(S.linecolor, {'none'}), + error('LineColor ''none'' not allowed: set Line to ''none'' instead.'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +memhold=ishold; % take hold state +if ~memhold + cla; +end +hold on; + +% Set surf if it exist + +if ~isempty(S.surf), + for i=1:3, + s(:,:,i)=reshape(S.coord(:,i),S.msize); + end + s(:,:,4:3+size(S.surf,2))=reshape(S.surf,[S.msize size(S.surf,2)]); + s=surf(s(:,:,1),s(:,:,2),s(:,:,3),s(:,:,4:end)); + set(s,'EdgeColor','none','Marker','none','FaceColor','interp'); +end + + +if fixedline, + % Line properties are fixed: draw fast, but + % if line is set to 'none' set empty handle ans skip + if strcmp(S.line,'none') + l={}; + else + p1=reshape(S.coord, [S.msize 3]); + p2=zeros(size(p1)-[0 1 0]); + p2(1:2:end,:,:)=p1(1:2:end,2:end,:); + p2(2:2:end,:,:)=p1(2:2:end,1:end-1,:); + + l{1}=plot3(p1(:,:,1), p1(:,:,2), p1(:,:,3), ... + 'Color', S.linecolor(1,:), ... + 'LineWidth', S.linewidth(1), ... + 'LineStyle', S.line); + l{2}=plot3(p1(:,:,1)', p1(:,:,2)', p1(:,:,3)', ... + 'Color', S.linecolor(1,:), ... + 'LineWidth', S.linewidth(1), ... + 'LineStyle', S.line); + if hexa, + l{3}=plot3(p2(:,:,1), p2(:,:,2), p2(:,:,3), ... + 'Color', S.linecolor(1,:), ... + 'LineWidth', S.linewidth(1), ... + 'LineStyle', S.line); + end + end + l=cat(1,l{:}); +else + % Variable properties: draw connection by connection + + [I,J,lw]=find(S.lattice); + x=[S.coord(I,1)'; S.coord(J,1)']; + y=[S.coord(I,2)'; S.coord(J,2)']; + z=[S.coord(I,3)'; S.coord(J,3)']; + if S.linewidth(1)==0, + linewidth=0.5; + else + linewidth=S.linewidth(1); + end + if ndims(S.linecolor) ~= 3 + if isstr(S.linecolor) + l=plot3(x, y, z, ... + 'Color', S.linecolor, ... + 'LineWidth', linewidth, ... + 'LineStyle',S.line); + else + if iscell(S.linecolor) + lcolor=[S.linecolor{1}(1,1) S.linecolor{2}(1,1) S.linecolor{3}(1,1)]; + l=plot3(x, y, z, ... + 'Color', lcolor, ... + 'LineWidth', linewidth, ... + 'LineStyle',S.line); + else + l=plot3(x, y, z, ... + 'Color', S.linecolor(1,:), ... + 'LineWidth', linewidth, ... + 'LineStyle',S.line); + end + end + else + l=plot3(x, y, z, ... + 'Color', S.linecolor(1,1,:), ... + 'LineWidth', linewidth, ... + 'LineStyle',S.line); + end +end + +if fixedmarker, + + % If marker is set to 'none' skip and set empty handle + if strcmp(S.marker,'none') + m=[]; + else + % Fixed markers: draw all in one command + + m=plot3(S.coord(:,1), S.coord(:,2), S.coord(:,3), ... + 'LineStyle', 'none', ... + 'Marker', S.marker, ... + 'MarkerSize', S.markersize(1), ... + 'MarkerFaceColor', S.markercolor(1,:), ... + 'MarkerEdgeColor', S.markercolor(1,:)); + end +else + % Variable marker properties: draw marker by marker + + x=[S.coord(:,1)'; S.coord(:,1)']; + y=[S.coord(:,2)'; S.coord(:,2)']; + z=[S.coord(:,3)'; S.coord(:,3)']; + if iscell(S.marker) + marker=S.marker{1}; + else + marker=S.marker(1); + end + sz=max(S.markersize(1),0.1); + m=plot3(x, y, z, ... + 'LineStyle', 'none', ... + 'Marker', marker, ... + 'MarkerSize', sz, ... + 'MarkerFaceColor', S.markercolor(1,:), ... + 'MarkerEdgeColor', S.markercolor(1,:)); +end + +L=length(l); +n=munits; + +%%% Set variable properties %%% + +% Line width + +if length(S.linewidth)>1 + lwidth=diag(S.linewidth(I,J)); + + % Handle zero width + iszero=(lwidth == 0);lwidth(iszero)=0.5; + for i=1:length(l), + set(l(i),'LineWidth', lwidth(i)); + end + if ~isempty(iszero), % zero width + set(l(iszero),'Visible','off'); + end +end + +% Line color + +if size(S.linecolor,1)>1 | iscell(S.linecolor) + if length(size(S.linecolor)) == 3 | iscell(S.linecolor) + if ~iscell(S.linecolor) + for i=1:L + set(l(i),'Color',S.linecolor(I(i),J(i),:)); + end + else + for i=1:L + lcolor=[S.linecolor{1}(I(i),J(i)),... + S.linecolor{2}(I(i),J(i)),... + S.linecolor{3}(I(i),J(i))]; + set(l(i),'Color',lcolor); + end + end + else + for i=1:L, + set(l(i),'Color', S.linecolor(I(i),:)); + end + end +end + +% Marker size + +if length(S.markersize)>1 + % handle zero size + iszero=find(~S.markersize); + S.markersize(iszero)=1; + for i=1:n, + set(m(i),'MarkerSize', S.markersize(i)); + end + if ~isempty(iszero), % zero size + set(m(iszero),'Visible','off'); + end +end + +% Marker type + +if size(S.marker,1)>1 + S.marker=char(S.marker); + for i=1:n, + set(m(i),'Marker', S.marker(i)); + end +end + +% Marker color + +if size(S.markercolor,1)>1 + for i=1:n, + set(m(i),'MarkerFaceColor', S.markercolor(i,:), ... + 'MarkerEdgeColor', S.markercolor(i,:)); + end +end + +% Set labels if they exist + +if ~isempty(S.label) + if vis_valuetype(S.labelcolor,{'xor'}), + S.labelcolor='g'; + XOR=1; + else + XOR=0; + end + if vis_valuetype(S.labelcolor,{'none'}), + S.labelcolor='g'; + VIS = 1; + else + VIS = 0; + end + for i=1:size(S.label,1), + L=cat(1,S.label(i,:)); + for j=length(L):-1:1, + if isempty(L{j}), + L=L(1:end-1); + end + end + + if isempty(L), + L=''; + end + t(i)=text(S.coord(i,1), S.coord(i,2), S.coord(i,3), L,... + 'FontSize', S.labelsize, 'Color',S.labelcolor, ... + 'HorizontalAlignment', 'center'); + end + if XOR + set(t,'EraseMode','xor'); + end + if VIS + set(t,'Visible','off'); + end +else + t=[]; +end + +%% Set hold state + +if ~memhold, + hold off; +end + +if nargout==0, + clear S m l t s; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_gui.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3130 @@ +function som_gui(varargin) + +%SOM_GUI A GUI for initialization and training of SOM. +% +% som_gui([sD]) +% +% som_gui +% som_gui(sD) +% +% Input and output arguments ([]'s are optional) +% [sD] (struct) SOM data struct +% (matrix) a data matrix, size dlen x dim +% +% Actually, there are more arguments the function takes, but +% they are for internal action of the function only. DO NOT use +% them. +% +% For a more throughout description, see the online documentation. +% See also PREPROCESS. + +%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% IN FILES: som_gui.html,browsewin.jpg,wspace.jpg,loadgui.jpg,initgui.jpg,questdlg.jpg,paragui.jpg,mwindow.jpg,visgui.gif,reload.gif,savemap.gif,browse.gif +% + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Mika Pollari +% Copyright (c) by Mika Pollari and SOM Toolbox Team +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Mika Pollari 31.1.2000 vs 1.1 + +global NEWMAP NEWST MAPSAVED MAP DATA LOAD_NAME LOAD_DATA; +global SAVEMAP ALGORITHM HANDLE2 STOPOLINIT INIT_TYPE; +global STRAIN1 STRAIN2 SOTHERS; + +if nargin == 0 + main_gui; + action = 'dummy'; +elseif nargin == 1 + temp = varargin{1}; + if isstruct(temp), + DATA = temp; + main_gui; + action = 'input_data'; + elseif isnumeric(temp), + DATA = som_data_struct(temp); + main_gui; + action = 'input_data'; + else + action = temp; + end +end + + +switch(action) + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% LOAD %%%%%%%%%%%%%%%%%%%%%%%%%% + case 'load_data' + loadgui3; %%% Activates load GUI + case 'workspace' + workspace; %%% Workspace selected + case 'file' + file; %%% File Selected + case 'file_select' + file_select; + case 'missing' + Handle = findobj(gcf,'Tag','Checkbox1'); + set(Handle,'Value',1); + case 'load_ok' %%% <Load OK> pushed + load_ok; + case 'input_data' %%% GUI activated with data as arg1 + input_data; %%% eg. som_gui(data) + + case 'browse' %%% Activates Browse GUI + browse; %%% Browse files or workspace variables + + case 'works_ok' %%% <OK> pushed in (workspace) browse GUI + works_ok; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%% Initialization %%%%%%%%%%%%%%%%%%%%%%%%%%%% + + case 'def_initialization' %%% Finds default initialization ... + def_initialization; %%% parameters + + case 'change_initialization' %%% Activates change (init) parameters GUI + change_initialization; + + case 'change_initialization_ok'%%% Set new init. parameters + change_initialization_ok; + + case 'change_initialization_cancel' + close(gcf); + return; + + case 'map_size' %%% Checks that 'map_size' is given in correct form + map_size; + + case 'munits' %%% Checks that 'munits' is given in correct form + munits; + + case 'init' %%% Initialize Map + init; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%% Train %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + case 'def_values_others' + def_values_others; + + case 'def_values_train' + STRAIN1 = som_train_struct('algorithm',ALGORITHM,'phase','rough','data',DATA); + STRAIN2 = som_train_struct('previous',STRAIN1); + + case 'fill_fields' %%% Fill text fields in GUI + fill_fields; + + case 'def_train' %%% Train Map + def_train; + + case 'change_def' %%% Change default training parameters + change_def; %%% Activate GUI + + case 'fill_new_defaults' + fill_new_defaults; + + case 'set_batch_mask' + set_batch_mask; + + case 'set_new_parameters' + set_new_parameters; + + case 'only_finetune' %%% Train only once with finetune parameters + only_finetune; + +%%%%%%% Next function check correctnes of new training parameters. + + case 'check_rough_radini' + check_rough_radini; + case 'check_fine_radini' + check_fine_radini; + case 'check_rough_radfin' + check_rough_radfin; + case 'check_fine_radfin' + check_fine_radfin; + case 'check_rough_alphaini' + check_rough_alphaini; + case 'check_fine_alphaini' + check_fine_alphaini; + case 'check_rough_trainlen' + check_rough_trainlen; + case 'check_fine_trainlen' + check_fine_trainlen; + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%% Save Map %%%%%%%%%%%%%%%%%%%%%% + + case 'savemap' %%% Save as <.cod> file + savemap; + case 'save_workspace' %%% Save in workspace + save_workspace; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%% Help & Info %%%%%%%%%%%%%%%%%%%%%%% + case 'help' + web file:///share/somtoolbox/vs2/html/som_GUI.html; + case 'helpwin' + helpwin1; + case 'helpwin2' + helpwin som_gui; + case 'data_info' + data_info; %%% Info about data + case 'map_info' %%% Info about map + map_info; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%% Other Functions %%%%%%%%%%%%%%%%%%%%%%% + + case 'preprocess' + preprocess_gui; %%%%% Call preprocess GUI + case 'visualize' + visualize; %%%%% Call visualization GUI + + case 'clear_all' %%%%% Clear all filds + clear_all; + + case 'close' + close_fig; %%%%% Close active GUI + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%% END OF SWITCH-STATEMENT %%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + + + + + + + + + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%% (SUB) FUNCTIONS + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% LOAD SECTION STARTS %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [] = workspace() +Handle = findobj(gcbf,'Tag','Radiobutton2'); +Value = get(Handle,'Value'); +HandleTemp = findobj(gcbf,'Tag','Radiobutton1'); +if Value == 1 + set(HandleTemp,'Value',0); + HandleBar = findobj(gcbf,'Tag','PopupMenu1'); + set(HandleBar,'Enable','off'); + set(HandleBar,'Visible','off'); + Handle3 = findobj(gcbf,'Tag','StaticText3'); + set(Handle3,'Visible','off'); + Handle3 = findobj(gcbf,'Tag','Checkbox1'); + set(Handle3,'Visible','off'); + Handle3 = findobj(gcbf,'Tag','EditText3'); + set(Handle3,'Visible','off'); + Handle = findobj(gcbf,'Tag','EditText2'); + set(Handle,'String',''); +end + + + +function [] = file() +Handle = findobj(gcbf,'Tag','Radiobutton1'); +Value = get(Handle,'Value'); +HandleTemp = findobj(gcbf,'Tag','Radiobutton2'); +if Value == 1 + set(HandleTemp,'Value',0); + HandleBar = findobj(gcbf,'Tag','PopupMenu1'); + set(HandleBar,'Enable','on'); + set(HandleBar,'Visible','on'); + Handle3 = findobj(gcbf,'Tag','StaticText3'); + set(Handle3,'Visible','on'); + Handle3 = findobj(gcbf,'Tag','Checkbox1'); + set(Handle3,'Visible','on'); + Handle3 = findobj(gcbf,'Tag','EditText3'); + set(Handle3,'Visible','on'); + Handle = findobj(gcbf,'Tag','EditText1'); + set(Handle,'String',''); +end + +function [] = file_select() +Handle = findobj(gcbf,'Tag','PopupMenu1'); +temp = get(Handle,'String'); +val = get(Handle,'Value'); +Handle1 = findobj(gcbf,'Tag','Checkbox1'); +Handle2 = findobj(gcbf,'Tag','EditText3'); +if strcmp(temp{val},'dat file') + set(Handle2,'String','x'); + set(Handle1,'Enable','on'); + set(Handle2,'Enable','on'); + set(Handle1,'Visible','on'); + set(Handle2,'Visible','on'); + else + set(Handle1,'Value',0); + set(Handle1,'Enable','off'); + set(Handle2,'Enable','off'); + set(Handle1,'Visible','off'); + set(Handle2,'Visible','off'); +end + +function [] = load_ok() +global MAP DATA LOAD_DATA LOAD_NAME; +Handle1 = findobj(gcbf,'Tag','EditText1'); +Handle2 = findobj(gcbf,'Tag','EditText2'); +Name1 = get(Handle1,'String'); +Name2 = get(Handle2,'String'); +if isempty(Name1) & not(isempty(Name2)) + Handle = findobj(gcbf,'Tag','PopupMenu1') + type = get(Handle,'String'); + val = get(Handle,'Value'); + type = type{val}; + if strcmp(type,'mat file') + ltemp = 'load:::'; + ltemp = strcat(ltemp,Name2); + ltemp = strrep(ltemp,':::',' '); + evalin('base',ltemp); + DATA = evalin('base','sD'); + LOAD_DATA = evalin('base','sD.data'); + LOAD_NAME = evalin('base','sD.name'); + LOAD_NAME = strrep(LOAD_NAME,'.','_'); + load_labels = evalin('base','sD.labels'); + load_comp_names = evalin('base','sD.comp_names'); + DATA = som_data_struct(LOAD_DATA); + DATA.name = LOAD_NAME; + DATA.comp_names = load_comp_names; + DATA.labels = load_labels; + else + Handle = findobj(gcbf,'Tag','Checkbox1'); + value = get(Handle,'Value'); + if value == 0 + temp = 'som_read_data('''; + temp = strcat(temp,Name2,''');'); + else + Handle = findobj(gcbf,'Tag','EditText3'); + missing = get(Handle,'String'); + if not(isempty(missing)) + temp = 'som_read_data('''; + temp = strcat(temp,Name2,'''',',','''',missing,''');'); + else + temp = 'som_read_data('''; + temp = strcat(temp,Name2,''');'); + end + end + evalin('base',temp); + DATA = evalin('base','ans'); + name = DATA.name; + temp = findstr('/',name); + if not(isempty(temp)) + name = name(temp(end)+1:end); + end + name = strrep(name,'.','_'); + LOAD_NAME = name; + DATA.name = name; + end +elseif isempty(Name2) & not(isempty(Name1)) + LOAD_DATA = evalin('base',Name1); + if not(isstruct(LOAD_DATA)) + DATA = som_data_struct(LOAD_DATA); + LOAD_NAME = Name1; + DATA.name = Name1; + else + DATA = LOAD_DATA; + name = DATA.name; + temp = findstr('/',name); + if not(isempty(temp)) + name = name(temp(end)+1:end); + end + name = strrep(name,'.','_'); + LOAD_NAME = name; + DATA.name = name; + end +else + errmsg = {'Give name of data before loading'}; + errordlg(errmsg,'Empty data name!'); + return; +end +close(gcbf); +if not(isempty(MAP)) + clear MAP; + global MAP; + str1 = 'Map: <empty>'; + str2 = 'Train'; + Handle = findobj(gcf,'Tag','StaticText3'); + set(Handle,'String',str1); + Handle = findobj(gcf,'Tag','StaticText8'); + set(Handle,'String',str2); +end +temp = 'Data:'; +temp = strcat(temp,' <',LOAD_NAME,'>'); +Handle = findobj(gcf,'Tag','StaticText4'); +set(Handle,'String',temp); +som_gui('def_initialization'); +Handle = findobj(gcf,'Tag','Pushbutton2'); +set(Handle,'Enable','off'); +Handle = findobj(gcf,'Tag','Pushbutton4'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','Pushbutton9'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','Subuimenu2'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','&Help/InfoHelp windowuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','&Init&Trainuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','&Init&TrainInitialize1'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','Subuimenu1'); +set(Handle,'Enable','off'); %%%%%%???????? +Handle = findobj(gcf,'Tag','StaticText10'); +set(Handle,'String','Status <data loaded>'); + + + +function [] = input_data() +global DATA; +name = DATA.name; +newname = strrep(name,'.','_'); +DATA.name = newname; +temp = strcat('Data: <',newname,'>'); +Handle = findobj(gcf,'Tag','StaticText4'); +set(Handle,'String',temp); +som_gui('def_initialization'); +Handle = findobj(gcf,'Tag','Pushbutton2'); +set(Handle,'Enable','off'); +Handle = findobj(gcf,'Tag','Pushbutton4'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','Pushbutton9'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','Subuimenu2'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','&Help/InfoHelp windowuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','&Init&Trainuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','&Init&TrainInitialize1'); +set(Handle,'Enable','on'); +Handle = findobj(gcf,'Tag','Subuimenu1'); +set(Handle,'Enable','off'); %%%%%%???????? +Handle = findobj(gcf,'Tag','StaticText10'); +set(Handle,'String','Status <data loaded>'); + + +function [] = browse() +global HANDLE2; +HandleWorkspace = findobj(gcbf,'Tag','Radiobutton2'); +HandleFile = findobj(gcbf,'Tag','Radiobutton1'); +WorkspaceVal = get(HandleWorkspace,'Value'); +FileVal = get(HandleFile,'Value'); +if FileVal == 1 + Handle = findobj(gcbf,'Tag','PopupMenu1'); + str = get(Handle,'String'); + value = get(Handle,'Value'); + str = str{value}; + if strcmp(str,'mat file') + filtter = '*.mat'; + else + filtter = '*.dat*'; + end + [filename pathname] = uigetfile(filtter,'Load file.'); + temp = strcat(pathname,filename); + Handle = findobj(gcbf,'Tag','EditText2'); + set(Handle,'String',temp); +elseif WorkspaceVal == 1 + HANDLE2 = gcf; + works; + temp = evalin('base','who'); + index2 = 1; + names = ''; + for index = 1:length(temp) + if isnumeric(evalin('base',temp{index})) + test = size(evalin('base',temp{index})); + if test(1) ~= 1 & test(2) ~= 1 + names{index2} = temp{index}; + index2 = index2 + 1; + end + end + end + for index = 1:length(temp) + variable = evalin('base',temp{index}); + if isstruct(variable) + fnames = fieldnames(variable); + if size(fnames,1) == 6 & strcmp(fnames(1),'type') & strcmp(variable.type,'som_data') + names{index2} = temp{index}; + index2 = index2 + 1; + end + end + end + Handle = findobj(gcf,'Tag','Listbox1'); + %%%%%% if is empty string#%%% + set(Handle,'String',names); +else + errmsg = 'Select browse type: Workspace or file.'; + errordlg(errmsg,'Browse error!'); + return; +end + + +function [] = works_ok() +global HANDLE2; +Handle = findobj(gcbf,'Tag','Listbox1'); +temp = get(Handle,'String'); +val = get(Handle,'Value'); +data = temp{val}; +Handle = findobj(HANDLE2,'Tag','EditText1'); +set(Handle,'String',data); +close; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% END OF LOAD SECTION %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% START OF INITIALIZATION %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [] = def_initialization() +global DATA STOPOLINIT INIT_TYPE; +sTopol = som_topol_struct('data',DATA); +Handle = findobj(gcf,'Tag','StaticText5'); +temp = num2str(sTopol.msize); +temp = strcat('map size:',' [',temp,']'); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText6'); +set(Handle,'String','type: linear'); +Handle = findobj(gcf,'Tag','StaticText20'); +temp = strcat('lattice:',sTopol.lattice); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText21'); +temp = strcat('shape:',sTopol.shape); +set(Handle,'String',temp); +STOPOLINIT = sTopol; +INIT_TYPE = 'linear'; + +function [] = change_initialization() +global INIT_TYPE STOPOLINIT; +initialization2; +Handle = findobj(gcf,'Tag','PopupMenu1'); +temp = get(Handle,'String'); +val = loop(temp,INIT_TYPE); +set(Handle,'Value',val); +Handle = findobj(gcf,'Tag','PopupMenu2'); +temp = get(Handle,'String'); +val = loop(temp,STOPOLINIT.lattice); +set(Handle,'Value',val); +Handle = findobj(gcf,'Tag','PopupMenu3'); +temp = get(Handle,'String'); +val = loop(temp,STOPOLINIT.shape); +set(Handle,'Value',val); +Handle = findobj(gcf,'Tag','EditText1'); +temp = num2str(STOPOLINIT.msize); +msize = strcat('[',temp,']'); +set(Handle,'String',msize); + +function [] = change_initialization_ok() +Handle = findobj(gcbf,'Tag','PopupMenu1'); +temp = get(Handle,'String'); +val = get(Handle,'Value'); +INIT_TYPE = temp{val}; +Handle = findobj(gcbf,'Tag','PopupMenu2'); +temp = get(Handle,'String'); +val = get(Handle,'Value'); +lattice = temp{val}; +Handle = findobj(gcbf,'Tag','PopupMenu3'); +temp = get(Handle,'String'); +val = get(Handle,'Value'); +shape = temp{val}; +Handle = findobj(gcbf,'Tag','EditText1'); +temp = get(Handle,'String'); +msize = str2num(temp); +STOPOLINIT = som_set('som_topol','msize',msize,'lattice',lattice,'shape',shape); +close(gcf); +Handle = findobj(gcf,'Tag','StaticText5'); +temp = num2str(STOPOLINIT.msize); +temp = strcat('map size:',' [',temp,']'); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText6'); +temp = strcat('type:',INIT_TYPE); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText20'); +temp = strcat('lattice:',STOPOLINIT.lattice); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText21'); +temp = strcat('shape:',STOPOLINIT.shape); +set(Handle,'String',temp); + + +function [] = def_values_others() +global SOTHERS; +Handle = findobj(gcf,'Tag','StaticText19'); +temp = strcat('tracking:',SOTHERS.tracking); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText12'); +temp = strcat('order:',SOTHERS.oder); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText14'); +temp = strcat('length_type:',SOTHERS.length_type); +set(Handle,'String',temp); + + + +function [] = fill_fields() +global STRAIN1 STRAIN2 ALGORITHM + +neigh = STRAIN1.neigh; +mask = STRAIN1.mask; +rad_ini1 = STRAIN1.radius_ini; +rad_ini2 = STRAIN2.radius_ini; +rad_fin1 = STRAIN1.radius_fin; +rad_fin2 = STRAIN2.radius_fin; +trainlen1 = num2str(STRAIN1.trainlen); +trainlen2 = num2str(STRAIN2.trainlen); +alpha_ini1 = num2str(STRAIN1.alpha_ini); +alpha_ini2 = num2str(STRAIN2.alpha_ini); +if strcmp(ALGORITHM,'seq') + alpha_type = STRAIN1.alpha_type; %%% only in sequential + Handle = findobj(gcf,'Tag','StaticText28'); + temp = strcat('alpha type:',alpha_type); + set(Handle,'String',temp); +end +Handle = findobj(gcf,'Tag','StaticText11'); +temp = strcat('neigh: ',neigh); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText22'); +temp = num2str(rad_fin1); +temp = strcat('radius final:',temp); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText25'); +temp = num2str(rad_fin2); +temp = strcat('radius final:',temp); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText11'); +temp = strcat('neigh: ',neigh); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText17'); +temp = num2str(rad_ini1); +temp = strcat('radius initial:',temp); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText24'); +temp = num2str(rad_ini2); +temp = strcat('radius initial:',temp); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText16'); +temp = num2str(trainlen1); +temp = strcat('training length:',temp); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText23'); +temp = num2str(trainlen2); +temp = strcat('training length:',temp); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText26'); +temp = strcat('alpha initial:',alpha_ini1); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText27'); +temp = strcat('alpha initial:',alpha_ini2); +set(Handle,'String',temp); + + +function [] = init(); +global INIT_TYPE MAP NEWMAP ALGORITHM SOTHERS DATA STOPOLINIT; +if strcmp(INIT_TYPE,'random') + MAP = som_randinit(DATA,STOPOLINIT); +else + MAP = som_lininit(DATA,STOPOLINIT); +end +NEWMAP = MAP; +temp = 'Map:'; +temp = strcat(temp,' <',MAP.name,'>'); +Handle = findobj(gcbf,'Tag','StaticText3'); +set(Handle,'String',temp); +Handle = findobj(gcbf,'Tag','StaticText10'); +set(Handle,'String','Status <map initialized>'); +ALGORITHM = 'batch'; +Handle = findobj(gcbf,'Tag','Pushbutton4'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Pushbutton6'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','Pushbutton5'); +set(Handle,'Enable','on'); +SOTHERS.tracking = '1'; +SOTHERS.length_type = 'epochs'; +SOTHERS.oder = 'random'; +som_gui('def_values_topol'); +som_gui('def_values_train'); +som_gui('def_values_others'); +som_gui('fill_fields'); +Handle = findobj(gcbf,'Tag','Pushbutton4'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Pushbutton9'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Radiobutton1'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','&Init&TrainTrain1'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','&Help/InfoData infouimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','Subuimenu2'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Init&Trainuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Init&TrainInitialize1'); +set(Handle,'Enable','off'); %%%%%%%%%%%?????????? +Handle = findobj(gcbf,'Tag','StaticText9'); +set(Handle,'String','training type: batch'); + + + +function [] = set_batch_mask() +Handle = findobj(gcbf,'Tag','Listbox2'); +temp = get(Handle,'String'); +mask = str2num(temp); +Handle = findobj(gcbf,'Tag','Listbox1'); +replace = get(Handle,'Value'); +Handle = findobj(gcbf,'Tag','EditText2'); +temp = get(Handle,'String'); +value = str2num(temp); +if not(isempty(value)) + mask(replace) = value; + Handle = findobj(gcbf,'Tag','Listbox2'); + temp = num2str(mask); + set(Handle,'String',temp); +end + +function [] = munits() +global DATA STOPOLINIT; +msgs = {'Correct map units is number';'Correct map units is number'}; +[msgs_nro, value] = check_ok('EditText2'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect map units!') + return; +end +STOPOLINIT = som_topol_struct('munits',value,'data',DATA); +Handle = findobj(gcbf,'Tag','EditText1'); +temp = num2str(STOPOLINIT.msize); +msize = strcat('[',temp,']'); +set(Handle,'String',msize); + +function [] = map_size() + global STOPOLINIT; + msgs = {'Map size must be in form [x y]';... + 'Map size must be in form [x y]'}; + [msgs_nro, value, Handle] = msize_ok('EditText1'); + if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect map size!'); + temp = num2str(STOPOLINIT.msize); + temp = strcat('[',temp,']'); + set(Handle,'String',temp); + return; + end + STOPOLINIT.msize = value; + Handle = findobj(gcbf,'Tag','EditText2'); + set(Handle,'String',''); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% END OF INITIALIZATION %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% START OF TRAINING %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +function [] = def_train() +global SOTHERS ALGORITHM MAP NEWST DATA STRAIN1 STRAIN2 MAPSAVED; +tlen_type = SOTHERS.length_type; +sample_order = SOTHERS.oder; +tracking = SOTHERS.tracking; +test = str2num(tracking); +Handle = findobj(gcbf,'Tag','Radiobutton1'); +tempval = get(Handle,'Value'); +if strcmp(ALGORITHM,'seq') + if tempval ~= 1 + [MAP NEWST] = som_seqtrain(MAP,DATA,'train',STRAIN1,tlen_type,sample_order); + end + if test > 1 + figure; + set(gcf,'Name',MAP.name); + set(gcf,'NumberTitle','off'); + end + [NEWMAP NEWST] = som_seqtrain(MAP,DATA,'train',STRAIN2,'tracking',test,tlen_type,sample_order); +else + if tempval ~= 1 + [MAP NEWST] = som_batchtrain(MAP,DATA,'train',STRAIN1); + end + if test > 1 + figure; + set(gcf,'Name',MAP.name); + set(gcf,'NumberTitle','off'); + end + [NEWMAP NEWST] = som_batchtrain(MAP,DATA,'train',STRAIN2,'tracking',test); +end +MAP = NEWMAP; +clear MAPSAVED; +Handle = findobj(gcbf,'Tag','StaticText10'); +set(Handle,'String','Status <map trained>'); +Handle = findobj(gcbf,'Tag','Load/SaveSubuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','Load/SaveSave mapuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','&Load/SaveSave mapSave in workspaceuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','&ToolsSubuimenu1'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Init&TrainTrain1'); +set(Handle,'Enable','off'); + + +function [] = change_def() +global ALGORITHM STRAIN1 DATA; + +ButtonName = questdlg('Select training type!',... + 'Change values.',... + 'Batch','Sequential','Cancel',... + 'Batch'); +if strcmp(ButtonName,'Sequential') + Handle = findobj(gcbf,'Visible','off'); + set(Handle,'Visible','on'); + ALGORITHM = 'seq'; + Handle = findobj(gcf,'Tag','StaticText9'); + set(Handle,'String','training type: sequential'); + new_para2_2; + Handle = findobj(gcf,'Tag','StaticText1'); + set(Handle,'String','Change parameters for sequential training'); + Handle = findobj(gcf,'Enable','off'); + set(Handle,'Enable','on'); + Handle = findobj(gcf,'Visible','off'); + set(Handle,'Visible','on'); +elseif strcmp(ButtonName,'Batch') + ALGORITHM = 'batch'; + Handle = findobj(gcbf,'Tag','StaticText26'); + set(Handle,'Visible','off'); + Handle = findobj(gcbf,'Tag','StaticText27'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','StaticText9'); + set(Handle,'String','training type: batch'); + Handle = findobj(gcf,'Tag','StaticText12'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','StaticText28'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','StaticText14'); + set(Handle,'Visible','off'); + new_para2_2; + Handle = findobj(gcf,'Tag','StaticText1'); + set(Handle,'String','Change parameters for batch training'); + Handle = findobj(gcf,'Tag','PopupMenu3'); + set(Handle,'Enable','off'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','PopupMenu4'); + set(Handle,'Enable','off'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','PopupMenu5'); + set(Handle,'Enable','off'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','StaticText17'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','StaticText18'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','StaticText19'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','StaticText13'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','StaticText14'); + set(Handle,'Visible','off'); + Handle = findobj(gcf,'Tag','EditText6'); + set(Handle,'Visible','off'); + set(Handle,'Enable','off'); + Handle = findobj(gcf,'Tag','EditText10'); + set(Handle,'Visible','off'); + set(Handle,'Enable','off'); +else + return; +end +som_gui('def_values_train'); +mask = STRAIN1.mask; +Handle = findobj(gcf,'Tag','Listbox1'); +set(Handle,'String',DATA.comp_names); +som_gui('fill_new_defaults'); + + +function [] = fill_new_defaults() +global STRAIN1 STRAIN2 SOTHERS ALGORITHM; + +Handle = findobj(gcf,'Tag','EditText4'); +temp = num2str(STRAIN1.radius_ini); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','EditText8'); +temp = num2str(STRAIN2.radius_ini); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','EditText5'); +temp = num2str(STRAIN1.radius_fin); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','EditText9'); +temp = num2str(STRAIN2.radius_fin); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','EditText6'); +temp = num2str(STRAIN1.alpha_ini); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','EditText10'); +temp = num2str(STRAIN2.alpha_ini); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','EditText7'); +temp = num2str(STRAIN1.trainlen); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','EditText11'); +temp = num2str(STRAIN2.trainlen); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','Listbox2'); +temp = num2str(STRAIN1.mask'); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','PopupMenu2'); +string = get(Handle,'String'); +val = loop(string,SOTHERS.tracking); +set(Handle,'Value',val); +Handle = findobj(gcf,'Tag','PopupMenu1'); +string = get(Handle,'String'); +val = loop(string,STRAIN1.neigh); +set(Handle,'Value',val); +if strcmp(ALGORITHM,'seq') + Handle = findobj(gcf,'Tag','PopupMenu3'); + string = get(Handle,'String'); + val = loop(string,SOTHERS.length_type); + set(Handle,'Value',val); + Handle = findobj(gcf,'Tag','PopupMenu4'); + string = get(Handle,'String'); + val = loop(string,SOTHERS.oder); + set(Handle,'Value',val); + Handle = findobj(gcf,'Tag','PopupMenu5'); + string = get(Handle,'String'); + val = loop(string,STRAIN1.alpha_type); + set(Handle,'Value',val); +end + + + +function [] = set_new_parameters() +global STRAIN1 STRAIN2 ALGORITHM SOTHERS; + +Handle = findobj(gcbf,'Tag','Listbox2'); +temp = get(Handle,'String'); +mask = str2num(temp); %%%%%%%%%%%%% Do somthing +mask = mask'; +Handle = findobj(gcbf,'Tag','PopupMenu1'); +temp = get(Handle,'String'); +val = get(Handle,'Value'); +neigh = temp{val}; +Handle = findobj(gcbf,'Tag','PopupMenu2'); +temp = get(Handle,'String'); +val = get(Handle,'Value'); +SOTHERS.tracking = temp{val}; %%%%% finetune phase! +Handle = findobj(gcbf,'Tag','EditText4'); +temp = get(Handle,'String'); +rad_ini1 = str2num(temp); +Handle = findobj(gcbf,'Tag','EditText8'); +temp = get(Handle,'String'); +rad_ini2 = str2num(temp); +Handle = findobj(gcbf,'Tag','EditText5'); +temp = get(Handle,'String'); +rad_fin1 = str2num(temp); +Handle = findobj(gcbf,'Tag','EditText9'); +temp = get(Handle,'String'); +rad_fin2 = str2num(temp); +Handle = findobj(gcbf,'Tag','EditText6'); +temp = get(Handle,'String'); +alpha_ini1 = str2num(temp); +Handle = findobj(gcbf,'Tag','EditText10'); +temp = get(Handle,'String'); +alpha_ini2 = str2num(temp); +Handle = findobj(gcbf,'Tag','EditText7'); +temp = get(Handle,'String'); +train_length1 = str2num(temp); +Handle = findobj(gcbf,'Tag','EditText11'); +temp = get(Handle,'String'); +train_length2 = str2num(temp); +if strcmp(ALGORITHM,'seq') + Handle = findobj(gcbf,'Tag','PopupMenu3'); + temp = get(Handle,'String'); + val = get(Handle,'Value'); + SOTHERS.length_type = temp{val}; + Handle = findobj(gcbf,'Tag','PopupMenu4'); + temp = get(Handle,'String'); + val = get(Handle,'Value'); + SOTHERS.oder= temp{val}; + Handle = findobj(gcbf,'Tag','PopupMenu5'); + temp = get(Handle,'String'); + val = get(Handle,'Value'); + alpha_type = temp{val}; +else + alpha_type = 'inv'; +end +STRAIN1.neigh = neigh; +STRAIN2.neigh = neigh; +STRAIN1.mask = mask; +STRAIN2.mask = mask; +STRAIN1.radius_ini = rad_ini1; +STRAIN2.radius_ini = rad_ini2; +STRAIN1.radius_fin = rad_fin1; +STRAIN2.radius_fin = rad_fin2; +STRAIN1.alpha_ini = alpha_ini1; +STRAIN2.alpha_ini = alpha_ini2; +STRAIN1.alpha_type = alpha_type; +STRAIN2.alpha_type = alpha_type; +STRAIN1.trainlen = train_length1; +STRAIN2.trainlen = train_length2; +close(gcbf); +som_gui('fill_fields'); +som_gui('def_values_others'); + +function [] = only_finetune() + +Handle = findobj(gcbf,'Tag','Radiobutton1'); +test = get(Handle,'Value'); +if test == 1 + Handle = findobj(gcbf,'Tag','StaticText16'); + set(Handle,'Enable','off'); + Handle = findobj(gcbf,'Tag','StaticText17'); + set(Handle,'Enable','off'); + Handle = findobj(gcbf,'Tag','StaticText22'); + set(Handle,'Enable','off'); + Handle = findobj(gcbf,'Tag','StaticText26'); + set(Handle,'Enable','off'); +else + Handle = findobj(gcbf,'Tag','StaticText16'); + set(Handle,'Enable','on'); + Handle = findobj(gcbf,'Tag','StaticText17'); + set(Handle,'Enable','on'); + Handle = findobj(gcbf,'Tag','StaticText22'); + set(Handle,'Enable','on'); + Handle = findobj(gcbf,'Tag','StaticText26'); + set(Handle,'Enable','on'); +end + + +function [] = check_rough_radini() +global STRAIN1; +msgs = {'Initial radius must be number!';... + 'Initial radius must be single valued number!'}; +[msgs_nro, value, Handle] = check_ok('EditText4'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect initial radius!') + temp = num2str(STRAIN1.radius_ini); + set(Handle,'String',temp); + return; +end + + +function [] = check_fine_radini() +global STRAIN2; +msgs = {'Initial radius must be number!';... + 'Initial radius must be single valued number!'}; +[msgs_nro, value, Handle] = check_ok('EditText8'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect initial radius!') + temp = num2str(STRAIN2.radius_ini); + set(Handle,'String',temp); + return; +end + +function [] = check_rough_radfin() +global STRAIN1; +msgs = {'Final radius must be number!';... + 'Final radius must be single valued number!'}; +[msgs_nro, value, Handle] = check_ok('EditText5'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect final radius!') + temp = num2str(STRAIN1.radius_fin); + set(Handle,'String',temp); + return; +end + +function [] = check_fine_radfin() +global STRAIN2; +msgs = {'Final radius must be number!';... + 'Final radius must be single valued number!'}; +[msgs_nro, value, Handle] = check_ok('EditText9'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect final radius!') + temp = num2str(STRAIN2.radius_fin); + set(Handle,'String',temp); + return; +end + +function [] = check_rough_alphaini() +global STRAIN1; +msgs = {'Alpha initial must be number!';... + 'Alpha initial must be single valued number!'}; +[msgs_nro, value, Handle] = check_ok('EditText6'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect initial alpha!') + temp = num2str(STRAIN1.alpha_ini); + set(Handle,'String',temp); + return; +end + +function [] = check_fine_alphaini() +global STRAIN2; +msgs = {'Alpha initial must be number!';... + 'Alpha initial must be single valued number!'}; +[msgs_nro, value, Handle] = check_ok('EditText10'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect initial alpha!') + temp = num2str(STRAIN2.alpha_ini); + set(Handle,'String',temp); + return; +end + +function [] = check_rough_trainlen() +global STRAIN1; +msgs = {'Training length must be number!';... + 'Training length must be single valued number!'}; +[msgs_nro, value, Handle] = check_ok('EditText7'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect training length!') + temp = num2str(STRAIN1.trainlen); + set(Handle,'String',temp); + return; +end + +function [] = check_fine_trainlen() +global STRAIN2; + +msgs = {'Training length must be number!';... + 'Training length must be single valued number!'}; +[msgs_nro, value, Handle] = check_ok('EditText11'); +if msgs_nro > 0 + errordlg({msgs{msgs_nro}},'Incorrect training length!') + temp = num2str(STRAIN2.trainlen); + set(Handle,'String',temp); + return; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% END OF TRAINING %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% START OF SAVING %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +function [] = savemap() +global MAP MAPSAVED; +if isempty(MAP) + str = {'There is no map to be saved! Train map before saving.'}; + helpdlg(str,'Empty map!'); + return; +end +[FileName Path] = uiputfile('*.cod','Save file!'); +if FileName ~= 0 + temp = strcat(Path,FileName); + som_write_cod(MAP,temp); + MAPSAVED = 'SAVED'; +end +Handle = findobj(gcf,'Tag','StaticText10'); +set(Handle,'String','Status <map saved>'); + +function [] = save_workspace() +global MAP MAPSAVED; +if isempty(MAP) + str = {'There is no map to be saved! Train map before saving.'}; + helpdlg(str,'Empty map!'); + return; +else + prompt = {'Save map as?'}; + title = 'Save map!'; + lineNo = 1; + answer = inputdlg(prompt,title,lineNo); + if isempty(answer) + return; + end + if not(isempty(answer{1})) + ws_variable = evalin('base','who'); + max_length = 0; + for index = 1:size(ws_variable,1) + if max_length < size(ws_variable{index},2) + max_length = size(ws_variable{index},2); + end + end + length = max_length + 1; + tempfoo(1:1:length) = 'A'; + assignin('base',tempfoo,answer{1}); + str = ['exist(' tempfoo ')']; + temp = evalin('base',str); %%%%%%%%%%@@@@@@@@@ + evalin('base',['clear ' tempfoo ]) + if temp == 0 + assignin('base',answer{1},MAP); + MAPSAVED = 'SAVED'; + elseif temp ~= 0 + Questmsg = strcat('Variable',' ''',answer{1},'''',... + ' exist. Overwrite?'); + ButtonName = questdlg(Questmsg); + switch(ButtonName) + case 'Yes' + assignin('base',answer{1},MAP); + MAPSAVED = 'SAVED'; + case 'No' + som_gui('save_workspace'); + end + end + else + helpmsg = {'There cannot be any empty field in ''save'''}; + helpdlg(helpmsg,'Help Save!'); + som_gui('save'); + end +end +Handle = findobj(gcf,'Tag','StaticText10'); +set(Handle,'String','Status <map saved>'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% END OF SAVING %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% START OF HELP & INFO %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%HEREXX + +function [] = data_info() +global DATA; +if isempty(DATA) + helpmsg = 'Load data first!'; + helpdlg(helpmsg,'Empty data!'); + return; +end +file_name = tempname; +file_name = strcat(file_name,'.m'); +fid = fopen(file_name,'w'); +fprintf(fid,'%% %+35s\n','DATA INFO'); +fprintf(fid,'%%\n'); +print_info(DATA,2,fid); +directory = tempdir; +addpath (directory); +helpwin (file_name); +fclose(fid); +delete(file_name); +rmpath (directory); + + +function [] = map_info() +global MAP; +if isempty(MAP) + helpmsg = 'There is no map!'; + helpdlg(helpmsg,'Empty map!'); + return; +end +file_name = tempname; +file_name = strcat(file_name,'.m'); +fid = fopen(file_name,'w'); +fprintf(fid,'%% %+35s\n','MAP INFO'); +fprintf(fid,'%%\n'); +print_info(MAP,2,fid); +directory = tempdir; +addpath (directory); +helpwin (file_name); +fclose(fid); +delete(file_name); +rmpath (directory); + + +function [] = helpwin1() +file1 = tempname; +file1 = strcat(file1,'.m'); +directory = tempdir; +html2tex('file:///share/somtoolbox/vs2/html/som_GUI.html',file1); +addpath (directory); +helpwin (file1); +rmpath (directory); +delete (file1); + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% END OF HELP & INFO %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% START OF OTHER FUNC %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +function [msgs_nro, value, Handle] = check_ok(Tag) +Handle = findobj(gcbf,'Tag',Tag); +temp = get(Handle,'String'); +value = str2num(temp); +if isempty(value) + msgs_nro = 1; + return; +end +[test1 test2] = size(value); +if test1 ~= 1 | test2 ~= 1 + msgs_nro = 2; + return; +end +msgs_nro = 0; + + +function [msgs_nro, value, Handle] = msize_ok(Tag) +Handle = findobj(gcbf,'Tag',Tag); +temp = get(Handle,'String'); +value = str2num(temp); +if isempty(value) + msgs_nro = 1; + return; +end +[test1 test2] = size(value); +if test1 ~= 1 | test2 ~= 2 + msgs_nro = 2; + return; +end +msgs_nro = 0; + + +%%% Changed 1.2.2000 + +function [] = visualize() +global MAP; +if isempty(MAP) + helpmsg = {'Train map before tryinig to visualize it!'}; + helpdlg(helpmsg,'Empty Map!'); + return; +end + +dim = size(MAP.codebook,2); +odim = 2; +[P,V] = pcaproj(MAP.codebook,odim); +ccode = som_colorcode(MAP, 'rgb1'); + +figure; +som_show(MAP,'umat','all','comp',1:dim,'norm','d'); +figure; +subplot(1,2,1) +som_grid(MAP,'Coord',P,'MarkerColor',ccode,'Markersize',5, ... + 'Linewidth',1,'Linecolor','k'); +xlabel('PC1'), ylabel('PC2') +title('PCA-projection (on the left), color coding (on the right)') +axis tight, axis equal +subplot(1,2,2) +som_cplane(MAP.topol.lattice,MAP.topol.msize,ccode); + +%msgbox('Save map in workspace. Load it from there.'); +%som_gui('save_workspace'); +%som_comp_vis; + +%%%%%%%%%%%%%%%% + + +function [] = clear_all() + +Handle = findobj(gcbf,'Enable','off'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','Radiobutton1'); +set(Handle,'Value',0); +Handle = findobj(gcbf,'Tag','StaticText10'); +set(Handle,'String','Status <no action>'); +Handle = findobj(gcbf,'Tag','StaticText3'); +set(Handle,'String','Map: <empty>'); +Handle = findobj(gcbf,'Tag','StaticText4'); +set(Handle,'String','Data: <empty>'); +Handle = findobj(gcbf,'Tag','StaticText20'); +set(Handle,'String','lattice:'); +Handle = findobj(gcbf,'Tag','StaticText11'); +set(Handle,'String','neigh:'); +Handle = findobj(gcbf,'Tag','StaticText16'); +set(Handle,'String','training length:'); +Handle = findobj(gcbf,'Tag','StaticText23'); +set(Handle,'String','training length:'); +Handle = findobj(gcbf,'Tag','StaticText17'); +set(Handle,'String','radius initial:'); +Handle = findobj(gcbf,'Tag','StaticText24'); +set(Handle,'String','radius initial:'); +Handle = findobj(gcbf,'Tag','StaticText5'); +set(Handle,'String','map size:'); +Handle = findobj(gcbf,'Tag','StaticText21'); +set(Handle,'String','shape:'); +Handle = findobj(gcbf,'Tag','StaticText12'); +set(Handle,'String','order:'); +set(Handle,'Visible','off'); +Handle = findobj(gcbf,'Tag','StaticText14'); +set(Handle,'String','length type:'); +set(Handle,'Visible','off'); +Handle = findobj(gcbf,'Tag','StaticText22'); +set(Handle,'String','radius final:'); +Handle = findobj(gcbf,'Tag','StaticText25'); +set(Handle,'String','radius final:'); +Handle = findobj(gcbf,'Tag','StaticText19'); +set(Handle,'String','tracking:'); +Handle = findobj(gcbf,'Tag','StaticText7'); +set(Handle,'String','Initialization'); +Handle = findobj(gcbf,'Tag','StaticText28'); +set(Handle,'String','alpha type:'); +set(Handle,'Visible','off'); +Handle = findobj(gcbf,'Tag','StaticText26'); +set(Handle,'String','alpha initial:'); +Handle = findobj(gcbf,'Tag','StaticText27'); +set(Handle,'String','alpha initial:'); +Handle = findobj(gcbf,'Tag','StaticText6'); +set(Handle,'String','type:'); +Handle = findobj(gcbf,'Tag','StaticText9'); +set(Handle,'String','training type:'); +Handle = findobj(gcbf,'Tag','Pushbutton9'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Pushbutton6'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Pushbutton4'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Pushbutton5'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Pushbutton2'); +set(Handle,'Enable','on'); +Handle = findobj(gcbf,'Tag','Radiobutton1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Load/SaveSave mapuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Load/SaveSave mapSave in workspaceuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Subuimenu2'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&ToolsSubuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Help/InfoHelp windowuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Help/InfoData infouimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Init&Trainuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Init&TrainInitialize1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Init&TrainChange initialization valuesuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','&Init&TrainTrain1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'Tag','Load/SaveSubuimenu1'); +set(Handle,'Enable','off'); +Handle = findobj(gcbf,'String','alpha initial:'); +set(Handle,'Visible','off'); +clear; +clear global; + + +function [] = close_fig() +global MAPSAVED NEWMAP; +if isempty(MAPSAVED) + if not(isempty(NEWMAP)) + quest = 'Save map before closing?'; + ButtonName = questdlg(quest); + switch ButtonName + case 'Yes' + som_gui('savemap'); + som_gui('clear'); + clear global; + close(gcbf); + case 'No' + som_gui('clear'); + clear global; + close(gcbf); + case 'Cancel' + end + else + som_gui('clear'); + clear global; + close(gcbf); + end +else + som_gui('clear'); + clear global; + close(gcbf); +end + + +function [] = preprocess_gui() +global DATA; +if isempty(DATA) + helpmsg = {'Load data before tryinig to preprocess!'}; + helpdlg(helpmsg,'Empty Data!'); + return; +end +preprocess(DATA); +waitfor(gcf); +prompt = {'Name of preprocessed data in workspace?'}; +tittle = 'Reload preprocessed data!'; +lineNo = 1; +def = {DATA.name}; +answer = inputdlg(prompt,tittle,lineNo,def); +if isempty(answer) + return; +end +data = answer{1}; +new_name = retname; +assignin('base',new_name,data); +str = ['exist(' new_name ')']; +temp = evalin('base',str); +if temp ~= 1 + temp = strcat('Variable ''',data,''' doesn''t exist in workspace.',... + 'Old Data which is not preprocessed will be used.'); + errordlg(temp,'Unknown variable!'); + return; +end +evalin('base',['clear ' new_name ]) +Handle = findobj(gcf,'Tag','StaticText4'); +temp = strcat('Data: <',data,'>'); +set(Handle,'String',temp); +Handle = findobj(gcf,'Tag','StaticText10'); +set(Handle,'String','Status <data preprocessed>'); +temp = evalin('base',data); +DATA.data = temp; +som_gui('def_initialization'); + + +function [val] = loop(cell_data, search_data) +for val = 1: length(cell_data) + if strcmp(cell_data{val},search_data) + break; + end +end +if not(strcmp(cell_data{val},search_data)) + val = -1; +end + + +function [] = comp_names(names,fid) +last = size(names); +for index=1:last + fprintf(fid,'%% %s\n',names{index}) +end + +function [] = fill_field(names,mask,fid) +last = size(mask); +for index=1:last + num = num2str(mask(index)) + fprintf(fid,'%% %-15s %-2s\n',names{index},num) +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% END OF OTHER FUNC %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + +function fig = main_gui() + +v = version; +ver_53_or_newer = (str2num(v(1:3)) >= 5.3); + +h0 = figure('Units','normalized', ... + 'Color',[0.85 0.85 0.85], ... + 'Name','SOM Toolbox -- Initialization & Training', ... + 'NumberTitle','off', ... + 'PaperPosition',[18 180 576 432], ... + 'PaperUnits','points', ... + 'Position',[0.3296875 0.28125 0.3828125 0.576171875], ... + 'Tag','Fig1'); +if ver_53_or_newer, set(h0,'ToolBar','none'); end + +h1 = uimenu('Parent',h0, ... + 'Label','&Load/Save', ... + 'Tag','uimenu1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''load_data'');',... + 'Label','Load Data', ... + 'Tag','Subuimenu1'); +h2 = uimenu('Parent',h1, ... + 'Label','Save map', ... + 'Enable','off',... + 'Tag','Load/SaveSubuimenu1'); +h3 = uimenu('Parent',h2, ... + 'Callback','som_gui(''save_workspace'');', ... + 'Enable','off', ... + 'Label','Save in workspace', ... + 'Tag','Load/SaveSave mapuimenu1'); +h3 = uimenu('Parent',h2, ... + 'Callback','som_gui(''savemap'');', ... + 'Enable','off', ... + 'Label','Write cod-file', ... + 'Tag','&Load/SaveSave mapSave in workspaceuimenu1'); +h1 = uimenu('Parent',h0, ... + 'Label','&Utilities', ... + 'Tag','uimenu2'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''preprocess'');', ... + 'Enable','off', ... + 'Label','Preprocess Data', ... + 'Tag','Subuimenu2'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''visualize'');', ... + 'Enable','off', ... + 'Label','Visualize Map', ... + 'Tag','&ToolsSubuimenu1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''clear_all'');', ... + 'Label','Clear all', ... + 'Tag','&ToolsSubuimenu2'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''close'');', ... + 'Label','Close Figure', ... + 'Tag','&ToolsClear alluimenu1'); +h1 = uimenu('Parent',h0, ... + 'Label','&Info', ... + 'Tag','&ToolsClose Figureuimenu1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''help'');', ... + 'Label','WWW Help', ... + 'Tag','Helpuimenu1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''helpwin'');', ... + 'Label','Help window', ... + 'Tag','Helpuimenu2'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''helpwin2'');', ... + 'Label','About GUI', ... + 'Tag','&Help/InfoHelp windowuimenu2'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''data_info'');', ... + 'Enable','off', ... + 'Label','Data info', ... + 'Tag','&Help/InfoHelp windowuimenu1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''map_info'');', ... + 'Enable','off', ... + 'Label','Map info', ... + 'Tag','&Help/InfoData infouimenu1'); +h1 = uimenu('Parent',h0, ... + 'Label','&Init/Train', ... + 'Tag','&Init/Train1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''change_initialization'');', ... + 'Enable','off', ... + 'Label','Change initialization values', ... + 'Tag','&Init&Trainuimenu1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''init'');', ... + 'Enable','off', ... + 'Label','Initialize', ... + 'Tag','&Init&TrainInitialize1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''change_def'');', ... + 'Enable','off', ... + 'Label','Change training values', ... + 'Tag','&Init&TrainChange initialization valuesuimenu1'); +h2 = uimenu('Parent',h1, ... + 'Callback','som_gui(''def_train'');', ... + 'Enable','off', ... + 'Label','Train', ... + 'Tag','&Init&TrainTrain1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.04081632653061224 0.01129943502824859 0.7619047619047619 0.9717514124293786], ... + 'Style','frame', ... + 'Tag','Frame1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.06802721088435373 0.7909604519774012 0.7074829931972788 0.1807909604519774], ... + 'Style','frame', ... + 'Tag','Frame2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.8527570621468927 0.6530612244897959 0.03389830508474576], ... + 'FontUnits','normalized',... + 'String','Map <empty>', ... + 'Style','text', ... + 'Tag','StaticText3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.8075593220338984 0.6530612244897959 0.03389830508474576], ... + 'String','Data <empty>', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText4'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.06802721088435373 0.5988700564971752 0.7074829931972788 0.1694915254237288], ... + 'Style','frame', ... + 'Tag','Frame3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.1041 0.7356 0.6286 0.0271], ... + 'String','Initialization', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'FontWeight','bold', ... + 'Tag','StaticText7'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4489795918367346 0.7005649717514124 0.2993197278911565 0.03389830508474576], ... + 'String','map size:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText5'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.6553672316384182 0.2993197278911565 0.03389830508474576], ... + 'String','lattice:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText20'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.7000000000000001 0.2993197278911565 0.03389830508474576], ... + 'String','type:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText6'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4489795918367346 0.6553672316384182 0.2993197278911565 0.03389830508474576], ... + 'String','shape:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText21'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.3129251700680272 0.6101694915254238 0.217687074829932 0.03389830508474576], ... + 'String','Change values', ... + 'FontUnits','normalized',... + 'Callback','som_gui(''change_initialization'');', ... + 'Enable','off', ... + 'Tag','Pushbutton9'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.06802721088435373 0.02259887005649718 0.7074829931972788 0.5536723163841808], ... + 'Style','frame', ... + 'Tag','Frame4'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.1041 0.5316 0.6429 0.0339], ... + 'String','Training', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'FontWeight','bold', ... + 'Tag','StaticText8'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.4971751412429379 0.6530612244897959 0.03389830508474576], ... + 'String','training type', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText9'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4489795918367346 0.4519774011299435 0.2993197278911565 0.03389830508474576], ... + 'String','tracking:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText19'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.4519774011299435 0.2993197278911565 0.03389830508474576], ... + 'String','neigh:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText11'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.36519774011299435 0.2993197278911565 0.03389830508474576], ... + 'String','alpha type:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Visible','off',... + 'Tag','StaticText28'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'ListboxTop',0, ... + 'HorizontalAlignment','left', ... + 'Position',[0.09523809523809523 0.4067796610169492 0.2993197278911565 0.03389830508474576], ... + 'String','length type:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Visible','off',... + 'Tag','StaticText14'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4489795918367346 0.4067796610169492 0.2993197278911565 0.03389830508474576], ... + 'String','order:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Visible','off',... + 'Tag','StaticText12'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.07909604519774012 0.2993197278911565 0.2711864406779661], ... + 'Style','frame', ... + 'Tag','Frame5'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.4353741496598639 0.07909604519774012 0.2993197278911565 0.2711864406779661], ... + 'Style','frame', ... + 'Tag','Frame6'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.108843537414966 0.3050847457627119 0.2721088435374149 0.03389830508474576], ... + 'String','Rough', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText13'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.4489795918367346 0.3050847457627119 0.2721088435374149 0.03389830508474576], ... + 'String','Finetune', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText15'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.108843537414966 0.1807909604519774 0.2721088435374149 0.03389830508474576], ... + 'String','training length:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText16'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.108843537414966 0.2694915254237288 0.2714285714285714 0.03389830508474576], ... + 'String','radius initial:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText17'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.1088 0.2260 0.2721 0.0339], ... + 'String','radius final:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText22'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'ListboxTop',0, ... + 'Position',[0.108843537414966 0.13694915254237288 0.2714285714285714 0.03389830508474576], ... + 'String','alpha initial:', ... + 'FontUnits','normalized',... + 'HorizontalAlignment','left', ... + 'Style','text', ... + 'Visible','off',... + 'Tag','StaticText26'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4489795918367346 0.1807909604519774 0.2721088435374149 0.03389830508474576], ... + 'String','training length:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText23'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4489795918367346 0.2711864406779661 0.2721088435374149 0.03389830508474576], ... + 'String','radius initial:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText24'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4490 0.2260 0.2721 0.0339], ... + 'String','radius final:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText25'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'ListboxTop',0, ... + 'Position',[0.4489795918367346 0.13694915254237288 0.2721088435374149 0.03389830508474576], ... + 'String','alpha initial:', ... + 'FontUnits','normalized',... + 'HorizontalAlignment','left', ... + 'Style','text', ... + 'Visible','off',... + 'Tag','StaticText27'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.3129251700680272 0.03389830508474576 0.217687074829932 0.03389830508474576], ... + 'String','Change values', ... + 'FontUnits','normalized',... + 'Callback','som_gui(''change_def'');', ... + 'Enable','off', ... + 'Tag','Pushbutton6'); +if ver_53_or_newer, set(h1,'TooltipString','Change default values in training.'); end + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.8163265306122448 0.8152542372881356 0.163265306122449 0.05593220338983051], ... + 'String','LOAD', ... + 'FontUnits','normalized',... + 'Callback','som_gui(''load_data'');', ... + 'Tag','Pushbutton2'); +if ver_53_or_newer, set(h1,'TooltipString','Load data file.'); end + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.8163265306122448 0.6457627118644068 0.163265306122449 0.05593220338983051], ... + 'String','INITIALIZE', ... + 'FontUnits','normalized',... + 'Callback','som_gui(''init'');', ... + 'Enable','off', ... + 'Tag','Pushbutton4'); +if ver_53_or_newer, set(h1,'TooltipString','Initialize map.'); end + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.8163265306122448 0.384180790960452 0.163265306122449 0.05649717514124294], ... + 'String','TRAIN', ... + 'FontUnits','normalized',... + 'Callback','som_gui(''def_train'');', ... + 'Enable','off', ... + 'Tag','Pushbutton5'); +if ver_53_or_newer, set(h1,'TooltipString','Train map whit default values.'); end + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.752941176470588 0.752941176470588 0.752941176470588], ... + 'ListboxTop',0, ... + 'Position',[0.8163265306122448 0.06779661016949153 0.163265306122449 0.05649717514124294], ... + 'Callback','som_gui(''close'');', ... + 'String','CLOSE', ... + 'FontUnits','normalized',... + 'Tag','Pushbutton8'); +if ver_53_or_newer, set(h1,'TooltipString','Close figure.'); end + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.9 0.9 0.9], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09387755102040815 0.897954802259887 0.6530612244897959 0.03389830508474576], ... + 'String','Status <no action>', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText10'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.108843537414966 0.0903954802259887 0.2721088435374149 0.03389830508474576], ... + 'String','Only finetune', ... + 'FontUnits','normalized',... + 'Callback','som_gui(''only_finetune'');', ... + 'Enable','off', ... + 'Style','radiobutton', ... + 'Tag','Radiobutton1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.09523809523809523 0.9418531073446328 0.6530612244897959 0.0259887005649718], ... + 'String','Information', ... + 'FontUnits','normalized',... + 'FontWeight','bold', ... + 'Style','text', ... + 'Tag','StaticText18'); +if nargout > 0, fig = h0; end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function fig = loadgui3() + +temp = {'dat file';'mat file'}; + +h0 = figure('Units','normalized', ... + 'Color',[0.8 0.8 0.8], ... + 'Name','Load data!', ... + 'NumberTitle','off', ... + 'PaperType','a4letter', ... + 'Position',[0.3828125 0.5 0.3421875 0.189453125], ... + 'Tag','Fig1'); + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.02853881278538813 0.06443298969072164 0.7705479452054794 0.8698453608247422], ... + 'Style','frame', ... + 'Tag','Frame1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.04337899543378995 0.547680412371134 0.7420091324200913 0.354381443298969], ... + 'Style','frame', ... + 'Tag','Frame2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.04280821917808219 0.09664948453608246 0.7420091324200913 0.4188144329896907], ... + 'Style','frame', ... + 'Tag','Frame3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'FontWeight','bold', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.05717762557077625 0.7881958762886597 0.2853881278538812 0.09664948453608246], ... + 'String','From', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'Callback','som_gui(''workspace'');', ... + 'ListboxTop',0, ... + 'Position',[0.05107762557077625 0.7087628865979381 0.1997716894977169 0.09664948453608246], ... + 'String','Ws', ... + 'FontUnits','normalized',... + 'Style','radiobutton', ... + 'Tag','Radiobutton2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'Callback','som_gui(''file'');', ... + 'ListboxTop',0, ... + 'Position',[0.05107762557077625 0.5773195876288659 0.2009132420091324 0.09793814432989689], ... + 'String','File', ... + 'FontUnits','normalized',... + 'Style','radiobutton', ... + 'Tag','Radiobutton1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','Handle = findobj(gcbf,''Tag'',''EditText2'');set(Handle,''String'','''');',... + 'FontUnits','normalized',... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.2893881278538812 0.7087628865979381 0.3139269406392694 0.09664948453608246], ... + 'Style','edit', ... + 'Tag','EditText1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','Handle = findobj(gcbf,''Tag'',''EditText1'');set(Handle,''String'','''');',... + 'FontUnits','normalized',... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.2893881278538812 0.5798969072164948 0.3139269406392694 0.09664948453608246], ... + 'Style','edit', ... + 'Tag','EditText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','som_gui(''browse'');', ... + 'ListboxTop',0, ... + 'Position',[0.6279 0.5799 0.1427 0.2255], ... + 'String','Browse', ... + 'FontUnits','normalized',... + 'Tag','Pushbutton1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','som_gui(''load_ok'');', ... + 'ListboxTop',0, ... + 'Position',[0.8276 0.5577 0.1427 0.2255], ... + 'String','Load', ... + 'FontUnits','normalized',... + 'Tag','Pushbutton2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','close;',... + 'ListboxTop',0, ... + 'Position',[0.8276 0.2577 0.1427 0.2255], ... + 'String','Cancel', ... + 'FontUnits','normalized',... + 'Tag','Pushbutton3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','som_gui(''file_select'');', ... + 'ListboxTop',0, ... + 'Max',2, ... + 'Min',1, ... + 'String',temp,... + 'FontUnits','normalized',... + 'Position',[0.3995433789954338 0.2977319587628866 0.1997716894977169 0.08664948453608246], ... + 'Style','popupmenu', ... + 'Tag','PopupMenu1', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'FontWeight','bold', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.05707762557077625 0.3865979381443299 0.7134703196347032 0.09664948453608246], ... + 'String','Parameters for file', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.05707762557077625 0.2777319587628866 0.2568493150684931 0.09664948453608246], ... + 'String','File type ', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.05707762557077625 0.1288659793814433 0.2996575342465753 0.09664948453608246], ... + 'String','Missing value', ... + 'Style','checkbox', ... + 'FontUnits','normalized',... + 'Tag','Checkbox1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''missing'');',... + 'ListboxTop',0, ... + 'Position',[0.5136986301369862 0.1258659793814433 0.08561643835616438 0.10664948453608246], ... + 'String','x', ... + 'FontUnits','normalized',... + 'Style','edit', ... + 'Tag','EditText3'); +if nargout > 0, fig = h0; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + +function fig = works() + +v = version; +ver_53_or_newer = (str2num(v(1:3)) >= 5.3); + +h0 = figure('Units','normalized', ... + 'Color',[0.8 0.8 0.8], ... + 'Name','Load from workspace!', ... + 'NumberTitle','off', ... + 'PaperPosition',[18 180 576 432], ... + 'PaperType','a4letter', ... + 'PaperUnits','points', ... + 'Position',[0.5390625 0.2490234375 0.203125 0.251953125], ... + 'Tag','Fig1'); +if ver_53_or_newer, set(h0,'ToolBar','none'); end + +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.05384615384615385 0.1472868217054263 0.9076923076923078 0.8255813953488372], ... + 'Style','frame', ... + 'Tag','Frame1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','som_gui(''works_ok'');', ... + 'ListboxTop',0, ... + 'Position',[0.1077 0.0194 0.2885 0.1202], ... + 'String','OK', ... + 'FontUnits','normalized',... + 'Tag','Pushbutton1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','close;', ... + 'ListboxTop',0, ... + 'Position',[0.6115 0.0155 0.2885 0.1202], ... + 'String','Cancel', ... + 'FontUnits','normalized',... + 'Tag','Pushbutton2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Position',[0.1192 0.1977 0.7692 0.6395], ... + 'String',' ', ... + 'FontUnits','normalized',... + 'Style','listbox', ... + 'Tag','Listbox1', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'FontWeight','bold', ... + 'ListboxTop',0, ... + 'Position',[0.2115384615384616 0.8720930232558139 0.576923076923077 0.06976744186046512], ... + 'String','Your options', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText1'); +if nargout > 0, fig = h0; end + + + + + + +function fig = initialization2() + + +temp1 = {'random';'linear'}; +temp2 = {'hexa';'rect'}; +temp3 = {'sheet';'cyl';'toroid'}; + + + +% position bug in following corrected 1.12.04 KimmoR +h0 = figure('Units','normalized', ... + 'Color',[0.8 0.8 0.8], ... + 'Name','Change initialization parameters!', ... + 'NumberTitle','off', ... + 'PaperType','a4letter', ... + 'Position',[0.48828125 0.4267578125 0.3515625 0.146484375], ... + 'Tag','Fig1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.02777777777777778 0.08333333333333333 0.8055555555555556 0.8333333333333334], ... + 'Style','frame', ... + 'Tag','Frame1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','som_gui(''change_initialization_ok'');', ... + 'ListboxTop',0, ... + 'Position',[0.8472222222222222 0.55 0.125 0.25], ... + 'FontUnits','normalized',... + 'String','OK', ... + 'Tag','Pushbutton1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','som_gui(''change_initialization_cancel'');', ... + 'ListboxTop',0, ... + 'Position',[0.8472222222222222 0.25 0.125 0.25], ... + 'FontUnits','normalized',... + 'String','Cancel', ... + 'Tag','Pushbutton2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'FontWeight','bold', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.08333333333333334 0.6666666666666666 0.7066666666666667 0.1933333333333333], ... + 'String','Initialization parameters:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.0556 0.200 0.1667 0.1250],... + 'String','type:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'ListboxTop',0, ... + 'Max',2, ... + 'Min',1, ... + 'Position',[0.2500 0.200 0.1667 0.1250], ... + 'String',temp1, ... + 'FontUnits','normalized',... + 'Style','popupmenu', ... + 'Tag','PopupMenu1', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.05555555555555556 0.6 0.1666666666666667 0.125], ... + 'String','map size:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''map_size'');', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.25 0.6 0.1666666666666667 0.125], ... + 'FontUnits','normalized',... + 'Style','edit', ... + 'Tag','EditText1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.05555555555555556 0.4033333333333333 0.1666666666666667 0.125], ... + 'String','lattice:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Max',2, ... + 'Min',1, ... + 'Position',[0.25 0.4333333333333333 0.1666666666666667 0.125], ... + 'String',temp2, ... + 'FontUnits','normalized',... + 'Style','popupmenu', ... + 'Tag','PopupMenu2', ... + 'Value',2); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4444444444444445 0.4033333333333333 0.1666666666666667 0.125], ... + 'String','shape:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText4'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Max',3, ... + 'Min',1, ... + 'Position',[0.638888888888889 0.4333333333333333 0.1666666666666667 0.125], ... + 'String',temp3, ... + 'FontUnits','normalized',... + 'Style','popupmenu', ... + 'Tag','PopupMenu3', ... + 'Value',2); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.4444444444444445 0.6 0.1666666666666667 0.125], ... + 'FontUnits','normalized',... + 'String','munits:', ... + 'Style','text', ... + 'Tag','StaticText5'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''munits'');', ... + 'ListboxTop',0, ... + 'Position',[0.638888888888889 0.6 0.1666666666666667 0.125], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText2'); +if nargout > 0, fig = h0; end + + + + + + + +function fig = new_para2_2() + +temp1 = {'0';'1';'2';'3'}; +temp2 = {'gaussian';'cutgauss';'ep';'bubble'}; +temp3 = {'epochs';'samples'}; +temp4 = {'random';'ordered'}; +temp5 = {'inv';'linear';'power'}; + +v = version; +ver_53_or_newer = (str2num(v(1:3)) >= 5.3); + +h0 = figure('Units','normalized', ... + 'Color',[0.8 0.8 0.8], ... + 'Name','Change training parameters!', ... + 'NumberTitle','off', ... + 'PaperPosition',[18 180 576 432], ... + 'PaperType','a4letter', ... + 'PaperUnits','points', ... + 'Position',[0.59140625 0.4560546875 0.3046875 0.4619140625], ... + 'Tag','Fig3'); +if ver_53_or_newer, set(h0,'ToolBar','none'); end +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.02051282051282051 0.08456659619450317 0.9641025641025641 0.8921775898520086], ... + 'Style','frame', ... + 'Tag','Frame1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.5308 0.1374 0.4000 0.3742], ... + 'Style','frame', ... + 'Tag','Frame3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'ListboxTop',0, ... + 'Position',[0.08012820512820512 0.1416490486257928 0.4102564102564102 0.3699788583509514], ... + 'Style','frame', ... + 'Tag','Frame2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','close(gcbf);', ... + 'ListboxTop',0, ... + 'Position',[0.6410 0.0036 0.2897 0.0740], ... + 'FontUnits','normalized',... + 'String','Cancel', ... + 'Tag','Pushbutton2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','som_gui(''set_new_parameters'');', ... + 'ListboxTop',0, ... + 'Position',[0.1026 0.0036 0.2897 0.0740], ... + 'String','Set parameters', ... + 'FontUnits','normalized',... + 'Tag','Pushbutton1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'ListboxTop',0, ... + 'Max',4, ... + 'Min',1, ... + 'Position',[0.7051282051282051 0.6723044397463003 0.1923076923076923 0.040169133192389], ... + 'String',temp1, ... + 'FontUnits','normalized',... + 'Style','popupmenu', ... + 'Tag','PopupMenu2', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'ListboxTop',0, ... + 'Max',4, ... + 'Min',1, ... + 'Position',[0.2948717948717949 0.6670190274841438 0.1923076923076923 0.03964059196617336], ... + 'String',temp2, ... + 'FontUnits','normalized',... + 'Style','popupmenu', ... + 'Tag','PopupMenu1', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'Callback','som_gui(''batch_cancel'');', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.5076923076923077 0.6575052854122622 0.1923076923076923 0.05285412262156448], ... + 'String','tracking', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText6'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'Callback','som_gui(''batch_cancel'');', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09615384615384615 0.6553911205073996 0.1923076923076923 0.05285412262156448], ... + 'String','neigh.', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText5'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09615384615384615 0.7526427061310783 0.09487179487179487 0.04228329809725159], ... + 'String','mask:', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Position',[0.2948717948717949 0.7399577167019028 0.6025641025641025 0.07399577167019028], ... + 'String',' ', ... + 'FontUnits','normalized',... + 'Style','listbox', ... + 'Tag','Listbox2', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.0962 0.8060 0.1154 0.0529], ... + 'FontUnits','normalized',... + 'String','Set', ... + 'Style','text', ... + 'Tag','StaticText3'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''set_batch_mask'');', ... + 'Position',[0.2948717948717949 0.8165961945031712 0.3205128205128205 0.05285412262156448], ... + 'String',' ', ... + 'FontUnits','normalized',... + 'Style','listbox', ... + 'Tag','Listbox1', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.6250 0.8060 0.1603 0.0529], ... + 'String','to value', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText4'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''set_batch_mask'');', ... + 'ListboxTop',0, ... + 'Position',[0.7923076923076923 0.8181818181818182 0.09487179487179487 0.05285412262156448], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText2'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''check_fine_trainlen'');', ... + 'ListboxTop',0, ... + 'Position',[0.7923 0.2352 0.0974 0.0402], ... + 'FontUnits','normalized',... + 'Style','edit', ... + 'Tag','EditText11'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''check_fine_alphaini'');', ... + 'Enable','off', ... + 'ListboxTop',0, ... + 'Position',[0.7923076923076923 0.1664904862579281 0.09743589743589742 0.03805496828752643], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText10', ... + 'Visible','off'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''check_fine_radfin'');', ... + 'ListboxTop',0, ... + 'Position',[0.7923076923076923 0.3002114164904862 0.09743589743589742 0.040169133192389], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText9'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''check_fine_radini'');', ... + 'ListboxTop',0, ... + 'Position',[0.7923076923076923 0.3657505285412262 0.09743589743589742 0.040169133192389], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText8'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.5590 0.2326 0.2179 0.0402], ... + 'String','training length', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText16'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.5590 0.1665 0.2179 0.0381], ... + 'String','alpha initial', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText14', ... + 'Visible','off'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.5590 0.2981 0.2179 0.0402], ... + 'String','radius final', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText12'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.5590 0.3636 0.2179 0.0402], ... + 'String','radius initial', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText10'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''check_rough_trainlen'');', ... + 'ListboxTop',0, ... + 'Position',[0.3590 0.2352 0.0949 0.0402], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText7'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''check_rough_alphaini'');', ... + 'Enable','off', ... + 'ListboxTop',0, ... + 'Position',[0.3590 0.1691 0.0949 0.0381], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText6', ... + 'Visible','off'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''check_rough_radfin'');', ... + 'ListboxTop',0, ... + 'Position',[0.358974358974359 0.3044397463002114 0.09487179487179487 0.040169133192389], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText5'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','som_gui(''check_rough_radini'');', ... + 'ListboxTop',0, ... + 'Position',[0.358974358974359 0.3699788583509514 0.09487179487179487 0.040169133192389], ... + 'Style','edit', ... + 'FontUnits','normalized',... + 'Tag','EditText4'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.0962 0.2326 0.2179 0.0402], ... + 'String','training length', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText15'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.0962 0.1691 0.2179 0.0381], ... + 'String','alpha initial', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText13', ... + 'Visible','off'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.0962 0.3023 0.2179 0.0402], ... + 'String','radius final', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText11'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.0962 0.3679 0.2179 0.0402], ... + 'FontUnits','normalized',... + 'String','radius initial', ... + 'Style','text', ... + 'Tag','StaticText9'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'ListboxTop',0, ... + 'Position',[0.5948717948717949 0.4291754756871036 0.2871794871794872 0.05285412262156448], ... + 'String','Finetune', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText8'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'ListboxTop',0, ... + 'Position',[0.1205128205128205 0.4355179704016914 0.3153846153846154 0.04862579281183932], ... + 'String','Rough', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText7'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'FontWeight','bold', ... + 'ListboxTop',0, ... + 'Position',[0.1641025641025641 0.8900634249471459 0.7025641025641025 0.05285412262156448], ... + 'String','Change parameters for batch training', ... + 'Style','text', ... + 'FontUnits','normalized',... + 'Tag','StaticText1'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09615384615384615 0.6025369978858351 0.1743589743589744 0.040169133192389], ... + 'String','length type:', ... + 'Style','text', ... + 'FontUnits','normalized',... + 'Tag','StaticText17'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'ListboxTop',0, ... + 'Max',2, ... + 'Min',1, ... + 'Position',[0.2948717948717949 0.6062367864693446 0.1923076923076923 0.03964059196617336], ... + 'String',temp3, ... + 'FontUnits','normalized',... + 'Style','popupmenu', ... + 'Tag','PopupMenu3', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.5102564102564102 0.6004228329809724 0.1641025641025641 0.040169133192389], ... + 'String','order', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText18'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Max',2, ... + 'Min',1, ... + 'Position',[0.7051282051282051 0.6109936575052853 0.1923076923076923 0.040169133192389], ... + 'String',temp4, ... + 'FontUnits','normalized',... + 'Style','popupmenu', ... + 'Tag','PopupMenu4', ... + 'Value',1); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'ListboxTop',0, ... + 'Position',[0.09615384615384615 0.5369978858350951 0.2051282051282051 0.040169133192389], ... + 'String','learning func', ... + 'FontUnits','normalized',... + 'Style','text', ... + 'Tag','StaticText19'); +h1 = uicontrol('Parent',h0, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701960784313725 0.701960784313725 0.701960784313725], ... + 'ListboxTop',0, ... + 'Max',3, ... + 'Min',1, ... + 'Position',[0.2948717948717949 0.5454545454545455 0.1923076923076923 0.03964059196617336], ... + 'String',temp5, ... + 'FontUnits','normalized',... + 'Style','popupmenu', ... + 'Tag','PopupMenu5', ... + 'Value',1); +if nargout > 0, fig = h0; end + + +function print_info(sS,level,fid) + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + + +%error(nargchk(1, 2, nargin)) % check no. of input args is correct + +if ~isstruct(sS), + if ~iscell(sS) | ~isstruct(sS{1}), + error('Input argument is not a struct or a cell array of structs.') + end + csS = sS; +else + csS = {sS}; +end + +if nargin<2 | isempty(level) | isnan(level), level = 1; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% print struct information + +for c=1:length(csS), + sS = csS{c}; + + switch sS.type, + case 'som_map', + mdim = length(sS.topol.msize); + [munits dim] = size(sS.codebook); + t = length(sS.trainhist); + if t==0, st='uninitialized'; + elseif t==1, st = 'initialized'; + else st = sprintf('initialized, trained %d times',t-1); + end + + % level 1 + fprintf(fid,'%% Struct type : %s\n', sS.type); + fprintf(fid,'%% Map name : %s\n', sS.name); + fprintf(fid,'%% Input dimension : %d\n', dim); + fprintf(fid,'%% Map grid size : '); + for i = 1:mdim - 1, fprintf(fid,'%d x ',sS.topol.msize(i)); end + fprintf(fid,'%d\n', sS.topol.msize(mdim)); + fprintf(fid,'%% Lattice type (rect/hexa) : %s\n', sS.topol.lattice); + fprintf(fid,'%% Shape (sheet/cyl/toroid) : %s\n', sS.topol.shape); + fprintf(fid,'%% Neighborhood type : %s\n', sS.neigh); + fprintf(fid,'%% Mask : '); + if dim, + for i = 1:dim-1, fprintf(fid,'%d ',sS.mask(i)); end; + fprintf(fid,'%d\n',sS.mask(dim)); + else fprintf(fid,'%% \n'); + end + fprintf(fid,'%% Training status : %s\n', st); + + % level 1, + status = cell(dim,1); + for i=1:dim, + n = length(sS.comp_norm{i}); + if n, + uninit = strcmp('uninit',{sS.comp_norm{i}.status}); + done = strcmp('done',{sS.comp_norm{i}.status}); + undone = strcmp('undone',{sS.comp_norm{i}.status}); + if sum(uninit)==n, status{i} = 'no normalization'; + elseif sum(done)==n, status{i} = 'normalized'; + elseif sum(undone)==n, status{i} = 'denormalized'; + else status{i} = 'partial'; + end + else status{i} = 'no normalization'; end + end + if level>1, + fprintf(fid,'%% Vector components\n'); + M = sS.codebook; + fprintf(fid,'%% # name mask min mean max std status\n'); + fprintf(fid,'%% --- ------------ ---- ------ ------ ------ ------ ------\n'); + for i = 1:dim, + fprintf(fid,'%% %-3d %-12s %-4.2f %6.1g %6.1g %6.1g %6.1g %s\n', ... + i,sS.comp_names{i}, sS.mask(i), ... + min(M(:,i)),mean(M(:,i)),max(M(:,i)),std(M(:,i)),status{i}); + end + end + + % level 3 + if level>2, + fprintf(fid,'%% Vector component normalizations\n'); + fprintf(fid,'%% # name method (i=uninit,u=undone,d=done)\n'); + fprintf(fid,'%% --- ------------ ---------------------------------------\n'); + for i=1:dim, + fprintf(fid,'%% %-3d %-12s ',i,sS.comp_names{i}); + n = length(sS.comp_norm{i}); + for j=1:n, + m = sS.comp_norm{i}(j).method; + s = sS.comp_norm{i}(j).status; + if strcmp(s,'uninit'), c='i'; + elseif strcmp(s,'undone'), c='u'; + else c='d'; + end + fprintf(fid,'%% %s[%s] ',m,c); + end + fprintf(fid,'%% \n'); + end + end + + % level 4 + if level>3, + fprintf(fid,'%% Training history\n'); + for i=1:t, + sT = sS.trainhist(i); + fprintf(fid,'%% * Algorithm: %8s Data: %13s Trainlen: %8d\n',... + sT.algorithm,sT.data_name,sT.trainlen); + %if i>1, + fprintf(fid,'%% Neighborh: %8s Mask: ',sT.neigh); + for i = 1:dim-1, fprintf(fid,'%% %d ',sT.mask(i)); end; + fprintf(fid,'%% %d\n',sT.mask(mdim)); + fprintf(fid,'%% Radius: %4.2f->%4.2f Alpha: %5.3f (%s)\n', ... + sT.radius_ini,sT.radius_fin,sT.alpha_ini,sT.alpha_type); + %end + fprintf(fid,'%% Time: %s\n',sT.time); + end + end + + case 'som_data', + + [dlen dim] = size(sS.data); + if dlen*dim + ind = find(~isnan(sum(sS.data),2)); + else ind = []; end + complete = size(sS.data(ind,:),1); + partial = dlen - complete; + values = prod(size(sS.data)); + missing = sum(sum(isnan(sS.data))); + + % level 1 + fprintf(fid,'%% Struct type : %s\n', sS.type); + fprintf(fid,'%% Data name : %s\n', sS.name); + fprintf(fid,'%% Vector dimension : %d\n', dim); + fprintf(fid,'%% Number of data vectors : %d\n', dlen); + fprintf(fid,'%% Complete data vectors : %d\n', complete); + fprintf(fid,'%% Partial data vectors : %d\n', partial); + if values, r = floor(100 * (values - missing) / values); else r = 0; end + fprintf(fid,'%% Complete values : %d of %d (%d%%)\n', ... + values-missing, values, r); + + % level 2, + status = cell(dim,1); + for i=1:dim, + n = length(sS.comp_norm{i}); + if n, + uninit = strcmp('uninit',{sS.comp_norm{i}.status}); + done = strcmp('done',{sS.comp_norm{i}.status}); + undone = strcmp('undone',{sS.comp_norm{i}.status}); + if sum(uninit)==n, status{i} = 'no normalization'; + elseif sum(done)==n, status{i} = 'normalized'; + elseif sum(undone)==n, status{i} = 'denormalized'; + else status{i} = 'partial'; + end + else status{i} = 'no normalization'; end + end + if level>1, + fprintf(fid,'%% Vector components\n'); + D = sS.data; + fprintf(fid,'%% # name min mean max std missing status\n'); + fprintf(fid,'%% --- ------------ ------ ------ ------ ------ ----------- ------\n'); + for i = 1:dim, + known = find(~isnan(D(:,i))); + miss = dlen-length(known); + fprintf(fid,'%% %-3d %-12s %6.1g %6.1g %6.1g %6.1g %5d (%2d%%) %s\n', ... + i,sS.comp_names{i}, ... + min(D(known,i)),mean(D(known,i)),max(D(known,i)),std(D(known,i)), ... + miss,floor(100*miss/dlen),status{i}); + end + end + + % level 3 + if level>2, + fprintf(fid,'%% Vector component normalizations\n'); + fprintf(fid,'%% # name method (i=uninit,u=undone,d=done)\n'); + fprintf(fid,'%% --- ------------ ---------------------------------------\n'); + for i=1:dim, + fprintf(fid,'%% %-3d %-12s ',i,sS.comp_names{i}); + n = length(sS.comp_norm{i}); + for j=1:n, + m = sS.comp_norm{i}(j).method; + s = sS.comp_norm{i}(j).status; + if strcmp(s,'uninit'), c='i'; + elseif strcmp(s,'undone'), c='u'; + else c='d'; + end + fprintf(fid,'%% %s[%s] ',m,c); + end + fprintf(fid,'%% \n'); + end + end + + case 'som_topol', + + mdim = length(sS.msize); + + % level 1 + fprintf(fid,'%% Struct type : %s\n',sS.type); + fprintf(fid,'%% Map grid size : '); + for i = 1:mdim - 1, fprintf(fid,'%% %d x ',sS.msize(i)); end + fprintf(fid,'%% %d\n', sS.msize(mdim)); + fprintf(fid,'%% Lattice type (rect/hexa) : %s\n', sS.lattice); + fprintf(fid,'%% Shape (sheet/cyl/toroid) : %s\n', sS.shape); + + case 'som_train', + + % level 1 + fprintf(fid,'%% Struct type : %s\n',sS.type); + fprintf(fid,'%% Training algorithm : %s\n',sS.algorithm); + fprintf(fid,'%% Training data : %s\n',sS.data_name); + fprintf(fid,'%% Neighborhood function : %s\n',sS.neigh); + fprintf(fid,'%% Mask : '); + dim = length(sS.mask); + if dim, + for i = 1:dim-1, fprintf(fid,'%% %d ',sS.mask(i)); end; + fprintf(fid,'%% %d\n',sS.mask(end)); + else fprintf(fid,'%% \n'); end + fprintf(fid,'%% Initial radius : %-6.1f\n',sS.radius_ini); + fprintf(fid,'%% Final radius : %-6.1f\n',sS.radius_fin); + fprintf(fid,'%% Initial learning rate (alpha) : %-6.1f\n',sS.alpha_ini); + fprintf(fid,'%% Alpha function type (linear/inv) : %s\n',sS.alpha_type); + fprintf(fid,'%% Training length : %d\n',sS.trainlen); + fprintf(fid,'%% Average quantization error : %-6.1f\n',sS.qerror); + fprintf(fid,'%% When training was done : %s\n',sS.time); + + case 'som_norm', + + % level 1 + fprintf(fid,'%% Struct type : %s\n',sS.type); + fprintf(fid,'%% Normalization method : %s\n',sS.method); + fprintf(fid,'%% Status : %s\n',sS.status); + + % level 2 + if level>1, + fprintf(fid,'%% Parameters:\n'); + sS.params + end + end +end + +function [] = html2tex(html_addres,texfile) + +tempfile = tempname; +fid = fopen(texfile,'w'); +eval(['!lynx -dump ' html_addres ' > ' tempfile]); +fid2 = fopen(tempfile,'r'); +while not(feof(fid2)) + line = fgets(fid2); + line = strcat('%',line); + fprintf(fid,'%s',line); +end + fclose(fid); + fclose(fid2); + delete (tempfile); + + +function [name] = retname +resnames = who; +if size(resnames,1) > 0 + max_length = size(resnames{1},2); + for index = 1:size(resnames,1) + if size(resnames{index},2) > max_length + max_length = size(resnames{index},2); + end + end + length = max_length + 1; + name(:,1:1:length) = 'A' +else + name = 'A'; +end +%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_hits.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,217 @@ +function [hits] = som_hits(sMap, sData, mode) + +%SOM_HITS Calculate the response of the given data on the map. +% +% hits = som_hits(sMap, sData, [mode]) +% +% h = som_hits(sMap,sData); +% h = som_hits(sMap,sData,'fuzzy'); +% +% Input and output arguments ([]'s are optional): +% sMap (struct) map struct +% (matrix) codebook matrix, size munits x dim +% sData (struct) data struct +% (matrix) data matrix, size dlen x dim +% [mode] (string) 'crisp' (default), 'kernel', 'fuzzy' +% +% hits (vector) the number of hits in each map unit, length = munits +% +% The response of the data on the map can be calculated e.g. in +% three ways, selected with the mode argument: +% 'crisp' traditional hit histogram +% 'kernel' a sum of dlen neighborhood kernels, where kernel +% is positioned on the BMU of each data sample. The +% neighborhood function is sMap.neigh and the +% neighborhood width is sMap.trainhist(end).radius_fin +% or 1 if this is empty or NaN +% 'fuzzy' fuzzy response calculated by summing 1./(1+(q/a)^2) +% for each data sample, where q is a vector containing +% distance from the data sample to each map unit and +% a is average quantization error +% +% For more help, try 'type som_hits' or check out online documentation. +% See also SOM_AUTOLABEL, SOM_BMUS. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_hits +% +% PURPOSE +% +% Calculate the response of the given data on the map. +% +% SYNTAX +% +% hits = som_hits(sMap, sData) +% hits = som_hits(M, D) +% hits = som_hits(..., mode) +% +% DESCRIPTION +% +% Returns a vector indicating the response of the map to the data. +% The response of the data on the map can be calculated e.g. in +% three ways, selected with the mode argument: +% 'crisp' traditional hit histogram: how many times each map unit +% was the BMU for the data set +% 'kernel' a sum of neighborhood kernels, where a kernel +% is positioned on the BMU of each data sample. The +% neighborhood function is sMap.neigh and the +% neighborhood width is sMap.trainhist(end).radius_fin +% or 1 if this is not available +% 'fuzzy' fuzzy response calculated by summing +% +% 1 +% ------------ +% 1 + (q/a)^2 +% +% for each data sample, where q is a vector containing +% distance from the data sample to each map unit and +% a is average quantization error +% +% REQUIRED INPUT ARGUMENTS +% +% sMap The vectors from among which the BMUs are searched +% for. These must not have any unknown components (NaNs). +% (struct) map struct +% (matrix) codebook matrix, size munits x dim +% +% sData The data vector(s) for which the BMUs are searched. +% (struct) data struct +% (matrix) data matrix, size dlen x dim +% +% OPTIONAL INPUT ARGUMENTS +% +% mode (string) The respond mode: 'crisp' (default), 'kernel' +% or 'fuzzy'. 'kernel' can only be used if +% the first argument (sMap) is a map struct. +% +% OUTPUT ARGUMENTS +% +% hits (vector) The number of hits in each map unit. +% +% EXAMPLES +% +% hits = som_hits(sM,D); +% hits = som_hits(sM,D,'kernel'); +% hits = som_hits(sM,D,'fuzzy'); +% +% SEE ALSO +% +% som_bmus Find BMUs and quantization errors for a given data set. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 220997 +% Version 2.0beta juuso 161199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(2, 3, nargin)); % check no. of input args is correct + +if isstruct(sMap), + switch sMap.type, + case 'som_map', munits = prod(sMap.topol.msize); + case 'som_data', munits = size(sMap.data,1); + otherwise, + error('Illegal struct for 1st argument.') + end +else + munits = size(sMap,1); +end +hits = zeros(munits,1); + +if nargin<3, mode = 'crisp'; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +% calculate BMUs +[bmus,qerrs] = som_bmus(sMap,sData,1); + +switch mode, +case 'crisp', + + % for each unit, check how many hits it got + for i=1:munits, hits(i) = sum(bmus == i); end + +case 'kernel', + + % check that sMap really is a map + if ~isstruct(sMap) & ~strcmp(sMap.type,'som_map'), + error('Kernel mode can only be used for maps.'); + end + + % calculate neighborhood kernel + Ud = som_unit_dists(sMap.topol).^2; + sTrain = sMap.trainhist(end); + if ~isempty(sTrain), + rad = sTrain.radius_fin; + if isempty(rad) | isnan(rad), rad = 1; end + else + rad = 1; + end + rad = rad^2; + if rad==0, rad = eps; end % to avoid divide-by-0 errors + switch sTrain.neigh, + case 'bubble', H = (Ud<=rad); + case 'gaussian', H = exp(-Ud/(2*rad)); + case 'cutgauss', H = exp(-Ud/(2*rad)) .* (Ud<=rad); + case 'ep', H = (1-Ud/rad) .* (Ud<=rad); + end + + % weight hits with neighborhood kernel + hits = sum(H(bmus,:),1)'; + +case 'fuzzy', + + % extract the two matrices (M, D) and the mask + mask = []; + if isstruct(sMap), + if strcmp(sMap.type,'som_data'), M = sMap.data; + else M = sMap.codebook; mask = sMap.mask; + end + else M = sMap; + end + if any(isnan(M(:))), + error('Data in first argument must not have any NaNs.'); + end + + if isstruct(sData), + switch sData.type, + case 'som_map', + D = sData.codebook; + if isempty(mask), mask = sData.mask; end + case 'som_data', D = sData.data; + otherwise, error('Illegal 2nd argument.'); + end + else D = sData; + end + [dlen dim] = size(D); + if isempty(mask), mask = ones(dim,1); end + + % scaling factor + a = mean(qerrs).^2; + + % calculate distances & bmus + % (this is better explained in som_batchtrain and som_bmus) + Known = ~isnan(D); D(find(~Known)) = 0; % unknown components + blen = min(munits,dlen); % block size + W1 = mask*ones(1,blen); W2 = ones(munits,1)*mask'; D = D'; Known = Known'; + i0 = 0; + while i0+1<=dlen, + inds = [(i0+1):min(dlen,i0+blen)]; i0 = i0+blen; % indeces + Dist = (M.^2)*(W1(:,1:length(inds)).*Known(:,inds)) ... + + W2*(D(:,inds).^2) ... + - 2*M*diag(mask)*D(:,inds); % squared distances + hits = hits + sum(1./(1+Dist/a),2); + end + + otherwise, + error(['Unknown mode: ' mode]); + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_ind2cod.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function cind = som_ind2cod(msize,ind) + +%SOM_IND2COD SOM_PAK style linear indeces from Matlab linear index. +% +% Cind = som_ind2cod(msize,inds) +% +% cind = som_ind2cod([10 15],44); +% cind = som_ind2cod(sMap,44); +% cind = som_ind2cod(sMap.msize,44); +% Cind = som_ind2cod([10 15],[44 13 91]'); +% +% Input and output arguments: +% msize (struct) map or topology struct +% (vector) size 1 x m, specifies the map grid size +% ind (vector) size n x 1, linear indeces of n map units +% +% cind (matrix) size n x 1, SOM_PAK style linear indeces +% (row first, then column) +% +% See also SOM_COD2IND. + +% Contributed to SOM Toolbox vs2, January 14th, 2002 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 140102 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(msize), + if strcmp(msize.type,'som_map'), msize = msize.topol.msize; + elseif strcmp(msize.type,'som_topol'), msize = msize.msize; + else error('Invalid first argument.'); end +end + +if nargin<2, ind = 1:prod(msize); end + +Co = som_unit_coords(msize,'rect','sheet'); + +switch size(Co,2), +case 1, I2C = [1:prod(msize)]; +case 2, I2C = 1 + Co(:,1) + Co(:,2)*msize(2); +case 3, I2C = 1 + Co(:,1) + Co(:,2)*msize(2) + Co(:,3)*msize(1)*msize(2); % ????? +end + +cind = I2C(ind); +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_ind2sub.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +function Subs = som_ind2sub(msize,inds) + +%SOM_IND2SUB Map grid subscripts from linear index. +% +% Subs = som_ind2sub(msize,inds) +% +% sub = som_ind2sub([10 15],44); +% sub = som_ind2sub(sMap,44); +% sub = som_ind2sub(sMap.msize,44); +% Subs = som_ind2sub([10 15],[44 13 91]'); +% +% Input and output arguments: +% msize (struct) map or topology struct +% (vector) size 1 x m, specifies the map grid size +% inds (vector) size n x 1, linear indeces of n map units +% +% Subs (matrix) size n x m, the subscripts +% +% See also SOM_SUB2IND. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 300798 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(msize), + if strcmp(msize.type,'som_map'), msize = msize.topol.msize; + elseif strcmp(msize.type,'som_topol'), msize = msize.msize; + else error('Invalid first argument.'); end +end + +n = length(msize); +k = [1 cumprod(msize(1:end-1))]; +inds = inds - 1; +for i = n:-1:1, + Subs(:,i) = floor(inds/k(i))+1; + inds = rem(inds,k(i)); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_info.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,403 @@ +function som_info(sS,level) + +%SOM_INFO Displays information on the given SOM Toolbox struct. +% +% som_info(sS,[level]) +% +% som_info(sMap); +% som_info(sData,3); +% som_info({sMap,sData}); +% som_info(sMap.comp_norm{2}); +% +% Input and output arguments ([]'s are optional): +% sS (struct) SOM Toolbox struct +% (cell array of structs) several structs in a cell array +% [level] (scalar) detail level (1-4), default = 1 +% +% For more help, try 'type som_info' or check out online documentation. +% See also SOM_SET. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_info +% +% PURPOSE +% +% Display information of the given SOM Toolbox struct(s). +% +% SYNTAX +% +% som_info(sM) +% som_info({sM,sD}) +% som_info(...,level) +% +% DESCRIPTION +% +% Display the contents of the given SOM Toolbox struct(s). Information +% of several structs can be shown if the structs are given in a cell +% array. The level of detail can be varied with the second argument. +% The number of different levels varies between structs. For map and +% data structs, not only the fields, but also some statistics of the +% vectors ('.data' and '.codebook' fields) is displayed. +% +% map struct +% level 1: name, dimension, topology, dimension, neigborhood function, +% mask and training status +% level 2: ..., training history +% level 3: ..., vector component names, statistics and normalization status +% level 4: ..., vector component normalizations +% +% data struct: +% level 1: name, dimension, data set completeness statistics +% level 2: ..., vector component names, statistics and normalization status +% level 3: ..., vector component normalizations +% level 4: ..., label statistics +% +% topology struct: +% level 1: all fields +% +% train struct: +% level 1: all fields +% +% normalization struct: +% level 1: method, status +% level 2: ..., parameters +% +% REQUIRED INPUT ARGUMENTS +% +% sS (struct) SOM Toolbox struct +% (cell array of structs) several structs in a cell array +% +% OPTIONAL INPUT ARGUMENTS +% +% level (scalar) detail level (1-4), default = 1 +% +% EXAMPLES +% +% som_info(sM) +% som_info(sM,4) +% som_info(sM.trainhist) +% som_info(sM.comp_norm{3}) +% +% SEE ALSO +% +% som_set Set fields and create SOM Toolbox structs. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 110997 +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(1, 2, nargin)) % check no. of input args is correct + +if ~isstruct(sS), + if ~iscell(sS) | ~isstruct(sS{1}), + error('Invalid first input argument.') + end + csS = sS; +else + l = length(sS); + csS = cell(l,1); + for i=1:l, csS{i} = sS(i); end +end + +if nargin<2 | isempty(level) | isnan(level), level = 1; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% print struct information + +for c=1:length(csS), + sS = csS{c}; + fprintf(1,'\n'); + + switch sS.type, + case 'som_map', + mdim = length(sS.topol.msize); + [munits dim] = size(sS.codebook); + t = length(sS.trainhist); + if t==0, st='uninitialized'; + elseif t==1, st = 'initialized'; + else st = sprintf('initialized, trained %d times',t-1); + end + + % level 1 + fprintf(1,'Struct type : %s\n', sS.type); + fprintf(1,'Map name : %s\n', sS.name); + fprintf(1,'Input dimension : %d\n', dim); + fprintf(1,'Map grid size : '); + for i = 1:mdim - 1, fprintf(1,'%d x ',sS.topol.msize(i)); end + fprintf(1,'%d\n', sS.topol.msize(mdim)); + fprintf(1,'Lattice type (rect/hexa) : %s\n', sS.topol.lattice); + fprintf(1,'Shape (sheet/cyl/toroid) : %s\n', sS.topol.shape); + fprintf(1,'Neighborhood type : %s\n', sS.neigh); + fprintf(1,'Mask : '); + if dim, + for i = 1:dim-1, fprintf(1,'%d ',sS.mask(i)); end; + fprintf(1,'%d\n',sS.mask(dim)); + else fprintf(1,'\n'); + end + fprintf(1,'Training status : %s\n', st); + + % level 1, + status = cell(dim,1); + for i=1:dim, + n = length(sS.comp_norm{i}); + if n, + uninit = strcmp('uninit',{sS.comp_norm{i}.status}); + done = strcmp('done',{sS.comp_norm{i}.status}); + undone = strcmp('undone',{sS.comp_norm{i}.status}); + if sum(uninit)==n, status{i} = 'none'; + elseif sum(done)==n, status{i} = 'done'; + elseif sum(undone)==n, status{i} = 'undone'; + else status{i} = 'partial'; + end + else status{i} = 'no normalization'; end + end + if level>1, + fprintf(1,'\nVector components\n'); + M = sS.codebook; + fprintf(1,' # name mask min mean max std normalization\n'); + fprintf(1,' --- ------------ ---- ------ ------ ------ ------ -------------\n'); + for i = 1:dim, + fprintf(1,' %-3d %-12s %-4.2f %6.2g %6.2g %6.2g %6.2g %s\n', ... + i,sS.comp_names{i}, sS.mask(i), ... + min(M(:,i)),mean(M(:,i)),max(M(:,i)),std(M(:,i)),status{i}); + end + end + + % level 3 + if level>2, + fprintf(1,'\nVector component normalizations\n'); + fprintf(1,' # name method (i=uninit,u=undone,d=done)\n'); + fprintf(1,' --- ------------ ---------------------------------------\n'); + for i=1:dim, + fprintf(1,' %-3d %-12s ',i,sS.comp_names{i}); + n = length(sS.comp_norm{i}); + for j=1:n, + m = sS.comp_norm{i}(j).method; + s = sS.comp_norm{i}(j).status; + if strcmp(s,'uninit'), c='i'; + elseif strcmp(s,'undone'), c='u'; + else c='d'; + end + fprintf(1,'%s[%s] ',m,c); + end + fprintf(1,'\n'); + end + end + + % level 4 + if level>3, + fprintf(1,'\nTraining history\n'); + fprintf(1,'Algorithm Data Trainlen Neigh.f. Radius Alpha (type) Date\n'); + fprintf(1,'--------- ------------- -------- -------- ---------- -------------- --------------------\n'); + for i=1:t, + sT = sS.trainhist(i); + fprintf(1,'%8s %13s %8d %8s %4.2f->%4.2f %5.3f (%6s) %s\n',... + sT.algorithm,sT.data_name,sT.trainlen,... + sT.neigh,sT.radius_ini,sT.radius_fin,sT.alpha_ini,sT.alpha_type,sT.time); + %for j = 1:length(sT.mask)-1, fprintf(1,'%d ',sT.mask(j)); end; + %if ~isempty(sT.mask), fprintf(1,'%d\n',sT.mask(end)); else fprintf(1,'\n'); end + end + end + + case 'som_data', + + [dlen dim] = size(sS.data); + if dlen*dim + if dim>1, ind = find(~isnan(sum(sS.data,2))); + else ind = find(~isnan(sS.data)); + end + else ind = []; end + complete = size(sS.data(ind,:),1); + partial = dlen - complete; + values = prod(size(sS.data)); + missing = sum(sum(isnan(sS.data))); + + % level 1 + fprintf(1,'Struct type : %s\n', sS.type); + fprintf(1,'Data name : %s\n', sS.name); + fprintf(1,'Vector dimension : %d\n', dim); + fprintf(1,'Number of data vectors : %d\n', dlen); + fprintf(1,'Complete data vectors : %d\n', complete); + fprintf(1,'Partial data vectors : %d\n', partial); + if values, r = floor(100 * (values - missing) / values); else r = 0; end + fprintf(1,'Complete values : %d of %d (%d%%)\n', ... + values-missing, values, r); + + % level 2, + status = cell(dim,1); + for i=1:dim, + n = length(sS.comp_norm{i}); + if n, + uninit = strcmp('uninit',{sS.comp_norm{i}.status}); + done = strcmp('done',{sS.comp_norm{i}.status}); + undone = strcmp('undone',{sS.comp_norm{i}.status}); + if sum(uninit)==n, status{i} = 'none'; + elseif sum(done)==n, status{i} = 'done'; + elseif sum(undone)==n, status{i} = 'undone'; + else status{i} = 'partial'; + end + else status{i} = 'no normalization'; end + end + if level>1, + fprintf(1,'\nVector components\n'); + D = sS.data; + fprintf(1,' # name min mean max std missing normalization\n'); + fprintf(1,' --- ------------ ------ ------ ------ ------ ----------- -------------\n'); + for i = 1:dim, + known = find(~isnan(D(:,i))); + miss = dlen-length(known); + switch length(known), + case 0, mi = NaN; me = NaN; ma = NaN; st = NaN; + case 1, mi = D(known,i); me = mi; ma = mi; st = 0; + otherwise, + mi = min(D(known,i)); ma = max(D(known,i)); + me = mean(D(known,i)); st = std(D(known,i)); + end + fprintf(1,' %-3d %-12s %6.2g %6.2g %6.2g %6.2g %5d (%2d%%) %s\n', ... + i,sS.comp_names{i},mi,me,ma,st,miss,floor(100*miss/dlen),status{i}); + end + end + + % level 3 + if level>2, + fprintf(1,'\nVector component normalizations\n'); + fprintf(1,' # name method (i=uninit,u=undone,d=done)\n'); + fprintf(1,' --- ------------ ---------------------------------------\n'); + for i=1:dim, + fprintf(1,' %-3d %-12s ',i,sS.comp_names{i}); + n = length(sS.comp_norm{i}); + for j=1:n, + m = sS.comp_norm{i}(j).method; + s = sS.comp_norm{i}(j).status; + if strcmp(s,'uninit'), c='i'; + elseif strcmp(s,'undone'), c='u'; + else c='d'; + end + fprintf(1,'%s[%s] ',m,c); + end + fprintf(1,'\n'); + end + end + + % level 4 + if level>3, + m = size(sS.labels,2); + fprintf(1,'\nLabels\n'); + if isempty(sS.label_names), + labs = {''}; freq = 0; + for i=1:dlen*m, + l = sS.labels{i}; + if isempty(l), freq(1) = freq(1)+1; + else + k = find(strcmp(labs,l)); + if isempty(k), labs{end+1} = l; freq(end+1) = 1; + else freq(k)=freq(k)+1; + end + end + end + emp = freq(1); + uni = length(freq)-1; + if uni>0, tot = sum(freq(2:end)); else tot = 0; end + fprintf(1,' Total: %d\n Empty: %d\n Unique: %d\n',tot,emp,uni); + else + for j=1:m, + labs = {''}; freq = 0; + for i=1:dlen, + l = sS.labels{i,j}; + if isempty(l), freq(1) = freq(1)+1; + else + k = find(strcmp(labs,l)); + if isempty(k), labs{end+1} = l; freq(end+1) = 1; + else freq(k)=freq(k)+1; + end + end + end + emp = freq(1); + uni = length(freq)-1; + if uni>0, tot = sum(freq(2:end)); else tot = 0; end + fprintf(1,' [%s] Total / empty / unique: %d / %d / %d\n', ... + sS.label_names{j},tot,emp,uni); + end + end + end + + case 'som_topol', + + mdim = length(sS.msize); + + % level 1 + fprintf(1,'Struct type : %s\n',sS.type); + fprintf(1,'Map grid size : '); + for i = 1:mdim - 1, fprintf(1,'%d x ',sS.msize(i)); end + fprintf(1,'%d\n', sS.msize(mdim)); + fprintf(1,'Lattice type (rect/hexa) : %s\n', sS.lattice); + fprintf(1,'Shape (sheet/cyl/toroid) : %s\n', sS.shape); + + case 'som_train', + + % level 1 + fprintf(1,'Struct type : %s\n',sS.type); + fprintf(1,'Training algorithm : %s\n',sS.algorithm); + fprintf(1,'Training data : %s\n',sS.data_name); + fprintf(1,'Neighborhood function : %s\n',sS.neigh); + fprintf(1,'Mask : '); + dim = length(sS.mask); + if dim, + for i = 1:dim-1, fprintf(1,'%d ',sS.mask(i)); end; + fprintf(1,'%d\n',sS.mask(end)); + else fprintf(1,'\n'); end + fprintf(1,'Initial radius : %-6.1f\n',sS.radius_ini); + fprintf(1,'Final radius : %-6.1f\n',sS.radius_fin); + fprintf(1,'Initial learning rate (alpha) : %-6.1f\n',sS.alpha_ini); + fprintf(1,'Alpha function type (linear/inv) : %s\n',sS.alpha_type); + fprintf(1,'Training length : %d\n',sS.trainlen); + fprintf(1,'When training was done : %s\n',sS.time); + + case 'som_norm', + + % level 1 + fprintf(1,'Struct type : %s\n',sS.type); + fprintf(1,'Normalization method : %s\n',sS.method); + fprintf(1,'Status : %s\n',sS.status); + + % level 2 + if level>1, + fprintf(1,'Parameters:\n'); + sS.params + end + + case 'som_grid', + + % level 1 + fprintf(1,'Struct type : %s\n',sS.type); + if ischar(sS.neigh), + fprintf(1,'Connections : [%d %d], %s, %s\n',... + sS.msize(1),sS.msize(2),sS.neigh,sS.shape); + else + fprintf(1,'Connections : [%d %d] %d lines\n',... + sS.msize(1),sS.msize(2),sum(sS.neigh)); + end + fprintf(1,'Line : %s\n',sS.line); + if length(sS.marker)==1, + fprintf(1,'Marker : %s\n',sS.marker); + else + fprintf(1,'Marker : varies\n'); + end + fprintf(1,'Surf : '); + if isempty(sS.surf), fprintf(1,'off\n'); else fprintf(1,'on\n'); end + fprintf(1,'Labels : '); + if isempty(sS.label), fprintf(1,'off\n'); + else fprintf(1,'on (%d)\n',sS.labelsize); end + + end + + fprintf(1,'\n'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_kmeans.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,145 @@ +function [codes,clusters,err] = som_kmeans(method, D, k, epochs, verbose) + +% SOM_KMEANS K-means algorithm. +% +% [codes,clusters,err] = som_kmeans(method, D, k, [epochs], [verbose]) +% +% Input and output arguments ([]'s are optional): +% method (string) k-means algorithm type: 'batch' or 'seq' +% D (matrix) data matrix +% (struct) data or map struct +% k (scalar) number of centroids +% [epochs] (scalar) number of training epochs +% [verbose] (scalar) if <> 0 display additonal information +% +% codes (matrix) codebook vectors +% clusters (vector) cluster number for each sample +% err (scalar) total quantization error for the data set +% +% See also KMEANS_CLUSTERS, SOM_MAKE, SOM_BATCHTRAIN, SOM_SEQTRAIN. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Function has been renamed by Kimmo Raivio, because matlab65 also have +% kmeans function 1.10.02 +%% input arguments + +if isstruct(D), + switch D.type, + case 'som_map', data = D.codebook; + case 'som_data', data = D.data; + end +else + data = D; +end +[l dim] = size(data); + +if nargin < 4 | isempty(epochs) | isnan(epochs), epochs = 100; end +if nargin < 5, verbose = 0; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +rand('state', sum(100*clock)); % init rand generator + +lr = 0.5; % learning rate for sequential k-means +temp = randperm(l); +centroids = data(temp(1:k),:); +res = zeros(k,l); +clusters = zeros(1, l); + +if dim==1, + [codes,clusters,err] = scalar_kmeans(data,k,epochs); + return; +end + +switch method + case 'seq', + len = epochs * l; + l_rate = linspace(lr,0,len); + order = randperm(l); + for iter = 1:len + x = D(order(rem(iter,l)+1),:); + dx = x(ones(k,1),:) - centroids; + [dist nearest] = min(sum(dx.^2,2)); + centroids(nearest,:) = centroids(nearest,:) + l_rate(iter)*dx(nearest,:); + end + [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ... + ones(l, 1) * sum((centroids.^2)',1) - ... + 2.*(data*(centroids')))'); + + case 'batch', + iter = 0; + old_clusters = zeros(k, 1); + while iter<epochs + + [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ... + ones(l, 1) * sum((centroids.^2)',1) - ... + 2.*(data*(centroids')))'); + + for i = 1:k + f = find(clusters==i); + s = length(f); + if s, centroids(i,:) = sum(data(f,:)) / s; end + end + + if iter + if sum(old_clusters==clusters)==0 + if verbose, fprintf(1, 'Convergence in %d iterations\n', iter); end + break; + end + end + + old_clusters = clusters; + iter = iter + 1; + end + + [dummy clusters] = min(((ones(k, 1) * sum((data.^2)', 1))' + ... + ones(l, 1) * sum((centroids.^2)',1) - ... + 2.*(data*(centroids')))'); + otherwise, + fprintf(2, 'Unknown method\n'); +end + +err = 0; +for i = 1:k + f = find(clusters==i); + s = length(f); + if s, err = err + sum(sum((data(f,:)-ones(s,1)*centroids(i,:)).^2,2)); end +end + +codes = centroids; +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [y,bm,qe] = scalar_kmeans(x,k,maxepochs) + + nans = ~isfinite(x); + x(nans) = []; + n = length(x); + mi = min(x); ma = max(x) + y = linspace(mi,ma,k)'; + bm = ones(n,1); + bmold = zeros(n,1); + i = 0; + while ~all(bm==bmold) & i<maxepochs, + bmold = bm; + [c bm] = histc(x,[-Inf; (y(2:end)+y(1:end-1))/2; Inf]); + y = full(sum(sparse(bm,1:n,x,k,n),2)); + zh = (c(1:end-1)==0); + y(~zh) = y(~zh)./c(~zh); + inds = find(zh)'; + for j=inds, if j==1, y(j) = mi; else y(j) = y(j-1) + eps; end, end + i=i+1; + end + if i==maxepochs, [c bm] = histc(x,[-Inf; (y(2:end)+y(1:end-1))/2; Inf]); end + if nargout>2, qe = sum(abs(x-y(bm)))/n; end + if any(nans), + notnan = find(~nans); n = length(nans); + y = full(sparse(notnan,1,y ,n,1)); y(nans) = NaN; + bm = full(sparse(notnan,1,bm,n,1)); bm(nans) = NaN; + if nargout>2, qe = full(sparse(notnan,1,qe,n,1)); qe(nans) = NaN; end + end + + return; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_kmeanscolor.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,139 @@ +function [color,best,kmeans]=som_kmeanscolor(sM,C,initRGB,contrast) + +% SOM_KMEANSCOLOR Map unit color code according to K-means clustering +% +% [color, best, kmeans] = som_kmeanscolor(sM, C, [initRGB],[contrast]) +% +% color = som_kmeanscolor(sM,15,som_colorcode(sM,'rgb1'),'enhance'); +% [color,best] = som_kmeanscolor(sM,15,[],'normal'); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map struct +% C (scalar) maximum number of clusters +% initRGB (string, matrix) color code string accepted by SOM_COLORCODE +% or an Mx3 matrix of RGB triples, where M is the number +% of map units. Default: SOM_COLORCODEs default +% contrast (string) 'flat', 'enhanced' color contrast mode, default: +% 'enhanced' +% +% color (matrix) MxCx3 of RGB triples +% best (scalar) index for "best" clustering according to +% Davies-Boulding index; color(:,:,best) includes the +% corresponding color code. +% kmeans (cell) output of KMEANS_CLUSTERS in a cell array. +% +% The function gives a set of color codings according to K-means +% clustering. For clustering, it uses function KMEANS_CLUSTERS for map units, +% and it calculates color codings for 1,2,...,C clusters. +% The idea of coloring is that the color of a cluster is the mean of the +% original colors (RGB values) of the map units belonging to that cluster, +% see SOM_CLUSTERCOLOR. The original colors are defined by SOM_COLORCODE +% by default. Input 'contrast' simply specifies whether or not +% to linearly redistribute R,G, and B values so that minimum is 0 and +% maximum 1 ('enahanced') or to use directly the output of +% SOM_CLUSTERCOLOR ('flat'). KMEANS_CLUSTERS uses certain heuristics to +% select the best of 5 trials for each number of clusters. Evaluating the +% clustering multiple times may take some time. +% +% EXAMPLE +% +% load iris; % or any other map struct sM +% [color,b]=som_kmeanscolor(sM,10); +% som_show(sM,'color',color,'color',{color(:,:,b),'"Best clustering"'); +% +% See also SOM_SHOW, SOM_COLORCODE, SOM_CLUSTERCOLOR, KMEANS_CLUSTERS + +% Contributed to SOM Toolbox 2.0, April 1st, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% corrected help text 11032005 johan + +%%% Check number of inputs + +error(nargchk(2, 4, nargin)); % check no. of input args + +%%% Check input args & set defaults + +if isstruct(sM) & isfield(sM,'type') & strcmp(sM.type,'som_map'), + [tmp,lattice,msize]=vis_planeGetArgs(sM); + munits=prod(msize); + if length(msize)>2 + error('Does not work with 3D maps.') + end +else + error('Map struct requires for first input argument!'); +end + +if ~vis_valuetype(C,{'1x1'}), + error('Scalar value expect for maximum number of clusters.'); +end + +% check initial color coding +if nargin<3 | isempty(initRGB) + initRGB=som_colorcode(sM); +end + +% check contrast checking +if nargin<4 | isempty(contrast), + contrast='enhanced'; +end + +if ~ischar(contrast), + error('String input expected for input arg. ''contrast''.'); +else + switch lower(contrast) + case {'flat','enhanced'} + ; + otherwise + error(['''flat'' or ''enhanced'' expected for '... + 'input argument ''contrast''.']); + end +end + +if ischar(initRGB), + try + initRGB=som_colorcode(sM,initRGB); + catch + error(['Color code ' initRGB ... + 'was not recognized by SOM_COLORCODE.']); + end +elseif vis_valuetype(initRGB,{'nx3rgb',[munits 3]},'all'), + ; +else + error(['The initial color code must be a string '... + 'or an Mx3 matrix of RGB triples.']); +end + +%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +disp('Wait...'); +[c,p,err,ind]=kmeans_clusters(sM,C,5,0); % use 5 trials, verbose off + +% Store outputs to kmeans +kmeans{1}=c; +kmeans{2}=p; +kmeans{3}=err; +kmeans{4}=ind; + +%%% Build output +color=som_clustercolor(sM,cat(2,p{:}),initRGB); +[tmp,best]=min(ind); + +switch contrast +case 'flat' + ; +case 'enhanced' + warning off; + ncolor=maxnorm(color); + ncolor(~isfinite(ncolor))=color(~isfinite(ncolor)); + color=ncolor; + warning on; +end + +%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function X=maxnorm(x) +% normalize columns of x between [0,1] + +x=x-repmat(min(x),[size(x,1) 1 1]); +X=x./repmat(max(x),[size(x,1) 1 1]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_kmeanscolor2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,193 @@ +function [color,centroids]=som_kmeanscolor2(mode,sM,C,initRGB,contrast,R) + +% SOM_KMEANSCOLOR2 Color codes a SOM according to averaged or best K-means clustering +% +% color = som_kmeanscolor2('average',sM, C, [initRGB], [contrast],[R]) +% +% color=som_kmeanscolor2('average',sM,[2 4 8 16],som_colorcode(sM,'rgb1'),'enhanced'); +% [color,centroid]=som_kmeanscolor2('best',sM,15,[],'flat',R); +% +% Input and output arguments ([]'s are optional): +% +% mode (string) 'average' or 'best', defalut: 'average' +% sM (struct) a map struct +% C (vector) number of clusters +% [initRGB] (string, matrix) a color code string accepted by SOM_COLORCODE +% or an Mx3 matrix of RGB triples, where M is the number +% of map units. Default: SOM_COLORCODEs default +% [contrast] (string) 'flat', 'enhanced' color contrast mode, default: +% 'enhanced'. +% [R] (scalar) number of K-means trials, default: 30. +% color (matrix) Mx3xC of RGB triples +% centroid (array of matrices) centroid{i} includes codebook for the best +% k-means for C(i) clusters, i.e. the cluster centroids corresponding to +% the color code color(:,:,i). +% +% The function gives a set of color codes for the SOM according to K-means +% clustering. It has two operation modes: +% +% 'average': The idea of coloring is that the color of the units belonging to the same +% cluster is the mean of the original RGB values (see SOM_COLORCODE) of the map units +% belonging to the cluster (see SOM_CLUSTERCOLOR). The K-means clustering is made, +% by default, 30 times and the resulting color codes are averaged for +% each specified number of clusters C(i), i=1,...,k. In a way, the resulting averaged color +% codes reflect the stability of the K-means clustering made on the map units. +% +% 'best': runs the k-means R times for C(i), i=1,...,n clusters as in previous mode, +% but instead of averaging all the R color codes, it picks the one that corresponds to the +% best k-means clustering for each C(i). The 'best' is the one with the lowest +% quantization error. The result may differ from run to run. +% +% EXAMPLE +% +% load iris; % or any other map struct sM +% color=som_kmeanscolor2('average',sM,[2:6]); +% som_show(sM,'umat','all','color',color); +% +% See also SOM_KMEANS, SOM_SHOW, SOM_COLORCODE, SOM_CLUSTERCOLOR, SOM_KMEANSCOLOR + +% Contributed to SOM Toolbox 2.0, 2001 February by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +%%% Check number of inputs + +error(nargchk(3, 6, nargin)); % check no. of input args + +%%% Check input args & set defaults + +if ~vis_valuetype(mode,{'string'}), + error('Mode must be a string.'); +end +switch lower(mode), +case{'average','best'} + ; +otherwise + error('Mode must be string ''average'' or ''best''.'); +end + +if isstruct(sM) & isfield(sM,'type') & strcmp(sM.type,'som_map'), + [tmp,lattice,msize]=vis_planeGetArgs(sM); + munits=prod(msize); + if length(msize)>2 + error('Does not work with 3D maps.') + end +else + error('Map struct required for the second input argument!'); +end + +if ~vis_valuetype(C,{'1xn','nx1'}), + error('Vector value expected for cluster number.'); +end + +% Round C and check +C=round(C(:)'); + +if any(C<2), + error('Cluster number must be 2 or more.'); +end + +% check initial color coding +if nargin<4 | isempty(initRGB) + initRGB=som_colorcode(sM); +end + +% check contrast checking +if nargin<5 | isempty(contrast), + contrast='enhanced'; +end + +if ~ischar(contrast), + error('String input expected for input arg. ''contrast''.'); +else + switch lower(contrast) + case {'flat','enhanced'} + ; + otherwise + error(['''flat'' or ''enhanced'' expected for '... + 'input argument ''contrast''.']); + end +end + +if ischar(initRGB), + try + initRGB=som_colorcode(sM,initRGB); + catch + error(['Color code ' initRGB ... + 'was not recognized by SOM_COLORCODE.']); + end +elseif vis_valuetype(initRGB,{'nx3rgb',[munits 3]},'all'), + ; +else + error(['The initial color code must be a string '... + 'or an Mx3 matrix of RGB triples.']); +end + +if nargin<6|isempty(R), + R=30; +end + +if ~vis_valuetype(R,{'1x1'}), + error('''R'' must be scalar.'); +end + +%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +disp('Wait...'); +index=0; hit_=zeros(munits,munits); + +switch mode, + %% Averaged k-means coloring +case 'average' + for k=C, + disp(['Running K-means for ' num2str(k) ' clusters...']); + color_=zeros(munits,3); + colord_=color_; + % Average R k-means colorings for C clusters + for j=1:R, + [dummy,c]=som_kmeans('batch',sM,k,100,0); % max 100 iterations, verbose off + color_=color_+som_clustercolor(sM,c,initRGB); + end + index=index+1; + color(:,:,index)=color_./R; + end + + %% coloring for 'best' k-means coloring +case 'best' + for k=C, + disp(['Running K-means for ' num2str(k) ' clusters...']); + c=[];err=Inf; div=[]; + %% look for the best k-means among R trials + for i=1:R, + [c_,div_,err_(i)]=som_kmeans('batch',sM,k,100,0); % max 100 iterations, verbose off + if err_(i)<err, + err=err_(i); c=c_; div=div_; + end + end + % record the 'best' k-means for C clusters + index=index+1; + color(:,:,index)=som_clustercolor(sM,div,initRGB); + centroid{index}=c; + end +end + +%%% Build output + +switch contrast +case 'flat' + ; +case 'enhanced' + warning off; + ncolor=maxnorm(color); + ncolor(~isfinite(ncolor))=color(~isfinite(ncolor)); + color=ncolor; + warning on; +end + + +%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function X=maxnorm(x) +% normalize columns of x between [0,1] + +x=x-repmat(min(x),[size(x,1) 1 1]); +X=x./repmat(max(x),[size(x,1) 1 1]);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_label.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,283 @@ +function [sTo] = som_label(sTo, mode, inds, labels) + +%SOM_LABEL Give/clear labels to/from map or data struct. +% +% sTo = som_label(sTo, mode, inds [, labels]) +% +% sD = som_label(sD,'add',20,'a_label'); +% sM = som_label(sM,'replace',[2 4],'a_label'); +% sM = som_label(sM,'add',som_bmus(sM,x),'BMU'); +% sD = som_label(sD,'prune',[1:10]'); +% sM = som_label(sM,'clear','all'); +% +% Input and output arguments ([]'s are optional): +% sTo (struct) data or map struct to which the labels are put +% mode (string) 'add' or 'replace' or 'prune' or 'clear' +% inds (vector) indeces of the vectors to which the labels +% are put. Note: this must be a column vector! +% (matrix) subscript indeces to the '.labels' field. The vector +% is given by the first index (e.g. inds(i,1)). +% (string) for 'prune' and 'clear' modes, the string 'all' +% means that all vectors should be pruned/cleared +% [labels] The labels themselves. The number of rows much match +% the number of given indeces, except if there is either +% only one index or only one label. If mode is +% 'prune' or 'clear', labels argument is ignored. +% (string) Label. +% (string array) Each row is a label. +% (cell array of strings) All labels in a cell are handled +% as a group and are applied to the same vector given +% on the corresponding row of inds. +% +% Note: If there is only one label/index, it is used for each specified +% index/label. +% +% For more help, try 'type som_label' or check out online documentation. +% See also SOM_AUTOLABEL, SOM_SHOW_ADD, SOM_SHOW. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_label +% +% PURPOSE +% +% Add (or remove) labels to (from) map and data structs. +% +% SYNTAX +% +% sTo = som_label(sTo, 'clear', inds) +% sTo = som_label(sTo, 'prune', inds) +% sTo = som_label(sTo, 'add', inds, labels) +% sTo = som_label(sTo, 'replace', inds, labels) +% +% DESCRIPTION +% +% This function can be used to give and remove labels in map and data +% structs. Of course the same operation could be done by hand, but this +% function offers an alternative and hopefully slightly user-friendlier +% way to do it. +% +% REQUIRED INPUT ARGUMENTS +% +% sTo (struct) data or map struct to which the labels are put +% mode (string) The mode of operation. +% 'add' : adds the given labels +% 'clear' : removes labels +% 'replace' : replaces current labels with given +% labels; basically same as 'clear' +% followed by 'add' +% 'prune' : removes empty labels ('') from between +% non-empty labels, e.g. if the labels of +% a vector were {'A','','','B','','C'} +% they'd become {'A','B','C'}. Some empty +% labels may be left at the end of the list. +% +% inds Identifies the vectors to which the operation +% (given by mode) is applied to. +% (vector) Linear indexes of the vectors, size n x 1. +% Notice! This should be a column vector! +% (matrix) The labels are in a cell matrix. By giving matrix +% argument for inds, you can address this matrix +% directly. The first index gives the vector and the +% second index the vertical position of the label in +% the labels array. Size n x 2, where n is the +% number of labels. +% (string) for 'prune' and 'clear' modes, the string 'all' +% means that all vectors should be pruned/cleared +% +% OPTIONAL INPUT ARGUMENTS +% +% [labels] The labels themselves. The number of rows much match +% the number of given indeces, except if there is either +% only one index or only one label. +% (string) Label, e.g. 'label' +% (string array) Each row is a label, +% e.g. ['label1'; 'label2'; 'label3'] +% (cell array of strings) All labels in a cell are handled +% as a group and are applied to the same vector given +% on the corresponding row of inds. +% e.g. three labels: {'label1'; 'label2'; 'label3'} +% e.g. a group of labels: {'label1', 'label2', 'label3'} +% e.g. three groups: {{'la1'},{'la21','la22'},{'la3'} +% +% OUTPUT ARGUMENTS +% +% sTo (struct) the given data/map struct with modified labels +% +% EXAMPLES +% +% This is the basic way to add a label to map structure: +% sMap = som_label(sMap,'add',3,'label'); +% +% The following examples have identical results: +% sMap = som_label(sMap,'add',[4; 13], ['label1'; 'label2']); +% sMap = som_label(sMap,'add',[4; 13], {{'label1'};{'label2'}}); +% +% Labeling the BMU of a vector x (and removing any old labels) +% sMap = som_label(sMap,'replace',som_bmus(sMap,x),'BMU'); +% +% Pruning labels +% sMap = som_label(sMap,'prune','all'); +% +% Clearing labels from a struct +% sMap = som_label(sMap,'clear','all'); +% sMap = som_label(sMap,'clear',[1:4, 9:30]'); +% +% SEE ALSO +% +% som_autolabel Automatically label a map/data set. +% som_show Show map planes. +% som_show_add Add for example labels to the SOM_SHOW visualization. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(3, 4, nargin)); % check no. of input args is correct + +% sTo +switch sTo.type, +case 'som_map', [dlen dim] = size(sTo.codebook); +case 'som_data', [dlen dim] = size(sTo.data); +end +maxl = size(sTo.labels,2); % maximum number of labels for a single vector + +% inds +if ischar(inds) & strcmp(inds,'all'), + inds = [1:dlen]'; +end +if length(inds)>2 & size(inds,2)>2, inds = inds'; end +ni = size(inds,1); +n = ni; + +% labels +if nargin==4, + % convert labels to a cell array of cells + if ischar(labels), labels = cellstr(labels); end + if iscellstr(labels), + tmplab = labels; + nl = size(labels,1); + labels = cell(nl,1); + for i=1:nl, + if ~iscell(tmplab{i}) + if ~isempty(tmplab{i}), labels{i} = tmplab(i,:); + else labels{i} = {}; end + else + labels(i) = tmplab(i); + end + end + clear tmplab; + end + nl = size(labels,1); +end + +% the case of a single label/index +if any(strcmp(mode,{'add','replace'})), + n = max(nl,ni); + if n>1, + if ni==1, + inds = zeros(n,1)+inds(1); + elseif nl==1, + label = labels{1}; + labels = cell(n,1); + for i=1:n, labels{i} = label; end + elseif ni ~= nl, + error('The number of labels and indexes does not match.'); + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +switch mode, + case 'clear', + if size(inds,2)>2, + inds = inds(find(inds(:,2)<=maxl),:); % ignore if subindex is out-of-range + inds = sub2ind([dlen maxl],inds(:,1),inds(:,2)); + sTo.labels{inds} = []; + else + sTo.labels(inds,:) = cell(n,maxl); + end + case 'prune', + if size(inds,2)==1, + % subindex gives the index from which the pruning is started + inds = [inds, ones(n,1)]; % from 1 by default + end + select = ones(1,maxl); + for i=1:n, + v = inds(i,1); s = inds(i,2); select(:) = 1; + for j=s:maxl, select(j) = ~isempty(sTo.labels{v,j}); end + if ~all(select), + labs = cell(1,maxl); + labs(1:sum(select)) = sTo.labels(v,find(select)); + sTo.labels(v,:) = labs; + end + end + case 'add', + if size(inds,2)==1, + % subindex gives the index from which the adding is started + inds = [inds, ones(n,1)]; % from 1 by default + end + for i=1:n, + v = inds(i,1); s = inds(i,2); l = length(labels{i}); + for j=1:l, + while s<=size(sTo.labels,2) & ~isempty(sTo.labels{v,s}), s=s+1; end + sTo.labels{v,s} = labels{i}{j}; + s=s+1; + end + end + case 'replace', + if size(inds,2)==1, + % subindex gives the index from which the replacing is started + inds = [inds, ones(n,1)]; % from 1 by default + end + for i=1:n, + v = inds(i,1); s = inds(i,2); l = length(labels(i)); + for j=1:l, sTo.labels{v,s-1+j} = labels{i}{j}; end + end + otherwise + error(['Unrecognized mode: ' mode]); +end + +sTo.labels = remove_empty_columns(sTo.labels); + +[dlen maxl] = size(sTo.labels); +for i=1:dlen, + for j=1:maxl, + if isempty(sTo.labels{i,j}) & ~ischar(sTo.labels{i,j}), + sTo.labels{i,j} = ''; + end + end +end + +return; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function labels = remove_empty_columns(labels) + + [dlen maxl] = size(labels); + + % find which columns are empty + cols = zeros(1,maxl); + for i=1:dlen, + for j=1:maxl, + cols(j) = cols(j) + ~isempty(labels{i,j}); + end + end + while maxl>0 & cols(maxl)==0, maxl = maxl-1; end % check starting from end + + if maxl==0, labels = cell(dlen,1); + elseif maxl<size(labels,2), labels = labels(:,1:maxl); + else % ok + end + % end of remove_empty_columns + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_label2num.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +function [nos,names] = som_label2num(L) + +%SOM_LABEL2NUM Recodes textual data labels to interger class labels +% +% [class,names]=class2num(L) +% +% [class,names]=class2num(sData) +% [class,names]=class2num(sMap) +% [class,names]=class2num(sData.labels); +% +% Input and output arguments ([]'s are optional): +% +% L (map struct, data struct, +% Nx1 cell array of strings, +% a Nxn char array) textual labels +% class (vector) Nx1 vector of integers where N is the number of original text labels +% names (cell) kx1 array of strings where names(i) correspons to integer label i +% +% See also KNN + +% Contributed to SOM Toolbox 2.0, October 29th, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 291000 + +%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(L); + if isfield(L,'type') & ischar(L.type), + ; + else + error('Invalid map/data struct?'); + end + switch L.type + case {'som_map', 'som_data'} + class=L.labels(:,1); + otherwise error('Invalid map/data struct?'); + end +elseif vis_valuetype(L,{'cellcolumn_of_char'}), + class=L; +elseif vis_valuetype(L,{'chararray'}), + class=cellstr(L); +else + error('Input must be an Nx1 cell array of strings, a char array, a map struct or a data struct.'); +end + +names = {}; +nos = zeros(length(class),1); +for i=1:length(class), + if ~isempty(class{i}) & ~any(strcmp(class{i},names)), + names=cat(1,names,class(i)); + end +end + +tmp_nos = (1:length(names))'; +for i=1:length(class), + if ~isempty(class{i}), + nos(i,1) = find(strcmp(class{i},names)); + end +end + +if any(nos==0), + nos=nos+1; + names(2:end+1)=names; + names{1}=''; +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_lininit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,285 @@ +function sMap = som_lininit(D, varargin) + +%SOM_LININIT Initialize a Self-Organizing Map linearly. +% +% sMap = som_lininit(D, [[argID,] value, ...]) +% +% sMap = som_lininit(D); +% sMap = som_lininit(D,sMap); +% sMap = som_lininit(D,'munits',100,'hexa'); +% +% Input and output arguments ([]'s are optional): +% D The training data. +% (struct) data struct +% (matrix) data matrix, size dlen x dim +% [argID, (string) Parameters affecting the map topology are given +% value] (varies) as argument ID - argument value pairs, listed below. +% sMap (struct) map struct +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'munits' (scalar) number of map units +% 'msize' (vector) map size +% 'lattice' *(string) map lattice: 'hexa' or 'rect' +% 'shape' *(string) map shape: 'sheet', 'cyl' or 'toroid' +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'map' *(struct) map struct +% 'som_map','sMap' = 'map' +% +% For more help, try 'type som_lininit' or check out online documentation. +% See also SOM_MAP_STRUCT, SOM_RANDINIT, SOM_MAKE. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_lininit +% +% PURPOSE +% +% Initializes a SOM linearly along its greatest eigenvectors. +% +% SYNTAX +% +% sMap = som_lininit(D); +% sMap = som_lininit(D,sMap); +% sMap = som_lininit(D,'munits',100,'hexa'); +% +% DESCRIPTION +% +% Initializes a SOM linearly. If necessary, a map struct is created +% first. The initialization is made by first calculating the eigenvalues +% and eigenvectors of the training data. Then, the map is initialized +% along the mdim greatest eigenvectors of the training data, where +% mdim is the dimension of the map grid. +% +% REFERENCES +% +% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, +% Berlin, 1995, pp. 106-107. +% +% REQUIRED INPUT ARGUMENTS +% +% D The training data. +% (struct) Data struct. If this is given, its '.comp_names' and +% '.comp_norm' fields are copied to the map struct. +% (matrix) data matrix, size dlen x dim +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is used. +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'dlen' (scalar) length of the training data +% 'data' (matrix) the training data +% *(struct) the training data +% 'munits' (scalar) number of map units +% 'msize' (vector) map size +% 'lattice' *(string) map lattice: 'hexa' or 'rect' +% 'shape' *(string) map shape: 'sheet', 'cyl' or 'toroid' +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'map' *(struct) map struct +% 'som_map','sMap' = 'map' +% +% OUTPUT ARGUMENTS +% +% sMap (struct) The initialized map struct. +% +% EXAMPLES +% +% sMap = som_lininit(D); +% sMap = som_lininit(D,sMap); +% sMap = som_lininit(D,'msize',[10 10]); +% sMap = som_lininit(D,'munits',100,'rect'); +% +% SEE ALSO +% +% som_map_struct Create a map struct. +% som_randinit Initialize a map with random values. +% som_make Initialize and train self-organizing map. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 100997 +% Version 2.0beta 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% data +if isstruct(D), + data_name = D.name; + comp_names = D.comp_names; + comp_norm = D.comp_norm; + D = D.data; + struct_mode = 1; +else + data_name = inputname(1); + struct_mode = 0; +end +[dlen dim] = size(D); + +% varargin +sMap = []; +sTopol = som_topol_struct; +sTopol.msize = 0; +munits = NaN; +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + case 'munits', i=i+1; munits = varargin{i}; sTopol.msize = 0; + case 'msize', i=i+1; sTopol.msize = varargin{i}; + munits = prod(sTopol.msize); + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case 'shape', i=i+1; sTopol.shape = varargin{i}; + case {'som_topol','sTopol','topol'}, i=i+1; sTopol = varargin{i}; + case {'som_map','sMap','map'}, i=i+1; sMap = varargin{i}; sTopol = sMap.topol; + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}.type, + case 'som_topol', + sTopol = varargin{i}; + case 'som_map', + sMap = varargin{i}; + sTopol = sMap.topol; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_topol_struct) Ignoring invalid argument #' num2str(i)]); + end + i = i+1; +end + +if length(sTopol.msize)==1, sTopol.msize = [sTopol.msize 1]; end + +if ~isempty(sMap), + [munits dim2] = size(sMap.codebook); + if dim2 ~= dim, error('Map and data must have the same dimension.'); end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% create map + +% map struct +if ~isempty(sMap), + sMap = som_set(sMap,'topol',sTopol); +else + if ~prod(sTopol.msize), + if isnan(munits), + sTopol = som_topol_struct('data',D,sTopol); + else + sTopol = som_topol_struct('data',D,'munits',munits,sTopol); + end + end + sMap = som_map_struct(dim, sTopol); +end + +if struct_mode, + sMap = som_set(sMap,'comp_names',comp_names,'comp_norm',comp_norm); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialization + +% train struct +sTrain = som_train_struct('algorithm','lininit'); +sTrain = som_set(sTrain,'data_name',data_name); + +msize = sMap.topol.msize; +mdim = length(msize); +munits = prod(msize); + +[dlen dim] = size(D); +if dlen<2, + %if dlen==1, sMap.codebook = (sMap.codebook - 0.5)*diag(D); end + error(['Linear map initialization requires at least two NaN-free' ... + ' samples.']); + return; +end + +% compute principle components +if dim > 1 & sum(msize > 1) > 1, + % calculate mdim largest eigenvalues and their corresponding + % eigenvectors + + % autocorrelation matrix + A = zeros(dim); + me = zeros(1,dim); + for i=1:dim, + me(i) = mean(D(isfinite(D(:,i)),i)); + D(:,i) = D(:,i) - me(i); + end + for i=1:dim, + for j=i:dim, + c = D(:,i).*D(:,j); c = c(isfinite(c)); + A(i,j) = sum(c)/length(c); A(j,i) = A(i,j); + end + end + + % take mdim first eigenvectors with the greatest eigenvalues + [V,S] = eig(A); + eigval = diag(S); + [y,ind] = sort(eigval); + eigval = eigval(flipud(ind)); + V = V(:,flipud(ind)); + V = V(:,1:mdim); + eigval = eigval(1:mdim); + + % normalize eigenvectors to unit length and multiply them by + % corresponding (square-root-of-)eigenvalues + for i=1:mdim, V(:,i) = (V(:,i) / norm(V(:,i))) * sqrt(eigval(i)); end + +else + + me = zeros(1,dim); + V = zeros(1,dim); + for i=1:dim, + inds = find(~isnan(D(:,i))); + me(i) = mean(D(inds,i),1); + V(i) = std(D(inds,i),1); + end + +end + +% initialize codebook vectors +if dim>1, + sMap.codebook = me(ones(munits,1),:); + Coords = som_unit_coords(msize,'rect','sheet'); + cox = Coords(:,1); Coords(:,1) = Coords(:,2); Coords(:,2) = cox; + for i=1:mdim, + ma = max(Coords(:,i)); mi = min(Coords(:,i)); + if ma>mi, Coords(:,i) = (Coords(:,i)-mi)/(ma-mi); else Coords(:,i) = 0.5; end + end + Coords = (Coords-0.5)*2; + for n = 1:munits, + for d = 1:mdim, + sMap.codebook(n,:) = sMap.codebook(n,:)+Coords(n,d)*V(:, d)'; + end + end +else + sMap.codebook = [0:(munits-1)]'/(munits-1)*(max(D)-min(D))+min(D); +end + +% training struct +sTrain = som_set(sTrain,'time',datestr(now,0)); +sMap.trainhist = sTrain; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_linkage.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,291 @@ +function [Z,order,Md] = som_linkage(sM,varargin) + +%SOM_LINKAGE Make a hierarchical linkage of the SOM map units. +% +% [Z,order,Dist] = som_linkage(sM, [[argID,] value, ...]) +% +% Z = som_linkage(sM); +% Z = som_linkage(D,'complete'); +% Z = som_linkage(sM,'single','ignore',find(~som_hits(sM,D))); +% Z = som_linkage(sM,pdist(sM.codebook,'mahal')); +% som_dendrogram(Z); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map or data struct to be clustered +% (matrix) size dlen x dim, a data set: the matrix must not +% contain any NaN's! +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% Z (matrix) size dlen-1 x 3, the linkage info +% Z(i,1) and Z(i,2) hold the indeces of clusters +% combined on level i (starting from bottom). The new +% cluster has index dlen+i. The initial cluster +% index of each unit is its linear index in the +% original data matrix. Z(i,3) is the distance +% between the combined clusters. See LINKAGE +% function in the Statistics Toolbox. +% The ignored samples are listed at the +% end of the Z-matrix and have Z(*,3) == Inf +% Dist (matrix) size dlen x dlen, pairwise distance matrix +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'linkage' *(string) the linkage criteria to use: 'single' (the +% default), 'average' or 'complete' +% 'topol' *(struct) topology struct +% 'connect' *(string) 'neighbors' or 'any' (default), whether the +% connections should be allowed only between +% neighbors or between any vectors +% (matrix) size dlen x dlen indicating the connections +% between vectors +% (scalar) the N-neighborhood upto which the connections +% should be formed (implies 'neighbors') +% 'ignore' (vector) the units/vectors which should be ignored +% 'dist' (matrix) size dlen x dlen, pairwise distance matrix to +% be used instead of euclidian distances +% (vector) as the output of PDIST function +% (scalar) distance norm to use (euclidian = 2) +% 'mask' (vector) size dim x 1, the search mask used to +% weight distance calculation. By default +% sM.mask or a vector of ones is used. +% +% Note that together 'connect'='neighbors' and 'ignore' may form +% areas on the map which will never be connected: connections +% across the ignored map units simply do not exist. +% +% See also KMEANS_CLUSTERS, LINKAGE, PDIST, DENDROGRAM. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 160600 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +% the data +if isstruct(sM), + if isfield(sM,'data'), D = sM.data; sT = []; mask = []; + else D = sM.codebook; sT = sM.topol; mask = sM.mask; + end +else + D = sM; sT = []; mask = []; +end +[dlen dim] = size(D); +if isempty(mask), mask = ones(dim,1); end +if any(isnan(D(:))), error('Data matrix must not have any NaNs.'); end + +% varargin +Md = 2; +linkage = 'single'; +ignore_units = []; +constrained = 0; +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case {'topol','som_topol','sTopol'}, i=i+1; sT = varargin{i}; + case 'connect', i=i+1; + if ischar(varargin{i}), constrained = ~strcmp(varargin{i},'any'); + else constrained = varargin{i}; end + case 'ignore', i=i+1; ignore_units = varargin{i}; + case 'dist', i=i+1; Md = varargin{i}; + case 'linkage', i=i+1; linkage = varargin{i}; + case 'mask', i=i+1; mask = varargin{i}; + case 'tracking',i=i+1; tracking = varargin{i}; + % unambiguous values + case 'neighbors', constrained = 1; + case 'any', constrained = 0; + case {'single','average','complete'}, linkage = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', sTopol = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_linkage) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% distance matrix + +% given distance matrix % jh corrected this place totally 27.3. 03 +if (prod(size(Md))==1), % no explicit distance matrix, set flag + q=2; % 17.2.03 kr added some brackets +else + if (prod(size(Md))<dlen^2), % check pdist form + Md = squareform(Md); % transform to ordinary square diastance matrix + end + % jh: 27.3. 03 "calculate pairwise dist. matrix" see approx. 20 lines below + % sets self-distance to Inf! This must be set here also, + % otherwise clustering fails for user defined dist. matrix! + Md(eye(dlen)==1)=Inf; +end + +% neighborhood constraint +if length(constrained)==1 & constrained>0, + Ne1 = som_unit_neighs(sT); + Conn = som_neighborhood(Ne1,constrained); + Conn(~isfinite(Conn(:))) = 0; +else Conn = constrained; end +if ~isempty(Conn), for i=1:dlen, C(i,i) = 1; end, end + +% pairwise distance matrix across connected units +n = size(D,1); +if prod(size(Md))>1, + % remove distances between non-neighbors + if constrained, for i = 1:n, Md(i,find(Conn(i,:)==0)) = Inf; end, end +else + % calculate pairwise distance matrix + q = Md; + Md = zeros(n,n)+Inf; + if ~constrained & q==2, % fast for the default case + for i = 1:n-1, + x = D(i,:); + inds = [(i+1):n]; + Diff = D(inds,:) - x(ones(n-i,1),:); + Md(inds,i) = sqrt((Diff.^2)*mask); + Md(i,inds) = Md(inds,i)'; + end + else + for i = 1:n-1, + inds = find(Conn(i,:)==1); + inds = inds(find(inds>i)); + Diff = abs(D(inds,:) - D(i*ones(length(inds),1),:)); + switch q, + case 1, dist = Diff*mask; + case 2, dist = sqrt((Diff.^2)*mask); + case Inf, dist = max(Diff,[],2); + otherwise, dist = ((Diff.^q)*mask).^(1/q); + end + Md(inds,i) = dist; + Md(i,inds) = dist'; + end + end +end + +% set distances to ignored units to Inf +if ~isempty(ignore_units), + Md(ignore_units,:) = Inf; + Md(:,ignore_units) = Inf; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% construct dendrogram + +Z = zeros(n-1,3)+NaN; % merged clusters and distance for each step +clusters = 1:dlen; % each vector is at first in its own cluster +Cd = Md; % distances between clusters + +h = waitbar(0,'Constructing hierarchical clustering'); + +for i=1:n-1, + + % tracking + waitbar(i/(n-1),h); + + %% combine two closest clusters + % find the clusters which are closest to each other (c1 and c2) + [d,ind] = min(min(Cd)); + if ~isfinite(d), break; end % no more connected clusters + [d,c1] = min(Cd(:,ind)); % cluster1 + c2 = clusters(ind); % cluster2 + % combine the two clusters + c1_inds = find(clusters==c1); % vectors belonging to c1 + c2_inds = find(clusters==c2); % vectors belonging to c2 + c_inds = [c1_inds, c2_inds]; % members of the new cluster + % new cluster index = bigger cluster + if length(c2_inds)>length(c1_inds), c=c2; k=c1; else c=c1; k=c2; end + clusters(c_inds) = c; % update cluster info + Z(i,:) = [c, k, d]; % save info into Z + + %% update cluster distances + % remove the subclusters from the Cd table + Cd(c_inds,c_inds) = Inf; % distance of the cluster to its members = Inf + k_inds = c_inds(c_inds ~= c); % vectors of the smaller cluster + Cd(k_inds,:) = Inf; % distance of the subclusters to + Cd(:,k_inds) = Inf; % other clusters = Inf + % update the distance of this cluster to the other clusters + cl = unique(clusters(clusters ~= c)); % indeces of all other clusters + if ~isempty(cl), % added since v6.5 works differently than 6.1 + for l=cl, + o_inds = find(clusters==l); % indeces belonging to cluster k + vd = Md(c_inds,o_inds); % distances between vectors in c and k + vd = vd(isfinite(vd(:))); % remove infinite distances (no connection) + len = length(vd); + if ~len, % if the two clusters are not connected, their distance in Inf + sd = Inf; + else % otherwise, calculate the distance between them + switch linkage, + case 'single', sd = min(vd); + case 'average', sd = sum(vd)/len; + case 'complete', sd = max(vd); + otherwise, error(['Unknown set distance: ' linkage]); + end + end + Cd(c,l) = sd; Cd(l,c) = sd; + end + end +end +close(h); + +last = Z(i,1); +if isnan(last), + last = Z(i-1,1); + rest = setdiff(unique(clusters),last); + Z(i:n-1,1) = rest'; + Z(i:n-1,2) = last; + Z(i:n-1,3) = Inf; + i = i - 1; +else + rest = []; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% return values + +% calculate the order of samples +order = last; +% go through the combination matrix from top to down +for k=i:-1:1, + c = Z(k,1); k = Z(k,2); % what (k) change to what (c) + j = find(order==c); % the occurance of c in order + if j == length(order), order = [order k]; % put k behind c + else order = [order(1:j) k order(j+1:end)]; + end +end +order = [rest, order]; + +% to maintain compatibility with Statistics Toolbox, the values in +% Z must be yet transformed so that they are similar to the output +% of LINKAGE function + +Zs = Z; +current_cluster = 1:dlen; +for i=1:size(Z,1), + Zs(i,1) = current_cluster(Z(i,1)); + Zs(i,2) = current_cluster(Z(i,2)); + current_cluster(Z(i,[1 2])) = dlen+i; +end + +Z = Zs; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_make.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,339 @@ +function sMap = som_make(D, varargin) + +%SOM_MAKE Create, initialize and train Self-Organizing Map. +% +% sMap = som_make(D, [[argID,] value, ...]) +% +% sMap = som_make(D); +% sMap = som_make(D, 'munits', 20); +% sMap = som_make(D, 'munits', 20, 'hexa', 'sheet'); +% sMap = som_make(D, 'msize', [4 6 7], 'lattice', 'rect'); +% +% Input and output arguments ([]'s are optional): +% D (matrix) training data, size dlen x dim +% (struct) data struct +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% sMap (struct) map struct +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'init' *(string) initialization: 'randinit' or 'lininit' (default) +% 'algorithm' *(string) training: 'seq' or 'batch' (default) or 'sompak' +% 'munits' (scalar) the preferred number of map units +% 'msize' (vector) map grid size +% 'mapsize' *(string) do you want a 'small', 'normal' or 'big' map +% Any explicit settings of munits or msize override this. +% 'lattice' *(string) map lattice, 'hexa' or 'rect' +% 'shape' *(string) map shape, 'sheet', 'cyl' or 'toroid' +% 'neigh' *(string) neighborhood function, 'gaussian', 'cutgauss', +% 'ep' or 'bubble' +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'mask' (vector) BMU search mask, size dim x 1 +% 'name' (string) map name +% 'comp_names' (string array / cellstr) component names, size dim x 1 +% 'tracking' (scalar) how much to report, default = 1 +% 'training' (string) 'short', 'default', 'long' +% (vector) size 1 x 2, first length of rough training in epochs, +% and then length of finetuning in epochs +% +% For more help, try 'type som_make' or check out online documentation. +% See also SOM_MAP_STRUCT, SOM_TOPOL_STRUCT, SOM_TRAIN_STRUCT, +% SOM_RANDINIT, SOM_LININIT, SOM_SEQTRAIN, SOM_BATCHTRAIN. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_make +% +% PURPOSE +% +% Creates, initializes and trains a SOM using default parameters. +% +% SYNTAX +% +% sMap = som_make(D); +% sMap = som_make(...,'argID',value,...); +% sMap = som_make(...,value,...); +% +% DESCRIPTION +% +% Creates, initializes and trains a SOM with default parameters. Uses functions +% SOM_TOPOL_STRUCT, SOM_TRAIN_STRUCT, SOM_DATA_STRUCT and SOM_MAP_STRUCT to come +% up with the default values. +% +% First, the number of map units is determined. Unless they are +% explicitly defined, function SOM_TOPOL_STRUCT is used to determine this. +% It uses a heuristic formula of 'munits = 5*dlen^0.54321'. The 'mapsize' +% argument influences the final number of map units: a 'big' map has +% x4 the default number of map units and a 'small' map has x0.25 the +% default number of map units. +% +% After the number of map units has been determined, the map size is +% determined. Basically, the two biggest eigenvalues of the training +% data are calculated and the ratio between sidelengths of the map grid +% is set to this ratio. The actual sidelengths are then set so that +% their product is as close to the desired number of map units as +% possible. +% +% Then the SOM is initialized. First, linear initialization along two +% greatest eigenvectors is tried, but if this can't be done (the +% eigenvectors cannot be calculated), random initialization is used +% instead. After initialization, the SOM is trained in two phases: +% first rough training and then fine-tuning. If the 'tracking' +% argument is greater than zero, the average quantization error and +% topographic error of the final map are calculated. +% +% REQUIRED INPUT ARGUMENTS +% +% D The data to use in the training. +% (struct) A data struct. If a struct is given, '.comp_names' field as +% well as '.comp_norm' field is copied to the map struct. +% (matrix) A data matrix, size dlen x dim. The data matrix may +% contain unknown values, indicated by NaNs. +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'init' *(string) initialization: 'randinit' or 'lininit' (default) +% 'algorithm' *(string) training: 'seq' or 'batch' (default) or 'sompak' +% 'munits' (scalar) the preferred number of map units +% 'msize' (vector) map grid size +% 'mapsize' *(string) do you want a 'small', 'normal' or 'big' map +% Any explicit settings of munits or msize override this. +% 'lattice' *(string) map lattice, 'hexa' or 'rect' +% 'shape' *(string) map shape, 'sheet', 'cyl' or 'toroid' +% 'neigh' *(string) neighborhood function, 'gaussian', 'cutgauss', +% 'ep' or 'bubble' +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'mask' (vector) BMU search mask, size dim x 1 +% 'name' (string) map name +% 'comp_names' (string array / cellstr) component names, size dim x 1 +% 'tracking' (scalar) how much to report, default = 1 +% 'training' (string) 'short', 'default' or 'long' +% (vector) size 1 x 2, first length of rough training in epochs, +% and then length of finetuning in epochs +% +% OUTPUT ARGUMENTS +% +% sMap (struct) the trained map struct +% +% EXAMPLES +% +% To simply train a map with default parameters: +% +% sMap = som_make(D); +% +% With the optional arguments, the initialization and training can be +% influenced. To change map size, use 'msize', 'munits' or 'mapsize' +% arguments: +% +% sMap = som_make(D,'mapsize','big'); or sMap=som_make(D,'big'); +% sMap = som_make(D,'munits', 100); +% sMap = som_make(D,'msize', [20 10]); +% +% Argument 'algorithm' can be used to switch between 'seq' and 'batch' +% algorithms. 'batch' is the default, so to use 'seq' algorithm: +% +% sMap = som_make(D,'algorithm','seq'); or sMap = som_make(D,'seq'); +% +% The 'tracking' argument can be used to control the amout of reporting +% during training. The argument is used in this function, and it is +% passed to the training functions. To make the function work silently +% set it to 0. +% +% sMap = som_make(D,'tracking',0); +% +% SEE ALSO +% +% som_map_struct Create a map struct. +% som_topol_struct Default values for SOM topology. +% som_train_struct Default values for SOM training parameters. +% som_randinint Random initialization algorithm. +% som_lininit Linear initialization algorithm. +% som_seqtrain Sequential training algorithm. +% som_batchtrain Batch training algorithm. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 111199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% D +if isstruct(D) + data_name = D.name; + comp_names = D.comp_names; + comp_norm = D.comp_norm; + D = D.data; +else + data_name = inputname(1); + sDummy = som_data_struct(D(1,:)); + comp_names = sDummy.comp_names; + comp_norm = sDummy.comp_norm; +end +[dlen dim] = size(D); + +% defaults +mapsize = ''; +sM = som_map_struct(dim); +sTopol = sM.topol; +munits = prod(sTopol.msize); % should be zero +mask = sM.mask; +name = sM.name; +neigh = sM.neigh; +tracking = 1; +algorithm = 'batch'; +initalg = 'lininit'; +training = 'default'; + +% varargin +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'mask', i=i+1; mask = varargin{i}; + case 'munits', i=i+1; munits = varargin{i}; + case 'msize', i=i+1; sTopol.msize = varargin{i}; + munits = prod(sTopol.msize); + case 'mapsize', i=i+1; mapsize = varargin{i}; + case 'name', i=i+1; name = varargin{i}; + case 'comp_names', i=i+1; comp_names = varargin{i}; + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case 'shape', i=i+1; sTopol.shape = varargin{i}; + case {'topol','som_topol','sTopol'}, + i=i+1; sTopol = varargin{i}; munits = prod(sTopol.msize); + case 'neigh', i=i+1; neigh = varargin{i}; + case 'tracking', i=i+1; tracking = varargin{i}; + case 'algorithm', i=i+1; algorithm = varargin{i}; + case 'init', i=i+1; initalg = varargin{i}; + case 'training', i=i+1; training = varargin{i}; + % unambiguous values + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; + case {'gaussian','cutgauss','ep','bubble'}, neigh = varargin{i}; + case {'seq','batch','sompak'}, algorithm = varargin{i}; + case {'small','normal','big'}, mapsize = varargin{i}; + case {'randinit','lininit'}, initalg = varargin{i}; + case {'short','default','long'}, training = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', sTopol = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_make) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% make the map struct + +%% map size +if isempty(sTopol.msize) | ~prod(sTopol.msize), + if tracking>0, fprintf(1,'Determining map size...\n'); end + if ~munits, + sTemp = som_topol_struct('dlen',dlen); + munits = prod(sTemp.msize); + switch mapsize, + case 'small', munits = max(9,ceil(munits/4)); + case 'big', munits = munits*4; + otherwise % nil + end + end + sTemp = som_topol_struct('data',D,'munits',munits); + sTopol.msize = sTemp.msize; + if tracking>0, + fprintf(1,' map size [%d, %d]\n',sTopol.msize(1), sTopol.msize(2)); + end +end + +% map struct +sMap = som_map_struct(dim,sTopol,neigh,'mask',mask,'name',name, ... + 'comp_names', comp_names, 'comp_norm', comp_norm); + +% function +if strcmp(algorithm,'sompak'), + algorithm = 'seq'; + func = 'sompak'; +else + func = algorithm; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialization + +if tracking>0, fprintf(1,'Initialization...\n'); end + +switch initalg, + case 'randinit', sMap = som_randinit(D, sMap); + case 'lininit', sMap = som_lininit(D, sMap); +end +sMap.trainhist(1) = som_set(sMap.trainhist(1),'data_name',data_name); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% training + +if tracking>0, fprintf(1,'Training using %s algorithm...\n',algorithm); end + +% rough train +if tracking>0, fprintf(1,'Rough training phase...\n'); end +sTrain = som_train_struct(sMap,'dlen',dlen,'algorithm',algorithm,'phase','rough'); +sTrain = som_set(sTrain,'data_name',data_name); +if isnumeric(training), sTrain.trainlen = training(1); +else + switch training, + case 'short', sTrain.trainlen = max(1,sTrain.trainlen/4); + case 'long', sTrain.trainlen = sTrain.trainlen*4; + end +end +switch func, + case 'seq', sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); + case 'sompak', sMap = som_sompaktrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); + case 'batch', sMap = som_batchtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); +end + +% finetune +if tracking>0, fprintf(1,'Finetuning phase...\n'); end +sTrain = som_train_struct(sMap,'dlen',dlen,'phase','finetune'); +sTrain = som_set(sTrain,'data_name',data_name,'algorithm',algorithm); +if isnumeric(training), sTrain.trainlen = training(2); +else + switch training, + case 'short', sTrain.trainlen = max(1,sTrain.trainlen/4); + case 'long', sTrain.trainlen = sTrain.trainlen*4; + end +end +switch func, + case 'seq', sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); + case 'sompak', sMap = som_sompaktrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); + case 'batch', sMap = som_batchtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask); +end + +% quality +if tracking>0, + [mqe,tge] = som_quality(sMap,D); + fprintf(1,'Final quantization error: %5.3f\n',mqe) + fprintf(1,'Final topographic error: %5.3f\n',tge) +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_map_struct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,231 @@ +function sMap = som_map_struct(dim, varargin) + +%SOM_MAP_STRUCT Create map struct. +% +% sMap = som_map_struct(dim, [[argID,] value, ...]) +% +% sMap = som_map_struct(4); +% sMap = som_map_struct(4,'msize',[3 4],'hexa','sheet'); +% sMap = som_map_struct(4,'msize',[3 4 5],'rect','name','a 3D-SOM'); +% sMap = som_map_struct(4,'msize',[3 4],'bubble','mask',[1 1 1 0]); +% +% Input and output arguments ([]'s are optional): +% dim (scalar) input space dimension +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% sMap (struct) self-organizing map struct +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'mask' (vector) BMU search mask, size dim x 1 +% 'msize' (vector) map grid size, default is [0] +% 'labels' (string array / cellstr) labels for each map unit, +% length=prod(msize) +% 'name' (string) map name +% 'comp_names' (string array / cellstr) component names, size dim x 1 +% 'comp_norm' (cell array) normalization operations for each +% component, size dim x 1. Each cell is either empty, +% or a cell array of normalization structs. +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'lattice' *(string) map lattice, 'hexa' or 'rect' +% 'shape' *(string) map shape, 'sheet', 'cyl' or 'toroid' +% 'neigh' *(string) neighborhood function, 'gaussian', 'cutgauss', +% 'ep' or 'bubble' +% +% For more help, try 'type som_map_struct' or check out online documentation. +% See also SOM_SET, SOM_INFO, SOM_DATA_STRUCT, SOM_TOPOL_STRUCT, SOM_MAKE. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_map_struct +% +% PURPOSE +% +% Creates a self-organizing map structure. +% +% SYNTAX +% +% sM = som_map_struct(dim) +% sM = som_map_struct(...,'argID',value,...); +% sM = som_map_struct(...,value,...); +% +% DESCRIPTION +% +% Creates a self-organizing map struct. The struct contains the map +% codebook, labels, topology, information on normalization and training, +% as well as component names and a name for the map. The obligatory +% parameter is the map dimension. Most of the other fields can be +% given values using optional arguments. If they are left unspecified, +% default values are used. +% +% Field Type Size / default value (munits = prod(msize)) +% ------------------------------------------------------------------------ +% .type (string) 'som_map' +% .name (string) 'SOM date' +% .codebook (matrix) rand(munits, dim) +% .topol (struct) topology struct, with the following fields +% .type (string) 'som_topol' +% .msize (vector) size k x 1, [0] +% .lattice (string) 'hexa' +% .shape (string) 'sheet' +% .labels (cellstr) size munits x m, {''; ''; ... ''} +% .neigh (string) 'gaussian' +% .mask (vector) size dim x 1, [1; 1; ...; 1] +% .trainhist (cell array) size tl x 1, [] +% .comp_names (cellstr) size dim x 1, {'Variable1', 'Variable2', ...} +% .comp_norm (cell array) size dim x 1, {[], [], ... []} +% +% '.type' field is the struct identifier. Do not change it. +% '.name' field is the identifier for the whole map struct +% '.codebook' field is the codebook matrix, each row corresponds to one unit +% '.topol' field is the topology of the map. This struct has three fields: +% '.msize' field is the dimensions of the map grid. Note that the +% matrix notation of indeces is used. +% '.lattice' field is the map grid lattice +% '.shape' field is the map grid shape +% '.labels' field contains the labels for each of the vectors. The ith row +% of '.labels' contains the labels for ith map unit. Note that +% if some vectors have more labels than others, the others are +% are given empty labels ('') to pad the '.labels' array up. +% '.neigh' field is the neighborhood function. +% '.mask' field is the BMU search mask. +% '.trainhist' field contains information on the training. It is a cell +% array of training structs. The first training struct contains +% information on initialization, the others on actual trainings. +% If the map has not been initialized, '.trainhist' is empty ([]). +% '.comp_names' field contains the names of the vector components +% '.comp_norm' field contains normalization information for each +% component. Each cell of '.comp_norm' is itself a cell array of +% normalization structs. If no normalizations are performed for +% the particular component, the cell is empty ([]). +% +% REQUIRED INPUT ARGUMENTS +% +% dim (scalar) Input space dimension. +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments are given as 'argID',value -pairs. If the +% value is unambiguous (marked below with '*'), it can be given +% without the preceeding argID. If an argument is given value +% multiple times, the last one is used. +% +% 'mask' (vector) BMU search mask, size dim x 1 +% 'msize' (vector) map grid size, default is [0] +% 'labels' (string array / cellstr) labels for each map unit, +% length=prod(msize) +% 'name' (string) map name +% 'comp_names' (string array / cellstr) component names, size dim x 1 +% 'comp_norm' (cell array) normalization operations for each +% component, size dim x 1. Each cell is either empty, +% or a cell array of normalization structs. +% 'lattice' *(string) map lattice, 'hexa' or 'rect' +% 'shape' *(string) map shape, 'sheet', 'cyl' or 'toroid' +% 'topol' *(struct) topology struct, sets msize, lattice and shape +% 'som_topol','sTopol' = 'topol' +% 'neigh' *(string) neighborhood function, 'gaussian', 'cutgauss', +% 'ep' or 'bubble' +% +% OUTPUT ARGUMENTS +% +% sMap (struct) the map struct +% +% EXAMPLES +% +% Simplest case: +% sMap = som_map_struct(3); +% +% With optional arguments, the other fields can be given values: +% sTo = som_set('som_topol','msize',[10 5]); +% labs = cell(50, 1); labs{1, 1} = 'first_unit'; +% cnames = {'first'; 'second'; 'third'}; +% sN = som_set('som_norm'); +% csN = {sN; sN; sN}; +% +% sMap = som_map_struct(3,'msize',[10 5],'rect'); +% sMap = som_map_struct(3,'msize',[10 5],'lattice','rect'); +% sMap = som_map_struct(3,sTo,'bubble','labels',labs); +% sMap = som_map_struct(3,sTo,'comp_names',cnames); +% sMap = som_map_struct(3,sTo,'name','a data struct'); +% sMap = som_map_struct(3,sTo,'comp_norm',csN,'mask',[1 0 0.5]); +% +% SEE ALSO +% +% som_set Set values and create SOM Toolbox structs. +% som_data_struct Create a data struct. +% som_make Initialize and train self-organizing map. +% som_topol_struct Default values for map topology. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 100997 +% Version 2.0beta juuso 101199 130300 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% default values +sTopol = som_set('som_topol','lattice','hexa','shape','sheet'); +neigh = 'gaussian'; +mask = ones(dim,1); +name = sprintf('SOM %s', datestr(now, 1)); +labels = cell(prod(sTopol.msize),1); +for i=1:length(labels), labels{i} = ''; end +comp_names = cell(dim,1); +for i = 1:dim, comp_names{i} = sprintf('Variable%d', i); end +comp_norm = cell(dim,1); + +% varargin +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'mask', i=i+1; mask = varargin{i}; + case 'msize', i=i+1; sTopol.msize = varargin{i}; + case 'labels', i=i+1; labels = varargin{i}; + case 'name', i=i+1; name = varargin{i}; + case 'comp_names', i=i+1; comp_names = varargin{i}; + case 'comp_norm', i=i+1; comp_norm = varargin{i}; + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case 'shape', i=i+1; sTopol.shape = varargin{i}; + case {'topol','som_topol','sTopol'}, i=i+1; sTopol = varargin{i}; + case 'neigh', i=i+1; neigh = varargin{i}; + % unambiguous values + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; + case {'gaussian','cutgauss','ep','bubble'}, neigh = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', sTopol = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_map_struct) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +% create the SOM +codebook = rand(prod(sTopol.msize),dim); +sTrain = som_set('som_train','time',datestr(now,0),'mask',mask); +sMap = som_set('som_map','codebook',codebook,'topol',sTopol,... + 'neigh',neigh,'labels',labels,'mask',mask,... + 'comp_names',comp_names,'name',name,... + 'comp_norm',comp_norm,'trainhist',sTrain); + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_mdist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,89 @@ +function Md = som_mdist(D,q,mask,Ne) + +% SOM_MDIST Mutual (or pairwise) distance matrix for the given data. +% +% Md = som_mdist(D,[q],[mask],[Ne]) +% +% Md = som_mdist(D); +% Md = som_mdist(D,Inf); +% Md = som_mdist(D,2,Ne); +% +% Input and output arguments ([]'s are optional): +% D (matrix) size dlen x dim, the data set +% (struct) map or data struct +% [q] (scalar) distance norm, default = 2 +% [mask] (vector) size dim x 1, the weighting mask +% [Ne] (matrix) size dlen x dlen, sparse matrix +% indicating which distances should be +% calculated (ie. less than Infinite) +% +% See also PDIST. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on XXX by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 220800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% mask +if nargin<3, mask = []; end + +% the data +if isstruct(D), + switch D.type, + case 'som_map', if isempty(mask), mask = D.mask; end, D = D.codebook; + case 'som_data', D = D.data; + otherwise, error('Bad first argument'); + end +end +nans = sum(isnan(D),2); +if any(nans>0), + D(find(nans>0),:) = 0; + warning('Distances of vectors with NaNs are not calculated.'); +end +[dlen dim] = size(D); + +% distance norm +if nargin<2 | isempty(q) | isnan(q), q = 2; end + +% mask +if isempty(mask), mask = ones(dim,1); end + +% connections +if nargin<4, Ne = []; end +if ~isempty(Ne), + l = size(Ne,1); Ne([0:l-1]*l+[1:l]) = 1; % set diagonal elements = 1 +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +m = mask; +o = ones(dlen,1); +l = dlen; +Md = zeros(dlen); +calculate_all = isempty(Ne); + +if ~calculate_all, Md(Ne==0) = Inf; end + +for i=1:l-1, + j=(i+1):l; + if ~calculate_all, j=find(Ne(i,j))+i; end + C=D(j,:)-D(i*o(1:length(j)),:); + switch q, + case 1, Md(j,i)=abs(C)*m; + case 2, Md(j,i)=sqrt((C.^2)*m); + case Inf, Md(j,i)=max(diag(m)*abs(C),[],2); + otherwise, Md(j,i)=((abs(C).^q)*m).^(1/q); + end + Md(i,j) = Md(j,i)'; +end + +Md(find(nans>0),:) = NaN; +Md(:,find(nans>0)) = NaN; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_modify_dataset.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,247 @@ +function sD = som_modify_dataset(sD,action,varargin) + +%SOM_MODIFY_DATASET Add or remove components or samples to/from a data struct. +% +% sD = som_modify_dataset(sD, 'addcomp', D, [indsto], [cnames}) +% sD = som_modify_dataset(sD, 'addsamp', D, [indsto], ['norm']) +% sD = som_modify_dataset(sD, 'removecomp', [inds]) +% sD = som_modify_dataset(sD, 'removesamp', inds) +% sD = som_modify_dataset(sD, 'extractcomp', [inds]) +% sD = som_modify_dataset(sD, 'extractsamp', inds) +% sD = som_modify_dataset(sD, 'movecomp', inds, indsto) +% sD = som_modify_dataset(sD, 'movesamp', inds, indsto) +% +% Input and output arguments ([]'s are optional) +% sD (struct) data struct +% action (string) 'addcomp', 'addsamp', 'removecomp', 'removesamp', +% 'extractcomp', 'extractsamp', 'movecomp', or 'movesamp' +% +% other input arguments depend on the action +% +% 'addcomp': +% D (matrix) data matrix, size [dlen x d] +% (struct) data struct, size of .data field [dlen x d] +% [indsto] (vector) new indeces of the components, length=d +% [cnames] (cellstr) of size d x 1, the component names +% +% 'addsamp': +% D (matrix) data matrix, size [n x dim] +% [indsto] (vector) new indeces of the samples, length=n +% ['norm'] (string) specified if the normalization procedure +% should be applied to the new samples +% +% 'removecomp', 'extractcomp': +% [inds] (vector) indeces of the components to be removed/extracted. +% If not given, a prompt will appear from which the +% user can select the appropriate components. +% +% 'removesamp', 'extractsamp': +% inds (vector) indeces of the samples to be removed/extracted +% +% 'movecomp', 'movesamp': +% inds (vector) indeces of the components/samples to be moved +% indsto (vector) new indeces of the components/samples +% +% See also SOM_DATA_STRUCT. + +% Copyright (c) 2000 by Juha Vesanto +% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 200400 160600 280800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +[dlen dim] = size(sD.data); + +switch action, + case 'addcomp', + D = varargin{1}; + if isstruct(D), [n d] = size(D.data); else [n d] = size(D); end + if n ~= dlen, error('The number of samples in the data struct and new data should match.'); end + indsto = []; + cnames = []; + for i=2:length(varargin), + if isnumeric(varargin{i}), + indsto = varargin{i}; + if length(indsto) ~= d, + error('The number of indeces should match the number of new components'); + end + else + if ischar(varargin{i}), cnames = cellstr(varargin{i}); + elseif iscellstr(varargin{i}), cnames = varargin{i}; + else + error(['[som_modify_dataset] Unrecognized argument #' num2str(i+1)]); + end + if length(cnames) ~= d, + error('The number of component names should match the number of new components'); + end + end + end + case 'addsamp', + D = varargin{1}; + if isstruct(D), + lab = D.labels; + if isfield(D,'data'), D = D.data; else D = D.codebook; end + else lab = []; + end + [n d] = size(D); + if d ~= dim, + error(['The dimensions of the old and new data sets should match.']); + end + norm = 0; + indsto = []; + for i=2:length(varargin), + if ischar(varargin{i}) & strcmp(varargin{i},'norm'), norm = 1; + elseif isnumeric(varargin{i}), + indsto = varargin{i}; + if length(indsto) ~= n, + error(['The number of new indeces should match the number of new' ... + ' samples']); + end + else + warning(['[som_modify_dataset] Ignoring unrecognized argument #', ... + num2str(i+2)]); + end + end + case 'removecomp', + if length(varargin)>0, + inds = varargin{1}; + else + [inds, ok] = listdlg('ListString',sD.comp_names, 'PromptString', ... + 'Components', 'Name', 'Remove components', 'uh', 25); + if ~ok, return; end + end + if min(inds)<1 | max(inds)>dim, + error('The component indeces must be within [1,dim]'); + end + case 'removesamp', + inds = varargin{1}; + if min(inds)<1 | max(inds)>dlen, + error('The sample indeces must be within [1,dlen]'); + end + case 'extractcomp', + if length(varargin)>0, + inds = varargin{1}; + else + [inds, ok] = listdlg('ListString',sD.comp_names, 'PromptString',... + 'Components', 'Name', 'Extract components', 'uh', 25); + if ~ok, return; end + end + if min(inds)<1 | max(inds)>dim, + error('The component indeces must be within [1,dim]'); + end + case 'extractsamp', + inds = varargin{1}; + if min(inds)<1 | max(inds)>dlen, + error('The sample indeces must be within [1,dlen]'); + end + case 'movecomp', + inds = varargin{1}; + indsto = varargin{2}; + if min(inds)<1 | max(inds)>dim | min(indsto)<1 | max(indsto)>dim, + error('The component indeces must be within [1,dim]'); + end + case 'movesamp', + inds = varargin{1}; + indsto = varargin{2}; + if min(inds)<1 | max(inds)>dlen | min(indsto)<1 | max(indsto)>dlen, + error('The sample indeces must be within [1,dlen]'); + end + otherwise, + error('Unrecognized action mode'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +switch action, + case 'addcomp', + if isstruct(D), + sD.data = [sD.data, D.data]; + sD.comp_names(dim+[1:d]) = D.comp_names; + sD.comp_norm(dim+[1:d]) = D.comp_norm; + sD = som_label(sD,'add',1:dlen,D.labels); + else + sD.data = [sD.data, D]; + if isempty(cnames), + for i=1:d, sD.comp_names(dim+i) = {sprintf('Variable%d',i+dim)}; end + else + sD.comp_names(dim+[1:d]) = cnames; + end + for i=1:d, sD.comp_norm(dim+i) = {[]}; end + end + if ~isempty(indsto), + sD = som_modify_dataset(sD,'movecomp',dim+[1:d],indsto); + end + + case 'addsamp', + if norm, D = som_normalize(D,sD); end + sD.data = [sD.data; D]; + nl = size(sD.labels,2); + sD.labels(dlen+[1:n],1:nl) = {''}; + if ~isempty(lab), + nl2 = size(lab,2); + if nl2>nl, sD.labels(1:dlen,nl+[1:(nl2-nl)]) = {''}; end + sD.labels(dlen+[1:n],1:nl2) = lab; + end + if ~isempty(indsto), + sD = som_modify_dataset(sD,'movesamp',dlen+[1:n],indsto); + end + + case 'removecomp', + includeinds = 1:dim; + includeinds(inds) = 0; + sD = som_modify_dataset(sD,'extractcomp',find(includeinds)); + + case 'removesamp', + includeinds = 1:dlen; + includeinds(inds) = 0; + sD = som_modify_dataset(sD,'extractsamp',find(includeinds)); + + case 'extractcomp', + sD.data = sD.data(:,inds); + sD.comp_names = sD.comp_names(inds); + sD.comp_norm = sD.comp_norm(inds); + + case 'extractsamp', + sD.data = sD.data(inds,:); + sD.labels = sD.labels(inds,:); + + case 'movecomp', + [indsto,order] = sort(indsto); + inds = inds(order); + oldinds = 1:dim; + oldinds(inds) = 0; + newinds = oldinds(oldinds>0); + for i=1:length(indsto), + ifrom = inds(i); ito = indsto(i); + if ito==1, newinds = [ifrom, newinds]; + else newinds = [newinds(1:ito-1), ifrom, newinds(ito:end)]; + end + end + sD.data = sD.data(:,newinds); + sD.comp_names = sD.comp_names(:,newinds); + sD.comp_norm = sD.comp_norm(:,newinds); + + case 'movesamp', + [indsto,order] = sort(indsto); + inds = inds(order); + oldinds = 1:dim; + oldinds(inds) = 0; + newinds = oldinds(oldinds>0); + for i=1:length(indsto), + ifrom = inds(i); ito = indsto(i); + if ito==1, newinds = [ifrom, newinds]; + else newinds = [newinds(1:ito-1), ifrom, newinds(ito:end)]; + end + end + sD.data = sD.data(newinds,:); + sD.labels = sD.labels(newinds,:); + +end + +%som_set(sD); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_neighborhood.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,157 @@ +function Ne = som_neighborhood(Ne1,n) + +%SOM_NEIGHBORHOOD Calculate neighborhood matrix. +% +% Ne = som_neighborhood(Ne1,n) +% +% Ne = som_neighborhood(Ne1); +% Ne = som_neighborhood(som_unit_neighs(topol),2); +% +% Input and output arguments ([]'s are optional): +% Ne1 (matrix, size [munits m]) a sparse matrix indicating +% the units in 1-neighborhood for each map unit +% [n] (scalar) maximum neighborhood which is calculated, default=Inf +% +% Ne (matrix, size [munits munits]) neighborhood matrix, +% each row (and column) contains neighborhood +% values from the specific map unit to all other +% map units, or Inf if the value is unknown. +% +% For more help, try 'type som_neighborhood' or check out online documentation. +% See also SOM_UNIT_NEIGHS, SOM_UNIT_DISTS, SOM_UNIT_COORDS, SOM_CONNECTION. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_neighborhood +% +% PURPOSE +% +% Calculate to which neighborhood each map unit belongs to relative to +% each other map unit, given the units in 1-neighborhood of each unit. +% +% SYNTAX +% +% Ne = som_neighborhood(Ne1); +% Ne = som_neighborhood(Ne1,n); +% +% DESCRIPTION +% +% For each map unit, finds the minimum neighborhood to which it belongs +% to relative to each other map unit. Or, equivalently, for each map +% unit, finds which units form its k-neighborhood, where k goes from +% 0 to n. +% +% The neighborhood is calculated iteratively using the reflexivity of +% neighborhood. +% let N1i be the 1-neighborhood set a unit i +% and let N11i be the set of units in the 1-neighborhood of any unit j in N1i +% then N2i (the 2-neighborhood set of unit i) is N11i \ N1i +% +% Consider, for example, the case of a 5x5 map. The neighborhood in case of +% 'rect' and 'hexa' lattices (and 'sheet' shape) for the unit at the +% center of the map are depicted below: +% +% 'rect' lattice 'hexa' lattice +% -------------- -------------- +% 4 3 2 3 4 3 2 2 2 3 +% 3 2 1 2 3 2 1 1 2 3 +% 2 1 0 1 2 2 1 0 1 2 +% 3 2 1 2 3 2 1 1 2 3 +% 4 3 2 3 4 3 2 2 2 3 +% +% Because the iterative procedure is rather slow, the neighborhoods +% are calculated upto given maximal value. The uncalculated values +% in the returned matrix are Inf:s. +% +% REQUIRED INPUT ARGUMENTS +% +% Ne1 (matrix) Each row contains 1, if the corresponding unit is adjacent +% for that map unit, 0 otherwise. This can be calculated +% using SOM_UNIT_NEIGHS. The matrix can be sparse. +% Size munits x munits. +% +% OPTIONAL INPUT ARGUMENTS +% +% n (scalar) Maximal neighborhood value which is calculated, +% Inf by default (all neighborhoods). +% +% OUTPUT ARGUMENTS +% +% Ne (matrix) neighborhood values for each map unit, size is +% [munits, munits]. The matrix contains the minimum +% neighborhood of unit i, to which unit j belongs, +% or Inf, if the neighborhood was bigger than n. +% +% EXAMPLES +% +% Ne = som_neighborhood(Ne1,1); % upto 1-neighborhood +% Ne = som_neighborhood(Ne1,Inf); % all neighborhoods +% Ne = som_neighborhood(som_unit_neighs(topol),4); +% +% SEE ALSO +% +% som_unit_neighs Calculate units in 1-neighborhood for each map unit. +% som_unit_coords Calculate grid coordinates. +% som_unit_dists Calculate interunit distances. +% som_connection Connection matrix. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 141097 +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +error(nargchk(1, 2, nargin)); + +if nargin<2, n=Inf; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +% initialize +if issparse(Ne1), Ne = full(Ne1); else Ne = Ne1; end +clear Ne1 +[munits dummy] = size(Ne); +Ne(find(Ne==0)) = NaN; +for i=1:munits, Ne(i,i)=0; end + +% Calculate neighborhood distance for each unit using reflexsivity +% of neighborhood: +% let N1i be the 1-neighborhood set a unit i +% then N2i is the union of all map units, belonging to the +% 1-neighborhood of any unit j in N1i, not already in N1i +k=1; +if n>1, + fprintf(1,'Calculating neighborhood: 1 '); + N1 = Ne; + N1(find(N1~=1)) = 0; +end +while k<n & any(isnan(Ne(:))), + k=k+1; + fprintf(1,'%d ',k); + for i=1:munits, + candidates = isnan(Ne(i,:)); % units not in any neighborhood yet + if any(candidates), + prevneigh = find(Ne(i,:)==k-1); % neighborhood (k-1) + N1_of_prevneigh = any(N1(prevneigh,:)); % union of their N1:s + Nn = find(N1_of_prevneigh & candidates); + if length(Nn), Ne(i,Nn) = k; Ne(Nn,i) = k; end + end + end +end +if n>1, fprintf(1,'\n'); end + +% finally replace all uncalculated distance values with Inf +Ne(find(isnan(Ne))) = Inf; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% faster version? + +l = size(Ne1,1); Ne1([0:l-1]*(l+1)+1) = 1; Ne = full(Ne1); M0 = Ne1; k = 2; +while any(Ne(:)==0), M1=(M0*Ne1>0); Ne(find(M1-M0))=k; M0=M1; k=k+1; end +Ne([0:l-1]*(l+1)+1) = 0;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_neighbors.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +function Ne = som_neighbors(sM,neigh) + +% Ne = som_neighbors(sM,neigh) +% +% sM (struct) map or data struct +% (matrix) data matrix, size n x dim +% [neigh] (string) 'kNN' or 'Nk' (which is valid for a SOM only) +% for example '6NN' or 'N1' +% default is '10NN' for a data set and 'N1' for SOM +% +% Ne (matrix) size n x n, a sparse matrix +% indicating the neighbors of each sample by value 1 +% (note: the unit itself also has value 0) + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(sM), + switch sM.type, + case 'som_map', M = sM.codebook; + case 'som_data', M = sM.data; sM = []; + end +else + M = sM; + sM = []; +end + +n = size(M,1); + +if nargin<2, + if isempty(sM), neigh = '10NN'; else neigh = 'N1'; end +end + +if strcmp(neigh(end-1:end),'NN'), + k = str2num(neigh(1:end-2)); + kmus = som_bmus(M,M,1:k+1); + Ne = sparse(n,n); + for i=1:n, Ne(i,kmus(i,:)) = 1; end +else + if ~isstruct(sM), error('Prototypes must be in a map struct.'); end + k = str2num(neigh(2:end)); + N1 = som_unit_neighs(sM); + Ne = sparse(som_neighborhood(N1,k)<=k); +end +Ne([0:n-1]*n+[1:n]) = 0; % remove self from neighbors + +return; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_neighf.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,117 @@ +function H = som_neighf(sMap,radius,neigh,ntype) + +%SOM_NEIGHF Return neighborhood function values. +% +% H = som_neighf(sMap,[radius],[neigh],[ntype]); +% +% Input and output arguments ([]'s are optional): +% sMap (struct) map or topology struct +% [radius] (scalar) neighborhood radius (by default, the last used value +% in sMap.trainhist is used, or 1 if that is unavailable) +% [neigh] (string) neighborhood function type (by default, ..., or +% 'gaussian' if that is unavailable) +% [ntype] (string) 'normal' (default), 'probability' or 'mirror' +% +% H (matrix) [munits x munits] neighborhood function values from +% each map unit to each other map unit +% +% For more help, try 'type som_batchtrain' or check out online documentation. +% See also SOM_MAKE, SOM_SEQTRAIN, SOM_TRAIN_STRUCT. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +% defaults +rdefault = 1; +ndefault = 'gaussian'; +tdefault = 'normal'; + +% map +switch sMap.type, + case 'som_map', + sTopol = sMap.topol; + sTrain = sMap.trainhist(end); + if isempty(sTrain.radius_fin) | isnan(sTrain.radius_fin), + rdefault = 1; + else + rdefault = sTrain.radius_fin; + end + if ~isempty(sTrain.neigh) & ~isnan(sTrain.neigh), + ndefault = sTrain.neigh; + end + case 'som_topol', sTopol = sMap; +end +munits = prod(sTopol.msize); + +% other parameters +if nargin<2 | isempty(radius), radius = rdefault; end +if nargin<3 | isempty(neigh), neigh = ndefault; end +if nargin<4 | isempty(ntype), ntype = tdefault; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize + +% basic neighborhood +Ud = som_unit_dists(sTopol); +Ud = Ud.^2; +radius = radius.^2; +if radius==0, radius = eps; end % zero neighborhood radius may cause div-by-zero error + +switch ntype, +case 'normal', + H = neighf(neigh,Ud,radius); +case 'probability', + H = neighf(neigh,Ud,radius); + for i=1:munits, H(i,:) = H(i,:)/sum(H(i,:)); end +case 'mirror', % only works for 2-dim grid!!! + H = zeros(munits,munits); + Co = som_unit_coords(sTopol); + for i=-1:1, + for j=-1:1, + Ud = gridmirrordist(Co,i,j); + H = H + neighf(neigh,Ud,radius); + end + end +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function H = neighf(neigh,Ud,radius) + + switch neigh, + case 'bubble', H = (Ud<=radius); + case 'gaussian', H = exp(-Ud/(2*radius)); + case 'cutgauss', H = exp(-Ud/(2*radius)) .* (Ud<=radius); + case 'ep', H = (1-Ud/radius) .* (Ud<=radius); + end + return; + +function Ud = gridmirrordist(Co,mirrorx,mirrory) + + [munits,mdim] = size(Co); + if mdim>2, error('Mirrored neighborhood only works for 2-dim map grids.'); end + + % width and height of the grid + dx = max(Co(:,1))-min(Co(:,1)); + dy = max(Co(:,2))-min(Co(:,2)); + + % calculate distance from each location to each other location + Ud = zeros(munits,munits); + for i=1:munits, + inds = [i:munits]; + coi = Co(i,:); % take hexagonal shift into account + coi(1) = coi(1)*(1-2*(mirrorx~=0)) + 2*dx*(mirrorx==1); % +mirrorx * step + coi(2) = coi(2)*(1-2*(mirrory~=0)) + 2*dy*(mirrory==1); % +mirrory * step + Dco = (Co(inds,:) - coi(ones(munits-i+1,1),:))'; + Ud(i,inds) = sqrt(sum(Dco.^2)); + Ud(inds,i) = Ud(i,inds)'; + end + return; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_norm_variable.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,533 @@ +function [x,sNorm] = som_norm_variable(x, method, operation) + +%SOM_NORM_VARIABLE Normalize or denormalize a scalar variable. +% +% [x,sNorm] = som_norm_variable(x, method, operation) +% +% xnew = som_norm_variable(x,'var','do'); +% [dummy,sN] = som_norm_variable(x,'log','init'); +% [xnew,sN] = som_norm_variable(x,sN,'do'); +% xorig = som_norm_variable(xnew,sN,'undo'); +% +% Input and output arguments: +% x (vector) a set of values of a scalar variable for +% which the (de)normalization is performed. +% The processed values are returned. +% method (string) identifier for a normalization method: 'var', +% 'range', 'log', 'logistic', 'histD', or 'histC'. +% A normalization struct with default values is created. +% (struct) normalization struct, or an array of such +% (cellstr) first string gives normalization operation, and the +% second gives denormalization operation, with x +% representing the variable, for example: +% {'x+2','x-2}, or {'exp(-x)','-log(x)'} or {'round(x)'}. +% Note that in the last case, no denorm operation is +% defined. +% operation (string) the operation to be performed: 'init', 'do' or 'undo' +% +% sNorm (struct) updated normalization struct/struct array +% +% For more help, try 'type som_norm_variable' or check out online documentation. +% See also SOM_NORMALIZE, SOM_DENORMALIZE. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_norm_variable +% +% PURPOSE +% +% Initialize, apply and undo normalizations on a given vector of +% scalar values. +% +% SYNTAX +% +% xnew = som_norm_variable(x,method,operation) +% xnew = som_norm_variable(x,sNorm,operation) +% [xnew,sNorm] = som_norm_variable(...) +% +% DESCRIPTION +% +% This function is used to initialize, apply and undo normalizations +% on scalar variables. It is the low-level function that upper-level +% functions SOM_NORMALIZE and SOM_DENORMALIZE utilize to actually (un)do +% the normalizations. +% +% Normalizations are typically performed to control the variance of +% vector components. If some vector components have variance which is +% significantly higher than the variance of other components, those +% components will dominate the map organization. Normalization of +% the variance of vector components (method 'var') is used to prevent +% that. In addition to variance normalization, other methods have +% been implemented as well (see list below). +% +% Usually normalizations convert the variable values so that they no +% longer make any sense: the values are still ordered, but their range +% may have changed so radically that interpreting the numbers in the +% original context is very hard. For this reason all implemented methods +% are (more or less) revertible. The normalizations are monotonic +% and information is saved so that they can be undone. Also, the saved +% information makes it possible to apply the EXACTLY SAME normalization +% to another set of values. The normalization information is determined +% with 'init' operation, while 'do' and 'undo' operations are used to +% apply or revert the normalization. +% +% The normalization information is saved in a normalization struct, +% which is returned as the second argument of this function. Note that +% normalization operations may be stacked. In this case, normalization +% structs are positioned in a struct array. When applied, the array is +% gone through from start to end, and when undone, in reverse order. +% +% method description +% +% 'var' Variance normalization. A linear transformation which +% scales the values such that their variance=1. This is +% convenient way to use Mahalanobis distance measure without +% actually changing the distance calculation procedure. +% +% 'range' Normalization of range of values. A linear transformation +% which scales the values between [0,1]. +% +% 'log' Logarithmic normalization. In many cases the values of +% a vector component are exponentially distributed. This +% normalization is a good way to get more resolution to +% (the low end of) that vector component. What this +% actually does is a non-linear transformation: +% x_new = log(x_old - m + 1) +% where m=min(x_old) and log is the natural logarithm. +% Applying the transformation to a value which is lower +% than m-1 will give problems, as the result is then complex. +% If the minimum for values is known a priori, +% it might be a good idea to initialize the normalization with +% [dummy,sN] = som_norm_variable(minimum,'log','init'); +% and normalize only after this: +% x_new = som_norm_variable(x,sN,'do'); +% +% 'logistic' or softmax normalization. This normalization ensures +% that all values in the future, too, are within the range +% [0,1]. The transformation is more-or-less linear in the +% middle range (around mean value), and has a smooth +% nonlinearity at both ends which ensures that all values +% are within the range. The data is first scaled as in +% variance normalization: +% x_scaled = (x_old - mean(x_old))/std(x_old) +% and then transformed with the logistic function +% x_new = 1/(1+exp(-x_scaled)) +% +% 'histD' Discrete histogram equalization. Non-linear. Orders the +% values and replaces each value by its ordinal number. +% Finally, scales the values such that they are between [0,1]. +% Useful for both discrete and continuous variables, but as +% the saved normalization information consists of all +% unique values of the initialization data set, it may use +% considerable amounts of memory. If the variable can get +% more than a few values (say, 20), it might be better to +% use 'histC' method below. Another important note is that +% this method is not exactly revertible if it is applied +% to values which are not part of the original value set. +% +% 'histC' Continuous histogram equalization. Actually, a partially +% linear transformation which tries to do something like +% histogram equalization. The value range is divided to +% a number of bins such that the number of values in each +% bin is (almost) the same. The values are transformed +% linearly in each bin. For example, values in bin number 3 +% are scaled between [3,4[. Finally, all values are scaled +% between [0,1]. The number of bins is the square root +% of the number of unique values in the initialization set, +% rounded up. The resulting histogram equalization is not +% as good as the one that 'histD' makes, but the benefit +% is that it is exactly revertible - even outside the +% original value range (although the results may be funny). +% +% 'eval' With this method, freeform normalization operations can be +% specified. The parameter field contains strings to be +% evaluated with 'eval' function, with variable name 'x' +% representing the variable itself. The first string is +% the normalization operation, and the second is a +% denormalization operation. If the denormalization operation +% is empty, it is ignored. +% +% INPUT ARGUMENTS +% +% x (vector) The scalar values to which the normalization +% operation is applied. +% +% method The normalization specification. +% (string) Identifier for a normalization method: 'var', +% 'range', 'log', 'logistic', 'histD' or 'histC'. +% Corresponding default normalization struct is created. +% (struct) normalization struct +% (struct array) of normalization structs, applied to +% x one after the other +% (cellstr) of length +% (cellstr array) first string gives normalization operation, and +% the second gives denormalization operation, with x +% representing the variable, for example: +% {'x+2','x-2}, or {'exp(-x)','-log(x)'} or {'round(x)'}. +% Note that in the last case, no denorm operation is +% defined. +% +% note: if the method is given as struct(s), it is +% applied (done or undone, as specified by operation) +% regardless of what the value of '.status' field +% is in the struct(s). Only if the status is +% 'uninit', the undoing operation is halted. +% Anyhow, the '.status' fields in the returned +% normalization struct(s) is set to approriate value. +% +% operation (string) The operation to perform: 'init' to initialize +% the normalization struct, 'do' to perform the +% normalization, 'undo' to undo the normalization, +% if possible. If operation 'do' is given, but the +% normalization struct has not yet been initialized, +% it is initialized using the given data (x). +% +% OUTPUT ARGUMENTS +% +% x (vector) Appropriately processed values. +% +% sNorm (struct) Updated normalization struct/struct array. If any, +% the '.status' and '.params' fields are updated. +% +% EXAMPLES +% +% To initialize and apply a normalization on a set of scalar values: +% +% [x_new,sN] = som_norm_variable(x_old,'var','do'); +% +% To just initialize, use: +% +% [dummy,sN] = som_norm_variable(x_old,'var','init'); +% +% To undo the normalization(s): +% +% x_orig = som_norm_variable(x_new,sN,'undo'); +% +% Typically, normalizations of data structs/sets are handled using +% functions SOM_NORMALIZE and SOM_DENORMALIZE. However, when only the +% values of a single variable are of interest, SOM_NORM_VARIABLE may +% be useful. For example, assume one wants to apply the normalization +% done on a component (i) of a data struct (sD) to a new set of values +% (x) of that component. With SOM_NORM_VARIABLE this can be done with: +% +% x_new = som_norm_variable(x,sD.comp_norm{i},'do'); +% +% Now, as the normalizations in sD.comp_norm{i} have already been +% initialized with the original data set (presumably sD.data), +% the EXACTLY SAME normalization(s) can be applied to the new values. +% The same thing can be done with SOM_NORMALIZE function, too: +% +% x_new = som_normalize(x,sD.comp_norm{i}); +% +% Or, if the new data set were in variable D - a matrix of same +% dimension as the original data set: +% +% D_new = som_normalize(D,sD,i); +% +% SEE ALSO +% +% som_normalize Add/apply/redo normalizations for a data struct/set. +% som_denormalize Undo normalizations of a data struct/set. + +% Copyright (c) 1998-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 151199 170400 150500 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(3, 3, nargin)); % check no. of input arguments is correct + +% method +sNorm = []; +if ischar(method) + if any(strcmp(method,{'var','range','log','logistic','histD','histC'})), + sNorm = som_set('som_norm','method',method); + else + method = cellstr(method); + end +end +if iscell(method), + if length(method)==1 & isstruct(method{1}), sNorm = method{1}; + else + if length(method)==1 | isempty(method{2}), method{2} = 'x'; end + sNorm = som_set('som_norm','method','eval','params',method); + end +else + sNorm = method; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +order = [1:length(sNorm)]; +if length(order)>1 & strcmp(operation,'undo'), order = order(end:-1:1); end + +for i=order, + + % initialize + if strcmp(operation,'init') | ... + (strcmp(operation,'do') & strcmp(sNorm(i).status,'uninit')), + + % case method = 'hist' + if strcmp(sNorm(i).method,'hist'), + inds = find(~isnan(x) & ~isinf(x)); + if length(unique(x(inds)))>20, sNorm(i).method = 'histC'; + else sNorm{i}.method = 'histD'; end + end + + switch(sNorm(i).method), + case 'var', params = norm_variance_init(x); + case 'range', params = norm_scale01_init(x); + case 'log', params = norm_log_init(x); + case 'logistic', params = norm_logistic_init(x); + case 'histD', params = norm_histeqD_init(x); + case 'histC', params = norm_histeqC_init(x); + case 'eval', params = sNorm(i).params; + otherwise, + error(['Unrecognized method: ' sNorm(i).method]); + end + sNorm(i).params = params; + sNorm(i).status = 'undone'; + end + + % do / undo + if strcmp(operation,'do'), + switch(sNorm(i).method), + case 'var', x = norm_scale_do(x,sNorm(i).params); + case 'range', x = norm_scale_do(x,sNorm(i).params); + case 'log', x = norm_log_do(x,sNorm(i).params); + case 'logistic', x = norm_logistic_do(x,sNorm(i).params); + case 'histD', x = norm_histeqD_do(x,sNorm(i).params); + case 'histC', x = norm_histeqC_do(x,sNorm(i).params); + case 'eval', x = norm_eval_do(x,sNorm(i).params); + otherwise, + error(['Unrecognized method: ' sNorm(i).method]); + end + sNorm(i).status = 'done'; + + elseif strcmp(operation,'undo'), + + if strcmp(sNorm(i).status,'uninit'), + warning('Could not undo: uninitialized normalization struct.') + break; + end + switch(sNorm(i).method), + case 'var', x = norm_scale_undo(x,sNorm(i).params); + case 'range', x = norm_scale_undo(x,sNorm(i).params); + case 'log', x = norm_log_undo(x,sNorm(i).params); + case 'logistic', x = norm_logistic_undo(x,sNorm(i).params); + case 'histD', x = norm_histeqD_undo(x,sNorm(i).params); + case 'histC', x = norm_histeqC_undo(x,sNorm(i).params); + case 'eval', x = norm_eval_undo(x,sNorm(i).params); + otherwise, + error(['Unrecognized method: ' sNorm(i).method]); + end + sNorm(i).status = 'undone'; + + elseif ~strcmp(operation,'init'), + + error(['Unrecognized operation: ' operation]) + + end +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +% linear scaling + +function p = norm_variance_init(x) + inds = find(~isnan(x) & isfinite(x)); + p = [mean(x(inds)), std(x(inds))]; + if p(2) == 0, p(2) = 1; end + %end of norm_variance_init + +function p = norm_scale01_init(x) + inds = find(~isnan(x) & isfinite(x)); + mi = min(x(inds)); + ma = max(x(inds)); + if mi == ma, p = [mi, 1]; else p = [mi, ma-mi]; end + %end of norm_scale01_init + +function x = norm_scale_do(x,p) + x = (x - p(1)) / p(2); + % end of norm_scale_do + +function x = norm_scale_undo(x,p) + x = x * p(2) + p(1); + % end of norm_scale_undo + +% logarithm + +function p = norm_log_init(x) + inds = find(~isnan(x) & isfinite(x)); + p = min(x(inds)); + % end of norm_log_init + +function x = norm_log_do(x,p) + x = log(x - p +1); + % if any(~isreal(x)), ok = 0; end + % end of norm_log_do + +function x = norm_log_undo(x,p) + x = exp(x) -1 + p; + % end of norm_log_undo + +% logistic + +function p = norm_logistic_init(x) + inds = find(~isnan(x) & isfinite(x)); + p = [mean(x(inds)), std(x(inds))]; + if p(2)==0, p(2) = 1; end + % end of norm_logistic_init + +function x = norm_logistic_do(x,p) + x = (x-p(1))/p(2); + x = 1./(1+exp(-x)); + % end of norm_logistic_do + +function x = norm_logistic_undo(x,p) + x = log(x./(1-x)); + x = x*p(2)+p(1); + % end of norm_logistic_undo + +% histogram equalization for discrete values + +function p = norm_histeqD_init(x) + inds = find(~isnan(x) & ~isinf(x)); + p = unique(x(inds)); + % end of norm_histeqD_init + +function x = norm_histeqD_do(x,p) + bins = length(p); + inds = find(~isnan(x) & ~isinf(x))'; + for i = inds, + [dummy ind] = min(abs(x(i) - p)); + % data item closer to the left-hand bin wall is indexed after RH wall + if x(i) > p(ind) & ind < bins, + x(i) = ind + 1; + else + x(i) = ind; + end + end + x = (x-1)/(bins-1); % normalization between [0,1] + % end of norm_histeqD_do + +function x = norm_histeqD_undo(x,p) + bins = length(p); + x = round(x*(bins-1)+1); + inds = find(~isnan(x) & ~isinf(x)); + x(inds) = p(x(inds)); + % end of norm_histeqD_undo + +% histogram equalization with partially linear functions + +function p = norm_histeqC_init(x) + % investigate x + inds = find(~isnan(x) & ~isinf(x)); + samples = length(inds); + xs = unique(x(inds)); + mi = xs(1); + ma = xs(end); + % decide number of limits + lims = ceil(sqrt(length(xs))); % 2->2,100->10,1000->32,10000->100 + % decide limits + if lims==1, + p = [mi, mi+1]; + lims = 2; + elseif lims==2, + p = [mi, ma]; + else + p = zeros(lims,1); + p(1) = mi; + p(end) = ma; + binsize = zeros(lims-1,1); b = 1; avebinsize = samples/(lims-1); + for i=1:(length(xs)-1), + binsize(b) = binsize(b) + sum(x==xs(i)); + if binsize(b) >= avebinsize, + b = b + 1; + p(b) = (xs(i)+xs(i+1))/2; + end + if b==(lims-1), + binsize(b) = samples-sum(binsize); break; + else + avebinsize = (samples-sum(binsize))/(lims-1-b); + end + end + end + % end of norm_histeqC_init + +function x = norm_histeqC_do(x,p) + xnew = x; + lims = length(p); + % handle values below minimum + r = p(2)-p(1); + inds = find(x<=p(1) & isfinite(x)); + if any(inds), xnew(inds) = 0-(p(1)-x(inds))/r; end + % handle values above maximum + r = p(end)-p(end-1); + inds = find(x>p(end) & isfinite(x)); + if any(inds), xnew(inds) = lims-1+(x(inds)-p(end))/r; end + % handle all other values + for i=1:(lims-1), + r0 = p(i); r1 = p(i+1); r = r1-r0; + inds = find(x>r0 & x<=r1); + if any(inds), xnew(inds) = i-1+(x(inds)-r0)/r; end + end + % scale so that minimum and maximum correspond to 0 and 1 + x = xnew/(lims-1); + % end of norm_histeqC_do + +function x = norm_histeqC_undo(x,p) + xnew = x; + lims = length(p); + % scale so that 0 and 1 correspond to minimum and maximum + x = x*(lims-1); + + % handle values below minimum + r = p(2)-p(1); + inds = find(x<=0 & isfinite(x)); + if any(inds), xnew(inds) = x(inds)*r + p(1); end + % handle values above maximum + r = p(end)-p(end-1); + inds = find(x>lims-1 & isfinite(x)); + if any(inds), xnew(inds) = (x(inds)-(lims-1))*r+p(end); end + % handle all other values + for i=1:(lims-1), + r0 = p(i); r1 = p(i+1); r = r1-r0; + inds = find(x>i-1 & x<=i); + if any(inds), xnew(inds) = (x(inds)-(i-1))*r + r0; end + end + x = xnew; + % end of norm_histeqC_undo + +% eval + +function p = norm_eval_init(method) + p = method; + %end of norm_eval_init + +function x = norm_eval_do(x,p) + x_tmp = eval(p{1}); + if size(x_tmp,1)>=1 & size(x,1)>=1 & ... + size(x_tmp,2)==1 & size(x,2)==1, + x = x_tmp; + end + %end of norm_eval_do + +function x = norm_eval_undo(x,p) + x_tmp = eval(p{2}); + if size(x_tmp,1)>=1 & size(x,1)>=1 & ... + size(x_tmp,2)==1 & size(x,2)==1, + x = x_tmp; + end + %end of norm_eval_undo + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_normalize.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,319 @@ +function sD = som_normalize(sD,method,comps) + +%SOM_NORMALIZE (Re)normalize data or add new normalizations. +% +% sS = som_normalize(sS,[method],[comps]) +% +% sS = som_normalize(sD) +% sS = som_normalize(sS,sNorm) +% D = som_normalize(D,'var') +% sS = som_normalize(sS,'histC',[1:3 10]) +% +% Input and output arguments ([]'s are optional): +% sS The data to which the normalization is applied. +% The modified and updated data is returned. +% (struct) data or map struct +% (matrix) data matrix (a matrix is also returned) +% [method] The normalization method(s) to add/use. If missing, +% or an empty variable ('') is given, the +% normalizations in sS are used. +% (string) identifier for a normalization method to be added: +% 'var', 'range', 'log', 'logistic', 'histD' or 'histC'. +% (struct) Normalization struct, or an array of such. +% Alternatively, a map/data struct can be given +% in which case its '.comp_norm' field is used +% (see below). +% (cell array) Of normalization structs. Typically, the +% '.comp_norm' field of a map/data struct. The +% length of the array must be equal to data dimension. +% (cellstr array) norm and denorm operations in a cellstr array +% which are evaluated with EVAL command with variable +% name 'x' reserved for the variable. +% [comps] (vector) the components to which the normalization is +% applied, default is [1:dim] ie. all components +% +% For more help, try 'type som_normalize' or check out online documentation. +% See also SOM_DENORMALIZE, SOM_NORM_VARIABLE, SOM_INFO. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_normalize +% +% PURPOSE +% +% Add/apply/redo normalization on data structs/sets. +% +% SYNTAX +% +% sS = som_normalize(sS) +% sS = som_normalize(sS,method) +% D = som_normalize(D,sNorm) +% sS = som_normalize(sS,csNorm) +% sS = som_normalize(...,comps) +% +% DESCRIPTION +% +% This function is used to (initialize and) add, redo and apply +% normalizations on data/map structs/sets. If a data/map struct is given, +% the specified normalizations are added to the '.comp_norm' field of the +% struct after ensuring that all normalizations specified therein have +% status 'done'. SOM_NORMALIZE actually uses function SOM_NORM_VARIABLE +% to handle the normalization operations, and only handles the data +% struct/set specific stuff itself. +% +% The different normalization methods are listed below. For more +% detailed descriptions, see SOM_NORM_VARIABLE. +% +% method description +% 'var' Variance is normalized to one (linear operation). +% 'range' Values are normalized between [0,1] (linear operation). +% 'log' Natural logarithm is applied to the values: +% xnew = log(x-m+1) +% where m = min(x). +% 'logistic' Logistic or softmax trasformation which scales all +% possible values between [0,1]. +% 'histD' Histogram equalization, values scaled between [0,1]. +% 'histC' Approximate histogram equalization with partially +% linear operations. Values scaled between [0,1]. +% 'eval' freeform operations +% +% To enable undoing and applying the exactly same normalization to +% other data sets, normalization information is saved into a +% normalization struct, which has the fields: +% +% .type ; struct type, ='som_norm' +% .method ; normalization method, a string +% .params ; normalization parameters +% .status ; string: 'uninit', 'undone' or 'done' +% +% Normalizations are always one-variable operations. In the data and map +% structs the normalization information for each component is saved in the +% '.comp_norm' field, which is a cell array of length dim. Each cell +% contains normalizations for one vector component in a struct array of +% normalization structs. Each component may have different amounts of +% different kinds of normalizations. Typically, all normalizations are +% either 'undone' or 'done', but in special situations this may not be the +% case. The easiest way to check out the status of the normalizations is to +% use function SOM_INFO, e.g. som_info(sS,3) +% +% REQUIRED INPUT ARGUMENTS +% +% sS The data to which the normalization is applied. +% (struct) Data or map struct. Before adding any new +% normalizations, it is ensured that the +% normalizations for the specified components in the +% '.comp_norm' field have status 'done'. +% (matrix) data matrix +% +% OPTIONAL INPUT ARGUMENTS +% +% method The normalization(s) to add/use. If missing, +% or an empty variable ('' or []) is given, the +% normalizations in the data struct are used. +% (string) Identifier for a normalization method to be added: +% 'var', 'range', 'log', 'logistic', 'histD' or 'histC'. The +% same method is applied to all specified components +% (given in comps). The normalizations are first +% initialized (for each component separately, of +% course) and then applied. +% (struct) Normalization struct, or an array of structs, which +% is applied to all specified components. If the +% '.status' field of the struct(s) is 'uninit', +% the normalization(s) is initialized first. +% Alternatively, the struct may be map or data struct +% in which case its '.comp_norm' field is used +% (see the cell array option below). +% (cell array) In practice, the '.comp_norm' field of +% a data/map struct. The length of the array +% must be equal to the dimension of the given +% data set (sS). Each cell contains the +% normalization(s) for one component. Only the +% normalizations listed in comps argument are +% applied though. +% (cellstr array) norm and denorm operations in a cellstr array +% which are evaluated with EVAL command with variable +% name 'x' reserved for the variable. +% +% comps (vector) The components to which the normalization(s) is +% applied. Default is to apply to all components. +% +% OUTPUT ARGUMENTS +% +% sS Modified and/or updated data. +% (struct) If a struct was given as input argument, the +% same struct is returned with normalized data and +% updated '.comp_norm' fields. +% (matrix) If a matrix was given as input argument, the +% normalized data matrix is returned. +% +% EXAMPLES +% +% To add (initialize and apply) a normalization to a data struct: +% +% sS = som_normalize(sS,'var'); +% +% This uses 'var'-method to all components. To add a method only to +% a few selected components, use the comps argument: +% +% sS = som_normalize(sS,'log',[1 3:5]); +% +% To ensure that all normalization operations have indeed been done: +% +% sS = som_normalize(sS); +% +% The same for only a few components: +% +% sS = som_normalize(sS,'',[1 3:5]); +% +% To apply the normalizations of a data struct sS to a new data set D: +% +% D = som_normalize(D,sS); +% or +% D = som_normalize(D,sS.comp_norm); +% +% To normalize a data set: +% +% D = som_normalize(D,'histD'); +% +% Note that in this case the normalization information is lost. +% +% To check out the status of normalization in a struct use SOM_INFO: +% +% som_info(sS,3) +% +% +% SEE ALSO +% +% som_denormalize Undo normalizations of a data struct/set. +% som_norm_variable Normalization operations for a set of scalar values. +% som_info User-friendly information of SOM Toolbox structs. + +% Copyright (c) 1998-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 151199 150500 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(1, 3, nargin)); % check no. of input arguments is correct + +% sD +struct_mode = isstruct(sD); +if struct_mode, + switch sD.type + case 'som_map', D = sD.codebook; + case 'som_data', D = sD.data; + otherwise, error('Illegal struct.') + end +else + D = sD; +end +[dlen dim] = size(D); + +% comps +if nargin<3 | (ischar(comps) & strcmp(comps,'all')), + comps = [1:dim]; +end +if isempty(comps), return; end +if size(comps,1)>1, comps = comps'; end % make it a row vector + +% method +csNorm = cell(dim,1); +if nargin<2 | isempty(method), + if ~struct_mode, + warning('No normalization method given. Data left unchanged.'); + return; + end + method = ''; +else + % check out the given method + % (and if necessary, copy it for each specified component) + if ischar(method), + switch method, + case {'var','range','log','histD','histC','logistic'}, + sN = som_set('som_norm','method',method); + otherwise, + error(['Unrecognized method: ' method]); + end + for i=comps, csNorm{i} = sN; end + elseif isstruct(method), + switch method(1).type, + case {'som_map','som_data'}, csNorm = method(1).comp_norm; + case {'som_norm'}, for i=comps, csNorm{i} = method; end + otherwise, + error('Invalid struct given as normalization method.') + end + elseif iscellstr(method), + [dummy,sN] = som_norm_variable(1,method,'init'); + for i=comps, csNorm{i} = sN; end + elseif iscell(method), + csNorm = method; + else + error('Illegal method argument.') + end + % check the size of csNorm is the same as data dimension + if length(csNorm) ~= dim, + error('Given number of normalizations does not match data dimension.') + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize + +% make sure all the current normalizations for current +% components have been done +if struct_mode, + alldone = 1; + for i = comps, + for j=1:length(sD.comp_norm{i}), + sN = sD.comp_norm{i}(j); + if ~strcmp(sN.status,'done'), + alldone = 0; + [x,sN] = som_norm_variable(D(:,i), sN, 'do'); + D(:,i) = x; + sD.comp_norm{i}(j) = sN; + end + end + end + if isempty(method), + if alldone, + warning('No ''undone'' normalizations found. Data left unchanged.'); + else + fprintf(1,'Normalizations have been redone.\n'); + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +% add the new normalizations to the old ones +for i = comps, + if ~isempty(csNorm{i}), + [x,sN] = som_norm_variable(D(:,i), csNorm{i}, 'do'); + D(:,i) = x; + if struct_mode, + if isempty(sD.comp_norm{i}), sD.comp_norm{i} = sN; + else sD.comp_norm{i} = [sD.comp_norm{i}, sN]; end + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% output + +if struct_mode, + switch sD.type + case 'som_map', sD.codebook = D; + case 'som_data', sD.data = D; + otherwise, error('Illegal struct.') + end +else + sD = D; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_normcolor.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,89 @@ +function color = som_normcolor(data, clrmap) + +%SOM_NORMCOLOR RGB values of indexed colors for a given dataset and colormap +% +% color = som_normcolor(data, [clrmap]) +% +% color = som_normcolor(data); +% color = som_normcolor(data,jet(64)); +% +% Input and output arguments ([]'s are optional): +% data (struct) map or data struct +% (matrix) size N x dim +% [clrmap] (matrix) size N x 3, a valid colormap (an RGB value matrix) +% Default is current colormap. See COLORMAP. +% +% color (matrix) size N x 3 x dim, RGB matrix +% +% Purpose of this function is to calculate fixed RGB colors that are similar +% to indexed colors with the specified colormap. This is because some +% SOM Toolbox visualization functions (as SOM_GRID) do not use indexed colors +% if the underlying Matlab function (e.g. PLOT) do not use indexed colors +% +% EXAMPLE +% +% %%% Visualize three variables in a map using som_grid: +% %%% Give coordinates for the markers according to variables 1 and 2, and +% %%% 'indexed colors' according to variable 3. +% +% som_grid(map.topol.lattice,map.topol.msize,'Coord',map.codebook(:,1:2), ... +% 'markercolor', som_normcolor(map.codebook(:,3))); + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan Himberg +% Copyright (c) by Johan Himberg +% http://www.cis.hut.fi/projects/somtoolbox/ + +% juha 150799 johan 010999 + +%%%% check possible errors %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(1,2,nargin)); + +if nargin < 2| isempty(clrmap), + clrmap=colormap; +elseif ~vis_valuetype(clrmap,{'nx3rgb'}), + error('The specified colormap is invalid!'); +end + +d=size(clrmap,1); + +if isstruct(data), + m_names={'type';'codebook';'topol';'labels';'neigh';'mask';'trainhist';... + 'name';'comp_names';'comp_norm'}; + d_names=fieldnames(vis_struct); + if length(fieldnames(data)) ~= length(d_names) % data is not som_data_struct + if length(fieldnames(data)) ~= length(m_names) % and not som_map_struct + error('Input argument is not a ''som_vis'' or ''som_map'' struct.') + elseif ~all(strcmp(fieldnames(data),m_names)) + error('Input argument is not a ''som_vis'' or ''som_map'' struct.') + else + data=data.codebook; + end + elseif ~all(strcmp(fieldnames(data),dnames)) + error('Input argument is not a ''som_vis'' or ''som_map'' struct.') + else + data=data.data; + end +end + +if ~isnumeric(data) | ndims(data) ~= 2 + error('Data is not 2 dimensional numeric matrix.'); +end + +%%% action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +data=som_normalize(data,'range'); + +for i=1:size(data,2), + inds=~isnan(data(:,i)); + color(inds,:,i)=clrmap(round(data(inds,i)*(d-1))+1,:); + color(~inds,:,i)=NaN; +end + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_order_cplanes.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,293 @@ +function P = som_order_cplanes(sM, varargin) + +%SOM_ORDER_CPLANES Orders and shows the SOM component planes. +% +% P = som_order_cplanes(sM, [[argID,] value, ...]) +% +% som_order_cplanes(sM); +% som_order_cplanes(sM,'comp',1:30,'simil',C,'pca'); +% P = som_order_cplanes(sM); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map or data struct +% (matrix) a data matrix, size * x dim +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% P (matrix) size n x * (typically n x 2), the projection coordinates +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'comp' (vector) size 1 x n, which components to project, 1:dim by default +% 'simil' *(string) similarity measure to use +% 'corr' linear correlation between component planes +% 'abs(corr)' absolute value of correlation (default) +% 'umat' as 'abs(corr)' but calculated from U-matrices +% 'mutu' mutual information (not implemented yet) +% (matrix) size n x n, a similarity matrix to be used +% 'proj' *(string) projection method to use: 'SOM' (default), +% 'pca', 'sammon', 'cca', 'order', 'ring' +% 'msize' (vector) size of the SOM that is used for projection +% 'show' *(string) how visualization is done: 'planes' (default), +% 'names', or 'none' +% 'mask' (vector) dim x 1, the mask to use, ones(dim,1) by default +% 'comp_names' (cell array) of strings, size dim x 1, the component names +% +% The visualized objects have a callback associated with them: by +% clicking on the object, the index and name of the component are printed +% to the standard output. +% +% See also SOM_SHOW. + +% Copyright (c) 2000 by the SOM toolbox programming team. +% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 120600 070601 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% sM +if isstruct(sM), + switch sM.type + case 'som_map', + D = sM.codebook; dim = size(D,2); cnames = sM.comp_names; mask = sM.mask; + ismap = 1; + case 'som_data', + D = sM.data; dim = size(D,2); cnames = sM.comp_names; mask = ones(dim,1); + ismap = 0; + otherwise, error('Invalid first argument.'); + end +else + D = sM; + dim = size(D,2); mask = ones(dim,1); + cnames = cell(dim,1); + for i = 1:dim, cnames{i} = sprintf('Variable%d',i); end + ismap = 0; +end + +% defaults +comps = 1:dim; +simil = 'abs(corr)'; +proj = 'SOM'; +show = 'planes'; +mapsize = NaN; + +% varargin +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'mask', i=i+1; mask = varargin{i}; + case 'comp_names', i=i+1; cnames = varargin{i}; + case 'comp', i=i+1; comps = varargin{i}; + case 'proj', i=i+1; proj = varargin{i}; + case 'show', i=i+1; show = varargin{i}; + case 'simil', i=i+1; simil = varargin{i}; + case 'msize', i=i+1; mapsize = varargin{i}; + % unambiguous values + case {'corr','abs(corr)','umat','mutu'}, simil = varargin{i}; + case {'SOM','pca','sammon','cca','order','ring'}, proj = varargin{i}; + case {'planes','names','none'}, show = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_order_cplanes) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +if strcmp(show,'planes') & ~ismap, + warning('Given data is not a map: using ''names'' visualization.'); + show = 'names'; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% similarity matrix + +fprintf(1,'Calculating similarity matrix\n'); + +% use U-matrix +if strcmp(simil,'umat'), + if ~ismap, error('Given data is not a map: cannot use U-matrix similarity.'); end + U = som_umat(sM); + D = zeros(prod(size(U)),dim); + m = zeros(dim,1); + for i=1:dim, m=m*0; m(i)=1; U = som_umat(sM,'mask',m); D(:,i) = U(:); end +end + +% components +D = D(:,comps); +cnames = cnames(comps); +mask = mask(comps); +dim = length(comps); + +% similarity matrix +if ischar(simil), + switch simil, + case {'corr','abs(corr)','umat'}, + A = zeros(dim); + me = zeros(1,dim); + for i=1:dim, + me(i) = mean(D(isfinite(D(:,i)),i)); D(:,i) = D(:,i) - me(i); + end + for i=1:dim, + for j=i:dim, + c = D(:,i).*D(:,j); c = c(isfinite(c)); + A(i,j) = sum(c)/length(c); A(j,i) = A(i,j); + end + end + s = diag(A); + A = A./sqrt(s*s'); + switch simil, + case {'abs(corr)','umat'}, A = abs(A); + case 'corr', A = A + 1; + end + case 'mutu', + error('Mutual information not implemented yet.'); + end +else + A = simil; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% projection + +fprintf(1,'Projection\n'); + +mu = 2*dim; + +switch proj, + case 'SOM', + + if isnan(mapsize), + sMtmp = som_randinit(A,'munits',mu); + msize = sMtmp.topol.msize; + else + msize = mapsize; + end + + sM2 = som_make(A,'msize',msize,'rect','tracking',0); + bm = assign_unique_bm(sM2,A); + Co = som_unit_coords(sM2); + P = Co(bm,:); + + case 'ring', + + if isnan(mapsize), msize = [1 mu]; else msize = mapsize; end + + sM2 = som_make(A,'msize',msize,'cyl','rect','tracking',0); + bm = assign_unique_bm(sM2,A); + Co = som_unit_coords(sM2); + P = Co(bm,[1 3]); + + case 'order', + + if isnan(mapsize), msize = [1 mu]; else msize = mapsize; end + + sM2 = som_make(A,'msize',msize,'tracking',0); + bm = assign_unique_bm(sM2,A); + [dummy,i] = sort(bm); + [dummy,P] = sort(i); + if size(P,2)>1, P = P'; end + if size(P,2)==1, P(:,2) = zeros(length(P),1); end + + case {'pca','sammon','cca'}, + P = pcaproj(A,2); + if strcmp(proj,'sammon'), P = sammon(A,P,50,'steps'); + elseif strcmp(proj,'cca'), P = cca(A,P,50); + end + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% visualization + +if ~strcmp(show,'none'), + fprintf(1,'Visualization\n'); + cla + hold on + if strcmp(show,'planes') + s = findscaling(sM.topol.msize,P); + for i=1:dim, + C = som_normcolor(D(:,i)); + if strcmp(simil,'umat'), + h=som_cplane([sM.topol.lattice 'U'],sM.topol.msize,C,1,s*P(i,:)); + else + h=som_cplane(sM,C,1,s*P(i,:)); + end + set(h,'edgecolor','none','Userdata',sprintf('[%d] %s',i,cnames{i})); + set(h,'ButtonDownFcn','fprintf(1,''%s\n'',get(gco,''UserData''))'); + end + else + s=1; + a=[min(P(:,1))-1 max(P(:,1))+1 min(P(:,2))-1-3 max(P(:,2))+1-3]; + axis(s*a); + end + h=text(s*P(:,1),s*P(:,2)-3,cnames); + for i=1:length(h), set(h(i),'Userdata',sprintf('[%d] %s',i,cnames{i})); end + set(h,'ButtonDownFcn','fprintf(1,''%s\n'',get(gco,''UserData''))'); + hold off + + axis on; axis equal; axis tight; set(gca,'XTick',[],'YTick',[],'Box','on'); +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 +%% subfunctions + +function bm = assign_unique_bm(sM,D) + + munits = size(sM.codebook,1); + [dlen dim] = size(D); + margin = max(0,dlen-munits); + + [bm,qers] = som_bmus(sM,D); + bmi=ones(dim,1); + hits = som_hits(sM,D); + mult = find(hits>1); + while any(mult) & sum(hits(mult))-length(mult)>margin, + choices = find(bm==mult(1)); + while length(choices)>1, + [dummy,mv] = max(qers(choices)); mv = choices(mv); + [mv_to,q] = som_bmus(sM,D(mv,:),bmi(mv)); + bmi(mv)=bmi(mv)+1; qers(mv) = q; bm(mv) = mv_to; + choices = find(bm==mv_to); + end + for i=1:length(hits), hits(i)=sum(bm==i); end + mult = find(hits>1); + end + return; + +function s = findscaling(msize,P) + + d1 = median(abs(diff(unique(sort(P(:,1)))))); + d2 = median(abs(diff(unique(sort(P(:,2)))))); + if d1>0, s1 = 1.5*msize(2)/d1; else s1 = 0; end + if d2>0, s2 = 1.5*msize(1)/d2; else s2 = 0; end + s = max(s1,s2); + if s==0, s=1; end + return; + +function alternative_SOM_plane_vis(sT,bm,simil,D,cnames) + + clf + for i=1:size(D,2), + subplot(sT.msize(2),sT.msize(1),bm(i)); + if strcmp(simil,'umat'), h=som_cplane([sT.lattice 'U'],sT.msize,D(:,i)); + else h=som_cplane(sT,D(:,i)); + end + set(h,'edgecolor','none'); + title(cnames{i}); + axis off + end + return; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_pieplane.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,325 @@ +function h=som_pieplane(varargin) + +%SOM_PIEPLANE Visualize the map prototype vectors as pie charts +% +% h=som_pieplane(lattice, msize, data, [color], [s], [pos]) +% h=som_pieplane(topol, data, [color], [s], [pos]) +% +% som_pieplane('hexa',[5 5], rand(25,4), jet(4), rand(25,1)) +% som_pieplane(sM, sM.codebook); +% +% Input and output arguments ([]'s are optional): +% lattice (string) grid 'hexa' or 'rect' +% msize (vector) size 1x2, defines the grid, M=msize(1)*msize(2) +% (matrix) size Mx2, gives explicit coordinates for each node: in +% this case the lattice does not matter. +% topol (struct) map or topology struct +% data (matrix) size Mxd, Mth row is the data for Mth pie. The +% values will be normalized to have unit sum in each row. +% [color] (matrix) size dx3, RGB triples. The first row is the +% color of the first slice in each pie etc. Default is hsv(d). +% (string) ColorSpec or 'none' gives the same color for each slice. +% [s] (matrix) size Mx1, gives an individual size scaling for each node. +% (scalar) gives the same size for each node. Default is 0.8. +% [pos] (vectors) a 1x2 vector that determines position for the +% origin, i.e. upper left corner. Default is no translation. +% +% h (scalar) the object handle to the PATCH object +% +% The data will be linearly scaled so that its sum is 1 in each unit. +% Negative values are invalid. Axis are set as in som_cplane. +% +% For more help, try 'type som_pieplane' or check out online documentation. +% See also SOM_CPLANE, SOM_PLOTPLANE, SOM_BARPLANE + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_pieplane +% +% PURPOSE +% +% Visualizes the map prototype vectors as pie charts. +% +% SYNTAX +% +% h = som_pieplane(topol, data) +% h = som_pieplane(lattice, msize, data) +% h = som_pieplane(..., color) +% h = som_pieplane(..., color, s) +% h = som_pieplane(..., color, s, pos) +% +% DESCRIPTION +% +% Visualizes the map prototype vectors as pie charts. +% +% KNOWN BUGS +% +% It is not possible to specify explicit coordinates for map +% consisting of just one unit as then the msize is interpreted as +% map size. +% +% FEATURES +% +% - negative values in data cause an error +% +% - the colors are fixed: changing colormap in the figure (see help +% colormap) will not affect the coloring of the slices. +% +% - if input variable s has size Nxd it gives each slice an individual +% scaling factor. This may be used to create a glyph where +% the radius of the slice, not the angle, shows the variable +% try, e.g., som_pieplane('rect',[5 4],ones(20,4),'w',rand(20,4)); +% +% REQUIRED INPUT ARGUMENTS +% +% lattice The basic shape of the map units +% +% (string) 'hexa' or 'rect' positions the pies according to hexagonal or +% rectangular map lattice. +% +% msize The size of the map grid +% +% (vector) [n1 n2] vector defines the map size (height n1 units, +% width n2 units, total M=n1xn2 units). The units will +% be placed to their topological locations to form a +% uniform hexagonal or rectangular grid. +% (matrix) Mx2 matrix defines arbitary coordinates for the M units. In +% this case the argument 'lattice' has no effect. +% +% topol Topology of the map grid +% +% (struct) map or topology struct from which the topology is taken +% +% data The data to be visualized +% +% (matrix) Mxd matrix of data vectors. Negative values are invalid. +% +% OPTIONAL INPUT ARGUMENTS +% +% If value is unspecified or empty ([] or ''), the default values +% are used for optional input arguments. +% +% s The size scaling factors for the units +% +% (scalar) gives each unit the same size scaling: +% 0 unit disappears (edges can be seen as a dot) +% ... default size is 0.8 +% >1 unit overlaps others +% (matrix) Mx1 double: each unit gets individual size scaling +% +% color The color of the slices in each pie +% +% (string) ColorSpec or 'none' gives the same color for each slice +% (matrix) dx3 matrix assigns an RGB color determined by the dth row of +% the matrix to the dth slice (variable) in each pie plot +% +% pos Position of origin +% +% (vector) size 1x2: this is meant for drawing the plane in arbitary +% location in a figure. Note the operation: if this argument is +% given, the axis limits setting part in the routine is skipped and +% the limits setting will be left to be done by +% MATLAB's defaults. Default is no translation. +% +% OUTPUT ARGUMENTS +% +% h (scalar) Handle to the created patch object. +% +% OBJECT TAGS +% +% One object handle is returned: field Tag is set to 'planePie' +% +% EXAMPLES +% +% %%% Create the data and make a map +% +% data=rand(100,5); map=som_make(data); +% +% %%% Create a 'jet' colormap that has as many rows as the data has variables +% +% colors=jet(5); +% +% %%% Draw pies +% +% som_pieplane(map, map.codebook, colors); +% +% %%% Calculate the hits of data on the map and normalize them between [0,1] +% +% hit=som_hits(map,data); hit=hit./max(max(hit)); +% +% %%% Draw the pies so that their size tells the hit count +% +% som_pieplane(map, map.codebook, colors, hit); +% +% %%% Try this! (see section FEATURES) +% +% som_pieplane('rect',[5 4],ones(20,4),'w',rand(20,4)); +% +% SEE ALSO +% +% som_cplane Visualize a 2D component plane, u-matrix or color plane +% som_barplane Visualize the map prototype vectors as bar diagrams +% som_plotplane Visualize the map prototype vectors as line graphs + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 140799 juuso 310300 070600 + +%%% Check & Init arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +[nargin, lattice, msize, data, color, s, pos] = vis_planeGetArgs(varargin{:}); +error(nargchk(3, 6, nargin)); % check no. of input args is correct + +% check pos + +if nargin < 6 | isempty(pos) + pos=NaN; % default value for pos (no translation) +elseif ~vis_valuetype(pos,{'1x2'}) + error('Position of origin has to be given as an 1x2 vector'); +end + +% check msize + +if ~vis_valuetype(msize,{'1x2','nx2'}), + error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.'); +end + +% check data + +if ~isnumeric(data), + error('Data matrix must be numeric.'); +elseif length(size((data)))>2 + error('Data matrix has too many dimensions!'); +else + d=size(data,2); + N=size(data,1); +end + +if any(data(:)<0) + error('Negative data values not allowed in pie plots!'); +end + +% Check lattice +if ~ischar(lattice) | ~any(strcmp(lattice,{'hexa','rect'})), + error('Invalid lattice.'); +end + +%% Calculate patch coordinates for slices + +for i=1:N, + [nx,ny]=vis_piepatch(data(i,:)); + piesx(:,(1+(i-1)*d):(i*d))=nx; + piesy(:,(1+(i-1)*d):(i*d))=ny; +end +l=size(piesx,1); + +if size(msize,1) == 1, + if prod(msize) ~= N + error('Data matrix has wrong size.'); + else + coord=som_vis_coords(lattice, msize); + end +else + if N ~= size(msize,1), + error('Data matrix has wrong size.'); + end + coord=msize; + % This turns the axis tightening off, + % as now we don't now the limits (no fixed grid) + if isnan(pos); pos=[0 0]; end +end +x=reshape(repmat(coord(:,1),1,l*d)',l,d*N); +y=reshape(repmat(coord(:,2),1,l*d)',l,d*N); + +% Check size + +if nargin < 5 | isempty(s), + s=0.8; % default value for scaling +elseif ~vis_valuetype(s, {'1x1', [N 1], [N d]}), + error('Size matrix does not match with the data matrix.'); +elseif size(s) == [N 1], + s=reshape(repmat(s,1,l*d)',l,d*N); +elseif all(size(s) ~= [1 1]), + s=reshape(repmat(reshape(s',d*N,1),1,l)',l,d*N); +end + +% Check color +% C_FLAG is a flag for color 'none' + +if nargin < 4 | isempty(color) + color=hsv(d); C_FLAG=0; % default n hsv colors +end + +if ~(vis_valuetype(color, {[d 3], 'nx3rgb'},'all')) & ... + ~vis_valuetype(color,{'colorstyle','1x3rgb'}), + error('The color matrix has wrong size or contains invalid values.'); +elseif ischar(color) & strcmp(color,'none'), + C_FLAG=1; % check for color 'none' + color='w'; +else + C_FLAG=0; % valid color string or colormap +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Size zero would cause division by zero. eps is as good (node disappears) +% The edge may be visible, though. (NaN causes some other problems) + +s(s==0)=eps; + +%% 1. Scaling +x=(x./s+piesx).*s; y=(y./s+piesy).*s; + +%% 2. Translation +if ~isnan(pos) + x=x+pos(1);y=y+pos(2); +end + +%% 3. Rearrange dx3 color matrix + +if ~isstr(color) & size(color,1)~=1, + color=reshape(repmat(color,N,1),[1 N*d 3]); +end + +%% Set axes properties +ax=newplot; % get current axis +vis_PlaneAxisProperties(ax,lattice, msize, pos); + +%% Draw the plane! + +h_=patch(x,y,color); + +if C_FLAG + set(h_,'FaceColor','none'); +end + +set(h_,'Tag','planePie'); % tag the object + +%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargout>0, h=h_; end % Set h only if + % there really is output +%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [x,y]=vis_piepatch(v) + +% Do a pie (see e.g. the MathWorks function PIE). +% Origin is at (0,0) and the radius is .5. + +N=25; + +if sum(v)==0, v_is_zero = 1; v(1) = 1; else v_is_zero = 0; end + +v(v==0) = eps; % Matlab 5.2 version of linspace doesn't work otherwise + +phi=[0 2*pi*cumsum(v./sum(v))]; + +for i=2:length(phi), + [xi,yi]=pol2cart(linspace(phi(i-1),phi(i),N),0.5); + x(:,i-1)=[0 xi 0]'; + y(:,i-1)=[0 yi 0]'; +end + +if v_is_zero, x = x*0; y = y*0; end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_plotmatrix.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,122 @@ +function som_plotmatrix(sM,D,Col,comps) + +%SOM_PLOTMATRIX Visualize pairwise scatter plots and histograms. +% +% som_plotmatrix(sM,[sD],[Col],[comps]) +% +% Input and output arguments ([]'s are optional): +% sM (struct) map struct +% [sD] (struct) data struct, corresponding to the map +% (matrix) data matrix (size dlen x dim) +% [Col] (matrix) size munits x 3, color for each map unit +% [comps] (vector) which components to plot (1:dim by default) +% +% See also: SOM_SHOW, SOM_ORDER_CPLANES. + +% Copyright (c) 2000 by the SOM toolbox programming team. +% Contributed to SOM Toolbox on June 16th, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 140600 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55 + +% sM +[munits dim] = size(sM.codebook); +M = sM.codebook; + +% sD +if nargin>1 & ~isempty(D), + if isstruct(D), D = D.data; end + bmus = som_bmus(sM,D); +else D = []; bmus = []; +end + +% Col +if nargin<3 | isempty(Col), Col = som_colorcode(sM); end +if ischar(Col), Col = som_colorcode(sM,Col); end + +% comps +if nargin<4 | isempty(comps), comps = 1:dim; end +n = length(comps)+1; + +% histogram bins +if ~isempty(D), C=D; else C=M; end +cHbins = cell(dim,1); +cAxis = cell(dim,1); +for i=1:dim, + if ~isempty(D), mima = [min(D(:,i)),max(D(:,i))]; + else mima = [min(M(:,i)),max(M(:,i))]; + end + cAxis{i} = mima; + [dummy,cHbins{i}] = hist(mima,20); +end + +nt = 4; % number of ticks in scatter plots + +% visualization +clf +for i=1:n, + for j=1:n, + subplot(n,n,(i-1)*n+j); + if j==1 & i==1, + h=som_cplane(sM,Col); set(h,'edgecolor','none') + elseif i==1, + ind = comps(j-1); + b = cHbins{ind}; + hs = hist(M(:,ind),b); + h = bar(b,hs,0.8); set(h,'EdgeColor','none','FaceColor','k'); + axis on, axis tight + set(gca,'XTick',[],'Box','on'); + title(sM.comp_names{ind}); + elseif j==1, + ind = comps(i-1); + if ~isempty(D), + b = cHbins{ind}; + hs = hist(D(:,ind),b); + h = bar(b,hs,0.8); set(h,'EdgeColor','none','FaceColor','k'); + axis on, axis tight + set(gca,'XTick',[],'Box','on'); + ylabel(sM.comp_names{ind}) + else + text(0.5,0.5,sM.comp_names{ind}); + axis off + end + elseif i==j, + ind = comps(i-1); + h=som_cplane(sM,M(:,ind)); + set(h,'edgecolor','none') + a = cAxis{ind}; + caxis(a); v = unique([a, min(M(:,ind)), max(M(:,ind))]); + vn=som_denormalize(v,sM.comp_norm{ind})'; + h=colorbar('vert'); + set(h,'YTick',v,'YTickLabel',cellstr(num2str(vn,2))); + elseif i<j | ~isempty(D), + if i>j, i1 = i-1; i2 = j-1; else i1 = j-1; i2 = i-1; end + ind1 = comps(i1); ind2 = comps(i2); + if i<j, + som_grid(sM,'coord',M(:,[ind1 ind2]),'markersize',2,'MarkerColor',Col); + else + som_grid('rect',[size(D,1) 1],'coord',D(:,[ind1 ind2]),... + 'Line','none','MarkerColor',Col(bmus,:),'Markersize',2); + %cla; hold on + %for k=1:max(bmus), + % inds = find(bmus==k); + % if any(inds), + % som_grid('rect',[length(inds) 1],'coord',D(inds,[ind1 ind2]),... + % 'Line','none','MarkerColor',Col(k,:),'Markersize',2); + % end + %end + end + a = [cAxis{ind1} cAxis{ind2}]; axis(a); + x = linspace(a(1),a(2),nt); xn = som_denormalize(x,sM.comp_norm{ind1})'; + set(gca,'XTick',x,'XTickLabel',cellstr(num2str(xn,2))); + y = linspace(a(3),a(4),nt); yn = som_denormalize(y,sM.comp_norm{ind2})'; + set(gca,'YTick',y,'YTickLabel',cellstr(num2str(yn,2))); + xlabel(sM.comp_names{ind1}), ylabel(sM.comp_names{ind2}) + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_plotplane.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,285 @@ +function h=som_plotplane(varargin) + +%SOM_PLOTPLANE Visualize the map prototype vectors as line graphs +% +% h=som_plotplane(lattice, msize, data, [color], [scaling], [pos]) +% h=som_plotplane(topol, data, [color], [scaling], [pos]) +% +% som_plotplane('hexa',[5 5], rand(25,4), jet(25)) +% som_plotplane(sM, sM.codebook) +% +% Input and output arguments ([]'s are optional) +% lattice (string) grid 'hexa' or 'rect' +% msize (vector) size 1x2, defines the grid size +% (matrix) size Mx2, defines explicit coordinates: in +% this case the first argument does not matter +% topol (struct) map or topology struct +% data (matrix) Mxd matrix, M=prod(msize) +% [color] (matrix) size Mx3, gives an individual color for each graph +% (string) ColorSpec gives the same color for each +% graph, default is 'k' (black) +% [scaling] (string) 'on' or 'off', default is 'on' +% [pos] (vector) 1x2 vector that determines translation. +% Default is no translation. +% +% h (vector) the object handles for the LINE objects +% +% If scaling is set on, the data will be linearly scaled in each +% unit so that min and max values span from lower to upper edge +% in each unit. If scaling is 'off', the proper scaling is left to +% the user: values in range [-.5,.5] will be plotted within the limits of the +% unit while values exceeding this range will be out of the unit. +% Axis are set as in SOM_CPLANE. +% +% For more help, try 'type som_plotplane' or check out online documentation. +% See also SOM_PLANE, SOM_PIEPLANE, SOM_BARPLANE + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_plotplane +% +% PURPOSE +% +% Visualizes the map prototype vectors as line graph +% +% SYNTAX +% +% h = som_plotplane(topol, data) +% h = som_plotplane(lattice, msize, data) +% h = som_plotplane(..., color) +% h = som_plotplane(..., color, scaling) +% h = som_plotplane(..., color, scaling, pos) +% +% DESCRIPTION +% +% Visualizes the map prototype vectors as line graph +% +% KNOWN BUGS +% +% It is not possible to specify explicit coordinates for map +% consistig of just one unit as then the msize is interpreted as +% map size. +% +% FEATURES +% +% - the colors are fixed: changing colormap in the figure (see +% COLORMAP) will not affect the coloring of the plots +% +% REQUIRED INPUT ARGUMENTS +% +% lattice The basic topology +% +% (string) 'hexa' or 'rect' positions the plots according to hexagonal or +% rectangular map lattice. +% +% msize The size of the map grid +% +% (vector) [n1 n2] vector defines the map size (height n1 units, width n2 +% units, total M=n1 x n2 units). The units will be placed to their +% topological locations in order to form a uniform hexagonal or +% rectangular grid. +% (matrix) Mx2 matrix defines arbitary coordinates for the M units. +% In this case the argument 'lattice' has no effect. +% +% topol Topology of the map grid +% +% (struct) map or topology struct from which the topology is taken +% +% data The data to be visualized +% +% (matrix) Mxd matrix of data vectors. +% +% OPTIONAL INPUT ARGUMENTS +% +% If unspecified or given empty values ('' or []), default values +% will be used for optional input arguments. +% +% color The color of the plots +% +% (string) Matlab's ColorSpec (see help plot) string gives the same color +% for each line. +% +% (matrix) Mx3 matrix assigns an RGB color determined by the Nth row of +% the matrix to the Nth plot. +% +% (vector) 1x3 RGB vector gives the same color for each line. +% +% scaling The data scaling mode +% +% (string) 'on or 'off': if scaling is set on, the data will be +% linearly scaled in each unit so that min and max values span from +% lower to upper edge in each unit. If scaling is 'off', the proper +% scaling is left to the user: values in range [-.5,.5] will be plotted +% within the limits of the unit while values exceeding this +% range will be out of the unit. +% +% pos Position of the origin +% +% (vector) This is meant for drawing the plane in arbitary location in a +% figure. Note the operation: if this argument is given, the +% axis limits setting part in the routine is skipped and the limits +% setting will be left to be done by MATLAB's +% defaults. By default no translation is done. +% +% OUTPUT ARGUMENTS +% +% h (scalar) Handle to the created patch object +% +% OBJECT TAG +% +% Object property 'Tag' is set to 'planePlot'. +% +% EXAMPLES +% +% %%% Create the data and make a map +% +% data=rand(1000,20); map=som_make(data); +% +% %%% Create a 'gray' colormap that has 64 levels +% +% color_map=gray(64); +% +% %%% Draw plots using red color +% +% som_plotplane(map, map.codebook,'r'); +% +% %%% Calculate hits on the map and calculate colors so that +% black = min. number of hits and white = max. number of hits +% +% hit=som_hits(map,data); color=som_normcolor(hit(:),color_map); +% +% %%% Draw plots again. Now the gray level indicates the number of hits to +% each node +% +% som_plotplane(map, map.codebook, color); +% +% SEE ALSO +% +% som_cplane Visualize a 2D component plane, u-matrix or color plane +% som_barplane Visualize the map prototype vectors as bar diagrams. +% som_pieplane Visualize the map prototype vectors as pie charts + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 160799 juuso 151199 070600 + +%%% Init & Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +[nargin, lattice, msize, data, color, scaling, pos] = vis_planeGetArgs(varargin{:}); +error(nargchk(3, 5, nargin)); % check no. of input args is correct + +s=0.8; % size of plot + +if nargin < 6 | isempty(pos) + pos=NaN; +end + +if nargin < 5 | isempty(scaling) + scaling='on'; +elseif ~vis_valuetype(scaling,{'string'}) | ... + ~any(strcmp(scaling,{'on','off'})), + error('Scaling should be string ''on'' or ''off''.'); +end + +l=size(data,2); + +if ~isnumeric(msize) | ndims(msize) ~= 2 | size(msize,2)~=2, + error('msize has to be 1x2 grid size vector or a Nx2 coordinate matrix.'); +elseif size(msize,1) == 1, + xdim=msize(2); + ydim=msize(1); + N=xdim*ydim; + y=repmat(repmat([1:ydim]',xdim,1),1,l); + x=reshape(repmat([1:xdim],ydim*l,1),l,N)'; +else + x=repmat(msize(:,1),1,l);y=repmat(msize(:,2),1,l); + N=size(msize,1); + lattice='rect'; + if isnan(pos), + pos=[0 0]; + end +end + +switch lattice +case {'hexa', 'rect'} + ; +otherwise + error(['Lattice' lattice ' not implemented!']); +end + +if ~isnumeric(data) | size(data,1) ~= N + error('Data matrix is invalid or has wrong size!'); +end + +if nargin < 4 | isempty(color), + color='k'; +elseif vis_valuetype(color, {'colorstyle',[N 3]}), + if ischar(color) & strcmp(color,'none'), + error('Colorstyle ''none'' not allowed in som_plotplane.'); + end +elseif vis_valuetype(color,{'1x3rgb'}) + ; +elseif ~vis_valuetype(color,{'nx3rgb',[N 3]},'all'), + error('The color matrix has wrong size or contains invalid RGB values or colorstyle.'); +end + +[linesx, linesy]=vis_line(data,scaling); + +%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Making the lattice. +% Command view([0 90]) shows the map in 2D properly oriented + +switch lattice +case 'hexa' + t=find(rem(y(:,1),2)); % move even rows by .5 + x(t,:)=x(t,:)-.5; + x=(x./s+linesx).*s+.5; y=(y./s+linesy).*s; % scale with s +case 'rect' + x=(x./s+linesx).*s; y=(y./s+linesy).*s; % scale with s +end + +%% Draw the map! ... + +h_=plot(x',y'); + +if size(color,1) == 1 + set(h_,'Color',color); +else + for i=1:N, + set(h_(i,:),'Color',color(i,:)); + end +end + +if ~isnan(pos) + x=x+pos(1);y=y+pos(2); % move upper left corner +end % to pos(1),pos(2) + +%% Set axes properties + +ax=gca; +vis_PlaneAxisProperties(ax, lattice, msize, pos); + +%%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +set(h_,'Tag','planePlot'); % tag the lineobject + +if nargout>0, h=h_; end % Set h only, + % if there really is output + +%% Subfuntion %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [x,y]=vis_line(data, scaling) + +s=size(data); +% normalization between [0,1] if scaling is on +if strcmp(scaling,'on') + mn=repmat(min(data,[],2),1,s(2)); + mx=repmat(max(data,[],2),1,s(2)); + y=-((data-mn)./(mx-mn))+.5; +else % -sign is beacuse we do axis ij + y=-data; +end + +x=repmat(linspace(-.5, .5, size(data,2)), size(data,1),1);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_probability_gmm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,100 @@ +function [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) + +%SOM_PROBABILITY_GMM Probabilities based on a gaussian mixture model. +% +% [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) +% +% [K,P] = som_estimate_gmm(sM,D); +% [pd,Pdm,pmd] = som_probability_gmm(D,sM,K,P); +% som_show(sM,'color',pmd(:,1),'color',Pdm(:,1)) +% +% Input and output arguments: +% D (matrix) size dlen x dim, the data for which the +% (struct) data struct, probabilities are calculated +% sM (struct) map struct +% (matrix) size munits x dim, the kernel centers +% K (matrix) size munits x dim, kernel width parameters +% computed by SOM_ESTIMATE_GMM +% P (matrix) size 1 x munits, a priori probabilities for each +% kernel computed by SOM_ESTIMATE_GMM +% +% pd (vector) size dlen x 1, probability of each data vector in +% terms of the whole gaussian mixture model +% Pdm (matrix) size munits x dlen, probability of each vector in +% terms of each kernel +% pmd (matrix) size munits x dlen, probability of each vector to +% have been generated by each kernel +% +% See also SOM_ESTIMATE_GMM. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Esa Alhoniemi +% Copyright (c) by Esa Alhoniemi +% http://www.cis.hut.fi/projects/somtoolbox/ + +% ecco 180298 juuso 050100 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% input arguments +if isstruct(sM), M = sM.codebook; else M = sM; end +[c dim] = size(M); + +if isstruct(D), D = D.data; end +dlen = size(D,1); + +% reserve space for output variables +pd = zeros(dlen,1); +if nargout>=2, Pdm = zeros(c,dlen); end +if nargout==3, pmd = zeros(c,dlen); end + +% the parameters of each kernel +cCoeff = cell(c,1); +cCoinv = cell(c,1); +for m=1:c, + co = diag(K(m,:)); + cCoinv{m} = inv(co); + cCoeff{m} = 1 / ((2*pi)^(dim/2)*det(co)^.5); +end + +% go through the vectors one by one +for i=1:dlen, + + x = D(i,:); + + % compute p(x|m) + pxm = zeros(c,1); + for m = 1:c, + dx = M(m,:) - x; + pxm(m) = cCoeff{m} * exp(-.5 * dx * cCoinv{m} * dx'); + %pxm(m) = normal(dx, zeros(1,dim), diag(K(m,:))); + end + pxm(isnan(pxm(:))) = 0; + + % p(x|m) + if nargin>=2, Pdm(:,i) = pxm; end + + % P(x) = P(x|M) = sum( P(m) * p(x|m) ) + pd(i) = P*pxm; + + % p(m|x) = p(x|m) * P(m) / P(x) + if nargout==3, pmd(:,i) = (P' .* pxm) / pd(i); end + +end + + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% subfunction normal +% +% computes probability of x when mean and covariance matrix +% of a distribution are known + +function result = normal(x, mu, co) + +[l dim] = size(x); +coinv = inv(co); +coeff = 1 / ((2*pi)^(dim/2)*det(co)^.5); +diff = x - mu; +result = coeff * exp(-.5 * diff * coinv * diff');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_projections.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +function [cPCAarg, Pdata, Pproto] = som_projections(D,sM,bmus) + +% SOM_PROJECTIONS Makes different kinds of projections for the data and the prototypes. +% +% [cPCAarg, Pdata, Pproto] = som_projections(D,sM,[bmus]) +% +% sD (struct) data struct +% (matrix) size dlen x dim +% sM (struct) map struct +% (matrix) size munits x dim: prototype vectors +% [bmus] (vector) BMU for each data vector (calculated if not specified) +% +% cPCAarg (cell array) PCA arguments: {V, me, l} from pcaproj function +% Pdata (matrix) size dlen x 7, consisting of 3 projection coordinates from PCA, +% 1 residual from the rest of the PCA-projection coordinates, +% and 3 color components +% Pproto (matrix) size dlen x 7, consisting of 3 projection coordinates from PCA, +% 1 residual from the rest of the PCA-projection coordinates, +% 3 color components, and 3 projection coordinates from CCA +% +% See also PCAPROJ, CCA, SAMMON, SOM_PROJECTIONS_PLOT, SOM_COLORING, SOM_COLORCODE, +% SOM_CLUSTERCOLOR, SOM_KMEANCOLOR. + +if isstruct(D), + cn = D.comp_names; + D = D.data; +end +[dlen dim] = size(D); +if nargin<3, bmus = som_bmus(sM,D); end + +% projection of data + +[P0,V,me,l] = pcaproj(D,dim); +D1 = som_fillnans(D,sM); +P1 = pcaproj(D1,V,me); +Res4 = zeros(dlen,1); +if dim<=3, + Res4 = zeros(dlen,1); +else + Res4 = sqrt(sum(P1(:,4:end).*P1(:,4:end),2)); +end +P1 = P1(:,1:min(3,dim)); +if dim<3, P1 = [P1, zeros(dlen,3-dim)]; end + +% projection of codebook vectors + +P1_m = pcaproj(sM,V,me); +Res4_m = zeros(dlen,1); +if dim<=3, + Res4_m = zeros(dlen,1); +else + Res4_m = sum(P1_m(:,4:end).*P1_m(:,4:end),2); +end +P1_m = P1_m(:,1:min(3,dim)); +if dim<3, P1_m = [P1_m, zeros(size(P1_m,1),3-dim)]; end + +P2_m = cca(sM,P1_m,20); + +PCol_m = som_coloring(sM); + +PCol = PCol_m(bmus,:); + +% output + +cPCAarg = {V,me,l}; +Pdata = [P1, Res4, PCol]; +Pproto = [P1_m, Res4_m, PCol_m, P2_m]; + +return; + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_projections_plot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function som_projections_plot(pmode,varargin) + +% SOM_PROJECTIONS_PLOT Projection visualizations. +% +% som_projections_plot(pmode,varargin) +% +% [cPCAarg, Pdata, Pproto] = som_projections(D,sM); +% som_projections_plot('scatter',Pdata(:,1:3)) +% som_projections_plot('scatter',Pproto(:,1:3),Pproto(:,5:7),5,sM) +% som_projections_plot('residuals',Pdata(:,1:4)) +% som_projections_plot('scree',cPCAarg{3}) +% +% The other arguments depend on the pmode: +% +% pmode = 'scatter' +% +% arg1: Co (matrix) coordinates +% arg2: color (matrix) color vectors +% (string) colorstring ('k' by default) +% arg3: psize (scalar) point size +% arg4: sT (struct) topology struct, if map grid is drawn +% +% pmode = 'residuals' +% +% arg1: Co (matrix) coordinates (2 first columns) + residuals (the rest) +% arg2: color (string) colorstring ('k' by default) +% +% pmode = 'scree' +% +% arg1: eigval (vector) vector of eigenvalues +% +% See also SOM_PROJECTIONS. + +switch pmode, +case 'scatter', + Co = varargin{1}; + if length(varargin)>1, color = varargin{2}; else color = 'k'; end + if length(varargin)>2, psize = varargin{3}; else psize = 5; end + if length(varargin)>3, sT = varargin{4}; else sT = []; end + if isstruct(sT) & strcmp(sT.type,'som_map'), sT = sT.topol; end + + if isempty(sT), + som_grid({'rect',[size(Co,1) 1]},'Coord',Co,'Markercolor',color,'line','none','markersize',psize); + else + if ischar(color), lcolor = color; else lcolor = 'k'; end + som_grid(sT,'Coord',Co,'Markercolor',color,'markersize',psize,'linecolor',lcolor); + end + +case 'residuals', + CoRes = varargin{1}; + n = size(CoRes,1); + if length(varargin)>1, color = varargin{2}; else color = 'k'; end + Co = CoRes(:,1:2); Co(end+1,:) = NaN; + res = sqrt(sum(CoRes(:,3:end).*CoRes(:,3:end),2)); + h=plot(Co(:,1),Co(:,2),'k.'); set(h,'color',color); + Co(end+1,:) = NaN; + res = [res; 0; NaN]; + i = [1:n; 1:n; (n+1)+zeros(1,n)]; i = i(:); + j = [(n+1)+zeros(1,n); 1:n; (n+2)+zeros(1,n)]; j = j(:); + h = line(Co(i,1),Co(i,2),res(j)); set(h,'color',color); + axis tight, axis equal, view(3) + +case 'scree', + eigval = varargin{1}; + if size(eigval,1)>1, eigval = eigval'; end + eigval = eigval / sum(eigval); + cumeig = cumsum(eigval); + bar(cumeig,0.1) + i = find(cumeig>=0.75); i75 = i(1); + hold on + plot([0 2],cumeig([2 2]),'r-',[0 i75],cumeig([i75 i75]),'r-'); + set(gca,'YTick',unique([0:0.1:1 cumeig(2) cumeig(i75)])); + axis tight, grid on + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_prototrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,155 @@ +function [sM,sTrain] = som_prototrain(sM, D) + +%SOM_PROTOTRAIN Use sequential algorithm to train the Self-Organizing Map. +% +% [sM,sT] = som_prototrain(sM, D) +% +% sM = som_prototrain(sM,D); +% +% Input and output arguments: +% sM (struct) map struct, the trained and updated map is returned +% (matrix) codebook matrix of a self-organizing map +% size munits x dim or msize(1) x ... x msize(k) x dim +% The trained map codebook is returned. +% D (struct) training data; data struct +% (matrix) training data, size dlen x dim +% +% This function is otherwise just like SOM_SEQTRAIN except that +% the implementation of the sequential training algorithm is very +% straightforward (and slower). This should make it easy for you +% to modify the algorithm, if you want to. +% +% For help on input and output parameters, try +% 'type som_prototrain' or check out the help for SOM_SEQTRAIN. +% See also SOM_SEQTRAIN, SOM_BATCHTRAIN. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 080200 130300 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check input arguments + +% map +struct_mode = isstruct(sM); +if struct_mode, + M = sM.codebook; + sTopol = sM.topol; + mask = sM.mask; + msize = sTopol.msize; + neigh = sM.neigh; +else + M = sM; orig_size = size(M); + if ndims(sM) > 2, + si = size(sM); dim = si(end); msize = si(1:end-1); + M = reshape(sM,[prod(msize) dim]); + else + msize = [orig_size(1) 1]; dim = orig_size(2); + end + sM = som_map_struct(dim,'msize',msize); sTopol = sM.topol; + mask = ones(dim,1); + neigh = 'gaussian'; +end +[munits dim] = size(M); + +% data +if isstruct(D), data_name = D.name; D = D.data; +else data_name = inputname(2); +end +D = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data +[dlen ddim] = size(D); % check input dimension +if dim ~= ddim, error('Map and data input space dimensions disagree.'); end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize (these are default values, change as you will) + +% training length +trainlen = 20*dlen; % 20 epochs by default + +% neighborhood radius +radius_type = 'linear'; +rini = max(msize)/2; +rfin = 1; + +% learning rate +alpha_type = 'inv'; +alpha_ini = 0.2; + +% initialize random number generator +rand('state',sum(100*clock)); + +% tracking +start = clock; trackstep = 100; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +Ud = som_unit_dists(sTopol); % distance between map units on the grid +mu_x_1 = ones(munits,1); % this is used pretty often + +for t = 1:trainlen, + + %% find BMU + ind = ceil(dlen*rand(1)+eps); % select one vector + x = D(ind,:); % pick it up + known = ~isnan(x); % its known components + Dx = M(:,known) - x(mu_x_1,known); % each map unit minus the vector + dist2 = (Dx.^2)*mask(known); % squared distances + [qerr bmu] = min(dist2); % find BMU + + %% neighborhood + switch radius_type, % radius + case 'linear', r = rini+(rfin-rini)*(t-1)/(trainlen-1); + end + if ~r, r=eps; end % zero neighborhood radius may cause div-by-zero error + switch neigh, % neighborhood function + case 'bubble', h = (Ud(:,bmu) <= r); + case 'gaussian', h = exp(-(Ud(:,bmu).^2)/(2*r*r)); + case 'cutgauss', h = exp(-(Ud(:,bmu).^2)/(2*r*r)) .* (Ud(:,bmu) <= r); + case 'ep', h = (1 - (Ud(:,bmu).^2)/(r*r)) .* (Ud(:,bmu) <= r); + end + + %% learning rate + switch alpha_type, + case 'linear', a = (1-t/trainlen)*alpha_ini; + case 'inv', a = alpha_ini / (1 + 99*(t-1)/(trainlen-1)); + case 'power', a = alpha_ini * (0.005/alpha_ini)^((t-1)/trainlen); + end + + %% update + M(:,known) = M(:,known) - a*h(:,ones(sum(known),1)).*Dx; + + %% tracking + if t==1 | ~rem(t,trackstep), + elap_t = etime(clock,start); tot_t = elap_t*trainlen/t; + fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t) + end + +end; % for t = 1:trainlen +fprintf(1,'\n'); + +% outputs +sTrain = som_set('som_train','algorithm','proto',... + 'data_name',data_name,... + 'neigh',neigh,... + 'mask',mask,... + 'radius_ini',rini,... + 'radius_fin',rfin,... + 'alpha_ini',alpha_ini,... + 'alpha_type',alpha_type,... + 'trainlen',trainlen,... + 'time',datestr(now,0)); + +if struct_mode, + sM = som_set(sM,'codebook',M,'mask',mask,'neigh',neigh); + sM.trainhist(end+1) = sTrain; +else + sM = reshape(M,orig_size); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_quality.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,156 @@ +function [mqe,tge] = som_quality(sMap, D) + +%SOM_QUALITY Calculate the mean quantization and topographic error. +% +% [qe,te] = som_quality(sMap, D) +% +% qe = som_quality(sMap,D); +% [qe,te] = som_quality(sMap,sD); +% +% Input and output arguments: +% sMap (struct) a map struct +% D the data +% (struct) a data struct +% (matrix) a data matrix, size dlen x dim +% +% qe (scalar) mean quantization error +% te (scalar) topographic error +% +% The issue of SOM quality is a complicated one. Typically two +% evaluation criterias are used: resolution and topology preservation. +% If the dimension of the data set is higher than the dimension of the +% map grid, these usually become contradictory goals. +% +% The first value returned by this function measures resolution and the +% second the topology preservation. +% qe : Average distance between each data vector and its BMU. +% te : Topographic error, the proportion of all data vectors +% for which first and second BMUs are not adjacent units. +% +% NOTE: when calculating BMUs of data vectors, the mask of the given +% map is used. +% +% For more help, try 'type som_quality' or check out the online documentation. +% See also SOM_BMUS. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_quality +% +% PURPOSE +% +% Calculates two quality measures for the given map. +% +% SYNTAX +% +% qe = som_quality(sM,sD); +% qe = som_quality(sM,D); +% [qe,te] = som_quality(...); +% +% DESCRIPTION +% +% This function measures the quality of the given map. The measures are +% data-dependent: they measure the map in terms of the given +% data. Typically, the quality of the map is measured in terms of the +% training data. The returned quality measures are average quantization +% error and topographic error. +% +% The issue of SOM quality is a complicated one. Typically two evaluation +% criterias are used: resolution and topology preservation. There are +% many ways to measure them. The ones implemented here were chosen for +% their simplicity. +% +% qe : Average distance between each data vector and its BMU. +% Measures map resolution. +% te : Topographic error, the proportion of all data vectors +% for which first and second BMUs are not adjacent units. +% Measures topology preservation. +% +% NOTE: when calculating BMUs of data vectors, the mask of the given +% map is used. The mask affects the quantization errors, too. +% If you want the quantization errors without the weighting given +% by the mask, you can use the following code: +% bmus = som_bmus(sMap,D); % this uses the mask in finding the BMUs +% for i=1:length(bmus), +% dx = sMap.codebook(bmus(i),:)-D(i,:); % m - x +% dx(isnan(dx)) = 0; % remove NaNs +% qerr(i) = sqrt(sum(dx.^2)); % euclidian distance +% end +% qe = mean(qerr); % average quantization error +% +% Please note that you should _not_ trust the measures blindly. Generally, +% both measures give the best results when the map has overfitted the +% data. This may happen when the number of map units is as large or larger +% than the number of training samples. Beware when you have such a case. +% +% REFERENCES +% +% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, +% Berlin, 1995, pp. 113. +% Kiviluoto, K., "Topology Preservation in Self-Organizing Maps", +% in the proceeding of International Conference on Neural +% Networks (ICNN), 1996, pp. 294-299. +% +% INPUT ARGUMENTS +% +% sMap (struct) Map struct. +% D The data to be used. +% (matrix) A data matrix, size dlen x dim. +% (struct) A data struct. +% +% OUTPUT ARGUMENTS +% +% qe (scalar) mean quantization error +% te (scalar) topographic error +% +% EXAMPLES +% +% qe = som_quality(sMap,D); +% [qe,te] = som_quality(sMap,sD); +% +% SEE ALSO +% +% som_bmus Find BMUs for the given set of data vectors. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 220997 +% Version 2.0beta juuso 151199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% input arguments +if nargin < 2, error('Not enough input arguments.'); end + +% data +if isstruct(D), D = D.data; end +[dlen dim] = size(D); + +% calculate topographic error, too? +if nargout==1, b=1; else b=[1:2]; end +[bmus qerrs]= som_bmus(sMap,D,b); +inds = find(~isnan(bmus(:,1))); +bmus = bmus(inds,:); +qerrs = qerrs(inds,:); +l = length(inds); +if ~l, error('Empty data set.'); end + +% mean quantization error +mqe = mean(qerrs(:,1)); + +if length(b)==2, % topographic error + Ne = full(som_unit_neighs(sMap.topol)); + tge = 0; + for i=1:l, tge = tge+(Ne(bmus(i,1),bmus(i,2)) ~= 1); end + tge = tge / l; +else + tge = NaN; +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_randinit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,215 @@ +function sMap = som_randinit(D, varargin) + +%SOM_RANDINIT Initialize a Self-Organizing Map with random values. +% +% sMap = som_randinit(D, [[argID,] value, ...]) +% +% sMap = som_randinit(D); +% sMap = som_randinit(D,sMap); +% sMap = som_randinit(D,'munits',100,'hexa'); +% +% Input and output arguments ([]'s are optional): +% D The training data. +% (struct) data struct +% (matrix) data matrix, size dlen x dim +% [argID, (string) Parameters affecting the map topology are given +% value] (varies) as argument ID - argument value pairs, listed below. +% +% sMap (struct) map struct +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'munits' (scalar) number of map units +% 'msize' (vector) map size +% 'lattice' *(string) map lattice: 'hexa' or 'rect' +% 'shape' *(string) map shape: 'sheet', 'cyl' or 'toroid' +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'map' *(struct) map struct +% 'som_map','sMap' = 'map' +% +% For more help, try 'type som_randinit' or check out online documentation. +% See also SOM_MAP_STRUCT, SOM_LININIT, SOM_MAKE. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_randinit +% +% PURPOSE +% +% Initializes a SOM with random values. +% +% SYNTAX +% +% sMap = som_randinit(D) +% sMap = som_randinit(D,sMap); +% sMap = som_randinit(D,'munits',100,'hexa'); +% +% DESCRIPTION +% +% Initializes a SOM with random values. If necessary, a map struct +% is created first. For each component (xi), the values are uniformly +% distributed in the range of [min(xi) max(xi)]. +% +% REQUIRED INPUT ARGUMENTS +% +% D The training data. +% (struct) Data struct. If this is given, its '.comp_names' and +% '.comp_norm' fields are copied to the map struct. +% (matrix) data matrix, size dlen x dim +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is used. +% +% Here are the valid argument IDs and corresponding values. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% 'dlen' (scalar) length of the training data +% 'data' (matrix) the training data +% *(struct) the training data +% 'munits' (scalar) number of map units +% 'msize' (vector) map size +% 'lattice' *(string) map lattice: 'hexa' or 'rect' +% 'shape' *(string) map shape: 'sheet', 'cyl' or 'toroid' +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'map' *(struct) map struct +% 'som_map','sMap' = 'map' +% +% OUTPUT ARGUMENTS +% +% sMap (struct) The initialized map struct. +% +% EXAMPLES +% +% sMap = som_randinit(D); +% sMap = som_randinit(D,sMap); +% sMap = som_randinit(D,sTopol); +% sMap = som_randinit(D,'msize',[10 10]); +% sMap = som_randinit(D,'munits',100,'hexa'); +% +% SEE ALSO +% +% som_map_struct Create a map struct. +% som_lininit Initialize a map using linear initialization algorithm. +% som_make Initialize and train self-organizing map. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 100997 +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% data +if isstruct(D), + data_name = D.name; + comp_names = D.comp_names; + comp_norm = D.comp_norm; + D = D.data; + struct_mode = 1; +else + data_name = inputname(1); + struct_mode = 0; +end +[dlen dim] = size(D); + +% varargin +sMap = []; +sTopol = som_topol_struct; +sTopol.msize = 0; +munits = NaN; +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + case 'munits', i=i+1; munits = varargin{i}; sTopol.msize = 0; + case 'msize', i=i+1; sTopol.msize = varargin{i}; + munits = prod(sTopol.msize); + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case 'shape', i=i+1; sTopol.shape = varargin{i}; + case {'som_topol','sTopol','topol'}, i=i+1; sTopol = varargin{i}; + case {'som_map','sMap','map'}, i=i+1; sMap = varargin{i}; sTopol = sMap.topol; + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}.type, + case 'som_topol', + sTopol = varargin{i}; + case 'som_map', + sMap = varargin{i}; + sTopol = sMap.topol; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_topol_struct) Ignoring invalid argument #' num2str(i)]); + end + i = i+1; +end + +if ~isempty(sMap), + [munits dim2] = size(sMap.codebook); + if dim2 ~= dim, error('Map and data must have the same dimension.'); end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% create map + +% map struct +if ~isempty(sMap), + sMap = som_set(sMap,'topol',sTopol); +else + if ~prod(sTopol.msize), + if isnan(munits), + sTopol = som_topol_struct('data',D,sTopol); + else + sTopol = som_topol_struct('data',D,'munits',munits,sTopol); + end + end + sMap = som_map_struct(dim, sTopol); +end + +if struct_mode, + sMap = som_set(sMap,'comp_names',comp_names,'comp_norm',comp_norm); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialization + +% train struct +sTrain = som_train_struct('algorithm','randinit'); +sTrain = som_set(sTrain,'data_name',data_name); + +munits = prod(sMap.topol.msize); +sMap.codebook = rand([munits dim]); + +% set interval of each component to correct value +for i = 1:dim, + inds = find(~isnan(D(:,i)) & ~isinf(D(:,i))); + if isempty(inds), mi = 0; ma = 1; + else ma = max(D(inds,i)); mi = min(D(inds,i)); + end + sMap.codebook(:,i) = (ma - mi) * sMap.codebook(:,i) + mi; +end + +% training struct +sTrain = som_set(sTrain,'time',datestr(now,0)); +sMap.trainhist = sTrain; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_read_cod.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,215 @@ +function sMap = som_read_cod(filename) + +%SOM_READ_COD Reads a SOM_PAK format codebook file. +% +% sMap = som_read_cod(filename); +% +% sMap = som_read_cod('map1.cod'); +% +% Input and output arguments: +% filename (string) name of input file +% sMap (struct) self-organizing map structure +% +% The file must be in SOM_PAK format. Empty lines and lines starting +% with a '#' are ignored, except the ones starting with '#n'. The strings +% after '#n' are read to field 'comp_names' of the map structure. +% +% For more help, try 'type som_read_cod' or check out online documentation. +% See also SOM_WRITE_COD, SOM_READ_DATA, SOM_WRITE_DATA, SOM_MAP_STRUCT. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_read_cod +% +% PURPOSE +% +% Reads a Self-Organizing Map from an ascii file in SOM_PAK format. +% +% SYNTAX +% +% sMap = som_read_cod(filename); +% +% DESCRIPTION +% +% This function is offered for compatibility with SOM_PAK, a SOM +% software package in C. It reads map files written in SOM_PAK format. +% +% The SOM_PAK map file format is as follows. The first line must contain +% the input space dimension, lattice type ('rect' or 'hexa'), map grid +% size in x-direction, map grid size in y-direction, and neighborhood +% function ('bubble' or 'gaussian'), in that order. The following lines +% are comment lines, empty lines or data lines. +% +% Each data line contains the weight vector of one map unit and its +% labels. From the beginning of the line, first are values of the vector +% components separated by whitespaces, then labels, again separated by +% whitespaces. The order of map units in the file are one row at a time +% from right to left, from the top to the bottom of the map (x-direction +% first, then y-direction). +% +% Comment lines start with '#'. Comment lines as well as empty lines are +% ignored, except if the comment line starts with '#n'. In that case the +% line should contain names of the vector components separated by +% whitespaces. +% +% In the returned map struct, several fields has to be set to default +% values, since the SOM_PAK file does not contain information on +% them. These include map shape ('sheet'), mask ([1 ... 1]), +% normalizations (none), trainhist (two entries, first with algorithm +% 'init' and the second with 'seq', both with data name 'unknown'), +% possibly also component names ('Var1',...). +% +% REQUIRED INPUT PARAMETERS +% +% filename (string) the name of the input file +% +% OUTPUT ARGUMENTS +% +% sMap (struct) the resulting SOM struct +% +% EXAMPLES +% +% sMap = som_read_cod('map1.cod'); +% +% SEE ALSO +% +% som_write_cod Writes a map struct into a file in SOM_PAK format. +% som_read_data Reads data from an ascii file. +% som_write_data Writes data struct into a file in SOM_PAK format. +% som_map_struct Creates map structs. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 221097 +% Version 2.0beta juuso 151199 250400 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(1, 1, nargin)) % check no. of input args is correct + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize variables + +lnum = 0; % codebook vector counter +comment_start = '#'; % the char a SOM_PAK command line starts with +comp_name_line = '#n'; % string used to start a special command line, + % which contains names of each component + +% open input file + +fid = fopen(filename); +if fid < 0, error(['Cannot open ' filename]); end + +% read header line + +ok_cnt = 0; +lin = fgetl(fid); li = lin; +[dim c err n] = sscanf(li, '%d%[^ \t]'); ok_cnt=ok_cnt+c; li = li(n:end); +[lattice c err n] = sscanf(li,'%s%[^ \t]'); ok_cnt=ok_cnt+c; li = li(n:end); +[msize(2) c err n] = sscanf(li, '%d%[^ \t]'); ok_cnt=ok_cnt+c; li = li(n:end); +[msize(1) c err n] = sscanf(li, '%d%[^ \t]'); ok_cnt=ok_cnt+c; li = li(n:end); +[neigh c err n] = sscanf(li, '%s%[^ \t\n]'); ok_cnt=ok_cnt+c; + +if ok_cnt ~= 5 + error([ 'Invalid header line: ' lin ]); +end + +% create map struct and set its fields according to header line + +munits = prod(msize); +sMap = som_map_struct(dim, 'msize', msize, ... + lattice, 'sheet', 'neigh', neigh); +[sT0, ok] = som_set('som_train','algorithm','init','data_name','unknown'); +sT1 = som_set('som_train','algorithm','seq','data_name','unknown',... + 'neigh',neigh,'mask',ones(dim,1)); +[sMap, ok, msgs] = som_set(sMap,'name',filename,'trainhist',{sT0,sT1}); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% read codebook from the file + +codebook = zeros(munits,dim); +labels = cell(munits,1); +comp_names = sMap.comp_names; +form = [repmat('%f',[1 dim-1]) '%f%[^ \t]']; + +while 1, + li = fgetl(fid); % read next line + if ~isstr(li), break, end; % is this the end of file? + + [data, c, err, n] = sscanf(li, form); + if c < dim % if there were less numbers than dim on the input file line + if c == 0 + if strncmp(li, comp_name_line, 2) % component name line? + li = li(3:end); i = 0; c = 1; + while c + [s, c, e, n] = sscanf(li, '%s%[^ \t]'); + if ~isempty(s), i = i + 1; comp_names{i} = s; li = li(n:end); end + end + + if i ~= dim + error(['Illegal number of component names: ' num2str(i) ... + ' (dimension is ' num2str(dim) ')']); + end + elseif ~strncmp(li, comment_start, 1) % not a comment, is it error? + [s, c, e, n] = sscanf(li, '%s%[^ \t]'); + if c + error(['Invalid vector on input file line ' ... + num2str(lnum+1) ': [' deblank(li) ']']), + end + end + else + error(['Only ' num2str(c) ' vector components on input file line ' ... + num2str(lnum+1) ' (dimension is ' num2str(dim) ')']); + end + + else + + lnum = lnum + 1; % this was a line containing data vector + codebook(lnum, 1:dim) = data'; % add data to struct + + % read labels + + if n < length(li) + li = li(n:end); + i = 0; n = 1; c = 1; + while c + [s, c, e, n_new] = sscanf(li(n:end), '%s%[^ \t]'); + if c, i = i + 1; labels{lnum, i} = s; n = n + n_new - 1; end + end + end + end +end + +% close the input file + +if fclose(fid) < 0 + error(['Cannot close file ' filename]); +else + fprintf(2, '\rmap read ok \n'); +end + +% check that the number of lines read was correct + +if lnum ~= munits + error(['Illegal number of map units: ' num2str(lnum) ' (should be ' num2str(munits) ').']); +end + +% set values + +% in SOM_PAK the xy-indexing is used, while in Matlab ij-indexing +% therefore, the codebook vectors have to be reorganized + +order = reshape([1:munits],msize); +order = reshape(order',[munits 1]); +codebook(order,:) = codebook; +labels(order,:) = labels; + +sMap.codebook = codebook; +sMap.labels = labels; +sMap.comp_names = comp_names; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_read_data.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,288 @@ +function sData = som_read_data(filename, varargin) + +%SOM_READ_DATA Read data from an ascii file in SOM_PAK format. +% +% sD = som_read_data(filename, dim, [missing]) +% sD = som_read_data(filename, [missing]) +% +% sD = som_read_data('system.data'); +% sD = som_read_data('system.data',10); +% sD = som_read_data('system.data','*'); +% sD = som_read_data('system.data',10,'*'); +% +% Input and output arguments ([]'s are optional): +% filename (string) input file name +% dim (scalar) input space dimension +% [missing] (string) string which indicates a missing component +% value, 'NaN' by default +% +% sD (struct) data struct +% +% Reads data from an ascii file. The file must be in SOM_PAK format, +% except that it may lack the input space dimension from the first +% line. +% +% For more help, try 'type som_read_data' or check out online documentation. +% See also SOM_WRITE_DATA, SOM_READ_COD, SOM_WRITE_COD, SOM_DATA_STRUCT. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_read_data +% +% PURPOSE +% +% Reads data from an ascii file in SOM_PAK format. +% +% SYNTAX +% +% sD = som_read_data(filename) +% sD = som_read_data(..., dim) +% sD = som_read_data(..., 'missing') +% sD = som_read_data(..., dim, 'missing') +% +% DESCRIPTION +% +% This function is offered for compatibility with SOM_PAK, a SOM software +% package in C. It reads data from a file in SOM_PAK format. +% +% The SOM_PAK data file format is as follows. The first line must +% contain the input space dimension and nothing else. The following +% lines are comment lines, empty lines or data lines. Unlike programs +% in SOM_PAK, this function can also determine the input dimension +% from the first data lines, if the input space dimension line is +% missing. Note that the SOM_PAK format is not fully supported: data +% vector 'weight' and 'fixed' properties are ignored (they are treated +% as labels). +% +% Each data line contains one data vector and its labels. From the beginning +% of the line, first are values of the vector components separated by +% whitespaces, then labels also separated by whitespaces. If there are +% missing values in the vector, the missing value marker needs to be +% specified as the last input argument ('NaN' by default). The missing +% values are stored as NaNs in the data struct. +% +% Comment lines start with '#'. Comment lines as well as empty lines are +% ignored, except if the comment lines that start with '#n' or '#l'. In that +% case the line should contain names of the vector components or label names +% separated by whitespaces. +% +% NOTE: The minimum value Matlab is able to deal with (realmax) +% should not appear in the input file. This is because function sscanf is +% not able to read NaNs: the NaNs are in the read phase converted to value +% realmax. +% +% REQUIRED INPUT ARGUMENTS +% +% filename (string) input filename +% +% OPTIONAL INPUT ARGUMENTS +% +% dim (scalar) input space dimension +% missing (string) string used to denote missing components (NaNs); +% default is 'NaN' +% +% OUTPUT ARGUMENTS +% +% sD (struct) the resulting data struct +% +% EXAMPLES +% +% The basic usage is: +% sD = som_read_data('system.data'); +% +% If you know the input space dimension beforehand, and the file does +% not contain it on the first line, it helps if you specify it as the +% second argument: +% sD = som_read_data('system.data',9); +% +% If the missing components in the data are marked with some other +% characters than with 'NaN', you can specify it with the last argument: +% sD = som_read_data('system.data',9,'*') +% sD = som_read_data('system.data','NaN') +% +% Here's an example data file: +% +% 5 +% #n one two three four five +% #l ID +% 10 2 3 4 5 1stline label +% 0.4 0.3 0.2 0.5 0.1 2ndline label1 label2 +% # comment line: missing components are indicated by 'x':s +% 1 x 1 x 1 3rdline missing_components +% x 1 2 2 2 +% x x x x x 5thline emptyline +% +% SEE ALSO +% +% som_write_data Writes data structs/matrices to a file in SOM_PAK format. +% som_read_cod Read a map from a file in SOM_PAK format. +% som_write_cod Writes data struct into a file in SOM_PAK format. +% som_data_struct Creates data structs. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 221097 +% Version 2.0beta ecco 060899, juuso 151199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(1, 3, nargin)) % check no. of input args is correct + +dont_care = 'NaN'; % default don't care string +comment_start = '#'; % the char a SOM_PAK command line starts with +comp_name_line = '#n'; % string denoting a special command line, + % which contains names of each component +label_name_line = '#l'; % string denoting a special command line, + % which contains names of each label +block_size = 1000; % block size used in file read + +kludge = num2str(realmax, 100); % used in sscanf + + +% open input file + +fid = fopen(filename); +if fid < 0 + error(['Cannot open ' filename]); +end + +% process input arguments + +if nargin == 2 + if isstr(varargin{1}) + dont_care = varargin{1}; + else + dim = varargin{1}; + end +elseif nargin == 3 + dim = varargin{1}; + dont_care = varargin{2}; +end + +% if the data dimension is not specified, find out what it is + +if nargin == 1 | (nargin == 2 & isstr(varargin{1})) + + fpos1 = ftell(fid); c1 = 0; % read first non-comment line + while c1 == 0, + line1 = strrep(fgetl(fid), dont_care, kludge); + [l1, c1] = sscanf(line1, '%f '); + end + + fpos2 = ftell(fid); c2 = 0; % read second non-comment line + while c2 == 0, + line2 = strrep(fgetl(fid), dont_care, kludge); + [l2, c2] = sscanf(line2, '%f '); + end + + if (c1 == 1 & c2 ~= 1) | (c1 == c2 & c1 == 1 & l1 == 1) + dim = l1; + fseek(fid, fpos2, -1); + elseif (c1 == c2) + dim = c1; + fseek(fid, fpos1, -1); + warning on + warning(['Automatically determined data dimension is ' ... + num2str(dim) '. Is it correct?']); + else + error(['Invalid header line: ' line1]); + end +end + +% check the dimension is valid + +if dim < 1 | dim ~= round(dim) + error(['Illegal data dimension: ' num2str(dim)]); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% read data + +sData = som_data_struct(zeros(1, dim), 'name', filename); +lnum = 0; % data vector counter +data_temp = zeros(block_size, dim); +labs_temp = cell(block_size, 1); +comp_names = sData.comp_names; +label_names = sData.label_names; +form = [repmat('%g',[1 dim-1]) '%g%[^ \t]']; + +limit = block_size; +while 1, + li = fgetl(fid); % read next line + if ~isstr(li), break, end; % is this the end of file? + + % all missing vectors are replaced by value realmax because + % sscanf is not able to read NaNs + li = strrep(li, dont_care, kludge); + [data, c, err, n] = sscanf(li, form); + if c < dim % if there were less numbers than dim on the input file line + if c == 0 + if strncmp(li, comp_name_line, 2) % component name line? + li = strrep(li(3:end), kludge, dont_care); i = 0; c = 1; + while c + [s, c, e, n] = sscanf(li, '%s%[^ \t]'); + if ~isempty(s), i = i + 1; comp_names{i} = s; li = li(n:end); end + end + + if i ~= dim + error(['Illegal number of component names: ' num2str(i) ... + ' (dimension is ' num2str(dim) ')']); + end + elseif strncmp(li, label_name_line, 2) % label name line? + li = strrep(li(3:end), kludge, dont_care); i = 0; c = 1; + while c + [s, c, e, n] = sscanf(li, '%s%[^ \t]'); + if ~isempty(s), i = i + 1; label_names{i} = s; li = li(n:end); end + end + elseif ~strncmp(li, comment_start, 1) % not a comment, is it error? + [s, c, e, n] = sscanf(li, '%s%[^ \t]'); + if c + error(['Invalid vector on input file data line ' ... + num2str(lnum+1) ': [' deblank(li) ']']), + end + end + else + error(['Only ' num2str(c) ' vector components on input file data line ' ... + num2str(lnum+1) ' (dimension is ' num2str(dim) ')']); + end + + else + + lnum = lnum + 1; % this was a line containing data vector + data_temp(lnum, 1:dim) = data'; % add data to struct + + if lnum == limit % reserve more memory if necessary + data_temp(lnum+1:lnum+block_size, 1:dim) = zeros(block_size, dim); + [dummy nl] = size(labs_temp); + labs_temp(lnum+1:lnum+block_size,1:nl) = cell(block_size, nl); + limit = limit + block_size; + end + + % read labels + + if n < length(li) + li = strrep(li(n:end), kludge, dont_care); i = 0; n = 1; c = 1; + while c + [s, c, e, n_new] = sscanf(li(n:end), '%s%[^ \t]'); + if c, i = i + 1; labs_temp{lnum, i} = s; n = n + n_new - 1; end + end + end + end +end + +% close input file +if fclose(fid) < 0, error(['Cannot close file ' filename]); +else fprintf(2, '\rdata read ok \n'); end + +% set values +data_temp(data_temp == realmax) = NaN; +sData.data = data_temp(1:lnum,:); +sData.labels = labs_temp(1:lnum,:); +sData.comp_names = comp_names; +sData.label_names = label_names; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_recolorbar.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,351 @@ +function h=som_recolorbar(p, ticks, scale, labels) + +%SOM_RECOLORBAR Refresh and rescale colorbars in the current SOM_SHOW fig. +% +% h = som_recolorbar([p], [ticks], [scaling], [labels]) +% +% colormap(jet); som_recolorbar +% +% Input and output arguments ([]'s are optional) +% [p] (vector) subplot number vector +% (string) 'all' (the default), 'comp' to process only +% component planes +% [ticks] (string) 'auto' or 'border', default: 'auto' +% (cell array) p x 1 cell array of p row vectors +% (vector) the same ticks are applied to all given subplots +% (scalar) value is at least 2: the number of ticks to show, +% evenly spaced between and including minimum and maximum +% [scale] (string) 'denormalized' or 'normalized' (the default) +% [labels] (cell array) p x 1 cell array of cells containing strings +% +% h (vector) handles to the colorbar objects. +% +% This function refreshes the colorbars in the figure created by SOM_SHOW. +% Refreshing is necessary if you have changed the colormap. +% Each colorbar has letter 'd' or 'n' and possibly 'u' as label. Letter 'd' means +% that the scale is denormalized, letter 'n' that the scale is +% normalized, and 'u' is for user specified labels. +% +% For more help, try 'type som_recolorbar' or check out online documentation. +% See also SOM_SHOW + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_recolorbar +% +% PURPOSE +% +% Refreshes the the colorbars in the figure. +% +% SYNTAX +% +% h = som_recolorbar +% h = som_recolorbar(p) +% h = som_recolorbar(p, ticks) +% h = som_recolorbar(p, ticks, scaling) +% h = som_recolorbar(p, ticks, scaling, labels) +% +% DESCRIPTION +% +% This function refreshes the colorbars in the figure created by SOM_SHOW. +% Refreshing is necessary if you have changed the colormap. Each colorbar +% has letter 'd' or 'n' and possibly 'u' as label. Letter 'd' means that the +% scale is denormalized, letter 'n' that the scale is normalized, and 'u' is +% for user specified labels. +% +% Different argument combinations: +% +% 1. Argument 'ticks' has string values: +% - 'auto' for input argument ticks sets the automatic tick +% marking on (factory default). +% - 'border' sets the tick marks to the color borders. This is +% convenient if there are only few colors in use. +% +% Argument scale controls the scaling of the tick mark label values. +% 'normalized' means that the tick mark labels are directly the values +% of the ticks, that is, they refer to the map codebook values. +% Value 'denormalized' scales the tick mark label values back to the original +% data scaling. This is made using som_denormalize_data. +% +% 2. Argument 'ticks' is a cell array of vectors: +% The values are set to be the tick marks to the colorbar specified by p. +% - if arg. scale is 'normalized' the ticks are set directly to the colorbar. +% - if arg. scale is 'denormalized' the tick values are first normalized +% in the same way as the data. +% +% 3. Argument 'ticks' is a vector +% As above, but the same values are used for all (given) subplots. +% +% 4. Argument 'ticks' is a scalar +% The ticks are set to equally spaced values between (and including) +% minimum and maximum. +% +% Argument 'labels' specify user defined labels to the tick marks +% +% NOTE: ticks are rounded to contain three significant digits. +% +% OPTIONAL INPUT ARGUMENTS +% +% p (vector) subplot number vector +% (string) 'all' (the default), 'comp' to effect only +% component planes +% +% ticks (string) 'auto' or 'border', default: 'auto' +% (cell array) p x 1 cell array of p row vectors +% (vector) as the cell array, but the same vector is +% applied to all given subplots +% (scalar) the number of ticks to show: these are +% evenly space between minimum and maximum +% +% scale (string) 'denormalized' or 'normalized' (the default) +% +% labels (cell array) p x 1 cell array of cells containing strings +% +% OUTPUT ARGUMENTS +% +% h (vector) handles to the colorbar objects. +% +% EXAMPLE +% +% colormap(jet(5)); som_recolorbar('all','border','denormalized') +% % Uses five colors and sets the ticks on the color borders. +% % Tick label values are denormalized back to the original data scaling +% +% colormap(copper(64));som_recolorbar +% % changes to colormap copper and resets default ticking and labeling +% +% som_recolorbar('all',3) +% % To put 3 ticks to each colorbar so that minimum, mean and +% % maximum values on the colorbar are shown. +% +% som_recolorbar([1 3],{[0.1 0.2 0.3];[0.2 0.4]},'denormalized') +% % Ticks colorbar 1 by first normalizing values 0.1, 0.2, 0.3 and +% % then setting the ticks to the colorbar. Labels are of course +% % 0.1, 0.2 and 0.3. Ticks colorbar 3 in the same way using values +% % 0.2 and 0.4. +% +% som_recolorbar([2 4],{[0.1 0.2];[-1.2 3]},'normalized',{{'1' '2'};{'a' 'b'}}) +% % Ticks colorbar 2 and 4 directly to the specified values. Sets labels +% % '1' '2' and 'a' 'b' to the ticks. +% +% som_recolorbar([2 4],{[0.1 0.2];[-1.2 3]},'normalized',{{'1' '2'};{'a' 'b'}}) +% % as previous one, but normalizes tick values first +% +% SEE ALSO +% +% som_show Basic SOM visualization. +% som_normalize Normalization operations. +% som_denormalize Denormalization operations. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta Johan 061197 +% Version 2.0beta juuso 151199 130300 160600 181101 + +%% Init & check %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(0, 4, nargin)) % check no. of input args + +% Check the subplot vector p and get the handles, exit if error +% Default subplot vector is 'all' + +if nargin < 1 | isempty(p) % default p + p= 'all'; +end + +% check SOM_SHOW and get the figure data. Exit, if error + +[handles, msg, lattice, msize, dim, normalization, comps]= ... + vis_som_show_data(p, gcf); +error(msg); + +if nargin < 2 | isempty(ticks) % default tick mode is 'auto' + ticks = 'auto'; +elseif isa(ticks,'cell') % check for cell + tickValues = ticks; + ticks= 'explicit'; +elseif isa(ticks,'double') & length(ticks)>1, + tickValues = {ticks}; + ticks = 'explicit'; +elseif isa(ticks,'double') & length(ticks)==1, + tickValues = max(2,round(ticks)); + ticks = 'evenspace'; +end +if ~ischar(ticks) % invalid argument + error('The second argument should be a string or a cell array of vectors.'); +end + +switch ticks % check ticks + case {'auto','border'}, % nill + case 'evenspace', + tickValues_tmp = cell(length(handles),1); + for i=1:length(handles), tickValues_tmp{i} = tickValues; end + tickValues = tickValues_tmp; + case 'explicit', + if length(tickValues)==1 & length(handles)>1, + tickValues_tmp = cell(length(handles),1); + for i=1:length(handles), tickValues_tmp{i} = tickValues{1}; end + tickValues = tickValues_tmp; + end + if length(tickValues) ~= length(handles), + error('Cell containing the ticks has wrong size.') + end +otherwise + error('''auto'' or ''border'' expected for the second argument.'); +end + +if nargin < 3 | isempty(scale) % default mode is normalized + scale= 'normalized'; +end +if ~ischar(scale) % check scale type + error('The third argument should be a string.'); +end +switch scale % check the string + case { 'normalized', 'denormalized'} % ok + case 'n', scale = 'normalized'; + case 'd', scale = 'denormalized'; + otherwise + error('''normalized'' or ''denormalized'' expected for the third argument.') +end + +if nargin < 4 | isempty(labels) % default is autolabeling + labels = 'auto'; +elseif ~isa(labels,'cell') % check type + error('The fourth argument should be a cell array of cells containing strings.') +else + labelValues=labels; % set labels + labels = 'explicit'; + if length(labelValues) == length(handles) % check size + ; + else + error('Cell containing the labels has wrong size') + end +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +n = size(colormap,1)+1; % number of colors+1 +h_ = zeros(length(handles),1); + +for i=1:length(handles), % MAIN LOOP BEGINS + axes(handles(i)); % set axes, refres colorbar and + if comps(i)>=0, + h_(i)=colorbar; % get colorbar handles + + colorbardir=get(h_(i),'YaxisLocation'); + switch colorbardir % get colorbar direction & + case 'left' % set some strings + Tick='Xtick'; Lim='Xlim'; LabelMode='XTickLabelMode'; Label='XtickLabel'; + case 'right' + Tick='Ytick'; Lim='Ylim'; LabelMode='YTickLabelMode'; Label='YtickLabel'; + otherwise + error('Internal error: unknown value for YaxisLocation'); % fatal + end + + switch ticks + case 'auto' + set(h_(i),LabelMode,'auto'); % factory default ticking + tickValues{i}=get(h_(i),Tick); % get tick values + case 'border' + limit=caxis; + t=linspace(limit(1),limit(2),n); % set n ticks between min and max + t([1 length(t)])=get(h_(i),Lim); % <- caxis is not necerraily the same + tickValues{i}=t; % as the colorbar min & max values + case 'evenspace' + limit = caxis; + t = linspace(limit(1),limit(2),tickValues{i}); + t([1 length(t)])=get(h_(i),Lim); + tickValues{i}=t; + case 'explicit' + if comps(i)>0, + if strcmp(scale,'normalized') % normalize tick values + tickValues{i} = som_normalize(tickValues{i},normalization{comps(i)}); + end + end + + otherwise + error('Internal error: unknown tick type') % this shouldn't happen + end + + %tickValues{i} = epsto0(tickValues{i}); + + switch labels + case 'auto' + switch scale + case 'normalized' + labelValues{i} = round2(tickValues{i}); % use the raw ones + case 'denormalized' % denormalize tick values + if comps(i)>0, + labelValues{i} = som_denormalize(tickValues{i},normalization{comps(i)}); + labelValues{i} = round2(labelValues{i}); % round the scale + else + labelValues{i} = round2(tickValues{i}); + end + otherwise + error('Internal error: unknown scale type'); % this shouldn't happen + end + case 'explicit' + ; % they are there already + otherwise + error('Internal error: unknown label type'); % this shouldn't happen + end + + set(h_(i),Tick,tickValues{i}); % set ticks and labels + set(h_(i),Label,labelValues{i}); + + if comps(i)>0, + % Label the colorbar with letter 'n' if normalized, with letter 'd' + % if denormalized and 'u' if the labels are user specified + mem_axes=gca; axes(h_(i)); + ch=' '; + if strcmp(scale,'normalized'), ch(1)='n'; end + if strcmp(scale,'denormalized'), ch(1)='d'; end + if strcmp(labels,'explicit'), ch(2)='u'; end + xlabel(ch); + axes(mem_axes); + end + end +end % MAIN LOOP ENDS + + +%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargout>0 + h=h_; +end + +return; + +%% Subfunction: ROUND2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ROUND2 rounds the labels to tol significant digits + +function r=round2(d) + +tol=3; + +zero=(d==0); +d(zero)=1; +k=floor(log10(abs(d)))-(tol-1); +r=round(d./10.^k).*10.^k; +r(zero)=0; +%r=epsto0(r); + +%% Subfunction: ISVECTOR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function t=isvector(v) +% ISVECTOR checks if a matrix is a vector or not + +t=(ndims(v) == 2 & min(size(v)) == 1) & isnumeric(v); + +%% Subfunction: EPSTO0 + +function t=epsto0(t) +% EPSTO0 checks whether first tick value is *very* close to zero, +% if so sets it to zero. + +if (t(end)-t(1))/t(end) > 1-0.005 & abs(t(1))<1, t(1) = 0; end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_select.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,716 @@ +function varargout=som_select(c_vect,plane_h,arg) + +%SOM_SELECT Manual selection of map units from a visualization. +% +% som_select(c_vect,[plane_h]) +% +% som_select(3) +% som_select(sM.labels(:,1)) +% +% Input arguments ([]'s are optional): +% c_vect (scalar) number of classes +% (vector) initial class identifiers +% (cell array) of strings, class names +% (matrix) size * x 3, the color of each class +% [plane_h] (scalar) handle of the plane (axes) to be marked. +% By default, the current axes is used (GCA). +% For the function to work, the plot in the +% axes must have been created with the +% SOM_CPLANE function (or SOM_SHOW). +% +% Launches a GUI which allows user to select nodes from plane by +% clicking them or by choosing a region (a polygon). +% +% Middle mouse button: selects (or clears selection of) a single node +% Left mouse button: lets user draw a polygon +% Right mouse button: selects (or clears selection of) the units +% inside the polygon +% +% From the GUI, the color (class) is selected as well as whether +% but buttons select or clear the selection from the units. The +% buttons on the bottom have the following actions: +% +% 'OK' Assigns the class identifiers to the 'ans' variable in +% workspace. The value is an array of class identifiers: +% strings (cellstr) if the c_vect was an array of +% strings, a vector otherwise. +% 'Clear' Removes marks from the plane. +% 'Close' Closes the application. +% +% See also SOM_SHOW, SOM_CPLANE. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100, juuso 010200 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +if nargin < 2, plane_h = gca; end +if(isempty(gcbo)), arg='start'; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +switch arg + case 'start' + patch_h=find_patch(plane_h); + lattice=getfield(size(get(patch_h,'XData')),{1}); + msize(1)=floor(getfield(get(plane_h,'YLim'),{2})); + msize(2)=floor(getfield(get(plane_h,'XLim'),{2})-0.5); + if lattice==6 + lattice='hexa'; + else + lattice='rect'; + end + + if any(strcmp(get(patch_h,'Tag'),{'planeBar','planePie'})) + tmp_dim=size(get(patch_h,'XData'),2)/prod(msize); + tmp_xdata=get(patch_h,'XData'); + tmp_x=tmp_xdata(:,(msize(1)*(msize(2)-1)+2)*tmp_dim); + if floor(tmp_x(1)) ~= round(tmp_x(1)) + lattice = 'hexa'; + else + lattice = 'rect'; + end + elseif strcmp(get(patch_h,'Tag'),'planePlot') + tmp_lines_h=get(gca,'Children'); + test_x=mean(get(tmp_lines_h(2),'XData')); + if round(test_x) ~= floor(test_x) + lattice = 'hexa'; + else + lattice = 'rect'; + end + form=0.5*vis_patch('hexa'); + l = size(form,1); + + nx = repmat(form(:,1),1,prod(msize)); + ny = repmat(form(:,2),1,prod(msize)); + + x=reshape(repmat(1:msize(2),l*msize(1),1),l,prod(msize)); + y=repmat(repmat(1:msize(1),l,1),1,msize(2)); + + if strcmp(lattice,'hexa') + t = find(~rem(y(1,:),2)); + x(:,t)=x(:,t)+.5; + end + x=x+nx; + y=y+ny; + + colors=reshape(ones(prod(msize),1)*[NaN NaN NaN],... + [1 prod(msize) 3]); + v=caxis; + patch_h=patch(x,y,colors,... + 'EdgeColor','none',... + 'ButtonDownFcn',... + 'som_select([],[],''click'')',... + 'Tag','planePlot'); + set([gca gcf],'ButtonDownFcn','som_select([],[],''click'')'); + caxis(v) + end + + c_colors = []; + if iscell(c_vect) + [c_vect,c_names,c_classes]=class2num(c_vect); + if length(c_classes)<prod(msize), + c_classes = zeros(prod(msize),1); + end + else + if all(size(c_vect)>1), + c_colors = c_vect; + c_names = 1:size(c_vect,1); + c_vect = size(c_vect,1); + c_classes = zeros(prod(msize),1); + elseif length(c_vect)==prod(msize), + c_classes = c_vect; + u = unique(c_classes(isfinite(c_classes) & c_classes>0)); + c_names = u; + c_vect = length(u); + elseif length(c_vect)>1, + c_names = c_vect; + c_vect = length(c_vect); + c_classes = zeros(prod(msize),1); + elseif length(c_vect)==1, + c_names = 1:c_vect; + c_classes = zeros(prod(msize),1); + end + end + + udata.lattice=lattice; + udata.patch_h=patch_h; + udata.plane_h=plane_h; + udata.type=get(udata.patch_h,'Tag'); + udata.msize=msize; + set(patch_h,'UserData',udata); + if strcmp(udata.type,'planePlot') + set([gca gcf],'UserData',udata); + end + str=cat(2,'som_select([],[],''click'')'); + set(patch_h,'ButtonDownFcn',str); + + draw_colorselection(c_names,c_colors); + tmp_data=findobj(get(0,'Children'),'Tag','SELECT_GUI'); + tmp_data=get(tmp_data,'UserData'); + tmp_data.c_names=c_names; + tmp_data.mat=reshape(c_classes,msize); + tmp_data.patch_h=patch_h; + tmp_data.plane_h=plane_h; + tmp_data.type=get(udata.patch_h,'Tag'); + tmp_data.lattice=lattice; + tmp_data.coords=[]; + tmp_data.poly_h=[]; + tmp_data.msize=msize; + tmp_data.mode='select'; + set(tmp_data.fig_h,'UserData',tmp_data); + draw_classes; + + case 'click' + switch get(gcf,'SelectionType') + case 'open' + return; + case {'normal','alt'} + draw_poly; + case 'extend' + click; + end + case 'choose' + draw_colorselection(0,0,'choose'); + case 'close' + close_gui; + case 'clear' + clear_plane; + case 'rb' + rb_control; + case 'ret_mat' + gui=findobj(get(0,'Children'),'Tag','SELECT_GUI'); + gui=get(gui,'UserData'); + mat=reshape(gui.mat,prod(size(gui.mat)),1); + if ~isempty(gui.c_names) + if isnumeric(gui.c_names), tmp=zeros(length(mat),1); + else tmp=cell(length(mat),1); + end + for i=1:length(gui.c_names) + inds=find(mat==i); + tmp(inds)=gui.c_names(i); + end + mat=tmp; + end + varargout{1}=mat; + %gui.mat=zeros(size(gui.mat)); + %set(gui.fig_h,'UserData',gui); + %h=findobj(get(gui.plane_h,'Children'),'Tag','SEL_PATCH'); + %delete(h); +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function rb_control; + +h=findobj(get(gcf,'Children'),'Style','radiobutton'); +set(h,'Value',0); +set(gcbo,'Value',1); + +udata=get(gcf,'UserData'); +if strcmp(get(gcbo,'Tag'),'Radiobutton1') + udata.mode='select'; +else + udata.mode='clear'; +end + +set(gcf,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function clear_plane + +h=findobj(get(0,'Children'),'Tag','SELECT_GUI'); +gui=get(h,'UserData'); + +if strcmp(get(gui.patch_h,'Tag'),'planePlot') + colors=reshape(get(gui.patch_h,'FaceVertexCData'),[prod(gui.msize) 3]); + colors(:,:)=NaN; + set(gui.patch_h,'FaceVertexCData',colors); +end + +h=findobj(get(gui.plane_h,'Children'),'Tag','SEL_PATCH'); +gui.mat=zeros(gui.msize); +set(gui.fig_h,'UserData',gui); +delete(h); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function click + +udata=get(gcbo,'UserData'); + +udata=get(udata.patch_h,'UserData'); +coords=get(gca,'CurrentPoint'); +row=round(coords(1,2)); +if row > udata.msize(1), row = udata.msize(1); end +if row < 1, row = 1; end +if any(strcmp(udata.lattice,{'hexa','hexaU'})) & ~mod(row,2), + col=floor(coords(1,1))+0.5; + if col > udata.msize(2)+0.5, col=udata.msize(2)+0.5; end +else + col=round(coords(1,1)); + if col > udata.msize(2), col=udata.msize(2); end +end +if col < 1, col = 1; end + +if strcmp(udata.type,'planePlot') + + if ~mod(row,2) & strcmp(udata.lattice,'hexa'), col=round(col-0.5); end + + ind=sub2ind(udata.msize,row,col); + colors=reshape(get(udata.patch_h,'FaceVertexCData'),[prod(udata.msize) 3]); + gui=findobj(get(0,'Children'),'Tag','SELECT_GUI'); + gui=get(gui,'UserData'); + + if ~isempty(gui.curr_col) & all(~isnan(colors(ind,1,:))), + if ~strcmp(gui.mode,'clear') & ~all(gui.curr_col == colors(ind,:)) + colors(ind,:)=gui.curr_col; + gui.mat(row,col)=gui.class; + else + colors(ind,:)=[NaN NaN NaN]; + gui.mat(row,col)=0; + end + elseif strcmp(gui.mode,'clear') + colors(ind,:)=[NaN NaN NaN]; + gui.mat(row,col)=0; + elseif isempty(gui.curr_col) + return; + else + gui.mat(row,col)=gui.class; + colors(ind,:)=gui.curr_col; + end + set(udata.patch_h,'FaceVertexCData',colors); + set(gui.fig_h,'UserData',gui); + return; +end + +if any(strcmp(udata.type,{'planePie','planeBar'})) + [x,y]=pol2cart(0:0.1:2*pi,0.5); + coords=[x';0.5]*0.7; + coords(:,2)=[y';0]*0.7; +elseif strcmp(udata.lattice,'hexa'); + coords=0.7*vis_patch('hexa'); +else + coords=0.7*vis_patch('rect'); +end +coords(:,1)=coords(:,1)+col; +coords(:,2)=coords(:,2)+row; +if ~mod(row,2) & strcmp(udata.lattice,'hexa'), col=round(col-0.5); end + +hold on; +if gco == udata.patch_h + gui=findobj(get(0,'Children'),'Tag','SELECT_GUI'); + gui=get(gui,'UserData'); + if isnan(gui.curr_col) | strcmp(gui.mode,'clear'), return; end + h=fill(coords(:,1),coords(:,2),gui.curr_col); + str=cat(2,'som_select([],[],''click'')'); + set(h,'ButtonDownFcn',str,'Tag','SEL_PATCH'); + tmp.patch_h=udata.patch_h; + set(h,'UserData',tmp); + gui.mat(row,col)=gui.class; + set(gui.fig_h,'UserData',gui); +else + gui=findobj(get(0,'Children'),'Tag','SELECT_GUI'); + gui=get(gui,'UserData'); + if ~all(get(gcbo,'FaceColor') == gui.curr_col) & ~strcmp(gui.mode,'clear'), + if ~isnan(gui.curr_col), + set(gcbo,'FaceColor',gui.curr_col); + gui.mat(row,col) = gui.class; + end + else + gui.mat(row,col)=0; + delete(gco); + end + set(gui.fig_h,'UserData',gui); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function draw_colorselection(varargin) + +if length(varargin)==2, + + if length(varargin{1})==1, + n = varargin{1}; + names = 1:n; + else + n = length(varargin{1}); + names = varargin{1}; + end + colors = varargin{2}; + + shape=[0.5 -0.5;0.5 0.5;1.5 0.5;1.5 -0.5]; + rep_x=repmat(shape(:,1),1,n); + rep_y=repmat(shape(:,2),1,n); + for i=0:getfield(size(rep_y,2))-1, rep_x(:,i+1)=rep_x(:,i+1)+i; end + if isempty(colors), colors=jet(n); end + data=som_select_gui; + data.colors=colors; + data.curr_col=NaN; + data.class=0; + set(0,'CurrentFigure',data.fig_h); + hold on; + tmp=fill(rep_x,rep_y,0.8); + for i=1:n + set(tmp(i),... + 'EdgeColor',[0 0 0],... + 'FaceColor',colors(i,:),... + 'ButtonDownFcn','som_select([],0,''choose'');'); + end + axis('equal'); + axis('on'); + set(gca,'XTick',1:n,'XTickLabel',names,'XAxisLocation','top'); + set(data.a_h,'YLim',[-0.5,0.5],... + 'XLim',[0.5 n+0.5],... + 'YTickLabel',''); + set(data.fig_h,'UserData',data); + +elseif strcmp(varargin{3},'choose') + + udata=get(gcf,'UserData'); + if strcmp(get(gcbo,'Selected'),'off') + old=findobj(get(gca,'Children'),'Type','patch'); + set(old,'Selected','off'); + set(gcbo,'Selected','on'); + udata.curr_col=udata.colors(round(mean(get(gcbo,'XData'))),:); + udata.class=mean(get(gcbo,'XData')); + else + set(gcbo,'Selected','off'); + udata.curr_col=NaN; + udata.class=0; + end + set(gcf,'UserData',udata); + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function data=som_select_gui() + + +a = figure('Color',[0.8 0.8 0.8], ... + 'PaperType','a4letter', ... + 'Position',[586 584 560 210], ... + 'Tag','SELECT_GUI'); + +data.fig_h=a; + +b = axes('Parent',a, ... + 'Box','on', ... + 'CameraUpVector',[0 1 0], ... + 'Color',[1 1 1], ... + 'DataAspectRatioMode','manual', ... + 'PlotBoxAspectRatio',[20 1 2], ... + 'PlotBoxAspectRatioMode','manual', ... + 'Position',[0.13 0.11 0.775 0.815], ... + 'Tag','Axes1', ... + 'WarpToFill','off', ... + 'XColor',[0 0 0], ... + 'XLimMode','manual', ... + 'YColor',[0 0 0], ... + 'YLimMode','manual', ... + 'YTickLabelMode','manual', ... + 'ZColor',[0 0 0]); + +data.a_h=b; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','som_select([],[],''close'')', ... + 'FontWeight','demi', ... + 'Position',[150 12 50 20], ... + 'String','CLOSE', ... + 'Tag','Pushbutton1'); + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','som_select([],0,''ret_mat'')',... + 'FontWeight','demi', ... + 'Position',[365 12 50 20], ... + 'String','OK', ... + 'Tag','Pushbutton2'); + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','som_select([],0,''clear'')',... + 'FontWeight','demi', ... + 'Position',[257.5 12 50 20], ... + 'String','CLEAR', ... + 'Tag','Pushbutton3'); + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'Position',[50 27 17 16], ... + 'Callback','som_select([],[],''rb'')',... + 'Style','radiobutton', ... + 'Tag','Radiobutton1', ... + 'Value',1); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','som_select([],[],''rb'')',... + 'Position',[50 7 17 16], ... + 'Style','radiobutton', ... + 'Tag','Radiobutton2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',9, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[72 25 28 15], ... + 'String','Select', ... + 'Style','text', ... + 'Tag','StaticText1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',9, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[72 7 25 13.6], ... + 'String','Clear', ... + 'Style','text', ... + 'Tag','StaticText2'); + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function close_gui + +udata=get(get(gcbo,'Parent'),'UserData'); + +if strcmp(udata.type,'planePlot'); + set(udata.plane_h,'ButtonDownFcn','','UserData',[]); + set(get(udata.plane_h,'Parent'),'ButtonDownFcn',''); + delete(udata.patch_h); + return; +end + +h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH'); +set(udata.patch_h,'ButtonDownFcn','','UserData',[]); +delete(h); +close(udata.fig_h); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function draw_poly + +udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'),'UserData'); + +if isempty(udata.coords) & strcmp(get(gcf,'SelectionType'),'alt') + return; +end + +coords(1,1) = getfield(get(gca,'CurrentPoint'),{3}); +coords(1,2) = getfield(get(gca,'CurrentPoint'),{1}); +udata.coords = cat(1,udata.coords,coords); +delete(udata.poly_h); +subplot(udata.plane_h); + +hold on; +switch get(gcf,'SelectionType'); + case 'normal' + udata.poly_h=plot(udata.coords(:,2),udata.coords(:,1),'black',... + 'ButtonDownFcn','som_select([],[],''click'')',... + 'LineWidth',2); + set(udata.fig_h,'UserData',udata); + case 'alt' + udata.coords=cat(1,udata.coords,udata.coords(1,:)); + udata.poly_h=plot(udata.coords(:,2),udata.coords(:,1),'black',... + 'LineWidth',2); + delete(udata.poly_h); + if ~isnan(udata.curr_col) + tmp=sort(repmat((1:udata.msize(1))',udata.msize(2),1)); + tmp(:,2)=repmat((1:udata.msize(2))',udata.msize(1),1); + tmp2=tmp; + if strcmp(udata.type,'planePlot') + in=find(inpolygon(tmp(:,2),tmp(:,1),... + udata.coords(:,2),udata.coords(:,1))); + row=tmp2(in,1); + col=tmp2(in,2); + in=sub2ind(udata.msize,row,col); + colors=reshape(get(udata.patch_h,'FaceVertexCData'),... + [prod(udata.msize) 3]); + if ~isnan(udata.curr_col) & ~strcmp(udata.mode,'clear') + colors(in,:)=ones(length(in),1)*udata.curr_col; + udata.mat(row,col)=udata.class; + elseif strcmp(udata.mode,'clear') + colors(in,:)=[NaN NaN NaN]; + udata.mat(row,col)=0; + end + udata.poly_h=[]; + udata.coords=[]; + set(udata.patch_h,'FaceVertexCData',colors); + set(udata.fig_h,'UserData',udata); + return; + end + if strcmp(udata.lattice,'hexa'); + t=find(~rem(tmp(:,1),2)); + tmp(t,2)=tmp(t,2)+0.5; + if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'})) + p=0.7*vis_patch('hexa'); + else + [x,y]=pol2cart(0:0.1:2*pi,0.5); + p=[x';0.5]*0.7; + p(:,2)=[y';0]*0.7; + end + else + if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'})) + p=0.7*vis_patch('rect'); + else + [x,y]=pol2cart(0:0.1:2*pi,0.5); + p=[x';0.5]*0.7; + p(:,2)=[y';0]*0.7; + end + end + in=find(inpolygon(tmp(:,2),tmp(:,1),udata.coords(:,2),udata.coords(:,1))); + set(udata.fig_h,'UserData',udata); + if strcmp(udata.mode,'select') + remove_selpatches; + udata=get(udata.fig_h,'UserData'); + for i=1:length(in) + udat.patch_h=udata.patch_h; + h=patch(p(:,1)+tmp(in(i),2),p(:,2)+tmp(in(i),1),... + udata.curr_col,... + 'EdgeColor','black',... + 'ButtonDownFcn','som_select([],[],''click'')', ... + 'Tag','SEL_PATCH',... + 'UserData',udat); + udata.mat(tmp2(in(i),1),tmp2(in(i),2))=udata.class; + end + else + remove_selpatches; + udata=get(udata.fig_h,'UserData'); + %h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH'); + %for i=1:length(h) + % if all(get(h(i),'FaceColor')==udata.curr_col) & ... + % inpolygon(mean(get(h(i),'XData')),mean(get(h(i),'YData')),... + % udata.coords(:,2),udata.coords(:,1)) + % coords=[floor(mean(get(h(i),'YData')))... + % floor(mean(get(h(i),'XData')))]; + % udata.mat(coords(1),coords(2))=0; + % delete(h(i)); + % end + %end + end + end + udata.poly_h=[]; + udata.coords=[]; + set(udata.fig_h,'UserData',udata); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function remove_selpatches + +udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'),'UserData'); +h=findobj(get(udata.plane_h,'Children'),'Tag','SEL_PATCH'); +for i=1:length(h) + if inpolygon(mean(get(h(i),'XData')),mean(get(h(i),'YData')),... + udata.coords(:,2),udata.coords(:,1)); + coords=[floor(mean(get(h(i),'YData')))... + floor(mean(get(h(i),'XData')))]; + udata.mat(coords(1),coords(2))=0; + delete(h(i)); + end +end + +set(udata.fig_h,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [n,names,classes]=class2num(class) + +names = {}; +classes = zeros(length(class),1); +for i=1:length(class) + if ~isempty(class{i}), + a = find(strcmp(class{i},names)); + if isempty(a), + names=cat(1,names,class(i)); + classes(i) = length(names); + else + classes(i) = a; + end + end +end +n=length(names); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function h=find_patch(a_h) + +h=[]; + +tags={'planeC','planeU','planePie','planeBar','planePlot'}; + +for i=1:5 + if ~isempty(findobj(get(a_h,'Children'),'Tag',tags{i})) + h=findobj(get(gca,'Children'),'Tag',tags{i}); + if length(h) > 1 + h=h(1); + end + return; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function draw_classes + +udata=get(findobj(get(0,'Children'),'Tag','SELECT_GUI'), ... + 'UserData'); +figure(get(udata.plane_h,'Parent')) +subplot(udata.plane_h); + +colors=zeros(prod(udata.msize),3)+NaN; +c_map=jet(length(udata.c_names)); +inds = find(udata.mat); +for i=1:length(inds), + colors(inds(i),:) = c_map(udata.mat(inds(i)),:); +end + +if strcmp(udata.type,'planePlot'), + + set(udata.patch_h,'FaceVertexCData',colors); + set(udata.fig_h,'UserData',udata); + +else + + hold on + co = som_vis_coords(udata.lattice,udata.msize); + if any(strcmp(get(udata.patch_h,'Tag'),{'planeC','planeU'})) + p=0.7*vis_patch(udata.lattice); + else + [x,y]=pol2cart(0:0.1:2*pi,0.5); + p=[x';0.5]*0.7; + p(:,2)=[y';0]*0.7; + end + for i=1:length(inds), + udat.patch_h=udata.patch_h; + h=patch(p(:,1)+co(inds(i),1),p(:,2)+co(inds(i),2),... + colors(inds(i),:),... + 'EdgeColor','black',... + 'ButtonDownFcn','som_select([],[],''click'')', ... + 'Tag','SEL_PATCH',... + 'UserData',udat); + end + +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_seqtrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,557 @@ +function [sMap, sTrain] = som_seqtrain(sMap, D, varargin) + +%SOM_SEQTRAIN Use sequential algorithm to train the Self-Organizing Map. +% +% [sM,sT] = som_seqtrain(sM, D, [[argID,] value, ...]) +% +% sM = som_seqtrain(sM,D); +% sM = som_seqtrain(sM,sD,'alpha_type','power','tracking',3); +% [M,sT] = som_seqtrain(M,D,'ep','trainlen',10,'inv','hexa'); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map struct, the trained and updated map is returned +% (matrix) codebook matrix of a self-organizing map +% size munits x dim or msize(1) x ... x msize(k) x dim +% The trained map codebook is returned. +% D (struct) training data; data struct +% (matrix) training data, size dlen x dim +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% sT (struct) learning parameters used during the training +% +% Here are the valid argument IDs and corresponding values. The values which +% are unambiguous (marked with '*') can be given without the preceeding argID. +% 'mask' (vector) BMU search mask, size dim x 1 +% 'msize' (vector) map size +% 'radius' (vector) neighborhood radiuses, length 1, 2 or trainlen +% 'radius_ini' (scalar) initial training radius +% 'radius_fin' (scalar) final training radius +% 'alpha' (vector) learning rates, length trainlen +% 'alpha_ini' (scalar) initial learning rate +% 'tracking' (scalar) tracking level, 0-3 +% 'trainlen' (scalar) training length +% 'trainlen_type' *(string) is the given trainlen 'samples' or 'epochs' +% 'train' *(struct) train struct, parameters for training +% 'sTrain','som_train ' = 'train' +% 'alpha_type' *(string) learning rate function, 'inv', 'linear' or 'power' +% 'sample_order'*(string) order of samples: 'random' or 'ordered' +% 'neigh' *(string) neighborhood function, 'gaussian', 'cutgauss', +% 'ep' or 'bubble' +% 'topol' *(struct) topology struct +% 'som_topol','sTopo l' = 'topol' +% 'lattice' *(string) map lattice, 'hexa' or 'rect' +% 'shape' *(string) map shape, 'sheet', 'cyl' or 'toroid' +% +% For more help, try 'type som_seqtrain' or check out online documentation. +% See also SOM_MAKE, SOM_BATCHTRAIN, SOM_TRAIN_STRUCT. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_seqtrain +% +% PURPOSE +% +% Trains a Self-Organizing Map using the sequential algorithm. +% +% SYNTAX +% +% sM = som_seqtrain(sM,D); +% sM = som_seqtrain(sM,sD); +% sM = som_seqtrain(...,'argID',value,...); +% sM = som_seqtrain(...,value,...); +% [sM,sT] = som_seqtrain(M,D,...); +% +% DESCRIPTION +% +% Trains the given SOM (sM or M above) with the given training data +% (sD or D) using sequential SOM training algorithm. If no optional +% arguments (argID, value) are given, a default training is done, the +% parameters are obtained from SOM_TRAIN_STRUCT function. Using +% optional arguments the training parameters can be specified. Returns +% the trained and updated SOM and a train struct which contains +% information on the training. +% +% REFERENCES +% +% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, +% Berlin, 1995, pp. 78-82. +% Kohonen, T., "Clustering, Taxonomy, and Topological Maps of +% Patterns", International Conference on Pattern Recognition +% (ICPR), 1982, pp. 114-128. +% Kohonen, T., "Self-Organized formation of topologically correct +% feature maps", Biological Cybernetics 43, 1982, pp. 59-69. +% +% REQUIRED INPUT ARGUMENTS +% +% sM The map to be trained. +% (struct) map struct +% (matrix) codebook matrix (field .data of map struct) +% Size is either [munits dim], in which case the map grid +% dimensions (msize) should be specified with optional arguments, +% or [msize(1) ... msize(k) dim] in which case the map +% grid dimensions are taken from the size of the matrix. +% Lattice, by default, is 'rect' and shape 'sheet'. +% D Training data. +% (struct) data struct +% (matrix) data matrix, size [dlen dim] +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is +% used. The valid IDs and corresponding values are listed below. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% +% 'mask' (vector) BMU search mask, size dim x 1. Default is +% the one in sM (field '.mask') or a vector of +% ones if only a codebook matrix was given. +% 'msize' (vector) map grid dimensions. Default is the one +% in sM (field sM.topol.msize) or +% 'si = size(sM); msize = si(1:end-1);' +% if only a codebook matrix was given. +% 'radius' (vector) neighborhood radius +% length = 1: radius_ini = radius +% length = 2: [radius_ini radius_fin] = radius +% length > 2: the vector given neighborhood +% radius for each step separately +% trainlen = length(radius) +% 'radius_ini' (scalar) initial training radius +% 'radius_fin' (scalar) final training radius +% 'alpha' (vector) learning rate +% length = 1: alpha_ini = alpha +% length > 1: the vector gives learning rate +% for each step separately +% trainlen is set to length(alpha) +% alpha_type is set to 'user defined' +% 'alpha_ini' (scalar) initial learning rate +% 'tracking' (scalar) tracking level: 0, 1 (default), 2 or 3 +% 0 - estimate time +% 1 - track time and quantization error +% 2 - plot quantization error +% 3 - plot quantization error and two first +% components +% 'trainlen' (scalar) training length (see also 'tlen_type') +% 'trainlen_type' *(string) is the trainlen argument given in 'epochs' +% or in 'samples'. Default is 'epochs'. +% 'sample_order'*(string) is the sample order 'random' (which is the +% the default) or 'ordered' in which case +% samples are taken in the order in which they +% appear in the data set +% 'train' *(struct) train struct, parameters for training. +% Default parameters, unless specified, +% are acquired using SOM_TRAIN_STRUCT (this +% also applies for 'trainlen', 'alpha_type', +% 'alpha_ini', 'radius_ini' and 'radius_fin'). +% 'sTrain', 'som_train' (struct) = 'train' +% 'neigh' *(string) The used neighborhood function. Default is +% the one in sM (field '.neigh') or 'gaussian' +% if only a codebook matrix was given. Other +% possible values is 'cutgauss', 'ep' and 'bubble'. +% 'topol' *(struct) topology of the map. Default is the one +% in sM (field '.topol'). +% 'sTopol', 'som_topol' (struct) = 'topol' +% 'alpha_type'*(string) learning rate function, 'inv', 'linear' or 'power' +% 'lattice' *(string) map lattice. Default is the one in sM +% (field sM.topol.lattice) or 'rect' +% if only a codebook matrix was given. +% 'shape' *(string) map shape. Default is the one in sM +% (field sM.topol.shape) or 'sheet' +% if only a codebook matrix was given. +% +% OUTPUT ARGUMENTS +% +% sM the trained map +% (struct) if a map struct was given as input argument, a +% map struct is also returned. The current training +% is added to the training history (sM.trainhist). +% The 'neigh' and 'mask' fields of the map struct +% are updated to match those of the training. +% (matrix) if a matrix was given as input argument, a matrix +% is also returned with the same size as the input +% argument. +% sT (struct) train struct; information of the accomplished training +% +% EXAMPLES +% +% Simplest case: +% sM = som_seqtrain(sM,D); +% sM = som_seqtrain(sM,sD); +% +% To change the tracking level, 'tracking' argument is specified: +% sM = som_seqtrain(sM,D,'tracking',3); +% +% The change training parameters, the optional arguments 'train', +% 'neigh','mask','trainlen','radius','radius_ini', 'radius_fin', +% 'alpha', 'alpha_type' and 'alpha_ini' are used. +% sM = som_seqtrain(sM,D,'neigh','cutgauss','trainlen',10,'radius_fin',0); +% +% Another way to specify training parameters is to create a train struct: +% sTrain = som_train_struct(sM,'dlen',size(D,1),'algorithm','seq'); +% sTrain = som_set(sTrain,'neigh','cutgauss'); +% sM = som_seqtrain(sM,D,sTrain); +% +% By default the neighborhood radius goes linearly from radius_ini to +% radius_fin. If you want to change this, you can use the 'radius' argument +% to specify the neighborhood radius for each step separately: +% sM = som_seqtrain(sM,D,'radius',[5 3 1 1 1 1 0.5 0.5 0.5]); +% +% By default the learning rate (alpha) goes from the alpha_ini to 0 +% along the function defined by alpha_type. If you want to change this, +% you can use the 'alpha' argument to specify the learning rate +% for each step separately: +% alpha = 0.2*(1 - log([1:100])); +% sM = som_seqtrain(sM,D,'alpha',alpha); +% +% You don't necessarily have to use the map struct, but you can operate +% directly with codebook matrices. However, in this case you have to +% specify the topology of the map in the optional arguments. The +% following commads are identical (M is originally a 200 x dim sized matrix): +% M = som_seqtrain(M,D,'msize',[20 10],'lattice','hexa','shape','cyl'); +% +% M = som_seqtrain(M,D,'msize',[20 10],'hexa','cyl'); +% +% sT= som_set('som_topol','msize',[20 10],'lattice','hexa','shape','cyl'); +% M = som_seqtrain(M,D,sT); +% +% M = reshape(M,[20 10 dim]); +% M = som_seqtrain(M,D,'hexa','cyl'); +% +% The som_seqtrain also returns a train struct with information on the +% accomplished training. This is the same one as is added to the end of the +% trainhist field of map struct, in case a map struct is given. +% [M,sTrain] = som_seqtrain(M,D,'msize',[20 10]); +% +% [sM,sTrain] = som_seqtrain(sM,D); % sM.trainhist{end}==sTrain +% +% SEE ALSO +% +% som_make Initialize and train a SOM using default parameters. +% som_batchtrain Train SOM with batch algorithm. +% som_train_struct Determine default training parameters. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 220997 +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +error(nargchk(2, Inf, nargin)); % check the number of input arguments + +% map +struct_mode = isstruct(sMap); +if struct_mode, + sTopol = sMap.topol; +else + orig_size = size(sMap); + if ndims(sMap) > 2, + si = size(sMap); dim = si(end); msize = si(1:end-1); + M = reshape(sMap,[prod(msize) dim]); + else + msize = [orig_size(1) 1]; + dim = orig_size(2); + end + sMap = som_map_struct(dim,'msize',msize); + sTopol = sMap.topol; +end +[munits dim] = size(sMap.codebook); + +% data +if isstruct(D), + data_name = D.name; + D = D.data; +else + data_name = inputname(2); +end +D = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data +[dlen ddim] = size(D); % check input dimension +if dim ~= ddim, error('Map and data input space dimensions disagree.'); end + +% varargin +sTrain = som_set('som_train','algorithm','seq','neigh', ... + sMap.neigh,'mask',sMap.mask,'data_name',data_name); +radius = []; +alpha = []; +tracking = 1; +sample_order_type = 'random'; +tlen_type = 'epochs'; + +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'msize', i=i+1; sTopol.msize = varargin{i}; + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case 'shape', i=i+1; sTopol.shape = varargin{i}; + case 'mask', i=i+1; sTrain.mask = varargin{i}; + case 'neigh', i=i+1; sTrain.neigh = varargin{i}; + case 'trainlen', i=i+1; sTrain.trainlen = varargin{i}; + case 'trainlen_type', i=i+1; tlen_type = varargin{i}; + case 'tracking', i=i+1; tracking = varargin{i}; + case 'sample_order', i=i+1; sample_order_type = varargin{i}; + case 'radius_ini', i=i+1; sTrain.radius_ini = varargin{i}; + case 'radius_fin', i=i+1; sTrain.radius_fin = varargin{i}; + case 'radius', + i=i+1; + l = length(varargin{i}); + if l==1, + sTrain.radius_ini = varargin{i}; + else + sTrain.radius_ini = varargin{i}(1); + sTrain.radius_fin = varargin{i}(end); + if l>2, radius = varargin{i}; tlen_type = 'samples'; end + end + case 'alpha_type', i=i+1; sTrain.alpha_type = varargin{i}; + case 'alpha_ini', i=i+1; sTrain.alpha_ini = varargin{i}; + case 'alpha', + i=i+1; + sTrain.alpha_ini = varargin{i}(1); + if length(varargin{i})>1, + alpha = varargin{i}; tlen_type = 'samples'; + sTrain.alpha_type = 'user defined'; + end + case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i}; + case {'topol','sTopol','som_topol'}, + i=i+1; + sTopol = varargin{i}; + if prod(sTopol.msize) ~= munits, + error('Given map grid size does not match the codebook size.'); + end + % unambiguous values + case {'inv','linear','power'}, sTrain.alpha_type = varargin{i}; + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; + case {'gaussian','cutgauss','ep','bubble'}, sTrain.neigh = varargin{i}; + case {'epochs','samples'}, tlen_type = varargin{i}; + case {'random', 'ordered'}, sample_order_type = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', + sTopol = varargin{i}; + if prod(sTopol.msize) ~= munits, + error('Given map grid size does not match the codebook size.'); + end + case 'som_train', sTrain = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_seqtrain) Ignoring invalid argument #' num2str(i+2)]); + end + i = i+1; +end + +% training length +if ~isempty(radius) | ~isempty(alpha), + lr = length(radius); + la = length(alpha); + if lr>2 | la>1, + tlen_type = 'samples'; + if lr> 2 & la<=1, sTrain.trainlen = lr; + elseif lr<=2 & la> 1, sTrain.trainlen = la; + elseif lr==la, sTrain.trainlen = la; + else + error('Mismatch between radius and learning rate vector lengths.') + end + end +end +if strcmp(tlen_type,'samples'), sTrain.trainlen = sTrain.trainlen/dlen; end + +% check topology +if struct_mode, + if ~strcmp(sTopol.lattice,sMap.topol.lattice) | ... + ~strcmp(sTopol.shape,sMap.topol.shape) | ... + any(sTopol.msize ~= sMap.topol.msize), + warning('Changing the original map topology.'); + end +end +sMap.topol = sTopol; + +% complement the training struct +sTrain = som_train_struct(sTrain,sMap,'dlen',dlen); +if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize + +M = sMap.codebook; +mask = sTrain.mask; +trainlen = sTrain.trainlen*dlen; + +% neighborhood radius +if length(radius)>2, + radius_type = 'user defined'; +else + radius = [sTrain.radius_ini sTrain.radius_fin]; + rini = radius(1); + rstep = (radius(end)-radius(1))/(trainlen-1); + radius_type = 'linear'; +end + +% learning rate +if length(alpha)>1, + sTrain.alpha_type ='user defined'; + if length(alpha) ~= trainlen, + error('Trainlen and length of neighborhood radius vector do not match.') + end + if any(isnan(alpha)), + error('NaN is an illegal learning rate.') + end +else + if isempty(alpha), alpha = sTrain.alpha_ini; end + if strcmp(sTrain.alpha_type,'inv'), + % alpha(t) = a / (t+b), where a and b are chosen suitably + % below, they are chosen so that alpha_fin = alpha_ini/100 + b = (trainlen - 1) / (100 - 1); + a = b * alpha; + end +end + +% initialize random number generator +rand('state',sum(100*clock)); + +% distance between map units in the output space +% Since in the case of gaussian and ep neighborhood functions, the +% equations utilize squares of the unit distances and in bubble case +% it doesn't matter which is used, the unitdistances and neighborhood +% radiuses are squared. +Ud = som_unit_dists(sTopol).^2; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +update_step = 100; +mu_x_1 = ones(munits,1); +samples = ones(update_step,1); +r = samples; +alfa = samples; + +qe = 0; +start = clock; +if tracking > 0, % initialize tracking + track_table = zeros(update_step,1); + qe = zeros(floor(trainlen/update_step),1); +end + +for t = 1:trainlen, + + % Every update_step, new values for sample indeces, neighborhood + % radius and learning rate are calculated. This could be done + % every step, but this way it is more efficient. Or this could + % be done all at once outside the loop, but it would require much + % more memory. + ind = rem(t,update_step); if ind==0, ind = update_step; end + if ind==1, + steps = [t:min(trainlen,t+update_step-1)]; + % sample order + switch sample_order_type, + case 'ordered', samples = rem(steps,dlen)+1; + case 'random', samples = ceil(dlen*rand(update_step,1)+eps); + end + + % neighborhood radius + switch radius_type, + case 'linear', r = rini+(steps-1)*rstep; + case 'user defined', r = radius(steps); + end + r=r.^2; % squared radius (see notes about Ud above) + r(r==0) = eps; % zero radius might cause div-by-zero error + + % learning rate + switch sTrain.alpha_type, + case 'linear', alfa = (1-steps/trainlen)*alpha; + case 'inv', alfa = a ./ (b + steps-1); + case 'power', alfa = alpha * (0.005/alpha).^((steps-1)/trainlen); + case 'user defined', alfa = alpha(steps); + end + end + + % find BMU + x = D(samples(ind),:); % pick one sample vector + known = ~isnan(x); % its known components + Dx = M(:,known) - x(mu_x_1,known); % each map unit minus the vector + [qerr bmu] = min((Dx.^2)*mask(known)); % minimum distance(^2) and the BMU + + % tracking + if tracking>0, + track_table(ind) = sqrt(qerr); + if ind==update_step, + n = ceil(t/update_step); + qe(n) = mean(track_table); + trackplot(M,D,tracking,start,n,qe); + end + end + + % neighborhood & learning rate + % notice that the elements Ud and radius have been squared! + % (see notes about Ud above) + switch sTrain.neigh, + case 'bubble', h = (Ud(:,bmu)<=r(ind)); + case 'gaussian', h = exp(-Ud(:,bmu)/(2*r(ind))); + case 'cutgauss', h = exp(-Ud(:,bmu)/(2*r(ind))) .* (Ud(:,bmu)<=r(ind)); + case 'ep', h = (1-Ud(:,bmu)/r(ind)) .* (Ud(:,bmu)<=r(ind)); + end + h = h*alfa(ind); + + % update M + M(:,known) = M(:,known) - h(:,ones(sum(known),1)).*Dx; + +end; % for t = 1:trainlen + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Build / clean up the return arguments + +if tracking, fprintf(1,'\n'); end + +% update structures +sTrain = som_set(sTrain,'time',datestr(now,0)); +if struct_mode, + sMap = som_set(sMap,'codebook',M,'mask',sTrain.mask,'neigh',sTrain.neigh); + tl = length(sMap.trainhist); + sMap.trainhist(tl+1) = sTrain; +else + sMap = reshape(M,orig_size); +end + +return; + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +%%%%%%%% +function [] = trackplot(M,D,tracking,start,n,qe) + + l = length(qe); + elap_t = etime(clock,start); + tot_t = elap_t*l/n; + fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t) + switch tracking + case 1, + case 2, + plot(1:n,qe(1:n),(n+1):l,qe((n+1):l)) + title('Quantization errors for latest samples') + drawnow + otherwise, + subplot(2,1,1), plot(1:n,qe(1:n),(n+1):l,qe((n+1):l)) + title('Quantization error for latest samples'); + subplot(2,1,2), plot(M(:,1),M(:,2),'ro',D(:,1),D(:,2),'b.'); + title('First two components of map units (o) and data vectors (+)'); + drawnow + end + % end of trackplot +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_set.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,777 @@ +function [sS, ok, msgs] = som_set(sS, varargin) + +%SOM_SET Create and check SOM Toolbox structs, give values to their fields. +% +% [sS, ok, msgs] = som_set(sS, [field, contents, ...]) +% +% sM = som_set(sM,'name','SOM#1.1'); +% [dummy,ok,msgs] = som_set(sData); +% sT = som_set('som_topol','msize',[10 10],'lattice','hexa'); +% [sTrain,ok] = som_set(sTrain,'algorithm','lininit'); +% [sN,ok,msgs] = som_set('som_norm'); +% +% Input and output arguments ([]'s are optional): +% sS the target struct +% (struct) a SOM Toolbox structure (not visualization struct) +% (string) structure identifier (see below) +% the updated/created structure is returned +% [field, (string) field to be given value to (see below) +% contents] (varies) the contents for the field +% +% ok (vector) status for each field-contents pair (1=ok) +% msgs (cellstr) status string for each field-contents pair (''=ok) +% +% There can be arbitrarily many field-contents pairs. If there +% are _no_ field-content pairs, and the first argument is a struct, +% the fields of the struct are checked for validity. +% +% Valid struct and corresponding field identifiers: +% 'som_map' : 'codebook', 'labels', 'mask', 'neigh', 'name', +% 'topol', 'msize, 'lattice', 'shape', +% 'trainhist', 'comp_names', 'comp_norm', +% 'som_data' : 'data', 'labels', 'name', 'comp_names', 'comp_norm', +% 'label_names' +% 'som_topol' : 'msize', 'lattice', 'shape' +% 'som_norm' : 'method', 'params', 'status' +% 'som_train' : 'algorithm', 'data_name', 'mask', 'neigh', +% 'radius_ini', 'radius_fin', 'alpha_ini', 'alpha_type', +% 'trainlen', 'time' +% 'som_grid' : 'lattice', 'shape', 'msize', 'coord', +% 'line', 'linecolor', 'linewidth', +% 'marker', 'markersize', 'markercolor', 'surf', +% 'label', 'labelcolor', 'labelsize' +% checking given values has not been implemented yet! +% +% For more help, try 'type som_set' or check out online documentation. +% See also SOM_INFO, SOM_MAP_STRUCT, SOM_DATA_STRUCT, SOM_VS1TO2. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_set +% +% PURPOSE +% +% Create and set values for fields of SOM Toolbox structs (except +% visualization struct). Can also be used to check the validity of structs. +% +% SYNTAX +% +% sMap = som_set('som_map'); +% sData = som_set(sData); +% sNorm = som_set(...,'field',contents,...); +% [sTopol,ok] = som_set(sTopol,...); +% [sTrain,ok,msgs] = som_set('som_train',...); +% +% DESCRIPTION +% +% The function is used to create and set values for fields of SOM +% Toolbox structs, except visualization structs. The given values are +% first checked for validity, and if they are not valid, an error +% message is returned. The function can also be used to check the +% validity of all the fields of the struct by supplying a struct as +% the first and only argument. +% +% NOTE: Using SOM_SET to create structures does _not_ guarantee that the +% structs are valid (try e.g. sM = som_set('som_map'); som_set(sM)). The +% initial values that the function gives to the fields of the structs are +% typically invalid. It is recommended that when creating map or data +% structs, the corresponding functions SOM_MAP_STRUCT and SOM_DATA_STRUCT +% are used instead of SOM_SET. However, when giving values for the fields, +% SOM_SET tries to guarantee that the values are valid. +% +% If a string is given as the first argument, the corresponding +% structure is first created and the field-content pairs are then +% applied to it. +% +% There can be arbitrarily many field-contents pairs. The pairs +% are processed sequentially one pair at a time. For each pair, +% the validity of the contents is checked and the corresponding +% items in the returned 'ok'-vector and 'msgs'-cellstring are set. +% - if the contents is ok, the status is set to 1 and message to '' +% - if the contents is suspicious, status is set to 1, but a +% message is produced +% - if the contents is invalid, status is set to 0 and an error +% message is produced. The contents are _not_ given to the field. +% If there is only one output argument, the status and messages +% for each pair are printed to standard output. +% +% The different field-contents pairs have no effect on each other. +% If a field is given a value multiple times, the last valid one +% stays in effect. +% +% In some cases, the order of the given fields is significant. +% For example in the case of 'som_map', the validity of some fields, +% like '.comp_names', depends on the input space dimension, which is +% checked from the '.data' field (dim = size(sD.data,2) to be specific). +% Therefore, the '.data' field (or '.codebook' field in case of map +% struct) should always be given a value first. Below is a list of +% this kind of dependancies: +% +% som_map: 'comp_names', 'comp_norm', 'msize', 'topol.msize', +% 'labels' and 'mask' depend on 'codebook' +% new value for 'codebook' should have equal size to the old +% one (unless the old one was empty) +% som_data: 'comp_names' and 'comp_norm' depend on 'data' +% new value for 'data' should have equal dimension (size(data,2)) +% as the old one (unless the old one was empty) +% +% KNOWN BUGS +% +% Checking the values given to som_grid struct has not been +% implemented. Use SOM_GRID function to give the values. +% +% REQUIRED INPUT ARGUMENTS +% +% sS The struct. +% (struct) A SOM Toolbox struct. +% (string) Identifier of a SOM Toolbox struct: 'som_map', +% 'som_data', 'som_topol', 'som_norm' or 'som_train' +% +% OPTIONAL INPUT ARGUMENTS +% +% field (string) Field identifier string (see below). +% contents (varies) Value for the field (see below). +% +% Below is the list of valid field identifiers for the different +% SOM Toolbox structs. +% +% 'som_map' (map struct) +% 'codebook' : matrix, size [munits, dim] +% 'labels' : cell array of strings, +% size [munits, maximum_number_of_labels] +% 'topol' : topology struct (prod(topol.msize)=munits) +% 'mask' : vector, size [dim, 1] +% 'neigh' : string ('gaussian' or 'cutgauss' or 'bubble' or 'ep') +% 'trainhist' : struct array of train structs +% 'name' : string +% 'comp_names' : cellstr, size [dim, 1], e.g. {'c1','c2','c3'} +% 'comp_norm' : cell array, size [dim, 1], of cell arrays +% of normalization structs +% Also the following can be used (although they are fields +% of the topology struct) +% 'msize' : vector (prod(msize)=munits) +% 'lattice' : string ('rect' or 'hexa') +% 'shape' : string ('sheet' or 'cyl' or 'toroid') +% +% 'som_data' (data struct) +% 'data' : matrix, size [dlen, dim] +% 'name' : string +% 'labels' : cell array of strings, +% size [dlen, m] +% 'comp_names' : cellstr, size [dim, 1], e.g. {'c1','c2','c3'} +% 'comp_norm' : cell array, size [dim, 1], of cell arrays +% of normalization structs +% 'label_names' : cellstr, size [m, 1] +% +% 'som_topol' (topology struct) +% 'msize' : vector +% 'lattice' : string ('rect' or 'hexa') +% 'shape' : string ('sheet' or 'cyl' or 'toroid') +% +% 'som_norm' (normalization struct) +% 'method' : string +% 'params' : varies +% 'status' : string ('done' or 'undone' or 'uninit') +% +% 'som_train' (train struct) +% 'algorithm' : string ('seq' or 'batch' or 'lininit' or 'randinit') +% 'data_name' : string +% 'mask' : vector, size [dim, 1] +% 'neigh' : string ('gaussian' or 'cutgauss' or 'bubble' or 'ep') +% 'radius_ini' : scalar +% 'radius_fin' : scalar +% 'alpha_ini' : scalar +% 'alpha_type' : string ('linear' or 'inv' or 'power') +% 'trainlen' : scalar +% 'time' : string +% +% 'som_grid' (grid struct) : checking the values has not been implemented yet! +% 'lattice' : string ('rect' or 'hexa') or +% (sparce) matrix, size munits x munits +% 'shape' : string ('sheet' or 'cyl' or 'toroid') +% 'msize' : vector, size 1x2 +% 'coord' : matrix, size munits x 2 or munits x 3 +% 'line' : string (linespec, e.g. '-', or 'none') +% 'linecolor' : RGB triple or string (colorspec, e.g. 'k') or +% munits x munits x 3 (sparce) matrix or cell +% array of RGB triples +% 'linewidth' : scalar or munits x munits (sparce) matrix +% 'marker' : string (markerspec, e.g. 'o', or 'none') or +% munits x 1 cell or char array of these +% 'markersize' : scalar or munits x 1 vector +% 'markercolor' : RGB triple or string (colorspec, e.g. 'k') +% 'surf' : [], munits x 1 or munits x 3 matrix of RGB triples +% 'label' : [] or munits x 1 char array or +% munits x l cell array of strings +% 'labelcolor' : RGB triple or string (colorspec, e.g. 'g' or 'none') +% 'labelsize' : scalar +% +% OUTPUT ARGUMENTS +% +% sS (struct) the created / updated struct +% ok (vector) length = number of field-contents pairs, gives +% validity status for each pair (0=invalid, 1 otherwise) +% msgs (cellstr) length = number of field-contents pairs, gives +% error/warning message for each pair ('' if ok) +% +% EXAMPLES +% +% To create a struct: +% sM = som_set('som_map'); +% sD = som_set('som_data'); +% sTo = som_set('som_topol'); +% sTr = som_set('som_train'); +% sN = som_set('som_norm'); +% sG = som_set('som_grid'); +% +% To check the the contents of a struct: +% som_set(sS); +% [dummy,ok] = som_set(sS); +% [dummy,ok,msgs] = som_set(sS); +% +% To give values to fields: +% sTo = som_set(sTo,'msize',[10 10],'lattice','hexa','shape','toroid'); +% sM = som_set('som_map','codebook',rand(100,4),'topol',sTo); +% +% SEE ALSO +% +% som_info Prints information the given struct. +% som_map_struct Create map struct. +% som_data_struct Create data struct. +% som_topol_struct Create topology struct. +% som_train_struct Create training struct. +% som_grid Create and visualize grid struct. +% som_vs1to2 Conversion from version 1.0 structs to 2.0. +% som_vs2to1 Conversion from version 2.0 structs to 1.0. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 101199 130300 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% create struct if necessary + +if ischar(sS), + switch sS + case 'som_map', + sS=struct('type', 'som_map', ... + 'codebook', [], ... + 'topol', som_set('som_topol'), ... + 'labels', cell(1), ... + 'neigh', 'gaussian', ... + 'mask', [], ... + 'trainhist', cell(1), ... + 'name', '',... + 'comp_names', {''}, ... + 'comp_norm', cell(1)); + case 'som_data', + sS=struct('type', 'som_data', ... + 'data', [], ... + 'labels', cell(1), ... + 'name', '', ... + 'comp_names', {''}, ... + 'comp_norm', cell(1), ... + 'label_names', []); + case 'som_topol', + sS=struct('type', 'som_topol', ... + 'msize', 0, ... + 'lattice', 'hexa', ... + 'shape', 'sheet'); + case 'som_train', + sS=struct('type', 'som_train', ... + 'algorithm', '', ... + 'data_name', '', ... + 'neigh', 'gaussian', ... + 'mask', [], ... + 'radius_ini', NaN, ... + 'radius_fin', NaN, ... + 'alpha_ini', NaN, ... + 'alpha_type', 'inv', ... + 'trainlen', NaN, ... + 'time', ''); + case 'som_norm', + sS=struct('type', 'som_norm', ... + 'method', 'var', ... + 'params', [], ... + 'status', 'uninit'); + case 'som_grid', + sS=struct('type','som_grid',... + 'lattice','hexa',... + 'shape','sheet',... + 'msize',[1 1],... + 'coord',[],... + 'line','-',... + 'linecolor',[.9 .9 .9],... + 'linewidth',0.5,... + 'marker','o',... + 'markersize',6,... + 'markercolor','k',... + 'surf',[],... + 'label',[],... + 'labelcolor','g',... + 'labelsize',12); + otherwise + ok=0; msgs = {['Unrecognized struct type: ' sS]}; sS = []; + return; + end + +elseif isstruct(sS) & length(varargin)==0, + + % check all fields + fields = fieldnames(sS); + if ~any(strcmp('type',fields)), + error('The struct has no ''type'' field.'); + end + k = 0; + for i=1:length(fields), + contents = getfield(sS,fields{i}); + if ~strcmp(fields{i},'type'), + varargin{k+1} = fields{i}; + varargin{k+2} = contents; + k = k + 2; + else + if ~any(strcmp(contents, ... + {'som_map','som_data','som_topol','som_train','som_norm'})), + error(['Unknown struct type: ' contents]); + end + end + end + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% set field values + +p = ceil(length(varargin)/2); +ok = ones(p,1); +msgs = cell(p,1); + +for i=1:p, + field = varargin{2*i-1}; + content = varargin{2*i}; + msg = ''; + isok = 0; + + si = size(content); + isscalar = (prod(si)==1); + isvector = (sum(si>1)==1); + isrowvector = (isvector & si(1)==1); + if isnumeric(content), + iscomplete = all(~isnan(content(:))); + ispositive = all(content(:)>0); + isinteger = all(content(:)==ceil(content(:))); + isrgb = all(content(:)>=0 & content(:)<=1) & size(content,2)==3; + end + + switch sS.type, + case 'som_map', + [munits dim] = size(sS.codebook); + switch field, + case 'codebook', + if ~isnumeric(content), + msg = '''codebook'' should be a numeric matrix'; + elseif size(content) ~= size(sS.codebook) & ~isempty(sS.codebook), + msg = 'New ''codebook'' must be equal in size to the old one.'; + elseif ~iscomplete, + msg = 'Map codebook must not contain NaN''s.'; + else + sS.codebook = content; isok=1; + end + case 'labels', + if isempty(content), + sS.labels = cell(munits,1); isok = 1; + elseif size(content,1) ~= munits, + msg = 'Length of labels array must be equal to the number of map units.'; + elseif ~iscell(content) & ~ischar(content), + msg = '''labels'' must be a string array or a cell array/matrix.'; + else + isok = 1; + if ischar(content), content = cellstr(content); + elseif ~iscellstr(content), + l = prod(size(content)); + for j=1:l, + if ischar(content{j}), + if ~isempty(content{j}), + msg = 'Invalid ''labels'' array.'; + isok = 0; + break; + else + content{j} = ''; + end + end + end + end + if isok, sS.labels = content; end + end + case 'topol', + if ~isstruct(content), + msg = '''topol'' should be a topology struct.'; + elseif ~isfield(content,'msize') | ... + ~isfield(content,'lattice') | ... + ~isfield(content,'shape'), + msg = '''topol'' is not a valid topology struct.'; + elseif prod(content.msize) ~= munits, + msg = '''topol''.msize does not match the number of map units.'; + else + sS.topol = content; isok = 1; + end + case 'msize', + if ~isnumeric(content) | ~isvector | ~ispositive | ~isinteger, + msg = '''msize'' should be a vector with positive integer elements.'; + elseif prod(content) ~= munits, + msg = '''msize'' does not match the map size.'; + else + sS.topol.msize = content; isok = 1; + end + case 'lattice', + if ~ischar(content), + msg = '''lattice'' should be a string'; + elseif ~strcmp(content,'rect') & ~strcmp(content,'hexa'), + msg = ['Unknown lattice type: ' content]; + sS.topol.lattice = content; isok = 1; + else + sS.topol.lattice = content; isok = 1; + end + case 'shape', + if ~ischar(content), + msg = '''shape'' should be a string'; + elseif ~strcmp(content,'sheet') & ~strcmp(content,'cyl') & ... + ~strcmp(content,'toroid'), + msg = ['Unknown shape type:' content]; + sS.topol.shape = content; isok = 1; + else + sS.topol.shape = content; isok = 1; + end + case 'neigh', + if ~ischar(content), + msg = '''neigh'' should be a string'; + elseif ~strcmp(content,'gaussian') & ~strcmp(content,'ep') & ... + ~strcmp(content,'cutgauss') & ~strcmp(content,'bubble'), + msg = ['Unknown neighborhood function: ' content]; + sS.neigh = content; isok = 1; + else + sS.neigh = content; isok = 1; + end + case 'mask', + if size(content,1) == 1, content = content'; end + if ~isnumeric(content) | size(content) ~= [dim 1], + msg = '''mask'' should be a column vector (size dim x 1).'; + else + sS.mask = content; isok = 1; + end + case 'name', + if ~ischar(content), + msg = '''name'' should be a string.'; + else + sS.name = content; isok = 1; + end + case 'comp_names', + if ~iscell(content) & ~ischar(content), + msg = '''comp_names'' should be a cell string or a string array.'; + elseif length(content) ~= dim, + msg = 'Length of ''comp_names'' should be equal to dim.'; + else + if ischar(content), content = cellstr(content); end + if size(content,1)==1, content = content'; end + sS.comp_names = content; + isok = 1; + end + case 'comp_norm', + if ~iscell(content) & length(content)>0, + msg = '''comp_norm'' should be a cell array.'; + elseif length(content) ~= dim, + msg = 'Length of ''comp_norm'' should be equal to dim.'; + else + isok = 1; + for j=1:length(content), + if ~isempty(content{j}) & (~isfield(content{j}(1),'type') | ... + ~strcmp(content{j}(1).type,'som_norm')), + msg = 'Each cell in ''comp_norm'' should be either empty or type ''som_norm''.'; + isok = 0; + break; + end + end + if isok, sS.comp_norm = content; end + end + case 'trainhist', + if ~isstruct(content) & ~isempty(content), + msg = '''trainhist'' should be a struct array or empty.'; + else + isok = 1; + for j=1:length(content), + if ~isfield(content(j),'type') | ~strcmp(content(j).type,'som_train'), + msg = 'Each cell in ''trainhist'' should be of type ''som_train''.'; + isok = 0; + break; + end + end + if isok, sS.trainhist = content; end + end + otherwise, + msg = ['Invalid field for map struct: ' field]; + end + + case 'som_data', + [dlen dim] = size(sS.data); + switch field, + case 'data', + [dummy dim2] = size(content); + if prod(si)==0, + msg = '''data'' is empty'; + elseif ~isnumeric(content), + msg = '''data'' should be numeric matrix.'; + elseif dim ~= dim2 & ~isempty(sS.data), + msg = 'New ''data'' must have the same dimension as old one.'; + else + sS.data = content; isok = 1; + end + case 'labels', + if isempty(content), + sS.labels = cell(dlen,1); isok = 1; + elseif size(content,1) ~= dlen, + msg = 'Length of ''labels'' must be equal to the number of data vectors.'; + elseif ~iscell(content) & ~ischar(content), + msg = '''labels'' must be a string array or a cell array/matrix.'; + else + isok = 1; + if ischar(content), content = cellstr(content); + elseif ~iscellstr(content), + l = prod(size(content)); + for j=1:l, + if ~ischar(content{j}), + if ~isempty(content{j}), + msg = 'Invalid ''labels'' array.'; + isok = 0; j + break; + else + content{j} = ''; + end + end + end + end + if isok, sS.labels = content; end + end + case 'name', + if ~ischar(content), + msg = '''name'' should be a string.'; + else + sS.name = content; isok = 1; + end + case 'comp_names', + if ~iscell(content) & ~ischar(content), + msg = '''comp_names'' should be a cell string or a string array.'; + elseif length(content) ~= dim, + msg = 'Length of ''comp_names'' should be equal to dim.'; + else + if ischar(content), content = cellstr(content); end + if size(content,1)==1, content = content'; end + sS.comp_names = content; + isok = 1; + end + case 'comp_norm', + if ~iscell(content) & length(content)>0, + msg = '''comp_norm'' should be a cell array.'; + elseif length(content) ~= dim, + msg = 'Length of ''comp_norm'' should be equal to dim.'; + else + isok = 1; + for j=1:length(content), + if ~isempty(content{j}) & (~isfield(content{j}(1),'type') | ... + ~strcmp(content{j}(1).type,'som_norm')), + msg = 'Each cell in ''comp_norm'' should be either empty or type ''som_norm''.'; + isok = 0; + break; + end + end + if isok, sS.comp_norm = content; end + end + case 'label_names', + if ~iscell(content) & ~ischar(content) & ~isempty(content), + msg = ['''label_names'' should be a cell string, a string array or' ... + ' empty.']; + else + if ~isempty(content), + if ischar(content), content = cellstr(content); end + if size(content,1)==1, content = content'; end + end + sS.label_names = content; + isok = 1; + end + otherwise, + msg = ['Invalid field for data struct: ' field]; + end + + case 'som_topol', + switch field, + case 'msize', + if ~isnumeric(content) | ~isvector | ~ispositive | ~isinteger, + msg = '''msize'' should be a vector with positive integer elements.'; + else + sS.msize = content; isok=1; + end + case 'lattice', + if ~ischar(content), + msg = '''lattice'' should be a string'; + elseif ~strcmp(content,'rect') & ~strcmp(content,'hexa'), + msg = ['Unknown lattice type: ' content]; + sS.lattice = content; isok = 1; + else + sS.lattice = content; isok = 1; + end + case 'shape', + if ~ischar(content), + msg = '''shape'' should be a string'; + elseif ~strcmp(content,'sheet') & ~strcmp(content,'cyl') & ... + ~strcmp(content,'toroid'), + msg = ['Unknown shape type: ' content]; + sS.shape = content; isok = 1; + else + sS.shape = content; isok = 1; + end + otherwise, + msg = ['Invalid field for topology struct: ' field]; + end + + case 'som_train', + switch field, + case 'algorithm', + if ~ischar(content), + msg = '''algorithm'' should be a string.'; + else + sS.algorithm = content; isok = 1; + end + case 'data_name', + if ~ischar(content), + msg = '''data_name'' should be a string'; + else + sS.data_name = content; isok = 1; + end + case 'neigh', + if ~ischar(content), + msg = '''neigh'' should be a string'; + elseif ~isempty(content) & ~strcmp(content,'gaussian') & ~strcmp(content,'ep') & ... + ~strcmp(content,'cutgauss') & ~strcmp(content,'bubble'), + msg = ['Unknown neighborhood function: ' content]; + sS.neigh = content; isok = 1; + else + sS.neigh = content; isok = 1; + end + case 'mask', + if size(content,1) == 1, content = content'; end + dim = size(content,1); %[munits dim] = size(sS.data); + if ~isnumeric(content) | size(content) ~= [dim 1], + msg = '''mask'' should be a column vector (size dim x 1).'; + else + sS.mask = content; isok = 1; + end + case 'radius_ini', + if ~isnumeric(content) | ~isscalar, + msg = '''radius_ini'' should be a scalar.'; + else + sS.radius_ini = content; isok = 1; + end + case 'radius_fin', + if ~isnumeric(content) | ~isscalar, + msg = '''radius_fin'' should be a scalar.'; + else + sS.radius_fin = content; isok = 1; + end + case 'alpha_ini', + if ~isnumeric(content) | ~isscalar, + msg = '''alpha_ini'' should be a scalar.'; + else + sS.alpha_ini = content; isok = 1; + end + case 'alpha_type', + if ~ischar(content), + msg = '''alpha_type'' should be a string'; + elseif ~strcmp(content,'linear') & ~strcmp(content,'inv') & ... + ~strcmp(content,'power') & ~strcmp(content,'constant') & ~strcmp(content,''), + msg = ['Unknown alpha type: ' content]; + sS.alpha_type = content; isok = 1; + else + sS.alpha_type = content; isok = 1; + end + case 'trainlen', + if ~isnumeric(content) | ~isscalar, + msg = '''trainlen'' should be a scalar.'; + else + sS.trainlen = content; isok = 1; + end + case 'time', + if ~ischar(content), + msg = '''time'' should be a string'; + else + sS.time = content; isok = 1; + end + otherwise, + msg = ['Invalid field for train struct: ' field]; + end + + case 'som_norm', + switch field, + case 'method', + if ~ischar(field), + msg = '''method'' should be a string.'; + else + sS.method = content; isok = 1; + end + case 'params', + sS.params = content; isok = 1; + case 'status', + if ~ischar(content), + msg = '''status'' should be a string'; + elseif ~strcmp(content,'done') & ~strcmp(content,'undone') & ... + ~strcmp(content,'uninit'), + msg = ['Unknown status type: ' content]; + sS.status = content; isok = 1; + else + sS.status = content; isok = 1; + end + otherwise, + msg = ['Invalid field for normalization struct: ' field]; + end + + case 'som_grid', + if any(strcmp(field,{'lattice', 'shape', 'msize', 'coord',... + 'line', 'linecolor', 'linewidth', ... + 'marker', 'markersize', 'markercolor', 'surf', ... + 'label', 'labelcolor', 'labelsize'})), + warning('No checking done on field identifier or content.'); + sS = setfield(sS,field,content); + isok = 1; + else + msg = ['Invalid field for grid struct: ' field]; + end + + otherwise, + error('Unrecognized structure.'); + + end + + msgs{i} = msg; + ok(i) = isok; + +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% return + +if nargout < 2, + for i=1:p, + if ~isempty(msgs{i}), + if ~ok(i), fprintf(1,'[Error! '); + else fprintf(1,'[Notice '); + end + fprintf(1,'in setting %s] ',varargin{2*i-1}); + fprintf(1,'%s\n',msgs{i}); + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_show.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,815 @@ +function h=som_show(sMap, varargin) + +% SOM_SHOW Basic SOM visualizations: component planes, u-matrix etc. +% +% h = som_show(sMap, ['argID', value, ...]) +% +% som_show(sMap); +% som_show(sMap,'bar','none'); +% som_show(sMap,'comp',[1:3],'umat','all'); +% som_show(sMap,'comp',[1 2],'umat',{[1 2],'1,2 only'},'comp',[3:6]); +% som_show(sMap,'size',m,'bar','vert','edge','off'); +% +% Input and output arguments ([]'s are optional): +% sMap (struct) map struct +% [argID, (string) Additional parameters are given as argID, value +% value] (varies) pairs. See below for list of valid IDs and values. +% +% h (struct) struct with the following fields: +% .plane (vector) handles to the axes objecets (subplots) +% .colorbar (vector) handles to the colorbars. Colorbars for empty +% grids & RGB color planes do not exist: the +% value for them in the vector is -1. +% .label (vector) handles to the axis labels +% +% Here are the valid argument IDs and corresponding values. M is +% the number of map units +% 'comp' Which component planes to draw, title is +% the name of the component (from sMap.comp_names) +% (vector) a vector of component indices +% (string) 'all' (or '' or []) for all components +% 'compi' as 'comp' but uses interpolated shading +% 'umat' Show u-matrix calculated using specified +% components +% (vector) a vector of component indeces +% (string) 'all' (or '' or []) to use all components +% (cell) of form {v, str} uses v as the vector, and put +% str as title instead of the default 'U-matrix' +% 'umati' as 'umat' but uses interpolated shading of colors +% 'empty' (string) Make an empty plane using given string as title +% 'color' Set arbitrary unit colors explicitly +% (matrix) size Mx1 or Mx3, Mx1 matrix uses indexed +% coloring; Mx3 matrix (RGB triples as rows) +% defines fixed unit colors +% (cell) of from {color, str}. 'color' is the Mx1 +% or Mx3 RGB triple matrix and 'str' is title +% string +% 'colori' as 'color' but uses interpolated shading of colors +% 'norm' (string) 'n' or 'd': Whether to show normalized 'n' or +% denormalized 'd' data values on the +% colorbar. By default denormalized values are used. +% 'bar' (string) Colorbar direction: 'horiz', 'vert' (default) +% or 'none' +% 'size' size of the units +% (scalar) same size for each unit, default is 1 +% (vector) size Mx1, individual size for each unit +% 'edge' (string) Unit edges on component planes 'on' +% (default) or 'off' +% 'footnote' (string) Footnote string, sMap.name by default +% 'colormap' (matrix) user defined colormap +% 'subplots' (vector) size 1 x 2, the number of subplots in y- and +% and x-directions (as in SUBPLOT command) +% +% If identifiers 'comp', 'compi', 'umat', 'umati', 'color', 'colori' +% or 'empty' are not specified at all, e.g. som_show(sMap) or +% som_show(sMap,'bar','none'), the U-matrix and all component planes +% are shown. +% +% For more help, try 'type som_show' or check out online documentation. +% See also SOM_SHOW_ADD, SOM_SHOW_CLEAR, SOM_UMAT, SOM_CPLANE, SOM_GRID. + +%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_show +% +% PURPOSE +% +% Shows basic visualizations of SOM: component planes, unified distance +% matrices as well as empty planes and fixed color planes. +% +% SYNTAX +% +% h = som_show(sMap) +% h = som_show(sMap, 'argID', value, ...) +% +% DESCRIPTION +% +% This function is used for basic visualization of the SOM. Four +% kinds of SOM planes can be shown: +% +% 1. U-matrix (see SOM_UMAT) which shows clustering structure of +% the SOM. Either all or just part of the components can +% be used in calculating the U-matrix. +% 2. component planes: each component plane shows the values of +% one variable in each map unit +% 3. an empty plane which may be used as a base for, e.g., hit +% histogram visualization or labeling (see SOM_SHOW_ADD) +% 4. a fixed or indexed color representation for showing color coding or +% clustering +% +% The component planes and u-matrices may have colorbars showing the +% scale for the variable. The scale shows by default the values that +% variables have in the map struct. It may be changed to show the +% original data values (estimated by SOM_DENORMALIZE). In this case a +% small 'd' appears below the colorbar. The orientation of these +% colorbars may be changed, or they can be removed. +% +% By default the u-matrix - calculated using all variables - and all +% component planes are shown. This is achieved by giving command +% som_show(sMap) without any further arguments +% +% REQUIRED INPUT ARGUMENTS +% +% sMap (struct) Map to be shown. If only this argument is +% specified, the function draws first the u-matrix +% calculated using all the variables followed by all +% the component planes. +% +% OPTIONAL INPUT ARGUMENTS +% +% (M is the number of map units) +% +% Optional arguments must be given as 'argID',value -pairs +% +% 'comp' Defines the variabels to be shown as component planes. +% (vector) 1xN or Nx1 vector with integer positive numbers ranging +% from 1 to the number of variables in the map codebook +% (dim). This vector determines the variables to be show +% as component planes and their order. Note that the same +% component plane (the same variable index) is allowed to +% occur several times. +% (string) 'all' or '' or []. This uses all variables, that is, it's +% the same that using value [1:dim] where dim is the +% number of variables in the codebook. +% +% 'compi' Same as 'comp' but uses a Gouraud shaded color plane +% (made using SOM_GRID function) instead of the cell-like +% visualization of 'comp' (made using SOM_CPLANE). Note +% that the color interpolation doesn't work strictly +% correctly on 'hexa' grid, as it uses rectangular grid +% (see SURF). +% +% 'umat' Show U-matrix: value defines the variables to be used +% for calculating a u-matrix. +% (vector) as in 'comps'. However, multiple occurences of the +% same variable (same variable index) are ignored. +% (string) 'all' or '' or []. This uses all variables, that is, +% is the same as using value [1:dim] where dim is the +% number of variables in the codebook. +% (cell) of form {v, str} where v is a valid index vector for 'umat' +% (see above) and str is a string that is used as a title +% for the u-matrix instead of the default title +% 'U-matrix'. This may be useful if several u-matrices +% are shown in the same figure. +% +% 'umati' Same as 'umat' but uses shaded color plane (see 'compi'). +% +% 'empty' Show an empty plane (patch edges only) +% (string) value is used as title +% +% 'color' Define fixed RGB colors for the map units +% (matrix) a Mx3 matrix of RGB triples as rows +% (vector) a Mx1 vector of any values: sets indexed coloring using +% the current colormap (as SURF does) +% (matrix) a Mx3xN matrix of RGB triples as rows. This gives N +% color planes. +% (matrix) a Mx1xN matrix of any values: sets indexed coloring using +% the current colormap (as SURF does). This gives N +% color planes. +% (cell) of form {rgb, str} where rgb is a Mx3 (xN) matrix of RGB +% triples as rows and str is a string that is used as +% title(s). +% (cell) of form {v, str} where v is a Mx1(xN) matrix of values +% and str is a string that is used as title(s). +% +% 'colori' Same as 'color' but uses shaded color plane (see 'compi'). +% +% 'norm' Defines whether to use normalized or denormalized +% values in the colorbar. If denormalized values are +% used, they are acquired from SOM_DENORMALIZE function +% using sMap.comp_norm field. +% (string) 'd' (default) for denormalized values and 'n' for +% normalized values. The corresponding letter appears +% below the colorbar. +% +% 'bar' Define the direction of the colorbars for component planes +% and U-matrices or turn them completely off. +% (string) 'vert' (default), 'horiz' or 'none'. 'vert' gives +% vertical and 'horiz' horizontal colorbars. 'none' +% shows no colorbars at all. +% +% 'size' Define sizes of the units. +% (scalar) all units have the same size (1 by default) +% (vector) size Mx1, each unit gets individual size scaling +% (as in SOM_CPLANE) +% +% 'edge' Unit edges on component plane visualizations. +% (string) 'on' or 'off' determines whether the unit edges on component +% planes ('comp') are shown or not. Default is 'off'. Note that +% U-matrix and color planes are _always_ drawn without edges. +% +% 'footnote' Text on the figure +% (string) is printed as a movable text object on the figure +% where it may be moved using mouse. Default value is the +% string in the sMap.name field. Note: value [] gives the +% string, but input value '' gives no footnote a all. +% See VIS_FOOTNOTE for more information on the text object +% and ways to change its font size. +% +% 'colormap' som_show ghages the colormap by default to a gray-level map +% (matrix) This argument is used to set some other colormap. +% +% 'subplots' the number of subplots in y- and x-directions, as in +% (vector) command SUBPLOT +% +% OUTPUT ARGUMENTS +% +% h (struct) +% .plane (vector) handles to the axes objects (subplots) +% .colorbar (vector) handles to the colorbars. Colorbars of empty +% & color planes do not exist: the corresponding +% value in the vector is -1 +% .label (vector) handles to the axis labels +% +% OBJECT TAGS +% +% The property field 'Tag' of the axis objects created by this function +% are set to contain string 'Cplane' if the axis contains component plane +% ('comp'), color plane ('color') or empty plane ('empty') and string +% 'Uplane' if it contains a u-matrix ('umat'). The tag is set to +% 'CplaneI' for planes created using 'compi' and 'colori', and +% 'UplaneI' for 'umati'. +% +% FEATURES +% +% Note that when interpolated shading is used in coloring ('compi' and +% 'colori') the standard built-in bilinear Gouraud interpolation for a +% SURF object is used. If the lattice is hexagonal - or anything else than +% rectangular in general - the result is not strictly what is looked +% for, especially if the map is small. +% +% EXAMPLES +% +%% Make random data, normalize it, and give component names +%% Make a map +% +% data=som_data_struct(rand(1000,3),'comp_names',{'One','Two','Three'}); +% data=som_normalize(data,'var'); +% map=som_make(data); +% +%% Do the basic visualization with som_show: u-matrix and all +%% component planes +% +% som_show(map); +% +%% The values shown in the colorbar are denormalized codebook values +%% (if denormalization is possible). To view the actual values, use +%% the ..., 'norm', 'n' argument pair. +% +% som_show(map,'norm','n') +% +%% Something more complex: +%% Show 1-2. Component planes 1 and 2 (variables 'One' and 'Two') +%% 3. U-matrix that is calculated only using variables +%% 'One' and 'Two' +%% with title '1,2 only' +%% 4. U-matrix that is calculated using all variables with the +%% deafult title 'U-matrix' +%% 5. The color code (in c) with title 'Color code' +%% 6. Component plane 3 (variable 'Three') +%% and use vertical colorbars and and the values +%% But first: make a continuous color code (see som_colorcode) +% +% c=som_colorcode(map,'rgb1'); +% +% som_show(map,'comp',[1 2],'umat',{1:2,'1,2 only'},'umat','all', ... +% 'color',{c,'Color code'},'bar','vert','norm','n','comp',3) +% +% SEE ALSO +% +% som_show_add Show hits, labels and trajectories on SOM_SHOW visualization. +% som_show_clear Clear hit marks, labels or trajectories from current figure. +% som_umat Compute unified distance matrix of self-organizing map. +% som_grid Visualization of a SOM grid. +% som_cplane Visualization of component, u-matrix and color planes. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta johan 100298 +% Version 2.0beta johan 201099 juuso 181199 johan 011299-100200 +% juuso 130300 190600 + +%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(1,Inf,nargin)) % check no. of input args + +if isstruct(sMap), % check map + [tmp,ok,tmp]=som_set(sMap); + if all(ok) & strcmp(sMap.type,'som_map') + ; + else + error('Map struct is invalid!'); + end +else + error('Requires a map struct!') +end + +munits=size(sMap.codebook,1); % numb. of map units +d=size(sMap.codebook,2); % numb. of components +msize=sMap.topol.msize; % size of the map +lattice=sMap.topol.lattice; % lattice + +if length(msize)>2 + error('This visualizes only 2D maps!') +end + +if rem(length(varargin),2) + error('Mismatch in identifier-value pairs.'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% read in optional arguments + +if isempty(varargin), + varargin = { 'umat','all','comp','all'}; +end + +%% check the varargin and build visualization infostrcuts +% Vis: what kind of planes, in which order, what are the values in +% the units +% Vis_param: general properties +% see subfunction + +% The try-catch construction is here just for avoiding the +% possible termination to happen in subfunction because an error +% message containing subfunction line numbers etc. might be confusing, as +% there probably is nothing wrong with the subfunction but with the +% input. Ok, this isn't proper programming sytle... + +try + [Plane, General]= check_varargin(varargin, munits, d, sMap.name); +catch + error(lasterr); +end + +% Set default values for missing ones + +% No planes at all (only general properties given in varargin): +% set default visualization + +if isempty(Plane) + varargin = [varargin, { 'umat','all','comp','all'}]; + % and again we go... + try + [Plane, General]= check_varargin(varargin, munits, d, sMap.name); + catch + error(lasterr); + end +end + +% set defaults for general properties + +if isempty(General.colorbardir) + General.colorbardir='vert'; +end + +if isempty(General.scale) + General.scale='denormalized'; +end + +if isempty(General.size) + General.size=1; +end + +if isempty(General.edgecolor) + General.edgecolor='none'; +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% get rid of an annoying warning: "RGB color data not yet supported in +% Painter's mode." +%set(gcf, 'renderer','zbuffer'); +%% -> a much more annoying thing results: the output to PostScript is +%% as bitmap, making the files over 6MB in size... + +n=length(Plane); % the number of subfigures + +% get the unique component indices +c=General.comp(General.comp>0); +c=setdiff(unique(c),[0 -1]); +c=c(~isnan(c)); + +% estimate the suitable dimension for +if isempty(General.subplots), + y=ceil(sqrt(n)); % subplots + x=ceil(n/y); +else + y = General.subplots(2); + x = General.subplots(1); + if y*x<n, + error(['Given subplots grid size is too small: should be >=' num2str(n)]); + end +end + +clf; % clear figure + +for i=1:n, % main loop + h_axes(i,1)=subplot(x,y,i); % open a new subplot + + % Main switch: select function according to the flags set in comps + + switch Plane{i}.mode + + case 'comp' + %%% Component plane + + tmp_h=som_cplane(lattice,msize, sMap.codebook(:,General.comp(i)), ... + General.size); + set(tmp_h,'EdgeColor', General.edgecolor); + set(h_axes(i),'Tag','Cplane'); + h_label(i,1)=xlabel(sMap.comp_names{General.comp(i)}); + + + case 'compi' + %%% Component plane (interpolated shading) + + tmp_h=som_grid(lattice, msize, 'surf', sMap.codebook(:,Plane{i}.value), ... + 'Marker', 'none', 'Line', 'none'); + set(h_axes(i),'Tag','CplaneI'); + h_label(i,1)=xlabel(sMap.comp_names(Plane{i}.value)); + vis_PlaneAxisProperties(gca,lattice,msize,NaN); + + case 'color' + %%% Color plane + + tmp_h=som_cplane(lattice,msize,Plane{i}.value,General.size); + set(tmp_h,'EdgeColor','none'); + set(h_axes(i),'Tag','Cplane'); + h_label(i,1)=xlabel(Plane{i}.name); + + + case 'colori' + %%% Color plane (interpolated shading) + + tmp_h=som_grid(lattice, msize, 'surf', Plane{i}.value, 'Marker', 'none', ... + 'Line', 'none'); + set(h_axes(i),'Tag','CplaneI'); + h_label(i,1)=xlabel(Plane{i}.name); + vis_PlaneAxisProperties(gca,lattice,msize,NaN); + + case 'empty' + %%% Empty plane + + tmp_h=som_cplane(lattice,msize,'none'); + h_label(i,1)=xlabel(Plane{i}.name); + set(h_axes(i),'Tag','Cplane'); + + case 'umat' + %%% Umatrix + + u=som_umat(sMap.codebook(:,Plane{i}.value),sMap.topol,'median',... + 'mask',sMap.mask(Plane{i}.value)); u=u(:); + tmp_h=som_cplane([lattice 'U'],msize,u); + set(tmp_h,'EdgeColor','none'); + set(h_axes(i),'Tag','Uplane'); + h_label(i,1)=xlabel(Plane{i}.name); + + case 'umati' + %%% Umatrix (interpolated shading) + + u=som_umat(sMap.codebook(:,Plane{i}.value),sMap.topol,'mean',... + 'mask',sMap.mask(Plane{i}.value)); u=u(1:2:end,1:2:end); + u=u(:); + tmp_h=som_grid('rect', msize, 'surf', u, ... + 'Marker', 'none', 'Line', 'none', ... + 'coord', som_vis_coords(lattice,msize)); + set(h_axes(i),'Tag','UplaneI'); + h_label(i,1)=xlabel(Plane{i}.name); + vis_PlaneAxisProperties(gca,lattice,msize,NaN); + + otherwise + error('INTERNAL ERROR: unknown visualization mode.'); + end + + %%% Adjust axis ratios to optimal (only 2D!) and put the + %%% title as close to axis as possible + + set(h_label,'Visible','on','verticalalignment','top'); + set(gca,'plotboxaspectratio',[msize(2) msize(1) msize(1)]); + + %%% Draw colorbars if they are turned on and the plane is umat or c-plane + + if General.comp(i)> -1 & ~strcmp(General.colorbardir,'none'), + h_colorbar(i,1)=colorbar(General.colorbardir); % colorbars + else + h_colorbar(i,1)=-1; + General.comp(i)=-1; + end +end %% main loop ends + +% Set window name + +set(gcf,'Name',[ 'Map name: ' sMap.name]); + +%% Set axes handles to the UserData field (for som_addxxx functions +%% and som_recolorbar) +%% set component indexes and normalization struct for som_recolorbar + +SOM_SHOW.subplotorder=h_axes; +SOM_SHOW.msize=msize; +SOM_SHOW.lattice=lattice; +SOM_SHOW.dim=d; +SOM_SHOW.comps=General.comp; +SOM_SHOW.comp_norm=sMap.comp_norm; %(General.comp(find(General.comp>0))); + +set(gcf,'UserData', SOM_SHOW); + +% Set text property 'interp' to 'none' in title texts + +set(h_label,'interpreter','none'); + +h_colorbar=som_recolorbar('all', 3, General.scale); %refresh colorbars + +% Set a movable text to lower corner pointsize 12. + +vis_footnote(General.footnote); vis_footnote(12); + +% set colormap +colormap(General.colormap); + +%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargout > 0 + h.plane=h_axes; h.colorbar=h_colorbar; h.label=h_label; +end + + +%%%%%% SUBFUNCTIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [Plane, General]=check_varargin(args, munits, dim, name) + +% args: varargin of the main function +% munits: number of map units +% dim: map codebook dimension +% name: map name +% Define some variables (they must exist later) + +Plane={}; % stores the visualization data for each subplot +General.comp=[]; % information stored on SOM_SHOW figure (which component) +General.size=[]; % unit size +General.scale=[]; % normalization +General.colorbardir=[]; % colorbar direction +General.edgecolor=[]; % edge colors +General.footnote=name; % footnote text +General.colormap=colormap; % default colormap (used to be gray(64).^.5;) +General.subplots=[]; % number of subplots in y- and x-directions + +for i=1:2:length(args), + %% Check that all argument types are strings + + if ~ischar(args{i}), + error('Invalid input identifier names or input argument order.'); + end + + %% Lower/uppercase in identifier types doesn't matter: + + identifier=lower(args{i}); % identifier (lowercase) + value=args{i+1}; + + %%% Check first the identifiers that define planes and get values + %%% to the visualization data struct array Plane. + %%% (comps,compi,umat,color,empty) Note that name, value and comp_ + %%% must be specified in these cases + %%% comp_ are collected to comp in order. This is stored to the + %%% SOM_SHOW user property field to give information for SOM_RECOLROBAR + %%% how to operate, i.e., which component is in which subplot: + %%% comp(i)=0: draw colorbar, but no normalization (umat) + %%% comp(i)=1...N: a component plane of variable comp(i) + %%% comp(i)=-1: no colorbar (color or empty plane) + + switch identifier + case {'comp','compi'} + %%% Component planes: check values & set defaults + + if ~vis_valuetype(value,{'nx1','1xn','string'}) & ~isempty(value), + error([ 'A vector argument or string ''all'' expected for ''' ... + identifier '''.']) + end + if isempty(value) + value=1:dim; + elseif ischar(value), + if ~strcmp(value,'all') + error([ 'Only string value ''all'' is valid for ''' ... + identifier '''.']); + else + value=1:dim; + end + else + value=round(value); + if min(value)<1 | max(value)>dim, + error([ 'Component indices out of range in ''' identifier '''.']) + end + end + if size(value,1)==1, value=value';end + comp_=value; + name=[]; % name is taken form sMap by index in main loop + + case {'umat','umati'} + %%% Check first the possible cell input + + if iscell(value), + if ndims(value) ~= 2 | any(size(value) ~= [1 2]) | ... + ~vis_valuetype(value{2},{'string'}), + error('Cell input for ''umat'' has to be of form {vector, string}.'); + else + name=value{2}; value=value{1}; + end + else + name='U-matrix'; % no cell: default title is set + end + if ~vis_valuetype(value,{'nx1','1xn','string'}) & ~isempty(value), + error('Vector, string ''all'', or cell {vector, string} expected for ''umat''.') + end + if isempty(value) + value=1:dim; + elseif ischar(value), + if ~strcmp(value,'all') + error('Only string value ''all'' is valid for ''umat''.') + else + value=1:dim; + end + else + value=unique(round(value)); + end + if min(value)<1 | max(value)>dim, + error('Component indices out of range in ''umat''.') + end + + if size(value,1)==1, value=value';end + comp_=0; + + case 'empty' + %%% Empty plane: check values & set defaults + + if ~vis_valuetype(value,{'string'}), + error('A string value for title name expected for ''empty''.'); + end + name=value; + comp_=-1; + + case { 'color','colori'} + %%% Color plane: check values & set defaults + + % Check first the possible cell input + if iscell(value), + if ndims(value)~=2 | any(size(value) ~= [1 2]) | ... + ~vis_valuetype(value{2},{'string'}), + error([ 'Cell input for ''' identifier ... + ''' has to be of form {M, string}.']); + else + name=value{2}; value=value{1}; + end + else + name='Color code'; % no cell: default title is set + end + if size(value,1)~=munits | ... + (~vis_valuetype(value,{'nx3rgb'}) & ... + ~vis_valuetype(value,{'nx1'}) & ... + ~vis_valuetype(value,{'nx1xm'}) & ... + ~vis_valuetype(value,{'nx3xdimrgb'})), + error(['Mx3 or Mx3xN RGBmatrix, Mx1 or Mx1xN matrix, cell '... + '{RGBmatrix, string},' ... + ' or {matrix, string} expected for ''' identifier '''.']); + end + + % if colormap is fixed, we don't draw colorbar (comp_ flag is -1) + % if colormap is indexed, we draw colorbar as in umat (comp_=0) + + if size(value,2)==3 + comp_=-1; + else + comp_=0; + end + + %%%% The next things are general properties of the visualization--- + %%%% + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + case 'size' + %%% Unit size: check & set + + if ~vis_valuetype(value,{'1x1',[munits 1]}) + error('A munits x 1 vector or a scalar expected for ''size''.') + end + if isempty(value), + General.size=1; + else + General.size=value; + end + + case 'bar' + %%% Colorbar existence & direction: check & set + + if ~vis_valuetype(value,{'string'}) + error('String value expected for ''bar''.') + elseif isempty(value) + value='vert'; + end + if any(strcmp(value,{'vert','horiz','none'})), + General.colorbardir=value; + else + error('String ''vert'', ''horiz'' or ''none'' expected for ''bar''.'); + end + + case 'norm' + %%% Value normalization: check & set + + if ~vis_valuetype(value,{'string'}) + error('String ''n'' or ''d'' expected for ''norm''.'); + elseif isempty(value) + value='n'; + end + if strcmp(value(1),'n'), + General.scale='normalized'; + elseif strcmp(value(1),'d'), + General.scale='denormalized'; + else + error('String ''n(ormalized)'' or ''d(enormalized)'' expected for ''norm''.'); + end + + case 'edge' + %%% Edge on or off : check % set + + if ~vis_valuetype(value,{'string'}) & ~isempty(value), + error('String value expected for ''edge''.') + elseif ~isempty(value), + switch value + case 'on' + General.edgecolor='k'; + case 'off' + General.edgecolor='none'; + otherwise + error('String value ''on'' or ''off'' expected for ''edge''.') + end + end + + case 'footnote' + %%% Set the movable footnote text + + if ~vis_valuetype(value,{'string'}) + if ~isempty(value), + error('String value expected for ''footnote''.'); + else + General.footnote=sMap.name; + end + else + General.footnote=value; + end + + case 'colormap' + %%% Set the colormap + if isempty(value) + General.colormap=gray(64).^2; + elseif ~vis_valuetype(value,{'nx3rgb'}) + error('Colormap is invalid!'); + else + General.colormap=value; + end + + case 'subplots' + %%% set the number of subplots + if ~vis_valuetype(value,{'1x2'}) & ~vis_valuetype(value,{'2x1'}) + error('Subplots grid size is invalid!'); + else + General.subplots=value; + end + + otherwise + %%% Unknown identifier + + error(['Invalid argument identifier ''' identifier '''!']); + end + + %%% Set new entry to the Plane array if the indentifier means + %%% making a new plane/planes + + tail=length(Plane); + switch identifier + case {'comp','compi'} + for i=1:length(value) + Plane{tail+i}.mode=identifier; + Plane{tail+i}.value=value(i); + Plane{tail+i}.name=name; % not used actually + end + General.comp = [General.comp; comp_]; + case {'umat','umati','empty'} + Plane{tail+1}.mode=identifier; + Plane{tail+1}.value=value; + Plane{tail+1}.name=name; + General.comp = [General.comp; comp_]; + case {'color','colori'}, + for i=1:size(value,3), + Plane{tail+i}.mode=identifier; + Plane{tail+i}.name=[name '_' num2str(i)]; + Plane{tail+i}.value=value(:,:,i); + General.comp = [General.comp; comp_]; + end + if size(value,3)==1, + Plane{tail+1}.name=name; + end + otherwise + ; % do nothing + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_show_add.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1610 @@ +function h=som_show_add(mode,D,varargin) + +%SOM_SHOW_ADD Shows hits, labels and trajectories on SOM_SHOW visualization +% +% h = som_show_add(mode, D, ['argID',value,...]) +% +% som_show_add('label',sMap) +% som_show_add('hit',som_hits(sMap,sD)) +% som_show_add('traj',som_bmus(sMap,sD)) +% som_show_add('comet',som_bmus(sMap,sD)) +% som_show_add('comet',inds, 'markersize', [1 0.2]) +% +% Input and output arguments ([]'s are optional): +% mode (string) operation mode 'label', 'hit', 'traj', 'comet' +% D (varies) depending on operation mode +% In 'label' mode gives the labels +% (struct) map struct, the .labels field of which is used +% (cell array of strings) size munits x number_of_labels +% In 'hit' mode gives the hit histogram(s) +% (matrix) size munits x k, if k>1, D gives hit histograms +% for k different sets of data (e.g. k classes). +% In 'traj' and 'comet' modes gives the trace of the trajectory +% (vector) size N x 1, D(1) is the current and D(end) +% is oldest item of the trajectory +% [argID, (string) Additional arguments are given as argID, value +% value] (varies) pairs. Depend on the operation mode (see below). +% +% h (vector) handles to the created objects +% +% Here are the valid argument IDs and corresponding values. Most of +% them depend on the operation mode: +% +% all modes +% 'SubPlot' (vector) which subplots are affected (default: current) +% (string) 'all': all subplots are affected +% mode = 'label' +% 'TextSize' (scalar) text size in points +% 'TextColor' (string) ColorSpec, 'xor' or 'none': label color +% +% mode = 'hit' +% 'EdgeColor' (string) ColorSpec, 'none' +% 'MarkerSize' (scalar) maximum marker size +% if k == 1, +% 'Marker' (string) 'lattice', Matlab's built-in markerstyles, 'none' +% 'MarkerColor'(string) Colorspec, 'none': fill color for markers +% 'Text' (string) 'on', 'off': whether to write the number of hits +% 'TextColor' (string) ColorSpec, 'xor': text color if Text is 'on' +% 'TextSize' (scalar) text font size in points if Text is 'on' +% if k > 1, +% 'SizeFactor' (string) 'common', 'separate': size scaling +% 'Marker' (string) 'lattice', Matlab's built-in markerstyles, 'pie', 'none' +% (cell array) size k x 1, marker style for each histogram +% 'MarkerColor'(string) Colorspec, 'none': fill color for markers +% (matrix) size k x 3, color for each histogram +% +% mode = 'traj' +% 'TrajWidth' (scalar) basic trajectory line width in points +% 'WidthFactor'(string) 'hit' or 'equal': effect of hits on line width +% 'TrajColor' (string) ColorSpec, 'xor': color for trajectory line +% 'Marker' (string) 'lattice', Matlab's built-in markerstyles, 'none' +% 'MarkerSize' (scalar) basic marker size (in points) +% 'SizeFactor' (string) 'equal', 'hit' (equal size/size depends on freq.) +% 'MarkerColor'(string) Colorspec, 'none': color of markers +% 'EdgeColor' (string) ColorSpec, 'none': edgecolor of markers +% +% mode = 'comet' +% 'Marker' (string) 'lattice', Matlab's built-in markerstyles +% 'MarkerColor'(string) ColorSpec, 'none': color for the markers +% (matrix) size N x 3, RGB color for each step +% 'EdgeColor' (string) ColorSpec, 'none': edgecolor for markers +% 'MarkerSize' (vector) size 1 x 2, size of comet core and tail +% +% For more help, try 'type som_show_add' or check out online documentation. +% See also SOM_SHOW. + +%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_show_add +% +% PURPOSE +% +% Shows hits, labels and trajectories on SOM_SHOW visualization +% +% SYNTAX +% +% h = som_show_add(mode, D); +% h = som_show_add(..., 'argID', value); +% +% DESCRIPTION +% +% The SOM_SHOW function makes the basic visualization of the SOM. +% With SOM_SHOW_ADD one can set labels, hit histogarms or different +% trajectories on this visualization. +% +% labels (mode = 'label') +% +% Labels are strings describing the units. They may be, e.g., a result +% of SOM_AUTOLABEL function. Labels are centered on the unit so that +% multiple labels are in a column. +% +% hit histograms (mode = 'hit') +% +% Hit histograms indicate how the best matching units of a data +% set/some data sets are distribited on a SOM. The hit histogram can +% be calculated using function SOM_HITS. +% +% trajectories (mode = 'traj' or mode = 'comet') +% +% Trajectories show the best matching units for a data set that is +% time (or any ordered) series. It may be either a line connecting the +% consecutive best matching units ('traj' mode) or a "comet" +% trajectory where the current (first sample in D) best matching unit +% has biggest marker and the oldest (last sample) has smallest +% marker ('comet' mode). +% +% NOTE: that the SOM_SHOW_ADD function can only be applied to +% figures that have been drawn by SOM_SHOW. +% +% KNOWN BUGS +% +% for 'hit' mode, if the given hit matrix is all zeros, a series of +% error messages is generated +% +% REQUIRED INPUT ARGUMENTS +% +% mode (string) Visuzalization mode +% 'label' map labeling +% 'hit' hit histograms +% 'traj' line style trajectory +% 'comet' comet style trajectory +% +% D (vector, map struct, cell array of strings) Data +% +% The valid value of D depends on the visualization mode: +% +% Mode Valid D +% 'label' map struct or Mxl cell array of strings, where +% M is number of map units and l maximum numer of +% labels in unit. +% +% 'hit' Mx1 vector or MxK matrix, where M is number of map +% units and K is number of hit histograms (of K +% different classes of data) to be shown +% +% 'comet' Lx1 vector of best matchig unit indices that have to +% 'traj' be in range of the map that is in the figure. L is +% the length of trajectory +% +% OPTIONAL INPUT ARGUMENTS +% +% Optional arguments must be given as 'argument identifier', value +% -pairs. This section is divided in four parts because each mode +% functions in a different way, though they may have same identifier +% names. +% +% If user specifies an identifier that is not operational in the +% specified mode, the functions gives a warning message. If the +% identifier does not exist in any mode the execution is terminated +% and an error message is returned. +% +% GENERAL OPTIONAL INPUT ARGUMENTS (in all modes) +% +% 'SubPlot' Target subplots in the figure +% (vector) Subplots' ordinal numbers in a vector. By default +% the target is the current subplot (see GCA). +% (string) String 'all' means all subplots. +% +% 'Marker' Data marker (not in use in 'label' mode) +% (string) 'none': sets the markers off +% 'lattice': sets the marker shape according to the +% lattice of the underlying map, i.e. it gives +% rectangles if underlying map lattice is 'rect' and +% hexagons for 'hexa', respectively +% any of the Matlab's built-in marker styles: 'o', 's', +% 'd', 'v', '^', '<' ,'> ', 'p', 'h', 'x', '.', '*', '+' +% +% NOTE that '.','x','+' or '*' are not recommended since +% they have only edgecolor and many visualizations are +% based on _face_ color. +% +% NOTE there is an important difference between built-in +% markers. If figure size is changed the 'lattice' +% markers are rescaled but the built-in markers stay at +% fixed size, and consequently, the size unit for +% 'lattice' markers is normalized but for built-in +% markers the size is given in points. For 'lattice' +% markers size 1 means the size of the map unit. +% +% NOTE that in 'hit' mode there are some additional features. +% +% 'EdgeColor' Sets edgecolor for the markers (not in use in 'label' mode) +% (string) ColorSpec, e.g. 'r', gives each edge the specified color +% 'none': sets markers edges invisible +% Default is 'none' - except if MarkerColor is set to 'none' the +% defaults is 'black'. +% +% OPTIONAL INPUT ARGUMENTS mode 'label' +% +% Labels are centered on the unit so that multiple labels are in +% a single column. +% +% 'SubPlot' see General Optional Input Arguments +% +% 'TextSize' Text size for labels +% (scalar) Text size in points. Default is 10. +% +% 'TextColor' Text color +% (string) ColorSpec specifies the text color for all labels +% 'xor': gives Matlab's "xor" text color mode where the +% label color depends on background color +% 'none': sets labels invisble (but creates the objects) +% +% OPTIONAL INPUT ARGUMENTS mode 'hit' +% +% The function in mode 'hit' depends on the input argument size. If +% only one hit histogram is drawn (K==1), it is possible to show the +% hits using numbers. This is not possible for multiple hit +% histograms (K>1). +% +% 'SubPlot' see General Optional Input Arguments +% +% 'Marker' Marker style(s) +% (string) As in General Optional Input Arguments. In addition +% 'pie': sets pie charts for markers. The size of the +% pie in each unit describes the number of total hits in the +% unit and the share of each sector is the relative amount of +% hits in each class (requires multiple histograms). Color for +% each class is set by MarkerColor. Default coloring +% is hsv(K), where K is the number of hit histograms (classes). +% (cell array) size K x 1, of built-in marker style characters. K is +% number of histograms (classes), i.e., same as size(D,2) +% where D is the second input argument. Cell value is +% valid only if multiple histograms are specified (K>1). +% +% NOTE if multiple histograms (classes) are specified +% and Marker is one of the built-in marker styles or +% 'lattice', the markers are drawn in size order from +% largest to smallest. This insures that all markers are +% visible (or at least their edges are). But if two +% markers for different classes in the same node were of +% same size, the other would be totally hidden. In order +% to prevent this, the markers for different classes are +% shifted different amounts from the exact centre of the +% unit. (Evidently, if Marker is 'pie' this problem does +% not exist.) +% +% Default marker is 'lattice' for one histogram and +% 'pie' for multiple histograms. +% +% 'MarkerColor' Marker color(s) +% (string) ColorSpec gives all markers the same color +% 'none': leaves the markes transparent (only edges are visible) +% (matrix) size K x 3, RGB triples for each histogram class +% giving each hit histogram an own color +% +% NOTE that markers '*','+','x', or '.' cannot use +% MarkerColor since these objects have no face (fill) +% color. For them only EdgeColor matters. +% +% 'MarkerSize' Maximum size for marker +% (scalar) set the _maximum_ marker size that corresponds to +% maximum hit count. If Marker is 'pie' or 'lattice' the +% MarkerSize is in normalized scale: 1 correspons to unit size. +% If Marker is one of the built-in styles, MarkerSize is given +% in points. +% +% Marker Default MarkerSize +% 'lattice' 1 (normalized units) +% 'pie' 1 (normalized units) +% 'o','s', etc. 6 (points) +% +% 'SizeFactor' Defines the scaling of the marker sizes in multiple +% histogram case (when Marker is one of the built-in marker +% styles or 'lattice'). +% (string) 'separate' (the default) means that marker size shows +% the share of the data which hits the unit compared to +% amount of data in that class. That is, the size of +% markers show the relative distribution of data on the map +% in each class separately. The maximum size is SizeFactor. +% 'common' means that marker size shows the distribution of +% the data in the different classes compared to +% _the total amount of data_. +% +% 'EdgeColor' Sets edgecolor for the markers, see General +% Optional Input Arguments. Default is 'none' - +% except if MarkerColor is 'none' or Marker is +% 'x','*,'x', or '.'. In these cases default EdgeColor is 'black'. +% +% 'Text' Write/don't write the number of hits on the +% units. This option is not in use for multiple histograms. +% (string) 'on' or 'off' (the default) +% +% 'TextColor' Text color +% (string) ColorSpec gives each letter the same color +% 'xor' gives a "xor" coloring for the text +% +% 'TextSize' Text size (in points) +% (scalar) text size in points, default is 10 +% +% OPTIONAL INPUT ARGUMENTS mode 'traj' +% +% Input D is a Nx1 vector of N BMU indices that describe the trace of the +% comet. First element D(1) is "newest" and D(end) "oldest". Note +% that at least two indeces are expected: size of D must be at +% least 2x1. +% +% 'SubPlot' see General Optional Input Arguments +% +% 'TrajColor' Color for trajectory line +% (string) ColorSpec gives each marker the same color, 'w' by default +% 'none' sets the marker fill invisible: only edges are shown +% +% 'TrajWidth' Maximum width of trajectory line +% (scalar) width in points. Default is 3. +% +% 'WidthFactor' Shows how often edge between two units has been traversed. +% (string) 'hit': the size of the marker shows how frequent the +% trajectory visits the unit (TrajWidth sets the +% maximum size). This is the default. +% 'equal': all lines have the same width (=TrajWidth) +% +% 'Marker' Marker style, see General Optional Input +% Arguments. Default is 'o'. +% +% NOTE Marker style 'lattice' is not valid in mode 'traj'. +% NOTE Markers can be turned off by setting MarkerSize to zero. +% +% 'MarkerSize' Maximum size of markers +% (scalar) Default is 12 (points). +% +% 'SizeFactor' Sets the frequency based marker size or constant marker size. +% (string) 'hit': the size of the marker shows how frequent the +% trajectory visits the unit (MarkerSize sets the +% maximum size). This is the default. +% 'equal': all markers have th esame size (=MarkerSize) +% +% 'MarkerColor' The fill color(s) for hit markers +% (string) ColorSpec gives each marker the same color, default is 'w' +% 'none' sets the marker fill invisible: only edges are shown +% +% NOTE markers '*','+','x', or '.' can't use MarkerColor since +% these objects have no face (fill) color: only EdgeColor +% matters for these markers. +% +% 'EdgeColor' see General Optional Input Arguments. Default is +% 'none' - except if MarkerColor is 'none' or Marker +% is 'x','*','x', or '.'. In these cases default +% EdgeColor is 'white'. +% +% OPTIONAL INPUT ARGUMENTS mode 'comet' +% +% Input D is a Nx1 vector of N BMU indices that describe the trace of +% the comet. First element D(1) is "newest" and D(end) "oldest". Note +% that at least two indeces are expected: size of D must be at least +% 2x1. +% +% 'SubPlot' see General Optional Input Arguments +% +% 'Marker' Marker style, see General Optional Input +% Arguments. Default is 'lattice'. +% +% 'MarkerColor' The fill color(s) for comet markers +% (string) ColorSpec gives each marker the same color, default is 'w' +% 'none' sets the marker fill invisible: only edges are shown +% (matrix) size N x 3, consisting of RGB triples as rows +% sets different color for each marker. This may be +% used to code the time series using color/grayscale. +% +% NOTE Markers '*','+','x', or '.' can't use MarkerColor +% since these objects have no face (fill) color: only +% EdgeColor matters for these markers. +% +% 'EdgeColor' see General Optional Input Arguments. Default is +% 'none' - except if MarkerColor is 'none' or Marker +% is 'x','*,'x', or '.'. In these cases default +% EdgeColor is 'white'. +% +% 'MarkerSize' The size of "comet core" and tail +% (vector) size 1 x 2: first element sets the size for the marker +% representing D(1) and the second set size for D(end) +% the size (area) of the markes between these changes linearly. +% Note that size units for 'lattice' marker style are +% normalized so that 1 means map unit size but for built-in +% marker styles the size is given points. +% +% Marker default value +% 'lattice' [0.8 0.1] +% 'o','v', etc. [20 4] +% +% OUTPUT ARGUMENTS +% +% h (vector) handles to all objects created by the function +% +% OBJECT TAGS +% +% Field Tag in every object is set to +% +% 'Lab' for objects created in mode 'label' +% 'Hit' -"- 'hit' +% 'Traj' -"- 'traj' +% 'Comet' -"- 'comet' +% +% EXAMPLES +% +% Not yet ready +% +% SEE ALSO +% +% som_show Basic map visualization +% som_show_clear Clear hit marks, labels or trajectories from current figure. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 131199 + +%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(2,Inf,nargin)) % check no. of input args + +% Get data from the SOM_SHOW figure, exit if error + +[handles,msg,lattice,msize,dim]=vis_som_show_data('all',gcf); +error(msg); + +munits=prod(msize); +% Initialize some variables: these must exist later; +% the default values are set by subfunctions + +Property=init_properties; +Property.handles=handles; + +%%% Check mode and that D is of right type & size for that mode +% mode has to be string +if ~vis_valuetype(mode,{'string'}), + error('String value expected for first input argument (mode).'); +else + mode=lower(mode); % case insensitive + mode_=mode; % 'mode' is internal variable; + % for program constructs 'mode_' is shown to + % user in some error messags +end + +switch mode % check mode + case 'hit' + %%% Hit histogram visualization: vector [msize k] + + if ~vis_valuetype(D,{'nxm'}), + error('Hit visualization: a matrix expected for data input.'); + elseif size(D,1) ~= prod(msize) + error('Hit visualization: data and map size do not match.'); + end + % Multiple hit histograms + if size(D,2)>1 + mode='mhit'; + % Hit count musn't be negative + if any(D(:)<0), + error('Hit visualization: negative hit count in data not allowed!'); + end + end + + case {'traj','comet'} + %%% Trajectory like visualizations + + if ~vis_valuetype(D,{'nx1'}), + error('Trajectory/Comet: a Nx1 vector expected for data input.'); + elseif any(D>prod(msize))| any(D<1), + error('Trajectory/Comet: BMU indices out of range in data input.'); + elseif any(fix(D)~=D), + warning('Trajectory/Comet: BMU indices not integer. Rounding...'); + elseif size(D,1)<2 + error('At least two BMU indexes expected.'); + end + + case 'label' + %%% Label visualizations + + if isstruct(D), % check if D is a map + [tmp,ok,tmp]=som_set(D); + if all(ok) & strcmp(D.type,'som_map') + ; + else + error('Map struct is invalid!'); + end + % Size check + if length(msize) ~= length(D.topol.msize) | ... + munits ~= prod(D.topol.msize), + error(['The size of the input map and the map in the figure' ... + ' do not match.']); + end + D=D.labels; + % Cell input + elseif vis_valuetype(D,{'2Dcellarray_of_char'}) + ; + % Char input + elseif vis_valuetype(D,{'char_array'}), + D=cellstr(D); + else + error(['Labels has to be in a map struct or in a cell array' ... + ' of strings']); + end + if size(D,1) ~= munits + error(['The number of labels does not match the size of the map' ... + ' in the figure.']); + end + otherwise + error('Invalid visualization mode.'); +end + +if rem(length(varargin),2) + error('Mismatch in identifier-value pairs or wrong input argument order.'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% read in optional arguments + +for i=1:2:length(varargin), + %% Check that all argument types are strings + + if ~ischar(varargin{i}) + error('Invalid identifier name or input argument order.'); + end + + %% Lower/uppercase in identifier types doesn't matter: + + identifier=lower(varargin{i}); % identifier (lowercase) + value=varargin{i+1}; + + % Check property identifiers and values and store the values. + % Struct used_in is set to initiate warning messages: + % if a don't care propersty is set, the user is warned. + + switch identifier + case 'marker' + %%% Marker for hits or trajectories + switch mode + case 'mhit' + if vis_valuetype(value,{'markerstyle'}) | ... + (vis_valuetype(value,{'string'}) & ... + any(strcmp(value,{'lattice','pie'}))), + ; % ok + elseif vis_valuetype(value,{'cellcolumn_of_char'}), + if size(value,1) ~= size(D,2) + error([' If a cell of Markers is specified its size must be' ... + ' number_of_hit_histograms x 1.']); + else + for i=1:size(D,2), + if ~vis_valuetype(value{i},{'markerstyle'}) + error('Cell input for ''Marker'' contains invalid styles.') + end + end + end + else + error([' Invalid ''Marker'' in case of multiple hit histograms.' ... + char(10) ' See detailed documentation.']) + end + case {'comet','hit'} + if vis_valuetype(value,{'markerstyle'}) | isempty(value), + % ok; + elseif ischar(value) & strcmp(value,'lattice'), + % ok; + else + error(['Marker must be Matlab''s marker style, or string' ... + ' ''lattice''.']); + end + case 'traj' + if ~vis_valuetype(value,{'markerstyle'}) & ~isempty(value), + error('In mode ''traj'' Marker must be one of Matlab''s built-in marker styles'); + end + end + used_in.comet=1; % Set relevance flags + used_in.traj=1; + used_in.label=0; + used_in.hit=1; + used_in.mhit=1; + + case 'markersize' + %%% Marker for hits or trajectories + switch mode + case 'comet' + if ~vis_valuetype(value,{'1x2'}) & ~isempty(value), + error('In mode ''comet'' MarkerSize'' must be a 1x2 vector.'); + end + case {'hit','traj'} + if ~vis_valuetype(value,{'1x1'}) & ~isempty(value), + error(['In mode ''' mode_ ... + ''' ''MarkerSize'' must be a scalar.']); + end + end + used_in.comet=1; % Set relevance flags + used_in.traj=1; + used_in.label=0; + used_in.hit=1; + used_in.mhit=1; + + case 'sizefactor' + %%% Hit dependent size factor + switch mode + case 'traj' + if ~vis_valuetype(value,{'string'}) | ... + ~any(strcmp(value,{'hit', 'equal'})), + error(['In mode ''traj'' ''SizeFactor'' must be ' ... + 'string ''equal'' or ''hit''.']); + end + case 'mhit' + if ~vis_valuetype(value,{'string'}) | ... + ~any(strcmp(value,{'common', 'separate'})), + error(['In mode ''hit'' ''SizeFactor'' must be ' ... + 'string ''common'' or ''separate''.']); + end + end + used_in.comet=0; % Set relevance flags + used_in.traj=1; + used_in.label=0; + used_in.hit=0; + used_in.mhit=1; + + case 'markercolor' + %%% Markercolor + switch mode + case 'comet' + if ~vis_valuetype(value,{'colorstyle','1x3rgb'}) & ... + ~vis_valuetype(value,{'nx3rgb',[size(D,1) 3]},'all') & ... + ~isempty(value), + error(['MarkerColor in mode ''comet'' must be a ColorSpec,' ... + ' string ''none'' or Mx3 matrix of RGB triples.']); + end + case 'mhit' + if ~vis_valuetype(value,{[size(D,2) 3],'nx3rgb'},'all') & ... + ~vis_valuetype(value,{'colorstyle','1x3rgb'}), + error([' If multiple hit histograms in mode ''hit'' are' ... + char(10) ... + ' given MarkerColor must be ColorSpec or a Kx3 matrix' ... + char(10)... + ' of RGB triples where K is the number of histograms.']); + end + case 'hit' + if ~vis_valuetype(value,{'colorstyle','1x3rgb'}) & ... + ~isempty(value), + error(['MarkerColor in mode ''hit'' ' ... + 'must be a ColorSpec or string ''none''.']); + end + case 'traj' + if ~vis_valuetype(value,{'colorstyle','1x3rgb'}) & ... + ~isempty(value), + error(['MarkerColor in mode ''traj'' ' ... + 'must be a ColorSpec or string ''none''.']); + end + end + + used_in.comet=1; % Set relevance flags + used_in.traj=1; + used_in.label=0; + used_in.hit=1; + used_in.mhit=1; + + case 'edgecolor' + %%% Color for marker edges + if ~vis_valuetype(value,{'colorstyle','1x3rgb'}) & ~isempty(value), + error('''EdgeColor'' must be a ColorSpec or string ''none''.') + end + + used_in.comet=1; % Set relevance flags + used_in.traj=1; + used_in.label=0; + used_in.hit=1; + used_in.mhit=1; + + case 'text' + %%% Labeling for trajectories/hits + switch mode + case 'hit' + %%% Hit count using numbers? + if isempty(value), + value='off'; + elseif vis_valuetype(value,{'string'}) & ... + ~any(strcmp(value,{'on','off'})), + error('Value for Text in mode ''hit'' should be ''on'' or ''off''.'); + else + ; % ok + end + %case 'traj','comet' + % if ~vis_valuetype(value,{'char_array','cellcolumn_of_char'}) & ... + % ~isempty(value) + % error('Value for Text is of wrong type or size.') + % elseif ischar(value) + % value=strcell(value) % ok, convert to cell + % end + % if size(traj_label,1)~=size(D,1) + % error(['The number of labels in Text and the length of the' ... + % ' trajectory do not match.']); + % end + case 'label' + ; % not used + end + used_in.comet=0; % Set relevance flags + used_in.traj=0; + used_in.label=0; + used_in.hit=1; + used_in.mhit=0; + + case 'textsize' + %%% Text size for labels + + if ~vis_valuetype(value,{'1x1'}) & ~isempty(value), + error('TextSize must be scalar.'); + end + used_in.comet=0; % Set relevance flags + used_in.traj=0; + used_in.label=1; + used_in.hit=1; + used_in.mhit=0; + + case 'textcolor' + %%% Color for labels + + if ~vis_valuetype(value,{'colorstyle','1x3rgb','xor'}) & ~isempty(value), + error('''TextColor'' must be ColorSpec, ''xor'' or ''none''.') + end + used_in.comet=0; % Set relevance flags + used_in.traj=0; + used_in.label=1; + used_in.hit=1; + used_in.mhit=0; + + case 'trajwidth' + %%% Basic line width for a line trajectory + if ~vis_valuetype(value,{'1x1'}) & ~isempty(value), + error('TrajWidth must be a scalar.'); + end + used_in.comet=0; % Set relevance flags + used_in.traj=1; + used_in.label=0; + used_in.hit=0; + used_in.mhit=0; + + case 'widthfactor' + %%% Hit factor for a line trajectory + if ~vis_valuetype(value,{'string'}) | ... + ~any(strcmp(value,{'hit', 'equal'})), + error(['In mode ''traj'' ''WidthFactor'' must be ' ... + 'string ''equal'' or ''hit''.']); + end + used_in.comet=0; % Set relevance flags + used_in.traj=1; + used_in.label=0; + used_in.hit=0; + used_in.mhit=0; + + case 'trajcolor' + %%% Color for trajectory line + + if ~vis_valuetype(value,{'colorstyle','1x3rgb','xor'}) & ~isempty(value), + error('''TrajColor'' must be a ColorSpec or string ''xor''.') + end + used_in.comet=0; % Set relevance flags + used_in.traj=1; + used_in.label=0; + used_in.hit=0; + used_in.mhit=0; + + case 'uselabel' + %%% Which labels to show + error('Not yet implemented.'); + + case 'shift' + if ~vis_valuetype(value,{'1x1'}) | ((value < 0) | (value > 1)), + error('''Shift'' must be a scalar in range [0,1].') + end + used_in.comet=0; % Set relevance flags + used_in.traj=0; + used_in.label=0; + used_in.hit=0; + used_in.mhit=1; + + case 'subplot' + %%% The subplots which are affected + + if vis_valuetype(value,{'1xn','nx1','string'}), + if ischar(value), + if ~strcmp(value,'all'), + error('Only valid string value for subplot indices is ''all''.'); + else + value=1:length(handles); + end + elseif any(value<1) | any(value>length(handles)), + error('Subplot indices must be in range 1...number_of_subplots!'); + end + elseif ~isempty(value) + error('Invalid subplot indices!'); + end + used_in.comet=1; % Set relevance flags + used_in.traj=1; + used_in.label=1; + used_in.hit=1; + used_in.mhit=1; + + otherwise + error([ 'Unknown identifier ''' identifier '''.']); + end + + % Warn user if the property that was set has no effect in the + % selected visuzlization mode + + if ~getfield(used_in, mode), + warning(['Property ''' identifier ''' has no effect in mode ''' ... + mode_ '''.']); + else + Property=setfield(Property,identifier,value); + end +end + +% set default subplot +if isempty(Property.subplot) + % search the subplot number for current axis + value=find(gca==handles); + if isempty(value) | value>length(handles) + error('SubPlot default value setting: current axis is not in the figure!'); + else + Property.subplot=value; + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%% Main switch: select the right subfunction %%%%%%%%%%%%%%%%%%% + +switch mode + case 'hit' + h_=hit(D, lattice, msize, Property); + case 'mhit' + h_=mhit(D, lattice, msize, Property); + case 'label' + h_=label(D, lattice, msize, Property); + case 'traj' + h_=traj(D, lattice, msize, Property); + case 'comet' + %error('Not yet implemented.'); + h_=comet(D, lattice, msize, Property); + otherwise + error('Whoops! Internal error: unknown mode!'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Build output if necessary %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargout>0 + h=h_; +end + +%%%% SUBFUNCTIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function h_=hit(Hits, lattice, msize, Property); + +% number of map units +munits=prod(msize); + +% subplots +p=Property.subplot; +handles=Property.handles; + +% Set default marker +if isempty(Property.marker), + if strcmp(Property.text,'on') + Property.marker='none'; + else + Property.marker='lattice'; + end +end + +% Set default markersize +if isempty(Property.markersize) + if strcmp(Property.marker,'none'), + warning('MarkerSize is not meaningful since Marker is set to ''none''.'); + elseif strcmp(Property.marker,'lattice'), + Property.markersize=1; % normalized size + else + Property.markersize=12; % points + end +end + +% Set default colors +if ~isempty(Property.markercolor), + if strcmp(Property.marker,'none') + warning('MarkerColor is not used since Marker is set to ''none''.'); + Property.markercolor=[]; % not used + else + ; % ok + end +elseif any(strcmp(Property.marker,{'+','*','.','x'})), + % these don't use fill color: 'none' will cause default + % edgecolor to be 'k'. + Property.markercolor='none'; +else + Property.markercolor='k'; +end + +if ~isempty(Property.edgecolor), + if strcmp(Property.marker,'none') + warning(['EdgeColor is not used since Marker is set to' ... + ' ''none''.']); + else + ; %ok + end +elseif ~strcmp(Property.markercolor,'none'), + Property.edgecolor='none'; +else + Property.edgecolor='k'; +end + +% Set default text +if isempty(Property.text), + Property.text='off'; +end + +% Set default textsize +if isempty(Property.textsize) + Property.textsize=10; +elseif strcmp(Property.text,'off') + warning('TextSize not used as hits are not set to be shown as numbers.'); +end + +% Set default textcolor +if isempty(Property.textcolor) + Property.textcolor='w'; +elseif strcmp(Property.text,'off') + warning('TextColor not used as hits are not set to be shown as numbers.'); +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +h_=[]; % this variable is for collecting the object handles + +% Select the drawing mode + +if ~strcmp(Property.marker,'none') + + %%%%% Draw spots %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + % unit coordinates + coord=som_vis_coords(lattice,msize); + + % Calculate the size of the spots + mx=max(Hits); + + if mx==0, + % nothing to draw! + h_=[]; + return + else + Size=sqrt(Hits./mx); + end + % coordinates for non-zero hits (only those are drawn) + coord=coord(Size~=0,:); + Size=Size(Size~=0); + N=size(Size,1); + + % som_cplane can't draw one unit with arbitrary + % coordinates as it its mixed with msize: + if size(coord,1)==1 & strcmp(Property.marker,'lattice'), + Size=[Size;Size]; + coord=[coord;coord]; + end + + for i=1:length(p), + % Set axes + axes(handles(p(i))); + % Get hold state and caxis + memhold=ishold; cax=caxis; + hold on; + + switch Property.marker + case 'lattice' + h_(i,1)=som_cplane(lattice, coord, Property.markercolor, ... + Property.markersize*Size); + otherwise + [S,m]=som_grid(lattice, [N 1],... + 'Coord',coord, ... + 'Line','none',... + 'Marker',Property.marker,... + 'MarkerColor',Property.markercolor,... + 'MarkerSize', Size*Property.markersize); + h_=[h_;m(:)]; + end + + % Restore hold state + if ~memhold + hold off; + end + end + + % Set edgecolor + if strcmp(Property.marker,'lattice') + set(h_,'edgecolor',Property.edgecolor); + else + set(h_,'markeredgecolor',Property.edgecolor); + end +end + +if strcmp(Property.text,'on'), + %%%%% Draw numbers %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + % Do numbers + Hits=reshape(Hits,[munits 1]); + labels=cell([munits 1]); + for i=1:length(Hits) + if Hits(i) % zero hit won't be shown + labels(i)={num2str(Hits(i))}; + end + end + + for i=1:length(p), + axes(handles(p(i))); % Set axes + memhold=ishold; % Get hold state + hold on; + [S,m,l,t]=som_grid(lattice, msize, ... + 'Line','none',... + 'Marker','none', ... + 'Label',labels, ... + 'LabelColor', Property.textcolor, ... + 'LabelSize', Property.textsize); + % Get handles + h_=[h_;t(:)]; + + % Restore hold state and caxis + if ~memhold + hold off; + end + caxis(cax); + end + + % Remove zero object handles (missing objects) + h_=setdiff(h_,0); +end + +%% Set object tags (for som_show_clear) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +set(h_,'Tag','Hit') + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function h_=mhit(Hits, lattice, msize, Property); + +% number of map units +munits=prod(msize); + +% subplots +p=Property.subplot; +handles=Property.handles; + + +% Set default marker +if isempty(Property.marker), + Property.marker=lattice; +end + +% variable 'mode' indicates which kind of markers are used: + +if iscell(Property.marker), + mode='marker'; +elseif vis_valuetype(Property.marker,{'markerstyle'}), + mode='marker'; +elseif strcmp(Property.marker,'pie'), + mode='pie'; +else + mode='lattice'; +end + +% Set default size scaling +if isempty(Property.sizefactor) + Property.sizefactor='separate'; +end + +% Set default markersize +if isempty(Property.markersize) + if any(strcmp(mode,{'lattice','pie'})), + Property.markersize=1; % normalized + else + Property.markersize=12; % points + end +end + +% Set default colors + +if isempty(Property.markercolor), + Property.markercolor=hsv(size(Hits,2)); +end + +if isempty(Property.edgecolor), + if vis_valuetype(Property.markercolor,{'none'}), + Property.edgecolor='k'; + else + Property.edgecolor='none'; + end +end + +% Set default shift +if isempty(Property.shift) + Property.shift=0; +end + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +h_=[]; % this variable is for collecting the object handles + +switch mode + case {'marker','lattice'} + % Number of hits histograms + n_Hits=size(Hits,2); + % Calculate the size of the spots + + if strcmp(Property.sizefactor,'common') + mx=max(max(Hits)); + if mx==0 % nothing to draw! + h_=[]; return + end + spotSize=sqrt(Hits./mx); + else + mx=repmat(max(Hits),munits,1); + mx(mx==0)=1; % Prevent division by zero + spotSize=sqrt(Hits./mx); + end + + %%% Make spotSize + + %reshape Size to a vector [spotSizeforHist(:,1); spotSizeforHist(:,2);...] + spotSize=spotSize(:); + + % indices for non-zero hits (only those are drawn) + notZero=find(spotSize ~= 0); + + % Drop zeros away from spotSize + spotSize=spotSize(notZero); + + % Order spots so that bigger will be drawn first, so that they + % won't hide smaller ones + [dummy, sizeOrder]=sort(spotSize); sizeOrder=sizeOrder(end:-1:1); + spotSize=spotSize(sizeOrder); + + %%% Make unit coordinates + coord=som_vis_coords(lattice,msize); + + move=repmat(linspace(-.1,.1,n_Hits),size(coord,1),1)*Property.shift; + move=repmat(move(:),1,2); + + % do n_Hits copies of unit coordinates so that they match spotSize + coord=repmat(coord,n_Hits,1)+move; + + % Drop zeros away from coords and order + coord=coord(notZero,:); + coord=coord(sizeOrder,:); + + %%% Make unit colors + + if vis_valuetype(Property.markercolor,{'nx3'}), + % If multiple colors Copy unit colors so that they match spotSize + color=Property.markercolor(reshape(repmat([1:n_Hits]',1,munits)',... + munits*n_Hits,1),:); + % drop zeros away & order + color=color(notZero,:); + color=color(sizeOrder,:); + else + % only on color + color=Property.markercolor; + end + + %%% Make unit markers + + if iscell(Property.marker), + %marker shows class: + marker=char(Property.marker); + marker=marker(reshape(repmat([1:n_Hits]',1,munits)',... + munits*n_Hits,1),:); + % Drop zeros, order & make to cell array (for som_grid) + marker=marker(notZero,:); + marker=cellstr(marker(sizeOrder,:)); + else + marker=Property.marker; + end + + % som_cplane can't draw one unit with arbitrary + % coordinates as it its mixed with msize: + if size(coord,1)==1 & strcmp(mode,'lattice'), + spotSize = [spotSize; spotSize]; + coord = [coord; coord]; + end + + N=length(notZero); % for som_grid visuzalization routine + case 'pie' + % marker 'pie' requires size parameter totHits + if strcmp(mode,'pie') + coord=som_vis_coords(lattice, msize); + notZero=sum(Hits,2)>0; + Hits=Hits(notZero,:); + coord=coord(notZero,:); + N=size(notZero,1); + totHits=sqrt(sum(Hits,2)./max(sum(Hits,2))); + end + + % som_pieplane can't draw one unit with arbitrary + % coordinates as it its mixed with msize: + if size(coord,1)==1, + Hits= [Hits; Hits]; + coord = [coord; coord]; + end + otherwise + error('Whoops: internal error. Bad mode in subfunction mhit'); +end + +for i=1:length(p), %%% Main loop begins + % Set axis + axes(handles(p(i))); + % Get hold state and caxis + memhold=ishold; cax=caxis; + hold on; + + switch mode + case 'lattice' + h_(i,1)=som_cplane(lattice, coord, color, spotSize*Property.markersize); + case 'marker' + [S,m]=som_grid(lattice, [N 1],... + 'Coord',coord, ... + 'Line','none',... + 'Marker',marker,... + 'MarkerColor',color,... + 'MarkerSize', spotSize*Property.markersize); + h_=[h_;m(:)]; + case 'pie' + h_(i)=som_pieplane(lattice, coord, ... + Hits, Property.markercolor, ... + totHits*Property.markersize); + end + + % Restore hold state and caxis + if ~memhold + hold off; + end + caxis(cax); +end + +% Set edgecolor +if any(strcmp(mode,{'lattice','pie'})), + set(h_,'edgecolor',Property.edgecolor); +else + set(h_,'markeredgecolor',Property.edgecolor); +end + +%% Set object tags (for som_show_clear) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +set(h_,'Tag','Hit') + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function h_=label(Labels, lattice, msize, Property) + +% number of map units +munits=prod(msize); + +% subplots and handles +p=Property.subplot; +handles= Property.handles; + +% Set default text size +if isempty(Property.textsize) % default point size + Property.textsize=10; +end + +% Check color/set default +if isempty(Property.textcolor), + Property.textcolor='k'; +end + +% handles will be collected in h_ for output +h_=[]; + +%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +for i=1:length(p); + % set axes + axes(handles(p(i))); + % store hold state and caxis (for some reason matlab may + % change caxis(!?) + memhold=ishold; + hold on; + cax=caxis; + + % Write labels + [S,m,l,t]=som_grid(lattice, msize, ... + 'Line','none', ... + 'Marker', 'none', ... + 'Label', Labels, ... + 'LabelColor', Property.textcolor, ... + 'LabelSize', Property.textsize); + % Get handles + h_=[h_;m(:);l(:);t(:)]; + + % reset hold state and caxis + if ~memhold + hold off; + end + caxis(cax); +end + +%%% Set object tags %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +set(h_,'Tag','Lab'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function h_=traj(bmu, lattice, msize, Property) + +% number of map units +munits=prod(msize); + +% subplots and handles +p=Property.subplot; +handles=Property.handles; + +% Set default text color +%if isempty(Property.textcolor), +% Property.textcolor='k'; +%end + +% Set default text size +%if isempty(Property.textsize) +% Property.textsize=10; +%end + +% Set default marker +if isempty(Property.marker) + Property.marker='o'; +end + +% Set default markersize +if isempty(Property.markersize) + Property.markersize=10; +end + +% Set default markercolor +if isempty(Property.markercolor) + Property.markercolor='w'; +end + +% Set default sizefactor +if isempty(Property.sizefactor) + %Property.sizefactor=0; + Property.sizefactor='hit'; +end + +% Set default trajwidth +if isempty(Property.trajwidth) + Property.trajwidth=3; +end + +% Set default widthfactor +if isempty(Property.widthfactor) + Property.widthfactor='hit'; +end + +% Set default trajcolor +if isempty(Property.trajcolor) + Property.trajcolor='w'; +end + +% if no labels, do a empty cell array for syntax reasons +%if isempty(Property.text), +% Property.text=cell(munits,1); +%end + +h_=[]; % handles will be collected in h_ for output +l=length(bmu); % length of trajectory +C=sparse(munits, munits); % init a connection matrix + +%%%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Calculate the connection matrix that describes the trajectory +for i=1:l-1, + % The following if structure removes the possible redundancy due + % to travels in both directions between two nodes of trajectory + % (i.e. reflexivity) + I=bmu(i+1);J=bmu(i); + %if bmu(i)>bmu(i+1) + + %else + % I=bmu(i);J=bmu(i+1); + %end + C(I,J)=C(I,J)+1; +end + +% transitive connections are equal +C=C+C'; +% drop reflexive conncetions away +C=spdiags(zeros(munits,1),0,C); + +% Do labels of trajectory nodes + +%traj_lab=cell(munits,1); +hits=zeros(munits,1); + +for i=1:l, +% traj_lab{bmu(i)}=strvcat(traj_lab{bmu(i)},Property.text{i}); + hits(bmu(i))=(hits(bmu(i))+1); +end + +% Calculate unit coordinates +unit_coord=som_vis_coords(lattice, msize); + +% Calculate line width +if strcmp(Property.widthfactor,'equal') + TrajWidth=(C>0)*Property.trajwidth; +else + TrajWidth=Property.trajwidth.*sqrt(C./max(max(C))); +end + +% Calculate marker sizes +if strcmp(Property.sizefactor,'hit') + MarkerSize=Property.markersize*sqrt(hits/max(hits)); +else + MarkerSize=Property.markersize*(hits>0); +end + +for i=1:length(p), + axes(handles(p(i))); + % Get hold state and caxis + memhold=ishold; cax=caxis; + hold on; + + %'Label', traj_lab, ... + %'LabelColor', Property.textcolor, ... + %'LabelSize', Property.textsize, ... + + % Draw + [S,m,l,t,s]=som_grid(C,msize,'coord',unit_coord,... + 'Line','-', ... + 'LineColor', Property.trajcolor, ... + 'LineWidth', TrajWidth, ... + 'Marker', Property.marker, ... + 'MarkerColor', Property.markercolor, ... + 'MarkerSize', MarkerSize); + + % Restore hold state and caxis + if ~memhold + hold off; + end + caxis(cax); + % Get handles + h_=[h_;m(:);l(:);t(:);s(:)]; +end + +%% Set object tags %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +set(h_,'Tag','Traj'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function h_=comet(bmu, lattice, msize, Property) + +% number of map units +munits=prod(msize); + +% subplots and handles +p=Property.subplot; +handles=Property.handles; + +% Set default text color +%if isempty(Property.textcolor), +% Property.textcolor='k'; +%end + +%% Set default text size +%if isempty(Property.textsize) +% Property.textsize=10; +%end + +% Set default marker +if isempty(Property.marker) + Property.marker='o'; +end + +% Set default markersize +if isempty(Property.markersize), + if strcmp(Property.marker,'lattice'), + Property.markersize=linspace(0.8,0.1,length(bmu))'; + else + Property.markersize=sqrt(linspace(400,16,length(bmu)))'; + end +else + if strcmp(Property.marker,'lattice'), + Property.markersize=linspace(Property.markersize(1),... + Property.markersize(2), ... + length(bmu))'; + else + Property.markersize=sqrt(linspace(Property.markersize(1).^2,... + Property.markersize(2).^2, ... + length(bmu)))'; + + end +end + +% Set default markercolor +if isempty(Property.markercolor) + Property.markercolor='w'; +end + +% Set default edgecolor +if isempty(Property.edgecolor), + if vis_valuetype(Property.markercolor,{'nx3rgb'}), + Property.edgecolor='none'; + else + Property.edgecolor=Property.markercolor; + end +end + +h_=[];l_=[]; % handles will be collected in h_ for output +N_bmus=length(bmu); % length of trajectory + +% if no labels, do a empty cell array for syntax reasons +%if isempty(Property.text), +% Property.text=cell(N_bmus,1); +%end + +%%%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Calculate unit coordinates for trajectory points +unit_coord=som_vis_coords(lattice, msize); +coord=unit_coord(bmu,:); + +% Make labels for the _unique_ units that the comet hits + +unique_bmu=unique(bmu); % count units +%N_labels=length(unique_bmu); +%traj_lab=cell(N_labels,1); % cell for labels +%label_coord=unit_coord(unique_bmu,:); % label coordinates + +% Make labels +%for i=1:N_bmus, +% index=find(unique_bmu==bmu(i)); +% traj_lab{index}=strvcat(traj_lab{index},Property.text{i}); +%end + +%Main loop for drawing comets +for i=1:length(p), + % set axis + axes(handles(p(i))); + + % Get hold state and caxis + memhold=ishold; cax=caxis; + hold on; + + if strcmp(Property.marker,'lattice'), + % Draw: marker is a patch ('hexa','rect') + l_=som_cplane(lattice, coord, Property.markercolor, ... + Property.markersize); + + % Set edgecolor + set(l_,'edgecolor',Property.edgecolor); + else + % Draw: other markers than 'hexa' or 'rect' + [S,m,l,t,s]=som_grid(lattice, [N_bmus 1], 'coord', coord,... + 'Line','none', ... + 'Marker', Property.marker, ... + 'MarkerColor', Property.markercolor, ... + 'MarkerSize',Property.markersize); + + % Set edgecolor + set(m, 'markeredgecolor', Property.edgecolor); + + % Get handles from markers + h_=[h_;l_(:);m(:);l(:);t(:);s(:)]; + end + + % Set labels + %[S,m,l,t,s]=som_grid(lattice, [N_labels 1], 'coord', label_coord,... + % 'Marker','none','Line','none',... + % 'Label', traj_lab, ... + % 'LabelColor', Property.textcolor, ... + % 'LabelSize', Property.textsize); + % Get handles from labels + %h_=[h_;m(:);l(:);t(:);s(:)]; + + % Restore hold state and caxis + if ~memhold + hold off; + end + caxis(cax); +end + +%% Set object tags %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +set(h_,'Tag','Comet'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +function P=init_properties; + +% Initialize an empty property struct + +P.marker=[]; +P.markersize=[]; +P.sizefactor=[]; +P.markercolor=[]; +P.edgecolor=[]; +P.trajwidth=[]; +P.widthfactor=[]; +P.trajcolor=[]; +P.text=[]; +P.textsize=[]; +P.textcolor=[]; +P.subplot=[]; +P.shift=[]; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_show_clear.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,142 @@ +function som_show_clear(type, p) + +%SOM_SHOW_CLEAR Clear hit marks, labels or trajectories from current figure. +% +% som_show_clear([type], [p]) +% +% som_show_clear +% som_show_clear('Traj',[1 2]) +% +% Input arguments ([]'s are optional): +% [type] (string) which markers to delete (case insensitive) +% 'hit' to remove hit marks +% 'lab' to remove labels +% 'traj' to remove line trajectories +% 'comet' to remove comet trajectories +% 'all' to remove all (the default) +% [p] (vector) subplot number vector +% (string) 'all' for all subplots (the default) +% +% This function removes the objects made by SOM_SHOW_ADD from a +% figure. If no value is given for p, the function operates on every +% axis in the current figure. It simply searches for the objects with +% certain values in the 'Tag' field. It does not matter if the figure +% objects are created by SOM Toolbox -functions or not. However, if +% vector p or string 'all' _is_ given, the figure has to have been +% created by SOM_SHOW. +% +% For more help, try 'type som_show_clear' or check out the helpdesk. +% See also SOM_SHOW_ADD, SOM_SHOW. + +%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_show_clear +% +% PURPOSE +% +% Clear hit marks, labels or trajectories created by SOM_SHOW_ADD +% from the current figure. +% +% SYNTAX +% +% som_show_clear +% som_show_clear([type],[p]) +% +% DESCRIPTION +% +% The function SOM_SHOW_ADD creates some markers on the top of +% visualizations made by SOM_SHOW. These objects may be removed using +% SOM_SHOW_CLEAR even if the object handles are not known. The function +% removes the objects based on certain tags written to the 'Tag' property +% field of the objects. +% +% If the function if called without input arguments it searches for +% every object in the current figure that have string +% 'Hit','Lab','Traj' or 'Comet' in their Tag property field and +% deletes them. +% +% If input argument p is not specified, the function does not check that the +% figure is created by function SOM_SHOW. +% +% OPTIONAL INPUT ARGUMENTS +% +% type (string) Which type of markers to delete +% 'Hit' for removing hit marks +% 'Lab' labels +% 'Traj' line trajectories +% 'Comet' comet trajectories +% 'All' all (the default) +% Strings are case insensitive. +% +% p (vector) Subplots from which the markers are removed +% Specifies the subplots from which the markers are removed. +% The valid values are 1...N where N is the number of subplots. +% It is required that the figure has been created by +% the SOM_SHOW function. +% +% EXAMPLES +% +% som_show_clear; +% % deletes all labels, hit marks and trajectories in the figure +% som_show_clear('hit'); +% % deletes all the hit marks in the current figure +% som_show_clear('lab',[1 2]); +% % deletes labels in SOM_SHOW figure subplots 1 and 2. +% +% SEE ALSO +% +% som_show Basic map visualizations: component planes, u-matrix etc. +% som_show_add Show hits, labels and trajectories on SOM_SHOW visualization. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta Johan 061197 +% Version 2.0beta Johan 061099 juuso 181199 + +%%% Check number of arguments + +error(nargchk(0,2, nargin)) % check no. of input args is correct + +%%% Initialize & check & action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargin == 0 | isempty(type) | strcmp(type,'all') % delete everything + % in the gcf + delete(findobj(gcf,'Tag','Hit')); + delete(findobj(gcf, 'Tag','Lab')); + delete(findobj(gcf, 'Tag','Traj')); + delete(findobj(gcf, 'Tag','Comet')); + return +end + +if nargin < 2 | isempty(p) % check handles + handle=gcf; +else % check subplot handles if p is given + [handle,msg]=vis_som_show_data(p,gcf); + if ~isempty(msg) + error('2nd argument invalid or figure not made by SOM_SHOW: try SOM_SHOW_CLEAR without arguments.'); + end +end + +switch lower(type) % check type & make proper tag names +case 'hit' + tag = 'Hit'; +case 'lab' + tag = 'Lab'; +case 'traj' + tag = 'Traj'; +case 'comet' + tag = 'Comet'; +otherwise + error('Invalid object tag. Must be {lab | hit | traj | comet}'); +end + +%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +for i=1:length(handle), + h=findobj(handle(i),'Tag',tag); % find object handles + delete(h); % delete objects +end + +%%% No output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_show_gui.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,708 @@ +function fig = som_show_gui(input,varargin) + +%SOM_SHOW_GUI A GUI for using SOM_SHOW and associated functions. +% +% h = som_show_gui(sM); +% +% Input and output arguments: +% sM (struct) a map struct: the SOM to visualize +% h (scalar) a handle to the GUI figure +% +% This is a graphical user interface to make the usage of SOM_SHOW and +% associated functions somewhat easier for beginning users of the SOM +% Toolbox. +% +% How to use the GUI: +% 1. Start the GUI by giving command som_show_gui(sM); +% 2. Build a list of visualization planes using the buttons +% ('Add components', etc.) on the right +% - the options associated with each of the planes can be +% modified by selecting a plane from the list, and pressing +% the 'Plane options' button +% - the controls below the list apply to all planes +% - the subplot grid size can be controlled using the 'subplots' +% field on top right corner, e.g. '4 3' to get 4 times 3 grid +% 3. To visualize the planes, press the 'Visualize' button on the bottom. +% 4. To add hits, labels, trajectories (or comets) to the +% visualization, or clear them, or reset the colorbars, +% see the tools available from the 'Tools' menu. +% - the arguments to those tools are either given in the tool, +% or read from the workspace ('Select variable' buttons) +% - the tools always apply to the latest figure created +% by the GUI +% 5. To quit, press the 'Close' button on the bottom. +% +% Known bugs: +% - Especially when using the adding tools, you can easily +% give arguments which do not fit each other, and this +% results in a lengthy (and often cryptic) error message. +% In such a case, check the arguments you are giving to see +% if there's something wrong with them. See function +% SOM_SHOW_ADD for more information on how the options +% can be set. +% - The default values in the adding tools may not be +% very reasonable: you may have to try out different +% values for e.g. markersize before getting the kind of +% result you want. +% +% SOM_SHOW_GUI has two subfunctions: VIS_SHOW_GUI_COMP and +% VIS_SHOW_GUI_TOOL. These are for internal use of SOM_SHOW_GUI. +% +% See also SOM_SHOW, SOM_SHOW_ADD, SOM_SHOW_CLEAR, SOM_RECOLORBAR. + +% Copyright (c) 2000 by Roman Feldman and Juha Vesanto +% Contributed to SOM Toolbox on August 22nd, 2000 +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta roman 160800 juuso 220800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% MAIN % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +warning off; +if (nargin < 1) + errordlg({'Make sure you have SOM as input argument'; ''; ... + 'example: som_show_gui(sMap)'},'Error in SOM_VIS: input arguments'); + return +end + +if isstruct(input) + fig_h = create_main_gui(input); + if (nargout > 0) fig = fig_h; end + return; + +elseif ischar(input) + action = lower(input); + + % + udata = get(varargin{1},'UserData'); + plot_array = udata.plot_array; + l = length(plot_array); + list1_h = udata.h(1); + + if (strcmp(action,'')) + errordlg('','Error in SOM_VIS: input arguments'); + return; + + %%%%%%%%%%%%%%%%%%%% + % add_selected_comp + % + elseif (strcmp(action,'add_selected_comp')) + if isempty(plot_array(1).string), tmp = 1; else tmp = l+1; end + [sel,ok] = listdlg('ListString',udata.sM.comp_names,... + 'Name','Component selection',... + 'PromptString','Select components to add'); + if ok & ~isempty(sel), + for i=1:length(sel), + plot_array(tmp+i-1).string = udata.sM.comp_names{sel(i)}; + plot_array(tmp+i-1).args = {'comp' sel(i)}; + udata.property{tmp+i-1} = {0}; + end + set(list1_h,'Value',tmp+i-1, ... + 'String',{plot_array(:).string}); + end + + udata.plot_array = plot_array; + set(varargin{1},'UserData',udata); + + %%%%%%%%%%%%%%%%%%%% + % add_all_comps + % + elseif (strcmp(action,'add_all_comps')) + if (strcmp(plot_array(1).string,'')) + tmp = 1; + else + tmp = l+1; + end + indx = length(udata.sM.comp_names); + for (i=1:indx) + plot_array(tmp+i-1).string = udata.sM.comp_names{i}; + plot_array(tmp+i-1).args = {'comp' i}; + udata.property{tmp+i-1} = {0}; + end + % update list + set(list1_h,'Value',tmp+indx-1, ... + 'String',{plot_array(:).string}); + + udata.plot_array = plot_array; + set(varargin{1},'UserData',udata); + + %%%%%%%%%%%%%%%%%%%% + % add_u_matrix + % + elseif (strcmp(action,'add_u_matrix')) + if (strcmp(plot_array(1).string,'')) + tmp = 1; + else + tmp = l+1; + end + plot_array(tmp).string = 'U-matrix'; + plot_array(tmp).args = {'umat' 'all'}; + udata.property{tmp} = {0 'U-matrix' 1:length(udata.sM.comp_names)}; + % update list + set(list1_h,'Value',tmp, ... + 'String',{plot_array(:).string}); + + udata.plot_array = plot_array; + set(varargin{1},'UserData',udata); + + %%%%%%%%%%%%%%%%%%%% + % add_colorplane + % + elseif (strcmp(action,'add_colorplane')) + if (strcmp(plot_array(1).string,'')) + tmp = 1; + else + tmp = l+1; + end + plot_array(tmp).string = 'color plane'; + c = som_colorcode(udata.sM); + plot_array(tmp).args = {'color' c}; + udata.property{tmp} = {0 'Color code' {'rgb1' 'rgb2' 'rgb3' 'rgb4' 'hsv' '-variable-'} 1}; + % update list + set(list1_h,'Value',tmp, ... + 'String',{plot_array(:).string}); + + udata.plot_array = plot_array; + set(varargin{1},'UserData',udata); + + %%%%%%%%%%%%%%%%%%%% + % add_empty + % + elseif (strcmp(action,'add_empty')) + if (strcmp(plot_array(1).string,'')) + tmp = 1; + else + tmp = l+1; + end + plot_array(tmp).string = 'empty plane'; + plot_array(tmp).args = {'empty' ''}; + udata.property{tmp} = {''}; + % update list + set(list1_h,'Value',tmp, ... + 'String',{plot_array(:).string}); + + udata.plot_array = plot_array; + set(varargin{1},'UserData',udata); + + %%%%%%%%%%%%%%%%%%%% + % remove + % + elseif (strcmp(action,'remove')) + rm_indx = get(list1_h,'Value'); + rm_l = length(rm_indx); + % rebuild array + incl_inds = setdiff(1:length(plot_array),rm_indx); + if isempty(incl_inds), + clear plot_array; + plot_array(1).args = {}; + plot_array(1).string = ''; + udata.property = {}; + udata.property{1} = {}; + else + plot_array = plot_array(incl_inds); + udata.property = udata.property(incl_inds); + end + set(list1_h,'Value',length(plot_array), ... + 'String',{plot_array(:).string}); + + udata.plot_array = plot_array; + set(varargin{1},'UserData',udata); + + %%%%%%%%%%%%%%%%%%%% + % remove_all + % + elseif (strcmp(action,'remove_all')) + plot_array = []; + plot_array(1).args = {}; + plot_array(1).string = ''; + udata.property = {}; + set(list1_h,'Value',1, ... + 'String',{plot_array(:).string}); + + udata.plot_array = plot_array; + set(varargin{1},'UserData',udata); + + %%%%%%%%%%%%%%%%%%%% + % more_options + % + elseif (strcmp(action,'more_options')) + vis_show_gui_comp(varargin{1},get(list1_h,'Value'),'init'); + + %%%%%%%%%%%%%%%%%%%% + % close + % + elseif (strcmp(action,'close')) + close(varargin{1}); + + %%%%%%%%%%%%%%%%%%%% + % visualize + % + elseif (strcmp(action,'visualize')) %% s = {k k.^2}; plot(s{:}); + current_fig = varargin{1}; + figure; + args = [{udata.sM} plot_array(:).args]; + % edge + tmp = get(udata.h(2),'UserData'); + i = get(udata.h(2),'Value'); + args = [args {'edge' tmp{i}}]; + % bar + tmp = get(udata.h(3),'UserData'); + i = get(udata.h(3),'Value'); + args = [args {'bar' tmp{i}}]; + % norm + tmp = get(udata.h(4),'UserData'); + i = get(udata.h(4),'Value'); + args = [args {'norm' tmp{i}}]; + % size + tmp = get(udata.h(5),'String'); + args = [args {'size' eval(tmp)}]; + % colormap + tmp = get(udata.h(6),'String'); + if ~isempty(tmp) + args = [args {'colormap' eval(tmp)}]; + end + % footnote + tmp = get(udata.h(7),'String'); + args = [args {'footnote' tmp}]; + % subplots + tmp = get(udata.h(8),'String'); + if ~(strcmp(tmp,'default') | isempty(tmp)) + tmp2 = sscanf(tmp,'%i %i'); + if length(tmp2)<2, tmp2 = sscanf(tmp,'%ix%i'); end + if length(tmp2)<2, tmp = eval(tmp); + else tmp = tmp2'; + end + if length(tmp)<2, tmp(2) = 1; end + if tmp(1)*tmp(2)<length(get(udata.h(1),'string')), + close(varargin{1}); + errordlg('Too small subplot size', ... + 'Error in SOM_VIS: subplot size'); + return; + end + args = [args {'subplots' tmp}]; + end + + som_show(args{:}); + + % udata.vis_h = varargin{1}; + % first refresh plot info + udata.vis_h = setdiff( ... + udata.vis_h, ... + setdiff(udata.vis_h,get(0,'children'))); + udata.vis_h = [udata.vis_h gcf]; + set(current_fig,'UserData',udata); + + else + ; + end + +else + errordlg({'Make sure you have SOM as input argument'; ''; ... + 'example: som_show_gui(sMap)'},'Error in SOM_VIS: input arguments'); +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% ---------------------- SUBFUNCTIONS ----------------------- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% CREATE_MAIN_GUI % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function fig_h = create_main_gui(sM) + +oldFigNumber=watchon; + +% init variables +FIGURENAME = 'SOM_SHOW_GUI'; +plot_array = []; +plot_array(1).args = {}; +plot_array(1).string = ''; + +% colors +fig_color = [0.8 0.8 0.8]; +bg_color1 = [0.701960784313725 0.701960784313725 0.701960784313725]; +bg_color2 = [0.9 0.9 0.9]; + +%%%% positions %%%%% +%--------- figure +fig_i = [0.02 0.25]; +fig_s = [0.24 0.55]; +%--------- +ue = 0.02; +th = 0.03; +hint_text_pos = [0.05 0.94 0.8 th]; +big_frame_pos = [ue 0.38 (1-2*ue) 0.56]; +planes_listbox_text_pos = [0.07 0.87 0.3 th]; +planes_listbox_pos = [(ue+0.03) 0.395 0.46 0.47]; +subplots_text_pos = [0.53 0.885 0.2 th]; +subplots_pos = [0.73 0.88 0.22 0.05]; ah = 0.045; d = (planes_listbox_pos(4) - 10*ah)/7; +add_components_pos = [0.53 (sum(planes_listbox_pos([2 4]))-ah) 0.42 ah]; tmp = add_components_pos(2)-(d+ah); +add_all_components_pos = [0.53 tmp 0.42 ah]; tmp = add_all_components_pos(2)-(d+ah); +add_u_matrix_pos = [0.53 tmp 0.42 ah]; tmp = add_u_matrix_pos(2)-(d+ah); +add_colorplane_pos = [0.53 tmp 0.42 ah]; tmp = add_colorplane_pos(2)-(d+ah); +add_empty_pos = [0.53 tmp 0.42 ah]; tmp = add_empty_pos(2)-2*(d+ah)+d; +remove_pos = [0.53 tmp 0.42 ah]; tmp = remove_pos(2)-(d+ah); +remove_all_pos = [0.53 tmp 0.42 ah]; tmp = remove_all_pos(2)-2*(d+ah)+d; +plane_options_pos = [0.53 tmp 0.42 ah]; +ph = 0.041; +dd = (ph-th)/2; +tmp = big_frame_pos(2) - (planes_listbox_pos(2)-big_frame_pos(2)) - ph; +ie1 = 0.25; +tw = 0.21; +iw = 0.28; +unit_edges_text_pos = [ue (tmp+dd) tw th]; +unit_edges_pos = [ie1 tmp iw ph]; tmp = unit_edges_pos(2)-(d+ph) - d; +unit_sizes_text_pos = [ue (tmp+dd) tw th]; +unit_sizes_pos = [ie1 tmp iw ph]; tmp = unit_sizes_pos(2)-(d+ph) - d; +colorbar_dir_text_pos = [ue (tmp+dd) tw th]; +colorbar_dir_pos = [ie1 tmp iw ph]; tmp2 = sum(colorbar_dir_pos([1 3])); +colorbar_norm_text_pos = [(tmp2) (tmp+dd) 0.11 th]; +colorbar_norm_pos = [(1-ue-(iw+0.06)) tmp (iw+0.06) ph]; tmp = colorbar_norm_pos(2)-(d+ph) - d; +colormap_text_pos = [ue (tmp+dd) tw th]; +colormap_pos = [ie1 tmp iw ph]; tmp = colormap_pos(2)-(d+ph) - d; +footnote_text_pos = [ue (tmp+dd) tw th]; +footnote_pos = [ie1 tmp (1-ue-ie1) ph]; +tmp = planes_listbox_pos(2)-big_frame_pos(2); +tmp2 = ah+2*tmp; +little_frame_pos = [ue tmp (1-2*ue) tmp2]; tmp2 = little_frame_pos(2)+tmp; +ddd = 0.1; +bw = (little_frame_pos(3)-2*0.03-ddd)/2; +visualize_pos = [(ue+0.03) tmp2 bw ah]; +close_pos = [(sum(visualize_pos([1 3]))+ddd) tmp2 bw ah]; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% main figure +% +fig_h = figure( ... + 'Units','normalized', ... + 'Color',fig_color, ... + 'PaperPosition',[18 180 576 432], ... + 'PaperType','A4', ... + 'PaperUnits','normalized', ... + 'Position',[fig_i fig_s], ... + 'ToolBar','none', ... + 'NumberTitle','off', ... + 'Name',FIGURENAME, ... + 'Visible','off'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% hint text +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','left', ... + 'Position',hint_text_pos, ... + 'String','Add planes and then visualize', ... + 'Style','text'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% planes listbox +% +uicontrol( ... + 'Units','normalized', ... + 'Position',big_frame_pos, ... + 'Style','frame'); + +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color1, ... + 'HorizontalAlignment','left', ... + 'Position',planes_listbox_text_pos, ... + 'String','Planes', ... + 'Style','text'); + +list1_h = uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color2, ... + 'Position',planes_listbox_pos, ... + 'String',{plot_array(:).string}, ... + 'Style','listbox', ... + 'Max',2, ... + 'Value',1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% edit subplots +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color1, ... + 'HorizontalAlignment','center', ... + 'Position',subplots_text_pos, ... + 'String','subplots', ... + 'Style','text'); + +edit4_h = uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color2, ... + 'Position',subplots_pos, ... + 'FontSize',14, ... + 'String','', ... + 'Style','edit'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Add components' +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color1, ... + 'HorizontalAlignment','left', ... + 'Position',add_components_pos, ... + 'String',' Add components', ... + 'Callback',['som_show_gui(''add_selected_comp'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Add all components' +% +uicontrol( ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'Position',add_all_components_pos, ... + 'String',' Add all components', ... + 'Callback',['som_show_gui(''add_all_comps'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Add U-matrix' +% +uicontrol( ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'Position',add_u_matrix_pos, ... + 'String',' Add U-matrix', ... + 'Callback',['som_show_gui(''add_u_matrix'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Add colorplane' +% +uicontrol( ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'Position',add_colorplane_pos, ... + 'String',' Add colorplane', ... + 'Callback',['som_show_gui(''add_colorplane'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Add empty' +% +uicontrol( ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'Position',add_empty_pos, ... + 'String',' Add empty', ... + 'Callback',['som_show_gui(''add_empty'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Remove' +% +uicontrol( ... + 'Units','normalized', ... + 'HorizontalAlignment','left', ... + 'Position',remove_pos, ... + 'String',' Remove', ... + 'Callback',['som_show_gui(''remove'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% creat pushbutton 'Remove all' +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color1, ... + 'HorizontalAlignment','left', ... + 'Position',remove_all_pos, ... + 'String',' Remove all', ... + 'Callback',['som_show_gui(''remove_all'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Plane options' +% +uicontrol( ... + 'Units','normalized', ... + 'Position',plane_options_pos, ... + 'String',' Plane options', ... + 'Callback',['som_show_gui(''more_options'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% popupmenu unitedges +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','left', ... + 'Position',unit_edges_text_pos, ... + 'String','unit edges are', ... + 'Style','text'); + +popup1_h = uicontrol( ... + 'Units','normalized', ... + 'Max',2, ... + 'Min',1, ... + 'Position',unit_edges_pos, ... + 'UserData',{'off' 'on'}, ... + 'String',{'off' 'on'}, ... + 'Style','popupmenu', ... + 'Value',1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% unit sizes edit +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','left', ... + 'Position',unit_sizes_text_pos, ... + 'String','unit sizes', ... + 'Style','text'); + +edit1_h = uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color2, ... + 'Position',unit_sizes_pos, ... + 'FontSize',12, ... + 'String','1', ... + 'Style','edit'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% popupmenu colorbardir +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','left', ... + 'Position',colorbar_dir_text_pos, ... + 'String','colorbar is', ... + 'Style','text'); + +popup2_h = uicontrol( ... + 'Units','normalized', ... + 'Max',3, ... + 'Min',1, ... + 'Position',colorbar_dir_pos, ... + 'UserData', {'vert' 'horiz' 'none'}, ... + 'String','vert| horiz| none', ... + 'Style','popupmenu', ... + 'Value',1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% popupmenu colorbarnorm +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','left', ... + 'Position',colorbar_norm_text_pos, ... + 'String',' and ', ... + 'Style','text'); + +popup3_h = uicontrol( ... + 'Units','normalized', ... + 'Max',2, ... + 'Min',1, ... + 'Position',colorbar_norm_pos, ... + 'UserData', {'d' 'n'}, ... + 'String',{'denormalized','normalized'}, ... + 'Style','popupmenu', ... + 'Value',1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% colormap edittext +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','left', ... + 'Position',colormap_text_pos, ... + 'String','colormap', ... + 'Style','text'); + +edit2_h = uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color2, ... + 'Position',colormap_pos, ... + 'FontSize',12, ... + 'String','', ... + 'Style','edit'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% footnote edittext +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','left', ... + 'Position',footnote_text_pos, ... + 'String','footnote', ... + 'Style','text'); + +edit3_h = uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color2, ... + 'Position',footnote_pos, ... + 'FontSize',12, ... + 'String',sM.name, ... + 'Style','edit'); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Visualize' +% +uicontrol( ... + 'Units','normalized', ... + 'Position',little_frame_pos, ... + 'Style','frame'); + +uicontrol( ... + 'Units','normalized', ... + 'Position',visualize_pos, ... + 'String','Visualize', ... + 'Callback',['som_show_gui(''visualize'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% pushbutton 'Close' +% +uicontrol( ... + 'Units','normalized', ... + 'BackgroundColor',bg_color1, ... + 'Position',close_pos, ... + 'String','Close', ... + 'Callback',['som_show_gui(''close'',' mat2str(gcf) ')']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% menus +% +uimenu('Parent',fig_h','Label',' ','Enable','off'); +m = uimenu('Parent',fig_h,'Label','Tools'); + a = uimenu('Parent',m,'Label','Add'); + s = strcat('vis_show_gui_tool(',mat2str(gcf),',''add_label'')'); + uimenu('Parent',a,'Label','label','Callback',s); + s = strcat('vis_show_gui_tool(',mat2str(gcf),',''add_hit'')'); + uimenu('Parent',a,'Label','hit','Callback',s); + s = strcat('vis_show_gui_tool(',mat2str(gcf),',''add_traj'')'); + uimenu('Parent',a,'Label','traj','Callback',s); + s = strcat('vis_show_gui_tool(',mat2str(gcf),',''add_comet'')'); + uimenu('Parent',a,'Label','comet','Callback',s); + s = ['vis_show_gui_tool(',mat2str(gcf),',''clear'')']; + c = uimenu('Parent',m,'Label','Clear','Separator','on','callback',s); + s = strcat('vis_show_gui_tool(',mat2str(gcf),',''recolorbar'')'); + r = uimenu('Parent',m,'Label','Recolorbar','Separator','on', ... + 'Callback',s); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% end +% + +ud.sM = sM; +ud.plot_array = plot_array; +ud.property = {}; +ud.vis_h = []; +ud.h = [list1_h popup1_h popup2_h popup3_h ... + edit1_h edit2_h edit3_h edit4_h]; + +watchoff(oldFigNumber); +set(fig_h,'Visible','on', ... + 'UserData', ud, ... + 'handlevisibility','off'); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_sompaktrain.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,443 @@ +function [sMap, sTrain] = som_sompaktrain(sMap, D, varargin) + +%SOM_SOMPAKTRAIN Use SOM_PAK to train the Self-Organizing Map. +% +% [sM,sT] = som_sompaktrain(sM, D, [[argID,] value, ...]) +% +% sM = som_sompaktrain(sM,D); +% sM = som_sompaktrain(sM,sD,'alpha_type','inv'); +% [M,sT] = som_sompaktrain(M,D,'bubble','trainlen',10,'inv','hexa'); +% +% Input and output arguments ([]'s are optional): +% sM (struct) map struct, the trained and updated map is returned +% (matrix) codebook matrix of a self-organizing map +% size munits x dim or msize(1) x ... x msize(k) x dim +% The trained map codebook is returned. +% D (struct) training data; data struct +% (matrix) training data, size dlen x dim +% (string) name of data file +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% sT (struct) learning parameters used during the training +% +% Here are the valid argument IDs and corresponding values. The values which +% are unambiguous (marked with '*') can be given without the preceeding argID. +% 'msize' (vector) map size +% 'radius_ini' (scalar) neighborhood radius +% 'radius' = 'radius_ini' +% 'alpha_ini' (scalar) initial learning rate +% 'alpha' = 'alpha_ini' +% 'trainlen' (scalar) training length +% 'seed' (scalar) seed for random number generator +% 'snapfile' (string) base name for snapshot files +% 'snapinterval' (scalar) snapshot interval +% 'tlen_type' *(string) is the given trainlen 'samples' or 'epochs' +% 'train' *(struct) train struct, parameters for training +% 'sTrain','som_train' = 'train' +% 'alpha_type' *(string) learning rate function, 'inv' or 'linear' +% 'neigh' *(string) neighborhood function, 'gaussian' or 'bubble' +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'lattice' *(string) map lattice, 'hexa' or 'rect' +% +% For more help, try 'type som_sompaktrain' or check out online documentation. +% See also SOM_MAKE, SOM_SEQTRAIN, SOM_BATCHTRAIN, SOM_TRAIN_STRUCT. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_sompaktrain +% +% PURPOSE +% +% Use SOM_PAK to train the Self-Organizing Map. +% +% SYNTAX +% +% sM = som_sompaktrain(sM,D); +% sM = som_sompaktrain(sM,sD); +% sM = som_sompaktrain(...,'argID',value,...); +% sM = som_sompaktrain(...,value,...); +% [sM,sT] = som_sompaktrain(M,D,...); +% +% DESCRIPTION +% +% Trains the given SOM (sM or M above) with the given training data (sD or +% D) using SOM_PAK. If no optional arguments (argID, value) are +% given, a default training is done, the parameters are obtained from +% SOM_TRAIN_STRUCT function. Using optional arguments the training +% parameters can be specified. Returns the trained and updated SOM and a +% train struct which contains information on the training. +% +% Notice that the SOM_PAK program 'vsom' must be in the search path of your +% shell. Alternatively, you can set a variable 'SOM_PAKDIR' in the Matlab +% workspace to tell the som_sompaktrain where to find the 'vsom' program. +% +% Notice also that many of the training parameters are much more limited in +% values than when using SOM Toolbox function for training: +% - the map shape is always 'sheet' +% - only initial value for neighborhood radius can be given +% - neighborhood function can only be 'bubble' or 'gaussian' +% - only initial value for learning rate can be given +% - learning rate can only be 'linear' or 'inv' +% - mask cannot be used: all variables are always used in BMU search +% Any parameters not confirming to these restrictions will be converted +% so that they do before training. On the other hand, there are some +% additional options that are not present in the SOM Toolbox: +% - random seed +% - snapshot file and interval +% +% REQUIRED INPUT ARGUMENTS +% +% sM The map to be trained. +% (struct) map struct +% (matrix) codebook matrix (field .data of map struct) +% Size is either [munits dim], in which case the map grid +% dimensions (msize) should be specified with optional arguments, +% or [msize(1) ... msize(k) dim] in which case the map +% grid dimensions are taken from the size of the matrix. +% Lattice, by default, is 'rect' and shape 'sheet'. +% D Training data. +% (struct) data struct +% (matrix) data matrix, size [dlen dim] +% (string) name of data file +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is +% used. The valid IDs and corresponding values are listed below. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% +% 'msize' (vector) map grid dimensions. Default is the one +% in sM (field sM.topol.msize) or +% 'si = size(sM); msize = si(1:end-1);' +% if only a codebook matrix was given. +% 'radius_ini' (scalar) initial neighborhood radius +% 'radius' (scalar) = 'radius_ini' +% 'alpha_ini' (vector) initial learning rate +% 'alpha' (scalar) = 'alpha_ini' +% 'trainlen' (scalar) training length (see also 'tlen_type') +% 'seed' (scalar) seed for random number generator +% 'snapfile' (string) base name for snapshot files +% 'snapinterval' (scalar) snapshot interval +% 'tlen_type' *(string) is the trainlen argument given in 'epochs' or +% in 'samples'. Default is 'epochs'. +% 'train' *(struct) train struct, parameters for training. +% Default parameters, unless specified, +% are acquired using SOM_TRAIN_STRUCT (this +% also applies for 'trainlen', 'alpha_type', +% 'alpha_ini', 'radius_ini' and 'radius_fin'). +% 'sTrain', 'som_topol' (struct) = 'train' +% 'neigh' *(string) The used neighborhood function. Default is +% the one in sM (field '.neigh') or 'gaussian' +% if only a codebook matrix was given. The other +% possible value is 'bubble'. +% 'topol' *(struct) topology of the map. Default is the one +% in sM (field '.topol'). +% 'sTopol', 'som_topol' (struct) = 'topol' +% 'alpha_type' *(string) learning rate function, 'inv' or 'linear' +% 'lattice' *(string) map lattice. Default is the one in sM +% (field sM.topol.lattice) or 'rect' +% if only a codebook matrix was given. +% +% OUTPUT ARGUMENTS +% +% sM the trained map +% (struct) if a map struct was given as input argument, a +% map struct is also returned. The current training +% is added to the training history (sM.trainhist). +% The 'neigh' and 'mask' fields of the map struct +% are updated to match those of the training. +% (matrix) if a matrix was given as input argument, a matrix +% is also returned with the same size as the input +% argument. +% sT (struct) train struct; information of the accomplished training +% +% EXAMPLES +% +% Simplest case: +% sM = som_sompaktrain(sM,D); +% sM = som_sompaktrain(sM,sD); +% +% The change training parameters, the optional arguments 'train', +% 'neigh','mask','trainlen','radius','radius_ini', 'alpha', +% 'alpha_type' and 'alpha_ini' are used. +% sM = som_sompaktrain(sM,D,'bubble','trainlen',10,'radius_ini',3); +% +% Another way to specify training parameters is to create a train struct: +% sTrain = som_train_struct(sM,'dlen',size(D,1),'algorithm','seq'); +% sTrain = som_set(sTrain,'neigh','gaussian'); +% sM = som_sompaktrain(sM,D,sTrain); +% +% You don't necessarily have to use the map struct, but you can operate +% directly with codebook matrices. However, in this case you have to +% specify the topology of the map in the optional arguments. The +% following commads are identical (M is originally a 200 x dim sized matrix): +% M = som_sompaktrain(M,D,'msize',[20 10],'lattice','hexa'); +% +% M = som_sompaktrain(M,D,'msize',[20 10],'hexa'); +% +% sT= som_set('som_topol','msize',[20 10],'lattice','hexa'); +% M = som_sompaktrain(M,D,sT); +% +% M = reshape(M,[20 10 dim]); +% M = som_sompaktrain(M,D,'hexa'); +% +% The som_sompaktrain also returns a train struct with information on the +% accomplished training. This is the same one as is added to the end of the +% trainhist field of map struct, in case a map struct is given. +% [M,sTrain] = som_sompaktrain(M,D,'msize',[20 10]); +% +% [sM,sTrain] = som_sompaktrain(sM,D); % sM.trainhist(end)==sTrain +% +% SEE ALSO +% +% som_make Initialize and train a SOM using default parameters. +% som_seqtrain Train SOM with sequential algorithm. +% som_batchtrain Train SOM with batch algorithm. +% som_train_struct Determine default training parameters. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 151199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +error(nargchk(2, Inf, nargin)); % check the number of input arguments + +% map +struct_mode = isstruct(sMap); +if struct_mode, + sTopol = sMap.topol; +else + orig_size = size(sMap); + if ndims(sMap) > 2, + si = size(sMap); dim = si(end); msize = si(1:end-1); + M = reshape(sMap,[prod(msize) dim]); + else + msize = [orig_size(1) 1]; + dim = orig_size(2); + end + sMap = som_map_struct(dim,'msize',msize); + sTopol = sMap.topol; +end +[munits dim] = size(sMap.codebook); + +% data +givendatafile = ''; +if ischar(D), + data_name = D; + givendatafile = D; + D = []; + dlen = NaN; +else + if isstruct(D), + data_name = D.name; + D = D.data; + else + data_name = inputname(2); + end + D = D(find(sum(isnan(D),2) < dim),:); % remove empty vectors from the data + [dlen ddim] = size(D); % check input dimension + if ddim ~= dim, error('Map and data dimensions must agree.'); end +end + +% varargin +sTrain = som_set('som_train','algorithm','seq',... + 'neigh',sMap.neigh,... + 'mask',ones(dim,1),... + 'data_name',data_name); +tlen_type = 'epochs'; +random_seed = 0; +snapshotname = ''; +snapshotinterval = 0; + +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'msize', i=i+1; sTopol.msize = varargin{i}; + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case 'neigh', i=i+1; sTrain.neigh = varargin{i}; + case 'trainlen', i=i+1; sTrain.trainlen = varargin{i}; + case 'tlen_type', i=i+1; tlen_type = varargin{i}; + case 'radius_ini', i=i+1; sTrain.radius_ini = varargin{i}; + case 'radius', i=i+1; sTrain.radius_ini = varargin{i}(1); + case 'alpha_type', i=i+1; sTrain.alpha_type = varargin{i}; + case 'alpha_ini', i=i+1; sTrain.alpha_ini = varargin{i}; + case 'alpha', i=i+1; sTrain.alpha_ini = varargin{i}(1); + case 'seed', i=i+1; random_seed = varargin{i}; + case 'snapshotname',i=i+1; snapshotname = varargin{i}; + case 'snapshotinterval',i=i+1; snapshotinterval = varargin{i}; + case {'sTrain','train','som_train'}, i=i+1; sTrain = varargin{i}; + case {'topol','sTopol','som_topol'}, + i=i+1; + sTopol = varargin{i}; + if prod(sTopol.msize) ~= munits, + error('Given map grid size does not match the codebook size.'); + end + % unambiguous values + case {'inv','linear'}, sTrain.alpha_type = varargin{i}; + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'gaussian','bubble'}, sTrain.neigh = varargin{i}; + case {'epochs','samples'}, tlen_type = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', + sTopol = varargin{i}; + if prod(sTopol.msize) ~= munits, + error('Given map grid size does not match the codebook size.'); + end + case 'som_train', sTrain = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_sompaktrain) Ignoring invalid argument #' num2str(i+2)]); + end + i = i+1; +end + +% check topology +if struct_mode, + if ~strcmp(sTopol.lattice,sMap.topol.lattice) | ... + ~strcmp(sTopol.shape,sMap.topol.shape) | ... + any(sTopol.msize ~= sMap.topol.msize), + warning('Changing the original map topology.'); + end +end +sMap.topol = sTopol; + +% complement the training struct +if ~isnan(dlen), + sTrain = som_train_struct(sTrain,sMap,'dlen',dlen); +else + sTrain = som_train_struct(sTrain,sMap); +end +if isempty(sTrain.mask), sTrain.mask = ones(dim,1); end + +% training length +if strcmp(tlen_type,'epochs'), + if isnan(dlen), + error('Training length given as epochs, but data length is not known.\n'); + else + rlen = sTrain.trainlen*dlen; + end +else + rlen = sTrain.trainlen; + sTrain.trainlen = sTrain.trainlen/dlen; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% mask +if any(sTrain.mask~=1), + sTrain.mask = ones(dim,1); + fprintf(1,'Ignoring given mask.\n'); +end + +% learning rate +if strcmp(sTrain.alpha_type,'power'), + sTrain.alpha_type = 'inv'; + fprintf(1,'Using ''inv'' learning rate type instead of ''power''\n'); +end + +% neighborhood +if any(strcmp(sTrain.neigh,{'cutgauss','ep'})), + fprintf(1,'Using ''gaussian'' neighborhood function instead of %s.\n',sTrain.neigh); + sTrain.neigh = 'gaussian'; +end + +% map shape +if ~strcmp(sMap.topol.shape,'sheet'), + fprintf(1,'Using ''sheet'' map shape of %s.\n',sMap.topol.shape); + sMap.topol.shape = 'sheet'; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +% write files +if ~isempty(givendatafile), + temp_din = givendatafile; +else + temp_din = tempname; + som_write_data(D, temp_din, 'x') +end +temp_cin = tempname; +som_write_cod(sMap, temp_cin) +temp_cout = tempname; + +% check if the environment variable 'SOM_PAKDIR' has been defined +if any(strcmp('SOM_PAKDIR', evalin('base', 'who'))) + som_pak_dir = evalin('base', 'SOM_PAKDIR'); +else + som_pak_dir = ''; +end +if ~isempty(som_pak_dir) & ~strncmp(som_pak_dir(end), '/', 1) + som_pak_dir(end + 1) = '/'; +end + +aini = sTrain.alpha_ini; +atype = sTrain.alpha_type; +if strcmp(atype,'inv'), atype = 'inverse_t'; end +rad = sTrain.radius_ini; +str = [som_pak_dir 'vsom ' ... + sprintf('-cin %s -din %s -cout %s', temp_cin, temp_din, temp_cout) ... + sprintf(' -rlen %d -alpha %g -alpha_type %s', rlen, aini, atype) ... + sprintf(' -radius %g -rand %g ',rad,random_seed)]; +if ~isempty(snapshotname) & snapinterval>0, + str = [str, sprintf(' -snapfile %s -snapinterval %d',snapshotname,snapshotinterval)]; +end + +fprintf(1,'Execute: %s\n',str); +if isunix, + [status,w] = unix(str); + if status, fprintf(1,'Execution failed.\n'); end + if ~isempty(w), fprintf(1,'%s\n',w); end +else + [status,w] = dos(str); + if status, fprintf(1,'Execution failed.\n'); end + if ~isempty(w), fprintf(1,'%s\n',w); end +end + +sMap_temp = som_read_cod(temp_cout); +M = sMap_temp.codebook; + +if isunix + unix(['/bin/rm -f ' temp_din ' ' temp_cin ' ' temp_cout]); +else + dos(['del ' temp_din ' ' temp_cin ' ' temp_cout]); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Build / clean up the return arguments + +% update structures +sTrain = som_set(sTrain,'time',datestr(now,0)); +if struct_mode, + sMap = som_set(sMap,'codebook',M,'mask',sTrain.mask,'neigh',sTrain.neigh); + tl = length(sMap.trainhist); + sMap.trainhist(tl+1) = sTrain; +else + sMap = reshape(M,orig_size); +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_stats.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,257 @@ +function csS = som_stats(D,varargin) + +%SOM_STATS Calculate descriptive statistics for the data. +% +% csS = som_stats(D,[sort]); +% +% csS = som_stats(D); +% csS = som_stats(D,'nosort'); +% som_table_print(som_stats_table(csS)) +% +% Input and output arguments ([]'s are optional): +% D (matrix) a matrix, size dlen x dim +% (struct) data or map struct +% [sort] (string) 'sort' (default) or 'nosort' +% If 'nosort' is specified, the data is not +% sorted, and therefore the values of +% nunique, uvalues, ucount, fvalues, fcount, and tiles fields +% are not calculated. This may be useful if +% there is a very large amount of data, and +% one wants to reduce calculation time. +% +% csS (cell array) size dim x 1, of statistics structs with +% the following fields +% .type (string) 'som_stat' +% .name (string) name of the variable +% .normalization (struct array) variable normalization (see SOM_NORMALIZE) +% .ntotal (scalar) total number of values +% .nvalid (scalar) number of valid values (not Inf or NaN) +% .min (scalar) minimum value +% .max (scalar) maximum value +% .mean (scalar) mean value (not Inf or NaN) +% .std (scalar) standard deviation (not Inf or NaN) +% .nunique (scalar) number of unique values +% .mfvalue (vector) most frequent value +% .mfcount (vector) number of occurances of most frequent value +% .values (vector) at most MAXDISCRETE (see below) sample values +% .counts (vector) number of occurances for each sampled value +% .tiles (vector) NT-tile values, for example +% NT=4 for quartiles: 25%, 50% and 75% +% NT=100 for percentiles: 1%, 2%, ... and 99% +% .hist (struct) histogram struct with the following fields +% .type (string) 'som_hist' +% .bins (vector) histogram bin centers +% .counts (vector) count of values in each bin +% .binlabels (cellstr) labels for the bins (denormalized bin +% center values) +% .binlabels2 (cellstr) labels for the bins (denormalized bin +% edge values, e.g. '[1.4,2.5[' +% +% Constants: +% MAXDISCRETE = 10 +% NT = 10 +% +% See also SOM_STATS_PLOT, SOM_STATS_TABLE, SOM_TABLE_PRINT, SOM_STATS_REPORT. + +% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 311201 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 +%% arguments + +% default values +nosort = 0; +nbins = 10; +maxdiscrete = 20; +ntiles = 10; + +% first argument +if isstruct(D), + switch D.type, + case 'som_map', cn = D.comp_names; sN = D.comp_norm; D = D.codebook; + case 'som_data', cn = D.comp_names; sN = D.comp_norm; D = D.data; + otherwise, error('Invalid first argument') + end +else + cn = cell(size(D,2),1); + cn(:) = {'Variable'}; + for i=1:length(cn), cn{i} = sprintf('%s%d',cn{i},i); end + sN = cell(size(D,2),1); +end +[dlen dim] = size(D); + +% other arguments + +if length(varargin)>0, + if strcmp(varargin{1},'nosort'), nosort = 1; end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 +%% action + +sStat = struct('type','som_stat','name','','normalization',[],... + 'min',NaN,'max',NaN,'mean',NaN,'std',NaN,... + 'nunique',NaN,'values',[],'counts',[],'mfvalue',NaN,'mfcount',NaN,'tiles',[],... + 'ntotal',dlen,'nvalid',NaN,'hist',[]); +csS = cell(0); + +for i=1:dim, + sS = sStat; + sS.name = cn{i}; + sS.normalization = sN{i}; + x = D(:,i); + x(find(~isfinite(x))) = []; + % basic descriptive statistics + sS.nvalid = length(x); + if length(x), + sS.min = min(x); + sS.max = max(x); + sS.mean = mean(x); + sS.std = std(x); + bins = []; + if ~nosort, + xsorted = sort(x); + % number of unique values + repeated = (xsorted(1:end-1)==xsorted(2:end)); + j = [1; find(~repeated)+1]; + xunique = xsorted(j); + sS.nunique = length(xunique); + ucount = diff([j; length(xsorted)+1]); + % most frequent value + [fcount,j] = max(ucount); + sS.mfvalue = xunique(j); + sS.mfcount = fcount; + % -tiles (k*100/ntiles % of values, k=1..) + pickind = round(linspace(1,sS.nvalid,ntiles+1)); + pickind = pickind(2:end-1); + sS.tiles = xsorted(pickind); + if sS.nunique <= sS.nvalid/2, + % unique values + sS.values = xunique; + sS.counts = ucount; + bins = sS.values; + else + % just maxdiscrete values, evenly picked + pickind = round(linspace(1,sS.nunique,maxdiscrete)); + sS.values = xunique(pickind); + sS.counts = ucount(pickind); + + %% OPTION 2: maxdiscrete most frequent values + %[v,j] = sort(ucount); + %pickind = j(1:maxdiscrete); + %sS.values = xunique(pickind); + %sS.counts = ucount(pickind); + + % OPTION 3: representative values - calculated using k-means + %[y,bm,qe] = kmeans(x,maxdiscrete); + %sS.values = y; + %sS.counts = full(sum(sparse(bm,1:length(bm),1,maxdiscrete,length(bm)),2)); + end + end + if isempty(bins), + bins = linspace(sS.min,sS.max,nbins+1); + bins = (bins(1:end-1)+bins(2:end))/2; + end + sS.hist = som_hist(x,bins,sS.normalization); + else + sS.hist = som_hist(x,0); + end + csS{end+1} = sS; +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 +%% subfunctions + +function sH = som_hist(x,bins,sN) + + binlabels = []; + binlabels2 = []; + if nargin<2 | isempty(bins) | isnan(bins), + bins = linspace(min(x),max(x),10); + end + if isstruct(bins), + bins = sH.bins; + binlabels = sH.binlabels; + binlabels2 = sH.binlabels2; + end + if nargin<3, sN = []; end + + sH = struct('type','som_hist','bins',bins,'counts',[],... + 'binlabels',binlabels,'binlabels2',binlabels2); + + if length(bins)==1, + sH.counts = [length(x)]; + edges = bins; + elseif length(x), + edges = (bins(1:end-1)+bins(2:end))/2; + counts = histc(x,[-Inf; edges(:); Inf]); + sH.counts = counts(1:end-1); + end + + if isempty(sH.binlabels), + b = som_denormalize(bins(:),sN); + sH.binlabels = numtostring(b,4); + end + + if isempty(sH.binlabels2), + if length(edges)==1, + sH.binlabels2 = numtostring(som_denormalize(edges,sN),2); + if length(bins)>1, + sH.binlabels2 = sH.binlabels2([1 1]); + sH.binlabels2{1} = [']' sH.binlabels2{1} '[']; + sH.binlabels2{2} = ['[' sH.binlabels2{2} '[']; + end + else + if size(edges,1)==1, edges = edges'; end + bstr = numtostring(som_denormalize(edges,sN),4); + sH.binlabels2 = bstr([1:end end]); + sH.binlabels2{1} = [bstr{1} '[']; + for i=2:length(sH.binlabels2)-1, + sH.binlabels2{i} = ['[' bstr{i-1} ',' bstr{i} '[']; + end + sH.binlabels2{end} = ['[' bstr{end}]; + end + end + + if 0, + if length(bins)==1, sH.binlabels2 = {'constant'}; + else + ntiles = 10; + plim = [1:ntiles-1] / ntiles; + cp = cumsum(sH.counts)/sum(sH.counts); + [dummy,i] = histc(cp,[-Inf plim Inf]); + l2 = cell(length(bins),1); + for j=1:length(bins), l2{j} = sprintf('Q%d',i(j)); end + if i(1) > 1, l2{1} = ['...' l2{1}]; end + k = 0; + for j=2:length(bins), + if i(j)==i(j-1), + if k==0, l2{j-1} = [l2{j-1} '.1']; k = 1; end + k = k + 1; + l2{j} = [l2{j} '.' num2str(k)]; + else k = 0; end + end + if i(end) < ntiles, l2{end} = [l2{end} '...']; end + sH.binlabels2 = l2; + end + end + + return; + +function vstr = numtostring(v,d) + + r = max(v)-min(v); + if r==0, r=1; end + nearzero = (abs(v)/r < 10.^-d); + i1 = find(v > 0 & nearzero); + i2 = find(v < 0 & nearzero); + vstr = strrep(cellstr(num2str(v,d)),' ',''); + vstr(i1) = {'0.0'}; + vstr(i2) = {'-0.0'}; + return; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_stats_plot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,144 @@ +function som_stats_plot(csS,plottype,varargin) + +%SOM_STATS_PLOT Plots of data set statistics. +% +% som_stats_plot(csS, plottype, [argID, value, ...]) +% +% som_stats_plot(csS,'stats') +% som_stats_plot(csS,'stats','p','vert','color','r') +% +% Input and output arguments ([]'s are optional): +% csS (cell array) of statistics structs +% (struct) a statistics struct +% plottype (string) some of the following +% 'hist' histogram +% 'box' min, max, mean, and std shown as a boxplot +% 'stats' both histogram (with black) and the boxplot +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% Here are the valid argument IDs and corresponding values. The values which +% are unambiguous (marked with '*') can be given without the preceeding argID. +% 'counts' *(string) 'c' (for counts, the default) or 'p' (for percentages) +% 'color' (vector) size 1 x 3, color to be used +% (string) a color string +% 'title' (string) 'on' (default) or 'off' +% 'orientation' *(string) 'horiz' or 'vert' (default): orientation for the +% bin values (horizontally or vertically) +% +% See also SOM_STATS, SOM_STATS_TABLE, SOM_TABLE_PRINT, SOM_STATS_REPORT. + +% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 311201 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% arguments + +% statistics +if isstruct(csS), csS = {csS}; end + +% default values +useprob = 0; +color = [0 0 1]; +showtitle = 1; +horiz = 0; + +% varargin +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'counts', i=i+1; useprob = strcmp(varargin{i}(1),'p'); + case 'color', i=i+1; color = varargin{i}; + case 'title', i=i+1; showtitle = strcmp(varargin{i},'on'); + case 'orientation', i=i+1; horiz = strcmp(varargin{i},'horiz'); + % unambiguous values + case {'horiz','vert'}, horiz = strcmp(varargin{i},'horiz'); + case {'c','p'}, useprob = strcmp(varargin{i}(1),'p'); + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + argok = 0; + else + argok = 0; + end + if ~argok, + disp(['(som_stats_plot) Ignoring invalid argument #' num2str(i+2)]); + end + i = i+1; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 +%% action + +ss = ceil(sqrt(length(csS))); ss = [ss, ceil(length(csS)/ss)]; + +for j = 1:length(csS), + sS = csS{j}; + subplot(ss(1),ss(2),j); + switch plottype, + case 'stats', + cla, hold on + Counts = sS.hist.counts; + if useprob, for i=1:size(Counts,2), Counts(:,i) = Counts(:,i)/sum(Counts(:,i)); end, end + hist_plot(sS.hist.bins,sS.hist.binlabels,Counts,color); + box_plot(sS.min,sS.max,sS.mean,sS.std,[0 0 0]); + case 'hist', + cla, hold on + Counts = sS.hist.counts; + if useprob, for i=1:size(Counts,2), Counts(:,i) = Counts(:,i)/sum(Counts(:,i)); end, end + hist_plot(sS.hist.bins,sS.hist.binlabels,Counts,color); + case 'box', + cla + box_plot(sS.min,sS.max,sS.mean,sS.std,color); + end + if showtitle, title(sprintf('%s (valid: %d/%d)',sS.name,sS.nvalid,sS.ntotal)); end + if ~horiz, view(90,-90); end + a = axis; a(1) = sS.min; a(2) = sS.max; axis(a); +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 +%% subfunctions + +function hist_plot(bins,binlabels,Counts,color) + + if nargin<4, color = jet(size(Counts,2)); end + h = bar(bins,Counts); + for j=1:length(h), set(h(j),'facecolor',color(j,:),'edgecolor','none'); end + a = axis; a(3:4) = [0 max(Counts(:))]; axis(a); + set(gca,'XTick',bins,'XTickLabel',binlabels); + return; + +function vstr = numtostring(v,d) + + nearzero = (abs(v)/(max(v)-min(v)) < 10.^-d); + i1 = find(v > 0 & nearzero); + i2 = find(v < 0 & nearzero); + vstr = strrep(cellstr(num2str(v,d)),' ',''); + vstr(i1) = {'0.0'}; + vstr(i2) = {'-0.0'}; + return; + +function box_plot(mi,ma,me,st,Color) + + if nargin < 5, Color = jet(length(mi)); end + a = axis; + y = linspace(a(3),a(4),length(mi)+2); y = y(2:end); + d = (y(2)-y(1))/20; + for i=1:length(mi), + h1 = line([mi(i) ma(i)],[y(i) y(i)]); + h2 = line([mi(i) mi(i) NaN ma(i) ma(i)],[y(i)-d y(i)+d NaN y(i)-d y(i)+d]); + h3 = line([me(i)-st(i) me(i)+st(i)],[y(i) y(i)]); + h4 = line([me(i) me(i)],[y(i)-2*d y(i)+2*d]); + set([h1 h2 h3 h4],'color',Color(i,:)); + set([h1 h2],'linewidth',1); + set([h3 h4],'linewidth',3); + end + return;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_stats_report.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,113 @@ +function som_stats_report(csS,fname,fmt,texonly) + +% SOM_STATS_REPORT Make report of the statistics. +% +% som_stats_report(csS, fname, fmt, [standalone]) +% +% som_stats_report(csS, 'data_stats', 'ps') +% +% Input and output arguments ([]'s are optional): +% csS (cell array) of statistics structs +% (struct) a statistics struct +% fname (string) output file name (without extension) +% (cellstr) {direc, fname} +% fmt (string) report format: 'ps', 'pdf', 'html' or 'txt' +% [texonly] (any) for 'ps' and 'pdf' formats: if 4th argument +% is given, only the tex file is written +% (w/o document start/end), and it is not compiled +% +% See also SOM_STATS, SOM_STATS_PLOT, SOM_STATS_TABLE, SOM_TABLE_PRINT, REP_UTILS. + +% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 311201 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% input arguments + +if isstruct(csS), csS = {csS}; end +dim = length(csS); +if iscell(fname), direc = fname{1}; fname = fname{2}; else direc = '.'; end +if nargin<4, texonly = 0; else texonly = 1; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +% additional analysis +continuity = zeros(dim,1); +for i=1:dim, continuity(i) = csS{i}.nunique / csS{i}.nvalid; end + +entropy_rel = zeros(dim,1); +for i=1:dim, + c = csS{i}.hist.counts; + if length(c) < 2 | all(c==0), entropy(i) = 0; + else + maxent = log(length(c)); + c = c(c>0)/sum(c); + entropy_rel(i) = -sum(c.*log(c)) / maxent; + end +end + +% meta-statistics +values = {'Number of variables',dim; ... + 'Number of samples',csS{1}.ntotal; ... + 'Valid values',c_and_p_str(count_total(csS,'nvalid'),dim*csS{1}.ntotal); ... + 'Mean(#unique / #valid)',mean(continuity); ... + 'Mean relative entropy',mean(entropy_rel)}; + %'Dataset name',sD.name; 'Report generated',datestr(now); +sTdset = som_table_struct(values); + +% statistics tables +[sTstats,csThist] = som_stats_table(csS); +sTstats = som_table_modify(sTstats,'addcol',entropy_rel,{'entropy'}); + +% write report +if isempty(fname), fid = 1; +else + switch fmt, + case {'ps','pdf'}, ending = '.tex'; + case 'html', ending = '.html'; + case 'txt', ending = '.txt'; + end + fid = fopen([direc '/' fname ending],'w'); +end +if ~texonly, rep_utils('header',fmt,fid); end + +rep_utils({'inserttable',sTdset,1,0},fmt,fid); +rep_utils({'insertbreak'},fmt,fid); +rep_utils({'inserttable',sTstats,1,0},fmt,fid); +rep_utils({'insertbreak'},fmt,fid); +som_stats_plot(csS,'stats'); +rep_utils({'printfigure',[direc '/histograms']},fmt); +rep_utils({'insertfigure','histograms'},fmt,fid); +for i=1:dim, + rep_utils({'insertbreak'},fmt,fid); + rep_utils({'inserttable',csThist{i},1,0},fmt,fid); +end + +if ~texonly, rep_utils('footer',fmt,fid); end +if fid~=1, fclose(fid); end + +if ~texonly & any(strcmp(fmt,{'ps','pdf'})), rep_utils('compile',fmt); end +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function a = count_total(csS,field) + % count total of the field values + a = 0; for i=1:length(csS), a = a + getfield(csS{i},field); end + return; + +function str = c_and_p_str(n,m) + % return a string of form # (%), e.g. '23 (12%)' + if n==m, p = '100'; + elseif n==0, p = '0'; + else p = sprintf('%.2g',100*n/m); + end + str = sprintf('%d (%s%%)',round(n),p); + return; + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_stats_table.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,116 @@ +function [sTstats,csThist] = som_stats_table(csS,histlabel) + +%SOM_STATS_TABLE Statistics table. +% +% [sTstats,csThist] = som_stats_table(csS) +% +% sTstats = som_stats_table(csS); +% som_table_print(sTstats); +% +% Input and output arguments ([]'s are optional): +% csS (cell array) of statistics structs +% (struct) a statistics struct +% +% sTstats (struct) a table struct with basic descriptive +% statistics for each variable +% csThist (cell array) of table structs, with histograms for +% each variable +% +% See also SOM_STATS, SOM_STATS_PLOT, SOM_TABLE_PRINT, SOM_STATS_REPORT. + +% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 311201 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% arguments + +if isstruct(csS), csS = {csS}; end +dim = length(csS); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 +%% action + +sTable = struct('colfmt','','headers',[],'values',[],'span',[]); + +% summary table of all variables +sT = sTable; +sT.headers = {'name','min','mean','max','std','missing'}; +if ~isnan(csS{1}.nunique), sT.headers{end+1} = 'unique'; end +%if length(col_values), sT.headers = [sT.headers, col_headers]; end +sT.values = cell(dim,length(sT.headers)); +sT.span = ones([size(sT.values) 2]); + +%if length(col_values), sT.values(:,end-size(col_values,2)+1:end) = col_values; end +%if length(col_spans), sT.span(:,end-size(col_spans,2)+1:end,:) = col_spans; end + +for i=1:dim, + sT.values{i,1} = csS{i}.name; + v = [csS{i}.min,csS{i}.mean,csS{i}.max,csS{i}.std]; + v = som_denormalize(v,csS{i}.normalization); + vstr = numtostring(v,6); + sT.values(i,2:5) = vstr'; + sT.values{i,6} = c_and_p_str(csS{i}.ntotal-csS{i}.nvalid,csS{i}.ntotal); + if ~isnan(csS{1}.nunique), + sT.values{i,7} = c_and_p_str(csS{i}.nunique,csS{i}.nvalid); + end +end +sTstats = sT; + +% histograms +csThist = cell(dim,1); +for i=1:dim, + sH = csS{i}.hist; + nvalid = csS{i}.nvalid; + nbins = length(sH.bins); + sT = sTable; + sT.headers = {[csS{i}.name ' values'],'frequency #','frequency %'}; + sT.values = cell(nbins,length(sT.headers)); + sT.span = ones(nbins,length(sT.headers),2); + for j=1:nbins, + if length(sH.bins) < csS{i}.nunique, sT.values{j,1} = sH.binlabels2{j}; + else sT.values{j,1} = sH.binlabels{j}; end + sT.values{j,2} = sprintf('%d',round(sH.counts(j))); + sT.values{j,3} = p_str(sH.counts(j)/nvalid); + end + csThist{i} = sT; +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 +%% subfunctions + +function vstr = numtostring(v,d) + + tp = (size(v,2)>1); + if tp, v = v'; end + nearzero = (abs(v)/(max(v)-min(v)) < 10.^-d); + i1 = find(v > 0 & nearzero); + i2 = find(v < 0 & nearzero); + vstr = strrep(cellstr(num2str(v,d)),' ',''); + vstr(i1) = {'0.0'}; + vstr(i2) = {'-0.0'}; + if tp, vstr = vstr'; end + return; + +function str = c_and_p_str(n,m) + % return a string of form # (%), e.g. '23 (12%)' + if n==m, p = '100'; + elseif n==0, p = '0'; + else p = sprintf('%.2g',100*n/m); + end + str = sprintf('%d (%s%%)',round(n),p); + return; + +function str = p_str(p) + % return a string of form %, e.g. '12%' + if round(p*100)>100, p = sprintf('%3g',100*p); + elseif p==1, p = '100'; + elseif abs(p)<eps, p = '0'; + else p = sprintf('%.2g',100*p); + end + str = sprintf('%s%%',p); + return;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_sub2ind.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +function inds = som_sub2ind(msize,Subs) + +%SOM_SUB2IND Linear index from map grid subscripts. +% +% ind = som_sub2ind(msize,Subs) +% +% ind = som_sub2ind([10 15],[4 5]); +% ind = som_sub2ind(sMap,[4 5]); +% ind = som_sub2ind(sMap.msize,[4 5]); +% inds = som_sub2ind([10 15],[4 5; 3 2; 1 10]); +% +% Input and output arguments: +% msize (struct) map or topology struct +% (vector) size 1 x m, specifies the map grid size +% Subs (matrix) size n x m, the subscripts of n vectors +% +% inds (vector) size n x 1, corresponding linear indeces +% +% See also SOM_IND2SUB. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% juuso 300798 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if isstruct(msize), + if strcmp(msize.type,'som_map'), msize = msize.topol.msize; + elseif strcmp(msize.type,'som_topol'), msize = msize.msize; + else error('Invalid first argument.'); end +end + +% check off-limits +[n d] = size(Subs); +offl = find(Subs < 1 | Subs > msize(ones(n,1),1:d)); +Subs(offl) = NaN; + +% indexes +k = [1 cumprod(msize(1:end-1))]'; +inds = 1 + (Subs-1)*k; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_supervised.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,337 @@ +function sM = som_supervised(sData,varargin) + +%SOM_SUPERVISED SOM training which utilizes class information. +% +% sM = som_supervised(sData, [ArgID, value,...])) +% +% Input and output arguments ([]'s are optional) +% sData (struct) data struct, the class information is +% taken from the first column of .labels field +% [argID, (string) See below. These are given as +% value] (varies) 'argID', value -pairs. +% +% sMap (struct) map struct +% +% Here are the argument IDs and corresponding values: +% 'munits' (scalar) the preferred number of map units +% 'msize' (vector) map grid size +% 'mask' (vector) BMU search mask, size dim x 1 +% 'name' (string) map name +% 'comp_names' (string array / cellstr) component names, size dim x 1 +% 'tracking' (scalar) how much to report, default = 1 +% The following values are unambiguous and can therefore +% be given without the preceeding argument ID: +% 'algorithm' (string) training algorithm: 'seq' or 'batch' +% 'mapsize' (string) do you want a 'small', 'normal' or 'big' map +% Any explicit settings of munits or msize override this. +% 'topol' (struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'lattice' (string) map lattice, 'hexa' or 'rect' +% 'shape' (string) map shape, 'sheet', 'cyl' or 'toroid' +% 'neigh' (string) neighborhood function, 'gaussian', 'cutgauss', +% 'ep' or 'bubble' +% +% For more help, try 'type som_supervised', or check out online documentation. +% See also SOM_MAKE, SOM_AUTOLABEL. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_supervised +% +% PURPOSE +% +% Creates, initializes and trains a supervised SOM by taking the +% class-identity into account. +% +% SYNTAX +% +% sMap = som_supervised(sData); +% sMap = som_supervised(...,'argID',value,...) +% sMap = som_make(...,value,...); +% +% DESCRIPTION +% +% Creates, initializes and trains a supervised SOM. It constructs the +% training data by adding 1-of-N -coded matrix to the original data +% based on the class information in the .labels field. The dimension +% of vectors after the process is (the old dimension + number of +% different classes). In each vector, one of the new components has +% value '1' (this depends on the class of the vector), and others '0'. +% Calls SOM_MAKE to construct the map. Then the class of each map unit +% is determined by taking maximum over these added components, and a +% label is give accordingly. Finally, the extra components (the +% 1-of-N -coded ones) are removed. +% +% REFERENCES +% +% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, +% Berlin, 1995, pp. 160-161. +% Kohonen, T., Mäkivasara, K., Saramäki, T., "Phonetic Maps - +% Insightful Representation of Phonological Features For +% Speech Recognition", In proceedings of International +% Conference on Pattern Recognition (ICPR), Montreal, Canada, +% 1984, pp. 182-185. +% +% REQUIRED INPUT ARGUMENTS +% +% sData The data to use in the training. +% (struct) A data struct. '.comp_names' as well as '.name' +% is copied to the map. The class information is +% taken from the first column of '.labels' field. +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is used. +% Here are the argument IDs and corresponding values: +% 'munits' (scalar) the preferred number of map units - this may +% change a bit, depending on the properties of the data +% 'msize' (vector) map grid size +% 'mask' (vector) BMU search mask, size dim x 1 +% 'name' (string) map name +% 'comp_names' (string array / cellstr) component names, size dim x 1 +% 'tracking' (scalar) how much to report, default = 1. This parameter +% is also passed to the training functions. +% The following values are unambiguous and can therefore +% be given without the preceeding argument ID: +% 'algorithm' (string) training algorithm: 'seq' or 'batch' (default) +% 'mapsize' (string) do you want a 'small', 'normal' or 'big' map +% Any explicit settings of munits or msize (or topol) +% override this. +% 'topol' (struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'lattice' (string) map lattice, 'hexa' or 'rect' +% 'shape' (string) map shape, 'sheet', 'cyl' or 'toroid' +% 'neigh' (string) neighborhood function, 'gaussian', 'cutgauss', +% 'ep' or 'bubble' +% +% OUTPUT ARGUMENTS +% +% sMap (struct) SOM -map struct +% +% EXAMPLES +% +% To simply train a map with default parameters: +% +% sMap = som_supervised(sData); +% +% With the optional arguments, the initialization and training can be +% influenced. To change map size, use 'msize', 'munits' or 'mapsize' +% arguments: +% +% sMap = som_supervised(D,'mapsize','big'); or +% sMap = som_supervised(D,'big'); +% sMap = som_supervised(D,'munits', 100); +% sMap = som_supervised(D,'msize', [20 10]); +% +% Argument 'algorithm' can be used to switch between 'seq' and 'batch' +% algorithms. 'batch' is the default, so to use 'seq' algorithm: +% +% sMap = som_supervised(D,'algorithm','seq'); or +% sMap = som_supervised(D,'seq'); +% +% The 'tracking' argument can be used to control the amout of reporting +% during training. The argument is used in this function, and it is +% passed to the training functions. To make the function work silently +% set it to 0. +% +% sMap = som_supervised(D,'tracking',0); +% +% SEE ALSO +% +% som_make Create, initialize and train Self-Organizing map. +% som_autolabel Label SOM/data set based on another SOM/data set. + +% Contributed to SOM Toolbox vs2, Feb 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +D0 = sData.data; +[c,n,classlabels] = class2num(sData.labels(:,1)); + +%%%%%%%% Checking arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if ~isstruct(sData) + error('Argument ''sData'' must be a ''som_data'' -struct.'); +else + data_name = sData.name; + comp_names = sData.comp_names; + comp_norm = sData.comp_norm; +end + +[dlen,dim] = size(sData.data); + +% defaults + +mapsize = ''; +sM = som_map_struct(dim+n); +sTopol = sM.topol; +munits = prod(sTopol.msize); % should be zero +mask = sM.mask; +name = sM.name; +neigh = sM.neigh; +tracking = 1; +algorithm = 'batch'; + +%%%% changes to defaults (checking varargin) %%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +i=1; +while i <= length(varargin) + argok = 1; + if ischar(varargin{i}) + switch varargin{i}, + % argument IDs + case 'mask', + i=i+1; + mask = varargin{i}; + case 'munits', + i=i+1; + munits = varargin{i}; + case 'msize', + i=i+1; + sTopol.msize = varargin{i}; + munits = prod(sTopol.msize); + case 'mapsize', + i=i+1; + mapsize = varargin{i}; + case 'name', + i=i+1; + name = varargin{i}; + case 'comp_names', + i=i+1; + comp_names = varargin{i}; + case 'lattice', + i=i+1; + sTopol.lattice = varargin{i}; + case 'shape', + i=i+1; + sTopol.shape = varargin{i}; + case {'topol','som_topol','sTopol'}, + i=i+1; + sTopol = varargin{i}; + munits = prod(sTopol.msize); + case 'neigh', + i=i+1; + neigh = varargin{i}; + case 'tracking', + i=i+1; + tracking = varargin{i}; + case 'algorithm', + i=i+1; + algorithm = varargin{i}; + % unambiguous values + case {'hexa','rect'}, + sTopol.lattice = varargin{i}; + case {'sheet','cyl','toroid'}, + sTopol.shape = varargin{i}; + case {'gaussian','cutgauss','ep','bubble'}, + neigh = varargin{i}; + case {'seq','batch'}, + algorithm = varargin{i}; + case {'small','normal','big'}, + mapsize = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', + sTopol = varargin{i}; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_supervised) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +%%%%%%%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + +% constructing the training data by adding 1-of-N -coded matrix to the +% original data. + +[dlen,dim] = size(D0); + +Dc = zeros(dlen,n); + +for i=1:dlen + if c(i) + Dc(i,c(i)) = 1; + end +end + +D = [D0, Dc]; + +% initialization and training + +sD = som_data_struct(D,... + 'name',data_name); + +sM = som_make(sD,... + 'mask',mask,... + 'munits',munits,... + 'name',data_name,... + 'tracking',tracking,... + 'algorithm',algorithm,... + 'mapsize',mapsize,... + 'topol',sTopol,... + 'neigh',neigh); + +% add labels + +for i=1:prod(sM.topol.msize), + [dummy,class] = max(sM.codebook(i,dim+[1:n])); + sM.labels{i} = classlabels{class}; +end + +%sD.labels = sData.labels; +%sM = som_autolabel(sM,sD,'vote'); + +% remove extra components and modify map -struct + +sM.codebook = sM.codebook(:,1:dim); +sM.mask = sM.mask(1:dim); +sM.comp_names = sData.comp_names; +sM.comp_norm = sData.comp_norm; + +% remove extras from sM.trainhist + +for i=1:length(sM.trainhist) + if sM.trainhist(i).mask + sM.trainhist(i).mask = sM.trainhist(i).mask(1:dim); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [numbers, n, names] = class2num(class) + +names = {}; +numbers = zeros(length(class),1); + +for i=1:length(class) + if ~isempty(class{i}) & ~any(strcmp(class{i},names)) + names=cat(1,names,class(i)); + end +end + +n=length(names); + +tmp_numbers = (1:n)'; + +for i=1:length(class) + if ~isempty(class{i}) + numbers(i,1) = find(strcmp(class{i},names)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_table_modify.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,91 @@ +function sT = som_table_modify(sT,action,arg1,arg2,arg3) + +%SOM_TABLE_MODIFY Modify table: add or remove columns or rows. +% +% sTable = som_table_modify(sTable,action,arg1,[arg2],[arg3]) +% +% Input and output arguments ([]'s are optional): +% sTable (struct) table struct +% action (string) action id (see below). +% arg1 (varies) Depending on action, 1 to 3 arguments +% [arg2] (varies) are needed. See below. +% [arg3] (varies) +% +% sTable (struct) the modified table struct +% +% Actions and their arguments: +% 'addcol' Add one or several new columns. +% arg1 (cell array) new values +% (char) new values (a single column can be given) +% (matrix) new values +% arg2 (cell array) new headers +% arg3 (scalar) at which position the new columns +% should be inserted (at the end by default) +% 'addrow' Add one or several new rows. +% arg1 (cell array) new values +% (char) new values (a single row can be given) +% (matrix) new values +% arg2 (scalar) at which position the new rows +% should be inserted (at the end by default) +% 'removecol' Remove one or several columns. +% arg1 (vector) indeces of columns to be removed +% 'removerow' Remove one or several rows. +% arg1 (vector) indeces of rows to be removed +% +% See also SOM_TABLE_STRUCT, SOM_TABLE_PRINT. + +% Contributed to SOM Toolbox 2.0, January 4th, 2002 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 040102 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +[nrT,ncT] = size(sT.values); + +switch action, + case 'addcol', + values = arg1; + if ischar(values), values = cellstr(values); end + if isnumeric(values), values = num2cell(values); end + spans = ones([size(values) 2]); + [nr,nc] = size(values); + if nargin<4, header = cell(1,nc); header(:) = {''}; else header = arg2; end + if ischar(header), header = cellstr(header); end + if nargin<5, where = ncT+1; else where = arg3; end + if nrT ~= nr, + error('Mismatch between sizes of given table and additional columns') + else + sT.headers = [sT.headers(:,1:where-1), header, sT.headers(:,where:end)]; + sT.values = [sT.values(:,1:where-1), values, sT.values(:,where:end)]; + sT.span = [sT.span(:,1:where-1,:), spans, sT.span(:,where:end,:)]; + end + case 'addrow', + values = arg1; + if ischar(values), values = cellstr(values); end + if isnumeric(values), values = num2cell(values); end + [nr,nc] = size(values); + spans = ones([size(values) 2]); + if nargin<4, where = nrT+1; else where = arg2; end + if ncT ~= nc, + error('Mismatch between sizes of given table and additional rows') + else + sT.values = [sT.values(1:where-1,:); values; sT.values(where:end,:)]; + sT.span = [sT.span(1:where-1,:,:); spans; sT.span(where:end,:,:)]; + end + case 'removecol', + where = setdiff(1:ncT,arg1); + sT.values = sT.values(:,where); + sT.headers = sT.headers(:,where); + sT.span = sT.span(:,where,:); + case 'removerow', + where = setdiff(1:nrT,arg1); + sT.values = sT.values(where,:); + sT.headers = sT.headers(where,:); + sT.span = sT.span(where,:,:); +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_table_print.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +function T = som_table_print(sTable,fid,fmt) + +%SOM_TABLE_PRINT Print a table to a file / standard output. +% +% som_table_print(sTable,[fid],[fmt]) +% +% som_table_print(sTable) +% som_table_print(sTable,fid,'html') +% +% Input and output arguments ([]'s are optional): +% sTable (struct) a table struct (see SOM_TABLE_STRUCT) +% [fid] (scalar) a file id (from FOPEN, for example) +% (empty) by default standard output (fid=1) is used +% [fmt] (string) 'txt' (default), 'ps', 'pdf' or 'html' +% the output format type +% +% See also SOM_TABLE_STRUCT, SOM_STATS_TABLE, SOM_STATS_REPORT. + +% Contributed to SOM Toolbox 2.0, January 2nd, 2002 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 020102 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargin<2 | isempty(fid) | isnan(fid), fid = 1; end +if nargin<3 | isempty(fmt) | isnan(fmt), fmt = 'txt'; end + +rowlines = 0; +longtable = 0; + +T = rep_utils({'inserttable',sTable,rowlines,longtable},fmt,fid); + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_table_struct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +function sTable = som_table_struct(values,headers,span,colfmt) + +%SOM_TABLE_STRUCT Create a table struct. +% +% sTable = som_table_struct(values,[headers],[span],[colfmt]) +% +% Input and output arguments ([]'s are optional): +% values (cell array) size nrow x ncol, the contents of the table +% (char array) size nrow x * +% (matrix) size nrow x ncol +% [headers] (cell array) size 1 x ncol, header row of the table +% (empty) by default, empty headers are used ('') +% [span] (matrix) size nrow x ncol x 2, span of each cell of the +% table: span(:,:,1) gives horizontal span and +% span(:,:,2) gives vertical span. If the value +% for a cell is greater than 1, it should be +% followed by a corresponding number of zeros +% for the following cells (left or down) +% (empty) by default ones(nrow,ncol,1) +% [colfmt] (string) the format of each column as given in LaTeX, +% only used if the table is printed as 'ps' or 'pdf', +% by default colfmt = '' +% +% sTable (struct) the table struct, with the following fields: +% .headers (cell array) header row, size 1 x ncol +% .values (cell array) values, size nrow x ncol +% .span (matrix) span of each cell, size nrow x ncol x 2 +% +% See also SOM_TABLE_MODIFY, SOM_TABLE_PRINT. + +% Contributed to SOM Toolbox 2.0, December 31st, 2001 by Juha Vesanto +% Copyright (c) by Juha Vesanto +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 311201 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if ischar(values), values = cellstr(values); +elseif isnumeric(values), values = num2cell(values); +end +[nrow,ncol] = size(values); + +if nargin<2 | isempty(headers), headers = cell(1,ncol); headers(:) = {''}; end +if ischar(headers), headers = cellstr(headers); end + +if nargin<3 | isempty(span), span = ones(nrow,ncol,2); end +if sum(span(:)) > 2*nrow*ncol, + warning('span matrix has overlapping cells') +elseif sum(span(:)) < 2*nrow*ncol, + warning('span matrix has noncontinuous cells') +end + +if nargin<4 | isempty(colfmt), colfmt = ''; end + +sTable = struct('colfmt','','headers',[],'values',[],'span',[]); +sTable.colfmt = colfmt; +sTable.headers = headers; +sTable.span = span; +sTable.values = values; + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_topol_struct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,272 @@ +function sTopol = som_topol_struct(varargin) + +%SOM_TOPOL_STRUCT Default values for SOM topology. +% +% sT = som_topol_struct([[argID,] value, ...]) +% +% sTopol = som_topol_struct('data',D); +% sTopol = som_topol_struct('data',D,'munits',200); +% sTopol = som_topol_struct(sTopol); +% sTopol = som_topol_struct; +% +% Input and output arguments ([]'s are optional): +% [argID, (string) Default map topology depends on a number of +% value] (varies) factors (see below). These are given as a +% argument ID - argument value pairs, listed below. +% +% sT (struct) The ready topology struct. +% +% Topology struct contains values for map size, lattice (default is 'hexa') +% and shape (default is 'sheet'). Map size depends on training data and the +% number of map units. The number of map units depends on number of training +% samples. +% +% Here are the valid argument IDs and corresponding values. The values which +% are unambiguous (marked with '*') can be given without the preceeding argID. +% 'dlen' (scalar) length of the training data +% 'data' (matrix) the training data +% *(struct) the training data +% 'munits' (scalar) number of map units +% 'msize' (vector) map size +% 'lattice' *(string) map lattice: 'hexa' or 'rect' +% 'shape' *(string) map shape: 'sheet', 'cyl' or 'toroid' +% 'topol' *(struct) incomplete topology struct: its empty fields +% will be given values +% 'som_topol','sTopol' = 'topol' +% +% For more help, try 'type som_topol_struct' or check out online documentation. +% See also SOM_SET, SOM_TRAIN_STRUCT, SOM_MAKE. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_topol_struct +% +% PURPOSE +% +% Default values for map topology and training parameters. +% +% SYNTAX +% +% sT = som_topol_struct('argID',value,...); +% sT = som_topol_struct(value,...); +% +% DESCRIPTION +% +% This function is used to give sensible values for map topology (ie. map +% size). The topology struct is returned. +% +% The topology struct has three fields: '.msize', '.lattice' and +% '.shape'. Of these, default value for '.lattice' is 'hexa' and for +% '.shape' 'sheet'. Only the '.msize' field depends on the optional +% arguments: 'dlen', 'munits' and 'data'. The value for '.msize' field is +% determined as follows. +% +% First, the number of map units is determined (unless it is given). A +% heuristic formula of 'munits = 5*sqrt(dlen)' is used to calculate +% it. After this, the map size is determined. Basically, the two biggest +% eigenvalues of the training data are calculated and the ratio between +% sidelengths of the map grid is set to the square root of this ratio. The +% actual sidelengths are then set so that their product is as close to the +% desired number of map units as possible. If the lattice of the grid is +% 'hexa', the ratio is modified a bit to take it into account. If the +% lattice is 'hexa' and shape is 'toroid', the map size along the first axis +% must be even. +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is +% used. The valid IDs and corresponding values are listed below. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% +% 'dlen' (scalar) length of the training data +% 'data' (matrix) the training data +% *(struct) the training data +% 'munits' (scalar) number of map units +% 'msize' (vector) map size +% 'lattice' *(string) map lattice: 'hexa' or 'rect' +% 'shape' *(string) map shape: 'sheet', 'cyl' or 'toroid' +% 'topol' *(struct) incomplete topology struct: its empty fields +% will be given values +% 'som_topol','sTopol' = 'topol' +% +% OUTPUT ARGUMENTS +% +% sT (struct) The topology struct. +% +% EXAMPLES +% +% The most important optional argument for the default topology is 'data'. +% To get a default topology (given data) use: +% +% sTopol = som_topol_struct('data',D); +% +% This sets lattice to its default value 'hexa'. If you want to have a +% 'rect' lattice instead: +% +% sTopol = som_topol_struct('data',D,'lattice','rect'); +% or +% sTopol = som_topol_struct('data',D,'rect'); +% +% If you want to have (close to) a specific number of map units, e.g. 100: +% +% sTopol = som_topol_struct('data',D,'munits',100); +% +% SEE ALSO +% +% som_make Initialize and train a map using default parameters. +% som_train_struct Default training parameters. +% som_randinint Random initialization algorithm. +% som_lininit Linear initialization algorithm. +% som_seqtrain Sequential training algorithm. +% som_batchtrain Batch training algorithm. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0alpha juuso 060898 250399 070499 050899 240801 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% initialize +sTopol = som_set('som_topol','lattice','hexa','shape','sheet'); +D = []; +dlen = NaN; +dim = 2; +munits = NaN; + +% varargin +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + case 'dlen', i=i+1; dlen = varargin{i}; + case 'munits', i=i+1; munits = varargin{i}; sTopol.msize = 0; + case 'msize', i=i+1; sTopol.msize = varargin{i}; + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case 'shape', i=i+1; sTopol.shape = varargin{i}; + case 'data', + i=i+1; + if isstruct(varargin{i}), D = varargin{i}.data; + else D = varargin{i}; + end + [dlen dim] = size(D); + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; + case {'som_topol','sTopol','topol'}, + i=i+1; + if ~isempty(varargin{i}.msize) & prod(varargin{i}.msize), + sTopol.msize = varargin{i}.msize; + end + if ~isempty(varargin{i}.lattice), sTopol.lattice = varargin{i}.lattice; end + if ~isempty(varargin{i}.shape), sTopol.shape = varargin{i}.shape; end + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}.type, + case 'som_topol', + if ~isempty(varargin{i}.msize) & prod(varargin{i}.msize), + sTopol.msize = varargin{i}.msize; + end + if ~isempty(varargin{i}.lattice), sTopol.lattice = varargin{i}.lattice; end + if ~isempty(varargin{i}.shape), sTopol.shape = varargin{i}.shape; end + case 'som_data', + D = varargin{i}.data; + [dlen dim] = size(D); + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_topol_struct) Ignoring invalid argument #' num2str(i)]); + end + i = i+1; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action - topology struct + +% lattice and shape set already, so if msize is also set, there's +% nothing else to do +if prod(sTopol.msize) & ~isempty(sTopol.msize), return; end + +% otherwise, decide msize +% first (if necessary) determine the number of map units (munits) +if isnan(munits), + if ~isnan(dlen), + munits = ceil(5 * dlen^0.5); % this is just one way to make a guess... + else + munits = 100; % just a convenient value + end +end + +% then determine the map size (msize) +if dim == 1, % 1-D data + + sTopol.msize = [1 ceil(munits)]; + +elseif size(D,1)<2, % eigenvalues cannot be determined since there's no data + + sTopol.msize = round(sqrt(munits)); + sTopol.msize(2) = round(munits/sTopol.msize(1)); + +else % determine map size based on eigenvalues + + % initialize xdim/ydim ratio using principal components of the input + % space; the ratio is the square root of ratio of two largest eigenvalues + + % autocorrelation matrix + A = zeros(dim)+Inf; + for i=1:dim, D(:,i) = D(:,i) - mean(D(isfinite(D(:,i)),i)); end + for i=1:dim, + for j=i:dim, + c = D(:,i).*D(:,j); c = c(isfinite(c)); + A(i,j) = sum(c)/length(c); A(j,i) = A(i,j); + end + end + % take mdim first eigenvectors with the greatest eigenvalues + [V,S] = eig(A); + eigval = diag(S); + [y,ind] = sort(eigval); + eigval = eigval(ind); + + %me = mean(D); + %D = D - me(ones(length(ind),1),:); % remove mean from data + %eigval = sort(eig((D'*D)./size(D,1))); + if eigval(end)==0 | eigval(end-1)*munits<eigval(end), + ratio = 1; + else + ratio = sqrt(eigval(end)/eigval(end-1)); % ratio between map sidelengths + end + + % in hexagonal lattice, the sidelengths are not directly + % proportional to the number of units since the units on the + % y-axis are squeezed together by a factor of sqrt(0.75) + if strcmp(sTopol.lattice,'hexa'), + sTopol.msize(2) = min(munits, round(sqrt(munits / ratio * sqrt(0.75)))); + else + sTopol.msize(2) = min(munits, round(sqrt(munits / ratio))); + end + sTopol.msize(1) = round(munits / sTopol.msize(2)); + + % if actual dimension of the data is 1, make the map 1-D + if min(sTopol.msize) == 1, sTopol.msize = [1 max(sTopol.msize)]; end; + + % a special case: if the map is toroid with hexa lattice, + % size along first axis must be even + if strcmp(sTopol.lattice,'hexa') & strcmp(sTopol.shape,'toroid'), + if mod(sTopol.msize(1),2), sTopol.msize(1) = sTopol.msize(1) + 1; end + end + +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_train_struct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,389 @@ +function sTrain = som_train_struct(varargin) + +%SOM_TRAIN_STRUCT Default values for SOM training parameters. +% +% sT = som_train_struct([[argID,] value, ...]) +% +% sTrain = som_train_struct('train',sM,sD); +% sTrain = som_train_struct('finetune','data',D); +% sTrain = som_train_struct('previous',sT0); +% +% Input and output arguments ([]'s are optional): +% [argID, (string) Several default values depend on other SOM parameters +% value] (varies) or on the proporties of a data set. See below for a +% a list of required and optional arguments for +% different parameters, and well as the list of valid +% argIDs and associated values. The values which are +% unambiguous can be given without the preceeding argID. +% +% sT (struct) The training struct. +% +% Training struct contains values for training and initialization +% parameters. These parameters depend on the number of training samples, +% phase of training, the training algorithm. +% +% Here are the valid argument IDs and corresponding values. The values which +% are unambiguous (marked with '*') can be given without the preceeding rgID. +% 'dim' (scalar) input space dimension +% 'dlen' (scalar) length of the training data +% 'data' (matrix / struct) the training data +% 'munits' (scalar) number of map units +% 'msize' (vector) map size +% 'previous' (struct) previous training struct can be given in +% conjunction with 'finetune' phase (see below) +% 'phase' *(string) training phase: 'init', 'train', 'rough' or 'finetune' +% 'algorithm' *(string) algorithm to use: 'lininit', 'randinit', 'batch' or 'seq' +% 'map' *(struct) If a map struct is given, the last training struct +% in '.trainhist' field is used as the previous training +% struct. The map size and input space dimension are +% extracted from the map struct. +% 'sTrain' *(struct) a train struct, the empty fields of which are +% filled with sensible values +% +% For more help, try 'type som_train_struct' or check out online documentation. +% See also SOM_SET, SOM_TOPOL_STRUCT, SOM_MAKE. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_train_struct +% +% PURPOSE +% +% Default values for SOM training parameters. +% +% SYNTAX +% +% sT = som_train_struct('argID',value,...); +% sT = som_train_struct(value,...); +% +% DESCRIPTION +% +% This function is used to give sensible values for SOM training +% parameters and returns a training struct. Often, the parameters +% depend on the properties of the map and the training data. These are +% given as optional arguments to the function. If a partially filled +% train struct is given, its empty fields (field value is [] or '' or +% NaN) are supplimented with default values. +% +% The training struct has a number of fields which depend on each other +% and the optional arguments in complex ways. The most important argument +% is 'phase' which can be either 'init', 'train', 'rough' or 'finetune'. +% +% 'init' Map initialization. +% 'train' Map training in a onepass operation, as opposed to the +% rough-finetune combination. +% 'rough' Rough organization of the map: large neighborhood, big +% initial value for learning coefficient. Short training. +% 'finetune' Finetuning the map after rough organization phase. Small +% neighborhood, learning coefficient is small already at +% the beginning. Long training. +% +% The fields of training struct set by this function are listed below. +% +% '.mask' Basically, a column vector of ones. But if a previous +% train or map struct is given, it is copied from there. +% '.neigh' Default value is 'gaussian' but if a previous train or map +% struct is given, it is copied from there. +% '.alpha_type' Default value is 'inv' but if a previous training struct +% is given, it is copied from there. +% '.alpha_ini' For 'train' and 'rough' phases, this is 0.5, for +% 'finetune' it is 0.05. +% '.radius_ini' Depends on the previous training operation and the +% maximum sidelength of the map ms = max(msize). +% if there isn't one, or it is 'randinit', rad_ini = max(1,ms/2) +% if it is 'lininit', rad_ini = max(1,ms/8) +% otherwise, rad_ini = rad_fin of the previous training +% '.radius_fin' Default value is 1, but if the training phase is +% 'rough', rad_fin = max(1,rad_ini/4). +% '.trainlen' For 'train' phase this is 20 x mpd epochs, for 'rough' +% phase 4 x mpd epochs and for 'finetune' 16 x mpd +% epochs, where mpd = munits/dlen. If mpd cannot be +% calculated, it is set to be = 0.5. In any case, +% trainlen is at least one epoch. +% '.algorithm' Default training algorithm is 'batch' and default +% initialization algorithm is 'lininit'. +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments can be given as 'argID',value -pairs. If an +% argument is given value multiple times, the last one is used. The +% valid IDs and corresponding values are listed below. The values +% which are unambiguous (marked with '*') can be given without the +% preceeding argID. +% +% 'dim' (scalar) input space dimension +% 'dlen' (scalar) length of the training data +% 'data' (matrix / struct) the training data +% 'munits' (scalar) number of map units +% 'msize' (vector) map size +% 'previous' (struct) previous training struct can be given in +% conjunction with 'finetune' phase. +% 'phase' *(string) training phase: 'init', 'train', 'rough' or 'finetune' +% 'algorithm' *(string) algorithm to use: 'lininit', 'randinit', +% 'batch' or 'seq' +% 'map' *(struct) If a map struc is given, the last training struct +% in '.trainhist' field is used as the previous training +% struct. The map size and input space dimension are +% extracted from the map struct. +% 'sTrain' *(struct) a train struct, the empty fields of which are +% filled with sensible values +% +% OUTPUT ARGUMENTS +% +% sT (struct) The training struct. +% +% EXAMPLES +% +% The most important optional argument for the training parameters is +% 'phase'. The second most important are 'previous' and/or 'map'. +% +% To get default initialization parameters, use: +% +% sTrain = som_train_struct('phase','init'); +% or +% sTrain = som_train_struct('init'); +% +% To get default training parameters, use: +% +% sTrain = som_train_struct('phase','train','data',D,'map',sMap); +% or +% sTrain = som_train_struct('train','data',D,sMap); +% or +% sTrain = som_train_struct('train','dlen',dlen, ... +% 'msize',sMap.topol.msize,'dim',dim); +% +% If you want to first rough train and then finetune, do like this: +% +% sT1 = som_train_struct('rough','dlen',length(D),sMap); % rough training +% sT2 = som_train_struct('finetune','previous',sT1); % finetuning +% +% SEE ALSO +% +% som_make Initialize and train a map using default parameters. +% som_topol_struct Default map topology. +% som_randinint Random initialization algorithm. +% som_lininit Linear initialization algorithm. +% som_seqtrain Sequential training algorithm. +% som_batchtrain Batch training algorithm. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 101199 090200 210301 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +% initial default structs +sTrain = som_set('som_train'); + +% initialize optional parameters +dlen = NaN; +msize = 0; +munits = NaN; +sTprev = []; +dim = NaN; +phase = ''; + +% varargin +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + case 'dim', i=i+1; dim = varargin{i}; + case 'dlen', i=i+1; dlen = varargin{i}; + case 'msize', i=i+1; msize = varargin{i}; + case 'munits', i=i+1; munits = varargin{i}; msize = 0; + case 'phase', i=i+1; phase = varargin{i}; + case 'algorithm', i=i+1; sTrain.algorithm = varargin{i}; + case 'mask', i=i+1; sTrain.mask = varargin{i}; + case {'previous','map'}, + i=i+1; + if strcmp(varargin{i}.type,'som_map'), + if length(varargin{i}.trainhist), + sTprev = varargin{i}.trainhist(end); + msize = varargin{i}.topol.msize; + end + elseif strcmp(varargin{i}.type,'som_train'), + sTprev = varargin{i}; + end + case 'data', + i=i+1; + if isstruct(varargin{i}), [dlen dim] = size(varargin{i}.data); + else [dlen dim] = size(varargin{i}); + end + case {'init','train','rough','finetune'}, phase = varargin{i}; + case {'lininit','randinit','seq','batch'}, sTrain.algorithm = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}.type, + case 'som_train', + sT = varargin{i}; + if ~isempty(sT.algorithm), sTrain.algorithm = sT.algorithm; end + if ~isempty(sT.neigh), sTrain.neigh = sT.neigh; end + if ~isempty(sT.mask), sTrain.mask = sT.mask; end + if ~isnan(sT.radius_ini), sTrain.radius_ini = sT.radius_ini; end + if ~isnan(sT.radius_fin), sTrain.radius_fin = sT.radius_fin; end + if ~isnan(sT.alpha_ini), sTrain.alpha_ini = sT.alpha_ini; end + if ~isempty(sT.alpha_type), sTrain.alpha_type = sT.alpha_type; end + if ~isnan(sT.trainlen), sTrain.trainlen = sT.trainlen; end + if ~isempty(sT.data_name), sTrain.data_name = sT.data_name; end + if ~isempty(sT.time), sTrain.time = sT.time; end + case 'som_map', + if strcmp(varargin{i}.type,'som_map'), + if length(varargin{i}.trainhist), + sTprev = varargin{i}.trainhist(end); + msize = varargin{i}.topol.msize; + end + if ~isempty(varargin{i}.neigh) & isempty(sTrain.neigh), + sTrain.neigh = varargin{i}.neigh; + end + if ~isempty(varargin{i}.mask) & isempty(sTrain.mask), + sTrain.mask = varargin{i}.mask; + end + elseif strcmp(varargin{i}.type,'som_train'), + sTprev = varargin{i}; + end + case 'som_topol', msize = varargin{i}.msize; + case 'som_data', [dlen dim] = size(varargin{i}.data); + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_train_struct) Ignoring invalid argument #' num2str(i)]); + end + i = i+1; +end + +% dim +if ~isempty(sTprev) & isnan(dim), dim = length(sTprev.mask); end + +% mask +if isempty(sTrain.mask) & ~isnan(dim), sTrain.mask = ones(dim,1); end + +% msize, munits +if ~msize | isempty(msize), + if isnan(munits), msize = [10 10]; + else s = round(sqrt(munits)); msize = [s round(munits/s)]; + end +end +munits = prod(msize); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% action + +% previous training +prevalg = ''; +if ~isempty(sTprev), + if any(findstr(sTprev.algorithm,'init')), prevalg = 'init'; + else prevalg = sTprev.algorithm; + end +end + +% first determine phase +if isempty(phase), + switch sTrain.algorithm, + case {'lininit','randinit'}, phase = 'init'; + case {'batch','seq',''}, + if isempty(sTprev), phase = 'rough'; + elseif strcmp(prevalg,'init'), phase = 'rough'; + else phase = 'finetune'; + end + otherwise, phase = 'train'; + end +end + +% then determine algorithm +if isempty(sTrain.algorithm), + if strcmp(phase,'init'), sTrain.algorithm = 'lininit'; + elseif any(strcmp(prevalg,{'init',''})), sTrain.algorithm = 'batch'; + else sTrain.algorithm = sTprev.algorithm; + end +end + +% mask +if isempty(sTrain.mask), + if ~isempty(sTprev), sTrain.mask = sTprev.mask; + elseif ~isnan(dim), sTrain.mask = ones(dim,1); + end +end + +% neighborhood function +if isempty(sTrain.neigh), + if ~isempty(sTprev) & ~isempty(sTprev.neigh), sTrain.neigh = sTprev.neigh; + else sTrain.neigh = 'gaussian'; + end +end + +if strcmp(phase,'init'), + sTrain.alpha_ini = NaN; + sTrain.alpha_type = ''; + sTrain.radius_ini = NaN; + sTrain.radius_fin = NaN; + sTrain.trainlen = NaN; + sTrain.neigh = ''; +else + mode = [phase, '-', sTrain.algorithm]; + + % learning rate + if isnan(sTrain.alpha_ini), + if strcmp(sTrain.algorithm,'batch'), sTrain.alpha_ini = NaN; + else + switch phase, + case {'train','rough'}, sTrain.alpha_ini = 0.5; + case 'finetune', sTrain.alpha_ini = 0.05; + end + end + end + if isempty(sTrain.alpha_type), + if ~isempty(sTprev) & ~isempty(sTprev.alpha_type) ... + & ~strcmp(sTrain.algorithm,'batch'), + sTrain.alpha_type = sTprev.alpha_type; + elseif strcmp(sTrain.algorithm,'seq'), + sTrain.alpha_type = 'inv'; + end + end + + % radius + ms = max(msize); + if isnan(sTrain.radius_ini), + if isempty(sTprev) | strcmp(sTprev.algorithm,'randinit'), + sTrain.radius_ini = max(1,ceil(ms/4)); + elseif strcmp(sTprev.algorithm,'lininit') | isnan(sTprev.radius_fin), + sTrain.radius_ini = max(1,ceil(ms/8)); + else + sTrain.radius_ini = sTprev.radius_fin; + end + end + if isnan(sTrain.radius_fin), + if strcmp(phase,'rough'), + sTrain.radius_fin = max(1,sTrain.radius_ini/4); + else + sTrain.radius_fin = 1; + end + end + + % trainlen + if isnan(sTrain.trainlen), + mpd = munits/dlen; + if isnan(mpd), mpd = 0.5; end + switch phase, + case 'train', sTrain.trainlen = ceil(50*mpd); + case 'rough', sTrain.trainlen = ceil(10*mpd); + case 'finetune', sTrain.trainlen = ceil(40*mpd); + end + sTrain.trainlen = max(1,sTrain.trainlen); + end + +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_trajectory.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,285 @@ +function som_trajectory(bmus,varargin) + +%SOM_TRAJECTORY Launch a "comet" trajectory visualization GUI. +% +% som_show(sM,'umat','all') +% bmus = som_bmus(sM,sD); +% som_trajectory(bmus) +% som_trajectory(bmus, 'data1', sD, 'trajsize', [12 6 3 1]') +% som_trajectory(bmus, 'data1', sD.data(:,[1 2 3]), 'name1', {'fii' 'faa' 'foo'}) +% +% Input arguments ([]'s are optional): +% bmus (matrix) size Nx1, vector of BMUS +% ['argID', (string) Other arguments can be given as 'argID', value +% value] (varies) pairs. See list below for valid values. +% +% NOTE: the GUI only works on a figure which has been made with SOM_SHOW. +% +% Here are the valid argument IDs (case insensitive) and associated values: +% 'color' string 'xor' or ColorSpec, default: 'xor'. +% (default: according to lattice as in som_cplane) +% 'TrajSize' vector of size Nx1 to define the length of comet +% (N) and size of the comet dots in points. +% default: [16 12 10 8 6 4]' +% 'Data1' SOM Toolbox data struct or matrix. The size of +% data matrix (in data struct the field .data) is +% Nxd, where N must be the same as the amount of +% BMUS given in the first input argument 'bmus' +% This data is shown in a new window in d subplots. +% Default: BMU indices (first input argument) +% 'Name1' cell array of d strings which contains names +% for the components in 'Data1'. If 'Data1' is a SOM +% Toolbox data struct, the existing component names +% are overdone. +% 'Figure' scalar that must be a handle to an existing figure +% which has been made using SOM_SHOW function. +% Default: current active figure (gcf). +% +% The following tools can be found in the 'Tools' -menu. +% +% Remove Trajectory: removes trajectory from the map. +% Dye Nodes : opens GUI for selecting color for the nodes +% and points selected. +% Clear Markers : removes markers from map and data figure. +% Save : saves the current situation as a struct. +% Load : loads the struct from workspace and draws markers. +% +% Mouse operation +% +% In data window: Left button is used to drag the operation point ruler +% if left button is on blank area, it starts +% In map window : Left button starts a polygon; right button +% finishes; middle button toggles a unit. +% +% SOM_TRAJECTORY is an application for observing trajectory behavior. +% +% Using mouse the line in data figure can be dragged and the +% trajectory moves in the SOM SHOW figure. It is also possible to move +% trajectory by pressing keys '>' and '<' when mouse pointer is above +% data figure. +% +% Regions can be chosen from the data and the points in that region +% are mapped to the component planes. Regions can be chosen also in +% the map. In this situation data points and map nodes are also +% marked (Left mouse button adds point to the polygon indicating the +% region and right button finals the polygon). By clicking a node (the +% middle button) that node is either added or removed from selection. +% +% It should be noticed that choosing intervals from data may cause +% situations that seem to be bugs. If there exisist marks of different +% color, removing them by clicking the map may left some marks in the +% data, because more than one point in the data is mapped to the same +% node in the map and the removing operation depends on the color of +% the marks. However, all the marks can be removed by using the 'Clear +% Markers' -operation. +% +% FEATURES +% +% The first input argument 'bmus' may also be a munits x N matrix +% In this case each column defines a "fuzzy response". That is, +% each column defines a hit histogram function). The element +% bmus(i,t) sets the size of marker on unit i at time t. +% NOTE: - in this case no regions can be selcted on the map! +% - only > and < keys can be used to move the operation point +% line: it can't be dragged +% - "fuzzy response is always black (hope this will be fixed) +% +% It is possible to open a second data window showing different data: +% use indetifiers 'Data2' (and 'Name2'). The argument syntax is +% identical to 'Data1' (and 'Name1'). +% +% See also SOM_SHOW, SOM_SHOW_ADD, SOM_BMUS. + +% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Johan +% Himberg and Juha Parhankangas +% Copyright (c) 2000 by the Johan Himberg and Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Check arguments + +error(nargchk(1,Inf,nargin)); % Check no. of input arguments + +%% Init input argument struct (see subfunction) +Traj=iniTraj(bmus); + +% Check tentative BMU input validity + +if ~vis_valuetype(bmus,{'nxm'}), + error(['First input should be a vector of BMU indices or' ... + ' a "response matrix"']); +end + +%% Check optional arguments +for i=1:2:length(varargin) + identifier=lower(varargin{i}); + value=varargin{i+1}; + + % Trajectory color + switch identifier + case 'color' + if isempty(value) + value='xor'; + end + if vis_valuetype(value,{'colorstyle','xor'}) + Traj.color=value; + else + error('''Color'' has to be ColorSpec or string ''xor''.'); + end + + % 1st data + case 'data1' + if isempty(value), + value=[]; + elseif vis_valuetype(value,{'nxm'}) + Traj.primary_data=value; + elseif isstruct(value) & isfield(value,'type') & ... + ischar(value.type) & strcmp(value.type,'som_data'), + Traj.primary_data=value.data; + if isempty(Traj.primary_names), + Traj.primary_names=value.comp_names; + end + end + + % 2nd data + case 'data2' + if isempty(value), + value=[]; + elseif vis_valuetype(value,{'nxm'}) + Traj.secondary_data=value; + elseif isstruct(value) & isfield(value,'type') & ... + ischar(value.type) & strcmp(value.type,'som_data'), + Traj.secondary_data=value.data; + if isempty(Traj.secondary_names), + Traj.secondary_names=value.comp_names; + end + end + + % Trajectory length & size + case 'trajsize' + if isempty(value), + Traj.size=[16 12 10 8 6 4]'; + end + if vis_valuetype(value,{'nx1'}) + Traj.size=value + else + error('''TrajSize'' has to be a nx1 vector.'); + end + + % Names for first data variables + case 'name1' + if isempty(value), + Traj.primary_names=[]; + elseif ~vis_valuetype(value,{'cellcolumn_of_char'}), + error('''Name1'': variable names must be in a cell column array.') + else + Traj.primary_names = value; + end + % Names for 2nd data variables + case 'name2' + if isempty(value), + Traj.secondary_names=[]; + elseif ~vis_valuetype(value,{'cellcolumn_of_char'}), + error('''Name2'': variable names must be in a cell column array.') + else + Traj.secondary_names = value; + end + + % Figure number + case 'figure' + if isempty(value) + Traj.figure='gcf'; + end + if vis_valuetype(value,{'1x1'}) + Traj.figure=value; + else + error('''Figure'' should be number of an existing figure.') + end + end +end + +%% Get SOM data from figure +[h,msg,lattice,msize,dim]=vis_som_show_data('all',Traj.figure); + +%% Not a SOM_SHOW figure? +if ~isempty(msg); + error('Figure is invalid: use SOM_SHOW to draw the figure.'); +end + +% Get map size from figure data +Traj.lattice=lattice; +Traj.msize=msize; +if length(msize)>2, + error(['This function works only with 2D maps: figure contains' ... + ' something else.']); +end +munits=prod(msize); + +% Check BMU (or response) and map match + +if vis_valuetype(bmus,{'nx1'}); + if max(bmus)>prod(msize) | min(bmus) <1 + error('BMU indexes out of range.') + elseif any(round(bmus)~=bmus) + error('BMU indexes must be integer.'); + elseif isempty(Traj.primary_data), + Traj.primary_data=bmus; + end +elseif size(bmus,1) ~= munits + error(['Response matrix column number must match with the number of' ... + ' map units.']); +else + bmus=bmus'; + if isempty(Traj.primary_data), + Traj.primary_data=[1:size(bmus,1)]'; + Traj.primary_names={'BMU Index'}; + end +end + +size1=size(Traj.primary_data); +size2=size(Traj.secondary_data); + +% Data2 must not be defined alone + +if isempty(Traj.primary_data)&~isempty(Traj.secondary_data), + error('If ''Data2'' is specified ''Data1'' must be specified, too.'); +elseif ~isempty(Traj.secondary_data) ... + & size1~= size2 + % If data1 and data2 exist both, check data1 and data2 match + error('''Data1'' and ''Data2'' have different amount of data vectors.') +end + +% Check BMU and data1 match (data2 matches with 1 anyway) + +if ~isempty(Traj.primary_data) & size(bmus,1) ~= size1, + error(['The number of data vectors in ''data1'' must match with' ... + ' the number of rows in the first input argument (bmus).']); +end + +% Check that number of names and data dimension is consistent + +if ~isempty(Traj.primary_names) & (size1(2)~=length(Traj.primary_names)), + error('Number of component names and ''Data1'' dimension mismatch.'); +end +if ~isempty(Traj.secondary_names) & ... + (size2(2)~=length(Traj.secondary_names)), + error('Number of component names and ''Data2'' dimension mismatch.'); +end + +%% Call the function that does the job +vis_trajgui(Traj); + +%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function Traj=iniTraj(bmus) + +Traj.figure=gcf; +Traj.primary_data=[]; +Traj.secondary_data=[]; +Traj.primary_names = []; +Traj.secondary_names = []; +Traj.size=[16 12 10 8 6 4]'; +Traj.bmus=bmus; +Traj.color='xor'; +Traj.msize=[]; +Traj.lattice=[]; +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_umat.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,362 @@ +function U = som_umat(sMap, varargin) + +%SOM_UMAT Compute unified distance matrix of self-organizing map. +% +% U = som_umat(sMap, [argID, value, ...]) +% +% U = som_umat(sMap); +% U = som_umat(M,sTopol,'median','mask',[1 1 0 1]); +% +% Input and output arguments ([]'s are optional): +% sMap (struct) map struct or +% (matrix) the codebook matrix of the map +% [argID, (string) See below. The values which are unambiguous can +% value] (varies) be given without the preceeding argID. +% +% U (matrix) u-matrix of the self-organizing map +% +% Here are the valid argument IDs and corresponding values. The values which +% are unambiguous (marked with '*') can be given without the preceeding argID. +% 'mask' (vector) size dim x 1, weighting factors for different +% components (same as BMU search mask) +% 'msize' (vector) map grid size +% 'topol' *(struct) topology struct +% 'som_topol','sTopol' = 'topol' +% 'lattice' *(string) map lattice, 'hexa' or 'rect' +% 'mode' *(string) 'min','mean','median','max', default is 'median' +% +% NOTE! the U-matrix is always calculated for 'sheet'-shaped map and +% the map grid must be at most 2-dimensional. +% +% For more help, try 'type som_umat' or check out online documentation. +% See also SOM_SHOW, SOM_CPLANE. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_umat +% +% PURPOSE +% +% Computes the unified distance matrix of a SOM. +% +% SYNTAX +% +% U = som_umat(sM) +% U = som_umat(...,'argID',value,...) +% U = som_umat(...,value,...) +% +% DESCRIPTION +% +% Compute and return the unified distance matrix of a SOM. +% For example a case of 5x1 -sized map: +% m(1) m(2) m(3) m(4) m(5) +% where m(i) denotes one map unit. The u-matrix is a 9x1 vector: +% u(1) u(1,2) u(2) u(2,3) u(3) u(3,4) u(4) u(4,5) u(5) +% where u(i,j) is the distance between map units m(i) and m(j) +% and u(k) is the mean (or minimum, maximum or median) of the +% surrounding values, e.g. u(3) = (u(2,3) + u(3,4))/2. +% +% Note that the u-matrix is always calculated for 'sheet'-shaped map and +% the map grid must be at most 2-dimensional. +% +% REFERENCES +% +% Ultsch, A., Siemon, H.P., "Kohonen's Self-Organizing Feature Maps +% for Exploratory Data Analysis", in Proc. of INNC'90, +% International Neural Network Conference, Dordrecht, +% Netherlands, 1990, pp. 305-308. +% Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, +% Berlin, 1995, pp. 117-119. +% Iivarinen, J., Kohonen, T., Kangas, J., Kaski, S., "Visualizing +% the Clusters on the Self-Organizing Map", in proceedings of +% Conference on Artificial Intelligence Research in Finland, +% Helsinki, Finland, 1994, pp. 122-126. +% Kraaijveld, M.A., Mao, J., Jain, A.K., "A Nonlinear Projection +% Method Based on Kohonen's Topology Preserving Maps", IEEE +% Transactions on Neural Networks, vol. 6, no. 3, 1995, pp. 548-559. +% +% REQUIRED INPUT ARGUMENTS +% +% sM (struct) SOM Toolbox struct or the codebook matrix of the map. +% (matrix) The matrix may be 3-dimensional in which case the first +% two dimensions are taken for the map grid dimensions (msize). +% +% OPTIONAL INPUT ARGUMENTS +% +% argID (string) Argument identifier string (see below). +% value (varies) Value for the argument (see below). +% +% The optional arguments are given as 'argID',value -pairs. If the +% value is unambiguous, it can be given without the preceeding argID. +% If an argument is given value multiple times, the last one is used. +% +% Below is the list of valid arguments: +% 'mask' (vector) mask to be used in calculating +% the interunit distances, size [dim 1]. Default is +% the one in sM (field sM.mask) or a vector of +% ones if only a codebook matrix was given. +% 'topol' (struct) topology of the map. Default is the one +% in sM (field sM.topol). +% 'sTopol','som_topol' (struct) = 'topol' +% 'msize' (vector) map grid dimensions +% 'lattice' (string) map lattice 'rect' or 'hexa' +% 'mode' (string) 'min', 'mean', 'median' or 'max' +% Map unit value computation method. In fact, +% eval-function is used to evaluate this, so +% you can give other computation methods as well. +% Default is 'median'. +% +% OUTPUT ARGUMENTS +% +% U (matrix) the unified distance matrix of the SOM +% size 2*n1-1 x 2*n2-1, where n1 = msize(1) and n2 = msize(2) +% +% EXAMPLES +% +% U = som_umat(sM); +% U = som_umat(sM.codebook,sM.topol,'median','mask',[1 1 0 1]); +% U = som_umat(rand(10,10,4),'hexa','rect'); +% +% SEE ALSO +% +% som_show show the selected component planes and the u-matrix +% som_cplane draw a 2D unified distance matrix + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 260997 +% Version 2.0beta juuso 151199, 151299, 200900 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(1, Inf, nargin)); % check no. of input arguments is correct + +% sMap +if isstruct(sMap), + M = sMap.codebook; + sTopol = sMap.topol; + mask = sMap.mask; +elseif isnumeric(sMap), + M = sMap; + si = size(M); + dim = si(end); + if length(si)>2, msize = si(1:end-1); + else msize = [si(1) 1]; + end + munits = prod(msize); + sTopol = som_set('som_topol','msize',msize,'lattice','rect','shape','sheet'); + mask = ones(dim,1); + M = reshape(M,[munits,dim]); +end +mode = 'median'; + +% varargin +i=1; +while i<=length(varargin), + argok = 1; + if ischar(varargin{i}), + switch varargin{i}, + % argument IDs + case 'mask', i=i+1; mask = varargin{i}; + case 'msize', i=i+1; sTopol.msize = varargin{i}; + case 'lattice', i=i+1; sTopol.lattice = varargin{i}; + case {'topol','som_topol','sTopol'}, i=i+1; sTopol = varargin{i}; + case 'mode', i=i+1; mode = varargin{i}; + % unambiguous values + case {'hexa','rect'}, sTopol.lattice = varargin{i}; + case {'min','mean','median','max'}, mode = varargin{i}; + otherwise argok=0; + end + elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), + switch varargin{i}(1).type, + case 'som_topol', sTopol = varargin{i}; + case 'som_map', sTopol = varargin{i}.topol; + otherwise argok=0; + end + else + argok = 0; + end + if ~argok, + disp(['(som_umat) Ignoring invalid argument #' num2str(i+1)]); + end + i = i+1; +end + +% check +[munits dim] = size(M); +if prod(sTopol.msize)~=munits, + error('Map grid size does not match the number of map units.') +end +if length(sTopol.msize)>2, + error('Can only handle 1- and 2-dimensional map grids.') +end +if prod(sTopol.msize)==1, + warning('Only one codebook vector.'); U = []; return; +end +if ~strcmp(sTopol.shape,'sheet'), + disp(['The ' sTopol.shape ' shape of the map ignored. Using sheet instead.']); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% initialize variables + +y = sTopol.msize(1); +x = sTopol.msize(2); +lattice = sTopol.lattice; +shape = sTopol.shape; +M = reshape(M,[y x dim]); + +ux = 2 * x - 1; +uy = 2 * y - 1; +U = zeros(uy, ux); + +calc = sprintf('%s(a)',mode); + +if size(mask,2)>1, mask = mask'; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% u-matrix computation + +% distances between map units + +if strcmp(lattice, 'rect'), % rectangular lattice + + for j=1:y, for i=1:x, + if i<x, + dx = (M(j,i,:) - M(j,i+1,:)).^2; % horizontal + U(2*j-1,2*i) = sqrt(mask'*dx(:)); + end + if j<y, + dy = (M(j,i,:) - M(j+1,i,:)).^2; % vertical + U(2*j,2*i-1) = sqrt(mask'*dy(:)); + end + if j<y & i<x, + dz1 = (M(j,i,:) - M(j+1,i+1,:)).^2; % diagonals + dz2 = (M(j+1,i,:) - M(j,i+1,:)).^2; + U(2*j,2*i) = (sqrt(mask'*dz1(:))+sqrt(mask'*dz2(:)))/(2 * sqrt(2)); + end + end + end + +elseif strcmp(lattice, 'hexa') % hexagonal lattice + + for j=1:y, + for i=1:x, + if i<x, + dx = (M(j,i,:) - M(j,i+1,:)).^2; % horizontal + U(2*j-1,2*i) = sqrt(mask'*dx(:)); + end + + if j<y, % diagonals + dy = (M(j,i,:) - M(j+1,i,:)).^2; + U(2*j,2*i-1) = sqrt(mask'*dy(:)); + + if rem(j,2)==0 & i<x, + dz= (M(j,i,:) - M(j+1,i+1,:)).^2; + U(2*j,2*i) = sqrt(mask'*dz(:)); + elseif rem(j,2)==1 & i>1, + dz = (M(j,i,:) - M(j+1,i-1,:)).^2; + U(2*j,2*i-2) = sqrt(mask'*dz(:)); + end + end + end + end + +end + +% values on the units + +if (uy == 1 | ux == 1), + % in 1-D case, mean is equal to median + + ma = max([ux uy]); + for i = 1:2:ma, + if i>1 & i<ma, + a = [U(i-1) U(i+1)]; + U(i) = eval(calc); + elseif i==1, U(i) = U(i+1); + else U(i) = U(i-1); % i==ma + end + end + +elseif strcmp(lattice, 'rect') + + for j=1:2:uy, + for i=1:2:ux, + if i>1 & j>1 & i<ux & j<uy, % middle part of the map + a = [U(j,i-1) U(j,i+1) U(j-1,i) U(j+1,i)]; + elseif j==1 & i>1 & i<ux, % upper edge + a = [U(j,i-1) U(j,i+1) U(j+1,i)]; + elseif j==uy & i>1 & i<ux, % lower edge + a = [U(j,i-1) U(j,i+1) U(j-1,i)]; + elseif i==1 & j>1 & j<uy, % left edge + a = [U(j,i+1) U(j-1,i) U(j+1,i)]; + elseif i==ux & j>1 & j<uy, % right edge + a = [U(j,i-1) U(j-1,i) U(j+1,i)]; + elseif i==1 & j==1, % top left corner + a = [U(j,i+1) U(j+1,i)]; + elseif i==ux & j==1, % top right corner + a = [U(j,i-1) U(j+1,i)]; + elseif i==1 & j==uy, % bottom left corner + a = [U(j,i+1) U(j-1,i)]; + elseif i==ux & j==uy, % bottom right corner + a = [U(j,i-1) U(j-1,i)]; + else + a = 0; + end + U(j,i) = eval(calc); + end + end + +elseif strcmp(lattice, 'hexa') + + for j=1:2:uy, + for i=1:2:ux, + if i>1 & j>1 & i<ux & j<uy, % middle part of the map + a = [U(j,i-1) U(j,i+1)]; + if rem(j-1,4)==0, a = [a, U(j-1,i-1) U(j-1,i) U(j+1,i-1) U(j+1,i)]; + else a = [a, U(j-1,i) U(j-1,i+1) U(j+1,i) U(j+1,i+1)]; end + elseif j==1 & i>1 & i<ux, % upper edge + a = [U(j,i-1) U(j,i+1) U(j+1,i-1) U(j+1,i)]; + elseif j==uy & i>1 & i<ux, % lower edge + a = [U(j,i-1) U(j,i+1)]; + if rem(j-1,4)==0, a = [a, U(j-1,i-1) U(j-1,i)]; + else a = [a, U(j-1,i) U(j-1,i+1)]; end + elseif i==1 & j>1 & j<uy, % left edge + a = U(j,i+1); + if rem(j-1,4)==0, a = [a, U(j-1,i) U(j+1,i)]; + else a = [a, U(j-1,i) U(j-1,i+1) U(j+1,i) U(j+1,i+1)]; end + elseif i==ux & j>1 & j<uy, % right edge + a = U(j,i-1); + if rem(j-1,4)==0, a=[a, U(j-1,i) U(j-1,i-1) U(j+1,i) U(j+1,i-1)]; + else a = [a, U(j-1,i) U(j+1,i)]; end + elseif i==1 & j==1, % top left corner + a = [U(j,i+1) U(j+1,i)]; + elseif i==ux & j==1, % top right corner + a = [U(j,i-1) U(j+1,i-1) U(j+1,i)]; + elseif i==1 & j==uy, % bottom left corner + if rem(j-1,4)==0, a = [U(j,i+1) U(j-1,i)]; + else a = [U(j,i+1) U(j-1,i) U(j-1,i+1)]; end + elseif i==ux & j==uy, % bottom right corner + if rem(j-1,4)==0, a = [U(j,i-1) U(j-1,i) U(j-1,i-1)]; + else a = [U(j,i-1) U(j-1,i)]; end + else + a=0; + end + U(j,i) = eval(calc); + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% normalization between [0,1] + +% U = U - min(min(U)); +% ma = max(max(U)); if ma > 0, U = U / ma; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_unit_coords.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,252 @@ +function Coords = som_unit_coords(topol,lattice,shape) + +%SOM_UNIT_COORDS Locations of units on the SOM grid. +% +% Co = som_unit_coords(topol, [lattice], [shape]) +% +% Co = som_unit_coords(sMap); +% Co = som_unit_coords(sMap.topol); +% Co = som_unit_coords(msize, 'hexa', 'cyl'); +% Co = som_unit_coords([10 4 4], 'rect', 'toroid'); +% +% Input and output arguments ([]'s are optional): +% topol topology of the SOM grid +% (struct) topology or map struct +% (vector) the 'msize' field of topology struct +% [lattice] (string) map lattice, 'rect' by default +% [shape] (string) map shape, 'sheet' by default +% +% Co (matrix, size [munits k]) coordinates for each map unit +% +% For more help, try 'type som_unit_coords' or check out online documentation. +% See also SOM_UNIT_DISTS, SOM_UNIT_NEIGHS. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_unit_coords +% +% PURPOSE +% +% Returns map grid coordinates for the units of a Self-Organizing Map. +% +% SYNTAX +% +% Co = som_unit_coords(sTopol); +% Co = som_unit_coords(sM.topol); +% Co = som_unit_coords(msize); +% Co = som_unit_coords(msize,'hexa'); +% Co = som_unit_coords(msize,'rect','toroid'); +% +% DESCRIPTION +% +% Calculates the map grid coordinates of the units of a SOM based on +% the given topology. The coordinates are such that they can be used to +% position map units in space. In case of 'sheet' shape they can be +% (and are) used to measure interunit distances. +% +% NOTE: for 'hexa' lattice, the x-coordinates of every other row are shifted +% by +0.5, and the y-coordinates are multiplied by sqrt(0.75). This is done +% to make distances of a unit to all its six neighbors equal. It is not +% possible to use 'hexa' lattice with higher than 2-dimensional map grids. +% +% 'cyl' and 'toroid' shapes: the coordinates are initially determined as +% in case of 'sheet' shape, but are then bended around the x- or the +% x- and then y-axes to get the desired shape. +% +% POSSIBLE BUGS +% +% I don't know if the bending operation works ok for high-dimensional +% map grids. Anyway, if anyone wants to make a 4-dimensional +% toroid map, (s)he deserves it. +% +% REQUIRED INPUT ARGUMENTS +% +% topol Map grid dimensions. +% (struct) topology struct or map struct, the topology +% (msize, lattice, shape) of the map is taken from +% the appropriate fields (see e.g. SOM_SET) +% (vector) the vector which gives the size of the map grid +% (msize-field of the topology struct). +% +% OPTIONAL INPUT ARGUMENTS +% +% lattice (string) The map lattice, either 'rect' or 'hexa'. Default +% is 'rect'. 'hexa' can only be used with 1- or +% 2-dimensional map grids. +% shape (string) The map shape, either 'sheet', 'cyl' or 'toroid'. +% Default is 'sheet'. +% +% OUTPUT ARGUMENTS +% +% Co (matrix) coordinates for each map units, size is [munits k] +% where k is 2, or more if the map grid is higher +% dimensional or the shape is 'cyl' or 'toroid' +% +% EXAMPLES +% +% Simplest case: +% Co = som_unit_coords(sTopol); +% Co = som_unit_coords(sMap.topol); +% Co = som_unit_coords(msize); +% Co = som_unit_coords([10 10]); +% +% If topology is given as vector, lattice is 'rect' and shape is 'sheet' +% by default. To change these, you can use the optional arguments: +% Co = som_unit_coords(msize, 'hexa', 'toroid'); +% +% The coordinates can also be calculated for high-dimensional grids: +% Co = som_unit_coords([4 4 4 4 4 4]); +% +% SEE ALSO +% +% som_unit_dists Calculate interunit distance along the map grid. +% som_unit_neighs Calculate neighborhoods of map units. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 110997 +% Version 2.0beta juuso 101199 070600 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +error(nargchk(1, 3, nargin)); + +% default values +sTopol = som_set('som_topol','lattice','rect'); + +% topol +if isstruct(topol), + switch topol.type, + case 'som_map', sTopol = topol.topol; + case 'som_topol', sTopol = topol; + end +elseif iscell(topol), + for i=1:length(topol), + if isnumeric(topol{i}), sTopol.msize = topol{i}; + elseif ischar(topol{i}), + switch topol{i}, + case {'rect','hexa'}, sTopol.lattice = topol{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = topol{i}; + end + end + end +else + sTopol.msize = topol; +end +if prod(sTopol.msize)==0, error('Map size is 0.'); end + +% lattice +if nargin>1 & ~isempty(lattice) & ~isnan(lattice), sTopol.lattice = lattice; end + +% shape +if nargin>2 & ~isempty(shape) & ~isnan(shape), sTopol.shape = shape; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +msize = sTopol.msize; +lattice = sTopol.lattice; +shape = sTopol.shape; + +% init variables + +if length(msize)==1, msize = [msize 1]; end +munits = prod(msize); +mdim = length(msize); +Coords = zeros(munits,mdim); + +% initial coordinates for each map unit ('rect' lattice, 'sheet' shape) +k = [1 cumprod(msize(1:end-1))]; +inds = [0:(munits-1)]'; +for i = mdim:-1:1, + Coords(:,i) = floor(inds/k(i)); % these are subscripts in matrix-notation + inds = rem(inds,k(i)); +end +% change subscripts to coordinates (move from (ij)-notation to (xy)-notation) +Coords(:,[1 2]) = fliplr(Coords(:,[1 2])); + +% 'hexa' lattice +if strcmp(lattice,'hexa'), + % check + if mdim > 2, + error('You can only use hexa lattice with 1- or 2-dimensional maps.'); + end + % offset x-coordinates of every other row + inds_for_row = (cumsum(ones(msize(2),1))-1)*msize(1); + for i=2:2:msize(1), + Coords(i+inds_for_row,1) = Coords(i+inds_for_row,1) + 0.5; + end +end + +% shapes +switch shape, +case 'sheet', + if strcmp(lattice,'hexa'), + % this correction is made to make distances to all + % neighboring units equal + Coords(:,2) = Coords(:,2)*sqrt(0.75); + end + +case 'cyl', + % to make cylinder the coordinates must lie in 3D space, at least + if mdim<3, Coords = [Coords ones(munits,1)]; mdim = 3; end + + % Bend the coordinates to a circle in the plane formed by x- and + % and z-axis. Notice that the angle to which the last coordinates + % are bended is _not_ 360 degrees, because that would be equal to + % the angle of the first coordinates (0 degrees). + + Coords(:,1) = Coords(:,1)/max(Coords(:,1)); + Coords(:,1) = 2*pi * Coords(:,1) * msize(2)/(msize(2)+1); + Coords(:,[1 3]) = [cos(Coords(:,1)) sin(Coords(:,1))]; + +case 'toroid', + + % NOTE: if lattice is 'hexa', the msize(1) should be even, otherwise + % the bending the upper and lower edges of the map do not match + % to each other + if strcmp(lattice,'hexa') & rem(msize(1),2)==1, + warning('Map size along y-coordinate is not even.'); + end + + % to make toroid the coordinates must lie in 3D space, at least + if mdim<3, Coords = [Coords ones(munits,1)]; mdim = 3; end + + % First bend the coordinates to a circle in the plane formed + % by x- and z-axis. Then bend in the plane formed by y- and + % z-axis. (See also the notes in 'cyl'). + + Coords(:,1) = Coords(:,1)/max(Coords(:,1)); + Coords(:,1) = 2*pi * Coords(:,1) * msize(2)/(msize(2)+1); + Coords(:,[1 3]) = [cos(Coords(:,1)) sin(Coords(:,1))]; + + Coords(:,2) = Coords(:,2)/max(Coords(:,2)); + Coords(:,2) = 2*pi * Coords(:,2) * msize(1)/(msize(1)+1); + Coords(:,3) = Coords(:,3) - min(Coords(:,3)) + 1; + Coords(:,[2 3]) = Coords(:,[3 3]) .* [cos(Coords(:,2)) sin(Coords(:,2))]; + +end + +return; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function C = bend(cx,cy,angle,xishexa) + + dx = max(cx) - min(cx); + if dx ~= 0, + % in case of hexagonal lattice it must be taken into account that + % coordinates of every second row are +0.5 off to the right + if xishexa, dx = dx-0.5; end + cx = angle*(cx - min(cx))/dx; + end + C(:,1) = (cy - min(cy)+1) .* cos(cx); + C(:,2) = (cy - min(cy)+1) .* sin(cx); + +% end of bend + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_unit_dists.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,268 @@ +function Ud = som_unit_dists(topol,lattice,shape) + +%SOM_UNIT_DISTS Distances between unit-locations on the map grid. +% +% Ud = som_unit_dists(topol,[lattice],[shape]) +% +% Ud = som_unit_dists(sMap); +% Ud = som_unit_dists(sMap.topol); +% Ud = som_unit_dists(msize, 'hexa', 'cyl'); +% Ud = som_unit_dists([10 4 4], 'rect', 'toroid'); +% +% Input and output arguments ([]'s are optional): +% topol topology of the SOM grid +% (struct) topology or map struct +% (vector) the 'msize' field of topology struct +% [lattice] (string) map lattice, 'rect' by default +% [shape] (string) map shape, 'sheet' by default +% +% Ud (matrix, size [munits munits]) distance from each map unit +% to each map unit +% +% For more help, try 'type som_unit_dists' or check out online documentation. +% See also SOM_UNIT_COORDS, SOM_UNIT_NEIGHS. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_unit_dists +% +% PURPOSE +% +% Returns interunit distances between the units of a Self-Organizing Map +% along the map grid. +% +% SYNTAX +% +% Ud = som_unit_dists(sTopol); +% Ud = som_unit_dists(sM.topol); +% Ud = som_unit_dists(msize); +% Ud = som_unit_dists(msize,'hexa'); +% Ud = som_unit_dists(msize,'rect','toroid'); +% +% DESCRIPTION +% +% Calculates the distances between the units of a SOM based on the +% given topology. The distance are euclidian and they are measured +% along the map grid (in the output space). +% +% In case of 'sheet' shape, the distances can be measured directly +% from the unit coordinates given by SOM_UNIT_COORDS. +% +% In case of 'cyl' and 'toroid' shapes this is not so. In these cases +% the coordinates are calculated as in the case of 'sheet' shape and +% the shape is then taken into account by shifting the map grid into +% different positions. +% +% Consider, for example, a 4x3 map. The basic position of map units +% is shown on the left (with '1' - 'C' each denoting one map unit). +% In case of a 'cyl' shape, units on the left and right edges are +% neighbors, so for this purpose the map is copied on the left and +% right sides of the map, as on right. +% +% basic left basic right +% ------- ------- ------- ------- +% 1 5 9 1 5 9 1 5 9 1 5 9 +% 2 6 a 2 6 a 2 6 a 2 6 a +% 3 7 b 3 7 b 3 7 b 3 7 b +% 4 8 c 4 8 c 4 8 c 4 8 c +% +% For the 'toroid' shape a similar trick is done, except that the +% copies are placed all around the basic position: +% +% 1 5 9 1 5 9 1 5 9 +% 2 6 a 2 6 a 2 6 a +% 3 7 b 3 7 b 3 7 b +% 4 8 c 4 8 c 4 8 c +% 1 5 9 1 5 9 1 5 9 +% 2 6 a 2 6 a 2 6 a +% 3 7 b 3 7 b 3 7 b +% 4 8 c 4 8 c 4 8 c +% 1 5 9 1 5 9 1 5 9 +% 2 6 a 2 6 a 2 6 a +% 3 7 b 3 7 b 3 7 b +% 4 8 c 4 8 c 4 8 c +% +% From this we can see that the distance from unit '1' is 1 to units +% '9','2','4' and '5', and sqrt(2) to units 'C','A','8' and '6'. Notice +% that in the case of a 'hexa' lattice and 'toroid' shape, the size +% of the map in y-direction should be even. The reason can be clearly +% seen from the two figures below. On the left the basic positions for +% a 3x3 map. If the map is copied above itself, it can be seen that the +% lattice is broken (on the right): +% +% basic positions example of broken lattice +% --------------- ------------------------- +% 1 4 7 +% 2 5 8 +% 3 6 9 +% 1 4 7 1 4 7 +% 2 5 8 2 5 8 +% 3 6 9 3 6 9 +% +% +% REQUIRED INPUT ARGUMENTS +% +% topol Map grid dimensions. +% (struct) topology struct or map struct, the topology +% (msize, lattice, shape) of the map is taken from +% the appropriate fields (see e.g. SOM_SET) +% (vector) the vector which gives the size of the map grid +% (msize-field of the topology struct). +% +% OPTIONAL INPUT ARGUMENTS +% +% lattice (string) The map lattice, either 'rect' or 'hexa'. Default +% is 'rect'. 'hexa' can only be used with 1- or +% 2-dimensional map grids. +% shape (string) The map shape, either 'sheet', 'cyl' or 'toroid'. +% Default is 'sheet'. +% +% OUTPUT ARGUMENTS +% +% Ud (matrix) distances from each map unit to each other map unit, +% size is [munits munits] +% +% EXAMPLES +% +% Simplest case: +% Ud = som_unit_dists(sTopol); +% Ud = som_unit_dists(sMap.topol); +% Ud = som_unit_dists(msize); +% Ud = som_unit_dists([10 10]); +% +% If topology is given as vector, lattice is 'rect' and shape is 'sheet' +% by default. To change these, you can use the optional arguments: +% Ud = som_unit_dists(msize, 'hexa', 'toroid'); +% +% The distances can also be calculated for high-dimensional grids: +% Ud = som_unit_dists([4 4 4 4 4 4]); +% +% SEE ALSO +% +% som_unit_coords Calculate grid coordinates. +% som_unit_neighs Calculate neighborhoods of map units. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 110997 +% Version 2.0beta juuso 101199 170400 070600 130600 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +error(nargchk(1, 3, nargin)); + +% default values +sTopol = som_set('som_topol','lattice','rect'); + +% topol +if isstruct(topol), + switch topol.type, + case 'som_map', sTopol = topol.topol; + case 'som_topol', sTopol = topol; + end +elseif iscell(topol), + for i=1:length(topol), + if isnumeric(topol{i}), sTopol.msize = topol{i}; + elseif ischar(topol{i}), + switch topol{i}, + case {'rect','hexa'}, sTopol.lattice = topol{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = topol{i}; + end + end + end +else + sTopol.msize = topol; +end +if prod(sTopol.msize)==0, error('Map size is 0.'); end + +% lattice +if nargin>1 & ~isempty(lattice) & ~isnan(lattice), sTopol.lattice = lattice; end + +% shape +if nargin>2 & ~isempty(shape) & ~isnan(shape), sTopol.shape = shape; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +msize = sTopol.msize; +lattice = sTopol.lattice; +shape = sTopol.shape; + +munits = prod(msize); +Ud = zeros(munits,munits); + +% free topology +if strcmp(lattice,'free'), + N1 = sTopol.connection; + Ud = som_neighborhood(N1,Inf); +end + +% coordinates of map units when the grid is spread on a plane +Coords = som_unit_coords(msize,lattice,'sheet'); + +% width and height of the grid +dx = max(Coords(:,1))-min(Coords(:,1)); +if msize(1)>1, dx = dx*msize(1)/(msize(1)-1); else dx = dx+1; end +dy = max(Coords(:,2))-min(Coords(:,2)); +if msize(2)>1, dy = dy*msize(2)/(msize(2)-1); else dy = dy+1; end + +% calculate distance from each location to each other location +switch shape, +case 'sheet', + for i=1:(munits-1), + inds = [(i+1):munits]; + Dco = (Coords(inds,:) - Coords(ones(munits-i,1)*i,:))'; + Ud(i,inds) = sqrt(sum(Dco.^2)); + end + +case 'cyl', + for i=1:(munits-1), + inds = [(i+1):munits]; + Dco = (Coords(inds,:) - Coords(ones(munits-i,1)*i,:))'; + dist = sum(Dco.^2); + % The cylinder shape is taken into account by adding and substracting + % the width of the map (dx) from the x-coordinate (ie. shifting the + % map right and left). + DcoS = Dco; DcoS(1,:) = DcoS(1,:) + dx; %East (x+dx) + dist = min(dist,sum(DcoS.^2)); + DcoS = Dco; DcoS(1,:) = DcoS(1,:) - dx; %West (x-dx) + dist = min(dist,sum(DcoS.^2)); + Ud(i,inds) = sqrt(dist); + end + +case 'toroid', + for i=1:(munits-1), + inds = [(i+1):munits]; + Dco = (Coords(inds,:) - Coords(ones(munits-i,1)*i,:))'; + dist = sum(Dco.^2); + % The toroid shape is taken into account as the cylinder shape was + % (see above), except that the map is shifted also vertically. + DcoS = Dco; DcoS(1,:) = DcoS(1,:) + dx; %East (x+dx) + dist = min(dist,sum(DcoS.^2)); + DcoS = Dco; DcoS(1,:) = DcoS(1,:) - dx; %West (x+dx) + dist = min(dist,sum(DcoS.^2)); + DcoS = Dco; DcoS(2,:) = DcoS(2,:) + dy; %South (y+dy) + dist = min(dist,sum(DcoS.^2)); + DcoS = Dco; DcoS(2,:) = DcoS(2,:) - dy; %North (y-dy) + dist = min(dist,sum(DcoS.^2)); + DcoS = Dco; DcoS(1,:) = DcoS(1,:) + dx; DcoS(2,:) = DcoS(2,:) - dy; %NorthEast (x+dx, y-dy) + dist = min(dist,sum(DcoS.^2)); + DcoS = Dco; DcoS(1,:) = DcoS(1,:) + dx; DcoS(2,:) = DcoS(2,:) + dy; %SouthEast (x+dx, y+dy) + dist = min(dist,sum(DcoS.^2)); + DcoS = Dco; DcoS(1,:) = DcoS(1,:) - dx; DcoS(2,:) = DcoS(2,:) + dy; %SouthWest (x-dx, y+dy) + dist = min(dist,sum(DcoS.^2)); + DcoS = Dco; DcoS(1,:) = DcoS(1,:) - dx; DcoS(2,:) = DcoS(2,:) - dy; %NorthWest (x-dx, y-dy) + dist = min(dist,sum(DcoS.^2)); + Ud(i,inds) = sqrt(dist); + end + +otherwise, + error (['Unknown shape: ', shape]); + +end + +Ud = Ud + Ud'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_unit_neighs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,164 @@ +function Ne1 = som_unit_neighs(topol,lattice,shape) + +%SOM_UNIT_NEIGHS Matrix indicating units in 1-neighborhood for each map unit. +% +% Ne1 = som_unit_neighs(topol,[lattice],[shape]) +% +% Ne1 = som_unit_neighs(sTopol); +% Ne1 = som_unit_neighs(sMap.topol); +% Ne1 = som_unit_neighs([10 4], 'hexa', 'cyl'); +% Ne1 = som_unit_neighs(msize, 'rect', 'toroid'); +% +% Input and output arguments ([]'s are optional): +% topol topology of the SOM grid +% (struct) topology or map struct +% (vector) the 'msize' field of topology struct +% [lattice] (string) map lattice, 'rect' by default +% [shape] (string) map shape, 'sheet' by default +% +% Ne1 (matrix, size [munits munits]) a sparse matrix +% indicating the map units in 1-neighborhood +% by value 1 (note: the unit itself also has value 0) +% +% For more help, try 'type som_unit_neighs' or check out online documentation. +% See also SOM_NEIGHBORHOOD, SOM_UNIT_DISTS, SOM_UNIT_COORDS, SOM_CONNECTION. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_unit_neighs +% +% PURPOSE +% +% Find the adjacent (in 1-neighborhood) units for each map unit of a SOM +% based on given topology. +% +% SYNTAX +% +% Ne1 = som_unit_neighs(sMap); +% Ne1 = som_unit_neighs(sM.topol); +% Ne1 = som_unit_neighs(msize); +% Ne1 = som_unit_neighs(msize,'hexa'); +% Ne1 = som_unit_neighs(msize,'rect','toroid'); +% +% DESCRIPTION +% +% For each map unit, find the units the distance of which from +% the map unit is equal to 1. The distances are calculated +% along the map grid. Consider, for example, the case of a 4x3 map. +% The unit ('1' to 'C') positions for 'rect' and 'hexa' lattice (and +% 'sheet' shape) are depicted below: +% +% 'rect' lattice 'hexa' lattice +% -------------- -------------- +% 1 5 9 1 5 9 +% 2 6 a 2 6 a +% 3 7 b 3 7 b +% 4 8 c 4 8 c +% +% The units in 1-neighborhood (adjacent units) for unit '6' are '2','5','7' +% and 'a' in the 'rect' case and '5','2','7','9','a' and 'b' in the 'hexa' +% case. The function returns a sparse matrix having value 1 for these units. +% Notice that not all units have equal number of neighbors. Unit '1' has only +% units '2' and '5' in its 1-neighborhood. +% +% REQUIRED INPUT ARGUMENTS +% +% topol Map grid dimensions. +% (struct) topology struct or map struct, the topology +% (msize, lattice, shape) of the map is taken from +% the appropriate fields (see e.g. SOM_SET) +% (vector) the vector which gives the size of the map grid +% (msize-field of the topology struct). +% +% OPTIONAL INPUT ARGUMENTS +% +% lattice (string) The map lattice, either 'rect' or 'hexa'. Default +% is 'rect'. 'hexa' can only be used with 1- or +% 2-dimensional map grids. +% shape (string) The map shape, either 'sheet', 'cyl' or 'toroid'. +% Default is 'sheet'. +% +% OUTPUT ARGUMENTS +% +% Ne1 (matrix) sparse matrix indicating units in 1-neighborhood +% by 1, all others have value 0 (including the unit itself!), +% size is [munits munits] +% +% EXAMPLES +% +% Simplest case: +% Ne1 = som_unit_neighs(sTopol); +% Ne1 = som_unit_neighs(sMap.topol); +% Ne1 = som_unit_neighs(msize); +% Ne1 = som_unit_neighs([10 10]); +% +% If topology is given as vector, lattice is 'rect' and shape is 'sheet' +% by default. To change these, you can use the optional arguments: +% Ne1 = som_unit_neighs(msize, 'hexa', 'toroid'); +% +% The neighbors can also be calculated for high-dimensional grids: +% Ne1 = som_unit_neighs([4 4 4 4 4 4]); +% +% SEE ALSO +% +% som_neighborhood Calculate N-neighborhoods of map units. +% som_unit_coords Calculate grid coordinates. +% som_unit_dists Calculate interunit distances. +% som_connection Connection matrix. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta juuso 141097 +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Check arguments + +error(nargchk(1, 3, nargin)); + +% default values +sTopol = som_set('som_topol','lattice','rect'); + +% topol +if isstruct(topol), + switch topol.type, + case 'som_map', sTopol = topol.topol; + case 'som_topol', sTopol = topol; + end +elseif iscell(topol), + for i=1:length(topol), + if isnumeric(topol{i}), sTopol.msize = topol{i}; + elseif ischar(topol{i}), + switch topol{i}, + case {'rect','hexa'}, sTopol.lattice = topol{i}; + case {'sheet','cyl','toroid'}, sTopol.shape = topol{i}; + end + end + end +else + sTopol.msize = topol; +end +if prod(sTopol.msize)==0, error('Map size is 0.'); end + +% lattice +if nargin>1 & ~isempty(lattice) & ~isnan(lattice), sTopol.lattice = lattice; end + +% shape +if nargin>2 & ~isempty(shape) & ~isnan(shape), sTopol.shape = shape; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Action + +% distances between each map unit +Ud = som_unit_dists(sTopol); + +% 1-neighborhood are those units the distance of which is equal to 1 +munits = prod(sTopol.msize); +Ne1 = sparse(zeros(munits)); +for i=1:munits, + inds = find(Ud(i,:)<1.01 & Ud(i,:)>0); % allow for rounding error + Ne1(i,inds) = 1; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_use_vs1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function som_use_vs1() + +% SOM_USE_VS1 Use SOM Toolbox version 1. +% +% Removes SOM Toolbox version 2 from the path and adds version 1 there +% instead. Before doing this, remember to convert any version 2 structs you +% will need to the corresponding version 1 structs using function SOM_VS2TO1. +% +% See also SOM_USE_VS2, SOM_VS1TO2, SOM_VS2TO1, PATHTOOL. + +rmpath /share/somtoolbox/www/package/codes2 + +s=path; +p=findstr(s,'/share/matlab5'); p=p(1); +i=p+12; while ~strcmp(s(i),'/'), i=i+1; end +r=strcat(s(p:i),'toolbox/somtoolbox'); +addpath(r) + +fprintf(1,'Version 1 of SOM Toolbox now in use.\n'); +fprintf(1,'Use som_vs2to1 function to convert any vs2 structs to vs1.\n')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_use_vs2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +function som_use_vs2() + +% SOM_USE_VS2 Use SOM Toolbox version 2. +% +% Removes SOM Toolbox version 1 from the path and adds version 2 +% there instead. You can use function SOM_VS1TO2 to convert +% any version 1 structs to the corresponding version 2 structs. +% +% See also SOM_USE_VS1, SOM_VS1TO2, SOM_VS2TO1, PATHTOOL. + +s=path; p=findstr(s,'somtoolbox'); +while any(p), + p=p(1); + i=p; while i<length(s) & ~strcmp(s(i),':'), i=i+1; end + if strcmp(s(i),':'), i=i-1; end + j=p; while j>1 & ~strcmp(s(j),':'), j=j-1; end + if strcmp(s(j),':'), j=j+1; end + r=s(j:i); + rmpath(r); + s=path; p=findstr(s,'somtoolbox'); +end + +addpath /share/somtoolbox/www/package/codes2 + +fprintf(1,'Version 2 of SOM Toolbox now in use.\n'); +fprintf(1,'Latest changes to SOM Toolbox: August 22nd 2000\n'); +fprintf(1,' see /share/somtoolbox/vs2/changelog.txt\n'); +fprintf(1,'Use som_vs1to2 function to convert any vs1 structs to vs2.\n') +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_vis_coords.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,153 @@ +function unit_coord=som_vis_coords(lattice, msize) + +%SOM_VIS_COORDS Unit coordinates used in visualizations. +% +% Co = som_vis_coords(lattice, msize) +% +% Co = som_vis_coords('hexa',[10 7]) +% Co = som_vis_coords('rectU',[10 7]) +% +% Input and output arguments: +% lattice (string) 'hexa', 'rect', 'hexaU' or 'rectU' +% msize (vector) grid size in a 1x2 vector +% +% Co (matrix) Mx2 matrix of unit coordinates, where +% M=prod(msize) for 'hexa' and 'rect', and +% M=(2*msize(1)-1)*(2*msize(2)-1) for 'hexaU' and 'rectU' +% +% This function calculates the coordinates of map units on a 'sheet' +% shaped map with either 'hexa' or 'rect' lattice as used in the +% visualizations. Note that this slightly different from the +% coordinates provided by SOM_UNIT_COORDS function. +% +% 'rectU' and 'hexaU' gives the coordinates of both units and the +% connections for u-matrix visualizations. +% +% For more help, try 'type som_vis_coords' or check out online documentation. +% See also SOM_UNIT_COORDS, SOM_UMAT, SOM_CPLANE, SOM_GRID. + +%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% PURPOSE +% +% Returns coordinates of the map units for map visualization +% +% SYNTAX +% +% Co = som_vis_coords(lattice, msize) +% +% DESCRIPTION +% +% This function calculates the coordinates of map units in 'hexa' and +% 'rect' lattices in 'sheet' shaped map for visualization purposes. It +% differs from SOM_UNIT_COORDS in the sense that hexagonal lattice is +% calculated in a "wrong" way in order to get integer coordinates for +% the units. Another difference is that it may be used to calculate +% the coordinates of units _and_ the center points of the lines +% connecting them (edges) by using 'hexaU' or 'rectU' for lattice. +% This property may be used for drawing u-matrices. +% +% The unit number 1 is set to (ij) coordinates (1,1)+shift +% 2 (2,1)+shift +% +% ... columnwise +% +% n-1th (n1-1,n2)+shift +% nth (n1,n2)+shift +% +% where grid size = [n1 n2] and shift is zero, except for +% the even lines of 'hexa' lattice, for which it is +0.5. +% +% For 'rectU' and 'hexaU' the unit coordinates are the same and the +% coordinates for connections are set according to these. In this case +% the ordering of the coordinates is the following: +% let +% U = som_umat(sMap); U=U(:); % make U a column vector +% Uc = som_vis_coords(sMap.topol.lattice, sMap.topol.msize); +% now the kth row of matrix Uc, i.e. Uc(k,:), contains the coordinates +% for value U(k). +% +% REQUIRED INPUT ARGUMENTS +% +% lattice (string) The local topology of the units: +% 'hexa', 'rect', 'hexaU' or 'rectU' +% msize (vector) size 1x2, defining the map grid size. +% Notice that only 2-dimensional grids +% are allowed. +% +% OUTPUT ARGUMENTS +% +% Co (matrix) size Mx2, giving the coordinates for each unit. +% M=prod(msize) for 'hexa' and 'rect', and +% M=(2*msize(1)-1)*(2*msize(2)-1) for 'hexaU' and 'rectU' +% +% FEATURES +% +% Only 'sheet' shaped maps are considered. If coordinates for 'toroid' +% or 'cyl' topologies are required, you must use SOM_UNIT_COORDS +% instead. +% +% EXAMPLES +% +% Though this is mainly a subroutine for visualizations it may be +% used, e.g., in the following manner: +% +% % This makes a hexagonal lattice, where the units are rectangular +% % instead of hexagons. +% som_cplane('rect',som_vis_coords('hexa',[10 7]),'none'); +% +% % Let's make a map and calculate a u-matrix: +% sM=som_make(data,'msize',[10 7],'lattice','hexa'); +% u=som_umat(sM); u=u(:); +% % Now, these produce equivalent results: +% som_cplane('hexaU',[10 7],u); +% som_cplane(vis_patch('hexa')/2,som_vis_coords('hexaU',[10 7]),u); +% +% SEE ALSO +% +% som_grid Visualization of a SOM grid +% som_cplane Visualize a 2D component plane, u-matrix or color plane +% som_barplane Visualize the map prototype vectors as bar diagrams +% som_plotplane Visualize the map prototype vectors as line graphs +% som_pieplane Visualize the map prototype vectors as pie charts +% som_unit_coords Locations of units on the SOM grid + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 201099 juuso 261199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if ~vis_valuetype(msize,{'1x2'}), + error('msize must be a 1x2 vector.') +end + +if vis_valuetype(lattice,{'string'}) + switch lattice + case {'hexa', 'rect'} + munits=prod(msize); + unit_coord(:,1)=reshape(repmat([1:msize(2)],msize(1),1),1,munits)'; + unit_coord(:,2)=repmat([1:msize(1)]',msize(2),1); + if strcmp(lattice,'hexa') + % Move even rows by .5 + d=rem(unit_coord(:,2),2) == 0; + unit_coord(d,1)=unit_coord(d,1)+.5; + end + case {'hexaU','rectU'} + msize=2*msize-1; munits=prod(msize); + unit_coord(:,1)=reshape(repmat([1:msize(2)],msize(1),1),1,munits)'; + unit_coord(:,2)=repmat([1:msize(1)]',msize(2),1); + if strcmp(lattice,'hexaU') + d=rem(unit_coord(:,2),2) == 0; + unit_coord(d,1)=unit_coord(d,1)+.5; + d=rem(unit_coord(:,2)+1,4) == 0; + unit_coord(d,1)=unit_coord(d,1)+1; + end + unit_coord=unit_coord/2+.5; + otherwise + error([ 'Unknown lattice ''' lattice '''.']); + end +else + error('Lattice must be a string.'); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_vs1to2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,262 @@ +function sS = som_vs1to2(sS) + +%SOM_VS1TO2 Convert version 1 structure to version 2. +% +% sSnew = som_vs1to2(sSold) +% +% sMnew = som_vs1to2(sMold); +% sDnew = som_vs1to2(sDold); +% +% Input and output arguments: +% sSold (struct) a SOM Toolbox version 1 structure +% sSnew (struct) a SOM Toolbox version 2 structure +% +% For more help, try 'type som_vs1to2' or check out online documentation. +% See also SOM_SET, SOM_VS2TO1. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_vs1to2 +% +% PURPOSE +% +% Transforms SOM Toolbox 1 version structs from to 2 version structs. +% +% SYNTAX +% +% sS2 = som_vs1to2(sS1) +% +% DESCRIPTION +% +% This function is offered to allow the change of old map and data structs +% to new ones. There are quite a lot of changes between the versions, +% especially in the map struct, and this function makes it easy to update +% the structs. +% +% WARNING! +% +% 'som_unit_norm' normalization type is not supported by version 2, +% so this type of normalization will be lost. +% +% REQUIRED INPUT ARGUMENTS +% +% sS1 (struct) any SOM Toolbox version 1 struct (map, data, +% training or normalization struct) +% +% OUTPUT ARGUMENTS +% +% sS2 (struct) the corresponding SOM Toolbox 2 version struct +% +% EXAMPLES +% +% sM = som_vs1to2(sMold); +% sD = som_vs1to2(sDold); +% sT = som_vs1to2(sMold.train_sequence{1}); +% sN = som_vs1to2(sDold.normalization); +% +% SEE ALSO +% +% som_set Set values and create SOM Toolbox structs. +% som_vs2to1 Transform structs from version 2.0 to 1.0. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(1, 1, nargin)); % check no. of input arguments is correct + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% set field values + +if isfield(sS,'codebook'), type='som_map'; +elseif isfield(sS,'data'), type='som_data'; +elseif isfield(sS,'algorithm'), type = 'som_train'; +elseif isfield(sS,'inv_params'), type = 'som_norm'; +else + error('Unrecognized input struct.'); +end + +switch type, + case 'som_map', + msize = sS.msize; munits = prod(msize); dim = prod(size(sS.codebook))/munits; + M = reshape(sS.codebook,[munits dim]); + + % topology + if strcmp(sS.shape,'rect'), shape = 'sheet'; else shape = sS.shape; end + sTopol = struct('type','som_topol','msize',msize,'lattice',sS.lattice,'shape',shape); + + % labels + labels = cell(munits,1); + for i=1:munits, + for j=1:length(sS.labels{i}), labels{i,j} = sS.labels{i}{j}; end + end + + % trainhist + tl = length(sS.train_sequence); + if strcmp(sS.init_type,'linear'); alg = 'lininit'; else alg = 'randinit'; end + trh = struct('type','som_train'); + trh.algorithm = alg; + trh.neigh = sS.neigh; + trh.mask = sS.mask; + trh.data_name = sS.data_name; + trh.radius_ini = NaN; + trh.radius_fin = NaN; + trh.alpha_ini = NaN; + trh.alpha_type = ''; + trh.trainlen = NaN; + trh.time = ''; + for i=1:tl, + trh(i+1) = som_vs1to2(sS.train_sequence{i}); + trh(i+1).mask = sS.mask; + trh(i+1).neigh = sS.neigh; + trh(i+1).data_name = sS.data_name; + end + + % component normalizations + cnorm = som_vs1to2(sS.normalization); + if isempty(cnorm), + cnorm = cell(dim,1); + elseif length(cnorm) ~= dim, + warning('Incorrect number of normalizations. Normalizations ignored.\n'); + cnorm = cell(dim,1); + else + if strcmp(cnorm{1}.method,'histD'), + M = redo_hist_norm(M,sS.normalization.inv_params,cnorm); + end + end + + % map + sSnew = struct('type','som_map'); + sSnew.codebook = M; + sSnew.topol = sTopol; + sSnew.labels = labels; + sSnew.neigh = sS.neigh; + sSnew.mask = sS.mask; + sSnew.trainhist = trh; + sSnew.name = sS.name; + sSnew.comp_norm = cnorm; + sSnew.comp_names = sS.comp_names; + + case 'som_data', + [dlen dim] = size(sS.data); + + % component normalizations + cnorm = som_vs1to2(sS.normalization); + if isempty(cnorm), + cnorm = cell(dim,1); + elseif length(cnorm) ~= dim, + warning('Incorrect number of normalizations. Normalizations ignored.\n'); + cnorm = cell(dim,1); + else + if strcmp(cnorm{1}.method,'histD'), + sS.data = redo_hist_norm(sS.data,sS.normalization.inv_params,cnorm); + end + end + + % data + sSnew = struct('type','som_data'); + sSnew.data = sS.data; + sSnew.name = sS.name; + sSnew.labels = sS.labels; + sSnew.comp_names = sS.comp_names; + sSnew.comp_norm = cnorm; + sSnew.label_names = []; + + case 'som_norm', + if isempty(sS.inv_params), + sSnew = []; + else + dim = size(sS.inv_params,2); + sSnew = cell(dim,1); + switch sS.name, + case 'som_var_norm', method = 'var'; + case 'som_lin_norm', method = 'range'; + case 'som_hist_norm', method = 'histD'; + case 'som_unit_norm', method = ''; + warning(['Normalization method ''som_unit_norm'' is not available' ... + ' in version 2 of SOM Toolbox.\n']); + end + if ~isempty(method), + for i=1:dim, + sSnew{i} = struct('type','som_norm'); + sSnew{i}.method = method; + sSnew{i}.params = []; + sSnew{i}.status = 'done'; + switch method, + case 'var', + me = sS.inv_params(1,i); st = sS.inv_params(2,i); + sSnew{i}.params = [me, st]; + case 'range', + mi = sS.inv_params(1,i); ma = sS.inv_params(2,i); + sSnew{i}.params = [mi, ma-mi]; + case 'histD', + vals = sS.inv_params(1:(end-1),i); + bins = sum(isfinite(vals)); + vals = vals(1:bins); + sSnew{i}.params = vals; + end + end + end + end + + case 'som_train', + sSnew = struct('type','som_train'); + sSnew.algorithm = sS.algorithm; + sSnew.neigh = 'gaussian'; + sSnew.mask = []; + sSnew.data_name = 'unknown'; + sSnew.radius_ini = sS.radius_ini; + sSnew.radius_fin = sS.radius_fin; + sSnew.alpha_ini = sS.alpha_ini; + sSnew.alpha_type = sS.alpha_type; + sSnew.trainlen = sS.trainlen; + sSnew.time = sS.time; + + case 'som_topol', + disp('Version 1.0 of SOM Toolbox did not have topology structure.\n'); + + case {'som_grid','som_vis'} + disp('Version 1.0 of SOM Toolbox did not have visualization structs.\n'); + + otherwise, + + error('Unrecognized struct.'); +end + +sS = sSnew; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function D = redo_hist_norm(D,inv_params,cnorm) + + dim = size(D,2); + + % first - undo the old way + n_bins = inv_params(end,:); + D = round(D * sparse(diag(n_bins))); + for i = 1:dim, + if any(isnan(D(:, i))), D(isnan(D(:, i)), i) = n_bins(i); end + D(:, i) = inv_params(D(:, i), i); + end + % then - redo the new way + for i=1:dim, + bins = length(cnorm{i}.params); + x = D(:,i); + inds = find(~isnan(x) & ~isinf(x))'; + for j = inds, + [dummy ind] = min(abs(x(j) - cnorm{i}.params)); + if x(j) > cnorm{i}.params(ind) & ind < bins, x(j) = ind + 1; + else x(j) = ind; + end + end + D(:,i) = (x-1)/(bins-1); + end + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_vs2to1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,298 @@ +function sS = som_vs2to1(sS) + +%SOM_VS2TO1 Convert version 2 struct to version 1. +% +% sSold = som_vs2to1(sSnew) +% +% sMold = som_vs2to1(sMnew); +% sDold = som_vs2to1(sDnew); +% +% Input and output arguments: +% sSnew (struct) a SOM Toolbox version 2 struct +% sSold (struct) a SOM Toolbox version 1 struct +% +% For more help, try 'type som_vs2to1' or check out online documentation. +% See also SOM_SET, SOM_VS1TO2. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_vs2to1 +% +% PURPOSE +% +% Converts SOM Toolbox version 2 structs to version 1 structs. +% +% SYNTAX +% +% sS1 = som_vs2to1(sS2) +% +% DESCRIPTION +% +% This function is offered to allow the change of new map and data structs +% to old ones. There are quite a lot of changes between the versions, +% especially in the map struct, and this function makes it possible to +% use the old functions with new structs. +% +% Note that part of the information is lost in the conversion. Especially, +% training history is lost, and the normalization is, except in the simplest +% cases (like all have 'range' or 'var' normalization) screwed up. +% +% REQUIRED INPUT ARGUMENTS +% +% sS2 (struct) som SOM Toolbox version 2.0 struct (map, data, +% training or normalization struct) +% +% OUTPUT ARGUMENTS +% +% sS1 (struct) the corresponding SOM Toolbox version 2.0 struct +% +% EXAMPLES +% +% sM = som_vs2to1(sMnew); +% sD = som_vs2to1(sDnew); +% sT = som_vs2to1(sMnew.trainhist(1)); +% +% SEE ALSO +% +% som_set Set values and create SOM Toolbox structs. +% som_vs1to2 Transform structs from 1.0 version to 2.0. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juuso 101199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(1, 1, nargin)); % check no. of input arguments is correct + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% set field values + +switch sS.type, + case 'som_map', + msize = sS.topol.msize; + [munits dim] = size(sS.codebook); + + % topology + if strcmp(sS.topol.shape,'sheet'), shape = 'rect'; + else shape = sS.shape; + end + + % labels + labels = cell(munits,1); + nl = size(sS.labels,2); + for i=1:munits, + labels{i} = cell(nl,1); + for j=1:nl, labels{i}{j} = sS.labels{i,j}; end + end + + % trainhist + tl = length(sS.trainhist); + if tl==0 | strcmp(sS.trainhist(1).algorithm,'lininit'), + init_type = 'linear'; + else + init_type = 'random'; + end + if tl>1, + for i=2:tl, + train_seq{i-1} = som_vs2to1(sS.trainhist(i)); + end + train_type = sS.trainhist(tl).algorithm; + else + train_seq = []; + train_type = 'batch'; + end + if tl>0, data_name = sS.trainhist(tl).data_name; else data_name = ''; end + + % component normalizations + sN = convert_normalizations(sS.comp_norm); + if strcmp(sN.name,'som_hist_norm'), + sS.codebook = redo_hist_norm(sS.codebook,sS.comp_norm,sN); + end + + % map + sSnew = struct('init_type', 'linear', 'train_type', 'batch', 'lattice' ,... + 'hexa', 'shape', 'rect', 'neigh', 'gaussian', 'msize', msize, ... + 'train_sequence', [], 'codebook', [], 'labels', [], ... + 'mask', [], 'data_name', 'unnamed', 'normalization', [], ... + 'comp_names', [], 'name', 'unnamed'); + sSnew.init_type = init_type; + sSnew.train_type = train_type; + sSnew.lattice = sS.topol.lattice; + sSnew.shape = shape; + sSnew.neigh = sS.neigh; + sSnew.msize = sS.topol.msize; + sSnew.train_sequence = train_seq; + sSnew.codebook = reshape(sS.codebook,[sS.topol.msize dim]); + sSnew.labels = labels; + sSnew.mask = sS.mask; + sSnew.data_name = data_name; + sSnew.normalization = sN; + sSnew.comp_names = sS.comp_names; + sSnew.name = sS.name; + + case 'som_data', + [dlen dim] = size(sS.data); + + % component normalizations + sN = convert_normalizations(sS.comp_norm); + if strcmp(sN.name,'som_hist_norm'), + sS.codebook = redo_hist_norm(sS.codebook,sS.comp_norm,sN); + end + + % data + sSnew = struct('data', [], 'name', '', 'labels' , [], 'comp_names', ... + [], 'normalization', []); + sSnew.data = sS.data; + sSnew.name = sS.name; + sSnew.labels = sS.labels; + sSnew.comp_names = sS.comp_names; + sSnew.normalization = sN; + + case 'som_norm', + sSnew = struct('name','som_var_norm','inv_params',[]); + + switch sS.method, + case 'var', sSnew.name = 'som_var_norm'; + case 'range', sSnew.name = 'som_lin_norm'; + case 'histD', sSnew.name = 'som_hist_norm'; + otherwise, + warning(['Method ' method ' does not exist in version 1.']) + end + + if strcmp(sS.status,'done'), + switch sS.method, + case 'var', + sSnew.inv_params = zeros(2,1); + sSnew.inv_params(1) = sS.params(1); + sSnew.inv_params(2) = sS.params(2); + case 'range', + sSnew.inv_params = zeros(2,1); + sSnew.inv_params(1) = sS.params(1); + sSnew.inv_params(2) = sS.params(2) + sS.params(1);; + case 'histD', + bins = length(sS.params); + sSnew.inv_params = zeros(bins+1,1) + Inf; + sSnew.inv_params(1:bins,i) = sS.params; + sSnew.inv_params(end,i) = bins; + end + end + + case 'som_train', + sSnew = struct('algorithm', sS.algorithm, 'radius_ini', ... + sS.radius_ini, 'radius_fin', sS.radius_fin, 'alpha_ini', ... + sS.alpha_ini, 'alpha_type', sS.alpha_type, 'trainlen', sS.trainlen, ... + 'qerror', NaN, 'time', sS.time); + + case 'som_topol', + disp('Version 1 of SOM Toolbox did not have topology structure.\n'); + + otherwise, + + error('Unrecognized struct.'); +end + +sS = sSnew; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% subfunctions + +function sN = convert_normalizations(cnorm) + + dim = length(cnorm); + sN = struct('name','som_var_norm','inv_params',[]); + + % check that there is exactly one normalization per component + % and that their status and method is the same + ok = 1; + nof = zeros(dim,1); + for i=1:dim, nof(i) = length(cnorm{i}); end + if any(nof>1), ok=0; + elseif any(nof==1) & any(nof==0), ok=0; + elseif any(nof>0), + status = cnorm{1}.status; + method = cnorm{1}.method; + for i=2:dim, + if ~strcmp(cnorm{i}.status,status) | ~strcmp(cnorm{i}.method,method), + ok = 0; + end + end + elseif all(nof==0), + return; + end + if ~ok, + warning(['Normalization could not be converted. All variables can' ... + ' only be normalized with a single, and same, method.']); + return; + end + + % method name + switch method, + case 'var', sN.name = 'som_var_norm'; + case 'range', sN.name = 'som_lin_norm'; + case 'histD', sN.name = 'som_hist_norm'; + otherwise, + warning(['Normalization could not be converted. Method ' method ... + 'does not exist in version 1.']); + return; + end + + % if not done, inv_params is empty + if ~strcmp(status,'done'), return; end + + % ok, make the conversion + switch method, + case 'var', + sN.inv_params = zeros(2,dim); + for i=1:dim, + sN.inv_params(1,i) = cnorm{i}.params(1); + sN.inv_params(2,i) = cnorm{i}.params(2); + end + case 'range', + sN.inv_params = zeros(2,dim); + for i=1:dim, + sN.inv_params(1,i) = cnorm{i}.params(1); + sN.inv_params(2,i) = cnorm{i}.params(2) + cnorm{i}.params(1); + end + case 'histD', + bins = zeros(dim,1); + for i=1:dim, bins(i) = length(cnorm{i}.params); end + m = max(bins); + sN.inv_params = zeros(m+1,dim) + Inf; + for i=1:dim, + sN.inv_params(1:bins(i),i) = cnorm{i}.params; + if bins(i)<m, sN.inv_params(bins(i)+1,i) = NaN; end + sN.inv_params(end,i) = bins(i); + end + end + +function D = redo_hist_norm(D,cnorm,sN) + + dim = size(D,2); + + % first - undo the new way + for i=1:dim, + bins = length(cnorm{i}.params); + D(:,i) = round(D(:,i)*(bins-1)+1); + inds = find(~isnan(D(:,i)) & ~isinf(D(:,i))); + D(inds,i) = cnorm{i}.params(D(inds,i)); + end + % then - redo the old way + n_bins = sN.inv_params(size(sN.inv_params,1),:); + for j = 1:dim, + for i = 1:size(D, 1) + if ~isnan(D(i, j)), + [d ind] = min(abs(D(i, j) - sN.inv_params(1:n_bins(j), j))); + if (D(i, j) - sN.inv_params(ind, j)) > 0 & ind < n_bins(j), + D(i, j) = ind + 1; + else + D(i, j) = ind; + end + end + end + end + D = D * sparse(diag(1 ./ n_bins)); + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_write_cod.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,187 @@ +function som_write_cod(sMap, filename) + +%SOM_WRITE_COD Writes a map struct to ascii file in SOM_PAK format. +% +% som_write_cod(sMap,filename) +% +% som_write_cod(sMap,'map1.cod'); +% +% Input and output arguments: +% sMap (struct) self-organizing map structure +% filename (string) name of input file +% +% Note that much of the information in the map struct is lost. +% Typically, when saving map structs into files use the 'save' command. +% +% For more help, try 'type som_write_cod' or check out online documentation. +% See also SOM_READ_COD, SOM_READ_DATA, SOM_WRITE_DATA. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_write_cod +% +% PURPOSE +% +% Writes a self-organizing map struct to a file in SOM_PAK format. +% +% SYNTAX +% +% som_write_cod(sMap,filename); +% +% DESCRIPTION +% +% This function is offered for compatibility with SOM_PAK, a SOM +% software package in C. It writes map struct to files in SOM_PAK format. +% +% See SOM_READ_COD for description of the SOM_PAK map file format. +% Because the SOM_PAK package does not support many of the features and +% options of the SOM Toolbox, some of the information is changed, or even +% lost. +% +% SOM_PAK does not support 3- or higher dimensional map grids. These cannot +% be exported using this function. +% SOM_PAK always supposes that the map has 'sheet' shape. +% SOM_PAK only supports 'bubble' and 'gaussian' neighborhood functions. +% Any other neighborhood function is changed to 'gaussian'. +% SOM_PAK doesn't support component names. However, the component names are +% written on a comment line which begins with '#n '. Any spaces (' ') +% in the component names are replaced with underscores ('_'). +% Information on map name, mask, training history and normalizations is lost. +% +% This function is only offered for compatibility with SOM_PAK. In general, +% when saving map structs in files, use 'save filename.mat sMap'. This is +% faster and retains all information of the map. +% +% REQUIRED INPUT ARGUMENTS +% +% sMap (struct) the SOM struct to be written +% filename (string) the name of the input file +% +% EXAMPLES +% +% som_write_cod(sMap,'map1.cod'); +% +% SEE ALSO +% +% som_read_cod Read a map from a file in SOM_PAK format. +% som_read_data Reads data from an ascii file. +% som_write_data Writes data struct into a file in SOM_PAK format. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 221097 +% Version 2.0beta ecco 030899, juuso 151199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments and initialize + +error(nargchk(2, 2, nargin)) % check no. of input args is correct + +% sMap +msize = sMap.topol.msize; % map grid size +mdim = length(msize); % map grid dimension +[munits dim] = size(sMap.codebook); % input space dimension + +% map dimension check: +% map dimensions higher than 2 are not supported by SOM_PAK +if mdim > 2, + error('Cannot write maps with higher dimension than two'); +end + +% in SOM_PAK the xy-indexing is used, while in Matlab ij-indexing +% therefore, the codebook vectors have to be reorganized +order = reshape([1:munits],msize); +order = reshape(order',[munits 1]); +msize = fliplr(msize); + +% open output file +fid = fopen(filename, 'w'); +if fid < 0, + error(['Cannot open file ' filename]); +end + +% check version +v = version; +ver_53_or_newer = (str2num(v(1:3)) >= 5.3); + + +[lines numlabs] = size(sMap.labels); +has_labels = zeros(lines, 1); +if ver_53_or_newer + has_labels = sum((~(cellfun('isempty', sMap.labels))), 2); +else + for i = 1:lines + for j = 1:numlabs + if ~isempty(sMap.labels{i,j}) + has_labels(i) = 1; break; + end + end + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% write map into a file + +% write header + +fprintf(fid, '%d %s ', dim, sMap.topol.lattice); % dimension and lattice +fprintf(fid, '%d ', msize); % map size +% neighborhood type ('ep' and 'cutgauss' are not supported by SOM_PAK; +% they are converted to 'gaussian') +if strcmp(sMap.neigh,'bubble'), fprintf(fid, 'bubble\n'); +else + if ~strcmp(sMap.neigh,'gaussian'), + warning(['Neighborhood type ''' sMap.neigh ''' converted to ''gaussian''']); + end + fprintf(fid,'gaussian\n'); +end + +% write the component names as a SOM_PAK comment line + +fprintf(fid,'#n '); +for i=1:dim, fprintf(fid, '%s ', strrep(sMap.comp_names{i},' ','_')); end +fprintf(fid,'\n'); + +% write codebook + +form = [repmat('%g ',[1 dim]) '\n']; +if ~has_labels % no labels; fast + fprintf(fid, form, sMap.codebook(order,:)'); +else % has labels; slow + for i=1:munits, + fprintf(fid, '%g ', sMap.codebook(order(i),:)); + + if has_labels(order(i)) + temp = ''; + if ver_53_or_newer + nonempty = ~(cellfun('isempty', sMap.labels(i,:))); + else + for j = 1:numlabs, nonempty(j) = ~isempty(sMap.labels{i, j}); end + end + labs = char(sMap.labels{order(i), nonempty}); + labs(:,end + 1) = ' '; + temp = reshape(labs',[1 prod(size(labs))]); + temp(findstr(' ', temp))=''; + fprintf(fid, '%s\n', temp(1:end-1)); + else + fprintf(fid, '\n'); + end + end +end + +% close file + +if fclose(fid) + error(['Cannot close file ' filename]); +else + fprintf(2, 'map write ok\n'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_write_data.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,210 @@ +function som_write_data(sData, filename, missing) + +%SOM_WRITE_DATA Writes data structs/matrices to a file in SOM_PAK format. +% +% som_write_data(data,filename,[missing]) +% +% som_write_data(sD,'system.data') +% som_write_data(D,'system.data','*') +% +% Input and output arguments ([]'s are optional): +% data (struct) data struct to be written in the file +% (matrix) data matrix +% filename (string) output filename +% [missing] (string) string used to denote missing components (NaNs); +% default is 'NaN' +% +% Note that much of the information in the data struct is lost. +% Typically, when saving data structs into files use the 'save' command. +% +% For more help, try 'type som_write_data' or check out online documentation. +% See also SOM_READ_DATA, SOM_READ_COD, SOM_WRITE_COD. + +%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% som_write_data +% +% PURPOSE +% +% Writes data structs/matrices to a file in SOM_PAK format. +% +% SYNTAX +% +% som_write_data(sD,'filename') +% som_write_data(D,'filename') +% som_write_data(...,'missing') +% +% DESCRIPTION +% +% This function is offered for compatibility with SOM_PAK, a SOM software +% package in C. It writes data structs/matrices to a file in SOM_PAK format. +% +% See SOM_READ_DATA for a decription of the SOM_PAK data file format. Since +% the format does not support information on normalizations, that +% information is lost, as well as the data name. The component names are +% written on a comment line which begins with '#n' and label names on +% comment line which begins with '#l', respectively. Any spaces (' ') in the +% component names and in the label names are replaced with +% underscores ('_'). +% +% This function is only offered for compatibility with SOM_PAK. In +% general, when saving data in files, use 'save filename.mat sData'. This is +% faster and retains all information of the data struct. +% +% The string to use for missing values (NaNs) in the written file can +% be given with the last argument. Notice that if you use SOM_PAK to +% read the files, you need to set the SOM_PAK environment variable +% LVQSOM_MASK_STR accordingly, e.g. to 'NaN' if you use the default +% replacement. For more information, see the SOM_PAK instructions. +% +% REQUIRED INPUT ARGUMENTS +% +% data (struct or matrix) data to be written +% filename (string) output filename +% +% OPTIONAL INPUT ARGUMENTS +% +% missing (string) string used to denote missing components (NaNs); +% default is 'NaN' +% +% EXAMPLES +% +% The basic usage is: +% som_write_data(sData,'system.data') +% +% To write a data matrix to a file in plain SOM_PAK format, give a +% data matrix as the first argument: +% som_write_data(D,'system.data') +% +% By default, all NaNs in the data matrix are written as 'NaN':s. The +% third argument can be used to change this: +% som_write_data(sData,'system.data','+') +% som_write_data(sData,'system.data','missing') +% +% SEE ALSO +% +% som_read_data Reads data from an ascii file. +% som_read_cod Read a map from a file in SOM_PAK format. +% som_write_cod Writes data struct into a file in SOM_PAK format. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 1.0beta ecco 131197 +% Version 2.0beta ecco 030899 juuso 151199 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% check arguments + +error(nargchk(2, 3, nargin)); % check no. of input args is correct + +% data +if isstruct(sData) + is_struct = 1; + D = sData.data; +else + is_struct = 0; + D = sData; +end +[samples dim] = size(D); + +% missing +if nargin == 2, missing = 'NaN'; end + +% open output file +fid = fopen(filename, 'w'); +if fid < 0, error(['Cannot open file ' filename]); end + +% check version +v = version; +ver_53_or_newer = (str2num(v(1:3)) >= 5.3); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% write data + +% write dimension + +fprintf(fid, '%d\n', dim); + +% write component names as a SOM_PAK comment line +if is_struct, + fprintf(fid,'#n '); + for i = 1:dim, fprintf(fid, '%s ', strrep(sData.comp_names{i},' ','_')); end + fprintf(fid,'\n'); + if ~isempty(sData.label_names) + fprintf(fid,'#l '); + l = length(sData.label_names); + for i = 1:l, fprintf(fid, '%s ', strrep(sData.label_names{i},' ','_')); end + fprintf(fid,'\n'); + end +end + +% are there NaNs and/or labels? + +has_nans = isnan(sum(D,2)) * (~strcmp(missing, 'NaN')); + +has_labels = 0; +if is_struct + [lines numlabs] = size(sData.labels); + has_labels = zeros(lines, 1); + if ver_53_or_newer + has_labels = sum((~(cellfun('isempty', sData.labels))), 2); + else + for i = 1:lines + for j = 1:numlabs + if ~isempty(sData.labels{i,j}) + has_labels(i) = 1; break; + end + end + end + end +end + +% write data + +form = [repmat('%g ',[1 dim-1]) '%g\n']; + +if ~sum(has_labels) & ~sum(has_nans) % no NaNs, no labels + fprintf(fid, form, D'); +elseif ~sum(has_labels) % no labels, NaNs + fprintf(fid, '%s', strrep(sprintf(form, D'), 'NaN', missing)); +else % labels and NaNs + for i = 1:samples + if has_nans(i) + fprintf(fid, strrep(sprintf('%g ', D(i,:)), 'NaN', missing)); + else + fprintf(fid, '%g ', D(i,:)); + end + + if has_labels(i) + temp = ''; + if ver_53_or_newer + nonempty = ~(cellfun('isempty', sData.labels(i,:))); + else + for j = 1:numlabs, nonempty(j) = ~isempty(sData.labels{i, j}); end + end + labs = char(sData.labels{i, nonempty}); + labs(:,end + 1) = ' '; + temp = reshape(labs',[1 prod(size(labs))]); + temp(findstr(' ', temp))=''; + fprintf(fid, '%s', temp(1:end-1)); + end + fprintf(fid,'\n'); + end +end + +% close file + +if fclose(fid), + error(['Cannot close file ' filename]); +else + fprintf(2, 'data write ok\n'); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sompak_gui.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,69 @@ +function sompak_gui() + +%SOMPAK_GUI A GUI for using SOM_PAK functions from Matlab. +% +% sompak_gui +% +% Launches a GUI which allows the use of SOM_PAK functions from +% Matlab. Notice that to use this function, the SOM_PAK programs must +% be in your search path, or the variable 'SOM_PAKDIR' which is a +% string containing the program path, must be defined in the +% workspace. SOM_PAK programs can be found from: +% http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% See also SOM_GUI, SOMPAK_INIT_GUI, SOMPAK_SAMMON_GUI, SOMPAK_TRAIN_GUI. + +% Contributed to SOM Toolbox 2.0, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +h=findobj(get(0,'Children'),'Tag','sompak_gui'); +if ~isempty(h) + figure(h); + return; +end + +a = figure('Color',[0.8 0.8 0.8], ... + 'PaperType','a4letter', ... + 'Position',[689 536 210 70], ... + 'Tag','sompak_gui'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_init_gui',... + 'FontWeight','demi', ... + 'Position',[8 30 50 20], ... + 'String','INIT', ... + 'Tag','Pushbutton1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_train_gui',... + 'FontWeight','demi', ... + 'Position',[63 30 50 20], ... + 'String','TRAIN', ... + 'Tag','Pushbutton2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_sammon_gui',... + 'FontWeight','demi', ... + 'Position',[118 30 50 20], ... + 'String','SAMMON', ... + 'Tag','Pushbutton3'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','close gcf', ... + 'FontSize',9, ... + 'FontWeight','demi', ... + 'Position',[128 5 40 15], ... + 'String','Close', ... + 'Tag','Pushbutton4'); + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sompak_init.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,211 @@ +function sMap=sompak_init(sData,ft,init_type,cout,ct,xdim,ydim,topol,neigh) + +%SOMPAK_INIT Call SOM_PAK initialization programs from Matlab. +% +% sMap=sompak_init(sData,ft,init_type,cout,ct,xdim,ydim,topol,neigh) +% +% ARGUMENTS ([]'s are optional and can be given as empty: [] or '') +% sData (struct) data struct +% (matrix) data matrix +% (string) filename +% [ft] (string) 'pak' or 'box'. Argument must be defined, if input +% file is used. +% init_type (string) string 'rand' or 'linear' +% [cout] (string) filename for output SOM, if argument is not defined +% (i.e. argument is '[]') temporary file '__abcdef' is +% used in operations and *it_is_removed* after +% operations!!! +% [ct] (string) 'pak' or 'box'. Argument must be defined, if output +% file is used. +% xdim (scalar) Number of units of the map in x-direction. +% ydim (scalar) Number of units of the map in y-direction. +% topol (string) string 'hexa' or 'rect' +% neigh (string) string 'bubble' or 'gaussian'. +% +% RETURNS +% sMap (struct) map struct +% +% Calls SOM_PAK initialization programs (randinit and lininit) from +% Matlab. Notice that to use this function, the SOM_PAK programs must +% be in your search path, or the variable 'SOM_PAKDIR' which is a +% string containing the program path, must be defined in the +% workspace. SOM_PAK programs can be found from: +% http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% See also SOMPAK_TRAIN, SOMPAK_SAMMON, SOMPAK_INIT_GUI, +% SOMPAK_GUI, SOM_LININIT, SOM_RANDINIT. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +nargchk(9,9,nargin); + +NO_FILE = 0; +if isstruct(sData); + sData=sData.data; +elseif ~(isreal(sData) | isstr(sData)) + error('Argument ''sData'' must be a struct or a real matrix.'); +else + if isempty(ft) + if isstr(sData) + error('Argument ''file_type'' must be defined when input file is used.'); + end + elseif strcmp(ft,'pak'); + sData=som_read_data(sData); + elseif strcmp(ft,'box') + new_var=diff_varname; + varnames=evalin('base','who'); + loadname=eval(cat(2,'who(''-file'',''',sData,''')')); + if any(strcmp(loadname{1},evalin('base','who'))) + assignin('base',new_var,evalin('base',loadname{1})); + evalin('base',cat(2,'load(''',sData,''');')); + new_var2=diff_varname; + + assignin('base',new_var2,evalin('base',loadname{1})); + assignin('base',loadname{1},evalin('base',new_var)); + evalin('base',cat(2,'clear ',new_var)); + sData=evalin('base',new_var2); + evalin('base',cat(2,'clear ',new_var2)); + else + evalin('base',cat(2,'load(''',sData,''');')); + sData=evalin('base',loadname{1}); + evalin('base',cat(2,'clear ',loadname{1})); + end + else + error('Argument ''ft'' must be a string ''pak'' or ''box''.'); + end +end +if isstr(init_type) + if strcmp(init_type,'rand') + if any(strcmp('SOM_PAKDIR',evalin('base','who'))) + init_command=cat(2,evalin('base','SOM_PAKDIR'),'randinit'); + else + init_command='randinit'; + end + elseif strcmp(init_type,'linear') + if any(strcmp('SOM_PAKDIR',evalin('base','who'))) + init_command=cat(2,evalin('base','SOM_PAKDIR'),'lininit'); + else + init_command='lininit'; + end + else + error('Argument ''init_type'' must be string ''rand'' or ''linear''.'); + end +else + error('Argument ''init_type'' must be string ''rand'' or ''linear''.'); +end + +if (isstr(cout) & isempty(cout)) | (~isstr(cout) & isempty(cout)) + NO_FILE = 1; + cout = '__abcdef'; +elseif ~isstr(cout) & ~isempty(cout) + error('Argument ''cout'' must be a string or ''[]''.'); +end + +if ~is_positive_integer(xdim) + error('Argument ''xdim'' must be a positive integer.'); +end + +if ~is_positive_integer(ydim) + error('Argument ''ydim'' must be a positive integer.'); +end + +if isstr(topol) + if isempty(topol) | (~strcmp(topol,'hexa') & ~strcmp(topol,'rect')) + error ('Argument ''topol'' must be either a string ''hexa'' or ''rect''.'); + end +else + error ('Argument ''topol'' must be either a string ''hexa'' or ''rect''.'); +end + +if isstr(neigh) + if isempty(neigh) | (~strcmp(neigh,'bubble') & ~strcmp(neigh,'gaussian')) + error(sprintf(cat(2,'Argument ''neigh'' must be either a string ',... + '''bubble'' or ''gaussian''.'))); + end +else + error(sprintf(cat(2,'Argument ''neigh'' must be either a string ',... + '''bubble'' or ''gaussian''.'))); +end + +som_write_data(sData, cout); +str=cat(2,init_command,sprintf(' -din %s -cout %s ', cout ,cout),... + sprintf('-topol %s ',topol),... + sprintf('-neigh %s ',neigh),... + sprintf('-xdim %d -ydim %d',xdim,ydim)); + +if isunix + unix(str); +else + dos(str); +end + +sMap=som_read_cod(cout); + +if ~NO_FILE + if isunix + unix(cat(2,'/bin/rm ',cout)); + else + dos(cat(2,'del ',cout)); + end + if strcmp(ct,'pak') + som_write_cod(sMap,cout); + disp(cat(2,'Output written to the file ',cout,'.')); + elseif strcmp(ct,'box') + eval(cat(2,'save ',cout,' sMap')); + disp(cat(2,'Output written to the file ',sprintf('''%s.mat''.',cout))); + end +else + sMap.name=cat(2,'SOM ',date); + if isunix + unix('/bin/rm __abcdef'); + else + dos('del __abcdef'); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function bool = is_positive_integer(x) + +bool = ~isempty(x) & isreal(x) & all(size(x) == 1) & x > 0; +if ~isempty(bool) + if bool & x~=round(x) + bool = 0; + end +else + bool = 0; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function str = diff_varname(); + +array=evalin('base','who'); + +if isempty(array) + str='a'; + return; +end + +for i=1:length(array) + lens(i)=length(array{i}); +end + + +ind=max(lens); + +str(1:ind+1)='a'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sompak_init_gui.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,369 @@ +function sompak_init_gui() + +%SOMPAK_INIT_GUI A GUI for using SOM_PAK initialization programs from Matlab. +% +% sompak_init_gui +% +% Launches a GUI which allows the use of SOM_PAK initialization +% programs (randinit and lininit) from Matlab. Notice that to use this +% function, the SOM_PAK programs must be in your search path, or the +% variable 'SOM_PAKDIR' which is a string containing the program path, +% must be defined in the workspace. SOM_PAK programs can be found +% from: http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% See also SOMPAK_INIT, SOMPAK_GUI, SOMPAK_SAMMON_GUI, +% SOMPAK_TRAIN_GUI, SOM_GUI. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +h=findobj(get(0,'Children'),'Tag','InitGUI'); + +if ~isempty(h) + figure(h); + return; +end + +a = figure('Color',[0.8 0.8 0.8], ... + 'PaperType','a4letter', ... + 'Position',[483 407 172 440], ... + 'Tag','InitGUI'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','close gcf',... + 'FontWeight','demi', ... + 'Position',[8 20 50 20], ... + 'String','CLOSE', ... + 'Tag','Pushbutton1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_rb_control init_ok', ... + 'FontWeight','demi', ... + 'Position',[86 20 50 20], ... + 'String','OK', ... + 'Tag','Pushbutton2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Position',[8 50 130 300], ... + 'Style','frame', ... + 'Tag','Frame1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 54 122 40], ... + 'Style','frame', ... + 'Tag','Frame2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'Position',[45.5 78 55 12], ... + 'String','INIT TYPE', ... + 'Style','text', ... + 'Tag','StaticText1'); +b = uicontrol('Parent',a, ... + 'Callback','sompak_rb_control linear',... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Position',[14 56 17 16], ... + 'Style','radiobutton', ... + 'Tag','LINEAR', ... + 'Value',1); + +udata.inittype = 'linear'; + +b = uicontrol('Parent',a, ... + 'Callback','sompak_rb_control rand',... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Position',[73 56 17 16], ... + 'Style','radiobutton', ... + 'Tag','RANDOM'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[33 58 30 12], ... + 'String','Linear', ... + 'Style','text', ... + 'Tag','StaticText2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[91 58 37 12], ... + 'String','Random', ... + 'Style','text', ... + 'Tag','StaticText3'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 96 122 40], ... + 'Style','frame', ... + 'Tag','Frame3'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'Position',[36.4235 118.588 74 12], ... + 'String','NEIGHBORHOOD', ... + 'Style','text', ... + 'Tag','StaticText4'); +b = uicontrol('Parent',a, ... + 'Callback','sompak_rb_control bubble',... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Position',[14 98 17 16], ... + 'Style','radiobutton', ... + 'Tag','BUBBLE', ... + 'Value',1); + +udata.neigh='bubble'; + +b = uicontrol('Parent',a, ... + 'Callback','sompak_rb_control gaussian',... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Position',[73 98 17 16], ... + 'Style','radiobutton', ... + 'Tag','GAUSSIAN'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[33 100 32 12], ... + 'String','Bubble', ... + 'Style','text', ... + 'Tag','StaticText5'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[89.5 100 43 12], ... + 'String','Gaussian', ... + 'Style','text', ... + 'Tag','StaticText6'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 138 122 40], ... + 'Style','frame', ... + 'Tag','Frame4'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'Position',[45 162 55 12], ... + 'String','TOPOLOGY', ... + 'Style','text', ... + 'Tag','StaticText7'); +b = uicontrol('Parent',a, ... + 'Callback','sompak_rb_control hexa',... + 'Units','points', ... + 'Position',[14 140 17 16], ... + 'Style','radiobutton', ... + 'Tag','HEXA', ... + 'Value',1); + +udata.topol='hexa'; + +b = uicontrol('Parent',a, ... + 'Callback','sompak_rb_control rect',... + 'Units','points', ... + 'Position',[73 140 17 16], ... + 'Style','radiobutton', ... + 'Tag','RECT'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[33 142 30 12], ... + 'String','Hexa', ... + 'Style','text', ... + 'Tag','StaticText8'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[90 142 30 12], ... + 'String','Rect', ... + 'Style','text', ... + 'Tag','StaticText9'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 180 122 40], ... + 'Style','frame', ... + 'Tag','Frame5'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[25 202 33 12], ... + 'String','X-dim', ... + 'Style','text', ... + 'Tag','StaticText10'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[89 202 33 12], ... + 'String','Y-dim', ... + 'Style','text', ... + 'Tag','StaticText11'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control xdim',... + 'Position',[20 183 40 20], ... + 'Style','edit', ... + 'Tag','XDIM'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control ydim',... + 'Position',[85 183 40 20], ... + 'Style','edit', ... + 'Tag','YDIM'); + +udata.xdim=[]; +udata.ydim=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 222 122 40], ... + 'Style','frame', ... + 'Tag','Frame6'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'Position',[25 245 95 12], ... + 'String','OUTPUT VARIABLE', ... + 'Style','text', ... + 'Tag','StaticText12'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control out_var',... + 'Position',[22 226 102 20], ... + 'String','''ans''', ... + 'Style','edit', ... + 'Tag','OUT_VAR'); + +udata.out_var='ans'; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 264 122 40], ... + 'Style','frame', ... + 'Tag','Frame7'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control out_file',... + 'Position',[15 267 50 20], ... + 'Style','edit', ... + 'Tag','OUT_FILE'); + +udata.out_file=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[40 288 60 12], ... + 'String','OUTPUT FILE', ... + 'Style','text', ... + 'Tag','StaticText13'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_rb_control out_ft',... + 'FontSize',9, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Max',3, ... + 'Min',1, ... + 'Position',[70 272 62 15], ... + 'String',{'No File';'mat-file';'cod-file'}, ... + 'Style','popupmenu', ... + 'Tag','OUT_FILE_TYPE', ... + 'Value',1); + +udata.out_file_type=''; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 306 122 40], ... + 'Style','frame', ... + 'Tag','Frame8'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[57 330 30 12], ... + 'String','DATA', ... + 'Style','text', ... + 'Tag','StaticText14'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control data',... + 'Position',[15 309 50 20], ... + 'Style','edit', ... + 'Tag','DATA'); + +udata.data=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'Callback','sompak_rb_control input_ft',... + 'FontSize',9, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Max',3, ... + 'Min',1, ... + 'Position',[70 314 62 15], ... + 'String',{'Variable';'mat-file';'dat-file'}, ... + 'Style','popupmenu', ... + 'Tag','INPUT_FILE_TYPE', ... + 'Value',1); +udata.input_file_type=''; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',12, ... + 'FontWeight','bold', ... + 'HorizontalAlignment','left', ... + 'Position',[57 355 30 12], ... + 'String','INIT', ... + 'Style','text', ... + 'Tag','StaticText15'); + +set(gcf,'UserData',udata); + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sompak_rb_control.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,252 @@ +function varargout=sompak_rb_control(str) + +%SOMPAK_RB_CONTROL An auxiliary function for SOMPAK_*_GUI functions. +% +% This is an auxiliary function for SOMPAK_GUI, SOMPAK_INIT_GUI, +% SOMPAK_SAMMON_GUI and SOMPAK_TRAIN_GUI functions. It controls the +% radio buttons in the GUIs. +% +% See also SOMPAK_GUI, SOMPAK_INIT_GUI, SOMPAK_SAMMON_GUI, SOMPAK_TRAIN_GUI. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +data=get(gcf,'UserData'); +switch str + case {'rand','linear'} + h=cat(2,findobj(get(gcf,'Children'),'Tag','RANDOM'),... + findobj(get(gcf,'Children'),'Tag','LINEAR')); + set(h,'Value',0); + set(gcbo,'Value',1); + data.inittype=str; + case {'bubble','gaussian'} + h=cat(2,findobj(get(gcf,'Children'),'Tag','BUBBLE'),... + findobj(get(gcf,'Children'),'Tag','GAUSSIAN')); + set(h,'Value',0); + set(gcbo,'Value',1); + data.neigh=str; + case {'hexa','rect'} + h=cat(2,findobj(get(gcf,'Children'),'Tag','HEXA'),... + findobj(get(gcf,'Children'),'Tag','RECT')); + set(h,'Value',0); + set(gcbo,'Value',1); + data.topol=str; + case {'out_ft'} + value=get(gcbo,'Value'); + switch value + case 1 + h=findobj(get(gcf,'Children'),'Tag','OUT_FILE'); + data.out_file_type=''; + set(h,'String',''); + case 2 + data.out_file_type='box'; + case 3 + data.out_file_type='pak'; + end + case {'input_ft'} + value=get(gcbo,'Value'); + switch value + case 1 + data.input_file_type=''; + case 2 + data.input_file_type='box'; + case 3 + data.input_file_type='pak'; + end + case {'map_ft'} + value=get(gcbo,'Value'); + switch value + case 1 + data.map_type=''; + case 2 + data.map_type='box'; + case 3 + data.map_type='pak'; + end + case {'out_file'} + if isempty(data.out_file_type) + data.out_file=''; + h=findobj(get(gcf,'Children'),'Tag','OUT_FILE'); + set(h,'String',''); + else + data.out_file=get(findobj(get(gcf,'Children'),'Tag','OUT_FILE'),'String'); + if isempty(data.out_file) + h=findobj(get(gcf,'Children'),'Tag','OUT_FILE_TYPE'); + set(h,'Value',1); + end + end + + case {'out_var'} + h=findobj(get(gcf,'Children'),'Tag','OUT_VAR'); + if ~isempty(get(h,'String')) + data.out_var=get(h,'String'); + else + data.out_var=[]; + set(h,'String','''ans'''); + end + case {'xdim'} + h=findobj(get(gcf,'Children'),'Tag','XDIM'); + data.xdim=str2num(get(h,'String')); + case {'ydim'} + h=findobj(get(gcf,'Children'),'Tag','YDIM'); + data.ydim=str2num(get(h,'String')); + case {'radius'} + h=findobj(get(gcf,'Children'),'Tag','RADIUS'); + data.radius=str2num(get(h,'String')); + case {'data'} + h=findobj(get(gcf,'Children'),'Tag','DATA'); + data.data=get(h,'String'); + case {'rlen'} + h=findobj(get(gcf,'Children'),'Tag','RLEN'); + data.rlen=str2num(get(h,'String')); + case {'alpha'} + h=findobj(get(gcf,'Children'),'Tag','ALPHA'); + data.alpha=str2num(get(h,'String')); + case {'map'} + h=findobj(get(gcf,'Children'),'Tag','MAP'); + data.map=get(h,'String'); + case 'init_ok' + if isempty(data.xdim) | ~is_positive_integer(data.xdim) + errordlg('Argument ''xdim'' must be positive integer.'); + return; + end + if isempty(data.ydim) | ~is_positive_integer(data.ydim) + errordlg('Argument ''ydim'' must be positive integer.'); + return; + end + if isempty(data.data) + errordlg('Argument ''Workspace data'' must be a string.'); + return; + end + + if isempty(data.input_file_type) + sData=evalin('base',data.data); + else + sData=data.data; + end + if isempty(data.out_file) + if ~isempty(data.out_file_type) + errordlg('Argument ''Output file'' is not defined.'); + return; + end + data.out_file=[]; + end + answer=sompak_init(sData,... + data.input_file_type,... + data.inittype,... + data.out_file,... + data.out_file_type,... + data.xdim,... + data.ydim,... + data.topol,... + data.neigh); + if any(strcmp(data.out_var,{'ans','''ans'''})) | isstr(answer) + varargout{1}=answer; + else + assignin('base',data.out_var,answer); + disp(sprintf('Map is set to workspace as ''%s''.',data.out_var)); + end + close(findobj(get(0,'Children'),'Tag','InitGUI')); + return; + case 'train_ok' + if isempty(data.rlen) | ~is_positive_integer(data.rlen) + errordlg('Argument ''Running Length'' must be positive integer.'); + return; + end + if isempty(data.alpha) | data.alpha <= 0 + errordlg('Argument ''Initial Alpha Value'' must be a positive float.'); + return; + end + if isempty(data.radius) | data.radius <= 0 + errordlg('Argument ''Neighborhood Radius'' must be a positive float.'); + return; + end + if isempty(data.data) + errordlg('Argument ''Teaching Data'' must be a string.'); + return; + end + if isempty(data.input_file_type) + sData=evalin('base',data.data); + else + sData=data.data; + end + if isempty(data.out_file); + data.outfile = []; + end + if isempty(data.map) + errordlg('Argument ''Workspace Map'' must be a string.'); + return; + end + if isempty(data.map_type) + sMap=evalin('base',data.map); + else + sMap=data.map; + end + + answer=sompak_train(sMap,... + data.map_type,... + data.out_file,... + data.out_file_type,... + data.data,... + data.input_file_type,... + data.rlen,... + data.alpha,... + data.radius); + if any(strcmp(data.out_var,{'''ans''','ans'})) | isstr(answer) + varargout{1}=answer; + else + assignin('base',data.out_var,answer); + disp(sprintf('Map is set to workspace as ''%s''.',data.out_var)); + end + close(findobj(get(0,'Children'),'Tag','TrainGUI')); + return; + case 'sammon_ok' + if isempty(data.map) + errordlg('Argument ''Workspace Map'' must be a string.'); + return; + end + if isempty(data.map_type) + sMap=evalin('base',data.map); + else + sMap=data.map; + end + if isempty(data.out_file); + data.outfile = []; + end + answer=sompak_sammon(sMap,... + data.map_type,... + data.out_file,... + data.out_file_type,... + data.rlen); + if strcmp(data.out_var,'''ans''')|strcmp(data.out_var,'ans')|isstr(answer) + varargout{1}=answer; + else + assignin('base',data.out_var,answer); + disp(sprintf('Codebook is set to workspace as ''%s''.',data.out_var)); + end + close(findobj(get(0,'Children'),'Tag','SammonGUI')); + return; +end + +set(gcf,'UserData',data); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function bool = is_positive_integer(x) + +bool = ~isempty(x) & isreal(x) & all(size(x) == 1) & x > 0; +if ~isempty(bool) + if bool & x~=round(x) + bool = 0; + end +else + bool = 0; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sompak_sammon.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,172 @@ +function sMap=sompak_sammon(sMap,ft,cout,ct,rlen) + +%SOMPAK_SAMMON Call SOM_PAK Sammon's mapping program from Matlab. +% +% P = sompak_sammon(sMap,ft,cout,ct,rlen) +% +% ARGUMENTS ([]'s are optional and can be given as empty: [] or '') +% sMap (struct) map struct +% (string) filename +% [ft] (string) 'pak' or 'box'. Argument must be defined, if +% input file is used. +% [cout] (string) output file name. If argument is not defined +% (i.e argument is '[]') temporary file '__abcdef' is +% used in operations and *it_is_removed* after +% operations!!! +% [ct] (string) 'pak' or 'box'. Argument must be defined, if +% output file is used. +% rlen (scalar) running length +% +% RETURNS: +% P (matrix) the mapping coordinates +% +% Calls SOM_PAK Sammon's mapping program (sammon) from Matlab. Notice +% that to use this function, the SOM_PAK programs must be in your +% search path, or the variable 'SOM_PAKDIR' which is a string +% containing the program path, must be defined in the workspace. +% SOM_PAK programs can be found from: +% http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% See also SOMPAK_INIT, SOMPAK_SAMMON, SOMPAK_SAMMON_GUI, +% SOMPAK_GUI, SAMMON. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +NO_FILE = 0; + +nargchk(5,5,nargin); + +if ~(isstruct(sMap) | isstr(sMap)) + error('Argument ''sMap'' must be a struct or filename.'); +end + +if isstr(sMap) + if isempty(ft) | ~isstr(ft) | ~(strcmp(ft,'pak') | strcmp(ft,'box')) + error('Argument ''ft'' must be string ''pak'' or ''box''.'); + end + if strcmp(ft,'pak') + sMap=som_read_cod(sMap); + else + new_var=diff_varname; + varnames=evalin('base','who'); + loadname=eval(cat(2,'who(''-file'',''',sMap,''')')); + if any(strcmp(loadname{1},evalin('base','who'))) + assignin('base',new_var,evalin('base',loadname{1})); + evalin('base',cat(2,'load(''',sMap,''');')); + new_var2=diff_varname; + + assignin('base',new_var2,evalin('base',loadname{1})); + assignin('base',loadname{1},evalin('base',new_var)); + evalin('base',cat(2,'clear ',new_var)); + sMap=evalin('base',new_var2); + evalin('base',cat(2,'clear ',new_var2)); + else + evalin('base',cat(2,'load(''',sMap,''');')); + sMap=evalin('base',loadname{1}); + evalin('base',cat(2,'clear ',loadname{1})); + end + end +end + +if ~isstr(cout) & isempty(cout) + NO_FILE = 1; + cout = '__abcdef'; +elseif ~isstr(cout) | isempty(cout) + error('Argument ''cout'' must be a string or ''[]''.'); +end + +if ~NO_FILE & (isempty(ct) | ~(strcmp(ct,'pak') | strcmp(ct,'box'))) + error('Argument ''ct'' must be string ''pak'' or ''box''.'); +end + +som_write_cod(sMap,cout); + +if ~is_positive_integer(rlen) + error('Argument ''rlen'' must be a positive integer.'); +end + +if any(strcmp('SOM_PAKDIR',evalin('base','who'))) + command=cat(2,evalin('base','SOM_PAKDIR'),'sammon '); +else + command='sammon '; +end + +str = sprintf('%s -cin %s -cout %s -rlen %d',command,cout,cout,rlen); + +if isunix + unix(str); +else + dos(str); +end + +sMap=som_read_cod(cout); + +if ~NO_FILE + if isunix + unix(cat(2,'/bin/rm ',cout)); + else + dos(cat(2,'del ',cout)); + end + if strcmp(ct,'box'); + sMap=sMap.codebook; + eval(cat(2,'save ',cout,' sMap')); + disp(cat(2,'Output is saved to the file ',sprintf('''%s.mat''.',cout))); + else + som_write_cod(sMap,cout); + sMap=sMap.codebook; + disp(cat(2,'Output is saved to the file ',cout,'.')); + end +else + if isunix + unix('/bin/rm __abcdef'); + else + dos('del __abcdef'); + end +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function bool = is_positive_integer(x) + +bool = ~isempty(x) & isreal(x) & all(size(x) == 1) & x > 0; +if ~isempty(bool) + if bool & x~=round(x) + bool = 0; + end +else + bool = 0; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function str = diff_varname(); + +array=evalin('base','who'); + +if isempty(array) + str='a'; + return; +end + +for i=1:length(array) + lens(i)=length(array{i}); +end + + +ind=max(lens); + +str(1:ind+1)='a'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sompak_sammon_gui.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,200 @@ +function sompak_sammon_gui() + +%SOMPAK_SAMMON_GUI A GUI for using SOM_PAK Sammon's mapping program +% from Matlab. +% +% sompak_sammon_gui +% +% Launches a GUI which allows the use of SOM_PAK Sammon's mapping +% program (sammon) from Matlab. Notice that to use this function, the +% SOM_PAK programs must be in your search path, or the variable +% 'SOM_PAKDIR' which is a string containing the program path, must be +% defined in the workspace. SOM_PAK programs can be found from: +% http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% See also SOMPAK_SAMMON, SOMPAK_GUI, SOMPAK_INIT_GUI, +% SOMPAK_TRAIN_GUI, SOM_GUI. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +h=findobj(get(0,'Children'),'Tag','SammonGUI'); + +if ~isempty(h) + figure(h); + return; +end + + +a = figure('Color',[0.8 0.8 0.8], ... + 'PaperType','a4letter', ... + 'Position',[665 517 175 295], ... + 'Tag','SammonGUI'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','close gcf', ... + 'FontWeight','demi', ... + 'Position',[8 20 50 20], ... + 'String','CLOSE', ... + 'Tag','Pushbutton1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_rb_control sammon_ok',... + 'FontWeight','demi', ... + 'Position',[86 20 50 20], ... + 'String','OK', ... + 'Tag','Pushbutton2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Position',[8 50 130 175], ... + 'Style','frame', ... + 'Tag','Frame1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 54 122 40], ... + 'Style','frame', ... + 'Tag','Frame2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[30 78 90 12], ... + 'String','RUNNING LENGTH', ... + 'Style','text', ... + 'Tag','StaticText1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control rlen',... + 'Position',[48 57 50 20], ... + 'Style','edit', ... + 'Tag','RLEN'); + +udata.rlen=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 96 122 40], ... + 'Style','frame', ... + 'Tag','Frame3'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[33 119 90 12], ... + 'String','OUTPUT VARIABLE', ... + 'Style','text', ... + 'Tag','StaticText2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control out_var',... + 'Position',[48 99 50 20], ... + 'String','''ans''', ... + 'Style','edit', ... + 'Tag','OUT_VAR'); + +udata.out_var='ans'; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 138 122 40], ... + 'Style','frame', ... + 'Tag','Frame4'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[43 162 60 12], ... + 'String','OUTPUT FILE', ... + 'Style','text', ... + 'Tag','StaticText3'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control out_file',... + 'Position',[15 141 50 20], ... + 'Style','edit', ... + 'Tag','OUT_FILE'); + +udata.out_file=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'Callback','sompak_rb_control out_ft',... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Max',3, ... + 'Min',1, ... + 'Position',[70 146 62 15], ... + 'String',{'No File';'mat-file';'cod-file'}, ... + 'Style','popupmenu', ... + 'Tag','OUT_FILE_TYPE', ... + 'Value',1); + +udata.out_file_type=''; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 180 122 40], ... + 'Style','frame', ... + 'Tag','Frame5'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[60 203 25 12], ... + 'String','MAP', ... + 'Style','text', ... + 'Tag','StaticText4'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control map',... + 'Position',[15 183 50 20], ... + 'Style','edit', ... + 'Tag','MAP'); + +udata.map=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'Callback','sompak_rb_control map_ft',... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Max',3, ... + 'Min',1, ... + 'Position',[70 188 62 15], ... + 'String',{'Variable';'mat-file';'cod-file'}, ... + 'Style','popupmenu', ... + 'Tag','MAP_TYPE', ... + 'Value',1); + +udata.map_type=''; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',12, ... + 'FontWeight','demi', ... + 'Position',[41 230 62 12], ... + 'String','SAMMON', ... + 'Style','text', ... + 'Tag','StaticText5'); + + +set(gcf,'UserData',udata);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sompak_train.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,243 @@ +function sMap=sompak_train(sMap,ft,cout,ct,din,dt,rlen,alpha,radius) + +%SOMPAK_TRAIN Call SOM_PAK training program from Matlab. +% +% sMap=sompak_train(sMap,ft,cout,ct,din,dt,rlen,alpha,radius) +% +% ARGUMENTS ([]'s are optional and can be given as empty: [] or '') +% sMap (struct) map struct +% (string) filename +% [ft] (string) 'pak' or 'box'. Argument must be defined, if input file +% is used. +% [cout] (string) filename for output SOM, if argument is not defined +% (i.e. argument is '[]') temporary file '__abcdef' is +% used in operations and *it_is_removed* after +% operations!!! +% [ct] (string) 'pak' or 'box'. Argument must be defined, if output +% file is used. +% din (struct) data struct to be used in teaching +% (matrix) data matrix +% (string) filename +% If argument is not a filename or file is .mat -file, +% temporary file '__din' is used in operations +% and *it_is_removed* after operations!!! +% [dt] (string) 'pak' or 'box'. Argument must be defined, if input file +% is used. +% rlen (scalar) running length of teaching +% alpha (float) initial alpha value +% radius (float) initial radius of neighborhood +% +% RETURNS +% sMap (struct) map struct +% +% Calls SOM_PAK training program (vsom) from Matlab. Notice that to +% use this function, the SOM_PAK programs must be in your search path, +% or the variable 'SOM_PAKDIR' which is a string containing the +% program path, must be defined in the workspace. SOM_PAK programs can +% be found from: http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% See also SOMPAK_TRAIN, SOMPAK_SAMMON, SOMPAK_TRAIN_GUI, +% SOMPAK_GUI, SOM_SEQTRAIN. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + + +nargchk(9,9,nargin); + +NO_FILE=0; +DIN_FILE = 0; + +if ~isstruct(sMap) & ~isstr(sMap) + error('Argument ''sMap'' must be a struct or string.'); +end + +if isstr(sMap) + if isempty(ft) + error('Argument ''ft'' must be defined.'); + end + if strcmp(ft,'pak') + sMap=som_read_cod(sMap); + elseif strcmp(ft,'box') + new_var=diff_varname; + varnames=evalin('base','who'); + loadname=eval(cat(2,'who(''-file'',''',sMap,''')')); + if any(strcmp(loadname{1},evalin('base','who'))) + assignin('base',new_var,evalin('base',loadname{1})); + evalin('base',cat(2,'load(''',sMap,''');')); + new_var2=diff_varname; + + assignin('base',new_var2,evalin('base',loadname{1})); + assignin('base',loadname{1},evalin('base',new_var)); + evalin('base',cat(2,'clear ',new_var)); + sMap=evalin('base',new_var2); + evalin('base',cat(2,'clear ',new_var2)); + else + evalin('base',cat(2,'load(''',sMap,''');')); + sMap=evalin('base',loadname{1}); + evalin('base',cat(2,'clear ',loadname{1})); + end + + end +end +if ~isstr(cout) & isempty(cout) + cout = '__abcdef'; + NO_FILE = 1; +elseif ~isstr(cout) | (isstr(cout) & isempty(cout)) + error('Argument ''cout'' must be a string or ''[]''.'); +end + +if ~NO_FILE & (isempty(ct) | ~(~isempty(ct) & ... + (strcmp(ct,'pak') | strcmp(ct,'box')))) + error('Argument ''ct'' must be string ''pak'' or ''box''.'); +end + +map_name=sMap.name; +som_write_cod(sMap,cout); + +if ~isempty(din) + som_write_data(din, '__din'); + DIN_FILE = 1; + din = '__din'; +else + DIN_FILE=0; +end + +if ~DIN_FILE + if isempty(dt) | ~isstr(dt) | ~(strcmp(dt,'box') | strcmp(dt,'pak')) + error('Argument ''dt'' must be string ''pak'' or ''box''.'); + end + if strcmp(dt,'box'); + DIN_FILE = 1; + din_var=diff_varname; + varnames=evalin('base','who'); + loadname=eval(cat(2,'who(''-file'',''',din,''')')); + if any(strcmp(loadname{1},evalin('base','who'))) + assignin('base',din_var,evalin('base',loadname{1})); + evalin('base',cat(2,'load(''',din,''');')); + din_var2=diff_varname; + + assignin('base',new_var2,evalin('base',loadname{1})); + assignin('base',loadname{1},evalin('base',din_var)); + evalin('base',cat(2,'clear ',din_var)); + din=evalin('base',din_var2); + else + evalin('base',cat(2,'load(''',din,''')')); + din=evalin('base',loadname{1}); + evalin('base',cat(2,'clear ',loadname{1})); + end + som_write_data(din,'__din'); + din = '__din'; + end +end +if ~is_positive_integer(rlen) + error('Argument ''rlen'' must be positive integer.'); +end + +if ~(isreal(alpha) & all(size(alpha)==1)) + error('Argument ''alpha'' must be a floating point number.'); +end + +if ~(isreal(radius) & all(size(radius)==1) & radius > 0) + error('Argument ''radius'' must be a positive floating point number.'); +end + +if any(strcmp('SOM_PAKDIR',evalin('base','who'))) + traincommand=cat(2,evalin('base','SOM_PAKDIR'),'vsom '); +else + traincommand='vsom '; +end + +str=cat(2,traincommand,sprintf('-cin %s -din %s -cout %s ',cout,din,cout),... + sprintf(' -rlen %d -alpha %f -radius %f',rlen,alpha,radius)); +if isunix + unix(str); +else + dos(str); +end + +sMap=som_read_cod(cout); +sMap.name=map_name; + +if ~NO_FILE + if isunix + unix(cat(2,'/bin/rm ',cout)); + else + dos(cat(2,'del ',cout)); + end + if isempty(ct) | ~isstr(ct) | ~(strcmp(ct,'pak') | strcmp(ct,'box')) + error('Argument ''ct'' must be string ''pak'' or ''box''.'); + elseif strcmp(ct,'box'); + eval(cat(2,'save ',cout,' sMap')); + disp(cat(2,'Output written to the file ',sprintf('''%s.mat''.',cout))); + else + som_write_cod(sMap,cout); + end +else + if isunix + unix('/bin/rm __abcdef'); + else + dos('del __abcdef'); + end +end + +if DIN_FILE + if isunix + unix('/bin/rm __din'); + else + dos('del __abcdef'); + end +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function bool = is_positive_integer(x) + +bool = ~isempty(x) & isreal(x) & all(size(x) == 1) & x > 0; +if ~isempty(bool) + if bool & x~=round(x) + bool = 0; + end +else + bool = 0; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function str = diff_varname(); + +array=evalin('base','who'); + +if isempty(array) + str='a'; + return; +end + +for i=1:length(array) + lens(i)=length(array{i}); +end + + +ind=max(lens); + +str(1:ind+1)='a'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/sompak_train_gui.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,290 @@ +function sompak_train_gui() + +%SOMPAK_TRAIN_GUI A GUI for using SOM_PAK training program from Matlab. +% +% sompak_train_gui +% +% Launches a GUI which allows the use of SOM_PAK training program +% (vsom) from Matlab. Notice that to use this function, the SOM_PAK +% programs must be in your search path, or the variable 'SOM_PAKDIR' +% which is a string containing the program path, must be defined in +% the workspace. SOM_PAK programs can be found from: +% http://www.cis.hut.fi/research/som_lvq_pak.shtml +% +% See also SOMPAK_TRAIN, SOMPAK_GUI, SOMPAK_INIT_GUI, +% SOMPAK_SAMMON_GUI, SOM_GUI. + +% Contributed to SOM Toolbox vs2, February 2nd, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Juha Parhankangas 050100 + +h=findobj(get(0,'Children'),'Tag','TrainGUI'); + +if ~isempty(h) + figure(h); + return; +end + +a = figure('Color',[0.8 0.8 0.8],... + 'PaperType','a4letter', ... + 'Position',[558 207 173 445], ... + 'Tag','TrainGUI'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','close gcf', ... + 'FontWeight','demi', ... + 'Position',[8 20 50 20], ... + 'String','CLOSE', ... + 'Tag','Pushbutton1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_rb_control train_ok',... + 'FontWeight','demi', ... + 'Position',[86 20 50 20], ... + 'String','OK', ... + 'Tag','Pushbutton2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Position',[8 50 130 300], ... + 'Style','frame', ... + 'Tag','Frame1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 54 122 40], ... + 'Style','frame', ... + 'Tag','Frame2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[18 78 110 12], ... + 'String','NEIGHBORHOOD RADIUS', ... + 'Style','text', ... + 'Tag','StaticText1'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control radius', ... + 'Position',[48 56 50 20], ... + 'Style','edit', ... + 'Tag','RADIUS'); + +udata.radius=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 96 122 40], ... + 'Style','frame', ... + 'Tag','Frame3'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[22 120 101 12], ... + 'String','INITIAL ALPHA VALUE', ... + 'Style','text', ... + 'Tag','StaticText2'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control alpha',... + 'Position',[48 99 50 20], ... + 'Style','edit', ... + 'Tag','ALPHA'); + + +udata.alpha=[]; + + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 138 122 40], ... + 'Style','frame', ... + 'Tag','Frame4'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[30 163 90 12], ... + 'String','RUNNING LENGTH', ... + 'Style','text', ... + 'Tag','StaticText3'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control rlen',... + 'Position',[48 141 50 20], ... + 'Style','edit', ... + 'Tag','RLEN'); + +udata.rlen=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 180 122 40], ... + 'Style','frame', ... + 'Tag','Frame5'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[30 204 90 11], ... + 'String','OUTPUT VARIABLE', ... + 'Style','text', ... + 'Tag','StaticText4'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control out_var',... + 'Position',[48 183 50 20], ... + 'String','''ans''',... + 'Style','edit', ... + 'Tag','OUT_VAR'); + +udata.out_var='ans'; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 222 122 40], ... + 'Style','frame', ... + 'Tag','Frame6'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[42 245 65 12], ... + 'String','OUTPUT FILE', ... + 'Style','text', ... + 'Tag','StaticText5'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control out_file',... + 'Position',[15 225 50 20], ... + 'Style','edit', ... + 'Tag','OUT_FILE'); + +udata.out_file=[]; + +b = uicontrol('Parent',a, ... + 'Callback','sompak_rb_control out_ft',... + 'Units','points', ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Max',3, ... + 'Min',1, ... + 'Position',[70 230 62 15], ... + 'String',{'No File';'mat-file';'cod-file'}, ... + 'Style','popupmenu', ... + 'Tag','OUT_FILE_TYPE', ... + 'Value',1); + +udata.out_file_type=''; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 264 122 40], ... + 'Style','frame', ... + 'Tag','Frame7'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[34 288 85 12], ... + 'String','TEACHING DATA', ... + 'Style','text', ... + 'Tag','StaticText6'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control data',... + 'Position',[15 267 50 20], ... + 'Style','edit', ... + 'Tag','DATA'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_rb_control input_ft',... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Max',3, ... + 'Min',1, ... + 'Position',[70 272 62 15], ... + 'String',{'Variable';'mat-file';'dat-file'}, ... + 'Style','popupmenu', ... + 'Tag','INPUT_FILE_TYPE', ... + 'Value',1); + +udata.input_file_type=''; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[12 306 122 40], ... + 'Style','frame', ... + 'Tag','Frame8'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[60 331 25 12], ... + 'String','MAP', ... + 'Style','text', ... + 'Tag','StaticText7'); +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[1 1 1], ... + 'Callback','sompak_rb_control map',... + 'Position',[15 309 50 20], ... + 'Style','edit', ... + 'Tag','MAP'); + +udata.map=[]; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Callback','sompak_rb_control map_ft',... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Max',3, ... + 'Min',1, ... + 'Position',[70 314 62 15], ... + 'String',{'Variable','mat-file','cod-file'}, ... + 'Style','popupmenu', ... + 'Tag','MAP_TYPE', ... + 'Value',1); + +udata.map_type=''; + +b = uicontrol('Parent',a, ... + 'Units','points', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontSize',12, ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[50 359 48 12], ... + 'String','TRAIN', ... + 'Style','text', ... + 'Tag','StaticText8'); + +set(gcf,'UserData',udata); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/somtoolbox.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,196 @@ +% SOM Toolbox +% Version 2.0beta, May 30 2002 +% +% Copyright 1997-2000 by +% Esa Alhoniemi, Johan Himberg, Juha Parhankangas and Juha Vesanto +% Contributed files may contain copyrights of their own. +% +% SOM Toolbox comes with ABSOLUTELY NO WARRANTY; for details +% see License.txt in the program package. This is free software, +% and you are welcome to redistribute it under certain conditions; +% see License.txt for details. +% +% +% Demos +% +% som_demo1 SOM Toolbox demo 1: basic properties +% som_demo2 SOM Toolbox demo 2: basic usage +% som_demo3 SOM Toolbox demo 3: visualization +% som_demo4 SOM Toolbox demo 4: data analysis +% +% Creation of structs +% +% som_set create & set (& check) values to structs +% som_info print out information on a given struct +% som_data_struct create & initialize a data struct +% som_map_struct create & initialize a map struct +% som_topol_struct create & initialize a topology struct +% som_train_struct create & initialize a train struct +% som_clstruct create a cluster struct +% som_clset set properties in a cluster struct +% som_clget get stuff from a cluster struct +% +% Struct conversion and file I/O +% +% som_vs1to2 converts a version 1.0 struct to version 2.0 struct +% som_vs2to1 converts a version 2.0 struct to version 1.0 struct +% som_read_data reads a (SOM_PAK format) ASCII data file +% som_write_data writes a SOM_PAK format codebook file +% som_write_cod writes a SOM_PAK format data file +% som_read_cod reads a SOM_PAK format codebook file +% +% Data preprocessing +% +% som_normalize normalize data set +% som_denormalize denormalize data set +% som_norm_variable (de)normalize one variable +% preprocess preprocessing GUI +% +% Initialization and training functions +% +% som_make create, initialize and train a SOM +% som_randinit random initialization algorithm +% som_lininit linear initialization algorithm +% som_seqtrain sequential training algorithm +% som_batchtrain batch training algorithm +% som_gui SOM initialization and training GUI +% som_prototrain a simple version of sequential training: easy to modify +% +% Clustering algorithms +% +% som_kmeans k-means algorithm (was earlier kmeans) +% kmeans_clusters try and evaluate several k-means clusterings +% neural_gas neural gas vector quantization algorithm +% som_linkage hierarchical clustering algorithms +% som_cllinkage hierarchical clustering of SOM +% som_dmatminima local minima from distance (or U-) matrix +% som_dmatclusters distance (or U-) matrix based clustering +% som_clspread spreads clusters to unassinged map units +% som_cldist calculate distances between clusters +% som_gapindex gap validity index of clustering +% db_index Davies-Bouldin validity index of clustering +% +% Supervised/classification algorithms +% +% som_supervised supervised SOM algorithm +% lvq1 LVQ1 algorithm +% lvq3 LVQ3 algorithm +% knn k-NN classification algorithm +% knn_old k-NN classification algorithm (old version) +% +% SOM error measures +% +% som_quality quantization and topographic error of SOM +% som_distortion SOM distortion measure +% som_distortion3 elements of the SOM distortion measure +% +% Auxiliary functions +% +% som_bmus calculates BMUs for given data vectors +% som_eucdist2 pairwise squared euclidian distances between vectors +% som_mdist calculates pairwise distances between vectors +% som_divide extract subsets of data based on map +% som_label give labels to map units +% som_label2num rcodes string data labels to interger class labels +% som_autolabel automatically labels the SOM based on given data +% som_unit_coords calculates coordinates in output space for map units +% som_unit_dists distances in output space between map units +% som_unit_neighs units in 1-neighborhood for each map unit +% som_neighborhood calculates neighborhood matrix for the given map +% som_neighbors calculates different kinds of neighborhoods +% som_neighf calculates neighborhood function values +% som_select GUI for manual selection of map units +% som_estimate_gmm create Gaussian mixture model on top of SOM +% som_probability_gmm evaluate Gaussian mixture model +% som_ind2sub from linear index to subscript index +% som_sub2ind from subscript index to linear index +% som_ind2cod from linear index to SOM_PAK linear index +% som_cod2ind from SOM_linear index to SOM_PAK linear index +% nanstats mean, std and median which ignore NaNs +% som_modify_dataset add, remove, or extract samples and components +% som_fillnans fill NaNs in a data set based on given SOM +% som_stats statistics of a data set +% som_drmake calculate descriptive rules for a cluster +% som_dreval evaluate descriptive rules for a cluster +% som_drsignif rule significance measures +% +% Using SOM_PAK from Matlab +% +% som_sompaktrain uses SOM_PAK to train a map +% sompak_gui GUI for using SOM_PAK from Matlab +% sompak_init call SOM_PAK's initialization programs from Matlab +% sompak_init_gui GUI for using SOM_PAK's initialization from Matlab +% sompak_rb_control an auxiliary function for sompak_*_gui functions. +% sompak_sammon call SOM_PAK's Sammon program from Matlab +% sompak_sammon_gui GUI for using SOM_PAK's Sammon program from Matlab +% sompak_train call SOM_PAK's training program from Matlab +% sompak_train_gui GUI for using SOM_PAK's training program from Matlab +% +% Visualization +% +% som_show basic visualization +% som_show_add add labels, hits and trajectories +% som_show_clear remove extra markers +% som_recolorbar refresh/reconfigure colorbars +% som_show_gui GUI for using som_show and associated functions +% som_grid visualization of SOM grid +% som_cplane component planes and U-matrices +% som_barplane bar chart visualization of map +% som_pieplane pie chart visualization of map +% som_plotplane plot chart visualization of map +% som_trajectory launches a GUI for presenting comet-trajectories +% som_dendrogram visualization of clustering tree +% som_plotmatrix pairwise scatter plots and histograms +% som_order_cplanes order and visualize the component planes +% som_clplot plots of clusters (based on cluster struct) +% som_projections_plot projections plots (see som_projections) +% som_stats_plot plots of statistics (see som_stats) +% +% Auxiliary functions for visualization +% +% hits calculates hits, or sum of values for each map unit +% som_hits calculates the response of data on the map +% som_umat calculates the U-matrix +% cca curvilinear component analysis projection algorithm +% pcaproj principal component projection algorithm +% sammon Sammon's mapping projection algorithm +% som_connection connection matrix for map +% som_vis_coords map unit coordinates used in visualizations +% som_colorcode create color coding for map/2D data +% som_bmucolor colors of the BMUs from a given map color code +% som_normcolor simulate indexed colormap +% som_clustercolor color coding which depends on clustering structure +% som_kmeanscolor color coding according to k-means clustering +% som_kmeanscolor2 a newer version of the som_kmeanscolor function +% som_fuzzycolor a fuzzy color coding +% som_coloring a SOM-based color coding +% som_projections calculates a default set of projections +% +% Report generation stuff +% +% som_table_struct creates a table struct +% som_table_modify modifies a table struct +% som_table_print print a table in various formats +% rep_utils various utilities for printing report elements +% som_stats_table a table of data set statistics +% som_stats_report report on data set statistics +% +% Low level routines used by visualization functions +% +% vis_patch defines hexagonal and rectangular patches +% vis_som_show_data returns UserData and subplot handles stored by som_show.m +% vis_valuetype used for type checks +% vis_footnote adds a movable text to the current figure +% vis_trajgui the actual GUI started by som_trajectory.m +% vis_PlaneAxisProperties set axis properties in visualization functions +% vis_footnoteButtonDownFcn callback function for vis_footnote.m +% vis_planeGetArgs converts topol struct to lattice, msize argument pair +% vis_show_gui_comp internal function used by som_show_gui.m +% vis_show_gui_tool internal function used by som_show_gui.m +% +% Other +% +% somtoolbox this file +% iris.data IRIS data set (used in demos) +% License.txt GNU General Public License +% Copyright.txt Copyright notice
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_PlaneAxisProperties.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +function vis_PlaneAxisProperties(ax,lattice,msize,pos) + +% VIS_PLANEAXISPROPERTIES Set axis properties for SOM_CPLANE, +% SOM_PIEPLANE, SOM_BARPLANE and SOM_PLOTPLANE. +% +% vis_PlaneAxisProperties(ax,lattice,msize,pos) +% +% Input arguments: +% ax (scalar) axis handle +% lattice (string) 'hexa', 'rect', 'hexaU' or 'rectU' +% (matrix) defines the patch, see e.g. help vis_patch +% msize (vector) a 1x2 vector defining the grid size +% pos (vector) a 1x2 vector that determines position of +% origin or NaN which means default operation: +% origin to [1 1] and tighten axis limits +% according to the grid size. +% +% This is a subfunction for SOM_CPLANE, SOM_PIEPLANE, SOM_BARPLANE and +% SOM_PLOTPLANE. This subfunction sets the proper values for axis. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 060799 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +xdim=msize(1);ydim=msize(2); +set(ax,'Visible','off'); +set(get(ax,'Title'),'Visible','on'); +set(ax,'XaxisLocation','Top'); % axis orientation +set(ax,'xdir','normal'); % = axis ij = matrix mode +set(ax,'ydir','reverse'); + +switch lattice +case {'rect', 'rectU'} + lelim=-.51; rilim=.51; uplim=-.51; lolim=.51; % axis limits + set(ax,'DataAspectRatio', [1 1 1]); % =axis equal +case {'hexa','hexaU'} + lelim=-.51; rilim=1.01; uplim=-.67; lolim=.67; % axis limits + set(ax,'DataAspectRatio',[0.9015 1 1]); % this corrects hexagons +end + +% Nan: default origin [1 1] & tighten the axis +if isnan(pos) + set(ax,'XLim',[1+lelim ydim+rilim],'YLim',[1+uplim xdim+lolim], ... + 'XLimMode','manual','YLimMode','manual'); % tighten the axis +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_footnote.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +function h=vis_footnote(txt) + +% VIS_FOOTNOTE Adds a movable text to the current figure +% +% h = vis_footnote(T) +% +% Input and output arguments ([]'s are optional) +% [T] (string) text to be written +% (scalar) font size to use in all strings +% +% h (vector) handles to axis objects created by this function +% +% This function sets a text to the current figure. If T is a string, +% it's written as it is to the same place. If T is a scalar, the font +% size of all text objects created by this function are changed to the +% pointsize T. If no input argument is given the function only returns +% the handles to all objects created by this function. The texts may +% be dragged to a new location at any time using mouse. Note that the +% current axis will be the parent of the text object after dragging. +% +% String 'Info' is set to the Tag property field of the objects. +% +% EXAMPLES +% +% % add movable texts to the current figure and change their +% % fontsize to 20 points +% vis_footnote('Faa'); vis_footnote('Foo'); vis_footnote(20); +% +% % delete all objects created by this function from the current figure +% delete(vis_footnote); +% +% See also SOM_SHOW. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 080698 + +%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(0, 1, nargin)) % check no. of input args + +%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Get the handles to the existing Info-axes objects + +h_infotxt=findobj(gcf,'tag','Info','type','text'); +h_infoax=findobj(gcf,'tag','Info','type','axes'); + +%% Action %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% If no arguments are given, return the old axes handles + +if nargin == 0 | isempty(txt), + ; +elseif ischar(txt) % text: set new text + [t,h_]=movetext(txt); + h_infoax=[h_; h_infoax]; +elseif vis_valuetype(txt,{'1x1'}) % scalar: change font size + set(h_infotxt,'fontunits','points'); + set(h_infotxt,'fontsize',txt); +else + error('Input argument should be a string or a scalar.'); +end + +%% Build output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if nargout>0 % output only if necessary + h=h_infoax; +end + +%%% SUBFUNCTIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [t,h]=movetext(txt) +% Moves the text. See also VIS_FOOTNOTEBUTTONDOWNFCN +% +% +initpos=[0.05 0.05 0.01 0.01]; + +memaxes = gca; % Memorize the gca + +%% Create new axis on the lower left corner. +%% This will be the parent for the text object + +h = axes('position',initpos,'units','normalized'); +set(h,'visible','off'); % hide axis + +t = text(0,0,txt); % write text +set(t,'tag','Info'); % set tag +set(h,'tag','Info'); % set tag + +set(t,'verticalalignment','bottom'); % set text alignment +set(t,'horizontalalignment','left'); + +%% Set ButtonDownFcn + +set(t,'buttondownfcn','vis_footnoteButtonDownFcn') + +axes(memaxes); % Reset original gca + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_footnoteButtonDownFcn.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +function vis_footnoteButtonDownFcn + +% VIS_FOOTNOTEBUTTONDOWNFCN Callback set by VIS_FOOTNOTE +% +% som_showtitleButtonDownFcn +% +% Moves the axis of current callback object using DRAGRECT +% command. This callback is set to all texts added to figures by +% VIS_FOOTNOTE function. +% +% See also DRAGRECT, SOM_SHOWTITLE. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 080698 + +% Action + +[txt,fig]=gcbo; % Get text and figure handles +ax=get(txt,'parent'); % Get axis handle + +memunits_fig=get(fig,'units'); % Get figure size in pixels +set(gcf,'units','pixels'); +pos_fig=get(fig,'position'); + +memunits_txt=get(txt,'units'); % Get text field size in pixels +set(txt,'units','pixels'); +text_size=get(txt,'extent'); + +memunits_ax=get(ax,'units'); % Get axis position in pixels +set(ax,'units','pixels'); +pos_ax=get(ax,'position'); + +%%% Move text + +pos_final=dragrect([pos_ax(1:2) text_size(3:4)]); + +%%% Keep the text inside the figure! + +pos_final(1)=max(pos_final(1),0); +pos_final(2)=max(pos_final(2),0); +pos_final(1)=min(pos_final(1),pos_fig(3)-text_size(3)); +pos_final(2)=min(pos_final(2),pos_fig(4)-text_size(4)); + +%%% Set new position + +new_pos=[pos_final(1:2) pos_ax(3:4)]; +set(ax,'position', new_pos); + +%%% Set the text on the top of the object stack + +children=get(gcf,'children'); +i=find(ismember(children,ax)); +new_i=[i 1:i-1 i+1:length(children)]; +set(gcf,'children',children(new_i)); + +set(txt,'position',[0 0 0]); +set(fig,'units',memunits_fig); +set(ax,'units',memunits_ax); +set(txt,'units',memunits_txt); + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_grid_struct.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +function S=vis_grid_struct + +% VIS_GRID_STRUCT Initiates and returns a default grid struct +% +% S = vis_grid_struct +% +% Output arguments: +% S (struct) som_grid struct +% +% Fields and their initial values: +% S.type='som_grid'; +% S.neigh=neigh; +% S.shape='sheet'; +% S.msize=msize; +% S.coord=[]; +% S.line='-'; +% S.linecolor=[.8 .8 .8]; +% S.linewidth=0.5; +% S.marker='o'; +% S.markersize=6; +% S.markercolor='k'; +% S.surf=[]; +% S.label=[]; +% S.labelcolor='g'; +% S.labelsize=12; +% +% This is a subfunction for SOM_GRID. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 090499 + +S.type='som_grid'; +S.neigh='hexa'; +S.shape='sheet'; +S.msize=[1 1]; +S.coord=[]; +S.line='-'; +S.linecolor=[.8 .8 .8]; +S.linewidth=0.5; +S.marker='o'; +S.markersize=6; +S.markercolor='k'; +S.surf=[]; +S.label=[]; +S.labelcolor='g'; +S.labelsize=12;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_patch.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,117 @@ +function p=vis_patch(lattice) + +% VIS_PATCH Defines the basic patches (hexa and rect) used in SOM_CPLANE +% +% p = vis_patch(lattice) +% +% Input and output arguments: +% lattice (string) 'rect', 'hexa' or 'hexagon' +% +% p (matrix) size Lx2, defines the vertices of the patch +% +% This function produces vertex coordinates for a patch presenting +% a map unit in hexagonal or rectangular lattice with its centre in (0,0). +% +% For more help, try 'type vis_patch' or check out online documentation. +% See also SOM_CPLANE, PATCH. + +%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% vis_patch +% +% SYNTAX +% +% p = vis_patch(lattice) +% +% DESCRIPTION +% +% Forms a map unit patch for SOM_CPLANE function. Mainly a subroutine +% of SOM_CPLANE, although can be used for on its own as well. +% +% REQUIRED INPUT ARGUMENTS +% +% lattice (string) +% +% 'hexa' produces vertex coordiantes for a hexagoanl patch which +% has its center on (0,0), unit width, and a height of +% 1.3334 units. This is not a regular hexagon but such that +% the node which has topological coordinates (a,b) has its +% center in the visualization at coordinates (a,b) for odd +% a and at (a,b+.5) for even a. The non-regular look of the +% patch is taken care simply by changing the axis ratio. +% +% 'rect' produces vertex coordinates for a uniform rectangular patch. +% having its center on (0,0) and unit sidelength. Used as a +% subfunction in SOM_CPLANE. +% +% 'hexagon' produces vertex coordinates for a regular hexagonal patch. +% It may be used in som_cplane if, for some reason, truly +% regular hexagons are needed instead of the default unit +% markers which are not uniform, but have integer +% y-coordinates in the lattice. +% +% OUTPUT ARGUMENTS +% +% p (matrix) The 2-dimensional vertex coordinates: +% +% case 'rect' case 'hexa' case 'hexagon' +% p=[[-.5 -.5];... p=[[0 0.6667];... p=[[0 0.5774];... +% [-.5 .5];... [0.5 0.3333];... [0.5 0.2887];... +% [ .5 .5];... [0.5 -0.3333];... [0.5 -0.2887];... +% [ .5 -.5]]; [0 -0.6667];... [0 -0.5774];... +% [-0.5 -0.3333];... [-0.5 -0.2887];... +% [-0.5 0.3333]]; [-0.5 0.2887]]; +% +% EXAMPLES +% +% som_cplane(vis_patch('rect'),[6 5],'none'); +% % this produces the same result as som_cplane('rect',[6 5], 'none') +% +% som_cplane(vis_patch('hexa'), vis_unit_coord('hexa',[6 5]), 'none'); +% % produces in principle the same result as +% % som_cplane(vis_patch('hexa'),[6 5],'none'), +% % _but_ in this case the axis are not rescaled and the non-regular +% % shape of hexagons can be seen. +% +% som_cplane(vis_patch('hexagon'), som_unit_coords([6 5],'hexa'), 'none'); +% % produces a truly regular hexa lattice +% +% SEE ALSO +% +% vis_unit_coord The default 'hexa' and 'rect' coordinates in visualizations +% som_unit_coords Locations of units on the SOM grid. + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 041099 + +if ~ischar(lattice) + error('Input argument should be a string') +else + switch lattice + case 'rect' + p=[[-.5 -.5]; ... + [-.5 .5];... + [.5 .5];... + [.5 -.5]]; + case 'hexagon' + p=[[0 0.5774];... + [0.5 0.2887];... + [0.5 -0.2887];... + [0 -0.5774];... + [-0.5 -0.2887];... + [-0.5 0.2887]]; + case 'hexa' + p=[[0 0.6667];... + [0.5 0.3333];... + [0.5 -0.3333];... + [0 -0.6667];... + [-0.5 -0.3333];... + [-0.5 0.3333]]; + otherwise + error('Unknown lattice'); + end +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_planeGetArgs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +function [nargin_,varargout]=vis_planeGetArgs(varargin) + +% VIS_PLANEGETARGS Subfunction for som_*plane: extracts topolopy +% information from the first arguments. +% +% [nargin,varargout]=vis_planeGetArgs(varargin) +% +% Input and output arguments: +% varargin (varies) arguments given to som_*plane function +% nargin_ (scalar) number of arguments that nargchk of som_*plane "should see" +% +number_of_varargins if varargin{1} is not map/topol struct +% +number_of_varargins+1 if varargin{2} is a map/topol struct +% varargout (varies) the arguments that som_*plane "should see" +% +% Basically, this function allows topology information to be given +% in various ways: either as a map/topology struct, or as a argument pair: +% lattice, msize. The topology is always converted into the (lattice, msize) +% argument pair. +% - if first input argument (varargin{1}) is a map or topol struct +% the function extracts lattice and msize fields to two first +% output variables after 'nargin_'. +% - otherwise it copies the input arguments to the output arguments +% after 'nargin_'. +% If there are too many inputs (as compared to number of outputs), the +% last ones are ignored. If too few, they are replaced by empty values +% in outputs. +% +% Example of usage: +% function definition: h = som_cplane(varargin) +% first code line: [nargin,lattice,msize,color,size,pos]=vis_planeGetArgs(varargin); +% +% See also SOM_CPLANE, SOM_BARPLANE, SOM_PLOTPLANE, SOM_PIEPLANE. + +% Copyright (c) 2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 240300 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +nout=nargout-1; + +% Set first all varargins to contain empty (==default values in som_*plane) + +for i=1:nout, varargout{i}=[]; end + +nargin_ = nargin; +% Struct: might be map or topol + +if isstruct(varargin{1}), + + % Get topol from topol field + if isfield(varargin{1},'topol'), topol=varargin{1}.topol; + else topol=varargin{1}; % assume that this is topol struct + end + + if ~isstruct(topol), + % topol not a struct !? + warning('Field ''topol'' is not a struct.'); + varargout{1}=varargin{1}; + varargoutC=2; + nargin_ = nargin; + elseif ~isfield(topol,'msize') | ~isfield(topol,'lattice'), + % Field missing?! + warning('Invalid topology struct.'); + varargout{1}=topol; + varargoutC=2; + nargin_ = nargin; + else + varargout{1}=topol.lattice; + varargout{2}=topol.msize; + % increment input arg. counter + varargoutC=3; + nargin_ = nargin+1; + end + +elseif iscell(varargin{1}), + + c = varargin{1}; + lattice = 'hexa'; shape = 'sheet'; msize = [1 1]; + for i=1:length(c), + if ischar(c{i}), + switch c{i}, + case {'hexa','hexaU','rect','rectU'}, lattice = c{i}; + case {'sheet','cyl','toroid'}, shape = c{i}; + end + else + msize = c{i}; + end + end + varargout{1} = lattice; + varargout{2} = msize; + varargoutC=3; + nargin_ = nargin+1; + +else + + % should be a lattice (string) + varargout{1}=varargin{1}; + varargoutC=2; + nargin_=nargin; + +end + +for i=2:nargin, varargout{varargoutC+i-2}=varargin{i}; end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_show_gui_comp.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,702 @@ +function vis_show_gui_comp(h, indx, action,varargin) + +%VIS_SHOW_GUI_COMP is a subfunction of SOM_SHOW_GUI. +% +% vis_show_gui_comp(handle, indx, action, varargin) +% +% Input arguments: +% handle (struct) +% indx (scalar) +% action (string) +% varargin (varies) +% +% See also SOM_SHOW_GUI. + +% Copyright (c) 2000 by Roman Feldman and Juha Vesanto +% Contributed to SOM Toolbox on August 22nd, 2000 +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta roman 160800 juuso 220800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% MAIN % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +udata = get(h,'UserData'); +plot_array = udata.plot_array; +l = length(plot_array); + + %%%%%% +% init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%% + +if (strcmp(action,'init')) + + %--- color vars --- + fig_color = [0.8 0.8 0.8]; + bg_color1 = [0.701960784313725 0.701960784313725 0.701960784313725]; + bg_color2 = [0.9 0.9 0.9]; + %--- object position vars (in pixels) --- + % calculations based on case 'comp' + %% hint text + hint_dist1 = 98.17-(61.27+36.9); % hint text lower edge and next lower frame upper edge + hint_dist2 = 123-(98.17+18.45); % figure upper edge and hint text upper edge + %% general + dist1 = 67.42-61.27; % general distance between frame edge and object in frame + %% frame + ok / cancel + frames_dist = 61.27-(8.38+36.9); + frames_dist2 = 8.38; + f_fr = [7.9 8.38 216.2 36.9]; % final frame + ok_pb = [17.1 15.76 75.9 22.14]; + cancel_pb = [139 15.76 75.9 22.14]; + %% objects + hint_txt = [230 18.45]; % hint text width and height + interp_cb = [98.9 24.6]; + title_txt = [35 hint_txt(2)]; + title_edit_h = 23.083; + var_pop = interp_cb; + list_lt = [130 130]; + calc_txt = [list_lt(1) hint_txt(2)]; + useall_pb = [50 interp_cb(2)]; + + if (length(indx) > 1) + errordlg({'Same options for multiple subplots', ... + 'not yet available'},'Error in SOM_VIS: options'); + return; + + elseif (isempty(plot_array(1).args)) + errordlg({'Try to select subplot first'}, ... + 'Error in SOM_VIS: options'); + return; + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % 'comp' + % + elseif (strncmp('comp',plot_array(indx).args{1},4)) + old_fig_n = watchon; + W = 230; H = 123; + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = o1+dist1; + o3 = 2*dist1+interp_cb(2); + o4 = o1+o3+hint_dist1; + hint_text_pos = [1 o4 hint_txt]; + frame1_pos = [f_fr(1) o1 f_fr(3) o3]; + interp_pos = [ok_pb(1) o2 interp_cb]; + frame2_pos = f_fr; + ok_pos = ok_pb; + cancel_pos = cancel_pb; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','component', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for component plane', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + pr = udata.property{indx}; + chkb_h = uicontrol( ... %% interpolated + 'Units','pixels', ... + 'HorizontalAlignment','center', ... + 'Position',interp_pos, ... + 'Style','checkbox', ... + 'String','interpolated', ... + 'Value',pr{1}); + set(chkb_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_comp(', ... + mat2str(h), ',', mat2str(indx), ... + ',''comp'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [chkb_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData',tmp_udata, ... + 'handlevisibility','off'); + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % 'umat' + % + elseif (strncmp('umat',plot_array(indx).args{1},4)) + old_fig_n = watchon; + W = 230; H = 335.893; + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = o1+dist1; + o3 = 2*dist1+list_lt(2)+calc_txt(2); + o4 = o2+list_lt(2); + o5 = sum(cancel_pb([1 3]))-useall_pb(1); + o6 = o2+o3/2-useall_pb(2)/2; + o7 = o1+o3+frames_dist2; + o8 = 2*dist1+interp_cb(2); + o9 = o7+dist1; + o10 = o7+o8+frames_dist2;; + o11 = 2*dist1+max(title_edit_h,title_txt(2)); + o12 = W-(title_txt(1)+2*ok_pb(1)); + o13 = o10+dist1+(title_edit_h-title_txt(2))/2; + o14 = o10+dist1; + if title_txt(2)>title_edit_h, + o13 = o10+dist1; + o14 = o13+(title_edit_h-title_txt(2))/2; + end + o15 = ok_pb(1)+title_txt(1); + o16 = o10+o11+hint_dist1; + hint_text_pos = [1 o16 hint_txt]; + frame1_pos = [f_fr(1) o10 f_fr(3) o11]; + title_text_pos = [ok_pb(1) o13 title_txt]; + title_pos = [o15 o14 o12 title_edit_h]; + frame2_pos = [f_fr(1) o7 f_fr(3) o8]; + interp_pos = [ok_pb(1) o9 interp_cb]; + frame3_pos = [f_fr(1) o1 f_fr(3) o3]; + list_text_pos = [ok_pb(1) o4 calc_txt]; + list_pos = [ok_pb(1) o2 list_lt]; + use_all_pos = [o5 o6 useall_pb]; + frame4_pos = f_fr; + ok_pos = ok_pb; + cancel_pos = cancel_pb; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','U-matrix', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for U-matrix', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% title + 'Units','pixels', ... + 'Position',title_text_pos, ... + 'BackgroundColor',bg_color1, ... + 'HorizontalAlignment','left', ... + 'Style','text', ... + 'String','Title'),'units','normalized'); + + pr = udata.property{indx}; + ed_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'Position',title_pos, ... + 'FontSize',12, ... + 'Style','edit', ... + 'String',pr{2}, ... + 'BackgroundColor',bg_color2); + set(ed_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + chkb_h = uicontrol( ... %% interpolated + 'Units','pixels', ... + 'Position',interp_pos, ... + 'Style','checkbox', ... + 'String','interpolated', ... + 'Value',pr{1}); + set(chkb_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame3_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% calculated from + 'Units','pixels', ... + 'BackgroundColor',bg_color1, ... + 'HorizontalAlignment','left', ... + 'Position',list_text_pos, ... + 'String','calculated from', ... + 'Style','text'),'units','normalized'); + + list1_h = uicontrol( ... %% [listbox] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',list_pos, ... + 'String',udata.sM.comp_names, ... + 'Style','listbox', ... + 'Max',2, ... + 'Value',pr{3}); + set(list1_h,'units','normalized'); + + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');set(tmp(3),''value'',' ... + mat2str(1:length(udata.sM.comp_names)) ');']; + set( ... + uicontrol( ... %% Use all + 'Units','pixels', ... + 'Position',use_all_pos, ... + 'String','Use all', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame4_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_comp(' ... + mat2str(h) ',' mat2str(indx) ... + ',''umat'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [chkb_h ed_h list1_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData', tmp_udata, ... + 'handlevisibility','off'); + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % 'color' + % + elseif (strncmp('color',plot_array(indx).args{1},5)) + old_fig_n = watchon; + W = 230; H = 212.043; + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = o1+dist1; + o3 = 2*dist1+var_pop(2); + o4 = o1+o3+frames_dist2; + o5 = o4+dist1; + o6 = 2*dist1+interp_cb(2); + o7 = o4+o6+frames_dist2; + o8 = 2*dist1+max(title_edit_h,title_txt(2)); + o9 = W-(title_txt(1)+2*ok_pb(1)); + o10 = o7+dist1+(title_edit_h-title_txt(2))/2; + o11 = o7+dist1; + if title_txt(2)>title_edit_h, + o10 = o7+dist1; + o11 = o10+(title_edit_h-title_txt(2))/2; + end + o12 = ok_pb(1)+title_txt(1); + o13 = o7+o8+hint_dist1; + hint_text_pos = [1 o13 hint_txt]; + frame1_pos = [f_fr(1) o7 f_fr(3) o8]; + title_text_pos = [ok_pb(1) o10 title_txt]; + title_pos = [o12 o11 o9 title_edit_h]; + frame2_pos = [f_fr(1) o4 f_fr(3) o6]; + interp_pos = [ok_pb(1) o5 interp_cb]; + frame3_pos = [f_fr(1) o1 f_fr(3) o3]; + popup_pos = [ok_pb(1) o2 var_pop]; + frame4_pos = f_fr; + ok_pos = ok_pb; + cancel_pos = cancel_pb; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position',fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','color plane', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for colorplane', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% title + 'Units','pixels', ... + 'Position',title_text_pos, ... + 'HorizontalAlignment','left', ... + 'Style','text', ... + 'String','Title'),'units','normalized'); + + pr = udata.property{indx}; + ed_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'Position',title_pos, ... + 'FontSize',12, ... + 'Style','edit', ... + 'String',pr{2}, ... + 'BackgroundColor',bg_color2); + set(ed_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + chkb_h = uicontrol( ... %% interpolated + 'Units','pixels', ... + 'Position',interp_pos, ... + 'Style','checkbox', ... + 'String','interpolated', ... + 'Value',pr{1}); + set(chkb_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame3_pos, ... + 'Style','frame'),'units','normalized'); + + popup1_h = uicontrol( ... %% [popup] + 'Units','pixels', ... + 'Max',2, ... + 'Min',1, ... + 'Position',popup_pos, ... + 'String',pr{3}, ... + 'Style','popupmenu', ... + 'Value',pr{4}); + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool([tmp(3) 6],''popup_select'');' ... + 'u=get(' mat2str(h) ',''UserData'');' ... + 'v=' mat2str(indx) ';' ... + 'pr=u.property{v};' ... + 'pr{3}=get(tmp(3),''string'');' ... + 'pr{4}=get(tmp(3),''value'');' ... + 'u.property{v}=pr;' ... + 'set(' mat2str(h) ',''userdata'',u)']; + set(popup1_h,'units','normalized', ... + 'Callback',s); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame4_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_comp(', ... + mat2str(h), ',', mat2str(indx), ... + ',''color'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [chkb_h ed_h popup1_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData', tmp_udata, ... + 'handlevisibility','off'); + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % 'empty' + % + elseif (strncmp('empty',plot_array(indx).args{1},5)) + old_fig_n = watchon; + W = 230; H = 121.483; + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = 2*dist1+max(title_edit_h,title_txt(2)); + o3 = 2*dist1+interp_cb(2); + o4 = W-(title_txt(1)+2*ok_pb(1)); + o5 = o1+dist1+(title_edit_h-title_txt(2))/2; + o6 = o1+dist1; + if title_txt(2)>title_edit_h, + o5 = o1+dist1; + o6 = o5+(title_edit_h-title_txt(2))/2; + end + o7 = ok_pb(1)+title_txt(1); + o8 = o1+o2+hint_dist1; + hint_text_pos = [1 o8 hint_txt]; + frame1_pos = [f_fr(1) o1 f_fr(3) o3]; + title_text_pos = [ok_pb(1) o5 title_txt]; + title_pos = [o7 o6 o4 title_edit_h]; + frame2_pos = f_fr; + ok_pos = ok_pb; + cancel_pos = cancel_pb; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','empty plane', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for empty plane', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% title + 'Units','pixels', ... + 'Position',title_text_pos, ... + 'HorizontalAlignment','left', ... + 'Style','text', ... + 'String','Title'),'units','normalized'); + + pr = udata.property{indx}; + ed_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'Position',title_pos, ... + 'Style','edit', ... + 'FontSize',12, ... + 'String',pr{1}, ... + 'BackgroundColor',bg_color2); + set(ed_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_comp(', ... + mat2str(h) ',' mat2str(indx), ... + ',''empty'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [ed_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData', tmp_udata, ... + 'handlevisibility','off'); + + end + + %%%%%% +% comp %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%% +elseif (strcmp(action,'comp')) + tmp_h = get(varargin{1},'UserData'); + v = get(tmp_h,'Value'); + pr = udata.property{indx}; + if (v) + v = 'compi'; + pr{1} = 1; + else + v = 'comp'; + pr{1} = 0; + end + plot_array(indx).args{1} = v; + udata.plot_array = plot_array; + udata.property{indx} = pr; + set(h,'UserData',udata); + close(varargin{1}); + + %%%%%% +% umat %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%% +elseif (strcmp(action,'umat')) + tmp_h = get(varargin{1},'UserData'); + v = get(tmp_h(1),'Value'); + pr = udata.property{indx}; + if (v) + v = 'umati'; + pr{1} = 1; + else + v = 'umat'; + pr{1} = 0; + end + plot_array(indx).args{1} = v; + + s = get(tmp_h(2),'String'); + v = get(tmp_h(3),'Value'); % userdata + plot_array(indx).string = s; + if strcmp(s,'U-matrix') + plot_array(indx).args{2} = v; + else + plot_array(indx).args{2} = {v s}; + end + pr{2} = s; + pr{3} = v; + udata.plot_array = plot_array; + udata.property{indx} = pr; + set(h,'UserData',udata); + set(udata.h(1),'String',{plot_array(:).string}); + close(varargin{1}); + + %%%%%%% +% color %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%% +elseif (strcmp(action,'color')) + tmp_h = get(varargin{1},'UserData'); % tmp_h = [chkb_h ed_h popup1_h]; + + v = get(tmp_h(1),'Value'); + pr = udata.property{indx}; + if (v) + v = 'colori'; + pr{1} = 1; + else + v = 'color'; + pr{1} = 0; + end + plot_array(indx).args{1} = v; + + v = get(tmp_h(3),'Value'); + s = get(tmp_h(3),'string'); + if v>5 & ~strcmp(s{v},'-variable-'), + m = evalin('base',s{v}); + elseif ~strcmp(s{v},'-variable-'), + m = som_colorcode(udata.sM,s{v}); + end + plot_array(indx).args{2} = m; + pr{3} = s; + s = get(tmp_h(2),'String'); + plot_array(indx).string = s; + if ~strcmp(s,'Color code') + plot_array(indx).args{2} = ... + {plot_array(indx).args{2} s}; + end + pr{2} = s; + pr{4} = v; + udata.plot_array = plot_array; + udata.property{indx} = pr; + set(h,'UserData',udata); + set(udata.h(1),'String',{plot_array(:).string}); + close(varargin{1}); + + %%%%%%% +% empty %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%% +elseif (strcmp(action,'empty')) + pr = udata.property{indx}; + tmp_h = get(varargin{1},'UserData'); + s = get(tmp_h,'String'); + pr{1} = s; + plot_array(indx).string = s; + plot_array(indx).args{2} = s; + udata.plot_array = plot_array; + udata.property{indx} = pr; + set(h,'UserData',udata); + set(udata.h(1),'String',{plot_array(:).string}); + close(varargin{1}); + +else + +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_show_gui_tool.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2085 @@ +function r=vis_show_gui_tool(h,action,varargin) + +%VIS_SHOW_GUI_TOOL is a subfunction of SOM_SHOW_GUI. +% +% r = vis_show_gui_tool(handle, action, varargin) +% +% Input arguments: +% handle (struct) +% action (string) +% varargin (varies) +% +% See also SOM_SHOW_GUI. + +% Copyright (c) 2000 by Roman Feldman and Juha Vesanto +% Contributed to SOM Toolbox on August 22nd, 2000 +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta roman 160800 juuso 220800 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if ishandle(h(1)), + udata = get(h(1),'UserData'); + if isfield(udata,'plot_array'), + plot_array = udata.plot_array; + if isfield(udata,'vis_h') + % get most recent plotted + children = get(0,'children'); + % first refresh plot info + udata.vis_h = setdiff(udata.vis_h,setdiff(udata.vis_h,children)); + set(h(1),'userdata',udata); + for i=1:length(children), + if any(children(i)==udata.vis_h), + child = children(i); + [handles,msg,lattice,msize,dim]=vis_som_show_data('all',children(i)); + break; + elseif i==length(children), + errordlg({'Plot not found', ... + 'Try to visualize first'},'Error in SOM_VIS: tools'); + return; + end + end + else + errordlg({'Plot not found', ... + 'Try to visualize first'},'Error in SOM_VIS: tools'); + return; + end + end +end + +%--- color vars --- + fig_color = [0.8 0.8 0.8]; + bg_color1 = [0.701960784313725 0.701960784313725 0.701960784313725]; + bg_color2 = [0.9 0.9 0.9]; +%--- object position vars (in pixels) --- + % calculations based on case 'comp' + %% hint text + hint_dist1 = 98.17-(61.27+36.9); % hint text lower edge and next lower frame upper edge + hint_dist2 = 123-(98.17+18.45); % figure upper edge and hint text upper edge + %% general + dist1 = 67.42-61.27; % general distance between frame edge and object in frame + %% frame + ok / cancel + frames_dist = 61.27-(8.38+36.9); + frames_dist2 = 8.38; + f_fr = [7.9 8.38 216.2 36.9]; % final frame + ok_pb = [17.1 15.76 75.9 22.14]; + cancel_pb = [139 15.76 75.9 22.14]; + %% objects + hint_txt = [460 18.45]; % hint text width and height + interp_cb = [98.9 24.6]; + title_txt = [90 hint_txt(2)-3]; + title_edit_h = 23.083; + var_pop = [interp_cb(1) ok_pb(4)]; + list_lt = [130 130]; + calc_txt = [list_lt(1) hint_txt(2)]; + selvar_pb = [110 ok_pb(4)]; + strd_ed = [var_pop(1) title_edit_h]; + radio_rb = selvar_pb; + + %%%%%%%%%%% +% add_label %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%%%%% +if strcmp(action,'add_label') + old_fig_n = watchon; + W = 345.4; H = 222.769; + o21 = zeros(1,4); + o21(1) = (W-f_fr(3))/2; + f_fr = [7.9 8.38 (W-2*7.9) 36.9]; % final frame + cancel_pb = [(W-(ok_pb(1)+75.9)) 15.76 75.9 22.14]; + hint_txt = [W 18.45]; % hint text width and height + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = 4*dist1+2*max([title_edit_h,title_txt(2)]) ... + +max([title_txt(2),var_pop(2)]); + o6 = max([title_txt(2),var_pop(2)]); + o7 = max([title_txt(2),title_edit_h]); + o8 = max([title_txt(2),title_edit_h,selvar_pb(2)]); + o3 = o1+0.5*o6+dist1-title_txt(2)/2; + o4 = o1+0.5*o6+dist1-var_pop(2)/2; + o5 = ok_pb(1)+title_txt(1); + o9 = o1+o6+0.5*o7+2*dist1-title_txt(2)/2; + o10 = o1+o6+0.5*o7+2*dist1-title_edit_h/2;; + o11 = o1+o6+o7+0.5*o8+3*dist1-title_txt(2)/2; + o12 = o1+o6+o7+0.5*o8+3*dist1-title_edit_h/2; + o13 = o1+o6+o7+0.5*o8+3*dist1-selvar_pb(2)/2; + o15 = o1+o2+frames_dist2; + o16 = 2*dist1+o7; + o17 = o5+strd_ed(1)+2*dist1; % W-ok_pb(1)-selvar_pb(1); + o18 = o15+0.5*o7+dist1-title_txt(2)/2; + o19 = o15+0.5*o7+dist1-title_edit_h/2; + o20 = o15+o16+hint_dist1; + hint_text_pos = [1 o20 hint_txt]; + frame1_pos = [f_fr(1) o15 f_fr(3) o16]; + subplots_text_pos = [ok_pb(1) o18 title_txt]; + subplots_pos = [o5 o19 strd_ed]; + frame2_pos = [f_fr(1) o1 f_fr(3) o2]; + labels_text_pos = [ok_pb(1) o11 title_txt]; + labels_pos = [o5 o12 strd_ed]; + sel_var_pos = [o17 o13 selvar_pb]; + text_size_text_pos = [ok_pb(1) o9 title_txt]; + text_size_pos = [o5 o10 strd_ed]; + text_color_text_pos = [ok_pb(1) o3 title_txt]; + text_color_pos = [o5 o4 var_pop]; + f_fr = [7.9 8.38 216.2 36.9]; % final frame + cancel_pb = [139 15.76 75.9 22.14]; + frame3_pos = f_fr+o21; + ok_pos = ok_pb+o21; + cancel_pos = cancel_pb+o21; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','add label', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for adding labels', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% To subplot(s) + 'Units','pixels', ... + 'Position',subplots_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','To subplot(s)', ... + 'Style','text'),'units','normalized'); + + ed1_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',subplots_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed1_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Labels + 'Units','pixels', ... + 'Position',labels_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Labels', ... + 'Style','text'),'units','normalized'); + + ed2_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',labels_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed2_h,'units','normalized'); + + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool(tmp(2),''select'')']; + set( ... + uicontrol( ... %% Select variable + 'Units','pixels', ... + 'Position',sel_var_pos, ... + 'String','Select variable', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Text size + 'Units','pixels', ... + 'Position',text_size_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Text size', ... + 'Style','text'),'units','normalized'); + + ed3_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',text_size_pos, ... + 'String','10', ... + 'FontSize',12, ... + 'Style','edit'); + set(ed3_h,'units','normalized'); + + set( ... + uicontrol( ... %% Text color + 'Units','pixels', ... + 'Position',text_color_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Text color', ... + 'Style','text'),'units','normalized'); + + ud = {'k' 'w' 'y' 'm' 'c' 'r' 'g' 'b' 'xor' 'none'}; + s = {'black' 'white' 'yellow' 'magenta' 'cyan' 'red' 'green' ... + 'blue' 'xor' 'none'}; + p_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',text_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + set(p_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame3_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_tool(' mat2str(h) ',''label'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + ud = [ed1_h ed2_h ed3_h p_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData',ud, ... + 'handlevisibility','off'); + + %%%%%%%%% +% add_hit %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%%% +elseif strcmp(action,'add_hit') + old_fig_n = watchon; + W = 424.3; H = 337.642; + o37 = zeros(1,4); + o37(1) = (W-f_fr(3))/2; + f_fr = [7.9 8.38 (W-2*7.9) 36.9]; % final frame + cancel_pb = [(W-(ok_pb(1)+75.9)) 15.76 75.9 22.14]; + hint_txt = [W 18.45]; % hint text width and height + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = o1+dist1; + o6 = max([title_txt(2),var_pop(2)]); + o7 = max([title_txt(2),title_edit_h]); + o8 = max([title_txt(2),title_edit_h,var_pop(2)]); + o38 = max([title_txt(2),title_edit_h,selvar_pb(2)]); + o3 = o8+o6+3*dist1; + o4 = o1+0.5*o8+dist1-title_txt(2)/2; + o5 = o1+0.5*o8+dist1-var_pop(2)/2; + o9 = o1+0.5*o8+dist1-title_edit_h/2; + o21 = o1+o8+0.5*o6+2*dist1-title_txt(2)/2; + o22 = o1+o8+0.5*o6+2*dist1-var_pop(2)/2; + o20 = ok_pb(1)+title_txt(1); + o10 = o20+var_pop(1)+2*dist1; + o11 = o10+title_txt(1); + o12 = o2+o8+dist1+0.5*o6-title_txt(2)/2; + o13 = o2+o8+dist1+0.5*o6-var_pop(2)/2; + o14 = o1+o3+frames_dist2; + o15 = 2*o6+o8+4*dist1; + o16 = o14+dist1+0.5*o6-title_txt(2)/2; + o17 = o14+dist1+0.5*o6-var_pop(2)/2; + o18 = o14+o6+2*dist1+0.5*o8-title_txt(2)/2; + o19 = o14+o6+2*dist1+0.5*o8-title_edit_h/2; + o23 = o14+o6+2*dist1+0.5*o8-var_pop(2)/2; + o24 = o14+o6+3*dist1+o8+0.5*o6-title_txt(2)/2; + o25 = o14+o6+3*dist1+o8+0.5*o6-var_pop(2)/2; + o26 = o14+o15+frames_dist2; + o27 = o8+2*dist1; + o28 = o26+dist1+0.5*o38-title_txt(2)/2; + o29 = o26+dist1+0.5*o38-title_edit_h/2; + o30 = o26+dist1+0.5*o38-selvar_pb(2)/2; + o31 = o10; %W-(ok_pb(1)+selvar_pb(1)); + o32 = o26+o27+frames_dist2; + o33 = o7+2*dist1; + o34 = o32+dist1+0.5*o7-title_txt(2)/2; + o35 = o32+dist1+0.5*o7-title_edit_h/2; + o36 = o32+o33+hint_dist1; + hint_text_pos = [1 o36 hint_txt]; + frame1_pos = [f_fr(1) o32 f_fr(3) o33]; + subplots_text_pos = [ok_pb(1) o34 title_txt]; + subplots_pos = [o20 o35 strd_ed]; + frame2_pos = [f_fr(1) o26 f_fr(3) o27]; + trace_vect_text_pos = [ok_pb(1) o28 title_txt]; + trace_vect_pos = [o20 o29 strd_ed]; + sel_vect_pos = [o31 o30 selvar_pb]; + frame3_pos = [f_fr(1) o14 f_fr(3) o15]; + marker_text_pos = [ok_pb(1) o24 title_txt]; + marker_pos = [o20 o25 var_pop]; + marker_size_text_pos = [ok_pb(1) o18 title_txt]; + marker_size_pos = [o20 o19 strd_ed]; + size_fact_text_pos = [o10 o18 title_txt]; + size_fact_pos = [o11 o23 var_pop]; + marker_color_text_pos = [ok_pb(1) o16 title_txt]; + marker_color_pos = [o20 o17 var_pop]; + edge_color_text_pos = [o10 o16 title_txt]; + edge_color_pos = [o11 o17 var_pop]; + frame4_pos = [f_fr(1) o1 f_fr(3) o3]; + text_text_pos = [ok_pb(1) o21 title_txt]; + text_pos = [o20 o22 var_pop]; + text_color_text_pos = [ok_pb(1) o4 title_txt]; + text_color_pos = [o20 o5 var_pop]; + text_size_text_pos = [o10 o4 title_txt]; + text_size_pos = [o11 o9 strd_ed]; + f_fr = [7.9 8.38 216.2 36.9]; % final frame + cancel_pb = [139 15.76 75.9 22.14]; + frame5_pos = f_fr+o37; + ok_pos = ok_pb+o37; + cancel_pos = cancel_pb+o37; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','add hit histogram', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for adding hit histogram', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% To subplot(s) + 'Units','pixels', ... + 'Position',subplots_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','To subplot(s)', ... + 'Style','text'),'units','normalized'); + + ed1_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',subplots_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed1_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Trace vector + 'Units','pixels', ... + 'Position',trace_vect_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Hit vector', ... + 'Style','text'),'units','normalized'); + + ed2_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',trace_vect_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed2_h,'units','normalized'); + + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool(tmp(2),''select'')']; + set( ... + uicontrol( ... %% Select variable + 'Units','pixels', ... + 'Position',sel_vect_pos, ... + 'String','Select variable', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame3_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Marker + 'Units','pixels', ... + 'Position',marker_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Marker', ... + 'Style','text'),'units','normalized'); + + s = {'lattice' 'o' '.' 'x' '+' '*' 's' 'd' 'v' '^' ... + '<' '>' 'p' 'h' 'none' 'cell array'}; + p3_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',marker_pos, ... + 'String',s, ... + 'Style','popupmenu'); + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool([tmp(6),16],''popup_select'')']; + set(p3_h,'units','normalized','Callback',s); + + set( ... + uicontrol( ... %% Marker size + 'Units','pixels', ... + 'HorizontalAlignment','left', ... + 'Position',marker_size_text_pos, ... + 'String','Marker size', ... + 'Style','text'),'units','normalized'); + + ed4_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',marker_size_pos, ... + 'String','12', ... + 'FontSize',12, ... + 'Style','edit'); + set(ed4_h,'units','normalized','Callback',s); + + set( ... + uicontrol( ... %% Size factor + 'Units','pixels', ... + 'HorizontalAlignment','left', ... + 'Position',size_fact_text_pos, ... + 'String','Size factor', ... + 'Style','text'),'units','normalized'); + + s = {'common' 'separate'}; + p4_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',size_fact_pos, ... + 'String',s, ... + 'Style','popupmenu'); + set(p4_h,'units','normalized'); + + + set( ... + uicontrol( ... %% Marker color + 'Units','pixels', ... + 'HorizontalAlignment','left', ... + 'Position',marker_color_text_pos, ... + 'String','Marker color', ... + 'Style','text'),'units','normalized'); + + ud = {'w' 'k' 'y' 'm' 'c' 'r' 'g' 'b' 'none' ''}; + s = {'white' 'black' 'yellow' 'magenta' 'cyan' ... + 'red' 'green' 'blue' 'none' '-matrix-'}; + p5_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',marker_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool([tmp(9) 10],''popup_select'')']; + set(p5_h,'units','normalized','Callback',s); + + set( ... + uicontrol( ... %% EdgeColor + 'Units','pixels', ... + 'Position',edge_color_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Edge color', ... + 'Style','text'),'units','normalized'); + + ud = {'none' 'w' 'k' 'y' 'm' 'c' 'r' 'g' 'b'}; + s = {'none' 'white' 'black' 'yellow' 'magenta' 'cyan' ... + 'red' 'green' 'blue'}; + p6_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',edge_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + set(p6_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame4_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Text + 'Units','pixels', ... + 'Position',text_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Text', ... + 'Style','text'),'units','normalized'); + + s = {'off' 'on'}; + p2_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',text_pos, ... + 'String',s, ... + 'Style','popupmenu'); + set(p2_h,'units','normalized'); + + set( ... + uicontrol( ... %% Text + 'Units','pixels', ... + 'Position',text_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Text', ... + 'Style','text'),'units','normalized'); + + s = {'off' 'on'}; + p2_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',text_pos, ... + 'String',s, ... + 'Style','popupmenu'); + set(p2_h,'units','normalized'); + + set( ... + uicontrol( ... %% Text color + 'Units','pixels', ... + 'Position',text_color_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Text color', ... + 'Style','text'),'units','normalized'); + + ud = {'k' 'w' 'y' 'm' 'c' 'r' 'g' 'b' 'xor'}; + s = {'black' 'white' 'yellow' 'magenta' 'cyan' ... + 'red' 'green' 'blue' 'xor'}; + p1_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',text_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + set(p1_h,'units','normalized'); + + set( ... + uicontrol( ... %% Text size + 'Units','pixels', ... + 'Position',text_size_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Text size', ... + 'Style','text'),'units','normalized'); + + ed3_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',text_size_pos, ... + 'String','3', ... + 'FontSize',12, ... + 'Style','edit'); + set(ed3_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame5_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_tool(' mat2str(h) ',''hit'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [ed1_h ed2_h p1_h ed3_h p2_h p3_h ed4_h p4_h p5_h p6_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData',tmp_udata, ... + 'handlevisibility','off'); + + %%%%%%%%%% +% add_traj %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%%%% +elseif strcmp(action,'add_traj') + old_fig_n = watchon; + W = 424.3; H = 337.642; + o34 = zeros(1,4); + o34(1) = (W-f_fr(3))/2; + f_fr = [7.9 8.38 (W-2*7.9) 36.9]; % final frame + cancel_pb = [(W-(ok_pb(1)+75.9)) 15.76 75.9 22.14]; + hint_txt = [W 18.45]; % hint text width and height + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = o1+dist1; + o6 = max([title_txt(2),var_pop(2)]); + o7 = max([title_txt(2),title_edit_h]); + o8 = max([title_txt(2),title_edit_h,var_pop(2)]); + o38 = max([title_txt(2),title_edit_h,selvar_pb(2)]); + o20 = ok_pb(1)+title_txt(1); + o10 = o20+var_pop(1)+2*dist1; + o11 = o10+title_txt(1); + o3 = 2*o6+o8+4*dist1; + o4 = o1+dist1+0.5*o6-title_txt(2)/2; + o5 = o1+dist1+0.5*o6-var_pop(2)/2; + o9 = o1+2*dist1+o6+0.5*o8-title_txt(2)/2; + o12 = o1+2*dist1+o6+0.5*o8-title_edit_h/2; + o13 = o1+2*dist1+o6+0.5*o8-var_pop(2)/2; + o14 = o1+3*dist1+o6+o8+0.5*o6-title_edit_h/2; + o15 = o1+3*dist1+o6+o8+0.5*o6-var_pop(2)/2; + o16 = o1+o3+frames_dist2; + o17 = o8+o6+3*dist1; + o18 = o16+dist1+0.5*o8-title_txt(2)/2; + o19 = o16+dist1+0.5*o8-title_edit_h/2; + o21 = o16+dist1+0.5*o8-var_pop(2)/2; + o22 = o16+2*dist1+o8+0.5*o6-title_txt(2)/2; + o23 = o16+2*dist1+o8+0.5*o6-var_pop(2)/2; + o24 = o16+o17+frames_dist2; + o25 = o38+2*dist1; + o26 = o24+dist1+0.5*o38-title_txt(2)/2; + o27 = o24+dist1+0.5*o38-title_edit_h/2; + o28 = o24+dist1+0.5*o38-selvar_pb(2)/2; + o29 = o24+o25+frames_dist2; + o30 = o7+2*dist1; + o31 = o29+dist1+0.5*o7-title_txt(2)/2; + o32 = o29+dist1+0.5*o7-title_edit_h/2; + o33 = o29+o30+hint_dist1; + hint_text_pos = [1 o33 hint_txt]; + frame1_pos = [f_fr(1) o29 f_fr(3) o30]; + subplots_text_pos = [ok_pb(1) o31 title_txt]; + subplots_pos = [o20 o32 strd_ed]; + frame2_pos = [f_fr(1) o24 f_fr(3) o25]; + trace_vect_text_pos = [ok_pb(1) o26 title_txt]; + trace_vect_pos = [o20 o27 strd_ed]; + sel_vect_pos = [o10 o28 selvar_pb]; + frame3_pos = [f_fr(1) o16 f_fr(3) o17]; + line_color_text_pos = [ok_pb(1) o22 title_txt]; + line_color_pos = [o20 o23 var_pop]; + line_width_text_pos = [ok_pb(1) o18 title_txt]; + line_width_pos = [o20 o19 strd_ed]; + width_factor_text_pos = [o10 o18 title_txt]; + width_factor_pos = [o11 o19 var_pop]; + frame4_pos = [f_fr(1) o1 f_fr(3) o3]; + marker_text_pos = [ok_pb(1) o14 title_txt]; + marker_pos = [o20 o15 var_pop]; + marker_size_text_pos = [ok_pb(1) o9 title_txt]; + marker_size_pos = [o20 o12 strd_ed]; + size_factor_text_pos = [o10 o9 title_txt]; + size_factor_pos = [o11 o13 var_pop]; + marker_color_text_pos = [ok_pb(1) o4 title_txt]; + marker_color_pos = [o20 o5 var_pop]; + edge_color_text_pos = [o10 o4 title_txt]; + edge_color_pos = [o11 o5 var_pop]; + f_fr = [7.9 8.38 216.2 36.9]; % final frame + cancel_pb = [139 15.76 75.9 22.14]; + frame5_pos = f_fr+o34; + ok_pos = ok_pb+o34; + cancel_pos = cancel_pb+o34; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','add trajectory', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for adding trajectory', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... + 'Units','pixels', ... %% To subplot(s) + 'Position',subplots_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','To subplot(s)', ... + 'Style','text'),'units','normalized'); + + ed1_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',subplots_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed1_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Trace vector + 'Units','pixels', ... + 'Position',trace_vect_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Trajectory', ... + 'Style','text'),'units','normalized'); + + ed2_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',trace_vect_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed2_h,'units','normalized'); + + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool(tmp(2),''select'')']; + set( ... + uicontrol( ... %% Select variable + 'Units','pixels', ... + 'Position',sel_vect_pos, ... + 'String','Select variable', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame3_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Line color + 'Units','pixels', ... + 'Position',line_color_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Line color', ... + 'Style','text'),'units','normalized'); + + ud = {'w' 'k' 'y' 'm' 'c' 'r' 'g' 'b' 'xor'}; + s = {'white' 'black' 'yellow' 'magenta' 'cyan' ... + 'red' 'green' 'blue' 'xor'}; + p1_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',line_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + set(p1_h,'units','normalized'); + + set( ... + uicontrol( ... %% Line width + 'Units','pixels', ... + 'Position',line_width_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Line width', ... + 'Style','text'),'units','normalized'); + + ed3_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',line_width_pos, ... + 'String','3', ... + 'FontSize',12, ... + 'Style','edit'); + set(ed3_h,'units','normalized'); + + set( ... + uicontrol( ... %% Factor + 'Units','pixels', ... + 'Position',width_factor_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Factor', ... + 'Style','text'),'units','normalized'); + + s = {'hit' 'equal'}; + p2_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',width_factor_pos, ... + 'String',s, ... + 'Style','popupmenu'); + set(p2_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame4_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Marker + 'Units','pixels', ... + 'Position',marker_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Marker', ... + 'Style','text'),'units','normalized'); + + s = {'o' '.' 'x' '+' '*' 's' 'd' 'v' '^' ... + '<' '>' 'p' 'h' 'none'}; + p3_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',marker_pos, ... + 'String',s, ... + 'Style','popupmenu'); + set(p3_h,'units','normalized'); + + set( ... + uicontrol( ... %% Marker size + 'Units','pixels', ... + 'HorizontalAlignment','left', ... + 'Position',marker_size_text_pos, ... + 'String','Marker size', ... + 'Style','text'),'units','normalized'); + + ed4_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',marker_size_pos, ... + 'String','10', ... + 'FontSize',12, ... + 'Style','edit'); + set(ed4_h,'units','normalized'); + + set( ... + uicontrol( ... %% Factor + 'Units','pixels', ... + 'HorizontalAlignment','left', ... + 'Position',size_factor_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Factor', ... + 'Style','text'),'units','normalized'); + + s = {'hit' 'equal'}; + p4_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',size_factor_pos, ... + 'String',s, ... + 'Style','popupmenu'); + set(p4_h,'units','normalized'); + + set( ... + uicontrol( ... %% Marker color + 'Units','pixels', ... + 'HorizontalAlignment','left', ... + 'Position',marker_color_text_pos, ... + 'String','Marker color', ... + 'Style','text'),'units','normalized'); + + ud = {'w' 'k' 'y' 'm' 'c' 'r' 'g' 'b' 'none'}; + s = {'white' 'black' 'yellow' 'magenta' 'cyan' ... + 'red' 'green' 'blue' 'none'}; + p5_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',marker_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + set(p5_h,'units','normalized'); + + set( ... + uicontrol( ... %% Edge color + 'Units','pixels', ... + 'Position',edge_color_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Edge color', ... + 'Style','text'),'units','normalized'); + + ud = {'none' 'w' 'k' 'y' 'm' 'c' 'r' 'g' 'b'}; + s = {'none' 'white' 'black' 'yellow' 'magenta' 'cyan' ... + 'red' 'green' 'blue'}; + p6_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',edge_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + set(p6_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame5_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_tool(' mat2str(h) ',''traj'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [ed1_h ed2_h p1_h ed3_h p2_h p3_h ed4_h p4_h p5_h p6_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData',tmp_udata, ... + 'handlevisibility','off'); + + %%%%%%%%%%% +% add_comet %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%%%%% +elseif strcmp(action,'add_comet') + old_fig_n = watchon; + W = 424.3; H = 265.589; + o26 = zeros(1,4); + o26(1) = (W-f_fr(3))/2; + f_fr = [7.9 8.38 (W-2*7.9) 36.9]; % final frame + cancel_pb = [(W-(ok_pb(1)+75.9)) 15.76 75.9 22.14]; + hint_txt = [W 18.45]; % hint text width and height + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = o1+dist1; + o6 = max([title_txt(2),var_pop(2)]); + o7 = max([title_txt(2),title_edit_h]); + o8 = max([title_txt(2),title_edit_h,var_pop(2)]); + o38 = max([title_txt(2),title_edit_h,selvar_pb(2)]); + o20 = ok_pb(1)+title_txt(1); + o10 = o20+var_pop(1)+2*dist1; + o11 = o10+title_txt(1); + o3 = 2*o6+o7+4*dist1; + o4 = o1+dist1+0.5*o6-title_txt(2)/2; + o5 = o1+dist1+0.5*o6-var_pop(2)/2; + o9 = o1+2*dist1+o6+0.5*o7-title_txt(2)/2; + o12 = o1+2*dist1+o6+0.5*o7-title_edit_h/2; + o13 = o1+3*dist1+o7+1.5*o6-title_txt(2)/2; + o14 = o1+3*dist1+o7+1.5*o6-var_pop(2)/2; + o15 = o1+o3+frames_dist2; + o16 = o38+2*dist1; + o17 = o15+dist1+0.5*o38-title_txt(2)/2; + o18 = o15+dist1+0.5*o38-title_edit_h/2; + o19 = o15+dist1+0.5*o38-selvar_pb(2)/2; + o21 = o15+o16+frames_dist2; + o22 = o7+2*dist1; + o23 = o21+dist1+0.5*o7-title_txt(2)/2; + o24 = o21+dist1+0.5*o7-title_edit_h/2; + o25 = o21+o22+hint_dist1; + hint_text_pos = [1 o25 hint_txt]; + frame1_pos = [f_fr(1) o21 f_fr(3) o22]; + subplots_text_pos = [ok_pb(1) o23 title_txt]; + subplots_pos = [o20 o24 strd_ed]; + frame2_pos = [f_fr(1) o15 f_fr(3) o16]; + trace_vect_text_pos = [ok_pb(1) o17 title_txt]; + trace_vect_pos = [o20 o18 strd_ed]; + sel_var_pos = [o10 o19 selvar_pb]; + frame3_pos = [f_fr(1) o1 f_fr(3) o3]; + marker_text_pos = [ok_pb(1) o13 title_txt]; + marker_pos = [o20 o14 var_pop]; + marker_size_text_pos = [ok_pb(1) o9 title_txt]; + marker_size_pos = [o20 o12 var_pop]; + marker_color_text_pos = [ok_pb(1) o4 title_txt]; + marker_color_pos = [o20 o5 var_pop]; + edge_color_text_pos = [o10 o4 title_txt]; + edge_color_pos = [o11 o5 var_pop]; + f_fr = [7.9 8.38 216.2 36.9]; % final frame + cancel_pb = [139 15.76 75.9 22.14]; + frame4_pos = f_fr+o26; + ok_pos = ok_pb+o26; + cancel_pos = cancel_pb+o26; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','add comet', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for adding comet', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% To subplot(s) + 'Units','pixels', ... + 'Position',subplots_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','To subplot(s)', ... + 'Style','text'),'units','normalized'); + + ed1_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',subplots_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed1_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Trace vector + 'Units','pixels', ... + 'Position',trace_vect_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Trace vector', ... + 'Style','text'),'units','normalized'); + + ed2_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',trace_vect_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed2_h,'units','normalized'); + + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool(tmp(2),''select'')']; + set( ... + uicontrol( ... %% Select variable + 'Units','pixels', ... + 'Position',sel_var_pos, ... + 'String','Select variable', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame3_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Marker + 'Units','pixels', ... + 'Position',marker_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Marker', ... + 'Style','text'),'units','normalized'); + + s = {'o' '.' 'x' '+' '*' 's' 'd' 'v' '^' ... + '<' '>' 'p' 'h' 'lattice' 'none'}; + p1_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',marker_pos, ... + 'String',s, ... + 'Style','popupmenu'); + set(p1_h,'units','normalized'); + + set( ... + uicontrol( ... %% Marker size + 'Units','pixels', ... + 'HorizontalAlignment','left', ... + 'Position',marker_size_text_pos, ... + 'String','Marker size', ... + 'Style','text'),'units','normalized'); + + ed3_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',marker_size_pos, ... + 'String','[20 4]', ... + 'FontSize',12, ... + 'Style','edit'); + set(ed3_h,'units','normalized'); + + set( ... + uicontrol( ... %% Marker color + 'Units','pixels', ... + 'HorizontalAlignment','left', ... + 'Position',marker_color_text_pos, ... + 'String','Marker color', ... + 'Style','text'),'units','normalized'); + + ud = {'w' 'k' 'y' 'm' 'c' 'r' 'g' 'b' 'none' ''}; + s = {'white' 'black' 'yellow' 'magenta' 'cyan' ... + 'red' 'green' 'blue' 'none' '-matrix-'}; + p2_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',marker_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool([tmp(5) 10],''popup_select'')']; + set(p2_h,'units','normalized','Callback',s); + + set( ... + uicontrol( ... %% Edge color + 'Units','pixels', ... + 'Position',edge_color_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Edge color', ... + 'Style','text'),'units','normalized'); + + ud = {'none' 'w' 'k' 'y' 'm' 'c' 'r' 'g' 'b'}; + s = {'none' 'white' 'black' 'yellow' 'magenta' 'cyan' ... + 'red' 'green' 'blue'}; + p3_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',edge_color_pos, ... + 'UserData',ud, ... + 'String',s, ... + 'Style','popupmenu'); + set(p3_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame4_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_tool(' mat2str(h) ',''comet'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [ed1_h ed2_h p1_h ed3_h p2_h p3_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData',tmp_udata, ... + 'handlevisibility','off'); + + %%%%%%%%%%%% +% recolorbar %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%%%%%% +elseif strcmp(action,'recolorbar') + old_fig_n = watchon; + W = 345.4; H = 237.299; + o25 = zeros(1,4); + o25(1) = (W-f_fr(3))/2; + f_fr = [7.9 8.38 (W-2*7.9) 36.9]; % final frame + cancel_pb = [(W-(ok_pb(1)+75.9)) 15.76 75.9 22.14]; + hint_txt = [W 18.45]; % hint text width and height + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = o1+dist1; + o6 = max([title_txt(2),var_pop(2)]); + o7 = max([title_txt(2),title_edit_h]); + o8 = max([title_txt(2),title_edit_h,selvar_pb(2)]); + o20 = ok_pb(1)+title_txt(1); + o10 = o20+var_pop(1)+2*dist1; + o11 = o10+title_txt(1); + o3 = o6+2*dist1; + o4 = o1+dist1+0.5*o6-title_txt(2)/2; + o5 = o1+dist1+0.5*o6-var_pop(2)/2; + o9 = o1+o3+frames_dist2; + o12 = 2*o8+3*dist1; + o13 = o9+dist1+0.5*o8-title_txt(2)/2; + o14 = o9+dist1+0.5*o8-title_edit_h/2; + o15 = o9+dist1+0.5*o8-var_pop(2)/2; + o16 = o9+2*dist1+1.5*o8-title_txt(2)/2; + o17 = o9+2*dist1+1.5*o8-title_edit_h/2; + o18 = o9+2*dist1+1.5*o8-var_pop(2)/2; + o19 = o9+o12+frames_dist2; + o21 = o7+2*dist1; + o22 = o19+dist1+0.5*o7-title_txt(2)/2; + o23 = o19+dist1+0.5*o7-title_edit_h/2; + o24 = o19+o21+hint_dist1; + hint_text_pos = [1 o24 hint_txt]; + frame1_pos = [f_fr(1) o19 f_fr(3) o21]; + subplots_text_pos = [ok_pb(1) o22 title_txt]; + subplots_pos = [o20 o23 strd_ed]; + frame2_pos = [f_fr(1) o9 f_fr(3) o12]; + tics_text_pos = [ok_pb(1) o16 title_txt]; + tics_pos = [o20 o17 strd_ed]; + sel_var2_pos = [o10 o18 var_pop]; + labels_text_pos = [ok_pb(1) o13 title_txt]; + labels_pos = [o20 o14 strd_ed]; + sel_var1_pos = [o10 o15 var_pop]; + frame3_pos = [f_fr(1) o1 f_fr(3) o3]; + scale_text_pos = [ok_pb(1) o4 title_txt]; + scale_pos = [o20 o5 1.3*var_pop(1) var_pop(2)]; + f_fr = [7.9 8.38 216.2 36.9]; % final frame + cancel_pb = [139 15.76 75.9 22.14]; + frame4_pos = f_fr+o25; + ok_pos = ok_pb+o25; + cancel_pos = cancel_pb+o25; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','recolorbar', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options, recolorbar', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% In subplot(s) + 'Units','pixels', ... + 'Position',subplots_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','In subplot(s)', ... + 'Style','text'),'units','normalized'); + + ed1_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',subplots_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed1_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Ticks + 'Units','pixels', ... + 'Position',tics_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Ticks', ... + 'Style','text'),'units','normalized'); + + ed3_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',tics_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed3_h,'units','normalized'); + + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool(tmp(3),''select'')']; + set( ... + uicontrol( ... %% Select variable + 'Units','pixels', ... + 'Position',sel_var2_pos, ... + 'String','Select variable', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Labels (cell array) + 'Units','pixels', ... + 'Position',labels_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Labels', ... + 'Style','text'),'units','normalized'); + + ed2_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',labels_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed2_h,'units','normalized'); + + s = ['tmp=get(' mat2str(fig_h) ',''userdata'');' ... + 'vis_show_gui_tool(tmp(2),''select'')']; + set( ... + uicontrol( ... %% Select variable + 'Units','pixels', ... + 'Position',sel_var1_pos, ... + 'String','Select variable', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame3_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% Scale + 'Units','pixels', ... + 'Position',scale_text_pos, ... + 'HorizontalAlignment','left', ... + 'String','Scale', ... + 'Style','text'),'units','normalized'); + + s = {'denormalized' 'normalized'}; + p1_h = uicontrol( ... %% [popupmenu] + 'Units','pixels', ... + 'Position',scale_pos, ... + 'String',s, ... + 'Style','popupmenu'); + set(p1_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame4_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_tool(' mat2str(h) ',''do_recolorbar'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [ed1_h ed2_h ed3_h p1_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData',tmp_udata, ... + 'handlevisibility','off'); + + %%%%%%% +% clear %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%% +elseif strcmp(action,'clear') + old_fig_n = watchon; + W = 232; H = 277.463; + o25 = zeros(1,4); + o25(1) = (W-f_fr(3))/2; + f_fr = [7.9 8.38 (W-2*7.9) 36.9]; % final frame + cancel_pb = [(W-(ok_pb(1)+75.9)) 15.76 75.9 22.14]; + hint_txt = [W 18.45]; % hint text width and height + units = get(h,'units'); + set(h,'units','pixels'); + fp = get(h,'Position'); + fig_pos = [(fp(1)+fp(3)) ... + (fp(2)+fp(2)+fp(4)/2-H/2) ... + W ... + H]; + o1 = sum(f_fr([2 4]))+frames_dist; + o2 = o1+dist1; + o3 = (W-radio_rb(1))/2; + o4 = o2+radio_rb(2)+dist1; + o5 = o4+radio_rb(2)+dist1; + o6 = o5+radio_rb(2)+dist1; + o7 = o6+radio_rb(2)+dist1; + o8 = 5*radio_rb(2)+6*dist1; + o9 = o1+o8+frames_dist2; + o10 = max([title_txt(2),title_edit_h]); + o11 = o9+dist1+0.5*o10-title_txt(2)/2; + o12 = o9+dist1+0.5*o10-strd_ed(2)/2; + o20 = W-(ok_pb(1)+strd_ed(1)); + o13 = o10+2*dist1; + o24 = o9+o13+hint_dist1; + hint_text_pos = [1 o24 hint_txt]; + frame1_pos = [f_fr(1) o9 f_fr(3) o13]; + subplots_text_pos = [ok_pb(1) o11 title_txt]; + subplots_pos = [o20 o12 strd_ed]; + frame2_pos = [f_fr(1) o1 f_fr(3) o8]; + radiob1_pos = [o3 o7 selvar_pb]; + radiob2_pos = [o3 o6 selvar_pb]; + radiob3_pos = [o3 o5 selvar_pb]; + radiob4_pos = [o3 o4 selvar_pb]; + radiob5_pos = [o3 o2 selvar_pb]; + f_fr = [7.9 8.38 216.2 36.9]; % final frame + cancel_pb = [139 15.76 75.9 22.14]; + frame3_pos = f_fr;%+o25; + ok_pos = ok_pb;%+o25; + cancel_pos = cancel_pb;%+o25; + + fig_h = figure( ... + 'Units','pixels', ... + 'Position', fig_pos, ... + 'Color',fig_color, ... + 'NumberTitle','off', ... + 'Name','clear label', ... + 'MenuBar','none', ... + 'Visible','off'); + + set( ... + uicontrol( ... %% hint + 'Units','pixels', ... + 'BackgroundColor',fig_color, ... + 'HorizontalAlignment','center', ... + 'Position',hint_text_pos, ... + 'String','Options for clearing', ... + 'Style','text'),'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame1_pos, ... + 'Style','frame'),'units','normalized'); + + set( ... + uicontrol( ... %% From subplots + 'Units','pixels', ... + 'Position',subplots_text_pos, ... + 'String','From subplots', ... + 'Style','text'),'units','normalized'); + + ed1_h = uicontrol( ... %% [edit] + 'Units','pixels', ... + 'BackgroundColor',bg_color2, ... + 'Position',subplots_pos, ... + 'FontSize',12, ... + 'Style','edit'); + set(ed1_h,'units','normalized'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame2_pos, ... + 'Style','frame'),'units','normalized'); + + rb1_h = uicontrol( ... %% + 'Units','pixels', ... + 'Position',radiob1_pos, ... + 'style','radiobutton', ... + 'String','All', ... + 'value',1); + + rb2_h = uicontrol( ... %% + 'Units','pixels', ... + 'Position',radiob2_pos, ... + 'style','radiobutton', ... + 'String','Comet'); + + rb3_h = uicontrol( ... %% + 'Units','pixels', ... + 'Position',radiob3_pos, ... + 'style','radiobutton', ... + 'String','Hit'); + + rb4_h = uicontrol( ... %% + 'Units','pixels', ... + 'Position',radiob4_pos, ... + 'style','radiobutton', ... + 'String','Label'); + + rb5_h = uicontrol( ... %% + 'Units','pixels', ... + 'Position',radiob5_pos, ... + 'style','radiobutton', ... + 'String','Trajectory'); + + set( ... + uicontrol( ... %% [frame] + 'Units','pixels', ... + 'Position',frame3_pos, ... + 'Style','frame'),'units','normalized'); + + s = ['vis_show_gui_tool(' mat2str(h) ',''c_clear'',' mat2str(fig_h) ')']; + set( ... + uicontrol( ... %% OK + 'Units','pixels', ... + 'Position',ok_pos, ... + 'String','OK', ... + 'Callback',s),'units','normalized'); + + set( ... + uicontrol( ... %% Cancel + 'Units','pixels', ... + 'Position',cancel_pos, ... + 'String','Cancel', ... + 'Callback',['close(' mat2str(fig_h) ')']),'units','normalized'); + + watchoff(old_fig_n); + tmp_udata = [ed1_h rb1_h rb2_h rb3_h rb4_h rb5_h]; + set(fig_h,'units','normalized', ... + 'Visible','on', ... + 'UserData',tmp_udata, ... + 'handlevisibility','off'); + + %%%%%%%%% +% c_clear %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%%% +elseif strcmp(action,'c_clear') + thisfig = varargin{1}; + u = get(varargin{1},'UserData'); + s = get(u(1),'String'); + if ~isempty(s) + if s(1)=='[' | (s(1)>='1' & s(1)<='9') + sprintf(s,'[%s]',s); + e = eval(s); else e = s; + end + if vis_valuetype(e,{'1xn','nx1','string'}), + if ischar(e), + if ~strcmp(e,'all'), + errordlg({'Only valid string value' ... + 'for subplot indices is ''all''.'}, ... + 'Error in SOM_VIS: tools'); + return; + else + e=1:length(handles); + end + elseif any(e<1) | any(e>length(handles)), + errordlg({'Subplot indices must be in', ... + 'range 1...number_of_subplots!'}, ... + 'Error in SOM_VIS: tools'); + return; + end + elseif ~isempty(e) + errordlg('Invalid subplot indices!','Error in SOM_VIS: tools'); + return; + end + else, + e = ''; + end + set(0,'currentfigure',child) + v = get(u(2),'value'); + if v, + if ~isempty(e), + som_show_clear('lab',e); + som_show_clear('hit',e); + som_show_clear('traj',e); + som_show_clear('comet',e); + else + som_show_clear('lab'); + som_show_clear('hit'); + som_show_clear('traj'); + som_show_clear('comet'); + end + end + v = get(u(3),'value'); + if v, + if ~isempty(e) + som_show_clear('comet',e); + else + som_show_clear('comet'); + end + end + v = get(u(4),'value'); + if v, + if ~isempty(e), + som_show_clear('hit',e); + else + som_show_clear('hit'); + end + end + v = get(u(5),'value'); + if v, + if ~isempty(e), + som_show_clear('lab',e); + else + som_show_clear('lab'); + end + end + v = get(u(6),'value'); + if v, + if ~isempty(e), + som_show_clear('traj',e); + else + som_show_clear('traj'); + end + end + close(thisfig); + + %%%%%%%%%%%%%% +% do_recolorbar %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%%%%%%%% +elseif strcmp(action,'do_recolorbar') + args = {}; + ud = get(varargin{1},'UserData'); + s = get(ud(1),'String'); + e = 1:length(handles); + if ~isempty(s) + if s(1)=='[' | (s(1)>='1' & s(1)<='9') + sprintf(s,'[%s]',s); + e = eval(s); else e = s; + end + if vis_valuetype(e,{'1xn','nx1','string'}), + if ischar(e) & ~strcmp(e,'all'), + errordlg({'Only valid string value' ... + 'for subplot indices is ''all''.'}, ... + 'Error in SOM_VIS: tools'); + return; + elseif any(e<1) | any(e>length(handles)), + errordlg({'Subplot indices must be in', ... + 'range 1...number_of_subplots.'}, ... + 'Error in SOM_VIS: tools'); + return; + end + elseif ~isempty(e) + errordlg('Invalid subplot indices!','Error in SOM_VIS: tools'); + return; + end + end + args = [args {e}]; + s = get(ud(3),'String'); + tmp = 'auto'; + if strcmp(s,'auto') | strcmp(s,'border'), + tmp = s; + elseif ~isempty(s) + tmp = evalin('base',s); + end + args = [args {tmp}]; + v = get(ud(4),'Value'); + s = get(ud(4),'String'); + args = [args {s{v}}]; + s = get(ud(2),'String'); + if ~isempty(s) + args = [args {evalin('base',s)}]; + end + thisfig = varargin{1}; + % set(0,'CurrentFigure',udata.vis_h); + set(0,'currentfigure',child) + som_recolorbar(args{:}); + close(thisfig); + + %%%%%%% +% label %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%% +elseif strcmp(action,'label') + args = {'label'}; + ud = get(varargin{1},'UserData'); + s = get(ud(2),'String'); + if ~isempty(s) + args = [args {evalin('base',s)}]; + else + args = [args {udata.sM}]; + end + s = get(ud(3),'String'); + if ~isempty(s) + args = [args {'TextSize' eval(s)}]; + end + v = get(ud(4),'Value'); + if v>1, + s = get(ud(4),'UserData'); + args = [args {'TextColor' s{v}}]; + end + s = get(ud(1),'String'); + if ~isempty(s) + if s(1)=='[' | (s(1)>='1' & s(1)<='9') + sprintf(s,'[%s]',s); + e = eval(s); else e = s; + end + if vis_valuetype(e,{'1xn','nx1','string'}), + if ischar(e), + if ~strcmp(e,'all'), + errordlg({'Only valid string value' ... + 'for subplot indices is ''all''.'}, ... + 'Error in SOM_VIS: tools'); + return; + else + e=1:length(handles); + end + elseif any(e<1) | any(e>length(handles)), + errordlg({'Subplot indices must be in', ... + 'range 1...number_of_subplots!'}, ... + 'Error in SOM_VIS: tools'); + return; + end + elseif ~isempty(e) + errordlg('Invalid subplot indices!','Error in SOM_VIS: tools'); + return; + end + args = [args {'SubPlot' e}]; + end + thisfig = varargin{1}; + % set(0,'CurrentFigure',udata.vis_h); + set(0,'currentfigure',child) + som_show_add(args{:}); + close(thisfig); + + %%%%% +% hit %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%% +elseif strcmp(action,'hit') + args = {'hit'}; + ud = get(varargin{1},'UserData'); + s = get(ud(2),'String'); + e = evalin('base',s); + [dummy k] = size(e); + args = [args {e}]; + v = get(ud(10),'Value'); + s = get(ud(10),'UserData'); + args = [args {'EdgeColor' s{v}}]; + % + s = get(ud(7),'String'); + if ~isempty(s) + args = [args {'MarkerSize' eval(s)}]; + end + if k == 1, + v = get(ud(6),'Value'); + if v<16, + s = get(ud(6),'String'); + args = [args {'Marker' s{v}}]; + else + % errordlg(); + return; + end + v = get(ud(9),'Value'); + if v<10, + s = get(ud(9),'UserData'); + args = [args {'MarkerColor' s{v}}]; + else + % errordlg(); + return; + end + % + v = get(ud(5),'Value'); + s = get(ud(5),'String'); + args = [args {'Text' s{v}}]; + if v>1, % 1='off' + v = get(ud(3),'Value'); + s = get(ud(3),'UserData'); + args = [args {'TextColor' s{v}}]; + % + s = get(ud(4),'String'); + args = [args {'TextSize' eval(s)}]; + end + else + v = get(ud(8),'Value'); + s = get(ud(8),'String'); + args = [args {'SizeFactor' s{v}}]; + % + v = get(ud(6),'Value'); + s = get(ud(6),'String'); + args = [args {'Marker' s{v}}]; + % + v = get(ud(9),'Value'); + s = get(ud(9),'UserData'); + args = [args {'MarkerColor' s{v}}]; + end + + s = get(ud(1),'String'); + if ~isempty(s) + if s(1)=='[' | (s(1)>='1' & s(1)<='9') + sprintf(s,'[%s]',s); + e = eval(s); else e = s; + end + if vis_valuetype(e,{'1xn','nx1','string'}), + if ischar(e), + if ~strcmp(e,'all'), + errordlg({'Only valid string value' ... + 'for subplot indices is ''all''.'}, ... + 'Error in SOM_VIS: tools'); + return; + else + e=1:length(handles); + end + elseif any(e<1) | any(e>length(handles)), + errordlg({'Subplot indices must be in', ... + 'range 1...number_of_subplots!'}, ... + 'Error in SOM_VIS: tools'); + return; + end + elseif ~isempty(e) + errordlg('Invalid subplot indices!','Error in SOM_VIS: tools'); + return; + end + args = [args {'SubPlot' e}]; + end + thisfig = varargin{1}; + % set(0,'CurrentFigure',udata.vis_h); + set(0,'currentfigure',child) + som_show_add(args{:}); + close(thisfig); + + %%%%%% +% traj %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%% +elseif strcmp(action,'traj') + args = {'traj'}; + ud = get(varargin{1},'UserData'); + s = get(ud(2),'String'); + args = [args {evalin('base',s)}]; + s = get(ud(4),'String'); + if ~isempty(s) + args = [args {'TrajWidth' eval(s)}]; + end + v = get(ud(5),'Value'); + if v>1, + s = get(ud(5),'String'); + args = [args {'WidthFactor' s{v}}]; + end + v = get(ud(3),'Value'); + if v>1, + s = get(ud(3),'UserData'); + args = [args {'TrajColor' s{v}}]; + end + v = get(ud(6),'Value'); + if v>1, + s = get(ud(6),'String'); + args = [args {'Marker' s{v}}]; + end + s = get(ud(7),'String'); + if ~isempty(s) + args = [args {'MarkerSize' eval(s)}]; + end + v = get(ud(8),'Value'); + if v>1, + s = get(ud(8),'String'); + args = [args {'SizeFactor' s{v}}]; + end + v = get(ud(9),'Value'); + if v>1, + s = get(ud(9),'UserData'); + args = [args {'MarkerColor' s{v}}]; + end + v = get(ud(10),'Value'); + if v>1, + s = get(ud(10),'UserData'); + args = [args {'EdgeColor' s{v}}]; + end + s = get(ud(1),'String'); + if ~isempty(s) + if s(1)=='[' | (s(1)>='1' & s(1)<='9') + sprintf(s,'[%s]',s); + e = eval(s); else e = s; + end + if vis_valuetype(e,{'1xn','nx1','string'}), + if ischar(e), + if ~strcmp(e,'all'), + errordlg({'Only valid string value' ... + 'for subplot indices is ''all''.'}, ... + 'Error in SOM_VIS: tools'); + return; + else + e=1:length(handles); + end + elseif any(e<1) | any(e>length(handles)), + errordlg({'Subplot indices must be in', ... + 'range 1...number_of_subplots!'}, ... + 'Error in SOM_VIS: tools'); + return; + end + elseif ~isempty(e) + errordlg('Invalid subplot indices!','Error in SOM_VIS: tools'); + return; + end + args = [args {'SubPlot' e}]; + end + thisfig = varargin{1}; + % set(0,'CurrentFigure',udata.vis_h); + set(0,'currentfigure',child) + som_show_add(args{:}); + close(thisfig); + + %%%%%%% +% comet %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%% +elseif strcmp(action,'comet') + args = {'comet'}; + ud = get(varargin{1},'UserData'); + s = get(ud(2),'String'); + args = [args {evalin('base',s)}]; + v = get(ud(3),'Value'); + if v>1, + s = get(ud(3),'String'); + args = [args {'Marker' s{v}}]; + end + s = get(ud(4),'String'); + if ~isempty(s) + args = [args {'MarkerSize' eval(s)}]; + end + v = get(ud(5),'Value'); + s = get(ud(5),'UserData'); + if v==10, + args = [args {'MarkerColor' evalin('base',s{10})}]; + else + args = [args {'MarkerColor' s{v}}]; + end + v = get(ud(6),'Value'); + if v>1, + s = get(ud(6),'UserData'); + args = [args {'EdgeColor' s{v}}]; + end + s = get(ud(1),'String'); + if ~isempty(s) + if s(1)=='[' | (s(1)>='1' & s(1)<='9') + sprintf(s,'[%s]',s); + e = eval(s); else e = s; + end + if vis_valuetype(e,{'1xn','nx1','string'}), + if ischar(e), + if ~strcmp(e,'all'), + errordlg({'Only valid string value' ... + 'for subplot indices is ''all''.'}, ... + 'Error in SOM_VIS: tools'); + return; + else + e=1:length(handles); + end + elseif any(e<1) | any(e>length(handles)), + errordlg({'Subplot indices must be in', ... + 'range 1...number_of_subplots!'}, ... + 'Error in SOM_VIS: tools'); + return; + end + elseif ~isempty(e) + errordlg('Invalid subplot indices!','Error in SOM_VIS: tools'); + return; + end + args = [args {'SubPlot' e}]; + end + thisfig = varargin{1}; + % set(0,'CurrentFigure',udata.vis_h); + set(0,'currentfigure',child) + som_show_add(args{:}); + close(thisfig); + + %%%%%%%% +% select %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%% +elseif strcmp(action,'select') + %% NOTE: input h is not main_gui_handle + %% but edit menu handle + w = evalin('base','whos'); + for (i=1:length(w)) + size_frst{i} = mat2str(w(i).size(1)); + x(i,1) = 'x'; + size_scnd{i} = mat2str(w(i).size(2)); + end + names = strvcat(w.name); + [dummy vert] = size(names); + for (i=1:length(w)) + for (j=1:8) + sp(i,j) = ' '; + end + end + size_M = strjust(strvcat(size_frst{:}),'right'); + size_N = strjust(strvcat(size_scnd{:}),'left'); + classes = strvcat(w.class); + s = [cellstr([names sp size_M x size_N sp classes])]'; + + [sel,ok] = listdlg('ListString',s,... + 'Name','Variable selection',... + 'PromptString','Select variable', ... + 'SelectionMode','single'); + if ok & ~isempty(sel), + w = {w.name}; + if ishandle(h), + set(h,'String',w{sel}); + elseif nargout==1, + r = w{sel}; + end + elseif nargout==1, + r = ''; + end + + %%%%%%%%%%%%%% +% popup_select %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + %%%%%%%%%%%%%% +elseif strcmp(action,'popup_select') + %% NOTE: input h is not main_gui_handle + %% but h(1) is popupmenu handle and h(2:end) are indeces + v = get(h(1),'Value'); + if any(v==h(2:end)), + w = evalin('base','whos'); + for (i=1:length(w)) + size_frst{i} = mat2str(w(i).size(1)); + x(i,1) = 'x'; + size_scnd{i} = mat2str(w(i).size(2)); + end + names = strvcat(w.name); + [dummy vert] = size(names); + for (i=1:length(w)) + for (j=1:8) + sp(i,j) = ' '; + end + end + size_M = strjust(strvcat(size_frst{:}),'right'); + size_N = strjust(strvcat(size_scnd{:}),'left'); + classes = strvcat(w.class); + s = [cellstr([names sp size_M x size_N sp classes])]'; + [sel,ok] = listdlg('ListString',s,... + 'Name','Variable selection',... + 'PromptString','Select variable', ... + 'SelectionMode','single'); + if ok & ~isempty(sel), + w = {w.name}; + i = find(v==h(2:end)); + s = get(h(1),'String'); + s{h(i+1)} = w{sel}; + set(h(1),'String',s); + if nargout==1, + r = w{sel}; + end + elseif nargout==1, + r = ''; + end + end + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_som_show_data.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,151 @@ +function [handles,msg,lattice,msize,dim,normalization,comps]=vis_som_show_data(p,f) + +% VIS_SOM_SHOW_DATA Checks and returns UserData and subplot handles stored by SOM_SHOW +% +% [handles,msg,lattice,msize,dim,normalization,comps] = vis_som_show_data(p, f) +% +% Input and output arguments ([]'s are optional): +% [p] (vector) subplot numbers +% (string) 'all' to process all subplots, this is default +% 'comp' to process only subplots which have +% component planes +% [f] (double) figure handle, default is current figure +% +% handles (vector) handles of requested subplots +% msg (string) error message or empty string (no error) +% lattice (string) map lattice: 'hexa' or 'rect' +% msize (vector) map grid size in figure +% dim (scalar) map data dimension in figure +% normalization (struct) normalization struct used in the map in figure +% comps (vector) the component indexes in figure +% +% This function gets the handles of component planes and u-matrices in +% subplots p from figure f. SOM_SHOW writes the handles into the +% UserData field of the figure where their order won't be mixed +% up. This function reads the data according to the vector p. If the +% figure has been manipulated (original planes are missing) the function +% warns user or returns error string. +% +% The main purpose for this is to be a subfuncion for SOM_SHOW_ADD, +% SOM_SHOW_CLEAR and SOM_RECOLORBAR functions, but it may be used on +% command line in the followong manner: +% +% % plots text on the fifth plane +% axes(vis_som_show_data(5)); hold on; text(1,3,'I am here'); +% +% See also SOM_SHOW, SOM_SHOW_ADD. + +% Copyright (c) 1997-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 201099 juuso 160600 + +%% Check input args %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +error(nargchk(0, 2, nargin)) % check no. of input args + +%% Init %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +handles=[]; % initialize output +normalize=[]; +comps=[]; +dim=[]; +msize=[]; +lattice=[]; +msg=[]; + +cr=sprintf('\n'); % carriage return + +if nargin < 2 | isempty(f) + f=gcf; % default figure +end + +if nargin < 1 | isempty(p) % default p + p= 'all'; +end + +% Find component planes and u-matrices from the figure and get the +% UserData field where the handles for the components are +% in the original order. +% If the fields are corrupted, return an error message. + +h_real = [findobj(f, 'Tag', 'Cplane'); ... + findobj(f, 'Tag', 'Uplane'); ... + findobj(f,'Tag','CplaneI'); ... + findobj(f,'Tag','UplaneI')]; +eval( 'h_stored=getfield(get(f,''UserData''),''subplotorder'');' , ... + 'msg=[ msg cr '' Missing SOM_SHOW.subplotorder''];'); +eval( 'normalization=getfield(get(f,''UserData''),''comp_norm'');' , ... + 'msg=[msg cr '' Missing SOM_SHOW.comp_norm''];'); +eval( 'comps=getfield(get(f,''UserData''),''comps'');' , ... + 'msg=[msg cr '' Missing SOM_SHOW.comps''];'); +eval( 'msize=getfield(get(f,''UserData''),''msize'');' , ... + 'msg=[msg cr '' Missing SOM_SHOW.msize''];'); +eval( 'dim=getfield(get(f,''UserData''),''dim'');' , ... + 'msg=[msg cr '' Missing SOM_SHOW.dim''];'); +eval( 'lattice=getfield(get(f,''UserData''),''lattice'');' , ... + 'msg=[msg cr '' Missing SOM_SHOW.lattice''];'); +if ~isempty(msg), + msg=['The figure does not contain SOM_SHOW visualization or is corrupted.'... + cr msg cr cr ... + 'This command may be applied only to a SOM_SHOW visualization.']; + return; +end + +%% Check arguments %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +index=ismember(h_stored, h_real); % the original order for plot axes + +if ~prod(double(index)) % missing planes?! + % double added by kr 1.10.02 + l1= 'Some of the original planes seems to be missing.'; + l2= 'Subplot numbers now refer to the existing ones.'; + warning([l1 cr l2]); +end + +if ~prod(double(ismember(h_real, h_stored))) % extra planes?! + % double added by kr 5.9.02 + warning('There seems to be new planes. Subplot numbers refer to the old ones.'); +end + +h_stored=h_stored(index); % existing original plots in original order + +if ischar(p) % check if p is 'all' + switch(p) + case 'all' + p=1:size(h_stored,1); % all original subplots + case 'comp' + p=find(comps>0); + otherwise + msg= 'String value for subplot number vector has to be ''all''!'; + return; + end +end + +if ~vis_valuetype(p,{ '1xn','nx1'}) % check the size + msg= 'Subplot numbers (argument p in help text) have to be in a vector!'; + return +end + +if min(p) < 1 % check for invalid values + msg= 'Subplot numbers (argument p in help text) must be at least 1!'; + return +end + +%% p is too large + +if max(p) > size(h_stored,1) + l1= 'There are not so many existing subplots created by SOM_SHOW in the'; + l2= 'figure as you are trying to refer with subplot numbers.'; + l3= 'This is probably caused by having a too large subplot number.'; + l4= 'However, the reason may be invalid manipulation of'; + l5= 'this figure object or a program failure, too.'; + msg=([l1 cr l2 cr l3 cr cr l4 cr l5]); + return; +end + +%% Action and building output %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +handles=h_stored(p); +comps=comps(p); +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_trajgui.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1647 @@ +function vis_trajgui(trajStruct,arg) + +% VIS_TRAJGUI subfuntion for SOM_TRAJECTORY +% +% This function is the actual GUI called by SOM_TRAJECTORY +% function. +% +% See also SOM_TRAJECTORY. + +% Contributed code to SOM Toolbox 2.0, February 11th, 2000 by Juha Parhankangas +% Copyright (c) by Juha Parhankangas. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta juha 180699 + +if nargin == 1 + + sM_h=trajStruct.figure; + + if size(trajStruct.bmus,1) ~= 1 & size(trajStruct.bmus,2) ~= 1 + fuzzy_traj(trajStruct,[]); + return; + end + + + if size(trajStruct.bmus,1) == 1 | size(trajStruct.bmus,2) == 1 + + udata.bmus = trajStruct.bmus; + udata.a_h=[findobj(get(sM_h,'Children'),'Tag','Uplane');... + findobj(get(sM_h,'Children'),'Tag','Cplane')]; + udata.sM_h=trajStruct.figure; + udata.traj=[]; + data1 = trajStruct.primary_data; + if ~isempty(trajStruct.primary_names) + names=trajStruct.primary_names; + else + for i=1:size(data1,2) + names{i,1}=sprintf('Var%d',i); + end + end + + udata.lattice=trajStruct.lattice; + form = 0.7*vis_patch(udata.lattice); + udata.msize = trajStruct.msize; + + + %%%%%%%%%%%%%%%%%%%%%%%% + % + % forming a patch object, which is placed above every component plane + % + + + l = size(form,1); + + nx = repmat(form(:,1),1,prod(udata.msize)); + ny = repmat(form(:,2),1,prod(udata.msize)); + + x=reshape(repmat(1:udata.msize(2),l*udata.msize(1),1),l,prod(udata.msize)); + y=repmat(repmat(1:udata.msize(1),l,1),1,udata.msize(2)); + + if strcmp(udata.lattice,'hexa') + t = find(~rem(y(1,:),2)); + x(:,t)=x(:,t)+.5; + end + x=x+nx; + y=y+ny; + + colors=reshape(ones(prod(udata.msize),1)*[NaN NaN NaN],... + [1 prod(udata.msize) 3]); + + set(0,'CurrentFigure',udata.sM_h); + + %%%%%%%%%%%%%%%%%%%%%% + % + % drawing patch + % + % caxis -commands keep the colormap of the original patch unchanged. + % + + for i=1:length(udata.a_h) + udata.real_patch(i)=get(udata.a_h(i),'Children'); + set(udata.real_patch(i),'ButtonDownFcn',... + 'vis_trajgui([],''click'')'); + subplot(udata.a_h(i)); + v=caxis; + udata.tmp_patch(i)=patch(x,y,colors,'EdgeColor','none',... + 'ButtonDownFcn',... + 'vis_trajgui([],''click'')',... + 'Tag','TmpPatch'); + caxis(v); + end + + %%%%%%%%%%%%%%%%%%%% + + + + udata.length_of_traj=length(trajStruct.size); + udata.size=trajStruct.size; + udata.color=trajStruct.color; + udata.poly.x=[]; + udata.poly.y=[]; + udata.poly.h=[]; + udata.new_marks=[]; + udata.all_marks=[]; + udata.d_mark2=[]; + udata.fig1 = figure; + set(udata.fig1,'KeyPressFcn','vis_trajgui([],''key'')',... + 'Name','Primary Data'); + + + %%%%%%%%%%%%%%%%%%%% + % + % making the 'Tools' -menu + % + + udata.m_i=uimenu(udata.fig1,'Label','Trajectoy T&ools'); + udata.m_i(2)=uimenu(udata.m_i,'Label','&Remove Trajectory',... + 'Callback',... + 'vis_trajgui([],''remove_traj'')'); + udata.m_i(3)=uimenu(udata.m_i(1),'Label','&Dye Nodes',... + 'Callback',... + 'vis_trajgui([],''dye_gui'')'); + udata.m_i(4)=uimenu(udata.m_i(1),'Label','&Clear Markers',... + 'Callback',... + 'vis_trajgui([],''clear'')'); + udata.m_i(5)=uimenu(udata.m_i(1),'Label','&Save',... + 'Callback',... + 'vis_trajgui([],''save'')'); + udata.m_i(6)=uimenu(udata.m_i(1),'Label','&Load',... + 'Callback',... + 'vis_trajgui([],''load'')'); + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % + % drawing data components to the figure .... + % + % + + if nargin < 5 | isempty(comps) | (isstr(comps) & strcmp(comps,'all')) + comps = 1:size(data1,2); + end + + x=1:size(data1,1); + + for i=1:length(comps) + subplot(length(comps),1,i); + udata.h(i)=gca; + udata.d_mark(i).h=[]; + + udata.d(i)=plot(x,data1(:,comps(i)),... + 'ButtonDownFcn',... + 'vis_trajgui([],''line_down'')'); + set(gca,'XLim',[1 size(data1,1)],... + 'XTick',[],... + 'ButtonDownFcn','vis_trajgui([],''line_down'')'); %,... + %'YLim',[min(data1(:,comps(i))) max(data1(:,comps(i)))]); + + ylabel(names{comps(i)}); + hold on; + ymin=get(udata.h(i),'YLim'); + pos=mean(get(udata.h(i),'XLim')); + udata.l(i) = line([pos pos],[ymin(1) ymin(2)],... + 'Color','red',... + 'ButtonDownFcn',... + 'vis_trajgui([],''down'')'); + end + udata.text1=[]; + + udata.fig2=[]; + udata.h2=[]; + udata.l2=[]; + udata.text2=[]; + + %%%%%%%%%%%%%%%%%%%%%%%%%%% + % + % ... and to the figure 2. + % + + + if ~isempty(trajStruct.secondary_data) + data2=trajStruct.secondary_data; + if isempty(trajStruct.secondary_names) + for i=1:size(data1,2) + names2{i,1}=sprintf('Var%d',i); + end + else + names2=trajStruct.secondary_names; + end + + udata.fig2 = figure; + set(udata.fig2,'Name','Secondary Data'); + set(udata.fig2,'KeyPressFcn',... + 'vis_trajgui([],''key'')'); + for i=1:size(data2,2) + subplot(size(data2,2),1,i); + udata.h2(i) = gca; + udata.d_mark2(i).h=[]; + udata.d2(i) = plot(x,data2(:,i),... + 'ButtonDownFcn',... + 'vis_trajgui([],''line_down'')'); + set(gca,'XLim',[1 size(data1,1)],'XTick',[],'ButtonDownFcn',... + 'vis_trajgui([],[],[],[],[],[],''line_down'')'); + ylabel(names2{i}); + hold on; + ymin = get(udata.h2(i),'YLim'); + pos = mean(get(udata.h2(i),'XLim')); + udata.l2(i) = line([pos pos],ymin,'Color','red',... + 'ButtonDownFcn','vis_trajgui([],''down'')'); + end + end + + %%%%%%%%%%%%%%%%%%%%%%%%%% + + set(udata.fig1,'UserData',udata); + if ~isempty(udata.fig2); + tmp.fig1=udata.fig1; + set(udata.fig2,'UserData',tmp); + end + tmp=get(udata.sM_h,'UserData'); + tmp.fig1=udata.fig1; + set(udata.sM_h,'UserData',tmp); + set_numbers(round(pos)); + return; + end + +end + +%%%%%%%%%%%%%%%%% +% +% if figures have been drawn, the only function calls that may exist +% are the ones that change the state of the application. +% +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +switch arg + case 'fuzzy' + fuzzy_traj(tS,[]); + return; + case 'move_fuzzy' + fuzzy_traj([],'move'); + return; + case 'remove_traj' + remove_traj; + return; + case 'line_down' + line_bdf('down'); + return; + case 'line_drag' + line_bdf('drag'); + return; + case 'line_up' + line_bdf('up'); + return; + case 'dye_gui'; + color_gui(udata.fig1); + return; + case {'dye','cyan','magenta','yellow','red','green','blue','white','grey'} + dye_nodes(arg); + return; + case 'clear' + clear_markers; + return; + case 'key' + key_bdf; + return; + case 'click' + click; + return; + case 'save' + save_data; + return; + case 'load' + load_data; + return; +end + + +%%%%%%%%%% +% +% lines in the data figure(s) are dragged ... +% + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + + +lims=get(gca,'XLim'); +x = getfield(get(gca,'CurrentPoint'),{1}); % the location of the line + +if x < lims(1) + x=lims(1); +elseif x > lims(2) + x=lims(2); +end + +old = gcf; + +switch arg + case 'down',... + + % mouse button is pressed down above the line + + set(gcf,'WindowButtonMotionFcn','vis_trajgui([],''drag'')'); + set(gcf,'WindowButtonUpFcn','vis_trajgui([],''up'')'); + set(udata.l,'EraseMode','xor'); + delete(udata.text1); + if ~isempty(udata.l2); + set(udata.l2,'EraseMode','xor'); + delete(udata.text2); + end + set(gcf,'Pointer','crosshair'); + + case 'drag' + % change the location of the lines + + set(0,'CurrentFigure',udata.fig1); + set(udata.l,'XData',[x x]); + if ~isempty(udata.fig2) + set(0,'CurrentFigure',udata.fig2); + set(udata.l2,'XData',[x x]); + end + draw_traj(round(x)); + set(0,'CurrentFigure',old); + case 'up' + + % draw trajectory and set figure to the normal state. + + set(udata.l,'EraseMode','normal'); + set(gcf,'Pointer','arrow','WindowButtonMotionFcn','',... + 'WindowButtonUpFcn',''); + draw_traj(round(x)); + set_numbers(round(x)); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function draw_traj(point) + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); +color=udata.color; +eMode='normal'; +if isstr(udata.color) & strcmp(udata.color,'xor') + eMode='xor'; + color='black'; +end +ind=udata.bmus(point); +[i j] = ind2sub(udata.msize,ind); +if ~mod(i,2) + j=j+0.5; +end +old = gcf; +set(0,'CurrentFigure',udata.sM_h); +hold on; +if isempty(udata.traj) | length(udata.traj.h) ~= length(udata.a_h) + + % trajectory does not exist + + for i=1:length(udata.a_h) + subplot(udata.a_h(i)); + hold on; + new.h = plot(j,i,'Color',color,'EraseMode',eMode,'LineStyle','none'); + udata.traj.h(i)=new; + udata.traj.j=j; + udata.traj.i=i; + end +else + if length(udata.traj.j) == udata.length_of_traj + % if the length of trajectory == ..., + udata.traj.j(1) = []; % the first (the oldest) coordinate pair + udata.traj.i(1) = []; % is removed. + end + udata.traj.j=[udata.traj.j;j]; % the new point is added to the + udata.traj.i=[udata.traj.i;i]; % end of coordinate vectors (i and j) + for i=1:length(udata.a_h) + subplot(udata.a_h(i)); % remove the existing trajectory + delete(udata.traj.h(i).h); % and plot the new one. + for j=1:length(udata.traj.j) + udata.traj.h(i).h(j)=plot(udata.traj.j(j),udata.traj.i(j),... + 'Color',color,... + 'EraseMode',eMode,'Marker','o','LineWidth',2,... + 'MarkerSize',udata.size(udata.length_of_traj-j+1),... + 'LineStyle','none'); + end + end +end +set(0,'CurrentFigure',udata.fig1); +set(udata.fig1,'UserData',udata); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function set_numbers(x); + +% This function writes the numbers beside of the pointer lines + + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +xlim = get(gca,'XLim'); +ylim = get(gca,'YLim'); +p = ylim(1) + 0.9*(ylim(2)-ylim(1)); + +old = gcf; +set(0,'CurrentFigure',udata.fig1); + + +for i=1:length(udata.h) + subplot(udata.h(i)); + + % check if the text is placed to the left side of the line... + + if abs(x-xlim(1)) > (abs(x-xlim(2))) + udata.text1(i)=text(x-1,p,sprintf('%d ->',x),... + 'VerticalAlignment','top',... + 'HorizontalAlignment','right',... + 'FontWeight','demi'); + else + + % or to the right side. + + udata.text1(i)=text(x+1,p,sprintf('<- %d',x),... + 'VerticalAlignment','top',... + 'FontWeight','demi'); + end +end + +if ~isempty(udata.fig2) + set(0,'CurrentFigure',udata.fig2); + + for i=1:length(udata.h2) + subplot(udata.h2(i)); + + if abs(x-xlim(1)) > (abs(x-xlim(2))) + udata.text2(i)=text(x-1,p,sprintf('%d ->',x),... + 'VerticalAlignment','top',... + 'HorizontalAlignment','right',... + 'FontWeight','demi'); + else + udata.text2(i)=text(x+1,p,sprintf('<- %d',x),... + 'VerticalAlignment','top',... + 'FontWeight','demi'); + end + end +end + +set(0,'CurrentFigure',old); +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function remove_traj() + +% delete trajectory -object from every component plane. + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +if isempty(udata.traj) + return; +end + + +for i=1:length(udata.traj.h) + delete(udata.traj.h(i).h); +end + +udata.traj=[]; +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function line_bdf(arg) + +% this function takes care of action when region is selected in the +% data figure. + + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); +xlim=get(gca,'XLim'); + +if ~(any(strcmp('THIS',fieldnames(udata)))) + p = getfield(get(gca,'CurrentPoint'),{1}); +else + p = getfield(get(udata.THIS,'CurrentPoint'),{1}); +end + +if p < xlim(1) + p = xlim(1); +elseif p > xlim(2) + p = xlim(2); +end + + + +switch arg + case 'down' + + % the mouse button is pressed down, the pointer lines are drawn. + % and the state of the figure is changed. + + udata.THIS=gca; + set(gcf,'WindowButtonMotionFcn',... + 'vis_trajgui([],''line_drag'')',... + 'WindowButtonUpFcn','vis_trajgui([],''line_up'')'); + udata.start_p=p; + + old = gcf; + set(0,'CurrentFigure',udata.fig1); + + for i=1:length(udata.h) + subplot(udata.h(i)); + udata.t_line.h(i)=line([p p],get(gca,'YLim'),'Color','red'); + udata.t_line.h2(i)=line([p p],get(gca,'YLim'),'Color','red',... + 'EraseMode','xor'); + end + if ~isempty(udata.h2) + set(0,'CurrentFigure',udata.fig2); + for i=1:length(udata.h2) + subplot(udata.h2(i)); + udata.t_line2.h(i)=line([p p],get(gca,'YLim'),'Color','red'); + udata.t_line2.h2(i)=line([p p],get(gca,'YLim'),'Color','red',... + 'EraseMode','xor'); + end + end + + case 'drag' + + % change the position of the pointer lines + + old = gcf; + set(0,'CurrentFigure',udata.fig1); + set(udata.t_line.h2,'XData',[p p]); + if ~isempty(udata.fig2) + set(0,'CurrentFigure',udata.fig2); + set(udata.t_line2.h2,'XData',[p p]); + end + set(0,'CurrentFigure',old); + case 'up' + + + % sort the 'points' -vector and draw the markers to the data and nodes + + points=sort([round(udata.start_p) round(p)]); + draw_markers(points(1):points(2)); + udata=get(udata.fig1,'UserData'); + udata.new_marks=unique([udata.new_marks ;(points(1):points(2))']); + delete([udata.t_line.h2 udata.t_line.h]); + if ~isempty(udata.fig2) + delete([udata.t_line2.h2 udata.t_line2.h]); + end + set(get(udata.THIS,'Parent'),'WindowButtonMotionFcn','',... + 'WindowButtonUpFcn',''); + udata=rmfield(udata,'THIS'); +end + +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function draw_markers(x); + +plot2data(x); +plot2plane(x); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function plot2data(x); + +% plot black markers to the data figure(s) + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +old = gcf; + +set(0,'CurrentFigure',udata.fig1); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% if there already exist points in the positions that are members +% of the set x, then the old points are removed from data figures... + +for i=1:length(udata.d_mark(1).h) + tmp1 = get(udata.d_mark(1).h(i),'XData'); + tmp2 = setdiff(tmp1,x); + if length(tmp1) ~= length(tmp2) + inds=[]; + for j=1:length(tmp2); + inds=[inds find(tmp2(j)==tmp1)]; + end + for j=1:length(udata.d_mark) + ydata=getfield(get(udata.d_mark(j).h(i),'YData'),{inds}); + set(udata.d_mark(j).h(i),'XData',tmp2,'YData',ydata); + end + if ~isempty(udata.fig2) + for j=1:length(udata.d_mark2) + ydata=getfield(get(udata.d_mark2(j).h(i),'YData'),{inds}); + set(udata.d_mark2(j).h(i),'XData',tmp2,'YData',ydata); + end + end + end +end + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% ... and the new ones are plotted. + +for i=1:length(udata.h) + subplot(udata.h(i)); + h=plot(x,getfield(get(udata.d(i),'YData'),{x}),'oblack',... + 'ButtonDownFcn',... + 'vis_trajgui([],''line_down'')'); + udata.d_mark(i).h=[udata.d_mark(i).h;h]; +end + +if ~isempty(udata.h2) + set(0,'CurrentFigure',udata.fig2); + + for i=1:length(udata.h2) + subplot(udata.h2(i)); + h=plot(x,getfield(get(udata.d2(i),'YData'),{x}),'oblack',... + 'ButtonDownFcn',... + 'vis_trajgui([],''line_down'')'); + udata.d_mark2(i).h=[udata.d_mark2(i).h;h]; + end +end + +set(0,'CurrentFigure',old); +set(udata.fig1,'UserData',udata); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function plot2plane(x); + +% sets markers to the component planes. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% actually new markers are never plotted, but the color of the patch +% lying above the original component plane patch is changed black in +% the right positions. + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +udata.new_marks=unique([udata.new_marks ;x']); + +for i=1:length(udata.a_h) + col=get(udata.tmp_patch(i),'FaceVertexCData'); + if length(size(col)) == 3 + col = reshape(col,[size(col,1) 3]); + end + for j=1:length(udata.new_marks) + col(udata.bmus(udata.new_marks(j)),:)=[0 0 0]; + end + set(udata.tmp_patch(i),'FaceVertexCData',col); +end + +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function color_gui(fig1) + +% construct the graphical user interface for changing the color of the +% black (marked) nodes. + + +udata=get(fig1,'UserData'); + +a = figure('Color',[0.8 0.8 0.8], ... + 'Name','Colors', ... + 'PaperType','a4letter', ... + 'Position',[518 456 120 311], ... + 'Tag','Fig1'); + +udata.c_struct.fig=a; + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.701961 0.701961 0.701961], ... + 'Position',[0.0700415 0.28956 0.830492 0.594566], ... + 'Style','frame', ... + 'Tag','Frame1'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[0.100059 0.301143 0.770456 0.571399], ... + 'Style','frame', ... + 'Tag','Frame2'); + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''cyan'')', ... + 'Position',[0.130077 0.795326 0.170101 0.0617729], ... + 'Style','radiobutton', ... + 'Tag','cyan', ... + 'Value',1); + + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''magenta'')', ... + 'Position',[0.130077 0.733553 0.170101 0.057912], ... + 'Style','radiobutton', ... + 'Tag','magenta'); + + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''yellow'')', ... + 'Position',[0.130077 0.664059 0.170101 0.0617729], ... + 'Style','radiobutton', ... + 'Tag','yellow'); + + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''red'')', ... + 'Position',[0.130077 0.590703 0.170101 0.0617729], ... + 'Style','radiobutton', ... + 'Tag','red'); + + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''green'')', ... + 'Position',[0.130077 0.525068 0.170101 0.057912], ... + 'Style','radiobutton', ... + 'Tag','green'); + + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''blue'')', ... + 'Position',[0.130077 0.455575 0.170101 0.0617729], ... + 'Style','radiobutton', ... + 'Tag','blue'); + + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''white'')', ... + 'Position',[0.130077 0.38608 0.170101 0.0617729], ... + 'Style','radiobutton', ... + 'Tag','white'); + + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''grey'')', ... + 'Position',[0.130077 0.320447 0.170101 0.057912], ... + 'Style','radiobutton', ... + 'Tag','grey'); + + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.32019 0.795326 0.470278 0.0501905], ... + 'String','Cyan', ... + 'Style','text', ... + 'Tag','StaticText1'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.32019 0.733553 0.520308 0.0463296], ... + 'String','Magenta', ... + 'Style','text', ... + 'Tag','StaticText2'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.32019 0.664059 0.470278 0.0501905], ... + 'String','Yellow', ... + 'Style','text', ... + 'Tag','StaticText3'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.32019 0.590703 0.470278 0.0501905], ... + 'String','Red', ... + 'Style','text', ... + 'Tag','StaticText4'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.32019 0.525068 0.470278 0.0463296], ... + 'String','Green', ... + 'Style','text', ... + 'Tag','StaticText5'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.32019 0.455575 0.470278 0.0463296], ... + 'String','Blue', ... + 'Style','text', ... + 'Tag','StaticText6'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.32019 0.38608 0.470278 0.0501905], ... + 'String','White', ... + 'Style','text', ... + 'Tag','StaticText7'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.32019 0.320447 0.470278 0.0463296], ... + 'String','Grey', ... + 'Style','text', ... + 'Tag','StaticText8'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Position',[0.0700415 0.146711 0.830492 0.135128], ... + 'Style','frame', ... + 'Tag','Frame3'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'Position',[0.100059 0.158293 0.770456 0.111963], ... + 'Style','frame', ... + 'Tag','Frame4'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[0.8 0.8 0.8], ... + 'FontWeight','demi', ... + 'HorizontalAlignment','left', ... + 'Position',[0.130077 0.177597 0.270833 0.0617729], ... + 'String','RGB', ... + 'Style','text', ... + 'Tag','StaticText9'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'BackgroundColor',[1 1 1], ... + 'Position',[0.410243 0.173736 0.420249 0.0810768], ... + 'Style','edit', ... + 'Tag','EditText1'); + +udata.c_struct.RGB=b; + +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','vis_trajgui([],''dye'')', ... + 'FontWeight','demi', ... + 'Position',[0.0700415 0.0270256 0.360214 0.0772162], ... + 'String','OK', ... + 'Tag','Pushbutton1'); +b = uicontrol('Parent',a, ... + 'Units','normalized', ... + 'Callback','close gcf', ... + 'FontWeight','demi', ... + 'Position',[0.54032 0.0270256 0.360214 0.0772162], ... + 'String','Close', ... + 'Tag','Pushbutton2'); + +udata.c_struct.color=[0 1 1]; + +tmp.fig1=fig1; +set(a,'UserData',tmp); +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function dye_nodes(arg) + +% takes care of the action, when radiobuttons are pressed +% (or the RGB value is set) in the color_gui -figure. +% It also handles the starting of dying nodes and plots. + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + + +switch arg + case {'cyan','magenta','yellow','red','green','blue','white','grey'} + h=findobj(get(gcf,'Children'),'Style','radiobutton'); + set(h,'Value',0); + set(gcbo,'Value',1); +end + + +switch arg + case 'cyan' + RGB = [0 1 1]; + case 'magenta' + RGB = [1 0 1]; + case 'yellow' + RGB = [1 1 0]; + case 'red' + RGB = [1 0 0]; + case 'green' + RGB = [0 1 0]; + case 'blue' + RGB = [0 0 1]; + case 'white' + RGB = [1 1 1]; + case 'grey' + RGB = [0.4 0.4 0.4]; + case 'dye' + + RGB = get(udata.c_struct.RGB,'String'); + if isempty(RGB) + dye; + return; + else + str1='The value of RGB must be vector containing three scalars'; + str2='between 0 and 1.'; + color = str2num(RGB); + set(udata.c_struct.RGB,'String',''); + if isempty(color) + close gcf; + udata=rmfield(udata,'c_struct'); + set(udata.fig1,'UserData',udata); + errordlg([{str1};{str2}]); + return; + end + if ~all([1 3] == size(color)) & ~all([3 1] == size(color)) + close gcf; + errordlg([{str1};{str2}]); + udata=rmfield(udata,'c_struct',udata); + set(udata.fig1,'UserData',udata); + return; + end + if ~isempty(cat(2,find(color>1),find(color<0))) + close gcf + errordlg([{str1};{str2}]); + udata=rmfield(udata,'c_struct',udata); + set(udata.fig1,'UserData',udata); + return; + end + udata.c_struct.color=color; + set(udata.fig1,'UserData',udata); + dye; + return; + end +end + +udata.c_struct.color=RGB; +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function dye() + +% dyes black markers in the component planes and in the data figures + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +inds=unique([udata.all_marks ; udata.new_marks]); + + +for i=1:length(udata.d_mark); + for j=1:length(udata.d_mark(i).h) + if all(get(udata.d_mark(i).h(j),'Color') == [0 0 0]) + set(udata.d_mark(i).h(j),'Color',udata.c_struct.color); + end + end +end + +if ~isempty(udata.fig2); + for i=1:length(udata.d_mark2) + for j=1:length(udata.d_mark2(i).h) + if all(get(udata.d_mark2(i).h(j),'Color') == [0 0 0]) + set(udata.d_mark2(i).h(j),'Color',udata.c_struct.color); + end + end + end +end + + +for i=1:length(udata.a_h) + col=get(udata.tmp_patch(i),'FaceVertexCData'); + for j=1:length(udata.new_marks) + col(udata.bmus(udata.new_marks(j)),:)=udata.c_struct.color; + end + set(udata.tmp_patch(i),'FaceVertexCData',col); +end + + +udata.all_marks=unique([udata.all_marks;udata.new_marks]); +udata.new_marks=[]; +close gcf; +udata=rmfield(udata,'c_struct'); +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function clear_markers() + +% removes markers from the componentplanes and the data figure(s). + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +for i=1:length(udata.d_mark) + delete(udata.d_mark(i).h); + udata.d_mark(i).h=[]; +end + +for i=1:length(udata.d_mark2) + delete(udata.d_mark2(i).h); + udata.d_mark2(i).h=[]; +end + +col=NaN*get(udata.tmp_patch(1),'FaceVertexCData'); +col=reshape(col,[size(col,1) 3]); + +for i=1:length(udata.tmp_patch) + set(udata.tmp_patch(i),'FaceVertexCData',col); +end + +udata.new_marks=[]; +udata.all_marks=[]; + + +if any(strcmp('c_struct',fieldnames(udata))) + udata=rmfield(udata,'c_struct'); +end + +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function key_bdf + +% moves trajectory and pointer lines, when either of +% the keys '>' or '<' is pressed. + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +key=get(gcbo,'CurrentCharacter'); + +% The easiest way to get a new coordinates is to get them from the texts... +% The texts are either '<- x' or 'x ->' + +x=get(udata.text1(1),'String'); +x=str2num(x(4:length(x))); + +if isempty(x) + x=get(udata.text1(1),'String'); + x=str2num(x(1:length(x)-3)); +end + +switch(key) + case '<' + if x ~= 1 + x= x-1; + end + case '>' + if x ~= getfield(get(get(udata.text1(1),'Parent'),'XLim'),{2}) + x = x+1; + end + otherwise + return; +end + +set(udata.l,'XData',[x x]); +if ~isempty(udata.fig2) + set(udata.l2,'XData',[x x]); +end + +delete(udata.text1); +delete(udata.text2); + +set_numbers(x); +draw_traj(x); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function click() + + +switch get(gcf,'SelectionType') + case 'open' + return; + case {'normal','alt'} + draw_poly; + case 'extend' + click_node; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function click_node() + +% takes care of the action, when the middle mouse button is +% pressed (mouse pointer is above some component plane). + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); +new_marks=[]; + +old=gcf; + +NEW=0; +AGAIN = 0; + + +coords=get(gca,'CurrentPoint'); +row=round(coords(1,2)); + +if strcmp(udata.lattice,'hexa') & ~mod(row,2) + col = round(coords(1,1) - 0.5); +else + col = round(coords(1,1)); +end + +ind = sub2ind(udata.msize,row,col); +new_marks=find(udata.bmus==ind); + +if strcmp(get(gcbo,'Tag'),'TmpPatch'); + + % if the callback is made via temporary patch object, node is marked + % (node is black) => the mark is to be removed + + node_color = getfield(get(gcbo,'FaceVertexCData'),{ind,[1:3]}); + AGAIN = 1; +end + + + +for i=1:length(udata.tmp_patch) + color = get(udata.tmp_patch(i),'FaceVertexCData'); + if length(size(color)) ~= 2 + color = reshape(color,[size(color,1) 3]); + end + if all(isnan(color(ind,:))) + NEW=1; + color(ind,:)=[0 0 0]; + else + color(ind,:)=[NaN NaN NaN]; + end + set(udata.tmp_patch(i),'FaceVertexCData',color); +end + +set(0,'CurrentFigure',udata.fig1); + +for j=1:length(udata.h) + subplot(udata.h(j)); + if NEW + y=getfield(get(udata.d(j),'YData'),{new_marks}); + udata.d_mark(j).h=[udata.d_mark(j).h;plot(new_marks,y,'Color',[0 0 0],... + 'LineStyle','none',... + 'Marker','o')]; + end +end + + +if ~isempty(udata.fig2) + set(0,'CurrentFigure',udata.fig2); + for j=1:length(udata.h2); + subplot(udata.h2(j)); + if NEW + y=getfield(get(udata.d2(j),'YData'),{new_marks}); + udata.d_mark2(j).h=[udata.d_mark2(j).h;plot(new_marks,y,... + 'LineStyle','none',... + 'Color','black',... + 'Marker','o')]; + end + end +end + +if NEW + udata.new_marks=[udata.new_marks; new_marks]; +end + +if AGAIN + + % find marks from the data that map to the clicked node. if the color + % of the mark(s) is the same as the node's color, remove mark(s), else + % let mark be unchanged. + + for i=1:length(udata.d_mark(1).h) + if all(node_color==get(udata.d_mark(1).h(i),'Color')) + tmp1 = get(udata.d_mark(1).h(i),'XData'); + tmp2 = setdiff(tmp1,new_marks); + if length(tmp1) ~= length(tmp2) + inds=[]; + for j=1:length(tmp2); + inds=[inds find(tmp2(j)==tmp1)]; + end + for j=1:length(udata.d_mark) + ydata=getfield(get(udata.d_mark(j).h(i),'YData'),{inds}); + set(udata.d_mark(j).h(i),'XData',tmp2,'YData',ydata); + end + if ~isempty(udata.fig2) + for j=1:length(udata.d_mark2) + ydata=getfield(get(udata.d_mark2(j).h(i),'YData'),{inds}); + set(udata.d_mark2(j).h(i),'XData',tmp2,'YData',ydata); + end + end + end + end + end + udata.new_marks=setdiff(udata.new_marks, new_marks); + udata.all_marks=setdiff(udata.all_marks,new_marks); +end + +set(udata.fig1,'UserData',udata); +set(0,'CurrentFigure',old); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function draw_poly() + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +if isempty(udata.poly.x) + if strcmp(get(gcf,'SelectionType'),'alt') + return; + end + udata.poly.THIS = gca; +end + +% 'THIS' indicates what was the axes where the polygon was meant to +% drawn. It is not possible to add points, that lie in another axes, to the +% polygon. + + +if gca ~= udata.poly.THIS + return; +end + +coords(1,1) = getfield(get(gca,'CurrentPoint'),{3}); +coords(1,2) = getfield(get(gca,'CurrentPoint'),{1}); + +udata.poly.x=cat(1,udata.poly.x,coords(2)); +udata.poly.y=cat(1,udata.poly.y,coords(1)); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% remove old 'polygon' from axis + +delete(udata.poly.h); + + +switch get(gcf,'SelectionType') + case 'normal' + + % add point to the 'polygon' and draw it + + for i=1:length(udata.a_h) + subplot(udata.a_h(i)); + hold on; + udata.poly.h(i) = plot(udata.poly.x,udata.poly.y,'black',... + 'EraseMode','xor',... + 'ButtonDownFcn',... + 'vis_trajgui([],''click'')',... + 'LineWidth',2); + end + case 'alt' + + % The polygon is ready. + + + udata.poly.x=cat(1,udata.poly.x,udata.poly.x(1)); + udata.poly.y=cat(1,udata.poly.y,udata.poly.y(1)); + + for i=1:length(udata.a_h) + subplot(udata.a_h(i)); + udata.poly.h(i) = plot(udata.poly.x,udata.poly.y,'black',... + 'EraseMode','xor',... + 'ButtonDownFcn',... + 'vis_trajgui([],''click'')',... + 'LineWidth',2); + end + + tmp=sort(repmat((1:udata.msize(1))',udata.msize(2),1)); + tmp(:,2)=repmat((1:udata.msize(2))',udata.msize(1),1); + tmp2=tmp; + if strcmp(udata.lattice,'hexa'); + t=find(~rem(tmp(:,1),2)); + tmp(t,2)=tmp(t,2)+0.5; + end + + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % find the nodes that lie inside polygon and change coordinates to + % linear indices. + + in = find(inpolygon(tmp(:,2),tmp(:,1),udata.poly.x,udata.poly.y)); + in = sub2ind(udata.msize,tmp2(in,1),tmp2(in,2)); + + colors=get(udata.tmp_patch(1),'FaceVertexCData'); + colors=reshape(colors,[size(colors,1) 3]); + tmp=ones(length(in),1); + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % set the color of the nodes just selected, black. + + colors(in,:)=tmp*[0 0 0]; + set(udata.tmp_patch,'FaceVertexCData',colors); + + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + % find the points mapping to the nodes from data + + inds = []; + for i=1:length(in) + inds=[inds;find(in(i) == udata.bmus)]; + end + + %%%%%%%%%%%%%%%%%%% + % plot marks to data + + set(udata.fig1,'UserData',udata); + plot2data(inds); + udata=get(udata.fig1,'UserData'); + udata.new_marks=union(udata.new_marks,inds); + delete(udata.poly.h); + udata.poly.h=[]; + udata.poly.x=[]; + udata.poly.y=[]; + udata.poly.THIS=[]; +end + +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function save_data() + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +data.points=[]; +data.nodes=[]; +k=1; + +for i=1:length(udata.d_mark(1).h) + data.points(i).inds=get(udata.d_mark(1).h(i),'XData'); + data.points(i).color=get(udata.d_mark(1).h(i),'Color'); +end + +color=get(udata.tmp_patch(1),'FaceVertexCData'); +color=reshape(color,[size(color,1) 3]); + +for i=1:size(color,1) + if all(~isnan(color(i,:))) + tmp.ind=i; + tmp.color=color(i,:); + data.nodes(k)=tmp; + k=k+1; + end +end + +answer=inputdlg('Enter the name of the output variable:','',1); + +if isempty(answer) | isempty(answer{1}) + msgbox('Output is not set to workspace.'); + return; +else + assignin('base',answer{1},data); + disp(sprintf('Struct is set to the workspace as ''%s''.',answer{1})); +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function load_data() + +answer = inputdlg('Enter the name of the struct to be loaded:','',1); + +if isempty(answer) | isempty(answer{1}) + msgbox('Data is not loaded.'); + return; +end + +data=evalin('base',answer{1}); + +if ~isstruct(data) + errordlg('Input variable must be a struct.'); + return; +end + +tmp1 = fieldnames(data); +tmp2 = {'nodes','points'}; + +for i=1:length(tmp1) + for j=1:length(tmp2); + if ~any(strcmp(tmp2{j},tmp1)) + errordlg('Wrong type of struct.'); + return; + end + end +end + +if ~isempty(data.points) + tmp1=fieldnames(data.points(1)); +end +if ~isempty(data.nodes) + tmp2=fieldnames(data.nodes(1)); +end + +for i=1:length(tmp1) + if ~any(strcmp(tmp1{i},{'inds','color'})) + errordlg('Wrong type of struct.'); + return; + end +end + +for i=1:length(tmp2) + if ~any(strcmp(tmp2{i},{'ind','color'})) + errordlg('Wrong type of struct.'); + return; + end +end + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +clear_markers; +remove_traj; + +old = gcf; + +for i=1:length(data.points) + for j=1:length(udata.h); + set(0,'CurrentFigure',udata.fig1); + subplot(udata.h(j)); + ydata=getfield(get(udata.d(j),'YData'),{data.points(i).inds}); + udata.d_mark(j).h=[udata.d_mark(j).h;... + plot(data.points(i).inds,ydata,... + 'Color',data.points(i).color,... + 'LineStyle','none',... + 'Marker','o',... + 'ButtonDownFcn',... + 'vis_trajgui([],''line_down'')')]; + if all(data.points(i).color == [0 0 0]) + udata.new_marks=unique([udata.new_marks; (data.points(i).inds)']); + else + udata.all_marks=unique([udata.all_marks; (data.points(i).inds)']); + end + end + if ~isempty(udata.fig2) + set(0,'CurrentFigure',udata.fig2); + for j=1:length(udata.h2) + subplot(udata.h2(j)); + ydata=getfield(get(udata.d2(j),'YData'),{data.points(i).inds}); + udata.d_mark2(j).h=[udata.d_mark2(j).h;... + plot(data.points(i).inds,ydata,... + 'Color',data.points(i).color,... + 'LineStyle','none',... + 'Marker','o',... + 'ButtonDownFcn',... + 'vis_trajgui([],''line_down'')')]; + end + end +end + + +set(0,'CurrentFigure',udata.sM_h); +color=get(udata.tmp_patch(1),'FaceVertexCData'); +color=reshape(color,[size(color,1) 3]); +for i=1:length(data.nodes) + color(data.nodes(i).ind,:)=data.nodes(i).color; +end +for i=1:length(udata.tmp_patch); + set(udata.tmp_patch(i),'FaceVertexCData',color); +end + +set(0,'CurrentFigure',old); +set(udata.fig1,'UserData',udata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function fuzzy_traj(trajStruct,arg); +%function fuzzy_traj(sM_h,sM,sD,interval,arg) + + +if isempty(arg) + + if strcmp(trajStruct.lattice,'hexa') + udata.lattice='hexa'; + udata.form=vis_patch('hexa'); + else + data.lattice='rect'; + udata.form=vis_patch('rect'); + end + + % interval=[1 size(trajStruct.primary_data,1)]; + + l=size(udata.form,1); + dim = size(trajStruct.primary_data,2); + udata.a_h=[findobj(get(trajStruct.figure,'Children'),'Tag','Uplane');... + findobj(get(trajStruct.figure,'Children'),'Tag','Cplane')]; + + udata.sM_h=trajStruct.figure; + udata.msize=trajStruct.msize; + + %%%%%%%%%%%%%%% + % + % constructing patch that is drawn above every plane in map + % + + nx = repmat(udata.form(:,1),1,prod(udata.msize)); + ny = repmat(udata.form(:,2),1,prod(udata.msize)); + + x_c=reshape(repmat(1:udata.msize(2),l*udata.msize(1),1),l,prod(udata.msize)); + y_c=repmat(repmat(1:udata.msize(1),l,1),1,udata.msize(2)); + + if strcmp(udata.lattice,'hexa') + t = find(~rem(y_c(1,:),2)); + x_c(:,t)=x_c(:,t)+.5; + end + + x_c=x_c+nx; + y_c=y_c+ny; + + udata.orig_c=ones(prod(udata.msize),1)*[NaN NaN NaN]; + colors=reshape(udata.orig_c,[1 size(udata.orig_c,1) 3]); + set(0,'CurrentFigure',trajStruct.figure); + + %%%%%%%%% + % drawing + + for i=1:length(udata.a_h); + subplot(udata.a_h(i)); + v=caxis; + udata.patch_h(i) =patch(x_c,y_c,colors,'EdgeColor','none'); + caxis(v); + end + + + udata.orig_x=get(udata.patch_h(1),'XData'); + udata.orig_y=get(udata.patch_h(1),'YData'); + + % if interval(1) < 1 | interval(2) > size(trajStruct.primary_data,1) + % error('Invalid argument ''interval''.'); + % end + + x=1:size(trajStruct.primary_data,1); + udata.fig1=figure; + set(udata.fig1,'KeyPressFcn',... + 'vis_trajgui([],''move_fuzzy'')'); + for i=1:size(trajStruct.primary_data,2) + subplot(size(trajStruct.primary_data,2),1,i); + udata.h(i)=gca; + set(udata.h(1),'XTick',[]); + udata.d(i)=plot(x,trajStruct.primary_data(:,i)); + l_x=1; + lims(1) = round(min(trajStruct.primary_data(:,i))); + lims(2) = round(max(trajStruct.primary_data(:,i))); + udata.l(i) = line([l_x l_x],lims,'Color','red','EraseMode','xor'); + end + + udata.l_x = l_x; + udata.interval=[1 size(trajStruct.bmus,2)]; + + tmp.fig1=udata.fig1; + % [K,P] = estimate_kernels(sM,sD); + % udata.K=K; + % udata.P=P; + % udata.codebook=sM.codebook; + % udata.data=sD.data; + udata.bmus=trajStruct.bmus; + set(udata.fig1,'UserData',udata); + set(trajStruct.figure,'UserData',tmp); + draw_fuzzy(l_x); + return; +end + +move_fuzzy; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function draw_fuzzy(x) + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +inds = find(udata.bmus(:,x)); +[row col] = ind2sub(udata.msize,inds); +if strcmp(udata.lattice,'hexa') + t=find(~mod(row,2)); + col(t)=col(t)+0.5; +end + +color=udata.orig_c; +color=reshape(color,[size(color,1) 3]); +xdata=udata.orig_x; +ydata=udata.orig_y; +tmp= ones(size(xdata(:,1),1),1)*udata.bmus(inds,x)'; +color(inds,:) = ones(length(inds),1)*[0 0 0]; +xdata(:,inds) = udata.form(:,1)*ones(1,length(inds)).*tmp+ones(6,1)*col'; +ydata(:,inds) = udata.form(:,2)*ones(1,length(inds)).*tmp+ones(6,1)*row'; + +set(udata.patch_h,'FaceVertexCData',color,'XData',xdata,'YData',ydata); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function move_fuzzy + +% moves pointer lines and draws fuzzy response. + +udata=get(gcf,'UserData'); +udata=get(udata.fig1,'UserData'); + +switch get(gcf,'CurrentCharacter'); + case {'<','>'} + key = get(gcf,'CurrentCharacter'); + if key == '>' + if udata.l_x + 1 > udata.interval(2) + return; + end + l_x = udata.l_x + 1; + else + if udata.l_x - 1 < udata.interval(1) + return; + end + l_x = udata.l_x - 1; + end + draw_fuzzy(l_x); + set(udata.l,'XData',[l_x l_x]); + udata.l_x=l_x; + set(udata.fig1,'UserData',udata); + otherwise + return; +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/vis_valuetype.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,269 @@ +function flag=vis_valuetype(value, valid, str); + +% VIS_VALUETYPE Used for type checks in SOM Toolbox visualization routines +% +% flag = vis_valuetype(value, valid, str) +% +% Input and output arguments: +% value (varies) variable to be checked +% valid (cell array) size 1xN, cells are strings or vectors (see below) +% str (string) 'all' or 'any' (default), determines whether +% all or just any of the types listed in argument 'valid' +% should be true for 'value' +% +% flag (scalar) 1 or 0 (true or false) +% +% This is an internal function of SOM Toolbox visualization. It makes +% various type checks. For example: +% +% % Return 1 if X is a numeric scalar otherwise 0: +% f=vis_valuetype(X,{'1x1'}); +% +% % Return 1 if X is a ColorSpec, that is, a 1x3 vector presenting an RGB +% % value or any of strings 'red','blue','green','yellow','magenta','cyan', +% % 'white' or 'black' or their shortenings 'r','g','b','y','m','c','w','k': +% f=vis_valueype(X,{'1x3rgb','colorstyle'}) +% +% % Return 1 if X is _both_ 10x3 size numeric matrix and has RGB values as rows +% f=vis_valuetype(X,{'nx3rgb',[10 3]},'all') +% +% Strings that may be used in argument valid: +% id is true if value is +% +% [n1 n2 ... nn] any n1 x n2 x ... x nn sized numeric matrix +% '1x1' scalar (numeric) +% '1x2' 1x2 vector (numeric) +% 'nx1' any nx1 numeric vector +% 'nx2' nx2 +% 'nx3' nx3 +% 'nxn' any numeric square matrix +% 'nxn[0,1]' numeric square matrix with values in interval [0,1] +% 'nxm' any numeric matrix +% '1xn' any 1xn numeric vector +% '1x3rgb' 1x3 vector v for which all(v>=0 & v<=1), e.g., a RGB code +% 'nx3rgb' nx3 numeric matrix that contains n RGB values as rows +% 'nx3dimrgb' nx3xdim numeric matrix that contains RGB values +% 'nxnx3rgb' nxnx3 numeric matrix of nxn RGB triples +% 'none' string 'none' +% 'xor' string 'xor' +% 'indexed' string 'indexed' +% 'colorstyle' strings 'red','blue','green','yellow','magenta','cyan','white' +% or 'black', or 'r','g','b','y','m','c','w','k' +% 'markerstyle' any of Matlab's marker chars '.','o','x','+','*','s','d','v', +% '^','<','>','p'or 'h' +% 'linestyle' any or Matlab's line style strings '-',':','--', or '-.' +% 'cellcolumn' a nx1 cell array +% 'topol_cell' {lattice, msize, shape} +% 'topol_cell_no_shape' {lattice, msize} +% 'string' any string (1xn array of char) +% 'chararray' any MxN char array + +% Copyright (c) 1999-2000 by the SOM toolbox programming team. +% http://www.cis.hut.fi/projects/somtoolbox/ + +% Version 2.0beta Johan 201099 juuso 280800 + +if nargin == 2 + str='any'; +end + +flag=0; +sz=size(value); +dims=ndims(value); + +% isnumeric +numeric=isnumeric(value); +character=ischar(value); + +% main loop: go through all types in arg. 'valid' +for i=1:length(valid), + if isnumeric(valid{i}), % numeric size for double matrix + if numeric & length(valid{i}) == dims, + flag(i)=all(sz == valid{i}); + else + flag(i)=0; % not numeric or wrong dimension + end + else + msg=''; % for a error message inside try + try + switch valid{i} + + % scalar + case '1x1' + flag(i)=numeric & dims == 2 & sz(1)==1 & sz(2) ==1; + + % 1x2 numeric vector + case '1x2' + flag(i)=numeric & dims == 2 & sz(1)==1 & sz(2) == 2; + + % 1xn numeric vector + case '1xn' + flag(i)=numeric & dims == 2 & sz(1) == 1; + + % any numeric matrix + case 'nxm' + flag(i)=numeric & dims == 2; + + % nx3 numeric matrix + case 'nx3' + flag(i)=numeric & dims == 2 & sz(2) == 3; + + % nx2 numeric matrix + case 'nx2' + flag(i)=numeric & dims == 2 & sz(2) == 2; + + % nx1 numeric vector + case 'nx1' + flag(i)=numeric & dims == 2 & sz(2) == 1; + + % nx1xm numric matrix + case 'nx1xm' + flag(i)=numeric & dims == 3 & sz(2) == 1; + + % nx3 matrix of RGB triples + case 'nx3rgb' + flag(i)=numeric & dims == 2 & sz(2) == 3 & in0_1(value); + + % RGB triple (ColorSpec vector) + case '1x3rgb' + flag(i) = numeric & dims == 2 & sz(1)==1 & sz(2) == 3 & in0_1(value); + + % any square matrix + case 'nxn' + flag(i)=numeric & dims == 2 & sz(1) == sz(2); + + % nx3xdim array of nxdim RGB triples + case 'nx3xdimrgb' + flag(i)=numeric & dims == 3 & sz(2) == 3 & in0_1(value); + + % nxnx3 array of nxn RGB triples + case 'nxnx3rgb' + flag(i)= numeric & dims == 3 & sz(1) == sz(2) & sz(3) == 3 ... + & in0_1(value); + + % nxn matrix of values between [0,1] + case 'nxn[0,1]' + + flag(i)=numeric & dims == 2 & sz(1) == sz(2) & in0_1(value); + + % string 'indexed' + case 'indexed' + flag(i) = ischar(value) & strcmp(value,'indexed'); + + % string 'none' + case 'none' + flag(i) = character & strcmp(value,'none'); + + % string 'xor' + case 'xor' + flag(i) = character & strcmp(value,'xor'); + + % any string (1xn char array) + case 'string' + flag(i) = character & dims == 2 & sz(1)<=1; + + % any char array + case 'chararray' + flag(i) = character & dims == 2 & sz(1)>0; + + % ColorSpec string + case 'colorstyle' + flag(i)=(character & sz(1) == 1 & sz(2) == 1 & ... + any(ismember('ymcrgbwk',value))) | ... + (ischar(value) & any(strcmp(value,{'none','yellow','magenta',... + 'cyan','red','green','blue','white','black'}))); + + % any valid Matlab's Marker + case 'markerstyle' + flag(i)=character & sz(1) == 1 & sz(2) == 1 & ... + any(ismember('.ox+*sdv^<>ph',value)); + + % any valid Matlab's LineStyle + case 'linestyle' + str=strrep(strrep(strrep(value,'z','1'),'--','z'),'-.','z'); + flag(i)=character & any(ismember(str,'z-:')) & sz(1)==1 & (sz(2)==1 | sz(2)==2); + + % any struct + case 'struct' + flag(i)=isstruct(value); + + % nx1 cell array of strings + case 'cellcolumn_of_char' + flag(i)=iscell(value) & dims == 2 & sz(2)==1; + try, char(value); catch, flag(i)=0; end + + % mxn cell array of strings + case '2Dcellarray_of_char' + flag(i)=iscell(value) & dims == 2; + try, char(cat(2,value{:})); catch, flag(i)=0; end + + % valid {lattice, msize} + case 'topol_cell_no_shape' + flag(i)=1; + if ~iscell(value) | length(size(value)) ~= 2 | size(value,2)~=2 + flag(i)=0; + else + if vis_valuetype(value{1},{'string'}), + switch value{1} + case { 'hexa','rect'} + ; + otherwise + flag(i)=0; + end + end + if ~vis_valuetype(value{2},{'1xn'}), + flag(i)=0; + end + end + + % valid {lattice, msize, shape} + case 'topol_cell' + flag(i)=1; + if ~iscell(value) | length(size(value)) ~= 2 | size(value,2) ~= 3, + flag(i)=0; + else + if vis_valuetype(value{1},{'string'}), + switch value{1} + case { 'hexa','rect'} + ; + otherwise + flag(i)=0; + end + end + if ~vis_valuetype(value{2},{'1xn'}) + flag(i)=0; + end + if ~vis_valuetype(value{3},{'string'}) + flag(i)=0; + else + switch value{3} + case { 'sheet','cyl', 'toroid'} + ; + otherwise + flag(i)=0; + end + end + end + otherwise + msg='Unknown valuetype!'; + end + catch + % error during type check is due to wrong type of value: + % lets set flag(i) to 0 + flag(i)=0; + end + % Unknown indetifier? + error(msg); + end + % set flag according to 3rd parameter (all ~ AND, any ~ OR) + if strcmp(str,'all'); + flag=all(flag); + else + flag=any(flag); + end +end + + +function f=in0_1(value) + +f=all(value(:) >= 0 & value(:)<=1); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/Main.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Main script of KRBM-Knowledge-based Restricted Boltzmann Machine % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% loading background knowledge +% bk_file = 'XOR.bk'; +% w = 5; +W = rand(0,0); +mW = rand(0,0); +% [W mW nNames] = encode_bk(bk_file,w) +%%% visualize_system(W,nNames,w,100,100); +%% Loading data +PRO_DIR = 'C://Pros/'; +% dat_file = 'XOR.dat'; +% dat_file = 'DNA.dat'; +% train_file = strcat(PRO_DIR,'/Data/ICDAR/digit_train_data.mat'; +% train_label = strcat(PRO_DIR,'/Data/ICDAR/digit_train_label.mat'; +% test_file = strcat(PRO_DIR,'/Data/ICDAR/digit_test_data.mat'; +% test_label = strcat(PRO_DIR,'/Data/ICDAR/digit_test_label.mat'; +%----------------------------------------------------- +% train_file = strcat(PRO_DIR,'/Data/MNIST/mnist_train_data.mat'; +% train_label = strcat(PRO_DIR,'/Data/MNIST/mnist_train_label.mat'; +% test_file = strcat(PRO_DIR,'/Data/MNIST/mnist_test_data.mat'; +% test_label = strcat(PRO_DIR,'/Data/MNIST/mnist_test_label.mat'; +%----------------------------------------------------- +% train_file = strcat(PRO_DIR,'/RBMLIB/train_dat.mat'); +% train_label = strcat(PRO_DIR,'/RBMLIB/train_lab.mat'); +% test_file = strcat(PRO_DIR,'/RBMLIB/test_dat.mat'); +% test_label = strcat(PRO_DIR,'/RBMLIB/test_lab.mat'); +%----------------------------------------------------- +% train_file = strcat(PRO_DIR,'/RBMLIB/train_dat.mat'); +data_file = strcat(PRO_DIR,'/DATA/XOR/xorall.mat'); +mod_file = strcat(PRO_DIR,'/DATA/XOR/model.mat'); +%% Move weights encoded from rule to right position of input if needed + +%% Setting parameters +conf.hidNum = 4; +conf.eNum = 50; % number of epoch +conf.bNum = 1; % number of batches devides the data set +conf.sNum = 4; % number of samples per batch +conf.gNum = 1; % number of iteration for gibb sampling +conf.params = [0.5 0.5 0.1 0.00002]; % [lr1 lr2 momentum cost] + % lr1: learning rate in first 10 epoches + % lr2: learning rate in the other epoches +%% Training system +% training_kbrm(conf,W,mW,dat_file); % unsupevised learning +% [W visB hidB] = gen_training_kbrbm(conf,W,mW,train_file,train_label); % unsupervised learning for generative classification +% dis_training_kbrm(conf,W,mW,dat_file,dat_label); % unsupervised learning for discriminative classification +[W visB hidB] = training_rbm(conf,W,mW,data_file); +save(mod_file,'W','mW','visB','hidB'); +nNames = {'X' 'Y' 'Z'}; +visualize_system(W,nNames,100,100); +%% Classify (if wanted) +% gen_classify(W, visB, hidB,mW,test_file,test_label); +%% Generate features (if wanted) +% in_f_file = '../Data/ICDAR/images.bin'; +% out_f_file = '../Data/ICDAR/feature.mat'; +% input_features = mnist2mat(in_f_file); +% output_features = input_features*W; +% save(out_f_file,'output_features'); +%% Extract knowledge (if wanted) +% name_file = 'names.txt'; % File contains name of input +% rule_file = 'rules.txt'; +% extract_knowledge(rule_file,W); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% END%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +clear; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/classify.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function [ output_args ] = classify( input_args ) +%CLASSIFY Summary of this function goes here +% Detailed explanation goes here + + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/discrete2softmax.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function S = discrete2softmax( D,Vals ) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% convert a col-array of discrete value to softmax % +% D: col-array % +% Vals: list of possible values % +% sontran2012 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +S = zeros(size(D,1),size(Vals,2)); +for i=1:size(D,1) + S(i,find(Vals==D(i))) = 1; +end + +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/gen_classify.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function [count total] = gen_classify(W, visB, hidB,mW,test_file,test_label); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% RBM for generative classification % +% conf: training setting % +% WW = [W visB hidB] % +% mW: mask of connections % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Loading data & label +vars = whos('-file', test_file); +A = load(test_file,vars(1).name); +data = A.(vars(1).name); +vars = whos('-file', test_label); +A = load(test_label,vars(1).name); +label = A.(vars(1).name); +assert(size(data,1) == size(label,1),'[KRBM-GEN] Number of data and label mismatch'); +Classes = unique(label)'; +lNum = size(Classes,2); +%% Loading structure +[total visNum]= size(data); +sNum = 500; % fix +bNum = floor(total/sNum); % divide the testing samples to batches of sNum +%% Classifying +count = 0; +for i=1:bNum + % Find the position of softmax unit which make lowest free energy + L = lowest_fenergy(data((i-1)*sNum + 1:i*sNum,:),W, visB, hidB,Classes); + count = count + sum(L==label((i-1)*sNum + 1:i*sNum)); +end +L = lowest_fenergy(data(bNum*sNum + 1:total,:),W, visB, hidB,Classes); +count = count + sum(L==label(bNum*sNum + 1:total)); +fprintf('Correct rate: %f (%d/%d)\n',count/total,count,total); +clear A vars data lNum sNum bNum out; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/gen_evaluate.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function [ output_args ] = gen_evaluate( data,label,WW,Classes) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% generative classification % +% WW = [W visB hidB] % +% mW: mask of connections % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% Loading data & label +assert(size(data,1) == size(label,1),'[KRBM-GEN] Number of data and label mismatch'); +lNum = size(Classes); +%% Loading structure +[total visNum]= size(data); +sNum = 500; % fix +bNum = ceil(total/sNum); % divide the testing samples to batches of sNum +%% Classifying +count = 0; +for i=1:bNum + % Find the position of softmax unit which make lowest free energy + L = lowest_fenergy(data((i-1)*sNum + 1:i*sNum,:),WW,lNum,Classes); + count = count + sum(L==label((i-1)*sNum + 1:i*sNum)); +end +L = lowest_fenergy(data(bNum*sNum + 1:total,:),WW,Classes); +count = count + sum(L==label(bNum*sNum + 1:total)); +fprintf('Correct rate: %f (%d/%d)',count/total,count,total; +clear A vars data lNum sNum bNum out; +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/gen_training_rbm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function [W visB hidB] = gen_training_krbm(conf,W,mW,train_file,train_label) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Training Knowledge Based RBM for generative classification % +% conf: training setting % +% W: weights of connections % +% mW: mask of connections % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% load data +vars = whos('-file', train_file); +A = load(train_file,vars(1).name); +data = A.(vars(1).name); +vars = whos('-file', train_label); +A = load(train_label,vars(1).name); +label = A.(vars(1).name); +assert(~isempty(data),'[KRBM-GEN] Data is empty'); +assert(size(data,1) == size(label,1),'[KRBM-GEN] Number of data and label mismatch'); +Classes = unique(label)'; +lNum = size(Classes,2); +data = [data discrete2softmax(label,Classes)] +%% initialization +visNum = size(data,2); +hidNum = conf.hidNum; +sNum = conf.sNum; +lr = conf.params(1); +N = 10; % Number of epoch training with lr_1 +W = [W;0.1*randn(visNum - size(W,1),size(W,2))]; +W = [W 0.1*randn(size(W,1),hidNum-size(W,2))]; + +DW = zeros(size(W)); +visB = zeros(1,visNum); +DVB = zeros(1,visNum); +hidB = zeros(1,hidNum); +DHB = zeros(1,hidNum); +visP = zeros(sNum,visNum); +visN = zeros(sNum,visNum); +visNs = zeros(sNum,visNum); +hidP = zeros(sNum,hidNum); +hidPs = zeros(sNum,hidNum); +hidN = zeros(sNum,hidNum); +hidNs = zeros(sNum,hidNum); +%% Reconstruction error & evaluation error & early stopping +mse = 0; +omse = 0; +inc_count = 0; +MAX_INC = 3; % If the error increase MAX_INC times continuously, then stop training +%% Average best settings +n_best = 1; +aW = size(W); +aVB = size(visB); +aHB = size(hidB); +%% ==================== Start training =========================== %% +for i=1:conf.eNum + if i== N+1 + lr = conf.params(2); + end + omse = mse; + mse = 0; + for j=1:conf.bNum + visP = data((j-1)*conf.sNum+1:j*conf.sNum,:); + %up + hidP = logistic(visP*W + repmat(hidB,sNum,1)); + hidPs = 1*(hidP >rand(sNum,hidNum)); + hidNs = hidPs; + for k=1:conf.gNum + % down + visN = hidNs*W' + repmat(visB,sNum,1); + visN(:,1:visNum-lNum) = logistic(visN(:,1:visNum-lNum)); + visN(:,visNum-lNum+1:visNum) = softmax_activation(visN(:,visNum-lNum+1:visNum)); + visNs = [1*(visN(:,1:visNum-lNum)>rand(sNum,visNum-lNum)) visN(:,visNum-lNum+1:visNum)]; + if j==5 && k==1, observe_reconstruction(visN(:,1:visNum-lNum),sNum,i,28,28); end + % up + hidN = logistic(visNs*W + repmat(hidB,sNum,1)); + hidNs = 1*(hidN>rand(sNum,hidNum)); + end + % Compute MSE for reconstruction + rdiff = (visP - visN); + mse = mse + sum(sum(rdiff.*rdiff))/(sNum*visNum); + % Update W,visB,hidB + diff = (visP'*hidP - visNs'*hidN)/sNum; + DW = lr*(diff - conf.params(4)*W) + conf.params(3)*DW; + W = W + DW; +% W = W.*mW; + DVB = lr*sum(visP - visN,1)/sNum + conf.params(3)*DVB; + visB = visB + DVB; + DHB = lr*sum(hidP - hidN,1)/sNum + conf.params(3)*DHB; + hidB = hidB + DHB; + end + if mse > omse + inc_count = inc_count + 1 + else + inc_count = 0; + end + if inc_count> MAX_INC, break; end; + fprintf('Epoch %d : MSE = %f\n',i,mse); +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/license.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,678 @@ + + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. [http://fsf.org/] + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. 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Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + {one line to give the program's name and a brief idea of what it does.} + Copyright (C) {year} {name of author} + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see [http://www.gnu.org/licenses/]. + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + {project} Copyright (C) {year} {fullname} + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +[http://www.gnu.org/licenses/]. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +[http://www.gnu.org/philosophy/why-not-lgpl.html]. + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/logistic.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function A = logistic( A ) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Logistic function % +% sontran2012 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +A = 1./(1+exp(-A)); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/lowest_fenergy.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function L = lowest_fenergy(V,W,visB,hidB,Classes) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Return the labels L(Classes) to the lowest energy softmax unit % +% visP: hidden states % +% visN: reconstructed data (with softmax) % +% WW = [W visB hidB] % +% F(v) = -\sum_i v_ia_i - \sum_jlog(1+e^(x_j)) % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +lNum = size(Classes,2); +sNum = size(V,1); +F = zeros(sNum,lNum); +out = zeros(sNum,lNum); +for i=1:lNum + F(:,i) = 1; + X = [V F]*W + repmat(hidB,sNum,1); + out(:,i) = sum(repmat(visB,sNum,1).*[V F],2) + sum(log(1+exp(X)),2); + F(:,i) = 0; +end +L = softmax2discrete(out,Classes); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/observe_reconstruction.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function observe_reconstruction(Is,num,g,img_row,img_col) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Saving images to files % +% Is: Images % +% sNum: Number of images to save % +% g: indices: imaged group gth % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if num>100, return; end; +dir = 'C://Pros/tmp/obsv/'; +col = floor(sqrt(num)); +row = ceil(num/col); +gap = 2; +pos = ones(1,col*row); +pos = cumsum(pos); +pos = vec2mat(pos,col); +bigImg = zeros(row*img_row + (row-1)*gap,col*img_col + (col-1)*gap); +for i=1:row + for j=1:col + y = 1 + (i-1)*(img_row+gap); + x = 1 + (j-1)*(img_col+gap); + if pos(i,j) <= num, bigImg(y:y+img_row-1,x:(x+img_col-1)) = vec2mat(Is(pos(i,j),:),img_col); end; + end +end +imwrite(bigImg,strcat(dir,'img_',num2str(g),'.bmp'),'BMP'); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/softmax2discrete.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +function D = softmax2discrete( S,Vals ) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% convert softmax to col-array of discrete value % +% S: softmax % +% Vals: list of possible values % +% sontran2012 % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +[dummy out] = max(S'); +out = out'; +D = zeros(size(out)); +for i=1:size(out,1) + D(i) = Vals(out(i)); +end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/softmax_activation.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function A = softmax_activation( softmax_group) +A = exp(softmax_group); +A = A./repmat(sum(A,2),1,size(A,2)); +for i=1:size(A,1) + A(i,:) = cumsum(A(i,:))>rand(); + m = max(A(i,:)); + inx = find(A(i,:)==m,1,'first'); + A(i,:) = 0; + A(i,inx) = 1; +end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/RBM/training_rbm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +function [W visB hidB] = training_rbm(conf,W,data_file) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Training RBM % +% conf: training setting % +% W: weights of connections % +% -*-sontran2012-*- % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% load data +vars = whos('-file', data_file); +A = load(data_file,vars(1).name); +data = A.(vars(1).name); +assert(~isempty(data),'[KBRBM] Data is empty'); +%% initialization +visNum = size(data,2); +hidNum = conf.hidNum; +sNum = conf.sNum; +lr = conf.params(1); +N = 10; % Number of epoch training with lr_1 +W = 0.1*randn(visNum - size(W,1),size(W,2)); +W = 0.1*randn(size(W,1),hidNum-size(W,2)); + +DW = zeros(size(W)); +visB = zeros(1,visNum); +DVB = zeros(1,visNum); +hidB = zeros(1,hidNum); +DHB = zeros(1,hidNum); +visP = zeros(sNum,visNum); +visN = zeros(sNum,visNum); +visNs = zeros(sNum,visNum); +hidP = zeros(sNum,hidNum); +hidPs = zeros(sNum,hidNum); +hidN = zeros(sNum,hidNum); +hidNs = zeros(sNum,hidNum); +%% Reconstruction error & evaluation error & early stopping +mse = 0; +omse = 0; +inc_count = 0; +MAX_INC = 3000; % If the error increase MAX_INC times continuously, then stop training +%% Average best settings +n_best = 1; +aW = size(W); +aVB = size(visB); +aHB = size(hidB); +%% Plotting +h = plot(nan); +%% ==================== Start training =========================== %% +for i=1:conf.eNum + if i== N+1 + lr = conf.params(2); + end + omse = mse; + mse = 0; + for j=1:conf.bNum + visP = data((j-1)*conf.sNum+1:j*conf.sNum,:); + %up + hidP = logistic(visP*W + repmat(hidB,sNum,1)); + hidPs = 1*(hidP >rand(sNum,hidNum)); + hidNs = hidPs; + for k=1:conf.gNum + % down + visN = logistic(hidNs*W' + repmat(visB,sNum,1)); + visNs = 1*(visN>rand(sNum,visNum)); + if j==5 && k==1, observe_reconstruction(visN,sNum,i,28,28); end + % up + hidN = logistic(visNs*W + repmat(hidB,sNum,1)); + hidNs = 1*(hidN>rand(sNum,hidNum)); + end + % Compute MSE for reconstruction + rdiff = (visP - visN); + mse = mse + sum(sum(rdiff.*rdiff))/(sNum*visNum); + % Update W,visB,hidB + diff = (visP'*hidP - visNs'*hidN)/sNum; + DW = lr*(diff - conf.params(4)*W) + conf.params(3)*DW; + W = W + DW; +% W = W.*mW; + DVB = lr*sum(visP - visN,1)/sNum + conf.params(3)*DVB; + visB = visB + DVB; + DHB = lr*sum(hidP - hidN,1)/sNum + conf.params(3)*DHB; + hidB = hidB + DHB; + end + %% + mse_plot(i) = mse; + axis([0 (conf.eNum+1) 0 2]); + set(h,'YData',mse_plot); + drawnow; +% plot(mse_plot,'XDataSource','real(mse_plot)','YDataSource','imag(mse_plot)') +% linkdata on; + + if mse > omse + inc_count = inc_count + 1 + else + inc_count = 0; + end + if inc_count> MAX_INC, break; end; + fprintf('Epoch %d : MSE = %f\n',i,mse); +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/LICENSE.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +SVM-Light +--------- + +Available at http://svmlight.joachims.org/ + +Author: Thorsten Joachims + thorsten@joachims.org + + Cornell University + Department of Computer Science + 4153 Upson Hall + Ithaca, NY 14853 + USA + +LICENSING TERMS + +This program is granted free of charge for research and education +purposes. However you must obtain a license from the author to use it +for commercial purposes. + +Scientific results produced using the software provided shall +acknowledge the use of SVM-Light. Please cite as + + T. Joachims, Making large-Scale SVM Learning + Practical. Advances in Kernel Methods - Support Vector + Learning, B. Schölkopf and C. Burges and A. Smola (ed.), + MIT-Press, 1999. + http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_99a.pdf + +Moreover shall the author of SVM-Light be informed about the +publication. + +The software must not be modified and distributed without prior +permission of the author. + +By using SVM-Light you agree to the licensing terms. + + +NO WARRANTY + +BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY +FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT +WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER +PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, +EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE +PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME +THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + +IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR +REDISTRIBUTE THE PROGRAM, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF +THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO +LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY +YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY +OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED +OF THE POSSIBILITY OF SUCH DAMAGES.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/Readme_optimization_relative_constraints.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +Solving general optimization problems +------------------------------------- + +You can use SVM-light to solve general optimzation problems of the form: + + min 0.5 w*w + C sum_i C_i \xi_i + s.t. x_i * w > rhs_i - \xi_i + +Use the option "-z o". This allows specifying a training set where the examples are the inequality constraints. For example, to specify the problem + + min 0.5 w*w + 10 (1000 \xi_1 + 1 \xi_2 + 1 \xi_3 + 1 \xi_4) + s.t. 1 w_1 >= 0 - \xi_1 + -2 w_1 >= 1 - \xi_2 + 2 w_3 >= 2 - \xi_3 + 2 w_2 + 1 w_3 >= 3 - \xi_4 + +you can use the training set + +0 cost:10000 1:1 +1 1:-2 +2 3:2 +3 2:3 3:1 + +and run + + svm_learn -c 10 -z o train.dat model + +The format is just like the normal SVM-light format. Each line corresponds to one inequality. However, the first element of each line is the right-hand side of the inequality. The remainder of the line specifies the left-hand side. The parameter cost:<value> is optional and lets you specify a factor by which the value of the slack variable is weighted in the objective. The general regularization parameter (10 in the example) is specified with the option -c <value> on the command line. + +To classify new inequalities, you can use svm_classify in the normal way. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/SVM-Light Support Vector Machine.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Frameset//EN" + "http://www.w3.org/TR/html4/frameset.dtd"> +<html> +<head> + <title>SVM-Light Support Vector Machine</title> + <meta http-equiv="content-type" content="text/html; charset=iso-8859-15"> + <meta name="description" content="SVM-Light Support Vector Machine"> + <meta name="keywords" content="Thorsten Joachims, SVM-light, SVM light, SVMlight, Support Vector Machine, Text Classification, Training Support Vector Machines, Classification, Regression, Estimators, Leave-one-out, loo, Estimator, Algorithm, Learning, Machine Learning, Training"> + <meta name="google-site-verification" content=""> +</head> +<frameset rows="*,0" border="0" frameborder="0" style="border: none"> + <frame src="http://www.cs.cornell.edu/People/tj/svm_light" noresize scrolling="auto"> + <noframes> + <body> + Hier finden Sie Informationen zu den folgenden Themen:<BR/><UL><LI>Thorsten Joachims</LI> +<LI>SVM-light</LI> +<LI>SVM light</LI> +<LI>SVMlight</LI> +<LI>Support Vector Machine</LI> +<LI>Text Classification</LI> +<LI>Training Support Vector Machines</LI> +<LI>Classification</LI> +<LI>Regression</LI> +<LI>Estimators</LI> +<LI>Leave-one-out</LI> +<LI>loo</LI> +<LI>Estimator</LI> +<LI>Algorithm</LI> +<LI>Learning</LI> +<LI>Machine Learning</LI> +<LI>Training</LI> +</UL> <p><a href="http://www.cs.cornell.edu/People/tj/svm_light">Weiter zur Startseite</a></p> + </body> + </noframes> +</frameset> +</html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/example1/test.dat Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,601 @@ +# Reuters category "corporate aquisitions" (test examples: 300 positive/ 300 negative) ++1 6:0.0342598670723747 26:0.148286149621374 27:0.0570037235976456 31:0.0373086482671729 33:0.0270832794680822 63:0.0317368459004657 67:0.138424991237843 75:0.0297571881179897 96:0.0303237495966756 142:0.0241139382095992 144:0.0581948804675796 185:0.0285004985793364 199:0.0228776475252599 208:0.0366675566391316 274:0.0528930062061687 308:0.0361623318128513 337:0.0374174808347037 351:0.0347329937800643 387:0.0690970538458777 408:0.0288195477724883 423:0.0741629177979597 480:0.0719961218888683 565:0.0520577748209694 580:0.0442849093862884 593:0.329982711875242 598:0.0517245325094578 613:0.0452655621746453 641:0.0387269206869957 643:0.0398205809532254 644:0.0466353065571088 657:0.0508331832990127 717:0.0495981406619795 727:0.104798994968809 764:0.0452655621746453 827:0.0418050310923008 1027:0.05114477444793 1281:0.0633241153685135 1340:0.0657101916402099 1395:0.0522617631894159 1433:0.0471872599750513 1502:0.840963375098259 1506:0.0686138465829187 1558:0.0589627036028818 1598:0.0512079697459134 1726:0.0660884976719923 1836:0.0521934221969394 1943:0.0587388821544177 2433:0.0666767220421155 2646:0.0729483627336339 2731:0.071437898589286 2771:0.0706069752753547 3553:0.0783933439550538 3589:0.0774668403369963 ++1 179:0.183380155787579 199:0.157247710191107 408:0.198088894016017 587:0.426066443224553 1990:0.447885839011823 2134:0.458297202655782 3005:0.5569874337712 ++1 6:0.00482709726364712 17:0.0526067936384763 26:0.10446503857735 27:0.0281106990768854 28:0.0356456751828835 63:0.0313012858968506 72:0.0300401635230985 83:0.012507314153985 88:0.0737185781091803 102:0.0919905568442081 142:0.00876215636178203 155:0.100271086073242 172:0.0378545536912375 179:0.0526268994083645 199:0.0225636721456629 204:0.0579559481364334 306:0.0378786130534899 308:0.106998107844821 337:0.0369039591653246 357:0.0446845288778611 437:0.130255551720447 475:0.0391145025392082 538:0.0422966045382641 539:0.0448465452746061 587:0.0611368109939652 598:0.0510146592713463 755:0.100761643621509 808:0.0503808218107545 823:0.0549991894617621 871:0.0554451802614129 1990:0.128535407197547 2026:0.348920957396963 2134:0.460331503040801 2736:0.315429331039326 2804:0.315429331039326 2975:0.0686186332932993 3005:0.559459802671567 3813:0.0839264409437691 3835:0.0782990010086473 5570:0.079922828953081 ++1 6:0.00797462873810959 26:0.0345163916642208 27:0.0696606189381528 33:0.0441290005061936 67:0.0451094300865861 142:0.0165434990048971 172:0.0625377934701842 204:0.047873198353993 308:0.117844484878955 437:0.0717297656206316 455:0.0582264995123673 473:0.0702120797712105 480:0.0586545824892089 546:0.0688988300164216 764:0.0737548795917481 808:0.166463747265811 882:0.05988570937618 1710:0.111056350005715 2976:0.107683390065739 5954:0.457701287906402 7029:0.316132177802849 7836:0.741910821304179 8399:0.165428413714452 ++1 6:0.020398477445271 11:0.0458319874879689 15:0.0349435129583541 26:0.0441451169688967 28:0.0753162264530517 29:0.0370416466621968 30:0.0613060121073632 48:0.0623952610910362 63:0.0881825438769616 75:0.0826819575927399 80:0.0664082539832147 81:0.0620988371567663 83:0.0528537445458926 106:0.0713919290192784 142:0.0264481114418981 179:0.0555980417403573 216:0.266211025112519 227:0.0724969884807626 229:0.106824100071866 243:0.0961696532496726 284:0.0619384650702658 345:0.0748473146787298 384:0.11052547545743 414:0.0753592469880539 437:0.0917395689641771 455:0.372347094775744 505:0.0733654174810628 507:0.0545348021447396 557:0.0959891051878867 689:0.254925792581784 710:0.117536871080518 967:0.130689853323039 1043:0.570342575896572 1055:0.111292231708947 1057:0.13894866046608 1081:0.113390061284155 1788:0.125587611447188 2423:0.131638583246465 4488:0.396846996835276 ++1 6:0.0331457691319788 31:0.126333924126773 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7664:0.125774657637267 ++1 26:0.149686330296799 63:0.0747518768524216 111:0.216276366406951 142:0.00896796977071743 322:0.299717516579412 2581:0.538344179991304 7170:0.738583253537661 ++1 6:0.0185684472431948 27:0.0540668650608559 29:0.0337184902007242 31:0.0353864892353714 37:0.0878561585615086 53:0.0715011039639436 56:0.0857129951890861 63:0.0200678324907244 64:0.161048903877005 75:0.0188160558998091 80:0.0604504999899329 83:0.0481120206073627 132:0.0563090342810332 142:0.012037672010292 160:0.0841505827460929 299:0.0768582150127175 325:0.0833688857206104 330:0.0923352097874877 337:0.070979429396105 351:0.0658870673474337 369:0.083021346662991 398:0.0722137159638078 434:0.160674739877467 501:0.0965455908234389 506:0.0519401978219796 507:0.0496422637061778 539:0.0862558453340445 733:0.104790360856065 890:0.0975024814545307 1007:0.107169682733815 1014:0.227706414303925 1149:0.0890707951890151 1194:0.142306925835349 1411:0.117587815984974 1433:0.0895123004830511 1455:0.12327222341366 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/example2/test.dat Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,601 @@ +# Reuters category "corporate aquisitions" (test examples: 300 positive/ 300 negative) ++1 6:0.0342598670723747 26:0.148286149621374 27:0.0570037235976456 31:0.0373086482671729 33:0.0270832794680822 63:0.0317368459004657 67:0.138424991237843 75:0.0297571881179897 96:0.0303237495966756 142:0.0241139382095992 144:0.0581948804675796 185:0.0285004985793364 199:0.0228776475252599 208:0.0366675566391316 274:0.0528930062061687 308:0.0361623318128513 337:0.0374174808347037 351:0.0347329937800643 387:0.0690970538458777 408:0.0288195477724883 423:0.0741629177979597 480:0.0719961218888683 565:0.0520577748209694 580:0.0442849093862884 593:0.329982711875242 598:0.0517245325094578 613:0.0452655621746453 641:0.0387269206869957 643:0.0398205809532254 644:0.0466353065571088 657:0.0508331832990127 717:0.0495981406619795 727:0.104798994968809 764:0.0452655621746453 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6:0.0290139351665723 15:0.0497021810665094 26:0.188370465162329 27:0.0844816207295095 63:0.0627135681507134 64:0.251645298852195 94:0.0870880588221888 97:0.0905212736259047 134:0.207796525247574 142:0.0564280196435502 144:0.172493918283761 174:0.106339594024885 176:0.0751959734763113 186:0.0823218193968935 192:0.117156280341252 198:0.126314676402126 204:0.261264176466566 226:0.112485473325213 265:0.137046135797307 286:0.0812861973902194 317:0.0864646687577001 337:0.110908173186837 351:0.102951155543425 808:0.151410445845402 834:0.146188408722028 862:0.139589134971201 965:0.144904282021311 991:0.154103562862921 1046:0.162985674890717 1607:0.187697348873378 1610:0.602165661571285 2736:0.189593158383877 2804:0.189593158383877 ++1 6:0.0113377941339566 17:0.123561835145194 29:0.0411766579506265 78:0.0871025726199448 79:0.230662360746054 96:0.07024636163921 139:0.0927530848247882 146:0.305427562498221 216:0.0986428458370067 271:0.125130222355737 274:0.245058166721627 278:0.195760672136747 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/example2/train_induction.dat Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +# Reuters category "corporate aquisitions" (training examples: 5 positive/ 5 negative) +1 6:0.0198403253586671 15:0.0339873732306071 29:0.0360280968798065 31:0.0378103484117687 41:0.0456787263779904 63:0.021442413608662 74:0.0813238108919922 75:0.0201048944012214 81:0.0603996615380116 142:0.0102897706466067 172:0.0777948548082322 174:0.072717200608936 179:0.054076743737027 180:0.0764456665578607 186:0.112586705083256 187:0.0925011727805475 205:0.156990770998115 229:0.0519505660963924 255:0.0923321093879611 308:0.0732972342194965 318:0.119740882706743 408:0.058414185072804 409:0.0506626198519805 465:0.0843545829662396 480:0.0729642744872502 519:0.118611296605205 664:0.112142083701314 679:0.374387819227881 720:0.0987664035972632 768:0.123975200617516 922:0.141018083523918 977:0.136393581474495 1018:0.107648758381437 1305:0.180449632267364 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/example2/train_transduction.dat Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,611 @@ +# Reuters category "corporate aquisitions" (training examples: 5 positive/ 5 negative / 600 unlabeled) +1 6:0.0198403253586671 15:0.0339873732306071 29:0.0360280968798065 31:0.0378103484117687 41:0.0456787263779904 63:0.021442413608662 74:0.0813238108919922 75:0.0201048944012214 81:0.0603996615380116 142:0.0102897706466067 172:0.0777948548082322 174:0.072717200608936 179:0.054076743737027 180:0.0764456665578607 186:0.112586705083256 187:0.0925011727805475 205:0.156990770998115 229:0.0519505660963924 255:0.0923321093879611 308:0.0732972342194965 318:0.119740882706743 408:0.058414185072804 409:0.0506626198519805 465:0.0843545829662396 480:0.0729642744872502 519:0.118611296605205 664:0.112142083701314 679:0.374387819227881 720:0.0987664035972632 768:0.123975200617516 922:0.141018083523918 977:0.136393581474495 1018:0.107648758381437 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/example3/test.dat Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +4 qid:4 1:1 2:0 3:0 4:0.2 5:1 +3 qid:4 1:1 2:1 3:0 4:0.3 5:0 +2 qid:4 1:0 2:0 3:0 4:0.2 5:1 +1 qid:4 1:0 2:0 3:1 4:0.2 5:0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/example3/train.dat Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +# query 1 +3 qid:1 1:1 2:1 3:0 4:0.2 5:0 +2 qid:1 1:0 2:0 3:1 4:0.1 5:1 +1 qid:1 1:0 2:1 3:0 4:0.4 5:0 +1 qid:1 1:0 2:0 3:1 4:0.3 5:0 +# query 2 +1 qid:2 1:0 2:0 3:1 4:0.2 5:0 +2 qid:2 1:1 2:0 3:1 4:0.4 5:0 +1 qid:2 1:0 2:0 3:1 4:0.1 5:0 +1 qid:2 1:0 2:0 3:1 4:0.2 5:0 +# query 3 +2 qid:3 1:0 2:0 3:1 4:0.1 5:1 +3 qid:3 1:1 2:1 3:0 4:0.3 5:0 +4 qid:3 1:1 2:0 3:0 4:0.4 5:1 +1 qid:3 1:0 2:1 3:1 4:0.5 5:0
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/LICENSE.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +SVM-Light +--------- + +Available at http://svmlight.joachims.org/ + +Author: Thorsten Joachims + thorsten@joachims.org + + Cornell University + Department of Computer Science + 4153 Upson Hall + Ithaca, NY 14853 + USA + +LICENSING TERMS + +This program is granted free of charge for research and education +purposes. However you must obtain a license from the author to use it +for commercial purposes. + +Scientific results produced using the software provided shall +acknowledge the use of SVM-Light. Please cite as + + T. Joachims, Making large-Scale SVM Learning + Practical. Advances in Kernel Methods - Support Vector + Learning, B. Schölkopf and C. Burges and A. Smola (ed.), + MIT-Press, 1999. + http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_99a.pdf + +Moreover shall the author of SVM-Light be informed about the +publication. + +The software must not be modified and distributed without prior +permission of the author. + +By using SVM-Light you agree to the licensing terms. + + +NO WARRANTY + +BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY +FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT +WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER +PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, +EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE +PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME +THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + +IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR +REDISTRIBUTE THE PROGRAM, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF +THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO +LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY +YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY +OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED +OF THE POSSIBILITY OF SUCH DAMAGES.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/Makefile Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,88 @@ +# +# makefile for svm_light +# +# Thorsten Joachims, 2002 +# +# Thanks to Ralf Herbrich for the initial version. +# + +CC=gcc # used C-compiler +CFLAGS= -O3 # release C-Compiler flags +#CFLAGS= -g -Wall -pedantic # debugging C-Compiler flags +LD=gcc # used linker +LFLAGS=-O3 # linker flags +LIBS=-L. -lm # used libraries + + +all: svm_learn_hideo svm_classify + +tidy: + rm -f *.o + rm -f donlp2/*.o + rm -f donlp2_c/*.o + rm -f pr_loqo/*.o + +clean: tidy + rm -f svm_learn + rm -f svm_classify + +help: info + +info: + @echo + @echo "make for SVM-light Thorsten Joachims, 1998" + @echo + @echo "Thanks to Ralf Herbrich for the initial version." + @echo + @echo "USAGE: make [svm_learn | svm_learn_loqo | svm_learn_hideo | " + @echo " svm_classify | all | clean | tidy]" + @echo + @echo " svm_learn builds the learning module (prefers HIDEO)" + @echo " svm_learn_hideo builds the learning module using HIDEO" + @echo " svm_learn_loqo builds the learning module using PR_LOQO" + @echo " svm_classify builds the classfication module" + @echo " all (default) builds svm_learn + svm_classify" + @echo " clean removes .o and target files" + @echo " tidy removes .o files" + @echo + + +svm_learn_hideo_noexe: svm_learn_main.o svm_learn.o svm_common.o svm_hideo.o + +svm_learn_hideo: svm_learn_main.o svm_learn.o svm_common.o svm_hideo.o + $(LD) $(LFLAGS) svm_learn_main.o svm_learn.o svm_common.o svm_hideo.o -o svm_learn $(LIBS) + +svm_learn_loqo_noexe: svm_learn_main.o svm_learn.o svm_common.o svm_loqo.o loqo + +svm_learn_loqo: svm_learn_main.o svm_learn.o svm_common.o svm_loqo.o loqo + $(LD) $(LFLAGS) svm_learn_main.o svm_learn.o svm_common.o svm_loqo.o pr_loqo/pr_loqo.o -o svm_learn $(LIBS) + + +svm_hideo.o: svm_hideo.c + $(CC) -c $(CFLAGS) svm_hideo.c -o svm_hideo.o + +svm_loqo.o: svm_loqo.c + $(CC) -c $(CFLAGS) svm_loqo.c -o svm_loqo.o + +svm_common.o: svm_common.c svm_common.h kernel.h + $(CC) -c $(CFLAGS) svm_common.c -o svm_common.o + +svm_learn.o: svm_learn.c svm_common.h + $(CC) -c $(CFLAGS) svm_learn.c -o svm_learn.o + +svm_learn_main.o: svm_learn_main.c svm_learn.h svm_common.h + $(CC) -c $(CFLAGS) svm_learn_main.c -o svm_learn_main.o + + +svm_classify: svm_classify.o svm_common.o + $(LD) $(LFLAGS) svm_classify.o svm_common.o -o svm_classify $(LIBS) + +svm_classify.o: svm_classify.c svm_common.h kernel.h + $(CC) -c $(CFLAGS) svm_classify.c -o svm_classify.o + + +loqo: pr_loqo/pr_loqo.o + +pr_loqo/pr_loqo.o: pr_loqo/pr_loqo.c + $(CC) -c $(CFLAGS) pr_loqo/pr_loqo.c -o pr_loqo/pr_loqo.o +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/kernel.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,40 @@ +/************************************************************************/ +/* */ +/* kernel.h */ +/* */ +/* User defined kernel function. Feel free to plug in your own. */ +/* */ +/* Copyright: Thorsten Joachims */ +/* Date: 16.12.97 */ +/* */ +/************************************************************************/ + +/* KERNEL_PARM is defined in svm_common.h The field 'custom' is reserved for */ +/* parameters of the user defined kernel. You can also access and use */ +/* the parameters of the other kernels. Just replace the line + return((double)(1.0)); + with your own kernel. */ + + /* Example: The following computes the polynomial kernel. sprod_ss + computes the inner product between two sparse vectors. + + return((CFLOAT)pow(kernel_parm->coef_lin*sprod_ss(a->words,b->words) + +kernel_parm->coef_const,(double)kernel_parm->poly_degree)); + */ + +/* If you are implementing a kernel that is not based on a + feature/value representation, you might want to make use of the + field "userdefined" in SVECTOR. By default, this field will contain + whatever string you put behind a # sign in the example file. So, if + a line in your training file looks like + + -1 1:3 5:6 #abcdefg + + then the SVECTOR field "words" will contain the vector 1:3 5:6, and + "userdefined" will contain the string "abcdefg". */ + +double custom_kernel(KERNEL_PARM *kernel_parm, SVECTOR *a, SVECTOR *b) + /* plug in you favorite kernel */ +{ + return((double)(1.0)); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/svm_classify.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,195 @@ +/***********************************************************************/ +/* */ +/* svm_classify.c */ +/* */ +/* Classification module of Support Vector Machine. */ +/* */ +/* Author: Thorsten Joachims */ +/* Date: 02.07.02 */ +/* */ +/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */ +/* */ +/* This software is available for non-commercial use only. It must */ +/* not be modified and distributed without prior permission of the */ +/* author. The author is not responsible for implications from the */ +/* use of this software. */ +/* */ +/************************************************************************/ + +# include "svm_common.h" + +char docfile[200]; +char modelfile[200]; +char predictionsfile[200]; + +void read_input_parameters(int, char **, char *, char *, char *, long *, + long *); +void print_help(void); + + +int main (int argc, char* argv[]) +{ + DOC *doc; /* test example */ + WORD *words; + long max_docs,max_words_doc,lld; + long totdoc=0,queryid,slackid; + long correct=0,incorrect=0,no_accuracy=0; + long res_a=0,res_b=0,res_c=0,res_d=0,wnum,pred_format; + long j; + double t1,runtime=0; + double dist,doc_label,costfactor; + char *line,*comment; + FILE *predfl,*docfl; + MODEL *model; + + read_input_parameters(argc,argv,docfile,modelfile,predictionsfile, + &verbosity,&pred_format); + + nol_ll(docfile,&max_docs,&max_words_doc,&lld); /* scan size of input file */ + max_words_doc+=2; + lld+=2; + + line = (char *)my_malloc(sizeof(char)*lld); + words = (WORD *)my_malloc(sizeof(WORD)*(max_words_doc+10)); + + model=read_model(modelfile); + + if(model->kernel_parm.kernel_type == 0) { /* linear kernel */ + /* compute weight vector */ + add_weight_vector_to_linear_model(model); + } + + if(verbosity>=2) { + printf("Classifying test examples.."); fflush(stdout); + } + + if ((docfl = fopen (docfile, "r")) == NULL) + { perror (docfile); exit (1); } + if ((predfl = fopen (predictionsfile, "w")) == NULL) + { perror (predictionsfile); exit (1); } + + while((!feof(docfl)) && fgets(line,(int)lld,docfl)) { + if(line[0] == '#') continue; /* line contains comments */ + parse_document(line,words,&doc_label,&queryid,&slackid,&costfactor,&wnum, + max_words_doc,&comment); + totdoc++; + if(model->kernel_parm.kernel_type == 0) { /* linear kernel */ + for(j=0;(words[j]).wnum != 0;j++) { /* Check if feature numbers */ + if((words[j]).wnum>model->totwords) /* are not larger than in */ + (words[j]).wnum=0; /* model. Remove feature if */ + } /* necessary. */ + doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0)); + t1=get_runtime(); + dist=classify_example_linear(model,doc); + runtime+=(get_runtime()-t1); + free_example(doc,1); + } + else { /* non-linear kernel */ + doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0)); + t1=get_runtime(); + dist=classify_example(model,doc); + runtime+=(get_runtime()-t1); + free_example(doc,1); + } + if(dist>0) { + if(pred_format==0) { /* old weired output format */ + fprintf(predfl,"%.8g:+1 %.8g:-1\n",dist,-dist); + } + if(doc_label>0) correct++; else incorrect++; + if(doc_label>0) res_a++; else res_b++; + } + else { + if(pred_format==0) { /* old weired output format */ + fprintf(predfl,"%.8g:-1 %.8g:+1\n",-dist,dist); + } + if(doc_label<0) correct++; else incorrect++; + if(doc_label>0) res_c++; else res_d++; + } + if(pred_format==1) { /* output the value of decision function */ + fprintf(predfl,"%.8g\n",dist); + } + if((int)(0.01+(doc_label*doc_label)) != 1) + { no_accuracy=1; } /* test data is not binary labeled */ + if(verbosity>=2) { + if(totdoc % 100 == 0) { + printf("%ld..",totdoc); fflush(stdout); + } + } + } + free(line); + free(words); + free_model(model,1); + + if(verbosity>=2) { + printf("done\n"); + +/* Note by Gary Boone Date: 29 April 2000 */ +/* o Timing is inaccurate. The timer has 0.01 second resolution. */ +/* Because classification of a single vector takes less than */ +/* 0.01 secs, the timer was underflowing. */ + printf("Runtime (without IO) in cpu-seconds: %.2f\n", + (float)(runtime/100.0)); + + } + if((!no_accuracy) && (verbosity>=1)) { + printf("Accuracy on test set: %.2f%% (%ld correct, %ld incorrect, %ld total)\n",(float)(correct)*100.0/totdoc,correct,incorrect,totdoc); + printf("Precision/recall on test set: %.2f%%/%.2f%%\n",(float)(res_a)*100.0/(res_a+res_b),(float)(res_a)*100.0/(res_a+res_c)); + } + + return(0); +} + +void read_input_parameters(int argc, char **argv, char *docfile, + char *modelfile, char *predictionsfile, + long int *verbosity, long int *pred_format) +{ + long i; + + /* set default */ + strcpy (modelfile, "svm_model"); + strcpy (predictionsfile, "svm_predictions"); + (*verbosity)=2; + (*pred_format)=1; + + for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) { + switch ((argv[i])[1]) + { + case 'h': print_help(); exit(0); + case 'v': i++; (*verbosity)=atol(argv[i]); break; + case 'f': i++; (*pred_format)=atol(argv[i]); break; + default: printf("\nUnrecognized option %s!\n\n",argv[i]); + print_help(); + exit(0); + } + } + if((i+1)>=argc) { + printf("\nNot enough input parameters!\n\n"); + print_help(); + exit(0); + } + strcpy (docfile, argv[i]); + strcpy (modelfile, argv[i+1]); + if((i+2)<argc) { + strcpy (predictionsfile, argv[i+2]); + } + if(((*pred_format) != 0) && ((*pred_format) != 1)) { + printf("\nOutput format can only take the values 0 or 1!\n\n"); + print_help(); + exit(0); + } +} + +void print_help(void) +{ + printf("\nSVM-light %s: Support Vector Machine, classification module %s\n",VERSION,VERSION_DATE); + copyright_notice(); + printf(" usage: svm_classify [options] example_file model_file output_file\n\n"); + printf("options: -h -> this help\n"); + printf(" -v [0..3] -> verbosity level (default 2)\n"); + printf(" -f [0,1] -> 0: old output format of V1.0\n"); + printf(" -> 1: output the value of decision function (default)\n\n"); +} + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/svm_common.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,984 @@ +/************************************************************************/ +/* */ +/* svm_common.c */ +/* */ +/* Definitions and functions used in both svm_learn and svm_classify. */ +/* */ +/* Author: Thorsten Joachims */ +/* Date: 02.07.04 */ +/* */ +/* Copyright (c) 2004 Thorsten Joachims - All rights reserved */ +/* */ +/* This software is available for non-commercial use only. It must */ +/* not be modified and distributed without prior permission of the */ +/* author. The author is not responsible for implications from the */ +/* use of this software. */ +/* */ +/************************************************************************/ + +# include "ctype.h" +# include "svm_common.h" +# include "kernel.h" /* this contains a user supplied kernel */ + +long verbosity; /* verbosity level (0-4) */ +long kernel_cache_statistic; + +double classify_example(MODEL *model, DOC *ex) + /* classifies one example */ +{ + register long i; + register double dist; + + if((model->kernel_parm.kernel_type == LINEAR) && (model->lin_weights)) + return(classify_example_linear(model,ex)); + + dist=0; + for(i=1;i<model->sv_num;i++) { + dist+=kernel(&model->kernel_parm,model->supvec[i],ex)*model->alpha[i]; + } + return(dist-model->b); +} + +double classify_example_linear(MODEL *model, DOC *ex) + /* classifies example for linear kernel */ + + /* important: the model must have the linear weight vector computed */ + /* use: add_weight_vector_to_linear_model(&model); */ + + + /* important: the feature numbers in the example to classify must */ + /* not be larger than the weight vector! */ +{ + double sum=0; + SVECTOR *f; + + for(f=ex->fvec;f;f=f->next) + sum+=f->factor*sprod_ns(model->lin_weights,f); + return(sum-model->b); +} + + +CFLOAT kernel(KERNEL_PARM *kernel_parm, DOC *a, DOC *b) + /* calculate the kernel function */ +{ + double sum=0; + SVECTOR *fa,*fb; + + /* in case the constraints are sums of feature vector as represented + as a list of SVECTOR's with their coefficient factor in the sum, + take the kernel between all pairs */ + for(fa=a->fvec;fa;fa=fa->next) { + for(fb=b->fvec;fb;fb=fb->next) { + if(fa->kernel_id == fb->kernel_id) + sum+=fa->factor*fb->factor*single_kernel(kernel_parm,fa,fb); + } + } + return(sum); +} + +CFLOAT single_kernel(KERNEL_PARM *kernel_parm, SVECTOR *a, SVECTOR *b) + /* calculate the kernel function between two vectors */ +{ + kernel_cache_statistic++; + switch(kernel_parm->kernel_type) { + case 0: /* linear */ + return((CFLOAT)sprod_ss(a,b)); + case 1: /* polynomial */ + return((CFLOAT)pow(kernel_parm->coef_lin*sprod_ss(a,b)+kernel_parm->coef_const,(double)kernel_parm->poly_degree)); + case 2: /* radial basis function */ + return((CFLOAT)exp(-kernel_parm->rbf_gamma*(a->twonorm_sq-2*sprod_ss(a,b)+b->twonorm_sq))); + case 3: /* sigmoid neural net */ + return((CFLOAT)tanh(kernel_parm->coef_lin*sprod_ss(a,b)+kernel_parm->coef_const)); + case 4: /* custom-kernel supplied in file kernel.h*/ + return((CFLOAT)custom_kernel(kernel_parm,a,b)); + default: printf("Error: Unknown kernel function\n"); exit(1); + } +} + + +SVECTOR *create_svector(WORD *words,char *userdefined,double factor) +{ + SVECTOR *vec; + long fnum,i; + + fnum=0; + while(words[fnum].wnum) { + fnum++; + } + fnum++; + vec = (SVECTOR *)my_malloc(sizeof(SVECTOR)); + vec->words = (WORD *)my_malloc(sizeof(WORD)*(fnum)); + for(i=0;i<fnum;i++) { + vec->words[i]=words[i]; + } + vec->twonorm_sq=sprod_ss(vec,vec); + + fnum=0; + while(userdefined[fnum]) { + fnum++; + } + fnum++; + vec->userdefined = (char *)my_malloc(sizeof(char)*(fnum)); + for(i=0;i<fnum;i++) { + vec->userdefined[i]=userdefined[i]; + } + vec->kernel_id=0; + vec->next=NULL; + vec->factor=factor; + return(vec); +} + +SVECTOR *copy_svector(SVECTOR *vec) +{ + SVECTOR *newvec=NULL; + if(vec) { + newvec=create_svector(vec->words,vec->userdefined,vec->factor); + newvec->next=copy_svector(vec->next); + } + return(newvec); +} + +void free_svector(SVECTOR *vec) +{ + if(vec) { + free(vec->words); + if(vec->userdefined) + free(vec->userdefined); + free_svector(vec->next); + free(vec); + } +} + +double sprod_ss(SVECTOR *a, SVECTOR *b) + /* compute the inner product of two sparse vectors */ +{ + register CFLOAT sum=0; + register WORD *ai,*bj; + ai=a->words; + bj=b->words; + while (ai->wnum && bj->wnum) { + if(ai->wnum > bj->wnum) { + bj++; + } + else if (ai->wnum < bj->wnum) { + ai++; + } + else { + sum+=(CFLOAT)(ai->weight) * (CFLOAT)(bj->weight); + ai++; + bj++; + } + } + return((double)sum); +} + +SVECTOR* sub_ss(SVECTOR *a, SVECTOR *b) + /* compute the difference a-b of two sparse vectors */ + /* Note: SVECTOR lists are not followed, but only the first + SVECTOR is used */ +{ + SVECTOR *vec; + register WORD *sum,*sumi; + register WORD *ai,*bj; + long veclength; + + ai=a->words; + bj=b->words; + veclength=0; + while (ai->wnum && bj->wnum) { + if(ai->wnum > bj->wnum) { + veclength++; + bj++; + } + else if (ai->wnum < bj->wnum) { + veclength++; + ai++; + } + else { + veclength++; + ai++; + bj++; + } + } + while (bj->wnum) { + veclength++; + bj++; + } + while (ai->wnum) { + veclength++; + ai++; + } + veclength++; + + sum=(WORD *)my_malloc(sizeof(WORD)*veclength); + sumi=sum; + ai=a->words; + bj=b->words; + while (ai->wnum && bj->wnum) { + if(ai->wnum > bj->wnum) { + (*sumi)=(*bj); + sumi->weight*=(-1); + sumi++; + bj++; + } + else if (ai->wnum < bj->wnum) { + (*sumi)=(*ai); + sumi++; + ai++; + } + else { + (*sumi)=(*ai); + sumi->weight-=bj->weight; + if(sumi->weight != 0) + sumi++; + ai++; + bj++; + } + } + while (bj->wnum) { + (*sumi)=(*bj); + sumi->weight*=(-1); + sumi++; + bj++; + } + while (ai->wnum) { + (*sumi)=(*ai); + sumi++; + ai++; + } + sumi->wnum=0; + + vec=create_svector(sum,"",1.0); + free(sum); + + return(vec); +} + +SVECTOR* add_ss(SVECTOR *a, SVECTOR *b) + /* compute the sum a+b of two sparse vectors */ + /* Note: SVECTOR lists are not followed, but only the first + SVECTOR is used */ +{ + SVECTOR *vec; + register WORD *sum,*sumi; + register WORD *ai,*bj; + long veclength; + + ai=a->words; + bj=b->words; + veclength=0; + while (ai->wnum && bj->wnum) { + if(ai->wnum > bj->wnum) { + veclength++; + bj++; + } + else if (ai->wnum < bj->wnum) { + veclength++; + ai++; + } + else { + veclength++; + ai++; + bj++; + } + } + while (bj->wnum) { + veclength++; + bj++; + } + while (ai->wnum) { + veclength++; + ai++; + } + veclength++; + + /*** is veclength=lengSequence(a)+lengthSequence(b)? ***/ + + sum=(WORD *)my_malloc(sizeof(WORD)*veclength); + sumi=sum; + ai=a->words; + bj=b->words; + while (ai->wnum && bj->wnum) { + if(ai->wnum > bj->wnum) { + (*sumi)=(*bj); + sumi++; + bj++; + } + else if (ai->wnum < bj->wnum) { + (*sumi)=(*ai); + sumi++; + ai++; + } + else { + (*sumi)=(*ai); + sumi->weight+=bj->weight; + if(sumi->weight != 0) + sumi++; + ai++; + bj++; + } + } + while (bj->wnum) { + (*sumi)=(*bj); + sumi++; + bj++; + } + while (ai->wnum) { + (*sumi)=(*ai); + sumi++; + ai++; + } + sumi->wnum=0; + + vec=create_svector(sum,"",1.0); + free(sum); + + return(vec); +} + +SVECTOR* add_list_ss(SVECTOR *a) + /* computes the linear combination of the SVECTOR list weighted + by the factor of each SVECTOR */ +{ + SVECTOR *scaled,*oldsum,*sum,*f; + WORD empty[2]; + + if(a){ + sum=smult_s(a,a->factor); + for(f=a->next;f;f=f->next) { + scaled=smult_s(f,f->factor); + oldsum=sum; + sum=add_ss(sum,scaled); + free_svector(oldsum); + free_svector(scaled); + } + sum->factor=1.0; + } + else { + empty[0].wnum=0; + sum=create_svector(empty,"",1.0); + } + return(sum); +} + +void append_svector_list(SVECTOR *a, SVECTOR *b) + /* appends SVECTOR b to the end of SVECTOR a. */ +{ + SVECTOR *f; + + for(f=a;f->next;f=f->next); /* find end of first vector list */ + f->next=b; /* append the two vector lists */ +} + +SVECTOR* smult_s(SVECTOR *a, double factor) + /* scale sparse vector a by factor */ +{ + SVECTOR *vec; + register WORD *sum,*sumi; + register WORD *ai; + long veclength; + + ai=a->words; + veclength=0; + while (ai->wnum) { + veclength++; + ai++; + } + veclength++; + + sum=(WORD *)my_malloc(sizeof(WORD)*veclength); + sumi=sum; + ai=a->words; + while (ai->wnum) { + (*sumi)=(*ai); + sumi->weight*=factor; + if(sumi->weight != 0) + sumi++; + ai++; + } + sumi->wnum=0; + + vec=create_svector(sum,a->userdefined,a->factor); + free(sum); + + return(vec); +} + +int featvec_eq(SVECTOR *a, SVECTOR *b) + /* tests two sparse vectors for equality */ +{ + register WORD *ai,*bj; + ai=a->words; + bj=b->words; + while (ai->wnum && bj->wnum) { + if(ai->wnum > bj->wnum) { + if((CFLOAT)(bj->weight) != 0) + return(0); + bj++; + } + else if (ai->wnum < bj->wnum) { + if((CFLOAT)(ai->weight) != 0) + return(0); + ai++; + } + else { + if((CFLOAT)(ai->weight) != (CFLOAT)(bj->weight)) + return(0); + ai++; + bj++; + } + } + return(1); +} + +double model_length_s(MODEL *model, KERNEL_PARM *kernel_parm) + /* compute length of weight vector */ +{ + register long i,j; + register double sum=0,alphai; + register DOC *supveci; + + for(i=1;i<model->sv_num;i++) { + alphai=model->alpha[i]; + supveci=model->supvec[i]; + for(j=1;j<model->sv_num;j++) { + sum+=alphai*model->alpha[j] + *kernel(kernel_parm,supveci,model->supvec[j]); + } + } + return(sqrt(sum)); +} + +void clear_vector_n(double *vec, long int n) +{ + register long i; + for(i=0;i<=n;i++) vec[i]=0; +} + +void add_vector_ns(double *vec_n, SVECTOR *vec_s, double faktor) +{ + register WORD *ai; + ai=vec_s->words; + while (ai->wnum) { + vec_n[ai->wnum]+=(faktor*ai->weight); + ai++; + } +} + +double sprod_ns(double *vec_n, SVECTOR *vec_s) +{ + register double sum=0; + register WORD *ai; + ai=vec_s->words; + while (ai->wnum) { + sum+=(vec_n[ai->wnum]*ai->weight); + ai++; + } + return(sum); +} + +void add_weight_vector_to_linear_model(MODEL *model) + /* compute weight vector in linear case and add to model */ +{ + long i; + SVECTOR *f; + + model->lin_weights=(double *)my_malloc(sizeof(double)*(model->totwords+1)); + clear_vector_n(model->lin_weights,model->totwords); + for(i=1;i<model->sv_num;i++) { + for(f=(model->supvec[i])->fvec;f;f=f->next) + add_vector_ns(model->lin_weights,f,f->factor*model->alpha[i]); + } +} + + +DOC *create_example(long docnum, long queryid, long slackid, + double costfactor, SVECTOR *fvec) +{ + DOC *example; + example = (DOC *)my_malloc(sizeof(DOC)); + example->docnum=docnum; + example->queryid=queryid; + example->slackid=slackid; + example->costfactor=costfactor; + example->fvec=fvec; + return(example); +} + +void free_example(DOC *example, long deep) +{ + if(example) { + if(deep) { + if(example->fvec) + free_svector(example->fvec); + } + free(example); + } +} + +void write_model(char *modelfile, MODEL *model) +{ + FILE *modelfl; + long j,i,sv_num; + SVECTOR *v; + + if(verbosity>=1) { + printf("Writing model file..."); fflush(stdout); + } + if ((modelfl = fopen (modelfile, "w")) == NULL) + { perror (modelfile); exit (1); } + fprintf(modelfl,"SVM-light Version %s\n",VERSION); + fprintf(modelfl,"%ld # kernel type\n", + model->kernel_parm.kernel_type); + fprintf(modelfl,"%ld # kernel parameter -d \n", + model->kernel_parm.poly_degree); + fprintf(modelfl,"%.8g # kernel parameter -g \n", + model->kernel_parm.rbf_gamma); + fprintf(modelfl,"%.8g # kernel parameter -s \n", + model->kernel_parm.coef_lin); + fprintf(modelfl,"%.8g # kernel parameter -r \n", + model->kernel_parm.coef_const); + fprintf(modelfl,"%s# kernel parameter -u \n",model->kernel_parm.custom); + fprintf(modelfl,"%ld # highest feature index \n",model->totwords); + fprintf(modelfl,"%ld # number of training documents \n",model->totdoc); + + sv_num=1; + for(i=1;i<model->sv_num;i++) { + for(v=model->supvec[i]->fvec;v;v=v->next) + sv_num++; + } + fprintf(modelfl,"%ld # number of support vectors plus 1 \n",sv_num); + fprintf(modelfl,"%.8g # threshold b, each following line is a SV (starting with alpha*y)\n",model->b); + + for(i=1;i<model->sv_num;i++) { + for(v=model->supvec[i]->fvec;v;v=v->next) { + fprintf(modelfl,"%.32g ",model->alpha[i]*v->factor); + for (j=0; (v->words[j]).wnum; j++) { + fprintf(modelfl,"%ld:%.8g ", + (long)(v->words[j]).wnum, + (double)(v->words[j]).weight); + } + fprintf(modelfl,"#%s\n",v->userdefined); + /* NOTE: this could be made more efficient by summing the + alpha's of identical vectors before writing them to the + file. */ + } + } + fclose(modelfl); + if(verbosity>=1) { + printf("done\n"); + } +} + + +MODEL *read_model(char *modelfile) +{ + FILE *modelfl; + long i,queryid,slackid; + double costfactor; + long max_sv,max_words,ll,wpos; + char *line,*comment; + WORD *words; + char version_buffer[100]; + MODEL *model; + + if(verbosity>=1) { + printf("Reading model..."); fflush(stdout); + } + + nol_ll(modelfile,&max_sv,&max_words,&ll); /* scan size of model file */ + max_words+=2; + ll+=2; + + words = (WORD *)my_malloc(sizeof(WORD)*(max_words+10)); + line = (char *)my_malloc(sizeof(char)*ll); + model = (MODEL *)my_malloc(sizeof(MODEL)); + + if ((modelfl = fopen (modelfile, "r")) == NULL) + { perror (modelfile); exit (1); } + + fscanf(modelfl,"SVM-light Version %s\n",version_buffer); + if(strcmp(version_buffer,VERSION)) { + perror ("Version of model-file does not match version of svm_classify!"); + exit (1); + } + fscanf(modelfl,"%ld%*[^\n]\n", &model->kernel_parm.kernel_type); + fscanf(modelfl,"%ld%*[^\n]\n", &model->kernel_parm.poly_degree); + fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.rbf_gamma); + fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.coef_lin); + fscanf(modelfl,"%lf%*[^\n]\n", &model->kernel_parm.coef_const); + fscanf(modelfl,"%[^#]%*[^\n]\n", model->kernel_parm.custom); + + fscanf(modelfl,"%ld%*[^\n]\n", &model->totwords); + fscanf(modelfl,"%ld%*[^\n]\n", &model->totdoc); + fscanf(modelfl,"%ld%*[^\n]\n", &model->sv_num); + fscanf(modelfl,"%lf%*[^\n]\n", &model->b); + + model->supvec = (DOC **)my_malloc(sizeof(DOC *)*model->sv_num); + model->alpha = (double *)my_malloc(sizeof(double)*model->sv_num); + model->index=NULL; + model->lin_weights=NULL; + + for(i=1;i<model->sv_num;i++) { + fgets(line,(int)ll,modelfl); + if(!parse_document(line,words,&(model->alpha[i]),&queryid,&slackid, + &costfactor,&wpos,max_words,&comment)) { + printf("\nParsing error while reading model file in SV %ld!\n%s", + i,line); + exit(1); + } + model->supvec[i] = create_example(-1, + 0,0, + 0.0, + create_svector(words,comment,1.0)); + } + fclose(modelfl); + free(line); + free(words); + if(verbosity>=1) { + fprintf(stdout, "OK. (%d support vectors read)\n",(int)(model->sv_num-1)); + } + return(model); +} + +MODEL *copy_model(MODEL *model) +{ + MODEL *newmodel; + long i; + + newmodel=(MODEL *)my_malloc(sizeof(MODEL)); + (*newmodel)=(*model); + newmodel->supvec = (DOC **)my_malloc(sizeof(DOC *)*model->sv_num); + newmodel->alpha = (double *)my_malloc(sizeof(double)*model->sv_num); + newmodel->index = NULL; /* index is not copied */ + newmodel->supvec[0] = NULL; + newmodel->alpha[0] = 0; + for(i=1;i<model->sv_num;i++) { + newmodel->alpha[i]=model->alpha[i]; + newmodel->supvec[i]=create_example(model->supvec[i]->docnum, + model->supvec[i]->queryid,0, + model->supvec[i]->costfactor, + copy_svector(model->supvec[i]->fvec)); + } + if(model->lin_weights) { + newmodel->lin_weights = (double *)my_malloc(sizeof(double)*(model->totwords+1)); + for(i=0;i<model->totwords+1;i++) + newmodel->lin_weights[i]=model->lin_weights[i]; + } + return(newmodel); +} + +void free_model(MODEL *model, int deep) +{ + long i; + + if(model->supvec) { + if(deep) { + for(i=1;i<model->sv_num;i++) { + free_example(model->supvec[i],1); + } + } + free(model->supvec); + } + if(model->alpha) free(model->alpha); + if(model->index) free(model->index); + if(model->lin_weights) free(model->lin_weights); + free(model); +} + + +void read_documents(char *docfile, DOC ***docs, double **label, + long int *totwords, long int *totdoc) +{ + char *line,*comment; + WORD *words; + long dnum=0,wpos,dpos=0,dneg=0,dunlab=0,queryid,slackid,max_docs; + long max_words_doc, ll; + double doc_label,costfactor; + FILE *docfl; + + if(verbosity>=1) { + printf("Scanning examples..."); fflush(stdout); + } + nol_ll(docfile,&max_docs,&max_words_doc,&ll); /* scan size of input file */ + max_words_doc+=2; + ll+=2; + max_docs+=2; + if(verbosity>=1) { + printf("done\n"); fflush(stdout); + } + + (*docs) = (DOC **)my_malloc(sizeof(DOC *)*max_docs); /* feature vectors */ + (*label) = (double *)my_malloc(sizeof(double)*max_docs); /* target values */ + line = (char *)my_malloc(sizeof(char)*ll); + + if ((docfl = fopen (docfile, "r")) == NULL) + { perror (docfile); exit (1); } + + words = (WORD *)my_malloc(sizeof(WORD)*(max_words_doc+10)); + if(verbosity>=1) { + printf("Reading examples into memory..."); fflush(stdout); + } + dnum=0; + (*totwords)=0; + while((!feof(docfl)) && fgets(line,(int)ll,docfl)) { + if(line[0] == '#') continue; /* line contains comments */ + if(!parse_document(line,words,&doc_label,&queryid,&slackid,&costfactor, + &wpos,max_words_doc,&comment)) { + printf("\nParsing error in line %ld!\n%s",dnum,line); + exit(1); + } + (*label)[dnum]=doc_label; + /* printf("docnum=%ld: Class=%f ",dnum,doc_label); */ + if(doc_label > 0) dpos++; + if (doc_label < 0) dneg++; + if (doc_label == 0) dunlab++; + if((wpos>1) && ((words[wpos-2]).wnum>(*totwords))) + (*totwords)=(words[wpos-2]).wnum; + if((*totwords) > MAXFEATNUM) { + printf("\nMaximum feature number exceeds limit defined in MAXFEATNUM!\n"); + printf("LINE: %s\n",line); + exit(1); + } + (*docs)[dnum] = create_example(dnum,queryid,slackid,costfactor, + create_svector(words,comment,1.0)); + /* printf("\nNorm=%f\n",((*docs)[dnum]->fvec)->twonorm_sq); */ + dnum++; + if(verbosity>=1) { + if((dnum % 100) == 0) { + printf("%ld..",dnum); fflush(stdout); + } + } + } + + fclose(docfl); + free(line); + free(words); + if(verbosity>=1) { + fprintf(stdout, "OK. (%ld examples read)\n", dnum); + } + (*totdoc)=dnum; +} + +int parse_document(char *line, WORD *words, double *label, + long *queryid, long *slackid, double *costfactor, + long int *numwords, long int max_words_doc, + char **comment) +{ + register long wpos,pos; + long wnum; + double weight; + int numread; + char featurepair[1000],junk[1000]; + + (*queryid)=0; + (*slackid)=0; + (*costfactor)=1; + + pos=0; + (*comment)=NULL; + while(line[pos] ) { /* cut off comments */ + if((line[pos] == '#') && (!(*comment))) { + line[pos]=0; + (*comment)=&(line[pos+1]); + } + if(line[pos] == '\n') { /* strip the CR */ + line[pos]=0; + } + pos++; + } + if(!(*comment)) (*comment)=&(line[pos]); + /* printf("Comment: '%s'\n",(*comment)); */ + + wpos=0; + /* check, that line starts with target value or zero, but not with + feature pair */ + if(sscanf(line,"%s",featurepair) == EOF) return(0); + pos=0; + while((featurepair[pos] != ':') && featurepair[pos]) pos++; + if(featurepair[pos] == ':') { + perror ("Line must start with label or 0!!!\n"); + printf("LINE: %s\n",line); + exit (1); + } + /* read the target value */ + if(sscanf(line,"%lf",label) == EOF) return(0); + pos=0; + while(space_or_null((int)line[pos])) pos++; + while((!space_or_null((int)line[pos])) && line[pos]) pos++; + while(((numread=sscanf(line+pos,"%s",featurepair)) != EOF) && + (numread > 0) && + (wpos<max_words_doc)) { + /* printf("%s\n",featurepair); */ + while(space_or_null((int)line[pos])) pos++; + while((!space_or_null((int)line[pos])) && line[pos]) pos++; + if(sscanf(featurepair,"qid:%ld%s",&wnum,junk)==1) { + /* it is the query id */ + (*queryid)=(long)wnum; + } + else if(sscanf(featurepair,"sid:%ld%s",&wnum,junk)==1) { + /* it is the slack id */ + if(wnum > 0) + (*slackid)=(long)wnum; + else { + perror ("Slack-id must be greater or equal to 1!!!\n"); + printf("LINE: %s\n",line); + exit (1); + } + } + else if(sscanf(featurepair,"cost:%lf%s",&weight,junk)==1) { + /* it is the example-dependent cost factor */ + (*costfactor)=(double)weight; + } + else if(sscanf(featurepair,"%ld:%lf%s",&wnum,&weight,junk)==2) { + /* it is a regular feature */ + if(wnum<=0) { + perror ("Feature numbers must be larger or equal to 1!!!\n"); + printf("LINE: %s\n",line); + exit (1); + } + if((wpos>0) && ((words[wpos-1]).wnum >= wnum)) { + perror ("Features must be in increasing order!!!\n"); + printf("LINE: %s\n",line); + exit (1); + } + (words[wpos]).wnum=wnum; + (words[wpos]).weight=(FVAL)weight; + wpos++; + } + else { + perror ("Cannot parse feature/value pair!!!\n"); + printf("'%s' in LINE: %s\n",featurepair,line); + exit (1); + } + } + (words[wpos]).wnum=0; + (*numwords)=wpos+1; + return(1); +} + +double *read_alphas(char *alphafile,long totdoc) + /* reads the alpha vector from a file as written by the + write_alphas function */ +{ + FILE *fl; + double *alpha; + long dnum; + + if ((fl = fopen (alphafile, "r")) == NULL) + { perror (alphafile); exit (1); } + + alpha = (double *)my_malloc(sizeof(double)*totdoc); + if(verbosity>=1) { + printf("Reading alphas..."); fflush(stdout); + } + dnum=0; + while((!feof(fl)) && fscanf(fl,"%lf\n",&alpha[dnum]) && (dnum<totdoc)) { + dnum++; + } + if(dnum != totdoc) + { perror ("\nNot enough values in alpha file!"); exit (1); } + fclose(fl); + + if(verbosity>=1) { + printf("done\n"); fflush(stdout); + } + + return(alpha); +} + +void nol_ll(char *file, long int *nol, long int *wol, long int *ll) + /* Grep through file and count number of lines, maximum number of + spaces per line, and longest line. */ +{ + FILE *fl; + int ic; + char c; + long current_length,current_wol; + + if ((fl = fopen (file, "r")) == NULL) + { perror (file); exit (1); } + current_length=0; + current_wol=0; + (*ll)=0; + (*nol)=1; + (*wol)=0; + while((ic=getc(fl)) != EOF) { + c=(char)ic; + current_length++; + if(space_or_null((int)c)) { + current_wol++; + } + if(c == '\n') { + (*nol)++; + if(current_length>(*ll)) { + (*ll)=current_length; + } + if(current_wol>(*wol)) { + (*wol)=current_wol; + } + current_length=0; + current_wol=0; + } + } + fclose(fl); +} + +long minl(long int a, long int b) +{ + if(a<b) + return(a); + else + return(b); +} + +long maxl(long int a, long int b) +{ + if(a>b) + return(a); + else + return(b); +} + +long get_runtime(void) +{ + clock_t start; + start = clock(); + return((long)((double)start*100.0/(double)CLOCKS_PER_SEC)); +} + + +# ifdef _MSC_VER + +int isnan(double a) +{ + return(_isnan(a)); +} + +# endif + +int space_or_null(int c) { + if (c==0) + return 1; + return isspace(c); +} + +void *my_malloc(size_t size) +{ + void *ptr; + ptr=(void *)malloc(size); + if(!ptr) { + perror ("Out of memory!\n"); + exit (1); + } + return(ptr); +} + +void copyright_notice(void) +{ + printf("\nCopyright: Thorsten Joachims, thorsten@joachims.org\n\n"); + printf("This software is available for non-commercial use only. It must not\n"); + printf("be modified and distributed without prior permission of the author.\n"); + printf("The author is not responsible for implications from the use of this\n"); + printf("software.\n\n"); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/svm_common.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,301 @@ +/************************************************************************/ +/* */ +/* svm_common.h */ +/* */ +/* Definitions and functions used in both svm_learn and svm_classify. */ +/* */ +/* Author: Thorsten Joachims */ +/* Date: 02.07.02 */ +/* */ +/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */ +/* */ +/* This software is available for non-commercial use only. It must */ +/* not be modified and distributed without prior permission of the */ +/* author. The author is not responsible for implications from the */ +/* use of this software. */ +/* */ +/************************************************************************/ + +#ifndef SVM_COMMON +#define SVM_COMMON + +# define MAXSHRINK 50000 /* maximum number of shrinking rounds */ +# define MAXFEATNUM 99999999 /* maximum feature number (must be in + valid range of long int type!) */ + +# include <stdio.h> +# include <ctype.h> +# include <math.h> +# include <string.h> +# include <stdlib.h> +# include <time.h> +# include <float.h> + +# define VERSION "V6.01" +# define VERSION_DATE "01.09.04" + +# define CFLOAT float /* the type of float to use for caching */ + /* kernel evaluations. Using float saves */ + /* us some memory, but you can use double, too */ +# define FNUM long /* the type used for storing feature ids */ +# define FVAL float /* the type used for storing feature values */ + +# define LINEAR 0 /* linear kernel type */ +# define POLY 1 /* polynoial kernel type */ +# define RBF 2 /* rbf kernel type */ +# define SIGMOID 3 /* sigmoid kernel type */ + +# define CLASSIFICATION 1 /* train classification model */ +# define REGRESSION 2 /* train regression model */ +# define RANKING 3 /* train ranking model */ +# define OPTIMIZATION 4 /* train on general set of constraints */ + +typedef struct word { + FNUM wnum; /* word number */ + FVAL weight; /* word weight */ +} WORD; + +typedef struct svector { + WORD *words; /* The features/values in the vector by + increasing feature-number. Feature + numbers that are skipped are + interpreted as having value zero. */ + double twonorm_sq; /* The squared euclidian length of the + vector. Used to speed up the RBF kernel. */ + char *userdefined; /* You can put additional information + here. This can be useful, if you are + implementing your own kernel that + does not work with feature/values + representations (for example a + string kernel). By default, + svm-light will put here the string + after the # sign from each line of + the input file. */ + long kernel_id; /* Feature vectors with different + kernel_id's are orthogonal (ie. the + feature number do not match). This + is used for computing component + kernels for linear constraints which + are a sum of several different + weight vectors. (currently not + implemented). */ + struct svector *next; /* Let's you set up a list of SVECTOR's + for linear constraints which are a + sum of multiple feature + vectors. List is terminated by + NULL. */ + double factor; /* Factor by which this feature vector + is multiplied in the sum. */ +} SVECTOR; + +typedef struct doc { + long docnum; /* Document ID. This has to be the position of + the document in the training set array. */ + long queryid; /* for learning rankings, constraints are + generated for documents with the same + queryID. */ + double costfactor; /* Scales the cost of misclassifying this + document by this factor. The effect of this + value is, that the upper bound on the alpha + for this example is scaled by this factor. + The factors are set by the feature + 'cost:<val>' in the training data. */ + long slackid; /* Index of the slack variable + corresponding to this + constraint. All constraints with the + same slackid share the same slack + variable. This can only be used for + svm_learn_optimization. */ + SVECTOR *fvec; /* Feature vector of the example. The + feature vector can actually be a + list of feature vectors. For + example, the list will have two + elements, if this DOC is a + preference constraint. The one + vector that is supposed to be ranked + higher, will have a factor of +1, + the lower ranked one should have a + factor of -1. */ +} DOC; + +typedef struct learn_parm { + long type; /* selects between regression and + classification */ + double svm_c; /* upper bound C on alphas */ + double eps; /* regression epsilon (eps=1.0 for + classification */ + double svm_costratio; /* factor to multiply C for positive examples */ + double transduction_posratio;/* fraction of unlabeled examples to be */ + /* classified as positives */ + long biased_hyperplane; /* if nonzero, use hyperplane w*x+b=0 + otherwise w*x=0 */ + long sharedslack; /* if nonzero, it will use the shared + slack variable mode in + svm_learn_optimization. It requires + that the slackid is set for every + training example */ + long svm_maxqpsize; /* size q of working set */ + long svm_newvarsinqp; /* new variables to enter the working set + in each iteration */ + long kernel_cache_size; /* size of kernel cache in megabytes */ + double epsilon_crit; /* tolerable error for distances used + in stopping criterion */ + double epsilon_shrink; /* how much a multiplier should be above + zero for shrinking */ + long svm_iter_to_shrink; /* iterations h after which an example can + be removed by shrinking */ + long maxiter; /* number of iterations after which the + optimizer terminates, if there was + no progress in maxdiff */ + long remove_inconsistent; /* exclude examples with alpha at C and + retrain */ + long skip_final_opt_check; /* do not check KT-Conditions at the end of + optimization for examples removed by + shrinking. WARNING: This might lead to + sub-optimal solutions! */ + long compute_loo; /* if nonzero, computes leave-one-out + estimates */ + double rho; /* parameter in xi/alpha-estimates and for + pruning leave-one-out range [1..2] */ + long xa_depth; /* parameter in xi/alpha-estimates upper + bounding the number of SV the current + alpha_t is distributed over */ + char predfile[200]; /* file for predicitions on unlabeled examples + in transduction */ + char alphafile[200]; /* file to store optimal alphas in. use + empty string if alphas should not be + output */ + + /* you probably do not want to touch the following */ + double epsilon_const; /* tolerable error on eq-constraint */ + double epsilon_a; /* tolerable error on alphas at bounds */ + double opt_precision; /* precision of solver, set to e.g. 1e-21 + if you get convergence problems */ + + /* the following are only for internal use */ + long svm_c_steps; /* do so many steps for finding optimal C */ + double svm_c_factor; /* increase C by this factor every step */ + double svm_costratio_unlab; + double svm_unlabbound; + double *svm_cost; /* individual upper bounds for each var */ + long totwords; /* number of features */ +} LEARN_PARM; + +typedef struct kernel_parm { + long kernel_type; /* 0=linear, 1=poly, 2=rbf, 3=sigmoid, 4=custom */ + long poly_degree; + double rbf_gamma; + double coef_lin; + double coef_const; + char custom[50]; /* for user supplied kernel */ +} KERNEL_PARM; + +typedef struct model { + long sv_num; + long at_upper_bound; + double b; + DOC **supvec; + double *alpha; + long *index; /* index from docnum to position in model */ + long totwords; /* number of features */ + long totdoc; /* number of training documents */ + KERNEL_PARM kernel_parm; /* kernel */ + + /* the following values are not written to file */ + double loo_error,loo_recall,loo_precision; /* leave-one-out estimates */ + double xa_error,xa_recall,xa_precision; /* xi/alpha estimates */ + double *lin_weights; /* weights for linear case using + folding */ + double maxdiff; /* precision, up to which this + model is accurate */ +} MODEL; + +typedef struct quadratic_program { + long opt_n; /* number of variables */ + long opt_m; /* number of linear equality constraints */ + double *opt_ce,*opt_ce0; /* linear equality constraints */ + double *opt_g; /* hessian of objective */ + double *opt_g0; /* linear part of objective */ + double *opt_xinit; /* initial value for variables */ + double *opt_low,*opt_up; /* box constraints */ +} QP; + +typedef struct kernel_cache { + long *index; /* cache some kernel evalutations */ + CFLOAT *buffer; /* to improve speed */ + long *invindex; + long *active2totdoc; + long *totdoc2active; + long *lru; + long *occu; + long elems; + long max_elems; + long time; + long activenum; + long buffsize; +} KERNEL_CACHE; + + +typedef struct timing_profile { + long time_kernel; + long time_opti; + long time_shrink; + long time_update; + long time_model; + long time_check; + long time_select; +} TIMING; + +typedef struct shrink_state { + long *active; + long *inactive_since; + long deactnum; + double **a_history; /* for shrinking with non-linear kernel */ + long maxhistory; + double *last_a; /* for shrinking with linear kernel */ + double *last_lin; /* for shrinking with linear kernel */ +} SHRINK_STATE; + +double classify_example(MODEL *, DOC *); +double classify_example_linear(MODEL *, DOC *); +CFLOAT kernel(KERNEL_PARM *, DOC *, DOC *); +CFLOAT single_kernel(KERNEL_PARM *, SVECTOR *, SVECTOR *); +double custom_kernel(KERNEL_PARM *, SVECTOR *, SVECTOR *); +SVECTOR *create_svector(WORD *, char *, double); +SVECTOR *copy_svector(SVECTOR *); +void free_svector(SVECTOR *); +double sprod_ss(SVECTOR *, SVECTOR *); +SVECTOR* sub_ss(SVECTOR *, SVECTOR *); +SVECTOR* add_ss(SVECTOR *, SVECTOR *); +SVECTOR* add_list_ss(SVECTOR *); +void append_svector_list(SVECTOR *a, SVECTOR *b); +SVECTOR* smult_s(SVECTOR *, double); +int featvec_eq(SVECTOR *, SVECTOR *); +double model_length_s(MODEL *, KERNEL_PARM *); +void clear_vector_n(double *, long); +void add_vector_ns(double *, SVECTOR *, double); +double sprod_ns(double *, SVECTOR *); +void add_weight_vector_to_linear_model(MODEL *); +DOC *create_example(long, long, long, double, SVECTOR *); +void free_example(DOC *, long); +MODEL *read_model(char *); +MODEL *copy_model(MODEL *); +void free_model(MODEL *, int); +void read_documents(char *, DOC ***, double **, long *, long *); +int parse_document(char *, WORD *, double *, long *, long *, double *, long *, long, char **); +double *read_alphas(char *,long); +void nol_ll(char *, long *, long *, long *); +long minl(long, long); +long maxl(long, long); +long get_runtime(void); +int space_or_null(int); +void *my_malloc(size_t); +void copyright_notice(void); +# ifdef _MSC_VER + int isnan(double); +# endif + +extern long verbosity; /* verbosity level (0-4) */ +extern long kernel_cache_statistic; + +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/svm_hideo.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1062 @@ +/***********************************************************************/ +/* */ +/* svm_hideo.c */ +/* */ +/* The Hildreth and D'Espo solver specialized for SVMs. */ +/* */ +/* Author: Thorsten Joachims */ +/* Date: 02.07.02 */ +/* */ +/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */ +/* */ +/* This software is available for non-commercial use only. It must */ +/* not be modified and distributed without prior permission of the */ +/* author. The author is not responsible for implications from the */ +/* use of this software. */ +/* */ +/***********************************************************************/ + +# include <math.h> +# include "svm_common.h" + +/* + solve the quadratic programming problem + + minimize g0 * x + 1/2 x' * G * x + subject to ce*x = ce0 + l <= x <= u + + The linear constraint vector ce can only have -1/+1 as entries +*/ + +/* Common Block Declarations */ + +long verbosity; + +# define PRIMAL_OPTIMAL 1 +# define DUAL_OPTIMAL 2 +# define MAXITER_EXCEEDED 3 +# define NAN_SOLUTION 4 +# define ONLY_ONE_VARIABLE 5 + +# define LARGEROUND 0 +# define SMALLROUND 1 + +/* /////////////////////////////////////////////////////////////// */ + +# define DEF_PRECISION 1E-5 +# define DEF_MAX_ITERATIONS 200 +# define DEF_LINDEP_SENSITIVITY 1E-8 +# define EPSILON_HIDEO 1E-20 +# define EPSILON_EQ 1E-5 + +double *optimize_qp(QP *, double *, long, double *, LEARN_PARM *); +double *primal=0,*dual=0; +long precision_violations=0; +double opt_precision=DEF_PRECISION; +long maxiter=DEF_MAX_ITERATIONS; +double lindep_sensitivity=DEF_LINDEP_SENSITIVITY; +double *buffer; +long *nonoptimal; + +long smallroundcount=0; +long roundnumber=0; + +/* /////////////////////////////////////////////////////////////// */ + +void *my_malloc(); + +int optimize_hildreth_despo(long,long,double,double,double,long,long,long,double,double *, + double *,double *,double *,double *,double *, + double *,double *,double *,long *,double *,double *); +int solve_dual(long,long,double,double,long,double *,double *,double *, + double *,double *,double *,double *,double *,double *, + double *,double *,double *,double *,long); + +void linvert_matrix(double *, long, double *, double, long *); +void lprint_matrix(double *, long); +void ladd_matrix(double *, long, double); +void lcopy_matrix(double *, long, double *); +void lswitch_rows_matrix(double *, long, long, long); +void lswitchrk_matrix(double *, long, long, long); + +double calculate_qp_objective(long, double *, double *, double *); + + + +double *optimize_qp(qp,epsilon_crit,nx,threshold,learn_parm) +QP *qp; +double *epsilon_crit; +long nx; /* Maximum number of variables in QP */ +double *threshold; +LEARN_PARM *learn_parm; +/* start the optimizer and return the optimal values */ +/* The HIDEO optimizer does not necessarily fully solve the problem. */ +/* Since it requires a strictly positive definite hessian, the solution */ +/* is restricted to a linear independent subset in case the matrix is */ +/* only semi-definite. */ +{ + long i,j; + int result; + double eq,progress; + + roundnumber++; + + if(!primal) { /* allocate memory at first call */ + primal=(double *)my_malloc(sizeof(double)*nx); + dual=(double *)my_malloc(sizeof(double)*((nx+1)*2)); + nonoptimal=(long *)my_malloc(sizeof(long)*(nx)); + buffer=(double *)my_malloc(sizeof(double)*((nx+1)*2*(nx+1)*2+ + nx*nx+2*(nx+1)*2+2*nx+1+2*nx+ + nx+nx+nx*nx)); + (*threshold)=0; + for(i=0;i<nx;i++) { + primal[i]=0; + } + } + + if(verbosity>=4) { /* really verbose */ + printf("\n\n"); + eq=qp->opt_ce0[0]; + for(i=0;i<qp->opt_n;i++) { + eq+=qp->opt_xinit[i]*qp->opt_ce[i]; + printf("%f: ",qp->opt_g0[i]); + for(j=0;j<qp->opt_n;j++) { + printf("%f ",qp->opt_g[i*qp->opt_n+j]); + } + printf(": a=%.10f < %f",qp->opt_xinit[i],qp->opt_up[i]); + printf(": y=%f\n",qp->opt_ce[i]); + } + if(qp->opt_m) { + printf("EQ: %f*x0",qp->opt_ce[0]); + for(i=1;i<qp->opt_n;i++) { + printf(" + %f*x%ld",qp->opt_ce[i],i); + } + printf(" = %f\n\n",-qp->opt_ce0[0]); + } + } + + result=optimize_hildreth_despo(qp->opt_n,qp->opt_m, + opt_precision,(*epsilon_crit), + learn_parm->epsilon_a,maxiter, + /* (long)PRIMAL_OPTIMAL, */ + (long)0, (long)0, + lindep_sensitivity, + qp->opt_g,qp->opt_g0,qp->opt_ce,qp->opt_ce0, + qp->opt_low,qp->opt_up,primal,qp->opt_xinit, + dual,nonoptimal,buffer,&progress); + if(verbosity>=3) { + printf("return(%d)...",result); + } + + if(learn_parm->totwords < learn_parm->svm_maxqpsize) { + /* larger working sets will be linear dependent anyway */ + learn_parm->svm_maxqpsize=maxl(learn_parm->totwords,(long)2); + } + + if(result == NAN_SOLUTION) { + lindep_sensitivity*=2; /* throw out linear dependent examples more */ + /* generously */ + if(learn_parm->svm_maxqpsize>2) { + learn_parm->svm_maxqpsize--; /* decrease size of qp-subproblems */ + } + precision_violations++; + } + + /* take one round of only two variable to get unstuck */ + if((result != PRIMAL_OPTIMAL) || (!(roundnumber % 31)) || (progress <= 0)) { + + smallroundcount++; + + result=optimize_hildreth_despo(qp->opt_n,qp->opt_m, + opt_precision,(*epsilon_crit), + learn_parm->epsilon_a,(long)maxiter, + (long)PRIMAL_OPTIMAL,(long)SMALLROUND, + lindep_sensitivity, + qp->opt_g,qp->opt_g0,qp->opt_ce,qp->opt_ce0, + qp->opt_low,qp->opt_up,primal,qp->opt_xinit, + dual,nonoptimal,buffer,&progress); + if(verbosity>=3) { + printf("return_srd(%d)...",result); + } + + if(result != PRIMAL_OPTIMAL) { + if(result != ONLY_ONE_VARIABLE) + precision_violations++; + if(result == MAXITER_EXCEEDED) + maxiter+=100; + if(result == NAN_SOLUTION) { + lindep_sensitivity*=2; /* throw out linear dependent examples more */ + /* generously */ + /* results not valid, so return inital values */ + for(i=0;i<qp->opt_n;i++) { + primal[i]=qp->opt_xinit[i]; + } + } + } + } + + + if(precision_violations > 50) { + precision_violations=0; + (*epsilon_crit)*=10.0; + if(verbosity>=1) { + printf("\nWARNING: Relaxing epsilon on KT-Conditions (%f).\n", + (*epsilon_crit)); + } + } + + if((qp->opt_m>0) && (result != NAN_SOLUTION) && (!isnan(dual[1]-dual[0]))) + (*threshold)=dual[1]-dual[0]; + else + (*threshold)=0; + + if(verbosity>=4) { /* really verbose */ + printf("\n\n"); + eq=qp->opt_ce0[0]; + for(i=0;i<qp->opt_n;i++) { + eq+=primal[i]*qp->opt_ce[i]; + printf("%f: ",qp->opt_g0[i]); + for(j=0;j<qp->opt_n;j++) { + printf("%f ",qp->opt_g[i*qp->opt_n+j]); + } + printf(": a=%.30f",primal[i]); + printf(": nonopti=%ld",nonoptimal[i]); + printf(": y=%f\n",qp->opt_ce[i]); + } + printf("eq-constraint=%.30f\n",eq); + printf("b=%f\n",(*threshold)); + printf(" smallroundcount=%ld ",smallroundcount); + } + + return(primal); +} + + + +int optimize_hildreth_despo(n,m,precision,epsilon_crit,epsilon_a,maxiter,goal, + smallround,lindep_sensitivity,g,g0,ce,ce0,low,up, + primal,init,dual,lin_dependent,buffer,progress) + long n; /* number of variables */ + long m; /* number of linear equality constraints [0,1] */ + double precision; /* solve at least to this dual precision */ + double epsilon_crit; /* stop, if KT-Conditions approx fulfilled */ + double epsilon_a; /* precision of alphas at bounds */ + long maxiter; /* stop after this many iterations */ + long goal; /* keep going until goal fulfilled */ + long smallround; /* use only two variables of steepest descent */ + double lindep_sensitivity; /* epsilon for detecting linear dependent ex */ + double *g; /* hessian of objective */ + double *g0; /* linear part of objective */ + double *ce,*ce0; /* linear equality constraints */ + double *low,*up; /* box constraints */ + double *primal; /* primal variables */ + double *init; /* initial values of primal */ + double *dual; /* dual variables */ + long *lin_dependent; + double *buffer; + double *progress; /* delta in the objective function between + before and after */ +{ + long i,j,k,from,to,n_indep,changed; + double sum,bmin=0,bmax=0; + double *d,*d0,*ig,*dual_old,*temp,*start; + double *g0_new,*g_new,*ce_new,*ce0_new,*low_new,*up_new; + double add,t; + int result; + double obj_before,obj_after; + long b1,b2; + double g0_b1,g0_b2,ce0_b; + + g0_new=&(buffer[0]); /* claim regions of buffer */ + d=&(buffer[n]); + d0=&(buffer[n+(n+m)*2*(n+m)*2]); + ce_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2]); + ce0_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n]); + ig=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m]); + dual_old=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n]); + low_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2]); + up_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2+n]); + start=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2+n+n]); + g_new=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2+n+n+n]); + temp=&(buffer[n+(n+m)*2*(n+m)*2+(n+m)*2+n+m+n*n+(n+m)*2+n+n+n+n*n]); + + b1=-1; + b2=-1; + for(i=0;i<n;i++) { /* get variables with steepest feasible descent */ + sum=g0[i]; + for(j=0;j<n;j++) + sum+=init[j]*g[i*n+j]; + sum=sum*ce[i]; + if(((b1==-1) || (sum<bmin)) + && (!((init[i]<=(low[i]+epsilon_a)) && (ce[i]<0.0))) + && (!((init[i]>=( up[i]-epsilon_a)) && (ce[i]>0.0))) + ) { + bmin=sum; + b1=i; + } + if(((b2==-1) || (sum>=bmax)) + && (!((init[i]<=(low[i]+epsilon_a)) && (ce[i]>0.0))) + && (!((init[i]>=( up[i]-epsilon_a)) && (ce[i]<0.0))) + ) { + bmax=sum; + b2=i; + } + } + /* in case of unbiased hyperplane, the previous projection on */ + /* equality constraint can lead to b1 or b2 being -1. */ + if((b1 == -1) || (b2 == -1)) { + b1=maxl(b1,b2); + b2=maxl(b1,b2); + } + + for(i=0;i<n;i++) { + start[i]=init[i]; + } + + /* in case both example vectors are linearly dependent */ + /* WARNING: Assumes that ce[] in {-1,1} */ + add=0; + changed=0; + if((b1 != b2) && (m==1)) { + for(i=0;i<n;i++) { /* fix other vectors */ + if(i==b1) + g0_b1=g0[i]; + if(i==b2) + g0_b2=g0[i]; + } + ce0_b=ce0[0]; + for(i=0;i<n;i++) { + if((i!=b1) && (i!=b2)) { + for(j=0;j<n;j++) { + if(j==b1) + g0_b1+=start[i]*g[i*n+j]; + if(j==b2) + g0_b2+=start[i]*g[i*n+j]; + } + ce0_b-=(start[i]*ce[i]); + } + } + if((g[b1*n+b2] == g[b1*n+b1]) && (g[b1*n+b2] == g[b2*n+b2])) { + /* printf("euqal\n"); */ + if(ce[b1] == ce[b2]) { + if(g0_b1 <= g0_b2) { /* set b1 to upper bound */ + /* printf("case +=<\n"); */ + changed=1; + t=up[b1]-init[b1]; + if((init[b2]-low[b2]) < t) { + t=init[b2]-low[b2]; + } + start[b1]=init[b1]+t; + start[b2]=init[b2]-t; + } + else if(g0_b1 > g0_b2) { /* set b2 to upper bound */ + /* printf("case +=>\n"); */ + changed=1; + t=up[b2]-init[b2]; + if((init[b1]-low[b1]) < t) { + t=init[b1]-low[b1]; + } + start[b1]=init[b1]-t; + start[b2]=init[b2]+t; + } + } + else if(((g[b1*n+b1]>0) || (g[b2*n+b2]>0))) { /* (ce[b1] != ce[b2]) */ + /* printf("case +!\n"); */ + t=((ce[b2]/ce[b1])*g0[b1]-g0[b2]+ce0[0]*(g[b1*n+b1]*ce[b2]/ce[b1]-g[b1*n+b2]/ce[b1]))/((ce[b2]*ce[b2]/(ce[b1]*ce[b1]))*g[b1*n+b1]+g[b2*n+b2]-2*(g[b1*n+b2]*ce[b2]/ce[b1]))-init[b2]; + changed=1; + if((up[b2]-init[b2]) < t) { + t=up[b2]-init[b2]; + } + if((init[b2]-low[b2]) < -t) { + t=-(init[b2]-low[b2]); + } + if((up[b1]-init[b1]) < t) { + t=(up[b1]-init[b1]); + } + if((init[b1]-low[b1]) < -t) { + t=-(init[b1]-low[b1]); + } + start[b1]=init[b1]+t; + start[b2]=init[b2]+t; + } + } + if((-g[b1*n+b2] == g[b1*n+b1]) && (-g[b1*n+b2] == g[b2*n+b2])) { + /* printf("diffeuqal\n"); */ + if(ce[b1] != ce[b2]) { + if((g0_b1+g0_b2) < 0) { /* set b1 and b2 to upper bound */ + /* printf("case -!<\n"); */ + changed=1; + t=up[b1]-init[b1]; + if((up[b2]-init[b2]) < t) { + t=up[b2]-init[b2]; + } + start[b1]=init[b1]+t; + start[b2]=init[b2]+t; + } + else if((g0_b1+g0_b2) >= 0) { /* set b1 and b2 to lower bound */ + /* printf("case -!>\n"); */ + changed=1; + t=init[b1]-low[b1]; + if((init[b2]-low[b2]) < t) { + t=init[b2]-low[b2]; + } + start[b1]=init[b1]-t; + start[b2]=init[b2]-t; + } + } + else if(((g[b1*n+b1]>0) || (g[b2*n+b2]>0))) { /* (ce[b1]==ce[b2]) */ + /* printf("case -=\n"); */ + t=((ce[b2]/ce[b1])*g0[b1]-g0[b2]+ce0[0]*(g[b1*n+b1]*ce[b2]/ce[b1]-g[b1*n+b2]/ce[b1]))/((ce[b2]*ce[b2]/(ce[b1]*ce[b1]))*g[b1*n+b1]+g[b2*n+b2]-2*(g[b1*n+b2]*ce[b2]/ce[b1]))-init[b2]; + changed=1; + if((up[b2]-init[b2]) < t) { + t=up[b2]-init[b2]; + } + if((init[b2]-low[b2]) < -t) { + t=-(init[b2]-low[b2]); + } + if((up[b1]-init[b1]) < -t) { + t=-(up[b1]-init[b1]); + } + if((init[b1]-low[b1]) < t) { + t=init[b1]-low[b1]; + } + start[b1]=init[b1]-t; + start[b2]=init[b2]+t; + } + } + } + /* if we have a biased hyperplane, then adding a constant to the */ + /* hessian does not change the solution. So that is done for examples */ + /* with zero diagonal entry, since HIDEO cannot handle them. */ + if((m>0) + && ((fabs(g[b1*n+b1]) < lindep_sensitivity) + || (fabs(g[b2*n+b2]) < lindep_sensitivity))) { + /* printf("Case 0\n"); */ + add+=0.093274; + } + /* in case both examples are linear dependent */ + else if((m>0) + && (g[b1*n+b2] != 0 && g[b2*n+b2] != 0) + && (fabs(g[b1*n+b1]/g[b1*n+b2] - g[b1*n+b2]/g[b2*n+b2]) + < lindep_sensitivity)) { + /* printf("Case lindep\n"); */ + add+=0.078274; + } + + /* special case for zero diagonal entry on unbiased hyperplane */ + if((m==0) && (b1>=0)) { + if(fabs(g[b1*n+b1]) < lindep_sensitivity) { + /* printf("Case 0b1\n"); */ + for(i=0;i<n;i++) { /* fix other vectors */ + if(i==b1) + g0_b1=g0[i]; + } + for(i=0;i<n;i++) { + if(i!=b1) { + for(j=0;j<n;j++) { + if(j==b1) + g0_b1+=start[i]*g[i*n+j]; + } + } + } + if(g0_b1<0) + start[b1]=up[b1]; + if(g0_b1>=0) + start[b1]=low[b1]; + } + } + if((m==0) && (b2>=0)) { + if(fabs(g[b2*n+b2]) < lindep_sensitivity) { + /* printf("Case 0b2\n"); */ + for(i=0;i<n;i++) { /* fix other vectors */ + if(i==b2) + g0_b2=g0[i]; + } + for(i=0;i<n;i++) { + if(i!=b2) { + for(j=0;j<n;j++) { + if(j==b2) + g0_b2+=start[i]*g[i*n+j]; + } + } + } + if(g0_b2<0) + start[b2]=up[b2]; + if(g0_b2>=0) + start[b2]=low[b2]; + } + } + + /* printf("b1=%ld,b2=%ld\n",b1,b2); */ + + lcopy_matrix(g,n,d); + if((m==1) && (add>0.0)) { + for(j=0;j<n;j++) { + for(k=0;k<n;k++) { + d[j*n+k]+=add*ce[j]*ce[k]; + } + } + } + else { + add=0.0; + } + + if(n>2) { /* switch, so that variables are better mixed */ + lswitchrk_matrix(d,n,b1,(long)0); + if(b2 == 0) + lswitchrk_matrix(d,n,b1,(long)1); + else + lswitchrk_matrix(d,n,b2,(long)1); + } + if(smallround == SMALLROUND) { + for(i=2;i<n;i++) { + lin_dependent[i]=1; + } + if(m>0) { /* for biased hyperplane, pick two variables */ + lin_dependent[0]=0; + lin_dependent[1]=0; + } + else { /* for unbiased hyperplane, pick only one variable */ + lin_dependent[0]=smallroundcount % 2; + lin_dependent[1]=(smallroundcount+1) % 2; + } + } + else { + for(i=0;i<n;i++) { + lin_dependent[i]=0; + } + } + linvert_matrix(d,n,ig,lindep_sensitivity,lin_dependent); + if(n>2) { /* now switch back */ + if(b2 == 0) { + lswitchrk_matrix(ig,n,b1,(long)1); + i=lin_dependent[1]; + lin_dependent[1]=lin_dependent[b1]; + lin_dependent[b1]=i; + } + else { + lswitchrk_matrix(ig,n,b2,(long)1); + i=lin_dependent[1]; + lin_dependent[1]=lin_dependent[b2]; + lin_dependent[b2]=i; + } + lswitchrk_matrix(ig,n,b1,(long)0); + i=lin_dependent[0]; + lin_dependent[0]=lin_dependent[b1]; + lin_dependent[b1]=i; + } + /* lprint_matrix(d,n); */ + /* lprint_matrix(ig,n); */ + + lcopy_matrix(g,n,g_new); /* restore g_new matrix */ + if(add>0) + for(j=0;j<n;j++) { + for(k=0;k<n;k++) { + g_new[j*n+k]+=add*ce[j]*ce[k]; + } + } + + for(i=0;i<n;i++) { /* fix linear dependent vectors */ + g0_new[i]=g0[i]+add*ce0[0]*ce[i]; + } + if(m>0) ce0_new[0]=-ce0[0]; + for(i=0;i<n;i++) { /* fix linear dependent vectors */ + if(lin_dependent[i]) { + for(j=0;j<n;j++) { + if(!lin_dependent[j]) { + g0_new[j]+=start[i]*g_new[i*n+j]; + } + } + if(m>0) ce0_new[0]-=(start[i]*ce[i]); + } + } + from=0; /* remove linear dependent vectors */ + to=0; + n_indep=0; + for(i=0;i<n;i++) { + if(!lin_dependent[i]) { + g0_new[n_indep]=g0_new[i]; + ce_new[n_indep]=ce[i]; + low_new[n_indep]=low[i]; + up_new[n_indep]=up[i]; + primal[n_indep]=start[i]; + n_indep++; + } + for(j=0;j<n;j++) { + if((!lin_dependent[i]) && (!lin_dependent[j])) { + ig[to]=ig[from]; + g_new[to]=g_new[from]; + to++; + } + from++; + } + } + + if(verbosity>=3) { + printf("real_qp_size(%ld)...",n_indep); + } + + /* cannot optimize with only one variable */ + if((n_indep<=1) && (m>0) && (!changed)) { + for(i=n-1;i>=0;i--) { + primal[i]=init[i]; + } + return((int)ONLY_ONE_VARIABLE); + } + + if((!changed) || (n_indep>1)) { + result=solve_dual(n_indep,m,precision,epsilon_crit,maxiter,g_new,g0_new, + ce_new,ce0_new,low_new,up_new,primal,d,d0,ig, + dual,dual_old,temp,goal); + } + else { + result=PRIMAL_OPTIMAL; + } + + j=n_indep; + for(i=n-1;i>=0;i--) { + if(!lin_dependent[i]) { + j--; + primal[i]=primal[j]; + } + else { + primal[i]=start[i]; /* leave as is */ + } + temp[i]=primal[i]; + } + + obj_before=calculate_qp_objective(n,g,g0,init); + obj_after=calculate_qp_objective(n,g,g0,primal); + (*progress)=obj_before-obj_after; + if(verbosity>=3) { + printf("before(%.30f)...after(%.30f)...result_sd(%d)...", + obj_before,obj_after,result); + } + + return((int)result); +} + + +int solve_dual(n,m,precision,epsilon_crit,maxiter,g,g0,ce,ce0,low,up,primal, + d,d0,ig,dual,dual_old,temp,goal) + /* Solves the dual using the method of Hildreth and D'Espo. */ + /* Can only handle problems with zero or exactly one */ + /* equality constraints. */ + + long n; /* number of variables */ + long m; /* number of linear equality constraints */ + double precision; /* solve at least to this dual precision */ + double epsilon_crit; /* stop, if KT-Conditions approx fulfilled */ + long maxiter; /* stop after that many iterations */ + double *g; + double *g0; /* linear part of objective */ + double *ce,*ce0; /* linear equality constraints */ + double *low,*up; /* box constraints */ + double *primal; /* variables (with initial values) */ + double *d,*d0,*ig,*dual,*dual_old,*temp; /* buffer */ + long goal; +{ + long i,j,k,iter; + double sum,w,maxviol,viol,temp1,temp2,isnantest; + double model_b,dist; + long retrain,maxfaktor,primal_optimal=0,at_bound,scalemaxiter; + double epsilon_a=1E-15,epsilon_hideo; + double eq; + + if((m<0) || (m>1)) + perror("SOLVE DUAL: inappropriate number of eq-constrains!"); + + /* + printf("\n"); + for(i=0;i<n;i++) { + printf("%f: ",g0[i]); + for(j=0;j<n;j++) { + printf("%f ",g[i*n+j]); + } + printf(": a=%.30f",primal[i]); + printf(": y=%f\n",ce[i]); + } + */ + + for(i=0;i<2*(n+m);i++) { + dual[i]=0; + dual_old[i]=0; + } + for(i=0;i<n;i++) { + for(j=0;j<n;j++) { /* dual hessian for box constraints */ + d[i*2*(n+m)+j]=ig[i*n+j]; + d[(i+n)*2*(n+m)+j]=-ig[i*n+j]; + d[i*2*(n+m)+j+n]=-ig[i*n+j]; + d[(i+n)*2*(n+m)+j+n]=ig[i*n+j]; + } + if(m>0) { + sum=0; /* dual hessian for eq constraints */ + for(j=0;j<n;j++) { + sum+=(ce[j]*ig[i*n+j]); + } + d[i*2*(n+m)+2*n]=sum; + d[i*2*(n+m)+2*n+1]=-sum; + d[(n+i)*2*(n+m)+2*n]=-sum; + d[(n+i)*2*(n+m)+2*n+1]=sum; + d[(n+n)*2*(n+m)+i]=sum; + d[(n+n+1)*2*(n+m)+i]=-sum; + d[(n+n)*2*(n+m)+(n+i)]=-sum; + d[(n+n+1)*2*(n+m)+(n+i)]=sum; + + sum=0; + for(j=0;j<n;j++) { + for(k=0;k<n;k++) { + sum+=(ce[k]*ce[j]*ig[j*n+k]); + } + } + d[(n+n)*2*(n+m)+2*n]=sum; + d[(n+n)*2*(n+m)+2*n+1]=-sum; + d[(n+n+1)*2*(n+m)+2*n]=-sum; + d[(n+n+1)*2*(n+m)+2*n+1]=sum; + } + } + + for(i=0;i<n;i++) { /* dual linear component for the box constraints */ + w=0; + for(j=0;j<n;j++) { + w+=(ig[i*n+j]*g0[j]); + } + d0[i]=up[i]+w; + d0[i+n]=-low[i]-w; + } + + if(m>0) { + sum=0; /* dual linear component for eq constraints */ + for(j=0;j<n;j++) { + for(k=0;k<n;k++) { + sum+=(ce[k]*ig[k*n+j]*g0[j]); + } + } + d0[2*n]=ce0[0]+sum; + d0[2*n+1]=-ce0[0]-sum; + } + + maxviol=999999; + iter=0; + retrain=1; + maxfaktor=1; + scalemaxiter=maxiter/5; + while((retrain) && (maxviol > 0) && (iter < (scalemaxiter*maxfaktor))) { + iter++; + + while((maxviol > precision) && (iter < (scalemaxiter*maxfaktor))) { + iter++; + maxviol=0; + for(i=0;i<2*(n+m);i++) { + sum=d0[i]; + for(j=0;j<2*(n+m);j++) { + sum+=d[i*2*(n+m)+j]*dual_old[j]; + } + sum-=d[i*2*(n+m)+i]*dual_old[i]; + dual[i]=-sum/d[i*2*(n+m)+i]; + if(dual[i]<0) dual[i]=0; + + viol=fabs(dual[i]-dual_old[i]); + if(viol>maxviol) + maxviol=viol; + dual_old[i]=dual[i]; + } + /* + printf("%d) maxviol=%20f precision=%f\n",iter,maxviol,precision); + */ + } + + if(m>0) { + for(i=0;i<n;i++) { + temp[i]=dual[i]-dual[i+n]+ce[i]*(dual[n+n]-dual[n+n+1])+g0[i]; + } + } + else { + for(i=0;i<n;i++) { + temp[i]=dual[i]-dual[i+n]+g0[i]; + } + } + for(i=0;i<n;i++) { + primal[i]=0; /* calc value of primal variables */ + for(j=0;j<n;j++) { + primal[i]+=ig[i*n+j]*temp[j]; + } + primal[i]*=-1.0; + if(primal[i]<=(low[i])) { /* clip conservatively */ + primal[i]=low[i]; + } + else if(primal[i]>=(up[i])) { + primal[i]=up[i]; + } + } + + if(m>0) + model_b=dual[n+n+1]-dual[n+n]; + else + model_b=0; + + epsilon_hideo=EPSILON_HIDEO; + for(i=0;i<n;i++) { /* check precision of alphas */ + dist=-model_b*ce[i]; + dist+=(g0[i]+1.0); + for(j=0;j<i;j++) { + dist+=(primal[j]*g[j*n+i]); + } + for(j=i;j<n;j++) { + dist+=(primal[j]*g[i*n+j]); + } + if((primal[i]<(up[i]-epsilon_hideo)) && (dist < (1.0-epsilon_crit))) { + epsilon_hideo=(up[i]-primal[i])*2.0; + } + else if((primal[i]>(low[i]+epsilon_hideo)) &&(dist>(1.0+epsilon_crit))) { + epsilon_hideo=(primal[i]-low[i])*2.0; + } + } + /* printf("\nEPSILON_HIDEO=%.30f\n",epsilon_hideo); */ + + for(i=0;i<n;i++) { /* clip alphas to bounds */ + if(primal[i]<=(low[i]+epsilon_hideo)) { + primal[i]=low[i]; + } + else if(primal[i]>=(up[i]-epsilon_hideo)) { + primal[i]=up[i]; + } + } + + retrain=0; + primal_optimal=1; + at_bound=0; + for(i=0;(i<n);i++) { /* check primal KT-Conditions */ + dist=-model_b*ce[i]; + dist+=(g0[i]+1.0); + for(j=0;j<i;j++) { + dist+=(primal[j]*g[j*n+i]); + } + for(j=i;j<n;j++) { + dist+=(primal[j]*g[i*n+j]); + } + if((primal[i]<(up[i]-epsilon_a)) && (dist < (1.0-epsilon_crit))) { + retrain=1; + primal_optimal=0; + } + else if((primal[i]>(low[i]+epsilon_a)) && (dist > (1.0+epsilon_crit))) { + retrain=1; + primal_optimal=0; + } + if((primal[i]<=(low[i]+epsilon_a)) || (primal[i]>=(up[i]-epsilon_a))) { + at_bound++; + } + /* printf("HIDEOtemp: a[%ld]=%.30f, dist=%.6f, b=%f, at_bound=%ld\n",i,primal[i],dist,model_b,at_bound); */ + } + if(m>0) { + eq=-ce0[0]; /* check precision of eq-constraint */ + for(i=0;i<n;i++) { + eq+=(ce[i]*primal[i]); + } + if((EPSILON_EQ < fabs(eq)) + /* + && !((goal==PRIMAL_OPTIMAL) + && (at_bound==n)) */ + ) { + retrain=1; + primal_optimal=0; + } + /* printf("\n eq=%.30f ce0=%f at-bound=%ld\n",eq,ce0[0],at_bound); */ + } + + if(retrain) { + precision/=10; + if(((goal == PRIMAL_OPTIMAL) && (maxfaktor < 50000)) + || (maxfaktor < 5)) { + maxfaktor++; + } + } + } + + if(!primal_optimal) { + for(i=0;i<n;i++) { + primal[i]=0; /* calc value of primal variables */ + for(j=0;j<n;j++) { + primal[i]+=ig[i*n+j]*temp[j]; + } + primal[i]*=-1.0; + if(primal[i]<=(low[i]+epsilon_a)) { /* clip conservatively */ + primal[i]=low[i]; + } + else if(primal[i]>=(up[i]-epsilon_a)) { + primal[i]=up[i]; + } + } + } + + isnantest=0; + for(i=0;i<n;i++) { /* check for isnan */ + isnantest+=primal[i]; + } + + if(m>0) { + temp1=dual[n+n+1]; /* copy the dual variables for the eq */ + temp2=dual[n+n]; /* constraints to a handier location */ + for(i=n+n+1;i>=2;i--) { + dual[i]=dual[i-2]; + } + dual[0]=temp2; + dual[1]=temp1; + isnantest+=temp1+temp2; + } + + if(isnan(isnantest)) { + return((int)NAN_SOLUTION); + } + else if(primal_optimal) { + return((int)PRIMAL_OPTIMAL); + } + else if(maxviol == 0.0) { + return((int)DUAL_OPTIMAL); + } + else { + return((int)MAXITER_EXCEEDED); + } +} + + +void linvert_matrix(matrix,depth,inverse,lindep_sensitivity,lin_dependent) +double *matrix; +long depth; +double *inverse,lindep_sensitivity; +long *lin_dependent; /* indicates the active parts of matrix on + input and output*/ +{ + long i,j,k; + double factor; + + for(i=0;i<depth;i++) { + /* lin_dependent[i]=0; */ + for(j=0;j<depth;j++) { + inverse[i*depth+j]=0.0; + } + inverse[i*depth+i]=1.0; + } + for(i=0;i<depth;i++) { + if(lin_dependent[i] || (fabs(matrix[i*depth+i])<lindep_sensitivity)) { + lin_dependent[i]=1; + } + else { + for(j=i+1;j<depth;j++) { + factor=matrix[j*depth+i]/matrix[i*depth+i]; + for(k=i;k<depth;k++) { + matrix[j*depth+k]-=(factor*matrix[i*depth+k]); + } + for(k=0;k<depth;k++) { + inverse[j*depth+k]-=(factor*inverse[i*depth+k]); + } + } + } + } + for(i=depth-1;i>=0;i--) { + if(!lin_dependent[i]) { + factor=1/matrix[i*depth+i]; + for(k=0;k<depth;k++) { + inverse[i*depth+k]*=factor; + } + matrix[i*depth+i]=1; + for(j=i-1;j>=0;j--) { + factor=matrix[j*depth+i]; + matrix[j*depth+i]=0; + for(k=0;k<depth;k++) { + inverse[j*depth+k]-=(factor*inverse[i*depth+k]); + } + } + } + } +} + +void lprint_matrix(matrix,depth) +double *matrix; +long depth; +{ + long i,j; + for(i=0;i<depth;i++) { + for(j=0;j<depth;j++) { + printf("%5.2f ",(double)(matrix[i*depth+j])); + } + printf("\n"); + } + printf("\n"); +} + +void ladd_matrix(matrix,depth,scalar) +double *matrix; +long depth; +double scalar; +{ + long i,j; + for(i=0;i<depth;i++) { + for(j=0;j<depth;j++) { + matrix[i*depth+j]+=scalar; + } + } +} + +void lcopy_matrix(matrix,depth,matrix2) +double *matrix; +long depth; +double *matrix2; +{ + long i; + + for(i=0;i<(depth)*(depth);i++) { + matrix2[i]=matrix[i]; + } +} + +void lswitch_rows_matrix(matrix,depth,r1,r2) +double *matrix; +long depth,r1,r2; +{ + long i; + double temp; + + for(i=0;i<depth;i++) { + temp=matrix[r1*depth+i]; + matrix[r1*depth+i]=matrix[r2*depth+i]; + matrix[r2*depth+i]=temp; + } +} + +void lswitchrk_matrix(matrix,depth,rk1,rk2) +double *matrix; +long depth,rk1,rk2; +{ + long i; + double temp; + + for(i=0;i<depth;i++) { + temp=matrix[rk1*depth+i]; + matrix[rk1*depth+i]=matrix[rk2*depth+i]; + matrix[rk2*depth+i]=temp; + } + for(i=0;i<depth;i++) { + temp=matrix[i*depth+rk1]; + matrix[i*depth+rk1]=matrix[i*depth+rk2]; + matrix[i*depth+rk2]=temp; + } +} + +double calculate_qp_objective(opt_n,opt_g,opt_g0,alpha) +long opt_n; +double *opt_g,*opt_g0,*alpha; +{ + double obj; + long i,j; + obj=0; /* calculate objective */ + for(i=0;i<opt_n;i++) { + obj+=(opt_g0[i]*alpha[i]); + obj+=(0.5*alpha[i]*alpha[i]*opt_g[i*opt_n+i]); + for(j=0;j<i;j++) { + obj+=(alpha[j]*alpha[i]*opt_g[j*opt_n+i]); + } + } + return(obj); +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/svm_learn.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4147 @@ +/***********************************************************************/ +/* */ +/* svm_learn.c */ +/* */ +/* Learning module of Support Vector Machine. */ +/* */ +/* Author: Thorsten Joachims */ +/* Date: 02.07.02 */ +/* */ +/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */ +/* */ +/* This software is available for non-commercial use only. It must */ +/* not be modified and distributed without prior permission of the */ +/* author. The author is not responsible for implications from the */ +/* use of this software. */ +/* */ +/***********************************************************************/ + + +# include "svm_common.h" +# include "svm_learn.h" + + +/* interface to QP-solver */ +double *optimize_qp(QP *, double *, long, double *, LEARN_PARM *); + +/*---------------------------------------------------------------------------*/ + +/* Learns an SVM classification model based on the training data in + docs/label. The resulting model is returned in the structure + model. */ + +void svm_learn_classification(DOC **docs, double *class, long int + totdoc, long int totwords, + LEARN_PARM *learn_parm, + KERNEL_PARM *kernel_parm, + KERNEL_CACHE *kernel_cache, + MODEL *model, + double *alpha) + /* docs: Training vectors (x-part) */ + /* class: Training labels (y-part, zero if test example for + transduction) */ + /* totdoc: Number of examples in docs/label */ + /* totwords: Number of features (i.e. highest feature index) */ + /* learn_parm: Learning paramenters */ + /* kernel_parm: Kernel paramenters */ + /* kernel_cache:Initialized Cache of size totdoc, if using a kernel. + NULL if linear.*/ + /* model: Returns learning result (assumed empty before called) */ + /* alpha: Start values for the alpha variables or NULL + pointer. The new alpha values are returned after + optimization if not NULL. Array must be of size totdoc. */ +{ + long *inconsistent,i,*label; + long inconsistentnum; + long misclassified,upsupvecnum; + double loss,model_length,example_length; + double maxdiff,*lin,*a,*c; + long runtime_start,runtime_end; + long iterations; + long *unlabeled,transduction; + long heldout; + long loo_count=0,loo_count_pos=0,loo_count_neg=0,trainpos=0,trainneg=0; + long loocomputed=0,runtime_start_loo=0,runtime_start_xa=0; + double heldout_c=0,r_delta_sq=0,r_delta,r_delta_avg; + long *index,*index2dnum; + double *weights; + CFLOAT *aicache; /* buffer to keep one row of hessian */ + + double *xi_fullset; /* buffer for storing xi on full sample in loo */ + double *a_fullset; /* buffer for storing alpha on full sample in loo */ + TIMING timing_profile; + SHRINK_STATE shrink_state; + + runtime_start=get_runtime(); + timing_profile.time_kernel=0; + timing_profile.time_opti=0; + timing_profile.time_shrink=0; + timing_profile.time_update=0; + timing_profile.time_model=0; + timing_profile.time_check=0; + timing_profile.time_select=0; + kernel_cache_statistic=0; + + learn_parm->totwords=totwords; + + /* make sure -n value is reasonable */ + if((learn_parm->svm_newvarsinqp < 2) + || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) { + learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize; + } + + init_shrink_state(&shrink_state,totdoc,(long)MAXSHRINK); + + label = (long *)my_malloc(sizeof(long)*totdoc); + inconsistent = (long *)my_malloc(sizeof(long)*totdoc); + unlabeled = (long *)my_malloc(sizeof(long)*totdoc); + c = (double *)my_malloc(sizeof(double)*totdoc); + a = (double *)my_malloc(sizeof(double)*totdoc); + a_fullset = (double *)my_malloc(sizeof(double)*totdoc); + xi_fullset = (double *)my_malloc(sizeof(double)*totdoc); + lin = (double *)my_malloc(sizeof(double)*totdoc); + learn_parm->svm_cost = (double *)my_malloc(sizeof(double)*totdoc); + model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2)); + model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2)); + model->index = (long *)my_malloc(sizeof(long)*(totdoc+2)); + + model->at_upper_bound=0; + model->b=0; + model->supvec[0]=0; /* element 0 reserved and empty for now */ + model->alpha[0]=0; + model->lin_weights=NULL; + model->totwords=totwords; + model->totdoc=totdoc; + model->kernel_parm=(*kernel_parm); + model->sv_num=1; + model->loo_error=-1; + model->loo_recall=-1; + model->loo_precision=-1; + model->xa_error=-1; + model->xa_recall=-1; + model->xa_precision=-1; + inconsistentnum=0; + transduction=0; + + r_delta=estimate_r_delta(docs,totdoc,kernel_parm); + r_delta_sq=r_delta*r_delta; + + r_delta_avg=estimate_r_delta_average(docs,totdoc,kernel_parm); + if(learn_parm->svm_c == 0.0) { /* default value for C */ + learn_parm->svm_c=1.0/(r_delta_avg*r_delta_avg); + if(verbosity>=1) + printf("Setting default regularization parameter C=%.4f\n", + learn_parm->svm_c); + } + + learn_parm->eps=-1.0; /* equivalent regression epsilon for + classification */ + + for(i=0;i<totdoc;i++) { /* various inits */ + docs[i]->docnum=i; + inconsistent[i]=0; + a[i]=0; + lin[i]=0; + c[i]=0.0; + unlabeled[i]=0; + if(class[i] == 0) { + unlabeled[i]=1; + label[i]=0; + transduction=1; + } + if(class[i] > 0) { + learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio* + docs[i]->costfactor; + label[i]=1; + trainpos++; + } + else if(class[i] < 0) { + learn_parm->svm_cost[i]=learn_parm->svm_c*docs[i]->costfactor; + label[i]=-1; + trainneg++; + } + else { + learn_parm->svm_cost[i]=0; + } + } + if(verbosity>=2) { + printf("%ld positive, %ld negative, and %ld unlabeled examples.\n",trainpos,trainneg,totdoc-trainpos-trainneg); fflush(stdout); + } + + /* caching makes no sense for linear kernel */ + if(kernel_parm->kernel_type == LINEAR) { + kernel_cache = NULL; + } + + /* compute starting state for initial alpha values */ + if(alpha) { + if(verbosity>=1) { + printf("Computing starting state..."); fflush(stdout); + } + index = (long *)my_malloc(sizeof(long)*totdoc); + index2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11)); + weights=(double *)my_malloc(sizeof(double)*(totwords+1)); + aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc); + for(i=0;i<totdoc;i++) { /* create full index and clip alphas */ + index[i]=1; + alpha[i]=fabs(alpha[i]); + if(alpha[i]<0) alpha[i]=0; + if(alpha[i]>learn_parm->svm_cost[i]) alpha[i]=learn_parm->svm_cost[i]; + } + if(kernel_parm->kernel_type != LINEAR) { + for(i=0;i<totdoc;i++) /* fill kernel cache with unbounded SV */ + if((alpha[i]>0) && (alpha[i]<learn_parm->svm_cost[i]) + && (kernel_cache_space_available(kernel_cache))) + cache_kernel_row(kernel_cache,docs,i,kernel_parm); + for(i=0;i<totdoc;i++) /* fill rest of kernel cache with bounded SV */ + if((alpha[i]==learn_parm->svm_cost[i]) + && (kernel_cache_space_available(kernel_cache))) + cache_kernel_row(kernel_cache,docs,i,kernel_parm); + } + (void)compute_index(index,totdoc,index2dnum); + update_linear_component(docs,label,index2dnum,alpha,a,index2dnum,totdoc, + totwords,kernel_parm,kernel_cache,lin,aicache, + weights); + (void)calculate_svm_model(docs,label,unlabeled,lin,alpha,a,c, + learn_parm,index2dnum,index2dnum,model); + for(i=0;i<totdoc;i++) { /* copy initial alphas */ + a[i]=alpha[i]; + } + free(index); + free(index2dnum); + free(weights); + free(aicache); + if(verbosity>=1) { + printf("done.\n"); fflush(stdout); + } + } + + if(transduction) { + learn_parm->svm_iter_to_shrink=99999999; + if(verbosity >= 1) + printf("\nDeactivating Shrinking due to an incompatibility with the transductive \nlearner in the current version.\n\n"); + } + + if(transduction && learn_parm->compute_loo) { + learn_parm->compute_loo=0; + if(verbosity >= 1) + printf("\nCannot compute leave-one-out estimates for transductive learner.\n\n"); + } + + if(learn_parm->remove_inconsistent && learn_parm->compute_loo) { + learn_parm->compute_loo=0; + printf("\nCannot compute leave-one-out estimates when removing inconsistent examples.\n\n"); + } + + if(learn_parm->compute_loo && ((trainpos == 1) || (trainneg == 1))) { + learn_parm->compute_loo=0; + printf("\nCannot compute leave-one-out with only one example in one class.\n\n"); + } + + + if(verbosity==1) { + printf("Optimizing"); fflush(stdout); + } + + /* train the svm */ + iterations=optimize_to_convergence(docs,label,totdoc,totwords,learn_parm, + kernel_parm,kernel_cache,&shrink_state,model, + inconsistent,unlabeled,a,lin, + c,&timing_profile, + &maxdiff,(long)-1, + (long)1); + + if(verbosity>=1) { + if(verbosity==1) printf("done. (%ld iterations)\n",iterations); + + misclassified=0; + for(i=0;(i<totdoc);i++) { /* get final statistic */ + if((lin[i]-model->b)*(double)label[i] <= 0.0) + misclassified++; + } + + printf("Optimization finished (%ld misclassified, maxdiff=%.5f).\n", + misclassified,maxdiff); + + runtime_end=get_runtime(); + if(verbosity>=2) { + printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n", + ((float)runtime_end-(float)runtime_start)/100.0, + (100.0*timing_profile.time_kernel)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_opti)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_shrink)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_update)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_model)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_check)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_select)/(float)(runtime_end-runtime_start)); + } + else { + printf("Runtime in cpu-seconds: %.2f\n", + (runtime_end-runtime_start)/100.0); + } + + if(learn_parm->remove_inconsistent) { + inconsistentnum=0; + for(i=0;i<totdoc;i++) + if(inconsistent[i]) + inconsistentnum++; + printf("Number of SV: %ld (plus %ld inconsistent examples)\n", + model->sv_num-1,inconsistentnum); + } + else { + upsupvecnum=0; + for(i=1;i<model->sv_num;i++) { + if(fabs(model->alpha[i]) >= + (learn_parm->svm_cost[(model->supvec[i])->docnum]- + learn_parm->epsilon_a)) + upsupvecnum++; + } + printf("Number of SV: %ld (including %ld at upper bound)\n", + model->sv_num-1,upsupvecnum); + } + + if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) { + loss=0; + model_length=0; + for(i=0;i<totdoc;i++) { + if((lin[i]-model->b)*(double)label[i] < 1.0-learn_parm->epsilon_crit) + loss+=1.0-(lin[i]-model->b)*(double)label[i]; + model_length+=a[i]*label[i]*lin[i]; + } + model_length=sqrt(model_length); + fprintf(stdout,"L1 loss: loss=%.5f\n",loss); + fprintf(stdout,"Norm of weight vector: |w|=%.5f\n",model_length); + example_length=estimate_sphere(model,kernel_parm); + fprintf(stdout,"Norm of longest example vector: |x|=%.5f\n", + length_of_longest_document_vector(docs,totdoc,kernel_parm)); + fprintf(stdout,"Estimated VCdim of classifier: VCdim<=%.5f\n", + estimate_margin_vcdim(model,model_length,example_length, + kernel_parm)); + if((!learn_parm->remove_inconsistent) && (!transduction)) { + runtime_start_xa=get_runtime(); + if(verbosity>=1) { + printf("Computing XiAlpha-estimates..."); fflush(stdout); + } + compute_xa_estimates(model,label,unlabeled,totdoc,docs,lin,a, + kernel_parm,learn_parm,&(model->xa_error), + &(model->xa_recall),&(model->xa_precision)); + if(verbosity>=1) { + printf("done\n"); + } + printf("Runtime for XiAlpha-estimates in cpu-seconds: %.2f\n", + (get_runtime()-runtime_start_xa)/100.0); + + fprintf(stdout,"XiAlpha-estimate of the error: error<=%.2f%% (rho=%.2f,depth=%ld)\n", + model->xa_error,learn_parm->rho,learn_parm->xa_depth); + fprintf(stdout,"XiAlpha-estimate of the recall: recall=>%.2f%% (rho=%.2f,depth=%ld)\n", + model->xa_recall,learn_parm->rho,learn_parm->xa_depth); + fprintf(stdout,"XiAlpha-estimate of the precision: precision=>%.2f%% (rho=%.2f,depth=%ld)\n", + model->xa_precision,learn_parm->rho,learn_parm->xa_depth); + } + else if(!learn_parm->remove_inconsistent) { + estimate_transduction_quality(model,label,unlabeled,totdoc,docs,lin); + } + } + if(verbosity>=1) { + printf("Number of kernel evaluations: %ld\n",kernel_cache_statistic); + } + } + + + /* leave-one-out testing starts now */ + if(learn_parm->compute_loo) { + /* save results of training on full dataset for leave-one-out */ + runtime_start_loo=get_runtime(); + for(i=0;i<totdoc;i++) { + xi_fullset[i]=1.0-((lin[i]-model->b)*(double)label[i]); + if(xi_fullset[i]<0) xi_fullset[i]=0; + a_fullset[i]=a[i]; + } + if(verbosity>=1) { + printf("Computing leave-one-out"); + } + + /* repeat this loop for every held-out example */ + for(heldout=0;(heldout<totdoc);heldout++) { + if(learn_parm->rho*a_fullset[heldout]*r_delta_sq+xi_fullset[heldout] + < 1.0) { + /* guaranteed to not produce a leave-one-out error */ + if(verbosity==1) { + printf("+"); fflush(stdout); + } + } + else if(xi_fullset[heldout] > 1.0) { + /* guaranteed to produce a leave-one-out error */ + loo_count++; + if(label[heldout] > 0) loo_count_pos++; else loo_count_neg++; + if(verbosity==1) { + printf("-"); fflush(stdout); + } + } + else { + loocomputed++; + heldout_c=learn_parm->svm_cost[heldout]; /* set upper bound to zero */ + learn_parm->svm_cost[heldout]=0; + /* make sure heldout example is not currently */ + /* shrunk away. Assumes that lin is up to date! */ + shrink_state.active[heldout]=1; + if(verbosity>=2) + printf("\nLeave-One-Out test on example %ld\n",heldout); + if(verbosity>=1) { + printf("(?[%ld]",heldout); fflush(stdout); + } + + optimize_to_convergence(docs,label,totdoc,totwords,learn_parm, + kernel_parm, + kernel_cache,&shrink_state,model,inconsistent,unlabeled, + a,lin,c,&timing_profile, + &maxdiff,heldout,(long)2); + + /* printf("%.20f\n",(lin[heldout]-model->b)*(double)label[heldout]); */ + + if(((lin[heldout]-model->b)*(double)label[heldout]) <= 0.0) { + loo_count++; /* there was a loo-error */ + if(label[heldout] > 0) loo_count_pos++; else loo_count_neg++; + if(verbosity>=1) { + printf("-)"); fflush(stdout); + } + } + else { + if(verbosity>=1) { + printf("+)"); fflush(stdout); + } + } + /* now we need to restore the original data set*/ + learn_parm->svm_cost[heldout]=heldout_c; /* restore upper bound */ + } + } /* end of leave-one-out loop */ + + + if(verbosity>=1) { + printf("\nRetrain on full problem"); fflush(stdout); + } + optimize_to_convergence(docs,label,totdoc,totwords,learn_parm, + kernel_parm, + kernel_cache,&shrink_state,model,inconsistent,unlabeled, + a,lin,c,&timing_profile, + &maxdiff,(long)-1,(long)1); + if(verbosity >= 1) + printf("done.\n"); + + + /* after all leave-one-out computed */ + model->loo_error=100.0*loo_count/(double)totdoc; + model->loo_recall=(1.0-(double)loo_count_pos/(double)trainpos)*100.0; + model->loo_precision=(trainpos-loo_count_pos)/ + (double)(trainpos-loo_count_pos+loo_count_neg)*100.0; + if(verbosity >= 1) { + fprintf(stdout,"Leave-one-out estimate of the error: error=%.2f%%\n", + model->loo_error); + fprintf(stdout,"Leave-one-out estimate of the recall: recall=%.2f%%\n", + model->loo_recall); + fprintf(stdout,"Leave-one-out estimate of the precision: precision=%.2f%%\n", + model->loo_precision); + fprintf(stdout,"Actual leave-one-outs computed: %ld (rho=%.2f)\n", + loocomputed,learn_parm->rho); + printf("Runtime for leave-one-out in cpu-seconds: %.2f\n", + (double)(get_runtime()-runtime_start_loo)/100.0); + } + } + + if(learn_parm->alphafile[0]) + write_alphas(learn_parm->alphafile,a,label,totdoc); + + shrink_state_cleanup(&shrink_state); + free(label); + free(inconsistent); + free(unlabeled); + free(c); + free(a); + free(a_fullset); + free(xi_fullset); + free(lin); + free(learn_parm->svm_cost); +} + + +/* Learns an SVM regression model based on the training data in + docs/label. The resulting model is returned in the structure + model. */ + +void svm_learn_regression(DOC **docs, double *value, long int totdoc, + long int totwords, LEARN_PARM *learn_parm, + KERNEL_PARM *kernel_parm, + KERNEL_CACHE **kernel_cache, MODEL *model) + /* docs: Training vectors (x-part) */ + /* class: Training value (y-part) */ + /* totdoc: Number of examples in docs/label */ + /* totwords: Number of features (i.e. highest feature index) */ + /* learn_parm: Learning paramenters */ + /* kernel_parm: Kernel paramenters */ + /* kernel_cache:Initialized Cache, if using a kernel. NULL if + linear. Note that it will be free'd and reassigned */ + /* model: Returns learning result (assumed empty before called) */ +{ + long *inconsistent,i,j; + long inconsistentnum; + long upsupvecnum; + double loss,model_length,example_length; + double maxdiff,*lin,*a,*c; + long runtime_start,runtime_end; + long iterations,kernel_cache_size; + long *unlabeled; + double r_delta_sq=0,r_delta,r_delta_avg; + double *xi_fullset; /* buffer for storing xi on full sample in loo */ + double *a_fullset; /* buffer for storing alpha on full sample in loo */ + TIMING timing_profile; + SHRINK_STATE shrink_state; + DOC **docs_org; + long *label; + + /* set up regression problem in standard form */ + docs_org=docs; + docs = (DOC **)my_malloc(sizeof(DOC)*2*totdoc); + label = (long *)my_malloc(sizeof(long)*2*totdoc); + c = (double *)my_malloc(sizeof(double)*2*totdoc); + for(i=0;i<totdoc;i++) { + j=2*totdoc-1-i; + docs[i]=create_example(i,0,0,docs_org[i]->costfactor,docs_org[i]->fvec); + label[i]=+1; + c[i]=value[i]; + docs[j]=create_example(j,0,0,docs_org[i]->costfactor,docs_org[i]->fvec); + label[j]=-1; + c[j]=value[i]; + } + totdoc*=2; + + /* need to get a bigger kernel cache */ + if(*kernel_cache) { + kernel_cache_size=(*kernel_cache)->buffsize*sizeof(CFLOAT)/(1024*1024); + kernel_cache_cleanup(*kernel_cache); + (*kernel_cache)=kernel_cache_init(totdoc,kernel_cache_size); + } + + runtime_start=get_runtime(); + timing_profile.time_kernel=0; + timing_profile.time_opti=0; + timing_profile.time_shrink=0; + timing_profile.time_update=0; + timing_profile.time_model=0; + timing_profile.time_check=0; + timing_profile.time_select=0; + kernel_cache_statistic=0; + + learn_parm->totwords=totwords; + + /* make sure -n value is reasonable */ + if((learn_parm->svm_newvarsinqp < 2) + || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) { + learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize; + } + + init_shrink_state(&shrink_state,totdoc,(long)MAXSHRINK); + + inconsistent = (long *)my_malloc(sizeof(long)*totdoc); + unlabeled = (long *)my_malloc(sizeof(long)*totdoc); + a = (double *)my_malloc(sizeof(double)*totdoc); + a_fullset = (double *)my_malloc(sizeof(double)*totdoc); + xi_fullset = (double *)my_malloc(sizeof(double)*totdoc); + lin = (double *)my_malloc(sizeof(double)*totdoc); + learn_parm->svm_cost = (double *)my_malloc(sizeof(double)*totdoc); + model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2)); + model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2)); + model->index = (long *)my_malloc(sizeof(long)*(totdoc+2)); + + model->at_upper_bound=0; + model->b=0; + model->supvec[0]=0; /* element 0 reserved and empty for now */ + model->alpha[0]=0; + model->lin_weights=NULL; + model->totwords=totwords; + model->totdoc=totdoc; + model->kernel_parm=(*kernel_parm); + model->sv_num=1; + model->loo_error=-1; + model->loo_recall=-1; + model->loo_precision=-1; + model->xa_error=-1; + model->xa_recall=-1; + model->xa_precision=-1; + inconsistentnum=0; + + r_delta=estimate_r_delta(docs,totdoc,kernel_parm); + r_delta_sq=r_delta*r_delta; + + r_delta_avg=estimate_r_delta_average(docs,totdoc,kernel_parm); + if(learn_parm->svm_c == 0.0) { /* default value for C */ + learn_parm->svm_c=1.0/(r_delta_avg*r_delta_avg); + if(verbosity>=1) + printf("Setting default regularization parameter C=%.4f\n", + learn_parm->svm_c); + } + + for(i=0;i<totdoc;i++) { /* various inits */ + inconsistent[i]=0; + a[i]=0; + lin[i]=0; + unlabeled[i]=0; + if(label[i] > 0) { + learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio* + docs[i]->costfactor; + } + else if(label[i] < 0) { + learn_parm->svm_cost[i]=learn_parm->svm_c*docs[i]->costfactor; + } + } + + /* caching makes no sense for linear kernel */ + if((kernel_parm->kernel_type == LINEAR) && (*kernel_cache)) { + printf("WARNING: Using a kernel cache for linear case will slow optimization down!\n"); + } + + if(verbosity==1) { + printf("Optimizing"); fflush(stdout); + } + + /* train the svm */ + iterations=optimize_to_convergence(docs,label,totdoc,totwords,learn_parm, + kernel_parm,*kernel_cache,&shrink_state, + model,inconsistent,unlabeled,a,lin,c, + &timing_profile,&maxdiff,(long)-1, + (long)1); + + if(verbosity>=1) { + if(verbosity==1) printf("done. (%ld iterations)\n",iterations); + + printf("Optimization finished (maxdiff=%.5f).\n",maxdiff); + + runtime_end=get_runtime(); + if(verbosity>=2) { + printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n", + ((float)runtime_end-(float)runtime_start)/100.0, + (100.0*timing_profile.time_kernel)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_opti)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_shrink)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_update)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_model)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_check)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_select)/(float)(runtime_end-runtime_start)); + } + else { + printf("Runtime in cpu-seconds: %.2f\n", + (runtime_end-runtime_start)/100.0); + } + + if(learn_parm->remove_inconsistent) { + inconsistentnum=0; + for(i=0;i<totdoc;i++) + if(inconsistent[i]) + inconsistentnum++; + printf("Number of SV: %ld (plus %ld inconsistent examples)\n", + model->sv_num-1,inconsistentnum); + } + else { + upsupvecnum=0; + for(i=1;i<model->sv_num;i++) { + if(fabs(model->alpha[i]) >= + (learn_parm->svm_cost[(model->supvec[i])->docnum]- + learn_parm->epsilon_a)) + upsupvecnum++; + } + printf("Number of SV: %ld (including %ld at upper bound)\n", + model->sv_num-1,upsupvecnum); + } + + if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) { + loss=0; + model_length=0; + for(i=0;i<totdoc;i++) { + if((lin[i]-model->b)*(double)label[i] < (-learn_parm->eps+(double)label[i]*c[i])-learn_parm->epsilon_crit) + loss+=-learn_parm->eps+(double)label[i]*c[i]-(lin[i]-model->b)*(double)label[i]; + model_length+=a[i]*label[i]*lin[i]; + } + model_length=sqrt(model_length); + fprintf(stdout,"L1 loss: loss=%.5f\n",loss); + fprintf(stdout,"Norm of weight vector: |w|=%.5f\n",model_length); + example_length=estimate_sphere(model,kernel_parm); + fprintf(stdout,"Norm of longest example vector: |x|=%.5f\n", + length_of_longest_document_vector(docs,totdoc,kernel_parm)); + } + if(verbosity>=1) { + printf("Number of kernel evaluations: %ld\n",kernel_cache_statistic); + } + } + + if(learn_parm->alphafile[0]) + write_alphas(learn_parm->alphafile,a,label,totdoc); + + /* this makes sure the model we return does not contain pointers to the + temporary documents */ + for(i=1;i<model->sv_num;i++) { + j=model->supvec[i]->docnum; + if(j >= (totdoc/2)) { + j=totdoc-j-1; + } + model->supvec[i]=docs_org[j]; + } + + shrink_state_cleanup(&shrink_state); + for(i=0;i<totdoc;i++) + free_example(docs[i],0); + free(docs); + free(label); + free(inconsistent); + free(unlabeled); + free(c); + free(a); + free(a_fullset); + free(xi_fullset); + free(lin); + free(learn_parm->svm_cost); +} + +void svm_learn_ranking(DOC **docs, double *rankvalue, long int totdoc, + long int totwords, LEARN_PARM *learn_parm, + KERNEL_PARM *kernel_parm, KERNEL_CACHE **kernel_cache, + MODEL *model) + /* docs: Training vectors (x-part) */ + /* rankvalue: Training target values that determine the ranking */ + /* totdoc: Number of examples in docs/label */ + /* totwords: Number of features (i.e. highest feature index) */ + /* learn_parm: Learning paramenters */ + /* kernel_parm: Kernel paramenters */ + /* kernel_cache:Initialized pointer to Cache of size 1*totdoc, if + using a kernel. NULL if linear. NOTE: Cache is + getting reinitialized in this function */ + /* model: Returns learning result (assumed empty before called) */ +{ + DOC **docdiff; + long i,j,k,totpair,kernel_cache_size; + double *target,*alpha,cost; + long *greater,*lesser; + MODEL *pairmodel; + SVECTOR *flow,*fhigh; + + totpair=0; + for(i=0;i<totdoc;i++) { + for(j=i+1;j<totdoc;j++) { + if((docs[i]->queryid==docs[j]->queryid) && (rankvalue[i] != rankvalue[j])) { + totpair++; + } + } + } + + printf("Constructing %ld rank constraints...",totpair); fflush(stdout); + docdiff=(DOC **)my_malloc(sizeof(DOC)*totpair); + target=(double *)my_malloc(sizeof(double)*totpair); + greater=(long *)my_malloc(sizeof(long)*totpair); + lesser=(long *)my_malloc(sizeof(long)*totpair); + + k=0; + for(i=0;i<totdoc;i++) { + for(j=i+1;j<totdoc;j++) { + if(docs[i]->queryid == docs[j]->queryid) { + cost=(docs[i]->costfactor+docs[j]->costfactor)/2.0; + if(rankvalue[i] > rankvalue[j]) { + if(kernel_parm->kernel_type == LINEAR) + docdiff[k]=create_example(k,0,0,cost, + sub_ss(docs[i]->fvec,docs[j]->fvec)); + else { + flow=copy_svector(docs[j]->fvec); + flow->factor=-1.0; + flow->next=NULL; + fhigh=copy_svector(docs[i]->fvec); + fhigh->factor=1.0; + fhigh->next=flow; + docdiff[k]=create_example(k,0,0,cost,fhigh); + } + target[k]=1; + greater[k]=i; + lesser[k]=j; + k++; + } + else if(rankvalue[i] < rankvalue[j]) { + if(kernel_parm->kernel_type == LINEAR) + docdiff[k]=create_example(k,0,0,cost, + sub_ss(docs[i]->fvec,docs[j]->fvec)); + else { + flow=copy_svector(docs[j]->fvec); + flow->factor=-1.0; + flow->next=NULL; + fhigh=copy_svector(docs[i]->fvec); + fhigh->factor=1.0; + fhigh->next=flow; + docdiff[k]=create_example(k,0,0,cost,fhigh); + } + target[k]=-1; + greater[k]=i; + lesser[k]=j; + k++; + } + } + } + } + printf("done.\n"); fflush(stdout); + + /* need to get a bigger kernel cache */ + if(*kernel_cache) { + kernel_cache_size=(*kernel_cache)->buffsize*sizeof(CFLOAT)/(1024*1024); + kernel_cache_cleanup(*kernel_cache); + (*kernel_cache)=kernel_cache_init(totpair,kernel_cache_size); + } + + /* must use unbiased hyperplane on difference vectors */ + learn_parm->biased_hyperplane=0; + pairmodel=(MODEL *)my_malloc(sizeof(MODEL)); + svm_learn_classification(docdiff,target,totpair,totwords,learn_parm, + kernel_parm,(*kernel_cache),pairmodel,NULL); + + /* Transfer the result into a more compact model. If you would like + to output the original model on pairs of documents, see below. */ + alpha=(double *)my_malloc(sizeof(double)*totdoc); + for(i=0;i<totdoc;i++) { + alpha[i]=0; + } + for(i=1;i<pairmodel->sv_num;i++) { + alpha[lesser[(pairmodel->supvec[i])->docnum]]-=pairmodel->alpha[i]; + alpha[greater[(pairmodel->supvec[i])->docnum]]+=pairmodel->alpha[i]; + } + model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2)); + model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2)); + model->index = (long *)my_malloc(sizeof(long)*(totdoc+2)); + model->supvec[0]=0; /* element 0 reserved and empty for now */ + model->alpha[0]=0; + model->sv_num=1; + for(i=0;i<totdoc;i++) { + if(alpha[i]) { + model->supvec[model->sv_num]=docs[i]; + model->alpha[model->sv_num]=alpha[i]; + model->index[i]=model->sv_num; + model->sv_num++; + } + else { + model->index[i]=-1; + } + } + model->at_upper_bound=0; + model->b=0; + model->lin_weights=NULL; + model->totwords=totwords; + model->totdoc=totdoc; + model->kernel_parm=(*kernel_parm); + model->loo_error=-1; + model->loo_recall=-1; + model->loo_precision=-1; + model->xa_error=-1; + model->xa_recall=-1; + model->xa_precision=-1; + + free(alpha); + free(greater); + free(lesser); + free(target); + + /* If you would like to output the original model on pairs of + document, replace the following lines with '(*model)=(*pairmodel);' */ + for(i=0;i<totpair;i++) + free_example(docdiff[i],1); + free(docdiff); + free_model(pairmodel,0); +} + + +/* The following solves a freely defined and given set of + inequalities. The optimization problem is of the following form: + + min 0.5 w*w + C sum_i C_i \xi_i + s.t. x_i * w > rhs_i - \xi_i + + This corresponds to the -z o option. */ + +void svm_learn_optimization(DOC **docs, double *rhs, long int + totdoc, long int totwords, + LEARN_PARM *learn_parm, + KERNEL_PARM *kernel_parm, + KERNEL_CACHE *kernel_cache, MODEL *model, + double *alpha) + /* docs: Left-hand side of inequalities (x-part) */ + /* rhs: Right-hand side of inequalities */ + /* totdoc: Number of examples in docs/label */ + /* totwords: Number of features (i.e. highest feature index) */ + /* learn_parm: Learning paramenters */ + /* kernel_parm: Kernel paramenters */ + /* kernel_cache:Initialized Cache of size 1*totdoc, if using a kernel. + NULL if linear.*/ + /* model: Returns solution as SV expansion (assumed empty before called) */ + /* alpha: Start values for the alpha variables or NULL + pointer. The new alpha values are returned after + optimization if not NULL. Array must be of size totdoc. */ +{ + long i,*label; + long misclassified,upsupvecnum; + double loss,model_length,example_length; + double maxdiff,*lin,*a,*c; + long runtime_start,runtime_end; + long iterations,maxslackid,svsetnum; + long *unlabeled,*inconsistent; + double r_delta_sq=0,r_delta,r_delta_avg; + long *index,*index2dnum; + double *weights,*slack,*alphaslack; + CFLOAT *aicache; /* buffer to keep one row of hessian */ + + TIMING timing_profile; + SHRINK_STATE shrink_state; + + runtime_start=get_runtime(); + timing_profile.time_kernel=0; + timing_profile.time_opti=0; + timing_profile.time_shrink=0; + timing_profile.time_update=0; + timing_profile.time_model=0; + timing_profile.time_check=0; + timing_profile.time_select=0; + kernel_cache_statistic=0; + + learn_parm->totwords=totwords; + + /* make sure -n value is reasonable */ + if((learn_parm->svm_newvarsinqp < 2) + || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) { + learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize; + } + + init_shrink_state(&shrink_state,totdoc,(long)MAXSHRINK); + + label = (long *)my_malloc(sizeof(long)*totdoc); + unlabeled = (long *)my_malloc(sizeof(long)*totdoc); + inconsistent = (long *)my_malloc(sizeof(long)*totdoc); + c = (double *)my_malloc(sizeof(double)*totdoc); + a = (double *)my_malloc(sizeof(double)*totdoc); + lin = (double *)my_malloc(sizeof(double)*totdoc); + learn_parm->svm_cost = (double *)my_malloc(sizeof(double)*totdoc); + model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2)); + model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2)); + model->index = (long *)my_malloc(sizeof(long)*(totdoc+2)); + + model->at_upper_bound=0; + model->b=0; + model->supvec[0]=0; /* element 0 reserved and empty for now */ + model->alpha[0]=0; + model->lin_weights=NULL; + model->totwords=totwords; + model->totdoc=totdoc; + model->kernel_parm=(*kernel_parm); + model->sv_num=1; + model->loo_error=-1; + model->loo_recall=-1; + model->loo_precision=-1; + model->xa_error=-1; + model->xa_recall=-1; + model->xa_precision=-1; + + r_delta=estimate_r_delta(docs,totdoc,kernel_parm); + r_delta_sq=r_delta*r_delta; + + r_delta_avg=estimate_r_delta_average(docs,totdoc,kernel_parm); + if(learn_parm->svm_c == 0.0) { /* default value for C */ + learn_parm->svm_c=1.0/(r_delta_avg*r_delta_avg); + if(verbosity>=1) + printf("Setting default regularization parameter C=%.4f\n", + learn_parm->svm_c); + } + + learn_parm->biased_hyperplane=0; /* learn an unbiased hyperplane */ + + learn_parm->eps=0.0; /* No margin, unless explicitly handcoded + in the right-hand side in the training + set. */ + + for(i=0;i<totdoc;i++) { /* various inits */ + docs[i]->docnum=i; + a[i]=0; + lin[i]=0; + c[i]=rhs[i]; /* set right-hand side */ + unlabeled[i]=0; + inconsistent[i]=0; + learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio* + docs[i]->costfactor; + label[i]=1; + } + if(learn_parm->sharedslack) /* if shared slacks are used, they must */ + for(i=0;i<totdoc;i++) /* be used on every constraint */ + if(!docs[i]->slackid) { + perror("Error: Missing shared slacks definitions in some of the examples."); + exit(0); + } + + /* compute starting state for initial alpha values */ + if(alpha) { + if(verbosity>=1) { + printf("Computing starting state..."); fflush(stdout); + } + index = (long *)my_malloc(sizeof(long)*totdoc); + index2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11)); + weights=(double *)my_malloc(sizeof(double)*(totwords+1)); + aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc); + for(i=0;i<totdoc;i++) { /* create full index and clip alphas */ + index[i]=1; + alpha[i]=fabs(alpha[i]); + if(alpha[i]<0) alpha[i]=0; + if(alpha[i]>learn_parm->svm_cost[i]) alpha[i]=learn_parm->svm_cost[i]; + } + if(kernel_parm->kernel_type != LINEAR) { + for(i=0;i<totdoc;i++) /* fill kernel cache with unbounded SV */ + if((alpha[i]>0) && (alpha[i]<learn_parm->svm_cost[i]) + && (kernel_cache_space_available(kernel_cache))) + cache_kernel_row(kernel_cache,docs,i,kernel_parm); + for(i=0;i<totdoc;i++) /* fill rest of kernel cache with bounded SV */ + if((alpha[i]==learn_parm->svm_cost[i]) + && (kernel_cache_space_available(kernel_cache))) + cache_kernel_row(kernel_cache,docs,i,kernel_parm); + } + (void)compute_index(index,totdoc,index2dnum); + update_linear_component(docs,label,index2dnum,alpha,a,index2dnum,totdoc, + totwords,kernel_parm,kernel_cache,lin,aicache, + weights); + (void)calculate_svm_model(docs,label,unlabeled,lin,alpha,a,c, + learn_parm,index2dnum,index2dnum,model); + for(i=0;i<totdoc;i++) { /* copy initial alphas */ + a[i]=alpha[i]; + } + free(index); + free(index2dnum); + free(weights); + free(aicache); + if(verbosity>=1) { + printf("done.\n"); fflush(stdout); + } + } + + /* removing inconsistent does not work for general optimization problem */ + if(learn_parm->remove_inconsistent) { + learn_parm->remove_inconsistent = 0; + printf("'remove inconsistent' not available in this mode. Switching option off!"); fflush(stdout); + } + + /* caching makes no sense for linear kernel */ + if(kernel_parm->kernel_type == LINEAR) { + kernel_cache = NULL; + } + + if(verbosity==1) { + printf("Optimizing"); fflush(stdout); + } + + /* train the svm */ + if(learn_parm->sharedslack) + iterations=optimize_to_convergence_sharedslack(docs,label,totdoc, + totwords,learn_parm,kernel_parm, + kernel_cache,&shrink_state,model, + a,lin,c,&timing_profile, + &maxdiff); + else + iterations=optimize_to_convergence(docs,label,totdoc, + totwords,learn_parm,kernel_parm, + kernel_cache,&shrink_state,model, + inconsistent,unlabeled, + a,lin,c,&timing_profile, + &maxdiff,(long)-1,(long)1); + + if(verbosity>=1) { + if(verbosity==1) printf("done. (%ld iterations)\n",iterations); + + misclassified=0; + for(i=0;(i<totdoc);i++) { /* get final statistic */ + if((lin[i]-model->b)*(double)label[i] <= 0.0) + misclassified++; + } + + printf("Optimization finished (maxdiff=%.5f).\n",maxdiff); + + runtime_end=get_runtime(); + if(verbosity>=2) { + printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n", + ((float)runtime_end-(float)runtime_start)/100.0, + (100.0*timing_profile.time_kernel)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_opti)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_shrink)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_update)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_model)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_check)/(float)(runtime_end-runtime_start), + (100.0*timing_profile.time_select)/(float)(runtime_end-runtime_start)); + } + else { + printf("Runtime in cpu-seconds: %.2f\n", + (runtime_end-runtime_start)/100.0); + } + } + if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) { + loss=0; + model_length=0; + for(i=0;i<totdoc;i++) { + if((lin[i]-model->b)*(double)label[i] < c[i]-learn_parm->epsilon_crit) + loss+=c[i]-(lin[i]-model->b)*(double)label[i]; + model_length+=a[i]*label[i]*lin[i]; + } + model_length=sqrt(model_length); + fprintf(stdout,"Norm of weight vector: |w|=%.5f\n",model_length); + } + + if(learn_parm->sharedslack) { + index = (long *)my_malloc(sizeof(long)*totdoc); + index2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11)); + maxslackid=0; + for(i=0;i<totdoc;i++) { /* create full index */ + index[i]=1; + if(maxslackid<docs[i]->slackid) + maxslackid=docs[i]->slackid; + } + (void)compute_index(index,totdoc,index2dnum); + slack=(double *)my_malloc(sizeof(double)*(maxslackid+1)); + alphaslack=(double *)my_malloc(sizeof(double)*(maxslackid+1)); + for(i=0;i<=maxslackid;i++) { /* init shared slacks */ + slack[i]=0; + alphaslack[i]=0; + } + compute_shared_slacks(docs,label,a,lin,c,index2dnum,learn_parm, + slack,alphaslack); + loss=0; + model->at_upper_bound=0; + svsetnum=0; + for(i=0;i<=maxslackid;i++) { /* create full index */ + loss+=slack[i]; + if(alphaslack[i] > (learn_parm->svm_c - learn_parm->epsilon_a)) + model->at_upper_bound++; + if(alphaslack[i] > learn_parm->epsilon_a) + svsetnum++; + } + free(index); + free(index2dnum); + free(slack); + free(alphaslack); + } + + if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) { + if(learn_parm->sharedslack) { + printf("Number of SV: %ld\n", + model->sv_num-1); + printf("Number of non-zero slack variables: %ld (out of %ld)\n", + model->at_upper_bound,svsetnum); + fprintf(stdout,"L1 loss: loss=%.5f\n",loss); + } + else { + upsupvecnum=0; + for(i=1;i<model->sv_num;i++) { + if(fabs(model->alpha[i]) >= + (learn_parm->svm_cost[(model->supvec[i])->docnum]- + learn_parm->epsilon_a)) + upsupvecnum++; + } + printf("Number of SV: %ld (including %ld at upper bound)\n", + model->sv_num-1,upsupvecnum); + fprintf(stdout,"L1 loss: loss=%.5f\n",loss); + } + example_length=estimate_sphere(model,kernel_parm); + fprintf(stdout,"Norm of longest example vector: |x|=%.5f\n", + length_of_longest_document_vector(docs,totdoc,kernel_parm)); + } + if(verbosity>=1) { + printf("Number of kernel evaluations: %ld\n",kernel_cache_statistic); + } + + if(alpha) { + for(i=0;i<totdoc;i++) { /* copy final alphas */ + alpha[i]=a[i]; + } + } + + if(learn_parm->alphafile[0]) + write_alphas(learn_parm->alphafile,a,label,totdoc); + + shrink_state_cleanup(&shrink_state); + free(label); + free(unlabeled); + free(inconsistent); + free(c); + free(a); + free(lin); + free(learn_parm->svm_cost); +} + + +long optimize_to_convergence(DOC **docs, long int *label, long int totdoc, + long int totwords, LEARN_PARM *learn_parm, + KERNEL_PARM *kernel_parm, + KERNEL_CACHE *kernel_cache, + SHRINK_STATE *shrink_state, MODEL *model, + long int *inconsistent, long int *unlabeled, + double *a, double *lin, double *c, + TIMING *timing_profile, double *maxdiff, + long int heldout, long int retrain) + /* docs: Training vectors (x-part) */ + /* label: Training labels/value (y-part, zero if test example for + transduction) */ + /* totdoc: Number of examples in docs/label */ + /* totwords: Number of features (i.e. highest feature index) */ + /* laern_parm: Learning paramenters */ + /* kernel_parm: Kernel paramenters */ + /* kernel_cache: Initialized/partly filled Cache, if using a kernel. + NULL if linear. */ + /* shrink_state: State of active variables */ + /* model: Returns learning result */ + /* inconsistent: examples thrown out as inconstistent */ + /* unlabeled: test examples for transduction */ + /* a: alphas */ + /* lin: linear component of gradient */ + /* c: right hand side of inequalities (margin) */ + /* maxdiff: returns maximum violation of KT-conditions */ + /* heldout: marks held-out example for leave-one-out (or -1) */ + /* retrain: selects training mode (1=regular / 2=holdout) */ +{ + long *chosen,*key,i,j,jj,*last_suboptimal_at,noshrink; + long inconsistentnum,choosenum,already_chosen=0,iteration; + long misclassified,supvecnum=0,*active2dnum,inactivenum; + long *working2dnum,*selexam; + long activenum; + double criterion,eq; + double *a_old; + long t0=0,t1=0,t2=0,t3=0,t4=0,t5=0,t6=0; /* timing */ + long transductcycle; + long transduction; + double epsilon_crit_org; + double bestmaxdiff; + long bestmaxdiffiter,terminate; + + double *selcrit; /* buffer for sorting */ + CFLOAT *aicache; /* buffer to keep one row of hessian */ + double *weights; /* buffer for weight vector in linear case */ + QP qp; /* buffer for one quadratic program */ + + epsilon_crit_org=learn_parm->epsilon_crit; /* save org */ + if(kernel_parm->kernel_type == LINEAR) { + learn_parm->epsilon_crit=2.0; + kernel_cache=NULL; /* caching makes no sense for linear kernel */ + } + learn_parm->epsilon_shrink=2; + (*maxdiff)=1; + + learn_parm->totwords=totwords; + + chosen = (long *)my_malloc(sizeof(long)*totdoc); + last_suboptimal_at = (long *)my_malloc(sizeof(long)*totdoc); + key = (long *)my_malloc(sizeof(long)*(totdoc+11)); + selcrit = (double *)my_malloc(sizeof(double)*totdoc); + selexam = (long *)my_malloc(sizeof(long)*totdoc); + a_old = (double *)my_malloc(sizeof(double)*totdoc); + aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc); + working2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11)); + active2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11)); + qp.opt_ce = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + qp.opt_ce0 = (double *)my_malloc(sizeof(double)); + qp.opt_g = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize + *learn_parm->svm_maxqpsize); + qp.opt_g0 = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + qp.opt_xinit = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + qp.opt_low=(double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + qp.opt_up=(double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + weights=(double *)my_malloc(sizeof(double)*(totwords+1)); + + choosenum=0; + inconsistentnum=0; + transductcycle=0; + transduction=0; + if(!retrain) retrain=1; + iteration=1; + bestmaxdiffiter=1; + bestmaxdiff=999999999; + terminate=0; + + if(kernel_cache) { + kernel_cache->time=iteration; /* for lru cache */ + kernel_cache_reset_lru(kernel_cache); + } + + for(i=0;i<totdoc;i++) { /* various inits */ + chosen[i]=0; + a_old[i]=a[i]; + last_suboptimal_at[i]=1; + if(inconsistent[i]) + inconsistentnum++; + if(unlabeled[i]) { + transduction=1; + } + } + activenum=compute_index(shrink_state->active,totdoc,active2dnum); + inactivenum=totdoc-activenum; + clear_index(working2dnum); + + /* repeat this loop until we have convergence */ + for(;retrain && (!terminate);iteration++) { + + if(kernel_cache) + kernel_cache->time=iteration; /* for lru cache */ + if(verbosity>=2) { + printf( + "Iteration %ld: ",iteration); fflush(stdout); + } + else if(verbosity==1) { + printf("."); fflush(stdout); + } + + if(verbosity>=2) t0=get_runtime(); + if(verbosity>=3) { + printf("\nSelecting working set... "); fflush(stdout); + } + + if(learn_parm->svm_newvarsinqp>learn_parm->svm_maxqpsize) + learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize; + + i=0; + for(jj=0;(j=working2dnum[jj])>=0;jj++) { /* clear working set */ + if((chosen[j]>=(learn_parm->svm_maxqpsize/ + minl(learn_parm->svm_maxqpsize, + learn_parm->svm_newvarsinqp))) + || (inconsistent[j]) + || (j == heldout)) { + chosen[j]=0; + choosenum--; + } + else { + chosen[j]++; + working2dnum[i++]=j; + } + } + working2dnum[i]=-1; + + if(retrain == 2) { + choosenum=0; + for(jj=0;(j=working2dnum[jj])>=0;jj++) { /* fully clear working set */ + chosen[j]=0; + } + clear_index(working2dnum); + for(i=0;i<totdoc;i++) { /* set inconsistent examples to zero (-i 1) */ + if((inconsistent[i] || (heldout==i)) && (a[i] != 0.0)) { + chosen[i]=99999; + choosenum++; + a[i]=0; + } + } + if(learn_parm->biased_hyperplane) { + eq=0; + for(i=0;i<totdoc;i++) { /* make sure we fulfill equality constraint */ + eq+=a[i]*label[i]; + } + for(i=0;(i<totdoc) && (fabs(eq) > learn_parm->epsilon_a);i++) { + if((eq*label[i] > 0) && (a[i] > 0)) { + chosen[i]=88888; + choosenum++; + if((eq*label[i]) > a[i]) { + eq-=(a[i]*label[i]); + a[i]=0; + } + else { + a[i]-=(eq*label[i]); + eq=0; + } + } + } + } + compute_index(chosen,totdoc,working2dnum); + } + else { /* select working set according to steepest gradient */ + if(iteration % 101) { + already_chosen=0; + if((minl(learn_parm->svm_newvarsinqp, + learn_parm->svm_maxqpsize-choosenum)>=4) + && (kernel_parm->kernel_type != LINEAR)) { + /* select part of the working set from cache */ + already_chosen=select_next_qp_subproblem_grad( + label,unlabeled,a,lin,c,totdoc, + (long)(minl(learn_parm->svm_maxqpsize-choosenum, + learn_parm->svm_newvarsinqp) + /2), + learn_parm,inconsistent,active2dnum, + working2dnum,selcrit,selexam,kernel_cache,1, + key,chosen); + choosenum+=already_chosen; + } + choosenum+=select_next_qp_subproblem_grad( + label,unlabeled,a,lin,c,totdoc, + minl(learn_parm->svm_maxqpsize-choosenum, + learn_parm->svm_newvarsinqp-already_chosen), + learn_parm,inconsistent,active2dnum, + working2dnum,selcrit,selexam,kernel_cache,0,key, + chosen); + } + else { /* once in a while, select a somewhat random working set + to get unlocked of infinite loops due to numerical + inaccuracies in the core qp-solver */ + choosenum+=select_next_qp_subproblem_rand( + label,unlabeled,a,lin,c,totdoc, + minl(learn_parm->svm_maxqpsize-choosenum, + learn_parm->svm_newvarsinqp), + learn_parm,inconsistent,active2dnum, + working2dnum,selcrit,selexam,kernel_cache,key, + chosen,iteration); + } + } + + if(verbosity>=2) { + printf(" %ld vectors chosen\n",choosenum); fflush(stdout); + } + + if(verbosity>=2) t1=get_runtime(); + + if(kernel_cache) + cache_multiple_kernel_rows(kernel_cache,docs,working2dnum, + choosenum,kernel_parm); + + if(verbosity>=2) t2=get_runtime(); + if(retrain != 2) { + optimize_svm(docs,label,unlabeled,inconsistent,0.0,chosen,active2dnum, + model,totdoc,working2dnum,choosenum,a,lin,c,learn_parm, + aicache,kernel_parm,&qp,&epsilon_crit_org); + } + + if(verbosity>=2) t3=get_runtime(); + update_linear_component(docs,label,active2dnum,a,a_old,working2dnum,totdoc, + totwords,kernel_parm,kernel_cache,lin,aicache, + weights); + + if(verbosity>=2) t4=get_runtime(); + supvecnum=calculate_svm_model(docs,label,unlabeled,lin,a,a_old,c, + learn_parm,working2dnum,active2dnum,model); + + if(verbosity>=2) t5=get_runtime(); + + /* The following computation of the objective function works only */ + /* relative to the active variables */ + if(verbosity>=3) { + criterion=compute_objective_function(a,lin,c,learn_parm->eps,label, + active2dnum); + printf("Objective function (over active variables): %.16f\n",criterion); + fflush(stdout); + } + + for(jj=0;(i=working2dnum[jj])>=0;jj++) { + a_old[i]=a[i]; + } + + if(retrain == 2) { /* reset inconsistent unlabeled examples */ + for(i=0;(i<totdoc);i++) { + if(inconsistent[i] && unlabeled[i]) { + inconsistent[i]=0; + label[i]=0; + } + } + } + + retrain=check_optimality(model,label,unlabeled,a,lin,c,totdoc,learn_parm, + maxdiff,epsilon_crit_org,&misclassified, + inconsistent,active2dnum,last_suboptimal_at, + iteration,kernel_parm); + + if(verbosity>=2) { + t6=get_runtime(); + timing_profile->time_select+=t1-t0; + timing_profile->time_kernel+=t2-t1; + timing_profile->time_opti+=t3-t2; + timing_profile->time_update+=t4-t3; + timing_profile->time_model+=t5-t4; + timing_profile->time_check+=t6-t5; + } + + /* checking whether optimizer got stuck */ + if((*maxdiff) < bestmaxdiff) { + bestmaxdiff=(*maxdiff); + bestmaxdiffiter=iteration; + } + if(iteration > (bestmaxdiffiter+learn_parm->maxiter)) { + /* long time no progress? */ + terminate=1; + retrain=0; + if(verbosity>=1) + printf("\nWARNING: Relaxing KT-Conditions due to slow progress! Terminating!\n"); + } + + noshrink=0; + if((!retrain) && (inactivenum>0) + && ((!learn_parm->skip_final_opt_check) + || (kernel_parm->kernel_type == LINEAR))) { + if(((verbosity>=1) && (kernel_parm->kernel_type != LINEAR)) + || (verbosity>=2)) { + if(verbosity==1) { + printf("\n"); + } + printf(" Checking optimality of inactive variables..."); + fflush(stdout); + } + t1=get_runtime(); + reactivate_inactive_examples(label,unlabeled,a,shrink_state,lin,c,totdoc, + totwords,iteration,learn_parm,inconsistent, + docs,kernel_parm,kernel_cache,model,aicache, + weights,maxdiff); + /* Update to new active variables. */ + activenum=compute_index(shrink_state->active,totdoc,active2dnum); + inactivenum=totdoc-activenum; + /* reset watchdog */ + bestmaxdiff=(*maxdiff); + bestmaxdiffiter=iteration; + /* termination criterion */ + noshrink=1; + retrain=0; + if((*maxdiff) > learn_parm->epsilon_crit) + retrain=1; + timing_profile->time_shrink+=get_runtime()-t1; + if(((verbosity>=1) && (kernel_parm->kernel_type != LINEAR)) + || (verbosity>=2)) { + printf("done.\n"); fflush(stdout); + printf(" Number of inactive variables = %ld\n",inactivenum); + } + } + + if((!retrain) && (learn_parm->epsilon_crit>(*maxdiff))) + learn_parm->epsilon_crit=(*maxdiff); + if((!retrain) && (learn_parm->epsilon_crit>epsilon_crit_org)) { + learn_parm->epsilon_crit/=2.0; + retrain=1; + noshrink=1; + } + if(learn_parm->epsilon_crit<epsilon_crit_org) + learn_parm->epsilon_crit=epsilon_crit_org; + + if(verbosity>=2) { + printf(" => (%ld SV (incl. %ld SV at u-bound), max violation=%.5f)\n", + supvecnum,model->at_upper_bound,(*maxdiff)); + fflush(stdout); + } + if(verbosity>=3) { + printf("\n"); + } + + if((!retrain) && (transduction)) { + for(i=0;(i<totdoc);i++) { + shrink_state->active[i]=1; + } + activenum=compute_index(shrink_state->active,totdoc,active2dnum); + inactivenum=0; + if(verbosity==1) printf("done\n"); + retrain=incorporate_unlabeled_examples(model,label,inconsistent, + unlabeled,a,lin,totdoc, + selcrit,selexam,key, + transductcycle,kernel_parm, + learn_parm); + epsilon_crit_org=learn_parm->epsilon_crit; + if(kernel_parm->kernel_type == LINEAR) + learn_parm->epsilon_crit=1; + transductcycle++; + /* reset watchdog */ + bestmaxdiff=(*maxdiff); + bestmaxdiffiter=iteration; + } + else if(((iteration % 10) == 0) && (!noshrink)) { + activenum=shrink_problem(docs,learn_parm,shrink_state,kernel_parm, + active2dnum,last_suboptimal_at,iteration,totdoc, + maxl((long)(activenum/10), + maxl((long)(totdoc/500),100)), + a,inconsistent); + inactivenum=totdoc-activenum; + if((kernel_cache) + && (supvecnum>kernel_cache->max_elems) + && ((kernel_cache->activenum-activenum)>maxl((long)(activenum/10),500))) { + kernel_cache_shrink(kernel_cache,totdoc, + minl((kernel_cache->activenum-activenum), + (kernel_cache->activenum-supvecnum)), + shrink_state->active); + } + } + + if((!retrain) && learn_parm->remove_inconsistent) { + if(verbosity>=1) { + printf(" Moving training errors to inconsistent examples..."); + fflush(stdout); + } + if(learn_parm->remove_inconsistent == 1) { + retrain=identify_inconsistent(a,label,unlabeled,totdoc,learn_parm, + &inconsistentnum,inconsistent); + } + else if(learn_parm->remove_inconsistent == 2) { + retrain=identify_misclassified(lin,label,unlabeled,totdoc, + model,&inconsistentnum,inconsistent); + } + else if(learn_parm->remove_inconsistent == 3) { + retrain=identify_one_misclassified(lin,label,unlabeled,totdoc, + model,&inconsistentnum,inconsistent); + } + if(retrain) { + if(kernel_parm->kernel_type == LINEAR) { /* reinit shrinking */ + learn_parm->epsilon_crit=2.0; + } + } + if(verbosity>=1) { + printf("done.\n"); + if(retrain) { + printf(" Now %ld inconsistent examples.\n",inconsistentnum); + } + } + } + } /* end of loop */ + + free(chosen); + free(last_suboptimal_at); + free(key); + free(selcrit); + free(selexam); + free(a_old); + free(aicache); + free(working2dnum); + free(active2dnum); + free(qp.opt_ce); + free(qp.opt_ce0); + free(qp.opt_g); + free(qp.opt_g0); + free(qp.opt_xinit); + free(qp.opt_low); + free(qp.opt_up); + free(weights); + + learn_parm->epsilon_crit=epsilon_crit_org; /* restore org */ + model->maxdiff=(*maxdiff); + + return(iteration); +} + +long optimize_to_convergence_sharedslack(DOC **docs, long int *label, + long int totdoc, + long int totwords, LEARN_PARM *learn_parm, + KERNEL_PARM *kernel_parm, + KERNEL_CACHE *kernel_cache, + SHRINK_STATE *shrink_state, MODEL *model, + double *a, double *lin, double *c, + TIMING *timing_profile, double *maxdiff) + /* docs: Training vectors (x-part) */ + /* label: Training labels/value (y-part, zero if test example for + transduction) */ + /* totdoc: Number of examples in docs/label */ + /* totwords: Number of features (i.e. highest feature index) */ + /* learn_parm: Learning paramenters */ + /* kernel_parm: Kernel paramenters */ + /* kernel_cache: Initialized/partly filled Cache, if using a kernel. + NULL if linear. */ + /* shrink_state: State of active variables */ + /* model: Returns learning result */ + /* a: alphas */ + /* lin: linear component of gradient */ + /* c: right hand side of inequalities (margin) */ + /* maxdiff: returns maximum violation of KT-conditions */ +{ + long *chosen,*key,i,j,jj,*last_suboptimal_at,noshrink,*unlabeled; + long *inconsistent,choosenum,already_chosen=0,iteration; + long misclassified,supvecnum=0,*active2dnum,inactivenum; + long *working2dnum,*selexam,*ignore; + long activenum,retrain,maxslackid,slackset,jointstep; + double criterion,eq_target; + double *a_old,*alphaslack; + long t0=0,t1=0,t2=0,t3=0,t4=0,t5=0,t6=0; /* timing */ + double epsilon_crit_org,maxsharedviol; + double bestmaxdiff; + long bestmaxdiffiter,terminate; + + double *selcrit; /* buffer for sorting */ + CFLOAT *aicache; /* buffer to keep one row of hessian */ + double *weights; /* buffer for weight vector in linear case */ + QP qp; /* buffer for one quadratic program */ + double *slack; /* vector of slack variables for optimization with + shared slacks */ + + epsilon_crit_org=learn_parm->epsilon_crit; /* save org */ + if(kernel_parm->kernel_type == LINEAR) { + learn_parm->epsilon_crit=2.0; + kernel_cache=NULL; /* caching makes no sense for linear kernel */ + } + learn_parm->epsilon_shrink=2; + (*maxdiff)=1; + + learn_parm->totwords=totwords; + + chosen = (long *)my_malloc(sizeof(long)*totdoc); + unlabeled = (long *)my_malloc(sizeof(long)*totdoc); + inconsistent = (long *)my_malloc(sizeof(long)*totdoc); + ignore = (long *)my_malloc(sizeof(long)*totdoc); + key = (long *)my_malloc(sizeof(long)*(totdoc+11)); + selcrit = (double *)my_malloc(sizeof(double)*totdoc); + selexam = (long *)my_malloc(sizeof(long)*totdoc); + a_old = (double *)my_malloc(sizeof(double)*totdoc); + aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc); + working2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11)); + active2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11)); + qp.opt_ce = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + qp.opt_ce0 = (double *)my_malloc(sizeof(double)); + qp.opt_g = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize + *learn_parm->svm_maxqpsize); + qp.opt_g0 = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + qp.opt_xinit = (double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + qp.opt_low=(double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + qp.opt_up=(double *)my_malloc(sizeof(double)*learn_parm->svm_maxqpsize); + weights=(double *)my_malloc(sizeof(double)*(totwords+1)); + maxslackid=0; + for(i=0;i<totdoc;i++) { /* determine size of slack array */ + if(maxslackid<docs[i]->slackid) + maxslackid=docs[i]->slackid; + } + slack=(double *)my_malloc(sizeof(double)*(maxslackid+1)); + alphaslack=(double *)my_malloc(sizeof(double)*(maxslackid+1)); + last_suboptimal_at = (long *)my_malloc(sizeof(long)*(maxslackid+1)); + for(i=0;i<=maxslackid;i++) { /* init shared slacks */ + slack[i]=0; + alphaslack[i]=0; + last_suboptimal_at[i]=1; + } + + choosenum=0; + retrain=1; + iteration=1; + bestmaxdiffiter=1; + bestmaxdiff=999999999; + terminate=0; + + if(kernel_cache) { + kernel_cache->time=iteration; /* for lru cache */ + kernel_cache_reset_lru(kernel_cache); + } + + for(i=0;i<totdoc;i++) { /* various inits */ + chosen[i]=0; + unlabeled[i]=0; + inconsistent[i]=0; + ignore[i]=0; + a_old[i]=a[i]; + } + activenum=compute_index(shrink_state->active,totdoc,active2dnum); + inactivenum=totdoc-activenum; + clear_index(working2dnum); + + /* call to init slack and alphaslack */ + compute_shared_slacks(docs,label,a,lin,c,active2dnum,learn_parm, + slack,alphaslack); + + /* repeat this loop until we have convergence */ + for(;retrain && (!terminate);iteration++) { + + if(kernel_cache) + kernel_cache->time=iteration; /* for lru cache */ + if(verbosity>=2) { + printf( + "Iteration %ld: ",iteration); fflush(stdout); + } + else if(verbosity==1) { + printf("."); fflush(stdout); + } + + if(verbosity>=2) t0=get_runtime(); + if(verbosity>=3) { + printf("\nSelecting working set... "); fflush(stdout); + } + + if(learn_parm->svm_newvarsinqp>learn_parm->svm_maxqpsize) + learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize; + + /* select working set according to steepest gradient */ + jointstep=0; + eq_target=0; + if(iteration % 101) { + slackset=select_next_qp_slackset(docs,label,a,lin,slack,alphaslack,c, + learn_parm,active2dnum,&maxsharedviol); + if((iteration % 2) + || (!slackset) || (maxsharedviol<learn_parm->epsilon_crit)){ + /* do a step with examples from different slack sets */ + if(verbosity >= 2) { + printf("(i-step)"); fflush(stdout); + } + i=0; + for(jj=0;(j=working2dnum[jj])>=0;jj++) { /* clear old part of working set */ + if((chosen[j]>=(learn_parm->svm_maxqpsize/ + minl(learn_parm->svm_maxqpsize, + learn_parm->svm_newvarsinqp)))) { + chosen[j]=0; + choosenum--; + } + else { + chosen[j]++; + working2dnum[i++]=j; + } + } + working2dnum[i]=-1; + + already_chosen=0; + if((minl(learn_parm->svm_newvarsinqp, + learn_parm->svm_maxqpsize-choosenum)>=4) + && (kernel_parm->kernel_type != LINEAR)) { + /* select part of the working set from cache */ + already_chosen=select_next_qp_subproblem_grad( + label,unlabeled,a,lin,c,totdoc, + (long)(minl(learn_parm->svm_maxqpsize-choosenum, + learn_parm->svm_newvarsinqp) + /2), + learn_parm,inconsistent,active2dnum, + working2dnum,selcrit,selexam,kernel_cache, + (long)1,key,chosen); + choosenum+=already_chosen; + } + choosenum+=select_next_qp_subproblem_grad( + label,unlabeled,a,lin,c,totdoc, + minl(learn_parm->svm_maxqpsize-choosenum, + learn_parm->svm_newvarsinqp-already_chosen), + learn_parm,inconsistent,active2dnum, + working2dnum,selcrit,selexam,kernel_cache, + (long)0,key,chosen); + } + else { /* do a step with all examples from same slack set */ + if(verbosity >= 2) { + printf("(j-step on %ld)",slackset); fflush(stdout); + } + jointstep=1; + for(jj=0;(j=working2dnum[jj])>=0;jj++) { /* clear working set */ + chosen[j]=0; + } + working2dnum[0]=-1; + eq_target=alphaslack[slackset]; + for(j=0;j<totdoc;j++) { /* mask all but slackset */ + /* for(jj=0;(j=active2dnum[jj])>=0;jj++) { */ + if(docs[j]->slackid != slackset) + ignore[j]=1; + else { + ignore[j]=0; + learn_parm->svm_cost[j]=learn_parm->svm_c; + /* printf("Inslackset(%ld,%ld)",j,shrink_state->active[j]); */ + } + } + learn_parm->biased_hyperplane=1; + choosenum=select_next_qp_subproblem_grad( + label,unlabeled,a,lin,c,totdoc, + learn_parm->svm_maxqpsize, + learn_parm,ignore,active2dnum, + working2dnum,selcrit,selexam,kernel_cache, + (long)0,key,chosen); + learn_parm->biased_hyperplane=0; + } + } + else { /* once in a while, select a somewhat random working set + to get unlocked of infinite loops due to numerical + inaccuracies in the core qp-solver */ + choosenum+=select_next_qp_subproblem_rand( + label,unlabeled,a,lin,c,totdoc, + minl(learn_parm->svm_maxqpsize-choosenum, + learn_parm->svm_newvarsinqp), + learn_parm,inconsistent,active2dnum, + working2dnum,selcrit,selexam,kernel_cache,key, + chosen,iteration); + } + + if(verbosity>=2) { + printf(" %ld vectors chosen\n",choosenum); fflush(stdout); + } + + if(verbosity>=2) t1=get_runtime(); + + if(kernel_cache) + cache_multiple_kernel_rows(kernel_cache,docs,working2dnum, + choosenum,kernel_parm); + + if(verbosity>=2) t2=get_runtime(); + if(jointstep) learn_parm->biased_hyperplane=1; + optimize_svm(docs,label,unlabeled,ignore,eq_target,chosen,active2dnum, + model,totdoc,working2dnum,choosenum,a,lin,c,learn_parm, + aicache,kernel_parm,&qp,&epsilon_crit_org); + learn_parm->biased_hyperplane=0; + + for(jj=0;(i=working2dnum[jj])>=0;jj++) /* recompute sums of alphas */ + alphaslack[docs[i]->slackid]+=(a[i]-a_old[i]); + for(jj=0;(i=working2dnum[jj])>=0;jj++) { /* reduce alpha to fulfill + constraints */ + if(alphaslack[docs[i]->slackid] > learn_parm->svm_c) { + if(a[i] < (alphaslack[docs[i]->slackid]-learn_parm->svm_c)) { + alphaslack[docs[i]->slackid]-=a[i]; + a[i]=0; + } + else { + a[i]-=(alphaslack[docs[i]->slackid]-learn_parm->svm_c); + alphaslack[docs[i]->slackid]=learn_parm->svm_c; + } + } + } + for(jj=0;(i=active2dnum[jj])>=0;jj++) + learn_parm->svm_cost[i]=a[i]+(learn_parm->svm_c + -alphaslack[docs[i]->slackid]); + + if(verbosity>=2) t3=get_runtime(); + update_linear_component(docs,label,active2dnum,a,a_old,working2dnum,totdoc, + totwords,kernel_parm,kernel_cache,lin,aicache, + weights); + compute_shared_slacks(docs,label,a,lin,c,active2dnum,learn_parm, + slack,alphaslack); + + if(verbosity>=2) t4=get_runtime(); + supvecnum=calculate_svm_model(docs,label,unlabeled,lin,a,a_old,c, + learn_parm,working2dnum,active2dnum,model); + + if(verbosity>=2) t5=get_runtime(); + + /* The following computation of the objective function works only */ + /* relative to the active variables */ + if(verbosity>=3) { + criterion=compute_objective_function(a,lin,c,learn_parm->eps,label, + active2dnum); + printf("Objective function (over active variables): %.16f\n",criterion); + fflush(stdout); + } + + for(jj=0;(i=working2dnum[jj])>=0;jj++) { + a_old[i]=a[i]; + } + + retrain=check_optimality_sharedslack(docs,model,label,a,lin,c, + slack,alphaslack,totdoc,learn_parm, + maxdiff,epsilon_crit_org,&misclassified, + active2dnum,last_suboptimal_at, + iteration,kernel_parm); + + if(verbosity>=2) { + t6=get_runtime(); + timing_profile->time_select+=t1-t0; + timing_profile->time_kernel+=t2-t1; + timing_profile->time_opti+=t3-t2; + timing_profile->time_update+=t4-t3; + timing_profile->time_model+=t5-t4; + timing_profile->time_check+=t6-t5; + } + + /* checking whether optimizer got stuck */ + if((*maxdiff) < bestmaxdiff) { + bestmaxdiff=(*maxdiff); + bestmaxdiffiter=iteration; + } + if(iteration > (bestmaxdiffiter+learn_parm->maxiter)) { + /* long time no progress? */ + terminate=1; + retrain=0; + if(verbosity>=1) + printf("\nWARNING: Relaxing KT-Conditions due to slow progress! Terminating!\n"); + } + + noshrink=0; + + if((!retrain) && (inactivenum>0) + && ((!learn_parm->skip_final_opt_check) + || (kernel_parm->kernel_type == LINEAR))) { + if(((verbosity>=1) && (kernel_parm->kernel_type != LINEAR)) + || (verbosity>=2)) { + if(verbosity==1) { + printf("\n"); + } + printf(" Checking optimality of inactive variables..."); + fflush(stdout); + } + t1=get_runtime(); + reactivate_inactive_examples(label,unlabeled,a,shrink_state,lin,c,totdoc, + totwords,iteration,learn_parm,inconsistent, + docs,kernel_parm,kernel_cache,model,aicache, + weights,maxdiff); + /* Update to new active variables. */ + activenum=compute_index(shrink_state->active,totdoc,active2dnum); + inactivenum=totdoc-activenum; + /* check optimality, since check in reactivate does not work for + sharedslacks */ + retrain=check_optimality_sharedslack(docs,model,label,a,lin,c, + slack,alphaslack,totdoc,learn_parm, + maxdiff,epsilon_crit_org,&misclassified, + active2dnum,last_suboptimal_at, + iteration,kernel_parm); + + /* reset watchdog */ + bestmaxdiff=(*maxdiff); + bestmaxdiffiter=iteration; + /* termination criterion */ + noshrink=1; + retrain=0; + if((*maxdiff) > learn_parm->epsilon_crit) + retrain=1; + timing_profile->time_shrink+=get_runtime()-t1; + if(((verbosity>=1) && (kernel_parm->kernel_type != LINEAR)) + || (verbosity>=2)) { + printf("done.\n"); fflush(stdout); + printf(" Number of inactive variables = %ld\n",inactivenum); + } + } + + if((!retrain) && (learn_parm->epsilon_crit>(*maxdiff))) + learn_parm->epsilon_crit=(*maxdiff); + if((!retrain) && (learn_parm->epsilon_crit>epsilon_crit_org)) { + learn_parm->epsilon_crit/=2.0; + retrain=1; + noshrink=1; + } + if(learn_parm->epsilon_crit<epsilon_crit_org) + learn_parm->epsilon_crit=epsilon_crit_org; + + if(verbosity>=2) { + printf(" => (%ld SV (incl. %ld SV at u-bound), max violation=%.5f)\n", + supvecnum,model->at_upper_bound,(*maxdiff)); + fflush(stdout); + } + if(verbosity>=3) { + printf("\n"); + } + + if(((iteration % 10) == 0) && (!noshrink)) { + activenum=shrink_problem(docs,learn_parm,shrink_state, + kernel_parm,active2dnum, + last_suboptimal_at,iteration,totdoc, + maxl((long)(activenum/10), + maxl((long)(totdoc/500),100)), + a,inconsistent); + inactivenum=totdoc-activenum; + if((kernel_cache) + && (supvecnum>kernel_cache->max_elems) + && ((kernel_cache->activenum-activenum)>maxl((long)(activenum/10),500))) { + kernel_cache_shrink(kernel_cache,totdoc, + minl((kernel_cache->activenum-activenum), + (kernel_cache->activenum-supvecnum)), + shrink_state->active); + } + } + + } /* end of loop */ + + + free(alphaslack); + free(slack); + free(chosen); + free(unlabeled); + free(inconsistent); + free(ignore); + free(last_suboptimal_at); + free(key); + free(selcrit); + free(selexam); + free(a_old); + free(aicache); + free(working2dnum); + free(active2dnum); + free(qp.opt_ce); + free(qp.opt_ce0); + free(qp.opt_g); + free(qp.opt_g0); + free(qp.opt_xinit); + free(qp.opt_low); + free(qp.opt_up); + free(weights); + + learn_parm->epsilon_crit=epsilon_crit_org; /* restore org */ + model->maxdiff=(*maxdiff); + + return(iteration); +} + + +double compute_objective_function(double *a, double *lin, double *c, + double eps, long int *label, + long int *active2dnum) + /* Return value of objective function. */ + /* Works only relative to the active variables! */ +{ + long i,ii; + double criterion; + /* calculate value of objective function */ + criterion=0; + for(ii=0;active2dnum[ii]>=0;ii++) { + i=active2dnum[ii]; + criterion=criterion+(eps-(double)label[i]*c[i])*a[i]+0.5*a[i]*label[i]*lin[i]; + } + return(criterion); +} + +void clear_index(long int *index) + /* initializes and empties index */ +{ + index[0]=-1; +} + +void add_to_index(long int *index, long int elem) + /* initializes and empties index */ +{ + register long i; + for(i=0;index[i] != -1;i++); + index[i]=elem; + index[i+1]=-1; +} + +long compute_index(long int *binfeature, long int range, long int *index) + /* create an inverted index of binfeature */ +{ + register long i,ii; + + ii=0; + for(i=0;i<range;i++) { + if(binfeature[i]) { + index[ii]=i; + ii++; + } + } + for(i=0;i<4;i++) { + index[ii+i]=-1; + } + return(ii); +} + + +void optimize_svm(DOC **docs, long int *label, long int *unlabeled, + long int *exclude_from_eq_const, double eq_target, + long int *chosen, long int *active2dnum, MODEL *model, + long int totdoc, long int *working2dnum, long int varnum, + double *a, double *lin, double *c, LEARN_PARM *learn_parm, + CFLOAT *aicache, KERNEL_PARM *kernel_parm, QP *qp, + double *epsilon_crit_target) + /* Do optimization on the working set. */ +{ + long i; + double *a_v; + + compute_matrices_for_optimization(docs,label,unlabeled, + exclude_from_eq_const,eq_target,chosen, + active2dnum,working2dnum,model,a,lin,c, + varnum,totdoc,learn_parm,aicache, + kernel_parm,qp); + + if(verbosity>=3) { + printf("Running optimizer..."); fflush(stdout); + } + /* call the qp-subsolver */ + a_v=optimize_qp(qp,epsilon_crit_target, + learn_parm->svm_maxqpsize, + &(model->b), /* in case the optimizer gives us */ + /* the threshold for free. otherwise */ + /* b is calculated in calculate_model. */ + learn_parm); + if(verbosity>=3) { + printf("done\n"); + } + + for(i=0;i<varnum;i++) { + a[working2dnum[i]]=a_v[i]; + /* + if(a_v[i]<=(0+learn_parm->epsilon_a)) { + a[working2dnum[i]]=0; + } + else if(a_v[i]>=(learn_parm->svm_cost[working2dnum[i]]-learn_parm->epsilon_a)) { + a[working2dnum[i]]=learn_parm->svm_cost[working2dnum[i]]; + } + */ + } +} + +void compute_matrices_for_optimization(DOC **docs, long int *label, + long int *unlabeled, long *exclude_from_eq_const, double eq_target, + long int *chosen, long int *active2dnum, + long int *key, MODEL *model, double *a, double *lin, double *c, + long int varnum, long int totdoc, LEARN_PARM *learn_parm, + CFLOAT *aicache, KERNEL_PARM *kernel_parm, QP *qp) +{ + register long ki,kj,i,j; + register double kernel_temp; + + if(verbosity>=3) { + fprintf(stdout,"Computing qp-matrices (type %ld kernel [degree %ld, rbf_gamma %f, coef_lin %f, coef_const %f])...",kernel_parm->kernel_type,kernel_parm->poly_degree,kernel_parm->rbf_gamma,kernel_parm->coef_lin,kernel_parm->coef_const); + fflush(stdout); + } + + qp->opt_n=varnum; + qp->opt_ce0[0]=-eq_target; /* compute the constant for equality constraint */ + for(j=1;j<model->sv_num;j++) { /* start at 1 */ + if((!chosen[(model->supvec[j])->docnum]) + && (!exclude_from_eq_const[(model->supvec[j])->docnum])) { + qp->opt_ce0[0]+=model->alpha[j]; + } + } + if(learn_parm->biased_hyperplane) + qp->opt_m=1; + else + qp->opt_m=0; /* eq-constraint will be ignored */ + + /* init linear part of objective function */ + for(i=0;i<varnum;i++) { + qp->opt_g0[i]=lin[key[i]]; + } + + for(i=0;i<varnum;i++) { + ki=key[i]; + + /* Compute the matrix for equality constraints */ + qp->opt_ce[i]=label[ki]; + qp->opt_low[i]=0; + qp->opt_up[i]=learn_parm->svm_cost[ki]; + + kernel_temp=(double)kernel(kernel_parm,docs[ki],docs[ki]); + /* compute linear part of objective function */ + qp->opt_g0[i]-=(kernel_temp*a[ki]*(double)label[ki]); + /* compute quadratic part of objective function */ + qp->opt_g[varnum*i+i]=kernel_temp; + for(j=i+1;j<varnum;j++) { + kj=key[j]; + kernel_temp=(double)kernel(kernel_parm,docs[ki],docs[kj]); + /* compute linear part of objective function */ + qp->opt_g0[i]-=(kernel_temp*a[kj]*(double)label[kj]); + qp->opt_g0[j]-=(kernel_temp*a[ki]*(double)label[ki]); + /* compute quadratic part of objective function */ + qp->opt_g[varnum*i+j]=(double)label[ki]*(double)label[kj]*kernel_temp; + qp->opt_g[varnum*j+i]=(double)label[ki]*(double)label[kj]*kernel_temp; + } + + if(verbosity>=3) { + if(i % 20 == 0) { + fprintf(stdout,"%ld..",i); fflush(stdout); + } + } + } + + for(i=0;i<varnum;i++) { + /* assure starting at feasible point */ + qp->opt_xinit[i]=a[key[i]]; + /* set linear part of objective function */ + qp->opt_g0[i]=(learn_parm->eps-(double)label[key[i]]*c[key[i]])+qp->opt_g0[i]*(double)label[key[i]]; + } + + if(verbosity>=3) { + fprintf(stdout,"done\n"); + } +} + +long calculate_svm_model(DOC **docs, long int *label, long int *unlabeled, + double *lin, double *a, double *a_old, double *c, + LEARN_PARM *learn_parm, long int *working2dnum, + long int *active2dnum, MODEL *model) + /* Compute decision function based on current values */ + /* of alpha. */ +{ + long i,ii,pos,b_calculated=0,first_low,first_high; + double ex_c,b_temp,b_low,b_high; + + if(verbosity>=3) { + printf("Calculating model..."); fflush(stdout); + } + + if(!learn_parm->biased_hyperplane) { + model->b=0; + b_calculated=1; + } + + for(ii=0;(i=working2dnum[ii])>=0;ii++) { + if((a_old[i]>0) && (a[i]==0)) { /* remove from model */ + pos=model->index[i]; + model->index[i]=-1; + (model->sv_num)--; + model->supvec[pos]=model->supvec[model->sv_num]; + model->alpha[pos]=model->alpha[model->sv_num]; + model->index[(model->supvec[pos])->docnum]=pos; + } + else if((a_old[i]==0) && (a[i]>0)) { /* add to model */ + model->supvec[model->sv_num]=docs[i]; + model->alpha[model->sv_num]=a[i]*(double)label[i]; + model->index[i]=model->sv_num; + (model->sv_num)++; + } + else if(a_old[i]==a[i]) { /* nothing to do */ + } + else { /* just update alpha */ + model->alpha[model->index[i]]=a[i]*(double)label[i]; + } + + ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a; + if((a_old[i]>=ex_c) && (a[i]<ex_c)) { + (model->at_upper_bound)--; + } + else if((a_old[i]<ex_c) && (a[i]>=ex_c)) { + (model->at_upper_bound)++; + } + + if((!b_calculated) + && (a[i]>learn_parm->epsilon_a) && (a[i]<ex_c)) { /* calculate b */ + model->b=((double)label[i]*learn_parm->eps-c[i]+lin[i]); + /* model->b=(-(double)label[i]+lin[i]); */ + b_calculated=1; + } + } + + /* No alpha in the working set not at bounds, so b was not + calculated in the usual way. The following handles this special + case. */ + if(learn_parm->biased_hyperplane + && (!b_calculated) + && (model->sv_num-1 == model->at_upper_bound)) { + first_low=1; + first_high=1; + b_low=0; + b_high=0; + for(ii=0;(i=active2dnum[ii])>=0;ii++) { + ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a; + if(a[i]<ex_c) { + if(label[i]>0) { + b_temp=-(learn_parm->eps-c[i]+lin[i]); + if((b_temp>b_low) || (first_low)) { + b_low=b_temp; + first_low=0; + } + } + else { + b_temp=-(-learn_parm->eps-c[i]+lin[i]); + if((b_temp<b_high) || (first_high)) { + b_high=b_temp; + first_high=0; + } + } + } + else { + if(label[i]<0) { + b_temp=-(-learn_parm->eps-c[i]+lin[i]); + if((b_temp>b_low) || (first_low)) { + b_low=b_temp; + first_low=0; + } + } + else { + b_temp=-(learn_parm->eps-c[i]+lin[i]); + if((b_temp<b_high) || (first_high)) { + b_high=b_temp; + first_high=0; + } + } + } + } + if(first_high) { + model->b=-b_low; + } + else if(first_low) { + model->b=-b_high; + } + else { + model->b=-(b_high+b_low)/2.0; /* select b as the middle of range */ + /* printf("\nb_low=%f, b_high=%f,b=%f\n",b_low,b_high,model->b); */ + } + } + + if(verbosity>=3) { + printf("done\n"); fflush(stdout); + } + + return(model->sv_num-1); /* have to substract one, since element 0 is empty*/ +} + +long check_optimality(MODEL *model, long int *label, long int *unlabeled, + double *a, double *lin, double *c, long int totdoc, + LEARN_PARM *learn_parm, double *maxdiff, + double epsilon_crit_org, long int *misclassified, + long int *inconsistent, long int *active2dnum, + long int *last_suboptimal_at, + long int iteration, KERNEL_PARM *kernel_parm) + /* Check KT-conditions */ +{ + long i,ii,retrain; + double dist,ex_c,target; + + if(kernel_parm->kernel_type == LINEAR) { /* be optimistic */ + learn_parm->epsilon_shrink=-learn_parm->epsilon_crit+epsilon_crit_org; + } + else { /* be conservative */ + learn_parm->epsilon_shrink=learn_parm->epsilon_shrink*0.7+(*maxdiff)*0.3; + } + retrain=0; + (*maxdiff)=0; + (*misclassified)=0; + for(ii=0;(i=active2dnum[ii])>=0;ii++) { + if((!inconsistent[i]) && label[i]) { + dist=(lin[i]-model->b)*(double)label[i];/* 'distance' from + hyperplane*/ + target=-(learn_parm->eps-(double)label[i]*c[i]); + ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a; + if(dist <= 0) { + (*misclassified)++; /* does not work due to deactivation of var */ + } + if((a[i]>learn_parm->epsilon_a) && (dist > target)) { + if((dist-target)>(*maxdiff)) /* largest violation */ + (*maxdiff)=dist-target; + } + else if((a[i]<ex_c) && (dist < target)) { + if((target-dist)>(*maxdiff)) /* largest violation */ + (*maxdiff)=target-dist; + } + /* Count how long a variable was at lower/upper bound (and optimal).*/ + /* Variables, which were at the bound and optimal for a long */ + /* time are unlikely to become support vectors. In case our */ + /* cache is filled up, those variables are excluded to save */ + /* kernel evaluations. (See chapter 'Shrinking').*/ + if((a[i]>(learn_parm->epsilon_a)) + && (a[i]<ex_c)) { + last_suboptimal_at[i]=iteration; /* not at bound */ + } + else if((a[i]<=(learn_parm->epsilon_a)) + && (dist < (target+learn_parm->epsilon_shrink))) { + last_suboptimal_at[i]=iteration; /* not likely optimal */ + } + else if((a[i]>=ex_c) + && (dist > (target-learn_parm->epsilon_shrink))) { + last_suboptimal_at[i]=iteration; /* not likely optimal */ + } + } + } + /* termination criterion */ + if((!retrain) && ((*maxdiff) > learn_parm->epsilon_crit)) { + retrain=1; + } + return(retrain); +} + +long check_optimality_sharedslack(DOC **docs, MODEL *model, long int *label, + double *a, double *lin, double *c, double *slack, + double *alphaslack, + long int totdoc, + LEARN_PARM *learn_parm, double *maxdiff, + double epsilon_crit_org, long int *misclassified, + long int *active2dnum, + long int *last_suboptimal_at, + long int iteration, KERNEL_PARM *kernel_parm) + /* Check KT-conditions */ +{ + long i,ii,retrain; + double dist,ex_c=0,target; + + if(kernel_parm->kernel_type == LINEAR) { /* be optimistic */ + learn_parm->epsilon_shrink=-learn_parm->epsilon_crit+epsilon_crit_org; + } + else { /* be conservative */ + learn_parm->epsilon_shrink=learn_parm->epsilon_shrink*0.7+(*maxdiff)*0.3; + } + + retrain=0; + (*maxdiff)=0; + (*misclassified)=0; + for(ii=0;(i=active2dnum[ii])>=0;ii++) { + /* 'distance' from hyperplane*/ + dist=(lin[i]-model->b)*(double)label[i]+slack[docs[i]->slackid]; + target=-(learn_parm->eps-(double)label[i]*c[i]); + ex_c=learn_parm->svm_c-learn_parm->epsilon_a; + if((a[i]>learn_parm->epsilon_a) && (dist > target)) { + if((dist-target)>(*maxdiff)) { /* largest violation */ + (*maxdiff)=dist-target; + if(verbosity>=5) printf("sid %ld: dist=%.2f, target=%.2f, slack=%.2f, a=%f, alphaslack=%f\n",docs[i]->slackid,dist,target,slack[docs[i]->slackid],a[i],alphaslack[docs[i]->slackid]); + if(verbosity>=5) printf(" (single %f)\n",(*maxdiff)); + } + } + if((alphaslack[docs[i]->slackid]<ex_c) && (slack[docs[i]->slackid]>0)) { + if((slack[docs[i]->slackid])>(*maxdiff)) { /* largest violation */ + (*maxdiff)=slack[docs[i]->slackid]; + if(verbosity>=5) printf("sid %ld: dist=%.2f, target=%.2f, slack=%.2f, a=%f, alphaslack=%f\n",docs[i]->slackid,dist,target,slack[docs[i]->slackid],a[i],alphaslack[docs[i]->slackid]); + if(verbosity>=5) printf(" (joint %f)\n",(*maxdiff)); + } + } + /* Count how long a variable was at lower/upper bound (and optimal).*/ + /* Variables, which were at the bound and optimal for a long */ + /* time are unlikely to become support vectors. In case our */ + /* cache is filled up, those variables are excluded to save */ + /* kernel evaluations. (See chapter 'Shrinking').*/ + if((a[i]>(learn_parm->epsilon_a)) + && (a[i]<ex_c)) { + last_suboptimal_at[docs[i]->slackid]=iteration; /* not at bound */ + } + else if((a[i]<=(learn_parm->epsilon_a)) + && (dist < (target+learn_parm->epsilon_shrink))) { + last_suboptimal_at[docs[i]->slackid]=iteration; /* not likely optimal */ + } + else if((a[i]>=ex_c) + && (slack[docs[i]->slackid] < learn_parm->epsilon_shrink)) { + last_suboptimal_at[docs[i]->slackid]=iteration; /* not likely optimal */ + } + } + /* termination criterion */ + if((!retrain) && ((*maxdiff) > learn_parm->epsilon_crit)) { + retrain=1; + } + return(retrain); +} + +void compute_shared_slacks(DOC **docs, long int *label, + double *a, double *lin, + double *c, long int *active2dnum, + LEARN_PARM *learn_parm, + double *slack, double *alphaslack) + /* compute the value of shared slacks and the joint alphas */ +{ + long jj,i; + double dist,target; + + for(jj=0;(i=active2dnum[jj])>=0;jj++) { /* clear slack variables */ + slack[docs[i]->slackid]=0.0; + alphaslack[docs[i]->slackid]=0.0; + } + for(jj=0;(i=active2dnum[jj])>=0;jj++) { /* recompute slack variables */ + dist=(lin[i])*(double)label[i]; + target=-(learn_parm->eps-(double)label[i]*c[i]); + if((target-dist) > slack[docs[i]->slackid]) + slack[docs[i]->slackid]=target-dist; + alphaslack[docs[i]->slackid]+=a[i]; + } +} + + +long identify_inconsistent(double *a, long int *label, + long int *unlabeled, long int totdoc, + LEARN_PARM *learn_parm, + long int *inconsistentnum, long int *inconsistent) +{ + long i,retrain; + + /* Throw out examples with multipliers at upper bound. This */ + /* corresponds to the -i 1 option. */ + /* ATTENTION: this is just a heuristic for finding a close */ + /* to minimum number of examples to exclude to */ + /* make the problem separable with desired margin */ + retrain=0; + for(i=0;i<totdoc;i++) { + if((!inconsistent[i]) && (!unlabeled[i]) + && (a[i]>=(learn_parm->svm_cost[i]-learn_parm->epsilon_a))) { + (*inconsistentnum)++; + inconsistent[i]=1; /* never choose again */ + retrain=2; /* start over */ + if(verbosity>=3) { + printf("inconsistent(%ld)..",i); fflush(stdout); + } + } + } + return(retrain); +} + +long identify_misclassified(double *lin, long int *label, + long int *unlabeled, long int totdoc, + MODEL *model, long int *inconsistentnum, + long int *inconsistent) +{ + long i,retrain; + double dist; + + /* Throw out misclassified examples. This */ + /* corresponds to the -i 2 option. */ + /* ATTENTION: this is just a heuristic for finding a close */ + /* to minimum number of examples to exclude to */ + /* make the problem separable with desired margin */ + retrain=0; + for(i=0;i<totdoc;i++) { + dist=(lin[i]-model->b)*(double)label[i]; /* 'distance' from hyperplane*/ + if((!inconsistent[i]) && (!unlabeled[i]) && (dist <= 0)) { + (*inconsistentnum)++; + inconsistent[i]=1; /* never choose again */ + retrain=2; /* start over */ + if(verbosity>=3) { + printf("inconsistent(%ld)..",i); fflush(stdout); + } + } + } + return(retrain); +} + +long identify_one_misclassified(double *lin, long int *label, + long int *unlabeled, + long int totdoc, MODEL *model, + long int *inconsistentnum, + long int *inconsistent) +{ + long i,retrain,maxex=-1; + double dist,maxdist=0; + + /* Throw out the 'most misclassified' example. This */ + /* corresponds to the -i 3 option. */ + /* ATTENTION: this is just a heuristic for finding a close */ + /* to minimum number of examples to exclude to */ + /* make the problem separable with desired margin */ + retrain=0; + for(i=0;i<totdoc;i++) { + if((!inconsistent[i]) && (!unlabeled[i])) { + dist=(lin[i]-model->b)*(double)label[i];/* 'distance' from hyperplane*/ + if(dist<maxdist) { + maxdist=dist; + maxex=i; + } + } + } + if(maxex>=0) { + (*inconsistentnum)++; + inconsistent[maxex]=1; /* never choose again */ + retrain=2; /* start over */ + if(verbosity>=3) { + printf("inconsistent(%ld)..",i); fflush(stdout); + } + } + return(retrain); +} + +void update_linear_component(DOC **docs, long int *label, + long int *active2dnum, double *a, + double *a_old, long int *working2dnum, + long int totdoc, long int totwords, + KERNEL_PARM *kernel_parm, + KERNEL_CACHE *kernel_cache, + double *lin, CFLOAT *aicache, double *weights) + /* keep track of the linear component */ + /* lin of the gradient etc. by updating */ + /* based on the change of the variables */ + /* in the current working set */ +{ + register long i,ii,j,jj; + register double tec; + SVECTOR *f; + + if(kernel_parm->kernel_type==0) { /* special linear case */ + clear_vector_n(weights,totwords); + for(ii=0;(i=working2dnum[ii])>=0;ii++) { + if(a[i] != a_old[i]) { + for(f=docs[i]->fvec;f;f=f->next) + add_vector_ns(weights,f, + f->factor*((a[i]-a_old[i])*(double)label[i])); + } + } + for(jj=0;(j=active2dnum[jj])>=0;jj++) { + for(f=docs[j]->fvec;f;f=f->next) + lin[j]+=f->factor*sprod_ns(weights,f); + } + } + else { /* general case */ + for(jj=0;(i=working2dnum[jj])>=0;jj++) { + if(a[i] != a_old[i]) { + get_kernel_row(kernel_cache,docs,i,totdoc,active2dnum,aicache, + kernel_parm); + for(ii=0;(j=active2dnum[ii])>=0;ii++) { + tec=aicache[j]; + lin[j]+=(((a[i]*tec)-(a_old[i]*tec))*(double)label[i]); + } + } + } + } +} + + +long incorporate_unlabeled_examples(MODEL *model, long int *label, + long int *inconsistent, + long int *unlabeled, + double *a, double *lin, + long int totdoc, double *selcrit, + long int *select, long int *key, + long int transductcycle, + KERNEL_PARM *kernel_parm, + LEARN_PARM *learn_parm) +{ + long i,j,k,j1,j2,j3,j4,unsupaddnum1=0,unsupaddnum2=0; + long pos,neg,upos,uneg,orgpos,orgneg,nolabel,newpos,newneg,allunlab; + double dist,model_length,posratio,negratio; + long check_every=2; + double loss; + static double switchsens=0.0,switchsensorg=0.0; + double umin,umax,sumalpha; + long imin=0,imax=0; + static long switchnum=0; + + switchsens/=1.2; + + /* assumes that lin[] is up to date -> no inactive vars */ + + orgpos=0; + orgneg=0; + newpos=0; + newneg=0; + nolabel=0; + allunlab=0; + for(i=0;i<totdoc;i++) { + if(!unlabeled[i]) { + if(label[i] > 0) { + orgpos++; + } + else { + orgneg++; + } + } + else { + allunlab++; + if(unlabeled[i]) { + if(label[i] > 0) { + newpos++; + } + else if(label[i] < 0) { + newneg++; + } + } + } + if(label[i]==0) { + nolabel++; + } + } + + if(learn_parm->transduction_posratio >= 0) { + posratio=learn_parm->transduction_posratio; + } + else { + posratio=(double)orgpos/(double)(orgpos+orgneg); /* use ratio of pos/neg */ + } /* in training data */ + negratio=1.0-posratio; + + learn_parm->svm_costratio=1.0; /* global */ + if(posratio>0) { + learn_parm->svm_costratio_unlab=negratio/posratio; + } + else { + learn_parm->svm_costratio_unlab=1.0; + } + + pos=0; + neg=0; + upos=0; + uneg=0; + for(i=0;i<totdoc;i++) { + dist=(lin[i]-model->b); /* 'distance' from hyperplane*/ + if(dist>0) { + pos++; + } + else { + neg++; + } + if(unlabeled[i]) { + if(dist>0) { + upos++; + } + else { + uneg++; + } + } + if((!unlabeled[i]) && (a[i]>(learn_parm->svm_cost[i]-learn_parm->epsilon_a))) { + /* printf("Ubounded %ld (class %ld, unlabeled %ld)\n",i,label[i],unlabeled[i]); */ + } + } + if(verbosity>=2) { + printf("POS=%ld, ORGPOS=%ld, ORGNEG=%ld\n",pos,orgpos,orgneg); + printf("POS=%ld, NEWPOS=%ld, NEWNEG=%ld\n",pos,newpos,newneg); + printf("pos ratio = %f (%f).\n",(double)(upos)/(double)(allunlab),posratio); + fflush(stdout); + } + + if(transductcycle == 0) { + j1=0; + j2=0; + j4=0; + for(i=0;i<totdoc;i++) { + dist=(lin[i]-model->b); /* 'distance' from hyperplane*/ + if((label[i]==0) && (unlabeled[i])) { + selcrit[j4]=dist; + key[j4]=i; + j4++; + } + } + unsupaddnum1=0; + unsupaddnum2=0; + select_top_n(selcrit,j4,select,(long)(allunlab*posratio+0.5)); + for(k=0;(k<(long)(allunlab*posratio+0.5));k++) { + i=key[select[k]]; + label[i]=1; + unsupaddnum1++; + j1++; + } + for(i=0;i<totdoc;i++) { + if((label[i]==0) && (unlabeled[i])) { + label[i]=-1; + j2++; + unsupaddnum2++; + } + } + for(i=0;i<totdoc;i++) { /* set upper bounds on vars */ + if(unlabeled[i]) { + if(label[i] == 1) { + learn_parm->svm_cost[i]=learn_parm->svm_c* + learn_parm->svm_costratio_unlab*learn_parm->svm_unlabbound; + } + else if(label[i] == -1) { + learn_parm->svm_cost[i]=learn_parm->svm_c* + learn_parm->svm_unlabbound; + } + } + } + if(verbosity>=1) { + /* printf("costratio %f, costratio_unlab %f, unlabbound %f\n", + learn_parm->svm_costratio,learn_parm->svm_costratio_unlab, + learn_parm->svm_unlabbound); */ + printf("Classifying unlabeled data as %ld POS / %ld NEG.\n", + unsupaddnum1,unsupaddnum2); + fflush(stdout); + } + if(verbosity >= 1) + printf("Retraining."); + if(verbosity >= 2) printf("\n"); + return((long)3); + } + if((transductcycle % check_every) == 0) { + if(verbosity >= 1) + printf("Retraining."); + if(verbosity >= 2) printf("\n"); + j1=0; + j2=0; + unsupaddnum1=0; + unsupaddnum2=0; + for(i=0;i<totdoc;i++) { + if((unlabeled[i] == 2)) { + unlabeled[i]=1; + label[i]=1; + j1++; + unsupaddnum1++; + } + else if((unlabeled[i] == 3)) { + unlabeled[i]=1; + label[i]=-1; + j2++; + unsupaddnum2++; + } + } + for(i=0;i<totdoc;i++) { /* set upper bounds on vars */ + if(unlabeled[i]) { + if(label[i] == 1) { + learn_parm->svm_cost[i]=learn_parm->svm_c* + learn_parm->svm_costratio_unlab*learn_parm->svm_unlabbound; + } + else if(label[i] == -1) { + learn_parm->svm_cost[i]=learn_parm->svm_c* + learn_parm->svm_unlabbound; + } + } + } + + if(verbosity>=2) { + /* printf("costratio %f, costratio_unlab %f, unlabbound %f\n", + learn_parm->svm_costratio,learn_parm->svm_costratio_unlab, + learn_parm->svm_unlabbound); */ + printf("%ld positive -> Added %ld POS / %ld NEG unlabeled examples.\n", + upos,unsupaddnum1,unsupaddnum2); + fflush(stdout); + } + + if(learn_parm->svm_unlabbound == 1) { + learn_parm->epsilon_crit=0.001; /* do the last run right */ + } + else { + learn_parm->epsilon_crit=0.01; /* otherwise, no need to be so picky */ + } + + return((long)3); + } + else if(((transductcycle % check_every) < check_every)) { + model_length=0; + sumalpha=0; + loss=0; + for(i=0;i<totdoc;i++) { + model_length+=a[i]*label[i]*lin[i]; + sumalpha+=a[i]; + dist=(lin[i]-model->b); /* 'distance' from hyperplane*/ + if((label[i]*dist)<(1.0-learn_parm->epsilon_crit)) { + loss+=(1.0-(label[i]*dist))*learn_parm->svm_cost[i]; + } + } + model_length=sqrt(model_length); + if(verbosity>=2) { + printf("Model-length = %f (%f), loss = %f, objective = %f\n", + model_length,sumalpha,loss,loss+0.5*model_length*model_length); + fflush(stdout); + } + j1=0; + j2=0; + j3=0; + j4=0; + unsupaddnum1=0; + unsupaddnum2=0; + umin=99999; + umax=-99999; + j4=1; + while(j4) { + umin=99999; + umax=-99999; + for(i=0;(i<totdoc);i++) { + dist=(lin[i]-model->b); + if((label[i]>0) && (unlabeled[i]) && (!inconsistent[i]) + && (dist<umin)) { + umin=dist; + imin=i; + } + if((label[i]<0) && (unlabeled[i]) && (!inconsistent[i]) + && (dist>umax)) { + umax=dist; + imax=i; + } + } + if((umin < (umax+switchsens-1E-4))) { + j1++; + j2++; + unsupaddnum1++; + unlabeled[imin]=3; + inconsistent[imin]=1; + unsupaddnum2++; + unlabeled[imax]=2; + inconsistent[imax]=1; + } + else + j4=0; + j4=0; + } + for(j=0;(j<totdoc);j++) { + if(unlabeled[j] && (!inconsistent[j])) { + if(label[j]>0) { + unlabeled[j]=2; + } + else if(label[j]<0) { + unlabeled[j]=3; + } + /* inconsistent[j]=1; */ + j3++; + } + } + switchnum+=unsupaddnum1+unsupaddnum2; + + /* stop and print out current margin + printf("switchnum %ld %ld\n",switchnum,kernel_parm->poly_degree); + if(switchnum == 2*kernel_parm->poly_degree) { + learn_parm->svm_unlabbound=1; + } + */ + + if((!unsupaddnum1) && (!unsupaddnum2)) { + if((learn_parm->svm_unlabbound>=1) && ((newpos+newneg) == allunlab)) { + for(j=0;(j<totdoc);j++) { + inconsistent[j]=0; + if(unlabeled[j]) unlabeled[j]=1; + } + write_prediction(learn_parm->predfile,model,lin,a,unlabeled,label, + totdoc,learn_parm); + if(verbosity>=1) + printf("Number of switches: %ld\n",switchnum); + return((long)0); + } + switchsens=switchsensorg; + learn_parm->svm_unlabbound*=1.5; + if(learn_parm->svm_unlabbound>1) { + learn_parm->svm_unlabbound=1; + } + model->at_upper_bound=0; /* since upper bound increased */ + if(verbosity>=1) + printf("Increasing influence of unlabeled examples to %f%% .", + learn_parm->svm_unlabbound*100.0); + } + else if(verbosity>=1) { + printf("%ld positive -> Switching labels of %ld POS / %ld NEG unlabeled examples.", + upos,unsupaddnum1,unsupaddnum2); + fflush(stdout); + } + + if(verbosity >= 2) printf("\n"); + + learn_parm->epsilon_crit=0.5; /* don't need to be so picky */ + + for(i=0;i<totdoc;i++) { /* set upper bounds on vars */ + if(unlabeled[i]) { + if(label[i] == 1) { + learn_parm->svm_cost[i]=learn_parm->svm_c* + learn_parm->svm_costratio_unlab*learn_parm->svm_unlabbound; + } + else if(label[i] == -1) { + learn_parm->svm_cost[i]=learn_parm->svm_c* + learn_parm->svm_unlabbound; + } + } + } + + return((long)2); + } + + return((long)0); +} + +/*************************** Working set selection ***************************/ + +long select_next_qp_subproblem_grad(long int *label, + long int *unlabeled, + double *a, double *lin, + double *c, long int totdoc, + long int qp_size, + LEARN_PARM *learn_parm, + long int *inconsistent, + long int *active2dnum, + long int *working2dnum, + double *selcrit, + long int *select, + KERNEL_CACHE *kernel_cache, + long int cache_only, + long int *key, long int *chosen) + /* Use the feasible direction approach to select the next + qp-subproblem (see chapter 'Selecting a good working set'). If + 'cache_only' is true, then the variables are selected only among + those for which the kernel evaluations are cached. */ +{ + long choosenum,i,j,k,activedoc,inum,valid; + double s; + + for(inum=0;working2dnum[inum]>=0;inum++); /* find end of index */ + choosenum=0; + activedoc=0; + for(i=0;(j=active2dnum[i])>=0;i++) { + s=-label[j]; + if(kernel_cache && cache_only) + valid=(kernel_cache->index[j]>=0); + else + valid=1; + if(valid + && (!((a[j]<=(0+learn_parm->epsilon_a)) && (s<0))) + && (!((a[j]>=(learn_parm->svm_cost[j]-learn_parm->epsilon_a)) + && (s>0))) + && (!chosen[j]) + && (label[j]) + && (!inconsistent[j])) + { + selcrit[activedoc]=(double)label[j]*(learn_parm->eps-(double)label[j]*c[j]+(double)label[j]*lin[j]); + /* selcrit[activedoc]=(double)label[j]*(-1.0+(double)label[j]*lin[j]); */ + key[activedoc]=j; + activedoc++; + } + } + select_top_n(selcrit,activedoc,select,(long)(qp_size/2)); + for(k=0;(choosenum<(qp_size/2)) && (k<(qp_size/2)) && (k<activedoc);k++) { + /* if(learn_parm->biased_hyperplane || (selcrit[select[k]] > 0)) { */ + i=key[select[k]]; + chosen[i]=1; + working2dnum[inum+choosenum]=i; + choosenum+=1; + if(kernel_cache) + kernel_cache_touch(kernel_cache,i); /* make sure it does not get + kicked out of cache */ + /* } */ + } + + activedoc=0; + for(i=0;(j=active2dnum[i])>=0;i++) { + s=label[j]; + if(kernel_cache && cache_only) + valid=(kernel_cache->index[j]>=0); + else + valid=1; + if(valid + && (!((a[j]<=(0+learn_parm->epsilon_a)) && (s<0))) + && (!((a[j]>=(learn_parm->svm_cost[j]-learn_parm->epsilon_a)) + && (s>0))) + && (!chosen[j]) + && (label[j]) + && (!inconsistent[j])) + { + selcrit[activedoc]=-(double)label[j]*(learn_parm->eps-(double)label[j]*c[j]+(double)label[j]*lin[j]); + /* selcrit[activedoc]=-(double)(label[j]*(-1.0+(double)label[j]*lin[j])); */ + key[activedoc]=j; + activedoc++; + } + } + select_top_n(selcrit,activedoc,select,(long)(qp_size/2)); + for(k=0;(choosenum<qp_size) && (k<(qp_size/2)) && (k<activedoc);k++) { + /* if(learn_parm->biased_hyperplane || (selcrit[select[k]] > 0)) { */ + i=key[select[k]]; + chosen[i]=1; + working2dnum[inum+choosenum]=i; + choosenum+=1; + if(kernel_cache) + kernel_cache_touch(kernel_cache,i); /* make sure it does not get + kicked out of cache */ + /* } */ + } + working2dnum[inum+choosenum]=-1; /* complete index */ + return(choosenum); +} + +long select_next_qp_subproblem_rand(long int *label, + long int *unlabeled, + double *a, double *lin, + double *c, long int totdoc, + long int qp_size, + LEARN_PARM *learn_parm, + long int *inconsistent, + long int *active2dnum, + long int *working2dnum, + double *selcrit, + long int *select, + KERNEL_CACHE *kernel_cache, + long int *key, + long int *chosen, + long int iteration) +/* Use the feasible direction approach to select the next + qp-subproblem (see section 'Selecting a good working set'). Chooses + a feasible direction at (pseudo) random to help jump over numerical + problem. */ +{ + long choosenum,i,j,k,activedoc,inum; + double s; + + for(inum=0;working2dnum[inum]>=0;inum++); /* find end of index */ + choosenum=0; + activedoc=0; + for(i=0;(j=active2dnum[i])>=0;i++) { + s=-label[j]; + if((!((a[j]<=(0+learn_parm->epsilon_a)) && (s<0))) + && (!((a[j]>=(learn_parm->svm_cost[j]-learn_parm->epsilon_a)) + && (s>0))) + && (!inconsistent[j]) + && (label[j]) + && (!chosen[j])) { + selcrit[activedoc]=(j+iteration) % totdoc; + key[activedoc]=j; + activedoc++; + } + } + select_top_n(selcrit,activedoc,select,(long)(qp_size/2)); + for(k=0;(choosenum<(qp_size/2)) && (k<(qp_size/2)) && (k<activedoc);k++) { + i=key[select[k]]; + chosen[i]=1; + working2dnum[inum+choosenum]=i; + choosenum+=1; + kernel_cache_touch(kernel_cache,i); /* make sure it does not get kicked */ + /* out of cache */ + } + + activedoc=0; + for(i=0;(j=active2dnum[i])>=0;i++) { + s=label[j]; + if((!((a[j]<=(0+learn_parm->epsilon_a)) && (s<0))) + && (!((a[j]>=(learn_parm->svm_cost[j]-learn_parm->epsilon_a)) + && (s>0))) + && (!inconsistent[j]) + && (label[j]) + && (!chosen[j])) { + selcrit[activedoc]=(j+iteration) % totdoc; + key[activedoc]=j; + activedoc++; + } + } + select_top_n(selcrit,activedoc,select,(long)(qp_size/2)); + for(k=0;(choosenum<qp_size) && (k<(qp_size/2)) && (k<activedoc);k++) { + i=key[select[k]]; + chosen[i]=1; + working2dnum[inum+choosenum]=i; + choosenum+=1; + kernel_cache_touch(kernel_cache,i); /* make sure it does not get kicked */ + /* out of cache */ + } + working2dnum[inum+choosenum]=-1; /* complete index */ + return(choosenum); +} + +long select_next_qp_slackset(DOC **docs, long int *label, + double *a, double *lin, + double *slack, double *alphaslack, + double *c, + LEARN_PARM *learn_parm, + long int *active2dnum, double *maxviol) + /* returns the slackset with the largest internal violation */ +{ + long i,ii,maxdiffid; + double dist,target,maxdiff,ex_c; + + maxdiff=0; + maxdiffid=0; + for(ii=0;(i=active2dnum[ii])>=0;ii++) { + ex_c=learn_parm->svm_c-learn_parm->epsilon_a; + if(alphaslack[docs[i]->slackid] >= ex_c) { + dist=(lin[i])*(double)label[i]+slack[docs[i]->slackid]; /* distance */ + target=-(learn_parm->eps-(double)label[i]*c[i]); /* rhs of constraint */ + if((a[i]>learn_parm->epsilon_a) && (dist > target)) { + if((dist-target)>maxdiff) { /* largest violation */ + maxdiff=dist-target; + maxdiffid=docs[i]->slackid; + } + } + } + } + (*maxviol)=maxdiff; + return(maxdiffid); +} + + +void select_top_n(double *selcrit, long int range, long int *select, + long int n) +{ + register long i,j; + + for(i=0;(i<n) && (i<range);i++) { /* Initialize with the first n elements */ + for(j=i;j>=0;j--) { + if((j>0) && (selcrit[select[j-1]]<selcrit[i])){ + select[j]=select[j-1]; + } + else { + select[j]=i; + j=-1; + } + } + } + if(n>0) { + for(i=n;i<range;i++) { + if(selcrit[i]>selcrit[select[n-1]]) { + for(j=n-1;j>=0;j--) { + if((j>0) && (selcrit[select[j-1]]<selcrit[i])) { + select[j]=select[j-1]; + } + else { + select[j]=i; + j=-1; + } + } + } + } + } +} + + +/******************************** Shrinking *********************************/ + +void init_shrink_state(SHRINK_STATE *shrink_state, long int totdoc, + long int maxhistory) +{ + long i; + + shrink_state->deactnum=0; + shrink_state->active = (long *)my_malloc(sizeof(long)*totdoc); + shrink_state->inactive_since = (long *)my_malloc(sizeof(long)*totdoc); + shrink_state->a_history = (double **)my_malloc(sizeof(double *)*maxhistory); + shrink_state->maxhistory=maxhistory; + shrink_state->last_lin = (double *)my_malloc(sizeof(double)*totdoc); + shrink_state->last_a = (double *)my_malloc(sizeof(double)*totdoc); + + for(i=0;i<totdoc;i++) { + shrink_state->active[i]=1; + shrink_state->inactive_since[i]=0; + shrink_state->last_a[i]=0; + shrink_state->last_lin[i]=0; + } +} + +void shrink_state_cleanup(SHRINK_STATE *shrink_state) +{ + free(shrink_state->active); + free(shrink_state->inactive_since); + if(shrink_state->deactnum > 0) + free(shrink_state->a_history[shrink_state->deactnum-1]); + free(shrink_state->a_history); + free(shrink_state->last_a); + free(shrink_state->last_lin); +} + +long shrink_problem(DOC **docs, + LEARN_PARM *learn_parm, + SHRINK_STATE *shrink_state, + KERNEL_PARM *kernel_parm, + long int *active2dnum, + long int *last_suboptimal_at, + long int iteration, + long int totdoc, + long int minshrink, + double *a, + long int *inconsistent) + /* Shrink some variables away. Do the shrinking only if at least + minshrink variables can be removed. */ +{ + long i,ii,change,activenum,lastiter; + double *a_old; + + activenum=0; + change=0; + for(ii=0;active2dnum[ii]>=0;ii++) { + i=active2dnum[ii]; + activenum++; + if(learn_parm->sharedslack) + lastiter=last_suboptimal_at[docs[i]->slackid]; + else + lastiter=last_suboptimal_at[i]; + if(((iteration-lastiter) > learn_parm->svm_iter_to_shrink) + || (inconsistent[i])) { + change++; + } + } + if((change>=minshrink) /* shrink only if sufficiently many candidates */ + && (shrink_state->deactnum<shrink_state->maxhistory)) { /* and enough memory */ + /* Shrink problem by removing those variables which are */ + /* optimal at a bound for a minimum number of iterations */ + if(verbosity>=2) { + printf(" Shrinking..."); fflush(stdout); + } + if(kernel_parm->kernel_type != LINEAR) { /* non-linear case save alphas */ + a_old=(double *)my_malloc(sizeof(double)*totdoc); + shrink_state->a_history[shrink_state->deactnum]=a_old; + for(i=0;i<totdoc;i++) { + a_old[i]=a[i]; + } + } + for(ii=0;active2dnum[ii]>=0;ii++) { + i=active2dnum[ii]; + if(learn_parm->sharedslack) + lastiter=last_suboptimal_at[docs[i]->slackid]; + else + lastiter=last_suboptimal_at[i]; + if(((iteration-lastiter) > learn_parm->svm_iter_to_shrink) + || (inconsistent[i])) { + shrink_state->active[i]=0; + shrink_state->inactive_since[i]=shrink_state->deactnum; + } + } + activenum=compute_index(shrink_state->active,totdoc,active2dnum); + shrink_state->deactnum++; + if(kernel_parm->kernel_type == LINEAR) { + shrink_state->deactnum=0; + } + if(verbosity>=2) { + printf("done.\n"); fflush(stdout); + printf(" Number of inactive variables = %ld\n",totdoc-activenum); + } + } + return(activenum); +} + + +void reactivate_inactive_examples(long int *label, + long int *unlabeled, + double *a, + SHRINK_STATE *shrink_state, + double *lin, + double *c, + long int totdoc, + long int totwords, + long int iteration, + LEARN_PARM *learn_parm, + long int *inconsistent, + DOC **docs, + KERNEL_PARM *kernel_parm, + KERNEL_CACHE *kernel_cache, + MODEL *model, + CFLOAT *aicache, + double *weights, + double *maxdiff) + /* Make all variables active again which had been removed by + shrinking. */ + /* Computes lin for those variables from scratch. */ +{ + register long i,j,ii,jj,t,*changed2dnum,*inactive2dnum; + long *changed,*inactive; + register double kernel_val,*a_old,dist; + double ex_c,target; + SVECTOR *f; + + if(kernel_parm->kernel_type == LINEAR) { /* special linear case */ + a_old=shrink_state->last_a; + clear_vector_n(weights,totwords); + for(i=0;i<totdoc;i++) { + if(a[i] != a_old[i]) { + for(f=docs[i]->fvec;f;f=f->next) + add_vector_ns(weights,f, + f->factor*((a[i]-a_old[i])*(double)label[i])); + a_old[i]=a[i]; + } + } + for(i=0;i<totdoc;i++) { + if(!shrink_state->active[i]) { + for(f=docs[i]->fvec;f;f=f->next) + lin[i]=shrink_state->last_lin[i]+f->factor*sprod_ns(weights,f); + } + shrink_state->last_lin[i]=lin[i]; + } + } + else { + changed=(long *)my_malloc(sizeof(long)*totdoc); + changed2dnum=(long *)my_malloc(sizeof(long)*(totdoc+11)); + inactive=(long *)my_malloc(sizeof(long)*totdoc); + inactive2dnum=(long *)my_malloc(sizeof(long)*(totdoc+11)); + for(t=shrink_state->deactnum-1;(t>=0) && shrink_state->a_history[t];t--) { + if(verbosity>=2) { + printf("%ld..",t); fflush(stdout); + } + a_old=shrink_state->a_history[t]; + for(i=0;i<totdoc;i++) { + inactive[i]=((!shrink_state->active[i]) + && (shrink_state->inactive_since[i] == t)); + changed[i]= (a[i] != a_old[i]); + } + compute_index(inactive,totdoc,inactive2dnum); + compute_index(changed,totdoc,changed2dnum); + + for(ii=0;(i=changed2dnum[ii])>=0;ii++) { + get_kernel_row(kernel_cache,docs,i,totdoc,inactive2dnum,aicache, + kernel_parm); + for(jj=0;(j=inactive2dnum[jj])>=0;jj++) { + kernel_val=aicache[j]; + lin[j]+=(((a[i]*kernel_val)-(a_old[i]*kernel_val))*(double)label[i]); + } + } + } + free(changed); + free(changed2dnum); + free(inactive); + free(inactive2dnum); + } + (*maxdiff)=0; + for(i=0;i<totdoc;i++) { + shrink_state->inactive_since[i]=shrink_state->deactnum-1; + if(!inconsistent[i]) { + dist=(lin[i]-model->b)*(double)label[i]; + target=-(learn_parm->eps-(double)label[i]*c[i]); + ex_c=learn_parm->svm_cost[i]-learn_parm->epsilon_a; + if((a[i]>learn_parm->epsilon_a) && (dist > target)) { + if((dist-target)>(*maxdiff)) /* largest violation */ + (*maxdiff)=dist-target; + } + else if((a[i]<ex_c) && (dist < target)) { + if((target-dist)>(*maxdiff)) /* largest violation */ + (*maxdiff)=target-dist; + } + if((a[i]>(0+learn_parm->epsilon_a)) + && (a[i]<ex_c)) { + shrink_state->active[i]=1; /* not at bound */ + } + else if((a[i]<=(0+learn_parm->epsilon_a)) && (dist < (target+learn_parm->epsilon_shrink))) { + shrink_state->active[i]=1; + } + else if((a[i]>=ex_c) + && (dist > (target-learn_parm->epsilon_shrink))) { + shrink_state->active[i]=1; + } + else if(learn_parm->sharedslack) { /* make all active when sharedslack */ + shrink_state->active[i]=1; + } + } + } + if(kernel_parm->kernel_type != LINEAR) { /* update history for non-linear */ + for(i=0;i<totdoc;i++) { + (shrink_state->a_history[shrink_state->deactnum-1])[i]=a[i]; + } + for(t=shrink_state->deactnum-2;(t>=0) && shrink_state->a_history[t];t--) { + free(shrink_state->a_history[t]); + shrink_state->a_history[t]=0; + } + } +} + +/****************************** Cache handling *******************************/ + +void get_kernel_row(KERNEL_CACHE *kernel_cache, DOC **docs, + long int docnum, long int totdoc, + long int *active2dnum, CFLOAT *buffer, + KERNEL_PARM *kernel_parm) + /* Get's a row of the matrix of kernel values This matrix has the + same form as the Hessian, just that the elements are not + multiplied by */ + /* y_i * y_j * a_i * a_j */ + /* Takes the values from the cache if available. */ +{ + register long i,j,start; + DOC *ex; + + ex=docs[docnum]; + + if(kernel_cache->index[docnum] != -1) { /* row is cached? */ + kernel_cache->lru[kernel_cache->index[docnum]]=kernel_cache->time; /* lru */ + start=kernel_cache->activenum*kernel_cache->index[docnum]; + for(i=0;(j=active2dnum[i])>=0;i++) { + if(kernel_cache->totdoc2active[j] >= 0) { /* column is cached? */ + buffer[j]=kernel_cache->buffer[start+kernel_cache->totdoc2active[j]]; + } + else { + buffer[j]=(CFLOAT)kernel(kernel_parm,ex,docs[j]); + } + } + } + else { + for(i=0;(j=active2dnum[i])>=0;i++) { + buffer[j]=(CFLOAT)kernel(kernel_parm,ex,docs[j]); + } + } +} + + +void cache_kernel_row(KERNEL_CACHE *kernel_cache, DOC **docs, + long int m, KERNEL_PARM *kernel_parm) + /* Fills cache for the row m */ +{ + register DOC *ex; + register long j,k,l; + register CFLOAT *cache; + + if(!kernel_cache_check(kernel_cache,m)) { /* not cached yet*/ + cache = kernel_cache_clean_and_malloc(kernel_cache,m); + if(cache) { + l=kernel_cache->totdoc2active[m]; + ex=docs[m]; + for(j=0;j<kernel_cache->activenum;j++) { /* fill cache */ + k=kernel_cache->active2totdoc[j]; + if((kernel_cache->index[k] != -1) && (l != -1) && (k != m)) { + cache[j]=kernel_cache->buffer[kernel_cache->activenum + *kernel_cache->index[k]+l]; + } + else { + cache[j]=kernel(kernel_parm,ex,docs[k]); + } + } + } + else { + perror("Error: Kernel cache full! => increase cache size"); + } + } +} + + +void cache_multiple_kernel_rows(KERNEL_CACHE *kernel_cache, DOC **docs, + long int *key, long int varnum, + KERNEL_PARM *kernel_parm) + /* Fills cache for the rows in key */ +{ + register long i; + + for(i=0;i<varnum;i++) { /* fill up kernel cache */ + cache_kernel_row(kernel_cache,docs,key[i],kernel_parm); + } +} + + +void kernel_cache_shrink(KERNEL_CACHE *kernel_cache, long int totdoc, + long int numshrink, long int *after) + /* Remove numshrink columns in the cache which correspond to + examples marked 0 in after. */ +{ + register long i,j,jj,from=0,to=0,scount; + long *keep; + + if(verbosity>=2) { + printf(" Reorganizing cache..."); fflush(stdout); + } + + keep=(long *)my_malloc(sizeof(long)*totdoc); + for(j=0;j<totdoc;j++) { + keep[j]=1; + } + scount=0; + for(jj=0;(jj<kernel_cache->activenum) && (scount<numshrink);jj++) { + j=kernel_cache->active2totdoc[jj]; + if(!after[j]) { + scount++; + keep[j]=0; + } + } + + for(i=0;i<kernel_cache->max_elems;i++) { + for(jj=0;jj<kernel_cache->activenum;jj++) { + j=kernel_cache->active2totdoc[jj]; + if(!keep[j]) { + from++; + } + else { + kernel_cache->buffer[to]=kernel_cache->buffer[from]; + to++; + from++; + } + } + } + + kernel_cache->activenum=0; + for(j=0;j<totdoc;j++) { + if((keep[j]) && (kernel_cache->totdoc2active[j] != -1)) { + kernel_cache->active2totdoc[kernel_cache->activenum]=j; + kernel_cache->totdoc2active[j]=kernel_cache->activenum; + kernel_cache->activenum++; + } + else { + kernel_cache->totdoc2active[j]=-1; + } + } + + kernel_cache->max_elems=(long)(kernel_cache->buffsize/kernel_cache->activenum); + if(kernel_cache->max_elems>totdoc) { + kernel_cache->max_elems=totdoc; + } + + free(keep); + + if(verbosity>=2) { + printf("done.\n"); fflush(stdout); + printf(" Cache-size in rows = %ld\n",kernel_cache->max_elems); + } +} + +KERNEL_CACHE *kernel_cache_init(long int totdoc, long int buffsize) +{ + long i; + KERNEL_CACHE *kernel_cache; + + kernel_cache=(KERNEL_CACHE *)my_malloc(sizeof(KERNEL_CACHE)); + kernel_cache->index = (long *)my_malloc(sizeof(long)*totdoc); + kernel_cache->occu = (long *)my_malloc(sizeof(long)*totdoc); + kernel_cache->lru = (long *)my_malloc(sizeof(long)*totdoc); + kernel_cache->invindex = (long *)my_malloc(sizeof(long)*totdoc); + kernel_cache->active2totdoc = (long *)my_malloc(sizeof(long)*totdoc); + kernel_cache->totdoc2active = (long *)my_malloc(sizeof(long)*totdoc); + kernel_cache->buffer = (CFLOAT *)my_malloc((size_t)(buffsize)*1024*1024); + + kernel_cache->buffsize=(long)(buffsize/sizeof(CFLOAT)*1024*1024); + + kernel_cache->max_elems=(long)(kernel_cache->buffsize/totdoc); + if(kernel_cache->max_elems>totdoc) { + kernel_cache->max_elems=totdoc; + } + + if(verbosity>=2) { + printf(" Cache-size in rows = %ld\n",kernel_cache->max_elems); + printf(" Kernel evals so far: %ld\n",kernel_cache_statistic); + } + + kernel_cache->elems=0; /* initialize cache */ + for(i=0;i<totdoc;i++) { + kernel_cache->index[i]=-1; + kernel_cache->lru[i]=0; + } + for(i=0;i<totdoc;i++) { + kernel_cache->occu[i]=0; + kernel_cache->invindex[i]=-1; + } + + kernel_cache->activenum=totdoc;; + for(i=0;i<totdoc;i++) { + kernel_cache->active2totdoc[i]=i; + kernel_cache->totdoc2active[i]=i; + } + + kernel_cache->time=0; + + return(kernel_cache); +} + +void kernel_cache_reset_lru(KERNEL_CACHE *kernel_cache) +{ + long maxlru=0,k; + + for(k=0;k<kernel_cache->max_elems;k++) { + if(maxlru < kernel_cache->lru[k]) + maxlru=kernel_cache->lru[k]; + } + for(k=0;k<kernel_cache->max_elems;k++) { + kernel_cache->lru[k]-=maxlru; + } +} + +void kernel_cache_cleanup(KERNEL_CACHE *kernel_cache) +{ + free(kernel_cache->index); + free(kernel_cache->occu); + free(kernel_cache->lru); + free(kernel_cache->invindex); + free(kernel_cache->active2totdoc); + free(kernel_cache->totdoc2active); + free(kernel_cache->buffer); + free(kernel_cache); +} + +long kernel_cache_malloc(KERNEL_CACHE *kernel_cache) +{ + long i; + + if(kernel_cache_space_available(kernel_cache)) { + for(i=0;i<kernel_cache->max_elems;i++) { + if(!kernel_cache->occu[i]) { + kernel_cache->occu[i]=1; + kernel_cache->elems++; + return(i); + } + } + } + return(-1); +} + +void kernel_cache_free(KERNEL_CACHE *kernel_cache, long int i) +{ + kernel_cache->occu[i]=0; + kernel_cache->elems--; +} + +long kernel_cache_free_lru(KERNEL_CACHE *kernel_cache) + /* remove least recently used cache element */ +{ + register long k,least_elem=-1,least_time; + + least_time=kernel_cache->time+1; + for(k=0;k<kernel_cache->max_elems;k++) { + if(kernel_cache->invindex[k] != -1) { + if(kernel_cache->lru[k]<least_time) { + least_time=kernel_cache->lru[k]; + least_elem=k; + } + } + } + if(least_elem != -1) { + kernel_cache_free(kernel_cache,least_elem); + kernel_cache->index[kernel_cache->invindex[least_elem]]=-1; + kernel_cache->invindex[least_elem]=-1; + return(1); + } + return(0); +} + + +CFLOAT *kernel_cache_clean_and_malloc(KERNEL_CACHE *kernel_cache, + long int docnum) + /* Get a free cache entry. In case cache is full, the lru element + is removed. */ +{ + long result; + if((result = kernel_cache_malloc(kernel_cache)) == -1) { + if(kernel_cache_free_lru(kernel_cache)) { + result = kernel_cache_malloc(kernel_cache); + } + } + kernel_cache->index[docnum]=result; + if(result == -1) { + return(0); + } + kernel_cache->invindex[result]=docnum; + kernel_cache->lru[kernel_cache->index[docnum]]=kernel_cache->time; /* lru */ + return((CFLOAT *)((long)kernel_cache->buffer + +(kernel_cache->activenum*sizeof(CFLOAT)* + kernel_cache->index[docnum]))); +} + +long kernel_cache_touch(KERNEL_CACHE *kernel_cache, long int docnum) + /* Update lru time to avoid removal from cache. */ +{ + if(kernel_cache && kernel_cache->index[docnum] != -1) { + kernel_cache->lru[kernel_cache->index[docnum]]=kernel_cache->time; /* lru */ + return(1); + } + return(0); +} + +long kernel_cache_check(KERNEL_CACHE *kernel_cache, long int docnum) + /* Is that row cached? */ +{ + return(kernel_cache->index[docnum] != -1); +} + +long kernel_cache_space_available(KERNEL_CACHE *kernel_cache) + /* Is there room for one more row? */ +{ + return(kernel_cache->elems < kernel_cache->max_elems); +} + +/************************** Compute estimates ******************************/ + +void compute_xa_estimates(MODEL *model, long int *label, + long int *unlabeled, long int totdoc, + DOC **docs, double *lin, double *a, + KERNEL_PARM *kernel_parm, + LEARN_PARM *learn_parm, double *error, + double *recall, double *precision) + /* Computes xa-estimate of error rate, recall, and precision. See + T. Joachims, Estimating the Generalization Performance of an SVM + Efficiently, IMCL, 2000. */ +{ + long i,looerror,looposerror,loonegerror; + long totex,totposex; + double xi,r_delta,r_delta_sq,sim=0; + long *sv2dnum=NULL,*sv=NULL,svnum; + + r_delta=estimate_r_delta(docs,totdoc,kernel_parm); + r_delta_sq=r_delta*r_delta; + + looerror=0; + looposerror=0; + loonegerror=0; + totex=0; + totposex=0; + svnum=0; + + if(learn_parm->xa_depth > 0) { + sv = (long *)my_malloc(sizeof(long)*(totdoc+11)); + for(i=0;i<totdoc;i++) + sv[i]=0; + for(i=1;i<model->sv_num;i++) + if(a[model->supvec[i]->docnum] + < (learn_parm->svm_cost[model->supvec[i]->docnum] + -learn_parm->epsilon_a)) { + sv[model->supvec[i]->docnum]=1; + svnum++; + } + sv2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11)); + clear_index(sv2dnum); + compute_index(sv,totdoc,sv2dnum); + } + + for(i=0;i<totdoc;i++) { + if(unlabeled[i]) { + /* ignore it */ + } + else { + xi=1.0-((lin[i]-model->b)*(double)label[i]); + if(xi<0) xi=0; + if(label[i]>0) { + totposex++; + } + if((learn_parm->rho*a[i]*r_delta_sq+xi) >= 1.0) { + if(learn_parm->xa_depth > 0) { /* makes assumptions */ + sim=distribute_alpha_t_greedily(sv2dnum,svnum,docs,a,i,label, + kernel_parm,learn_parm, + (double)((1.0-xi-a[i]*r_delta_sq)/(2.0*a[i]))); + } + if((learn_parm->xa_depth == 0) || + ((a[i]*kernel(kernel_parm,docs[i],docs[i])+a[i]*2.0*sim+xi) >= 1.0)) { + looerror++; + if(label[i]>0) { + looposerror++; + } + else { + loonegerror++; + } + } + } + totex++; + } + } + + (*error)=((double)looerror/(double)totex)*100.0; + (*recall)=(1.0-(double)looposerror/(double)totposex)*100.0; + (*precision)=(((double)totposex-(double)looposerror) + /((double)totposex-(double)looposerror+(double)loonegerror))*100.0; + + free(sv); + free(sv2dnum); +} + + +double distribute_alpha_t_greedily(long int *sv2dnum, long int svnum, + DOC **docs, double *a, + long int docnum, + long int *label, + KERNEL_PARM *kernel_parm, + LEARN_PARM *learn_parm, double thresh) + /* Experimental Code improving plain XiAlpha Estimates by + computing a better bound using a greedy optimzation strategy. */ +{ + long best_depth=0; + long i,j,k,d,skip,allskip; + double best,best_val[101],val,init_val_sq,init_val_lin; + long best_ex[101]; + CFLOAT *cache,*trow; + + cache=(CFLOAT *)my_malloc(sizeof(CFLOAT)*learn_parm->xa_depth*svnum); + trow = (CFLOAT *)my_malloc(sizeof(CFLOAT)*svnum); + + for(k=0;k<svnum;k++) { + trow[k]=kernel(kernel_parm,docs[docnum],docs[sv2dnum[k]]); + } + + init_val_sq=0; + init_val_lin=0; + best=0; + + for(d=0;d<learn_parm->xa_depth;d++) { + allskip=1; + if(d>=1) { + init_val_sq+=cache[best_ex[d-1]+svnum*(d-1)]; + for(k=0;k<d-1;k++) { + init_val_sq+=2.0*cache[best_ex[k]+svnum*(d-1)]; + } + init_val_lin+=trow[best_ex[d-1]]; + } + for(i=0;i<svnum;i++) { + skip=0; + if(sv2dnum[i] == docnum) skip=1; + for(j=0;j<d;j++) { + if(i == best_ex[j]) skip=1; + } + + if(!skip) { + val=init_val_sq; + if(kernel_parm->kernel_type == LINEAR) + val+=docs[sv2dnum[i]]->fvec->twonorm_sq; + else + val+=kernel(kernel_parm,docs[sv2dnum[i]],docs[sv2dnum[i]]); + for(j=0;j<d;j++) { + val+=2.0*cache[i+j*svnum]; + } + val*=(1.0/(2.0*(d+1.0)*(d+1.0))); + val-=((init_val_lin+trow[i])/(d+1.0)); + + if(allskip || (val < best_val[d])) { + best_val[d]=val; + best_ex[d]=i; + } + allskip=0; + if(val < thresh) { + i=svnum; + /* printf("EARLY"); */ + } + } + } + if(!allskip) { + for(k=0;k<svnum;k++) { + cache[d*svnum+k]=kernel(kernel_parm, + docs[sv2dnum[best_ex[d]]], + docs[sv2dnum[k]]); + } + } + if((!allskip) && ((best_val[d] < best) || (d == 0))) { + best=best_val[d]; + best_depth=d; + } + if(allskip || (best < thresh)) { + d=learn_parm->xa_depth; + } + } + + free(cache); + free(trow); + + /* printf("Distribute[%ld](%ld)=%f, ",docnum,best_depth,best); */ + return(best); +} + + +void estimate_transduction_quality(MODEL *model, long int *label, + long int *unlabeled, + long int totdoc, DOC **docs, double *lin) + /* Loo-bound based on observation that loo-errors must have an + equal distribution in both training and test examples, given + that the test examples are classified correctly. Compare + chapter "Constraints on the Transductive Hyperplane" in my + Dissertation. */ +{ + long i,j,l=0,ulab=0,lab=0,labpos=0,labneg=0,ulabpos=0,ulabneg=0,totulab=0; + double totlab=0,totlabpos=0,totlabneg=0,labsum=0,ulabsum=0; + double r_delta,r_delta_sq,xi,xisum=0,asum=0; + + r_delta=estimate_r_delta(docs,totdoc,&(model->kernel_parm)); + r_delta_sq=r_delta*r_delta; + + for(j=0;j<totdoc;j++) { + if(unlabeled[j]) { + totulab++; + } + else { + totlab++; + if(label[j] > 0) + totlabpos++; + else + totlabneg++; + } + } + for(j=1;j<model->sv_num;j++) { + i=model->supvec[j]->docnum; + xi=1.0-((lin[i]-model->b)*(double)label[i]); + if(xi<0) xi=0; + + xisum+=xi; + asum+=fabs(model->alpha[j]); + if(unlabeled[i]) { + ulabsum+=(fabs(model->alpha[j])*r_delta_sq+xi); + } + else { + labsum+=(fabs(model->alpha[j])*r_delta_sq+xi); + } + if((fabs(model->alpha[j])*r_delta_sq+xi) >= 1) { + l++; + if(unlabeled[model->supvec[j]->docnum]) { + ulab++; + if(model->alpha[j] > 0) + ulabpos++; + else + ulabneg++; + } + else { + lab++; + if(model->alpha[j] > 0) + labpos++; + else + labneg++; + } + } + } + printf("xacrit>=1: labeledpos=%.5f labeledneg=%.5f default=%.5f\n",(double)labpos/(double)totlab*100.0,(double)labneg/(double)totlab*100.0,(double)totlabpos/(double)(totlab)*100.0); + printf("xacrit>=1: unlabelpos=%.5f unlabelneg=%.5f\n",(double)ulabpos/(double)totulab*100.0,(double)ulabneg/(double)totulab*100.0); + printf("xacrit>=1: labeled=%.5f unlabled=%.5f all=%.5f\n",(double)lab/(double)totlab*100.0,(double)ulab/(double)totulab*100.0,(double)l/(double)(totdoc)*100.0); + printf("xacritsum: labeled=%.5f unlabled=%.5f all=%.5f\n",(double)labsum/(double)totlab*100.0,(double)ulabsum/(double)totulab*100.0,(double)(labsum+ulabsum)/(double)(totdoc)*100.0); + printf("r_delta_sq=%.5f xisum=%.5f asum=%.5f\n",r_delta_sq,xisum,asum); +} + +double estimate_margin_vcdim(MODEL *model, double w, double R, + KERNEL_PARM *kernel_parm) + /* optional: length of model vector in feature space */ + /* optional: radius of ball containing the data */ +{ + double h; + + /* follows chapter 5.6.4 in [Vapnik/95] */ + + if(w<0) { + w=model_length_s(model,kernel_parm); + } + if(R<0) { + R=estimate_sphere(model,kernel_parm); + } + h = w*w * R*R +1; + return(h); +} + +double estimate_sphere(MODEL *model, KERNEL_PARM *kernel_parm) + /* Approximates the radius of the ball containing */ + /* the support vectors by bounding it with the */ +{ /* length of the longest support vector. This is */ + register long j; /* pretty good for text categorization, since all */ + double xlen,maxxlen=0; /* documents have feature vectors of length 1. It */ + DOC *nulldoc; /* assumes that the center of the ball is at the */ + WORD nullword; /* origin of the space. */ + + nullword.wnum=0; + nulldoc=create_example(-2,0,0,0.0,create_svector(&nullword,"",1.0)); + + for(j=1;j<model->sv_num;j++) { + xlen=sqrt(kernel(kernel_parm,model->supvec[j],model->supvec[j]) + -2*kernel(kernel_parm,model->supvec[j],nulldoc) + +kernel(kernel_parm,nulldoc,nulldoc)); + if(xlen>maxxlen) { + maxxlen=xlen; + } + } + + free_example(nulldoc,1); + return(maxxlen); +} + +double estimate_r_delta(DOC **docs, long int totdoc, KERNEL_PARM *kernel_parm) +{ + long i; + double maxxlen,xlen; + DOC *nulldoc; /* assumes that the center of the ball is at the */ + WORD nullword; /* origin of the space. */ + + nullword.wnum=0; + nulldoc=create_example(-2,0,0,0.0,create_svector(&nullword,"",1.0)); + + maxxlen=0; + for(i=0;i<totdoc;i++) { + xlen=sqrt(kernel(kernel_parm,docs[i],docs[i]) + -2*kernel(kernel_parm,docs[i],nulldoc) + +kernel(kernel_parm,nulldoc,nulldoc)); + if(xlen>maxxlen) { + maxxlen=xlen; + } + } + + free_example(nulldoc,1); + return(maxxlen); +} + +double estimate_r_delta_average(DOC **docs, long int totdoc, + KERNEL_PARM *kernel_parm) +{ + long i; + double avgxlen; + DOC *nulldoc; /* assumes that the center of the ball is at the */ + WORD nullword; /* origin of the space. */ + + nullword.wnum=0; + nulldoc=create_example(-2,0,0,0.0,create_svector(&nullword,"",1.0)); + + avgxlen=0; + for(i=0;i<totdoc;i++) { + avgxlen+=sqrt(kernel(kernel_parm,docs[i],docs[i]) + -2*kernel(kernel_parm,docs[i],nulldoc) + +kernel(kernel_parm,nulldoc,nulldoc)); + } + + free_example(nulldoc,1); + return(avgxlen/totdoc); +} + +double length_of_longest_document_vector(DOC **docs, long int totdoc, + KERNEL_PARM *kernel_parm) +{ + long i; + double maxxlen,xlen; + + maxxlen=0; + for(i=0;i<totdoc;i++) { + xlen=sqrt(kernel(kernel_parm,docs[i],docs[i])); + if(xlen>maxxlen) { + maxxlen=xlen; + } + } + + return(maxxlen); +} + +/****************************** IO-handling **********************************/ + +void write_prediction(char *predfile, MODEL *model, double *lin, + double *a, long int *unlabeled, + long int *label, long int totdoc, + LEARN_PARM *learn_parm) +{ + FILE *predfl; + long i; + double dist,a_max; + + if(verbosity>=1) { + printf("Writing prediction file..."); fflush(stdout); + } + if ((predfl = fopen (predfile, "w")) == NULL) + { perror (predfile); exit (1); } + a_max=learn_parm->epsilon_a; + for(i=0;i<totdoc;i++) { + if((unlabeled[i]) && (a[i]>a_max)) { + a_max=a[i]; + } + } + for(i=0;i<totdoc;i++) { + if(unlabeled[i]) { + if((a[i]>(learn_parm->epsilon_a))) { + dist=(double)label[i]*(1.0-learn_parm->epsilon_crit-a[i]/(a_max*2.0)); + } + else { + dist=(lin[i]-model->b); + } + if(dist>0) { + fprintf(predfl,"%.8g:+1 %.8g:-1\n",dist,-dist); + } + else { + fprintf(predfl,"%.8g:-1 %.8g:+1\n",-dist,dist); + } + } + } + fclose(predfl); + if(verbosity>=1) { + printf("done\n"); + } +} + +void write_alphas(char *alphafile, double *a, + long int *label, long int totdoc) +{ + FILE *alphafl; + long i; + + if(verbosity>=1) { + printf("Writing alpha file..."); fflush(stdout); + } + if ((alphafl = fopen (alphafile, "w")) == NULL) + { perror (alphafile); exit (1); } + for(i=0;i<totdoc;i++) { + fprintf(alphafl,"%.18g\n",a[i]*(double)label[i]); + } + fclose(alphafl); + if(verbosity>=1) { + printf("done\n"); + } +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/svm_learn.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,169 @@ +/***********************************************************************/ +/* */ +/* svm_learn.h */ +/* */ +/* Declarations for learning module of Support Vector Machine. */ +/* */ +/* Author: Thorsten Joachims */ +/* Date: 02.07.02 */ +/* */ +/* Copyright (c) 2002 Thorsten Joachims - All rights reserved */ +/* */ +/* This software is available for non-commercial use only. It must */ +/* not be modified and distributed without prior permission of the */ +/* author. The author is not responsible for implications from the */ +/* use of this software. */ +/* */ +/***********************************************************************/ + +#ifndef SVM_LEARN +#define SVM_LEARN + +void svm_learn_classification(DOC **, double *, long, long, LEARN_PARM *, + KERNEL_PARM *, KERNEL_CACHE *, MODEL *, + double *); +void svm_learn_regression(DOC **, double *, long, long, LEARN_PARM *, + KERNEL_PARM *, KERNEL_CACHE **, MODEL *); +void svm_learn_ranking(DOC **, double *, long, long, LEARN_PARM *, + KERNEL_PARM *, KERNEL_CACHE **, MODEL *); +void svm_learn_optimization(DOC **, double *, long, long, LEARN_PARM *, + KERNEL_PARM *, KERNEL_CACHE *, MODEL *, + double *); +long optimize_to_convergence(DOC **, long *, long, long, LEARN_PARM *, + KERNEL_PARM *, KERNEL_CACHE *, SHRINK_STATE *, + MODEL *, long *, long *, double *, + double *, double *, + TIMING *, double *, long, long); +long optimize_to_convergence_sharedslack(DOC **, long *, long, long, + LEARN_PARM *, + KERNEL_PARM *, KERNEL_CACHE *, SHRINK_STATE *, + MODEL *, double *, double *, double *, + TIMING *, double *); +double compute_objective_function(double *, double *, double *, double, + long *, long *); +void clear_index(long *); +void add_to_index(long *, long); +long compute_index(long *,long, long *); +void optimize_svm(DOC **, long *, long *, long *, double, long *, long *, + MODEL *, + long, long *, long, double *, double *, double *, + LEARN_PARM *, CFLOAT *, KERNEL_PARM *, QP *, double *); +void compute_matrices_for_optimization(DOC **, long *, long *, long *, double, + long *, + long *, long *, MODEL *, double *, + double *, double *, long, long, LEARN_PARM *, + CFLOAT *, KERNEL_PARM *, QP *); +long calculate_svm_model(DOC **, long *, long *, double *, double *, + double *, double *, LEARN_PARM *, long *, + long *, MODEL *); +long check_optimality(MODEL *, long *, long *, double *, double *, + double *, long, + LEARN_PARM *,double *, double, long *, long *, long *, + long *, long, KERNEL_PARM *); +long check_optimality_sharedslack(DOC **docs, MODEL *model, long int *label, + double *a, double *lin, double *c, double *slack, + double *alphaslack, long int totdoc, + LEARN_PARM *learn_parm, double *maxdiff, + double epsilon_crit_org, long int *misclassified, + long int *active2dnum, + long int *last_suboptimal_at, + long int iteration, KERNEL_PARM *kernel_parm); +void compute_shared_slacks(DOC **docs, long int *label, double *a, + double *lin, double *c, long int *active2dnum, + LEARN_PARM *learn_parm, + double *slack, double *alphaslack); +long identify_inconsistent(double *, long *, long *, long, LEARN_PARM *, + long *, long *); +long identify_misclassified(double *, long *, long *, long, + MODEL *, long *, long *); +long identify_one_misclassified(double *, long *, long *, long, + MODEL *, long *, long *); +long incorporate_unlabeled_examples(MODEL *, long *,long *, long *, + double *, double *, long, double *, + long *, long *, long, KERNEL_PARM *, + LEARN_PARM *); +void update_linear_component(DOC **, long *, long *, double *, double *, + long *, long, long, KERNEL_PARM *, + KERNEL_CACHE *, double *, + CFLOAT *, double *); +long select_next_qp_subproblem_grad(long *, long *, double *, + double *, double *, long, + long, LEARN_PARM *, long *, long *, + long *, double *, long *, KERNEL_CACHE *, + long, long *, long *); +long select_next_qp_subproblem_rand(long *, long *, double *, + double *, double *, long, + long, LEARN_PARM *, long *, long *, + long *, double *, long *, KERNEL_CACHE *, + long *, long *, long); +long select_next_qp_slackset(DOC **docs, long int *label, double *a, + double *lin, double *slack, double *alphaslack, + double *c, LEARN_PARM *learn_parm, + long int *active2dnum, double *maxviol); +void select_top_n(double *, long, long *, long); +void init_shrink_state(SHRINK_STATE *, long, long); +void shrink_state_cleanup(SHRINK_STATE *); +long shrink_problem(DOC **, LEARN_PARM *, SHRINK_STATE *, KERNEL_PARM *, + long *, long *, long, long, long, double *, long *); +void reactivate_inactive_examples(long *, long *, double *, SHRINK_STATE *, + double *, double*, long, long, long, LEARN_PARM *, + long *, DOC **, KERNEL_PARM *, + KERNEL_CACHE *, MODEL *, CFLOAT *, + double *, double *); + +/* cache kernel evalutations to improve speed */ +KERNEL_CACHE *kernel_cache_init(long, long); +void kernel_cache_cleanup(KERNEL_CACHE *); +void get_kernel_row(KERNEL_CACHE *,DOC **, long, long, long *, CFLOAT *, + KERNEL_PARM *); +void cache_kernel_row(KERNEL_CACHE *,DOC **, long, KERNEL_PARM *); +void cache_multiple_kernel_rows(KERNEL_CACHE *,DOC **, long *, long, + KERNEL_PARM *); +void kernel_cache_shrink(KERNEL_CACHE *,long, long, long *); +void kernel_cache_reset_lru(KERNEL_CACHE *); +long kernel_cache_malloc(KERNEL_CACHE *); +void kernel_cache_free(KERNEL_CACHE *,long); +long kernel_cache_free_lru(KERNEL_CACHE *); +CFLOAT *kernel_cache_clean_and_malloc(KERNEL_CACHE *,long); +long kernel_cache_touch(KERNEL_CACHE *,long); +long kernel_cache_check(KERNEL_CACHE *,long); +long kernel_cache_space_available(KERNEL_CACHE *); + +void compute_xa_estimates(MODEL *, long *, long *, long, DOC **, + double *, double *, KERNEL_PARM *, + LEARN_PARM *, double *, double *, double *); +double xa_estimate_error(MODEL *, long *, long *, long, DOC **, + double *, double *, KERNEL_PARM *, + LEARN_PARM *); +double xa_estimate_recall(MODEL *, long *, long *, long, DOC **, + double *, double *, KERNEL_PARM *, + LEARN_PARM *); +double xa_estimate_precision(MODEL *, long *, long *, long, DOC **, + double *, double *, KERNEL_PARM *, + LEARN_PARM *); +void avg_similarity_of_sv_of_one_class(MODEL *, DOC **, double *, long *, KERNEL_PARM *, double *, double *); +double most_similar_sv_of_same_class(MODEL *, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *); +double distribute_alpha_t_greedily(long *, long, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *, double); +double distribute_alpha_t_greedily_noindex(MODEL *, DOC **, double *, long, long *, KERNEL_PARM *, LEARN_PARM *, double); +void estimate_transduction_quality(MODEL *, long *, long *, long, DOC **, double *); +double estimate_margin_vcdim(MODEL *, double, double, KERNEL_PARM *); +double estimate_sphere(MODEL *, KERNEL_PARM *); +double estimate_r_delta_average(DOC **, long, KERNEL_PARM *); +double estimate_r_delta(DOC **, long, KERNEL_PARM *); +double length_of_longest_document_vector(DOC **, long, KERNEL_PARM *); + +void write_model(char *, MODEL *); +void write_prediction(char *, MODEL *, double *, double *, long *, long *, + long, LEARN_PARM *); +void write_alphas(char *, double *, long *, long); + +typedef struct cache_parm_s { + KERNEL_CACHE *kernel_cache; + CFLOAT *cache; + DOC **docs; + long m; + KERNEL_PARM *kernel_parm; + long offset,stepsize; +} cache_parm_t; + +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/svm_learn_main.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,391 @@ +/***********************************************************************/ +/* */ +/* svm_learn_main.c */ +/* */ +/* Command line interface to the learning module of the */ +/* Support Vector Machine. */ +/* */ +/* Author: Thorsten Joachims */ +/* Date: 02.07.02 */ +/* */ +/* Copyright (c) 2000 Thorsten Joachims - All rights reserved */ +/* */ +/* This software is available for non-commercial use only. It must */ +/* not be modified and distributed without prior permission of the */ +/* author. The author is not responsible for implications from the */ +/* use of this software. */ +/* */ +/***********************************************************************/ + + +/* uncomment, if you want to use svm-learn out of C++ */ +/* extern "C" { */ +# include "svm_common.h" +# include "svm_learn.h" +/* } */ + +char docfile[200]; /* file with training examples */ +char modelfile[200]; /* file for resulting classifier */ +char restartfile[200]; /* file with initial alphas */ + +void read_input_parameters(int, char **, char *, char *, char *, long *, + LEARN_PARM *, KERNEL_PARM *); +void wait_any_key(); +void print_help(); + + + +int main (int argc, char* argv[]) +{ + DOC **docs; /* training examples */ + long totwords,totdoc,i; + double *target; + double *alpha_in=NULL; + KERNEL_CACHE *kernel_cache; + LEARN_PARM learn_parm; + KERNEL_PARM kernel_parm; + MODEL *model=(MODEL *)my_malloc(sizeof(MODEL)); + + read_input_parameters(argc,argv,docfile,modelfile,restartfile,&verbosity, + &learn_parm,&kernel_parm); + read_documents(docfile,&docs,&target,&totwords,&totdoc); + if(restartfile[0]) alpha_in=read_alphas(restartfile,totdoc); + + if(kernel_parm.kernel_type == LINEAR) { /* don't need the cache */ + kernel_cache=NULL; + } + else { + /* Always get a new kernel cache. It is not possible to use the + same cache for two different training runs */ + kernel_cache=kernel_cache_init(totdoc,learn_parm.kernel_cache_size); + } + + if(learn_parm.type == CLASSIFICATION) { + svm_learn_classification(docs,target,totdoc,totwords,&learn_parm, + &kernel_parm,kernel_cache,model,alpha_in); + } + else if(learn_parm.type == REGRESSION) { + svm_learn_regression(docs,target,totdoc,totwords,&learn_parm, + &kernel_parm,&kernel_cache,model); + } + else if(learn_parm.type == RANKING) { + svm_learn_ranking(docs,target,totdoc,totwords,&learn_parm, + &kernel_parm,&kernel_cache,model); + } + else if(learn_parm.type == OPTIMIZATION) { + svm_learn_optimization(docs,target,totdoc,totwords,&learn_parm, + &kernel_parm,kernel_cache,model,alpha_in); + } + + if(kernel_cache) { + /* Free the memory used for the cache. */ + kernel_cache_cleanup(kernel_cache); + } + + /* Warning: The model contains references to the original data 'docs'. + If you want to free the original data, and only keep the model, you + have to make a deep copy of 'model'. */ + /* deep_copy_of_model=copy_model(model); */ + write_model(modelfile,model); + + free(alpha_in); + free_model(model,0); + for(i=0;i<totdoc;i++) + free_example(docs[i],1); + free(docs); + free(target); + + return(0); +} + +/*---------------------------------------------------------------------------*/ + +void read_input_parameters(int argc,char *argv[],char *docfile,char *modelfile, + char *restartfile,long *verbosity, + LEARN_PARM *learn_parm,KERNEL_PARM *kernel_parm) +{ + long i; + char type[100]; + + /* set default */ + strcpy (modelfile, "svm_model"); + strcpy (learn_parm->predfile, "trans_predictions"); + strcpy (learn_parm->alphafile, ""); + strcpy (restartfile, ""); + (*verbosity)=1; + learn_parm->biased_hyperplane=1; + learn_parm->sharedslack=0; + learn_parm->remove_inconsistent=0; + learn_parm->skip_final_opt_check=0; + learn_parm->svm_maxqpsize=10; + learn_parm->svm_newvarsinqp=0; + learn_parm->svm_iter_to_shrink=-9999; + learn_parm->maxiter=100000; + learn_parm->kernel_cache_size=40; + learn_parm->svm_c=0.0; + learn_parm->eps=0.1; + learn_parm->transduction_posratio=-1.0; + learn_parm->svm_costratio=1.0; + learn_parm->svm_costratio_unlab=1.0; + learn_parm->svm_unlabbound=1E-5; + learn_parm->epsilon_crit=0.001; + learn_parm->epsilon_a=1E-15; + learn_parm->compute_loo=0; + learn_parm->rho=1.0; + learn_parm->xa_depth=0; + kernel_parm->kernel_type=0; + kernel_parm->poly_degree=3; + kernel_parm->rbf_gamma=1.0; + kernel_parm->coef_lin=1; + kernel_parm->coef_const=1; + strcpy(kernel_parm->custom,"empty"); + strcpy(type,"c"); + + for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) { + switch ((argv[i])[1]) + { + case '?': print_help(); exit(0); + case 'z': i++; strcpy(type,argv[i]); break; + case 'v': i++; (*verbosity)=atol(argv[i]); break; + case 'b': i++; learn_parm->biased_hyperplane=atol(argv[i]); break; + case 'i': i++; learn_parm->remove_inconsistent=atol(argv[i]); break; + case 'f': i++; learn_parm->skip_final_opt_check=!atol(argv[i]); break; + case 'q': i++; learn_parm->svm_maxqpsize=atol(argv[i]); break; + case 'n': i++; learn_parm->svm_newvarsinqp=atol(argv[i]); break; + case '#': i++; learn_parm->maxiter=atol(argv[i]); break; + case 'h': i++; learn_parm->svm_iter_to_shrink=atol(argv[i]); break; + case 'm': i++; learn_parm->kernel_cache_size=atol(argv[i]); break; + case 'c': i++; learn_parm->svm_c=atof(argv[i]); break; + case 'w': i++; learn_parm->eps=atof(argv[i]); break; + case 'p': i++; learn_parm->transduction_posratio=atof(argv[i]); break; + case 'j': i++; learn_parm->svm_costratio=atof(argv[i]); break; + case 'e': i++; learn_parm->epsilon_crit=atof(argv[i]); break; + case 'o': i++; learn_parm->rho=atof(argv[i]); break; + case 'k': i++; learn_parm->xa_depth=atol(argv[i]); break; + case 'x': i++; learn_parm->compute_loo=atol(argv[i]); break; + case 't': i++; kernel_parm->kernel_type=atol(argv[i]); break; + case 'd': i++; kernel_parm->poly_degree=atol(argv[i]); break; + case 'g': i++; kernel_parm->rbf_gamma=atof(argv[i]); break; + case 's': i++; kernel_parm->coef_lin=atof(argv[i]); break; + case 'r': i++; kernel_parm->coef_const=atof(argv[i]); break; + case 'u': i++; strcpy(kernel_parm->custom,argv[i]); break; + case 'l': i++; strcpy(learn_parm->predfile,argv[i]); break; + case 'a': i++; strcpy(learn_parm->alphafile,argv[i]); break; + case 'y': i++; strcpy(restartfile,argv[i]); break; + default: printf("\nUnrecognized option %s!\n\n",argv[i]); + print_help(); + exit(0); + } + } + if(i>=argc) { + printf("\nNot enough input parameters!\n\n"); + wait_any_key(); + print_help(); + exit(0); + } + strcpy (docfile, argv[i]); + if((i+1)<argc) { + strcpy (modelfile, argv[i+1]); + } + if(learn_parm->svm_iter_to_shrink == -9999) { + if(kernel_parm->kernel_type == LINEAR) + learn_parm->svm_iter_to_shrink=2; + else + learn_parm->svm_iter_to_shrink=100; + } + if(strcmp(type,"c")==0) { + learn_parm->type=CLASSIFICATION; + } + else if(strcmp(type,"r")==0) { + learn_parm->type=REGRESSION; + } + else if(strcmp(type,"p")==0) { + learn_parm->type=RANKING; + } + else if(strcmp(type,"o")==0) { + learn_parm->type=OPTIMIZATION; + } + else if(strcmp(type,"s")==0) { + learn_parm->type=OPTIMIZATION; + learn_parm->sharedslack=1; + } + else { + printf("\nUnknown type '%s': Valid types are 'c' (classification), 'r' regession, and 'p' preference ranking.\n",type); + wait_any_key(); + print_help(); + exit(0); + } + if((learn_parm->skip_final_opt_check) + && (kernel_parm->kernel_type == LINEAR)) { + printf("\nIt does not make sense to skip the final optimality check for linear kernels.\n\n"); + learn_parm->skip_final_opt_check=0; + } + if((learn_parm->skip_final_opt_check) + && (learn_parm->remove_inconsistent)) { + printf("\nIt is necessary to do the final optimality check when removing inconsistent \nexamples.\n"); + wait_any_key(); + print_help(); + exit(0); + } + if((learn_parm->svm_maxqpsize<2)) { + printf("\nMaximum size of QP-subproblems not in valid range: %ld [2..]\n",learn_parm->svm_maxqpsize); + wait_any_key(); + print_help(); + exit(0); + } + if((learn_parm->svm_maxqpsize<learn_parm->svm_newvarsinqp)) { + printf("\nMaximum size of QP-subproblems [%ld] must be larger than the number of\n",learn_parm->svm_maxqpsize); + printf("new variables [%ld] entering the working set in each iteration.\n",learn_parm->svm_newvarsinqp); + wait_any_key(); + print_help(); + exit(0); + } + if(learn_parm->svm_iter_to_shrink<1) { + printf("\nMaximum number of iterations for shrinking not in valid range: %ld [1,..]\n",learn_parm->svm_iter_to_shrink); + wait_any_key(); + print_help(); + exit(0); + } + if(learn_parm->svm_c<0) { + printf("\nThe C parameter must be greater than zero!\n\n"); + wait_any_key(); + print_help(); + exit(0); + } + if(learn_parm->transduction_posratio>1) { + printf("\nThe fraction of unlabeled examples to classify as positives must\n"); + printf("be less than 1.0 !!!\n\n"); + wait_any_key(); + print_help(); + exit(0); + } + if(learn_parm->svm_costratio<=0) { + printf("\nThe COSTRATIO parameter must be greater than zero!\n\n"); + wait_any_key(); + print_help(); + exit(0); + } + if(learn_parm->epsilon_crit<=0) { + printf("\nThe epsilon parameter must be greater than zero!\n\n"); + wait_any_key(); + print_help(); + exit(0); + } + if(learn_parm->rho<0) { + printf("\nThe parameter rho for xi/alpha-estimates and leave-one-out pruning must\n"); + printf("be greater than zero (typically 1.0 or 2.0, see T. Joachims, Estimating the\n"); + printf("Generalization Performance of an SVM Efficiently, ICML, 2000.)!\n\n"); + wait_any_key(); + print_help(); + exit(0); + } + if((learn_parm->xa_depth<0) || (learn_parm->xa_depth>100)) { + printf("\nThe parameter depth for ext. xi/alpha-estimates must be in [0..100] (zero\n"); + printf("for switching to the conventional xa/estimates described in T. Joachims,\n"); + printf("Estimating the Generalization Performance of an SVM Efficiently, ICML, 2000.)\n"); + wait_any_key(); + print_help(); + exit(0); + } +} + +void wait_any_key() +{ + printf("\n(more)\n"); + (void)getc(stdin); +} + +void print_help() +{ + printf("\nSVM-light %s: Support Vector Machine, learning module %s\n",VERSION,VERSION_DATE); + copyright_notice(); + printf(" usage: svm_learn [options] example_file model_file\n\n"); + printf("Arguments:\n"); + printf(" example_file-> file with training data\n"); + printf(" model_file -> file to store learned decision rule in\n"); + + printf("General options:\n"); + printf(" -? -> this help\n"); + printf(" -v [0..3] -> verbosity level (default 1)\n"); + printf("Learning options:\n"); + printf(" -z {c,r,p} -> select between classification (c), regression (r),\n"); + printf(" and preference ranking (p) (default classification)\n"); + printf(" -c float -> C: trade-off between training error\n"); + printf(" and margin (default [avg. x*x]^-1)\n"); + printf(" -w [0..] -> epsilon width of tube for regression\n"); + printf(" (default 0.1)\n"); + printf(" -j float -> Cost: cost-factor, by which training errors on\n"); + printf(" positive examples outweight errors on negative\n"); + printf(" examples (default 1) (see [4])\n"); + printf(" -b [0,1] -> use biased hyperplane (i.e. x*w+b>0) instead\n"); + printf(" of unbiased hyperplane (i.e. x*w>0) (default 1)\n"); + printf(" -i [0,1] -> remove inconsistent training examples\n"); + printf(" and retrain (default 0)\n"); + printf("Performance estimation options:\n"); + printf(" -x [0,1] -> compute leave-one-out estimates (default 0)\n"); + printf(" (see [5])\n"); + printf(" -o ]0..2] -> value of rho for XiAlpha-estimator and for pruning\n"); + printf(" leave-one-out computation (default 1.0) (see [2])\n"); + printf(" -k [0..100] -> search depth for extended XiAlpha-estimator \n"); + printf(" (default 0)\n"); + printf("Transduction options (see [3]):\n"); + printf(" -p [0..1] -> fraction of unlabeled examples to be classified\n"); + printf(" into the positive class (default is the ratio of\n"); + printf(" positive and negative examples in the training data)\n"); + printf("Kernel options:\n"); + printf(" -t int -> type of kernel function:\n"); + printf(" 0: linear (default)\n"); + printf(" 1: polynomial (s a*b+c)^d\n"); + printf(" 2: radial basis function exp(-gamma ||a-b||^2)\n"); + printf(" 3: sigmoid tanh(s a*b + c)\n"); + printf(" 4: user defined kernel from kernel.h\n"); + printf(" -d int -> parameter d in polynomial kernel\n"); + printf(" -g float -> parameter gamma in rbf kernel\n"); + printf(" -s float -> parameter s in sigmoid/poly kernel\n"); + printf(" -r float -> parameter c in sigmoid/poly kernel\n"); + printf(" -u string -> parameter of user defined kernel\n"); + printf("Optimization options (see [1]):\n"); + printf(" -q [2..] -> maximum size of QP-subproblems (default 10)\n"); + printf(" -n [2..q] -> number of new variables entering the working set\n"); + printf(" in each iteration (default n = q). Set n<q to prevent\n"); + printf(" zig-zagging.\n"); + printf(" -m [5..] -> size of cache for kernel evaluations in MB (default 40)\n"); + printf(" The larger the faster...\n"); + printf(" -e float -> eps: Allow that error for termination criterion\n"); + printf(" [y [w*x+b] - 1] >= eps (default 0.001)\n"); + printf(" -y [0,1] -> restart the optimization from alpha values in file\n"); + printf(" specified by -a option. (default 0)\n"); + printf(" -h [5..] -> number of iterations a variable needs to be\n"); + printf(" optimal before considered for shrinking (default 100)\n"); + printf(" -f [0,1] -> do final optimality check for variables removed\n"); + printf(" by shrinking. Although this test is usually \n"); + printf(" positive, there is no guarantee that the optimum\n"); + printf(" was found if the test is omitted. (default 1)\n"); + printf(" -y string -> if option is given, reads alphas from file with given\n"); + printf(" and uses them as starting point. (default 'disabled')\n"); + printf(" -# int -> terminate optimization, if no progress after this\n"); + printf(" number of iterations. (default 100000)\n"); + printf("Output options:\n"); + printf(" -l string -> file to write predicted labels of unlabeled\n"); + printf(" examples into after transductive learning\n"); + printf(" -a string -> write all alphas to this file after learning\n"); + printf(" (in the same order as in the training set)\n"); + wait_any_key(); + printf("\nMore details in:\n"); + printf("[1] T. Joachims, Making Large-Scale SVM Learning Practical. Advances in\n"); + printf(" Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and\n"); + printf(" A. Smola (ed.), MIT Press, 1999.\n"); + printf("[2] T. Joachims, Estimating the Generalization performance of an SVM\n"); + printf(" Efficiently. International Conference on Machine Learning (ICML), 2000.\n"); + printf("[3] T. Joachims, Transductive Inference for Text Classification using Support\n"); + printf(" Vector Machines. International Conference on Machine Learning (ICML),\n"); + printf(" 1999.\n"); + printf("[4] K. Morik, P. Brockhausen, and T. Joachims, Combining statistical learning\n"); + printf(" with a knowledge-based approach - A case study in intensive care \n"); + printf(" monitoring. International Conference on Machine Learning (ICML), 1999.\n"); + printf("[5] T. Joachims, Learning to Classify Text Using Support Vector\n"); + printf(" Machines: Methods, Theory, and Algorithms. Dissertation, Kluwer,\n"); + printf(" 2002.\n\n"); +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/src/svm_loqo.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,211 @@ +/***********************************************************************/ +/* */ +/* svm_loqo.c */ +/* */ +/* Interface to the PR_LOQO optimization package for SVM. */ +/* */ +/* Author: Thorsten Joachims */ +/* Date: 19.07.99 */ +/* */ +/* Copyright (c) 1999 Universitaet Dortmund - All rights reserved */ +/* */ +/* This software is available for non-commercial use only. It must */ +/* not be modified and distributed without prior permission of the */ +/* author. The author is not responsible for implications from the */ +/* use of this software. */ +/* */ +/***********************************************************************/ + +# include <math.h> +# include "pr_loqo/pr_loqo.h" +# include "svm_common.h" + +/* Common Block Declarations */ + +long verbosity; + +/* /////////////////////////////////////////////////////////////// */ + +# define DEF_PRECISION_LINEAR 1E-8 +# define DEF_PRECISION_NONLINEAR 1E-14 + +double *optimize_qp(); +double *primal=0,*dual=0; +double init_margin=0.15; +long init_iter=500,precision_violations=0; +double model_b; +double opt_precision=DEF_PRECISION_LINEAR; + +/* /////////////////////////////////////////////////////////////// */ + +void *my_malloc(); + +double *optimize_qp(qp,epsilon_crit,nx,threshold,learn_parm) +QP *qp; +double *epsilon_crit; +long nx; /* Maximum number of variables in QP */ +double *threshold; +LEARN_PARM *learn_parm; +/* start the optimizer and return the optimal values */ +{ + register long i,j,result; + double margin,obj_before,obj_after; + double sigdig,dist,epsilon_loqo; + int iter; + + if(!primal) { /* allocate memory at first call */ + primal=(double *)my_malloc(sizeof(double)*nx*3); + dual=(double *)my_malloc(sizeof(double)*(nx*2+1)); + } + + if(verbosity>=4) { /* really verbose */ + printf("\n\n"); + for(i=0;i<qp->opt_n;i++) { + printf("%f: ",qp->opt_g0[i]); + for(j=0;j<qp->opt_n;j++) { + printf("%f ",qp->opt_g[i*qp->opt_n+j]); + } + printf(": a%ld=%.10f < %f",i,qp->opt_xinit[i],qp->opt_up[i]); + printf(": y=%f\n",qp->opt_ce[i]); + } + for(j=0;j<qp->opt_m;j++) { + printf("EQ-%ld: %f*a0",j,qp->opt_ce[j]); + for(i=1;i<qp->opt_n;i++) { + printf(" + %f*a%ld",qp->opt_ce[i],i); + } + printf(" = %f\n\n",-qp->opt_ce0[0]); + } +} + + obj_before=0; /* calculate objective before optimization */ + for(i=0;i<qp->opt_n;i++) { + obj_before+=(qp->opt_g0[i]*qp->opt_xinit[i]); + obj_before+=(0.5*qp->opt_xinit[i]*qp->opt_xinit[i]*qp->opt_g[i*qp->opt_n+i]); + for(j=0;j<i;j++) { + obj_before+=(qp->opt_xinit[j]*qp->opt_xinit[i]*qp->opt_g[j*qp->opt_n+i]); + } + } + + result=STILL_RUNNING; + qp->opt_ce0[0]*=(-1.0); + /* Run pr_loqo. If a run fails, try again with parameters which lead */ + /* to a slower, but more robust setting. */ + for(margin=init_margin,iter=init_iter; + (margin<=0.9999999) && (result!=OPTIMAL_SOLUTION);) { + sigdig=-log10(opt_precision); + + result=pr_loqo((int)qp->opt_n,(int)qp->opt_m, + (double *)qp->opt_g0,(double *)qp->opt_g, + (double *)qp->opt_ce,(double *)qp->opt_ce0, + (double *)qp->opt_low,(double *)qp->opt_up, + (double *)primal,(double *)dual, + (int)(verbosity-2), + (double)sigdig,(int)iter, + (double)margin,(double)(qp->opt_up[0])/4.0,(int)0); + + if(isnan(dual[0])) { /* check for choldc problem */ + if(verbosity>=2) { + printf("NOTICE: Restarting PR_LOQO with more conservative parameters.\n"); + } + if(init_margin<0.80) { /* become more conservative in general */ + init_margin=(4.0*margin+1.0)/5.0; + } + margin=(margin+1.0)/2.0; + (opt_precision)*=10.0; /* reduce precision */ + if(verbosity>=2) { + printf("NOTICE: Reducing precision of PR_LOQO.\n"); + } + } + else if(result!=OPTIMAL_SOLUTION) { + iter+=2000; + init_iter+=10; + (opt_precision)*=10.0; /* reduce precision */ + if(verbosity>=2) { + printf("NOTICE: Reducing precision of PR_LOQO due to (%ld).\n",result); + } + } + } + + if(qp->opt_m) /* Thanks to Alex Smola for this hint */ + model_b=dual[0]; + else + model_b=0; + + /* Check the precision of the alphas. If results of current optimization */ + /* violate KT-Conditions, relax the epsilon on the bounds on alphas. */ + epsilon_loqo=1E-10; + for(i=0;i<qp->opt_n;i++) { + dist=-model_b*qp->opt_ce[i]; + dist+=(qp->opt_g0[i]+1.0); + for(j=0;j<i;j++) { + dist+=(primal[j]*qp->opt_g[j*qp->opt_n+i]); + } + for(j=i;j<qp->opt_n;j++) { + dist+=(primal[j]*qp->opt_g[i*qp->opt_n+j]); + } + /* printf("LOQO: a[%d]=%f, dist=%f, b=%f\n",i,primal[i],dist,dual[0]); */ + if((primal[i]<(qp->opt_up[i]-epsilon_loqo)) && (dist < (1.0-(*epsilon_crit)))) { + epsilon_loqo=(qp->opt_up[i]-primal[i])*2.0; + } + else if((primal[i]>(0+epsilon_loqo)) && (dist > (1.0+(*epsilon_crit)))) { + epsilon_loqo=primal[i]*2.0; + } + } + + for(i=0;i<qp->opt_n;i++) { /* clip alphas to bounds */ + if(primal[i]<=(0+epsilon_loqo)) { + primal[i]=0; + } + else if(primal[i]>=(qp->opt_up[i]-epsilon_loqo)) { + primal[i]=qp->opt_up[i]; + } + } + + obj_after=0; /* calculate objective after optimization */ + for(i=0;i<qp->opt_n;i++) { + obj_after+=(qp->opt_g0[i]*primal[i]); + obj_after+=(0.5*primal[i]*primal[i]*qp->opt_g[i*qp->opt_n+i]); + for(j=0;j<i;j++) { + obj_after+=(primal[j]*primal[i]*qp->opt_g[j*qp->opt_n+i]); + } + } + + /* if optimizer returned NAN values, reset and retry with smaller */ + /* working set. */ + if(isnan(obj_after) || isnan(model_b)) { + for(i=0;i<qp->opt_n;i++) { + primal[i]=qp->opt_xinit[i]; + } + model_b=0; + if(learn_parm->svm_maxqpsize>2) { + learn_parm->svm_maxqpsize--; /* decrease size of qp-subproblems */ + } + } + + if(obj_after >= obj_before) { /* check whether there was progress */ + (opt_precision)/=100.0; + precision_violations++; + if(verbosity>=2) { + printf("NOTICE: Increasing Precision of PR_LOQO.\n"); + } + } + + if(precision_violations > 500) { + (*epsilon_crit)*=10.0; + precision_violations=0; + if(verbosity>=1) { + printf("\nWARNING: Relaxing epsilon on KT-Conditions.\n"); + } + } + + (*threshold)=model_b; + + if(result!=OPTIMAL_SOLUTION) { + printf("\nERROR: PR_LOQO did not converge. \n"); + return(qp->opt_xinit); + } + else { + return(primal); + } +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/SVM-light/svm_light_faq.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,218 @@ +<HTML> +<HEAD> +<META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=windows-1252"> +<META NAME="Generator" CONTENT="Microsoft FrontPage 6.0"> +<TITLE>svm_light_faq</TITLE> +<META NAME="Version" CONTENT="8.0.3514"> +<META NAME="Date" CONTENT="11/26/96"> +<META NAME="Template" CONTENT="C:\Programme\Microsoft Office\Office\HTML.DOT"> +</HEAD> +<BODY LINK="#0000ff" VLINK="#800080" BGCOLOR="#ffffff"> + +<TABLE CELLSPACING=0 BORDER=0 CELLPADDING=5> +<TR><TD WIDTH="14%" VALIGN="TOP"> +<H2><a TARGET="_top" HREF="http://www-ai.cs.uni-dortmund.de/"><IMG SRC="eier_graybg.gif" BORDER=0 WIDTH=100 HEIGHT=81></A></H2></TD> +<TD WIDTH="75%" VALIGN="TOP"> +<H1 ALIGN="CENTER">SVM<I><SUP>light</SUP> &</I> SVM<sup><i>struct</i></sup><I> +</I> </H1> +<H1 ALIGN="CENTER">FAQ</H1> +<FONT COLOR="#000000"><P ALIGN="CENTER">Author: </FONT><a TARGET="_top" HREF="http://www.joachims.org/">Thorsten Joachims</A><FONT COLOR="#000000"> <</FONT><A HREF="mailto:thorsten@joachims.org">thorsten@joachims.org</A><FONT COLOR="#000000">> <BR> +</FONT><a TARGET="_top" HREF="http://www.cornell.edu/">Cornell University</A><FONT COLOR="#000000"> <BR> +</FONT><a TARGET="_top" HREF="http://www.cs.cornell.edu/">Department of Computer Science</A><FONT COLOR="#000000"> </P> +<P ALIGN="CENTER">Developed at: <BR> +</FONT><a TARGET="_top" HREF="http://www.uni-dortmund.de/">University of Dortmund</A><FONT COLOR="#000000">, </FONT><a TARGET="_top" HREF="http://www.informatik.uni-dortmund.de/">Informatik</A><FONT COLOR="#000000">, </FONT><a TARGET="_top" HREF="http://www-ai.informatik.uni-dortmund.de/">AI-Unit</A><FONT COLOR="#000000"> <BR> +</FONT><a TARGET="_top" HREF="http://www.sfb475.uni-dortmund.de/">Collaborative Research Center on 'Complexity Reduction in Multivariate Data' (SFB475)</A><FONT COLOR="#000000"> </FONT> </P> +<P ALIGN="CENTER"><FONT COLOR="#000000"> Date: 25. May, 2009</FONT></P></TD> +<TD WIDTH="11%" VALIGN="TOP"> +<H2><IMG SRC="http://www.joachims.org/images/culogo_125.gif" WIDTH=80 HEIGHT=80></H2></TD> +</TR> +</TABLE> + +<H2>How do I compile on a Windows PC?</H2> +<UL> + <LI>The following instructions apply to SVM<I><SUP>light</SUP></I>, SVM<sup><i>struct</i></sup>, SVM<sup><i>perf</i></sup>, SVM<sup><i>cfg</i></sup>, SVM<sup><i>multiclass</i></sup>, + and SVM<sup><i>hmm</i></sup>. </LI> + <LI>The easiest is to install <a href="http://www.cygwin.com">CYGWIN</a> and + use the gcc compiler that comes with the Cygwin distribution. Just open a + cygwin command window, change to the directory that contains file "Makefile" and type + <br> + make<br> + at the command prompt. This creates executables that run under Cygwin.</LI> + <LI>If you want execuables that do not need Cygwin to run, follow the same + instructions as above, but make sure that you download the MinGW package as + part of Cygwin. Then use the command <br> + make 'SFLAGS=-mno-cygwin'<br> + to create the executables.</LI> + <LI>You can also use the Visual C compiler, but it will be much more of a + hassle than using Cygwin. To use Visual C, you need to build the appropriate project file according to the provided <TT>"Makefile"</TT>. +Make sure you do not include the file "svm_loqo.c" in your project, unless you +want to use the PL_LOQO optimizer instead of the built-in optimizer.</p> +</UL> + +<H2>How do I compile on a PowerMac using Code Warrior?</H2> + +<UL> +<LI>This applies to SVM<I><SUP>light</SUP></I>, but should also work for SVM<sup><i>struct</i></sup>, SVM<sup><i>perf</i></sup>, SVM<sup><i>cfg</i></sup>, SVM<sup><i>multiclass</i></sup>, + and SVM<sup><i>hmm</i></sup>. </LI> +<LI>You need to modify the source code a little (as suggested by Jewgeni Starikow). Use <TT>#include "console.h"</TT> to emulate a UNIX shell. Then add <TT>argc=ccommand(&argv)</TT> as the first instruction of each <TT>main()</TT>. </LI> +<LI>CPU-timing might cause another problem. The timing routines are used to calculated the runtime of the program. If you do not need this feature, remove the body of <TT>get_runtime()</TT> in <TT>svm_common.c</TT>. Otherwise replace the body with the appropriate Mac routines from <TT>'time.h'</TT>. </LI></UL> +<H2>How do I integrate SVM<SUP>light</SUP> into C++ code?</H2> + +<UL> +<LI>Compile <TT>"svm_learn.c"</TT>, <TT>"svm_common.c"</TT>, and <TT>"svm_hideo.c"</TT> as C code.</LI> +<LI>The C++ program you want to call <TT>svm_learn/8</TT> and <TT>classify_example/2 </TT>(or <TT>classify_example_linear/2</TT>) from needs to include the following headers:<BR> +<FONT FACE="Courier New" SIZE=2>extern "C" {<BR> +# include "svm_common.h"<BR> +# include "svm_learn.h"<BR> +} </FONT> </LI> +<LI>Link <TT>"svm_learn.o"</TT>, <TT>"svm_common.o"</TT>, and <TT>"svm_hideo.o" </TT>to your program.</LI></UL> + +<h2>Is there an option for doing multi-class classification?</h2> +<ul> + <li>Not in SVM<I><SUP>light</SUP></I>. If you do not use kernel, you can + use <a href="svm_multiclass.html">SVM<sup><i>multiclass</i></sup></a> to + do multi-class classification. Otherwise, I recommend to split the task + into multiple binary classification tasks for multi-class or multi-label classification. For + example, you can do one-class-against-the-rest classification, or pairwise classification. Refer to Chapter 2 in [<a href="index.html#References">Joachims, + 2002a</a>].</li> +</ul> + +<h2>Is there an option for doing cross-validation?</h2> +<ul> + <li>Yes, there is such an option in SVM<I><SUP>light</SUP></I>, but not in SVM<sup><i>struct</i></sup>. + By setting the option "-x 1", SVM<I><SUP>light</SUP></I> computes the + leave-one-out estimates of the prediction error, recall, precision, and F1. By + default, it also outputs the XiAlpha-Estimates of these quantities. They are + strictly conservative approximations to the leave-one-out (i.e. they never + underestimate the leave-one-out error). Refer to Chapter 5 in [<a href="index.html#References">Joachims, + 2002a</a>].</li> +</ul> +<h2>How can I output the dual variables at the solution?</h2> +<ul> + <li>Use the option -a <filename>. The file contains the value of the + dual variables (multiplied by the label y) for each training example in the + order in which the examples appeared in the training file.</li> +</ul> +<h2>How can I get the weight vector of the hyperplane for a linear SVM?</h2> +<ul> + <li>There is a small <a href="svm2weight.pl.txt">PERL script to + compute the weight vector</a> (<a href="svm2weight.py.txt">similar script in + PYTHON by Ori Cohen</a>) based on the model file output by svm_learn + and svm_perf_learn. + Of course, it works only in the linear case and not for kernels. All the + script does is compute the weighted sum of the support vectors (first + element in line is alpha*y, what follows is the feature vector). For + further info, see the comment in the model file for its format. + </li> +</ul> + +<h2>What is the format of the learned model file?</h2> +<ul> + <li>The model files produced by svm_learn and svm_perf_learn are in ASCII and + easy to read. The first few lines contain parameters. The comments in the + model file are self-explanatory. The last of these is the threshold b of the + learned hyperplane classifier sign(w*x - b). The following lines each contain + a support vector, and the first element of the line is its coefficient (alpha_i + * y_i) in the kernel expansion. The support vectors are listed in random + order. Note that the term support vector has a different meaning in SVM<sup>perf</sup> + compared to SVM<SUP>light</SUP>.</li> +</ul> + + +<h2>How can I implement my own Kernel?</h2> +<ul> + <li>You can write your own kernel by extending the function "double + custom_kernel(KERNEL_PARM *kernel_parm, SVECTOR *a, SVECTOR *b)" in "kernel.h" + and then select it via the "-t 4" option. The "a" and "b" are pointers to the + examples you are computing the kernel for.</li> + <ul> + <li>If the data you are working with is vectorial, then you can use the + existing sparse vector operations defined in svm_common.c. For example,<br> + pow(sprod_ss(a->words,b->words),2.0) <br> + implements the homogeneous polynomial kernel of degree 2.</li> + <li>If the data is non-vectorial (e.g. strings), then you can use the + following functionality. In the training and test data files, whatever is + written behind the "#" is copied into the "userdefined" field of the + internal datastructure "SVECTOR" as a string. So, for the line<br> + +1 #abcdefg<br> + you will find "abcdefg" in "svector->userdefined".</li> + </ul> +</ul> + + +<H2>Error messages and known problems:</H2> + +<UL> +<TT><LI>ERROR: terminating optimizer - choldc failed, matrix not positive definite</TT> </LI> + +<UL> +<LI>If the program terminates after this message, get the lastest version of PR_LOQO and SVM-Light V2.01 (or later). </LI> +<LI>It the program continues after this error message, don't worry :-) </LI></UL> + +<I><LI>The CPU-time is negative or looks bogus:</I> </LI> + +<UL> +<LI>To be compatible with Windows, I used the <TT>clock/0</TT> function to measure CPU-time. This function returns a long integer and counts microseconds, so that it wraps around after about 30 minutes. You could use timing routines more appropriate for your system. For example on Solaris, you might want to use the routines from <TT>'sys/times.h'</TT>.</LI></UL> +</UL> + +<H2>The program hangs when ...</H2> + +<UL> +<I><LI>... reading in the examples.</I> </LI> + +<UL> +<LI>Get version 3.02 or later. </LI></UL> +</UL> + +<H2>Convergence during learning is very slow!</H2> + +<UL> +<I><LI>In verbose mode 2 I observe that <TT>max violation</TT> bounces around and does not really converge.</I> </LI> + +<UL> +<LI>Use a smaller value for the option -n (default n=q). This makes sure that only n new variables enter the working set in each iteration. This can prevent zig-zagging behavior.</LI> +<LI>Use a smaller or larger value for the size of the working set (option -q). Reasonable values are in the range [2:50].</LI> +<LI>You might be using an excessively large value of C (option -c) in relation to your data. The value of C should typically be less than 1000 times 1.0/(the maximum squared Euclidian length of your feature vectors). </LI> +<LI>You have weired data and the convergence simply IS very slow <FONT FACE="Wingdings">J</FONT> +. Sorry, not much you can do about it. </LI></UL> + +<I><LI>Nearly all my training examples end up as support vectors.</I> </LI> + +<UL> +<LI>Use a "stiffer" kernel (e g. a lower value of gamma for the RBF-kernel). If you pick a kernel which is very far away from the optimum, you will not get good generalization performance anyway. </LI> +<LI>Your data is really difficult to separate. Think about a better representation of your data. It is a bad idea - for example - if different features have values in very much different orders of magnitude. You might want to normalize all features to the range [-1,+1]. </LI></UL> +</UL> + +<H2>It does not converge!</H2> + +<UL> +<LI>If you are using the built-in HIDEO optimizer, get the version 3.50 or later. There used to be problems for low dimensional data sets for the old HIDEO optimizer.</LI> +<LI>It should always converge, unless there are numerical problems :-) </LI> +<LI>Numerical problems. </LI> + +<UL> +<LI>Make sure your data is properly scaled. It is a bad idea - for example - if different features have values in different orders of magnitude. You might want to normalize all features to the range [-1,+1], especially for problems with more than 100 features. Or even better, normalize all feature vectors to Euclidian length 1.</LI></UL> +</UL> + +<h2>It crashes!</h2> +<ul> + <li><i>It crashes during learning when using a non-linear kernel.</i> + <ul> + <li>Get version 5.00 or later. There was an initialization bug in the + kernel cache.</li> + </ul> + </li> +</ul> +<h2>The results look bogus!</h2> +<ul> + <li><i>When doing transductive learning, for some training set sizes it does + not obey the -p parameter and the results look bogus.</i> + <ul> + <li>Get version 5.00 or later. There was a bug introduced in version 4.00.</li> + </ul> + </li> +</ul> + +<FONT COLOR="#000000"> +<P>Last modified July 1st, 2007 by </FONT><a TARGET="_top" HREF="http://www.joachims.org">Thorsten Joachims</A><FONT COLOR="#000000"> <</FONT><a HREF="mailto:thorsten@joachims.org">thorsten@joachims.org</a><FONT COLOR="#000000">></P></FONT></BODY> +</HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/_python_lib/pylast/.build Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +9 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/_python_lib/pylast/COPYING Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ + Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. Definitions. + +"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. + +"Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. + +"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/_python_lib/pylast/INSTALL Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +Installation Instructions +========================= + +Execute "python setup.py install" as a super user.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/_python_lib/pylast/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +pylast +------ + +A python interface to Last.fm. Try using the pydoc utility for help +on usage. +For more info check out the project's home page at http://code.google.com/p/pylast/ +or the mailing list http://groups.google.com/group/pylast/
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/_python_lib/pylast/pylast.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3834 @@ +# -*- coding: utf-8 -*- +# +# pylast - A Python interface to Last.fm (and other API compatible social networks) +# +# Copyright 2008-2010 Amr Hassan +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# http://code.google.com/p/pylast/ + +__version__ = '0.5' +__author__ = 'Amr Hassan' +__copyright__ = "Copyright (C) 2008-2010 Amr Hassan" +__license__ = "apache2" +__email__ = 'amr.hassan@gmail.com' + +import hashlib +from xml.dom import minidom +import xml.dom +import time +import shelve +import tempfile +import sys +import collections +import warnings + +def _deprecation_warning(message): + warnings.warn(message, DeprecationWarning) + +if sys.version_info[0] == 3: + from http.client import HTTPConnection + import html.entities as htmlentitydefs + from urllib.parse import splithost as url_split_host + from urllib.parse import quote_plus as url_quote_plus + + unichr = chr + +elif sys.version_info[0] == 2: + from httplib import HTTPConnection + import htmlentitydefs + from urllib import splithost as url_split_host + from urllib import quote_plus as url_quote_plus + +STATUS_INVALID_SERVICE = 2 +STATUS_INVALID_METHOD = 3 +STATUS_AUTH_FAILED = 4 +STATUS_INVALID_FORMAT = 5 +STATUS_INVALID_PARAMS = 6 +STATUS_INVALID_RESOURCE = 7 +STATUS_TOKEN_ERROR = 8 +STATUS_INVALID_SK = 9 +STATUS_INVALID_API_KEY = 10 +STATUS_OFFLINE = 11 +STATUS_SUBSCRIBERS_ONLY = 12 +STATUS_INVALID_SIGNATURE = 13 +STATUS_TOKEN_UNAUTHORIZED = 14 +STATUS_TOKEN_EXPIRED = 15 + +EVENT_ATTENDING = '0' +EVENT_MAYBE_ATTENDING = '1' +EVENT_NOT_ATTENDING = '2' + +PERIOD_OVERALL = 'overall' +PERIOD_7DAYS = "7day" +PERIOD_3MONTHS = '3month' +PERIOD_6MONTHS = '6month' +PERIOD_12MONTHS = '12month' + +DOMAIN_ENGLISH = 0 +DOMAIN_GERMAN = 1 +DOMAIN_SPANISH = 2 +DOMAIN_FRENCH = 3 +DOMAIN_ITALIAN = 4 +DOMAIN_POLISH = 5 +DOMAIN_PORTUGUESE = 6 +DOMAIN_SWEDISH = 7 +DOMAIN_TURKISH = 8 +DOMAIN_RUSSIAN = 9 +DOMAIN_JAPANESE = 10 +DOMAIN_CHINESE = 11 + +COVER_SMALL = 0 +COVER_MEDIUM = 1 +COVER_LARGE = 2 +COVER_EXTRA_LARGE = 3 +COVER_MEGA = 4 + +IMAGES_ORDER_POPULARITY = "popularity" +IMAGES_ORDER_DATE = "dateadded" + + +USER_MALE = 'Male' +USER_FEMALE = 'Female' + +SCROBBLE_SOURCE_USER = "P" +SCROBBLE_SOURCE_NON_PERSONALIZED_BROADCAST = "R" +SCROBBLE_SOURCE_PERSONALIZED_BROADCAST = "E" +SCROBBLE_SOURCE_LASTFM = "L" +SCROBBLE_SOURCE_UNKNOWN = "U" + +SCROBBLE_MODE_PLAYED = "" +SCROBBLE_MODE_LOVED = "L" +SCROBBLE_MODE_BANNED = "B" +SCROBBLE_MODE_SKIPPED = "S" + +class _Network(object): + """ + A music social network website that is Last.fm or one exposing a Last.fm compatible API + """ + + def __init__(self, name, homepage, ws_server, api_key, api_secret, session_key, submission_server, username, password_hash, + domain_names, urls): + """ + name: the name of the network + homepage: the homepage url + ws_server: the url of the webservices server + api_key: a provided API_KEY + api_secret: a provided API_SECRET + session_key: a generated session_key or None + submission_server: the url of the server to which tracks are submitted (scrobbled) + username: a username of a valid user + password_hash: the output of pylast.md5(password) where password is the user's password + domain_names: a dict mapping each DOMAIN_* value to a string domain name + urls: a dict mapping types to urls + + if username and password_hash were provided and not session_key, session_key will be + generated automatically when needed. + + Either a valid session_key or a combination of username and password_hash must be present for scrobbling. + + You should use a preconfigured network object through a get_*_network(...) method instead of creating an object + of this class, unless you know what you're doing. + """ + + self.name = name + self.homepage = homepage + self.ws_server = ws_server + self.api_key = api_key + self.api_secret = api_secret + self.session_key = session_key + self.submission_server = submission_server + self.username = username + self.password_hash = password_hash + self.domain_names = domain_names + self.urls = urls + + self.cache_backend = None + self.proxy_enabled = False + self.proxy = None + self.last_call_time = 0 + + #generate a session_key if necessary + if (self.api_key and self.api_secret) and not self.session_key and (self.username and self.password_hash): + sk_gen = SessionKeyGenerator(self) + self.session_key = sk_gen.get_session_key(self.username, self.password_hash) + + """def __repr__(self): + attributes = ("name", "homepage", "ws_server", "api_key", "api_secret", "session_key", "submission_server", + "username", "password_hash", "domain_names", "urls") + + text = "pylast._Network(%s)" + args = [] + for attr in attributes: + args.append("=".join((attr, repr(getattr(self, attr))))) + + return text % ", ".join(args) + """ + + def __str__(self): + return "The %s Network" %self.name + + def get_artist(self, artist_name): + """ + Return an Artist object + """ + + return Artist(artist_name, self) + + def get_track(self, artist, title): + """ + Return a Track object + """ + + return Track(artist, title, self) + + def get_album(self, artist, title): + """ + Return an Album object + """ + + return Album(artist, title, self) + + def get_authenticated_user(self): + """ + Returns the authenticated user + """ + + return AuthenticatedUser(self) + + def get_country(self, country_name): + """ + Returns a country object + """ + + return Country(country_name, self) + + def get_group(self, name): + """ + Returns a Group object + """ + + return Group(name, self) + + def get_user(self, username): + """ + Returns a user object + """ + + return User(username, self) + + def get_tag(self, name): + """ + Returns a tag object + """ + + return Tag(name, self) + + def get_scrobbler(self, client_id, client_version): + """ + Returns a Scrobbler object used for submitting tracks to the server + + Quote from http://www.last.fm/api/submissions: + ======== + Client identifiers are used to provide a centrally managed database of + the client versions, allowing clients to be banned if they are found to + be behaving undesirably. The client ID is associated with a version + number on the server, however these are only incremented if a client is + banned and do not have to reflect the version of the actual client application. + + During development, clients which have not been allocated an identifier should + use the identifier tst, with a version number of 1.0. Do not distribute code or + client implementations which use this test identifier. Do not use the identifiers + used by other clients. + ========= + + To obtain a new client identifier please contact: + * Last.fm: submissions@last.fm + * # TODO: list others + + ...and provide us with the name of your client and its homepage address. + """ + + _deprecation_warning("Use _Network.scrobble(...), _Network.scrobble_many(...), and Netowrk.update_now_playing(...) instead") + + return Scrobbler(self, client_id, client_version) + + def _get_language_domain(self, domain_language): + """ + Returns the mapped domain name of the network to a DOMAIN_* value + """ + + if domain_language in self.domain_names: + return self.domain_names[domain_language] + + def _get_url(self, domain, type): + return "http://%s/%s" %(self._get_language_domain(domain), self.urls[type]) + + def _get_ws_auth(self): + """ + Returns a (API_KEY, API_SECRET, SESSION_KEY) tuple. + """ + return (self.api_key, self.api_secret, self.session_key) + + def _delay_call(self): + """ + Makes sure that web service calls are at least a second apart + """ + + # delay time in seconds + DELAY_TIME = 1.0 + now = time.time() + + if (now - self.last_call_time) < DELAY_TIME: + time.sleep(1) + + self.last_call_time = now + + def create_new_playlist(self, title, description): + """ + Creates a playlist for the authenticated user and returns it + title: The title of the new playlist. + description: The description of the new playlist. + """ + + params = {} + params['title'] = title + params['description'] = description + + doc = _Request(self, 'playlist.create', params).execute(False) + + e_id = doc.getElementsByTagName("id")[0].firstChild.data + user = doc.getElementsByTagName('playlists')[0].getAttribute('user') + + return Playlist(user, e_id, self) + + def get_top_tags(self, limit=None): + """Returns a sequence of the most used tags as a sequence of TopItem objects.""" + + doc = _Request(self, "tag.getTopTags").execute(True) + seq = [] + for node in doc.getElementsByTagName("tag"): + tag = Tag(_extract(node, "name"), self) + weight = _number(_extract(node, "count")) + + seq.append(TopItem(tag, weight)) + + if limit: + seq = seq[:limit] + + return seq + + def enable_proxy(self, host, port): + """Enable a default web proxy""" + + self.proxy = [host, _number(port)] + self.proxy_enabled = True + + def disable_proxy(self): + """Disable using the web proxy""" + + self.proxy_enabled = False + + def is_proxy_enabled(self): + """Returns True if a web proxy is enabled.""" + + return self.proxy_enabled + + def _get_proxy(self): + """Returns proxy details.""" + + return self.proxy + + def enable_caching(self, file_path = None): + """Enables caching request-wide for all cachable calls. + In choosing the backend used for caching, it will try _SqliteCacheBackend first if + the module sqlite3 is present. If not, it will fallback to _ShelfCacheBackend which uses shelve.Shelf objects. + + * file_path: A file path for the backend storage file. If + None set, a temp file would probably be created, according the backend. + """ + + if not file_path: + file_path = tempfile.mktemp(prefix="pylast_tmp_") + + self.cache_backend = _ShelfCacheBackend(file_path) + + def disable_caching(self): + """Disables all caching features.""" + + self.cache_backend = None + + def is_caching_enabled(self): + """Returns True if caching is enabled.""" + + return not (self.cache_backend == None) + + def _get_cache_backend(self): + + return self.cache_backend + + def search_for_album(self, album_name): + """Searches for an album by its name. Returns a AlbumSearch object. + Use get_next_page() to retreive sequences of results.""" + + return AlbumSearch(album_name, self) + + def search_for_artist(self, artist_name): + """Searches of an artist by its name. Returns a ArtistSearch object. + Use get_next_page() to retreive sequences of results.""" + + return ArtistSearch(artist_name, self) + + def search_for_tag(self, tag_name): + """Searches of a tag by its name. Returns a TagSearch object. + Use get_next_page() to retreive sequences of results.""" + + return TagSearch(tag_name, self) + + def search_for_track(self, artist_name, track_name): + """Searches of a track by its name and its artist. Set artist to an empty string if not available. + Returns a TrackSearch object. + Use get_next_page() to retreive sequences of results.""" + + return TrackSearch(artist_name, track_name, self) + + def search_for_venue(self, venue_name, country_name): + """Searches of a venue by its name and its country. Set country_name to an empty string if not available. + Returns a VenueSearch object. + Use get_next_page() to retreive sequences of results.""" + + return VenueSearch(venue_name, country_name, self) + + def get_track_by_mbid(self, mbid): + """Looks up a track by its MusicBrainz ID""" + + params = {"mbid": mbid} + + doc = _Request(self, "track.getInfo", params).execute(True) + + return Track(_extract(doc, "name", 1), _extract(doc, "name"), self) + + def get_artist_by_mbid(self, mbid): + """Loooks up an artist by its MusicBrainz ID""" + + params = {"mbid": mbid} + + doc = _Request(self, "artist.getInfo", params).execute(True) + + return Artist(_extract(doc, "name"), self) + + def get_album_by_mbid(self, mbid): + """Looks up an album by its MusicBrainz ID""" + + params = {"mbid": mbid} + + doc = _Request(self, "album.getInfo", params).execute(True) + + return Album(_extract(doc, "artist"), _extract(doc, "name"), self) + + def update_now_playing(self, artist, title, album = None, album_artist = None, + duration = None, track_number = None, mbid = None, context = None): + """ + Used to notify Last.fm that a user has started listening to a track. + + Parameters: + artist (Required) : The artist name + title (Required) : The track title + album (Optional) : The album name. + album_artist (Optional) : The album artist - if this differs from the track artist. + duration (Optional) : The length of the track in seconds. + track_number (Optional) : The track number of the track on the album. + mbid (Optional) : The MusicBrainz Track ID. + context (Optional) : Sub-client version (not public, only enabled for certain API keys) + """ + + params = {"track": title, "artist": artist} + + if album: params["album"] = album + if album_artist: params["albumArtist"] = album_artist + if context: params["context"] = context + if track_number: params["trackNumber"] = track_number + if mbid: params["mbid"] = mbid + if duration: params["duration"] = duration + + _Request(self, "track.updateNowPlaying", params).execute() + + def scrobble(self, artist, title, timestamp, album = None, album_artist = None, track_number = None, + duration = None, stream_id = None, context = None, mbid = None): + + """Used to add a track-play to a user's profile. + + Parameters: + artist (Required) : The artist name. + title (Required) : The track name. + timestamp (Required) : The time the track started playing, in UNIX timestamp format (integer number of seconds since 00:00:00, January 1st 1970 UTC). This must be in the UTC time zone. + album (Optional) : The album name. + album_artist (Optional) : The album artist - if this differs from the track artist. + context (Optional) : Sub-client version (not public, only enabled for certain API keys) + stream_id (Optional) : The stream id for this track received from the radio.getPlaylist service. + track_number (Optional) : The track number of the track on the album. + mbid (Optional) : The MusicBrainz Track ID. + duration (Optional) : The length of the track in seconds. + """ + + return self.scrobble_many(({"artist": artist, "title": title, "timestamp": timestamp, "album": album, "album_artist": album_artist, + "track_number": track_number, "duration": duration, "stream_id": stream_id, "context": context, "mbid": mbid},)) + + def scrobble_many(self, tracks): + """ + Used to scrobble a batch of tracks at once. The parameter tracks is a sequence of dicts per + track containing the keyword arguments as if passed to the scrobble() method. + """ + + tracks_to_scrobble = tracks[:50] + if len(tracks) > 50: + remaining_tracks = tracks[50:] + else: + remaining_tracks = None + + params = {} + for i in range(len(tracks_to_scrobble)): + + params["artist[%d]" % i] = tracks_to_scrobble[i]["artist"] + params["track[%d]" % i] = tracks_to_scrobble[i]["title"] + + additional_args = ("timestamp", "album", "album_artist", "context", "stream_id", "track_number", "mbid", "duration") + args_map_to = {"album_artist": "albumArtist", "track_number": "trackNumber", "stream_id": "streamID"} # so friggin lazy + + for arg in additional_args: + + if arg in tracks_to_scrobble[i] and tracks_to_scrobble[i][arg]: + if arg in args_map_to: + maps_to = args_map_to[arg] + else: + maps_to = arg + + params["%s[%d]" %(maps_to, i)] = tracks_to_scrobble[i][arg] + + + _Request(self, "track.scrobble", params).execute() + + if remaining_tracks: + self.scrobble_many(remaining_tracks) + +class LastFMNetwork(_Network): + + """A Last.fm network object + + api_key: a provided API_KEY + api_secret: a provided API_SECRET + session_key: a generated session_key or None + username: a username of a valid user + password_hash: the output of pylast.md5(password) where password is the user's password + + if username and password_hash were provided and not session_key, session_key will be + generated automatically when needed. + + Either a valid session_key or a combination of username and password_hash must be present for scrobbling. + + Most read-only webservices only require an api_key and an api_secret, see about obtaining them from: + http://www.last.fm/api/account + """ + + def __init__(self, api_key="", api_secret="", session_key="", username="", password_hash=""): + _Network.__init__(self, + name = "Last.fm", + homepage = "http://last.fm", + ws_server = ("ws.audioscrobbler.com", "/2.0/"), + api_key = api_key, + api_secret = api_secret, + session_key = session_key, + submission_server = "http://post.audioscrobbler.com:80/", + username = username, + password_hash = password_hash, + domain_names = { + DOMAIN_ENGLISH: 'www.last.fm', + DOMAIN_GERMAN: 'www.lastfm.de', + DOMAIN_SPANISH: 'www.lastfm.es', + DOMAIN_FRENCH: 'www.lastfm.fr', + DOMAIN_ITALIAN: 'www.lastfm.it', + DOMAIN_POLISH: 'www.lastfm.pl', + DOMAIN_PORTUGUESE: 'www.lastfm.com.br', + DOMAIN_SWEDISH: 'www.lastfm.se', + DOMAIN_TURKISH: 'www.lastfm.com.tr', + DOMAIN_RUSSIAN: 'www.lastfm.ru', + DOMAIN_JAPANESE: 'www.lastfm.jp', + DOMAIN_CHINESE: 'cn.last.fm', + }, + urls = { + "album": "music/%(artist)s/%(album)s", + "artist": "music/%(artist)s", + "event": "event/%(id)s", + "country": "place/%(country_name)s", + "playlist": "user/%(user)s/library/playlists/%(appendix)s", + "tag": "tag/%(name)s", + "track": "music/%(artist)s/_/%(title)s", + "group": "group/%(name)s", + "user": "user/%(name)s", + } + ) + + def __repr__(self): + return "pylast.LastFMNetwork(%s)" %(", ".join(("'%s'" %self.api_key, "'%s'" %self.api_secret, "'%s'" %self.session_key, + "'%s'" %self.username, "'%s'" %self.password_hash))) + + def __str__(self): + return "LastFM Network" + +def get_lastfm_network(api_key="", api_secret="", session_key = "", username = "", password_hash = ""): + """ + Returns a preconfigured _Network object for Last.fm + + api_key: a provided API_KEY + api_secret: a provided API_SECRET + session_key: a generated session_key or None + username: a username of a valid user + password_hash: the output of pylast.md5(password) where password is the user's password + + if username and password_hash were provided and not session_key, session_key will be + generated automatically when needed. + + Either a valid session_key or a combination of username and password_hash must be present for scrobbling. + + Most read-only webservices only require an api_key and an api_secret, see about obtaining them from: + http://www.last.fm/api/account + """ + + _deprecation_warning("Create a LastFMNetwork object instead") + + return LastFMNetwork(api_key, api_secret, session_key, username, password_hash) + +class LibreFMNetwork(_Network): + """ + A preconfigured _Network object for Libre.fm + + api_key: a provided API_KEY + api_secret: a provided API_SECRET + session_key: a generated session_key or None + username: a username of a valid user + password_hash: the output of pylast.md5(password) where password is the user's password + + if username and password_hash were provided and not session_key, session_key will be + generated automatically when needed. + """ + + def __init__(self, api_key="", api_secret="", session_key = "", username = "", password_hash = ""): + + _Network.__init__(self, + name = "Libre.fm", + homepage = "http://alpha.dev.libre.fm", + ws_server = ("alpha.dev.libre.fm", "/2.0/"), + api_key = api_key, + api_secret = api_secret, + session_key = session_key, + submission_server = "http://turtle.libre.fm:80/", + username = username, + password_hash = password_hash, + domain_names = { + DOMAIN_ENGLISH: "alpha.dev.libre.fm", + DOMAIN_GERMAN: "alpha.dev.libre.fm", + DOMAIN_SPANISH: "alpha.dev.libre.fm", + DOMAIN_FRENCH: "alpha.dev.libre.fm", + DOMAIN_ITALIAN: "alpha.dev.libre.fm", + DOMAIN_POLISH: "alpha.dev.libre.fm", + DOMAIN_PORTUGUESE: "alpha.dev.libre.fm", + DOMAIN_SWEDISH: "alpha.dev.libre.fm", + DOMAIN_TURKISH: "alpha.dev.libre.fm", + DOMAIN_RUSSIAN: "alpha.dev.libre.fm", + DOMAIN_JAPANESE: "alpha.dev.libre.fm", + DOMAIN_CHINESE: "alpha.dev.libre.fm", + }, + urls = { + "album": "artist/%(artist)s/album/%(album)s", + "artist": "artist/%(artist)s", + "event": "event/%(id)s", + "country": "place/%(country_name)s", + "playlist": "user/%(user)s/library/playlists/%(appendix)s", + "tag": "tag/%(name)s", + "track": "music/%(artist)s/_/%(title)s", + "group": "group/%(name)s", + "user": "user/%(name)s", + } + ) + + def __repr__(self): + return "pylast.LibreFMNetwork(%s)" %(", ".join(("'%s'" %self.api_key, "'%s'" %self.api_secret, "'%s'" %self.session_key, + "'%s'" %self.username, "'%s'" %self.password_hash))) + + def __str__(self): + return "Libre.fm Network" + +def get_librefm_network(api_key="", api_secret="", session_key = "", username = "", password_hash = ""): + """ + Returns a preconfigured _Network object for Libre.fm + + api_key: a provided API_KEY + api_secret: a provided API_SECRET + session_key: a generated session_key or None + username: a username of a valid user + password_hash: the output of pylast.md5(password) where password is the user's password + + if username and password_hash were provided and not session_key, session_key will be + generated automatically when needed. + """ + + _deprecation_warning("DeprecationWarning: Create a LibreFMNetwork object instead") + + return LibreFMNetwork(api_key, api_secret, session_key, username, password_hash) + +class _ShelfCacheBackend(object): + """Used as a backend for caching cacheable requests.""" + def __init__(self, file_path = None): + self.shelf = shelve.open(file_path) + + def get_xml(self, key): + return self.shelf[key] + + def set_xml(self, key, xml_string): + self.shelf[key] = xml_string + + def has_key(self, key): + return key in self.shelf.keys() + +class _Request(object): + """Representing an abstract web service operation.""" + + def __init__(self, network, method_name, params = {}): + + self.network = network + self.params = {} + + for key in params: + self.params[key] = _unicode(params[key]) + + (self.api_key, self.api_secret, self.session_key) = network._get_ws_auth() + + self.params["api_key"] = self.api_key + self.params["method"] = method_name + + if network.is_caching_enabled(): + self.cache = network._get_cache_backend() + + if self.session_key: + self.params["sk"] = self.session_key + self.sign_it() + + def sign_it(self): + """Sign this request.""" + + if not "api_sig" in self.params.keys(): + self.params['api_sig'] = self._get_signature() + + def _get_signature(self): + """Returns a 32-character hexadecimal md5 hash of the signature string.""" + + keys = list(self.params.keys()) + + keys.sort() + + string = "" + + for name in keys: + string += name + string += self.params[name] + + string += self.api_secret + + return md5(string) + + def _get_cache_key(self): + """The cache key is a string of concatenated sorted names and values.""" + + keys = list(self.params.keys()) + keys.sort() + + cache_key = str() + + for key in keys: + if key != "api_sig" and key != "api_key" and key != "sk": + cache_key += key + _string(self.params[key]) + + return hashlib.sha1(cache_key).hexdigest() + + def _get_cached_response(self): + """Returns a file object of the cached response.""" + + if not self._is_cached(): + response = self._download_response() + self.cache.set_xml(self._get_cache_key(), response) + + return self.cache.get_xml(self._get_cache_key()) + + def _is_cached(self): + """Returns True if the request is already in cache.""" + + return self.cache.has_key(self._get_cache_key()) + + def _download_response(self): + """Returns a response body string from the server.""" + + # Delay the call if necessary + #self.network._delay_call() # enable it if you want. + + data = [] + for name in self.params.keys(): + data.append('='.join((name, url_quote_plus(_string(self.params[name]))))) + data = '&'.join(data) + + headers = { + "Content-type": "application/x-www-form-urlencoded", + 'Accept-Charset': 'utf-8', + 'User-Agent': "pylast" + '/' + __version__ + } + + (HOST_NAME, HOST_SUBDIR) = self.network.ws_server + + if self.network.is_proxy_enabled(): + conn = HTTPConnection(host = self._get_proxy()[0], port = self._get_proxy()[1]) + + try: + conn.request(method='POST', url="http://" + HOST_NAME + HOST_SUBDIR, + body=data, headers=headers) + except Exception as e: + raise NetworkError(self.network, e) + + else: + conn = HTTPConnection(host=HOST_NAME) + + try: + conn.request(method='POST', url=HOST_SUBDIR, body=data, headers=headers) + except Exception as e: + raise NetworkError(self.network, e) + + try: + response_text = _unicode(conn.getresponse().read()) + except Exception as e: + raise MalformedResponseError(self.network, e) + + self._check_response_for_errors(response_text) + return response_text + + def execute(self, cacheable = False): + """Returns the XML DOM response of the POST Request from the server""" + + if self.network.is_caching_enabled() and cacheable: + response = self._get_cached_response() + else: + response = self._download_response() + + return minidom.parseString(_string(response)) + + def _check_response_for_errors(self, response): + """Checks the response for errors and raises one if any exists.""" + + try: + doc = minidom.parseString(_string(response)) + except Exception as e: + raise MalformedResponseError(self.network, e) + + e = doc.getElementsByTagName('lfm')[0] + + if e.getAttribute('status') != "ok": + e = doc.getElementsByTagName('error')[0] + status = e.getAttribute('code') + details = e.firstChild.data.strip() + raise WSError(self.network, status, details) + +class SessionKeyGenerator(object): + """Methods of generating a session key: + 1) Web Authentication: + a. network = get_*_network(API_KEY, API_SECRET) + b. sg = SessionKeyGenerator(network) + c. url = sg.get_web_auth_url() + d. Ask the user to open the url and authorize you, and wait for it. + e. session_key = sg.get_web_auth_session_key(url) + 2) Username and Password Authentication: + a. network = get_*_network(API_KEY, API_SECRET) + b. username = raw_input("Please enter your username: ") + c. password_hash = pylast.md5(raw_input("Please enter your password: ") + d. session_key = SessionKeyGenerator(network).get_session_key(username, password_hash) + + A session key's lifetime is infinie, unless the user provokes the rights of the given API Key. + + If you create a Network object with just a API_KEY and API_SECRET and a username and a password_hash, a + SESSION_KEY will be automatically generated for that network and stored in it so you don't have to do this + manually, unless you want to. + """ + + def __init__(self, network): + self.network = network + self.web_auth_tokens = {} + + def _get_web_auth_token(self): + """Retrieves a token from the network for web authentication. + The token then has to be authorized from getAuthURL before creating session. + """ + + request = _Request(self.network, 'auth.getToken') + + # default action is that a request is signed only when + # a session key is provided. + request.sign_it() + + doc = request.execute() + + e = doc.getElementsByTagName('token')[0] + return e.firstChild.data + + def get_web_auth_url(self): + """The user must open this page, and you first, then call get_web_auth_session_key(url) after that.""" + + token = self._get_web_auth_token() + + url = '%(homepage)s/api/auth/?api_key=%(api)s&token=%(token)s' % \ + {"homepage": self.network.homepage, "api": self.network.api_key, "token": token} + + self.web_auth_tokens[url] = token + + return url + + def get_web_auth_session_key(self, url): + """Retrieves the session key of a web authorization process by its url.""" + + if url in self.web_auth_tokens.keys(): + token = self.web_auth_tokens[url] + else: + token = "" #that's gonna raise a WSError of an unauthorized token when the request is executed. + + request = _Request(self.network, 'auth.getSession', {'token': token}) + + # default action is that a request is signed only when + # a session key is provided. + request.sign_it() + + doc = request.execute() + + return doc.getElementsByTagName('key')[0].firstChild.data + + def get_session_key(self, username, password_hash): + """Retrieve a session key with a username and a md5 hash of the user's password.""" + + params = {"username": username, "authToken": md5(username + password_hash)} + request = _Request(self.network, "auth.getMobileSession", params) + + # default action is that a request is signed only when + # a session key is provided. + request.sign_it() + + doc = request.execute() + + return _extract(doc, "key") + +TopItem = collections.namedtuple("TopItem", ["item", "weight"]) +SimilarItem = collections.namedtuple("SimilarItem", ["item", "match"]) +LibraryItem = collections.namedtuple("LibraryItem", ["item", "playcount", "tagcount"]) +PlayedTrack = collections.namedtuple("PlayedTrack", ["track", "playback_date", "timestamp"]) +LovedTrack = collections.namedtuple("LovedTrack", ["track", "date", "timestamp"]) +ImageSizes = collections.namedtuple("ImageSizes", ["original", "large", "largesquare", "medium", "small", "extralarge"]) +Image = collections.namedtuple("Image", ["title", "url", "dateadded", "format", "owner", "sizes", "votes"]) +Shout = collections.namedtuple("Shout", ["body", "author", "date"]) + +def _string_output(funct): + def r(*args): + return _string(funct(*args)) + + return r + +def _pad_list(given_list, desired_length, padding = None): + """ + Pads a list to be of the desired_length. + """ + + while len(given_list) < desired_length: + given_list.append(padding) + + return given_list + +class _BaseObject(object): + """An abstract webservices object.""" + + network = None + + def __init__(self, network): + self.network = network + + def _request(self, method_name, cacheable = False, params = None): + if not params: + params = self._get_params() + + return _Request(self.network, method_name, params).execute(cacheable) + + def _get_params(self): + """Returns the most common set of parameters between all objects.""" + + return {} + + def __hash__(self): + return hash(self.network) + \ + hash(str(type(self)) + "".join(list(self._get_params().keys()) + list(self._get_params().values())).lower()) + +class _Taggable(object): + """Common functions for classes with tags.""" + + def __init__(self, ws_prefix): + self.ws_prefix = ws_prefix + + def add_tags(self, tags): + """Adds one or several tags. + * tags: A sequence of tag names or Tag objects. + """ + + for tag in tags: + self.add_tag(tag) + + def add_tag(self, tag): + """Adds one tag. + * tag: a tag name or a Tag object. + """ + + if isinstance(tag, Tag): + tag = tag.get_name() + + params = self._get_params() + params['tags'] = tag + + self._request(self.ws_prefix + '.addTags', False, params) + + def remove_tag(self, tag): + """Remove a user's tag from this object.""" + + if isinstance(tag, Tag): + tag = tag.get_name() + + params = self._get_params() + params['tag'] = tag + + self._request(self.ws_prefix + '.removeTag', False, params) + + def get_tags(self): + """Returns a list of the tags set by the user to this object.""" + + # Uncacheable because it can be dynamically changed by the user. + params = self._get_params() + + doc = self._request(self.ws_prefix + '.getTags', False, params) + tag_names = _extract_all(doc, 'name') + tags = [] + for tag in tag_names: + tags.append(Tag(tag, self.network)) + + return tags + + def remove_tags(self, tags): + """Removes one or several tags from this object. + * tags: a sequence of tag names or Tag objects. + """ + + for tag in tags: + self.remove_tag(tag) + + def clear_tags(self): + """Clears all the user-set tags. """ + + self.remove_tags(*(self.get_tags())) + + def set_tags(self, tags): + """Sets this object's tags to only those tags. + * tags: a sequence of tag names or Tag objects. + """ + + c_old_tags = [] + old_tags = [] + c_new_tags = [] + new_tags = [] + + to_remove = [] + to_add = [] + + tags_on_server = self.get_tags() + + for tag in tags_on_server: + c_old_tags.append(tag.get_name().lower()) + old_tags.append(tag.get_name()) + + for tag in tags: + c_new_tags.append(tag.lower()) + new_tags.append(tag) + + for i in range(0, len(old_tags)): + if not c_old_tags[i] in c_new_tags: + to_remove.append(old_tags[i]) + + for i in range(0, len(new_tags)): + if not c_new_tags[i] in c_old_tags: + to_add.append(new_tags[i]) + + self.remove_tags(to_remove) + self.add_tags(to_add) + + def get_top_tags(self, limit=None): + """Returns a list of the most frequently used Tags on this object.""" + + doc = self._request(self.ws_prefix + '.getTopTags', True) + + elements = doc.getElementsByTagName('tag') + seq = [] + + for element in elements: + tag_name = _extract(element, 'name') + tagcount = _extract(element, 'count') + + seq.append(TopItem(Tag(tag_name, self.network), tagcount)) + + if limit: + seq = seq[:limit] + + return seq + +class WSError(Exception): + """Exception related to the Network web service""" + + def __init__(self, network, status, details): + self.status = status + self.details = details + self.network = network + + @_string_output + def __str__(self): + return self.details + + def get_id(self): + """Returns the exception ID, from one of the following: + STATUS_INVALID_SERVICE = 2 + STATUS_INVALID_METHOD = 3 + STATUS_AUTH_FAILED = 4 + STATUS_INVALID_FORMAT = 5 + STATUS_INVALID_PARAMS = 6 + STATUS_INVALID_RESOURCE = 7 + STATUS_TOKEN_ERROR = 8 + STATUS_INVALID_SK = 9 + STATUS_INVALID_API_KEY = 10 + STATUS_OFFLINE = 11 + STATUS_SUBSCRIBERS_ONLY = 12 + STATUS_TOKEN_UNAUTHORIZED = 14 + STATUS_TOKEN_EXPIRED = 15 + """ + + return self.status + +class MalformedResponseError(Exception): + """Exception conveying a malformed response from Last.fm.""" + + def __init__(self, network, underlying_error): + self.network = network + self.underlying_error = underlying_error + + def __str__(self): + return "Malformed response from Last.fm. Underlying error: %s" %str(self.underlying_error) + +class NetworkError(Exception): + """Exception conveying a problem in sending a request to Last.fm""" + + def __init__(self, network, underlying_error): + self.network = network + self.underlying_error = underlying_error + + def __str__(self): + return "NetworkError: %s" %str(self.underlying_error) + +class Album(_BaseObject, _Taggable): + """An album.""" + + title = None + artist = None + + def __init__(self, artist, title, network): + """ + Create an album instance. + # Parameters: + * artist: An artist name or an Artist object. + * title: The album title. + """ + + _BaseObject.__init__(self, network) + _Taggable.__init__(self, 'album') + + if isinstance(artist, Artist): + self.artist = artist + else: + self.artist = Artist(artist, self.network) + + self.title = title + + def __repr__(self): + return "pylast.Album(%s, %s, %s)" %(repr(self.artist.name), repr(self.title), repr(self.network)) + + @_string_output + def __str__(self): + return _unicode("%s - %s") %(self.get_artist().get_name(), self.get_title()) + + def __eq__(self, other): + return (self.get_title().lower() == other.get_title().lower()) and (self.get_artist().get_name().lower() == other.get_artist().get_name().lower()) + + def __ne__(self, other): + return (self.get_title().lower() != other.get_title().lower()) or (self.get_artist().get_name().lower() != other.get_artist().get_name().lower()) + + def _get_params(self): + return {'artist': self.get_artist().get_name(), 'album': self.get_title(), } + + def get_artist(self): + """Returns the associated Artist object.""" + + return self.artist + + def get_title(self): + """Returns the album title.""" + + return self.title + + def get_name(self): + """Returns the album title (alias to Album.get_title).""" + + return self.get_title() + + def get_release_date(self): + """Retruns the release date of the album.""" + + return _extract(self._request("album.getInfo", cacheable = True), "releasedate") + + def get_cover_image(self, size = COVER_EXTRA_LARGE): + """ + Returns a uri to the cover image + size can be one of: + COVER_EXTRA_LARGE + COVER_LARGE + COVER_MEDIUM + COVER_SMALL + """ + + return _extract_all(self._request("album.getInfo", cacheable = True), 'image')[size] + + def get_id(self): + """Returns the ID""" + + return _extract(self._request("album.getInfo", cacheable = True), "id") + + def get_playcount(self): + """Returns the number of plays on the network""" + + return _number(_extract(self._request("album.getInfo", cacheable = True), "playcount")) + + def get_listener_count(self): + """Returns the number of liteners on the network""" + + return _number(_extract(self._request("album.getInfo", cacheable = True), "listeners")) + + def get_top_tags(self, limit=None): + """Returns a list of the most-applied tags to this album.""" + + doc = self._request("album.getInfo", True) + e = doc.getElementsByTagName("toptags")[0] + + seq = [] + for name in _extract_all(e, "name"): + seq.append(Tag(name, self.network)) + + if limit: + seq = seq[:limit] + + return seq + + def get_tracks(self): + """Returns the list of Tracks on this album.""" + + uri = 'lastfm://playlist/album/%s' %self.get_id() + + return XSPF(uri, self.network).get_tracks() + + def get_mbid(self): + """Returns the MusicBrainz id of the album.""" + + return _extract(self._request("album.getInfo", cacheable = True), "mbid") + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the album page on the network. + # Parameters: + * domain_name str: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + artist = _url_safe(self.get_artist().get_name()) + album = _url_safe(self.get_title()) + + return self.network._get_url(domain_name, "album") %{'artist': artist, 'album': album} + + def get_wiki_published_date(self): + """Returns the date of publishing this version of the wiki.""" + + doc = self._request("album.getInfo", True) + + if len(doc.getElementsByTagName("wiki")) == 0: + return + + node = doc.getElementsByTagName("wiki")[0] + + return _extract(node, "published") + + def get_wiki_summary(self): + """Returns the summary of the wiki.""" + + doc = self._request("album.getInfo", True) + + if len(doc.getElementsByTagName("wiki")) == 0: + return + + node = doc.getElementsByTagName("wiki")[0] + + return _extract(node, "summary") + + def get_wiki_content(self): + """Returns the content of the wiki.""" + + doc = self._request("album.getInfo", True) + + if len(doc.getElementsByTagName("wiki")) == 0: + return + + node = doc.getElementsByTagName("wiki")[0] + + return _extract(node, "content") + +class Artist(_BaseObject, _Taggable): + """An artist.""" + + name = None + + def __init__(self, name, network): + """Create an artist object. + # Parameters: + * name str: The artist's name. + """ + + _BaseObject.__init__(self, network) + _Taggable.__init__(self, 'artist') + + self.name = name + + def __repr__(self): + return "pylast.Artist(%s, %s)" %(repr(self.get_name()), repr(self.network)) + + @_string_output + def __str__(self): + return self.get_name() + + def __eq__(self, other): + return self.get_name().lower() == other.get_name().lower() + + def __ne__(self, other): + return self.get_name().lower() != other.get_name().lower() + + def _get_params(self): + return {'artist': self.get_name()} + + def get_name(self, properly_capitalized=False): + """Returns the name of the artist. + If properly_capitalized was asserted then the name would be downloaded + overwriting the given one.""" + + if properly_capitalized: + self.name = _extract(self._request("artist.getInfo", True), "name") + + return self.name + + def get_cover_image(self, size = COVER_MEGA): + """ + Returns a uri to the cover image + size can be one of: + COVER_MEGA + COVER_EXTRA_LARGE + COVER_LARGE + COVER_MEDIUM + COVER_SMALL + """ + + return _extract_all(self._request("artist.getInfo", True), "image")[size] + + def get_playcount(self): + """Returns the number of plays on the network.""" + + return _number(_extract(self._request("artist.getInfo", True), "playcount")) + + def get_mbid(self): + """Returns the MusicBrainz ID of this artist.""" + + doc = self._request("artist.getInfo", True) + + return _extract(doc, "mbid") + + def get_listener_count(self): + """Returns the number of liteners on the network.""" + + if hasattr(self, "listener_count"): + return self.listener_count + else: + self.listener_count = _number(_extract(self._request("artist.getInfo", True), "listeners")) + return self.listener_count + + def is_streamable(self): + """Returns True if the artist is streamable.""" + + return bool(_number(_extract(self._request("artist.getInfo", True), "streamable"))) + + def get_bio_published_date(self): + """Returns the date on which the artist's biography was published.""" + + return _extract(self._request("artist.getInfo", True), "published") + + def get_bio_summary(self): + """Returns the summary of the artist's biography.""" + + return _extract(self._request("artist.getInfo", True), "summary") + + def get_bio_content(self): + """Returns the content of the artist's biography.""" + + return _extract(self._request("artist.getInfo", True), "content") + + def get_upcoming_events(self): + """Returns a list of the upcoming Events for this artist.""" + + doc = self._request('artist.getEvents', True) + + ids = _extract_all(doc, 'id') + + events = [] + for e_id in ids: + events.append(Event(e_id, self.network)) + + return events + + def get_similar(self, limit = None): + """Returns the similar artists on the network.""" + + params = self._get_params() + if limit: + params['limit'] = limit + + doc = self._request('artist.getSimilar', True, params) + + names = _extract_all(doc, "name") + matches = _extract_all(doc, "match") + + artists = [] + for i in range(0, len(names)): + artists.append(SimilarItem(Artist(names[i], self.network), _number(matches[i]))) + + return artists + + def compare_with_artist(self, artist, shared_artists_limit = None): + """Compare this artist with another Last.fm artist. + Seems like the tasteometer only returns stats on subsets of artist sets... + Returns a sequence (tasteometer_score, (shared_artist1, shared_artist2, ...)) + artist: A artist object or a artistname string/unicode object. + """ + + if isinstance(artist, Artist): + artist = artist.get_name() + + params = self._get_params() + if shared_artists_limit: + params['limit'] = shared_artists_limit + params['type1'] = 'artists' + params['type2'] = 'artists' + params['value1'] = self.get_name() + params['value2'] = artist + + doc = self._request('tasteometer.compare', False, params) + + score = _extract(doc, 'score') + + artists = doc.getElementsByTagName('artists')[0] + shared_artists_names = _extract_all(artists, 'name') + + shared_artists_seq = [] + + for name in shared_artists_names: + shared_artists_seq.append(Artist(name, self.network)) + + return (score, shared_artists_seq) + + def get_top_albums(self): + """Retuns a list of the top albums.""" + + doc = self._request('artist.getTopAlbums', True) + + seq = [] + + for node in doc.getElementsByTagName("album"): + name = _extract(node, "name") + artist = _extract(node, "name", 1) + playcount = _extract(node, "playcount") + + seq.append(TopItem(Album(artist, name, self.network), playcount)) + + return seq + + def get_top_tracks(self): + """Returns a list of the most played Tracks by this artist.""" + + doc = self._request("artist.getTopTracks", True) + + seq = [] + for track in doc.getElementsByTagName('track'): + + title = _extract(track, "name") + artist = _extract(track, "name", 1) + playcount = _number(_extract(track, "playcount")) + + seq.append( TopItem(Track(artist, title, self.network), playcount) ) + + return seq + + def get_top_fans(self, limit = None): + """Returns a list of the Users who played this artist the most. + # Parameters: + * limit int: Max elements. + """ + + doc = self._request('artist.getTopFans', True) + + seq = [] + + elements = doc.getElementsByTagName('user') + + for element in elements: + if limit and len(seq) >= limit: + break + + name = _extract(element, 'name') + weight = _number(_extract(element, 'weight')) + + seq.append(TopItem(User(name, self.network), weight)) + + return seq + + def share(self, users, message = None): + """Shares this artist (sends out recommendations). + # Parameters: + * users [User|str,]: A list that can contain usernames, emails, User objects, or all of them. + * message str: A message to include in the recommendation message. + """ + + #last.fm currently accepts a max of 10 recipient at a time + while(len(users) > 10): + section = users[0:9] + users = users[9:] + self.share(section, message) + + nusers = [] + for user in users: + if isinstance(user, User): + nusers.append(user.get_name()) + else: + nusers.append(user) + + params = self._get_params() + recipients = ','.join(nusers) + params['recipient'] = recipients + if message: + params['message'] = message + + self._request('artist.share', False, params) + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the artist page on the network. + # Parameters: + * domain_name: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + artist = _url_safe(self.get_name()) + + return self.network._get_url(domain_name, "artist") %{'artist': artist} + + def get_images(self, order=IMAGES_ORDER_POPULARITY, limit=None): + """ + Returns a sequence of Image objects + if limit is None it will return all + order can be IMAGES_ORDER_POPULARITY or IMAGES_ORDER_DATE. + + If limit==None, it will try to pull all the available data. + """ + + images = [] + + params = self._get_params() + params["order"] = order + nodes = _collect_nodes(limit, self, "artist.getImages", True, params) + for e in nodes: + if _extract(e, "name"): + user = User(_extract(e, "name"), self.network) + else: + user = None + + images.append(Image( + _extract(e, "title"), + _extract(e, "url"), + _extract(e, "dateadded"), + _extract(e, "format"), + user, + ImageSizes(*_extract_all(e, "size")), + (_extract(e, "thumbsup"), _extract(e, "thumbsdown")) + ) + ) + return images + + def get_shouts(self, limit=50): + """ + Returns a sequqence of Shout objects + """ + + shouts = [] + for node in _collect_nodes(limit, self, "artist.getShouts", False): + shouts.append(Shout( + _extract(node, "body"), + User(_extract(node, "author"), self.network), + _extract(node, "date") + ) + ) + return shouts + + def shout(self, message): + """ + Post a shout + """ + + params = self._get_params() + params["message"] = message + + self._request("artist.Shout", False, params) + + +class Event(_BaseObject): + """An event.""" + + id = None + + def __init__(self, event_id, network): + _BaseObject.__init__(self, network) + + self.id = event_id + + def __repr__(self): + return "pylast.Event(%s, %s)" %(repr(self.id), repr(self.network)) + + @_string_output + def __str__(self): + return "Event #" + self.get_id() + + def __eq__(self, other): + return self.get_id() == other.get_id() + + def __ne__(self, other): + return self.get_id() != other.get_id() + + def _get_params(self): + return {'event': self.get_id()} + + def attend(self, attending_status): + """Sets the attending status. + * attending_status: The attending status. Possible values: + o EVENT_ATTENDING + o EVENT_MAYBE_ATTENDING + o EVENT_NOT_ATTENDING + """ + + params = self._get_params() + params['status'] = attending_status + + self._request('event.attend', False, params) + + def get_attendees(self): + """ + Get a list of attendees for an event + """ + + doc = self._request("event.getAttendees", False) + + users = [] + for name in _extract_all(doc, "name"): + users.append(User(name, self.network)) + + return users + + def get_id(self): + """Returns the id of the event on the network. """ + + return self.id + + def get_title(self): + """Returns the title of the event. """ + + doc = self._request("event.getInfo", True) + + return _extract(doc, "title") + + def get_headliner(self): + """Returns the headliner of the event. """ + + doc = self._request("event.getInfo", True) + + return Artist(_extract(doc, "headliner"), self.network) + + def get_artists(self): + """Returns a list of the participating Artists. """ + + doc = self._request("event.getInfo", True) + names = _extract_all(doc, "artist") + + artists = [] + for name in names: + artists.append(Artist(name, self.network)) + + return artists + + def get_venue(self): + """Returns the venue where the event is held.""" + + doc = self._request("event.getInfo", True) + + v = doc.getElementsByTagName("venue")[0] + venue_id = _number(_extract(v, "id")) + + return Venue(venue_id, self.network) + + def get_start_date(self): + """Returns the date when the event starts.""" + + doc = self._request("event.getInfo", True) + + return _extract(doc, "startDate") + + def get_description(self): + """Returns the description of the event. """ + + doc = self._request("event.getInfo", True) + + return _extract(doc, "description") + + def get_cover_image(self, size = COVER_MEGA): + """ + Returns a uri to the cover image + size can be one of: + COVER_MEGA + COVER_EXTRA_LARGE + COVER_LARGE + COVER_MEDIUM + COVER_SMALL + """ + + doc = self._request("event.getInfo", True) + + return _extract_all(doc, "image")[size] + + def get_attendance_count(self): + """Returns the number of attending people. """ + + doc = self._request("event.getInfo", True) + + return _number(_extract(doc, "attendance")) + + def get_review_count(self): + """Returns the number of available reviews for this event. """ + + doc = self._request("event.getInfo", True) + + return _number(_extract(doc, "reviews")) + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the event page on the network. + * domain_name: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + return self.network._get_url(domain_name, "event") %{'id': self.get_id()} + + def share(self, users, message = None): + """Shares this event (sends out recommendations). + * users: A list that can contain usernames, emails, User objects, or all of them. + * message: A message to include in the recommendation message. + """ + + #last.fm currently accepts a max of 10 recipient at a time + while(len(users) > 10): + section = users[0:9] + users = users[9:] + self.share(section, message) + + nusers = [] + for user in users: + if isinstance(user, User): + nusers.append(user.get_name()) + else: + nusers.append(user) + + params = self._get_params() + recipients = ','.join(nusers) + params['recipient'] = recipients + if message: + params['message'] = message + + self._request('event.share', False, params) + + def get_shouts(self, limit=50): + """ + Returns a sequqence of Shout objects + """ + + shouts = [] + for node in _collect_nodes(limit, self, "event.getShouts", False): + shouts.append(Shout( + _extract(node, "body"), + User(_extract(node, "author"), self.network), + _extract(node, "date") + ) + ) + return shouts + + def shout(self, message): + """ + Post a shout + """ + + params = self._get_params() + params["message"] = message + + self._request("event.Shout", False, params) + +class Country(_BaseObject): + """A country at Last.fm.""" + + name = None + + def __init__(self, name, network): + _BaseObject.__init__(self, network) + + self.name = name + + def __repr__(self): + return "pylast.Country(%s, %s)" %(repr(self.name), repr(self.network)) + + @_string_output + def __str__(self): + return self.get_name() + + def __eq__(self, other): + return self.get_name().lower() == other.get_name().lower() + + def __ne__(self, other): + return self.get_name() != other.get_name() + + def _get_params(self): + return {'country': self.get_name()} + + def _get_name_from_code(self, alpha2code): + # TODO: Have this function lookup the alpha-2 code and return the country name. + + return alpha2code + + def get_name(self): + """Returns the country name. """ + + return self.name + + def get_top_artists(self): + """Returns a sequence of the most played artists.""" + + doc = self._request('geo.getTopArtists', True) + + seq = [] + for node in doc.getElementsByTagName("artist"): + name = _extract(node, 'name') + playcount = _extract(node, "playcount") + + seq.append(TopItem(Artist(name, self.network), playcount)) + + return seq + + def get_top_tracks(self): + """Returns a sequence of the most played tracks""" + + doc = self._request("geo.getTopTracks", True) + + seq = [] + + for n in doc.getElementsByTagName('track'): + + title = _extract(n, 'name') + artist = _extract(n, 'name', 1) + playcount = _number(_extract(n, "playcount")) + + seq.append( TopItem(Track(artist, title, self.network), playcount)) + + return seq + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the event page on the network. + * domain_name: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + country_name = _url_safe(self.get_name()) + + return self.network._get_url(domain_name, "country") %{'country_name': country_name} + + +class Library(_BaseObject): + """A user's Last.fm library.""" + + user = None + + def __init__(self, user, network): + _BaseObject.__init__(self, network) + + if isinstance(user, User): + self.user = user + else: + self.user = User(user, self.network) + + self._albums_index = 0 + self._artists_index = 0 + self._tracks_index = 0 + + def __repr__(self): + return "pylast.Library(%s, %s)" %(repr(self.user), repr(self.network)) + + @_string_output + def __str__(self): + return repr(self.get_user()) + "'s Library" + + def _get_params(self): + return {'user': self.user.get_name()} + + def get_user(self): + """Returns the user who owns this library.""" + + return self.user + + def add_album(self, album): + """Add an album to this library.""" + + params = self._get_params() + params["artist"] = album.get_artist.get_name() + params["album"] = album.get_name() + + self._request("library.addAlbum", False, params) + + def add_artist(self, artist): + """Add an artist to this library.""" + + params = self._get_params() + params["artist"] = artist.get_name() + + self._request("library.addArtist", False, params) + + def add_track(self, track): + """Add a track to this library.""" + + params = self._get_params() + params["track"] = track.get_title() + + self._request("library.addTrack", False, params) + + def get_albums(self, artist=None, limit=50): + """ + Returns a sequence of Album objects + If no artist is specified, it will return all, sorted by playcount descendingly. + If limit==None it will return all (may take a while) + """ + + params = self._get_params() + if artist: + params["artist"] = artist + + seq = [] + for node in _collect_nodes(limit, self, "library.getAlbums", True, params): + name = _extract(node, "name") + artist = _extract(node, "name", 1) + playcount = _number(_extract(node, "playcount")) + tagcount = _number(_extract(node, "tagcount")) + + seq.append(LibraryItem(Album(artist, name, self.network), playcount, tagcount)) + + return seq + + def get_artists(self, limit=50): + """ + Returns a sequence of Album objects + if limit==None it will return all (may take a while) + """ + + seq = [] + for node in _collect_nodes(limit, self, "library.getArtists", True): + name = _extract(node, "name") + + playcount = _number(_extract(node, "playcount")) + tagcount = _number(_extract(node, "tagcount")) + + seq.append(LibraryItem(Artist(name, self.network), playcount, tagcount)) + + return seq + + def get_tracks(self, artist=None, album=None, limit=50): + """ + Returns a sequence of Album objects + If limit==None it will return all (may take a while) + """ + + params = self._get_params() + if artist: + params["artist"] = artist + if album: + params["album"] = album + + seq = [] + for node in _collect_nodes(limit, self, "library.getTracks", True, params): + name = _extract(node, "name") + artist = _extract(node, "name", 1) + playcount = _number(_extract(node, "playcount")) + tagcount = _number(_extract(node, "tagcount")) + + seq.append(LibraryItem(Track(artist, name, self.network), playcount, tagcount)) + + return seq + + +class Playlist(_BaseObject): + """A Last.fm user playlist.""" + + id = None + user = None + + def __init__(self, user, id, network): + _BaseObject.__init__(self, network) + + if isinstance(user, User): + self.user = user + else: + self.user = User(user, self.network) + + self.id = id + + @_string_output + def __str__(self): + return repr(self.user) + "'s playlist # " + repr(self.id) + + def _get_info_node(self): + """Returns the node from user.getPlaylists where this playlist's info is.""" + + doc = self._request("user.getPlaylists", True) + + for node in doc.getElementsByTagName("playlist"): + if _extract(node, "id") == str(self.get_id()): + return node + + def _get_params(self): + return {'user': self.user.get_name(), 'playlistID': self.get_id()} + + def get_id(self): + """Returns the playlist id.""" + + return self.id + + def get_user(self): + """Returns the owner user of this playlist.""" + + return self.user + + def get_tracks(self): + """Returns a list of the tracks on this user playlist.""" + + uri = _unicode('lastfm://playlist/%s') %self.get_id() + + return XSPF(uri, self.network).get_tracks() + + def add_track(self, track): + """Adds a Track to this Playlist.""" + + params = self._get_params() + params['artist'] = track.get_artist().get_name() + params['track'] = track.get_title() + + self._request('playlist.addTrack', False, params) + + def get_title(self): + """Returns the title of this playlist.""" + + return _extract(self._get_info_node(), "title") + + def get_creation_date(self): + """Returns the creation date of this playlist.""" + + return _extract(self._get_info_node(), "date") + + def get_size(self): + """Returns the number of tracks in this playlist.""" + + return _number(_extract(self._get_info_node(), "size")) + + def get_description(self): + """Returns the description of this playlist.""" + + return _extract(self._get_info_node(), "description") + + def get_duration(self): + """Returns the duration of this playlist in milliseconds.""" + + return _number(_extract(self._get_info_node(), "duration")) + + def is_streamable(self): + """Returns True if the playlist is streamable. + For a playlist to be streamable, it needs at least 45 tracks by 15 different artists.""" + + if _extract(self._get_info_node(), "streamable") == '1': + return True + else: + return False + + def has_track(self, track): + """Checks to see if track is already in the playlist. + * track: Any Track object. + """ + + return track in self.get_tracks() + + def get_cover_image(self, size = COVER_EXTRA_LARGE): + """ + Returns a uri to the cover image + size can be one of: + COVER_MEGA + COVER_EXTRA_LARGE + COVER_LARGE + COVER_MEDIUM + COVER_SMALL + """ + + return _extract(self._get_info_node(), "image")[size] + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the playlist on the network. + * domain_name: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + english_url = _extract(self._get_info_node(), "url") + appendix = english_url[english_url.rfind("/") + 1:] + + return self.network._get_url(domain_name, "playlist") %{'appendix': appendix, "user": self.get_user().get_name()} + + +class Tag(_BaseObject): + """A Last.fm object tag.""" + + # TODO: getWeeklyArtistChart (too lazy, i'll wait for when someone requests it) + + name = None + + def __init__(self, name, network): + _BaseObject.__init__(self, network) + + self.name = name + + def __repr__(self): + return "pylast.Tag(%s, %s)" %(repr(self.name), repr(self.network)) + + @_string_output + def __str__(self): + return self.get_name() + + def __eq__(self, other): + return self.get_name().lower() == other.get_name().lower() + + def __ne__(self, other): + return self.get_name().lower() != other.get_name().lower() + + def _get_params(self): + return {'tag': self.get_name()} + + def get_name(self, properly_capitalized=False): + """Returns the name of the tag. """ + + if properly_capitalized: + self.name = _extract(self._request("tag.getInfo", True), "name") + + return self.name + + def get_similar(self): + """Returns the tags similar to this one, ordered by similarity. """ + + doc = self._request('tag.getSimilar', True) + + seq = [] + names = _extract_all(doc, 'name') + for name in names: + seq.append(Tag(name, self.network)) + + return seq + + def get_top_albums(self): + """Retuns a list of the top albums.""" + + doc = self._request('tag.getTopAlbums', True) + + seq = [] + + for node in doc.getElementsByTagName("album"): + name = _extract(node, "name") + artist = _extract(node, "name", 1) + playcount = _extract(node, "playcount") + + seq.append(TopItem(Album(artist, name, self.network), playcount)) + + return seq + + def get_top_tracks(self): + """Returns a list of the most played Tracks by this artist.""" + + doc = self._request("tag.getTopTracks", True) + + seq = [] + for track in doc.getElementsByTagName('track'): + + title = _extract(track, "name") + artist = _extract(track, "name", 1) + playcount = _number(_extract(track, "playcount")) + + seq.append( TopItem(Track(artist, title, self.network), playcount) ) + + return seq + + def get_top_artists(self): + """Returns a sequence of the most played artists.""" + + doc = self._request('tag.getTopArtists', True) + + seq = [] + for node in doc.getElementsByTagName("artist"): + name = _extract(node, 'name') + playcount = _extract(node, "playcount") + + seq.append(TopItem(Artist(name, self.network), playcount)) + + return seq + + def get_weekly_chart_dates(self): + """Returns a list of From and To tuples for the available charts.""" + + doc = self._request("tag.getWeeklyChartList", True) + + seq = [] + for node in doc.getElementsByTagName("chart"): + seq.append( (node.getAttribute("from"), node.getAttribute("to")) ) + + return seq + + def get_weekly_artist_charts(self, from_date = None, to_date = None): + """Returns the weekly artist charts for the week starting from the from_date value to the to_date value.""" + + params = self._get_params() + if from_date and to_date: + params["from"] = from_date + params["to"] = to_date + + doc = self._request("tag.getWeeklyArtistChart", True, params) + + seq = [] + for node in doc.getElementsByTagName("artist"): + item = Artist(_extract(node, "name"), self.network) + weight = _number(_extract(node, "weight")) + seq.append(TopItem(item, weight)) + + return seq + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the tag page on the network. + * domain_name: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + name = _url_safe(self.get_name()) + + return self.network._get_url(domain_name, "tag") %{'name': name} + +class Track(_BaseObject, _Taggable): + """A Last.fm track.""" + + artist = None + title = None + + def __init__(self, artist, title, network): + _BaseObject.__init__(self, network) + _Taggable.__init__(self, 'track') + + if isinstance(artist, Artist): + self.artist = artist + else: + self.artist = Artist(artist, self.network) + + self.title = title + + def __repr__(self): + return "pylast.Track(%s, %s, %s)" %(repr(self.artist.name), repr(self.title), repr(self.network)) + + @_string_output + def __str__(self): + return self.get_artist().get_name() + ' - ' + self.get_title() + + def __eq__(self, other): + return (self.get_title().lower() == other.get_title().lower()) and (self.get_artist().get_name().lower() == other.get_artist().get_name().lower()) + + def __ne__(self, other): + return (self.get_title().lower() != other.get_title().lower()) or (self.get_artist().get_name().lower() != other.get_artist().get_name().lower()) + + def _get_params(self): + return {'artist': self.get_artist().get_name(), 'track': self.get_title()} + + def get_artist(self): + """Returns the associated Artist object.""" + + return self.artist + + def get_title(self, properly_capitalized=False): + """Returns the track title.""" + + if properly_capitalized: + self.title = _extract(self._request("track.getInfo", True), "name") + + return self.title + + def get_name(self, properly_capitalized=False): + """Returns the track title (alias to Track.get_title).""" + + return self.get_title(properly_capitalized) + + def get_id(self): + """Returns the track id on the network.""" + + doc = self._request("track.getInfo", True) + + return _extract(doc, "id") + + def get_duration(self): + """Returns the track duration.""" + + doc = self._request("track.getInfo", True) + + return _number(_extract(doc, "duration")) + + def get_mbid(self): + """Returns the MusicBrainz ID of this track.""" + + doc = self._request("track.getInfo", True) + + return _extract(doc, "mbid") + + def get_listener_count(self): + """Returns the listener count.""" + + if hasattr(self, "listener_count"): + return self.listener_count + else: + doc = self._request("track.getInfo", True) + self.listener_count = _number(_extract(doc, "listeners")) + return self.listener_count + + def get_playcount(self): + """Returns the play count.""" + + doc = self._request("track.getInfo", True) + return _number(_extract(doc, "playcount")) + + def is_streamable(self): + """Returns True if the track is available at Last.fm.""" + + doc = self._request("track.getInfo", True) + return _extract(doc, "streamable") == "1" + + def is_fulltrack_available(self): + """Returns True if the fulltrack is available for streaming.""" + + doc = self._request("track.getInfo", True) + return doc.getElementsByTagName("streamable")[0].getAttribute("fulltrack") == "1" + + def get_album(self): + """Returns the album object of this track.""" + + doc = self._request("track.getInfo", True) + + albums = doc.getElementsByTagName("album") + + if len(albums) == 0: + return + + node = doc.getElementsByTagName("album")[0] + return Album(_extract(node, "artist"), _extract(node, "title"), self.network) + + def get_wiki_published_date(self): + """Returns the date of publishing this version of the wiki.""" + + doc = self._request("track.getInfo", True) + + if len(doc.getElementsByTagName("wiki")) == 0: + return + + node = doc.getElementsByTagName("wiki")[0] + + return _extract(node, "published") + + def get_wiki_summary(self): + """Returns the summary of the wiki.""" + + doc = self._request("track.getInfo", True) + + if len(doc.getElementsByTagName("wiki")) == 0: + return + + node = doc.getElementsByTagName("wiki")[0] + + return _extract(node, "summary") + + def get_wiki_content(self): + """Returns the content of the wiki.""" + + doc = self._request("track.getInfo", True) + + if len(doc.getElementsByTagName("wiki")) == 0: + return + + node = doc.getElementsByTagName("wiki")[0] + + return _extract(node, "content") + + def love(self): + """Adds the track to the user's loved tracks. """ + + self._request('track.love') + + def ban(self): + """Ban this track from ever playing on the radio. """ + + self._request('track.ban') + + def get_similar(self): + """Returns similar tracks for this track on the network, based on listening data. """ + + doc = self._request('track.getSimilar', True) + + seq = [] + for node in doc.getElementsByTagName("track"): + title = _extract(node, 'name') + artist = _extract(node, 'name', 1) + match = _number(_extract(node, "match")) + + seq.append(SimilarItem(Track(artist, title, self.network), match)) + + return seq + + def get_top_fans(self, limit = None): + """Returns a list of the Users who played this track.""" + + doc = self._request('track.getTopFans', True) + + seq = [] + + elements = doc.getElementsByTagName('user') + + for element in elements: + if limit and len(seq) >= limit: + break + + name = _extract(element, 'name') + weight = _number(_extract(element, 'weight')) + + seq.append(TopItem(User(name, self.network), weight)) + + return seq + + def share(self, users, message = None): + """Shares this track (sends out recommendations). + * users: A list that can contain usernames, emails, User objects, or all of them. + * message: A message to include in the recommendation message. + """ + + #last.fm currently accepts a max of 10 recipient at a time + while(len(users) > 10): + section = users[0:9] + users = users[9:] + self.share(section, message) + + nusers = [] + for user in users: + if isinstance(user, User): + nusers.append(user.get_name()) + else: + nusers.append(user) + + params = self._get_params() + recipients = ','.join(nusers) + params['recipient'] = recipients + if message: + params['message'] = message + + self._request('track.share', False, params) + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the track page on the network. + * domain_name: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + artist = _url_safe(self.get_artist().get_name()) + title = _url_safe(self.get_title()) + + return self.network._get_url(domain_name, "track") %{'domain': self.network._get_language_domain(domain_name), 'artist': artist, 'title': title} + + def get_shouts(self, limit=50): + """ + Returns a sequqence of Shout objects + """ + + shouts = [] + for node in _collect_nodes(limit, self, "track.getShouts", False): + shouts.append(Shout( + _extract(node, "body"), + User(_extract(node, "author"), self.network), + _extract(node, "date") + ) + ) + return shouts + +class Group(_BaseObject): + """A Last.fm group.""" + + name = None + + def __init__(self, group_name, network): + _BaseObject.__init__(self, network) + + self.name = group_name + + def __repr__(self): + return "pylast.Group(%s, %s)" %(repr(self.name), repr(self.network)) + + @_string_output + def __str__(self): + return self.get_name() + + def __eq__(self, other): + return self.get_name().lower() == other.get_name().lower() + + def __ne__(self, other): + return self.get_name() != other.get_name() + + def _get_params(self): + return {'group': self.get_name()} + + def get_name(self): + """Returns the group name. """ + return self.name + + def get_weekly_chart_dates(self): + """Returns a list of From and To tuples for the available charts.""" + + doc = self._request("group.getWeeklyChartList", True) + + seq = [] + for node in doc.getElementsByTagName("chart"): + seq.append( (node.getAttribute("from"), node.getAttribute("to")) ) + + return seq + + def get_weekly_artist_charts(self, from_date = None, to_date = None): + """Returns the weekly artist charts for the week starting from the from_date value to the to_date value.""" + + params = self._get_params() + if from_date and to_date: + params["from"] = from_date + params["to"] = to_date + + doc = self._request("group.getWeeklyArtistChart", True, params) + + seq = [] + for node in doc.getElementsByTagName("artist"): + item = Artist(_extract(node, "name"), self.network) + weight = _number(_extract(node, "playcount")) + seq.append(TopItem(item, weight)) + + return seq + + def get_weekly_album_charts(self, from_date = None, to_date = None): + """Returns the weekly album charts for the week starting from the from_date value to the to_date value.""" + + params = self._get_params() + if from_date and to_date: + params["from"] = from_date + params["to"] = to_date + + doc = self._request("group.getWeeklyAlbumChart", True, params) + + seq = [] + for node in doc.getElementsByTagName("album"): + item = Album(_extract(node, "artist"), _extract(node, "name"), self.network) + weight = _number(_extract(node, "playcount")) + seq.append(TopItem(item, weight)) + + return seq + + def get_weekly_track_charts(self, from_date = None, to_date = None): + """Returns the weekly track charts for the week starting from the from_date value to the to_date value.""" + + params = self._get_params() + if from_date and to_date: + params["from"] = from_date + params["to"] = to_date + + doc = self._request("group.getWeeklyTrackChart", True, params) + + seq = [] + for node in doc.getElementsByTagName("track"): + item = Track(_extract(node, "artist"), _extract(node, "name"), self.network) + weight = _number(_extract(node, "playcount")) + seq.append(TopItem(item, weight)) + + return seq + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the group page on the network. + * domain_name: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + name = _url_safe(self.get_name()) + + return self.network._get_url(domain_name, "group") %{'name': name} + + def get_members(self, limit=50): + """ + Returns a sequence of User objects + if limit==None it will return all + """ + + nodes = _collect_nodes(limit, self, "group.getMembers", False) + + users = [] + + for node in nodes: + users.append(User(_extract(node, "name"), self.network)) + + return users + +class XSPF(_BaseObject): + "A Last.fm XSPF playlist.""" + + uri = None + + def __init__(self, uri, network): + _BaseObject.__init__(self, network) + + self.uri = uri + + def _get_params(self): + return {'playlistURL': self.get_uri()} + + @_string_output + def __str__(self): + return self.get_uri() + + def __eq__(self, other): + return self.get_uri() == other.get_uri() + + def __ne__(self, other): + return self.get_uri() != other.get_uri() + + def get_uri(self): + """Returns the Last.fm playlist URI. """ + + return self.uri + + def get_tracks(self): + """Returns the tracks on this playlist.""" + + doc = self._request('playlist.fetch', True) + + seq = [] + for n in doc.getElementsByTagName('track'): + title = _extract(n, 'title') + artist = _extract(n, 'creator') + + seq.append(Track(artist, title, self.network)) + + return seq + +class User(_BaseObject): + """A Last.fm user.""" + + name = None + + def __init__(self, user_name, network): + _BaseObject.__init__(self, network) + + self.name = user_name + + self._past_events_index = 0 + self._recommended_events_index = 0 + self._recommended_artists_index = 0 + + def __repr__(self): + return "pylast.User(%s, %s)" %(repr(self.name), repr(self.network)) + + @_string_output + def __str__(self): + return self.get_name() + + def __eq__(self, another): + return self.get_name() == another.get_name() + + def __ne__(self, another): + return self.get_name() != another.get_name() + + def _get_params(self): + return {"user": self.get_name()} + + def get_name(self, properly_capitalized=False): + """Returns the nuser name.""" + + if properly_capitalized: + self.name = _extract(self._request("user.getInfo", True), "name") + + return self.name + + def get_upcoming_events(self): + """Returns all the upcoming events for this user. """ + + doc = self._request('user.getEvents', True) + + ids = _extract_all(doc, 'id') + events = [] + + for e_id in ids: + events.append(Event(e_id, self.network)) + + return events + + def get_friends(self, limit = 50): + """Returns a list of the user's friends. """ + + seq = [] + for node in _collect_nodes(limit, self, "user.getFriends", False): + seq.append(User(_extract(node, "name"), self.network)) + + return seq + + def get_loved_tracks(self, limit=50): + """Returns this user's loved track as a sequence of LovedTrack objects + in reverse order of their timestamp, all the way back to the first track. + + If limit==None, it will try to pull all the available data. + + This method uses caching. Enable caching only if you're pulling a + large amount of data. + + Use extract_items() with the return of this function to + get only a sequence of Track objects with no playback dates. """ + + params = self._get_params() + if limit: + params['limit'] = limit + + seq = [] + for track in _collect_nodes(limit, self, "user.getLovedTracks", True, params): + + title = _extract(track, "name") + artist = _extract(track, "name", 1) + date = _extract(track, "date") + timestamp = track.getElementsByTagName("date")[0].getAttribute("uts") + + seq.append(LovedTrack(Track(artist, title, self.network), date, timestamp)) + + return seq + + def get_neighbours(self, limit = 50): + """Returns a list of the user's friends.""" + + params = self._get_params() + if limit: + params['limit'] = limit + + doc = self._request('user.getNeighbours', True, params) + + seq = [] + names = _extract_all(doc, 'name') + + for name in names: + seq.append(User(name, self.network)) + + return seq + + def get_past_events(self, limit=50): + """ + Returns a sequence of Event objects + if limit==None it will return all + """ + + seq = [] + for n in _collect_nodes(limit, self, "user.getPastEvents", False): + seq.append(Event(_extract(n, "id"), self.network)) + + return seq + + def get_playlists(self): + """Returns a list of Playlists that this user owns.""" + + doc = self._request("user.getPlaylists", True) + + playlists = [] + for playlist_id in _extract_all(doc, "id"): + playlists.append(Playlist(self.get_name(), playlist_id, self.network)) + + return playlists + + def get_now_playing(self): + """Returns the currently playing track, or None if nothing is playing. """ + + params = self._get_params() + params['limit'] = '1' + + doc = self._request('user.getRecentTracks', False, params) + + e = doc.getElementsByTagName('track')[0] + + if not e.hasAttribute('nowplaying'): + return None + + artist = _extract(e, 'artist') + title = _extract(e, 'name') + + return Track(artist, title, self.network) + + + def get_recent_tracks(self, limit = 10): + """Returns this user's played track as a sequence of PlayedTrack objects + in reverse order of their playtime, all the way back to the first track. + + If limit==None, it will try to pull all the available data. + + This method uses caching. Enable caching only if you're pulling a + large amount of data. + + Use extract_items() with the return of this function to + get only a sequence of Track objects with no playback dates. """ + + params = self._get_params() + if limit: + params['limit'] = limit + + seq = [] + for track in _collect_nodes(limit, self, "user.getRecentTracks", True, params): + + if track.hasAttribute('nowplaying'): + continue #to prevent the now playing track from sneaking in here + + title = _extract(track, "name") + artist = _extract(track, "artist") + date = _extract(track, "date") + timestamp = track.getElementsByTagName("date")[0].getAttribute("uts") + + seq.append(PlayedTrack(Track(artist, title, self.network), date, timestamp)) + + return seq + + def get_id(self): + """Returns the user id.""" + + doc = self._request("user.getInfo", True) + + return _extract(doc, "id") + + def get_language(self): + """Returns the language code of the language used by the user.""" + + doc = self._request("user.getInfo", True) + + return _extract(doc, "lang") + + def get_country(self): + """Returns the name of the country of the user.""" + + doc = self._request("user.getInfo", True) + + return Country(_extract(doc, "country"), self.network) + + def get_age(self): + """Returns the user's age.""" + + doc = self._request("user.getInfo", True) + + return _number(_extract(doc, "age")) + + def get_gender(self): + """Returns the user's gender. Either USER_MALE or USER_FEMALE.""" + + doc = self._request("user.getInfo", True) + + value = _extract(doc, "gender") + + if value == 'm': + return USER_MALE + elif value == 'f': + return USER_FEMALE + + return None + + def is_subscriber(self): + """Returns whether the user is a subscriber or not. True or False.""" + + doc = self._request("user.getInfo", True) + + return _extract(doc, "subscriber") == "1" + + def get_playcount(self): + """Returns the user's playcount so far.""" + + doc = self._request("user.getInfo", True) + + return _number(_extract(doc, "playcount")) + + def get_top_albums(self, period = PERIOD_OVERALL): + """Returns the top albums played by a user. + * period: The period of time. Possible values: + o PERIOD_OVERALL + o PERIOD_7DAYS + o PERIOD_3MONTHS + o PERIOD_6MONTHS + o PERIOD_12MONTHS + """ + + params = self._get_params() + params['period'] = period + + doc = self._request('user.getTopAlbums', True, params) + + seq = [] + for album in doc.getElementsByTagName('album'): + name = _extract(album, 'name') + artist = _extract(album, 'name', 1) + playcount = _extract(album, "playcount") + + seq.append(TopItem(Album(artist, name, self.network), playcount)) + + return seq + + def get_top_artists(self, period = PERIOD_OVERALL): + """Returns the top artists played by a user. + * period: The period of time. Possible values: + o PERIOD_OVERALL + o PERIOD_7DAYS + o PERIOD_3MONTHS + o PERIOD_6MONTHS + o PERIOD_12MONTHS + """ + + params = self._get_params() + params['period'] = period + + doc = self._request('user.getTopArtists', True, params) + + seq = [] + for node in doc.getElementsByTagName('artist'): + name = _extract(node, 'name') + playcount = _extract(node, "playcount") + + seq.append(TopItem(Artist(name, self.network), playcount)) + + return seq + + def get_top_tags(self, limit=None): + """Returns a sequence of the top tags used by this user with their counts as TopItem objects. + * limit: The limit of how many tags to return. + """ + + doc = self._request("user.getTopTags", True) + + seq = [] + for node in doc.getElementsByTagName("tag"): + seq.append(TopItem(Tag(_extract(node, "name"), self.network), _extract(node, "count"))) + + if limit: + seq = seq[:limit] + + return seq + + def get_top_tracks(self, period = PERIOD_OVERALL): + """Returns the top tracks played by a user. + * period: The period of time. Possible values: + o PERIOD_OVERALL + o PERIOD_7DAYS + o PERIOD_3MONTHS + o PERIOD_6MONTHS + o PERIOD_12MONTHS + """ + + params = self._get_params() + params['period'] = period + + doc = self._request('user.getTopTracks', True, params) + + seq = [] + for track in doc.getElementsByTagName('track'): + name = _extract(track, 'name') + artist = _extract(track, 'name', 1) + playcount = _extract(track, "playcount") + + seq.append(TopItem(Track(artist, name, self.network), playcount)) + + return seq + + def get_weekly_chart_dates(self): + """Returns a list of From and To tuples for the available charts.""" + + doc = self._request("user.getWeeklyChartList", True) + + seq = [] + for node in doc.getElementsByTagName("chart"): + seq.append( (node.getAttribute("from"), node.getAttribute("to")) ) + + return seq + + def get_weekly_artist_charts(self, from_date = None, to_date = None): + """Returns the weekly artist charts for the week starting from the from_date value to the to_date value.""" + + params = self._get_params() + if from_date and to_date: + params["from"] = from_date + params["to"] = to_date + + doc = self._request("user.getWeeklyArtistChart", True, params) + + seq = [] + for node in doc.getElementsByTagName("artist"): + item = Artist(_extract(node, "name"), self.network) + weight = _number(_extract(node, "playcount")) + seq.append(TopItem(item, weight)) + + return seq + + def get_weekly_album_charts(self, from_date = None, to_date = None): + """Returns the weekly album charts for the week starting from the from_date value to the to_date value.""" + + params = self._get_params() + if from_date and to_date: + params["from"] = from_date + params["to"] = to_date + + doc = self._request("user.getWeeklyAlbumChart", True, params) + + seq = [] + for node in doc.getElementsByTagName("album"): + item = Album(_extract(node, "artist"), _extract(node, "name"), self.network) + weight = _number(_extract(node, "playcount")) + seq.append(TopItem(item, weight)) + + return seq + + def get_weekly_track_charts(self, from_date = None, to_date = None): + """Returns the weekly track charts for the week starting from the from_date value to the to_date value.""" + + params = self._get_params() + if from_date and to_date: + params["from"] = from_date + params["to"] = to_date + + doc = self._request("user.getWeeklyTrackChart", True, params) + + seq = [] + for node in doc.getElementsByTagName("track"): + item = Track(_extract(node, "artist"), _extract(node, "name"), self.network) + weight = _number(_extract(node, "playcount")) + seq.append(TopItem(item, weight)) + + return seq + + def compare_with_user(self, user, shared_artists_limit = None): + """Compare this user with another Last.fm user. + Returns a sequence (tasteometer_score, (shared_artist1, shared_artist2, ...)) + user: A User object or a username string/unicode object. + """ + + if isinstance(user, User): + user = user.get_name() + + params = self._get_params() + if shared_artists_limit: + params['limit'] = shared_artists_limit + params['type1'] = 'user' + params['type2'] = 'user' + params['value1'] = self.get_name() + params['value2'] = user + + doc = self._request('tasteometer.compare', False, params) + + score = _extract(doc, 'score') + + artists = doc.getElementsByTagName('artists')[0] + shared_artists_names = _extract_all(artists, 'name') + + shared_artists_seq = [] + + for name in shared_artists_names: + shared_artists_seq.append(Artist(name, self.network)) + + return (score, shared_artists_seq) + + def get_image(self): + """Returns the user's avatar.""" + + doc = self._request("user.getInfo", True) + + return _extract(doc, "image") + + def get_url(self, domain_name = DOMAIN_ENGLISH): + """Returns the url of the user page on the network. + * domain_name: The network's language domain. Possible values: + o DOMAIN_ENGLISH + o DOMAIN_GERMAN + o DOMAIN_SPANISH + o DOMAIN_FRENCH + o DOMAIN_ITALIAN + o DOMAIN_POLISH + o DOMAIN_PORTUGUESE + o DOMAIN_SWEDISH + o DOMAIN_TURKISH + o DOMAIN_RUSSIAN + o DOMAIN_JAPANESE + o DOMAIN_CHINESE + """ + + name = _url_safe(self.get_name()) + + return self.network._get_url(domain_name, "user") %{'name': name} + + def get_library(self): + """Returns the associated Library object. """ + + return Library(self, self.network) + + def get_shouts(self, limit=50): + """ + Returns a sequqence of Shout objects + """ + + shouts = [] + for node in _collect_nodes(limit, self, "user.getShouts", False): + shouts.append(Shout( + _extract(node, "body"), + User(_extract(node, "author"), self.network), + _extract(node, "date") + ) + ) + return shouts + + def shout(self, message): + """ + Post a shout + """ + + params = self._get_params() + params["message"] = message + + self._request("user.Shout", False, params) + +class AuthenticatedUser(User): + def __init__(self, network): + User.__init__(self, "", network); + + def _get_params(self): + return {"user": self.get_name()} + + def get_name(self): + """Returns the name of the authenticated user.""" + + doc = self._request("user.getInfo", True, {"user": ""}) # hack + + self.name = _extract(doc, "name") + return self.name + + def get_recommended_events(self, limit=50): + """ + Returns a sequence of Event objects + if limit==None it will return all + """ + + seq = [] + for node in _collect_nodes(limit, self, "user.getRecommendedEvents", False): + seq.append(Event(_extract(node, "id"), self.network)) + + return seq + + def get_recommended_artists(self, limit=50): + """ + Returns a sequence of Event objects + if limit==None it will return all + """ + + seq = [] + for node in _collect_nodes(limit, self, "user.getRecommendedArtists", False): + seq.append(Artist(_extract(node, "name"), self.network)) + + return seq + +class _Search(_BaseObject): + """An abstract class. Use one of its derivatives.""" + + def __init__(self, ws_prefix, search_terms, network): + _BaseObject.__init__(self, network) + + self._ws_prefix = ws_prefix + self.search_terms = search_terms + + self._last_page_index = 0 + + def _get_params(self): + params = {} + + for key in self.search_terms.keys(): + params[key] = self.search_terms[key] + + return params + + def get_total_result_count(self): + """Returns the total count of all the results.""" + + doc = self._request(self._ws_prefix + ".search", True) + + return _extract(doc, "opensearch:totalResults") + + def _retreive_page(self, page_index): + """Returns the node of matches to be processed""" + + params = self._get_params() + params["page"] = str(page_index) + doc = self._request(self._ws_prefix + ".search", True, params) + + return doc.getElementsByTagName(self._ws_prefix + "matches")[0] + + def _retrieve_next_page(self): + self._last_page_index += 1 + return self._retreive_page(self._last_page_index) + +class AlbumSearch(_Search): + """Search for an album by name.""" + + def __init__(self, album_name, network): + + _Search.__init__(self, "album", {"album": album_name}, network) + + def get_next_page(self): + """Returns the next page of results as a sequence of Album objects.""" + + master_node = self._retrieve_next_page() + + seq = [] + for node in master_node.getElementsByTagName("album"): + seq.append(Album(_extract(node, "artist"), _extract(node, "name"), self.network)) + + return seq + +class ArtistSearch(_Search): + """Search for an artist by artist name.""" + + def __init__(self, artist_name, network): + _Search.__init__(self, "artist", {"artist": artist_name}, network) + + def get_next_page(self): + """Returns the next page of results as a sequence of Artist objects.""" + + master_node = self._retrieve_next_page() + + seq = [] + for node in master_node.getElementsByTagName("artist"): + artist = Artist(_extract(node, "name"), self.network) + artist.listener_count = _number(_extract(node, "listeners")) + seq.append(artist) + + return seq + +class TagSearch(_Search): + """Search for a tag by tag name.""" + + def __init__(self, tag_name, network): + + _Search.__init__(self, "tag", {"tag": tag_name}, network) + + def get_next_page(self): + """Returns the next page of results as a sequence of Tag objects.""" + + master_node = self._retrieve_next_page() + + seq = [] + for node in master_node.getElementsByTagName("tag"): + tag = Tag(_extract(node, "name"), self.network) + tag.tag_count = _number(_extract(node, "count")) + seq.append(tag) + + return seq + +class TrackSearch(_Search): + """Search for a track by track title. If you don't wanna narrow the results down + by specifying the artist name, set it to empty string.""" + + def __init__(self, artist_name, track_title, network): + + _Search.__init__(self, "track", {"track": track_title, "artist": artist_name}, network) + + def get_next_page(self): + """Returns the next page of results as a sequence of Track objects.""" + + master_node = self._retrieve_next_page() + + seq = [] + for node in master_node.getElementsByTagName("track"): + track = Track(_extract(node, "artist"), _extract(node, "name"), self.network) + track.listener_count = _number(_extract(node, "listeners")) + seq.append(track) + + return seq + +class VenueSearch(_Search): + """Search for a venue by its name. If you don't wanna narrow the results down + by specifying a country, set it to empty string.""" + + def __init__(self, venue_name, country_name, network): + + _Search.__init__(self, "venue", {"venue": venue_name, "country": country_name}, network) + + def get_next_page(self): + """Returns the next page of results as a sequence of Track objects.""" + + master_node = self._retrieve_next_page() + + seq = [] + for node in master_node.getElementsByTagName("venue"): + seq.append(Venue(_extract(node, "id"), self.network)) + + return seq + +class Venue(_BaseObject): + """A venue where events are held.""" + + # TODO: waiting for a venue.getInfo web service to use. + + id = None + + def __init__(self, id, network): + _BaseObject.__init__(self, network) + + self.id = _number(id) + + def __repr__(self): + return "pylast.Venue(%s, %s)" %(repr(self.id), repr(self.network)) + + @_string_output + def __str__(self): + return "Venue #" + str(self.id) + + def __eq__(self, other): + return self.get_id() == other.get_id() + + def _get_params(self): + return {"venue": self.get_id()} + + def get_id(self): + """Returns the id of the venue.""" + + return self.id + + def get_upcoming_events(self): + """Returns the upcoming events in this venue.""" + + doc = self._request("venue.getEvents", True) + + seq = [] + for node in doc.getElementsByTagName("event"): + seq.append(Event(_extract(node, "id"), self.network)) + + return seq + + def get_past_events(self): + """Returns the past events held in this venue.""" + + doc = self._request("venue.getEvents", True) + + seq = [] + for node in doc.getElementsByTagName("event"): + seq.append(Event(_extract(node, "id"), self.network)) + + return seq + +def md5(text): + """Returns the md5 hash of a string.""" + + h = hashlib.md5() + h.update(_unicode(text).encode("utf-8")) + + return h.hexdigest() + +def _unicode(text): + if sys.version_info[0] == 3: + if type(text) in (bytes, bytearray): + return str(text, "utf-8") + elif type(text) == str: + return text + else: + return str(text) + + elif sys.version_info[0] ==2: + if type(text) in (str,): + return unicode(text, "utf-8") + elif type(text) == unicode: + return text + else: + return unicode(text) + +def _string(text): + """For Python2 routines that can only process str type.""" + + if sys.version_info[0] == 3: + if type(text) != str: + return str(text) + else: + return text + + elif sys.version_info[0] == 2: + if type(text) == str: + return text + + if type(text) == int: + return str(text) + + return text.encode("utf-8") + +def _collect_nodes(limit, sender, method_name, cacheable, params=None): + """ + Returns a sequqnce of dom.Node objects about as close to + limit as possible + """ + + if not params: + params = sender._get_params() + + nodes = [] + page = 1 + end_of_pages = False + + while not end_of_pages and (not limit or (limit and len(nodes) < limit)): + params["page"] = str(page) + doc = sender._request(method_name, cacheable, params) + + main = doc.documentElement.childNodes[1] + + if main.hasAttribute("totalPages"): + total_pages = _number(main.getAttribute("totalPages")) + elif main.hasAttribute("totalpages"): + total_pages = _number(main.getAttribute("totalpages")) + else: + raise Exception("No total pages attribute") + + for node in main.childNodes: + if not node.nodeType == xml.dom.Node.TEXT_NODE and len(nodes) < limit: + nodes.append(node) + + if page >= total_pages: + end_of_pages = True + + page += 1 + + return nodes + +def _extract(node, name, index = 0): + """Extracts a value from the xml string""" + + nodes = node.getElementsByTagName(name) + + if len(nodes): + if nodes[index].firstChild: + return _unescape_htmlentity(nodes[index].firstChild.data.strip()) + else: + return None + +def _extract_all(node, name, limit_count = None): + """Extracts all the values from the xml string. returning a list.""" + + seq = [] + + for i in range(0, len(node.getElementsByTagName(name))): + if len(seq) == limit_count: + break + + seq.append(_extract(node, name, i)) + + return seq + +def _url_safe(text): + """Does all kinds of tricks on a text to make it safe to use in a url.""" + + return url_quote_plus(url_quote_plus(_string(text))).lower() + +def _number(string): + """ + Extracts an int from a string. Returns a 0 if None or an empty string was passed + """ + + if not string: + return 0 + elif string == "": + return 0 + else: + try: + return int(string) + except ValueError: + return float(string) + +def _unescape_htmlentity(string): + + #string = _unicode(string) + + mapping = htmlentitydefs.name2codepoint + for key in mapping: + string = string.replace("&%s;" %key, unichr(mapping[key])) + + return string + +def extract_items(topitems_or_libraryitems): + """Extracts a sequence of items from a sequence of TopItem or LibraryItem objects.""" + + seq = [] + for i in topitems_or_libraryitems: + seq.append(i.item) + + return seq + +class ScrobblingError(Exception): + def __init__(self, message): + Exception.__init__(self) + self.message = message + + @_string_output + def __str__(self): + return self.message + +class BannedClientError(ScrobblingError): + def __init__(self): + ScrobblingError.__init__(self, "This version of the client has been banned") + +class BadAuthenticationError(ScrobblingError): + def __init__(self): + ScrobblingError.__init__(self, "Bad authentication token") + +class BadTimeError(ScrobblingError): + def __init__(self): + ScrobblingError.__init__(self, "Time provided is not close enough to current time") + +class BadSessionError(ScrobblingError): + def __init__(self): + ScrobblingError.__init__(self, "Bad session id, consider re-handshaking") + +class _ScrobblerRequest(object): + + def __init__(self, url, params, network, type="POST"): + + for key in params: + params[key] = str(params[key]) + + self.params = params + self.type = type + (self.hostname, self.subdir) = url_split_host(url[len("http:"):]) + self.network = network + + def execute(self): + """Returns a string response of this request.""" + + connection = HTTPConnection(self.hostname) + + data = [] + for name in self.params.keys(): + value = url_quote_plus(self.params[name]) + data.append('='.join((name, value))) + data = "&".join(data) + + headers = { + "Content-type": "application/x-www-form-urlencoded", + "Accept-Charset": "utf-8", + "User-Agent": "pylast" + "/" + __version__, + "HOST": self.hostname + } + + if self.type == "GET": + connection.request("GET", self.subdir + "?" + data, headers = headers) + else: + connection.request("POST", self.subdir, data, headers) + response = _unicode(connection.getresponse().read()) + + self._check_response_for_errors(response) + + return response + + def _check_response_for_errors(self, response): + """When passed a string response it checks for erros, raising + any exceptions as necessary.""" + + lines = response.split("\n") + status_line = lines[0] + + if status_line == "OK": + return + elif status_line == "BANNED": + raise BannedClientError() + elif status_line == "BADAUTH": + raise BadAuthenticationError() + elif status_line == "BADTIME": + raise BadTimeError() + elif status_line == "BADSESSION": + raise BadSessionError() + elif status_line.startswith("FAILED "): + reason = status_line[status_line.find("FAILED ")+len("FAILED "):] + raise ScrobblingError(reason) + +class Scrobbler(object): + """A class for scrobbling tracks to Last.fm""" + + session_id = None + nowplaying_url = None + submissions_url = None + + def __init__(self, network, client_id, client_version): + self.client_id = client_id + self.client_version = client_version + self.username = network.username + self.password = network.password_hash + self.network = network + + def _do_handshake(self): + """Handshakes with the server""" + + timestamp = str(int(time.time())) + + if self.password and self.username: + token = md5(self.password + timestamp) + elif self.network.api_key and self.network.api_secret and self.network.session_key: + if not self.username: + self.username = self.network.get_authenticated_user().get_name() + token = md5(self.network.api_secret + timestamp) + + params = {"hs": "true", "p": "1.2.1", "c": self.client_id, + "v": self.client_version, "u": self.username, "t": timestamp, + "a": token} + + if self.network.session_key and self.network.api_key: + params["sk"] = self.network.session_key + params["api_key"] = self.network.api_key + + server = self.network.submission_server + response = _ScrobblerRequest(server, params, self.network, "GET").execute().split("\n") + + self.session_id = response[1] + self.nowplaying_url = response[2] + self.submissions_url = response[3] + + def _get_session_id(self, new = False): + """Returns a handshake. If new is true, then it will be requested from the server + even if one was cached.""" + + if not self.session_id or new: + self._do_handshake() + + return self.session_id + + def report_now_playing(self, artist, title, album = "", duration = "", track_number = "", mbid = ""): + + _deprecation_warning("DeprecationWarning: Use Netowrk.update_now_playing(...) instead") + + params = {"s": self._get_session_id(), "a": artist, "t": title, + "b": album, "l": duration, "n": track_number, "m": mbid} + + try: + _ScrobblerRequest(self.nowplaying_url, params, self.network).execute() + except BadSessionError: + self._do_handshake() + self.report_now_playing(artist, title, album, duration, track_number, mbid) + + def scrobble(self, artist, title, time_started, source, mode, duration, album="", track_number="", mbid=""): + """Scrobble a track. parameters: + artist: Artist name. + title: Track title. + time_started: UTC timestamp of when the track started playing. + source: The source of the track + SCROBBLE_SOURCE_USER: Chosen by the user (the most common value, unless you have a reason for choosing otherwise, use this). + SCROBBLE_SOURCE_NON_PERSONALIZED_BROADCAST: Non-personalised broadcast (e.g. Shoutcast, BBC Radio 1). + SCROBBLE_SOURCE_PERSONALIZED_BROADCAST: Personalised recommendation except Last.fm (e.g. Pandora, Launchcast). + SCROBBLE_SOURCE_LASTFM: ast.fm (any mode). In this case, the 5-digit recommendation_key value must be set. + SCROBBLE_SOURCE_UNKNOWN: Source unknown. + mode: The submission mode + SCROBBLE_MODE_PLAYED: The track was played. + SCROBBLE_MODE_LOVED: The user manually loved the track (implies a listen) + SCROBBLE_MODE_SKIPPED: The track was skipped (Only if source was Last.fm) + SCROBBLE_MODE_BANNED: The track was banned (Only if source was Last.fm) + duration: Track duration in seconds. + album: The album name. + track_number: The track number on the album. + mbid: MusicBrainz ID. + """ + + _deprecation_warning("DeprecationWarning: Use Network.scrobble(...) instead") + + params = {"s": self._get_session_id(), "a[0]": _string(artist), "t[0]": _string(title), + "i[0]": str(time_started), "o[0]": source, "r[0]": mode, "l[0]": str(duration), + "b[0]": _string(album), "n[0]": track_number, "m[0]": mbid} + + _ScrobblerRequest(self.submissions_url, params, self.network).execute() + + def scrobble_many(self, tracks): + """ + Scrobble several tracks at once. + + tracks: A sequence of a sequence of parameters for each trach. The order of parameters + is the same as if passed to the scrobble() method. + """ + + _deprecation_warning("DeprecationWarning: Use Network.scrobble_many(...) instead") + + remainder = [] + + if len(tracks) > 50: + remainder = tracks[50:] + tracks = tracks[:50] + + params = {"s": self._get_session_id()} + + i = 0 + for t in tracks: + _pad_list(t, 9, "") + params["a[%s]" % str(i)] = _string(t[0]) + params["t[%s]" % str(i)] = _string(t[1]) + params["i[%s]" % str(i)] = str(t[2]) + params["o[%s]" % str(i)] = t[3] + params["r[%s]" % str(i)] = t[4] + params["l[%s]" % str(i)] = str(t[5]) + params["b[%s]" % str(i)] = _string(t[6]) + params["n[%s]" % str(i)] = t[7] + params["m[%s]" % str(i)] = t[8] + + i += 1 + + _ScrobblerRequest(self.submissions_url, params, self.network).execute() + + if remainder: + self.scrobble_many(remainder)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/_python_lib/pylast/setup.py Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +#!/usr/bin/env python + +from distutils.core import setup + +import os +def get_build(): + path = "./.build" + + if os.path.exists(path): + fp = open(path, "r") + build = eval(fp.read()) + if os.path.exists("./.increase_build"): + build += 1 + fp.close() + else: + build = 1 + + fp = open(path, "w") + fp.write(str(build)) + fp.close() + + return str(build) + +setup(name = "pylast", + version = "0.5." + get_build(), + author = "Amr Hassan <amr.hassan@gmail.com>", + description = "A Python interface to Last.fm (and other API compatible social networks)", + author_email = "amr.hassan@gmail.com", + url = "http://code.google.com/p/pylast/", + py_modules = ("pylast",), + license = "Apache2" + )
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/conversion/extract_buchfink_xls.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,128 @@ + + +%seglengths +%vols +%periods +%name + +% +% datlen=size(data,1); +% maxseglen=10; +% clear segvol; +% clear seglengths; +% clear repetitions; +% clear name; +% +% filectr=0; +% lineptr=15; +% +% while lineptr <= size(textdata,1) +% if ~strcmp('',textdata{lineptr,1}) +% path = textdata{lineptr,1}; +% end +% if ~strcmp('',textdata{lineptr,2}) +% filectr=filectr+1; +% name{filectr} = strcat(path,'\',textdata{lineptr,2}); +% datapos=lineptr-14; +% +% segctr=0; +% while ~isnan(data(datapos,2))&& datapos < datlen +% segctr=segctr+1; +% seglengths(filectr,segctr)= data(datapos,2); +% if ~isnan(data(datapos,3)) +% repetitions(filectr,segctr)=data(datapos,3); +% end +% segvol(filectr,segctr)=data(datapos,4); +% datapos=datapos+1; +% lineptr = lineptr+1; +% end +% end +% lineptr = lineptr+1; +% end + + +compl_len=sum(seglengths,2);%ins secs +compl_std=std(compl_len); +compl_mean=mean(compl_len); + +numsegs=seglengths>0; +numsegs=sum(numsegs,2); +meannumsegs=mean(numsegs) +minnumsegs=min(numsegs) +maxnumsegs=max(numsegs) + +for i=1:maxnumsegs %segmentlänge, betrachtet werden jeweils alle segs + % mit ausreichender länge + valid_cols=find(numsegs>=i); + mean_seglen(i)=mean(seglengths(valid_cols,i)); + min_seglen(i)=min(seglengths(valid_cols,i)); + max_seglen(i)=max(seglengths(valid_cols,i)); + std_seglen(i)=std(seglengths(valid_cols,i));%in s +end + +% valid_cols=find(numsegs==floor(meannumsegs)); +% for i=1:floor(mean(numsegs)) %segmentlänge, betrachtet werden jeweils alle segs +% % mit genau floor(mittlerer) länge +% mean_seglen2(i)=mean(seglengths(valid_cols,i)); +% min_seglen2(i)=min(seglengths(valid_cols,i)); +% max_seglen2(i)=max(seglengths(valid_cols,i)); +% std_seglen2(i)=std(seglengths(valid_cols,i));%in s +% end + +%längen +seglen_all=reshape(seglengths,115*6,1); +goodlens=find(seglen_all>0); +mean_seglen_all=mean(seglen_all(goodlens)); +std_seglen_all=std(seglen_all(goodlens)); +for i=1:maxnumsegs + segs(i)=sum(numsegs==i); +end + +period_segs=sum(repetitions>0,2); +for i=1:maxnumsegs + havenumperiodsegs(i)=sum(period_segs==i); +end +segs_period=(repetitions>0); +sum((period_segs==3)&(numsegs==4)) +sum((period_segs==2)&(numsegs==4)) +sum(((period_segs==4)&(numsegs==5))) +sum(((period_segs==3)&(numsegs==5))) + +better_half_ind=find((period_segs==3)&(numsegs==4)); +better_half_len=seglengths(better_half_ind,:); +better_half_mean=mean(better_half_len,1); +better_half_std=std(better_half_len); + +%wiederholungen +period_all=reshape(repetitions,115*6,1); +goodreps=find(period_all>0); +period_all=period_all(goodreps); +peri_all_mean=mean(period_all); +peri_all_std=std(period_all); + +%frequenzen +freqs = repetitions./(seglengths./1000); +freqs_all=reshape(freqs,115*6,1); +freqs_all=freqs_all(find(~isnan(freqs_all) & (freqs_all>0))); +freqs_all_mean=mean(freqs_all); +freqs_all_std=std(freqs_all); + + +better_h_f=repetitions(better_half_ind,2:4)./(seglengths(better_half_ind,2:4)./1000); +better_h_f_mean=mean(better_h_f); +better_h_f_std=std(better_h_f); +better_h_f_min=min(better_h_f); +better_h_f_max=max(better_h_f); + +better_half2_ind=find((period_segs==4)&(numsegs==5)); + +better_h2_f=repetitions(better_half2_ind,2:5)./(seglengths(better_half2_ind,2:5)./1000); +better_h2_f_mean=mean(better_h2_f); +better_h2_f_std=std(better_h2_f); +better_h2_f_min=min(better_h2_f); +better_h2_f_max=max(better_h2_f); + +abweichung_f=better_h_f-repmat(better_h_f_mean,size(better_h_f,1),1); +corrcoef(abweichung_f) +abweichung2_f=better_h2_f-repmat(better_h2_f_mean,size(better_h2_f,1),1); +corrcoef(abweichung2_f)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/conversion/found_time_make_txt.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,75 @@ +%makes textfiles for already found wavs + +clear +global globalvars; +addpath('..\vogelst\k-band250905') +sourcedir='F:\Aufnahmen'; +%sourcedir='F:\Ample'; +%sourcedir='M:\PrograMathe\matlab\work\hiwi\vogelst\testmaterial\online2'; + +segmentsdir='F:\viele_einzelne_strophen\blaumeise\typ1'; + +%segmentsdir='F:\viele_einzelne_strophen\fringila_coelebs_buchfink\tst'; +%segmentsdir='M:\PrograMathe\matlab\work\hiwi\vogelst\testmaterial\online'; + + + +%---------------------------------------------z-------------zugehörige dateien suchen +allpaths=genpath(sourcedir); +data_names=dir(strcat(segmentsdir,'\*.wav')); +psourcedirs = explode(';',allpaths); +for i = 1:length(data_names) + ldata_names{i} = data_names(i).name; +end + +names_done = []; +relsource_files = {}; +for i = 1:length(psourcedirs) + actdir = dir(strcat(psourcedirs{i},'\*.wav')); + ldata_names = ldata_names(setdiff(1:length(ldata_names),names_done)); + for j = 1:length(ldata_names) + for k = 1:length(actdir) + if(strncmp(actdir(k).name,ldata_names{j},length(ldata_names{j})-(7+2*6+1+3))) && ... + strncmp(substr(ldata_names{j},-(7+4),1),'-',1) + names_done = [names_done j]; + relsource_files= cat(1,relsource_files,{[strcat(psourcedirs{i},'\',actdir(k).name)] [strcat(segmentsdir,'\',ldata_names{j})]}); + end + end + end +end + + +for i=1:size(relsource_files,1) + newfilename=char(relsource_files(i,2)); + position(i,2)= hhmmss_to_s(substr(newfilename,-(7+3),6)); + position(i,1)= hhmmss_to_s(substr(newfilename,-(7+3+7),6)); + + filetoopen=strcat(newfilename,'.txt'); + ftoex=dir(filetoopen); + if(length(ftoex)==0) + FID = fopen(filetoopen,'w'); + fprintf(FID,'%s','annotations for '); + fprintf(FID,'%s',newfilename); + fprintf(FID,'\n'); + fprintf(FID,'%s','sourcefile available at'); + fprintf(FID,'\n'); + fprintf(FID,'%s',char(relsource_files(i,1))); + fprintf(FID,'\n'); + fprintf(FID,'%s','time of occurrence in hhmmss format:'); + fprintf(FID,'\n'); + fprintf(FID,'%s',s_to_hhmmss(position(i,1))); + fprintf(FID,'%s','-'); + fprintf(FID,'%s',s_to_hhmmss(position(i,2))); + fprintf(FID,'\n'); + fprintf(FID,'%s','in seconds:'); + fprintf(FID,'\n'); + fprintf(FID,'%s',sprintf('%d',position(i,1))); + fprintf(FID,'\n'); + fprintf(FID,'%s',sprintf('%d',position(i,2))); + fprintf(FID,'\n'); + fprintf(FID,'%s','comments:'); + + fclose(FID); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/conversion/hhmmss_to_s.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +function out=hhmmss_to_s(in) + +h = sscanf(substr(in,0,2),'%d'); +m = sscanf(substr(in,2,2),'%d'); +s = sscanf(substr(in,4,2),'%d'); + +out=h*3600+m*60+s; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/conversion/process_options.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,132 @@ +% PROCESS_OPTIONS - Processes options passed to a Matlab function. +% This function provides a simple means of +% parsing attribute-value options. Each option is +% named by a unique string and is given a default +% value. +% +% Usage: [var1, var2, ..., varn[, unused]] = ... +% process_options(args, ... +% str1, def1, str2, def2, ..., strn, defn) +% +% Arguments: +% args - a cell array of input arguments, such +% as that provided by VARARGIN. Its contents +% should alternate between strings and +% values. +% str1, ..., strn - Strings that are associated with a +% particular variable +% def1, ..., defn - Default values returned if no option +% is supplied +% +% Returns: +% var1, ..., varn - values to be assigned to variables +% unused - an optional cell array of those +% string-value pairs that were unused; +% if this is not supplied, then a +% warning will be issued for each +% option in args that lacked a match. +% +% Examples: +% +% Suppose we wish to define a Matlab function 'func' that has +% required parameters x and y, and optional arguments 'u' and 'v'. +% With the definition +% +% function y = func(x, y, varargin) +% +% [u, v] = process_options(varargin, 'u', 0, 'v', 1); +% +% calling func(0, 1, 'v', 2) will assign 0 to x, 1 to y, 0 to u, and 2 +% to v. The parameter names are insensitive to case; calling +% func(0, 1, 'V', 2) has the same effect. The function call +% +% func(0, 1, 'u', 5, 'z', 2); +% +% will result in u having the value 5 and v having value 1, but +% will issue a warning that the 'z' option has not been used. On +% the other hand, if func is defined as +% +% function y = func(x, y, varargin) +% +% [u, v, unused_args] = process_options(varargin, 'u', 0, 'v', 1); +% +% then the call func(0, 1, 'u', 5, 'z', 2) will yield no warning, +% and unused_args will have the value {'z', 2}. This behaviour is +% useful for functions with options that invoke other functions +% with options; all options can be passed to the outer function and +% its unprocessed arguments can be passed to the inner function. + +% Copyright (C) 2002 Mark A. Paskin +% +% This program is free software; you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation; either version 2 of the License, or +% (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, but +% WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +% General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 +% USA. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +function [varargout] = process_options(args, varargin) + +% Check the number of input arguments +n = length(varargin); +if (mod(n, 2)) + error('Each option must be a string/value pair.'); +end + +% Check the number of supplied output arguments +if (nargout < (n / 2)) + error('Insufficient number of output arguments given'); +elseif (nargout == (n / 2)) + warn = 1; + nout = n / 2; +else + warn = 0; + nout = n / 2 + 1; +end + +% Set outputs to be defaults +varargout = cell(1, nout); +for i=2:2:n + varargout{i/2} = varargin{i}; +end + +% Now process all arguments +nunused = 0; +for i=1:2:length(args) + found = 0; + for j=1:2:n + if strcmpi(args{i}, varargin{j}) + varargout{(j + 1)/2} = args{i + 1}; + found = 1; + break; + end + end + if (~found) + if (warn) + warning(sprintf('Option ''%s'' not used.', args{i})); + args{i} + else + nunused = nunused + 1; + unused{2 * nunused - 1} = args{i}; + unused{2 * nunused} = args{i + 1}; + end + end +end + +% Assign the unused arguments +if (~warn) + if (nunused) + varargout{nout} = unused; + else + varargout{nout} = cell(0); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/conversion/s_to_hhmmss.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function out = s_to_hhmmss(in) +%input: time in s (integer) +%output: string in hhmmss format + +hours = div(in,3600); +mins = div(mod(in,3600),60); +secs = mod(in,60); + +out = strcat(tostr(hours),tostr(mins),tostr(secs)); + + +function u=tostr(v) +if(v<10) + u=sprintf('0%d',v); +else + u=sprintf('%d',v); +end + +function u=div(v,w) +u=(floor(v/w)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/filesys/find_time_and_rename.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,177 @@ +%sucht die datei (1) "coelebs_aufn1_ch1"unter sourcedir bei datei (2) +% coelebs_aufn1_ch1_01 im segmentsdir. +% daraufhin wird die genaue stelle des vorkommens von (2) in (1) gesucht, +% und in sekunden an den dateinamen angehängt; + +clear +global globalvars; +addpath('..\vogelst\k-band250905') +sourcedir='F:\Aufnahmen\2006-Juni-3-Remscheid-Daniel'; +%sourcedir='F:\Ample'; +%sourcedir='M:\PrograMathe\matlab\work\hiwi\vogelst\testmaterial\online2'; + +segmentsdir='F:\viele_einzelne_strophen\fringila_coelebs_buchfink\typ1'; + +%segmentsdir='F:\viele_einzelne_strophen\fringila_coelebs_buchfink\tst'; +%segmentsdir='M:\PrograMathe\matlab\work\hiwi\vogelst\testmaterial\online'; + + + +%---------------------------------------------z-------------zugehörige dateien suchen +allpaths=genpath(sourcedir); +data_names=dir(strcat(segmentsdir,'\*.wav')); +psourcedirs = explode(';',allpaths); +for i = 1:length(data_names) + ldata_names{i} = data_names(i).name; +end + +names_done = []; +relsource_files = {}; +for i = 1:length(psourcedirs) + actdir = dir(strcat(psourcedirs{i},'\*.wav')); + ldata_names = ldata_names(setdiff(1:length(ldata_names),names_done)); + for j = 1:length(ldata_names) + for k = 1:length(actdir) + if(strncmp(actdir(k).name,ldata_names{j},length(ldata_names{j})-7)) + names_done = [names_done j]; + relsource_files= cat(1,relsource_files,{[strcat(psourcedirs{i},'\',actdir(k).name)] [strcat(segmentsdir,'\',ldata_names{j})]}); + end + end + end +end + + + +oldsource=''; +oldsamplesource=0; +k=1; +position=zeros(size(relsource_files,1),2); + +i=1; +while i <= size(relsource_files,1) + aktsource =char(relsource_files(i,1)); + aktsamplesource=i; +%--------------------------------------------------features generieren und overhead + + if ~strcmp(aktsource,oldsource) || (aktsamplesource~=oldsamplesource) %neue quelldatei + aktmajstart=1; + siz=WAVREAD(aktsource,'size'); + siz=siz(1,1); + aktmajpointer = min(siz,globalvars.maxaudiodatalen); + [aktdata,sr,nbits]=wavread(aktsource,[aktmajstart aktmajpointer]); + oldsource=aktsource; + if length(aktdata(1,:)) > 1 + aktdata = aktdata(:,1); + end + windowsz=floor(sr/5); + fprintf('generating features for fast search \n'); + aktfeat =resample(abs(specgram(aktdata,windowsz,sr,windowsz,floor(windowsz/2))),1,40); + elseif (aktmajpointer < siz) && (aktsamplesource==oldsamplesource) %alte quelldatei zu lang + aktmajstart=aktmajstart+floor(globalvars.maxaudiodatalen/2); + aktmajpointer=min(aktmajpointer+floor(globalvars.maxaudiodatalen/2),siz); + if (aktmajstart-aktmajpointer) < windowsz + aktmajstart=max(aktmajstart-windowsz,1); + end + [aktdata,sr,nbits]=wavread(aktsource,[aktmajstart aktmajpointer]); + if length(aktdata(1,:)) > 1 + aktdata = aktdata(:,1); + end + fprintf('generating features for fast search \n'); + aktfeat =resample(abs(specgram(aktdata,windowsz,sr,windowsz,floor(windowsz/2))),1,40); + end + + + if (aktsamplesource~=oldsamplesource)%neue suchdatei + [aktsample,sr,nbits]=wavread(char(relsource_files(i,2))); + oldsamplesource=aktsamplesource; + if length(aktsample(1,:)) > 1 + aktsample = aktsample(:,1); + end + + if(length(aktsample)>=windowsz) + aktsamplefeat =resample(abs(specgram(aktsample(1:windowsz),windowsz,sr,windowsz,floor(windowsz/2))),1,40); + for j=1:size(aktfeat,2) + di(j)=dist(aktfeat(:,j),aktsamplefeat(:,1)); + end + bdi = di>mean(di); + else + fprintf('fast search not possible \n'); + bdi=ones(1,size(aktfeat,2)) + end + fprintf(strcat('searching for \n', escape(aktsource ),' in \n' ,escape(char(relsource_files(i,2))),'\n' )); + end + + +%---------------------- bestandteil lokalisieren (k mismatches), ausreichende minmatches + minmatches=length(aktsample); + found=0; + aktpointer=1; + aktstart=1; + len = length(aktdata); + len2=length(aktsample); + while ((aktstart+len2)<=len) &&~found + mismatches=0; + aktstart=aktstart+1; + aktpointer=aktstart; + while (aktpointer <= len)&&(mismatches <k)&&((aktpointer-(aktstart)+1)<=len2) + if(aktdata(aktpointer) ~= aktsample(aktpointer-(aktstart)+1)) + mismatches = mismatches+1; + if(~bdi(floor(aktpointer/(windowsz/2))+1)) + aktpointer=aktpointer+(windowsz/2); + end + end + aktpointer=aktpointer+1; + if (aktpointer-aktstart) >= minmatches + fprintf('found'); + position(i,:)=[floor((aktmajstart-1+aktstart)/sr) floor((aktmajstart-1+aktpointer)/sr)] + found=1; + break + end + end + end + if(position(i,2)~=0) + +%---------------------------------------------zugehörige infos in dateien schreiben + newfilename= strcat(substr(char(relsource_files(i,2)),0,length(relsource_files(i,2))-5),'__',... + s_to_hhmmss(position(i,1)),'-',s_to_hhmmss(position(i,2)),'.wav'); + movefile(char(relsource_files(i,2)),newfilename); + + filetoopen=strcat(newfilename,'.txt'); + ftoex=dir(filetoopen); + if(length(ftoex)==0) + FID = fopen(filetoopen,'w'); + fprintf(FID,'%s','annotations for '); + fprintf(FID,'%s',newfilename); + fprintf(FID,'\n'); + fprintf(FID,'%s','sourcefile available at'); + fprintf(FID,'\n'); + fprintf(FID,'%s',char(relsource_files(i,1))); + fprintf(FID,'\n'); + fprintf(FID,'%s','time of occurrence in hhmmss format:'); + fprintf(FID,'\n'); + fprintf(FID,'%s',s_to_hhmmss(position(i,1))); + fprintf(FID,'%s','-'); + fprintf(FID,'%s',s_to_hhmmss(position(i,2))); + fprintf(FID,'\n'); + fprintf(FID,'%s','in seconds:'); + fprintf(FID,'\n'); + fprintf(FID,'%s',sprintf('%d',position(i,1))); + fprintf(FID,'\n'); + fprintf(FID,'%s',sprintf('%d',position(i,2))); + fprintf(FID,'\n'); + fprintf(FID,'%s','comments:'); + + fclose(FID); + end + + i=i+1; + elseif(aktmajpointer==siz) + i=i+1; + end +end + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/filesys/forAllFilesInDir.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,68 @@ +function y = forAllFilesInDir(startdir, filetypes, func, recursive, y, varargin) +% y = forAllFilesInDir(startdir, filetypes, func, recursive, y, varargin) +% +% Traverse directory structure, performing for each file of a specified filetype +% an action defined by a function handle. +% +% INPUT: +% ------ +% startdir ..... (string) Start directory. +% Any directory containing a file named "skip" will be skipped. +% filetypes .... (cell array of strings) File type filter, e.g. {'*.amc' '*.asf'} +% or {'*.*'} for all files +% func ......... (function handle) Worker function to be called for each file +% The function designated by func will be passed the current complete +% filename (including pathname) as the first argument. If the parameter list +% varargin is nonempty, a single cell array with the current filename as the +% first entry followed by the remaining parameters in varargin will be passed. +% recursive .... (boolean) Flag indicating whether recursive traversal is desired or not. +% y ............ (matrix or cell array) Initial value for output container. Usually empty ([]). +% varargin ..... (cell array of parameters) Parameters to be passed to func, see "func". +% +% OUTPUT: +% ------- +% y ............ (matrix or cell array) Collected outputs of calls to func, vertically concatenated. +% +% EXAMPLE: +% -------- +% y = forAllFilesInDir('S:\data_MoCap\MoCaDaDB\AMC',{'*.amc'},@getAMCLengthInSeconds,true,[]); +% y = forAllFilesInDir('S:\data_MoCap\MoCaDaDB\AMC',{'*.amc'},@getAMCLengthInSeconds,true,[],param1,...,paramn); + +if (startdir(end)~='\') + startdir = [startdir '\']; +end + +files = dir(startdir); +if (isempty(strmatch('skip',{files.name},'exact'))) % only process dir if no "skip" file is present! + disp(['Entered directory ' startdir]); + files = []; + for k=1:length(filetypes) + fls = dir([startdir filetypes{k}]); + files = [files; fls]; + end + + for k=1:length(files) + disp(['Processing file ' files(k).name]); + if (nargin>5) + args = horzcat({startdir files(k).name}, varargin); % changed by -fk to pass both dir + filename + else + args = {startdir files(k).name}; % changed by -fk to pass both dir + filename + end + a = feval(func,args{:}); + y = [y; a]; + end + + if (recursive) + dirs = dir(startdir); + isdir = cell2mat({dirs.isdir}); + dirs = dirs(find(isdir)); + for k = 1:length(dirs) + if (~strcmp(dirs(k).name,'.') & ~strcmp(dirs(k).name,'..')) + y = forAllFilesInDir([startdir dirs(k).name], filetypes, func, recursive, y, varargin{:}); + end + end + end + disp(['Left directory ' startdir]); +else + disp(['Skipped directory ' startdir]); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/filesys/pathsdiff.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function path_out=pathsdiff(root_path,substr_notwanted) +path_out=''; +root_path=explode(';',root_path); +for i=1:size(root_path,2) + tmp_path=root_path{i}; + if(isempty(findstr(tmp_path,substr_notwanted))) + path_out=strcat(tmp_path,';',path_out ); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/filesys/pathsdiff_mac.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function path_out=pathsdiff(root_path,substr_notwanted) +path_out=''; +root_path=explode(':',root_path); +for i=1:size(root_path,2) + tmp_path=root_path{i}; + if(isempty(findstr(tmp_path,substr_notwanted))) + path_out=strcat(tmp_path,':',path_out ); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/math/dist.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +function out = dist(a,b) + +out = sqrt(sum((a-b).^2)); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/math/maxsum.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function [startf,stopf,maxsofar]=maxsum(in) + +maxsofar=0; +maxendinghere=0; +startf=1; +stopf=1; +aktstartf=1; + +for i=1:length(in) + if (maxendinghere+in(i) < 0) + maxendinghere=0; + aktstartf=i+1; + else + maxendinghere=maxendinghere+in(i); + end + + if(maxendinghere > maxsofar) + maxsofar=maxendinghere; + stopf=i; + startf=aktstartf; + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/sp/acorr2peaks.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function out=acorr2peaks(acorr) +for i=1:size(acorr,2) +p{i}=peaks(acorr(:,i)); +end +out=zeros(size(acorr)); +for i=1:size(acorr,2) +out(p{i},i)=acorr(p{i},i); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/sp/peaks.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function out = peaks(in, hw) + +% this shall be the great peak picking algo. +% 1. lowpass +% 2. hillvalley +% 3. plateaus +% 4. filter + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/sp/seq_wavwrite.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,407 @@ +function seq_wavwrite(in_files,wavefile,varargin) +% Combines wav-data from folder or specific files in one file. +% +% seq_wavwrite(path_string,wavefile) combines all .wav files in +% path_string in the file wavefile. +% i.e.: seq_wavwrite('C:\wav\','C:\combined.wav') +% +% The file format will depend on the first file processed, the other +% files will be transfered to this format by resampling, dithering and +% channel repetition/rejection. You may use some of the extra-options 'Fs','nbits' +% or 'channels' to override these settings. +% i.e.: seq_wavwrite('C:\wav\','C:\combined.wav','Fs',44100,'nbits',16,'channels',1) +% will produce a mono file with 44.1 kHz samle rate and 16bits per sample +% +% seq_wavwrite(files_cellarray,wavefile) only combines the files specified +% in files_cellarray. +% i.e.: files={'C:\wav\test1.wav','C:\other_wav\test2.wav'}; +% seq_wavwrite(files,'C:\combined.wav'); +% +% You may want to copy only some parts of the files. +% Therefore use the extra-option 'sequences': +% seq_wavwrite(files,'C:\combined','sequences',segments); +% ,where segments is an cell array the same size as the files_cellarray, +% witch contains the position of the parts for every file. +% Every cell row contains a struct array with the following fields: +% abs_startspl and abs_stopspl +% or +% abs_startms and abs_stopms +% You may also specify the channels that are to be copied in the field +% channels +% i.e.: files={'C:\wav\test1.wav','C:\other_wav\test2.wav'}; +% segsforfile1(1).abs_startspl=1; +% segsforfile1(1).abs_stopspl=44100; +% segsforfile1(2).abs_startspl=88200; +% segsforfile1(2).abs_stopspl=200000; +% segsforfile2(1).abs_startms=1; +% segsforfile2(1).abs_stopms=2000; +% segsforfile2(1).channels=[1 2]; <- use the first two channels +% segments={segsforfile1,segsforfile2}; +% seq_wavwrite(files,'C:\combined','sequences',segments); +% +% If you want to copy specific files as a whole, just omit their abs_... +% values. +% +% seq_wavwrite uses blockwise file processing to be able to copy large +% amounts of data. The option 'max_chunksize' allows you to specify the +% blocksize in samples. Keep in mind that in multichannel mode the actual +% blocksize will be chunksize times channels. +% i.e.: seq_wavwrite('C:\wav\','C:\combined.wav','max_chunksize',44100*60) + +% Parse inputs: + +[slash,leftargs]=process_options(varargin,'systemslash','\'); + +if ischar(in_files) + data_names=dir(strcat(in_files,slash,'*.wav')); + in_files=strcat(in_files,{data_names.name}); +end + +[tmp_sig,tmp_Fs,tmp_nbits]=wavread(char(in_files{1}),[1 2]); +tmp_channels=size(tmp_sig,2); + +def_max_chunk_size = 44100*60*2;%chunksize nearly one minute at 44,1 khz sample rate +[sequences,Fs,nbits,channels,max_chunk_size]=process_options(leftargs,'sequences',[],'Fs',... + tmp_Fs,'nbits',tmp_nbits,'channels',tmp_channels,'max_chunksize',def_max_chunk_size); + +if ischar(in_files) && ~isempty(sequences) + warning('segment parameters ignored in directory-input mode') + sequences=[]; +end + +% Determine number of bytes in chunks +% (not including pad bytes, if needed): +% ---------------------------------- +% 'RIFF' 4 bytes +% size 4 bytes +% 'WAVE' 4 bytes +% 'fmt ' 4 bytes +% size 4 bytes +% <wave-format> 14 bytes +% <format_specific> 2 bytes (PCM) +% 'data' 4 bytes +% size 4 bytes +% <wave-data> N bytes +% ---------------------------------- + +bytes_per_sample = ceil(nbits/8); +fmt_cksize = 16; % Don't include 'fmt ' or its size field + +% Open file for output: +[fid,err] = OpenWaveWrite(wavefile); +error(err); +try + % Prepare basic chunk structure fields: + ck=[]; ck.fid=fid; ck.filename = wavefile; + + fwrite(fid,zeros(1,20),'uchar'); %skip previous chunks + % Write <wave-format>: + fmt.filename = wavefile; + if nbits == 32, + fmt.wFormatTag = 3; % Data encoding format (1=PCM, 3=Type 3 32-bit) + else + fmt.wFormatTag = 1; + end + fmt.nSamplesPerSec = Fs; % Samples per second + fmt.nAvgBytesPerSec = channels*bytes_per_sample*Fs; % Avg transfer rate + fmt.nBlockAlign = channels*bytes_per_sample; % Block alignment + fmt.nBitsPerSample = nbits; % standard <PCM-format-specific> info + fmt.nChannels = channels; % Number of channels + error(write_wavefmt(fid,fmt)); + + fwrite(fid,zeros(1,8),'uchar'); %skip following chunks + + % Write all audio data + sample_sum=0; + for filei=1:size(in_files,2) + resamplewarn=0; + channelwarn=0; + if ~isempty(sequences)&& ~isempty(sequences{filei}) + numsegs=size(sequences{filei},2); + else numsegs=1; + end + for seqi=1:numsegs; + tmp_fsiz=wavread(char(in_files{filei}),'size'); + tmp_fsiz=tmp_fsiz(1); + [y,tmp_fs,null]=wavread(char(in_files{filei}),[1 2]);%read data + if ~isempty(sequences) && ~isempty(sequences{filei}) + if isfield(sequences{filei}(seqi),'abs_startspl') + spl_seq=[sequences{filei}(seqi).abs_startspl sequences{filei}(seqi).abs_stopspl]; + elseif isfield(sequences{filei}(seqi),'abs_startms') + spl_seq=floor([sequences{filei}(seqi).abs_startms sequences{filei}(seqi).abs_stopms].*tmp_fs./1000); + else + spl_seq=[1 tmp_fsiz]; + end + if (spl_seq(1)< 1) || (spl_seq(2) > tmp_fsiz) + warning('correcting segment range, not necessary critical in miliseconds-mode') + spl_seq(1)=max(spl_seq(1),1); + spl_seq(2)=min(spl_seq(2),tmp_fsiz); + end + else + spl_seq=[1 tmp_fsiz]; + end + win_start=spl_seq(1); + win_stop=(min(spl_seq(2),spl_seq(1)+max_chunk_size-1)); + while win_stop <= spl_seq(2) + [y,tmp_fs,null]=wavread(char(in_files{filei}),[win_start win_stop]);%read data + if (size(y,2) > 1) && ~isempty(sequences) && isfield(sequences{filei}(seqi),'channels') %choose channel + if size(y,2) >= max(sequences{filei}(seqi).channels) + y=y(:,sequences{filei}(seqi).channels); + else + if ~channelwarn + warning('ignoring errorneous channel field'); + channelwarn=1; + end + end + end + if (tmp_fs ~= Fs) %resample data if necessary + if ~resamplewarn + fprintf('seq_wavwrite.m: resampling from %d to %d Hz. \n',tmp_fs,Fs); + resamplewarn=1; + end + y=resample(y,Fs,tmp_fs); + end + [samples,akt_channels] = size(y); + if akt_channels > channels % if necessary make equivalent channelnum + y=y(:,1:channels); + elseif akt_channels < channels + y=[y repmat(y(:,end),1,channels-akt_channels)]; + end + error(write_wavedat(fid,fmt,y)); + sample_sum=sample_sum+samples; + + if win_stop == spl_seq(2), break; + end + win_start=win_start+max_chunk_size; + win_stop=(min(spl_seq(2),win_stop+max_chunk_size)); + end + end + end + clear y; + + total_samples = sample_sum * channels; + total_bytes = total_samples * bytes_per_sample; + data_cksize = total_bytes; + + riff_cksize = 36+total_bytes; + + % Determine pad bytes: + % Determine if a pad-byte must be appended to data chunk: + if rem(data_cksize, 2) ~= 0, + fwrite(fid,0,'uchar'); + end + data_pad = rem(data_cksize,2); + riff_cksize = riff_cksize + data_pad; % + fmt_pad, always 0 + + % Write RIFF chunk: + fseek(fid,0,'bof'); + ck.ID = 'RIFF'; + ck.Size = riff_cksize; + error(write_ckinfo(ck)); + + % Write WAVE subchunk: + ck.ID = 'WAVE'; + ck.Size = []; % Indicate a subchunk (no chunk size) + error(write_ckinfo(ck)); + + % Write <fmt-ck>: + ck.ID = 'fmt '; + ck.Size = fmt_cksize; + error(write_ckinfo(ck)); + + % Write <data-ck>: + fseek(fid,36,'bof'); + ck.ID = 'data'; + ck.Size = data_cksize; + error(write_ckinfo(ck)); + err=''; +catch + err=lasterr; +end +% Close file: +fclose(fid); + +error(err); +% end of wavwrite() + + +% ------------------------------------------------------------------------ +% Private functions: +% ------------------------------------------------------------------------ + + +% ------------------------------------------------------------------------ +function [fid,err] = OpenWaveWrite(wavefile) +% OpenWaveWrite +% Open WAV file for writing. +% If filename does not contain an extension, add ".wav" + +fid = []; +err = ''; +if ~isstr(wavefile), + err='Wave file name must be a string.'; return; +end +if isempty(findstr(wavefile,'.')), + wavefile=[wavefile '.wav']; +end +% Open file, little-endian: +[fid,err] = fopen(wavefile,'wb','l'); + +return + + +% ------------------------------------------------------------------------ +function err = write_ckinfo(ck) +% WRITE_CKINFO: Writes next RIFF chunk, but not the chunk data. +% Assumes the following fields in ck: +% .fid File ID to an open file +% .ID 4-character string chunk identifier +% .Size Size of chunk (empty if subchunk) +% +% +% Expects an open FID pointing to first byte of chunk header, +% and a chunk structure. +% ck.fid, ck.ID, ck.Size, ck.Data + +errmsg = ['Failed to write ' ck.ID ' chunk to WAVE file: ' ck.filename]; +err = ''; + +if (fwrite(ck.fid, ck.ID, 'char') ~= 4), + err=errmsg; return; +end + +if ~isempty(ck.Size), + % Write chunk size: + if (fwrite(ck.fid, ck.Size, 'uint32') ~= 1), + err=errmsg; return; + end +end + +return + +% ------------------------------------------------------------------------ +function err = write_wavefmt(fid, fmt) +% WRITE_WAVEFMT: Write WAVE format chunk. +% Assumes fid points to the wave-format subchunk. +% Requires chunk structure to be passed, indicating +% the length of the chunk. + +errmsg = ['Failed to write WAVE format chunk to file' fmt.filename]; +err = ''; + +% Create <wave-format> data: +if (fwrite(fid, fmt.wFormatTag, 'uint16') ~= 1) | ... + (fwrite(fid, fmt.nChannels, 'uint16') ~= 1) | ... + (fwrite(fid, fmt.nSamplesPerSec, 'uint32' ) ~= 1) | ... + (fwrite(fid, fmt.nAvgBytesPerSec, 'uint32' ) ~= 1) | ... + (fwrite(fid, fmt.nBlockAlign, 'uint16') ~= 1), + err=errmsg; return; +end + +% Write format-specific info: +if fmt.wFormatTag==1 | fmt.wFormatTag==3, + % Write standard <PCM-format-specific> info: + if (fwrite(fid, fmt.nBitsPerSample, 'uint16') ~= 1), + err=errmsg; return; + end + +else + err='Unknown data format.'; +end + +return + + +% ----------------------------------------------------------------------- +function y = PCM_Quantize(x, fmt) +% PCM_Quantize: +% Scale and quantize input data, from [-1, +1] range to +% either an 8-, 16-, or 24-bit data range. + +% Clip data to normalized range [-1,+1]: +ClipMsg = ['Data clipped during write to file:' fmt.filename]; +ClipWarn = 0; + +% Determine slope (m) and bias (b) for data scaling: +nbits = fmt.nBitsPerSample; +m = 2.^(nbits-1); + +switch nbits +case 8, + b=128; +case {16,24}, + b=0; +otherwise, + error('Invalid number of bits specified.'); +end + +y = round(m .* x + b); + +% Determine quantized data limits, based on the +% presumed input data limits of [-1, +1]: +ylim = [-1 +1]; +qlim = m * ylim + b; +qlim(2) = qlim(2)-1; + +% Clip data to quantizer limits: +i = find(y < qlim(1)); +if ~isempty(i), + warning(ClipMsg); ClipWarn=1; + y(i) = qlim(1); +end + +i = find(y > qlim(2)); +if ~isempty(i), + if ~ClipWarn, warning(ClipMsg); end + y(i) = qlim(2); +end + +return + + +% ----------------------------------------------------------------------- +function err = write_wavedat(fid,fmt,data) +% WRITE_WAVEDAT: Write WAVE data chunk +% Assumes fid points to the wave-data chunk +% Requires <wave-format> structure to be passed. + +err = ''; + +if fmt.wFormatTag==1 | fmt.wFormatTag==3, + % PCM Format + + % 32-bit Type 3 is normalized, so no scaling needed. + if fmt.nBitsPerSample ~= 32, + data = PCM_Quantize(data, fmt); + end + + switch fmt.nBitsPerSample + case 8, + dtype='uchar'; % unsigned 8-bit + case 16, + dtype='int16'; % signed 16-bit + case 24, + dtype='bit24'; % signed 24-bit + case 32, + dtype='float'; % normalized 32-bit floating point + otherwise, + err = 'Invalid number of bits specified.'; return; + end + + % Write data, one row at a time (one sample from each channel): + [samples,channels] = size(data); + total_samples = samples*channels; + + if (fwrite(fid, reshape(data',total_samples,1), dtype) ~= total_samples), + err = 'Failed to write PCM data samples.'; return; + end + + +else + % Unknown wave-format for data. + err = 'Unsupported data format.'; +end + +return + +% end of wavwrite.m
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/strings/escape.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function out = escape(in) +j=1; +for i=1:length(in) + if(in(i)=='\') + out(j) = in(i); + out(j+1) = '\'; + j=j+2; + else + out(j) = in(i); + j=j+1; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/strings/explode.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function out = explode(divider,input) +%out = explode(divider,input) +out = {}; +len = length(input); +start = 1; +stop = 1; + +segment = 1; +while (stop <= len) + while (stop <= len)&& (~strcmp(input(stop),divider)) + stop = stop+1; + end + out{segment} = input(start:stop-1); + segment = segment+1; + stop= stop+1; + start = stop; +end + + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/strings/substr.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +function outstr = substr(str, offset, len, repl) +%SUBSTR Extract a substring out of a string. +% +% SUBSTR(STRING, OFFSET, LENGTH) extracts a substring out of STRING with +% given LENGTH starting at the given OFFSET. First character is at offset 0. +% If OFFSET is negative, starts that far from the end of the string. If +% LENGTH is omitted, returns everything to the end of the string. If LENGTH +% is negative, removes that many characters from the end of the string. +% +% SUBSTR(STRING, OFFSET, LENGTH, REPLACEMENT) will not return the substring +% as specified by STRING, OFFSET, and LENGTH (see above) but rather replace +% it by REPLACEMENT and return the result. +% +% Examples: +% +% Get first character: substr(string, 0, 1) +% Get last character: substr(string, -1, 1) +% Remove first character: substr(string, 1) +% Remove last character: substr(string, 0, -1) +% Remove first and last character: substr(string, 1, -1) +% +% SUBSTR is a MATLAB version of the Perl operator with the same name. +% However, unlike Perl's SUBSTR, no warning is produced if the substring is +% totally outside the string. + +% Author: Peter J. Acklam +% Time-stamp: 2004-02-21 22:49:14 +0100 +% E-mail: pjacklam@online.no +% URL: http://home.online.no/~pjacklam + + % Check number of input arguments. + error(nargchk(2, 4, nargin)); + + n = length(str); + + % Get lower index. + lb = offset + 1; % offset from beginning of string + if offset < 0 + lb = lb + n; % offset from end of string + end + lb = max(lb, 1); + + % Get upper index. + if nargin == 2 % SUBSTR(STR, OFFSET) + ub = n; + elseif nargin > 2 % SUBSTR(STR, OFFSET, LEN) + if len >= 0 + ub = lb + len - 1; + else + ub = n + len; + end + ub = min(ub, n); + end + + % Extract or replace substring. + if nargin < 4 + outstr = str(lb : ub); % extract substring + else + outstr = [str(1:lb-1) repl str(ub+1:end)]; % replace substring + end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/visualisation/datarawplot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,45 @@ +function [h,out] = datarawplot(wavin, varargin) +%[h,out] =datarawplot(data,'minx',-1, 'maxx',-1,'color','k','against',-1); + + +[miny,maxy,minx,maxx,color,against,unused]=process_options(varargin,... + 'miny',-1,'maxy',-1,'minx',-1,'maxx',-1,'color','k','against',-1); +out= wavin; + + +m=min(wavin); +ma=max(wavin); +add=(ma-m)*0.1; +if miny ==-1 + miny=m-add; +end +if maxy==-1 + maxy=ma+add; +end + +wavlens=length(wavin); + +if against==-1 + h=plot(out,color); +else + h=plot(against,out,color); +end + +if minx ==-1 + minx=0; + if against ~=-1 + minx=min(against); + end +end +if maxx==-1 + maxx=wavlens; + if against ~=-1 + maxx=max(against); + end +end +axis([minx maxx miny maxy]); +set(gca,'FontSize',8); +h=get(0,'CurrentFigure'); +axis xy; +xlabel('Time[samples]','FontSize',8) +%ylabel('Amplitude','FontSize',8)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/visualisation/dataspecgram.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,65 @@ +function [h,out] = dataspecgram(wavin, varargin) +%[h,spec] = dataspecgram(data,'sr',fs,'fftlen',2048,'stepw',1024,'winsize',2048,'min_fkhz',0.5,'max_fkhz',15,'nolog',0,'clip',0,'logadd',0); + + +[sr,fftlen, stepw,winsize,min_fkhz,max_fkhz,nolog,clip,logadd,fontsize,cutoff,colormode,unused]=process_options(varargin,... + 'sr',44800,'fftlen',2048,'stepw',1024,'winsize',2048,'min_fkhz',0.5,'max_fkhz',15,... + 'nolog',0,'clip',0,'logadd',0,'fontsize',8,'cutoff',0,'colormode','jet'); +out= specgram(wavin,fftlen,sr,winsize,winsize-stepw); + +wavlens=round(length(wavin)/sr*100)/100; + +firstindex= max(round(fftlen/(sr)*min_fkhz*1000),1); +lastindex= min(round(fftlen/(sr)*max_fkhz*1000),fftlen/2); + +out=out(firstindex:lastindex,:); +if nolog + out=abs(out); + out=sqrt(abs(out)); + out=out./max(max(out)); +else + out=log(0.000000001+logadd+abs(out)); + out=out./max(max(abs(out))); + out=out+1; +end + +%cut off small values +if cutoff + for i=1:size(out,2) + out((out(:,i)<cutoff),i)=0; + end +end + + + +if clip > 0 + h=image([0 wavlens],[min_fkhz max_fkhz],out*100*clip); +else + h=imagesc([0 wavlens],[min_fkhz max_fkhz],out); +end + +switch colormode + case 'inv' + color= 'w'; + bgcolor= 'k'; + colormap(hot); + case 'bw' + color= 'black'; + colormap(1-grey); + bgcolor= 'w'; + case 'jet' + color= 'black'; + colormap(jet); + bgcolor= 'w'; +end + +set(gca,'FontSize',fontsize,'xcolor',color); +set(gca,'FontSize',fontsize,'ycolor',color); + +h=get(0,'CurrentFigure'); +axis xy; +set(gcf, 'color', bgcolor); +set(gcf, 'InvertHardCopy', 'off'); + +xlabel('Time[s]','FontSize',fontsize,'Color',color) +ylabel('Frequency[kHz]','FontSize',fontsize,'Color',color)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/visualisation/datawavplot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +function [h,out] = datawavplot(wavin, varargin) +%[h,out] = datawavplot(data,'sr',fs,'minx',-1, 'maxx',-1,'color','k'); + + +[sr,miny,maxy,minx,maxx,color,unused]=process_options(varargin,... + 'sr',44800,'miny',-1,'maxy',-1,'minx',-1,'maxx',-1,'color','k'); +out= wavin; + + +m=min(wavin); +ma=max(wavin); +add=(ma-m)*0.1; +if miny ==-1 + miny=m-add; +end +if maxy==-1 + maxy=ma+add; +end + +wavlens=round(length(wavin)/sr*100)/100; +timetbl=([0:length(wavin)-1]./length(wavin))*wavlens; +h=plot(timetbl,out,color); + +if minx ==-1 + minx=0; +end +if maxx==-1 + maxx=wavlens; +end +axis([minx maxx miny maxy]); +set(gca,'FontSize',8); +h=get(0,'CurrentFigure'); +axis xy; +xlabel('Time[s]','FontSize',8) +ylabel('Amplitude','FontSize',8)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/visualisation/datawavstem.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +function [h,out] = datawavstem(wavin, varargin) +%[h,out] = datawavplot(data) + + +[sr,miny,maxy,minx,maxx,unused]=process_options(varargin,... + 'sr',44800,'miny',-1,'maxy',-1,'minx',-1,'maxx',-1); +out= wavin; + +m=min(wavin); +ma=max(wavin); +add=(ma-m)*0.1; +if miny ==-1 + miny=m-add; +end +if maxy==-1 + maxy=ma+add; +end +wavlens=round(length(wavin)/sr*100)/100; +timetbl=([0:length(wavin)-1]./length(wavin))*wavlens; +h=stem(timetbl,out,'r'); + +if minx ==-1 + minx=0; +end +if maxx==-1 + maxx=wavlens; +end +axis([minx maxx miny maxy]); +set(gca,'FontSize',8); +h=get(0,'CurrentFigure'); +axis xy; +xlabel('Time[s]','FontSize',8) +ylabel('Amplitude','FontSize',8) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/bioakustik_tools/visualisation/filespecgram.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,19 @@ +function out=filespecgram(working_file, varargin) + +[starts,stops, unused]=process_options(varargin,'starts',0,'stops',-1); + + +filesize=wavread(working_file,'size'); +audiodatalen=filesize(1); +num_channels=filesize(2); +[src,sr,bits] = wavread(working_file,[1 2]); + + +startspl=max(1,starts*sr); +if stops =-1, + stopspl=audiodatalen; +else + stopspl=max(1,starts*sr); +end + +sgram=dataspecgram(src(startspl:stopspl,1),'sr',sr); \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/.gitignore Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +*.mex*
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/LICENSE Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/> + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/MANIFEST Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,56 @@ +./cuttingPlane/cpGradientDiag.m +./cuttingPlane/cpGradientDiagMKL.m +./cuttingPlane/cpGradientFull.m +./cuttingPlane/cpGradientFullMKL.m +./cuttingPlane/cuttingPlaneFull.m +./cuttingPlane/cuttingPlaneRandom.m +./distance/distanceDiag.m +./distance/distanceDiagMKL.m +./distance/distanceFull.m +./distance/distanceFullMKL.m +./distance/setDistanceDiag.m +./distance/setDistanceDiagMKL.m +./distance/setDistanceFull.m +./distance/setDistanceFullMKL.m +./feasible/feasibleDiag.m +./feasible/feasibleDiagMKL.m +./feasible/feasibleFull.m +./feasible/feasibleFullMKL.m +./initialize/initializeDiag.m +./initialize/initializeDiagMKL.m +./initialize/initializeFull.m +./initialize/initializeFullMKL.m +./LICENSE +./loss/lossHingeDiagMKL.m +./loss/lossHingeFullMKL.m +./loss/lossHinge.m +./MANIFEST +./metricPsi/metricPsiPO.m +./mlr_demo.m +./mlr_plot.m +./mlr_test.m +./mlr_train.m +./README +./regularize/regularizeKernel.m +./regularize/regularizeMKLDiag.m +./regularize/regularizeMKLFull.m +./regularize/regularizeNone.m +./regularize/regularizeTraceDiag.m +./regularize/regularizeTraceFull.m +./regularize/regularizeTwoDiag.m +./regularize/regularizeTwoFull.m +./separationOracle/separationOracleAUC.m +./separationOracle/separationOracleKNN.m +./separationOracle/separationOracleMAP.m +./separationOracle/separationOracleMRR.m +./separationOracle/separationOracleNDCG.m +./separationOracle/separationOraclePrecAtK.m +./util/binarysearch.c +./util/cummax.c +./util/dbprint.m +./util/distToFrom.m +./util/Makefile +./util/mlr_solver.m +./util/PsdToEdm.m +./util/soft_classify.m +./Wine.mat
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,236 @@ +Metric Learning to Rank (mlr-1.2) +http://www-cse.ucsd.edu/~bmcfee/code/mlr/ + +AUTHORS: +Brian McFee <bmcfee@cs.ucsd.edu> +Daryl Lim <dklim@ucsd.edu> + +This code is distributed under the GNU GPL license. See LICENSE for details +or http://www.gnu.org/licenses/gpl-3.0.txt + + + +INTRODUCTION +------------ +This package contains the MATLAB code for Metric Learning to Rank (MLR). +The latest version of this software can be found at the URL above. + +The software included here implements the algorithm described in + +[1] McFee, Brian and Lanckriet, G.R.G. Metric learning to rank. + Proceedings of the 27th annual International Conference + on Machine Learning (ICML), 2010. + +Please cite this paper if you use this code. + +If you use the Robust MLR code (rmlr_train), please cite: + +[2] Lim, D.K.H., McFee, B. and Lanckriet, G.R.G. Robust structural metric learning. + Proceedings of the 30th annual International Conference on Machine Learning (ICML), + 2013. + + +INSTALLATION +------------ + +1. Requirements + +This software requires MATLAB R2007a or later. Because it makes extensive use +of the "bsxfun" function, earlier versions of Matlab will not work. + +If you have an older Matlab installation, you can install an alternative bsxfun +implementation by going to + +http://www.mathworks.com/matlabcentral/fileexchange/23005 , + +however, this may not work, and it will certainly be slower than the native +bsxfun implementation. + + +2. Compiling MEX functions + +MLR includes two auxiliary functions "cummax" and "binarysearch" to accelerate +certain steps of the optimization procedure. The source code for these +functions is located in the "util" subdirectory. + +A makefile is provided, so that (on Unix systems), you can simply type + +cd util +make +cd .. + + +3. Running the demo + +To test the installation, first add the path to MLR to your Matlab environment: + + >> addpath(genpath('/path/to/mlr/')); + +Then, run the demo script: + + >> mlr_demo + +from within Matlab. The demo generates a random training/test split of the Wine data set +(http://archive.ics.uci.edu/ml/datasets/Wine) +and learns a metric with MLR. Both native and learned metrics are displayed in a figure +with a scatter plot. + + +TRAINING +-------- + +There are several modes of operation for training metrics with MLR. In the +simplest mode, training data is contained in a matrix X (each column is a +training vector), and Y contains the labels/relevance for the training data +(see below). + + [W, Xi, D] = mlr_train(X, Y, C,...) + + X = d*n data matrix + Y = either n-by-1 label of vectors + OR + n-by-2 cell array where + Y{q,1} contains relevant indices for q, and + Y{q,2} contains irrelevant indices for q + + C >= 0 slack trade-off parameter (default=1) + + W = the learned metric, i.e., the inner product matrix of the learned + space can be computed by X' * W * X + Xi = slack value on the learned metric (see [1]) + D = diagnostics + + +By default, MLR optimizes for Area Under the ROC Curve (AUC). This can be +changed by setting the "LOSS" parameter to one of several ranking loss +measures: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS) + where LOSS is one of: + 'AUC': Area under ROC curve (default) + 'KNN': KNN accuracy* + 'Prec@k': Precision-at-k + 'MAP': Mean Average Precision + 'MRR': Mean Reciprocal Rank + 'NDCG': Normalized Discounted Cumulative Gain + + *Note: KNN is correct only for binary classification problems; in + practice, Prec@k is usually a better alternative. + + +For KNN/Prec@k/NDCG, a threshold k may be set to determine the truncation of +the ranked list. Thiscan be done by setting the k parameter: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS, k) + where k is the number of neighbors for Prec@k or NDCG + (default=3) + + +By default, MLR regularizes the metric W by the trace, i.e., the 1-norm of the +eigenvalues. This can be changed to one of several alternatives: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG) + where REG defines the regularization on W, and is one of: + 0: no regularization + 1: 1-norm: trace(W) (default) + 2: 2-norm: trace(W' * W) + 3: Kernel: trace(W * X), assumes X is square + and positive-definite + + The last setting, "kernel", is appropriate for regularizing metrics learned + from kernel matrices. + + +W corresponds to a linear projection matrix (rotation and scaling). To learn a +restricted model which may only scale, but not rotate the data, W can be +constrained to diagonal matrices by setting the "Diagonal" parameter to 1: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal) + Diagonal = 0: learn a full d-by-d W (default) + Diagonal = 1: learn diagonally-constrained W (d-by-1) + + Note: the W returned in this case will be the d-by-1 vector corresponding + to the main diagonal of a full metric, not the full d-by-d matrix. + + + +Finally, we provide a stochastic gradient descent implementation to handle +large-scale problems. Rather than estimating gradients from the entire +training set, this variant uses a random subset of size B (see below) at each +call to the cutting plane subroutine. This results in faster, but less +accurate, optimization: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal, B) + where B > 0 enables stochastic optimization with batch size B + + + +TESTING +------- + +Once a metric has been trained by "mlr_train", you can evaluate performance +across all measures by using the "mlr_test" function: + + Perf = mlr_test(W, test_k, Xtrain, Ytrain, Xtest, Ytest) + + W = d-by-d positive semi-definite matrix + test_k = vector of k-values to use for KNN/Prec@k/NDCG + Xtrain = d-by-n matrix of training data + Ytrain = n-by-1 vector of training labels + OR + n-by-2 cell array where + Y{q,1} contains relevant indices (in 1..n) for point q + Y{q,2} contains irrelevant indices (in 1..n) for point q + Xtest = d-by-m matrix of testing data + Ytest = m-by-1 vector of training labels, or m-by-2 cell array + If using the cell version, indices correspond to + the training set, and must lie in the range (1..n) + + + The output structure Perf contains the mean score for: + AUC, KNN, Prec@k, MAP, MRR, NDCG, + as well as the effective dimensionality of W, and + the best-performing k-value for KNN, Prec@k, and NDCG. + + For information retrieval settings, consider Xtrain/Ytrain as the corpus + and Xtest/Ytest as the queries. + +By testing with + Wnative = [] +you can quickly evaluate the performance of the native (Euclidean) metric. + + +MULTIPLE KERNEL LEARNING +------------------------ + +As of version 1.1, MLR supports learning (multiple) kernel metrics, using the method described in + +[3] Galleguillos, Carolina, McFee, Brian, Belongie, Serge, and Lanckriet, G.R.G. + From region similarity to category discovery. + Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. + +For m kernels, input data must be provided in the form of an n-by-n-by-m matrix K, where K(:,:,i) +contains the i'th kernel matrix of the training data. Training the metric is similar to the +single-kernel case, except that the regularization parameter should be set to 3 (kernel regularization), +eg: + + >> [W, Xi, D] = mlr_train(K, Y, C, 'auc', 1, 3); + +returns an n-by-n-by-m matrix W which can be used directly with mlr_test. Multiple kernel learning also supports +diagonally constrained learning, eg: + + >> [W, Xi, D] = mlr_train(K, Y, C, 'auc', 1, 3, 1); + +returns an n-by-m matrix W, where W(:,i) is the main diagonal of the i'th kernels metric. Again, this W can be fed +directly into mlr_test. + +For testing with multiple kernel data, Ktest should be an n-by-nTest-by-m matrix, where K(:,:,i) is the +training-by-test slice of the i'th kernel matrix. + +FEEDBACK +-------- + +Please send any bug reports, source code contributions, etc. to + +Brian McFee <bmcfee@cs.ucsd.edu> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/cuttingPlane/cpGradientDODMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function dPsi = cpGradientDODMKL(X, S) + + [d,n,m] = size(X); + + dPsi = zeros(m,m,d); + + for i = 1:m + dPsi(i,i,:) = diag(X(:,:,i) * S * X(:,:,i)'); + for j = (i+1):m + Q = X(:,:,i) + X(:,:,j); + dPsi(i,j,:) = diag( Q * S * Q'); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/cuttingPlane/cpGradientDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function dPsi = cpGradientDiag(X, S, batchsize) + + dPsi = diag(X * S * X') / batchsize; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/cuttingPlane/cpGradientDiagMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function dPsi = cpGradientDiagMKL(X, S, batchsize) + + [d,n,m] = size(X); + + dPsi = zeros(d,m); + + for i = 1:m + dPsi(:,i) = diag(X(:,:,i) * S * X(:,:,i)'); + end + + dPsi = dPsi / batchsize; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/cuttingPlane/cpGradientFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function dPsi = cpGradientFull(X, S, batchSize) + + dPsi = X * S * X' / batchSize; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/cuttingPlane/cpGradientFullMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function dPsi = cpGradientFullMKL(X, S, batchsize) + + [d,n,m] = size(X); + + dPsi = zeros(d,d,m); + for i = 1:m + dPsi(:,:,i) = X(:,:,i) * S * X(:,:,i)'; + end + + dPsi = dPsi / batchsize; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/cuttingPlane/cuttingPlaneFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,96 @@ +function [dPsi, M, SO_time] = cuttingPlaneFull(k, X, W, Ypos, Yneg, batchSize, SAMPLES, ClassScores) +% +% [dPsi, M, SO_time] = cuttingPlaneFull(k, X, W, Yp, Yn, batchSize, SAMPLES, ClassScores) +% +% k = k parameter for the SO +% X = d*n data matrix +% W = d*d PSD metric +% Yp = cell-array of relevant results for each point +% Yn = cell-array of irrelevant results for each point +% batchSize = number of points to use in the constraint batch +% SAMPLES = indices of valid points to include in the batch +% ClassScores = structure for synthetic constraints +% +% dPsi = dPsi vector for this batch +% M = mean loss on this batch +% SO_time = time spent in separation oracle + + global SO PSI DISTANCE CPGRADIENT; + + [d,n,m] = size(X); + D = DISTANCE(W, X); + + M = 0; + S = zeros(n); + dIndex = sub2ind([n n], 1:n, 1:n); + + SO_time = 0; + + if isempty(ClassScores) + TS = zeros(batchSize, n); + parfor i = 1:batchSize + if i <= length(SAMPLES) + j = SAMPLES(i); + + if isempty(Ypos{j}) + continue; + end + if isempty(Yneg) + % Construct a negative set + Ynegative = setdiff((1:n)', [j ; Ypos{j}]); + else + Ynegative = Yneg{j}; + end + SO_start = tic(); + [yi, li] = SO(j, D, Ypos{j}, Ynegative, k); + SO_time = SO_time + toc(SO_start); + + M = M + li /batchSize; + TS(i,:) = PSI(j, yi', n, Ypos{j}, Ynegative); + end + end + + % Reconstruct the S matrix from TS + S(SAMPLES,:) = TS; + S(:,SAMPLES) = S(:,SAMPLES) + TS'; + S(dIndex) = S(dIndex) - sum(TS, 1); + else + + % Do it class-wise for efficiency + batchSize = 0; + for j = 1:length(ClassScores.classes) + c = ClassScores.classes(j); + points = find(ClassScores.Y == c); + + Yneg = find(ClassScores.Yneg{j}); + yp = ClassScores.Ypos{j}; + + if length(points) <= 1 + continue; + end + + batchSize = batchSize + length(points); + TS = zeros(length(points), n); + parfor x = 1:length(points) + i = points(x); + yl = yp; + yl(i) = 0; + Ypos = find(yl); + SO_start = tic(); + [yi, li] = SO(i, D, Ypos, Yneg, k); + SO_time = SO_time + toc(SO_start); + + M = M + li; + TS(x,:) = PSI(i, yi', n, Ypos, Yneg); + end + + S(points,:) = S(points,:) + TS; + S(:,points) = S(:,points) + TS'; + S(dIndex) = S(dIndex) - sum(TS, 1); + end + M = M / batchSize; + end + + dPsi = CPGRADIENT(X, S, batchSize); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/cuttingPlane/cuttingPlaneRandom.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,105 @@ +function [dPsi, M, SO_time] = cuttingPlaneRandom(k, X, W, Ypos, Yneg, batchSize, SAMPLES, ClassScores) +% +% [dPsi, M, SO_time] = cuttingPlaneRandom(k, X, W, Yp, Yn, batchSize, SAMPLES, ClassScores) +% +% k = k parameter for the SO +% X = d*n data matrix +% W = d*d PSD metric +% Yp = cell-array of relevant results for each point +% Yn = cell-array of irrelevant results for each point +% batchSize = number of points to use in the constraint batch +% SAMPLES = indices of valid points to include in the batch +% ClassScores = structure for synthetic constraints +% +% dPsi = dPsi vector for this batch +% M = mean loss on this batch +% SO_time = time spent in separation oracle + + global SO PSI SETDISTANCE CPGRADIENT; + + [d,n] = size(X); + + + if length(SAMPLES) == n + % All samples are fair game (full data) + Batch = randperm(n); + Batch = Batch(1:batchSize); + D = SETDISTANCE(X, W, Batch); + + else + Batch = randperm(length(SAMPLES)); + Batch = SAMPLES(Batch(1:batchSize)); + + Ito = sparse(n,1); + + if isempty(ClassScores) + for i = Batch + Ito(Ypos{i}) = 1; + Ito(Yneg{i}) = 1; + end + D = SETDISTANCE(X, W, Batch, find(Ito)); + else + D = SETDISTANCE(X, W, Batch, 1:n); + end + end + + + M = 0; + S = zeros(n); + dIndex = sub2ind([n n], 1:n, 1:n); + + SO_time = 0; + + + if isempty(ClassScores) + TS = zeros(batchSize, n); + parfor j = 1:batchSize + i = Batch(j); + if isempty(Yneg) + Ynegative = setdiff((1:n)', [i ; Ypos{i}]); + else + Ynegative = Yneg{i}; + end + SO_start = tic(); + [yi, li] = SO(i, D, Ypos{i}, Ynegative, k); + SO_time = SO_time + toc(SO_start); + + M = M + li /batchSize; + TS(j,:) = PSI(i, yi', n, Ypos{i}, Ynegative); + end + S(Batch,:) = TS; + S(:,Batch) = S(:,Batch) + TS'; + S(dIndex) = S(dIndex) - sum(TS, 1); + else + for j = 1:length(ClassScores.classes) + c = ClassScores.classes(j); + points = find(ClassScores.Y(Batch) == c); + if ~any(points) + continue; + end + + Yneg = find(ClassScores.Yneg{j}); + yp = ClassScores.Ypos{j}; + + TS = zeros(length(points), n); + parfor x = 1:length(points) + i = Batch(points(x)); + yl = yp; + yl(i) = 0; + Ypos = find(yl); + SO_start = tic(); + [yi, li] = SO(i, D, Ypos, Yneg, k); + SO_time = SO_time + toc(SO_start); + + M = M + li /batchSize; + TS(x,:) = PSI(i, yi', n, Ypos, Yneg); + end + S(Batch(points),:) = S(Batch(points),:) + TS; + S(:,Batch(points)) = S(:,Batch(points)) + TS'; + S(dIndex) = S(dIndex) - sum(TS, 1); + end + end + + dPsi = CPGRADIENT(X, S, batchSize); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/distanceDODMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function D = distanceDODMKL(W, X) + + [d, n, m] = size(X); + + D = 0; + for i = 1:m + D = D + PsdToEdm(X(:,:,i)' * bsxfun(@times, squeeze(W(i,i,:)), X(:,:,i))); + for j = (i+1):m + Q = X(:,:,i) + X(:,:,j); + D = D + PsdToEdm(Q' * bsxfun(@times, squeeze(W(i,j,:)), Q)); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/distanceDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function D = distanceDiag(W, X) + + D = PsdToEdm(X' * bsxfun(@times, W, X)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/distanceDiagMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function D = distanceDiagMKL(W, X) + + [d, n, m] = size(X); + + D = zeros(n); + for i = 1:m + D = D + PsdToEdm(X(:,:,i)' * bsxfun(@times, W(:,i), X(:,:,i))); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/distanceFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function D = distanceFull(W, X) + + D = PsdToEdm(X' * W * X); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/distanceFullMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function D = distanceFullMKL(W, X) + + [d, n, m] = size(X); + + D = zeros(n); + parfor i = 1:m + D = D + PsdToEdm(X(:,:,i)' * W(:,:,i) * X(:,:,i)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/setDistanceDODMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,39 @@ +function D = setDistanceDODMKL(X, W, Ifrom, Ito) +% +% D = setDistanceDODMKL(X, W, Ifrom, Ito) +% +% X = d-by-n data matrix +% W = m-by-m-by-n PSD matrix +% Ifrom = k-by-1 vector of source points +% Ito = j-by-1 vector of destination points +% +% D = n-by-n matrix of squared euclidean distances from Ifrom to Ito +% D is sparse, and only the rows corresponding to Ifrom and +% columns corresponding to Ito are populated. + + [d,n,m] = size(X); + L = W.^0.5; + + D = 0; + for i = 1:m + for j = i:m + Vfrom = bsxfun(@times, squeeze(L(i,j)), X(:,Ifrom,i) + X(:,Ifrom,j)); + + if nargin == 4 + Vto = bsxfun(@times, squeeze(L(i,j)), X(:,Ito,i) + X(:,Ito,j)); + else + Vto = bsxfun(@times, squeeze(L(i,j)), X(:,:,i) + X(:,:,j)); + Ito = 1:n; + end + + if i == j + s = 0.5; + else + s = 1; + end + + D = D + s * distToFrom(n, Vto, Vfrom, Ito, Ifrom); + end + end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/setDistanceDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function D = setDistanceDiag(X, W, Ifrom, Ito) +% +% D = setDistanceDiag(X, W, Ifrom, Ito) +% +% X = d-by-n data matrix +% W = d-by-1 PSD matrix +% Ifrom = k-by-1 vector of source points +% Ito = j-by-1 vector of destination points +% +% D = n-by-n matrix of squared euclidean distances from Ifrom to Ito +% D is sparse, and only the rows corresponding to Ifrom and +% columns corresponding to Ito are populated. + + [d,n] = size(X); + L = W.^0.5; + + Vfrom = bsxfun(@times, L, X(:,Ifrom)); + + if nargin == 4 + Vto = bsxfun(@times, L, X(:,Ito)); + else + Vto = bsxfun(@times, L, X); + Ito = 1:n; + end + + D = distToFrom(n, Vto, Vfrom, Ito, Ifrom); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/setDistanceDiagMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +function D = setDistanceDiagMKL(X, W, Ifrom, Ito) +% +% D = setDistanceDiagMKL(X, W, Ifrom, Ito) +% +% X = d-by-n data matrix +% W = d-by-1 PSD matrix +% Ifrom = k-by-1 vector of source points +% Ito = j-by-1 vector of destination points +% +% D = n-by-n matrix of squared euclidean distances from Ifrom to Ito +% D is sparse, and only the rows corresponding to Ifrom and +% columns corresponding to Ito are populated. + + [d,n,m] = size(X); + L = W.^0.5; + + D = 0; + for i = 1:m + Vfrom = bsxfun(@times, L(:,i), X(:,Ifrom,i)); + + if nargin == 4 + Vto = bsxfun(@times, L(:,i), X(:,Ito,i)); + else + Vto = bsxfun(@times, L(:,i), X(:,:,i)); + Ito = 1:n; + end + + D = D + distToFrom(n, Vto, Vfrom, Ito, Ifrom); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/setDistanceFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,28 @@ +function D = setDistanceFull(X, W, Ifrom, Ito) +% +% D = setDistanceFull(X, W, Ifrom, Ito) +% +% X = d-by-n data matrix +% W = d-by-d PSD matrix +% Ifrom = k-by-1 vector of source points +% Ito = j-by-1 vector of destination points +% +% D = n-by-n matrix of squared euclidean distances from Ifrom to Ito +% D is sparse, and only the rows corresponding to Ifrom and +% columns corresponding to Ito are populated. + + [d,n] = size(X); + [vecs,vals] = eig(0.5 * (W + W')); + L = real(abs(vals)).^0.5 * vecs'; + + Vfrom = L * X(:,Ifrom); + + if nargin == 4 + Vto = L * X(:,Ito); + else + Vto = L * X; + Ito = 1:n; + end + + D = distToFrom(n, Vto, Vfrom, Ito, Ifrom); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/distance/setDistanceFullMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +function D = setDistanceFullMKL(X, W, Ifrom, Ito) +% +% D = setDistanceFullMKL(X, W, Ifrom, Ito) +% +% X = d-by-n-by-m data matrix +% W = d-by-d-by-m PSD matrix +% Ifrom = k-by-1 vector of source points +% Ito = j-by-1 vector of destination points +% +% D = n-by-n matrix of squared euclidean distances from Ifrom to Ito +% D is sparse, and only the rows corresponding to Ifrom and +% columns corresponding to Ito are populated. + + [d,n,m] = size(X); + + D = 0; + + if nargin < 4 + Ito = 1:n; + end + + parfor i = 1:m + [vecs,vals] = eig(0.5 * (W(:,:,i) + W(:,:,i)')); + L = real(abs(vals)).^0.5 * vecs'; + + Vfrom = L * X(:,Ifrom,i); + + Vto = L * X(:,Ito,i); + + D = D + distToFrom(n, Vto, Vfrom, Ito, Ifrom); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/dual/dualWDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function W = dualWDiag(alpha, Z, U, RHO, K) + + global PsiR; + d = size(K, 1); + m = length(alpha); + + W = Z - U - ones(d,1) / (d * RHO); + for i = 1:m + W = W + alpha(i) * PsiR{i} / RHO; + end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/dual/dualWDiagMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function W = dualWDiagMKL(alpha, Z, U, RHO, K) + + global PsiR; + d = size(K, 1); + m = length(alpha); + + nKernels = size(K,3); + + W = Z - U; + for p = 1:nKernels + W(:,p) = W(:,p) - diag(K(:,:,p)) / RHO; + end + + for i = 1:m + W = W + alpha(i) * PsiR{i} / RHO; + end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/dual/dualWLinear.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function W = dualWlinear(alpha, Z, U, RHO, K) + + global PsiR; + d = size(K, 1); + m = length(alpha); + + W = Z - U - eye(d) /(d * RHO); + for i = 1:m + W = W + alpha(i) * PsiR{i} / RHO; + end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/dual/dualWMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function W = dualWMKL(alpha, Z, U, RHO, K) + + global PsiR; + d = size(K, 1); + m = length(alpha); + + nKernels = size(K,3); + + W = Z - U - K / RHO; + for i = 1:m + W = W + alpha(i) * PsiR{i} / RHO; + end +% for p = 1:nKernels +% W(:,:,p) = W(:,:,p) - K(:,:,p) / RHO; +% for i = 1:m +% W(:,:,p) = W(:,:,p) + alpha(i) * PsiR{i}(:,:,p) / RHO; +% end +% end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/dual/structKernelDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function H = structKernelDiag(Psi1, Psi2) + + H = Psi1' * Psi2; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/dual/structKernelDiagMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function H = structKernelDiagMKL(Psi1, Psi2) + + H = Psi1(:)' * Psi2(:); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/dual/structKernelLinear.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function H = structKernelLinear(Psi1, Psi2) + + H = Psi1(:)' * Psi2(:); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/dual/structKernelMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function H = structKernelMKL(Psi1, Psi2) + + H = Psi1(:)' * Psi2(:); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/feasible/feasibleDODMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function W = feasibleDODMKL(W) +% +% W = feasibleFull(W) +% +% Projects a single d*1 matrix onto the PSD cone +% + W = max(0,W); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/feasible/feasibleDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function W = feasibleDiag(W) +% +% W = feasibleDiag(W) +% +% Projects a single d*1 matrix onto the PSD cone +% + global FEASIBLE_COUNT; + FEASIBLE_COUNT = FEASIBLE_COUNT + 1; + W = max(0,W); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/feasible/feasibleDiagMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function W = feasibleFull(W) +% +% W = feasibleFull(W) +% +% Projects a single d*1 matrix onto the PSD cone +% + global FEASIBLE_COUNT; + FEASIBLE_COUNT = FEASIBLE_COUNT + 1; + W = max(0,W); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/feasible/feasibleFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,14 @@ +function W = feasibleFull(W) +% +% W = feasibleFull(W) +% +% Projects a single d*d matrix onto the PSD cone +% + + global FEASIBLE_COUNT; + FEASIBLE_COUNT = FEASIBLE_COUNT + 1; + + [v,d] = eig(0.5 * (W + W')); + W = v * bsxfun(@times, max(real(diag(d)),0), v'); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/feasible/feasibleFullMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function W = feasibleFullMKL(W) +% +% W = feasibleFullMKL(W) +% +% Projects a single d*d matrix onto the PSD cone +% + global FEASIBLE_COUNT; + m = size(W,3); + FEASIBLE_COUNT = FEASIBLE_COUNT + m; + + parfor i = 1:m + [v,d] = eig(0.5 * (W(:,:,i) + W(:,:,i)')); + W(:,:,i) = v * bsxfun(@times, max(real(diag(d)),0), v'); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/initialize/initializeDODMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function W = initializeDODMKL(X) + + [d,n,m] = size(X); + + W = zeros(m,m,d); + for i = 1:m + for j = i:m + W(i,j,:) = ones(d,1); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/initialize/initializeDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function W = initializeDiag(X) + + [d,n,m] = size(X); + +% W = ones(d, 1); + W = 1./std(X,1,2); + W(isinf(W)) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/initialize/initializeDiagMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function W = initializeDiagMKL(X) + + [d,n,m] = size(X); + + W = zeros(d,m); + for i = 1:m + W(:,i) = 1./std(X(:,:,i),1,2); + end + W(isinf(W)) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/initialize/initializeFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function W = initializeFull(X) + + [d,n,m] = size(X); + + W = diag(1./std(X,1,2)); + W(isinf(W)) = 1; + W(isnan(W)) = 1; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/initialize/initializeFullMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +function W = initializeFull(X) + + [d,n,m] = size(X); + + W = zeros(d,d,m); + for i = 1:m + W(:,:,i) = diag(1./std(X(:,:,i),1,2)); + end + W(isinf(W)) = 1; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/loss/lossHinge.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function Xi = lossHinge(W, Psi, M, gradient) +% +% Xi = lossHinge(W, Psi, M, gradient) +% +% W: d*d metric +% Psi: d*d feature matrix +% M: the desired margin +% gradient: if 0, returns the loss value +% if 1, returns the gradient of the loss WRT W + + Xi = max(0, M - sum(sum(W .* Psi))); + + if gradient & Xi > 0 + Xi = -Psi; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/loss/lossHingeDODMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,18 @@ +function Xi = lossHingeDODMKL(W, Psi, M, gradient) +% +% Xi = lossHingeDODMKL(W, Psi, M, gradient) +% +% W: m*m*d matrix of diagonal metrics +% Psi: m*m*d feature matrix +% M: the desired margin +% gradient: if 0, returns the loss value +% if 1, returns the gradient of the loss WRT W + + m = size(W,1); + + Xi = max(0, M - sum(sum(sum(W .* Psi)))); + + if gradient & Xi > 0 + Xi = -Psi; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/loss/lossHingeDiagMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,16 @@ +function Xi = lossHingeDiagMKL(W, Psi, M, gradient) +% +% Xi = lossHingeDiagMKL(W, Psi, M, gradient) +% +% W: d*m matrix of diagonal metrics +% Psi: d*m feature matrix +% M: the desired margin +% gradient: if 0, returns the loss value +% if 1, returns the gradient of the loss WRT W + + Xi = max(0, M - trace(W' * Psi)); + + if gradient & Xi > 0 + Xi = -Psi; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/loss/lossHingeFullMKL.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +function Xi = lossHingeMKLFull(W, Psi, M, gradient) +% +% Xi = lossHingeMKLFull(W, Psi, M, gradient) +% +% W: d*d*m metric +% Psi: d*d*m feature matrix +% M: the desired margin +% gradient: if 0, returns the loss value +% if 1, returns the gradient of the loss WRT W + + m = size(W, 3); + + Xi = M; + for i = 1:m + Xi = Xi - sum(sum(W(:,:,i) .* Psi(:,:,i))); + end + Xi = max(0, Xi); + + if gradient & Xi > 0 + Xi = -Psi; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/metricPsi/metricPsiMRR.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +function S = metricPsiMRR(q, y, n, pos, neg) +% +% S = metricPsiMRR(q, y, n, d, pos, neg) +% +% q = index of the query point +% y = the ordering to compute +% n = number of points in the data set +% pos = indices of relevant results for q +% neg = indices of irrelevant results for q +% +% S is the vector of weights for q + + yp = ismember(y, pos); + % Truncate yp after the first 1 + yp(find(yp,1)+1:end) = 0; + NumPOS = sum(yp); + PosAfter = NumPOS - cumsum(yp); + + yn = ~yp; + NumNEG = sum(yn); + NegBefore = cumsum(yn) - yn; + + S = zeros(n,1); + + S(y) = 2 * (yp .* NegBefore - yn .* PosAfter) / (NumNEG * NumPOS); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/metricPsi/metricPsiPO.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +function S = metricPsiPO(q, y, n, pos, neg) +% +% S = metricPsiPO(q, y, n, d, pos, neg) +% +% q = index of the query point +% y = the ordering to compute +% n = number of points in the data set +% pos = indices of relevant results for q +% neg = indices of irrelevant results for q +% +% S is the vector of weights for q + + yp = ismember(y, pos); + NumPOS = sum(yp); + PosAfter = NumPOS - cumsum(yp); + + yn = ~yp; + NumNEG = sum(yn); + NegBefore = cumsum(yn) - yn; + + S = zeros(n,1); + + S(y) = 2 * (yp .* NegBefore - yn .* PosAfter) / (NumNEG * NumPOS); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/mlr_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,70 @@ +function mlr_demo() + + display('Loading Wine data'); + load Wine; + + % z-score the input dimensions + display('z-scoring features'); + X = zscore(X')'; + + [d,n] = size(X); + + % Generate a random training/test split + display('Generating a 80/20 training/test split'); + P = randperm(n); + Xtrain = X(:,P(1:floor(0.8 * n))); + Ytrain = Y(P(1:floor(0.8*n))); + Xtest = X(:,P((1+floor(0.8*n)):end)); + Ytest = Y(P((1+floor(0.8*n)):end)); + + + % Optimize W for AUC + C = 1e-2; + display(sprintf('Training with C=%.2e, Delta=mAP', C)); + [W, Xi, Diagnostics] = mlr_train(Xtrain, Ytrain, C, 'map'); +% [W, Xi, Diagnostics] = mlr_train_primal(Xtrain, Ytrain, C, 'map'); + + display('Test performance in the native (normalized) metric'); + mlr_test(eye(d), 3, Xtrain, Ytrain, Xtest, Ytest) + + display('Test performance with MLR metric'); + mlr_test(W, 3, Xtrain, Ytrain, Xtest, Ytest) + + % Scatter-plot + figure; + subplot(1,2,1), drawData(eye(d), Xtrain, Ytrain, Xtest, Ytest), title('Native metric (z-scored)'); + subplot(1,2,2), drawData(W, Xtrain, Ytrain, Xtest, Ytest), title('Learned metric (MLR-mAP)'); + + Diagnostics + +end + + +function drawData(W, Xtrain, Ytrain, Xtest, Ytest); + + n = length(Ytrain); + m = length(Ytest); + + if size(W,2) == 1 + W = diag(W); + end + % PCA the learned metric + Z = [Xtrain Xtest]; + A = Z' * W * Z; + [v,d] = eig(A); + + L = (d.^0.5) * v'; + L = L(1:2,:); + + % Draw training points + hold on; + trmarkers = {'b+', 'r+', 'g+'}; + tsmarkers = {'bo', 'ro', 'go'}; + for i = min(Ytrain):max(Ytrain) + points = find(Ytrain == i); + scatter(L(1,points), L(2,points), trmarkers{i}); + points = n + find(Ytest == i); + scatter(L(1,points), L(2,points), tsmarkers{i}); + end + legend({'Training', 'Test'}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/mlr_plot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +function mlr_plot(X, Y, W, D) + + [d, n] = size(X); + %%% + % Color-coding + CODES = { 'b.', 'r.', 'g.', 'c.', 'k.', 'm.', 'y.', ... + 'b+', 'r+', 'g+', 'c+', 'k+', 'm+', 'y+', ... + 'b^', 'r^', 'g^', 'c^', 'k^', 'm^', 'y^', ... + 'bx', 'rx', 'gx', 'cx', 'kx', 'mx', 'yx', ... + 'bo', 'ro', 'go', 'co', 'ko', 'mo', 'yo'}; + + %%% + % First, PCA-plot of X + + figure; + + z = sum(X,3); + subplot(3,2,[1 3]), pcaplot(z, eye(d), Y, CODES), title('Native'); + subplot(3,2,[2 4]), pcaplot(X, W, Y, CODES), title('Learned'); + + [vecs, vals] = eig(z * z'); + subplot(3,2,5), bar(sort(real(diag(vals)), 'descend')), title('X*X'' spectrum'), axis tight; + + if size(X,3) == 1 + [vecs, vals] = eig(W); + vals = real(diag(vals)); + else + if size(W,3) == 1 + vals = real(W(:)); + else + vals = []; + for i = 1:size(W,3) + [vecs, vals2] = eig(W(:,:,i)); + vals = [vals ; real(diag(vals2))]; + end + end + end + subplot(3,2,6), bar(sort(vals, 'descend')), title('W spectrum'), axis tight; + + if nargin < 4 + return; + end + + %%% + % Now show some diagnostics + + figure; + subplot(2,1,1), semilogy(D.f), title('Objective'); + subplot(2,1,2), barh([D.time_SO, D.time_solver D.time_total]), ... + title('Time% in SO/solver/total'); + + +function pcaplot(X, W, Y, CODES) + if size(X,3) == 1 + A = X' * W * X; + else + A = 0; + if size(W,3) == 1 + for i = 1:size(X,3) + A = A + X(:,:,i)' * bsxfun(@times, W(:,i), X(:,:,i)); + end + else + for i = 1:size(X,3) + A = A + X(:,:,i)' * W(:,:,i) * X(:,:,i); + end + end + end + [v,d] = eigs(A, 3); + X2 = d.^0.5 * v'; + hold on; + for y = 1:max(Y) + z = Y == y; + scatter3(X2(1, z), X2(2, z), X2(3,z), CODES{y}); + end + + axis equal;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/mlr_test.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,283 @@ +function Perf = mlr_test(W, test_k, Xtrain, Ytrain, Xtest, Ytest) +% Perf = mlr_test(W, test_k, Xtrain, Ytrain, Xtest, Ytest) +% +% W = d-by-d positive semi-definite matrix +% test_k = vector of k-values to use for KNN/Prec@k/NDCG +% Xtrain = d-by-n matrix of training data +% Ytrain = n-by-1 vector of training labels +% OR +% n-by-2 cell array where +% Y{q,1} contains relevant indices (in 1..n) for point q +% Y{q,2} contains irrelevant indices (in 1..n) for point q +% Xtest = d-by-m matrix of testing data +% Ytest = m-by-1 vector of training labels, or m-by-2 cell array +% +% +% The output structure Perf contains the mean score for: +% AUC, KNN, Prec@k, MAP, MRR, NDCG, +% as well as the effective dimensionality of W, and +% the best-performing k-value for KNN, Prec@k, and NDCG. +% + + Perf = struct( ... + 'AUC', [], ... + 'KNN', [], ... + 'PrecAtK', [], ... + 'MAP', [], ... + 'MRR', [], ... + 'NDCG', [], ... + 'dimensionality', [], ... + 'KNNk', [], ... + 'PrecAtKk', [], ... + 'NDCGk', [] ... + ); + + [d, nTrain, nKernel] = size(Xtrain); + % Compute dimensionality of the learned metric + Perf.dimensionality = mlr_test_dimension(W, nTrain, nKernel); + test_k = min(test_k, nTrain); + + if nargin > 5 + % Knock out the points with no labels + if ~iscell(Ytest) + Ibad = find(isnan(Ytrain)); + Xtrain(:,Ibad,:) = inf; + end + + % Build the distance matrix + [D, I] = mlr_test_distance(W, Xtrain, Xtest); + else + % Leave-one-out validation + + if nargin > 4 + % In this case, Xtest is a subset of training indices to test on + testRange = Xtest; + else + testRange = 1:nTrain; + end + Xtest = Xtrain(:,testRange,:); + Ytest = Ytrain(testRange); + + % compute self-distance + [D, I] = mlr_test_distance(W, Xtrain, Xtest); + % clear out the self-link (distance = 0) + I = I(2:end,:); + D = D(2:end,:); + end + + nTest = length(Ytest); + + % Compute label agreement + if ~iscell(Ytest) + % First, knock out the points with no label + Labels = Ytrain(I); + Agree = bsxfun(@eq, Ytest', Labels); + + % We only compute KNN error if Y are labels + [Perf.KNN, Perf.KNNk] = mlr_test_knn(Labels, Ytest, test_k); + else + if nargin > 5 + Agree = zeros(nTrain, nTest); + else + Agree = zeros(nTrain-1, nTest); + end + for i = 1:nTest + Agree(:,i) = ismember(I(:,i), Ytest{i,1}); + end + + Agree = reduceAgreement(Agree); + end + + % Compute AUC score + Perf.AUC = mlr_test_auc(Agree); + + % Compute MAP score + Perf.MAP = mlr_test_map(Agree); + + % Compute MRR score + Perf.MRR = mlr_test_mrr(Agree); + + % Compute prec@k + [Perf.PrecAtK, Perf.PrecAtKk] = mlr_test_preck(Agree, test_k); + + % Compute NDCG score + [Perf.NDCG, Perf.NDCGk] = mlr_test_ndcg(Agree, test_k); + +end + + +function [D,I] = mlr_test_distance(W, Xtrain, Xtest) + + % CASES: + % Raw: W = [] + + % Linear, full: W = d-by-d + % Single Kernel, full: W = n-by-n + % MKL, full: W = n-by-n-by-m + + % Linear, diagonal: W = d-by-1 + % Single Kernel, diagonal: W = n-by-1 + % MKL, diag: W = n-by-m + % MKL, diag-off-diag: W = m-by-m-by-n + + [d, nTrain, nKernel] = size(Xtrain); + nTest = size(Xtest, 2); + + if isempty(W) + % W = [] => native euclidean distances + D = mlr_test_distance_raw(Xtrain, Xtest); + + elseif size(W,1) == d && size(W,2) == d + % We're in a full-projection case + D = setDistanceFullMKL([Xtrain Xtest], W, nTrain + (1:nTest), 1:nTrain); + + elseif size(W,1) == d && size(W,2) == nKernel + % We're in a simple diagonal case + D = setDistanceDiagMKL([Xtrain Xtest], W, nTrain + (1:nTest), 1:nTrain); + + else + % Error? + error('Cannot determine metric mode.'); + + end + + D = full(D(1:nTrain, nTrain + (1:nTest))); + [v,I] = sort(D, 1); +end + + + +function dimension = mlr_test_dimension(W, nTrain, nKernel) + + % CASES: + % Raw: W = [] + + % Linear, full: W = d-by-d + % Single Kernel, full: W = n-by-n + % MKL, full: W = n-by-n-by-m + + % Linear, diagonal: W = d-by-1 + % Single Kernel, diagonal: W = n-by-1 + % MKL, diag: W = n-by-m + % MKL, diag-off-diag: W = m-by-m-by-n + + + if size(W,1) == size(W,2) + dim = []; + for i = 1:nKernel + [v,d] = eig(0.5 * (W(:,:,i) + W(:,:,i)')); + dim = [dim ; abs(real(diag(d)))]; + end + else + dim = W(:); + end + + cd = cumsum(dim) / sum(dim); + dimension = find(cd >= 0.95, 1); + if isempty(dimension) + dimension = 0; + end +end + +function [NDCG, NDCGk] = mlr_test_ndcg(Agree, test_k) + + nTrain = size(Agree, 1); + + Discount = zeros(1, nTrain); + Discount(1:2) = 1; + + NDCG = -Inf; + NDCGk = 0; + for k = test_k + + Discount(3:k) = 1 ./ log2(3:k); + Discount = Discount / sum(Discount); + + b = mean(Discount * Agree); + if b > NDCG + NDCG = b; + NDCGk = k; + end + end +end + +function [PrecAtK, PrecAtKk] = mlr_test_preck(Agree, test_k) + + PrecAtK = -Inf; + PrecAtKk = 0; + for k = test_k + b = mean( mean( Agree(1:k, :), 1 ) ); + if b > PrecAtK + PrecAtK = b; + PrecAtKk = k; + end + end +end + +function [KNN, KNNk] = mlr_test_knn(Labels, Ytest, test_k) + + KNN = -Inf; + KNNk = 0; + for k = test_k + % FIXME: 2012-02-07 16:51:59 by Brian McFee <bmcfee@cs.ucsd.edu> + % fix these to discount nans + + b = mean( mode( Labels(1:k,:), 1 ) == Ytest'); + if b > KNN + KNN = b; + KNNk = k; + end + end +end + +function MAP = mlr_test_map(Agree); + + nTrain = size(Agree, 1); + MAP = bsxfun(@ldivide, (1:nTrain)', cumsum(Agree, 1)); + MAP = mean(sum(MAP .* Agree, 1)./ sum(Agree, 1)); +end + +function MRR = mlr_test_mrr(Agree); + + nTest = size(Agree, 2); + MRR = 0; + for i = 1:nTest + MRR = MRR + (1 / find(Agree(:,i), 1)); + end + MRR = MRR / nTest; +end + +function AUC = mlr_test_auc(Agree) + + TPR = cumsum(Agree, 1); + FPR = cumsum(~Agree, 1); + + numPos = TPR(end,:); + numNeg = FPR(end,:); + + TPR = mean(bsxfun(@rdivide, TPR, numPos),2); + FPR = mean(bsxfun(@rdivide, FPR, numNeg),2); + AUC = diff([0 FPR']) * TPR; +end + + +function D = mlr_test_distance_raw(Xtrain, Xtest) + + [d, nTrain, nKernel] = size(Xtrain); + nTest = size(Xtest, 2); + + % Not in kernel mode, compute distances directly + D = 0; + for i = 1:nKernel + D = D + setDistanceDiag([Xtrain(:,:,i) Xtest(:,:,i)], ones(d,1), ... + nTrain + (1:nTest), 1:nTrain); + end +end + +function A = reduceAgreement(Agree) + nPos = sum(Agree,1); + nNeg = sum(~Agree,1); + + goodI = find(nPos > 0 & nNeg > 0); + A = Agree(:,goodI); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/mlr_train.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,507 @@ +function [W, Xi, Diagnostics] = mlr_train(X, Y, Cslack, varargin) +% +% [W, Xi, D] = mlr_train(X, Y, C,...) +% +% X = d*n data matrix +% Y = either n-by-1 label of vectors +% OR +% n-by-2 cell array where +% Y{q,1} contains relevant indices for q, and +% Y{q,2} contains irrelevant indices for q +% +% C >= 0 slack trade-off parameter (default=1) +% +% W = the learned metric +% Xi = slack value on the learned metric +% D = diagnostics +% +% Optional arguments: +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS) +% where LOSS is one of: +% 'AUC': Area under ROC curve (default) +% 'KNN': KNN accuracy +% 'Prec@k': Precision-at-k +% 'MAP': Mean Average Precision +% 'MRR': Mean Reciprocal Rank +% 'NDCG': Normalized Discounted Cumulative Gain +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k) +% where k is the number of neighbors for Prec@k or NDCG +% (default=3) +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG) +% where REG defines the regularization on W, and is one of: +% 0: no regularization +% 1: 1-norm: trace(W) (default) +% 2: 2-norm: trace(W' * W) +% 3: Kernel: trace(W * X), assumes X is square and positive-definite +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal) +% Diagonal = 0: learn a full d-by-d W (default) +% Diagonal = 1: learn diagonally-constrained W (d-by-1) +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal, B) +% where B > 0 enables stochastic optimization with batch size B +% +% // added by Daniel Wolff +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal, B, CC) +% Set ConstraintClock to CC (default: 20, 100) +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal, B, CC, E) +% Set ConstraintClock to E (default: 1e-3) +% + + TIME_START = tic(); + + global C; + C = Cslack; + + [d,n,m] = size(X); + + if m > 1 + MKL = 1; + else + MKL = 0; + end + + if nargin < 3 + C = 1; + end + + %%% + % Default options: + + global CP SO PSI REG FEASIBLE LOSS DISTANCE SETDISTANCE CPGRADIENT STRUCTKERNEL DUALW INIT; + + global FEASIBLE_COUNT; + FEASIBLE_COUNT = 0; + + CP = @cuttingPlaneFull; + SO = @separationOracleAUC; + PSI = @metricPsiPO; + + if ~MKL + INIT = @initializeFull; + REG = @regularizeTraceFull; + STRUCTKERNEL= @structKernelLinear; + DUALW = @dualWLinear; + FEASIBLE = @feasibleFull; + CPGRADIENT = @cpGradientFull; + DISTANCE = @distanceFull; + SETDISTANCE = @setDistanceFull; + LOSS = @lossHinge; + Regularizer = 'Trace'; + else + INIT = @initializeFullMKL; + REG = @regularizeMKLFull; + STRUCTKERNEL= @structKernelMKL; + DUALW = @dualWMKL; + FEASIBLE = @feasibleFullMKL; + CPGRADIENT = @cpGradientFullMKL; + DISTANCE = @distanceFullMKL; + SETDISTANCE = @setDistanceFullMKL; + LOSS = @lossHingeFullMKL; + Regularizer = 'Trace'; + end + + + Loss = 'AUC'; + Feature = 'metricPsiPO'; + + + %%% + % Default k for prec@k, ndcg + k = 3; + + %%% + % Stochastic violator selection? + STOCHASTIC = 0; + batchSize = n; + SAMPLES = 1:n; + + + if nargin > 3 + switch lower(varargin{1}) + case {'auc'} + SO = @separationOracleAUC; + PSI = @metricPsiPO; + Loss = 'AUC'; + Feature = 'metricPsiPO'; + case {'knn'} + SO = @separationOracleKNN; + PSI = @metricPsiPO; + Loss = 'KNN'; + Feature = 'metricPsiPO'; + case {'prec@k'} + SO = @separationOraclePrecAtK; + PSI = @metricPsiPO; + Loss = 'Prec@k'; + Feature = 'metricPsiPO'; + case {'map'} + SO = @separationOracleMAP; + PSI = @metricPsiPO; + Loss = 'MAP'; + Feature = 'metricPsiPO'; + case {'mrr'} + SO = @separationOracleMRR; + PSI = @metricPsiPO; + Loss = 'MRR'; + Feature = 'metricPsiPO'; + case {'ndcg'} + SO = @separationOracleNDCG; + PSI = @metricPsiPO; + Loss = 'NDCG'; + Feature = 'metricPsiPO'; + otherwise + error('MLR:LOSS', ... + 'Unknown loss function: %s', varargin{1}); + end + end + + if nargin > 4 + k = varargin{2}; + end + + Diagonal = 0; + if nargin > 6 & varargin{4} > 0 + Diagonal = varargin{4}; + + if ~MKL + INIT = @initializeDiag; + REG = @regularizeTraceDiag; + STRUCTKERNEL= @structKernelDiag; + DUALW = @dualWDiag; + FEASIBLE = @feasibleDiag; + CPGRADIENT = @cpGradientDiag; + DISTANCE = @distanceDiag; + SETDISTANCE = @setDistanceDiag; + Regularizer = 'Trace'; + else + INIT = @initializeDiagMKL; + REG = @regularizeMKLDiag; + STRUCTKERNEL= @structKernelDiagMKL; + DUALW = @dualWDiagMKL; + FEASIBLE = @feasibleDiagMKL; + CPGRADIENT = @cpGradientDiagMKL; + DISTANCE = @distanceDiagMKL; + SETDISTANCE = @setDistanceDiagMKL; + LOSS = @lossHingeDiagMKL; + Regularizer = 'Trace'; + end + end + + if nargin > 5 + switch(varargin{3}) + case {0} + REG = @regularizeNone; + Regularizer = 'None'; + + case {1} + if MKL + if Diagonal == 0 + REG = @regularizeMKLFull; + STRUCTKERNEL= @structKernelMKL; + DUALW = @dualWMKL; + elseif Diagonal == 1 + REG = @regularizeMKLDiag; + STRUCTKERNEL= @structKernelDiagMKL; + DUALW = @dualWDiagMKL; + end + else + if Diagonal + REG = @regularizeTraceDiag; + STRUCTKERNEL= @structKernelDiag; + DUALW = @dualWDiag; + else + REG = @regularizeTraceFull; + STRUCTKERNEL= @structKernelLinear; + DUALW = @dualWLinear; + end + end + Regularizer = 'Trace'; + + case {2} + if Diagonal + REG = @regularizeTwoDiag; + else + REG = @regularizeTwoFull; + end + Regularizer = '2-norm'; + error('MLR:REGULARIZER', '2-norm regularization no longer supported'); + + + case {3} + if MKL + if Diagonal == 0 + REG = @regularizeMKLFull; + STRUCTKERNEL= @structKernelMKL; + DUALW = @dualWMKL; + elseif Diagonal == 1 + REG = @regularizeMKLDiag; + STRUCTKERNEL= @structKernelDiagMKL; + DUALW = @dualWDiagMKL; + end + else + if Diagonal + REG = @regularizeMKLDiag; + STRUCTKERNEL= @structKernelDiagMKL; + DUALW = @dualWDiagMKL; + else + REG = @regularizeKernel; + STRUCTKERNEL= @structKernelMKL; + DUALW = @dualWMKL; + end + end + Regularizer = 'Kernel'; + + otherwise + error('MLR:REGULARIZER', ... + 'Unknown regularization: %s', varargin{3}); + end + end + + + % Are we in stochastic optimization mode? + if nargin > 7 && varargin{5} > 0 + if varargin{5} < n + STOCHASTIC = 1; + CP = @cuttingPlaneRandom; + batchSize = varargin{5}; + end + end + + % Algorithm + % + % Working <- [] + % + % repeat: + % (W, Xi) <- solver(X, Y, C, Working) + % + % for i = 1:|X| + % y^_i <- argmax_y^ ( Delta(y*_i, y^) + w' Psi(x_i, y^) ) + % + % Working <- Working + (y^_1,y^_2,...,y^_n) + % until mean(Delta(y*_i, y_i)) - mean(w' (Psi(x_i,y_i) - Psi(x_i,y^_i))) + % <= Xi + epsilon + + global DEBUG; + + if isempty(DEBUG) + DEBUG = 0; + end + + %%% + % Timer to eliminate old constraints + + %%% + % Timer to eliminate old constraints + ConstraintClock = 100; % standard: 100 + + if nargin > 8 && varargin{6} > 0 + ConstraintClock = varargin{6}; + end + + %%% + % Convergence criteria for worst-violated constraint + E = 1e-3; + if nargin > 9 && varargin{7} > 0 + E = varargin{7}; + end + + + %XXX: 2012-01-31 21:29:50 by Brian McFee <bmcfee@cs.ucsd.edu> + % no longer belongs here + % Initialize + W = INIT(X); + + + global ADMM_Z ADMM_U RHO; + ADMM_Z = W; + ADMM_U = 0 * ADMM_Z; + + %%% + % Augmented lagrangian factor + RHO = 1; + + ClassScores = []; + + if isa(Y, 'double') + Ypos = []; + Yneg = []; + ClassScores = synthesizeRelevance(Y); + + elseif isa(Y, 'cell') && size(Y,1) == n && size(Y,2) == 2 + dbprint(2, 'Using supplied Ypos/synthesized Yneg'); + Ypos = Y(:,1); + Yneg = Y(:,2); + + % Compute the valid samples + SAMPLES = find( ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2)))); + elseif isa(Y, 'cell') && size(Y,1) == n && size(Y,2) == 1 + dbprint(2, 'Using supplied Ypos/synthesized Yneg'); + Ypos = Y(:,1); + Yneg = []; + SAMPLES = find( ~(cellfun(@isempty, Y(:,1)))); + else + error('MLR:LABELS', 'Incorrect format for Y.'); + end + + %% + % If we don't have enough data to make the batch, cut the batch + batchSize = min([batchSize, length(SAMPLES)]); + + + Diagnostics = struct( 'loss', Loss, ... % Which loss are we optimizing? + 'feature', Feature, ... % Which ranking feature is used? + 'k', k, ... % What is the ranking length? + 'regularizer', Regularizer, ... % What regularization is used? + 'diagonal', Diagonal, ... % 0 for full metric, 1 for diagonal + 'num_calls_SO', 0, ... % Calls to separation oracle + 'num_calls_solver', 0, ... % Calls to solver + 'time_SO', 0, ... % Time in separation oracle + 'time_solver', 0, ... % Time in solver + 'time_total', 0, ... % Total time + 'f', [], ... % Objective value + 'num_steps', [], ... % Number of steps for each solver run + 'num_constraints', [], ... % Number of constraints for each run + 'Xi', [], ... % Slack achieved for each run + 'Delta', [], ... % Mean loss for each SO call + 'gap', [], ... % Gap between loss and slack + 'C', C, ... % Slack trade-off + 'epsilon', E, ... % Convergence threshold + 'feasible_count', FEASIBLE_COUNT, ... % Counter for # svd's + 'constraint_timer', ConstraintClock); % Time before evicting old constraints + + + + global PsiR; + global PsiClock; + + PsiR = {}; + PsiClock = []; + + Xi = -Inf; + Margins = []; + H = []; + Q = []; + + if STOCHASTIC + dbprint(1, 'STOCHASTIC OPTIMIZATION: Batch size is %d/%d', batchSize, n); + end + + MAXITER = 200; +% while 1 + while Diagnostics.num_calls_solver < MAXITER + dbprint(2, 'Round %03d', Diagnostics.num_calls_solver); + % Generate a constraint set + Termination = 0; + + + dbprint(2, 'Calling separation oracle...'); + + [PsiNew, Mnew, SO_time] = CP(k, X, W, Ypos, Yneg, batchSize, SAMPLES, ClassScores); + Termination = LOSS(W, PsiNew, Mnew, 0); + + Diagnostics.num_calls_SO = Diagnostics.num_calls_SO + 1; + Diagnostics.time_SO = Diagnostics.time_SO + SO_time; + + Margins = cat(1, Margins, Mnew); + PsiR = cat(1, PsiR, PsiNew); + PsiClock = cat(1, PsiClock, 0); + H = expandKernel(H); + Q = expandRegularizer(Q, X, W); + + + dbprint(2, '\n\tActive constraints : %d', length(PsiClock)); + dbprint(2, '\t Mean loss : %0.4f', Mnew); + dbprint(2, '\t Current loss Xi : %0.4f', Xi); + dbprint(2, '\t Termination -Xi < E : %0.4f <? %.04f\n', Termination - Xi, E); + + Diagnostics.gap = cat(1, Diagnostics.gap, Termination - Xi); + Diagnostics.Delta = cat(1, Diagnostics.Delta, Mnew); + + if Termination <= Xi + E + dbprint(2, 'Done.'); + break; + end + + dbprint(2, 'Calling solver...'); + PsiClock = PsiClock + 1; + Solver_time = tic(); + [W, Xi, Dsolver] = mlr_admm(C, X, Margins, H, Q); + Diagnostics.time_solver = Diagnostics.time_solver + toc(Solver_time); + Diagnostics.num_calls_solver = Diagnostics.num_calls_solver + 1; + + Diagnostics.Xi = cat(1, Diagnostics.Xi, Xi); + Diagnostics.f = cat(1, Diagnostics.f, Dsolver.f); + Diagnostics.num_steps = cat(1, Diagnostics.num_steps, Dsolver.num_steps); + + %%% + % Cull the old constraints + GC = PsiClock < ConstraintClock; + Margins = Margins(GC); + PsiR = PsiR(GC); + PsiClock = PsiClock(GC); + H = H(GC, GC); + Q = Q(GC); + + Diagnostics.num_constraints = cat(1, Diagnostics.num_constraints, length(PsiR)); + end + + + % Finish diagnostics + + Diagnostics.time_total = toc(TIME_START); + Diagnostics.feasible_count = FEASIBLE_COUNT; +end + +function H = expandKernel(H) + + global STRUCTKERNEL; + global PsiR; + + m = length(H); + H = padarray(H, [1 1], 0, 'post'); + + + for i = 1:m+1 + H(i,m+1) = STRUCTKERNEL( PsiR{i}, PsiR{m+1} ); + H(m+1, i) = H(i, m+1); + end +end + +function Q = expandRegularizer(Q, K, W) + + % FIXME: 2012-01-31 21:34:15 by Brian McFee <bmcfee@cs.ucsd.edu> + % does not support unregularized learning + + global PsiR; + global STRUCTKERNEL REG; + + m = length(Q); + Q(m+1,1) = STRUCTKERNEL(REG(W,K,1), PsiR{m+1}); + +end + +function ClassScores = synthesizeRelevance(Y) + + classes = unique(Y); + nClasses = length(classes); + + ClassScores = struct( 'Y', Y, ... + 'classes', classes, ... + 'Ypos', [], ... + 'Yneg', []); + + Ypos = cell(nClasses, 1); + Yneg = cell(nClasses, 1); + for c = 1:nClasses + Ypos{c} = (Y == classes(c)); + Yneg{c} = ~Ypos{c}; + end + + ClassScores.Ypos = Ypos; + ClassScores.Yneg = Yneg; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/mlr_train_primal.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,413 @@ +function [W, Xi, Diagnostics] = mlr_train(X, Y, Cslack, varargin) +% +% [W, Xi, D] = mlr_train(X, Y, C,...) +% +% X = d*n data matrix +% Y = either n-by-1 label of vectors +% OR +% n-by-2 cell array where +% Y{q,1} contains relevant indices for q, and +% Y{q,2} contains irrelevant indices for q +% +% C >= 0 slack trade-off parameter (default=1) +% +% W = the learned metric +% Xi = slack value on the learned metric +% D = diagnostics +% +% Optional arguments: +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS) +% where LOSS is one of: +% 'AUC': Area under ROC curve (default) +% 'KNN': KNN accuracy +% 'Prec@k': Precision-at-k +% 'MAP': Mean Average Precision +% 'MRR': Mean Reciprocal Rank +% 'NDCG': Normalized Discounted Cumulative Gain +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k) +% where k is the number of neighbors for Prec@k or NDCG +% (default=3) +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG) +% where REG defines the regularization on W, and is one of: +% 0: no regularization +% 1: 1-norm: trace(W) (default) +% 2: 2-norm: trace(W' * W) +% 3: Kernel: trace(W * X), assumes X is square and positive-definite +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal) +% Diagonal = 0: learn a full d-by-d W (default) +% Diagonal = 1: learn diagonally-constrained W (d-by-1) +% +% [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal, B) +% where B > 0 enables stochastic optimization with batch size B +% + + TIME_START = tic(); + + global C; + C = Cslack; + + [d,n,m] = size(X); + + if m > 1 + MKL = 1; + else + MKL = 0; + end + + if nargin < 3 + C = 1; + end + + %%% + % Default options: + + global CP SO PSI REG FEASIBLE LOSS DISTANCE SETDISTANCE CPGRADIENT; + global FEASIBLE_COUNT; + FEASIBLE_COUNT = 0; + + CP = @cuttingPlaneFull; + SO = @separationOracleAUC; + PSI = @metricPsiPO; + + if ~MKL + INIT = @initializeFull; + REG = @regularizeTraceFull; + FEASIBLE = @feasibleFull; + CPGRADIENT = @cpGradientFull; + DISTANCE = @distanceFull; + SETDISTANCE = @setDistanceFull; + LOSS = @lossHinge; + Regularizer = 'Trace'; + else + INIT = @initializeFullMKL; + REG = @regularizeMKLFull; + FEASIBLE = @feasibleFullMKL; + CPGRADIENT = @cpGradientFullMKL; + DISTANCE = @distanceFullMKL; + SETDISTANCE = @setDistanceFullMKL; + LOSS = @lossHingeFullMKL; + Regularizer = 'Trace'; + end + + + Loss = 'AUC'; + Feature = 'metricPsiPO'; + + + %%% + % Default k for prec@k, ndcg + k = 3; + + %%% + % Stochastic violator selection? + STOCHASTIC = 0; + batchSize = n; + SAMPLES = 1:n; + + + if nargin > 3 + switch lower(varargin{1}) + case {'auc'} + SO = @separationOracleAUC; + PSI = @metricPsiPO; + Loss = 'AUC'; + Feature = 'metricPsiPO'; + case {'knn'} + SO = @separationOracleKNN; + PSI = @metricPsiPO; + Loss = 'KNN'; + Feature = 'metricPsiPO'; + case {'prec@k'} + SO = @separationOraclePrecAtK; + PSI = @metricPsiPO; + Loss = 'Prec@k'; + Feature = 'metricPsiPO'; + case {'map'} + SO = @separationOracleMAP; + PSI = @metricPsiPO; + Loss = 'MAP'; + Feature = 'metricPsiPO'; + case {'mrr'} + SO = @separationOracleMRR; + PSI = @metricPsiPO; + Loss = 'MRR'; + Feature = 'metricPsiPO'; + case {'ndcg'} + SO = @separationOracleNDCG; + PSI = @metricPsiPO; + Loss = 'NDCG'; + Feature = 'metricPsiPO'; + otherwise + error('MLR:LOSS', ... + 'Unknown loss function: %s', varargin{1}); + end + end + + if nargin > 4 + k = varargin{2}; + end + + Diagonal = 0; + if nargin > 6 & varargin{4} > 0 + Diagonal = varargin{4}; + + if ~MKL + INIT = @initializeDiag; + REG = @regularizeTraceDiag; + FEASIBLE = @feasibleDiag; + CPGRADIENT = @cpGradientDiag; + DISTANCE = @distanceDiag; + SETDISTANCE = @setDistanceDiag; + Regularizer = 'Trace'; + else + INIT = @initializeDiagMKL; + REG = @regularizeMKLDiag; + FEASIBLE = @feasibleDiagMKL; + CPGRADIENT = @cpGradientDiagMKL; + DISTANCE = @distanceDiagMKL; + SETDISTANCE = @setDistanceDiagMKL; + LOSS = @lossHingeDiagMKL; + Regularizer = 'Trace'; + end + end + + if nargin > 5 + switch(varargin{3}) + case {0} + REG = @regularizeNone; + Regularizer = 'None'; + + case {1} + if MKL + if Diagonal == 0 + REG = @regularizeMKLFull; + elseif Diagonal == 1 + REG = @regularizeMKLDiag; + end + else + if Diagonal + REG = @regularizeTraceDiag; + else + REG = @regularizeTraceFull; + end + end + Regularizer = 'Trace'; + + case {2} + if Diagonal + REG = @regularizeTwoDiag; + else + REG = @regularizeTwoFull; + end + Regularizer = '2-norm'; + + case {3} + if MKL + if Diagonal == 0 + REG = @regularizeMKLFull; + elseif Diagonal == 1 + REG = @regularizeMKLDiag; + end + else + if Diagonal + REG = @regularizeMKLDiag; + else + REG = @regularizeKernel; + end + end + Regularizer = 'Kernel'; + + otherwise + error('MLR:REGULARIZER', ... + 'Unknown regularization: %s', varargin{3}); + end + end + + + % Are we in stochastic optimization mode? + if nargin > 7 && varargin{5} > 0 + if varargin{5} < n + STOCHASTIC = 1; + CP = @cuttingPlaneRandom; + batchSize = varargin{5}; + end + end + % Algorithm + % + % Working <- [] + % + % repeat: + % (W, Xi) <- solver(X, Y, C, Working) + % + % for i = 1:|X| + % y^_i <- argmax_y^ ( Delta(y*_i, y^) + w' Psi(x_i, y^) ) + % + % Working <- Working + (y^_1,y^_2,...,y^_n) + % until mean(Delta(y*_i, y_i)) - mean(w' (Psi(x_i,y_i) - Psi(x_i,y^_i))) + % <= Xi + epsilon + + global DEBUG; + + if isempty(DEBUG) + DEBUG = 0; + end + + %%% + % Timer to eliminate old constraints + ConstraintClock = 100; + + %%% + % Convergence criteria for worst-violated constraint + E = 1e-3; + + % Initialize + W = INIT(X); + + ClassScores = []; + + if isa(Y, 'double') + Ypos = []; + Yneg = []; + ClassScores = synthesizeRelevance(Y); + + elseif isa(Y, 'cell') && size(Y,1) == n && size(Y,2) == 2 + dbprint(1, 'Using supplied Ypos/Yneg'); + Ypos = Y(:,1); + Yneg = Y(:,2); + + % Compute the valid samples + SAMPLES = find( ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2)))); + elseif isa(Y, 'cell') && size(Y,1) == n && size(Y,2) == 1 + dbprint(1, 'Using supplied Ypos/synthesized Yneg'); + Ypos = Y(:,1); + Yneg = []; + SAMPLES = find( ~(cellfun(@isempty, Y(:,1)))); + else + error('MLR:LABELS', 'Incorrect format for Y.'); + end + + %% + % If we don't have enough data to make the batch, cut the batch + batchSize = min([batchSize, length(SAMPLES)]); + + + Diagnostics = struct( 'loss', Loss, ... % Which loss are we optimizing? + 'feature', Feature, ... % Which ranking feature is used? + 'k', k, ... % What is the ranking length? + 'regularizer', Regularizer, ... % What regularization is used? + 'diagonal', Diagonal, ... % 0 for full metric, 1 for diagonal + 'num_calls_SO', 0, ... % Calls to separation oracle + 'num_calls_solver', 0, ... % Calls to solver + 'time_SO', 0, ... % Time in separation oracle + 'time_solver', 0, ... % Time in solver + 'time_total', 0, ... % Total time + 'f', [], ... % Objective value + 'num_steps', [], ... % Number of steps for each solver run + 'num_constraints', [], ... % Number of constraints for each run + 'Xi', [], ... % Slack achieved for each run + 'Delta', [], ... % Mean loss for each SO call + 'gap', [], ... % Gap between loss and slack + 'C', C, ... % Slack trade-off + 'epsilon', E, ... % Convergence threshold + 'feasible_count', 0, ... % Counter for projections + 'constraint_timer', ConstraintClock); % Time before evicting old constraints + + + + global PsiR; + global PsiClock; + + PsiR = {}; + PsiClock = []; + + Xi = -Inf; + Margins = []; + + if STOCHASTIC + dbprint(1, 'STOCHASTIC OPTIMIZATION: Batch size is %d/%d', batchSize, n); + end + + while 1 + dbprint(1, 'Round %03d', Diagnostics.num_calls_solver); + % Generate a constraint set + Termination = 0; + + + dbprint(2, 'Calling separation oracle...'); + + [PsiNew, Mnew, SO_time] = CP(k, X, W, Ypos, Yneg, batchSize, SAMPLES, ClassScores); + Termination = LOSS(W, PsiNew, Mnew, 0); + + Diagnostics.num_calls_SO = Diagnostics.num_calls_SO + 1; + Diagnostics.time_SO = Diagnostics.time_SO + SO_time; + + Margins = cat(1, Margins, Mnew); + PsiR = cat(1, PsiR, PsiNew); + PsiClock = cat(1, PsiClock, 0); + + dbprint(2, '\n\tActive constraints : %d', length(PsiClock)); + dbprint(2, '\t Mean loss : %0.4f', Mnew); + dbprint(2, '\t Termination -Xi < E : %0.4f <? %.04f\n', Termination - Xi, E); + + Diagnostics.gap = cat(1, Diagnostics.gap, Termination - Xi); + Diagnostics.Delta = cat(1, Diagnostics.Delta, Mnew); + + if Termination <= Xi + E + dbprint(1, 'Done.'); + break; + end + + dbprint(1, 'Calling solver...'); + PsiClock = PsiClock + 1; + Solver_time = tic(); + [W, Xi, Dsolver] = mlr_solver(C, Margins, W, X); + Diagnostics.time_solver = Diagnostics.time_solver + toc(Solver_time); + Diagnostics.num_calls_solver = Diagnostics.num_calls_solver + 1; + + Diagnostics.Xi = cat(1, Diagnostics.Xi, Xi); + Diagnostics.f = cat(1, Diagnostics.f, Dsolver.f); + Diagnostics.num_steps = cat(1, Diagnostics.num_steps, Dsolver.num_steps); + + %%% + % Cull the old constraints + GC = PsiClock < ConstraintClock; + Margins = Margins(GC); + PsiR = PsiR(GC); + PsiClock = PsiClock(GC); + + Diagnostics.num_constraints = cat(1, Diagnostics.num_constraints, length(PsiR)); + end + + + % Finish diagnostics + + Diagnostics.time_total = toc(TIME_START); + Diagnostics.feasible_count = FEASIBLE_COUNT; +end + + +function ClassScores = synthesizeRelevance(Y) + + classes = unique(Y); + nClasses = length(classes); + + ClassScores = struct( 'Y', Y, ... + 'classes', classes, ... + 'Ypos', [], ... + 'Yneg', []); + + Ypos = cell(nClasses, 1); + Yneg = cell(nClasses, 1); + for c = 1:nClasses + Ypos{c} = (Y == classes(c)); + Yneg{c} = ~Ypos{c}; + end + + ClassScores.Ypos = Ypos; + ClassScores.Yneg = Yneg; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/readme.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,198 @@ +Metric Learning to Rank (mlr-1.0) +http://www-cse.ucsd.edu/~bmcfee/code/mlr/ + +Brian McFee <bmcfee@cs.ucsd.edu>, 2010. + +This code is distributed under the GNU GPL license. See LICENSE for details, +or http://www.gnu.org/licenses/gpl-3.0.txt . + + + +INTRODUCTION +------------ +This package contains the MATLAB code for Metric Learning to Rank (MLR). +The latest version of this software can be found at the URL above. + +The software included here implements the algorithm described in + +[1] Mcfee, Brian and Lanckriet, G.R.G. Metric learning to rank. + Proceedings of the 27th annual International Conference + on Machine Learning (ICML), 2010. + +Please cite this paper if you use this code. + + + +INSTALLATION +------------ + +1. Requirements + +This software requires MATLAB R2007a or later. Because it makes extensive use +of the "bsxfun" function, earlier versions of Matlab will not work. + +If you have an older Matlab installation, you can install an alternative bsxfun +implementation by going to + +http://www.mathworks.com/matlabcentral/fileexchange/23005 , + +however, this may not work, and it will certainly be slower than the native +bsxfun implementation. + + +2. Compiling MEX functions + +MLR includes two auxiliary functions "cummax" and "binarysearch" to accelerate +certain steps of the optimization procedure. The source code for these +functions is located in the "util" subdirectory. + +A makefile is provided, so that (on Unix systems), you can simply type + +cd util +make +cd .. + + +3. Running the demo + +To test the installation, run + + mlr_demo + +from within Matlab. The demo generates a random training/test split of the Wine data set +http://archive.ics.uci.edu/ml/datasets/Wine +and learns a metric with MLR. Both native and learned metrics are displayed in a figure +with a scatter plot. + + +TRAINING +-------- + +There are several modes of operation for training metrics with MLR. In the +simplest mode, training data is contained in a matrix X (each column is a +training vector), and Y contains the labels/relevance for the training data +(see below). + + [W, Xi, D] = mlr_train(X, Y, C,...) + + X = d*n data matrix + Y = either n-by-1 label of vectors + OR + n-by-2 cell array where + Y{q,1} contains relevant indices for q, and + Y{q,2} contains irrelevant indices for q + + C >= 0 slack trade-off parameter (default=1) + + W = the learned metric, i.e., the inner product matrix of the learned + space can be computed by X' * W * X + Xi = slack value on the learned metric (see [1]) + D = diagnostics + + +By default, MLR optimizes for Area Under the ROC Curve (AUC). This can be +changed by setting the "LOSS" parameter to one of several ranking loss +measures: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS) + where LOSS is one of: + 'AUC': Area under ROC curve (default) + 'KNN': KNN accuracy* + 'Prec@k': Precision-at-k + 'MAP': Mean Average Precision + 'MRR': Mean Reciprocal Rank + 'NDCG': Normalized Discounted Cumulative Gain + + *Note: KNN is correct only for binary classification problems; in + practice, Prec@k is usually a better alternative. + + +For KNN/Prec@k/NDCG, a threshold k may be set to determine the truncation of +the ranked list. Thiscan be done by setting the k parameter: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS, k) + where k is the number of neighbors for Prec@k or NDCG + (default=3) + + +By default, MLR regularizes the metric W by the trace, i.e., the 1-norm of the +eigenvalues. This can be changed to one of several alternatives: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG) + where REG defines the regularization on W, and is one of: + 0: no regularization + 1: 1-norm: trace(W) (default) + 2: 2-norm: trace(W' * W) + 3: Kernel: trace(W * X), assumes X is square + and positive-definite + + The last setting, "kernel", is appropriate for regularizing metrics learned + from kernel matrices. + + +W corresponds to a linear projection matrix (rotation and scaling). To learn a +restricted model which may only scale, but not rotate the data, W can be +constrained to diagonal matrices by setting the "Diagonal" parameter to 1: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal) + Diagonal = 0: learn a full d-by-d W (default) + Diagonal = 1: learn diagonally-constrained W (d-by-1) + + Note: the W returned in this case will be the d-by-1 vector corresponding + to the main diagonal of a full metric, not the full d-by-d matrix. + + + +Finally, we provide a stochastic gradient descent implementation to handle +large-scale problems. Rather than estimating gradients from the entire +training set, this variant uses a random subset of size B (see below) at each +call to the cutting plane subroutine. This results in faster, but less +accurate, optimization: + + [W, Xi, D] = mlr_train(X, Y, C, LOSS, k, REG, Diagonal, B) + where B > 0 enables stochastic optimization with batch size B + + + +TESTING +------- + +Once a metric has been trained by "mlr_train", you can evaluate performance +across all measures by using the "mlr_test" function: + + Perf = mlr_test(W, test_k, Xtrain, Ytrain, Xtest, Ytest) + + W = d-by-d positive semi-definite matrix + test_k = vector of k-values to use for KNN/Prec@k/NDCG + Xtrain = d-by-n matrix of training data + Ytrain = n-by-1 vector of training labels + OR + n-by-2 cell array where + Y{q,1} contains relevant indices (in 1..n) for point q + Y{q,2} contains irrelevant indices (in 1..n) for point q + Xtest = d-by-m matrix of testing data + Ytest = m-by-1 vector of training labels, or m-by-2 cell array + If using the cell version, indices correspond to + the training set, and must lie in the range (1..n) + + + The output structure Perf contains the mean score for: + AUC, KNN, Prec@k, MAP, MRR, NDCG, + as well as the effective dimensionality of W, and + the best-performing k-value for KNN, Prec@k, and NDCG. + + For information retrieval settings, consider Xtrain/Ytrain as the corpus + and Xtest/Ytest as the queries. + +By testing with + Wnative = eye(size(X,1)) +you can quickly evaluate the performance of the native (Euclidean) metric. + + +FEEDBACK +-------- + +Please send any bug reports, source code contributions, etc. to + +Brian McFee <bmcfee@cs.ucsd.edu> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeKernel.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function r = regularizeKernel(W, X, gradient) +% +% r = regularizeKernel(W, X, gradient) +% +% + if gradient + r = X; + else + r = sum(sum(W .* X)); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeMKLDOD.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +function r = regularizeMKLDOD(W, X, gradient) +% +% r = regularizeMKLDOD(W, X, gradient) +% +% + + [d,n,m] = size(X); + + if gradient + r = zeros(m,m,d); + for i = 1:m + r(i,i,:) = diag(X(:,:,i)); + for j = (i+1):m + r(i,j,:) = diag(X(:,:,i)) + diag(X(:,:,j)); + end + end + else + r = 0; + for i = 1:m + r = r + squeeze(W(i,i,:))' * diag(X(:,:,i)); + for j = (i+1):m + r = r + squeeze(W(i,j,:))' * (diag(X(:,:,i)) + diag(X(:,:,j))); + end + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeMKLDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +function r = regularizeMKLDiag(W, X, gradient) +% +% r = regularizeMKL(W, X, gradient) +% +% + + [d,n,m] = size(X); + + if gradient + r = zeros(d,m); + for i = 1:m + r(:,i) = diag(X(:,:,i)); + end + else + r = 0; + for i = 1:m + r = r + W(:,i)' * diag(X(:,:,i)); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeMKLFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +function r = regularizeMKLFull(W, X, gradient) +% +% r = regularizMKL(W, X, gradient) +% +% + + [d,n,m] = size(X); + + if gradient + r = X; + else + r = 0; + for i = 1:m + r = r + sum(sum(W(:,:,i) .* X(:,:,i))); + end + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeNone.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function r = regularizeNone(W, X, gradient) +% +% r = regularizeNone(W, X, gradient) +% +% Always returns 0 + + if gradient + r = zeros(size(W)); + else + r = 0; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeTraceDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function r = regularizeTraceDiag(W, X, gradient) +% +% r = regularizeTraceDiag(W, X, gradient) +% +% + d = length(W); + + if gradient + r = ones(d,1) / d; + else + r = sum(W) / d; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeTraceFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function r = regularizeTraceFull(W, X, gradient) +% +% r = regularizeTraceFull(W, X, gradient) +% +% + d = length(W); + + if gradient + r = eye(d) / d; + else + r = trace(W) / d; + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeTwoDiag.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function r = regularizeTwoDiag(W, X, gradient) +% +% r = regularizeTwoDiag(W, X, gradient) +% +% + d = length(W); + + if gradient + r = W / d; + else + r = sum(sum(W.^2)) / (2 * d); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/regularize/regularizeTwoFull.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +function r = regularizeTwoFull(W, X, gradient) +% +% r = regularizeTwoFull(W, X, gradient) +% +% + d = length(W); + + if gradient + r = W / d; + else + r = sum(sum(W.^2)) / (2 * d); + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/rmlr_demo.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,87 @@ +function rmlr_demo() + + display('Loading Wine data'); + load Wine; + + noisedim = 96; + [d,n] = size(X); + d = d + noisedim; + + %create covariance matrix + var = randn(noisedim); var = var'*var; + noise = sqrtm(var)* randn(noisedim, n); + X = [X; noise]; + + % z-score the input dimensions + display('z-scoring features'); + X = zscore(X')'; + + display('Generating a 80/20 training/test split'); + P = randperm(n); + Xtrain = X(:,P(1:floor(0.8 * n))); + Ytrain = Y(P(1:floor(0.8*n))); + Xtest = X(:,P((1+floor(0.8*n)):end)); + Ytest = Y(P((1+floor(0.8*n)):end)); + + C = 1e2; + lam = 0.5; + + display(sprintf('Training with C=%.2e, Delta=MAP', C)); + %learn metric with R-MLR + [W_rmlr, Xi, Diagnostics_rmlr] = rmlr_train(Xtrain, Ytrain, C, 'map',3,1,0,0,lam); + + %learn metric with MLR + [W_mlr, Xi, Diagnostics_mlr] = mlr_train(Xtrain, Ytrain, C, 'map'); + + display('Test performance in the native (normalized) metric'); + mlr_test(eye(d), 3, Xtrain, Ytrain, Xtest, Ytest) + + display('Test performance with R-MLR metric'); + mlr_test(W_rmlr, 3, Xtrain, Ytrain, Xtest, Ytest) + + display('Test performance with MLR metric'); + mlr_test(W_mlr, 3, Xtrain, Ytrain, Xtest, Ytest) + + % Scatter-plot + figure; + subplot(1,3,1), drawData(eye(d), Xtrain, Ytrain, Xtest, Ytest), title('Native metric (z-scored)'); + subplot(1,3,2), drawData(W_mlr, Xtrain, Ytrain, Xtest, Ytest), title('Learned metric (MLR)'); + subplot(1,3,3), drawData(W_rmlr, Xtrain, Ytrain, Xtest, Ytest), title('Learned metric (RMLR)'); + + figure; + subplot(121), imagesc(W_mlr), title('W: MLR'); + subplot(122), imagesc(W_rmlr), title('W: RMLR'); + Diagnostics_rmlr + Diagnostics_mlr + +end + + +function drawData(W, Xtrain, Ytrain, Xtest, Ytest); + + n = length(Ytrain); + m = length(Ytest); + + if size(W,2) == 1 + W = diag(W); + end + % PCA the learned metric + Z = [Xtrain Xtest]; + A = Z' * W * Z; + [v,d] = eig(A); + + L = (d.^0.5) * v'; + L = L(1:2,:); + + % Draw training points + hold on; + trmarkers = {'b+', 'r+', 'g+'}; + tsmarkers = {'bo', 'ro', 'go'}; + for i = min(Ytrain):max(Ytrain) + points = find(Ytrain == i); + scatter(L(1,points), L(2,points), trmarkers{i}); + points = n + find(Ytest == i); + scatter(L(1,points), L(2,points), tsmarkers{i}); + end + legend({'Training', 'Test'}); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/rmlr_train.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,482 @@ +function [W, Xi, Diagnostics] = rmlr_train(X, Y, Cslack, varargin) +%[W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, stochastic, lam, STRUCTREG) +% +% [W, Xi, D] = rmlr_train(X, Y, C,...) +% +% X = d*n data matrix +% Y = either n-by-1 label of vectors +% OR +% n-by-2 cell array where +% Y{q,1} contains relevant indices for q, and +% Y{q,2} contains irrelevant indices for q +% +% C >= 0 slack trade-off parameter (default=1) +% +% W = the learned metric +% Xi = slack value on the learned metric +% D = diagnostics +% +% Optional arguments: +% +% [W, Xi, D] = rmlr_train(X, Y, C, LOSS) +% where LOSS is one of: +% 'AUC': Area under ROC curve (default) +% 'KNN': KNN accuracy +% 'Prec@k': Precision-at-k +% 'MAP': Mean Average Precision +% 'MRR': Mean Reciprocal Rank +% 'NDCG': Normalized Discounted Cumulative Gain +% +% [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k) +% where k is the number of neighbors for Prec@k or NDCG +% (default=3) +% +% [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG) +% where REG defines the regularization on W, and is one of: +% 0: no regularization +% 1: 1-norm: trace(W) (default) +% 2: 2-norm: trace(W' * W) +% 3: Kernel: trace(W * X), assumes X is square and positive-definite +% +% [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal) +% Not implemented as learning a diagonal W metric just reduces to MLR because ||W||_2,1 = trace(W) when W is diagonal. +% +% [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, B) +% where B > 0 enables stochastic optimization with batch size B +% +% [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, B, lambda) +% lambda is the desired value of the hyperparameter which is the coefficient of ||W||_2,1. Default is 1 if lambda is not set +% [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, B, lambda, CC) +% Set ConstraintClock to CC (default: 20, 100) +% +% [W, Xi, D] = rmlr_train(X, Y, C, LOSS, k, REG, Diagonal, B, lambda, CC, E) +% Set ConstraintClock to E (default: 1e-3) +% + + + TIME_START = tic(); + + global C; + C = Cslack; + + [d,n,m] = size(X); + + if m > 1 + MKL = 1; + else + MKL = 0; + end + + if nargin < 3 + C = 1; + end + + %%% + % Default options: + + global CP SO PSI REG FEASIBLE LOSS DISTANCE SETDISTANCE CPGRADIENT STRUCTKERNEL DUALW THRESH INIT; + global RHO; + + + %%% + % Augmented lagrangian factor + RHO = 1; + + % </modified> + + global FEASIBLE_COUNT; + FEASIBLE_COUNT = 0; + + CP = @cuttingPlaneFull; + SO = @separationOracleAUC; + PSI = @metricPsiPO; + + if ~MKL + INIT = @initializeFull; + REG = @regularizeTraceFull; + STRUCTKERNEL= @structKernelLinear; + DUALW = @dualWLinear; + FEASIBLE = @feasibleFull; + THRESH = @threshFull_admmMixed; + CPGRADIENT = @cpGradientFull; + DISTANCE = @distanceFull; + SETDISTANCE = @setDistanceFull; + LOSS = @lossHinge; + Regularizer = 'Trace'; + else + INIT = @initializeFullMKL; + REG = @regularizeMKLFull; + STRUCTKERNEL= @structKernelMKL; + DUALW = @dualWMKL; + FEASIBLE = @feasibleFullMKL; + THRESH = @threshFull_admmMixed; + CPGRADIENT = @cpGradientFullMKL; + DISTANCE = @distanceFullMKL; + SETDISTANCE = @setDistanceFullMKL; + LOSS = @lossHingeFullMKL; + Regularizer = 'Trace'; + end + + + Loss = 'AUC'; + Feature = 'metricPsiPO'; + + + %%% + % Default k for prec@k, ndcg + k = 3; + + %%% + % Stochastic violator selection? + STOCHASTIC = 0; + batchSize = n; + SAMPLES = 1:n; + + + if nargin > 3 + switch lower(varargin{1}) + case {'auc'} + SO = @separationOracleAUC; + PSI = @metricPsiPO; + Loss = 'AUC'; + Feature = 'metricPsiPO'; + case {'knn'} + SO = @separationOracleKNN; + PSI = @metricPsiPO; + Loss = 'KNN'; + Feature = 'metricPsiPO'; + case {'prec@k'} + SO = @separationOraclePrecAtK; + PSI = @metricPsiPO; + Loss = 'Prec@k'; + Feature = 'metricPsiPO'; + case {'map'} + SO = @separationOracleMAP; + PSI = @metricPsiPO; + Loss = 'MAP'; + Feature = 'metricPsiPO'; + case {'mrr'} + SO = @separationOracleMRR; + PSI = @metricPsiPO; + Loss = 'MRR'; + Feature = 'metricPsiPO'; + case {'ndcg'} + SO = @separationOracleNDCG; + PSI = @metricPsiPO; + Loss = 'NDCG'; + Feature = 'metricPsiPO'; + otherwise + error('MLR:LOSS', ... + 'Unknown loss function: %s', varargin{1}); + end + end + + if nargin > 4 + k = varargin{2}; + end + + Diagonal = 0; + %Diagonal case is not implemented. Use mlr_train for that. + + if nargin > 5 + switch(varargin{3}) + case {0} + REG = @regularizeNone; + Regularizer = 'None'; + THRESH = @threshFull_admmMixed; + case {1} + if MKL + REG = @regularizeMKLFull; + STRUCTKERNEL= @structKernelMKL; + DUALW = @dualWMKL; + else + REG = @regularizeTraceFull; + STRUCTKERNEL= @structKernelLinear; + DUALW = @dualWLinear; + end + Regularizer = 'Trace'; + + case {2} + REG = @regularizeTwoFull; + Regularizer = '2-norm'; + error('MLR:REGULARIZER', '2-norm regularization no longer supported'); + + + case {3} + if MKL + REG = @regularizeMKLFull; + STRUCTKERNEL= @structKernelMKL; + DUALW = @dualWMKL; + else + REG = @regularizeKernel; + STRUCTKERNEL= @structKernelMKL; + DUALW = @dualWMKL; + end + Regularizer = 'Kernel'; + + + otherwise + error('MLR:REGULARIZER', ... + 'Unknown regularization: %s', varargin{3}); + end + end + + + % Are we in stochastic optimization mode? + if nargin > 7 && varargin{5} > 0 + if varargin{5} < n + STOCHASTIC = 1; + CP = @cuttingPlaneRandom; + batchSize = varargin{5}; + end + end + % Algorithm + % + % Working <- [] + % + % repeat: + % (W, Xi) <- solver(X, Y, C, Working) + % + % for i = 1:|X| + % y^_i <- argmax_y^ ( Delta(y*_i, y^) + w' Psi(x_i, y^) ) + % + % Working <- Working + (y^_1,y^_2,...,y^_n) + % until mean(Delta(y*_i, y_i)) - mean(w' (Psi(x_i,y_i) - Psi(x_i,y^_i))) + % <= Xi + epsilon + + if nargin > 8 + lam = varargin{6}; + else + lam = 1; + end + + disp(['lam = ' num2str(lam)]) + + global DEBUG; + + if isempty(DEBUG) + DEBUG = 0; + end + + DEBUG = 1; + + %%% + % Max calls to seperation oracle + MAX_CALLS = 200; + MIN_CALLS = 10; + + %%% + % Timer to eliminate old constraints + ConstraintClock = 500; % standard: 500 + + if nargin > 9 && varargin{7} > 0 + ConstraintClock = varargin{7}; + end + + %%% + % Convergence criteria for worst-violated constraint + E = 1e-3; + if nargin > 10 && varargin{8} > 0 + E = varargin{8}; + end + + %XXX: 2012-01-31 21:29:50 by Brian McFee <bmcfee@cs.ucsd.edu> + % no longer belongs here + % Initialize + W = INIT(X); + + + global ADMM_Z ADMM_V ADMM_UW ADMM_UV; + ADMM_Z = W; + ADMM_V = W; + ADMM_UW = 0 * ADMM_Z; + ADMM_UV = 0 * ADMM_Z; + + ClassScores = []; + + if isa(Y, 'double') + Ypos = []; + Yneg = []; + ClassScores = synthesizeRelevance(Y); + + elseif isa(Y, 'cell') && size(Y,1) == n && size(Y,2) == 2 + dbprint(2, 'Using supplied Ypos/Yneg'); + Ypos = Y(:,1); + Yneg = Y(:,2); + + % Compute the valid samples + SAMPLES = find( ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2)))); + elseif isa(Y, 'cell') && size(Y,1) == n && size(Y,2) == 1 + dbprint(2, 'Using supplied Ypos/synthesized Yneg'); + Ypos = Y(:,1); + Yneg = []; + SAMPLES = find( ~(cellfun(@isempty, Y(:,1)))); + else + error('MLR:LABELS', 'Incorrect format for Y.'); + end + %% + % If we don't have enough data to make the batch, cut the batch + batchSize = min([batchSize, length(SAMPLES)]); + + Diagnostics = struct( 'loss', Loss, ... % Which loss are we optimizing? + 'feature', Feature, ... % Which ranking feature is used? + 'k', k, ... % What is the ranking length? + 'regularizer', Regularizer, ... % What regularization is used? + 'diagonal', Diagonal, ... % 0 for full metric, 1 for diagonal + 'num_calls_SO', 0, ... % Calls to separation oracle + 'num_calls_solver', 0, ... % Calls to solver + 'time_SO', 0, ... % Time in separation oracle + 'time_solver', 0, ... % Time in solver + 'time_total', 0, ... % Total time + 'f', [], ... % Objective value + 'num_steps', [], ... % Number of steps for each solver run + 'num_constraints', [], ... % Number of constraints for each run + 'Xi', [], ... % Slack achieved for each run + 'Delta', [], ... % Mean loss for each SO call + 'gap', [], ... % Gap between loss and slack + 'C', C, ... % Slack trade-off + 'epsilon', E, ... % Convergence threshold + 'feasible_count', FEASIBLE_COUNT, ... % Counter for # svd's + 'constraint_timer', ConstraintClock); % Time before evicting old constraints + + + + global PsiR; + global PsiClock; + + PsiR = {}; + PsiClock = []; + + Xi = -Inf; + Margins = []; + H = []; + Q = []; + + if STOCHASTIC + dbprint(2, 'STOCHASTIC OPTIMIZATION: Batch size is %d/%d', batchSize, n); + end + + dbprint(2,['Regularizer is "' Regularizer '"']); + while 1 + if Diagnostics.num_calls_solver > MAX_CALLS + dbprint(2,['Calls to SO >= ' num2str(MAX_CALLS)]); + break; + end + + dbprint(2, 'Round %03d', Diagnostics.num_calls_solver); + % Generate a constraint set + Termination = -Inf; + + + dbprint(2, 'Calling separation oracle...'); + [PsiNew, Mnew, SO_time] = CP(k, X, W, Ypos, Yneg, batchSize, SAMPLES, ClassScores); + + Termination = LOSS(W, PsiNew, Mnew, 0); + Diagnostics.num_calls_SO = Diagnostics.num_calls_SO + 1; + Diagnostics.time_SO = Diagnostics.time_SO + SO_time; + + Margins = cat(1, Margins, Mnew); + PsiR = cat(1, PsiR, PsiNew); + PsiClock = cat(1, PsiClock, 0); + H = expandKernel(H); + Q = expandRegularizer(Q, X, W); + + + dbprint(2, '\n\tActive constraints : %d', length(PsiClock)); + dbprint(2, '\t Mean loss : %0.4f', Mnew); + dbprint(2, '\t Current loss Xi : %0.4f', Xi); + dbprint(2, '\t Termination -Xi < E : %0.4f <? %.04f\n', Termination - Xi, E); + + Diagnostics.gap = cat(1, Diagnostics.gap, Termination - Xi); + Diagnostics.Delta = cat(1, Diagnostics.Delta, Mnew); + + %if Termination <= Xi + E + if Termination <= Xi + E && Diagnostics.num_calls_solver > MIN_CALLS + %if Termination - Xi <= E + dbprint(1, 'Done.'); + break; + end + + + + dbprint(1, 'Calling solver...'); + PsiClock = PsiClock + 1; + Solver_time = tic(); +% disp('Robust MLR') + [W, Xi, Dsolver] = rmlr_admm(C, X, Margins, H, Q, lam); + + Diagnostics.time_solver = Diagnostics.time_solver + toc(Solver_time); + Diagnostics.num_calls_solver = Diagnostics.num_calls_solver + 1; + + Diagnostics.Xi = cat(1, Diagnostics.Xi, Xi); + Diagnostics.f = cat(1, Diagnostics.f, Dsolver.f); + Diagnostics.num_steps = cat(1, Diagnostics.num_steps, Dsolver.num_steps); + + %%% + % Cull the old constraints + GC = PsiClock < ConstraintClock; + Margins = Margins(GC); + PsiR = PsiR(GC); + PsiClock = PsiClock(GC); + H = H(GC, GC); + Q = Q(GC); + + Diagnostics.num_constraints = cat(1, Diagnostics.num_constraints, length(PsiR)); + end + + + % Finish diagnostics + + Diagnostics.time_total = toc(TIME_START); + Diagnostics.feasible_count = FEASIBLE_COUNT; +end + +function H = expandKernel(H) + + global STRUCTKERNEL; + global PsiR; + + m = length(H); + H = padarray(H, [1 1], 0, 'post'); + + + for i = 1:m+1 + H(i,m+1) = STRUCTKERNEL( PsiR{i}, PsiR{m+1} ); + H(m+1, i) = H(i, m+1); + end +end + +function Q = expandRegularizer(Q, K, W) + + % FIXME: 2012-01-31 21:34:15 by Brian McFee <bmcfee@cs.ucsd.edu> + % does not support unregularized learning + + global PsiR; + global STRUCTKERNEL REG; + + m = length(Q); + Q(m+1,1) = STRUCTKERNEL(REG(W,K,1), PsiR{m+1}); + +end + +function ClassScores = synthesizeRelevance(Y) + + classes = unique(Y); + nClasses = length(classes); + + ClassScores = struct( 'Y', Y, ... + 'classes', classes, ... + 'Ypos', [], ... + 'Yneg', []); + + Ypos = cell(nClasses, 1); + Yneg = cell(nClasses, 1); + for c = 1:nClasses + Ypos{c} = (Y == classes(c)); + Yneg{c} = ~Ypos{c}; + end + + ClassScores.Ypos = Ypos; + ClassScores.Yneg = Yneg; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/separationOracle/separationOracleAUC.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +function [Y, Loss] = separationOracleAUC(q, D, pos, neg, k) +% +% [Y,Loss] = separationOracleAUC(q, D, pos, neg, k) +% +% q = index of the query point +% D = the current distance matrix +% pos = indices of relevant results for q +% neg = indices of irrelevant results for q +% k = length of the list to consider (unused in AUC) +% +% Y is a permutation 1:n corresponding to the maximally +% violated constraint +% +% Loss is the loss for Y, in this case, 1-AUC(Y) + + + % First, sort the documents in descending order of W'Phi(q,x) + % Phi = - (X(q) - X(x)) * (X(q) - X(x))' + + % Sort the positive documents + ScorePos = - D(pos,q); + [Vpos, Ipos] = sort(full(ScorePos'), 'descend'); + Ipos = pos(Ipos); + + % Sort the negative documents + ScoreNeg = - D(neg,q); + [Vneg, Ineg] = sort(full(ScoreNeg'), 'descend'); + Ineg = neg(Ineg); + + + % How many pos and neg documents are we using here? + numPos = length(pos); + numNeg = length(neg); + n = numPos + numNeg; + + + NegsBefore = sum(bsxfun(@lt, Vpos, Vneg' + 0.5),1); + + % Construct Y from NegsBefore + Y = nan * ones(n,1); + Y((1:numPos) + NegsBefore) = Ipos; + Y(isnan(Y)) = Ineg; + + % Compute AUC loss for this ranking + Loss = 1 - sum(NegsBefore) / (numPos * numNeg * 2); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/separationOracle/separationOracleKNN.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,98 @@ +function [Y, Loss] = separationOracleKNN(q, D, pos, neg, k) +% +% [Y,Loss] = separationOracleKNN(q, D, pos, neg, k) +% +% q = index of the query point +% D = the current distance matrix +% pos = indices of relevant results for q +% neg = indices of irrelevant results for q +% k = length of the list to consider +% +% Y is a permutation 1:n corresponding to the maximally +% violated constraint +% +% Loss is the loss for Y, in this case, 1-Prec@k(Y) + + + % First, sort the documents in descending order of W'Phi(q,x) + % Phi = - (X(q) - X(x)) * (X(q) - X(x))' + + % Sort the positive documents + ScorePos = - D(pos,q); + [Vpos, Ipos] = sort(full(ScorePos'), 'descend'); + Ipos = pos(Ipos); + + % Sort the negative documents + ScoreNeg = - D(neg,q); + [Vneg, Ineg] = sort(full(ScoreNeg'), 'descend'); + Ineg = neg(Ineg); + + % Now, solve the DP for the interleaving + + numPos = length(pos); + numNeg = length(neg); + n = numPos + numNeg; + + cVpos = cumsum(Vpos); + cVneg = cumsum(Vneg); + + + % If we don't have enough positive (or negative) examples, scale k down + k = min([k, numPos, numNeg]); + + % Algorithm: + % For each precision score in 0, 1/k, 2/k, ... 1 + % Calculate maximum discriminant score for that precision level + KNN = (0:(1/k):1)' > 0.5; + Discriminant = zeros(k+1, 1); + NegsBefore = zeros(numPos,k+1); + + % For 0 precision, all positives go after the first k negatives + + NegsBefore(:,1) = k + binarysearch(Vpos, Vneg(k+1:end)); + Discriminant(1) = Vpos * (numNeg - 2 * NegsBefore(:,1)) + numPos * cVneg(end) ... + - 2 * sum(cVneg(NegsBefore((NegsBefore(:,1) > 0),1))); + + + + % For precision (a-1)/k, swap the (a-1)'th positive doc + % into the top (k-a) negative docs + + for a = 2:(k+1) + NegsBefore(:,a) = NegsBefore(:,a-1); + + % We have a-1 positives, and k - (a-1) negatives + NegsBefore(a-1, a) = binarysearch(Vpos(a-1), Vneg(1:(k-a+1))); + + % There were NegsBefore(a-1,a-1) negatives before (a-1) + % Now there are NegsBefore(a,a-1) + + Discriminant(a) = Discriminant(a-1) ... + + 2 * (NegsBefore(a-1,a-1) - NegsBefore(a-1,a)) * Vpos(a-1); + + if NegsBefore(a-1,a-1) > 0 + Discriminant(a) = Discriminant(a) + 2 * cVneg(NegsBefore(a-1,a-1)); + end + if NegsBefore(a-1,a) > 0 + Discriminant(a) = Discriminant(a) - 2 * cVneg(NegsBefore(a-1,a)); + end + end + + % Normalize discriminant scores + Discriminant = Discriminant / (numPos * numNeg); + [s, x] = max(Discriminant - KNN); + + % Now we know that there are x-1 relevant docs in the max ranking + % Construct Y from NegsBefore(x,:) + + Y = nan * ones(n,1); + Y((1:numPos)' + NegsBefore(:,x)) = Ipos; + if sum(isnan(Y)) ~= length(Ineg) + keyboard; + end + Y(isnan(Y)) = Ineg; + + % Compute loss for this list + Loss = 1 - KNN(x); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/separationOracle/separationOracleMAP.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,134 @@ +function [Y, Loss] = separationOracleMAP(q, D, pos, neg, k) +% +% [Y,Loss] = separationOracleMAP(q, D, pos, neg, k) +% +% q = index of the query point +% D = the current distance matrix +% pos = indices of relevant results for q +% neg = indices of irrelevant results for q +% k = length of the list to consider (unused in MAP) +% +% Y is a permutation 1:n corresponding to the maximally +% violated constraint +% +% Loss is the loss for Y, in this case, 1-AP(Y) + + + % First, sort the documents in descending order of W'Phi(q,x) + % Phi = - (X(q) - X(x)) * (X(q) - X(x))' + + % Sort the positive documents + ScorePos = - D(pos,q); + [Vpos, Ipos] = sort(full(ScorePos'), 'descend'); + Ipos = pos(Ipos); + + % Sort the negative documents + ScoreNeg = - D(neg,q); + [Vneg, Ineg] = sort(full(ScoreNeg'), 'descend'); + Ineg = neg(Ineg); + + % Now, solve the DP for the interleaving + + numPos = length(pos); + numNeg = length(neg); + n = numPos + numNeg; + + + % Pre-generate the precision scores +% H = triu(1./bsxfun(@minus, (0:(numPos-1))', 1:n)); + H = tril(1./bsxfun(@minus, 0:(numPos-1), (1:n)')); + + % Padded cumulative Vneg + pcVneg = cumsum([0 Vneg]); + + % Generate the discriminant scores + H = H + scoreChangeMatrix(Vpos, Vneg, n, pcVneg); + + % Cost of inserting the first + at position b + P = zeros(size(H)); + + % Now recurse + for a = 2:numPos + + % Fill in the back-pointers + [m,p] = cummax(H(:,a-1)); + % The best point is the previous row, up to b-1 + H(a:n,a) = H(a:n,a) + (a-1)/a .* m(a-1:n-1)'; + P(a+1:n,a) = p(a:n-1); + P(a,a) = a-1; + end + + % Now reconstruct the permutation from the DP table + Y = nan * ones(n,1); + [m,p] = max(H(:,numPos)); + Y(p) = Ipos(numPos); + + for a = numPos:-1:2 + p = P(p,a); + Y(p) = Ipos(a-1); + end + Y(isnan(Y)) = Ineg; + + % Compute loss for this list + Loss = 1 - AP(Y, pos); +end + +function C = scoreChangeMatrix(Vpos, Vneg, n, pcVneg) + numNeg = length(Vneg); + numPos = length(Vpos); + + % Inserting the a'th relevant document at position b + % There are (b - (a - 1)) negative docs before a + % And (numNeg - (b - (a - 1))) negative docs after + % + % The change in score is proportional to: + % + % sum_{negative j} (Vpos(a) - Vneg(j)) * y_{aj} + % + % = (numNeg - (b - (a - 1))) * Vpos(a) # Negatives after a + % - (cVneg(end) - cVneg(b - (a - 1))) Weight of negs after a + % - (b - (a - 1)) * Vpos(a) # Negatives before a + % + cVneg(b - (a - 1)) Weight of negs before a + % + % Rearrange: + % + % (numNeg - 2 * (b - a + 1)) * Vpos(a) + % - cVneg(end) + 2 * cVneg(b - a + 1) + % + % Valid range of a: 1:numPos + % Valid range of b: a:n + + D = bsxfun(@plus, 1-(1:numPos), (1:n)'); + C = numNeg - 2 * D; + C = bsxfun(@times, Vpos, C); + + D(D < 1) = 1; + D(D > length(pcVneg)) = length(pcVneg); + + % FIXME: 2011-01-28 21:13:37 by Brian McFee <bmcfee@cs.ucsd.edu> + % brutal hack to get around matlab's screwy matrix reshaping + if numPos == 1 + pcVneg = pcVneg'; + end + + C = C + 2 * pcVneg(D) - pcVneg(end); + + % Normalize + C = bsxfun(@ldivide, (1:numPos) * numNeg, C); + + % -Inf out the infeasible regions + C = C - triu(Inf * bsxfun(@gt, (1:numPos), (1:n)'),1); + + +end + +function x = AP(Y, pos) + % Indicator for relevant documents + rel = ismember(Y, pos); + + % Prec@k for all k + Prec = cumsum(rel)' ./ (1:length(Y)); + + % Prec@k averaged over relevant positions + x = mean(Prec(rel)); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/separationOracle/separationOracleMRR.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,77 @@ +function [Y, Loss] = separationOracleMRR(q, D, pos, neg, k) +% +% [Y,Loss] = separationOracleMRR(q, D, pos, neg, k) +% +% q = index of the query point +% D = the current distance matrix +% pos = indices of relevant results for q +% neg = indices of irrelevant results for q +% k = length of the list to consider (unused in MRR) +% +% Y is a permutation 1:n corresponding to the maximally +% violated constraint +% +% Loss is the loss for Y, in this case, 1-MRR(Y) + + + % First, sort the documents in descending order of W'Phi(q,x) + % Phi = - (X(q) - X(x)) * (X(q) - X(x))' + + % Sort the positive documents + ScorePos = - D(pos,q); + [Vpos, Ipos] = sort(full(ScorePos'), 'descend'); + Ipos = pos(Ipos); + + % Sort the negative documents + ScoreNeg = -D(neg,q); + [Vneg, Ineg] = sort(full(ScoreNeg'), 'descend'); + Ineg = neg(Ineg); + + % Now, solve the DP for the interleaving + + numPos = length(pos); + numNeg = length(neg); + n = numPos + numNeg; + + cVpos = cumsum(Vpos); + cVneg = cumsum(Vneg); + + + % Algorithm: + % For each RR score in 1/1, 1/2, ..., 1/(numNeg+1) + % Calculate maximum discriminant score for that precision level + MRR = ((1:(numNeg+1)).^-1)'; + + + Discriminant = zeros(numNeg+1, 1); + Discriminant(end) = numPos * cVneg(end) - numNeg * cVpos(end); + + % For the rest of the positions, we're interleaving one more negative + % example into the 2nd-through-last positives + offsets = 1 + binarysearch(Vneg, Vpos(2:end)); + + % How many of the remaining positives go before Vneg(a)? + NegsBefore = -bsxfun(@ge, offsets, (1:length(Vpos))'); + + % For the last position, all negatives come before all positives + NegsBefore(:,numNeg+1) = numNeg; + + Discriminant(1:numNeg) = -2 * (offsets .* Vneg - cVpos(offsets)); + Discriminant = sum(Discriminant) - cumsum(Discriminant) + Discriminant; + + + % Normalize discriminant scores + Discriminant = Discriminant / (numPos * numNeg); + [s, x] = max(Discriminant - MRR); + + % Now we know that there are x-1 relevant docs in the max ranking + % Construct Y from NegsBefore(x,:) + + Y = nan * ones(n,1); + Y((1:numPos)' + sum(NegsBefore(:,x:end),2)) = Ipos; + Y(isnan(Y)) = Ineg; + + % Compute loss for this list + Loss = 1 - MRR(x); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/separationOracle/separationOracleNDCG.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,100 @@ +function [Y, Loss] = separationOracleNDCG(q, D, pos, neg, k) +% +% [Y,Loss] = separationOracleNDCG(q, D, pos, neg, k) +% +% q = index of the query point +% D = the current distance matrix +% pos = indices of relevant results for q +% neg = indices of irrelevant results for q +% k = length of the list to consider +% +% Y is a permutation 1:n corresponding to the maximally +% violated constraint +% +% Loss is the loss for Y, in this case, 1-NDCG(Y) + + + % First, sort the documents in descending order of W'Phi(q,x) + % Phi = - (X(q) - X(x)) * (X(q) - X(x))' + + % Sort the positive documents + ScorePos = - D(pos, q); + [Vpos, Ipos] = sort(full(ScorePos'), 'descend'); + Ipos = pos(Ipos); + + % Sort the negative documents + ScoreNeg = - D(neg, q); + [Vneg, Ineg] = sort(full(ScoreNeg'), 'descend'); + Ineg = neg(Ineg); + + % Now, solve the DP for the interleaving + + numPos = length(pos); + numNeg = length(neg); + n = numPos + numNeg; + + % From Chakrabarti (KDD08) + k = min(k, numPos); + + cVneg = cumsum(Vneg); + + Discount = zeros(k, 1); + Discount(1:2) = 1; + Discount(3:k) = 1./ log2(3:k); + + DCGstar = sum(Discount); + + + % Pre-compute the loss table + LossTab = padarray( hankel(- Discount / DCGstar), ... + max(0, [numNeg numPos] - k), 0, 'post'); + if sum(size(LossTab) > [numNeg, numPos]) + LossTab = LossTab(1:numNeg, 1:numPos); + end + + % 2010-01-17 09:13:41 by Brian McFee <bmcfee@cs.ucsd.edu> + % initialize the score table + + pcVneg = [0 cVneg]; + % Pre-compute cellScore + cellValue = bsxfun(@times, Vpos / (numPos * numNeg), numNeg - 2 * ((1:numNeg)-1)'); + cellValue = bsxfun(@plus, (2 * pcVneg(1:numNeg) - cVneg(end))' / (numPos * numNeg), cellValue); + cellValue = cellValue + LossTab; + + S = zeros(numNeg, numPos); + P = zeros(numNeg, numPos); + + % Initialize first column + P(:,1) = 1; + S(:,1) = cellValue(:,1); + + % Initialize first row + P(1,:) = 1; + S(1,:) = cumsum(cellValue(1,:)); + + % For the rest, use the recurrence + + for g = 2:numPos + [m, pointer] = cummax(S(:,g-1)); + P(:,g) = pointer; + S(:,g) = m' + cellValue(:,g); + end + + % Now reconstruct the permutation from the DP table + Y = nan * ones(n,1); + [m,p] = max(S(:,numPos)); + + Loss = 1 + LossTab(p,numPos); + + NegsBefore = zeros(numPos,1); + NegsBefore(numPos) = p-1; + + for a = numPos:-1:2 + p = P(p,a); + NegsBefore(a-1) = p-1; + Loss = Loss + LossTab(p,a-1); + end + Y((1:numPos)' + NegsBefore) = Ipos; + Y(isnan(Y)) = Ineg; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/separationOracle/separationOraclePrecAtK.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +function [Y, Loss] = separationOraclePrecAtK(q, D, pos, neg, k) +% +% [Y,Loss] = separationOraclePrecAtK(q, D, pos, neg, k) +% +% q = index of the query point +% D = the current distance matrix +% pos = indices of relevant results for q +% neg = indices of irrelevant results for q +% k = length of the list to consider +% +% Y is a permutation 1:n corresponding to the maximally +% violated constraint +% +% Loss is the loss for Y, in this case, 1-Prec@k(Y) + + + % First, sort the documents in descending order of W'Phi(q,x) + % Phi = - (X(q) - X(x)) * (X(q) - X(x))' + + % Sort the positive documents + ScorePos = - D(pos,q); + [Vpos, Ipos] = sort(full(ScorePos'), 'descend'); + Ipos = pos(Ipos); + + % Sort the negative documents + ScoreNeg = -D(neg,q); + [Vneg, Ineg] = sort(full(ScoreNeg'), 'descend'); + Ineg = neg(Ineg); + + % Now, solve the DP for the interleaving + + numPos = length(pos); + numNeg = length(neg); + n = numPos + numNeg; + + cVpos = cumsum(Vpos); + cVneg = cumsum(Vneg); + + + % If we don't have enough positive (or negative) examples, scale k down + k = min([k, numPos, numNeg]); + + % Algorithm: + % For each precision score in 0, 1/k, 2/k, ... 1 + % Calculate maximum discriminant score for that precision level + Precision = (0:(1/k):1)'; + Discriminant = zeros(k+1, 1); + NegsBefore = zeros(numPos, k+1); + + % For 0 precision, all positives go after the first k negatives + + NegsBefore(:,1) = k + binarysearch(Vpos, Vneg(k+1:end)); + + Discriminant(1) = Vpos * (numNeg - 2 * NegsBefore(:,1)) + numPos * cVneg(end) ... + - 2 * sum(cVneg(NegsBefore((NegsBefore(:,1) > 0),1))); + + + + % For precision (a-1)/k, swap the (a-1)'th positive doc + % into the top (k-a) negative docs + + for a = 2:(k+1) + NegsBefore(:,a) = NegsBefore(:,a-1); + + % We have a-1 positives, and k - (a-1) negatives + NegsBefore(a-1, a) = binarysearch(Vpos(a-1), Vneg(1:(k-a+1))); + + % There were NegsBefore(a-1,a-1) negatives before (a-1) + % Now there are NegsBefore(a,a-1) + Discriminant(a) = Discriminant(a-1) ... + + 2 * (NegsBefore(a-1,a-1) - NegsBefore(a-1,a)) * Vpos(a-1); + + if NegsBefore(a-1,a-1) > 0 + Discriminant(a) = Discriminant(a) + 2 * cVneg(NegsBefore(a-1,a-1)); + end + if NegsBefore(a-1,a) > 0 + Discriminant(a) = Discriminant(a) - 2 * cVneg(NegsBefore(a-1,a)); + end + end + + % Normalize discriminant scores + Discriminant = Discriminant / (numPos * numNeg); + [s, x] = max(Discriminant - Precision); + + % Now we know that there are x-1 relevant docs in the max ranking + % Construct Y from NegsBefore(x,:) + + Y = nan * ones(n,1); + Y((1:numPos)' + NegsBefore(:,x)) = Ipos; + Y(isnan(Y)) = Ineg; + + % Compute loss for this list + Loss = 1 - Precision(x); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/thresh/threshFull_admmMixed.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +function result = threshFull_admmMixed(R,lam); +% return argmin_V lam*||V||_2,1 + 0.5 ||V - A||_F^2 +% symmetry constraint on V + + + + W = R; + V = eye(length(R)); + U = zeros(size(R)); + q = 1; + mu = 100; + mult = 2; + + p = numel(R); + n = numel(U); + tol = 1e-4; + ptol = tol * sqrt(p); + dtol = tol * sqrt(n); + + iter = 0; + while 1 + iter = iter + 1; + Vold = V; + W = threshFull_mixed(((1*R+q*(V-U))/(1+q)),lam/(1+q)); + V = feasible_symm(W+U); + U = U + W - V; + + pr = q * norm( V(:)-Vold(:)); + dr = norm((W(:) - V(:))); + + if pr < ptol && dr < dtol,break,end + + if pr/dr > mu + q = 1/mult * q; + U = mult * U; + % disp(['q = ' num2str(q)]); + elseif dr/pr > mu + q = mult * q; + U = 1/mult * U; + % disp(['q = ' num2str(q)]); + + end + end + %disp(['Iterations: ' num2str(iter)]); + %disp(['PR: ' num2str(pr) 'DR: ' num2str(dr)]) + result = V; +end + + + +function result = feasible_symm(A) +result = 0.5*(A+A'); +end + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/thresh/threshFull_mixed.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function result = threshFull_mixed(A, lam) +%return argmin_V lam*||V||_2,1 + 0.5 ||V - A||_F^2 +% no symmetry constraint on V + +norm = sqrt(sum(A.^2)); +result = bsxfun(@times, A, max(1 - (lam ./ norm),0)); + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/Makefile Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +MEXFLAGS= +MEX=mex + +all: binarysearch cummax + +binarysearch: binarysearch.c + ${MEX} ${MEXFLAGS} $< + +cummax: cummax.c + ${MEX} ${MEXFLAGS} $<
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/PsdToEdm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,4 @@ +function D = PsdToEdm(G) + + d = diag(G); + D = bsxfun(@plus, d, d') - 2 * G;
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/binarysearch.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +/* CREATED:2010-01-17 08:12:57 by Brian McFee <bmcfee@cs.ucsd.edu> */ +/* binarysearch.c + * + * binary search + * + * Compile: + * mex binarysearch.c + */ + +#include "mex.h" + + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + /* Declare variables */ + int nQuery, nData; /* number of elements */ + double *pQuery, *pData; /* input arrays */ + double *positions; /* output array(s) */ + + int i; + + if (nrhs != 2) { + mexErrMsgTxt("Two input arguments are required."); + } + if (nlhs > 1) { + mexErrMsgTxt("Too many output arguments."); + } + + if (!(mxIsDouble(prhs[0])) || !(mxIsDouble(prhs[1]))) { + mexErrMsgTxt("Input arrays must be of type double."); + } + + nQuery = mxGetNumberOfElements(prhs[0]); + nData = mxGetNumberOfElements(prhs[1]); + pQuery = (double *)mxGetPr(prhs[0]); + pData = (double *)mxGetPr(prhs[1]); + + plhs[0] = mxCreateDoubleMatrix(1, nQuery, mxREAL); + positions = mxGetPr(plhs[0]); + + /* Now for the meat */ + + for (i = 0; i < nQuery; i++) { + positions[i] = 0; + positions[i] = binarysearch(pQuery[i], pData, nData); + } + + mxSetN(plhs[0], nQuery); +} + +int binarysearch(double q, double *data, int n) { + int l = 0; + int u = n-1; + int pivot; + + while (l < u) { + pivot = l + (u - l) / 2; + + if (q > data[pivot]) { + u = pivot - 1; + } else { + l = pivot + 1; + } + } + + /* Break ties to the right */ + if (l == n || q > data[l]) { + return l; + } else { + return l + 1; + } + +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/cummax.c Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +/* CREATED:2010-01-08 17:25:37 by Brian McFee <bmcfee@cs.ucsd.edu> */ +/* cummax.c + * + * cumulative maximum (analogous to cumsum) + * + * Compile: + * mex cummax.c + */ + +#include "mex.h" + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + /* Declare variables */ + int n; /* number of elements */ + double *pi; /* input array */ + double *po1, *po2; /* output array(s) */ + double currentMax; + double currentMaxPointer; + int i; + + if (nrhs != 1) { + mexErrMsgTxt("Only one input argument is required."); + } + if (nlhs > 2) { + mexErrMsgTxt("Too many output arguments."); + } + + if (!(mxIsDouble(prhs[0]))) { + mexErrMsgTxt("Input array must be of type double."); + } + + n = mxGetNumberOfElements(prhs[0]); + pi = (double *)mxGetPr(prhs[0]); + + plhs[0] = mxCreateDoubleMatrix(1, n, mxREAL); + po1 = mxGetPr(plhs[0]); + + plhs[1] = mxCreateDoubleMatrix(1, n, mxREAL); + po2 = mxGetPr(plhs[1]); + + /* Now for the meat */ + + currentMax = pi[0]; + currentMaxPointer = 1; + for (i = 0; i < n; i++) { + if (pi[i] > currentMax) { + currentMax = pi[i]; + currentMaxPointer = i + 1; + } + po1[i] = currentMax; + po2[i] = currentMaxPointer; + } + + mxSetN(plhs[0], n); + mxSetN(plhs[1], n); + +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/dbprint.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +function dbprint(l, varargin) + + global DEBUG; + + if DEBUG >= l + display(sprintf(varargin{:})); + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/distToFrom.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function D = distToFrom(n, Vto, Vfrom, Ito, Ifrom) + Cross = -2 * Vto' * Vfrom; + Tonorm = sum(Vto .^2, 1)'; + Fromnorm = sum(Vfrom .^2, 1); + + D = zeros(n); + D(Ito,Ifrom) = bsxfun(@plus, bsxfun(@plus, Cross, Tonorm), Fromnorm); +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/lcprog.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,307 @@ +function [z, w, status, z0] = lcprog(M, q, varargin) +%LCPROG - Linear complementary programming with Lemke's algorithm. +% +% [z, w] = lcprog(M, q); +% [z, w, status, z0] = lcprog(M, q, varargin); +% +% Inputs: +% M - Matrix (n x n matrix) +% q - Vector (n vector) +% +% Optional Inputs: +% Optional name-value pairs are: +% 'maxiter' Maximum number of iterations before termination of algorithm (default is (1+n)^3). +% 'tiebreak' Method to break ties for pivot selection (default is 'first'). Options are: +% 'first' Select pivot with lowest index. +% 'last' Select pivot with highest index. +% 'random' Select a pivot at random. +% 'tolpiv' Pivot tolerance below which a number is considered to be zero (default is 1.0e-9). +% +% Outputs: +% z - Solution vector (n vector) +% w - Complementary solution vector (n vector) +% status - Status of optimization with values (integer) +% 6 Ray termination +% 1 Convergence to a solution (normal termination) +% 0 Termination prior to convergence +% -2 Infeasible problem +% -3 Bad pivot to an infeasible solution +% z0 - Artificial variable which is 0 upon normal termination (scalar) +% +% Comments: +% Given a matrix M and a vector q, the linear complementary programming (LCP) problem seeks an +% orthogonal pair of non-negative vectors w and z such that +% w = M * z + q +% with +% w' * z = 0 +% w >= 0 +% z >= 0 . +% +% The algorithm starts with the problem +% w = M * z + q + z0 * 1 +% with an artificial variable z0 > 0 such that q + z0*1 >= 0. The algorithm goes through a series +% of pivot steps that seeks to drive z0 to 0. The algorithm can stop in two ways with either +% normal termination or ray termination. +% +% With normal termination, the artificial variable z0 is zero so that the original problem is +% solved. +% +% With ray termination, the artificial variable z0 is non-negative with "ray" solution in the form +% w = wR + lambda * dw +% z = zR + lambda * dz +% z0 = z0R + lambda * dz0 +% that satisfies +% w = M * z + q + z0 * 1 +% w' * z = 0 +% for any lambda >= 0. If ray termination occurs, the returned variables w, z, and z0 are the +% points on the ray with lambda = 0. +% +% If tolpiv is too small, the algorithm may identify a pivot that is effectively zero. If this +% situation occurs, the status code -3 is triggered. Try a larger pivot tolerance 'tolpiv' although +% a pivot that is too large can trigger false convergence due to failure to find a pivot. +% + +% +% Copyright 2007 The MathWorks, Inc. +% $Revision: 1.1.6.4 $ $ Date: $ + +% initialization + +if nargin < 2 || isempty(M) || isempty(q) + error('finance:lcprog:MissingInputArgument', ... + 'One or more missing required input arguments M, q.'); +end + +q = q(:); +n = size(q,1); + +% process name-value pairs (if any) + +if mod(nargin-2,2) ~= 0 + error('finance:lcprog:InvalidNameValuePair', ... + 'Invalid name-value pairs with either a missing name or value.'); +end + +names = { 'maxiter', 'tiebreak', 'tolpiv' }; % names +values = { (1 + n)^3, 'first', 1.0e-9 }; % default values +try + [maxiter, tiebreak, tolpiv] = parsepvpairs(names, values, varargin{:}); + +catch E + E.throw +end + +% check optional arguments + +if maxiter <= 0 + error('finance:lcprog:NonPositiveInteger', ... + 'Maximum number of iterations (maxiter) must be a positive integer.'); +end + +if ~isempty(strmatch(tiebreak,{'default','first'})) + tb = 1; +elseif ~isempty(strmatch(tiebreak,'last')) + tb = 2; +elseif ~isempty(strmatch(tiebreak,'random')) + tb = 3; +else + tb = 1; +end + +if tolpiv < 2*eps + error('finance:lcprog:InvalidTolerance', ... + 'Unrealistically small tolerance (tolpiv) specified.'); +end + +% trivial solution + +if all(q >= 0) + z = zeros(n,1); + w = q; + status = 1; + z0 = 0; + return +end + +% set up general problem + +w = zeros(n,1); +z = zeros(n,1); +u = ones(n,1); +z0 = max(-q); + +tableau = [ eye(n), -M, -u, q]; +basic = (1:n)'; + +% initial pivot and initial basis + +r = find(q == min(q),1); % r are pivot rows +cx = r; % w(r) leaves the basis +c = 2*n + 1; % initial pivot column +basic(r) = c; % z0 enters the basis + +% pivot step + +pr = (1/tableau(r,c)) * tableau(r,:); +pc = tableau(:,c); +tableau = tableau - pc * pr; +tableau(r,:) = pr; + +% main loop + +for iter = 2:maxiter + + % get new basic variable + + if cx <= n + c = n + cx; + elseif cx <= 2*n + c = cx - n; + end + + % do ratio test + + d = tableau(:,c); + mind = NaN(n,1); + for i = 1:n + if d(i) > tolpiv + mind(i) = tableau(i,end)/d(i); + end + end + iind = find(mind == nanmin(mind)); + + % ray termination + + if isempty(iind) % cannot find a new basic variable + for i = 1:n + if basic(i) <= n + ii = basic(i); + w(ii) = tableau(i,end); + z(ii) = 0; + elseif basic(i) <= 2*n + ii = basic(i) - n; + w(ii) = 0; + z(ii) = tableau(i,end); + else + z0 = tableau(i,end); + end + end + if lcprealitycheck(M, q, w, z) + status = -3; + z = NaN(n,1); + w = NaN(n,1); + z0 = NaN; + else + status = 6; + end + return + end + + % identify next basic variable to be replaced using ratio test + + if tb == 3 + ii = ceil(rand()*numel(iind)); + if ii < 1 + ii = 1; + end + r = iind(ii); + elseif tb == 2 + r = iind(end); + elseif tb == 1 + r = iind(1); + end + for i = 1:numel(iind) % always select z0 if it is in the index set + if basic(iind(i)) == (2*n + 1) + r = iind(i); + end + end + + % new basis + + cx = basic(r); % get variable leaving the basis + basic(r) = c; % move new basic variable into the basis + + % pivot step + + pr = (1/tableau(r,c)) * tableau(r,:); + pc = tableau(:,c); + tableau = tableau - pc * pr; + tableau(r,:) = pr; + + if ~isfinite(tableau(1,end)) % trap overflow/underflow + warning('finance:lcprog:InfeasibleProblem', ... + 'Tableau overflow/underflow due to bad pivot.'); + status = -3; + z = NaN(n,1); + w = NaN(n,1); + z0 = NaN; + return + end + + % convergence test + + if all(basic <= 2*n) % test if z0 left the basis -> convergence + for i = 1:n + if basic(i) <= n + ii = basic(i); + w(ii) = tableau(i,end); + z(ii) = 0; + else + ii = basic(i) - n; + w(ii) = 0; + z(ii) = tableau(i,end); + end + end + z0 = 0; + if lcprealitycheck(M, q, w, z) + status = -3; + z = NaN(n,1); + w = NaN(n,1); + z0 = NaN; + else + status = 1; + end + return + end +end + +% too many iterations + +for i = 1:n + if basic(i) <= n + ii = basic(i); + w(ii) = tableau(i,end); + z(ii) = 0; + elseif basic(i) <= 2*n + ii = basic(i) - n; + w(ii) = 0; + z(ii) = tableau(i,end); + else + z0 = tableau(i,end); + end +end +if lcprealitycheck(M, q, w, z) + status = -3; + z = NaN(n,1); + w = NaN(n,1); + z0 = NaN; +else + status = 0; +end + + +function notok = lcprealitycheck(M, q, w, z) +%LCPREALITYCHECK - Make sure candidate solution z and w is complementary basic feasible. + +u = w - M*z - q; + +if (max(u) - min(u)) > 1 + warning('finance:lcprog:InfeasibleProblem', ... + 'Candidate solution is infeasible due to a bad pivot.'); + notok = true; +else + notok = false; +end + + +% [EOF]
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/mlr_admm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,181 @@ +function [W, Xi, Diagnostics] = mlr_admm(C, K, Delta, H, Q) +% [W, Xi, D] = mlr_admm(C, Delta, W, X) +% +% C >= 0 Slack trade-off parameter +% K = data matrix (or kernel) +% Delta = array of mean margin values +% H = structural kernel matrix +% Q = kernel-structure interaction vector +% +% W (output) = the learned metric +% Xi = 1-slack +% D = diagnostics + + global DEBUG REG FEASIBLE LOSS INIT STRUCTKERNEL DUALW; + + %%% + % Initialize the gradient directions for each constraint + % + global PsiR; + + global ADMM_Z ADMM_U; + + global ADMM_STEPS; + + global RHO; + + global ADMM_RELTOL; + + + numConstraints = length(PsiR); + + Diagnostics = struct( 'f', [], ... + 'num_steps', [], ... + 'stop_criteria', []); + + + % Convergence settings + if ~isempty(ADMM_STEPS) + MAX_ITER = ADMM_STEPS; + else + MAX_ITER = 10; + end + ABSTOL = 1e-4 * sqrt(numel(ADMM_Z)); + + if ~isempty(ADMM_RELTOL) + RELTOL = ADMM_RELTOL; + else + RELTOL = 1e-3; + end + + SCALE_THRESH = 10; + RHO_RESCALE = 2; + stopcriteria= 'MAX STEPS'; + + % Objective function + F = zeros(1,MAX_ITER); + + % how many constraints + + alpha = zeros(numConstraints, 1); + Gamma = zeros(numConstraints, 1); + + ln1 = 0; + ln2 = 0; + for step = 1:MAX_ITER + % do a w-update + % dubstep needs: + % C <-- static + % RHO <-- static + % H <-- static + % Q <-- static + % Delta <-- static + % Gamma <-- this one's dynamic + + for i = 1:numConstraints + Gamma(i) = STRUCTKERNEL(ADMM_Z-ADMM_U, PsiR{i}); + end + + alpha = mlr_dual(C, RHO, H, Q, Delta, Gamma, alpha); + + %%% + % 3) convert back to W + % + W = DUALW(alpha, ADMM_Z, ADMM_U, RHO, K); + + %figure(1), imagesc(W), drawnow; + % Update Z + Zold = ADMM_Z; + ADMM_Z = FEASIBLE(W + ADMM_U); + + % Update residuals + ADMM_U = ADMM_U + W - ADMM_Z; + + % Compute primal objective + % slack term + Xi = 0; + for R = numConstraints:-1:1 + Xi = max(Xi, LOSS(ADMM_Z, PsiR{R}, Delta(R), 0)); + end + F(step) = C * Xi + REG(ADMM_Z, K, 0); + +% figure(2), loglog(1:step, F(1:step)), xlim([0, MAX_ITER]), drawnow; + % Test for convergence + + N1 = norm(W(:)-ADMM_Z(:)); + N2 = RHO * norm(Zold(:) - ADMM_Z(:)); + + eps_primal = ABSTOL + RELTOL * max(norm(W(:)), norm(ADMM_Z(:))); + eps_dual = ABSTOL + RELTOL * RHO * norm(ADMM_U(:)); +% figure(2), loglog(step + (-1:0), [ln1, N1/eps_primal], 'b'), xlim([0, MAX_ITER]), hold('on'); +% figure(2), loglog(step + (-1:0), [ln2, N2/eps_dual], 'r-'), xlim([0, MAX_ITER]), hold('on'), drawnow; +% ln1 = N1/eps_primal; +% ln2 = N2/eps_dual; + if N1 < eps_primal && N2 < eps_dual + stopcriteria = 'CONVERGENCE'; + break; + end + + if N1 > SCALE_THRESH * N2 + dbprint(3, sprintf('RHO: %.2e UP %.2e', RHO, RHO * RHO_RESCALE)); + RHO = RHO * RHO_RESCALE; + ADMM_U = ADMM_U / RHO_RESCALE; + elseif N2 > SCALE_THRESH * N1 + dbprint(3, sprintf('RHO: %.2e DN %.2e', RHO, RHO / RHO_RESCALE)); + RHO = RHO / RHO_RESCALE; + ADMM_U = ADMM_U * RHO_RESCALE; + end + end +% figure(2), hold('off'); + + %%% + % Ensure feasibility + % + W = FEASIBLE(W); + + + %%% + % Compute the slack + % + Xi = 0; + for R = numConstraints:-1:1 + Xi = max(Xi, LOSS(W, PsiR{R}, Delta(R), 0)); + end + + %%% + % Update diagnostics + % + + Diagnostics.f = F(1:step)'; + Diagnostics.stop_criteria = stopcriteria; + Diagnostics.num_steps = step; + + dbprint(1, '\t%s after %d steps.\n', stopcriteria, step); +end + +function alpha = mlr_dual(C, RHO, H, Q, Delta, Gamma, alpha) + + global PsiClock; + + m = length(Delta); + + if nargin < 7 + alpha = zeros(m,1); + end + + %%% + % 1) construct the QP parameters + % + b = RHO * (Gamma - Delta) - Q; + + %%% + % 2) solve the QP + % + alpha = qplcprog(H, b, ones(1, m), C, [], [], 0, []); + + %%% + % 3) update the Psi clock + % + PsiClock(alpha > 0) = 0; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/mlr_solver.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,177 @@ +function [W, Xi, Diagnostics] = mlr_solver(C, Margins, W, K) +% [W, Xi, D] = mlr_solver(C, Margins, W, X) +% +% C >= 0 Slack trade-off parameter +% Margins = array of mean margin values +% W = initial value for W +% X = data matrix (or kernel) +% +% W (output) = the learned metric +% Xi = 1-slack +% D = diagnostics + + global DEBUG REG FEASIBLE LOSS; + + %%% + % Initialize the gradient directions for each constraint + % + global PsiR; + global PsiClock; + + numConstraints = length(PsiR); + + %%% + % Some optimization details + + % Armijo rule number + armijo = 1e-5; + + % Initial learning rate + lambda0 = 1e-4; + + % Increase/decrease after each iteration + lambdaup = ((1+sqrt(5))/2)^(1/3); + lambdadown = ((1+sqrt(5))/2)^(-1); + + % Maximum steps to take + maxsteps = 1e4; + + % Size of convergence window + frame = 10; + + % Convergence threshold + convthresh = 1e-5; + + % Maximum number of backtracks + maxbackcount = 100; + + + Diagnostics = struct( 'f', [], ... + 'num_steps', [], ... + 'stop_criteria', []); + + % Repeat until convergence: + % 1) Calculate f + % 2) Take a gradient step + % 3) Project W back onto PSD + + %%% + % Initialze + % + + f = inf; + dfdW = zeros(size(W)); + lambda = lambda0; + F = Inf * ones(1,maxsteps+1); + XiR = zeros(numConstraints,1); + + + stepcount = -1; + backcount = 0; + done = 0; + + + while 1 + fold = f; + Wold = W; + + %%% + % Count constraint violations and build the gradient + dbprint(3, 'Computing gradient'); + + %%% + % Calculate constraint violations + % + XiR(:) = 0; + for R = numConstraints:-1:1 + XiR(R) = LOSS(W, PsiR{R}, Margins(R), 0); + end + + %%% + % Find the most active constraint + % + [Xi, mgrad] = max(XiR); + Xi = max(Xi, 0); + + PsiClock(mgrad) = 0; + + %%% + % Evaluate f + % + + f = C * max(Xi, 0) ... + + REG(W, K, 0); + + %%% + % Test for convergence + % + objDiff = fold - f; + + if objDiff > armijo * lambda * (dfdW(:)' * dfdW(:)) + + stepcount = stepcount + 1; + + F(stepcount+1) = f; + + sdiff = inf; + if stepcount >= frame; + sdiff = log(F(stepcount+1-frame) / f); + end + + if stepcount >= maxsteps + done = 1; + stopcriteria = 'MAXSTEPS'; + elseif sdiff <= convthresh + done = 1; + stopcriteria = 'CONVERGENCE'; + else + %%% + % If it's positive, add the corresponding gradient + dfdW = C * LOSS(W, PsiR{mgrad}, Margins(mgrad), 1) ... + + REG(W, K, 1); + end + + dbprint(3, 'Lambda up!'); + Wold = W; + lambda = lambdaup * lambda; + backcount = 0; + + else + % Backtracking time, drop the learning rate + if backcount >= maxbackcount + W = Wold; + f = fold; + done = 1; + + stopcriteria = 'BACKTRACK'; + else + dbprint(3, 'Lambda down!'); + lambda = lambdadown * lambda; + backcount = backcount+1; + end + end + + %%% + % Take a gradient step + % + W = W - lambda * dfdW; + + %%% + % Project back onto the feasible set + % + + dbprint(3, 'Projecting onto feasible set'); + W = FEASIBLE(W); + if done + break; + end; + + end + + Diagnostics.f = F(2:(stepcount+1))'; + Diagnostics.stop_criteria = stopcriteria; + Diagnostics.num_steps = stepcount; + + dbprint(1, '\t%s after %d steps.\n', stopcriteria, stepcount); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/parsepvpairs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ +function varargout = parsepvpairs(names, defaults, varargin) +%PARSEPVPAIRS Validate parameter name/value pairs and throw errors if necessary. +% Given the cell array of valid parameter names, a corresponding cell array of +% parameter default values, and a variable length list of parameter name/value +% pairs, validate the specified name/value pairs and assign values to output +% parameters. +% +% [P1, P2, ...] = parsepvpairs(Names, Defaults, 'Name1', Value1, 'Name2', Value2, ...) +% +% Inputs: +% Names - Cell array of valid parameter names. +% +% Defaults - Cell array of default values for the parameters named in Names. +% +% Name# - Character strings of parameter names to be validated and +% assigned the corresponding value that immediately follows each +% in the input argument list. Parameter name validation is +% case-insensitive and partial string matches are allowed provided +% no ambiguities exist. +% +% Value# - The values assigned to the corresponding parameter that +% immediately precede each in the input argument list. +% +% Outputs: +% P# - Parameters assigned the parameter values Value1, Value2, ... +% in the same order as the names listed in Names. Parameters +% corresponding to entries in Names that are not specified in the +% name/value pairs are set to the corresponding value listed in +% Defaults. + +% Copyright 1995-2007 The MathWorks, Inc. +% $Revision: 1.1.6.2 $ $Date: 2008/12/21 01:51:18 $ + +% +% Short-circuit the input argument checking for performance purposes. Under the +% following specific circumstances, users may by-pass P-V pair validation when: +% +% (1) Values are assigned to all recognized parameters, +% (2) Parameters are specified in exactly the same order as in the input NAMES, +% (3) All parameters are completely specified (i.e., exact matches and no partial +% names allowed). +% + +if isequal(varargin(1:2:end-1), names) + varargout = varargin(2:2:end); + return +end + +% Initialize some variables. +nInputs = length(varargin); % # of input arguments +varargout = defaults; + +% Ensure parameter/value pairs. +if mod(nInputs, 2) ~= 0 + error('finance:parsepvpairs:incorrectNumberOfInputs', ... + 'Input parameters must be in name/value pair format.'); + +else + names = lower(names); + + % Process p/v pairs. + for j = 1:2:nInputs + pName = varargin{j}; + + if ~ischar(pName) + error('finance:parsepvpairs:nonTextString', ... + 'Parameter names must be character strings.'); + end + + i = strmatch(lower(pName), names); + + if isempty(i) + error('finance:parsepvpairs:invalidParameter', ... + 'Invalid parameter name: %s.', pName); + + elseif length(i) > 1 + % If ambiguities exist, check for exact match to narrow search. + i = strmatch(lower(pName), names, 'exact'); + if length(i) == 1 + varargout{i} = varargin{j+1}; + + else + error('finance:parsepvpairs:ambiguousParameter', ... + 'Ambiguous parameter name: %s.', pName); + end + + else + varargout{i} = varargin{j+1}; + end + end +end + + +% [EOF]
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/qplcprog.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,315 @@ +function [x, fval, status] = qplcprog(H, f, Ai, bi, Ae, be, lb, ub, varargin) +%QPLCPROG Positive-semidefinite quadratic programming based on linear complementary programming. +% +% x = qplcprog(H, f); +% [x, fval, status] = qplcprog(H, f, Ai, bi, Ae, be, lb, ub, varargin); +% +% Inputs: +% H - Hessian for objective function (n x n matrix) +% +% Optional Inputs: +% f - Vector for objective function, default is 0 (n x 1 vector) +% Ai - Linear inequality constraint matrix, default is [] (ni x n matrix) +% bi - Linear inequality constraint vector, default is [] (ni x 1 vector) +% Ae - Linear equality constraint matrix, default is [] (ne x n matrix) +% be - Linear equality constraint vector, default is [] (ne x 1 vector) +% lb - Lower-bound constraint, default is 0 (n x 1 vector) +% ub - Upper-bound constraint, default is [] (n x 1 vector) +% +% maxiter - LCP maximum iterations, default is 100000 +% tiebreak - Method to break ties for pivot selection (default is 'first'). Options are: +% 'first' Select pivot with lowest index. +% 'last' Select pivot with highest index. +% 'random' Select a pivot at random. +% tolpiv - LCP pivot selection tolerance, default is 1.0e-9 +% tolcon - QP constraint tolerance, default is 1.0e-6 +% +% Outputs: +% x - Solution vector (n x 1 vector) +% fval - Value of objective function at solution (scalar) +% status - Status of optimization with values (integer) +% 6 Ray termination +% 1 Convergence to a solution (normal termination) +% 0 Termination prior to convergence +% -2 Infeasible problem +% -3 Bad pivot to an infeasible solution +% +% Notes: +% Depending upon the inputs, various types of positive-semidefinite quadratic programs can be +% solved. The general problem is +% +% min 0.5 * x' * H * x + f' * x +% x +% +% subject to any or all of the following constraints +% +% Ai * x <= bi +% Ae * x = be +% lb <= x <= ub +% +% with the current requirement that lb must be specified and finite. +% +% It is assumed that H is positive-semidefinite. Note that if H = 0, qplcp solves a linear +% program. + +% Copyright 2008 The MathWorks, Inc. +% $Revision: 1.1.6.3 $ $ Date: $ + +if nargin < 1 + error('Finance:qplcprog:MissingInputArgument', ... + 'Missing required Hessian matrix for QP.'); +end +H = full(H); +n = size(H,1); + +if nargin < 2 || isempty(f) + f = zeros(n,1); +else + f = full(f); + f = f(:); +end + +if nargin == 3 + error('Finance:qplcprog:IncompleteConstraintSpecification', ... + 'Incomplete specification for linear inequality constraints.'); +end +if nargin < 3 + Ai = []; + bi = []; +else + Ai = full(Ai); + bi = full(bi); + bi = bi(:); +end + +if nargin == 5 + error('Finance:qplcprog:IncompleteConstraintSpecification', ... + 'Incomplete specification for linear equality constraints.'); +end +if nargin < 5 + Ae = []; + be = []; +else + Ae = full(Ae); + be = full(be); + be = be(:); +end + +if nargin < 7 || isempty(lb) + error('Finance:qplcprog:MissingBoundConstraint', ... + 'Lower-bound constraint required.'); +else + lb = full(lb); + lb = lb(:); + if any(~isfinite(lb)) + error('Finance:qplcprog:MissingBoundConstraint', ... + 'Finite lower-bound constraint required.'); + end +end + +if nargin < 8 + ub = []; +else + ub = full(ub); + ub = ub(:); + if all(ub == Inf) + ub = []; + end +end +if any(~isfinite(ub)) + error('Finance:qplcprog:InvalidBoundConstraint', ... + 'If specified, finite upper-bound constraint required.'); +end + +% process name-value pairs (if any) + +if nargin > 8 + if mod(nargin-8,2) ~= 0 + error('Finance:qplcprog:InvalidNameValuePair', ... + 'Invalid name-value pairs with either a missing name or value.'); + end + + names = { 'maxiter', 'tiebreak', 'tolcon', 'tolpiv' }; % names + values = { 10000, 'first', 1.0e-4, 1.0e-9 }; % default values + try + [maxiter, tiebreak, tolcon, tolpiv] = parsepvpairs(names, values, varargin{:}); + + catch E + E.throw + end +else + maxiter = 100000; + tiebreak = 'first'; + tolpiv = 1.0e-9; + tolcon = 1.0e-6; +end + +% check arguments + +if maxiter <= 0 + error('Finance:qplcprog:NonPositiveInteger', ... + 'Maximum number of iterations (maxiter) must be a positive integer.'); +end + +if tolcon < 2*eps + error('Finance:qplcprog:InvalidTolerance', ... + 'Unrealistically small constraint tolerance (tolcon) specified.'); +end + +if tolpiv < 2*eps + error('Finance:qplcprog:InvalidTolerance', ... + 'Unrealistically small pivot tolerance (tolpiv) specified.'); +end + +% set translation if necessary + +if norm(lb) ~= 0 + fx = f + H*lb; + if ~isempty(Ai) && ~isempty(bi) + bxi = bi - Ai*lb; + end + if ~isempty(Ae) && ~isempty(be) + bxe = be - Ae*lb; + end + if ~isempty(ub) + uxb = ub - lb; + end + translate = true; +else + fx = f; + if ~isempty(Ai) && ~isempty(bi) + bxi = bi; + end + if ~isempty(Ae) && ~isempty(be) + bxe = be; + end + if ~isempty(ub) + uxb = ub; + end + translate = false; +end + +% set up constraints + +if ~isempty(Ai) && ~isempty(bi) + if ~isempty(Ae) && ~isempty(be) + if ~isempty(ub) + A = [ Ai; Ae; -Ae; eye(n,n) ]; + b = [ bxi; bxe; -bxe; uxb ]; + else + A = [ Ai; Ae; -Ae ]; + b = [ bxi; bxe; -bxe ]; + end + else + if ~isempty(ub) + A = [ Ai; eye(n,n) ]; + b = [ bxi; uxb ]; + else + A = Ai; + b = bxi; + end + end +else + if ~isempty(Ae) && ~isempty(be) + if ~isempty(ub) + A = [ Ae; -Ae; eye(n,n) ]; + b = [ bxe; -bxe; uxb ]; + else + A = [ Ae; -Ae ]; + b = [ bxe; -bxe ]; + end + else + if ~isempty(ub) + A = eye(n,n); + b = uxb; + else + error('Finance:qplcprog:InvalidConstraints', ... + 'Insufficient or invalid constraints.'); + end + end +end + +% set up lcp problem + +ns = size(A,1); + +M = [ zeros(ns,ns) -A; A' H ]; +q = [ b; fx ]; + +% solve lcp problem + +if isempty(varargin) + [z, w, status] = lcprog(M, q); %#ok +else + [z, w, status] = lcprog(M, q, 'maxiter', maxiter, 'tiebreak', tiebreak, 'tolpiv', tolpiv); %#ok +end + +% solve qp problem + +if status > 0 + x = z(end-n+1:end); + if translate + x = x + lb; + end + fval = 0.5*x'*H*x + f'*x; + + if qplcprogrealitycheck(Ai, bi, Ae, be, lb, ub, x) + status = -2; + x = NaN(n,1); + fval = NaN; + end +else + x = NaN(n,1); + fval = NaN; +end + +function notok = qplcprogrealitycheck(Ai, bi, Ae, be, lb, ub, x, tolcon) +%QPLCPROGREALITYCHECK - Make sure candidate solution x is feasible. + +if nargin < 7 + error('Finance:qplcprog:MissingInputArgument', ... + 'Missing required input arguments Ai, bi, Ae, be, lb, ub, x.'); +end +if nargin < 8 || isempty(tolcon) + tolcon = 1.0e-6; +end + +notok = false; + +if ~isempty(Ai) + u = max(Ai*x - bi,0); + if u > tolcon + warning('Finance:qplcprog:InfeasibleProblem', ... + 'Candidate solution violates inequality constraints.'); + notok = true; + end +end + +if ~isempty(Ae) + if norm(be - Ae*x,inf) > tolcon + warning('Finance:qplcprog:InfeasibleProblem', ... + 'Candidate solution violates equality constraints.'); + notok = true; + end +end + +if ~isempty(lb) + u = x - lb; + if min(u) < -tolcon + warning('Finance:qplcprog:InfeasibleProblem', ... + 'Candidate solution violates lower-bound constraints.'); + notok = true; + end +end + +if ~isempty(ub) + u = ub - x; + if min(u) < -tolcon + warning('Finance:qplcprog:InfeasibleProblem', ... + 'Candidate solution violates upper-bound constraints.'); + notok = true; + end +end + + +% [EOF]
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/rmlr_admm.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,188 @@ +function [W, Xi, Diagnostics] = rmlr_admm(C, K, Delta, H, Q, lam) +% [W, Xi, D] = mlr_admm(C, Delta, W, X) +% +% C >= 0 Slack trade-off parameter +% K = data matrix (or kernel) +% Delta = array of mean margin values +% H = structural kernel matrix +% Q = kernel-structure interaction vector +% +% W (output) = the learned metric +% Xi = 1-slack +% D = diagnostics + +global DEBUG REG FEASIBLE LOSS INIT STRUCTKERNEL DUALW THRESH; + +%%% +% Initialize the gradient directions for each constraint +% +global PsiR; + +global ADMM_Z ADMM_V ADMM_UW ADMM_UV; + +global ADMM_STEPS; + +global RHO; + +numConstraints = length(PsiR); + +Diagnostics = struct( 'f', [], ... + 'num_steps', [], ... + 'stop_criteria', []); + + +% Convergence settings +if ~isempty(ADMM_STEPS) + MAX_ITER = ADMM_STEPS; +else + MAX_ITER = 10; +end +ABSTOL = 1e-4 * sqrt(numel(ADMM_Z)); +RELTOL = 1e-3; +SCALE_THRESH = 10; +RHO_RESCALE = 2; +stopcriteria= 'MAX STEPS'; + +% Objective function +F = zeros(1,MAX_ITER); + +% how many constraints + +alpha = zeros(numConstraints, 1); +Gamma = zeros(numConstraints, 1); + +ln1 = 0; +ln2 = 0; + +% figure(2) +% hold off +% plot(0) +% delete(abc) +% delete(abc2) +for step = 1:MAX_ITER + % do a w-update + % dubstep needs: + % C <-- static + % RHO <-- static + % H <-- static + % Q <-- static + % Delta <-- static + % Gamma <-- this one's dynamic + + for i = 1:numConstraints + Gamma(i) = STRUCTKERNEL(ADMM_Z-ADMM_UW, PsiR{i}); + end + % d = length(K); + alpha = mlr_dual(C, RHO, H, Q, Delta, Gamma, alpha); + + %%% + % 3) convert back to W + % + W = DUALW(alpha, ADMM_Z, ADMM_UW, RHO, K); + + % figure(1), imagesc(W), drawnow; + + % Update V + ADMM_V = THRESH(ADMM_Z - ADMM_UV, lam/RHO); + + % Update Z + Zold = ADMM_Z; + ADMM_Z = FEASIBLE(0.5* (W + ADMM_V + ADMM_UW + ADMM_UV)); + + % Update residuals + ADMM_UW = ADMM_UW + W - ADMM_Z; + ADMM_UV = ADMM_UV + ADMM_V - ADMM_Z; + + % Compute primal objective + % slack term + Xi = 0; + for R = numConstraints:-1:1 + Xi = max(Xi, LOSS(ADMM_Z, PsiR{R}, Delta(R), 0)); + end + F(step) = C * Xi + REG(W, K, 0) + lam * sum(sqrt(sum(W.^2))); + +% figure(2), loglog(1:step, F(1:step)), xlim([0, MAX_ITER]), drawnow; + % Test for convergence + + %WIP + N1 = norm(ADMM_V(:) + W(:) - 2* ADMM_Z(:)); + N2 = RHO * norm(2* (Zold(:) - ADMM_Z(:))); + + eps_primal = ABSTOL + RELTOL * max(norm(W(:)), norm(ADMM_Z(:))); + eps_dual = ABSTOL + RELTOL * RHO * norm(ADMM_UW(:)); + %end WIP + + +% figure(2), loglog(step + (-1:0), [ln1, N1/eps_primal], 'b'), xlim([0, MAX_ITER]), hold('on'); +% figure(2), loglog(step + (-1:0), [ln2, N2/eps_dual], 'r-'), xlim([0, MAX_ITER]), hold('on'), drawnow; +% ln1 = N1/eps_primal; +% ln2 = N2/eps_dual; + + if N1 < eps_primal && N2 < eps_dual + stopcriteria = 'CONVERGENCE'; + break; + end + + if N1 > SCALE_THRESH * N2 + dbprint(3, sprintf('RHO: %.2e UP %.2e', RHO, RHO * RHO_RESCALE)); + RHO = RHO * RHO_RESCALE; + ADMM_UW = ADMM_UW / RHO_RESCALE; + elseif N2 > SCALE_THRESH * N1 + dbprint(3, sprintf('RHO: %.2e DN %.2e', RHO, RHO / RHO_RESCALE)); + RHO = RHO / RHO_RESCALE; + ADMM_UW = ADMM_UW * RHO_RESCALE; + end +end +% figure(2), hold('off'); + +%%% +% Ensure feasibility +% +W = FEASIBLE(W); + + +%%% +% Compute the slack +% +Xi = 0; +for R = numConstraints:-1:1 + Xi = max(Xi, LOSS(W, PsiR{R}, Delta(R), 0)); +end + +%%% +% Update diagnostics +% + +Diagnostics.f = F(1:step)'; +Diagnostics.stop_criteria = stopcriteria; +Diagnostics.num_steps = step; + +dbprint(1, '\t%s after %d steps.\n', stopcriteria, step); +end + +function alpha = mlr_dual(C, RHO, H, Q, Delta, Gamma, alpha) + +global PsiClock; + +m = length(Delta); + +if nargin < 7 + alpha = zeros(m,1); +end + +%%% +% 1) construct the QP parameters +% +b = RHO * (Gamma - Delta) - Q; + +%%% +% 2) solve the QP +% +alpha = qplcprog(H, b, ones(1, m), C, [], [], 0, []); + +%%% +% 3) update the Psi clock +% +PsiClock(alpha > 0) = 0; + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/distance_learning/mlr/util/soft_classify.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,99 @@ +function Ypredict = soft_classify(W, test_k, Xtrain, Ytrain, Xtest, Testnorm) +% Ypredict = soft_classify(W, test_k, Xtrain, Ytrain, Xtest, Testnorm) +% +% W = d-by-d positive semi-definite matrix +% test_k = k-value to use for KNN +% Xtrain = d-by-n matrix of training data +% Ytrain = n-by-1 vector of training labels +% Xtest = d-by-m matrix of testing data +% Testnorm= m-by-#kernels vector of k(i,i) for each i in test +% + + addpath('cuttingPlane', 'distance', 'feasible', 'initialize', 'loss', ... + 'metricPsi', 'regularize', 'separationOracle', 'util'); + + [d, nTrain, nKernel] = size(Xtrain); + nTest = size(Xtest, 2); + test_k = min(test_k, nTrain); + + if nargin < 7 + Testnorm = []; + end + + % Build the distance matrix + [D, I] = mlr_test_distance(W, Xtrain, Xtest, Testnorm); + + % Compute label agreement + Ypredict = histc(Ytrain(I(1:test_k,:)), unique(Ytrain)'); + +end + + +function [D,I] = mlr_test_distance(W, Xtrain, Xtest, Testnorm) + + % CASES: + % Raw: W = [] + + % Linear, full: W = d-by-d + % Single Kernel, full: W = n-by-n + % MKL, full: W = n-by-n-by-m + + % Linear, diagonal: W = d-by-1 + % Single Kernel, diagonal: W = n-by-1 + % MKL, diag: W = n-by-m + % MKL, diag-off-diag: W = m-by-m-by-n + + [d, nTrain, nKernel] = size(Xtrain); + nTest = size(Xtest, 2); + + if isempty(W) + % W = [] => native euclidean distances + D = mlr_test_distance_raw(Xtrain, Xtest, Testnorm); + + elseif size(W,1) == d && size(W,2) == d + % We're in a full-projection case + D = setDistanceFullMKL([Xtrain Xtest], W, nTrain + (1:nTest), 1:nTrain); + + elseif size(W,1) == d && size(W,2) == nKernel + % We're in a simple diagonal case + D = setDistanceDiagMKL([Xtrain Xtest], W, nTrain + (1:nTest), 1:nTrain); + + elseif size(W,1) == nKernel && size(W,2) == nKernel && size(W,3) == nTrain + % We're in DOD mode + D = setDistanceDODMKL([Xtrain Xtest], W, nTrain + (1:nTest), 1:nTrain); + + else + % Error? + error('Cannot determine metric mode.'); + + end + + D = full(D(1:nTrain, nTrain + (1:nTest))); + [v,I] = sort(D, 1); +end + + + +function D = mlr_test_distance_raw(Xtrain, Xtest, Testnorm) + + [d, nTrain, nKernel] = size(Xtrain); + nTest = size(Xtest, 2); + + if isempty(Testnorm) + % Not in kernel mode, compute distances directly + D = 0; + for i = 1:nKernel + D = D + setDistanceDiag([Xtrain(:,:,i) Xtest(:,:,i)], ones(d,1), ... + nTrain + (1:nTest), 1:nTrain); + end + else + % We are in kernel mode + D = sparse(nTrain + nTest, nTrain + nTest); + for i = 1:nKernel + Trainnorm = diag(Xtrain(:,:,i)); + D(1:nTrain, nTrain + (1:nTest)) = D(1:nTrain, nTrain + (1:nTest)) ... + + bsxfun(@plus, Trainnorm, bsxfun(@plus, Testnorm(:,i)', -2 * Xtest(:,:,i))); + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/bin/config6 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,255 @@ +# This file was generated by "dot -c" at time of install. + +# You may temporarily disable a plugin by removing or commenting out +# a line in this file, or you can modify its "quality" value to affect +# default plugin selection. + +# Manual edits to this file **will be lost** on upgrade. + +gvplugin_core.dll core { + device { + dot:dot 1 + gv:dot 1 + canon:dot 1 + plain:dot 1 + plain-ext:dot 1 + xdot:xdot 1 + } + device { + fig:fig 1 + } + device { + ismap:map 1 + cmap:map 1 + imap:map 1 + cmapx:map 1 + imap_np:map 1 + cmapx_np:map 1 + } + device { + ps:ps 1 + ps2:ps 1 + eps:ps 1 + } + device { + svg:svg 1 + svgz:svg 1 + } + device { + tk:tk 1 + } + device { + vml:vml 1 + vmlz:vml 1 + } + render { + dot 1 + xdot 1 + } + render { + fig 1 + } + render { + map 1 + } + render { + ps 1 + } + render { + svg 1 + } + render { + tk 1 + } + render { + vml 1 + } + loadimage { + png:svg 1 + gif:svg 1 + jpeg:svg 1 + jpe:svg 1 + jpg:svg 1 + png:fig 1 + gif:fig 1 + jpeg:fig 1 + jpe:fig 1 + jpg:fig 1 + png:vrml 1 + gif:vrml 1 + jpeg:vrml 1 + jpe:vrml 1 + jpg:vrml 1 + eps:ps 1 +#FAILS eps:lasi 1 + ps:ps 1 +#FAILS ps:lasi 1 + (lib):ps 1 + png:map 1 + gif:map 1 + jpeg:map 1 + jpe:map 1 + jpg:map 1 + ps:map 1 + eps:map 1 + svg:map 1 + png:dot 1 + gif:dot 1 + jpeg:dot 1 + jpe:dot 1 + jpg:dot 1 + ps:dot 1 + eps:dot 1 + svg:dot 1 + png:xdot 1 + gif:xdot 1 + jpeg:xdot 1 + jpe:xdot 1 + jpg:xdot 1 + ps:xdot 1 + eps:xdot 1 + svg:xdot 1 + svg:svg 1 + } +} +gvplugin_dot_layout.dll dot_layout { + layout { + dot 0 + } +} +gvplugin_gd.dll gd { + render { + gd 1 + } + render { + vrml 1 + } + textlayout { + textlayout 2 + } + loadimage { + gd:gd 1 + gd2:gd 1 + gif:gd 1 + jpeg:gd 1 + jpe:gd 1 + jpg:gd 1 + png:gd 1 + gd:ps 1 +#FAILS gd:lasi 1 + gd2:ps 1 +#FAILS gd2:lasi 1 + gif:ps 1 +#FAILS gif:lasi 1 + jpeg:ps 1 + jpg:ps 1 + jpe:ps 1 +#FAILS jpeg:lasi 1 +#FAILS jpg:lasi 1 +#FAILS jpe:lasi 1 + png:ps 1 +#FAILS png:lasi 1 + gd:cairo 1 + gd2:cairo 1 + gif:cairo 1 + jpeg:cairo 1 + jpg:cairo 1 + jpe:cairo 1 + png:cairo -1 + } + device { + gif:cairo 10 + wbmp:cairo 5 + jpe:cairo 5 + jpeg:cairo 5 + jpg:cairo 5 + png:cairo 5 + gd:cairo 5 + gd2:cairo 5 + } + device { + gif:gd 1 + wbmp:gd 1 + jpe:gd 1 + jpeg:gd 1 + jpg:gd 1 + png:gd 1 + gd:gd 1 + gd2:gd 1 + } + device { + vrml:vrml 1 + } +} +gvplugin_gdiplus.dll gdiplus { + render { + gdiplus 1 + } + textlayout { + textlayout 8 + } + loadimage { + bmp:gdiplus 8 + gif:gdiplus 8 + jpe:gdiplus 8 + jpeg:gdiplus 8 + jpg:gdiplus 8 + png:gdiplus 8 + } + device { + metafile:gdiplus 8 + bmp:gdiplus 8 + emf:gdiplus 8 + emfplus:gdiplus 8 + gif:gdiplus 8 + jpe:gdiplus 8 + jpeg:gdiplus 8 + jpg:gdiplus 8 + png:gdiplus 8 + tif:gdiplus 8 + tiff:gdiplus 8 + } + device { + bmp:cairo 8 + gif:cairo 8 + jpe:cairo 8 + jpeg:cairo 8 + jpg:cairo 8 + png:cairo 8 + tif:cairo 8 + tiff:cairo 8 + } +} +gvplugin_neato_layout.dll neato_layout { + layout { + neato 0 + fdp 0 + sfdp 0 + twopi 0 + circo 0 + patchwork 0 + osage 0 + nop 0 + nop1 0 + nop2 0 + } +} +gvplugin_pango.dll cairo { + render { + cairo 10 + } + textlayout { + textlayout 10 + } + loadimage { + png:cairo 1 +#FAILS png:lasi 2 + png:ps 2 + } + device { + png:cairo 10 + ps:cairo -10 + pdf:cairo 10 + svg:cairo -10 + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/bin/props.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +"Damping", "0.99", GRAPH, NEATO +"Epsilon", "", GRAPH, NEATO +"URL", "", ANY_ELEMENT, ALL_ENGINES +"arrowhead", "normal", EDGE, ALL_ENGINES +"arrowsize", "1.0", EDGE, ALL_ENGINES +"arrowtail", "normal", EDGE, ALL_ENGINES +"bb", "", GRAPH, ALL_ENGINES +"bgcolor", "", GRAPH Or CLUSTER, ALL_ENGINES +"bottomlabel", "", NODE, ALL_ENGINES +"center", "false", GRAPH, ALL_ENGINES +"clusterrank", "local", GRAPH, DOT +"color", "black", EDGE Or NODE Or CLUSTER, ALL_ENGINES +"comment", "", EDGE Or NODE Or GRAPH, ALL_ENGINES +"compound", "false", GRAPH, DOT +"concentrate", "false", GRAPH, DOT +"constraint", "true", EDGE, DOT +"decorate", "false", EDGE, ALL_ENGINES +"dir", "forward", EDGE, ALL_ENGINES +"distortion", "0.0", NODE, ALL_ENGINES +"fillcolor", "lightgrey", NODE Or CLUSTER, ALL_ENGINES +"fixedsize", "false", NODE, ALL_ENGINES +"fontcolor", "black", EDGE Or NODE Or GRAPH Or CLUSTER, ALL_ENGINES +"fontname", "Times-Roman", EDGE Or NODE Or GRAPH Or CLUSTER, ALL_ENGINES +"fontpath", "", GRAPH, ALL_ENGINES +"fontsize", "14.0", EDGE Or NODE Or GRAPH Or CLUSTER, ALL_ENGINES +"group", "", NODE, DOT +"headURL", "", EDGE, ALL_ENGINES +"headlabel", "", EDGE, ALL_ENGINES +"headport", "center", EDGE, ALL_ENGINES +"height", "0.5", NODE, ALL_ENGINES +"label", "", EDGE Or NODE Or GRAPH Or CLUSTER, ALL_ENGINES +"labelangle", "-25.0", EDGE, ALL_ENGINES +"labeldistance", "1.0", EDGE, ALL_ENGINES +"labelfloat", "false", EDGE, ALL_ENGINES +"labelfontcolor", "black", EDGE, ALL_ENGINES +"labelfontname", "Times-Roman", EDGE, ALL_ENGINES +"labelfontsize", "11.0", EDGE, ALL_ENGINES +"labeljust", "", ANY_ELEMENT, DOT +"labelloc", "t", GRAPH Or CLUSTER, DOT +"layer", "", EDGE Or NODE, ALL_ENGINES +"layers", "", GRAPH, ALL_ENGINES +"len", "1.0", EDGE, NEATO +"lhead", "", EDGE, DOT +"lp", "", EDGE Or GRAPH Or CLUSTER, ALL_ENGINES +"ltail", "", EDGE, DOT +"margin", "", GRAPH, ALL_ENGINES +"maxiter", "", GRAPH, NEATO +"mclimit", "1.0", GRAPH, DOT +"minlen", "1", EDGE, DOT +"model", "", GRAPH, NEATO +"nodesep", "0.25", GRAPH, DOT +"normalize", "false", GRAPH, NEATO +"nslimit", "", GRAPH, DOT +"ordering", "", GRAPH, DOT +"orientation", "0.0", NODE, ALL_ENGINES +"orientation", "", GRAPH, ALL_ENGINES +"overlap", "", GRAPH, NEATO +"pack", "false", GRAPH, NEATO +"page", "", GRAPH, ALL_ENGINES +"pagedir", "", GRAPH, ALL_ENGINES +"pencolor", "black", CLUSTER, ALL_ENGINES +"peripheries", "0", NODE, ALL_ENGINES +"pin", "false", NODE, NEATO +"pos", "", EDGE Or NODE, ALL_ENGINES +"quantum", "0.0", GRAPH, ALL_ENGINES +"rank", "", SUBGRAPH, DOT +"rankdir", "", GRAPH, DOT +"ranksep", "", GRAPH, ALL_ENGINES +"ratio", "", GRAPH, ALL_ENGINES +"rects", "", NODE, ALL_ENGINES +"regular", "false", NODE, ALL_ENGINES +"remincross", "false", GRAPH, DOT +"rotate", "0", GRAPH, ALL_ENGINES +"samehead", "", EDGE, DOT +"sametail", "", EDGE, DOT +"samplepoints", "8", GRAPH, ALL_ENGINES +"searchsize", "30", GRAPH, DOT +"sep", "0.01", GRAPH, NEATO +"shape", "ellipse", NODE, ALL_ENGINES +"shapefile", "", NODE, ALL_ENGINES +"showboxes", "0", EDGE Or NODE Or GRAPH, DOT +"sides", "4", NODE, ALL_ENGINES +"size", "", GRAPH, ALL_ENGINES +"skew", "0.0", NODE, ALL_ENGINES +"splines", "false", GRAPH, NEATO +"start", "", GRAPH, ALL_ENGINES +"style", "", EDGE Or NODE, ALL_ENGINES +"stylesheet", "", GRAPH, ALL_ENGINES +"tailURL", "", EDGE, ALL_ENGINES +"taillabel", "", EDGE, ALL_ENGINES +"tailport", "center", EDGE, ALL_ENGINES +"toplabel", "", NODE, ALL_ENGINES +"vertices", "", NODE, ALL_ENGINES +"voro_margin", "0.05", GRAPH, NEATO +"weight", "", EDGE, DOT Or NEATO +"width", "0.75", NODE, ALL_ENGINES +"z", "0.0", NODE, ALL_ENGINES
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/10-autohint.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- debian/autohint.conf --> +<fontconfig> +<!-- Use the Autohinter --> + <match target="font"> + <edit name="autohint" mode="assign"><bool>true</bool></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/10-no-sub-pixel.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- Enable sub-pixel rendering --> + <match target="font"> + <edit name="rgba" mode="assign"><const>none</const></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/10-sub-pixel-bgr.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- Enable sub-pixel rendering --> + <match target="font"> + <edit name="rgba" mode="assign"><const>bgr</const></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/10-sub-pixel-rgb.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- Enable sub-pixel rendering --> + <match target="font"> + <edit name="rgba" mode="assign"><const>rgb</const></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/10-sub-pixel-vbgr.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- Enable sub-pixel rendering --> + <match target="font"> + <edit name="rgba" mode="assign"><const>vbgr</const></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/10-sub-pixel-vrgb.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- Enable sub-pixel rendering --> + <match target="font"> + <edit name="rgba" mode="assign"><const>vrgb</const></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/10-unhinted.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- debian/unhinted.conf --> +<fontconfig> +<!-- Disable hinting --> + <match target="font"> + <edit name="hinting" mode="assign"><bool>false</bool></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/20-fix-globaladvance.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- + Some Asian fonts misadvertise themselves as monospaced when + in fact they are dual-spaced (half and full). This makes + FreeType very confused as it forces all widths to match. + Undo this magic by disabling the width forcing code --> + <match target="font"> + <test name="family"><string>GulimChe</string></test> + <edit name="globaladvance"><bool>false</bool></edit> + </match> + + <match target="font"> + <test name="family"><string>DotumChe</string></test> + <edit name="globaladvance"><bool>false</bool></edit> + </match> + + <match target="font"> + <test name="family"><string>BatangChe</string></test> + <edit name="globaladvance"><bool>false</bool></edit> + </match> + + <match target="font"> + <test name="family"><string>GungsuhChe</string></test> + <edit name="globaladvance"><bool>false</bool></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/20-lohit-gujarati.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> + +<!-- Available Gujarti fonts look much better without hinting --> + + <match target="font"> + <test name="family"><string>Lohit Gujarati</string></test> + <edit name="hinting"><bool>false</bool></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/20-unhint-small-vera.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- + The Bitstream Vera fonts have GASP entries suggesting that hinting be + disabled below 8 ppem, but FreeType ignores those, preferring to use + the data found in the instructed hints. The initial Vera release + didn't include the right instructions in the 'prep' table. Fix this + by disabling hinting manually at smaller sizes (< 8ppem) + --> + + <match target="font"> + <test name="family"> + <string>Bitstream Vera Sans</string> + </test> + <test name="pixelsize" compare="less"> + <double>7.5</double> + </test> + <edit name="hinting"> + <bool>false</bool> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Bitstream Vera Serif</string> + </test> + <test name="pixelsize" compare="less"> + <double>7.5</double> + </test> + <edit name="hinting"> + <bool>false</bool> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Bitstream Vera Sans Mono</string> + </test> + <test name="pixelsize" compare="less"> + <double>7.5</double> + </test> + <edit name="hinting"> + <bool>false</bool> + </edit> + </match> + +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/30-amt-aliases.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- + AMT provides metric and shape compatible fonts for these three web font + families. + --> + <alias> + <family>Times New Roman</family> + <accept><family>Thorndale AMT</family></accept> + </alias> + <alias> + <family>Arial</family> + <accept><family>Albany AMT</family></accept> + </alias> + <alias> + <family>Courier New</family> + <accept><family>Cumberland AMT</family></accept> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/30-urw-aliases.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- + URW provides metric and shape compatible fonts for these 10 Adobe families. + --> + <alias> + <family>Avant Garde</family> + <accept><family>URW Gothic L</family></accept> + </alias> + <alias> + <family>Bookman</family> + <accept><family>URW Bookman L</family></accept> + </alias> + <alias> + <family>Courier</family> + <accept><family>Nimbus Mono L</family></accept> + </alias> + <alias> + <family>Helvetica</family> + <accept><family>Nimbus Sans L</family></accept> + </alias> + <alias> + <family>New Century Schoolbook</family> + <accept><family>Century Schoolbook L</family></accept> + </alias> + <alias> + <family>Palatino</family> + <accept><family>URW Palladio L</family></accept> + </alias> + <alias> + <family>Times</family> + <accept><family>Nimbus Roman No9 L</family></accept> + </alias> + <alias> + <family>Zapf Chancery</family> + <accept><family>URW Chancery L</family></accept> + </alias> + <alias> + <family>Zapf Dingbats</family> + <accept><family>Dingbats</family></accept> + </alias> + <match target="pattern"> + <test name="family"> + <string>Symbol</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Standard Symbols L</string> + </edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/40-generic.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> +<!-- + Mark common families with their generics so we'll get + something reasonable +--> + +<!-- + Serif faces + --> + <alias> + <family>Bitstream Vera Serif</family> + <family>DejaVu Serif</family> + <family>Times New Roman</family> + <family>Times</family> + <family>Nimbus Roman No9 L</family> + <family>Luxi Serif</family> + <family>Kochi Mincho</family> + <family>AR PL SungtiL GB</family> + <family>AR PL Mingti2L Big5</family> + <family>ï¼ï¼³ 明æœ</family> + <family>Baekmuk Batang</family> + <family>FreeSerif</family> + <family>MgOpen Canonica</family> + <default><family>serif</family></default> + </alias> +<!-- + Sans-serif faces + --> + <alias> + <family>Bitstream Vera Sans</family> + <family>DejaVu Sans</family> + <family>Helvetica</family> + <family>Arial</family> + <family>Verdana</family> + <family>Albany AMT</family> + <family>Nimbus Sans L</family> + <family>Luxi Sans</family> + <family>Kochi Gothic</family> + <family>AR PL KaitiM GB</family> + <family>AR PL KaitiM Big5</family> + <family>ï¼ï¼³ ゴシック</family> + <family>Baekmuk Dotum</family> + <family>SimSun</family> + <family>FreeSans</family> + <family>MgOpen Modata</family> + <default><family>sans-serif</family></default> + </alias> +<!-- + Monospace faces + --> + <alias> + <family>Bitstream Vera Sans Mono</family> + <family>DejaVu Sans Mono</family> + <family>Courier</family> + <family>Courier New</family> + <family>Andale Mono</family> + <family>Luxi Mono</family> + <family>Cumberland AMT</family> + <family>Nimbus Mono L</family> + <family>NSimSun</family> + <family>FreeMono</family> + <default><family>monospace</family></default> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/49-sansserif.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> +<!-- + If the font still has no generic name, add sans-serif + --> + <match target="pattern"> + <test qual="all" name="family" compare="not_eq"> + <string>sans-serif</string> + </test> + <test qual="all" name="family" compare="not_eq"> + <string>serif</string> + </test> + <test qual="all" name="family" compare="not_eq"> + <string>monospace</string> + </test> + <edit name="family" mode="append_last"> + <string>sans-serif</string> + </edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/50-user.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.avail/50-user.conf file to configure system font access --> +<fontconfig> + <!-- Load per-user customization file --> + <include ignore_missing="yes">~/.fonts.conf</include> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/51-local.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.avail/51-local.conf file to configure system font access --> +<fontconfig> + <!-- Load local system customization file --> + <include ignore_missing="yes">local.conf</include> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/60-latin.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> + <alias> + <family>serif</family> + <prefer> + <family>Bitstream Vera Serif</family> + <family>DejaVu Serif</family> + <family>Times New Roman</family> + <family>Thorndale AMT</family> + <family>Luxi Serif</family> + <family>Nimbus Roman No9 L</family> + <family>Times</family> + </prefer> + </alias> + <alias> + <family>sans-serif</family> + <prefer> + <family>Bitstream Vera Sans</family> + <family>DejaVu Sans</family> + <family>Verdana</family> + <family>Arial</family> + <family>Albany AMT</family> + <family>Luxi Sans</family> + <family>Nimbus Sans L</family> + <family>Helvetica</family> + </prefer> + </alias> + <alias> + <family>monospace</family> + <prefer> + <family>Bitstream Vera Sans Mono</family> + <family>DejaVu Sans Mono</family> + <family>Andale Mono</family> + <family>Courier New</family> + <family>Cumberland AMT</family> + <family>Luxi Mono</family> + <family>Nimbus Mono L</family> + <family>Courier</family> + </prefer> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/65-fonts-persian.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,539 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.d/10-fonts-persian.conf --> +<!-- + fonts-persian.conf + To configure Persian fonts from The FarsiWeb Project. + + Copyright (C) 2005 Sharif FarsiWeb, Inc. <license@farsiweb.info> + + Permission to use, copy, modify, distribute, and sell this software and its + documentation for any purpose is hereby granted without fee, provided that + the above copyright notice appear in all copies and that both that + copyright notice and this permission notice appear in supporting + documentation, and that the name of Sharif FarsiWeb, Inc. not be used in + advertising or publicity pertaining to distribution of the software without + specific, written prior permission. Sharif FarsiWeb, Inc. makes no + representations about the suitability of this software for any purpose. It + is provided "as is" without express or implied warranty. + + SHARIF FARSIWEB, INC. DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, + INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO + EVENT SHALL KEITH PACKARD BE LIABLE FOR ANY SPECIAL, INDIRECT OR + CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, + DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER + TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR + PERFORMANCE OF THIS SOFTWARE. + + ChangeLog: + 2005-04-03 Behdad Esfahbod: Initial revision. + 2005-10-09 Behdad Esfahbod: Turned off back-slant and Tahoma sections. + 2005-11-30 Behdad Esfahbod: Set Titr susbtitution size to 24 points. + + Todo: + Add generic font "fantasy". Add some fonts into that category. + Maybe add a "cursive" generic font. + --> +<fontconfig> + + +<!-- + We don't want weak bindings. So got to use the syntactical expansion + of alias tag to do binding=same :(. Please support that in alias. + --> + + +<!-- Deprecated fonts are discouraged --> + + <!-- Nesf[2] is officially deprecated and has problematic tables --> + <match> + <test name="family"> + <string>Nesf</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Nesf2</string> + </edit> + </match> + <match> + <test name="family"> + <string>Nesf2</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Persian_sansserif_default</string> + </edit> + </match> + + <!-- Tahoma looks awful (TURNED-OFF) --> + <!--match> + <test name="family"> + <string>Tahoma</string> + </test> + <edit name="family" mode="prepend" binding="same"> + <string>Persian_sansserif_default</string> + </edit> + </match--> + +<!-- Name changes and spelling variant aliases --> + + <!-- Changed due to trademark problems --> + <match> + <test name="family"> + <string>Nazanin</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Nazli</string> + </edit> + </match> + <match> + <test name="family"> + <string>Lotus</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Lotoos</string> + </edit> + </match> + + <!-- Changed due to transcription orthography --> + <match> + <test name="family"> + <string>Yaqut</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Yaghoot</string> + </edit> + </match> + <match> + <test name="family"> + <string>Yagut</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Yaghoot</string> + </edit> + </match> + <match> + <test name="family"> + <string>Yaghut</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Yaghoot</string> + </edit> + </match> + <match> + <test name="family"> + <string>Traffic</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Terafik</string> + </edit> + </match> + <match> + <test name="family"> + <string>Ferdowsi</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Ferdosi</string> + </edit> + </match> + <match> + <test name="family"> + <string>Fantezy</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Fantezi</string> + </edit> + </match> + + +<!-- Classify fonts. --> + + <!-- Persian_title class --> + <match> + <test name="family"> + <string>Jadid</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_title</string> + </edit> + </match> + <match> + <test name="family"> + <string>Titr</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_title</string> + </edit> + </match> + + <!-- Persian_fantasy class --> + <match> + <test name="family"> + <string>Kamran</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Homa</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Persian_fantasy</string> + </edit> + </match> + <match> + <test name="family"> + <string>Homa</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Kamran</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Persian_fantasy</string> + </edit> + </match> + <match> + <test name="family"> + <string>Fantezi</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_fantasy</string> + </edit> + </match> + <match> + <test name="family"> + <string>Tabassom</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_fantasy</string> + </edit> + </match> + + <!-- Persian_square class --> + <match> + <test name="family"> + <string>Arshia</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + <match> + <test name="family"> + <string>Nasim</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + <match> + <test name="family"> + <string>Elham</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Farnaz</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + <match> + <test name="family"> + <string>Farnaz</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Elham</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + <match> + <test name="family"> + <string>Sina</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + +<!-- Font ordering per class --> + + <!-- Persian_title class --> + <match> + <test name="family"> + <string>Persian_title</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_serif</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Jadid</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Titr</string> + </edit> + </match> + + <!-- Persian_fantasy class --> + <match> + <test name="family"> + <string>Persian_fantasy</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Tabassom</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Fantezi</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Kamran</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Homa</string> + </edit> + </match> + + <!-- Persian_square class --> + <match> + <test name="family"> + <string>Persian_square</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_serif</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Sina</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Nasim</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Farnaz</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Elham</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Arshia</string> + </edit> + </match> + +<!-- Register the fonts that we actually do have --> + + <match target="font"> + <test name="family"> + <string>Elham</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Homa</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Koodak</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Nazli</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Roya</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Terafik</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Titr</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + +<!-- Our fonts should oblique to the other side (TURNED-OFF) --> + + <match target="font"> + <test name="foundry"> + <!--string>farsiweb</string--> + <string>TURNED-OFF</string> + </test> + <test name="foundry"> + <string>farsiweb</string> + </test> + <!-- check to see if the font is roman --> + <test name="slant"> + <const>roman</const> + </test> + <!-- check to see if the pattern requested non-roman --> + <test target="pattern" name="slant" compare="not_eq"> + <const>roman</const> + </test> + <!-- multiply the matrix to slant the font --> + <edit name="matrix" mode="assign"> + <times> + <name>matrix</name> + <matrix><double>1</double><double>-0.2</double> + <double>0</double><double>1</double> + </matrix> + </times> + </edit> + <!-- pretend the font is oblique now --> + <edit name="slant" mode="assign"> + <const>oblique</const> + </edit> + </match> + + +<!-- + We can't hint our fonts well, so turn off hinting. + Moreover, the bitmaps we have designed (well, they + have designed), suck, so disable them too. + --> + + <match target="font"> + <test name="foundry"> + <string>farsiweb</string> + </test> + <edit name="autohint"> + <bool>false</bool> + </edit> + <edit name="hinting"> + <bool>false</bool> + </edit> + <edit name="embeddedbitmap"> + <bool>false</bool> + </edit> + </match> + + +<!-- Alias our fonts to common families --> + + <!-- Persian serif fonts --> + <alias> + <family>serif</family> + <accept> + <family>Nazli</family> + <family>Lotoos</family> + <family>Mitra</family> + <family>Ferdosi</family> + <family>Badr</family> + <family>Zar</family> + </accept> + </alias> + + <!-- Persian sans-serif fonts --> + <alias> + <family>sans-serif</family> + <accept> + <family>Roya</family> + <family>Koodak</family> + <family>Terafik</family> + </accept> + </alias> + + <!-- Persian monospace fonts --> + <alias> + <family>monospace</family> + <accept> + <!-- Not really monospace --> + <family>Terafik</family> + </accept> + </alias> + + +<!-- Use Titr in titles --> + + <!-- Both serif... --> + <match> + <test name="family"> + <string>serif</string> + </test> + <test name="weight" compare="more_eq"> + <int>200</int> + </test> + <test name="size" compare="more_eq"> + <double>24</double> + </test> + <edit name="family" mode="prepend"> + <string>Titr</string> + </edit> + </match> + + <!-- and sans-serif. --> + <match> + <test name="family"> + <string>sans-serif</string> + </test> + <test name="weight" compare="more_eq"> + <int>200</int> + </test> + <test name="size" compare="more_eq"> + <double>24</double> + </test> + <edit name="family" mode="prepend"> + <string>Titr</string> + </edit> + </match> + + <!-- and more. --> + <match> + <test name="family"> + <string>Persian_sansserif_default</string> + </test> + <test name="weight" compare="more_eq"> + <int>200</int> + </test> + <test name="size" compare="more_eq"> + <double>24</double> + </test> + <edit name="family" mode="prepend" binding="same"> + <string>Titr</string> + </edit> + </match> + + +<!-- Default substituted for deprecated sans-serif fonts --> + + <match> + <test name="family"> + <string>Persian_sansserif_default</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Roya</string> + </edit> + </match> + +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/65-nonlatin.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> + <alias> + <family>serif</family> + <prefer> + <family>Frank Ruehl</family> + <family>MgOpen Canonica</family> + <family>Kochi Mincho</family> + <family>AR PL SungtiL GB</family> + <family>AR PL Mingti2L Big5</family> + <family>ï¼ï¼³ 明æœ</family> + <family>Baekmuk Batang</family> + </prefer> + </alias> + <alias> + <family>sans-serif</family> + <prefer> + <family>Nachlieli</family> + <family>MgOpen Modata</family> + <family>Kochi Gothic</family> + <family>AR PL KaitiM GB</family> + <family>AR PL KaitiM Big5</family> + <family>ï¼ï¼³ ゴシック</family> + <family>Baekmuk Dotum</family> + <family>SimSun</family> + </prefer> + </alias> + <alias> + <family>monospace</family> + <prefer> + <family>Miriam Mono</family> + <family>Kochi Gothic</family> + <family>AR PL KaitiM GB</family> + <family>Baekmuk Dotum</family> + </prefer> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/69-unifont.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> + <alias> + <family>serif</family> + <prefer> + <family>FreeSerif</family> + <family>Code2000</family> + </prefer> + </alias> + <alias> + <family>sans-serif</family> + <prefer> + <family>FreeSans</family> + <family>Arial Unicode MS</family> + </prefer> + </alias> + <alias> + <family>monospace</family> + <prefer> + <family>FreeMono</family> + </prefer> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/70-no-bitmaps.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.d/no-bitmaps.conf --> +<fontconfig> +<!-- Reject bitmap fonts --> + <selectfont> + <rejectfont> + <pattern> + <patelt name="scalable"><bool>false</bool></patelt> + </pattern> + </rejectfont> + </selectfont> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/70-yes-bitmaps.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/yes-bitmaps.conf --> +<fontconfig> +<!-- Accept bitmap fonts --> + <selectfont> + <acceptfont> + <pattern> + <patelt name="scalable"><bool>false</bool></patelt> + </pattern> + </acceptfont> + </selectfont> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/80-delicious.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.d/60-delicious.conf --> +<fontconfig> +<!-- Fix-ups for Delicious family --> + +<!-- Delicious 'heavy' variant says its Medium weight --> +<match target="scan"> + <test name="family"> + <string>Delicious</string> + </test> + <test name="style"> + <string>Heavy</string> + </test> + <edit name="weight"> + <const>heavy</const> + </edit> +</match> + +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/90-synthetic.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> +<!-- + Artificial oblique for fonts without an italic or oblique version + --> + + <match target="font"> + <!-- check to see if the font is roman --> + <test name="slant"> + <const>roman</const> + </test> + <!-- check to see if the pattern requested non-roman --> + <test target="pattern" name="slant" compare="not_eq"> + <const>roman</const> + </test> + <!-- multiply the matrix to slant the font --> + <edit name="matrix" mode="assign"> + <times> + <name>matrix</name> + <matrix><double>1</double><double>0.2</double> + <double>0</double><double>1</double> + </matrix> + </times> + </edit> + <!-- pretend the font is oblique now --> + <edit name="slant" mode="assign"> + <const>oblique</const> + </edit> + <!-- and disable embedded bitmaps for artificial oblique --> + <edit name="embeddedbitmap" mode="assign"> + <bool>false</bool> + </edit> + </match> + +<!-- + Synthetic emboldening for fonts that do not have bold face available + --> + + <match target="font"> + <!-- check to see if the font is just regular --> + <test name="weight" compare="less_eq"> + <const>medium</const> + </test> + <!-- check to see if the pattern requests bold --> + <test target="pattern" name="weight" compare="more"> + <const>medium</const> + </test> + <!-- + set the embolden flag + needed for applications using cairo, e.g. gucharmap, gedit, ... + --> + <edit name="embolden" mode="assign"> + <bool>true</bool> + </edit> + <!-- + set weight to bold + needed for applications using Xft directly, e.g. Firefox, ... + --> + <edit name="weight" mode="assign"> + <const>bold</const> + </edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.avail/README Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +conf.d/README + +Each file in this directory is a fontconfig configuration file. Fontconfig +scans this directory, loading all files of the form [0-9][0-9]*. These files +are normally installed in ../conf.avail and then symlinked here, allowing +them to be easily installed and then enabled/disabled by adjusting the +symlinks. + +The files are loaded in numeric order, the structure of the configuration +has led to the following conventions in usage: + + Files begining with: Contain: + + 00 through 09 Font directories + 10 through 19 system rendering defaults (AA, etc) + 10-autohint.conf + 10-no-sub-pixel.conf + 10-sub-pixel-bgr.conf + 10-sub-pixel-rgb.conf + 10-sub-pixel-vbgr.conf + 10-sub-pixel-vrgb.conf + 10-unhinted.conf + 20 through 29 font rendering options + 20-fix-globaladvance.conf + 20-lohit-gujarati.conf + 20-unhint-small-vera.conf + 30 through 39 family substitution + 30-urw-aliases.conf + 30-amt-aliases.conf + 40 through 49 generic identification, map family->generic + 40-generic-id.conf + 49-sansserif.conf + 50 through 59 alternate config file loading + 50-user.conf Load ~/.fonts.conf + 51-local.conf Load local.conf + 60 through 69 generic aliases + 60-latin.conf + 65-fonts-persian.conf + 65-nonlatin.conf + 69-unifont.conf + 70 through 79 select font (adjust which fonts are available) + 70-no-bitmaps.conf + 70-yes-bitmaps.conf + 80 through 89 match target="scan" (modify scanned patterns) + 80-delicious.conf + 90 through 98 font synthesis + 90-synthetic.conf +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/20-fix-globaladvance.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,29 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- + Some Asian fonts misadvertise themselves as monospaced when + in fact they are dual-spaced (half and full). This makes + FreeType very confused as it forces all widths to match. + Undo this magic by disabling the width forcing code --> + <match target="font"> + <test name="family"><string>GulimChe</string></test> + <edit name="globaladvance"><bool>false</bool></edit> + </match> + + <match target="font"> + <test name="family"><string>DotumChe</string></test> + <edit name="globaladvance"><bool>false</bool></edit> + </match> + + <match target="font"> + <test name="family"><string>BatangChe</string></test> + <edit name="globaladvance"><bool>false</bool></edit> + </match> + + <match target="font"> + <test name="family"><string>GungsuhChe</string></test> + <edit name="globaladvance"><bool>false</bool></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/20-lohit-gujarati.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> + +<!-- Available Gujarti fonts look much better without hinting --> + + <match target="font"> + <test name="family"><string>Lohit Gujarati</string></test> + <edit name="hinting"><bool>false</bool></edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/20-unhint-small-vera.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- + The Bitstream Vera fonts have GASP entries suggesting that hinting be + disabled below 8 ppem, but FreeType ignores those, preferring to use + the data found in the instructed hints. The initial Vera release + didn't include the right instructions in the 'prep' table. Fix this + by disabling hinting manually at smaller sizes (< 8ppem) + --> + + <match target="font"> + <test name="family"> + <string>Bitstream Vera Sans</string> + </test> + <test name="pixelsize" compare="less"> + <double>7.5</double> + </test> + <edit name="hinting"> + <bool>false</bool> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Bitstream Vera Serif</string> + </test> + <test name="pixelsize" compare="less"> + <double>7.5</double> + </test> + <edit name="hinting"> + <bool>false</bool> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Bitstream Vera Sans Mono</string> + </test> + <test name="pixelsize" compare="less"> + <double>7.5</double> + </test> + <edit name="hinting"> + <bool>false</bool> + </edit> + </match> + +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/30-amt-aliases.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- + AMT provides metric and shape compatible fonts for these three web font + families. + --> + <alias> + <family>Times New Roman</family> + <accept><family>Thorndale AMT</family></accept> + </alias> + <alias> + <family>Arial</family> + <accept><family>Albany AMT</family></accept> + </alias> + <alias> + <family>Courier New</family> + <accept><family>Cumberland AMT</family></accept> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/30-urw-aliases.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,52 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- conf.d/sub-pixel.conf --> +<fontconfig> +<!-- + URW provides metric and shape compatible fonts for these 10 Adobe families. + --> + <alias> + <family>Avant Garde</family> + <accept><family>URW Gothic L</family></accept> + </alias> + <alias> + <family>Bookman</family> + <accept><family>URW Bookman L</family></accept> + </alias> + <alias> + <family>Courier</family> + <accept><family>Nimbus Mono L</family></accept> + </alias> + <alias> + <family>Helvetica</family> + <accept><family>Nimbus Sans L</family></accept> + </alias> + <alias> + <family>New Century Schoolbook</family> + <accept><family>Century Schoolbook L</family></accept> + </alias> + <alias> + <family>Palatino</family> + <accept><family>URW Palladio L</family></accept> + </alias> + <alias> + <family>Times</family> + <accept><family>Nimbus Roman No9 L</family></accept> + </alias> + <alias> + <family>Zapf Chancery</family> + <accept><family>URW Chancery L</family></accept> + </alias> + <alias> + <family>Zapf Dingbats</family> + <accept><family>Dingbats</family></accept> + </alias> + <match target="pattern"> + <test name="family"> + <string>Symbol</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Standard Symbols L</string> + </edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/40-generic.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> +<!-- + Mark common families with their generics so we'll get + something reasonable +--> + +<!-- + Serif faces + --> + <alias> + <family>Bitstream Vera Serif</family> + <family>DejaVu Serif</family> + <family>Times New Roman</family> + <family>Times</family> + <family>Nimbus Roman No9 L</family> + <family>Luxi Serif</family> + <family>Kochi Mincho</family> + <family>AR PL SungtiL GB</family> + <family>AR PL Mingti2L Big5</family> + <family>ï¼ï¼³ 明æœ</family> + <family>Baekmuk Batang</family> + <family>FreeSerif</family> + <family>MgOpen Canonica</family> + <default><family>serif</family></default> + </alias> +<!-- + Sans-serif faces + --> + <alias> + <family>Bitstream Vera Sans</family> + <family>DejaVu Sans</family> + <family>Helvetica</family> + <family>Arial</family> + <family>Verdana</family> + <family>Albany AMT</family> + <family>Nimbus Sans L</family> + <family>Luxi Sans</family> + <family>Kochi Gothic</family> + <family>AR PL KaitiM GB</family> + <family>AR PL KaitiM Big5</family> + <family>ï¼ï¼³ ゴシック</family> + <family>Baekmuk Dotum</family> + <family>SimSun</family> + <family>FreeSans</family> + <family>MgOpen Modata</family> + <default><family>sans-serif</family></default> + </alias> +<!-- + Monospace faces + --> + <alias> + <family>Bitstream Vera Sans Mono</family> + <family>DejaVu Sans Mono</family> + <family>Courier</family> + <family>Courier New</family> + <family>Andale Mono</family> + <family>Luxi Mono</family> + <family>Cumberland AMT</family> + <family>Nimbus Mono L</family> + <family>NSimSun</family> + <family>FreeMono</family> + <default><family>monospace</family></default> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/49-sansserif.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> +<!-- + If the font still has no generic name, add sans-serif + --> + <match target="pattern"> + <test qual="all" name="family" compare="not_eq"> + <string>sans-serif</string> + </test> + <test qual="all" name="family" compare="not_eq"> + <string>serif</string> + </test> + <test qual="all" name="family" compare="not_eq"> + <string>monospace</string> + </test> + <edit name="family" mode="append_last"> + <string>sans-serif</string> + </edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/50-user.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.avail/50-user.conf file to configure system font access --> +<fontconfig> + <!-- Load per-user customization file --> + <include ignore_missing="yes">~/.fonts.conf</include> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/51-local.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.avail/51-local.conf file to configure system font access --> +<fontconfig> + <!-- Load local system customization file --> + <include ignore_missing="yes">local.conf</include> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/60-latin.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> + <alias> + <family>serif</family> + <prefer> + <family>Bitstream Vera Serif</family> + <family>DejaVu Serif</family> + <family>Times New Roman</family> + <family>Thorndale AMT</family> + <family>Luxi Serif</family> + <family>Nimbus Roman No9 L</family> + <family>Times</family> + </prefer> + </alias> + <alias> + <family>sans-serif</family> + <prefer> + <family>Bitstream Vera Sans</family> + <family>DejaVu Sans</family> + <family>Verdana</family> + <family>Arial</family> + <family>Albany AMT</family> + <family>Luxi Sans</family> + <family>Nimbus Sans L</family> + <family>Helvetica</family> + </prefer> + </alias> + <alias> + <family>monospace</family> + <prefer> + <family>Bitstream Vera Sans Mono</family> + <family>DejaVu Sans Mono</family> + <family>Andale Mono</family> + <family>Courier New</family> + <family>Cumberland AMT</family> + <family>Luxi Mono</family> + <family>Nimbus Mono L</family> + <family>Courier</family> + </prefer> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/65-fonts-persian.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,539 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.d/10-fonts-persian.conf --> +<!-- + fonts-persian.conf + To configure Persian fonts from The FarsiWeb Project. + + Copyright (C) 2005 Sharif FarsiWeb, Inc. <license@farsiweb.info> + + Permission to use, copy, modify, distribute, and sell this software and its + documentation for any purpose is hereby granted without fee, provided that + the above copyright notice appear in all copies and that both that + copyright notice and this permission notice appear in supporting + documentation, and that the name of Sharif FarsiWeb, Inc. not be used in + advertising or publicity pertaining to distribution of the software without + specific, written prior permission. Sharif FarsiWeb, Inc. makes no + representations about the suitability of this software for any purpose. It + is provided "as is" without express or implied warranty. + + SHARIF FARSIWEB, INC. DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, + INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO + EVENT SHALL KEITH PACKARD BE LIABLE FOR ANY SPECIAL, INDIRECT OR + CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, + DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER + TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR + PERFORMANCE OF THIS SOFTWARE. + + ChangeLog: + 2005-04-03 Behdad Esfahbod: Initial revision. + 2005-10-09 Behdad Esfahbod: Turned off back-slant and Tahoma sections. + 2005-11-30 Behdad Esfahbod: Set Titr susbtitution size to 24 points. + + Todo: + Add generic font "fantasy". Add some fonts into that category. + Maybe add a "cursive" generic font. + --> +<fontconfig> + + +<!-- + We don't want weak bindings. So got to use the syntactical expansion + of alias tag to do binding=same :(. Please support that in alias. + --> + + +<!-- Deprecated fonts are discouraged --> + + <!-- Nesf[2] is officially deprecated and has problematic tables --> + <match> + <test name="family"> + <string>Nesf</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Nesf2</string> + </edit> + </match> + <match> + <test name="family"> + <string>Nesf2</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Persian_sansserif_default</string> + </edit> + </match> + + <!-- Tahoma looks awful (TURNED-OFF) --> + <!--match> + <test name="family"> + <string>Tahoma</string> + </test> + <edit name="family" mode="prepend" binding="same"> + <string>Persian_sansserif_default</string> + </edit> + </match--> + +<!-- Name changes and spelling variant aliases --> + + <!-- Changed due to trademark problems --> + <match> + <test name="family"> + <string>Nazanin</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Nazli</string> + </edit> + </match> + <match> + <test name="family"> + <string>Lotus</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Lotoos</string> + </edit> + </match> + + <!-- Changed due to transcription orthography --> + <match> + <test name="family"> + <string>Yaqut</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Yaghoot</string> + </edit> + </match> + <match> + <test name="family"> + <string>Yagut</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Yaghoot</string> + </edit> + </match> + <match> + <test name="family"> + <string>Yaghut</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Yaghoot</string> + </edit> + </match> + <match> + <test name="family"> + <string>Traffic</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Terafik</string> + </edit> + </match> + <match> + <test name="family"> + <string>Ferdowsi</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Ferdosi</string> + </edit> + </match> + <match> + <test name="family"> + <string>Fantezy</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Fantezi</string> + </edit> + </match> + + +<!-- Classify fonts. --> + + <!-- Persian_title class --> + <match> + <test name="family"> + <string>Jadid</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_title</string> + </edit> + </match> + <match> + <test name="family"> + <string>Titr</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_title</string> + </edit> + </match> + + <!-- Persian_fantasy class --> + <match> + <test name="family"> + <string>Kamran</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Homa</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Persian_fantasy</string> + </edit> + </match> + <match> + <test name="family"> + <string>Homa</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Kamran</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Persian_fantasy</string> + </edit> + </match> + <match> + <test name="family"> + <string>Fantezi</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_fantasy</string> + </edit> + </match> + <match> + <test name="family"> + <string>Tabassom</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_fantasy</string> + </edit> + </match> + + <!-- Persian_square class --> + <match> + <test name="family"> + <string>Arshia</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + <match> + <test name="family"> + <string>Nasim</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + <match> + <test name="family"> + <string>Elham</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Farnaz</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + <match> + <test name="family"> + <string>Farnaz</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Elham</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + <match> + <test name="family"> + <string>Sina</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + </match> + +<!-- Font ordering per class --> + + <!-- Persian_title class --> + <match> + <test name="family"> + <string>Persian_title</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_serif</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Jadid</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Titr</string> + </edit> + </match> + + <!-- Persian_fantasy class --> + <match> + <test name="family"> + <string>Persian_fantasy</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_square</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Tabassom</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Fantezi</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Kamran</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Homa</string> + </edit> + </match> + + <!-- Persian_square class --> + <match> + <test name="family"> + <string>Persian_square</string> + </test> + <edit name="family" mode="append" binding="same"> + <string>Persian_serif</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Sina</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Nasim</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Farnaz</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Elham</string> + </edit> + <edit name="family" mode="append" binding="same"> + <string>Arshia</string> + </edit> + </match> + +<!-- Register the fonts that we actually do have --> + + <match target="font"> + <test name="family"> + <string>Elham</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Homa</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Koodak</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Nazli</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Roya</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Terafik</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + <match target="font"> + <test name="family"> + <string>Titr</string> + </test> + <edit name="foundry"> + <string>farsiweb</string> + </edit> + </match> + + +<!-- Our fonts should oblique to the other side (TURNED-OFF) --> + + <match target="font"> + <test name="foundry"> + <!--string>farsiweb</string--> + <string>TURNED-OFF</string> + </test> + <test name="foundry"> + <string>farsiweb</string> + </test> + <!-- check to see if the font is roman --> + <test name="slant"> + <const>roman</const> + </test> + <!-- check to see if the pattern requested non-roman --> + <test target="pattern" name="slant" compare="not_eq"> + <const>roman</const> + </test> + <!-- multiply the matrix to slant the font --> + <edit name="matrix" mode="assign"> + <times> + <name>matrix</name> + <matrix><double>1</double><double>-0.2</double> + <double>0</double><double>1</double> + </matrix> + </times> + </edit> + <!-- pretend the font is oblique now --> + <edit name="slant" mode="assign"> + <const>oblique</const> + </edit> + </match> + + +<!-- + We can't hint our fonts well, so turn off hinting. + Moreover, the bitmaps we have designed (well, they + have designed), suck, so disable them too. + --> + + <match target="font"> + <test name="foundry"> + <string>farsiweb</string> + </test> + <edit name="autohint"> + <bool>false</bool> + </edit> + <edit name="hinting"> + <bool>false</bool> + </edit> + <edit name="embeddedbitmap"> + <bool>false</bool> + </edit> + </match> + + +<!-- Alias our fonts to common families --> + + <!-- Persian serif fonts --> + <alias> + <family>serif</family> + <accept> + <family>Nazli</family> + <family>Lotoos</family> + <family>Mitra</family> + <family>Ferdosi</family> + <family>Badr</family> + <family>Zar</family> + </accept> + </alias> + + <!-- Persian sans-serif fonts --> + <alias> + <family>sans-serif</family> + <accept> + <family>Roya</family> + <family>Koodak</family> + <family>Terafik</family> + </accept> + </alias> + + <!-- Persian monospace fonts --> + <alias> + <family>monospace</family> + <accept> + <!-- Not really monospace --> + <family>Terafik</family> + </accept> + </alias> + + +<!-- Use Titr in titles --> + + <!-- Both serif... --> + <match> + <test name="family"> + <string>serif</string> + </test> + <test name="weight" compare="more_eq"> + <int>200</int> + </test> + <test name="size" compare="more_eq"> + <double>24</double> + </test> + <edit name="family" mode="prepend"> + <string>Titr</string> + </edit> + </match> + + <!-- and sans-serif. --> + <match> + <test name="family"> + <string>sans-serif</string> + </test> + <test name="weight" compare="more_eq"> + <int>200</int> + </test> + <test name="size" compare="more_eq"> + <double>24</double> + </test> + <edit name="family" mode="prepend"> + <string>Titr</string> + </edit> + </match> + + <!-- and more. --> + <match> + <test name="family"> + <string>Persian_sansserif_default</string> + </test> + <test name="weight" compare="more_eq"> + <int>200</int> + </test> + <test name="size" compare="more_eq"> + <double>24</double> + </test> + <edit name="family" mode="prepend" binding="same"> + <string>Titr</string> + </edit> + </match> + + +<!-- Default substituted for deprecated sans-serif fonts --> + + <match> + <test name="family"> + <string>Persian_sansserif_default</string> + </test> + <edit name="family" mode="assign" binding="same"> + <string>Roya</string> + </edit> + </match> + +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/65-nonlatin.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,38 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> + <alias> + <family>serif</family> + <prefer> + <family>Frank Ruehl</family> + <family>MgOpen Canonica</family> + <family>Kochi Mincho</family> + <family>AR PL SungtiL GB</family> + <family>AR PL Mingti2L Big5</family> + <family>ï¼ï¼³ 明æœ</family> + <family>Baekmuk Batang</family> + </prefer> + </alias> + <alias> + <family>sans-serif</family> + <prefer> + <family>Nachlieli</family> + <family>MgOpen Modata</family> + <family>Kochi Gothic</family> + <family>AR PL KaitiM GB</family> + <family>AR PL KaitiM Big5</family> + <family>ï¼ï¼³ ゴシック</family> + <family>Baekmuk Dotum</family> + <family>SimSun</family> + </prefer> + </alias> + <alias> + <family>monospace</family> + <prefer> + <family>Miriam Mono</family> + <family>Kochi Gothic</family> + <family>AR PL KaitiM GB</family> + <family>Baekmuk Dotum</family> + </prefer> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/69-unifont.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> + <alias> + <family>serif</family> + <prefer> + <family>FreeSerif</family> + <family>Code2000</family> + </prefer> + </alias> + <alias> + <family>sans-serif</family> + <prefer> + <family>FreeSans</family> + <family>Arial Unicode MS</family> + </prefer> + </alias> + <alias> + <family>monospace</family> + <prefer> + <family>FreeMono</family> + </prefer> + </alias> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/80-delicious.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/conf.d/60-delicious.conf --> +<fontconfig> +<!-- Fix-ups for Delicious family --> + +<!-- Delicious 'heavy' variant says its Medium weight --> +<match target="scan"> + <test name="family"> + <string>Delicious</string> + </test> + <test name="style"> + <string>Heavy</string> + </test> + <edit name="weight"> + <const>heavy</const> + </edit> +</match> + +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/conf.d/90-synthetic.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<fontconfig> +<!-- + Artificial oblique for fonts without an italic or oblique version + --> + + <match target="font"> + <!-- check to see if the font is roman --> + <test name="slant"> + <const>roman</const> + </test> + <!-- check to see if the pattern requested non-roman --> + <test target="pattern" name="slant" compare="not_eq"> + <const>roman</const> + </test> + <!-- multiply the matrix to slant the font --> + <edit name="matrix" mode="assign"> + <times> + <name>matrix</name> + <matrix><double>1</double><double>0.2</double> + <double>0</double><double>1</double> + </matrix> + </times> + </edit> + <!-- pretend the font is oblique now --> + <edit name="slant" mode="assign"> + <const>oblique</const> + </edit> + <!-- and disable embedded bitmaps for artificial oblique --> + <edit name="embeddedbitmap" mode="assign"> + <bool>false</bool> + </edit> + </match> + +<!-- + Synthetic emboldening for fonts that do not have bold face available + --> + + <match target="font"> + <!-- check to see if the font is just regular --> + <test name="weight" compare="less_eq"> + <const>medium</const> + </test> + <!-- check to see if the pattern requests bold --> + <test target="pattern" name="weight" compare="more"> + <const>medium</const> + </test> + <!-- + set the embolden flag + needed for applications using cairo, e.g. gucharmap, gedit, ... + --> + <edit name="embolden" mode="assign"> + <bool>true</bool> + </edit> + <!-- + set weight to bold + needed for applications using Xft directly, e.g. Firefox, ... + --> + <edit name="weight" mode="assign"> + <const>bold</const> + </edit> + </match> +</fontconfig>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/fonts.conf Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,155 @@ +<?xml version="1.0"?> +<!DOCTYPE fontconfig SYSTEM "fonts.dtd"> +<!-- /etc/fonts/fonts.conf file to configure system font access --> +<fontconfig> + +<!-- + DO NOT EDIT THIS FILE. + IT WILL BE REPLACED WHEN FONTCONFIG IS UPDATED. + LOCAL CHANGES BELONG IN 'local.conf'. + + The intent of this standard configuration file is to be adequate for + most environments. If you have a reasonably normal environment and + have found problems with this configuration, they are probably + things that others will also want fixed. Please submit any + problems to the fontconfig bugzilla system located at fontconfig.org + + Note that the normal 'make install' procedure for fontconfig is to + replace any existing fonts.conf file with the new version. Place + any local customizations in local.conf which this file references. + + Keith Packard +--> + + +<!-- Font directory list --> + + <dir>C:\WINDOWS\Fonts</dir> + <dir>~/.fonts</dir> + +<!-- Font cache directory list--> + + <cachedir>c:/windows</cachedir> + <cachedir>~/.fontconfig</cachedir> + + +<!-- + Accept deprecated 'mono' alias, replacing it with 'monospace' +--> + <match target="pattern"> + <test qual="any" name="family"> + <string>mono</string> + </test> + <edit name="family" mode="assign"> + <string>monospace</string> + </edit> + </match> + + + + +<!-- + Accept alternate 'sans serif' spelling, replacing it with 'sans-serif' +--> + <match target="pattern"> + <test qual="any" name="family"> + <string>sans serif</string> + </test> + <edit name="family" mode="assign"> + <string>sans-serif</string> + </edit> + </match> + + +<!-- + Accept deprecated 'sans' alias, replacing it with 'sans-serif' +--> + <match target="pattern"> + <test qual="any" name="family"> + <string>sans</string> + </test> + <edit name="family" mode="assign"> + <string>sans-serif</string> + </edit> + </match> +<!-- + Load local system customization file +--> + <include ignore_missing="yes">conf.d</include> + + <config> +<!-- + These are the default Unicode chars that are expected to be blank + in fonts. All other blank chars are assumed to be broken and + won't appear in the resulting charsets + --> + <blank> + <int>0x0020</int> <!-- SPACE --> + <int>0x00A0</int> <!-- NO-BREAK SPACE --> + <int>0x00AD</int> <!-- SOFT HYPHEN --> + <int>0x034F</int> <!-- COMBINING GRAPHEME JOINER --> + <int>0x0600</int> <!-- ARABIC NUMBER SIGN --> + <int>0x0601</int> <!-- ARABIC SIGN SANAH --> + <int>0x0602</int> <!-- ARABIC FOOTNOTE MARKER --> + <int>0x0603</int> <!-- ARABIC SIGN SAFHA --> + <int>0x06DD</int> <!-- ARABIC END OF AYAH --> + <int>0x070F</int> <!-- SYRIAC ABBREVIATION MARK --> + <int>0x115F</int> <!-- HANGUL CHOSEONG FILLER --> + <int>0x1160</int> <!-- HANGUL JUNGSEONG FILLER --> + <int>0x1680</int> <!-- OGHAM SPACE MARK --> + <int>0x17B4</int> <!-- KHMER VOWEL INHERENT AQ --> + <int>0x17B5</int> <!-- KHMER VOWEL INHERENT AA --> + <int>0x180E</int> <!-- MONGOLIAN VOWEL SEPARATOR --> + <int>0x2000</int> <!-- EN QUAD --> + <int>0x2001</int> <!-- EM QUAD --> + <int>0x2002</int> <!-- EN SPACE --> + <int>0x2003</int> <!-- EM SPACE --> + <int>0x2004</int> <!-- THREE-PER-EM SPACE --> + <int>0x2005</int> <!-- FOUR-PER-EM SPACE --> + <int>0x2006</int> <!-- SIX-PER-EM SPACE --> + <int>0x2007</int> <!-- FIGURE SPACE --> + <int>0x2008</int> <!-- PUNCTUATION SPACE --> + <int>0x2009</int> <!-- THIN SPACE --> + <int>0x200A</int> <!-- HAIR SPACE --> + <int>0x200B</int> <!-- ZERO WIDTH SPACE --> + <int>0x200C</int> <!-- ZERO WIDTH NON-JOINER --> + <int>0x200D</int> <!-- ZERO WIDTH JOINER --> + <int>0x200E</int> <!-- LEFT-TO-RIGHT MARK --> + <int>0x200F</int> <!-- RIGHT-TO-LEFT MARK --> + <int>0x2028</int> <!-- LINE SEPARATOR --> + <int>0x2029</int> <!-- PARAGRAPH SEPARATOR --> + <int>0x202A</int> <!-- LEFT-TO-RIGHT EMBEDDING --> + <int>0x202B</int> <!-- RIGHT-TO-LEFT EMBEDDING --> + <int>0x202C</int> <!-- POP DIRECTIONAL FORMATTING --> + <int>0x202D</int> <!-- LEFT-TO-RIGHT OVERRIDE --> + <int>0x202E</int> <!-- RIGHT-TO-LEFT OVERRIDE --> + <int>0x202F</int> <!-- NARROW NO-BREAK SPACE --> + <int>0x205F</int> <!-- MEDIUM MATHEMATICAL SPACE --> + <int>0x2060</int> <!-- WORD JOINER --> + <int>0x2061</int> <!-- FUNCTION APPLICATION --> + <int>0x2062</int> <!-- INVISIBLE TIMES --> + <int>0x2063</int> <!-- INVISIBLE SEPARATOR --> + <int>0x206A</int> <!-- INHIBIT SYMMETRIC SWAPPING --> + <int>0x206B</int> <!-- ACTIVATE SYMMETRIC SWAPPING --> + <int>0x206C</int> <!-- INHIBIT ARABIC FORM SHAPING --> + <int>0x206D</int> <!-- ACTIVATE ARABIC FORM SHAPING --> + <int>0x206E</int> <!-- NATIONAL DIGIT SHAPES --> + <int>0x206F</int> <!-- NOMINAL DIGIT SHAPES --> + <int>0x3000</int> <!-- IDEOGRAPHIC SPACE --> + <int>0x3164</int> <!-- HANGUL FILLER --> + <int>0xFEFF</int> <!-- ZERO WIDTH NO-BREAK SPACE --> + <int>0xFFA0</int> <!-- HALFWIDTH HANGUL FILLER --> + <int>0xFFF9</int> <!-- INTERLINEAR ANNOTATION ANCHOR --> + <int>0xFFFA</int> <!-- INTERLINEAR ANNOTATION SEPARATOR --> + <int>0xFFFB</int> <!-- INTERLINEAR ANNOTATION TERMINATOR --> + </blank> +<!-- + Rescan configuration every 30 seconds when FcFontSetList is called + --> + <rescan> + <int>30</int> + </rescan> + </config> + +</fontconfig> +ÿ
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/fonts/fonts.dtd Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,222 @@ +<!-- This is the Document Type Definition for font configuration files --> +<!ELEMENT fontconfig (dir | + cache | + cachedir | + include | + config | + selectfont | + match | + alias)* > + +<!-- + Add a directory that provides fonts +--> +<!ELEMENT dir (#PCDATA)> +<!ATTLIST dir xml:space (default|preserve) 'preserve'> + +<!-- + Define the per-user file that holds cache font information. + + If the filename begins with '~', it is replaced with the users + home directory path. +--> +<!ELEMENT cache (#PCDATA)> +<!ATTLIST cache xml:space (default|preserve) 'preserve'> + +<!-- + Add a directory that is searched for font cache files. + These hold per-directory cache data and are searched in + order for each directory. When writing cache files, the first + directory which allows the cache file to be created is used. + + A leading '~' in a directory name is replaced with the users + home directory path. +--> +<!ELEMENT cachedir (#PCDATA)> +<!ATTLIST cachedir xml:space (default|preserve) 'preserve'> + +<!-- + Reference another configuration file; note that this + is another complete font configuration file and not + just a file included by the XML parser. + + Set 'ignore_missing' to 'yes' if errors are to be ignored. + + If the filename begins with '~', it is replaced with the users + home directory path. +--> +<!ELEMENT include (#PCDATA)> +<!ATTLIST include + ignore_missing (no|yes) "no" + xml:space (default|preserve) "preserve"> + +<!-- + Global library configuration data + --> +<!ELEMENT config (blank|rescan)*> + +<!-- + Specify the set of Unicode encoding values which + represent glyphs that are allowed to contain no + data. With this list, fontconfig can examine + fonts for broken glyphs and eliminate them from + the set of valid Unicode chars. This idea + was borrowed from Mozilla + --> +<!ELEMENT blank (int)*> + +<!-- + Aliases are just a special case for multiple match elements + + They are syntactically equivalent to: + + <match> + <test name="family"> + <string value=[family]/> + </test> + <edit name="family" mode="prepend"> + <string value=[prefer]/> + ... + </edit> + <edit name="family" mode="append"> + <string value=[accept]/> + ... + </edit> + <edit name="family" mode="append_last"> + <string value=[default]/> + ... + </edit> + </match> +--> +<!-- + Periodically rescan the font configuration and + directories to synch internal state with filesystem + --> +<!ELEMENT rescan (int)> + +<!-- + Edit list of available fonts at startup/reload time + --> +<!ELEMENT selectfont (rejectfont | acceptfont)* > + +<!ELEMENT rejectfont (glob | pattern)*> + +<!ELEMENT acceptfont (glob | pattern)*> + +<!ELEMENT glob (#PCDATA)> + +<!ELEMENT pattern (patelt)*> + +<!ENTITY % constant 'int|double|string|matrix|bool|charset|const'> + +<!ELEMENT patelt (%constant;)*> +<!ATTLIST patelt + name CDATA #REQUIRED> + +<!ELEMENT alias (family*, prefer?, accept?, default?)> +<!ELEMENT prefer (family)*> +<!ELEMENT accept (family)*> +<!ELEMENT default (family)*> +<!ELEMENT family (#PCDATA)> +<!ATTLIST family xml:space (default|preserve) 'preserve'> + +<!ENTITY % expr 'int|double|string|matrix|bool|charset + |name|const + |or|and|eq|not_eq|less|less_eq|more|more_eq|contains|not_contains + |plus|minus|times|divide|not|if|floor|ceil|round|trunc'> + +<!-- + Match and edit patterns. + + If 'target' is 'pattern', execute the match before selecting a font. + if 'target' is 'font', execute the match on the result of a font + selection. +--> +<!ELEMENT match (test*, edit*)> +<!ATTLIST match + target (pattern|font|scan) "pattern"> + +<!-- + Match a field in a pattern + + if 'qual' is 'any', then the match succeeds if any value in the field matches. + if 'qual' is 'all', then the match succeeds only if all values match. + if 'qual' is 'first', then the match succeeds only if the first value matches. + if 'qual' is 'not_first', then the match succeeds only if any value other than + the first matches. + For match elements with target=font, if test 'target' is 'pattern', + then the test is applied to the pattern used in matching rather than + to the resulting font. + + Match elements with target=scan are applied as fonts are scanned. + They edit the pattern generated from the scanned font and affect + what the fontconfig database contains. +--> +<!ELEMENT test (%expr;)*> +<!ATTLIST test + qual (any|all|first|not_first) "any" + name CDATA #REQUIRED + target (pattern|font|default) "default" + compare (eq|not_eq|less|less_eq|more|more_eq|contains|not_contains) "eq"> + +<!-- + Edit a field in a pattern + + The enclosed values are used together to edit the list of values + associated with 'name'. + + If 'name' matches one of those used in a test element for this match element: + if 'mode' is 'assign', replace the matched value. + if 'mode' is 'assign_replace', replace all of the values + if 'mode' is 'prepend', insert before the matched value + if 'mode' is 'append', insert after the matched value + if 'mode' is 'prepend_first', insert before all of the values + if 'mode' is 'append_last', insert after all of the values + If 'name' doesn't match any of those used in a test element: + if 'mode' is 'assign' or 'assign_replace, replace all of the values + if 'mode' is 'prepend' or 'prepend_first', insert before all of the values + if 'mode' is 'append' or 'append_last', insert after all of the values +--> +<!ELEMENT edit (%expr;)*> +<!ATTLIST edit + name CDATA #REQUIRED + mode (assign|assign_replace|prepend|append|prepend_first|append_last) "assign" + binding (weak|strong|same) "weak"> + +<!-- + Elements of expressions follow +--> +<!ELEMENT int (#PCDATA)> +<!ATTLIST int xml:space (default|preserve) 'preserve'> +<!ELEMENT double (#PCDATA)> +<!ATTLIST double xml:space (default|preserve) 'preserve'> +<!ELEMENT string (#PCDATA)> +<!ATTLIST string xml:space (default|preserve) 'preserve'> +<!ELEMENT matrix (double,double,double,double)> +<!ELEMENT bool (#PCDATA)> +<!ELEMENT charset (#PCDATA)> +<!ATTLIST charset xml:space (default|preserve) 'preserve'> +<!ELEMENT name (#PCDATA)> +<!ATTLIST name xml:space (default|preserve) 'preserve'> +<!ELEMENT const (#PCDATA)> +<!ATTLIST const xml:space (default|preserve) 'preserve'> +<!ELEMENT or (%expr;)*> +<!ELEMENT and (%expr;)*> +<!ELEMENT eq ((%expr;), (%expr;))> +<!ELEMENT not_eq ((%expr;), (%expr;))> +<!ELEMENT less ((%expr;), (%expr;))> +<!ELEMENT less_eq ((%expr;), (%expr;))> +<!ELEMENT more ((%expr;), (%expr;))> +<!ELEMENT more_eq ((%expr;), (%expr;))> +<!ELEMENT contains ((%expr;), (%expr;))> +<!ELEMENT not_contains ((%expr;), (%expr;))> +<!ELEMENT plus (%expr;)*> +<!ELEMENT minus (%expr;)*> +<!ELEMENT times (%expr;)*> +<!ELEMENT divide (%expr;)*> +<!ELEMENT not (%expr;)> +<!ELEMENT if ((%expr;), (%expr;), (%expr;))> +<!ELEMENT floor (%expr;)> +<!ELEMENT ceil (%expr;)> +<!ELEMENT round (%expr;)> +<!ELEMENT trunc (%expr;)>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/gtk-2.0/gdk-pixbuf.loaders Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +# GdkPixbuf Image Loader Modules file +# Automatically generated file, do not edit +# Created by gdk-pixbuf-query-loaders.exe from gtk+-2.10.6 +# +# LoaderDir = /target/lib/gtk-2.0/2.10.0/loaders +# +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-ani.dll" +"ani" 4 "gtk20" "The ANI image format" +"application/x-navi-animation" "" +"ani" "" +"RIFF ACON" " xxxx " 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-bmp.dll" +"bmp" 5 "gtk20" "The BMP image format" +"image/bmp" "image/x-bmp" "image/x-MS-bmp" "" +"bmp" "" +"BM" "" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-gif.dll" +"gif" 4 "gtk20" "The GIF image format" +"image/gif" "" +"gif" "" +"GIF8" "" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-ico.dll" +"ico" 5 "gtk20" "The ICO image format" +"image/x-icon" "" +"ico" "cur" "" +" \001 " "zz znz" 100 +" \002 " "zz znz" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-jpeg.dll" +"jpeg" 5 "gtk20" "The JPEG image format" +"image/jpeg" "" +"jpeg" "jpe" "jpg" "" +"\377\330" "" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-pcx.dll" +"pcx" 4 "gtk20" "The PCX image format" +"image/x-pcx" "" +"pcx" "" +"\n \001" "" 100 +"\n\002\001" "" 100 +"\n\003\001" "" 100 +"\n\004\001" "" 100 +"\n\005\001" "" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-png.dll" +"png" 5 "gtk20" "The PNG image format" +"image/png" "" +"png" "" +"\211PNG\r\n\032\n" "" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-pnm.dll" +"pnm" 4 "gtk20" "The PNM/PBM/PGM/PPM image format family" +"image/x-portable-anymap" "image/x-portable-bitmap" "image/x-portable-graymap" "image/x-portable-pixmap" "" +"pnm" "pbm" "pgm" "ppm" "" +"P1" "" 100 +"P2" "" 100 +"P3" "" 100 +"P4" "" 100 +"P5" "" 100 +"P6" "" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-ras.dll" +"ras" 4 "gtk20" "The Sun raster image format" +"image/x-cmu-raster" "image/x-sun-raster" "" +"ras" "" +"Y\246j\225" "" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-tga.dll" +"tga" 4 "gtk20" "The Targa image format" +"image/x-tga" "" +"tga" "targa" "" +" \001\001" "x " 100 +" \001\t" "x " 100 +" \002" "xz " 99 +" \003" "xz " 100 +" \n" "xz " 100 +" \013" "xz " 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-tiff.dll" +"tiff" 1 "gtk20" "The TIFF image format" +"image/tiff" "" +"tiff" "tif" "" +"MM *" " z " 100 +"II* " " z" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-wbmp.dll" +"wbmp" 4 "gtk20" "The WBMP image format" +"image/vnd.wap.wbmp" "" +"wbmp" "" +" " "z" 1 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-xbm.dll" +"xbm" 4 "gtk20" "The XBM image format" +"image/x-xbitmap" "" +"xbm" "" +"#define " "" 100 +"/*" "" 50 + +"/target/lib/gtk-2.0/2.10.0/loaders/libpixbufloader-xpm.dll" +"xpm" 4 "gtk20" "The XPM image format" +"image/x-xpixmap" "" +"xpm" "" +"/* XPM */" "" 100 + +"/target/lib/gtk-2.0/2.10.0/loaders/svg_loader.dll" +"svg" 2 "gtk20" "Scalable Vector Graphics" +"image/svg+xml" "image/svg" "image/svg-xml" "image/vnd.adobe.svg+xml" "text/xml-svg" "" +"svg" "svgz" "svg.gz" "" +" <svg" "* " 100 +" <!DOCTYPE svg" "* " 100 +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/gtk-2.0/gtk.immodules Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +# GTK+ Input Method Modules file +# Automatically generated file, do not edit +# Created by gtk-query-immodules-2.0.exe from gtk+-2.10.6 +# +# ModulesPath = /target/lib/gtk-2.0/2.4.0/immodules +# +"/target/lib/gtk-2.0/2.10.0/immodules/im-am-et.dll" +"am_et" "Amharic (EZ+)" "gtk20" "${prefix}/share/locale" "am" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-cedilla.dll" +"cedilla" "Cedilla" "gtk+" "${prefix}/share/locale" "az:ca:co:fr:gv:oc:pt:sq:tr:wa" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-cyrillic-translit.dll" +"cyrillic_translit" "Cyrillic (Transliterated)" "gtk20" "${prefix}/share/locale" "" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-ime.dll" +"ime" "Windows IME" "gtk+" "" "ja:ko:zh" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-inuktitut.dll" +"inuktitut" "Inuktitut (Transliterated)" "gtk20" "${prefix}/share/locale" "iu" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-ipa.dll" +"ipa" "IPA" "gtk20" "${prefix}/share/locale" "" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-thai-broken.dll" +"thai_broken" "Thai (Broken)" "gtk20" "${prefix}/share/locale" "" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-ti-er.dll" +"ti_er" "Tigrigna-Eritrean (EZ+)" "gtk20" "${prefix}/share/locale" "ti" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-ti-et.dll" +"ti_et" "Tigrigna-Ethiopian (EZ+)" "gtk20" "${prefix}/share/locale" "ti" + +"/target/lib/gtk-2.0/2.10.0/immodules/im-viqr.dll" +"viqr" "Vietnamese (VIQR)" "gtk20" "${prefix}/share/locale" "vi" +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/gtk-2.0/gtkrc Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1 @@ +gtk-theme-name = "MS-Windows"
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/pango/pango.aliases Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +courier = "courier new" + +tahoma = "tahoma,browallia new,mingliu,simhei,gulimche,ms gothic,latha,mangal" +sans = "arial,browallia new,mingliu,simhei,gulimche,ms gothic,latha,mangal" +serif = "times new roman,angsana new,mingliu,simsun,gulimche,ms gothic,latha,mangal" +monospace = "courier new,courier monothai,mingliu,simsun,gulimche,ms gothic,latha,mangal" +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/etc/pango/pango.modules Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,35 @@ +# Pango Modules file +# Automatically generated file, do not edit +# +# ModulesPath = /target/lib/pango/1.6.0/modules +# +"/target/lib/pango/1.6.0/modules/pango-arabic-fc.dll" ArabicScriptEngineFc PangoEngineShape PangoRenderFc arabic:* +"/target/lib/pango/1.6.0/modules/pango-arabic-lang.dll" ArabicScriptEngineLang PangoEngineLang PangoRenderNone arabic:* +"/target/lib/pango/1.6.0/modules/pango-basic-fc.dll" BasicScriptEngineFc PangoEngineShape PangoRenderFc armenian:* bopomofo:* cherokee:* coptic:* cyrillic:* deseret:* ethiopic:* georgian:* gothic:* greek:* han:* hiragana:* katakana:* latin:* ogham:* old-italic:* runic:* canadian-aboriginal:* yi:* braille:* cypriot:* limbu:* osmanya:* shavian:* linear-b:* ugaritic:* glagolitic:* cuneiform:* phoenician:* common: +"/target/lib/pango/1.6.0/modules/pango-basic-win32.dll" BasicScriptEngineWin32 PangoEngineShape PangoRenderWin32 common: +"/target/lib/pango/1.6.0/modules/pango-hangul-fc.dll" HangulScriptEngineFc PangoEngineShape PangoRenderFc hangul:* +"/target/lib/pango/1.6.0/modules/pango-hebrew-fc.dll" HebrewScriptEngineFc PangoEngineShape PangoRenderFc hebrew:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" devaScriptEngineFc PangoEngineShape PangoRenderFc devanagari:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" bengScriptEngineFc PangoEngineShape PangoRenderFc bengali:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" guruScriptEngineFc PangoEngineShape PangoRenderFc gurmukhi:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" gujrScriptEngineFc PangoEngineShape PangoRenderFc gujarati:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" oryaScriptEngineFc PangoEngineShape PangoRenderFc oriya:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" tamlScriptEngineFc PangoEngineShape PangoRenderFc tamil:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" teluScriptEngineFc PangoEngineShape PangoRenderFc telugu:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" kndaScriptEngineFc PangoEngineShape PangoRenderFc kannada:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" mlymScriptEngineFc PangoEngineShape PangoRenderFc malayalam:* +"/target/lib/pango/1.6.0/modules/pango-indic-fc.dll" sinhScriptEngineFc PangoEngineShape PangoRenderFc sinhala:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" devaIndicScriptEngineLang PangoEngineLang PangoRenderNone devanagari:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" bengIndicScriptEngineLang PangoEngineLang PangoRenderNone bengali:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" guruIndicScriptEngineLang PangoEngineLang PangoRenderNone gurmukhi:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" gujrIndicScriptEngineLang PangoEngineLang PangoRenderNone gujarati:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" oryaIndicScriptEngineLang PangoEngineLang PangoRenderNone oriya:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" tamlIndicScriptEngineLang PangoEngineLang PangoRenderNone tamil:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" teluIndicScriptEngineLang PangoEngineLang PangoRenderNone telugu:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" kndaIndicScriptEngineLang PangoEngineLang PangoRenderNone kannada:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" mlymIndicScriptEngineLang PangoEngineLang PangoRenderNone malayalam:* +"/target/lib/pango/1.6.0/modules/pango-indic-lang.dll" sinhIndicScriptEngineLang PangoEngineLang PangoRenderNone sinhala:* +"/target/lib/pango/1.6.0/modules/pango-khmer-fc.dll" KhmerScriptEngineFc PangoEngineShape PangoRenderFc khmer:* +"/target/lib/pango/1.6.0/modules/pango-syriac-fc.dll" SyriacScriptEngineFc PangoEngineShape PangoRenderFc syriac:* +"/target/lib/pango/1.6.0/modules/pango-thai-fc.dll" ThaiScriptEngineFc PangoEngineShape PangoRenderFc thai:* lao:* +"/target/lib/pango/1.6.0/modules/pango-tibetan-fc.dll" TibetanScriptEngineFc PangoEngineShape PangoRenderFc tibetan:*
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/demos/graphViz4MatlabDEMO1.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +load smallExample +nodeColors = {'g','b','r','c'}; % if too few specified, it will cycle through +edgeColors = {'Tom', 'Bill', 'r' + 'Bill' 'all' , 'g'}; + +graphViz4Matlab('-adjMat',adj,'-nodeLabels',names,'-layout',Treelayout,'-nodeColors',nodeColors,'-edgeColors', edgeColors);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/demos/graphViz4MatlabDEMO2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2 @@ +load largeExample +graphViz4Matlab(adj);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/demos/graphViz4MatlabDEMO3.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +adj = rand(4) > 0.5; +descriptions = {'First Random Node','Yet another node','The thrid one','The final node'}; +colors = [0 0.5 0.2]; % either use string names for colors or normalized rgb values +graphViz4Matlab('-adjMat',adj,'-nodeDescriptions',descriptions,'-nodeColors',colors); + +% double click on the nodes to see and edit descriptions. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/drawNetwork.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,11 @@ +function g = drawNetwork(varargin) +% Visualize a graph in a Matlab figure window by specifying an +% adjacency matrix and optionally node labels, descriptions, colors and the +% the layout algorithm. The best layout algorithms require that graphViz be +% installed, available free at <http://www.graphviz.org>. +%% +% Type doc graphViz4Matlab for more details. + + g = graphViz4Matlab(varargin{:}); + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/graphViz4Matlab.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1028 @@ +classdef graphViz4Matlab < handle + % Visualize a graph in a Matlab figure window by specifying an + % adjacency matrix and optionally node labels, descriptions, colors and the + % the layout algorithm. The best layout algorithms require that graphViz be + % installed, available free at <http://www.graphviz.org>. + % + % Matthew Dunham + % University of British Columbia + % Last Updated: April 24, 2010 + % Requires Matlab version 2008a or newer. + % + % Syntax (see more examples below): + % graphViz4Matlab('-adjMat',adj,'-nodeLabels',labels,'-nodeColors',colors); + % + % Self loops are removed and not represented on the graph. + % + % Once the graph is displayed, there are several operations you can perform + % with the mouse. + % (1) Move a single node around. + % (2) Draw a mouse box around several nodes and move them all together. + % (3) Enter a description for a node by double clicking it. + % (4) Shade the node by right clicking it + % (5) Display a node's properties on the console by shift clicking it. + % (6) Increase or decrease the font size + % (7) Increase or decrease the node size + % (8) Tighten the axes and relax the square aspect ratio. + % (9) Ask the current layout algorithm to layout the nodes as though the + % arrows were pointing the other way. This only affects some of the + % layouts. + % (10) Change the layout algorithm and refresh the graph + % + % Additionally, any operation you could do with a regular Matlab figure can + % be done here, e.g. annotating or saving as a pdf. + % + % Options are specified via name value pairs in any order. + % [] denote defaults. + % + % '-adjMat' [example matrix] The adjacency matrix + % + % '-layout' [Gvizlayout if graphViz installed, else Gridlayout] + % A layout object, i.e. Gvizlayout | Gridlayout | Circlelayout + % (See knownLayouts Property) + % + % '-nodeLabels' ['1':'n'] A cell array of labels for the nodes + % + % '-nodeDescriptions' [{}] Longer descriptions for the nodes, displayed when + % double clicking on a node. + % + % '-nodeColors' ['c'] A cell array or n-by-3 matrix specifying colors + % for the nodes. If fewer colors than nodes are specified, + % the specified colors are reused and cycled through. + % + % '-undirected' [false] If true, no arrows are displayed. + % + % '-edgeColors' [] An n-by-3 cell array listing + % {fromName,toName,color} for each row. You can + % list only the n < numel(edges) edges you want to + % color. If you do not label the nodes, graphViz4Matlab + % uses '1','2','3', etc, in which case use these. + % You can specify the text 'all' in place of toName, + % to mean all nodes, i.e. {fromName,'all',color} + % + % + % '-splitLabels' [true] If true, long node labels are split into + % several rows + % + % '-doubleClickFn' (by default, double clicking a node brings up + % an edit box for the node's description, but you + % can pass in a custom function handle. The function + % gets passed the node's label. + % Examples: + % + % adj = rand(5,5) > 0.8; + % labels = {'First','Second','Third','Fourth','Fifth'}; + % colors = {'g','b'}; % will cycle through + % s = graphViz4Matlab('-adjMat',adj,'-nodeLabels',labels,'-nodeColors',colors); + % freeze(s); % convert to an image + % + % If you are only specifying an adjacency matrix, you can omit the + % '-adjMat' name as in graphViz4Matlab(adj). + % + % Calling graphViz4Matlab without any parameters displays an example graph. + % + + + properties(GetAccess = 'public', SetAccess = 'private') + % read only + path = addpath(genpath(fileparts(which(mfilename)))); % automatically adds subdirectories to path + graphVizPath = setupPath(); + nnodes = 0; % The number of nodes + nedges = 0; % The number of edges + currentLayout= []; % The current layout object + layouts = []; % List currently added layout objects + adjMatrix = []; % The adjacency matrix + isvisible = false; % True iff the graph is being displayed + nodeArray = []; % The nodes + edgeArray = []; % The edges + fig = []; % The main window + ax = []; % The main axes + doubleClickFn = []; % function to execute when a user double clicks on a node, (must be a function handle that takes in the node name + selectedNode = []; % The selected node, if any + minNodeSize = []; % A minimum size for the nodes + maxNodeSize = []; % A maximum size for the nodes + undirected = false; % If undirected, arrows are not displayed + flipped = false; % If true, layout is done as though edge directions were reversed. + % (does not affect the logical layout). + knownLayouts = {Gvizlayout ,... % add your own layout here or use + Treelayout ,... % the addLayout() method + Radiallayout,... + Circularlayout,... + Springlayout,... + Circlelayout,... + Gridlayout ,... + Randlayout }; + defaultEdgeColor = [0,0,0];%[20,43,140]/255; + edgeColors; + square = true; % amounts to a the call "axis square" + splitLabels = true; + end + + properties(GetAccess = 'private', SetAccess = 'private') + % These values store the initial values not the current ones. + nodeLabels = {}; + nodeDescriptions = {}; + nodeColors = {}; + end + + properties(GetAccess = 'protected',SetAccess = 'protected') + toolbar; % The button toolbar + layoutButtons; % The layout buttons + fontSize; % last calculated optimal font size + selectedFontSize; % + + previousMouseLocation; % last mouse location relative to the axes + groupSelectionMode = 0; % current stage in a group selection task + groupSelectedNodes; % selected nodes in a group selection + groupSelectedDims; % size of enclosing rectangle of selected nodes + groupSelectedRect; % a bounding rectangle for the selected nodes + end + + methods + + function obj = graphViz4Matlab(varargin) + % graphViz4Matlab constructor + if(~exist('processArgs','file')), error('Requires processArgs() function'); end + obj.addKnownLayouts(); + obj.processInputs(varargin{:}) + obj.addNodes(); + obj.addEdges(); + obj.draw(); + end + + function draw(obj) + % Draw the graph + if(obj.isvisible) + obj.erase() + end + obj.createWindow(); + obj.calculateMinMaxNodeSize(); + obj.layoutNodes(); + obj.displayGraph(); + obj.isvisible = true; + obj.paperCrop(); + end + + function fig = freeze(obj) + % Freeze the current image into a regular Matlab figure + figure(obj.fig); + print tmp.png -dpng -r300 + fig = figure; + image(imread('tmp.png')); + axis off; + delete tmp.png; + close(obj.fig); + end + + function redraw(obj) + % Redraw the graph. (You could also call draw() again but then the + % window is recreated as well and it doesn't look as nice). + if(~obj.isvisible) + obj.draw(); + return; + end + cla(obj.ax); + obj.clearGroupSelection(); + obj.calculateMinMaxNodeSize(); + obj.layoutNodes(); + obj.displayGraph(); + end + + function flip(obj,varargin) + % Have the layout algorithms layout the graph as though the arrows + % were pointing in the opposite direction. The node connectivity + % remains the same and if node one pointed to node 2 before, it + % still does after. This is useful for tree layout, for example to + % turn the tree on its head. Calling it twice flips it back. + obj.flipped = ~obj.flipped; + if(obj.isvisible) + obj.redraw(); + end + end + + function erase(obj) + % Erase the graph but maintain the state so that it can be redrawn. + if(obj.isvisible) + obj.clearGroupSelection(); + delete(obj.fig); + obj.isvisible = false; + + end + end + + function nodeSelected(obj,node) + % This function is called by nodes when they are selected by the + % mouse. It should not be called manually. + if(obj.groupSelectionMode == 1) + obj.groupSelectionStage1(); + return; + end + if(~isempty(obj.selectedNode)) + node.deselect(); + obj.selectedNode = []; + return; + end + switch get(obj.fig,'SelectionType') + case 'normal' + obj.singleClick(node); + case 'open' + obj.doubleClick(node); + case 'alt' + obj.rightClick(node); + otherwise + obj.shiftClick(node); + end + end + + function addLayout(obj,layout) + % Let the graph know about a new layout you have created so that it + % will be available via a toolbar button. The layout object must be + % a descendant of the Abstractlayout class. This method does not have + % to be called for existing layouts, nor does it need to be called + % if you passed the new layout to the constructor or to the + % setLayout() method. It will not add two layouts with the same + % name property. + if(~ismember(layout.name,fieldnames(obj.layouts))) + if(layout.isavailable()) + obj.layouts.(layout.name) = layout; + if(obj.isvisible) + obj.addButtons(); + end + else + warning('graphViz4Matlab:layout','This layout is not available'); + end + end + end + + function setLayout(obj,layout) + % Set a new layout algorithm and refresh the graph. + if(layout.isavailable()) + obj.addLayout(layout); + obj.currentLayout = obj.layouts.(layout.name); + obj.redraw(); + else + warning('graphViz4Matlab:layout','Sorry, this layout is not available'); + end + end + + function squareAxes(obj,varargin) + % Toggle the axes from square to normal and vice versa. + obj.clearGroupSelection(); + if(obj.square) + axis(obj.ax,'normal'); + obj.square = false; + else + axis(obj.ax,'square'); + obj.square = true; + end + + end + + function tightenAxes(obj,varargin) + % Tighten the axes as much as possible. + obj.clearGroupSelection(); + xpos = vertcat(obj.nodeArray.xpos); + ypos = vertcat(obj.nodeArray.ypos); + r = obj.nodeArray(1).width/2; + axis(obj.ax,[min(xpos)-r,max(xpos)+r,min(ypos)-r,max(ypos)+r]); + axis normal; + end + + + + + end % end of public methods + + + methods(Access = 'protected') + + function addKnownLayouts(obj) + % Add all of the known layouts + obj.layouts = struct; + for i=1:numel(obj.knownLayouts) + layout = obj.knownLayouts{i}; + if(layout.isavailable()) + obj.layouts.(layout.name) = layout; + end + end + end + + function processInputs(obj,varargin) + % Process the inputs and perform error checking + labels = {'adj', 'adjMatrix', 'adjMat', 'layout', 'nodeLabels', 'nodeDescriptions', 'nodeColors', 'undirected', 'edgeColors', 'splitLabels', 'doubleClickFn'}; + for i=1:numel(varargin) + arg = varargin{i}; + if ~ischar(arg), continue; end + for j = 1:numel(labels) + if strcmpi(arg, labels{i}); + varargin{i} = ['-', arg]; + end + if strcmpi(arg, '-adj') || strcmpi(arg, '-adjMatrix') + varargin{i} = '-adjMat'; + end + end + end + + [adjMatrix, currentLayout, nodeLabels, nodeDescriptions, nodeColors,obj.undirected,obj.edgeColors,obj.splitLabels,obj.doubleClickFn] = processArgs(varargin,... + '-adjMat' , [] ,... + '-layout' , [] ,... + '-nodeLabels' , {} ,... + '-nodeDescriptions' , {} ,... + '-nodeColors' , {} ,... + '-undirected' , false ,... + '-edgeColors' , [] ,... + '-splitLabels' , true ,... + '-doubleClickFn' , [] ); + + + if(~isempty(currentLayout) && ~isavailable(currentLayout)) + currentLayout = []; + end + if(isempty(adjMatrix)) + adjMatrix = [0 0 0 0; 1 0 0 0; 1 1 0 0; 1 1 1 0]; % example graph + end + + if(isempty(currentLayout)) + fields = fieldnames(obj.layouts); + currentLayout = obj.layouts.(fields{1}); + else + obj.addLayout(currentLayout); + end + obj.nnodes = size(adjMatrix,1); + obj.adjMatrix = adjMatrix; + if(isempty(nodeDescriptions)) + nodeDescriptions = repmat({'Enter a description here...'},size(adjMatrix,1),1); + end + obj.nodeDescriptions = nodeDescriptions; + obj.nodeColors = nodeColors; + + if(isempty(nodeLabels)) + nodeLabels = cellfun(@(x)num2str(x),mat2cell(1:obj.nnodes,1,ones(1,obj.nnodes)),'UniformOutput',false); + end + + obj.nodeLabels = nodeLabels; + if(~isequal(numel(obj.nodeLabels),size(adjMatrix,1),size(adjMatrix,2))) + error('graphViz4Matlab:dimMismatch','The number of labels must match the dimensions of adjmatrix.'); + end + obj.currentLayout = currentLayout; + end + + function createWindow(obj) + % Create the main window + obj.fig = figure(floor(1000*rand) + 1000); + set(obj.fig,'Name','graphViz4Matlab',... + 'NumberTitle' ,'off',... + 'Color','w' ,'Toolbar','none'); + obj.createAxes(); + ssize = get(0,'ScreenSize'); + pos = [ssize(3)/2,50,-20+ssize(3)/2,ssize(4)-200]; + set(obj.fig,'Position',pos); + obj.setCallbacks(); + obj.addButtons(); + + end + + function createAxes(obj) + % Create the axes upon which the graph will be displayed. + obj.ax = axes('Parent',obj.fig,'box','on','UserData','main'); + outerpos = get(obj.ax,'OuterPosition'); + axpos = outerpos; + axpos(4) = 0.90; + axpos(2) = 0.03; + axis manual + if(obj.square) + axis square + end + set(obj.ax,'Position',axpos,'XTick',[],'YTick',[],'LineWidth',0.5); + set(obj.ax,'ButtonDownFcn',@obj.axPressed); + end + + + + function setCallbacks(obj) + % Set the callback functions for the figure, i.e. functions that + % will be called when the user performs various actions. + set(obj.fig,'ResizeFcn' ,@obj.windowResized); + set(obj.fig,'WindowButtonMotionFcn' ,@obj.mouseMoved); + set(obj.fig,'WindowButtonUpFcn' ,@obj.buttonUp); + set(obj.fig,'DeleteFcn' ,@obj.deleted); + end + + function addNodes(obj) + % Add all of the nodes to the graph structure, but don't display + % them yet. + obj.nodeArray = []; + for i=1:obj.nnodes + newnode = graphViz4MatlabNode(obj.nodeLabels{i}); + newnode.containingGraph = obj; + newnode.showFullLabel = ~obj.splitLabels; + obj.nodeArray = [obj.nodeArray newnode]; + end + obj.addNodeDescriptions(obj.nodeDescriptions); + obj.addNodeColors(obj.nodeColors); + end + + function addNodeDescriptions(obj,nodeDescriptions) + % Add any descriptions to the newly created nodes. + if(~isempty(nodeDescriptions)) + if(numel(nodeDescriptions) == 1) + nodeDescriptions = repmat(nodeDescriptions,obj.nnodes,1); + end + for i=1:obj.nnodes + obj.nodeArray(i).description = nodeDescriptions{i}; + end + end + end + + function addNodeColors(obj,nodeColors) + % Shade the nodes according to the specified colors. If too few + % colors are specified, they are cycled through. + if(~isempty(nodeColors)) + if(~iscell(nodeColors)) + nodeColors = mat2cell(nodeColors,ones(1,size(nodeColors,1)),size(nodeColors,2)); + end + if(size(nodeColors,2) > size(nodeColors,1)) + nodeColors = nodeColors'; + end + if(numel(nodeColors) < obj.nnodes) + nodeColors = repmat(nodeColors,ceil(obj.nnodes/numel(nodeColors)),1); + nodeColors = nodeColors(1:obj.nnodes); + end + for i=1:obj.nnodes + obj.nodeArray(i).shade(nodeColors{i}); + end + obj.nodeColors = nodeColors; + end + end + + function addEdges(obj) + % Add all of the edges to the graph structure, but don't display + % them yet. + if(any(diag(obj.adjMatrix))) + fprintf('\nRemoving Self Loops\n'); + obj.adjMatrix = obj.adjMatrix - diag(diag(obj.adjMatrix)); + end + obj.edgeArray = struct('from',[],'to',[],'arrow',[]); + counter = 1; + for i=1:obj.nnodes + for j=1:obj.nnodes + if(obj.adjMatrix(i,j)) + obj.edgeArray(counter) = struct('from',obj.nodeArray(i),'to',obj.nodeArray(j),'arrow',-1); + obj.nodeArray(i).outedges = [obj.nodeArray(i).outedges,counter]; + obj.nodeArray(j).inedges = [obj.nodeArray(j).inedges,counter]; + counter = counter + 1; + end + end + end + obj.nedges = counter -1; + end + + function calculateMinMaxNodeSize(obj) + % calculates the maximum and minimum node sizes in data units + SCREEN_PROPORTION_MAX = 1/10; + SCREEN_PROPORTION_MIN = 1/35; + units = get(0,'Units'); + set(0,'Units','pixels'); + screensize = get(0,'ScreenSize'); + set(0,'Units',units); + axunits = get(obj.ax,'Units'); + set(obj.ax,'Units','pixels'); + axsize = get(obj.ax,'Position'); + set(obj.ax,'Units',axunits); + if(screensize(3) < screensize(4)) + dataUnitsPerPixel = abs(diff(xlim))/axsize(3); + obj.minNodeSize = (SCREEN_PROPORTION_MIN*screensize(3))*dataUnitsPerPixel; + obj.maxNodeSize = (SCREEN_PROPORTION_MAX*screensize(3))*dataUnitsPerPixel; + else + dataUnitsPerPixel = abs(diff(ylim))/axsize(4); + obj.minNodeSize = (SCREEN_PROPORTION_MIN*screensize(4))*dataUnitsPerPixel; + obj.maxNodeSize = (SCREEN_PROPORTION_MAX*screensize(4))*dataUnitsPerPixel; + end + end + + function layoutNodes(obj) + % Layout the nodes and edges according to the current layout + % algorithm. + if(obj.flipped) + adj = obj.adjMatrix'; + else + adj = obj.adjMatrix; + end + obj.currentLayout.dolayout(adj,obj.ax,obj.maxNodeSize); + nodesize = obj.currentLayout.nodeSize(); + locs = obj.currentLayout.centers(); + for i=1:obj.nnodes + node = obj.nodeArray(i); + node.resize(nodesize); + node.move(locs(i,1),locs(i,2)); + end + end + + function displayGraph(obj) + % Display all of the nodes and edges. + cla(obj.ax); + obj.setFontSize(); + for i=1:obj.nnodes + node = obj.nodeArray(i); + node.fontSize = obj.fontSize; + node.draw(obj.ax); + end + displayEdges(obj); + end + + function displayEdges(obj,indices) + % Display or refresh the specified edges. If none specified, all + % are refreshed. Currently only works with round nodes. + figure(obj.fig); + if(nargin < 2) + indices = 1:obj.nedges; + else + indices = unique(indices); + end + for i=1:numel(indices) + edge = obj.edgeArray(indices(i)); + [X,Y,Xarrow,Yarrow] = obj.calcPositions(edge); + if(ishandle(edge.arrow)) + delete(edge.arrow) + end + hold on; + edgeColor = obj.defaultEdgeColor; + if ~isempty(obj.edgeColors) + candidates = obj.edgeColors(findString(edge.from.label,obj.edgeColors(:,1)),:); + if size(candidates,1)==1 && strcmpi(candidates(1,2),'all') + edgeColor = candidates{1,3}; + else + edgeCol = candidates(findString(edge.to.label,candidates(:,2)),3); + if ~isempty(edgeCol); edgeColor = edgeCol{1}; end + end + end + edge.arrow = plot(X,Y,'LineWidth',2,'HitTest','off','Color',edgeColor); + if(~obj.undirected) + arrowHead = obj.displayArrowHead(X,Y,Xarrow,Yarrow,edgeColor); + edge.arrow = [edge.arrow arrowHead]; + end + hold off; + obj.edgeArray(indices(i)) = edge; + end + end + + function arrowHead = displayArrowHead(obj,X,Y,Xarrow,Yarrow,arrowColor) %#ok + % Displays the arrow head given the appropriate coordinates + % calculated via the calcPositions() function. + + arrowHead = patch('Faces' ,[1,2,3] ,... + 'Vertices' ,[Xarrow(1) Yarrow(1); Xarrow(2) Yarrow(2) ;X(2) Y(2)],... + 'FaceColor' ,arrowColor); + end + + function [X,Y,Xarrow,Yarrow] = calcPositions(obj,edge) + % Helper function for displayEdges() - calculates edge and arrow + % start and end positions in data units. + X = [edge.from.xpos edge.to.xpos]; + Y = [edge.from.ypos edge.to.ypos]; + ratio = (Y(2) - Y(1))/(X(2)-X(1)); + if(isinf(ratio)) + ratio = realmax; + end + % dx: x-distance from node1 center to perimeter in direction of node2 + % dy: y-distance from node1 center to perimeter in direction of node2 + % ddx: x-distance from node1 perimeter to base of arrow head + % ddy: y-distance from node1 perimeter to base of arrow head + % dpx: x-offset away from edge in perpendicular direction, for arrow head + % dpy: y-offset away from edge in perpendicular direction, for arrow head + + arrowSize = obj.maxNodeSize/10; + [dx,dy] = pol2cart(atan(ratio),edge.from.width/2); + [ddx,ddy] = pol2cart(atan(ratio),arrowSize); + ratio = 1/ratio; % now work out perpendicular directions. + if(isinf(ratio)) + ratio = realmax; + end + [dpx dpy] = pol2cart(atan(ratio),arrowSize/2); + ddx = abs(ddx); ddy = abs(ddy); dpx = abs(dpx); dpy = abs(dpy); + dx = abs(dx); dy = abs(dy); + if(X(1) < X(2)) + X(1) = X(1) + dx; X(2) = X(2) - dx; + else + X(1) = X(1) - dx; X(2) = X(2) + dx; + end + if(Y(1) < Y(2)) + Y(1) = Y(1) + dy; Y(2) = Y(2) - dy; + else + Y(1) = Y(1) - dy; Y(2) = Y(2) + dy; + end + if(X(1) <= X(2) && Y(1) <= Y(2)) + Xarrow(1) = X(2) - ddx - dpx; Xarrow(2) = X(2) - ddx + dpx; + Yarrow(1) = Y(2) - ddy + dpy; Yarrow(2) = Y(2) - ddy - dpy; + elseif(X(1) <= X(2) && Y(1) >= Y(2)) + Xarrow(1) = X(2) - ddx - dpx; Xarrow(2) = X(2) - ddx + dpx; + Yarrow(1) = Y(2) + ddy - dpy; Yarrow(2) = Y(2) + ddy + dpy; + elseif(X(1) >= X(2) && Y(1) <= Y(2)) + Xarrow(1) = X(2) + ddx - dpx; Xarrow(2) = X(2) + ddx + dpx; + Yarrow(1) = Y(2) - ddy - dpy; Yarrow(2) = Y(2) - ddy + dpy; + else % (X(1) >= (X(2) && Y(1) >= Y(2)) + Xarrow(1) = X(2) + ddx - dpx; Xarrow(2) = X(2) + ddx + dpx; + Yarrow(1) = Y(2) + ddy + dpy; Yarrow(2) = Y(2) + ddy - dpy; + end + end + + function addButtons(obj) + % Add user interface buttons. + if(~isempty(obj.toolbar)) + if(ishandle(obj.toolbar)) + delete(obj.toolbar); + obj.toolbar = []; + end + end + obj.toolbar = uitoolbar(obj.fig); + + % button icons + load glicons; + + uipushtool(obj.toolbar,... + 'ClickedCallback' ,@obj.decreaseFontSize,... + 'TooltipString' ,'Decrease Font Size',... + 'CData' ,icons.downblue); + + uipushtool(obj.toolbar,... + 'ClickedCallback' ,@obj.increaseFontSize,... + 'TooltipString' ,'Increase Font Size',... + 'CData' ,icons.upblue); + + uipushtool(obj.toolbar,... + 'ClickedCallback' ,@obj.tightenAxes,... + 'TooltipString' ,'Tighten Axes',... + 'CData' ,icons.expand); + + uipushtool(obj.toolbar,... + 'ClickedCallback' ,@obj.flip,... + 'TooltipString' ,'Flip/Reset Layout',... + 'CData' , icons.flip); + + uipushtool(obj.toolbar,... + 'ClickedCallback' ,@obj.shrinkNodes,... + 'TooltipString' ,'Decrease Node Size',... + 'CData' , icons.downdarkblue); + + uipushtool(obj.toolbar,... + 'ClickedCallback' ,@obj.growNodes,... + 'TooltipString' ,'Increase Node Size',... + 'CData' , icons.updarkblue); + + if(~isempty(obj.layoutButtons)) + for i=1:numel(obj.layoutButtons) + if(ishandle(obj.layoutButtons(i))) + delete(obj.layoutButtons(i)); + end + end + obj.layoutButtons = []; + end + + layoutNames = fieldnames(obj.layouts); + for i=1:numel(layoutNames) + layout = obj.layouts.(layoutNames{i}); + layoutButton = uipushtool(obj.toolbar,... + 'ClickedCallback', @obj.layoutButtonPushed,... + 'TooltipString', layout.shortDescription,... + 'UserData' , layoutNames{i},... + 'Separator' , 'on',... + 'CData' , layout.image); + obj.layoutButtons = [obj.layoutButtons,layoutButton]; + end + end + + function setFontSize(obj) + % fontsize = obj.maxFontSize; + fontSize = 20; + maxchars = size(char(obj.nodeLabels),2); + width = obj.nodeArray(1).width; + height = obj.nodeArray(1).height; + xpos = -10; ypos = -10; + t = text(xpos,ypos,repmat('g',1,maxchars),... + 'FontUnits' , 'points' ,... + 'Units' , 'data' ,... + 'HorizontalAlignment' , 'center' ,... + 'VerticalAlignment' , 'middle' ,... + 'FontWeight' , 'demi' ,... + 'LineStyle' , 'none' ,... + 'Margin' , 0.01 ,... + 'FontSize' , fontSize ,... + 'Color' , 'w' ); + + extent = get(t,'Extent'); + + while(extent(3) > width || extent(4) > height) + fontSize = fontSize - 1; + if(fontSize < 2), break,end + set(t,'FontSize',fontSize); + extent = get(t,'Extent'); + end + obj.fontSize = fontSize; + + end + + function asp = aspectRatio(obj) + % Return the current aspect ratio of the figure, width/height + + units = get(obj.ax,'Units'); + set(obj.ax,'Units','pixels'); + pos = get(obj.ax,'Position'); + set(obj.ax,'Units',units); + asp = (pos(3)/pos(4)); + + end + + function paperCrop(obj) + % Make the papersize the same as the the figure size. This is + % useful when saving as pdf. + units = get(obj.fig,'Units'); + set(obj.fig,'Units','inches'); + pos = get(obj.fig,'Position'); + set(obj.fig,'Units',units); + set(obj.fig,'PaperPositionMode','auto','PaperSize',pos(3:4)); + end + %% + % Callbacks + + function layoutButtonPushed(obj,buttonPushed,varargin) + % Called when a layout button is pushed. + name = get(buttonPushed,'UserData'); + obj.currentLayout = obj.layouts.(name); + axis square; + obj.redraw; + end + + function windowResized(obj,varargin) + % This function is called whenever the window is resized. It + % redraws the whole graph. + if(obj.isvisible) + obj.redraw; + obj.paperCrop(); + end + + end + + function mouseMoved(obj,varargin) + % This function is called whenever the mouse moves within the + % figure. + if(obj.groupSelectionMode == 2) + % Move all of the nodes & rectangle + currentPoint = get(obj.ax,'CurrentPoint'); + xlimits = get(obj.ax,'XLim'); + ylimits = get(obj.ax,'YLim'); + sdims = obj.groupSelectedDims; + xdiff = currentPoint(1,1) - obj.previousMouseLocation(1,1); + ydiff = currentPoint(1,2) - obj.previousMouseLocation(1,2); + + if(xdiff <=0) + xdiff = max(xdiff,(xlimits(1)-sdims(1))); + else + xdiff = min(xdiff,xlimits(2)-sdims(2)); + end + if(ydiff <=0) + ydiff = max(ydiff,(ylimits(1)-sdims(3))); + else + ydiff = min(ydiff,ylimits(2)-sdims(4)); + end + xnodepos = vertcat(obj.groupSelectedNodes.xpos) + xdiff; + ynodepos = vertcat(obj.groupSelectedNodes.ypos) + ydiff; + for i=1:numel(obj.groupSelectedNodes) + obj.groupSelectedNodes(i).move(xnodepos(i),ynodepos(i)); + end + recpos = get(obj.groupSelectedRect,'Position'); + recpos(1) = recpos(1) + xdiff; + recpos(2) = recpos(2) + ydiff; + obj.groupSelectedDims = [recpos(1),recpos(1)+recpos(3),recpos(2),recpos(2)+recpos(4)]; + set(obj.groupSelectedRect,'Position',recpos); + edges = [obj.groupSelectedNodes.inedges,obj.groupSelectedNodes.outedges]; + obj.displayEdges(edges); + obj.previousMouseLocation = currentPoint; + else + if(isempty(obj.selectedNode)), return,end + currentPoint = get(obj.ax,'CurrentPoint'); + x = currentPoint(1,1); y = currentPoint(1,2); + xl = xlim + [obj.selectedNode.width,-obj.selectedNode.width]/2; + yl = ylim + [obj.selectedNode.height,-obj.selectedNode.height]/2; + x = min(max(xl(1),x),xl(2)); + y = min(max(yl(1),y),yl(2)); + obj.selectedNode.move(x,y); + obj.displayEdges([obj.selectedNode.inedges,obj.selectedNode.outedges]); + end + end + + function buttonUp(obj,varargin) + % This function executes when the mouse button is released. + if(obj.groupSelectionMode == 2) + obj.clearGroupSelection(); + return; + end + if(isempty(obj.selectedNode)),return,end + obj.selectedNode.deselect(); + + obj.selectedNode.useFullLabel = false; + obj.selectedNode.fontSize = obj.selectedFontSize; + obj.selectedNode.redraw(); + obj.selectedNode = []; + set(gcf,'Pointer','arrow'); + end + + function axPressed(obj,varargin) + % Called when the user selects the axes but not a node + switch obj.groupSelectionMode + case 0 % hasn't been selected yet + xpos = vertcat(obj.nodeArray.xpos); + ypos = vertcat(obj.nodeArray.ypos); + p1 = get(obj.ax,'CurrentPoint'); + rbbox; % returns after box drawn + p2 = get(obj.ax,'CurrentPoint'); + xleft = min(p1(1,1),p2(1,1)); + xright = max(p1(1,1),p2(1,1)); + ylower = min(p1(1,2),p2(1,2)); + yupper = max(p1(1,2),p2(1,2)); + selectedX = (xpos <= xright) & (xpos >= xleft); + selectedY = (ypos <= yupper) & (ypos >= ylower); + selected = selectedX & selectedY; + if(~any(selected)),return,end + obj.groupSelectionMode = 1; + obj.groupSelectedNodes = obj.nodeArray(selected); + for i=1:numel(obj.groupSelectedNodes) + node = obj.groupSelectedNodes(i); + node.select(); + node.redraw(); + end + + w = obj.groupSelectedNodes(1).width/2; + h = obj.groupSelectedNodes(1).height/2; + x = vertcat(obj.groupSelectedNodes.xpos); + y = vertcat(obj.groupSelectedNodes.ypos); + minx = min(x)-w; maxx = max(x)+w; miny = min(y)-h; maxy = max(y)+h; + obj.groupSelectedDims = [minx,maxx,miny,maxy]; + obj.groupSelectedRect = rectangle('Position',[minx,miny,maxx-minx,maxy-miny],'LineStyle','--','EdgeColor','r'); + case 1 % nodes selected + obj.groupSelectionStage1(); + case 2 %not ever reached in this function + obj.clearGroupSelection(); + end + end + + function groupSelectionStage1(obj) + % Called after a group of nodes has been selected and the mouse + % button has been pressed somewhere on the axes, (or on a node). + p = get(obj.ax,'CurrentPoint'); + obj.previousMouseLocation = p; + dims = obj.groupSelectedDims; + if(p(1,1) >= dims(1) && p(1,1) <= dims(2) && p(1,2) >= dims(3) && p(1,2) <=dims(4)) + set(gcf,'Pointer','hand'); + obj.groupSelectionMode = 2; + else + obj.clearGroupSelection(); + end + end + + function clearGroupSelection(obj) + % Clear a group selection + if(ishandle(obj.groupSelectedRect)) + delete(obj.groupSelectedRect); + end + obj.groupSelectedRect = []; + for i=1:numel(obj.groupSelectedNodes) + obj.groupSelectedNodes(i).deselect(); + end + obj.groupSelectedNodes = []; + obj.groupSelectedDims = []; + obj.groupSelectionMode = 0; + set(gcf,'Pointer','arrow'); + end + + function deleted(obj,varargin) + % Called when the figure is deleted by the user. + obj.isvisible = false; + obj.clearGroupSelection(); + end + + function singleClick(obj,node) + % Called when a user single clicks on a node. + obj.selectedNode = node; + node.select(); + set(gcf,'Pointer','hand'); + obj.selectedFontSize = node.fontSize; + node.useFullLabel = true; + node.fontSize = max(15,node.fontSize*1.5); + node.redraw(); + end + + function doubleClick(obj,node) + % Called when a user double clicks on a node + if isempty(obj.doubleClickFn) + description = node.description; + if(~iscell(description)) + description = {description}; + end + answer = inputdlg('',node.label,4,description); + if(~isempty(answer)) + node.description = answer; + end + else + obj.doubleClickFn(node.label); + end + end + + function rightClick(obj,node) %#ok + % Called when a user right clicks on a node + if(node.isshaded) + node.unshade(); + else + node.shade(); + end + end + + function shiftClick(obj,node) %#ok + % Called when a user shift clicks on a node + display(node); + end + + + end + + methods + % Callbacks that can be called by the user programmatically. + function shrinkNodes(obj,varargin) + % Shrink the nodes to 95% of their original size, (but not smaller + % than a calculated minimum. + obj.clearGroupSelection(); + s = max(0.8*obj.nodeArray(1).width,obj.minNodeSize); + obj.nodeArray(1).resize(s); + obj.setFontSize(); + for i=1:obj.nnodes + node = obj.nodeArray(i); + node.fontSize = obj.fontSize; + node.resize(s); + end + obj.displayEdges(); + end + + function growNodes(obj,varargin) + % Grow the nodes to 1/0.95 times their original size, (but not + % larger than a calculated maximum. + obj.clearGroupSelection(); + s = min(obj.nodeArray(1).width/0.8,1.5*obj.maxNodeSize); + obj.nodeArray(1).resize(s); + obj.setFontSize(); + for i=1:obj.nnodes + node = obj.nodeArray(i); + node.fontSize = obj.fontSize; + node.resize(s); + end + obj.displayEdges(); + end + + function increaseFontSize(obj,varargin) + % Increase the fontsize of all the nodes by 0.5 points. + current = get(obj.nodeArray(1).labelhandle,'FontSize'); + newsize = current + 1; + for i=1:numel(obj.nodeArray) + node = obj.nodeArray(i); + node.fontSize = newsize; + node.redraw(); + end + end + + function decreaseFontSize(obj,varargin) + % Decrease the fontsize of all the nodes by 0.5 points. + current = get(obj.nodeArray(1).labelhandle,'FontSize'); + newsize = max(current - 1,1); + for i=1:numel(obj.nodeArray) + node = obj.nodeArray(i); + node.fontSize = newsize; + node.redraw(); + end + end + + function XY = getNodePositions(obj) + % Return the current positions of the nodes. The bottom left + % corner is [0 0] and the top right is [1 1]. Node positions + % refer to the centre of a node. + XY = zeros(obj.nnodes, 2); + for i=1:obj.nnodes + XY(i, 1) = obj.nodeArray(i).xpos; + XY(i, 2) = obj.nodeArray(i).ypos; + end + end + + function setNodePositions(obj, XY) + % Programmatically set the node positions + % XY(i, 1) is the xposition of node i, XY(i, 2) is the yposition. + for i=1:obj.nnodes + obj.nodeArray(i).move(XY(i, 1), XY(i, 2)); + end + obj.displayGraph(); + end + + function moveNode(obj, nodeIndex, xpos, ypos) + % Programmatically set a node position. + obj.nodeArray(nodeIndex).move(xpos, ypos); + obj.displayGraph(); + end + + end +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/graphViz4MatlabRoot.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +function r = graphViz4MatlabRoot() +% Return directory name where graphlayout is stored +[pathstr,name,ext,versn] = fileparts(which(mfilename)); +r = pathstr; +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Abstractlayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +classdef Abstractlayout < handle +% This class represents an abstract graph layout. Subclass to create actual +% layouts. + + properties(Abstract = true) + xmin; % The left most point on the graph axis in data units + xmax; % The right most point on the graph axis in data units + ymin; % The bottom most point on the graph axis in data units + ymax; % The top most point on the graph axis in data units + adjMatrix; % The adjacency matrix + maxNodeSize; % The maximum diameter of a node in data units + image; % A 20-by-20-by-3 array specifying an image for + % the smartgraph button that will launch this + % layout. + name; % A unique name for instances of this class. + shortDescription; % A short description used for tooltips. + nodeSize; % The calculated node size, call dolayout() before accessing + centers; % The calculated node centers in an n-by-2 matrix + end + + methods + function dolayout(obj,adjMatrix,graphAxis,maxNodeSize) + % Calculate the node centers and an optimal size + obj.adjMatrix = adjMatrix; + obj.maxNodeSize = maxNodeSize; + xlimits = get(graphAxis,'XLim'); + ylimits = get(graphAxis,'YLim'); + obj.xmin = xlimits(1); obj.xmax = xlimits(2); + obj.ymin = xlimits(1); obj.ymax = ylimits(2); + obj.calcLayout(); + end + + function available = isavailable(obj) + % Test to see if this layout is available. + available = true; + end + + end + + methods(Access = 'protected') + + function setImage(obj,color) + % Sets the image to a grid with the specified color in rgb normalized + % units, i.e. [0.2 0.5 0.9]. + obj.image = cat(3,color(1)*ones(20,20),color(2)*ones(20,20),color(3)*ones(20,20)); + obj.image(1:4:20,:,:) = 0; + obj.image(:,1:4:20,:) = 0; + end + + + end + + methods(Abstract = true, Access = 'protected') + calcLayout(obj); % do the actual work + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Circlelayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +classdef Circlelayout < Abstractlayout +% A simple layout which places node onto a scaled unit circle. +% +% Matthew Dunham +% University of British Columbia + properties + xmin; % The left most point on the graph axis in data units + xmax; % The right most point on the graph axis in data units + ymin; % The bottom most point on the graph axis in data units + ymax; % The top most point on the graph axis in data units + adjMatrix; % The adjacency matrix + maxNodeSize; % The maximum diameter of a node in data units + image; % An image for the button that will lanuch this layout + name; % A unique name for instances of this class + shortDescription; % A description for use in the tooltips + nodeSize; % The calculated node size, call dolayout() before accessing + centers; % The calculated node centers in an n-by-2 matrix + end + + methods + function obj = Circlelayout(name) + % constructor + if(nargin < 1) + obj.name = 'circlelayout'; + else + obj.name = name; + end + load glicons; + obj.image = icons.circle; + obj.shortDescription = 'Simple Circle Layout'; + end + end + + methods(Access = 'protected') + + function calcLayout(obj) + nnodes = size(obj.adjMatrix,1); + step = 2*pi/(nnodes); + t = 0:step:2*pi; + x = 0.4*sin(t)+0.5; + y = 0.4*cos(t)+0.5; + + x = obj.xmin + x ./ (obj.xmax - obj.xmin); + y = obj.ymin + y ./ (obj.ymax - obj.ymin); + obj.centers = [x',y']; + d = sqrt((x(2)- x(1))^2 + (y(2) - y(1))^2); + obj.nodeSize = min(2*d/3,obj.maxNodeSize); + end + + end + + + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Circularlayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +classdef Circularlayout < Gvizlayout +% A layout that also uses graphviz but calls twopi instead of neato to +% to display the graph like a tree. +% +% Matthew Dunham +% University of British Columbia +% http://www.cs.ubc.ca/~mdunham/ + methods + function obj = Circularlayout(name) + if(nargin < 1) + obj.name = 'circularlayout'; + else + obj.name = name; + end + load glicons; + obj.image = icons.circular; + obj.shortDescription = 'Circular Layout (GraphViz)'; + end + + end + + methods(Access = 'protected') + + function callGraphViz(obj) + % Call GraphViz to determine an optimal layout. Write this layout in + % layout.dot for later parsing. + err = system(['circo -Tdot -Gmaxiter=5000 -Gstart=7 -o ',obj.layoutFile,' ',obj.adjFile]); + if(err),error('Sorry, unknown GraphViz failure, try another layout'); end + end + + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Gridlayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +classdef Gridlayout < Abstractlayout +% Provides a simple, (naive) grid layout for Graphlayout. +% An uninitialized instance can be created to pass to the GraphLayout +% constructor by just calling Gridlayout without any parameters, e.g. +% Graphlayout('-adjMat',[0 1; 0 0], '-layout',Gridlayout); +% +% Matthew Dunham +% University of British Columbia +% http://www.cs.ubc.ca/~mdunham/ + + properties + xmin; % The left most point on the graph axis in data units + xmax; % The right most point on the graph axis in data units + ymin; % The bottom most point on the graph axis in data units + ymax; % The top most point on the graph axis in data units + adjMatrix; % The adjacency matrix + maxNodeSize; % The maximum diameter of a node in data units + image; % An image for the button that will lanuch this layout + name; % A unique name for instances of this class + shortDescription; % A description for use in the tooltips + nodeSize; % The calculated node size, call dolayout() before accessing + centers; % The calculated node centers in an n-by-2 matrix + end + + methods + function obj = Gridlayout(name) + + % constructor + if(nargin < 1) + obj.name = 'Gridlayout'; + else + obj.name = name; + end + load glicons; + obj.image = icons.grid; + obj.shortDescription = 'Grid Layout'; + end + + end + + + methods(Access = 'protected') + + function calcLayout(obj) + + nnodes = size(obj.adjMatrix,1); + obj.centers = zeros(nnodes,2); + xspacePerNode = (obj.xmax - obj.xmin)/ceil(sqrt(nnodes)); + yspacePerNode = (obj.ymax - obj.ymin)/ceil(sqrt(nnodes)); + obj.nodeSize = min(min([xspacePerNode,yspacePerNode]./2),obj.maxNodeSize); + xstart = obj.xmin + (xspacePerNode)/2; + ystart = obj.ymin + (yspacePerNode)/2; + counter = 1; + for ypos=1:ceil(sqrt(nnodes)) + if(counter > nnodes),break,end + for xpos=1:ceil(sqrt(nnodes)) + obj.centers(counter,1) = xstart + (xpos-1)*xspacePerNode; + obj.centers(counter,2) = ystart + (ypos-1)*yspacePerNode; + positions(ypos,xpos) = counter; %#ok + counter = counter + 1; + end + end + + %swap out the center node for the node with the most edges, + %(total of in and out). + edgeCounts = sum(obj.adjMatrix,1)' + sum(obj.adjMatrix,2); + [vals ndx] = sort(edgeCounts,'descend'); + c = positions(ceil(size(positions,1)/2),ceil(size(positions,2)/2)); + if(c ~= 0) + store = obj.centers(c,:); + obj.centers(c,:) = obj.centers(ndx(1),:); + obj.centers(ndx(1),:) = store; + end + + end + + + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Gvizlayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,142 @@ +classdef Gvizlayout < Abstractlayout +% Use the graphVIZ package to determine the optimal layout for a graph. +% +% Matthew Dunham +% University of British Columbia +% http://www.cs.ubc.ca/~mdunham/ + properties + xmin; % The left most point on the graph axis in data units + xmax; % The right most point on the graph axis in data units + ymin; % The bottom most point on the graph axis in data units + ymax; % The top most point on the graph axis in data units + adjMatrix; % The adjacency matrix + maxNodeSize; % The maximum diameter of a node in data units + image; % An image for the button that will lanuch this layout + name; % A unique name for instances of this class. + shortDescription; % A short descriptions used in tooltips. + nodeSize; % The calculated node size, call dolayout() before accessing + centers; % The calculated node centers in an n-by-2 matrix + end + + properties(GetAccess = 'protected', SetAccess = 'protected') + layoutFile = 'layout.dot'; + adjFile = 'adjmat.dot'; + end + + methods + + function obj = Gvizlayout(name) + % constructor + if(nargin < 1) + obj.name = 'Gvizlayout'; + else + obj.name = name; + end + load glicons; + obj.image = icons.energy; + obj.shortDescription = 'Minimum Energy Layout (GraphViz)'; + end + + function available = isavailable(obj) + % Make sure graphViz is available. + available = Gvizlayout.queryGviz('neato'); + if(not(available)) + fprintf('Please install or upgrade graphViz\n'); + end + end + + + end + + + methods(Access = 'protected') + + function calcLayout(obj) + % Have graphViz calculate the layout + obj.writeDOTfile(); + obj.callGraphViz(); + obj.readLayout(); + obj.cleanup(); + end + + function writeDOTfile(obj) + % Write the adjacency matrix into a dot file that graphViz can + % understand. + fid = fopen('adjmat.dot','w'); + fprintf(fid,'digraph G {\ncenter = 1;\nsize="10,10";\n'); + n = size(obj.adjMatrix,1); + for i=1:n, fprintf(fid,'%d;\n',i); end + edgetxt = ' -> '; + for i=1:n + for j=1:n + if(obj.adjMatrix(i,j)) + fprintf(fid,'%d%s%d;\n',i,edgetxt,j); + end + end + end + fprintf(fid,'}'); + fclose(fid); + end + + function callGraphViz(obj) + % Call GraphViz to determine an optimal layout. Write this layout in + % layout.dot for later parsing. + err = system(['neato -Tdot -Gmaxiter=5000 -Gstart=7 -o ',obj.layoutFile,' ',obj.adjFile]); + if(err),error('Sorry, unknown GraphViz failure, try another layout'); end + end + + function readLayout(obj) + % Parse the layout.dot file for the graphViz node locations and + % dimensions. + fid = fopen(obj.layoutFile,'r'); + text = textscan(fid,'%s','delimiter','\n'); + fclose(fid); + text = text{:}; + [start,dims] = strtok(text{cellfun(@(x)~isempty(x),strfind(text,'graph [bb="'))},'"'); + dims = textscan(strtok(dims,'"'),'%n','delimiter',','); + dims = dims{:}'; + text(cellfun(@(x)~isempty(x),strfind(text,' -> ')))=[]; % delete edge info, we don't need it + text(cellfun(@(x)isempty(x),strfind(text,'pos')))=[]; % keep only positions + [start,remaining] = strtok(text,'"'); + [locations,remaining] = strtok(remaining,'"'); + locations = cellfun(@(str)textscan(str,'%n','delimiter',','),locations,'UniformOutput',false); + locations = [locations{:}]; + locations = [locations{:}]'; + obj.scaleLocations(locations,dims); + end + + function scaleLocations(obj,locations,graphVizDims) + % Scale the graphViz node locations to the smartgraph dimensions and + % set the node size. + dims = graphVizDims; loc = locations; + loc(:,1) = (loc(:,1)-dims(1)) ./ (dims(3)-dims(1))*(obj.xmax - obj.xmin) + obj.xmin; + loc(:,2) = (loc(:,2)-dims(2)) ./ (dims(4)-dims(2))*(obj.ymax - obj.ymin) + obj.ymin; + obj.centers = loc; + + a = min(abs(loc(:,1) - obj.xmin)); + b = min(abs(loc(:,1) - obj.xmax)); + c = min(abs(loc(:,2) - obj.ymin)); + d = min(abs(loc(:,2) - obj.ymax)); + obj.nodeSize = min(2*min([a,b,c,d]),obj.maxNodeSize); + + end + + function cleanup(obj) + % delete the temporary files. + delete(obj.adjFile); + delete(obj.layoutFile); + end + + end + + methods(Access = 'protected',Static = true) + + function available = queryGviz(name) + err = system([name,' -V']); + available = ~err; + end + + end + + +end % end of graphVIZlayout class \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Radiallayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +classdef Radiallayout < Gvizlayout +% A layout that also uses graphviz but calls twopi instead of neato to +% to display the graph like a tree. +% +% Matthew Dunham +% University of British Columbia +% http://www.cs.ubc.ca/~mdunham/ + methods + function obj = Radiallayout(name) + if(nargin < 1) + obj.name = 'Radiallayout'; + else + obj.name = name; + end + load glicons; + obj.image = icons.radial; + obj.shortDescription = 'Radial Layout (GraphViz)'; + end + + function available = isavailable(obj) + % Make sure graphViz is available. + available = Gvizlayout.queryGviz('twopi'); + if(not(available)) + fprintf('Please install or upgrade graphViz\n'); + end + end + + end + + methods(Access = 'protected') + + function callGraphViz(obj) + % Call GraphViz to determine an optimal layout. Write this layout in + % layout.dot for later parsing. + err = system(['twopi -Tdot -Gmaxiter=5000 -Gstart=7 -o ',obj.layoutFile,' ',obj.adjFile]); + if(err),error('Sorry, unknown GraphViz failure, try another layout'); end + end + + + + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Randlayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,134 @@ +classdef Randlayout < Abstractlayout +% A greedy, random layout - gives a different layout each time its called. +% Matthew Dunham +% University of British Columbia +% http://www.cs.ubc.ca/~mdunham/ + + properties + xmin; % The left most point on the graph axis in data units + xmax; % The right most point on the graph axis in data units + ymin; % The bottom most point on the graph axis in data units + ymax; % The top most point on the graph axis in data units + adjMatrix; % The adjacency matrix + maxNodeSize; % The maximum diameter of a node in data units + image; % An image for the button that will lanuch this layout + name; % A unique name for instances of this class + shortDescription; % A description for use in the tooltips + nodeSize; % The calculated node size, call dolayout() before accessing + centers; % The calculated node centers in an n-by-2 matrix + seed; % The seed used to generate the last layout. + end + + + + methods + + function obj = Randlayout(name,seed) + % constructor - seed is optional. + if(nargin < 1) + obj.name = 'Randlayout'; + else + obj.name = name; + end + if(nargin < 2) + seed = 31; + end + obj.seed = seed; + load glicons + obj.image = icons.random; + obj.shortDescription = 'Random Greedy Layout'; + + end + end + + methods(Access = 'protected') + + function calcLayout(obj) + rand('twister',obj.seed); randn('state',obj.seed); + obj.seed = obj.seed + 1; + nnodes = size(obj.adjMatrix,1); + obj.nodeSize = min(obj.maxNodeSize,2*(obj.xmax-obj.xmin)/nnodes); + npoints = 25; + locdist = ones(npoints,npoints); + c = floor(0.4*npoints):ceil(0.6*npoints); + locdist(c,c) = 1.5; % slightly favour the center + locdist = locdist./sum(locdist(:)); % discrete distribution over locations + locations = zeros(nnodes,2); % holds indices into locdist + nsize = ceil(npoints*(obj.nodeSize/2)/(obj.xmax - obj.xmin+obj.nodeSize)); + for i=1:nnodes + while(true) + [xcandidate,ycandidate] = obj.sample(locdist); + [valid,locdist] = obj.isfree(xcandidate,ycandidate,locdist,nsize); + if(valid) + locations(i,:) = [xcandidate,ycandidate]; + locdist = obj.radiate(locdist,locations,npoints); + break; + end + end + end + nedges = sum(obj.adjMatrix,1) + sum(obj.adjMatrix,2)'; + [val,perm] = sort(nedges,'descend'); + locations = locations(perm,:); + obj.mapLocationsToAxes(locations,npoints); + end + + function mapLocationsToAxes(obj,locations,npoints) + % Map sampled locations on a grid to actual points on the graph + % axes. + xndx = locations(:,1); yndx = locations(:,2); + xmin = obj.xmin + obj.nodeSize/2; + xmax = obj.xmax - obj.nodeSize/2; + ymin = obj.ymin + obj.nodeSize/2; + ymax = obj.ymax - obj.nodeSize/2; + xstep = (xmax - xmin) /npoints; + ystep = (ymax - ymin) /npoints; + xpos = xmin+xstep:xstep:xmax; + ypos = ymin+ystep:ystep:ymax; + obj.centers = [xpos(xndx)',ypos(yndx)']; + end + + function [valid,locdist2] = isfree(obj,xcandidate,ycandidate,locdist,nsize) + % Check if the sampled location is valid. Regardless, zero out the + % spot so that we don't sample from here again. + xndx = max(1,xcandidate-nsize):min(xcandidate+nsize,size(locdist,2)); + yndx = max(1,ycandidate-nsize):min(ycandidate+nsize,size(locdist,1)); + nodeArea = locdist(xndx,yndx); + valid = all(nodeArea(:)); + locdist2 = locdist; + locdist2(xndx,yndx) = 0; + locdist2 = locdist2 ./sum(locdist(:)); + end + + function locdist = radiate(obj,locdist,locations,npoints) + % Update the location probability distribution. + n = nnz(locations(:,1)); + loczeros = locdist == 0; + for i=1:n + [x y] = meshgrid(1:npoints,1:npoints); + x = x(:); y = y(:); + dist = sqrt((x-locations(i,1)).^2 + (y-locations(i,2)).^2); + locdist = locdist + reshape(dist,npoints,npoints)'; + end + locdist(loczeros) = 0; + locdist = locdist ./sum(locdist(:)); + end + + function [xndx,yndx] = sample(obj,dist) + % Sample a single value from the non-uniform discrete distribution. + if(isnan(dist(1))) + perm = randperm(size(dist,1)); + xndx = perm(1); + yndx = perm(2); + return; + end + r = rand; + cumprob = cumsum(dist(:)); + s = sum(r > cumprob(1:end-1)) + 1; + [xndx,yndx] = ind2sub(size(dist),s); + end + + end + + + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Springlayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +classdef Springlayout < Gvizlayout +% A layout that also uses graphviz but calls twopi instead of neato to +% to display the graph like a tree. +% +% Matthew Dunham +% University of British Columbia +% http://www.cs.ubc.ca/~mdunham/ + methods + function obj = Springlayout(name) + if(nargin < 1) + obj.name = 'springlayout'; + else + obj.name = name; + end + load glicons; + obj.image = icons.spring; + obj.shortDescription = 'Spring Layout (GraphViz)'; + end + + function available = isavailable(obj) + % Make sure graphViz is available. + available = Gvizlayout.queryGviz('fdp'); + if(not(available)) + fprintf('Please install or upgrade graphViz\n'); + end + end + + end + + methods(Access = 'protected') + + function callGraphViz(obj) + % Call GraphViz to determine an optimal layout. Write this layout in + % layout.dot for later parsing. + err = system(['fdp -Tdot -Gmaxiter=5000 -Gstart=7 -o ',obj.layoutFile,' ',obj.adjFile]); + if(err),error('Sorry, unknown GraphViz failure, try another layout'); end + end + + + + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/layouts/Treelayout.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +classdef Treelayout < Gvizlayout +% A layout that also uses graphviz but calls dot instead of neato to +% to display the graph like a tree. +% +% Matthew Dunham +% University of British Columbia +% http://www.cs.ubc.ca/~mdunham/ + + methods + function obj = Treelayout(name) + if(nargin < 1) + obj.name = 'Treelayout'; + else + obj.name = name; + end + %obj.setImage([0,153,102]/255); + load glicons; + obj.image = icons.tree; + obj.shortDescription = 'Tree Layout (GraphViz)'; + end + + function available = isavailable(obj) + % Make sure graphViz is available. + available = Gvizlayout.queryGviz('dot'); + if(not(available)) + fprintf('Please install or upgrade graphViz\n'); + end + end + + end + + methods(Access = 'protected') + + function callGraphViz(obj) + % Call GraphViz to determine an optimal layout. Write this layout in + % layout.dot for later parsing. + err = system(['dot -Tdot -Gmaxiter=5000 -Gstart=7 -o ',obj.layoutFile,' ',obj.adjFile]); + if(err),error('Sorry, unknown GraphViz failure, try another layout'); end + end + + + + end + + + + + + + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/util/addtosystempath.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function addtosystempath(p) +% Add p to the system path. If p is already on the path, this does nothing. +% Note, this change only persists for the duration of the Matlab session. +% Do not include path delimiters like ; or : . +% Example: +% addtosystempath('C:\Users\matt\bin'); + + if isempty(getenv('PATH')) || all(cellfun(@(c)isempty(c),strfind(tokenize(getenv('PATH'),pathsep()),p))); + setenv('PATH', [p, pathsep(), getenv('PATH'),]); + end + +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/util/adj2pajek2.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,86 @@ +function adj2pajek2(adj,filename, varargin) +% ADJ2PAJEK2 Converts an adjacency matrix representation to a Pajek .net read format +% adj2pajek2(adj, filename-stem, 'argname1', argval1, ...) +% +% Set A(i,j)=-1 to get a dotted line +% +% Optional arguments +% +% nodeNames - cell array, defaults to {'v1','v2,...} +% shapes - cell array, defaults to {'ellipse','ellipse',...} +% Choices are 'ellipse', 'box', 'diamond', 'triangle', 'cross', 'empty' +% partition - vector of integers, defaults to [1 1 ... 1] +% This will automatically color-code the vertices by their partition +% +% Run pajek (available from http://vlado.fmf.uni-lj.si/pub/networks/pajek/) +% Choose File->Network->Read from the menu +% Then press ctrl-G (Draw->Draw) +% Optional: additionally load the partition file then press ctrl-P (Draw->partition) +% +% Examples +% A=zeros(5,5);A(1,2)=-1;A(2,1)=-1;A(1,[3 4])=1;A(2,5)=1; +% adj2pajek2(A,'foo') % makes foo.net +% +% adj2pajek2(A,'foo','partition',[1 1 2 2 2]) % makes foo.net and foo.clu +% +% adj2pajek2(A,'foo',... +% 'nodeNames',{'TF1','TF2','G1','G2','G3'},... +% 'shapes',{'box','box','ellipse','ellipse','ellipse'}); +% +% +% The file format is documented on p68 of the pajek manual +% and good examples are on p58, p72 +% +% Written by Kevin Murphy, 30 May 2007 +% Based on adj2pajek by Gergana Bounova +% http://stuff.mit.edu/people/gerganaa/www/matlab/routines.html +% Fixes a small bug (opens files as 'wt' instead of 'w' so it works in windows) +% Also, simplified her code and added some features. + +N = length(adj); +for i=1:N + nodeNames{i} = strcat('"v',num2str(i),'"'); + shapes{i} = 'ellipse'; +end + +[nodeNames, shapes, partition] = process_options(varargin, ... + 'nodeNames', nodeNames, 'shapes', shapes, 'partition', []); + +if ~isempty(partition) + fid = fopen(sprintf('%s.clu', filename),'wt','native'); + fprintf(fid,'*Vertices %6i\n',N); + for i=1:N + fprintf(fid, '%d\n', partition(i)); + end + fclose(fid); +end + +fid = fopen(sprintf('%s.net', filename),'wt','native'); + +fprintf(fid,'*Vertices %6i\n',N); +for i=1:N + fprintf(fid,'%3i %s %s\n', i, nodeNames{i}, shapes{i}); +end + +%fprintf(fid,'*Edges\n'); +fprintf(fid,'*Arcs\n'); % directed +for i=1:N + for j=1:N + if adj(i,j) ~= 0 + fprintf(fid,' %4i %4i %2i\n',i,j,adj(i,j)); + end + end +end +fclose(fid) + + +if 0 +adj2pajek2(A,'foo',... + 'nodeNames',{'TF1','TF2','G1','G2','G3'},... + 'shapes',{'box','box','ellipse','ellipse','ellipse'}); + +N = 100; part = ones(1,N); part(intersect(reg.tfidxTest,1:N))=2; +G = reg.Atest(1:N, 1:N)'; +adj2pajek2(G, 'Ecoli100', 'partition', part) +end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/util/findString.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function idx = findString(str, cellArray,ignoreCase) +% Returns a boolean matrix the same size as cellArray with true everywhere +% the corresponding cell holds the specified string, str. + + if nargin < 3, ignoreCase = false; end + if ignoreCase, fn = @(c)strcmpi(c,str); else fn = @(c)strcmp(c,str); end + idx = cellfun(fn,cellArray); +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/util/graphViz4MatlabNode.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,298 @@ +classdef graphViz4MatlabNode < dynamicprops & hgsetget +% This class represents a drawable node in an arbitrary graph. +% +% Public properties can be set using the standard Matlab set method as in +% set(node,'curvature',[0,0],'linesytle','--','linecolor','g'). Call +% node.redraw to redraw it after changing properties. +% +% Nodes are not really designed to live on their own, they should be +% aggregated into a graph object responsible for layout. +% +% Matthew Dunham +% University of British Columbia +% http://www.cs.ubc.ca/~mdunham/ + + properties + label; % displayed name for the node + splitLabel; % label split in the middle + useFullLabel = false; % if true, the full non-split label is used regardles of how long it is. + description = ''; % text displayed when you click on the node + curvature = [1 1]; % curvature of the node, [1,1] = circle + lineStyle = '-'; % line style for the node + lineWidth = 2; % line wdth for the node + inedges = []; % indices of edges in + outedges = []; % indices of edges out + fontSize = 12; % The font size for the node + showFullLabel = false; % If true, node labels are not split onto multiple lines regardelss of how long + end + + properties + % Color properties + lineColor = 'k'; % line color for the node + selectedColor = [1 1 0.7]; % face color when selected with mouse + faceColor = [1 1 0.8]; % face color when not shaded + shadedColor = 'r'; % color when shaded, call shade() to shade + textColor = 'k'; % label's text color + containingGraph = []; % containing graph object + + end + + properties(GetAccess = 'public', SetAccess = 'protected') + % Read only properties + xpos = 0; % x-coordinate of node center relative to parent axes + ypos = 0; % y-coordinate of node center relative to parent axes + isvisible = false; % true iff the node is being displayed + isshaded = false; % is the node shaded or not? + width = 1; % width in data units + height = 1; % height in data units + end + + properties(GetAccess = 'public', SetAccess = 'protected') + % Handles to underlying Matlab graphics objects + rechandle = []; % handle to the underlying rectangle object + labelhandle = []; % handle to the underlying text object + parent = []; % handle to the parent axes object + isselected = false; % true iff, the node has been selected + end + + methods + + function obj = graphViz4MatlabNode(label) + % Node Constructor + obj.label = label; + obj.setSplitLabel(label); + + end + + function draw(obj,parent) + % Draw the node on the specified parent axes. If no parent is + % specified, the current axis is used. + if(obj.isvisible) + warning('GRAPHNODE:draw',['Node ',obj.label,' is already drawn, call redraw().']); + return; + end + if(nargin < 2) + if(isempty(obj.parent) || ~ishandle(obj.parent)) + obj.parent = gca; + end + else + obj.parent = parent; + end + obj.drawNode(); + obj.setText(); + obj.isvisible = true; + end + + function redraw(obj) + % Redraw the node, (must be called after node properties are + % changed). + if(obj.isvisible), obj.erase;end + obj.draw(); + end + + function erase(obj) + % Erase the node but do not delete it + if(~obj.isvisible) + warning('GRAPHNODE:erase',['Node ',obj.label,' is already erased']); + return; + end + delete(obj.rechandle); + delete(obj.labelhandle); + obj.rechandle = []; + end + + function shade(obj,color) + % Shade the node the specified color. The default color is used if + % none given. + obj.isshaded = true; + if(nargin == 2) + obj.shadedColor = color; + end + if(obj.isvisible) + set(obj.rechandle,'FaceColor',obj.shadedColor); + end + end + + function unshade(obj) + % Unshade the node + obj.isshaded = false; + if(obj.isvisible) + set(obj.rechandle,'FaceColor',obj.faceColor); + end + end + + function resize(obj,width,height) + % Resize the node by the specified proportion + if(nargin < 3) + if(~isempty(obj.containingGraph)) + height = width; + end + end + obj.width = width; + obj.height = height; + if(obj.isvisible), obj.redraw; end + end + + function move(obj,x,y) + % Move the node's center to the new x,y coordinates, (relative to + % the parent axes.) + obj.xpos = x; obj.ypos = y; + if(obj.isvisible),obj.redraw;end + end + + function select(obj) + % Call this function to set the node in a selected state. + obj.isselected = true; + if(obj.isvisible) + set(obj.rechandle,'faceColor',obj.selectedColor); + end + end + + + function deselect(obj) + % Call this function to deselect the node. + obj.isselected = false; + if(obj.isvisible) + obj.redraw; + end + end + + end % end of public methods + + methods(Access = 'protected') + + function nodePressed(obj,varargin) + % This function is called whenever the node is pressed. + if(~isempty(obj.containingGraph)) + obj.containingGraph.nodeSelected(obj); + end + end + + function nodeDeleted(obj) + % This function is called whenver the node is deleted, (perhaps + % because the figure window was closed for instance). + obj.isvisible = false; + obj.parent = []; + end + + function drawNode(obj) + % Draw the actual node + recxpos = obj.xpos - obj.width/2; + recypos = obj.ypos - obj.height/2; + lineColor = obj.lineColor; + lineWidth = obj.lineWidth; + if(obj.isselected) + color = obj.selectedColor; + lineColor = 'r'; + lineWidth = 1.5*lineWidth; + elseif(obj.isshaded) + color = obj.shadedColor; + else + color = obj.faceColor; + end + obj.rechandle = rectangle(... + 'Parent' ,obj.parent ,... + 'Position' ,[recxpos,recypos,obj.width,obj.height] ,... + 'Curvature' ,obj.curvature ,... + 'LineWidth' , lineWidth ,... + 'LineStyle' ,obj.lineStyle ,... + 'EdgeColor' ,lineColor ,... + 'faceColor' ,color ,... + 'DisplayName' ,obj.label ,... + 'Tag' ,obj.label ,... + 'ButtonDownFcn',@obj.nodePressed ,... + 'UserData' ,obj ,... + 'DeleteFcn' ,@(varargin)obj.nodeDeleted() ); + end + + function setText(obj) + % Draw the node's label + if((length(obj.label) < 10) || obj.useFullLabel || obj.showFullLabel) + label = obj.label; + else + label = obj.splitLabel; + end + obj.labelhandle = text(obj.xpos,obj.ypos,label ,... + 'FontUnits' , 'points' ,... + 'HitTest' , 'off' ,... + 'FontWeight' , 'demi' ,... + 'Margin' , 0.01 ,... + 'HorizontalAlignment' , 'center' ,... + 'BackGroundColor' , 'none' ,... + 'Selected' , 'off' ,... + 'VerticalAlignment' , 'middle' ,... + 'LineStyle' , 'none' ,... + 'FontSize' , obj.fontSize ,... + 'Color' , obj.textColor ); + if(obj.useFullLabel) + set(obj.labelhandle,'BackgroundColor',obj.selectedColor,'Margin',6,'EdgeColor','k','LineStyle','-'); + end + end + + function resizeText(obj) + % Resize the text to fill the node (too slow for large graphs) + fontsize = obj.maxFontSize; + set(obj.labelhandle,'FontSize',fontsize); + extent = get(obj.labelhandle,'Extent'); + + while((extent(1) < (obj.xpos - obj.width/2)) || (extent(2)+(extent(4)) > (obj.ypos + obj.height/2))) + fontsize = 0.95*fontsize; + set(obj.labelhandle,'FontSize',fontsize); + extent = get(obj.labelhandle,'Extent'); + end + end + + function setSplitLabel(obj,label) + + obj.splitLabel = splitString(label,8,10); + + end + + end % end of protected methods + +end % end of graphnode class + + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function S = splitString(varargin) +% Split a string into multiple lines based on camel case. +% +% Inputs +% +% '-S' the string to split +% '-minSize' does not split at all if length(S) < minSize +% '-maxSize' splits no matter what, (even if no camel case change) if length(S) > maxSize +% '-center' if true, [default], the string is center justified +% '-cellMode' if true, a cell array of strings is returned, instead of a char array. + + [S,minSize,maxSize,cellMode,center] = processArgs(varargin,'*+-S','','-minSize',8,'-maxSize',10,'-cellMode',false,'-center',true); + + S = splitInTwo(S); + if center + S = strjust(S,'center'); + end + if cellMode + S = cellstr(S); + end + + function str = splitInTwo(str) + % recursively split a string into two based on camel case + isupper = isstrprop(str(2:end),'upper'); + if(size(str,2) >= minSize && any(isupper)) + first = find(isupper); first = first(1); + top = str(1:first); + bottom = str(first+1:end); + str = strvcat(splitInTwo(top),splitInTwo(bottom)); %#ok + elseif(size(str,2) > maxSize) + top = [str(1:floor(length(str)/2)),'-']; + bottom = str(floor(length(str)/2)+1:end); + str = strvcat(splitInTwo(top),splitInTwo(bottom)); %#ok + end + end +end + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/util/processArgs.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,346 @@ +function varargout = processArgs(userArgs, varargin) +% Process function arguments, allowing args to be passed by +% the user either as name/value pairs, or positionally, or both. +% +% This function also provides optional enforcement of required inputs, and +% optional type checking. Argument names must start with the '-' +% character and not precede any positional arguments. +% +% Matthew Dunham +% University of British Columbia +% Last updated January 14th, 2010 +% +%% USAGE: +% +% [out1,out2,...,outN] = processArgs(userArgs ,... +% '-name1' , default1 ,... +% '-name2' , default2 ,... +% '-nameN' , defaultN ); +% +% The 'userArgs' input is a cell array and in normal usage is simply the +% varargin cell array from the calling function. It contains 0 to N values +% or 0 to N name/value pairs. It may also contain a combination of +% positional and named arguments as long as no named argument precedes a +% positional one. +% +% Note, this function is CASE INSENSITIVE. +% +%% ENFORCING REQUIRED ARGUMENTS +% +% To ensure that certain arguments are passed in, (and error if not), add a +% '*' character to the corresponding name as in +% +% [out1,...] = processArgs(userArgs,'*-name1',default1,... +% +% +% Providing empty values, (e.g. {},[],'') for required arguments also +% errors unless these were explicitly passed as named arguments. +%% TYPE CHECKING +% +% To enforce that the input type, (class) is the same type as the default +% value, add a '+' character to the corresponding name as in +% +% [out1,...] = processArgs(userArgs,'+-name1',default1,... +% +% '+' and '*' can be combined as in +% +% [out1,...] = processArgs(userArgs,'*+-name1',default1,... +% +% or equivalently +% +% [out1,...] = processArgs(userArgs,'+*-name1',default1,... +%% OTHER CONSIDERATIONS +% +% If the user passes in arguments in positional mode, and uses [], {}, or +% '' as place holders, the default values are used in their place. When the +% user passes in values via name/value pairs, this behavior does not +% occur; the explicit value the user specified, (even if [], {}, '') is +% always used. +% +%% ADVANCED +% The programmer must specify the same number of output arguments as +% possible input arguments, OR exactly one output. If exactly one output +% is used, the outputs are all bundled into a single cell array and +% returned with each arg value preceded by its name. This can be useful for +% relaying some or all of the arguments to subsequent functions as is done +% with the extractArgs function. +% +%% DEPENDENCIES +% +% None +% +%% EXAMPLES +% These are all valid usages. Note that here the first and second arguments +% are required and the types of the second and fourth arguments are +% checked. +% +% outerFunction(obj,... +% '-first' , 1 ,... +% '-second' , MvnDist() ,... +% '-third' , 22 ,... +% '-fourth' , 10 ); +% +% outerFunction(obj,'-fourth',3,'-second',MvnDist(),'-first',12); +% outerFunction(obj,1,MvnDist(),3); +% outerFunction(obj,1,MvnDist(),3,[]); +% outerFunction(obj,'-first',1,'-second',DiscreteDist(),'-third',[]); +% outerFunction(obj,1,MvnDist(),'-fourth',10); +% +% +% function [a,b,c,d] = outerFunction(obj,varargin) +% [a,b,c,d] = processArgs(varargin ,... +% '*-first' , [] ,... +% '*+-second' , MvnDist() ,... +% '-third' , 18 ,... +% '+-fourth' , 23 ); +% end +%% CONSTANTS + PREFIX = '-'; % prefix that must precede the names of arguments. + REQ = '*'; % require the argument + TYPE = '+'; % check the type of the arg against the default type + + % set to true for more exhaustive error checking or false for faster + % execution. + FULL_ERROR_CHECK = true; + +%% PROCESS VARARGIN - PASSED BY PROGRAMMER + + %% Check Initial Inputs + if ~iscell(userArgs) ,throwAsCaller(MException('PROCESSARGS:noUserArgs','PROGRAMMER ERROR - you must pass in the user''s arguments in a cell array as in processArgs(varargin,''-name'',val,...)'));end + if isempty(varargin) ,throwAsCaller(MException('PROCESSARGS:emptyVarargin','PROGRAMMER ERROR - you have not passed in any name/default pairs to processArgs')); end + %% Extract Programmer Argument Names and Markers + progArgNames = varargin(1:2:end); + maxNargs = numel(progArgNames); + required = cellfun(@(c)any(REQ==c(1:min(3,end))),progArgNames); + typecheck = cellfun(@(c)any(TYPE==c(1:min(3,end))),progArgNames); + if ~iscellstr(progArgNames) ,throwAsCaller(MException('PROCESSARGS:notCellStr ',sprintf('PROGRAMMER ERROR - you must pass to processArgs name/default pairs'))); end + %% Remove * and + Markers + try + progArgNames(required | typecheck) = ... + cellfuncell(@(c)c(c~=REQ & c~=TYPE) ,... + progArgNames(required | typecheck)); + catch ME + if strcmp(ME.identifier,'MATLAB:UndefinedFunction') + err = MException('PROCESSARGS:missingExternalFunctions','ProcessArgs requires the following external functions available in PMTK2: catString, cellfuncell, interweave, isprefix. Please add these to your MATLAB path.'); + throw(addCause(err,ME)); + else + rethrow(ME); + end + end + %% Set Default Values + defaults = varargin(2:2:end); + varargout = defaults; + %% Check Programmer Supplied Arguments + if mod(numel(varargin),2) ,throwAsCaller(MException('PROCESSARGS:oddNumArgs',sprintf('PROGRAMMER ERROR - you have passed in an odd number of arguments to processArgs, which requires name/default pairs'))); end + if any(cellfun(@isempty,progArgNames)) ,throwAsCaller(MException('PROCESSARGS:emptyStrName ',sprintf('PROGRAMMER ERROR - empty-string names are not allowed')));end + if nargout ~= 1 && nargout ~= maxNargs ,throwAsCaller(MException('PROCESSARGS:wrongNumOutputs',sprintf('PROGRAMMER ERROR - processArgs requires the same number of output arguments as named/default input pairs'))); end + if ~isempty(PREFIX) && ... + ~all(cellfun(@(c)~isempty(c) &&... + c(1)==PREFIX,progArgNames)) + throwAsCaller(MException('PROCESSARGS:missingPrefix',sprintf('PROGRAMMER ERROR - processArgs requires that each argument name begin with the prefix %s',PREFIX))); + end + if FULL_ERROR_CHECK && ... + numel(unique(progArgNames)) ~= numel(progArgNames) ,throwAsCaller(MException('PROCESSARGS:duplicateName',sprintf('PROGRAMMER ERROR - you can not use the same argument name twice')));end +%% PROCESS USERARGS + + %% Error Check User Args + if numel(userArgs) == 0 && nargout > 1 + if any(required) ,throwAsCaller(MException('PROCESSARGS:missingReqArgs',sprintf('The following required arguments were not specified:\n%s',catString(progArgNames(required))))); + else return; + end + end + if FULL_ERROR_CHECK + % slow, but helpful in transition from process_options to processArgs + % checks for missing '-' + if ~isempty(PREFIX) + userstrings = lower(... + userArgs(cellfun(@(c)ischar(c) && size(c,1)==1,userArgs))); + problem = ismember(... + userstrings,cellfuncell(@(c)c(2:end),progArgNames)); + if any(problem) + if sum(problem) == 1, warning('processArgs:missingPrefix','The specified value ''%s'', matches an argument name, except for a missing prefix %s. It will be interpreted as a value, not a name.',userstrings{problem},PREFIX) + else warning('processArgs:missingPrefix','The following values match an argument name, except for missing prefixes %s:\n\n%s\n\nThey will be interpreted as values, not names.',PREFIX,catString(userstrings(problem))); + end + end + end + end + %% Find User Arg Names + userArgNamesNDX = find(cellfun(@(c)ischar(c) &&... + ~isempty(c) &&... + c(1)==PREFIX,userArgs)); + %% Check User Arg Names + if ~isempty(userArgNamesNDX) && ... + ~isequal(userArgNamesNDX,userArgNamesNDX(1):2:numel(userArgs)-1) + if isempty(PREFIX), throwAsCaller(MException('PROCESSARGS:missingVal',sprintf('\n(1) every named argument must be followed by its value\n(2) no positional argument may be used after the first named argument\n'))); + else throwAsCaller(MException('PROCESSARGS:posArgAfterNamedArg',sprintf('\n(1) every named argument must be followed by its value\n(2) no positional argument may be used after the first named argument\n(3) every argument name must begin with the ''%s'' character\n(4) values cannot be strings beginning with the %s character\n',PREFIX,PREFIX))); + end + end + if FULL_ERROR_CHECK && ... + ~isempty(userArgNamesNDX) && ... + numel(unique(userArgs(userArgNamesNDX))) ~= numel(userArgNamesNDX) + throwAsCaller(MException('PROCESSARGS:duplicateUserArg',sprintf('You have specified the same argument name twice'))); + end + %% Extract Positional Args + argsProvided = false(1,maxNargs); + if isempty(userArgNamesNDX) + positionalArgs = userArgs; + elseif userArgNamesNDX(1) == 1 + positionalArgs = {}; + else + positionalArgs = userArgs(1:userArgNamesNDX(1)-1); + end + %% Check For Too Many Inputs + if numel(positionalArgs) + numel(userArgNamesNDX) > maxNargs ,throwAsCaller(MException('PROCESSARGS:tooManyInputs',sprintf('You have specified %d too many arguments to the function',numel(positionalArgs)+numel(userArgNamesNDX)- maxNargs)));end + %% Process Positional Args + for i=1:numel(positionalArgs) + % don't overwrite default value if positional arg is + % empty, i.e. '',{},[] + if ~isempty(userArgs{i}) + argsProvided(i) = true; + if typecheck(i) && ~isa(userArgs{i},class(defaults{i})) ,throwAsCaller(MException('PROCESSARGS:argWrongType',sprintf('Argument %d must be of type %s',i,class(defaults{i})))); end + varargout{i} = userArgs{i}; + end + end + %% Process Named Args + userArgNames = userArgs(userArgNamesNDX); + userProgMap = zeros(1,numel(userArgNames)); + usedProgArgNames = false(1,numel(progArgNames)); + for i=1:numel(userArgNames) + for j=1:numel(progArgNames) + if ~usedProgArgNames(j) && strcmpi(userArgNames{i},progArgNames{j}) + userProgMap(i) = j; + usedProgArgNames(j) = true; + break; + end + end + end + %% Error Check User Args + if any(~userProgMap) ,throwAsCaller(MException('PROCESSARGS:invalidArgNames',sprintf('The following argument names are invalid: %s',catString(userArgNames(userProgMap == 0),' , ')))); end + if any(userProgMap <= numel(positionalArgs)) ,throwAsCaller(MException('PROCESSARGS:bothPosAndName' ,sprintf('You cannot specify an argument positionally, and by name in the same function call.')));end + %% Extract User Values + userValues = userArgs(userArgNamesNDX + 1); + %% Type Check User Args + if any(typecheck) + for i=1:numel(userArgNamesNDX) + if typecheck(userProgMap(i)) && ... + ~isa(userArgs{userArgNamesNDX(i)+1},... + class(defaults{userProgMap(i)})) + throwAsCaller(MException('PROCESSARGS:wrongType',sprintf('Argument %s must be of type %s',userArgs{userArgNamesNDX(i)},class(defaults{userProgMap(i)})))); + end + end + end + varargout(userProgMap) = userValues; + %% Check Required Args + argsProvided(userProgMap) = true; + if any(~argsProvided & required) ,throwAsCaller(MException('PROCESSARGS:emptyVals',sprintf('The following required arguments were either not specified, or were given empty values:\n%s',catString(progArgNames(~argsProvided & required))))); end + %% Relay Mode + if nargout == 1 && (numel(varargin) > 2 || (numel(varargin) == 1 && isprefix('-',varargin{1}))) + varargout = {interweave(progArgNames,varargout)}; + end + +end + + +function s = catString(c,delim) +% Converts a cell array of strings to a single string, (i.e. single-row +% character array). The specified delimiter, delim, is added between each +% entry. Include any spaces you want in delim. If delim is not specified, +% ', ' is used instead. If c is already a string, it is just returned. If c +% is empty, s = ''. +% +% EXAMPLE: +% +% s = catString({'touch /tmp/foo';'touch /tmp foo2';'mkdir /tmp/test'},' && ') +% s = +% touch /tmp/foo && touch /tmp foo2 && mkdir /tmp/test + + if nargin == 0; s = ''; return; end + if ischar(c), s=c; + if strcmp(s,','),s = '';end + return; + end + if isempty(c),s=''; return;end + if nargin < 2, delim = ', '; end + s = ''; + for i=1:numel(c) + s = [s, rowvec(c{i}),rowvec(delim)]; %#ok + end + s(end-numel(delim)+1:end) = []; + if strcmp(s,','),s = '';end +end + + +function out = cellfuncell(fun, C, varargin) + out = cellfun(fun, C, varargin{:},'UniformOutput',false); +end + +function C = interweave(A,B) +% If A, B are two cell arrays of length N1, N2, C is a cell array of length +% N1 + N2 where where C(1) = A(1), C(2) = B(1), C(3) = A(2), C(4) = B(2), ... etc +% Note, C is always a row vector. If one cell array is longer than the +% other the remaining elements of the longer cell array are added to the +% end of C. A and B are first converted to column vectors. + A = A(:); B = B(:); + C = cell(length(A)+length(B),1); + counter = 1; + while true + if ~isempty(A) + C(counter) = A(1); A(1) = []; + counter = counter + 1; + end + if ~isempty(B) + C(counter) = B(1); B(1) = []; + counter = counter + 1; + end + if isempty(A) && isempty(B) + break; + end + end + C = C'; +end + +function p = isprefix(short,long) +% ISPREFIX Tests if the first arg is a prefix of the second. +% The second arg may also be a cell array of strings, in which case, each +% is tested. CASE SENSITIVE! +% +% If the second argument is not a string, p = false, it does not error. +% +% EXAMPLES: +% +% isprefix('foo','foobar') +% ans = +% 1 +% +%isprefix('test_',{'test_MvnDist','test_DiscreteDist','UnitTest'}) +%ans = +% 1 1 0 + error(nargchk(2,2,nargin)); + if ischar(long) + p = strncmp(long,short,length(short)); + elseif iscell(long) + p = cellfun(@(c)isprefix(short,c),long); + else + p = false; + end +end + + +function x = rowvec(x) + x = x(:)'; +end + +function x = colvec(x) + x = x(:); +end + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/util/setupPath.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +function gvizPath = setupPath() +% Attempt to add GraphViz to the system path, (temporariliy within the +% matlab session only). + gvizPath = 'unknown'; + if ~ispc() + return + end + try %#ok + gvizPath = winqueryreg('HKEY_LOCAL_MACHINE','SOFTWARE\AT&T Research Labs\Graphviz', 'InstallPath'); + addtosystempath(fullfile(gvizPath, 'bin')); + end +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/graphViz4Matlab/util/tokenize.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +function tokens = tokenize(str,delimiter) +% tokenize a string + if(nargin < 2) + delimiter = ' ' ; + end + tokens = textscan(str,'%s','delimiter',delimiter, 'bufsize', 100000); + tokens = tokens{:}; +end \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/arith.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,106 @@ +/* $Id: arith.h,v 1.16 2009/06/03 01:10:51 ellson Exp $ $Revision: 1.16 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +/* geometric functions (e.g. on points and boxes) with application to, but + * no specific dependance on graphs */ + +#ifndef GV_ARITH_H +#define GV_ARITH_H + +/* for sincos */ +#ifndef _GNU_SOURCE +#define _GNU_SOURCE 1 +#endif + +#include <limits.h> +#ifdef HAVE_VALUES_H +#include <values.h> +#endif +#include <math.h> + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef MIN +#undef MIN +#endif +#define MIN(a,b) ((a)<(b)?(a):(b)) + +#ifdef MAX +#undef MAX +#endif +#define MAX(a,b) ((a)>(b)?(a):(b)) + +#ifdef ABS +#undef ABS +#endif +#define ABS(a) ((a) >= 0 ? (a) : -(a)) + +#define AVG(a,b) ((a + b) / 2) +#define SGN(a) (((a)<0)? -1 : 1) +#define CMP(a,b) (((a)<(b)) ? -1 : (((a)>(b)) ? 1 : 0)) + +#ifndef INT_MAX +#define INT_MAX ((int)(~(unsigned)0 >> 1)) +#endif + +#ifndef INT_MIN +#define INT_MIN (-INT_MAX - 1) +#endif + +#ifndef MAXSHORT +#define MAXSHORT (0x7fff) +#endif + +#ifndef MAXDOUBLE +#define MAXDOUBLE 1.7976931348623157e+308 +#endif + +#ifndef MAXFLOAT +#define MAXFLOAT ((float)3.40282347e+38) +#endif + +#ifdef BETWEEN +#undef BETWEEN +#endif +#define BETWEEN(a,b,c) (((a) <= (b)) && ((b) <= (c))) + +#ifndef M_PI +#define M_PI 3.14159265358979323846 +#endif + +#ifndef SQRT2 +#define SQRT2 1.41421356237309504880 +#endif + +#define ROUND(f) ((f>=0)?(int)(f + .5):(int)(f - .5)) +#define RADIANS(deg) ((deg)/180.0 * M_PI) +#define DEGREES(rad) ((rad)/M_PI * 180.0) + +#define SQR(a) ((a) * (a)) + +#ifdef HAVE_SINCOS + extern void sincos(double x, double *s, double *c); +#else +# define sincos(x,s,c) *s = sin(x); *c = cos(x) +#endif + +#ifdef __cplusplus +} +#endif + +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/cdt.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,261 @@ +/* $Id: cdt.h,v 1.11 2009/07/24 21:10:19 arif Exp $ $Revision: 1.11 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef _CDT_H +#define _CDT_H 1 + +/* Public interface for the dictionary library +** +** Written by Kiem-Phong Vo (05/25/96) +*/ + +#define CDT_VERSION 19991101L + +#define Void_t void +#define _ARG_(x) x +#ifndef NIL +#define NIL(type) ((type)0) +#endif + +#include <string.h> + +#if _PACKAGE_ast +# include <ast_std.h> +#elif _BLD_cdt +# include "ast_common.h" +#endif + +#ifdef __cplusplus +extern "C" { +#endif + + typedef struct _dtlink_s Dtlink_t; + typedef struct _dthold_s Dthold_t; + typedef struct _dtdisc_s Dtdisc_t; + typedef struct _dtmethod_s Dtmethod_t; + typedef struct _dtdata_s Dtdata_t; + typedef struct _dt_s Dt_t; + typedef struct _dt_s Dict_t; /* for libdict compatibility */ + typedef struct _dtstat_s Dtstat_t; + typedef Void_t *(*Dtsearch_f) _ARG_((Dt_t *, Void_t *, int)); + typedef Void_t *(*Dtmake_f) _ARG_((Dt_t *, Void_t *, Dtdisc_t *)); + typedef void (*Dtfree_f) _ARG_((Dt_t *, Void_t *, Dtdisc_t *)); + typedef int (*Dtcompar_f) + _ARG_((Dt_t *, Void_t *, Void_t *, Dtdisc_t *)); + typedef unsigned int (*Dthash_f) _ARG_((Dt_t *, Void_t *, Dtdisc_t *)); + typedef Void_t *(*Dtmemory_f) + _ARG_((Dt_t *, Void_t *, size_t, Dtdisc_t *)); + typedef int (*Dtevent_f) _ARG_((Dt_t *, int, Void_t *, Dtdisc_t *)); + + struct _dtlink_s { + Dtlink_t *right; /* right child */ + union { + unsigned int _hash; /* hash value */ + Dtlink_t *_left; /* left child */ + } hl; + }; + +/* private structure to hold an object */ + struct _dthold_s { + Dtlink_t hdr; /* header */ + Void_t *obj; /* user object */ + }; + +/* method to manipulate dictionary structure */ + struct _dtmethod_s { + Dtsearch_f searchf; /* search function */ + int type; /* type of operation */ + }; + +/* stuff that may be in shared memory */ + struct _dtdata_s { + int type; /* type of dictionary */ + Dtlink_t *here; /* finger to last search element */ + union { + Dtlink_t **_htab; /* hash table */ + Dtlink_t *_head; /* linked list */ + } hh; + int ntab; /* number of hash slots */ + int size; /* number of objects */ + int loop; /* number of nested loops */ + }; + +/* structure to hold methods that manipulate an object */ + struct _dtdisc_s { + int key; /* where the key begins in an object */ + int size; /* key size and type */ + int link; /* offset to Dtlink_t field */ + Dtmake_f makef; /* object constructor */ + Dtfree_f freef; /* object destructor */ + Dtcompar_f comparf; /* to compare two objects */ + Dthash_f hashf; /* to compute hash value of an object */ + Dtmemory_f memoryf; /* to allocate/free memory */ + Dtevent_f eventf; /* to process events */ + }; + +/* the dictionary structure itself */ + struct _dt_s { + Dtsearch_f searchf; /* search function */ + Dtdisc_t *disc; /* method to manipulate objs */ + Dtdata_t *data; /* sharable data */ + Dtmemory_f memoryf; /* function to alloc/free memory */ + Dtmethod_t *meth; /* dictionary method */ + int type; /* type information */ + int nview; /* number of parent view dictionaries */ + Dt_t *view; /* next on viewpath */ + Dt_t *walk; /* dictionary being walked */ + }; + +/* structure to get status of a dictionary */ + struct _dtstat_s { + int dt_meth; /* method type */ + int dt_size; /* number of elements */ + int dt_n; /* number of chains or levels */ + int dt_max; /* max size of a chain or a level */ + int *dt_count; /* counts of chains or levels by size */ + }; + +/* supported storage methods */ +#define DT_SET 0000001 /* set with unique elements */ +#define DT_BAG 0000002 /* multiset */ +#define DT_OSET 0000004 /* ordered set (self-adjusting tree) */ +#define DT_OBAG 0000010 /* ordered multiset */ +#define DT_LIST 0000020 /* linked list */ +#define DT_STACK 0000040 /* stack */ +#define DT_QUEUE 0000100 /* queue */ +#define DT_METHODS 0000177 /* all currently supported methods */ + +/* asserts to dtdisc() */ +#define DT_SAMECMP 0000001 /* compare methods equivalent */ +#define DT_SAMEHASH 0000002 /* hash methods equivalent */ + +/* types of search */ +#define DT_INSERT 0000001 /* insert object if not found */ +#define DT_DELETE 0000002 /* delete object if found */ +#define DT_SEARCH 0000004 /* look for an object */ +#define DT_NEXT 0000010 /* look for next element */ +#define DT_PREV 0000020 /* find previous element */ +#define DT_RENEW 0000040 /* renewing an object */ +#define DT_CLEAR 0000100 /* clearing all objects */ +#define DT_FIRST 0000200 /* get first object */ +#define DT_LAST 0000400 /* get last object */ +#define DT_MATCH 0001000 /* find object matching key */ +#define DT_VSEARCH 0002000 /* search using internal representation */ +#define DT_ATTACH 0004000 /* attach an object to the dictionary */ +#define DT_DETACH 0010000 /* attach an object to the dictionary */ + +/* events */ +#define DT_OPEN 1 /* a dictionary is being opened */ +#define DT_CLOSE 2 /* a dictionary is being closed */ +#define DT_DISC 3 /* discipline is about to be changed */ +#define DT_METH 4 /* method is about to be changed */ + + +#if _BLD_cdt && defined(__EXPORT__) +#define extern __EXPORT__ +#endif +#if !_BLD_cdt && defined(GVDLL) +#define extern __declspec(dllimport) +#endif +#if !_BLD_cdt && defined(__IMPORT__) +#define extern __IMPORT__ +#endif +/*visual studio*/ +#ifdef WIN32_DLL +#ifndef CDT_EXPORTS +#define extern __declspec(dllimport) +#else +#define extern __declspec(dllexport) +#endif +#endif +/*end visual studio*/ + + + extern Dtmethod_t *Dtset; + extern Dtmethod_t *Dtbag; + extern Dtmethod_t *Dtoset; + extern Dtmethod_t *Dtobag; + extern Dtmethod_t *Dtlist; + extern Dtmethod_t *Dtstack; + extern Dtmethod_t *Dtqueue; + +/* compatibility stuff; will go away */ +#ifndef KPVDEL + extern Dtmethod_t *Dtorder; + extern Dtmethod_t *Dttree; + extern Dtmethod_t *Dthash; + extern Dtmethod_t _Dttree; + extern Dtmethod_t _Dthash; + extern Dtmethod_t _Dtlist; + extern Dtmethod_t _Dtqueue; + extern Dtmethod_t _Dtstack; +#endif + +#undef extern +#if _BLD_cdt && defined(__EXPORT__) +#define extern __EXPORT__ +#endif +#if !_BLD_cdt && defined(__IMPORT__) && defined(__EXPORT__) +#define extern __IMPORT__ +#endif + extern Dt_t *dtopen _ARG_((Dtdisc_t *, Dtmethod_t *)); + extern int dtclose _ARG_((Dt_t *)); + extern Dt_t *dtview _ARG_((Dt_t *, Dt_t *)); + extern Dtdisc_t *dtdisc _ARG_((Dt_t * dt, Dtdisc_t *, int)); + extern Dtmethod_t *dtmethod _ARG_((Dt_t *, Dtmethod_t *)); + + extern Dtlink_t *dtflatten _ARG_((Dt_t *)); + extern Dtlink_t *dtextract _ARG_((Dt_t *)); + extern int dtrestore _ARG_((Dt_t *, Dtlink_t *)); + + extern int dtwalk + _ARG_((Dt_t *, int (*)(Dt_t *, Void_t *, Void_t *), Void_t *)); + + extern Void_t *dtrenew _ARG_((Dt_t *, Void_t *)); + + extern int dtsize _ARG_((Dt_t *)); + extern int dtstat _ARG_((Dt_t *, Dtstat_t *, int)); + extern unsigned int dtstrhash _ARG_((unsigned int, Void_t *, int)); + +#undef extern + +#define _DT_(d) ((Dt_t*)(d)) +#define dtvnext(d) (_DT_(d)->view) +#define dtvcount(d) (_DT_(d)->nview) +#define dtvhere(d) (_DT_(d)->walk) +#define dtlink(d,e) (((Dtlink_t*)(e))->right) +#define dtobj(d,e) ((_DT_(d)->disc->link < 0) ? (((Dthold_t*)(e))->obj) : \ + (Void_t*)((char*)(e) - _DT_(d)->disc->link) ) +#define dtfinger(d) (_DT_(d)->data->here ? dtobj((d),_DT_(d)->data->here) : \ + (Void_t*)(0) ) +#define dtfirst(d) (*(_DT_(d)->searchf))((d),(Void_t*)(0),DT_FIRST) +#define dtnext(d,o) (*(_DT_(d)->searchf))((d),(Void_t*)(o),DT_NEXT) +#define dtlast(d) (*(_DT_(d)->searchf))((d),(Void_t*)(0),DT_LAST) +#define dtprev(d,o) (*(_DT_(d)->searchf))((d),(Void_t*)(o),DT_PREV) +#define dtsearch(d,o) (*(_DT_(d)->searchf))((d),(Void_t*)(o),DT_SEARCH) +#define dtmatch(d,o) (*(_DT_(d)->searchf))((d),(Void_t*)(o),DT_MATCH) +#define dtinsert(d,o) (*(_DT_(d)->searchf))((d),(Void_t*)(o),DT_INSERT) +#define dtdelete(d,o) (*(_DT_(d)->searchf))((d),(Void_t*)(o),DT_DELETE) +#define dtattach(d,o) (*(_DT_(d)->searchf))((d),(Void_t*)(o),DT_ATTACH) +#define dtdetach(d,o) (*(_DT_(d)->searchf))((d),(Void_t*)(o),DT_DETACH) +#define dtclear(d) (*(_DT_(d)->searchf))((d),(Void_t*)(0),DT_CLEAR) +/* A linear congruential hash: h*17 + c + 97531 */ +#define dtcharhash(h,c) ((((unsigned int)(h))<<4) + ((unsigned int)(h)) + \ + ((unsigned char)(c)) + 97531 ) +#ifdef __cplusplus +} +#endif +#endif /* _CDT_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/cgraph.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,467 @@ +/* $Id: cgraph.h,v 1.16 2009/07/10 21:10:32 erg Exp $ $Revision: 1.16 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef ATT_GRAPH_H +#define ATT_GRAPH_H + +#include "cdt.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#ifndef FALSE +#define FALSE (0) +#endif +#ifndef TRUE +#define TRUE (!FALSE) +#endif +#ifndef NOT +#define NOT(x) (!(x)) +#endif +#ifndef NIL +#define NIL(type) ((type)0) +#endif +#define NILgraph NIL(Agraph_t*) +#define NILnode NIL(Agnode_t*) +#define NILedge NIL(Agedge_t*) +#define NILsym NIL(Agsym_t*) + +/* forward struct type declarations */ +typedef struct Agtag_s Agtag_t; +typedef struct Agobj_s Agobj_t; /* generic object header */ +typedef struct Agraph_s Agraph_t; /* graph, subgraph (or hyperedge) */ +typedef struct Agnode_s Agnode_t; /* node (atom) */ +typedef struct Agedge_s Agedge_t; /* node pair */ +typedef struct Agdesc_s Agdesc_t; /* graph descriptor */ +typedef struct Agmemdisc_s Agmemdisc_t; /* memory allocator */ +typedef struct Agiddisc_s Agiddisc_t; /* object ID allocator */ +typedef struct Agiodisc_s Agiodisc_t; /* IO services */ +typedef struct Agdisc_s Agdisc_t; /* union of client discipline methods */ +typedef struct Agdstate_s Agdstate_t; /* client state (closures) */ +typedef struct Agsym_s Agsym_t; /* string attribute descriptors */ +typedef struct Agattr_s Agattr_t; /* string attribute container */ +typedef struct Agcbdisc_s Agcbdisc_t; /* client event callbacks */ +typedef struct Agcbstack_s Agcbstack_t; /* enclosing state for cbdisc */ +typedef struct Agclos_s Agclos_t; /* common fields for graph/subgs */ +typedef struct Agrec_s Agrec_t; /* generic runtime record */ +typedef struct Agdatadict_s Agdatadict_t; /* set of dictionaries per graph */ +typedef struct Agedgepair_s Agedgepair_t; /* the edge object */ +typedef struct Agsubnode_s Agsubnode_t; + +/* Header of a user record. These records are attached by client programs +dynamically at runtime. A unique string ID must be given to each record +attached to the same object. Cgraph has functions to create, search for, +and delete these records. The records are maintained in a circular list, +with obj->data pointing somewhere in the list. The search function has +an option to lock this pointer on a given record. The application must +be written so only one such lock is outstanding at a time. */ + +struct Agrec_s { + char *name; + Agrec_t *next; + /* following this would be any programmer-defined data */ +}; + +/* Object tag for graphs, nodes, and edges. While there may be several structs +for a given node or edges, there is only one unique ID (per main graph). */ +struct Agtag_s { + unsigned objtype:2; /* see literal tags below */ + unsigned mtflock:1; /* move-to-front lock, see above */ + unsigned attrwf:1; /* attrs written (parity, write.c) */ + unsigned seq:(sizeof(unsigned) * 8 - 4); /* sequence no. */ + unsigned long id; /* client ID */ +}; + + /* object tags */ +#define AGRAPH 0 /* can't exceed 2 bits. see Agtag_t. */ +#define AGNODE 1 +#define AGOUTEDGE 2 +#define AGINEDGE 3 /* (1 << 1) indicates an edge tag. */ +#define AGEDGE AGOUTEDGE /* synonym in object kind args */ + + /* a generic graph/node/edge header */ +struct Agobj_s { + Agtag_t tag; + Agrec_t *data; +}; + +#define AGTAG(obj) (((Agobj_t*)(obj))->tag) +#define AGTYPE(obj) (AGTAG(obj).objtype) +#define AGID(obj) (AGTAG(obj).id) +#define AGSEQ(obj) (AGTAG(obj).seq) +#define AGATTRWF(obj) (AGTAG(obj).attrwf) +#define AGDATA(obj) (((Agobj_t*)(obj))->data) + +/* This is the node struct allocated per graph (or subgraph). It resides +in the n_dict of the graph. The node set is maintained by libdict, but +transparently to libgraph callers. Every node may be given an optional +string name at its time of creation, or it is permissible to pass NIL(char*) +for the name. */ + +struct Agsubnode_s { /* the node-per-graph-or-subgraph record */ + Dtlink_t seq_link; /* must be first */ + Dtlink_t id_link; + Agnode_t *node; /* the object */ + Dtlink_t *in_id, *out_id; /* by node/ID for random access */ + Dtlink_t *in_seq, *out_seq; /* by node/sequence for serial access */ +}; + +struct Agnode_s { + Agobj_t base; + Agraph_t *root; + Agsubnode_t mainsub; /* embedded for main graph */ +}; + +struct Agedge_s { + Agobj_t base; + Dtlink_t id_link; /* main graph only */ + Dtlink_t seq_link; + Agnode_t *node; /* the endpoint node */ +}; + +struct Agedgepair_s { + Agedge_t out, in; +}; + +struct Agdesc_s { /* graph descriptor */ + unsigned directed:1; /* if edges are asymmetric */ + unsigned strict:1; /* if multi-edges forbidden */ + unsigned no_loop:1; /* if no loops */ + unsigned maingraph:1; /* if this is the top level graph */ + unsigned flatlock:1; /* if sets are flattened into lists in cdt */ + unsigned no_write:1; /* if a temporary subgraph */ + unsigned has_attrs:1; /* if string attr tables should be initialized */ + unsigned has_cmpnd:1; /* if may contain collapsed nodes */ +}; + +/* disciplines for external resources needed by libgraph */ + +struct Agmemdisc_s { /* memory allocator */ + void *(*open) (void); /* independent of other resources */ + void *(*alloc) (void *state, size_t req); + void *(*resize) (void *state, void *ptr, size_t old, size_t req); + void (*free) (void *state, void *ptr); + void (*close) (void *state); +}; + +struct Agiddisc_s { /* object ID allocator */ + void *(*open) (Agraph_t * g); /* associated with a graph */ + long (*map) (void *state, int objtype, char *str, unsigned long *id, + int createflag); + long (*alloc) (void *state, int objtype, unsigned long id); + void (*free) (void *state, int objtype, unsigned long id); + char *(*print) (void *state, int objtype, unsigned long id); + void (*close) (void *state); +}; + +struct Agiodisc_s { + int (*afread) (void *chan, char *buf, int bufsize); + int (*putstr) (void *chan, const char *str); + int (*flush) (void *chan); /* sync */ + /* error messages? */ +}; + +struct Agdisc_s { /* user's discipline */ + Agmemdisc_t *mem; + Agiddisc_t *id; + Agiodisc_t *io; +}; + + /* default resource disciplines */ +#if !defined(_BLD_cgraph) && defined(GVDLL) +#define extern __declspec(dllimport) +#endif + +/*visual studio*/ +#ifdef WIN32_DLL +#ifndef CGRAPH_EXPORTS +#define extern __declspec(dllimport) +#endif +#endif +/*end visual studio*/ + +extern Agmemdisc_t AgMemDisc; +extern Agiddisc_t AgIdDisc; +extern Agiodisc_t AgIoDisc; + +extern Agdisc_t AgDefaultDisc; +#undef extern + +struct Agdstate_s { + void *mem; + void *id; + /* IO must be initialized and finalized outside Cgraph, + * and channels (FILES) are passed as void* arguments. */ +}; + +typedef void (*agobjfn_t) (Agraph_t * g, Agobj_t * obj, void *arg); +typedef void (*agobjupdfn_t) (Agraph_t * g, Agobj_t * obj, void *arg, + Agsym_t * sym); + +struct Agcbdisc_s { + struct { + agobjfn_t ins; + agobjupdfn_t mod; + agobjfn_t del; + } graph, node, edge; +}; + +struct Agcbstack_s { /* object event callbacks */ + Agcbdisc_t *f; /* methods */ + void *state; /* closure */ + Agcbstack_t *prev; /* kept in a stack, unlike other disciplines */ +}; + +struct Agclos_s { + Agdisc_t disc; /* resource discipline functions */ + Agdstate_t state; /* resource closures */ + Dict_t *strdict; /* shared string dict */ + unsigned long seq[3]; /* local object sequence number counter */ + Agcbstack_t *cb; /* user and system callback function stacks */ + unsigned char callbacks_enabled; /* issue user callbacks or hold them? */ + Dict_t *lookup_by_name[3]; + Dict_t *lookup_by_id[3]; +}; + +struct Agraph_s { + Agobj_t base; + Agdesc_t desc; + Dtlink_t link; + Dict_t *n_seq; /* the node set in sequence */ + Dict_t *n_id; /* the node set indexed by ID */ + Dict_t *e_seq, *e_id; /* holders for edge sets */ + Dict_t *g_dict; /* subgraphs - descendants */ + Agraph_t *parent, *root; /* subgraphs - ancestors */ + Agclos_t *clos; /* shared resources */ +}; + + +#if _PACKAGE_ast +/* fine control of object callbacks */ +# if defined(_BLD_cgraph) && defined(__EXPORT__) +# define extern __EXPORT__ +# endif +# if !defined(_BLD_cgraph) && defined(__IMPORT__) +# define extern __IMPORT__ +# endif +#endif + +extern void agpushdisc(Agraph_t * g, Agcbdisc_t * disc, void *state); +extern int agpopdisc(Agraph_t * g, Agcbdisc_t * disc); +extern int agcallbacks(Agraph_t * g, int flag); /* return prev value */ + +/* graphs */ +extern Agraph_t *agopen(char *name, Agdesc_t desc, Agdisc_t * disc); +extern int agclose(Agraph_t * g); +extern Agraph_t *agread(void *chan, Agdisc_t * disc); +extern void agreadline(int); +extern void agsetfile(char *); +extern Agraph_t *agconcat(Agraph_t * g, void *chan, Agdisc_t * disc); +extern int agwrite(Agraph_t * g, void *chan); +extern int agisdirected(Agraph_t * g); +extern int agisundirected(Agraph_t * g); +extern int agisstrict(Agraph_t * g); +extern int agissimple(Agraph_t * g); + +/* nodes */ +extern Agnode_t *agnode(Agraph_t * g, char *name, int createflag); +extern Agnode_t *agidnode(Agraph_t * g, unsigned long id, int createflag); +extern Agnode_t *agsubnode(Agraph_t * g, Agnode_t * n, int createflag); +extern Agnode_t *agfstnode(Agraph_t * g); +extern Agnode_t *agnxtnode(Agraph_t * g, Agnode_t * n); +extern Agnode_t *aglstnode(Agraph_t * g); +extern Agnode_t *agprvnode(Agraph_t * g, Agnode_t * n); + +extern Agsubnode_t *agsubrep(Agraph_t * g, Agnode_t * n); + +/* edges */ +extern Agedge_t *agedge(Agraph_t * g, Agnode_t * t, Agnode_t * h, + char *name, int createflag); +extern Agedge_t *agidedge(Agraph_t * g, Agnode_t * t, Agnode_t * h, + unsigned long id, int createflag); +extern Agedge_t *agsubedge(Agraph_t * g, Agedge_t * e, int createflag); +extern Agedge_t *agfstin(Agraph_t * g, Agnode_t * n); +extern Agedge_t *agnxtin(Agraph_t * g, Agedge_t * e); +extern Agedge_t *agfstout(Agraph_t * g, Agnode_t * n); +extern Agedge_t *agnxtout(Agraph_t * g, Agedge_t * e); +extern Agedge_t *agfstedge(Agraph_t * g, Agnode_t * n); +extern Agedge_t *agnxtedge(Agraph_t * g, Agedge_t * e, Agnode_t * n); + +/* generic */ +extern Agraph_t *agraphof(void* obj); +extern Agraph_t *agroot(void* obj); +extern int agcontains(Agraph_t *, void *); +extern char *agnameof(void *); +extern int agrelabel(void *obj, char *name); /* scary */ +extern int agrelabel_node(Agnode_t * n, char *newname); +extern int agdelete(Agraph_t * g, void *obj); +extern long agdelsubg(Agraph_t * g, Agraph_t * sub); /* could be agclose */ +extern int agdelnode(Agraph_t * g, Agnode_t * arg_n); +extern int agdeledge(Agraph_t * g, Agedge_t * arg_e); +extern int agobjkind(void *); + +/* strings */ +extern char *agstrdup(Agraph_t *, char *); +extern char *agstrdup_html(Agraph_t *, char *); +extern int aghtmlstr(char *); +extern char *agstrbind(Agraph_t * g, char *); +extern int agstrfree(Agraph_t *, char *); +extern char *agstrcanon(char *, char *); +char *agcanonStr(char *str); /* manages its own buf */ + +/* definitions for dynamic string attributes */ +struct Agattr_s { /* dynamic string attributes */ + Agrec_t h; /* common data header */ + Dict_t *dict; /* shared dict to interpret attr field */ + char **str; /* the attribute string values */ +}; + +struct Agsym_s { /* symbol in one of the above dictionaries */ + Dtlink_t link; + char *name; /* attribute's name */ + char *defval; /* its default value for initialization */ + int id; /* its index in attr[] */ + unsigned char kind; /* referent object type */ + unsigned char fixed; /* immutable value */ +}; + +struct Agdatadict_s { /* set of dictionaries per graph */ + Agrec_t h; /* installed in list of graph recs */ + struct { + Dict_t *n, *e, *g; + } dict; +}; + +extern Agsym_t *agattr(Agraph_t * g, int kind, char *name, char *value); +extern Agsym_t *agattrsym(void *obj, char *name); +extern Agsym_t *agnxtattr(Agraph_t * g, int kind, Agsym_t * attr); +extern int agcopyattr(void *oldobj, void *newobj); + +extern void *agbindrec(void *obj, char *name, unsigned int size, + int move_to_front); +extern Agrec_t *aggetrec(void *obj, char *name, int move_to_front); +extern int agdelrec(void *obj, char *name); +extern void aginit(Agraph_t * g, int kind, char *rec_name, int rec_size, + int move_to_front); +extern void agclean(Agraph_t * g, int kind, char *rec_name); + +extern char *agget(void *obj, char *name); +extern char *agxget(void *obj, Agsym_t * sym); +extern int agset(void *obj, char *name, char *value); +extern int agxset(void *obj, Agsym_t * sym, char *value); +extern int agsafeset(void* obj, char* name, char* value, char* def); + +/* defintions for subgraphs */ +extern Agraph_t *agsubg(Agraph_t * g, char *name, int cflag); /* constructor */ +extern Agraph_t *agidsubg(Agraph_t * g, unsigned long id, int cflag); /* constructor */ +extern Agraph_t *agfstsubg(Agraph_t * g), *agnxtsubg(Agraph_t * subg); +extern Agraph_t *agparent(Agraph_t * g); + +/* set cardinality */ +extern int agnnodes(Agraph_t * g), agnedges(Agraph_t * g); +extern int agdegree(Agraph_t * g, Agnode_t * n, int in, int out); +extern int agcountuniqedges(Agraph_t * g, Agnode_t * n, int in, int out); + +/* memory */ +extern void *agalloc(Agraph_t * g, size_t size); +extern void *agrealloc(Agraph_t * g, void *ptr, size_t oldsize, + size_t size); +extern void agfree(Agraph_t * g, void *ptr); +extern struct _vmalloc_s *agheap(Agraph_t * g); + +/* an engineering compromise is a joy forever */ +extern void aginternalmapclearlocalnames(Agraph_t * g); + +#define agnew(g,t) ((t*)agalloc(g,sizeof(t))) +#define agnnew(g,n,t) ((t*)agalloc(g,(n)*sizeof(t))) + +/* error handling */ +typedef enum { AGWARN, AGERR, AGMAX, AGPREV } agerrlevel_t; +extern agerrlevel_t agerrno; +extern void agseterr(agerrlevel_t); +extern char *aglasterr(void); +extern int agerr(agerrlevel_t level, char *fmt, ...); +extern void agerrorf(char *fmt, ...); +extern void agwarningf(char *fmt, ...); +extern int agerrors(void); + +/* data access macros */ +/* this assumes that e[0] is out and e[1] is inedge, see edgepair in edge.c */ +#define AGIN2OUT(e) ((e)-1) +#define AGOUT2IN(e) ((e)+1) +#define AGOPP(e) ((AGTYPE(e)==AGINEDGE)?AGIN2OUT(e):AGOUT2IN(e)) +#define AGMKOUT(e) (AGTYPE(e) == AGOUTEDGE? (e): AGIN2OUT(e)) +#define AGMKIN(e) (AGTYPE(e) == AGINEDGE? (e): AGOUT2IN(e)) +#define AGTAIL(e) (AGMKIN(e)->node) +#define AGHEAD(e) (AGMKOUT(e)->node) +#define agtail(e) AGTAIL(e) +#define aghead(e) AGHEAD(e) +#define agopp(e) AGOPP(e) + +#define TAILPORT_ID "tailport" +#define HEADPORT_ID "headport" + +#if _PACKAGE_ast +# if !defined(_BLD_cgraph) && defined(__IMPORT__) +# define extern __IMPORT__ +# endif +#endif +#if !defined(_BLD_cgraph) && defined(GVDLL) +#define extern __declspec(dllimport) +#endif + +extern Agdesc_t Agdirected, Agstrictdirected, Agundirected, + Agstrictundirected; + +#undef extern + +/* fast graphs */ +void agflatten(Agraph_t * g, int flag); +typedef Agsubnode_t Agnoderef_t; +typedef Dtlink_t Agedgeref_t; + +#define AGHEADPOINTER(g) ((Agnoderef_t*)(g->n_seq->data->hh._head)) +#define AGRIGHTPOINTER(rep) ((Agnoderef_t*)((rep)->seq_link.right?((void*)((rep)->seq_link.right) - offsetof(Agsubnode_t,seq_link)):0)) +#define AGLEFTPOINTER(rep) ((Agnoderef_t*)((rep)->seq_link.hl._left?((void*)((rep)->seq_link.hl._left) - offsetof(Agsubnode_t,seq_link)):0)) + +#define FIRSTNREF(g) (agflatten(g,1), AGHEADPOINTER(g)) + +#define NEXTNREF(g,rep) (AGRIGHTPOINTER(rep) == AGHEADPOINTER(g)?0:AGRIGHTPOINTER(rep)) + +#define PREVNREF(g,rep) (((rep)==AGHEADPOINTER(g))?0:(AGLEFTPOINTER(rep))) + +#define LASTNREF(g) (agflatten(g,1), AGHEADPOINTER(g)?AGLEFTPOINTER(AGHEADPOINTER(g)):0) +#define NODEOF(rep) ((rep)->node) + +#define FIRSTOUTREF(g,sn) (agflatten(g,1), (sn)->out_seq) +#define LASTOUTREF(g,sn) (agflatten(g,1), (Agedgeref_t*)dtlast(sn->out_seq)) +#define FIRSTINREF(g,sn) (agflatten(g,1), (sn)->in_seq) +#define NEXTEREF(g,rep) ((rep)->right) +#define PREVEREF(g,rep) ((rep)->hl._left) +/* this is expedient but a bit slimey because it "knows" that dict entries of both nodes +and edges are embedded in main graph objects but allocated separately in subgraphs */ +#define AGSNMAIN(sn) ((sn)==(&((sn)->node->mainsub))) +#define EDGEOF(sn,rep) (AGSNMAIN(sn)?((Agedge_t*)((unsigned char*)(rep) - offsetof(Agedge_t,seq_link))) : ((Dthold_t*)(rep))->obj) + +#undef extern +#if _PACKAGE_ast +_END_EXTERNS_ +#endif +#ifdef __cplusplus +} +#endif +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/color.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +/* $Id: color.h,v 1.7 2009/06/03 01:10:51 ellson Exp $ $Revision: 1.7 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GV_COLOR_H +#define GV_COLOR_H + +/* #include "arith.h" */ + +#ifdef __cplusplus +extern "C" { +#endif + +typedef struct hsvrgbacolor_t { + char *name; + unsigned char h, s, v; + unsigned char r, g, b, a; +} hsvrgbacolor_t; + +/* possible representations of color in gvcolor_t */ +typedef enum { HSVA_DOUBLE, RGBA_BYTE, RGBA_WORD, CMYK_BYTE, + RGBA_DOUBLE, COLOR_STRING, COLOR_INDEX } color_type_t; + +/* gvcolor_t can hold a color spec in a choice or representations */ +typedef struct color_s { + union { + double RGBA[4]; + double HSVA[4]; + unsigned char rgba[4]; + unsigned char cmyk[4]; + int rrggbbaa[4]; + char *string; + int index; + } u; + color_type_t type; +} gvcolor_t; + +#define COLOR_MALLOC_FAIL -1 +#define COLOR_UNKNOWN 1 +#define COLOR_OK 0 + +#ifdef __cplusplus +} +#endif +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/geom.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +/* $Id: geom.h,v 1.14 2009/06/03 01:10:52 ellson Exp $ $Revision: 1.14 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +/* geometric types and macros (e.g. points and boxes) with application to, but + * no specific dependance on graphs */ + +#ifndef GV_GEOM_H +#define GV_GEOM_H + +#ifdef HAVE_CONFIG_H +#include "arith.h" +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +typedef struct { int x, y; } point; + +typedef struct pointf_s { double x, y; } pointf; + +/* tell pathplan/pathgeom.h */ +#define HAVE_POINTF_S + +typedef struct { point LL, UR; } box; + +typedef struct { pointf LL, UR; } boxf; + +#ifdef HAVE_CONFIG_H + +/* true if point p is inside box b */ +#define INSIDE(p,b) (BETWEEN((b).LL.x,(p).x,(b).UR.x) && BETWEEN((b).LL.y,(p).y,(b).UR.y)) + +/* true if boxes b0 and b1 overlap */ +#define OVERLAP(b0,b1) (((b0).UR.x >= (b1).LL.x) && ((b1).UR.x >= (b0).LL.x) && ((b0).UR.y >= (b1).LL.y) && ((b1).UR.y >= (b0).LL.y)) + +/* true if box b0 completely contains b1*/ +#define CONTAINS(b0,b1) (((b0).UR.x >= (b1).UR.x) && ((b0).UR.y >= (b1).UR.y) && ((b0).LL.x <= (b1).LL.x) && ((b0).LL.y <= (b1).LL.y)) + +/* expand box b as needed to enclose point p */ +#define EXPANDBP(b, p) ((b).LL.x = MIN((b).LL.x, (p).x), (b).LL.y = MIN((b).LL.y, (p).y), (b).UR.x = MAX((b).UR.x, (p).x), (b).UR.y = MAX((b).UR.y, (p).y)) + +/* expand box b0 as needed to enclose box b1 */ +#define EXPANDBB(b0, b1) ((b0).LL.x = MIN((b0).LL.x, (b1).LL.x), (b0).LL.y = MIN((b0).LL.y, (b1).LL.y), (b0).UR.x = MAX((b0).UR.x, (b1).UR.x), (b0).UR.y = MAX((b0).UR.y, (b1).UR.y)) + +/* clip box b0 to fit box b1 */ +#define CLIPBB(b0, b1) ((b0).LL.x = MAX((b0).LL.x, (b1).LL.x), (b0).LL.y = MAX((b0).LL.y, (b1).LL.y), (b0).UR.x = MIN((b0).UR.x, (b1).UR.x), (b0).UR.y = MIN((b0).UR.y, (b1).UR.y)) + +#define LEN2(a,b) (SQR(a) + SQR(b)) +#define LEN(a,b) (sqrt(LEN2((a),(b)))) + +#define DIST2(p,q) (LEN2(((p).x - (q).x),((p).y - (q).y))) +#define DIST(p,q) (sqrt(DIST2((p),(q)))) + +#define POINTS_PER_INCH 72 +#define POINTS_PER_PC ((double)POINTS_PER_INCH / 6) +#define POINTS_PER_CM ((double)POINTS_PER_INCH * 0.393700787) +#define POINTS_PER_MM ((double)POINTS_PER_INCH * 0.0393700787) + +#define POINTS(a_inches) (ROUND((a_inches)*POINTS_PER_INCH)) +#define PS2INCH(a_points) ((a_points)/(double)POINTS_PER_INCH) + +#define P2PF(p,pf) ((pf).x = (p).x,(pf).y = (p).y) +#define PF2P(pf,p) ((p).x = ROUND((pf).x),(p).y = ROUND((pf).y)) + +#define B2BF(b,bf) (P2PF((b).LL,(bf).LL),P2PF((b).UR,(bf).UR)) +#define BF2B(bf,b) (PF2P((bf).LL,(b).LL),PF2P((bf).UR,(b).UR)) + +#define APPROXEQ(a,b,tol) (ABS((a) - (b)) < (tol)) +#define APPROXEQPT(p,q,tol) (DIST2((p),(q)) < SQR(tol)) + +/* some common tolerance values */ +#define MILLIPOINT .001 +#define MICROPOINT .000001 + +#endif + +#ifdef __cplusplus +} +#endif + +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/graph.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,236 @@ +/* $Id: graph.h,v 1.20 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.20 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + + + +#ifndef _GRAPH_H +#define _GRAPH_H 1 + +#if _PACKAGE_ast +#include <ast.h> +#else +#include <sys/types.h> +#include <stdlib.h> +#endif +#include <stdio.h> +#include "cdt.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define TAIL_ID "tailport" +#define HEAD_ID "headport" + + typedef struct Agraph_t Agraph_t; + typedef struct Agnode_t Agnode_t; + typedef struct Agedge_t Agedge_t; + typedef struct Agdict_t Agdict_t; + typedef struct Agsym_t Agsym_t; + typedef struct Agdata_t Agdata_t; + typedef struct Agproto_t Agproto_t; + + typedef char *(*gets_f) (char *ubuf, int n, FILE * fp); + +#define AGFLAG_DIRECTED (1<<0) +#define AGFLAG_STRICT (1<<1) +#define AGFLAG_METAGRAPH (1<<2) + +#define AGRAPH 0 +#define AGRAPHSTRICT (AGRAPH | AGFLAG_STRICT) +#define AGDIGRAPH AGFLAG_DIRECTED +#define AGDIGRAPHSTRICT (AGDIGRAPH | AGFLAG_STRICT) +#define AGMETAGRAPH (AGFLAG_DIRECTED | AGFLAG_STRICT | AGFLAG_METAGRAPH) + +#define AG_IS_DIRECTED(g) ((g)->kind & AGFLAG_DIRECTED) +#define AG_IS_STRICT(g) ((g)->kind & AGFLAG_STRICT) +#define AG_IS_METAGRAPH(g) ((g)->kind & AGFLAG_METAGRAPH) +#define aginit() aginitlib(sizeof(Agraph_t),sizeof(Agnode_t),sizeof(Agedge_t)) + + struct Agraph_t { + int tag:4; + int kind:4; + int handle:24; + char **attr; + char *didset; + char *name; + Agdata_t *univ; + Dict_t *nodes, *inedges, *outedges; + Agraph_t *root; + Agnode_t *meta_node; + Agproto_t *proto; + Agraphinfo_t u; + }; + + struct Agnode_t { + int tag:4; + int pad:4; + int handle:24; + char **attr; + char *didset; + char *name; + int id; + Agraph_t *graph; + Agnodeinfo_t u; + }; + + struct Agedge_t { + int tag:4; + int printkey:4; + int handle:24; + char **attr; + char *didset; + Agnode_t *head, *tail; + int id; + Agedgeinfo_t u; + }; + + struct Agdata_t { /* for main graph */ + Dict_t *node_dict; + Agdict_t *nodeattr; + Agdict_t *edgeattr; + Agdict_t *globattr; + int max_node_id, max_edge_id; + }; + + struct Agsym_t { + char *name, *value; + int index; + unsigned char printed; + unsigned char fixed; + }; + + struct Agdict_t { + char *name; + Dict_t *dict; + Agsym_t **list; + }; + + struct Agproto_t { + Agnode_t *n; + Agedge_t *e; + Agproto_t *prev; + }; + +#if _PACKAGE_ast + _BEGIN_EXTERNS_ /* public data */ +#if _BLD_graph && defined(__EXPORT__) +#define extern __EXPORT__ +#endif +#if !_BLD_graph && defined(__IMPORT__) && 0 +#define extern __IMPORT__ +#endif +#endif + +/*visual studio*/ +#ifdef WIN32_DLL +#ifndef GRAPH_EXPORTS +#define extern __declspec(dllimport) +#endif +#endif +/*end visual studio*/ + extern char *agstrcanon(char *, char *); + extern char *agcanonical(char *); + extern char *agcanon(char *); + extern int aghtmlstr(char *s); + extern char *agget(void *, char *); + extern char *agxget(void *, int); + extern int agset(void *, char *, char *); + extern int agsafeset(void *, char *, char *, char*); + extern int agxset(void *, int, char *); + extern int agindex(void *, char *); + + extern void aginitlib(int, int, int); + extern Agraph_t *agopen(char *, int); + extern Agraph_t *agsubg(Agraph_t *, char *); + extern Agraph_t *agfindsubg(Agraph_t *, char *); + extern void agclose(Agraph_t *); + extern Agraph_t *agread(FILE *); + extern Agraph_t *agread_usergets(FILE *, gets_f); + extern void agreadline(int); + extern void agsetfile(char *); + extern Agraph_t *agmemread(char *); + extern void agsetiodisc( + char * (*myfgets) (char *s, int size, FILE *stream), + size_t (*myfwrite) (const void *ptr, size_t size, size_t nmemb, FILE *stream), + int (*myferror) (FILE *stream) ); + extern int agputs(const char *s, FILE *fp); + extern int agputc(int c, FILE *fp); + extern int agwrite(Agraph_t *, FILE *); + extern int agerrors(void); + extern Agraph_t *agprotograph(void); + extern Agnode_t *agprotonode(Agraph_t *); + extern Agedge_t *agprotoedge(Agraph_t *); + extern Agraph_t *agusergraph(Agnode_t *); + extern int agnnodes(Agraph_t *); + extern int agnedges(Agraph_t *); + + extern void aginsert(Agraph_t *, void *); + extern void agdelete(Agraph_t *, void *); + extern int agcontains(Agraph_t *, void *); + + extern Agnode_t *agnode(Agraph_t *, char *); + extern Agnode_t *agfindnode(Agraph_t *, char *); + extern Agnode_t *agfstnode(Agraph_t *); + extern Agnode_t *agnxtnode(Agraph_t *, Agnode_t *); + extern Agnode_t *aglstnode(Agraph_t *); + extern Agnode_t *agprvnode(Agraph_t *, Agnode_t *); + + extern Agedge_t *agedge(Agraph_t *, Agnode_t *, Agnode_t *); + extern Agedge_t *agfindedge(Agraph_t *, Agnode_t *, Agnode_t *); + extern Agedge_t *agfstedge(Agraph_t *, Agnode_t *); + extern Agedge_t *agnxtedge(Agraph_t *, Agedge_t *, Agnode_t *); + extern Agedge_t *agfstin(Agraph_t *, Agnode_t *); + extern Agedge_t *agnxtin(Agraph_t *, Agedge_t *); + extern Agedge_t *agfstout(Agraph_t *, Agnode_t *); + extern Agedge_t *agnxtout(Agraph_t *, Agedge_t *); + + extern Agsym_t *agattr(void *, char *, char *); + extern Agsym_t *agraphattr(Agraph_t *, char *, char *); + extern Agsym_t *agnodeattr(Agraph_t *, char *, char *); + extern Agsym_t *agedgeattr(Agraph_t *, char *, char *); + extern Agsym_t *agfindattr(void *, char *); + extern Agsym_t *agfstattr(void *); + extern Agsym_t *agnxtattr(void *, Agsym_t *); + extern Agsym_t *aglstattr(void *); + extern Agsym_t *agprvattr(void *, Agsym_t *); + extern int agcopyattr(void *, void *); + + typedef enum { AGWARN, AGERR, AGMAX, AGPREV } agerrlevel_t; + extern agerrlevel_t agerrno; + extern void agseterr(agerrlevel_t); + extern char *aglasterr(void); + extern int agerr(agerrlevel_t level, char *fmt, ...); + extern void agerrorf(const char *fmt, ...); + extern void agwarningf(char *fmt, ...); + + extern char *agstrdup(char *); + extern char *agstrdup_html(char *s); + extern void agstrfree(char *); + + typedef enum { AGNODE = 1, AGEDGE, AGGRAPH } agobjkind_t; +#define agobjkind(p) ((agobjkind_t)(((Agraph_t*)(p))->tag)) + +#define agmetanode(g) ((g)->meta_node) + +#undef extern +#if _PACKAGE_ast + _END_EXTERNS_ +#endif +#ifdef __cplusplus +} +#endif +#endif /* _GRAPH_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvc.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,111 @@ +/* $Id: gvc.h,v 1.65 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.65 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GVC_H +#define GVC_H + +#include "types.h" +#include "gvplugin.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef GVDLL +#define extern __declspec(dllexport) +#else +#define extern +#endif + +/*visual studio*/ +#ifdef WIN32_DLL +#ifndef GVC_EXPORTS +#define extern __declspec(dllimport) +#endif +#endif +/*end visual studio*/ + +/* misc */ +/* FIXME - this needs eliminating or renaming */ +extern void gvToggle(int); + +/* set up a graphviz context */ +extern GVC_t *gvNEWcontext(const lt_symlist_t *builtins, int demand_loading); + +/* set up a graphviz context - and init graph - retaining old API */ +extern GVC_t *gvContext(void); +/* set up a graphviz context - and init graph - with builtins */ +extern GVC_t *gvContextPlugins(const lt_symlist_t *builtins, int demand_loading); + +/* get information associated with a graphviz context */ +extern char **gvcInfo(GVC_t*); +extern char *gvcVersion(GVC_t*); +extern char *gvcBuildDate(GVC_t*); + +/* parse command line args - minimally argv[0] sets layout engine */ +extern int gvParseArgs(GVC_t *gvc, int argc, char **argv); +extern graph_t *gvNextInputGraph(GVC_t *gvc); +extern graph_t *gvPluginsGraph(GVC_t *gvc); + +/* Compute a layout using a specified engine */ +extern int gvLayout(GVC_t *gvc, graph_t *g, const char *engine); + +/* Compute a layout using layout engine from command line args */ +extern int gvLayoutJobs(GVC_t *gvc, graph_t *g); + +/* Render layout into string attributes of the graph */ +extern void attach_attrs(graph_t *g); + +/* Parse an html string */ +#ifndef WITH_CGRAPH +extern char *agstrdup_html(char *s); +#endif +extern int aghtmlstr(char *s); + +/* Render layout in a specified format to an open FILE */ +extern int gvRender(GVC_t *gvc, graph_t *g, const char *format, FILE *out); + +/* Render layout in a specified format to an open FILE */ +extern int gvRenderFilename(GVC_t *gvc, graph_t *g, const char *format, const char *filename); + +/* Render layout in a specified format to an external context */ +extern int gvRenderContext(GVC_t *gvc, graph_t *g, const char *format, void *context); + +/* Render layout in a specified format to a malloc'ed string */ +extern int gvRenderData(GVC_t *gvc, graph_t *g, const char *format, char **result, unsigned int *length); + +/* Render layout according to -T and -o options found by gvParseArgs */ +extern int gvRenderJobs(GVC_t *gvc, graph_t *g); + +/* Clean up layout data structures - layouts are not nestable (yet) */ +extern int gvFreeLayout(GVC_t *gvc, graph_t *g); + +/* Clean up graphviz context */ +extern int gvFreeContext(GVC_t *gvc); + +/** Add a library from your user application + * @param gvc Graphviz context to add library to + * @param lib library to add + */ +extern void gvAddLibrary(GVC_t *gvc, gvplugin_library_t *lib); + +#undef extern + +#ifdef __cplusplus +} +#endif + +#endif /* GVC_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvcext.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ + +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +/* Common header used by both clients and plugins */ + +#ifndef GVCEXT_H +#define GVCEXT_H + +#ifdef __cplusplus +extern "C" { +#endif + +/* + * Define an apis array of name strings using an enumerated api_t as index. + * The enumerated type is defined here. The apis array is + * inititialized in gvplugin.c by redefining ELEM and reinvoking APIS. + */ +#define APIS ELEM(render) ELEM(layout) ELEM(textlayout) ELEM(device) ELEM(loadimage) + +/* + * Define api_t using names based on the plugin names with API_ prefixed. + */ +#define ELEM(x) API_##x, + typedef enum { APIS _DUMMY_ELEM_=0 } api_t; /* API_render, API_layout, ... */ + /* Stupid but true: The sole purpose of "_DUMMY_ELEM_=0" + * is to avoid a "," after the last element of the enum + * because some compilers when using "-pedantic" + * generate an error for about the dangling "," + * but only if this header is used from a .cpp file! + * Setting it to 0 makes sure that the enumeration + * does not define an extra value. (It does however + * define _DUMMY_ELEM_ as an enumeration symbol, + * but its value duplicates that of the first + * symbol in the enumeration - in this case "render".) + */ + + /* One could wonder why trailing "," in: + * int nums[]={1,2,3,}; + * is OK, but in: + * typedef enum {a,b,c,} abc_t; + * is not!!! + */ +#undef ELEM + + typedef struct GVJ_s GVJ_t; + typedef struct GVC_s GVC_t; + + typedef struct { + const char *name; + void* address; + } lt_symlist_t; + + typedef struct gvplugin_available_s gvplugin_available_t; + +/*visual studio*/ +#ifdef WIN32_DLL +#ifndef GVC_EXPORTS +__declspec(dllimport) lt_symlist_t lt_preloaded_symbols[]; +#else +__declspec(dllexport) lt_symlist_t lt_preloaded_symbols[]; +#endif +#endif +/*end visual studio*/ + + +#ifndef WIN32_DLL +#if defined(GVDLL) + __declspec(dllexport) lt_symlist_t lt_preloaded_symbols[]; +#else + extern lt_symlist_t lt_preloaded_symbols[]; +#endif +#endif + + +#ifdef __cplusplus +} +#endif + + + +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvcjob.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,374 @@ +/* $Id: gvcjob.h,v 1.82 2009/07/08 15:14:20 ellson Exp $ $Revision: 1.82 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +/* Common header used by both clients and plugins */ + +#ifndef GVCJOB_H +#define GVCJOB_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include "gvcommon.h" +#include "color.h" + +#define ARRAY_SIZE(A) (sizeof(A)/sizeof(A[0])) + + typedef struct gvdevice_engine_s gvdevice_engine_t; + typedef struct gvformatter_engine_s gvformatter_engine_t; + typedef struct gvrender_engine_s gvrender_engine_t; + typedef struct gvlayout_engine_s gvlayout_engine_t; + typedef struct gvtextlayout_engine_s gvtextlayout_engine_t; + typedef struct gvloadimage_engine_s gvloadimage_engine_t; + + typedef enum { PEN_NONE, PEN_DASHED, PEN_DOTTED, PEN_SOLID } pen_type; + typedef enum { FILL_NONE, FILL_SOLID } fill_type; + typedef enum { FONT_REGULAR, FONT_BOLD, FONT_ITALIC } font_type; + typedef enum { LABEL_PLAIN, LABEL_HTML } label_type; + +#define PENWIDTH_NORMAL 1. +#define PENWIDTH_BOLD 2. + typedef enum { GVATTR_STRING, GVATTR_BOOL, GVATTR_COLOR } gvattr_t; + +/* The -T output formats listed below are examples only, they are not definitive or inclusive, + other outputs may use the flags now, or in the future + + Default emit order is breadth first graph walk order + EMIT_SORTED emits nodes before edges + EMIT_COLORS emits colors before nodes or edge -Tfig + EMIT_CLUSTERS_LAST emits cluster after nodes and edges + EMIT_PREORDER emit in preorder traversal ??? + EMIT_EDGE_SORTED emits edges before nodes + + GVDEVICE_DOES_PAGES provides pagination support -Tps + GVDEVICE_DOES_LAYERS provides support for layers -Tps + GVDEVICE_EVENTS supports mouse events -Tgtk, -Txlib + GVDEVICE_DOES_TRUECOLOR supports alpha channel -Tpng, -Tgtk, -Txlib + GVDEVICE_BINARY_FORMAT Suppresses \r\n substitution for linends + GVDEVICE_COMPRESSED_FORMAT controls libz compression + GVDEVICE_NO_WRITER used when gvdevice is not used because device uses its own writer, -Tming, devil outputs (FIXME seems to overlap OUTPUT_NOT_REQUIRED) + + GVRENDER_Y_GOES_DOWN device origin top left, y goes down, otherwise + device origin lower left, y goes up + GVRENDER_DOES_TRANSFORM device uses scale, translate, rotate to do its own + coordinate transformations, otherwise coordinates + are pre-transformed + GVRENDER_DOES_ARROWS renderer has its own idea of arrow shapes (deprecated) + GVRENDER_DOES_LABELS basically, maps don't need labels + GVRENDER_DOES_MAPS renderer encodes mapping information for mouse events -Tcmapx -Tsvg + GVRENDER_DOES_MAP_RECTANGLE supports a 2 coord rectngle optimization + GVRENDER_DOES_MAP_CIRCLE supports a 1 coord + radius circle optimization + GVRENDER_DOES_MAP_POLYGON supports polygons (basically, -Tsvg uses anchors, so doesn't need to support any map shapes) + GVRENDER_DOES_MAP_ELLIPSE supports a 2 coord ellipse optimization + GVRENDER_DOES_MAP_BSPLINE supports mapping of splines + GVRENDER_DOES_TOOLTIPS can represent tooltip info -Tcmapx, -Tsvg + GVRENDER_DOES_TARGETS can represent target info (open link in a new tab or window) + GVRENDER_DOES_Z render support 2.5D representation -Tvrml + GVRENDER_NO_WHITE_BG don't paint white background, assumes white paper -Tps + LAYOUT_NOT_REQUIRED don't perform layout -Tcanon + OUTPUT_NOT_REQUIRED don't use gvdevice for output (basically when agwrite() used instead) -Tcanon, -Txdot + */ + + +#define EMIT_SORTED (1<<0) +#define EMIT_COLORS (1<<1) +#define EMIT_CLUSTERS_LAST (1<<2) +#define EMIT_PREORDER (1<<3) +#define EMIT_EDGE_SORTED (1<<4) +#define GVDEVICE_DOES_PAGES (1<<5) +#define GVDEVICE_DOES_LAYERS (1<<6) +#define GVDEVICE_EVENTS (1<<7) +#define GVDEVICE_DOES_TRUECOLOR (1<<8) +#define GVDEVICE_BINARY_FORMAT (1<<9) +#define GVDEVICE_COMPRESSED_FORMAT (1<<10) +#define GVDEVICE_NO_WRITER (1<<11) +#define GVRENDER_Y_GOES_DOWN (1<<12) +#define GVRENDER_DOES_TRANSFORM (1<<13) +#define GVRENDER_DOES_ARROWS (1<<14) +#define GVRENDER_DOES_LABELS (1<<15) +#define GVRENDER_DOES_MAPS (1<<16) +#define GVRENDER_DOES_MAP_RECTANGLE (1<<17) +#define GVRENDER_DOES_MAP_CIRCLE (1<<18) +#define GVRENDER_DOES_MAP_POLYGON (1<<19) +#define GVRENDER_DOES_MAP_ELLIPSE (1<<20) +#define GVRENDER_DOES_MAP_BSPLINE (1<<21) +#define GVRENDER_DOES_TOOLTIPS (1<<22) +#define GVRENDER_DOES_TARGETS (1<<23) +#define GVRENDER_DOES_Z (1<<24) +#define GVRENDER_NO_WHITE_BG (1<<25) +#define LAYOUT_NOT_REQUIRED (1<<26) +#define OUTPUT_NOT_REQUIRED (1<<27) + + typedef struct { + int flags; + double default_pad; /* graph units */ + char **knowncolors; + int sz_knowncolors; + color_type_t color_type; + } gvrender_features_t; + + typedef struct { + int flags; + pointf default_margin; /* left/right, top/bottom - points */ + pointf default_pagesize;/* default page width, height - points */ + pointf default_dpi; + } gvdevice_features_t; + +#define LAYOUT_USES_RANKDIR (1<<0) + + typedef struct gvplugin_active_device_s { + gvdevice_engine_t *engine; + int id; + gvdevice_features_t *features; + const char *type; + } gvplugin_active_device_t; + + typedef struct gvplugin_active_render_s { + gvrender_engine_t *engine; + int id; + gvrender_features_t *features; + const char *type; + } gvplugin_active_render_t; + + typedef struct gvplugin_active_loadimage_t { + gvloadimage_engine_t *engine; + int id; + const char *type; + } gvplugin_active_loadimage_t; + + typedef struct gv_argvlist_s { + char **argv; + int argc; + int alloc; + } gv_argvlist_t; + + typedef struct gvdevice_callbacks_s { + void (*refresh) (GVJ_t * job); + void (*button_press) (GVJ_t * job, int button, pointf pointer); + void (*button_release) (GVJ_t * job, int button, pointf pointer); + void (*motion) (GVJ_t * job, pointf pointer); + void (*modify) (GVJ_t * job, const char *name, const char *value); + void (*del) (GVJ_t * job); /* can't use "delete" 'cos C++ stole it */ + void (*read) (GVJ_t * job, const char *filename, const char *layout); + void (*layout) (GVJ_t * job, const char *layout); + void (*render) (GVJ_t * job, const char *format, const char *filename); + } gvdevice_callbacks_t; + + typedef int (*gvevent_key_callback_t) (GVJ_t * job); + + typedef struct gvevent_key_binding_s { + char *keystring; + gvevent_key_callback_t callback; + } gvevent_key_binding_t; + + typedef enum {MAP_RECTANGLE, MAP_CIRCLE, MAP_POLYGON, } map_shape_t; + + typedef enum {ROOTGRAPH_OBJTYPE, CLUSTER_OBJTYPE, NODE_OBJTYPE, EDGE_OBJTYPE} obj_type; + + /* See comment in gvrender_core_dot.c */ + typedef enum { + EMIT_GDRAW, EMIT_CDRAW, EMIT_TDRAW, EMIT_HDRAW, + EMIT_GLABEL, EMIT_CLABEL, EMIT_TLABEL, EMIT_HLABEL, + EMIT_NDRAW, EMIT_EDRAW, EMIT_NLABEL, EMIT_ELABEL, + } emit_state_t; + + typedef struct obj_state_s obj_state_t; + + struct obj_state_s { + obj_state_t *parent; + + obj_type type; + union { + graph_t *g; + graph_t *sg; + node_t *n; + edge_t *e; + } u; + + emit_state_t emit_state; + + gvcolor_t pencolor, fillcolor; + pen_type pen; + fill_type fill; + double penwidth; + char **rawstyle; + + double z, tail_z, head_z; /* z depths for 2.5D renderers such as vrml */ + + /* fully substituted text strings */ + char *label; + char *taillabel; + char *headlabel; + + char *url; /* if GVRENDER_DOES_MAPS */ + char *id; + char *labelurl; + char *tailurl; + char *headurl; + + char *tooltip; /* if GVRENDER_DOES_TOOLTIPS */ + char *labeltooltip; + char *tailtooltip; + char *headtooltip; + + char *target; /* if GVRENDER_DOES_TARGETS */ + char *labeltarget; + char *tailtarget; + char *headtarget; + + int explicit_tooltip:1; + int explicit_tailtooltip:1; + int explicit_headtooltip:1; + int explicit_labeltooltip:1; + int explicit_tailtarget:1; + int explicit_headtarget:1; + int explicit_edgetarget:1; + int explicit_tailurl:1; + int explicit_headurl:1; + + /* primary mapped region - node shape, edge labels */ + map_shape_t url_map_shape; + int url_map_n; /* number of points for url map if GVRENDER_DOES_MAPS */ + pointf *url_map_p; + + /* additonal mapped regions for edges */ + int url_bsplinemap_poly_n; /* number of polygons in url bspline map + if GVRENDER_DOES_MAPS && GVRENDER_DOES_MAP_BSPLINES */ + int *url_bsplinemap_n; /* array of url_bsplinemap_poly_n ints + of number of points in each polygon */ + pointf *url_bsplinemap_p; /* all the polygon points */ + + int tailendurl_map_n; /* tail end intersection with node */ + pointf *tailendurl_map_p; + + int headendurl_map_n; /* head end intersection with node */ + pointf *headendurl_map_p; + }; + +/* Note on units: + * points - a physical distance (1/72 inch) unaffected by zoom or dpi. + * graph units - related to physical distance by zoom. Equals points at zoom=1 + * device units - related to physical distance in points by dpi/72 + */ + + struct GVJ_s { + GVC_t *gvc; /* parent gvc */ + GVJ_t *next; /* linked list of jobs */ + GVJ_t *next_active; /* linked list of active jobs (e.g. multiple windows) */ + + GVCOMMON_t *common; + + obj_state_t *obj; /* objects can be nested (at least clusters can) + so keep object state on a stack */ + char *input_filename; + int graph_index; + + const char *layout_type; + + const char *output_filename; + FILE *output_file; + char *output_data; + unsigned int output_data_allocated; + unsigned int output_data_position; + + const char *output_langname; + int output_lang; + + gvplugin_active_render_t render; + gvplugin_active_device_t device; + gvplugin_active_loadimage_t loadimage; + gvdevice_callbacks_t *callbacks; + pointf device_dpi; + boolean device_sets_dpi; + + void *display; + int screen; + + void *context; /* gd or cairo surface */ + boolean external_context; /* context belongs to caller */ + char *imagedata; /* location of imagedata */ + + int flags; /* emit_graph flags */ + + int numLayers; /* number of layers */ + int layerNum; /* current layer - 1 based*/ + + point pagesArraySize; /* 2D size of page array */ + point pagesArrayFirst;/* 2D starting corner in */ + point pagesArrayMajor;/* 2D major increment */ + point pagesArrayMinor;/* 2D minor increment */ + point pagesArrayElem; /* 2D coord of current page - 0,0 based */ + int numPages; /* number of pages */ + + boxf bb; /* graph bb with padding - graph units */ + pointf pad; /* padding around bb - graph units */ + boxf clip; /* clip region in graph units */ + boxf pageBox; /* current page in graph units */ + pointf pageSize; /* page size in graph units */ + pointf focus; /* viewport focus - graph units */ + + double zoom; /* viewport zoom factor (points per graph unit) */ + int rotation; /* viewport rotation (degrees) 0=portrait, 90=landscape */ + + pointf view; /* viewport size - points */ + boxf canvasBox; /* viewport area - points */ + pointf margin; /* job-specific margin - points */ + + pointf dpi; /* device resolution device-units-per-inch */ + + unsigned int width; /* device width - device units */ + unsigned int height; /* device height - device units */ + box pageBoundingBox;/* rotated boundingBox - device units */ + box boundingBox; /* cumulative boundingBox over all pages - device units */ + + pointf scale; /* composite device to graph units (zoom and dpi) */ + pointf translation; /* composite translation */ + pointf devscale; /* composite device to points: dpi, y_goes_down */ + + boolean fit_mode, + needs_refresh, + click, + has_grown, + has_been_rendered; + + unsigned char button; /* active button */ + pointf pointer; /* pointer position in device units */ + pointf oldpointer; /* old pointer position in device units */ + + void *current_obj; /* graph object that pointer is in currently */ + + void *selected_obj; /* graph object that has been selected */ + /* (e.g. button 1 clicked on current obj) */ + char *active_tooltip; /* tooltip of active object - or NULL */ + char *selected_href; /* href of selected object - or NULL */ + gv_argvlist_t selected_obj_type_name; /* (e.g. "edge" "node3" "e" "->" "node5" "") */ + gv_argvlist_t selected_obj_attributes; /* attribute triplets: name, value, type */ + /* e.g. "color", "red", GVATTR_COLOR, + "style", "filled", GVATTR_BOOL, */ + + void *window; /* display-specific data for gvrender plugin */ + + /* keybindings for keyboard events */ + gvevent_key_binding_t *keybindings; + int numkeys; + void *keycodes; + }; + +#ifdef __cplusplus +} +#endif +#endif /* GVCJOB_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvcommon.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,43 @@ +/* $Id: gvcommon.h,v 1.14 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.14 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GVCOMMON_H +#define GVCOMMON_H + +#ifdef __cplusplus +extern "C" { +#endif + + typedef struct GVCOMMON_s { + char **info; + char *cmdname; + int verbose; + boolean config, auto_outfile_names; + void (*errorfn) (const char *fmt, ...); + const char **show_boxes; /* emit code for correct box coordinates */ + const char **lib; + + /* rendering state */ + int viewNum; /* current view - 1 based count of views, + all pages in all layers */ + const lt_symlist_t *builtins; + int demand_loading; + } GVCOMMON_t; + +#ifdef __cplusplus +} +#endif +#endif /* GVCOMMON_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvconfig.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,33 @@ +/* $Id: gvconfig.h,v 1.4 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.4 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +/* Header used by plugins */ + +#ifndef GVCONFIG_H +#define GVCONFIG_H + +#include "gvplugin.h" + +#ifdef __cplusplus +extern "C" { +#endif + +extern void gvconfig_plugin_install_from_library(GVC_t * gvc, char *path, gvplugin_library_t *library); + +#ifdef __cplusplus +} +#endif +#endif /* GVCONFIG_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvplugin.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +/* $Id: gvplugin.h,v 1.31 2009/07/14 13:18:07 ellson Exp $ $Revision: 1.31 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +/* Header used by plugins */ + +#ifndef GVPLUGIN_H +#define GVPLUGIN_H + +#ifdef __cplusplus +extern "C" { +#endif + +#include "gvcext.h" + +/* + * Terminology: + * + * package - e.g. libgvplugin_cairo.so + * api - e.g. render + * type - e.g. "png", "ps" + */ + + typedef struct { + int id; /* an id that is only unique within a package + of plugins of the same api. + A renderer-type such as "png" in the cairo package + has an id that is different from the "ps" type + in the same package */ + const char *type; /* a string name, such as "png" or "ps" that + distinguishes different types withing the same + (renderer in this case) */ + int quality; /* an arbitrary integer used for ordering plugins of + the same type from different packages */ + void *engine; /* pointer to the jump table for the plugin */ + void *features; /* pointer to the feature description + void* because type varies by api */ + } gvplugin_installed_t; + + typedef struct { + api_t api; + gvplugin_installed_t *types; + } gvplugin_api_t; + + typedef struct { + char *packagename; /* used when this plugin is builtin and has + no pathname */ + gvplugin_api_t *apis; + } gvplugin_library_t; + +#ifdef __cplusplus +} +#endif +#endif /* GVPLUGIN_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvplugin_device.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,37 @@ +/* $Id: gvplugin_device.h,v 1.18 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.18 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GVDEVICE_PLUGIN_H +#define GVDEVICE_PLUGIN_H + +#include "types.h" +#include "gvplugin.h" +#include "gvcjob.h" + +#ifdef __cplusplus +extern "C" { +#endif + + struct gvdevice_engine_s { + void (*initialize) (GVJ_t * firstjob); + void (*format) (GVJ_t * firstjob); + void (*finalize) (GVJ_t * firstjob); + }; + +#ifdef __cplusplus +} +#endif +#endif /* GVDEVICE_PLUGIN_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvplugin_layout.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +/* $Id: gvplugin_layout.h,v 1.8 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.8 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GVPLUGIN_LAYOUT_H +#define GVPLUGIN_LAYOUT_H + +#include "types.h" +#include "gvplugin.h" +#include "gvcjob.h" + +#ifdef __cplusplus +extern "C" { +#endif + + struct gvlayout_engine_s { + void (*layout) (graph_t * g); + void (*cleanup) (graph_t * g); + }; + +#ifdef __cplusplus +} +#endif +#endif /* GVPLUGIN_LAYOUT_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvplugin_loadimage.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +/* $Id: gvplugin_loadimage.h,v 1.12 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.12 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GVPLUGIN_IMAGELOAD_H +#define GVPLUGIN_IMAGELOAD_H + +#include "types.h" +#include "gvplugin.h" +#include "gvcjob.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef GVDLL +# define extern __declspec(dllexport) +#endif + +/*visual studio*/ +#ifdef WIN32_DLL +#ifndef GVC_EXPORTS +#define extern __declspec(dllimport) +#endif +#endif +/*end visual studio*/ + +extern boolean gvusershape_file_access(usershape_t *us); +extern void gvusershape_file_release(usershape_t *us); + + struct gvloadimage_engine_s { + void (*loadimage) (GVJ_t *job, usershape_t *us, boxf b, boolean filled); + }; + +#ifdef extern +#undef extern +#endif + +#ifdef __cplusplus +} +#endif +#endif /* GVPLUGIN_IMAGELOAD_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvplugin_render.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +/* $Id: gvplugin_render.h,v 1.36 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.36 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GVPLUGIN_RENDER_H +#define GVPLUGIN_RENDER_H + +#include "types.h" +#include "gvplugin.h" +#include "gvcjob.h" + +#ifdef __cplusplus +extern "C" { +#endif + + struct gvrender_engine_s { + void (*begin_job) (GVJ_t * job); + void (*end_job) (GVJ_t * job); + void (*begin_graph) (GVJ_t * job); + void (*end_graph) (GVJ_t * job); + void (*begin_layer) (GVJ_t * job, char *layername, + int layerNum, int numLayers); + void (*end_layer) (GVJ_t * job); + void (*begin_page) (GVJ_t * job); + void (*end_page) (GVJ_t * job); + void (*begin_cluster) (GVJ_t * job); + void (*end_cluster) (GVJ_t * job); + void (*begin_nodes) (GVJ_t * job); + void (*end_nodes) (GVJ_t * job); + void (*begin_edges) (GVJ_t * job); + void (*end_edges) (GVJ_t * job); + void (*begin_node) (GVJ_t * job); + void (*end_node) (GVJ_t * job); + void (*begin_edge) (GVJ_t * job); + void (*end_edge) (GVJ_t * job); + void (*begin_anchor) (GVJ_t * job, + char *href, char *tooltip, char *target, char *id); + void (*end_anchor) (GVJ_t * job); + void (*begin_label) (GVJ_t * job, label_type type); + void (*end_label) (GVJ_t * job); + void (*textpara) (GVJ_t * job, pointf p, textpara_t * str); + void (*resolve_color) (GVJ_t * job, gvcolor_t * color); + void (*ellipse) (GVJ_t * job, pointf * A, int filled); + void (*polygon) (GVJ_t * job, pointf * A, int n, int filled); + void (*beziercurve) (GVJ_t * job, pointf * A, int n, + int arrow_at_start, int arrow_at_end, int); + void (*polyline) (GVJ_t * job, pointf * A, int n); + void (*comment) (GVJ_t * job, char *comment); + void (*library_shape) (GVJ_t * job, char *name, pointf * A, int n, int filled); + }; + +#ifdef __cplusplus +} +#endif +#endif /* GVPLUGIN_RENDER_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/gvplugin_textlayout.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,36 @@ +/* $Id: gvplugin_textlayout.h,v 1.16 2009/06/03 01:10:53 ellson Exp $ $Revision: 1.16 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GVPLUGIN_TEXTLAYOUT_H +#define GVPLUGIN_TEXTLAYOUT_H + +#include "types.h" +#include "gvplugin.h" +#include "gvcjob.h" +#include "gvcommon.h" + +#ifdef __cplusplus +extern "C" { +#endif + + struct gvtextlayout_engine_s { + boolean (*textlayout) (textpara_t *para, char** fontpath); + }; + +#ifdef __cplusplus +} +#endif +#endif /* GVPLUGIN_TEXTLAYOUT_H */
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/pack.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,95 @@ +/* $Id: pack.h,v 1.17 2009/08/11 21:23:23 erg Exp $ $Revision: 1.17 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + + + +#ifndef _PACK_H +#define _PACK_H 1 + +#ifdef __cplusplus +extern "C" { +#endif + +#include "types.h" + +/* Type indicating granularity and method + * l_undef - unspecified + * l_node - polyomino using nodes and edges + * l_clust - polyomino using nodes and edges and top-level clusters + * (assumes ND_clust(n) unused by application) + * l_graph - polyomino using computer graph bounding box + * l_array - array based on graph bounding boxes + * l_aspect - tiling based on graph bounding boxes preserving aspect ratio + * l_hull - polyomino using convex hull (unimplemented) + * l_tile - tiling using graph bounding box (unimplemented) + * l_bisect - alternate bisection using graph bounding box (unimplemented) + */ + typedef enum { l_undef, l_clust, l_node, l_graph, l_array, l_aspect } pack_mode; + +#define PK_COL_MAJOR 1 +#define PK_USER_VALS 2 + +typedef unsigned char packval_t; + + typedef struct { + float aspect; /* desired aspect ratio */ + int sz; /* row/column size size */ + unsigned int margin; /* margin left around objects, in points */ + int doSplines; /* use splines in constructing graph shape */ + pack_mode mode; /* granularity and method */ + boolean *fixed; /* fixed[i] == true implies g[i] should not be moved */ + packval_t* vals; /* for arrays, sort numbers */ + int flags; + } pack_info; +#ifdef GVDLL +#define extern __declspec(dllexport) +#else +#define extern +#endif + +/*visual studio*/ +#ifdef WIN32_DLL +#ifndef GVC_EXPORTS +#define extern __declspec(dllimport) +#endif +#endif +/*end visual studio*/ + + extern point *putRects(int ng, boxf* bbs, pack_info* pinfo); + extern int packRects(int ng, boxf* bbs, pack_info* pinfo); + + extern point *putGraphs(int, Agraph_t **, Agraph_t *, pack_info *); + extern int packGraphs(int, Agraph_t **, Agraph_t *, pack_info *); + extern int packSubgraphs(int, Agraph_t **, Agraph_t *, pack_info *); + extern int pack_graph(int ng, Agraph_t** gs, Agraph_t* root, boolean* fixed); + + extern int shiftGraphs(int, Agraph_t**, point*, Agraph_t*, int); + + extern pack_mode getPackMode(Agraph_t * g, pack_mode dflt); + extern int getPack(Agraph_t *, int not_def, int dflt); + extern pack_mode getPackInfo(Agraph_t * g, pack_mode dflt, int dfltMargin, pack_info*); + extern pack_mode getPackModeInfo(Agraph_t * g, pack_mode dflt, pack_info*); + extern pack_mode parsePackModeInfo(char* p, pack_mode dflt, pack_info* pinfo); + + extern int isConnected(Agraph_t *); + extern Agraph_t **ccomps(Agraph_t *, int *, char *); + extern Agraph_t **pccomps(Agraph_t *, int *, char *, boolean *); + extern int nodeInduce(Agraph_t *); +#undef extern +#ifdef __cplusplus +} +#endif +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/pathgeom.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,54 @@ +/* $Id: pathgeom.h,v 1.4 2009/06/03 01:10:54 ellson Exp $ $Revision: 1.4 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + + +#ifndef _PATHGEOM_INCLUDE +#define _PATHGEOM_INCLUDE + +#ifdef __cplusplus +extern "C" { +#endif + +#ifdef HAVE_POINTF_S + typedef struct pointf_s Ppoint_t; + typedef struct pointf_s Pvector_t; +#else + typedef struct Pxy_t { + double x, y; + } Pxy_t; + + typedef struct Pxy_t Ppoint_t; + typedef struct Pxy_t Pvector_t; +#endif + + typedef struct Ppoly_t { + Ppoint_t *ps; + int pn; + } Ppoly_t; + + typedef Ppoly_t Ppolyline_t; + + typedef struct Pedge_t { + Ppoint_t a, b; + } Pedge_t; + +/* opaque state handle for visibility graph operations */ + typedef struct vconfig_s vconfig_t; + +#ifdef __cplusplus +} +#endif +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/pathplan.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +/* $Id: pathplan.h,v 1.5 2009/06/03 01:10:54 ellson Exp $ $Revision: 1.5 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + + + +#ifndef _PATH_INCLUDE +#define _PATH_INCLUDE + +#include "pathgeom.h" + +#ifdef __cplusplus +extern "C" { +#endif + + +#if defined(_BLD_pathplan) && defined(__EXPORT__) +# define extern __EXPORT__ +#endif + +/* find shortest euclidean path within a simple polygon */ + extern int Pshortestpath(Ppoly_t * boundary, Ppoint_t endpoints[2], + Ppolyline_t * output_route); + +/* fit a spline to an input polyline, without touching barrier segments */ + extern int Proutespline(Pedge_t * barriers, int n_barriers, + Ppolyline_t input_route, + Pvector_t endpoint_slopes[2], + Ppolyline_t * output_route); + +/* utility function to convert from a set of polygonal obstacles to barriers */ + extern int Ppolybarriers(Ppoly_t ** polys, int npolys, + Pedge_t ** barriers, int *n_barriers); + +/* function to convert a polyline into a spline representation */ + extern void make_polyline(Ppolyline_t line, Ppolyline_t* sline); + +#undef extern + +#ifdef __cplusplus +} +#endif +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/textpara.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +/* $Id: textpara.h,v 1.13 2009/06/03 01:10:52 ellson Exp $ $Revision: 1.13 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef TEXTPARA_H +#define TEXTPARA_H + +#ifdef __cplusplus +extern "C" { +#endif + + typedef struct _PostscriptAlias { + char* name; + char* family; + char* weight; + char* stretch; + char* style; + int xfig_code; + char* svg_font_family; + char* svg_font_weight; + char* svg_font_style; + } PostscriptAlias; + + typedef struct textpara_t { + char *str; /* stored in utf-8 */ + char *fontname; + PostscriptAlias *postscript_alias; + void *layout; + void (*free_layout) (void *layout); /* FIXME - this is ugly */ + double fontsize, width, height, yoffset_layout, yoffset_centerline; + char just; /* 'l' 'n' 'r' */ + } textpara_t; + +#ifdef __cplusplus +} +#endif +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/types.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,752 @@ +/* $Id: types.h,v 1.101 2009/08/28 19:46:42 erg Exp $ $Revision: 1.101 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef GV_TYPES_H +#define GV_TYPES_H + +#include <stdio.h> +#include <assert.h> +#include <signal.h> + +typedef unsigned char boolean; +#ifndef NOT +#define NOT(v) (!(v)) +#endif +#ifndef FALSE +#define FALSE 0 +#endif +#ifndef TRUE +#define TRUE NOT(FALSE) +#endif + +#include "geom.h" +#include "gvcext.h" +#include "pathgeom.h" +#include "textpara.h" + +#ifdef __cplusplus +extern "C" { +#endif + + typedef int (*qsort_cmpf) (const void *, const void *); + typedef int (*bsearch_cmpf) (const void *, const void *); + +#ifdef WITH_CGRAPH +#include <cgraph.h> + typedef struct Agraph_s graph_t; + typedef struct Agnode_s node_t; + typedef struct Agedge_s edge_t; + typedef struct Agsym_s attrsym_t; +#define TAIL_ID "tailport" +#define HEAD_ID "headport" +#else + typedef struct Agraph_t graph_t; + typedef struct Agnode_t node_t; + typedef struct Agedge_t edge_t; + typedef struct Agsym_t attrsym_t; +#endif + + typedef struct htmllabel_t htmllabel_t; + + typedef union inside_t { + struct { + pointf* p; + double* r; + } a; + struct { + node_t* n; + boxf* bp; + } s; + } inside_t; + + typedef struct port { /* internal edge endpoint specification */ + pointf p; /* aiming point relative to node center */ + double theta; /* slope in radians */ + boxf *bp; /* if not null, points to bbox of + * rectangular area that is port target + */ + boolean defined; /* if true, edge has port info at this end */ + boolean constrained; /* if true, constraints such as theta are set */ + boolean clip; /* if true, clip end to node/port shape */ + boolean dyna; /* if true, assign compass point dynamically */ + unsigned char order; /* for mincross */ + unsigned char side; /* if port is on perimeter of node, this + * contains the bitwise OR of the sides (TOP, + * BOTTOM, etc.) it is on. + */ + char *name; /* port name, if it was explicitly given, otherwise NULL */ + } port; + + typedef struct { + boolean(*swapEnds) (edge_t * e); /* Should head and tail be swapped? */ + boolean(*splineMerge) (node_t * n); /* Is n a node in the middle of an edge? */ + boolean ignoreSwap; /* Test for swapped edges if false */ + } splineInfo; + + typedef struct pathend_t { + boxf nb; /* the node box */ + pointf np; /* node port */ + int sidemask; + int boxn; + boxf boxes[20]; + } pathend_t; + + typedef struct path { /* internal specification for an edge spline */ + port start, end; + int nbox; /* number of subdivisions */ + boxf *boxes; /* rectangular regions of subdivision */ + void *data; + } path; + + typedef struct bezier { + pointf *list; + int size; + int sflag, eflag; + pointf sp, ep; + } bezier; + + typedef struct splines { + bezier *list; + int size; + boxf bb; + } splines; + + typedef struct textlabel_t { + char *text, *fontname, *fontcolor; + int charset; + double fontsize; + pointf dimen; /* the diagonal size of the label (estimated by layout) */ + pointf space; /* the diagonal size of the space for the label */ + /* the rendered label is aligned in this box */ + /* space does not include pad or margin */ + pointf pos; /* the center of the space for the label */ + union { + struct { + textpara_t *para; + short nparas; + } txt; + htmllabel_t *html; + } u; + char valign; /* 't' 'c' 'b' */ + boolean set; /* true if position is set */ + boolean html; /* true if html label */ + } textlabel_t; + + typedef struct polygon_t { /* mutable shape information for a node */ + int regular; /* true for symmetric shapes */ + int peripheries; /* number of periphery lines */ + int sides; /* number of sides */ + double orientation; /* orientation of shape (+ve degrees) */ + double distortion; /* distortion factor - as in trapezium */ + double skew; /* skew factor - as in parallelogram */ + int option; /* ROUNDED, DIAGONAL corners, etc. */ + pointf *vertices; /* array of vertex points */ + } polygon_t; + + typedef struct stroke_t { /* information about a single stroke */ + /* we would have called it a path if that term wasn't already used */ + int nvertices; /* number of points in the stroke */ + int flags; /* stroke style flags */ + pointf *vertices; /* array of vertex points */ + } stroke_t; + +/* flag definitions for stroke_t */ +#define STROKE_CLOSED (1 << 0) +#define STROKE_FILLED (1 << 1) +#define STROKE_PENDOWN (1 << 2) +#define STROKE_VERTICES_ALLOCATED (1 << 3) + + typedef struct shape_t { /* mutable shape information for a node */ + int nstrokes; /* number of strokes in array */ + stroke_t *strokes; /* array of strokes */ + /* The last stroke must always be closed, but can be pen_up. + * It is used as the clipping path */ + } shape_t; + + typedef struct shape_functions { /* read-only shape functions */ + void (*initfn) (node_t *); /* initializes shape from node u.shape_info structure */ + void (*freefn) (node_t *); /* frees shape from node u.shape_info structure */ + port(*portfn) (node_t *, char *, char *); /* finds aiming point and slope of port */ + boolean(*insidefn) (inside_t * inside_context, pointf); /* clips incident gvc->e spline on shape of gvc->n */ + int (*pboxfn)(node_t* n, port* p, int side, boxf rv[], int *kptr); /* finds box path to reach port */ + void (*codefn) (GVJ_t * job, node_t * n); /* emits graphics code for node */ + } shape_functions; + + typedef enum { SH_UNSET, SH_POLY, SH_RECORD, SH_POINT, SH_EPSF} shape_kind; + + typedef struct shape_desc { /* read-only shape descriptor */ + char *name; /* as read from graph file */ + shape_functions *fns; + polygon_t *polygon; /* base polygon info */ + boolean usershape; + } shape_desc; + +#include "usershape.h" /* usershapes needed by gvc */ + + typedef struct nodequeue { + node_t **store, **limit, **head, **tail; + } nodequeue; + + typedef struct adjmatrix_t { + int nrows, ncols; + char *data; + } adjmatrix_t; + + typedef struct rank_t { + int n; /* number of nodes in this rank */ + node_t **v; /* ordered list of nodes in rank */ + int an; /* globally allocated number of nodes */ + node_t **av; /* allocated list of nodes in rank */ + int ht1, ht2; /* height below/above centerline */ + int pht1, pht2; /* as above, but only primitive nodes */ + boolean candidate; /* for transpose () */ + boolean valid; + int cache_nc; /* caches number of crossings */ + adjmatrix_t *flat; + } rank_t; + + typedef enum { R_NONE = + 0, R_VALUE, R_FILL, R_COMPRESS, R_AUTO, R_EXPAND } ratio_t; + + typedef struct layout_t { + double quantum; + double scale; + double ratio; /* set only if ratio_kind == R_VALUE */ + double dpi; + pointf margin; + pointf page; + pointf size; + boolean filled; + boolean landscape; + boolean centered; + ratio_t ratio_kind; + void* xdots; + } layout_t; + +/* for "record" shapes */ + typedef struct field_t { + pointf size; /* its dimension */ + boxf b; /* its placement in node's coordinates */ + int n_flds; + textlabel_t *lp; /* n_flds == 0 */ + struct field_t **fld; /* n_flds > 0 */ + char *id; /* user's identifier */ + unsigned char LR; /* if box list is horizontal (left to right) */ + unsigned char sides; /* sides of node exposed to field */ + } field_t; + + typedef struct nlist_t { + node_t **list; + int size; + } nlist_t; + + typedef struct elist { + edge_t **list; + int size; + } elist; + +#define GUI_STATE_ACTIVE (1<<0) +#define GUI_STATE_SELECTED (1<<1) +#define GUI_STATE_VISITED (1<<2) +#define GUI_STATE_DELETED (1<<3) + +#define elist_fastapp(item,L) do {L.list[L.size++] = item; L.list[L.size] = NULL;} while(0) +#define elist_append(item,L) do {L.list = ALLOC(L.size + 2,L.list,edge_t*); L.list[L.size++] = item; L.list[L.size] = NULL;} while(0) +#define alloc_elist(n,L) do {L.size = 0; L.list = N_NEW(n + 1,edge_t*); } while (0) +#define free_list(L) do {if (L.list) free(L.list);} while (0) + +typedef enum {NATIVEFONTS,PSFONTS,SVGFONTS} fontname_kind; + + typedef struct Agraphinfo_t { +#ifdef WITH_CGRAPH + Agrec_t hdr; +#endif + /* to generate code */ + layout_t *drawing; + textlabel_t *label; /* if the cluster has a title */ + boxf bb; /* bounding box */ + pointf border[4]; /* sizes of margins for graph labels */ + unsigned char gui_state; /* Graph state for GUI ops */ + unsigned char has_labels; + boolean has_images; + unsigned char charset; /* input character set */ + int rankdir; + int ht1, ht2; /* below and above extremal ranks */ + unsigned short flags; + void *alg; + GVC_t *gvc; /* context for "globals" over multiple graphs */ + void (*cleanup) (graph_t * g); /* function to deallocate layout-specific data */ + +#ifndef DOT_ONLY + /* to place nodes */ + node_t **neato_nlist; + int move; + double **dist, **spring, **sum_t, ***t; + unsigned short ndim; + unsigned short odim; +#endif +#ifndef NEATO_ONLY + /* to have subgraphs */ + int n_cluster; + graph_t **clust; /* clusters are in clust[1..n_cluster] !!! */ + node_t *nlist; + rank_t *rank; + /* fast graph node list */ + nlist_t comp; + /* connected components */ + node_t *minset, *maxset; /* set leaders */ + long n_nodes; + /* includes virtual */ + short minrank, maxrank; + + /* various flags */ + boolean has_flat_edges; + unsigned char showboxes; + boolean cluster_was_collapsed; + fontname_kind fontnames; /* to override mangling in SVG */ + + int nodesep, ranksep; + node_t *ln, *rn; /* left, right nodes of bounding box */ + + + /* for clusters */ + node_t *leader, **rankleader; + boolean expanded; + char installed; + char set_type; + char label_pos; + boolean exact_ranksep; +#endif + + } Agraphinfo_t; + +#ifdef WITH_CGRAPH +#define GD_u(g)(((Agraphinfo_t*)AGDATA(g))) +#define GD_drawing(g) (((Agraphinfo_t*)AGDATA(g))->drawing) +#define GD_bb(g) (((Agraphinfo_t*)AGDATA(g))->bb) +#define GD_gvc(g) (((Agraphinfo_t*)AGDATA(g))->gvc) +#define GD_cleanup(g) (((Agraphinfo_t*)AGDATA(g))->cleanup) +#define GD_dist(g) (((Agraphinfo_t*)AGDATA(g))->dist) +#define GD_alg(g) (((Agraphinfo_t*)AGDATA(g))->alg) +#define GD_border(g) (((Agraphinfo_t*)AGDATA(g))->border) +#define GD_cl_cnt(g) (((Agraphinfo_t*)AGDATA(g))->cl_nt) +#define GD_clust(g) (((Agraphinfo_t*)AGDATA(g))->clust) +#define GD_cluster_was_collapsed(g) (((Agraphinfo_t*)AGDATA(g))->cluster_was_collapsed) +#define GD_comp(g) (((Agraphinfo_t*)AGDATA(g))->comp) +#define GD_exact_ranksep(g) (((Agraphinfo_t*)AGDATA(g))->exact_ranksep) +#define GD_expanded(g) (((Agraphinfo_t*)AGDATA(g))->expanded) +#define GD_flags(g) (((Agraphinfo_t*)AGDATA(g))->flags) +#define GD_gui_state(g) (((Agraphinfo_t*)AGDATA(g))->gui_state) +#define GD_charset(g) (((Agraphinfo_t*)AGDATA(g))->charset) +#define GD_has_labels(g) (((Agraphinfo_t*)AGDATA(g))->has_labels) +#define GD_has_images(g) (((Agraphinfo_t*)AGDATA(g))->has_images) +#define GD_has_flat_edges(g) (((Agraphinfo_t*)AGDATA(g))->has_flat_edges) +#define GD_ht1(g) (((Agraphinfo_t*)AGDATA(g))->ht1) +#define GD_ht2(g) (((Agraphinfo_t*)AGDATA(g))->ht2) +#define GD_inleaf(g) (((Agraphinfo_t*)AGDATA(g))->inleaf) +#define GD_installed(g) (((Agraphinfo_t*)AGDATA(g))->installed) +#define GD_label(g) (((Agraphinfo_t*)AGDATA(g))->label) +#define GD_leader(g) (((Agraphinfo_t*)AGDATA(g))->leader) +#define GD_rankdir2(g) (((Agraphinfo_t*)AGDATA(g))->rankdir) +#define GD_rankdir(g) (((Agraphinfo_t*)AGDATA(g))->rankdir & 0x3) +#define GD_flip(g) (GD_rankdir(g) & 1) +#define GD_realrankdir(g) ((((Agraphinfo_t*)AGDATA(g))->rankdir) >> 2) +#define GD_realflip(g) (GD_realrankdir(g) & 1) +#define GD_ln(g) (((Agraphinfo_t*)AGDATA(g))->ln) +#define GD_maxrank(g) (((Agraphinfo_t*)AGDATA(g))->maxrank) +#define GD_maxset(g) (((Agraphinfo_t*)AGDATA(g))->maxset) +#define GD_minrank(g) (((Agraphinfo_t*)AGDATA(g))->minrank) +#define GD_minset(g) (((Agraphinfo_t*)AGDATA(g))->minset) +#define GD_move(g) (((Agraphinfo_t*)AGDATA(g))->move) +#define GD_n_cluster(g) (((Agraphinfo_t*)AGDATA(g))->n_cluster) +#define GD_n_nodes(g) (((Agraphinfo_t*)AGDATA(g))->n_nodes) +#define GD_ndim(g) (((Agraphinfo_t*)AGDATA(g))->ndim) +#define GD_odim(g) (((Agraphinfo_t*)AGDATA(g))->odim) +#define GD_neato_nlist(g) (((Agraphinfo_t*)AGDATA(g))->neato_nlist) +#define GD_nlist(g) (((Agraphinfo_t*)AGDATA(g))->nlist) +#define GD_nodesep(g) (((Agraphinfo_t*)AGDATA(g))->nodesep) +#define GD_outleaf(g) (((Agraphinfo_t*)AGDATA(g))->outleaf) +#define GD_rank(g) (((Agraphinfo_t*)AGDATA(g))->rank) +#define GD_rankleader(g) (((Agraphinfo_t*)AGDATA(g))->rankleader) +#define GD_ranksep(g) (((Agraphinfo_t*)AGDATA(g))->ranksep) +#define GD_rn(g) (((Agraphinfo_t*)AGDATA(g))->rn) +#define GD_set_type(g) (((Agraphinfo_t*)AGDATA(g))->set_type) +#define GD_label_pos(g) (((Agraphinfo_t*)AGDATA(g))->label_pos) +#define GD_showboxes(g) (((Agraphinfo_t*)AGDATA(g))->showboxes) +#define GD_fontnames(g) (((Agraphinfo_t*)AGDATA(g))->fontnames) +#define GD_spring(g) (((Agraphinfo_t*)AGDATA(g))->spring) +#define GD_sum_t(g) (((Agraphinfo_t*)AGDATA(g))->sum_t) +#define GD_t(g) (((Agraphinfo_t*)AGDATA(g))->t) + +#else + +#define GD_alg(g) (g)->u.alg +#define GD_bb(g) (g)->u.bb +#define GD_border(g) (g)->u.border +#define GD_cl_cnt(g) (g)->u.cl_cnt +#define GD_cleanup(g) (g)->u.cleanup +#define GD_clust(g) (g)->u.clust +#define GD_cluster_was_collapsed(g) (g)->u.cluster_was_collapsed +#define GD_comp(g) (g)->u.comp +#define GD_dist(g) (g)->u.dist +#define GD_drawing(g) (g)->u.drawing +#define GD_exact_ranksep(g) (g)->u.exact_ranksep +#define GD_expanded(g) (g)->u.expanded +#define GD_flags(g) (g)->u.flags +#define GD_gui_state(g) (g)->u.gui_state +#define GD_gvc(g) (g)->u.gvc +#define GD_charset(g) (g)->u.charset +#define GD_has_labels(g) (g)->u.has_labels +#define GD_has_images(g) (g)->u.has_images +#define GD_has_flat_edges(g) (g)->u.has_flat_edges +#define GD_ht1(g) (g)->u.ht1 +#define GD_ht2(g) (g)->u.ht2 +#define GD_inleaf(g) (g)->u.inleaf +#define GD_installed(g) (g)->u.installed +#define GD_label(g) (g)->u.label +#define GD_leader(g) (g)->u.leader +#define GD_rankdir(g) ((g)->u.rankdir & 0x3) +#define GD_flip(g) (GD_rankdir(g) & 1) +#define GD_realrankdir(g) ((g)->u.rankdir >> 2) +#define GD_realflip(g) (GD_realrankdir(g) & 1) +#define GD_ln(g) (g)->u.ln +#define GD_maxrank(g) (g)->u.maxrank +#define GD_maxset(g) (g)->u.maxset +#define GD_minrank(g) (g)->u.minrank +#define GD_minset(g) (g)->u.minset +#define GD_move(g) (g)->u.move +#define GD_n_cluster(g) (g)->u.n_cluster +#define GD_n_nodes(g) (g)->u.n_nodes +#define GD_ndim(g) (g)->u.ndim +#define GD_odim(g) (g)->u.odim +#define GD_neato_nlist(g) (g)->u.neato_nlist +#define GD_nlist(g) (g)->u.nlist +#define GD_nodesep(g) (g)->u.nodesep +#define GD_outleaf(g) (g)->u.outleaf +#define GD_rank(g) (g)->u.rank +#define GD_rankleader(g) (g)->u.rankleader +#define GD_ranksep(g) (g)->u.ranksep +#define GD_rn(g) (g)->u.rn +#define GD_set_type(g) (g)->u.set_type +#define GD_label_pos(g) (g)->u.label_pos +#define GD_showboxes(g) (g)->u.showboxes +#define GD_fontnames(g) (g)->u.fontnames +#define GD_spring(g) (g)->u.spring +#define GD_sum_t(g) (g)->u.sum_t +#define GD_t(g) (g)->u.t +#endif + + typedef struct Agnodeinfo_t { +#ifdef WITH_CGRAPH + Agrec_t hdr; +#endif + shape_desc *shape; + void *shape_info; + pointf coord; + double width, height; /* inches */ + boxf bb; + double ht, lw, rw; + textlabel_t *label; + textlabel_t *xlabel; + void *alg; + char state; + unsigned char gui_state; /* Node state for GUI ops */ + boolean clustnode; + +#ifndef DOT_ONLY + unsigned char pinned; + short xsize, ysize; + int id, heapindex, hops; + double *pos, dist; +#endif +#ifndef NEATO_ONLY + unsigned char showboxes; + boolean has_port; + + /* fast graph */ + char node_type, mark, onstack; + char ranktype, weight_class; + node_t *next, *prev; + elist in, out, flat_out, flat_in, other; + graph_t *clust; + + /* for union-find and collapsing nodes */ + int UF_size; + node_t *UF_parent; + node_t *inleaf, *outleaf; + + /* for placing nodes */ + int rank, order; /* initially, order = 1 for ordered edges */ + int mval; + elist save_in, save_out; + + /* for network-simplex */ + elist tree_in, tree_out; + edge_t *par; + int low, lim; + int priority; + + double pad[1]; +#endif + + } Agnodeinfo_t; + +#ifdef WITH_CGRAPH +#define ND_id(n) (((Agnodeinfo_t*)AGDATA(n))->id) +#define ND_alg(n) (((Agnodeinfo_t*)AGDATA(n))->alg) +#define ND_UF_parent(n) (((Agnodeinfo_t*)AGDATA(n))->UF_parent) +#define ND_UF_size(n) (((Agnodeinfo_t*)AGDATA(n))->UF_size) +#define ND_bb(n) (((Agnodeinfo_t*)AGDATA(n))->bb) +#define ND_clust(n) (((Agnodeinfo_t*)AGDATA(n))->clust) +#define ND_coord(n) (((Agnodeinfo_t*)AGDATA(n))->coord) +#define ND_dist(n) (((Agnodeinfo_t*)AGDATA(n))->dist) +#define ND_flat_in(n) (((Agnodeinfo_t*)AGDATA(n))->flat_in) +#define ND_flat_out(n) (((Agnodeinfo_t*)AGDATA(n))->flat_out) +#define ND_gui_state(n) (((Agnodeinfo_t*)AGDATA(n))->gui_state) +#define ND_has_port(n) (((Agnodeinfo_t*)AGDATA(n))->has_port) +#define ND_heapindex(n) (((Agnodeinfo_t*)AGDATA(n))->heapindex) +#define ND_height(n) (((Agnodeinfo_t*)AGDATA(n))->height) +#define ND_hops(n) (((Agnodeinfo_t*)AGDATA(n))->hops) +#define ND_ht(n) (((Agnodeinfo_t*)AGDATA(n))->ht) +#define ND_in(n) (((Agnodeinfo_t*)AGDATA(n))->in) +#define ND_inleaf(n) (((Agnodeinfo_t*)AGDATA(n))->inleaf) +#define ND_label(n) (((Agnodeinfo_t*)AGDATA(n))->label) +#define ND_xlabel(n) (((Agnodeinfo_t*)AGDATA(n))->xlabel) +#define ND_lim(n) (((Agnodeinfo_t*)AGDATA(n))->lim) +#define ND_low(n) (((Agnodeinfo_t*)AGDATA(n))->low) +#define ND_lw(n) (((Agnodeinfo_t*)AGDATA(n))->lw) +#define ND_mark(n) (((Agnodeinfo_t*)AGDATA(n))->mark) +#define ND_mval(n) (((Agnodeinfo_t*)AGDATA(n))->mval) +#define ND_n_cluster(n) (((Agnodeinfo_t*)AGDATA(n))->n_cluster) +#define ND_next(n) (((Agnodeinfo_t*)AGDATA(n))->next) +#define ND_node_type(n) (((Agnodeinfo_t*)AGDATA(n))->node_type) +#define ND_onstack(n) (((Agnodeinfo_t*)AGDATA(n))->onstack) +#define ND_order(n) (((Agnodeinfo_t*)AGDATA(n))->order) +#define ND_other(n) (((Agnodeinfo_t*)AGDATA(n))->other) +#define ND_out(n) (((Agnodeinfo_t*)AGDATA(n))->out) +#define ND_outleaf(n) (((Agnodeinfo_t*)AGDATA(n))->outleaf) +#define ND_par(n) (((Agnodeinfo_t*)AGDATA(n))->par) +#define ND_pinned(n) (((Agnodeinfo_t*)AGDATA(n))->pinned) +#define ND_pos(n) (((Agnodeinfo_t*)AGDATA(n))->pos) +#define ND_prev(n) (((Agnodeinfo_t*)AGDATA(n))->prev) +#define ND_priority(n) (((Agnodeinfo_t*)AGDATA(n))->priority) +#define ND_rank(n) (((Agnodeinfo_t*)AGDATA(n))->rank) +#define ND_ranktype(n) (((Agnodeinfo_t*)AGDATA(n))->ranktype) +#define ND_rw(n) (((Agnodeinfo_t*)AGDATA(n))->rw) +#define ND_save_in(n) (((Agnodeinfo_t*)AGDATA(n))->save_in) +#define ND_save_out(n) (((Agnodeinfo_t*)AGDATA(n))->save_out) +#define ND_shape(n) (((Agnodeinfo_t*)AGDATA(n))->shape) +#define ND_shape_info(n) (((Agnodeinfo_t*)AGDATA(n))->shape_info) +#define ND_showboxes(n) (((Agnodeinfo_t*)AGDATA(n))->showboxes) +#define ND_state(n) (((Agnodeinfo_t*)AGDATA(n))->state) +#define ND_clustnode(n) (((Agnodeinfo_t*)AGDATA(n))->clustnode) +#define ND_tree_in(n) (((Agnodeinfo_t*)AGDATA(n))->tree_in) +#define ND_tree_out(n) (((Agnodeinfo_t*)AGDATA(n))->tree_out) +#define ND_weight_class(n) (((Agnodeinfo_t*)AGDATA(n))->weight_class) +#define ND_width(n) (((Agnodeinfo_t*)AGDATA(n))->width) +#define ND_xsize(n) (((Agnodeinfo_t*)AGDATA(n))->xsize) +#define ND_ysize(n) (((Agnodeinfo_t*)AGDATA(n))->ysize) + +#else + +#define ND_UF_parent(n) (n)->u.UF_parent +#define ND_UF_size(n) (n)->u.UF_size +#define ND_alg(n) (n)->u.alg +#define ND_bb(n) (n)->u.bb +#define ND_clust(n) (n)->u.clust +#define ND_coord(n) (n)->u.coord +#define ND_dist(n) (n)->u.dist +#define ND_flat_in(n) (n)->u.flat_in +#define ND_flat_out(n) (n)->u.flat_out +#define ND_gui_state(n) (n)->u.gui_state +#define ND_has_port(n) (n)->u.has_port +#define ND_heapindex(n) (n)->u.heapindex +#define ND_height(n) (n)->u.height +#define ND_hops(n) (n)->u.hops +#define ND_ht(n) (n)->u.ht +#define ND_id(n) (n)->u.id +#define ND_in(n) (n)->u.in +#define ND_inleaf(n) (n)->u.inleaf +#define ND_label(n) (n)->u.label +#define ND_xlabel(n) (n)->u.xlabel +#define ND_lim(n) (n)->u.lim +#define ND_low(n) (n)->u.low +#define ND_lw(n) (n)->u.lw +#define ND_mark(n) (n)->u.mark +#define ND_mval(n) (n)->u.mval +#define ND_n_cluster(n) (n)->u.n_cluster +#define ND_next(n) (n)->u.next +#define ND_node_type(n) (n)->u.node_type +#define ND_onstack(n) (n)->u.onstack +#define ND_order(n) (n)->u.order +#define ND_other(n) (n)->u.other +#define ND_out(n) (n)->u.out +#define ND_outleaf(n) (n)->u.outleaf +#define ND_par(n) (n)->u.par +#define ND_pinned(n) (n)->u.pinned +#define ND_pos(n) (n)->u.pos +#define ND_prev(n) (n)->u.prev +#define ND_priority(n) (n)->u.priority +#define ND_rank(n) (n)->u.rank +#define ND_ranktype(n) (n)->u.ranktype +#define ND_rw(n) (n)->u.rw +#define ND_save_in(n) (n)->u.save_in +#define ND_save_out(n) (n)->u.save_out +#define ND_shape(n) (n)->u.shape +#define ND_shape_info(n) (n)->u.shape_info +#define ND_showboxes(n) (n)->u.showboxes +#define ND_state(n) (n)->u.state +#define ND_clustnode(n) (n)->u.clustnode +#define ND_tree_in(n) (n)->u.tree_in +#define ND_tree_out(n) (n)->u.tree_out +#define ND_weight_class(n) (n)->u.weight_class +#define ND_width(n) (n)->u.width +#define ND_xsize(n) (n)->u.xsize +#define ND_ysize(n) (n)->u.ysize +#endif + + typedef struct Agedgeinfo_t { +#ifdef WITH_CGRAPH + Agrec_t hdr; +#endif + splines *spl; + port tail_port, head_port; + textlabel_t *label, *head_label, *tail_label, *xlabel; + char edge_type; + char adjacent; /* true for flat edge with adjacent nodes */ + char label_ontop; + unsigned char gui_state; /* Edge state for GUI ops */ + edge_t *to_orig; /* for dot's shapes.c */ + void *alg; + +#ifndef DOT_ONLY + double factor; + double dist; + Ppolyline_t path; +#endif +#ifndef NEATO_ONLY + unsigned char showboxes; + boolean conc_opp_flag; + short xpenalty; + int weight; + int cutvalue, tree_index; + short count; + unsigned short minlen; + edge_t *to_virt; +#endif + } Agedgeinfo_t; + +#ifdef WITH_CGRAPH +#define ED_alg(e) (((Agedgeinfo_t*)AGDATA(e))->alg) +#define ED_conc_opp_flag(e) (((Agedgeinfo_t*)AGDATA(e))->conc_opp_flag) +#define ED_count(e) (((Agedgeinfo_t*)AGDATA(e))->count) +#define ED_cutvalue(e) (((Agedgeinfo_t*)AGDATA(e))->cutvalue) +#define ED_edge_type(e) (((Agedgeinfo_t*)AGDATA(e))->edge_type) +#define ED_adjacent(e) (((Agedgeinfo_t*)AGDATA(e))->adjacent) +#define ED_factor(e) (((Agedgeinfo_t*)AGDATA(e))->factor) +#define ED_gui_state(e) (((Agedgeinfo_t*)AGDATA(e))->gui_state) +#define ED_head_label(e) (((Agedgeinfo_t*)AGDATA(e))->head_label) +#define ED_head_port(e) (((Agedgeinfo_t*)AGDATA(e))->head_port) +#define ED_label(e) (((Agedgeinfo_t*)AGDATA(e))->label) +#define ED_xlabel(e) (((Agedgeinfo_t*)AGDATA(e))->xlabel) +#define ED_label_ontop(e) (((Agedgeinfo_t*)AGDATA(e))->label_ontop) +#define ED_minlen(e) (((Agedgeinfo_t*)AGDATA(e))->minlen) +#define ED_path(e) (((Agedgeinfo_t*)AGDATA(e))->path) +#define ED_showboxes(e) (((Agedgeinfo_t*)AGDATA(e))->showboxes) +#define ED_spl(e) (((Agedgeinfo_t*)AGDATA(e))->spl) +#define ED_tail_label(e) (((Agedgeinfo_t*)AGDATA(e))->tail_label) +#define ED_tail_port(e) (((Agedgeinfo_t*)AGDATA(e))->tail_port) +#define ED_to_orig(e) (((Agedgeinfo_t*)AGDATA(e))->to_orig) +#define ED_to_virt(e) (((Agedgeinfo_t*)AGDATA(e))->to_virt) +#define ED_tree_index(e) (((Agedgeinfo_t*)AGDATA(e))->tree_index) +#define ED_xpenalty(e) (((Agedgeinfo_t*)AGDATA(e))->xpenalty) +#define ED_dist(e) (((Agedgeinfo_t*)AGDATA(e))->dist) +#define ED_weight(e) (((Agedgeinfo_t*)AGDATA(e))->weight) + +#else + +#define ED_alg(e) (e)->u.alg +#define ED_conc_opp_flag(e) (e)->u.conc_opp_flag +#define ED_count(e) (e)->u.count +#define ED_cutvalue(e) (e)->u.cutvalue +#define ED_dist(e) (e)->u.dist +#define ED_edge_type(e) (e)->u.edge_type +#define ED_adjacent(e) (e)->u.adjacent +#define ED_factor(e) (e)->u.factor +#define ED_gui_state(e) (e)->u.gui_state +#define ED_head_label(e) (e)->u.head_label +#define ED_head_port(e) (e)->u.head_port +#define ED_label(e) (e)->u.label +#define ED_xlabel(e) (e)->u.xlabel +#define ED_label_ontop(e) (e)->u.label_ontop +#define ED_minlen(e) (e)->u.minlen +#define ED_path(e) (e)->u.path +#define ED_showboxes(e) (e)->u.showboxes +#define ED_spl(e) (e)->u.spl +#define ED_tail_label(e) (e)->u.tail_label +#define ED_tail_port(e) (e)->u.tail_port +#define ED_to_orig(e) (e)->u.to_orig +#define ED_to_virt(e) (e)->u.to_virt +#define ED_tree_index(e) (e)->u.tree_index +#define ED_weight(e) (e)->u.weight +#define ED_xpenalty(e) (e)->u.xpenalty +#endif + +#ifdef WITH_CGRAPH +#include "cgraph.h" +#define SET_RANKDIR(g,rd) (GD_rankdir2(g) = rd) +#define agfindedge(g,t,h) (agedge(g,t,h,NULL,0)) +#define agfindnode(g,n) (agnode(g,n,0)) +#define agfindgraphattr(g,a) (agattr(g,AGRAPH,a,NULL)) +#define agfindnodeattr(g,a) (agattr(g,AGNODE,a,NULL)) +#define agfindedgeattr(g,a) (agattr(g,AGEDGE,a,NULL)) +#else +#include "graph.h" +#define SET_RANKDIR(g,rd) ((g)->u.rankdir = (rd)) +#define agnameof(x) ((x)->name) + /* warning, agraphof doesn't work for edges */ +#define agraphof(n) ((n)->graph) +#define agroot(g) ((g)->root) +#define aghead(e) ((e)->head) +#define agtail(e) ((e)->tail) +#define agisdirected(g) ((g)->kind & AGFLAG_DIRECTED) +#define AGID(x) ((x)->id) +#define agfindgraphattr(g,a) agfindattr((g)->root,a) +#define agfindnodeattr(g,a) agfindattr((g)->proto->n,a) +#define agfindedgeattr(g,a) agfindattr((g)->proto->e,a) +#define agcanonStr(s) agcanonical(s) +#endif + + + typedef struct { + int useGrid; /* use grid for speed up */ + int useNew; /* encode x-K into attractive force */ + int numIters; /* actual iterations in layout */ + int unscaled; /* % of iterations used in pass 1 */ + double C; /* Repulsion factor in xLayout */ + double Tfact; /* scale temp from default expression */ + double K; /* spring constant; ideal distance */ + double T0; /* initial temperature */ + } fdpParms_t; + + typedef struct { + int flags; + } gvlayout_features_t; + +#ifdef __cplusplus +} +#endif +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/include/graphviz/usershape.h Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,59 @@ +/* $Id: usershape.h,v 1.15 2009/06/03 01:10:52 ellson Exp $ $Revision: 1.15 $ */ +/* vim:set shiftwidth=4 ts=8: */ + +/********************************************************** +* This software is part of the graphviz package * +* http://www.graphviz.org/ * +* * +* Copyright (c) 1994-2004 AT&T Corp. * +* and is licensed under the * +* Common Public License, Version 1.0 * +* by AT&T Corp. * +* * +* Information and Software Systems Research * +* AT&T Research, Florham Park NJ * +**********************************************************/ + +#ifndef USERSHAPE_H +#define USERSHAPE_H + +#include "cdt.h" + +#ifdef __cplusplus +extern "C" { +#endif + + typedef enum { FT_NULL, + FT_BMP, FT_GIF, FT_PNG, FT_JPEG, + FT_PDF, FT_PS, FT_EPS, FT_SVG, FT_XML + } imagetype_t; + + typedef enum { + IMAGESCALE_FALSE, /* no image scaling */ + IMAGESCALE_TRUE, /* scale image to fit but keep aspect ratio */ + IMAGESCALE_WIDTH, /* scale image width to fit, keep height fixed */ + IMAGESCALE_HEIGHT, /* scale image height to fit, keep width fixed */ + IMAGESCALE_BOTH /* scale image to fit without regard for aspect ratio */ + } imagescale_t; + + typedef struct usershape_s usershape_t; + + struct usershape_s { + Dtlink_t link; + const char *name; + int macro_id; + boolean must_inline; + boolean nocache; + FILE *f; + imagetype_t type; + char *stringtype; + int x, y, w, h, dpi; + void *data; /* data loaded by a renderer */ + size_t datasize; /* size of data (if mmap'ed) */ + void (*datafree)(usershape_t *us); /* renderer's function for freeing data */ + }; + +#ifdef __cplusplus +} +#endif +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/box.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,157 @@ +load ('def.lefty'); +definit (); +# +# initialize window data +# +canvas = defcanvas; +wrect = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 400; 'y' = 500;];]; +setwidgetattr (canvas, ['window' = wrect;]); +# +# data structures +# +objarray = []; +objnum = 0; +# +# misc functions +# +min = function (a, b) { + if (a <= b) + return a; + return b; +}; +max = function (a, b) { + if (b <= a) + return a; + return b; +}; +rectof = function (p1, p2) { + return [ + 0 = ['x' = min (p1.x, p2.x); 'y' = min (p1.y, p2.y);]; + 1 = ['x' = max (p1.x, p2.x); 'y' = max (p1.y, p2.y);]; + ]; +}; +pointadd = function (p1, p2) { + return ['x' = p2.x + p1.x; 'y' = p2.y + p1.y;]; +}; +pointsub = function (p1, p2) { + return ['x' = p2.x - p1.x; 'y' = p2.y - p1.y;]; +}; +# +# rendering functions +# +drawbox = function (obj, color) { + box (canvas, obj, obj.rect, ['color' = color;]); +}; +redrawboxes = function () { + local i; + clear (canvas); + for (i = 0; i < objnum; i = i + 1) + drawbox (objarray[i], 1); +}; +redraw = function (canvas) { + redrawboxes (); +}; +# +# editing functions +# +new = function (rect) { + objarray[objnum] = [ + 'rect' = rect; + 'id' = objnum; + ]; + objnum = objnum + 1; + return objarray[objnum - 1]; +}; +reshape = function (obj, rect) { + obj.rect = rect; + return obj; +}; +move = function (obj, p) { + obj.rect[0] = pointadd (obj.rect[0], p); + obj.rect[1] = pointadd (obj.rect[1], p); + return obj; +}; +delete = function (obj) { + if (obj.id ~= objnum - 1) { + objarray[obj.id] = objarray[objnum - 1]; + objarray[obj.id].id = obj.id; + } + remove (objnum - 1, objarray); + objnum = objnum - 1; +}; +# +# user interface functions +# +# left mouse button creates new box +# middle button moves a box +# right button deletes a box +# +leftdown = function (data) { + if (data.obj ~= null) + return; + leftbox = new (rectof (data.pos, data.pos)); + drawbox (leftbox, 1); + setgfxattr (canvas, ['mode' = 'xor';]); +}; +leftmove = function (data) { + if (~leftbox) + return; + drawbox (leftbox, 1); + clearpick (canvas, leftbox); + reshape (leftbox, rectof (data.ppos, data.pos)); + drawbox (leftbox, 1); +}; +leftup = function (data) { + if (~leftbox) + return; + drawbox (leftbox, 1); + clearpick (canvas, leftbox); + reshape (leftbox, rectof (data.ppos, data.pos)); + setgfxattr (canvas, ['mode' = 'src';]); + drawbox (leftbox, 1); + remove ('leftbox'); +}; +middledown = function (data) { + if (data.obj == null) + return; + middlebox = data.obj; + middlepos = data.pos; + setgfxattr (canvas, ['mode' = 'xor';]); +}; +middlemove = function (data) { + if (~middlebox) + return; + drawbox (middlebox, 1); + clearpick (canvas, middlebox); + move (middlebox, pointsub (middlepos, data.pos)); + middlepos = data.pos; + drawbox (middlebox, 1); +}; +middleup = function (data) { + if (~middlebox) + return; + drawbox (middlebox, 1); + clearpick (canvas, middlebox); + move (middlebox, pointsub (middlepos, data.pos)); + setgfxattr (canvas, ['mode' = 'src';]); + drawbox (middlebox, 1); + remove ('middlepos'); + remove ('middlebox'); +}; +rightup = function (data) { + if (data.pobj == null) + return; + drawbox (data.obj, 0); + clearpick (canvas, data.obj); + delete (data.obj); +}; +dops = function () { + local s; + + s = ['x' = 8 * 300; 'y' = 10.5 * 300;]; + canvas = createwidget (-1, ['type' = 'ps'; 'size' = s;]); + setwidgetattr (canvas, ['window' = wrect;]); + redraw (canvas); + destroywidget (canvas); + canvas=defcanvas; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/def.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +definit = function () { + defview = createwidget (-1, [ + 'type' = 'view'; + 'name' = 'graphics view'; + 'origin' = ['x' = 1; 'y' = 1;]; + 'size' = ['x' = 400; 'y' = 500;]; + ]); + defscroll = createwidget (defview, ['type' = 'scroll';]); + defcanvas = createwidget (defscroll, [ + 'type' = 'canvas'; + 'origin' = ['x' = 1; 'y' = 1;]; + 'size' = ['x' = 400; 'y' = 500;]; + 'borderwidth' = 1; + ]); +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/dotty.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,735 @@ +# +# DOTTY +# +dotty = [ + 'keys' = [ + 'nid' = 'nid'; + 'eid' = 'eid'; + 'gid' = 'gid'; + 'name' = 'name'; + 'attr' = 'attr'; + 'gattr' = 'graphattr'; + 'eattr' = 'edgeattr'; + 'nattr' = 'nodeattr'; + 'edges' = 'edges'; + 'tail' = 'tail'; + 'tport' = 'tport'; + 'head' = 'head'; + 'hport' = 'hport'; + 'pos' = 'pos'; + 'size' = 'size'; + 'rect' = 'rect'; + 'fname' = 'fontname'; + 'fsize' = 'fontsize'; + 'fcolor' = 'fontcolor'; + 'dcolor' = 'drawcolor'; + 'bcolor' = 'fillcolor'; + ]; + 'maps' = [ + 'X11' = [ + 'fontmap' = [ + 'Times-Roman' = '-*-times-medium-r-*--%d-*-*-*-*-*-*-1'; + 'Times-Italic' = '-*-times-medium-i-*--%d-*-*-*-*-*-*-1'; + 'Times-Bold' = '-*-times-bold-r-*--%d-*-*-*-*-*-*-1'; + 'Courier' = '-*-courier-bold-r-*--%d-*-*-*-*-*-*-1'; + 'Courier-Bold' = '-*-courier-bold-r-*--%d-*-*-*-*-*-*-1'; + 'Helvetica' = ( + '-*-helvetica-medium-r-normal--%d-*-*-*-p-*-iso8859-1' + ); + 'Helvetica-Bold' = ( + '-*-helvetica-bold-r-normal--%d-*-*-*-p-*-iso8859-1' + ); + ]; + 'psfontmap' = [ + 'Times-Roman' = 'Times-Roman'; + 'Times-Italic' = 'Times-Italic'; + 'Times-Bold' = 'Times-Bold'; + 'Courier' = 'Courier'; + 'Courier-Bold' = 'Courier-Bold'; + 'Helvetica' = 'Helvetica'; + 'Helvetica-Bold' = 'Helvetica-Bold'; + ]; + ]; + 'mswin' = [ + 'fontmap' = [ + 'Times-Roman' = 'Times New Roman'; + 'Times-Italic' = 'Times New Roman Italic'; + 'Times-Bold' = 'Times New Roman Bold'; + 'Courier' = 'Courier New'; + 'Courier-Bold' = 'Courier New Bold'; + 'Helvetica' = 'Arial'; + 'Helvetica-Bold' = 'Arial Bold'; + ]; + 'psfontmap' = [ + 'Times-Roman' = 'Times New Roman'; + 'Times-Italic' = 'Times New Roman Italic'; + 'Times-Bold' = 'Times New Roman Bold'; + 'Courier' = 'Courier New'; + 'Courier-Bold' = 'Courier New Bold'; + 'Helvetica' = 'Arial'; + 'Helvetica-Bold' = 'Arial Bold'; + ]; + ]; + ]; + 'protogt' = [ + 'graph' = [ + 'graphattr' = [ + 'fontsize' = '14'; + 'fontname' = 'Times-Roman'; + 'fontcolor' = 'black'; + ]; + 'nodeattr' = [ + 'shape' = 'ellipse'; + 'fontsize' = '14'; + 'fontname' = 'Times-Roman'; + 'fontcolor' = 'black'; + 'style' = 'solid'; + ]; + 'edgeattr' = [ + 'fontsize' = '14'; + 'fontname' = 'Times-Roman'; + 'fontcolor' = 'black'; + 'style' = 'solid'; + ]; + 'graphdict' = []; + 'nodedict' = []; + 'graphs' = []; + 'nodes' = []; + 'edges' = []; + 'maxgid' = 0; + 'maxnid' = 0; + 'maxeid' = 0; + 'type' = 'digraph'; + ]; + 'layoutmode' = 'sync'; + 'lserver' = 'dot'; + 'edgehandles' = 1; + 'noundo' = 0; + ]; + 'lservers' = []; + 'mlevel' = 0; + 'graphs' = []; + 'views' = []; + 'protovt' = [ + 'normal' = [ + 'name' = 'DOTTY'; + 'orig' = ['x' = 1; 'y' = 1;]; + 'size' = ['x' = 420; 'y' = 520;]; + 'wrect' = [ + 0 = ['x' = 0; 'y' = 0;]; + 1 = ['x' = 400; 'y' = 500;]; + ]; + 'vsize' = ['x' = 400; 'y' = 500;]; + 'w2v' = 1; + ]; + 'birdseye' = [ + 'type' = 'birdseye'; + 'name' = 'DOTTY birdseye view'; + 'orig' = ['x' = 1; 'y' = 1;]; + 'size' = ['x' = 220; 'y' = 260;]; + 'wrect' = [ + 0 = ['x' = 0; 'y' = 0;]; + 1 = ['x' = 200; 'y' = 250;]; + ]; + 'vsize' = ['x' = 200; 'y' = 250;]; + 'w2v' = 1; + ]; + ]; + 'pagesizes' = [ + '8.5x11' = ['x' = 8; 'y' = 10.5;]; + '11x17' = ['x' = 10.5; 'y' = 16.5;]; + '36x50' = ['x' = 35.5; 'y' = 49.5;]; + ]; +]; +load ('dotty_draw.lefty'); +load ('dotty_edit.lefty'); +load ('dotty_layout.lefty'); +load ('dotty_ui.lefty'); +# +# initialization functions +# +dotty.init = function () { + dotty.fontmap = dotty.maps[getenv ('LEFTYWINSYS')].fontmap; + dotty.clipgt = dotty.protogt.creategraph (['noundo' = 1;]); + dotty.inited = 1; +}; +dotty.simple = function (file) { + if (dotty.inited ~= 1) + dotty.init (); + dotty.createviewandgraph (file, 'file', null, null); + txtview ('off'); +}; +# +# main operations +# +dotty.protogt.creategraph = function (protogt) { + local gt, id, gtid; + + if (~protogt) + protogt = dotty.protogt; + for (gtid = 0; dotty.graphs[gtid]; gtid = gtid + 1) + ; + gt = (dotty.graphs[gtid] = []); + if (protogt.mode ~= 'replace') { + for (id in dotty.protogt) + gt[id] = copy (dotty.protogt[id]); + } + for (id in protogt) + gt[id] = copy (protogt[id]); + gt.gtid = gtid; + gt.views = []; + gt.undoarray = ['level' = 0; 'entries' = [];]; + gt.busy = 0; + return gt; +}; +dotty.protogt.copygraph = function (ogt) { + local gt, gtid, id; + + for (gtid = 0; dotty.graphs[gtid]; gtid = gtid + 1) + ; + gt = (dotty.graphs[gtid] = []); + for (id in ogt) + gt[id] = copy (ogt[id]); + gt.gtid = gtid; + gt.views = []; + gt.undoarray = ['level' = 0; 'entries' = [];]; + gt.busy = 0; + return gt; +}; +dotty.protogt.destroygraph = function (gt) { + local vid, vlist; + + if (gt.layoutpending > 0) + gt.cancellayout (gt); + for (vid in gt.views) + vlist[vid] = gt.views[vid]; + for (vid in gt.views) + gt.destroyview (gt, vlist[vid]); + remove (gt.gtid, dotty.graphs); +}; +dotty.protogt.loadgraph = function (gt, name, type, protograph, layoutflag) { + local fd, vid, vt, graph, nid, eid, gid; + + if (gt.layoutpending > 0) + gt.cancellayout (gt); + if (~name) + if (~(name = ask ('file name:', 'file', ''))) + return; + dotty.pushbusy (gt, gt.views); + dotty.message (1, 'loading'); + if (~protograph) + protograph = dotty.protogt.graph; + if ( + ~((fd = dotty.openio (name, type, 'r')) >= 0) | + ~(graph = readgraph (fd, protograph)) + ) { + dotty.message (0, 'cannot load graph'); + dotty.popbusy (gt, gt.views); + return; + } + for (vid in gt.views) { + vt = gt.views[vid]; + vt.colors = []; + vt.colorn = 2; + } + gt.graph = graph; + gt.name = name; + gt.type = type; + gt.undoarray = ['level' = 0; 'entries' = [];]; + if (~(type == 'file' & name == '-')) + closeio (fd); + graph.maxgid = tablesize (graph.graphs); + graph.maxnid = tablesize (graph.nodes); + graph.maxeid = tablesize (graph.edges); + for (nid in graph.nodes) + graph.nodes[nid][dotty.keys.nid] = nid; + for (eid in graph.edges) + graph.edges[eid][dotty.keys.eid] = eid; + for (gid in graph.graphs) + graph.graphs[gid][dotty.keys.gid] = gid; + gt.unpackattr (gt); + if (layoutflag) { + dotty.message (1, 'generating layout'); + gt.layoutgraph (gt); + } + dotty.popbusy (gt, gt.views); + return gt.graph; +}; +dotty.protogt.savegraph = function (gt, name, type) { + local fd; + + if (~name) + if (~(name = ask ('file name:', 'file', ''))) + return; + if ( + ~((fd = dotty.openio (name, type, 'w')) >= 0) | + ~writegraph (fd, gt.graph, 0) + ) { + dotty.message (0, 'cannot save graph'); + return; + } + if (~(type == 'file' & name == '-')) + closeio (fd); +}; +dotty.protogt.setgraph = function (gt, graph) { + local vid, vt, nid, eid, gid; + + if (gt.layoutpending > 0) + gt.cancellayout (gt); + for (vid in gt.views) { + vt = gt.views[vid]; + vt.colors = []; + vt.colorn = 2; + } + gt.graph = copy (graph); + gt.undoarray = ['level' = 0; 'entries' = [];]; + gt.unpackattr (gt); + gt.graph.maxgid = tablesize (graph.graphs); + gt.graph.maxnid = tablesize (graph.nodes); + gt.graph.maxeid = tablesize (graph.edges); + for (nid in gt.graph.nodes) + gt.graph.nodes[nid][dotty.keys.nid] = nid; + for (eid in gt.graph.edges) + gt.graph.edges[eid][dotty.keys.eid] = eid; + for (gid in gt.graph.graphs) + gt.graph.graphs[gid][dotty.keys.gid] = gid; + gt.unpackattr (gt); + dotty.message (1, 'generating layout'); + gt.layoutgraph (gt); + return gt.graph; +}; +dotty.protogt.erasegraph = function (gt, protogt, protovt) { + local vid, vt; + + if (gt.layoutpending > 0) + gt.cancellayout (gt); + for (vid in gt.views) { + vt = gt.views[vid]; + vt.colors = []; + vt.colorn = 2; + clear (vt.canvas); + } + if (~protogt) + protogt = dotty.protogt; + gt.graph = copy (protogt.graph); + gt.undoarray = ['level' = 0; 'entries' = [];]; +}; +dotty.protogt.layoutgraph = function (gt) { + if (gt.graph.graphattr.xdotversion) { + gt.unpacklayout (gt, gt.graph); + gt.setviewsize (gt.views, gt.graph.rect); + gt.redrawgraph (gt, gt.views); + return; + } + if (gt.layoutmode == 'async') { + if (~gt.haveinput) { + gt.startlayout (gt); + return; + } + if (~gt.finishlayout (gt)) + return; + gt.setviewsize (gt.views, gt.graph.rect); + gt.redrawgraph (gt, gt.views); + } else { + if (~gt.startlayout (gt)) + return; + else + while (~gt.finishlayout (gt)) + ; + gt.setviewsize (gt.views, gt.graph.rect); + gt.redrawgraph (gt, gt.views); + } +}; +dotty.protogt.createview = function (gt, protovt) { + local vt, ovt, id, t; + + vt = []; + vt.colors = []; + vt.colorn = 2; + if (~protovt) + protovt = dotty.protovt.normal; + if (protovt.mode ~= 'replace') { + for (id in dotty.protovt[protovt.type]) + vt[id] = copy (dotty.protovt[protovt.type][id]); + } + for (id in protovt) + vt[id] = copy (protovt[id]); + if (~(vt.parent >= 0)) { + vt.view = createwidget (-1, [ + 'type' = 'view'; + 'name' = vt.name; + 'origin' = vt.orig; + 'size' = vt.size; + ]); + vt.scroll = createwidget (vt.view, ['type' = 'scroll';]); + } else { + vt.view = -1; + vt.scroll = createwidget (vt.parent, [ + 'type' = 'scroll'; + 'size' = vt.size; + ]); + } + vt.canvas = createwidget (vt.scroll, [ + 'type' = 'canvas'; + 'color' = [0 = protovt.bgcolor; 1 = protovt.fgcolor;]; + ]); + setwidgetattr (vt.canvas, [ + 'window' = vt.wrect; + 'viewport' = vt.vsize; + ]); + clear (vt.canvas); + dotty.views[vt.canvas] = vt; + vt.vtid = vt.canvas; + vt.gtid = gt.gtid; + gt.views[vt.vtid] = vt; + dotty.views[vt.scroll] = vt; + if (vt.view ~= -1) + dotty.views[vt.view] = vt; + if (protovt.colors & tablesize (protovt.colors) > 0) { + for (id in protovt.colors) + if (id == '_bgcolor_') + setwidgetattr (vt.canvas, [ + 'color' = [0 = protovt.colors[id];]; + ]); + else if (setwidgetattr (vt.canvas, ['color' = [ + protovt.colors[id] = id; + ];]) ~= 1) { + t = split (id, ' '); + if (tablesize (t) ~= 3 | setwidgetattr (vt.canvas, [ + 'color' = [protovt.colors[id] = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];]; + ]) ~= 1) { + dotty.message ( + 0, concat ('unknown color ', id, ' using #1') + ); + } + } + vt.colors = copy (protovt.colors); + vt.colorn = protovt.colorn; + } else if (tablesize (gt.views) > 1) { + for (id in gt.views) + if (gt.views[id] ~= vt) + break; + ovt = gt.views[id]; + for (id in ovt.colors) + if (id == '_bgcolor_') + setwidgetattr (vt.canvas, ['color' = [0 = ovt.colors[id];];]); + else if (setwidgetattr (vt.canvas, ['color' = [ + ovt.colors[id] = id; + ];]) ~= 1) { + t = split (id, ' '); + if (tablesize (t) ~= 3 | setwidgetattr (vt.canvas, [ + 'color' = [ovt.colors[id] = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];]; + ]) ~= 1) { + dotty.message ( + 0, concat ('unknown color ', id, ' using #1') + ); + } + } + vt.colors = copy (ovt.colors); + vt.colorn = ovt.colorn; + } + if (gt.graph.rect) + gt.setviewsize ([vt.vtid = vt;], gt.graph.rect); + gt.drawgraph (gt, [vt.vtid = vt;]); + for (id in vt.uifuncs) + if (id == 'closeview') + widgets[vt.view][id] = vt.uifuncs[id]; + else + widgets[vt.canvas][id] = vt.uifuncs[id]; + return vt; +}; +dotty.protogt.destroyview = function (gt, vt) { + destroywidget (vt.canvas); + destroywidget (vt.scroll); + if (vt.view ~= -1) { + destroywidget (vt.view); + remove (vt.view, dotty.views); + } + remove (vt.scroll, dotty.views); + remove (vt.canvas, dotty.views); + if (vt.gtid >= 0) + remove (vt.vtid, gt.views); + if (tablesize (dotty.views) == 0) + exit (); +}; +dotty.protogt.zoom = function (gt, vt, factor, pos) { + gt.setviewscale ([vt.vtid = vt;], factor); + if (pos) + gt.setviewcenter ([vt.vtid = vt;], pos); + gt.redrawgraph (gt, [vt.vtid = vt;]); +}; +dotty.protogt.findnode = function (gt, vt) { + local key, node, node1, nid; + + if (~(key = ask ('give node name or label'))) + return; + if (gt.graph.nodedict[key] >= 0) + node = gt.graph.nodes[gt.graph.nodedict[key]]; + else if (gt.graph.nodedict[ston (key)] >= 0) + node = gt.graph.nodes[gt.graph.nodedict[ston (key)]]; + else { + for (nid in gt.graph.nodes) { + node1 = gt.graph.nodes[nid]; + if (node1.attr.label == key | node1.attr.label == ston (key)) { + node = node1; + break; + } + } + } + if (~node) { + dotty.message (0, concat ('cannot find node: ', key)); + return; + } + gt.setviewcenter ([vt.vtid = vt;], node.pos); +}; +dotty.protogt.setattr = function (gt, obj) { + local kv, t, attr, value; + + if (~(kv = ask ('give attr/value, eg. color=blue'))) + return; + t = split (kv, '='); + attr = t[0]; + value = t[1]; + if ( + obj.attr == gt.graph.graphattr | + obj.attr == gt.graph.edgeattr | + obj.attr == gt.graph.nodeattr + ) { + obj.attr[attr] = value; + return; + } + if (obj.nid >= 0) { + gt.undrawnode (gt, gt.views, obj); + obj.attr[attr] = value; + gt.unpacknodeattr (gt, obj); + gt.drawnode (gt, gt.views, obj); + } else if (obj.eid >= 0) { + gt.undrawedge (gt, gt.views, obj); + obj.attr[attr] = value; + gt.unpackedgeattr (gt, obj); + gt.drawedge (gt, gt.views, obj); + } +}; +dotty.protogt.getattr = function (gt, node) { + local kv; + + if (~(kv.key = ask ('give attr name'))) + return null; + if ((kv.val = node.attr[kv.key])) + return kv; + return null; +}; +# +# utilities +# +dotty.createviewandgraph = function (name, type, protogt, protovt) { + local vt, gt; + + if (~protogt) + protogt = dotty.protogt; + if (protogt.creategraph) + gt = protogt.creategraph (protogt); + else + gt = dotty.protogt.creategraph (protogt); + vt = gt.createview (gt, protovt); + if (~protogt.graph) + protogt.graph = copy (dotty.protogt.graph); + if (name) + gt.loadgraph (gt, name, type, protogt.graph, 1); + return ['gt' = gt; 'vt' = vt;]; +}; +dotty.openio = function (name, type, mode) { + local fd; + + if (~name) + return null; + if (type == 'file') { + if (name == '-') { + if (mode == 'r' | mode == 'r+') + fd = 0; + else + fd = 1; + } else if (~((fd = openio ('file', name, mode)) >= 0)) { + dotty.message (0, concat ('cannot open file: ', name)); + return null; + } + } else if (type == 'pipe') { + if (~((fd = openio ( + 'pipe', 'ksh', mode, concat ("%e ", name) + )) >= 0)) { + dotty.message (0, concat ('cannot run command: ', name)); + return null; + } + } else + return null; + return fd; +}; +dotty.pushbusy = function (gt, views) { + local vid; + + if (gt.busy == 0) + for (vid in gt.views) + setwidgetattr (vid, ['cursor' = 'watch';]); + gt.busy = gt.busy + 1; +}; +dotty.popbusy = function (gt, views) { + local vid; + + gt.busy = gt.busy - 1; + if (gt.busy == 0) + for (vid in gt.views) + setwidgetattr (vid, ['cursor' = 'default';]); +}; +dotty.message = function (level, text) { + if (level <= dotty.mlevel) + echo ('dotty.lefty: ', text); +}; +# +# printing or saving to file +# +dotty.protogt.printorsave = function (gt, vt, otype, name, mode, ptype) { + local pr, wrect, vsize, xy, psize, canvas, pscanvas, cid, cname, t; + local graph, edgehandles, fontmap, eid, edge, nid, node, gid, sgraph; + local did, draw, i; + + if (~otype) + if (~(otype = ask ('print to', 'choice', 'file|printer'))) + return; + if (otype == 'printer') { + if (~getenv ('TMPDIR')) + name = concat (getenv ('HOME'), '/.dottyout.ps'); + else + name = concat (getenv ('TMPDIR'), '/.dottyout.ps', random (10000)); + if (getenv ('LEFTYWINSYS') ~= 'mswin' & ~pr) + if (~(pr = ask ('printer command', 'string', 'lpr'))) + return; + } + if (~name) + if (~(name = ask ('postscript file', 'file', 'out.ps'))) + return; + if (~ptype) + if (~(ptype = ask ('page size', 'choice', '8.5x11|11x17|36x50'))) + return; + if (~mode) + if (~(mode = ask ('mode', 'choice', 'portrait|landscape|best fit'))) + return; + wrect = copy (vt.wrect); + wrect[0].x = wrect[0].x - 1; + wrect[1].x = wrect[1].x + 1; + wrect[0].y = wrect[0].y - 1; + wrect[1].y = wrect[1].y + 1; + vsize = copy (vt.vsize); + if (vsize.x == 0) + vsize.x = 1; + if (vsize.y == 0) + vsize.y = 1; + xy = vsize.x / vsize.y; + if (mode == 'best fit') { + if (xy < 1) + mode = 'portrait'; + else + mode = 'landscape'; + } + psize = dotty.pagesizes[ptype]; + if (mode == 'portrait') { + if (xy < psize.x / psize.y) { + vsize.y = psize.y * 300; + vsize.x = vsize.y * xy; + } else { + vsize.x = psize.x * 300; + vsize.y = vsize.x / xy; + } + } else { + if (xy < psize.y / psize.x) { + vsize.y = psize.x * 300; + vsize.x = vsize.y * xy; + } else { + vsize.x = psize.y * 300; + vsize.y = vsize.x / xy; + } + } + if (~((pscanvas = createwidget (-1, [ + 'type' = 'ps'; + 'origin' = ['x' = 0; 'y' = 0;]; + 'size' = vsize; + 'mode' = mode; + 'name' = name; + ])) >= 0)) { + dotty.message (0, 'cannot open printer device'); + return; + } + for (cname in vt.colors) { + cid = vt.colors[cname]; + if (cname == '_bgcolor_') + setwidgetattr (pscanvas, ['color' = [0 = cid;];]); + else if (setwidgetattr (pscanvas, ['color' = [cid = cname;];]) ~= 1) { + t = split (cname, ' '); + if (tablesize (t) ~= 3 | setwidgetattr (pscanvas, [ + 'color' = [cid = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];]; + ]) ~= 1) { + dotty.message ( + 0, concat ('unknown color ', cname, ' using #1') + ); + } + } + } + setwidgetattr (pscanvas, ['window' = wrect;]); + graph = copy (gt.graph); + canvas = vt.canvas; + vt.canvas = pscanvas; + edgehandles = gt.edgehandles; + gt.edgehandles = 0; + fontmap = dotty.maps[getenv ('LEFTYWINSYS')].psfontmap; + for (eid in graph.edges) { + edge = graph.edges[eid]; + edge.fontname = fontmap[edge.attr.fontname]; + for (did in edge.draws) { + if (did == 'ep') + continue; + draw = edge.draws[did]; + for (i = 0; draw[i]; i = i + 1) + if (draw[i].type == 'F') + draw[i].fn = fontmap[draw[i].ofn]; + } + gt.drawedge (gt, [0 = vt;], edge); + } + for (nid in graph.nodes) { + node = graph.nodes[nid]; + node.fontname = fontmap[node.attr.fontname]; + for (did in node.draws) { + if (did == 'ep') + continue; + draw = node.draws[did]; + for (i = 0; draw[i]; i = i + 1) + if (draw[i].type == 'F') + draw[i].fn = fontmap[draw[i].ofn]; + } + gt.drawnode (gt, [0 = vt;], node); + } + for (gid in graph.graphs) { + sgraph = graph.graphs[gid]; + sgraph.fontname = fontmap[sgraph.graphattr.fontname]; + for (did in sgraph.draws) { + if (did == 'ep') + continue; + draw = sgraph.draws[did]; + for (i = 0; draw[i]; i = i + 1) + if (draw[i].type == 'F') + draw[i].fn = fontmap[draw[i].ofn]; + } + gt.drawsgraph (gt, [0 = vt;], sgraph); + } + graph.fontname = fontmap[graph.graphattr.fontname]; + gt.drawsgraph (gt, [0 = vt;], graph); + gt.edgehandles = edgehandles; + vt.canvas = canvas; + destroywidget (pscanvas); + if (otype == 'printer' & getenv ('LEFTYWINSYS') ~= 'mswin') + system (concat (pr, ' ', name, '; rm ',name)); +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/dotty_draw.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,596 @@ +# +# dotty_draw: drawing functions and data structures +# +dotty.protogt.drawgraph = function (gt, views) { + local gid, eid, nid, graph; + + graph = gt.graph; + gt.drawsgraph (gt, views, graph); + for (gid in graph.graphs) + gt.drawsgraph (gt, views, graph.graphs[gid]); + for (eid in graph.edges) + gt.drawedge (gt, views, graph.edges[eid]); + for (nid in graph.nodes) + gt.drawnode (gt, views, graph.nodes[nid]); +}; +dotty.protogt.redrawgraph = function (gt, views) { + local vid; + + for (vid in views) + clear (views[vid].canvas); + gt.drawgraph (gt, views); +}; +dotty.protogt.setviewsize = function (views, r) { + local vid, vt, w2v, scale, attr; + + for (vid in views) { + vt = views[vid]; + vt.wrect = copy (r); + if (r[1].x == 0 | r[1].y == 0) { + attr = getwidgetattr (vt.scroll, [0 = 'size';]); + vt.wrect[1] = copy (attr.size); + } + if (vt.type == 'birdseye') { + attr = getwidgetattr (vt.scroll, [0 = 'size';]); + scale.x = (vt.wrect[1].x - vt.wrect[0].x) / attr.size.x; + scale.y = (vt.wrect[1].y - vt.wrect[0].y) / attr.size.y; + if (scale.x > 1 & scale.x > scale.y) + vt.w2v = scale.x; + else if (scale.y > 1) + vt.w2v = scale.y; + else + vt.w2v = 1; + } + w2v = vt.w2v; + vt.vsize = [ + 'x' = toint ((vt.wrect[1].x - vt.wrect[0].x) / w2v); + 'y' = toint ((vt.wrect[1].y - vt.wrect[0].y) / w2v); + ]; + setwidgetattr (vt.canvas, [ + 'window' = vt.wrect; + 'viewport' = vt.vsize; + ]); + attr = getwidgetattr (vt.canvas, [0 = 'viewport';]); + vt.vsize = copy (attr.viewport); + } +}; +dotty.protogt.setviewscale = function (views, factor) { + local vid, vt, w2v, attr; + + for (vid in views) { + vt = views[vid]; + if ((w2v = vt.w2v * factor) < 0.01) { + dotty.message (0, 'cannot zoom any closer'); + return; + } + vt.w2v = w2v; + vt.vsize = [ + 'x' = (vt.wrect[1].x - vt.wrect[0].x) / w2v; + 'y' = (vt.wrect[1].y - vt.wrect[0].y) / w2v; + ]; + setwidgetattr (vt.canvas, ['viewport' = vt.vsize;]); + attr = getwidgetattr (vt.canvas, [0 = 'viewport';]); + vt.vsize = copy (attr.viewport); + } +}; +dotty.protogt.setviewcenter = function (views, center) { + local vid, vt, pos; + + for (vid in views) { + vt = views[vid]; + pos = [ + 'x' = center.x * vt.vsize.x / (vt.wrect[1].x - vt.wrect[0].x); + 'y' = ( + (vt.wrect[1].y - center.y) * vt.vsize.y / + (vt.wrect[1].y - vt.wrect[0].y) + ); + ]; + setwidgetattr (vt.scroll, ['childcenter' = pos;]); + } +}; +# +# draw graph components +# +dotty.protogt.drawsgraph = function (gt, views, sgraph) { + sgraph.draw = 1; + if (~sgraph.draws) + return; + gt.execalldraw (gt, views, null, sgraph.draws, [ + 'fontname' = sgraph.fontname; + 'fontsize' = sgraph.fontsize; + 'fontcolor' = sgraph.fontcolor; + 'drawcolor' = sgraph.drawcolor; + 'fillcolor' = sgraph.fillcolor; + ]); +}; +dotty.protogt.undrawsgraph = function (gt, views, sgraph) { + sgraph.drawn = 0; + if (~sgraph.draws) + return; + gt.execalldraw (gt, views, null, sgraph.draws, [ + 'fontname' = sgraph.fontname; + 'fontsize' = sgraph.fontsize; + 'fontcolor' = sgraph.fontcolor; + 'drawcolor' = 0; + 'fillcolor' = 0; + ]); +}; +dotty.protogt.drawnode = function (gt, views, node) { + local vid; + + node.drawn = 1; + if (~node.draws) + return; + gt.execalldraw (gt, views, node, node.draws, [ + 'fontname' = node.fontname; + 'fontsize' = node.fontsize; + 'fontcolor' = node.fontcolor; + 'drawcolor' = node.drawcolor; + 'fillcolor' = node.fillcolor; + ]); + for (vid in views) + setpick (views[vid].canvas, node, node.rect); +}; +dotty.protogt.undrawnode = function (gt, views, node) { + local vid; + + if (~node.drawn) + return; + node.drawn = 0; + if (~node.pos) + return; + gt.execalldraw (gt, views, node, node.draws, [ + 'nooverride' = 1; + 'fontname' = node.fontname; + 'fontsize' = node.fontsize; + 'fontcolor' = 0; + 'drawcolor' = 0; + 'fillcolor' = 0; + ]); + for (vid in views) + clearpick (views[vid].canvas, node); +}; +dotty.protogt.movenode = function (gt, node, pos) { + local dp, eid, edge; + + dp.x = pos.x - node.pos.x; + dp.y = pos.y - node.pos.y; + gt.undrawnode (gt, gt.views, node); + node.pos.x = pos.x; + node.pos.y = pos.y; + gt.movenodedraw (node.draws, dp); + for (eid in node.edges) { + edge = node.edges[eid]; + gt.undrawedge (gt, gt.views, edge); + gt.moveedgedraw (edge.draws, edge.tail.pos, edge.head.pos); + gt.drawedge (gt, gt.views, edge); + } + gt.drawnode (gt, gt.views, node); +}; +dotty.protogt.drawedge = function (gt, views, edge) { + local vid, canvas; + + edge.drawn = 1; + if (~edge.draws) + return; + gt.execalldraw (gt, views, edge, edge.draws, [ + 'fontname' = edge.fontname; + 'fontsize' = edge.fontsize; + 'fontcolor' = edge.fontcolor; + 'drawcolor' = edge.drawcolor; + 'fillcolor' = edge.fillcolor; + ]); + for (vid in views) { + canvas = views[vid].canvas; + if (gt.edgehandles == 0 | ~edge.draws.ep) + continue; + arc (canvas, edge, edge.draws.ep, ['x' = 5; 'y' = 5;], ['color' = 1;]); + } +}; +dotty.protogt.undrawedge = function (gt, views, edge) { + local vid, canvas; + + if (~edge.drawn) + return; + edge.drawn = 0; + if (~edge.draws) + return; + gt.execalldraw (gt, views, edge, edge.draws, [ + 'nooverride' = 1; + 'fontname' = edge.fontname; + 'fontsize' = edge.fontsize; + 'fontcolor' = 0; + 'drawcolor' = 0; + 'fillcolor' = 0; + ]); + for (vid in views) { + canvas = views[vid].canvas; + if (gt.edgehandles == 0 | ~edge.draws.ep) + continue; + arc (canvas, edge, edge.draws.ep, ['x' = 5; 'y' = 5;], ['color' = 0;]); + clearpick (canvas, edge); + } +}; +# +# draw directives +# +dotty.protogt.execalldraw = function (gt, views, obj, draws, gc) { + local vid, vt, did, draw, i, func; + + for (vid in views) { + vt = views[vid]; + for (did in draws) { + if (did == 'ep') + continue; + draw = draws[did]; + for (i = 0; draw[i]; i = i + 1) + if ((func = gt.drawfunc[draw[i].type])) + func (gt, vt.canvas, obj, draw[i], gc); + } + } +}; +dotty.protogt.drawfunc.E = function (gt, canvas, obj, data, gc) { + arc (canvas, obj, data.c, data.s, [ + 'color' = gc.fillcolor; 'style' = gc.style; 'width' = gc.width; + 'fill' = 'on'; + ]); + arc (canvas, obj, data.c, data.s, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.e = function (gt, canvas, obj, data, gc) { + arc (canvas, obj, data.c, data.s, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.P = function (gt, canvas, obj, data, gc) { + polygon (canvas, obj, data.ps, [ + 'color' = gc.fillcolor; 'style' = gc.style; 'width' = gc.width; + 'fill' = 'on'; + ]); + polygon (canvas, obj, data.ps, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.p = function (gt, canvas, obj, data, gc) { + polygon (canvas, obj, data.ps, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.L = function (gt, canvas, obj, data, gc) { + polygon (canvas, obj, data.ps, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.b = function (gt, canvas, obj, data, gc) { + splinegon (canvas, obj, data.ps, [ + 'color' = gc.fillcolor; 'style' = gc.style; 'width' = gc.width; + 'fill' = 'on'; + ]); +}; +dotty.protogt.drawfunc.B = function (gt, canvas, obj, data, gc) { + splinegon (canvas, obj, data.ps, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.T = function (gt, canvas, obj, data, gc) { + text (canvas, obj, data.p, data.s, gc.fontname, gc.fontsize, data.j, [ + 'color' = gc.fontcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.C = function (gt, canvas, obj, data, gc) { + if (gc.nooverride ~= 1) + gc.fillcolor = data.fillcolor; +}; +dotty.protogt.drawfunc.c = function (gt, canvas, obj, data, gc) { + if (gc.nooverride ~= 1) { + gc.drawcolor = data.drawcolor; + gc.fontcolor = data.drawcolor; + } +}; +dotty.protogt.drawfunc.F = function (gt, canvas, obj, data, gc) { + gc.fontname = data.fn; + gc.fontsize = data.fs; +}; +dotty.protogt.drawfunc.S = function (gt, canvas, obj, data, gc) { + gc.style = data.style; + gc.width = data.width; +}; +dotty.protogt.movenodedraw = function (draws, dp) { + local did, draw, i, j; + + for (did in draws) { + if (did == 'ep') + continue; + draw = draws[did]; + for (i = 0; draw[i]; i = i + 1) { + if (draw[i].type == 'E' | draw[i].type == 'e') { + draw[i].c.x = draw[i].c.x + dp.x; + draw[i].c.y = draw[i].c.y + dp.y; + } else if (draw[i].type == 'P' | draw[i].type == 'p') { + for (j = 1; draw[i].ps[j]; j = j + 1) { + draw[i].ps[j].x = draw[i].ps[j].x + dp.x; + draw[i].ps[j].y = draw[i].ps[j].y + dp.y; + } + } else if (draw[i].type == 'L' | draw[i].type == 'B') { + for (j = 0; draw[i].ps[j]; j = j + 1) { + draw[i].ps[j].x = draw[i].ps[j].x + dp.x; + draw[i].ps[j].y = draw[i].ps[j].y + dp.y; + } + } else if (draw[i].type == 'T') { + draw[i].p.x = draw[i].p.x + dp.x; + draw[i].p.y = draw[i].p.y + dp.y; + } + } + } +}; +dotty.protogt.moveedgedraw = function (draws, tp, hp) { + local draws2, did; + + for (did in draws) + draws2[did] = draws[did]; + for (did in draws2) + remove (did, draws); + draws[0] = [ + 0 = [ + 'type' = 'L'; + 'n' = 2; + 'ps' = [ + 0 = copy (tp); + 1 = copy (hp); + ]; + ]; + 'ep' = ['x' = (tp.x + hp.x) / 2; 'y' = (tp.y + hp.y) / 2;]; + ]; +}; +dotty.protogt.simplenodedraw = function (node, c, s) { + local draws; + + if (node.attr.shape == 'ellipse') + draws[0] = [ + 0 = [ + 'type' = 'e'; + 'c' = copy (c); + 's' = ['x' = s.x / 2; 'y' = s.y / 2;]; + ]; + ]; + else + draws[0] = [ + 0 = [ + 'type' = 'p'; + 'n' = 5; + 'ps' = [ + 0 = ['x' = c.x - s.x / 2; 'y' = c.y - s.y / 2;]; + 1 = ['x' = c.x + s.x / 2; 'y' = c.y - s.y / 2;]; + 2 = ['x' = c.x + s.x / 2; 'y' = c.y + s.y / 2;]; + 3 = ['x' = c.x - s.x / 2; 'y' = c.y + s.y / 2;]; + 4 = ['x' = c.x - s.x / 2; 'y' = c.y - s.y / 2;]; + ]; + ]; + ]; + return draws; +}; +dotty.protogt.simpleedgedraw = function (edge, tp, hp) { + local draws; + + draws[0] = [ + 0 = [ + 'type' = 'L'; + 'n' = 2; + 'ps' = [ + 0 = copy (tp); + 1 = copy (hp); + ]; + ]; + 'ep' = ['x' = (tp.x + hp.x) / 2; 'y' = (tp.y + hp.y) / 2;]; + ]; + return draws; +}; +# +# utilities +# +dotty.protogt.getcolor = function (views, name) { + local vid, vt, color, t; + + for (vid in views) { + vt = views[vid]; + if (~(color >= 0)) { + if (~(vt.colors[name] >= 0)) + color = (vt.colors[name] = vt.colorn); + else { + color = vt.colors[name]; + break; + } + } else if (~(vt.colors[name] >= 0)) + vt.colors[name] = color; + else if (vt.colors[name] ~= color) + dotty.message (0, concat ('inconsistent color ids for ', name)); + if (setwidgetattr (vt.canvas, ['color' = [color = name;];]) ~= 1) { + t = split (name, ' '); + if (tablesize (t) ~= 3 | + setwidgetattr (vt.canvas, ['color' = [color = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];];]) ~= 1) { + dotty.message (0, concat ('unknown color ', name, ' using #1')); + return 1; + } + } + vt.colorn = color + 1; + } + return color; +}; +dotty.protogt.setbgcolor = function (views, name) { + local vid, vt, t; + + for (vid in views) { + vt = views[vid]; + if (setwidgetattr (vt.canvas, ['color' = [0 = name;];]) ~= 1) { + t = split (name, ' '); + if (tablesize (t) ~= 3 | + setwidgetattr (vt.canvas, ['color' = [0 = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];];]) ~= 1) { + dotty.message (0, concat ('unknown bgcolor ', name)); + return; + } + } + vt.colors['_bgcolor_'] = name; + } +}; +dotty.protogt.unpacksgraphattr = function (gt, sgraph) { + local attr; + + attr = sgraph.graphattr; + if (dotty.fontmap[attr.fontname]) + sgraph[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + sgraph[dotty.keys.fname] = attr.fontname; + sgraph[dotty.keys.fsize] = ston (attr.fontsize); + sgraph[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + sgraph[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + sgraph[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.fillcolor); + else if (attr.color) + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } +}; +dotty.protogt.unpacknodeattr = function (gt, node) { + local attr; + + attr = node.attr; + if (dotty.fontmap[attr.fontname]) + node[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + node[dotty.keys.fname] = attr.fontname; + node[dotty.keys.fsize] = ston (attr.fontsize); + node[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + node[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + node[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + node[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.fillcolor); + else if (attr.color) + node[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + node[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } +}; +dotty.protogt.unpackedgeattr = function (gt, edge) { + local attr; + + attr = edge.attr; + if (dotty.fontmap[attr.fontname]) + edge[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + edge[dotty.keys.fname] = attr.fontname; + edge[dotty.keys.fsize] = ston (attr.fontsize); + edge[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + edge[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + edge[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + edge[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.fillcolor); + else if (attr.color) + edge[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + edge[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } +}; +dotty.protogt.unpackattr = function (gt) { + local gid, sgraph, nid, node, eid, edge, graph, attr; + + graph = gt.graph; + attr = graph.graphattr; + if (dotty.fontmap[attr.fontname]) + graph[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + graph[dotty.keys.fname] = attr.fontname; + graph[dotty.keys.fsize] = ston (attr.fontsize); + graph[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + graph[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + graph[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + graph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.fillcolor); + else if (attr.color) + graph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + graph[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } + if (attr.bgcolor & attr.bgcolor ~= '') + gt.setbgcolor (gt.views, attr.bgcolor); + for (gid in graph.graphdict) { + sgraph = graph.graphs[graph.graphdict[gid]]; + attr = sgraph.graphattr; + if (dotty.fontmap[attr.fontname]) + sgraph[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + sgraph[dotty.keys.fname] = attr.fontname; + sgraph[dotty.keys.fsize] = ston (attr.fontsize); + sgraph[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + sgraph[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + sgraph[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + sgraph[dotty.keys.bcolor] = gt.getcolor ( + gt.views, attr.fillcolor + ); + else if (attr.color) + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } + } + for (nid in graph.nodedict) { + node = graph.nodes[graph.nodedict[nid]]; + attr = node.attr; + if (dotty.fontmap[attr.fontname]) + node[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + node[dotty.keys.fname] = attr.fontname; + node[dotty.keys.fsize] = ston (attr.fontsize); + node[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + node[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + node[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + node[dotty.keys.bcolor] = gt.getcolor ( + gt.views, attr.fillcolor + ); + else if (attr.color) + node[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + node[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } + } + for (eid in graph.edges) { + edge = graph.edges[eid]; + attr = edge.attr; + if (dotty.fontmap[attr.fontname]) + edge[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + edge[dotty.keys.fname] = attr.fontname; + edge[dotty.keys.fsize] = ston (attr.fontsize); + edge[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + edge[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + edge[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + } +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/dotty_edit.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,593 @@ +# +# dotty_edit: editing functions and data structures +# +dotty.protogt.getnodesbyattr = function (gt, key, val) { + local nid, node, nlist; + + nlist = []; + for (nid in gt.graph.nodes) { + node = gt.graph.nodes[nid]; + if (node.attr[key] == val) + nlist[nid] = node; + } + return nlist; +}; +dotty.protogt.reachablenodes = function (gt, node) { + local nlist, stack, eid, edge, i; + + stack[0] = node; + i = 1; + while (i > 0) { + node = stack[i - 1]; + i = i - 1; + nlist[node.nid] = node; + for (eid in node.edges) { + edge = node.edges[eid]; + if (~nlist[edge.head.nid]) { + nlist[edge.head.nid] = edge.head; + stack[i] = edge.head; + i = i + 1; + } + } + } + return nlist; +}; +dotty.protogt.mergegraph = function (gt, graph, show) { + local nameid, onode, pos, size, eid, eid2, tnode, hnode, oedge; + + if (~gt.noundo) + gt.startadd2undo (gt); + for (nameid in graph.nodedict) { + pos = null; + size = null; + onode = graph.nodes[graph.nodedict[nameid]]; + if (onode.pos) + pos = node.pos; + if (onode.size) + size = node.size; + if (~(gt.graph.nodedict[nameid] >= 0)) { + pos = null; + size = null; + if (onode.pos) + pos = node.pos; + if (onode.size) + size = node.size; + gt.insertnode (gt, pos, size, nameid, onode.attr, show); + } + } + for (eid in graph.edges) { + oedge = graph.edges[eid]; + tnode = gt.graph.nodes[gt.graph.nodedict[oedge.tail.name]]; + hnode = gt.graph.nodes[gt.graph.nodedict[oedge.head.name]]; + for (eid2 in tnode.edges) + if ( + tnode.edges[eid2].tail == tnode & + tnode.edges[eid2].head == hnode + ) { + oedge = null; + break; + } + if (oedge) + gt.insertedge (gt, tnode, null, hnode, null, oedge.attr, show); + } + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.insertsgraph = function (gt, name, attr, show) { + local gid, sgraph, aid; + + if (~gt) + return null; + gid = gt.graph.maxgid; + if (~name) { + while (gt.graph.graphdict[(name = concat ('g', gid))] >= 0) + gid = gid + 1; + } else if (gt.graph.graphdict[name]) { + dotty.message (0, concat ('graph: ', name, ' exists')); + return null; + } + gt.graph.graphdict[name] = gid; + gt.graph.maxgid = gid + 1; + gt.graph.graphs[gid] = [ + dotty.keys.gid = gid; + dotty.keys.name = name; + dotty.keys.gattr = copy (gt.graph.graphattr); + dotty.keys.nattr = copy (gt.graph.nodeattr); + dotty.keys.eattr = copy (gt.graph.edgeattr); + ]; + sgraph = gt.graph.graphs[gid]; + if (~attr) + attr = []; + if (~attr.label) + attr.label = '\N'; + for (aid in attr) + sgraph.graphattr[aid] = attr[aid]; + gt.unpacksgraphattr (gt, sgraph); + if (show) + gt.drawsgraph (gt, gt.views, sgraph); + return sgraph; +}; +dotty.protogt.removesgraph = function (gt, sgraph) { + gt.undrawsgraph (gt, gt.views, sgraph); + remove (sgraph.name, gt.graph.graphdict); + remove (sgraph.gid, gt.graph.graphs); +}; +dotty.protogt.insertnode = function (gt, pos, size, name, attr, show) { + local nid, node, aid; + + nid = gt.graph.maxnid; + if (~name) { + while (gt.graph.nodedict[(name = concat ('n', nid))] >= 0) + nid = nid + 1; + } else if (gt.graph.nodedict[name] >= 0) { + dotty.message (0, concat ('node: ', name, ' exists')); + return null; + } + gt.graph.nodedict[name] = nid; + gt.graph.maxnid = nid + 1; + gt.graph.nodes[nid] = [ + dotty.keys.nid = nid; + dotty.keys.name = name; + dotty.keys.attr = copy (gt.graph.nodeattr); + dotty.keys.edges = []; + ]; + node = gt.graph.nodes[nid]; + if (~attr) + attr = []; + if (~attr.label) + attr.label = '\N'; + for (aid in attr) + node.attr[aid] = attr[aid]; + gt.unpacknodeattr (gt, node); + if (~pos) + pos = ['x' = 10; 'y' = 10;]; + node[dotty.keys.pos] = copy (pos); + if (~size) + size = ['x' = strlen (attr.label) * 30; 'y' = 30;]; + if (size.x == 0) + size.x = 30; + node[dotty.keys.size] = copy (size); + node[dotty.keys.rect] = [ + 0 = ['x' = pos.x - size.x / 2; 'y' = pos.y - size.y / 2;]; + 1 = ['x' = pos.x + size.x / 2; 'y' = pos.y + size.y / 2;]; + ]; + node.draws = gt.simplenodedraw (node, pos, size); + if (show) + gt.drawnode (gt, gt.views, node); + if (~gt.noundo) { + gt.startadd2undo (gt); + gt.currundo.inserted.nodes[nid] = node; + gt.endadd2undo (gt); + } + return node; +}; +dotty.protogt.removenode = function (gt, node) { + local eid, list, edge, gid; + + if (~gt.noundo) + gt.startadd2undo (gt); + for (eid in node.edges) + list[eid] = node.edges[eid]; + for (eid in list) + gt.removeedge (gt, list[eid]); + gt.undrawnode (gt, gt.views, node); + for (gid in gt.graph.graphs) + remove (node.nid, gt.graph.graphs[gid].nodes); + remove (node.name, gt.graph.nodedict); + remove (node.nid, gt.graph.nodes); + if (~gt.noundo) { + gt.currundo.deleted.nodes[node.nid] = node; + gt.endadd2undo (gt); + } +}; +dotty.protogt.insertedge = function ( + gt, nodea, porta, nodeb, portb, attr, show +) { + local eid, edge, aid, tport, hport; + + if (~nodea | ~nodeb) + return null; + if (porta) + tport = porta; + if (portb) + hport = portb; + eid = gt.graph.maxeid; + while (gt.graph.edges[eid]) + eid = eid + 1; + gt.graph.maxeid = eid + 1; + gt.graph.edges[eid] = [ + dotty.keys.eid = eid; + dotty.keys.tail = nodea; + dotty.keys.tport = porta; + dotty.keys.head = nodeb; + dotty.keys.hport = portb; + dotty.keys.attr = copy (gt.graph.edgeattr); + ]; + edge = gt.graph.edges[eid]; + if (~attr) + attr = []; + for (aid in attr) + edge.attr[aid] = attr[aid]; + nodea.edges[eid] = edge; + nodeb.edges[eid] = edge; + gt.unpackedgeattr (gt, edge); + edge.draws = gt.simpleedgedraw (edge, nodea.pos, nodeb.pos); + if (show) + gt.drawedge (gt, gt.views, edge); + if (~gt.noundo) { + gt.startadd2undo (gt); + gt.currundo.inserted.edges[eid] = edge; + gt.endadd2undo (gt); + } + return edge; +}; +dotty.protogt.removeedge = function (gt, edge) { + local head, tail; + + if (~gt.noundo) + gt.startadd2undo (gt); + if (edge.head.attr.support == 1) + head = edge.head; + if (edge.tail.attr.support == 1) + if (head ~= edge.tail) + tail = edge.tail; + gt.undrawedge (gt, gt.views, edge); + remove (edge.eid, edge.head.edges); + remove (edge.eid, edge.tail.edges); + remove (edge.eid, gt.graph.edges); + if (head & tablesize (head.edges) == 0) + gt.removenode (gt, head); + if (tail & tablesize (tail.edges) == 0) + gt.removenode (gt, tail); + if (~gt.noundo) { + gt.currundo.deleted.edges[edge.eid] = edge; + gt.endadd2undo (gt); + } +}; +dotty.protogt.swapedgeids = function (gt, edge1, edge2) { + local eid1, eid2; + + if (edge1.eid == edge2.eid) + return; + if (~gt.noundo) + gt.startadd2undo (gt); + eid1 = edge1.eid; + eid2 = edge2.eid; + gt.graph.edges[eid1] = edge2; + gt.graph.edges[eid2] = edge1; + remove (eid1, edge1.tail.edges); + remove (eid1, edge1.head.edges); + remove (eid2, edge2.tail.edges); + remove (eid2, edge2.head.edges); + edge1.tail.edges[eid2] = edge1; + edge1.head.edges[eid2] = edge1; + edge2.tail.edges[eid1] = edge2; + edge2.head.edges[eid1] = edge2; + edge1.eid = eid2; + edge2.eid = eid1; + if (~gt.noundo) { + gt.currundo.swapped.edges[eid1] = edge1; + gt.currundo.swapped.edges[eid2] = edge2; + gt.endadd2undo (gt); + } +}; +dotty.protogt.removesubtree = function (gt, obj) { + local nlist, node, head, nid, edge, eid; + + if (~gt.noundo) + gt.startadd2undo (gt); + if (obj.nid >= 0) + node = obj; + else if (obj.eid >= 0) { + node = obj.head; + gt.removeedge (gt, obj); + if (~gt.graph.nodes[node.nid]) { + if (~gt.noundo) + gt.endadd2undo (gt); + return; + } + for (eid in node.edges) { + edge = node.edges[eid]; + if (edge.head == node & edge.tail ~= node) { + if (~gt.noundo) + gt.endadd2undo (gt); + return; + } + } + } else { + dotty.message (0, 'bad object type in gt.removesubtree'); + return; + } + nlist = [node.nid = node;]; + while (node) { + for (eid in node.edges) { + head = node.edges[eid].head; + if (head ~= node) + nlist[head.nid] = head; + } + gt.removenode (gt, node); + remove (node.nid, nlist); + node = null; + for (nid in nlist) { + node = nlist[nid]; + for (eid in node.edges) { + edge = node.edges[eid]; + if (edge.head == node & edge.tail ~= node) { + node = null; + break; + } + } + if (node) + break; + } + } + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.removenodesbyattr = function (gt, key, val) { + local nlist, nid; + + if (~gt.noundo) + gt.startadd2undo (gt); + nlist = gt.getnodesbyattr (gt, key, val); + for (nid in nlist) + gt.removenode (gt, nlist[nid]); + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.removesubtreesbyattr = function (gt, key, val) { + local nlist, nid; + + if (~gt.noundo) + gt.startadd2undo (gt); + nlist = gt.getnodesbyattr (gt, key, val); + for (nid in nlist) + if (gt.graph.nodes[nid]) + gt.removesubtree (gt, nlist[nid]); + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.groupnodes = function ( + gt, nlist, gnode, pos, size, attr, keepmulti, show +) { + local nid, node, elist, eid, edge, nodea, nodeb, inlist, outlist; + + if (~nlist | tablesize (nlist) == 0) + return; + if (gnode.attr.support) { + dotty.message (0, 'cannot group nodes in a support node'); + return; + } + if (~gt.noundo) + gt.startadd2undo (gt); + if (~gnode) + gnode = gt.insertnode (gt, pos, size, null, attr, show); + inlist = []; + outlist = []; + for (nid in nlist) { + if ((node = nlist[nid]) == gnode) + continue; + elist = []; + for (eid in node.edges) + elist[eid] = node.edges[eid]; + for (eid in elist) { + edge = elist[eid]; + if (edge.head == node) { + nodea = edge.tail; + nodeb = gnode; + if (~keepmulti) { + if (inlist[nodea.nid]) + continue; + inlist[nodea.nid] = nodea; + if (nodea == gnode) + outlist[nodea.nid] = nodea; + } + } else { + nodea = gnode; + nodeb = edge.head; + if (~keepmulti) { + if (outlist[nodeb.nid]) + continue; + outlist[nodeb.nid] = nodeb; + if (nodeb == gnode) + inlist[nodeb.nid] = nodeb; + } + } + gt.insertedge (gt, nodea, null, nodeb, null, edge.attr, show); + } + gt.removenode (gt, node); + } + if (~gt.noundo) + gt.endadd2undo (gt); + return gnode; +}; +dotty.protogt.groupnodesbyattr = function ( + gt, key, val, attr, keepmulti, show +) { + local nlist, nid, pos, size; + + pos = null; + size = null; + nlist = gt.getnodesbyattr (gt, key, val); + if (show) + for (nid in nlist) { + pos = nlist[nid].pos; + size = nlist[nid].size; + break; + } + return gt.groupnodes (gt, nlist, null, pos, size, attr, keepmulti, show); +}; +dotty.protogt.cut = function (gt, obj, set, mode, op) { + local clipgt, list, node, nid, edge, eid, clipnode; + + clipgt = dotty.clipgt; + clipgt.graph = copy (dotty.protogt.graph); + if (obj.eid >= 0) { # it's an edge + list.edges[obj.eid] = obj; + node = obj.head; + } else if (obj.nid >= 0) { + list.nodes[obj.nid] = obj; + node = obj; + for (eid in node.edges) + list.edges[eid] = node.edges[eid]; + } else { + dotty.message (0, 'unknown object type in gt.cut'); + return; + } + if (set == 'reachable') { + list.nodes = gt.reachablenodes (gt, node); + for (nid in list.nodes) { + node = list.nodes[nid]; + for (eid in node.edges) { + edge = node.edges[eid]; + list.edges[edge.eid] = edge; + } + } + } + if (mode == 'support') { + for (eid in list.edges) { + edge = list.edges[eid]; + if (~list.nodes[edge.tail.nid]) { + list.support[edge.tail.nid] = edge.tail; + list.nodes[edge.tail.nid] = edge.tail; + } + if (~list.nodes[edge.head.nid]) { + list.support[edge.head.nid] = edge.head; + list.nodes[edge.head.nid] = edge.head; + } + } + } + for (nid = 0; nid < gt.graph.maxnid; nid = nid + 1) { + if (~list.nodes[nid]) + continue; + node = list.nodes[nid]; + clipnode = gt.insertnode (clipgt, null, null, node.name, node.attr, 0); + if (list.support[nid]) + clipnode.support = 1; + list.clipnodes[nid] = clipnode; + } + for (eid = 0; eid < gt.graph.maxeid; eid = eid + 1) { + if (~list.edges[eid]) + continue; + edge = list.edges[eid]; + if (~list.nodes[edge.tail.nid] | ~list.nodes[edge.head.nid]) + continue; + gt.insertedge ( + clipgt, list.clipnodes[edge.tail.nid], null, + list.clipnodes[edge.head.nid], null, edge.attr, 0 + ); + } + if (op ~= 'cut') + return; + if (~gt.noundo) + gt.startadd2undo (gt); + for (eid in list.edges) + gt.removeedge (gt, list.edges[eid]); + for (nid in list.nodes) + if (~list.support[nid] & gt.graph.nodes[nid]) + gt.removenode (gt, list.nodes[nid]); + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.paste = function (gt, pos, show) { + local clipgt, offset, center, nid, node, eid, edge, nodes; + + if (~gt.noundo) + gt.startadd2undo (gt); + clipgt = dotty.clipgt; + if (clipgt.graph.rect) + center = [ + 'x' = (clipgt.graph.rect[1].x + clipgt.graph.rect[0].x) / 2; + 'y' = (clipgt.graph.rect[1].y + clipgt.graph.rect[0].y) / 2; + ]; + else + center = pos; + offset = [ + 'x' = center.x - pos.x; + 'y' = center.y - pos.y; + ]; + for (nid = 0; clipgt.graph.nodes[nid]; nid = nid + 1) { + node = clipgt.graph.nodes[nid]; + if (node.attr.label == '\N' | ~node.attr.label) + node.attr.label = node.name; + if (node.support == 1) + nodes[nid] = gt.insertnode (gt, [ + 'x' = node.pos.x - offset.x; + 'y' = node.pos.y - offset.y; + ], null, null, [ + 'support' = 1; 'shape' = 'circle'; + 'label' = ''; 'width' = 0.2; + ], show); + else + nodes[nid] = gt.insertnode (gt, [ + 'x' = node.pos.x - offset.x; + 'y' = node.pos.y - offset.y; + ], node.size, null, node.attr, show); + } + for (eid = 0; clipgt.graph.edges[eid]; eid = eid + 1) { + edge = clipgt.graph.edges[eid]; + gt.insertedge ( + gt, nodes[edge.tail.nid], null, + nodes[edge.head.nid], null, edge.attr, show + ); + } + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.startadd2undo = function (gt) { + if (~gt.undoarray.level) + gt.currundo = ( + gt.undoarray.entries[tablesize (gt.undoarray.entries)] = [] + ); + gt.undoarray.level = gt.undoarray.level + 1; +}; +dotty.protogt.endadd2undo = function (gt) { + gt.undoarray.level = gt.undoarray.level - 1; +}; +dotty.protogt.undo = function (gt, show) { + local entry, n, eid, edge, nid, node, edges; + + if ((n = tablesize (gt.undoarray.entries)) < 1) + return; + entry = gt.undoarray.entries[n - 1]; + remove (n - 1, gt.undoarray.entries); + remove ('currundo', gt); + gt.noundo = 1; + # hardwire nodes and edges back with the same id's as the originals + for (nid in entry.deleted.nodes) { + node = entry.deleted.nodes[nid]; + gt.graph.nodedict[node.name] = node.nid; + gt.graph.nodes[node.nid] = node; + node.edges = []; + if (show) + gt.drawnode (gt, gt.views, node); + } + for (eid in entry.deleted.edges) { + edge = entry.deleted.edges[eid]; + gt.graph.edges[edge.eid] = edge; + edge.head.edges[edge.eid] = edge; + edge.tail.edges[edge.eid] = edge; + if (show) + gt.drawedge (gt, gt.views, edge); + } + if (entry.swapped.edges) { + if (tablesize (entry.swapped.edges) == 2) { + n = 0; + for (eid in entry.swapped.edges) { + edges[n] = entry.swapped.edges[eid]; + n = n + 1; + } + gt.swapedgeids (gt, edges[0], edges[1]); + } else + dotty.message (0, 'cannot handle undoing swap of > 2 edges'); + } + for (eid in entry.inserted.edges) { + edge = entry.inserted.edges[eid]; + gt.removeedge (gt, edge); + } + for (nid in entry.inserted.nodes) { + node = entry.inserted.nodes[nid]; + gt.removenode (gt, node); + } + gt.noundo = 0; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/dotty_layout.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,449 @@ +# +# dotty_layout: layout functions and data structures +# +dotty.grablserver = function (lserver) { + local fd; + + if (~dotty.lservers[lserver] | tablesize (dotty.lservers[lserver]) == 0) { + if (~((fd = openio ('pipe', lserver, 'r+', '%e -Txdot')) >= 0)) { + dotty.message (0, concat ('cannot start ', lserver)); + return null; + } + dotty.lservers[lserver][fd] = [ + 'fd' = fd; + 'count' = 0; + ]; + } + for (fd in dotty.lservers[lserver]) { + dotty.lservers[lserver][fd].count = dotty.lservers[ + lserver + ][fd].count + 1; + dotty.lservers.inuse[fd] = dotty.lservers[lserver][fd]; + remove (fd, dotty.lservers[lserver]); + return fd; + } +}; +dotty.releaselserver = function (lserver, fd, state) { + if (state == 'bad' | dotty.lservers.inuse[fd].count > 40) { + closeio (fd, 'kill'); + remove (fd, dotty.lservers.inuse); + return; + } + dotty.lservers[lserver][fd] = dotty.lservers.inuse[fd]; + remove (fd, dotty.lservers.inuse); +}; +dotty.protogt.startlayout = function (gt) { + local lpt, fd; + + if (gt.layoutpending >= 1) { + lpt = dotty.layoutpending[gt.gtid]; + if (gt.layoutmode == 'async') + monitor ('off', lpt.fd); + dotty.releaselserver (gt.lserver, lpt.fd, 'bad'); + remove (gt.gtid, dotty.layoutpending); + gt.layoutpending = 0; + gt.haveinput = 0; + dotty.popbusy (gt, gt.views); + } + if (~((fd = dotty.grablserver (gt.lserver)) >= 0)) + return null; + dotty.pushbusy (gt, gt.views); + writegraph (fd, gt.graph, 1); + gt.layoutpending = 1; + dotty.layoutpending[gt.gtid] = [ + 'fd' = fd; + 'gtid' = gt.gtid; + ]; + if (gt.layoutmode == 'async') + monitor ('on', fd); + return 1; +}; +dotty.protogt.finishlayout = function (gt) { + local graph, lpt, fd; + + if (~(gt.layoutpending >= 1)) { + dotty.message (0, concat ('no layout pending for graph ', gt.gtid)); + return null; + } + lpt = dotty.layoutpending[gt.gtid]; + if (~(graph = readgraph (lpt.fd))) { + if (gt.layoutmode == 'async') + monitor ('off', lpt.fd); + dotty.releaselserver (gt.lserver, lpt.fd, 'bad'); + if (gt.layoutpending == 2) { + dotty.message (0, concat ('giving up on ', gt.lserver)); + if ((fd = openio ('file', 'dottybug.dot', 'w+')) >= 0) { + writegraph (fd, gt.graph, 0); + closeio (fd); + dotty.message ( + 0, concat ('graph that causes ', gt.lserver) + ); + dotty.message ( + 0, 'to fail has been saved in file dottybug.dot' + ); + dotty.message ( + 0, 'please fill out a bug report at' + ); + dotty.message ( + 0, 'http://www.graphviz.org/bugs/bugform.html' + ); + } + dotty.popbusy (gt, gt.views); + gt.layoutpending = 0; + gt.haveinput = 0; + return 1; + } + dotty.message ( + 1, concat ('lost connection to ', gt.lserver, ', restarting...') + ); + lpt.fd = dotty.grablserver (gt.lserver); + writegraph (lpt.fd, gt.graph, 1); + if (gt.layoutmode == 'async') + monitor ('on', lpt.fd); + gt.layoutpending = 2; + gt.haveinput = 0; + return null; + } + if (gt.layoutmode == 'async') + monitor ('off', lpt.fd); + dotty.releaselserver (gt.lserver, lpt.fd, null); + remove (gt.gtid, dotty.layoutpending); + gt.layoutpending = 0; + gt.haveinput = 0; + gt.unpacklayout (gt, graph); + dotty.popbusy (gt, gt.views); + return 1; +}; +dotty.protogt.cancellayout = function (gt) { + local lpt, vid; + + if (gt.layoutpending >= 1) { + lpt = dotty.layoutpending[gt.gtid]; + if (gt.layoutmode == 'async') + monitor ('off', lpt.fd); + dotty.releaselserver (gt.lserver, lpt.fd, 'bad'); + remove (gt.gtid, dotty.layoutpending); + gt.layoutpending = 0; + gt.haveinput = 0; + dotty.popbusy (gt, gt.views); + } +}; +dotty.protogt.unpacklayout = function (gt, graph2) { + local graph, gid, sgraph1, sgraph2, nid, node1, node2, eid, edge1, edge2; + local t1, pos, size; + + graph = gt.graph; + for (gid in graph2.graphdict) { + if (~(sgraph1 = graph.graphs[graph.graphdict[gid]])) + continue; + sgraph2 = graph2.graphs[graph2.graphdict[gid]]; + sgraph1.draws = gt.unpackalldraw (gt, sgraph2.graphattr); + } + for (nid in graph2.nodedict) { + if (~(node1 = graph.nodes[graph.nodedict[nid]])) + continue; + node2 = graph2.nodes[graph2.nodedict[nid]]; + node1.draws = gt.unpackalldraw (gt, node2.attr); + t1 = split (node2.attr.pos, ','); + pos = ['x' = ston (t1[0]); 'y' = ston (t1[1]);]; + size = [ + 'x' = ston (node2.attr.width) * 72; + 'y' = ston (node2.attr.height) * 72; + ]; + node1.pos = pos; + node1.size = size; + node1.rect = [ + 0 = ['x' = pos.x - size.x / 2; 'y' = pos.y - size.y / 2;]; + 1 = ['x' = pos.x + size.x / 2; 'y' = pos.y + size.y / 2;]; + ]; + } + for (eid in graph2.edges) { + edge2 = graph2.edges[eid]; + if (edge2.attr.id) { + if (~(edge1 = graph.edges[ston (edge2.attr.id)])) + continue; + } else if (graph == graph2) + edge1 = edge2; + edge1.draws = gt.unpackalldraw (gt, edge2.attr); + } + graph.draws = gt.unpackalldraw (gt, graph2.graphattr); + t1 = split (graph2.graphattr.bb, ','); + graph.rect[0].x = ston (t1[0]); + graph.rect[0].y = ston (t1[1]); + graph.rect[1].x = ston (t1[2]); + graph.rect[1].y = ston (t1[3]); + if (gt.graph ~= graph2) + return; + # strip position and size info from the attributes + for (gid in graph2.graphdict) { + sgraph2 = graph2.graphs[graph2.graphdict[gid]]; + gt.removealldraw (gt, sgraph2.graphattr); + if (sgraph2.graphattr.bb) + remove ('bb', sgraph2.graphattr); + } + for (nid in graph2.nodedict) { + node2 = graph2.nodes[graph2.nodedict[nid]]; + gt.removealldraw (gt, node2.attr); + if (node2.attr.rects) + remove ('rects', node2.attr); + remove ('pos', node2.attr); + remove ('width', node2.attr); + remove ('height', node2.attr); + } + for (eid in graph2.edges) { + edge2 = graph2.edges[eid]; + gt.removealldraw (gt, edge2.attr); + if (edge2.attr.pos) + remove ('pos', edge2.attr); + if (edge2.attr.lp) + remove ('lp', edge2.attr); + } + gt.removealldraw (gt, graph2.graphattr); + remove ('bb', graph2.graphattr); + if (graph2.graphattr.lp) + remove ('lp', graph2.graphattr); +}; +# +# draw directive parsing +# +dotty.protogt.unpackalldraw = function (gt, attr) { + local o, did; + + o = []; + if (attr._draw_) + o._draw_ = gt.unpackdraw (gt, attr._draw_); + if (attr._ldraw_) + o._ldraw_ = gt.unpackdraw (gt, attr._ldraw_); + if (attr._hdraw_) + o._hdraw_ = gt.unpackdraw (gt, attr._hdraw_); + if (attr._tdraw_) + o._tdraw_ = gt.unpackdraw (gt, attr._tdraw_); + if (attr._hldraw_) + o._hldraw_ = gt.unpackdraw (gt, attr._hldraw_); + if (attr._tldraw_) + o._tldraw_ = gt.unpackdraw (gt, attr._tldraw_); + for (did in o) + if (o[did].ep) { + o.ep = o[did].ep; + break; + } + return o; +}; +dotty.protogt.removealldraw = function (gt, attr) { + if (attr._draw_) + remove ('_draw_', attr); + if (attr._ldraw_) + remove ('_ldraw_', attr); + if (attr._hdraw_) + remove ('_hdraw_', attr); + if (attr._tdraw_) + remove ('_tdraw_', attr); + if (attr._hldraw_) + remove ('_hldraw_', attr); + if (attr._tldraw_) + remove ('_tldraw_', attr); +}; +dotty.protogt.unpackdraw = function (gt, attr) { + local oo, o, tt, t, n, i, j, s, l, ep; + + oo = []; + t = split (attr, ' ', 0); + n = tablesize (t); + if (t[n - 1] == '') { + remove (n - 1, t); + n = n - 1; + } + i = 0; + while (i < n) { + o = []; + if (t[i] == 'E') { + o.type = t[i]; + o.c.x = ston (t[i + 1]); + o.c.y = ston (t[i + 2]); + o.s.x = ston (t[i + 3]); + o.s.y = ston (t[i + 4]); + i = i + 5; + } else if (t[i] == 'e') { + o.type = t[i]; + o.c.x = ston (t[i + 1]); + o.c.y = ston (t[i + 2]); + o.s.x = ston (t[i + 3]); + o.s.y = ston (t[i + 4]); + i = i + 5; + } else if (t[i] == 'P') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + o.ps[o.n] = o.ps[0]; + o.n = o.n + 1; + } else if (t[i] == 'p') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + o.ps[o.n] = o.ps[0]; + o.n = o.n + 1; + } else if (t[i] == 'L') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + if (~ep) + ep = copy (o.ps[1]); + } else if (t[i] == 'B') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + if (~ep) + ep = copy (o.ps[1]); + } else if (t[i] == 'b') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + if (~ep) + ep = copy (o.ps[1]); + } else if (t[i] == 'T') { + o.type = t[i]; + o.p.x = ston (t[i + 1]); + o.p.y = ston (t[i + 2]); + o.j = ston (t[i + 3]); + if (o.j == -1) + o.j = 'lb'; + else if (o.j == 1) + o.j = 'rb'; + else if (o.j == 0) + o.j = 'cb'; + o.w = ston (t[i + 4]); + o.n = ston (t[i + 5]); + i = i + 6; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + o.s = s; + } else if (t[i] == 'C') { + o.type = t[i]; + o.n = ston (t[i + 1]); + i = i + 2; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + o.fillcolor = gt.getcolor (gt.views, s); + } else if (t[i] == 'c') { + o.type = t[i]; + o.n = ston (t[i + 1]); + i = i + 2; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + o.drawcolor = gt.getcolor (gt.views, s); + } else if (t[i] == 'F') { + o.type = t[i]; + o.fs = ston (t[i + 1]); + o.n = ston (t[i + 2]); + i = i + 3; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + o.ofn = s; + o.fn = dotty.fontmap[s]; + } else if (t[i] == 'S') { + o.type = t[i]; + o.n = ston (t[i + 1]); + i = i + 2; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + if ( + s == 'solid' | s == 'dashed' | s == 'dotted' | + s == 'longdashed' | s == 'shortdashed' + ) + o.style = s; + else if (s == 'bold') + o.width = 3; + else { + tt = split (s, '('); + if (tt[0] == 'setlinewidth') { + tt = split (tt[1], ')'); + o.width = ston (tt[0]); + } else + continue; + } + } else if (t[i] == 'I') { + i = i + 7; + } else { + dotty.message (0, concat ('draw language parser error: ', t[i])); + return null; + } + oo[tablesize (oo)] = o; + } + oo.ep = ep; + return oo; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/dotty_ui.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,468 @@ +# +# dotty_ui: user interface functions and data structures +# +dotty.protogt.doaction = function (data, s) { + local vt, gt; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + data.menuitem = s; + if (data.obj.nid >= 0) { + if (gt.actions.node[s]) { + gt.actions.node[s] (gt, vt, data.obj, data); + return; + } + } else if (data.obj.eid >= 0) { + if (gt.actions.edge[s]) { + gt.actions.edge[s] (gt, vt, data.obj, data); + return; + } + } + if (gt.actions.general[s]) + gt.actions.general[s] (gt, vt, data); +}; +dotty.protogt.actions.general = [ + "undo" = function (gt, vt, data) { + gt.undo (gt, 1); + }; + "paste" = function (gt, vt, data) { + gt.paste (gt, data.pos, 1); + }; + "do layout" = function (gt, vt, data) { + gt.layoutgraph (gt); + }; + "cancel layout" = function (gt, vt, data) { + gt.cancellayout (gt); + }; + "redraw" = function (gt, vt, data) { + gt.redrawgraph (gt, [vt.vtid = vt;]); + }; + "new graph" = function (gt, vt, data) { + gt.erasegraph (gt, null, null); + }; + "load graph" = function (gt, vt, data) { + gt.loadgraph (gt, null, 'file', dotty.protogt.graph, 1); + }; + "reload graph" = function (gt, vt, data) { + gt.loadgraph (gt, gt.name, gt.type, gt.graph, 1); + }; + "save graph" = function (gt, vt, data) { + gt.savegraph (gt, gt.name, gt.type); + }; + "save graph as" = function (gt, vt, data) { + gt.savegraph (gt, null, 'file'); + }; + "open view" = function (gt, vt, data) { + gt = dotty.protogt.creategraph (null); + gt.createview (gt, null); + }; + "copy view" = function (gt, vt, data) { + gt = gt.copygraph (gt); + gt.createview (gt, vt); + }; + "birdseye view" = function (gt, vt, data) { + gt.createview (gt, dotty.protovt.birdseye); + }; + "clone view" = function (gt, vt, data) { + gt.createview (gt, vt); + }; + "close view" = function (gt, vt, data) { + gt.destroyview (gt, vt); + if (tablesize (gt.views) == 0) + gt.destroygraph (gt); + }; + "set graph attr" = function (gt, vt, data) { + gt.setattr (gt, ['attr' = gt.graph.graphattr;]); + }; + "set node attr" = function (gt, vt, data) { + gt.setattr (gt, ['attr' = gt.graph.nodeattr;]); + }; + "set edge attr" = function (gt, vt, data) { + gt.setattr (gt, ['attr' = gt.graph.edgeattr;]); + }; + "zoom in" = function (gt, vt, data) { + gt.zoom (gt, vt, 0.5, data.pos); + }; + "zoom out" = function (gt, vt, data) { + gt.zoom (gt, vt, 2, data.pos); + }; + "zoom in slowly" = function (gt, vt, data) { + gt.zoom (gt, vt, 0.9, data.pos); + }; + "zoom out slowly" = function (gt, vt, data) { + gt.zoom (gt, vt, 1.1, data.pos); + }; + "scroll horizontally" = function (gt, vt, data) { + vt.scrollmode = 'h'; + }; + "scroll vertically" = function (gt, vt, data) { + vt.scrollmode = 'v'; + }; + "find node" = function (gt, vt, data) { + gt.findnode (gt, vt); + }; + "print graph" = function (gt, vt, data) { + gt.printorsave (gt, vt, null, null, null, null); + }; + "text view" = function (gt, vt, data) { + if (dotty.txtview == 'on') + dotty.txtview = 'off'; + else + dotty.txtview = 'on'; + txtview (dotty.txtview); + }; + "quit" = function (gt, vt, data) { + exit (); + }; +]; +dotty.protogt.actions.node = [ + "cut" = function (gt, vt, obj, data) { + gt.cut (gt, obj, 'one', 'support', 'cut'); + dotty.clipgt.layoutgraph (dotty.clipgt); + }; + "Cut" = function (gt, vt, obj, data) { + gt.cut (gt, obj, 'reachable', 'support', 'cut'); + dotty.clipgt.layoutgraph (dotty.clipgt); + }; + "copy" = function (gt, vt, obj, data) { + gt.cut (gt, obj, 'one', 'support', 'copy'); + dotty.clipgt.layoutgraph (dotty.clipgt); + }; + "Copy" = function (gt, vt, obj, data) { + gt.cut (gt, obj, 'reachable', 'support', 'copy'); + dotty.clipgt.layoutgraph (dotty.clipgt); + }; + "group" = function (gt, vt, obj, data) { + local kv; + + if ((kv = gt.getattr (gt, obj))) + gt.groupnodesbyattr (gt, kv.key, kv.val, [ + 'label' = kv.val; kv.key = kv.val; + ], 1, 1); + }; + "Group" = function (gt, vt, obj, data) { + local kv; + + if ((kv = gt.getattr (gt, obj))) + gt.groupnodesbyattr (gt, kv.key, kv.val, [ + 'label' = kv.val; kv.key = kv.val; + ], 0, 1); + }; + "delete" = function (gt, vt, obj, data) { + if (obj.eid >= 0) + gt.removeedge (gt, obj); + else + gt.removenode (gt, obj); + }; + "Delete" = function (gt, vt, obj, data) { + gt.removesubtree (gt, obj); + }; + "remove" = function (gt, vt, obj, data) { + if (obj.nid >= 0) + if ((kv = gt.getattr (gt, obj))) + gt.removenodesbyattr (gt, kv.key, kv.val); + }; + "Remove" = function (gt, vt, obj, data) { + if (obj.nid >= 0) + if ((kv = gt.getattr (gt, obj))) + gt.removesubtreesbyattr (gt, kv.key, kv.val); + }; + "set attr" = function (gt, vt, obj, data) { + gt.setattr (gt, obj); + }; + "print attr" = function (gt, vt, obj, data) { + if (obj.nid >= 0) + echo ('node: ', obj.name); + dump (obj.attr); + }; +]; +dotty.protogt.actions.edge = dotty.protogt.actions.node; +dotty.protovt.normal.menus = [ + 'general' = [ + 0 = "undo"; + 1 = "paste"; + 2 = "do layout"; + 3 = "cancel layout"; + 4 = "redraw"; + 5 = "new graph"; + 6 = "load graph"; + 7 = "reload graph"; + 8 = "save graph"; + 9 = "save graph as"; + 10 = "open view"; + 11 = "copy view"; + 12 = "clone view"; + 13 = "birdseye view"; + 14 = "close view"; + 15 = "set graph attr"; + 16 = "set node attr"; + 17 = "set edge attr"; + 18 = "zoom in"; + 19 = "zoom out"; + 20 = "find node"; + 21 = "print graph"; + 22 = "text view"; + 23 = "quit"; + ]; + 'node' = [ + 0 = "cut"; + 1 = "Cut"; + 2 = "copy"; + 3 = "Copy"; + 4 = "group"; + 5 = "Group"; + 6 = "delete"; + 7 = "Delete"; + 8 = "remove"; + 9 = "Remove"; + 10 = "set attr"; + 11 = "print attr"; + ]; + 'edge' = [ + 0 = "cut"; + 1 = "Cut"; + 2 = "copy"; + 3 = "Copy"; + 4 = "delete"; + 5 = "Delete"; + 6 = "set attr"; + 7 = "print attr"; + ]; +]; +dotty.protovt.normal.keys = [ + 'general' = [ + 'u' = "undo"; + 'p' = "paste"; + 'l' = "do layout"; + 'k' = "cancel layout"; + ' ' = "redraw"; + 'L' = "reload graph"; + 's' = "save graph"; + 'Z' = "zoom in slowly"; + 'z' = "zoom out slowly"; + 'h' = "scroll horizontally"; + 'v' = "scroll vertically"; + ]; + 'node' = [ + 'c' = "copy"; + 'C' = "Copy"; + 'g' = "group"; + 'G' = "Group"; + 'd' = "delete"; + 'D' = "Delete"; + 'r' = "remove"; + 'R' = "Remove"; + 'a' = "set attr"; + ]; + 'edge' = [ + 'c' = "copy"; + 'C' = "Copy"; + 'd' = "delete"; + 'D' = "Delete"; + 'a' = "set attr"; + ]; +]; +dotty.protovt.birdseye.menus = dotty.protovt.normal.menus; +dotty.protovt.birdseye.keys = dotty.protovt.normal.keys; +dotty.protovt.normal.uifuncs = [ + 'leftdown' = function (data) { + local gt; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + if (data.obj.nid >= 0) { + dotty.node2move = data.obj; + dotty.movewidget = data.widget; + dotty.rp2 = data.pos; + } + }; + 'leftmove' = function (data) { + local gt; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + if (dotty.node2move & ( + dotty.rp2.x ~= data.pos.x | dotty.rp2.y ~= data.pos.y + )) { + gt.movenode (gt, dotty.node2move, data.pos); + dotty.rp2 = data.pos; + } + }; + 'leftup' = function (data) { + local gt; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + if (dotty.node2move) { + if (dotty.movewidget == data.widget) + gt.movenode (gt, dotty.node2move, data.pos); + dotty.node2move = 0; + } else if (~data.obj) + gt.insertnode (gt, data.pos, null, null, null, 1); + }; + 'middledown' = function (data) { + if (~(data.obj.nid >= 0)) + return; + dotty.rubberband = 1; + dotty.movewidget = data.widget; + setgfxattr (data.widget, ['mode' = 'xor';]); + dotty.rp1 = data.pos; + dotty.rp2 = data.pos; + line (data.widget, null, dotty.rp1, dotty.rp2, ['color' = 1;]); + }; + 'middlemove' = function (data) { + if (dotty.rubberband ~= 1 | ( + dotty.rp2.x == data.pos.x & dotty.rp2.y == data.pos.y + )) + return; + line (data.widget, null, dotty.rp1, dotty.rp2, ['color' = 1;]); + dotty.rp2 = data.pos; + line (data.widget, null, dotty.rp1, dotty.rp2, ['color' = 1;]); + }; + 'middleup' = function (data) { + local gt; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + if (dotty.rubberband ~= 1) + return; + dotty.rubberband = 0; + line (dotty.movewidget, null, dotty.rp1, dotty.rp2, ['color' = 1;]); + setgfxattr (dotty.movewidget, ['mode' = 'src';]); + if (dotty.movewidget ~= data.widget | ~( + data.pobj.nid >= 0) | ~(data.obj.nid >= 0 + )) + return; + if (data.pobj.attr.support) + gt.groupnodes (gt, [ + data.obj.nid = data.obj; + data.pobj.nid = data.pobj; + ], data.obj, null, null, null, 1, 1); + else if (data.obj.attr.support) + gt.groupnodes (gt, [ + data.obj.nid = data.obj; + data.pobj.nid = data.pobj; + ], data.pobj, null, null, null, 1, 1); + else + gt.insertedge (gt, data.pobj, null, data.obj, null, null, 1); + }; + 'rightdown' = function (data) { + local vt, gt, menu, i; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + if (~data.obj) + menu = vt.menus.general; + else if (data.obj.nid >= 0) + menu = vt.menus.node; + else if (data.obj.eid >= 0) + menu = vt.menus.edge; + if ((i = displaymenu (data.widget, menu)) >= 0) + gt.doaction (data, menu[i]); + }; + 'button3up' = function (data) { + local vt, attr; + + vt = dotty.views[data.widget]; + attr = getwidgetattr (vt.scroll, [0 = 'childcenter';]); + if (vt.scrollmode == 'h') + attr.childcenter.x = attr.childcenter.x - 40; + else + attr.childcenter.y = attr.childcenter.y - 40; + setwidgetattr (vt.scroll, ['childcenter' = attr.childcenter;]); + }; + 'button4up' = function (data) { + local vt, attr; + + vt = dotty.views[data.widget]; + attr = getwidgetattr (vt.scroll, [0 = 'childcenter';]); + if (vt.scrollmode == 'h') + attr.childcenter.x = attr.childcenter.x + 40; + else + attr.childcenter.y = attr.childcenter.y + 40; + setwidgetattr (vt.scroll, ['childcenter' = attr.childcenter;]); + }; + 'keyup' = function (data) { + local vt, gt, action; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + if (data.obj.nid >= 0) { + if (vt.keys.node[data.key]) + action = vt.keys.node[data.key]; + } else if (data.obj.eid >= 0) { + if (vt.keys.edge[data.key]) + action = vt.keys.edge[data.key]; + } + if (~action) + if (vt.keys.general[data.key]) + action = vt.keys.general[data.key]; + if (action) + gt.doaction (data, action); + }; + 'redraw' = function (data) { + local vt, gt; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + gt.drawgraph (gt, [vt.vtid = vt;]); + }; + 'closeview' = function (data) { + local vt, gt; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + gt.destroyview (gt, vt); + if (tablesize (gt.views) == 0) + gt.destroygraph (gt); + }; +]; +dotty.protovt.birdseye.uifuncs = [ + 'leftdown' = function (data) { + local gt, vid; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + for (vid in gt.views) { + vt = gt.views[vid]; + if (vt.type ~= 'birdseye') + gt.setviewcenter ([vid = vt;], data.pos); + } + }; + 'leftmove' = function (data) { + local gt, vid; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + for (vid in gt.views) { + vt = gt.views[vid]; + if (vt.type ~= 'birdseye') + gt.setviewcenter ([vid = vt;], data.pos); + } + }; + 'leftup' = function (data) { + local gt, vid; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + for (vid in gt.views) { + vt = gt.views[vid]; + if (vt.type ~= 'birdseye') + gt.setviewcenter ([vid = vt;], data.pos); + } + }; + 'middledown' = dotty.protovt.normal.uifuncs.middledown; + 'middlemove' = dotty.protovt.normal.uifuncs.middlemove; + 'middleup' = dotty.protovt.normal.uifuncs.middleup; + 'rightdown' = dotty.protovt.normal.uifuncs.rightdown; + 'keyup' = dotty.protovt.normal.uifuncs.keyup; + 'redraw' = dotty.protovt.normal.uifuncs.redraw; + 'closeview' = dotty.protovt.normal.uifuncs.closeview; +]; +dotty.monitorfile = function (data) { + local gtid, gt, lpt; + + for (gtid in dotty.layoutpending) { + lpt = dotty.layoutpending[gtid]; + if (lpt.fd == data.fd) { + gt = dotty.graphs[lpt.gtid]; + gt.haveinput = 1; + gt.layoutgraph (gt); + return 1; + } + } + return 0; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/fractal.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +load ('def.lefty'); +definit (); +# +# initialize window data +# +canvas = defcanvas; +wrect = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 400; 'y' = 500;];]; +setwidgetattr (canvas, ['window' = wrect;]); + +sq = function (x) { + return x * x; +}; + +# data structures +# +length = 300; +center = ['x' = 200; 'y' = 250;]; +radius = 2 * length / sqrt (12); +fractalangle = 0; +maxlevel = 2; + +# drawing functions +# +# draw a Koch curve (a ``snowflake'' fractal) +# +# start with a triangle and keep replacing edges +# with the construct: _/\_ +# until the recursion level reaches 'maxlevel' +# +fractal = function (level, length, angle) { + local nlength, newpenpos; + + if (level >= maxlevel) { + newpenpos.x = penpos.x + length * cos (angle); + newpenpos.y = penpos.y + length * sin (angle); + line (canvas, null, penpos, newpenpos, ['color' = 1;]); + penpos = newpenpos; + return; + } + nlength = length / 3; + fractal (level + 1, nlength, angle); + fractal (level + 1, nlength, angle + 60); + fractal (level + 1, nlength, angle - 60); + fractal (level + 1, nlength, angle); +}; +drawfractal = function () { + clear (canvas); + setpick (canvas, center, wrect); + penpos = [ + 'x' = center.x + cos (fractalangle + 210) * radius; + 'y' = center.y + sin (fractalangle + 210) * radius; + ]; + fractal (0, length, fractalangle + 60); + fractal (0, length, fractalangle - 60); + fractal (0, length, fractalangle - 180); + remove ('penpos'); +}; + +# editing functions +# +# transform the fractal. +# +# map point 'prevpoint' to point 'currpoint' +# with respect to the center of the fractal. +# +transformfractal = function (prevpoint, currpoint) { + local prevtan, currtan, prevradius, currradius; + + prevtan = atan (prevpoint.y - center.y, prevpoint.x - center.x); + currtan = atan (currpoint.y - center.y, currpoint.x - center.x); + fractalangle = fractalangle + (currtan - prevtan); + prevradius = sqrt ( + sq (prevpoint.y - center.y) + sq (prevpoint.x - center.x) + ); + currradius = sqrt ( + sq (currpoint.y - center.y) + sq (currpoint.x - center.x) + ); + radius = radius / prevradius * currradius; + length = radius / 2 * sqrt (12); +}; + +# user interface functions +# +# bind changes to the fractal to user actions +# +leftup = function (data) { + transformfractal (data.ppos, data.pos); + drawfractal (); +}; +dops = function () { + local s; + + s = ['x' = 8 * 300; 'y' = 10.5 * 300;]; + canvas = createwidget (-1, ['type' = 'ps'; 'size' = s;]); + setwidgetattr (canvas, ['window' = wrect;]); + drawfractal (); + destroywidget (canvas); + canvas=defcanvas; +}; +transformfractal (['x' = 0; 'y' = 0;], ['x' = 0; 'y' = 0;]); +drawfractal ();
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/fractal2.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,218 @@ +# +# data structures +# +length = 300; +center = ['x' = 200; 'y' = 250;]; +radius = 2 * length / sqrt (12); +fractalangle = 0; +maxlevel = 2; +sizes = [ + 'button' = [ 'x' = 100; 'y' = 40; ]; + 'canvas' = [ 'x' = 400; 'y' = 500; ]; + 'view' = [ 'x' = 400; 'y' = 600; ]; +]; +sq = function (x) { + return x * x; +}; +# +# create view and other widgets +# +init = function () { + view = createwidget (-1, [ + 'type' = 'view'; 'name' = 'fractal'; 'size' = sizes.view; + ]); + + array1 = createwidget (view, [ + 'type' = 'array'; 'borderwidth' = 1; 'mode' = 'vertical'; + ]); + widgets[array1].resize = resize; + + array2 = createwidget (array1, [ + 'type' = 'array'; 'borderwidth' = 1; 'mode' = 'horizontal'; + ]); + widgets[array2].resize = resize; + + array3 = createwidget (array2, [ + 'type' = 'array'; 'borderwidth' = 1; 'mode' = 'vertical'; + ]); + widgets[array3].resize = resize; + + morebutton = createwidget (array3, [ + 'type' = 'button'; 'text' = 'more'; + ]); + widgets[morebutton].pressed = pressed; + lessbutton = createwidget (array3, [ + 'type' = 'button'; 'text' = 'less'; + ]); + widgets[lessbutton].pressed = pressed; + setwidgetattr (morebutton, ['size' = sizes.button;]); + setwidgetattr (lessbutton, ['size' = sizes.button;]); + + atext = createwidget (array2, [ + 'type' = 'text'; 'mode' = 'oneline'; + ]); + widgets[atext].oneline = oneline; + setwidgetattr (atext, [ + 'size' = ['x' = sizes.button.x; 'y' = sizes.button.y * 2;]; + ]); + + scroll = createwidget (array1, ['type' = 'scroll';]); + canvas = createwidget (scroll, ['type' = 'canvas';]); + wrect = [0 = ['x' = 0; 'y' = 0;]; 1 = sizes.canvas;]; + setwidgetattr (canvas, ['window' = wrect; 'viewport' = wrect[1];]); +}; +# +# drawing functions +# +# draw a Koch curve (a ``snowflake'' fractal) +# +# start with a triangle and keep replacing edges +# with the construct: _/\_ +# until the recursion level reaches 'maxlevel' +# +fractal = function (level, length, angle) { + local nlength, newpenpos; + + if (level >= maxlevel) { + newpenpos.x = penpos.x + length * cos (angle); + newpenpos.y = penpos.y + length * sin (angle); + line (canvas, null, penpos, newpenpos, ['color' = 1;]); + penpos = newpenpos; + return; + } + nlength = length / 3; + fractal (level + 1, nlength, angle); + fractal (level + 1, nlength, angle + 60); + fractal (level + 1, nlength, angle - 60); + fractal (level + 1, nlength, angle); +}; +redrawfractal = function () { + clear (canvas); + setpick (canvas, center, wrect); + penpos = [ + 'x' = center.x + cos (fractalangle + 210) * radius; + 'y' = center.y + sin (fractalangle + 210) * radius; + ]; + fractal (0, length, fractalangle + 60); + fractal (0, length, fractalangle - 60); + fractal (0, length, fractalangle - 180); + remove ('penpos'); +}; +# +# editing functions +# +# transform the fractal. +# +# map point 'prevpoint' to point 'currpoint' +# with respect to the center of the fractal. +# +transformfractal = function (prevpoint, currpoint) { + local prevtan, currtan, prevradius, currradius; + + prevtan = atan (prevpoint.y - center.y, prevpoint.x - center.x); + currtan = atan (currpoint.y - center.y, currpoint.x - center.x); + fractalangle = fractalangle + (currtan - prevtan); + prevradius = sqrt ( + sq (prevpoint.y - center.y) + sq (prevpoint.x - center.x) + ); + currradius = sqrt ( + sq (currpoint.y - center.y) + sq (currpoint.x - center.x) + ); + radius = radius / prevradius * currradius; + length = radius / 2 * sqrt (12); +}; +# +# main actions +# +redraw = function (data) { + redrawfractal (); +}; +changemaxlevel = function (dn) { + maxlevel = maxlevel + dn; + if (maxlevel < 0) + maxlevel = 0; + redrawfractal (); +}; +resize = function (data) { + local ret; + if (data.widget == array1) { + ret = [ + array2 = [ + 'x' = data.size.x; + 'y' = sizes.button.y * 2; + ]; + scroll = [ + 'x' = data.size.x; + 'y' = data.size.y - sizes.button.y * 2; + ]; + ]; + } else if (data.widget == array2) { + ret = [ + array3 = [ + 'x' = sizes.button.x; + 'y' = 2 * sizes.button.y; + ]; + atext = [ + 'x' = data.size.x - sizes.button.x; + 'y' = 2 * sizes.button.y; + ]; + ]; + } else if (data.widget == array3) { + ret = [ + morebutton = sizes.button; + lessbutton = sizes.button; + ]; + } + return ret; +}; +# +# user interface functions +# +# bind changes to the fractal to user actions +# +leftup = function (data) { + transformfractal (data.ppos, data.pos); + redrawfractal (); +}; +menu = [ + 0 = 'more'; + 1 = 'less'; +]; +domenu = function (i) { + local s; + s = menu[i]; + if (s == 'more') + changemaxlevel (1); + else if (s == 'less') + changemaxlevel (-1); +}; +rightdown = function (data) { + domenu (displaymenu (canvas, menu)); +}; +pressed = function (data) { + if (data.widget == morebutton) + changemaxlevel (1); + else if (data.widget == lessbutton) + changemaxlevel (-1); +}; +oneline = function (data) { + local dn; + dn = ston (data.text); + if (dn > 0 | dn < 0) + changemaxlevel (dn - maxlevel); +}; +# +# postscript generation +# +dops = function () { + local r; + + r = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 8 * 300; 'y' = 10.5 * 300;];]; + canvas = opencanvas ('pscanvas', '', r); + setwidgetattr (canvas, ['window' = wrect;]); + redraw (); + closecanvas (canvas); + canvas=defcanvas; +}; +init (); +#txtview ('off');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/lefty.psp Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +/BOX { + /boxy1 exch def /boxx1 exch def /boxy0 exch def /boxx0 exch def + boxx0 boxy0 moveto boxx1 boxy0 lineto + boxx1 boxy1 lineto boxx0 boxy1 lineto + closepath +} def +/SCP { stroke initclip newpath BOX clip newpath } def +/CL { stroke setrgbcolor } def +/DO { stroke } def +/NP { newpath } def +/FI { fill } def +/LI { moveto lineto } def +/CT { curveto } def +/AR { + /ang2 exch def /ang1 exch def + /radius exch def /y2x exch def /cy exch def /cx exch def + gsave + cx cy translate 1 y2x scale 0 0 radius ang1 ang2 arc stroke + grestore +} def +/ARF { + /ang2 exch def /ang1 exch def + /radius exch def /y2x exch def /cy exch def /cx exch def + gsave + cx cy translate 1 y2x scale 0 0 radius ang1 ang2 arc fill + grestore +} def +/TXT { + /texth exch def + /textf exch def + /textn exch def + /texts exch def + /textyj exch def /texty exch def + /textxj exch def /textx exch def + textf findfont texth scalefont dup setfont + /FontBBox get 1 get 1000 div texth mul /textbl exch def + /textth texth textn mul def /texttw 0 def + 0 1 textn 1 sub { + texts exch get 0 get stringwidth pop + dup texttw gt { /texttw exch def } { pop } ifelse + } for + textyj (b) eq { /ty texty textth add textbl add def } if + textyj (d) eq { /ty texty textth add def } if + textyj (c) eq { /ty texty textth 2 div add def } if + textyj (u) eq { /ty texty def } if + /ty ty textbl sub def + textxj (l) eq { /tx textx def } if + textxj (c) eq { /tx textx texttw 2 div sub def } if + textxj (r) eq { /tx textx texttw sub def } if + 0 1 textn 1 sub { + /ty ty texth sub def + texts exch get dup 0 get /ts exch def 1 get /tj exch def + tj (l) eq { tx ty moveto ts show } if + tj (n) eq { + tx texttw ts stringwidth pop sub 2 div add ty moveto ts show + } if + tj (r) eq { + tx texttw ts stringwidth pop sub add ty moveto ts show + } if + } for +} def + +/colorimage where { + pop +} { + /bwproc { + rgbproc dup length 3 idiv string 0 3 0 5 -1 roll { + add 2 1 roll 1 sub dup 0 eq { + pop 3 idiv 3 -1 roll dup 4 -1 roll + dup 3 1 roll 5 -1 roll put 1 add 3 0 + } { + 2 1 roll + } ifelse + } forall + pop pop pop + } def + /colorimage { + pop pop /rgbproc exch def {bwproc} image + } bind def +} ifelse
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/slides.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,109 @@ +load ('def.lefty'); +definit (); +# +# initialize window data +# +canvas = defcanvas; +wrect = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 800; 'y' = 1000;];]; +lmargin = 100; +width = 800; +height = 1000; +setwidgetattr (canvas, ['window' = wrect;]); +fonts = [ + 'timr' = [ + 14 = 'timr14'; + 18 = 'timr18'; + 24 = 'timr24'; + ]; + 'courr' = [ + 14 = 'courr14'; + 18 = 'courr18'; + 24 = 'courr24'; + ]; +]; +x2ps = [ + 'timr24' = 'Times-Roman'; + 'timr18' = 'Times-Roman'; + 'timr14' = 'Times-Roman'; + 'courr24' = 'Courier'; + 'courr18' = 'Courier'; + 'courr14' = 'Courier'; +]; + +calc = function () { + local i, j, cpos, tsiz, dist, slidep; + tsiz = ['x' = 0; 'y' = 0;]; + for (i = 0; slides[i]; i = i + 1) { + slidep = slides[i]; + if (slidep.skip) { + tsiz.y = tsiz.y + slidep.skip; + } else { + for (j = 0; slidep.text[j]; j = j + 1) { + if (j > 0) + tsiz.y = tsiz.y + 5; + if (~slidep.font) + slidep.font = slides.font; + if (~slidep.size) + slidep.size = slides.size; + if (~slidep.just) + slidep.just = slides.just; + slidep.tsiz[j] = textsize (canvas, slidep.text[j], + fonts[slidep.font][slidep.size], 0); + tsiz.y = tsiz.y + slidep.tsiz[j].y; + } + } + } + dist = (height - tsiz.y) / (i + 1); + cpos = ['x' = lmargin; 'y' = height - dist;]; + for (i = 0; slides[i]; i = i + 1) { + slidep = slides[i]; + if (slidep.skip) { + cpos.y = cpos.y - slidep.skip - dist; + } else { + for (j = 0; slidep.text[j]; j = j + 1) { + if (j > 0) + cpos.y = cpos.y - 5; + if (slidep.just == 0) + slidep.tpos[j].x = cpos.x; + else + slidep.tpos[j].x = width / 2; + cpos.y = cpos.y - slidep.tsiz[j].y; + slidep.tpos[j].y = cpos.y; + } + cpos.y = cpos.y - dist; + } + } +}; +redraw = function () { + local i, j, slidep; + for (i = 0; slides[i]; i = i + 1) { + slidep = slides[i]; + if (~slidep.skip) + for (j = 0; slidep.text[j]; j = j + 1) + text (canvas, slidep, slidep.tpos[j], slidep.text[j], + fonts[slidep.font][slidep.size], 0, slidep.just); + } +}; +dops = function () { + local i, j, slidep, r; + + r = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 8 * 300; 'y' = 10.5 * 300;];]; + canvas = createwidget (-1, ['type' = 'ps'; 'origin' = r[0]; 'size' = r[1] +;]); + setwidgetattr (canvas, ['window' = wrect;]); + for (i = 0; slides[i]; i = i + 1) { + slidep = slides[i]; + if (~slidep.skip) + for (j = 0; slidep.text[j]; j = j + 1) + text (canvas, slidep, slidep.tpos[j], slidep.text[j], + x2ps[fonts[slidep.font][slidep.size]], + slidep.tsiz[j].y, slidep.just, 1); + } + destroywidget (canvas); + canvas=defcanvas; +}; +doit = function () { + calc (); + redraw (); + dops (); +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/lefty/tree.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,195 @@ +load ('def.lefty'); +definit (); +# +# initialize window data +# +canvas = defcanvas; +wrect = [0 = ['x' = -5; 'y' = 0;]; 1 = ['x' = 410; 'y' = 500;];]; +setwidgetattr (canvas, ['window' = wrect;]); +# +# data structures +# +nodearray = []; +nodenum = 0; +dist = ['x' = 40; 'y' = 40;]; +defsize = ['x' = 10; 'y' = 10;]; +fontname = 'fixed'; +fontsize = 18; +tree = null; + +# drawing functions +# +boxnode = function (node) { + local center; + box (canvas, node, node.rect, ['color' = 0; 'fill' = 'on';]); + box (canvas, node, node.rect); + center = [ + 'x' = (node.rect[0].x + node.rect[1].x) / 2; + 'y' = (node.rect[0].y + node.rect[1].y) / 2; + ]; + if (node.name) + text (canvas, node, center, node.name, fontname, fontsize, 'cc'); +}; +circlenode = function (node) { + local center, radius; + center = [ + 'x' = (node.rect[0].x + node.rect[1].x) / 2; + 'y' = (node.rect[0].y + node.rect[1].y) / 2; + ]; + radius = [ + 'x' = center.x - node.rect[0].x; + 'y' = center.y - node.rect[0].y; + ]; + arc (canvas, node, center, radius, ['color' = 0; 'fill' = 'on';]); + arc (canvas, node, center, radius); + if (node.name) + text (canvas, node, center, node.name, fontname, fontsize, 'cc'); +}; +drawnode = boxnode; +drawedge = function (node1, node2) { + line (canvas, null, + [ + 'x' = (node1.rect[1].x + node1.rect[0].x) / 2; + 'y' = node1.rect[0].y; + ], [ + 'x' = (node2.rect[1].x + node2.rect[0].x) / 2; + 'y' = node2.rect[1].y; + ]); +}; +drawtree = function (node) { + local i; + for (i in nodearray) + drawnode (nodearray[i]); + drawtreerec (node); +}; +drawtreerec = function (node) { + local i, n; + if ((n = tablesize (node.ch)) > 0) { + for (i = 0; i < n; i = i + 1) { + drawedge (node, node.ch[i]); + drawtreerec (node.ch[i]); + } + } +}; +redraw = function (c) { + if (tree) + drawtree (tree); +}; + +# layout functions +# +complayout = function () { + leafx = 0; + leafrank = 0; + dolayout (tree, wrect[1].y - 10); + remove ('leafx'); + remove ('leafrank'); +}; +dolayout = function (node, pary) { + local r, n, i, size, lchp, rchp; + size = nodesize (node); + if (node.chn > 0) { + for (i = 0; i < node.chn; i = i + 1) + dolayout (node.ch[i], pary - size.y - dist.y); + node.rank = (node.ch[0].rank + node.ch[node.chn - 1].rank) / 2; + lchp = node.ch[0].rect; + rchp = node.ch[node.chn - 1].rect; + r[0].x = lchp[0].x + ((rchp[1].x - lchp[0].x) - size.x) / 2; + r[0].y = pary - size.y; + r[1].x = r[0].x + size.x; + r[1].y = pary; + node.rect = r; + } else { + node.rank = leafrank; + r[0].x = leafx; + r[0].y = pary - size.y; + r[1].x = r[0].x + size.x; + r[1].y = pary; + leafrank = leafrank + 1; + leafx = r[1].x + dist.x; + node.rect = r; + } +}; + +# editing functions +# +inode = function (point, name) { + local i, nnum, size; + nnum = nodenum; + if (~name) + name = ask ('give name of node:'); + nodearray[nnum].ch = []; + nodearray[nnum].chn = 0; + nodearray[nnum].name = name; + size = nodesize (nodearray[nnum]); + nodearray[nnum].rect[0] = point; + nodearray[nnum].rect[1] = ['x' = point.x + size.x; 'y' = point.y + size.y;]; + nodenum = nodenum + 1; + if (~tree) { + tree = nodearray[nnum]; + tree.depth = 0; + complayout (); + drawtree (tree); + } else + drawtree (nodearray[nnum]); + return nodearray[nnum]; +}; +iedge = function (node1, node2) { + node1.ch[node1.chn] = node2; + node1.chn = node1.chn + 1; + node2.depth = node1.depth + 1; + complayout (); + clear (canvas); + drawtree (tree); +}; +fix = function (node, op, np) { + if (node.depth ~= 0) + dist.y = dist.y + (op.y - np.y) / node.depth; + if (node.rank ~= 0) + dist.x = dist.x + (np.x - op.x) / node.rank; + complayout (); + clear (canvas); + drawtree (tree); +}; +nodesize = function (node) { + local siz; + if (~(siz = textsize (canvas, node.name, fontname, fontsize))) + siz = defsize; + else { + siz.x = siz.x + 8; + siz.y = siz.y + 8; + } + return siz; +}; +changenode = function (nodestyle) { + drawnode = nodestyle; + clear (canvas); + drawtree (tree); +}; + +# user interface functions +# +leftdown = function (data) { + if (~data.obj) + inode (data.pos, null); +}; +leftup = function (data) { + if (data.pobj) + fix (data.pobj, data.ppos, data.pos); +}; +middleup = function (data) { + if (data.pobj & data.obj) + iedge (data.pobj, data.obj); +}; +dops = function () { + local s; + + s = ['x' = 8 * 300; 'y' = 10.5 * 300;]; + fontname = 'Times-Roman'; + canvas = createwidget (-1, ['type' = 'ps'; 'size' = s;]); + setwidgetattr (canvas, ['window' = wrect;]); + drawtree (tree); + destroywidget (canvas); + canvas=defcanvas; + fontname = 'fixed'; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/pkgconfig/libcdt.pc Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +prefix=/c/graphviz-mingw/local.2.20.3 +exec_prefix=${prefix} +libdir=${exec_prefix}/lib +includedir=${prefix}/include/graphviz + +Name: libcdt +Description: Container DataType library +Version: 2.20.3 +Libs: -L${libdir} -lcdt +Cflags: -I${includedir}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/pkgconfig/libcgraph.pc Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +prefix=/c/graphviz-mingw/local.2.20.3 +exec_prefix=${prefix} +libdir=${exec_prefix}/lib +includedir=${prefix}/include/graphviz + +Name: libcgraph +Description: Graph library (file i/o, dot language parsing, graph, subgraph, node, edge, attribute, data structure manipulation) +Version: 2.20.3 +Libs: -L${libdir} -lcgraph -lcdt +Cflags: -I${includedir}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/pkgconfig/libgraph.pc Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +prefix=/c/graphviz-mingw/local.2.20.3 +exec_prefix=${prefix} +libdir=${exec_prefix}/lib +includedir=${prefix}/include/graphviz + +Name: libgraph +Description: Graph library (file i/o, dot language parsing, graph, subgraph, node, edge, attribute, data structure manipulation) +Version: 2.20.3 +Libs: -L${libdir} -lgraph -lcdt +Cflags: -I${includedir}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/pkgconfig/libgvc.pc Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,12 @@ +prefix=/c/graphviz-mingw/local.2.20.3 +exec_prefix=${prefix} +libdir=${exec_prefix}/lib +includedir=${prefix}/include/graphviz +plugins=5 + +Name: libgvc +Description: The GraphVizContext library +Version: 2.20.3 +Libs: -L${libdir} -lgvc -lgraph -lcdt +Cflags: -I${includedir} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/lib/pkgconfig/libpathplan.pc Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,10 @@ +prefix=/c/graphviz-mingw/local.2.20.3 +exec_prefix=${prefix} +libdir=${exec_prefix}/lib +includedir=${prefix}/include/graphviz + +Name: libpathplan +Description: Library for planning polyline and bezier paths around polygon obstacles +Version: 2.20.3 +Libs: -L${libdir} -lpathplan +Cflags: -I${includedir}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/AUTHORS Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +Primary contact. Please send patches or enhancements to: + Graphviz project <gviz-bugs@research.att.com> + +--------------- +Authors: (In alphabetical order.) + + David Dobkin <dpd@cs.princeton.edu> + John Ellson <ellson@graphviz.org> + Emden Gansner <erg@research.att.com> + Eleftherios Koutsofios <ek@research.att.com> + Stephen North <north@research.att.com> + Kiem-Phong Vo <kpv@research.att.com> + Gordon Woodhull <gmcw@worldnet.att.net> + +--------------- + +Recommended packages: + +FreeType: <http://www.freetype.org/> + David TURNER (david.turner@freetype.org) + Robert WILHELM (robert.wilhelm@freetype.org) + and others. + +GD: <http://www.boutell.com/gd/> + Tom Boutell
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/COPYING Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,213 @@ +Common Public License Version 1.0 + +THE ACCOMPANYING PROGRAM IS PROVIDED UNDER THE TERMS OF THIS COMMON PUBLIC +LICENSE ("AGREEMENT"). ANY USE, REPRODUCTION OR DISTRIBUTION OF THE PROGRAM +CONSTITUTES RECIPIENT'S ACCEPTANCE OF THIS AGREEMENT. + +1. DEFINITIONS + +"Contribution" means: + + a) in the case of the initial Contributor, the initial code and +documentation distributed under this Agreement, and + + b) in the case of each subsequent Contributor: + + i) changes to the Program, and + + ii) additions to the Program; + + where such changes and/or additions to the Program originate from and are +distributed by that particular Contributor. A Contribution 'originates' from a +Contributor if it was added to the Program by such Contributor itself or anyone +acting on such Contributor's behalf. 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/ChangeLog Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,960 @@ +June 16, 2009 + - Release 2.24.0 + - Core: + - add new layout engine for large graphs: sfdp + - Fixed bugs: + 1515, 1590, 1598, 1601, 1605, 1607, 1609, 1610, 1611, + 1614, 1615, 1617, 1625, 1628, 1634, 1635, 1640, 1641, + 1642, 1646, 1649, 1651, 1652 + +March 13, 2009 + - Release 2.22.2 + - fix for buffer overflow (present in 2.22.0 and 2.22.1) + - Fixed bugs: + 1602 + +March 9, 2009 + - Release 2.22.1 + - build fixes for Visual Studio and for FreeBSD +March 3, 2009 + - Release 2.22.0 + - Core: + - libgvc api changed, version bumped. Affects third party + applications using libgvc. + - plugin api changed, version bumped. Affects third party + plugins for graphviz. + - 90% conversion to cgraph has been done, but not enabled yet, + (and yes, its true what they say about the last 10% ) + - drop libagraph from distribution (use libcgraph) + - layout code completely converted to floating point. + - new "dot -P" option for generating a graph of available + plugins. + - registered MIME type: text/vnd.graphviz for .gv files + - rename files from .dot to .gv to avoid conflict with + Word templates. .dot still supported, but deprecated. + - new command: mm2gv (matrix-market graph file conversion) + - rename commands: dot2gxl -> gv2gxl + gxl2dot -> gxl2gv + - Plugins: + - new rsvg plugin for support of node shapes in SVG format + - new gs plugin for support of node shapes in PS format + - new lasi plugin for support of UTF-8 characters in PS output + (the above thee plugins are Linux only, at the moment) + - new quartz plugin (MacOSx only) + - new gdiplus plugin (Windows only) + - new -Tvml support in core plugin (thanks Steve Roush) + - new -Ttk support in core plugin (also used by Tcldot and + gv_tcl language bindings.) + - disabled old style codegens completely + - Linux: + - new Ubuntu8 builds + - new Fedora 10 and 11 builds + - MacOSx: + - Universal binary for Leopard: i386, x86_64, ppc, ppc64 + - Should not conflict with parallel install of MacPorts + version of graphviz + - Improved GUI + - Windows: + - VisualC project files now available, in addition to the GNU + Makefiles that are used the mingw builds. + - Language Bindings: + - fixed problem with writing dot, xdot, plain, canon to + memory or to Tcl_Channels + - renamed man pages to Debian style: gv.3tcl, gv.3perl, etc + - Fixed bugs: + 827, 1365, 1366, 1367, 1368, 1374, 1375, 1376, 1378, 1380, 1382, + 1383, 1385, 1386, 1388, 1390, 1391, 1392, 1394, 1395, 1397, + 1398, 1399, 1405, 1407, 1410, 1412, 1414, 1415, 1416, 1421, + 1424, 1425, 1427, 1429, 1431, 1433, 1435, 1436, 1437, 1438, + 1440, 1441, 1444, 1446, 1451, 1452, 1453, 1456, 1457, 1459, + 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1470, 1474, 1475, + 1476, 1477, 1478, 1484, 1485, 1489, 1490, 1492, 1493, 1495, + 1496, 1499, 1500, 1501, 1502, 1503, 1505, 1509, 1513, 1521, + 1523, 1525, 1530, 1531, 1532, 1533, 1535, 1536, 1539, 1540, + 1542, 1543, 1546, 1547, 1551, 1553, 1554, 1561, 1565, 1566, + 1568, 1569, 1570, 1571, 1573, 1577, 1578, 1579, 1580, 1581, + 1582, 1584, 1586, + +June 25, 2008 + - Release 2.20.2 + - Fix bug in HTML-like labels +June 23, 2008 + - Release 2.20.1 + - Fix bug in ccomps related to conversion to cgraph +June 20, 2008 + - Release 2.20.0 + - Preparing for Release 2.20 + - Fixed bugs: 1315, 1317, 1324, 1336, 1343, 1364 + - Add new "folder" shape for nodes. + - Migration of gvpr tools to libcgraph. + - New output format -Teps (encapsulated postscript) + - Various NetBSD and SuSE fixes incorporated + - ./configure now provides a summary + - RPM specfile updates for fedora-10 (no more string comparisons) + - Add MacOS support (Glen Low) +March 10, 2008 + - Release 2.18 + - Fixed bugs: 1249, 1255, 1256, 1268, 1276, 1289, 1295, 1300, + Fedora BZ#247376, + - in -Tps use a new number formatter that suppresses trailing 0. + - support tcl/tk-8.5 + - support gcc-4.3 + - support for node usershapes/images in svg format (thanks Alex Poylisher) + - install: perl, php, python, ruby, tcl, bindings in language-specified directories + - add arrowhead scaling with edge penwidth + - add "folder" node shape (thanks Pander) + - many windows and mac fixes (thanks Glen) + - add "smyna" large graph view (thanks Arif) (not yet included in binary distros) +December 12, 2007 + - Release 2.16.1 + - Fixed bugs: 1228, 1234, 1238, 1239, 1245 + - Improvements to PHP binding + - Improvements to OCAML binding + - Make regression tests run from the build tree, rather than require installation + - Repair freetype detection on RedHat-7 (Yes, people still use it!!) + - Fix zoom-at-mouse-location in -Txlib and -Tgtk + - Fix some dotty regressions +November 9, 2007 + - Release 2.16 + - Fixed bugs: 456, 473, 1021, 1153, 1154, 1155, 1159, 1160, 1162, + 1165, 1166, 1168, 1169, 1170, 1172, 1173, 1174, 1175, 1177, 1178, + 1179, 1181, 1182, 1183, 1185, 1187, 1189, 1192, 1193, 1195, 1196, + 1199, 1204, 1207, 1210, 1215, 1216, 1217, 1218, 1219, 1220, 1223 + - new regression test suite + - new cgraph library (will eventually replace graph and agraph) + - add "image" and "imagescale" for simpler support for images in nodes + - add "tab" "box3d" and "component" shapes. - Diomidis Spinellis + - replace arith.h in distro + - add functions to access version info to avoid need for gvcint.h + - Fix problem with irregular character spacing at 96dpi in pango/cairo output formats. + - Add gdk_pixbuf plugin providing: .bmp .ico .jpg .png .tif + - Add DevIL plugin providing: .bmp .jpg .png .tif .tga + - Extend GD plugin to provide a backend to cairo for: .gif .jpg .png .gd .gd2 .wbmp <- gifs are now antialiased + - Rework plugin framework to separate device from renderer, and to autoload load dependendent plugins + - show defaults in output from: ./configure --help + - add more info to dot -v and dot -v2 debug outputs + - various issues with CR/LF in windows, but not in binary outputs. +August 15, 2007 + - release 2.14.1 + - Fixed bugs: 1163, 1167 + - Windows build fixes + - Add xdot parsing library to source distros + - graphviz.spec fixes for rpm distros from Gareth Armstrong + - moved language binding man pages to mann (gv_php.n, gv_ocaml.n, etc.) + - New access functions for version info in GVC_t - permits gvcint.h to + be private. +August 2, 2007 + - release 2.14 + - Fixed (or otherwise closed) bugs: + 74, 130, 162, 184, 190, 197, 219, 223, 281, 295, 311, 316, + 324, 352, 364, 385, 393, 404, 420, 447, 455, 474, 489, 507, + 530, 532, 537, 543, 551, 564, 571, 574, 577, 583, 587, 588, + 590, 592, 595, 599, 638, 647, 650, 660, 675, 667, 668, 669, + 676, 684, 685, 686, 721, 725, 734, 740, 746, 747, 748, 749, + 752, 755, 756, 765, 778, 780, 781, 782, 785, 794, 803, 814, + 822, 828, 836, 840, 847, 852, 862, 866, 868, 893, 928, 944, + 948, 950, 955, 961, 976, 985, 992, 1024, 1057, 1064, 1065, + 1066, 1069, 1072, 1074, 1079, 1085, 1086, 1089, 1091, 1092, + 1093, 1094, 1096, 1107, 1111, 1123, 1124, 1130, 1138, 1145, + 1151, 1152, 1156 + - Fixed Redhat bugs: 218191, 237497 + - Fixed Debian bugs: 321128, 422862, 422873 + - Fixed Gentoo bugs: 173676 + - Using system version of libgd if gd-2.0.34 or later. (Fedora 7 and 8 distros) + internal copy of gd updated to gd-2.0.35. + - Updated GVGUI viewer for Windows + - Windows build process now uses GNU autoconf and UWIN + - Added support for selection of edge routing types: + line, polyline, orthogonal, spline + - Added -Tvml support +December 5, 2006 + - release 2.12 + - Bug fix release for 2.10 + - The gd plugin for font handlers was not being used at all if the build + did not use fontconfig, e.g., on Windows. In addition, the code had + dropped the name mapping to Windows font names. + - PostScript output had an extraneous '%' character on the first line, + which would cause printing to fail. + - Text handling, during both sizing and layout, incorrectly handled + empty lines such as label="\nabc". + - HTML-like tables had been changed to use too much vertical space, + to possibly use the wrong font in calculating the height of a line, + and to use the wrong offset when moving the baseline from one line to + the next. +November 27, 2006 + - release 2.10 + - dot - New pango+cairo renderer plugin (was in separate graphviz-cairo tree). + -- -Tpng now uses cairo (-Tpng:gd for old gd based renderer) + -- -Tpdf now available + -- -Tps:cairo now available (-Tps is a direct ps renderer not based on cairo) + -- -Tsvg:cairo now available (-Tsvg is a direct svg renderer not based on cairo) + -- -Txlib now available -- "dot -Tx11 foo.dot" watches foo.dot with inotify and updates + -- -Tgtk now available -- eventually to provide a graph editing capability - not fully working + -- -Tswf "Flash" now available using the ming library. Currently has incomplete font support and not yet in Fedora rpms because ming not yet available as rpm. + - remove hard gd dependencies from dot. gd renderers now provided + as optional plugin. Deprecated, but required for -Tjpg, -Tgif and -Tvrml. + - gvpr - Add kindOf function, plus functions to set and get default values + - dot - Implement esep attribute to allow graph to specify room + around nodes for spline routing. + - neato - add vpsc library and DIGCOLA + - neato - add IPSEPCOLA additions from Tim Dwyer + - move: -Tps, -Tfig, -Tsvg, -Timap/ismap/cmap/cmapx, -Tdot/xdot, + from codegens to a "core" plugin. + - dot - new usershape plugin mechanism potentially supporting + a wider range of input shape format -> output format combinations. + display on changes + - Fixes for builds on Mac OS/X + - dot - new -O switch to automatically generate output file + names based on the input filename and the -T value. + e.g. "dot -Tpng -O *.dot" + Also works for case of multiple graphs in a single input file. + - add support for "Brewer" color nameset + - move reusable .so libraries to $PREFIX/lib per frequent request + from Debian community. Plugin .so's remain in $PREFIX/lib/graphviz. + - Fix bugs 882 884 886 896 902 905 906 911 918 919 933 936 938 940 + 948 955 958 967 979 987 993 1005 1006 1011 1012 1013 1014 1016 + 1018 1025 1030 1034 1035 1039 1040 debian#37300 + +February 3, 2006 + - release 2.8 + - (POTENTIAL INCOMPATIBILITY) The default input scaling, in the + absence of a "-s" switch, has been changed from inches to points. + The new behavior of "neato" is equivalent to "neato -s72". + The old behavior can be restored with "neato -s1". + The purpose of this change is to avoid a Frequently-Made-Mistake + when using "neato -n" to process a previously generated layout. + Previously it was necessary to use "neato -n -s72", but with this + change the default matches dot's output and the "-s72" is not required. + - Added pseudo layout engines: "dot -Knop" and dot -Knop1" equivalent + to "neato -n" + - Added pseodo layout engine: "dot -Knop2" equivalent to "neato -n2" + - Add support for color namespaces; add Brewer color data + - Add support for simulated duplex edges using parallel edges: + head arrow takes first color, tail arrow takes second color. + - source code management moved back to CVS until GIT matures a bit more + - distribute separe rpms for binares of language bindings : + - Add a small pad region around graph renderings to allow for finite + penwidths at the drawing edges + - Add protonode(g) and E=protoedge(g) functions to simplify + language bindings. + - Add special purpose code to deal with html labels from language + bindings. + - Various portability fixes for: HPUX, Mac OS/X, Cygwin, Windows. + - Fix bugs 784, 786, 787, 788, 789, 790, 791, 793, 795, 796, 798, 799, + 800, 801, 804, 806, 811, 812, 817, 820, 821, 823, 824, 825, 830, + 837, 839, 841, 842, 843, 848, 850, 851, 854, 855, 856, 857, 858, + 859, 861, 863, 866, 867, 869, 872, 874, 876, 877 + +August 28, 2005 + - release 2.6 + - experimentally moved source code management from CVS to GIT + - added iterator functions to script bindings + - more C-API tuning + - add "-c" switch to dot to explicitly generate plugin "config" file + instead of generating it as a side-effect of "dot -V" + - better support for binary relocation. + - plugin versioning and version checking + - clean up of header files + - provide statically linked "dot_static" (not incl. in rpms) + - additional "event" support for GUIs (e.g. "DotEdit" graphviz-cairo) + - add some information about plugins to "dot -v" output. + - lefty/dotty fixes + - fix bugs 746, 750, 752, 753, 754, 756, 761, 763, 764, 765, 768, + 771, 772, 773, 774, 775, 776, 777, 778 + - not a bug 757, 760, 770 +July 20, 2005 + - release 2.4 + - major code restructuring + - new plugin architecture (e.g. see separate package: graphviz-cairo ) + - new script-language bindings using swig (perl, ruby, python, tcl, java ... ) + - C-API now in libgvc (no more dotneato.[ch] or dotneato-config.sh] + - pkgconfig now used for reusable libraries + - lefty upgrade + - fix bugs 156, 255, 492, 631, 641, 647, 659, 662, 665, 670, 690, 691, + 701, 702, 703, 705, 730, 731, 732, 741, 743 +April 7, 2005 + - release 2.2.1 + - correct license headers to CPL in .cpp files + - undo indentation cleanup to dynagraph .h files + - fix bugs: 183, 247, 419, 615, 616, 625, 626, 627, 643, + 646, 651, 658, 661, 664, 674 + - fix buffer overrun in Gvfilepath construction +January 19, 2005 + - release 2.2 + - fix bugs: 86, 345, 517, 579, 580, 597, 600, 601, 604 + - use the original cpl1.0.txt as the license master, instead of CPL.html - fix for bug generating in memory bitmaps that was affecting webdot + - fixes for windows builds + - documentation updates +December 11, 2004 + - release 2.0 + - new CPL license + - re indent all sources +December 11, 2004 + - release 1.18 + dotneato + - fix bugs: 451, 536, 545, 547, 548, 559, 561, 565, 572 + - increase max size of HTML tables. + - spline cluster edges in fdp + - center userimages in nodes + - support user images in HTML table cells + - syntax extension for node:port:compass as well as node:compass + - FreeBSD fixes + - sync with gd-2.0.32 + - attempt to catch some out-of-memory conditions with very large graphs + - support background and node-fill partial transparency when truecolor=true + +September 14, 2004 + - release 1.16 + dotneato + - fix bugs: 275, 523, 526, 527, 529, 534 +August 30, 2004 + - release 1.14 + dotneato + - the official gd now has support support for GIFs again - the + internal gd is now closely sync'ed with the official version + and will eventually be removed in favor of using a + separate installation of the official version. + - gd has new support for FontConfig (thanks to Dag Lem) + NB. the fontname attribute in graphs is now a font pattern + as understood by fontconfig (e.g. fontname="Times-Italic"), + unless it contains a '/' in which case it is interpreted as + a font path as before. + - gd provides support for html4 entities in decimal, hex or named, e.g "<" + - "dot -v" debugging output now reports fontname -> fontpath resolutions + + - PostScript generated by -Tps now uses "xshow" operator for strings + for better matching of bitmap and PostScript outputs. + + - ability to use an external gd-2.0.29 version of libgd (EXPERIMENTAL) + + - new feature: parallel edges by using a ":" separated list of edge colors + - new feature: rankdir=BT and rankdir=RL (thanks to Dag Lem) + + - new layout engine: fdp - force directed placement (EXPERIMENTAL) + a neato-like undirected layout engine that produces + clustered symmetric layouts. + Supports edges between clusters and nodes. + + - updated neato engine: now using stress majorization as the default, + which avoids the potential for cycling + - model=subset in neato provides a third distance function, where + two nodes sharing many nodes will be place farther apart + - shape=none now equivalent to shape=plaintext + - fix label justification with \l and \r + - first cut at <FONT> support added to html labels + - various color transparency fixes + - various fixes for UTF8 and Latin[12] character encodings. + - various cluster fixes. + - improved hyperlink support in -Tsvg + - support tooltips on clusters in client-side imagemaps + + gvpr + - add support for scanf and friends + + general + - greater use of shared libraries. + - pkg-config files provided for shared libraries (EXPERIMENTAL) + - "./configure --disable-shared --enable-static" works if needed + - C++ wrappers on all header files (thanks to Victor Wodecki) + - various configuration and portablity fixes + - provide pdf version of man pages + - Windows package provides graphviz libraries and header files + - Closed bugs: 195, 198, 234, 321, 330, 399, 401, 406, 410, 411, + 412, 413, 415, 416, 417, 423, 424, 427, 430, 431, 433, 434, 435, + 438, 441, 442, 444, 445, 449, 450, 452, 454, 457, 458, 462, 463, + 464, 467, 468, 469, 471, 475, 480, 482, 485, 495, 496, 498, 499, + 500, 501, 504, 508, 511, 512, 514 + +March 5, 2004 + - added glyphwidths.ps support utility + +March 1, 2004 + - release 1.12 + - general + - rename bcc -> bcomps to avoid name conflict with "Bruce's C Compiler" + on Redhat distributions. + - all build without X11 (fix problem in lefty tree) + - remove from distribution: + dag, fdp, geo, grid, incr, shape, tcldgr, tcldgl + - dotneato + - fix "brown-bag" problem resulting in PNG and JPEG errors on RH8 and RH9. +February 23, 2004 + - release 1.11 + - general + - fix windows builds + - add tool "bcc" to distribution + - dotneato + - add -Gviewport="X,Y,Z,x,y" where XY are the dimensions of a viewport + in device coordinates (pixels), Z is a zooming factor, x,y is the + location of the center of the viewport in graph coordinates. + Supported in bitmap and imagemap outputs only. + - fix memory leak in gd/gdft.c + - clean up calculation of whitespace around labels + - dotty, lefty + - fix for bug #400 +December 23, 2003 + - added dijkstra (single source distance) filter +September 10, 2003 + - general + - removed CVS directories from .tar.gz distributions + - add "config" directory to contain some of the autoconf clutter + - only remove flex products with "make maintainer-clean" to + avoid trying to regenerate them after "make distclean" + basically this is to avoid the broken flex on Debian. + - suppress complaints from ./configure about config.rpath + - doc/build.html updated with notes about Windows builds + - build fixes for Forte 6sp2 compiler on Sun -xarch=v9a (64bit) + - build fixes for OpenBSD + - improved configure testing for Tcl/Tk + - various bug fixes, internal restructuring, etc + - dotneato + - fix problem with extra escape chars in .fig output + - support for "setlinewidth" in -Tfig + - improved splines in -Tfig + - add manpage for dotneato-config + - neato: add defaultdist graph attribute to set distance + between components + - first cut at html table formatter add. not ready for use yet + as the syntax is going to change some more. + - tools + - renamed "colorize" to "gvcolor" to avoid conflict on Debian + - renamed "gpr" to "gvpr" to avoid conflict on Debian + - add fflush() to acyclic, ccomps, gvcolor, tred, dot2gxl + to try to fix truncated output when used in php or perl cgi scripts +July 9, 2003 + - rerelease 1.10 with ast_common.h fix in -devel rpms +July 3, 2003 + - declare this version 1.10 + - general + - "mkdir obj;cd obj;../configure;make" now works (bug #293) + - "make prefix=xxx" now works (bug #274) + - "--with-wish=xxx" now works (bug #270) + - remove generated file: ast_common.h from source distributions + - make GIF support configurable + - added .cvsignore thoughout source tree to reduce CVS noise + - FAQ updates + - documentation updates for gpr + - improve portability of dotneato-config, but requires libtool now + - improvements to error processing for library users + -gd + - sync with gd-2.0.15 + - optimize line drawing code + - dot, neato, twopi + - fix bugs 240,270,274,293,298,303 + - support "peripheries=0" without crashing + - add support for "dia" output format (-Tdia) + - espf fixes (use of showpage) + - svg fixes (coordinates and viewBox) + - ismap/imap, fixes (quoting of label strings) + - fix to "point" shape + - improve (m|c|re)alloc usage + - improve handling of very-small fonts in bitmap outputs. + - various fixes for multiple -T -o feature + - add support for splines to records and ports (neato) + - various improvements to libpack + - dot_init_graph and neato_init_graph external for library users + - cluster improvements (neato) + - fix support for truecolor + - normalize splines so that they now always go from tail to head + - add some simple help text for any unrecognized option + (e.g. -? -h --help) + - tools + - extend gpr language to allow access to command-line arguments + - add sqrt() function to gpr + - add new tool - gvpack + - tcldot + - use .dll extension if on windows + - doted demo + - use tcl's file requestor instead of homebrew + - add zooming controlled by mousewheel + - support additonal export formats + +January 31, 2003 + - declare this version 1.9 + (3-level version numbering has been dropped now + that we have nightly snapshot builds with their + own extended numbering.) + - general + - config.h is no longer installed. config.h is generated by + ./configure for the current build only. It may not be + applicable for derivative builds. + - improve ICONV configure tests + - lots of janitor-work to clean up warning messages from -Wall + - use @OBJEXT@ in Makefile.am so that .obj is used under cygwin + - fixes for Solaris builds + - use libpng-config if available + - reduce long build times due to touching ast_common.h too often + - improve dependency tracking. "make -j8" now works with distcc + - autogen.sh fixes to work on RH7.3, RH8.0, and Solaris. + - eliminate use of suffix rules which confused some makes. + - DOT language allows '+' for concatenation of quoted strings + - dot, neato, twopi + - fix bugs 209, 210, 214, 216, 217, 222, 224, 225, 229, + 230, 233, 236, 237 + - update gd into alignment with gd-2.0.9 + - change to make libagraph output compatible with libgraph input + - add shapes: septagon, pentagon, a_ediamond, rect, rectangle + - introduce "ND_...", "ED_...", "GD_...", node/edge/graph-data + accessor macros in partial preparation for use of + libagraph in dot. + - add libdotneato.so, dotneato.h, dotneato-config + to aid use of dot libraries by user apps based + on installed graphviz-devel rpm and without access + to graphviz sources. + - new xdot output format providing detailed drawing instructions + - new -y command line flag, inverts y coordinates + - support multple -T when -o given, as in: + cat xxx.dot | dot -Tpng -Tcmap -o xxx + which produces xxx.png and xxx.cmap from a single + layout computation. Intended for use in CGI programs. + - agraph + - correct callback ordering for deletions + - tools + - add gxl2dot and dot2gxl for GXL language conversions + - gvui now provides *map output + - tcldot, tcldgr, tcldgl + - improve tcl8.4 support + - extend search path for tcl.h to include /usr/local/include/tcl8.4/ + in support of BSD install conventions. + - dynagraph + - many fixes + - change to not build dynagraph by default (use --with-dynagraph) + - docs + - dotguide updates +September 27, 2002 + - declare this version 1.8.10 + - general + - various configure.in fixes and simplifications + - change configure to now build dynagraph by default + "--without-dynagraph" is supported + - fix graphviz.spec.in to partition packages properly + graphviz no longer depends on graphviz-tcl. + - Makefile.old cleanups + - configure.old now set version number automatically from + configure.in + - dot, neato, twopi + - Initial support for image node shapes + URL fetch. + - Made number of dimensions a runtime variable in neato. + - Bug fix in vrmlgen for degenerate splines. + - Bug fix - ordering=in should now work + - Bug fix - layers no numbered from 0 to match PS requirements + - Bug fix - don't draw arrows on invisible edges + - Bug fix - when pack=true and ratio is set + - Bug fix - agraph/scan.l to work with latest flex beta + +August 2, 2002 + - declare this version 1.8.9 + - general + - split rpm into: + graphviz, graphviz-tcl, graphviz-graphs, graphviz-devel + - gcc3 warning cleanup + - Install lincdt, libgraph, libagraph, libgd, libpathplan, libexp, + and libpack so that they can be used by other programs. + Headers and man3 in graphviz-devel + - dynagraph, graphsearch + - New tools based on libagraph and written in C++ + - dot, neato, twopi + - Add node and edge tooltips for use with -Tcmap + \N,\E,\H,\T substitutions also work in tooltips. + - Add alt="label_string" to -Tcmap + - Add edge-label and port mappings to -Tps and -Tps2 so + that edges can be hyperlinked in PDF documents. + - Add support for \E (edge name), \H (head-node name), + \T (tail-node name) substitutions in edge labels and edge URLs + - Add support for stylesheet="file.css" for use in -Tsvg + - Fix -Tpic to work with recent gpic (Bruce Lilly) + - Fix alignment of imagemaps to images. + - Fix "transparent" color support in -Tsvg + - Fix support for graph [URL="default.html"] in -Tsvg and -Tcmap. + - Fix '&' escaping in URLs in -Tsvg + - Fix infinite loop in dot layout algorithm + - Fix text rotations again (hopefully freetype is stable now.) + - Cluster layout improvements + - Clean up warning messages from pathplan + - Consolidation of mapping code from imapgen.c and ismapgen.c into mapgen.c + - gpr + - Added additional mode to extract components based sharing an + edge or a cluster + - Fix test for getopt + - tcl-based tools + - Disable tcl-based tool building if tcl/tk not available + with stubs support. + - documentation updates: FAQ, dotguide, dot.1 +July 5, 2002 + - declare 1.8.7 a "brown bag" release + and declare this version 1.8.8 + - remove wrong assert in gdgen.c + - fix graph centering in bitmap outputs + - provide enough margins + - fix line widths after scaling + (test with directed/proc3d.dot) + - fix text rotations (requires libfreetype.so.6.3.1) + (test with directed/NaN.dot) +July 5, 2002 + - declare this version 1.8.7 + - Fix missing "]" in ihi demo. +July 2, 2002 + - Add URL mappings for clusters: svg,svgz,ps,ismap,imap,cmap. + - Fix to avoid white edges in bitmap outputs when bgcolor is set. + - Improve sizing and position of strings in bitmap outputs + when using builtin fonts (when font file not found). + - Fix \N substitution in edge URLs in imap and cmap outputs. + - Add -Tcmap for client-side imagemaps. + - Generate warnings instead of access violation for EPSF file problems. + - Various spline fixes in neato. + - Fixes to pack.c + - Add feature to ccomps to allow extraction of individual component + by number or node. + - Cdt make to use iffe provided in the tools directory. + - Various Makefile.old fixes. + - Use HAVE_LIBZ to remove GD2 format if libz not available. + Now bare-bones programs can be built without any add-on libraries. + - Modified dot grammar to allow simple name attributes in attribute + lists. Thus, [splines] is equivalent to [splines=true]. Adopted + the same convention for command line attributes -G, -E and -N. + In addition, such command line attributes now override any + competing initial attribute statements. + - HP-UX 11.11 build fixes for struct dioattr. + - Fix for bug #158 "Nodes disappear with ports" + - Various Windows-specific #ifdefs + - Fix edge coordinates in -Tplain. + +May 24, 2002 + - declare this version 1.8.6 +May 19, 2002 + - Fixed segfault from use of bgcolor in clusters. +May 15, 2002 + - Changed install location of architecture-independent demo + scripts and graphs to <prefix>/share/graphviz/ to conform to FHS. + - Avoid multiple linking of libfreetype (and others) which caused + problems on SunOS-2.8. +May 6, 2002 + - Factored out some duplicated arrow code from dotgen/splines.c + and neatorgen/splines.c into common/arrows.c. + - Added new arrow types: halfopen, box, obox, crow. + - Touched up the arrow designs so that they look better at default size. + - Modified/extended graphs/directed/newarrows.dot to show new arrows. +May 3, 2002 + - Added some UML arrow types from Diomidis Spinellis <dds@aueb.gr> + empty, invempty, open, diamond, odiamond. +May 2, 2002 + - Added new pack option to neato. This causes each connected component + to be laid out separately, and then the resulting graphs are packed + together in a single layout. + - Amended neato to accept new tee arrowhead. +April 19, 2002 + - Coords of rectangles changed to left/top right/bottom in -Timap. + - Generate COPYING from LICENSE.html during ./authogen.sh, + remove COPYING from CVS. +April 16, 2002 + - Minor license file patches. + - Corrected one of those reversed flat edge bugs again. + +April 11, 2002 + - declared this version 1.8.5 + - various portability fixes + - various SVG fixes and optimizations +April 5, 2002: + - declared this version 1.8.4 + - SVG renderer: + - make graph|node|edge ids unique, particularly for multiedges + - put graph|node|edge names in <title>...</title> + - use some property inheritance to reduce size of output + - fix compile errors when no zlib + - updated DTD reference + - GD renderer: + - Minimal Type1 font support: + - look in /usr/lib/X11/fonts/Type1/ + - look for .pfa or .pfb font files based on fontname + - run gdgen.c through dos2unix - problems with gcc on SuSE + - fix Mac-OSX build problems: + - improve strto[u]ll configure tests + - add -fno-common for extern problem + - function renamed to avoid conflicts (vis -> visibility) + - add configure tests for search.h, malloc.h, getopt.h, errno.h + - improve configure tests for FILE struct features + - add configure tests for lrand48 + - add new demo graphs: + - graphs/undirected/Heawood.dot + - graphs/undirected/Petersen.dot + - neato: + - fix for -x implementation in neato (Bug 77) + - fix spline problem (Bug 87) + - fix some divide-by-zero problems + - twopi: + - fix Bug 117 + - update man pages for unconnected graphs capability + - added arrowhead or arrowtail = tee +March 22, 2002: + - add dotneato/pack code to twopi + - add contrib/prune to gnu build and install +March 20, 2002: + - declared this version 1.8.3 + - fixed parse error for lines starting with '#' in .dot files + - fixed a recently introduced bug that caused failure of: + digraph G { {rank = same; A -> B; B -> A } } + - updated DOCTYPE header in SVG outputs + - added dotneato/common/xbuf.[ch] for dynamic string handling + to avoid sprintf buffer overruns. + - twopigen - handle special case of graphs with < 3 nodes. + - neato - handle point shapes + - added fontcolor support to svg +March 14, 2002: + - Fixed bug 109 + - Removed duplicate definitions for str[n]casecmp + - Added missing declarations needed for Windows + - Cleaned up warning messages from set but unused variables + - Removed use of DOS preprocessor variable; uniformly replaced by MSWIN32 +March 8, 2002: + - declared this version 1.8.2 + - Mainly to fix a missed static buffer problem which trips up the + Windows community +March 1, 2002: + - declared this version 1.8.1 + - Bug fixes reported from user testing of 1.8.0, especially problem + with SVG output +February 25, 2002: + - updated dotguide.tex and moved to LaTeX article format + - added webdot.cgi perl script, enhanced to accept the same + argument format as John's tcl version (so it can also + serve neato and twopi graph layouts). + +February 7, 2002: graphviz-1.8.0 pre + - declared this version 1.8.0 + +February 5, 2002: graphviz-1.7.17-0 + - various 64bit portability fixes + - various bug fixes +January 2, 2002: graphviz-1.7.16-0 + - dotneato + - fix bugs in -Tps output due to pen/fill color changes + - various -Tfig.c fixes + - various portability fixes +December 28, 2001: graphviz-1.7.15-0 + -dotneato + - introduce damping factor into neato's solver + - clean up pencolor v fillcolor code so that filled polygons are drawn + just once if the renderer is capable (e.g. svg, fig) + - complete -Tfig support (xfig format) +December 11, 2001: graphviz-1.7.14-0 + -dotneato + - add -Tsvgz (compressed SVG) support +December 11, 2001: graphviz-1.7.13-0 + - dotneato + - fontwidth fixes + - remove some potential buffer overruns + - escape '&' in SVG, unless it is already part of a UTF entity sequence + - recognize Times_New_Roman and Courier_New as default font names. + - improve -liconv support in configure + - clean up some compiler warnings + - dynagraph + - change "round" to "ROUND" to avoid conflict with system headers on linux +December 03, 2001: graphviz-1.7.12-0 + - dotneato + - add -Tplain-ext which includes port identifiers edge records + - escape '>' with '>' in edge ids and edge URLs in -Tsvg. + - spline fixes + - mincross fixes + - improved text alignment in nodes - particularly in bitmap outputs. + - fixed text scaling problems for 8-bit characters (e.g. umlauts) + - add graph lexer and postscript support for extended characters + - lefty + - fix for X11 displays + - pathplan + - added workaround for gcc-0.96 bug when "-O2 -mcpu=686 -ffast-math" +October 22, 2001: graphviz-1.7.11-0 + - dotneato + - svg - fix landscape "y" direction + - fix text rotation (works in batik, not yet in sodipodi or amaya) + - fix linewidth + - fix xmnls:xlink reference + - doc + - Dot.ref - updated + - graphs/directed + - newarrows.dot expanded + - honda-tokoro.dot added +October 21, 2001: graphviz-1.7.10-0 + - lefty & dotty + - realign code with EK's master tree. + includes fix for dirty trails when dragging nodes in dotty. + - dotneato + - svg - kludge escape of "<" & ">" characters in labels. + - general + - generate doxygen documentation on http://www.graphviz.org/ +August 20, 2001: graphviz-1.7.9-0 + - general + - first release from relocated cvs server + - dotneato + - fix for abort from spline code + - fix for crash from gd tiling code +August 15, 2001: graphviz-1.7.8-0 + - general + - Update gd to gd-2.0.1 with extensions + - dotneato + - more spline fixes + - add suport for "#rgb" color specification + - add twopi layout engine (circular layouts) +July 13, 2001: graphviz-1.7.7-0 + - Synchronization release prior to relocating CVS server. + - general + - some Makefile fixes for OpenBSD + - some FAQ updates + - dotneato + - self-edge fixes + - spline fixes + - libgraph + - parser fixes +July 1, 2001: graphviz-1.7.6-3 + - general + - portability fixes (including 14 charater file names !) + - memory leak fixes + - "make test" targets in graphs/directed, graphs/undirected + - configure + - add support for building without X11, Tk, Tcl + - add hooks for dmalloc and ElectricFence debugging + - dotneato + - spline fixes + - cluster fixes + - fix label centering + - fix support for graph margins in bitmapped outputs + - correction to PostScript preamble + - SVG generator improvement - now works with Amaya and SodiPodi + - tcldot + - now uses Tcl Channels properly for input + - fixes for linewidth support + - command extensions + - listattributes now accepts list + - queryattributes now accepts list + - setattributes now accepts list + - queryattributevalues - new command + - generates list of pairs compatible with setattributes + - dotty + - passthrough keyboard events + - doted + - fix resizing problems + - add PNG and SVG output formats + +April 27, 2001: graphviz-1.7.6 + + NEW FEATURES + + Added a collection of graph processing tools: + + acyclic : a filter that takes a directed graph as input + and outputs a copy of the graph with sufficient edges + reversed to make the graph acyclic. + + ccomps : decomposes graphs into their connected components, + printing the components to standard output. + + colorize : is a filter that sets node colors from initial + seed values. Colors flow along edges from tail to head. + + gc : a graph analogue to wc in that it prints to standard + output the number of nodes, edges, connected components or + clusters contained in the input files. + + gpr : a graph stream editor inspired by awk. It copies + input graphs to its output, possibly transforming their + structure and attributes, creating new graphs, or + printing arbitrary information. + + nop : reads a stream of graphs and prints each in + pretty-printed (canonical) format on stdout. + + sccmap : decomposes digraphs into strongly connected components + and an auxiliary map of the relationship between components. + + tred : computes the transitive reduction of directed graphs, + and prints the resulting graphs to standard output. This + removes edges implied by transitivity. + + unflatten : is a preprocessor to dot that is used to improve + the aspect ratio of graphs having many leaves or disconnected + nodes. The usual layout for such a graph is generally very + wide or tall. unflatten inserts invisible edges or adjusts + the minlen on edges to improve layout compaction. + + + FIXES + + Add FAQ + + Change PNG default background color from transparent to white + because of the difficulty some viewers have with transparency. + + Add support for [color=transparent] + + Fix broken support for specific capitalized fontnames + (Times Helvetica Arial Courier) + + Fix broken support for DOTFONTPATH + + Some bitmap font scaling fixes - we're still not happy with + bitmap font scaling as some labels still exceed the area + allocated by the layout engines. + + Some -Timap fixes for mouse sensitive graphs on web pages + + Some cluster layout fixes + + Fix for [rankdir=LR] problems when using neato layout engine + + Some neato layout fixes + + Updates to unix.dot + + Various OS and distro fixes + + +December 23, 2000: graphviz-1.7.5 + + - update to gd-1.8.4 and freetype2 + - add support for font paths + + +December 15, 2000: graphviz-1.7.4 + -various cluster fixes + -separate support for node fillcolor from pencolor (see dot.1) + -add support for dotted and dashed lines to bitmap renderers (PNG, GIF etc) + -add support for varying linewidth to bitmap renderers + -remove libtcldot dependence on lingdtclft (already statically included) + -various fixes to build processes, GNU and non-GNU + + +graphviz-1.7.3 ..... + +May 3, 2000: removed webdot into its own CVS module and rpm package + +April 16, 2000: Use check for "gdImagePng" to make sure that we have + recent version of libgd. <ellson@graphviz.org> + +April 14, 2000: Add Tcldgl and dge demo <ellson@graphviz.org> + +April 14, 2000: Add dynagraph libraries <north@research.att.com> + +April 14, 2000: Flatten directory hierachy of sources <ellson@graphviz.org> + +April 14, 2000: Fix X11 library detection for lefty: + src/configure.in, src/lefty/Makefile.in + <ellson@graphviz.org> + +April 14, 2000: Fix pic support: + src/dotneato/picgen.c, + src/dotneato/emit.c, + webdot/tcl/webdot.tcl + <Bruce Lilly> + +April 7, 2000: Upgrade webdot installation process: + webdot/Makefile, webdot/README + <ellson@graphviz.org> + +March 13, 2000: Support for virtual hosts in webdot/webdot.tcl, add + "puts $skt "Host: $server" Michael Tillberg <mt@proteome.com> + +March 13, 2000: Fix to src/graph/parser.y line 149 + "if ((e->head == t->node) && !(Agraph_type & AGDIGRAPH)) {" + Stephen North <north@research.att.com> + +March 13, 2000: Use AM_PROG_LIBTOOL instead of AC_PROG_LIBTOOL + in configure.in. John Ellson <ellson@graphviz.org>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/Dot.ref Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,870 @@ + Quick Reference Guide for GraphViz + 20 February 2002 + + +Command-line Invocation + + ------------------------------------------------------------------------ +All GraphViz programs have a similar invocation: + cmd [ flags ] [ input files ] +If no input files are supplied, the program reads from stdin. + +Flags + +-Gname=value + Set a graph attribute. +-Nname=value + Set a default node attribute. +-Ename=value + Set a default edge attribute. +-Tformat + Set output language to one of the supported formats. By default, + attributed dot is produced. +-V + Emit version information and exit. +-llibfile + User-supplied, device-dependent library files. Multiple libraries may + be given. These names are passed to the code generator at the beginning + of output. +-n[num] + Sets no-op flag in neato. If set, neato assumes nodes have already been + positioned and have a pos attribute giving this position. It then just + computes the edge layouts and emits the graph in the appropriate + format. If num is supplied and > 1, neato checks for previously + positioned edges, with control points provided in the pos attribute. It + uses these if given; otherwise, it computes the edge layout. +-ooutfile + Write output to file outfile. By default, output goes to stdout. +-s[scale] + Set input scale to scale. If this value is omitted, 72.0 is used. This + number is used to convert the point coordinate units used in the pos + attribute into inches. For example, dot -Tdot produces pos values in + points, with 72 per inch. Thus, to use this output as input to neato + -n, one needs -s (equivalently, -s72). +-v + Verbose mode +-x + In neato, on input, prune isolated nodes and peninsulas. This removes + uninteresting graph structure and produces a less cluttered drawing. + +Note that the file contents override the -G, -N and -E flags. In addition, +these flags cause the related attributes to be permanently attached to the +graph. Thus, if attributed dot is used for output, the graph will have these +attributes. + + ------------------------------------------------------------------------ + +The DOT Language + + ------------------------------------------------------------------------ +digraph G {foo -> bar; ... } or graph G { foo -- bar; ...} + +To connect all pairs: + {foo bar}->{baz bart} + +To make a chain of links: + foo->bar->baz + +Commas inside [...] and semicolons inside {...} are optional. + +Quotes are needed on attribute values to protect spaces, commas, periods, etc. + +// C++ like comment: to end of line +/* C-like comment: between delimiters */ + +For a complete language description, see + + http://www.research.att.com/~erg/graphviz/info/lang.html + + ------------------------------------------------------------------------ + +Attributes + + ------------------------------------------------------------------------ + + Name Used By Type Default Minimum Notes + Damping G double 0.99 0.0 neato only + Epsilon G double (.0001 * # nodes) neato only + URL ENGC string + arrowhead E arrowType normal + arrowsize E double 1.0 0.0 + arrowtail E arrowType normal + bb G rect write-only + bgcolor G color + bottomlabel N string "" + center G bool false + clusterrank G clusterMode local dot only + color ENC color black + comment ENG string "" + compound G bool false dot only + concentrate G bool false dot only + constraint E bool true dot only + decorate E bool false + + dir E dirType forward(directed) + none(undirected) + distortion N double 0.0 -100.0 + + fillcolor NC color lightgrey(nodes) + black(clusters) + fixedsize N bool false + fontcolor ENGC color black + fontname ENGC string "Times-Roman" + fontpath G string "" + fontsize ENGC double 14.0 1.0 + group N string "" dot only + headURL E string "" + headlabel E string "" + headport E portPos center + height N double 0.5 0.02 + K G double 1.0 0.0 sfdp only + + label ENGC string "" + "\N" (nodes) + labelangle E double -25.0 -180.0 + labeldistance E double 1.0 0.0 + labelfloat E bool false + labelfontcolor E color black + labelfontname E string "Times-Roman" + labelfontsize E double 11.0 1.0 + labeljust C string "" dot only + + labelloc GC string "t"(clusters) dot only + "b"(root graphs) + layer EN layerRange "" + layers G layerList "" + len E double 1.0 neato only + levels G int MAXINT 0 sfdp only + lhead E string "" dot only + lp EGC point write-only + ltail E string "" dot only + + margin G double + pointf + maxiter G int MAXINT neato only + mclimit G double 1.0 dot only + minlen E int 1 0 dot only + model G string "" neato only + nodesep G double 0.25 0.02 dot only + normalize G bool false neato only + nslimit + nslimit1 G double dot only + ordering G string "" dot only + orientation N double 0.0 360.0 + orientation G string "" + + overlap G string "" neato/sfdp only + bool + page G pointf + pagedir G pagedir BL + peripheries N int 0 + pin N bool neato only + + pos EN point + splineType + quadtree G string "" sfdp only + quantum G double 0.0 0.0 + rank S rankType dot only + rankdir G rankdir TB dot only + ranksep G double 0.5 0.02 + ratio G double + rects N rect write-only + regular N bool false + remincross G bool false dot only + repulsiveforce G double 1 0.0 sfdp only + rotate G int 0 + samehead E string "" dot only + sametail E string "" dot only + samplepoints G int 8 + searchsize G int 30 dot only + sep G double 0.01 neato only + shape N shape ellipse + shapefile N string "" + showboxes ENG int 0 0 dot only + sides N int 4 0 + size G pointf + skew N double 0.0 -100.0 + smoothing G string "" sfdp only + splines G bool false neato only + + start G string "" neato only + int + style ENC style + styleheet G style + tailURL E string "" + taillabel E string "" + tailport E portPos center + toplabel N string "" + vertices N pointfList write-only + voro_margin G double 0.05 0.0 neato only + w E double 1.0 neato only + + weight E double 1.0 0(dot) + 1(neato) + width N double 0.75 0.01 + + z N double 0.0 -MAXFLOAT + -1000 + ------------------------------------------------------ +For the bool type, TRUE values are represented by "true" (case-insensitive) +and any non-zero integer, and FALSE values by "false" (case-insensitive) and +zero. + +In the Used By field, the characters E, N, G and C represent edges, nodes, +the root graph and cluster subgraphs, respectively. This field indicates +which graph component uses the attribute. + ------------------------------------------------------------------------ + +Attribute Descriptions + +Damping + Factor damping force motions. On each iteration, a nodes movement is + limited to this factor of its potential motion. By being less than 1.0, + the system tends to ``cool'', thereby preventing cycling. +Epsilon + Terminating condition. If the length squared of all energy gradients + are < Epsilon, the algorithm stops. +URL + Incorporated into device-dependent output. At present, used for ps2, + i*map and svg. For ps2, URLs can be attached to nodes and the root + graph. When distilled to PDF, such nodes are active links and the graph + URL can be used as a base URL. For i*map, URLs can be attached to + nodes, edges and the root graph. If the node or edge URL contains "\N", + this is replaced by the object's name. For svg, URLs can be attached to + nodes, edges and clusters, with "\N" replacement done for all three. +arrowhead + Style of arrowhead on head of edge. +arrowsize + Multiplicative scale factor for arrowheads. +arrowtail + Style of arrowhead on tail of edge. +bb + Bounding box of drawing in integer points. +bgcolor + Color used as background for canvas, plus initial fill color. +bottomlabel + Additional label near bottom of nodes of shape M*. +center + If true, the drawing is centered in the output canvas. +clusterrank + Mode used for handling clusters. If clusterrank is "local", a subgraph + whose name begins with "cluster" is given special treatment. The + subgraph is laid out separately, and then integrated as a unit into its + parent graph, with a bounding rectangle drawn about it. If the cluster + has a label parameter, this label is displayed within the rectangle. + Note also that there can be clusters within clusters. At present, the + modes "global" and "none" appear to be identical, both turning off the + special cluster processing. +color + Basic drawing color for graphics. +comment + Comments are inserted into output. Device-dependent +compound + If true, allow edges between clusters. (See lhead and ltail below.) +concentrate + If true, use edge concentrators. +constraint + If false, the edge is not used in ranking the nodes. For example, in + the graph + + digraph G { + a -> c; + a -> b; + b -> c [constraint=false]; + } + + + the edge b -> c does not add a constraint during rank assignment, so + the only constraints are that a be above b and c. + +decorate + If true, attach edge label to edge by a 2-segment polyline, underlining + the label, then going to the closest point of spline. +dir + Set edge type for drawing arrowheads. +distortion + Distortion factor for shape=polygon. Positive values cause top part to + be larger than bottom; negative values do the opposite. +fillcolor + If fillcolor is not defined, color is used. If this is not defined, the + default is used, except for shape=point or when the output format is + MIF, which use black by default. +fixedsize + If true, the node size is kept fixed and not expanded to contain the + text label. +fontcolor + Color used for text. +fontname + Font used for text. +fontpath + Directory list used by libgd to search for bitmap fonts. If fontpath is + not set, the environment variable DOTFONTPATH is checked. If that is + not set, GDFONTPATH is checked. If not set, libgd uses its compiled-in + font path. +fontsize + Font size, in points, used for text. +group + If the end points of an edge belong to the same group, i.e., have the + same group attribute, parameters are set to avoid crossings and keep + the edges straight. +headURL + For the output format imap, or cmap, if headURL is defined, it is + output as part of the head label of the edge. If headURL contains + "\N", this will be replaced by the head label text. +headlabel + Text label to be placed near head of edge. +headport + Indicates where on the head node to attach the head of the edge. In the + default case, the edge is aimed towards the center of the node, and + then clipped at the node boundary. +height + Height of node, in inches. +K constant used in the Fruchterman-Reingold force directed model. +label + Text label attached to objects. The escape sequences "\n", "\l" and + "\r" divide the label into lines, centered, left-justified and + right-justified, respectively. The escape sequence "\N" represents a + node's name. If a node's shape is record, then the label can have a + special format which describes the record layout. +labelangle + Angle, in degrees, that the headlabel(taillabel) is rotated from the + angle the edge makes incident with the head(tail) node, respectively. +labeldistance + Multiplicative scaling factor adjusting the distance that the + headlabel(taillabel) is the from head(tail) node. +labelfloat + If true, allows edge labels to be less constrained in position. In + particular, it may appear on top of other edges. +labelfontcolor + Color used for headlabel and taillabel. +labelfontname + Font used for headlabel and taillabel. +labelfontsize + Font size, in points, used for headlabel and taillabel. +labeljust + Justification for cluster labels. If "r", the label is right-justified + within bounding rectangle; otherwise, left-justified. +labelloc + Top/bottom placement of graph and cluster labels. If the attribute is + "t", place label at the top; if the attribute is "b", place label at + the bottom. By default, root graph labels go on the bottom and cluster + labels go on the top. +layer + Specifies layers in which the node or edge is present. +layers + Specifies a linearly ordered list of layer names attached to the graph + The graph is then output in separate layers. Only those components + belonging to the current output layer appear. For more information, see + the page How to use drawing layers (overlays). +len + Preferred edge length, in inches. +levels + Number of levels allowed in the multilevel scheme. Default is MAXINT. +lhead + Logical head of an edge. When compound is true, if lhead is defined and + is the name of a cluster containing the real head, the edge is clipped + to the boundary of the cluster. +lp + Label position, in points. +ltail + Logical tail of an edge. When compound is true, if ltail is defined and + is the name of a cluster containing the real tail, the edge is clipped + to the boundary of the cluster. +margin + Set x and y margins of canvas, in inches. In first case, both margins + are set equal to the given value. +maxiter + Sets the number of iterations used. +mclimit + Multiplicative scale factor used to alter the MinQuit (default = 8) and + MaxIter (default = 24) parameters used during crossing minimization. + These correspond to the number of tries without improvement before + quitting and the maximum number of iterations in each pass. +minlen + Minimum edge length (rank difference between head and tail). +model + If "circuit", use circuit resistance model to compute dissimilarity + values; otherwise, use shortest path model. +nodesep + Minimum space between two adjacent nodes in the same rank, in inches. +normalize + If set, normalize coordinates of final layout so that the first point + is at the origin, and then rotate the layout so that the first edge is + horizontal. +nslimit , +nslimit1 + Used to set number of iterations in network simplex applications. + nslimit is used in computing node x coordinates, nslimit1 for ranking + nodes. If defined, # iterations = nslimit(1) * # nodes; otherwise, # + iterations = MAXINT. +ordering + If "out" for a graph G, and n is a node in G, then edges n->* appear + left-to-right in the same order in which they are defined. +orientation + Angle, in degrees, used to rotate node shapes. +orientation + If "[lL]*", set graph orientation to landscape Used only if rotate is + not defined. +overlap + If "scale", remove node overlaps by scaling; if converts to false, + remove node overlaps by the Voronoi technique; otherwise, leave + overlaps. +page + Width and height of output pages, in inches. If this is set and is + smaller than the size of the layout, a rectangular array of pages of + the specified page size is overlaid on the layout, with origins aligned + in the lower-left corner, thereby partitioning the layout into pages. + The pages are then produced one at a time, in pagedir order. +pagedir + If the page attribute is set and applicable, this attribute specifies + the order in which the pages are emitted. This is limited to one of the + 8 row or column major orders. +peripheries + Set number of peripheries used in polygonal shapes. +pin + If true and the node has a pos attribute on input, neato prevents the + node from moving from the input position. This property can also be + specified in the pos attribute itself (cf. the point type). +pos + Position of node, or spline control points, in points. In neato, pos + can be used to set initial position of a node. Concerning this, see the + -s command line flag. +quantum + If quantum > 0.0, node label dimensions will be rounded to integral + multiples of the quantum. +quadtree + Quadtree scheme to use. Values are "normal" (default), "fast" or "none". + "fast" gives about 2-4 overall speedup compared with "normal", though + layout quality can suffer a little. +rank + Rank constraints on the nodes in a subgraph. If rank="same", all nodes + are placed on the same rank. If rank="min", all nodes are placed on the + minimum rank. If rank="source", all nodes are placed on the minimum + rank, and the only nodes on the minimum rank belong to some subgraph + whose rank attribute is "source" or "min". Analogous criteria hold for + rank="max" and rank="sink". (Note: the minimum rank is topmost or + leftmost, and the maximum rank is bottommost or rightmost.) +rankdir + Sets direction of graph layout. If rankdir="LR", the graph is laid out + from left to right, i.e., directed edges tend to go from left to right. + By default, graphs are laid out from top to bottom. +ranksep + In dot, this the gives desired rank separation, in inches. This is the + minimum vertical distance between the bottom of the nodes in one rank + and the tops of nodes in the next. If the value contains "equally", the + centers of all ranks are spaced equally apart. Note that both settings + are possible, e.g., ranksep = "1.2 equally". In twopi, specifies radial + separation of ranks. +ratio + Sets the aspect ratio (drawing height/drawing width) for the drawing. + Note that this is adjusted before the size attribute constraints are + enforced. + + If ratio is numeric, it is taken as the desired aspect ratio. Then, if + the actual aspect ratio is less than the desired ratio, the drawing + height is scaled up to achieve the desired ratio; if the actual ratio + is greater than that desired ratio, the drawing width is scaled up. + + If ratio = "fill" and the size attribute is set, the drawing is scaled + to achieve the aspect ratio implied by size. As size is set, when the + drawing is later scaled to fit that rectangle, the resulting picture + will fill the rectangle. + + If ratio = "compress" and the size attribute is set, dot attempts to + compress the initial layout to fit in the given size. This achieves a + tighter packing of nodes but reduces the balance and symmetry. + + If ratio = "auto", the page attribute is set and the graph cannot be + drawn on a single page, then size is set to an ``ideal'' value. In + particular, the size in a given dimension will be the smallest integral + multiple of the page size in that dimension which is at least half the + current size. The two dimensions are then scaled independently to the + new size. +rects + Rectangles for fields of records, in points. +regular + If true, force polygon to be regular. +repulsiveforce + The power of repulsive force used in an extended Fruchterman-Reingold + force directed model. Default is 1. Values larger than 1 tend to reduce + the warping effect at the expensive of less clustering. +remincross + If true and there are multiple clusters, run cross minimization a + second time. +rotate + If 90, set drawing orientation to landscape. +samehead + Edges with the same head and the same samehead value are aimed at the + same point on the head. +sametail + Edges with the same tail and the same sametail value are aimed at the + same point on the tail. +samplepoints + If the input graph defines the vertices attribute, and output is plain* + or attributed dot, this give the number of points used to represent + circles and ellipses. It plays the same role in neato, when adjusting + the layout to avoid overlapping nodes. +searchsize + During network simplex, maximum number of edges with negative cut + values to search when looking for one with minimum cut value. +sep + Fraction to increase polygons (multiply coordinates by 1 + sep) for + purposes of determining overlap. Guarantees a minimal non-zero distance + between nodes. +shape + Set shape of node. +shapefile + For output format is ps* or svg*, if non-empty, taken as a filename + containing a device-dependent description of a node's shape. +showboxes + Print guide boxes in PostScript at the beginning of routesplines if 1, + or at the end if 2. (Debugging) +sides + Number of sides if shape=polygon. +size + Maximum width and height of drawing, inches. If defined and the drawing + is too large, the drawing is uniformly scaled down so that it fits + within the given size. Note that there is some interaction between the + size and ratio attributes. +skew + Skew factor for shape=polygon. Positive values skew top of polygon to + right; negative to left. +smoothing + A post processing step to smooth out uneven distribution of nodes. Possible + values are "none" (default), "avg_dist", "graph_dist", "power_dist", "rng", + "spring" and "triangle". +splines + If true, draw edges as splines. This requires non-overlapping nodes + (cf. overlap). +start + Parameter used to determine the initial layout of nodes. By default, + nodes are randomly placed in a square whose sides have length (# + nodes). The same seed is always used for the random number generator, + so the initial placement is repeatable. If start converts to an + integer, this is used as a seed value for the random number generator. + If start is "regular", the nodes are placed regularly about a circle. + Finally, if start is defined but is not one of the above cases, the + current time is used to pick a seed. +style + Set style for node or edge. For cluster subgraph, if "filled", the + cluster box's background is filled. +stylesheet + The URL of a CSS stylesheet to be referenced from the output file. + Relevant only for SVG output. +tailURL + When the output format is imap, or cmap, if tailURL is defined, it is + output as part of the tail label of the edge. If tailURL + contains "\N", this will be replaced by the tail label text. +taillabel + Text label to be placed near tail of edge. +tailport + Indicates where on the tail node to attach the tail of the edge. +toplabel + Additional label near top of nodes of shape M*. +vertices + If the input graph defines this attribute, the node is polynomial, and + output is plain* or attributed dot, this attribute provides the + coordinates of the vertices of the node's polynomial, in points. If the + node is an ellipse or circle, the samplepoints attribute affects the + output. +voro_margin + Factor to scale up drawing to allow margin for expansion in Voronoi + technique. dim' = (1+2*margin)*dim. +w + Redundant definition of weight in neato, cf. bug 9. +weight + Weight of edge. Usually, the heavier the weight, the shorter (and, in + dot, straighter and more vertical) an edge is. +width + Width of node, in inches. +z + Provides z coordinate for the node when output format is VRML. + + ------------------------------------------------------------------------ + +Attribute Type Descriptions + +arrowType + "normal" "inv" + + "dot" "invdot" + + "odot" "invodot" + + "none" +clusterMode + "local","global","none" +color + "#%2x%2x%2x" RGB + "#%2x%2x%2x%2x"RGBA + "%lf,%lf,%lf" HSV 0.0 <= values <= 1.0 + string color name +dirType + For an edge T -> H; + + "forward" "back" + + "both" "none" + + For undirected edges T -- H;, one of the nodes, usually the righthand + one, is treated as the head for the purpose of interpreting "forward" + and "back". +layerList + list of strings separated by colons, tabs or spaces, defining layer + names and implicitly numbered 1,2,... +layerRange + layerId or layerId[:\t ]layerId, + where layerId = "all", decimal integer or layer name. (An integer i + corresponds to layer i.) +pagedir + "BL", "BR", "TL", "TR", "RB", "RT", "LB", "LT". These specify the 8 row + or column major orders for traversing a rectangular array, the first + character corresponding to the major order and the second to the minor + order. Thus, for "BL", the major order is from bottom to top, and the + minor order is from left to right. This means the bottom row is + traversed first, from left to right, then the next row up, from left to + right, and so on, until the topmost row is traversed. +point + "%d,%d"('!') representing the point (x,y). The optional '!' indicates + the node position should not change (input-only). +pointf + "%lf,%lf" representing the point (x,y). +pointfList + list of pointf, separated by spaces. +portPos + "n","ne","e","se","s","sw","w","nw" These correspond to the obvious + compass points on the node. +rankType + "same", "min", "source", "max", "sink" +rankdir + "LR". Any other value corresponds to a top-down layout. +rect + "%d,%d,%d,%d" The rect llx,lly,urx,ury gives the coordinates, in + points, of the lower-left corner (llx,lly) and the upper-right corner + (urx,ury). +shape + A string specifying the shape of a node. There are three main types of + shapes : polygon-based, record-based and PostScript-based. +splineType + spline ( ';' spline )* + where spline= (endp)? (startp)? point (triple)]* + and triple = point point point + and endp = "e,%d,%d" + and startp = "s,%d,%d" + If a spline has points p1 p2 p3 ... pn, (n = 1 (mod 3)), the points + correspond to the control points of a B-spline from p1 to pn. If startp + is given, it touches one node of the edge, and the arrowhead goes from + p1 to startp. If startp is not given, p1 touches a node. Similarly for + pn and endp. +style + styleItem ( ',' styleItem )* + where styleItem= name or name'('args')' + and args = name ( ',' name )* + and name = [^)(, ][^)(,]* + At present, the recognized style names comprise "dashed", "dotted", + "solid", "invis" and "bold" for nodes and edges, and "filled", + "diagonals" and "rounded" for nodes only. Additional styles are + available in device-dependent form. Style lists are passed to device + drivers, which can use this to generate appropriate output. + + Note that, at present, "rounded" cancels "filled", except for circles + and ellipses. This holds for the Mrecord shape, which is rounded by + definition. + + ------------------------------------------------------------------------ + +Output Formats + + ------------------------------------------------------------------------ +The output format is specified with the -Tlang flag on the command line, + where lang is one of the following: + Command-line + parameter Format + canon + cmap Client-side imagemap + dot DOT + fig FIG + gd + gd2 GD/GD2 formats + gif GIF + hpgl HP-GL/2 + imap Server-side imagemap + ismap Server-side imagemap (deprecated) + jpg + jpeg JPEG + mif FrameMaker MIF format + mp MetaPost + pcl PCL + pic PIC + plain + plain-ext Simple text format + png Portable Network Graphics format + ps PostScript + ps2 PostScript for PDF + svg + svgz Scalable Vector Graphics + vrml VRML + vtx Visual Thought format + wbmp Wireless BitMap format + ------------------------------------------------------------------------ + +Format Descriptions + +canon , +dot + These two formats produce output in variations of the dot language. + Using canon produces a prettyprinted version of the input, with no + layout performed. + + dot corresponds to attributed dot, and is the default output format. It + reproduces the input, along with layout information for the graph. In + particular, a bb attribute is attached to the graph, specifying the + bounding box of the drawing. If the graph has a label, its position is + specified by the lp attribute. + + Each node gets pos, width and height attributes. If the node is a + record, the record rectangles are given in the rects attribute. If the + node is a polygon and the vertices attribute is defined, this attribute + contains the vertices of the node. + + Every edge is assigned a pos attribute, and if the edge has a label, + the label position is given in lp. +fig + Outputs graphs in the FIG graphics language. +gd , +gd2 + Output images in the GD and GD2 format. These are the internal formats + used by the gd library. The latter is compressed. +gif + Outputs GIF bitmap images. +hpgl + Produces output in the HP-GL/2 vector graphic printer language. +imap +cmap + Produces map files for server-side and client-side image maps, + respectively. These can be used in a web page with a graphical + form of the output, e.g. in JPEG or GIF format, to attach links + to nodes and edges. For example, given the dot file + + /* x.dot */ + digraph G { + URL="http://www.research.att.com/"; + command [URL="command.html"]; + command -> output [URL="colors.html"]; + } + + + this can be processed twice: + + dot -Timap > x.map + dot -Tgif > x.gif + + + and then be referred to in a web page, such as + + <A HREF="x.map"> + <IMG SRC="x.gif" ISMAP> + </A> + + + URLs can be attached to the root graph, nodes and edges. If the URL of + a node contains the escape sequence "\N", it will be replaced by the + node's name. If an edge has a URL, various points along the edge (but + not the head or tail) will link to it. In addition, if the edge has a + label, that will link to the URL. + + As for the head of the edge, this is linked to the headURL, if set. + Otherwise, it is linked to the edge's URL if that is defined. If the + headURL contains the escape sequence "\N", it will be replaced by the + headlabel, if defined. The analogous description holds for the tail, + tailURL and taillabel. +ismap + Produces HTML image map files. This is a predecessor (circa 1994) of + the IMAP format. Most servers now user the latter. URLs can be attached + to the root graph, nodes and edges. Since edge links are attached to + edge labels, an edge must have a label for its URL to be used. For both + nodes and edges, if the URL has the escape sequence "\N" embedded in + its string, this will be replaced with the node or edge name. +jpg , +jpeg + Output JPEG compressed image files. This format relies on the + installation having the JPEG library. +mif + Generates Frame Maker MIF files. +mp + Produces MetaPost output. +pcl + Produces output in the PCL printer language. HP-GL is a subset of PCL, + so that PCL output is the same as HP-GL, wrapped with some initial and + final commands to set the printer to and from HP-GL mode. +pic + Outputs in PIC, the picture description language in the troff-family +plain , +plain-ext + The plain and plain-ext formats produce output using a simple, + line-based language. The latter format differs in that, on edges, it + provides port names on head and tail nodes when applicable. + + There are four types of statements. + + graph scale width height + node name x y width height label style shape color fillcolor + edge tail head n x1 y1 .. xn yn [label xl yl] style color + stop + + + graph + The width and height values give the width and height of the + drawing. The lower left corner of the drawing is at the origin. + The scale value indicates how the drawing should be scaled if a + size attribute was given and the drawing needs to be scaled to + conform to that size. If no scaling is necessary, it will be set + to 1.0. Note that all graph, node and edge coordinates and lengths + are given unscaled. + node + The name value is the name of the node, and x and y give the + node's position. The width and height are the width and height of + the node. The label, style, shape, color and fillcolor give the + node's label, style, shape, color and fillcolor, respectively, + using attribute default values where necessary. If the node does + not have a style attribute, "solid" is used. + edge + The tail and head values give the names of the head and tail + nodes. n is the number of control points defining the B-spline + forming the edge. This is followed by 2*n numbers giving the x and + y coordinates of the control points in order from tail to head. If + the edge has a label, this comes next followed by the x and y + coordinates of the label's position. The edge description is + completed by the edge's style and color. As with nodes, if a style + is not defined, "solid" is used. + The output consists of one graph line, a sequence of node lines, one + per node, a sequence of edge lines, one per edge, and a final stop + line. All units are in inches, represented by a floating point number. +png + Produces output in the PNG (Portable Network Graphics) format. PNG + relies on having libz and the PNG library. +ps + Produces PostScript output. +ps2 + Produces PostScript output with PDF notations. It is assumed the output + will be directly converted into PDF format. The notations include PDF + bounding box information, so that the resulting PDF file can be + correctly used with pdf tools, such as pdflatex. In addition, if a node + has a URL attribute, this gets translated into PDF code such that the + node, when viewed in a PDF-viewer, e.g., acroread, is a link to the + given URL. If a URL is attached to the graph, this serves as a base, + such that relative URLs on nodes are derived from it. +svg , +svgz + Produce SVG output, the latter in compressed format. SVG relies on + having libz. +vrml + Outputs graphs in the VRML format. VRML relies on having the PNG + library. +vtx + Generates graph diagrams in the format for Confluents's Visual Thought. +wbmp + Produces output in the Wireless BitMap (WBMP) format, optimized for + mobile computing. + + ------------------------------------------------------------------------
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/NEWS Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3 @@ +Notes on 2.0 + +This release is licensed under the CPL.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/addingLayout.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,297 @@ +To create a new layout plugin called xxx, you first need +to provide two functions: xxx_layout and xxx_cleanup. The +semantics of these are described below. + +======================== + + void xxx_layout(Agraph_t * g) + + Initialize the graph. + - If the algorithm will use the common edge routing code, it should + call setEdgeType (g, ...); + + - For each node, call common_init_node and gv_nodesize. + + If the algorithm will use spline_edges() to route the edges, the + node coordinates need to be stored in ND_pos, so this should be + allocated here. This, and the two calls mentioned above, are all + handled by a call to neato_init_node(). + + - For each edge, call common_init_edge + + - The algorithm should allocate whatever other data structures it + needs. This can involve fields in the A*info_t fields. In addition, + each of these fields contains a void* alg; subfield that the algorithm + can use the store additional data. + Once we move to cgraph, this will all be replace with + algorithm specific records. + + Layout the graph. When finished, each node should have its coordinates + stored in points in ND_coord_i(n), each edge should have its layout + described in ED_spl(e). + (N.B. As of version 2.21, ND_coord_i has been replaced by ND_coord, + which are now floating point coordinates.) + + To add edges, there are 3 functions available: + + - spline_edges1 (Agraph_t*, int edgeType) + Assumes the node coordinates are stored in ND_coord_i, and that + GD_bb is set. For each edge, this function constructs the appropriate + data and stores it in ED_spl. + - spline_edges0 (Agraph_t*) + Assumes the node coordinates are stored in ND_pos, and that + GD_bb is set. This function uses the ratio attribute if set, + copies the values in ND_pos to ND_coord_i (converting from + inches to points); and calls spline_edges1 using the edge type + specified by setEdgeType(). + - spline_edges (Agraph_t*) + Assumes the node coordinates are stored in ND_pos. This + function calculates the bounding box of g and stores it in GD_bb, + then calls spline_edges0(). + + If the algorithm only works with connected components, the code can + use the pack library to get components, lay them out individually, and + pack them together based on user specifications. A typical schema is + given below. One can look at the code for twopi, circo, neato or fdp + for more detailed examples. + + Agraph_t **ccs; + Agraph_t *sg; + Agnode_t *c = NULL; + int ncc; + int i; + + ccs = ccomps(g, &ncc, 0); + if (ncc == 1) { + /* layout nodes of g */ + adjustNodes(g); /* if you need to remove overlaps */ + spline_edges(g); /* generic edge routing code */ + + } else { + pack_info pinfo; + pack_mode pmode = getPackMode(g, l_node); + + for (i = 0; i < ncc; i++) { + sg = ccs[i]; + /* layout sg */ + adjustNodes(sg); /* if you need to remove overlaps */ + } + spline_edges(g); /* generic edge routing */ + + /* initialize packing info, e.g. */ + pinfo.margin = getPack(g, CL_OFFSET, CL_OFFSET); + pinfo.doSplines = 1; + pinfo.mode = pmode; + pinfo.fixed = 0; + packSubgraphs(ncc, ccs, g, &pinfo); + } + for (i = 0; i < ncc; i++) { + agdelete(g, ccs[i]); + } + + free(ccs); + + Be careful in laying of subgraphs if you rely on attributes that have + only been set in the root graph. With connected components, edges can + be added with each component, before packing (as above) or after the + components have been packed (see circo). + + It good to check for trivial cases where the graph has 0 or 1 nodes, + or no edges. + + At the end of xxx_layout, call + + dotneato_postprocess(g); + + The following template will work in most cases, ignoring the problems of + handling disconnected graphs and removing node overlaps: + + static void + xxx_init_node(node_t * n) + { + neato_init_node(n); + /* add algorithm-specific data, if desired */ + } + + static void + xxx_init_edge(edge_t * e) + { + common_init_edge(e); + /* add algorithm-specific data, if desired */ + } + + static void + xxx_init_node_edge(graph_t * g) + { + node_t *n; + edge_t *e; + + for (n = agfstnode(g); n; n = agnxtnode(g, n)) { + xxx_init_node(n); + } + for (n = agfstnode(g); n; n = agnxtnode(g, n)) { + for (e = agfstout(g, n); e; e = agnxtout(g, e)){ + xxx_init_edge(e); + } + } + } + + void + xxx_layout (Agraph_t* g) + { + xxx_init_node_edge(g); + /* Set ND_pos(n) for each node n */ + spline_edges(g); + dotneato_postprocess(g); + } + +====================== + + void xxx_cleanup(Agraph_t * g) + + Free up any resources allocated in the layout. + + Finish with calls to gv_cleanup_node and gv_cleanup_edge for + each node and edge. This cleans up splines labels, ND_pos, shapes + and 0's out the A*info_t, so these have to occur last, but could be + part of explicit xxx_cleanup_node and xxx_cleanup_edge, if desired. + At the end, you should do + + if (g != g->root) memset(&(g->u), 0, sizeof(Agraphinfo_t)); + + This is necessary for the graph to be laid out again, as the layout + code assumes this structure is clean. + + libgvc does a final cleanup to the root graph, freeing any drawing, + freeing its label, and zeroing out Agraphinfo_t of the root graph. + + The following template will work in most cases: + + static void xxx_cleanup_graph(Agraph_t * g) + { + /* Free any algorithm-specific data attached to the graph */ + if (g != g->root) memset(&(g->u), 0, sizeof(Agraphinfo_t)); + } + + static void xxx_cleanup_edge (Agedge_t* e) + { + /* Free any algorithm-specific data attached to the edge */ + gv_cleanup_edge(e); + } + + static void xxx_cleanup_node (Agnode_t* n) + { + /* Free any algorithm-specific data attached to the node */ + gv_cleanup_node(e); + } + + void xxx_cleanup(Agraph_t * g) + { + Agnode_t *n; + Agedge_t *e; + + for (n = agfstnode(g); n; n = agnxtnode(g, n)) { + for (e = agfstout(g, n); e; e = agnxtout(g, e)) { + xxx_cleanup_edge(e); + } + xxx_cleanup_node(n); + } + xxx_cleanup_graph(g); + } + +================== + +Most layouts use auxiliary routines similar to neato, so +the entry points can be added in plugin/neato_layout + +Add to gvlayout_neato_layout.c: + +gvlayout_engine_t xxxgen_engine = { + xxx_layout, + xxx_cleanup, +}; + +and the line + + {LAYOUT_XXX, "xxx", 0, &xxxgen_engine, &neatogen_features}, + +to gvlayout_neato_types and a new emum + + LAYOUT_XXX + +to layout_type in that file. + +The above allows the new layout to piggyback on top of the neato +plugin, but requires rebuilding the plugin. In general, a user +can (and probably should) build a layout plugin totally separately. + +To do this, after writing xxx_layout and xxx_cleanup, it is necessary to: + + - add the types and data structures + +typedef enum { LAYOUT_XXX } layout_type; + +static gvlayout_features_t xxxgen_features = { + 0 +}; +gvlayout_engine_t xxxgen_engine = { + xxx_layout, + xxx_cleanup, +}; +static gvplugin_installed_t gvlayout_xxx_types[] = { + {LAYOUT_XXX, "xxx", 0, &xxxgen_engine, &xxxgen_features}, + {0, NULL, 0, NULL, NULL} +}; +static gvplugin_api_t apis[] = { + {API_layout, &gvlayout_xxx_types}, + {(api_t)0, 0}, +}; +gvplugin_library_t gvplugin_xxx_layout_LTX_library = { "xxx_layout", apis }; + + - combine all of this into a dynamic library whose name contains the + string "gvplugin_" and install the library in the same directory as the + other Graphviz plugins. For example, on Linux systems, the dot layout + plugin is in the library libgvplugin_dot_layout.so. + + - run + dot -c + to regenerate the config file. + +NOTES: + - Additional layouts can be added as extra lines in gvlayout_xxx_types. + - Obviously, most of the names and strings can be arbitrary. One + constraint is that external identifier for the gvplugin_library_t + type must end in "_LTX_library". In addition, the string "xxx" in + each entry of gvlayout_xxx_types is the name used to identify the + layout algorithm, so needs to be distinct from any other layout name. + - The features of a layout algorithm are currently limited to a + flag of bits, and the only flag supported is LAYOUT_USES_RANKDIR, + which enables the layout to the rankdir attribute. + +Changes need to be made to any applications, such as gvedit, that +statically know about layout algorithms. + +================== + +Software configuration - automake + +If you want to integrate your code into the Graphviz software +and use its build system, follow the instructions below. +You can certainly build and install your plugin using your own +build software. + +0. Put your software in lib/xxxgen, and added the hooks describe above +into gvlayout_neato_layout.c +1. In lib/xxxgen, provide a Makefile.am (based on a simple example +like lib/fdpgen/Makefile.am) +3. In lib/Makefile.am, add xxxgen to SUBDIRS +2. In configure.ac, add lib/xxxgen/Makefile to AC_CONFIG_FILES. +4. In lib/plugin/neato_layout/Makefile.am, insert + $(top_builddir)/lib/xxxgen/libxxxgen_C.la + in libgvplugin_neato_layout_C_la_LIBADD +5. Remember to run autogen.sh because on its own configure can guess wrong. + +This also assumes you have a good version of the various automake tools +on your system. + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/cpl1.0.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,213 @@ +Common Public License Version 1.0 + +THE ACCOMPANYING PROGRAM IS PROVIDED UNDER THE TERMS OF THIS COMMON PUBLIC +LICENSE ("AGREEMENT"). ANY USE, REPRODUCTION OR DISTRIBUTION OF THE PROGRAM +CONSTITUTES RECIPIENT'S ACCEPTANCE OF THIS AGREEMENT. + +1. DEFINITIONS + +"Contribution" means: + + a) in the case of the initial Contributor, the initial code and +documentation distributed under this Agreement, and + + b) in the case of each subsequent Contributor: + + i) changes to the Program, and + + ii) additions to the Program; + + where such changes and/or additions to the Program originate from and are +distributed by that particular Contributor. A Contribution 'originates' from a +Contributor if it was added to the Program by such Contributor itself or anyone +acting on such Contributor's behalf. 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/fontfaq.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,319 @@ +Graphviz and fonts. +=================== + +Before we launch into the gory details, we would like to explain +why this is a hard problem. The naming and rendering of text fonts +in Graphviz (and other programs) is complicated. There are several reasons: + +- Graphviz runs on a wide range of systems: Linux and other Unix +variants, Microsoft Windows, and Mac. +- Graphviz has a wide range of output formats: raster-oriented formats +like PNG and GIF; path-based ones like Postscript, PDF and SVG; some +idiosyncractic legacy formats, like troff PIC and HPGL. +- Often, output will be downloaded and displayed on a computer or other +device, different than the one where the layout was created. +- Graphviz layouts should be identical in size and appearance, +regardless of the output format. +- Graphviz can run on external libraries that help with naming and +rendering text fonts, but they are not required, and stripped-down +Graphviz tools can be built without them. In fact, Graphviz may have +to run on systems with no font files installed. +- There are several major font file formats to be supported. +- Non-Western, international character sets should be supported. +- Graphviz should provide a good set of standard fonts. +- It should be easy to specify standard fonts. +- Users should be able to load their own custom fonts. +- Output should be small to download quickly. +- Output should allow the best rendering possible in a given format. +- Output files should be easy to postprocess, for example, retaining +the objects of the original graph if possible. +- It is very helpful to work around known bugs or missing features +in support libraries and popular external tools. + +This is a tall order. Some of the goals conflict. Generally our +approach has been to define defaults that favor convenience and good +looking output, and give the user options to override the defaults. + +===Overview=== + +In the following, we will assume a ''standard'' version of Graphviz +with the full set of support libraries (fontconfig, gd, Cairo and Pango), +running on a desktop system or server with a standard installation of +font files. + +The graphviz layout engines (dot, neato, etc) create layouts with nodes +sized to enclose the text labels. This requires knowing the size of +the text blocks, which in turn requires knowing the metrics of the font +glyphs and their composition into words, taking into account wordspacing, +kerning, hinting, etc. So the overall process is: font specification, +then text layout, followed by Graphviz output (and final rendering on +the target display or device, which may or may not be by a Graphviz tool.) + + +A font is usually selected by family name ("fontname") and other properties +(see below: "Font selection"). Then fontconfig matches the request +to a system font. [Note: in older versions of Graphviz, fontname was +simply a file name. This required exact file name matching (with a little +bit of helpful name mangling under the hood, e.g. translating Times-Roman +to Times, or Helvetica to Arial on Windows systems (and yes we know +there is a difference). Under fontconfig, fontnames are family names, +which fontconfig matches to the closest font it finds. This always +"succeeds", but unfortunately produces surprising results if fontconfig's +idea of "close" doesn't match yours. This can happen when you specify +a custom (or just nonexistent) font, like Steve-North-Handwriting, +and fontconfig silently falls back to something safe like a typewriter +font.] + +Text layout is performed by pango, which accepts text and computes a +layout with metrics that determine node sizes. + +Though line drawing is provided by cairo for many output formats (and +likely more in the future), for raster output formats, font rendering +is passed though cairo to freetype. Freetype is also called if gd is +used for drawing. (gd can also be requested explicitly, e.g. dot -Tpng:gd, +or by default when Graphviz is built without cairo). Freetype provides +antialiasing, hinting, kerning, and other low-level font features. + +Font metrics are obtained from the fonts installed on the system running +Graphviz. Results are guaranteed when Graphviz outputs raster formats, +because freetype immediately renders the fonts into pixels. On the +other hand, with path-based formats like Postscript (-Tps) and SVG (-Tsvg), +final rendering may be done on a different platform altogether, with +different font files installed. Clearly, Your Milage May Vary. In the +case of Postscript, the driver in Graphviz passes the expected metrics +of the text block down to the renderer, and asks it to make a final stretch +(or squeeze) to force the text to fit the metrics that were in effect at +layout time. In Graphviz SVG, there is only a hope and a prayer that +the SVG rendering program's fonts match the ones fontconfig and freetype +used when Graphviz was run. (More about this later.) + +Default fonts and PostScript fonts. =================================== + +The default font in graphviz is, and always has been, Times-Roman. + +Graphviz has historically supported some ``standard'' Postscript +fonts, initially, Times-Roman, Helvetica, Courier and Symbol. +This list was later enlarged by Adobe to include 35 fonts, which are: + AvantGarde-Book AvantGarde-BookOblique AvantGarde-Demi + AvantGarde-DemiOblique Bookman-Demi Bookman-DemiItalic + Bookman-Light Bookman-LightItalic Courier Courier-Bold + Courier-BoldOblique Courier-Oblique Helvetica + Helvetica-Bold Helvetica-BoldOblique Helvetica-Narrow + Helvetica-Narrow-Bold Helvetica-Narrow-BoldOblique + Helvetica-Narrow-Oblique Helvetica-Oblique NewCenturySchlbk-Bold + NewCenturySchlbk-BoldItalic NewCenturySchlbk-Italic + NewCenturySchlbk-Roman Palatino-Bold Palatino-BoldItalic + Palatino-Italic Palatino-Roman Symbol Times-Bold Times-BoldItalic + Times-Italic Times-Roman ZapfChancery-MediumItalic ZapfDingbats + +Unfortunately, fontconfig doesn't recognize PostScript-style font +names directly, so Graphviz makes custom mappings from its list of +PostScipt names into fontconfig family names for use in all cairo +and gd based renderers. In -Tps output, these fonts are used without +name translation. + +Font selection. =============== + +The fontname attribute in .dot graphs is a fontconfig style specification. +From: http://www.fontconfig.org/fontconfig-user.html + + Fontconfig provides a textual representation for patterns that + the library can both accept and generate. The representation is + in three parts, first a family name list, second list of point sizes, + and finally a list of additional properties: + + <families>-<point sizes>:<name1>=<values1>:<name2>=<values2>... + + Values in a list are separated with commas. The name needn't + include either a family or point size; they can be elided. In + addition, there are symbolic constants that simultaneously + indicate both a name and a value. Here are some examples: + + Name Meaning + ---------------------------------------------------------- + Times-12 12 point Times Roman + Times-12:bold 12 point Times Bold + Courier:italic Courier Italic in the default size + Monospace:matrix=1 .1 0 The users preferred monospace font + with artificial obliquing + +Graphviz currently has a seperate attribute for specififying fontsize. + +[ FIXME + We should allow the fontconfig style specification. "Times-20" does + not currently result in a 20pt font. + + This is probably because of special treatment of '-' for postscript + font names. +] + +[ FIXME + We seem to have a bug with use of ':' in fontnames, probably because + of special treatment for filenames in Windows. + + In fontnames, use <space> instead of ':' to separate values. + + -Nfontname="Courier:italic" doesn't produce an italic font in + graphviz-2.16.1, but: -Nfontname="Courier italic" works, but + -Nfontname="Monospace matrix=1 .1 0 1" doesn't. +] + + +Font management with fontconfig. ================================ + +How can I tell what fonts are available? + $ fc-list + +How can I tell what fonts dot is using; + $ dot foo.dot -Tpng -o foo.png -v 2>&1 | grep font + +How can I add a custom font? + In the current version of Graphviz with fontconfig, Cairo and + Pango, this cannot be done by simply putting a file in the + current directory or setting the DOTFONTPATH path variable. + Your custom font must be explicitly installed by fontconfig tools. + + For a single font, e.g., foo.ttf: + $ mkdir -p ~/.fonts + $ cp foo.ttf ~/.fonts/ + + One can run fc-cache to speed up the use of fontconfig. + $ fc-cache + + For Windows users, one can go to the C:\windows\fonts + folder and use File -> Install New Font from the pull-down menus + to install the font. + + For a new font directory, e.g., /Library/Fonts, add a new <dir> element + + <dir>/Library/Fonts</dir> + + to a .conf file. Note that the file must have a correct xml structure + as specified by the fontconfig fonts.dtd. Possible choices for the + .conf file are local.conf in the same directory as the system-wide + fonts.conf file, or .fonts.conf in your home directory. + +How can I ... font? + See: http://www.fontconfig.org/fontconfig-user.html + +Can I specifiy a font by filename instead of by familyname? + Sorry, the answer is no. {The reason is that for this to + work, Graphviz has to intercept the font lookup before + fontconfig is called, and this can't be done when fonts + are being looked up by Pango.) + + Some versions of fontconfig appear to recognize pathnames and + attempt to use that, but this isn't always the case. + +How can I be sure that a specific font is selected? + Provide enough specification in the fontname, and test it + with fc-match to ensure that your desired font is selected. + (Note, this will not ensure that the same font is used in -Tps + or -Tsvg renderings where we rely on the fonts available on the + final printer or computer.) + + Note the downside, as mentioned previously, is that Graphviz cannot + do much to warn you when fontconfig didn't find a very + good match, because fontconfig just cheerfully falls back + to some standard font. It would be really nice if the + fontconfig developers could provide a metric reflecting the + quality of the font match in their API. + +What about SVG fonts? + Graphviz has a native SVG driver that we wrote (which is the + default), and cairo's SVG driver (which you get with -Tsvg:cairo). + + Graphviz' native SVG driver generates Windows compliant names + like "Times New Roman" or Arial by default. The names work in a + lot of situations (like Firefox running on Windows), but are + not guaranteed to be portable. If you set -Gfontnames=ps, + you get Postscript names like Times-Roman. If you set -Gfontnames=svg + you are guaranteed to get rock solid standards compliant SVG. + The SVG standard says that the legal generic font names + are Serif, Sans-Serif, and Monospace (plus Cursive and + Fantasy which we don't use in Graphviz). We generate those names. + The bad news is that various downstream renderers and editors + may resolve the generic font names differently, so it's not + quite clear how your SVG will look. Many W3C examples show + how to use CSS (Cascading Style Sheets) to get around this + problem by giving a list of font family names in order of + lookup precedence, but some downstream processors (like the + inkscape editor in Linux) don't implement CSS, so we're up a tree here. + + The cairo SVG driver solves this in an effective though brute + force way: it simply encodes embeds the needed fonts as lines and + curves in the target SVG. For small examples, -Tsvg:cairo is + about 10 times bigger than -Tsvg, but maybe it's worth it for + correctness. The other problem is that such SVG is much much + slower to render, no doubt because it bypasses any system + font rendering services, and does it the old fashioned way. + +What about Postscript fonts? + + say something here. What about non-ASCII like Latin1. + what about loading your own fonts via -L like in the old + days with the weird outline font example. + +==="What if" issues for nonstandard Graphviz builds=== +The following only apply if you build your own version of Graphviz +by configuring and compiling the source code to build your own +custom executable. If you don't know what this means, it +definitely does not mean you. + +No freetype. ============ + +When graphviz is built on systems without freetype, then only the gd +renderer will be available for bitmap outputs, and the only available +fonts are a small set of builtin bitmap fonts. The poor quality of +these fonts will be evident, also, "dot ... -v 2>&1 | grep font" will +say that the font is "<internal>". This may actually be desirable +for installing minimal graphviz programs on a server where fonts +may not even be installed. + + +No fontconfig. ============== + +If graphviz is built on systems without fontconfig (e.g. Redhat-7) then +the fontname attribute will be interpreted as a font file name. The +system directories will be searched for this, or the directories can +be specified with the GDFONTPATH environment variable (or DOTFONTPATH +for historical reasons). Graphviz will use gd and freetype to obtain +metrics and render text. No pango/cairo renderers will be available +without fontconfig support. + + +Disabling fontconfig. ===================== + +Pango/cairo depends on fontconfig, so to disable fontconfig you also have +to disable pango/cairo. The easiest way to do this temporarily is to +edit /usr/lib/graphviz/config and remove the entire "libpango" block. +[Note that any changes to this file will be lost the next time graphviz +is updated, or "dot -c" is run with installer priviledges.] + +With pango disabled, graphviz will use gd which, even if it was built with +fontconfig support, will still allow fontnames to be given as filenames. + +You can also disable cairopango at build time with configure script options. + + +No gd. ===== + +Cairopango works without gd. We are moving graphviz to the pango/cairo +libraries, but gd still offers some features that are hard to replace, +such as JPEGs, GIFs and paletted color bitmap outputs. However, font support +is fully functional without gd so long as pango, cairo, fontconfig, +freetype are available. + +No pango/cairo. =============== + +Without pango/cairo, some of the key renderers are only available +with gd, which produces lower quality (but smaller) output. + +Looking forward, we expect to depend more on pango for things like: +line wrapping, multiple fonts per label, bidirectional text and +other internationalization features. + +No gd and no cairopango ===== +This is basically the original Graphviz without any external fonts. +It cannot render any raster formats, so it's mainly good for Postscript. +It relies on a few internal font tables
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/FAQ.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1089 @@ +<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" + "http://www.w3.org/TR/html4/loose.dtd"> +<HTML><HEAD><TITLE>Graphviz FAQ 2008-06-06</TITLE> +<META HTTP-EQUIV="content-type" CONTENT="text/html; charset=utf-8"> +</HEAD><BODY> +<H1>Graphviz FAQ 2008-06-06</H1> + +<A HREF="mailto:gviz-bugs@research.att.com">The Graphviz Project</A> +<p> +<b>Note</b>: +This is not a tutorial; to understand the following, you should +know how to use the basic features of the tools and +languages involved. Please see the +<A HREF="http://www.graphviz.org/Documentation.php"> +user guides and documentation</A> for further information or the +<A HREF="http://www.graphviz.org/Resources.php">resources page</A> +for a partial list of compatible tools and packages. + +<h2>General</h2> + +<A name=Q1 HREF=#Q1> +<B>Q1</A>. Where can I see a list of all the attributes that control dot or neato?</B> + +</A> +<P> +See <A HREF="info/attrs.html"> +Graph Attributes</A>. There is also information on +<A HREF="info/command.html"> +command-line usage</A> and +<A HREF="info/output.html"> +output formats</A>. +<p> +<a name="mailinglist"></a> +<A name=Q2 HREF=#Q2> +<B>Q2</A>. Where can I discuss Graphviz?</B> + +<p> +We run a mailing list. +<p> +To subscribe or unsubscribe, visit the +<A HREF="https://mailman.research.att.com/mailman/listinfo/graphviz-interest">graphviz-interest</A> <em>mailman</em> control page. See also the general +<A HREF="http://www.list.org/mailman-member/index.html"> +instructions</A> for mailman. +<p> +You can also see the +<A HREF="https://mailman.research.att.com/pipermail/graphviz-interest/"> +archive</A>. +<p> +You may wish to use a Yahoo or Hotmail account if you're concerned +about spam. We also run anti-spam filters, and rewrite <tt>@</tt> +as <tt>at</tt> to keep verbatim addresses out of the archive. +<p> +Please, please, please, do not torment the mailing list with beginner's +questions. First, check this FAQ and the +<A HREF="https://mailman.research.att.com/pipermail/graphviz-interest/"> +message archive</A> carefully. +If you are desperate, or better yet, if you have constructive advice, +please send a message to the <A HREF="https://mailman.research.att.com/mailman/listinfo/graphviz-devel">graphviz-devel mailing list</A>. +<p> +Also, if a program crashes or you get an abort or something strange occurs +and you are fairly comfortable using the tools: +<UL> +<LI> +Check the +<A HREF="http://www.research.att.com/~erg/graphviz/bugs/openbugs.html"> open +bug list</A> to see if a similar bug has already been reported. You might +also consider checking the +<A HREF="http://www.research.att.com/~erg/graphviz/bugs/buglist.html"> full +bug list</A>, since your bug may have been reported and fixed in the working +version. +<LI> +Submit a <A HREF="http://www.research.att.com/~erg/graphviz/bugform.html">bug +report</A>. If you prefer, you can download a +<A HREF="http://www.research.att.com/~erg/graphviz/bugform.txt">report in +text form</A>, fill in the fields, and email it to +<a href="mailto:gviz-bugs@research.att.com">gviz-bugs@research.att.com</a>. +</UL> +<p> +<A name=Q3 HREF=#Q3> +<B>Q3</A>. I'm trying to make a layout larger. How?</B> + +<p> +There are various ways to increase the size of a layout. In doing this, +one has to decide if the sizes of the nodes and text should be +increased as well. +<p> +One approach is to adjust individual +parameters such as <tt>fontsize, nodesep</tt> and <tt>ranksep</tt>. +For example, +<pre> + digraph G { + graph [fontsize=24]; + edge [fontsize=24]; + node [fontsize=24]; + ranksep = 1.5; + nodesep = .25; + edge [style="setlinewidth(3)"]; + a -> b -> c; + } +</pre> +If you do this, make sure you are not fighting a conflicting graph +size setting, like <tt>size="6,6"</tt>, which will then scale +everything back down. +<p> +If you are using fdp or neato, increasing the edge len will tend to +expand the layout. +<pre> + graph G { + edge [len=3] + a -- { b c d } + } +</pre> +For twopi and circo, there are other parameters such as +<tt>ranksep</tt> which can be used. See the +<A HREF="info/attrs.html"> +graph attributes</A>. +<p> +You can also use the <tt>ratio</tt> attribute. If you set the <tt>size</tt> +attribute to the desired drawing size, and then set <tt>ratio=fill</tt>, node +positions are scaled separately in x and y until the drawing fills the +specified <tt>size</tt>. Note that node sizes stay the same. If, instead, +you set <tt>ratio=expand</tt>, the layout is uniformly scaled up in x and y +until at least one dimension fits <tt>size</tt>. +<p> +If you specify the <tt>size</tt> attribute but end it with an exclamation +mark (!), the final drawing will be scaled up uniformly in x and y +until at least one dimension fits <tt>size</tt>. Note that everything is +scaled up, including text and node sizes. +<p> +If you're using Postscript, you can just scale up the output by +manually adding a command such as <tt>2 2 scale</tt> where the +Postscript environment is set up. Make sure to adjust the +<tt>BoundingBox</tt> too if your tools look at this header. +<p> +<A name=Q4 HREF=#Q4> +<B>Q4</A>. How can I join or merge certain edge routes in dot?</B> + +<p> +You can try running <tt>dot -Gconcentrate=true</tt> or you can +introduce your own virtual nodes drawn as tiny circles where +you want to split or join edges: + +<pre> +digraph G { + yourvirtualnode [shape=circle,width=.01,height=.01,label=""]; + a -> yourvirtualnode [arrowhead=none] + yourvirtualnode -> {b;c} +} +</pre> + +<P> +<A name=Q4a> +<B>Q. How can I generate graph layouts in PDF?</B> +</A> +<P> +Recent versions of graphviz with the CairoPango based drivers +can generate PDF directly with the <tt>-Tpdf</tt> command line option. +This this first. +<P> +Otherwise, create Postscript output, then use an external converter from +Postscript to PDF. +For example,<BR> +<tt>dot -Tps | epsf2pdf -o file.pdf</tt><br> +Note that URL tags are respected, to allow clickable PDF objects. +<P> +If your intention is to use the figure as PDF in some document preparation +system, such as pdflatex, it is very important to use -Tps2 rather than +-Tps. In general, if you really want PDF output, that is, you would like +to have a -Tpdf flag, use -Tps2 before converting to PDF. +<P> +In the diagram below, the shaded nodes will contain bad output.<BR> +<IMG src="pspdf.png"> +<P> +<A name=Q4b> +<B>Q. How can I make duplicate nodes?</B> +</A> +<P> +Make unique nodes with duplicate labels. +<pre> + digraph G { + node001 [label = "A"]; + node002 [label = "A"]; + node001 -> node002; + } +</pre> +<P> +<A name=Q4c> +<B>Q. How can I set a graph or cluster label without its propagating to all sub-clusters?</B> +</A> +<P> +Set the label at the end of the graph (before the closing brace), after all +its contents have been defined. (We admit it seems desirable to define some +special syntax for non-inherited attribute settings.) +<p> +<A name=Q5 HREF=#Q5> +<B>Q5</A>. How can I draw multiple parallel edges in neato?</B> + +<p> +This is possible when the <tt>splines</tt> attribute is false, which +is the default. When <tt>splines=true</tt>, we have no good answer but +we are working on it. One trick which is sometimes sufficient is to +specify multiple colors for the edge. This will a produce set of tightly +parallel splines, each in its specified color. Read about the +<A HREF="info/attrs.html#d:color">color +attribute</A> for more information. + +<h2>Clusters</h2> + +<A name=Q5a> +<B>Q. How can I create edges between cluster boxes?</B> +</A> +<p> +This only works in Graphviz version 1.7 and higher. +To make edges between clusters, first set the +graph attribute <tt>compound=true</tt>. +Then, you can specify a cluster by name as +a <i>logical head or tail</i> to an edge. This will +cause the edge joining the two nodes to be +clipped to the exterior of the box around the +given cluster. +<p> +For example, + +<pre> + digraph G { + compound=true; + nodesep=1.0; + subgraph cluster_A { + a -> b; + a -> c; + } + subgraph cluster_B { + d -> e; + f -> e; + } + a -> e [ ltail=cluster_A, + lhead=cluster_B ]; + } +</pre> + +has an edge going from <tt>cluster_A</tt> to +<tt>cluster_B</tt>. If, instead, you say + +<pre> + a -> e [ltail=cluster_A]; +</pre> + +this gives you an edge from <tt>cluster_A</tt> to node +<tt>e</tt>. Or you could just specify +an <tt>lhead</tt> attribute. + +The program warns if a cluster specified as a +logical node is not defined. +Also, if a cluster is specified as a logical +head for an edge, the real +head must be contained in the cluster, and +the real tail must not be. +A similar check is done for logical tails. In +these cases, the edge +is drawn between the real nodes as usual. +<p> +<A name=Q6 HREF=#Q6> +<B>Q6</A>. Clusters are hard to see.</B> + +<P> +Set <tt>bgcolor=grey</tt> +(or some other color) +in the cluster. +<P> +<A name=Q7 HREF=#Q7> +<B>Q7</A>. How can I symmetrize (balance) tree layouts?</B> + +<P> +When a tree node has an even number of children, it isn't necessarily +centered above the two middle ones. If you know the order of the children, +a simple hack is to introduce new, invisible middle nodes to re-balance +the layout. The connecting edges should also be invisible. For example: +<pre> +digraph G { +a -> b0; +xb [label="",width=.1,style=invis] +a -> xb [style=invis]; +a -> b1; +{rank=same b0 -> xb -> b1 [style=invis]} +b0 -> c0; +xc [label="",width=.1,style=invis] +b0 -> xc [style=invis]; +b0 -> c1; +{rank=same c0 -> xc -> c1 [style=invis]} +} +</pre> +This trick really ought to be build into our solver (and made +independent of the order of the children, and available for +layouts other than trees, too). + +<H2>Output features</H2> + +<A name=Q8 HREF=#Q8> +<B>Q8</A>. How can I get high quality (antialiased) output?</B> + +<P> +The easiest thing may be to make the layout in Postscript (option <tt>-Tps</tt>), +then run through <A HREF="http://www.cs.wisc.edu/~ghost/">Ghostview</A> with +antialiasing enabled. The important command line options are +<tt>-dTextAlphaBits=4 -dGraphicsAlphaBits=4</tt> +(4 is the highest level of antialiasing allowed - see the +<A HREF="http://www.cs.wisc.edu/~ghost/doc/GPL/8.15/Use.htm">Ghostview documentation</A>). +The full command line to render a raster could be something like: +<pre> +gs -q -dNOPAUSE -dBATCH -dTextAlphaBits=4 -dGraphicsAlphaBits=4 -sDEVICE=png16m -sOutputFile=file.png file.ps +</pre> +<P> +On Mac OS X, the <A HREF="http://www.pixelglow.com/graphviz/">pixelglow</A> port +uses Apple's Quartz renderer, which enables antialiasing. It also provides a beautiful document container for its user interface. (One downside is +that you can't run Pixelglow Graphviz as a web server or other background +process if your Mac has 3D graphics, because Quartz wants to get this resource +to accelerate rendering. Another problem is that as of this writing, +Pixelglow Graphviz hasn't been updated in a long time, maybe mid 2004.) +<P> +On the Linux bleeding edge, Graphviz has an optional plugin to use +the <A HREF="http://www.cairographics.org">cairo</A> back end, +which has antialiased, path-based graphics. If you want this, +you must install cairo, which is not part of Graphviz. Cairo is +available in recent versions of Fedora linux, or it can be built +from source. +<P> +<A name=Q9 HREF=#Q9> +<B>Q9</A>. I can only get 11x17 output.</B> + +<P> +It's not us! It's probably your printer setup. If you don't +believe this, run <tt>dot -Tps</tt> and look at the +<tt>BoundingBox</tt> header. The coords are in 1/72ths of an inch. + +<P> +<A name=Q10 HREF=#Q10> +<B>Q10</A>. How do I create special symbols and accents in labels?</B> + +<P> +The following solution only works with the +raster drivers that load Truetype or Type1 +fonts! (That means, <tt>-Tgif, -Tpng, -Tjpeg</tt>, and possibly +<tt>-Tbmp</tt> or <tt>-Txbm</tt> if enabled). + +Use UTF8 coding, <i>e.g.</i> &#165; for the Yen currency symbol ¥. +Example: + +<pre> + graph G { + yen [label="&#165;"] + } +</pre> +<P> +You can look up other examples in this handy +<A HREF="http://www.graphviz.org/doc/char.html"> +character set reference</A>. +<P> +<A name=Q10b> +<B>Q. More generally, how do I use non-ASCII character sets?</B> +</A> +<P> +The following applies to Graphviz 2.8 and later. (In older versions +of Graphviz, you can sometimes get away with simply putting +Latin-1 or other UTF-8 characters in the input stream, but the +results are not always correct.) +<P> +<B>Input:</B> the general idea is to find the +<A HREF="http://en.wikipedia.org/wiki/Unicode">Unicode</A> +value for the glyph you want, and enter it within a text +string "...." or HTML-like label <...>. +<P> +For example, the mathematical <it>forall</it> sign (∀) has the value 0x2200. +There are several ways this can be inserted into a file. +One is to write out the ASCII representation: "&#<nnn>;" where <nnn> +is the decimal representation of the value. The decimal value of 0x2200 is 8704, +so the character can be specified as "&#8704;" . Alternatively, Graphviz +accepts UTF-8 encoded input. In the case of forall, its UTF-8 representation +is 3 bytes whose decimal values are 226 136 128. For convenience, you +would probably enter this using your favorite editor, tuned to your character set +of choice. You can then use the <A HREF="http://www.gnu.org/software/libiconv/#TOCdownloading"> +iconv</A> program to map the graph from your character set to UTF-8 or Latin-1. +<P> +We also accept the HTML symbolic names for Latin-1 characters as suggested +<A HREF="#Q10">above</A>. +(Go to http://www.research.att.com/~john/docs/html/index.htm and click +on Special symbols and Entities) For example, the cent sign (unicode +and Latin-1 value decimal 162 can be inserted as +<pre> +&cent; +</pre> +<P> +Note that <b>the graph file must always be a plain text document</b> +not a Word or other rich format file. Any characters not enclosed in "..." +or <...> must be ordinary ASCII characters. In particular, all of the DOT +keywords such as <tt>digraph</tt> or <tt>subgraph</tt> must be ASCII. +<P> +Because we cannot always guess the encoding, you should set the graph +attribute <tt>charset</tt> to +<A HREF="http://en.wikipedia.org/wiki/UTF-8">UTF-8</A>, +<A HREF="http://en.wikipedia.org/wiki/Latin-1">Latin1</A> +(alias ISO-8859-1 or ISO-IR-100) +or +<A HREF="http://en.wikipedia.org/wiki/Big-5">Big-5</A> for +Traditional Chinese. This can be done in the graph file or on the command line. +For example <tt>charset=Latin1</tt>. +<P> +<B>Output:</B> It is essential that a font which has the glyphs for your +specified characters is available at final rendering time. +The choice of this font depends on the target code generator. +For the gd-based raster generators (PNG, GIF, etc.) you need a +TrueType or Type-1 font file on the machine running the Graphviz program. +If Graphviz is built with the <tt>fontconfig</tt> +library, it will be used to find the specified font. Otherwise, Graphviz will +look in various default directories for the font. The directories to be +searched include those specified by the <tt>fontpath</tt> attribute, +related environment or shell variables +(see the <a href=http://www.graphviz.org/doc/info/attrs.html#d:fontpath>fontpath</A> entry), +and known system font directories. +The table +<A HREF="http://www.graphviz.org/doc/char.html"> +http://www.graphviz.org/doc/char.html</A> +points out that these glyphs are from the <tt>times.ttf</tt> font. +With fontconfig, it's hard to specify this font. <tt>Times</tt> usually gets +resolved to Adobe Type1 times, which doesn't have all the glyphs seen on that page.) +<!--- can someone explain whether Cairo differs from libgd here? ---> +<P> +For Postscript, the input must be either the ASCII subset of UTF-8 +or Latin-1. (We have looked for more general solutions, but it +appears that UTF-8 and Unicode are handled differently for every +kind of font type in Postscript, and we don't have time to hack +this case-by-case. If someone wants to volunteer to work on this, let us know.) +<P> +For SVG output, we just pass the raw UTF-8 (or other encoding) +straight through to the generated code. +<P> +Non-ASCII characters probably won't ever work in Grappa +or dotty, which have their own back end rendering. +(Though, Java supports UTF-8, so there's a chance +Grappa also handles raw UTF-8 strings.) +<P> +As you can see, this is a sad state of affairs. +Our plan is to eventually migrate Graphviz to the +<A HREF="http://www.pango.org/">pango</A> text formatting +library, to ameliorate the worst of these complications. +<P> +<A name = Q11> +<B>Q. How do I get font and color changes in record labels or other labels?</B> +</A> +<P> +This is not possible in record shapes. However, you can do this using +<A HREF="info/shapes.html#html"> +HTML-like labels</A>. The granularity of changes is still at the cell level, +but by playing with cell spacing and padding, you can get pretty much +the effect you want. The intention is to support arbitrary font changes +within running text in the not-too-distant future. + +<P> +<A name=Q12 HREF=#Q12> +<B>Q12</A>. In plain format, splines do not touch the nodes (arrowheads are missing).</B> + +<P> +Edges are specified as the main spline and, if necessary, arrowheads +which actually abut the node. If the arrowheads are not given, drawing +the edge spline will leave a gap between the edge and the node. +This is a bug which has now solidified into a feature. +A workaround is to set + +<pre> + edge [dir=none] +</pre> +Since the edges have no arrowheads, the spline specification will go +all the way to both nodes. +<P> +<A name=Q13 HREF=#Q13> +<B>Q13</A>. Record nodes are drawn differently in dot and neato when rankdir=LR.</B> + +<P> +It's true. dot -Grankdir=LR rotates record nodes so that their top level +fields are still listed across levels. rankdir=LR has no effect in neato. +One workaround is +<A HREF="info/shapes.html#html"> +HTML-like records</A> (they don't rotate; the downside is that +you have to write in XML). Another workaround is to enclose +record labels in { } to rotate/unrotate the record contents. See also, +<A HREF="http://msdn.microsoft.com/library/default.asp?url=/library/en-us/dnhfact/html/hfactor8_5.asp">How To Avoid Foolish Consistency</A> +by Scott Berkun (Microsoft Corp.) +<P> +<A name=Q14 HREF=#Q14> +<B>Q14</A>. How can I print a big graph on multiple pages?</B> + +<P> +The <tt>page</tt> attribute, if set, tells Graphviz to print the +graph as an array of pages of the given size. Thus, the graph +<pre> +digraph G { + page="8.5,11"; + ... +} +</pre> +will be emitted as 8.5 by 11 inch pages. When printed, the +pages can be tiled to make a drawing of the entire graph. +At present, the feature only works with PostScript output. +<P> +Alternatively, there are various tools and viewers which will take +a large picture and allow you to extract page-size pieces, which can +then be printed. +<P> +<A name=Q15> +<B>Q. When I have a red edge it shows up as a +solid red in PNG and GIF formats, but has a +black border when rendered to JPEG. </B> +</A> +<P> +This is an artifact of JPEG's lossy +compression algorithm. JPEG isn't very good +for line drawings. PNG is bitmap format of +choice. John Ellson wants to deprecate and +eventually remove the JPEG driver, but North +is reluctant to change anything that people +might already rely on. +<P> +<A name=Q16 HREF=#Q16> +<B>Q16</A>. How can I get custom shapes or images in my graph?</B> + +<P> +Please see the +<A HREF="http://www.graphviz.org/Documentation/html/shapehowto.html"> +Shape HowTo</A> for some approaches. There is no easy way to create +custom shapes that work with dot/neato, dotty +(Unix or MS-Windows) and Grappa (the Java +front end), because they don't share any universal back end structure. +We're thinking about it. +<P> +<A name=Q17> +<B>Q. Sometimes in dotty, right mouse click shows the global menu +but none of the items can be selected.</B> +</A> +<P> +Check that the NUMLOCK key is off. It's a +<A HREF="http://www.research.att.com/~erg/graphviz/bugs/b524.html"> +known bug</A>. +<P> +<A name=Q18 HREF=#Q18> +<B>Q18</A>. Why does dotty report a syntax error on a legal dot file?</B> + +<P> +Typically this error is reported as: +<pre> +>> graph parser: syntax error near line 14 +>> context: >>> <<< digraph G { +>> dotty.lefty: giving up on dot +>> dotty.lefty: graph that causes dot +>> dotty.lefty: to fail has been saved in file dottybug.dot +</pre> +Probably there is a command in your shell environment (such as +.alias or .profile) that does output even for non-interactive shells. +When this occurs, those characters go in the pipe to the dot parser +and cause this problem. An easy check is whether other users have +the same problem. +<P> +<A name=Q20> +<B>Q. How can I get some display feature (such +as bold lines) in dotty?</B> +</A> +<P> +<A NAME="dotty_note">Dotty</A> has not really changed for many years. Therefore, there are +myriad features available in Graphviz which it cannot handle. +In some cases, you can use +<A HREF="http://www.research.att.com/~john/Grappa/">Grappa</A> +or <A HREF="http://www.graphviz.org/webdot/">webdot</A> +for display instead of dotty. +For example, Grappa has generalized polygons +(<tt>node [shape=polygon]</tt>) that dotty lacks. +There are additional interactive viewers available. For example, see +<A HREF="http://www.graphviz.org/Resources.php">Graphical Interfaces</A> +and <A HREF="http://www.graphviz.org/About.php">Viewers</A>. If you +are using Mac OS X, the <A HREF="http://www.pixelglow.com/graphviz/">Mac +version</A> of Graphviz has a highly recommended GUI. +<P> +If the display attribute that you need isn't there already, in dotty, +there's probably no easy way to do it except by rolling up +your sleeves and hacking the dotty code (a lefty script) that +interprets and renders graphical attributes. This is problematic +for the same reason as above: there's no universal low-level driver +layer shared across all the Graphviz tools. We recently added an +intermediate rendering language to the layout tools, but the +various front ends don't use it yet. This would be a good project +for someone who wants to get involved here (along with porting +dotty to GTK.) +<P> +<A name=Q21> +<B>Q. How can I get rid of the little circles on +edges ("edge handles") in dotty?</B> +</A> +<P> +Edit the file dotty.lefty and change the +line that says: 'edgehandles' = 1; to 'edgehandles' = 0; +it's around line 110. +<P> +<A name=Q22> +<B>Q. I already have all the coordinates for the +nodes and edges of my graph and just want to +use dot, neato, or dotty to render it. How?</B> +</A> +<P> +Put the graph with layout attributes into a dot +file. + +Then run <tt>neato -s -n2</tt>. For example: +<pre> +neato -s -n2 -Tgif file.dot -o file.gif +</pre> +Note that if an edge does not have a <TT>pos</TT> attribute +defined, neato will perform whatever edge routing it would +normally do. All of the usual backend attributes (<TT>size</TT>, +<TT>overlap</TT>, <TT>page</TT>, etc.) are available. +<P> +<A name=Q23> +<B>Q. I already have all the coordinates for the +nodes, and I want dot or neato to route the edges.</B> +</A> +<P> +It's not really too convenient to use dot for this. +It is possible to use neato for this, +running neato -s -n For example: +<pre> +neato -s -n -Tgif file.dot -o file.gif +</pre> +neato will use the node positions, but use its technique +for routing the edges. There are several things to note. First, +the neato edge router is different from dot's. Without the built-in +top-down bias, it doesn't do as good a job of avoiding edge overlaps +and, at present, it doesn't handle spline multi-edges at all. Second, by +default, neato uses straight lines as edges. To get spline routing, +you have to specify -Gsplines=true. And this will only work if none of +the nodes overlap. Since the input graph supplies fixed node positions, +it is the user's task to insure this. +<P> +<A name=Q24> +<B>Q. I already have all the coordinates for the +nodes and edges of my graph and just want to +use dotty to render it. How?</B> +</A> +<P> +Just run dotty on it. Dotty will use the given pos attributes. +<P> +<A name=Q25 HREF=#Q25> +<B>Q25</A>. Same as above, but I have only node coords, not edges.</B> + +<P> +<tt>neato -n</tt> is some help, but neato doesn't handle +spline-based parallel edges. +<P> +<A name=Q26 HREF=#Q26> +<B>Q26</A>. How can I make client-side image maps?</B> + +<P> +Use the -Tcmap command line option (only version 1.8.9 and beyond!) +<P> +<A name=Q27 HREF=#Q27> +<B>Q27</A>. Why aren't my server-side maps being recognized? I've checked the HTML!</B> + +<P> +Make sure that your server has map files enabled. For example, if running +apache, check that httpd.conf has a line like the following: +<pre> +AddHandler imap-file map +</pre> +and that it is not commented out! +<P> +<A name=Q28> +<B>Q. I've installed Debian Graphviz and it works just fine on the command line, +but when I execute a Perl/CGI script through Apache, no output is generated.</A> +For example, the code +<tt> +system("/usr/local/bin/dot -Tpng /tmp/tree.dot -o /tmp/tree.png"); +</tt> +produces no file <tt>/tmp/tree.png</tt>.</B> +<P> +As best as we can tell, dot dies with no stdout or stderr messages on Debian +systems when run from an Apache cgi program +with no HOME set. The workaround is to provide a HOME directory in the +Apache userid's environment. +<P> +Someone has also suggested using the +<A HREF="http://search.cpan.org/search?query=graphviz&mode=all"> +Perl module for Graphviz</A>. +<P> +<A name=Q29 HREF=#Q29> +<B>Q29</A>. How can I get 3D output?</B> + +<P> +The Graphviz authors have qualms about the gratuitous use of 3D. +<p> +Nonetheless, dot -Tvrml generates VRML files. There's no Z coordinate +layout - you specify Z coords yourself in the <tt>z</tt> attribute of nodes, +and the Z coordinates of edges are interpolated. If someone +contributes a driver for a newer, more useful format (OpenGL Performer +scene graphs? Open Scene Graphs? Java3D programs?) we'd like to try it. +<p> +neato internally supports layouts in higher dimensions through the <tt>dim</tt> +attribute, e.g. <tt>neato -Gdim=7</tt> but there's no way to get the output +unless you invoke neato as a library and inspect ND_pos(n)[i] +where n is a pointer to the relevant node. +This would need some (minor) driver work and a good 7-dimensional viewer. Well, +<tt>dim=3</tt> ought to be possible. + +<H2>Problems</H2> +<A name=Q30 HREF=#Q30> +<B>Q30</A>. How can I avoid node overlaps in neato?</B> + +<P> +Use the graph attribute <A HREF="info/attrs.html#d:overlap"><tt>overlap</tt></A>. +<P> +<A name=Q31 HREF=#Q31> +<B>Q31</A>. How can I avoid node-edge overlaps in neato?</B> + +<P> +Use the <tt>overlap</tt> attribute to leave room among the nodes, then +use <tt>-Gsplines=true</tt>. +<pre> +neato -Goverlap=... -Gsplines=true -Gsep=.1 +</pre> +<P> +The <tt>sep</tt> argument is the node-edge separation as +a ratio of a node's bounding box. That is, <tt>sep=.1</tt> means +each node is treated as though it is 1.1 times larger than it is. +The actual value may require some tinkering. +(Don't ask why this isn't just a constant!) Note that this option really +slows down neato, so should be used sparingly and only +with modest-sized graphs. +<P> +<A name=Q32 HREF=#Q32> +<B>Q32</A>. Neato runs forever on a certain example.</B> + +<P> +First, how big is your graph? Neato is a quadratic algorithm, roughly +equivalent to statistical multidimensional scaling. If you +feed it a graph with thousands of nodes and edges, it can easily take +hours or days. The first thing to check is to run <tt>neato -v</tt> to +get a trace of the output. If the numbers you see are generally +getting smaller, the layout is just taking a long time. You can set +certain parameters, such as <tt>epsilon</tt> or <tt>maxiter</tt> to +shorten the layout time, at the expense of layout quality. But if your +graph is big, who's going to notice? +<P> +If you see +the numbers repeating, or fluctuating up and down, then neato is +cycling, especially if your graph is small. +This should never happen by default for versions later than 1.13. If it +does, please report it as a bug. +<P> +If you are using an earlier version of neato, or you used <tt>mode=KK</tt>, +cycling is indeed possible. This cycling is very sensitive to the +initial layout. By using the <tt>start</tt> attribute, for example, +<pre> +neato -Gstart=3 +neato -Gstart=rand +</pre> +the cycling will most likely disappear. Or you can employ the parameters used +for large graphs to stop the layout earlier: +<pre> +neato -Gepsilon=.01 +neato -Gmaxiter=500 +</pre> +<P> +Note that, if you have a large graph, the generation of edges as splines +is a cubic algorithm, so you would do well to avoid using <tt>splines=true</tt>. +(This commment applies to circo, fdp and twopi as well.) +<P> +<A name=Q33 HREF=#Q33> +<B>Q33</A>. Edge label placement in neato is bad.</b> + +<p> +Difficult problem. We're working on it. +If anyone has some general +label placement code (e.g. a simulated annealer based on the Marks et al. +technique in <I>Graphics Gems IV</I>), please get in touch. +<P> +<A name=Q34 HREF=#Q34> +<B>Q34</A>. Dot runs forever on a certain example.</B> + +<p> +Try dot -v to observe its progress. +<p> +Note that it's possible to make graphs whose layout or even parsing +is quadratic in the input size. For example, in dot, + +<pre> +digraph G { + a -> b -> c -> .... -> x -> y -> z + a -> z + b -> z + c -> z + /* and so on... */ + x -> z +} +</pre> + +The total edge length (therefore the layout time) of +this as a ranked graph is quadratic in the number of nodes. + + +You probably won't encounter the following, but it is also possible +to construct graphs whose parsing takes quadratic time in the number +of attributes, by appending attributes to nodes and edges after the +graph has been loaded. For example: + +<pre> +digraph G { + /* really big graph goes here...with N+1 nodes */ + n0 -> n1 -> ... -> nN; + + n0 [attr0="whatever", + attr1="something else", + /* and so on with many more attributes */ + attrM="something again"] +} +</pre> +When an attribute first appears, each object is visited with possible cost +proportional to the number of previously declared attributes. Thus, +the running time for the above would be <I>cN</I> O(<I>M</I>) +for some constant <I>c</I>. If there is any concern about this, the +graph should specify the attributes first before declaring nodes or +edges. In practice, this problem is neglible. +<P> +<A name=Q34a> +<B>Q. Twopi runs forever on a certain example.</B> +</A> +<p> +Is your graph is large (many thousands of edges), +and did you set <pre>splines=true</pre>? It takes +a lot of cycles to fit all those splines! +<p> +<A name=Q35> +<B>Q. Neato has unnecessary edge crossings, or has missed an +obvious chance to make a much nicer layout.</B> +</A> +<P> +Neato and all similar virtual physical model algorithms rely +on heuristic solutions of optimization problems. The better +the solution, the longer it takes to find. Unfortunately, it +is also possible for these heuristics to get stuck in local +minima. Also, it is heavily influenced by the initial position +of the nodes. It is quite possible that if you run neato again, +but with a different random seed value, +or more iterations, you'll get a better layout. For example: +<pre> +neato -Gstart=5 file.dot -Tps -o file.ps +neato -Gepsilon=.0000001 file.dot -Tps -o file.ps +</pre> +<P> +In particular, note that there are no guarantees that neato will produce +a planar layout of a planar graph, or expose all or most of a graph's +symmetries. +<P> +<A name=Q36 HREF=#Q36> +<B>Q36</A>. Webdot doesn't work.</B> + +<P> +We assume you're using Apache and have <A HREF="http://www.tcl.tk/">TCL</A> installed. +If you don't, it's probably better to just use the +<A HREF="http://www.graphviz.org/Misc/webdot.pl"> +webdot perl script</A>. +<P> +To debug webdot, first test whether <tt>tclsh</tt> can load the +Tcldot shared library. Try: +<pre> +$ tclsh +% load <b>$prefix</b>/lib/graphviz/libtcldot.so.0 +% +</pre> +where <b>$prefix</b> is the installation prefix for graphviz; usually /usr +or /usr/local. +<p> +Then test whether webdot runs from a shell command. (With webdot we provide +a helper script scaffold.tcl or scaffold.sh that sets up an environment +like the one Apache provides.) For example +<pre> +$ scaffold.tcl >out.gif +can't read "LIBTCLDOT": no such variable + while executing +"file mtime $LIBTCLDOT" + invoked from within +"set t1 [file mtime $LIBTCLDOT]" + (file "cgi-bin/webdot" line 67) + invoked from within +"source cgi-bin/webdot +" + (file "scaffold.tcl" line 22) +</pre> +The above is a strong clue that webdot is not configured properly. +<P> +Finally, test whether webdot runs as a cgi-bin program. +It may help to examine the cgi-bin environment using a +simple cgi-bin tcl script like: +<pre> + #!/bin/env tclsh + puts "Content-type: text/plain" + puts "" + foreach e [lsort [array names env]] {puts "$e: $env($e)"} +</pre> +Save this script as .../cgi-bin/test.tcl, make it executable, then +look at: <a href="http://localhost/cgi-bin/test.tcl">http://localhost/cgi-bin/test.tcl</a> +<P> +Also, if you see something like: +<pre> +WebDot Error: + +Response Code = 403 +</pre> +This usually means that webdot ran succesfully, but was not able +to fetch the remote graph from the URL you gave as an argument. +The reason is probably that your server is behind a firewall that +blocks the webdot server, so it cannot get the graph file. +You can either change firewall permissions, put the graph on a +different server, or install webdot locally so you don't need a +remote server to fetch your graph data. +<P> +It would be nice if someone hacked webdot to take the contents +of a graph as a cgi-bin argument, so it wouldn't need +permission to fetch a graph remotely. +This is left as an exercise for the Open Source Community. +<P> +<A name=Q37 HREF=#Q37> +<B>Q37</A>. I have "Font not found" errors, or text labels missing in webdot.</B> + +<P> +Firstly, recent versions of graphviz will use fontconfig if it is available +on your platform. With fontconfig, this error should not occur, so you +may want to see if an upgrade to graphviz is available, or if a rebuild +will add fontconfig support. +<p> +If fontconfig is not available then graphviz tries to resolve fontnames +to fontpaths itself, and uses DOTFONTPATH (or GDFONTPATH) to indicate where it should look. +<p> +For copyright reasons, Graphviz doesn't come with its own fonts. +On a Windows machine, it knows to search in <tt>C:\Windows\Fonts</tt>. +On a Unix machine, you need to set up a directory that contains +Truetype fonts. You can get a copy of some fonts +<A HREF="http://www.graphviz.org/pub/graphviz/webfonts-1.0-5.noarch.rpm">here</A>. +<P> +The default DOTFONTPATH is: +<pre> +#define DEFAULT_FONTPATH "/usr/X11R6/lib/X11/fonts/TrueType:/usr/X11R6/lib/X11/fonts/truetype:/usr/X11R6/lib/X11/fonts/TTF:/usr/share/fonts/TrueType:/usr/share/fonts/truetype:/usr/openwin/lib/X11/fonts/TrueType:/usr/X11R6/lib/X11/fonts/Type1" +</pre> +If your fonts are somewhere else, then you must set that directory in +the webdot script, or recompile Graphviz with the correct DEFAULT_FONTPATH +(or set <tt>fontpath="/your/font/directory"</tt> in every graph you lay out, +but that's pretty clumsy.) +<P> +<A name=Q38 HREF=#Q38> +<B>Q38</A>. My browser doesn't recognize SVG.</B> + +<P> +The correct MIME type for svg images is: <tt>image/svg+xml</tt> (note "+" not "-"). +<P> +SVG is not built into all browsers; you can get +<A HREF="http://www.adobe.com/svg/viewer/install/main.html">plugins</A> +from Adobe for Windows, Linux and some other operating systems. +<A HREF="http://www.mozilla.com/firefox/"> +Firefox 1.5</A> has a <A href="http://developer.mozilla.org/en/docs/SVG_in_Firefox_1.5">large subset of SVG</A> and renders graphviz -Tsvg output +though until graphviz 2.8, the fonts may be too large (thanks for +Phil Colbourne at the RailCorp of New South Wales for this advice). +<P> +For help with embedding SVG in HTML pages, see +<A HREF="http://www.graphviz.org/webdot/svgembed.html">here</A>. +<P> +There are several good standalone viewers and editors for SVG. +We like <A HREF="http://www.inkscape.org">inkscape</A>. +<A HREF="http://www.gnome.org/projects/evince/">evince</A> +is the standard Gnome document viewer that handles SVG, at least +since version 0.5 (though Phil C. reports output is blurred) +(see also <A HREF="">eog</A> (Eye of Gnome)). +Commercial tools like Adobe Illustrator and Microsoft Visio +can import SVG (the better to deal with your content, my dear!) +If you are using an older (less bloated) Unix system, you +may find tools like <A HREF="http://xml.apache.org/batik/">Batik</A> +(an SVG renderer in Java) or <A HREF="http://www.sodipodi.com">sodipodi</A> +useful, though it seems they are no longer very actively maintained. +sodipodi is faster but both make sharp images - isn't that the +beauty of path-based graphics? +<P> +<A name=Q39> +<B>Q39. libexpat is reported as containing a virus or as a security hole. +Is this a real problem?</B> +</A> +<P> +No, this is a false positive reported by various security software. +See <A HREF="http://www.pcreview.co.uk/forums/thread-1689630.php">http://www.pcreview.co.uk/forums/thread-1689630.php</A> or +<A HREF="http://spywareblog.com/index.php/2004/11/24/is_libexpat_dll_spyware">http://spywareblog.com/index.php/2004/11/24/is_libexpat_dll_spyware</A>. +<P> +<A name=Q40 HREF=#Q40> +<B>Q40</A>. What is the coordinate transformation between the graph bb and a .png image?</B> +<OL> +<LI> +The bb is expanded by 4 graph-units in all directions (pad) to allow for finite line widths. +<LI> +Then it is zoomed and/or rotated according to -Gviewport, -Gsize, -Glandscape, -Gorientation options. +At the default scaling of 1:1, one graph unit = 1 point (1/72 inch). +<LI> +Then it is paginated, if requested by -Gpage and if the output format supports it. Not the -Tpng renderer, yet. +<LI> +Then a margin is added, -Gmargin, in absolute units (inches). +The top/bottom margin can be set independently of the left/right margin. +<LI> +Then it is converted to device units, according to -Gdpi, +or a dpi value that is given by the output device, +or a default that is provided by each render. +There are separate dpi values for x and y to allow for non-square pixels. +Some renderers invert the Y axis and need an offset to place the +origin in the top left corner. +The default dpi for -Tpng is 96dpi (approximating the resolution +of most computer monitors) so this is where the scaling by 96/72 (4/3) +comes from. +</OL> +<P> +At the renderer api, plugins have a choice of coordinate representation: +<UL> +<LI> +coordinates in graph-units, and composite transformation data consisting +of: scaling, rotation, and translation. (used by svg, cairo, ps, renderers) +<LI> +coordinates pre-transformed into device units. +</UL> +<P> +<A name=Q41 HREF=#Q41> +<B>Q41</A>. File associations are broken in Mac OSX. Clicking on a <tt>dot</tt> file doesn't open Graphviz.</B> +<p> +The immediate fix is to rebuild the Launch Services database like this: +<p> +1. Trash all other versions of Graphviz.app on your system, except for the just installed one. You can use either of these command lines to find it: +<p> +<pre>locate Graphviz.app</pre> +<p> +or +<p> +<pre>find / -name "Graphviz.app"</pre> +<p> +2. Run the following command line: +<p> +<pre>/System/Library/Frameworks/CoreServices.framework/Versions/A/Frameworks/LaunchServices.framework/Versions/A/Support/lsregister -kill -r / </pre> +<p> +or +<p> +<pre>/System/Library/Frameworks/CoreServices.framework/Versions/A/Frameworks/LaunchServices.framework/Versions/A/Support/lsregister -kill -r /Applications</pre> +<p> +which deletes the Launch Services database and rebuilds it from existing apps. You may need to sudo to do this. +<p> +3. Verify that the Graphviz.app can now open .dot files and Microsoft Word can still open its own .dot files. +<p> +One artifact of this will be that Microsoft .dot files may appear with the Graphviz document icon. Unfortunately there doesn't seem any a priori way of getting the system to determine whether an arbitrary .dot file belongs to Word or Graphviz -- you can choose which application to open with by right-clicking or control-clicking on the document icon and choosing the app. +<p> +As for why the Launch Services database doesn't automatically register Graphviz, +we're not entirely sure but suspect this only happens if both conditions +hold true: +<p> +A. The user had installed Microsoft Word.<br> +B. There is also another version of Graphviz.app present in the system. (Possibly the previous version 1.13 released by Pixelglow Software)<br> +<A name=Q42 HREF=#Q42><B>Q41</A>. What do all these plugin libraries do?</B> +<p> +<ul> + <li><b>lasi</b> - Adds support for UTF8 characters, beyond ASCII, in postScript output. This table provides a feture comparison of the various PosScript renderers: + <table> + <tr> + <th></th> + <th>UTF8</th> + <th>hyperlinks</th> + <th>human readable PostScript</th> + </tr> + <tr> + <th>-Tps:core</th><td>no</td><td>yes</td><td>++</td> + </tr> + <tr> + <th>-Tps:lasi</th><td>yes</td><td>yes</td><td>+</td> + </tr> + <tr> + <th>-Tps:cairo</th><td>yes</td><td>no</td><td>--</td> + </tr> + </table> + <li><b>gs</b> - Provides support for usershapes in PostScript that can be embedded in all output formats. It interprets the format and renders to a cairo surface. Requires a recent version of ghostscript. (Not needed for PostScript in PostScript) + <li><b>rsvg</b> - Provides support for usershapes in SVG that can be embedded in all output formats. It interprets the format and renders to a cairo surface. (Not needed for SVG in SVG) + <li><b>glitz</b> - OpenGL output. But this may not be the right way to dot it, and my glitz plugin is unfinished. Not useful at this time. + <li><b>ming</b> - Flash output. Was sort of working at one point, not now. Needs development. Might be interesting opportunities for a dynamic graph output. Not useful at this time. +</ul> + + +</BODY> +</HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/GVedit.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,305 @@ +<html> + +<head> +<meta http-equiv=Content-Type content="text/html; charset=windows-1252"> +<meta name=Generator content="Microsoft Word 11 (filtered)"> +<title>GVedit Help</title> +<style> +<!-- + /* Style Definitions */ + p.MsoNormal, li.MsoNormal, div.MsoNormal + {margin:0in; + margin-bottom:.0001pt; + font-size:12.0pt; + font-family:"Times New Roman";} +@page Section1 + {size:8.5in 11.0in; + margin:1.0in 1.25in 1.0in 1.25in;} +div.Section1 + {page:Section1;} +--> +</style> + +</head> + +<body lang=EN-US> + +<div class=Section1> + +<p class=MsoNormal align=center style='text-align:center'><b><u>Introduction</u></b></p> + +<p class=MsoNormal style='text-indent:.5in'>GVedit<span style='color:black'> is a +Graphviz tool for creating, viewing, editing and processing DOT files. It +allows users to set attributes of graphs with dialogue boxes and save them +for future use. GVedit users can also take advantage of the simple +instant previewing feature of the program.</span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal align=center style='text-align:center'><b><u><span +style='color:black'>Menu system</span></u></b></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>GVedit's main menu bar +provides basic file options such as <b>Open</b>, <b>Save</b>, <b>Save As</b> and simple editing +operations such as <b>Cut</b>, <b>Copy</b> and <b>Paste</b>. Most of the common commands +also have standard keyboard shortcuts. +Below you will find a list of the menu items and brief information on +each of them.</span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> +<p class=MsoNormal><b><span style='color:black'>File:</span></b></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>New: Creates a blank text +window. You can use it to write a description of a graph in the DOT language. +New graphs are not saved to disk until you save them manually.</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Open: Opens a file +dialogue box to select and load a DOT file into the editor. Each file is opened in a +new window and does not affect other open graphs.</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Save: Saves the active +graph to disk. If the active graph is new and does not have a name, +it opens a <b>Save As</b> dialogue to allow you +to specify the file's name before saving.</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Save as: Opens a <b>Save As</b> +dialogue to save the active graph. You can save a graph under different names but +you will always be working with the most recently saved graph.</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +Close: Closes the active graph window. If there are any unsaved changes, +you get a dialogue box asking if you want to +save the changes before closing the graph.</span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal><b><span style='color:black'>Edit:</span></b></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Undo : This command +rolls back the last thing that you have done within the active graph. </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Cut : Removes selected +text and keeps it in the clipboard. </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Copy: Copies selected +text to the clipboard. </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +Paste: Inserts the text from the clipboard into the cursor position on the +active graph.</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Find: Opens the find +dialogue box to allow you to locate desired text in a graph.</span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> +<p class=MsoNormal><b><span style='color:black'>Graphviz</span></b></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Run: Processes the active +graph with the last attributes that were used for it. If the active graph has +never been processed before, it opens the settings window.</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Settings: Opens the +settings window. Details of this window are given below. +</span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> +<p class=MsoNormal><b><span style='color:black'>View</span></b></p> +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Cascade: +Sets the style for handling multiple windows to cascade, in which each +new window overlaps the previous one. +</span></p> +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Tile: +Sets the style for handling multiple windows to tiling, in which the +open graph windows partition the root window. +</span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> +<p class=MsoNormal><b><span style='color:black'>Help</span></b></p> +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +Help: Opens a manual page describing how to use this program. +</span></p> +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +Graphviz on net: Opens a browser window on the Graphviz home page. +</span></p> +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +About GVedit: Provides version information about the program. +</span></p> + +<p class=MsoNormal align=center style='text-align:center'><b><u><span +style='color:black'>Tool bar</span></u></b></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>Under the menu bar, +there is a toolbar with five buttons on it. These buttons are nothing but shortcuts +to some menu items. Each button has a tool tip attached to it to +indicate its purpose.</span></p> + +<p class=MsoNormal align=center style='text-align:center'><b><u><span +style='color:black'><span style='text-decoration:none'> </span></span></u></b></p> + +<p class=MsoNormal align=center style='text-align:center'><b><u><span +style='color:black'>Working with multiple graphs; the active graph</span></u></b><span +style='color:black'>.</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +GVedit allows you to have multiple graphs open at the same time. At any +time, there is a single active graph. +Anything you do using either the GVedit menus or the settings window affects the +active graph. When you go to the settings window, you always see the +settings of active graph. +The settings of each graph are maintained separately. +</span></p> +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +To make a graph active, click on its window. +The background of the active graph window is white +while all others are gray. +</span></p> + +<p class=MsoNormal align=center style='text-align:center'><b><u><span +style='color:black'><span style='text-decoration:none'> </span></span></u></b></p> +<p class=MsoNormal align=center style='text-align:center'><b><u><span +style='color:black'>Settings window</span></u></b></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +With the settings window, +you can select the layout engine, set the output type and file, +and set any attributes that are available with the Graphviz release +installed on your computer.</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'><b>Layout engine:</b> +Graphviz provides five different graph layouts. You can select +the one which best fits your needs. More information about these layouts +can be found at the <A HREF="http://www.graphviz.org">Graphviz home page</A> +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'><b>Output file name:</b> +This field allows you to specify the output file name. +You can either type in the name, or use the +small button by the text box to open a file +dialogue box. +This field corresponds to the –o option of Graphviz. +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><b><span style='color:black'>Output +file type:</span></b><span style='color:black'> This drop down list allows +you to select the output file type. The output file name's extension is changed +accordingly. +This field corresponds to the –T option of Graphviz. +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><b><span style='color:black'>Graphviz +bin folder:</span></b><span style='color:black'>You usually do not need to +enter anything here. During the Graphviz installation, locations of the Graphviz +files are stored in the registry and GVedit retrieves it from there. If, +for some reason, Graphviz files are not where they are supposed to be +and GVedit can not +find them, you can edit this information manually. +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><b><span style='color:black'> +Preview Output file:</span></b><span style='color:black'> +If this box is checked, +GVedit creates a low-quality preview of the output and shows it in a +separate window. It is a convenient way to preview the results without +executing another viewing program. +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><b><span style='color:black'> +Apply cairo filter:</span></b><span style='color:black'> +If this output format is supported by the cairo library, this box becomes +active. If checked, output will be generated using the cairo library +rather than the default renderer. +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><b><span style='color:black'>Attributes:</span></b><span style='color:black'> +On the lower half of the settings window, you will see two dropdown lists, +one small text box, and a larger text editing area. GVedit processes the +contents of the large box as additional, generic graph, node or edge attributes. +(These correspond to the –G, –N, and –E command-line options of Graphviz.) +You can either type directly in the large text box, +or use the drop down lists to pick +an allowed scope and one of the common attribute names. +For details of these attributes, refer to the +<A HREF="http://www.graphviz.org/doc/info/attrs.html">page of Graphviz attributes.</A> +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +If you specify an attribute using the drop down lists, you need to click on the +<b>Add</b> button to register the attribute. +If you do not click on +<b>Add</b> button, the attribute that you entered will not be +added to the large box, hence will not be processed. +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'>As an +example, assume that we would like to have a graph drawn +with a green background. +First, click on the scope list and select <b>graph</b>; +then click on the name list and +select <b>bgcolor</b>; and then type <tt>green</tt> to the value box. +Finally, click on the <b>Add</b> button to send the attribute to large box. +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +Belowe the <b>Add</b> button is a <b>Help</b> button. Clicking on this +will open a browser window on the Graphviz page describing the supported +attributes. +</span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> +Under the large box, there are 3 small buttons, plus +<b>Cancel</b> and <b>OK</b> buttons. The first button is used to +clear the attributes box; +the second one is used to load an existing text file into the attributes box; +and the last one is used to save the attributes to a text file for later use. +The <b>Cancel</b> button closes the window without processing anything; +the <b>OK</b> button processes the +active document file with the settings on the screen and closes the settings +window. If you check the preview check box, you will see the preview of the +graph layout on the screen.</span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal align=center style='text-align:center'><b><u><span +style='color:black'>Console window</span></u></b></p> + +<p class=MsoNormalstyle='text-indent:.5in'><span style='color:black'> +At the bottom of the +main window, there is a console window where you can see the results of your +actions. This provides a trace of your layout actions, showing the +Graphviz command corresponding to the active graph's settings. It also +displays warnings from GVedit, as well as the output of the +executed Graphviz programs. +By right clicking on the console window, you will get a menu providing some +additional functions, such as clearing or saving the console logs.</span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> </span></p> + +<p class=MsoNormal style='text-indent:.5in'><span style='color:black'> </span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +<p class=MsoNormal><span style='color:black'> </span></p> + +</div> + +</body> + +</html>
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/build.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,382 @@ +<html> +<head> +<title>graphviz build notes</title> +</head> +<body bgcolor=#ffffff> +<h1>graphviz build notes</h1> +<h2>External packages</h2> +The full build requires external libraries that you must +obtain elsewhere. Most, if not all of these are optional, +and configure will build graphviz with reduced functionality +if an external library is not found. Newer versions of these +libraries should be fine - if not, please let us know. +<ul> +<li>GD (generic raster graphics driver for PNG, GIF, JPEG) +<a href="http://www.boutell.com/gd/http/gd-2.0.28.tar.gz"> +http://www.boutell.com/gd/http/gd-2.0.28.tar.gz</a> +<br>Graphviz also contains a copy of GD 2.0.4, but we will +remove it eventually so the external library is preferred. +<li>ZLIB (raster image compression) +<a href="http://www.zlib.net/zlib-1.2.3.tar.gz"> +http://www.zlib.net/zlib-1.2.3.tar.gz</a> +<br> +NB: Use "./configure -s" to build shared libraries. (See notes in zlib-1.2.3/Makefile) +<li>FREETYPE (renders text in raster graphics formats) +<a href="http://prdownloads.sourceforge.net/freetype/freetype-2.1.3.tar.gz"> +http://prdownloads.sourceforge.net/freetype/freetype-2.1.3.tar.gz</a> +<li>PNG (driver for Portable Network Graphics raster format) +<a href="http://prdownloads.sourceforge.net/libpng/libpng-1.2.5.tar.gz"> +http://prdownloads.sourceforge.net/libpng/libpng-1.2.5.tar.gz</a> +<li>JPEG (a popular raster format with lossy compression, not critical here) +<a href="ftp://ftp.uu.net/graphics/jpeg/jpegsrc.v6b.tar.gz"> +ftp://ftp.uu.net/graphics/jpeg/jpegsrc.v6b.tar.gz</a> +<li>EXPAT (XML parser) +<a href="http://prdownloads.sourceforge.net/expat/expat-1.95.5.tar.gz"> +http://prdownloads.sourceforge.net/expat/expat-1.95.5.tar.gz</a> +<li>GETTEXT (gcc compatibility library) +<a href="http://ftp.gnu.org/gnu/gettext/gettext-0.11.5.tar.gz"> +http://ftp.gnu.org/gnu/gettext/gettext-0.11.5.tar.gz</a> +<li>ICONV (string converstion for text internationalization) +(Many systems already provide iconv in glibc. Check to see if you already +have a working /usr/bin/iconv before installing this extra package.) +<br> +<a href="http://ftp.gnu.org/gnu/libiconv/libiconv-1.8.tar.gz"> +http://ftp.gnu.org/gnu/libiconv/libiconv-1.8.tar.gz</a> +<li>TCL (a command interpreter for the tcldot GUI) +<a href="http://prdownloads.sourceforge.net/tcl/tcl8.3.5-src.tar.gz"> +http://prdownloads.sourceforge.net/tcl/tcl8.3.5-src.tar.gz</a> +<li>LIBTOOL (generic library support) +<a href="ftp://ftp.gnu.org/pub/gnu/libtool/libtool-1.5.22.tar.gz"> +ftp://ftp.gnu.org/pub/gnu/libtool/libtool-1.5.22.tar.gz +</a> +</ul> +<P> +These libraries (either static or dynamic) and their +interface header files must already be on your system. +It's OK if you install your own copies of these packages, +though you may need to tell make or configure where to find them. +Run "configure --help" for details. +<P> +On most Linux distributions these packages are already installed +or installable from packages included in the distribution. +You might need to install some -devel packages to get the include +header files. + +<h2>Abbreviated Build Instructions (GNU tools)</h2> + +<p>If you are using GNU tools, the recommended method for building +graphviz is the usual: +<pre> + + ./configure + make + make install +</pre> + +<p>If you are building from CVS sources, then you must have +recent versions of "libtool", "automake", and "autoconf". +Build with: +<pre> + + ./autogen.sh + ./configure + make + make install +</pre> + +<p>If you are not building with GNU tools, then there are some +tried-and-true old-style Makefiles that can be used instead. +Details below. + +<h2>Detailed Build Instructions</h2> + +<P> +There are a handful of preprocessor defines that control the +image driver configuration. These preprocessor defines need to +agree with the libraries used in the Makefile. +<p> +The generic raster driver is <tt>gd</tt> and it can be configured to +generate GIF (no compression), PNG (lossless compression), JPEG +(lossy compression), and wireless bitmap (WBMP) files. +The compressors all need <tt>zlib</tt>. In the current build we +have a top-level config.h file that defines various symbols, such as: +<P> + HAVE_JPEG<br> + HAVE_PNG<br> +<P> +If you don't enable the <tt>gd</tt> based drivers, graphviz will +still have the <tt>printf</tt> style drivers for Postscript and SVG. +(However, even these benefit from better text size estimation via +Freetype, when it is available.) +<P> +<h2>Build tools for Unix</h2> +You have several choices. +<P> +1. (Recommended for Linux) Use configure generated by GNU autoconf. +This often works well on a vanilla Linux distribution with +Tcl/Tk, freetype-devel and libjpeg already installed by root +under /usr. Probably works OK with Solaris, too. +Otherwise some adjusting of command line arguments +to <tt>configure</tt> will be needed. +For Linux you can also just pick up the source tarball +or RPMs <A HREF="http://www.graphviz.org/pub/graphviz/">here</A>. +<P> +First, if you are using sources from CVS, run "./autogen.sh" +to generate the "configure" script. If you are using sources +from graphviz-<version>.tar.gz this autogen.sh step should not be +needed. +<p> +Next, run configure. For help on possible configure options, run: +<br> +<pre> +./configure --help +</pre> +<p> +For example, I use <br> +<pre> +./configure --prefix=$HOME/arch/sgi.mips3 \ + --with-freetypeincludedir=$HOME/arch/$ARCH/include/freetype2 \ + --with-freetypelibdir=$HOME/arch/$ARCH/lib \ + --with-tcl=/usr/common/tcl8.3.3 </pre> +<P> +Obviously you would change the pathnames to reflect your installation. +<p> +Note that the directory ${prefix}/include is automatically searched +for headers, and ${prefix}/lib for libraries. +<P> +If you have problems with one or more of the optional script language bindings, +they can be disabled with e.g. --disable-perl. +<p> +2. (Recommend for other Unix platforms.) As above, you need +the external packages to be installed somewhere. +<ul> +<li> +Run configure.old from the root graphviz directory. +<li> +Edit Config.mk for your architecture, tools, and installation directory. +In particular, set the ARCH make variable. +<li> +If desired, check settings in makearch/$(ARCH). +See below for further notes on individual platforms. +<li> +Run make. +</ul> +<pre> +make +make install +make clean +</pre> + +<P> +3. (Recommended for Dave Korn and Eleftheris Koutsofios) Use AT&T/Lucent +<A HREF="http://www.research.att.com/sw/tools/reuse/packages/astkit.html">nmake</A>. +<P> +You have to install the AST tools first. +<P> +Then edit Makeargs in the top source directory appropriately +and <tt>nmake install</tt>. Some fiddling with X11 libs is probably +necessary to compile 'lefty'. This usually involes editing +$INSTALLROOT/lib/lib/Xt or X11 (or less portably, edit nmakefile). +You may also need... +<pre> +export PACKAGE_tcl=/where/tcl/is/installed +</pre> +<P> +When nmake works, it's absolutely great, but it's usually +some work to get the environment right including the +actions of tools like <tt>iffe</tt> (the AST equivalent +of what GNU <tt>configure</tt> does). If you're Lefty +or Dave Korn, you already knew that. +<P> +<h2>Build tools for win32</h2> +<p>See separate +<A HREF="http://www.graphviz.org/pub/scm/graphviz2/doc/winbuild.html"> +build notes</A> for Microsoft Windows (in native mode). +<p> +With some persistence you can likely get Graphviz to build on +<A HREF="http://www.research.att.com/sw/tools/uwin">UWIN</A> +or <A HREF="http://www.cygwin.com/">Cygwin</A> installed on +Microsoft Windows. You will still need various third-party packages +to enable all the Graphviz drivers. + +<!-------------------------------------------------------------------------- +Currently we build graphviz on Windows using the MS Visual C++ compiler only. +The project and makefiles can be found in both the CVS tree and the source +distribution package in the $ROOT/windows directory. ($ROOT refers +to the directory where the source tree has been installed.) +<p> +Follow the step-by-step procedure given below: +<p> +<ol> +<li>Open the WSH script file $ROOT/windows/winmake.wsf using a text editor +and set the "vc" variable to the location of MS VC++ on your machine.</li> +<p> +<li>Download the third-party libraries/header files from +<a href=http://www.research.att.com/sw/tools/graphviz/dist/third-party.zip>http: +//www.research.att.com/sw/tools/graphviz/dist/third-party.zip</a>. +Unzip the file in $ROOT directory.</li> +<p> +<li>Execute the $ROOT/windows/winmake.wsf file by double-clicking on the +file within Windows Explorer.</li> +<p> +<li>If the script throws errors, you'll need to open the graphviz project in +$ROOT/makearch/win32/static/graphviz.dsw using the MS Visual Studio. +Switch to the "File View" within the MS Visual Studio and trigger project +builds individually.</li> +<p> +<li>Follow step 4 for building tools in +$ROOT/makearch/win32/static/tools.dsw.</li> +</ol> + +<P> +If you're ambitious, we have also used nmake from +<A HREF="http://www.research.att.com/sw/tools/uwin"> +AT&T UWIN</A> to build for both the UWIN environment +and the native win32 platform. +<pre> + export nativepp=-1 <br> + nmake CC=ncc MSWIN32==1 +</pre> +(the nativepp thing seems fixed in UWIN 2.25). +<p> +Obviously you need to have native versions +of zlib, libpng, jpeg and freetype. (See step 2 above. Or build them +yourself; see notes below.) +<P> +If you're running UWIN, of course, you can always just go +with either the old binaries of graphviz for UWIN +(from Dave Korn's <A HREF="http://www.research.att.com/sw/tools/uwin">UWIN +site </A>) or build graphviz as a UWIN program using nmake or gmake. +<br> +<P> +More win32 notes... +<il> +<P> +<li> +Add extra fluff to globals.h to pull in astwin32.h but #undef _UWIN +when compiling with CC=ncc. +</li> +<P> +<li> +MSVC 5.0 C compiler may die when -O is invoked on neato/splines.c; +this required nmakefile CC.NOOPTIMIZE. +</li> +<P> +<li> +Get cygwin versions of: zlib, png, jpeg-6b (required minor +source mods), freetype.1.3.1. +</li> +<P> +<li> +Build zlib by <tt>configure CC=ncc --prefix=/home/users/north/src/usr</tt> +then <tt>gmake install</tt> +</li> +<P> +<li> +Build png by copying scripts/makefile.gcc to makefile and editing slightly +then gmake install +</li> +<P> +<li> +Build jpeg same as zlib: <tt> gmake install-lib install-headers </tt> +</li> +<P> +<li> +Needed to hand-craft a lib/lib/z to go with our libz.a in order +to not unintentionally pull in /usr/lib/ast.lib (because of mistakenly +binding the lib/lib/z that goes with /usr/lib/z.lib). +</li> +<P> +<li> +-----------------------------------------------------------> +We haven't tried to build tcldot or webdot in win32. +</li> +<h2>Unix platform-specific build notes</h2> +<h2>AIX</h2> +Matt Fago tells us that "--enable-shared=no" is required or the +executables segfault after seemingly correct compiles. (Bug #421) +<h2>SGI</h2> +The default Irix libjpeg is obsolescent. We need at least +version 62. It wasn't at all clear to me where to get sources for +this - eventually I found the source for v61 and patches for v62 +using a search engine. We may eventually remove JPEG support +(lossy compression seems good for photos, not technical diagrams) +so if you can't get this to work, the loss of -Tjpeg is no big deal. +<P> + +We found that on some platforms (Solaris?) +gd/dotneato and freetype MUST be compiled with the +same C compiler (e.g. gcc, or the native cc). +Otherwise there are are weird stack argument errors in +the call to TT_Open_Face. I don't have time to try +to figure out what's wrong. + +<h2>Solaris</h2> +Put /usr/ccs/bin in PATH + +<h2>HP-UX</h2> +The X11 package must include /usr/contrib/X11R6 +as well as the base stuff. For some reason GNU autoconf doesn't seem to find +X11 in this location so you may need to add the following to the +./configure line: +<pre> +--with-Xawincludedir=/usr/contrib/X11R6/include --with-Xawlibdir=/usr/contrib/X11R6/lib +</pre> + +<h2>Apple Mac OS/X</h2> +<P> +Currently, PixelGlow (Glen Low) provides a full featured +<A HREF="http://www.pixelglow.com/graphviz/"> +port of Graphviz</A> including a very nice Aqua-based graph +document container. Highly recommended. We merged his patches +into our source, but we don't build or test on OS X. Talk to Glen. +<P> +The following are old pre-Pixelglow build notes: +<P> +dot and neato build with old make. (Didn't bother with tcldot.) +<P> +There could still be problems with case-sensitive names, +though we fixed all the ones we could find. +<P> +It's weird that Mac OSX declares lrand48() but it's not +defined in any of the standard system library functions. + +<h2>GNU style building</h2> + +<pre> +<!-- +To produce graphviz-(ver).tar.gz from CVS sources. + + cvs checkout graphviz + cd graphviz + ./autogen.sh + make dist +--> +To produce graphviz-(ver).tar.gz from GIT sources using cogito. + cg clone http://www.graphviz.org/pub/scm/graphviz2/.git + cd graphviz2 +or to update from GIT + cd graphviz2 + cg update +then + ./autogen.sh + make dist + +(Cogito & GIT are available from http://www.kernel.org/pub/software/scm/ +or as binary packages in Fedora-Extras ) + +To build source and binary rpms (results are left in /usr/src/redhat/ ): + + rpm -ta graphviz-(ver).tar.gz + +To build locally: + + zcat graphviz-(ver).tar.gz | tar xfvo - + cd graphviz-(ver) + ./configure + make + make install +</pre> + +</body> +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/char.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ +<html> +<head> +<title>Character Set Reference</title> +</head> +<body bgcolor=white> +<h1>Character Set Reference</h1> +<img src="Gdtclft2.2.5.example.png" + alt="character glyphs"> +</body></html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/gd.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5593 @@ +<!-- REMEMBER TO EDIT index.html.source --> +<head> +<TITLE>gd 2.0.34</TITLE> +</head> +<body bgcolor="#FFFFFF"> +<!-- BANNER HERE --> +<H2>gd 2.0.33</H2> +<H3>A graphics library for fast image creation</H3> +<H3>Follow this link to the +<A HREF="http://libgd.org">latest version +of this document</A>.</H3> +<blockquote> +<strong>UPGRADING UNIX USERS: READ THIS FIRST!</strong> +Modern versions of gd install by default to /usr/local/lib and +/usr/local/include. If you already have an older version of gd +in /usr/lib and /usr/include, you may wish to use: +<pre> +./configure --prefix=/usr</pre> +To ensure that your +new installation overwrites the old. +<p> +<strong>GIF support has been restored in gd 2.0.28 and above.</strong> +The well-known patents on LZW compression held by Unisys +have expired in all countries. British Telecom and IBM may hold related +patents but have never chosen to require royalties for GIF applications, +to the best of my knowledge. I am not a lawyer and cannot give +legal advice regarding this issue. PNG remains a superior format especially +if lossless truecolor images are needed. +<p> +When building from soruce, gd 2.0.33 <strong>requires</strong> that the +following libraries also be installed, in order to produce the related +image formats. The win32 binary release (bgd) already contains the +appropriate libraries. +You may skip libraries associated with formats you do not use: +<p> +libpng (see the <a href="http://www.libpng.org/pub/png/">libpng home page</a>), if you want PNG +<p> +zlib (see the <a href="http://www.info-zip.org/pub/infozip/zlib/">info-zip home page</a>), if you want PNG +<p> +jpeg-6b or later, if desired (see the <a href="http://www.ijg.org/">Independent JPEG Group home page</a>), if you want JPEG +<p> +If you want to use the TrueType font support, you must also +install the <strong>FreeType 2.x library</strong>, including +the header files. See the <a href="http://www.freetype.org/">Freetype +Home Page</a>, or <a href="http://freetype.sourceforge.net/">SourceForge</a>. +No, I cannot explain why that site is down on a particular day, and no, I +can't send you a copy. +<p> +If you want to use the Xpm color bitmap loading support, you must also +have the X Window System and the Xpm library installed (Xpm is often +included in modern X distributions). Most of the time you won't +need Xpm. +<p> +Please read the documentation and install the required libraries. +Do not send email asking why <code>png.h</code> is not found. +Do not send email asking why <code>libgd.so</code> is not found, either. +See the <a href="#required">requirements section</a> for more +information. Thank you! +</blockquote> +<H3>Table of Contents</H3> +<UL> +<LI><A HREF="#notice">Credits and license terms</A> +<LI><A HREF="#whatsnew2.0.34">What's new in version "XYZ" of GD?</A> +<LI><A HREF="#whatis">What is gd?</A> +<LI><A HREF="#gdother">What if I want to use another programming language?</A> +<LI><A HREF="#required">What else do I need to use gd?</A> +<LI><A HREF="#getgd">How do I get gd?</A> +<LI><A HREF="#buildgd">How do I build gd?</A> +<LI><A HREF="#basics">gd basics: using gd in your program</A> +<LI><A HREF="#webpng">webpng: a useful example</A> +<LI><A HREF="#reference">Function and type reference by category</A> +<LI><A HREF="#gdformat">About the additional .gd image file format</A> +<LI><A HREF="#informing"><strong>Please</strong> + tell us you're using gd!</A> +<LI><A HREF="#support">How do I get support?</A> +<LI><A HREF="#issues">How do I report issues, bugs or features request?</A> +<LI><A HREF="#index">Alphabetical quick index</A> +</UL> +<P><A HREF="http://www.libgd.org/"> +Up to the <EM>LibGD Homepage</EM></A> +<A NAME="notice"><H3>Credits and license terms</A></H3> +<P> +In order to resolve any possible confusion regarding the authorship +of gd, the following copyright statement covers all of the authors +who have required such a statement. <strong>If you are aware of any oversights +in this copyright notice, please contact <A HREF="http://libgd.org/Contact">Pierre-A. Joye</A> who will be +pleased to correct them.</strong> +<pre> +COPYRIGHT STATEMENT FOLLOWS THIS LINE +</pre> +<blockquote> + +Portions copyright 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004 by Cold Spring +Harbor Laboratory. Funded under Grant P41-RR02188 by the National +Institutes of Health. +<P> +Portions copyright 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004 by Boutell.Com, Inc. +<p> +Portions relating to GD2 format copyright 1999, 2000, 2001, 2002, 2003, 2004 Philip Warner. +<p> +Portions relating to PNG copyright 1999, 2000, 2001, 2002, 2003, 2004 Greg Roelofs. +<p> +Portions relating to gdttf.c copyright 1999, 2000, 2001, 2002, 2003, 2004 John Ellson (ellson@graphviz.org). +<p> +Portions relating to gdft.c copyright 2001, 2002, 2003, 2004 John Ellson (ellson@graphviz.org). +<p> +Portions copyright 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007 Pierre-Alain Joye (pierre@libgd.org). + +<p> +Portions relating to JPEG and to color quantization copyright 2000, 2001, 2002, 2003, 2004, Doug Becker and copyright (C) 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004 Thomas G. Lane. This software is based +in part on the work of the Independent JPEG Group. See the file +README-JPEG.TXT for more information. +<p> +Portions relating to GIF compression copyright 1989 by Jef +Poskanzer and David Rowley, with modifications for thread safety +by Thomas Boutell. +<p> +Portions relating to GIF decompression copyright 1990, 1991, 1993 +by David Koblas, with modifications for thread safety by +Thomas Boutell. +<p> +Portions relating to WBMP copyright 2000, 2001, 2002, 2003, 2004 Maurice Szmurlo and Johan Van +den Brande. +<p> +Portions relating to GIF animations copyright 2004 Jaakko Hyvätti (jaakko.hyvatti@iki.fi) +<p> +<strong>Permission has been granted to copy, distribute and modify gd in any +context without fee, including a commercial application, provided that this notice +is present in user-accessible supporting documentation.</strong> +<p> +This does not affect your ownership of the derived work itself, and the intent +is to assure proper credit for the authors of gd, not to interfere +with your productive use of gd. If you have questions, ask. +"Derived works" includes all programs that utilize the library. +Credit must be given in user-accessible documentation. +<p> +<strong>This software is provided "AS IS."</strong> +The copyright holders disclaim all warranties, either express or implied, +including but not limited to implied warranties of merchantability and +fitness for a particular purpose, with respect to this code and accompanying +documentation. +<p> +Although their code does not appear in the current release, the authors +also wish to thank Hutchison Avenue Software Corporation for their +prior contributions. +</blockquote> +<pre> +END OF COPYRIGHT STATEMENT +</pre> +<A NAME="whatis"><H3>What is gd?</H3></A> +<P> +gd is a graphics library. It allows your code to quickly +draw images complete with lines, arcs, text, multiple +colors, cut and paste from other images, and flood fills, and +write out the result as a PNG or JPEG file. This is particularly +useful in World Wide Web applications, where PNG and JPEG are two +of the formats accepted for inline images by most browsers. +<P> +gd is not a paint program. +If you are looking for a paint program, you are looking in +the wrong place. If you are not a programmer, you are looking +in the wrong place, unless you are installing a required +library in order to run an application. +<P> +gd does not provide for every possible desirable graphics +operation. It is not necessary or desirable for gd to become +a kitchen-sink graphics package, but version 2.0 does include +most frequently requested features, including both truecolor and +palette images, resampling (smooth resizing of truecolor images) +and so forth. +<P> +<A NAME="gdother"><H3>What if I want to use another programming +language?</h3></A> +Not all of these tools are necessarily up to date and fully compatible +with 2.0.33. +<h4>PHP</h4> +A variant of gd 2.x is included in PHP 4.3.0. It is also possible +to patch PHP 4.2.3 for use with gd 2.0.33; see the +<a href="http://www.libgd.org/">gd home page</a> for a link to +that information. It would be a Good Idea to merge all of the things +that are better in mainstream gd and all of the things that are +better in PHP gd at some point in the near future. +<h4>Perl</h4> +gd can also be used from Perl, courtesy of +Lincoln Stein's +<a href="http://stein.cshl.org/WWW/software/GD/"> +GD.pm</a> library, which uses gd as the basis for a set of +Perl 5.x classes. Highly recommended. +<h4>OCaml</h4> +gd can be used from OCaml, thanks to +<a href="http://gd4o.sourceforge.net/">Matt Gushee's GD4O project</a>. +<h4>Tcl</h4> +gd can be used from Tcl with John Ellson's +<a href=http://www.graphviz.org/pub/>Gdtclft</a> +dynamically loaded extension package. +<h4>Pascal</h4> +Pascal enthusiasts should look into the +<a href="http://www.freepascal.org/">freepascal</a> project, a +free Pascal compiler that includes gd support. +<h4>REXX</h4> +A +<a href="http://www.lightlink.com/hessling/RexxGd/index.html">gd interface +for the REXX language</a> is available. +<h4>Any Language</h4> +The "fly" interpreter performs gd operations specified in a text file. +You can output the desired commands to a simple +text file from whatever scripting language you prefer to use, then +invoke the interpreter. +<p> +<ul> +<li><a href="http://martin.gleeson.com/fly/">fly</a>, by Martin Gleeson +</ul> +<p> +<A NAME="whatsnew2.0.34"><h3>What's new in version 2.0.34?</h3></a> +<p> +From 2.0.34 and later, please check the ISSUES and ChangeLog as well as +the releases announcements. +<p> +<A NAME="whatsnew2.0.33"><h3>What's new in version 2.0.33?</h3></a> +<p> +Version 2.0.33 restores compatibility with older releases +of Freetype 2.x in addition to the latest release. Thanks to +John Ellson and the graphviz project. +<p> +<A NAME="whatsnew2.0.32"><h3>What's new in version 2.0.32?</h3></a> +<p> +Version 2.0.32 restores correct detection of Unicode character sets +for freetype fonts, which repairs a bug that prevented umlauts from +displaying properly. Thanks to John Ellson and the graphviz project. +Also, version 2.0.32 builds all test programs +smoothly in the absence of libpng. +<p> +<A NAME="whatsnew2.0.31"><h3>What's new in version 2.0.31?</h3></a> +<p> +A minor type naming conflict prevented bgd.dll from compiling, and it +was left out of the distribution as a result. This has been corrected. +<p> +<A NAME="whatsnew2.0.30"><h3>What's new in version 2.0.30?</h3></a> +<p> +2.0.29 did not compile correctly when freetype was not available. +This has been corrected. Thanks to Alessandro Ranellucci. +<p> +<A NAME="whatsnew2.0.29"><h3>What's new in version 2.0.29?</h3></a> +<p> +<ul> +<li>A 32-bit multiplication overflow vulnerability reported on +the Bugtraq mailing list has been corrected, along with a number +of similar issues. These bugs come into play only when attempting +to deal with images with <i>extremely large</i> dimensions. +The relevant functions now fail gracefully when such extreme +parameters are specified. The code in question is also +correct for systems with larger bit depths. Thanks to Phil Knirsch, +Alan Cox and infamous41md. +Since exploits are theoretically possible, upgrading is recommended. +<li>Support for the fontconfig library, when available. +When fontconfig is available and gdFTUseFontConfig(1) has been invoked +or the gdFTEX_FONTCONFIG flag has been set for a particular call, fontconfig +patterns can be used to fetch the best available font. +For instance, "arial:bold:italic" does the right thing (or as close as +the available fonts permit). Also, standard +PostScript font names can be mapped to an appropriate font by +gdImageStringFTEx and relatives. +When fontconfig is available <tt>gdlib-config --features</tt> will list +the GD_FONTCONFIG feature. For more information about fontconfig, see the +<a href="http://freedesktop.org/software/fontconfig">fontconfig pages</a>. +<p> +The actual resolved font filename can be returned in the gdFTStringExtra +structure as the fontpath element if the gdFTEX_RETURNFONTPATHNAME flag +is set. Also, a +vector of character position advances can be retrieved if gdFTEX_XSHOW is set +in the flags element. .afm files (font metrics) are now used to adjust size +calculations +when available. When fontconfig is not available, gd falls back to its +usual behavior and requires a specific font file name. One can +still fetch fonts by filename when gdFTUseFontConfig(1) is in effect, by +setting the gdFTEX_FONTPATHNAME flag +in the flag element of the gdFTStringExtra structure. Thanks to +Dag Lem and John Ellson. +<li>Additional freetype fixes: fixed width fonts are now the right +size, horizontal advance calculations now better match the +PostScript equivalent, and various compiler warning fixes. Also, +a fix to the encoding table selection in the was made, addressing a problem +with latin1 font encodings. Thanks to Dag Lem and John Ellson. +<li>Improved tolerance when reading JPEG files containing some garbage as well +as valid image data. +<li>Easier compilation on Windows: no errno.h in gd_gd2.c. +<li>Support for creating optimized GIF animations has been added + by Jaakko Hyvätti. See + <A HREF="#gdImageGifAnimAdd">gdImageGifAnimAdd</A>, + <A HREF="#gdImageGifAnimAddCtx">gdImageGifAnimAddCtx</A>, + <A HREF="#gdImageGifAnimAddPtr">gdImageGifAnimAddPtr</A>, + <A HREF="#gdImageGifAnimBegin">gdImageGifAnimBegin</A>, + <A HREF="#gdImageGifAnimBeginCtx">gdImageGifAnimBeginCtx</A>, + <A HREF="#gdImageGifAnimBeginPtr">gdImageGifAnimBeginPtr</A>, + <A HREF="#gdImageGifAnimEnd">gdImageGifAnimEnd</A>, + <A HREF="#gdImageGifAnimEndCtx">gdImageGifAnimEndCtx</A>, and + <A HREF="#gdImageGifAnimEndPtr">gdImageGifAnimEndPtr</A>. +<li><A HREF="#gdImageOpenPolygon">gdImageOpenPolygon</A> has been + added to allow consecutive line segments to be drawn without + connecting the end points to form a closed polygon. Thanks to +Jaakko Hyvätti. +<li>Better alpha channel blending when the destination color +contains an alpha channel. Also, quicker handling of the +most common cases. Thanks to Frank Warmerdam. +</ul> +<P> +<A NAME="whatsnew2.0.28"><H3>What's new in version 2.0.28?</H3></A> +<P> +<ul> +<li>GIF support has been restored. See +<a href="#gdImageGif">gdImageGif</a>, +<a href="#gdImageGifCtx">gdImageGifCtx</a>, +<a href="#gdImageGifPtr">gdImageGifPtr</a>, +<a href="#gdImageCreateFromGif">gdImageCreateFromGif</a>, +<a href="#gdImageCreateFromGifCtx">gdImageCreateFromGifCtx</a>, +and <a href="#gdImageCreateFromGifPtr">gdImageCreateFromGifPtr</a>. +These functions are now thread-safe, similar to the PNG and JPEG +manipulation functions. +<li>The new <a href="#gdImageCreatePaletteFromTrueColor">gdImageCreatePaletteFromTrueColor</a> function is identical to <a href="#gdImageTrueColorToPalette">gdImageTrueColorToPalette</a>, except that it returns a new image rather than permanently modifying the original. +</ul> +<P> +<A NAME="whatsnew2.0.27"><H3>What's new in version 2.0.27?</H3></A> +<P> +<ul> +<li>In gd 2.0.26, there was potential for out of bounds fills, and therefore +crashes, in the horizontalLine function used by gdImageFilledPolygon. +Fixed by John Ellson. +<li>The order of the points returned in the bounding rectangle by +gdImageStringFT was incorrect in version 2.0.26. This has been +corrected in version 2.0.27. Thanks to Riccardo Cohen for pointing +this out, and to John Ellson for verifying and fixing it. +</ul> +<P> +<A NAME="whatsnew2.0.26"><H3>What's new in version 2.0.26?</H3></A> +<P> +The following enhancements and fixes: +<ul> +<li>Drastically faster, less memory-intensive antialiased drawing, thanks to +Pierre-Alain Joye. This code was imported from the PHP "fork" +of gd. The API for antialiased drawing has not changed, however the +implementation has been completely replaced. +Antialiased line drawing does not support widths other +than 1, however this did not work properly with the other +implementation of antialiasing either. Support has been included +for the "non-blending color" option introduced by the previous +implementation of antialiased drawing. +<li><code>gdlib-config</code>, which has been installed by +<code>make install</code> for some time now, has gained +a <code>--features</code> option. This option produces a space-separated +list of optional features with which the gd library was compiled. +Typical usage looks like this: +<pre> +% gdlib-config --features +GD_XPM GD_JPEG GD_FREETYPE GD_PNG GD_GIF +</pre> +Other <code>configure</code> scripts can conveniently define +preprocessor symbols based on this list in order to conditionally +compile code. For instance, if +GD_PNG is not reported by --features, then gdImagePng is not +included in the library. +<p> +Thanks to Lars Hecking and Lincoln Stein for their advice on +implementing this feature. Any blame for the actual implementation +is entirely due to me (TBB). +<li>Fixes to the behavior of the bounding rectangle returned by +gdImageStringFT and relatives when the string is rotated. +See fontwheeltest.c. Thanks to John Ellson. +<li>Previously, gdImageStringFT and friends accepted either +a full path to a font file, or the name of a font with no +extension, in which case the GDFONTPATH environment variable +and then the compiled-in DEFAULT_FONTPATH was searched. In addition, +a font filename with an extension but no full path can now be +automatically searched for in the same fashion. Thanks to John Ellson. + +<li>Fixes to freetype antialiased text against a transparent +background. See testtr.c. Thanks to John Ellson. + +<li>Support for named entities like &amp; and hex-coded +entities such as &#x6C34; in text +strings passed to gdImageStringFT and relatives, adding to the +previous support for decimal-coded entities like &#197;. +These were extracted from entities.html (from the W3C) via +the script entities.tcl, which is included for the curious and +those with other entities they need support for. Thanks to John Ellson. + +<li>Optimization: gdImageSetPixel no longer calls gdImageAlphaBlend +when either the source or the destination pixel is 100% transparent. +Thanks to John Ellson. + +<li>Optimization: gdImageLine is potentially faster now in the most +common cases. +Thanks to John Ellson. +<li>Documentation of the entities feature of gdImageStringFT. +<li>autoconf/configure fixes. Thanks to many who pointed out an oversight +in handling libpng flags. +</ul> +<P> +<A NAME="whatsnew2.0.25"><H3>What's new in version 2.0.25?</H3></A> +<P> +Owing to an oversight while making changes to better accommodate the use +of gd as a DLL, the <b>extern</b> qualifier was dropped from the +declarations of font pointers in 2.0.24. This has been corrected. +Thanks to Richard ("OpenMacNews"). +<P> +<A NAME="whatsnew2.0.24"><H3>What's new in version 2.0.24?</H3></A> +<P> +<b>Windows DLL now uses __stdcall calling convention.</b> Existing +applications will require a recompile, using the new version of gd.h, +in order to use this version of the DLL. However, Visual BASIC and other +non-C programmers will now be able to use the DLL, which is an enormous +benefit and justifies the one-time inconvenience to existing DLL users. +<p> +The elaborate #ifdef test for older versions of Freetype without +FT_ENCODING_MS_SYMBOL was needed in a second place also. Thanks to +David R. Morrison. +<p> +An off-by-one error in gdImageToPalette caused transparency to be applied +to the wrong pixels. Thanks to "Super Pikeman." +<P> +<A NAME="whatsnew2.0.23"><H3>What's new in version 2.0.23?</H3></A> +<P> +Output dpi specification option added to the +<code>gdFTStringExtra</code> structure, thanks to +Mark Shackelford. See <a href="#gdImageStringFTEx">gdImageStringFTEx</a>. +<P> +<A NAME="whatsnew2.0.22"><H3>What's new in version 2.0.22?</H3></A> +<P> +<ul> +<li>Win32 DLL users: working with pointers exported by DLLs is +difficult and causes unexpected results. gd 2.0.22 exports new +functions for retrieving the basic gd fonts: +<a href="#gdFontGetTiny">gdFontGetTiny()</a>, +<a href="#gdFontGetSmall">gdFontGetSmall()</a>, +<a href="#gdFontGetMediumBold">gdFontGetMediumBold()</a>, +<a href="#gdFontGetLarge">gdFontGetLarge()</a>, and +<a href="#gdFontGetHuge">gdFontGetHuge()</a>. You may safely assign the +return values from these functions to a local <code>gdFontPtr</code>. +Direct use of <code>gdFontLarge</code>, etc. is strongly deprecated +for users of <code>bgd.dll</code>; use these new functions instead. +<li>Basic support for loading CMYK-colorspace JPEG images. They are +of course converted to RGB which is a lossy process, however the +results do look quite good and are certainly fine for thumbnails and +web previews of DTP work. +<li>"make" no longer fails on <code>circletexttest</code> if +PNG support is missing. +<li>Small performance improvements to gdImageCopyResampled; larger +improvements are forthcoming. +</ul> +<P> +<A NAME="whatsnew2.0.21"><H3>What's new in version 2.0.21?</H3></A> +<P> +<ul> +<li>Version 2.0.21 adds a <code>gdImageCreateFrom*Ptr</code> family +of functions which make it convenient to load an image in any +GD-supported format directly from memory. +<li>The new <code>gdNewDynamicCtxEx</code> function was added to +support the easy implementation of the above functions and to +correct a design problem which made life unpleasant for those passing +in memory not originally allocated by gd to the +<code>gdNewDynamicCtx</code> function by provoding a way to specify +that gd should never free or reallocate a particular block of memory. +The <code>gdNewDynamicCtx</code> function and its relatives, although +still exported for ABI compatibility, are now <b>deprecated</b> except +for internal use, in favor of <a href="#gdImageCreateFromPngPtr"><code>gdImageCreateFromPngPtr</code></a> +and its relatives. +<li>Version 2.0.21 includes a new patch from Ethan A. Merritt to +correct a bug in the conditional compilation of support for +symbol fonts in gdft.c. Symbol fonts should now work correctly. +Thanks to Mr. Merritt. +<li>Version 2.0.20 restores the <code>gdFreeFontCache</code> function, +an undocumented function added in an earlier release which now simply +calls <code>gdFontCacheShutdown</code> for backwards compatibility. +This repairs build problems when compiling PHP against the latest gd. +<li>Documentation improvements. +</ul> +<P> +<A NAME="whatsnew2.0.20"><H3>What's new in version 2.0.20?</H3></A> +<P> +<ul> +<li>Version 2.0.20 restores the <code>gdFreeFontCache</code> function, +an undocumented function added in an earlier release which now simply +calls <code>gdFontCacheShutdown</code> for backwards compatibility. +This repairs build problems when compiling PHP against the latest gd. +</ul> +<P> +<A NAME="whatsnew2.0.19"><H3>What's new in version 2.0.19?</H3></A> +<P> +<ul> +<li>Version 2.0.19 restored <code>extern</code> declarations for the +gd font pointers inadvertently removed in 2.0.18. +</ul> +<P> +<A NAME="whatsnew2.0.18"><H3>What's new in version 2.0.18?</H3></A> +<P> +<ul> +<li>A Win32 binary distribution of "bgd.dll," built with mingw32 and +tested with win32 versions of the demo programs as console applications, +is now available. +<li>Semicolon rather than space used as the default separator of +alternative font file paths in <a href="#gdImageStringFT">gdImageStringFT</a>, +for better compatibility with Windows and other environments where +spaces are common in paths. +<li>The circletexttest demo no longer fails to compile when JPEG +support happens to be absent. +</ul> +<P> +<A NAME="whatsnew2.0.17"><H3>What's new in version 2.0.17?</H3></A> +<P> +Minor compilation and packaging problems with 2.0.16 were corrected. +If 2.0.16 compiled without errors for you, then you don't need +to upgrade to 2.0.17. +<P> +<A NAME="whatsnew2.0.16"><H3>What's new in version 2.0.16?</H3></A> +<P> +<ul> +<li>Thread safety for freetype text output. Background: all gd functions +were already thread safe, as long as only one thread manipulates each +image -- except for gdImageStringFT and gdImageStringFTEx. This is because +of a shared freetype font cache. Sharing the cache between images +is worthwhile, so "configure" now detects pthreads and uses it to +wrap freetype text output in a critical section if available. There is +also critical section support under WIN32. Those who wish to be +strictly thread-safe should call the new function +<a href="#gdFontCacheSetup">gdFontCacheSetup</a> before allowing any +thread to use freetype text calls. Otherwise this function is automatically +invoked on the first use of freetype, with a very small but real chance +of a race condition. +<li><a href="#gdImageSquareToCircle">gdImageSquareToCircle</a> performs +a "polar coordinate transform," returning a new image in which the +X axis of the original has been remapped to theta (angle) and the +Y axis of the original has been remapped to rho (distance from center). +<li><a href="#gdImageStringFTCircle">gdImageStringFTCircle</a> wraps +text in a circle around a specified center point. This function +takes advantage of <a href="#gdImageSquareToCircle">gdImageSquareToCircle</a>. +The result is very smooth, although it takes some time to compute. +Thanks to Steve Bassi for sponsoring this work. +<li><a href="#gdImageSharpen">gdImageSharpen</a>, contributed by +Paul Troughton. Thank you. +<li>Christophe Thomas corrected gdft.c to include freetype header +files in the way that is now mandatory in freetype 2.1.6 and above. +<li>Gustavo Scotti fixed a memory leak in gdft.c. +<li>Clipping rectangle respected in freetype text output. Thanks to Matt +McNabb. +<li>Paul den Dulk found a degenerate case that crashes +gdImageToPalette. Fixed. +<li>Optimization by Ilia Chipitsine to avoid wasting time with +offscreen scanlines during polygon rasterization. +<li>Optimized PNG saving by Phong Tran. Speeds up saves a +little bit. +<li>Bug in gdImageCopyResized fixed by Mao Morimoto. +</ul> +<P> +<A NAME="whatsnew2.0.15"><H3>What's new in version 2.0.15?</H3></A> +<P> +<ul> +<li>gd.c in 2.0.14 contained an instance of declaring variables +after the first line of executable code appears. This is of course +not allowed by ANSI C, although many compilers accept it. +My apologies. Thanks to Jeff Vendetti for reporting this quickly. +</ul> +<P> +<A NAME="whatsnew2.0.14"><H3>What's new in version 2.0.14?</H3></A> +<P> +<ul> +<li>2.0.13 was available for mere minutes due to a typo +in the new bounds-checking code for antialiased line drawing. Fixed. +<li>Not all platforms -- notably msys/mingw -- have an ssize_t type. +We now call an int an int in gd_jpeg.c, with good results. +(Note: ssize_t is signed, unlike size_t, and it needs to be here.) +</ul> +<P> +<A NAME="whatsnew2.0.13"><H3>What's new in version 2.0.13?</H3></A> +<P> +<ul> +<li>The <code>main()</code> function of one of the test programs +was accidentally included in the gd shared library, causing problems +on some platforms. This has been corrected. Thanks to many people +who pointed this out. +<li>The antialiased drawing functions now have proper bounds +checking. Thanks to Arne Jorgensen. +<li>A void function returned a value in gd_png.c, causing warnings +and, on some platforms, compilation errors but no reported runtime problems. +Thanks to Kevin Smith, among others. +<li>Autohinting was being forced ON for freetype text output. This is +apparently meant only for testing freetype and does not look as good +as the default behavior (FT_LOAD_DEFAULT). Thanks to Bob Ostermann. +<li>penf.x is properly reset when newlines are encountered in freetype +text output. Thanks to Christopher J. Grayce. +</ul> +<P> +<A NAME="whatsnew2.0.12"><H3>What's new in version 2.0.12?</H3></A> +<P> +<ul> +<li>Small but numerous code cleanups by Dr. Martin Zinser. +<li><a href="#gdImageSetClip">gdImageSetClip</a> and +<a href="#gdImageGetClip">gdImageGetClip</a> have been added. All +drawing routines now stay within the specified clipping rectangle. +Note that the <a href="#gdImageBoundsSafe">gdImageBoundsSafe</a> function +now returns true only if the specified location is within the +clipping rectangle. Of course, the default clipping area is the +entire image. The behavior of existing gd applications does not change. +<li>Support for fast drawing of antialiased lines and polygons, +by Bright Fulton and Frank Faubert. To learn more about this feature, +read about the <a href="#gdImageSetAntiAliased">gdImageSetAntiAliased</a> +function, which is used to set the foreground color for antialiasing, +as well as the <a href="#gdAntiAliased">gdAntiAliased</a> constant, which +is passed to line- and polygon-drawing functions in place of a color. +This code does not currently support an alpha channel component in the +specified foreground color, or in the existing background image, +but <em>does</em> perform alpha blending against an opaque background. +Also see the +<a href="#gdImageSetAntiAliasedDontBlend">gdImageSetAntiAliasedDontBlend</a> +function, which allows the specification of a special background +color that should never be blended with the foreground. +<li>Fixes to color mapping in <a href="#gdImageCopyMergeGray">gdImageCopyMergeGray</a>. Thanks to Philip Warner. +<li><a href="#gdImageStringFTEx">gdImageStringFTEx</a> now supports +explicit specification of the desired character mapping. +This is useful when a font offers more than one of Unicode, +Shift_JIS, and Big5. +<li>The PNG compression level can now be specified when writing PNG +images. See the new <a href="#gdImagePngEx">gdImagePngEx</a>, +<a href="#gdImagePngEx">gdImagePngEx</a>, +<a href="#gdImagePngCtxEx">gdImagePngCtxEx</a>, and +<a href="#gdImagePngPtrEx">gdImagePngPtrEx</a> functions. +<li>The annotate utility builds without error in the absence of +freetype, although of course it is not useful without freetype. +<li>Thorben Kundinger fixed a bug relating to the use of palette-based +images as brushes when drawing on truecolor images. +<li>Michael Schwartz corrected a problem with his code for drawing +thick lines. +<li>Prior to 2.0.12, any alpha channel component in the +<em>destination</em> image was ignored when drawing with +alpha blending in effect (see +<a href="#gdImageAlphaBlending">gdImageAlphaBlending</a>). 2.0.12 +correctly preserves an appropriate proportion of the alpha component +of the destination, just as it preserves an appropriate proportion +of the red, green and blue components, depending on the opacity +of the foreground. Thanks to Frank Warmerdam for pointing out the issue. +<li>Memory leaks on failed attempts to load fonts +in <a href="#gdImageStringFTEx">gdImageStringFTEx</a> were corrected. +Thanks to Frank Faubert. +<li>The impact of kerning is now correctly included in the calculation +of the bounding box returned by the freetype text routines. This issue +was pointed out by several individuals. +<li>Color problems with the <code>gd2</code> file format routines +were fixed by Steven Brown. These problems were due to the +incorrect use of a signed integer. +<li>Version 2.0.12 supports the <code>gd</code> file format correctly +for truecolor images. Truecolor <code>gd</code> files created with +earlier releases in the 2.0 series must be written again. The <code>gd</code> +file format is used to quickly load an entire uncompressed image, typically +an existing background to which additional material will be added; it is not a +general purpose file format. More advanced capabilities are also available +via the <code>gd2</code> format. Thanks to Andreas Pfaller for reporting +the problem. +<li>Signed vs. unsigned problem caused misbehavior when attempting to +load a bad JPEG image. Thanks to Geert Jansen. +<li>Existing truecolor PNG images with simple single-color transparency are +now loaded properly, thanks to Slaven Rezic. +<li>The <a href="#gdImageTrueColorToPalette">gdImageTrueColorToPalette</a> +function no longer attempts to preserve an alpha channel in the original. +My attempt to do so resulted in significantly inferior output even if no +alpha channel was present in the original. Thanks to Barend Gehrels for +submitting a new adaptation of Tom Lane's jquant2.c which does a very +high-quality job of palette conversion. Thanks also to Steven Brown, who +submitted patches allowing a single 100% transparent color in the +original truecolor image to be preserved. In practice, more complex +alpha channels in palettes are ill-supported and difficult to +allocate skillfully. +</ul> +<P> +<A NAME="whatsnew2.0.11"><H3>What's new in version 2.0.11?</H3></A> +<P> +<ul> +<li>Support for the "gd2" file format, which allows fast loading of all or +only part of an existing image, has been properly debugged for use with +truecolor images. (Palette images already worked properly, except for a +bug when loading from a regular file with gdImageCreateFromGd2Part, which +has also been fixed.) .gd2 files can be either compressed or uncompressed, +and they allow useful tricks such as fast loading of a 500x500 pixel +region of a 6000x3000 pixel image, without uncompressing <em>all</em> of the +image. .gd2 is NOT a general purpose file format and should only be used +where quick loading of a background image or subset of a larger image +is required. For more information, see +<a href="#gdImageGd2">gdImageGd2</a>, +<a href="#gdImageCreateFromGd2">gdImageCreateFromGd2</a>, +and +<a href="#gdImageCreateFromGd2Part">gdImageCreateFromGd2Part</a>. +<li>The gd2topng utility has been extended to support extraction of +only part of an image from a .gd2 file. This is both a demonstration and +a practical tool. +<li>Additional <code>configure</code> improvements by Lars Hecking. +</ul> +<P> +<A NAME="whatsnew2.0.10"><H3>What's new in version 2.0.10?</H3></A> +<P> +<ul> +<li>gdImageLine now clips to the edges of the image before drawing +lines, which greatly improves performance when many lines extend +outside or are entirely outside the actual image. Thanks to +Nick Atty for this code. +<li>gdImageBoundsSafe is replaced with a macro when called internally; +this improves the performance of gdImageSetPixel and gdImageGetPixel +a little bit, and therefore everything else as well. Thanks to +Nicky Atty for the idea. +<li>Transparent indexes are handled properly with non-truecolor +source images in gdImageCopy. Thanks to Frank Warmerdam. +<li>floor() replaced with a cast to long in gdImageCopyResampled, +for a roughly 35% performance boost. Thanks to John Buckman. +<li>gdft.c builds correctly on WIN32 without patches. +<li>Much faster gdImageCreateFromJpeg routines, thanks to Christian +Aberger for more efficient pointer arithmetic. +<li>gdtestft correctly builds without PNG tests if PNG support is not present. +Thanks to Gabriele Verzeletti. +</ul> +<P> +<A NAME="whatsnew2.0.9"><H3>What's new in version 2.0.9?</H3></A> +<P> +<ul> +<li>Version 2.0.9 contains a fix to gdImageCopyResized which allows +correct results when copying a palette-based image with a single +transparent index into a truecolor image. Thanks to Thorben +Kundinger. +<li>More <code>configure</code> fixes from Lars Hecking. Thanks, Lars. +</ul> +<P> +<A NAME="whatsnew2.0.8"><H3>What's new in version 2.0.8?</H3></A> +<P> +<ul> +<li>Version 2.0.8 contains additional fixes to the 'configure' script, +allowing a clean out-of-the-box build on more systems. +<li>Version 2.0.8 adds the new +<a href="#gdImageCopyRotated">gdImageCopyRotated</a> function, which +can rotate any rectangular image region by an arbitrary number of degrees. +</ul> +<P> +<A NAME="whatsnew2.0.7"><H3>What's new in version 2.0.7?</H3></A> +<P> +Version 2.0.7 corrects a problem which caused 'configure' to complain +that the directory NONE was not found, in various places, causing +the configuration process to stop. There are no code changes. +<P> +<A NAME="whatsnew2.0.6"><H3>What's new in version 2.0.6?</H3></A> +<P> +<ul> +<li> +Fixed a compilation problem with gdft.c. A declaration appeared +after executable code, and gcc let it slide by, so it made it +out the door. My apologies! +<li>As penance, I have seen to it that the entire library +now compiles cleanly with the <code>-Wall</code>, <code>-ansi</code> +and <code>-pedantic</code> options enabled. +</ul> +<p> +<A NAME="whatsnew2.0.5"><H3>What's new in version 2.0.5?</H3></A> +<ul> +<li>libgd 2.0.5 INSTALLS IN /usr/local/lib BY DEFAULT. IF YOU WANT +IT TO INSTALL IN /usr/lib, YOU MUST SPECIFY THIS at +<code>configure</code> time using this syntax: +<p> +<code>./configure --prefix=/usr</code> +<li>gd now uses GNU autoconf. This means that the provided +<code>configure</code> script should be compatible with all standard +GNU configure options and will figure out the correct settings for a +much wider range of operating systems. Many, many thanks to +Lars Hecking for taking care of this. +<li>The <a href="#gdImageStringFTEx">gdImageStringFTEx</a> function +is now included, thanks to Wez Furlong. My apologies to Wez for the +unreasonable amount of time this patch has sat in my queue. +<li>Additional fixes from Wez Furlong. +<li>Arithmetic cleanup by Doug Claar. +<li>Corrections to loading and saving of alpha channel +information in PNG files, by Andrew Hull. +<li>gdImageTrueColorToPalette does not contain +unneeded test code. +<li>gdImageCopyResized works properly again when copying +from a non-truecolor source. +</ul> +<P> +<A NAME="whatsnew2.0.4"><H3>What's new in version 2.0.4?</H3></A> +The following contributions from John Ellson: +<ul> +<li>Various test programs now compile in the absence +of PNG support +<li>gdIOCtx correctly calls gdFree rather than free +<li>Various cleanups to pass -Wall without warnings +<li>Support for Adobe-style Type 1 fonts (.pfa and .pfb files) +via freetype +<li>gdImageColorResolve and gdImageColorResolveAlpha will not +attempt to resolve a color request to the transparent color index +(for palette-based images) +<li>Improved font search path support +<li>Antialiased freetype text on palette images works properly +when more than one image is created in a single program lifetime +with different color indexes +<li>Better threshold for two-color "mono" images +<li>Memory leak fixes +<li>Text rotation fix +<li>More extensive default font path +<li>fontwheeltest and fontsizetest test programs for freetype +</ul> +And the following additional fixes: +<ul> +<li><code>configure</code> now correctly detects and provides +support for the Xpm library and its dependencies (Len Makin) +</ul> +<A NAME="whatsnew2.0.3"><H3>What's new in version 2.0.3?</H3></A> +<ul> +<li>The <code>configure</code> script has been extensively modified +to work properly in tests with both Solaris and Linux. Other platforms +should also work based on feedback received and integrated to date. +<li>The <code>--prefix</code> option to <code>configure</code> +works properly. +<li>The <code>annotate</code> utility has been added. This is a +very handy tool for adding freetype text to existing JPEGs. After +<code>make install</code>, type <code>annotate -h</code> for more +information. Thanks to Joel Dubiner. +</ul> +<P> +<A NAME="whatsnew2.0.2"><H3>What's new in version 2.0.2?</H3></A> +<ul> +<li>A "configure" script has been added. After wrestling with GNU +autoconf for a while, I got tired of trying to make it detect libraries +but accept their absence gracefully, and so on. Instead, I wrote a short +Perl script which does the job and builds a reasonable Makefile. Those +who find it doesn't detect their system's needs properly are welcome +to contribute patches or the necessary commands. +<li>Antialiased freetype text output now works properly in both +truecolor and non-truecolor contexts! Hurrah! On a truecolor image +it is possible, for instance, to draw antialiased text on an arbitrarily +complex background with 50% alpha blending (transparency), and get the +expected pretty results. Thanks to Joel Dubiner for his support of this work. +<li><strong>By default, alpha blending is now done within the library.</strong> +Also, by default, alpha channel is not saved with PNG images. This means +that programmers who try loading a JPEG, scribbling some pretty antialiased +text on it, and saving the JPEG again will now get the results they +expected. It also means that, by default, users will not run afoul of +the fact that many web browsers don't properly support full PNG alpha +channel. +<li>Various submitted bug fixes have been incorporated. +<li>Various other submitted changes have not been incorporated. Sorry. +The interval between 2.0.1 and 2.0.2 was simply too long, and changes +accumulated which were not mutually compatible. I'll do better in +the future, especially with bug fixes. +</ul> +<P><A NAME="whatsnew2.0.1"><H3>What's new in version 2.0.1?</H3></A> +<ul> +<li>Workaround for a bug in gcc, apparently found in gcc 2.7.2 and up. +I reproduced and fixed it while using gcc 2.9.5.2. The bug occurred only +when the -g option was in use. This problem caused gcc to spew +internal error messages unrelated to the correctness of the code +in gd_gd2.c. Howard Jones was first to report it. +<li><a href="#gdImageFilledEllipse">gdImageFilledEllipse</a> documented +and altered; no longer requires a superfluous style argument. Thanks to +Francis James Franklin. +<li>The Makefile now offers the correct syntax for +optionally creating a static library. Thanks to Jean-Lous Regez, +among others. +<li>A nested comment, an attempt to return the value of a void function, +and a potentially significant error in gdImageCopyResampled were fixed +thanks to Joseph Shirley. +<li>A bug preventing proper truecolor text rendering was fixed, +thanks to Jason Gallagher. +<li><a href="#gdImageStringFT">gdImageStringFT</a> (FreeType) should +now work better against a transparent or semitransparent background, +and should act in a manner consistent with the most recent +<a href="#gdImageAlphaBlending">gdImageAlphaBlending</a> setting. +Antialiasing is now done via the alpha channel mechanism if the +image is a truecolor image. +<li>Bugs in the output of gdImageArc and gdImageFilledArc were reported +by Bruce Verderaime. A simple and correct but inefficient implementation +has been substituted until fixes are contributed for the faster code, +which is in gd_arc_f_buggy.c along with the test program that reproduces +the bug(s). +<li><a href="#gdImageFilledArc">gdImageFilledArc</a> now offers additional +style options, which can be combined to produce various effects. +<li>Masahito Yamaga (ma@yama-ga.com) sent a patch to improve +support for Japanese output via <a href="#gdImageStringFT">gdImageStringFT</a>. +He also added a new <code>readme.jpn</code> file. +<li>Zillions of documentation fixes. +</ul> +<P><A NAME="whatsnew2.0"><H3>What's new in version 2.0?</H3></A> +<ul> +<li><strong>Support for truecolor images!</strong> Version 2.0 can +load truecolor PNGs with no loss of color information, and almost +no loss of alpha channel information. Version 2.0 can also load +truecolor JPEGs with as little loss as possible; however, bear in +mind that JPEG is a lossy format, so repeated load/save cycles +always reduce image quality. This is not a bug. To create +a truecolor image from scratch, call the new +<a href="#gdImageCreateTrueColor">gdImageCreateTrueColor</a> +function. The <a href="#gdImageCreate">gdImageCreate</a> function +is still available to create palette images, and may also be +referred to as <a href="#gdImageCreatePalette">gdImageCreatePalette</a>. +<li><strong>Support for alpha channels!</strong> In addition to +24 bits of color information for each pixel (eight bits of +red, green, and blue respectively), version 2.0 supports +7 bits of "alpha channel" information. This is used to determine +exactly how transparent the pixel should be. There is also support +for a full 7 bits of transparency for each individual palette index +in a palette-based image. Please note that, as of this writing, +only Macintosh Internet Explorer 5.x and Mozilla/Netscape 6.x +display partial transparency properly. +<li>The new <a href="#gdImageAlphaBlending">gdImageAlphaBlending</a> +function allows for two different modes of drawing. In blending mode, +the alpha channel component of the color supplied to all drawing +functions, such as <a href="#gdImageSetPixel">gdImageSetPixel</a>, +determines how much of the underlying color should be allowed to +shine through. The resulting image is not transparent. In non-blending +mode, drawing color is copied literally with the alpha channel +information, resulting in a transparent image. Blending mode is +not available when drawing on palette images. +<li>The <a href="#gdImageCopyResampled">gdImageCopyResampled</a> +function provides "smooth" copying from a large image to a smaller +one, using a weighted average of the pixels of the source area rather +than selecting one representative pixel. This function is identical +to <a href="#gdImageCopyResized">gdImageCopyResized</a> when the +destination image is a palette image. +<li>The <a href="#gdImageTrueColorToPalette">gdImageTrueColorToPalette</a> +function converts a truecolor image to a palette image. The code for +this function was originally drawn from the Independent JPEG Group library +code, which is excellent. The code has been modified to preserve as much +alpha channel information as possible in the resulting palette, in addition +to preserving colors as well as possible. This does not work as well as +might be hoped. It is usually best to simply produce a truecolor +output image instead, which guarantees the highest output quality. +<li>A very high degree of backwards compatibility with existing +gd 1.x code has been maintained, at both the source code and binary +level. <strong>Code which directly accesses the <code>pixels</code> array +will fail only if it encounters an existing truecolor image</strong>, which may +happen if the code attempts to open and modify an existing JPEG or +truecolor PNG. Such code should be modified to check the +<code>trueColor</code> flag of the <code>gdImage</code> structure, and +refer to the <code>tpixels</code> array instead when it is set. +<li>gd is now compiled and installed as a shared library. However, +gd still does not use autoconf, because I (TBB) have very limited +patience with autoconf. These days, most Unix systems provide a fairly +POSIX-standard environment, and the provided Makefile is likely to work well +if users read it and follow the instructions at the top. +<li>Support for line thickness was added by Michael Schwartz. My apologies +to him for sitting on his patches for so long. See the new +<a href="#gdImageSetThickness">gdImageSetThickness</a> function, which +affects all standard gd functions that draw lines and curves. In addition, +Michael added a convenient <a href="#gdImageEllipse">gdImageEllipse</a> +function. +<li>The new <a href="#gdImageFilledArc">gdImageFilledArc</a> function +provides a straightforward way to draw filled arcs. Also, +<a href="#gdImageFilledEllipse">gdImageFilledEllipse</a> is a +convenient way to fill an ellipse without specifying starting +and ending angles. Thanks go out to F J Franklin. +<li>To put an end to the confusion, TrueType 1.x support has been +removed in favor of TrueType 2.x support. The old +gdImageStringTTF function simply invokes gdImageStringFT. +<li>The specialized .gd and .gd2 file formats have been upgraded to support +truecolor. New images written by the versions of these functions +found in 2.0 will be rejected, with varying degrees of grace, by +older versions of gd. THIS AFFECTS THE .GD and .GD2 FORMATS ONLY. IF YOU +ARE CONFUSED BY THIS PARAGRAPH, IT PROBABLY DOESN'T APPLY TO ANYTHING +YOU WILL EVER ENCOUNTER. Since these file formats are absolutely, +positively *not* designed for distributing images, just for +preprocessing them, this should not be a big problem. gd 2.0 should +read old .gd and .gd2 files correctly. +</ul> +<P><A NAME="whatsnew1.8.4"><H3>What's new in version 1.8.4?</H3></A> +<ul> +<li>Add support for FreeType2 (John Ellson ellson@graphviz.org) +<li>Add support for finding in fonts in a builtin DEFAULT_FONTPATH, +or in a path from the GDFONTPATH environment variable. +<li>remove some unused symbols to reduce compiler warnings +<li>bugfix in size comparisons in gdImageCompare +<li>REXX now mentioned +<li>All memory allocation functions are now wrapped within the +library; gdFree is exported and recommended for freeing memory +returned by the gdImage(Something)Ptr family of functions. +</ul> +<P><A NAME="whatsnew1.8.3"><H3>What's new in version 1.8.3?</H3></A> +<ul> +<li>WBMP output memory leak fixed +<li><code>#include <gd.h></code> corrected to <code>#include "gd.h"</code> in gd_wbmp.c +<li>Documented the fact that the source and output images shouldn't +match in the WBMP test except for black and white source images +</ul> +<P> +<A NAME="whatsnew1.8.2"><H3>What's new in version 1.8.2?</H3></A> +<ul> +<li>WBMP support debugged and improved by Johann Van den Brande +<li>WBMP tests added to gdtest.c by Thomas Boutell +<li>Use of platform-dependent 'install' command removed by Thomas Boutell +<li>Comments added to Makefile warning users to juggle the order of the +libraries if the linker complains; is there any portable way to do this +automatically, short of using autoconf? +<li>Documentation of <a href="#gdImageCreateFromXpm">gdImageCreateFromXpm</a> +corrected +<li>Updated links to fast-moving, always dodging libpng and zlib web sites +</ul> +<P><A NAME="whatsnew1.8.1"><H3>What's new in version 1.8.1?</H3></A> +<ul> +<li>Optional components no longer built by default (following the +documentation) +<li>JPEG code no longer requires inappropriate header files +<li>Win32 patches from Joe Gregorio +<li>16-bit font support for bdftogd, from Honza Pazdziora +</ul> +<P><A NAME="whatsnew1.8"><H3>What's new in version 1.8?</H3></A> +<ul> +<li>Support for JPEG output, courtesy of Doug Becker +<li>A link to Michael Bradbery's Pascal wrapper +<li>Support for WBMP output, courtesy of Maurice Szmurlo +<li>gdImageColorClosestHWB function based on hue, whiteness, blackness, +superior to the regular gdImageColorClosest function, courtesy +of Philip Warner +<li>License clarification: yes, you can modify gd +</ul> +<h4>Additional JPEG Information</h4> +Support for reading and writing JPEG-format images is courtesy +of Doug Becker and the Independent JPEG Group / Thomas G. Lane. You +can get the latest version of the IJG JPEG software from <A +HREF="ftp://ftp.uu.net/graphics/jpeg/">ftp://ftp.uu.net/graphics/jpeg/</A> +(e.g., the <A +HREF="ftp://ftp.uu.net/graphics/jpeg/jpegsrc.v6b.tar.gz">jpegsrc.v6b.tar.gz</A> +file). You <strong>must</strong> use +version 6b or later of the IJG JPEG software. You might also consult +the <A HREF="http://www.faqs.org/faqs/jpeg-faq/">JPEG FAQ</A> at +<A HREF="http://www.faqs.org/faqs/jpeg-faq/">http://www.faqs.org/faqs/jpeg-faq/</A>. +<P><A NAME="whatsnew1.7.3"><H3>What's new in version 1.7.3?</H3></A> +Another attempt at Makefile fixes to permit +linking with all libraries required on platforms with order- +dependent linkers. Perhaps it will work this time. +<P><A NAME="whatsnew1.7.2"><H3>What's new in version 1.7.2?</H3></A> +An uninitialized-pointer bug in <code>gdtestttf.c</code> was corrected. +This bug caused crashes at the end of each call to gdImageStringTTF on +some platforms. Thanks to Wolfgang Haefelinger. +<p> +Documentation fixes. Thanks to Dohn Arms. +<p> +Makefile fixes to permit +linking with all libraries required on platforms with order- +dependent linkers. +<P><A NAME="whatsnew1.7.1"><H3>What's new in version 1.7.1?</H3></A> +A minor buglet in the Makefile was corrected, as well as an inaccurate +error message in <code>gdtestttf.c</code>. Thanks to Masahito Yamaga. +<P><A NAME="whatsnew1.7"><H3>What's new in version 1.7?</H3></A> +Version 1.7 contains the following changes: +<ul> +<li>Japanese language support for the TrueType functions. +Thanks to Masahito Yamaga. +<li><code>autoconf</code> and <code>configure</code> have been removed, in favor of a +carefully designed Makefile which produces and properly installs +the library and the binaries. System-dependent variables are +at the top of the Makefile for easy modification. I'm sorry, +folks, but autoconf generated <strong>many, many confused email +messages</strong> from people who didn't have things where autoconf +expected to find them. I am not an autoconf/automake wizard, and +gd is a simple, very compact library which does not need to +be a shared library. I <strong>did</strong> make many improvements +over the old gd 1.3 Makefile, which were directly inspired by the +autoconf version found in the 1.6 series (thanks to John Ellson). +<li>Completely ANSI C compliant, according to the <code>-pedantic-errors</code> +flag of gcc. Several pieces of not-quite-ANSI-C code were causing problems +for those with non-gcc compilers. +<li><code>gdttf.c</code> patched to allow the use of Windows symbol +fonts, when present (thanks to Joseph Peppin). +<li><code>extern "C"</code> wrappers added to <code>gd.h</code> and the +font header files for the convenience of C++ programmers. +<code>bdftogd</code> was also modified to automatically insert these +wrappers into future font header files. Thanks to John Lindal. +<li>Compiles correctly on platforms that don't define <code>SEEK_SET</code>. +Thanks to Robert Bonomi. +<li>Loads Xpm images via the +<a href="#gdImageCreateFromXpm"><code>gdImageCreateFromXpm</code></a> +function, if the Xpm library is available. Thanks to Caolan McNamara. +</ul> +<P><A NAME="whatsnew1.6.3"><H3>What's new in version 1.6.3?</H3></A> +Version 1.6.3 corrects a memory leak in gd_png.c. This leak caused +a significant amount of memory to be allocated and not freed when +writing a PNG image. +<P><A NAME="whatsnew1.6.2"><H3>What's new in version 1.6.2?</H3></A> +Version 1.6.2 from John Ellson <ellson@graphviz.org> adds two new functions: +<ul> +<li>gdImageStringTTF - scalable, rotatable, anti-aliased, TrueType strings using +the FreeType library, but only if libttf is found by configure. +<strong>We do not provide TrueType fonts. Obtaining them +is entirely up to you.</strong> +<li>gdImageColorResolve - an efficient alternative for the +common code fragment: +<pre> + + if ((color=gdImageColorExact(im,R,G,B)) < 0) + if ((color=gdImageColorAllocate(im,R,G,B)) < 0) + color=gdImageColorClosest(im,R,G,B); +</pre> +</ul> +<p> +Also in this release the build process has been converted to +GNU autoconf/automake/libtool conventions so that both (or either) +static and shared libraries can be built. +<P><A NAME="whatsnew1.6.1"><H3>What's new in version 1.6.1?</H3></A> +Version 1.6.1 incorporates superior PNG reading and writing code +from Greg Roelofs, with minor modifications by Tom Boutell. +Specifically, I altered his code to read non-palette images +(converting them to palette images badly, by dithering them), +and to tolerate palette images with types of transparency that +gd doesn't actually support (it just ignores the advanced +transparency features). Any bugs in this area are therefore my +fault, not Greg's. +<p> +Unlike gd 1.6, users should have no trouble linking with +gd 1.6.1 if they follow the instructions and install all of +the pieces. However, <strong>If you get undefined symbol errors, +be sure to check for older versions of libpng in your +library directories!</strong> +<P><A NAME="whatsnew1.6"><H3>What's new in version 1.6?</H3></A> +Version 1.6 features the following changes: +<p> +<strong>Support for 8-bit palette PNG images has been added. +Support for GIF has been removed.</strong> This step was taken +to completely avoid the legal controversy regarding the LZW +compression algorithm used in GIF. Unisys holds a patent which +is relevant to LZW compression. PNG is a superior image format +in any case. Now that PNG is supported by both Microsoft +Internet Explorer and Netscape (in their recent releases), +we highly recommend that GD users upgrade in order to get +well-compressed images in a format which is legally unemcumbered. + +<P><A NAME="whatsnew1.5"><H3>What's new in version 1.5?</H3></A> + +Version 1.5 featured the following changes: + +<dl> +<dt><b>New GD2 format</b> +<dd> An improvement over the GD format, the GD2 format uses the zlib + compression library to compress the image in chunks. This results + in file sizes comparable to GIFs, with the ability to access parts + of large images without having to read the entire image into memory. +<p> + This format also supports version numbers and rudimentary validity + checks, so it should be more 'supportable' than the previous GD format. +<p> +<dt><b>Re-arranged source files</b> +<dd> gd.c has been broken into constituant parts: io, gif, gd, gd2 and + graphics functions are now in separate files. +<p> +<dt><b>Extended I/O capabilities.</b> +<dd> The source/sink feature has been extended to support GD2 file formats (which + require seek/tell functions; seek must return 1 for success, 0 for failure), and to allow more general non-file I/O. +<p> +<dt><b>Better support for Lincoln Stein's Perl Module</b> +<dd> The new gdImage*Ptr function returns the chosen format stored in a block of memory. + This can be directly used by the GD perl module. +<p> +<dt><b>Added functions</b> +<dd>gdImageCreateFromGd2Part - allows retrieval of part of an image (good for huge images, like maps), +<br>gdImagePaletteCopy - Copies a palette from one image to another, doing it's best to match the colors in the target image to the colors in the source palette. +<br>gdImageGd2, gdImageCreateFromGd2 - Support for new format +<br>gdImageCopyMerge - Merges two images (useful to highlight part of an image) +<br>gdImageCopyMergeGray - Similar to gdImageCopyMerge, but tries to preserve source image hue. +<br>gdImagePngPtr, gdImageJpegPtr, gdImageWBMPPtr, gdImageGdPtr, gdImageGd2Ptr - return memory blocks for each type of image. +<br>gdImageCreateFromPngCtx, gdImageCreateFromGdCtx, gdImageCreateFromGd2Ctx, gdImageCreateFromGd2PartCtx - Support for new I/O context. + +</dl> + +<b>NOTE:</b> In fairness to Thomas Boutell, any bug/problems with any of the above features should +probably be reported to <a href=mailto:pjw@rhyme.com.au>Philip Warner</a>. + +<P><A NAME="whatsnew1.4"><H3>What's new in version 1.4?</H3></A> + +Version 1.4 features the following changes: +<dl> +<dt>Fixed polygon fill routine (again) +<dd>Thanks to Kirsten Schulz, version 1.4 is able to fill +numerous types of polygons that caused problems with +previous releases, including version 1.3. +<dt>Support for alternate data sources +<dd>Programmers who wish to load a GIF from something other +than a stdio FILE * stream can use the new +<a href="#gdImageCreateFromPngSource">gdImageCreateFromPngSource</a> function. +<dt>Support for alternate data destinations +<dd>Programmers who wish to write a GIF to something other +than a stdio FILE * stream can use the new +<a href="#gdImagePngToSink">gdImagePngToSink</a> function. +<dt>More tolerant when reading GIFs +<dd> +Version 1.4 does not crash when reading certain animated GIFs, +although it still only reads the first frame. Version 1.4 also has +overflow testing code to prevent crashes when reading +damaged GIFs. +</dl> +<P><A NAME="whatsnew1.3"><H3>What's new in version 1.3?</H3></A> +Version 1.3 features the following changes: +<dl> +<dt>Non-LZW-based GIF compression code +<dd> +Version 1.3 contained GIF compression code that uses simple Run Length +Encoding instead of LZW compression, while still retaining compatibility +with normal LZW-based GIF decoders (your browser will still like your GIFs). +<strong>LZW compression is patented by Unisys. We are currently reevaluating +the approach taken by gd 1.3. The current release of gd does not support +this approach. We recommend that you use the current release, and generate +PNG images.</strong> Thanks to +Hutchison Avenue Software Corporation for contributing +the RLE GIF code. +<dt>8-bit fonts, and 8-bit font support +<dd>This improves support for European languages. Thanks are due +to Honza Pazdziora <adelton@informatics.muni.cz> and also to +Jan Pazdziora <adelton@fi.muni.cz>. Also see the provided bdftogd +Perl script if you wish to convert fixed-width X11 fonts +to gd fonts. +<dt>16-bit font support (no fonts provided) +<dd>Although no such fonts are provided in the distribution, +fonts containing more than 256 characters should work if the +gdImageString16 and gdImageStringUp16 routines are used. +<dt>Improvements to the "webpng" example/utility +<dd>The "webpng" utility is now a slightly more useful application. Thanks to +Brian Dowling for this code. +<dt>Corrections to the color resolution field of GIF output +<dd>Thanks to Bruno Aureli. +<dt>Fixed polygon fills +<dd>A one-line patch for the infamous polygon fill bug, courtesy +of Jim Mason. I believe this fix is sufficient. However, if you +find a situation where polygon fills still fail to behave properly, +please send code that demonstrates the problem, <em>and</em> a fix if +you have one. Verifying the fix is important. +<dt>Row-major, not column-major +<dd>Internally, gd now represents the array of pixels as +an array of rows of pixels, rather than an array of columns +of pixels. This improves the performance of compression and +decompression routines slightly, because horizontally adjacent +pixels are now next to each other in memory. <strong>This should +not affect properly written gd applications, but applications that +directly manipulate the <code>pixels</code> array will require +changes.</strong> +</dl> +<A NAME="required"><H3>What else do I need to use gd?</H3></A> +<P> +To use gd, you will need an ANSI C compiler. <strong>All popular +Windows 95 and NT C compilers are ANSI C compliant.</strong> Any +full-ANSI-standard C compiler should be adequate. <strong>The cc +compiler released with SunOS 4.1.3 is not an ANSI C compiler. +Most Unix users who do not already have gcc should get it. +gcc is free, ANSI compliant and a de facto industry standard. +Ask your ISP why it is missing.</strong> +<P> +As of version 1.6, you also need the zlib compression library, +and the libpng library. As of version 1.6.2, you can draw text +using antialiased TrueType fonts if you also have the libttf +library installed, but this is not mandatory. +zlib is available for a variety of platforms from +<a href="http://www.freesoftware.com/pub/infozip/index.html">the zlib web site</a>. +libpng is available for a variety of platforms from +<a href="http://www.cdrom.com/pub/png/">the PNG web site</a>. + +<P> +You will also want a PNG viewer, if you do not already have +one for your system, since you will need a good way to check the +results of your work. Netscape 4.04 and higher, and Microsoft +Internet Explorer 4.0 or higher, both support PNG. +<strong>Not every PNG-compatible viewer supports alpha channel +transparency,</strong> which is why gd 2.0.2 and above do alpha +blending in the library by default; it is possible to turn on the +saving of alpha channel information to the file instead. +<P> +<A NAME="getgd"><H3>How do I get gd?</H3></A> +<h4>Binaries (DLL for Windows programmers):</h4> +<ul> +<li><a href="http://www.libgd.org/Downloads">.ZIP File of DLL, Headers, Et Cetera</a> +<p> +</ul> +<h4>Source Code:</h4> +<ul> +<li><a href="http://www.libgd.org/Downloads">Gzipped Tar File (Unix)</a> +<li><a href="http://www.boutell.com/gd/http/gd-2.0.33.tar.gz">.ZIP File of SOURCE CODE (Windows)</a> +</ul> +<P> +<A NAME="buildgd"><H3>How do I build gd?</H3></A> +<blockquote> +Win32 DLL users: if you are using MSVC, use the provided batch file +<code>makemsvcimport.bat</code> to make a bgd.lib import library +corresponding to the provided bgd.dll. Copy bgd.dll to your +application directory, or to your Windows sytem directory. In the +settings of your MSVC project, you <b>MUST</b> choose the +"multithreaded DLL" library option under "code generation." +mingw32 and cygwin users can simply link with the provided libbgd.a +stub library in order to use the DLL. +</blockquote> +Building gd From the Source +<p> +In order to build gd, you must first unpack the archive you have +downloaded. If you are not familiar with <code>tar</code> and +<code>gunzip</code> (Unix) or <code>ZIP</code> (Windows), please +consult with an experienced user of your system. Sorry, we cannot +answer questions about basic Internet skills. +<p> +Unpacking the archive will produce a directory called "gd-2.0.33". +<p> +<h4>For Unix</h4> +<code>cd</code> to the 2.0.33 directory and type: +<p> +<code>./configure</code> +<P> +<blockquote> +<STRONG>NOTE: BY DEFAULT, THE LIBRARY IS INSTALLED IN +<code>/usr/local/lib</code></strong> and the include files are +installed in <code>/usr/local/include</code>. IF YOU ARE +UPGRADING, you may wish to use: +<pre> +./configure --prefix=/usr +</pre> +Rather than just <code>./configure</code>, before typing +<code>make</code> and <code>make install</code>. +</blockquote> +<p> +If all goes well, this will create a Makefile. If all does not go well -- +for instance, if neither the the JPEG nor the PNG and ZLIB libraries +are found -- you will need to install those libraries, then come back +and run <code>configure</code> again. +<p> +If necessary, make changes to the resulting Makefile. Then, +type "make". If there are no errors, follow this with "make install". +Because gd 2.0 and above installs as a shared library, it is necessary to +install the library properly before running gd-based programs. +<p> +If you get errors, type <code>./configure --help</code> for more +information about the available options. In the unlikely event +that the GNU autoconf-produced configure script does not work well +for you, you may wish to try <code>configure.pl</code>, a +simple Perl script with similar but less complete capabilities. +If all else fails, try renaming <code>makefile.sample</code> +to <code>Makefile</code>. However, <code>./configure</code> is +almost always your best bet. +<blockquote> +<h4>For Windows</h4> +Use the DLL version! See the paragraph at the beginning of this sectino. +If you really want to compile it yourself for some strange reason, read on. +<p> +Create a project using your favorite programming environment. +Copy all of the gd files to the project directory. Add <code>gd.c</code> +to your project. Add other source files as appropriate. Learning the +basic skills of creating projects with your chosen C environment +is up to you. Alternatively, use the free <code>mingw32</code> +or <code>cygwin</code> tools, which may prove to be compatible +with the provided <code>configure</code> script. +</blockquote> +<P> +If you wish to test the library, type "make test" AFTER you have +successfully executed "make install". This will build +several test programs, including "gddemo". (Not all of these +programs are expected to print completely successful messages, +depending on the nature of the image formats with which some of +the tests are tried; for instance, WBMP is a black and white +format, so loss of color information is expected there.) +Run gddemo to see some of the capabilities of gd. Run +gdtestft to play with the freetype support, if you have built +gd with it and have access to truetype fonts. +<P> +gddemo should execute without incident, creating the file +demoout.png. (Note there is also a file named demoin.png, +which is provided in the package as part of the demonstration.) +<P> +Display demoout.png in your PNG viewer. The image should +be 128x128 pixels and should contain an image of the +space shuttle with quite a lot of graphical elements drawn +on top of it. +<P> +(If you are missing the demoin.png file, the other items +should appear anyway.) +<P> +Look at demoin.png to see the original space shuttle +image which was scaled and copied into the output image. +<P> +<A NAME="basics"><H3>gd basics: using gd in your program</H3></A> +gd lets you create PNG or JPEG images on the fly. To use gd in your +program, include the file gd.h, and link with the gd +library and the other required libraries; the syntax for +most Unix flavors is: +<pre> +-lgd -lpng -lz -ljpeg -lfreetype -lm +</pre> +Assuming that all of these libraries are available. +<P> +If you want to use the provided simple fonts, include +gdfontt.h, gdfonts.h, gdfontmb.h, gdfontl.h and/or gdfontg.h. For +more impressive results, install FreeType 2.x and use the +<a href="#gdImageStringFT">gdImageStringFT</a> +function. If you are not using the provided Makefile and/or a +library-based approach, be sure to include the source modules as well in your +project. (They may be too large for 16-bit memory models, +that is, 16-bit DOS and Windows.) +<P> +Here is a short example program. <strong>(For a more advanced example, +see gddemo.c, included in the distribution. gddemo.c is NOT the same program; +it demonstrates additional features!)</strong> +<P> +<PRE> +/* Bring in gd library functions */ +#include "gd.h" + +/* Bring in standard I/O so we can output the PNG to a file */ +#include <stdio.h> + +int main() { + /* Declare the image */ + <A HREF="#gdImagePtr">gdImagePtr</A> im; + /* Declare output files */ + FILE *pngout, *jpegout; + /* Declare color indexes */ + int black; + int white; + + /* Allocate the image: 64 pixels across by 64 pixels tall */ + im = <A HREF="#gdImageCreate">gdImageCreate</A>(64, 64); + + /* Allocate the color black (red, green and blue all minimum). + Since this is the first color in a new image, it will + be the background color. */ + black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); + + /* Allocate the color white (red, green and blue all maximum). */ + white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); + + /* Draw a line from the upper left to the lower right, + using white color index. */ + <A HREF="#gdImageLine">gdImageLine</A>(im, 0, 0, 63, 63, white); + + /* Open a file for writing. "wb" means "write binary", important + under MSDOS, harmless under Unix. */ + pngout = fopen("test.png", "wb"); + + /* Do the same for a JPEG-format file. */ + jpegout = fopen("test.jpg", "wb"); + + /* Output the image to the disk file in PNG format. */ + <A HREF="#gdImagePng">gdImagePng</A>(im, pngout); + + /* Output the same image in JPEG format, using the default + JPEG quality setting. */ + <A HREF="#gdImageJpeg">gdImageJpeg</A>(im, jpegout, -1); + + /* Close the files. */ + fclose(pngout); + fclose(jpegout); + + /* Destroy the image in memory. */ + <A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +} +</PRE> +When executed, this program creates an image, allocates +two colors (the first color allocated becomes the background +color), draws a diagonal line (note that 0, 0 is the upper +left corner), writes the image to PNG and JPEG files, and +destroys the image. +<P> +The above example program should +give you an idea of how the package works. +gd provides many additional functions, which are listed +in the following reference chapters, complete with code +snippets demonstrating each. There is also an +<A HREF="#index">alphabetical index</A>. +<H3><A NAME="webpng">Webpng: a more powerful gd example</A></H3> +Webpng is a simple utility program to manipulate PNGs from the +command line. It is written for Unix and similar command-line +systems, but should be easily adapted for other environments. +Webpng allows you to set transparency and interlacing and +output interesting information about the PNG in question. +<P> +webpng.c is provided in the distribution. Unix users can +simply type "make webpng" to compile the program. Type +"webpng" with no arguments to see the available options. +<H2><A NAME="reference">Function and type reference</A></H2> +<UL> +<LI><A HREF="#types">Types</A></LI> +<LI><A HREF="#creating">Image creation, destruction, loading and saving</A></LI> +<LI><A HREF="#drawing">Drawing, styling, brushing, tiling and +filling functions</A></LI> +<LI><A HREF="#query">Query functions (not color-related)</A></LI> +<LI><A HREF="#fonts">Font and text-handling functions</A></LI> +<LI><A HREF="#colors">Color handling functions</A></LI> +<LI><A HREF="#copying">Copying, resizing, rotating, deformation and filter +functions</A></LI> +<LI><A HREF="#misc">Miscellaneous Functions</A></LI> +<LI><A HREF="#constants">Constants</A></LI> +</UL> +<H3><A NAME="types">Types</A></H3> +<DL> +<DT><A NAME="gdImage"><code>gdImage</code><strong>(TYPE)</strong></A> +<DD> +The data structure in which gd stores images. <A HREF="#gdImageCreate"> +gdImageCreate</A>, <a href="#gdImageCreateTrueColor">gdImageCreateTrueColor</a> +and the various image file-loading functions return +a pointer to this type, and the other functions expect to receive +a pointer to this type as their first argument. It is reasonably safe to +examine any of the members of this structure. It is also reasonably +safe to modify individual pixels within the <code>pixels</code> +or <code>tpixels</code> arrays. If the <code>trueColor</code> flag +is set, the <code>tpixels</code> array is valid; otherwise the +<code>pixels</code> array is valid. +<p> +The <code>colorsTotal</code>, <code>red</code>, <code>green</code>, +<code>blue</code>, <code>alpha</code> and <code>open</code> arrays +manage the palette. They are valid only when the <code>trueColor</code> +flag is not set. +The <code>transparent</code> value contains the palette index of the first +transparent color as read-only information for backwards compatibility; +gd 2.0 stores this information in the <code>alpha</code> array so that +variable transparency can be supported for each palette entry. However, +for truecolor images, <code>transparent</code> represents a single +RGB color which is <strong>always 100% transparent</strong>, and this +feature is generally supported by browsers which do not support +full alpha channels. +<PRE> +typedef struct { + /* Palette-based image pixels */ + unsigned char ** pixels; + int sx; + int sy; + /* These are valid in palette images only. See also + /* 'alpha', which appears later in the structure to + preserve binary backwards compatibility */ + int colorsTotal; + int red[gdMaxColors]; + int green[gdMaxColors]; + int blue[gdMaxColors]; + int open[gdMaxColors]; + /* For backwards compatibility, this is set to the + first palette entry with 100% transparency, + and is also set and reset by the + gdImageColorTransparent function. Newer + applications can allocate palette entries + with any desired level of transparency; however, + bear in mind that many viewers, notably + many web browsers, fail to implement + full alpha channel for PNG and provide + support for full opacity or transparency only. */ + int transparent; + int *polyInts; + int polyAllocated; + struct gdImageStruct *brush; + struct gdImageStruct *tile; + int brushColorMap[gdMaxColors]; + int tileColorMap[gdMaxColors]; + int styleLength; + int stylePos; + int *style; + int interlace; + /* New in 2.0: alpha channel for palettes. Note that only + Macintosh Internet Explorer and (possibly) Netscape 6 + really support multiple levels of transparency in + palettes, to my knowledge, as of 2/15/01. Most + common browsers will display 100% opaque and + 100% transparent correctly, and do something + unpredictable and/or undesirable for levels + in between. TBB */ + int alpha[gdMaxColors]; + /* Truecolor flag and pixels. New 2.0 fields appear here at the + end to minimize breakage of existing object code. */ + int trueColor; + int ** tpixels; + /* Should alpha channel be copied, or applied, each time a + pixel is drawn? This applies to truecolor images only. + No attempt is made to alpha-blend in palette images, + even if semitransparent palette entries exist. + To do that, build your image as a truecolor image, + then quantize down to 8 bits. */ + int alphaBlendingFlag; + /* Should the alpha channel of the image be saved? This affects + PNG at the moment; other future formats may also + have that capability. JPEG doesn't. */ + int saveAlphaFlag; +} gdImage; +</PRE> +<p> +The order of the structure members may appear confusing, but was chosen +deliberately to increase backwards compatibility with existing gd 1.x-based +binary code that references particular structure members. +<DT><A NAME="gdImagePtr">gdImagePtr</A> <strong>(TYPE)</strong> +<DD> +A pointer to an image structure. <A HREF="#gdImageCreate">gdImageCreate</A> +returns this type, and the other functions expect it as the first +argument. +<DT><A NAME="gdIoCtx">gdIOCtx</a> <strong>(TYPE)</strong> +<DD> +Most of the gd functions that read and write files, such as +<a href="#gdImagePng">gdImagePng</a> and <a href="#gdImageCreateFromJpeg"></a>, +also have variants that accept a gdIOCtx structure; see +<a href="#gdImagePngCtx">gdImagePngCtx</a> and +<a href="#gdImageCreateFromJpegCtx">gdImageCreateFromJpegCtx</a>. Those who wish to provide +their own custom routines to read and write images can populate a +gdIOCtx structure with functions of their own devising to +to read and write data. For image reading, the only mandatory +functions are getC and getBuf, which must return the number of +characters actually read, or a negative value on error or EOF. +These functions must read the number of characters requested +unless at the end of the file. For image writing, the only mandatory +functions are putC and putBuf, which return the number of +characters written; these functions must write the number of +characters requested except in the event of an error. The seek +and tell functions are only required in conjunction with the +<code>gd2</code> file format, which supports quick loading of +partial images. The gd_free function will not be invoked when +calling the standard Ctx functions; it is an implementation +convenience when adding new data types to gd. For examples, +see gd_png.c, gd_gd2.c, gd_jpeg.c, etc., all of which rely +on gdIOCtx to implement the standard image read and write functions. + +<pre> +typedef struct gdIOCtx +{ + int (*getC) (struct gdIOCtx *); + int (*getBuf) (struct gdIOCtx *, void *, int wanted); + + void (*putC) (struct gdIOCtx *, int); + int (*putBuf) (struct gdIOCtx *, const void *, int wanted); + + /* seek must return 1 on SUCCESS, 0 on FAILURE. Unlike fseek! */ + int (*seek) (struct gdIOCtx *, const int); + + long (*tell) (struct gdIOCtx *); + + void (*gd_free) (struct gdIOCtx *); + +} gdIOCtx; +</pre> + + +<DT><A NAME="gdFont">gdFont</A> <strong>(TYPE)</strong> +<DD> +A font structure. Used to declare the characteristics of a font. +Please see the files gdfontl.c and gdfontl.h for an example of the +proper declaration of this structure. You can provide your +own font data by providing such a structure and the associated +pixel array. You can determine the width and height of a single +character in a font by examining the w and h members of the +structure. If you will not be creating your own fonts, you will +not need to concern yourself with the rest of the components of this +structure. +<PRE> +typedef struct { + /* # of characters in font */ + int nchars; + /* First character is numbered... (usually 32 = space) */ + int offset; + /* Character width and height */ + int w; + int h; + /* Font data; array of characters, one row after another. + Easily included in code, also easily loaded from + data files. */ + char *data; +} gdFont; +</PRE> +<DT><A NAME="gdFontPtr">gdFontPtr</A> <strong>(TYPE)</strong> +<DD> +A pointer to a font structure. Text-output functions expect these +as their second argument, following the <A HREF="#gdImagePtr"> +gdImagePtr</A> argument. Two such pointers are declared in the +provided include files gdfonts.h and gdfontl.h. +<DT><A NAME="gdPoint">gdPoint</A> <strong>(TYPE)</strong> +<DD> +Represents a point in the coordinate space of the image; used +by <A HREF="#gdImagePolygon">gdImagePolygon</A>, +<A HREF="#gdImageOpenPolygon">gdImageOpenPolygon</A> and +<A HREF="#gdImageFilledPolygon">gdImageFilledPolygon</A>. +<PRE> +typedef struct { + int x, y; +} gdPoint, *gdPointPtr; +</PRE> +<DT><A NAME="gdPointPtr">gdPointPtr</A> <strong>(TYPE)</strong> +<DD> +A pointer to a <A HREF="#gdPoint">gdPoint</A> structure; passed +as an argument to <A HREF="#gdImagePolygon">gdImagePolygon</A>, +<A HREF="#gdImageOpenPolygon">gdImageOpenPolygon</A> +and <A HREF="#gdImageFilledPolygon">gdImageFilledPolygon</A>. +</DL> +<DT><A NAME="gdFTStringExtra">gdFTStringExtra</a> <strong>(TYPE)</strong> +<DD> +A structure used to pass additional parameters to the +<a href="#gdImageStringFTEx">gdImageStringFTEx</a> function. See +<a href="#gdImageStringFTEx">gdImageStringFTEx</a> for the +structure definition. +</DD> +<DT><A NAME="gdFTStringExtraPtr">gdFTStringExtraPtr</a> <strong>(TYPE)</strong> +<DD> +A pointer to a structure used to pass additional parameters to the +<a href="#gdImageStringFTEx">gdImageStringFTEx</a> function. See +<a href="#gdImageStringFTEx">gdImageStringFTEx</a> for the +structure definition. +</DD> +<DT><A NAME="gdSource">gdSource</A> <strong>(TYPE)</strong> +<DD> +<pre> +typedef struct { + int (*source) (void *context, char *buffer, int len); + void *context; +} gdSource, *gdSourcePtr; +</pre> +Represents a source from which a PNG can be read. +Programmers who do not wish to read PNGs from a file can provide +their own alternate input mechanism, using the +<a href="#gdImageCreateFromPngSource">gdImageCreateFromPngSource</a> function. +See the documentation of that function for an example of the +proper use of this type. +<DT><A NAME="gdSink">gdSink</A> <strong>(TYPE)</strong> +<DD> +<PRE> +typedef struct { + int (*sink) (void *context, char *buffer, int len); + void *context; +} gdSink, *gdSinkPtr; +</PRE> +Represents a "sink" (destination) to which a PNG can be written. +Programmers who do not wish to write PNGs to a file can provide +their own alternate output mechanism, using the +<a href="#gdImagePngToSink">gdImagePngToSink</a> function. +See the documentation of that function for an example of the +proper use of this type. +<H3><A NAME="creating">Image creation, destruction, loading and saving</A></H3> +<DL> +<DT><A NAME="gdImageCreate">gdImageCreate(sx, sy)</A> +<strong>(FUNCTION)</strong> +<DD> +gdImageCreate is called to create palette-based images, with no +more than 256 colors. Invoke gdImageCreate +with the x and y dimensions of the desired image. gdImageCreate +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or +NULL if unable to +allocate the image. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +im = gdImageCreate(64, 64); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageCreateTrueColor">gdImageCreateTrueColor(sx, sy)</A> +<strong>(FUNCTION)</strong> +<DD> +gdImageCreateTrueColor is called to create truecolor images, with +an essentially unlimited number of colors. Invoke gdImageCreateTrueColor +with the x and y dimensions of the desired image. gdImageCreateTrueColor +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or +NULL if unable to +allocate the image. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<p> +Truecolor images are always filled with black at creation time. +There is no concept of a "background" color index. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +im = gdImageCreateTrueColor(64, 64); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageCreateFromJpeg">gdImageCreateFromJpeg(FILE *in)</A> +<strong>(FUNCTION)</strong> +<br> +<A NAME="gdImageCreateFromJpegPtr">gdImageCreateFromJpegPtr(int size, void *data)</A> +<strong>(FUNCTION)</strong> +<br> +<A NAME="gdImageCreateFromJpegCtx">gdImageCreateFromJpegCtx(gdIOCtx *in)</A> +<strong>(FUNCTION)</strong> +<p> +<DD> +gdImageCreateFromJpeg is called to load truecolor images from JPEG format files. +Invoke gdImageCreateFromJpeg with an already opened pointer to a file +containing the desired image. +gdImageCreateFromJpeg +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new +truecolor image, or NULL +if unable to load the image (most often because the file is corrupt or +does not contain a JPEG image). gdImageCreateFromJpeg does <em>not</em> +close the file. You can inspect the sx and sy members of the +image to determine its size. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. <strong>The +returned image is always a truecolor image.</strong> +<p> +If you already have the +image file in memory, pass the size of the file and a pointer to the +file's data to gdImageCreateFromJpegPtr, which is otherwise identical +to gdImageCreateFromJpeg. +<p> +<PRE> +<A HREF="#gdImagePtr">gdImagePtr</A> im; +... inside a function ... +FILE *in; +in = fopen("myjpeg.jpg", "rb"); +im = gdImageCreateFromJpeg(in); +fclose(in); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageCreateFromPng">gdImageCreateFromPng(FILE *in)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromPngPtr">gdImageCreateFromPngPtr(int size, void *data)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromPngCtx">gdImageCreateFromPngCtx(<a href=#gdioctx>gdIOCtx</a> *in)</A> +<strong>(FUNCTION)</strong> +<p> +<DD> +gdImageCreateFromPng is called to load images from PNG format files. +Invoke gdImageCreateFromPng with an already opened pointer to a file +containing the desired image. +gdImageCreateFromPng +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or NULL +if unable to load the image (most often because the file is corrupt or +does not contain a PNG image). gdImageCreateFromPng does <em>not</em> +close the file. You can inspect the sx and sy members of the +image to determine its size. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<p> +If you already have the +image file in memory, pass the size of the file and a pointer to the +file's data to gdImageCreateFromPngPtr, which is otherwise identical +to gdImageCreateFromPng. +<p> +If the PNG image being loaded is a truecolor image, the resulting +gdImagePtr will refer to a truecolor image. If the PNG image +being loaded is a palette or grayscale image, the resulting +gdImagePtr will refer to a palette image. gd retains only 8 bits +of resolution for each of the red, green and blue channels, and +only 7 bits of resolution for the alpha channel. The former +restriction affects only a handful of very rare 48-bit color +and 16-bit grayscale PNG images. The second restriction affects +all semitransparent PNG images, but the difference is essentially +invisible to the eye. 7 bits of alpha channel resolution is, +in practice, quite a lot. +<PRE> +<A HREF="#gdImagePtr">gdImagePtr</A> im; +... inside a function ... +FILE *in; +in = fopen("mypng.png", "rb"); +im = gdImageCreateFromPng(in); +fclose(in); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageCreateFromPngSource">gdImageCreateFromPngSource(gdSourcePtr in)</A> +<strong>(FUNCTION)</strong> +<dd> +<b>Deprecated</b> in favor of +<a href="#gdImageCreateFromPngCtx">gdImageCreateFromPngCtx</a>. Should +not be used in new applications. +<p> +gdImageCreateFromPngSource is called to load a PNG from +a data source other than a file. Usage is very similar to +the <a href="#gdImageCreateFromPng">gdImageCreateFromPng</a> function, +except that the programmer provides a custom data source. +<p> +The programmer must write an input function which accepts +a context pointer, a buffer, and a number of bytes to be +read as arguments. This function must read the number of +bytes requested, unless the end of the file has been reached, +in which case the function should return zero, or an error +has occurred, in which case the function should return +<code>-1</code>. The programmer then creates a +<a href="#gdSource">gdSource</a> structure and sets +the <code>source</code> pointer to the input function and +the context pointer to any value which is useful to the +programmer. +<p> +The example below +implements <a href="#gdImageCreateFromPng">gdImageCreateFromPng</a> +by creating a custom data source and invoking gdImageCreateFromPngSource. +<pre> +static int freadWrapper(void *context, char *buf, int len); + +gdImagePtr gdImageCreateFromPng(FILE *in) +{ + gdSource s; + s.source = freadWrapper; + s.context = in; + return gdImageCreateFromPngSource(&s); +} + +static int freadWrapper(void *context, char *buf, int len) +{ + int got = fread(buf, 1, len, (FILE *) context); + return got; +} +</pre> +<DT><A NAME="gdImageCreateFromGif">gdImageCreateFromGif(FILE *in)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromGifPtr">gdImageCreateFromGifPtr(int size, void *data)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromGifCtx">gdImageCreateFromGifCtx(<a href=#gdioctx>gdIOCtx</a> *in)</A> +<strong>(FUNCTION)</strong> +<p> +<DD> +gdImageCreateFromGif is called to load images from GIF format files. +Invoke gdImageCreateFromGif with an already opened pointer to a file +containing the desired image. +gdImageCreateFromGif +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or NULL +if unable to load the image (most often because the file is corrupt or +does not contain a GIF image). gdImageCreateFromGif does <em>not</em> +close the file. You can inspect the sx and sy members of the +image to determine its size. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<p> +If you already have the +image file in memory, pass the size of the file and a pointer to the +file's data to gdImageCreateFromGifPtr, which is otherwise identical +to gdImageCreateFromGif. +<PRE> +<A HREF="#gdImagePtr">gdImagePtr</A> im; +... inside a function ... +FILE *in; +in = fopen("mygif.gif", "rb"); +im = gdImageCreateFromGif(in); +fclose(in); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageCreateFromGd">gdImageCreateFromGd(FILE *in)</A> +<strong>(FUNCTION)</strong> +<br><DT><A NAME="gdImageCreateFromGdPtr">gdImageCreateFromGdPtr(int size, void *data)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromGdCtx">gdImageCreateFromGdCtx(<a href=#gdioctx>gdIOCtx</a> *in)</A> +<strong>(FUNCTION)</strong> +<p> +<DD> +gdImageCreateFromGd is called to load images from gd format files. +Invoke gdImageCreateFromGd +with an already opened pointer to a file containing the desired image +in the <A HREF="#gdformat">gd file format</A>, which is specific to +gd and intended for very fast loading. (It is <em>not</em> intended for +compression; for compression, use PNG or JPEG.) +<p> +If you already have the +image file in memory, pass the size of the file and a pointer to the +file's data to gdImageCreateFromGdPtr, which is otherwise identical +to gdImageCreateFromGd. +<p> +gdImageCreateFromGd +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or NULL +if unable to load the image (most often because the file is corrupt or +does not contain a gd format image). gdImageCreateFromGd does <em>not</em> +close the file. You can inspect the sx and sy members of the +image to determine its size. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +FILE *in; +in = fopen("mygd.gd", "rb"); +im = gdImageCreateFromGd(in); +fclose(in); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> + +<DT><A NAME="gdImageCreateFromGd2">gdImageCreateFromGd2(FILE *in)</A> +<strong>(FUNCTION)</strong> +<br><A NAME="gdImageCreateFromGd2Ptr">gdImageCreateFromGd2Ptr(int size, void *data)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromGd2Ctx">gdImageCreateFromGd2Ctx(<a href=#gdioctx>gdIOCtx</a> *in)</A> +<strong>(FUNCTION)</strong> +<p> + +<DD> +gdImageCreateFromGd2 is called to load images from gd2 format files. +Invoke gdImageCreateFromGd2 +with an already opened pointer to a file containing the desired image +in the <A HREF="#gdformat">gd2 file format</A>, which is specific to +gd2 and intended for fast loading of parts of large images. +(It is a compressed format, but generally not as good as maximum +compression of the entire image would be.) +<p> +If you already have the +image file in memory, pass the size of the file and a pointer to the +file's data to gdImageCreateFromGd2Ptr, which is otherwise identical +to gdImageCreateFromGd2. +<p> +gdImageCreateFromGd2 +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or NULL +if unable to load the image (most often because the file is corrupt or +does not contain a gd format image). gdImageCreateFromGd2 does <em>not</em> +close the file. You can inspect the sx and sy members of the +image to determine its size. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +FILE *in; +in = fopen("mygd.gd2", "rb"); +im = gdImageCreateFromGd2(in); +fclose(in); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> + +<DT><A NAME="gdImageCreateFromGd2Part">gdImageCreateFromGd2Part(FILE *in, int srcX, int srcY, int w, int h)</A> +<strong>(FUNCTION)</strong> +<br><A NAME="gdImageCreateFromGd2PartPtr">gdImageCreateFromGd2PartPtr(int size, void *data, int srcX, int srcY, int w, int h)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromGd2PartCtx">gdImageCreateFromGd2PartCtx(<a href=#gdioctx>gdIOCtx</a> *in)</A> +<strong>(FUNCTION)</strong> +<p> + +<DD> +gdImageCreateFromGd2Part is called to load parts of images from <A HREF="#gdformat">gd2 format files</a>. +Invoked in the same way as <a href=#gdImageCreateFromGd2>gdImageCreateFromGd2</a>, +but with extra parameters +indicating the source (x, y) and width/height of the desired image. +gdImageCreateFromGd2Part returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the +new image, or NULL if unable to load the image. +The image must eventually be destroyed using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<p> +If you already have the image file in memory, you may use +gdImageCreateFromGd2PartPtr. Pass the size of the image file, +in bytes, as the first argument and the pointer to the image file data +as the second argument. +<p> +<DT><A NAME="gdImageCreateFromWBMP">gdImageCreateFromWBMP(FILE *in)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromWBMPPtr">gdImageCreateFromWBMPPtr(int size, void *data)</A> +<strong>(FUNCTION)</strong> +<BR><A NAME="gdImageCreateFromWBMPCtx">gdImageCreateFromWBMPCtx(<a href=#gdioctx>gdIOCtx</a> *in)</A> +<strong>(FUNCTION)</strong> +<p> +<DD> +gdImageCreateFromWBMP is called to load images from WBMP format files. +Invoke gdImageCreateFromWBMP with an already opened pointer to a file +containing the desired image. +gdImageCreateFromWBMP +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or NULL +if unable to load the image (most often because the file is corrupt or +does not contain a PNG image). gdImageCreateFromWBMP does <em>not</em> +close the file. You can inspect the sx and sy members of the +image to determine its size. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<p> +If you already have the +image file in memory, pass the size of the file and a pointer to the +file's data to gdImageCreateFromWBMPPtr, which is otherwise identical +to gdImageCreateFromWBMP. +<PRE> +<A HREF="#gdImagePtr">gdImagePtr</A> im; +... inside a function ... +FILE *in; +in = fopen("mywbmp.wbmp", "rb"); +im = gdImageCreateFromWBMP(in); +fclose(in); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<p> +<DT><A NAME="gdImageCreateFromXbm">gdImageCreateFromXbm(FILE *in)</A> +<strong>(FUNCTION)</strong> +<DD> +gdImageCreateFromXbm is called to load images from X bitmap format +files. Invoke gdImageCreateFromXbm +with an already opened pointer to a file containing the desired image. +gdImageCreateFromXbm +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or NULL +if unable to load the image (most often because the file is corrupt or +does not contain an X bitmap format image). gdImageCreateFromXbm does +<em>not</em> close the file. You can inspect the sx and sy members of the +image to determine its size. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +FILE *in; +in = fopen("myxbm.xbm", "rb"); +im = gdImageCreateFromXbm(in); +fclose(in); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageCreateFromXpm">gdImageCreateFromXpm(char *filename)</A> +<strong>(FUNCTION)</strong> +<DD> +gdImageCreateFromXbm is called to load images from XPM X Window System +color bitmap format files. This function is available only if HAVE_XPM +is selected in the Makefile and the Xpm library is linked with the +application. Unlike most gd file functions, the Xpm functions require +filenames, not file pointers. +gdImageCreateFromXpm +returns a <A HREF="#gdImagePtr">gdImagePtr</A> to the new image, or NULL +if unable to load the image (most often because the file is corrupt or +does not contain an XPM bitmap format image). You can inspect the sx and sy members of the +image to determine its size. The image must eventually be destroyed +using <A HREF="#gdImageDestroy">gdImageDestroy()</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +FILE *in; +in = fopen("myxpm.xpm", "rb"); +im = gdImageCreateFromXpm(in); +fclose(in); +/* ... Use the image ... */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageDestroy">gdImageDestroy(gdImagePtr im)</A> <STRONG>(FUNCTION)</STRONG> +<DD>gdImageDestroy is used to free the memory associated with +an image. It is important to invoke gdImageDestroy before +exiting your program or assigning a new image to +a <A HREF="#gdImagePtr">gdImagePtr</A> variable. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(10, 10); +/* ... Use the image ... */ +/* Now destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageJpeg"> +void gdImageJpeg(gdImagePtr im, FILE *out, int quality)</A> +<STRONG>(FUNCTION)</STRONG><BR> +<a name="gdImageJpegCtx">void gdImageJpegCtx(gdImagePtr im, gdIOCtx *out, int quality)</A> +<STRONG>(FUNCTION)</STRONG><BR> +<DD> +gdImageJpeg outputs the specified image to the specified +file in JPEG format. The file must be open for writing. Under MSDOS +and all versions of Windows, it is important to use "wb" as opposed +to simply "w" as the mode when opening the file, and under Unix there +is no penalty for doing so. gdImageJpeg does <em>not</em> +close the file; your code must do so. +<P> +If quality is negative, the default IJG JPEG quality value (which +should yield a good general quality / size tradeoff for most +situations) is used. Otherwise, for practical purposes, quality +should be a value in the range 0-95, higher quality values usually +implying both higher quality and larger image sizes. +<P> +If you have set image interlacing using +<A HREF="#gdImageInterlace">gdImageInterlace</A>, this function will +interpret that to mean you wish to output a progressive JPEG. Some +programs (e.g., Web browsers) can display progressive JPEGs +incrementally; this can be useful when browsing over a relatively slow +communications link, for example. Progressive JPEGs can also be +slightly smaller than sequential (non-progressive) JPEGs. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black, white; +FILE *out; +/* Create the image */ +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Allocate background */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate drawing color */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Draw rectangle */ +<A HREF="#gdImageRectangle">gdImageRectangle</A>(im, 0, 0, 99, 99, black); +/* Open output file in binary mode */ +out = fopen("rect.jpg", "wb"); +/* Write JPEG using default quality */ +gdImageJpeg(im, out, -1); +/* Close file */ +fclose(out); +/* Destroy image */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageJpegPtr"> +void* gdImageJpegPtr(gdImagePtr im, int *size, int quality)</A> +<STRONG>(FUNCTION)</STRONG> +<DD>Identical to gdImageJpeg except that it returns a pointer to a memory +area with the JPEG data. This memory must be freed by the caller when it is +no longer needed. <strong>The caller must invoke gdFree(), not free(), +unless the caller is absolutely certain that the same implementations of +malloc, free, etc. are used both at library build time and at application +build time.</strong> The 'size' parameter receives the total size of the block +of memory. +<DT><A NAME="gdImageGif"> +void gdImageGif(gdImagePtr im, FILE *out)</A> +<br> +<A NAME="gdImageGifCtx"> +void gdImageGifCtx(gdImagePtr im, gdIOCtx *out)</A> + +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageGif outputs the specified image to the specified +file in GIF format. The file must be open for writing. Under MSDOS +and all versions of Windows, it is important to use "wb" as opposed +to simply "w" as the mode when opening the file, and under Unix there +is no penalty for doing so. gdImageGif does <em>not</em> +close the file; your code must do so. +<p> +GIF does not support true color; GIF images can contain a maximum +of 256 colors. If the image to be written is a +truecolor image, such as those created with +<a href="#gdImageCreateTrueColor">gdImageCreateTrueColor</a> or loaded +from a JPEG or a truecolor PNG image file, a palette-based +temporary image will automatically be created internally using the +<a href="#gdImageCreatePaletteFromTrueColor">gdImageCreatePaletteFromTrueColor</a> function. The original image pixels are not modified. This conversion +produces high quality palettes but does require some CPU time. If you are +regularly converting truecolor to palette in this way, you should consider +creating your image as a palette-based image in the first place. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black, white; +FILE *out; +/* Create the image */ +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Allocate background */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate drawing color */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Draw rectangle */ +<A HREF="#gdImageRectangle">gdImageRectangle</A>(im, 0, 0, 99, 99, black); +/* Open output file in binary mode */ +out = fopen("rect.gif", "wb"); +/* Write GIF */ +gdImageGif(im, out); +/* Close file */ +fclose(out); +/* Destroy image */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageGifPtr"> +void* gdImageGifPtr(gdImagePtr im, int *size)</A> +<STRONG>(FUNCTION)</STRONG> +<DD>Identical to gdImageGif except that it returns a pointer to a memory +area with the GIF data. This memory must be freed by the caller when it is +no longer needed. <strong>The caller must invoke gdFree(), not free(), +unless the caller is absolutely certain that the same implementations of +malloc, free, etc. are used both at library build time and at application +build time.</strong> The 'size' parameter receives the total size of the block +of memory. + +<DT><A NAME="gdImageGifAnimBegin"> +void gdImageGifAnimBegin(gdImagePtr im, FILE *out, int GlobalCM, int Loops)</A> +<br> +<A NAME="gdImageGifAnimBeginCtx"> +void gdImageGifAnimBeginCtx(gdImagePtr im, gdIOCtx *out, int GlobalCM, int Loops)</A> + +<STRONG>(FUNCTION)</STRONG> + +<DD>This function must be called as the first function when creating a +GIF animation. It writes the correct GIF file headers to selected +file output, and prepares for frames to be added for the animation. +The image argument is not used to produce an image frame to the file, +it is only used to establish the GIF animation frame size, interlacing +options and the color palette. gdImageGifAnimAdd is used to +add the first and subsequent frames to the animation, and the animation +must be terminated by writing a semicolon character (;) to it or by using +gdImageGifAnimEnd to do that. +<p> + +The GlobalCM flag indicates if a global color map (or palette) is used +in the GIF89A header. A nonzero value specifies that a global color +map should be used to reduce the size of the animation. +Of course, if the color maps of +individual frames differ greatly, a global color map may not be a good idea. +GlobalCM=1 means write global color map, GlobalCM=0 means do not, and +GlobalCM=-1 means to do the default, which currently is to use a global +color map. + +<p> + +If Loops is 0 or greater, the Netscape 2.0 extension for animation +loop count is written. 0 means infinite loop count. -1 means that +the extension is not added which results in no looping. -1 is the +default. + +<DT><A NAME="gdImageGifAnimBeginPtr"> +void* gdImageGifAnimBeginPtr(gdImagePtr im, int *size, int GlobalCM, int Loops)</A> +<STRONG>(FUNCTION)</STRONG> +<DD>Identical to gdImageGifAnimBegin except that it returns a pointer +to a memory area with the GIF data. This memory must be freed by the +caller when it is no longer needed. <strong>The caller must invoke +gdFree(), not free(), unless the caller is absolutely certain that the +same implementations of malloc, free, etc. are used both at library +build time and at application build time.</strong> The 'size' +parameter receives the total size of the block of memory. + +<DT><A NAME="gdImageGifAnimAdd"> +void gdImageGifAnimAdd(gdImagePtr im, FILE *out, int LocalCM, int LeftOfs, int TopOfs, int Delay, int Disposal, gdImagePtr previm)</A> +<br> +<A NAME="gdImageGifAnimAddCtx"> +void gdImageGifAnimAddCtx(gdImagePtr im, gdIOCtx *out, int LocalCM, int LeftOfs, int TopOfs, int Delay, int Disposal, gdImagePtr previm)</A> + +<STRONG>(FUNCTION)</STRONG> + +<DD> This function writes GIF animation frames to GIF animation, which +was initialized with <a +href="#gdImageGifAnimBegin">gdImageGifAnimBegin</a>. With LeftOfs and +TopOfs you can place this frame in different offset than (0,0) inside +the image screen as defined in gdImageGifAnimBegin. Delay between the +previous frame and this frame is in 1/100s units. Disposal is usually +<code>gdDisposalNone</code>, meaning that the pixels changed by this +frame should remain on the display when the next frame begins to render, but +can also be <code>gdDisposalUnknown</code> (not recommended), +<code>gdDisposalRestoreBackground</code> (restores the first +allocated color of the global palette), or +<code>gdDisposalRestorePrevious</code> (restores the appearance of the +affected area before the frame was rendered). Only +<code>gdDisposalNone</code> is a sensible choice for the first frame. +If <code>previm</code> is +passed, the built-in GIF optimizer will always use <code>gdDisposalNone</code> +regardless of the Disposal parameter. +<p> +Setting the LocalCM flag to 1 adds a local palette for this image to the +animation. Otherwise the global palette is assumed and the user must make +sure the palettes match. Use <A HREF="#gdImagePaletteCopy">gdImagePaletteCopy</A> to do that. + +<p> + +Automatic optimization is activated by giving the previous image as a +parameter. This function then compares the images and only writes the changed +pixels to the new frame in animation. The Disposal parameter for +optimized animations must be set to 1, also for the first frame. +LeftOfs and TopOfs parameters are ignored for optimized frames. To +achieve good optimization, it is usually best to use a single global +color map. To allow gdImageGifAnimAdd to compress unchanged pixels via +the use of a transparent color, the image must include a transparent color. + +<PRE> +... inside a function ... +gdImagePtr im, im2, im3; +int black, white, trans; +FILE *out; +/* Create the image */ +im = gdImageCreate(100, 100); +/* Allocate background */ +white = gdImageColorAllocate(im, 255, 255, 255); +/* Allocate drawing color */ +black = gdImageColorAllocate(im, 0, 0, 0); +/* Allocate transparent color for animation compression */ +trans = gdImageColorAllocate(im, 1, 1, 1); +/* Draw rectangle */ +gdImageRectangle(im, 0, 0, 10, 10, black); +/* Open output file in binary mode */ +out = fopen("anim.gif", "wb"); +/* Write GIF header. Use global color map. Loop a few times */ +gdImageGifAnimBegin(im, out, 1, 3); +/* Write the first frame. No local color map. Delay = 1s */ +gdImageGifAnimAdd(im, out, 0, 0, 0, 100, 1, NULL); +/* construct the second frame */ +im2 = gdImageCreate(100, 100); +/* Allocate background to make it white */ +(void)gdImageColorAllocate(im2, 255, 255, 255); +/* Make sure the palette is identical */ +gdImagePaletteCopy (im2, im); +/* Draw something */ +gdImageRectangle(im2, 0, 0, 15, 15, black); +/* Allow animation compression with transparent pixels */ +gdImageColorTransparent (im2, trans); +/* Add the second frame */ +gdImageGifAnimAdd(im2, out, 0, 0, 0, 100, 1, im); +/* construct the second frame */ +im3 = gdImageCreate(100, 100); +/* Allocate background to make it white */ +(void)gdImageColorAllocate(im3, 255, 255, 255); +/* Make sure the palette is identical */ +gdImagePaletteCopy (im3, im); +/* Draw something */ +gdImageRectangle(im3, 0, 0, 15, 20, black); +/* Allow animation compression with transparent pixels */ +gdImageColorTransparent (im3, trans); +/* Add the third frame, compressing against the second one */ +gdImageGifAnimAdd(im3, out, 0, 0, 0, 100, 1, im2); +/* Write the end marker */ +/* gdImageGifAnimEnd(out); is the same as the following: */ +putc (';', out); +/* Close file */ +fclose(out); +/* Destroy images */ +gdImageDestroy(im); +gdImageDestroy(im2); +gdImageDestroy(im3); +</PRE> + +<DT><A NAME="gdImageGifAnimAddPtr"> +void* gdImageGifAnimAddPtr(gdImagePtr im, int *size, int LocalCM, int LeftOfs, int TopOfs, int Delay, int Disposal, gdImagePtr previm)</A> +<STRONG>(FUNCTION)</STRONG> +<DD>Identical to gdImageGifAnimAdd except that it returns a pointer +to a memory area with the GIF data. This memory must be freed by the +caller when it is no longer needed. <strong>The caller must invoke +gdFree(), not free(), unless the caller is absolutely certain that the +same implementations of malloc, free, etc. are used both at library +build time and at application build time.</strong> The 'size' +parameter receives the total size of the block of memory. + +<DT><A NAME="gdImageGifAnimEnd"> +void gdImageGifAnimEnd(FILE *out)</A> +<br> +<A NAME="gdImageGifAnimEndCtx"> +void gdImageGifAnimEndCtx(gdIOCtx *out)</A> + +<STRONG>(FUNCTION)</STRONG> + +<DD>Writes semicolon character (;) to the output file. This +terminates the GIF file properly. You can omit the call to +gdImageGifAnimEnd and just print out the semicolon. + +<DT><A NAME="gdImageGifAnimEndPtr"> +void* gdImageGifAnimEndPtr(int *size)</A> +<STRONG>(FUNCTION)</STRONG> + +<DD>Returns a one byte string containing the semicolon character (;). +Returns a pointer to a memory area with that string. This memory must +be freed by the caller when it is no longer needed. <strong>The caller +must invoke gdFree(), not free(), unless the caller is absolutely +certain that the same implementations of malloc, free, etc. are used +both at library build time and at application build time.</strong> The +'size' parameter receives the total size of the block of memory. The +string ";" can be used in place of this function. + +<DT><A NAME="gdImagePng"> +void gdImagePng(gdImagePtr im, FILE *out)</A> +<br> +<A NAME="gdImagePngCtx"> +void gdImagePngCtx(gdImagePtr im, gdIOCtx *out)</A> + +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImagePng outputs the specified image to the specified +file in PNG format. The file must be open for writing. Under MSDOS +and all versions of Windows, it is important to use "wb" as opposed +to simply "w" as the mode when opening the file, and under Unix there +is no penalty for doing so. gdImagePng does <em>not</em> +close the file; your code must do so. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black, white; +FILE *out; +/* Create the image */ +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Allocate background */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate drawing color */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Draw rectangle */ +<A HREF="#gdImageRectangle">gdImageRectangle</A>(im, 0, 0, 99, 99, black); +/* Open output file in binary mode */ +out = fopen("rect.png", "wb"); +/* Write PNG */ +gdImagePng(im, out); +/* Close file */ +fclose(out); +/* Destroy image */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImagePngEx"> +void gdImagePngEx(gdImagePtr im, FILE *out, int level)</A> +<br> +<A NAME="gdImagePngCtxEx"> +void gdImagePngCtxEx(gdImagePtr im, gdIOCtx *out, int level)</A> + +<STRONG>(FUNCTION)</STRONG> +<DD> +Like <a href="#gdImagePng">gdImagePng</a>, gdImagePngEx outputs the +specified image to the specified file in PNG format. In addition, +gdImagePngEx allows the level of compression to be specified. A compression +level of 0 means "no compression." A compression level of 1 means +"compressed, but as quickly as possible." A compression level of 9 +means "compressed as much as possible to produce the smallest possible +file." A compression level of -1 will use the default compression level +at the time zlib was compiled on your system. +<p> +For more information, see <a href="#gdImagePng">gdImagePng</a>. +<DT><A NAME="gdImagePngPtr"> +void* gdImagePngPtr(gdImagePtr im, int *size)</A> +<STRONG>(FUNCTION)</STRONG> +<DD>Identical to gdImagePng except that it returns a pointer to a memory +area with the PNG data. This memory must be freed by the caller when it is +no longer needed. <strong>The caller must invoke gdFree(), not free(), +unless the caller is absolutely certain that the same implementations of +malloc, free, etc. are used both at library build time and at application +build time.</strong> The 'size' parameter receives the total size of the block +of memory. +<DT><A NAME="gdImagePngPtrEx"> +void* gdImagePngPtrEx(gdImagePtr im, int *size, int level)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +Like <a href="#gdImagePngPtr">gdImagePngPtr</a>, gdImagePngPtrEx returns a +pointer to a PNG image in allocated memory. +In addition, gdImagePngPtrEx allows the level of compression to be +specified. A compression level of 0 means "no compression." A compression level of 1 means +"compressed, but as quickly as possible." A compression level of 9 +means "compressed as much as possible to produce the smallest possible +file." A compression level of -1 will use the default compression level +at the time zlib was compiled on your system. +<p> +For more information, see <a href="#gdImagePngPtr">gdImagePngPtr</a>. +<DT><A NAME="gdImagePngToSink">gdImagePngToSink(gdImagePtr im, gdSinkPtr out)</A> +<strong>(FUNCTION)</strong> +<dd> +gdImagePngToSink is called to write a PNG to +a data "sink" (destination) other than a file. Usage is very similar to +the <a href="#gdImagePng">gdImagePng</a> function, +except that the programmer provides a custom data sink. +<p> +The programmer must write an output function which accepts +a context pointer, a buffer, and a number of bytes to be +written as arguments. This function must write the number of +bytes requested and return that number, unless an error +has occurred, in which case the function should return +<code>-1</code>. The programmer then creates a +<a href="#gdSink">gdSink</a> structure and sets +the <code>sink</code> pointer to the output function and +the context pointer to any value which is useful to the +programmer. +<p> +The example below +implements <a href="#gdImagePng">gdImagePng</a> +by creating a custom data source and invoking gdImagePngFromSink. +<pre> +static int stdioSink(void *context, char *buffer, int len) +{ + return fwrite(buffer, 1, len, (FILE *) context); +} + +void gdImagePng(gdImagePtr im, FILE *out) +{ + gdSink mySink; + mySink.context = (void *) out; + mySink.sink = stdioSink; + gdImagePngToSink(im, &mySink); +} +</pre> +<DT><A NAME="gdImageWBMP"> +void gdImageWBMP(gdImagePtr im, int fg, FILE *out)</A> +<BR><A NAME="gdImageWBMPCtx">gdImageWBMPCtx(<a href=#gdioctx>gdIOCtx</a> *out)</A> +<strong>(FUNCTION)</strong><STRONG>(FUNCTION)</STRONG> +<DD> +gdImageWBMP outputs the specified image to the specified +file in WBMP format. The file must be open for writing. Under MSDOS +and all versions of Windows, it is important to use "wb" as opposed +to simply "w" as the mode when opening the file, and under Unix there +is no penalty for doing so. gdImageWBMP does <em>not</em> +close the file; your code must do so. +<p> +<strong>WBMP file support is black and white only. The color index +specified by the fg argument is the "foreground," and only pixels +of this color will be set in the WBMP file.</strong> All other pixels +will be considered "background." +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black, white; +FILE *out; +/* Create the image */ +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Allocate background */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate drawing color */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Draw rectangle */ +<A HREF="#gdImageRectangle">gdImageRectangle</A>(im, 0, 0, 99, 99, black); +/* Open output file in binary mode */ +out = fopen("rect.wbmp", "wb"); +/* Write WBMP, with black as foreground */ +gdImageWBMP(im, black, out); +/* Close file */ +fclose(out); +/* Destroy image */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageWBMPPtr"> +void* gdImageWBMPPtr(gdImagePtr im, int *size)</A> +<STRONG>(FUNCTION)</STRONG> +<DD>Identical to gdImageWBMP except that it returns a pointer to a memory +area with the WBMP data. This memory must be freed by the caller when it is +no longer needed. <strong>The caller must invoke gdFree(), not free(), +unless the caller is absolutely certain that the same implementations of +malloc, free, etc. are used both at library build time and at application +build time.</strong> The 'size' parameter receives the total size of the block +of memory. +<DT><A NAME="gdImageGd"> +void gdImageGd(gdImagePtr im, FILE *out)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageGd outputs the specified image to the specified +file in the <A HREF="#gdformat">gd image format</A>. The file must +be open for writing. Under MSDOS and all versions of Windows, it is +important to use "wb" as +opposed to simply "w" as the mode when opening the file, and under +Unix there is no penalty for doing so. gdImagePng does <em>not</em> +close the file; your code must do so. +<P> +The gd image format is intended for fast reads and writes of +images your program will need frequently to build other +images. It is <em>not</em> a compressed format, and is not intended +for general use. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black, white; +FILE *out; +/* Create the image */ +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Allocate background */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate drawing color */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Draw rectangle */ +<A HREF="#gdImageRectangle">gdImageRectangle</A>(im, 0, 0, 99, 99, black); +/* Open output file in binary mode */ +out = fopen("rect.gd", "wb"); +/* Write gd format file */ +gdImageGd(im, out); +/* Close file */ +fclose(out); +/* Destroy image */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> + +<DT><A NAME="gdImageGdPtr"> +void* gdImageGdPtr(gdImagePtr im, int *size)</A> +<STRONG>(FUNCTION)</STRONG> +<DD>Identical to gdImageGd except that it returns a pointer to a memory +area with the GD data. This memory must be freed by the caller when it is +no longer needed. <strong>The caller must invoke gdFree(), not free(), +unless the caller is absolutely certain that the same implementations of +malloc, free, etc. are used both at library build time and at application +build time.</strong> The 'size' parameter receives the total size of the block +of memory. + +<DT><A NAME="gdImageGd2"> +void gdImageGd2(gdImagePtr im, FILE *out, int chunkSize, int fmt)</A> +<br> +<A NAME="gdImageGd2Ctx"> +void gdImageGd2Ctx(gdImagePtr im, gdIOCtx *out, int chunkSize, int fmt)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageGd2 outputs the specified image to the specified +file in the <A HREF="#gd2format">gd2 image format</A>. The file must +be open for writing. Under MSDOS and all versions of Windows, it is +important to use "wb" as +opposed to simply "w" as the mode when opening the file, and under +Unix there is no penalty for doing so. gdImageGd2 does <em>not</em> +close the file; your code must do so. +<P> +The gd2 image format is intended for fast reads and writes of +parts of images. +It is a compressed format, and well suited to retrieving smll sections of +much larger images. + +The third and fourth parameters are the 'chunk size' and format resposectively. +<p> +The file is stored as a series of compressed subimages, and the +<strong>Chunk Size</strong> determines the sub-image size - a value of +zero causes the GD library to use the default. +<p> +It is also possible to store GD2 files in an uncompressed format, in which case the +fourth parameter should be GD2_FMT_RAW. + +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black, white; +FILE *out; +/* Create the image */ +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Allocate background */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate drawing color */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Draw rectangle */ +<A HREF="#gdImageRectangle">gdImageRectangle</A>(im, 0, 0, 99, 99, black); +/* Open output file in binary mode */ +out = fopen("rect.gd", "wb"); +/* Write gd2 format file */ +gdImageGd2(im, out, 0, GD2_FMT_COMPRESSED); +/* Close file */ +fclose(out); +/* Destroy image */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> + +<DT><A NAME="gdImageGd2Ptr"> +void* gdImageGd2Ptr(gdImagePtr im, int chunkSize, int fmt, int *size)</A> +<STRONG>(FUNCTION)</STRONG> +<DD>Identical to gdImageGd2 except that it returns a pointer to a memory +area with the GD2 data. This memory must be freed by the caller when it is +no longer needed. <strong>The caller must invoke gdFree(), not free(), +unless the caller is absolutely certain that the same implementations of +malloc, free, etc. are used both at library build time and at application +build time.</strong> The 'size' parameter receives the total size of the block +of memory. +<DT><A NAME="gdImageTrueColorToPalette"> +void gdImageTrueColorToPalette(gdImagePtr im, int ditherFlag, int colorsWanted)</A> +<br> +<A NAME="gdImageCreatePaletteFromTrueColor"> +gdImagePtr gdImageCreatePaletteFromTrueColor(gdImagePtr im, int ditherFlag, int colorsWanted)</A> + +<STRONG>(FUNCTION)</STRONG> +<DD> +<blockquote> +gdImageCreatePaletteFromTrueColor returns a <b>new</b> +image. gdImageTrueColorToPalette permanently converts the +<b>existing</b> image. The two functions are otherwise identical. +</blockquote> +<p> +The function converts a truecolor image to a palette-based image, +using a high-quality two-pass quantization routine. +If ditherFlag is set, the image will be +dithered to approximate colors better, at the expense +of some obvious "speckling." colorsWanted can be +anything up to 256. If the original source image +includes photographic information or anything that +came out of a JPEG, 256 is strongly recommended. +100% transparency of a single transparent color in the +original truecolor image will be preserved. There is no other +support for preservation of alpha channel or transparency in +the destination image. +<p> +For best results, don't use this function -- write real +truecolor PNGs and JPEGs. The disk space gain of +conversion to palette is not great (for small images +it can be negative) and the quality loss is ugly. However, +the version of this function included in version 2.0.12 and later does +do a better job than the version included prior to 2.0.12. +</DL> +<H3><A NAME="drawing">Drawing Functions</A></H3> +<DL> +<DT><A NAME="gdImageSetPixel">void gdImageSetPixel(gdImagePtr im, int x, int y, int color)</A> <STRONG>(FUNCTION)</STRONG> +<DD>gdImageSetPixel sets a pixel to a particular color index. Always use +this function or one of the other drawing functions to access pixels; +do not access the pixels of the <A HREF="#gdImage">gdImage</A> structure +directly. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Set a pixel near the center. */ +gdImageSetPixel(im, 50, 50, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageLine">void gdImageLine(gdImagePtr im, int x1, int y1, int x2, int y2, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageLine is used to draw a line between two endpoints (x1,y1 and x2, y2). +The line is drawn using the color index specified. Note that the color +index can be an actual color returned by <A HREF="#gdImageColorAllocate"> +gdImageColorAllocate</A> or one of <A HREF="#gdStyled">gdStyled</A>, +<A HREF="#gdBrushed">gdBrushed</A> or <A HREF="#gdStyledBrushed"> +gdStyledBrushed</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green + and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a line from the upper left corner to the + lower right corner. */ +gdImageLine(im, 0, 0, 99, 99, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageDashedLine">void gdImageDashedLine(gdImagePtr im, int x1, int y1, int x2, int y2, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageDashedLine is provided <strong>solely for backwards compatibility +</strong> with gd 1.0. New programs should draw dashed lines using +the normal <A HREF="#gdImageLine">gdImageLine</A> function and the +new <A HREF="#gdImageSetStyle">gdImageSetStyle</A> function. +<P> +gdImageDashedLine is used to draw a dashed line between two endpoints +(x1,y1 and x2, y2). +The line is drawn using the color index specified. The portions of the line +that are not drawn are left transparent so the background is visible. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue + all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a dashed line from the upper left corner + to the lower right corner. */ +gdImageDashedLine(im, 0, 0, 99, 99); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImagePolygon">void gdImagePolygon(gdImagePtr im, gdPointPtr points, int pointsTotal, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImagePolygon is used to draw a polygon with the verticies +(at least 3) specified, using the color index specified. +See also <A HREF="#gdImageFilledPolygon">gdImageFilledPolygon</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +/* Points of polygon */ +<A HREF="#gdPoint">gdPoint</A> points[3]; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and + blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a triangle. */ +points[0].x = 50; +points[0].y = 0; +points[1].x = 99; +points[1].y = 99; +points[2].x = 0; +points[2].y = 99; +gdImagePolygon(im, points, 3, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageOpenPolygon">void gdImageOpenPolygon(gdImagePtr im, gdPointPtr points, int pointsTotal, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageOpenPolygon is used to draw a sequence of lines with the verticies +(at least 3) specified, using the color index specified. Unlike +<A HREF="#gdImagePolygon">gdImagePolygon</A>, the enpoints of the line +sequence are not connected to a closed polygon. +<DT><A NAME="gdImageRectangle">void gdImageRectangle(gdImagePtr im, int x1, int y1, int x2, int y2, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageRectangle is used to draw a rectangle with the two corners +(upper left first, then lower right) specified, using the +color index specified. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a rectangle occupying the central area. */ +gdImageRectangle(im, 25, 25, 74, 74, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageFilledPolygon">void gdImageFilledPolygon(gdImagePtr im, gdPointPtr points, int pointsTotal, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageFilledPolygon is used to fill a polygon with the verticies +(at least 3) specified, using the color index specified. +See also <A HREF="#gdImageFilledPolygon">gdImagePolygon</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +int red; +/* Points of polygon */ +<A HREF="#gdPoint">gdPoint</A> points[3]; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate the color red. */ +red = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 0, 0); +/* Draw a triangle. */ +points[0].x = 50; +points[0].y = 0; +points[1].x = 99; +points[1].y = 99; +points[2].x = 0; +points[2].y = 99; +/* Paint it in white */ +gdImageFilledPolygon(im, points, 3, white); +/* Outline it in red; must be done second */ +<A HREF="#gdImagePolygon">gdImagePolygon</A>(im, points, 3, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageFilledRectangle">void gdImageFilledRectangle(gdImagePtr im, int x1, int y1, int x2, int y2, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageFilledRectangle is used to draw a solid rectangle with the two corners +(upper left first, then lower right) specified, using the +color index specified. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">int gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a filled rectangle occupying the central area. */ +gdImageFilledRectangle(im, 25, 25, 74, 74, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageArc">void gdImageArc(gdImagePtr im, int cx, int cy, int w, int h, int s, int e, int color)</A> +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageArc is used to draw a partial ellipse centered at the given point, +with the specified width and height in pixels. The arc begins at +the position in degrees specified by <code>s</code> and ends at +the position specified by <code>e</code>. The arc is drawn in +the color specified by the last argument. A circle can be drawn +by beginning from 0 degrees and ending at 360 degrees, with +width and height being equal. e must be greater than s. Values greater +than 360 are interpreted modulo 360. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 50); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Inscribe an ellipse in the image. */ +gdImageArc(im, 50, 25, 98, 48, 0, 360, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageFilledArc">void gdImageFilledArc(gdImagePtr im, int cx, int cy, int w, int h, int s, int e, int color, int style)</A> +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageFilledArc is used to draw a partial ellipse centered at the given point, +with the specified width and height in pixels. The arc begins at +the position in degrees specified by <code>s</code> and ends at +the position specified by <code>e</code>. The arc is filled in +the color specified by the second to last argument. A circle can be drawn +by beginning from 0 degrees and ending at 360 degrees, with +width and height being equal. e must be greater than s. Values greater +than 360 are interpreted modulo 360. The last argument is a bitwise +OR of the following possibilities: +<ul> +<li>gdArc +<li>gdChord +<li>gdPie (synonym for gdChord) +<li>gdNoFill +<li>gdEdged +</ul> +gdArc and gdChord are mutually exclusive; +gdChord just connects the starting and ending +angles with a straight line, while gdArc produces +a rounded edge. gdPie is a synonym for gdArc. +gdNoFill indicates that the arc or chord should be +outlined, not filled. gdEdged, used together with +gdNoFill, indicates that the beginning and ending +angles should be connected to the center; this is +a good way to outline (rather than fill) a +'pie slice'. + +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 50); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Inscribe a filled pie slice in the image. */ +gdImageFilledArc(im, 50, 25, 98, 48, 0, 45, white, gdArc); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageFilledEllipse">void gdImageFilledEllipse(gdImagePtr im, int cx, int cy, int w, int h, int color)</A> +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageFilledEllipse is used to draw an ellipse centered at the given point, +with the specified width and height in pixels. The ellipse is filled in +the color specified by the last argument. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 50); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Inscribe a filled ellipse in the image. */ +gdImageFilledEllipse(im, 50, 25, 98, 48, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageFillToBorder">void gdImageFillToBorder(gdImagePtr im, int x, int y, int border, int color) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageFillToBorder floods a portion of the image with the specified +<code>color</code>, beginning at the specified point and stopping at +the specified <code>border</code> color. For a way of flooding an +area defined by the color of the starting point, see +<A HREF="#gdImageFill">gdImageFill</A>. +<P> +The border color <em>cannot</em> be a special color +such as <A HREF="#gdTiled">gdTiled</A>; it must be a proper +solid color. The fill color can be, however. +<P> +Note that gdImageFillToBorder is recursive. It is not the most +naive implementation possible, and the implementation is +expected to improve, but there will always be degenerate +cases in which the stack can become very deep. This can be +a problem in MSDOS and MS Windows 3.1 environments. (Of course, +in a Unix or Windows 95/98/NT environment with a proper stack, this is +not a problem at all.) +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +int red; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 50); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate the color red. */ +red = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 0, 0); +/* Inscribe an ellipse in the image. */ +gdImageArc(im, 50, 25, 98, 48, 0, 360, white); +/* Flood-fill the ellipse. Fill color is red, border color is + white (ellipse). */ +gdImageFillToBorder(im, 50, 50, white, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageFill">void gdImageFill(gdImagePtr im, int x, int y, int color) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageFill floods a portion of the image with the specified +<code>color</code>, beginning at the specified point and flooding the +surrounding region of the same color as the starting point. +For a way of flooding a region defined by a specific border +color rather than by its interior color, see +<A HREF="#gdImageFillToBorder">gdImageFillToBorder</A>. +<P> +The fill color can be <A HREF="#gdTiled">gdTiled</A>, resulting +in a tile fill using another image as the tile. However, +the tile image cannot be transparent. If the image you wish +to fill with has a transparent color index, call +<A HREF="#gdImageTransparent">gdImageTransparent</A> on the +tile image and set the transparent color index to -1 +to turn off its transparency. +<P> +Note that gdImageFill is recursive. It is not the most +naive implementation possible, and the implementation is +expected to improve, but there will always be degenerate +cases in which the stack can become very deep. This can be +a problem in MSDOS and MS Windows environments. (Of course, +in a Unix or Windows 95/98/NT environment with a proper stack, this is +not a problem at all.) +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +int red; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 50); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Allocate the color red. */ +red = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 0, 0); +/* Inscribe an ellipse in the image. */ +gdImageArc(im, 50, 25, 98, 48, 0, 360, white); +/* Flood-fill the ellipse. Fill color is red, and will replace the + black interior of the ellipse. */ +gdImageFill(im, 50, 50, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageSetAntiAliased">void gdImageSetAntiAliased(gdImagePtr im, int c)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +"Antialiasing" is a process by which jagged edges associated with line +drawing can be reduced by blending the foreground color with an appropriate +percentage of the background, depending on how much of the pixel in question +is actually within the boundaries of the line being drawn. +All line-drawing functions, +such as <A HREF="#gdImageLine">gdImageLine</A>, +<A HREF="#gdImageOpenPolygon">gdImageOpenPolygon</A> and +<A HREF="#gdImagePolygon">gdImagePolygon</A>, will draw antialiased lines +if the special "color" <A HREF="#gdAntiAliased"> +gdAntiAliased</A> is used when calling them. +<P> +gdImageSetAntiAliased is used to specify the actual foreground color +to be used when drawing antialiased lines. You may set any color to +be the foreground, however as of version 2.0.12 an alpha channel +component is not supported. +<p> +Antialiased lines can be drawn on both truecolor and palette-based +images. However, attempts to draw antialiased lines on +highly complex palette-based backgrounds may not give satisfactory +results, due to the limited number of colors available in the +palette. Antialiased line-drawing on simple backgrounds should +work well with palette-based images; otherwise create or fetch +a truecolor image instead. +<P> +You need not take any special action when you are finished +with antialised line drawing. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im, brush; +int black; +int blue; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +blue = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 255); +gdImageSetAntiAliased(im, blue); +/* Draw a smooth line from the upper left corner to the + lower right corner. */ +<A HREF="#gdImageLine">gdImageLine</A>(im, 0, 0, 99, 99, <A HREF="#gdBrushed">gdAntiAliased</A>); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +See also <a href="#gdAntiAliased">gdAntiAliased</a> and +<a href="#gdSetAntiAliasedDontBlend">gdSetAntiAliasedDontBlend</a>. +<DT><A NAME="gdImageSetAntiAliasedDontBlend">void gdImageSetAntiAliasedDontBlend(gdImagePtr im, int c)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +Normally, when drawing lines with the special +<a href="#gdAntiAliased">gdAntiAliased</a> "color," blending with the +background to reduce jagged edges is the desired behavior. However, when +it is desired that lines not be blended with one particular color when +it is encountered in the background, the +gdImageSetAntiAliasedDontBlend function can be used to indicate the +special color that the foreground should stand out more clearly against. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im, brush; +int black; +int blue; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +blue = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 255); +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); + +gdImageSetAntiAliased(im, blue); +/* The portion of the line that crosses this white rectangle will + not be blended smoothly */ +gdImageSetAntiAliasedDontBlend(im, white); +gdImageFilledRectangle(im, 25, 25, 75, 75, white); +/* Draw a smooth line from the upper left corner + to the lower right corner. */ +<A HREF="#gdImageLine">gdImageLine</A>(im, 0, 0, 99, 99, <A HREF="#gdBrushed">gdAntiAliased</A>); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +See also <a href="#gdAntiAliased">gdAntiAliased</a> and +<a href="#gdSetAntiAliased">gdSetAntiAliased</a>. +<DT><A NAME="gdImageSetBrush">void gdImageSetBrush(gdImagePtr im, gdImagePtr brush)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +A "brush" is an image used to draw wide, shaped strokes in another image. Just +as a paintbrush is not a single point, a brush image need not be +a single pixel. <em>Any</em> gd image can be used as a brush, and by +setting the transparent color index of the brush image with +<A HREF="#gdImageColorTransparent">gdImageColorTransparent</A>, +a brush of any shape can be created. All line-drawing functions, +such as <A HREF="#gdImageLine">gdImageLine</A>, +<A HREF="#gdImageOpenPolygon">gdImageOpenPolygon</A> and +<A HREF="#gdImagePolygon">gdImagePolygon</A>, will use the +current brush if the special "color" <A HREF="#gdBrushed"> +gdBrushed</A> or <A HREF="#gdStyledBrushed">gdStyledBrushed</A> +is used when calling them. +<P> +gdImageSetBrush is used to specify the brush to be used in a +particular image. You can set any image to be the brush. +If the brush image does not have the same color map as the +first image, any colors missing from the first image +will be allocated. If not enough colors can be allocated, +the closest colors already available will be used. This +allows arbitrary PNGs to be used as brush images. It also +means, however, that you should not set a brush unless you +will actually use it; if you set a rapid succession of +different brush images, you can quickly fill your color map, +and the results will not be optimal. +<P> +You need not take any special action when you are finished +with a brush. As for any other image, if you will not +be using the brush image for any further purpose, +you should call <A HREF="#gdImageDestroy">gdImageDestroy</A>. +You must not use the color <A HREF="#gdBrushed">gdBrushed</A> +if the current brush has been destroyed; you can of +course set a new brush to replace it. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im, brush; +FILE *in; +int black; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Open the brush PNG. For best results, portions of the + brush that should be transparent (ie, not part of the + brush shape) should have the transparent color index. */ +in = fopen("star.png", "rb"); +brush = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +gdImageSetBrush(im, brush); +/* Draw a line from the upper left corner to the lower right corner + using the brush. */ +<A HREF="#gdImageLine">gdImageLine</A>(im, 0, 0, 99, 99, <A HREF="#gdBrushed">gdBrushed</A>); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +/* Destroy the brush image */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(brush); +</PRE> +<DT><A NAME="gdImageSetTile">void gdImageSetTile(gdImagePtr im, gdImagePtr tile)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +A "tile" is an image used to fill an area with a repeated pattern. +<em>Any</em> gd image can be used as a tile, and by +setting the transparent color index of the tile image with +<A HREF="#gdImageColorTransparent">gdImageColorTransparent</A>, +a tile that allows certain parts of the underlying area to shine +through can be created. All region-filling functions, +such as <A HREF="#gdImageFill">gdImageFill</A> and +<A HREF="#gdImageFilledPolygon">gdImageFilledPolygon</A>, will use the +current tile if the special "color" <A HREF="#gdTiled"> +gdTiled</A> is used when calling them. +<P> +gdImageSetTile is used to specify the tile to be used in a +particular image. You can set any image to be the tile. +If the tile image does not have the same color map as the +first image, any colors missing from the first image +will be allocated. If not enough colors can be allocated, +the closest colors already available will be used. This +allows arbitrary PNGs to be used as tile images. It also +means, however, that you should not set a tile unless you +will actually use it; if you set a rapid succession of +different tile images, you can quickly fill your color map, +and the results will not be optimal. +<P> +You need not take any special action when you are finished +with a tile. As for any other image, if you will not +be using the tile image for any further purpose, +you should call <A HREF="#gdImageDestroy">gdImageDestroy</A>. +You must not use the color <A HREF="#gdBrushed">gdTiled</A> +if the current tile has been destroyed; you can of +course set a new tile to replace it. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im, tile; +FILE *in; +int black; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Open the tile PNG. For best results, portions of the + tile that should be transparent (ie, allowing the + background to shine through) should have the transparent + color index. */ +in = fopen("star.png", "rb"); +tile = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +gdImageSetTile(im, tile); +/* Fill an area using the tile. */ +<A HREF="#gdImageFilledRectangle">gdImageFilledRectangle</A>(im, 25, 25, 75, 75, <A HREF="#gdTiled">gdTiled</A>); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +/* Destroy the tile image */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(tile); +</PRE> +<DT><A NAME="gdImageSetStyle">void gdImageSetStyle(gdImagePtr im, int *style, int styleLength)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +It is often desirable to draw dashed lines, dotted lines, and other +variations on a broken line. gdImageSetStyle can be used to set +any desired series of colors, including a special color that +leaves the background intact, to be repeated during the drawing +of a line. +<P> +To use gdImageSetStyle, create an array of integers and assign +them the desired series of color values to be repeated. +You can assign the special color value <A HREF="#gdTransparent"> +gdTransparent</A> to indicate that the existing color should +be left unchanged for that particular pixel (allowing a dashed +line to be attractively drawn over an existing image). +<P> +Then, to draw a line using the style, use the normal +<A HREF="#gdImageLine">gdImageLine</A> function with the +special color value <A HREF="#gdStyled">gdStyled</A>. +<P> +As of <A HREF="#whatsnew1.1.1">version 1.1.1</A>, the style +array is copied when you set the style, so you need not +be concerned with keeping the array around indefinitely. +This should not break existing code that assumes styles +are not copied. +<P> +You can also combine styles and brushes to draw the brush +image at intervals instead of in a continuous stroke. +When creating a style for use with a brush, the +style values are interpreted differently: zero (0) indicates +pixels at which the brush should not be drawn, while one (1) +indicates pixels at which the brush should be drawn. +To draw a styled, brushed line, you must use the +special color value <A HREF="#gdStyledBrushed"> +gdStyledBrushed</A>. For an example of this feature +in use, see gddemo.c (provided in the distribution). +<PRE> +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int styleDotted[2], styleDashed[6]; +FILE *in; +int black; +int red; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +red = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 0, 0); +/* Set up dotted style. Leave every other pixel alone. */ +styleDotted[0] = red; +styleDotted[1] = gdTransparent; +/* Set up dashed style. Three on, three off. */ +styleDashed[0] = red; +styleDashed[1] = red; +styleDashed[2] = red; +styleDashed[3] = gdTransparent; +styleDashed[4] = gdTransparent; +styleDashed[5] = gdTransparent; +/* Set dotted style. Note that we have to specify how many pixels are + in the style! */ +gdImageSetStyle(im, styleDotted, 2); +/* Draw a line from the upper left corner to the lower right corner. */ +<A HREF="#gdImageLine">gdImageLine</A>(im, 0, 0, 99, 99, <A HREF="#gdStyled">gdStyled</A>); +/* Now the dashed line. */ +gdImageSetStyle(im, styleDashed, 6); +<A HREF="#gdImageLine">gdImageLine</A>(im, 0, 99, 0, 99, <A HREF="#gdStyled">gdStyled</A>); + +/* ... Do something with the image, such as + saving it to a file ... */ + +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageSetThickness">void gdImageSetThickness(gdImagePtr im, int thickness)</A> <STRONG>(FUNCTION)</STRONG> +<DD>gdImageSetThickness determines the width of lines drawn by the +<a href="#gdImageLine">gdImageLine</a>, <a href="#gdImagePolygon">gdImagePolygon</a>, <a href="#gdImageOpenPolygon">gdImageOpenPolygon</a> +and related functions, in pixels. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Set thickness. */ +gdImageSetThickness(im, 4); +/* Draw a fat line from the upper left corner to the lower right corner. */ +gdImageLine(im, 0, 0, 99, 99, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageAlphaBlending">void gdImageAlphaBlending(gdImagePtr im, int blending)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +The <a href="#gdImageAlphaBlending">gdImageAlphaBlending</a> +function allows for two different modes of drawing on truecolor +images. In blending mode, which is <strong>on by default (gd 2.0.2 +and above)</strong>, the alpha channel component of the color +supplied to all drawing functions, such as +<a href="#gdImageSetPixel">gdImageSetPixel</a>, determines how much of +the underlying color should be allowed to shine through. As a result, +gd automatically blends the existing color at that point with the +drawing color, and stores the result in the image. The resulting pixel +is opaque. In non-blending mode, the drawing color is copied literally +with its alpha channel information, replacing the destination pixel. +Blending mode is not available when drawing on palette images. +<PRE> +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int red, blue; +im = <A HREF="#gdImageCreate">gdImageCreateTrueColor</A>(100, 100); +/* Background color */ +red = <A HREF="#gdTrueColor">gdTrueColor</A>(255, 0, 0); +gdImageFilledRectangle(im, 0, 0, 100, 100, red); +/* Drawing color. Full transparency would be an alpha channel value + of 127 (gd has a 7 bit alpha chnanel). 0 is opaque, + 127 is transparent. So cut gdAlphaTransparent in half to get + 50% blending. */ +blue = <A HREF="#gdTrueColor">gdTrueColorAlpha</A>(0, 0, 255, gdAlphaTransparent / 2); +/* Draw with blending. Result will be 50% red, 50% blue: yellow + (emitted light, remember, not reflected light. What you learned + in Kindergarten is wrong here). */ +gdImageAlphaBlending(im, 1); +<a href="#gdImageFilledRectangle">gdImageFilledRectangle</a>(im, 0, 0, 25, 25, blue); +/* Draw without blending. Result will be 50% blue, 50% + the background color of the image viewer or web browser + used; results in browsers that don't support + semi-transparent pixels are unpredictable! */ +gdImageAlphaBlending(im, 0); +<a href="#gdImageFilledRectangle">gdImageFilledRectangle</a>(im, 75, 75, 25, 25, blue); +/* Write the image to disk, etc. */ +</pre> +<DT><A NAME="gdImageSaveAlpha"> +void gdImageSaveAlpha(gdImagePtr im, int saveFlag)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +By default, gd 2.0.2 and above do not attempt to save full alpha channel information +(as opposed to single-color transparency) when saving PNG images. (PNG +is currently the only output format supported by gd which can accommodate +alpa channel information.) This saves space in the output file. If you wish +to create an image with alpha channel information for use with tools that +support it, call gdImageSaveAlpha(im, 1) to turn on saving of such +information, and call <a href="#gdImageAlphaBlending">gdImageAlphaBlending(im, 0)</a> +to turn off alpha blending within the library so that alpha channel +information is actually stored in the image rather than being composited +immediately at the time that drawing functions are invoked. +<DT><A NAME="gdImageSetClip"> +void gdImageSetClip(gdImagePtr im, int x1, int y1, int x2, int y2)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +Establishes a clipping rectangle. Once gdImageSetClip has been called, +all future drawing operations will remain within the specified clipping +area, until a new gdImageSetClip call takes place. For instance, +if a clipping rectangle of 25, 25, 75, 75 has been set within a +100x100 image, a diagonal line from 0,0 to 99,99 will appear only +between 25,25 and 75,75. +<p> +If gdImageSetClip is never called, the clipping area will be the +entire image. +<p> +The parameters passed to gdImageSetClip are checked against the dimensions +of the image and limited to "safe" values. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Set the clipping rectangle. */ +gdImageSetClip(im, 25, 25, 75, 75); +/* Draw a line from the upper left corner to the lower right corner. + Only the part within the clipping rectangle will appear. */ +<a href="#gdImageLine">gdImageLine</a>(im, 0, 0, 99, 99, white); +/* ... Do something with the image, such as + saving it to a file ... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +See also <a href="#gdImageGetClip">gdImageGetClip</a>. +<DT><A NAME="gdImageGetClip"> +void gdImageGetClip(gdImagePtr im, int *x1P, int *y1P, int *x2P, int *y2P)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +Fetches the boundaries of the current clipping rectangle. +<pre> +... Inside a function ... +gdImagePtr im = <a href="#gdImageCreateTrueColor">gdImageCreateTrueColor</a>(100, 100); +int x1, y1, x2, y2; +gdImageSetClip(im, 25, 25, 75, 75); +gdImageGetClip(im, &x1, &y1, &x2, &y2); +printf("%d %d %d %d\n", x1, y1, x2, y2); +</pre> +The above code would print: +<pre> +25 25 75 75 +</pre> +See also <a href="#gdImageSetClip">gdImageSetClip</a>. +</DL> +<H3><A NAME="query">Query Functions</A></H3> +<DL> +<DT><A NAME="gdImageBlue"> +int gdImageAlpha(gdImagePtr im, int color)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageAlpha is a macro which returns the alpha channel component of +the specified color index. Alpha channel values vary between +0 (gdAlphaOpaque), which does not blend at all with the background, +through 127 (gdAlphaTransparent), which allows the background to +shine through 100%. Use this macro rather than accessing the +structure members directly. +int gdImageBlue(gdImagePtr im, int color)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageBlue is a macro which returns the blue component of +the specified color index. Use this macro rather than accessing the +structure members directly. +<DT><A NAME="gdImageGetPixel">int gdImageGetPixel(gdImagePtr im, int x, int y)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageGetPixel() retrieves the color index of a particular +pixel. Always use this function to query pixels; +do not access the pixels of the <A HREF="#gdImage">gdImage</A> structure +directly. +<PRE> +... inside a function ... +FILE *in; +gdImagePtr im; +int c; +in = fopen("mypng.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +c = gdImageGetPixel(im, gdImageSX(im) / 2, gdImageSY(im) / 2); +printf("The value of the center pixel is %d; RGB values are %d,%d,%d\n", + c, im->red[c], im->green[c], im->blue[c]); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageBoundsSafe"> +int gdImageBoundsSafe(gdImagePtr im, int x, int y)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageBoundsSafe returns true (1) if the specified point is within the +current clipping rectangle, false (0) if not. The clipping rectangle is +set by <a href="#gdImageSetClip">gdImageSetClip</a> and defaults +to the entire image. This function is intended primarily for +use by those who wish to add functions to gd. All of the gd drawing +functions already clip safely using this function or its macro +equivalent in gd.c, gdImageBoundsSafeMacro. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +if (gdImageBoundsSafe(im, 50, 50)) { + printf("50, 50 is within the image bounds\n"); +} else { + printf("50, 50 is outside the image bounds\n"); +} +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageGreen"> +int gdImageGreen(gdImagePtr im, int color)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageGreen is a macro which returns the green component of +the specified color index. Use this macro rather than accessing the +structure members directly. +<DT><A NAME="gdImageRed"> +int gdImageRed(gdImagePtr im, int color)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageRed is a macro which returns the red component of +the specified color index. Use this macro rather than accessing the +structure members directly. +<DT><A NAME="gdImageSX"> +int gdImageSX(gdImagePtr im)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageSX is a macro which returns the width of the image +in pixels. Use this macro rather than accessing the +structure members directly. +<DT><A NAME="gdImageSY"> +int gdImageSY(gdImagePtr im)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageSY is a macro which returns the height of the image +in pixels. Use this macro rather than accessing the +structure members directly. +</DL> +<H3><A NAME="fonts">Fonts and text-handling functions</A></H3> +<DL> +<DT><A NAME="gdFontGetSmall"> +gdFontPtr gdFontGetSmall(void)</a> +<STRONG>(FUNCTION)</STRONG> +<DD> +Returns a font pointer for the "small" gd font. Your code must +include the header file <code>gdfonts.h</code> before +calling this function. Under Windows, due to the nature of DLLs, +the use of this function is strongly recommended rather than attempting +to use the <code>gdFontSmall</code> pointer directly. (You may +safely assign the result to a local <code>gdFontPtr</code> variable +in your own code.) +<p> +See <a href="#gdImageString">gdImageString</a> for more information +and examples, or <a href="#gdImageStringFT">gdImageStringFT</a> for a +freetype-based alternative that supports truetype fonts. +<DT><A NAME="gdFontGetLarge"> +gdFontPtr gdFontGetLarge(void)</a> +<STRONG>(FUNCTION)</STRONG> +<DD> +Returns a font pointer for the "large" gd font. Your code must +include the header file <code>gdfontl.h</code> before +calling this function. Under Windows, due to the nature of DLLs, +the use of this function is strongly recommended rather than attempting +to use the <code>gdFontLarge</code> pointer directly. (You may +safely assign the result to a local <code>gdFontPtr</code> variable +in your own code.) +<p> +See <a href="#gdImageString">gdImageString</a> for more information +and examples, or <a href="#gdImageStringFT">gdImageStringFT</a> for a +freetype-based alternative that supports truetype fonts. +<DT><A NAME="gdFontGetMediumBold"> +gdFontPtr gdFontGetMediumBold(void)</a> +<STRONG>(FUNCTION)</STRONG> +<DD> +Returns a font pointer for the "medium bold" gd font. Your code must +include the header file <code>gdfontmb.h</code> before +calling this function. Under Windows, due to the nature of DLLs, +the use of this function is strongly recommended rather than attempting +to use the <code>gdFontMediumBold</code> pointer directly. (You may +safely assign the result to a local <code>gdFontPtr</code> variable +in your own code.) +<p> +See <a href="#gdImageString">gdImageString</a> for more information +and examples, or <a href="#gdImageStringFT">gdImageStringFT</a> for a +freetype-based alternative that supports truetype fonts. +<DT><A NAME="gdFontGetGiant"> +gdFontPtr gdFontGetGiant(void)</a> +<STRONG>(FUNCTION)</STRONG> +<DD> +Returns a font pointer for the "giant" gd font. Your code must +include the header file <code>gdfontg.h</code> before +calling this function. Under Windows, due to the nature of DLLs, +the use of this function is strongly recommended rather than attempting +to use the <code>gdFontGiant</code> pointer directly. (You may +safely assign the result to a local <code>gdFontPtr</code> variable +in your own code.) +<p> +See <a href="#gdImageString">gdImageString</a> for more information +and examples, or <a href="#gdImageStringFT">gdImageStringFT</a> for a +freetype-based alternative that supports truetype fonts. +<DT><A NAME="gdFontGetTiny"> +gdFontPtr gdFontGetTiny(void)</a> +<STRONG>(FUNCTION)</STRONG> +<DD> +Returns a font pointer for the "tiny" gd font. Your code must +include the header file <code>gdfontt.h</code> before +calling this function. Under Windows, due to the nature of DLLs, +the use of this function is strongly recommended rather than attempting +to use the <code>gdFontTiny</code> pointer directly. (You may +safely assign the result to a local <code>gdFontPtr</code> variable +in your own code.) +<p> +See <a href="#gdImageString">gdImageString</a> for more information +and examples, or <a href="#gdImageStringFT">gdImageStringFT</a> for a +freetype-based alternative that supports truetype fonts. +<DT><A NAME="gdImageChar"> +void gdImageChar(gdImagePtr im, gdFontPtr font, int x, int y, + int c, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageChar is used to draw single characters on the image. +(To draw multiple characters, use <A HREF="#gdImageString"> +gdImageString</A> or <A HREF="#gdImageString16"> +gdImageString16</A>. +See also <A HREF="#gdImageStringFT">gdImageStringFT</A> for a high quality +solution.) +The second argument is a pointer to a font definition structure; five fonts are +provided with gd, gdFontTiny, gdFontSmall, gdFontMediumBold, +gdFontLarge, and gdFontGiant. +<p> +You must include the files "gdfontt.h", "gdfonts.h", "gdfontmb.h", +"gdfontl.h" and "gdfontg.h" respectively +and (if you are not using a library-based approach) link with the +corresponding .c files to use the provided fonts. +<p> +<blockquote> +<b>Windows DLL users:</b> although you can use +these DLL-exported pointers directly, you cannot easily assign them to other +pointers. This will cause hard-to-debug problems. To avoid such troubles, you +should call the functions gdFontGetTiny(), gdFontGetSmall(), +gdFontGetMediumBold(), gdFontGetLarge(), and gdFontGetGiant() in order to +obtain pointers to the fonts under Windows. +</blockquote> +<p> +The character specified by the fifth +argument is drawn from left to right in the specified +color. (See <A HREF="#gdImageCharUp">gdImageCharUp</A> for a way +of drawing vertical text.) Pixels not +set by a particular character retain their previous color. +<PRE> +#include "gd.h" +#include "gdfontl.h" +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a character. */ +gdImageChar(im, gdFontGetLarge(), 0, 0, 'Q', white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageCharUp"> +void gdImageCharUp(gdImagePtr im, gdFontPtr font, int x, int y, +int c, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageCharUp is used to draw single characters on the image, +rotated 90 degrees. +(To draw multiple characters, use <A HREF="#gdImageStringUp"> +gdImageStringUp</A> or <A HREF="#gdImageStringUp16"> +gdImageStringUp16</A>.) The second argument is a +pointer to a font definition structure; five fonts are +provided with gd, gdFontTiny, gdFontSmall, gdFontMediumBold, +gdFontLarge, and gdFontGiant. You must +include the files "gdfontt.h", "gdfonts.h", "gdfontmb.h", +"gdfontl.h" and "gdfontg.h" respectively +and (if you are not using a library-based approach) link with the +corresponding .c files to use the provided fonts. +<p> +<blockquote> +<b>Windows DLL users:</b> although you can use +these DLL-exported pointers directly, you cannot easily assign them to other +pointers. This will cause hard-to-debug problems. To avoid such troubles, you +should call the functions gdFontGetTiny(), gdFontGetSmall(), +gdFontGetMediumBold(), gdFontGetLarge(), and gdFontGetGiant() in order to +obtain pointers to the fonts under Windows. +</blockquote> +<p> +The character specified by +the fifth argument is drawn +from bottom to top, rotated at a 90-degree angle, in the specified +color. (See <A HREF="#gdImageChar">gdImageChar</A> for a way +of drawing horizontal text.) Pixels not +set by a particular character retain their previous color. +<PRE> +#include "gd.h" +#include "gdfontl.h" +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a character upwards so it rests against the top of the image. */ +gdImageCharUp(im, gdFontGetLarge(), + 0, gdFontGetLarge()->h, 'Q', white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageString"> +void gdImageString(gdImagePtr im, gdFontPtr font, int x, int y, +unsigned char *s, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageString is used to draw multiple characters on the image. +(To draw single characters, use <A HREF="#gdImageChar"> +gdImageChar</A>.) The second argument is a +pointer to a font definition structure; five fonts are +provided with gd, gdFontTiny, gdFontSmall, gdFontMediumBold, +gdFontLarge, and gdFontGiant. You must +include the files "gdfontt.h", "gdfonts.h", "gdfontmb.h", +"gdfontl.h" and "gdfontg.h" respectively +and (if you are not using a library-based approach) link with the +corresponding .c files to use the provided fonts. +<p> +<blockquote> +<b>Windows DLL users:</b> although you can use +these DLL-exported pointers directly, you cannot easily assign them to other +pointers. This will cause hard-to-debug problems. To avoid such troubles, you +should call the functions gdFontGetTiny(), gdFontGetSmall(), +gdFontGetMediumBold(), gdFontGetLarge(), and gdFontGetGiant() in order to +obtain pointers to the fonts under Windows. +</blockquote> +The null-terminated C string specified +by the fifth argument is drawn from left to right in the specified +color. (See <A HREF="#gdImageStringUp">gdImageStringUp</A> for a way +of drawing vertical text. +See also <A HREF="#gdImageStringFT">gdImageStringFT</A> for a high +quality solution.) +Pixels not set by a particular character retain their previous color. +<PRE> +#include "gd.h" +#include "gdfontl.h" +#include <string.h> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +/* String to draw. */ +char *s = "Hello."; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a centered string. */ +gdImageString(im, gdFontGetLarge(), + im->sx / 2 - (strlen(s) * gdFontGetLarge()->w / 2), + im->sy / 2 - gdFontGetLarge()->h / 2, + s, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageString16"> +void gdImageString16(gdImagePtr im, gdFontPtr font, int x, int y, +unsigned short *s, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageString16 is used to draw multiple 16-bit characters on the image. +(To draw single characters, use <A HREF="#gdImageChar"> +gdImageChar16</A>.) The second argument is a +pointer to a font definition structure; no 16-bit fonts are +provided with gd as standard equipment and there does not seem to be +much momentum to create them although the bdftogd script can do so. The +preferred solution is <a href="#gdImageStringFT">gdImageStringFT</a>, which +uses freetype to provide truetype font support. +<p> +<blockquote> +<b>Windows DLL users:</b> although you can use +these DLL-exported pointers directly, you cannot easily assign them to other +pointers. This will cause hard-to-debug problems. To avoid such troubles, you +should call the functions gdFontGetTiny(), gdFontGetSmall(), +gdFontGetMediumBold(), gdFontGetLarge(), and gdFontGetGiant() in order to +obtain pointers to the fonts under Windows. +</blockquote> +The null-terminated string of characters represented as 16-bit unsigned +short integers specified by the fifth argument is drawn from left to right +in the specified +color. (See <A HREF="#gdImageStringUp16">gdImageStringUp16</A> for a way +of drawing vertical text.) Pixels not +set by a particular character retain their previous color. +<p> +This function was added in gd1.3 to provide a means of rendering +fonts with more than 256 characters for those who have them. A +more frequently used routine is <a href="#gdImageString">gdImageString</a>. +<DT><A NAME="gdImageStringUp"> +void gdImageStringUp(gdImagePtr im, gdFontPtr font, int x, int y, +unsigned char *s, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageStringUp is used to draw multiple characters on the image, +rotated 90 degrees. +(To draw single characters, use <A HREF="#gdImageCharUp"> +gdImageCharUp</A>.) The second argument is a +pointer to a font definition structure; five fonts are +provided with gd, gdFontTiny, gdFontSmall, gdFontMediumBold, +gdFontLarge, and gdFontGiant. You must +include the files "gdfontt.h", "gdfonts.h", "gdfontmb.h", +"gdfontl.h" and "gdfontg.h" respectively +and (if you are not using a library-based approach) link with the +corresponding .c files to use the provided fonts. +<blockquote> +<b>Windows DLL users:</b> although you can use +these DLL-exported pointers directly, you cannot easily assign them to other +pointers. This will cause hard-to-debug problems. To avoid such troubles, you +should call the functions gdFontGetTiny(), gdFontGetSmall(), +gdFontGetMediumBold(), gdFontGetLarge(), and gdFontGetGiant() in order to +obtain pointers to the fonts under Windows. +</blockquote> + +The null-terminated C string specified +by the fifth argument is drawn from bottom to top (rotated +90 degrees) in the specified color. (See +<A HREF="#gdImageString">gdImageString</A> for a way +of drawing horizontal text.) Pixels not +set by a particular character retain their previous color. +<PRE> +#include "gd.h" +#include "gdfontl.h" +#include <string.h> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +/* String to draw. */ +char *s = "Hello."; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color white (red, green and blue all maximum). */ +white = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 255, 255); +/* Draw a centered string going upwards. Axes are reversed, + and Y axis is decreasing as the string is drawn. */ +gdImageStringUp(im, gdFontGetLarge(), + im->w / 2 - gdFontGetLarge()->h / 2, + im->h / 2 + (strlen(s) * gdFontGetLarge()->w / 2), + s, white); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageStringUp16"> +void gdImageStringUp16(gdImagePtr im, gdFontPtr font, int x, int y, +unsigned short *s, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageString is used to draw multiple 16-bit characters vertically on +the image. (To draw single characters, use <A HREF="#gdImageChar"> +gdImageChar</A>.) The second argument is a +pointer to a font definition structure; five fonts are +provided with gd, gdFontTiny, gdFontSmall, gdFontMediumBold, +gdFontLarge, and gdFontGiant. You must +include the files "gdfontt.h", "gdfonts.h", "gdfontmb.h", +"gdfontl.h" and "gdfontg.h" respectively +and (if you are not using a library-based approach) link with the +corresponding .c files to use the provided fonts. +<blockquote> +<b>Windows DLL users:</b> although you can use +these DLL-exported pointers directly, you cannot easily assign them to other +pointers. This will cause hard-to-debug problems. To avoid such troubles, you +should call the functions gdFontGetTiny(), gdFontGetSmall(), +gdFontGetMediumBold(), gdFontGetLarge(), and gdFontGetGiant() in order to +obtain pointers to the fonts under Windows. +</blockquote> +The null-terminated string of characters represented as 16-bit unsigned +short integers specified by the fifth argument is drawn from bottom to top +in the specified color. +(See <A HREF="#gdImageStringUp16">gdImageStringUp16</A> for a way +of drawing horizontal text.) Pixels not +set by a particular character retain their previous color. +<p> +This function was added in gd1.3 to provide a means of rendering +fonts with more than 256 characters for those who have them. A +more frequently used routine is <a href="#gdImageStringUp">gdImageStringUp</a>. +<DT><A NAME="gdFTUseFontConfig">int gdFTUseFontConfig(int flag)</a> +<STRONG>(FUNCTION)</STRONG> +<DD> +GD 2.0.29 introduced the ability to use +<a href="http://freedesktop.org/software/fontconfig">fontconfig patterns</a> +rather than font file names as parameters to +<a href="#gdImageStringFT">gdImageStringFT</a>, +<a href="#gdImageStringFTEx">gdImageStringFTEx</a> and +<a href="#gdImageStringFTEx">gdImageStringFTCircle</a>. +For backwards compatibility reasons, the fontlist parameter to those +functions is still expected to be a full or partial font file path name +or list thereof by default. However, as a convenience, a single call +to gdFTUseFontConfig with a nonzero parameter configures gd to expect +the fontlist parameter to be a fontconfig pattern. Regardless of whether +the flag argument is nonzero, this function returns true when the +fontconfig library is available and false when it is not. When fontconfig +is not available, the fontlist parameter always behaves as in previous +versions of GD. +<pre> +#include "gd.h" +#include <string.h> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +int brect[8]; +int x, y; +char *err; + +char *s = "Hello."; /* String to draw. */ +double sz = 40.; +char *fc = "times:bold:italic"; /* fontconfig pattern */ + +/* Signal that all freetype font calls in this program will receive + fontconfig patterns rather than filenames of font files */ +gdUseFontConfig(1); + +/* obtain brect so that we can size the image */ +err = <A HREF="#gdImageStringFT">gdImageStringFT</A>(NULL,&brect[0],0,fc,sz,0.,0,0,s); +if (err) {fprintf(stderr,err); return 1;} + +/* create an image big enough for the string plus a little whitespace */ +x = brect[2]-brect[6] + 6; +y = brect[3]-brect[7] + 6; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(x,y); + +/* Background color (first allocated) */ +white = <A HREF="#gdImageColorResolve">gdImageColorResolve</A>(im, 255, 255, 255); +black = <A HREF="#gdImageColorResolve">gdImageColorResolve</A>(im, 0, 0, 0); + +/* render the string, offset origin to center string*/ +/* note that we use top-left coordinate for adjustment + * since gd origin is in top-left with y increasing downwards. */ +x = 3 - brect[6]; +y = 3 - brect[7]; +err = <A HREF="#gdImageStringFT">gdImageStringFT</A>(im,&brect[0],black,fc,sz,0.0,x,y,s); +if (err) {fprintf(stderr,err); return 1;} +</pre> +<DT><A NAME="gdImageStringFT"> +char *gdImageStringFT(gdImagePtr im, int *brect, + int fg, char *fontname, double ptsize, double angle, + int x, int y, char *string)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +<strong>RECOMMENDED. New in 1.8.4.</strong> gdImageStringFT draws text using the +FreeType 2.x library. +<p> +gdImageStringFT draws a string of anti-aliased characters on the image using +the <A HREF=http://www.freetype.org/>FreeType</A> +library to render user-supplied TrueType fonts. <strong>We do not provide +TrueType fonts (.ttf and .ttc files). Obtaining them is entirely up to +you.</strong> The string is anti-aliased, meaning that there should be +fewer "jaggies" visible. The fontname is the full pathname to a TrueType +font file, or a font face name if the GDFONTPATH environment variable +or the compiled-in DEFAULT_FONTPATH macro of gdft.c have been set intelligently. In the absence of a full path, the font face name may be presented with or without extension (2.0.26). +<p> +The null-terminated <b>string</b> argument is considered to be encoded via the UTF_8 +standard; also, HTML entities are supported, including decimal, +hexadecimal, and named entities (2.0.26). Those who are passing +ordinary ASCII strings may have difficulty with the & +character unless encoded correctly as & but should have no +other difficulties. +<p> +The string may be arbitrarily scaled (ptsize) and rotated (angle in radians). +The direction of rotation is counter-clockwise, with 0 radians (0 degrees) +at 3 o'clock and PI/2 radians (90 degrees) at 12 o'clock. + +<p> +The user-supplied int brect[8] array is filled on return from gdImageStringFT +with the 8 elements representing the 4 corner coordinates of the +bounding rectangle (the smallest rectangle that completely surrounds the +rendered string and does not intersect any pixel of the rendered string). + +<TABLE BORDER="1"> +<TR><TD ALIGN="LEFT" VALIGN="TOP">0</TD><TD ALIGN="LEFT" VALIGN="TOP"> +lower left corner, X position</TD></TR> +<TR><TD ALIGN="LEFT" VALIGN="TOP">1</TD><TD ALIGN="LEFT" VALIGN="TOP"> +lower left corner, Y position</TD></TR> +<TR><TD ALIGN="LEFT" VALIGN="TOP">2</TD><TD ALIGN="LEFT" VALIGN="TOP"> +lower right corner, X position</TD></TR> +<TR><TD ALIGN="LEFT" VALIGN="TOP">3</TD><TD ALIGN="LEFT" VALIGN="TOP"> +lower right corner, Y position</TD></TR> +<TR><TD ALIGN="LEFT" VALIGN="TOP">4</TD><TD ALIGN="LEFT" VALIGN="TOP"> +upper right corner, X position</TD></TR> +<TR><TD ALIGN="LEFT" VALIGN="TOP">5</TD><TD ALIGN="LEFT" VALIGN="TOP"> +upper right corner, Y position</TD></TR> +<TR><TD ALIGN="LEFT" VALIGN="TOP">6</TD><TD ALIGN="LEFT" VALIGN="TOP"> +upper left corner, X position</TD></TR> +<TR><TD ALIGN="LEFT" VALIGN="TOP">7</TD><TD ALIGN="LEFT" VALIGN="TOP"> +upper left corner, Y position</TD></TR> +</TABLE> +<p> +The points are relative to the text regardless of the angle, so "upper left" +means in the top left-hand corner seeing the text horizontally. +<p> +Use a NULL gdImagePtr to get the bounding rectangle without rendering. +This is a relatively cheap operation if followed by a rendering of the same +string, because of the caching of the partial rendering during bounding +rectangle calculation. +<p> +The string is rendered in the color indicated by the gf color index. +<strong>Use the negative of the desired color index to +disable anti-aliasing.</strong> +<p> +The string may contain UTF-8 sequences like: "&#192;" +<p> +gdImageStringFT will return a null char* on success, or an error +string on failure. +<PRE> +#include "gd.h" +#include <string.h> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int white; +int brect[8]; +int x, y; +char *err; + +char *s = "Hello."; /* String to draw. */ +double sz = 40.; +char *f = "/usr/local/share/ttf/Times.ttf"; /* User supplied font */ + +/* obtain brect so that we can size the image */ +err = <A HREF="#gdImageStringFT">gdImageStringFT</A>(NULL,&brect[0],0,f,sz,0.,0,0,s); +if (err) {fprintf(stderr,err); return 1;} + +/* create an image big enough for the string plus a little whitespace */ +x = brect[2]-brect[6] + 6; +y = brect[3]-brect[7] + 6; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(x,y); + +/* Background color (first allocated) */ +white = <A HREF="#gdImageColorResolve">gdImageColorResolve</A>(im, 255, 255, 255); +black = <A HREF="#gdImageColorResolve">gdImageColorResolve</A>(im, 0, 0, 0); + +/* render the string, offset origin to center string*/ +/* note that we use top-left coordinate for adjustment + * since gd origin is in top-left with y increasing downwards. */ +x = 3 - brect[6]; +y = 3 - brect[7]; +err = <A HREF="#gdImageStringFT">gdImageStringFT</A>(im,&brect[0],black,f,sz,0.0,x,y,s); +if (err) {fprintf(stderr,err); return 1;} + +/* Write img to stdout */ +<A HREF="#gdImagePng">gdImagePng</A>(im, stdout); + +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +See also <a href="#gdImageStringFTEx">gdImageStringFTEx</a>. +<DT><A NAME="gdImageStringFTEx"> +char *gdImageStringFTEx(gdImagePtr im, int *brect, + int fg, char *fontname, double ptsize, double angle, + int x, int y, char *string, gdFTStringExtraPtr strex)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +<strong>New in 2.0.5,</strong> also found in common third-party versions +of gd. gdImageStringFTEx extends the capabilities of +<a href="#gdImageStringFT">gdImageStringFT</a> by providing a +way to pass additional parameters. +<p> +If the <code>strex</code> parameter is not null, it must point to a +<code>gdFTStringExtra</code> structure. As of gd 2.0.5, this structure +is defined as follows: +<pre> +typedef struct { + /* logical OR of gdFTEX_ values */ + int flags; + /* fine tune line spacing for '\n' */ + double linespacing; + /* Preferred character mapping */ + int charmap; + /* Rendering resolution */ + int hdpi; + int vdpi; + char *xshow; + char *fontpath; +} gdFTStringExtra, *gdFTStringExtraPtr; +</pre> +To output multiline text with a specific line spacing, +include <code>gdFTEX_LINESPACE</code> in the setting of +<code>flags</code>: +<pre> +flags |= gdFTEX_LINESPACE; +</pre> +And also set <code>linespacing</code> to the desired spacing, expressed as a +multiple of the font height. Thus a line spacing of 1.0 is the +minimum to guarantee that lines of text do not collide. +<p> +If <code>gdFTEX_LINESPACE</code> is not present, or +<code>strex</code> is null, or <a href="#gdImageStringFT">gdImageStringFT</a> +is called, <code>linespacing</code> defaults to 1.05. +<p> +To specify a preference for Unicode, Shift_JIS Big5 character encoding, +set or To output multiline text with a specific line spacing, +include <code>gdFTEX_CHARMAP</code> in the setting of +<code>flags</code>: +<pre> +flags |= gdFTEX_CHARMAP; +</pre> +And set <code>charmap</code> to the desired value, which can be +any of gdFTEX_Unicode, gdFTEX_Shift_JIS, gdFTEX_Big5, or gdFTEX_Adobe_Custom. +If you do not specify a preference, Unicode will be tried first. If the preferred +character mapping is not found in the font, other character mappings +are attempted. +<p> +GD operates on the assumption that the output image will be +rendered to a computer screen. By default, gd passes a +resolution of 96 dpi to the freetype text rendering engine. +This influences the "hinting" decisions made by the renderer. To +specify a different resolution, set hdpi and vdpi accordingly +(in dots per inch) and add <code>gdFTEX_RESOLUTION</code> to <code>flags</code>: +<pre> +flags | gdFTEX_RESOLUTION; +</pre> +GD 2.0.29 and later will normally attempt to apply kerning tables, if +fontconfig is available, to adjust the relative positions of consecutive +characters more ideally for that pair of characters. This can be turn off by +specifying the gdFTEX_DISABLE_KERNING flag: +<pre> +flags | gdFTEX_DISABLE_KERNING; +</pre> +GD 2.0.29 and later can return a vector of individual character +position advances, occasionally useful in applications that must know +exactly where each character begins. This is returned in the xshow +element of the gdFTStringExtra structure if the gdFTEX_XSHOW +flag is set: +<pre> +flags | gdFTEX_XSHOW; +</pre> +<b>The caller is responsible for calling gdFree() on the xshow +element after the call</b> if gdFTEX_XSHOW is set. +<p> +GD 2.0.29 and later can also return the path to the actual font file +used if the gdFTEX_RETURNFONTPATHNAME flag is set. This is useful because +GD 2.0.29 and above are capable of +selecting a font automatically based on a fontconfig font pattern +when fontconfig is available. This information is returned in the +fontpath element of the gdFTStringExtra structure. +<pre> +flags | gdFTEX_RETURNFONTPATHNAME; +</pre> +<b>The caller is responsible for calling gdFree() on the fontpath +element after the call</b> if gdFTEX_RETURNFONTPATHNAME is set. +<p> +GD 2.0.29 and later can use fontconfig to resolve +font names, including fontconfig patterns, if the gdFTEX_FONTCONFIG +flag is set. As a convenience, this behavior can be made the default +by calling <a href="#gdFTUseFontConfig">gdFTUseFontConfig</a> with +a nonzero value. In that situation it is not necessary to set the +gdFTEX_FONTCONFIG flag on every call; however explicit font path names +can still be used if the gdFTEX_FONTPATHNAME flag is set: +<pre> +flags | gdFTEX_FONTPATHNAME; +</pre> +<p> +Unless <a href="#gdFTUseFontConfig">gdFTUseFontConfig</a> has been +called with a nonzero value, GD 2.0.29 and later will still expect +the fontlist argument to the freetype text output functions to be +a font file name or list thereof as in previous versions. If you do +not wish to make fontconfig the default, it is +still possible to force the use of fontconfig for a single call to +the freetype text output functions by setting the gdFTEX_FONTCONFIG +flag: +<pre> +flags | gdFTEX_FONTCONFIG; +</pre> +GD 2.0.29 and above can use fontconfig to resolve +font names, including fontconfig patterns, if the gdFTEX_FONTCONFIG +flag is set. As a convenience, this behavior can be made the default +by calling <a href="#gdFTUseFontConfig">gdFTUseFontConfig</a> with +a nonzero value. In that situation it is not necessary to set the +gdFTEX_FONTCONFIG flag on every call; however explicit font path names +can still be used if the gdFTEX_FONTPATHNAME flag is set: +<pre> +flags | gdFTEX_FONTPATHNAME; +</pre> +For more information, see <a href="#gdImageStringFT">gdImageStringFT</a>. +<DT><A NAME="gdImageStringFTCircle"> +char *gdImageStringFTCircle(gdImagePtr im, + int cx, + int cy, + double radius, + double textRadius, + double fillPortion, + char *font, + double points, + char *top, + char *bottom, + int fgcolor)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +Draws the text strings specified by <code>top</code> and <code>bottom</code> +on <code>im</code>, curved along the edge of a circle of radius +<code>radius</code>, with its center at <code>cx</code> and <code>cy</code>. +<code>top</code> is written clockwise +along the top; <code>bottom</code> is written counterclockwise +along the bottom. <code>textRadius</code> determines the "height" +of each character; if <code>textRadius</code> is 1/2 of +<code>radius</code>, +characters extend halfway from the edge to the center. +<code>fillPortion</code> varies from 0 to 1.0, with useful values +from about 0.4 to 0.9, and determines how much of the +180 degrees of arc assigned to each section of text +is actually occupied by text; 0.9 looks better than +1.0 which is rather crowded. <code>font</code> is a freetype +font; see gdImageStringFT. <code>points</code> is passed to the +freetype engine and has an effect on hinting; although +the size of the text is determined by <code>radius</code>, +<code>textRadius</code>, and <code>fillPortion</code>, you should +pass a point size that +"hints" appropriately -- if you know the text will be +large, pass a large point size such as 24.0 to get the +best results. <code>fgcolor</code> can be any color, and may have +an alpha component, do blending, etc. +<p> +Returns 0 on success, or an error string otherwise. +<pre> +#include <stdio.h> +#include <gd.h> + +int main (int argc, char *argv[]) +{ + FILE *in; + FILE *out; + gdImagePtr im; + int radius; + /* Create an image of text on a circle, with an + alpha channel so that we can copy it onto a + background */ + in = fopen("mypicture.jpg", "rb"); + if (!in) { + im = gdImageCreateTrueColor(300, 300); + } else { + im = gdImageCreateFromJpeg(in); + fclose(in); + } + if (gdImageSX(im) < gdImageSY(im)) { + radius = gdImageSX(im) / 2; + } else { + radius = gdImageSY(im) / 2; + } + gdStringFTCircle( + im, + gdImageSX(im) / 2, + gdImageSY(im) / 2, + radius, + radius / 2, + 0.8, + "arial", + 24, + "top text", + "bottom text", + gdTrueColorAlpha(240, 240, 255, 32)); + out = fopen("gdfx.png", "wb"); + if (!out) { + fprintf(stderr, "Can't create gdfx.png\n"); + return 1; + } + gdImagePng(im, out); + fclose(out); + gdImageDestroy(im); + return 0; +} +</pre> + +<p> +For more information, see <a href="#gdImageStringFTEx">gdImageStringFTEx</a> +and <a href="#gdImageSquareToCircle">gdImageSquareToCircle</a>. +<DT><A NAME="gdImageStringTTF"> +char *gdImageStringTTF(gdImagePtr im, int *brect, + int fg, char *fontname, double ptsize, double angle, + int x, int y, char *string)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +<strong>DEPRECATED.</strong> This function simply invokes +<a href="#gdImageStringFT">gdImageStringFT</a> for backwards +compatibility with old code that was written with FreeType 1.x. +<DT><A NAME="gdFontCacheSetup"> +int gdFontCacheSetup(void)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +This function initializes the font cache for freetype text output +functions such as <a href="#gdImageStringFTEx">gdImageStringFTEx</a>. +If this function is not called by the programmer, it is invoked +automatically on the first truetype text output call, which is +perfectly safe <b>unless</b> the application is multithreaded. +Multithreaded applications should directly invoke this function before +allowing any thread to use freetype text output. Returns 0 on success, +nonzero if the freetype library fails to initialize. +<DT><A NAME="gdFontCacheShutdown"> +void gdFontCacheShutdown(void)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +This function releases the memory used by the freetype font cache +and the text output mutex. Applications that use gd for their +entire lifetime, then exit, need not call this function. +</DL> +<H3><A NAME="colors">Color-handling functions</A></H3> +<DL> +<DT><A NAME="gdImageColorAllocate"> +int gdImageColorAllocate(gdImagePtr im, int r, int g, int b)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorAllocate finds the first available color index in +the image specified, sets its RGB values to those requested +(255 is the maximum for each), +and returns the index of the new color table entry, or an RGBA +value in the case of a truecolor image; in either case you can +then use the returned value as a parameter to drawing functions. When +creating a new palette-based image, the first time you invoke this function, +you are setting the background color for that image. +<P> +In the event that all <A HREF="#gdMaxColors">gdMaxColors</A> colors +(256) have already been allocated, gdImageColorAllocate will +return -1 to indicate failure. (This is not uncommon when +working with existing PNG files that already use 256 colors.) +Note that gdImageColorAllocate +does not check for existing colors that match your request; +see <A HREF="#gdImageColorExact">gdImageColorExact</A>, +<A HREF="#gdImageColorClosest">gdImageColorClosest</A> and +<A HREF="#gdImageColorClosestHWB">gdImageColorClosestHWB</A> +for ways to locate existing colors that approximate the +color desired in situations where a new color is not available. +Also see <A HREF="#gdImageColorResolve">gdImageColorResolve</A>, +new in gd-1.6.2. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int red; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color red. */ +red = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 0, 0); +/* Draw a dashed line from the upper left corner + to the lower right corner. */ +gdImageDashedLine(im, 0, 0, 99, 99, red); +/* ... Do something with the image, such as saving + it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorAllocateAlpha"> +int gdImageColorAllocateAlpha(gdImagePtr im, int r, int g, int b, int a)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorAllocateAlpha finds the first available color index in +the image specified, sets its RGBA values to those requested +(255 is the maximum for red, green and blue, and 127 represents +full transparency for alpha), +and returns the index of the new color table entry, or an RGBA +value in the case of a truecolor image; in either case you can +then use the returned value as a parameter to drawing functions. When +creating a new palette-based image, the first time you invoke this function, +you are setting the background color for that image. +<P> +In the event that all <A HREF="#gdMaxColors">gdMaxColors</A> colors +(256) have already been allocated, gdImageColorAllocate will +return -1 to indicate failure. (This is not uncommon when +working with existing palette-based PNG files that already use 256 colors.) +Note that gdImageColorAllocateAlpha +does not check for existing colors that match your request; +see <A HREF="#gdImageColorExactAlpha">gdImageColorExactAlpha</A> and +<A HREF="#gdImageColorClosestAlpha">gdImageColorClosestAlpha</A> +for ways to locate existing colors that approximate the +color desired in situations where a new color is not available. +Also see <A HREF="#gdImageColorResolveAlpha">gdImageColorResolveAlpha</A>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +int red; +im = <A HREF="#gdImageCreate">gdImageCreate</A>(100, 100); +/* Background color (first allocated) */ +black = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 0, 0, 0); +/* Allocate the color red, 50% transparent. */ +red = <A HREF="#gdImageColorAllocateAlpha">gdImageColorAllocateAlpha</A>(im, 255, 0, 0, 64); +/* Draw a dashed line from the upper left corner to the lower right corner. */ +gdImageDashedLine(im, 0, 0, 99, 99, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorClosest"> +int gdImageColorClosest(gdImagePtr im, int r, int g, int b)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorClosest searches the colors which have been +defined thus far in the image specified and returns the +index of the color with RGB values closest to those of the +request. (Closeness is determined by Euclidian distance, +which is used to determine the distance in three-dimensional color +space between colors.) +<P> +If no colors have yet been allocated in the image, +gdImageColorClosest returns -1. +<p> +When applied to a truecolor image, this function always +succeeds in returning the desired color. +<P> +This function is most useful as a backup method for choosing +a drawing color when an image already contains +<A HREF="#gdMaxColors">gdMaxColors</A> (256) colors and +no more can be allocated. (This is not uncommon when +working with existing PNG files that already use many colors.) +See <A HREF="#gdImageColorExact">gdImageColorExact</A> +for a method of locating exact matches only. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +FILE *in; +int red; +/* Let's suppose that photo.png is a scanned photograph with + many colors. */ +in = fopen("photo.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Try to allocate red directly */ +red = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 0, 0); +/* If we fail to allocate red... */ +if (red == (-1)) { + /* Find the <em>closest</em> color instead. */ + red = gdImageColorClosest(im, 255, 0, 0); +} +/* Draw a dashed line from the upper left corner to the lower right corner */ +gdImageDashedLine(im, 0, 0, 99, 99, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorClosestAlpha"> +int gdImageColorClosestAlpha(gdImagePtr im, int r, int g, int b, int a)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorClosest searches the colors which have been +defined thus far in the image specified and returns the +index of the color with RGBA values closest to those of the +request. (Closeness is determined by Euclidian distance, +which is used to determine the distance in four-dimensional color/alpha +space between colors.) +<P> +If no colors have yet been allocated in the image, +gdImageColorClosestAlpha returns -1. +<p> +When applied to a truecolor image, this function always +succeeds in returning the desired color. +<P> +This function is most useful as a backup method for choosing +a drawing color when a palette-based image already contains +<A HREF="#gdMaxColors">gdMaxColors</A> (256) colors and +no more can be allocated. (This is not uncommon when +working with existing palette-based PNG files that already use many colors.) +See <A HREF="#gdImageColorExactAlpha">gdImageColorExactAlpha</A> +for a method of locating exact matches only. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +FILE *in; +int red; +/* Let's suppose that photo.png is a scanned photograph with + many colors. */ +in = fopen("photo.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Try to allocate red, 50% transparent, directly */ +red = <A HREF="#gdImageColorAllocateAlpha">gdImageColorAllocateAlpha</A>(im, 255, 0, 0, 64); +/* If we fail to allocate red... */ +if (red == (-1)) { + /* Find the <em>closest</em> color instead. */ + red = gdImageColorClosestAlpha(im, 255, 0, 0, 64); +} +/* Draw a dashed line from the upper left corner to the lower right corner */ +gdImageDashedLine(im, 0, 0, 99, 99, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorClosestHWB"> +int gdImageColorClosestHWB(gdImagePtr im, int r, int g, int b)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorClosestHWB searches the colors which have been +defined thus far in the image specified and returns the +index of the color with hue, whiteness and blackness closest to the +requested color. This scheme is typically superior to the +Euclidian distance scheme used by +<a href="#gdImageColorClosest">gdImageColorClosest</a>. +<P> +If no colors have yet been allocated in the image, +gdImageColorClosestHWB returns -1. +<p> +When applied to a truecolor image, this function always +succeeds in returning the desired color. +<P> +This function is most useful as a backup method for choosing +a drawing color when an image already contains +<A HREF="#gdMaxColors">gdMaxColors</A> (256) colors and +no more can be allocated. (This is not uncommon when +working with existing PNG files that already use many colors.) +See <A HREF="#gdImageColorExact">gdImageColorExact</A> +for a method of locating exact matches only. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +FILE *in; +int red; +/* Let's suppose that photo.png is a scanned photograph with + many colors. */ +in = fopen("photo.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Try to allocate red directly */ +red = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 0, 0); +/* If we fail to allocate red... */ +if (red == (-1)) { + /* Find the <em>closest</em> color instead. */ + red = gdImageColorClosestHWB(im, 255, 0, 0); +} +/* Draw a dashed line from the upper left corner to the lower right corner */ +gdImageDashedLine(im, 0, 0, 99, 99, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorExact"> +int gdImageColorExact(gdImagePtr im, int r, int g, int b)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorExact searches the colors which have been +defined thus far in the image specified and returns the +index of the first color with RGB values which exactly +match those of the request. If no allocated color matches the +request precisely, gdImageColorExact returns -1. +See <A HREF="#gdImageColorClosest">gdImageColorClosest</A> +for a way to find the color closest to the color requested. +<p> +When applied to a truecolor image, this function always +succeeds in returning the desired color. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int red; +in = fopen("photo.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* The image may already contain red; if it does, we'll save a slot + in the color table by using that color. */ +/* Try to allocate red directly */ +red = gdImageColorExact(im, 255, 0, 0); +/* If red isn't already present... */ +if (red == (-1)) { + /* Second best: try to allocate it directly. */ + red = <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>(im, 255, 0, 0); + /* Out of colors, so find the <em>closest</em> color instead. */ + red = gdImageColorClosest(im, 255, 0, 0); +} +/* Draw a dashed line from the upper left corner to the lower right corner */ +gdImageDashedLine(im, 0, 0, 99, 99, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorResolve"> +int gdImageColorResolve(gdImagePtr im, int r, int g, int b)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorResolve searches the colors which have been +defined thus far in the image specified and returns the +index of the first color with RGB values which exactly +match those of the request. If no allocated color matches the +request precisely, then gdImageColorResolve tries to allocate the +exact color. If there is no space left in the color table then +gdImageColorResolve returns the closest color (as in gdImageColorClosest). +This function always returns an index of a color. +<p> +When applied to a truecolor image, this function always +succeeds in returning the desired color. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int red; +in = fopen("photo.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* The image may already contain red; if it does, we'll save a slot + in the color table by using that color. */ +/* Get index of red, or color closest to red */ +red = gdImageColorResolve(im, 255, 0, 0); +/* Draw a dashed line from the upper left corner to the lower right corner */ +gdImageDashedLine(im, 0, 0, 99, 99, red); +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorResolveAlpha"> +int gdImageColorResolveAlpha(gdImagePtr im, int r, int g, int b, int a)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorResolveAlpha searches the colors which have been +defined thus far in the image specified and returns the +index of the first color with RGBA values which exactly +match those of the request. If no allocated color matches the +request precisely, then gdImageColorResolveAlpha tries to allocate the +exact color. If there is no space left in the color table then +gdImageColorResolveAlpha returns the closest color (as in gdImageColorClosestAlpha). +This function always returns an index of a color. +<p> +When applied to a truecolor image, this function always +succeeds in returning the desired color. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int red; +in = fopen("photo.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* The image may already contain red; if it does, + we'll save a slot in the color table by using that color. */ +/* Get index of red, 50% transparent, or the next best thing */ +red = gdImageColorResolveAlpha(im, 255, 0, 0, 64); +/* Draw a dashed line from the upper left corner to the lower right corner */ +gdImageDashedLine(im, 0, 0, 99, 99, red); +/* ... Do something with the image, such as saving + it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorsTotal"> +int gdImageColorsTotal(gdImagePtr im)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageColorsTotal is a macro which returns the number of +colors currently allocated in a palette image. For truecolor +images, the result of this call is undefined and should not +be used. +<DT><A NAME="gdImageRed"> +int gdImageRed(gdImagePtr im, int c)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageRed is a macro which returns the red portion +of the specified color in the image. This macro works +for both palette and truecolor images. +<DT><A NAME="gdImageGreen"> +int gdImageGreen(gdImagePtr im, int c)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageGreen is a macro which returns the green portion +of the specified color in the image. This macro works +for both palette and truecolor images. +<DT><A NAME="gdImageBlue"> +int gdImageBlue(gdImagePtr im, int c)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageBlue is a macro which returns the blue portion +of the specified color in the image. This macro works +for both palette and truecolor images. +<DT><A NAME="gdImageGetInterlaced"> +int gdImageGetInterlaced(gdImagePtr im)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageGetInterlaced is a macro which returns true (1) +if the image is interlaced, false (0) if not. +Use this macro to obtain this information; do not +access the structure directly. +See <A HREF="#gdImageInterlace">gdImageInterlace</A> for +a means of interlacing images. +<DT><A NAME="gdImageGetTransparent"> +int gdImageGetTransparent(gdImagePtr im)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageGetTransparent is a macro which returns the +current transparent color index in the image. +If there is no transparent color, gdImageGetTransparent +returns -1. Use this macro to obtain this information; do not +access the structure directly. +<DT><A NAME="gdImageColorDeallocate"> +void gdImageColorDeallocate(gdImagePtr im, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorDeallocate marks the specified color as being +available for reuse. It does not attempt to determine whether +the color index is still in use in the image. After a call +to this function, the next call to +<A HREF="#gdImageColorAllocate">gdImageColorAllocate</A> +for the same image will set new RGB values for that +color index, changing the color of any pixels which +have that index as a result. If multiple calls to +gdImageColorDeallocate are made consecutively, the lowest-numbered +index among them will be reused by the next +<A HREF="#gdImageColorAllocate"> gdImageColorAllocate</A> call. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int red, blue; +in = fopen("photo.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Look for red in the color table. */ +red = gdImageColorExact(im, 255, 0, 0); +/* If red is present... */ +if (red != (-1)) { + /* Deallocate it. */ + gdImageColorDeallocate(im, red); + /* Allocate blue, reusing slot in table. + Existing red pixels will change color. */ + blue = gdImageColorAllocate(im, 0, 0, 255); +} +/* ... Do something with the image, such as + saving it to a file... */ +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageColorTransparent"> +void gdImageColorTransparent(gdImagePtr im, int color)</A> +<STRONG>(FUNCTION)</STRONG> +<DD> +gdImageColorTransparent sets the transparent color index +for the specified image to the specified index. To indicate +that there should be <em>no</em> transparent color, invoke +gdImageColorTransparent with a color index of -1. Note that +JPEG images do not support transparency, so this setting has no effect +when writing JPEG images. +<P> +The color index used should be an index +allocated by <A HREF="#gdImageColorAllocate">gdImageColorAllocate</A>, +whether explicitly invoked by your code or implicitly +invoked by loading an image. +In order to ensure that your image has a reasonable appearance +when viewed by users who do not have transparent background +capabilities (or when you are writing a JPEG-format file, which does +not support transparency), be sure to give reasonable RGB values to the +color you allocate for use as a transparent color, +<em>even though it will be transparent on systems +that support PNG transparency</em>. +<PRE> +... inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im; +int black; +FILE *in, *out; +in = fopen("photo.png", "rb"); +im = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Look for black in the color table and make it transparent. */ +black = <A HREF="#gdImageColorExact">gdImageColorExact</A>(im, 0, 0, 0); +/* If black is present... */ +if (black != (-1)) { + /* Make it transparent */ + gdImageColorTransparent(im, black); +} +/* Save the newly-transparent image back to the file */ +out = fopen("photo.png", "wb"); +<A HREF="#gdImagePng">gdImagePng</A>(im, out); +fclose(out); +/* Destroy it */ +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdImageTrueColor"> +void gdImageTrueColor(int red, int green, int blue)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdImageTrueColor returns an RGBA color value for use when +drawing on a truecolor image. Red, green, and blue are all +in the range between 0 (off) and 255 (maximum). This macro should +not be used with palette-based images. If you need to write +code which is compatible with both palette-based and +truecolor images, use <a href="#gdImageColorResolve">gdImageColorResolve</a>. +<DT><A NAME="gdTrueColorAlpha"> +void gdTrueColorAlpha(int red, int green, int blue, int alpha)</A> +<STRONG>(MACRO)</STRONG> +<DD> +gdTrueColorAlpha returns an RGBA color value for use when +drawing on a truecolor image with alpha channel transparency. Red, +green, and blue are all +in the range between 0 (off) and 255 (maximum). Alpha is in the +range between 0 (opaque) and 127 (fully transparent). This macro should +not be used with palette-based images. If you need to write +code which is compatible with both palette-based and +truecolor images, use <a href="#gdImageColorResolveAlpha">gdImageColorResolveAlpha</a>.</DL> +<H3><A NAME="copying">Copying and resizing functions</A></H3> +<DL> + +<DT><A NAME="gdImageCopy">void gdImageCopy(gdImagePtr dst, gdImagePtr src, int dstX, int dstY, int srcX, int srcY, int w, int h) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageCopy is used to copy a rectangular portion of one image to +another image. (For a way of stretching or shrinking the image +in the process, see <A HREF="#gdImageCopyResized"> +gdImageCopyResized</A>.) +<P> +The <code>dst</code> argument is the destination image to which the +region will be copied. The <code>src</code> argument is the source +image from which the region is copied. The <code>dstX</code> +and <code>dstY</code> arguments specify the point in the destination +image to which the region will be copied. The <code>srcX</code> +and <code>srcY</code> arguments specify the upper left corner +of the region in the source image. The <code>w</code> +and <code>h</code> arguments specify the width and height +of the region. +<P> +When you copy a region from one location in an image to another +location in the same image, gdImageCopy will perform as expected +unless the regions overlap, in which case the result is +unpredictable. +<P> +<strong>Important note on copying between images:</strong> since +different images do +not necessarily have the same color tables, pixels are not simply set to the +same color index values to copy them. gdImageCopy will attempt +to find an identical RGB value in the destination image for +each pixel in the copied portion of the source image by +invoking <A HREF="#gdImageColorExact">gdImageColorExact</A>. If +such a value is not found, gdImageCopy will attempt to +allocate colors as needed using <A HREF="#gdImageColorAllocate"> +gdImageColorAllocate</A>. If both of these methods fail, +gdImageCopy will invoke <A HREF="#gdImageColorClosest"> +gdImageColorClosest</A> to find the color in the destination +image which most closely approximates the color of the +pixel being copied. +<PRE> +... Inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im_in; +<A HREF="#gdImagePtr">gdImagePtr</A> im_out; +int x, y; +FILE *in; +FILE *out; +/* Load a small png to tile the larger one with */ +in = fopen("small.png", "rb"); +im_in = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Make the output image four times as large on both axes */ +im_out = <A HREF="#gdImageCreate">gdImageCreate</A>(im_in->sx * 4, im_in->sy * 4); +/* Now tile the larger image using the smaller one */ +for (y = 0; (y < 4); y++) { + for (x = 0; (x < 4); x++) { + gdImageCopy(im_out, im_in, + x * im_in->sx, y * im_in->sy, + 0, 0, + im_in->sx, im_in->sy); + } +} +out = fopen("tiled.png", "wb"); +<A HREF="#gdImagePng">gdImagePng</A>(im_out, out); +fclose(out); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im_in); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im_out); +</PRE> +<DT><A NAME="gdImageCopyResized">void gdImageCopyResized(gdImagePtr dst, gdImagePtr src, int dstX, int dstY, int srcX, int srcY, int destW, int destH, int srcW, int srcH) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageCopyResized is used to copy a rectangular portion of one image to +another image. The X and Y dimensions of the original region and the +destination region can vary, resulting in stretching or shrinking of +the region as appropriate. (For a simpler version of this function +which does not deal with resizing, see <A HREF="#gdImageCopy"> +gdImageCopy</A>.) +<P> +The <code>dst</code> argument is the destination image to which the +region will be copied. The <code>src</code> argument is the source +image from which the region is copied. The <code>dstX</code> +and <code>dstY</code> arguments specify the point in the destination +image to which the region will be copied. The <code>srcX</code> +and <code>srcY</code> arguments specify the upper left corner +of the region in the source image. The <code>dstW</code> +and <code>dstH</code> arguments specify the width and height +of the destination region. The <code>srcW</code> +and <code>srcH</code> arguments specify the width and height +of the source region and can differ from the destination size, +allowing a region to be scaled during the copying process. +<P> +When you copy a region from one location in an image to another +location in the same image, gdImageCopy will perform as expected +unless the regions overlap, in which case the result is +unpredictable. If this presents a problem, create a scratch image +in which to keep intermediate results. +<P> +<strong>Important note on copying between images:</strong> since images +do not necessarily have the same color tables, pixels are not simply set +to the same color index values to copy them. gdImageCopy will attempt +to find an identical RGB value in the destination image for +each pixel in the copied portion of the source image by +invoking <A HREF="#gdImageColorExact">gdImageColorExact</A>. If +such a value is not found, gdImageCopy will attempt to +allocate colors as needed using <A HREF="#gdImageColorAllocate"> +gdImageColorAllocate</A>. If both of these methods fail, +gdImageCopy will invoke <A HREF="#gdImageColorClosest"> +gdImageColorClosest</A> to find the color in the destination +image which most closely approximates the color of the +pixel being copied. +<PRE> +... Inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im_in; +<A HREF="#gdImagePtr">gdImagePtr</A> im_out; +int x, y; +FILE *in; +FILE *out; +/* Load a small png to expand in the larger one */ +in = fopen("small.png", "rb"); +im_in = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Make the output image four times as large on both axes */ +im_out = <A HREF="#gdImageCreate">gdImageCreate</A>(im_in->sx * 4, im_in->sy * 4); +/* Now copy the smaller image, but four times larger */ +gdImageCopyResized(im_out, im_in, 0, 0, 0, 0, + im_out->sx, im_out->sy, + im_in->sx, im_in->sy); +out = fopen("large.png", "wb"); +<A HREF="#gdImagePng">gdImagePng</A>(im_out, out); +fclose(out); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im_in); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im_out); +</PRE> +<DT><A NAME="gdImageCopyResampled">void gdImageCopyResampled(gdImagePtr dst, gdImagePtr src, int dstX, int dstY, int srcX, int srcY, int destW, int destH, int srcW, int srcH) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageCopyResampled is used to copy a rectangular portion of one image to +another image, smoothly interpolating pixel values so that, in particular, +reducing the size of an image still retains a great deal of clarity. The +X and Y dimensions of the original region and the +destination region can vary, resulting in stretching or shrinking of +the region as appropriate. (For a simpler version of this function +which does not deal with resizing, see <A HREF="#gdImageCopy"> +gdImageCopy</A>. For a version which does not interpolate pixel values, +see <A HREF="#gdImageCopyResized">gdImageCopyResized</A>. +<p> +Pixel values are only interpolated if the destination image is a +truecolor image. Otherwise, +<a href="#gdImageCopyResized">gdImageCopyResized</a> is +automatically invoked. +<P> +The <code>dst</code> argument is the destination image to which the +region will be copied. The <code>src</code> argument is the source +image from which the region is copied. The <code>dstX</code> +and <code>dstY</code> arguments specify the point in the destination +image to which the region will be copied. The <code>srcX</code> +and <code>srcY</code> arguments specify the upper left corner +of the region in the source image. The <code>dstW</code> +and <code>dstH</code> arguments specify the width and height +of the destination region. The <code>srcW</code> +and <code>srcH</code> arguments specify the width and height +of the source region and can differ from the destination size, +allowing a region to be scaled during the copying process. +<P> +When you copy a region from one location in an image to another +location in the same image, gdImageCopy will perform as expected +unless the regions overlap, in which case the result is +unpredictable. If this presents a problem, create a scratch image +in which to keep intermediate results. +<P> +<strong>Important note on copying between images:</strong> since images +do not necessarily have the same color tables, pixels are not simply set +to the same color index values to copy them. If the destination image +is a palette image, gd will use the +<a href="#gdImageColorResolve">gdImageColorResolve</a> function to +determine the best color available. +<PRE> +... Inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im_in; +<A HREF="#gdImagePtr">gdImagePtr</A> im_out; +int x, y; +FILE *in; +FILE *out; +/* Load a large png to shrink in the smaller one */ +in = fopen("large.png", "rb"); +im_in = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Make the output image four times as small on both axes. Use + a true color image so that we can interpolate colors. */ +im_out = <A HREF="#gdImageCreate">gdImageCreateTrueColor</A>(im_in->sx / 4, im_in->sy / 4); +/* Now copy the large image, but four times smaller */ +gdImageCopyResampled(im_out, im_in, 0, 0, 0, 0, + im_out->sx, im_out->sy, + im_in->sx, im_in->sy); +out = fopen("large.png", "wb"); +<A HREF="#gdImagePng">gdImagePng</A>(im_out, out); +fclose(out); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im_in); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im_out); +</PRE> +<DT><A NAME="gdImageCopyRotated">void gdImageCopyRotated(gdImagePtr dst, gdImagePtr src, double dstX, double dstY, int srcX, int srcY, int srcW, int srcH, int angle) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageCopyRotated is used to copy a rectangular portion of one image to +another image, or to another region of the same image. <strong>The srcX and +srcY coordinates specify the upper left corner of the source area; however, +the dstX and dstY coordinates specify the CENTER of the destination area. +</strong> This important distinction is made because the rotated rectangle may +may or may not be parallel to the X and Y axes. The destination coordinates +may be floating point, as the center of the desired destination area may lie +at the center of a pixel (0.5 pixels) rather than its upper left corner. +The angle specified is an integer number of degrees, between 0 and 360, +with 0 degrees causing no change, and counterclockwise rotation as +the angle increases. +<P> +When you copy a region from one location in an image to another +location in the same image, gdImageCopyRotated will perform as expected +unless the regions overlap, in which case the result is +unpredictable. If this presents a problem, create a scratch image +in which to keep intermediate results. +<P> +<strong>Important note on copying between images:</strong> since +palette-based images do not necessarily have the same color tables, pixels +are not simply set to the same color index values to copy them. +If the destination image is not a truecolor image, +<a href="#gdImageColorResolveAlpha">gdImageColorResolveAlpha</a> is +used to choose the destination pixel. +<PRE> +... Inside a function ... +<A HREF="#gdImagePtr">gdImagePtr</A> im_in; +<A HREF="#gdImagePtr">gdImagePtr</A> im_out; +int x, y; +int a; +FILE *in; +FILE *out; +/* Load a small png to rotate in the larger one */ +in = fopen("small.png", "rb"); +im_in = <A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A>(in); +fclose(in); +/* Make the output image four times as large on both axes */ +im_out = <A HREF="#gdImageCreate">gdImageCreate</A>(im_in->sx * 4, im_in->sy * 4); +/* Now rotate the smaller image */ +for (a = 0; (a < 360); a += 45) { + double x = cos(a * .0174532925) * gdImageSX(im_out) / 2; + double y = -sin(a * .0174532925) * gdImageSY(im_out) / 2; + gdImageCopyRotated(im_out, im_in, + gdImageSX(im_out) / 2 + x, + gdImageSY(im_out) / 2 + y, + 0, 0, + gdImageSX(im_in), + gdImageSY(im_in), + a); +} +out = fopen("large.png", "wb"); +<A HREF="#gdImagePng">gdImagePng</A>(im_out, out); +fclose(out); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im_in); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im_out); +</PRE> + +<DT><A NAME="gdImageCopyMerge">void gdImageCopyMerge(gdImagePtr dst, gdImagePtr src, int dstX, int dstY, int srcX, int srcY, int w, int h, int pct) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageCopyMerge is almost identical to <a href=#gdImageCopy>gdImageCopy</a>, except that +it 'merges' the two images by an amount specified in the last parameter. If the last +parameter is 100, then it will function identically to gdImageCopy - the source image replaces +the pixels in the destination. +<p> +If, however, the <strong>pct</strong> parameter is less than 100, then the two images are merged. +With pct = 0, no action is taken. +<p>This feature is most useful to 'highlight' sections of an image by merging a solid color with +pct = 50: +<PRE> +... Inside a function ... +gdImageCopyMerge(im_out, im_in, 100, 200, 0, 0, 30, 50, 50); +</PRE> + +<DT><A NAME="gdImageCopyMergeGray">void gdImageCopyMergeGray(gdImagePtr dst, gdImagePtr src, int dstX, int dstY, int srcX, int srcY, int + w, int h, int pct) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageCopyMergeGray is almost identical to <a href=#gdImageCopyMerge>gdImageCopyMerge</a>, +except that when merging images it preserves the hue of the source by converting the destination +pixels to grey scale before the copy operation. +<PRE> +... Inside a function ... +gdImageCopyMergeGray(im_out, im_in, 100, 200, 0, 0, 30, 50, 50); +</PRE> + +<DT><A NAME="gdImagePaletteCopy">void gdImagePaletteCopy(gdImagePtr dst, gdImagePtr src) +<STRONG> (FUNCTION)</STRONG> +<DD> +Copies a palette from one image to another, attempting to match the colors in the target image +to the colors +in the source palette. +<DT><A NAME="gdImageSquareToCircle">void gdImageSquareToCircle(gdImagePtr im, int radius)</a> +<STRONG> (FUNCTION)</STRONG> +<DD> +<b>im MUST be square, but can have any size.</b> Returns a new image +of width and height radius * 2, in which the X axis of +the original has been remapped to theta (angle) and the Y axis +of the original has been remapped to rho (distance from center). +This is known as a "polar coordinate transform." +See also <a href="#gdImageStringFTCircle">gdImageStringFTCircle</a>, which +uses this function internally. +<DT><A NAME="gdImageSharpen">void gdImageSharpen(gdImagePtr im, int pct)</a> +<STRONG> (FUNCTION)</STRONG> +<DD> +Sharpens the specified image. pct is a sharpening percentage, and +can be greater than 100. Silently does nothing to non-truecolor images. +Silently does nothing for pct<0. Transparency/alpha channel are not +altered. +</DL> +<H3><A NAME="misc">Miscellaneous Functions</A></H3> +<DL> + +<DT><A NAME="gdImageCompare">int gdImageCompare(gdImagePtr im1, gdImagePtr im2) +<STRONG> (FUNCTION)</STRONG> +<DD> +gdImageCompare returns a bitmap indicating if the two images are different. The members of the +bitmap are defined in gd.h, but the most important is GD_CMP_IMAGE, which indicated that the images +will actually appear different when displayed. Other, less important, differences relate to pallette +entries. Any difference in the transparent colour is assumed to make images display differently, +even if the transparent colour is not used. +<PRE> +... Inside a function ... +cmpMask = gdImageCompare(im1, im2); +</PRE> + +<DT><A NAME="gdImageInterlace">gdImageInterlace(gdImagePtr im, int interlace)</A> <strong>(FUNCTION)</strong> +<DD> +gdImageInterlace is used to determine whether an image should be stored +in a linear fashion, in which lines will appear on the display from +first to last, or in an interlaced fashion, in which the image +will "fade in" over several passes. By default, images are not +interlaced. (When writing JPEG images, interlacing implies generating +progressive JPEG files, which are represented as a series of scans of +increasing quality. Noninterlaced gd images result in regular +[sequential] JPEG data streams.) +<P> +A nonzero value for the interlace argument turns on interlace; +a zero value turns it off. Note that interlace has no effect +on other functions, and has no meaning unless you save the +image in PNG or JPEG format; the gd and xbm formats do not support +interlace. +<P> +When a PNG is loaded with +<A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A> or a JPEG is +loaded with +<A HREF="#gdImageCreateFromJpeg">gdImageCreateFromJpeg</A>, interlace +will be set according to the setting in the PNG or JPEG file. +<P> +Note that many PNG and JPEG viewers and web browsers do <em>not</em> +support interlace or the incremental display of progressive +JPEGs. However, the interlaced PNG or progressive JPEG should still +display; it will simply appear all at once, just as other images do. +<PRE> +gdImagePtr im; +FILE *out; +/* ... Create or load the image... */ + +/* Now turn on interlace */ +gdImageInterlace(im, 1); +/* And open an output file */ +out = fopen("test.png", "wb"); +/* And save the image -- could also use <A HREF="#gdImageJpeg">gdImageJpeg</A> */ +<A HREF="#gdImagePng">gdImagePng</A>(im, out); +fclose(out); +<A HREF="#gdImageDestroy">gdImageDestroy</A>(im); +</PRE> +<DT><A NAME="gdFree">gdFree(void *ptr)</A> <strong>(FUNCTION)</strong> +<DD> +gdFree provides a reliable way to free memory allocated by functions +such as <a href="#gdImagePngPtr">gdImagePngPtr</a> which return +blocks of memory. Use of this function guarantees that the +version of <code>free()</code> that is ultimately called will +be intended for use with the version of <code>malloc()</code> that +originally allocated the block. +</DL> +<H3><A NAME="constants">Constants</A></H3> +<DL> +<DT><A NAME="gdAntiAliased">gdAntiAliased</A> <strong>(CONSTANT)</strong> +<DD> +Used in place of a color when invoking a line-drawing +function such as <A HREF="#gdImageLine">gdImageLine</A> +or <A HREF="#gdImageRectangle">gdImageRectangle</A>. +When gdAntiAliased is used as the color, the foreground color +set with <a href="#gdImageSetAntiAliased">gdImageSetAntiAliased</a> +is used, with antialiasing mechanisms to minimize any +"jagged" appearance. +For more information, see +<a href="#gdImageSetAntiAliased">gdImageSetAntiAliased</a>. +<DT><A NAME="gdBrushed">gdBrushed</A> <strong>(CONSTANT)</strong> +<DD> +Used in place of a color when invoking a line-drawing +function such as <A HREF="#gdImageLine">gdImageLine</A> +or <A HREF="#gdImageRectangle">gdImageRectangle</A>. +When gdBrushed is used as the color, the brush +image set with <A HREF="#gdImageSetBrush">gdImageSetBrush</A> +is drawn in place of each pixel of the line (the brush is +usually larger than one pixel, creating the effect +of a wide paintbrush). See also +<A HREF="#gdStyledBrushed">gdStyledBrushed</A> for a way +to draw broken lines with a series of distinct copies of an image. +<DT><A NAME="gdMaxColors"><code>gdMaxColors</code><strong>(CONSTANT)</strong> +<DD> +The constant 256. This is the maximum number of colors in a palette-based +PNG file according to the PNG standard, and is also the maximum number of +colors in a palette-based gd image. This of course does not apply to +truecolor images. +<DT><A NAME="gdStyled">gdStyled</A> <strong>(CONSTANT)</strong> +<DD> +Used in place of a color when invoking a line-drawing +function such as <A HREF="#gdImageLine">gdImageLine</A> +or <A HREF="#gdImageRectangle">gdImageRectangle</A>. +When gdStyled is used as the color, the colors of the pixels are +drawn successively from the style that has been +set with <A HREF="#gdImageSetStyle">gdImageSetStyle</A>. +If the color of a pixel is equal to +<A HREF="#gdTransparent">gdTransparent</A>, that pixel +is not altered. (This mechanism is completely unrelated +to the "transparent color" of the image itself; see +<A HREF="#gdImageColorTransparent">gdImageColorTransparent</A> +gdImageColorTransparent for that mechanism.) See also +<A NAME="#gdStyledBrushed"> gdStyledBrushed</A>. +<DT><A NAME="gdStyledBrushed">gdStyledBrushed</A> <strong>(CONSTANT)</strong> +<DD> +Used in place of a color when invoking a line-drawing +function such as <A HREF="#gdImageLine">gdImageLine</A> +or <A HREF="#gdImageRectangle">gdImageRectangle</A>. +When gdStyledBrushed is used as the color, the brush +image set with <A HREF="#gdImageSetBrush">gdImageSetBrush</A> +is drawn at each pixel of the line, providing that the +style set with <A HREF="#gdImageSetStyle">gdImageSetStyle</A> +contains a nonzero value (OR gdTransparent, which +does not equal zero but is supported for consistency) +for the current pixel. (Pixels are drawn successively from the style as the +line is drawn, returning to the beginning when the +available pixels in the style are exhausted.) Note that +this differs from the behavior of <A HREF="#gdStyled">gdStyled</A>, +in which the values in the style are used as actual +pixel colors, except for gdTransparent. +<DT><A NAME="gdDashSize">gdDashSize</A> <strong>(CONSTANT)</strong> +<DD> +The length of a dash in a dashed line. Defined to be 4 for +backwards compatibility with programs that use +<A NAME="gdImageDashedLine">gdImageDashedLine</A>. New +programs should use <A NAME="gdImageSetStyle"> +gdImageSetStyle</A> and call the standard +<A NAME="gdImageLine">gdImageLine</A> function +with the special "color" <A NAME="gdStyled"> +gdStyled</A> or <A NAME="gdStyledBrushed">gdStyledBrushed</A>. +<DT><A NAME="gdTiled">gdTiled</A> <strong>(CONSTANT)</strong> +<DD> +Used in place of a normal color in <A HREF="#gdImageFilledRectangle"> +gdImageFilledRectangle</A>, <A HREF="#gdImageFilledPolygon"> +gdImageFilledPolygon</A>, +<A HREF="#gdImageFill">gdImageFill</A>, and <A HREF="#gdImageFillToBorder"> +gdImageFillToBorder</A>. gdTiled selects a pixel from the +tile image set with <A HREF="#gdImageSetTile">gdImageSetTile</A> +in such a way as to ensure that the filled area will be +tiled with copies of the tile image. See the discussions of +<A HREF="#gdImageFill">gdImageFill</A> and +<A HREF="#gdImageFillToBorder">gdImageFillToBorder</A> for special +restrictions regarding those functions. +<DT><A NAME="gdTransparent">gdTransparent</A> <strong>(CONSTANT)</strong> +<DD> +Used in place of a normal color in a style to be set with +<A HREF="#gdImageSetStyle">gdImageSetStyle</A>. +gdTransparent is <strong>not</strong> the transparent +color index of the image; for that functionality please +see <A HREF="#gdImageColorTransparent">gdImageColorTransparent</A>. +</DL> + +<A NAME="gdformat"><H3>About the additional .gd image file format</H3></A> +In addition to reading and writing the PNG and JPEG formats and reading the +X Bitmap format, gd has the capability to read and write its +own ".gd" format. This format is <em>not</em> intended for +general purpose use and should never be used to distribute +images. It is not a compressed format. Its purpose is solely to +allow very fast loading of images your program needs often in +order to build other images for output. If you are experiencing +performance problems when loading large, fixed PNG images your +program needs to produce its output images, you may wish +to examine the functions <A HREF="#gdImageCreateFromGd"> +gdImageCreateFromGd</A> and <A HREF="#gdImageGd">gdImageGd</A>, +which read and write .gd format images. + +<P> +The program "pngtogd.c" is provided as a simple way of converting +.png files to .gd format. I emphasize again that you will not +need to use this format unless you have a need for high-speed loading +of a few frequently-used images in your program. + +<A NAME="gd2format"><H3>About the .gd2 image file format</H3></A> +In addition to reading and writing the PNG format and reading the +X Bitmap format, gd has the capability to read and write its +own ".gd2" format. This format is <em>not</em> intended for +general purpose use and should never be used to distribute +images. It is a compressed format allowing pseudo-random access +to large image files. Its purpose is solely to +allow very fast loading of <strong>parts</strong> of images +If you are experiencing +performance problems when loading large, fixed PNG or JPEG images your +program needs to produce its output images, you may wish +to examine the functions <A HREF="#gdImageCreateFromGd2"> +gdImageCreateFromGd2</A>, <A HREF="#gdImageCreateFromGd2Part"> +gdImageCreateFromGd2Part</A> and <A HREF="#gdImageGd2">gdImageGd2</A>, +which read and write .gd2 format images. + +<P> +The program "pngtogd2.c" is provided as a simple way of converting +.png files to .gd2 format. + +<A NAME="gdioctx"><H3>About the gdIOCtx structure</H3></A> +Version 1.5 of GD added a new style of I/O based on an IOCtx +structure (the most up-to-date version can be found in gd_io.h): +<PRE> +typedef struct gdIOCtx { + int (*getC)(struct gdIOCtx*); + int (*getBuf)(struct gdIOCtx*, void*, int); + + void (*putC)(struct gdIOCtx*, int); + int (*putBuf)(struct gdIOCtx*, const void*, int); + + int (*seek)(struct gdIOCtx*, const int); /* Returns 1 on SUCCESS */ + long (*tell)(struct gdIOCtx*); + + void (*free)(struct gdIOCtx*); + +} gdIOCtx; +</PRE> + +Most functions that accepted files in previous versions now also have a +counterpart that accepts an I/O context. These functions have a 'Ctx' +suffix. +<p> +The <xxx>Ctx routines use the function pointers in the I/O context pointed to +by gdIOCtx to perform all I/O. Examples of how to implement an I/O context +can be found in io_file.c (which provides a wrapper for file routines), and +io_dp.c (which implements in-memory storage). +<p> +It is not necessary to implement all functions in an I/O context if you know +that it will only be used in limited cirsumstances. At the time of writing +(Version 1.6.1, July 1999), the known requirements are: +<p> +<Table> +<TR><TD>All</TD><td width=20> </td><TD>Must have 'free',</td></tr> +<TR><TD>Anything that reads from the context</TD><td></td><TD>Must have 'getC' and 'getBuf',</td></tr> +<TR><TD>Anything that writes to the context</TD><td></td><TD>Must have 'putC' and 'putBuf'.</td></tr> +<TR><TD>If gdCreateFromGd2Part is called</td><td></td><TD>Must also have 'seek' and 'tell'. Note: seek must return 1 on SUCCESS and 0 on FAILURE.</td></tr> +<TR><TD>If gdImageGd2 is called</td><td></td><TD>Must also have 'seek' and 'tell'.</td></tr> +</Table> + + + +<A NAME="informing"><H3>Please tell us you're using gd!</H3> +When you contact us and let us know you are using gd, +you help us justify the time spent in maintaining and improving +it. So please let us know. If the results are publicly +visible on the web, a URL is a wonderful thing to receive, but +if it's not a publicly visible project, a simple note is just +as welcome. + +<A NAME="support"><H3>How do I get support?</H3></A> +<h4>Free Support</h4> + +Anyone can mail questions about the gd library using the +<a href="http://www.libgd.org/Wiki/Support">LibGD support</a>. However, +we receive a very large volume of email on many subjects, and while we do +our best to respond to all queries this can take some time. Sometimes +the response must take the form of an eventual new release or + an addition to a FAQ or other document, as opposed to an detailed individual response. + +<h4>Hourly Support</h4> +Those requiring support in detail may arrange for direct support +from the maintaines, at the rate of $50/hr, billed +directly by credit card. Purchase orders are also accepted from +Fortune 500 corporations and institutions in good standing. +To make arrangements, contact <A HREF="http://www.libgd.org/Contact">Pierre-A. Joye</A>. To avoid delay +and/or confusion, be sure to specifically mention that you wish to purchase gd support at the +hourly rate above. + +<A NAME="issues"><H3>How do I report issues, bugs or features request?</H3></A> +Bugs, feature requests or other issues can be reported using the +<A HREF="http://bugs.libgd.org">libGD.org issues tracker.</A> as well as using +one of our support channels: <a href="http://www.libgd.org/Wiki/Support">LibGD support</a> + +<H3><A NAME="index">Alphabetical quick index</A></H3> +<A HREF="#gdAntiAliased">gdAntiAliased</A> | +<A HREF="#gdBrushed">gdBrushed</A> | +<A HREF="#gdDashSize">gdDashSize</A> | +<A HREF="#gdFont">gdFont</A> | +<A HREF="#gdFontGetHuge">gdFontGetHuge</A> | +<A HREF="#gdFontGetLarge">gdFontGetLarge</A> | +<A HREF="#gdFontGetMediumBold">gdFontGetMediumBold</A> | +<A HREF="#gdFontGetSmall">gdFontGetSmall</A> | +<A HREF="#gdFontGetTiny">gdFontGetTiny</A> | +<A HREF="#gdFontCacheSetup">gdFontCacheSetup</A> | +<A HREF="#gdFontCacheShutdown">gdFontCacheShutdown</A> | +<A HREF="#gdFontPtr">gdFontPtr</A> | +<A HREF="#gdFree">gdFree</A> | +<A HREF="#gdImage">gdImage</A> | +<A HREF="#gdImageAlphaBlending">gdImageAlphaBlending</A> | +<A HREF="#gdImageArc">gdImageArc</A> | +<A HREF="#gdImageBlue">gdImageBlue</A> | +<A HREF="#gdImageBoundsSafe">gdImageBoundsSafe</A> | +<A HREF="#gdImageChar">gdImageChar</A> | +<A HREF="#gdImageCharUp">gdImageCharUp</A> | +<A HREF="#gdImageColorAllocate">gdImageColorAllocate</A> | +<A HREF="#gdImageColorAllocateAlpha">gdImageColorAllocateAlpha</A> | +<A HREF="#gdImageColorClosest">gdImageColorClosest</A> | +<A HREF="#gdImageColorClosestAlpha">gdImageColorClosestAlpha</A> | +<A HREF="#gdImageColorClosestHWB">gdImageColorClosestHWB</A> | +<A HREF="#gdImageColorDeallocate">gdImageColorDeallocate</A> | +<A HREF="#gdImageColorExact">gdImageColorExact</A> | +<A HREF="#gdImageColorExactAlpha">gdImageColorExactAlpha</A> | +<A HREF="#gdImageColorResolve">gdImageColorResolve</A> | +<A HREF="#gdImageColorResolveAlpha">gdImageColorResolveAlpha</A> | +<A HREF="#gdImageColorTransparent">gdImageColorTransparent</A> | +<A HREF="#gdImageCopy">gdImageCopy</A> | +<A HREF="#gdImageCopyMerge">gdImageCopyMerge</A> | +<A HREF="#gdImageCopyMergeGray">gdImageMergeGray</A> | +<A HREF="#gdImageCopyResized">gdImageCopyResized</A> | +<A HREF="#gdImageCopyResampled">gdImageCopyResampled</A> | +<A HREF="#gdImageCopyRotated">gdImageCopyRotated</A> | +<A HREF="#gdImageCreate">gdImageCreate</A> | +<A HREF="#gdImageCreate">gdImageCreatePalette</A> | +<A HREF="#gdImageCreate">gdImageCreateTrueColor</A> | +<A HREF="#gdImageCreateFromGd">gdImageCreateFromGd</A> | +<A HREF="#gdImageCreateFromGdCtx">gdImageCreateFromGdCtx</A> | +<A HREF="#gdImageCreateFromGdPtr">gdImageCreateFromGdPtr</A> | +<A HREF="#gdImageCreateFromGd2">gdImageCreateFromGd2</A> | +<A HREF="#gdImageCreateFromGd2Ctx">gdImageCreateFromGd2Ctx</A> | +<A HREF="#gdImageCreateFromGd2Ptr">gdImageCreateFromGd2Ptr</A> | +<A HREF="#gdImageCreateFromGd2Part">gdImageCreateFromGd2Part</A> | +<A HREF="#gdImageCreateFromGd2PartCtx">gdImageCreateFromGd2PartCtx</A> | +<A HREF="#gdImageCreateFromGd2PartPtr">gdImageCreateFromGd2PartPtr</A> | +<A HREF="#gdImageCreateFromJpeg">gdImageCreateFromJpeg</A> | +<A HREF="#gdImageCreateFromJpegCtx">gdImageCreateFromJpegCtx</A> | +<A HREF="#gdImageCreateFromJpegPtr">gdImageCreateFromJpegPtr</A> | +<A HREF="#gdImageCreateFromPng">gdImageCreateFromPng</A> | +<A HREF="#gdImageCreateFromPngCtx">gdImageCreateFromPngCtx</A> | +<A HREF="#gdImageCreateFromPngPtr">gdImageCreateFromPngPtr</A> | +<A HREF="#gdImageCreateFromPngSource">gdImageCreateFromPngSource</A> | +<A HREF="#gdImageCreateFromWBMP">gdImageCreateFromWBMP</A> | +<A HREF="#gdImageCreateFromWBMPCtx">gdImageCreateFromWBMPCtx</A> | +<A HREF="#gdImageCreateFromWBMPPtr">gdImageCreateFromWBMPPtr</A> | +<A HREF="#gdImageCreateFromXbm">gdImageCreateFromXbm</A> | +<A HREF="#gdImageCreateFromXpm">gdImageCreateFromXpm</A> | +<A HREF="#gdImageDashedLine">gdImageDashedLine</A> | +<A HREF="#gdImageDestroy">gdImageDestroy</A> | +<A HREF="#gdImageFill">gdImageFill</A> | +<A HREF="#gdImageFilledArc">gdImageFilledArc</A> | +<A HREF="#gdImageFilledEllipse">gdImageFilledEllipse</A> | +<A HREF="#gdImageFillToBorder">gdImageFillToBorder</A> | +<A HREF="#gdImageFilledRectangle">gdImageFilledRectangle</A> | +<A HREF="#gdImageGd">gdImageGd</A> | +<A HREF="#gdImageGd2">gdImageGd2</A> | +<A HREF="#gdImageGetInterlaced">gdImageGetInterlaced</A> | +<A HREF="#gdImageGetPixel">gdImageGetPixel</A> | +<A HREF="#gdImageGetTransparent">gdImageGetTransparent</A> | +<A HREF="#gdImageGifAnimAdd">gdImageGifAnimAdd</A> | +<A HREF="#gdImageGifAnimAddCtx">gdImageGifAnimAddCtx</A> | +<A HREF="#gdImageGifAnimAddPtr">gdImageGifAnimAddPtr</A> | +<A HREF="#gdImageGifAnimBegin">gdImageGifAnimBegin</A> | +<A HREF="#gdImageGifAnimBeginCtx">gdImageGifAnimBeginCtx</A> | +<A HREF="#gdImageGifAnimBeginPtr">gdImageGifAnimBeginPtr</A> | +<A HREF="#gdImageGifAnimEnd">gdImageGifAnimEnd</A> | +<A HREF="#gdImageGifAnimEndCtx">gdImageGifAnimEndCtx</A> | +<A HREF="#gdImageGifAnimEndPtr">gdImageGifAnimEndPtr</A> | +<A HREF="#gdImageGreen">gdImageGreen</A> | +<A HREF="#gdImageInterlace">gdImageInterlace</A> | +<A HREF="#gdImageJpeg">gdImageJpeg</A> | +<A HREF="#gdImageJpegCtx">gdImageJpegCtx</A> | +<A HREF="#gdImageLine">gdImageLine</A> | +<A HREF="#gdImageFilledPolygon">gdImageFilledPolygon</A> | +<A HREF="#gdImageOpenPolygon">gdImageOpenPolygon</A> | +<A HREF="#gdImagePaletteCopy">gdImagePaletteCopy</A> | +<A HREF="#gdImagePng">gdImagePng</A> | +<A HREF="#gdImagePngEx">gdImagePngEx</A> | +<A HREF="#gdImagePngCtx">gdImagePngCtx</A> | +<A HREF="#gdImagePngCtxEx">gdImagePngCtxEx</A> | +<A HREF="#gdImagePngPtr">gdImagePngPtr</A> | +<A HREF="#gdImagePngPtrEx">gdImagePngPtrEx</A> | +<A HREF="#gdImagePngToSink">gdImagePngToSink</A> | +<A HREF="#gdImagePolygon">gdImagePolygon</A> | +<A HREF="#gdImagePtr">gdImagePtr</A> | +<A HREF="#gdImageWBMP">gdImageWBMP</A> | +<A HREF="#gdImageWBMPCtx">gdImageWBMPCtx</A> | +<A HREF="#gdImageRectangle">gdImageRectangle</A> | +<A HREF="#gdImageRed">gdImageRed</A> | +<A HREF="#gdImageSaveAlpha">gdImageSaveAlpha</A> | +<A HREF="#gdImageSetAntiAliased">gdImageSetAntiAliased</A> | +<A HREF="#gdImageSetAntiAliasedDontBlend">gdImageSetAntiAliasedDontBlend</A> | +<A HREF="#gdImageSetBrush">gdImageSetBrush</A> | +<A HREF="#gdImageSetPixel">gdImageSetPixel</A> | +<A HREF="#gdImageSetStyle">gdImageSetStyle</A> | +<A HREF="#gdImageSetThickness">gdImageSetThickness</A> | +<A HREF="#gdImageSetTile">gdImageSetTile</A> | +<A HREF="#gdImageSharpen">gdImageSharpen</A> | +<A HREF="#gdImageSquareToCircle">gdImageSquareToCircle</A> | +<A HREF="#gdImageString">gdImageString</A> | +<A HREF="#gdImageString16">gdImageString16</A> | +<A HREF="#gdImageStringFT">gdImageStringFT</A> | +<A HREF="#gdImageStringFTCircle">gdImageStringFTCircle</A> | +<A HREF="#gdImageStringFTEx">gdImageStringFTEx</A> | +<A HREF="#gdImageStringTTF">gdImageStringTTF</A> | +<A HREF="#gdImageStringUp">gdImageStringUp</A> | +<A HREF="#gdImageStringUp">gdImageStringUp16</A> | +<A HREF="#gdImageToPalette">gdImageToPalette</A> | +<A HREF="#gdImageWBMP">gdImageWBMP</A> | +<A HREF="#gdMaxColors">gdMaxColors</A> | +<A HREF="#gdPoint">gdPoint</A> | +<A HREF="#gdStyled">gdStyled</A> | +<A HREF="#gdStyledBrushed">gdStyledBrushed</A> | +<A HREF="#gdTiled">gdTiled</A> | +<A HREF="#gdTransparent">gdTransparent</A> +<P> +<em><A HREF="http://www.libgd.org/"> +www.libgd.org</A></em> +</body> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/index.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +<html> +<head> +<title>Graphviz Documentation Index</title> +</head> +<body> +<h1>Graphviz Documentation Index</h1> +<ul> +<li>Reference (HTML) + <ul> + <li><a href="info/index.html">Dot Language Reference</a> + </ul> +<li>Miscellaneous (HTML) + <ul> + <li><a href="FAQ.html">FAQ</a> + <li><a href="build.html">build</a> + <li><a href="gd.html">gd</a> + <li><a href="tcldot.html">tcldot</a> + </ul> +<li>Guides (PDF) + <ul> + <li><a href="../pdf/dotguide.pdf">dot guide</a> + <li><a href="../pdf/dottyguide.pdf">dotty guide</a> + <li><a href="../pdf/leftyguide.pdf">lefty guide</a> + <li><a href="../pdf/neatoguide.pdf">neato guide</a> + <li><a href="../pdf/libguide.pdf">library guide</a> + </ul> +<li>Command manual pages (PDF) + <ul> + <li><a href="../pdf/acyclic.pdf">acyclic.1</a> + <li><a href="../pdf/bcomps.pdf">bcmps.1</a> + <li><a href="../pdf/ccomps.pdf">ccomps.1</a> + <li><a href="../pdf/circo.pdf">circo.1</a> + <li><a href="../pdf/dijkstra.pdf">dijkstra.1</a> + <li><a href="../pdf/dot.pdf">dot.1</a> + <li><a href="../pdf/dot2gxl.pdf">dot2gxl.1</a> + <li><a href="../pdf/dotneato-config.pdf">dotneato-config.1</a> + <li><a href="../pdf/dotty.pdf">dotty.1</a> + <li><a href="../pdf/fdp.pdf">fdp.1</a> + <li><a href="../pdf/gc.pdf">gc.1</a> + <li><a href="../pdf/gvcolor.pdf">gvcolor.1</a> + <li><a href="../pdf/gvpack.pdf">gvpack.1</a> + <li><a href="../pdf/gvpr.pdf">gvpr.1</a> + <li><a href="../pdf/gxl2dot.pdf">gxl2dot.1</a> + <li><a href="../pdf/lefty.pdf">lefty.1</a> + <li><a href="../pdf/lneato.pdf">lneato.1</a> + <li><a href="../pdf/neato.pdf">neato.1</a> + <li><a href="../pdf/nop.pdf">nop.1</a> + <li><a href="../pdf/prune.pdf">prune.1</a> + <li><a href="../pdf/sccmap.pdf">sccmap.1</a> + <li><a href="../pdf/tred.pdf">tred.1</a> + <li><a href="../pdf/twopi.pdf">twopi.1</a> + <li><a href="../pdf/unflatten.pdf">unflatten.1</a> + </ul> +<li>Tcl package manual pages (PDF) + <ul> + <li><a href="../pdf/gdtclft.pdf">gdtclft.n</a> + <li><a href="../pdf/tcldot.pdf">tcldot.n</a> + <li><a href="../pdf/tkspline.pdf">tkspline.n</a> + </ul> +<li>Library manual pages (PDF) + <ul> + <li><a href="../pdf/agraph.pdf">agraph.3</a> + <li><a href="../pdf/cdt.pdf">cdt.3</a> + <li><a href="../pdf/expr.pdf">expr.3</a> + <li><a href="../pdf/graph.pdf">graph.3</a> + <li><a href="../pdf/pathplan.pdf">pathplan.3</a> + <li><a href="../pdf/pack.pdf">pack.3</a> + </ul> +</ul> +</body> +</html>
Binary file toolboxes/graph_visualisation/share/graphviz/doc/html/info/a_lteeoldiamond.gif has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/arrows.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,278 @@ +<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> +<!-- + This is a generated document. Do not edit. +--> +<HTML VERSION="2.0"> +<HEAD> +<TITLE>Arrow Shapes</TITLE> +</HEAD> +<BODY BGCOLOR=white> +<A NAME="top"></A> +<H1 ALIGN=CENTER>Arrow Shapes</H1> +<HR> +Arrow shapes can be specified and named using the following simple +grammar. +Terminals are shown in bold font and nonterminals in italics. +Literal characters are given in single quotes. +Square brackets [ and ] enclose optional items. +Vertical bars | separate alternatives. +<TABLE> +<TR> + <TD ALIGN=RIGHT><I>arrowname</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>aname</I> [ <I>aname</I> [ <I>aname</I> [ <I>aname</I> ] ] ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>aname</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>[ <I>modifiers</I> ] <I>shape</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>modifiers</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>[ <B>'o'</B> ] [ <I>side</I> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>side</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><B>'l'</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>'r'</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>shape</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><B>box</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>crow</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>diamond</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>dot</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>inv</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>none</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>normal</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>tee</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>vee</B></TD> +</TR> +</TABLE> +<P> +The primitive shapes are: +<CENTER> +<TABLE> + <TR> + <TD>"box"<TD><IMG SRC="a_box.gif"> + <TD>"crow"<TD><IMG SRC="a_crow.gif"> + <TD>"diamond"<TD><IMG SRC="a_diamond.gif"> + </TR> + <TR> + <TD>"dot"<TD><IMG SRC="a_dot.gif"> + <TD>"inv"<TD><IMG SRC="a_inv.gif"> + <TD>"none"<TD><IMG SRC="a_none.gif"> + </TR> + <TR> + <TD>"normal"<TD><IMG SRC="a_normal.gif"> + <TD>"tee"<TD><IMG SRC="a_tee.gif"> + <TD>"vee"<TD><IMG SRC="a_open.gif"> + </TR> +</TABLE> +</CENTER> +<P> +As for the modifiers: +<DL> +<DT>'l' + <DD> Clip the shape, leaving only the part to the left of the edge. +<DT>'r' + <DD> Clip the shape, leaving only the part to the right of the edge. +<DT>'o' + <DD> Use an open (non-filled) version of the shape. +</DL> +Left and right are defined as those directions determined by looking +from the edge towards the point where the arrow "touches" the node. +<P> +As an example, the arrow shape <TT>lteeoldiamond</TT> is parsed as +<TT>'l' 'tee' 'o' 'l' 'diamond'</TT> and corresponds to the shape +<CENTER> +<IMG SRC="a_lteeoldiamond.gif"> +</CENTER> +Note that the first arrow shape specified occurs closest to the node. +Subsequent arrow shapes, if specified, occur further from the node. +<P> +Not all syntactically legal combinations of modifiers are meaningful +or semantically valid. +For example, none of the modifiers make any sense with <TT>none</TT>. +The following table indicates which modifiers are allowed with which shapes. +<P> +<CENTER> +<TABLE BORDER=2> +<TR ALIGN=CENTER> + <TH>Modifier</TH> + <TH>box + <TH>crow + <TH>diamond + <TH>dot + <TH>inv + <TH>none + <TH>normal + <TH>tee + <TH>vee +</TR> +<TR ALIGN=CENTER><TD>'l'/'r'<TD>X<TD>X<TD>X<TD> <TD>X<TD> <TD>X<TD>X<TD>X</TR> +<TR ALIGN=CENTER><TD>'o' <TD>X<TD> <TD>X<TD>X<TD>X<TD> <TD>X<TD> <TD> </TR> +</TABLE> +</CENTER> +<P> +This yields 36 different arrow shapes. The optional second, third, fourth shapes +can independently be any of the 36, but since <TT>nonenone</TT> +is redundant, there are 35**4 + 35**3 + 35**2 + 35 + 1 = 1,544,761 different combinations. +<P> +The following display contains the 36 combinations possible with a single +arrow shape. The node attached to the arrow is not drawn but would appear +on the right side of the edge. +<P> +<CENTER> +<TABLE> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_box.gif"> + <TD><IMG SRC="aa_lbox.gif"> + <TD><IMG SRC="aa_rbox.gif"> + <TD><IMG SRC="aa_obox.gif"> + <TD><IMG SRC="aa_olbox.gif"> + <TD><IMG SRC="aa_orbox.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>box + <TD>lbox + <TD>rbox + <TD>obox + <TD>olbox + <TD>orbox + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_crow.gif"> + <TD><IMG SRC="aa_lcrow.gif"> + <TD><IMG SRC="aa_rcrow.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>crow + <TD>lcrow + <TD>rcrow + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_diamond.gif"> + <TD><IMG SRC="aa_ldiamond.gif"> + <TD><IMG SRC="aa_rdiamond.gif"> + <TD><IMG SRC="aa_odiamond.gif"> + <TD><IMG SRC="aa_oldiamond.gif"> + <TD><IMG SRC="aa_ordiamond.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>diamond + <TD>ldiamond + <TD>rdiamond + <TD>odiamond + <TD>oldiamond + <TD>ordiamond + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_dot.gif"> + <TD><IMG SRC="aa_odot.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>dot + <TD>odot + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_inv.gif"> + <TD><IMG SRC="aa_linv.gif"> + <TD><IMG SRC="aa_rinv.gif"> + <TD><IMG SRC="aa_oinv.gif"> + <TD><IMG SRC="aa_olinv.gif"> + <TD><IMG SRC="aa_orinv.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>inv + <TD>linv + <TD>rinv + <TD>oinv + <TD>olinv + <TD>orinv + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_none.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>none + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_normal.gif"> + <TD><IMG SRC="aa_lnormal.gif"> + <TD><IMG SRC="aa_rnormal.gif"> + <TD><IMG SRC="aa_onormal.gif"> + <TD><IMG SRC="aa_olnormal.gif"> + <TD><IMG SRC="aa_ornormal.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>normal + <TD>lnormal + <TD>rnormal + <TD>onormal + <TD>olnormal + <TD>ornormal + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_tee.gif"> + <TD><IMG SRC="aa_ltee.gif"> + <TD><IMG SRC="aa_rtee.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>tee + <TD>ltee + <TD>rtee + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC="aa_vee.gif"> + <TD><IMG SRC="aa_lvee.gif"> + <TD><IMG SRC="aa_rvee.gif"> + </TR> + <TR ALIGN=CENTER> + <TD>vee + <TD>lvee + <TD>rvee + </TR> +</TABLE> +</CENTER> +</BODY> +</HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/attrs.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2087 @@ +<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> +<!-- + This is a generated document. Do not edit. +--> +<HTML VERSION="2.0"> +<HEAD> +<TITLE>Node, Edge and Graph Attributes</TITLE> +</HEAD> +<BODY BGCOLOR=white> +<A NAME="top"></A> +<H1 align=CENTER>Node, Edge and Graph Attributes</H1> +<HR> +The table below describes the attributes used by various Graphviz tools. +The table gives the name of the attribute, the graph components (node, +edge, etc.) which use the attribute and the type of the attribute +(strings representing legal values of that type). Where applicable, the table +also gives a default value for the attribute, a minimum allowed setting +for numeric attributes, and certain restrictions on the use of the attribute. +<P> +All Graphviz attributes are specified by name-value pairs. Thus, to +set the fillcolor of a node <TT>abc</TT>, one would use +<TABLE> +<TR><TD><TT>abc [fillcolor = red]</TT></TR> +</TABLE> +Similarly, to set the arrowhead style of an edge <TT>abc -> def</TT>, +one would use +<TABLE> +<TR><TD><TT>abc -> def [arrowhead = diamond]</TT></TR> +</TABLE> +Further details concerning the setting of attributes can be found +in the description of the +<A HREF="http://www.graphviz.org/doc/info/lang.html">DOT language.</A> +<P> +At present, most device-independent units are either inches or +<A NAME="points"></A> +<A HREF="http://en.wikipedia.org/wiki/Point_(typography)">points</A>, +which we take as 72 points per inch. +<P> +<A NAME=h:undir_note><STRONG>Note:</STRONG></A> Some attributes, such as +<A HREF=#d:dir>dir</A> or <A HREF=#d:arrowtail>arrowtail</A>, are +ambiguous when used in +<A HREF="http://www.graphviz.org/doc/info/lang.html">DOT</A> +with an undirected graph since the head and tail of an edge are meaningless. +As a convention, the first time an undirected edge appears, the +<A HREF="http://www.graphviz.org/doc/info/lang.html">DOT</A> +parser will assign the left node as the tail node and the right node as +the head. For example, the edge <TT>A -- B</TT> will have tail <TT>A</TT> +and head <TT>B</TT>. It is the user's responsibility to handle such +edges consistently. If the edge appears later, in the format +<TABLE> +<TR><TD><TT>B -- A [taillabel = "tail"]</TT></TR> +</TABLE> +the drawing will attach the tail label to node <TT>A</TT>. +To avoid possible confusion when such attributes are required, the user +is encouraged to use a directed graph. +If it is important to make the graph appear undirected, this can be +done using the <A HREF=#d:dir>dir</A>, <A HREF=#d:arrowtail>arrowtail</A> +or <A HREF=#d:arrowhead>arrowhead</A> attributes. +<P> +The tools accept standard C representations for <EM>int</EM> and +<EM>double</EM> types. +For the <A NAME=k:bool><EM>bool</EM></A> type, TRUE values are +represented by "true" (case-insensitive) +and any non-zero integer, and FALSE values by "false" (case-insensitive) +and zero. +In addition, there are a variety of specialized types such as +<EM>arrowType</EM>, <EM>color</EM>, +<EM>pointf</EM> and <EM>rankdir</EM>. Legal values for these types are given +at the end. +<P> +In the <A NAME=h:uses><STRONG>Used By</STRONG></A> field, the +characters E, N, G, S and C +represent edges, nodes, the root graph, subgraphs +and cluster subgraphs, respectively. +This field indicates which graph component uses the attribute. +<HR ALIGN=CENTER WIDTH="70%" SIZE=3> +<TABLE ALIGN=CENTER> +<TR><TH>Name</TH><TH><A HREF=#h:uses>Used By</A></TH><TH>Type</TH><TH>Default</TH><TH>Minimum</TH><TH>Notes</TH></TR> + <TR><TD><A NAME=a:Damping HREF=#d:Damping>Damping</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">0.99</TD><TD>0.0</TD><TD>neato only</TD> </TR> + <TR><TD><A NAME=a:K HREF=#d:K>K</A> +</TD><TD>GC</TD><TD>double</TD><TD ALIGN="CENTER">0.3</TD><TD>0</TD><TD>sfdp, fdp only</TD> </TR> + <TR><TD><A NAME=a:URL HREF=#d:URL>URL</A> +</TD><TD>ENGC</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER"><none></TD><TD></TD><TD>svg, postscript, map only</TD> </TR> + <TR><TD><A NAME=a:arrowhead HREF=#d:arrowhead>arrowhead</A> +</TD><TD>E</TD><TD><A HREF=#k:arrowType>arrowType</A> +</TD><TD ALIGN="CENTER">normal</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:arrowsize HREF=#d:arrowsize>arrowsize</A> +</TD><TD>E</TD><TD>double</TD><TD ALIGN="CENTER">1.0</TD><TD>0.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:arrowtail HREF=#d:arrowtail>arrowtail</A> +</TD><TD>E</TD><TD><A HREF=#k:arrowType>arrowType</A> +</TD><TD ALIGN="CENTER">normal</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:aspect HREF=#d:aspect>aspect</A> +</TD><TD>G</TD><TD><A HREF=#k:aspectType>aspectType</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:bb HREF=#d:bb>bb</A> +</TD><TD>G</TD><TD><A HREF=#k:rect>rect</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD>write only</TD> </TR> + <TR><TD><A NAME=a:bgcolor HREF=#d:bgcolor>bgcolor</A> +</TD><TD>GC</TD><TD><A HREF=#k:color>color</A> +</TD><TD ALIGN="CENTER"><none></TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:center HREF=#d:center>center</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:charset HREF=#d:charset>charset</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">"UTF-8"</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:clusterrank HREF=#d:clusterrank>clusterrank</A> +</TD><TD>G</TD><TD><A HREF=#k:clusterMode>clusterMode</A> +</TD><TD ALIGN="CENTER">local</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:color HREF=#d:color>color</A> +</TD><TD>ENC</TD><TD><A HREF=#k:color>color</A> +<BR><A HREF=#k:colorList>colorList</A> +</TD><TD ALIGN="CENTER">black</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:colorscheme HREF=#d:colorscheme>colorscheme</A> +</TD><TD>ENCG</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:comment HREF=#d:comment>comment</A> +</TD><TD>ENG</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:compound HREF=#d:compound>compound</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:concentrate HREF=#d:concentrate>concentrate</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:constraint HREF=#d:constraint>constraint</A> +</TD><TD>E</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">true</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:decorate HREF=#d:decorate>decorate</A> +</TD><TD>E</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:defaultdist HREF=#d:defaultdist>defaultdist</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">1+(avg. len)*sqrt(|V|)</TD><TD>epsilon</TD><TD>neato only</TD> </TR> + <TR><TD><A NAME=a:dim HREF=#d:dim>dim</A> +</TD><TD>G</TD><TD>int</TD><TD ALIGN="CENTER">2</TD><TD>2</TD><TD>sfdp, fdp, neato only</TD> </TR> + <TR><TD><A NAME=a:dimen HREF=#d:dimen>dimen</A> +</TD><TD>G</TD><TD>int</TD><TD ALIGN="CENTER">2</TD><TD>2</TD><TD>sfdp, fdp, neato only</TD> </TR> + <TR><TD><A NAME=a:dir HREF=#d:dir>dir</A> +</TD><TD>E</TD><TD><A HREF=#k:dirType>dirType</A> +</TD><TD ALIGN="CENTER">forward(directed)<BR>none(undirected)</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:diredgeconstraints HREF=#d:diredgeconstraints>diredgeconstraints</A> +</TD><TD>G</TD><TD>string<BR><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD>neato only</TD> </TR> + <TR><TD><A NAME=a:distortion HREF=#d:distortion>distortion</A> +</TD><TD>N</TD><TD>double</TD><TD ALIGN="CENTER">0.0</TD><TD>-100.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:dpi HREF=#d:dpi>dpi</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">96.0<BR>0.0</TD><TD></TD><TD>svg, bitmap output only</TD> </TR> + <TR><TD><A NAME=a:edgeURL HREF=#d:edgeURL>edgeURL</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:edgehref HREF=#d:edgehref>edgehref</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:edgetarget HREF=#d:edgetarget>edgetarget</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER"><none></TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:edgetooltip HREF=#d:edgetooltip>edgetooltip</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, cmap only</TD> </TR> + <TR><TD><A NAME=a:epsilon HREF=#d:epsilon>epsilon</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">.0001 * # nodes(mode == KK)<BR>.0001(mode == major)</TD><TD></TD><TD>neato only</TD> </TR> + <TR><TD><A NAME=a:esep HREF=#d:esep>esep</A> +</TD><TD>G</TD><TD>double<BR><A HREF=#k:pointf>pointf</A> +</TD><TD ALIGN="CENTER">+3</TD><TD></TD><TD>not dot</TD> </TR> + <TR><TD><A NAME=a:fillcolor HREF=#d:fillcolor>fillcolor</A> +</TD><TD>NC</TD><TD><A HREF=#k:color>color</A> +</TD><TD ALIGN="CENTER">lightgrey(nodes)<BR>black(clusters)</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:fixedsize HREF=#d:fixedsize>fixedsize</A> +</TD><TD>N</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:fontcolor HREF=#d:fontcolor>fontcolor</A> +</TD><TD>ENGC</TD><TD><A HREF=#k:color>color</A> +</TD><TD ALIGN="CENTER">black</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:fontname HREF=#d:fontname>fontname</A> +</TD><TD>ENGC</TD><TD>string</TD><TD ALIGN="CENTER">"Times-Roman"</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:fontnames HREF=#d:fontnames>fontnames</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg only</TD> </TR> + <TR><TD><A NAME=a:fontpath HREF=#d:fontpath>fontpath</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">system-dependent</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:fontsize HREF=#d:fontsize>fontsize</A> +</TD><TD>ENGC</TD><TD>double</TD><TD ALIGN="CENTER">14.0</TD><TD>1.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:group HREF=#d:group>group</A> +</TD><TD>N</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:headURL HREF=#d:headURL>headURL</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:headclip HREF=#d:headclip>headclip</A> +</TD><TD>E</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">true</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:headhref HREF=#d:headhref>headhref</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:headlabel HREF=#d:headlabel>headlabel</A> +</TD><TD>E</TD><TD><A HREF=#k:lblString>lblString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:headport HREF=#d:headport>headport</A> +</TD><TD>E</TD><TD><A HREF=#k:portPos>portPos</A> +</TD><TD ALIGN="CENTER">center</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:headtarget HREF=#d:headtarget>headtarget</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER"><none></TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:headtooltip HREF=#d:headtooltip>headtooltip</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, cmap only</TD> </TR> + <TR><TD><A NAME=a:height HREF=#d:height>height</A> +</TD><TD>N</TD><TD>double</TD><TD ALIGN="CENTER">0.5</TD><TD>0.02</TD><TD></TD> </TR> + <TR><TD><A NAME=a:href HREF=#d:href>href</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, postscript, map only</TD> </TR> + <TR><TD><A NAME=a:id HREF=#d:id>id</A> +</TD><TD>GNE</TD><TD><A HREF=#k:lblString>lblString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, postscript, map only</TD> </TR> + <TR><TD><A NAME=a:image HREF=#d:image>image</A> +</TD><TD>N</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:imagescale HREF=#d:imagescale>imagescale</A> +</TD><TD>N</TD><TD><A HREF=#k:bool>bool</A> +<BR>string</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:label HREF=#d:label>label</A> +</TD><TD>ENGC</TD><TD><A HREF=#k:lblString>lblString</A> +</TD><TD ALIGN="CENTER">"\N" (nodes)<BR>"" (otherwise)</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:labelURL HREF=#d:labelURL>labelURL</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:labelangle HREF=#d:labelangle>labelangle</A> +</TD><TD>E</TD><TD>double</TD><TD ALIGN="CENTER">-25.0</TD><TD>-180.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:labeldistance HREF=#d:labeldistance>labeldistance</A> +</TD><TD>E</TD><TD>double</TD><TD ALIGN="CENTER">1.0</TD><TD>0.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:labelfloat HREF=#d:labelfloat>labelfloat</A> +</TD><TD>E</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:labelfontcolor HREF=#d:labelfontcolor>labelfontcolor</A> +</TD><TD>E</TD><TD><A HREF=#k:color>color</A> +</TD><TD ALIGN="CENTER">black</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:labelfontname HREF=#d:labelfontname>labelfontname</A> +</TD><TD>E</TD><TD>string</TD><TD ALIGN="CENTER">"Times-Roman"</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:labelfontsize HREF=#d:labelfontsize>labelfontsize</A> +</TD><TD>E</TD><TD>double</TD><TD ALIGN="CENTER">14.0</TD><TD>1.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:labelhref HREF=#d:labelhref>labelhref</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:labeljust HREF=#d:labeljust>labeljust</A> +</TD><TD>GC</TD><TD>string</TD><TD ALIGN="CENTER">"c"</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:labelloc HREF=#d:labelloc>labelloc</A> +</TD><TD>GC</TD><TD>string</TD><TD ALIGN="CENTER">"t"(clusters)<BR>"b"(root graphs)</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=aa:labelloc HREF=#dd:labelloc>labelloc</A> +</TD><TD>N</TD><TD>string</TD><TD ALIGN="CENTER">"c"(clusters)</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:labeltarget HREF=#d:labeltarget>labeltarget</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER"><none></TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:labeltooltip HREF=#d:labeltooltip>labeltooltip</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, cmap only</TD> </TR> + <TR><TD><A NAME=a:landscape HREF=#d:landscape>landscape</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:layer HREF=#d:layer>layer</A> +</TD><TD>EN</TD><TD><A HREF=#k:layerRange>layerRange</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:layers HREF=#d:layers>layers</A> +</TD><TD>G</TD><TD><A HREF=#k:layerList>layerList</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:layersep HREF=#d:layersep>layersep</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">" :\t"</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:layout HREF=#d:layout>layout</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:len HREF=#d:len>len</A> +</TD><TD>E</TD><TD>double</TD><TD ALIGN="CENTER">1.0(neato)<BR>0.3(fdp)</TD><TD></TD><TD>fdp, neato only</TD> </TR> + <TR><TD><A NAME=a:levels HREF=#d:levels>levels</A> +</TD><TD>G</TD><TD>int</TD><TD ALIGN="CENTER">MAXINT</TD><TD>0.0</TD><TD>sfdp only</TD> </TR> + <TR><TD><A NAME=a:levelsgap HREF=#d:levelsgap>levelsgap</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">0.0</TD><TD></TD><TD>neato only</TD> </TR> + <TR><TD><A NAME=a:lhead HREF=#d:lhead>lhead</A> +</TD><TD>E</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:lp HREF=#d:lp>lp</A> +</TD><TD>EGC</TD><TD><A HREF=#k:point>point</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD>write only</TD> </TR> + <TR><TD><A NAME=a:ltail HREF=#d:ltail>ltail</A> +</TD><TD>E</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:margin HREF=#d:margin>margin</A> +</TD><TD>NG</TD><TD>double<BR><A HREF=#k:pointf>pointf</A> +</TD><TD ALIGN="CENTER"><device-dependent></TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:maxiter HREF=#d:maxiter>maxiter</A> +</TD><TD>G</TD><TD>int</TD><TD ALIGN="CENTER">100 * # nodes(mode == KK)<BR>200(mode == major)<BR>600(fdp)</TD><TD></TD><TD>fdp, neato only</TD> </TR> + <TR><TD><A NAME=a:mclimit HREF=#d:mclimit>mclimit</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">1.0</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:mindist HREF=#d:mindist>mindist</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">1.0</TD><TD>0.0</TD><TD>circo only</TD> </TR> + <TR><TD><A NAME=a:minlen HREF=#d:minlen>minlen</A> +</TD><TD>E</TD><TD>int</TD><TD ALIGN="CENTER">1</TD><TD>0</TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:mode HREF=#d:mode>mode</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">"major"</TD><TD></TD><TD>neato only</TD> </TR> + <TR><TD><A NAME=a:model HREF=#d:model>model</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">"shortpath"</TD><TD></TD><TD>neato only</TD> </TR> + <TR><TD><A NAME=a:mosek HREF=#d:mosek>mosek</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD>neato only</TD> </TR> + <TR><TD><A NAME=a:nodesep HREF=#d:nodesep>nodesep</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">0.25</TD><TD>0.02</TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:nojustify HREF=#d:nojustify>nojustify</A> +</TD><TD>GCNE</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:normalize HREF=#d:normalize>normalize</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD>not dot</TD> </TR> + <TR><TD><A NAME=a:nslimit HREF=#d:nslimit>nslimit</A> +<BR><A NAME=a:nslimit1 HREF=#d:nslimit1>nslimit1</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER"></TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:ordering HREF=#d:ordering>ordering</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:orientation HREF=#d:orientation>orientation</A> +</TD><TD>N</TD><TD>double</TD><TD ALIGN="CENTER">0.0</TD><TD>360.0</TD><TD></TD> </TR> + <TR><TD><A NAME=aa:orientation HREF=#dd:orientation>orientation</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:outputorder HREF=#d:outputorder>outputorder</A> +</TD><TD>G</TD><TD><A HREF=#k:outputMode>outputMode</A> +</TD><TD ALIGN="CENTER">breadthfirst</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:overlap HREF=#d:overlap>overlap</A> +</TD><TD>G</TD><TD>string<BR><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">true</TD><TD></TD><TD>not dot</TD> </TR> + <TR><TD><A NAME=a:overlap_scaling HREF=#d:overlap_scaling>overlap_scaling</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">-4</TD><TD>-1.0e10</TD><TD>prism only</TD> </TR> + <TR><TD><A NAME=a:pack HREF=#d:pack>pack</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +<BR>int</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD>not dot</TD> </TR> + <TR><TD><A NAME=a:packmode HREF=#d:packmode>packmode</A> +</TD><TD>G</TD><TD><A HREF=#k:packMode>packMode</A> +</TD><TD ALIGN="CENTER">node</TD><TD></TD><TD>not dot</TD> </TR> + <TR><TD><A NAME=a:pad HREF=#d:pad>pad</A> +</TD><TD>G</TD><TD>double<BR><A HREF=#k:pointf>pointf</A> +</TD><TD ALIGN="CENTER">0.0555 (4 points)</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:page HREF=#d:page>page</A> +</TD><TD>G</TD><TD><A HREF=#k:pointf>pointf</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:pagedir HREF=#d:pagedir>pagedir</A> +</TD><TD>G</TD><TD><A HREF=#k:pagedir>pagedir</A> +</TD><TD ALIGN="CENTER">BL</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:pencolor HREF=#d:pencolor>pencolor</A> +</TD><TD>C</TD><TD><A HREF=#k:color>color</A> +</TD><TD ALIGN="CENTER">black</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:penwidth HREF=#d:penwidth>penwidth</A> +</TD><TD>CNE</TD><TD>double</TD><TD ALIGN="CENTER">1.0</TD><TD>0.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:peripheries HREF=#d:peripheries>peripheries</A> +</TD><TD>NC</TD><TD>int</TD><TD ALIGN="CENTER">shape default(nodes)<BR>1(clusters)</TD><TD>0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:pin HREF=#d:pin>pin</A> +</TD><TD>N</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD>fdp, neato only</TD> </TR> + <TR><TD><A NAME=a:pos HREF=#d:pos>pos</A> +</TD><TD>EN</TD><TD><A HREF=#k:point>point</A> +<BR><A HREF=#k:splineType>splineType</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:quadtree HREF=#d:quadtree>quadtree</A> +</TD><TD>G</TD><TD><A HREF=#k:quadType>quadType</A> +<BR><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">"normal"</TD><TD></TD><TD>sfdp only</TD> </TR> + <TR><TD><A NAME=a:quantum HREF=#d:quantum>quantum</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">0.0</TD><TD>0.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:rank HREF=#d:rank>rank</A> +</TD><TD>S</TD><TD><A HREF=#k:rankType>rankType</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:rankdir HREF=#d:rankdir>rankdir</A> +</TD><TD>G</TD><TD><A HREF=#k:rankdir>rankdir</A> +</TD><TD ALIGN="CENTER">TB</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:ranksep HREF=#d:ranksep>ranksep</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">0.5(dot)<BR>1.0(twopi)</TD><TD>0.02</TD><TD>twopi, dot only</TD> </TR> + <TR><TD><A NAME=a:ratio HREF=#d:ratio>ratio</A> +</TD><TD>G</TD><TD>double<BR>string</TD><TD ALIGN="CENTER"></TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:rects HREF=#d:rects>rects</A> +</TD><TD>N</TD><TD><A HREF=#k:rect>rect</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD>write only</TD> </TR> + <TR><TD><A NAME=a:regular HREF=#d:regular>regular</A> +</TD><TD>N</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:remincross HREF=#d:remincross>remincross</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">false</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:repulsiveforce HREF=#d:repulsiveforce>repulsiveforce</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">1.0</TD><TD>0.0</TD><TD>sfdp only</TD> </TR> + <TR><TD><A NAME=a:resolution HREF=#d:resolution>resolution</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">96.0<BR>0.0</TD><TD></TD><TD>svg, bitmap output only</TD> </TR> + <TR><TD><A NAME=a:root HREF=#d:root>root</A> +</TD><TD>GN</TD><TD>string<BR><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">""(graphs)<BR>false(nodes)</TD><TD></TD><TD>circo, twopi only</TD> </TR> + <TR><TD><A NAME=a:rotate HREF=#d:rotate>rotate</A> +</TD><TD>G</TD><TD>int</TD><TD ALIGN="CENTER">0</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:samehead HREF=#d:samehead>samehead</A> +</TD><TD>E</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:sametail HREF=#d:sametail>sametail</A> +</TD><TD>E</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:samplepoints HREF=#d:samplepoints>samplepoints</A> +</TD><TD>N</TD><TD>int</TD><TD ALIGN="CENTER">8(output)<BR>20(overlap and image maps)</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:searchsize HREF=#d:searchsize>searchsize</A> +</TD><TD>G</TD><TD>int</TD><TD ALIGN="CENTER">30</TD><TD></TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:sep HREF=#d:sep>sep</A> +</TD><TD>G</TD><TD>double<BR><A HREF=#k:pointf>pointf</A> +</TD><TD ALIGN="CENTER">+4</TD><TD></TD><TD>not dot</TD> </TR> + <TR><TD><A NAME=a:shape HREF=#d:shape>shape</A> +</TD><TD>N</TD><TD><A HREF=#k:shape>shape</A> +</TD><TD ALIGN="CENTER">ellipse</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:shapefile HREF=#d:shapefile>shapefile</A> +</TD><TD>N</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:showboxes HREF=#d:showboxes>showboxes</A> +</TD><TD>ENG</TD><TD>int</TD><TD ALIGN="CENTER">0</TD><TD>0</TD><TD>dot only</TD> </TR> + <TR><TD><A NAME=a:sides HREF=#d:sides>sides</A> +</TD><TD>N</TD><TD>int</TD><TD ALIGN="CENTER">4</TD><TD>0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:size HREF=#d:size>size</A> +</TD><TD>G</TD><TD><A HREF=#k:pointf>pointf</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:skew HREF=#d:skew>skew</A> +</TD><TD>N</TD><TD>double</TD><TD ALIGN="CENTER">0.0</TD><TD>-100.0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:smoothing HREF=#d:smoothing>smoothing</A> +</TD><TD>G</TD><TD><A HREF=#k:smoothType>smoothType</A> +</TD><TD ALIGN="CENTER">"none"</TD><TD></TD><TD>sfdp only</TD> </TR> + <TR><TD><A NAME=a:sortv HREF=#d:sortv>sortv</A> +</TD><TD>GCN</TD><TD>int</TD><TD ALIGN="CENTER">0</TD><TD>0</TD><TD></TD> </TR> + <TR><TD><A NAME=a:splines HREF=#d:splines>splines</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +<BR>string</TD><TD ALIGN="CENTER"></TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:start HREF=#d:start>start</A> +</TD><TD>G</TD><TD><A HREF=#k:startType>startType</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>fdp, neato only</TD> </TR> + <TR><TD><A NAME=a:style HREF=#d:style>style</A> +</TD><TD>ENC</TD><TD><A HREF=#k:style>style</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:stylesheet HREF=#d:stylesheet>stylesheet</A> +</TD><TD>G</TD><TD>string</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg only</TD> </TR> + <TR><TD><A NAME=a:tailURL HREF=#d:tailURL>tailURL</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:tailclip HREF=#d:tailclip>tailclip</A> +</TD><TD>E</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER">true</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:tailhref HREF=#d:tailhref>tailhref</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:taillabel HREF=#d:taillabel>taillabel</A> +</TD><TD>E</TD><TD><A HREF=#k:lblString>lblString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:tailport HREF=#d:tailport>tailport</A> +</TD><TD>E</TD><TD><A HREF=#k:portPos>portPos</A> +</TD><TD ALIGN="CENTER">center</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:tailtarget HREF=#d:tailtarget>tailtarget</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER"><none></TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:tailtooltip HREF=#d:tailtooltip>tailtooltip</A> +</TD><TD>E</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, cmap only</TD> </TR> + <TR><TD><A NAME=a:target HREF=#d:target>target</A> +</TD><TD>ENGC</TD><TD><A HREF=#k:escString>escString</A> +<BR>string</TD><TD ALIGN="CENTER"><none></TD><TD></TD><TD>svg, map only</TD> </TR> + <TR><TD><A NAME=a:tooltip HREF=#d:tooltip>tooltip</A> +</TD><TD>NEC</TD><TD><A HREF=#k:escString>escString</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD>svg, cmap only</TD> </TR> + <TR><TD><A NAME=a:truecolor HREF=#d:truecolor>truecolor</A> +</TD><TD>G</TD><TD><A HREF=#k:bool>bool</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD>bitmap output only</TD> </TR> + <TR><TD><A NAME=a:vertices HREF=#d:vertices>vertices</A> +</TD><TD>N</TD><TD><A HREF=#k:pointfList>pointfList</A> +</TD><TD ALIGN="CENTER"></TD><TD></TD><TD>write only</TD> </TR> + <TR><TD><A NAME=a:viewport HREF=#d:viewport>viewport</A> +</TD><TD>G</TD><TD><A HREF=#k:viewPort>viewPort</A> +</TD><TD ALIGN="CENTER">""</TD><TD></TD><TD></TD> </TR> + <TR><TD><A NAME=a:voro_margin HREF=#d:voro_margin>voro_margin</A> +</TD><TD>G</TD><TD>double</TD><TD ALIGN="CENTER">0.05</TD><TD>0.0</TD><TD>not dot</TD> </TR> + <TR><TD><A NAME=a:weight HREF=#d:weight>weight</A> +</TD><TD>E</TD><TD>double</TD><TD ALIGN="CENTER">1.0</TD><TD>0(dot)<BR>1(neato,fdp,sfdp)</TD><TD></TD> </TR> + <TR><TD><A NAME=a:width HREF=#d:width>width</A> +</TD><TD>N</TD><TD>double</TD><TD ALIGN="CENTER">0.75</TD><TD>0.01</TD><TD></TD> </TR> + <TR><TD><A NAME=a:z HREF=#d:z>z</A> +</TD><TD>N</TD><TD>double</TD><TD ALIGN="CENTER">0.0</TD><TD>-MAXFLOAT<BR>-1000</TD><TD></TD> </TR> +</TABLE> +<HR> +<H1>Attribute Descriptions</H1> +<DL> +<DT><A NAME=d:Damping HREF=#a:Damping><STRONG>Damping</STRONG></A> +<DD> Factor damping force motions. On each iteration, a nodes movement + is limited to this factor of its potential motion. By being less than + 1.0, the system tends to ``cool'', thereby preventing cycling. + +<DT><A NAME=d:K HREF=#a:K><STRONG>K</STRONG></A> +<DD> Spring constant used in virtual physical model. It roughly corresponds + to an ideal edge length (in inches), in that increasing K tends to + increase the distance between nodes. + Note that the edge attribute <A HREF=#d:len>len</A> can be used to + override this value for adjacent nodes. + +<DT><A NAME=d:URL HREF=#a:URL><STRONG>URL</STRONG></A> +<DD> Hyperlinks incorporated into device-dependent output. + At present, used in ps2, cmap, i*map and svg formats. + For all these formats, URLs can be attached to nodes, edges and + clusters. URL attributes can also be attached to the root graph in ps2, + cmap and i*map formats. This serves as the base URL for relative URLs in the + former, and as the default image map file in the latter. + <P> + For svg, cmapx and imap output, the active area for a node is its + visible image. + For example, an unfilled + node with no drawn boundary will only be active on its label. + For other output, the active area is its bounding box. + The active area for a cluster is its bounding box. + For edges, the active areas are small circles where the edge contacts its head + and tail nodes. In addition, for svg, cmapx and imap, the active area + includes a thin polygon approximating the edge. The circles may + overlap the related node, and the edge URL dominates. + If the edge has a label, this will also be active. + Finally, if the edge has a head or tail label, this will also be active. + <P> + Note that, for edges, the attributes <A HREF=#d:headURL>headURL</A>, + <A HREF=#d:tailURL>tailURL</A>, <A HREF=#d:labelURL>labelURL</A> and + <A HREF=#d:edgeURL>edgeURL</A> allow control of various parts of an + edge. + Also note that, if active areas of two edges overlap, it is unspecified + which area dominates. + +<DT><A NAME=d:arrowhead HREF=#a:arrowhead><STRONG>arrowhead</STRONG></A> +<DD> Style of arrowhead on the head node of an edge. + See also the <A HREF=#d:dir>dir</A> attribute, + and a <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:arrowsize HREF=#a:arrowsize><STRONG>arrowsize</STRONG></A> +<DD> Multiplicative scale factor for arrowheads. + +<DT><A NAME=d:arrowtail HREF=#a:arrowtail><STRONG>arrowtail</STRONG></A> +<DD> Style of arrowhead on the tail node of an edge. + See also the <A HREF=#d:dir>dir</A> attribute, + and a <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:aspect HREF=#a:aspect><STRONG>aspect</STRONG></A> +<DD> Target aspect ratio (width of the layout divided by the height) + of the graph drawing. If unset, dot minimizes the total edge length. + For certain graphs, like those with large fan-in or fan-out, this can + lead to very wide layouts. Setting <TT>aspect</TT> will cause dot to try + to adjust the layout to get an aspect ratio close to that specified + by <TT>aspect</TT>. + <P> + By default, dot will do 5 passes attempting to achieve the desired + aspect ratio. For certain graphs, more passes will be needed to get + close enough. The <TT>aspect</TT> attribute can also be used to + specify the maximum number of passes to try. + <P> + At present, there is no mechanism for widening a very tall layout. + Also, the algorithm doesn't handle clusters, nor disconnected graphs. + For the latter case, one can split the pipeline<BR> + <TT>ccomps -x | dot | gvpack | neato -n2</TT> + to get a similar effect. + +<DT><A NAME=d:bb HREF=#a:bb><STRONG>bb</STRONG></A> +<DD> Bounding box of drawing in points. + +<DT><A NAME=d:bgcolor HREF=#a:bgcolor><STRONG>bgcolor</STRONG></A> +<DD> When attached to the root graph, this color is used as the background for + entire canvas. When a cluster attribute, it is used as the initial + background for the cluster. If a cluster has a filled + <A HREF=#d:style>style</A>, the + cluster's <A HREF=#d:fillcolor>fillcolor</A> will overlay the + background color. + <P> + If no background color is specified for the root graph, no graphics + operation are performed on the background. This works fine for + PostScript but for bitmap output, all bits are initialized to something. + This means that when the bitmap output is included in some other + document, all of the bits within the bitmap's bounding box will be + set, overwriting whatever color or graphics where already on the page. + If this effect is not desired, and you only want to set bits explicitly + assigned in drawing the graph, set <B>bgcolor</B>="transparent". + +<DT><A NAME=d:center HREF=#a:center><STRONG>center</STRONG></A> +<DD> If true, the drawing is centered in the output canvas. + +<DT><A NAME=d:charset HREF=#a:charset><STRONG>charset</STRONG></A> +<DD> Specifies the character encoding used when interpreting string input + as a text label. The default value is <TT>"UTF-8"</TT>. + The other legal value is <TT>"iso-8859-1"</TT> or, + equivalently, + <TT>"Latin1"</TT>. The <B>charset</B> attribute is case-insensitive. + Note that if the character encoding used in the input does not + match the <B>charset</B> value, the resulting output may be very strange. + +<DT><A NAME=d:clusterrank HREF=#a:clusterrank><STRONG>clusterrank</STRONG></A> +<DD> Mode used for handling clusters. If <B>clusterrank</B> is "local", a + subgraph whose name begins with "cluster" is given special treatment. + The subgraph is laid out separately, and then integrated as a unit into + its parent graph, with a bounding rectangle drawn about it. + If the cluster has a <A HREF=#d:label>label</A> parameter, this label + is displayed within the rectangle. + Note also that there can be clusters within clusters. + At present, the modes "global" and "none" + appear to be identical, both turning off the special cluster processing. + +<DT><A NAME=d:color HREF=#a:color><STRONG>color</STRONG></A> +<DD> Basic drawing color for graphics, not text. For the latter, use the + <A HREF=#d.fontcolor>fontcolor</A> attribute. + <P> + For edges, the value + can either be a single color or a <A HREF=#k:colorList>colorList</A>. + In the latter case, the edge is drawn using parallel splines or lines, + one for each color in the list, in the order given. + The head arrow, if any, is drawn using the first color in the list, + and the tail arrow, if any, the second color. This supports the common + case of drawing opposing edges, but using parallel splines instead of + separately routed multiedges. For example, the graph + <PRE> + digraph G { + a -> b [dir=both color="red:blue"] + } + </PRE> + yields<BR> + <IMG SRC="colorlist.gif"> + +<DT><A NAME=d:colorscheme HREF=#a:colorscheme><STRONG>colorscheme</STRONG></A> +<DD> This attribute specifies a color scheme namespace. If defined, it specifies + the context for interpreting color names. In particular, if a + <A HREF=#k:color>color</A> value has form <TT>"xxx"</TT> or <TT>"//xxx"</TT>, + then the + color <TT>xxx</TT> will be evaluated according to the current color scheme. + If no color scheme is set, the standard X11 naming is used. + For example, if <TT>colorscheme=bugn9</TT>, then <TT>color=7</TT> + is interpreted as <TT>"/bugn9/7"</TT>. + +<DT><A NAME=d:comment HREF=#a:comment><STRONG>comment</STRONG></A> +<DD> Comments are inserted into output. Device-dependent + +<DT><A NAME=d:compound HREF=#a:compound><STRONG>compound</STRONG></A> +<DD> If true, allow edges between clusters. (See <A HREF=#d:lhead>lhead</A> + and <A HREF=#d:ltail>ltail</A> below.) + +<DT><A NAME=d:concentrate HREF=#a:concentrate><STRONG>concentrate</STRONG></A> +<DD> If true, use edge concentrators. + This merges multiedges into a single edge and causes partially parallel + edges to share part of their paths. The latter feature is not yet available + outside of dot. + +<DT><A NAME=d:constraint HREF=#a:constraint><STRONG>constraint</STRONG></A> +<DD> If false, the edge is not used in ranking the nodes. For example, + in the graph + <PRE> + digraph G { + a -> c; + a -> b; + b -> c [constraint=false]; + } + </PRE> + the edge <CODE>b -> c</CODE> does not add a constraint during rank + assignment, so the only constraints are that a be above b and c, + yielding the graph:<BR> + <IMG SRC="constraint.gif"> + +<DT><A NAME=d:decorate HREF=#a:decorate><STRONG>decorate</STRONG></A> +<DD> If true, attach edge label to edge by a 2-segment + polyline, underlining the label, then going to the closest point of spline. + +<DT><A NAME=d:defaultdist HREF=#a:defaultdist><STRONG>defaultdist</STRONG></A> +<DD> This specifies the distance between nodes in separate connected + components. If set too small, connected components may overlap. + Only applicable if <A HREF=#d:pack>pack</A>=false. + +<DT><A NAME=d:dim HREF=#a:dim><STRONG>dim</STRONG></A> +<DD> Set the number of dimensions used for the layout. The maximum value + allowed is 10. + +<DT><A NAME=d:dimen HREF=#a:dimen><STRONG>dimen</STRONG></A> +<DD> Set the number of dimensions used for rendering. + The maximum value allowed is 10. + If both <TT>dimen</TT> and <TT>dim</TT> are set, the latter specifies + the dimension used for layout, and the former for rendering. + If only <TT>dimen</TT> is set, this is used for both layout and rendering + dimensions. + +<DT><A NAME=d:dir HREF=#a:dir><STRONG>dir</STRONG></A> +<DD> Set edge type for drawing arrowheads. This indicates which ends of the + edge should be decorated with an arrowhead. The actual style of the + arrowhead can be specified using the <A HREF=#d:arrowhead>arrowhead</A> + and <A HREF=#d:arrowtail>arrowtail</A> attributes. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:diredgeconstraints HREF=#a:diredgeconstraints><STRONG>diredgeconstraints</STRONG></A> +<DD> Only valid when <A HREF=#d:mode>mode</A>="ipsep". + If true, constraints are generated for each edge in the largest (heuristic) + directed acyclic subgraph such that the edge must point downwards. + If "hier", generates level constraints similar to those used with + <A HREF=#d:mode>mode</A>="hier". The main difference is that, in the latter + case, only these constraints are involved, so a faster solver can be used. + +<DT><A NAME=d:distortion HREF=#a:distortion><STRONG>distortion</STRONG></A> +<DD> Distortion factor for <A HREF=#d:shape><B>shape</B></A>=polygon. + Positive values cause top part to + be larger than bottom; negative values do the opposite. + +<DT><A NAME=d:dpi HREF=#a:dpi><STRONG>dpi</STRONG></A> +<DD> This specifies the expected number of pixels per inch on a display device. + For bitmap output, this guarantees that text rendering will be + done more accurately, both in size and in placement. For SVG output, + it is used to guarantee that the dimensions in the output correspond to + the correct number of points or inches. + +<DT><A NAME=d:edgeURL HREF=#a:edgeURL><STRONG>edgeURL</STRONG></A> +<DD> If <B>edgeURL</B> is defined, this is the link used for the non-label + parts of an edge. This value overrides any <A HREF=#d:URL>URL</A> + defined for the edge. + Also, this value is used near the head or tail node unless overridden + by a <A HREF=#d:headURL>headURL</A> or <A HREF=#d:tailURL>tailURL</A> value, + respectively. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:edgehref HREF=#a:edgehref><STRONG>edgehref</STRONG></A> +<DD> Synonym for <A HREF=#d:edgeURL>edgeURL</A>. + +<DT><A NAME=d:edgetarget HREF=#a:edgetarget><STRONG>edgetarget</STRONG></A> +<DD> If the edge has a <A HREF=#d:URL>URL</A> or <A HREF=#d:edgeURL>edgeURL</A> + attribute, this attribute determines which window of the + browser is used + for the URL attached to the non-label part of the edge. + Setting it to "_graphviz" will open a new window if it + doesn't already exist, or reuse it if it does. + If undefined, the value of the <A HREF=#d:target>target</A> is used. + +<DT><A NAME=d:edgetooltip HREF=#a:edgetooltip><STRONG>edgetooltip</STRONG></A> +<DD> Tooltip annotation attached to the non-label part of an edge. + This is used only if the edge has a <A HREF=#d:URL>URL</A> + or <A HREF=#d:edgeURL>edgeURL</A> attribute. + +<DT><A NAME=d:epsilon HREF=#a:epsilon><STRONG>epsilon</STRONG></A> +<DD> Terminating condition. If the length squared of all energy gradients are + < <B>epsilon</B>, the algorithm stops. + +<DT><A NAME=d:esep HREF=#a:esep><STRONG>esep</STRONG></A> +<DD> Margin used around polygons for purposes of spline edge routing. + The interpretation is the same as given for <A HREF=#d:sep>sep</A>. + This should normally be strictly less than <A HREF=#d:sep>sep</A>. + +<DT><A NAME=d:fillcolor HREF=#a:fillcolor><STRONG>fillcolor</STRONG></A> +<DD> Color used to fill the background of a node or cluster + assuming <A HREF=#d:style>style</A>=filled. + If <B>fillcolor</B> is not defined, <A HREF=#d:color>color</A> is + used. (For clusters, if <B>color</B> is not defined, + <A HREF=#d:bgcolor>bgcolor</A> is used.) If this is not defined, + the default is used, except for + <A HREF=#d:shape><B>shape</B></A>=point or when the output + format is MIF, + which use black by default. + <P> + Note that a cluster inherits the root graph's attributes if defined. + Thus, if the root graph has defined a <B>fillcolor</B>, this will override a + <B>color</B> or <B>bgcolor</B> attribute set for the cluster. + +<DT><A NAME=d:fixedsize HREF=#a:fixedsize><STRONG>fixedsize</STRONG></A> +<DD> If true, the node size is specified by the values of the + <A HREF=#d:width><B>width</B></A> + and <A HREF=#d:height><B>height</B></A> attributes only + and is not expanded to contain the text label. + +<DT><A NAME=d:fontcolor HREF=#a:fontcolor><STRONG>fontcolor</STRONG></A> +<DD> Color used for text. + +<DT><A NAME=d:fontname HREF=#a:fontname><STRONG>fontname</STRONG></A> +<DD> Font used for text. This very much depends on the output format and, for + non-bitmap output such as PostScript or SVG, the availability of the font + when the graph is displayed or printed. As such, it is best to rely on + font faces that are generally available, such as Times-Roman, Helvetica or + Courier. + <P> + If Graphviz was built using the + <A HREF=http://pdx.freedesktop.org/~fontconfig/fontconfig-user.html>fontconfig library</A>, the latter library + will be used to search for the font. However, if the <TT>fontname</TT> string + contains a slash character "/", it is treated as a pathname for the font + file, though font lookup will append the usual font suffixes. + <P> + If Graphviz does not use fontconfig, <TT>fontname</TT> will be + considered the name of a Type 1 or True Type font file. + If you specify <TT>fontname=schlbk</TT>, the tool will look for a + file named <TT>schlbk.ttf</TT> or <TT>schlbk.pfa</TT> or <TT>schlbk.pfb</TT> + in one of the directories specified by + the <A HREF=#d:fontpath>fontpath</A> attribute. + The lookup does support various aliases for the common fonts. + +<DT><A NAME=d:fontnames HREF=#a:fontnames><STRONG>fontnames</STRONG></A> +<DD> Allows user control of how basic fontnames are represented in SVG output. + If <TT>fontnames</TT> is undefined or <TT>"svg"</TT>, + the output will try to use known SVG fontnames. For example, the + default font <TT>"Times-Roman"</TT> will be mapped to the + basic SVG font <TT>"serif"</TT>. This can be overridden by setting + <TT>fontnames</TT> to <TT>"ps"</TT> or <TT>"gd"</TT>. + In the former case, known PostScript font names such as + <TT>"Times-Roman"</TT> will be used in the output. + In the latter case, the fontconfig font conventions + are used. Thus, <TT>"Times-Roman"</TT> would be treated as + <TT>"Nimbus Roman No9 L"</TT>. These last two options are useful + with SVG viewers that support these richer fontname spaces. + +<DT><A NAME=d:fontpath HREF=#a:fontpath><STRONG>fontpath</STRONG></A> +<DD> Directory list used by libgd to search for bitmap fonts if Graphviz + was not built with the fontconfig library. + If <B>fontpath</B> is not set, the environment + variable <TT>DOTFONTPATH</TT> is checked. + If that is not set, <TT>GDFONTPATH</TT> is checked. + If not set, libgd uses its compiled-in font path. + Note that fontpath is an attribute of the root graph. + +<DT><A NAME=d:fontsize HREF=#a:fontsize><STRONG>fontsize</STRONG></A> +<DD> Font size, <A HREF=#points>in points</A>, used for text. + +<DT><A NAME=d:group HREF=#a:group><STRONG>group</STRONG></A> +<DD> If the end points of an edge belong to the same group, i.e., have the + same group attribute, parameters are set to avoid crossings and keep + the edges straight. + +<DT><A NAME=d:headURL HREF=#a:headURL><STRONG>headURL</STRONG></A> +<DD> If <B>headURL</B> is defined, it is + output as part of the head label of the edge. + Also, this value is used near the head node, overriding any + <A HREF=#d:URL>URL</A> value. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:headclip HREF=#a:headclip><STRONG>headclip</STRONG></A> +<DD> If true, the head of an edge is clipped to the boundary of the head node; + otherwise, the end of the edge goes to the center of the node, or the + center of a port, if applicable. + +<DT><A NAME=d:headhref HREF=#a:headhref><STRONG>headhref</STRONG></A> +<DD> Synonym for <A HREF=#d:headURL>headURL</A>. + +<DT><A NAME=d:headlabel HREF=#a:headlabel><STRONG>headlabel</STRONG></A> +<DD> Text label to be placed near head of edge. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:headport HREF=#a:headport><STRONG>headport</STRONG></A> +<DD> Indicates where on the head node to attach the head of the edge. + In the default case, the edge is aimed towards the center of the node, + and then clipped at the node boundary. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:headtarget HREF=#a:headtarget><STRONG>headtarget</STRONG></A> +<DD> If the edge has a <A HREF=#d:headURL>headURL</A>, + this attribute determines which window of the + browser is used + for the URL. Setting it to "_graphviz" will open a new window if it + doesn't already exist, or reuse it if it does. + If undefined, the value of the <A HREF=#d:target>target</A> is used. + +<DT><A NAME=d:headtooltip HREF=#a:headtooltip><STRONG>headtooltip</STRONG></A> +<DD> Tooltip annotation attached to the head of an edge. This is used only + if the edge has a <A HREF=#d:headURL>headURL</A> attribute. + +<DT><A NAME=d:height HREF=#a:height><STRONG>height</STRONG></A> +<DD> Height of node, in inches. This is taken as the initial, minimum height + of the node. If <A HREF=#d:fixedsize><B>fixedsize</B></A> is true, this + will be the final height of the node. Otherwise, if the node label + requires more height to fit, the node's height will be increased to + contain the label. Note also that, if the output format is dot, the + value given to <B>height</B> will be the final value. + +<DT><A NAME=d:href HREF=#a:href><STRONG>href</STRONG></A> +<DD> Synonym for <A HREF=#d:URL>URL</A>. + +<DT><A NAME=d:id HREF=#a:id><STRONG>id</STRONG></A> +<DD> Allows the graph author to provide an id for graph objects which is to be included in the output. + Normal "N", "E", "G" substitutions are applied. + If provided, it is the reponsiblity of the provider to keep + its values sufficiently unique for its intended downstream use. + Note, in particular, that "E" does not provide a unique id for multi-edges. + If no id attribute is provided, then a unique internal id is used, however this value is unpredictable by the graph writer. + An externally provided id is not used internally. + +<DT><A NAME=d:image HREF=#a:image><STRONG>image</STRONG></A> +<DD> Gives the name of a file containing an image to be displayed inside + a node. The image file must be in one of the recognized + <A HREF=output.html#d:image_fmts>formats</A>, + typically JPEG, PNG, GIF or Postscript, and be able to be converted + into the desired output format. + <P> + Unlike with the <A HREF=#d:shapefile>shapefile</A> attribute, + the image is treated as node + content rather than the entire node. In particular, an image can + be contained in a node of any shape, not just a rectangle. + +<DT><A NAME=d:imagescale HREF=#a:imagescale><STRONG>imagescale</STRONG></A> +<DD> Attribute controlling how an image fills its + containing node. In general, the image is given its natural size, + (cf. <A HREF=#d:dpi>dpi</A>), + and the node size is made large enough to contain its image, its + label, its margin, and its peripheries. + Its width and height will also be at least as large as its + minimum <A HREF=#d:width>width</A> and <A HREF=#d:height>height</A>. + If, however, <TT>fixedsize=true</TT>, + the width and height attributes specify the exact size of the node. + <P> + During rendering, in the default case (<TT>imagescale=false</TT>), + the image retains its natural size. + If <TT>imagescale=true</TT>, + the image is uniformly scaled (i.e., its aspect ratio is + preserved) to fit inside the node. + At least one dimension of the image will be as large as possible + given the size of the node. + When <TT>imagescale=width</TT>, + the width of the image is scaled to fill the node width. + The corresponding property holds when <TT>imagescale=height</TT>. + When <TT>imagescale=both</TT>, + both the height and the width are scaled separately to fill the node. + <P> + In all cases, if a dimension of the image is larger than the + corresponding dimension of the node, that dimension of the + image is scaled down to fit the node. As with the case of + expansion, if <TT>imagescale=true</TT>, width and height are + scaled uniformly. + +<DT><A NAME=d:label HREF=#a:label><STRONG>label</STRONG></A> +<DD> Text label attached to objects. + If a node's <A HREF=#d:shape>shape</A> is record, then the label can + have a <A HREF=shapes.html#record>special format</A> + which describes the record layout. + +<DT><A NAME=d:labelURL HREF=#a:labelURL><STRONG>labelURL</STRONG></A> +<DD> If <B>labelURL</B> is defined, this is the link used for the label + of an edge. This value overrides any <A HREF=#d:URL>URL</A> + defined for the edge. + +<DT><A NAME=d:labelangle HREF=#a:labelangle><STRONG>labelangle</STRONG></A> +<DD> This, along with <A HREF=#d:labeldistance>labeldistance</A>, determine + where the + headlabel (taillabel) are placed with respect to the head (tail) + in polar coordinates. The origin in the coordinate system is + the point where the edge touches the node. The ray of 0 degrees + goes from the origin back along the edge, parallel to the edge + at the origin. + <P> + The angle, in degrees, specifies the rotation from the 0 degree ray, + with positive angles moving counterclockwise and negative angles + moving clockwise. + +<DT><A NAME=d:labeldistance HREF=#a:labeldistance><STRONG>labeldistance</STRONG></A> +<DD> Multiplicative scaling factor adjusting the distance that + the headlabel(taillabel) is from the head(tail) node. + The default distance is 10 points. See <A HREF=#d:labelangle>labelangle</A> + for more details. + +<DT><A NAME=d:labelfloat HREF=#a:labelfloat><STRONG>labelfloat</STRONG></A> +<DD> If true, allows edge labels to be less constrained in position. + In particular, it may appear on top of other edges. + +<DT><A NAME=d:labelfontcolor HREF=#a:labelfontcolor><STRONG>labelfontcolor</STRONG></A> +<DD> Color used for headlabel and taillabel. + If not set, defaults to edge's fontcolor. + +<DT><A NAME=d:labelfontname HREF=#a:labelfontname><STRONG>labelfontname</STRONG></A> +<DD> Font used for headlabel and taillabel. + If not set, defaults to edge's fontname. + +<DT><A NAME=d:labelfontsize HREF=#a:labelfontsize><STRONG>labelfontsize</STRONG></A> +<DD> Font size, <A HREF=#points>in points</A>, used for headlabel and taillabel. + If not set, defaults to edge's fontsize. + +<DT><A NAME=d:labelhref HREF=#a:labelhref><STRONG>labelhref</STRONG></A> +<DD> Synonym for <A HREF=#d:labelURL>labelURL</A>. + +<DT><A NAME=d:labeljust HREF=#a:labeljust><STRONG>labeljust</STRONG></A> +<DD> Justification for cluster labels. If "r", the label + is right-justified within bounding rectangle; if "l", left-justified; + else the label is centered. + Note that a subgraph inherits attributes from its parent. Thus, if + the root graph sets <B>labeljust</B> to "l", the subgraph inherits + this value. + +<DT><A NAME=d:labelloc HREF=#a:labelloc><STRONG>labelloc</STRONG></A> +<DD> Top/bottom placement of graph and cluster labels. + If the attribute is "t", place label at the top; + if the attribute is "b", place label at the bottom. + By default, root + graph labels go on the bottom and cluster labels go on the top. + Note that a subgraph inherits attributes from its parent. Thus, if + the root graph sets <B>labelloc</B> to "b", the subgraph inherits + this value. + +<DT><A NAME=dd:labelloc HREF=#aa:labelloc><STRONG>labelloc</STRONG></A> +<DD> Vertical placement of node label. By default, if the height of a node + is larger than the height of its label, the label is vertically centered. + If <TT>labelloc</TT> is set to "t", "c", or "b", the label is aligned + with the top, centered, or aligned with the bottom of the node, respectively. + +<DT><A NAME=d:labeltarget HREF=#a:labeltarget><STRONG>labeltarget</STRONG></A> +<DD> If the edge has a <A HREF=#d:URL>URL</A> or <A HREF=#d:labelURL>labelURL</A> + attribute, this attribute determines which window of the + browser is used + for the URL attached to the label. + Setting it to "_graphviz" will open a new window if it + doesn't already exist, or reuse it if it does. + If undefined, the value of the <A HREF=#d:target>target</A> is used. + +<DT><A NAME=d:labeltooltip HREF=#a:labeltooltip><STRONG>labeltooltip</STRONG></A> +<DD> Tooltip annotation attached to label of an edge. + This is used only if the edge has a <A HREF=#d:URL>URL</A> + or <A HREF=#d:labelURL>labelURL</A> attribute. + +<DT><A NAME=d:landscape HREF=#a:landscape><STRONG>landscape</STRONG></A> +<DD> If true, the graph is rendered in landscape mode. Synonymous with + <A HREF=#d:rotate><TT>rotate=90</TT></A> or + <A HREF=#d:orientation><TT>orientation=landscape</TT></A>. + +<DT><A NAME=d:layer HREF=#a:layer><STRONG>layer</STRONG></A> +<DD> Specifies layers in which the node or edge is present. + +<DT><A NAME=d:layers HREF=#a:layers><STRONG>layers</STRONG></A> +<DD> Specifies a linearly ordered list of layer names attached to the graph + The graph is then output in separate layers. Only those components + belonging to the current output layer appear. For more information, + see the page <A HREF="http://www.graphviz.org/Documentation/html/layers/">How to use drawing layers (overlays)</A>. + +<DT><A NAME=d:layersep HREF=#a:layersep><STRONG>layersep</STRONG></A> +<DD> Specifies the separator characters used to split the + <A HREF=#d:layers>layers </A>attribute into a list of layer names. + +<DT><A NAME=d:layout HREF=#a:layout><STRONG>layout</STRONG></A> +<DD> Specifies the name of the layout algorithm to use, such as "dot" + or "neato". Normally, graphs should be kept independent of a type of + layout. In some cases, however, it can be convenient to embed the type + of layout desired within the graph. For example, a graph containing + position information from a layout might want to record what the + associated layout algorithm was. + <P> + This attribute takes precedence over + the <A HREF=command.html#minusK>-K flag</A> + or the actual command name used. + +<DT><A NAME=d:len HREF=#a:len><STRONG>len</STRONG></A> +<DD> Preferred edge length, in inches. + +<DT><A NAME=d:levels HREF=#a:levels><STRONG>levels</STRONG></A> +<DD> Number of levels allowed in the multilevel scheme. + +<DT><A NAME=d:levelsgap HREF=#a:levelsgap><STRONG>levelsgap</STRONG></A> +<DD> Specifies strictness of level constraints in neato + when <TT><A HREF=#d:mode>mode</A>="ipsep" or "hier"</TT>. + Larger positive values mean stricter constraints, which demand more + separation between levels. On the other hand, negative values will relax + the constraints by allowing some overlap between the levels. + +<DT><A NAME=d:lhead HREF=#a:lhead><STRONG>lhead</STRONG></A> +<DD> Logical head of an edge. When <A HREF=#d:compound><B>compound</B></A> is true, + if <B>lhead</B> is defined and is the name of a cluster containing + the real head, + the edge is clipped to the boundary of the cluster. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:lp HREF=#a:lp><STRONG>lp</STRONG></A> +<DD> Label position, <A HREF=#points>in points</A>. + The position indicates the center of the label. + +<DT><A NAME=d:ltail HREF=#a:ltail><STRONG>ltail</STRONG></A> +<DD> Logical tail of an edge. When <A HREF=#d:compound><B>compound</B></A> is true, + if <B>ltail</B> is defined and is the name of a cluster + containing the real tail, + the edge is clipped to the boundary of the cluster. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:margin HREF=#a:margin><STRONG>margin</STRONG></A> +<DD> For graphs, this sets x and y margins of canvas, in inches. If the margin + is a single double, both margins are set equal to the given value. + <P> + Note that the margin is not part of the drawing but just empty space + left around the drawing. It basically corresponds to a translation of + drawing, as would be necessary to center a drawing on a page. Nothing + is actually drawn in the margin. To actually extend the background of + a drawing, see the <A HREF=#d:pad>pad</A> attribute. + <P> + For nodes, this attribute specifies space left around the node's label. + By default, the value is <TT>0.11,0.055</TT>. + +<DT><A NAME=d:maxiter HREF=#a:maxiter><STRONG>maxiter</STRONG></A> +<DD> Sets the number of iterations used. + +<DT><A NAME=d:mclimit HREF=#a:mclimit><STRONG>mclimit</STRONG></A> +<DD> Multiplicative scale factor used to alter the MinQuit (default = 8) + and MaxIter (default = 24) parameters used during crossing + minimization. These correspond to the + number of tries without improvement before quitting and the + maximum number of iterations in each pass. + +<DT><A NAME=d:mindist HREF=#a:mindist><STRONG>mindist</STRONG></A> +<DD> Specifies the minimum separation between all nodes. + +<DT><A NAME=d:minlen HREF=#a:minlen><STRONG>minlen</STRONG></A> +<DD> Minimum edge length (rank difference between head and tail). + +<DT><A NAME=d:mode HREF=#a:mode><STRONG>mode</STRONG></A> +<DD> Technique for optimizing the layout. If <B>mode</B> is <TT>"major"</TT>, + neato uses stress majorization. If <B>mode</B> is <TT>"KK"</TT>, + neato uses a version of the gradient descent method. The only advantage + to the latter technique is that it is sometimes appreciably faster for + small (number of nodes < 100) graphs. A significant disadvantage is that + it may cycle. + <P> + There are two new, experimental modes in neato, "hier", which adds a top-down + directionality similar to the layout used in dot, and "ipsep", which + allows the graph to specify minimum vertical and horizontal distances + between nodes. (See the <A HREF=#d:sep>sep</A> attribute.) + +<DT><A NAME=d:model HREF=#a:model><STRONG>model</STRONG></A> +<DD> This value specifies how the distance matrix is computed for the input + graph. The distance matrix specifies the ideal distance between every + pair of nodes. neato attemps to find a layout which best achieves + these distances. By default, it uses the length of the shortest path, + where the length of each edge is given by its <A HREF=#d:len>len</A> + attribute. If <B>model</B> is <TT>"circuit"</TT>, neato uses the + circuit resistance + model to compute the distances. This tends to emphasize clusters. If + <B>model</B> is <TT>"subset"</TT>, neato uses the subset model. This sets the + edge length to be the number of nodes that are neighbors of exactly one + of the end points, and then calculates the shortest paths. This helps + to separate nodes with high degree. + +<DT><A NAME=d:mosek HREF=#a:mosek><STRONG>mosek</STRONG></A> +<DD> If Graphviz is built with MOSEK defined, mode=ipsep and mosek=true, + the Mosek software (www.mosek.com) is use to solve the ipsep constraints. + +<DT><A NAME=d:nodesep HREF=#a:nodesep><STRONG>nodesep</STRONG></A> +<DD> Minimum space between two adjacent nodes in the same rank, in inches. + +<DT><A NAME=d:nojustify HREF=#a:nojustify><STRONG>nojustify</STRONG></A> +<DD> By default, the justification of multi-line labels is done within the + largest context that makes sense. Thus, in the label of a polygonal + node, a left-justified line will align with the left side of the node + (shifted by the prescribed <A HREF=#d:margin>margin</A>). + In record nodes, left-justified + line will line up with the left side of the enclosing column of fields. + If <B>nojustify</B> is <TT>"true"</TT>, multi-line labels will be justified + in the context of itself. For example, if the attribute is set, + the first label line is long, and the second is shorter and left-justified, + the second will align with the left-most character in the first line, + regardless of how large the node might be. + +<DT><A NAME=d:normalize HREF=#a:normalize><STRONG>normalize</STRONG></A> +<DD> If set, normalize coordinates of final + layout so that the first point is at the origin, and then rotate the + layout so that the first edge is horizontal. + +<DT><A NAME=d:nslimit HREF=#a:nslimit><STRONG>nslimit</STRONG></A> +,<DT><A NAME=d:nslimit1 HREF=#a:nslimit1><STRONG>nslimit1</STRONG></A> +<DD> Used to set number of iterations in + network simplex applications. <B>nslimit</B> is used in + computing node x coordinates, <B>nslimit1</B> for ranking nodes. + If defined, # iterations = <B>nslimit(1)</B> * # nodes; + otherwise, # iterations = MAXINT. + +<DT><A NAME=d:ordering HREF=#a:ordering><STRONG>ordering</STRONG></A> +<DD> If "out" for a graph G, and n is a node in G, then edges n->* appear + left-to-right in the same order in which they are defined. + If "in", the edges *->n appear + left-to-right in the same order in which they are defined for all + nodes n. + +<DT><A NAME=d:orientation HREF=#a:orientation><STRONG>orientation</STRONG></A> +<DD> Angle, in degrees, used to rotate polygon node shapes. For any number of polygon sides, 0 degrees rotation results in a flat base. + +<DT><A NAME=dd:orientation HREF=#aa:orientation><STRONG>orientation</STRONG></A> +<DD> If "[lL]*", set graph orientation to landscape + Used only if <A HREF=#d:rotate><B>rotate</B></A> is not defined. + +<DT><A NAME=d:outputorder HREF=#a:outputorder><STRONG>outputorder</STRONG></A> +<DD> Specify order in which nodes and edges are drawn. + +<DT><A NAME=d:overlap HREF=#a:overlap><STRONG>overlap</STRONG></A> +<DD> Determines if and how node overlaps should be removed. Nodes are first + enlarged using the <A HREF=#d:sep><B>sep</B></A> attribute. + If "true" , overlaps are retained. + If the value is "scale", overlaps are removed by uniformly scaling in x and y. + If the value converts to "false", node overlaps are removed by a + Voronoi-based technique. + If the value is "scalexy", x and y are separately + scaled to remove overlaps. + <P> + If sfdp is available, one can set overlap to "prism" to use a + proximity graph-based algorithm for overlap removal. + This is the preferred technique, + though "scale" and "false" can work well with small graphs. + This technique starts with a + small scaling up, controlled by the + <A HREF="#d:overlap_scaling"><TT>overlap_scaling</TT></A> attribute, + which can remove a significant portion of the overlap. + The prism option also accepts an optional non-negative integer suffix. + This can be used to control the number of attempts made at overlap + removal. By default, <TT>overlap="prism"</TT> is equivalent to + <TT>overlap="prism1000"</TT>. Setting <TT>overlap="prism0"</TT> + causes only the scaling phase to be run. + <P> + If the value is "compress", the layout will be scaled down as much as + possible without introducing any overlaps, obviously assuming there are + none to begin with. + <P> + <B>N.B.</B>The remaining allowed values of <TT>overlap</TT> + correspond to algorithms which, at present, can produce bad aspect ratios. + In addition, we deprecate the use of the "ortho*" and "portho*". + <P> + If the value is "vpsc", overlap removal is done as a + quadratic optimization to minimize node displacement while removing + node overlaps. + <P> + If the value is "orthoxy" or "orthoyx", overlaps + are moved by optimizing two constraint problems, one for the x axis and + one for the y. The suffix indicates which axis is processed first. + If the value is "ortho", the technique is similar to "orthoxy" except a + heuristic is used to reduce the bias between the two passes. + If the value is "ortho_yx", the technique is the same as "ortho", except + the roles of x and y are reversed. + The values "portho", "porthoxy", "porthoxy", and "portho_yx" are similar + to the previous four, except only pseudo-orthogonal ordering is + enforced. + <P> + If the layout is done by neato with <A HREF=#d:mode>mode</A>="ipsep", + then one can use <TT>overlap=ipsep</TT>. + In this case, the overlap removal constraints are + incorporated into the layout algorithm itself. + N.B. At present, this only supports one level of clustering. + <P> + Except for fdp and sfdp, the layouts assume <TT>overlap="true"</TT> as the default. + Fdp first uses a number of passes using built-in, force-directed technique + to remove overlaps. Thus, fdp accepts <B>overlap</B> with an integer + prefix followed by a colon, specifying the number of tries. If there is + no prefix, no initial tries will be performed. If there is nothing following + a colon, none of the above methods will be attempted. By default, fdp + uses <TT>overlap="9:portho"</TT>. Note that <TT>overlap="true"</TT>, + <TT>overlap="0:true"</TT> and <TT>overlap="0:"</TT> all turn off all overlap + removal. + <P> + By default, sfdp uses <TT>overlap="prism0"</TT>. + <P> + Except for the Voronoi and prism methods, all of these transforms preserve the + orthogonal ordering of the original layout. That is, if the x coordinates + of two nodes are originally the same, they will remain the same, and if + the x coordinate of one node is originally less than the x coordinate of + another, this relation will still hold in the transformed layout. The + similar properties hold for the y coordinates. + This is not quite true for the "porth*" cases. For these, orthogonal + ordering is only preserved among nodes related by an edge. + +<DT><A NAME=d:overlap_scaling HREF=#a:overlap_scaling><STRONG>overlap_scaling</STRONG></A> +<DD> When <TT>overlap=prism</TT>, the layout is scaled by this factor, thereby + removing a fair amount of node overlap, and making node overlap removal + faster and better able to retain the graph's shape. + <P> + If <TT>overlap_scaling</TT> is negative, the layout is scaled by + <TT>-1*overlap_scaling</TT> times the average label size. + If <TT>overlap_scaling</TT> is positive, the layout is scaled by + <TT>overlap_scaling</TT>. + If <TT>overlap_scaling</TT> is zero, no scaling is done. + +<DT><A NAME=d:pack HREF=#a:pack><STRONG>pack</STRONG></A> +<DD> This is true if the value of pack is "true" (case-insensitive) or a + non-negative integer. If true, each connected component of the graph is + laid out separately, and then the graphs are packed together. + If pack has an integral value, this is used as the size, + in <A HREF=#points>points</A>, of + a margin around each part; otherwise, a default margin of 8 is used. + If pack is interpreted as false, the entire graph is laid out together. + The granularity and method of packing is influenced by the + <A HREF=#d:packmode>packmode</A> attribute. + <P> + For layouts which always do packing, such a twopi, the <B>pack</B> + attribute is just used to set the margin. + +<DT><A NAME=d:packmode HREF=#a:packmode><STRONG>packmode</STRONG></A> +<DD> This indicates how connected components should be packed + (cf. <A HREF=#k:packMode>packMode</A>). Note that defining + <B>packmode</B> will automatically turn on packing as though one had + set <B>pack=true</B>. + +<DT><A NAME=d:pad HREF=#a:pad><STRONG>pad</STRONG></A> +<DD> The pad attribute specifies how much, in inches, to extend the + drawing area around the minimal area needed to draw the graph. + If the pad is a single double, both the x and y pad values are set + equal to the given value. This area is part of the + drawing and will be filled with the background color, if appropriate. + <P> + Normally, a small pad is used for aesthetic reasons, especially when + a background color is used, to avoid having nodes and edges abutting + the boundary of the drawn region. + +<DT><A NAME=d:page HREF=#a:page><STRONG>page</STRONG></A> +<DD> Width and height of output pages, in inches. If this is set and is + smaller than the size of the layout, a rectangular array of pages of + the specified page size is overlaid on the layout, with origins + aligned in the lower-left corner, thereby partitioning the layout + into pages. The pages are then produced one at a time, in + <A HREF=#d:pagedir>pagedir</A> order. + <P> + At present, this only works for PostScript output. For other types of + output, one should use another tool to split the output into multiple + output files. Or use the <A HREF=#d:viewport>viewport</A> to generate + multiple files. + +<DT><A NAME=d:pagedir HREF=#a:pagedir><STRONG>pagedir</STRONG></A> +<DD> If the <A HREF=#d:page>page</A> attribute is set and applicable, + this attribute specifies the order in which the pages are emitted. + This is limited to one of the 8 row or column major orders. + +<DT><A NAME=d:pencolor HREF=#a:pencolor><STRONG>pencolor</STRONG></A> +<DD> Color used to draw the bounding box around a cluster. + If <B>pencolor</B> is not defined, <A HREF=#d:color><B>color</B></A> is + used. If this is not defined, <A HREF=#d:bgcolor>bgcolor</A> is used. + If this is not defined, the default is used. + <P> + Note that a cluster inherits the root graph's attributes if defined. + Thus, if the root graph has defined a <B>pencolor</B>, this will override a + <B>color</B> or <B>bgcolor</B> attribute set for the cluster. + +<DT><A NAME=d:penwidth HREF=#a:penwidth><STRONG>penwidth</STRONG></A> +<DD> Specifies the width of the pen, in points, used to draw lines and curves, + including the boundaries of edges and clusters. The value is inherited + by subclusters. + It has no effect on text. + <P> + Previous to 31 January 2008, the effect of <TT>penwidth=<I>W</I></TT> + was achieved by including <TT>setlinewidth(<I>W</I>)</TT> + as part of a <A HREF=#d:style><TT>style</TT></A> specification. + If both are used, <TT>penwidth</TT> will be used. + +<DT><A NAME=d:peripheries HREF=#a:peripheries><STRONG>peripheries</STRONG></A> +<DD> Set number of peripheries used in polygonal shapes and cluster + boundaries. Note that + <A HREF=shapes.html#epsf>user-defined shapes</A> are treated as a + form of box shape, so the default + peripheries value is 1 and the user-defined shape will be drawn in + a bounding rectangle. Setting <TT>peripheries=0</TT> will turn this off. + Also, 1 is the maximum peripheries value for clusters. + +<DT><A NAME=d:pin HREF=#a:pin><STRONG>pin</STRONG></A> +<DD> If true and the node has a pos attribute on input, neato or fdp prevents the + node from moving from the input position. This property can also be specified + in the pos attribute itself (cf. the <A HREF=#d:point>point</A> type). + <P> + <B>Note:</B> Due to an artifact of the implementation, final coordinates + are translated to the origin. Thus, if you look at the output coordinates + given in the (x)dot or plain format, pinned nodes will not have the same + output coordinates as were given on input. If this is important, a + simple workaround is to maintain the coordinates of a pinned node. The vector + difference between the old and new coordinates will give the translation, + which can then be subtracted from all of the appropriate coordinates. + +<DT><A NAME=d:pos HREF=#a:pos><STRONG>pos</STRONG></A> +<DD> Position of node, or spline control points. + For nodes, the position indicates the center of the node. + On output, the coordinates are in <A HREF=#points>points</A>. + <P> + In neato and fdp, pos can be used to set the initial position of a node. + By default, the coordinates are assumed to be in inches. However, the + <A HREF=command.html#d:s>-s</A> command line flag can be used to specify + different units. As the output coordinates are in points, + feeding the output of a graph laid out by a Graphviz program into + neato or fdp will almost always require the -s flag. + <P> + When the <A HREF=command.html#d:n>-n</A> command line flag is used with + neato, it is assumed the positions have been set by one of the layout + programs, and are therefore in points. Thus, <TT>neato -n</TT> can accept + input correctly without requiring a <TT>-s</TT> flag and, in fact, + ignores any such flag. + +<DT><A NAME=d:quadtree HREF=#a:quadtree><STRONG>quadtree</STRONG></A> +<DD> Quadtree scheme to use. + <P> + A TRUE bool value corresponds to "normal"; + a FALSE bool value corresponds to "none". + As a slight exception to the normal interpretation of bool, + a value of "2" corresponds to "fast". + +<DT><A NAME=d:quantum HREF=#a:quantum><STRONG>quantum</STRONG></A> +<DD> If <B>quantum</B> > 0.0, node label dimensions + will be rounded to integral multiples of the quantum. + +<DT><A NAME=d:rank HREF=#a:rank><STRONG>rank</STRONG></A> +<DD> Rank constraints on the nodes in a subgraph. + If <B>rank</B>="same", all nodes are placed on the same rank. + If <B>rank</B>="min", all nodes are placed on the minimum rank. + If <B>rank</B>="source", all nodes are placed on the minimum rank, and + the only nodes on the minimum rank belong to some subgraph whose + rank attribute is "source" or "min". + Analogous criteria hold for <B>rank</B>="max" and <B>rank</B>="sink". + (Note: the + minimum rank is topmost or leftmost, and the maximum rank is bottommost + or rightmost.) + +<DT><A NAME=d:rankdir HREF=#a:rankdir><STRONG>rankdir</STRONG></A> +<DD> Sets direction of graph layout. For example, if <B>rankdir</B>="LR", + and barring cycles, an edge <CODE>T -> H;</CODE> will go + from left to right. By default, graphs are laid out from top to bottom. + <P> + This attribute also has a side-effect in determining how record nodes + are interpreted. See <A HREF="shapes.html#record">record shapes</A>. + +<DT><A NAME=d:ranksep HREF=#a:ranksep><STRONG>ranksep</STRONG></A> +<DD> In dot, this gives the desired rank separation, in inches. This is + the minimum vertical distance between the bottom of the nodes in one + rank and the tops of nodes in the next. If the value + contains "equally", the centers of all ranks are spaced equally apart. + Note that both + settings are possible, e.g., ranksep = "1.2 equally". + In twopi, specifies radial separation of concentric circles. + +<DT><A NAME=d:ratio HREF=#a:ratio><STRONG>ratio</STRONG></A> +<DD> Sets the aspect ratio (drawing height/drawing width) for the drawing. + Note that this is adjusted before + the <A HREF=#d:size><B>size</B></A> attribute constraints are enforced. + <P> + If <B>ratio</B> is numeric, it is taken as the desired aspect ratio. + Then, if the actual aspect ratio is less than the desired ratio, + the drawing height is scaled up to achieve the + desired ratio; if the actual ratio is greater than that desired ratio, + the drawing width is scaled up. + <P> + If <B>ratio</B> = "fill" and the <A HREF=#d:size><B>size</B></A> + attribute is set, node positions are scaled, separately in both x + and y, so that the final drawing exactly fills the specified size. + <P> + If <B>ratio</B> = "compress" and the <A HREF=#d:size><B>size</B></A> + attribute is set, dot attempts to compress the initial layout to fit + in the given size. This achieves a tighter packing of nodes but + reduces the balance and symmetry. This feature only works in dot. + <P> + If <B>ratio</B> = "expand", the <A HREF=#d:size><B>size</B></A> + attribute is set, and both the width and the height of the graph are + less than the value in <A HREF=#d:size><B>size</B></A>, node positions are scaled + uniformly until at least + one dimension fits <A HREF=#d:size><B>size</B></A> exactly. + Note that this is distinct from using <A HREF=#d:size><B>size</B></A> as the + desired size, as here the drawing is expanded before edges are generated and + all node and text sizes remain unchanged. + <P> + If <B>ratio</B> = "auto", the <A HREF=#d:page><B>page</B></A> + attribute is set and the graph cannot be drawn on a single page, + then <A HREF=#d:size><B>size</B></A> is set to an ``ideal'' value. + In particular, the size in a given dimension will be the smallest integral + multiple of the page size in that dimension which is at least half the + current size. The two dimensions are then scaled independently to the + new size. This feature only works in dot. + +<DT><A NAME=d:rects HREF=#a:rects><STRONG>rects</STRONG></A> +<DD> Rectangles for fields of records, <A HREF=#points>in points</A>. + +<DT><A NAME=d:regular HREF=#a:regular><STRONG>regular</STRONG></A> +<DD> If true, force polygon to be regular. + +<DT><A NAME=d:remincross HREF=#a:remincross><STRONG>remincross</STRONG></A> +<DD> If true and there are multiple clusters, run cross + minimization a second time. + +<DT><A NAME=d:repulsiveforce HREF=#a:repulsiveforce><STRONG>repulsiveforce</STRONG></A> +<DD> The power of the repulsive force used in an extended Fruchterman-Reingold + force directed model. Values larger than 1 tend to reduce + the warping effect at the expense of less clustering. + +<DT><A NAME=d:resolution HREF=#a:resolution><STRONG>resolution</STRONG></A> +<DD> This is a synonym for the <A HREF=#d:dpi>dpi</A> attribute. + +<DT><A NAME=d:root HREF=#a:root><STRONG>root</STRONG></A> +<DD> This specifies nodes to be used as the center of the + layout and the root of the generated spanning tree. As a graph attribute, + this gives the name of the node. As a node attribute (circo only), it + specifies that the node should be used as a central node. In twopi, + this will actually be the central node. In circo, the block containing + the node will be central in the drawing of its connected component. + If not defined, + twopi will pick a most central node, and circo will pick a random node. + +<DT><A NAME=d:rotate HREF=#a:rotate><STRONG>rotate</STRONG></A> +<DD> If 90, set drawing orientation to landscape. + +<DT><A NAME=d:samehead HREF=#a:samehead><STRONG>samehead</STRONG></A> +<DD> Edges with the same head and the same <B>samehead</B> value are aimed + at the same point on the head. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:sametail HREF=#a:sametail><STRONG>sametail</STRONG></A> +<DD> Edges with the same tail and the same <B>sametail</B> value are aimed + at the same point on the tail. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:samplepoints HREF=#a:samplepoints><STRONG>samplepoints</STRONG></A> +<DD> If the input graph defines the <A HREF=#d:vertices><B>vertices</B></A> + attribute, and output is dot or xdot, this gives + the number of points used for a node whose shape is a circle or ellipse. + It plays the same role in neato, when adjusting the layout to avoid + overlapping nodes, and in image maps. + +<DT><A NAME=d:searchsize HREF=#a:searchsize><STRONG>searchsize</STRONG></A> +<DD> During network simplex, maximum number of edges with negative cut values + to search when looking for one with minimum cut value. + +<DT><A NAME=d:sep HREF=#a:sep><STRONG>sep</STRONG></A> +<DD> Specifies margin to leave around nodes when removing node overlap. + This guarantees a minimal non-zero distance between nodes. + <P> + If the attribute begins with a plus sign '+', an additive margin is + specified. That is, "+w,h" causes the node's bounding box to be increased + by w + points on the left and right sides, and by h points on the top and bottom. + Without a plus sign, the node is scaled by 1 + w in the x coordinate + and 1 + h in the y coordinate. + <P> + If only a single number is given, this is used for both dimensions. + <P> + If unset but <A HREF=#d:esep>esep</A> is defined, the <tt>sep</tt> values + will be set to the <tt>esep</tt> values divided by <tt>0.8</tt>. + If <tt>esep</tt> is unset, the default value is used. + +<DT><A NAME=d:shape HREF=#a:shape><STRONG>shape</STRONG></A> +<DD> Set the shape of a node. + +<DT><A NAME=d:shapefile HREF=#a:shapefile><STRONG>shapefile</STRONG></A> +<DD> (Deprecated) If defined, shapefile specifies a file containing user-supplied node content. + The <A HREF=#d:shape><B>shape</B></A> of the node is set to box. + The image in the shapefile must be + rectangular. The image formats supported as well as the precise semantics of + how the file is used depends on the + <A HREF="http://www.graphviz.org/doc/info/output.html">output format</A>. For further details, see + <A HREF=output.html#d:image_fmts>Image Formats</A> and + <A HREF=http://www.graphviz.org/Documentation/html/shapehowto.html#ext_image>External PostScript files</A>. + <P> + There is one exception to this usage. + If <B>shape</B> is set to "epsf", shapefile gives + a filename containing a definition of the node in PostScript. + The graphics defined must be contain all of the + node content, including any desired boundaries. + For further details, see + <A HREF=http://www.graphviz.org/Documentation/html/shapehowto.html#ext_ps> + External PostScript files</A>. + +<DT><A NAME=d:showboxes HREF=#a:showboxes><STRONG>showboxes</STRONG></A> +<DD> Print guide boxes in PostScript at the beginning of + routesplines if 1, or at the end if 2. (Debugging) + +<DT><A NAME=d:sides HREF=#a:sides><STRONG>sides</STRONG></A> +<DD> Number of sides if <A HREF=#d:shape><B>shape</B></A>=polygon. + +<DT><A NAME=d:size HREF=#a:size><STRONG>size</STRONG></A> +<DD> Maximum width and height of drawing, in inches. + If defined and the drawing is too large, the drawing is uniformly + scaled down so that it fits within the given size. + <P> + If <TT>size</TT> ends in an exclamation point (<TT>!</TT>), + then it is taken to be + the desired size. In this case, if both dimensions of the drawing are + less than <TT>size</TT>, the drawing is scaled up uniformly until at + least one dimension equals its dimension in <TT>size</TT>. + <P> + Note that there is some interaction between the <B>size</B> and + <A HREF=#d:ratio><B>ratio</B></A> attributes. + +<DT><A NAME=d:skew HREF=#a:skew><STRONG>skew</STRONG></A> +<DD> Skew factor for <A HREF=#d:shape><B>shape</B></A>=polygon. Positive values + skew top of polygon to right; negative to left. + +<DT><A NAME=d:smoothing HREF=#a:smoothing><STRONG>smoothing</STRONG></A> +<DD> Specifies a post-processing step used to smooth out an uneven distribution + of nodes. + +<DT><A NAME=d:sortv HREF=#a:sortv><STRONG>sortv</STRONG></A> +<DD> If <A HREF="#d:packmode">packmode</A> indicates an array packing, + this attribute specifies an + insertion order among the components, with smaller values inserted first. + +<DT><A NAME=d:splines HREF=#a:splines><STRONG>splines</STRONG></A> +<DD> Controls how, and if, edges are represented. If true, edges are drawn as + splines routed around nodes; if false, edges are drawn as line segments. + If set to "", no edges are drawn at all. + <P> + (1 March 2007) The values <TT>line</TT> and <TT>spline</TT> can be + used as synonyms for <TT>false</TT> and <TT>true</TT>, respectively. + In addition, the value <TT>polyline</TT> specifies that edges should be + drawn as polylines. + <P> + By default, the attribute is unset. How this is interpreted depends on + the layout. For dot, the default is to draw edges as splines. For all + other layouts, the default is to draw edges as line segments. Note that + for these latter layouts, if <TT>splines="true"</TT>, this + requires non-overlapping nodes (cf. <A HREF=#d:overlap><B>overlap</B></A>). + If fdp is used for layout and <TT>splines="compound"</TT>, then the edges are + drawn to avoid clusters as well as nodes. + +<DT><A NAME=d:start HREF=#a:start><STRONG>start</STRONG></A> +<DD> Parameter used to determine the initial layout of nodes. If unset, the + nodes are randomly placed in a unit square with + the same seed is always used for the random number generator, so the + initial placement is repeatable. + +<DT><A NAME=d:style HREF=#a:style><STRONG>style</STRONG></A> +<DD> Set style for node or edge. For cluster subgraph, if "filled", the + cluster box's background is filled. + +<DT><A NAME=d:stylesheet HREF=#a:stylesheet><STRONG>stylesheet</STRONG></A> +<DD> A URL or pathname specifying an XML style sheet, used in SVG output. + +<DT><A NAME=d:tailURL HREF=#a:tailURL><STRONG>tailURL</STRONG></A> +<DD> If <B>tailURL</B> is defined, it is + output as part of the tail label of the edge. + Also, this value is used near the tail node, overriding any + <A HREF=#d:URL>URL</A> value. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:tailclip HREF=#a:tailclip><STRONG>tailclip</STRONG></A> +<DD> If true, the tail of an edge is clipped to the boundary of the tail node; + otherwise, the end of the edge goes to the center of the node, or the + center of a port, if applicable. + +<DT><A NAME=d:tailhref HREF=#a:tailhref><STRONG>tailhref</STRONG></A> +<DD> Synonym for <A HREF=#d:tailURL>tailURL</A>. + +<DT><A NAME=d:taillabel HREF=#a:taillabel><STRONG>taillabel</STRONG></A> +<DD> Text label to be placed near tail of edge. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:tailport HREF=#a:tailport><STRONG>tailport</STRONG></A> +<DD> Indicates where on the tail node to attach the tail of the edge. + See <A HREF=#h:undir_note>limitation</A>. + +<DT><A NAME=d:tailtarget HREF=#a:tailtarget><STRONG>tailtarget</STRONG></A> +<DD> If the edge has a <A HREF=#d:tailURL>tailURL</A>, + this attribute determines which window of the + browser is used + for the URL. Setting it to "_graphviz" will open a new window if it + doesn't already exist, or reuse it if it does. + If undefined, the value of the <A HREF=#d:target>target</A> is used. + +<DT><A NAME=d:tailtooltip HREF=#a:tailtooltip><STRONG>tailtooltip</STRONG></A> +<DD> Tooltip annotation attached to the tail of an edge. This is used only + if the edge has a <A HREF=#d:tailURL>tailURL</A> attribute. + +<DT><A NAME=d:target HREF=#a:target><STRONG>target</STRONG></A> +<DD> If the object has a URL, this attribute determines which window + of the browser is used for the URL. + See <A HREF="http://www.w3.org/TR/html401/present/frames.html#adef-target">W3C documentation</A>. + +<DT><A NAME=d:tooltip HREF=#a:tooltip><STRONG>tooltip</STRONG></A> +<DD> Tooltip annotation attached to the node or edge. If unset, Graphviz + will use the object's <A HREF=#d:label>label</A> if defined. + Note that if the label is a record specification or an HTML-like + label, the resulting tooltip may be unhelpful. In this case, if + tooltips will be generated, the user should set a <TT>tooltip</TT> + attribute explicitly. + +<DT><A NAME=d:truecolor HREF=#a:truecolor><STRONG>truecolor</STRONG></A> +<DD> If set explicitly to true or false, the value determines whether or not + internal bitmap rendering relies on a truecolor color model or uses + a color palette. + If the attribute is unset, truecolor is not used + unless there is a <A HREF=#d:shapefile>shapefile</A> property + for some node in the graph. + The output model will use the input model when possible. + <P> + Use of color palettes results in less memory usage during creation of the + bitmaps and smaller output files. + <P> + Usually, the only time it is necessary to specify the truetype model + is if the graph uses more than 256 colors. + However, if one uses <A HREF=#d:bgcolor>bgcolor</A>=transparent with + a color palette, font + antialiasing can show up as a fuzzy white area around characters. + Using <B>truecolor</B>=true avoids this problem. + +<DT><A NAME=d:vertices HREF=#a:vertices><STRONG>vertices</STRONG></A> +<DD> If the input graph defines this attribute, the node is polygonal, + and output is dot or xdot, this attribute provides the + coordinates of the vertices of the node's polygon, in inches. + If the node is an ellipse or circle, the + <A HREF=#d:samplepoints>samplepoints</A> attribute affects + the output. + +<DT><A NAME=d:viewport HREF=#a:viewport><STRONG>viewport</STRONG></A> +<DD> Clipping window on final drawing. + +<DT><A NAME=d:voro_margin HREF=#a:voro_margin><STRONG>voro_margin</STRONG></A> +<DD> Factor to scale up drawing to allow margin for expansion in + Voronoi technique. dim' = (1+2*margin)*dim. + +<DT><A NAME=d:weight HREF=#a:weight><STRONG>weight</STRONG></A> +<DD> Weight of edge. In dot, the heavier the weight, the shorter, + straighter and more vertical the edge is. + +<DT><A NAME=d:width HREF=#a:width><STRONG>width</STRONG></A> +<DD> Width of node, in inches. This is taken as the initial, minimum width + of the node. If <A HREF=#d:fixedsize><B>fixedsize</B></A> is true, this + will be the final width of the node. Otherwise, if the node label + requires more width to fit, the node's width will be increased to + contain the label. Note also that, if the output format is dot, the + value given to <B>width</B> will be the final value. + +<DT><A NAME=d:z HREF=#a:z><STRONG>z</STRONG></A> +<DD> <B>Deprecated:</B>Use <A HREF=#d:pos><B>pos</B></A> attribute, along + with <A HREF=#d:dimen><B>dimen</B></A> and/or <A HREF=#d:dim><B>dim</B></A> + to specify dimensions. + <P> + Provides z coordinate value for 3D layouts and displays. If the + graph has <A HREF=#d:dim><B>dim</B></A> set to 3 (or more), + neato will use a node's <B>z</B> value + for the z coordinate of its initial position if + its <A HREF=#d:pos><B>pos</B></A> attribute is also defined. + <P> + Even if no <B>z</B> values are specified in the input, it is necessary to + declare a <B>z</B> attribute for nodes, e.g, using <TT>node[z=""]</TT> + in order to get z values on output. + Thus, setting <TT>dim=3</TT> but not declaring <B>z</B> will + cause <TT>neato -Tvrml</TT> to + layout the graph in 3D but project the layout onto the xy-plane + for the rendering. If the <B>z</B> attribute is declared, the final rendering + will be in 3D. + +</DL> +<HR> +<H1>Attribute Type Descriptions</H1> +The following list gives the legal strings corresponding to values of +the given types. +<DL> +<DT><A NAME=k:arrowType><STRONG>arrowType</STRONG></A> +<DD><TABLE> + <TR><TD>"normal"<TD><IMG SRC="a_normal.gif"> + <TD>"inv"<TD><IMG SRC="a_inv.gif"></TR> + <TR><TD>"dot"<TD><IMG SRC="a_dot.gif"> + <TD>"invdot"<TD><IMG SRC="a_invdot.gif"></TR> + <TR><TD>"odot"<TD><IMG SRC="a_odot.gif"> + <TD>"invodot"<TD><IMG SRC="a_invodot.gif"></TR> + <TR><TD>"none"<TD><IMG SRC="a_none.gif"> + <TD>"tee"<TD><IMG SRC="a_tee.gif"></TR> + <TR><TD>"empty"<TD><IMG SRC="a_empty.gif"> + <TD>"invempty"<TD><IMG SRC="a_invempty.gif"></TR> + <TR><TD>"diamond"<TD><IMG SRC="a_diamond.gif"> + <TD>"odiamond"<TD><IMG SRC="a_odiamond.gif"></TR> + <TR><TD>"ediamond"<TD><IMG SRC="a_ediamond.gif"> + <TD>"crow"<TD><IMG SRC="a_crow.gif"></TR> + <TR><TD>"box"<TD><IMG SRC="a_box.gif"> + <TD>"obox"<TD><IMG SRC="a_obox.gif"></TR> + <TR><TD>"open"<TD><IMG SRC="a_open.gif"> + <TD>"halfopen"<TD><IMG SRC="a_halfopen.gif"></TR> + <TR><TD>"vee"<TD><IMG SRC="a_open.gif"> + </TABLE> + <P> + These are the basic set of backward-compatible arrow shapes. In addition, + there is a grammar of <A HREF=arrows.html>arrow shapes</A> + which can be used to describe a collection of 1,544,761 arrow combinations of the 36 varations of the primitive set of 9 arrows. The basic arrows shown + above contain all of the primitive shapes + (<TT>box</TT>, <TT>crow</TT>, <TT>diamond</TT>, + <TT>dot</TT>, <TT>inv</TT>, <TT>none</tt>, + <TT>normal</tt>, <TT>tee</TT>, <TT>vee</TT>) + plus ones that can be derived from the grammar + (<TT>odot</TT>, <TT>invdot</TT>, <TT>invodot</TT>, + <TT>obox</TT>, <TT>odiamond</TT>) + plus some supported as special cases for backward-compatibility + (<TT>ediamond</TT>, <TT>open</TT>, <TT>halfopen</TT>, + <TT>empty</TT>, <TT>invempty</TT>). + +<DT><A NAME=k:aspectType><STRONG>aspectType</STRONG></A> +<DD><p>An aspect ratio, double, followed optionally by a ',' and a maximum pass + count. If the aspect ratio is given, but no maximum pass count, the + latter defaults to 5. + +<DT><A NAME=k:clusterMode><STRONG>clusterMode</STRONG></A> +<DD>"local","global","none" + +<DT><A NAME=k:color><STRONG>color</STRONG></A> +<DD>Colors can be specified using one of four formats. + <TABLE><TR><TD>"#%2x%2x%2x"<TD>Red-Green-Blue (RGB)</TR> + <TR><TD>"#%2x%2x%2x%2x"<TD>Red-Green-Blue-Alpha (RGBA)</TR> + <TR><TD>H[, ]+S[, ]+V<TD>Hue-Saturation-Value (HSV) 0.0 <= H,S,V <= 1.0</TR> + <TR><TD>string<TD><A HREF=colors.html>color name</A></TR></TABLE> + The specification for the RGB and RGBA formats are the format strings used by + <TT>sscanf</TT> to scan the color value. Thus, these values have the form + "#RGB" or "#RGBA", where R, G, B, and A each consist of 2 hexidecimal + digits, and can be separated by whitespace. HSV colors have the form of 3 + numbers between 0 and 1, separated by whitespace or commas. + <P> + String-valued color specifications are case-insensitive and interpreted + in the context of the current color scheme, as specified by the + <A HREF=#d:colorscheme>colorscheme</A> attribute. If this is undefined, + the X11 naming scheme will be used. + An initial <TT>"/"</TT> character can be used to override the use of + the <TT>colorscheme</TT> attribute. In particular, a single initial + <TT>"/"</TT> will cause the string to be evaluated using the default + X11 naming. If the color value has the form <TT>"/ssss/yyyy"</TT>, + the name <TT>yyyy</TT> is interpreted using the schema <TT>ssss</TT>. + If the color scheme name is empty, i.e., the color has the + form <TT>"//yyyy"</TT>, the <TT>colorscheme</TT> attribute is used. + Thus, the forms <TT>"yyyy"</TT> and <TT>"//yyyy"</TT> are + equivalent. + <P>At present, Graphviz recognizes the default color scheme <TT>X11</TT>, + and the <A HREF="http://www.personal.psu.edu/faculty/c/a/cab38/ColorBrewer/ColorBrewer_intro.html">Brewer color schemes</A>. Please note that Brewer + color schemes are covered by this <A HREF=colors.html#brewer_license>license</A>. + <P> + Examples:<BR> + <TABLE border=1> + <TR><TH>Color<TH>RGB<TH>HSV<TH>String</TR> + <TR><TD align=center bgcolor=#ffffff>White<TD>"#ffffff"<TD>"0.000 0.000 1.000"<TD>"white"</TR> + <TR><TD align=center bgcolor=#000000><font color=white>Black</font><TD>"#000000"<TD>"0.000 0.000 0.000"<TD>"black"</TR> + <TR><TD align=center bgcolor=#ff0000>Red<TD>"#ff0000"<TD>"0.000 1.000 1.000"<TD>"red"</TR> + <TR><TD align=center bgcolor=#40e0d0>Turquoise<TD>"#40e0d0"<TD>"0.482 0.714 0.878"<TD>"turquoise"</TR> + <TR><TD align=center bgcolor=#a0522d>Sienna<TD>"#a0522d"<TD>"0.051 0.718 0.627"<TD>"sienna"</TR> + </TABLE> + <P> + Note that some output formats, e.g., <A HREF="output.html#a:mif">mif</A>, + only support a limited number of specific colors. + <P> + The string value <TT>transparent</TT> can be used to indicate no color. + This is only available in the output formats + ps, svg, fig, vmrl, and the bitmap formats. It can be used whenever a + color is needed but is most useful with + the <A HREF=#d:bgcolor>bgcolor</A> attribute. + Usually, the same effect can be achieved by setting + <A HREF=#d:style>style</A> to <TT>invis</TT>. + +<DT><A NAME=k:colorList><STRONG>colorList</STRONG></A> +<DD>A colon-separated list of color values: <I>C</I>(:<I>C</I>)* + where each <I>C</I> is a <A HREF=#k:color>color</A> value. + +<DT><A NAME=k:dirType><STRONG>dirType</STRONG></A> +<DD>For an edge <CODE>T -> H;</CODE> + <TABLE> + <TR><TD>"forward"<TD><IMG SRC="forward.gif"> + <TD>"back"<TD><IMG SRC="back.gif"></TR> + <TR><TD>"both"<TD><IMG SRC="both.gif"> + <TD>"none"<TD><IMG SRC="nohead.gif"></TR> + </TABLE> + For undirected edges <CODE>T -- H;</CODE>, one of the nodes, usually + the righthand one, is treated as the head for the purpose of + interpreting "forward" and "back". + +<DT><A NAME=k:escString><STRONG>escString</STRONG></A> +<DD>string allowing escape sequences which are replaced according + to the context. + For node attributes, the substring "\N" is replaced by the name of the node, + and the substring "\G" by the name of the graph. + For graph or cluster attributes, the substring "\G" is replaced by the + name of the graph or cluster. + For edge attributes, the substring "\E" is replaced by the name of the edge, + the substring "\G" is replaced by the name of the graph or cluster, + and the substrings "\T" and "\H" by the names of + the tail and head nodes, respectively. + The name of an edge is the string formed from the name of the + tail node, the appropriate edge operator ("--" or "->") and the name of the + head node. + In all cases, the substring "\L" is replaced by the object's label attribute. + <P> + In addition, if the associated attribute is + <A HREF=#a:label>label</A>, + <A HREF=#a:headlabel>headlabel</A> or <A HREF=#a:taillabel>taillabel</A>, + the escape sequences "\n", "\l" and "\r" + divide the label into lines, centered, left-justified, and right-justified, + respectively. + +<DT><A NAME=k:layerList><STRONG>layerList</STRONG></A> +<DD>list of strings separated by characters from the + <A HREF=#a:layersep>layersep</A> attribute (by default, colons, + tabs or spaces), defining <A HREF=#a:layer>layer</A> + names and implicitly numbered 1,2,... + +<DT><A NAME=k:layerRange><STRONG>layerRange</STRONG></A> +<DD>layerId or layerId<B>s</B>layerId,<BR> where layerId = "all", + a decimal integer or a <A HREF=#a:layer>layer</A> name. + (An integer i corresponds to layer i.) + The string <B>s</B> consists of 1 or more separator characters specified + by the <A HREF=#a:layersep>layersep</A> attribute. + +<DT><A NAME=k:lblString><STRONG>lblString</STRONG></A> +<DD>an <A HREF=#k:escString>escString</A> + or an <A HREF=shapes.html#html>HTML label</A>. + +<DT><A NAME=k:outputMode><STRONG>outputMode</STRONG></A> +<DD>"breadthfirst","nodesfirst","edgesfirst" + These specify the order in which nodes and edges are drawn in concrete + output. The default "breadthfirst" is the simplest, but when the graph + layout does not avoid edge-node overlap, this mode will sometimes have + edges drawn over nodes and sometimes on top of nodes. If the mode + "nodesfirst" is chosen, all nodes are drawn first, followed by the + edges. This guarantees an edge-node overlap will not be mistaken for + an edge ending at a node. On the other hand, usually for aesthetic + reasons, it may be desirable that all edges appear beneath nodes, even + if the resulting drawing is ambiguous. This can be achieved by choosing + "edgesfirst". + +<DT><A NAME=k:packMode><STRONG>packMode</STRONG></A> +<DD>"node", "clust" , "graph" , "array[_flags][%d]" + <P> + The modes "node", "clust" or "graph" + specify that the components should be packed together tightly, using + the specified granularity. + A value of "node" causes + packing at the node and edge level, with no overlapping of these objects. + This produces a layout with the least area, but it also allows interleaving, + where a node of one component may lie between two nodes in another + component. A value of "graph" does a packing using the bounding box of the + component. Thus, there will be a rectangular region around a component + free of elements of any other component. + A value of "clust" guarantees that top-level clusters are kept intact. + What effect a value has also depends on the layout algorithm. For + example, neato does not support clusters, so a value of "clust" will + have the same effect as the default "node" value. + <P> + The mode "array[_flags][%d]" + indicates that the components should be packed at the + graph level into an array of graphs. By default, the components + are in row-major order, with the number of columns roughly the + square root of the number of components. If the optional flags + contains "c", then column-major order is used. Finally, if the + optional integer suffix is used, this specifies the number of + columns for row-major or the number of rows for column-major. + Thus, the mode "array_c4" indicates array packing, with 4 rows, + starting in the upper left and going down the first column, then + down the second column, etc., until all components are used. + <P> + If the optional flags contains "u", this causes the insertion + order of elements in the array to be determined by user-supplied + values. Each component can specify its sort value by a + non-negative integer using the <A HREF="#d:sortv">sortv</A> attribute. + Components are + inserted in order, starting with the one with the smallest + sort value. If no sort value is specified, zero is used. + +<DT><A NAME=k:pagedir><STRONG>pagedir</STRONG></A> +<DD>"BL", "BR", "TL", "TR", "RB", "RT", "LB", "LT". + These specify the 8 row or column major orders for traversing a + rectangular array, the first character corresponding to the major + order and the second to the minor order. Thus, for "BL", the + major order is from bottom to top, and the minor order is from left + to right. This means the bottom row is traversed first, from left + to right, then the next row up, from left to right, and so on, + until the topmost row is traversed. + +<DT><A NAME=k:point><STRONG>point</STRONG></A> +<DD>"%f,%f"('!') representing the point (x,y). The + optional '!' indicates the + node position should not change (input-only). + <P> + If <A HREF=#d:dim>dim</A> is 3 or more, <B>point</B> may also have + the format "%f,%f,%f"('!') to represent the point (x,y,z). + +<DT><A NAME=k:pointf><STRONG>pointf</STRONG></A> +<DD>"%lf,%lf" representing the point (x,y). + +<DT><A NAME=k:pointfList><STRONG>pointfList</STRONG></A> +<DD>list of pointf, separated by spaces. + +<DT><A NAME=k:portPos><STRONG>portPos</STRONG></A> +<DD>modifier indicating where on a node an edge should be aimed. + It has the form <TT><I>portname</I>[:<I>compass_point</I>]</TT> + or <TT><I>compass_point</I></TT>. + If the first form is used, the corresponding node must either have + <A HREF=shapes.html#record>record</A> shape with one of its fields + having the given <I>portname</I>, + or have an <A HREF=shapes.html#html>HTML-like label</A>, one of + whose components has a <TT>PORT</TT> attribute set to <I>portname</I>. + <P> + If a compass point is used, it must have the form + <TT>"n","ne","e","se","s","sw","w","nw","c","_"</TT>. This modifies the edge + placement to aim for the corresponding compass point on the port or, + in the second form where no <I>portname</I> is supplied, on the node + itself. The compass point "c" specifies the center of the node or port. + The compass point "_" specifies that an appropriate side of the port + adjacent to the exterior of the node should be used, if such exists. + Otherwise, the center is used. + If no compass point is used with a portname, the default value is "_". + <P> + <P> + This attribute can be attached to an edge using the + <A HREF=#k:headport>headport</A> and + <A HREF=#k:tailport>tailport</A> attributes, or as part of the + edge description as in + <CENTER> + <TT>node1:port1 -> node2:port5:nw;</TT> + </CENTER> + <P> + Note that it is legal to have a portname the same as one of + the compass points. In this case, this reference will be resolved to + the port. Thus, if node <TT>A</TT> has a port <TT>w</TT>, then + <TT>headport=w</TT> will refer to the port and not the compass point. + At present, in this case, there is no way to specify that the compass + point should be used. + +<DT><A NAME=k:quadType><STRONG>quadType</STRONG></A> +<DD>"normal", "fast", "none". + <P> + Using "fast" gives about a 2-4 times overall speedup compared with "normal", + though layout quality can suffer a little. + +<DT><A NAME=k:rankType><STRONG>rankType</STRONG></A> +<DD>"same", "min", "source", "max", "sink" + +<DT><A NAME=k:rankdir><STRONG>rankdir</STRONG></A> +<DD>"TB", "LR", "BT", "RL", corresponding to directed graphs drawn + from top to bottom, from left to right, from bottom to top, and from + right to left, respectively. + +<DT><A NAME=k:rect><STRONG>rect</STRONG></A> +<DD>"%f,%f,%f,%f" The rectangle llx,lly,urx,ury gives the coordinates, in + points, of the lower-left corner (llx,lly) and the upper-right corner + (urx,ury). + +<DT><A NAME=k:shape><STRONG>shape</STRONG></A> +<DD>A string specifying the <A HREF=shapes.html>shape</A> of a node. + There are three + main types of shapes : + <A HREF=shapes.html#polygon>polygon-based</A>, + <A HREF=shapes.html#record>record-based</A> and + <A HREF=shapes.html#epsf>user-defined</A>. + +<DT><A NAME=k:smoothType><STRONG>smoothType</STRONG></A> +<DD>Values are "none", "avg_dist", "graph_dist", "power_dist", "rng", + "spring" and "triangle". + +<DT><A NAME=k:splineType><STRONG>splineType</STRONG></A> +<DD>spline ( ';' spline )*<BR> + <TABLE> + <TR><TD ALIGN=right>where spline<TD>=<TD>(endp)? (startp)? point (triple)+</TR> + <TR><TD ALIGN=right>and triple<TD>=<TD>point point point</TR> + <TR><TD ALIGN=right>and endp<TD>=<TD>"e,%f,%f"</TR> + <TR><TD ALIGN=right>and startp<TD>=<TD>"s,%f,%f"</TR> + </TABLE> + If a spline has points p<SUB>1</SUB> p<SUB>2</SUB> p<SUB>3</SUB> ... p<SUB>n</SUB>, (n = 1 (mod 3)), the points + correspond to the control points of a B-spline from p<SUB>1</SUB> to p<SUB>n</SUB>. If startp + is given, it touches one node of the edge, and the arrowhead + goes from p<SUB>1</SUB> to startp. If startp is not given, p<SUB>1</SUB> touches a node. + Similarly for p<SUB>n</SUB> and endp. + +<DT><A NAME=k:startType><STRONG>startType</STRONG></A> +<DD>has the syntax <TT>[<I>style</I>][<I>seed</I>]</TT>. + <P> + If <I>style</I> is present, it must be one of the strings <TT>"regular"</TT>, + <TT>"self"</TT>, or <TT>"random"</TT>. In the first case, the nodes are + placed regularly about a circle. In the second case, + an abbreviated version of neato is run to obtain the initial layout. + In the last case, the nodes are placed randomly in a unit square. + <P> + If <I>seed</I> is present, it specifies a seed for the random number + generator. If <I>seed</I> is a positive number, this is used as the + seed. If it is anything else, + the current time, and possibly the process id, is used to pick a seed, + thereby making the choice more random. In this case, the seed value + is stored in the graph. + <P> + If the value is just <TT>"random"</TT>, a time-based seed is chosen. + <P> + Note that input positions, specified by a node's + <A HREF=#d:pos>pos</A> + attribute, are only used when the style is <TT>"random"</TT>. + +<DT><A NAME=k:style><STRONG>style</STRONG></A> +<DD>styleItem ( ',' styleItem )*<BR> + <TABLE> + <TR><TD ALIGN=right>where styleItem<TD>=<TD>name or name'('args')'</TR> + <TR><TD ALIGN=right>and args<TD>=<TD>name ( ',' name )*</TR> + <TR><TD ALIGN=right>and name<TD>=<TD>[^)(, ][^)(,]*</TR> + </TABLE> + Note that whitespace characters are ignored, except for names which + can contain them. A + name cannot contain any comma, or left or right parenthesis, and it cannot + begin with a whitespace character. + <P> + At present, the recognized style names are + "dashed", "dotted", "solid", "invis" and "bold" for nodes and edges, + and "filled", "diagonals" and "rounded" for nodes only. + The styles "filled" and "rounded" are recognized for clusters. + Additional styles are available in + device-dependent form. Style lists are passed to device drivers, which + can use this to generate appropriate output. + <P> + The <TT>setlinewidth</TT> style value can be + used for more control over the width of node borders and edges than is + allowed by <TT>bold</TT>. This style value takes an argument, specifying the + width of the line in <A HREF=#points>points</A>. For example, <TT>style="bold"</TT> is + equivalent to <TT>style="setlinewidth(2)"</TT>. + <B>The use of <TT>setlinewidth</TT> is deprecated; one should use the + <A HREF=#d:penwidth><TT>penwidth</TT></A> attribute instead.</B> + +<DT><A NAME=k:viewPort><STRONG>viewPort</STRONG></A> +<DD>"%lf,%lf,%lf,%lf,%lf" or "%lf,%lf,%lf,'%s'" + <P> + The viewPort <TT>W,H,Z,x,y</TT> or <TT>W,H,Z,N</TT> + specifies a viewport for the final image. The pair (W,H) gives the + dimensions (width and height) of the final image, in + <A HREF=#points>points</A>. + The optional Z is the zoom factor, i.e., the image in the original layout will be + W/Z by H/Z points in size. By default, Z is 1. + The optional last part is either a pair (x,y) giving a position in the original layout of the + graph, in + <A HREF=#points>points</A>, of the center of the viewport, or the name N + of a node whose center should used as the focus. + By default, the focus is the center of the graph bounding box, i.e., + (bbx/2,bby/2), where "bbx,bby" is the + value of the bounding box attribute <A HREF=#a:bb>bb</A>. + <P> + Sample values: <TT>50,50,.5,'2.8 BSD'</T> or <TT>100,100,2,450,300</TT>. + The first will take the 100x100 point square centered on the node <TT>2.8 BSD</TT> + and scale it down by 0.5, yielding a 50x50 point final image. + +</DL> +</BODY> +</HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/colors.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,3721 @@ +<!DOCTYPE HTML PUBLIC \"-//IETF//DTD HTML//EN\"> +<!-- +This is a generated document. Do not edit. +--> +<HTML VERSION=\"2.0\"> +<HEAD> +<TITLE>Color Names</TITLE> +</HEAD> +<BODY BGCOLOR=#ffffff> + +<H1 ALIGN=CENTER>Color Names</H1> +Color names are case-insensitive. +<P> +Use of all color schemes below except X11 are covered by this +<A HREF=#brewer_license>license</A>. +<HR> +X11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f0f8ff><a title=#f0f8ff>aliceblue</a></td> +<td bgcolor=#faebd7><a title=#faebd7>antiquewhite</a></td> +<td bgcolor=#ffefdb><a title=#ffefdb>antiquewhite1</a></td> +<td bgcolor=#eedfcc><a title=#eedfcc>antiquewhite2</a></td> +<td bgcolor=#cdc0b0><a title=#cdc0b0>antiquewhite3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b8378><a title=#8b8378>antiquewhite4</a></td> +<td bgcolor=#7fffd4><a title=#7fffd4>aquamarine</a></td> +<td bgcolor=#7fffd4><a title=#7fffd4>aquamarine1</a></td> +<td bgcolor=#76eec6><a title=#76eec6>aquamarine2</a></td> +<td bgcolor=#66cdaa><a title=#66cdaa>aquamarine3</a></td> +</tr> +<tr align=center> +<td bgcolor=#458b74><a title=#458b74>aquamarine4</a></td> +<td bgcolor=#f0ffff><a title=#f0ffff>azure</a></td> +<td bgcolor=#f0ffff><a title=#f0ffff>azure1</a></td> +<td bgcolor=#e0eeee><a title=#e0eeee>azure2</a></td> +<td bgcolor=#c1cdcd><a title=#c1cdcd>azure3</a></td> +</tr> +<tr align=center> +<td bgcolor=#838b8b><a title=#838b8b>azure4</a></td> +<td bgcolor=#f5f5dc><a title=#f5f5dc>beige</a></td> +<td bgcolor=#ffe4c4><a title=#ffe4c4>bisque</a></td> +<td bgcolor=#ffe4c4><a title=#ffe4c4>bisque1</a></td> +<td bgcolor=#eed5b7><a title=#eed5b7>bisque2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cdb79e><a title=#cdb79e>bisque3</a></td> +<td bgcolor=#8b7d6b><a title=#8b7d6b>bisque4</a></td> +<td bgcolor=#000000><a title=#000000><font color=white>black</font></a></td> +<td bgcolor=#ffebcd><a title=#ffebcd>blanchedalmond</a></td> +<td bgcolor=#0000ff><a title=#0000ff> blue </a></td> +</tr> +<tr align=center> +<td bgcolor=#0000ff><a title=#0000ff>blue1</a></td> +<td bgcolor=#0000ee><a title=#0000ee>blue2</a></td> +<td bgcolor=#0000cd><a title=#0000cd>blue3</a></td> +<td bgcolor=#00008b><a title=#00008b>blue4</a></td> +<td bgcolor=#8a2be2><a title=#8a2be2>blueviolet</a></td> +</tr> +<tr align=center> +<td bgcolor=#a52a2a><a title=#a52a2a>brown</a></td> +<td bgcolor=#ff4040><a title=#ff4040>brown1</a></td> +<td bgcolor=#ee3b3b><a title=#ee3b3b>brown2</a></td> +<td bgcolor=#cd3333><a title=#cd3333>brown3</a></td> +<td bgcolor=#8b2323><a title=#8b2323>brown4</a></td> +</tr> +<tr align=center> +<td bgcolor=#deb887><a title=#deb887>burlywood</a></td> +<td bgcolor=#ffd39b><a title=#ffd39b>burlywood1</a></td> +<td bgcolor=#eec591><a title=#eec591>burlywood2</a></td> +<td bgcolor=#cdaa7d><a title=#cdaa7d>burlywood3</a></td> +<td bgcolor=#8b7355><a title=#8b7355>burlywood4</a></td> +</tr> +<tr align=center> +<td bgcolor=#5f9ea0><a title=#5f9ea0>cadetblue</a></td> +<td bgcolor=#98f5ff><a title=#98f5ff>cadetblue1</a></td> +<td bgcolor=#8ee5ee><a title=#8ee5ee>cadetblue2</a></td> +<td bgcolor=#7ac5cd><a title=#7ac5cd>cadetblue3</a></td> +<td bgcolor=#53868b><a title=#53868b>cadetblue4</a></td> +</tr> +<tr align=center> +<td bgcolor=#7fff00><a title=#7fff00>chartreuse</a></td> +<td bgcolor=#7fff00><a title=#7fff00>chartreuse1</a></td> +<td bgcolor=#76ee00><a title=#76ee00>chartreuse2</a></td> +<td bgcolor=#66cd00><a title=#66cd00>chartreuse3</a></td> +<td bgcolor=#458b00><a title=#458b00>chartreuse4</a></td> +</tr> +<tr align=center> +<td bgcolor=#d2691e><a title=#d2691e>chocolate</a></td> +<td bgcolor=#ff7f24><a title=#ff7f24>chocolate1</a></td> +<td bgcolor=#ee7621><a title=#ee7621>chocolate2</a></td> +<td bgcolor=#cd661d><a title=#cd661d>chocolate3</a></td> +<td bgcolor=#8b4513><a title=#8b4513>chocolate4</a></td> +</tr> +<tr align=center> +<td bgcolor=#ff7f50><a title=#ff7f50>coral</a></td> +<td bgcolor=#ff7256><a title=#ff7256>coral1</a></td> +<td bgcolor=#ee6a50><a title=#ee6a50>coral2</a></td> +<td bgcolor=#cd5b45><a title=#cd5b45>coral3</a></td> +<td bgcolor=#8b3e2f><a title=#8b3e2f>coral4</a></td> +</tr> +<tr align=center> +<td bgcolor=#6495ed><a title=#6495ed>cornflowerblue</a></td> +<td bgcolor=#fff8dc><a title=#fff8dc>cornsilk</a></td> +<td bgcolor=#fff8dc><a title=#fff8dc>cornsilk1</a></td> +<td bgcolor=#eee8cd><a title=#eee8cd>cornsilk2</a></td> +<td bgcolor=#cdc8b1><a title=#cdc8b1>cornsilk3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b8878><a title=#8b8878>cornsilk4</a></td> +<td bgcolor=#dc143c><a title=#dc143c>crimson</a></td> +<td bgcolor=#00ffff><a title=#00ffff> cyan </a></td> +<td bgcolor=#00ffff><a title=#00ffff>cyan1</a></td> +<td bgcolor=#00eeee><a title=#00eeee>cyan2</a></td> +</tr> +<tr align=center> +<td bgcolor=#00cdcd><a title=#00cdcd>cyan3</a></td> +<td bgcolor=#008b8b><a title=#008b8b>cyan4</a></td> +<td bgcolor=#b8860b><a title=#b8860b>darkgoldenrod</a></td> +<td bgcolor=#ffb90f><a title=#ffb90f>darkgoldenrod1</a></td> +<td bgcolor=#eead0e><a title=#eead0e>darkgoldenrod2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cd950c><a title=#cd950c>darkgoldenrod3</a></td> +<td bgcolor=#8b6508><a title=#8b6508>darkgoldenrod4</a></td> +<td bgcolor=#006400><a title=#006400><font color=white>darkgreen</font></a></td> +<td bgcolor=#bdb76b><a title=#bdb76b>darkkhaki</a></td> +<td bgcolor=#556b2f><a title=#556b2f><font color=white>darkolivegreen</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#caff70><a title=#caff70>darkolivegreen1</a></td> +<td bgcolor=#bcee68><a title=#bcee68>darkolivegreen2</a></td> +<td bgcolor=#a2cd5a><a title=#a2cd5a>darkolivegreen3</a></td> +<td bgcolor=#6e8b3d><a title=#6e8b3d>darkolivegreen4</a></td> +<td bgcolor=#ff8c00><a title=#ff8c00>darkorange</a></td> +</tr> +<tr align=center> +<td bgcolor=#ff7f00><a title=#ff7f00>darkorange1</a></td> +<td bgcolor=#ee7600><a title=#ee7600>darkorange2</a></td> +<td bgcolor=#cd6600><a title=#cd6600>darkorange3</a></td> +<td bgcolor=#8b4500><a title=#8b4500>darkorange4</a></td> +<td bgcolor=#9932cc><a title=#9932cc>darkorchid</a></td> +</tr> +<tr align=center> +<td bgcolor=#bf3eff><a title=#bf3eff>darkorchid1</a></td> +<td bgcolor=#b23aee><a title=#b23aee>darkorchid2</a></td> +<td bgcolor=#9a32cd><a title=#9a32cd>darkorchid3</a></td> +<td bgcolor=#68228b><a title=#68228b>darkorchid4</a></td> +<td bgcolor=#e9967a><a title=#e9967a>darksalmon</a></td> +</tr> +<tr align=center> +<td bgcolor=#8fbc8f><a title=#8fbc8f>darkseagreen</a></td> +<td bgcolor=#c1ffc1><a title=#c1ffc1>darkseagreen1</a></td> +<td bgcolor=#b4eeb4><a title=#b4eeb4>darkseagreen2</a></td> +<td bgcolor=#9bcd9b><a title=#9bcd9b>darkseagreen3</a></td> +<td bgcolor=#698b69><a title=#698b69>darkseagreen4</a></td> +</tr> +<tr align=center> +<td bgcolor=#483d8b><a title=#483d8b>darkslateblue</a></td> +<td bgcolor=#2f4f4f><a title=#2f4f4f><font color=white>darkslategray</font></a></td> +<td bgcolor=#97ffff><a title=#97ffff>darkslategray1</a></td> +<td bgcolor=#8deeee><a title=#8deeee>darkslategray2</a></td> +<td bgcolor=#79cdcd><a title=#79cdcd>darkslategray3</a></td> +</tr> +<tr align=center> +<td bgcolor=#528b8b><a title=#528b8b>darkslategray4</a></td> +<td bgcolor=#2f4f4f><a title=#2f4f4f><font color=white>darkslategrey</font></a></td> +<td bgcolor=#00ced1><a title=#00ced1>darkturquoise</a></td> +<td bgcolor=#9400d3><a title=#9400d3>darkviolet</a></td> +<td bgcolor=#ff1493><a title=#ff1493>deeppink</a></td> +</tr> +<tr align=center> +<td bgcolor=#ff1493><a title=#ff1493>deeppink1</a></td> +<td bgcolor=#ee1289><a title=#ee1289>deeppink2</a></td> +<td bgcolor=#cd1076><a title=#cd1076>deeppink3</a></td> +<td bgcolor=#8b0a50><a title=#8b0a50>deeppink4</a></td> +<td bgcolor=#00bfff><a title=#00bfff>deepskyblue</a></td> +</tr> +<tr align=center> +<td bgcolor=#00bfff><a title=#00bfff>deepskyblue1</a></td> +<td bgcolor=#00b2ee><a title=#00b2ee>deepskyblue2</a></td> +<td bgcolor=#009acd><a title=#009acd>deepskyblue3</a></td> +<td bgcolor=#00688b><a title=#00688b>deepskyblue4</a></td> +<td bgcolor=#696969><a title=#696969><font color=white>dimgray</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#696969><a title=#696969><font color=white>dimgrey</font></a></td> +<td bgcolor=#1e90ff><a title=#1e90ff>dodgerblue</a></td> +<td bgcolor=#1e90ff><a title=#1e90ff>dodgerblue1</a></td> +<td bgcolor=#1c86ee><a title=#1c86ee>dodgerblue2</a></td> +<td bgcolor=#1874cd><a title=#1874cd>dodgerblue3</a></td> +</tr> +<tr align=center> +<td bgcolor=#104e8b><a title=#104e8b>dodgerblue4</a></td> +<td bgcolor=#b22222><a title=#b22222>firebrick</a></td> +<td bgcolor=#ff3030><a title=#ff3030>firebrick1</a></td> +<td bgcolor=#ee2c2c><a title=#ee2c2c>firebrick2</a></td> +<td bgcolor=#cd2626><a title=#cd2626>firebrick3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b1a1a><a title=#8b1a1a>firebrick4</a></td> +<td bgcolor=#fffaf0><a title=#fffaf0>floralwhite</a></td> +<td bgcolor=#228b22><a title=#228b22>forestgreen</a></td> +<td bgcolor=#dcdcdc><a title=#dcdcdc>gainsboro</a></td> +<td bgcolor=#f8f8ff><a title=#f8f8ff>ghostwhite</a></td> +</tr> +<tr align=center> +<td bgcolor=#ffd700><a title=#ffd700> gold </a></td> +<td bgcolor=#ffd700><a title=#ffd700>gold1</a></td> +<td bgcolor=#eec900><a title=#eec900>gold2</a></td> +<td bgcolor=#cdad00><a title=#cdad00>gold3</a></td> +<td bgcolor=#8b7500><a title=#8b7500>gold4</a></td> +</tr> +<tr align=center> +<td bgcolor=#daa520><a title=#daa520>goldenrod</a></td> +<td bgcolor=#ffc125><a title=#ffc125>goldenrod1</a></td> +<td bgcolor=#eeb422><a title=#eeb422>goldenrod2</a></td> +<td bgcolor=#cd9b1d><a title=#cd9b1d>goldenrod3</a></td> +<td bgcolor=#8b6914><a title=#8b6914>goldenrod4</a></td> +</tr> +<tr align=center> +<td bgcolor=#c0c0c0><a title=#c0c0c0> gray </a></td> +<td bgcolor=#000000><a title=#000000><font color=white>gray0</font></a></td> +<td bgcolor=#030303><a title=#030303><font color=white>gray1</font></a></td> +<td bgcolor=#050505><a title=#050505><font color=white>gray2</font></a></td> +<td bgcolor=#080808><a title=#080808><font color=white>gray3</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#0a0a0a><a title=#0a0a0a><font color=white>gray4</font></a></td> +<td bgcolor=#0d0d0d><a title=#0d0d0d><font color=white>gray5</font></a></td> +<td bgcolor=#0f0f0f><a title=#0f0f0f><font color=white>gray6</font></a></td> +<td bgcolor=#121212><a title=#121212><font color=white>gray7</font></a></td> +<td bgcolor=#141414><a title=#141414><font color=white>gray8</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#171717><a title=#171717><font color=white>gray9</font></a></td> +<td bgcolor=#1a1a1a><a title=#1a1a1a><font color=white>gray10</font></a></td> +<td bgcolor=#1c1c1c><a title=#1c1c1c><font color=white>gray11</font></a></td> +<td bgcolor=#1f1f1f><a title=#1f1f1f><font color=white>gray12</font></a></td> +<td bgcolor=#212121><a title=#212121><font color=white>gray13</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#242424><a title=#242424><font color=white>gray14</font></a></td> +<td bgcolor=#262626><a title=#262626><font color=white>gray15</font></a></td> +<td bgcolor=#292929><a title=#292929><font color=white>gray16</font></a></td> +<td bgcolor=#2b2b2b><a title=#2b2b2b><font color=white>gray17</font></a></td> +<td bgcolor=#2e2e2e><a title=#2e2e2e><font color=white>gray18</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#303030><a title=#303030><font color=white>gray19</font></a></td> +<td bgcolor=#333333><a title=#333333><font color=white>gray20</font></a></td> +<td bgcolor=#363636><a title=#363636><font color=white>gray21</font></a></td> +<td bgcolor=#383838><a title=#383838><font color=white>gray22</font></a></td> +<td bgcolor=#3b3b3b><a title=#3b3b3b><font color=white>gray23</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#3d3d3d><a title=#3d3d3d><font color=white>gray24</font></a></td> +<td bgcolor=#404040><a title=#404040><font color=white>gray25</font></a></td> +<td bgcolor=#424242><a 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title=#616161><font color=white>gray38</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#636363><a title=#636363><font color=white>gray39</font></a></td> +<td bgcolor=#666666><a title=#666666><font color=white>gray40</font></a></td> +<td bgcolor=#696969><a title=#696969><font color=white>gray41</font></a></td> +<td bgcolor=#6b6b6b><a title=#6b6b6b><font color=white>gray42</font></a></td> +<td bgcolor=#6e6e6e><a title=#6e6e6e><font color=white>gray43</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#707070><a title=#707070><font color=white>gray44</font></a></td> +<td bgcolor=#737373><a title=#737373><font color=white>gray45</font></a></td> +<td bgcolor=#757575><a title=#757575><font color=white>gray46</font></a></td> +<td bgcolor=#787878><a title=#787878><font color=white>gray47</font></a></td> +<td bgcolor=#7a7a7a><a title=#7a7a7a><font color=white>gray48</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#7d7d7d><a title=#7d7d7d><font color=white>gray49</font></a></td> +<td bgcolor=#7f7f7f><a title=#7f7f7f><font color=white>gray50</font></a></td> +<td bgcolor=#828282><a title=#828282><font color=white>gray51</font></a></td> +<td bgcolor=#858585><a title=#858585>gray52</a></td> +<td bgcolor=#878787><a title=#878787>gray53</a></td> +</tr> +<tr align=center> +<td bgcolor=#8a8a8a><a title=#8a8a8a>gray54</a></td> +<td bgcolor=#8c8c8c><a title=#8c8c8c>gray55</a></td> +<td bgcolor=#8f8f8f><a title=#8f8f8f>gray56</a></td> +<td bgcolor=#919191><a title=#919191>gray57</a></td> +<td bgcolor=#949494><a title=#949494>gray58</a></td> +</tr> +<tr align=center> +<td bgcolor=#969696><a title=#969696>gray59</a></td> +<td bgcolor=#999999><a title=#999999>gray60</a></td> +<td bgcolor=#9c9c9c><a title=#9c9c9c>gray61</a></td> +<td bgcolor=#9e9e9e><a title=#9e9e9e>gray62</a></td> +<td bgcolor=#a1a1a1><a title=#a1a1a1>gray63</a></td> +</tr> +<tr align=center> +<td bgcolor=#a3a3a3><a title=#a3a3a3>gray64</a></td> +<td bgcolor=#a6a6a6><a title=#a6a6a6>gray65</a></td> +<td bgcolor=#a8a8a8><a title=#a8a8a8>gray66</a></td> +<td bgcolor=#ababab><a title=#ababab>gray67</a></td> +<td bgcolor=#adadad><a title=#adadad>gray68</a></td> +</tr> +<tr align=center> +<td bgcolor=#b0b0b0><a title=#b0b0b0>gray69</a></td> +<td bgcolor=#b3b3b3><a title=#b3b3b3>gray70</a></td> +<td bgcolor=#b5b5b5><a title=#b5b5b5>gray71</a></td> +<td bgcolor=#b8b8b8><a title=#b8b8b8>gray72</a></td> +<td bgcolor=#bababa><a title=#bababa>gray73</a></td> +</tr> +<tr align=center> +<td bgcolor=#bdbdbd><a title=#bdbdbd>gray74</a></td> +<td bgcolor=#bfbfbf><a title=#bfbfbf>gray75</a></td> +<td bgcolor=#c2c2c2><a title=#c2c2c2>gray76</a></td> +<td bgcolor=#c4c4c4><a title=#c4c4c4>gray77</a></td> +<td bgcolor=#c7c7c7><a title=#c7c7c7>gray78</a></td> +</tr> +<tr align=center> +<td bgcolor=#c9c9c9><a title=#c9c9c9>gray79</a></td> +<td bgcolor=#cccccc><a title=#cccccc>gray80</a></td> +<td bgcolor=#cfcfcf><a title=#cfcfcf>gray81</a></td> +<td bgcolor=#d1d1d1><a title=#d1d1d1>gray82</a></td> +<td bgcolor=#d4d4d4><a title=#d4d4d4>gray83</a></td> +</tr> +<tr align=center> +<td bgcolor=#d6d6d6><a title=#d6d6d6>gray84</a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9>gray85</a></td> +<td bgcolor=#dbdbdb><a title=#dbdbdb>gray86</a></td> +<td bgcolor=#dedede><a title=#dedede>gray87</a></td> +<td bgcolor=#e0e0e0><a title=#e0e0e0>gray88</a></td> +</tr> +<tr align=center> +<td bgcolor=#e3e3e3><a title=#e3e3e3>gray89</a></td> +<td bgcolor=#e5e5e5><a title=#e5e5e5>gray90</a></td> +<td bgcolor=#e8e8e8><a title=#e8e8e8>gray91</a></td> +<td bgcolor=#ebebeb><a title=#ebebeb>gray92</a></td> +<td bgcolor=#ededed><a title=#ededed>gray93</a></td> +</tr> +<tr align=center> +<td bgcolor=#f0f0f0><a title=#f0f0f0>gray94</a></td> +<td bgcolor=#f2f2f2><a title=#f2f2f2>gray95</a></td> +<td bgcolor=#f5f5f5><a title=#f5f5f5>gray96</a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7>gray97</a></td> +<td bgcolor=#fafafa><a title=#fafafa>gray98</a></td> +</tr> +<tr align=center> 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color=white>grey5</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#0f0f0f><a title=#0f0f0f><font color=white>grey6</font></a></td> +<td bgcolor=#121212><a title=#121212><font color=white>grey7</font></a></td> +<td bgcolor=#141414><a title=#141414><font color=white>grey8</font></a></td> +<td bgcolor=#171717><a title=#171717><font color=white>grey9</font></a></td> +<td bgcolor=#1a1a1a><a title=#1a1a1a><font color=white>grey10</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#1c1c1c><a title=#1c1c1c><font color=white>grey11</font></a></td> +<td bgcolor=#1f1f1f><a title=#1f1f1f><font color=white>grey12</font></a></td> +<td bgcolor=#212121><a title=#212121><font color=white>grey13</font></a></td> +<td bgcolor=#242424><a title=#242424><font color=white>grey14</font></a></td> +<td bgcolor=#262626><a title=#262626><font color=white>grey15</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#292929><a title=#292929><font color=white>grey16</font></a></td> +<td bgcolor=#2b2b2b><a title=#2b2b2b><font color=white>grey17</font></a></td> +<td bgcolor=#2e2e2e><a title=#2e2e2e><font color=white>grey18</font></a></td> +<td bgcolor=#303030><a title=#303030><font color=white>grey19</font></a></td> +<td bgcolor=#333333><a title=#333333><font color=white>grey20</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#363636><a title=#363636><font color=white>grey21</font></a></td> +<td bgcolor=#383838><a title=#383838><font color=white>grey22</font></a></td> +<td bgcolor=#3b3b3b><a title=#3b3b3b><font color=white>grey23</font></a></td> +<td bgcolor=#3d3d3d><a title=#3d3d3d><font color=white>grey24</font></a></td> +<td bgcolor=#404040><a title=#404040><font color=white>grey25</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#424242><a title=#424242><font color=white>grey26</font></a></td> +<td bgcolor=#454545><a title=#454545><font color=white>grey27</font></a></td> +<td bgcolor=#474747><a title=#474747><font color=white>grey28</font></a></td> +<td bgcolor=#4a4a4a><a title=#4a4a4a><font color=white>grey29</font></a></td> +<td bgcolor=#4d4d4d><a title=#4d4d4d><font color=white>grey30</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#4f4f4f><a title=#4f4f4f><font color=white>grey31</font></a></td> +<td bgcolor=#525252><a title=#525252><font color=white>grey32</font></a></td> +<td bgcolor=#545454><a title=#545454><font color=white>grey33</font></a></td> +<td bgcolor=#575757><a title=#575757><font color=white>grey34</font></a></td> +<td bgcolor=#595959><a title=#595959><font color=white>grey35</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#5c5c5c><a title=#5c5c5c><font color=white>grey36</font></a></td> +<td bgcolor=#5e5e5e><a title=#5e5e5e><font color=white>grey37</font></a></td> +<td bgcolor=#616161><a title=#616161><font color=white>grey38</font></a></td> +<td bgcolor=#636363><a title=#636363><font color=white>grey39</font></a></td> +<td bgcolor=#666666><a title=#666666><font color=white>grey40</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#696969><a title=#696969><font color=white>grey41</font></a></td> +<td bgcolor=#6b6b6b><a title=#6b6b6b><font color=white>grey42</font></a></td> +<td bgcolor=#6e6e6e><a title=#6e6e6e><font color=white>grey43</font></a></td> +<td bgcolor=#707070><a title=#707070><font color=white>grey44</font></a></td> +<td bgcolor=#737373><a title=#737373><font color=white>grey45</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#757575><a title=#757575><font color=white>grey46</font></a></td> +<td bgcolor=#787878><a title=#787878><font color=white>grey47</font></a></td> +<td bgcolor=#7a7a7a><a title=#7a7a7a><font color=white>grey48</font></a></td> +<td bgcolor=#7d7d7d><a title=#7d7d7d><font color=white>grey49</font></a></td> +<td bgcolor=#7f7f7f><a title=#7f7f7f><font color=white>grey50</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#828282><a title=#828282><font color=white>grey51</font></a></td> +<td bgcolor=#858585><a title=#858585>grey52</a></td> +<td bgcolor=#878787><a title=#878787>grey53</a></td> +<td bgcolor=#8a8a8a><a title=#8a8a8a>grey54</a></td> +<td bgcolor=#8c8c8c><a title=#8c8c8c>grey55</a></td> +</tr> +<tr align=center> +<td bgcolor=#8f8f8f><a title=#8f8f8f>grey56</a></td> +<td bgcolor=#919191><a title=#919191>grey57</a></td> +<td bgcolor=#949494><a title=#949494>grey58</a></td> +<td bgcolor=#969696><a title=#969696>grey59</a></td> +<td bgcolor=#999999><a title=#999999>grey60</a></td> +</tr> +<tr align=center> +<td bgcolor=#9c9c9c><a title=#9c9c9c>grey61</a></td> +<td bgcolor=#9e9e9e><a title=#9e9e9e>grey62</a></td> +<td bgcolor=#a1a1a1><a title=#a1a1a1>grey63</a></td> +<td bgcolor=#a3a3a3><a title=#a3a3a3>grey64</a></td> +<td bgcolor=#a6a6a6><a title=#a6a6a6>grey65</a></td> +</tr> +<tr align=center> +<td bgcolor=#a8a8a8><a title=#a8a8a8>grey66</a></td> +<td bgcolor=#ababab><a title=#ababab>grey67</a></td> +<td bgcolor=#adadad><a title=#adadad>grey68</a></td> +<td bgcolor=#b0b0b0><a title=#b0b0b0>grey69</a></td> +<td bgcolor=#b3b3b3><a title=#b3b3b3>grey70</a></td> +</tr> +<tr align=center> +<td bgcolor=#b5b5b5><a title=#b5b5b5>grey71</a></td> +<td bgcolor=#b8b8b8><a title=#b8b8b8>grey72</a></td> +<td bgcolor=#bababa><a title=#bababa>grey73</a></td> +<td bgcolor=#bdbdbd><a title=#bdbdbd>grey74</a></td> +<td bgcolor=#bfbfbf><a title=#bfbfbf>grey75</a></td> +</tr> +<tr align=center> +<td bgcolor=#c2c2c2><a title=#c2c2c2>grey76</a></td> +<td bgcolor=#c4c4c4><a title=#c4c4c4>grey77</a></td> +<td bgcolor=#c7c7c7><a title=#c7c7c7>grey78</a></td> +<td bgcolor=#c9c9c9><a title=#c9c9c9>grey79</a></td> +<td bgcolor=#cccccc><a title=#cccccc>grey80</a></td> +</tr> +<tr align=center> +<td bgcolor=#cfcfcf><a title=#cfcfcf>grey81</a></td> +<td bgcolor=#d1d1d1><a title=#d1d1d1>grey82</a></td> +<td bgcolor=#d4d4d4><a title=#d4d4d4>grey83</a></td> +<td bgcolor=#d6d6d6><a title=#d6d6d6>grey84</a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9>grey85</a></td> +</tr> +<tr align=center> +<td bgcolor=#dbdbdb><a title=#dbdbdb>grey86</a></td> +<td bgcolor=#dedede><a title=#dedede>grey87</a></td> +<td bgcolor=#e0e0e0><a title=#e0e0e0>grey88</a></td> +<td bgcolor=#e3e3e3><a title=#e3e3e3>grey89</a></td> +<td bgcolor=#e5e5e5><a title=#e5e5e5>grey90</a></td> +</tr> +<tr align=center> +<td bgcolor=#e8e8e8><a title=#e8e8e8>grey91</a></td> +<td bgcolor=#ebebeb><a title=#ebebeb>grey92</a></td> +<td bgcolor=#ededed><a title=#ededed>grey93</a></td> +<td bgcolor=#f0f0f0><a title=#f0f0f0>grey94</a></td> +<td bgcolor=#f2f2f2><a title=#f2f2f2>grey95</a></td> +</tr> +<tr align=center> +<td bgcolor=#f5f5f5><a title=#f5f5f5>grey96</a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7>grey97</a></td> +<td bgcolor=#fafafa><a title=#fafafa>grey98</a></td> +<td bgcolor=#fcfcfc><a title=#fcfcfc>grey99</a></td> +<td bgcolor=#ffffff><a title=#ffffff>grey100</a></td> +</tr> +<tr align=center> +<td bgcolor=#f0fff0><a title=#f0fff0>honeydew</a></td> +<td bgcolor=#f0fff0><a title=#f0fff0>honeydew1</a></td> +<td bgcolor=#e0eee0><a title=#e0eee0>honeydew2</a></td> +<td bgcolor=#c1cdc1><a title=#c1cdc1>honeydew3</a></td> +<td bgcolor=#838b83><a title=#838b83>honeydew4</a></td> +</tr> +<tr align=center> +<td bgcolor=#ff69b4><a title=#ff69b4>hotpink</a></td> +<td bgcolor=#ff6eb4><a title=#ff6eb4>hotpink1</a></td> +<td bgcolor=#ee6aa7><a title=#ee6aa7>hotpink2</a></td> +<td bgcolor=#cd6090><a title=#cd6090>hotpink3</a></td> +<td bgcolor=#8b3a62><a title=#8b3a62>hotpink4</a></td> +</tr> +<tr align=center> +<td bgcolor=#cd5c5c><a title=#cd5c5c>indianred</a></td> +<td bgcolor=#ff6a6a><a title=#ff6a6a>indianred1</a></td> +<td bgcolor=#ee6363><a title=#ee6363>indianred2</a></td> +<td bgcolor=#cd5555><a title=#cd5555>indianred3</a></td> +<td bgcolor=#8b3a3a><a title=#8b3a3a>indianred4</a></td> +</tr> +<tr align=center> +<td bgcolor=#4b0082><a title=#4b0082><font color=white>indigo</font></a></td> +<td bgcolor=#fffff0><a title=#fffff0>ivory</a></td> +<td bgcolor=#fffff0><a title=#fffff0>ivory1</a></td> +<td bgcolor=#eeeee0><a title=#eeeee0>ivory2</a></td> +<td bgcolor=#cdcdc1><a title=#cdcdc1>ivory3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b8b83><a title=#8b8b83>ivory4</a></td> +<td bgcolor=#f0e68c><a title=#f0e68c>khaki</a></td> +<td bgcolor=#fff68f><a title=#fff68f>khaki1</a></td> +<td bgcolor=#eee685><a title=#eee685>khaki2</a></td> +<td bgcolor=#cdc673><a title=#cdc673>khaki3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b864e><a title=#8b864e>khaki4</a></td> +<td bgcolor=#e6e6fa><a title=#e6e6fa>lavender</a></td> +<td bgcolor=#fff0f5><a title=#fff0f5>lavenderblush</a></td> +<td bgcolor=#fff0f5><a title=#fff0f5>lavenderblush1</a></td> +<td bgcolor=#eee0e5><a title=#eee0e5>lavenderblush2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cdc1c5><a title=#cdc1c5>lavenderblush3</a></td> +<td bgcolor=#8b8386><a title=#8b8386>lavenderblush4</a></td> +<td bgcolor=#7cfc00><a title=#7cfc00>lawngreen</a></td> +<td bgcolor=#fffacd><a title=#fffacd>lemonchiffon</a></td> +<td bgcolor=#fffacd><a title=#fffacd>lemonchiffon1</a></td> +</tr> +<tr align=center> +<td bgcolor=#eee9bf><a title=#eee9bf>lemonchiffon2</a></td> +<td bgcolor=#cdc9a5><a title=#cdc9a5>lemonchiffon3</a></td> +<td bgcolor=#8b8970><a title=#8b8970>lemonchiffon4</a></td> +<td bgcolor=#add8e6><a title=#add8e6>lightblue</a></td> +<td bgcolor=#bfefff><a title=#bfefff>lightblue1</a></td> +</tr> +<tr align=center> +<td bgcolor=#b2dfee><a title=#b2dfee>lightblue2</a></td> +<td bgcolor=#9ac0cd><a title=#9ac0cd>lightblue3</a></td> +<td bgcolor=#68838b><a title=#68838b>lightblue4</a></td> +<td bgcolor=#f08080><a title=#f08080>lightcoral</a></td> +<td bgcolor=#e0ffff><a title=#e0ffff>lightcyan</a></td> +</tr> +<tr align=center> +<td bgcolor=#e0ffff><a title=#e0ffff>lightcyan1</a></td> +<td bgcolor=#d1eeee><a title=#d1eeee>lightcyan2</a></td> +<td bgcolor=#b4cdcd><a title=#b4cdcd>lightcyan3</a></td> +<td bgcolor=#7a8b8b><a title=#7a8b8b>lightcyan4</a></td> +<td bgcolor=#eedd82><a title=#eedd82>lightgoldenrod</a></td> +</tr> +<tr align=center> +<td bgcolor=#ffec8b><a title=#ffec8b>lightgoldenrod1</a></td> +<td bgcolor=#eedc82><a title=#eedc82>lightgoldenrod2</a></td> +<td bgcolor=#cdbe70><a title=#cdbe70>lightgoldenrod3</a></td> +<td bgcolor=#8b814c><a title=#8b814c>lightgoldenrod4</a></td> +<td bgcolor=#fafad2><a title=#fafad2>lightgoldenrodyellow</a></td> +</tr> +<tr align=center> +<td bgcolor=#d3d3d3><a title=#d3d3d3>lightgray</a></td> +<td bgcolor=#d3d3d3><a title=#d3d3d3>lightgrey</a></td> +<td bgcolor=#ffb6c1><a title=#ffb6c1>lightpink</a></td> +<td bgcolor=#ffaeb9><a title=#ffaeb9>lightpink1</a></td> +<td bgcolor=#eea2ad><a title=#eea2ad>lightpink2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cd8c95><a title=#cd8c95>lightpink3</a></td> +<td bgcolor=#8b5f65><a title=#8b5f65>lightpink4</a></td> +<td bgcolor=#ffa07a><a title=#ffa07a>lightsalmon</a></td> +<td bgcolor=#ffa07a><a title=#ffa07a>lightsalmon1</a></td> +<td bgcolor=#ee9572><a title=#ee9572>lightsalmon2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cd8162><a title=#cd8162>lightsalmon3</a></td> +<td bgcolor=#8b5742><a title=#8b5742>lightsalmon4</a></td> +<td bgcolor=#20b2aa><a title=#20b2aa>lightseagreen</a></td> +<td bgcolor=#87cefa><a title=#87cefa>lightskyblue</a></td> +<td bgcolor=#b0e2ff><a title=#b0e2ff>lightskyblue1</a></td> +</tr> +<tr align=center> +<td bgcolor=#a4d3ee><a title=#a4d3ee>lightskyblue2</a></td> +<td bgcolor=#8db6cd><a title=#8db6cd>lightskyblue3</a></td> +<td bgcolor=#607b8b><a title=#607b8b>lightskyblue4</a></td> +<td bgcolor=#8470ff><a title=#8470ff>lightslateblue</a></td> +<td bgcolor=#778899><a title=#778899>lightslategray</a></td> +</tr> +<tr align=center> +<td bgcolor=#778899><a title=#778899>lightslategrey</a></td> +<td bgcolor=#b0c4de><a title=#b0c4de>lightsteelblue</a></td> +<td bgcolor=#cae1ff><a title=#cae1ff>lightsteelblue1</a></td> +<td bgcolor=#bcd2ee><a title=#bcd2ee>lightsteelblue2</a></td> +<td bgcolor=#a2b5cd><a title=#a2b5cd>lightsteelblue3</a></td> +</tr> +<tr align=center> +<td bgcolor=#6e7b8b><a title=#6e7b8b>lightsteelblue4</a></td> +<td bgcolor=#ffffe0><a title=#ffffe0>lightyellow</a></td> +<td bgcolor=#ffffe0><a title=#ffffe0>lightyellow1</a></td> +<td bgcolor=#eeeed1><a title=#eeeed1>lightyellow2</a></td> +<td bgcolor=#cdcdb4><a title=#cdcdb4>lightyellow3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b8b7a><a title=#8b8b7a>lightyellow4</a></td> +<td bgcolor=#32cd32><a title=#32cd32>limegreen</a></td> +<td bgcolor=#faf0e6><a title=#faf0e6>linen</a></td> +<td bgcolor=#ff00ff><a title=#ff00ff>magenta</a></td> +<td bgcolor=#ff00ff><a title=#ff00ff>magenta1</a></td> +</tr> +<tr align=center> +<td bgcolor=#ee00ee><a title=#ee00ee>magenta2</a></td> +<td bgcolor=#cd00cd><a title=#cd00cd>magenta3</a></td> +<td bgcolor=#8b008b><a title=#8b008b>magenta4</a></td> +<td bgcolor=#b03060><a title=#b03060>maroon</a></td> +<td bgcolor=#ff34b3><a title=#ff34b3>maroon1</a></td> +</tr> +<tr align=center> +<td bgcolor=#ee30a7><a title=#ee30a7>maroon2</a></td> +<td bgcolor=#cd2990><a title=#cd2990>maroon3</a></td> +<td bgcolor=#8b1c62><a title=#8b1c62>maroon4</a></td> +<td bgcolor=#66cdaa><a title=#66cdaa>mediumaquamarine</a></td> +<td bgcolor=#0000cd><a title=#0000cd>mediumblue</a></td> +</tr> +<tr align=center> +<td bgcolor=#ba55d3><a title=#ba55d3>mediumorchid</a></td> +<td bgcolor=#e066ff><a title=#e066ff>mediumorchid1</a></td> +<td bgcolor=#d15fee><a title=#d15fee>mediumorchid2</a></td> +<td bgcolor=#b452cd><a title=#b452cd>mediumorchid3</a></td> +<td bgcolor=#7a378b><a title=#7a378b>mediumorchid4</a></td> +</tr> +<tr align=center> +<td bgcolor=#9370db><a title=#9370db>mediumpurple</a></td> +<td bgcolor=#ab82ff><a title=#ab82ff>mediumpurple1</a></td> +<td bgcolor=#9f79ee><a title=#9f79ee>mediumpurple2</a></td> +<td bgcolor=#8968cd><a title=#8968cd>mediumpurple3</a></td> +<td bgcolor=#5d478b><a title=#5d478b>mediumpurple4</a></td> +</tr> +<tr align=center> +<td bgcolor=#3cb371><a title=#3cb371>mediumseagreen</a></td> +<td bgcolor=#7b68ee><a title=#7b68ee>mediumslateblue</a></td> +<td bgcolor=#00fa9a><a title=#00fa9a>mediumspringgreen</a></td> +<td bgcolor=#48d1cc><a title=#48d1cc>mediumturquoise</a></td> +<td bgcolor=#c71585><a title=#c71585>mediumvioletred</a></td> +</tr> +<tr align=center> +<td bgcolor=#191970><a title=#191970><font color=white>midnightblue</font></a></td> +<td bgcolor=#f5fffa><a title=#f5fffa>mintcream</a></td> +<td bgcolor=#ffe4e1><a title=#ffe4e1>mistyrose</a></td> +<td bgcolor=#ffe4e1><a title=#ffe4e1>mistyrose1</a></td> +<td bgcolor=#eed5d2><a title=#eed5d2>mistyrose2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cdb7b5><a title=#cdb7b5>mistyrose3</a></td> +<td bgcolor=#8b7d7b><a title=#8b7d7b>mistyrose4</a></td> +<td bgcolor=#ffe4b5><a title=#ffe4b5>moccasin</a></td> +<td bgcolor=#ffdead><a title=#ffdead>navajowhite</a></td> +<td bgcolor=#ffdead><a title=#ffdead>navajowhite1</a></td> +</tr> +<tr align=center> +<td bgcolor=#eecfa1><a title=#eecfa1>navajowhite2</a></td> +<td bgcolor=#cdb38b><a title=#cdb38b>navajowhite3</a></td> +<td bgcolor=#8b795e><a title=#8b795e>navajowhite4</a></td> +<td bgcolor=#000080><a title=#000080><font color=white> navy </font></a></td> +<td bgcolor=#000080><a title=#000080><font color=white>navyblue</font></a></td> +</tr> +<tr align=center> +<td bgcolor=#fdf5e6><a title=#fdf5e6>oldlace</a></td> +<td bgcolor=#6b8e23><a title=#6b8e23>olivedrab</a></td> +<td bgcolor=#c0ff3e><a title=#c0ff3e>olivedrab1</a></td> +<td bgcolor=#b3ee3a><a title=#b3ee3a>olivedrab2</a></td> +<td bgcolor=#9acd32><a title=#9acd32>olivedrab3</a></td> +</tr> +<tr align=center> +<td bgcolor=#698b22><a title=#698b22>olivedrab4</a></td> +<td bgcolor=#ffa500><a title=#ffa500>orange</a></td> +<td bgcolor=#ffa500><a title=#ffa500>orange1</a></td> +<td bgcolor=#ee9a00><a title=#ee9a00>orange2</a></td> +<td bgcolor=#cd8500><a title=#cd8500>orange3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b5a00><a title=#8b5a00>orange4</a></td> +<td bgcolor=#ff4500><a title=#ff4500>orangered</a></td> +<td bgcolor=#ff4500><a title=#ff4500>orangered1</a></td> +<td bgcolor=#ee4000><a title=#ee4000>orangered2</a></td> +<td bgcolor=#cd3700><a title=#cd3700>orangered3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b2500><a title=#8b2500>orangered4</a></td> +<td bgcolor=#da70d6><a title=#da70d6>orchid</a></td> +<td bgcolor=#ff83fa><a title=#ff83fa>orchid1</a></td> +<td bgcolor=#ee7ae9><a title=#ee7ae9>orchid2</a></td> +<td bgcolor=#cd69c9><a title=#cd69c9>orchid3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b4789><a title=#8b4789>orchid4</a></td> +<td bgcolor=#eee8aa><a title=#eee8aa>palegoldenrod</a></td> +<td bgcolor=#98fb98><a title=#98fb98>palegreen</a></td> +<td bgcolor=#9aff9a><a title=#9aff9a>palegreen1</a></td> +<td bgcolor=#90ee90><a title=#90ee90>palegreen2</a></td> +</tr> +<tr align=center> +<td bgcolor=#7ccd7c><a title=#7ccd7c>palegreen3</a></td> +<td bgcolor=#548b54><a title=#548b54>palegreen4</a></td> +<td bgcolor=#afeeee><a title=#afeeee>paleturquoise</a></td> +<td bgcolor=#bbffff><a title=#bbffff>paleturquoise1</a></td> +<td bgcolor=#aeeeee><a title=#aeeeee>paleturquoise2</a></td> +</tr> +<tr align=center> +<td bgcolor=#96cdcd><a title=#96cdcd>paleturquoise3</a></td> +<td bgcolor=#668b8b><a title=#668b8b>paleturquoise4</a></td> +<td bgcolor=#db7093><a title=#db7093>palevioletred</a></td> +<td bgcolor=#ff82ab><a title=#ff82ab>palevioletred1</a></td> +<td bgcolor=#ee799f><a title=#ee799f>palevioletred2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cd6889><a title=#cd6889>palevioletred3</a></td> +<td bgcolor=#8b475d><a title=#8b475d>palevioletred4</a></td> +<td bgcolor=#ffefd5><a title=#ffefd5>papayawhip</a></td> +<td bgcolor=#ffdab9><a title=#ffdab9>peachpuff</a></td> +<td bgcolor=#ffdab9><a title=#ffdab9>peachpuff1</a></td> +</tr> +<tr align=center> +<td bgcolor=#eecbad><a title=#eecbad>peachpuff2</a></td> +<td bgcolor=#cdaf95><a title=#cdaf95>peachpuff3</a></td> +<td bgcolor=#8b7765><a title=#8b7765>peachpuff4</a></td> +<td bgcolor=#cd853f><a title=#cd853f> peru </a></td> +<td bgcolor=#ffc0cb><a title=#ffc0cb> pink </a></td> +</tr> +<tr align=center> +<td bgcolor=#ffb5c5><a title=#ffb5c5>pink1</a></td> +<td bgcolor=#eea9b8><a title=#eea9b8>pink2</a></td> +<td bgcolor=#cd919e><a title=#cd919e>pink3</a></td> +<td bgcolor=#8b636c><a title=#8b636c>pink4</a></td> +<td bgcolor=#dda0dd><a title=#dda0dd> plum </a></td> +</tr> +<tr align=center> +<td bgcolor=#ffbbff><a title=#ffbbff>plum1</a></td> +<td bgcolor=#eeaeee><a title=#eeaeee>plum2</a></td> +<td bgcolor=#cd96cd><a title=#cd96cd>plum3</a></td> +<td bgcolor=#8b668b><a title=#8b668b>plum4</a></td> +<td bgcolor=#b0e0e6><a title=#b0e0e6>powderblue</a></td> +</tr> +<tr align=center> +<td bgcolor=#a020f0><a title=#a020f0>purple</a></td> +<td bgcolor=#9b30ff><a title=#9b30ff>purple1</a></td> +<td bgcolor=#912cee><a title=#912cee>purple2</a></td> +<td bgcolor=#7d26cd><a title=#7d26cd>purple3</a></td> +<td bgcolor=#551a8b><a title=#551a8b>purple4</a></td> +</tr> +<tr align=center> +<td bgcolor=#ff0000><a title=#ff0000> red </a></td> +<td bgcolor=#ff0000><a title=#ff0000> red1 </a></td> +<td bgcolor=#ee0000><a title=#ee0000> red2 </a></td> +<td bgcolor=#cd0000><a title=#cd0000> red3 </a></td> +<td bgcolor=#8b0000><a title=#8b0000> red4 </a></td> +</tr> +<tr align=center> +<td bgcolor=#bc8f8f><a title=#bc8f8f>rosybrown</a></td> +<td bgcolor=#ffc1c1><a title=#ffc1c1>rosybrown1</a></td> +<td bgcolor=#eeb4b4><a title=#eeb4b4>rosybrown2</a></td> +<td bgcolor=#cd9b9b><a title=#cd9b9b>rosybrown3</a></td> +<td bgcolor=#8b6969><a title=#8b6969>rosybrown4</a></td> +</tr> +<tr align=center> +<td bgcolor=#4169e1><a title=#4169e1>royalblue</a></td> +<td bgcolor=#4876ff><a title=#4876ff>royalblue1</a></td> +<td bgcolor=#436eee><a title=#436eee>royalblue2</a></td> +<td bgcolor=#3a5fcd><a title=#3a5fcd>royalblue3</a></td> +<td bgcolor=#27408b><a title=#27408b>royalblue4</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b4513><a title=#8b4513>saddlebrown</a></td> +<td bgcolor=#fa8072><a title=#fa8072>salmon</a></td> +<td bgcolor=#ff8c69><a title=#ff8c69>salmon1</a></td> +<td bgcolor=#ee8262><a title=#ee8262>salmon2</a></td> +<td bgcolor=#cd7054><a title=#cd7054>salmon3</a></td> +</tr> +<tr align=center> +<td bgcolor=#8b4c39><a title=#8b4c39>salmon4</a></td> +<td bgcolor=#f4a460><a title=#f4a460>sandybrown</a></td> +<td bgcolor=#2e8b57><a title=#2e8b57>seagreen</a></td> +<td bgcolor=#54ff9f><a title=#54ff9f>seagreen1</a></td> +<td bgcolor=#4eee94><a title=#4eee94>seagreen2</a></td> +</tr> +<tr align=center> +<td bgcolor=#43cd80><a title=#43cd80>seagreen3</a></td> +<td bgcolor=#2e8b57><a title=#2e8b57>seagreen4</a></td> +<td bgcolor=#fff5ee><a title=#fff5ee>seashell</a></td> +<td bgcolor=#fff5ee><a title=#fff5ee>seashell1</a></td> +<td bgcolor=#eee5de><a title=#eee5de>seashell2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cdc5bf><a title=#cdc5bf>seashell3</a></td> +<td bgcolor=#8b8682><a title=#8b8682>seashell4</a></td> +<td bgcolor=#a0522d><a title=#a0522d>sienna</a></td> +<td bgcolor=#ff8247><a title=#ff8247>sienna1</a></td> +<td bgcolor=#ee7942><a title=#ee7942>sienna2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cd6839><a title=#cd6839>sienna3</a></td> +<td bgcolor=#8b4726><a title=#8b4726>sienna4</a></td> +<td bgcolor=#87ceeb><a title=#87ceeb>skyblue</a></td> +<td bgcolor=#87ceff><a title=#87ceff>skyblue1</a></td> +<td bgcolor=#7ec0ee><a title=#7ec0ee>skyblue2</a></td> +</tr> +<tr align=center> +<td bgcolor=#6ca6cd><a title=#6ca6cd>skyblue3</a></td> +<td bgcolor=#4a708b><a title=#4a708b>skyblue4</a></td> +<td bgcolor=#6a5acd><a title=#6a5acd>slateblue</a></td> +<td bgcolor=#836fff><a title=#836fff>slateblue1</a></td> +<td bgcolor=#7a67ee><a title=#7a67ee>slateblue2</a></td> +</tr> +<tr align=center> +<td bgcolor=#6959cd><a title=#6959cd>slateblue3</a></td> +<td bgcolor=#473c8b><a title=#473c8b>slateblue4</a></td> +<td bgcolor=#708090><a title=#708090>slategray</a></td> +<td bgcolor=#c6e2ff><a title=#c6e2ff>slategray1</a></td> +<td bgcolor=#b9d3ee><a title=#b9d3ee>slategray2</a></td> +</tr> +<tr align=center> +<td bgcolor=#9fb6cd><a title=#9fb6cd>slategray3</a></td> +<td bgcolor=#6c7b8b><a title=#6c7b8b>slategray4</a></td> +<td bgcolor=#708090><a title=#708090>slategrey</a></td> +<td bgcolor=#fffafa><a title=#fffafa> snow </a></td> +<td bgcolor=#fffafa><a title=#fffafa>snow1</a></td> +</tr> +<tr align=center> +<td bgcolor=#eee9e9><a title=#eee9e9>snow2</a></td> +<td bgcolor=#cdc9c9><a title=#cdc9c9>snow3</a></td> +<td bgcolor=#8b8989><a title=#8b8989>snow4</a></td> +<td bgcolor=#00ff7f><a title=#00ff7f>springgreen</a></td> +<td bgcolor=#00ff7f><a title=#00ff7f>springgreen1</a></td> +</tr> +<tr align=center> +<td bgcolor=#00ee76><a title=#00ee76>springgreen2</a></td> +<td bgcolor=#00cd66><a title=#00cd66>springgreen3</a></td> +<td bgcolor=#008b45><a title=#008b45>springgreen4</a></td> +<td bgcolor=#4682b4><a title=#4682b4>steelblue</a></td> +<td bgcolor=#63b8ff><a title=#63b8ff>steelblue1</a></td> +</tr> +<tr align=center> +<td bgcolor=#5cacee><a title=#5cacee>steelblue2</a></td> +<td bgcolor=#4f94cd><a title=#4f94cd>steelblue3</a></td> +<td bgcolor=#36648b><a title=#36648b>steelblue4</a></td> +<td bgcolor=#d2b48c><a title=#d2b48c> tan </a></td> +<td bgcolor=#ffa54f><a title=#ffa54f> tan1 </a></td> +</tr> +<tr align=center> +<td bgcolor=#ee9a49><a title=#ee9a49> tan2 </a></td> +<td bgcolor=#cd853f><a title=#cd853f> tan3 </a></td> +<td bgcolor=#8b5a2b><a title=#8b5a2b> tan4 </a></td> +<td bgcolor=#d8bfd8><a title=#d8bfd8>thistle</a></td> +<td bgcolor=#ffe1ff><a title=#ffe1ff>thistle1</a></td> +</tr> +<tr align=center> +<td bgcolor=#eed2ee><a title=#eed2ee>thistle2</a></td> +<td bgcolor=#cdb5cd><a title=#cdb5cd>thistle3</a></td> +<td bgcolor=#8b7b8b><a title=#8b7b8b>thistle4</a></td> +<td bgcolor=#ff6347><a title=#ff6347>tomato</a></td> +<td bgcolor=#ff6347><a title=#ff6347>tomato1</a></td> +</tr> +<tr align=center> +<td bgcolor=#ee5c42><a title=#ee5c42>tomato2</a></td> +<td bgcolor=#cd4f39><a title=#cd4f39>tomato3</a></td> +<td bgcolor=#8b3626><a title=#8b3626>tomato4</a></td> +<td bgcolor=#fffffe><a title=#fffffe>transparent</a></td> +<td bgcolor=#40e0d0><a title=#40e0d0>turquoise</a></td> +</tr> +<tr align=center> +<td bgcolor=#00f5ff><a title=#00f5ff>turquoise1</a></td> +<td bgcolor=#00e5ee><a title=#00e5ee>turquoise2</a></td> +<td bgcolor=#00c5cd><a title=#00c5cd>turquoise3</a></td> +<td bgcolor=#00868b><a title=#00868b>turquoise4</a></td> +<td bgcolor=#ee82ee><a title=#ee82ee>violet</a></td> +</tr> +<tr align=center> +<td bgcolor=#d02090><a title=#d02090>violetred</a></td> +<td bgcolor=#ff3e96><a title=#ff3e96>violetred1</a></td> +<td bgcolor=#ee3a8c><a title=#ee3a8c>violetred2</a></td> +<td bgcolor=#cd3278><a title=#cd3278>violetred3</a></td> +<td bgcolor=#8b2252><a title=#8b2252>violetred4</a></td> +</tr> +<tr align=center> +<td bgcolor=#f5deb3><a title=#f5deb3>wheat</a></td> +<td bgcolor=#ffe7ba><a title=#ffe7ba>wheat1</a></td> +<td bgcolor=#eed8ae><a title=#eed8ae>wheat2</a></td> +<td bgcolor=#cdba96><a title=#cdba96>wheat3</a></td> +<td bgcolor=#8b7e66><a title=#8b7e66>wheat4</a></td> +</tr> +<tr align=center> +<td bgcolor=#ffffff><a title=#ffffff>white</a></td> +<td bgcolor=#f5f5f5><a title=#f5f5f5>whitesmoke</a></td> +<td bgcolor=#ffff00><a title=#ffff00>yellow</a></td> +<td bgcolor=#ffff00><a title=#ffff00>yellow1</a></td> +<td bgcolor=#eeee00><a title=#eeee00>yellow2</a></td> +</tr> +<tr align=center> +<td bgcolor=#cdcd00><a title=#cdcd00>yellow3</a></td> +<td bgcolor=#8b8b00><a title=#8b8b00>yellow4</a></td> +<td bgcolor=#9acd32><a title=#9acd32>yellowgreen</a></td> +</table><HR> +accent3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7fc97f><a title=#7fc97f> 1 </a></td> +<td bgcolor=#beaed4><a title=#beaed4> 2 </a></td> +<td bgcolor=#fdc086><a title=#fdc086> 3 </a></td> +</table><HR> +accent4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7fc97f><a title=#7fc97f> 1 </a></td> +<td bgcolor=#beaed4><a title=#beaed4> 2 </a></td> +<td bgcolor=#fdc086><a title=#fdc086> 3 </a></td> +<td bgcolor=#ffff99><a title=#ffff99> 4 </a></td> +</table><HR> +accent5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7fc97f><a title=#7fc97f> 1 </a></td> +<td bgcolor=#beaed4><a title=#beaed4> 2 </a></td> +<td bgcolor=#fdc086><a title=#fdc086> 3 </a></td> +<td bgcolor=#ffff99><a title=#ffff99> 4 </a></td> +<td bgcolor=#386cb0><a title=#386cb0> 5 </a></td> +</table><HR> +accent6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7fc97f><a title=#7fc97f> 1 </a></td> +<td bgcolor=#beaed4><a title=#beaed4> 2 </a></td> +<td bgcolor=#fdc086><a title=#fdc086> 3 </a></td> +<td bgcolor=#ffff99><a title=#ffff99> 4 </a></td> +<td bgcolor=#386cb0><a title=#386cb0> 5 </a></td> +<td bgcolor=#f0027f><a title=#f0027f> 6 </a></td> +</table><HR> +accent7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7fc97f><a title=#7fc97f> 1 </a></td> +<td bgcolor=#beaed4><a title=#beaed4> 2 </a></td> +<td bgcolor=#fdc086><a title=#fdc086> 3 </a></td> +<td bgcolor=#ffff99><a title=#ffff99> 4 </a></td> +<td bgcolor=#386cb0><a title=#386cb0> 5 </a></td> +<td bgcolor=#f0027f><a title=#f0027f> 6 </a></td> +<td bgcolor=#bf5b17><a title=#bf5b17> 7 </a></td> +</table><HR> +accent8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7fc97f><a title=#7fc97f> 1 </a></td> +<td bgcolor=#beaed4><a title=#beaed4> 2 </a></td> +<td bgcolor=#fdc086><a title=#fdc086> 3 </a></td> +<td bgcolor=#ffff99><a title=#ffff99> 4 </a></td> +<td bgcolor=#386cb0><a title=#386cb0> 5 </a></td> +<td bgcolor=#f0027f><a title=#f0027f> 6 </a></td> +<td bgcolor=#bf5b17><a title=#bf5b17> 7 </a></td> +<td bgcolor=#666666><a title=#666666><font color=white> 8 </font></a></td> +</table><HR> +blues3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#deebf7><a title=#deebf7> 1 </a></td> +<td bgcolor=#9ecae1><a title=#9ecae1> 2 </a></td> +<td bgcolor=#3182bd><a title=#3182bd> 3 </a></td> +</table><HR> +blues4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#eff3ff><a title=#eff3ff> 1 </a></td> +<td bgcolor=#bdd7e7><a title=#bdd7e7> 2 </a></td> +<td bgcolor=#6baed6><a title=#6baed6> 3 </a></td> +<td bgcolor=#2171b5><a title=#2171b5> 4 </a></td> +</table><HR> +blues5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#eff3ff><a title=#eff3ff> 1 </a></td> +<td bgcolor=#bdd7e7><a title=#bdd7e7> 2 </a></td> +<td bgcolor=#6baed6><a title=#6baed6> 3 </a></td> +<td bgcolor=#3182bd><a title=#3182bd> 4 </a></td> +<td bgcolor=#08519c><a title=#08519c> 5 </a></td> +</table><HR> +blues6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#eff3ff><a title=#eff3ff> 1 </a></td> +<td bgcolor=#c6dbef><a title=#c6dbef> 2 </a></td> +<td bgcolor=#9ecae1><a title=#9ecae1> 3 </a></td> +<td bgcolor=#6baed6><a title=#6baed6> 4 </a></td> +<td bgcolor=#3182bd><a title=#3182bd> 5 </a></td> +<td bgcolor=#08519c><a title=#08519c> 6 </a></td> +</table><HR> +blues7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#eff3ff><a title=#eff3ff> 1 </a></td> +<td bgcolor=#c6dbef><a title=#c6dbef> 2 </a></td> +<td bgcolor=#9ecae1><a title=#9ecae1> 3 </a></td> +<td bgcolor=#6baed6><a title=#6baed6> 4 </a></td> +<td bgcolor=#4292c6><a title=#4292c6> 5 </a></td> +<td bgcolor=#2171b5><a title=#2171b5> 6 </a></td> +<td bgcolor=#084594><a title=#084594> 7 </a></td> +</table><HR> +blues8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fbff><a title=#f7fbff> 1 </a></td> +<td bgcolor=#deebf7><a title=#deebf7> 2 </a></td> +<td bgcolor=#c6dbef><a title=#c6dbef> 3 </a></td> +<td bgcolor=#9ecae1><a title=#9ecae1> 4 </a></td> +<td bgcolor=#6baed6><a title=#6baed6> 5 </a></td> +<td bgcolor=#4292c6><a title=#4292c6> 6 </a></td> +<td bgcolor=#2171b5><a title=#2171b5> 7 </a></td> +<td bgcolor=#084594><a title=#084594> 8 </a></td> +</table><HR> +blues9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fbff><a title=#f7fbff> 1 </a></td> +<td bgcolor=#deebf7><a title=#deebf7> 2 </a></td> +<td bgcolor=#c6dbef><a title=#c6dbef> 3 </a></td> +<td bgcolor=#9ecae1><a title=#9ecae1> 4 </a></td> +<td bgcolor=#6baed6><a title=#6baed6> 5 </a></td> +<td bgcolor=#4292c6><a title=#4292c6> 6 </a></td> +<td bgcolor=#2171b5><a title=#2171b5> 7 </a></td> +<td bgcolor=#08519c><a title=#08519c> 8 </a></td> +<td bgcolor=#08306b><a title=#08306b><font color=white> 9 </font></a></td> +</table><HR> +brbg3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d8b365><a title=#d8b365> 1 </a></td> +<td bgcolor=#f5f5f5><a title=#f5f5f5> 2 </a></td> +<td bgcolor=#5ab4ac><a title=#5ab4ac> 3 </a></td> +</table><HR> +brbg4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6611a><a title=#a6611a> 1 </a></td> +<td bgcolor=#dfc27d><a title=#dfc27d> 2 </a></td> +<td bgcolor=#80cdc1><a title=#80cdc1> 3 </a></td> +<td bgcolor=#018571><a title=#018571> 4 </a></td> +</table><HR> +brbg5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6611a><a title=#a6611a> 1 </a></td> +<td bgcolor=#dfc27d><a title=#dfc27d> 2 </a></td> +<td bgcolor=#f5f5f5><a title=#f5f5f5> 3 </a></td> +<td bgcolor=#80cdc1><a title=#80cdc1> 4 </a></td> +<td bgcolor=#018571><a title=#018571> 5 </a></td> +</table><HR> +brbg6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8c510a><a title=#8c510a> 1 </a></td> +<td bgcolor=#d8b365><a title=#d8b365> 2 </a></td> +<td bgcolor=#f6e8c3><a title=#f6e8c3> 3 </a></td> +<td bgcolor=#c7eae5><a title=#c7eae5> 4 </a></td> +<td bgcolor=#5ab4ac><a title=#5ab4ac> 5 </a></td> +<td bgcolor=#01665e><a title=#01665e><font color=white> 6 </font></a></td> +</table><HR> +brbg7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8c510a><a title=#8c510a> 1 </a></td> +<td bgcolor=#d8b365><a title=#d8b365> 2 </a></td> +<td bgcolor=#f6e8c3><a title=#f6e8c3> 3 </a></td> +<td bgcolor=#f5f5f5><a title=#f5f5f5> 4 </a></td> +<td bgcolor=#c7eae5><a title=#c7eae5> 5 </a></td> +<td bgcolor=#5ab4ac><a title=#5ab4ac> 6 </a></td> +<td bgcolor=#01665e><a title=#01665e><font color=white> 7 </font></a></td> +</table><HR> +brbg8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8c510a><a title=#8c510a> 1 </a></td> +<td bgcolor=#bf812d><a title=#bf812d> 2 </a></td> +<td bgcolor=#dfc27d><a title=#dfc27d> 3 </a></td> +<td bgcolor=#f6e8c3><a title=#f6e8c3> 4 </a></td> +<td bgcolor=#c7eae5><a title=#c7eae5> 5 </a></td> +<td bgcolor=#80cdc1><a title=#80cdc1> 6 </a></td> +<td bgcolor=#35978f><a title=#35978f> 7 </a></td> +<td bgcolor=#01665e><a title=#01665e><font color=white> 8 </font></a></td> +</table><HR> +brbg9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8c510a><a title=#8c510a> 1 </a></td> +<td bgcolor=#bf812d><a title=#bf812d> 2 </a></td> +<td bgcolor=#dfc27d><a title=#dfc27d> 3 </a></td> +<td bgcolor=#f6e8c3><a title=#f6e8c3> 4 </a></td> +<td bgcolor=#f5f5f5><a title=#f5f5f5> 5 </a></td> +<td bgcolor=#c7eae5><a title=#c7eae5> 6 </a></td> +<td bgcolor=#80cdc1><a title=#80cdc1> 7 </a></td> +<td bgcolor=#35978f><a title=#35978f> 8 </a></td> +<td bgcolor=#01665e><a title=#01665e><font color=white> 9 </font></a></td> +</table><HR> +brbg10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#543005><a title=#543005><font color=white> 1 </font></a></td> +<td bgcolor=#8c510a><a title=#8c510a> 2 </a></td> +<td bgcolor=#bf812d><a title=#bf812d> 3 </a></td> +<td bgcolor=#dfc27d><a title=#dfc27d> 4 </a></td> +<td bgcolor=#f6e8c3><a title=#f6e8c3> 5 </a></td> +<td bgcolor=#c7eae5><a title=#c7eae5> 6 </a></td> +<td bgcolor=#80cdc1><a title=#80cdc1> 7 </a></td> +<td bgcolor=#35978f><a title=#35978f> 8 </a></td> +<td bgcolor=#01665e><a title=#01665e><font color=white> 9 </font></a></td> +<td bgcolor=#003c30><a title=#003c30><font color=white> 10 </font></a></td> +</table><HR> +brbg11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#543005><a title=#543005><font color=white> 1 </font></a></td> +<td bgcolor=#8c510a><a title=#8c510a> 2 </a></td> +<td bgcolor=#bf812d><a title=#bf812d> 3 </a></td> +<td bgcolor=#dfc27d><a title=#dfc27d> 4 </a></td> +<td bgcolor=#f6e8c3><a title=#f6e8c3> 5 </a></td> +<td bgcolor=#f5f5f5><a title=#f5f5f5> 6 </a></td> +<td bgcolor=#c7eae5><a title=#c7eae5> 7 </a></td> +<td bgcolor=#80cdc1><a title=#80cdc1> 8 </a></td> +<td bgcolor=#35978f><a title=#35978f> 9 </a></td> +<td bgcolor=#01665e><a title=#01665e><font color=white> 10 </font></a></td> +<td bgcolor=#003c30><a title=#003c30><font color=white> 11 </font></a></td> +</table><HR> +bugn3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e5f5f9><a title=#e5f5f9> 1 </a></td> +<td bgcolor=#99d8c9><a title=#99d8c9> 2 </a></td> +<td bgcolor=#2ca25f><a title=#2ca25f> 3 </a></td> +</table><HR> +bugn4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8fb><a title=#edf8fb> 1 </a></td> +<td bgcolor=#b2e2e2><a title=#b2e2e2> 2 </a></td> +<td bgcolor=#66c2a4><a title=#66c2a4> 3 </a></td> +<td bgcolor=#238b45><a title=#238b45> 4 </a></td> +</table><HR> +bugn5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8fb><a title=#edf8fb> 1 </a></td> +<td bgcolor=#b2e2e2><a title=#b2e2e2> 2 </a></td> +<td bgcolor=#66c2a4><a title=#66c2a4> 3 </a></td> +<td bgcolor=#2ca25f><a title=#2ca25f> 4 </a></td> +<td bgcolor=#006d2c><a title=#006d2c><font color=white> 5 </font></a></td> +</table><HR> +bugn6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8fb><a title=#edf8fb> 1 </a></td> +<td bgcolor=#ccece6><a title=#ccece6> 2 </a></td> +<td bgcolor=#99d8c9><a title=#99d8c9> 3 </a></td> +<td bgcolor=#66c2a4><a title=#66c2a4> 4 </a></td> +<td bgcolor=#2ca25f><a title=#2ca25f> 5 </a></td> +<td bgcolor=#006d2c><a title=#006d2c><font color=white> 6 </font></a></td> +</table><HR> +bugn7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8fb><a title=#edf8fb> 1 </a></td> +<td bgcolor=#ccece6><a title=#ccece6> 2 </a></td> +<td bgcolor=#99d8c9><a title=#99d8c9> 3 </a></td> +<td bgcolor=#66c2a4><a title=#66c2a4> 4 </a></td> +<td bgcolor=#41ae76><a title=#41ae76> 5 </a></td> +<td bgcolor=#238b45><a title=#238b45> 6 </a></td> +<td bgcolor=#005824><a title=#005824><font color=white> 7 </font></a></td> +</table><HR> +bugn8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcfd><a title=#f7fcfd> 1 </a></td> +<td bgcolor=#e5f5f9><a title=#e5f5f9> 2 </a></td> +<td bgcolor=#ccece6><a title=#ccece6> 3 </a></td> +<td bgcolor=#99d8c9><a title=#99d8c9> 4 </a></td> +<td bgcolor=#66c2a4><a title=#66c2a4> 5 </a></td> +<td bgcolor=#41ae76><a title=#41ae76> 6 </a></td> +<td bgcolor=#238b45><a title=#238b45> 7 </a></td> +<td bgcolor=#005824><a title=#005824><font color=white> 8 </font></a></td> +</table><HR> +bugn9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcfd><a title=#f7fcfd> 1 </a></td> +<td bgcolor=#e5f5f9><a title=#e5f5f9> 2 </a></td> +<td bgcolor=#ccece6><a title=#ccece6> 3 </a></td> +<td bgcolor=#99d8c9><a title=#99d8c9> 4 </a></td> +<td bgcolor=#66c2a4><a title=#66c2a4> 5 </a></td> +<td bgcolor=#41ae76><a title=#41ae76> 6 </a></td> +<td bgcolor=#238b45><a title=#238b45> 7 </a></td> +<td bgcolor=#006d2c><a title=#006d2c><font color=white> 8 </font></a></td> +<td bgcolor=#00441b><a title=#00441b><font color=white> 9 </font></a></td> +</table><HR> +bupu3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e0ecf4><a title=#e0ecf4> 1 </a></td> +<td bgcolor=#9ebcda><a title=#9ebcda> 2 </a></td> +<td bgcolor=#8856a7><a title=#8856a7> 3 </a></td> +</table><HR> +bupu4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8fb><a title=#edf8fb> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#8c96c6><a title=#8c96c6> 3 </a></td> +<td bgcolor=#88419d><a title=#88419d> 4 </a></td> +</table><HR> +bupu5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8fb><a title=#edf8fb> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#8c96c6><a title=#8c96c6> 3 </a></td> +<td bgcolor=#8856a7><a title=#8856a7> 4 </a></td> +<td bgcolor=#810f7c><a title=#810f7c><font color=white> 5 </font></a></td> +</table><HR> +bupu6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8fb><a title=#edf8fb> 1 </a></td> +<td bgcolor=#bfd3e6><a title=#bfd3e6> 2 </a></td> +<td bgcolor=#9ebcda><a title=#9ebcda> 3 </a></td> +<td bgcolor=#8c96c6><a title=#8c96c6> 4 </a></td> +<td bgcolor=#8856a7><a title=#8856a7> 5 </a></td> +<td bgcolor=#810f7c><a title=#810f7c><font color=white> 6 </font></a></td> +</table><HR> +bupu7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8fb><a title=#edf8fb> 1 </a></td> +<td bgcolor=#bfd3e6><a title=#bfd3e6> 2 </a></td> +<td bgcolor=#9ebcda><a title=#9ebcda> 3 </a></td> +<td bgcolor=#8c96c6><a title=#8c96c6> 4 </a></td> +<td bgcolor=#8c6bb1><a title=#8c6bb1> 5 </a></td> +<td bgcolor=#88419d><a title=#88419d> 6 </a></td> +<td bgcolor=#6e016b><a title=#6e016b><font color=white> 7 </font></a></td> +</table><HR> +bupu8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcfd><a title=#f7fcfd> 1 </a></td> +<td bgcolor=#e0ecf4><a title=#e0ecf4> 2 </a></td> +<td bgcolor=#bfd3e6><a title=#bfd3e6> 3 </a></td> +<td bgcolor=#9ebcda><a title=#9ebcda> 4 </a></td> +<td bgcolor=#8c96c6><a title=#8c96c6> 5 </a></td> +<td bgcolor=#8c6bb1><a title=#8c6bb1> 6 </a></td> +<td bgcolor=#88419d><a title=#88419d> 7 </a></td> +<td bgcolor=#6e016b><a title=#6e016b><font color=white> 8 </font></a></td> +</table><HR> +bupu9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcfd><a title=#f7fcfd> 1 </a></td> +<td bgcolor=#e0ecf4><a title=#e0ecf4> 2 </a></td> +<td bgcolor=#bfd3e6><a title=#bfd3e6> 3 </a></td> +<td bgcolor=#9ebcda><a title=#9ebcda> 4 </a></td> +<td bgcolor=#8c96c6><a title=#8c96c6> 5 </a></td> +<td bgcolor=#8c6bb1><a title=#8c6bb1> 6 </a></td> +<td bgcolor=#88419d><a title=#88419d> 7 </a></td> +<td bgcolor=#810f7c><a title=#810f7c><font color=white> 8 </font></a></td> +<td bgcolor=#4d004b><a title=#4d004b><font color=white> 9 </font></a></td> +</table><HR> +dark23 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#1b9e77><a title=#1b9e77> 1 </a></td> +<td bgcolor=#d95f02><a title=#d95f02> 2 </a></td> +<td bgcolor=#7570b3><a title=#7570b3> 3 </a></td> +</table><HR> +dark24 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#1b9e77><a title=#1b9e77> 1 </a></td> +<td bgcolor=#d95f02><a title=#d95f02> 2 </a></td> +<td bgcolor=#7570b3><a title=#7570b3> 3 </a></td> +<td bgcolor=#e7298a><a title=#e7298a> 4 </a></td> +</table><HR> +dark25 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#1b9e77><a title=#1b9e77> 1 </a></td> +<td bgcolor=#d95f02><a title=#d95f02> 2 </a></td> +<td bgcolor=#7570b3><a title=#7570b3> 3 </a></td> +<td bgcolor=#e7298a><a title=#e7298a> 4 </a></td> +<td bgcolor=#66a61e><a title=#66a61e> 5 </a></td> +</table><HR> +dark26 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#1b9e77><a title=#1b9e77> 1 </a></td> +<td bgcolor=#d95f02><a title=#d95f02> 2 </a></td> +<td bgcolor=#7570b3><a title=#7570b3> 3 </a></td> +<td bgcolor=#e7298a><a title=#e7298a> 4 </a></td> +<td bgcolor=#66a61e><a title=#66a61e> 5 </a></td> +<td bgcolor=#e6ab02><a title=#e6ab02> 6 </a></td> +</table><HR> +dark27 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#1b9e77><a title=#1b9e77> 1 </a></td> +<td bgcolor=#d95f02><a title=#d95f02> 2 </a></td> +<td bgcolor=#7570b3><a title=#7570b3> 3 </a></td> +<td bgcolor=#e7298a><a title=#e7298a> 4 </a></td> +<td bgcolor=#66a61e><a title=#66a61e> 5 </a></td> +<td bgcolor=#e6ab02><a title=#e6ab02> 6 </a></td> +<td bgcolor=#a6761d><a title=#a6761d> 7 </a></td> +</table><HR> +dark28 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#1b9e77><a title=#1b9e77> 1 </a></td> +<td bgcolor=#d95f02><a title=#d95f02> 2 </a></td> +<td bgcolor=#7570b3><a title=#7570b3> 3 </a></td> +<td bgcolor=#e7298a><a title=#e7298a> 4 </a></td> +<td bgcolor=#66a61e><a title=#66a61e> 5 </a></td> +<td bgcolor=#e6ab02><a title=#e6ab02> 6 </a></td> +<td bgcolor=#a6761d><a title=#a6761d> 7 </a></td> +<td bgcolor=#666666><a title=#666666><font color=white> 8 </font></a></td> +</table><HR> +gnbu3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e0f3db><a title=#e0f3db> 1 </a></td> +<td bgcolor=#a8ddb5><a title=#a8ddb5> 2 </a></td> +<td bgcolor=#43a2ca><a title=#43a2ca> 3 </a></td> +</table><HR> +gnbu4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f0f9e8><a title=#f0f9e8> 1 </a></td> +<td bgcolor=#bae4bc><a title=#bae4bc> 2 </a></td> +<td bgcolor=#7bccc4><a title=#7bccc4> 3 </a></td> +<td bgcolor=#2b8cbe><a title=#2b8cbe> 4 </a></td> +</table><HR> +gnbu5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f0f9e8><a title=#f0f9e8> 1 </a></td> +<td bgcolor=#bae4bc><a title=#bae4bc> 2 </a></td> +<td bgcolor=#7bccc4><a title=#7bccc4> 3 </a></td> +<td bgcolor=#43a2ca><a title=#43a2ca> 4 </a></td> +<td bgcolor=#0868ac><a title=#0868ac> 5 </a></td> +</table><HR> +gnbu6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f0f9e8><a title=#f0f9e8> 1 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 2 </a></td> +<td bgcolor=#a8ddb5><a title=#a8ddb5> 3 </a></td> +<td bgcolor=#7bccc4><a title=#7bccc4> 4 </a></td> +<td bgcolor=#43a2ca><a title=#43a2ca> 5 </a></td> +<td bgcolor=#0868ac><a title=#0868ac> 6 </a></td> +</table><HR> +gnbu7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f0f9e8><a title=#f0f9e8> 1 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 2 </a></td> +<td bgcolor=#a8ddb5><a title=#a8ddb5> 3 </a></td> +<td bgcolor=#7bccc4><a title=#7bccc4> 4 </a></td> +<td bgcolor=#4eb3d3><a title=#4eb3d3> 5 </a></td> +<td bgcolor=#2b8cbe><a title=#2b8cbe> 6 </a></td> +<td bgcolor=#08589e><a title=#08589e> 7 </a></td> +</table><HR> +gnbu8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcf0><a title=#f7fcf0> 1 </a></td> +<td bgcolor=#e0f3db><a title=#e0f3db> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +<td bgcolor=#a8ddb5><a title=#a8ddb5> 4 </a></td> +<td bgcolor=#7bccc4><a title=#7bccc4> 5 </a></td> +<td bgcolor=#4eb3d3><a title=#4eb3d3> 6 </a></td> +<td bgcolor=#2b8cbe><a title=#2b8cbe> 7 </a></td> +<td bgcolor=#08589e><a title=#08589e> 8 </a></td> +</table><HR> +gnbu9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcf0><a title=#f7fcf0> 1 </a></td> +<td bgcolor=#e0f3db><a title=#e0f3db> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +<td bgcolor=#a8ddb5><a title=#a8ddb5> 4 </a></td> +<td bgcolor=#7bccc4><a title=#7bccc4> 5 </a></td> +<td bgcolor=#4eb3d3><a title=#4eb3d3> 6 </a></td> +<td bgcolor=#2b8cbe><a title=#2b8cbe> 7 </a></td> +<td bgcolor=#0868ac><a title=#0868ac> 8 </a></td> +<td bgcolor=#084081><a title=#084081><font color=white> 9 </font></a></td> +</table><HR> +greens3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e5f5e0><a title=#e5f5e0> 1 </a></td> +<td bgcolor=#a1d99b><a title=#a1d99b> 2 </a></td> +<td bgcolor=#31a354><a title=#31a354> 3 </a></td> +</table><HR> +greens4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8e9><a title=#edf8e9> 1 </a></td> +<td bgcolor=#bae4b3><a title=#bae4b3> 2 </a></td> +<td bgcolor=#74c476><a title=#74c476> 3 </a></td> +<td bgcolor=#238b45><a title=#238b45> 4 </a></td> +</table><HR> +greens5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8e9><a title=#edf8e9> 1 </a></td> +<td bgcolor=#bae4b3><a title=#bae4b3> 2 </a></td> +<td bgcolor=#74c476><a title=#74c476> 3 </a></td> +<td bgcolor=#31a354><a title=#31a354> 4 </a></td> +<td bgcolor=#006d2c><a title=#006d2c><font color=white> 5 </font></a></td> +</table><HR> +greens6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8e9><a title=#edf8e9> 1 </a></td> +<td bgcolor=#c7e9c0><a title=#c7e9c0> 2 </a></td> +<td bgcolor=#a1d99b><a title=#a1d99b> 3 </a></td> +<td bgcolor=#74c476><a title=#74c476> 4 </a></td> +<td bgcolor=#31a354><a title=#31a354> 5 </a></td> +<td bgcolor=#006d2c><a title=#006d2c><font color=white> 6 </font></a></td> +</table><HR> +greens7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8e9><a title=#edf8e9> 1 </a></td> +<td bgcolor=#c7e9c0><a title=#c7e9c0> 2 </a></td> +<td bgcolor=#a1d99b><a title=#a1d99b> 3 </a></td> +<td bgcolor=#74c476><a title=#74c476> 4 </a></td> +<td bgcolor=#41ab5d><a title=#41ab5d> 5 </a></td> +<td bgcolor=#238b45><a title=#238b45> 6 </a></td> +<td bgcolor=#005a32><a title=#005a32><font color=white> 7 </font></a></td> +</table><HR> +greens8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcf5><a title=#f7fcf5> 1 </a></td> +<td bgcolor=#e5f5e0><a title=#e5f5e0> 2 </a></td> +<td bgcolor=#c7e9c0><a title=#c7e9c0> 3 </a></td> +<td bgcolor=#a1d99b><a title=#a1d99b> 4 </a></td> +<td bgcolor=#74c476><a title=#74c476> 5 </a></td> +<td bgcolor=#41ab5d><a title=#41ab5d> 6 </a></td> +<td bgcolor=#238b45><a title=#238b45> 7 </a></td> +<td bgcolor=#005a32><a title=#005a32><font color=white> 8 </font></a></td> +</table><HR> +greens9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcf5><a title=#f7fcf5> 1 </a></td> +<td bgcolor=#e5f5e0><a title=#e5f5e0> 2 </a></td> +<td bgcolor=#c7e9c0><a title=#c7e9c0> 3 </a></td> +<td bgcolor=#a1d99b><a title=#a1d99b> 4 </a></td> +<td bgcolor=#74c476><a title=#74c476> 5 </a></td> +<td bgcolor=#41ab5d><a title=#41ab5d> 6 </a></td> +<td bgcolor=#238b45><a title=#238b45> 7 </a></td> +<td bgcolor=#006d2c><a title=#006d2c><font color=white> 8 </font></a></td> +<td bgcolor=#00441b><a title=#00441b><font color=white> 9 </font></a></td> +</table><HR> +greys3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f0f0f0><a title=#f0f0f0> 1 </a></td> +<td bgcolor=#bdbdbd><a title=#bdbdbd> 2 </a></td> +<td bgcolor=#636363><a title=#636363><font color=white> 3 </font></a></td> +</table><HR> +greys4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 1 </a></td> +<td bgcolor=#cccccc><a title=#cccccc> 2 </a></td> +<td bgcolor=#969696><a title=#969696> 3 </a></td> +<td bgcolor=#525252><a title=#525252><font color=white> 4 </font></a></td> +</table><HR> +greys5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 1 </a></td> +<td bgcolor=#cccccc><a title=#cccccc> 2 </a></td> +<td bgcolor=#969696><a title=#969696> 3 </a></td> +<td bgcolor=#636363><a title=#636363><font color=white> 4 </font></a></td> +<td bgcolor=#252525><a title=#252525><font color=white> 5 </font></a></td> +</table><HR> +greys6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 1 </a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9> 2 </a></td> +<td bgcolor=#bdbdbd><a title=#bdbdbd> 3 </a></td> +<td bgcolor=#969696><a title=#969696> 4 </a></td> +<td bgcolor=#636363><a title=#636363><font color=white> 5 </font></a></td> +<td bgcolor=#252525><a title=#252525><font color=white> 6 </font></a></td> +</table><HR> +greys7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 1 </a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9> 2 </a></td> +<td bgcolor=#bdbdbd><a title=#bdbdbd> 3 </a></td> +<td bgcolor=#969696><a title=#969696> 4 </a></td> +<td bgcolor=#737373><a title=#737373><font color=white> 5 </font></a></td> +<td bgcolor=#525252><a title=#525252><font color=white> 6 </font></a></td> +<td bgcolor=#252525><a title=#252525><font color=white> 7 </font></a></td> +</table><HR> +greys8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffff><a title=#ffffff> 1 </a></td> +<td bgcolor=#f0f0f0><a title=#f0f0f0> 2 </a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9> 3 </a></td> +<td bgcolor=#bdbdbd><a title=#bdbdbd> 4 </a></td> +<td bgcolor=#969696><a title=#969696> 5 </a></td> +<td bgcolor=#737373><a title=#737373><font color=white> 6 </font></a></td> +<td bgcolor=#525252><a title=#525252><font color=white> 7 </font></a></td> +<td bgcolor=#252525><a title=#252525><font color=white> 8 </font></a></td> +</table><HR> +greys9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffff><a title=#ffffff> 1 </a></td> +<td bgcolor=#f0f0f0><a title=#f0f0f0> 2 </a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9> 3 </a></td> +<td bgcolor=#bdbdbd><a title=#bdbdbd> 4 </a></td> +<td bgcolor=#969696><a title=#969696> 5 </a></td> +<td bgcolor=#737373><a title=#737373><font color=white> 6 </font></a></td> +<td bgcolor=#525252><a title=#525252><font color=white> 7 </font></a></td> +<td bgcolor=#252525><a title=#252525><font color=white> 8 </font></a></td> +<td bgcolor=#000000><a title=#000000><font color=white> 9 </font></a></td> +</table><HR> +oranges3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fee6ce><a title=#fee6ce> 1 </a></td> +<td bgcolor=#fdae6b><a title=#fdae6b> 2 </a></td> +<td bgcolor=#e6550d><a title=#e6550d> 3 </a></td> +</table><HR> +oranges4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#feedde><a title=#feedde> 1 </a></td> +<td bgcolor=#fdbe85><a title=#fdbe85> 2 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 3 </a></td> +<td bgcolor=#d94701><a title=#d94701> 4 </a></td> +</table><HR> +oranges5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#feedde><a title=#feedde> 1 </a></td> +<td bgcolor=#fdbe85><a title=#fdbe85> 2 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 3 </a></td> +<td bgcolor=#e6550d><a title=#e6550d> 4 </a></td> +<td bgcolor=#a63603><a title=#a63603> 5 </a></td> +</table><HR> +oranges6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#feedde><a title=#feedde> 1 </a></td> +<td bgcolor=#fdd0a2><a title=#fdd0a2> 2 </a></td> +<td bgcolor=#fdae6b><a title=#fdae6b> 3 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 4 </a></td> +<td bgcolor=#e6550d><a title=#e6550d> 5 </a></td> +<td bgcolor=#a63603><a title=#a63603> 6 </a></td> +</table><HR> +oranges7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#feedde><a title=#feedde> 1 </a></td> +<td bgcolor=#fdd0a2><a title=#fdd0a2> 2 </a></td> +<td bgcolor=#fdae6b><a title=#fdae6b> 3 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 4 </a></td> +<td bgcolor=#f16913><a title=#f16913> 5 </a></td> +<td bgcolor=#d94801><a title=#d94801> 6 </a></td> +<td bgcolor=#8c2d04><a title=#8c2d04> 7 </a></td> +</table><HR> +oranges8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff5eb><a title=#fff5eb> 1 </a></td> +<td bgcolor=#fee6ce><a title=#fee6ce> 2 </a></td> +<td bgcolor=#fdd0a2><a title=#fdd0a2> 3 </a></td> +<td bgcolor=#fdae6b><a title=#fdae6b> 4 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 5 </a></td> +<td bgcolor=#f16913><a title=#f16913> 6 </a></td> +<td bgcolor=#d94801><a title=#d94801> 7 </a></td> +<td bgcolor=#8c2d04><a title=#8c2d04> 8 </a></td> +</table><HR> +oranges9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff5eb><a title=#fff5eb> 1 </a></td> +<td bgcolor=#fee6ce><a title=#fee6ce> 2 </a></td> +<td bgcolor=#fdd0a2><a title=#fdd0a2> 3 </a></td> +<td bgcolor=#fdae6b><a title=#fdae6b> 4 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 5 </a></td> +<td bgcolor=#f16913><a title=#f16913> 6 </a></td> +<td bgcolor=#d94801><a title=#d94801> 7 </a></td> +<td bgcolor=#a63603><a title=#a63603> 8 </a></td> +<td bgcolor=#7f2704><a title=#7f2704><font color=white> 9 </font></a></td> +</table><HR> +orrd3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fee8c8><a title=#fee8c8> 1 </a></td> +<td bgcolor=#fdbb84><a title=#fdbb84> 2 </a></td> +<td bgcolor=#e34a33><a title=#e34a33> 3 </a></td> +</table><HR> +orrd4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fef0d9><a title=#fef0d9> 1 </a></td> +<td bgcolor=#fdcc8a><a title=#fdcc8a> 2 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 3 </a></td> +<td bgcolor=#d7301f><a title=#d7301f> 4 </a></td> +</table><HR> +orrd5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fef0d9><a title=#fef0d9> 1 </a></td> +<td bgcolor=#fdcc8a><a title=#fdcc8a> 2 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 3 </a></td> +<td bgcolor=#e34a33><a title=#e34a33> 4 </a></td> +<td bgcolor=#b30000><a title=#b30000> 5 </a></td> +</table><HR> +orrd6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fef0d9><a title=#fef0d9> 1 </a></td> +<td bgcolor=#fdd49e><a title=#fdd49e> 2 </a></td> +<td bgcolor=#fdbb84><a title=#fdbb84> 3 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 4 </a></td> +<td bgcolor=#e34a33><a title=#e34a33> 5 </a></td> +<td bgcolor=#b30000><a title=#b30000> 6 </a></td> +</table><HR> +orrd7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fef0d9><a title=#fef0d9> 1 </a></td> +<td bgcolor=#fdd49e><a title=#fdd49e> 2 </a></td> +<td bgcolor=#fdbb84><a title=#fdbb84> 3 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 4 </a></td> +<td bgcolor=#ef6548><a title=#ef6548> 5 </a></td> +<td bgcolor=#d7301f><a title=#d7301f> 6 </a></td> +<td bgcolor=#990000><a title=#990000> 7 </a></td> +</table><HR> +orrd8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7ec><a title=#fff7ec> 1 </a></td> +<td bgcolor=#fee8c8><a title=#fee8c8> 2 </a></td> +<td bgcolor=#fdd49e><a title=#fdd49e> 3 </a></td> +<td bgcolor=#fdbb84><a title=#fdbb84> 4 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 5 </a></td> +<td bgcolor=#ef6548><a title=#ef6548> 6 </a></td> +<td bgcolor=#d7301f><a title=#d7301f> 7 </a></td> +<td bgcolor=#990000><a title=#990000> 8 </a></td> +</table><HR> +orrd9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7ec><a title=#fff7ec> 1 </a></td> +<td bgcolor=#fee8c8><a title=#fee8c8> 2 </a></td> +<td bgcolor=#fdd49e><a title=#fdd49e> 3 </a></td> +<td bgcolor=#fdbb84><a title=#fdbb84> 4 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 5 </a></td> +<td bgcolor=#ef6548><a title=#ef6548> 6 </a></td> +<td bgcolor=#d7301f><a title=#d7301f> 7 </a></td> +<td bgcolor=#b30000><a title=#b30000> 8 </a></td> +<td bgcolor=#7f0000><a title=#7f0000><font color=white> 9 </font></a></td> +</table><HR> +paired3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +</table><HR> +paired4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +</table><HR> +paired5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +<td bgcolor=#fb9a99><a title=#fb9a99> 5 </a></td> +</table><HR> +paired6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +<td bgcolor=#fb9a99><a title=#fb9a99> 5 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 6 </a></td> +</table><HR> +paired7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +<td bgcolor=#fb9a99><a title=#fb9a99> 5 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 6 </a></td> +<td bgcolor=#fdbf6f><a title=#fdbf6f> 7 </a></td> +</table><HR> +paired8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +<td bgcolor=#fb9a99><a title=#fb9a99> 5 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 6 </a></td> +<td bgcolor=#fdbf6f><a title=#fdbf6f> 7 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 8 </a></td> +</table><HR> +paired9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +<td bgcolor=#fb9a99><a title=#fb9a99> 5 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 6 </a></td> +<td bgcolor=#fdbf6f><a title=#fdbf6f> 7 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 8 </a></td> +<td bgcolor=#cab2d6><a title=#cab2d6> 9 </a></td> +</table><HR> +paired10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +<td bgcolor=#fb9a99><a title=#fb9a99> 5 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 6 </a></td> +<td bgcolor=#fdbf6f><a title=#fdbf6f> 7 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 8 </a></td> +<td bgcolor=#cab2d6><a title=#cab2d6> 9 </a></td> +<td bgcolor=#6a3d9a><a title=#6a3d9a> 10 </a></td> +</table><HR> +paired11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +<td bgcolor=#fb9a99><a title=#fb9a99> 5 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 6 </a></td> +<td bgcolor=#fdbf6f><a title=#fdbf6f> 7 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 8 </a></td> +<td bgcolor=#cab2d6><a title=#cab2d6> 9 </a></td> +<td bgcolor=#6a3d9a><a title=#6a3d9a> 10 </a></td> +<td bgcolor=#ffff99><a title=#ffff99> 11 </a></td> +</table><HR> +paired12 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a6cee3><a title=#a6cee3> 1 </a></td> +<td bgcolor=#1f78b4><a title=#1f78b4> 2 </a></td> +<td bgcolor=#b2df8a><a title=#b2df8a> 3 </a></td> +<td bgcolor=#33a02c><a title=#33a02c> 4 </a></td> +<td bgcolor=#fb9a99><a title=#fb9a99> 5 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 6 </a></td> +<td bgcolor=#fdbf6f><a title=#fdbf6f> 7 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 8 </a></td> +<td bgcolor=#cab2d6><a title=#cab2d6> 9 </a></td> +<td bgcolor=#6a3d9a><a title=#6a3d9a> 10 </a></td> +<td bgcolor=#ffff99><a title=#ffff99> 11 </a></td> +<td bgcolor=#b15928><a title=#b15928> 12 </a></td> +</table><HR> +pastel13 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fbb4ae><a title=#fbb4ae> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +</table><HR> +pastel14 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fbb4ae><a title=#fbb4ae> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +<td bgcolor=#decbe4><a title=#decbe4> 4 </a></td> +</table><HR> +pastel15 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fbb4ae><a title=#fbb4ae> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +<td bgcolor=#decbe4><a title=#decbe4> 4 </a></td> +<td bgcolor=#fed9a6><a title=#fed9a6> 5 </a></td> +</table><HR> +pastel16 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fbb4ae><a title=#fbb4ae> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +<td bgcolor=#decbe4><a title=#decbe4> 4 </a></td> +<td bgcolor=#fed9a6><a title=#fed9a6> 5 </a></td> +<td bgcolor=#ffffcc><a title=#ffffcc> 6 </a></td> +</table><HR> +pastel17 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fbb4ae><a title=#fbb4ae> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +<td bgcolor=#decbe4><a title=#decbe4> 4 </a></td> +<td bgcolor=#fed9a6><a title=#fed9a6> 5 </a></td> +<td bgcolor=#ffffcc><a title=#ffffcc> 6 </a></td> +<td bgcolor=#e5d8bd><a title=#e5d8bd> 7 </a></td> +</table><HR> +pastel18 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fbb4ae><a title=#fbb4ae> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +<td bgcolor=#decbe4><a title=#decbe4> 4 </a></td> +<td bgcolor=#fed9a6><a title=#fed9a6> 5 </a></td> +<td bgcolor=#ffffcc><a title=#ffffcc> 6 </a></td> +<td bgcolor=#e5d8bd><a title=#e5d8bd> 7 </a></td> +<td bgcolor=#fddaec><a title=#fddaec> 8 </a></td> +</table><HR> +pastel19 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fbb4ae><a title=#fbb4ae> 1 </a></td> +<td bgcolor=#b3cde3><a title=#b3cde3> 2 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 3 </a></td> +<td bgcolor=#decbe4><a title=#decbe4> 4 </a></td> +<td bgcolor=#fed9a6><a title=#fed9a6> 5 </a></td> +<td bgcolor=#ffffcc><a title=#ffffcc> 6 </a></td> +<td bgcolor=#e5d8bd><a title=#e5d8bd> 7 </a></td> +<td bgcolor=#fddaec><a title=#fddaec> 8 </a></td> +<td bgcolor=#f2f2f2><a title=#f2f2f2> 9 </a></td> +</table><HR> +pastel23 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b3e2cd><a title=#b3e2cd> 1 </a></td> +<td bgcolor=#fdcdac><a title=#fdcdac> 2 </a></td> +<td bgcolor=#cbd5e8><a title=#cbd5e8> 3 </a></td> +</table><HR> +pastel24 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b3e2cd><a title=#b3e2cd> 1 </a></td> +<td bgcolor=#fdcdac><a title=#fdcdac> 2 </a></td> +<td bgcolor=#cbd5e8><a title=#cbd5e8> 3 </a></td> +<td bgcolor=#f4cae4><a title=#f4cae4> 4 </a></td> +</table><HR> +pastel25 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b3e2cd><a title=#b3e2cd> 1 </a></td> +<td bgcolor=#fdcdac><a title=#fdcdac> 2 </a></td> +<td bgcolor=#cbd5e8><a title=#cbd5e8> 3 </a></td> +<td bgcolor=#f4cae4><a title=#f4cae4> 4 </a></td> +<td bgcolor=#e6f5c9><a title=#e6f5c9> 5 </a></td> +</table><HR> +pastel26 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b3e2cd><a title=#b3e2cd> 1 </a></td> +<td bgcolor=#fdcdac><a title=#fdcdac> 2 </a></td> +<td bgcolor=#cbd5e8><a title=#cbd5e8> 3 </a></td> +<td bgcolor=#f4cae4><a title=#f4cae4> 4 </a></td> +<td bgcolor=#e6f5c9><a title=#e6f5c9> 5 </a></td> +<td bgcolor=#fff2ae><a title=#fff2ae> 6 </a></td> +</table><HR> +pastel27 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b3e2cd><a title=#b3e2cd> 1 </a></td> +<td bgcolor=#fdcdac><a title=#fdcdac> 2 </a></td> +<td bgcolor=#cbd5e8><a title=#cbd5e8> 3 </a></td> +<td bgcolor=#f4cae4><a title=#f4cae4> 4 </a></td> +<td bgcolor=#e6f5c9><a title=#e6f5c9> 5 </a></td> +<td bgcolor=#fff2ae><a title=#fff2ae> 6 </a></td> +<td bgcolor=#f1e2cc><a title=#f1e2cc> 7 </a></td> +</table><HR> +pastel28 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b3e2cd><a title=#b3e2cd> 1 </a></td> +<td bgcolor=#fdcdac><a title=#fdcdac> 2 </a></td> +<td bgcolor=#cbd5e8><a title=#cbd5e8> 3 </a></td> +<td bgcolor=#f4cae4><a title=#f4cae4> 4 </a></td> +<td bgcolor=#e6f5c9><a title=#e6f5c9> 5 </a></td> +<td bgcolor=#fff2ae><a title=#fff2ae> 6 </a></td> +<td bgcolor=#f1e2cc><a title=#f1e2cc> 7 </a></td> +<td bgcolor=#cccccc><a title=#cccccc> 8 </a></td> +</table><HR> +piyg3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e9a3c9><a title=#e9a3c9> 1 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 2 </a></td> +<td bgcolor=#a1d76a><a title=#a1d76a> 3 </a></td> +</table><HR> +piyg4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d01c8b><a title=#d01c8b> 1 </a></td> +<td bgcolor=#f1b6da><a title=#f1b6da> 2 </a></td> +<td bgcolor=#b8e186><a title=#b8e186> 3 </a></td> +<td bgcolor=#4dac26><a title=#4dac26> 4 </a></td> +</table><HR> +piyg5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d01c8b><a title=#d01c8b> 1 </a></td> +<td bgcolor=#f1b6da><a title=#f1b6da> 2 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 3 </a></td> +<td bgcolor=#b8e186><a title=#b8e186> 4 </a></td> +<td bgcolor=#4dac26><a title=#4dac26> 5 </a></td> +</table><HR> +piyg6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#c51b7d><a title=#c51b7d> 1 </a></td> +<td bgcolor=#e9a3c9><a title=#e9a3c9> 2 </a></td> +<td bgcolor=#fde0ef><a title=#fde0ef> 3 </a></td> +<td bgcolor=#e6f5d0><a title=#e6f5d0> 4 </a></td> +<td bgcolor=#a1d76a><a title=#a1d76a> 5 </a></td> +<td bgcolor=#4d9221><a title=#4d9221> 6 </a></td> +</table><HR> +piyg7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#c51b7d><a title=#c51b7d> 1 </a></td> +<td bgcolor=#e9a3c9><a title=#e9a3c9> 2 </a></td> +<td bgcolor=#fde0ef><a title=#fde0ef> 3 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 4 </a></td> +<td bgcolor=#e6f5d0><a title=#e6f5d0> 5 </a></td> +<td bgcolor=#a1d76a><a title=#a1d76a> 6 </a></td> +<td bgcolor=#4d9221><a title=#4d9221> 7 </a></td> +</table><HR> +piyg8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#c51b7d><a title=#c51b7d> 1 </a></td> +<td bgcolor=#de77ae><a title=#de77ae> 2 </a></td> +<td bgcolor=#f1b6da><a title=#f1b6da> 3 </a></td> +<td bgcolor=#fde0ef><a title=#fde0ef> 4 </a></td> +<td bgcolor=#e6f5d0><a title=#e6f5d0> 5 </a></td> +<td bgcolor=#b8e186><a title=#b8e186> 6 </a></td> +<td bgcolor=#7fbc41><a title=#7fbc41> 7 </a></td> +<td bgcolor=#4d9221><a title=#4d9221> 8 </a></td> +</table><HR> +piyg9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#c51b7d><a title=#c51b7d> 1 </a></td> +<td bgcolor=#de77ae><a title=#de77ae> 2 </a></td> +<td bgcolor=#f1b6da><a title=#f1b6da> 3 </a></td> +<td bgcolor=#fde0ef><a title=#fde0ef> 4 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 5 </a></td> +<td bgcolor=#e6f5d0><a title=#e6f5d0> 6 </a></td> +<td bgcolor=#b8e186><a title=#b8e186> 7 </a></td> +<td bgcolor=#7fbc41><a title=#7fbc41> 8 </a></td> +<td bgcolor=#4d9221><a title=#4d9221> 9 </a></td> +</table><HR> +piyg10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8e0152><a title=#8e0152> 1 </a></td> +<td bgcolor=#c51b7d><a title=#c51b7d> 2 </a></td> +<td bgcolor=#de77ae><a title=#de77ae> 3 </a></td> +<td bgcolor=#f1b6da><a title=#f1b6da> 4 </a></td> +<td bgcolor=#fde0ef><a title=#fde0ef> 5 </a></td> +<td bgcolor=#e6f5d0><a title=#e6f5d0> 6 </a></td> +<td bgcolor=#b8e186><a title=#b8e186> 7 </a></td> +<td bgcolor=#7fbc41><a title=#7fbc41> 8 </a></td> +<td bgcolor=#4d9221><a title=#4d9221> 9 </a></td> +<td bgcolor=#276419><a title=#276419><font color=white> 10 </font></a></td> +</table><HR> +piyg11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8e0152><a title=#8e0152> 1 </a></td> +<td bgcolor=#c51b7d><a title=#c51b7d> 2 </a></td> +<td bgcolor=#de77ae><a title=#de77ae> 3 </a></td> +<td bgcolor=#f1b6da><a title=#f1b6da> 4 </a></td> +<td bgcolor=#fde0ef><a title=#fde0ef> 5 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 6 </a></td> +<td bgcolor=#e6f5d0><a title=#e6f5d0> 7 </a></td> +<td bgcolor=#b8e186><a title=#b8e186> 8 </a></td> +<td bgcolor=#7fbc41><a title=#7fbc41> 9 </a></td> +<td bgcolor=#4d9221><a title=#4d9221> 10 </a></td> +<td bgcolor=#276419><a title=#276419><font color=white> 11 </font></a></td> +</table><HR> +prgn3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#af8dc3><a title=#af8dc3> 1 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 2 </a></td> +<td bgcolor=#7fbf7b><a title=#7fbf7b> 3 </a></td> +</table><HR> +prgn4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7b3294><a title=#7b3294> 1 </a></td> +<td bgcolor=#c2a5cf><a title=#c2a5cf> 2 </a></td> +<td bgcolor=#a6dba0><a title=#a6dba0> 3 </a></td> +<td bgcolor=#008837><a title=#008837> 4 </a></td> +</table><HR> +prgn5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7b3294><a title=#7b3294> 1 </a></td> +<td bgcolor=#c2a5cf><a title=#c2a5cf> 2 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 3 </a></td> +<td bgcolor=#a6dba0><a title=#a6dba0> 4 </a></td> +<td bgcolor=#008837><a title=#008837> 5 </a></td> +</table><HR> +prgn6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#762a83><a title=#762a83> 1 </a></td> +<td bgcolor=#af8dc3><a title=#af8dc3> 2 </a></td> +<td bgcolor=#e7d4e8><a title=#e7d4e8> 3 </a></td> +<td bgcolor=#d9f0d3><a title=#d9f0d3> 4 </a></td> +<td bgcolor=#7fbf7b><a title=#7fbf7b> 5 </a></td> +<td bgcolor=#1b7837><a title=#1b7837><font color=white> 6 </font></a></td> +</table><HR> +prgn7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#762a83><a title=#762a83> 1 </a></td> +<td bgcolor=#af8dc3><a title=#af8dc3> 2 </a></td> +<td bgcolor=#e7d4e8><a title=#e7d4e8> 3 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 4 </a></td> +<td bgcolor=#d9f0d3><a title=#d9f0d3> 5 </a></td> +<td bgcolor=#7fbf7b><a title=#7fbf7b> 6 </a></td> +<td bgcolor=#1b7837><a title=#1b7837><font color=white> 7 </font></a></td> +</table><HR> +prgn8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#762a83><a title=#762a83> 1 </a></td> +<td bgcolor=#9970ab><a title=#9970ab> 2 </a></td> +<td bgcolor=#c2a5cf><a title=#c2a5cf> 3 </a></td> +<td bgcolor=#e7d4e8><a title=#e7d4e8> 4 </a></td> +<td bgcolor=#d9f0d3><a title=#d9f0d3> 5 </a></td> +<td bgcolor=#a6dba0><a title=#a6dba0> 6 </a></td> +<td bgcolor=#5aae61><a title=#5aae61> 7 </a></td> +<td bgcolor=#1b7837><a title=#1b7837><font color=white> 8 </font></a></td> +</table><HR> +prgn9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#762a83><a title=#762a83> 1 </a></td> +<td bgcolor=#9970ab><a title=#9970ab> 2 </a></td> +<td bgcolor=#c2a5cf><a title=#c2a5cf> 3 </a></td> +<td bgcolor=#e7d4e8><a title=#e7d4e8> 4 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 5 </a></td> +<td bgcolor=#d9f0d3><a title=#d9f0d3> 6 </a></td> +<td bgcolor=#a6dba0><a title=#a6dba0> 7 </a></td> +<td bgcolor=#5aae61><a title=#5aae61> 8 </a></td> +<td bgcolor=#1b7837><a title=#1b7837><font color=white> 9 </font></a></td> +</table><HR> +prgn10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#40004b><a title=#40004b><font color=white> 1 </font></a></td> +<td bgcolor=#762a83><a title=#762a83> 2 </a></td> +<td bgcolor=#9970ab><a title=#9970ab> 3 </a></td> +<td bgcolor=#c2a5cf><a title=#c2a5cf> 4 </a></td> +<td bgcolor=#e7d4e8><a title=#e7d4e8> 5 </a></td> +<td bgcolor=#d9f0d3><a title=#d9f0d3> 6 </a></td> +<td bgcolor=#a6dba0><a title=#a6dba0> 7 </a></td> +<td bgcolor=#5aae61><a title=#5aae61> 8 </a></td> +<td bgcolor=#1b7837><a title=#1b7837><font color=white> 9 </font></a></td> +<td bgcolor=#00441b><a title=#00441b><font color=white> 10 </font></a></td> +</table><HR> +prgn11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#40004b><a title=#40004b><font color=white> 1 </font></a></td> +<td bgcolor=#762a83><a title=#762a83> 2 </a></td> +<td bgcolor=#9970ab><a title=#9970ab> 3 </a></td> +<td bgcolor=#c2a5cf><a title=#c2a5cf> 4 </a></td> +<td bgcolor=#e7d4e8><a title=#e7d4e8> 5 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 6 </a></td> +<td bgcolor=#d9f0d3><a title=#d9f0d3> 7 </a></td> +<td bgcolor=#a6dba0><a title=#a6dba0> 8 </a></td> +<td bgcolor=#5aae61><a title=#5aae61> 9 </a></td> +<td bgcolor=#1b7837><a title=#1b7837><font color=white> 10 </font></a></td> +<td bgcolor=#00441b><a title=#00441b><font color=white> 11 </font></a></td> +</table><HR> +pubu3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ece7f2><a title=#ece7f2> 1 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 2 </a></td> +<td bgcolor=#2b8cbe><a title=#2b8cbe> 3 </a></td> +</table><HR> +pubu4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1eef6><a title=#f1eef6> 1 </a></td> +<td bgcolor=#bdc9e1><a title=#bdc9e1> 2 </a></td> +<td bgcolor=#74a9cf><a title=#74a9cf> 3 </a></td> +<td bgcolor=#0570b0><a title=#0570b0> 4 </a></td> +</table><HR> +pubu5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1eef6><a title=#f1eef6> 1 </a></td> +<td bgcolor=#bdc9e1><a title=#bdc9e1> 2 </a></td> +<td bgcolor=#74a9cf><a title=#74a9cf> 3 </a></td> +<td bgcolor=#2b8cbe><a title=#2b8cbe> 4 </a></td> +<td bgcolor=#045a8d><a title=#045a8d> 5 </a></td> +</table><HR> +pubu6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1eef6><a title=#f1eef6> 1 </a></td> +<td bgcolor=#d0d1e6><a title=#d0d1e6> 2 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 3 </a></td> +<td bgcolor=#74a9cf><a title=#74a9cf> 4 </a></td> +<td bgcolor=#2b8cbe><a title=#2b8cbe> 5 </a></td> +<td bgcolor=#045a8d><a title=#045a8d> 6 </a></td> +</table><HR> +pubu7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1eef6><a title=#f1eef6> 1 </a></td> +<td bgcolor=#d0d1e6><a title=#d0d1e6> 2 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 3 </a></td> +<td bgcolor=#74a9cf><a title=#74a9cf> 4 </a></td> +<td bgcolor=#3690c0><a title=#3690c0> 5 </a></td> +<td bgcolor=#0570b0><a title=#0570b0> 6 </a></td> +<td bgcolor=#034e7b><a title=#034e7b><font color=white> 7 </font></a></td> +</table><HR> +pubu8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7fb><a title=#fff7fb> 1 </a></td> +<td bgcolor=#ece7f2><a title=#ece7f2> 2 </a></td> +<td bgcolor=#d0d1e6><a title=#d0d1e6> 3 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 4 </a></td> +<td bgcolor=#74a9cf><a title=#74a9cf> 5 </a></td> +<td bgcolor=#3690c0><a title=#3690c0> 6 </a></td> +<td bgcolor=#0570b0><a title=#0570b0> 7 </a></td> +<td bgcolor=#034e7b><a title=#034e7b><font color=white> 8 </font></a></td> +</table><HR> +pubu9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7fb><a title=#fff7fb> 1 </a></td> +<td bgcolor=#ece7f2><a title=#ece7f2> 2 </a></td> +<td bgcolor=#d0d1e6><a title=#d0d1e6> 3 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 4 </a></td> +<td bgcolor=#74a9cf><a title=#74a9cf> 5 </a></td> +<td bgcolor=#3690c0><a title=#3690c0> 6 </a></td> +<td bgcolor=#0570b0><a title=#0570b0> 7 </a></td> +<td bgcolor=#045a8d><a title=#045a8d> 8 </a></td> +<td bgcolor=#023858><a title=#023858><font color=white> 9 </font></a></td> +</table><HR> +pubugn3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ece2f0><a title=#ece2f0> 1 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 2 </a></td> +<td bgcolor=#1c9099><a title=#1c9099> 3 </a></td> +</table><HR> +pubugn4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f6eff7><a title=#f6eff7> 1 </a></td> +<td bgcolor=#bdc9e1><a title=#bdc9e1> 2 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 3 </a></td> +<td bgcolor=#02818a><a title=#02818a> 4 </a></td> +</table><HR> +pubugn5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f6eff7><a title=#f6eff7> 1 </a></td> +<td bgcolor=#bdc9e1><a title=#bdc9e1> 2 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 3 </a></td> +<td bgcolor=#1c9099><a title=#1c9099> 4 </a></td> +<td bgcolor=#016c59><a title=#016c59><font color=white> 5 </font></a></td> +</table><HR> +pubugn6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f6eff7><a title=#f6eff7> 1 </a></td> +<td bgcolor=#d0d1e6><a title=#d0d1e6> 2 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 3 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 4 </a></td> +<td bgcolor=#1c9099><a title=#1c9099> 5 </a></td> +<td bgcolor=#016c59><a title=#016c59><font color=white> 6 </font></a></td> +</table><HR> +pubugn7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f6eff7><a title=#f6eff7> 1 </a></td> +<td bgcolor=#d0d1e6><a title=#d0d1e6> 2 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 3 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 4 </a></td> +<td bgcolor=#3690c0><a title=#3690c0> 5 </a></td> +<td bgcolor=#02818a><a title=#02818a> 6 </a></td> +<td bgcolor=#016450><a title=#016450><font color=white> 7 </font></a></td> +</table><HR> +pubugn8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7fb><a title=#fff7fb> 1 </a></td> +<td bgcolor=#ece2f0><a title=#ece2f0> 2 </a></td> +<td bgcolor=#d0d1e6><a title=#d0d1e6> 3 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 4 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 5 </a></td> +<td bgcolor=#3690c0><a title=#3690c0> 6 </a></td> +<td bgcolor=#02818a><a title=#02818a> 7 </a></td> +<td bgcolor=#016450><a title=#016450><font color=white> 8 </font></a></td> +</table><HR> +pubugn9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7fb><a title=#fff7fb> 1 </a></td> +<td bgcolor=#ece2f0><a title=#ece2f0> 2 </a></td> +<td bgcolor=#d0d1e6><a title=#d0d1e6> 3 </a></td> +<td bgcolor=#a6bddb><a title=#a6bddb> 4 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 5 </a></td> +<td bgcolor=#3690c0><a title=#3690c0> 6 </a></td> +<td bgcolor=#02818a><a title=#02818a> 7 </a></td> +<td bgcolor=#016c59><a title=#016c59><font color=white> 8 </font></a></td> +<td bgcolor=#014636><a title=#014636><font color=white> 9 </font></a></td> +</table><HR> +puor3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1a340><a title=#f1a340> 1 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 2 </a></td> +<td bgcolor=#998ec3><a title=#998ec3> 3 </a></td> +</table><HR> +puor4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e66101><a title=#e66101> 1 </a></td> +<td bgcolor=#fdb863><a title=#fdb863> 2 </a></td> +<td bgcolor=#b2abd2><a title=#b2abd2> 3 </a></td> +<td bgcolor=#5e3c99><a title=#5e3c99> 4 </a></td> +</table><HR> +puor5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e66101><a title=#e66101> 1 </a></td> +<td bgcolor=#fdb863><a title=#fdb863> 2 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 3 </a></td> +<td bgcolor=#b2abd2><a title=#b2abd2> 4 </a></td> +<td bgcolor=#5e3c99><a title=#5e3c99> 5 </a></td> +</table><HR> +puor6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b35806><a title=#b35806> 1 </a></td> +<td bgcolor=#f1a340><a title=#f1a340> 2 </a></td> +<td bgcolor=#fee0b6><a title=#fee0b6> 3 </a></td> +<td bgcolor=#d8daeb><a title=#d8daeb> 4 </a></td> +<td bgcolor=#998ec3><a title=#998ec3> 5 </a></td> +<td bgcolor=#542788><a title=#542788> 6 </a></td> +</table><HR> +puor7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b35806><a title=#b35806> 1 </a></td> +<td bgcolor=#f1a340><a title=#f1a340> 2 </a></td> +<td bgcolor=#fee0b6><a title=#fee0b6> 3 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 4 </a></td> +<td bgcolor=#d8daeb><a title=#d8daeb> 5 </a></td> +<td bgcolor=#998ec3><a title=#998ec3> 6 </a></td> +<td bgcolor=#542788><a title=#542788> 7 </a></td> +</table><HR> +puor8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b35806><a title=#b35806> 1 </a></td> +<td bgcolor=#e08214><a title=#e08214> 2 </a></td> +<td bgcolor=#fdb863><a title=#fdb863> 3 </a></td> +<td bgcolor=#fee0b6><a title=#fee0b6> 4 </a></td> +<td bgcolor=#d8daeb><a title=#d8daeb> 5 </a></td> +<td bgcolor=#b2abd2><a title=#b2abd2> 6 </a></td> +<td bgcolor=#8073ac><a title=#8073ac> 7 </a></td> +<td bgcolor=#542788><a title=#542788> 8 </a></td> +</table><HR> +puor9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b35806><a title=#b35806> 1 </a></td> +<td bgcolor=#e08214><a title=#e08214> 2 </a></td> +<td bgcolor=#fdb863><a title=#fdb863> 3 </a></td> +<td bgcolor=#fee0b6><a title=#fee0b6> 4 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 5 </a></td> +<td bgcolor=#d8daeb><a title=#d8daeb> 6 </a></td> +<td bgcolor=#b2abd2><a title=#b2abd2> 7 </a></td> +<td bgcolor=#8073ac><a title=#8073ac> 8 </a></td> +<td bgcolor=#542788><a title=#542788> 9 </a></td> +</table><HR> +purd3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e7e1ef><a title=#e7e1ef> 1 </a></td> +<td bgcolor=#c994c7><a title=#c994c7> 2 </a></td> +<td bgcolor=#dd1c77><a title=#dd1c77> 3 </a></td> +</table><HR> +purd4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1eef6><a title=#f1eef6> 1 </a></td> +<td bgcolor=#d7b5d8><a title=#d7b5d8> 2 </a></td> +<td bgcolor=#df65b0><a title=#df65b0> 3 </a></td> +<td bgcolor=#ce1256><a title=#ce1256> 4 </a></td> +</table><HR> +purd5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1eef6><a title=#f1eef6> 1 </a></td> +<td bgcolor=#d7b5d8><a title=#d7b5d8> 2 </a></td> +<td bgcolor=#df65b0><a title=#df65b0> 3 </a></td> +<td bgcolor=#dd1c77><a title=#dd1c77> 4 </a></td> +<td bgcolor=#980043><a title=#980043> 5 </a></td> +</table><HR> +purd6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1eef6><a title=#f1eef6> 1 </a></td> +<td bgcolor=#d4b9da><a title=#d4b9da> 2 </a></td> +<td bgcolor=#c994c7><a title=#c994c7> 3 </a></td> +<td bgcolor=#df65b0><a title=#df65b0> 4 </a></td> +<td bgcolor=#dd1c77><a title=#dd1c77> 5 </a></td> +<td bgcolor=#980043><a title=#980043> 6 </a></td> +</table><HR> +purd7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f1eef6><a title=#f1eef6> 1 </a></td> +<td bgcolor=#d4b9da><a title=#d4b9da> 2 </a></td> +<td bgcolor=#c994c7><a title=#c994c7> 3 </a></td> +<td bgcolor=#df65b0><a title=#df65b0> 4 </a></td> +<td bgcolor=#e7298a><a title=#e7298a> 5 </a></td> +<td bgcolor=#ce1256><a title=#ce1256> 6 </a></td> +<td bgcolor=#91003f><a title=#91003f> 7 </a></td> +</table><HR> +purd8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7f4f9><a title=#f7f4f9> 1 </a></td> +<td bgcolor=#e7e1ef><a title=#e7e1ef> 2 </a></td> +<td bgcolor=#d4b9da><a title=#d4b9da> 3 </a></td> +<td bgcolor=#c994c7><a title=#c994c7> 4 </a></td> +<td bgcolor=#df65b0><a title=#df65b0> 5 </a></td> +<td bgcolor=#e7298a><a title=#e7298a> 6 </a></td> +<td bgcolor=#ce1256><a title=#ce1256> 7 </a></td> +<td bgcolor=#91003f><a title=#91003f> 8 </a></td> +</table><HR> +purd9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7f4f9><a title=#f7f4f9> 1 </a></td> +<td bgcolor=#e7e1ef><a title=#e7e1ef> 2 </a></td> +<td bgcolor=#d4b9da><a title=#d4b9da> 3 </a></td> +<td bgcolor=#c994c7><a title=#c994c7> 4 </a></td> +<td bgcolor=#df65b0><a title=#df65b0> 5 </a></td> +<td bgcolor=#e7298a><a title=#e7298a> 6 </a></td> +<td bgcolor=#ce1256><a title=#ce1256> 7 </a></td> +<td bgcolor=#980043><a title=#980043> 8 </a></td> +<td bgcolor=#67001f><a title=#67001f><font color=white> 9 </font></a></td> +</table><HR> +puor10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7f3b08><a title=#7f3b08><font color=white> 1 </font></a></td> +<td bgcolor=#b35806><a title=#b35806> 2 </a></td> +<td bgcolor=#e08214><a title=#e08214> 3 </a></td> +<td bgcolor=#fdb863><a title=#fdb863> 4 </a></td> +<td bgcolor=#fee0b6><a title=#fee0b6> 5 </a></td> +<td bgcolor=#d8daeb><a title=#d8daeb> 6 </a></td> +<td bgcolor=#b2abd2><a title=#b2abd2> 7 </a></td> +<td bgcolor=#8073ac><a title=#8073ac> 8 </a></td> +<td bgcolor=#542788><a title=#542788> 9 </a></td> +<td bgcolor=#2d004b><a title=#2d004b><font color=white> 10 </font></a></td> +</table><HR> +puor11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#7f3b08><a title=#7f3b08><font color=white> 1 </font></a></td> +<td bgcolor=#b35806><a title=#b35806> 2 </a></td> +<td bgcolor=#e08214><a title=#e08214> 3 </a></td> +<td bgcolor=#fdb863><a title=#fdb863> 4 </a></td> +<td bgcolor=#fee0b6><a title=#fee0b6> 5 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 6 </a></td> +<td bgcolor=#d8daeb><a title=#d8daeb> 7 </a></td> +<td bgcolor=#b2abd2><a title=#b2abd2> 8 </a></td> +<td bgcolor=#8073ac><a title=#8073ac> 9 </a></td> +<td bgcolor=#542788><a title=#542788> 10 </a></td> +<td bgcolor=#2d004b><a title=#2d004b><font color=white> 11 </font></a></td> +</table><HR> +purples3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#efedf5><a title=#efedf5> 1 </a></td> +<td bgcolor=#bcbddc><a title=#bcbddc> 2 </a></td> +<td bgcolor=#756bb1><a title=#756bb1> 3 </a></td> +</table><HR> +purples4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f2f0f7><a title=#f2f0f7> 1 </a></td> +<td bgcolor=#cbc9e2><a title=#cbc9e2> 2 </a></td> +<td bgcolor=#9e9ac8><a title=#9e9ac8> 3 </a></td> +<td bgcolor=#6a51a3><a title=#6a51a3> 4 </a></td> +</table><HR> +purples5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f2f0f7><a title=#f2f0f7> 1 </a></td> +<td bgcolor=#cbc9e2><a title=#cbc9e2> 2 </a></td> +<td bgcolor=#9e9ac8><a title=#9e9ac8> 3 </a></td> +<td bgcolor=#756bb1><a title=#756bb1> 4 </a></td> +<td bgcolor=#54278f><a title=#54278f> 5 </a></td> +</table><HR> +purples6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f2f0f7><a title=#f2f0f7> 1 </a></td> +<td bgcolor=#dadaeb><a title=#dadaeb> 2 </a></td> +<td bgcolor=#bcbddc><a title=#bcbddc> 3 </a></td> +<td bgcolor=#9e9ac8><a title=#9e9ac8> 4 </a></td> +<td bgcolor=#756bb1><a title=#756bb1> 5 </a></td> +<td bgcolor=#54278f><a title=#54278f> 6 </a></td> +</table><HR> +purples7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f2f0f7><a title=#f2f0f7> 1 </a></td> +<td bgcolor=#dadaeb><a title=#dadaeb> 2 </a></td> +<td bgcolor=#bcbddc><a title=#bcbddc> 3 </a></td> +<td bgcolor=#9e9ac8><a title=#9e9ac8> 4 </a></td> +<td bgcolor=#807dba><a title=#807dba> 5 </a></td> +<td bgcolor=#6a51a3><a title=#6a51a3> 6 </a></td> +<td bgcolor=#4a1486><a title=#4a1486> 7 </a></td> +</table><HR> +purples8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fcfbfd><a title=#fcfbfd> 1 </a></td> +<td bgcolor=#efedf5><a title=#efedf5> 2 </a></td> +<td bgcolor=#dadaeb><a title=#dadaeb> 3 </a></td> +<td bgcolor=#bcbddc><a title=#bcbddc> 4 </a></td> +<td bgcolor=#9e9ac8><a title=#9e9ac8> 5 </a></td> +<td bgcolor=#807dba><a title=#807dba> 6 </a></td> +<td bgcolor=#6a51a3><a title=#6a51a3> 7 </a></td> +<td bgcolor=#4a1486><a title=#4a1486> 8 </a></td> +</table><HR> +purples9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fcfbfd><a title=#fcfbfd> 1 </a></td> +<td bgcolor=#efedf5><a title=#efedf5> 2 </a></td> +<td bgcolor=#dadaeb><a title=#dadaeb> 3 </a></td> +<td bgcolor=#bcbddc><a title=#bcbddc> 4 </a></td> +<td bgcolor=#9e9ac8><a title=#9e9ac8> 5 </a></td> +<td bgcolor=#807dba><a title=#807dba> 6 </a></td> +<td bgcolor=#6a51a3><a title=#6a51a3> 7 </a></td> +<td bgcolor=#54278f><a title=#54278f> 8 </a></td> +<td bgcolor=#3f007d><a title=#3f007d><font color=white> 9 </font></a></td> +</table><HR> +rdbu10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#67001f><a title=#67001f><font color=white> 1 </font></a></td> +<td bgcolor=#b2182b><a title=#b2182b> 2 </a></td> +<td bgcolor=#d6604d><a title=#d6604d> 3 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 4 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 5 </a></td> +<td bgcolor=#d1e5f0><a title=#d1e5f0> 6 </a></td> +<td bgcolor=#92c5de><a title=#92c5de> 7 </a></td> +<td bgcolor=#4393c3><a title=#4393c3> 8 </a></td> +<td bgcolor=#2166ac><a title=#2166ac> 9 </a></td> +<td bgcolor=#053061><a title=#053061><font color=white> 10 </font></a></td> +</table><HR> +rdbu11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#67001f><a title=#67001f><font color=white> 1 </font></a></td> +<td bgcolor=#b2182b><a title=#b2182b> 2 </a></td> +<td bgcolor=#d6604d><a title=#d6604d> 3 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 4 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 5 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 6 </a></td> +<td bgcolor=#d1e5f0><a title=#d1e5f0> 7 </a></td> +<td bgcolor=#92c5de><a title=#92c5de> 8 </a></td> +<td bgcolor=#4393c3><a title=#4393c3> 9 </a></td> +<td bgcolor=#2166ac><a title=#2166ac> 10 </a></td> +<td bgcolor=#053061><a title=#053061><font color=white> 11 </font></a></td> +</table><HR> +rdbu3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ef8a62><a title=#ef8a62> 1 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 2 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 3 </a></td> +</table><HR> +rdbu4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ca0020><a title=#ca0020> 1 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 2 </a></td> +<td bgcolor=#92c5de><a title=#92c5de> 3 </a></td> +<td bgcolor=#0571b0><a title=#0571b0> 4 </a></td> +</table><HR> +rdbu5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ca0020><a title=#ca0020> 1 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 2 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 3 </a></td> +<td bgcolor=#92c5de><a title=#92c5de> 4 </a></td> +<td bgcolor=#0571b0><a title=#0571b0> 5 </a></td> +</table><HR> +rdbu6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b2182b><a title=#b2182b> 1 </a></td> +<td bgcolor=#ef8a62><a title=#ef8a62> 2 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 3 </a></td> +<td bgcolor=#d1e5f0><a title=#d1e5f0> 4 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 5 </a></td> +<td bgcolor=#2166ac><a title=#2166ac> 6 </a></td> +</table><HR> +rdbu7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b2182b><a title=#b2182b> 1 </a></td> +<td bgcolor=#ef8a62><a title=#ef8a62> 2 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 3 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 4 </a></td> +<td bgcolor=#d1e5f0><a title=#d1e5f0> 5 </a></td> +<td bgcolor=#67a9cf><a title=#67a9cf> 6 </a></td> +<td bgcolor=#2166ac><a title=#2166ac> 7 </a></td> +</table><HR> +rdbu8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b2182b><a title=#b2182b> 1 </a></td> +<td bgcolor=#d6604d><a title=#d6604d> 2 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 3 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 4 </a></td> +<td bgcolor=#d1e5f0><a title=#d1e5f0> 5 </a></td> +<td bgcolor=#92c5de><a title=#92c5de> 6 </a></td> +<td bgcolor=#4393c3><a title=#4393c3> 7 </a></td> +<td bgcolor=#2166ac><a title=#2166ac> 8 </a></td> +</table><HR> +rdbu9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b2182b><a title=#b2182b> 1 </a></td> +<td bgcolor=#d6604d><a title=#d6604d> 2 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 3 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 4 </a></td> +<td bgcolor=#f7f7f7><a title=#f7f7f7> 5 </a></td> +<td bgcolor=#d1e5f0><a title=#d1e5f0> 6 </a></td> +<td bgcolor=#92c5de><a title=#92c5de> 7 </a></td> +<td bgcolor=#4393c3><a title=#4393c3> 8 </a></td> +<td bgcolor=#2166ac><a title=#2166ac> 9 </a></td> +</table><HR> +rdgy3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ef8a62><a title=#ef8a62> 1 </a></td> +<td bgcolor=#ffffff><a title=#ffffff> 2 </a></td> +<td bgcolor=#999999><a title=#999999> 3 </a></td> +</table><HR> +rdgy4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ca0020><a title=#ca0020> 1 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 2 </a></td> +<td bgcolor=#bababa><a title=#bababa> 3 </a></td> +<td bgcolor=#404040><a title=#404040><font color=white> 4 </font></a></td> +</table><HR> +rdgy5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ca0020><a title=#ca0020> 1 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 2 </a></td> +<td bgcolor=#ffffff><a title=#ffffff> 3 </a></td> +<td bgcolor=#bababa><a title=#bababa> 4 </a></td> +<td bgcolor=#404040><a title=#404040><font color=white> 5 </font></a></td> +</table><HR> +rdgy6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b2182b><a title=#b2182b> 1 </a></td> +<td bgcolor=#ef8a62><a title=#ef8a62> 2 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 3 </a></td> +<td bgcolor=#e0e0e0><a title=#e0e0e0> 4 </a></td> +<td bgcolor=#999999><a title=#999999> 5 </a></td> +<td bgcolor=#4d4d4d><a title=#4d4d4d><font color=white> 6 </font></a></td> +</table><HR> +rdgy7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b2182b><a title=#b2182b> 1 </a></td> +<td bgcolor=#ef8a62><a title=#ef8a62> 2 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 3 </a></td> +<td bgcolor=#ffffff><a title=#ffffff> 4 </a></td> +<td bgcolor=#e0e0e0><a title=#e0e0e0> 5 </a></td> +<td bgcolor=#999999><a title=#999999> 6 </a></td> +<td bgcolor=#4d4d4d><a title=#4d4d4d><font color=white> 7 </font></a></td> +</table><HR> +rdgy8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b2182b><a title=#b2182b> 1 </a></td> +<td bgcolor=#d6604d><a title=#d6604d> 2 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 3 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 4 </a></td> +<td bgcolor=#e0e0e0><a title=#e0e0e0> 5 </a></td> +<td bgcolor=#bababa><a title=#bababa> 6 </a></td> +<td bgcolor=#878787><a title=#878787> 7 </a></td> +<td bgcolor=#4d4d4d><a title=#4d4d4d><font color=white> 8 </font></a></td> +</table><HR> +rdgy9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#b2182b><a title=#b2182b> 1 </a></td> +<td bgcolor=#d6604d><a title=#d6604d> 2 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 3 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 4 </a></td> +<td bgcolor=#ffffff><a title=#ffffff> 5 </a></td> +<td bgcolor=#e0e0e0><a title=#e0e0e0> 6 </a></td> +<td bgcolor=#bababa><a title=#bababa> 7 </a></td> +<td bgcolor=#878787><a title=#878787> 8 </a></td> +<td bgcolor=#4d4d4d><a title=#4d4d4d><font color=white> 9 </font></a></td> +</table><HR> +rdpu3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fde0dd><a title=#fde0dd> 1 </a></td> +<td bgcolor=#fa9fb5><a title=#fa9fb5> 2 </a></td> +<td bgcolor=#c51b8a><a title=#c51b8a> 3 </a></td> +</table><HR> +rdpu4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#feebe2><a title=#feebe2> 1 </a></td> +<td bgcolor=#fbb4b9><a title=#fbb4b9> 2 </a></td> +<td bgcolor=#f768a1><a title=#f768a1> 3 </a></td> +<td bgcolor=#ae017e><a title=#ae017e> 4 </a></td> +</table><HR> +rdpu5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#feebe2><a title=#feebe2> 1 </a></td> +<td bgcolor=#fbb4b9><a title=#fbb4b9> 2 </a></td> +<td bgcolor=#f768a1><a title=#f768a1> 3 </a></td> +<td bgcolor=#c51b8a><a title=#c51b8a> 4 </a></td> +<td bgcolor=#7a0177><a title=#7a0177><font color=white> 5 </font></a></td> +</table><HR> +rdpu6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#feebe2><a title=#feebe2> 1 </a></td> +<td bgcolor=#fcc5c0><a title=#fcc5c0> 2 </a></td> +<td bgcolor=#fa9fb5><a title=#fa9fb5> 3 </a></td> +<td bgcolor=#f768a1><a title=#f768a1> 4 </a></td> +<td bgcolor=#c51b8a><a title=#c51b8a> 5 </a></td> +<td bgcolor=#7a0177><a title=#7a0177><font color=white> 6 </font></a></td> +</table><HR> +rdpu7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#feebe2><a title=#feebe2> 1 </a></td> +<td bgcolor=#fcc5c0><a title=#fcc5c0> 2 </a></td> +<td bgcolor=#fa9fb5><a title=#fa9fb5> 3 </a></td> +<td bgcolor=#f768a1><a title=#f768a1> 4 </a></td> +<td bgcolor=#dd3497><a title=#dd3497> 5 </a></td> +<td bgcolor=#ae017e><a title=#ae017e> 6 </a></td> +<td bgcolor=#7a0177><a title=#7a0177><font color=white> 7 </font></a></td> +</table><HR> +rdpu8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7f3><a title=#fff7f3> 1 </a></td> +<td bgcolor=#fde0dd><a title=#fde0dd> 2 </a></td> +<td bgcolor=#fcc5c0><a title=#fcc5c0> 3 </a></td> +<td bgcolor=#fa9fb5><a title=#fa9fb5> 4 </a></td> +<td bgcolor=#f768a1><a title=#f768a1> 5 </a></td> +<td bgcolor=#dd3497><a title=#dd3497> 6 </a></td> +<td bgcolor=#ae017e><a title=#ae017e> 7 </a></td> +<td bgcolor=#7a0177><a title=#7a0177><font color=white> 8 </font></a></td> +</table><HR> +rdpu9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7f3><a title=#fff7f3> 1 </a></td> +<td bgcolor=#fde0dd><a title=#fde0dd> 2 </a></td> +<td bgcolor=#fcc5c0><a title=#fcc5c0> 3 </a></td> +<td bgcolor=#fa9fb5><a title=#fa9fb5> 4 </a></td> +<td bgcolor=#f768a1><a title=#f768a1> 5 </a></td> +<td bgcolor=#dd3497><a title=#dd3497> 6 </a></td> +<td bgcolor=#ae017e><a title=#ae017e> 7 </a></td> +<td bgcolor=#7a0177><a title=#7a0177><font color=white> 8 </font></a></td> +<td bgcolor=#49006a><a title=#49006a><font color=white> 9 </font></a></td> +</table><HR> +rdgy10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#67001f><a title=#67001f><font color=white> 1 </font></a></td> +<td bgcolor=#b2182b><a title=#b2182b> 2 </a></td> +<td bgcolor=#d6604d><a title=#d6604d> 3 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 4 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 5 </a></td> +<td bgcolor=#e0e0e0><a title=#e0e0e0> 6 </a></td> +<td bgcolor=#bababa><a title=#bababa> 7 </a></td> +<td bgcolor=#878787><a title=#878787> 8 </a></td> +<td bgcolor=#4d4d4d><a title=#4d4d4d><font color=white> 9 </font></a></td> +<td bgcolor=#1a1a1a><a title=#1a1a1a><font color=white> 10 </font></a></td> +</table><HR> +rdgy11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#67001f><a title=#67001f><font color=white> 1 </font></a></td> +<td bgcolor=#b2182b><a title=#b2182b> 2 </a></td> +<td bgcolor=#d6604d><a title=#d6604d> 3 </a></td> +<td bgcolor=#f4a582><a title=#f4a582> 4 </a></td> +<td bgcolor=#fddbc7><a title=#fddbc7> 5 </a></td> +<td bgcolor=#ffffff><a title=#ffffff> 6 </a></td> +<td bgcolor=#e0e0e0><a title=#e0e0e0> 7 </a></td> +<td bgcolor=#bababa><a title=#bababa> 8 </a></td> +<td bgcolor=#878787><a title=#878787> 9 </a></td> +<td bgcolor=#4d4d4d><a title=#4d4d4d><font color=white> 10 </font></a></td> +<td bgcolor=#1a1a1a><a title=#1a1a1a><font color=white> 11 </font></a></td> +</table><HR> +rdylbu3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fc8d59><a title=#fc8d59> 1 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 2 </a></td> +<td bgcolor=#91bfdb><a title=#91bfdb> 3 </a></td> +</table><HR> +rdylbu4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d7191c><a title=#d7191c> 1 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 2 </a></td> +<td bgcolor=#abd9e9><a title=#abd9e9> 3 </a></td> +<td bgcolor=#2c7bb6><a title=#2c7bb6> 4 </a></td> +</table><HR> +rdylbu5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d7191c><a title=#d7191c> 1 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 2 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 3 </a></td> +<td bgcolor=#abd9e9><a title=#abd9e9> 4 </a></td> +<td bgcolor=#2c7bb6><a title=#2c7bb6> 5 </a></td> +</table><HR> +rdylbu6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d73027><a title=#d73027> 1 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 2 </a></td> +<td bgcolor=#fee090><a title=#fee090> 3 </a></td> +<td bgcolor=#e0f3f8><a title=#e0f3f8> 4 </a></td> +<td bgcolor=#91bfdb><a title=#91bfdb> 5 </a></td> +<td bgcolor=#4575b4><a title=#4575b4> 6 </a></td> +</table><HR> +rdylbu7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d73027><a title=#d73027> 1 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 2 </a></td> +<td bgcolor=#fee090><a title=#fee090> 3 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 4 </a></td> +<td bgcolor=#e0f3f8><a title=#e0f3f8> 5 </a></td> +<td bgcolor=#91bfdb><a title=#91bfdb> 6 </a></td> +<td bgcolor=#4575b4><a title=#4575b4> 7 </a></td> +</table><HR> +rdylbu8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d73027><a title=#d73027> 1 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 2 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 3 </a></td> +<td bgcolor=#fee090><a title=#fee090> 4 </a></td> +<td bgcolor=#e0f3f8><a title=#e0f3f8> 5 </a></td> +<td bgcolor=#abd9e9><a title=#abd9e9> 6 </a></td> +<td bgcolor=#74add1><a title=#74add1> 7 </a></td> +<td bgcolor=#4575b4><a title=#4575b4> 8 </a></td> +</table><HR> +rdylbu9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d73027><a title=#d73027> 1 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 2 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 3 </a></td> +<td bgcolor=#fee090><a title=#fee090> 4 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 5 </a></td> +<td bgcolor=#e0f3f8><a title=#e0f3f8> 6 </a></td> +<td bgcolor=#abd9e9><a title=#abd9e9> 7 </a></td> +<td bgcolor=#74add1><a title=#74add1> 8 </a></td> +<td bgcolor=#4575b4><a title=#4575b4> 9 </a></td> +</table><HR> +rdylbu10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a50026><a title=#a50026> 1 </a></td> +<td bgcolor=#d73027><a title=#d73027> 2 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 3 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 4 </a></td> +<td bgcolor=#fee090><a title=#fee090> 5 </a></td> +<td bgcolor=#e0f3f8><a title=#e0f3f8> 6 </a></td> +<td bgcolor=#abd9e9><a title=#abd9e9> 7 </a></td> +<td bgcolor=#74add1><a title=#74add1> 8 </a></td> +<td bgcolor=#4575b4><a title=#4575b4> 9 </a></td> +<td bgcolor=#313695><a title=#313695> 10 </a></td> +</table><HR> +rdylbu11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a50026><a title=#a50026> 1 </a></td> +<td bgcolor=#d73027><a title=#d73027> 2 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 3 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 4 </a></td> +<td bgcolor=#fee090><a title=#fee090> 5 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 6 </a></td> +<td bgcolor=#e0f3f8><a title=#e0f3f8> 7 </a></td> +<td bgcolor=#abd9e9><a title=#abd9e9> 8 </a></td> +<td bgcolor=#74add1><a title=#74add1> 9 </a></td> +<td bgcolor=#4575b4><a title=#4575b4> 10 </a></td> +<td bgcolor=#313695><a title=#313695> 11 </a></td> +</table><HR> +rdylgn3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fc8d59><a title=#fc8d59> 1 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 2 </a></td> +<td bgcolor=#91cf60><a title=#91cf60> 3 </a></td> +</table><HR> +rdylgn4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d7191c><a title=#d7191c> 1 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 2 </a></td> +<td bgcolor=#a6d96a><a title=#a6d96a> 3 </a></td> +<td bgcolor=#1a9641><a title=#1a9641> 4 </a></td> +</table><HR> +rdylgn5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d7191c><a title=#d7191c> 1 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 2 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 3 </a></td> +<td bgcolor=#a6d96a><a title=#a6d96a> 4 </a></td> +<td bgcolor=#1a9641><a title=#1a9641> 5 </a></td> +</table><HR> +rdylgn6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d73027><a title=#d73027> 1 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 2 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 3 </a></td> +<td bgcolor=#d9ef8b><a title=#d9ef8b> 4 </a></td> +<td bgcolor=#91cf60><a title=#91cf60> 5 </a></td> +<td bgcolor=#1a9850><a title=#1a9850> 6 </a></td> +</table><HR> +rdylgn7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d73027><a title=#d73027> 1 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 2 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 3 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 4 </a></td> +<td bgcolor=#d9ef8b><a title=#d9ef8b> 5 </a></td> +<td bgcolor=#91cf60><a title=#91cf60> 6 </a></td> +<td bgcolor=#1a9850><a title=#1a9850> 7 </a></td> +</table><HR> +rdylgn8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d73027><a title=#d73027> 1 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 2 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 3 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 4 </a></td> +<td bgcolor=#d9ef8b><a title=#d9ef8b> 5 </a></td> +<td bgcolor=#a6d96a><a title=#a6d96a> 6 </a></td> +<td bgcolor=#66bd63><a title=#66bd63> 7 </a></td> +<td bgcolor=#1a9850><a title=#1a9850> 8 </a></td> +</table><HR> +rdylgn9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d73027><a title=#d73027> 1 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 2 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 3 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 4 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 5 </a></td> +<td bgcolor=#d9ef8b><a title=#d9ef8b> 6 </a></td> +<td bgcolor=#a6d96a><a title=#a6d96a> 7 </a></td> +<td bgcolor=#66bd63><a title=#66bd63> 8 </a></td> +<td bgcolor=#1a9850><a title=#1a9850> 9 </a></td> +</table><HR> +rdylgn10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a50026><a title=#a50026> 1 </a></td> +<td bgcolor=#d73027><a title=#d73027> 2 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 3 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 4 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 5 </a></td> +<td bgcolor=#d9ef8b><a title=#d9ef8b> 6 </a></td> +<td bgcolor=#a6d96a><a title=#a6d96a> 7 </a></td> +<td bgcolor=#66bd63><a title=#66bd63> 8 </a></td> +<td bgcolor=#1a9850><a title=#1a9850> 9 </a></td> +<td bgcolor=#006837><a title=#006837><font color=white> 10 </font></a></td> +</table><HR> +rdylgn11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#a50026><a title=#a50026> 1 </a></td> +<td bgcolor=#d73027><a title=#d73027> 2 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 3 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 4 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 5 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 6 </a></td> +<td bgcolor=#d9ef8b><a title=#d9ef8b> 7 </a></td> +<td bgcolor=#a6d96a><a title=#a6d96a> 8 </a></td> +<td bgcolor=#66bd63><a title=#66bd63> 9 </a></td> +<td bgcolor=#1a9850><a title=#1a9850> 10 </a></td> +<td bgcolor=#006837><a title=#006837><font color=white> 11 </font></a></td> +</table><HR> +reds3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fee0d2><a title=#fee0d2> 1 </a></td> +<td bgcolor=#fc9272><a title=#fc9272> 2 </a></td> +<td bgcolor=#de2d26><a title=#de2d26> 3 </a></td> +</table><HR> +reds4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fee5d9><a title=#fee5d9> 1 </a></td> +<td bgcolor=#fcae91><a title=#fcae91> 2 </a></td> +<td bgcolor=#fb6a4a><a title=#fb6a4a> 3 </a></td> +<td bgcolor=#cb181d><a title=#cb181d> 4 </a></td> +</table><HR> +reds5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fee5d9><a title=#fee5d9> 1 </a></td> +<td bgcolor=#fcae91><a title=#fcae91> 2 </a></td> +<td bgcolor=#fb6a4a><a title=#fb6a4a> 3 </a></td> +<td bgcolor=#de2d26><a title=#de2d26> 4 </a></td> +<td bgcolor=#a50f15><a title=#a50f15> 5 </a></td> +</table><HR> +reds6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fee5d9><a title=#fee5d9> 1 </a></td> +<td bgcolor=#fcbba1><a title=#fcbba1> 2 </a></td> +<td bgcolor=#fc9272><a title=#fc9272> 3 </a></td> +<td bgcolor=#fb6a4a><a title=#fb6a4a> 4 </a></td> +<td bgcolor=#de2d26><a title=#de2d26> 5 </a></td> +<td bgcolor=#a50f15><a title=#a50f15> 6 </a></td> +</table><HR> +reds7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fee5d9><a title=#fee5d9> 1 </a></td> +<td bgcolor=#fcbba1><a title=#fcbba1> 2 </a></td> +<td bgcolor=#fc9272><a title=#fc9272> 3 </a></td> +<td bgcolor=#fb6a4a><a title=#fb6a4a> 4 </a></td> +<td bgcolor=#ef3b2c><a title=#ef3b2c> 5 </a></td> +<td bgcolor=#cb181d><a title=#cb181d> 6 </a></td> +<td bgcolor=#99000d><a title=#99000d> 7 </a></td> +</table><HR> +reds8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff5f0><a title=#fff5f0> 1 </a></td> +<td bgcolor=#fee0d2><a title=#fee0d2> 2 </a></td> +<td bgcolor=#fcbba1><a title=#fcbba1> 3 </a></td> +<td bgcolor=#fc9272><a title=#fc9272> 4 </a></td> +<td bgcolor=#fb6a4a><a title=#fb6a4a> 5 </a></td> +<td bgcolor=#ef3b2c><a title=#ef3b2c> 6 </a></td> +<td bgcolor=#cb181d><a title=#cb181d> 7 </a></td> +<td bgcolor=#99000d><a title=#99000d> 8 </a></td> +</table><HR> +reds9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff5f0><a title=#fff5f0> 1 </a></td> +<td bgcolor=#fee0d2><a title=#fee0d2> 2 </a></td> +<td bgcolor=#fcbba1><a title=#fcbba1> 3 </a></td> +<td bgcolor=#fc9272><a title=#fc9272> 4 </a></td> +<td bgcolor=#fb6a4a><a title=#fb6a4a> 5 </a></td> +<td bgcolor=#ef3b2c><a title=#ef3b2c> 6 </a></td> +<td bgcolor=#cb181d><a title=#cb181d> 7 </a></td> +<td bgcolor=#a50f15><a title=#a50f15> 8 </a></td> +<td bgcolor=#67000d><a title=#67000d><font color=white> 9 </font></a></td> +</table><HR> +set13 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e41a1c><a title=#e41a1c> 1 </a></td> +<td bgcolor=#377eb8><a title=#377eb8> 2 </a></td> +<td bgcolor=#4daf4a><a title=#4daf4a> 3 </a></td> +</table><HR> +set14 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e41a1c><a title=#e41a1c> 1 </a></td> +<td bgcolor=#377eb8><a title=#377eb8> 2 </a></td> +<td bgcolor=#4daf4a><a title=#4daf4a> 3 </a></td> +<td bgcolor=#984ea3><a title=#984ea3> 4 </a></td> +</table><HR> +set15 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e41a1c><a title=#e41a1c> 1 </a></td> +<td bgcolor=#377eb8><a title=#377eb8> 2 </a></td> +<td bgcolor=#4daf4a><a title=#4daf4a> 3 </a></td> +<td bgcolor=#984ea3><a title=#984ea3> 4 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 5 </a></td> +</table><HR> +set16 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e41a1c><a title=#e41a1c> 1 </a></td> +<td bgcolor=#377eb8><a title=#377eb8> 2 </a></td> +<td bgcolor=#4daf4a><a title=#4daf4a> 3 </a></td> +<td bgcolor=#984ea3><a title=#984ea3> 4 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 5 </a></td> +<td bgcolor=#ffff33><a title=#ffff33> 6 </a></td> +</table><HR> +set17 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e41a1c><a title=#e41a1c> 1 </a></td> +<td bgcolor=#377eb8><a title=#377eb8> 2 </a></td> +<td bgcolor=#4daf4a><a title=#4daf4a> 3 </a></td> +<td bgcolor=#984ea3><a title=#984ea3> 4 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 5 </a></td> +<td bgcolor=#ffff33><a title=#ffff33> 6 </a></td> +<td bgcolor=#a65628><a title=#a65628> 7 </a></td> +</table><HR> +set18 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e41a1c><a title=#e41a1c> 1 </a></td> +<td bgcolor=#377eb8><a title=#377eb8> 2 </a></td> +<td bgcolor=#4daf4a><a title=#4daf4a> 3 </a></td> +<td bgcolor=#984ea3><a title=#984ea3> 4 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 5 </a></td> +<td bgcolor=#ffff33><a title=#ffff33> 6 </a></td> +<td bgcolor=#a65628><a title=#a65628> 7 </a></td> +<td bgcolor=#f781bf><a title=#f781bf> 8 </a></td> +</table><HR> +set19 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#e41a1c><a title=#e41a1c> 1 </a></td> +<td bgcolor=#377eb8><a title=#377eb8> 2 </a></td> +<td bgcolor=#4daf4a><a title=#4daf4a> 3 </a></td> +<td bgcolor=#984ea3><a title=#984ea3> 4 </a></td> +<td bgcolor=#ff7f00><a title=#ff7f00> 5 </a></td> +<td bgcolor=#ffff33><a title=#ffff33> 6 </a></td> +<td bgcolor=#a65628><a title=#a65628> 7 </a></td> +<td bgcolor=#f781bf><a title=#f781bf> 8 </a></td> +<td bgcolor=#999999><a title=#999999> 9 </a></td> +</table><HR> +set23 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#66c2a5><a title=#66c2a5> 1 </a></td> +<td bgcolor=#fc8d62><a title=#fc8d62> 2 </a></td> +<td bgcolor=#8da0cb><a title=#8da0cb> 3 </a></td> +</table><HR> +set24 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#66c2a5><a title=#66c2a5> 1 </a></td> +<td bgcolor=#fc8d62><a title=#fc8d62> 2 </a></td> +<td bgcolor=#8da0cb><a title=#8da0cb> 3 </a></td> +<td bgcolor=#e78ac3><a title=#e78ac3> 4 </a></td> +</table><HR> +set25 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#66c2a5><a title=#66c2a5> 1 </a></td> +<td bgcolor=#fc8d62><a title=#fc8d62> 2 </a></td> +<td bgcolor=#8da0cb><a title=#8da0cb> 3 </a></td> +<td bgcolor=#e78ac3><a title=#e78ac3> 4 </a></td> +<td bgcolor=#a6d854><a title=#a6d854> 5 </a></td> +</table><HR> +set26 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#66c2a5><a title=#66c2a5> 1 </a></td> +<td bgcolor=#fc8d62><a title=#fc8d62> 2 </a></td> +<td bgcolor=#8da0cb><a title=#8da0cb> 3 </a></td> +<td bgcolor=#e78ac3><a title=#e78ac3> 4 </a></td> +<td bgcolor=#a6d854><a title=#a6d854> 5 </a></td> +<td bgcolor=#ffd92f><a title=#ffd92f> 6 </a></td> +</table><HR> +set27 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#66c2a5><a title=#66c2a5> 1 </a></td> +<td bgcolor=#fc8d62><a title=#fc8d62> 2 </a></td> +<td bgcolor=#8da0cb><a title=#8da0cb> 3 </a></td> +<td bgcolor=#e78ac3><a title=#e78ac3> 4 </a></td> +<td bgcolor=#a6d854><a title=#a6d854> 5 </a></td> +<td bgcolor=#ffd92f><a title=#ffd92f> 6 </a></td> +<td bgcolor=#e5c494><a title=#e5c494> 7 </a></td> +</table><HR> +set28 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#66c2a5><a title=#66c2a5> 1 </a></td> +<td bgcolor=#fc8d62><a title=#fc8d62> 2 </a></td> +<td bgcolor=#8da0cb><a title=#8da0cb> 3 </a></td> +<td bgcolor=#e78ac3><a title=#e78ac3> 4 </a></td> +<td bgcolor=#a6d854><a title=#a6d854> 5 </a></td> +<td bgcolor=#ffd92f><a title=#ffd92f> 6 </a></td> +<td bgcolor=#e5c494><a title=#e5c494> 7 </a></td> +<td bgcolor=#b3b3b3><a title=#b3b3b3> 8 </a></td> +</table><HR> +set33 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +</table><HR> +set34 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +</table><HR> +set35 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +<td bgcolor=#80b1d3><a title=#80b1d3> 5 </a></td> +</table><HR> +set36 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +<td bgcolor=#80b1d3><a title=#80b1d3> 5 </a></td> +<td bgcolor=#fdb462><a title=#fdb462> 6 </a></td> +</table><HR> +set37 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +<td bgcolor=#80b1d3><a title=#80b1d3> 5 </a></td> +<td bgcolor=#fdb462><a title=#fdb462> 6 </a></td> +<td bgcolor=#b3de69><a title=#b3de69> 7 </a></td> +</table><HR> +set38 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +<td bgcolor=#80b1d3><a title=#80b1d3> 5 </a></td> +<td bgcolor=#fdb462><a title=#fdb462> 6 </a></td> +<td bgcolor=#b3de69><a title=#b3de69> 7 </a></td> +<td bgcolor=#fccde5><a title=#fccde5> 8 </a></td> +</table><HR> +set39 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +<td bgcolor=#80b1d3><a title=#80b1d3> 5 </a></td> +<td bgcolor=#fdb462><a title=#fdb462> 6 </a></td> +<td bgcolor=#b3de69><a title=#b3de69> 7 </a></td> +<td bgcolor=#fccde5><a title=#fccde5> 8 </a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9> 9 </a></td> +</table><HR> +set310 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +<td bgcolor=#80b1d3><a title=#80b1d3> 5 </a></td> +<td bgcolor=#fdb462><a title=#fdb462> 6 </a></td> +<td bgcolor=#b3de69><a title=#b3de69> 7 </a></td> +<td bgcolor=#fccde5><a title=#fccde5> 8 </a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9> 9 </a></td> +<td bgcolor=#bc80bd><a title=#bc80bd> 10 </a></td> +</table><HR> +set311 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +<td bgcolor=#80b1d3><a title=#80b1d3> 5 </a></td> +<td bgcolor=#fdb462><a title=#fdb462> 6 </a></td> +<td bgcolor=#b3de69><a title=#b3de69> 7 </a></td> +<td bgcolor=#fccde5><a title=#fccde5> 8 </a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9> 9 </a></td> +<td bgcolor=#bc80bd><a title=#bc80bd> 10 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 11 </a></td> +</table><HR> +set312 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#8dd3c7><a title=#8dd3c7> 1 </a></td> +<td bgcolor=#ffffb3><a title=#ffffb3> 2 </a></td> +<td bgcolor=#bebada><a title=#bebada> 3 </a></td> +<td bgcolor=#fb8072><a title=#fb8072> 4 </a></td> +<td bgcolor=#80b1d3><a title=#80b1d3> 5 </a></td> +<td bgcolor=#fdb462><a title=#fdb462> 6 </a></td> +<td bgcolor=#b3de69><a title=#b3de69> 7 </a></td> +<td bgcolor=#fccde5><a title=#fccde5> 8 </a></td> +<td bgcolor=#d9d9d9><a title=#d9d9d9> 9 </a></td> +<td bgcolor=#bc80bd><a title=#bc80bd> 10 </a></td> +<td bgcolor=#ccebc5><a title=#ccebc5> 11 </a></td> +<td bgcolor=#ffed6f><a title=#ffed6f> 12 </a></td> +</table><HR> +spectral3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fc8d59><a title=#fc8d59> 1 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 2 </a></td> +<td bgcolor=#99d594><a title=#99d594> 3 </a></td> +</table><HR> +spectral4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d7191c><a title=#d7191c> 1 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 2 </a></td> +<td bgcolor=#abdda4><a title=#abdda4> 3 </a></td> +<td bgcolor=#2b83ba><a title=#2b83ba> 4 </a></td> +</table><HR> +spectral5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d7191c><a title=#d7191c> 1 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 2 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 3 </a></td> +<td bgcolor=#abdda4><a title=#abdda4> 4 </a></td> +<td bgcolor=#2b83ba><a title=#2b83ba> 5 </a></td> +</table><HR> +spectral6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d53e4f><a title=#d53e4f> 1 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 2 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 3 </a></td> +<td bgcolor=#e6f598><a title=#e6f598> 4 </a></td> +<td bgcolor=#99d594><a title=#99d594> 5 </a></td> +<td bgcolor=#3288bd><a title=#3288bd> 6 </a></td> +</table><HR> +spectral7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d53e4f><a title=#d53e4f> 1 </a></td> +<td bgcolor=#fc8d59><a title=#fc8d59> 2 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 3 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 4 </a></td> +<td bgcolor=#e6f598><a title=#e6f598> 5 </a></td> +<td bgcolor=#99d594><a title=#99d594> 6 </a></td> +<td bgcolor=#3288bd><a title=#3288bd> 7 </a></td> +</table><HR> +spectral8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d53e4f><a title=#d53e4f> 1 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 2 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 3 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 4 </a></td> +<td bgcolor=#e6f598><a title=#e6f598> 5 </a></td> +<td bgcolor=#abdda4><a title=#abdda4> 6 </a></td> +<td bgcolor=#66c2a5><a title=#66c2a5> 7 </a></td> +<td bgcolor=#3288bd><a title=#3288bd> 8 </a></td> +</table><HR> +spectral9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#d53e4f><a title=#d53e4f> 1 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 2 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 3 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 4 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 5 </a></td> +<td bgcolor=#e6f598><a title=#e6f598> 6 </a></td> +<td bgcolor=#abdda4><a title=#abdda4> 7 </a></td> +<td bgcolor=#66c2a5><a title=#66c2a5> 8 </a></td> +<td bgcolor=#3288bd><a title=#3288bd> 9 </a></td> +</table><HR> +spectral10 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#9e0142><a title=#9e0142> 1 </a></td> +<td bgcolor=#d53e4f><a title=#d53e4f> 2 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 3 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 4 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 5 </a></td> +<td bgcolor=#e6f598><a title=#e6f598> 6 </a></td> +<td bgcolor=#abdda4><a title=#abdda4> 7 </a></td> +<td bgcolor=#66c2a5><a title=#66c2a5> 8 </a></td> +<td bgcolor=#3288bd><a title=#3288bd> 9 </a></td> +<td bgcolor=#5e4fa2><a title=#5e4fa2> 10 </a></td> +</table><HR> +spectral11 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#9e0142><a title=#9e0142> 1 </a></td> +<td bgcolor=#d53e4f><a title=#d53e4f> 2 </a></td> +<td bgcolor=#f46d43><a title=#f46d43> 3 </a></td> +<td bgcolor=#fdae61><a title=#fdae61> 4 </a></td> +<td bgcolor=#fee08b><a title=#fee08b> 5 </a></td> +<td bgcolor=#ffffbf><a title=#ffffbf> 6 </a></td> +<td bgcolor=#e6f598><a title=#e6f598> 7 </a></td> +<td bgcolor=#abdda4><a title=#abdda4> 8 </a></td> +<td bgcolor=#66c2a5><a title=#66c2a5> 9 </a></td> +<td bgcolor=#3288bd><a title=#3288bd> 10 </a></td> +<td bgcolor=#5e4fa2><a title=#5e4fa2> 11 </a></td> +</table><HR> +ylgn3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#f7fcb9><a title=#f7fcb9> 1 </a></td> +<td bgcolor=#addd8e><a title=#addd8e> 2 </a></td> +<td bgcolor=#31a354><a title=#31a354> 3 </a></td> +</table><HR> +ylgn4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#c2e699><a title=#c2e699> 2 </a></td> +<td bgcolor=#78c679><a title=#78c679> 3 </a></td> +<td bgcolor=#238443><a title=#238443> 4 </a></td> +</table><HR> +ylgn5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#c2e699><a title=#c2e699> 2 </a></td> +<td bgcolor=#78c679><a title=#78c679> 3 </a></td> +<td bgcolor=#31a354><a title=#31a354> 4 </a></td> +<td bgcolor=#006837><a title=#006837><font color=white> 5 </font></a></td> +</table><HR> +ylgn6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#d9f0a3><a title=#d9f0a3> 2 </a></td> +<td bgcolor=#addd8e><a title=#addd8e> 3 </a></td> +<td bgcolor=#78c679><a title=#78c679> 4 </a></td> +<td bgcolor=#31a354><a title=#31a354> 5 </a></td> +<td bgcolor=#006837><a title=#006837><font color=white> 6 </font></a></td> +</table><HR> +ylgn7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#d9f0a3><a title=#d9f0a3> 2 </a></td> +<td bgcolor=#addd8e><a title=#addd8e> 3 </a></td> +<td bgcolor=#78c679><a title=#78c679> 4 </a></td> +<td bgcolor=#41ab5d><a title=#41ab5d> 5 </a></td> +<td bgcolor=#238443><a title=#238443> 6 </a></td> +<td bgcolor=#005a32><a title=#005a32><font color=white> 7 </font></a></td> +</table><HR> +ylgn8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffe5><a title=#ffffe5> 1 </a></td> +<td bgcolor=#f7fcb9><a title=#f7fcb9> 2 </a></td> +<td bgcolor=#d9f0a3><a title=#d9f0a3> 3 </a></td> +<td bgcolor=#addd8e><a title=#addd8e> 4 </a></td> +<td bgcolor=#78c679><a title=#78c679> 5 </a></td> +<td bgcolor=#41ab5d><a title=#41ab5d> 6 </a></td> +<td bgcolor=#238443><a title=#238443> 7 </a></td> +<td bgcolor=#005a32><a title=#005a32><font color=white> 8 </font></a></td> +</table><HR> +ylgn9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffe5><a title=#ffffe5> 1 </a></td> +<td bgcolor=#f7fcb9><a title=#f7fcb9> 2 </a></td> +<td bgcolor=#d9f0a3><a title=#d9f0a3> 3 </a></td> +<td bgcolor=#addd8e><a title=#addd8e> 4 </a></td> +<td bgcolor=#78c679><a title=#78c679> 5 </a></td> +<td bgcolor=#41ab5d><a title=#41ab5d> 6 </a></td> +<td bgcolor=#238443><a title=#238443> 7 </a></td> +<td bgcolor=#006837><a title=#006837><font color=white> 8 </font></a></td> +<td bgcolor=#004529><a title=#004529><font color=white> 9 </font></a></td> +</table><HR> +ylgnbu3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#edf8b1><a title=#edf8b1> 1 </a></td> +<td bgcolor=#7fcdbb><a title=#7fcdbb> 2 </a></td> +<td bgcolor=#2c7fb8><a title=#2c7fb8> 3 </a></td> +</table><HR> +ylgnbu4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#a1dab4><a title=#a1dab4> 2 </a></td> +<td bgcolor=#41b6c4><a title=#41b6c4> 3 </a></td> +<td bgcolor=#225ea8><a title=#225ea8> 4 </a></td> +</table><HR> +ylgnbu5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#a1dab4><a title=#a1dab4> 2 </a></td> +<td bgcolor=#41b6c4><a title=#41b6c4> 3 </a></td> +<td bgcolor=#2c7fb8><a title=#2c7fb8> 4 </a></td> +<td bgcolor=#253494><a title=#253494> 5 </a></td> +</table><HR> +ylgnbu6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#c7e9b4><a title=#c7e9b4> 2 </a></td> +<td bgcolor=#7fcdbb><a title=#7fcdbb> 3 </a></td> +<td bgcolor=#41b6c4><a title=#41b6c4> 4 </a></td> +<td bgcolor=#2c7fb8><a title=#2c7fb8> 5 </a></td> +<td bgcolor=#253494><a title=#253494> 6 </a></td> +</table><HR> +ylgnbu7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#c7e9b4><a title=#c7e9b4> 2 </a></td> +<td bgcolor=#7fcdbb><a title=#7fcdbb> 3 </a></td> +<td bgcolor=#41b6c4><a title=#41b6c4> 4 </a></td> +<td bgcolor=#1d91c0><a title=#1d91c0> 5 </a></td> +<td bgcolor=#225ea8><a title=#225ea8> 6 </a></td> +<td bgcolor=#0c2c84><a title=#0c2c84> 7 </a></td> +</table><HR> +ylgnbu8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffd9><a title=#ffffd9> 1 </a></td> +<td bgcolor=#edf8b1><a title=#edf8b1> 2 </a></td> +<td bgcolor=#c7e9b4><a title=#c7e9b4> 3 </a></td> +<td bgcolor=#7fcdbb><a title=#7fcdbb> 4 </a></td> +<td bgcolor=#41b6c4><a title=#41b6c4> 5 </a></td> +<td bgcolor=#1d91c0><a title=#1d91c0> 6 </a></td> +<td bgcolor=#225ea8><a title=#225ea8> 7 </a></td> +<td bgcolor=#0c2c84><a title=#0c2c84> 8 </a></td> +</table><HR> +ylgnbu9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffd9><a title=#ffffd9> 1 </a></td> +<td bgcolor=#edf8b1><a title=#edf8b1> 2 </a></td> +<td bgcolor=#c7e9b4><a title=#c7e9b4> 3 </a></td> +<td bgcolor=#7fcdbb><a title=#7fcdbb> 4 </a></td> +<td bgcolor=#41b6c4><a title=#41b6c4> 5 </a></td> +<td bgcolor=#1d91c0><a title=#1d91c0> 6 </a></td> +<td bgcolor=#225ea8><a title=#225ea8> 7 </a></td> +<td bgcolor=#253494><a title=#253494> 8 </a></td> +<td bgcolor=#081d58><a title=#081d58><font color=white> 9 </font></a></td> +</table><HR> +ylorbr3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#fff7bc><a title=#fff7bc> 1 </a></td> +<td bgcolor=#fec44f><a title=#fec44f> 2 </a></td> +<td bgcolor=#d95f0e><a title=#d95f0e> 3 </a></td> +</table><HR> +ylorbr4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffd4><a title=#ffffd4> 1 </a></td> +<td bgcolor=#fed98e><a title=#fed98e> 2 </a></td> +<td bgcolor=#fe9929><a title=#fe9929> 3 </a></td> +<td bgcolor=#cc4c02><a title=#cc4c02> 4 </a></td> +</table><HR> +ylorbr5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffd4><a title=#ffffd4> 1 </a></td> +<td bgcolor=#fed98e><a title=#fed98e> 2 </a></td> +<td bgcolor=#fe9929><a title=#fe9929> 3 </a></td> +<td bgcolor=#d95f0e><a title=#d95f0e> 4 </a></td> +<td bgcolor=#993404><a title=#993404> 5 </a></td> +</table><HR> +ylorbr6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffd4><a title=#ffffd4> 1 </a></td> +<td bgcolor=#fee391><a title=#fee391> 2 </a></td> +<td bgcolor=#fec44f><a title=#fec44f> 3 </a></td> +<td bgcolor=#fe9929><a title=#fe9929> 4 </a></td> +<td bgcolor=#d95f0e><a title=#d95f0e> 5 </a></td> +<td bgcolor=#993404><a title=#993404> 6 </a></td> +</table><HR> +ylorbr7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffd4><a title=#ffffd4> 1 </a></td> +<td bgcolor=#fee391><a title=#fee391> 2 </a></td> +<td bgcolor=#fec44f><a title=#fec44f> 3 </a></td> +<td bgcolor=#fe9929><a title=#fe9929> 4 </a></td> +<td bgcolor=#ec7014><a title=#ec7014> 5 </a></td> +<td bgcolor=#cc4c02><a title=#cc4c02> 6 </a></td> +<td bgcolor=#8c2d04><a title=#8c2d04> 7 </a></td> +</table><HR> +ylorbr8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffe5><a title=#ffffe5> 1 </a></td> +<td bgcolor=#fff7bc><a title=#fff7bc> 2 </a></td> +<td bgcolor=#fee391><a title=#fee391> 3 </a></td> +<td bgcolor=#fec44f><a title=#fec44f> 4 </a></td> +<td bgcolor=#fe9929><a title=#fe9929> 5 </a></td> +<td bgcolor=#ec7014><a title=#ec7014> 6 </a></td> +<td bgcolor=#cc4c02><a title=#cc4c02> 7 </a></td> +<td bgcolor=#8c2d04><a title=#8c2d04> 8 </a></td> +</table><HR> +ylorbr9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffe5><a title=#ffffe5> 1 </a></td> +<td bgcolor=#fff7bc><a title=#fff7bc> 2 </a></td> +<td bgcolor=#fee391><a title=#fee391> 3 </a></td> +<td bgcolor=#fec44f><a title=#fec44f> 4 </a></td> +<td bgcolor=#fe9929><a title=#fe9929> 5 </a></td> +<td bgcolor=#ec7014><a title=#ec7014> 6 </a></td> +<td bgcolor=#cc4c02><a title=#cc4c02> 7 </a></td> +<td bgcolor=#993404><a title=#993404> 8 </a></td> +<td bgcolor=#662506><a title=#662506><font color=white> 9 </font></a></td> +</table><HR> +ylorrd3 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffeda0><a title=#ffeda0> 1 </a></td> +<td bgcolor=#feb24c><a title=#feb24c> 2 </a></td> +<td bgcolor=#f03b20><a title=#f03b20> 3 </a></td> +</table><HR> +ylorrd4 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffb2><a title=#ffffb2> 1 </a></td> +<td bgcolor=#fecc5c><a title=#fecc5c> 2 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 3 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 4 </a></td> +</table><HR> +ylorrd5 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffb2><a title=#ffffb2> 1 </a></td> +<td bgcolor=#fecc5c><a title=#fecc5c> 2 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 3 </a></td> +<td bgcolor=#f03b20><a title=#f03b20> 4 </a></td> +<td bgcolor=#bd0026><a title=#bd0026> 5 </a></td> +</table><HR> +ylorrd6 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffb2><a title=#ffffb2> 1 </a></td> +<td bgcolor=#fed976><a title=#fed976> 2 </a></td> +<td bgcolor=#feb24c><a title=#feb24c> 3 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 4 </a></td> +<td bgcolor=#f03b20><a title=#f03b20> 5 </a></td> +<td bgcolor=#bd0026><a title=#bd0026> 6 </a></td> +</table><HR> +ylorrd7 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffb2><a title=#ffffb2> 1 </a></td> +<td bgcolor=#fed976><a title=#fed976> 2 </a></td> +<td bgcolor=#feb24c><a title=#feb24c> 3 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 4 </a></td> +<td bgcolor=#fc4e2a><a title=#fc4e2a> 5 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 6 </a></td> +<td bgcolor=#b10026><a title=#b10026> 7 </a></td> +</table><HR> +ylorrd8 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#ffeda0><a title=#ffeda0> 2 </a></td> +<td bgcolor=#fed976><a title=#fed976> 3 </a></td> +<td bgcolor=#feb24c><a title=#feb24c> 4 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 5 </a></td> +<td bgcolor=#fc4e2a><a title=#fc4e2a> 6 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 7 </a></td> +<td bgcolor=#b10026><a title=#b10026> 8 </a></td> +</table><HR> +ylorrd9 color scheme<BR> +<table border=1 align=center> +<tr align=center> +<td bgcolor=#ffffcc><a title=#ffffcc> 1 </a></td> +<td bgcolor=#ffeda0><a title=#ffeda0> 2 </a></td> +<td bgcolor=#fed976><a title=#fed976> 3 </a></td> +<td bgcolor=#feb24c><a title=#feb24c> 4 </a></td> +<td bgcolor=#fd8d3c><a title=#fd8d3c> 5 </a></td> +<td bgcolor=#fc4e2a><a title=#fc4e2a> 6 </a></td> +<td bgcolor=#e31a1c><a title=#e31a1c> 7 </a></td> +<td bgcolor=#bd0026><a title=#bd0026> 8 </a></td> +<td bgcolor=#800026><a title=#800026><font color=white> 9 </font></a></td> +</table><HR> +<BR> +<A NAME=brewer_license>Apache-Style Software License for ColorBrewer software and ColorBrewer +Color Schemes, Version 1.1<A> +<P> +Copyright (c) 2002 Cynthia Brewer, Mark Harrower, and The Pennsylvania +State University. All rights reserved. +<P> +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: +<OL> +<LI>Redistributions as source code must retain the above copyright notice, +this list of conditions and the following disclaimer. +<LI>The end-user documentation included with the redistribution, if any, +must include the following acknowledgment: +<P> +<I>This product includes color specifications and designs developed +by Cynthia Brewer (http://colorbrewer.org/)</I>. +<P> +Alternately, this acknowledgment may appear in the software itself, if and +wherever such third-party acknowledgments normally appear. +<LI>The name "ColorBrewer" must not be used to endorse or promote products +derived from this software without prior written permission. For written +permission, please contact Cynthia Brewer at cbrewer@psu.edu. +<LI>Products derived from this software may not be called "ColorBrewer", nor +may "ColorBrewer" appear in their name, without prior written permission +of Cynthia Brewer. +</OL> +THIS SOFTWARE IS PROVIDED "AS IS" AND ANY EXPRESSED OR IMPLIED WARRANTIES, +INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND +FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CYNTHIA +BREWER, MARK HARROWER, OR THE PENNSYLVANIA STATE UNIVERSITY BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +</body> +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/command.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,171 @@ +<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> +<HTML VERSION="2.0"> +<HEAD> +<TITLE>Command-line Usage</TITLE> +</HEAD> +<BODY BGCOLOR=white> +<A NAME="top"></A> +<H1 ALIGN=CENTER>Command-line Invocation</H1> +<HR> +All Graphviz programs have a similar invocation:<BR> +<TABLE><TR><TD> +<CODE><I>cmd</I> [ <I>flags</I> ] [ <I>input files</I> ]<CODE> +</TR></TABLE> +If no input files are supplied, the program reads from <STRONG>stdin</STRONG>. +<H3>Flags</H3> +<DL> +<DT><STRONG>-G</STRONG><I>name</I>[=<I>value</I>] +<DD>Set a graph attribute, with default <I>value</I> = <TT>true</TT>. +<DT><STRONG>-N</STRONG><I>name</I>[=<I>value</I>] +<DD>Set a default node attribute, with default <I>value</I> = <TT>true</TT>. +<DT><STRONG>-E</STRONG><I>name</I>[=<I>value</I>] +<DD>Set a default edge attribute, with default <I>value</I> = <TT>true</TT>. +<DT><A NAME=minusK><STRONG>-K</STRONG></A><I>layout</I> +<DD>Specifies which default layout algorithm to use, overriding the default from the command name. For example, running +<TT>dot -Kneato</TT> is equivalent to running <TT>neato</TT>. +<DT><A NAME=d:T><STRONG>-T</STRONG></A><I>format</I>[:<I>renderer</I>[:<I>formatter</I>]] +<DD>Set output language to one of the <A HREF=output.html>supported formats</A>. +By default, <A HREF=output.html#d:dot>attributed dot</A> is produced. +<P> +Depending on how Graphviz was built, there may be multiple renderers for +generating a particular output format, and multiple formatters for +creating the final output. For example, a typical installation +can produce <A HREF="http://www.graphviz.org/doc/info/output.html#d:png">PNG</A> +output using either the Cairo or GD library. The desired rendering engine +can be specified after a colon. If there are multiple formatting engines +available, the desired one can be specified in a similar fashion after +the rendering engine. Thus, <TT>-Tpng:cairo</TT> specifies PNG +output produced by Cairo (using the Cairo's default formatter), and +<TT>-Tpng:cairo:gd</TT> specifies PNG +output produced by Cairo formatted using the GD library. +<P> +If no renderer is specified, or a renderer but no formatter, the default one +is invoked. The flag <TT>-T<I>format</I>:</TT> produces a list of all +of the renderers available for the specified <I>format</I>, the first one +listed with a prefix matching <I>format</I> being the default. +Using the <TT>-v</TT> flag, described below, will print which format, +renderer, and formatter are actually used. +<DT><STRONG>-V</STRONG> +<DD>Emit version information and exit. +<DT><STRONG>-l</STRONG><I>library</I> +<DD>User-supplied, device-dependent library text. Multiple flags may +be given. These strings are passed to the code generator at the +beginning of output. +<P>For PostScript output, they are treated as file names +whose content will be included in the preamble after the standard preamble. +If <I>library</I> is the empty string <TT>""</TT>, the standard preamble +is not emitted. +<DT><A NAME=d:n><STRONG>-n</STRONG>[<I>num</I>]</A> +<DD>Sets no-op flag in <STRONG>neato</STRONG>. +If set, <STRONG>neato</STRONG> assumes nodes have already been +positioned and all nodes have a <A HREF=attrs.html#d:pos>pos</A> +attribute giving +the positions. It then performs an optional adjustment to remove node-node +overlap, depending on the value of the +<A HREF=attrs.html#d:overlap>overlap</A> attribute, computes the edge +layouts, depending on the value of the +<A HREF=attrs.html#d:splines>splines</A> attribute, and +emits the graph in the appropriate format. If <I>num</I> is supplied, +the following actions occur: +<DL> +<DT><I>num</I> = 1 +<DD>Equivalent to <STRONG>-n</STRONG>. +<DT><I>num</I> > 1 +<DD>Use node positions as specified, with no adjustment to +remove node-node overlaps, and use any edge layouts already specified +by the <A HREF=attrs.html#d:pos>pos</A> attribute. <STRONG>neato</STRONG> +computes an edge layout for any edge that does not have a <B>pos</B> attribute. +As usual, edge layout is guided by the +<A HREF=attrs.html#d:splines>splines</A> attribute. +</DL> +<DT><STRONG>-o</STRONG><I>outfile</I> +<DD>Write output to file <I>outfile</I>. By default, output goes to +<STRONG>stdout</STRONG>. +<DT><STRONG>-O</STRONG> +<DD>Automatically generate output file names based on the input +file name and the various output formats specified by the <STRONG>-T</STRONG> +flags. +<DT><STRONG>-P</STRONG> +<DD>Automatically generate a graph that shows the plugin configuration of +the current executable. e.g. <STRONG>dot -P -Tps | lpr</STRONG> +<DT><STRONG>-q</STRONG> +<DD>Suppress warning messages. +<DT><A NAME=d:s><STRONG>-s</STRONG></A>[<I>scale</I>] +<DD>Set input scale to <I>scale</I>. If this value is omitted, +72.0 is used. This number is used to convert the point coordinate +units used in the <A HREF=attrs.html#d:pos>pos</A> attribute +into inches, which is what is expected by neato and fdp. +Thus, feeding the output of a graph laid out by one program into +neato or fdp almost always requires this flag. +Ignored if the <STRONG>-n</STRONG> flag is used. +<DT><STRONG>-v</STRONG> +<DD>Verbose mode +<DT><STRONG>-x</STRONG> +<DD>In <STRONG>neato</STRONG>, on input, prune isolated nodes and peninsulas. +This removes uninteresting graph structure and produces a less cluttered +drawing. +<DT><STRONG>-y</STRONG> +<DD>By default, the coordinate system used in generic output formats, +such as <A HREF=output.html#d:dot>attributed dot</A>, +<A HREF=output.html#d:xdot>extended dot</A>, +<A HREF=output.html#d:plain>plain</A> and +<A HREF=output.html#d:plain-ext>plain-ext</A>, +is the standard cartesian system with the origin in the lower left corner, +and with increasing y coordinates as points move from bottom to top. +If the <CODE>-y</CODE> flag is used, the coordinate system is inverted, +so that increasing values of y correspond to movement from top to bottom. +<DT><STRONG>-?</STRONG> +<DD>Print usage information, then exit. +</DL> +If multiple <STRONG>-T</STRONG> flags are given, drawings of the graph +are emitted in each of the specified formats. Multiple <STRONG>-o</STRONG> +flags can be used to specify the output file for each format. If there +are more formats than files, the remaining formats are written to +<STRONG>stdout</STRONG>. +<P> +Note that the <STRONG>-G</STRONG>, +<STRONG>-N</STRONG> and +<STRONG>-E</STRONG> flags override any initial attribute declarations +in the input graph, +i.e., those attribute statements appearing before any node, edge or +subgraph definitions. +In addition, these flags cause the related attributes to be permanently +attached to the graph. Thus, if attributed dot is used for +output, the graph will have these attributes. +<H3>Environment Variables</H3> +<DL> +<DT><STRONG>GDFONTPATH</STRONG> +<DD> +List of pathnames giving directories which a program should search for fonts. +Overridden by <A HREF=#d:DOTFONTPATH>DOTFONTPATH</A>. +<IT>Used only if Graphviz is not built with the <TT>fontconfig</TT> library</IT> +<DT><A NAME=d:DOTFONTPATH><STRONG>DOTFONTPATH</STRONG></A> +<DD> +List of pathnames giving directories which a program should search for fonts. +Overridden by <A HREF=attrs.html#d:fontpath><STRONG>fontpath</STRONG></A>. +<IT>Used only if Graphviz is not built with the <TT>fontconfig</TT> library</IT> +<DT><A NAME=d:SERVER_NAME><STRONG>SERVER_NAME</STRONG></A> +<DD> +If defined, this indicates that the software is running as a web application, +which restricts access to image files. See +<A HREF=d:GV_FILE_PATH>GV_FILE_PATH</A>. +<DT><A NAME=d:GV_FILE_PATH><STRONG>GV_FILE_PATH</STRONG></A> +<DD> +If <A HREF=d:SERVER_NAME>SERVER_NAME</A> is defined, image files are +restricted to the directory specified by <TT>GV_FILE_PATH</TT>. +Note that sometimes, when using one of the layout programs in a web +script, it is not enough to use an export command but rather the +variables should be set when the command is run, for example,<BR> +<CODE> +SERVER_NAME=xxx GV_FILE_PATH="images/" dot -Tpng -o x.png x.dot +</CODE> +<DT><A NAME=d:GVBINDIR><STRONG>GVBINDIR</STRONG></A> +<DD> +Indicates which directory contains the Graphviz config file and +plug-in libraries. If it is defined, the value overrides any other +mechanism for finding this directory. If Graphviz is properly installed, +it should not be needed, though it can be useful for relocation on +platforms not running Linux or Windows. +</DL> +</BODY> +</HTML>
Binary file toolboxes/graph_visualisation/share/graphviz/doc/html/info/doubleoctagon.gif has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/grammar.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> +<HTML VERSION="2.0"> +<HEAD> +<TITLE>Command-line Invocation</TITLE> +</HEAD> +<BODY BGCOLOR=white> +<A NAME="top"></A> +<H1 align=CENTER>Command-line Invocation</H1> +<HR> +<H2>Flags</H1> +<DL> +<DT>-Gname=value +<DT>Set graph attribue +<DT>-Nname=value +<DT>Set default node attribue +<DT>-Ename=value +<DT>Set default edge attribue +<DT>-T<lang> +<DT>Set output language (see output_info) +<DT>-V +<DT>Emit version info and exit +<DT>-l<libfile> +<DT>User-supplied, device-depende library. Multiple libraries may +be given. These names are passed to the code generator at the +beginning of output. +<DT>-n[<num>] +<DT>Sets no-op flag in neato. In this case, neato assumes nodes +have already been positioned and have a pos attribute giving +this position. It then just computes the edge layouts and +emits the graph in the appropriate format. If <num> is used +and > 1, neato checks for previously positioned edges, with +control points provided in the pos attribute. It uses these +if given; otherwise, it computes the edge layout. +<DT>-o<outfile> +<DT>write output to outfile (default: stdout) +<DT>-s<scale> +<DT>Set input scale to <scale>. If this value is omitted, +72.0 is used. This number is used to convert pos point +units into inches. For example, dot -Tdot produces pos +values in points. To use this as input to neato -n, one +needs -s (equivalently, -s72). +<DT>-v +<DT>verbose mode +<DT>-x +<DT>In neato, on input, prune isolated nodes and peninsulas +</DL> +</BODY> +</HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/html2.dot Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,46 @@ +digraph G { + rankdir=LR + node [shape=plaintext] + a [ + label=< +<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0"> + <TR><TD ROWSPAN="3" BGCOLOR="yellow">class</TD></TR> + <TR><TD PORT="here" BGCOLOR="lightblue">qualifier</TD></TR> +</TABLE>> + ] + b [shape=ellipse style=filled + label=< +<TABLE BGCOLOR="bisque"> + <TR><TD COLSPAN="3">elephant</TD> + <TD ROWSPAN="2" BGCOLOR="chartreuse" + VALIGN="bottom" ALIGN="right">two</TD> </TR> + <TR><TD COLSPAN="2" ROWSPAN="2"> + <TABLE BGCOLOR="grey"> + <TR> <TD>corn</TD> </TR> + <TR> <TD BGCOLOR="yellow">c</TD> </TR> + <TR> <TD>f</TD> </TR> + </TABLE> </TD> + <TD BGCOLOR="white">penguin</TD> + </TR> + <TR> <TD COLSPAN="2" BORDER="4" ALIGN="right" PORT="there">4</TD> </TR> +</TABLE>> + ] + c [ + label=<long line 1<BR/>line 2<BR ALIGN="LEFT"/>line 3<BR ALIGN="RIGHT"/>> + ] + + subgraph { rank=same b c } + a:here -> b:there [arrowtail = diamond] + c -> b + d [shape=triangle] + d -> c [label=< +<TABLE> + <TR><TD BGCOLOR="red" WIDTH="10"> </TD> + <TD>Edge labels<BR/>also</TD> + <TD BGCOLOR="blue" WIDTH="10"> </TD> + </TR> +</TABLE>> + ] + +} +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/html3.dot Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +digraph structs { + node [shape=plaintext]; + + struct1 [label=<<TABLE> + <TR> + <TD>line 1</TD> + <TD BGCOLOR="blue"><FONT COLOR="white">line2</FONT></TD> + <TD BGCOLOR="gray"><FONT POINT-SIZE="24.0">line3</FONT></TD> + <TD BGCOLOR="yellow"><FONT POINT-SIZE="24.0" FACE="playbill">line4</FONT></TD> + <TD> + <TABLE CELLPADDING="0" BORDER="0" CELLSPACING="0"> + <TR> + <TD><FONT COLOR="green">Mixed</FONT></TD> + <TD><FONT COLOR="red">fonts</FONT></TD> + </TR> + </TABLE> + </TD> + </TR> + </TABLE>>]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/html4.dot Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +digraph structs { + node [shape=plaintext]; + + struct1 [label=<<TABLE> + <TR><TD><IMG SRC="eqn.png"/></TD></TR> + <TR><TD>caption</TD></TR> + </TABLE>>]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/index.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> +<HTML VERSION="2.0"> +<HEAD> +<TITLE>GraphViz Reference</TITLE> +</HEAD> +<BODY BGCOLOR=white> +<A NAME="top"></A> +<H1 align=CENTER>GraphViz Reference</H1> +<HR> +<A HREF=lang.html>The DOT Language</A><BR> +<A HREF=command.html>Command-line Usage</A><BR> +<A HREF=output.html>Output Formats</A><BR> +<A HREF=attrs.html>Graph Attributes</A><BR> +<A HREF=shapes.html>Node Shapes</A><BR> +<A HREF=colors.html>Colors</A><BR> +</BODY> +</HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/lang.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,215 @@ +<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> +<!-- + This is a generated document. Do not edit. +--> +<HTML VERSION="2.0"> +<HEAD> +<TITLE>The DOT Language</TITLE> +</HEAD> +<BODY BGCOLOR=white> +<A NAME="top"></A> +<H1 ALIGN=CENTER>The DOT Language</H1> +<HR> +The following is an abstract grammar defining the DOT language. +Terminals are shown in bold font and nonterminals in italics. +Literal characters are given in single quotes. +Parentheses ( and ) indicate grouping when needed. +Square brackets [ and ] enclose optional items. +Vertical bars | separate alternatives. +<TABLE> +<TR> + <TD ALIGN=RIGHT><I>graph</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>[ <B>strict</B> ] (<B>graph</B> | <B>digraph</B>) [ <I>ID</I> ] <B>'{'</B> <I>stmt_list</I> <B>'}'</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>stmt_list</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>[ <I>stmt</I> [ <B>';'</B> ] [ <I>stmt_list</I> ] ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>stmt</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>node_stmt</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><I>edge_stmt</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><I>attr_stmt</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><I>ID</I> <B>'='</B> <I>ID</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><I>subgraph</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>attr_stmt</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>(<B>graph</B> | <B>node</B> | <B>edge</B>) <I>attr_list</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>attr_list</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><B>'['</B> [ <I>a_list</I> ] <B>']'</B> [ <I>attr_list</I> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>a_list</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>ID</I> [ <B>'='</B> <I>ID</I> ] [ <B>','</B> ] [ <I>a_list</I> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>edge_stmt</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>(<I>node_id</I> | <I>subgraph</I>) <I>edgeRHS</I> [ <I>attr_list</I> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>edgeRHS</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>edgeop</I> (<I>node_id</I> | <I>subgraph</I>) [ <I>edgeRHS</I> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>node_stmt</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>node_id</I> [ <I>attr_list</I> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>node_id</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>ID</I> [ <I>port</I> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>port</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><B>':'</B> <I>ID</I> [ <B>':'</B> <I>compass_pt</I> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B>':'</B> <I>compass_pt</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>subgraph</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>[ <B>subgraph</B> [ <I>ID</I> ] ] <B>'{'</B> <I>stmt_list</I> <B>'}'</B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>compass_pt</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>(<B>n</B> | <B>ne</B> | <B>e</B> | <B>se</B> | <B>s</B> | <B>sw</B> | <B>w</B> | <B>nw</B> | <B>c</B> | <B>_</B>)</TD> +</TR> +</TABLE> +<P> +The keywords <B>node</B>, <B>edge</B>, <B>graph</B>, <B>digraph</B>, +<B>subgraph</B>, and <B>strict</B> are case-independent. +Note also that the allowed compass point values are not keywords, so +these strings can be used elsewhere as ordinary identifiers and, conversely, +the parser will actually accept any identifier. +<P> +An <I>ID</I> is one of the following: +<MENU> +<LI> Any string of alphabetic (<TT>[a-zA-Z\200-\377]</TT>) characters, underscores (<TT>'_'</TT>) or +digits (<TT>[0-9]</TT>), not beginning with a digit; +<LI> a number [<tt>-</tt>]<sup>?</sup>(<tt>.</tt>[<tt>0</tt>-<tt>9</tt>]<sup>+</sup> | [<tt>0</tt>-<tt>9</tt>]<sup>+</sup>(<tt>.</tt>[<tt>0</tt>-<tt>9</tt>]<sup>*</sup>)<sup>?</sup> ); +<LI> any double-quoted string ("...") possibly containing escaped +quotes (\")<SUP>1</SUP>; +<LI> an <A NAME=html>HTML string</A> (<...>). +</MENU> +Note that in HTML strings, angle +brackets must occur in matched pairs, and unescaped newlines are allowed. +In addition, the content must be legal XML, so that the special XML +escape sequences for ", &, <, and > may be necessary +in order to embed these characters in attribute values or raw text. +<P> +Both quoted strings and HTML strings are scanned as a unit, so +any embedded comments will be treated as part of the strings. +<P> +An <I>edgeop</I> is <TT>-></TT> in directed graphs and <TT>--</TT> in +undirected graphs. +<P> +An <I>a_list</I> clause of the form <I>ID</I> is equivalent to +<I>ID</I><TT>=true</TT>. +<P> +The language supports C++-style comments: <TT>/* */</TT> and <TT>//</TT>. +In addition, a line beginning with a '#' character is considered a line +output from a C preprocessor (e.g., # 34 to indicate line 34 ) and discarded. +<P> +Semicolons aid readability but are not required except in the rare case +that a named subgraph with no body immediately preceeds an anonymous +subgraph, since the precedence rules cause this sequence to be parsed as +a subgraph with a heading and a body. +Also, any amount of whitespace may be inserted between terminals. +<P> +As another aid for readability, dot allows single logical lines to +span multiple physical lines using the standard C convention of a +backslash immediately preceding a newline character. In addition, +double-quoted strings can be concatenated using a '+' operator. +As HTML strings can contain newline characters, they do not support the +concatenation operator. +<H2>Semantic Notes</H2> +If a default attribute is +defined using a <B>node</B>, <B>edge</B>, or <B>graph</B> statement, +or by an attribute assignment not attached to a node or edge, any object of the +appropriate type defined afterwards will inherit this attribute value. +This holds until the default attribute is set to a new value, from which +point the new value is used. Objects defined before a default attribute +is set will have an empty string value attached to the attribute once +the default attribute definition is made. +<P> +Note, in particular, that a subgraph receives the attribute settings of +its parent graph at the time of its definition. This can be useful; for +example, one can assign a font to the root graph and all subgraphs will +also use the font. For some attributes, however, this property is +undesirable. If one attaches a label to the root graph, it is probably +not the desired effect to have the label used by all subgraphs. Rather +than listing the graph attribute at the top of the graph, and the +resetting the attribute as needed in the subgraphs, one can simple defer +the attribute definition if the graph until the appropriate subgraphs +have been defined. +<P> +If an edge belongs to a cluster, its endpoints belong to that cluster. +Thus, where you put an edge can effect a layout, as clusters are sometimes +laid out recursively. +<H2>Character encodings</H2> +The DOT language assumes at least the ascii character set. +Quoted strings, both ordinary and HTML-like, may contain non-ascii characters. +In most cases, these strings are uninterpreted: they simply serve as +unique identifiers or values passed through untouched. Labels, however, +are meant to be displayed, which requires that the software be able to +compute the size of the text and determine the appropriate glyphs. +For this, it needs to know what character encoding is used. +<P> +By default, DOT assumes the UTF-8 character encoding. It also accepts +the Latin1 (ISO-8859-1) character set, assuming the input graph uses +the <B><A HREF=attrs.html#a:charset>charset</A></B> attribute to +specify this. For graphs using other +character sets, there are usually programs, such as <TT>iconv</TT>, which +will translate from one character set to another. +<P> +Another way to avoid non-ascii characters in labels is to use HTML entities +for special characters. During label evaluation, these entities are +translated into the underlying character. This +<a HREF="http://www.graphviz.org/doc/char.html"> +table</a> shows the supported entities, with their Unicode value, a typical +glyph, and the HTML entity name. Thus, to include a lower-case Greek beta +into a string, one can use the ascii sequence <TT>&beta;</TT>. +In general, one should only use entities that are allowed in the output +character set, and for which there is a glyph in the font. +<HR> +<OL TYPE="1"> +<LI> In quoted strings in DOT, the only escaped character is double-quote +("). That is, in quoted strings, the dyad \" is converted to "; all other +characters are left unchanged. In particular, \\ remains \\. Layout +engines may apply additional escape sequences. +</OL> +</BODY> +</HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/info/output.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,582 @@ +<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> +<!-- + This is a generated document. Do not edit. +--> +<HTML VERSION="2.0"> +<HEAD> +<TITLE>Output Formats</TITLE> +</HEAD> +<BODY BGCOLOR=white> +<A NAME="top"></A> +<H1 align=CENTER>Output Formats</H1> +<HR> +The output format is specified with the <STRONG>-T</STRONG><I>lang</I> +flag on the <A HREF=command.html>command line</A>, where <I>lang</I> +is one of the parameters listed below. +<P> +The formats actually available in a given Graphviz system depend on +how the system was built and the presence of additional libraries. +To see what formats <b>dot</b> supports, run <TT>dot -T?</TT>. +See the <A HREF=command.html#d.T> description of the -T</A> +flag for additional information. +<P> +Note that the internal coordinate system has the origin +in the lower left corner. +Thus, positions in the +<A HREF=#d:canon>canon</A>, +<A HREF=#d:dot>dot</A>, +<A HREF=#d:xdot>xdot</A>, +<A HREF=#d:plain>plain</A>, and +<A HREF=#d:plain-ext>plain-ext</A> +formats need to be interpreted in this manner. +<P> +<TABLE ALIGN=CENTER> +<TR><TH>Command-line<BR>parameter</TH><TH>Format</TH></TR> + <TR><TD ALIGN=CENTER><A NAME=a:bmp HREF=#d:bmp>bmp</A> +</TD><TD>Windows Bitmap Format</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:canon HREF=#d:canon>canon</A> +<BR><A NAME=a:dot HREF=#d:dot>dot</A> +<BR><A NAME=a:xdot HREF=#d:xdot>xdot</A> +</TD><TD>DOT</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:cmap HREF=#d:cmap>cmap</A> +</TD><TD>Client-side imagemap (deprecated)</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:dia HREF=#d:dia>dia</A> +</TD><TD>Dia format</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:eps HREF=#d:eps>eps</A> +</TD><TD>Encapsulated PostScript</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:fig HREF=#d:fig>fig</A> +</TD><TD>FIG</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:gd HREF=#d:gd>gd</A> +<BR><A NAME=a:gd2 HREF=#d:gd2>gd2</A> +</TD><TD>GD/GD2 formats</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:gif HREF=#d:gif>gif</A> +</TD><TD>GIF</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:gtk HREF=#d:gtk>gtk</A> +</TD><TD>GTK canvas</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:hpgl HREF=#d:hpgl>hpgl</A> +</TD><TD>HP-GL/2</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:ico HREF=#d:ico>ico</A> +</TD><TD>Icon Image File Format</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:imap HREF=#d:imap>imap</A> +<BR><A NAME=a:cmapx HREF=#d:cmapx>cmapx</A> +</TD><TD>Server-side and client-side imagemaps</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:imap_np HREF=#d:imap_np>imap_np</A> +<BR><A NAME=a:cmapx_np HREF=#d:cmapx_np>cmapx_np</A> +</TD><TD>Server-side and client-side imagemaps</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:ismap HREF=#d:ismap>ismap</A> +</TD><TD>Server-side imagemap (deprecated)</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:jpg HREF=#d:jpg>jpg</A> +<BR><A NAME=a:jpeg HREF=#d:jpeg>jpeg</A> +<BR><A NAME=a:jpe HREF=#d:jpe>jpe</A> +</TD><TD>JPEG</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:mif HREF=#d:mif>mif</A> +</TD><TD>FrameMaker MIF format</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:mp HREF=#d:mp>mp</A> +</TD><TD>MetaPost</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:pcl HREF=#d:pcl>pcl</A> +</TD><TD>PCL</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:pdf HREF=#d:pdf>pdf</A> +</TD><TD>Portable Document Format (PDF)</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:pic HREF=#d:pic>pic</A> +</TD><TD>PIC</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:plain HREF=#d:plain>plain</A> +<BR><A NAME=a:plain-ext HREF=#d:plain-ext>plain-ext</A> +</TD><TD>Simple text format</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:png HREF=#d:png>png</A> +</TD><TD>Portable Network Graphics format</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:ps HREF=#d:ps>ps</A> +</TD><TD>PostScript</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:ps2 HREF=#d:ps2>ps2</A> +</TD><TD>PostScript for PDF</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:svg HREF=#d:svg>svg</A> +<BR><A NAME=a:svgz HREF=#d:svgz>svgz</A> +</TD><TD>Scalable Vector Graphics</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:tga HREF=#d:tga>tga</A> +</TD><TD>Truevision Targa Format (TGA)</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:tif HREF=#d:tif>tif</A> +<BR><A NAME=a:tiff HREF=#d:tiff>tiff</A> +</TD><TD>TIFF (Tag Image File Format)</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:vml HREF=#d:vml>vml</A> +<BR><A NAME=a:vmlz HREF=#d:vmlz>vmlz</A> +</TD><TD>Vector Markup Language (VML)</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:vrml HREF=#d:vrml>vrml</A> +</TD><TD>VRML</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:vtx HREF=#d:vtx>vtx</A> +</TD><TD>Visual Thought format</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:wbmp HREF=#d:wbmp>wbmp</A> +</TD><TD>Wireless BitMap format</TD> </TR> + <TR><TD ALIGN=CENTER><A NAME=a:xlib HREF=#d:xlib>xlib</A> +</TD><TD>Xlib canvas</TD> </TR> +</TABLE> +<HR> +<H2>Format Descriptions</H2> +<DL> +<DT><A NAME=d:bmp HREF=#a:bmp><STRONG>bmp</STRONG></A> +<DD>Outputs images in the Windows <A HREF="http://en.wikipedia.org/wiki/Bitmap">BMP</A> format. + +<DT><A NAME=d:canon HREF=#a:canon><STRONG>canon</STRONG></A> +,<DT><A NAME=d:dot HREF=#a:dot><STRONG>dot</STRONG></A> +,<DT><A NAME=d:xdot HREF=#a:xdot><STRONG>xdot</STRONG></A> +<DD>These formats produce output in the +<A HREF=lang.html>dot language</A>. +Using <B>canon</B> produces a prettyprinted version of the input, +with no layout performed. +<P> +The <B>dot</B> option corresponds to attributed dot output, +and is the default output format. +It reproduces the input, along with layout information for the graph. +In particular, a <A HREF=attrs.html#d:bb>bb</A> attribute is +attached to the graph, specifying the bounding box of the drawing. +If the graph has a label, its position is specified by the +<A HREF=attrs.html#d:lp>lp</A> attribute. +<P> +Each node gets <A HREF=attrs.html#d:pos>pos</A>, +<A HREF=attrs.html#d:width>width</A> and +<A HREF=attrs.html#d:height>height</A> attributes. If the node is a record, +the record rectangles are given in the +<A HREF=attrs.html#d:rects>rects</A> attribute. +If the node is a polygon and the +<A HREF=attrs.html#d:vertices>vertices</A> attribute is defined, this +attribute contains the vertices of the node. +<P> +Every edge is +assigned a <A HREF=attrs.html#d:pos>pos</A> attribute, +and if the edge has a label, the label position +is given in <A HREF=attrs.html#d:lp>lp</A>. +<P> +The <B>xdot</B> format extends the +<B>dot</B> format by providing much more detailed information about +how graph components are drawn. It relies on additional attributes +for nodes, edges and graphs. +<P> +The format is preliminary; comments and +suggestions for better representations are welcome. +To allow for changes in the format, Graphviz attaches the attribute +<TT>xdotversion</TT> to the graph. +<P> +Additional drawing attributes can appear on nodes, edges, clusters and +on the graph itself. There are six new attributes: +<SPACER TYPE=VERTICAL size=10> +<TABLE border bgcolor=beige> +<TR><TD>_draw_<TD colspan=2>Drawing operations +<TR><TD>_ldraw_<TD colspan=2>Label drawing +<TR><TD>_hdraw_<TD>Head arrowhead<TD>Edge only +<TR><TD>_tdraw_<TD>Tail arrowhead<TD>Edge only +<TR><TD>_hldraw_<TD>Head label<TD>Edge only +<TR><TD>_tldraw_<TD>Tail label<TD>Edge only +</TABLE> +<P> +For a given graph object, one will typically a draw directive before the +label directive. For example, for a node, one would first use the commands +in <B>_draw_</B> followed by the commands in <B>_ldraw_</B>. +<P> +The value of these attributes consists of the concatenation of some +(multi-)set of the following 13 rendering or attribute operations. +(The number is parentheses gives the xdot version when the operation +was added to the format. If no version number is given, the operation +was in the original specification.) +<SPACER TYPE=VERTICAL size=10> +<TABLE border bgcolor=beige> +<TR><TD>E x<sub>0</sub> y<sub>0</sub> w h +<TD>Filled ellipse ((x-x<sub>0</sub>)/w)<sup>2</sup> + ((y-y<sub>0</sub>)/h)<sup>2</sup> = 1 +<TR><TD>e x<sub>0</sub> y<sub>0</sub> w h +<TD>Unfilled ellipse ((x-x<sub>0</sub>)/w)<sup>2</sup> + ((y-y<sub>0</sub>)/h)<sup>2</sup> = 1 +<TR><TD>P n x<sub>1</sub> y<sub>1</sub> ... x<sub>n</sub> y<sub>n</sub> +<TD>Filled polygon using the given n points +<TR><TD>p n x<sub>1</sub> y<sub>1</sub> ... x<sub>n</sub> y<sub>n</sub> +<TD>Unfilled polygon using the given n points +<TR><TD>L n x<sub>1</sub> y<sub>1</sub> ... x<sub>n</sub> y<sub>n</sub> +<TD>Polyline using the given n points +<TR><TD>B n x<sub>1</sub> y<sub>1</sub> ... x<sub>n</sub> y<sub>n</sub> +<TD>B-spline using the given n control points +<TR><TD>b n x<sub>1</sub> y<sub>1</sub> ... x<sub>n</sub> y<sub>n</sub> +<TD>Filled B-spline using the given n control points (1.1) +<TR><TD>T x y j w n -<I>b<sub>1</sub>b<sub>2</sub>...b<sub>n</sub></I> +<TD>Text drawn using the baseline point (x,y). The text consists of the +n bytes following '-'. The text should be left-aligned (centered, +right-aligned) on the point if j is -1 (0, 1), respectively. The value +w gives the width of the text as computed by the library. +<TR><TD>C n -<I>b<sub>1</sub>b<sub>2</sub>...b<sub>n</sub></I> +<TD>Set fill color. The color value consists of the +n bytes following '-'. (1.1) +<TR><TD>c n -<I>b<sub>1</sub>b<sub>2</sub>...b<sub>n</sub></I> +<TD>Set pen color. The color value consists of the +n bytes following '-'. (1.1) +<TR><TD>F s n -<I>b<sub>1</sub>b<sub>2</sub>...b<sub>n</sub></I> +<TD>Set font. The font size is s points. The font name consists of the +n bytes following '-'. (1.1) +<TR><TD>S n -<I>b<sub>1</sub>b<sub>2</sub>...b<sub>n</sub></I> +<TD>Set style attribute. The style value consists of the +n bytes following '-'. The syntax of the value is the same as +specified for a <B>styleItem</B> in <A HREF=attrs.html#k:style>style</A>. (1.1) +<TR><TD>I x y w h n -<I>b<sub>1</sub>b<sub>2</sub>...b<sub>n</sub></I> +<TD>Externally-specified image drawn in the box with lower left +corner (x,y) and upper right corner (x+w,y+h). The name of the image +consists of the n bytes following '-'. This is usually a bitmap +image. Note that the image size, even when converted from pixels to +points, might be different from the required size (w,h). It is +assumed the renderer will perform the necessary scaling. (1.2) +</TABLE> +<P> +Note that the filled figures (ellipses, polygons and B-Splines) +imply two operations: first, drawing the filled figure with the +current fill color; second, drawing an unfilled figure with the +current pen color, pen width and pen style. +the +<P> +Style values which can be incorporated in the graphics model do not +appear in xdot output. In particular, the style values +<TT>filled</TT>, <TT>rounded</TT>, <TT>diagonals</TT>, and <TT>invis</TT> +will not appear. Indeed, if style contains <TT>invis</TT>, +there will not be any xdot output at all. +<P> +In handling text alignment, the application may want to recompute the +string width using its own rendering primitives. +<P> +The text operation is only used in the label attributes. Normally, +the non-text operations are only used in the non-label attributes. +If, however, the <A HREF=attrs.html#d:decorate>decorate</A> +attribute is set on an edge, its label +attribute will also contain a polyline operation. +In addition, if a label is a complex, HTML-like label, it will also +contain non-text operations. +<P> +All coordinates and sizes are in points. +Note though that if +an edge or node is invisible, no drawing operations are attached to it. +<P> +Version info: +<TABLE border > +<TR><TH>Xdot version</TH><TH>Graphviz version</TH></TR> +<TR><TD>1.0</TD><TD>1.9</TD></TR> +<TR><TD>1.1</TD><TD>2.8</TD></TR> +<TR><TD>1.2</TD><TD>2.13</TD></TR> +</TABLE> + +<DT><A NAME=d:cmap HREF=#a:cmap><STRONG>cmap</STRONG></A> +<DD>Produces map files for client-side image maps. The cmap format is +mostly identical to cmapx, but the latter is well-formed XML amenable +to processing by XML tools. In particular, the cmapx output is wrapped in +<map></map>. +<P> +See <A HREF=#ID>Note</A>. + +<DT><A NAME=d:dia HREF=#a:dia><STRONG>dia</STRONG></A> +<DD>Produces <A HREF="http://www.gnome.org/projects/dia/">Dia</A> output. + +<DT><A NAME=d:eps HREF=#a:eps><STRONG>eps</STRONG></A> +<DD>Produces Encapsulated PostScript output. +At present, this is only guaranteed to be correct for a single +input graph since the Bounding Box information has to appear +at the beginning of the output, and this will be based on the first graph. + +<DT><A NAME=d:fig HREF=#a:fig><STRONG>fig</STRONG></A> +<DD>Outputs graphs in the FIG graphics language. + +<DT><A NAME=d:gd HREF=#a:gd><STRONG>gd</STRONG></A> +,<DT><A NAME=d:gd2 HREF=#a:gd2><STRONG>gd2</STRONG></A> +<DD>Output images in the GD and GD2 format. These are the internal +formats used by the gd library. The latter is compressed. + +<DT><A NAME=d:gif HREF=#a:gif><STRONG>gif</STRONG></A> +<DD>Outputs GIF bitmap images. + +<DT><A NAME=d:gtk HREF=#a:gtk><STRONG>gtk</STRONG></A> +<DD>Creates a <A HREF="http://www.gtk.org/">GTK</A> window and displays the output there. + +<DT><A NAME=d:hpgl HREF=#a:hpgl><STRONG>hpgl</STRONG></A> +<DD>Produces output in the HP-GL/2 vector graphic printer language. + +<DT><A NAME=d:ico HREF=#a:ico><STRONG>ico</STRONG></A> +<DD>Outputs images in the Windows <A HREF="http://en.wikipedia.org/wiki/ICO_(icon_image_file_format)">ICO format</A>. + +<DT><A NAME=d:imap HREF=#a:imap><STRONG>imap</STRONG></A> +,<DT><A NAME=d:cmapx HREF=#a:cmapx><STRONG>cmapx</STRONG></A> +<DD>Produces map files for server-side and client-side image maps, +These can be used in a web page with +a graphical form of the output, e.g. in JPEG or GIF format, to attach +links to nodes and edges. For example, to create a server-side map +given the dot file +<PRE> +/* x.dot */ +digraph mainmap { + URL="http://www.research.att.com/base.html"; + command [URL="http://www.research.att.com/command.html"]; + command -> output [URL="colors.html"]; +} +</PRE> +one would process the graph and generate two output files: +<PRE> +dot -Timap -ox.map -Tgif -ox.gif x.dot +</PRE> +and then refer to it in a web page: +<XMP> +<A HREF="x.map"><IMG SRC="x.gif" ismap="ismap" /></A> +</XMP> +For client-side maps, one again generates two output files: +<PRE> +dot -Tcmapx -ox.map -Tgif -ox.gif x.dot +</PRE> +and uses the HTML +<XMP> +<IMG SRC="x.gif" USEMAP="#mainmap" /> +... [content of x.map] ... +</XMP> +<A HREF=attrs.html#d:URL>URLs</A> can be attached to the root +graph, nodes and edges. If a node has a URL, clicking in the node +will activate the link. +If an edge has a URL, various +points along the edge (but not necessarily the head or tail) +will link to it. In addition, if the edge has a +<A HREF=attrs.html#d:label>label</A>, that will link +to the URL. +As for the head of the edge, this is linked to the +<A HREF=attrs.html#d:headURL>headURL</A>, if set. +Otherwise, it is linked to the edge's URL if that is defined. +The analogous description holds for the tail and the +<A HREF=attrs.html#d:tailURL>tailURL</A>. +A URL associated with the graph is used as a default link. +<P> +If the URL +of a node contains the escape sequence "\N", it will be replaced by +the node's name. +If the headURL is defined and contains the escape sequence "\N", +it will be replaced by +the <A HREF=attrs.html#d:headlabel>headlabel</A>, if defined. +The analogous result holds for the tailURL and the +<A HREF=attrs.html#d:taillabel>taillabel</A>. +<P> +See <A HREF=#ID>Note</A>. + +<DT><A NAME=d:imap_np HREF=#a:imap_np><STRONG>imap_np</STRONG></A> +,<DT><A NAME=d:cmapx_np HREF=#a:cmapx_np><STRONG>cmapx_np</STRONG></A> +<DD>These are identical to the imap and cmapx formats, except they +rely solely on rectangles as active areas. + +<DT><A NAME=d:ismap HREF=#a:ismap><STRONG>ismap</STRONG></A> +<DD>Produces HTML image map files. This is a predecessor (circa 1994) +of the IMAP format. Most servers now use the latter. +<A HREF=attrs.html#d:URL>URLs</A> can be attached to the root graph, +nodes and edges. Since edge +links are attached to edge labels, an edge must +have a <A HREF=attrs.html#d:label>label</A> for its +URL to be used. For both nodes and edges, if the URL has the escape +sequence "\N" embedded in its string, this will be replaced with the +node or edge name. + +<DT><A NAME=d:jpg HREF=#a:jpg><STRONG>jpg</STRONG></A> +,<DT><A NAME=d:jpeg HREF=#a:jpeg><STRONG>jpeg</STRONG></A> +,<DT><A NAME=d:jpe HREF=#a:jpe><STRONG>jpe</STRONG></A> +<DD>Output JPEG compressed image files. + +<DT><A NAME=d:mif HREF=#a:mif><STRONG>mif</STRONG></A> +<DD>Generates Frame Maker MIF files. + +<DT><A NAME=d:mp HREF=#a:mp><STRONG>mp</STRONG></A> +<DD>Produces <A HREF="http://cm.bell-labs.com/who/hobby/MetaPost.html">MetaPost</A> output. + +<DT><A NAME=d:pcl HREF=#a:pcl><STRONG>pcl</STRONG></A> +<DD>Produces output in the PCL printer language. +<A HREF=#d:hpgl>HP-GL</A> is a subset of +PCL, so that PCL output is the same as HP-GL, wrapped with some initial +and final commands to set the printer to and from HP-GL mode. + +<DT><A NAME=d:pdf HREF=#a:pdf><STRONG>pdf</STRONG></A> +<DD>Produces <A HREF="http://www.adobe.com/devnet/pdf/">PDF</A> output. +(This option assumes Graphviz includes the Cairo renderer.) +Alternatively, one can use the <A HREF="#d:ps2">ps2</A> option to +produce PDF-compatible PostScript, and then use a ps-to-pdf converter. +<P> +Note: At present, this option does not support anchors, etc. To get these +included in your PDF output, use <A HREF="#d:ps2">ps2</A>. + +<DT><A NAME=d:pic HREF=#a:pic><STRONG>pic</STRONG></A> +<DD>Outputs in PIC, the picture description language in the troff-family + +<DT><A NAME=d:plain HREF=#a:plain><STRONG>plain</STRONG></A> +,<DT><A NAME=d:plain-ext HREF=#a:plain-ext><STRONG>plain-ext</STRONG></A> +<DD>The plain and plain-ext formats produce output using +a simple, line-based language. +The latter format differs in that, on edges, it provides port names +on head and tail nodes when applicable. +<P> +There are four types of statements. +<PRE> + <STRONG>graph</STRONG> <I>scale</I> <I>width</I> <I>height</I> + <STRONG>node</STRONG> <I>name</I> <I>x</I> <I>y</I> <I>width</I> <I>height</I> <I>label</I> <I>style</I> <I>shape</I> <I>color</I> <I>fillcolor</I> + <STRONG>edge</STRONG> <I>tail</I> <I>head</I> <I>n</I> <I>x<sub>1</sub></I> <I>y<sub>1</sub></I> .. <I>x<sub>n</sub></I> <I>y<sub>n</sub></I> [<I>label</I> <I>xl</I> <I>yl</I>] <I>style</I> <I>color</I> + <STRONG>stop</STRONG> +</PRE> +<DL> +<DT><STRONG>graph</STRONG> +<DD>The <I>width</I> and <I>height</I> values give the width and height +of the drawing. The lower left corner of the drawing is at the origin. +The <I>scale</I> value indicates how the drawing should be scaled +if a <A HREF=attrs.html#d:size>size</A> attribute was given and the drawing +needs to be scaled to conform to that size. If no scaling is necessary, +it will be set to 1.0. Note that all graph, node and edge +coordinates and lengths are given unscaled. +<DT><STRONG>node</STRONG> +<DD>The <I>name</I> value is the name of the node, and <I>x</I> and <I>y</I> +give the node's position. The <I>width</I> and <I>height</I> are the +width and height of the node. +The <I>label</I>, +<I>style</I>, <I>shape</I>, <I>color</I> and <I>fillcolor</I> give the +node's <A HREF=attrs.html#d:label>label</A>, +<A HREF=attrs.html#d:style>style</A>, <A HREF=attrs.html#d:shape>shape</A>, +<A HREF=attrs.html#d:color>color</A> and +<A HREF=attrs.html#d:fillcolor>fillcolor</A>, +respectively, using attribute default values where necessary. If the +node does not have a style attribute, "solid" is used. +<DT><STRONG>edge</STRONG> +<DD>The <I>tail</I> and <I>head</I> values give the names of the head and +tail nodes. In plain-ext format, the head or tail name will be appended +with a colon and a portname if the edge connects to the node at a port. +<I>n</I> is the number of control points defining the +B-spline forming the edge. This is followed by 2*<I>n</I> numbers giving +the x and y coordinates of the control points in order from tail to head. +If the edge has a <A HREF=attrs.html#d:label>label</A>, this comes next +followed by the x and y coordinates of the label's position. +The edge description is completed by the edge's +<A HREF=attrs.html#d:style>style</A> and <A HREF=attrs.html#d:color>color</A>. +As with nodes, if a style is not defined, "solid" is used. +<P> +<B>Note:</B> The control points given in an edge statement define the +body of the edge. In particular, if the edge has an arrowhead to the +head or tail node, +there will be a gap between the last or first control points and the +boundary of the associated node. There are at least 3 possible ways +of handling this gap: +<UL> +<LI> Arrange that the input graph uses <TT>dir=none</TT>, +<TT>arrowhead=none</TT>, or <TT>arrowtail=none</TT> for all edges. +In this case, the terminating control points will always touch the node. +<LI> Consider the line segment joining the control point and the center +of the node, and determine the point where the segment intersects the +node's boundary. Then use the control point and the intersection point +as the main axis of an arrowhead. The problem with this approach is +that, if the edge has a port, the edge will not be pointing to the +center of the node. In this case, rather than use the control point +and center point, one can use the control point and its tangent. +<LI> Arrange that the input graph uses <TT>headclip=false</TT> or +<TT>tailclip=false</TT>. In this case, the edge will terminate at +the node's center rather than its boundary. If arrowheads are used, +there will still be a gap, but normally this will occur within the +node. The application will still need to clip the spline to the node +boundary. Also, as with the previous item, if the edge points to +a node port, this technique will fail. +</UL> +</DL> +The output consists of one <STRONG>graph</STRONG> line, a sequence of +<STRONG>node</STRONG> lines, one per node, a sequence of +<STRONG>edge</STRONG> lines, one per edge, and a final <STRONG>stop</STRONG> +line. All units are in inches, represented by a floating point number. +<P> +Note that the plain formats provide minimal information, really giving not +much more than node positions and sizes, and edge spline control points. +These formats are usually most useful to applications wanting just this +geometric information, and willing to fill in all of the graphical details. +The only real advantages to these formats is their terseness and their +ease of parsing. In general, the <A HREF=#d:dot>dot</A> and +<A HREF=#d:xdot>xdot</A> are preferable in terms of the quantity of +information provided. + +<DT><A NAME=d:png HREF=#a:png><STRONG>png</STRONG></A> +<DD>Produces output in the PNG (Portable Network Graphics) format. + +<DT><A NAME=d:ps HREF=#a:ps><STRONG>ps</STRONG></A> +<DD>Produces PostScript output. +<P> +Note: The default PostScript renderer can only handle the Latin-1 +character set. To get non-Latin-1 characters into PostScript output, +use <TT>-Tps:cairo</TT>, assuming your version was built with the +Cairo renderer. + +<DT><A NAME=d:ps2 HREF=#a:ps2><STRONG>ps2</STRONG></A> +<DD>Produces PostScript output with PDF notations. It is assumed the output +will be directly converted into PDF format. The notations include PDF +bounding box information, so that the resulting PDF file can be correctly +used with pdf tools, such as <STRONG>pdflatex</STRONG>. +In addition, if a node has a URL +attribute, this gets translated into PDF code such that the node, +when viewed in a PDF-viewer, e.g., +<STRONG>acroread</STRONG>, +is a link to the given URL. If a URL is attached to the graph, this serves +as a base, such that relative URLs on nodes are derived from it. + +<DT><A NAME=d:svg HREF=#a:svg><STRONG>svg</STRONG></A> +,<DT><A NAME=d:svgz HREF=#a:svgz><STRONG>svgz</STRONG></A> +<DD>Produce <A HREF="http://www.adobe.com/svg/">SVG</A> output, +the latter in compressed format. +<P> +See <A HREF=#ID>Note</A>. + +<DT><A NAME=d:tga HREF=#a:tga><STRONG>tga</STRONG></A> +<DD>Produces <A HREF="http://en.wikipedia.org/wiki/Truevision_TGA">Targa</A> output. + +<DT><A NAME=d:tif HREF=#a:tif><STRONG>tif</STRONG></A> +,<DT><A NAME=d:tiff HREF=#a:tiff><STRONG>tiff</STRONG></A> +<DD>Produces <A HREF="http://www.libtiff.org/">TIFF</A> output. + +<DT><A NAME=d:vml HREF=#a:vml><STRONG>vml</STRONG></A> +,<DT><A NAME=d:vmlz HREF=#a:vmlz><STRONG>vmlz</STRONG></A> +<DD>Produces <A HREF="http://www.w3.org/TR/NOTE-VML">VML</A> output, +the latter in compressed format. +<P> +See <A HREF=#ID>Note</A>. + +<DT><A NAME=d:vrml HREF=#a:vrml><STRONG>vrml</STRONG></A> +<DD>Outputs graphs in the <A HREF="http://www.vrml.org/">VRML</A> format. +To get a 3D embedding, nodes must have a <A HREF=attrs.html#d:z>z</A> +attribute. These can either be supplied as part of the input graph, or +be generated by neato provided <A HREF=attrs.html#d:dim>dim</A><TT>=3</TT> +and at least one node has a <B>z</B> value. +<P> +Line segments are drawn as cylinders. +In general, VRML output relies on having the PNG library to produce images +used to texture-fill the node shapes. However, if +<A HREF=attrs.html#d:shape>shape</A><TT>=point</TT>, +a node is drawn as a 3D sphere. + +<DT><A NAME=d:vtx HREF=#a:vtx><STRONG>vtx</STRONG></A> +<DD>Generates graph diagrams in the format for +<A HREF="http://www.bombshellstudios.com/samples/co/vt.html">Confluents's Visual Thought</A>. + +<DT><A NAME=d:wbmp HREF=#a:wbmp><STRONG>wbmp</STRONG></A> +<DD>Produces output in the Wireless BitMap (WBMP) format, optimized for +mobile computing. + +<DT><A NAME=d:xlib HREF=#a:xlib><STRONG>xlib</STRONG></A> +<DD>Creates an <A HREF="http://en.wikipedia.org/wiki/Xlib">Xlib</A> window and displays the output there. + +</DL> +<HR> +</BODY> +</HTML> +<H1 align=CENTER><A NAME=d:image_fmts>Image Formats</A></H1> +<HR> +The <A HREF=attrs.html#a:image>image</A> and <A HREF=attrs.html#a:shapefile>shapefile</A> attributes specify an image file to be included +as part of the final diagram. Not all image formats can be read. In addition, +even if read, not all image formats can necessarily be used in a given +output format. +<P> +The graph below shows what image formats can be used in which output formats, +and the required plugins. On the left are the supported image formats. +On the right are the supported output formats. +In the middle are the plugins: image loaders, renderers, drivers, arranged by +plugin library. +This presents the most general case. A given installation may not provide +one of the plugins, in which case, that transformation is not possible. +<BR> +<IMG WIDTH="80%" SRC="plugins.png"> +<HR> +<H2>Notes</H2> +<OL TYPE="1"> +<LI> +<A NAME=ID>In the formats: -Tcmap, -Tcmapx, -Tsvg, -Tvml, the output generates +'id="node#"' properties for nodes, 'id="edge#"' properties for edges, and 'id="cluster#"' properties for clusters, with the '#' replaced by an internally assigned integer. These strings can be provided instead by an externally provided "id=xxx" attribute on the object. +Normal "\N" "\E" "\G" substitutions are applied. +Externally provided id values are not used internally, and it is the use's reponsibilty to ensure +that they are sufficiently unique for their intended downstream use. +Note, in particular, that "\E" is not a unique id for multiedges. +</OL>
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Do not edit. +--> +<HTML VERSION="2.0"> +<HEAD> +<TITLE>Node Shapes</TITLE> +</HEAD> +<BODY BGCOLOR=white> +<A NAME="top"></A> +<H1 align=CENTER>Node Shapes</H1> +<HR> +The geometries of all node shapes are affected by +the node attributes +<A HREF=attrs.html#d:fixedsize><TT>fixedsize</TT></A>, +<A HREF=attrs.html#d:fontname><TT>fontname</TT></A>, +<A HREF=attrs.html#d:fontsize><TT>fontsize</TT></A>, +<A HREF=attrs.html#d:height><TT>height</TT></A>, +<A HREF=attrs.html#d:label><TT>label</TT></A>, +<A HREF=#d:style><TT>style</TT></A> and +<A HREF=attrs.html#d:width><TT>width</TT></A>. + +<H2><A NAME=polygon>Polygon-based Nodes</A></H2> +The possible polygon-based shapes are displayed below. +<TABLE ALIGN=CENTER> + <TR ALIGN=CENTER> + <TD><IMG SRC=box.gif> + <TD><IMG SRC=polygon.gif> + <TD><IMG SRC=ellipse.gif> + <TD><IMG SRC=circle.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:box>box</A> + <TD><A NAME=d:polygon>polygon</A> + <TD><A NAME=d:ellipse>ellipse</A> + <TD><A NAME=d:circle>circle</A> + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC=point.gif> + <TD><IMG SRC=egg.gif> + <TD><IMG SRC=triangle.gif> + <TD><IMG SRC=plaintext.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:point>point</A> + <TD><A NAME=d:egg>egg</A> + <TD><A NAME=d:triangle>triangle</A> + <TD><A NAME=d:plaintext>plaintext</A> + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC=diamond.gif> + <TD><IMG SRC=trapezium.gif> + <TD><IMG SRC=parallelogram.gif> + <TD><IMG SRC=house.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:diamond>diamond</A> + <TD><A NAME=d:trapezium>trapezium</A> + <TD><A NAME=d:parallelogram>parallelogram</A> + <TD><A NAME=d:house>house</A> + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC=pentagon.gif> + <TD><IMG SRC=hexagon.gif> + <TD><IMG SRC=septagon.gif> + <TD><IMG SRC=octagon.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:pentagon>pentagon</A> + <TD><A NAME=d:hexagon>hexagon</A> + <TD><A NAME=d:septagon>septagon</A> + <TD><A NAME=d:octagon>octagon</A> + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC=doublecircle.gif> + <TD><IMG SRC=doubleoctagon.gif> + <TD><IMG SRC=tripleoctagon.gif> + <TD><IMG SRC=invtriangle.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:doublecircle>doublecircle</A> + <TD><A NAME=d:doubleoctagon>doubleoctagon</A> + <TD><A NAME=d:tripleoctagon>tripleoctagon</A> + <TD><A NAME=d:invtriangle>invtriangle</A> + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC=invtrapezium.gif> + <TD><IMG SRC=invhouse.gif> + <TD><IMG SRC=Mdiamond.gif> + <TD><IMG SRC=Msquare.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:invtrapezium>invtrapezium</A> + <TD><A NAME=d:invhouse>invhouse</A> + <TD><A NAME=d:Mdiamond>Mdiamond</A> + <TD><A NAME=d:Msquare>Msquare</A> + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC=Mcircle.gif> + <TD><IMG SRC=rect.gif> + <TD><IMG SRC=rectangle.gif> + <TD><IMG SRC=none.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:Mcircle>Mcircle</A> + <TD><A NAME=d:rect>rect</A> + <TD><A NAME=d:rectangle>rectangle</A> + <TD><A NAME=d:none>none</A> + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC=note.gif> + <TD><IMG SRC=tab.gif> + <TD><IMG SRC=folder.gif> + <TD><IMG SRC=box3d.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:note>note</A> + <TD><A NAME=d:tab>tab</A> + <TD><A NAME=d:folder>folder</A> + <TD><A NAME=d:box3d>box3d</A> + </TR> + <TR ALIGN=CENTER> + <TD><IMG SRC=component.gif> + </TR> + <TR ALIGN=CENTER> + <TD><A NAME=d:component>component</A> + </TR> +</TABLE> +As the figures suggest, the shapes <TT>rect</TT> and <TT>rectangle</TT> are synonyms for <TT>box</TT>, and <TT>none</TT> is a synonym for <TT>plaintext</TT>. +Also, unlike the rest, we have shown these last two +without <TT>style=filled</TT> +to indicate the normal use. If fill were turned on, the label text would +appear in a filled rectangle. +<P> +The geometries of polygon-based shapes are also affected +by the node attributes +<A HREF=attrs.html#d:regular><TT>regular</TT></A>, +<A HREF=attrs.html#d:peripheries><TT>peripheries</TT></A> and +<A HREF=attrs.html#d:orientation><TT>orientation</TT></A>. +If <TT>shape="polygon"</TT>, the attributes +<A HREF=attrs.html#d:sides><TT>sides</TT></A>, +<A HREF=attrs.html#d:skew><TT>skew</TT></A> and +<A HREF=attrs.html#d:distortion><TT>distortion</TT></A> are also used. +If unset, they default to 4, 0.0 and 0.0, respectively. +In addition, the 3 M* shapes support auxiliary labels using +the <A HREF=attrs.html#d:toplabel><TT>toplabel</TT></A> and +<A HREF=attrs.html#d:bottomlabel><TT>bottomlabel</TT></A> attributes. +On the other hand, the point shape is special in that it is +only affected by the <A HREF=attrs.html#d:peripheries><TT>peripheries</TT></A>, +<A HREF=attrs.html#d:width><TT>width</TT></A> and +<A HREF=attrs.html#d:height><TT>height</TT></A> attributes. + +<H2><A NAME=record>Record-based Nodes</A></H2> +These are specified by shape values of "record" and "Mrecord". +The structure of a record-based node is determined by +its <A HREF=attrs.html#d:label><TT>label</TT></A>, +which has the following schema: + <TABLE> + <TR><TD ALIGN=right><I>rlabel</I><TD>=<TD><I>field</I> ( '|' <I>field</I> )*</TR> + <TR><TD ALIGN=right>where <I>field</I><TD>=<TD>fieldId</I> or '{' <I>rlabel</I> '}'</TR> + <TR><TD ALIGN=right>and <I>fieldId</I><TD>=<TD> [ '<' <I>string</I> '>'] [ <I>string</I> ]</TR> + </TABLE> +Literal braces, vertical bars and angle brackets must be escaped. +Spaces are interpreted as separators between tokens, +so they must be escaped if you want spaces in the text. +<P> +The first string in <I>fieldId</I> assigns a portname to the field and can +be combined with the node name to indicate where to attach an edge +to the node. (See <A HREF=attrs.html#k:portPos>portPos</A>.) +The second string is used as the text for the field; it supports the usual +<A HREF=attrs.html#k:escString>escape sequences</A> \n, \l and \r. +<P> +Visually, a record is a box, with fields represented by alternating +rows of horizontal or vertical subboxes. The Mrecord shape is identical +to a record shape, except that the outermost box has rounded corners. +Flipping between horizontal and vertical layouts is done by nesting +fields in braces "{...}". The top-level orientation in a record is +horizontal. Thus, a record with label "A | B | C | D" will have 4 fields +oriented left to right, while "{A | B | C | D}" will have them +from top to bottom and "A | { B | C } | D" will have "B" over "C", with +"A" to the left and "D" to the right of "B" and "C". +<P> +The initial orientation of a record node depends on the +<A HREF="attrs.html#d:rankdir">rankdir</A> attribute. If this attribute +is <TT>TB</TT> (the default) or <TT>TB</TT>, corresponding to vertical +layouts, the top-level fields in a record are displayed horizontally. +If, however, this attribute is <TT>LR</TT> or <TT>RL</TT>, +corresponding to horizontal layouts, the top-level fields are +displayed vertically. +<P> +As an example of a record node, the dot input +<XMP> +digraph structs { + node [shape=record]; + struct1 [label="<f0> left|<f1> mid\ dle|<f2> right"]; + struct2 [label="<f0> one|<f1> two"]; + struct3 [label="hello\nworld |{ b |{c|<here> d|e}| f}| g | h"]; + struct1:f1 -> struct2:f0; + struct1:f2 -> struct3:here; +} +</XMP> + +yields the figure<BR> +<IMG SRC=record.gif> +<P> +If we add the line +<XMP> + rankdir=LR +</XMP> +we get the layout<BR> +<IMG SRC=record2.gif> +<P> +If we change node <TT>struct1</TT> to have shape <TT>Mrecord</TT>, +it then looks like:<BR> +<IMG SRC=mrecord.gif> + +<H2><A NAME="d:style">Styles for Nodes</A></H2> +The <A HREF=attrs.html#d:style><TT>style</TT></A> +attribute can be used to modify the appearance of a node. +At present, there are 8 style values recognized: +<TT>filled</TT>, <TT>invisible</TT>, <TT>diagonals</TT>, <TT>rounded</TT>. +<TT>dashed</TT>, <TT>dotted</TT>, <TT>solid</TT> and <TT>bold</TT>. +As usual, the value of the <A HREF=attrs.html#d:style><TT>style</TT></A> +attribute can be a comma-separated list of any of these. If the +style contains conflicts (e.g, <TT>style="dotted, solid"</TT>), the last +attribute wins. +<DL> +<DT><A NAME=d:filled><TT>filled</TT> + <DD>This value indicates that the node's interior should be filled. +The color used is the node's <TT>fillcolor</TT> or, if that's not defined, its +<TT>color</TT>. For unfilled nodes, the interior of the node is transparent to +whatever color is the current graph or cluster background color. +Note that <TT>point</TT> shapes are always filled. +<P> +Thus, the code +<XMP> +digraph G { + rankdir=LR + node [shape=box, color=blue] + node1 [style=filled] + node2 [style=filled, fillcolor=red] + node0 -> node1 -> node2 +} +</XMP> +yields the figure<BR> +<IMG SRC=fill.gif> + +<DT><A NAME=d:invisible><TT>invisible</TT> + <DD>Setting this style causes the node not to be displayed at all. +Note that the node is still used in laying out the graph. + +<DT><A NAME=d:diagonals><TT>diagonals</TT> + <DD>The diagonals style causes small chords to be drawn near the vertices +of the node's polygon or, in case of circles and ellipses, two chords near +the top and the bottom of the shape. The special node shapes +<A HREF=#d:Msquare><TT>Msquare</TT></A>, +<A HREF=#d:Mcircle><TT>Mcircle</TT></A>, and +<A HREF=#d:Mdiamond><TT>Mdiamond</TT></A> +are simply an ordinary square, circle and +diamond with the diagonals style set. + +<DT><A NAME=d:rounded><TT>rounded</TT> + <DD>The rounded style causes the polygonal corners to be smoothed. +Note that this style also applies to record-based nodes. Indeed, +the <TT>Mrecord</TT> shape is simply shorthand for setting this style. +Also, prior to 26 April 2005, the rounded and filled styles were +mutually exclusive. +<P> +As an example of rounding, dot uses the graph +<XMP> +digraph R { + rankdir=LR + node [style=rounded] + node1 [shape=box] + node2 [fillcolor=yellow, style="rounded,filled", shape=diamond] + node3 [shape=record, label="{ a | b | c }"] + + node1 -> node2 -> node3 +} +</XMP> +to produce the figure<BR> +<IMG SRC=round.gif> +<DT><A NAME=d:dashed><TT>dashed</TT> + <DD>This style causes the node's border to be drawn as a dashed line. +<DT><A NAME=d:dotted><TT>dotted</TT> + <DD>This style causes the node's border to be drawn as a dotted line. +<DT><A NAME=d:solid><TT>solid</TT> + <DD>This style causes the node's border to be drawn as a solid line, +which is the default. +<DT><A NAME=d:bold><TT>bold</TT> + <DD>This style causes the node's border to be drawn as a bold line. +See also <A HREF=attrs.html#d:setlinewidth>setlinewidth</A>. + +</DL> + +<P> +Additional styles may be available with a specific code generator. +<H2><A NAME=html>HTML-Like Labels</A></H2> + +<B>NOTE:</B><EM>This feature is only available on versions of Graphviz +that are newer than mid-November 2003. In particular, it is not part +of release 1.10.</EM> +<P> +If the value of a label attribute +(<A HREF=attrs.html#d:label><TT>label</TT></A> for nodes, edges, clusters, and +graphs, and the +<A HREF=attrs.html#d:headlabel><TT>headlabel</TT></A> and +<A HREF=attrs.html#d:taillabel><TT>taillabel</TT></A> +attributes of an edge) is given as an +<A HREF=lang.html#html>HTML string</A>, +that is, delimited by <TT><...></TT> +rather than <TT>"..."</TT>, +the label is interpreted as +an HTML description. At their simplest, such labels +can describe multiple lines of variously aligned text as provided by ordinary +<A HREF=attrs.html#k:escString>string labels</A>. More generally, the +label can specify a table similar to those provided by HTML, +with different graphical attributes at each level. +<P> +<B>NOTE:</B> The features and syntax supported by these labels are +modeled on HTML. However, there are many aspects that are relevant +to Graphviz labels that are not in HTML and, conversely, HTML allows +various constructs which are meaningless in Graphviz. We will generally +refer to these labels as "HTML labels" rather than the cumbersome +"HTML-like labels" but the reader is warned that these are not really +HTML. The grammar below describes precisely what Graphviz will accept. +<P> +Although HTML labels are not, strictly speaking, a shape, they can be +viewed as a generalization of the record shapes described above. +In particular, if a node has set its +<A HREF=attrs.html#k:shape><TT>shape</TT></A> +attribute to <TT>plaintext</TT>, the HTML label will be the node's +shape. On the other hand, if the node has any other shape (except +<TT>point</TT>), the HTML label will be embedded within the node the +same way an ordinary label would be. +<P> +The following is an abstract grammar for HTML labels. +Terminals, corresponding to elements, are shown in bold font, +and nonterminals in italics. +Square brackets [ and ] enclose optional items. +Vertical bars | separate alternatives. +Note that, as in HTML, element and attribute names are case-insensitive. +(cf. sections 3.2.1 and 3.2.2 of the +<A HREF="http://www.w3.org/TR/html401">HTML 4.01 specification</A>). +<TABLE> +<TR> + <TD ALIGN=RIGHT><I>label</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>text</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><I>table</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>text</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>textitem</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><I>text</I> <I>textitem</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>textitem</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>string</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B><BR/></B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B><FONT></B> <I>text</I> <B></FONT></B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>table</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT>[ <B><FONT></B> ] <B><TABLE></B> <I>rows</I> <B></TABLE></B> [ <B></FONT></B> ]</TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>rows</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>row</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><I>rows</I> <I>row</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>row</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><B><TR></B> <I>cells</I> <B></TR></B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>cells</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><I>cell</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><I>cells</I> <I>cell</I></TD> +</TR> +<TR> + <TD ALIGN=RIGHT><I>cell</I></TD> + <TD ALIGN=LEFT>:</TD> + <TD ALIGN=LEFT><B><TD></B> <I>label</I> <B></TD></B></TD> +</TR> +<TR> + <TD ALIGN=RIGHT></TD> + <TD ALIGN=LEFT>|</TD> + <TD ALIGN=LEFT><B><TD></B> <B><IMG/></B> <B></TD></B></TD> +</TR> +</TABLE> +<P> +Above, a <I>string</I> is any collection of printable characters, including +all spaces. Note that outside of the body of a <TD> element, +whitespace characters are ignored; within a <TD> element, spaces +are preserved but all other white space characters are discarded. +HTML comments are allowed within an HTML string. They can occur anywhere +provided that, if they contain part of an HTML element, they must contain +the entire element. +<P> +As is obvious from the above description, the interpretation of white space +characters is one place where HTML-like labels is very different from +standard HTML. In HTML, any sequence of white space characters is +collapsed to a single space, If the user does not want this to happen, the +input must use non-breaking spaces "&nbsp;". This makes sense in +HTML, where text layout depends dynamically on the space available. In +Graphviz, the layout is statically determined by the input, so it is +reasonable to treat ordinary space characters as non-breaking. In addition, +ignoring tabs and newlines allows the input text to be formatted for +easier reading. +<P> +Each of the HTML elements has a set of optional attributes. +Attribute values must appear in double quotes. +<PRE><TABLE + ALIGN="CENTER|LEFT|RIGHT" + BGCOLOR="<I>color</I>" + BORDER="<I>value</I>" + CELLBORDER="<I>value</I>" + CELLPADDING="<I>value</I>" + CELLSPACING="<I>value</I>" + COLOR="<I>color</I>" + FIXEDSIZE="FALSE|TRUE" + HEIGHT="<I>value</I>" + HREF="<I>value</I>" + PORT="<I>portName</I>" + TARGET="<I>value</I>" + TITLE="<I>value</I>" + TOOLTIP="<I>value</I>" + VALIGN="MIDDLE|BOTTOM|TOP" + WIDTH="<I>value</I>" +> +</PRE> +<P> +<PRE><TR + <!-- No attributes --> +> +</PRE> +<P> +<PRE><TD + ALIGN="CENTER|LEFT|RIGHT|TEXT" + BALIGN="CENTER|LEFT|RIGHT" + BGCOLOR="<I>color</I>" + BORDER="<I>value</I>" + CELLPADDING="<I>value</I>" + CELLSPACING="<I>value</I>" + COLOR="<I>color</I>" + COLSPAN="<I>value</I>" + FIXEDSIZE="FALSE|TRUE" + HEIGHT="<I>value</I>" + HREF="<I>value</I>" + PORT="<I>portName</I>" + ROWSPAN="<I>value</I>" + TARGET="<I>value</I>" + TITLE="<I>value</I>" + TOOLTIP="<I>value</I>" + VALIGN="MIDDLE|BOTTOM|TOP" + WIDTH="<I>value</I>" +> +</PRE> + +<P> +<PRE><FONT + COLOR="<I>color</I>" + FACE="<I>fontname</I>" + POINT-SIZE="<I>value</I>" +> +</PRE> + +<P> +<PRE><BR + ALIGN="CENTER|LEFT|RIGHT" +> +</PRE> + +<P> +<PRE><IMG + SCALE="FALSE|TRUE|WIDTH|HEIGHT|BOTH" + SRC="<I>value</I>" +> +</PRE> + +<P> +<B><I><FONT SIZE=-1>ALIGN</FONT> </I></B> +<BLOCKQUOTE> +specifies horizontal placement. When an object is allocated +more space than required, this value determines where the extra space +is placed left and right of the object. +<P> +<ul> +<LI>CENTER aligns the object in the center. (Default) </LI> +<LI>LEFT aligns the object on the left.</LI> +<LI>RIGHT aligns the object on the right. </LI> +<LI>(<T><TD></T> only) TEXT aligns lines of text using the full +cell width. The alignment of a line is determined by its (possibly +implicit) associated <T><BR></T> element.</LI> +</ul> +<P> +The contents of a cell are normally aligned as a block. In particular, +lines of text are first aligned as a text block based on the width of +the widest line and the corresponding <T><BR></T> elements. Then, +the entire text block is aligned within a cell. If, however, the +cell's <B><I>ALIGN</I></B> value is <T>"TEXT"</T>, and the cell contains +lines of text, then the lines are justified using the entire available +width of the cell. If the cell does not contain text, then the contained +image or table is centered. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>BALIGN</FONT> </I></B> +<BLOCKQUOTE> +specifies the default alignment of <T><BR></T> elements contained +in the cell. That is, if a <T><BR></T> element has no +explicit <B><I>ALIGN</I></B> attribute, the attribute value is specified +by the value of <B><I>BALIGN</I></B>. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>BGCOLOR="color"</FONT></I></B> +<BLOCKQUOTE> +sets the color of the background. This color can be +overridden by a <B><I>BGCOLOR</I></B> attribute in descendents. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>BORDER="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the width of the border around the object in points. +A value of zero indicates no border. The default is 1. +The maximum value is 255. +If set in a table, and <B><I>CELLBORDER</I></B> is not set, +this value is also used for all cells in the table. +It can be overridden by a <B><I>BORDER</I></B> tag in a cell. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>CELLBORDER="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the width of the border for all cells in a table. +It can be overridden by a <B><I>BORDER</I></B> tag in a cell. +The maximum value is 255. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>CELLPADDING="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the space, in points, between a cell's border and its content. +The default is 2. +The maximum value is 255. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>CELLSPACING="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the space, in points, between cells in a table and between +a cell and the table's border. The default is 2. +The maximum value is 127. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>COLOR="color"</FONT></I></B> +<BLOCKQUOTE> +sets the color of the font within the scope of +<FONT>...</FONT>, or the border color +of the table or cell within the scope of +<TABLE>...</TABLE>, +or <TD>...</TD>. +This color can be +overridden by a <B><I>COLOR</I></B> attribute in descendents. +By default, the font color is determined by the +<A HREF="attrs.html#:fontcolor">fontcolor</A> attribute of +the corresponding node, edge or graph, and the border color +is determined by the +<A HREF="attrs.html#:color">color</A> attribute of +the corresponding node, edge or graph. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>COLSPAN="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the number of columns spanned by the cell. The default is 1. +The maximum value is 65535. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>FACE="fontname"</FONT></I></B> +<BLOCKQUOTE> +specifies the font to use within the scope of +<FONT>...</FONT>. +This can be +overridden by a <B><I>FACE</I></B> attribute in descendents. +By default, the font name is determined by the +<A HREF="attrs.html#:fontname">fontname</A> attribute of the corresponding +node, edge or graph. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>FIXEDSIZE</FONT> </I></B> +<BLOCKQUOTE> +specifies whether the values given by the <B><I>WIDTH</I></B> +and <B><I>HEIGHT</I></B> attributes are enforced. +<P> +<ul> +<LI>FALSE allows the object to grow so that all its contents will fit. (Default) </LI> +<LI>TRUE fixes the object size to its given <B><I>WIDTH</I></B> +and <B><I>HEIGHT</I></B>. +Both of these attributes must be supplied.</LI> +</ul> +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>HEIGHT="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the mininum height, in points, of the object. The height +includes the contents, any spacing and the border. Unless +<B><I>FIXEDSIZE</I></B> is true, the height will be expanded to allow +the contents to fit. +The maximum value is 65535. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>HREF="value"</FONT></I></B> +<BLOCKQUOTE> +attaches a URL to the object. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>POINT-SIZE="value"</FONT></I></B> +<BLOCKQUOTE> +sets the size of the font, in points, used within the scope of +<FONT>...</FONT>. +This can be +overridden by a <B><I>POINT-SIZE</I></B> attribute in descendents. +By default, the font size is determined by the +<A HREF="attrs.html#:fontsize">fontsize</A> attribute of the corresponding +node, edge or graph. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>PORT="value"</FONT></I></B> +<BLOCKQUOTE> +attaches a portname to the object. +(See <A HREF=attrs.html#k:portPos>portPos</A>.) +This can be used to modify the head +or tail of an edge, so that the end attaches directly to the object. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>ROWSPAN="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the number of rows spanned by the cell. The default is 1. +The maximum value is 65535. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>SCALE</FONT></I></B> +<BLOCKQUOTE> +specifies how an image will use any extra space available in its cell. +Allowed values are +<ul> +<LI>FALSE : keep image its natural size. (Default) </LI> +<LI>TRUE : scale image uniformly to fit.</LI> +<LI>WIDTH : expand image width to fill </LI> +<LI>HEIGHT : expand image height to fill </LI> +<LI>BOTH : expand both image width height to fill </LI> +</ul> +If this attribute is undefined, +the image inherits the <A HREF="attrs.html#d:imagescale">imagescale</A> +attribute of the graph object being drawn. +As with the <A HREF="attrs.html#d:imagescale">imagescale</A> +attribute, if the cell has a fixed size and the image is too large, +any offending dimension will be shrunk to fit the space, the +scaling being uniform in width and height if <I>SCALE=<TT>"true"</TT></I>. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>SRC="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the image file to be displayed in the cell. +Note that if the software is used as a web server, file system access +to images is more restricted. See <A HREF="#d:GV_FILE_PATH">GV_FILE_PATH</A> +and <A HREF="#d:SERVER_NAME">SERVER_NAME</A>. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>TARGET="value"</FONT></I></B> +<BLOCKQUOTE> +determines which window of the browser is used for the URL if the object +has one. +See <A HREF="http://www.w3.org/TR/html401/present/frames.html#adef-target">W3C documentation</A>. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>TITLE="value"</FONT></I></B> +<BLOCKQUOTE> +sets the tooltip annotation attached to the element. +This is used only if the element has a HREF attribute. +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>TOOLTIP="value"</FONT></I></B> +<BLOCKQUOTE> +is an alias for <B><I><FONT SIZE=-1>TITLE</FONT></I></B>. +</BLOCKQUOTE> + +<P> +<B><I><FONT SIZE=-1>VALIGN</FONT> </I></B> +<BLOCKQUOTE> +specifies vertical placement. When an object is allocated +more space than required, this value determines where the extra space +is placed above and below the object. +<P> +<ul> +<LI>MIDDLE aligns the object in the center. (Default) </LI> +<LI>LEFT aligns the object on the left.</LI> +<LI>RIGHT aligns the object on the right. </LI> +</ul> +</BLOCKQUOTE> + +<B><I><FONT SIZE=-1>WIDTH="value"</FONT></I></B> +<BLOCKQUOTE> +specifies the mininum width, in points, of the object. The width +includes the contents, any spacing and the border. Unless +<B><I>FIXEDSIZE</I></B> is true, the width will be expanded to allow +the contents to fit. +The maximum value is 65535. +</BLOCKQUOTE> + +<P> +There is some inheritance among the attributes. If a table specifies +a <I><B>CELLPADDING</B></I>, <I><B>CELLBORDER</B></I> or <I><B>BORDER</B></I> +value, this value is used by the table's +cells unless overridden. If a cell or table specifies a <I><B>BGCOLOR</B></I>, +this will be the background color for all of its descendents. +Of course, if a background or fill color is specified for the +graph object owning the label, this will be the original +background for the label. +The object's fontname, fontcolor and fontsize attributes +are the default for drawing text. These can be overridden by using +<I><B>FONT</B></I> to set new values. The new font values will hold +until overridden by an enclosed <I><B>FONT</B></I> element. +Finally, the pencolor or color of the graph object will be used as +the border color. +<P> +Because of certain limitations in handling tables in a device-independent +manner, when <I><B>BORDER</B></I> is 1 and both table and cell borders +are on and <I><B>CELLSPACING</B></I> is less than 2, anomalies can arise +in the output, such as gaps between sides of borders which should be +abutting or even collinear. The user can usual get around this by increasing +the border size or the spacing, or turning off the table border. +<P> +As an example of HTML labels, the dot input +<XMP> +digraph structs { + node [shape=plaintext] + struct1 [label=< +<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0"> + <TR><TD>left</TD><TD PORT="f1">mid dle</TD><TD PORT="f2">right</TD></TR> +</TABLE>>]; + struct2 [label=< +<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0"> + <TR><TD PORT="f0">one</TD><TD>two</TD></TR> +</TABLE>>]; + struct3 [label=< +<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="4"> + <TR> + <TD ROWSPAN="3">hello<BR/>world</TD> + <TD COLSPAN="3">b</TD> + <TD ROWSPAN="3">g</TD> + <TD ROWSPAN="3">h</TD> + </TR> + <TR> + <TD>c</TD><TD PORT="here">d</TD><TD>e</TD> + </TR> + <TR> + <TD COLSPAN="3">f</TD> + </TR> +</TABLE>>]; + struct1:f1 -> struct2:f0; + struct1:f2 -> struct3:here; +} +</XMP> +produces the HTML analogue of the record example above<BR> +<IMG SRC=html1.gif> +<P> +As usual, an HTML specification is more verbose. On the other hand, +HTML labels are much more general, as the following example shows: +<P> +<IMG SRC=html2.gif> +<P> +The source for this graph can be found <A HREF=html2.dot>here</A>. +<P> +Here is an example using <FONT> elements +<P> +<IMG SRC=html3.gif> +<P> +with the <A HREF=html3.dot>input graph</A>. +<P> +Here is an example using an <IMG> element +<P> +<IMG SRC=html4.gif> +<P> +with the <A HREF=html4.dot>input graph</A>. + +<H2><A NAME=epsf>User-defined Nodes</A></H2> + +There is a third type of node shape which is specified by the user. +Typically, these shapes rely on the details of a concrete graphics +format. At present, shapes can be described using PostScript, via a +file or add-on library, for use in PostScript output, or shapes can +be specified by a bitmap-image file for use with SVG or bitmap (jpeg, +gif, etc.) output. More information can be found on the page +<A HREF="http://www.graphviz.org/Documentation/html/shapehowto.html"> +How to create custom shapes</A>. + +<H2><A NAME=sdlshapes>SDL Shapes for PostScript</A></H2> +One example of user-defined node shapes is provided by Mark Rison of CSR. +These are the <A HREF=http://www.sdl-forum.org/SDL/index.htm>SDL</A> shapes. +These are available as PostScript functions whose use is described in +<A HREF="http://www.graphviz.org/Documentation/html/shapehowto.html#psprocs"> External PostScript procedures</A>. +The necessary PostScript library file and sample use can be found in the +<tt>contrib/sdlshapes</tt> directory in the release. Please note the +COPYRIGHT AND PERMISSION NOTICE contained in the library file <tt>sdl.ps</tt>. +<P> +The table below +gives the shape names and the corresponding node shapes. +<IMG SRC=sdlshapes.jpg> +</BODY> +</HTML>
Binary file toolboxes/graph_visualisation/share/graphviz/doc/html/info/tripleoctagon.gif has changed
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/schema/arguments.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +<?xml version="1.0"?> +<xsd:schema + xmlns:xsd="http://www.w3.org/2001/XMLSchema" + xmlns:html="http://www.w3.org/1999/xhtml"> + + <xsd:simpleType name="layout"> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="circo" /> + <xsd:enumeration value="dot" /> + <xsd:enumeration value="fdp" /> + <xsd:enumeration value="neato" /> + <xsd:enumeration value="twopi" /> + </xsd:restriction> + </xsd:simpleType> + + <xsd:simpleType name="format"> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="bmp" /> + <xsd:enumeration value="canon" /> + <xsd:enumeration value="dot" /> + <xsd:enumeration value="xdot" /> + <xsd:enumeration value="cmap" /> + <xsd:enumeration value="dia" /> + <xsd:enumeration value="fig" /> + <xsd:enumeration value="gd" /> + <xsd:enumeration value="gd2" /> + <xsd:enumeration value="gif" /> + <xsd:enumeration value="gtk" /> + <xsd:enumeration value="hpgl" /> + <xsd:enumeration value="ico" /> + <xsd:enumeration value="imap" /> + <xsd:enumeration value="cmapx" /> + <xsd:enumeration value="imap_np" /> + <xsd:enumeration value="cmpax_np" /> + <xsd:enumeration value="ismap" /> + <xsd:enumeration value="jpg" /> + <xsd:enumeration value="jpeg" /> + <xsd:enumeration value="jpe" /> + <xsd:enumeration value="mif" /> + <xsd:enumeration value="mp" /> + </xsd:restriction> + </xsd:simpleType> + +</xsd:schema> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/schema/attributes.xml Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,2624 @@ +<?xml version="1.0"?> +<?xml-stylesheet href="attributes.xslt" type="text/xsl"?> +<xsd:schema + xmlns:xsd="http://www.w3.org/2001/XMLSchema" + xmlns:html="http://www.w3.org/1999/xhtml" + xmlns:gv="urn:graphviz"> + + <!-- + microformats in xsd:documentation + ======================================== + + class + val: a value of the enclosing attribute + + rel + attr: link to an attribute + type: link to an attribute type + + --> + + <!-- NOTES --> + <xsd:annotation id="introduction"> + <xsd:documentation> + <html:p> + All Graphviz attributes are specified by name-value pairs. Thus, to + set the fillcolor of a node <html:code>abc</html:code>, one would use + </html:p> + <html:p> + <html:code>abc [fillcolor = red]</html:code> + </html:p> + <html:p> + Similarly, to set the arrowhead style of an edge <html:code>abc -> def</html:code>, + one would use + </html:p> + <html:p> + <html:code>abc -> def [arrowhead = diamond]</html:code> + </html:p> + <html:p> + Further details concerning the setting of attributes can be found + in the description of the + <html:a href="http://www.graphviz.org/doc/info/lang.html">DOT language.</html:a> + </html:p> + </xsd:documentation> </xsd:annotation> + + <xsd:annotation id="points"> + <xsd:documentation> + <html:p> + At present, most device-independent units are either inches or + <html:a href="http://en.wikipedia.org/wiki/Point_(typography)">points</html:a>, + which we take as 72 points per inch. + </html:p> + </xsd:documentation> + + </xsd:annotation> + + <xsd:annotation id="undirected"> + <xsd:documentation> + <html:p> + Some attributes, such as + <html:a rel="attr">dir</html:a> or <html:a rel="attr">arrowtail</html:a>, are + ambiguous when used in + <html:a href="http://www.graphviz.org/doc/info/lang.html">DOT</html:a> + with an undirected graph since the head and tail of an edge are meaningless. + As a convention, the first time an undirected edge appears, the + <html:a href="http://www.graphviz.org/doc/info/lang.html">DOT</html:a> + parser will assign the left node as the tail node and the right node as + the head. For example, the edge <html:code>A -- B</html:code> will have tail <html:code>A</html:code> + and head <html:code>B</html:code>. It is the user's responsibility to handle such + edges consistently. If the edge appears later, in the format + </html:p> + <html:p> + <html:code>B -- A [taillabel = "tail"]</html:code> + </html:p> + <html:p> + the drawing will attach the tail label to node <html:code>A</html:code>. + To avoid possible confusion when such attributes are required, the user + is encouraged to use a directed graph. + If it is important to make the graph appear undirected, this can be + done using the <html:a rel="attr">dir</html:a>, <html:a rel="attr">arrowtail</html:a> + or <html:a rel="attr">arrowhead</html:a> attributes. + </html:p> + </xsd:documentation> + </xsd:annotation> + + <!-- ATTRIBUTE TYPES --> + + <!-- + <xsd:simpleType name="arrowType"> + </xsd:simpleType> + --> + + <xsd:simpleType name="clusterMode"> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="local" /> + <xsd:enumeration value="global" /> + <xsd:enumeration value="none" /> + </xsd:restriction> + </xsd:simpleType> + + <!-- + <xsd:simpleType name="color"> + </xsd:simpleType> + --> + + <xsd:simpleType name="colorList"> + <xsd:list itemType="color" /> + </xsd:simpleType> + + <xsd:simpleType name="dirType"> + <xsd:annotation> + <xsd:documentation> + <html:p> + For undirected edges <html:code>T -- H;</html:code>, one of the nodes, usually + the righthand one, is treated as the head for the purpose of + interpreting <html:span class="val">forward</html:span> and <html:span class="val">back</html:span>. + </html:p> + </xsd:documentation> + </xsd:annotation> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="forward" /> + <xsd:enumeration value="back" /> + <xsd:enumeration value="both" /> + <xsd:enumeration value="none" /> + </xsd:restriction> + </xsd:simpleType> + + + <xsd:simpleType name="escString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + String allowing escape sequences which are replaced according + to the context. + For node attributes, the substring <html:code>\N</html:code> is replaced by the name of the node, + and the substring <html:code>\G</html:code> by the name of the graph. + For graph or cluster attributes, the substring <html:code>\G</html:code> is replaced by the + name of the graph or cluster. + For edge attributes, the substring <html:code>\E</html:code> is replaced by the name of the edge, + the substring <html:code>\G</html:code> is replaced by the name of the graph or cluster, + and the substrings <html:code>\T</html:code> and <html:code>\H</html:code> by the names of + the tail and head nodes, respectively. + The name of an edge is the string formed from the name of the + tail node, the appropriate edge operator (<html:code>--</html:code> or <html:code>-></html:code>) and the name of the + head node. + </html:p> + <html:p> + In addition, if the associated attribute is + <html:a rel="attr">label</html:a>, + <html:a rel="attr">headlabel</html:a> or <html:a rel="attr">taillabel</html:a>, + the escape sequences <html:code>\n</html:code>, <html:code>\l</html:code> and <html:code>\r</html:code> + divide the label into lines, centered, left-justified, and right-justified, + respectively. + </html:p> + </xsd:documentation> + </xsd:annotation> + <xsd:restriction base="xsd:string" /> + </xsd:simpleType> + <!-- + layerList + layerRange + lblString + --> + + <xsd:simpleType name="outputMode"> + <xsd:annotation> + <xsd:documentation> + <html:p> + These specify the order in which nodes and edges are drawn in concrete + output. The default <html:span class="val">breadthfirst</html:span> is the simplest, but when the graph + layout does not avoid edge-node overlap, this mode will sometimes have + edges drawn over nodes and sometimes on top of nodes. If the mode + <html:span class="val">nodesfirst</html:span> is chosen, all nodes are drawn first, followed by the + edges. This guarantees an edge-node overlap will not be mistaken for + an edge ending at a node. On the other hand, usually for aesthetic + reasons, it may be desirable that all edges appear beneath nodes, even + if the resulting drawing is ambiguous. This can be achieved by choosing + <html:span class="val">edgesfirst</html:span>. + </html:p> + </xsd:documentation> + </xsd:annotation> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="breadthfirst" /> + <xsd:enumeration value="nodesfirst" /> + <xsd:enumeration value="edgesfirst" /> + </xsd:restriction> + </xsd:simpleType> + + <xsd:simpleType name="packMode"> + <xsd:annotation> + <xsd:documentation> + <html:p> + These specify the granularity of packing connected components when + the <html:a rel="attr">pack</html:a> attribute is true. A value of <html:span class="val">node</html:span> causes + packing at the node and edge label, with no overlapping of these objects. + This produces a layout with the least area, but it also allows interleaving, + where a node of one component may lie between two nodes in another + component. A value of <html:span class="val">graph</html:span> does a packing using the bounding box of the + component. Thus, there will be a rectangular region around a component + free of elements of any other component. + A value of <html:span class="val">clust</html:span> guarantees that top-level clusters are kept intact. + What effect a value has also depends on the layout algorithm. For + example, neato does not support clusters, so a value of <html:span class="val">clust</html:span> will + have the same effect as the default <html:span class="val">node</html:span> value. + </html:p> + </xsd:documentation> + </xsd:annotation> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="node" /> + <xsd:enumeration value="clust" /> + <xsd:enumeration value="graph" /> + </xsd:restriction> + </xsd:simpleType> + + <xsd:simpleType name="pagedir"> + <xsd:annotation> + <xsd:documentation> + <html:p> + These specify the 8 row or column major orders for traversing a + rectangular array, the first character corresponding to the major + order and the second to the minor order. Thus, for "BL", the + major order is from bottom to top, and the minor order is from left + to right. This means the bottom row is traversed first, from left + to right, then the next row up, from left to right, and so on, + until the topmost row is traversed. + </html:p> + </xsd:documentation> + </xsd:annotation> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="BL" /> + <xsd:enumeration value="BR" /> + <xsd:enumeration value="TL" /> + <xsd:enumeration value="TR" /> + <xsd:enumeration value="RB" /> + <xsd:enumeration value="RT" /> + <xsd:enumeration value="LB" /> + <xsd:enumeration value="LT" /> + </xsd:restriction> + </xsd:simpleType> + + <!-- + point + pointf + --> + + <xsd:simpleType name="pointfList"> + <xsd:annotation> + <xsd:documentation> + <html:p> + List of <html:a rel="type">pointf</html:a>, separated by spaces. + </html:p> + </xsd:documentation> + </xsd:annotation> + <xsd:list itemType="pointf" /> + </xsd:simpleType> + <!-- + portPos + --> + + <xsd:simpleType name="rankType"> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="same" /> + <xsd:enumeration value="min" /> + <xsd:enumeration value="source" /> + <xsd:enumeration value="max" /> + <xsd:enumeration value="sink" /> + </xsd:restriction> + </xsd:simpleType> + + <xsd:simpleType name="rankdir"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Corresponding to directed graphs drawn + from top to bottom, from left to right, from bottom to top, and from + right to left, respectively. + </html:p> + </xsd:documentation> + </xsd:annotation> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="TB" /> + <xsd:enumeration value="LR" /> + <xsd:enumeration value="BT" /> + <xsd:enumeration value="RL" /> + </xsd:restriction> + </xsd:simpleType> + + <!-- + rect + --> + + <xsd:simpleType name="shape"> + <xsd:restriction base="xsd:string"> + <xsd:enumeration value="box" /> + <xsd:enumeration value="polygon" /> + <xsd:enumeration value="ellipse" /> + <xsd:enumeration value="circle" /> + <xsd:enumeration value="point" /> + <xsd:enumeration value="egg" /> + <xsd:enumeration value="triangle" /> + <xsd:enumeration value="plaintext" /> + <xsd:enumeration value="diamond" /> + <xsd:enumeration value="trapezium" /> + <xsd:enumeration value="parallelogram" /> + <xsd:enumeration value="house" /> + <xsd:enumeration value="pentagon" /> + <xsd:enumeration value="hexagon" /> + <xsd:enumeration value="septagon" /> + <xsd:enumeration value="octagon" /> + <xsd:enumeration value="doublecircle" /> + <xsd:enumeration value="doubleoctagon" /> + <xsd:enumeration value="tripleoctagon" /> + <xsd:enumeration value="invtriangle" /> + <xsd:enumeration value="invtrapezium" /> + <xsd:enumeration value="invhouse" /> + <xsd:enumeration value="Mdiamond" /> + <xsd:enumeration value="Msquare" /> + <xsd:enumeration value="Mcircle" /> + <xsd:enumeration value="rect" /> + <xsd:enumeration value="rectangle" /> + <xsd:enumeration value="none" /> + <xsd:enumeration value="note" /> + <xsd:enumeration value="tab" /> + <xsd:enumeration value="box3d" /> + <xsd:enumeration value="component" /> + </xsd:restriction> + </xsd:simpleType> + + <!-- + splineType + startType + style + viewPort + --> + + <!-- ATTRIBUTES --> + + <xsd:attribute name="Damping" type="xsd:decimal" default="0.99" gv:layouts="neato"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Factor damping force motions. On each iteration, a nodes movement + is limited to this factor of its potential motion. By being less than + 1.0, the system tends to "cool", thereby preventing cycling. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="K" type="xsd:decimal" default="0.3" gv:layouts="fdp"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Spring constant used in virtual physical model. It roughly corresponds + to an ideal edge length (in inches), in that increasing K tends to + increase the distance between nodes. + Note that the edge attribute <html:a rel="attr">len</html:a> can be used to + override this value for adjacent nodes. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="URL" type="xsd:anyURI" gv:formats="svg ps ps2 map"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Hyperlinks incorporated into device-dependent output. + At present, used in ps2, cmap, i*map and svg formats. + For all these formats, URLs can be attached to nodes, edges and + clusters. URL attributes can also be attached to the root graph in ps2, + cmap and i*map formats. This serves as the base URL for relative URLs in the + former, and as the default image map file in the latter. + </html:p> + <html:p> + For svg, cmapx and imap output, the active area for a node is its + visible image. + For example, an unfilled node with no drawn boundary will only be active on its label. + For other output, the active area is its bounding box. + The active area for a cluster is its bounding box. + For edges, the active areas are small circles where the edge contacts its head + and tail nodes. In addition, for svg, cmapx and imap, the active area + includes a thin polygon approximating the edge. The circles may + overlap the related node, and the edge URL dominates. + If the edge has a label, this will also be active. + Finally, if the edge has a head or tail label, this will also be active. + </html:p> + <html:p> + Note that, for edges, the attributes <html:a rel="attr">headURL</html:a>, + <html:a rel="attr">tailURL</html:a>, <html:a rel="attr">labelURL</html:a> and + <html:a rel="attr">edgeURL</html:a> allow control of various parts of an + edge. Also note that, if active areas of two edges overlap, it is unspecified + which area dominates. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="arrowhead" type="arrowType" default="normal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Style of arrowhead on the head node of an edge. + See also the <html:a rel="attr">dir</html:a> attribute, + and the <html:a rel="note">undirected</html:a> note. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="arrowsize" type="xsd:decimal" default="1.0"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Multiplicative scale factor for arrowheads. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="arrowtail" type="arrowType" default="normal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Style of arrowhead on the tail node of an edge. + See also the <html:a rel="attr">dir</html:a> attribute, + and the <html:a rel="note">undirected</html:a> note. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="bb" type="rect"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Bounding box of drawing in integer points. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="bgcolor" type="color"> + <xsd:annotation> + <xsd:documentation> + <html:p> + When attached to the root graph, this color is used as the background for + entire canvas. When a cluster attribute, it is used as the initial + background for the cluster. If a cluster has a filled + <html:a rel="attr">style</html:a>, the + cluster's <html:a rel="attr">fillcolor</html:a> will overlay the + background color. + </html:p> + <html:p> + If no background color is specified for the root graph, no graphics + operation are performed on the background. This works fine for + PostScript but for bitmap output, all bits are initialized to something. + This means that when the bitmap output is included in some other + document, all of the bits within the bitmap's bounding box will be + set, overwriting whatever color or graphics where already on the page. + If this effect is not desired, and you only want to set bits explicitly + assigned in drawing the graph, set <html:a rel="attr">bgcolor</html:a>="transparent". + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="center" type="xsd:boolean" default="false"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If true, the drawing is centered in the output canvas. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="charset" type="xsd:string" default="UTF-8"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Specifies the character encoding used when interpreting string input + as a text label. The default value is <html:span class="val">UTF-8</html:span>. + The other legal value is <html:span class="val">iso-8859-1</html:span> or, + equivalently, + <html:span class="val">Latin1</html:span>. The <html:a rel="attr">charset</html:a> attribute is case-insensitive. + Note that if the character encoding used in the input does not + match the <html:a rel="attr">charset</html:a> value, the resulting output may be very strange. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="clusterrank" type="clusterMode" default="local" gv:layouts="dot"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Mode used for handling clusters. If <html:a rel="attr">clusterrank</html:a> is <html:span class="val">local</html:span>, a + subgraph whose name begins with "cluster" is given special treatment. + The subgraph is laid out separately, and then integrated as a unit into + its parent graph, with a bounding rectangle drawn about it. + If the cluster has a <html:a rel="attr">label</html:a> parameter, this label + is displayed within the rectangle. + Note also that there can be clusters within clusters. + At present, the modes <html:span class="val">global</html:span> and <html:span class="val">none</html:span> + appear to be identical, both turning off the special cluster processing. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="color" type="colorList" default="black"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Basic drawing color for graphics, not text. For the latter, use the + <html:a rel="attr">fontcolor</html:a> attribute. + </html:p> + <html:p> + For edges, the value + can either be a single <html:a rel="type">color</html:a> or a <html:a rel="type">colorList</html:a>. + In the latter case, the edge is drawn using parallel splines or lines, + one for each color in the list, in the order given. + The head arrow, if any, is drawn using the first color in the list, + and the tail arrow, if any, the second color. This supports the common + case of drawing opposing edges, but using parallel splines instead of + separately routed multiedges. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="colorscheme" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This attribute specifies a color scheme namespace. If defined, it specifies + the context for interpreting color names. In particular, if a + <html:a rel="type">color</html:a> value has form <html:code>xxx</html:code> or <html:code>//xxx</html:code>, + then the color <html:code>xxx</html:code> will be evaluated according to the current color scheme. + If no color scheme is set, the standard X11 naming is used. + For example, if <html:code>colorscheme=bugn9</html:code>, then <html:code>color=7</html:code> + is interpreted as <html:code>/bugn9/7</html:code>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="comment" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Comments are inserted into output. Device-dependent. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="compound" type="xsd:boolean" default="false" gv:layouts="dot"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:span class="val">true</html:span>, allow edges between clusters. (See <html:a rel="attr">lhead</html:a> and <html:a rel="attr">ltail</html:a> below.) + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="concentrate" type="xsd:boolean" default="false" gv:layouts="dot"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:span class="val">true</html:span>, use edge concentrators. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="constraint" type="xsd:boolean" default="true" gv:layouts="dot"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:span class="val">false</html:span>, the edge is not used in ranking the nodes. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="decorate" type="xsd:boolean" default="false"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:span class="val">true</html:span>, attach edge label to edge by a 2-segment + polyline, underlining the label, then going to the closest point of spline. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="defaultdist" type="xsd:decimal" gv:layouts="neato"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This specifies the distance between nodes in separate connected + components. If set too small, connected components may overlap. + Only applicable if <html:a rel="attr">pack</html:a>=false. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="dim" type="xsd:integer" default="2" gv:layouts="fdp neato"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Set the number of dimensions used for the layout. The maximum value + allowed is 10. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="dir" type="dirType"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Set edge type for drawing arrowheads. This indicates which ends of the + edge should be decorated with an arrowhead. The actual style of the + arrowhead can be specified using the <html:a rel="attr">arrowhead</html:a> + and <html:a rel="attr">arrowtail</html:a> attributes. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="diredgeconstraints" type="xsd:string" default="false" gv:layouts="neato"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Only valid when <html:a rel="attr">mode</html:a>="ipsep". + If <html:span class="val">true</html:span>, constraints are generated for each edge in the largest (heuristic) + directed acyclic subgraph such that the edge must point downwards. + If <html:span class="val">hier</html:span>, generates level constraints similar to those used with + <html:a rel="attr">mode</html:a>="hier". The main difference is that, in the latter + case, only these constraints are involved, so a faster solver can be used. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="distortion" type="xsd:decimal" default="0.0"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Distortion factor for <html:a rel="attr">shape</html:a>=polygon. + Positive values cause top part to + be larger than bottom; negative values do the opposite. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="dpi" type="xsd:decimal" default="96.0"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This specifies the expected number of pixels per inch on a display device. + For bitmap output, this guarantees that text rendering will be + done more accurately, both in size and in placement. For SVG output, + it is used to guarantee that the dimensions in the output correspond to + the correct number of points or inches. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="edgeURL" type="xsd:anyURI" gv:formats="svg map"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:a rel="attr">edgeURL</html:a> is defined, this is the link used for the non-label + parts of an edge. This value overrides any <html:a rel="attr">URL</html:a> + defined for the edge. + Also, this value is used near the head or tail node unless overridden + by a <html:a rel="attr">headURL</html:a> or <html:a rel="attr">tailURL</html:a> value, + respectively. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="edgehref" type="xsd:anyURI" gv:formats="svg map"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Synonym for <html:a rel="attr">edgeURL</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="edgetarget" type="escString" gv:formats="svg map"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the edge has a <html:a rel="attr">URL</html:a> or <html:a rel="attr">edgeURL</html:a> + attribute, this attribute determines which window of the + browser is used + for the URL attached to the non-label part of the edge. + Setting it to "_graphviz" will open a new window if it + doesn't already exist, or reuse it if it does. + If undefined, the value of the <html:a rel="attr">target</html:a> is used. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="edgetooltip" type="escString" gv:formats="svg cmap"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Tooltip annotation attached to the non-label part of an edge. + This is used only if the edge has a <html:a rel="attr">URL</html:a> + or <html:a rel="attr">edgeURL</html:a> attribute. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="epsilon" type="xsd:decimal" gv:layouts="neato"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Terminating condition. If the length squared of all energy gradients are + < <html:a rel="attr">epsilon</html:a>, the algorithm stops. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="esep" type="xsd:decimal" gv:layouts="neato circo fdp"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Fraction to increase polygons (multiply + coordinates by 1 + esep) for purposes of spline edge routing. + This should normally be strictly less than + <html:a rel="attr">sep</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="fillcolor" type="color"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Color used to fill the background of a node or cluster + assuming <html:a rel="attr">style</html:a>=filled. + If <html:a rel="attr">fillcolor</html:a> is not defined, <html:a rel="attr">color</html:a> is + used. (For clusters, if <html:a rel="attr">color</html:a> is not defined, + <html:a rel="attr">bgcolor</html:a> is used.) If this is not defined, + the default is used, except for + <html:a rel="attr">shape</html:a>=point or when the output + format is MIF, + which use black by default. + </html:p> + <html:p> + Note that a cluster inherits the root graph's attributes if defined. + Thus, if the root graph has defined a <html:a rel="attr">fillcolor</html:a>, this will override a + <html:a rel="attr">color</html:a> or <html:a rel="attr">bgcolor</html:a> attribute set for the cluster. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="fixedsize" type="xsd:boolean" default="false"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If true, the node size is specified by the values of the + <html:a rel="attr">width</html:a> + and <html:a rel="attr">height</html:a> attributes only + and is not expanded to contain the text label. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="fontcolor" type="color" default="black"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Color used for text. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="fontname" type="xsd:string" default="Times-Roman"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Font used for text. This very much depends on the output format and, for + non-bitmap output such as PostScript or SVG, the availability of the font + when the graph is displayed or printed. As such, it is best to rely on + font faces that are generally available, such as Times-Roman, Helvetica or + Courier. + </html:p> + <html:p> + If Graphviz was built using the + <html:a href="http://pdx.freedesktop.org/~fontconfig/fontconfig-user.html">fontconfig library</html:a>, the latter library + will be used to search for the font. However, if the <html:a rel="attr">fontname</html:a> string + contains a slash character "/", it is treated as a pathname for the font + file, though font lookup will append the usual font suffixes. + </html:p> + <html:p> + If Graphviz does not use fontconfig, <html:a rel="attr">fontname</html:a> will be + considered the name of a Type 1 or True Type font file. + If you specify <html:code>fontname=schlbk</html:code>, the tool will look for a + file named <html:code>schlbk.ttf</html:code> or <html:code>schlbk.pfa</html:code> or <html:code>schlbk.pfb</html:code> + in one of the directories specified by + the <html:a rel="attr">fontpath</html:a> attribute. + The lookup does support various aliases for the common fonts. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="fontnames" type="xsd:string" gv:formats="svg"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Allows user control of how basic fontnames are represented in SVG output. + If <html:a rel="attr">fontnames</html:a> is undefined or <html:span class="val">svg</html:span>, + the output will try to use known SVG fontnames. For example, the + default font <html:code>Times-Roman</html:code> will be mapped to the + basic SVG font <html:code>serif</html:code>. This can be overridden by setting + <html:a rel="attr">fontnames</html:a> to <html:span class="val">ps</html:span> or <html:span class="val">gd</html:span>. + In the former case, known PostScript font names such as + <html:code>Times-Roman</html:code> will be used in the output. + In the latter case, the fontconfig font conventions + are used. Thus, <html:code>Times-Roman</html:code> would be treated as + <html:code>Nimbus Roman No9 L</html:code>. These last two options are useful + with SVG viewers that support these richer fontname spaces. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="fontpath" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Directory list used by libgd to search for bitmap fonts if Graphviz + was not built with the fontconfig library. + If <html:a rel="attr">fontpath</html:a> is not set, the environment + variable <html:code>DOTFONTPATH</html:code> is checked. + If that is not set, <html:code>GDFONTPATH</html:code> is checked. + If not set, libgd uses its compiled-in font path. + Note that fontpath is an attribute of the root graph. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="fontsize" type="xsd:decimal" default="14.0"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Font size, in <html:a rel="note">points</html:a>, used for text. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="group" type="xsd:string" gv:layouts="dot"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the end points of an edge belong to the same group, i.e., have the + same group attribute, parameters are set to avoid crossings and keep + the edges straight. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="headURL" type="xsd:anyURI" gv:formats="svg map"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:a rel="attr">headURL</html:a> is defined, it is + output as part of the head label of the edge. + Also, this value is used near the head node, overriding any + <html:a rel="attr">URL</html:a> value. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="headclip" type="xsd:boolean" default="true"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:span class="val">true</html:span>, the head of an edge is clipped to the boundary of the head node; + otherwise, the end of the edge goes to the center of the node, or the + center of a port, if applicable. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="headhref" type="xsd:anyURI" gv:formats="svg map"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Synonym for <html:a rel="attr">headURL</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="headlabel" type="lblString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Text label to be placed near head of edge. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="headport" type="portPos" default="center"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Indicates where on the head node to attach the head of the edge. + In the default case, the edge is aimed towards the center of the node, + and then clipped at the node boundary. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="headtarget" type="escString" gv:formats="svg map"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the edge has a <html:a rel="attr">headURL</html:a>, + this attribute determines which window of the + browser is used + for the URL. Setting it to "_graphviz" will open a new window if it + doesn't already exist, or reuse it if it does. + If undefined, the value of the <html:a rel="attr">target</html:a> is used. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="headtooltip" type="escString" gv:formats="svg cmap"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Tooltip annotation attached to the head of an edge. This is used only + if the edge has a <html:a rel="attr">headURL</html:a> attribute. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="height" type="xsd:decimal" default="0.5"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Height of node, in inches. This is taken as the initial, minimum height + of the node. If <html:a rel="attr">fixedsize</html:a> is true, this + will be the final height of the node. Otherwise, if the node label + requires more height to fit, the node's height will be increased to + contain the label. Note also that, if the output format is dot, the + value given to <html:a rel="attr">height</html:a> will be the final value. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="href" type="xsd:anyURI" gv:formats="svg ps ps2 map"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Synonym for <html:a rel="attr">URL</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="image" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Gives the name of a file containing an image to be displayed inside + a node. The image file must be in one of the recognized formats, + typically JPEG, PNG, GIF or Postscript, and be able to be converted + into the desired output format. + </html:p> + <html:p> + Unlike with the <html:a rel="attr">shapefile</html:a> attribute, + the image is treated as node + content rather than the entire node. In particular, an image can + be contained in a node of any shape, not just a rectangle. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="imagescale" type="xsd:string" default="false"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Attribute controlling how an image fills its + containing node. In general, the image is given its natural size, + (cf. <html:a rel="attr">dpi</html:a>), + and the node size is made large enough to contain its image, its + label, its margin, and its peripheries. + Its width and height will also be at least as large as its + minimum <html:a rel="attr">width</html:a> and <html:a rel="attr">height</html:a>. + If, however, <html:code>fixedsize=true</html:code>, + the width and height attributes specify the exact size of the node. + </html:p> + <html:p> + During rendering, in the default case (<html:code>imagescale=false</html:code>), + the image retains its natural size. + If <html:span class="val">true</html:span>, + the image is uniformly scaled (i.e., its aspect ratio is + preserved) to fit inside the node. + At least one dimension of the image will be as large as possible + given the size of the node. + When <html:span class="val">width</html:span>, + the width of the image is scaled to fill the node width. + The corresponding property holds when <html:tt>imagescale=height</html:tt>. + When <html:span class="val">both</html:span>, + both the height and the width are scaled separately to fill the node. + </html:p> + <html:p> + In all cases, if a dimension of the image is larger than the + corresponding dimension of the node, that dimension of the + image is scaled down to fit the node. As with the case of + expansion, if <html:code>imagescale=true</html:code>, width and height are + scaled uniformly. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="label" type="lblString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Text label attached to objects. + If a node's <html:a rel="attr">shape</html:a> is record, then the label can + have a <html:a href="http://www.graphviz.org/doc/info/shapes.html#record">special format</html:a> + which describes the record layout. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labelURL" type="xsd:anyURI"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:a rel="attr">labelURL</html:a> is defined, this is the link used for the label + of an edge. This value overrides any <html:a rel="attr">URL</html:a> + defined for the edge. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labelangle" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This, along with <html:a rel="attr">labeldistance</html:a>, determine + where the + headlabel (taillabel) are placed with respect to the head (tail) + in polar coordinates. The origin in the coordinate system is + the point where the edge touches the node. The ray of 0 degrees + goes from the origin back along the edge, parallel to the edge + at the origin. + </html:p> + <html:p> + The angle, in degrees, specifies the rotation from the 0 degree ray, + with positive angles moving counterclockwise and negative angles + moving clockwise. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labeldistance" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Multiplicative scaling factor adjusting the distance that + the headlabel (taillabel) is from the head (tail) node. + The default distance is 10 points. See <html:a rel="attr">labelangle</html:a> + for more details. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labelfloat" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If true, allows edge labels to be less constrained in position. + In particular, it may appear on top of other edges. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labelfontcolor" type="color"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Color used for headlabel and taillabel. + If not set, defaults to edge's fontcolor. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labelfontname" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Font used for headlabel and taillabel. + If not set, defaults to edge's fontname. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labelfontsize" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Font size, in <html:a rel="note">points</html:a>, used for headlabel and taillabel. + If not set, defaults to edge's fontsize. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labelhref" type="xsd:anyURI"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Synonym for <html:a rel="attr">labelURL</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labeljust" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Justification for cluster labels. If <html:span class="val">r</html:span>, the label + is right-justified within bounding rectangle; if <html:span class="val">l</html:span>, left-justified; + else the label is centered. + Note that a subgraph inherits attributes from its parent. Thus, if + the root graph sets <html:a rel="attr">labeljust</html:a> to <html:span class="val">l</html:span>, the subgraph inherits + this value. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labelloc" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Top/bottom placement of graph and cluster labels. + If the attribute is <html:span class="val">t</html:span>, place label at the top; + if the attribute is <html:span class="val">b</html:span>, place label at the bottom. + By default, root + graph labels go on the bottom and cluster labels go on the top. + Note that a subgraph inherits attributes from its parent. Thus, if + the root graph sets <html:a rel="attr">labelloc</html:a> to <html:span class="val">b</html:span>, the subgraph inherits + this value. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labeltarget" type="escString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the edge has a <html:a rel="attr">URL</html:a> or <html:a rel="attr">labelURL</html:a> + attribute, this attribute determines which window of the + browser is used + for the URL attached to the label. + Setting it to "_graphviz" will open a new window if it + doesn't already exist, or reuse it if it does. + If undefined, the value of the <html:a rel="attr">target</html:a> is used. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="labeltooltip" type="escString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Tooltip annotation attached to label of an edge. + This is used only if the edge has a <html:a rel="attr">URL</html:a> + or <html:a rel="attr">labelURL</html:a> attribute. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="landscape" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If true, the graph is rendered in landscape mode. Synonymous with + <html:code><html:a rel="attr">rotate</html:a>=90</html:code> or + <html:code><html:a rel="attr">orientation</html:a>=landscape</html:code>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="layer" type="layerRange"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Specifies layers in which the node or edge is present. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="layers" type="layerList"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Specifies a linearly ordered list of layer names attached to the graph + The graph is then output in separate layers. Only those components + belonging to the current output layer appear. For more information, + see the page <html:a href="http://www.graphviz.org/Documentation/html/layers/">How to use drawing layers (overlays)</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="layersep" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Specifies the separator characters used to split the + <html:a rel="attr">layers </html:a>attribute into a list of layer names. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="len" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Preferred edge length, in inches. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="levelsgap" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Specifies strictness of level constraints in neato + when <html:code><html:a rel="attr">mode</html:a>="ipsep" or "hier"</html:code>. + Larger positive values mean stricter constraints, which demand more + separation between levels. On the other hand, negative values will relax + the constraints by allowing some overlap between the levels. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="lhead" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Logical head of an edge. When <html:a rel="attr">compound</html:a> is true, + if <html:a rel="attr">lhead</html:a> is defined and is the name of a cluster containing + the real head, + the edge is clipped to the boundary of the cluster. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="lp" type="point"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Label position, in <html:a rel="note">points</html:a>. + The position indicates the center of the label. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="ltail" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Logical tail of an edge. When <html:a rel="attr">compound</html:a> is true, + if <html:a rel="attr">ltail</html:a> is defined and is the name of a cluster + containing the real tail, + the edge is clipped to the boundary of the cluster. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="margin" type="pointf"> + <xsd:annotation> + <xsd:documentation> + <html:p> + For graphs, this sets x and y margins of canvas, in inches. If the margin + is a single double, both margins are set equal to the given value. + </html:p> + <html:p> + Note that the margin is not part of the drawing but just empty space + left around the drawing. It basically corresponds to a translation of + drawing, as would be necessary to center a drawing on a page. Nothing + is actually drawn in the margin. To actually extend the background of + a drawing, see the <html:a rel="attr">pad</html:a> attribute. + </html:p> + <html:p> + For nodes, this attribute specifies space left around the node's label. + By default, the value is <html:code>0.11,0.055</html:code>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="maxiter" type="xsd:integer"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Sets the number of iterations used. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="mclimit" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Multiplicative scale factor used to alter the MinQuit (default = 8) + and MaxIter (default = 24) parameters used during crossing + minimization. These correspond to the + number of tries without improvement before quitting and the + maximum number of iterations in each pass. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="mindist" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Specifies the minimum separation between all nodes. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="minlen" type="xsd:integer"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Minimum edge length (rank difference between head and tail). + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="mode" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Technique for optimizing the layout. If <html:a rel="attr">mode</html:a> is <html:span class="val">major</html:span>, + neato uses stress majorization. If <html:a rel="attr">mode</html:a> is <html:span class="val">KK</html:span>, + neato uses a version of the gradient descent method. The only advantage + to the latter technique is that it is sometimes appreciably faster for + small (number of nodes < 100) graphs. A significant disadvantage is that + it may cycle. + </html:p> + <html:p> + There are two new, experimental modes in neato, <html:span class="val">hier</html:span>, which adds a top-down + directionality similar to the layout used in dot, and <html:span class="val">ipsep</html:span>, which + allows the graph to specify minimum vertical and horizontal distances + between nodes. (See the <html:a rel="attr">sep</html:a> attribute.) + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="model" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This value specifies how the distance matrix is computed for the input + graph. The distance matrix specifies the ideal distance between every + pair of nodes. neato attemps to find a layout which best achieves + these distances. By default, it uses the length of the shortest path, + where the length of each edge is given by its <html:a rel="attr">len</html:a> + attribute. If <html:a rel="attr">model</html:a> is <html:span class="val">circuit</html:span>, neato uses the + circuit resistance + model to compute the distances. This tends to emphasize clusters. If + <html:a rel="attr">model</html:a> is <html:span class="val">subset</html:span>, neato uses the subset model. This sets the + edge length to be the number of nodes that are neighbors of exactly one + of the end points, and then calculates the shortest paths. This helps + to separate nodes with high degree. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="mosek" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If Graphviz is built with MOSEK defined, mode=ipsep and mosek=true, + the Mosek software (www.mosek.com) is use to solve the ipsep constraints. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="nodesep" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Minimum space between two adjacent nodes in the same rank, in inches. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="nojustify" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + By default, the justification of multi-line labels is done within the + largest context that makes sense. Thus, in the label of a polygonal + node, a left-justified line will align with the left side of the node + (shifted by the prescribed <html:a rel="attr">margin</html:a>). + In record nodes, left-justified + line will line up with the left side of the enclosing column of fields. + If <html:a rel="attr">nojustify</html:a> is <html:span class="val">true</html:span>, multi-line labels will be justified + in the context of itself. For example, if the attribute is set, + the first label line is long, and the second is shorter and left-justified, + the second will align with the left-most character in the first line, + regardless of how large the node might be. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="normalize" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If set, normalize coordinates of final + layout so that the first point is at the origin, and then rotate the + layout so that the first edge is horizontal. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="nslimit" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Used to set number of iterations in + network simplex applications, used in + computing node x coordinates. + If defined, # iterations = <html:a rel="attr">nslimit</html:a> * # nodes; + otherwise, # iterations = MAXINT. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="nslimit1" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Used to set number of iterations in + network simplex applications, used for ranking nodes. + If defined, # iterations = <html:a rel="attr">nslimit1</html:a> * # nodes; + otherwise, # iterations = MAXINT. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="ordering" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If "out" for a graph G, and n is a node in G, then edges n->* appear + left-to-right in the same order in which they are defined. + If "in", the edges *->n appear + left-to-right in the same order in which they are defined for all + nodes n. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <!-- + <xsd:attribute name="orientation" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + Angle, in degrees, used to rotate node shapes. + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="orientation" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + If "[lL]*", set graph orientation to landscape + Used only if <html:a rel="attr">rotate</html:a> is not defined. + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + --> + + <xsd:attribute name="outputorder" type="outputMode"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Specify order in which nodes and edges are drawn. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="overlap" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Determines if and how node overlaps should be removed. Nodes are first + enlarged using the <html:a rel="attr">sep</html:a> attribute. + If <html:span class="val">true</html:span>, overlaps are retained. + If the value is <html:span class="val">scale</html:span>, overlaps are removed by uniformly scaling in x and y. + If the value converts to <html:span class="val">false</html:span>, node overlaps are removed by a + Voronoi-based technique. + If the value is <html:span class="val">scalexy</html:span>, x and y are separately + scaled to remove overlaps. + If the value is <html:span class="val">orthoxy</html:span> or <html:span class="val">orthoyx</html:span>, overlaps + are moved by optimizing two constraint problems, one for the x axis and + one for the y. The suffix indicates which axis is processed first. + If the value is <html:span class="val">ortho</html:span>, the technique is similar to <html:span class="val">orthoxy</html:span> except a + heuristic is used to reduce the bias between the two passes. + If the value is <html:span class="val">ortho_yx</html:span>, the technique is the same as <html:span class="val">ortho</html:span>, except + the roles of x and y are reversed. + The values <html:span class="val">portho</html:span>, <html:span class="val">porthoxy</html:span>, <html:span class="val">porthoxy</html:span>, and <html:span class="val">portho_yx</html:span> are similar + to the previous four, except only pseudo-orthogonal ordering is + enforced. + </html:p> + <html:p> + If the value is <html:span class="val">compress</html:span>, the layout will be scaled down as much as + possible without introducing any overlaps, obviously assuming there are + none to begin with. + </html:p> + <html:p> + If the value is <html:span class="val">ipsep</html:span>, and the layout is done by neato with + <html:a rel="attr">mode</html:a>="ipsep", the overlap removal constraints are + incorporated into the layout algorithm itself. + N.B. At present, this only supports one level of clustering. + </html:p> + <html:p> + If the value is <html:span class="val">vpsc</html:span>, overlap removal is similarly to <html:span class="val">ortho</html:span>, except + quadratic optimization is used to minimize node displacement. + N.B. At present, this mode only works when <html:a rel="attr">mode</html:a>="ipsep". + </html:p> + <html:p> + Except for fdp, the layouts assume <html:code>overlap="true"</html:code> as the default. + Fdp first uses a number of passes using built-in, force-directed technique + to remove overlaps. Thus, fdp accepts <html:a rel="attr">overlap</html:a> with an integer + prefix followed by a colon, specifying the number of tries. If there is + no prefix, no initial tries will be performed. If there is nothing following + a colon, none of the above methods will be attempted. By default, fdp + uses <html:code>overlap="9:portho"</html:code>. Note that <html:code>overlap="true"</html:code>, + <html:code>overlap="0:true"</html:code> and <html:code>overlap="0:"</html:code> all turn off all overlap + removal. + </html:p> + <html:p> + Except for the Voronoi method, all of these transforms preserve the + orthogonal ordering of the original layout. That is, if the x coordinates + of two nodes are originally the same, they will remain the same, and if + the x coordinate of one node is originally less than the x coordinate of + another, this relation will still hold in the transformed layout. The + similar properties hold for the y coordinates. + This is not quite true for the "porth*" cases. For these, orthogonal + ordering is only preserved among nodes related by an edge. + </html:p> + <html:p> + <html:b>NOTE</html:b>The methods <html:span class="val">orthoxy</html:span> and <html:span class="val">orthoyx</html:span> are still evolving. The semantics of these may change, or these methods may disappear altogether. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="pack" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This is true if the value of pack is <html:span class="val">true</html:span> (case-insensitive) or a + non-negative integer. If true, each connected component of the graph is + laid out separately, and then the graphs are packed tightly. + If pack has an integral value, this is used as the size, + in <html:a href="#points">points</html:a>, of + a margin around each part; otherwise, a default margin of 8 is used. + If pack is interpreted as false, the entire graph is laid out together. + The granularity and method of packing is influenced by the + <html:a rel="attr">packmode</html:a> attribute. + </html:p> + <html:p> + For layouts which always do packing, such a twopi, the <html:a rel="attr">pack</html:a> + attribute is just used to set the margin. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="packmode" type="packMode"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This indicates the granularity and method used for packing + (cf. <html:a rel="type">packMode</html:a>). Note that defining + <html:a rel="attr">packmode</html:a> will automatically turn on packing as though one had + set <html:code>pack=true</html:code>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="pad" type="pointf"> + <xsd:annotation> + <xsd:documentation> + <html:p> + The pad attribute specifies how much, in inches, to extend the + drawing area around the minimal area needed to draw the graph. + If the pad is a single double, both the x and y pad values are set + equal to the given value. This area is part of the + drawing and will be filled with the background color, if appropriate. + </html:p> + <html:p> + Normally, a small pad is used for aesthetic reasons, especially when + a background color is used, to avoid having nodes and edges abutting + the boundary of the drawn region. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="page" type="pointf"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Width and height of output pages, in inches. If this is set and is + smaller than the size of the layout, a rectangular array of pages of + the specified page size is overlaid on the layout, with origins + aligned in the lower-left corner, thereby partitioning the layout + into pages. The pages are then produced one at a time, in + <html:a rel="attr">pagedir</html:a> order. + </html:p> + <html:p> + At present, this only works for PostScript output. For other types of + output, one should use another tool to split the output into multiple + output files. Or use the <html:a rel="attr">viewport</html:a> to generate + multiple files. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="pagedir" type="pagedir"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the <html:a rel="attr">page</html:a> attribute is set and applicable, + this attribute specifies the order in which the pages are emitted. + This is limited to one of the 8 row or column major orders. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="pencolor" type="color"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Color used to draw the bounding box around a cluster. + If <html:a rel="attr">pencolor</html:a> is not defined, <html:a rel="attr">color</html:a> is + used. If this is not defined, <html:a rel="attr">bgcolor</html:a> is used. + If this is not defined, the default is used. + </html:p> + <html:p> + Note that a cluster inherits the root graph's attributes if defined. + Thus, if the root graph has defined a <html:a rel="attr">pencolor</html:a>, this will override a + <html:a rel="attr">color</html:a> or <html:a rel="attr">bgcolor</html:a> attribute set for the cluster. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="peripheries" type="xsd:integer"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Set number of peripheries used in polygonal shapes and cluster + boundaries. Note that + <html:a href="http://www.graphviz.org/doc/info/shapes.html#epsf">user-defined shapes</html:a> are treated as a + form of box shape, so the default + peripheries value is 1 and the user-defined shape will be drawn in + a bounding rectangle. Setting <html:code>peripheries=0</html:code> will turn this off. + Also, 1 is the maximum peripheries value for clusters. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="pin" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If true and the node has a pos attribute on input, neato prevents the + node from moving from the input position. This property can also be specified + in the pos attribute itself (cf. the <html:a rel="attr">point</html:a> type). + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="pos" type="splineType"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Position of node, or spline control points. + For nodes, the position indicates the center of the node. + On output, the coordinates are in <html:a href="#points">points</html:a>. + </html:p> + <html:p> + In neato and fdp, pos can be used to set the initial position of a node. + By default, the coordinates are assumed to be in inches. However, the + <html:a href="http://www.graphviz.org/doc/info/command.html#d:s">-s</html:a> command line flag can be used to specify + different units. + </html:p> + <html:p> + When the <html:a href="http://www.graphviz.org/doc/info/command.html#d:n">-n</html:a> command line flag is used with + neato, it is assumed the positions have been set by one of the layout + programs, and are therefore in points. Thus, <html:code>neato -n</html:code> can accept + input correctly without requiring a <html:code>-s</html:code> flag and, in fact, + ignores any such flag. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="quantum" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:a rel="attr">quantum</html:a> > 0.0, node label dimensions + will be rounded to integral multiples of the quantum. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="rank" type="rankType"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Rank constraints on the nodes in a subgraph. + If <html:span class="val">same</html:span>, all nodes are placed on the same rank. + If <html:span class="val">min</html:span>, all nodes are placed on the minimum rank. + If <html:span class="val">source</html:span>, all nodes are placed on the minimum rank, and + the only nodes on the minimum rank belong to some subgraph whose + rank attribute is "source" or "min". + Analogous criteria hold for <html:a rel="attr">rank</html:a>=<html:span class="val">max</html:span> and <html:a rel="attr">rank</html:a>=<html:span class="val">sink</html:span>. + (Note: the + minimum rank is topmost or leftmost, and the maximum rank is bottommost + or rightmost.) + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="rankdir" type="rankdir"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Sets direction of graph layout. For example, if <html:a rel="attr">rankdir</html:a>="LR", + and barring cycles, an edge <html:code>T -> H;</html:code> will go + from left to right. By default, graphs are laid out from top to bottom. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="ranksep" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + In dot, this gives the desired rank separation, in inches. This is + the minimum vertical distance between the bottom of the nodes in one + rank and the tops of nodes in the next. If the value + contains "equally", the centers of all ranks are spaced equally apart. + Note that both + settings are possible, e.g., ranksep = "1.2 equally". + In twopi, specifies radial separation of concentric circles. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="ratio" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Sets the aspect ratio (drawing height/drawing width) for the drawing. + Note that this is adjusted before + the <html:a rel="attr">size</html:a> attribute constraints are enforced. + </html:p> + <html:p> + If <html:a rel="attr">ratio</html:a> is numeric, it is taken as the desired aspect ratio. + Then, if the actual aspect ratio is less than the desired ratio, + the drawing height is scaled up to achieve the + desired ratio; if the actual ratio is greater than that desired ratio, + the drawing width is scaled up. + </html:p> + <html:p> + If <html:a rel="attr">ratio</html:a> = <html:span class="val">fill</html:span> and the <html:a rel="attr">size</html:a> + attribute is set, node positions are scaled, separately in both x + and y, so that the final drawing exactly fills the specified size. + </html:p> + <html:p> + If <html:a rel="attr">ratio</html:a> = <html:span class="val">compress</html:span> and the <html:a rel="attr">size</html:a> + attribute is set, dot attempts to compress the initial layout to fit + in the given size. This achieves a tighter packing of nodes but + reduces the balance and symmetry. This feature only works in dot. + </html:p> + <html:p> + If <html:a rel="attr">ratio</html:a> = <html:span class="val">expand</html:span>, the <html:a rel="attr">size</html:a> + attribute is set, and both the width and the height of the graph are + less than the value in <html:a rel="attr">size</html:a>, node positions are scaled + uniformly until at least + one dimension fits <html:a rel="attr">size</html:a> exactly. + Note that this is distinct from using <html:a rel="attr">size</html:a> as the + desired size, as here the drawing is expanded before edges are generated and + all node and text sizes remain unchanged. + </html:p> + <html:p> + If <html:a rel="attr">ratio</html:a> = <html:span class="val">auto</html:span>, the <html:a rel="attr">page</html:a> + attribute is set and the graph cannot be drawn on a single page, + then <html:a rel="attr">size</html:a> is set to an ``ideal'' value. + In particular, the size in a given dimension will be the smallest integral + multiple of the page size in that dimension which is at least half the + current size. The two dimensions are then scaled independently to the + new size. This feature only works in dot. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="rects" type="rect"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Rectangles for fields of records, in <html:a rel="note">points</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="regular" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If true, force polygon to be regular. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="remincross" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If true and there are multiple clusters, run cross + minimization a second time. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="resolution" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This is a synonym for the <html:a rel="attr">dpi</html:a> attribute. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="root" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + This specifies nodes to be used as the center of the + layout and the root of the generated spanning tree. As a graph attribute, + this gives the name of the node. As a node attribute (circo only), it + specifies that the node should be used as a central node. In twopi, + this will actually be the central node. In circo, the block containing + the node will be central in the drawing of its connected component. + If not defined, + twopi will pick a most central node, and circo will pick a random node. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="rotate" type="xsd:integer"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If 90, set drawing orientation to landscape. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="samehead" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Edges with the same head and the same <html:a rel="attr">samehead</html:a> value are aimed + at the same point on the head. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="sametail" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Edges with the same tail and the same <html:a rel="attr">sametail</html:a> value are aimed + at the same point on the tail. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="samplepoints" type="xsd:integer"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the input graph defines the <html:a rel="attr"><html:a rel="attr">vertices</html:a></html:a> + attribute, and output is dot or xdot, this gives + the number of points used for a node whose shape is a circle or ellipse. + It plays the same role in neato, when adjusting the layout to avoid + overlapping nodes, and in image maps. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="searchsize" type="xsd:integer"> + <xsd:annotation> + <xsd:documentation> + <html:p> + During network simplex, maximum number of edges with negative cut values + to search when looking for one with minimum cut value. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="sep" type="pointf"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Fraction to increase polygons (multiply + coordinates by 1 + sep) for purposes of determining overlap. Guarantees + a minimal non-zero distance between nodes. + If unset but <html:a rel="attr">esep</html:a> is defined, <html:a rel="attr">sep</html:a> will be + set to <html:code>esep/0.8</html:code>. If <html:a rel="attr">esep</html:a> is unset, the default value + is used. + </html:p> + <html:p> + When <html:a rel="attr">overlap</html:a>="ipsep" or "vpsc", + <html:a rel="attr">sep</html:a> gives a minimum distance, in inches, to be left between nodes. + In this case, if <html:a rel="attr">sep</html:a> is a pointf, the x and y separations can be + specified separately. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="shape" type="shape"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Set the shape of a node. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="shapefile" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + (Deprecated) If defined, shapefile specifies a file containing user-supplied node content. + The <html:a rel="attr">shape</html:a> of the node is set to box. + The image in the shapefile must be + rectangular. The image formats supported as well as the precise semantics of + how the file is used depends on the + <html:a href="http://www.graphviz.org/doc/info/output.html">output format</html:a>. For further details, see + <html:a href="http://www.graphviz.org/Documentation/html/shapehowto.html#ext_image">External PostScript files</html:a>. + </html:p> + <html:p> + There is one exception to this usage. + If <html:a rel="attr">shape</html:a> is set to "epsf", shapefile gives + a filename containing a definition of the node in PostScript. + The graphics defined must be contain all of the + node content, including any desired boundaries. + For further details, see + <html:a href="http://www.graphviz.org/Documentation/html/shapehowto.html#ext_ps"> + External PostScript files</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="showboxes" type="xsd:integer"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Print guide boxes in PostScript at the beginning of + routesplines if 1, or at the end if 2. (Debugging) + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="sides" type="xsd:integer"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Number of sides if <html:a rel="attr">shape</html:a>=polygon. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="size" type="pointf"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Maximum width and height of drawing, in inches. + If defined and the drawing is too large, the drawing is uniformly + scaled down so that it fits within the given size. + </html:p> + <html:p> + If <html:a rel="attr">size</html:a> ends in an exclamation point (<html:tt>!</html:tt>), + then it is taken to be + the desired size. In this case, if both dimensions of the drawing are + less than <html:a rel="attr">size</html:a>, the drawing is scaled up uniformly until at + least one dimension equals its dimension in <html:a rel="attr">size</html:a>. + </html:p> + <html:p> + Note that there is some interaction between the <html:a rel="attr">size</html:a> and + <html:a rel="attr">ratio</html:a> attributes. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="skew" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Skew factor for <html:a rel="attr">shape</html:a>=polygon. Positive values + skew top of polygon to right; negative to left. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="splines" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Controls how, and if, edges are represented. If true, edges are drawn as + splines routed around nodes; if false, edges are drawn as line segments. + If set to "", no edges are drawn at all. + </html:p> + <html:p> + (1 March 2007) The values <html:span class="val">line</html:span> and <html:span class="val">spline</html:span> can be + used as synonyms for <html:span class="val">false</html:span> and <html:span class="val">true</html:span>, respectively. + In addition, the value <html:span class="val">polyline</html:span> specifies that edges should be + drawn as polylines. + </html:p> + <html:p> + By default, the attribute is unset. How this is interpreted depends on + the layout. For dot, the default is to draw edges as splines. For all + other layouts, the default is to draw edges as line segments. Note that + for these latter layouts, if <html:code>splines="true"</html:code>, this + requires non-overlapping nodes (cf. <html:a rel="attr">overlap</html:a>). + If fdp is used for layout and <html:tt>splines="compound"</html:tt>, then the edges are + drawn to avoid clusters as well as nodes. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="start" type="startType"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Parameter used to determine the initial layout of nodes. If unset, the + nodes are randomly placed in a unit square with + the same seed is always used for the random number generator, so the + initial placement is repeatable. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="style" type="style"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Set style for node or edge. For cluster subgraph, if "filled", the + cluster box's background is filled. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="stylesheet" type="xsd:string"> + <xsd:annotation> + <xsd:documentation> + <html:p> + A URL or pathname specifying an XML style sheet, used in SVG output. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="tailURL" type="xsd:anyURI"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:a rel="attr">tailURL</html:a> is defined, it is + output as part of the tail label of the edge. + Also, this value is used near the tail node, overriding any + <html:a rel="attr">URL</html:a> value. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="tailclip" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If <html:span class="val">true</html:span>, the tail of an edge is clipped to the boundary of the tail node; + otherwise, the end of the edge goes to the center of the node, or the + center of a port, if applicable. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="tailhref" type="xsd:anyURI"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Synonym for <html:a rel="attr">tailURL</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="taillabel" type="lblString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Text label to be placed near tail of edge. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="tailport" type="portPos"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Indicates where on the tail node to attach the tail of the edge. + See <html:a rel="note">undirected</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="tailtarget" type="escString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the edge has a <html:a rel="attr">tailURL</html:a>, + this attribute determines which window of the + browser is used + for the URL. Setting it to "_graphviz" will open a new window if it + doesn't already exist, or reuse it if it does. + If undefined, the value of the <html:a rel="attr">target</html:a> is used. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="tailtooltip" type="escString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Tooltip annotation attached to the tail of an edge. This is used only + if the edge has a <html:a rel="attr">tailURL</html:a> attribute. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="target" type="escString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the object has a URL, this attribute determines which window + of the browser is used for the URL. + See <html:a href="http://www.w3.org/TR/html401/present/frames.html#adef-target">W3C documentation</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="tooltip" type="escString"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Tooltip annotation attached to the node or edge. If unset, Graphviz + will use the object's <html:a rel="attr">label</html:a> if defined. + Note that if the label is a record specification or an HTML-like + label, the resulting tooltip may be unhelpful. In this case, if + tooltips will be generated, the user should set a <html:tt>tooltip</html:tt> + attribute explicitly. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="truecolor" type="xsd:boolean"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If set explicitly to true or false, the value determines whether or not + internal bitmap rendering relies on a truecolor color model or uses + a color palette. + If the attribute is unset, truecolor is not used + unless there is a <html:a rel="attr">shapefile</html:a> property + for some node in the graph. + The output model will use the input model when possible. + </html:p> + <html:p> + Use of color palettes results in less memory usage during creation of the + bitmaps and smaller output files. + </html:p> + <html:p> + Usually, the only time it is necessary to specify the truetype model + is if the graph uses more than 256 colors. + However, if one uses <html:a rel="attr">bgcolor</html:a>=transparent with + a color palette, font + antialiasing can show up as a fuzzy white area around characters. + Using <html:a rel="attr">truecolor</html:a>=true avoids this problem. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="vertices" type="pointfList"> + <xsd:annotation> + <xsd:documentation> + <html:p> + If the input graph defines this attribute, the node is polygonal, + and output is dot or xdot, this attribute provides the + coordinates of the vertices of the node's polygon, in inches. + If the node is an ellipse or circle, the + <html:a rel="attr">samplepoints</html:a> attribute affects + the output. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="viewport" type="viewPort"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Clipping window on final drawing. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="voro_margin" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Factor to scale up drawing to allow margin for expansion in + Voronoi technique. dim' = (1+2*margin)*dim. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="weight" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Weight of edge. In dot, the heavier the weight, the shorter, + straighter and more vertical the edge is. In neato, the heavier the + weight, the more neato will try to place the end points so that the + length of the edge is <html:a rel="attr">len</html:a>. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="width" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Width of node, in inches. This is taken as the initial, minimum width + of the node. If <html:a rel="attr">fixedsize</html:a> is true, this + will be the final width of the node. Otherwise, if the node label + requires more width to fit, the node's width will be increased to + contain the label. Note also that, if the output format is dot, the + value given to <html:a rel="attr">width</html:a> will be the final value. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <xsd:attribute name="z" type="xsd:decimal"> + <xsd:annotation> + <xsd:documentation> + <html:p> + Provides z coordinate value for 3D layouts and displays. If the + graph has <html:a rel="attr">dim</html:a> set to 3 (or more), + neato will use a node's <html:a rel="attr">z</html:a> value + for the z coordinate of its initial position if + its <html:a rel="attr">pos</html:a> attribute is also defined. + </html:p> + <html:p> + Even if no <html:a rel="attr">z</html:a> values are specified in the input, it is necessary to + declare a <html:a rel="attr">z</html:a> attribute for nodes, e.g, using <html:tt>node[z=""]</html:tt> + in order to get z values on output. + Thus, setting <html:tt>dim=3</html:tt> but not declaring <html:a rel="attr">z</html:a> will + cause <html:tt>neato -Tvrml</html:tt> to + layout the graph in 3D but project the layout onto the xy-plane + for the rendering. If the <html:a rel="attr">z</html:a> attribute is declared, the final rendering + will be in 3D. + </html:p> + </xsd:documentation> + </xsd:annotation> + </xsd:attribute> + + <!-- COMPONENTS --> + + <xsd:complexType name="edge"> + <xsd:attribute ref="URL" /> + <xsd:attribute ref="arrowhead" default="normal" /> + <xsd:attribute ref="arrowsize" default="1.0" /> + <xsd:attribute ref="arrowtail" default="normal" /> + <xsd:attribute ref="color" default="black" /> + <xsd:attribute ref="colorscheme" /> + <xsd:attribute ref="comment" /> + <xsd:attribute ref="constraint" default="true" /> + <xsd:attribute ref="decorate" default="false" /> + <xsd:attribute ref="dir" /> + <xsd:attribute ref="edgeURL" /> + <xsd:attribute ref="edgehref"/> + <xsd:attribute ref="edgetarget" /> + <xsd:attribute ref="edgetooltip" /> + <xsd:attribute ref="fontcolor" default="black" /> + <xsd:attribute ref="fontname" default="Times-Roman" /> + <xsd:attribute ref="fontsize" default="14.0" /> + <xsd:attribute ref="headURL" /> + <xsd:attribute ref="headclip" default="true" /> + <xsd:attribute ref="headhref"/> + <xsd:attribute ref="headlabel" /> + <xsd:attribute ref="headport" default="center"/> + <xsd:attribute ref="headtarget" /> + <xsd:attribute ref="headtooltip" /> + <xsd:attribute ref="href" /> + <xsd:attribute ref="label" /> + <xsd:attribute ref="labelURL" /> + <xsd:attribute ref="labelangle" default="-25.0" /> + <xsd:attribute ref="labeldistance" default="1.0" /> + <xsd:attribute ref="labelfloat" default="false" /> + <xsd:attribute ref="labelfontcolor" default="black" /> + <xsd:attribute ref="labelfontname" default="Times-Roman" /> + <xsd:attribute ref="labelfontsize" default="14.0" /> + <xsd:attribute ref="labelhref" /> + <xsd:attribute ref="labeltarget" /> + <xsd:attribute ref="labeltooltip" /> + <xsd:attribute ref="layer" /> + <xsd:attribute ref="len" /> + <xsd:attribute ref="lhead" /> + <xsd:attribute ref="lp" /> + <xsd:attribute ref="ltail" /> + <xsd:attribute ref="minlen" default="1" /> + <xsd:attribute ref="nojustify" default="false" /> + <xsd:attribute ref="pos" /> + <xsd:attribute ref="samehead" /> + <xsd:attribute ref="sametail" /> + <xsd:attribute ref="showboxes" default="0" /> + <xsd:attribute ref="style" /> + <xsd:attribute ref="tailURL" /> + <xsd:attribute ref="tailclip" default="true" /> + <xsd:attribute ref="tailhref"/> + <xsd:attribute ref="taillabel" /> + <xsd:attribute ref="tailport" default="center"/> + <xsd:attribute ref="tailtarget" /> + <xsd:attribute ref="tailtooltip" /> + <xsd:attribute ref="target" /> + <xsd:attribute ref="tooltip" /> + <xsd:attribute ref="weight" default="1.0" /> + </xsd:complexType> + + <xsd:complexType name="node"> + <xsd:attribute ref="URL" /> + <xsd:attribute ref="color" default="black" /> + <xsd:attribute ref="colorscheme" /> + <xsd:attribute ref="comment" /> + <xsd:attribute ref="distortion" default="0.0" /> + <xsd:attribute ref="fillcolor" default="lightgrey" /> + <xsd:attribute ref="fixedsize" default="false" /> + <xsd:attribute ref="fontcolor" default="black" /> + <xsd:attribute ref="fontname" default="Times-Roman" /> + <xsd:attribute ref="fontsize" default="14.0" /> + <xsd:attribute ref="group" /> + <xsd:attribute ref="height" default="0.5" /> + <xsd:attribute ref="image" /> + <xsd:attribute ref="imagescale" default="false" /> + <xsd:attribute ref="label" default="\N" /> + <xsd:attribute ref="layer" /> + <xsd:attribute ref="margin" /> + <xsd:attribute ref="nojustify" default="false" /> + <!-- <xsd:attribute ref="orientation" default="0.0" /> --> + <xsd:attribute ref="peripheries" /> + <xsd:attribute ref="pin" default="false" /> + <xsd:attribute ref="pos" /> + <xsd:attribute ref="rects" /> + <xsd:attribute ref="regular" default="false" /> + <xsd:attribute ref="root" default="false" /> + <xsd:attribute ref="samplepoints" default="false" /> + <xsd:attribute ref="shape" default="ellipse" /> + <xsd:attribute ref="shapefile" /> + <xsd:attribute ref="showboxes" default="0" /> + <xsd:attribute ref="sides" default="4" /> + <xsd:attribute ref="skew" default="0.0" /> + <xsd:attribute ref="style" /> + <xsd:attribute ref="target" /> + <xsd:attribute ref="tooltip" /> + <xsd:attribute ref="vertices" /> + <xsd:attribute ref="width" default="0.75" /> + <xsd:attribute ref="z" default="0.0" /> + </xsd:complexType> + + <xsd:complexType name="graph"> + <xsd:attribute ref="Damping" default="0.99" /> + <xsd:attribute ref="K" default="0.3" /> + <xsd:attribute ref="URL" /> + <xsd:attribute ref="bb" /> + <xsd:attribute ref="bgcolor" /> + <xsd:attribute ref="center" /> + <xsd:attribute ref="charset" /> + <xsd:attribute ref="clusterrank" default="local" /> + <xsd:attribute ref="colorscheme" /> + <xsd:attribute ref="comment" /> + <xsd:attribute ref="compound" default="false" /> + <xsd:attribute ref="concentrate" default="false" /> + <xsd:attribute ref="defaultdist" /> + <xsd:attribute ref="dim" default="2" /> + <xsd:attribute ref="diredgeconstraints" default="false" /> + <xsd:attribute ref="dpi" default="96.0" /> + <xsd:attribute ref="epsilon" /> + <xsd:attribute ref="esep" /> + <xsd:attribute ref="fontcolor" default="black" /> + <xsd:attribute ref="fontname" default="Times-Roman" /> + <xsd:attribute ref="fontnames" /> + <xsd:attribute ref="fontpath" /> + <xsd:attribute ref="fontsize" default="14.0" /> + <xsd:attribute ref="label" /> + <xsd:attribute ref="labeljust" default="c" /> + <xsd:attribute ref="labelloc" default="b" /> + <xsd:attribute ref="landscape" default="false" /> + <xsd:attribute ref="layers" default="false" /> + <xsd:attribute ref="layersep" default=" : " /> + <xsd:attribute ref="levelsgap" default="0.0" /> + <xsd:attribute ref="lp" /> + <xsd:attribute ref="margin" /> + <xsd:attribute ref="maxiter" /> + <xsd:attribute ref="mclimit" default="1.0" /> + <xsd:attribute ref="mindist" default="1.0" /> + <xsd:attribute ref="mode" default="major" /> + <xsd:attribute ref="model" default="shortpath" /> + <xsd:attribute ref="mosek" default="false" /> + <xsd:attribute ref="nodesep" default="0.25" /> + <xsd:attribute ref="nojustify" default="false" /> + <xsd:attribute ref="normalize" default="false" /> + <xsd:attribute ref="nslimit" /> + <xsd:attribute ref="nslimit1" /> + <xsd:attribute ref="ordering" /> + <!-- <xsd:attribute ref="orientation" /> --> + <xsd:attribute ref="outputorder" default="breadthfirst" /> + <xsd:attribute ref="overlap" default="true" /> + <xsd:attribute ref="pack" default="false" /> + <xsd:attribute ref="packmode" default="node" /> + <xsd:attribute ref="pad" /> + <xsd:attribute ref="page" /> + <xsd:attribute ref="pagedir" /> + <xsd:attribute ref="quantum" default="0.0" /> + <xsd:attribute ref="rankdir" default="TB" /> + <xsd:attribute ref="ranksep" /> + <xsd:attribute ref="ratio" /> + <xsd:attribute ref="remincross" default="false" /> + <xsd:attribute ref="resolution" default="96.0" /> + <xsd:attribute ref="root" /> + <xsd:attribute ref="rotate" default="0" /> + <xsd:attribute ref="searchsize" default="30" /> + <xsd:attribute ref="sep" default="0.1" /> + <xsd:attribute ref="showboxes" default="0" /> + <xsd:attribute ref="size" /> + <xsd:attribute ref="splines" /> + <xsd:attribute ref="start" /> + <xsd:attribute ref="stylesheet" /> + <xsd:attribute ref="target" /> + <xsd:attribute ref="truecolor" /> + <xsd:attribute ref="viewport" /> + <xsd:attribute ref="voro_margin" default="0.05" /> + </xsd:complexType> + + <xsd:complexType name="subgraph"> + <xsd:attribute ref="rank" /> + </xsd:complexType> + + <xsd:complexType name="cluster"> + <xsd:attribute ref="K" /> + <xsd:attribute ref="URL" /> + <xsd:attribute ref="bgcolor" /> + <xsd:attribute ref="color" default="black" /> + <xsd:attribute ref="colorscheme" /> + <xsd:attribute ref="fillcolor" default="black" /> + <xsd:attribute ref="fixedsize" default="false" /> + <xsd:attribute ref="fontcolor" default="black" /> + <xsd:attribute ref="fontname" default="Times-Roman" /> + <xsd:attribute ref="fontsize" default="14.0" /> + <xsd:attribute ref="label" /> + <xsd:attribute ref="labeljust" default="c" /> + <xsd:attribute ref="labelloc" default="t" /> + <xsd:attribute ref="lp" /> + <xsd:attribute ref="nojustify" default="false" /> + <xsd:attribute ref="pencolor" default="black" /> + <xsd:attribute ref="style" /> + <xsd:attribute ref="target" /> + <xsd:attribute ref="tooltip" /> + </xsd:complexType> + +</xsd:schema>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/schema/attributes.xslt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,467 @@ +<?xml version="1.0"?> +<xsl:stylesheet + xmlns:xsl="http://www.w3.org/1999/XSL/Transform" + xmlns:xsd="http://www.w3.org/2001/XMLSchema" + xmlns="http://www.w3.org/1999/xhtml" + xmlns:gv="urn:graphviz" + version="1.0"> + + <xsl:output method="html" /> + + <xsl:variable name="arguments" select="document('arguments.xml')" /> + + <xsl:template match="/"> + <html> + <head> + <title>Node, Edge and Graph Attributes</title> + <style> + body + { + margin: 0px; + padding: 0px; + overflow: hidden; + + font-family: Georgia; + } + + code + { + font-family: Courier; + } + + a + { + text-decoration: none; + } + + a:hover + { + text-decoration: underline; + } + + #indices + { + position: absolute; + left: 0px; + width: 200px; + top: 0px; + bottom: 0px; /* IE doesn't use this */ + height: expression(offsetParent.offsetHeight); /* only IE uses this */ + overflow: auto; + + background: #EEEEEE + } + + #contents + { + position: absolute; + left: 210px; + right:0px; /* IE doesn't use this */ + width: expression(offsetParent.offsetWidth-210); /* only IE uses this */ + top: 0px; + bottom: 0px; /* IE doesn't use this */ + height: expression(offsetParent.offsetHeight); /* only IE uses this */ + overflow: auto; + } + + .val + { + font-family: Courier; + color: green; + } + + .attr + { + font-family: Courier; + font-weight: bold; + } + + .comp + { + } + + .layout + { + } + + .format + { + } + + .note + { + } + + .missing + { + color: lightgray; + } + + .heading + { + padding-left: 5px; + padding-right: 5px; + padding-top: 10px; + padding-bottom: 2px; + + color: gray; + font-weight: bold; + font-size: 60%; + } + + .text + { + padding-left: 20px; + padding-bottom: 10px; + padding-right: 10px; + } + + .index + { + padding-left: 20px; + padding-right: 5px; + padding-top: 2px; + padding-bottom: 2px; + + display: block; + font-size: 80%; + + color: blue; + } + + .index_selected + { + background: #8888FF; + color: white; + } + + .content + { + display: none; + } + + .content_selected + { + display: block; + } + </style> + + <script> + <xsl:text disable-output-escaping="yes"> + var lastSelected = null; + + function addClass (element, newClass) + { + if (element) + { + var classes = element.className.split (" "); + for (var i = 0; i < classes.length; ++i) + if (classes [i] == newClass) + break; + + if (i == classes.length) + { + classes.push (newClass); + element.className = classes.join (" "); + } + } + } + + function removeClass (element, oldClass) + { + if (element) + { + var classes = element.className.split (" "); + for (var i = 0; i < classes.length; ++i) + if (classes [i] == oldClass) + { + classes.splice (i, 1); + element.className = classes.join (" "); + break; + } + } + } + + + function select (selected) + { + if (selected != lastSelected) + { + // deselect the last selection + if (lastSelected) + { + removeClass (document.getElementById ("index_" + lastSelected.replace (':', '_')), "index_selected"); + removeClass (document.getElementById ("content_" + lastSelected.replace (':', '_')), "content_selected"); + } + + lastSelected = selected; + + var selectedIndex = document.getElementById ("index_" + lastSelected.replace (':', '_')); + addClass (selectedIndex, "index_selected"); + + // ensure selected index is visible in indices source list + if (selectedIndex) + { + var indexTop = selectedIndex.offsetTop; + var indexBottom = indexTop + selectedIndex.offsetHeight; + var indicesTop = selectedIndex.offsetParent.scrollTop; + var indicesBottom = indicesTop + selectedIndex.offsetParent.offsetHeight; + if (indexBottom < indicesTop || indexTop > indicesBottom) + selectedIndex.scrollIntoView (); + } + + // display the content associated with the selected index + addClass (document.getElementById ("content_" + lastSelected.replace (':', '_')), "content_selected"); + } + return true; + + } + + function hashed () + { + // check if we navigated to a new internal location e.g. via the back button + // if so we need to update the selection + // NOTE: this means there are no real anchors in the HTML at all, we're just simulating them + var hash = window.location.hash; + if (hash && hash.charAt (0) == '#') + select (hash.substr (1)); + else + select ("h:introduction"); + return true; + } + + function clicked (event) + { + // check to see if an internal link was clicked, if so we need to update the selection + // NOTE: this is not strictly necessary since hashed () will catch it but it helps with the responsiveness + var clickedElement = event.target; + if (!clickedElement) + clickedElement = event.srcElement; + switch (clickedElement.tagName) + { + case 'a': + case 'A': + var oldHref = window.location.href; + var oldHash = oldHref.indexOf ('#'); + if (oldHash == -1) + oldHash = oldHref.length; + + var newHref = clickedElement.href; + var newHash = newHref.indexOf ('#'); + if (newHash == -1) + newHash = newHref.length; + + if (oldHref.substr (0, oldHash) == newHref.substr (0, newHash)) + { + if (newHash < newHref.length) + select (newHref.substr (newHash + 1)); + else + select ("h:introduction"); + } + break; + } + return true; + } + + function loaded () + { + hashed (); + window.setInterval ("hashed ()", 1000); + return true; + } + </xsl:text> + </script> + + </head> + <body onload="return loaded ()" onclick="return clicked (event)"> + <div id="indices"> + + <!-- display all the attribute type names --> + <div class="heading">NOTES</div> + <xsl:for-each select="/xsd:schema/xsd:annotation[@id]"> + <a id="index_h_{@id}" class="index note" href="#h:{@id}"> + <xsl:value-of select="@id" /> + </a> + </xsl:for-each> + + <!-- display all the attribute names --> + <div class="heading">ATTRIBUTES</div> + <xsl:for-each select="/xsd:schema/xsd:attribute"> + <a id="index_d_{@name}" class="index attr" href="#d:{@name}"> + <xsl:value-of select="@name" /> + </a> + </xsl:for-each> + + <!-- display all the attribute type names --> + <div class="heading">TYPES</div> + <xsl:for-each select="/xsd:schema/xsd:simpleType"> + <a id="index_k_{@name}" class="index type" href="#k:{@name}"> + <xsl:value-of select="@name" /> + </a> + </xsl:for-each> + + + + </div> + <div id="contents"> + + <!-- note content --> + <xsl:for-each select="/xsd:schema/xsd:annotation[@id]"> + <div id="content_h_{@id}" class="content"> + <div class="text"> + <xsl:apply-templates select="xsd:documentation/*" mode="html" /> + </div> + </div> + </xsl:for-each> + + <!-- attribute content --> + <xsl:for-each select="/xsd:schema/xsd:attribute"> + <div id="content_d_{@name}" class="content"> + + <!-- display the layouts this attribute applies to --> + <div class="heading">LAYOUTS</div> + <div class="text"> + <xsl:variable name="attributeLayouts" select="@gv:layouts" /> + <xsl:for-each select="$arguments/xsd:schema/xsd:simpleType[@name='layout']/xsd:restriction/xsd:enumeration"> + <span class="layout"> + <xsl:attribute name="class">layout <xsl:if test="$attributeLayouts and not(contains(concat(' ',$attributeLayouts,' '),concat(' ',@value,' ')))">missing</xsl:if></xsl:attribute> + <xsl:value-of select="@value" /> + </span> + <xsl:text> </xsl:text> + </xsl:for-each> + </div> + + <!-- display the components this attribute is used by --> + <div class="heading">COMPONENTS</div> + <div class="text"> + <xsl:variable name="attributeName" select="@name" /> + <xsl:for-each select="/xsd:schema/xsd:complexType"> + <span class="comp"> + <xsl:attribute name="class">comp <xsl:if test="not(xsd:attribute[@ref=$attributeName])">missing</xsl:if></xsl:attribute> + <xsl:value-of select="@name" /> + </span> + <xsl:text> </xsl:text> + </xsl:for-each> + </div> + + <!-- display the formats this attribute can output --> + <div class="heading">FORMATS</div> + <div class="text"> + <xsl:choose> + <xsl:when test="@gv:formats"> + <span class="format"><xsl:value-of select="@gv:formats" /></span> + </xsl:when> + <xsl:otherwise> + <span class="format missing">all</span> + </xsl:otherwise> + </xsl:choose> + </div> + + <!-- display the type of this attribute --> + <div class="heading">TYPE</div> + <div class="text"> + <xsl:choose> + <xsl:when test="@type='xsd:anyURI'"> + <span class="type">URL</span> + </xsl:when> + <xsl:when test="@type='xsd:boolean'"> + <span class="type">bool</span> + </xsl:when> + <xsl:when test="@type='xsd:decimal'"> + <span class="type">double</span> + </xsl:when> + <xsl:when test="@type='xsd:integer'"> + <span class="type">int</span> + </xsl:when> + <xsl:when test="@type='xsd:string'"> + <span class="type">string</span> + </xsl:when> + <xsl:when test="not(contains(@type,':'))"> + <a class="type" href="#k:{@type}"><xsl:value-of select="@type" /></a> + </xsl:when> + </xsl:choose> + </div> + + <!-- display the attribute default or none if missing --> + <div class="heading">DEFAULT</div> + <div class="text"> + <xsl:choose> + <xsl:when test="@default"> + <span class="val"> + <xsl:value-of select="@default" /> + </span> + </xsl:when> + <xsl:otherwise> + <span class="val missing">none</span> + </xsl:otherwise> + </xsl:choose> + </div> + + <!-- display the description from the documentation bits --> + <div class="heading">DESCRIPTION</div> + <div class="text"> + <xsl:apply-templates select="xsd:annotation/xsd:documentation/*" mode="html" /> + </div> + + </div> + </xsl:for-each> + + <!-- attribute type content --> + <xsl:for-each select="/xsd:schema/xsd:simpleType"> + <div id="content_k_{@name}" class="content"> + + <!-- display the values if this is an enumeration --> + <xsl:if test="xsd:restriction/xsd:enumeration"> + <div class="heading">VALUES</div> + <div class="text"> + <xsl:for-each select="xsd:restriction/xsd:enumeration"> + <span class="val"><xsl:value-of select="@value" /></span> + <xsl:text> </xsl:text> + </xsl:for-each> + </div> + </xsl:if> + + <!-- display the description from the documentation bits --> + <div class="heading">DESCRIPTION</div> + <div class="text"> + <xsl:apply-templates select="xsd:annotation/xsd:documentation/*" mode="html" /> + </div> + </div> + </xsl:for-each> + + + + </div> + </body> + </html> + </xsl:template> + + + + + <xsl:template match="html:a[@rel='attr']" mode="html" xmlns:html="http://www.w3.org/1999/xhtml"> + <a href="#d:{text()}" class="attr"> + <xsl:apply-templates select="@*|node()" mode="html" /> + </a> + </xsl:template> + + <xsl:template match="html:a[@rel='type']" mode="html" xmlns:html="http://www.w3.org/1999/xhtml"> + <a href="#k:{text()}" class="type"> + <xsl:apply-templates select="@*|node()" mode="html" /> + </a> + </xsl:template> + + <xsl:template match="html:a[@rel='note']" mode="html" xmlns:html="http://www.w3.org/1999/xhtml"> + <a href="#h:{text()}" class="note"> + <xsl:apply-templates select="@*|node()" mode="html" /> + </a> + </xsl:template> + + <xsl:template match="@*|node()" mode="html"> + <xsl:copy> + <xsl:apply-templates select="@*|node()" mode="html" /> + </xsl:copy> + </xsl:template> + + +</xsl:stylesheet> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/todo.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,112 @@ +<html> +<head> +<title>Graphviz todo list 30-January-2009</title> +</head> +<body bgcolor=#ffffff> + +<h1>Graphviz todo list 30-January-2009</h1> +<P> +The 4 categories give a rough time as to when a task should be done, +and reflect an assessment of the task's ease multiplied by its importance. +Thus, orthogonal layouts are extremely important but not easy, +so they fall into category 2. The following denote owners:<br> +AB=<A HREF=mailto:arif@research.att.com>Arif Bilgin</A><br> +ERG=<A HREF=mailto:erg@graphviz.org>Emden Gansner</A><br> +JCE=<A HREF=mailto:ellson@graphviz.org>John Ellson</A><br> +SCN=<A HREF=mailto:north@graphviz.org>Stephen North</A><br> +YH=<A HREF=mailto:yifanhu@graphviz.org>Yifan Hu</A><br> + +<hr align=left width=66> +<H2>Category 0</H2> +<pre> +ALL - Revise the buglist +SCN - Update dot/neatoguide +</pre> +<hr align=left width=66> +<H2>Category 1</H2> +<pre> +SCN - Lightweight edge labels in dot +? - Lightweight node labels in dot, just placed outside nodes. +SCN - Treemaps +JCE - Extend attribute set for line styles +JCE - Allow arrowheads in the middle of edges +JCE - Support single, uniform font descriptors à la pango +? - Move generic node adjustment and spline routing out of neato +ERG - New text labels with font elements <B>, <I>, <TT>, etc. (support via fontconfig or egregious hacks) +AB - Create Graph editor for Topview/Graphviz with OpenGL and GTK. (smyrna - in process) (Deprecate use of lefty) +YH/ERG - Clusters in sfdp +YH/ERG - Fixed nodes in sfdp +</pre> +<hr align=left width=66> +<H2>Category 2</H2> +<pre> +SCN/AB - New dot core: + - cgraph + - new rank assignment (have prototype) + - new mincross + clusters (partial prototype) + - symmetrize layouts + - Ulrik's improvements with network simplex +JCE - Shapes + - SVG grammar or other grammar to specify node and arrow shapes + - user-defined nodes in all code generators +YH/ERG - additional graph packing strategies +? - Filters for GraphML and Chaco formats. +? - Library for bundled edges (Given two sets of nodes, with positions, +forming a bipartite graph, return splines to bundle the edges together) +? - better spacing of edges along the side of a node, especially +exposed port sides. Perhaps extend compass points to S, N, E, W to +mean anywhere along that side. +? - Tabular layout in rows and columns in dot (swim lanes) +? - Enable arbitrary depth clusters in neato using IPSEP +? - Allow mixed edge types within the same graph + +</pre> +<hr align=left width=66> +<H2>Category 3</H2> +<pre> +Generic label placement, with and without modifying graph layout. +Support for compound graphs. +Spline router improvement (Gordon's improvements, alpha adjustments, fewer + inflection points) +Fast incremental hierarchical layout +Edge routing with just two vnodes per edge (after Kaufmann) +Graph language improvements + local name spaces + subclassing +Shapes - new parallelogram shape where angle is fixed for varying text +Orthogonal layouts. +Support rank=min/max within the context of a cluster +</pre> +<H2>General R&D directions</H2> +<pre> +Incremental layouts +Dot with orthogonal, coalesced edges (e.g., <A HREF=http://upload.wikimedia.org/wikipedia/en/thumb/7/74/Uml_diagram.svg/700px-Uml_diagram.svg.png>this</A>) +Constraint-based layouts +Display of large-graphs (hair ball problem) + - relaxing constraining edges + - edge bundles +Venn diagrams +Integration of geographic info - maps +</pre> +<H2>Done</H2> +<pre> +JCE - Extend attribute set for line thickness +ERG - New text labels with font elements <FONT> +ERG - Allow edges as line segments and polylines +ERG - New multiedge code +YH/ERG - New overlap adjustment strategies +YH - Faster fdp (sfdp) +ERG - Variable port depending on layout +ERG - Better aspect ratio in dot +ERG - New orthogonal routing code +JCE - use floats not ints where appropriate +YH - Filters for Matrix Market +</pre> +<H2>Retired</H2> +<pre> +JCE - Revise GV_FILE_PATH to work as directory list; remove loose ends +ERG - eliminate the nonlinear adjustment + - use convex hull or alpha shapes +</pre> +</body> +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/html/winbuild.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,301 @@ +<html> +<head> +<title>Graphviz Build Instructions for Windows</title> +</head> +<body bgcolor=#ffffff> +<h2>Graphviz Build Instructions for Windows</h2> +<P> +<b>(Graphviz versions ≥ 2.22)</b> +<P> +We now build both Visual Studio and MinGW versions of Graphviz. +In addition, the source package comes with project files for Visual Studio, +so building from source should be fairly simple in either case. +<UL> +<LI> +The first step is to add the third-party libraries. These are listed +<A HREF="http://www.graphviz.org/Download_source.php">here</A>. +This is most easily done by installing +a Windows binary version of <A HREF="http://www.gtk.org">GTK 2.0</A> or later. +<LI> +Next, download and unwrap the Graphviz source package. Go to +<A HREF=http://www.graphviz.org/Download_source.php>http://www.graphviz.org/Download_source.php</A> +and copy over either the stable release +or the development source package. +</UL> +Let $ROOT be the root directory (folder) +in which you stored the Graphviz source. +<h3>Building with Visual Studio</h3> +<OL> +<LI> +Copy the file $ROOT/windows/config.h into $ROOT. +<LI> +Copy the folder $ROOT/windows/FEATURE into $ROOT. +<LI> +Start Visual Studio and read in $ROOT/graphviz.sln. +There are project files for all of the parts of Graphviz. +<LI> +Modify the various settings for include file and library directories to find +the third-party software. +<LI> +Modify the settings as to where you want the software installed. +<LI> +Build Graphviz. +</OL> +<h3>Building with MinGW</h3> +<OL> +<LI> Install MinGW and Msys. This gives a mini GNU-like compile environment +<LI> Set environment variables based on your machine, giving values to the +first six below: +<CODE> +<PRE> +export INSTALLROOT # Root install directory +export GTKDIR # Root of GTK tree +export FONTCONFIGINCLUDEDIR # Path of fontconfig include dir +export FONTCONFIGLIBDIR # Path of fontconfig lib dir +export FREETYPEINCLUDEDIR # Path of freetype2 include dir +export FREETYPELIBDIR # Path of freetype2 lib dir + +export CFLAGS="-DGVDLL=1" +export CPPFLAGS="-I$GTKDIR/include -I$GTKDIR/include/freetype2 " +export LDFLAGS="-L$GTKDIR/lib -no-undefined" +export FONTCONFIG_CFLAGS=-I$GTKDIR/include/ +export FONTCONFIG_LIBS="-L$FONTCONFIGLIBDIR -lfontconfig" +export FREETYPE2_CFLAGS=-I$FREETYPEINCLUDEDIR +export FREETYPE2_LIBS="-L$FREETYPELIBDIR +export PKG_CONFIG=$GTKDIR/bin/pkg-config +export PKG_CONFIG_PATH=$GTKDIR/lib/pkgconfig + +export PANGOFLAGS="--with-fontconfig --with-fontconfiglibdir$FONTCONFIGLIBDIR --with-fontconfigincludedir=$FONTCONFIGINCLUDEDIR --with-pangocairo" + +</PRE> +</CODE> +<LI> Change directory to $ROOT and run configure:<BR> +<CODE> +<PRE> +./configure -C --prefix=$INSTALLROOT --without-gdk-pixbuf --with-mylibgd --disable-swig --without-x --disable-tcl --without-ipsepcola --with-codegens --enable-shared --disable-static --with-freetype=$GTKDIR/lib --enable-ltdl $PANGOFLAGS --without-gtk --without-gtkgl + +</PRE> +</CODE> +When configure finishes, it lists all of the Graphviz features that have been +enabled. If there are problems, you can check for errors in <TT>config.log</TT>. +You may have to add or modify +other flags and environment variables for <TT>configure</TT> to give you the +settings you want. Run <TT>configure --help</TT> +to see the allowed options and environment variables. +<LI> Run <TT>make</TT> +<LI> Run <TT>make install</TT> +</OL> +These builds create a feature-full version of Graphviz. You can tailor +them to your wants. This is fairly simple for MinGW: just remove or reset +the flag of the option you don't want. For Visual Studio, you will probably +need to reset some of the values in <TT>config.h</TT> and some of the +compile-time options. +<HR> +<P> +<b>(2.21 >= Graphviz versions > 2.14)</b> +<P> +To simplify our build process, especially as most Windows users only +want a binary version of the software, the Graphviz build for Windows +now uses <A HREF="http://www.research.att.com/sw/tools/uwin/"><B>uwin</B></A>, +an open-source Unix layer on top of Windows. In this +environment, we can use essentially the same tools and process +used on Unix. In particular, we do not have to maintain multiple build +files. +<P> +To build Graphviz: +<OL> +<LI> +Install the third-party libraries. This is most easily done by installing +a Windows binary verion of <A HREF="http://www.gtk.org">GTK 2.0</A> or later. +<LI> +Download the Graphviz source package:<BR>Go to +<A HREF=http://www.graphviz.org/Download_source.php>http://www.graphviz.org/Download_source.php</A> +and copy over either the stable release <TT>graphviz-</TT><I>VERSION</I><TT>.tar.gz</TT> +or the development source package <TT>graphviz-working.tar.gz</TT>. +<LI> +Unwrap the package:<BR> +You can use, for example,<BR><BR> +<TT>gunzip < graphviz-working.tar.gz | tar xf -</TT><BR> +<SPACER TYPE=VERTICAL SIZE=10> +<BR> +Let <TT>$ROOT</TT> be the directory in which you stored the Graphviz source. +<LI> +Configure the package for your system:<BR><BR> +<TT>cd $ROOT</TT><BR> +<TT>configure --disable-shared --enable-static --with-mylibgd --disable-swig --without-x --without-tclsh --with-codegens --disable-ltdl</TT><BR><BR> +You will probably want to provide additional arguments to +<TT>configure</TT> to indicate where you have installed GTK, where +you want the results installed (<TT>--prefix</TT>) and which +additional optional packages you want built. Run <TT>configure --help</TT> +to see the available set of arguments. +<LI> +Build Graphviz:<BR><BR> +<TT>make install</TT><BR><BR> +</OL> +<P> +Presumably, a similar approach would work with Cygwin or MinGW. +The following are some notes by +<A HREF="https://mailman.research.att.com/pipermail/graphviz-devel/2007/000444.html">Steve Roush</A> +describing how he +made a static build of the libraries on MinGW. +<MENU> +<LI> Install MinGW and Msys. This gives a mini GNU-like compile environment +that produces Windows-compatible results. +<LI> You may need to configure and build in Windows "Safe mode". Try +./configure in normal mode; if it dies with "fork" problems, use Safe mode. +<LI> Edit the "configure" file.<BR> + after this line:<BR> + <CODE>DEFS=-DHAVE_CONFIG_H</CODE><BR> + add these lines:<BR> +<CODE> +<PRE> + case "$host_os" in + mingw*) + DEFS="$DEFS -DWIN32 -DMSWIN32" + ;; + esac +</PRE> +</CODE> +<LI> run configure:<BR> +<CODE> +<PRE> + ./configure --enable-static=yes --enable-shared=no +--prefix=/usr/local/ --with-libgd=no --enable-ltdl=no --disable-swig +--disable-sharp --disable-guile --disable-io --disable-java +--disable-lua --disable-ocaml --disable-perl --disable-php +--disable-python --disable-ruby --disable-tcl +</PRE> +</CODE> +<LI> edit lib/Makefile<BR> + replace these lines:<BR> +<CODE> +<PRE> + SUBDIRS = cdt graph agraph gd pathplan agutil sfio vmalloc ast vpsc \ + circogen dotgen fdpgen neatogen twopigen common pack gvc \ + ingraphs expr +</PRE> +</CODE> + + with these lines:<BR> +<CODE> +<PRE> + SUBDIRS = cdt graph agraph gd pathplan vpsc \ + circogen dotgen fdpgen neatogen twopigen common pack gvc \ + ingraphs + +make # builds most of "lib" (see Makefile changes), cmd/dot and +cmd/tools - stops on cmd/gvpr +make install +</PRE> +</CODE> +</MENU> +here is the command line I used to build "simple.c" (dot.demo/simple.c), +after adding a few lines based on +https://mailman.research.att.com/pipermail/graphviz-interest/2006q2/003586.html + + +<CODE> +<PRE> +X=simple;gcc -v -I'/usr/local/include/graphviz' -I'/usr/local/include' +-o $X -O $X.c -L/usr/local/lib -L/usr/local/lib/graphviz -lgvc -lgraph +-lpathplan -lcdt -lgvplugin_core -lgvplugin_dot_layout +-lgvplugin_neao_layout -lgvplugin_gd -lgvplugin_pango -lgvc -lpathplan +</PRE> +</CODE> +<P> +If you +desire to build directly using Visual Studio or other non-Unix-based +environment, you will need to derive the necessary information from the +supplied Makefiles. +<HR> +<P> +<b>(2.14 >= Graphviz versions >= 2.3)</b> +<P> +We build Graphviz on Windows using the MS Visual C++ 6.0 compiler +only. The simplest way to build the software from source on Windows is as +follows: +<OL> +<LI> +Download the Windows source package from: +<A HREF=http://www.graphviz.org/Download_windows.php>http://www.graphviz.org/Download_windows.php</A>. +</LI> +<SPACER TYPE=VERTICAL SIZE=10> +<LI> +Unwrap the package:<BR> +If you have Unix tools available, you can use +<SPACER TYPE=VERTICAL SIZE=10> +<TT>gunzip < graphviz-win.tgz | tar xf -</TT><BR> +<SPACER TYPE=VERTICAL SIZE=10> +If not, double click on the file and WinZip should start up and let you +store all of the files into a directory.<BR> +Let <TT>$ROOT</TT> be the directory in which you stored the Graphviz source. +</LI> +<SPACER TYPE=VERTICAL SIZE=10> +<LI> +Add the third-party libraries: +<SPACER TYPE=VERTICAL SIZE=10> +<UL TYPE=DISC> +<LI> +zlib 1.1.3 <A HREF=http://www.gzip.org/zlib/>http://www.gzip.org/zlib/</A> +<LI> +libpng 1.0.6 <A HREF=http://www.libpng.org/pub/png>http://www.libpng.org/pub/png</A> +<LI> +jpeg-6b 62 <A HREF=http://www.ijg.org/>http://www.ijg.org/</A> +<LI> +freetype 2.1.7 <A HREF=http://freetype.sourceforge.net/>http://freetype.sourceforge.net/</A> +<LI> +expat <A HREF=http://expat.sourceforge.net/>http://expat.sourceforge.net/</A> +</UL> +<SPACER TYPE=VERTICAL SIZE=10> +The versions should be at least the ones indicated. +You can download the files from +<a href=http://www.graphviz.org/Misc/third-party.zip>http://www.graphviz.org/Misc/third-party.zip</a>. +Unzip the package in the <TT>$ROOT</TT> directory; this will create a subdirectory called +<TT>third-party</TT>. +Or you can obtain the libraries yourself, and install +them in <TT>$ROOT\third-party</TT> +Also note that these libraries come with the binary release. +</LI> +<SPACER TYPE=VERTICAL SIZE=10> +<LI> +Build the software in one of the following ways:<BR> +<UL TYPE=DISC> +<LI> Run the script <TT>$ROOT\build.bat</TT> while in the <TT>$ROOT</TT> +directory. You'll first want to check +that the <TT>vc</TT> variable points to your local installation of visual C. +Obviously, this should be run in a DOS command window. +<LI> The file <TT>$ROOT\ws\graphviz.dsw</TT> +is the main workspace for the Graphviz libraries and graph drawing programs. +The file +<TT>$ROOT\ws\tools.dsw</TT> provides the workspace for the +additional Graphviz tools. Double-clicking on these files will open the +workspaces using the MS Visual Studio, from which you can use the Build +menu item to create the various libraries and programs. +<PP> +Note that we have avoided all dependencies (i.e., there are no .dep files) +because we found they tended to include absolute pathnames. +If necessary, follow the order used in <TT>build.bat</TT> to be safe. +<LI> If you have a Unix environment, with a real ksh, run the script +<TT>$ROOT\wmake.sh</TT> +after setting the PATH, LIB and INCLUDE shell variables to the paths where +the Visual C programs are installed on your machine, and the associated +library and include file directories. +</UL> +</OL> +<P> Once built, all of the libraries and programs will be found in +either the Release or Debug subdirectory of the corresponding source +directory. If you wish to install +the software somewhere, edit the file <TT>$ROOT\install.bat</TT> to +set the variable <TT>root</TT> to be the absolute pathname of the +directory where you want +the software installed, and then run the script <TT>$ROOT\install.bat</TT> +from the <TT>$ROOT</TT> directory. +<P> +If you want to change source files, you can just do it and rebuild. +If, however, you want to add new files or projects, you will +have to redo the makefiles or scripts. +<P> +If you have problems or questions, please contact us at +<A href=mailto:erg@research.att.com>erg@research.att.com</A>. +</body> +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/doc/latex_suggestions.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,51 @@ +--- vladimir@worklogic.com: this below isn't implemented yet! --- +--- Talk to me if you're interested in it and want to help --- + Latex/Psfrag Attributes+ +Only work in -Tps and if latex2e and psfrag.sty ver.3 are installed. + The resulting ps file will print latex commands unless you include it in a +tex file that does \usepackage[scanall]{psfrag}, or run it through the +supplied script "psfrag" (which wraps it in such a tex file). + +psfrag: string [GNE]. This text is passed through latex. + Example: node: a1 [psfrag="$\alpha_1$"] + edge: x->y [label="x -> y" psfrag="$x \rightarrow y$"] + The name/label is used only as a guide for positioning, but you better + specify them. E.g. if the edge had no label attribute, dot would have to + assume that the label is 17 characters wide. + The line breaks \l,\n,\r are *not* processed in the psfrag attribute. + If you want line breaks, arrange it with latex commands, and *also* specify + a label that emulates the breaks. +tailpsfrag/headpsfrag: string [E]. Same for tail/head of the edge. +fontname: [GNE] Use any of the commands described in latex2e's fntguide. + If the command sequence contains %s, the text is inserted there. Else the + text is preceded by the command, all surrounded by braces. For example, if + the text is "..." then \texttt{%s} becomes \texttt{...}, and \ttfamily + becomes {\ttfamily ...}. + If fontname matches "tt" then dot will use fixed font width for its + positioning (is 0.65 right??). If fontname matches "bf" then dot will + multiply the width for positioning purposes by ?? (for all families??). + You cannot change the font by simply giving a different font name. + E.g. to switch to Courier, you either have to use \texttt{%s} with a style + that uses Postscript fonts, or else use \fontfamily{pcr}\selectfont ("pcr" + stands for postscript courier). As a precaution, dot won't output a fontname + that does not contain \. + Often used commands are \rmfamily (roman), \sffamily (sans serif), + \ttfamily (typewriter), \mdseries (medium, i.e. normal), \bfseries (bold), + \upshape (upright, i.e. normal), \itshape (italic), \slshape (slanted), + \scshape (small caps); or their equivalents \textrm{%s}, ...; \boldmath; + \mathcal{%s}. +fontsize: "n\cmd". [GNE] Dot assumes for positioning purposes that the font is + n points high, and emits the font changing command \cmd to latex. Both n and + \cmd are optional. + If \cmd is missing then you are informing dot that the latex fontsize is n. + *Please* do this at the top level. The default 14 is probably wrong for you. + The correct setting for a 10pt article style is "digraph G {fontsize=10 ...}" + If you use "n!", dot will force this size to latex by emitting commands + \fontsize\selectfont. This is not smart unless you use scalable fonts, eg + postscript fonts. + If n is missing and \cmd is one of the standard commands \tiny, + \scriptsize, \footnotesize, \normalsize, \large, \Large, \LARGE, \huge or + \HUGE, dot will compute the new size automatically. Otherwise you have to + specify both. + If the command contains %s, the text will be inserted there, else the + text is preceded by the command, all surrounded by braces.
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/KW91.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +digraph G { + style=bold; + subgraph cluster_outer { + Act_1 -> Act_21; + Act_1 -> Act_23; + Act_25 -> Act_3; + subgraph cluster_inner { + label = " Act_2"; + {Act_21 -> Act_22 [minlen=2]; rank=same;} + Act_22 -> Act_23; + Act_22 -> Act_24; + {Act_23 -> Act_24 [minlen=2]; rank=same;} + Act_23 -> Act_25; + Act_24 -> Act_25; + } + } + Ext_1 -> Act_1; + Act_3 -> Ext_2; + Ext_3 -> Act_24; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/Latin1.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +digraph G { +graph [charset=latin1] +node [fontsize=10] +a [label = "áâãäåæçèéêëìíîïðñòóôõöøùúûü"]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/NaN.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +digraph xyz { +orientation=landscape; +ratio=compress; +size="16,10"; +AbstractMemory -> Memory; +AliasedMemory -> AliasedMemory; +AliasedMemory -> Memory; +Architecture -> ROOT; +Assembly -> ROOT; +AtomProperties -> NRAtom; +AtomWr -> Wr; +Break -> Break; +Break -> Target; +Breakpoint -> Breakpoint; +Breakpoint -> Event; +Breakpoint -> ROOT; +CDB -> Target; +CDB -> Thread; +CommonFrame -> Target; +ControlOps -> InterpF; +Displayed -> Displayed; +Displayed -> InterpTypes; +ETimer -> RTHeapRep; +Event -> Event; +Event -> ROOT; +Event -> Target; +EventHandler -> ROOT; +EventHandler -> StandardEvents; +Expression -> ROOT; +ExpressionServer -> Expression; +FollowBreakpoint -> Breakpoint; +Formatter -> ROOT; +Formatter -> Thread; +Frame -> Frame; +Frame -> Memory; +Frame -> Target; +FrameClass -> Frame; +IntIntTbl -> IntIntTbl; +IntIntTbl -> ROOT; +Interp -> InterpF; +Interp -> ROOT; +InterpF -> Interp; +InterpF -> InterpF; +InterpF -> ROOT; +InterpScan -> TokenStream; +InterpTypes -> InterpTypes; +InterpTypes -> ROOT; +List -> Thread; +LoadState -> LoadState; +LoadState -> LoadStateRep; +LoadState -> ROOT; +LoadStateRep -> LoadState; +LocationRep -> Memory; +MC68Frame -> CommonFrame; +MC68GCommonFrame -> EventHandler; +MUTEX -> ROOT; +Memory -> Displayed; +Memory -> InterpTypes; +MipsFrame -> CommonFrame; +MipsFrame -> InterpTypes; +MipsGCommonFrame -> EventHandler; +NRAtom -> AtomProperties; +NRAtom -> ROOT; +NopBreakpoint -> Breakpoint; +PSFormatter -> InterpTypes; +PSInterp -> InterpTypes; +PSLoadState -> InterpTypes; +PSMemory -> InterpTypes; +ProtectedWire -> ProtectedWire; +ProtectedWire -> Wire; +RTHeap -> RTHeapRep; +RTHeapRep -> ROOT; +Rd -> RdClass; +RdClass -> MUTEX; +RegisterMemory -> Memory; +Scope -> ROOT; +Scope -> Scope; +Scope -> Target; +SourceLoc -> Target; +SourceMap -> ROOT; +SparcFrame -> CommonFrame; +SparcGCommonFrame -> EventHandler; +StandardEvents -> StandardEvents; +StandardEvents -> Target; +StreamWire -> Wire; +Symbol -> Displayed; +Symbol -> Symbol; +TThread -> ROOT; +TThread -> Target; +Target -> Displayed; +Target -> Event; +Target -> FrameClass; +Target -> ROOT; +Target -> TThread; +Target -> Target; +Target -> TargetF; +Target -> Thread; +TargetF -> Target; +TargetState -> Assembly; +TextRd -> Rd; +TextWr -> Wr; +Thread -> ROOT; +Thread -> Thread; +TokenStream -> ROOT; +TokenStream -> TokenStream; +Trap -> ROOT; +TrapMemory -> Memory; +UFileRd -> Rd; +UFileRd -> UFileRd; +UFileWr -> UFileWr; +UFileWr -> Wr; +UnixHandler -> Event; +UnixHandler -> UnixHandler; +UserBreak -> Break; +UserBreak -> Breakpoint; +UserBreak -> Event; +UserBreak -> Trap; +UserBreak -> UserBreak; +VaxFrame -> CommonFrame; +VaxGCommonFrame -> EventHandler; +Wire -> ROOT; +Wire -> TrapMemory; +Wire -> Wire; +Wr -> WrClass; +WrClass -> MUTEX; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/abstract.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +digraph abstract { + size="6,6"; + S24 -> 27; + S24 -> 25; + S1 -> 10; + S1 -> 2; + S35 -> 36; + S35 -> 43; + S30 -> 31; + S30 -> 33; + 9 -> 42; + 9 -> T1; + 25 -> T1; + 25 -> 26; + 27 -> T24; + 2 -> 3; + 2 -> 16; + 2 -> 17; + 2 -> T1; + 2 -> 18; + 10 -> 11; + 10 -> 14; + 10 -> T1; + 10 -> 13; + 10 -> 12; + 31 -> T1; + 31 -> 32; + 33 -> T30; + 33 -> 34; + 42 -> 4; + 26 -> 4; + 3 -> 4; + 16 -> 15; + 17 -> 19; + 18 -> 29; + 11 -> 4; + 14 -> 15; + 37 -> 39; + 37 -> 41; + 37 -> 38; + 37 -> 40; + 13 -> 19; + 12 -> 29; + 43 -> 38; + 43 -> 40; + 36 -> 19; + 32 -> 23; + 34 -> 29; + 39 -> 15; + 41 -> 29; + 38 -> 4; + 40 -> 19; + 4 -> 5; + 19 -> 21; + 19 -> 20; + 19 -> 28; + 5 -> 6; + 5 -> T35; + 5 -> 23; + 21 -> 22; + 20 -> 15; + 28 -> 29; + 6 -> 7; + 15 -> T1; + 22 -> 23; + 22 -> T35; + 29 -> T30; + 7 -> T8; + 23 -> T24; + 23 -> T1; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/alf.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +digraph Alf { +size = "6,9"; +node [ shape = record ]; +Decl [ label = "\n\nDecl|{name|access|decl_flags|extern_c_linkage}"]; +Nontype_decl [ label = "Nontype_decl|{type}"]; +Defined_decl [ label = "Defined_decl|{linkage}"]; +Data_decl [ label = "Data_decl|{storage_class}"]; +Function_decl [ label = "Function_decl|{formals|defaults}"]; +Data [ label = "Data|{initializer}"]; +Function [ label = "Function|{body}"]; +Constructor [ label = "Constructor|{member_initializers}"]; +Aggregate -> Type_decl ; +Class -> Aggregate; +Union -> Aggregate; +Data -> Data_decl; +Data -> Defn; +Data_decl -> Defined_decl; +Data_member -> Nontype_decl ; +Defined_decl -> Nontype_decl; +Defn -> Defined_decl; +Enum -> Type_decl ; +Enumerator -> Nontype_decl ; +Function -> Defn; +Function -> Function_decl; +Constructor -> Function; +Destructor -> Function; +Function_decl -> Defined_decl; +Nontype_decl -> Decl ; +Template_type_arg -> Type_decl ; +Type_decl -> Decl ; +Typedef -> Type_decl ; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/arrows.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,77 @@ +digraph G { + graph [rankdir=LR nodesep=0] + node [shape=point label=""] + edge [fontsize=10] + _box -> box [arrowhead=box label=box] + box -> boxbox [arrowhead=boxbox label=boxbox] + _box -> lbox [arrowhead=lbox label=lbox] + lbox -> lboxlbox [arrowhead=lboxlbox label=lboxlbox] + _box -> rbox [arrowhead=rbox label=rbox] + rbox -> rboxrbox [arrowhead=rboxrbox label=rboxrbox] + _box -> olbox [arrowhead=olbox label=olbox] + olbox -> olboxolbox [arrowhead=olboxolbox label=olboxolbox] + _box -> orbox [arrowhead=orbox label=orbox] + orbox -> orboxorbox [arrowhead=orboxorbox label=orboxorbox] + _box -> obox [arrowhead=obox label=obox] + obox -> oboxobox [arrowhead=oboxobox label=oboxobox] + _crow -> crow [arrowhead=crow label=crow] + crow -> crowcrow [arrowhead=crowcrow label=crowcrow] + _crow -> lcrow [arrowhead=lcrow label=lcrow] + lcrow -> lcrowlcrow [arrowhead=lcrowlcrow label=lcrowlcrow] + _crow -> rcrow [arrowhead=rcrow label=rcrow] + rcrow -> rcrowrcrow [arrowhead=rcrowrcrow label=rcrowrcrow] + _diamond -> diamond [arrowhead=diamond label=diamond] + diamond -> diamonddiamond [arrowhead=diamonddiamond label=diamonddiamond] + _diamond -> ldiamond [arrowhead=ldiamond label=ldiamond] + ldiamond -> ldiamondldiamond [arrowhead=ldiamondldiamond label=ldiamondldiamond] + _diamond -> rdiamond [arrowhead=rdiamond label=rdiamond] + rdiamond -> rdiamondrdiamond [arrowhead=rdiamondrdiamond label=rdiamondrdiamond] + _diamond -> oldiamond [arrowhead=oldiamond label=oldiamond] + oldiamond -> oldiamondoldiamond [arrowhead=oldiamondoldiamond label=oldiamondoldiamond] + _diamond -> ordiamond [arrowhead=ordiamond label=ordiamond] + ordiamond -> ordiamondordiamond [arrowhead=ordiamondordiamond label=ordiamondordiamond] + _diamond -> odiamond [arrowhead=odiamond label=odiamond] + odiamond -> odiamondodiamond [arrowhead=odiamondodiamond label=odiamondodiamond] + _dot -> dot [arrowhead=dot label=dot] + dot -> dotdot [arrowhead=dotdot label=dotdot] + _dot -> odot [arrowhead=odot label=odot] + odot -> odotodot [arrowhead=odotodot label=odotodot] + _inv -> inv [arrowhead=inv label=inv] + inv -> invinv [arrowhead=invinv label=invinv] + _inv -> linv [arrowhead=linv label=linv] + linv -> linvlinv [arrowhead=linvlinv label=linvlinv] + _inv -> rinv [arrowhead=rinv label=rinv] + rinv -> rinvrinv [arrowhead=rinvrinv label=rinvrinv] + _inv -> olinv [arrowhead=olinv label=olinv] + olinv -> olinvolinv [arrowhead=olinvolinv label=olinvolinv] + _inv -> orinv [arrowhead=orinv label=orinv] + orinv -> orinvorinv [arrowhead=orinvorinv label=orinvorinv] + _inv -> oinv [arrowhead=oinv label=oinv] + oinv -> oinvoinv [arrowhead=oinvoinv label=oinvoinv] + _none -> none [arrowhead=none label=none] + none -> nonenone [arrowhead=nonenone label=nonenone] + _normal -> normal [arrowhead=normal label=normal] + normal -> normalnormal [arrowhead=normalnormal label=normalnormal] + _normal -> lnormal [arrowhead=lnormal label=lnormal] + lnormal -> lnormallnormal [arrowhead=lnormallnormal label=lnormallnormal] + _normal -> rnormal [arrowhead=rnormal label=rnormal] + rnormal -> rnormalrnormal [arrowhead=rnormalrnormal label=rnormalrnormal] + _normal -> olnormal [arrowhead=olnormal label=olnormal] + olnormal -> olnormalolnormal [arrowhead=olnormalolnormal label=olnormalolnormal] + _normal -> ornormal [arrowhead=ornormal label=ornormal] + ornormal -> ornormalornormal [arrowhead=ornormalornormal label=ornormalornormal] + _normal -> onormal [arrowhead=onormal label=onormal] + onormal -> onormalonormal [arrowhead=onormalonormal label=onormalonormal] + _tee -> tee [arrowhead=tee label=tee] + tee -> teetee [arrowhead=teetee label=teetee] + _tee -> ltee [arrowhead=ltee label=ltee] + ltee -> lteeltee [arrowhead=lteeltee label=lteeltee] + _tee -> rtee [arrowhead=rtee label=rtee] + rtee -> rteertee [arrowhead=rteertee label=rteertee] + _vee -> vee [arrowhead=vee label=vee] + vee -> veevee [arrowhead=veevee label=veevee] + _vee -> lvee [arrowhead=lvee label=lvee] + lvee -> lveelvee [arrowhead=lveelvee label=lveelvee] + _vee -> rvee [arrowhead=rvee label=rvee] + rvee -> rveervee [arrowhead=rveervee label=rveervee] +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/awilliams.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,188 @@ +digraph pvn { + ordering=out; + + node_1 -> node_2; + node_1 [label="ID: 1\ntype: 48\nnbr out: 0\nnbr chi: 11"]; + node_2 [label="ID: 2\ntype: 8\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_3; + node_3 [label="ID: 3\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_4; + node_4 [label="ID: 4\ntype: 6\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_5; + node_5 [label="ID: 5\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_6; + node_6 [label="ID: 6\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_7; + node_7 [label="ID: 7\ntype: 49\nnbr out: 0\nnbr chi: 0"]; + node_7 -> node_8; + node_8 [label="ID: 8\ntype: 45\nnbr out: 2\nnbr chi: 0"]; + node_8 -> node_9; + node_9 [label="ID: 9\ntype: 48\nnbr out: 0\nnbr chi: 4"]; + node_9 -> node_10; + node_10 [label="ID: 10\ntype: 8\nnbr out: 0\nnbr chi: 0"]; + node_9 -> node_11; + node_11 [label="ID: 11\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_9 -> node_12; + node_12 [label="ID: 12\ntype: 5\nnbr out: 0\nnbr chi: 0"]; + node_9 -> node_13; + node_13 [label="ID: 13\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_8 -> node_14; + node_14 [label="ID: 14\ntype: 39\nnbr out: 1\nnbr chi: 0"]; + node_14 -> node_15; + node_15 [label="ID: 15\ntype: 55\nnbr out: 0\nnbr chi: 0"]; + node_15 -> node_16; + node_16 [label="ID: 16\ntype: 48\nnbr out: 0\nnbr chi: 3"]; + node_16 -> node_17; + node_17 [label="ID: 17\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_16 -> node_18; + node_18 [label="ID: 18\ntype: 6\nnbr out: 0\nnbr chi: 0"]; + node_16 -> node_19; + node_19 [label="ID: 19\ntype: 45\nnbr out: 1\nnbr chi: 0"]; + node_19 -> node_20; + node_20 [label="ID: 20\ntype: 48\nnbr out: 0\nnbr chi: 5"]; + node_20 -> node_21; + node_21 [label="ID: 21\ntype: 38\nnbr out: 0\nnbr chi: 0"]; + node_20 -> node_22; + node_22 [label="ID: 22\ntype: 8\nnbr out: 0\nnbr chi: 0"]; + node_20 -> node_23; + node_23 [label="ID: 23\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_20 -> node_24; + node_24 [label="ID: 24\ntype: 5\nnbr out: 0\nnbr chi: 0"]; + node_20 -> node_25; + node_25 [label="ID: 25\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_19 -> node_26; + node_26 [label="ID: 26\ntype: 41\nnbr out: 12\nnbr chi: 0"]; + node_26 -> node_27; + node_27 [label="ID: 27\ntype: 48\nnbr out: 0\nnbr chi: 5"]; + node_27 -> node_28; + node_28 [label="ID: 28\ntype: 38\nnbr out: 0\nnbr chi: 0"]; + node_27 -> node_29; + node_29 [label="ID: 29\ntype: 8\nnbr out: 0\nnbr chi: 0"]; + node_27 -> node_30; + node_30 [label="ID: 30\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_27 -> node_31; + node_31 [label="ID: 31\ntype: 5\nnbr out: 0\nnbr chi: 0"]; + node_27 -> node_32; + node_32 [label="ID: 32\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_27; + node_26 -> node_33; + node_33 [label="ID: 33\ntype: 48\nnbr out: 0\nnbr chi: 5"]; + node_33 -> node_34; + node_34 [label="ID: 34\ntype: 38\nnbr out: 0\nnbr chi: 0"]; + node_33 -> node_35; + node_35 [label="ID: 35\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_33 -> node_36; + node_36 [label="ID: 36\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_33 -> node_37; + node_37 [label="ID: 37\ntype: 20\nnbr out: 0\nnbr chi: 0"]; + node_33 -> node_38; + node_38 [label="ID: 38\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_15 -> node_39; + node_39 [label="ID: 39\ntype: 45\nnbr out: 1\nnbr chi: 0"]; + node_39 -> node_40; + node_40 [label="ID: 40\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_39 -> node_41; + node_41 [label="ID: 41\ntype: 48\nnbr out: 0\nnbr chi: 3"]; + node_41 -> node_42; + node_42 [label="ID: 42\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_41 -> node_43; + node_43 [label="ID: 43\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_41 -> node_44; + node_44 [label="ID: 44\ntype: 6\nnbr out: 0\nnbr chi: 0"]; + node_15 -> node_45; + node_45 [label="ID: 45\ntype: 48\nnbr out: 0\nnbr chi: 4"]; + node_45 -> node_46; + node_46 [label="ID: 46\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_45 -> node_47; + node_47 [label="ID: 47\ntype: 45\nnbr out: 1\nnbr chi: 0"]; + node_47 -> node_48; + node_48 [label="ID: 48\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_47 -> node_49; + node_49 [label="ID: 49\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_45 -> node_50; + node_50 [label="ID: 50\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_45 -> node_51; + node_51 [label="ID: 51\ntype: 45\nnbr out: 1\nnbr chi: 0"]; + node_51 -> node_52; + node_52 [label="ID: 52\ntype: 45\nnbr out: 1\nnbr chi: 0"]; + node_52 -> node_53; + node_53 [label="ID: 53\ntype: 54\nnbr out: 0\nnbr chi: 0"]; + node_52 -> node_54; + node_54 [label="ID: 54\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_51 -> node_55; + node_55 [label="ID: 55\ntype: 48\nnbr out: 0\nnbr chi: 3"]; + node_55 -> node_56; + node_56 [label="ID: 56\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_55 -> node_57; + node_57 [label="ID: 57\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_55 -> node_58; + node_58 [label="ID: 58\ntype: 6\nnbr out: 0\nnbr chi: 0"]; + node_15 -> node_59; + node_59 [label="ID: 59\ntype: 48\nnbr out: 0\nnbr chi: 5"]; + node_59 -> node_60; + node_60 [label="ID: 60\ntype: 38\nnbr out: 0\nnbr chi: 0"]; + node_59 -> node_61; + node_61 [label="ID: 61\ntype: 8\nnbr out: 0\nnbr chi: 0"]; + node_59 -> node_62; + node_62 [label="ID: 62\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_59 -> node_63; + node_63 [label="ID: 63\ntype: 5\nnbr out: 0\nnbr chi: 0"]; + node_59 -> node_64; + node_64 [label="ID: 64\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_15 -> node_65; + node_65 [label="ID: 65\ntype: 48\nnbr out: 0\nnbr chi: 5"]; + node_65 -> node_66; + node_66 [label="ID: 66\ntype: 38\nnbr out: 0\nnbr chi: 0"]; + node_65 -> node_67; + node_67 [label="ID: 67\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_65 -> node_68; + node_68 [label="ID: 68\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_65 -> node_69; + node_69 [label="ID: 69\ntype: 20\nnbr out: 0\nnbr chi: 0"]; + node_65 -> node_70; + node_70 [label="ID: 70\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_14 -> node_71; + node_71 [label="ID: 71\ntype: 45\nnbr out: 1\nnbr chi: 0"]; + node_71 -> node_72; + node_72 [label="ID: 72\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_71 -> node_73; + node_73 [label="ID: 73\ntype: 48\nnbr out: 0\nnbr chi: 3"]; + node_73 -> node_74; + node_74 [label="ID: 74\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_73 -> node_75; + node_75 [label="ID: 75\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_73 -> node_76; + node_76 [label="ID: 76\ntype: 6\nnbr out: 0\nnbr chi: 0"]; + node_8 -> node_77; + node_77 [label="ID: 77\ntype: 45\nnbr out: 1\nnbr chi: 0"]; + node_77 -> node_78; + node_78 [label="ID: 78\ntype: 16\nnbr out: 0\nnbr chi: 0"]; + node_77 -> node_79; + node_79 [label="ID: 79\ntype: 48\nnbr out: 0\nnbr chi: 3"]; + node_79 -> node_80; + node_80 [label="ID: 80\ntype: 14\nnbr out: 0\nnbr chi: 0"]; + node_79 -> node_81; + node_81 [label="ID: 81\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_79 -> node_82; + node_82 [label="ID: 82\ntype: 6\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_83; + node_83 [label="ID: 83\ntype: 38\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_84; + node_84 [label="ID: 84\ntype: 8\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_85; + node_85 [label="ID: 85\ntype: 1\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_86; + node_86 [label="ID: 86\ntype: 5\nnbr out: 0\nnbr chi: 0"]; + node_1 -> node_87; + node_87 [label="ID: 87\ntype: 16\nnbr out: 0\nnbr chi: 0"]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/clust.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +digraph G { + subgraph cluster_0 { + label = "hello world"; + a -> b; + a -> c; + color = hotpink; + } + + subgraph cluster_1 { + label = "MSDOT"; + style= "dashed"; + color=purple; + x -> y; + x -> z; + y -> z; + y -> q; + } + + top -> a; + top -> y; + y -> b; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/clust1.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +digraph G { + subgraph cluster_c0 {a0 -> a1 -> a2 -> a3;} + subgraph cluster_c1 {b0 -> b1 -> b2 -> b3;} + x -> a0; + x -> b0; + a1 -> a3; + a3 -> a0; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/clust2.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +digraph G { + subgraph cluster_c0 {a0 -> a1 -> a2 -> a3;} + subgraph cluster_c1 {b0 -> b1 -> b2 -> b3;} + x -> a0; + x -> b0; + a1 -> b3; + b3 -> a1; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/clust3.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +digraph G { + subgraph cluster_c0 {a0 -> a1 -> a2 -> a3;} + subgraph cluster_c1 {b0 -> b1 -> b2 -> b3;} + x -> a0; + x -> b0; + a1 -> b3; + b1 -> a3; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/clust4.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +digraph G { + + subgraph cluster_0 { + style=filled; + color=lightgrey; + node [style=filled,color=white]; + a0 -> a1 -> a2 -> a3; + label = "process #1"; + } + + subgraph cluster_1 { + node [style=filled]; + b0 -> b1 -> b2 -> b3; + label = "process #2"; + color=blue + } + start -> a0; + start -> b0; + a1 -> b3; + b2 -> a3; + a3 -> a0; + a3 -> end; + b3 -> end; + + start [shape=Mdiamond]; + end [shape=Msquare]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/clust5.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,25 @@ +digraph G { +size="6,6"; + a -> b -> c; + + subgraph cluster0 { + x0 -> y0; + x0 -> z0; + } + + subgraph cluster1 { + x1 -> y1; + x1 -> z1; + } + + subgraph cluster2 { + x2 -> y2; + x2 -> z2; + } + + a -> x0; + b -> x1; + b -> x2; + a -> z2; + c -> z1; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/crazy.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,104 @@ +digraph "unix" { + graph [ fontname = "Helvetica-Oblique", + fontsize = 36, + label = "\n\n\n\nObject Oriented Graphs\nStephen North, 3/19/93", + size = "6,6" ]; + node [ shape = polygon, + sides = 4, + distortion = "0.0", + orientation = "0.0", + skew = "0.0", + color = white, + style = filled, + fontname = "Helvetica-Outline" ]; + "5th Edition" [sides=9, distortion="0.936354", orientation=28, skew="-0.126818", color=salmon2]; + "6th Edition" [sides=5, distortion="0.238792", orientation=11, skew="0.995935", color=deepskyblue]; + "PWB 1.0" [sides=8, distortion="0.019636", orientation=79, skew="-0.440424", color=goldenrod2]; + LSX [sides=9, distortion="-0.698271", orientation=22, skew="-0.195492", color=burlywood2]; + "1 BSD" [sides=7, distortion="0.265084", orientation=26, skew="0.403659", color=gold1]; + "Mini Unix" [distortion="0.039386", orientation=2, skew="-0.461120", color=greenyellow]; + Wollongong [sides=5, distortion="0.228564", orientation=63, skew="-0.062846", color=darkseagreen]; + Interdata [distortion="0.624013", orientation=56, skew="0.101396", color=dodgerblue1]; + "Unix/TS 3.0" [sides=8, distortion="0.731383", orientation=43, skew="-0.824612", color=thistle2]; + "PWB 2.0" [sides=6, distortion="0.592100", orientation=34, skew="-0.719269", color=darkolivegreen3]; + "7th Edition" [sides=10, distortion="0.298417", orientation=65, skew="0.310367", color=chocolate]; + "8th Edition" [distortion="-0.997093", orientation=50, skew="-0.061117", color=turquoise3]; + "32V" [sides=7, distortion="0.878516", orientation=19, skew="0.592905", color=steelblue3]; + V7M [sides=10, distortion="-0.960249", orientation=32, skew="0.460424", color=navy]; + "Ultrix-11" [sides=10, distortion="-0.633186", orientation=10, skew="0.333125", color=darkseagreen4]; + Xenix [sides=8, distortion="-0.337997", orientation=52, skew="-0.760726", color=coral]; + "UniPlus+" [sides=7, distortion="0.788483", orientation=39, skew="-0.526284", color=darkolivegreen3]; + "9th Edition" [sides=7, distortion="0.138690", orientation=55, skew="0.554049", color=coral3]; + "2 BSD" [sides=7, distortion="-0.010661", orientation=84, skew="0.179249", color=blanchedalmond]; + "2.8 BSD" [distortion="-0.239422", orientation=44, skew="0.053841", color=lightskyblue1]; + "2.9 BSD" [distortion="-0.843381", orientation=70, skew="-0.601395", color=aquamarine2]; + "3 BSD" [sides=10, distortion="0.251820", orientation=18, skew="-0.530618", color=lemonchiffon]; + "4 BSD" [sides=5, distortion="-0.772300", orientation=24, skew="-0.028475", color=darkorange1]; + "4.1 BSD" [distortion="-0.226170", orientation=38, skew="0.504053", color=lightyellow1]; + "4.2 BSD" [sides=10, distortion="-0.807349", orientation=50, skew="-0.908842", color=darkorchid4]; + "4.3 BSD" [sides=10, distortion="-0.030619", orientation=76, skew="0.985021", color=lemonchiffon2]; + "Ultrix-32" [distortion="-0.644209", orientation=21, skew="0.307836", color=goldenrod3]; + "PWB 1.2" [sides=7, distortion="0.640971", orientation=84, skew="-0.768455", color=cyan]; + "USG 1.0" [distortion="0.758942", orientation=42, skew="0.039886", color=blue]; + "CB Unix 1" [sides=9, distortion="-0.348692", orientation=42, skew="0.767058", color=firebrick]; + "USG 2.0" [distortion="0.748625", orientation=74, skew="-0.647656", color=chartreuse4]; + "CB Unix 2" [sides=10, distortion="0.851818", orientation=32, skew="-0.020120", color=greenyellow]; + "CB Unix 3" [sides=10, distortion="0.992237", orientation=29, skew="0.256102", color=bisque4]; + "Unix/TS++" [sides=6, distortion="0.545461", orientation=16, skew="0.313589", color=mistyrose2]; + "PDP-11 Sys V" [sides=9, distortion="-0.267769", orientation=40, skew="0.271226", color=cadetblue1]; + "USG 3.0" [distortion="-0.848455", orientation=44, skew="0.267152", color=bisque2]; + "Unix/TS 1.0" [distortion="0.305594", orientation=75, skew="0.070516", color=orangered]; + "TS 4.0" [sides=10, distortion="-0.641701", orientation=50, skew="-0.952502", color=crimson]; + "System V.0" [sides=9, distortion="0.021556", orientation=26, skew="-0.729938", color=darkorange1]; + "System V.2" [sides=6, distortion="0.985153", orientation=33, skew="-0.399752", color=darkolivegreen4]; + "System V.3" [sides=7, distortion="-0.687574", orientation=58, skew="-0.180116", color=lightsteelblue1]; + "5th Edition" -> "6th Edition"; + "5th Edition" -> "PWB 1.0"; + "6th Edition" -> LSX; + "6th Edition" -> "1 BSD"; + "6th Edition" -> "Mini Unix"; + "6th Edition" -> Wollongong; + "6th Edition" -> Interdata; + Interdata -> "Unix/TS 3.0"; + Interdata -> "PWB 2.0"; + Interdata -> "7th Edition"; + "7th Edition" -> "8th Edition"; + "7th Edition" -> "32V"; + "7th Edition" -> V7M; + "7th Edition" -> "Ultrix-11"; + "7th Edition" -> Xenix; + "7th Edition" -> "UniPlus+"; + V7M -> "Ultrix-11"; + "8th Edition" -> "9th Edition"; + "1 BSD" -> "2 BSD"; + "2 BSD" -> "2.8 BSD"; + "2.8 BSD" -> "Ultrix-11"; + "2.8 BSD" -> "2.9 BSD"; + "32V" -> "3 BSD"; + "3 BSD" -> "4 BSD"; + "4 BSD" -> "4.1 BSD"; + "4.1 BSD" -> "4.2 BSD"; + "4.1 BSD" -> "2.8 BSD"; + "4.1 BSD" -> "8th Edition"; + "4.2 BSD" -> "4.3 BSD"; + "4.2 BSD" -> "Ultrix-32"; + "PWB 1.0" -> "PWB 1.2"; + "PWB 1.0" -> "USG 1.0"; + "PWB 1.2" -> "PWB 2.0"; + "USG 1.0" -> "CB Unix 1"; + "USG 1.0" -> "USG 2.0"; + "CB Unix 1" -> "CB Unix 2"; + "CB Unix 2" -> "CB Unix 3"; + "CB Unix 3" -> "Unix/TS++"; + "CB Unix 3" -> "PDP-11 Sys V"; + "USG 2.0" -> "USG 3.0"; + "USG 3.0" -> "Unix/TS 3.0"; + "PWB 2.0" -> "Unix/TS 3.0"; + "Unix/TS 1.0" -> "Unix/TS 3.0"; + "Unix/TS 3.0" -> "TS 4.0"; + "Unix/TS++" -> "TS 4.0"; + "CB Unix 3" -> "TS 4.0"; + "TS 4.0" -> "System V.0"; + "System V.0" -> "System V.2"; + "System V.2" -> "System V.3"; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/ctext.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +digraph G { + xyz [label = "hello\nworld",color="slateblue",fontsize=24,fontname="Palatino-Italic",style=filled,fontcolor="hotpink"]; + node [style=filled]; + red [color=red]; + green [color=green]; + blue [color=blue,fontcolor=black]; + cyan [color=cyan]; + magenta [color=magenta]; + yellow [color=yellow]; + orange [color=orange]; + red -> green; + red -> blue; + blue -> cyan; + blue -> magenta; + green -> yellow; + green -> orange; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/dfa.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +digraph g { +"start" [ label = "MWGC-" ]; +"n1" [ label = "WC-MG" ]; +"n2" [ label = "MWC-G" ]; +"n3" [ label = "C-MWG" ]; +"n4" [ label = "W-MGC" ]; +"n5" [ label = "MGC-W" ]; +"n6" [ label = "MWG-C" ]; +"n7" [ label = "G-MWC" ]; +"n8" [ label = "MG-WC" ]; +"n9" [ label = "-MWGC" ]; +"start" -> "n1" [ label = "g" ]; +"n1" -> "start" [ label = "g" ]; +subgraph l { rank = same; "n3" "n4" } +subgraph r { rank = same; "n5" "n6" } +"n1" -> "n2" [ label = "m" ]; +"n2" -> "n1" [ label = "m" ]; +"n2" -> "n3" [ label = "w" ]; +"n3" -> "n2" [ label = "w" ]; +"n2" -> "n4" [ label = "c" ]; +"n4" -> "n2" [ label = "c" ]; +"n3" -> "n5" [ label = "g" ]; +"n5" -> "n3" [ label = "g" ]; +"n4" -> "n6" [ label = "g" ]; +"n6" -> "n4" [ label = "g" ]; +"n5" -> "n7" [ label = "c" ]; +"n7" -> "n5" [ label = "c" ]; +"n6" -> "n7" [ label = "w" ]; +"n7" -> "n6" [ label = "w" ]; +"n7" -> "n8" [ label = "m" ]; +"n8" -> "n7" [ label = "m" ]; +"n8" -> "n9" [ label = "g" ]; +"n9" -> "n8" [ label = "g" ]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/fig6.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,74 @@ +digraph G { + size = "8,8"; + {rank=min S8 S24 S1 S35 S30} + {rank=max T8 T24 T1 T35 T30} + S8 -> 9; + S24 -> 27; + S24 -> 25; + S1 -> 10; + S1 -> 2; + S35 -> 36; + S35 -> 43; + S30 -> 31; + S30 -> 33; + 9 -> 42; + 9 -> T1; + 25 -> T1; + 25 -> 26; + 27 -> T24; + 2 -> 3; + 2 -> 16; + 2 -> 17; + 2 -> T1; + 2 -> 18; + 10 -> 11; + 10 -> 14; + 10 -> T1; + 10 -> 13; + 10 -> 12; + 31 -> T1; + 31 -> 32; + 33 -> T30; + 33 -> 34; + 42 -> 4; + 26 -> 4; + 3 -> 4; + 16 -> 15; + 17 -> 19; + 18 -> 29; + 11 -> 4; + 14 -> 15; + 37 -> 39; + 37 -> 41; + 37 -> 38; + 37 -> 40; + 13 -> 19; + 12 -> 29; + 43 -> 38; + 43 -> 40; + 36 -> 19; + 32 -> 23; + 34 -> 29; + 39 -> 15; + 41 -> 29; + 38 -> 4; + 40 -> 19; + 4 -> 5; + 19 -> 21; + 19 -> 20; + 19 -> 28; + 5 -> 6; + 5 -> T35; + 5 -> 23; + 21 -> 22; + 20 -> 15; + 28 -> 29; + 6 -> 7; + 15 -> T1; + 22 -> 23; + 22 -> T35; + 29 -> T30; + 7 -> T8; + 23 -> T24; + 23 -> T1; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/fsm.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +digraph finite_state_machine { + + node [shape = doublecircle]; LR_0 LR_3 LR_4 LR_8; + node [shape = circle]; + rankdir=LR; + LR_0 -> LR_2 [ label = "SS(B)" ]; + LR_0 -> LR_1 [ label = "SS(S)" ]; + LR_1 -> LR_3 [ label = "S($end)" ]; + LR_2 -> LR_6 [ label = "SS(b)" ]; + LR_2 -> LR_5 [ label = "SS(a)" ]; + LR_2 -> LR_4 [ label = "S(A)" ]; + LR_5 -> LR_7 [ label = "S(b)" ]; + LR_5 -> LR_5 [ label = "S(a)" ]; + LR_6 -> LR_6 [ label = "S(b)" ]; + LR_6 -> LR_5 [ label = "S(a)" ]; + LR_7 -> LR_8 [ label = "S(b)" ]; + LR_7 -> LR_5 [ label = "S(a)" ]; + LR_8 -> LR_6 [ label = "S(b)" ]; + LR_8 -> LR_5 [ label = "S(a)" ]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/grammar.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,71 @@ +digraph L0 { + size = "8,8"; + ordering=out; + node [shape = box]; + + n0 [label="E"]; + n1 [label="T"]; + n2 [label="F"]; + n3 [label="IDENT : a "]; + n4 [label="+"]; + n5 [label="T"]; + n6 [label="F"]; + n7 [label="("]; + n8 [label="E"]; + n9 [label="T"]; + n10 [label="F"]; + n11 [label="IDENT : b "]; + n12 [label="*"]; + n13 [label="F"]; + n14 [label="IDENT : c "]; + n15 [label=")"]; + n16 [label="*"]; + n17 [label="F"]; + n18 [label="("]; + n19 [label="E"]; + n20 [label="T"]; + n21 [label="F"]; + n22 [label="IDENT : d "]; + n23 [label="*"]; + n24 [label="F"]; + n25 [label="IDENT : e "]; + n26 [label="+"]; + n27 [label="T"]; + n28 [label="F"]; + n29 [label="("]; + n30 [label="E"]; + n31 [label="T"]; + n32 [label="F"]; + n33 [label="IDENT : a "]; + n34 [label="*"]; + n35 [label="F"]; + n36 [label="IDENT : b "]; + n37 [label=")"]; + n38 [label=")"]; + n39 [label="+"]; + n40 [label="T"]; + n41 [label="F"]; + n42 [label="IDENT : q "]; + n0 -> { n1 n4 n5 n39 n40 }; + n1 -> n2 ; + n2 -> n3 ; + n5 -> { n6 n16 n17 }; + n6 -> { n7 n8 n15 }; + n8 -> n9 ; + n9 -> { n10 n12 n13 }; + n10 -> n11 ; + n13 -> n14 ; + n17 -> { n18 n19 n38 }; + n19 -> { n20 n26 n27 }; + n20 -> { n21 n23 n24 }; + n21 -> n22 ; + n24 -> n25 ; + n27 -> n28 ; + n28 -> { n29 n30 n37 }; + n30 -> n31 ; + n31 -> { n32 n34 n35 }; + n32 -> n33 ; + n35 -> n36 ; + n40 -> n41 ; + n41 -> n42 ; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/hashtable.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +digraph G { + nodesep=.05; + rankdir=LR; + node [shape=record,width=.1,height=.1]; + + node0 [label = "<f0> |<f1> |<f2> |<f3> |<f4> |<f5> |<f6> | ",height=2.0]; + node [width = 1.5]; + node1 [label = "{<n> n14 | 719 |<p> }"]; + node2 [label = "{<n> a1 | 805 |<p> }"]; + node3 [label = "{<n> i9 | 718 |<p> }"]; + node4 [label = "{<n> e5 | 989 |<p> }"]; + node5 [label = "{<n> t20 | 959 |<p> }"] ; + node6 [label = "{<n> o15 | 794 |<p> }"] ; + node7 [label = "{<n> s19 | 659 |<p> }"] ; + + node0:f0 -> node1:n; + node0:f1 -> node2:n; + node0:f2 -> node3:n; + node0:f5 -> node4:n; + node0:f6 -> node5:n; + node2:p -> node6:n; + node4:p -> node7:n; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/honda-tokoro.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +digraph "Honda-Tokoro" { +rankdir="LR" ranksep="0.2" edge[labelfontsize="8" fontsize="8" labeldistance="0.8" arrowsize="0.9" labelangle="-30" dir="none"] nodesep="0.2" node[width="0" height="0" fontsize="10"] + +/*Net net00*/ + +n000 [label="z"] +n001->n000 [headlabel=":s:" arrowhead="invdot"] +n001 [label="m"] +n002->n001 [samehead="m002" headlabel=":r:" samearrowhead="1" arrowhead="invdot" arrowtail="inv"] +n002 [label="p1"] +n003->n002 [headlabel=":s:" arrowhead="dot"] +n003 [label="b"] +n004->n003 +n004 [label="x1"] +n022->n004 [weight="0" headlabel=":s/r:" fontsize="8" arrowhead="invdot"] +n003->n002 [samehead="m000" fontsize="8" samearrowhead="1" arrowtail="inv"] +n005->n002 [samehead="m000" headlabel=":u:" fontsize="8" samearrowhead="1" arrowhead="dot" arrowtail="inv"] +n005->n001 [samehead="m002" samearrowhead="1"] +n005 [label="b"] +n006->n005 [arrowtail="inv"] +n006 [label="p2"] +n007->n006 [headlabel=":s:" arrowhead="dot"] +n007 [label="b"] +n008->n007 +n008 [label="x2"] +n022->n008 [weight="0" headlabel=":s/r:" fontsize="8" arrowhead="invdot"] +n007->n006 [samehead="m001" headlabel=":u:" fontsize="8" samearrowhead="1" arrowhead="dot" arrowtail="inv"] +n009->n006 [samehead="m001" samearrowhead="1" arrowtail="inv"] +n009 [label="b2"] +n022->n009 [fontsize="8"] +n022->n009 [fontsize="8"] +n010->n006 [samehead="m001" samearrowhead="1" arrowtail="inv"] +n010 [label="b2"] +n022->n010 [fontsize="8"] +n022->n010 [fontsize="8"] +n011->n000 [headlabel=":r:" arrowhead="invdot" arrowtail="inv"] +n011 [label="n"] +n012->n011 [samehead="m005" headlabel=":s:" samearrowhead="1" arrowhead="dot"] +n012 [label="b"] +n013->n012 +n013 [label="c1"] +n014->n013 [headlabel=":r:" arrowhead="invdot"] +n014 [label="b"] +n015->n014 [arrowtail="inv"] +n015 [label="y1"] +n023->n015 [weight="0" headlabel=":s/r:" fontsize="8" arrowhead="dot"] +n016->n015 [samehead="m003" headlabel=":u:" fontsize="8" samearrowhead="1" arrowhead="dot" arrowtail="inv"] +n018->n015 [samehead="m003" fontsize="8" samearrowhead="1" arrowtail="inv"] +n014->n011 [samehead="m006" headlabel=":u:" fontsize="8" samearrowhead="1" arrowhead="dot" arrowtail="inv"] +n012->n011 [samehead="m006" fontsize="8" samearrowhead="1" arrowtail="inv"] +n016->n011 [samehead="m005" samearrowhead="1"] +n016 [label="b"] +n017->n016 +n017 [label="c2"] +n018->n017 [headlabel=":r:" arrowhead="invdot"] +n018 [label="b"] +n019->n018 [arrowtail="inv"] +n019 [label="y2"] +n023->n019 [weight="0" headlabel=":s/r:" fontsize="8" arrowhead="dot"] +n020->n019 [samehead="m004" headlabel=":u:" samearrowhead="1" arrowhead="dot" arrowtail="inv"] +n020 [label="b2"] +n023->n020 [fontsize="8"] +n023->n020 [fontsize="8"] +n021->n019 [samehead="m004" samearrowhead="1" arrowtail="inv"] +n021 [label="b2"] +n023->n021 [fontsize="8"] +n023->n021 [fontsize="8"] +n022 [width="0.5" label="[P]" shape="box" style="dashed" height="0.35"] +n023 [width="0.5" label="[Q]" shape="box" style="dashed" height="0.35"] +{/*L=x1*/rank=same n004 n015} +{/*L=p1*/rank=same n002 n013} +{/*L=b*/rank=same n009 n010 n020 n021} +{/*L=x2*/rank=same n008 n019} +{/*L=p2*/rank=same n006 n017} +{/*L=m*/rank=same n001 n011} +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/jcctree.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,44 @@ +digraph "tree" { +// The problem disappeared when I removed the "ELEM3 -> ID5;" line! +//size="4,5"; +ordering=out; +node [shape=plaintext]; +SPEC -> DEF2; +SPEC -> DEF1; +DEF1 -> ID1; +DEF1 -> SET1; +DEF1 -> SC1; +DEF2 -> ID2; +DEF2 -> SET2; +DEF2 -> SC2; +SET1 -> OPEN1; +SET1 -> ELEM1; +SET1 -> SC3; +SET1 -> ELEM2; +SET1 -> CLOSE1; +ELEM1 -> ID3; +SET2 -> OPEN2; +SET2 -> ELEM3; +SET2 -> CLOSE2; +ELEM2 -> ID4; +ELEM3 -> ID5; +DEF1 [label=DEF]; +DEF2 [label=DEF]; +SET1 [label=SET]; +SC1 [label=";"]; +SC3 [label=";"]; +SET2 [label=SET]; +SC2 [label=";"]; +OPEN1 [label="{"]; +OPEN2 [label="{"]; +CLOSE1 [label="}"]; +CLOSE2 [label="}"]; +ELEM1 [label=ELEMENT]; +ELEM2 [label=ELEMENT]; +ELEM3 [label=ELEMENT]; +ID1 [label=cities]; +ID2 [label=insects]; +ID3 [label=andover]; +ID4 [label=boston]; +ID5 [label=fly]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/jsort.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,150 @@ +digraph prof { + size="6,4"; ratio = fill; + node [style=filled]; + start -> main [color="0.002 0.999 0.999"]; + start -> on_exit [color="0.649 0.701 0.701"]; + main -> sort [color="0.348 0.839 0.839"]; + main -> merge [color="0.515 0.762 0.762"]; + main -> term [color="0.647 0.702 0.702"]; + main -> signal [color="0.650 0.700 0.700"]; + main -> sbrk [color="0.650 0.700 0.700"]; + main -> unlink [color="0.650 0.700 0.700"]; + main -> newfile [color="0.650 0.700 0.700"]; + main -> fclose [color="0.650 0.700 0.700"]; + main -> close [color="0.650 0.700 0.700"]; + main -> brk [color="0.650 0.700 0.700"]; + main -> setbuf [color="0.650 0.700 0.700"]; + main -> copyproto [color="0.650 0.700 0.700"]; + main -> initree [color="0.650 0.700 0.700"]; + main -> safeoutfil [color="0.650 0.700 0.700"]; + main -> getpid [color="0.650 0.700 0.700"]; + main -> sprintf [color="0.650 0.700 0.700"]; + main -> creat [color="0.650 0.700 0.700"]; + main -> rem [color="0.650 0.700 0.700"]; + main -> oldfile [color="0.650 0.700 0.700"]; + sort -> msort [color="0.619 0.714 0.714"]; + sort -> filbuf [color="0.650 0.700 0.700"]; + sort -> newfile [color="0.650 0.700 0.700"]; + sort -> fclose [color="0.650 0.700 0.700"]; + sort -> setbuf [color="0.650 0.700 0.700"]; + sort -> setfil [color="0.650 0.700 0.700"]; + msort -> qsort [color="0.650 0.700 0.700"]; + msort -> insert [color="0.650 0.700 0.700"]; + msort -> wline [color="0.650 0.700 0.700"]; + msort -> div [color="0.650 0.700 0.700"]; + msort -> cmpsave [color="0.650 0.700 0.700"]; + merge -> insert [color="0.650 0.700 0.700"]; + merge -> rline [color="0.650 0.700 0.700"]; + merge -> wline [color="0.650 0.700 0.700"]; + merge -> unlink [color="0.650 0.700 0.700"]; + merge -> fopen [color="0.650 0.700 0.700"]; + merge -> fclose [color="0.650 0.700 0.700"]; + merge -> setfil [color="0.650 0.700 0.700"]; + merge -> mul [color="0.650 0.700 0.700"]; + merge -> setbuf [color="0.650 0.700 0.700"]; + merge -> cmpsave [color="0.650 0.700 0.700"]; + insert -> cmpa [color="0.650 0.700 0.700"]; + wline -> flsbuf [color="0.649 0.700 0.700"]; + qsort -> cmpa [color="0.650 0.700 0.700"]; + rline -> filbuf [color="0.649 0.700 0.700"]; + xflsbuf -> write [color="0.650 0.700 0.700"]; + flsbuf -> xflsbuf [color="0.649 0.700 0.700"]; + filbuf -> read [color="0.650 0.700 0.700"]; + term -> unlink [color="0.650 0.700 0.700"]; + term -> signal [color="0.650 0.700 0.700"]; + term -> setfil [color="0.650 0.700 0.700"]; + term -> exit [color="0.650 0.700 0.700"]; + endopen -> open [color="0.650 0.700 0.700"]; + fopen -> endopen [color="0.639 0.705 0.705"]; + fopen -> findiop [color="0.650 0.700 0.700"]; + newfile -> fopen [color="0.634 0.707 0.707"]; + newfile -> setfil [color="0.650 0.700 0.700"]; + fclose -> fflush [color="0.642 0.704 0.704"]; + fclose -> close [color="0.650 0.700 0.700"]; + fflush -> xflsbuf [color="0.635 0.707 0.707"]; + malloc -> morecore [color="0.325 0.850 0.850"]; + malloc -> demote [color="0.650 0.700 0.700"]; + morecore -> sbrk [color="0.650 0.700 0.700"]; + morecore -> getfreehdr [color="0.650 0.700 0.700"]; + morecore -> free [color="0.650 0.700 0.700"]; + morecore -> getpagesize [color="0.650 0.700 0.700"]; + morecore -> putfreehdr [color="0.650 0.700 0.700"]; + morecore -> udiv [color="0.650 0.700 0.700"]; + morecore -> umul [color="0.650 0.700 0.700"]; + on_exit -> malloc [color="0.325 0.850 0.850"]; + signal -> sigvec [color="0.650 0.700 0.700"]; + moncontrol -> profil [color="0.650 0.700 0.700"]; + getfreehdr -> sbrk [color="0.650 0.700 0.700"]; + free -> insert [color="0.650 0.700 0.700"]; + insert -> getfreehdr [color="0.650 0.700 0.700"]; + setfil -> div [color="0.650 0.700 0.700"]; + setfil -> rem [color="0.650 0.700 0.700"]; + sigvec -> sigblock [color="0.650 0.700 0.700"]; + sigvec -> sigsetmask [color="0.650 0.700 0.700"]; + doprnt -> urem [color="0.650 0.700 0.700"]; + doprnt -> udiv [color="0.650 0.700 0.700"]; + doprnt -> strlen [color="0.650 0.700 0.700"]; + doprnt -> localeconv [color="0.650 0.700 0.700"]; + sprintf -> doprnt [color="0.650 0.700 0.700"]; +cmpa [color="0.000 1.000 1.000"]; +wline [color="0.201 0.753 1.000"]; +insert [color="0.305 0.625 1.000"]; +rline [color="0.355 0.563 1.000"]; +sort [color="0.408 0.498 1.000"]; +qsort [color="0.449 0.447 1.000"]; +write [color="0.499 0.386 1.000"]; +read [color="0.578 0.289 1.000"]; +msort [color="0.590 0.273 1.000"]; +merge [color="0.603 0.258 1.000"]; +unlink [color="0.628 0.227 1.000"]; +filbuf [color="0.641 0.212 1.000"]; +open [color="0.641 0.212 1.000"]; +sbrk [color="0.647 0.204 1.000"]; +signal [color="0.647 0.204 1.000"]; +moncontrol [color="0.647 0.204 1.000"]; +xflsbuf [color="0.650 0.200 1.000"]; +flsbuf [color="0.650 0.200 1.000"]; +div [color="0.650 0.200 1.000"]; +cmpsave [color="0.650 0.200 1.000"]; +rem [color="0.650 0.200 1.000"]; +setfil [color="0.650 0.200 1.000"]; +close [color="0.650 0.200 1.000"]; +fclose [color="0.650 0.200 1.000"]; +fflush [color="0.650 0.200 1.000"]; +setbuf [color="0.650 0.200 1.000"]; +endopen [color="0.650 0.200 1.000"]; +findiop [color="0.650 0.200 1.000"]; +fopen [color="0.650 0.200 1.000"]; +mul [color="0.650 0.200 1.000"]; +newfile [color="0.650 0.200 1.000"]; +sigblock [color="0.650 0.200 1.000"]; +sigsetmask [color="0.650 0.200 1.000"]; +sigvec [color="0.650 0.200 1.000"]; +udiv [color="0.650 0.200 1.000"]; +urem [color="0.650 0.200 1.000"]; +brk [color="0.650 0.200 1.000"]; +getfreehdr [color="0.650 0.200 1.000"]; +strlen [color="0.650 0.200 1.000"]; +umul [color="0.650 0.200 1.000"]; +doprnt [color="0.650 0.200 1.000"]; +copyproto [color="0.650 0.200 1.000"]; +creat [color="0.650 0.200 1.000"]; +demote [color="0.650 0.200 1.000"]; +exit [color="0.650 0.200 1.000"]; +free [color="0.650 0.200 1.000"]; +getpagesize [color="0.650 0.200 1.000"]; +getpid [color="0.650 0.200 1.000"]; +initree [color="0.650 0.200 1.000"]; +insert [color="0.650 0.200 1.000"]; +localeconv [color="0.650 0.200 1.000"]; +main [color="0.650 0.200 1.000"]; +malloc [color="0.650 0.200 1.000"]; +morecore [color="0.650 0.200 1.000"]; +oldfile [color="0.650 0.200 1.000"]; +on_exit [color="0.650 0.200 1.000"]; +profil [color="0.650 0.200 1.000"]; +putfreehdr [color="0.650 0.200 1.000"]; +safeoutfil [color="0.650 0.200 1.000"]; +sprintf [color="0.650 0.200 1.000"]; +term [color="0.650 0.200 1.000"]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/ldbxtried.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,416 @@ +digraph g { +graph [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +node [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +shape = "box" +color = "black" +width = "0.5" +style = "filled" +]; +edge [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"n0" [ +label = "18519\n?" +color = "lightblue" +]; +"n1" [ +label = "4836" +shape = "ellipse" +color = "maroon1" +]; +"n2" [ +label = "ttyqa" +shape = "ellipse" +color = "maroon1" +]; +"n448" [ +label = "21079\nlefty" +color = "lightblue" +]; +"n449" [ +label = "tried.lefty" +shape = "ellipse" +color = "maroon1" +]; +"n454" [ +fontsize = "7" +label = "bunting\n6000" +shape = "doublecircle" +color = "green" +]; +"n460" [ +label = "" +shape = "doublecircle" +color = "yellow" +]; +"n461" [ +label = "" +shape = "doublecircle" +color = "yellow" +]; +"n462" [ +label = "21084\ntried" +color = "lightblue" +]; +"n464" [ +label = "21086\nldbx" +color = "lightblue" +]; +"n466" [ +label = "ldbx" +shape = "ellipse" +color = "maroon1" +]; +"n468" [ +label = "21087\nlefty" +color = "lightblue" +]; +"n469" [ +label = "sh21086.1" +shape = "ellipse" +color = "maroon1" +]; +"n474" [ +fontsize = "7" +label = "bunting\n6000" +shape = "doublecircle" +color = "green" +]; +"n479" [ +label = "ldbx.lefty" +shape = "ellipse" +color = "maroon1" +]; +"n482" [ +label = "" +shape = "doublecircle" +color = "yellow" +]; +"n483" [ +label = "" +shape = "doublecircle" +color = "yellow" +]; +"n484" [ +label = "21088\ndot" +color = "lightblue" +]; +"n486" [ +label = "" +shape = "doublecircle" +color = "yellow" +]; +"n487" [ +label = "" +shape = "doublecircle" +color = "yellow" +]; +"n488" [ +label = "21089\nxterm" +color = "lightblue" +]; +"n496" [ +fontsize = "7" +label = "bunting\n6000" +shape = "doublecircle" +color = "green" +]; +"n500" [ +label = "ptyq2" +shape = "ellipse" +color = "maroon1" +]; +"n503" [ +label = "21090\nldbxmp" +color = "lightblue" +]; +"n505" [ +label = "ttyq2" +shape = "ellipse" +color = "maroon1" +]; +"n512" [ +label = "ptyq5" +shape = "ellipse" +color = "maroon1" +]; +"n513" [ +label = "ttyq5" +shape = "ellipse" +color = "maroon1" +]; +"n514" [ +label = "21091\ndbx" +color = "lightblue" +]; +"n518" [ +label = "tty" +shape = "ellipse" +color = "maroon1" +]; +"n526" [ +label = "delaunay.c" +shape = "ellipse" +color = "maroon1" +]; +subgraph "cluster0" { +graph [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +label = "toucan" +color = "black" +]; +node [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +shape = "box" +color = "black" +width = "0.5" +style = "filled" +]; +edge [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"n0" +"n468" +"n486" +"n460" +"n487" +"n514" +"n461" +"n488" +"n462" +"n464" +"n482" +"n483" +"n448" +"n484" +"n503" +} +"n0" -> "n1" [ +dir = "both" +]; +"n0" -> "n2" [ +dir = "both" +]; +"n0" -> "n2" [ +dir = "both" +]; +"n0" -> "n2" [ +dir = "both" +]; +"n0" -> "n448" [ +style = "dotted" +]; +"n448" -> "n2" [ +dir = "both" +]; +"n448" -> "n2" [ +dir = "both" +]; +"n448" -> "n2" [ +dir = "both" +]; +"n448" -> "n449" [ +dir = "back" +]; +"n448" -> "n454" [ +dir = "both" +]; +"n448" -> "n460" [ +dir = "back" +]; +"n448" -> "n461" [ +dir = "forward" +]; +"n448" -> "n462" [ +style = "dotted" +]; +"n462" -> "n2" [ +dir = "both" +]; +"n462" -> "n2" [ +dir = "both" +]; +"n462" -> "n2" [ +dir = "both" +]; +"n462" -> "n449" [ +dir = "back" +]; +"n462" -> "n460" [ +dir = "forward" +]; +"n462" -> "n461" [ +dir = "back" +]; +"n462" -> "n460" [ +dir = "forward" +]; +"n462" -> "n461" [ +dir = "back" +]; +"n0" -> "n464" [ +style = "dotted" +]; +"n464" -> "n2" [ +dir = "both" +]; +"n464" -> "n2" [ +dir = "both" +]; +"n464" -> "n2" [ +dir = "both" +]; +"n464" -> "n466" [ +dir = "back" +]; +"n464" -> "n468" [ +style = "dotted" +]; +"n468" -> "n2" [ +dir = "both" +]; +"n468" -> "n2" [ +dir = "both" +]; +"n468" -> "n469" [ +dir = "back" +]; +"n468" -> "n474" [ +dir = "both" +]; +"n468" -> "n479" [ +dir = "back" +]; +"n468" -> "n482" [ +dir = "back" +]; +"n468" -> "n483" [ +dir = "forward" +]; +"n468" -> "n484" [ +style = "dotted" +]; +"n484" -> "n2" [ +dir = "both" +]; +"n484" -> "n483" [ +dir = "back" +]; +"n484" -> "n479" [ +dir = "back" +]; +"n484" -> "n482" [ +dir = "forward" +]; +"n468" -> "n486" [ +dir = "back" +]; +"n468" -> "n487" [ +dir = "forward" +]; +"n468" -> "n488" [ +style = "dotted" +]; +"n488" -> "n486" [ +dir = "forward" +]; +"n488" -> "n2" [ +dir = "both" +]; +"n488" -> "n487" [ +dir = "back" +]; +"n488" -> "n469" [ +dir = "back" +]; +"n488" -> "n2" [ +dir = "both" +]; +"n488" -> "n479" [ +dir = "back" +]; +"n488" -> "n496" [ +dir = "both" +]; +"n488" -> "n500" [ +dir = "both" +]; +"n488" -> "n503" [ +style = "dotted" +]; +"n503" -> "n479" [ +dir = "back" +]; +"n503" -> "n486" [ +dir = "forward" +]; +"n503" -> "n487" [ +dir = "back" +]; +"n503" -> "n505" [ +dir = "both" +]; +"n503" -> "n505" [ +dir = "both" +]; +"n503" -> "n505" [ +dir = "forward" +]; +"n503" -> "n512" [ +dir = "both" +]; +"n503" -> "n514" [ +style = "dotted" +]; +"n514" -> "n487" [ +dir = "back" +]; +"n514" -> "n486" [ +dir = "forward" +]; +"n514" -> "n479" [ +dir = "back" +]; +"n514" -> "n505" [ +dir = "forward" +]; +"n503" -> "n486" [ +dir = "forward" +]; +"n514" -> "n518" [ +dir = "back" +]; +"n514" -> "n513" [ +dir = "both" +]; +"n514" -> "n513" [ +dir = "both" +]; +"n514" -> "n518" [ +dir = "back" +]; +"n514" -> "n526" [ +dir = "back" +]; +"n503" -> "n487" [ +dir = "back" +]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/longflat.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,7 @@ +digraph if +{ +rankdir=LR; + {rank=same;b;c;} + a->b; + c->b[label="long long long"]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/mike.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +digraph mike{ +size = "8,8"; + a -> A; + a -> m; + a -> E; + t -> O; + r -> V; + r -> Q; + p -> B; + m -> R; + l -> C; + c -> C; + W -> X; + W -> D; + V -> W; + T -> U; + Q -> T; + Q -> H; + Q -> A; + O -> K; + L -> U; + K -> L; + K -> J; + K -> E; + J -> I; + R -> B; + P -> F; + H -> R; + H -> P; + U -> H; + G -> U; + E -> G; + C -> Z; + C -> D; + S -> D; + B -> N; + B -> D; + B -> S; + M -> B; + A -> M; + N -> Y; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/nhg.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,13 @@ +digraph automata_0 { + size ="8.5, 11"; + node [shape = circle]; + 0 [ style = filled, color=lightgrey ]; + 2 [ shape = doublecircle ]; + 0 -> 2 [ label = "a " ]; + 0 -> 1 [ label = "other " ]; + 1 -> 2 [ label = "a " ]; + 1 -> 1 [ label = "other " ]; + 2 -> 2 [ label = "a " ]; + 2 -> 1 [ label = "other " ]; + "Machine: a" [ shape = plaintext ]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/oldarrows.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +digraph G { + // leave some space for the head/taillabels + graph [ranksep=1.5 splines=true overlap=false] + + // to avoid confusion, remember this: + // it's spelt tail/head, but it's read start/end + + // emphasize theatrically + // show only explicitly given head/tails + // put head/tail labels farther from the node +// edge [arrowsize=2 dir=none labeldistance=3] + edge [dir=none labeldistance=3] + + // not interested in node labels + node [shape=circle width=0.5 label=""] + + { + edge [samehead=ahead samearrowhead=1] + a->Z [arrowtail=none taillabel=none] + b->Z [arrowtail=normal taillabel=normal] + c->Z [arrowtail=inv taillabel=inv] + d->Z [arrowtail=dot taillabel=dot] + e->Z [arrowtail=odot taillabel=odot] + f->Z [arrowtail=invdot taillabel=invdot] + g->Z [arrowtail=invodot taillabel=invodot] + h->Z [arrowtail=open taillabel=open] + i->Z [arrowtail=halfopen taillabel=halfopen arrowhead=inv headlabel=samehead] + j->Z [arrowtail=empty taillabel=empty] + k->Z [arrowtail=invempty taillabel=invempty] + l->Z [arrowtail=diamond taillabel=diamond] + m->Z [arrowtail=odiamond taillabel=odiamond] + n->Z [arrowtail=box taillabel=box] + o->Z [arrowtail=obox taillabel=obox] + p->Z [arrowtail=tee taillabel=tee] + q->Z [arrowtail=crow taillabel=crow] + } + { + edge [sametail=atail samearrowtail=1] + Z->A [arrowhead=none headlabel=none] + Z->B [arrowhead=normal headlabel=normal] + Z->C [arrowhead=inv headlabel=inv] + Z->D [arrowhead=dot headlabel=dot] + Z->E [arrowhead=odot headlabel=odot] + Z->F [arrowhead=invdot headlabel=invdot] + Z->G [arrowhead=invodot headlabel=invodot] + Z->H [arrowhead=open headlabel=open] + Z->I [arrowhead=halfopen headlabel=halfopen arrowtail=inv taillabel=sametail] + Z->J [arrowhead=empty headlabel=empty] + Z->K [arrowhead=invempty headlabel=invempty] + Z->L [arrowhead=diamond headlabel=diamond] + Z->M [arrowhead=odiamond headlabel=odiamond] + Z->N [arrowhead=box headlabel=box] + Z->O [arrowhead=obox headlabel=obox] + Z->P [arrowhead=tee headlabel=tee] + Z->Q [arrowhead=crow headlabel=crow] + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/pgram.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,91 @@ +digraph test { + + size="7,9.5"; + page="8,10.5"; + ratio=fill; + rankdir=LR; + + { rank=same; + node [shape=house]; + A;C;E;G;I;K;M;O;Q;S;U;W;Y; + node [shape=invhouse]; + B;D;F;H;J;L;N;P;R;T;V;X;Z; + } + + { rank=same; + node [shape=parallelogram]; + "Parallelogram" [label="This is a test\nof a multiline\nlabel in an\nparallelogram with approx\nsquare aspect"]; + "a ----- long thin parallelogram"; + "xx" [label="m"]; + "yy" [label="a\nb\nc\nd\ne\nf"]; + node [shape=octagon]; + "Octagon" [label="This is a test\nof a multiline\nlabel in an\noctagon with approx\nsquare aspect"]; + node [shape=parallelogram]; + "Parallelogram" [label="This is a test\nof a multiline\nlabel in an\nparallelogram with approx\nsquare aspect"]; + "a ----- long thin parallelogram"; + "zz" [label="m"]; + "qq" [label="a\nb\nc\nd\ne\nf"]; + ordering=out; + } + + Parallelogram -> A; + Parallelogram -> B; + Parallelogram -> C; + Parallelogram -> D; + Parallelogram -> E; + Parallelogram -> F; + Parallelogram -> G; + Parallelogram -> H; + Parallelogram -> I; + Parallelogram -> J; + Parallelogram -> K; + Parallelogram -> L; + Parallelogram -> M; + Parallelogram -> N; + Parallelogram -> O; + Parallelogram -> P; + Parallelogram -> Q; + Parallelogram -> R; + Parallelogram -> S; + Parallelogram -> T; + Parallelogram -> U; + Parallelogram -> V; + Parallelogram -> W; + Parallelogram -> X; + Parallelogram -> Y; + Parallelogram -> Z; + + { rank=same; + node [shape=triangle]; + a;c;e;g;i;k;m;o;q;s;u;w;y; + node [shape=tripleoctagon]; + b;d;f;h;j;l;n;p;r;t;v;x;z; + } + + a -> Parallelogram -> Octagon; + b -> Parallelogram -> Octagon; + c -> Parallelogram -> Octagon; + d -> Parallelogram -> Octagon; + e -> Parallelogram -> Octagon; + f -> Parallelogram -> Octagon; + g -> Parallelogram -> Octagon; + h -> Parallelogram -> Octagon; + i -> Parallelogram -> Octagon; + j -> Parallelogram -> Octagon; + k -> Parallelogram -> Octagon; + l -> Parallelogram -> Octagon; + m -> Parallelogram -> Octagon; + n -> Parallelogram -> Octagon; + o -> Parallelogram -> Octagon; + p -> Parallelogram -> Octagon; + q -> Parallelogram -> Octagon; + r -> Parallelogram -> Octagon; + s -> Parallelogram -> Octagon; + t -> Parallelogram -> Octagon; + u -> Parallelogram -> Octagon; + v -> Parallelogram -> Octagon; + w -> Parallelogram -> Octagon; + x -> Parallelogram -> Octagon; + y -> Parallelogram -> Octagon; + z -> Parallelogram -> Octagon; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/pm2way.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,126 @@ +digraph g { +graph [ +]; +node [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +shape = "box" +color = "black" +width = "0.5" +]; +edge [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"22690" [ +label = "22690\n?" +pname = "?" +kind = "proc" +]; +"22692" [ +label = "22692\ndotty" +pname = "dotty" +kind = "proc" +]; +"116842+2595" [ +label = "116842+2595\n/home/ek/work/sun4/bin/dotty" +fname = "/home/ek/work/sun4/bin/dotty" +shape = "ellipse" +kind = "file" +]; +"22693" [ +label = "22693\nlefty" +pname = "lefty" +kind = "proc" +]; +"182440-1" [ +label = "182440-1\n182441-1\npipe" +fontsize = "7" +fname = "pipe" +shape = "doublecircle" +subkind = "pipe" +kind = "file" +]; +"182442-1" [ +label = "182442-1\n182443-1\npipe" +fontsize = "7" +fname = "pipe" +shape = "doublecircle" +subkind = "pipe" +kind = "file" +]; +"22694" [ +label = "22694\ndot" +pname = "dot" +kind = "proc" +]; +"4761+2595" [ +label = "4761+2595\n/home/ek/pm2.dot" +fname = "/home/ek/pm2.dot" +shape = "ellipse" +kind = "file" +]; +"22690" -> "22692" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"22692" -> "116842+2595" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +"22692" -> "22693" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"22693" -> "182440-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +"22693" -> "182442-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "forward" +color = "black" +]; +"22693" -> "22694" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"22694" -> "182440-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "forward" +color = "black" +]; +"22694" -> "182442-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +"22693" -> "4761+2595" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/pmpipe.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,219 @@ +digraph g { +graph [ +]; +node [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +shape = "box" +color = "black" +width = "0.5" +]; +edge [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"23296" [ +label = "23296\n?" +pname = "?" +kind = "proc" +]; +"182948-1" [ +label = "182948-1\n182949-1\npipe" +fontsize = "7" +fname = "pipe" +shape = "doublecircle" +subkind = "pipe" +kind = "file" +]; +"23310" [ +label = "23310\ncat" +pname = "cat" +kind = "proc" +]; +"182950-1" [ +label = "182950-1\n182951-1\npipe" +fontsize = "7" +fname = "pipe" +shape = "doublecircle" +subkind = "pipe" +kind = "file" +]; +"23311" [ +label = "23311\ncat" +pname = "cat" +kind = "proc" +]; +"182952-1" [ +label = "182952-1\n182953-1\npipe" +fontsize = "7" +fname = "pipe" +shape = "doublecircle" +subkind = "pipe" +kind = "file" +]; +"23312" [ +label = "23312\ncat" +pname = "cat" +kind = "proc" +]; +"182954-1" [ +label = "182954-1\n182955-1\npipe" +fontsize = "7" +fname = "pipe" +shape = "doublecircle" +subkind = "pipe" +kind = "file" +]; +"23313" [ +label = "23313\ncat" +pname = "cat" +kind = "proc" +]; +"79893+2568" [ +label = "79893+2568\n/usr/share/lib/termcap" +fname = "/usr/share/lib/termcap" +shape = "ellipse" +kind = "file" +]; +"85+2560" [ +label = "85+2560\n?" +fname = "?" +shape = "ellipse" +kind = "file" +]; +"23314" [ +label = "23314\ncat" +pname = "cat" +kind = "proc" +]; +"4151865284+0" [ +label = "4151865284+0\n/tmp/termcap" +fname = "/tmp/termcap" +shape = "ellipse" +kind = "file" +]; +"23296" -> "23310" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"23296" -> "23311" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"23311" -> "182948-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +"23310" -> "182948-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "forward" +color = "black" +]; +"23296" -> "23312" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"23312" -> "182952-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "forward" +color = "black" +]; +"23312" -> "182950-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +"23296" -> "23313" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"23313" -> "182954-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "forward" +color = "black" +]; +"23311" -> "182950-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "forward" +color = "black" +]; +"23310" -> "79893+2568" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +"23296" -> "85+2560" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "both" +color = "black" +]; +"23296" -> "23314" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +color = "black" +]; +"23314" -> "85+2560" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "both" +color = "black" +]; +"23314" -> "182954-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +"23296" -> "85+2560" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "both" +color = "black" +]; +"23314" -> "4151865284+0" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "forward" +color = "black" +]; +"23313" -> "182952-1" [ +fontsize = "14" +fontname = "Times-Roman" +fontcolor = "black" +dir = "back" +color = "black" +]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/polypoly.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,162 @@ +digraph polypoly { + + size="7,9.5"; + page="8.5,11"; + ratio=fill; + node [shape=polygon]; + + { rank=same; + node [sides=0]; + node [peripheries=1]; + 0000 [label="M"]; + 0001 [label="MMMMMMMMMM"]; + 0002 [label="M\nM\nM\nM\nM\nM"]; + 0003 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + node [peripheries=2]; + 0010 [label="M"]; + 0011 [label="MMMMMMMMMM"]; + 0012 [label="M\nM\nM\nM\nM\nM"]; + 0013 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + node [distortion=-.3]; + 0110 [label="M"]; + 0111 [label="MMMMMMMMMM"]; + 0112 [label="M\nM\nM\nM\nM\nM"]; + 0113 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + } + { rank=same; + node [sides=3]; + node [peripheries=1]; + node [orientation=0]; + 3000 [label="M"]; + 3001 [label="MMMMMMMMMM"]; + 3002 [label="M\nM\nM\nM\nM\nM"]; + 3003 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + node [peripheries=2]; + node [orientation=60]; + 3110 [label="M"]; + 3111 [label="MMMMMMMMMM"]; + 3112 [label="M\nM\nM\nM\nM\nM"]; + 3113 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + } + 3000->0000; + { rank=same; + node [sides=4]; + node [peripheries=1]; + node [orientation=0]; + 4000 [label="M"]; + 4001 [label="MMMMMMMMMM"]; + 4002 [label="M\nM\nM\nM\nM\nM"]; + 4003 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + node [peripheries=2]; + node [orientation=45]; + 4110 [label="M"]; + 4111 [label="MMMMMMMMMM"]; + 4112 [label="M\nM\nM\nM\nM\nM"]; + 4113 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + } + 4000->3000; + { rank=same; + node [sides=5]; + node [peripheries=1]; + node [orientation=0]; + 5000 [label="M"]; + 5001 [label="MMMMMMMMMM"]; + 5002 [label="M\nM\nM\nM\nM\nM"]; + 5003 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + node [peripheries=2]; + node [orientation=36]; + 5110 [label="M"]; + 5111 [label="MMMMMMMMMM"]; + 5112 [label="M\nM\nM\nM\nM\nM"]; + 5113 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + } + 5000->4000; + { rank=same; + node [sides=6]; + node [peripheries=1]; + node [orientation=0]; + 6000 [label="M"]; + 6001 [label="MMMMMMMMMM"]; + 6002 [label="M\nM\nM\nM\nM\nM"]; + 6003 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + node [peripheries=2]; + node [orientation=30]; + 6110 [label="M"]; + 6111 [label="MMMMMMMMMM"]; + 6112 [label="M\nM\nM\nM\nM\nM"]; + 6113 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + } + 6000->5000; + { rank=same; + node [sides=7]; + node [peripheries=1]; + node [orientation=0]; + 7000 [label="M"]; + 7001 [label="MMMMMMMMMM"]; + 7002 [label="M\nM\nM\nM\nM\nM"]; + 7003 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + node [peripheries=2]; + node [orientation=25.7]; + 7110 [label="M"]; + 7111 [label="MMMMMMMMMM"]; + 7112 [label="M\nM\nM\nM\nM\nM"]; + 7113 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + } + 7000->6000; + { rank=same; + node [sides=8]; + node [peripheries=1]; + node [orientation=0]; + 8000 [label="M"]; + 8001 [label="MMMMMMMMMM"]; + 8002 [label="M\nM\nM\nM\nM\nM"]; + 8003 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + node [peripheries=2]; + node [orientation=22.5]; + 8110 [label="M"]; + 8111 [label="MMMMMMMMMM"]; + 8112 [label="M\nM\nM\nM\nM\nM"]; + 8113 [label="MMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM\nMMMMMMMMMM"]; + } + 8000->7000; + { rank=same; + node [sides=4]; + node [peripheries=1]; + node [regular=1]; + node [distortion=.5]; + node [orientation=0]; + 9000 [label="M"]; + node [orientation=45.]; + 9001 [label="M"]; + node [orientation=90.]; + 9002 [label="M"]; + node [orientation=135.]; + 9003 [label="M"]; + node [orientation=180.]; + 9004 [label="M"]; + node [orientation=225.]; + 9005 [label="M"]; + node [orientation=270.]; + 9006 [label="M"]; + node [orientation=315.]; + 9007 [label="M"]; + node [peripheries=2]; + node [orientation=0]; + 9010 [label="M"]; + node [orientation=45.]; + 9011 [label="M"]; + node [orientation=90.]; + 9012 [label="M"]; + node [orientation=135.]; + 9013 [label="M"]; + node [orientation=180.]; + 9014 [label="M"]; + node [orientation=225.]; + 9015 [label="M"]; + node [orientation=270.]; + 9016 [label="M"]; + node [orientation=315.]; + 9017 [label="M"]; + } + 9000->8000; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/proc3d.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,443 @@ +digraph g { +graph [ +fontname=Courier, +fontsize=24, +ranksep = 1.0, +size="10,7.5", +orientation=land, +style="setlinewidth(8)" +page = "8.5,11", +center=true +]; +node [ +shape = "box" +width = "0.5" +]; +edge [ +]; +subgraph cluster_0 { +label="gryphon" +"22342" +"22343" +"22346" +"22347" +"22351" +"22344" +"22345" +"22348" +"22350" +"22357" +} +subgraph cluster_1 { +label=toucan +"22349" +"22352" +"22356" +"22361" +"22369" +"22353" +"22355" +"22360" +"22365" +"22374" +} +subgraph cluster_2 { +label=parker +"22354" +"22359" +"22375" +} +subgraph cluster_3 { +label=condor +"22358" +"22362" +"22367" +"22373" +"22378" +} +subgraph cluster_4 { +label=kite +"22363" +"22366" +"22371" +"22376" +"22380" +} +subgraph cluster_5 { +label=coot +"22368" +"22372" +"22377" +"22379" +"22381" +} +"22316" [ +label = "22316\nksh" +pname = "ksh" +kind = "proc" +]; +"22324" [ +label = "22324\nnmake" +pname = "nmake" +kind = "proc" +]; +"22337" [ +label = "22337\nksh" +pname = "ksh" +kind = "proc" +]; +"22342" [ +label = "22342\nksh" +pname = "ksh" +kind = "proc" +]; +"22343" [ +label = "22343\ngcc" +pname = "gcc" +kind = "proc" +]; +"22344" [ +label = "22344\nksh" +pname = "ksh" +kind = "proc" +]; +"22345" [ +label = "22345\ngcc" +pname = "gcc" +kind = "proc" +]; +"22346" [ +label = "22346\ncpp" +pname = "cpp" +kind = "proc" +]; +"22347" [ +label = "22347\ncc1" +pname = "cc1" +kind = "proc" +]; +"22348" [ +label = "22348\ncpp" +pname = "cpp" +kind = "proc" +]; +"93736-32246" [ +label = "93736-32246\n/home/ek/work/src/lefty/lefty.c" +fname = "/home/ek/work/src/lefty/lefty.c" +shape = "ellipse" +kind = "file" +]; +"22349" [ +label = "22349\nksh" +pname = "ksh" +kind = "proc" +]; +"22350" [ +label = "22350\ncc1" +pname = "cc1" +kind = "proc" +]; +"93627-32246" [ +label = "93627-32246\n/home/ek/work/src/lefty/gfxview.c" +fname = "/home/ek/work/src/lefty/gfxview.c" +shape = "ellipse" +kind = "file" +]; +"22351" [ +label = "22351\nas" +pname = "as" +kind = "proc" +]; +"22352" [ +label = "22352\ngcc" +pname = "gcc" +kind = "proc" +]; +"22353" [ +label = "22353\nksh" +pname = "ksh" +kind = "proc" +]; +"22354" [ +label = "22354\nksh" +pname = "ksh" +kind = "proc" +]; +"22355" [ +label = "22355\ngcc" +pname = "gcc" +kind = "proc" +]; +"22356" [ +label = "22356\ncpp" +pname = "cpp" +kind = "proc" +]; +"22357" [ +label = "22357\nas" +pname = "as" +kind = "proc" +]; +"22358" [ +label = "22358\nksh" +pname = "ksh" +kind = "proc" +]; +"22359" [ +label = "22359\ngcc" +pname = "gcc" +kind = "proc" +]; +"22360" [ +label = "22360\ncpp" +pname = "cpp" +kind = "proc" +]; +"22361" [ +label = "22361\ncc1" +pname = "cc1" +kind = "proc" +]; +"93645-32246" [ +label = "93645-32246\n/home/ek/work/src/lefty/txtview.c" +fname = "/home/ek/work/src/lefty/txtview.c" +shape = "ellipse" +kind = "file" +]; +"22362" [ +label = "22362\ngcc" +pname = "gcc" +kind = "proc" +]; +"22363" [ +label = "22363\nksh" +pname = "ksh" +kind = "proc" +]; +"22365" [ +label = "22365\ncc1" +pname = "cc1" +kind = "proc" +]; +"22366" [ +label = "22366\ngcc" +pname = "gcc" +kind = "proc" +]; +"93638-32246" [ +label = "93638-32246\n/home/ek/work/src/lefty/internal.c" +fname = "/home/ek/work/src/lefty/internal.c" +shape = "ellipse" +kind = "file" +]; +"22367" [ +label = "22367\ncpp" +pname = "cpp" +kind = "proc" +]; +"22368" [ +label = "22368\nksh" +pname = "ksh" +kind = "proc" +]; +"22369" [ +label = "22369\nas" +pname = "as" +kind = "proc" +]; +"93642-32246" [ +label = "93642-32246\n/home/ek/work/src/lefty/lex.c" +fname = "/home/ek/work/src/lefty/lex.c" +shape = "ellipse" +kind = "file" +]; +"22371" [ +label = "22371\ncpp" +pname = "cpp" +kind = "proc" +]; +"22372" [ +label = "22372\ngcc" +pname = "gcc" +kind = "proc" +]; +"22373" [ +label = "22373\ncc1" +pname = "cc1" +kind = "proc" +]; +"88860-32246" [ +label = "88860-32246\n/home/ek/dev/src/lefty/stringify.c" +fname = "/home/ek/dev/src/lefty/stringify.c" +shape = "ellipse" +kind = "file" +]; +"22374" [ +label = "22374\nas" +pname = "as" +kind = "proc" +]; +"22375" [ +label = "22375\nas" +pname = "as" +kind = "proc" +]; +"22376" [ +label = "22376\ncc1" +pname = "cc1" +kind = "proc" +]; +"93626-32246" [ +label = "93626-32246\n/home/ek/work/src/lefty/exec.c" +fname = "/home/ek/work/src/lefty/exec.c" +shape = "ellipse" +kind = "file" +]; +"22377" [ +label = "22377\ncpp" +pname = "cpp" +kind = "proc" +]; +"22378" [ +label = "22378\nas" +pname = "as" +kind = "proc" +]; +"22379" [ +label = "22379\ncc1" +pname = "cc1" +kind = "proc" +]; +"93643-32246" [ +label = "93643-32246\n/home/ek/work/src/lefty/parse.c" +fname = "/home/ek/work/src/lefty/parse.c" +shape = "ellipse" +kind = "file" +]; +"22380" [ +label = "22380\nas" +pname = "as" +kind = "proc" +]; +"22381" [ +label = "22381\nas" +pname = "as" +kind = "proc" +]; +"37592-32246" [ +label = "37592-32246\n/home/ek/dev/src/lefty/exec.h" +fname = "/home/ek/dev/src/lefty/exec.h" +shape = "ellipse" +kind = "file" +]; +"135504-32246" [ +label = "135504-32246\n/home/ek/work/sun4/lefty/display.o" +fname = "/home/ek/work/sun4/lefty/display.o" +shape = "ellipse" +kind = "file" +]; +"22316" -> "22324" [ +]; +"22324" -> "22337" [ +]; +"22337" -> "22342" [ +]; +"22342" -> "22343" [ +]; +"22337" -> "22344" [ +]; +"22344" -> "22345" [ +]; +"22343" -> "22346" [ +]; +"22343" -> "22347" [ +]; +"22345" -> "22348" [ +]; +"22346" -> "93736-32246" [ +]; +"22337" -> "22349" [ +]; +"22345" -> "22350" [ +]; +"22348" -> "93627-32246" [ +]; +"22343" -> "22351" [ +]; +"22349" -> "22352" [ +]; +"22337" -> "22353" [ +]; +"22337" -> "22354" [ +]; +"22353" -> "22355" [ +]; +"22352" -> "22356" [ +]; +"22345" -> "22357" [ +]; +"22337" -> "22358" [ +]; +"22354" -> "22359" [ +]; +"22355" -> "22360" [ +]; +"22352" -> "22361" [ +]; +"22356" -> "93645-32246" [ +]; +"22358" -> "22362" [ +]; +"22337" -> "22363" [ +]; +"22355" -> "22365" [ +]; +"22363" -> "22366" [ +]; +"22360" -> "93638-32246" [ +]; +"22362" -> "22367" [ +]; +"22337" -> "22368" [ +]; +"22352" -> "22369" [ +]; +"22324" -> "93642-32246" [ +]; +"22366" -> "22371" [ +]; +"22368" -> "22372" [ +]; +"22362" -> "22373" [ +]; +"22367" -> "88860-32246" [ +]; +"22355" -> "22374" [ +]; +"22359" -> "22375" [ +]; +"22366" -> "22376" [ +]; +"22371" -> "93626-32246" [ +]; +"22372" -> "22377" [ +]; +"22362" -> "22378" [ +]; +"22372" -> "22379" [ +]; +"22377" -> "93643-32246" [ +]; +"22366" -> "22380" [ +]; +"22372" -> "22381" [ +]; +"22371" -> "37592-32246" [ +]; +"22375" -> "135504-32246" [ +]; + +/* hack to increase node separation */ +{ rank = same; "22337" -> "93642-32246" [style=invis,minlen=10]; } + +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/psfonttest.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +digraph G { +rankdir=LR +nodesep=0 +node [shape=none margin=0] +edge [color=white] +"AvantGarde-Book" [fontname="AvantGarde-Book"] +"AvantGarde-Demi" [fontname="AvantGarde-Demi"] +"AvantGarde-BookOblique" [fontname="AvantGarde-BookOblique"] +"AvantGarde-DemiOblique" [fontname="AvantGarde-DemiOblique"] + +"AvantGarde-Book" -> "AvantGarde-Demi" -> "AvantGarde-BookOblique" -> "AvantGarde-DemiOblique" + +"Bookman-Light" [fontname="Bookman-Light"] +"Bookman-Demi" [fontname="Bookman-Demi"] +"Bookman-LightItalic" [fontname="Bookman-LightItalic"] +"Bookman-DemiItalic" [fontname="Bookman-DemiItalic"] + +"Bookman-Light" -> "Bookman-Demi" -> "Bookman-LightItalic" -> "Bookman-DemiItalic" + +"Courier" [fontname="Courier"] +"Courier-Bold" [fontname="Courier-Bold"] +"Courier-Oblique" [fontname="Courier-Oblique"] +"Courier-BoldOblique" [fontname="Courier-BoldOblique"] + +"Courier" -> "Courier-Bold" -> "Courier-Oblique" -> "Courier-BoldOblique" + +"Helvetica" [fontname="Helvetica"] +"Helvetica-Bold" [fontname="Helvetica-Bold"] +"Helvetica-Oblique" [fontname="Helvetica-Oblique"] +"Helvetica-BoldOblique" [fontname="Helvetica-BoldOblique"] + +"Helvetica-Narrow" [fontname="Helvetica-Narrow"] +"Helvetica-Narrow-Bold" [fontname="Helvetica-Narrow-Bold"] +"Helvetica-Narrow-Oblique" [fontname="Helvetica-Narrow-Oblique"] +"Helvetica-Narrow-BoldOblique" [fontname="Helvetica-Narrow-BoldOblique"] + +"Helvetica" -> "Helvetica-Bold" -> "Helvetica-Oblique" -> "Helvetica-BoldOblique" + +"Helvetica-Narrow" -> "Helvetica-Narrow-Bold" -> "Helvetica-Narrow-Oblique" -> "Helvetica-Narrow-BoldOblique" + +"NewCenturySchlbk-Roman" [fontname="NewCenturySchlbk-Roman"] +"NewCenturySchlbk-Bold" [fontname="NewCenturySchlbk-Bold"] +"NewCenturySchlbk-Italic" [fontname="NewCenturySchlbk-Italic"] +"NewCenturySchlbk-BoldItalic" [fontname="NewCenturySchlbk-BoldItalic"] + +"NewCenturySchlbk-Roman" -> "NewCenturySchlbk-Bold" -> "NewCenturySchlbk-Italic" -> "NewCenturySchlbk-BoldItalic" + +"Palatino-Roman" [fontname="Palatino-Roman"] +"Palatino-Bold" [fontname="Palatino-Bold"] +"Palatino-Italic" [fontname="Palatino-Italic"] +"Palatino-BoldItalic" [fontname="Palatino-BoldItalic"] + +"Palatino-Roman" -> "Palatino-Bold" -> "Palatino-Italic" -> "Palatino-BoldItalic" + +"Times-Roman" [fontname="Times-Roman"] +"Times-Bold" [fontname="Times-Bold"] +"Times-Italic" [fontname="Times-Italic"] +"Times-BoldItalic" [fontname="Times-BoldItalic"] + +"Times-Roman" -> "Times-Bold" -> "Times-Italic" -> "Times-BoldItalic" + +"ZapfChancery-MediumItalic" [fontname="ZapfChancery-MediumItalic"] +"ZapfDingbats" [fontname="ZapfDingbats"] +"Symbol" [fontname="Symbol"] + +"Symbol" -> "ZapfDingbats" -> "ZapfChancery-MediumItalic" +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/record2.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,6 @@ +digraph G { + node [shape=record]; + a [label = "<f0> foo | x | <f1> bar"]; + b [label = "a | { <f0> foo | x | <f1> bar } | b"]; + a:f0 -> b:f1 +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/records.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,17 @@ +digraph G { + rankdir=LR; + node [shape=record]; + a [ label ="<bala> Graphs can\lbe fun\l|<f1> mid|<f2> right\r"]; + b [ label ="<left> |<mid> b | " ]; + c [ label ="<p1> | c |<p2> " ]; + x [ label ="<p1> | x |<p2> " ]; + y [ label ="<p1> | y |<p2> " ]; + z [ label =" | z |<p2> " ]; + a:bala -> b:left; + a:f1 -> d; + a:f2 -> y:"p1"; + c:"p1" -> d; + b:mid -> x:"p1"; + c:"p2" -> y:"p2"; + b:left -> z:"p2"; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/rowe.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,72 @@ +digraph rowe { + node [shape = box]; + size = "6,6"; + 1 -> 2; + 1 -> 10; + 10 -> 14; + 10 -> 12; + 10 -> 13; + 10 -> 11; + 2 -> 18; + 2 -> 17; + 2 -> 16; + 2 -> 3; + 11 -> 4; + 16 -> 4; + 3 -> 4; + 4 -> 5; + 13 -> 19; + 17 -> 19; + 5 -> 23; + 5 -> 35; + 5 -> 6; + 37 -> 39; + 37 -> 41; + 37 -> 40; + 37 -> 38; + 19 -> 20; + 19 -> 28; + 19 -> 21; + 12 -> 29; + 18 -> 29; + 41 -> 29; + 28 -> 29; + 29 -> 30; + 30 -> 31; + 30 -> 33; + 31 -> 32; + 21 -> 22; + 32 -> 23; + 22 -> 23; + 6 -> 7; + 23 -> 24; + 7 -> 8; + 24 -> 25; + 24 -> 27; + 35 -> 43; + 35 -> 36; + 8 -> 9; + 14 -> 15; + 39 -> 15; + 20 -> 15; + 33 -> 34; + 43 -> 40; + 43 -> 38; + 25 -> 26; + 9 -> 42; + 10 -> 1; + 15 -> 1; + 23 -> 1; + 31 -> 1; + 2 -> 1; + 25 -> 1; + 9 -> 1; + 38 -> 4; + 26 -> 4; + 42 -> 4; + 40 -> 19; + 36 -> 19; + 34 -> 29; + 33 -> 30; + 27 -> 24; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/russian.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,9 @@ + digraph G { +Контрагенты -> БанковÑкиеСчета; +Организации -> БанковÑкиеСчета; +ВопроÑыДлÑÐÐ½ÐºÐµÑ‚Ð¸Ñ€Ð¾Ð²Ð°Ð½Ð¸Ñ -> ВариантыОтветовОпроÑов; +Контрагенты -> ДоговорыВзаимораÑчетов; +Ðоменклатура -> ЕдиницыИзмерениÑ; +ÐоменклатурныеГруппы -> ЕдиницыИзмерениÑ; +СвойÑтваОбектов -> ЗначениÑСвойÑтвОбектов; +} \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/sdh.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,284 @@ +digraph G { + graph [bgcolor=black]; /* set background */ + edge [color=white]; + graph[page="8.5,11",size="7.5,7",ratio=fill,center=1]; + node[style=filled,label=""]; + subgraph ds3CTP { + rank = same; + node[shape=box,color=green]; + ds3CTP_1_1; + ds3CTP_1_2; + ds3CTP_5_1; + ds3CTP_5_2; + } + subgraph t3TTP { + rank = same; + node[shape=invtriangle,color=red]; + t3TTP_1_1; + t3TTP_5_2; + } + subgraph vc3TTP { + rank = same; + node[shape=invtriangle,color=red]; + vc3TTP_1_2; + vc3TTP_5_1; + } + subgraph fabric { + rank = same; + node[shape=hexagon,color=blue]; + fabric_1_2; + fabric_4_1; + fabric_5_1; + } + subgraph xp { + rank = same; + node[shape=diamond,color=blue]; + xp_1_2; + xp_4_1; + xp_5_1; + } + subgraph au3CTP { + rank = same; + node[shape=box,color=green]; + au3CTP_1_2; + au3CTP_4_1; + au3CTP_4_2; + au3CTP_5_1; + } + subgraph aug { + rank = same; + node[shape=invtrapezium,color=pink]; + aug_1_2; + aug_4_1; + aug_4_2; + aug_5_1; + } + subgraph protectionTTP { + rank = same; + node[shape=invtriangle,color=red]; + prTTP_1_2; + prTTP_4_1; + prTTP_4_2; + prTTP_5_1; + } + subgraph protectionGroup { + rank = same; + node[shape=hexagon,color=blue]; + pg_1_2; + pg_4_1; + pg_4_2; + pg_5_1; + } + subgraph protectionUnit { + rank = same; + node[shape=diamond,color=blue]; + pu_1_2; + pu_4_1; + pu_4_2; + pu_5_1; + } + subgraph protectionCTP { + node[shape=box,color=green]; + prCTP_1_2; + prCTP_4_1; + prCTP_4_2; + prCTP_5_1; + } + subgraph msTTP { + rank = same; + node[shape=invtriangle,color=red]; + msTTP_1_2; + msTTP_4_1; + msTTP_4_2; + msTTP_5_1; + } + subgraph msCTP { + rank = same; + node[shape=box,color=green]; + msCTP_1_2; + msCTP_3_1; + msCTP_3_2; + msCTP_4_1; + msCTP_4_2; + msCTP_5_1; + } + subgraph rsTTP { + rank = same; + node[shape=invtriangle,color=red]; + rsTTP_1_2; + rsTTP_3_1; + rsTTP_3_2; + rsTTP_4_1; + rsTTP_4_2; + rsTTP_5_1; + } + subgraph rsCTP { + rank = same; + node[shape=box,color=green]; + rsCTP_1_2; + rsCTP_2_1; + rsCTP_2_2; + rsCTP_3_1; + rsCTP_3_2; + rsCTP_4_1; + rsCTP_4_2; + rsCTP_5_1; + } + subgraph spiTTP { + rank = same; + node[shape=invtriangle,color=red]; + spiTTP_1_2; + spiTTP_2_1; + spiTTP_2_2; + spiTTP_3_1; + spiTTP_3_2; + spiTTP_4_1; + spiTTP_4_2; + spiTTP_5_1; + } + subgraph me { + rank = same; + node[shape=box,peripheries=2]; + me_1; + me_2; + me_3; + me_4; + me_5; + } + subgraph client_server { + edge[style=dotted,dir=none,weight=100]; + ds3CTP_1_1->t3TTP_1_1; + ds3CTP_1_2->vc3TTP_1_2; + au3CTP_1_2->aug_1_2->prTTP_1_2; + prCTP_1_2->msTTP_1_2; + msCTP_1_2->rsTTP_1_2; + rsCTP_1_2->spiTTP_1_2; + rsCTP_2_1->spiTTP_2_1; + rsCTP_2_2->spiTTP_2_2; + msCTP_3_1->rsTTP_3_1; + rsCTP_3_1->spiTTP_3_1; + msCTP_3_2->rsTTP_3_2; + rsCTP_3_2->spiTTP_3_2; + au3CTP_4_1->aug_4_1->prTTP_4_1; + prCTP_4_1->msTTP_4_1; + msCTP_4_1->rsTTP_4_1; + rsCTP_4_1->spiTTP_4_1; + au3CTP_4_2->aug_4_2->prTTP_4_2; + prCTP_4_2->msTTP_4_2; + msCTP_4_2->rsTTP_4_2; + rsCTP_4_2->spiTTP_4_2; + ds3CTP_5_1->vc3TTP_5_1; + au3CTP_5_1->aug_5_1->prTTP_5_1; + prCTP_5_1->msTTP_5_1; + msCTP_5_1->rsTTP_5_1; + rsCTP_5_1->spiTTP_5_1; + ds3CTP_5_2->t3TTP_5_2; + } + subgraph trail { + edge[style=dashed,dir=none]; + vc3TTP_1_2->vc3TTP_5_1; + prTTP_1_2->prTTP_4_1; + prTTP_4_2->prTTP_5_1; + msTTP_1_2->msTTP_4_1; + msTTP_4_2->msTTP_5_1; + rsTTP_1_2->rsTTP_3_1; + rsTTP_3_2->rsTTP_4_1; + rsTTP_4_2->rsTTP_5_1; + spiTTP_1_2->spiTTP_2_1; + spiTTP_2_2->spiTTP_3_1; + spiTTP_3_2->spiTTP_4_1; + spiTTP_4_2->spiTTP_5_1; + } + subgraph contain { + pu_1_2->pg_1_2; + pu_4_1->pg_4_1; + pu_4_2->pg_4_2; + pu_5_1->pg_5_1; + xp_1_2->fabric_1_2; + xp_4_1->fabric_4_1; + xp_5_1->fabric_5_1; + fabric_1_2->me_1; + fabric_4_1->me_4; + fabric_5_1->me_5; + pg_1_2->me_1; + pg_4_1->me_4; + pg_4_2->me_4; + pg_5_1->me_5; + t3TTP_1_1->me_1; + t3TTP_5_2->me_5; + vc3TTP_1_2->me_1; + vc3TTP_5_1->me_5; + prTTP_1_2->me_1; + prTTP_4_1->me_4; + prTTP_4_2->me_4; + prTTP_5_1->me_5; + msTTP_1_2->me_1; + msTTP_4_1->me_4; + msTTP_4_2->me_4; + msTTP_5_1->me_5; + rsTTP_1_2->me_1; + rsTTP_3_1->me_3; + rsTTP_3_2->me_3; + rsTTP_4_1->me_4; + rsTTP_4_2->me_4; + rsTTP_5_1->me_5; + spiTTP_1_2->me_1; + spiTTP_2_1->me_2; + spiTTP_2_2->me_2; + spiTTP_3_1->me_3; + spiTTP_3_2->me_3; + spiTTP_4_1->me_4; + spiTTP_4_2->me_4; + spiTTP_5_1->me_5; + } + subgraph connectedBy { + vc3TTP_1_2->fabric_1_2; + au3CTP_1_2->fabric_1_2; + au3CTP_4_1->fabric_4_1; + au3CTP_4_2->fabric_4_1; + vc3TTP_5_1->fabric_5_1; + au3CTP_5_1->fabric_5_1; + prTTP_1_2->pg_1_2; + prTTP_4_1->pg_4_1; + prTTP_4_2->pg_4_2; + prTTP_5_1->pg_5_1; + prCTP_1_2->pg_1_2; + prCTP_4_1->pg_4_1; + prCTP_4_2->pg_4_2; + prCTP_5_1->pg_5_1; + } + subgraph crossConnection { + edge[style=dotted,dir=none]; + vc3TTP_1_2->xp_1_2->au3CTP_1_2; + prTTP_1_2->pu_1_2->prCTP_1_2; + prTTP_4_1->pu_4_1->prCTP_4_1; + au3CTP_4_1->xp_4_1->au3CTP_4_2; + prTTP_4_2->pu_4_2->prCTP_4_2; + prTTP_5_1->pu_5_1->prCTP_5_1; + vc3TTP_5_1->xp_5_1->au3CTP_5_1; + } + subgraph bindingConnection { + edge[style=bold,dir=none,weight=100]; + ds3CTP_1_1->ds3CTP_1_2; + vc3TTP_1_2->au3CTP_1_2; + prTTP_1_2->prCTP_1_2; + msTTP_1_2->msCTP_1_2; + rsTTP_1_2->rsCTP_1_2; + rsCTP_2_1->rsCTP_2_2; + rsTTP_3_1->rsCTP_3_1; + msCTP_3_1->msCTP_3_2; + rsTTP_3_2->rsCTP_3_2; + prTTP_4_1->prCTP_4_1; + msTTP_4_1->msCTP_4_1; + rsTTP_4_1->rsCTP_4_1; + au3CTP_4_1->au3CTP_4_2; + prTTP_4_2->prCTP_4_2; + msTTP_4_2->msCTP_4_2; + rsTTP_4_2->rsCTP_4_2; + prTTP_5_1->prCTP_5_1; + msTTP_5_1->msCTP_5_1; + rsTTP_5_1->rsCTP_5_1; + ds3CTP_5_1->ds3CTP_5_2; + vc3TTP_5_1->au3CTP_5_1; + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/shells.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,55 @@ +digraph shells { + size="7,8"; + node [fontsize=24, shape = plaintext]; + + 1972 -> 1976; + 1976 -> 1978; + 1978 -> 1980; + 1980 -> 1982; + 1982 -> 1984; + 1984 -> 1986; + 1986 -> 1988; + 1988 -> 1990; + 1990 -> future; + + node [fontsize=20, shape = box]; + { rank=same; 1976 Mashey Bourne; } + { rank=same; 1978 Formshell csh; } + { rank=same; 1980 esh vsh; } + { rank=same; 1982 ksh "System-V"; } + { rank=same; 1984 v9sh tcsh; } + { rank=same; 1986 "ksh-i"; } + { rank=same; 1988 KornShell Perl rc; } + { rank=same; 1990 tcl Bash; } + { rank=same; "future" POSIX "ksh-POSIX"; } + + Thompson -> Mashey; + Thompson -> Bourne; + Thompson -> csh; + csh -> tcsh; + Bourne -> ksh; + Bourne -> esh; + Bourne -> vsh; + Bourne -> "System-V"; + Bourne -> v9sh; + v9sh -> rc; + Bourne -> Bash; + "ksh-i" -> Bash; + KornShell -> Bash; + esh -> ksh; + vsh -> ksh; + Formshell -> ksh; + csh -> ksh; + KornShell -> POSIX; + "System-V" -> POSIX; + ksh -> "ksh-i"; + "ksh-i" -> KornShell; + KornShell -> "ksh-POSIX"; + Bourne -> Formshell; + + edge [style=invis]; + 1984 -> v9sh -> tcsh ; + 1988 -> rc -> KornShell; + Formshell -> csh; + KornShell -> Perl; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/states.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,21 @@ +/* +The command line is + + dot -Tps -Grankdir=LR states.dot > states.ps + +and the file is: +*/ +digraph states { + size="3,2"; + rankdir=LR; + node [shape=ellipse]; + empty [label = "Empty"]; + stolen [label = "Stolen"]; + waiting [label = "Waiting"]; + full [label = "Full"]; + empty -> full [label = "return"] + empty -> stolen [label = "dispatch", wt=28] + stolen -> full [label = "return"]; + stolen -> waiting [label = "touch"]; + waiting -> full [label = "return"]; + }
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/structs.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,8 @@ +digraph structs { +node [shape=record]; + struct1 [shape=record,label="<f0> left|<f1> middle|<f2> right"]; + struct2 [shape=record,label="<f0> one|<f1> two"]; + struct3 [shape=record,label="hello\nworld |{ b |{c|<here> d|e}| f}| g | h"]; + struct1:f1 -> struct2:f0; + struct1:f2 -> struct3:here; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/switch.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,60 @@ +digraph G { + graph [center=true rankdir=LR bgcolor="#808080"] + edge [dir=none] + node [width=0.3 height=0.3 label=""] + { node [shape=circle style=invis] + 1 2 3 4 5 6 7 8 10 20 30 40 50 60 70 80 + } + { node [shape=circle] + a b c d e f g h i j k l m n o p q r s t u v w x + } + { node [shape=diamond] + A B C D E F G H I J K L M N O P Q R S T U V W X + } + 1 -> a -> {A B} [color="#0000ff"] + 2 -> b -> {B A} [color="#ff0000"] + 3 -> c -> {C D} [color="#ffff00"] + 4 -> d -> {D C} [color="#00ff00"] + 5 -> e -> {E F} [color="#000000"] + 6 -> f -> {F E} [color="#00ffff"] + 7 -> g -> {G H} [color="#ffffff"] + 8 -> h -> {H G} [color="#ff00ff"] + { edge [color="#ff0000:#0000ff"] + A -> i -> {I K} + B -> j -> {J L} + } + { edge [color="#00ff00:#ffff00"] + C -> k -> {K I} + D -> l -> {L J} + } + { edge [color="#00ffff:#000000"] + E -> m -> {M O} + F -> n -> {N P} + } + { edge [color="#ff00ff:#ffffff"] + G -> o -> {O M} + H -> p -> {P N} + } + { edge [color="#00ff00:#ffff00:#ff0000:#0000ff"] + I -> q -> {Q U} + J -> r -> {R V} + K -> s -> {S W} + L -> t -> {T X} + } + { edge [color="#ff00ff:#ffffff:#00ffff:#000000"] + M -> u -> {U Q} + N -> v -> {V R} + O -> w -> {W S} + P -> x -> {X T} + } + { edge [color="#ff00ff:#ffffff:#00ffff:#000000:#00ff00:#ffff00:#ff0000:#0000ff"] + Q -> 10 + R -> 20 + S -> 30 + T -> 40 + U -> 50 + V -> 60 + W -> 70 + X -> 80 + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/table.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,57 @@ +digraph structs { + node [shape=plaintext]; + + struct1 [label=<<TABLE CELLPADDING="10" BORDER="0"> + <TR> + <TD HEIGHT="30" WIDTH="90">a</TD> + <TD>b</TD> + <TD>c</TD> + </TR> + </TABLE>>]; + + struct2 [label=<<TABLE> + <TR> + <TD COLSPAN="3">elefantel</TD> + <TD ROWSPAN="2" VALIGN="bottom" ALIGN="right">two</TD> + </TR><TR> + <TD COLSPAN="2" ROWSPAN="2"><TABLE BGCOLOR="grey"> + <TR> + <TD>buca</TD> + </TR><TR> + <TD BGCOLOR="yellow">c</TD> + </TR><TR> + <TD>f</TD> + </TR> + </TABLE></TD> + <TD>patratos</TD> + </TR><TR> + <TD COLSPAN="2" ALIGN="right">4</TD> + </TR> + </TABLE> + >]; + + struct3 [label=<<TABLE CELLPADDING="5"> + <TR> + <TD ROWSPAN="3">Hello</TD> + <TD ROWSPAN="2" COLSPAN="3"><TABLE BORDER="4"> + <TR> + <TD COLSPAN="3">b</TD> + </TR><TR> + <TD>a</TD> + <TD>dino</TD> + <TD>y</TD> + </TR><TR> + <TD COLSPAN="3">rhino</TD> + </TR> + </TABLE></TD> + </TR><TR> + <TD COLSPAN="2">climb</TD> + <TD ROWSPAN="2">Up</TD> + </TR><TR> + <TD COLSPAN="3">low</TD> + </TR> + </TABLE>>]; + + struct1 -> struct3; + struct1 -> struct2; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/train11.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,30 @@ +digraph G { + size="6,6"; + node [shape=circle,fontsize=8]; + rankdir=LR; + st9 -> st9 [label="11/1"]; + st9 -> st10 [label="10/1"]; + st8 -> st8 [label="10/1"]; + st8 -> st0 [label="00/-"]; + st7 -> st8 [label="10/1"]; + st7 -> st7 [label="00/1"]; + st6 -> st6 [label="01/1"]; + st6 -> st0 [label="00/-"]; + st5 -> st6 [label="01/1"]; + st5 -> st5 [label="11/1"]; + st4 -> st4 [label="01/1"]; + st4 -> st0 [label="00/-"]; + st3 -> st4 [label="01/1"]; + st3 -> st3 [label="00/1"]; + st2 -> st9 [label="11/1"]; + st2 -> st7 [label="00/1"]; + st2 -> st2 [label="01/1"]; + st10 -> st10 [label="10/1"]; + st10 -> st0 [label="00/-"]; + st1 -> st5 [label="11/1"]; + st1 -> st3 [label="00/1"]; + st1 -> st1 [label="10/1"]; + st0 -> st2 [label="01/-"]; + st0 -> st1 [label="10/-"]; + st0 -> st0 [label="00/0"]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/trapeziumlr.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,79 @@ +digraph test { + + size="7,9.5"; + page="8,10.5"; + ratio=fill; + rankdir=LR; + + { rank=same; + node [shape=house]; + A;C;E;G;I;K;M;O;Q;S;U;W;Y; + node [shape=invhouse]; + B;D;F;H;J;L;N;P;R;T;V;X;Z; + } + + { rank=same; + node [shape=trapezium]; + "Trapezium"; + ordering=out; + } + + Trapezium -> A; + Trapezium -> B; + Trapezium -> C; + Trapezium -> D; + Trapezium -> E; + Trapezium -> F; + Trapezium -> G; + Trapezium -> H; + Trapezium -> I; + Trapezium -> J; + Trapezium -> K; + Trapezium -> L; + Trapezium -> M; + Trapezium -> N; + Trapezium -> O; + Trapezium -> P; + Trapezium -> Q; + Trapezium -> R; + Trapezium -> S; + Trapezium -> T; + Trapezium -> U; + Trapezium -> V; + Trapezium -> W; + Trapezium -> X; + Trapezium -> Y; + Trapezium -> Z; + + { rank=same; + node [shape=parallelogram]; + a;b;c;d;e;f;g;h;i;j;k;l;m;n;o;p;q;r;s;t;u;v;w;x;y;z; + } + + a -> Trapezium; + b -> Trapezium; + c -> Trapezium; + d -> Trapezium; + e -> Trapezium; + f -> Trapezium; + g -> Trapezium; + h -> Trapezium; + i -> Trapezium; + j -> Trapezium; + k -> Trapezium; + l -> Trapezium; + m -> Trapezium; + n -> Trapezium; + o -> Trapezium; + p -> Trapezium; + q -> Trapezium; + r -> Trapezium; + s -> Trapezium; + t -> Trapezium; + u -> Trapezium; + v -> Trapezium; + w -> Trapezium; + x -> Trapezium; + y -> Trapezium; + z -> Trapezium; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/tree.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +digraph g { +node [shape = record,height=.1]; +node0[label = "<f0> |<f1> G|<f2> "]; +node1[label = "<f0> |<f1> E|<f2> "]; +node2[label = "<f0> |<f1> B|<f2> "]; +node3[label = "<f0> |<f1> F|<f2> "]; +node4[label = "<f0> |<f1> R|<f2> "]; +node5[label = "<f0> |<f1> H|<f2> "]; +node6[label = "<f0> |<f1> Y|<f2> "]; +node7[label = "<f0> |<f1> A|<f2> "]; +node8[label = "<f0> |<f1> C|<f2> "]; +"node0":f2 -> "node4":f1; +"node0":f0 -> "node1":f1; +"node1":f0 -> "node2":f1; +"node1":f2 -> "node3":f1; +"node2":f2 -> "node8":f1; +"node2":f0 -> "node7":f1; +"node4":f2 -> "node6":f1; +"node4":f0 -> "node5":f1; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/triedds.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,114 @@ +digraph g { +graph [ +rankdir = "LR" +]; +node [ +fontsize = "16" +shape = "ellipse" +]; +edge [ +]; +"node0" [ +label = "<f0> 0x10ba8| <f1>" +shape = "record" +]; +"node1" [ +label = "<f0> 0xf7fc4380| <f1> | <f2> |-1" +shape = "record" +]; +"node2" [ +label = "<f0> 0xf7fc44b8| | |2" +shape = "record" +]; +"node3" [ +label = "<f0> 3.43322790286038071e-06|44.79998779296875|0" +shape = "record" +]; +"node4" [ +label = "<f0> 0xf7fc4380| <f1> | <f2> |2" +shape = "record" +]; +"node5" [ +label = "<f0> (nil)| | |-1" +shape = "record" +]; +"node6" [ +label = "<f0> 0xf7fc4380| <f1> | <f2> |1" +shape = "record" +]; +"node7" [ +label = "<f0> 0xf7fc4380| <f1> | <f2> |2" +shape = "record" +]; +"node8" [ +label = "<f0> (nil)| | |-1" +shape = "record" +]; +"node9" [ +label = "<f0> (nil)| | |-1" +shape = "record" +]; +"node10" [ +label = "<f0> (nil)| <f1> | <f2> |-1" +shape = "record" +]; +"node11" [ +label = "<f0> (nil)| <f1> | <f2> |-1" +shape = "record" +]; +"node12" [ +label = "<f0> 0xf7fc43e0| | |1" +shape = "record" +]; +"node0":f0 -> "node1":f0 [ +id = 0 +]; +"node0":f1 -> "node2":f0 [ +id = 1 +]; +"node1":f0 -> "node3":f0 [ +id = 2 +]; +"node1":f1 -> "node4":f0 [ +id = 3 +]; +"node1":f2 -> "node5":f0 [ +id = 4 +]; +"node4":f0 -> "node3":f0 [ +id = 5 +]; +"node4":f1 -> "node6":f0 [ +id = 6 +]; +"node4":f2 -> "node10":f0 [ +id = 7 +]; +"node6":f0 -> "node3":f0 [ +id = 8 +]; +"node6":f1 -> "node7":f0 [ +id = 9 +]; +"node6":f2 -> "node9":f0 [ +id = 10 +]; +"node7":f0 -> "node3":f0 [ +id = 11 +]; +"node7":f1 -> "node1":f0 [ +id = 12 +]; +"node7":f2 -> "node8":f0 [ +id = 13 +]; +"node10":f1 -> "node11":f0 [ +id = 14 +]; +"node10":f2 -> "node12":f0 [ +id = 15 +]; +"node11":f2 -> "node1":f0 [ +id = 16 +]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/try.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +digraph G { + subgraph cluster_small { + a -> b; + label=small; + } + + subgraph cluster_big { + p -> q -> r -> s -> t; + label=big; + t -> p; + } + + t -> a; + b -> q; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/unix.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,53 @@ +/* courtesy Ian Darwin and Geoff Collyer, Softquad Inc. */ +digraph unix { +size="6,6"; + "5th Edition" -> "6th Edition"; + "5th Edition" -> "PWB 1.0"; + "6th Edition" -> "LSX"; + "6th Edition" -> "1 BSD"; + "6th Edition" -> "Mini Unix"; + "6th Edition" -> "Wollongong"; + "6th Edition" -> "Interdata"; + "Interdata" -> "Unix/TS 3.0"; + "Interdata" -> "PWB 2.0"; + "Interdata" -> "7th Edition"; + "7th Edition" -> "8th Edition"; + "7th Edition" -> "32V"; + "7th Edition" -> "V7M"; + "7th Edition" -> "Ultrix-11"; + "7th Edition" -> "Xenix"; + "7th Edition" -> "UniPlus+"; + "V7M" -> "Ultrix-11"; + "8th Edition" -> "9th Edition"; + "1 BSD" -> "2 BSD"; + "2 BSD" -> "2.8 BSD"; + "2.8 BSD" -> "Ultrix-11"; + "2.8 BSD" -> "2.9 BSD"; + "32V" -> "3 BSD"; + "3 BSD" -> "4 BSD"; + "4 BSD" -> "4.1 BSD"; + "4.1 BSD" -> "4.2 BSD"; + "4.1 BSD" -> "2.8 BSD"; + "4.1 BSD" -> "8th Edition"; + "4.2 BSD" -> "4.3 BSD"; + "4.2 BSD" -> "Ultrix-32"; + "PWB 1.0" -> "PWB 1.2"; + "PWB 1.0" -> "USG 1.0"; + "PWB 1.2" -> "PWB 2.0"; + "USG 1.0" -> "CB Unix 1"; + "USG 1.0" -> "USG 2.0"; + "CB Unix 1" -> "CB Unix 2"; + "CB Unix 2" -> "CB Unix 3"; + "CB Unix 3" -> "Unix/TS++"; + "CB Unix 3" -> "PDP-11 Sys V"; + "USG 2.0" -> "USG 3.0"; + "USG 3.0" -> "Unix/TS 3.0"; + "PWB 2.0" -> "Unix/TS 3.0"; + "Unix/TS 1.0" -> "Unix/TS 3.0"; + "Unix/TS 3.0" -> "TS 4.0"; + "Unix/TS++" -> "TS 4.0"; + "CB Unix 3" -> "TS 4.0"; + "TS 4.0" -> "System V.0"; + "System V.0" -> "System V.2"; + "System V.2" -> "System V.3"; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/unix2.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,63 @@ +/* Courtesy of Ian Darwin <ian@darwinsys.com> + * and Geoff Collyer <geoff@plan9.bell-labs.com> + * Mildly updated by Ian Darwin in 2000. + */ +digraph unix { + size="6,6"; + node [color=lightblue2, style=filled]; + "5th Edition" -> "6th Edition"; + "5th Edition" -> "PWB 1.0"; + "6th Edition" -> "LSX"; + "6th Edition" -> "1 BSD"; + "6th Edition" -> "Mini Unix"; + "6th Edition" -> "Wollongong"; + "6th Edition" -> "Interdata"; + "Interdata" -> "Unix/TS 3.0"; + "Interdata" -> "PWB 2.0"; + "Interdata" -> "7th Edition"; + "7th Edition" -> "8th Edition"; + "7th Edition" -> "32V"; + "7th Edition" -> "V7M"; + "7th Edition" -> "Ultrix-11"; + "7th Edition" -> "Xenix"; + "7th Edition" -> "UniPlus+"; + "V7M" -> "Ultrix-11"; + "8th Edition" -> "9th Edition"; + "9th Edition" -> "10th Edition"; + "1 BSD" -> "2 BSD"; + "2 BSD" -> "2.8 BSD"; + "2.8 BSD" -> "Ultrix-11"; + "2.8 BSD" -> "2.9 BSD"; + "32V" -> "3 BSD"; + "3 BSD" -> "4 BSD"; + "4 BSD" -> "4.1 BSD"; + "4.1 BSD" -> "4.2 BSD"; + "4.1 BSD" -> "2.8 BSD"; + "4.1 BSD" -> "8th Edition"; + "4.2 BSD" -> "4.3 BSD"; + "4.2 BSD" -> "Ultrix-32"; + "4.3 BSD" -> "4.4 BSD"; + "4.4 BSD" -> "FreeBSD"; + "4.4 BSD" -> "NetBSD"; + "4.4 BSD" -> "OpenBSD"; + "PWB 1.0" -> "PWB 1.2"; + "PWB 1.0" -> "USG 1.0"; + "PWB 1.2" -> "PWB 2.0"; + "USG 1.0" -> "CB Unix 1"; + "USG 1.0" -> "USG 2.0"; + "CB Unix 1" -> "CB Unix 2"; + "CB Unix 2" -> "CB Unix 3"; + "CB Unix 3" -> "Unix/TS++"; + "CB Unix 3" -> "PDP-11 Sys V"; + "USG 2.0" -> "USG 3.0"; + "USG 3.0" -> "Unix/TS 3.0"; + "PWB 2.0" -> "Unix/TS 3.0"; + "Unix/TS 1.0" -> "Unix/TS 3.0"; + "Unix/TS 3.0" -> "TS 4.0"; + "Unix/TS++" -> "TS 4.0"; + "CB Unix 3" -> "TS 4.0"; + "TS 4.0" -> "System V.0"; + "System V.0" -> "System V.2"; + "System V.2" -> "System V.3"; + "System V.3" -> "System V.4"; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/viewfile.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +digraph Viewfile { +node [ style = filled ]; +atoi [color=green]; +chkmalloc [color=green]; +close [color=green]; +error [color=blue]; +exit [color=blue]; +fclose [color=green]; +fgets [color=red]; +fopen [color=green]; +fprintf [color=blue]; +free [color=blue]; +free_list [color=blue]; +fstat [color=green]; +getopt [color=green]; +init_list [color=green]; +insert_list [color=green]; +main [color=green]; +makeargs [color=blue]; +makepairs [color=green]; +malloc [color=green]; +open [color=green]; +printf [color=red]; +read [color=green]; +rewind [color=green]; +viewline [color=green]; +viewlines [color=green]; +walk_list [color=green]; +write [color=green]; +fclose -> close [color=green]; +fgets -> fstat [color=green]; +fgets -> read [color=green]; +fopen -> open [color=green]; +printf -> write [color=green]; +main -> fgets [color=blue]; +main -> getopt [color=green]; +main -> makeargs [color=blue]; +main -> makepairs [color=green]; +main -> chkmalloc [color=green]; +main -> error [color=blue]; +main -> viewlines [color=green]; +makeargs -> chkmalloc [color=blue]; +makepairs -> atoi [color=green]; +makepairs -> init_list [color=green]; +makepairs -> insert_list [color=green]; +makepairs -> chkmalloc [color=green]; +free_list -> free [color=blue]; +init_list -> chkmalloc [color=green]; +insert_list -> chkmalloc [color=green]; +walk_list -> error [color=blue]; +walk_list -> viewline [color=green]; +chkmalloc -> malloc [color=green]; +chkmalloc -> error [color=blue]; +error -> exit [color=blue]; +error -> fprintf [color=blue]; +error -> error [color=blue]; +viewline -> fgets [color=red]; +viewline -> printf [color=red]; +viewline -> rewind [color=green]; +viewlines -> fclose [color=green]; +viewlines -> fopen [color=green]; +viewlines -> walk_list [color=green]; +viewlines -> viewline [color=blue]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/directed/world.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,67 @@ +digraph world { +size="7,7"; + {rank=same; S8 S24 S1 S35 S30;} + {rank=same; T8 T24 T1 T35 T30;} + {rank=same; 43 37 36 10 2;} + {rank=same; 25 9 38 40 13 17 12 18;} + {rank=same; 26 42 11 3 33 19 39 14 16;} + {rank=same; 4 31 34 21 41 28 20;} + {rank=same; 27 5 22 32 29 15;} + {rank=same; 6 23;} + {rank=same; 7;} + + S8 -> 9; + S24 -> 25; + S24 -> 27; + S1 -> 2; + S1 -> 10; + S35 -> 43; + S35 -> 36; + S30 -> 31; + S30 -> 33; + 9 -> 42; + 9 -> T1; + 25 -> T1; + 25 -> 26; + 27 -> T24; + 2 -> {3 ; 16 ; 17 ; T1 ; 18} + 10 -> { 11 ; 14 ; T1 ; 13; 12;} + 31 -> T1; + 31 -> 32; + 33 -> T30; + 33 -> 34; + 42 -> 4; + 26 -> 4; + 3 -> 4; + 16 -> 15; + 17 -> 19; + 18 -> 29; + 11 -> 4; + 14 -> 15; + 37 -> {39 ; 41 ; 38 ; 40;} + 13 -> 19; + 12 -> 29; + 43 -> 38; + 43 -> 40; + 36 -> 19; + 32 -> 23; + 34 -> 29; + 39 -> 15; + 41 -> 29; + 38 -> 4; + 40 -> 19; + 4 -> 5; + 19 -> {21 ; 20 ; 28;} + 5 -> {6 ; T35 ; 23;} + 21 -> 22; + 20 -> 15; + 28 -> 29; + 6 -> 7; + 15 -> T1; + 22 -> T35; + 22 -> 23; + 29 -> T30; + 7 -> T8; + 23 -> T24; + 23 -> T1; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/undirected/ER.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,22 @@ +graph ER { + node [shape=box]; course; institute; student; + node [shape=ellipse]; {node [label="name"] name0; name1; name2;} + code; grade; number; + node [shape=diamond,style=filled,color=lightgrey]; "C-I"; "S-C"; "S-I"; + + name0 -- course; + code -- course; + course -- "C-I" [label="n",len=1.00]; + "C-I" -- institute [label="1",len=1.00]; + institute -- name1; + institute -- "S-I" [label="1",len=1.00]; + "S-I" -- student [label="n",len=1.00]; + student -- grade; + student -- name2; + student -- number; + student -- "S-C" [label="m",len=1.00]; + "S-C" -- course [label="n",len=1.00]; + + label = "\n\nEntity Relation Diagram\ndrawn by NEATO"; + fontsize=20; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/undirected/Heawood.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +/* + * The transitive 6-net, also known as Heawood's graph, + * can be used to test the "stability points" of the layout + * algorithm. + + * The "ideal" layout occurs when len="2.5". The layout + * loses the regularity when smaller values are used. + */ +graph "Heawood" { + node [ + fontname = "Arial" + label = "\N" + shape = "circle" + width = "0.50000" + height = "0.500000" + color = "black" + ] + edge [ + color = "black" + ] + /* The outer wheel */ + "0" -- "1" -- "2" -- "3" -- "4" -- "5" -- "6" -- "7" -- "8" -- "9" -- "10" -- "11" -- "12" -- "13" -- "0"; + /* The internal edges. The len = makes them internal */ + "0" -- "5" [len = 2.5]; + "2" -- "7" [len = 2.5]; + "4" -- "9" [len = 2.5]; + "6" -- "11" [len = 2.5]; + "8" -- "13" [len = 2.5]; + "10" -- "1" [len = 2.5]; + "12" -- "3" [len = 2.5]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/undirected/Petersen.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,48 @@ +/* + * The transitive 5-net, also known as Petersen's graph, + * can be used to test the "stability points" of the layout + * algorithm. + * + * The "ideal" layout is achieved for certain random seed + * values when len=1.5. For len=2.5 or above, the layout + * is stable. Sometimes, the graph is rendered "inside-out". + */ +graph "Petersen" { + node [ + fontname = "Arial" + label = "\N" + shape = "circle" + width = "0.400000" + height = "0.400000" + color = "black" + ] + edge [ + color = "black" + ] + /* Outer wheel. The len= is what makes it outer */ + "0" -- "1" -- "2" -- "3" -- "4" -- "0" [ + color = "blue" + len = 2.6 + ] + "0" -- "5" [ + color = "red" + weight = "5" + ] + "1" -- "6" [ + color = "red" + weight = "5" + ] + "2" -- "7" [ + color = "red" + weight = "5" + ] + "3" -- "8" [ + color = "red" + weight = "5" + ] + "4" -- "9" [ + color = "red" + weight = "5" + ] + "5" -- "7" -- "9" -- "6" -- "8" -- "5"; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/undirected/ngk10_4.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,103 @@ +graph G { + graph [splines=true overlap=false] + 1 -- 30 [f=1]; + 1 -- 40 [f=14]; + 8 -- 46 [f=1]; + 8 -- 16 [f=18]; + 10 -- 25 [f=1]; + 10 -- 19 [f=5]; + 10 -- 33 [f=1]; + 12 -- 8 [f=1]; + 12 -- 36 [f=5]; + 12 -- 17 [f=16]; + 13 -- 38 [f=1]; + 13 -- 24 [f=19]; + 24 -- 49 [f=1]; + 24 -- 13 [f=1]; + 24 -- 47 [f=12]; + 24 -- 12 [f=19]; + 25 -- 27 [f=1]; + 25 -- 12 [f=1]; + 27 -- 12 [f=1]; + 27 -- 14 [f=8]; + 29 -- 10 [f=1]; + 29 -- 8 [f=17]; + 30 -- 24 [f=1]; + 30 -- 44 [f=15]; + 38 -- 29 [f=1]; + 38 -- 35 [f=15]; + 2 -- 42 [f=2]; + 2 -- 35 [f=3]; + 2 -- 11 [f=19]; + 14 -- 18 [f=2]; + 14 -- 24 [f=15]; + 14 -- 38 [f=18]; + 18 -- 49 [f=2]; + 18 -- 47 [f=20]; + 26 -- 41 [f=2]; + 26 -- 42 [f=15]; + 31 -- 39 [f=2]; + 31 -- 47 [f=17]; + 31 -- 25 [f=14]; + 37 -- 26 [f=2]; + 37 -- 16 [f=14]; + 39 -- 50 [f=2]; + 39 -- 14 [f=2]; + 39 -- 18 [f=17]; + 39 -- 47 [f=10]; + 41 -- 31 [f=2]; + 41 -- 8 [f=16]; + 42 -- 44 [f=2]; + 42 -- 29 [f=12]; + 44 -- 37 [f=2]; + 44 -- 32 [f=15]; + 3 -- 20 [f=2]; + 3 -- 28 [f=19]; + 6 -- 45 [f=2]; + 6 -- 28 [f=10]; + 9 -- 6 [f=2]; + 9 -- 16 [f=1]; + 15 -- 16 [f=2]; + 15 -- 48 [f=2]; + 16 -- 50 [f=2]; + 16 -- 32 [f=14]; + 16 -- 39 [f=8]; + 20 -- 33 [f=2]; + 33 -- 9 [f=2]; + 33 -- 46 [f=3]; + 33 -- 48 [f=17]; + 45 -- 15 [f=2]; + 4 -- 17 [f=4]; + 4 -- 15 [f=6]; + 4 -- 12 [f=16]; + 17 -- 21 [f=4]; + 19 -- 35 [f=4]; + 19 -- 15 [f=9]; + 19 -- 43 [f=4]; + 21 -- 19 [f=4]; + 21 -- 50 [f=4]; + 23 -- 36 [f=4]; + 34 -- 23 [f=4]; + 34 -- 24 [f=11]; + 35 -- 34 [f=4]; + 35 -- 16 [f=6]; + 35 -- 18 [f=16]; + 36 -- 46 [f=4]; + 5 -- 7 [f=1]; + 5 -- 36 [f=6]; + 7 -- 32 [f=1]; + 7 -- 11 [f=2]; + 7 -- 14 [f=17]; + 11 -- 40 [f=1]; + 11 -- 50 [f=1]; + 22 -- 46 [f=1]; + 28 -- 43 [f=1]; + 28 -- 8 [f=18]; + 32 -- 28 [f=1]; + 32 -- 39 [f=13]; + 32 -- 42 [f=15]; + 40 -- 22 [f=1]; + 40 -- 47 [f=1]; + 43 -- 11 [f=1]; + 43 -- 17 [f=19]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/graphs/undirected/process.gv Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +graph G { + run -- intr; + intr -- runbl; + runbl -- run; + run -- kernel; + kernel -- zombie; + kernel -- sleep; + kernel -- runmem; + sleep -- swap; + swap -- runswap; + runswap -- new; + runswap -- runmem; + new -- runmem; + sleep -- runmem; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/box.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,157 @@ +load ('def.lefty'); +definit (); +# +# initialize window data +# +canvas = defcanvas; +wrect = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 400; 'y' = 500;];]; +setwidgetattr (canvas, ['window' = wrect;]); +# +# data structures +# +objarray = []; +objnum = 0; +# +# misc functions +# +min = function (a, b) { + if (a <= b) + return a; + return b; +}; +max = function (a, b) { + if (b <= a) + return a; + return b; +}; +rectof = function (p1, p2) { + return [ + 0 = ['x' = min (p1.x, p2.x); 'y' = min (p1.y, p2.y);]; + 1 = ['x' = max (p1.x, p2.x); 'y' = max (p1.y, p2.y);]; + ]; +}; +pointadd = function (p1, p2) { + return ['x' = p2.x + p1.x; 'y' = p2.y + p1.y;]; +}; +pointsub = function (p1, p2) { + return ['x' = p2.x - p1.x; 'y' = p2.y - p1.y;]; +}; +# +# rendering functions +# +drawbox = function (obj, color) { + box (canvas, obj, obj.rect, ['color' = color;]); +}; +redrawboxes = function () { + local i; + clear (canvas); + for (i = 0; i < objnum; i = i + 1) + drawbox (objarray[i], 1); +}; +redraw = function (canvas) { + redrawboxes (); +}; +# +# editing functions +# +new = function (rect) { + objarray[objnum] = [ + 'rect' = rect; + 'id' = objnum; + ]; + objnum = objnum + 1; + return objarray[objnum - 1]; +}; +reshape = function (obj, rect) { + obj.rect = rect; + return obj; +}; +move = function (obj, p) { + obj.rect[0] = pointadd (obj.rect[0], p); + obj.rect[1] = pointadd (obj.rect[1], p); + return obj; +}; +delete = function (obj) { + if (obj.id ~= objnum - 1) { + objarray[obj.id] = objarray[objnum - 1]; + objarray[obj.id].id = obj.id; + } + remove (objnum - 1, objarray); + objnum = objnum - 1; +}; +# +# user interface functions +# +# left mouse button creates new box +# middle button moves a box +# right button deletes a box +# +leftdown = function (data) { + if (data.obj ~= null) + return; + leftbox = new (rectof (data.pos, data.pos)); + drawbox (leftbox, 1); + setgfxattr (canvas, ['mode' = 'xor';]); +}; +leftmove = function (data) { + if (~leftbox) + return; + drawbox (leftbox, 1); + clearpick (canvas, leftbox); + reshape (leftbox, rectof (data.ppos, data.pos)); + drawbox (leftbox, 1); +}; +leftup = function (data) { + if (~leftbox) + return; + drawbox (leftbox, 1); + clearpick (canvas, leftbox); + reshape (leftbox, rectof (data.ppos, data.pos)); + setgfxattr (canvas, ['mode' = 'src';]); + drawbox (leftbox, 1); + remove ('leftbox'); +}; +middledown = function (data) { + if (data.obj == null) + return; + middlebox = data.obj; + middlepos = data.pos; + setgfxattr (canvas, ['mode' = 'xor';]); +}; +middlemove = function (data) { + if (~middlebox) + return; + drawbox (middlebox, 1); + clearpick (canvas, middlebox); + move (middlebox, pointsub (middlepos, data.pos)); + middlepos = data.pos; + drawbox (middlebox, 1); +}; +middleup = function (data) { + if (~middlebox) + return; + drawbox (middlebox, 1); + clearpick (canvas, middlebox); + move (middlebox, pointsub (middlepos, data.pos)); + setgfxattr (canvas, ['mode' = 'src';]); + drawbox (middlebox, 1); + remove ('middlepos'); + remove ('middlebox'); +}; +rightup = function (data) { + if (data.pobj == null) + return; + drawbox (data.obj, 0); + clearpick (canvas, data.obj); + delete (data.obj); +}; +dops = function () { + local s; + + s = ['x' = 8 * 300; 'y' = 10.5 * 300;]; + canvas = createwidget (-1, ['type' = 'ps'; 'size' = s;]); + setwidgetattr (canvas, ['window' = wrect;]); + redraw (canvas); + destroywidget (canvas); + canvas=defcanvas; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/def.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,15 @@ +definit = function () { + defview = createwidget (-1, [ + 'type' = 'view'; + 'name' = 'graphics view'; + 'origin' = ['x' = 1; 'y' = 1;]; + 'size' = ['x' = 400; 'y' = 500;]; + ]); + defscroll = createwidget (defview, ['type' = 'scroll';]); + defcanvas = createwidget (defscroll, [ + 'type' = 'canvas'; + 'origin' = ['x' = 1; 'y' = 1;]; + 'size' = ['x' = 400; 'y' = 500;]; + 'borderwidth' = 1; + ]); +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/dotty.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,748 @@ +# +# DOTTY +# +dotty = [ + 'keys' = [ + 'nid' = 'nid'; + 'eid' = 'eid'; + 'gid' = 'gid'; + 'name' = 'name'; + 'attr' = 'attr'; + 'gattr' = 'graphattr'; + 'eattr' = 'edgeattr'; + 'nattr' = 'nodeattr'; + 'edges' = 'edges'; + 'tail' = 'tail'; + 'tport' = 'tport'; + 'head' = 'head'; + 'hport' = 'hport'; + 'pos' = 'pos'; + 'size' = 'size'; + 'rect' = 'rect'; + 'fname' = 'fontname'; + 'fsize' = 'fontsize'; + 'fcolor' = 'fontcolor'; + 'dcolor' = 'drawcolor'; + 'bcolor' = 'fillcolor'; + ]; + 'maps' = [ + 'X11' = [ + 'fontmap' = [ + 'Times-Roman' = '-*-times-medium-r-*--%d-*-*-*-*-*-*-1'; + 'Times-Italic' = '-*-times-medium-i-*--%d-*-*-*-*-*-*-1'; + 'Times-Bold' = '-*-times-bold-r-*--%d-*-*-*-*-*-*-1'; + 'Courier' = '-*-courier-bold-r-*--%d-*-*-*-*-*-*-1'; + 'Courier-Bold' = '-*-courier-bold-r-*--%d-*-*-*-*-*-*-1'; + 'Helvetica' = ( + '-*-helvetica-medium-r-normal--%d-*-*-*-p-*-iso8859-1' + ); + 'Helvetica-Bold' = ( + '-*-helvetica-bold-r-normal--%d-*-*-*-p-*-iso8859-1' + ); + ]; + 'psfontmap' = [ + 'Times-Roman' = 'Times-Roman'; + 'Times-Italic' = 'Times-Italic'; + 'Times-Bold' = 'Times-Bold'; + 'Courier' = 'Courier'; + 'Courier-Bold' = 'Courier-Bold'; + 'Helvetica' = 'Helvetica'; + 'Helvetica-Bold' = 'Helvetica-Bold'; + ]; + ]; + 'mswin' = [ + 'fontmap' = [ + 'Times-Roman' = 'Times New Roman'; + 'Times-Italic' = 'Times New Roman Italic'; + 'Times-Bold' = 'Times New Roman Bold'; + 'Courier' = 'Courier New'; + 'Courier-Bold' = 'Courier New Bold'; + 'Helvetica' = 'Arial'; + 'Helvetica-Bold' = 'Arial Bold'; + ]; + 'psfontmap' = [ + 'Times-Roman' = 'Times New Roman'; + 'Times-Italic' = 'Times New Roman Italic'; + 'Times-Bold' = 'Times New Roman Bold'; + 'Courier' = 'Courier New'; + 'Courier-Bold' = 'Courier New Bold'; + 'Helvetica' = 'Arial'; + 'Helvetica-Bold' = 'Arial Bold'; + ]; + ]; + ]; + 'protogt' = [ + 'graph' = [ + 'graphattr' = [ + 'fontsize' = '14'; + 'fontname' = 'Times-Roman'; + 'fontcolor' = 'black'; + ]; + 'nodeattr' = [ + 'shape' = 'ellipse'; + 'fontsize' = '14'; + 'fontname' = 'Times-Roman'; + 'fontcolor' = 'black'; + 'style' = 'solid'; + ]; + 'edgeattr' = [ + 'fontsize' = '14'; + 'fontname' = 'Times-Roman'; + 'fontcolor' = 'black'; + 'style' = 'solid'; + ]; + 'graphdict' = []; + 'nodedict' = []; + 'graphs' = []; + 'nodes' = []; + 'edges' = []; + 'maxgid' = 0; + 'maxnid' = 0; + 'maxeid' = 0; + 'type' = 'digraph'; + ]; + 'layoutmode' = 'sync'; + 'lserver' = 'dot'; + 'edgehandles' = 1; + 'noundo' = 0; + ]; + 'lservers' = []; + 'mlevel' = 0; + 'graphs' = []; + 'views' = []; + 'protovt' = [ + 'normal' = [ + 'name' = 'DOTTY'; + 'orig' = ['x' = 1; 'y' = 1;]; + 'size' = ['x' = 420; 'y' = 520;]; + 'wrect' = [ + 0 = ['x' = 0; 'y' = 0;]; + 1 = ['x' = 400; 'y' = 500;]; + ]; + 'vsize' = ['x' = 400; 'y' = 500;]; + 'w2v' = 1; + ]; + 'birdseye' = [ + 'type' = 'birdseye'; + 'name' = 'DOTTY birdseye view'; + 'orig' = ['x' = 1; 'y' = 1;]; + 'size' = ['x' = 220; 'y' = 260;]; + 'wrect' = [ + 0 = ['x' = 0; 'y' = 0;]; + 1 = ['x' = 200; 'y' = 250;]; + ]; + 'vsize' = ['x' = 200; 'y' = 250;]; + 'w2v' = 1; + ]; + ]; + 'pagesizes' = [ + '8.5x11' = ['x' = 8; 'y' = 10.5;]; + '11x17' = ['x' = 10.5; 'y' = 16.5;]; + '36x50' = ['x' = 35.5; 'y' = 49.5;]; + ]; +]; +load ('dotty_draw.lefty'); +load ('dotty_edit.lefty'); +load ('dotty_layout.lefty'); +load ('dotty_ui.lefty'); +# +# initialization functions +# +dotty.init = function () { + dotty.fontmap = dotty.maps[getenv ('LEFTYWINSYS')].fontmap; + dotty.clipgt = dotty.protogt.creategraph (['noundo' = 1;]); + dotty.inited = 1; +}; +dotty.simple = function (file) { + if (dotty.inited ~= 1) + dotty.init (); + dotty.createviewandgraph (file, 'file', null, null); + txtview ('off'); +}; +# +# main operations +# +dotty.protogt.creategraph = function (protogt) { + local gt, id, gtid; + + if (~protogt) + protogt = dotty.protogt; + for (gtid = 0; dotty.graphs[gtid]; gtid = gtid + 1) + ; + gt = (dotty.graphs[gtid] = []); + if (protogt.mode ~= 'replace') { + for (id in dotty.protogt) + gt[id] = copy (dotty.protogt[id]); + } + for (id in protogt) + gt[id] = copy (protogt[id]); + gt.gtid = gtid; + gt.views = []; + gt.undoarray = ['level' = 0; 'entries' = [];]; + gt.busy = 0; + return gt; +}; +dotty.protogt.copygraph = function (ogt) { + local gt, gtid, id; + + for (gtid = 0; dotty.graphs[gtid]; gtid = gtid + 1) + ; + gt = (dotty.graphs[gtid] = []); + for (id in ogt) + gt[id] = copy (ogt[id]); + gt.gtid = gtid; + gt.views = []; + gt.undoarray = ['level' = 0; 'entries' = [];]; + gt.busy = 0; + return gt; +}; +dotty.protogt.destroygraph = function (gt) { + local vid, vlist; + + if (gt.layoutpending > 0) + gt.cancellayout (gt); + for (vid in gt.views) + vlist[vid] = gt.views[vid]; + for (vid in gt.views) + gt.destroyview (gt, vlist[vid]); + remove (gt.gtid, dotty.graphs); +}; +dotty.protogt.loadgraph = function (gt, name, type, protograph, layoutflag) { + local fd, vid, vt, graph, nid, eid, gid; + + if (gt.layoutpending > 0) + gt.cancellayout (gt); + if (~name) + if (~(name = ask ('file name:', 'file', ''))) + return; + dotty.pushbusy (gt, gt.views); + dotty.message (1, 'loading'); + if (~protograph) + protograph = dotty.protogt.graph; + if ( + ~((fd = dotty.openio (name, type, 'r')) >= 0) | + ~(graph = readgraph (fd, protograph)) + ) { + dotty.message (0, 'cannot load graph'); + dotty.popbusy (gt, gt.views); + return; + } + for (vid in gt.views) { + vt = gt.views[vid]; + vt.colors = []; + vt.colorn = 2; + } + gt.graph = graph; + gt.name = name; + gt.type = type; + gt.undoarray = ['level' = 0; 'entries' = [];]; + if (~(type == 'file' & name == '-')) + closeio (fd); + graph.maxgid = tablesize (graph.graphs); + graph.maxnid = tablesize (graph.nodes); + graph.maxeid = tablesize (graph.edges); + for (nid in graph.nodes) + graph.nodes[nid][dotty.keys.nid] = nid; + for (eid in graph.edges) + graph.edges[eid][dotty.keys.eid] = eid; + for (gid in graph.graphs) + graph.graphs[gid][dotty.keys.gid] = gid; + gt.unpackattr (gt); + if (layoutflag) { + dotty.message (1, 'generating layout'); + gt.layoutgraph (gt); + } + dotty.popbusy (gt, gt.views); + return gt.graph; +}; +dotty.protogt.savegraph = function (gt, name, type) { + local fd; + + if (~name) + if (~(name = ask ('file name:', 'file', ''))) + return; + if ( + ~((fd = dotty.openio (name, type, 'w')) >= 0) | + ~writegraph (fd, gt.graph, 0) + ) { + dotty.message (0, 'cannot save graph'); + return; + } + if (~(type == 'file' & name == '-')) + closeio (fd); +}; +dotty.protogt.setgraph = function (gt, graph) { + local vid, vt, nid, eid, gid; + + if (gt.layoutpending > 0) + gt.cancellayout (gt); + for (vid in gt.views) { + vt = gt.views[vid]; + vt.colors = []; + vt.colorn = 2; + } + gt.graph = copy (graph); + gt.undoarray = ['level' = 0; 'entries' = [];]; + gt.unpackattr (gt); + gt.graph.maxgid = tablesize (graph.graphs); + gt.graph.maxnid = tablesize (graph.nodes); + gt.graph.maxeid = tablesize (graph.edges); + for (nid in gt.graph.nodes) + gt.graph.nodes[nid][dotty.keys.nid] = nid; + for (eid in gt.graph.edges) + gt.graph.edges[eid][dotty.keys.eid] = eid; + for (gid in gt.graph.graphs) + gt.graph.graphs[gid][dotty.keys.gid] = gid; + gt.unpackattr (gt); + dotty.message (1, 'generating layout'); + gt.layoutgraph (gt); + return gt.graph; +}; +dotty.protogt.erasegraph = function (gt, protogt, protovt) { + local vid, vt; + + if (gt.layoutpending > 0) + gt.cancellayout (gt); + for (vid in gt.views) { + vt = gt.views[vid]; + vt.colors = []; + vt.colorn = 2; + clear (vt.canvas); + } + if (~protogt) + protogt = dotty.protogt; + gt.graph = copy (protogt.graph); + gt.undoarray = ['level' = 0; 'entries' = [];]; +}; +dotty.protogt.layoutgraph = function (gt) { + if (gt.graph.graphattr.xdotversion) { + gt.unpacklayout (gt, gt.graph); + gt.setviewsize (gt.views, gt.graph.rect); + gt.redrawgraph (gt, gt.views); + return; + } + if (gt.layoutmode == 'async') { + if (~gt.haveinput) { + gt.startlayout (gt); + return; + } + if (~gt.finishlayout (gt)) + return; + gt.setviewsize (gt.views, gt.graph.rect); + gt.redrawgraph (gt, gt.views); + } else { + if (~gt.startlayout (gt)) + return; + else + while (~gt.finishlayout (gt)) + ; + gt.setviewsize (gt.views, gt.graph.rect); + gt.redrawgraph (gt, gt.views); + } +}; +dotty.protogt.createview = function (gt, protovt) { + local vt, ovt, id, t; + + vt = []; + vt.colors = []; + vt.colorn = 2; + if (~protovt) + protovt = dotty.protovt.normal; + if (protovt.mode ~= 'replace') { + for (id in dotty.protovt[protovt.type]) + vt[id] = copy (dotty.protovt[protovt.type][id]); + } + for (id in protovt) + vt[id] = copy (protovt[id]); + if (~(vt.parent >= 0)) { + vt.view = createwidget (-1, [ + 'type' = 'view'; + 'name' = vt.name; + 'origin' = vt.orig; + 'size' = vt.size; + ]); + vt.scroll = createwidget (vt.view, ['type' = 'scroll';]); + } else { + vt.view = -1; + vt.scroll = createwidget (vt.parent, [ + 'type' = 'scroll'; + 'size' = vt.size; + ]); + } + vt.canvas = createwidget (vt.scroll, [ + 'type' = 'canvas'; + 'color' = [0 = protovt.bgcolor; 1 = protovt.fgcolor;]; + ]); + setwidgetattr (vt.canvas, [ + 'window' = vt.wrect; + 'viewport' = vt.vsize; + ]); + clear (vt.canvas); + dotty.views[vt.canvas] = vt; + vt.vtid = vt.canvas; + vt.gtid = gt.gtid; + gt.views[vt.vtid] = vt; + dotty.views[vt.scroll] = vt; + if (vt.view ~= -1) + dotty.views[vt.view] = vt; + if (protovt.colors & tablesize (protovt.colors) > 0) { + for (id in protovt.colors) + if (id == '_bgcolor_') + setwidgetattr (vt.canvas, [ + 'color' = [0 = protovt.colors[id];]; + ]); + else if (setwidgetattr (vt.canvas, ['color' = [ + protovt.colors[id] = id; + ];]) ~= 1) { + t = split (id, ' '); + if (tablesize (t) ~= 3 | setwidgetattr (vt.canvas, [ + 'color' = [protovt.colors[id] = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];]; + ]) ~= 1) { + dotty.message ( + 0, concat ('unknown color ', id, ' using #1') + ); + } + } + vt.colors = copy (protovt.colors); + vt.colorn = protovt.colorn; + } else if (tablesize (gt.views) > 1) { + for (id in gt.views) + if (gt.views[id] ~= vt) + break; + ovt = gt.views[id]; + for (id in ovt.colors) + if (id == '_bgcolor_') + setwidgetattr (vt.canvas, ['color' = [0 = ovt.colors[id];];]); + else if (setwidgetattr (vt.canvas, ['color' = [ + ovt.colors[id] = id; + ];]) ~= 1) { + t = split (id, ' '); + if (tablesize (t) ~= 3 | setwidgetattr (vt.canvas, [ + 'color' = [ovt.colors[id] = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];]; + ]) ~= 1) { + dotty.message ( + 0, concat ('unknown color ', id, ' using #1') + ); + } + } + vt.colors = copy (ovt.colors); + vt.colorn = ovt.colorn; + } + if (gt.graph.rect) + gt.setviewsize ([vt.vtid = vt;], gt.graph.rect); + gt.drawgraph (gt, [vt.vtid = vt;]); + for (id in vt.uifuncs) + if (id == 'closeview') + widgets[vt.view][id] = vt.uifuncs[id]; + else + widgets[vt.canvas][id] = vt.uifuncs[id]; + return vt; +}; +dotty.protogt.destroyview = function (gt, vt) { + destroywidget (vt.canvas); + destroywidget (vt.scroll); + if (vt.view ~= -1) { + destroywidget (vt.view); + remove (vt.view, dotty.views); + } + remove (vt.scroll, dotty.views); + remove (vt.canvas, dotty.views); + if (vt.gtid >= 0) + remove (vt.vtid, gt.views); + if (tablesize (dotty.views) == 0) + exit (); +}; +dotty.protogt.zoom = function (gt, vt, factor, pos) { + gt.setviewscale ([vt.vtid = vt;], factor); + if (pos) + gt.setviewcenter ([vt.vtid = vt;], pos); + gt.redrawgraph (gt, [vt.vtid = vt;]); +}; +dotty.protogt.findnode = function (gt, vt) { + local key, node, node1, nid; + + if (~(key = ask ('give node name or label'))) + return; + if (gt.graph.nodedict[key] >= 0) + node = gt.graph.nodes[gt.graph.nodedict[key]]; + else if (gt.graph.nodedict[ston (key)] >= 0) + node = gt.graph.nodes[gt.graph.nodedict[ston (key)]]; + else { + for (nid in gt.graph.nodes) { + node1 = gt.graph.nodes[nid]; + if (node1.attr.label == key | node1.attr.label == ston (key)) { + node = node1; + break; + } + } + } + if (~node) { + dotty.message (0, concat ('cannot find node: ', key)); + return; + } + gt.setviewcenter ([vt.vtid = vt;], node.pos); +}; +dotty.protogt.setattr = function (gt, obj) { + local kv, t, attr, value, n, i, s; + + if (~(kv = ask ('give attr/value, eg. color=blue'))) + return; + t = split (kv, '='); + attr = t[0]; + value = t[1]; + if ((n = tablesize (t)) > 2) + for (i = 2; i < n; i = i + 1) + value = concat (value, '=', t[i]); + # Check for HTML string and convert using lefty convention + s = split (value, ''); + n = tablesize (s); + if ((s[0] == '<') & (s[n-1] == '>')) { + s[0] = '>'; + s[n-1] = '<'; + value = s[0]; + for (i = 1; i < n; i = i + 1) + value = concat (value, s[i]); + } + if ( + obj.attr == gt.graph.graphattr | + obj.attr == gt.graph.edgeattr | + obj.attr == gt.graph.nodeattr + ) { + obj.attr[attr] = value; + return; + } + if (obj.nid >= 0) { + gt.undrawnode (gt, gt.views, obj); + obj.attr[attr] = value; + gt.unpacknodeattr (gt, obj); + gt.drawnode (gt, gt.views, obj); + } else if (obj.eid >= 0) { + gt.undrawedge (gt, gt.views, obj); + obj.attr[attr] = value; + gt.unpackedgeattr (gt, obj); + gt.drawedge (gt, gt.views, obj); + } +}; +dotty.protogt.getattr = function (gt, node) { + local kv; + + if (~(kv.key = ask ('give attr name'))) + return null; + if ((kv.val = node.attr[kv.key])) + return kv; + return null; +}; +# +# utilities +# +dotty.createviewandgraph = function (name, type, protogt, protovt) { + local vt, gt; + + if (~protogt) + protogt = dotty.protogt; + if (protogt.creategraph) + gt = protogt.creategraph (protogt); + else + gt = dotty.protogt.creategraph (protogt); + vt = gt.createview (gt, protovt); + if (~protogt.graph) + protogt.graph = copy (dotty.protogt.graph); + if (name) + gt.loadgraph (gt, name, type, protogt.graph, 1); + return ['gt' = gt; 'vt' = vt;]; +}; +dotty.openio = function (name, type, mode) { + local fd; + + if (~name) + return null; + if (type == 'file') { + if (name == '-') { + if (mode == 'r' | mode == 'r+') + fd = 0; + else + fd = 1; + } else if (~((fd = openio ('file', name, mode)) >= 0)) { + dotty.message (0, concat ('cannot open file: ', name)); + return null; + } + } else if (type == 'pipe') { + if (~((fd = openio ( + 'pipe', 'ksh', mode, concat ("%e ", name) + )) >= 0)) { + dotty.message (0, concat ('cannot run command: ', name)); + return null; + } + } else + return null; + return fd; +}; +dotty.pushbusy = function (gt, views) { + local vid; + + if (gt.busy == 0) + for (vid in gt.views) + setwidgetattr (vid, ['cursor' = 'watch';]); + gt.busy = gt.busy + 1; +}; +dotty.popbusy = function (gt, views) { + local vid; + + gt.busy = gt.busy - 1; + if (gt.busy == 0) + for (vid in gt.views) + setwidgetattr (vid, ['cursor' = 'default';]); +}; +dotty.message = function (level, text) { + if (level <= dotty.mlevel) + echo ('dotty.lefty: ', text); +}; +# +# printing or saving to file +# +dotty.protogt.printorsave = function (gt, vt, otype, name, mode, ptype) { + local pr, wrect, vsize, xy, psize, canvas, pscanvas, cid, cname, t; + local graph, edgehandles, fontmap, eid, edge, nid, node, gid, sgraph; + local did, draw, i; + + if (~otype) + if (~(otype = ask ('print to', 'choice', 'file|printer'))) + return; + if (otype == 'printer') { + if (~getenv ('TMPDIR')) + name = concat (getenv ('HOME'), '/.dottyout.ps'); + else + name = concat (getenv ('TMPDIR'), '/.dottyout.ps', random (10000)); + if (getenv ('LEFTYWINSYS') ~= 'mswin' & ~pr) + if (~(pr = ask ('printer command', 'string', 'lpr'))) + return; + } + if (~name) + if (~(name = ask ('postscript file', 'file', 'out.ps'))) + return; + if (~ptype) + if (~(ptype = ask ('page size', 'choice', '8.5x11|11x17|36x50'))) + return; + if (~mode) + if (~(mode = ask ('mode', 'choice', 'portrait|landscape|best fit'))) + return; + wrect = copy (vt.wrect); + wrect[0].x = wrect[0].x - 1; + wrect[1].x = wrect[1].x + 1; + wrect[0].y = wrect[0].y - 1; + wrect[1].y = wrect[1].y + 1; + vsize = copy (vt.vsize); + if (vsize.x == 0) + vsize.x = 1; + if (vsize.y == 0) + vsize.y = 1; + xy = vsize.x / vsize.y; + if (mode == 'best fit') { + if (xy < 1) + mode = 'portrait'; + else + mode = 'landscape'; + } + psize = dotty.pagesizes[ptype]; + if (mode == 'portrait') { + if (xy < psize.x / psize.y) { + vsize.y = psize.y * 300; + vsize.x = vsize.y * xy; + } else { + vsize.x = psize.x * 300; + vsize.y = vsize.x / xy; + } + } else { + if (xy < psize.y / psize.x) { + vsize.y = psize.x * 300; + vsize.x = vsize.y * xy; + } else { + vsize.x = psize.y * 300; + vsize.y = vsize.x / xy; + } + } + if (~((pscanvas = createwidget (-1, [ + 'type' = 'ps'; + 'origin' = ['x' = 0; 'y' = 0;]; + 'size' = vsize; + 'mode' = mode; + 'name' = name; + ])) >= 0)) { + dotty.message (0, 'cannot open printer device'); + return; + } + for (cname in vt.colors) { + cid = vt.colors[cname]; + if (cname == '_bgcolor_') + setwidgetattr (pscanvas, ['color' = [0 = cid;];]); + else if (setwidgetattr (pscanvas, ['color' = [cid = cname;];]) ~= 1) { + t = split (cname, ' '); + if (tablesize (t) ~= 3 | setwidgetattr (pscanvas, [ + 'color' = [cid = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];]; + ]) ~= 1) { + dotty.message ( + 0, concat ('unknown color ', cname, ' using #1') + ); + } + } + } + setwidgetattr (pscanvas, ['window' = wrect;]); + graph = copy (gt.graph); + canvas = vt.canvas; + vt.canvas = pscanvas; + edgehandles = gt.edgehandles; + gt.edgehandles = 0; + fontmap = dotty.maps[getenv ('LEFTYWINSYS')].psfontmap; + for (eid in graph.edges) { + edge = graph.edges[eid]; + edge.fontname = fontmap[edge.attr.fontname]; + for (did in edge.draws) { + if (did == 'ep') + continue; + draw = edge.draws[did]; + for (i = 0; draw[i]; i = i + 1) + if (draw[i].type == 'F') + draw[i].fn = fontmap[draw[i].ofn]; + } + gt.drawedge (gt, [0 = vt;], edge); + } + for (nid in graph.nodes) { + node = graph.nodes[nid]; + node.fontname = fontmap[node.attr.fontname]; + for (did in node.draws) { + if (did == 'ep') + continue; + draw = node.draws[did]; + for (i = 0; draw[i]; i = i + 1) + if (draw[i].type == 'F') + draw[i].fn = fontmap[draw[i].ofn]; + } + gt.drawnode (gt, [0 = vt;], node); + } + for (gid in graph.graphs) { + sgraph = graph.graphs[gid]; + sgraph.fontname = fontmap[sgraph.graphattr.fontname]; + for (did in sgraph.draws) { + if (did == 'ep') + continue; + draw = sgraph.draws[did]; + for (i = 0; draw[i]; i = i + 1) + if (draw[i].type == 'F') + draw[i].fn = fontmap[draw[i].ofn]; + } + gt.drawsgraph (gt, [0 = vt;], sgraph); + } + graph.fontname = fontmap[graph.graphattr.fontname]; + gt.drawsgraph (gt, [0 = vt;], graph); + gt.edgehandles = edgehandles; + vt.canvas = canvas; + destroywidget (pscanvas); + if (otype == 'printer' & getenv ('LEFTYWINSYS') ~= 'mswin') + system (concat (pr, ' ', name, '; rm ',name)); +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/dotty_draw.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,596 @@ +# +# dotty_draw: drawing functions and data structures +# +dotty.protogt.drawgraph = function (gt, views) { + local gid, eid, nid, graph; + + graph = gt.graph; + gt.drawsgraph (gt, views, graph); + for (gid in graph.graphs) + gt.drawsgraph (gt, views, graph.graphs[gid]); + for (eid in graph.edges) + gt.drawedge (gt, views, graph.edges[eid]); + for (nid in graph.nodes) + gt.drawnode (gt, views, graph.nodes[nid]); +}; +dotty.protogt.redrawgraph = function (gt, views) { + local vid; + + for (vid in views) + clear (views[vid].canvas); + gt.drawgraph (gt, views); +}; +dotty.protogt.setviewsize = function (views, r) { + local vid, vt, w2v, scale, attr; + + for (vid in views) { + vt = views[vid]; + vt.wrect = copy (r); + if (r[1].x == 0 | r[1].y == 0) { + attr = getwidgetattr (vt.scroll, [0 = 'size';]); + vt.wrect[1] = copy (attr.size); + } + if (vt.type == 'birdseye') { + attr = getwidgetattr (vt.scroll, [0 = 'size';]); + scale.x = (vt.wrect[1].x - vt.wrect[0].x) / attr.size.x; + scale.y = (vt.wrect[1].y - vt.wrect[0].y) / attr.size.y; + if (scale.x > 1 & scale.x > scale.y) + vt.w2v = scale.x; + else if (scale.y > 1) + vt.w2v = scale.y; + else + vt.w2v = 1; + } + w2v = vt.w2v; + vt.vsize = [ + 'x' = toint ((vt.wrect[1].x - vt.wrect[0].x) / w2v); + 'y' = toint ((vt.wrect[1].y - vt.wrect[0].y) / w2v); + ]; + setwidgetattr (vt.canvas, [ + 'window' = vt.wrect; + 'viewport' = vt.vsize; + ]); + attr = getwidgetattr (vt.canvas, [0 = 'viewport';]); + vt.vsize = copy (attr.viewport); + } +}; +dotty.protogt.setviewscale = function (views, factor) { + local vid, vt, w2v, attr; + + for (vid in views) { + vt = views[vid]; + if ((w2v = vt.w2v * factor) < 0.01) { + dotty.message (0, 'cannot zoom any closer'); + return; + } + vt.w2v = w2v; + vt.vsize = [ + 'x' = (vt.wrect[1].x - vt.wrect[0].x) / w2v; + 'y' = (vt.wrect[1].y - vt.wrect[0].y) / w2v; + ]; + setwidgetattr (vt.canvas, ['viewport' = vt.vsize;]); + attr = getwidgetattr (vt.canvas, [0 = 'viewport';]); + vt.vsize = copy (attr.viewport); + } +}; +dotty.protogt.setviewcenter = function (views, center) { + local vid, vt, pos; + + for (vid in views) { + vt = views[vid]; + pos = [ + 'x' = center.x * vt.vsize.x / (vt.wrect[1].x - vt.wrect[0].x); + 'y' = ( + (vt.wrect[1].y - center.y) * vt.vsize.y / + (vt.wrect[1].y - vt.wrect[0].y) + ); + ]; + setwidgetattr (vt.scroll, ['childcenter' = pos;]); + } +}; +# +# draw graph components +# +dotty.protogt.drawsgraph = function (gt, views, sgraph) { + sgraph.draw = 1; + if (~sgraph.draws) + return; + gt.execalldraw (gt, views, null, sgraph.draws, [ + 'fontname' = sgraph.fontname; + 'fontsize' = sgraph.fontsize; + 'fontcolor' = sgraph.fontcolor; + 'drawcolor' = sgraph.drawcolor; + 'fillcolor' = sgraph.fillcolor; + ]); +}; +dotty.protogt.undrawsgraph = function (gt, views, sgraph) { + sgraph.drawn = 0; + if (~sgraph.draws) + return; + gt.execalldraw (gt, views, null, sgraph.draws, [ + 'fontname' = sgraph.fontname; + 'fontsize' = sgraph.fontsize; + 'fontcolor' = sgraph.fontcolor; + 'drawcolor' = 0; + 'fillcolor' = 0; + ]); +}; +dotty.protogt.drawnode = function (gt, views, node) { + local vid; + + node.drawn = 1; + if (~node.draws) + return; + gt.execalldraw (gt, views, node, node.draws, [ + 'fontname' = node.fontname; + 'fontsize' = node.fontsize; + 'fontcolor' = node.fontcolor; + 'drawcolor' = node.drawcolor; + 'fillcolor' = node.fillcolor; + ]); + for (vid in views) + setpick (views[vid].canvas, node, node.rect); +}; +dotty.protogt.undrawnode = function (gt, views, node) { + local vid; + + if (~node.drawn) + return; + node.drawn = 0; + if (~node.pos) + return; + gt.execalldraw (gt, views, node, node.draws, [ + 'nooverride' = 1; + 'fontname' = node.fontname; + 'fontsize' = node.fontsize; + 'fontcolor' = 0; + 'drawcolor' = 0; + 'fillcolor' = 0; + ]); + for (vid in views) + clearpick (views[vid].canvas, node); +}; +dotty.protogt.movenode = function (gt, node, pos) { + local dp, eid, edge; + + dp.x = pos.x - node.pos.x; + dp.y = pos.y - node.pos.y; + gt.undrawnode (gt, gt.views, node); + node.pos.x = pos.x; + node.pos.y = pos.y; + gt.movenodedraw (node.draws, dp); + for (eid in node.edges) { + edge = node.edges[eid]; + gt.undrawedge (gt, gt.views, edge); + gt.moveedgedraw (edge.draws, edge.tail.pos, edge.head.pos); + gt.drawedge (gt, gt.views, edge); + } + gt.drawnode (gt, gt.views, node); +}; +dotty.protogt.drawedge = function (gt, views, edge) { + local vid, canvas; + + edge.drawn = 1; + if (~edge.draws) + return; + gt.execalldraw (gt, views, edge, edge.draws, [ + 'fontname' = edge.fontname; + 'fontsize' = edge.fontsize; + 'fontcolor' = edge.fontcolor; + 'drawcolor' = edge.drawcolor; + 'fillcolor' = edge.fillcolor; + ]); + for (vid in views) { + canvas = views[vid].canvas; + if (gt.edgehandles == 0 | ~edge.draws.ep) + continue; + arc (canvas, edge, edge.draws.ep, ['x' = 5; 'y' = 5;], ['color' = 1;]); + } +}; +dotty.protogt.undrawedge = function (gt, views, edge) { + local vid, canvas; + + if (~edge.drawn) + return; + edge.drawn = 0; + if (~edge.draws) + return; + gt.execalldraw (gt, views, edge, edge.draws, [ + 'nooverride' = 1; + 'fontname' = edge.fontname; + 'fontsize' = edge.fontsize; + 'fontcolor' = 0; + 'drawcolor' = 0; + 'fillcolor' = 0; + ]); + for (vid in views) { + canvas = views[vid].canvas; + if (gt.edgehandles == 0 | ~edge.draws.ep) + continue; + arc (canvas, edge, edge.draws.ep, ['x' = 5; 'y' = 5;], ['color' = 0;]); + clearpick (canvas, edge); + } +}; +# +# draw directives +# +dotty.protogt.execalldraw = function (gt, views, obj, draws, gc) { + local vid, vt, did, draw, i, func; + + for (vid in views) { + vt = views[vid]; + for (did in draws) { + if (did == 'ep') + continue; + draw = draws[did]; + for (i = 0; draw[i]; i = i + 1) + if ((func = gt.drawfunc[draw[i].type])) + func (gt, vt.canvas, obj, draw[i], gc); + } + } +}; +dotty.protogt.drawfunc.E = function (gt, canvas, obj, data, gc) { + arc (canvas, obj, data.c, data.s, [ + 'color' = gc.fillcolor; 'style' = gc.style; 'width' = gc.width; + 'fill' = 'on'; + ]); + arc (canvas, obj, data.c, data.s, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.e = function (gt, canvas, obj, data, gc) { + arc (canvas, obj, data.c, data.s, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.P = function (gt, canvas, obj, data, gc) { + polygon (canvas, obj, data.ps, [ + 'color' = gc.fillcolor; 'style' = gc.style; 'width' = gc.width; + 'fill' = 'on'; + ]); + polygon (canvas, obj, data.ps, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.p = function (gt, canvas, obj, data, gc) { + polygon (canvas, obj, data.ps, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.L = function (gt, canvas, obj, data, gc) { + polygon (canvas, obj, data.ps, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.b = function (gt, canvas, obj, data, gc) { + splinegon (canvas, obj, data.ps, [ + 'color' = gc.fillcolor; 'style' = gc.style; 'width' = gc.width; + 'fill' = 'on'; + ]); +}; +dotty.protogt.drawfunc.B = function (gt, canvas, obj, data, gc) { + splinegon (canvas, obj, data.ps, [ + 'color' = gc.drawcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.T = function (gt, canvas, obj, data, gc) { + text (canvas, obj, data.p, data.s, gc.fontname, gc.fontsize, data.j, [ + 'color' = gc.fontcolor; 'style' = gc.style; 'width' = gc.width; + ]); +}; +dotty.protogt.drawfunc.C = function (gt, canvas, obj, data, gc) { + if (gc.nooverride ~= 1) + gc.fillcolor = data.fillcolor; +}; +dotty.protogt.drawfunc.c = function (gt, canvas, obj, data, gc) { + if (gc.nooverride ~= 1) { + gc.drawcolor = data.drawcolor; + gc.fontcolor = data.drawcolor; + } +}; +dotty.protogt.drawfunc.F = function (gt, canvas, obj, data, gc) { + gc.fontname = data.fn; + gc.fontsize = data.fs; +}; +dotty.protogt.drawfunc.S = function (gt, canvas, obj, data, gc) { + gc.style = data.style; + gc.width = data.width; +}; +dotty.protogt.movenodedraw = function (draws, dp) { + local did, draw, i, j; + + for (did in draws) { + if (did == 'ep') + continue; + draw = draws[did]; + for (i = 0; draw[i]; i = i + 1) { + if (draw[i].type == 'E' | draw[i].type == 'e') { + draw[i].c.x = draw[i].c.x + dp.x; + draw[i].c.y = draw[i].c.y + dp.y; + } else if (draw[i].type == 'P' | draw[i].type == 'p') { + for (j = 1; draw[i].ps[j]; j = j + 1) { + draw[i].ps[j].x = draw[i].ps[j].x + dp.x; + draw[i].ps[j].y = draw[i].ps[j].y + dp.y; + } + } else if (draw[i].type == 'L' | draw[i].type == 'B') { + for (j = 0; draw[i].ps[j]; j = j + 1) { + draw[i].ps[j].x = draw[i].ps[j].x + dp.x; + draw[i].ps[j].y = draw[i].ps[j].y + dp.y; + } + } else if (draw[i].type == 'T') { + draw[i].p.x = draw[i].p.x + dp.x; + draw[i].p.y = draw[i].p.y + dp.y; + } + } + } +}; +dotty.protogt.moveedgedraw = function (draws, tp, hp) { + local draws2, did; + + for (did in draws) + draws2[did] = draws[did]; + for (did in draws2) + remove (did, draws); + draws[0] = [ + 0 = [ + 'type' = 'L'; + 'n' = 2; + 'ps' = [ + 0 = copy (tp); + 1 = copy (hp); + ]; + ]; + 'ep' = ['x' = (tp.x + hp.x) / 2; 'y' = (tp.y + hp.y) / 2;]; + ]; +}; +dotty.protogt.simplenodedraw = function (node, c, s) { + local draws; + + if (node.attr.shape == 'ellipse') + draws[0] = [ + 0 = [ + 'type' = 'e'; + 'c' = copy (c); + 's' = ['x' = s.x / 2; 'y' = s.y / 2;]; + ]; + ]; + else + draws[0] = [ + 0 = [ + 'type' = 'p'; + 'n' = 5; + 'ps' = [ + 0 = ['x' = c.x - s.x / 2; 'y' = c.y - s.y / 2;]; + 1 = ['x' = c.x + s.x / 2; 'y' = c.y - s.y / 2;]; + 2 = ['x' = c.x + s.x / 2; 'y' = c.y + s.y / 2;]; + 3 = ['x' = c.x - s.x / 2; 'y' = c.y + s.y / 2;]; + 4 = ['x' = c.x - s.x / 2; 'y' = c.y - s.y / 2;]; + ]; + ]; + ]; + return draws; +}; +dotty.protogt.simpleedgedraw = function (edge, tp, hp) { + local draws; + + draws[0] = [ + 0 = [ + 'type' = 'L'; + 'n' = 2; + 'ps' = [ + 0 = copy (tp); + 1 = copy (hp); + ]; + ]; + 'ep' = ['x' = (tp.x + hp.x) / 2; 'y' = (tp.y + hp.y) / 2;]; + ]; + return draws; +}; +# +# utilities +# +dotty.protogt.getcolor = function (views, name) { + local vid, vt, color, t; + + for (vid in views) { + vt = views[vid]; + if (~(color >= 0)) { + if (~(vt.colors[name] >= 0)) + color = (vt.colors[name] = vt.colorn); + else { + color = vt.colors[name]; + break; + } + } else if (~(vt.colors[name] >= 0)) + vt.colors[name] = color; + else if (vt.colors[name] ~= color) + dotty.message (0, concat ('inconsistent color ids for ', name)); + if (setwidgetattr (vt.canvas, ['color' = [color = name;];]) ~= 1) { + t = split (name, ' '); + if (tablesize (t) ~= 3 | + setwidgetattr (vt.canvas, ['color' = [color = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];];]) ~= 1) { + dotty.message (0, concat ('unknown color ', name, ' using #1')); + return 1; + } + } + vt.colorn = color + 1; + } + return color; +}; +dotty.protogt.setbgcolor = function (views, name) { + local vid, vt, t; + + for (vid in views) { + vt = views[vid]; + if (setwidgetattr (vt.canvas, ['color' = [0 = name;];]) ~= 1) { + t = split (name, ' '); + if (tablesize (t) ~= 3 | + setwidgetattr (vt.canvas, ['color' = [0 = [ + 'h' = ston (t[0]); 's' = ston (t[1]); 'v' = ston (t[2]); + ];];]) ~= 1) { + dotty.message (0, concat ('unknown bgcolor ', name)); + return; + } + } + vt.colors['_bgcolor_'] = name; + } +}; +dotty.protogt.unpacksgraphattr = function (gt, sgraph) { + local attr; + + attr = sgraph.graphattr; + if (dotty.fontmap[attr.fontname]) + sgraph[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + sgraph[dotty.keys.fname] = attr.fontname; + sgraph[dotty.keys.fsize] = ston (attr.fontsize); + sgraph[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + sgraph[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + sgraph[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.fillcolor); + else if (attr.color) + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } +}; +dotty.protogt.unpacknodeattr = function (gt, node) { + local attr; + + attr = node.attr; + if (dotty.fontmap[attr.fontname]) + node[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + node[dotty.keys.fname] = attr.fontname; + node[dotty.keys.fsize] = ston (attr.fontsize); + node[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + node[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + node[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + node[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.fillcolor); + else if (attr.color) + node[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + node[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } +}; +dotty.protogt.unpackedgeattr = function (gt, edge) { + local attr; + + attr = edge.attr; + if (dotty.fontmap[attr.fontname]) + edge[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + edge[dotty.keys.fname] = attr.fontname; + edge[dotty.keys.fsize] = ston (attr.fontsize); + edge[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + edge[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + edge[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + edge[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.fillcolor); + else if (attr.color) + edge[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + edge[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } +}; +dotty.protogt.unpackattr = function (gt) { + local gid, sgraph, nid, node, eid, edge, graph, attr; + + graph = gt.graph; + attr = graph.graphattr; + if (dotty.fontmap[attr.fontname]) + graph[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + graph[dotty.keys.fname] = attr.fontname; + graph[dotty.keys.fsize] = ston (attr.fontsize); + graph[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + graph[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + graph[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + graph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.fillcolor); + else if (attr.color) + graph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + graph[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } + if (attr.bgcolor & attr.bgcolor ~= '') + gt.setbgcolor (gt.views, attr.bgcolor); + for (gid in graph.graphdict) { + sgraph = graph.graphs[graph.graphdict[gid]]; + attr = sgraph.graphattr; + if (dotty.fontmap[attr.fontname]) + sgraph[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + sgraph[dotty.keys.fname] = attr.fontname; + sgraph[dotty.keys.fsize] = ston (attr.fontsize); + sgraph[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + sgraph[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + sgraph[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + sgraph[dotty.keys.bcolor] = gt.getcolor ( + gt.views, attr.fillcolor + ); + else if (attr.color) + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + sgraph[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } + } + for (nid in graph.nodedict) { + node = graph.nodes[graph.nodedict[nid]]; + attr = node.attr; + if (dotty.fontmap[attr.fontname]) + node[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + node[dotty.keys.fname] = attr.fontname; + node[dotty.keys.fsize] = ston (attr.fontsize); + node[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + node[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + node[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + if (attr.style == 'filled') { + if (attr.fillcolor) + node[dotty.keys.bcolor] = gt.getcolor ( + gt.views, attr.fillcolor + ); + else if (attr.color) + node[dotty.keys.bcolor] = gt.getcolor (gt.views, attr.color); + else + node[dotty.keys.bcolor] = gt.getcolor (gt.views, 'lightgrey'); + } + } + for (eid in graph.edges) { + edge = graph.edges[eid]; + attr = edge.attr; + if (dotty.fontmap[attr.fontname]) + edge[dotty.keys.fname] = dotty.fontmap[attr.fontname]; + else + edge[dotty.keys.fname] = attr.fontname; + edge[dotty.keys.fsize] = ston (attr.fontsize); + edge[dotty.keys.fcolor] = gt.getcolor (gt.views, attr.fontcolor); + if (attr.color) + edge[dotty.keys.dcolor] = gt.getcolor (gt.views, attr.color); + else + edge[dotty.keys.dcolor] = gt.getcolor (gt.views, 'black'); + } +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/dotty_edit.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,593 @@ +# +# dotty_edit: editing functions and data structures +# +dotty.protogt.getnodesbyattr = function (gt, key, val) { + local nid, node, nlist; + + nlist = []; + for (nid in gt.graph.nodes) { + node = gt.graph.nodes[nid]; + if (node.attr[key] == val) + nlist[nid] = node; + } + return nlist; +}; +dotty.protogt.reachablenodes = function (gt, node) { + local nlist, stack, eid, edge, i; + + stack[0] = node; + i = 1; + while (i > 0) { + node = stack[i - 1]; + i = i - 1; + nlist[node.nid] = node; + for (eid in node.edges) { + edge = node.edges[eid]; + if (~nlist[edge.head.nid]) { + nlist[edge.head.nid] = edge.head; + stack[i] = edge.head; + i = i + 1; + } + } + } + return nlist; +}; +dotty.protogt.mergegraph = function (gt, graph, show) { + local nameid, onode, pos, size, eid, eid2, tnode, hnode, oedge; + + if (~gt.noundo) + gt.startadd2undo (gt); + for (nameid in graph.nodedict) { + pos = null; + size = null; + onode = graph.nodes[graph.nodedict[nameid]]; + if (onode.pos) + pos = node.pos; + if (onode.size) + size = node.size; + if (~(gt.graph.nodedict[nameid] >= 0)) { + pos = null; + size = null; + if (onode.pos) + pos = node.pos; + if (onode.size) + size = node.size; + gt.insertnode (gt, pos, size, nameid, onode.attr, show); + } + } + for (eid in graph.edges) { + oedge = graph.edges[eid]; + tnode = gt.graph.nodes[gt.graph.nodedict[oedge.tail.name]]; + hnode = gt.graph.nodes[gt.graph.nodedict[oedge.head.name]]; + for (eid2 in tnode.edges) + if ( + tnode.edges[eid2].tail == tnode & + tnode.edges[eid2].head == hnode + ) { + oedge = null; + break; + } + if (oedge) + gt.insertedge (gt, tnode, null, hnode, null, oedge.attr, show); + } + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.insertsgraph = function (gt, name, attr, show) { + local gid, sgraph, aid; + + if (~gt) + return null; + gid = gt.graph.maxgid; + if (~name) { + while (gt.graph.graphdict[(name = concat ('g', gid))] >= 0) + gid = gid + 1; + } else if (gt.graph.graphdict[name]) { + dotty.message (0, concat ('graph: ', name, ' exists')); + return null; + } + gt.graph.graphdict[name] = gid; + gt.graph.maxgid = gid + 1; + gt.graph.graphs[gid] = [ + dotty.keys.gid = gid; + dotty.keys.name = name; + dotty.keys.gattr = copy (gt.graph.graphattr); + dotty.keys.nattr = copy (gt.graph.nodeattr); + dotty.keys.eattr = copy (gt.graph.edgeattr); + ]; + sgraph = gt.graph.graphs[gid]; + if (~attr) + attr = []; + if (~attr.label) + attr.label = '\N'; + for (aid in attr) + sgraph.graphattr[aid] = attr[aid]; + gt.unpacksgraphattr (gt, sgraph); + if (show) + gt.drawsgraph (gt, gt.views, sgraph); + return sgraph; +}; +dotty.protogt.removesgraph = function (gt, sgraph) { + gt.undrawsgraph (gt, gt.views, sgraph); + remove (sgraph.name, gt.graph.graphdict); + remove (sgraph.gid, gt.graph.graphs); +}; +dotty.protogt.insertnode = function (gt, pos, size, name, attr, show) { + local nid, node, aid; + + nid = gt.graph.maxnid; + if (~name) { + while (gt.graph.nodedict[(name = concat ('n', nid))] >= 0) + nid = nid + 1; + } else if (gt.graph.nodedict[name] >= 0) { + dotty.message (0, concat ('node: ', name, ' exists')); + return null; + } + gt.graph.nodedict[name] = nid; + gt.graph.maxnid = nid + 1; + gt.graph.nodes[nid] = [ + dotty.keys.nid = nid; + dotty.keys.name = name; + dotty.keys.attr = copy (gt.graph.nodeattr); + dotty.keys.edges = []; + ]; + node = gt.graph.nodes[nid]; + if (~attr) + attr = []; + if (~attr.label) + attr.label = '\N'; + for (aid in attr) + node.attr[aid] = attr[aid]; + gt.unpacknodeattr (gt, node); + if (~pos) + pos = ['x' = 10; 'y' = 10;]; + node[dotty.keys.pos] = copy (pos); + if (~size) + size = ['x' = strlen (attr.label) * 30; 'y' = 30;]; + if (size.x == 0) + size.x = 30; + node[dotty.keys.size] = copy (size); + node[dotty.keys.rect] = [ + 0 = ['x' = pos.x - size.x / 2; 'y' = pos.y - size.y / 2;]; + 1 = ['x' = pos.x + size.x / 2; 'y' = pos.y + size.y / 2;]; + ]; + node.draws = gt.simplenodedraw (node, pos, size); + if (show) + gt.drawnode (gt, gt.views, node); + if (~gt.noundo) { + gt.startadd2undo (gt); + gt.currundo.inserted.nodes[nid] = node; + gt.endadd2undo (gt); + } + return node; +}; +dotty.protogt.removenode = function (gt, node) { + local eid, list, edge, gid; + + if (~gt.noundo) + gt.startadd2undo (gt); + for (eid in node.edges) + list[eid] = node.edges[eid]; + for (eid in list) + gt.removeedge (gt, list[eid]); + gt.undrawnode (gt, gt.views, node); + for (gid in gt.graph.graphs) + remove (node.nid, gt.graph.graphs[gid].nodes); + remove (node.name, gt.graph.nodedict); + remove (node.nid, gt.graph.nodes); + if (~gt.noundo) { + gt.currundo.deleted.nodes[node.nid] = node; + gt.endadd2undo (gt); + } +}; +dotty.protogt.insertedge = function ( + gt, nodea, porta, nodeb, portb, attr, show +) { + local eid, edge, aid, tport, hport; + + if (~nodea | ~nodeb) + return null; + if (porta) + tport = porta; + if (portb) + hport = portb; + eid = gt.graph.maxeid; + while (gt.graph.edges[eid]) + eid = eid + 1; + gt.graph.maxeid = eid + 1; + gt.graph.edges[eid] = [ + dotty.keys.eid = eid; + dotty.keys.tail = nodea; + dotty.keys.tport = porta; + dotty.keys.head = nodeb; + dotty.keys.hport = portb; + dotty.keys.attr = copy (gt.graph.edgeattr); + ]; + edge = gt.graph.edges[eid]; + if (~attr) + attr = []; + for (aid in attr) + edge.attr[aid] = attr[aid]; + nodea.edges[eid] = edge; + nodeb.edges[eid] = edge; + gt.unpackedgeattr (gt, edge); + edge.draws = gt.simpleedgedraw (edge, nodea.pos, nodeb.pos); + if (show) + gt.drawedge (gt, gt.views, edge); + if (~gt.noundo) { + gt.startadd2undo (gt); + gt.currundo.inserted.edges[eid] = edge; + gt.endadd2undo (gt); + } + return edge; +}; +dotty.protogt.removeedge = function (gt, edge) { + local head, tail; + + if (~gt.noundo) + gt.startadd2undo (gt); + if (edge.head.attr.support == 1) + head = edge.head; + if (edge.tail.attr.support == 1) + if (head ~= edge.tail) + tail = edge.tail; + gt.undrawedge (gt, gt.views, edge); + remove (edge.eid, edge.head.edges); + remove (edge.eid, edge.tail.edges); + remove (edge.eid, gt.graph.edges); + if (head & tablesize (head.edges) == 0) + gt.removenode (gt, head); + if (tail & tablesize (tail.edges) == 0) + gt.removenode (gt, tail); + if (~gt.noundo) { + gt.currundo.deleted.edges[edge.eid] = edge; + gt.endadd2undo (gt); + } +}; +dotty.protogt.swapedgeids = function (gt, edge1, edge2) { + local eid1, eid2; + + if (edge1.eid == edge2.eid) + return; + if (~gt.noundo) + gt.startadd2undo (gt); + eid1 = edge1.eid; + eid2 = edge2.eid; + gt.graph.edges[eid1] = edge2; + gt.graph.edges[eid2] = edge1; + remove (eid1, edge1.tail.edges); + remove (eid1, edge1.head.edges); + remove (eid2, edge2.tail.edges); + remove (eid2, edge2.head.edges); + edge1.tail.edges[eid2] = edge1; + edge1.head.edges[eid2] = edge1; + edge2.tail.edges[eid1] = edge2; + edge2.head.edges[eid1] = edge2; + edge1.eid = eid2; + edge2.eid = eid1; + if (~gt.noundo) { + gt.currundo.swapped.edges[eid1] = edge1; + gt.currundo.swapped.edges[eid2] = edge2; + gt.endadd2undo (gt); + } +}; +dotty.protogt.removesubtree = function (gt, obj) { + local nlist, node, head, nid, edge, eid; + + if (~gt.noundo) + gt.startadd2undo (gt); + if (obj.nid >= 0) + node = obj; + else if (obj.eid >= 0) { + node = obj.head; + gt.removeedge (gt, obj); + if (~gt.graph.nodes[node.nid]) { + if (~gt.noundo) + gt.endadd2undo (gt); + return; + } + for (eid in node.edges) { + edge = node.edges[eid]; + if (edge.head == node & edge.tail ~= node) { + if (~gt.noundo) + gt.endadd2undo (gt); + return; + } + } + } else { + dotty.message (0, 'bad object type in gt.removesubtree'); + return; + } + nlist = [node.nid = node;]; + while (node) { + for (eid in node.edges) { + head = node.edges[eid].head; + if (head ~= node) + nlist[head.nid] = head; + } + gt.removenode (gt, node); + remove (node.nid, nlist); + node = null; + for (nid in nlist) { + node = nlist[nid]; + for (eid in node.edges) { + edge = node.edges[eid]; + if (edge.head == node & edge.tail ~= node) { + node = null; + break; + } + } + if (node) + break; + } + } + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.removenodesbyattr = function (gt, key, val) { + local nlist, nid; + + if (~gt.noundo) + gt.startadd2undo (gt); + nlist = gt.getnodesbyattr (gt, key, val); + for (nid in nlist) + gt.removenode (gt, nlist[nid]); + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.removesubtreesbyattr = function (gt, key, val) { + local nlist, nid; + + if (~gt.noundo) + gt.startadd2undo (gt); + nlist = gt.getnodesbyattr (gt, key, val); + for (nid in nlist) + if (gt.graph.nodes[nid]) + gt.removesubtree (gt, nlist[nid]); + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.groupnodes = function ( + gt, nlist, gnode, pos, size, attr, keepmulti, show +) { + local nid, node, elist, eid, edge, nodea, nodeb, inlist, outlist; + + if (~nlist | tablesize (nlist) == 0) + return; + if (gnode.attr.support) { + dotty.message (0, 'cannot group nodes in a support node'); + return; + } + if (~gt.noundo) + gt.startadd2undo (gt); + if (~gnode) + gnode = gt.insertnode (gt, pos, size, null, attr, show); + inlist = []; + outlist = []; + for (nid in nlist) { + if ((node = nlist[nid]) == gnode) + continue; + elist = []; + for (eid in node.edges) + elist[eid] = node.edges[eid]; + for (eid in elist) { + edge = elist[eid]; + if (edge.head == node) { + nodea = edge.tail; + nodeb = gnode; + if (~keepmulti) { + if (inlist[nodea.nid]) + continue; + inlist[nodea.nid] = nodea; + if (nodea == gnode) + outlist[nodea.nid] = nodea; + } + } else { + nodea = gnode; + nodeb = edge.head; + if (~keepmulti) { + if (outlist[nodeb.nid]) + continue; + outlist[nodeb.nid] = nodeb; + if (nodeb == gnode) + inlist[nodeb.nid] = nodeb; + } + } + gt.insertedge (gt, nodea, null, nodeb, null, edge.attr, show); + } + gt.removenode (gt, node); + } + if (~gt.noundo) + gt.endadd2undo (gt); + return gnode; +}; +dotty.protogt.groupnodesbyattr = function ( + gt, key, val, attr, keepmulti, show +) { + local nlist, nid, pos, size; + + pos = null; + size = null; + nlist = gt.getnodesbyattr (gt, key, val); + if (show) + for (nid in nlist) { + pos = nlist[nid].pos; + size = nlist[nid].size; + break; + } + return gt.groupnodes (gt, nlist, null, pos, size, attr, keepmulti, show); +}; +dotty.protogt.cut = function (gt, obj, set, mode, op) { + local clipgt, list, node, nid, edge, eid, clipnode; + + clipgt = dotty.clipgt; + clipgt.graph = copy (dotty.protogt.graph); + if (obj.eid >= 0) { # it's an edge + list.edges[obj.eid] = obj; + node = obj.head; + } else if (obj.nid >= 0) { + list.nodes[obj.nid] = obj; + node = obj; + for (eid in node.edges) + list.edges[eid] = node.edges[eid]; + } else { + dotty.message (0, 'unknown object type in gt.cut'); + return; + } + if (set == 'reachable') { + list.nodes = gt.reachablenodes (gt, node); + for (nid in list.nodes) { + node = list.nodes[nid]; + for (eid in node.edges) { + edge = node.edges[eid]; + list.edges[edge.eid] = edge; + } + } + } + if (mode == 'support') { + for (eid in list.edges) { + edge = list.edges[eid]; + if (~list.nodes[edge.tail.nid]) { + list.support[edge.tail.nid] = edge.tail; + list.nodes[edge.tail.nid] = edge.tail; + } + if (~list.nodes[edge.head.nid]) { + list.support[edge.head.nid] = edge.head; + list.nodes[edge.head.nid] = edge.head; + } + } + } + for (nid = 0; nid < gt.graph.maxnid; nid = nid + 1) { + if (~list.nodes[nid]) + continue; + node = list.nodes[nid]; + clipnode = gt.insertnode (clipgt, null, null, node.name, node.attr, 0); + if (list.support[nid]) + clipnode.support = 1; + list.clipnodes[nid] = clipnode; + } + for (eid = 0; eid < gt.graph.maxeid; eid = eid + 1) { + if (~list.edges[eid]) + continue; + edge = list.edges[eid]; + if (~list.nodes[edge.tail.nid] | ~list.nodes[edge.head.nid]) + continue; + gt.insertedge ( + clipgt, list.clipnodes[edge.tail.nid], null, + list.clipnodes[edge.head.nid], null, edge.attr, 0 + ); + } + if (op ~= 'cut') + return; + if (~gt.noundo) + gt.startadd2undo (gt); + for (eid in list.edges) + gt.removeedge (gt, list.edges[eid]); + for (nid in list.nodes) + if (~list.support[nid] & gt.graph.nodes[nid]) + gt.removenode (gt, list.nodes[nid]); + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.paste = function (gt, pos, show) { + local clipgt, offset, center, nid, node, eid, edge, nodes; + + if (~gt.noundo) + gt.startadd2undo (gt); + clipgt = dotty.clipgt; + if (clipgt.graph.rect) + center = [ + 'x' = (clipgt.graph.rect[1].x + clipgt.graph.rect[0].x) / 2; + 'y' = (clipgt.graph.rect[1].y + clipgt.graph.rect[0].y) / 2; + ]; + else + center = pos; + offset = [ + 'x' = center.x - pos.x; + 'y' = center.y - pos.y; + ]; + for (nid = 0; clipgt.graph.nodes[nid]; nid = nid + 1) { + node = clipgt.graph.nodes[nid]; + if (node.attr.label == '\N' | ~node.attr.label) + node.attr.label = node.name; + if (node.support == 1) + nodes[nid] = gt.insertnode (gt, [ + 'x' = node.pos.x - offset.x; + 'y' = node.pos.y - offset.y; + ], null, null, [ + 'support' = 1; 'shape' = 'circle'; + 'label' = ''; 'width' = 0.2; + ], show); + else + nodes[nid] = gt.insertnode (gt, [ + 'x' = node.pos.x - offset.x; + 'y' = node.pos.y - offset.y; + ], node.size, null, node.attr, show); + } + for (eid = 0; clipgt.graph.edges[eid]; eid = eid + 1) { + edge = clipgt.graph.edges[eid]; + gt.insertedge ( + gt, nodes[edge.tail.nid], null, + nodes[edge.head.nid], null, edge.attr, show + ); + } + if (~gt.noundo) + gt.endadd2undo (gt); +}; +dotty.protogt.startadd2undo = function (gt) { + if (~gt.undoarray.level) + gt.currundo = ( + gt.undoarray.entries[tablesize (gt.undoarray.entries)] = [] + ); + gt.undoarray.level = gt.undoarray.level + 1; +}; +dotty.protogt.endadd2undo = function (gt) { + gt.undoarray.level = gt.undoarray.level - 1; +}; +dotty.protogt.undo = function (gt, show) { + local entry, n, eid, edge, nid, node, edges; + + if ((n = tablesize (gt.undoarray.entries)) < 1) + return; + entry = gt.undoarray.entries[n - 1]; + remove (n - 1, gt.undoarray.entries); + remove ('currundo', gt); + gt.noundo = 1; + # hardwire nodes and edges back with the same id's as the originals + for (nid in entry.deleted.nodes) { + node = entry.deleted.nodes[nid]; + gt.graph.nodedict[node.name] = node.nid; + gt.graph.nodes[node.nid] = node; + node.edges = []; + if (show) + gt.drawnode (gt, gt.views, node); + } + for (eid in entry.deleted.edges) { + edge = entry.deleted.edges[eid]; + gt.graph.edges[edge.eid] = edge; + edge.head.edges[edge.eid] = edge; + edge.tail.edges[edge.eid] = edge; + if (show) + gt.drawedge (gt, gt.views, edge); + } + if (entry.swapped.edges) { + if (tablesize (entry.swapped.edges) == 2) { + n = 0; + for (eid in entry.swapped.edges) { + edges[n] = entry.swapped.edges[eid]; + n = n + 1; + } + gt.swapedgeids (gt, edges[0], edges[1]); + } else + dotty.message (0, 'cannot handle undoing swap of > 2 edges'); + } + for (eid in entry.inserted.edges) { + edge = entry.inserted.edges[eid]; + gt.removeedge (gt, edge); + } + for (nid in entry.inserted.nodes) { + node = entry.inserted.nodes[nid]; + gt.removenode (gt, node); + } + gt.noundo = 0; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/dotty_layout.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,449 @@ +# +# dotty_layout: layout functions and data structures +# +dotty.grablserver = function (lserver) { + local fd; + + if (~dotty.lservers[lserver] | tablesize (dotty.lservers[lserver]) == 0) { + if (~((fd = openio ('pipe', lserver, 'r+', '%e -Txdot')) >= 0)) { + dotty.message (0, concat ('cannot start ', lserver)); + return null; + } + dotty.lservers[lserver][fd] = [ + 'fd' = fd; + 'count' = 0; + ]; + } + for (fd in dotty.lservers[lserver]) { + dotty.lservers[lserver][fd].count = dotty.lservers[ + lserver + ][fd].count + 1; + dotty.lservers.inuse[fd] = dotty.lservers[lserver][fd]; + remove (fd, dotty.lservers[lserver]); + return fd; + } +}; +dotty.releaselserver = function (lserver, fd, state) { + if (state == 'bad' | dotty.lservers.inuse[fd].count > 40) { + closeio (fd, 'kill'); + remove (fd, dotty.lservers.inuse); + return; + } + dotty.lservers[lserver][fd] = dotty.lservers.inuse[fd]; + remove (fd, dotty.lservers.inuse); +}; +dotty.protogt.startlayout = function (gt) { + local lpt, fd; + + if (gt.layoutpending >= 1) { + lpt = dotty.layoutpending[gt.gtid]; + if (gt.layoutmode == 'async') + monitor ('off', lpt.fd); + dotty.releaselserver (gt.lserver, lpt.fd, 'bad'); + remove (gt.gtid, dotty.layoutpending); + gt.layoutpending = 0; + gt.haveinput = 0; + dotty.popbusy (gt, gt.views); + } + if (~((fd = dotty.grablserver (gt.lserver)) >= 0)) + return null; + dotty.pushbusy (gt, gt.views); + writegraph (fd, gt.graph, 1); + gt.layoutpending = 1; + dotty.layoutpending[gt.gtid] = [ + 'fd' = fd; + 'gtid' = gt.gtid; + ]; + if (gt.layoutmode == 'async') + monitor ('on', fd); + return 1; +}; +dotty.protogt.finishlayout = function (gt) { + local graph, lpt, fd; + + if (~(gt.layoutpending >= 1)) { + dotty.message (0, concat ('no layout pending for graph ', gt.gtid)); + return null; + } + lpt = dotty.layoutpending[gt.gtid]; + if (~(graph = readgraph (lpt.fd))) { + if (gt.layoutmode == 'async') + monitor ('off', lpt.fd); + dotty.releaselserver (gt.lserver, lpt.fd, 'bad'); + if (gt.layoutpending == 2) { + dotty.message (0, concat ('giving up on ', gt.lserver)); + if ((fd = openio ('file', 'dottybug.dot', 'w+')) >= 0) { + writegraph (fd, gt.graph, 0); + closeio (fd); + dotty.message ( + 0, concat ('graph that causes ', gt.lserver) + ); + dotty.message ( + 0, 'to fail has been saved in file dottybug.dot' + ); + dotty.message ( + 0, 'please fill out a bug report at' + ); + dotty.message ( + 0, 'http://www.graphviz.org/bugs/bugform.html' + ); + } + dotty.popbusy (gt, gt.views); + gt.layoutpending = 0; + gt.haveinput = 0; + return 1; + } + dotty.message ( + 1, concat ('lost connection to ', gt.lserver, ', restarting...') + ); + lpt.fd = dotty.grablserver (gt.lserver); + writegraph (lpt.fd, gt.graph, 1); + if (gt.layoutmode == 'async') + monitor ('on', lpt.fd); + gt.layoutpending = 2; + gt.haveinput = 0; + return null; + } + if (gt.layoutmode == 'async') + monitor ('off', lpt.fd); + dotty.releaselserver (gt.lserver, lpt.fd, null); + remove (gt.gtid, dotty.layoutpending); + gt.layoutpending = 0; + gt.haveinput = 0; + gt.unpacklayout (gt, graph); + dotty.popbusy (gt, gt.views); + return 1; +}; +dotty.protogt.cancellayout = function (gt) { + local lpt, vid; + + if (gt.layoutpending >= 1) { + lpt = dotty.layoutpending[gt.gtid]; + if (gt.layoutmode == 'async') + monitor ('off', lpt.fd); + dotty.releaselserver (gt.lserver, lpt.fd, 'bad'); + remove (gt.gtid, dotty.layoutpending); + gt.layoutpending = 0; + gt.haveinput = 0; + dotty.popbusy (gt, gt.views); + } +}; +dotty.protogt.unpacklayout = function (gt, graph2) { + local graph, gid, sgraph1, sgraph2, nid, node1, node2, eid, edge1, edge2; + local t1, pos, size; + + graph = gt.graph; + for (gid in graph2.graphdict) { + if (~(sgraph1 = graph.graphs[graph.graphdict[gid]])) + continue; + sgraph2 = graph2.graphs[graph2.graphdict[gid]]; + sgraph1.draws = gt.unpackalldraw (gt, sgraph2.graphattr); + } + for (nid in graph2.nodedict) { + if (~(node1 = graph.nodes[graph.nodedict[nid]])) + continue; + node2 = graph2.nodes[graph2.nodedict[nid]]; + node1.draws = gt.unpackalldraw (gt, node2.attr); + t1 = split (node2.attr.pos, ','); + pos = ['x' = ston (t1[0]); 'y' = ston (t1[1]);]; + size = [ + 'x' = ston (node2.attr.width) * 72; + 'y' = ston (node2.attr.height) * 72; + ]; + node1.pos = pos; + node1.size = size; + node1.rect = [ + 0 = ['x' = pos.x - size.x / 2; 'y' = pos.y - size.y / 2;]; + 1 = ['x' = pos.x + size.x / 2; 'y' = pos.y + size.y / 2;]; + ]; + } + for (eid in graph2.edges) { + edge2 = graph2.edges[eid]; + if (edge2.attr.id) { + if (~(edge1 = graph.edges[ston (edge2.attr.id)])) + continue; + } else if (graph == graph2) + edge1 = edge2; + edge1.draws = gt.unpackalldraw (gt, edge2.attr); + } + graph.draws = gt.unpackalldraw (gt, graph2.graphattr); + t1 = split (graph2.graphattr.bb, ','); + graph.rect[0].x = ston (t1[0]); + graph.rect[0].y = ston (t1[1]); + graph.rect[1].x = ston (t1[2]); + graph.rect[1].y = ston (t1[3]); + if (gt.graph ~= graph2) + return; + # strip position and size info from the attributes + for (gid in graph2.graphdict) { + sgraph2 = graph2.graphs[graph2.graphdict[gid]]; + gt.removealldraw (gt, sgraph2.graphattr); + if (sgraph2.graphattr.bb) + remove ('bb', sgraph2.graphattr); + } + for (nid in graph2.nodedict) { + node2 = graph2.nodes[graph2.nodedict[nid]]; + gt.removealldraw (gt, node2.attr); + if (node2.attr.rects) + remove ('rects', node2.attr); + remove ('pos', node2.attr); + remove ('width', node2.attr); + remove ('height', node2.attr); + } + for (eid in graph2.edges) { + edge2 = graph2.edges[eid]; + gt.removealldraw (gt, edge2.attr); + if (edge2.attr.pos) + remove ('pos', edge2.attr); + if (edge2.attr.lp) + remove ('lp', edge2.attr); + } + gt.removealldraw (gt, graph2.graphattr); + remove ('bb', graph2.graphattr); + if (graph2.graphattr.lp) + remove ('lp', graph2.graphattr); +}; +# +# draw directive parsing +# +dotty.protogt.unpackalldraw = function (gt, attr) { + local o, did; + + o = []; + if (attr._draw_) + o._draw_ = gt.unpackdraw (gt, attr._draw_); + if (attr._ldraw_) + o._ldraw_ = gt.unpackdraw (gt, attr._ldraw_); + if (attr._hdraw_) + o._hdraw_ = gt.unpackdraw (gt, attr._hdraw_); + if (attr._tdraw_) + o._tdraw_ = gt.unpackdraw (gt, attr._tdraw_); + if (attr._hldraw_) + o._hldraw_ = gt.unpackdraw (gt, attr._hldraw_); + if (attr._tldraw_) + o._tldraw_ = gt.unpackdraw (gt, attr._tldraw_); + for (did in o) + if (o[did].ep) { + o.ep = o[did].ep; + break; + } + return o; +}; +dotty.protogt.removealldraw = function (gt, attr) { + if (attr._draw_) + remove ('_draw_', attr); + if (attr._ldraw_) + remove ('_ldraw_', attr); + if (attr._hdraw_) + remove ('_hdraw_', attr); + if (attr._tdraw_) + remove ('_tdraw_', attr); + if (attr._hldraw_) + remove ('_hldraw_', attr); + if (attr._tldraw_) + remove ('_tldraw_', attr); +}; +dotty.protogt.unpackdraw = function (gt, attr) { + local oo, o, tt, t, n, i, j, s, l, ep; + + oo = []; + t = split (attr, ' ', 0); + n = tablesize (t); + if (t[n - 1] == '') { + remove (n - 1, t); + n = n - 1; + } + i = 0; + while (i < n) { + o = []; + if (t[i] == 'E') { + o.type = t[i]; + o.c.x = ston (t[i + 1]); + o.c.y = ston (t[i + 2]); + o.s.x = ston (t[i + 3]); + o.s.y = ston (t[i + 4]); + i = i + 5; + } else if (t[i] == 'e') { + o.type = t[i]; + o.c.x = ston (t[i + 1]); + o.c.y = ston (t[i + 2]); + o.s.x = ston (t[i + 3]); + o.s.y = ston (t[i + 4]); + i = i + 5; + } else if (t[i] == 'P') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + o.ps[o.n] = o.ps[0]; + o.n = o.n + 1; + } else if (t[i] == 'p') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + o.ps[o.n] = o.ps[0]; + o.n = o.n + 1; + } else if (t[i] == 'L') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + if (~ep) + ep = copy (o.ps[1]); + } else if (t[i] == 'B') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + if (~ep) + ep = copy (o.ps[1]); + } else if (t[i] == 'b') { + o.type = t[i]; + o.n = ston (t[i + 1]); + for (j = 0; j < o.n; j = j + 1) { + o.ps[j].x = ston (t[i + 2 + j * 2]); + o.ps[j].y = ston (t[i + 2 + j * 2 + 1]); + } + i = i + 2 + o.n * 2; + if (~ep) + ep = copy (o.ps[1]); + } else if (t[i] == 'T') { + o.type = t[i]; + o.p.x = ston (t[i + 1]); + o.p.y = ston (t[i + 2]); + o.j = ston (t[i + 3]); + if (o.j == -1) + o.j = 'lb'; + else if (o.j == 1) + o.j = 'rb'; + else if (o.j == 0) + o.j = 'cb'; + o.w = ston (t[i + 4]); + o.n = ston (t[i + 5]); + i = i + 6; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + o.s = s; + } else if (t[i] == 'C') { + o.type = t[i]; + o.n = ston (t[i + 1]); + i = i + 2; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + o.fillcolor = gt.getcolor (gt.views, s); + } else if (t[i] == 'c') { + o.type = t[i]; + o.n = ston (t[i + 1]); + i = i + 2; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + o.drawcolor = gt.getcolor (gt.views, s); + } else if (t[i] == 'F') { + o.type = t[i]; + o.fs = ston (t[i + 1]); + o.n = ston (t[i + 2]); + i = i + 3; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + o.ofn = s; + o.fn = dotty.fontmap[s]; + } else if (t[i] == 'S') { + o.type = t[i]; + o.n = ston (t[i + 1]); + i = i + 2; + s = t[i]; + i = i + 1; + l = strlen (s) - 1; + while (l < o.n) { + s = concat (s, ' ', t[i]); + l = l + 1 + strlen (t[i]); + i = i + 1; + } + tt = split (s, ''); + l = tablesize (tt); + s = ''; + for (j = 1; j < l; j = j + 1) + s = concat (s, tt[j]); + if ( + s == 'solid' | s == 'dashed' | s == 'dotted' | + s == 'longdashed' | s == 'shortdashed' + ) + o.style = s; + else if (s == 'bold') + o.width = 3; + else { + tt = split (s, '('); + if (tt[0] == 'setlinewidth') { + tt = split (tt[1], ')'); + o.width = ston (tt[0]); + } else + continue; + } + } else if (t[i] == 'I') { + i = i + 7; + } else { + dotty.message (0, concat ('draw language parser error: ', t[i])); + return null; + } + oo[tablesize (oo)] = o; + } + oo.ep = ep; + return oo; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/dotty_ui.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,468 @@ +# +# dotty_ui: user interface functions and data structures +# +dotty.protogt.doaction = function (data, s) { + local vt, gt; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + data.menuitem = s; + if (data.obj.nid >= 0) { + if (gt.actions.node[s]) { + gt.actions.node[s] (gt, vt, data.obj, data); + return; + } + } else if (data.obj.eid >= 0) { + if (gt.actions.edge[s]) { + gt.actions.edge[s] (gt, vt, data.obj, data); + return; + } + } + if (gt.actions.general[s]) + gt.actions.general[s] (gt, vt, data); +}; +dotty.protogt.actions.general = [ + "undo" = function (gt, vt, data) { + gt.undo (gt, 1); + }; + "paste" = function (gt, vt, data) { + gt.paste (gt, data.pos, 1); + }; + "do layout" = function (gt, vt, data) { + gt.layoutgraph (gt); + }; + "cancel layout" = function (gt, vt, data) { + gt.cancellayout (gt); + }; + "redraw" = function (gt, vt, data) { + gt.redrawgraph (gt, [vt.vtid = vt;]); + }; + "new graph" = function (gt, vt, data) { + gt.erasegraph (gt, null, null); + }; + "load graph" = function (gt, vt, data) { + gt.loadgraph (gt, null, 'file', dotty.protogt.graph, 1); + }; + "reload graph" = function (gt, vt, data) { + gt.loadgraph (gt, gt.name, gt.type, gt.graph, 1); + }; + "save graph" = function (gt, vt, data) { + gt.savegraph (gt, gt.name, gt.type); + }; + "save graph as" = function (gt, vt, data) { + gt.savegraph (gt, null, 'file'); + }; + "open view" = function (gt, vt, data) { + gt = dotty.protogt.creategraph (null); + gt.createview (gt, null); + }; + "copy view" = function (gt, vt, data) { + gt = gt.copygraph (gt); + gt.createview (gt, vt); + }; + "birdseye view" = function (gt, vt, data) { + gt.createview (gt, dotty.protovt.birdseye); + }; + "clone view" = function (gt, vt, data) { + gt.createview (gt, vt); + }; + "close view" = function (gt, vt, data) { + gt.destroyview (gt, vt); + if (tablesize (gt.views) == 0) + gt.destroygraph (gt); + }; + "set graph attr" = function (gt, vt, data) { + gt.setattr (gt, ['attr' = gt.graph.graphattr;]); + }; + "set node attr" = function (gt, vt, data) { + gt.setattr (gt, ['attr' = gt.graph.nodeattr;]); + }; + "set edge attr" = function (gt, vt, data) { + gt.setattr (gt, ['attr' = gt.graph.edgeattr;]); + }; + "zoom in" = function (gt, vt, data) { + gt.zoom (gt, vt, 0.5, data.pos); + }; + "zoom out" = function (gt, vt, data) { + gt.zoom (gt, vt, 2, data.pos); + }; + "zoom in slowly" = function (gt, vt, data) { + gt.zoom (gt, vt, 0.9, data.pos); + }; + "zoom out slowly" = function (gt, vt, data) { + gt.zoom (gt, vt, 1.1, data.pos); + }; + "scroll horizontally" = function (gt, vt, data) { + vt.scrollmode = 'h'; + }; + "scroll vertically" = function (gt, vt, data) { + vt.scrollmode = 'v'; + }; + "find node" = function (gt, vt, data) { + gt.findnode (gt, vt); + }; + "print graph" = function (gt, vt, data) { + gt.printorsave (gt, vt, null, null, null, null); + }; + "text view" = function (gt, vt, data) { + if (dotty.txtview == 'on') + dotty.txtview = 'off'; + else + dotty.txtview = 'on'; + txtview (dotty.txtview); + }; + "quit" = function (gt, vt, data) { + exit (); + }; +]; +dotty.protogt.actions.node = [ + "cut" = function (gt, vt, obj, data) { + gt.cut (gt, obj, 'one', 'support', 'cut'); + dotty.clipgt.layoutgraph (dotty.clipgt); + }; + "Cut" = function (gt, vt, obj, data) { + gt.cut (gt, obj, 'reachable', 'support', 'cut'); + dotty.clipgt.layoutgraph (dotty.clipgt); + }; + "copy" = function (gt, vt, obj, data) { + gt.cut (gt, obj, 'one', 'support', 'copy'); + dotty.clipgt.layoutgraph (dotty.clipgt); + }; + "Copy" = function (gt, vt, obj, data) { + gt.cut (gt, obj, 'reachable', 'support', 'copy'); + dotty.clipgt.layoutgraph (dotty.clipgt); + }; + "group" = function (gt, vt, obj, data) { + local kv; + + if ((kv = gt.getattr (gt, obj))) + gt.groupnodesbyattr (gt, kv.key, kv.val, [ + 'label' = kv.val; kv.key = kv.val; + ], 1, 1); + }; + "Group" = function (gt, vt, obj, data) { + local kv; + + if ((kv = gt.getattr (gt, obj))) + gt.groupnodesbyattr (gt, kv.key, kv.val, [ + 'label' = kv.val; kv.key = kv.val; + ], 0, 1); + }; + "delete" = function (gt, vt, obj, data) { + if (obj.eid >= 0) + gt.removeedge (gt, obj); + else + gt.removenode (gt, obj); + }; + "Delete" = function (gt, vt, obj, data) { + gt.removesubtree (gt, obj); + }; + "remove" = function (gt, vt, obj, data) { + if (obj.nid >= 0) + if ((kv = gt.getattr (gt, obj))) + gt.removenodesbyattr (gt, kv.key, kv.val); + }; + "Remove" = function (gt, vt, obj, data) { + if (obj.nid >= 0) + if ((kv = gt.getattr (gt, obj))) + gt.removesubtreesbyattr (gt, kv.key, kv.val); + }; + "set attr" = function (gt, vt, obj, data) { + gt.setattr (gt, obj); + }; + "print attr" = function (gt, vt, obj, data) { + if (obj.nid >= 0) + echo ('node: ', obj.name); + dump (obj.attr); + }; +]; +dotty.protogt.actions.edge = dotty.protogt.actions.node; +dotty.protovt.normal.menus = [ + 'general' = [ + 0 = "undo"; + 1 = "paste"; + 2 = "do layout"; + 3 = "cancel layout"; + 4 = "redraw"; + 5 = "new graph"; + 6 = "load graph"; + 7 = "reload graph"; + 8 = "save graph"; + 9 = "save graph as"; + 10 = "open view"; + 11 = "copy view"; + 12 = "clone view"; + 13 = "birdseye view"; + 14 = "close view"; + 15 = "set graph attr"; + 16 = "set node attr"; + 17 = "set edge attr"; + 18 = "zoom in"; + 19 = "zoom out"; + 20 = "find node"; + 21 = "print graph"; + 22 = "text view"; + 23 = "quit"; + ]; + 'node' = [ + 0 = "cut"; + 1 = "Cut"; + 2 = "copy"; + 3 = "Copy"; + 4 = "group"; + 5 = "Group"; + 6 = "delete"; + 7 = "Delete"; + 8 = "remove"; + 9 = "Remove"; + 10 = "set attr"; + 11 = "print attr"; + ]; + 'edge' = [ + 0 = "cut"; + 1 = "Cut"; + 2 = "copy"; + 3 = "Copy"; + 4 = "delete"; + 5 = "Delete"; + 6 = "set attr"; + 7 = "print attr"; + ]; +]; +dotty.protovt.normal.keys = [ + 'general' = [ + 'u' = "undo"; + 'p' = "paste"; + 'l' = "do layout"; + 'k' = "cancel layout"; + ' ' = "redraw"; + 'L' = "reload graph"; + 's' = "save graph"; + 'Z' = "zoom in slowly"; + 'z' = "zoom out slowly"; + 'h' = "scroll horizontally"; + 'v' = "scroll vertically"; + ]; + 'node' = [ + 'c' = "copy"; + 'C' = "Copy"; + 'g' = "group"; + 'G' = "Group"; + 'd' = "delete"; + 'D' = "Delete"; + 'r' = "remove"; + 'R' = "Remove"; + 'a' = "set attr"; + ]; + 'edge' = [ + 'c' = "copy"; + 'C' = "Copy"; + 'd' = "delete"; + 'D' = "Delete"; + 'a' = "set attr"; + ]; +]; +dotty.protovt.birdseye.menus = dotty.protovt.normal.menus; +dotty.protovt.birdseye.keys = dotty.protovt.normal.keys; +dotty.protovt.normal.uifuncs = [ + 'leftdown' = function (data) { + local gt; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + if (data.obj.nid >= 0) { + dotty.node2move = data.obj; + dotty.movewidget = data.widget; + dotty.rp2 = data.pos; + } + }; + 'leftmove' = function (data) { + local gt; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + if (dotty.node2move & ( + dotty.rp2.x ~= data.pos.x | dotty.rp2.y ~= data.pos.y + )) { + gt.movenode (gt, dotty.node2move, data.pos); + dotty.rp2 = data.pos; + } + }; + 'leftup' = function (data) { + local gt; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + if (dotty.node2move) { + if (dotty.movewidget == data.widget) + gt.movenode (gt, dotty.node2move, data.pos); + dotty.node2move = 0; + } else if (~data.obj) + gt.insertnode (gt, data.pos, null, null, null, 1); + }; + 'middledown' = function (data) { + if (~(data.obj.nid >= 0)) + return; + dotty.rubberband = 1; + dotty.movewidget = data.widget; + setgfxattr (data.widget, ['mode' = 'xor';]); + dotty.rp1 = data.pos; + dotty.rp2 = data.pos; + line (data.widget, null, dotty.rp1, dotty.rp2, ['color' = 1;]); + }; + 'middlemove' = function (data) { + if (dotty.rubberband ~= 1 | ( + dotty.rp2.x == data.pos.x & dotty.rp2.y == data.pos.y + )) + return; + line (data.widget, null, dotty.rp1, dotty.rp2, ['color' = 1;]); + dotty.rp2 = data.pos; + line (data.widget, null, dotty.rp1, dotty.rp2, ['color' = 1;]); + }; + 'middleup' = function (data) { + local gt; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + if (dotty.rubberband ~= 1) + return; + dotty.rubberband = 0; + line (dotty.movewidget, null, dotty.rp1, dotty.rp2, ['color' = 1;]); + setgfxattr (dotty.movewidget, ['mode' = 'src';]); + if (dotty.movewidget ~= data.widget | ~( + data.pobj.nid >= 0) | ~(data.obj.nid >= 0 + )) + return; + if (data.pobj.attr.support) + gt.groupnodes (gt, [ + data.obj.nid = data.obj; + data.pobj.nid = data.pobj; + ], data.obj, null, null, null, 1, 1); + else if (data.obj.attr.support) + gt.groupnodes (gt, [ + data.obj.nid = data.obj; + data.pobj.nid = data.pobj; + ], data.pobj, null, null, null, 1, 1); + else + gt.insertedge (gt, data.pobj, null, data.obj, null, null, 1); + }; + 'rightdown' = function (data) { + local vt, gt, menu, i; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + if (~data.obj) + menu = vt.menus.general; + else if (data.obj.nid >= 0) + menu = vt.menus.node; + else if (data.obj.eid >= 0) + menu = vt.menus.edge; + if ((i = displaymenu (data.widget, menu)) >= 0) + gt.doaction (data, menu[i]); + }; + 'button3up' = function (data) { + local vt, attr; + + vt = dotty.views[data.widget]; + attr = getwidgetattr (vt.scroll, [0 = 'childcenter';]); + if (vt.scrollmode == 'h') + attr.childcenter.x = attr.childcenter.x - 40; + else + attr.childcenter.y = attr.childcenter.y - 40; + setwidgetattr (vt.scroll, ['childcenter' = attr.childcenter;]); + }; + 'button4up' = function (data) { + local vt, attr; + + vt = dotty.views[data.widget]; + attr = getwidgetattr (vt.scroll, [0 = 'childcenter';]); + if (vt.scrollmode == 'h') + attr.childcenter.x = attr.childcenter.x + 40; + else + attr.childcenter.y = attr.childcenter.y + 40; + setwidgetattr (vt.scroll, ['childcenter' = attr.childcenter;]); + }; + 'keyup' = function (data) { + local vt, gt, action; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + if (data.obj.nid >= 0) { + if (vt.keys.node[data.key]) + action = vt.keys.node[data.key]; + } else if (data.obj.eid >= 0) { + if (vt.keys.edge[data.key]) + action = vt.keys.edge[data.key]; + } + if (~action) + if (vt.keys.general[data.key]) + action = vt.keys.general[data.key]; + if (action) + gt.doaction (data, action); + }; + 'redraw' = function (data) { + local vt, gt; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + gt.drawgraph (gt, [vt.vtid = vt;]); + }; + 'closeview' = function (data) { + local vt, gt; + + vt = dotty.views[data.widget]; + gt = dotty.graphs[vt.gtid]; + gt.destroyview (gt, vt); + if (tablesize (gt.views) == 0) + gt.destroygraph (gt); + }; +]; +dotty.protovt.birdseye.uifuncs = [ + 'leftdown' = function (data) { + local gt, vid; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + for (vid in gt.views) { + vt = gt.views[vid]; + if (vt.type ~= 'birdseye') + gt.setviewcenter ([vid = vt;], data.pos); + } + }; + 'leftmove' = function (data) { + local gt, vid; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + for (vid in gt.views) { + vt = gt.views[vid]; + if (vt.type ~= 'birdseye') + gt.setviewcenter ([vid = vt;], data.pos); + } + }; + 'leftup' = function (data) { + local gt, vid; + + gt = dotty.graphs[dotty.views[data.widget].gtid]; + for (vid in gt.views) { + vt = gt.views[vid]; + if (vt.type ~= 'birdseye') + gt.setviewcenter ([vid = vt;], data.pos); + } + }; + 'middledown' = dotty.protovt.normal.uifuncs.middledown; + 'middlemove' = dotty.protovt.normal.uifuncs.middlemove; + 'middleup' = dotty.protovt.normal.uifuncs.middleup; + 'rightdown' = dotty.protovt.normal.uifuncs.rightdown; + 'keyup' = dotty.protovt.normal.uifuncs.keyup; + 'redraw' = dotty.protovt.normal.uifuncs.redraw; + 'closeview' = dotty.protovt.normal.uifuncs.closeview; +]; +dotty.monitorfile = function (data) { + local gtid, gt, lpt; + + for (gtid in dotty.layoutpending) { + lpt = dotty.layoutpending[gtid]; + if (lpt.fd == data.fd) { + gt = dotty.graphs[lpt.gtid]; + gt.haveinput = 1; + gt.layoutgraph (gt); + return 1; + } + } + return 0; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/fractal.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +load ('def.lefty'); +definit (); +# +# initialize window data +# +canvas = defcanvas; +wrect = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 400; 'y' = 500;];]; +setwidgetattr (canvas, ['window' = wrect;]); + +sq = function (x) { + return x * x; +}; + +# data structures +# +length = 300; +center = ['x' = 200; 'y' = 250;]; +radius = 2 * length / sqrt (12); +fractalangle = 0; +maxlevel = 2; + +# drawing functions +# +# draw a Koch curve (a ``snowflake'' fractal) +# +# start with a triangle and keep replacing edges +# with the construct: _/\_ +# until the recursion level reaches 'maxlevel' +# +fractal = function (level, length, angle) { + local nlength, newpenpos; + + if (level >= maxlevel) { + newpenpos.x = penpos.x + length * cos (angle); + newpenpos.y = penpos.y + length * sin (angle); + line (canvas, null, penpos, newpenpos, ['color' = 1;]); + penpos = newpenpos; + return; + } + nlength = length / 3; + fractal (level + 1, nlength, angle); + fractal (level + 1, nlength, angle + 60); + fractal (level + 1, nlength, angle - 60); + fractal (level + 1, nlength, angle); +}; +drawfractal = function () { + clear (canvas); + setpick (canvas, center, wrect); + penpos = [ + 'x' = center.x + cos (fractalangle + 210) * radius; + 'y' = center.y + sin (fractalangle + 210) * radius; + ]; + fractal (0, length, fractalangle + 60); + fractal (0, length, fractalangle - 60); + fractal (0, length, fractalangle - 180); + remove ('penpos'); +}; + +# editing functions +# +# transform the fractal. +# +# map point 'prevpoint' to point 'currpoint' +# with respect to the center of the fractal. +# +transformfractal = function (prevpoint, currpoint) { + local prevtan, currtan, prevradius, currradius; + + prevtan = atan (prevpoint.y - center.y, prevpoint.x - center.x); + currtan = atan (currpoint.y - center.y, currpoint.x - center.x); + fractalangle = fractalangle + (currtan - prevtan); + prevradius = sqrt ( + sq (prevpoint.y - center.y) + sq (prevpoint.x - center.x) + ); + currradius = sqrt ( + sq (currpoint.y - center.y) + sq (currpoint.x - center.x) + ); + radius = radius / prevradius * currradius; + length = radius / 2 * sqrt (12); +}; + +# user interface functions +# +# bind changes to the fractal to user actions +# +leftup = function (data) { + transformfractal (data.ppos, data.pos); + drawfractal (); +}; +dops = function () { + local s; + + s = ['x' = 8 * 300; 'y' = 10.5 * 300;]; + canvas = createwidget (-1, ['type' = 'ps'; 'size' = s;]); + setwidgetattr (canvas, ['window' = wrect;]); + drawfractal (); + destroywidget (canvas); + canvas=defcanvas; +}; +transformfractal (['x' = 0; 'y' = 0;], ['x' = 0; 'y' = 0;]); +drawfractal ();
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/fractal2.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,218 @@ +# +# data structures +# +length = 300; +center = ['x' = 200; 'y' = 250;]; +radius = 2 * length / sqrt (12); +fractalangle = 0; +maxlevel = 2; +sizes = [ + 'button' = [ 'x' = 100; 'y' = 40; ]; + 'canvas' = [ 'x' = 400; 'y' = 500; ]; + 'view' = [ 'x' = 400; 'y' = 600; ]; +]; +sq = function (x) { + return x * x; +}; +# +# create view and other widgets +# +init = function () { + view = createwidget (-1, [ + 'type' = 'view'; 'name' = 'fractal'; 'size' = sizes.view; + ]); + + array1 = createwidget (view, [ + 'type' = 'array'; 'borderwidth' = 1; 'mode' = 'vertical'; + ]); + widgets[array1].resize = resize; + + array2 = createwidget (array1, [ + 'type' = 'array'; 'borderwidth' = 1; 'mode' = 'horizontal'; + ]); + widgets[array2].resize = resize; + + array3 = createwidget (array2, [ + 'type' = 'array'; 'borderwidth' = 1; 'mode' = 'vertical'; + ]); + widgets[array3].resize = resize; + + morebutton = createwidget (array3, [ + 'type' = 'button'; 'text' = 'more'; + ]); + widgets[morebutton].pressed = pressed; + lessbutton = createwidget (array3, [ + 'type' = 'button'; 'text' = 'less'; + ]); + widgets[lessbutton].pressed = pressed; + setwidgetattr (morebutton, ['size' = sizes.button;]); + setwidgetattr (lessbutton, ['size' = sizes.button;]); + + atext = createwidget (array2, [ + 'type' = 'text'; 'mode' = 'oneline'; + ]); + widgets[atext].oneline = oneline; + setwidgetattr (atext, [ + 'size' = ['x' = sizes.button.x; 'y' = sizes.button.y * 2;]; + ]); + + scroll = createwidget (array1, ['type' = 'scroll';]); + canvas = createwidget (scroll, ['type' = 'canvas';]); + wrect = [0 = ['x' = 0; 'y' = 0;]; 1 = sizes.canvas;]; + setwidgetattr (canvas, ['window' = wrect; 'viewport' = wrect[1];]); +}; +# +# drawing functions +# +# draw a Koch curve (a ``snowflake'' fractal) +# +# start with a triangle and keep replacing edges +# with the construct: _/\_ +# until the recursion level reaches 'maxlevel' +# +fractal = function (level, length, angle) { + local nlength, newpenpos; + + if (level >= maxlevel) { + newpenpos.x = penpos.x + length * cos (angle); + newpenpos.y = penpos.y + length * sin (angle); + line (canvas, null, penpos, newpenpos, ['color' = 1;]); + penpos = newpenpos; + return; + } + nlength = length / 3; + fractal (level + 1, nlength, angle); + fractal (level + 1, nlength, angle + 60); + fractal (level + 1, nlength, angle - 60); + fractal (level + 1, nlength, angle); +}; +redrawfractal = function () { + clear (canvas); + setpick (canvas, center, wrect); + penpos = [ + 'x' = center.x + cos (fractalangle + 210) * radius; + 'y' = center.y + sin (fractalangle + 210) * radius; + ]; + fractal (0, length, fractalangle + 60); + fractal (0, length, fractalangle - 60); + fractal (0, length, fractalangle - 180); + remove ('penpos'); +}; +# +# editing functions +# +# transform the fractal. +# +# map point 'prevpoint' to point 'currpoint' +# with respect to the center of the fractal. +# +transformfractal = function (prevpoint, currpoint) { + local prevtan, currtan, prevradius, currradius; + + prevtan = atan (prevpoint.y - center.y, prevpoint.x - center.x); + currtan = atan (currpoint.y - center.y, currpoint.x - center.x); + fractalangle = fractalangle + (currtan - prevtan); + prevradius = sqrt ( + sq (prevpoint.y - center.y) + sq (prevpoint.x - center.x) + ); + currradius = sqrt ( + sq (currpoint.y - center.y) + sq (currpoint.x - center.x) + ); + radius = radius / prevradius * currradius; + length = radius / 2 * sqrt (12); +}; +# +# main actions +# +redraw = function (data) { + redrawfractal (); +}; +changemaxlevel = function (dn) { + maxlevel = maxlevel + dn; + if (maxlevel < 0) + maxlevel = 0; + redrawfractal (); +}; +resize = function (data) { + local ret; + if (data.widget == array1) { + ret = [ + array2 = [ + 'x' = data.size.x; + 'y' = sizes.button.y * 2; + ]; + scroll = [ + 'x' = data.size.x; + 'y' = data.size.y - sizes.button.y * 2; + ]; + ]; + } else if (data.widget == array2) { + ret = [ + array3 = [ + 'x' = sizes.button.x; + 'y' = 2 * sizes.button.y; + ]; + atext = [ + 'x' = data.size.x - sizes.button.x; + 'y' = 2 * sizes.button.y; + ]; + ]; + } else if (data.widget == array3) { + ret = [ + morebutton = sizes.button; + lessbutton = sizes.button; + ]; + } + return ret; +}; +# +# user interface functions +# +# bind changes to the fractal to user actions +# +leftup = function (data) { + transformfractal (data.ppos, data.pos); + redrawfractal (); +}; +menu = [ + 0 = 'more'; + 1 = 'less'; +]; +domenu = function (i) { + local s; + s = menu[i]; + if (s == 'more') + changemaxlevel (1); + else if (s == 'less') + changemaxlevel (-1); +}; +rightdown = function (data) { + domenu (displaymenu (canvas, menu)); +}; +pressed = function (data) { + if (data.widget == morebutton) + changemaxlevel (1); + else if (data.widget == lessbutton) + changemaxlevel (-1); +}; +oneline = function (data) { + local dn; + dn = ston (data.text); + if (dn > 0 | dn < 0) + changemaxlevel (dn - maxlevel); +}; +# +# postscript generation +# +dops = function () { + local r; + + r = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 8 * 300; 'y' = 10.5 * 300;];]; + canvas = opencanvas ('pscanvas', '', r); + setwidgetattr (canvas, ['window' = wrect;]); + redraw (); + closecanvas (canvas); + canvas=defcanvas; +}; +init (); +#txtview ('off');
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/lefty.psp Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,80 @@ +/BOX { + /boxy1 exch def /boxx1 exch def /boxy0 exch def /boxx0 exch def + boxx0 boxy0 moveto boxx1 boxy0 lineto + boxx1 boxy1 lineto boxx0 boxy1 lineto + closepath +} def +/SCP { stroke initclip newpath BOX clip newpath } def +/CL { stroke setrgbcolor } def +/DO { stroke } def +/NP { newpath } def +/FI { fill } def +/LI { moveto lineto } def +/CT { curveto } def +/AR { + /ang2 exch def /ang1 exch def + /radius exch def /y2x exch def /cy exch def /cx exch def + gsave + cx cy translate 1 y2x scale 0 0 radius ang1 ang2 arc stroke + grestore +} def +/ARF { + /ang2 exch def /ang1 exch def + /radius exch def /y2x exch def /cy exch def /cx exch def + gsave + cx cy translate 1 y2x scale 0 0 radius ang1 ang2 arc fill + grestore +} def +/TXT { + /texth exch def + /textf exch def + /textn exch def + /texts exch def + /textyj exch def /texty exch def + /textxj exch def /textx exch def + textf findfont texth scalefont dup setfont + /FontBBox get 1 get 1000 div texth mul /textbl exch def + /textth texth textn mul def /texttw 0 def + 0 1 textn 1 sub { + texts exch get 0 get stringwidth pop + dup texttw gt { /texttw exch def } { pop } ifelse + } for + textyj (b) eq { /ty texty textth add textbl add def } if + textyj (d) eq { /ty texty textth add def } if + textyj (c) eq { /ty texty textth 2 div add def } if + textyj (u) eq { /ty texty def } if + /ty ty textbl sub def + textxj (l) eq { /tx textx def } if + textxj (c) eq { /tx textx texttw 2 div sub def } if + textxj (r) eq { /tx textx texttw sub def } if + 0 1 textn 1 sub { + /ty ty texth sub def + texts exch get dup 0 get /ts exch def 1 get /tj exch def + tj (l) eq { tx ty moveto ts show } if + tj (n) eq { + tx texttw ts stringwidth pop sub 2 div add ty moveto ts show + } if + tj (r) eq { + tx texttw ts stringwidth pop sub add ty moveto ts show + } if + } for +} def + +/colorimage where { + pop +} { + /bwproc { + rgbproc dup length 3 idiv string 0 3 0 5 -1 roll { + add 2 1 roll 1 sub dup 0 eq { + pop 3 idiv 3 -1 roll dup 4 -1 roll + dup 3 1 roll 5 -1 roll put 1 add 3 0 + } { + 2 1 roll + } ifelse + } forall + pop pop pop + } def + /colorimage { + pop pop /rgbproc exch def {bwproc} image + } bind def +} ifelse
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/slides.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,109 @@ +load ('def.lefty'); +definit (); +# +# initialize window data +# +canvas = defcanvas; +wrect = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 800; 'y' = 1000;];]; +lmargin = 100; +width = 800; +height = 1000; +setwidgetattr (canvas, ['window' = wrect;]); +fonts = [ + 'timr' = [ + 14 = 'timr14'; + 18 = 'timr18'; + 24 = 'timr24'; + ]; + 'courr' = [ + 14 = 'courr14'; + 18 = 'courr18'; + 24 = 'courr24'; + ]; +]; +x2ps = [ + 'timr24' = 'Times-Roman'; + 'timr18' = 'Times-Roman'; + 'timr14' = 'Times-Roman'; + 'courr24' = 'Courier'; + 'courr18' = 'Courier'; + 'courr14' = 'Courier'; +]; + +calc = function () { + local i, j, cpos, tsiz, dist, slidep; + tsiz = ['x' = 0; 'y' = 0;]; + for (i = 0; slides[i]; i = i + 1) { + slidep = slides[i]; + if (slidep.skip) { + tsiz.y = tsiz.y + slidep.skip; + } else { + for (j = 0; slidep.text[j]; j = j + 1) { + if (j > 0) + tsiz.y = tsiz.y + 5; + if (~slidep.font) + slidep.font = slides.font; + if (~slidep.size) + slidep.size = slides.size; + if (~slidep.just) + slidep.just = slides.just; + slidep.tsiz[j] = textsize (canvas, slidep.text[j], + fonts[slidep.font][slidep.size], 0); + tsiz.y = tsiz.y + slidep.tsiz[j].y; + } + } + } + dist = (height - tsiz.y) / (i + 1); + cpos = ['x' = lmargin; 'y' = height - dist;]; + for (i = 0; slides[i]; i = i + 1) { + slidep = slides[i]; + if (slidep.skip) { + cpos.y = cpos.y - slidep.skip - dist; + } else { + for (j = 0; slidep.text[j]; j = j + 1) { + if (j > 0) + cpos.y = cpos.y - 5; + if (slidep.just == 0) + slidep.tpos[j].x = cpos.x; + else + slidep.tpos[j].x = width / 2; + cpos.y = cpos.y - slidep.tsiz[j].y; + slidep.tpos[j].y = cpos.y; + } + cpos.y = cpos.y - dist; + } + } +}; +redraw = function () { + local i, j, slidep; + for (i = 0; slides[i]; i = i + 1) { + slidep = slides[i]; + if (~slidep.skip) + for (j = 0; slidep.text[j]; j = j + 1) + text (canvas, slidep, slidep.tpos[j], slidep.text[j], + fonts[slidep.font][slidep.size], 0, slidep.just); + } +}; +dops = function () { + local i, j, slidep, r; + + r = [0 = ['x' = 0; 'y' = 0;]; 1 = ['x' = 8 * 300; 'y' = 10.5 * 300;];]; + canvas = createwidget (-1, ['type' = 'ps'; 'origin' = r[0]; 'size' = r[1] +;]); + setwidgetattr (canvas, ['window' = wrect;]); + for (i = 0; slides[i]; i = i + 1) { + slidep = slides[i]; + if (~slidep.skip) + for (j = 0; slidep.text[j]; j = j + 1) + text (canvas, slidep, slidep.tpos[j], slidep.text[j], + x2ps[fonts[slidep.font][slidep.size]], + slidep.tsiz[j].y, slidep.just, 1); + } + destroywidget (canvas); + canvas=defcanvas; +}; +doit = function () { + calc (); + redraw (); + dops (); +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/lefty/tree.lefty Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,195 @@ +load ('def.lefty'); +definit (); +# +# initialize window data +# +canvas = defcanvas; +wrect = [0 = ['x' = -5; 'y' = 0;]; 1 = ['x' = 410; 'y' = 500;];]; +setwidgetattr (canvas, ['window' = wrect;]); +# +# data structures +# +nodearray = []; +nodenum = 0; +dist = ['x' = 40; 'y' = 40;]; +defsize = ['x' = 10; 'y' = 10;]; +fontname = 'fixed'; +fontsize = 18; +tree = null; + +# drawing functions +# +boxnode = function (node) { + local center; + box (canvas, node, node.rect, ['color' = 0; 'fill' = 'on';]); + box (canvas, node, node.rect); + center = [ + 'x' = (node.rect[0].x + node.rect[1].x) / 2; + 'y' = (node.rect[0].y + node.rect[1].y) / 2; + ]; + if (node.name) + text (canvas, node, center, node.name, fontname, fontsize, 'cc'); +}; +circlenode = function (node) { + local center, radius; + center = [ + 'x' = (node.rect[0].x + node.rect[1].x) / 2; + 'y' = (node.rect[0].y + node.rect[1].y) / 2; + ]; + radius = [ + 'x' = center.x - node.rect[0].x; + 'y' = center.y - node.rect[0].y; + ]; + arc (canvas, node, center, radius, ['color' = 0; 'fill' = 'on';]); + arc (canvas, node, center, radius); + if (node.name) + text (canvas, node, center, node.name, fontname, fontsize, 'cc'); +}; +drawnode = boxnode; +drawedge = function (node1, node2) { + line (canvas, null, + [ + 'x' = (node1.rect[1].x + node1.rect[0].x) / 2; + 'y' = node1.rect[0].y; + ], [ + 'x' = (node2.rect[1].x + node2.rect[0].x) / 2; + 'y' = node2.rect[1].y; + ]); +}; +drawtree = function (node) { + local i; + for (i in nodearray) + drawnode (nodearray[i]); + drawtreerec (node); +}; +drawtreerec = function (node) { + local i, n; + if ((n = tablesize (node.ch)) > 0) { + for (i = 0; i < n; i = i + 1) { + drawedge (node, node.ch[i]); + drawtreerec (node.ch[i]); + } + } +}; +redraw = function (c) { + if (tree) + drawtree (tree); +}; + +# layout functions +# +complayout = function () { + leafx = 0; + leafrank = 0; + dolayout (tree, wrect[1].y - 10); + remove ('leafx'); + remove ('leafrank'); +}; +dolayout = function (node, pary) { + local r, n, i, size, lchp, rchp; + size = nodesize (node); + if (node.chn > 0) { + for (i = 0; i < node.chn; i = i + 1) + dolayout (node.ch[i], pary - size.y - dist.y); + node.rank = (node.ch[0].rank + node.ch[node.chn - 1].rank) / 2; + lchp = node.ch[0].rect; + rchp = node.ch[node.chn - 1].rect; + r[0].x = lchp[0].x + ((rchp[1].x - lchp[0].x) - size.x) / 2; + r[0].y = pary - size.y; + r[1].x = r[0].x + size.x; + r[1].y = pary; + node.rect = r; + } else { + node.rank = leafrank; + r[0].x = leafx; + r[0].y = pary - size.y; + r[1].x = r[0].x + size.x; + r[1].y = pary; + leafrank = leafrank + 1; + leafx = r[1].x + dist.x; + node.rect = r; + } +}; + +# editing functions +# +inode = function (point, name) { + local i, nnum, size; + nnum = nodenum; + if (~name) + name = ask ('give name of node:'); + nodearray[nnum].ch = []; + nodearray[nnum].chn = 0; + nodearray[nnum].name = name; + size = nodesize (nodearray[nnum]); + nodearray[nnum].rect[0] = point; + nodearray[nnum].rect[1] = ['x' = point.x + size.x; 'y' = point.y + size.y;]; + nodenum = nodenum + 1; + if (~tree) { + tree = nodearray[nnum]; + tree.depth = 0; + complayout (); + drawtree (tree); + } else + drawtree (nodearray[nnum]); + return nodearray[nnum]; +}; +iedge = function (node1, node2) { + node1.ch[node1.chn] = node2; + node1.chn = node1.chn + 1; + node2.depth = node1.depth + 1; + complayout (); + clear (canvas); + drawtree (tree); +}; +fix = function (node, op, np) { + if (node.depth ~= 0) + dist.y = dist.y + (op.y - np.y) / node.depth; + if (node.rank ~= 0) + dist.x = dist.x + (np.x - op.x) / node.rank; + complayout (); + clear (canvas); + drawtree (tree); +}; +nodesize = function (node) { + local siz; + if (~(siz = textsize (canvas, node.name, fontname, fontsize))) + siz = defsize; + else { + siz.x = siz.x + 8; + siz.y = siz.y + 8; + } + return siz; +}; +changenode = function (nodestyle) { + drawnode = nodestyle; + clear (canvas); + drawtree (tree); +}; + +# user interface functions +# +leftdown = function (data) { + if (~data.obj) + inode (data.pos, null); +}; +leftup = function (data) { + if (data.pobj) + fix (data.pobj, data.ppos, data.pos); +}; +middleup = function (data) { + if (data.pobj & data.obj) + iedge (data.pobj, data.obj); +}; +dops = function () { + local s; + + s = ['x' = 8 * 300; 'y' = 10.5 * 300;]; + fontname = 'Times-Roman'; + canvas = createwidget (-1, ['type' = 'ps'; 'size' = s;]); + setwidgetattr (canvas, ['window' = wrect;]); + drawtree (tree); + destroywidget (canvas); + canvas=defcanvas; + fontname = 'fixed'; +};
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/smyrna/attr_widgets.dot Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,50 @@ +digraph smyrna_attr_widgets +{ + graph[color_button_bgcolor="settingsColorBtn1"]; + graph[color_button_bordercolor="settingsColorBtn2"]; + graph[color_button_gridcolor="settingsColorBtn3"]; + graph[color_button_selectednodecolor="settingsColorBtn8"]; + graph[color_button_selectededgecolor="settingsColorBtn9"]; + graph[color_button_defaultnodecolor="DefaultNodeCbtn"]; + graph[color_button_defaultedgecolor="DefaultEdgeCbtn"]; + graph[color_button_topologicaltopviewfinestcolor="settingsColorBtn9"]; + graph[color_button_topologicaltopviewcoarsestcolor="settingsColorBtn9"]; + graph[color_button_topologicalfisheyefinestcolor="settingsColorBtn10"]; + graph[color_button_topologicalfisheyecoarsestcolor="settingsColorBtn11"]; + graph[text_box_topologicalfisheyelabelattribute="finenodelabelattribute"]; + graph[check_box_bordervisible="settingsChkBox2"]; + graph[check_box_gridvisible="settingsChkBox3"]; + graph[check_box_drawnodes="settingsChkBox5-1"]; + graph[check_box_drawedges="settingsChkBox5-2"]; + graph[check_box_edgesselectable="chkboxEdgeSelect"]; + graph[check_box_nodesselectable="chkboxNodeSelect"]; + graph[combobox_labelglutfont="labelfont"]; + graph[combobox_defaultnodeshape="settingscombobox1"]; + graph[combobox_colortheme="colorthemecb"]; + graph[color_button_nodelabelcolor="nodelabelcolor"]; + graph[color_button_edgelabelcolor="edgelabelcolor"]; + graph[text_box_nodelabelattribute="labelnodeattribute"]; + graph[text_box_edgecolorattribute="edgecolortxt"]; + graph[text_box_edgelabelattribute="labeledgeattribute"]; + graph[check_box_labelwithdegree="labelwithdegree"]; + graph[spin_button_labelnumberofnodes="labelzoomfactor"]; + graph[check_box_shownodelabels="labelshownodes"]; + graph[check_box_showedgelabels="labelshowedges"]; + graph[check_box_usermode="settingsChkBox10"]; + graph[check_box_antialiasing="settingsChkBox12"]; + graph[check_box_topologicalfisheyedist2limit="settingsChkBox13"]; + graph[check_box_topologicalfisheyeanimate="settingsChkBox14"]; + graph[check_box_topologicalfisheyelabelfinenodes="settingsChkBox15"]; + graph[check_box_topologicalfisheyecolornodes="settingsChkBox16"]; + graph[check_box_topologicalfisheyecoloredges="settingsChkBox17"]; + graph[check_box_topologicalfisheyelabelfocus="settingsChkBox18"]; + graph[spin_button_defaultfisheyemagnifierradius="settingsspinbutton4"]; + graph[spin_button_defaultfisheyemagnifierdistort="settingsspinbutton5"]; + graph[spin_button_topologicalfisheyefinenodes="settingsspinbutton6"]; + graph[spin_button_topologicalfisheyecoarseningfactor="settingsspinbutton7"]; + graph[spin_button_topologicalfisheyedistortionfactor="settingsspinbutton8"]; + graph[scale_button_defaultnodealpha="settingsscale1"]; + graph[scale_button_defaultedgealpha="settingsscale2"]; + graph[scale_button_nodesize="nodesizescale"]; + graph[combobox_edgerender="edgerendercb"]; +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/smyrna/attrs.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +F,Damping, 0.99, GRAPH, NEATO +F,Epsilon, , GRAPH, NEATO +A,URL, , ANY_ELEMENT, ALL_ENGINES +A,arrowhead, normal, EDGE, ALL_ENGINES +F,arrowsize, 1.0, EDGE, ALL_ENGINES +A,arrowtail, normal, EDGE, ALL_ENGINES +A,bb, , GRAPH, ALL_ENGINES +C,bgcolor, white, GRAPH Or CLUSTER, ALL_ENGINES +A,bottomlabel, , NODE, ALL_ENGINES +B,center, false, GRAPH, ALL_ENGINES +A,clusterrank, local, GRAPH, DOT +C,color,, EDGE Or NODE Or CLUSTER, ALL_ENGINES +A,comment, , EDGE Or NODE Or GRAPH, ALL_ENGINES +B,compound, false, GRAPH, DOT +B,concentrate, false, GRAPH, DOT +B,constraint, true, EDGE, DOT +B,decorate, false, EDGE, ALL_ENGINES +D,dir, forward, EDGE, ALL_ENGINES +F,distortion, 0.0, NODE, ALL_ENGINES +C,fillcolor, lightgrey, NODE Or CLUSTER, ALL_ENGINES +B,fixedsize, false, NODE, ALL_ENGINES +C,fontcolor, green, EDGE Or NODE Or GRAPH Or CLUSTER, ALL_ENGINES +A,fontname, Times-Roman, EDGE Or NODE Or GRAPH Or CLUSTER, ALL_ENGINES +A,fontpath, , GRAPH, ALL_ENGINES +I,fontsize, 14.0, EDGE Or NODE Or GRAPH Or CLUSTER, ALL_ENGINES +A,group, , NODE, DOT +A,headURL, , EDGE, ALL_ENGINES +A,headlabel, , EDGE, ALL_ENGINES +A,headport, center, EDGE, ALL_ENGINES +F,height, 0.5, NODE, ALL_ENGINES +A,label, , EDGE Or NODE Or GRAPH Or CLUSTER, ALL_ENGINES +F,labelangle, -25.0, EDGE, ALL_ENGINES +F,labeldistance, 1.0, EDGE, ALL_ENGINES +B,labelfloat, false, EDGE, ALL_ENGINES +C,labelfontcolor, black, EDGE, ALL_ENGINES +A,labelfontname, Times-Roman, EDGE, ALL_ENGINES +I,labelfontsize, 11.0, EDGE, ALL_ENGINES +A,labeljust, , ANY_ELEMENT, DOT +A,labelloc, t, GRAPH Or CLUSTER, DOT +A,layer, , EDGE Or NODE, ALL_ENGINES +A,layers, , GRAPH, ALL_ENGINES +F,len, 1.0, EDGE, NEATO +A,lhead, , EDGE, DOT +A,lp, , EDGE Or GRAPH Or CLUSTER, ALL_ENGINES +A,ltail, , EDGE, DOT +F,margin, , GRAPH, ALL_ENGINES +I,maxiter, , GRAPH, NEATO +F,mclimit, 1.0, GRAPH, DOT +I,minlen, 1, EDGE, DOT +A,model, , GRAPH, NEATO +F,nodesep, 0.25, GRAPH, DOT +B,normalize, false, GRAPH, NEATO +F,nslimit, , GRAPH, DOT +A,ordering, , GRAPH, DOT +F,orientation, 0.0, NODE, ALL_ENGINES +A,orientation, , GRAPH, ALL_ENGINES +B,overlap, , GRAPH, NEATO +B,pack, false, GRAPH, NEATO +A,page, , GRAPH, ALL_ENGINES +A,pagedir, BL, GRAPH, ALL_ENGINES +C,pencolor, black, CLUSTER, ALL_ENGINES +I,peripheries, 0, NODE, ALL_ENGINES +B,pin, false, NODE, NEATO +A,pos, , EDGE Or NODE, ALL_ENGINES +F,quantum, 0.0, GRAPH, ALL_ENGINES +A,rank, , SUBGRAPH, DOT +A,rankdir, , GRAPH, DOT +F,ranksep, , GRAPH, ALL_ENGINES +F,ratio, , GRAPH, ALL_ENGINES +A,rects, , NODE, ALL_ENGINES +B,regular, false, NODE, ALL_ENGINES +B,remincross, false, GRAPH, DOT +I,rotate, 0, GRAPH, ALL_ENGINES +A,samehead, , EDGE, DOT +A,sametail, , EDGE, DOT +I,samplepoints, 8, GRAPH, ALL_ENGINES +I,searchsize, 30, GRAPH, DOT +F,sep, 0.01, GRAPH, NEATO +A,shape, ellipse, NODE, ALL_ENGINES +A,shapefile, , NODE, ALL_ENGINES +I,showboxes, 0, EDGE Or NODE Or GRAPH, DOT +I,sides, 4, NODE, ALL_ENGINES +F,size, , GRAPH, ALL_ENGINES +F,skew, 0.0, NODE, ALL_ENGINES +B,splines, false, GRAPH, NEATO +A,start, , GRAPH, ALL_ENGINES +A,style, , EDGE Or NODE, ALL_ENGINES +A,stylesheet, , GRAPH, ALL_ENGINES +A,tailURL, , EDGE, ALL_ENGINES +A,taillabel, , EDGE, ALL_ENGINES +A,tailport, center, EDGE, ALL_ENGINES +A,toplabel, , NODE, ALL_ENGINES +A,vertices, , NODE, ALL_ENGINES +F,voro_margin, 0.05, GRAPH, NEATO +F,weight, , EDGE, DOT Or NEATO +F,width, 0.75, NODE, ALL_ENGINES +F,z, 0.0, NODE, ALL_ENGINES
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/smyrna/mouse_actions.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,34 @@ +#Handled actions: +#MM_PAN +#MM_ROTATE +#MM_SINGLE_SELECT +#MM_RECTANGULAR_SELECT +#MM_MOVE +#MM_FISHEYE_MAGNIFIER +#keys:These are pre defined keys in the code , more keys can be added by using their GTK key event correspondants, 0 means no key +#B_LSHIFT Left Shift +#B_RSHIFT Right Shift +#B_LCTRL Left CTRL +#B_RCTRL Right CTRL +#view modes , use one from the following list for each line +#ALL Action is always active +#2D active only in 2D mode +#3D active in only 3D mode +#FISHEYE active in only fisheye mode +#NO_FISHEYE active when mode is not fisheye +#mouse_button +#LEFT +#RIGHT +#MIDDLE +#drag +#specify if action requires mouse to be dragged +#Line format +#Action,hotkey,view_mode,mouse_button,drag +#COMBO1 +MM_PAN,0,ALL,LEFT,1 +MM_ROTATE,B_LSHIFT,3D,LEFT,1 +MM_SINGLE_SELECT,0,NO_FISHEYE,RIGHT,0 +MM_RECTANGULAR_SELECT,0,ALL,RIGHT,1 +MM_MOVE,B_LCTRL,2D,LEFT,1 +MM_FISHEYE_MAGNIFIER,B_LSHIFT,2D,LEFT,1 +MM_FISHEYE_PICK,0,FISHEYE,RIGHT,0 \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/smyrna/original_smyrna.glade Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5317 @@ +<?xml version="1.0" standalone="no"?> <!--*- mode: xml -*--> +<!DOCTYPE glade-interface SYSTEM "http://glade.gnome.org/glade-2.0.dtd"> + +<glade-interface> + +<widget class="GtkWindow" id="frmMain"> + <property name="width_request">900</property> + <property name="height_request">700</property> + <property name="title" translatable="yes">Smyrna Graph visualizer ver: 0.99 ATT</property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_CENTER</property> + <property name="modal">False</property> + <property name="default_width">900</property> + <property name="default_height">700</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_NORMAL</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + + <child> + <widget class="GtkVBox" id="vbox2"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkHBox" id="hbox13"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkMenuBar" id="menubar1"> + <property name="width_request">540</property> + <property name="visible">True</property> + <property name="pack_direction">GTK_PACK_DIRECTION_LTR</property> + <property name="child_pack_direction">GTK_PACK_DIRECTION_LTR</property> + + <child> + <widget class="GtkMenuItem" id="menuitem1"> + <property name="visible">True</property> + <property name="label" translatable="yes">_File</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem1_menu"> + + <child> + <widget class="GtkImageMenuItem" id="mOpen"> + <property name="visible">True</property> + <property name="label">gtk-open</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mOpenSlot"/> + </widget> + </child> + + <child> + <widget class="GtkImageMenuItem" id="mSave"> + <property name="visible">True</property> + <property name="label">gtk-save</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mSaveSlot"/> + </widget> + </child> + + <child> + <widget class="GtkImageMenuItem" id="mSaveAs"> + <property name="visible">True</property> + <property name="label">gtk-save-as</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mSaveAsSlot"/> + </widget> + </child> + + <child> + <widget class="GtkSeparatorMenuItem" id="separatormenuitem1"> + <property name="visible">True</property> + </widget> + </child> + + <child> + <widget class="GtkImageMenuItem" id="mQuit"> + <property name="visible">True</property> + <property name="label">gtk-quit</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mQuitSlot"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem2"> + <property name="visible">True</property> + <property name="label" translatable="yes">_Edit</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem2_menu"> + + <child> + <widget class="GtkMenuItem" id="mTopviewSettings"> + <property name="visible">True</property> + <property name="label" translatable="yes">Smyrna Settings</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mTopviewSettingsSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="mAttributes"> + <property name="visible">True</property> + <property name="label" translatable="yes">Attributes</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mAttributesSlot" last_modification_time="Fri, 06 Nov 2009 17:03:12 GMT"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="node_list1"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node List</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mNodeListSlot" last_modification_time="Fri, 06 Nov 2009 17:02:05 GMT"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem3"> + <property name="visible">True</property> + <property name="label" translatable="yes">_View</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem3_menu"> + + <child> + <widget class="GtkMenuItem" id="show_console1"> + <property name="visible">True</property> + <property name="label" translatable="yes">Show Console</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mShowConsoleSlot" last_modification_time="Wed, 22 Jul 2009 16:17:32 GMT"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="hide_console1"> + <property name="visible">True</property> + <property name="label" translatable="yes">Hide Console</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mHideConsoleSlot" last_modification_time="Wed, 22 Jul 2009 16:17:32 GMT"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem6"> + <property name="visible">True</property> + <property name="label" translatable="yes">Select</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem6_menu"> + + <child> + <widget class="GtkImageMenuItem" id="mSelectAll"> + <property name="visible">True</property> + <property name="label">gtk-select-all</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mSelectAllSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="mUnselectAll"> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mUnselectAllSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem7"> + <property name="visible">True</property> + <property name="label" translatable="yes">Select All Nodes</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mSelectAllNodesSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem8"> + <property name="visible">True</property> + <property name="label" translatable="yes">Select All Edges</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mSelectAllEdgesSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem9"> + <property name="visible">True</property> + <property name="label" translatable="yes">Select All Clusters</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mSelectAllClustersSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem10"> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All Nodes</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mUnselectAllNodesSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem11"> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All Edges</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mUnselectAllEdgesSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem12"> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All Clusters</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mUnselectAllClustersSlot"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem4"> + <property name="visible">True</property> + <property name="label" translatable="yes">_Help</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem4_menu"> + + <child> + <widget class="GtkImageMenuItem" id="mAbout"> + <property name="visible">True</property> + <property name="label">gtk-about</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mAbout"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label128"> + <property name="visible">True</property> + <property name="label" translatable="yes">Active Graph:</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_RIGHT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">1</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">2</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="button10"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Remove</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + + <child> + <placeholder/> + </child> + + <child> + <widget class="GtkButton" id="btnActivateGraph"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Activate</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_btnActivateGraph_clicked" last_modification_time="Tue, 23 Jun 2009 16:15:08 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox11"> + <property name="height_request">0</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <placeholder/> + </child> + + <child> + <widget class="GtkVBox" id="vbox13"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkScrolledWindow" id="scrolledwindow7"> + <property name="width_request">215</property> + <property name="height_request">488</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="hscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="vscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="shadow_type">GTK_SHADOW_IN</property> + <property name="window_placement">GTK_CORNER_TOP_LEFT</property> + + <child> + <widget class="GtkTextView" id="mainconsole"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="overwrite">False</property> + <property name="accepts_tab">True</property> + <property name="justification">GTK_JUSTIFY_LEFT</property> + <property name="wrap_mode">GTK_WRAP_NONE</property> + <property name="cursor_visible">True</property> + <property name="pixels_above_lines">0</property> + <property name="pixels_below_lines">0</property> + <property name="pixels_inside_wrap">0</property> + <property name="left_margin">0</property> + <property name="right_margin">0</property> + <property name="indent">0</property> + <property name="text" translatable="yes"></property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table17"> + <property name="visible">True</property> + <property name="n_rows">1</property> + <property name="n_columns">4</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkButton" id="consoledecbtn"> + <property name="width_request">27</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">-</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_consoledecbtn_clicked" last_modification_time="Wed, 22 Jul 2009 15:24:50 GMT"/> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="consoleincbtn"> + <property name="width_request">27</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">+</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_consoleincbtn_clicked" last_modification_time="Thu, 23 Jul 2009 18:54:38 GMT"/> + </widget> + <packing> + <property name="left_attach">3</property> + <property name="right_attach">4</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="clearconsolebtn"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Clear</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_clearconsolebtn_clicked" last_modification_time="Wed, 22 Jul 2009 15:22:56 GMT"/> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="hideconsolebtn"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Hide</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_hideconsolebtn_clicked" last_modification_time="Wed, 22 Jul 2009 15:23:55 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="y_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkWindow" id="frmTVNodes"> + <property name="width_request">950</property> + <property name="height_request">680</property> + <property name="title" translatable="yes"></property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_MOUSE</property> + <property name="modal">False</property> + <property name="default_width">950</property> + <property name="default_height">680</property> + <property name="resizable">False</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_NORMAL</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + + <child> + <widget class="GtkVBox" id="vbox3"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkLayout" id="layout10"> + <property name="width_request">0</property> + <property name="height_request">66</property> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 950 0 855 950</property> + <property name="vadjustment">0 0 400 0 59.4 66</property> + + <child> + <widget class="GtkLabel" id="label8"> + <property name="width_request">60</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">ID</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">30</property> + <property name="y">222</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="cgbTVSelect"> + <property name="width_request">25</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NONE</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="toggled" handler="cgbTVSelect_toggled_cb"/> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="x">16</property> + <property name="y">48</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="lblTVData1"> + <property name="width_request">100</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">D</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">360</property> + <property name="y">47</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="lblTVData2"> + <property name="width_request">100</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Data2</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">566</property> + <property name="y">47</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label11"> + <property name="width_request">51</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Visible</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">130</property> + <property name="y">32</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label12"> + <property name="width_request">73</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Highlighted</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">184</property> + <property name="y">32</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="cgbTVVisible"> + <property name="width_request">20</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="toggled" handler="cgbTVVisible_toggled_cb"/> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="x">139</property> + <property name="y">48</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="cgbTVHighlighted"> + <property name="width_request">20</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="toggled" handler="cgbTVHighlighted_toggled_cb"/> + </widget> + <packing> + <property name="x">204</property> + <property name="y">48</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label20"> + <property name="width_request">53</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Selected</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">0</property> + <property name="y">32</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label21"> + <property name="width_request">28</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">ID</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">34</property> + <property name="y">47</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVSelectAll"> + <property name="width_request">75</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Select All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVSelectAll_clicked_cb"/> + </widget> + <packing> + <property name="x">3</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVUnselectAll"> + <property name="width_request">80</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVUnselectAll_clicked_cb"/> + </widget> + <packing> + <property name="x">80</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVShowAll"> + <property name="width_request">98</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Show All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVShowAll_clicked_cb"/> + </widget> + <packing> + <property name="x">176</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVHideAll"> + <property name="width_request">100</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Hide All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVHideAll_clicked_cb"/> + </widget> + <packing> + <property name="x">288</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVHideAll1"> + <property name="width_request">150</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Save As Subgraph</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVSaveAs_clicked_cb"/> + </widget> + <packing> + <property name="x">400</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label131"> + <property name="width_request">40</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">name</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">280</property> + <property name="y">48</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layoutTVData"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 950 0 855 950</property> + <property name="vadjustment">0 0 574 0 516.6 574</property> + + <child> + <widget class="GtkHSeparator" id="hseparator3"> + <property name="width_request">947</property> + <property name="height_request">20</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">4</property> + <property name="y">-9</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox1"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkLayout" id="layout13"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 400 0 304.2 338</property> + <property name="vadjustment">0 0 400 0 36 40</property> + + <child> + <widget class="GtkButton" id="btnTVFilter"> + <property name="width_request">80</property> + <property name="height_request">30</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Filter</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVFilter_clicked_cb"/> + </widget> + <packing> + <property name="x">3</property> + <property name="y">8</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout12"> + <property name="width_request">178</property> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 516 0 464.4 516</property> + <property name="vadjustment">0 0 400 0 36 40</property> + + <child> + <widget class="GtkButton" id="btnTVFirst"> + <property name="width_request">40</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes"><<</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVFirst_clicked_cb"/> + </widget> + <packing> + <property name="x">7</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVPrevious"> + <property name="width_request">40</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes"><</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVPrevious_clicked_cb"/> + </widget> + <packing> + <property name="x">51</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVNext"> + <property name="width_request">40</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVNext_clicked_cb"/> + </widget> + <packing> + <property name="x">95</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVLast"> + <property name="width_request">40</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">>></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVLast_clicked_cb"/> + </widget> + <packing> + <property name="x">140</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="lblTVPage"> + <property name="width_request">136</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Page (0/0)</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">180</property> + <property name="y">13</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVGotopage"> + <property name="width_request">90</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Go to page #</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVGotopage_clicked_cb"/> + </widget> + <packing> + <property name="x">317</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="spnTVGotopage"> + <property name="width_request">99</property> + <property name="height_request">23</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">0 0 1000000 1 10 10</property> + </widget> + <packing> + <property name="x">410</property> + <property name="y">11</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout9"> + <property name="width_request">96</property> + <property name="height_request">40</property> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 400 0 86.4 96</property> + <property name="vadjustment">0 0 400 0 36 40</property> + + <child> + <widget class="GtkButton" id="btnTVOK"> + <property name="width_request">80</property> + <property name="height_request">30</property> + <property name="visible">True</property> + <property name="label" translatable="yes">OK</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVOK_clicked_cb"/> + </widget> + <packing> + <property name="x">9</property> + <property name="y">7</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkDialog" id="dlgTVFilter"> + <property name="border_width">5</property> + <property name="width_request">590</property> + <property name="height_request">196</property> + <property name="title" translatable="yes"></property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_NONE</property> + <property name="modal">False</property> + <property name="default_width">590</property> + <property name="default_height">350</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_NORMAL</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + <property name="has_separator">False</property> + + <child internal-child="vbox"> + <widget class="GtkVBox" id="dialog-vbox2"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">2</property> + + <child internal-child="action_area"> + <widget class="GtkHButtonBox" id="dialog-action_area2"> + <property name="visible">True</property> + <property name="layout_style">GTK_BUTTONBOX_END</property> + + <child> + <widget class="GtkButton" id="btnTVFilterCancel"> + <property name="visible">True</property> + <property name="label" translatable="yes">Cancel</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">2</property> + </widget> + </child> + + <child> + <widget class="GtkButton" id="btnTVFilterApply"> + <property name="visible">True</property> + <property name="label" translatable="yes">Apply</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">1</property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout11"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 576 0 518.4 576</property> + <property name="vadjustment">0 0 400 0 128.7 143</property> + + <child> + <widget class="GtkTable" id="table1"> + <property name="width_request">419</property> + <property name="height_request">80</property> + <property name="visible">True</property> + <property name="n_rows">3</property> + <property name="n_columns">2</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkHBox" id="hbox2"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterSel1"> + <property name="width_request">15</property> + <property name="visible">True</property> + <property name="label" translatable="yes">All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterSel2"> + <property name="width_request">75</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Selected</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterSel1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterSel3"> + <property name="width_request">100</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Non-selected</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterSel1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label3"> + <property name="visible">True</property> + <property name="label" translatable="yes">Selection</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label9"> + <property name="visible">True</property> + <property name="label" translatable="yes">Visible</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label10"> + <property name="label" translatable="yes">Highlighted</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox3"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterVisible1"> + <property name="width_request">15</property> + <property name="visible">True</property> + <property name="label" translatable="yes">All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterVisible2"> + <property name="width_request">75</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Visible</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterVisible1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterVisible3"> + <property name="width_request">100</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Hidden</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterVisible1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox4"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterHigh1"> + <property name="width_request">15</property> + <property name="label" translatable="yes">All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterHigh2"> + <property name="width_request">75</property> + <property name="label" translatable="yes">Highlighted</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterHigh1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterHigh3"> + <property name="width_request">100</property> + <property name="label" translatable="yes">Not Highlighted</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterHigh1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + </widget> + <packing> + <property name="x">2</property> + <property name="y">30</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label13"> + <property name="width_request">181</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">FILTER CRITERIAS</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">0</property> + <property name="y">0</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator4"> + <property name="width_request">581</property> + <property name="height_request">20</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">0</property> + <property name="y">13</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVFilterClear"> + <property name="width_request">100</property> + <property name="height_request">29</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Clear Filter</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVFilterClear_clicked_cb"/> + </widget> + <packing> + <property name="x">467</property> + <property name="y">32</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkDialog" id="dlgSettings"> + <property name="border_width">5</property> + <property name="width_request">676</property> + <property name="height_request">505</property> + <property name="title" translatable="yes"></property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_NONE</property> + <property name="modal">False</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">True</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_NORMAL</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + <property name="has_separator">False</property> + <signal name="delete_event" handler="on_dlgSettings_close" last_modification_time="Thu, 03 Dec 2009 16:39:12 GMT"/> + + <child internal-child="vbox"> + <widget class="GtkVBox" id="dialog-vbox5"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">2</property> + + <child internal-child="action_area"> + <widget class="GtkHButtonBox" id="dialog-action_area5"> + <property name="visible">True</property> + <property name="layout_style">GTK_BUTTONBOX_END</property> + + <child> + <widget class="GtkButton" id="button15"> + <property name="visible">True</property> + <property name="label" translatable="yes">Cancel</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">0</property> + <signal name="clicked" handler="on_settingsCancelBtn_clicked" last_modification_time="Wed, 11 Nov 2009 20:53:28 GMT"/> + </widget> + </child> + + <child> + <widget class="GtkButton" id="button12"> + <property name="visible">True</property> + <property name="label" translatable="yes">Apply</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">0</property> + <signal name="clicked" handler="on_settingsApplyBtn_clicked" last_modification_time="Mon, 20 Jul 2009 16:39:55 GMT"/> + </widget> + </child> + + <child> + <widget class="GtkButton" id="button11"> + <property name="visible">True</property> + <property name="label" translatable="yes">OK</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">0</property> + <signal name="clicked" handler="on_settingsOKBtn_clicked" last_modification_time="Tue, 26 May 2009 19:00:55 GMT"/> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + + <child> + <widget class="GtkNotebook" id="notebook3"> + <property name="visible">True</property> + <property name="show_tabs">True</property> + <property name="show_border">True</property> + <property name="tab_pos">GTK_POS_TOP</property> + <property name="scrollable">False</property> + <property name="enable_popup">False</property> + + <child> + <widget class="GtkLayout" id="layout15"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkTable" id="table4"> + <property name="width_request">443</property> + <property name="height_request">408</property> + <property name="visible">True</property> + <property name="n_rows">14</property> + <property name="n_columns">3</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox3"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox2"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn3"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn2"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label41"> + <property name="visible">True</property> + <property name="label" translatable="yes">Default Edge Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label40"> + <property name="visible">True</property> + <property name="label" translatable="yes">Default Node Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label39"> + <property name="visible">True</property> + <property name="label" translatable="yes">Grid Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label32"> + <property name="visible">True</property> + <property name="label" translatable="yes">Border Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label31"> + <property name="visible">True</property> + <property name="label" translatable="yes">Background Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label81"> + <property name="visible">True</property> + <property name="label" translatable="yes">Draw Nodes</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">9</property> + <property name="bottom_attach">10</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label82"> + <property name="visible">True</property> + <property name="label" translatable="yes">Draw Edges</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">10</property> + <property name="bottom_attach">11</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox5-1"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">9</property> + <property name="bottom_attach">10</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox5-2"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">10</property> + <property name="bottom_attach">11</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label117"> + <property name="visible">True</property> + <property name="label" translatable="yes">Color Theme</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">11</property> + <property name="bottom_attach">12</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkComboBoxEntry" id="colorthemecb"> + <property name="visible">True</property> + <property name="items" translatable="yes">Ice Blue +All Pastel +Magma +Rain Forest</property> + <property name="add_tearoffs">False</property> + <property name="has_frame">True</property> + <property name="focus_on_click">True</property> + <signal name="changed" handler="color_change_request" last_modification_time="Tue, 17 Nov 2009 19:03:10 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">11</property> + <property name="bottom_attach">12</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="DefaultNodeCbtn"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label118"> + <property name="visible">True</property> + <property name="label" translatable="yes">Edge Attr. For Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">12</property> + <property name="bottom_attach">13</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="edgecolortxt"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + <signal name="changed" handler="color_change_request" last_modification_time="Tue, 17 Nov 2009 19:03:56 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">12</property> + <property name="bottom_attach">13</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="DefaultEdgeCbtn"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label44"> + <property name="visible">True</property> + <property name="label" translatable="yes">Selected Node Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn8"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label45"> + <property name="visible">True</property> + <property name="label" translatable="yes">Selected Edge Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn9"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn1"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label129"> + <property name="visible">True</property> + <property name="label" translatable="yes">Nodes Selectable?</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">7</property> + <property name="bottom_attach">8</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label130"> + <property name="visible">True</property> + <property name="label" translatable="yes">Edges Selectable?</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">8</property> + <property name="bottom_attach">9</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="chkboxNodeSelect"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">7</property> + <property name="bottom_attach">8</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="chkboxEdgeSelect"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">8</property> + <property name="bottom_attach">9</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label132"> + <property name="visible">True</property> + <property name="label" translatable="yes">Show Edges </property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">13</property> + <property name="bottom_attach">14</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkComboBoxEntry" id="edgerendercb"> + <property name="visible">True</property> + <property name="items" translatable="yes">Always +When head or tail is within viewport +</property> + <property name="add_tearoffs">False</property> + <property name="has_frame">True</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">13</property> + <property name="bottom_attach">14</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + </widget> + <packing> + <property name="x">10</property> + <property name="y">17</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label53"> + <property name="width_request">90</property> + <property name="height_request">21</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">152</property> + <property name="y">0</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label54"> + <property name="width_request">100</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Apply</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">15</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">408</property> + <property name="y">0</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label46"> + <property name="width_request">60</property> + <property name="visible">True</property> + <property name="label" translatable="yes">General</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout16"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkTable" id="table16"> + <property name="width_request">32</property> + <property name="height_request">80</property> + <property name="visible">True</property> + <property name="n_rows">8</property> + <property name="n_columns">3</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkComboBox" id="labelfont"> + <property name="visible">True</property> + <property name="items" translatable="yes"> GLUT_BITMAP_8_BY_13 +GLUT_BITMAP_9_BY_15 +GLUT_BITMAP_TIMES_ROMAN_10 +GLUT_BITMAP_TIMES_ROMAN_24 +GLUT_BITMAP_HELVETICA_10 +GLUT_BITMAP_HELVETICA_12 +GLUT_BITMAP_HELVETICA_18 +</property> + <property name="add_tearoffs">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="y_options">fill</property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="labelnodeattribute"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="labelzoomfactor"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">1200 0 5000 1 50 50</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label110"> + <property name="visible">True</property> + <property name="label" translatable="yes">Max # of nodes to display</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label109"> + <property name="visible">True</property> + <property name="label" translatable="yes">Label Visibility with degree</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label108"> + <property name="visible">True</property> + <property name="label" translatable="yes">Edge Label Attribute</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label107"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node Label Attribute</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label106"> + <property name="visible">True</property> + <property name="label" translatable="yes">Default Node Label Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label105"> + <property name="visible">True</property> + <property name="label" translatable="yes">Font</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label111"> + <property name="visible">True</property> + <property name="label" translatable="yes">Show Node Labels</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label112"> + <property name="visible">True</property> + <property name="label" translatable="yes">Show Edge Labels</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">7</property> + <property name="bottom_attach">8</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="labelshowedges"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">7</property> + <property name="bottom_attach">8</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="labelshownodes"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="labelwithdegree"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="labeledgeattribute"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox8"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkColorButton" id="nodelabelcolor"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label113"> + <property name="visible">True</property> + <property name="label" translatable="yes"> Default Edge Label Color </property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="edgelabelcolor"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + </widget> + <packing> + <property name="x">32</property> + <property name="y">26</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label47"> + <property name="width_request">61</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Labels</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout17"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkTable" id="table5"> + <property name="width_request">423</property> + <property name="height_request">308</property> + <property name="visible">True</property> + <property name="n_rows">7</property> + <property name="n_columns">2</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkLabel" id="label49"> + <property name="visible">True</property> + <property name="label" translatable="yes">Advanced Mode</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label50"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node Shape</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label52"> + <property name="visible">True</property> + <property name="label" translatable="yes">Anti-aliasing</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label84"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node Alpha</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label85"> + <property name="visible">True</property> + <property name="label" translatable="yes">Edge Alpha</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + </packing> + </child> + + <child> + <widget class="GtkHScale" id="settingsscale1"> + <property name="visible">True</property> + <property name="draw_value">True</property> + <property name="value_pos">GTK_POS_TOP</property> + <property name="digits">1</property> + <property name="update_policy">GTK_UPDATE_CONTINUOUS</property> + <property name="inverted">False</property> + <property name="adjustment">1 0 1 0.00999999977648 0 0</property> + <signal name="value_changed" handler="color_change_request" last_modification_time="Thu, 19 Nov 2009 17:09:07 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + </packing> + </child> + + <child> + <widget class="GtkHScale" id="settingsscale2"> + <property name="visible">True</property> + <property name="draw_value">True</property> + <property name="value_pos">GTK_POS_TOP</property> + <property name="digits">1</property> + <property name="update_policy">GTK_UPDATE_CONTINUOUS</property> + <property name="inverted">False</property> + <property name="adjustment">1 0 1 0.01 0 0</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + </packing> + </child> + + <child> + <widget class="GtkComboBox" id="settingscombobox1"> + <property name="visible">True</property> + <property name="items" translatable="yes">OpenGL dots +Spherical +Custom</property> + <property name="add_tearoffs">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox10"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox12"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label119"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node Size</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkHScale" id="nodesizescale"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="draw_value">True</property> + <property name="value_pos">GTK_POS_TOP</property> + <property name="digits">0</property> + <property name="update_policy">GTK_UPDATE_CONTINUOUS</property> + <property name="inverted">False</property> + <property name="adjustment">5 0 100 0 0 0</property> + <signal name="value_changed" handler="size_change_request" last_modification_time="Thu, 19 Nov 2009 16:56:12 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + </widget> + <packing> + <property name="x">28</property> + <property name="y">26</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label48"> + <property name="width_request">56</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Graph </property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout18"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkTable" id="table6"> + <property name="width_request">176</property> + <property name="height_request">84</property> + <property name="visible">True</property> + <property name="n_rows">3</property> + <property name="n_columns">2</property> + <property name="homogeneous">False</property> + <property name="row_spacing">2</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkLabel" id="label56"> + <property name="width_request">84</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Width</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton2"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">3 0 5000 1 50 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton1"> + <property name="width_request">128</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">3 0 5000 1 50 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label58"> + <property name="width_request">84</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Height</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label61"> + <property name="width_request">84</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Zoom X</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton3"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">2</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">2 2 20 1 50 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="x">40</property> + <property name="y">48</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label59"> + <property name="width_request">118</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Magnifier Settings</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">32</property> + <property name="y">15</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator10"> + <property name="width_request">401</property> + <property name="height_request">20</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">35</property> + <property name="y">29</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label60"> + <property name="width_request">194</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Fisheye Magnifier Settings</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">33</property> + <property name="y">159</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator11"> + <property name="width_request">400</property> + <property name="height_request">31</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">35</property> + <property name="y">166</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table7"> + <property name="width_request">124</property> + <property name="height_request">67</property> + <property name="visible">True</property> + <property name="n_rows">2</property> + <property name="n_columns">2</property> + <property name="homogeneous">False</property> + <property name="row_spacing">2</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkLabel" id="label63"> + <property name="width_request">84</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Distortion</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton4"> + <property name="width_request">128</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">5 0 5000 1 50 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton5"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">4 0 5000 1 50 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label65"> + <property name="width_request">84</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Radius</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="x">37</property> + <property name="y">192</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label55"> + <property name="visible">True</property> + <property name="label" translatable="yes">Magnifiers</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout19"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkVBox" id="vbox6"> + <property name="width_request">430</property> + <property name="height_request">405</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkTable" id="table9"> + <property name="height_request">76</property> + <property name="visible">True</property> + <property name="n_rows">4</property> + <property name="n_columns">3</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkLabel" id="label66"> + <property name="width_request">1</property> + <property name="visible">True</property> + <property name="label" translatable="yes"># of fine nodes</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton6"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">0 0 9999999 1 10 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label67"> + <property name="width_request">1</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Coarsening factor</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton7"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">4</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">0 0 9999999 0.1 10 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton8"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">4</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">0 0 9999999 0.1 10 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label102"> + <property name="visible">True</property> + <property name="label" translatable="yes"># Of Fine Nodes</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label104"> + <property name="visible">True</property> + <property name="label" translatable="yes">Distortion Factor</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label103"> + <property name="visible">True</property> + <property name="label" translatable="yes">Coarsening Factor</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label116"> + <property name="visible">True</property> + <property name="label" translatable="yes">Fine Node Label Attribute</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="finenodelabelattribute"> + <property name="visible">True</property> + <property name="tooltip" translatable="yes">leave blank to show node id</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="y_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator12"> + <property name="height_request">38</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table8"> + <property name="height_request">61</property> + <property name="visible">True</property> + <property name="n_rows">2</property> + <property name="n_columns">3</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox13"> + <property name="visible">True</property> + <property name="label" translatable="yes">Dist 2 limit</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox14"> + <property name="visible">True</property> + <property name="label" translatable="yes">Animate</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox15"> + <property name="visible">True</property> + <property name="label" translatable="yes">Label fine nodes</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox16"> + <property name="visible">True</property> + <property name="label" translatable="yes">Color nodes</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox17"> + <property name="visible">True</property> + <property name="label" translatable="yes">Label Coarse Nodes</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox18"> + <property name="visible">True</property> + <property name="label" translatable="yes">Label focus</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator13"> + <property name="height_request">46</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label69"> + <property name="visible">True</property> + <property name="label" translatable="yes">Color extremes</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">10</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table10"> + <property name="visible">True</property> + <property name="n_rows">1</property> + <property name="n_columns">5</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkLabel" id="label70"> + <property name="width_request">122</property> + <property name="height_request">32</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Finest:</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">30</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn10"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label71"> + <property name="width_request">122</property> + <property name="height_request">32</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Coarsest:</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">30</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">3</property> + <property name="right_attach">4</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn11"> + <property name="height_request">47</property> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">4</property> + <property name="right_attach">5</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="x">7</property> + <property name="y">8</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="lllllllllllll"> + <property name="visible">True</property> + <property name="label" translatable="yes">Topfish</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkVBox" id="vbox15"> + <property name="width_request">179</property> + <property name="height_request">323</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkScrolledWindow" id="scrolledwindow5"> + <property name="height_request">212</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="hscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="vscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="shadow_type">GTK_SHADOW_IN</property> + <property name="window_placement">GTK_CORNER_TOP_LEFT</property> + + <child> + <widget class="GtkTextView" id="gvprtextinput"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="overwrite">False</property> + <property name="accepts_tab">True</property> + <property name="justification">GTK_JUSTIFY_LEFT</property> + <property name="wrap_mode">GTK_WRAP_NONE</property> + <property name="cursor_visible">True</property> + <property name="pixels_above_lines">0</property> + <property name="pixels_below_lines">0</property> + <property name="pixels_inside_wrap">0</property> + <property name="left_margin">0</property> + <property name="right_margin">0</property> + <property name="indent">0</property> + <property name="text" translatable="yes"> </property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label20"> + <property name="visible">True</property> + <property name="label" translatable="yes">Arguments</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="gvprargs"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label114"> + <property name="visible">True</property> + <property name="label" translatable="yes">Output</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkScrolledWindow" id="scrolledwindow6"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="hscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="vscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="shadow_type">GTK_SHADOW_IN</property> + <property name="window_placement">GTK_CORNER_TOP_LEFT</property> + + <child> + <widget class="GtkTextView" id="gvprtextoutput"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="overwrite">False</property> + <property name="accepts_tab">True</property> + <property name="justification">GTK_JUSTIFY_LEFT</property> + <property name="wrap_mode">GTK_WRAP_NONE</property> + <property name="cursor_visible">True</property> + <property name="pixels_above_lines">0</property> + <property name="pixels_below_lines">0</property> + <property name="pixels_inside_wrap">0</property> + <property name="left_margin">0</property> + <property name="right_margin">0</property> + <property name="indent">0</property> + <property name="text" translatable="yes"></property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox9"> + <property name="height_request">34</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkCheckButton" id="gvprapplycb"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Clone Current Graph</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="gvprbuttonload"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Load</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_gvprbuttonload_clicked" last_modification_time="Fri, 22 May 2009 16:45:40 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="gvprbuttonsave"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Save</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_gvprbuttonsave_clicked" last_modification_time="Mon, 01 Jun 2009 16:23:07 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="button8"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Run gvpr</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="mTestgvpr" last_modification_time="Wed, 29 Apr 2009 14:54:06 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label86"> + <property name="visible">True</property> + <property name="label" translatable="yes">Script</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkVBox" id="vbox14"> + <property name="width_request">0</property> + <property name="height_request">0</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkFixed" id="fixed7"> + <property name="height_request">40</property> + <property name="visible">True</property> + + <child> + <widget class="GtkHSeparator" id="hseparator14"> + <property name="width_request">456</property> + <property name="height_request">16</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">8</property> + <property name="y">24</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label124"> + <property name="width_request">470</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">0 Nodes Selected</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">8</property> + <property name="y">8</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkFixed" id="fixed6"> + <property name="width_request">351</property> + <property name="height_request">381</property> + <property name="visible">True</property> + + <child> + <widget class="GtkHBox" id="hbox12"> + <property name="width_request">428</property> + <property name="height_request">26</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkRadioButton" id="attrRB0"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">GRAPH </property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="clicked" handler="on_attrRB0_clicked" last_modification_time="Thu, 29 Oct 2009 20:20:28 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="attrRB1"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">NODE </property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">attrRB0</property> + <signal name="clicked" handler="on_attrRB0_clicked" last_modification_time="Thu, 29 Oct 2009 20:20:28 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="attrRB2"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">EDGE</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">attrRB0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + </widget> + <packing> + <property name="x">11</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table18"> + <property name="width_request">562</property> + <property name="height_request">68</property> + <property name="visible">True</property> + <property name="n_rows">3</property> + <property name="n_columns">6</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkEntry" id="txtAttr"> + <property name="width_request">88</property> + <property name="height_request">21</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + <signal name="changed" handler="on_txtAttr_changed" last_modification_time="Thu, 22 Oct 2009 20:26:47 GMT"/> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="txtValue"> + <property name="width_request">88</property> + <property name="height_request">21</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="txtDefValue"> + <property name="width_request">88</property> + <property name="height_request">21</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label122"> + <property name="width_request">40</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Value</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label123"> + <property name="width_request">88</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Default Value</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label121"> + <property name="width_request">96</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Attribute Name</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label125"> + <property name="width_request">72</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Publish</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">3</property> + <property name="right_attach">4</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="attrProg"> + <property name="width_request">24</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="toggled" handler="on_attrProg_toggled" last_modification_time="Mon, 26 Oct 2009 19:57:29 GMT"/> + </widget> + <packing> + <property name="left_attach">3</property> + <property name="right_attach">4</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="attrAddBtn"> + <property name="width_request">32</property> + <property name="height_request">23</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">+</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_attrAddBtn_clicked" last_modification_time="Mon, 26 Oct 2009 22:04:52 GMT"/> + </widget> + <packing> + <property name="left_attach">5</property> + <property name="right_attach">6</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="attrApplyBtn"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Apply</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_attrApplyBtn_clicked" last_modification_time="Mon, 09 Nov 2009 23:05:37 GMT"/> + </widget> + <packing> + <property name="left_attach">4</property> + <property name="right_attach">5</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="attrSearchBtn"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Search</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_attrSearchBtn_clicked" last_modification_time="Wed, 11 Nov 2009 16:07:55 GMT"/> + </widget> + <packing> + <property name="left_attach">4</property> + <property name="right_attach">5</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="x">12</property> + <property name="y">41</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label127"> + <property name="visible">True</property> + <property name="label" translatable="yes">Attribute</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkWindow" id="frmWait"> + <property name="width_request">200</property> + <property name="height_request">178</property> + <property name="title" translatable="yes">Smyrna</property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_NONE</property> + <property name="modal">False</property> + <property name="default_width">274</property> + <property name="default_height">248</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">False</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_SPLASHSCREEN</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + + <child> + <widget class="GtkFixed" id="fixed4"> + <property name="visible">True</property> + + <child> + <widget class="GtkImage" id="image17"> + <property name="width_request">188</property> + <property name="height_request">176</property> + <property name="visible">True</property> + <property name="stock">gtk-missing-image</property> + <property name="icon_size">4</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + </widget> + <packing> + <property name="x">6</property> + <property name="y">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label80"> + <property name="width_request">100</property> + <property name="height_request">26</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Please Wait....</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">52</property> + <property name="y">122</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkDialog" id="findDialog"> + <property name="border_width">5</property> + <property name="title" translatable="yes"></property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_CENTER_ON_PARENT</property> + <property name="modal">False</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_DIALOG</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + <property name="has_separator">False</property> + + <child internal-child="vbox"> + <widget class="GtkVBox" id="dialog-vbox7"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">2</property> + + <child internal-child="action_area"> + <widget class="GtkHButtonBox" id="dialog-action_area7"> + <property name="visible">True</property> + <property name="layout_style">GTK_BUTTONBOX_END</property> + + <child> + <widget class="GtkButton" id="button3"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Cancel</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">2</property> + </widget> + </child> + + <child> + <widget class="GtkButton" id="button2"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">OK</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">1</property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + + <child> + <widget class="GtkVBox" id="vbox8"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkLabel" id="label94"> + <property name="visible">True</property> + <property name="label" translatable="yes"> +Search in +</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkComboBox" id="combobox1"> + <property name="visible">True</property> + <property name="add_tearoffs">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkVBox" id="vbox7"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkLabel" id="label93"> + <property name="visible">True</property> + <property name="label" translatable="yes">Regular Expression</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="findText"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +</glade-interface>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/smyrna/smyrna.glade Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5317 @@ +<?xml version="1.0" standalone="no"?> <!--*- mode: xml -*--> +<!DOCTYPE glade-interface SYSTEM "http://glade.gnome.org/glade-2.0.dtd"> + +<glade-interface> + +<widget class="GtkWindow" id="frmMain"> + <property name="width_request">900</property> + <property name="height_request">700</property> + <property name="title" translatable="yes">Smyrna Graph visualizer ver: 0.99 ATT</property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_CENTER</property> + <property name="modal">False</property> + <property name="default_width">900</property> + <property name="default_height">700</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_NORMAL</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + + <child> + <widget class="GtkVBox" id="vbox2"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkHBox" id="hbox13"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkMenuBar" id="menubar1"> + <property name="width_request">540</property> + <property name="visible">True</property> + <property name="pack_direction">GTK_PACK_DIRECTION_LTR</property> + <property name="child_pack_direction">GTK_PACK_DIRECTION_LTR</property> + + <child> + <widget class="GtkMenuItem" id="menuitem1"> + <property name="visible">True</property> + <property name="label" translatable="yes">_File</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem1_menu"> + + <child> + <widget class="GtkImageMenuItem" id="mOpen"> + <property name="visible">True</property> + <property name="label">gtk-open</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mOpenSlot"/> + </widget> + </child> + + <child> + <widget class="GtkImageMenuItem" id="mSave"> + <property name="visible">True</property> + <property name="label">gtk-save</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mSaveSlot"/> + </widget> + </child> + + <child> + <widget class="GtkImageMenuItem" id="mSaveAs"> + <property name="visible">True</property> + <property name="label">gtk-save-as</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mSaveAsSlot"/> + </widget> + </child> + + <child> + <widget class="GtkSeparatorMenuItem" id="separatormenuitem1"> + <property name="visible">True</property> + </widget> + </child> + + <child> + <widget class="GtkImageMenuItem" id="mQuit"> + <property name="visible">True</property> + <property name="label">gtk-quit</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mQuitSlot"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem2"> + <property name="visible">True</property> + <property name="label" translatable="yes">_Edit</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem2_menu"> + + <child> + <widget class="GtkMenuItem" id="mTopviewSettings"> + <property name="visible">True</property> + <property name="label" translatable="yes">Smyrna Settings</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mTopviewSettingsSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="mAttributes"> + <property name="visible">True</property> + <property name="label" translatable="yes">Attributes</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mAttributesSlot" last_modification_time="Fri, 06 Nov 2009 17:03:12 GMT"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="node_list1"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node List</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mNodeListSlot" last_modification_time="Fri, 06 Nov 2009 17:02:05 GMT"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem3"> + <property name="visible">True</property> + <property name="label" translatable="yes">_View</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem3_menu"> + + <child> + <widget class="GtkMenuItem" id="show_console1"> + <property name="visible">True</property> + <property name="label" translatable="yes">Show Console</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mShowConsoleSlot" last_modification_time="Wed, 22 Jul 2009 16:17:32 GMT"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="hide_console1"> + <property name="visible">True</property> + <property name="label" translatable="yes">Hide Console</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mHideConsoleSlot" last_modification_time="Wed, 22 Jul 2009 16:17:32 GMT"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem6"> + <property name="visible">True</property> + <property name="label" translatable="yes">Select</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem6_menu"> + + <child> + <widget class="GtkImageMenuItem" id="mSelectAll"> + <property name="visible">True</property> + <property name="label">gtk-select-all</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mSelectAllSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="mUnselectAll"> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mUnselectAllSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem7"> + <property name="visible">True</property> + <property name="label" translatable="yes">Select All Nodes</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mSelectAllNodesSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem8"> + <property name="visible">True</property> + <property name="label" translatable="yes">Select All Edges</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mSelectAllEdgesSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem9"> + <property name="visible">True</property> + <property name="label" translatable="yes">Select All Clusters</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mSelectAllClustersSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem10"> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All Nodes</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mUnselectAllNodesSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem11"> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All Edges</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mUnselectAllEdgesSlot"/> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem12"> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All Clusters</property> + <property name="use_underline">True</property> + <signal name="activate" handler="mUnselectAllClustersSlot"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + + <child> + <widget class="GtkMenuItem" id="menuitem4"> + <property name="visible">True</property> + <property name="label" translatable="yes">_Help</property> + <property name="use_underline">True</property> + + <child> + <widget class="GtkMenu" id="menuitem4_menu"> + + <child> + <widget class="GtkImageMenuItem" id="mAbout"> + <property name="visible">True</property> + <property name="label">gtk-about</property> + <property name="use_stock">True</property> + <signal name="activate" handler="mAbout"/> + </widget> + </child> + </widget> + </child> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label128"> + <property name="visible">True</property> + <property name="label" translatable="yes">Active Graph:</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_RIGHT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">1</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">2</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="button10"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Remove</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + + <child> + <placeholder/> + </child> + + <child> + <widget class="GtkButton" id="btnActivateGraph"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Activate</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_btnActivateGraph_clicked" last_modification_time="Tue, 23 Jun 2009 16:15:08 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox11"> + <property name="height_request">0</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <placeholder/> + </child> + + <child> + <widget class="GtkVBox" id="vbox13"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkScrolledWindow" id="scrolledwindow7"> + <property name="width_request">215</property> + <property name="height_request">488</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="hscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="vscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="shadow_type">GTK_SHADOW_IN</property> + <property name="window_placement">GTK_CORNER_TOP_LEFT</property> + + <child> + <widget class="GtkTextView" id="mainconsole"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="overwrite">False</property> + <property name="accepts_tab">True</property> + <property name="justification">GTK_JUSTIFY_LEFT</property> + <property name="wrap_mode">GTK_WRAP_NONE</property> + <property name="cursor_visible">True</property> + <property name="pixels_above_lines">0</property> + <property name="pixels_below_lines">0</property> + <property name="pixels_inside_wrap">0</property> + <property name="left_margin">0</property> + <property name="right_margin">0</property> + <property name="indent">0</property> + <property name="text" translatable="yes"></property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table17"> + <property name="visible">True</property> + <property name="n_rows">1</property> + <property name="n_columns">4</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkButton" id="consoledecbtn"> + <property name="width_request">27</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">-</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_consoledecbtn_clicked" last_modification_time="Wed, 22 Jul 2009 15:24:50 GMT"/> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="consoleincbtn"> + <property name="width_request">27</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">+</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_consoleincbtn_clicked" last_modification_time="Thu, 23 Jul 2009 18:54:38 GMT"/> + </widget> + <packing> + <property name="left_attach">3</property> + <property name="right_attach">4</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="clearconsolebtn"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Clear</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_clearconsolebtn_clicked" last_modification_time="Wed, 22 Jul 2009 15:22:56 GMT"/> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="hideconsolebtn"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Hide</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_hideconsolebtn_clicked" last_modification_time="Wed, 22 Jul 2009 15:23:55 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="y_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkWindow" id="frmTVNodes"> + <property name="width_request">950</property> + <property name="height_request">680</property> + <property name="title" translatable="yes"></property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_MOUSE</property> + <property name="modal">False</property> + <property name="default_width">950</property> + <property name="default_height">680</property> + <property name="resizable">False</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_NORMAL</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + + <child> + <widget class="GtkVBox" id="vbox3"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkLayout" id="layout10"> + <property name="width_request">0</property> + <property name="height_request">66</property> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 950 0 855 950</property> + <property name="vadjustment">0 0 400 0 59.4 66</property> + + <child> + <widget class="GtkLabel" id="label8"> + <property name="width_request">60</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">ID</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">30</property> + <property name="y">222</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="cgbTVSelect"> + <property name="width_request">25</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NONE</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="toggled" handler="cgbTVSelect_toggled_cb"/> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="x">16</property> + <property name="y">48</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="lblTVData1"> + <property name="width_request">100</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">D</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">360</property> + <property name="y">47</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="lblTVData2"> + <property name="width_request">100</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Data2</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">566</property> + <property name="y">47</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label11"> + <property name="width_request">51</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Visible</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">130</property> + <property name="y">32</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label12"> + <property name="width_request">73</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Highlighted</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">184</property> + <property name="y">32</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="cgbTVVisible"> + <property name="width_request">20</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="toggled" handler="cgbTVVisible_toggled_cb"/> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="x">139</property> + <property name="y">48</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="cgbTVHighlighted"> + <property name="width_request">20</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="toggled" handler="cgbTVHighlighted_toggled_cb"/> + </widget> + <packing> + <property name="x">204</property> + <property name="y">48</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label20"> + <property name="width_request">53</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Selected</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">0</property> + <property name="y">32</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label21"> + <property name="width_request">28</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">ID</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">34</property> + <property name="y">47</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVSelectAll"> + <property name="width_request">75</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Select All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVSelectAll_clicked_cb"/> + </widget> + <packing> + <property name="x">3</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVUnselectAll"> + <property name="width_request">80</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Unselect All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVUnselectAll_clicked_cb"/> + </widget> + <packing> + <property name="x">80</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVShowAll"> + <property name="width_request">98</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Show All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVShowAll_clicked_cb"/> + </widget> + <packing> + <property name="x">176</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVHideAll"> + <property name="width_request">100</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Hide All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVHideAll_clicked_cb"/> + </widget> + <packing> + <property name="x">288</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVHideAll1"> + <property name="width_request">150</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Save As Subgraph</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVSaveAs_clicked_cb"/> + </widget> + <packing> + <property name="x">400</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label131"> + <property name="width_request">40</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">name</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">280</property> + <property name="y">48</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layoutTVData"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 950 0 855 950</property> + <property name="vadjustment">0 0 574 0 516.6 574</property> + + <child> + <widget class="GtkHSeparator" id="hseparator3"> + <property name="width_request">947</property> + <property name="height_request">20</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">4</property> + <property name="y">-9</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox1"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkLayout" id="layout13"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 400 0 304.2 338</property> + <property name="vadjustment">0 0 400 0 36 40</property> + + <child> + <widget class="GtkButton" id="btnTVFilter"> + <property name="width_request">80</property> + <property name="height_request">30</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Filter</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVFilter_clicked_cb"/> + </widget> + <packing> + <property name="x">3</property> + <property name="y">8</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout12"> + <property name="width_request">178</property> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 516 0 464.4 516</property> + <property name="vadjustment">0 0 400 0 36 40</property> + + <child> + <widget class="GtkButton" id="btnTVFirst"> + <property name="width_request">40</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes"><<</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVFirst_clicked_cb"/> + </widget> + <packing> + <property name="x">7</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVPrevious"> + <property name="width_request">40</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes"><</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVPrevious_clicked_cb"/> + </widget> + <packing> + <property name="x">51</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVNext"> + <property name="width_request">40</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVNext_clicked_cb"/> + </widget> + <packing> + <property name="x">95</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVLast"> + <property name="width_request">40</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">>></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVLast_clicked_cb"/> + </widget> + <packing> + <property name="x">140</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="lblTVPage"> + <property name="width_request">136</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Page (0/0)</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">180</property> + <property name="y">13</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVGotopage"> + <property name="width_request">90</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Go to page #</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVGotopage_clicked_cb"/> + </widget> + <packing> + <property name="x">317</property> + <property name="y">10</property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="spnTVGotopage"> + <property name="width_request">99</property> + <property name="height_request">23</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">0 0 1000000 1 10 10</property> + </widget> + <packing> + <property name="x">410</property> + <property name="y">11</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout9"> + <property name="width_request">96</property> + <property name="height_request">40</property> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 400 0 86.4 96</property> + <property name="vadjustment">0 0 400 0 36 40</property> + + <child> + <widget class="GtkButton" id="btnTVOK"> + <property name="width_request">80</property> + <property name="height_request">30</property> + <property name="visible">True</property> + <property name="label" translatable="yes">OK</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVOK_clicked_cb"/> + </widget> + <packing> + <property name="x">9</property> + <property name="y">7</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkDialog" id="dlgTVFilter"> + <property name="border_width">5</property> + <property name="width_request">590</property> + <property name="height_request">196</property> + <property name="title" translatable="yes"></property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_NONE</property> + <property name="modal">False</property> + <property name="default_width">590</property> + <property name="default_height">350</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_NORMAL</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + <property name="has_separator">False</property> + + <child internal-child="vbox"> + <widget class="GtkVBox" id="dialog-vbox2"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">2</property> + + <child internal-child="action_area"> + <widget class="GtkHButtonBox" id="dialog-action_area2"> + <property name="visible">True</property> + <property name="layout_style">GTK_BUTTONBOX_END</property> + + <child> + <widget class="GtkButton" id="btnTVFilterCancel"> + <property name="visible">True</property> + <property name="label" translatable="yes">Cancel</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">2</property> + </widget> + </child> + + <child> + <widget class="GtkButton" id="btnTVFilterApply"> + <property name="visible">True</property> + <property name="label" translatable="yes">Apply</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">1</property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout11"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 576 0 518.4 576</property> + <property name="vadjustment">0 0 400 0 128.7 143</property> + + <child> + <widget class="GtkTable" id="table1"> + <property name="width_request">419</property> + <property name="height_request">80</property> + <property name="visible">True</property> + <property name="n_rows">3</property> + <property name="n_columns">2</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkHBox" id="hbox2"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterSel1"> + <property name="width_request">15</property> + <property name="visible">True</property> + <property name="label" translatable="yes">All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterSel2"> + <property name="width_request">75</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Selected</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterSel1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterSel3"> + <property name="width_request">100</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Non-selected</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterSel1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label3"> + <property name="visible">True</property> + <property name="label" translatable="yes">Selection</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label9"> + <property name="visible">True</property> + <property name="label" translatable="yes">Visible</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label10"> + <property name="label" translatable="yes">Highlighted</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox3"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterVisible1"> + <property name="width_request">15</property> + <property name="visible">True</property> + <property name="label" translatable="yes">All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterVisible2"> + <property name="width_request">75</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Visible</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterVisible1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterVisible3"> + <property name="width_request">100</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Hidden</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterVisible1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox4"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterHigh1"> + <property name="width_request">15</property> + <property name="label" translatable="yes">All</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterHigh2"> + <property name="width_request">75</property> + <property name="label" translatable="yes">Highlighted</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterHigh1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="rbTVFilterHigh3"> + <property name="width_request">100</property> + <property name="label" translatable="yes">Not Highlighted</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">rbTVFilterHigh1</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + </widget> + <packing> + <property name="x">2</property> + <property name="y">30</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label13"> + <property name="width_request">181</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">FILTER CRITERIAS</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">0</property> + <property name="y">0</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator4"> + <property name="width_request">581</property> + <property name="height_request">20</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">0</property> + <property name="y">13</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="btnTVFilterClear"> + <property name="width_request">100</property> + <property name="height_request">29</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Clear Filter</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="btnTVFilterClear_clicked_cb"/> + </widget> + <packing> + <property name="x">467</property> + <property name="y">32</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkDialog" id="dlgSettings"> + <property name="border_width">5</property> + <property name="width_request">676</property> + <property name="height_request">505</property> + <property name="title" translatable="yes"></property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_NONE</property> + <property name="modal">False</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">True</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_NORMAL</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + <property name="has_separator">False</property> + <signal name="delete_event" handler="on_dlgSettings_close" last_modification_time="Thu, 03 Dec 2009 16:39:12 GMT"/> + + <child internal-child="vbox"> + <widget class="GtkVBox" id="dialog-vbox5"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">2</property> + + <child internal-child="action_area"> + <widget class="GtkHButtonBox" id="dialog-action_area5"> + <property name="visible">True</property> + <property name="layout_style">GTK_BUTTONBOX_END</property> + + <child> + <widget class="GtkButton" id="button15"> + <property name="visible">True</property> + <property name="label" translatable="yes">Cancel</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">0</property> + <signal name="clicked" handler="on_settingsCancelBtn_clicked" last_modification_time="Wed, 11 Nov 2009 20:53:28 GMT"/> + </widget> + </child> + + <child> + <widget class="GtkButton" id="button12"> + <property name="visible">True</property> + <property name="label" translatable="yes">Apply</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">0</property> + <signal name="clicked" handler="on_settingsApplyBtn_clicked" last_modification_time="Mon, 20 Jul 2009 16:39:55 GMT"/> + </widget> + </child> + + <child> + <widget class="GtkButton" id="button11"> + <property name="visible">True</property> + <property name="label" translatable="yes">OK</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">0</property> + <signal name="clicked" handler="on_settingsOKBtn_clicked" last_modification_time="Tue, 26 May 2009 19:00:55 GMT"/> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + + <child> + <widget class="GtkNotebook" id="notebook3"> + <property name="visible">True</property> + <property name="show_tabs">True</property> + <property name="show_border">True</property> + <property name="tab_pos">GTK_POS_TOP</property> + <property name="scrollable">False</property> + <property name="enable_popup">False</property> + + <child> + <widget class="GtkLayout" id="layout15"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkTable" id="table4"> + <property name="width_request">443</property> + <property name="height_request">408</property> + <property name="visible">True</property> + <property name="n_rows">14</property> + <property name="n_columns">3</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox3"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox2"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn3"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn2"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label41"> + <property name="visible">True</property> + <property name="label" translatable="yes">Default Edge Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label40"> + <property name="visible">True</property> + <property name="label" translatable="yes">Default Node Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label39"> + <property name="visible">True</property> + <property name="label" translatable="yes">Grid Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label32"> + <property name="visible">True</property> + <property name="label" translatable="yes">Border Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label31"> + <property name="visible">True</property> + <property name="label" translatable="yes">Background Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label81"> + <property name="visible">True</property> + <property name="label" translatable="yes">Draw Nodes</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">9</property> + <property name="bottom_attach">10</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label82"> + <property name="visible">True</property> + <property name="label" translatable="yes">Draw Edges</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">10</property> + <property name="bottom_attach">11</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox5-1"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">9</property> + <property name="bottom_attach">10</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox5-2"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">10</property> + <property name="bottom_attach">11</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label117"> + <property name="visible">True</property> + <property name="label" translatable="yes">Color Theme</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">11</property> + <property name="bottom_attach">12</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkComboBoxEntry" id="colorthemecb"> + <property name="visible">True</property> + <property name="items" translatable="yes">Ice Blue +All Pastel +Magma +Rain Forest</property> + <property name="add_tearoffs">False</property> + <property name="has_frame">True</property> + <property name="focus_on_click">True</property> + <signal name="changed" handler="color_change_request" last_modification_time="Tue, 17 Nov 2009 19:03:10 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">11</property> + <property name="bottom_attach">12</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="DefaultNodeCbtn"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label118"> + <property name="visible">True</property> + <property name="label" translatable="yes">Edge Attr. For Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">12</property> + <property name="bottom_attach">13</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="edgecolortxt"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + <signal name="changed" handler="color_change_request" last_modification_time="Tue, 17 Nov 2009 19:03:56 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">12</property> + <property name="bottom_attach">13</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="DefaultEdgeCbtn"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label44"> + <property name="visible">True</property> + <property name="label" translatable="yes">Selected Node Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn8"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label45"> + <property name="visible">True</property> + <property name="label" translatable="yes">Selected Edge Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn9"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn1"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label129"> + <property name="visible">True</property> + <property name="label" translatable="yes">Nodes Selectable?</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">7</property> + <property name="bottom_attach">8</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label130"> + <property name="visible">True</property> + <property name="label" translatable="yes">Edges Selectable?</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">8</property> + <property name="bottom_attach">9</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="chkboxNodeSelect"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">7</property> + <property name="bottom_attach">8</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="chkboxEdgeSelect"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">8</property> + <property name="bottom_attach">9</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label132"> + <property name="visible">True</property> + <property name="label" translatable="yes">Show Edges </property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">13</property> + <property name="bottom_attach">14</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkComboBoxEntry" id="edgerendercb"> + <property name="visible">True</property> + <property name="items" translatable="yes">Always +When head or tail is within viewport +</property> + <property name="add_tearoffs">False</property> + <property name="has_frame">True</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">13</property> + <property name="bottom_attach">14</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + </widget> + <packing> + <property name="x">10</property> + <property name="y">17</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label53"> + <property name="width_request">90</property> + <property name="height_request">21</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">152</property> + <property name="y">0</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label54"> + <property name="width_request">100</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Apply</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">15</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">408</property> + <property name="y">0</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label46"> + <property name="width_request">60</property> + <property name="visible">True</property> + <property name="label" translatable="yes">General</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout16"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkTable" id="table16"> + <property name="width_request">32</property> + <property name="height_request">80</property> + <property name="visible">True</property> + <property name="n_rows">8</property> + <property name="n_columns">3</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkComboBox" id="labelfont"> + <property name="visible">True</property> + <property name="items" translatable="yes"> GLUT_BITMAP_8_BY_13 +GLUT_BITMAP_9_BY_15 +GLUT_BITMAP_TIMES_ROMAN_10 +GLUT_BITMAP_TIMES_ROMAN_24 +GLUT_BITMAP_HELVETICA_10 +GLUT_BITMAP_HELVETICA_12 +GLUT_BITMAP_HELVETICA_18 +</property> + <property name="add_tearoffs">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="y_options">fill</property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="labelnodeattribute"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="labelzoomfactor"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">1200 0 5000 1 50 50</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label110"> + <property name="visible">True</property> + <property name="label" translatable="yes">Max # of nodes to display</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label109"> + <property name="visible">True</property> + <property name="label" translatable="yes">Label Visibility with degree</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label108"> + <property name="visible">True</property> + <property name="label" translatable="yes">Edge Label Attribute</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label107"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node Label Attribute</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label106"> + <property name="visible">True</property> + <property name="label" translatable="yes">Default Node Label Color</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label105"> + <property name="visible">True</property> + <property name="label" translatable="yes">Font</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label111"> + <property name="visible">True</property> + <property name="label" translatable="yes">Show Node Labels</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label112"> + <property name="visible">True</property> + <property name="label" translatable="yes">Show Edge Labels</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">7</property> + <property name="bottom_attach">8</property> + <property name="x_padding">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="labelshowedges"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">7</property> + <property name="bottom_attach">8</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="labelshownodes"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="labelwithdegree"> + <property name="visible">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="labeledgeattribute"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox8"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkColorButton" id="nodelabelcolor"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label113"> + <property name="visible">True</property> + <property name="label" translatable="yes"> Default Edge Label Color </property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="edgelabelcolor"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + </widget> + <packing> + <property name="x">32</property> + <property name="y">26</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label47"> + <property name="width_request">61</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Labels</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout17"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkTable" id="table5"> + <property name="width_request">423</property> + <property name="height_request">308</property> + <property name="visible">True</property> + <property name="n_rows">7</property> + <property name="n_columns">2</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkLabel" id="label49"> + <property name="visible">True</property> + <property name="label" translatable="yes">Advanced Mode</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label50"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node Shape</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label52"> + <property name="visible">True</property> + <property name="label" translatable="yes">Anti-aliasing</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label84"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node Alpha</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label85"> + <property name="visible">True</property> + <property name="label" translatable="yes">Edge Alpha</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + </packing> + </child> + + <child> + <widget class="GtkHScale" id="settingsscale1"> + <property name="visible">True</property> + <property name="draw_value">True</property> + <property name="value_pos">GTK_POS_TOP</property> + <property name="digits">1</property> + <property name="update_policy">GTK_UPDATE_CONTINUOUS</property> + <property name="inverted">False</property> + <property name="adjustment">1 0 1 0.00999999977648 0 0</property> + <signal name="value_changed" handler="color_change_request" last_modification_time="Thu, 19 Nov 2009 17:09:07 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">4</property> + <property name="bottom_attach">5</property> + </packing> + </child> + + <child> + <widget class="GtkHScale" id="settingsscale2"> + <property name="visible">True</property> + <property name="draw_value">True</property> + <property name="value_pos">GTK_POS_TOP</property> + <property name="digits">1</property> + <property name="update_policy">GTK_UPDATE_CONTINUOUS</property> + <property name="inverted">False</property> + <property name="adjustment">1 0 1 0.01 0 0</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">5</property> + <property name="bottom_attach">6</property> + </packing> + </child> + + <child> + <widget class="GtkComboBox" id="settingscombobox1"> + <property name="visible">True</property> + <property name="items" translatable="yes">OpenGL dots +Spherical +Custom</property> + <property name="add_tearoffs">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox10"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox12"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label119"> + <property name="visible">True</property> + <property name="label" translatable="yes">Node Size</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkHScale" id="nodesizescale"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="draw_value">True</property> + <property name="value_pos">GTK_POS_TOP</property> + <property name="digits">0</property> + <property name="update_policy">GTK_UPDATE_CONTINUOUS</property> + <property name="inverted">False</property> + <property name="adjustment">5 0 100 0 0 0</property> + <signal name="value_changed" handler="size_change_request" last_modification_time="Thu, 19 Nov 2009 16:56:12 GMT"/> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">6</property> + <property name="bottom_attach">7</property> + <property name="x_options">fill</property> + <property name="y_options">fill</property> + </packing> + </child> + </widget> + <packing> + <property name="x">28</property> + <property name="y">26</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label48"> + <property name="width_request">56</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Graph </property> + <property name="use_underline">False</property> + <property 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<property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label61"> + <property name="width_request">84</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Zoom X</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton3"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">2</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">2 2 20 1 50 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="x">40</property> + <property name="y">48</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label59"> + <property name="width_request">118</property> + <property name="height_request">25</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Magnifier Settings</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">32</property> + <property name="y">15</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator10"> + <property name="width_request">401</property> + <property name="height_request">20</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">35</property> + <property name="y">29</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label60"> + <property name="width_request">194</property> + <property name="height_request">20</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Fisheye Magnifier Settings</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property 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<property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton4"> + <property name="width_request">128</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">5 0 5000 1 50 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton5"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">4 0 5000 1 50 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label65"> + <property name="width_request">84</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Radius</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="x">37</property> + <property name="y">192</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label55"> + <property name="visible">True</property> + <property name="label" translatable="yes">Magnifiers</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkLayout" id="layout19"> + <property name="visible">True</property> + <property name="width">400</property> + <property name="height">400</property> + <property name="hadjustment">0 0 658 0 592.2 658</property> + <property name="vadjustment">0 0 425 0 382.5 425</property> + + <child> + <widget class="GtkVBox" id="vbox6"> + <property name="width_request">430</property> + <property name="height_request">405</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkTable" id="table9"> + <property name="height_request">76</property> + <property name="visible">True</property> + <property name="n_rows">4</property> + <property name="n_columns">3</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkLabel" id="label66"> + <property name="width_request">1</property> + <property name="visible">True</property> + <property name="label" translatable="yes"># of fine nodes</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton6"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">0</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">0 0 9999999 1 10 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label67"> + <property name="width_request">1</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Coarsening factor</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">5</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options"></property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton7"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">4</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">0 0 9999999 0.1 10 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkSpinButton" id="settingsspinbutton8"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="climb_rate">1</property> + <property name="digits">4</property> + <property name="numeric">False</property> + <property name="update_policy">GTK_UPDATE_ALWAYS</property> + <property name="snap_to_ticks">False</property> + <property name="wrap">False</property> + <property name="adjustment">0 0 9999999 0.1 10 10</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label102"> + <property name="visible">True</property> + <property name="label" translatable="yes"># Of Fine Nodes</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label104"> + <property name="visible">True</property> + <property name="label" translatable="yes">Distortion Factor</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label103"> + <property name="visible">True</property> + <property name="label" translatable="yes">Coarsening Factor</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label116"> + <property name="visible">True</property> + <property name="label" translatable="yes">Fine Node Label Attribute</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="finenodelabelattribute"> + <property name="visible">True</property> + <property name="tooltip" translatable="yes">leave blank to show node id</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">3</property> + <property name="bottom_attach">4</property> + <property name="y_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator12"> + <property name="height_request">38</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table8"> + <property name="height_request">61</property> + <property name="visible">True</property> + <property name="n_rows">2</property> + <property name="n_columns">3</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox13"> + <property name="visible">True</property> + <property name="label" translatable="yes">Dist 2 limit</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox14"> + <property name="visible">True</property> + <property name="label" translatable="yes">Animate</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox15"> + <property name="visible">True</property> + <property name="label" translatable="yes">Label fine nodes</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox16"> + <property name="visible">True</property> + <property name="label" translatable="yes">Color nodes</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox17"> + <property name="visible">True</property> + <property name="label" translatable="yes">Label Coarse Nodes</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="settingsChkBox18"> + <property name="visible">True</property> + <property name="label" translatable="yes">Label focus</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkHSeparator" id="hseparator13"> + <property name="height_request">46</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label69"> + <property name="visible">True</property> + <property name="label" translatable="yes">Color extremes</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">10</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table10"> + <property name="visible">True</property> + <property name="n_rows">1</property> + <property name="n_columns">5</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkLabel" id="label70"> + <property name="width_request">122</property> + <property name="height_request">32</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Finest:</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">30</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn10"> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label71"> + <property name="width_request">122</property> + <property name="height_request">32</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Coarsest:</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">30</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">3</property> + <property name="right_attach">4</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options"></property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkColorButton" id="settingsColorBtn11"> + <property name="height_request">47</property> + <property name="visible">True</property> + <property name="use_alpha">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="left_attach">4</property> + <property name="right_attach">5</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <placeholder/> + </child> + </widget> + <packing> + <property name="x">7</property> + <property name="y">8</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="lllllllllllll"> + <property name="visible">True</property> + <property name="label" translatable="yes">Topfish</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkVBox" id="vbox15"> + <property name="width_request">179</property> + <property name="height_request">323</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkScrolledWindow" id="scrolledwindow5"> + <property name="height_request">212</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="hscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="vscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="shadow_type">GTK_SHADOW_IN</property> + <property name="window_placement">GTK_CORNER_TOP_LEFT</property> + + <child> + <widget class="GtkTextView" id="gvprtextinput"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="overwrite">False</property> + <property name="accepts_tab">True</property> + <property name="justification">GTK_JUSTIFY_LEFT</property> + <property name="wrap_mode">GTK_WRAP_NONE</property> + <property name="cursor_visible">True</property> + <property name="pixels_above_lines">0</property> + <property name="pixels_below_lines">0</property> + <property name="pixels_inside_wrap">0</property> + <property name="left_margin">0</property> + <property name="right_margin">0</property> + <property name="indent">0</property> + <property name="text" translatable="yes"> </property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label20"> + <property name="visible">True</property> + <property name="label" translatable="yes">Arguments</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="gvprargs"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label114"> + <property name="visible">True</property> + <property name="label" translatable="yes">Output</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkScrolledWindow" id="scrolledwindow6"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="hscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="vscrollbar_policy">GTK_POLICY_ALWAYS</property> + <property name="shadow_type">GTK_SHADOW_IN</property> + <property name="window_placement">GTK_CORNER_TOP_LEFT</property> + + <child> + <widget class="GtkTextView" id="gvprtextoutput"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="overwrite">False</property> + <property name="accepts_tab">True</property> + <property name="justification">GTK_JUSTIFY_LEFT</property> + <property name="wrap_mode">GTK_WRAP_NONE</property> + <property name="cursor_visible">True</property> + <property name="pixels_above_lines">0</property> + <property name="pixels_below_lines">0</property> + <property name="pixels_inside_wrap">0</property> + <property name="left_margin">0</property> + <property name="right_margin">0</property> + <property name="indent">0</property> + <property name="text" translatable="yes"></property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkHBox" id="hbox9"> + <property name="height_request">34</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkCheckButton" id="gvprapplycb"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Clone Current Graph</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="gvprbuttonload"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Load</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_gvprbuttonload_clicked" last_modification_time="Fri, 22 May 2009 16:45:40 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="gvprbuttonsave"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Save</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_gvprbuttonsave_clicked" last_modification_time="Mon, 01 Jun 2009 16:23:07 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="button8"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Run gvpr</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="mTestgvpr" last_modification_time="Wed, 29 Apr 2009 14:54:06 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label86"> + <property name="visible">True</property> + <property name="label" translatable="yes">Script</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + + <child> + <widget class="GtkVBox" id="vbox14"> + <property name="width_request">0</property> + <property name="height_request">0</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkFixed" id="fixed7"> + <property name="height_request">40</property> + <property name="visible">True</property> + + <child> + <widget class="GtkHSeparator" id="hseparator14"> + <property name="width_request">456</property> + <property name="height_request">16</property> + <property name="visible">True</property> + </widget> + <packing> + <property name="x">8</property> + <property name="y">24</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label124"> + <property name="width_request">470</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">0 Nodes Selected</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">8</property> + <property name="y">8</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkFixed" id="fixed6"> + <property name="width_request">351</property> + <property name="height_request">381</property> + <property name="visible">True</property> + + <child> + <widget class="GtkHBox" id="hbox12"> + <property name="width_request">428</property> + <property name="height_request">26</property> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkRadioButton" id="attrRB0"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">GRAPH </property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="clicked" handler="on_attrRB0_clicked" last_modification_time="Thu, 29 Oct 2009 20:20:28 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="attrRB1"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">NODE </property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">attrRB0</property> + <signal name="clicked" handler="on_attrRB0_clicked" last_modification_time="Thu, 29 Oct 2009 20:20:28 GMT"/> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + + <child> + <widget class="GtkRadioButton" id="attrRB2"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">EDGE</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <property name="group">attrRB0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">False</property> + </packing> + </child> + </widget> + <packing> + <property name="x">11</property> + <property name="y">4</property> + </packing> + </child> + + <child> + <widget class="GtkTable" id="table18"> + <property name="width_request">562</property> + <property name="height_request">68</property> + <property name="visible">True</property> + <property name="n_rows">3</property> + <property name="n_columns">6</property> + <property name="homogeneous">False</property> + <property name="row_spacing">0</property> + <property name="column_spacing">0</property> + + <child> + <widget class="GtkEntry" id="txtAttr"> + <property name="width_request">88</property> + <property name="height_request">21</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + <signal name="changed" handler="on_txtAttr_changed" last_modification_time="Thu, 22 Oct 2009 20:26:47 GMT"/> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="txtValue"> + <property name="width_request">88</property> + <property name="height_request">21</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="txtDefValue"> + <property name="width_request">88</property> + <property name="height_request">21</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label122"> + <property name="width_request">40</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Value</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">1</property> + <property name="right_attach">2</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label123"> + <property name="width_request">88</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Default Value</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">2</property> + <property name="right_attach">3</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label121"> + <property name="width_request">96</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Attribute Name</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">0</property> + <property name="right_attach">1</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label125"> + <property name="width_request">72</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Publish</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="left_attach">3</property> + <property name="right_attach">4</property> + <property name="top_attach">0</property> + <property name="bottom_attach">1</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkCheckButton" id="attrProg"> + <property name="width_request">24</property> + <property name="height_request">16</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes"></property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="active">False</property> + <property name="inconsistent">False</property> + <property name="draw_indicator">True</property> + <signal name="toggled" handler="on_attrProg_toggled" last_modification_time="Mon, 26 Oct 2009 19:57:29 GMT"/> + </widget> + <packing> + <property name="left_attach">3</property> + <property name="right_attach">4</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="attrAddBtn"> + <property name="width_request">32</property> + <property name="height_request">23</property> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">+</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_attrAddBtn_clicked" last_modification_time="Mon, 26 Oct 2009 22:04:52 GMT"/> + </widget> + <packing> + <property name="left_attach">5</property> + <property name="right_attach">6</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="attrApplyBtn"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Apply</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_attrApplyBtn_clicked" last_modification_time="Mon, 09 Nov 2009 23:05:37 GMT"/> + </widget> + <packing> + <property name="left_attach">4</property> + <property name="right_attach">5</property> + <property name="top_attach">1</property> + <property name="bottom_attach">2</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + + <child> + <widget class="GtkButton" id="attrSearchBtn"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Search</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <signal name="clicked" handler="on_attrSearchBtn_clicked" last_modification_time="Wed, 11 Nov 2009 16:07:55 GMT"/> + </widget> + <packing> + <property name="left_attach">4</property> + <property name="right_attach">5</property> + <property name="top_attach">2</property> + <property name="bottom_attach">3</property> + <property name="x_options">fill</property> + <property name="y_options"></property> + </packing> + </child> + </widget> + <packing> + <property name="x">12</property> + <property name="y">41</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="tab_expand">False</property> + <property name="tab_fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label127"> + <property name="visible">True</property> + <property name="label" translatable="yes">Attribute</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="type">tab</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkWindow" id="frmWait"> + <property name="width_request">200</property> + <property name="height_request">178</property> + <property name="title" translatable="yes">Smyrna</property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_NONE</property> + <property name="modal">False</property> + <property name="default_width">274</property> + <property name="default_height">248</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">False</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_SPLASHSCREEN</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + + <child> + <widget class="GtkFixed" id="fixed4"> + <property name="visible">True</property> + + <child> + <widget class="GtkImage" id="image17"> + <property name="width_request">188</property> + <property name="height_request">176</property> + <property name="visible">True</property> + <property name="stock">gtk-missing-image</property> + <property name="icon_size">4</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + </widget> + <packing> + <property name="x">6</property> + <property name="y">1</property> + </packing> + </child> + + <child> + <widget class="GtkLabel" id="label80"> + <property name="width_request">100</property> + <property name="height_request">26</property> + <property name="visible">True</property> + <property name="label" translatable="yes">Please Wait....</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="x">52</property> + <property name="y">122</property> + </packing> + </child> + </widget> + </child> +</widget> + +<widget class="GtkDialog" id="findDialog"> + <property name="border_width">5</property> + <property name="title" translatable="yes"></property> + <property name="type">GTK_WINDOW_TOPLEVEL</property> + <property name="window_position">GTK_WIN_POS_CENTER_ON_PARENT</property> + <property name="modal">False</property> + <property name="resizable">True</property> + <property name="destroy_with_parent">False</property> + <property name="decorated">True</property> + <property name="skip_taskbar_hint">False</property> + <property name="skip_pager_hint">False</property> + <property name="type_hint">GDK_WINDOW_TYPE_HINT_DIALOG</property> + <property name="gravity">GDK_GRAVITY_NORTH_WEST</property> + <property name="focus_on_map">True</property> + <property name="urgency_hint">False</property> + <property name="has_separator">False</property> + + <child internal-child="vbox"> + <widget class="GtkVBox" id="dialog-vbox7"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">2</property> + + <child internal-child="action_area"> + <widget class="GtkHButtonBox" id="dialog-action_area7"> + <property name="visible">True</property> + <property name="layout_style">GTK_BUTTONBOX_END</property> + + <child> + <widget class="GtkButton" id="button3"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">Cancel</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">2</property> + </widget> + </child> + + <child> + <widget class="GtkButton" id="button2"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="label" translatable="yes">OK</property> + <property name="use_underline">True</property> + <property name="relief">GTK_RELIEF_NORMAL</property> + <property name="focus_on_click">True</property> + <property name="response_id">1</property> + </widget> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + <property name="pack_type">GTK_PACK_END</property> + </packing> + </child> + + <child> + <widget class="GtkVBox" id="vbox8"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkLabel" id="label94"> + <property name="visible">True</property> + <property name="label" translatable="yes"> +Search in +</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkComboBox" id="combobox1"> + <property name="visible">True</property> + <property name="add_tearoffs">False</property> + <property name="focus_on_click">True</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkVBox" id="vbox7"> + <property name="visible">True</property> + <property name="homogeneous">False</property> + <property name="spacing">0</property> + + <child> + <widget class="GtkLabel" id="label93"> + <property name="visible">True</property> + <property name="label" translatable="yes">Regular Expression</property> + <property name="use_underline">False</property> + <property name="use_markup">False</property> + <property name="justify">GTK_JUSTIFY_LEFT</property> + <property name="wrap">False</property> + <property name="selectable">False</property> + <property name="xalign">0.5</property> + <property name="yalign">0.5</property> + <property name="xpad">0</property> + <property name="ypad">0</property> + <property name="ellipsize">PANGO_ELLIPSIZE_NONE</property> + <property name="width_chars">-1</property> + <property name="single_line_mode">False</property> + <property name="angle">0</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + + <child> + <widget class="GtkEntry" id="findText"> + <property name="visible">True</property> + <property name="can_focus">True</property> + <property name="editable">True</property> + <property name="visibility">True</property> + <property name="max_length">0</property> + <property name="text" translatable="yes"></property> + <property name="has_frame">True</property> + <property name="invisible_char">*</property> + <property name="activates_default">False</property> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">False</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + <packing> + <property name="padding">0</property> + <property name="expand">True</property> + <property name="fill">True</property> + </packing> + </child> + </widget> + </child> +</widget> + +</glade-interface>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/graphviz/smyrna/template.dot Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,73 @@ +strict digraph template { + graph [antialiasing=1, + bgcolor="#ffffff", + bordercolor="#38eb29", + bordercoloralpha="1", + bordervisible="1", + defaultfontname="1", + defaultfontsize="52", + gridcolor="#6033d8", + gridcoloralpha="1", + gridsize="30", + gridvisible="0", + highlightededgecolor="#c41b50", + highlightededgecoloralpha="1", + highlightednodecolor="#d1cd24", + highlightednodecoloralpha="1", + defaultlinewidth="1" + nodesizewithdegree="1", + selectededgecolor="#EE1F1C", + selectededgecoloralpha="1", + selectednodecolor="#8ce61d", + selectednodecoloralpha="1", + gridcoloralpha="1", + defaultmagnifierwidth="300", + defaultmagnifierheight="200", + defaultmagnifierkts="5", + defaultfisheyemagnifierradius="250" + defaultfisheyemagnifierdistort="5", + usermode="1", + topologicalfisheyefinenodes="50", + topologicalfisheyecoarseningfactor="2.5", + topologicalfisheyedistortionfactor="1", + topologicalfisheyedist2limit="1", + topologicalfisheyeanimate="1", + topologicalfisheyelabelfinenodes="1", + topologicalfisheyecolornodes="1", + topologicalfisheyecoloredges="1", + topologicalfisheyelabelfocus="1", + topologicalfisheyefinestcolor="red", + topologicalfisheyecoarsestcolor="green", + topologicalfisheyelabelattribute="label", + drawnodes="1", + drawedges="1", + drawlabels="1", + defaultnodealpha="0.8", + defaultedgealpha="0.5", + defaultnodeshape="0", + defaultselectionmethod="2" + + labelglutfont="0" + nodelabelcolor="#8ce61d" + edgelabelcolor="#8ce61d" + nodelabelattribute="name" + edgelabelattribute="name" + labelwithdegree="0" + labelnumberofnodes="45" + shownodelabels="1" + showedgelabels="1" + colortheme="1" + defaultnodecolor="blue" + defaultedgecolor="purple" + edgecolorattribute="" + nodesize="50" + nodesselectable="1" + edgesselectable="1" + edgerender="0" + + ]; + +A->B; +} + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/acyclic.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,62 @@ +.TH ACYCLIC 1 "21 March 2001" +.SH NAME +acyclic \- make directed graph acyclic +.SH SYNOPSIS +.B acyclic +[ +.B \-nv? +] [ +.B \-o +.I outfile +] +[ +.I file +] +.SH DESCRIPTION +.B acyclic +is a filter that takes a directed graph as input and outputs +a copy of the graph with sufficient edges reversed to make +the graph acyclic. The reversed edge inherits all of the attributes +of the original edge. The optional file argument specifies where the +the input graph is stored; by default, the program reads from \fBstdin\fP. +.SH OPTIONS +The following options are supported: +.TP +.B \-n +No output is produced, though the return value +will indicate whether the graph is acyclic or not. +.TP +.B \-v +Print information about whether the file is acyclic, has a cycle or +is undirected. +.TP +.BI \-o " outfile" +causes the output to be written to the specified file; by default, +output is written to \fBstdout\fP. +.TP +.B \-? +option causes the program to print usage information. +.SH RETURN CODES +.B acyclic +returns +.B 0 +if the graph is acyclic; +.B 1 +if the graph has a cycle; +.B 2 +if the graph is undirected; and +.B 255 +if there are any errors. +.SH BUGS +If the graph is strict and there is a cycle of length 2, +the attributes of the reversed edge are lost. +.PP +Some edge attributes are non-symmetric, referring to either the head +or tail node. At present, there is no mechanism or convention for +for correctly switching or renaming these. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +gc(1), dot(1), gvpr(1), gvcolor(1), ccomps(1), sccmap(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/bcomps.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,76 @@ +.TH BCOMPS 1 "18 November 2003" +.SH NAME +bcomps \- biconnected components filter for graphs +.SH SYNOPSIS +.B bcomps +[ +.B \-stvx? +] +[ +.BI -o outfile +] +[ +.I files +] +.SH DESCRIPTION +.B bcomps +decomposes graphs into their biconnected components, +printing the components to standard output. +.SH OPTIONS +The following options are supported: +.TP +.B \-s +No output graph is printed. Implies the +.B \-v +flag. +.TP +.B \-t +Print the underlying block-cutvertex tree. +.TP +.B \-x +Each biconnected component is printed as a separate root graph. +.TP +.B \-v +Prints number of blocks and cutvertices. +.TP +.BI \-o " outfile" +If specified, each root graph will be written to a different file +with the names derived from \fIoutfile\fP. In particular, +if both \fB-o\fP and \fB-x\fP flags are used, then each +block is written to a different file. If \fIoutfile\fP does +not have a suffix, the nth block of the ith graph is written +to \fIoutfile_n_i\fP. However, the 0th block of the 0th graph is written to +\fIoutfile\fP. +.sp +If \fIoutfile\fP has a suffix, i.e., has the form \fIbase.sfx\fP, +then the files will have the same name as above, except appended with \fI.sfx\fP. +.sp +The block-cutvertex tree of ith graph is written to \fIoutfile_n_T\fP, +with an appended suffix if specified. +.LP +By default, each input graph is printed, with each +block given as a subgraph whose name is a concatenation of +the name of the input graph, the string "_bcc_" and the +number of the block. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in dot format. +If no +.I files +operand is specified, +the standard input will be used. +.SH RETURN CODES +.B bcomps +returns +.B 0 +if all the input graphs are biconnected; and +non-zero if any graph has multiple blocks, or any error occurred. +.SH "BUGS" +It is possible, though unlikely, that the names used for connected +components and their subgraphs may conflict with existing subgraph names. +.SH AUTHORS +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +ccomps(1), gc(1), dot(1), gvpr(1), gvcolor(1), acyclic(1), sccmap(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/ccomps.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,107 @@ +.TH CCOMPS 1 "27 August 2008" +.SH NAME +ccomps \- connected components filter for graphs +.SH SYNOPSIS +.B ccomps +[ +.B \-sxvnzC? +] +[ +.BI -X [#]v +] +[ +.BI -o outfile +] +[ +.I files +] +.SH DESCRIPTION +.B ccomps +decomposes graphs into their connected components, +printing the components to standard output. +.SH OPTIONS +The following options are supported: +.TP +.B \-s +No output graph is printed. The return value can be used to +check if the graph is connected or not. +.TP +.B \-x +Only the connected components are printed, as separate graphs. +.TP +.B \-v +Counts of nodes, edges and connected components are printed. +.TP +.B \-z +Sort components by size, with the largest first. This is only +effective if either \fB-x\fP or \fB-X#v\fP is present. +Thus, \fB-zX#0\fP will cause the largest component to be printed. +.TP +.B \-C +Use clusters in computing components in addition to normal edge +connectivity. In essence, this gives the connected components of the +derived graph in which nodes top-level clusters and nodes in the +original graph. This maintains all subgraph structure within a +component, even if a subgraph does not contain any nodes. +.TP +.B \-n +Do not project subgraph structure. Normally, if +.B ccomps +produces components as graphs distinct from the input graph, it will +define subgraphs which are projections of subgraphs of the input graph +onto the component. (If the projection is empty, no subgraph is produced.) +If this flag is set, the component contains only the relevant nodes and +edges. +.TP +.BI \-X " node_name" +Prints only the component containing the node \fInode_name\fP, +if any. +.TP +.BI \-X# " index" +Prints only component number \fIindex\fP, if any, starting at 0. +.TP +.BI \-o " outfile" +If specified, each graph will be written to a different file +with the names derived from \fIoutfile\fP. In particular, +if both \fB-o\fP and \fB-x\fP flags are used, then each connected +component is written to a different file. If \fIoutfile\fP does +not have a suffix, the first file will have the name \fIoutfile\fP; +then next \fIoutfile_1\fP, then next \fIoutfile_2\fP, and so on. +If \fIoutfile\fP has a suffix, i.e., has the form \fIbase.sfx\fP, +then the files will be named \fIbase.sfx\fP, \fIbase_1.sfx\fP, +\fIbase_2.sfx\fP, etc. +.LP +By default, each input graph is printed, with each connected +component given as a subgraph whose name is a concatenation of +the name of the input graph, the string "_component_" and the +number of the component. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in dot format. +If no +.I files +operand is specified, +the standard input will be used. +.SH RETURN CODES +Unless used to extract a single connected component, +.B ccomps +returns +.B 0 +if all the input graphs are connected; and +non-zero if any graph has multiple components, or any error occurred. +If just extracting a single component, +.B ccomps +returns +.B 0 +on success and non-zero if an error occurred. +.SH "BUGS" +It is possible, though unlikely, that the names used for connected +components and their subgraphs may conflict with existing subgraph names. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +gc(1), dot(1), gvpr(1), gvcolor(1), acyclic(1), sccmap(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/circo.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,725 @@ +.TH DOT 1 "23 August 2004" \*(eX +.SH NAME +dot \- filter for drawing directed graphs +.br +neato \- filter for drawing undirected graphs +.br +twopi \- filter for radial layouts of graphs +.br +circo \- filter for circular layout of graphs +.br +fdp \- filter for drawing undirected graphs +.br +sfdp \- filter for drawing large undirected graphs +.SH SYNOPSIS +\fBdot\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBneato\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-n\fR[\fB1\fR|\fB2\fR]] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBtwopi\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBcirco\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBsfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.SH DESCRIPTION +.I dot +draws directed graphs. It works well on DAGs and other graphs +that can be drawn as hierarchies. +It reads attributed graph files and writes drawings. +By default, the output format +.I dot +is the input file with layout coordinates appended. +.PP +.I neato +draws undirected graphs using ``spring'' models (see Kamada and Kawai, +Information Processing Letters 31:1, April 1989). Input files must be +formatted in the +.I dot +attributed graph language. +By default, the output of +.I neato +is the input graph with layout coordinates appended. +.PP +.I twopi +draws graphs using a radial layout (see G. Wills, +Symposium on Graph Drawing GD'97, September, 1997). +Basically, one node is chosen as the center and put at the origin. +The remaining nodes are placed on a sequence of concentric circles +centered about the origin, each a fixed radial distance from the +previous circle. +All nodes distance 1 from the center are placed on the first circle; +all nodes distance 1 from a node on the first circle are placed on +the second circle; and so forth. +.PP +.I circo +draws graphs using a circular layout (see +Six and Tollis, GD '99 and ALENEX '99, and +Kaufmann and Wiese, GD '02.) +The tool identifies biconnected components and draws the nodes of +the component on a circle. The block\(hycutpoint tree +is then laid out using a recursive radial algorithm. Edge +crossings within a circle are minimized by placing as many edges on +the circle's perimeter as possible. +In particular, if the component is outerplanar, the component will +have a planar layout. +.PP +If a node belongs to multiple non\(hytrivial biconnected components, +the layout puts the node in one of them. By default, this is the first +non\(hytrivial component found in the search from the root component. +.PP +.I fdp +draws undirected graphs using a ``spring'' model. It relies on a +force\(hydirected approach in the spirit of Fruchterman and Reingold +(cf. Software\(hyPractice & Experience 21(11), 1991, pp. 1129\(hy1164). +.PP +.I sfdp +also draws undirected graphs using the ``spring'' model described +above, but it uses a multi-scale approach to produce layouts +of large graphs in a reasonably short time. +.SH OUTPUT FORMATS +Dot uses an extensible plugin mechanism for its output renderers, +so to see what output formats your installation of dot supports +you can use ``dot \-Txxx'' (where xxx is an unlikely format) +and check the warning message. +Also, The plugin mechanism supports multiple implementations +of the output formats. +To see what variants are available, use, for example: ``dot \-Tpng:'' +and to force a particular variant, use, for example: ``dot \-Tpng:gd'' +.P +Traditionally, dot supports the following: +\fB\-Tps\fP (PostScript), +\fB\-Tsvg\fP \fB\-Tsvgz\fP (Structured Vector Graphics), +\fB\-Tfig\fP (XFIG graphics), +\fB\-Tmif\fP (FrameMaker graphics), +\fB\-Thpgl\fP (HP pen plotters), and \fB\-Tpcl\fP (Laserjet printers), +\fB\-Tpng\fP \fB\-Tgif\fP (bitmap graphics), +\fB\-Tdia\fP (GTK+ based diagrams), +\fB\-Timap\fP (imagemap files for httpd servers for each node or edge +that has a non\(hynull "href" attribute.), +\fB\-Tcmapx\fP (client\(hyside imagemap for use in html and xhtml). +Additional less common or more special\(hypurpose output formats +can be found at http://www.graphviz.org/cvs/doc/info/output.html.) +.P +Alternative plugins providing support for a given output format +can be found from the error message resulting from appending a ':' to the format. e.g. \fB-Tpng:\fP +The first plugin listed is always the default. +.P +The \fB\-P\fP switch can be used to produce a graph of all output variants supported by plugins in the local installation of graphviz. +.SH GRAPH FILE LANGUAGE +Here is a synopsis of the graph file language, traditionally using the extension \fB.dot\fR, for graphs: +.PP +[\fBstrict\fR] (\fBgraph\fR|\fBdigraph\fR) \fIname\fP { \fIstatement\(hylist\fP }\fR +.br +Is the top level graph. If the graph is \fBstrict\fR then multiple edges are +not allowed between the same pairs of nodes. +If it is a directed graph, indicated by \fBdigraph\fR, +then the \fIedgeop\fR must be "\->". If it is an undirected \fBgraph\fR +then the \fIedgeop\fR must be "\-\-". +Statements may be: +.PP +\fIname\fB=\fIval\fB;\fR +.br +\fBnode [\fIname\fB=\fIval\fB];\fR +.br +\fBedge [\fIname\fB=\fIval\fB];\fR +.br +Set default graph, node, or edge attribute \fIname\fP to \fIval\fP. +Any subgraph, node, or edge appearing after this inherits the new +default attributes. +.PP +\fBn0 [\fIname0=val0,name1=val1,...\fB];\fR +Creates node \fBn0\fP (if it does not already exist) +and sets its attributes according to the optional list. +.PP +\fBn0 \fIedgeop\fR n1 \fIedgeop\fR \fI...\fB \fIedgeop\fR nn [\fIname0=val0,name1=val1,...\fB];\fR +.br +Creates edges between nodes \fBn0\fP, \fBn1\fP, ..., \fBnn\fP and sets +their attributes according to the optional list. +Creates nodes as necessary. +.PP +[\fBsubgraph \fIname\fB] { \fIstatement\(hylist \fB}\fR +.br +Creates a subgraph. Subgraphs may be used in place +of \fBn0\fP, ..., \fBnn\fP in the above statements to create edges. +[\fBsubgraph \fIname\fR] is optional; +if missing, the subgraph is assigned an internal name. +.PP +Comments may be /*C\(hylike*/ or //C++\(hylike. + +.PP +Attribute names and values are ordinary (C\(hystyle) strings. +The following sections describe attributes that control graph layout. + +.SH "GRAPH ATTRIBUTES" +.PP +\fBsize="\fIx,y\fP"\fR sets bounding box of drawing in inches. +.PP +\fBpage="\fIx,y\fP"\fR sets the PostScript pagination unit. +.PP +\fBratio=\fIf\fR sets the aspect ratio to \fIf\fP which may be +a floating point number, or one of the keywords \fBfill\fP, +\fBcompress\fP, or \fBauto\fP. +.PP +\fBlayout=\fIengine\fR indicates the preferred layout engine ("dot", "neato", fdp" etc) overriding the default from the basename of the command or the -K commandline option. +.PP +\fBmargin=\fIf\fR sets the page margin (included in the page size). +.PP +\fBnodesep=\fIf\fR sets the minimum separation between nodes. +.PP +\fBranksep=\fIf\fR sets the minimum separation between ranks. +.PP +\fBordering=out\fR constrains order of out\(hyedges in a subgraph +according to their file sequence. +.PP +\fBrankdir=LR|RL|BT\fR requests a left\(hyto\(hyright, right\(hyto\(hyleft, or bottom\(hyto\(hytop, drawing. +.PP +\fBpagedir=\fR[TBLR][TBLR] sets the major and minor order of pagination. +.PP +\fBrank=same\fR (or \fBmin\fP or \fBmax\fP) in a subgraph +constrains the rank assignment of its nodes. If a subgraph's +name has the prefix \fBcluster\fP, its nodes are drawn in +a distinct rectangle of the layout. Clusters may be nested. +.PP +\fBrotate=90\fR sets landscape mode. +(\fBorientation=land\fR is backward compatible but obsolete.) +.PP +\fBcenter=\fIn\fR a non\(hyzero value centers the drawing on the page. +.PP +\fBnslimit=\fIf\fR or \fBmclimit=\fIf\fR adjusts the bound on the +number of network simplex or mincross iterations by the given ratio. +For example, \fBmclimit=2.0\fP runs twice as long. +.PP +\fBlayers="\fIid:id:id:id\fR" is a sequence of layer identifiers for +overlay diagrams. The PostScript array variable \fIlayercolorseq\fR +sets the assignment of colors to layers. The least index is 1 and +each element must be a 3\(hyelement array to be interpreted as a color coordinate. +.PP +\fBcolor=\fIcolorvalue\fR sets foreground color (\fBbgcolor\fP for background). +.PP +\fBhref=\fI"url"\fR the default url for image map files; in PostScript files, +the base URL for all relative URLs, as recognized by Acrobat Distiller +3.0 and up. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBstylesheet=\fI"file.css"\fR includes a reference to a stylesheet +in \-Tsvg and \-Tsvgz outputs. Ignored by other formats. +.PP +\fBsplines\fR. If set to \fItrue\fR, edges are +drawn as splines. +If set to \fIpolyline\fR, edges are +drawn as polylines. +If set to \fIortho\fR, edges are +drawn as orthogonal polylines. +In all of these cases, the nodes may not overlap. +If \fBsplines=\fIfalse\fR or \fBsplines=\fIline\fR, edges are +drawn as line segments. +The default is \fItrue\fR for dot, and \fIfalse\fR for all other layouts. + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBstart=\fIval\fR. Requests random initial placement and seeds +the random number generator. If \fIval\fP is not an integer, +the process ID or current time is used as the seed. +.PP +\fBepsilon=\fIn\fR. Sets the cutoff for the solver. +The default is 0.1. + +.PP +\fB(twopi\(hyspecific attributes)\fR +.br +\fBroot=\fIctr\fR. This specifies the node to be used as the center of +the layout. If not specified, \fItwopi\fP will randomly pick one of the +nodes that are furthest from a leaf node, where a leaf node is a node +of degree 1. If no leaf nodes exists, an arbitrary node is picked as center. +.PP +\fBranksep=\fIval\fR. Specifies the radial distance in inches between +the sequence of rings. The default is 0.75. +.PP +\fBoverlap=\fImode\fR. This specifies what \fItwopi\fP should do if +any nodes overlap. If mode is \fI"false"\fP, the program uses Voronoi +diagrams to adjust the nodes to eliminate overlaps. If mode is \fI"scale"\fP, +the layout is uniformly scaled up, preserving node sizes, until nodes no +longer overlap. The latter technique removes overlaps while preserving +symmetry and structure, while the former removes overlaps more compactly +but destroys symmetries. +If mode is \fI"true"\fP (the default), no repositioning is done. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fInodename\fR. Specifies the name of a node occurring in the +root block. If the graph is disconnected, the \fBroot\fP node attribute +can be used to specify additional root blocks. +.PP +\fBmindist=\fIvalue\fR. Sets the minimum separation between all nodes. If not +specified then \fIcirco\fP uses a default value of 1.0. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBK=\fIval\fR. Sets the default ideal node separation +in the layout. +.PP +\fBmaxiter=\fIval\fR. Sets the maximum number of iterations used to +layout the graph. +.PP +\fBstart=\fIval\fR. Adjusts the random initial placement of nodes +with no specified position. If \fIval\fP is is an integer, +it is used as the seed for the random number generator. +If \fIval\fP is not an integer, a random system\(hygenerated integer, +such as the process ID or current time, is used as the seed. +.PP + +.SH "NODE ATTRIBUTES" +.PP +\fBheight=\fId\fR or \fBwidth=\fId\fR sets minimum height or width. +Adding \fBfixedsize=true\fP forces these to be the actual size +(text labels are ignored). +.PP +\fBshape=record polygon epsf \fIbuiltin_polygon\fR +.br +\fIbuiltin_polygon\fR is one of: \fBplaintext ellipse oval circle egg +triangle box diamond trapezium parallelogram house hexagon octagon +note tab box3d component.\fR +(Polygons are defined or modified by the following node attributes: +\fBregular\fR, \fBperipheries\fR, \fBsides\fR, \fBorientation\fR, +\fBdistortion\fR and \fBskew\fR.) \fBepsf\fR uses the node's +\fBshapefile\fR attribute as the path name of an external +EPSF file to be automatically loaded for the node shape. +.PP +\fBlabel=\fItext\fR where \fItext\fP may include escaped newlines +\\\|n, \\\|l, or \\\|r for center, left, and right justified lines. +The string '\\N' value will be replaced by the node name. +The string '\\G' value will be replaced by the graph name. +Record labels may contain recursive box lists delimited by { | }. +Port identifiers in labels are set off by angle brackets < >. +In the graph file, use colon (such as, \fBnode0:port28\fR). +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBcolor=\fIcolorvalue\fR sets the outline color, and the default fill color +if style=filled and \fBfillcolor\fR is not specified. +.PP +\fBfillcolor=\fIcolorvalue\fR sets the fill color +when style=filled. If not specified, the fillcolor when style=filled defaults +to be the same as the outline color. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +A \fIcolorvalue\fP may be "\fIh,s,v\fB"\fR (hue, saturation, brightness) +floating point numbers between 0 and 1, or an X11 color name such as +\fBwhite black red green blue yellow magenta cyan\fR or \fBburlywood\fR, +or a "\fI#rrggbb" (red, green, blue, 2 hex characters each) value. +.PP +\fBstyle=filled solid dashed dotted bold invis\fP or any Postscript code. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the node's active layers. +The empty string means no layers (invisible). +.PP +The following attributes apply only to polygon shape nodes: +.PP +\fBregular=\fIn\fR if \fIn\fR is non\(hyzero then the polygon is made +regular, i.e. symmetric about the x and y axis, otherwise the +polygon takes on the aspect ratio of the label. +\fIbuiltin_polygons\fR that are not already regular are made regular +by this attribute. +\fIbuiltin_polygons\fR that are already regular are not affected (i.e. +they cannot be made asymmetric). +.PP +\fBperipheries=\fIn\fR sets the number of periphery lines drawn around +the polygon. This value supersedes the number of periphery lines +of \fIbuiltin_polygons\fR. +.PP +\fBsides=\fIn\fR sets the number of sides to the polygon. \fIn\fR<3 +results in an ellipse. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBorientation=\fIf\fR sets the orientation of the first apex of the +polygon counterclockwise from the vertical, in degrees. +\fIf\fR may be a floating point number. +The orientation of labels is not affected by this attribute. +This attribute is added to the initial orientation of \fIbuiltin_polygons.\fR +.PP +\fBdistortion=\fIf\fR sets the amount of broadening of the top and +narrowing of the bottom of the polygon (relative to its orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBskew=\fIf\fR sets the amount of right\(hydisplacement of the top and +left\(hydisplacement of the bottom of the polygon (relative to its +orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. +The target string is used to determine which window of the browser is used +for the URL. Setting it to "_graphviz" will open a new window if it doesn't +already exist, or reuse it if it does. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. The tooltip string defaults to be the +same as the label string, but this attribute permits nodes without +labels to still have tooltips thus permitting denser graphs. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fItrue/false\fR. This specifies that the block containing the given +node be treated as the root of the spanning tree in the layout. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBpin=\fIval\fR. If \fIval\fR is "true", the node will remain at +its initial position. + +.SH "EDGE ATTRIBUTES" +\fBminlen=\fIn\fR where \fIn\fP is an integer factor that applies +to the edge length (ranks for normal edges, or minimum node separation +for flat edges). +.PP +\fBweight=\fIn\fR where \fIn\fP is the integer cost of the edge. +Values greater than 1 tend to shorten the edge. Weight 0 flat +edges are ignored for ordering nodes. +.PP +\fBlabel=\fItext\fR where \fItext\fR may include escaped newlines +\\\|n, \\\|l, or \\\|r for centered, left, or right justified lines. +If the substring '\\T' is found in a label it will be replaced by the tail_node name. +If the substring '\\H' is found in a label it will be replaced by the head_node name. +If the substring '\\E' value is found in a label it will be replaced by: tail_node_name\->head_node_name +If the substring '\\G' is found in a label it will be replaced by the graph name. +or by: tail_node_name\-\-head_node_name for undirected graphs. +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +\fBstyle=solid dashed dotted bold invis\fP +.PP +\fBcolor=\fIcolorvalue\fR sets the line color for edges. +.PP +\fBcolor=\fIcolorvaluelist\fR a ':' separated list of \fIcolorvalue\fR creates +parallel edges, one edge for each color. +.PP +\fBdir=forward back both none\fP controls arrow direction. +.PP +\fBtailclip,headclip=false\fP disables endpoint shape clipping. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edges have a URL. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when edges have a URL. The tooltip string defaults to be the +same as the edge label string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBarrowhead,arrowtail=none, normal, inv, dot, odot, invdot, invodot, +tee, empty, invempty, open, halfopen, diamond, odiamond, box, obox, crow\fP. +.PP +\fBarrowsize\fP (norm_length=10,norm_width=5, +inv_length=6,inv_width=7,dot_radius=2) +.PP +\fBheadlabel,taillabel=string\fP for port labels. +\fBlabelfontcolor\fP,\fBlabelfontname\fP,\fBlabelfontsize\fP +for head and tail labels. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadhref=\fI"url"\fR sets the url for the head port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadURL=\fI"url"\fR ("headURL" is a synonym for "headhref".) +.PP +\fBheadtarget=\fI"headtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge heads have a URL. +The headtarget string is used to determine which window of the browser is used +for the URL. If the headtarget string is empty, the default, +then headtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when head ports have a URL. The tooltip string defaults to be the +same as the headlabel string. +The substrings '\\T', '\\H', and '\\E' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailhref=\fI"url"\fR sets the url for the tail port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailURL=\fI"url"\fR ("tailURL" is a synonym for "tailhref".) +.PP +\fBtailtarget=\fI"tailtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge tails have a URL. +The tailtarget string is used to determine which window of the browser is used +for the URL. If the tailtarget string is empty, the default, +then tailtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when tail ports have a URL. The tooltip string defaults to be the +same as the taillabel string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBlabeldistance\fP and \fPport_label_distance\fP set distance; also +\fBlabelangle\fP (in degrees CCW) +.PP +\fBdecorate\fP draws line from edge to label. +.PP +\fBsamehead,sametail\fP aim edges having the same value to the +same port, using the average landing point. +.PP +\fBconstraint=false\fP causes an edge to be ignored for rank assignment. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the edge's active layers. +The empty string means no layers (invisible). + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBw=\fIf\fR sets the weight (spring constant) of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.PP +\fBlen=\fIf\fR sets the optimal length of an edge. +The default is 1.0. +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBweight=\fIf\fR sets the weight of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.SH "COMMAND LINE OPTIONS" +\fB\-G\fP sets a default graph attribute. +.br +\fB\-N\fP sets a default node attribute. +.br +\fB\-E\fP sets a default edge attribute. +Example: \fB\-Gsize="7,8" \-Nshape=box \-Efontsize=8\fR +.PP +\fB\-l\fIfile\fR loads custom PostScript library files. +Usually these define custom shapes or styles. +If \fB\-l\fP is given by itself, the standard library is omitted. +.PP +\fB\-T\fIlang\fR sets the output language as described above. + +.PP +\fB\-n\fR[\fB1\fR|\fB2\fR] (no\(hyop) +If set, neato assumes nodes have already been positioned and all +nodes have a pos attribute giving the positions. +It then performs an optional adjustment to remove node\(hynode overlap, +depending on the value of the overlap attribute, +computes the edge layouts, depending on the value of the \fBsplines\fR attribute, +and emits the graph in the appropriate format. +If num is supplied, the following actions occur: +.nf + num = 1 +.fi +Equivalent to \-n. +.nf + num > 1 +.fi +Use node positions as specified, +with no adjustment to remove node\(hynode overlaps, +and use any edge layouts already specified by the pos attribute. +neato computes an edge layout for any edge that does not have a pos attribute. +As usual, edge layout is guided by the \fBsplines\fR attribute. +.PP +\fB\-K\fIlayout\fR override the default layout engine implied by the command name. +.PP +\fB\-O\fP automatically generate output filenames based on the input filename and the -T format. +.PP +\fB\-P\fP generate a graph of the currently available plugins. +.PP +\fB\-v\fP (verbose) prints various information useful for debugging. +.PP +\fB\-V\fP (version) prints version information and exits. +.PP +\fB\-?\fP prints the usage and exits. +.SH "EXAMPLES" +.nf +digraph test123 { + a \-> b \-> c; + a \-> {x y}; + b [shape=box]; + c [label="hello\\\|nworld",color=blue,fontsize=24, + fontname="Palatino\-Italic",fontcolor=red,style=filled]; + a \-> z [label="hi", weight=100]; + x \-> z [label="multi\-line\\\|nlabel"]; + edge [style=dashed,color=red]; + b \-> x; + {rank=same; b x} +} +.fi +.PP +.nf +graph test123 { + a \-\- b \-\- c; + a \-\- {x y}; + x \-\- c [w=10.0]; + x \-\- y [w=5.0,len=3]; +} +.fi +.SH "CAVEATS" +Edge splines can overlap unintentionally. +.PP +Flat edge labels are slightly broken. +Intercluster edge labels are totally broken. +.PP +Because unconstrained optimization is employed, node boxes can +possibly overlap or touch unrelated edges. All existing +spring embedders seem to have this limitation. +.PP +Apparently reasonable attempts to pin nodes or adjust edge lengths +and weights can cause instability. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.br +John C. Ellson <ellson@research.att.com> +.PP +The bitmap driver (PNG, GIF etc) is by Thomas Boutell, <http://www.boutell.com/gd> +.PP +The Truetype font renderer is from +the Freetype Project (David Turner, Robert Wilhelm, and Werner Lemberg) +(who can be contacted at freetype\-devel@lists.lrz\-muenchen.de). +.SH "SEE ALSO" +This man page contains only a small amount of the information related +to the Graphviz layout programs. The most complete information can be +found at http://www.graphviz.org/Documentation.php, especially in the +on\(hyline reference pages. Most of these documents are also available in the +\fIdoc\fP and \fIdoc/info\fP subtrees in the source and binary distributions. +.PP +dotty(1) +.br +tcldot(n) +.br +xcolors(1) +.br +libgraph(3) +.PP +E. R. Gansner, S. C. North, K. P. Vo, "DAG \(hy A Program to Draw Directed Graphs", Software \(hy Practice and Experience 17(1), 1988, pp. 1047\(hy1062. +.br +E. R. Gansner, E. Koutsofios, S. C. North, K. P. Vo, "A Technique for Drawing Directed Graphs," IEEE Trans. on Soft. Eng. 19(3), 1993, pp. 214\(hy230. +.br +S. North and E. Koutsofios, "Applications of graph visualization", +Graphics Interface 94, pp. 234\(hy245. +.br +E. Koutsofios and S. C. North, "Drawing Graphs with dot," +Available on research.att.com in dist/drawdag/dotguide.ps.Z. +.br +S. C. North, "NEATO User's Manual". +Available on research.att.com in dist/drawdag/neatodoc.ps.Z.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/dijkstra.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,64 @@ +.TH DIJKSTRA 1 "21 March 2001" +.SH NAME +dijkstra \- single-source distance filter +.SH SYNOPSIS +.B dijkstra +[ +.B \-ap? +] +[ +.I sourcenode file +] +.SH DESCRIPTION +.B dijkstra +reads a stream of graphs and for each computes the distance of every +node from +.I sourcenode. +Edge length is given in the +.I len +attribute, and the default is 1. The +.I dist +attribute of every node is set to its distance from +.I sourcenode. +If the \fB-p\fP flag is used, the +.I prev +attribute of each node reachable from +.I sourcenode +is set to the name of the previous node on a shortest path. +The graph attribute +.I maxdist +is set to the maximum +.I dist +of all nodes in the graph. +.P +By default, if the graph is disconnected, the +.I dist +attribute of nodes unreachable from +.I sourcenode +are left untouched, and +.I maxdist +is set to the maximum of any previous value and the largest +distance recorded in this run. On the other hand, if +the \fB-a\fP flag is used, the +.I dist +attribute of an unreachable node is assigned a very large value, +and +.I maxdist +records the maximum distance found in the component containing +.I sourcenode. +.P +Any number of +.I sourcenode file +pairs may be given. +If the last +.I file +is missing, \fBstdin\fP is used. +All output is written to \fBstdout\fP. +.P +In a typical application, +.I dist +and +.I maxdist +can drive a downstream calculation of color or some other attribute. +.SH "SEE ALSO" +gvpr(1), gvcolor(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/dot.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,725 @@ +.TH DOT 1 "23 August 2004" \*(eX +.SH NAME +dot \- filter for drawing directed graphs +.br +neato \- filter for drawing undirected graphs +.br +twopi \- filter for radial layouts of graphs +.br +circo \- filter for circular layout of graphs +.br +fdp \- filter for drawing undirected graphs +.br +sfdp \- filter for drawing large undirected graphs +.SH SYNOPSIS +\fBdot\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBneato\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-n\fR[\fB1\fR|\fB2\fR]] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBtwopi\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBcirco\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBsfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.SH DESCRIPTION +.I dot +draws directed graphs. It works well on DAGs and other graphs +that can be drawn as hierarchies. +It reads attributed graph files and writes drawings. +By default, the output format +.I dot +is the input file with layout coordinates appended. +.PP +.I neato +draws undirected graphs using ``spring'' models (see Kamada and Kawai, +Information Processing Letters 31:1, April 1989). Input files must be +formatted in the +.I dot +attributed graph language. +By default, the output of +.I neato +is the input graph with layout coordinates appended. +.PP +.I twopi +draws graphs using a radial layout (see G. Wills, +Symposium on Graph Drawing GD'97, September, 1997). +Basically, one node is chosen as the center and put at the origin. +The remaining nodes are placed on a sequence of concentric circles +centered about the origin, each a fixed radial distance from the +previous circle. +All nodes distance 1 from the center are placed on the first circle; +all nodes distance 1 from a node on the first circle are placed on +the second circle; and so forth. +.PP +.I circo +draws graphs using a circular layout (see +Six and Tollis, GD '99 and ALENEX '99, and +Kaufmann and Wiese, GD '02.) +The tool identifies biconnected components and draws the nodes of +the component on a circle. The block\(hycutpoint tree +is then laid out using a recursive radial algorithm. Edge +crossings within a circle are minimized by placing as many edges on +the circle's perimeter as possible. +In particular, if the component is outerplanar, the component will +have a planar layout. +.PP +If a node belongs to multiple non\(hytrivial biconnected components, +the layout puts the node in one of them. By default, this is the first +non\(hytrivial component found in the search from the root component. +.PP +.I fdp +draws undirected graphs using a ``spring'' model. It relies on a +force\(hydirected approach in the spirit of Fruchterman and Reingold +(cf. Software\(hyPractice & Experience 21(11), 1991, pp. 1129\(hy1164). +.PP +.I sfdp +also draws undirected graphs using the ``spring'' model described +above, but it uses a multi-scale approach to produce layouts +of large graphs in a reasonably short time. +.SH OUTPUT FORMATS +Dot uses an extensible plugin mechanism for its output renderers, +so to see what output formats your installation of dot supports +you can use ``dot \-Txxx'' (where xxx is an unlikely format) +and check the warning message. +Also, The plugin mechanism supports multiple implementations +of the output formats. +To see what variants are available, use, for example: ``dot \-Tpng:'' +and to force a particular variant, use, for example: ``dot \-Tpng:gd'' +.P +Traditionally, dot supports the following: +\fB\-Tps\fP (PostScript), +\fB\-Tsvg\fP \fB\-Tsvgz\fP (Structured Vector Graphics), +\fB\-Tfig\fP (XFIG graphics), +\fB\-Tmif\fP (FrameMaker graphics), +\fB\-Thpgl\fP (HP pen plotters), and \fB\-Tpcl\fP (Laserjet printers), +\fB\-Tpng\fP \fB\-Tgif\fP (bitmap graphics), +\fB\-Tdia\fP (GTK+ based diagrams), +\fB\-Timap\fP (imagemap files for httpd servers for each node or edge +that has a non\(hynull "href" attribute.), +\fB\-Tcmapx\fP (client\(hyside imagemap for use in html and xhtml). +Additional less common or more special\(hypurpose output formats +can be found at http://www.graphviz.org/cvs/doc/info/output.html.) +.P +Alternative plugins providing support for a given output format +can be found from the error message resulting from appending a ':' to the format. e.g. \fB-Tpng:\fP +The first plugin listed is always the default. +.P +The \fB\-P\fP switch can be used to produce a graph of all output variants supported by plugins in the local installation of graphviz. +.SH GRAPH FILE LANGUAGE +Here is a synopsis of the graph file language, traditionally using the extension \fB.dot\fR, for graphs: +.PP +[\fBstrict\fR] (\fBgraph\fR|\fBdigraph\fR) \fIname\fP { \fIstatement\(hylist\fP }\fR +.br +Is the top level graph. If the graph is \fBstrict\fR then multiple edges are +not allowed between the same pairs of nodes. +If it is a directed graph, indicated by \fBdigraph\fR, +then the \fIedgeop\fR must be "\->". If it is an undirected \fBgraph\fR +then the \fIedgeop\fR must be "\-\-". +Statements may be: +.PP +\fIname\fB=\fIval\fB;\fR +.br +\fBnode [\fIname\fB=\fIval\fB];\fR +.br +\fBedge [\fIname\fB=\fIval\fB];\fR +.br +Set default graph, node, or edge attribute \fIname\fP to \fIval\fP. +Any subgraph, node, or edge appearing after this inherits the new +default attributes. +.PP +\fBn0 [\fIname0=val0,name1=val1,...\fB];\fR +Creates node \fBn0\fP (if it does not already exist) +and sets its attributes according to the optional list. +.PP +\fBn0 \fIedgeop\fR n1 \fIedgeop\fR \fI...\fB \fIedgeop\fR nn [\fIname0=val0,name1=val1,...\fB];\fR +.br +Creates edges between nodes \fBn0\fP, \fBn1\fP, ..., \fBnn\fP and sets +their attributes according to the optional list. +Creates nodes as necessary. +.PP +[\fBsubgraph \fIname\fB] { \fIstatement\(hylist \fB}\fR +.br +Creates a subgraph. Subgraphs may be used in place +of \fBn0\fP, ..., \fBnn\fP in the above statements to create edges. +[\fBsubgraph \fIname\fR] is optional; +if missing, the subgraph is assigned an internal name. +.PP +Comments may be /*C\(hylike*/ or //C++\(hylike. + +.PP +Attribute names and values are ordinary (C\(hystyle) strings. +The following sections describe attributes that control graph layout. + +.SH "GRAPH ATTRIBUTES" +.PP +\fBsize="\fIx,y\fP"\fR sets bounding box of drawing in inches. +.PP +\fBpage="\fIx,y\fP"\fR sets the PostScript pagination unit. +.PP +\fBratio=\fIf\fR sets the aspect ratio to \fIf\fP which may be +a floating point number, or one of the keywords \fBfill\fP, +\fBcompress\fP, or \fBauto\fP. +.PP +\fBlayout=\fIengine\fR indicates the preferred layout engine ("dot", "neato", fdp" etc) overriding the default from the basename of the command or the -K commandline option. +.PP +\fBmargin=\fIf\fR sets the page margin (included in the page size). +.PP +\fBnodesep=\fIf\fR sets the minimum separation between nodes. +.PP +\fBranksep=\fIf\fR sets the minimum separation between ranks. +.PP +\fBordering=out\fR constrains order of out\(hyedges in a subgraph +according to their file sequence. +.PP +\fBrankdir=LR|RL|BT\fR requests a left\(hyto\(hyright, right\(hyto\(hyleft, or bottom\(hyto\(hytop, drawing. +.PP +\fBpagedir=\fR[TBLR][TBLR] sets the major and minor order of pagination. +.PP +\fBrank=same\fR (or \fBmin\fP or \fBmax\fP) in a subgraph +constrains the rank assignment of its nodes. If a subgraph's +name has the prefix \fBcluster\fP, its nodes are drawn in +a distinct rectangle of the layout. Clusters may be nested. +.PP +\fBrotate=90\fR sets landscape mode. +(\fBorientation=land\fR is backward compatible but obsolete.) +.PP +\fBcenter=\fIn\fR a non\(hyzero value centers the drawing on the page. +.PP +\fBnslimit=\fIf\fR or \fBmclimit=\fIf\fR adjusts the bound on the +number of network simplex or mincross iterations by the given ratio. +For example, \fBmclimit=2.0\fP runs twice as long. +.PP +\fBlayers="\fIid:id:id:id\fR" is a sequence of layer identifiers for +overlay diagrams. The PostScript array variable \fIlayercolorseq\fR +sets the assignment of colors to layers. The least index is 1 and +each element must be a 3\(hyelement array to be interpreted as a color coordinate. +.PP +\fBcolor=\fIcolorvalue\fR sets foreground color (\fBbgcolor\fP for background). +.PP +\fBhref=\fI"url"\fR the default url for image map files; in PostScript files, +the base URL for all relative URLs, as recognized by Acrobat Distiller +3.0 and up. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBstylesheet=\fI"file.css"\fR includes a reference to a stylesheet +in \-Tsvg and \-Tsvgz outputs. Ignored by other formats. +.PP +\fBsplines\fR. If set to \fItrue\fR, edges are +drawn as splines. +If set to \fIpolyline\fR, edges are +drawn as polylines. +If set to \fIortho\fR, edges are +drawn as orthogonal polylines. +In all of these cases, the nodes may not overlap. +If \fBsplines=\fIfalse\fR or \fBsplines=\fIline\fR, edges are +drawn as line segments. +The default is \fItrue\fR for dot, and \fIfalse\fR for all other layouts. + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBstart=\fIval\fR. Requests random initial placement and seeds +the random number generator. If \fIval\fP is not an integer, +the process ID or current time is used as the seed. +.PP +\fBepsilon=\fIn\fR. Sets the cutoff for the solver. +The default is 0.1. + +.PP +\fB(twopi\(hyspecific attributes)\fR +.br +\fBroot=\fIctr\fR. This specifies the node to be used as the center of +the layout. If not specified, \fItwopi\fP will randomly pick one of the +nodes that are furthest from a leaf node, where a leaf node is a node +of degree 1. If no leaf nodes exists, an arbitrary node is picked as center. +.PP +\fBranksep=\fIval\fR. Specifies the radial distance in inches between +the sequence of rings. The default is 0.75. +.PP +\fBoverlap=\fImode\fR. This specifies what \fItwopi\fP should do if +any nodes overlap. If mode is \fI"false"\fP, the program uses Voronoi +diagrams to adjust the nodes to eliminate overlaps. If mode is \fI"scale"\fP, +the layout is uniformly scaled up, preserving node sizes, until nodes no +longer overlap. The latter technique removes overlaps while preserving +symmetry and structure, while the former removes overlaps more compactly +but destroys symmetries. +If mode is \fI"true"\fP (the default), no repositioning is done. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fInodename\fR. Specifies the name of a node occurring in the +root block. If the graph is disconnected, the \fBroot\fP node attribute +can be used to specify additional root blocks. +.PP +\fBmindist=\fIvalue\fR. Sets the minimum separation between all nodes. If not +specified then \fIcirco\fP uses a default value of 1.0. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBK=\fIval\fR. Sets the default ideal node separation +in the layout. +.PP +\fBmaxiter=\fIval\fR. Sets the maximum number of iterations used to +layout the graph. +.PP +\fBstart=\fIval\fR. Adjusts the random initial placement of nodes +with no specified position. If \fIval\fP is is an integer, +it is used as the seed for the random number generator. +If \fIval\fP is not an integer, a random system\(hygenerated integer, +such as the process ID or current time, is used as the seed. +.PP + +.SH "NODE ATTRIBUTES" +.PP +\fBheight=\fId\fR or \fBwidth=\fId\fR sets minimum height or width. +Adding \fBfixedsize=true\fP forces these to be the actual size +(text labels are ignored). +.PP +\fBshape=record polygon epsf \fIbuiltin_polygon\fR +.br +\fIbuiltin_polygon\fR is one of: \fBplaintext ellipse oval circle egg +triangle box diamond trapezium parallelogram house hexagon octagon +note tab box3d component.\fR +(Polygons are defined or modified by the following node attributes: +\fBregular\fR, \fBperipheries\fR, \fBsides\fR, \fBorientation\fR, +\fBdistortion\fR and \fBskew\fR.) \fBepsf\fR uses the node's +\fBshapefile\fR attribute as the path name of an external +EPSF file to be automatically loaded for the node shape. +.PP +\fBlabel=\fItext\fR where \fItext\fP may include escaped newlines +\\\|n, \\\|l, or \\\|r for center, left, and right justified lines. +The string '\\N' value will be replaced by the node name. +The string '\\G' value will be replaced by the graph name. +Record labels may contain recursive box lists delimited by { | }. +Port identifiers in labels are set off by angle brackets < >. +In the graph file, use colon (such as, \fBnode0:port28\fR). +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBcolor=\fIcolorvalue\fR sets the outline color, and the default fill color +if style=filled and \fBfillcolor\fR is not specified. +.PP +\fBfillcolor=\fIcolorvalue\fR sets the fill color +when style=filled. If not specified, the fillcolor when style=filled defaults +to be the same as the outline color. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +A \fIcolorvalue\fP may be "\fIh,s,v\fB"\fR (hue, saturation, brightness) +floating point numbers between 0 and 1, or an X11 color name such as +\fBwhite black red green blue yellow magenta cyan\fR or \fBburlywood\fR, +or a "\fI#rrggbb" (red, green, blue, 2 hex characters each) value. +.PP +\fBstyle=filled solid dashed dotted bold invis\fP or any Postscript code. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the node's active layers. +The empty string means no layers (invisible). +.PP +The following attributes apply only to polygon shape nodes: +.PP +\fBregular=\fIn\fR if \fIn\fR is non\(hyzero then the polygon is made +regular, i.e. symmetric about the x and y axis, otherwise the +polygon takes on the aspect ratio of the label. +\fIbuiltin_polygons\fR that are not already regular are made regular +by this attribute. +\fIbuiltin_polygons\fR that are already regular are not affected (i.e. +they cannot be made asymmetric). +.PP +\fBperipheries=\fIn\fR sets the number of periphery lines drawn around +the polygon. This value supersedes the number of periphery lines +of \fIbuiltin_polygons\fR. +.PP +\fBsides=\fIn\fR sets the number of sides to the polygon. \fIn\fR<3 +results in an ellipse. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBorientation=\fIf\fR sets the orientation of the first apex of the +polygon counterclockwise from the vertical, in degrees. +\fIf\fR may be a floating point number. +The orientation of labels is not affected by this attribute. +This attribute is added to the initial orientation of \fIbuiltin_polygons.\fR +.PP +\fBdistortion=\fIf\fR sets the amount of broadening of the top and +narrowing of the bottom of the polygon (relative to its orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBskew=\fIf\fR sets the amount of right\(hydisplacement of the top and +left\(hydisplacement of the bottom of the polygon (relative to its +orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. +The target string is used to determine which window of the browser is used +for the URL. Setting it to "_graphviz" will open a new window if it doesn't +already exist, or reuse it if it does. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. The tooltip string defaults to be the +same as the label string, but this attribute permits nodes without +labels to still have tooltips thus permitting denser graphs. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fItrue/false\fR. This specifies that the block containing the given +node be treated as the root of the spanning tree in the layout. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBpin=\fIval\fR. If \fIval\fR is "true", the node will remain at +its initial position. + +.SH "EDGE ATTRIBUTES" +\fBminlen=\fIn\fR where \fIn\fP is an integer factor that applies +to the edge length (ranks for normal edges, or minimum node separation +for flat edges). +.PP +\fBweight=\fIn\fR where \fIn\fP is the integer cost of the edge. +Values greater than 1 tend to shorten the edge. Weight 0 flat +edges are ignored for ordering nodes. +.PP +\fBlabel=\fItext\fR where \fItext\fR may include escaped newlines +\\\|n, \\\|l, or \\\|r for centered, left, or right justified lines. +If the substring '\\T' is found in a label it will be replaced by the tail_node name. +If the substring '\\H' is found in a label it will be replaced by the head_node name. +If the substring '\\E' value is found in a label it will be replaced by: tail_node_name\->head_node_name +If the substring '\\G' is found in a label it will be replaced by the graph name. +or by: tail_node_name\-\-head_node_name for undirected graphs. +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +\fBstyle=solid dashed dotted bold invis\fP +.PP +\fBcolor=\fIcolorvalue\fR sets the line color for edges. +.PP +\fBcolor=\fIcolorvaluelist\fR a ':' separated list of \fIcolorvalue\fR creates +parallel edges, one edge for each color. +.PP +\fBdir=forward back both none\fP controls arrow direction. +.PP +\fBtailclip,headclip=false\fP disables endpoint shape clipping. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edges have a URL. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when edges have a URL. The tooltip string defaults to be the +same as the edge label string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBarrowhead,arrowtail=none, normal, inv, dot, odot, invdot, invodot, +tee, empty, invempty, open, halfopen, diamond, odiamond, box, obox, crow\fP. +.PP +\fBarrowsize\fP (norm_length=10,norm_width=5, +inv_length=6,inv_width=7,dot_radius=2) +.PP +\fBheadlabel,taillabel=string\fP for port labels. +\fBlabelfontcolor\fP,\fBlabelfontname\fP,\fBlabelfontsize\fP +for head and tail labels. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadhref=\fI"url"\fR sets the url for the head port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadURL=\fI"url"\fR ("headURL" is a synonym for "headhref".) +.PP +\fBheadtarget=\fI"headtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge heads have a URL. +The headtarget string is used to determine which window of the browser is used +for the URL. If the headtarget string is empty, the default, +then headtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when head ports have a URL. The tooltip string defaults to be the +same as the headlabel string. +The substrings '\\T', '\\H', and '\\E' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailhref=\fI"url"\fR sets the url for the tail port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailURL=\fI"url"\fR ("tailURL" is a synonym for "tailhref".) +.PP +\fBtailtarget=\fI"tailtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge tails have a URL. +The tailtarget string is used to determine which window of the browser is used +for the URL. If the tailtarget string is empty, the default, +then tailtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when tail ports have a URL. The tooltip string defaults to be the +same as the taillabel string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBlabeldistance\fP and \fPport_label_distance\fP set distance; also +\fBlabelangle\fP (in degrees CCW) +.PP +\fBdecorate\fP draws line from edge to label. +.PP +\fBsamehead,sametail\fP aim edges having the same value to the +same port, using the average landing point. +.PP +\fBconstraint=false\fP causes an edge to be ignored for rank assignment. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the edge's active layers. +The empty string means no layers (invisible). + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBw=\fIf\fR sets the weight (spring constant) of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.PP +\fBlen=\fIf\fR sets the optimal length of an edge. +The default is 1.0. +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBweight=\fIf\fR sets the weight of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.SH "COMMAND LINE OPTIONS" +\fB\-G\fP sets a default graph attribute. +.br +\fB\-N\fP sets a default node attribute. +.br +\fB\-E\fP sets a default edge attribute. +Example: \fB\-Gsize="7,8" \-Nshape=box \-Efontsize=8\fR +.PP +\fB\-l\fIfile\fR loads custom PostScript library files. +Usually these define custom shapes or styles. +If \fB\-l\fP is given by itself, the standard library is omitted. +.PP +\fB\-T\fIlang\fR sets the output language as described above. + +.PP +\fB\-n\fR[\fB1\fR|\fB2\fR] (no\(hyop) +If set, neato assumes nodes have already been positioned and all +nodes have a pos attribute giving the positions. +It then performs an optional adjustment to remove node\(hynode overlap, +depending on the value of the overlap attribute, +computes the edge layouts, depending on the value of the \fBsplines\fR attribute, +and emits the graph in the appropriate format. +If num is supplied, the following actions occur: +.nf + num = 1 +.fi +Equivalent to \-n. +.nf + num > 1 +.fi +Use node positions as specified, +with no adjustment to remove node\(hynode overlaps, +and use any edge layouts already specified by the pos attribute. +neato computes an edge layout for any edge that does not have a pos attribute. +As usual, edge layout is guided by the \fBsplines\fR attribute. +.PP +\fB\-K\fIlayout\fR override the default layout engine implied by the command name. +.PP +\fB\-O\fP automatically generate output filenames based on the input filename and the -T format. +.PP +\fB\-P\fP generate a graph of the currently available plugins. +.PP +\fB\-v\fP (verbose) prints various information useful for debugging. +.PP +\fB\-V\fP (version) prints version information and exits. +.PP +\fB\-?\fP prints the usage and exits. +.SH "EXAMPLES" +.nf +digraph test123 { + a \-> b \-> c; + a \-> {x y}; + b [shape=box]; + c [label="hello\\\|nworld",color=blue,fontsize=24, + fontname="Palatino\-Italic",fontcolor=red,style=filled]; + a \-> z [label="hi", weight=100]; + x \-> z [label="multi\-line\\\|nlabel"]; + edge [style=dashed,color=red]; + b \-> x; + {rank=same; b x} +} +.fi +.PP +.nf +graph test123 { + a \-\- b \-\- c; + a \-\- {x y}; + x \-\- c [w=10.0]; + x \-\- y [w=5.0,len=3]; +} +.fi +.SH "CAVEATS" +Edge splines can overlap unintentionally. +.PP +Flat edge labels are slightly broken. +Intercluster edge labels are totally broken. +.PP +Because unconstrained optimization is employed, node boxes can +possibly overlap or touch unrelated edges. All existing +spring embedders seem to have this limitation. +.PP +Apparently reasonable attempts to pin nodes or adjust edge lengths +and weights can cause instability. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.br +John C. Ellson <ellson@research.att.com> +.PP +The bitmap driver (PNG, GIF etc) is by Thomas Boutell, <http://www.boutell.com/gd> +.PP +The Truetype font renderer is from +the Freetype Project (David Turner, Robert Wilhelm, and Werner Lemberg) +(who can be contacted at freetype\-devel@lists.lrz\-muenchen.de). +.SH "SEE ALSO" +This man page contains only a small amount of the information related +to the Graphviz layout programs. The most complete information can be +found at http://www.graphviz.org/Documentation.php, especially in the +on\(hyline reference pages. Most of these documents are also available in the +\fIdoc\fP and \fIdoc/info\fP subtrees in the source and binary distributions. +.PP +dotty(1) +.br +tcldot(n) +.br +xcolors(1) +.br +libgraph(3) +.PP +E. R. Gansner, S. C. North, K. P. Vo, "DAG \(hy A Program to Draw Directed Graphs", Software \(hy Practice and Experience 17(1), 1988, pp. 1047\(hy1062. +.br +E. R. Gansner, E. Koutsofios, S. C. North, K. P. Vo, "A Technique for Drawing Directed Graphs," IEEE Trans. on Soft. Eng. 19(3), 1993, pp. 214\(hy230. +.br +S. North and E. Koutsofios, "Applications of graph visualization", +Graphics Interface 94, pp. 234\(hy245. +.br +E. Koutsofios and S. C. North, "Drawing Graphs with dot," +Available on research.att.com in dist/drawdag/dotguide.ps.Z. +.br +S. C. North, "NEATO User's Manual". +Available on research.att.com in dist/drawdag/neatodoc.ps.Z.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/dotty.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +.TH DOTTY 1 +.SH NAME +dotty \- A Customizable Graph Editor +.SH SYNOPSIS +.B dotty +[ +.B -V +] [ +.I file +] +.SH DESCRIPTION +.B dotty +is a graph editor for the X Window System. It may be run as a standalone +editor, or as a front end for applications that use graphs. It can control +multiple windows viewing different graphs. + +.B dotty +is written on top of +.B dot +and +.BR lefty . +.B lefty +is a general-purpose programmable editor for technical pictures. It has an +interpretive programming language similar to AWK and C. The user interface and +graph editing operations of +.B dotty +are written as +.B lefty +functions. +Programmer-defined graph operations may be loaded as well. Graph layouts are +made by +.BR dot , +which runs as a separate process that communicates with +.B lefty +through pipes. +.SH USAGE +The file name is optional. If present, the graph contained in that file is +displayed in the +.B dotty +window. +.SH OPTIONS +.TP +.B -V +Prints the version. +.SH SEE ALSO +dot(1), lefty(1) +.br +.I dotty +user guide.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/fdp.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,725 @@ +.TH DOT 1 "23 August 2004" \*(eX +.SH NAME +dot \- filter for drawing directed graphs +.br +neato \- filter for drawing undirected graphs +.br +twopi \- filter for radial layouts of graphs +.br +circo \- filter for circular layout of graphs +.br +fdp \- filter for drawing undirected graphs +.br +sfdp \- filter for drawing large undirected graphs +.SH SYNOPSIS +\fBdot\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBneato\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-n\fR[\fB1\fR|\fB2\fR]] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBtwopi\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBcirco\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBsfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.SH DESCRIPTION +.I dot +draws directed graphs. It works well on DAGs and other graphs +that can be drawn as hierarchies. +It reads attributed graph files and writes drawings. +By default, the output format +.I dot +is the input file with layout coordinates appended. +.PP +.I neato +draws undirected graphs using ``spring'' models (see Kamada and Kawai, +Information Processing Letters 31:1, April 1989). Input files must be +formatted in the +.I dot +attributed graph language. +By default, the output of +.I neato +is the input graph with layout coordinates appended. +.PP +.I twopi +draws graphs using a radial layout (see G. Wills, +Symposium on Graph Drawing GD'97, September, 1997). +Basically, one node is chosen as the center and put at the origin. +The remaining nodes are placed on a sequence of concentric circles +centered about the origin, each a fixed radial distance from the +previous circle. +All nodes distance 1 from the center are placed on the first circle; +all nodes distance 1 from a node on the first circle are placed on +the second circle; and so forth. +.PP +.I circo +draws graphs using a circular layout (see +Six and Tollis, GD '99 and ALENEX '99, and +Kaufmann and Wiese, GD '02.) +The tool identifies biconnected components and draws the nodes of +the component on a circle. The block\(hycutpoint tree +is then laid out using a recursive radial algorithm. Edge +crossings within a circle are minimized by placing as many edges on +the circle's perimeter as possible. +In particular, if the component is outerplanar, the component will +have a planar layout. +.PP +If a node belongs to multiple non\(hytrivial biconnected components, +the layout puts the node in one of them. By default, this is the first +non\(hytrivial component found in the search from the root component. +.PP +.I fdp +draws undirected graphs using a ``spring'' model. It relies on a +force\(hydirected approach in the spirit of Fruchterman and Reingold +(cf. Software\(hyPractice & Experience 21(11), 1991, pp. 1129\(hy1164). +.PP +.I sfdp +also draws undirected graphs using the ``spring'' model described +above, but it uses a multi-scale approach to produce layouts +of large graphs in a reasonably short time. +.SH OUTPUT FORMATS +Dot uses an extensible plugin mechanism for its output renderers, +so to see what output formats your installation of dot supports +you can use ``dot \-Txxx'' (where xxx is an unlikely format) +and check the warning message. +Also, The plugin mechanism supports multiple implementations +of the output formats. +To see what variants are available, use, for example: ``dot \-Tpng:'' +and to force a particular variant, use, for example: ``dot \-Tpng:gd'' +.P +Traditionally, dot supports the following: +\fB\-Tps\fP (PostScript), +\fB\-Tsvg\fP \fB\-Tsvgz\fP (Structured Vector Graphics), +\fB\-Tfig\fP (XFIG graphics), +\fB\-Tmif\fP (FrameMaker graphics), +\fB\-Thpgl\fP (HP pen plotters), and \fB\-Tpcl\fP (Laserjet printers), +\fB\-Tpng\fP \fB\-Tgif\fP (bitmap graphics), +\fB\-Tdia\fP (GTK+ based diagrams), +\fB\-Timap\fP (imagemap files for httpd servers for each node or edge +that has a non\(hynull "href" attribute.), +\fB\-Tcmapx\fP (client\(hyside imagemap for use in html and xhtml). +Additional less common or more special\(hypurpose output formats +can be found at http://www.graphviz.org/cvs/doc/info/output.html.) +.P +Alternative plugins providing support for a given output format +can be found from the error message resulting from appending a ':' to the format. e.g. \fB-Tpng:\fP +The first plugin listed is always the default. +.P +The \fB\-P\fP switch can be used to produce a graph of all output variants supported by plugins in the local installation of graphviz. +.SH GRAPH FILE LANGUAGE +Here is a synopsis of the graph file language, traditionally using the extension \fB.dot\fR, for graphs: +.PP +[\fBstrict\fR] (\fBgraph\fR|\fBdigraph\fR) \fIname\fP { \fIstatement\(hylist\fP }\fR +.br +Is the top level graph. If the graph is \fBstrict\fR then multiple edges are +not allowed between the same pairs of nodes. +If it is a directed graph, indicated by \fBdigraph\fR, +then the \fIedgeop\fR must be "\->". If it is an undirected \fBgraph\fR +then the \fIedgeop\fR must be "\-\-". +Statements may be: +.PP +\fIname\fB=\fIval\fB;\fR +.br +\fBnode [\fIname\fB=\fIval\fB];\fR +.br +\fBedge [\fIname\fB=\fIval\fB];\fR +.br +Set default graph, node, or edge attribute \fIname\fP to \fIval\fP. +Any subgraph, node, or edge appearing after this inherits the new +default attributes. +.PP +\fBn0 [\fIname0=val0,name1=val1,...\fB];\fR +Creates node \fBn0\fP (if it does not already exist) +and sets its attributes according to the optional list. +.PP +\fBn0 \fIedgeop\fR n1 \fIedgeop\fR \fI...\fB \fIedgeop\fR nn [\fIname0=val0,name1=val1,...\fB];\fR +.br +Creates edges between nodes \fBn0\fP, \fBn1\fP, ..., \fBnn\fP and sets +their attributes according to the optional list. +Creates nodes as necessary. +.PP +[\fBsubgraph \fIname\fB] { \fIstatement\(hylist \fB}\fR +.br +Creates a subgraph. Subgraphs may be used in place +of \fBn0\fP, ..., \fBnn\fP in the above statements to create edges. +[\fBsubgraph \fIname\fR] is optional; +if missing, the subgraph is assigned an internal name. +.PP +Comments may be /*C\(hylike*/ or //C++\(hylike. + +.PP +Attribute names and values are ordinary (C\(hystyle) strings. +The following sections describe attributes that control graph layout. + +.SH "GRAPH ATTRIBUTES" +.PP +\fBsize="\fIx,y\fP"\fR sets bounding box of drawing in inches. +.PP +\fBpage="\fIx,y\fP"\fR sets the PostScript pagination unit. +.PP +\fBratio=\fIf\fR sets the aspect ratio to \fIf\fP which may be +a floating point number, or one of the keywords \fBfill\fP, +\fBcompress\fP, or \fBauto\fP. +.PP +\fBlayout=\fIengine\fR indicates the preferred layout engine ("dot", "neato", fdp" etc) overriding the default from the basename of the command or the -K commandline option. +.PP +\fBmargin=\fIf\fR sets the page margin (included in the page size). +.PP +\fBnodesep=\fIf\fR sets the minimum separation between nodes. +.PP +\fBranksep=\fIf\fR sets the minimum separation between ranks. +.PP +\fBordering=out\fR constrains order of out\(hyedges in a subgraph +according to their file sequence. +.PP +\fBrankdir=LR|RL|BT\fR requests a left\(hyto\(hyright, right\(hyto\(hyleft, or bottom\(hyto\(hytop, drawing. +.PP +\fBpagedir=\fR[TBLR][TBLR] sets the major and minor order of pagination. +.PP +\fBrank=same\fR (or \fBmin\fP or \fBmax\fP) in a subgraph +constrains the rank assignment of its nodes. If a subgraph's +name has the prefix \fBcluster\fP, its nodes are drawn in +a distinct rectangle of the layout. Clusters may be nested. +.PP +\fBrotate=90\fR sets landscape mode. +(\fBorientation=land\fR is backward compatible but obsolete.) +.PP +\fBcenter=\fIn\fR a non\(hyzero value centers the drawing on the page. +.PP +\fBnslimit=\fIf\fR or \fBmclimit=\fIf\fR adjusts the bound on the +number of network simplex or mincross iterations by the given ratio. +For example, \fBmclimit=2.0\fP runs twice as long. +.PP +\fBlayers="\fIid:id:id:id\fR" is a sequence of layer identifiers for +overlay diagrams. The PostScript array variable \fIlayercolorseq\fR +sets the assignment of colors to layers. The least index is 1 and +each element must be a 3\(hyelement array to be interpreted as a color coordinate. +.PP +\fBcolor=\fIcolorvalue\fR sets foreground color (\fBbgcolor\fP for background). +.PP +\fBhref=\fI"url"\fR the default url for image map files; in PostScript files, +the base URL for all relative URLs, as recognized by Acrobat Distiller +3.0 and up. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBstylesheet=\fI"file.css"\fR includes a reference to a stylesheet +in \-Tsvg and \-Tsvgz outputs. Ignored by other formats. +.PP +\fBsplines\fR. If set to \fItrue\fR, edges are +drawn as splines. +If set to \fIpolyline\fR, edges are +drawn as polylines. +If set to \fIortho\fR, edges are +drawn as orthogonal polylines. +In all of these cases, the nodes may not overlap. +If \fBsplines=\fIfalse\fR or \fBsplines=\fIline\fR, edges are +drawn as line segments. +The default is \fItrue\fR for dot, and \fIfalse\fR for all other layouts. + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBstart=\fIval\fR. Requests random initial placement and seeds +the random number generator. If \fIval\fP is not an integer, +the process ID or current time is used as the seed. +.PP +\fBepsilon=\fIn\fR. Sets the cutoff for the solver. +The default is 0.1. + +.PP +\fB(twopi\(hyspecific attributes)\fR +.br +\fBroot=\fIctr\fR. This specifies the node to be used as the center of +the layout. If not specified, \fItwopi\fP will randomly pick one of the +nodes that are furthest from a leaf node, where a leaf node is a node +of degree 1. If no leaf nodes exists, an arbitrary node is picked as center. +.PP +\fBranksep=\fIval\fR. Specifies the radial distance in inches between +the sequence of rings. The default is 0.75. +.PP +\fBoverlap=\fImode\fR. This specifies what \fItwopi\fP should do if +any nodes overlap. If mode is \fI"false"\fP, the program uses Voronoi +diagrams to adjust the nodes to eliminate overlaps. If mode is \fI"scale"\fP, +the layout is uniformly scaled up, preserving node sizes, until nodes no +longer overlap. The latter technique removes overlaps while preserving +symmetry and structure, while the former removes overlaps more compactly +but destroys symmetries. +If mode is \fI"true"\fP (the default), no repositioning is done. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fInodename\fR. Specifies the name of a node occurring in the +root block. If the graph is disconnected, the \fBroot\fP node attribute +can be used to specify additional root blocks. +.PP +\fBmindist=\fIvalue\fR. Sets the minimum separation between all nodes. If not +specified then \fIcirco\fP uses a default value of 1.0. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBK=\fIval\fR. Sets the default ideal node separation +in the layout. +.PP +\fBmaxiter=\fIval\fR. Sets the maximum number of iterations used to +layout the graph. +.PP +\fBstart=\fIval\fR. Adjusts the random initial placement of nodes +with no specified position. If \fIval\fP is is an integer, +it is used as the seed for the random number generator. +If \fIval\fP is not an integer, a random system\(hygenerated integer, +such as the process ID or current time, is used as the seed. +.PP + +.SH "NODE ATTRIBUTES" +.PP +\fBheight=\fId\fR or \fBwidth=\fId\fR sets minimum height or width. +Adding \fBfixedsize=true\fP forces these to be the actual size +(text labels are ignored). +.PP +\fBshape=record polygon epsf \fIbuiltin_polygon\fR +.br +\fIbuiltin_polygon\fR is one of: \fBplaintext ellipse oval circle egg +triangle box diamond trapezium parallelogram house hexagon octagon +note tab box3d component.\fR +(Polygons are defined or modified by the following node attributes: +\fBregular\fR, \fBperipheries\fR, \fBsides\fR, \fBorientation\fR, +\fBdistortion\fR and \fBskew\fR.) \fBepsf\fR uses the node's +\fBshapefile\fR attribute as the path name of an external +EPSF file to be automatically loaded for the node shape. +.PP +\fBlabel=\fItext\fR where \fItext\fP may include escaped newlines +\\\|n, \\\|l, or \\\|r for center, left, and right justified lines. +The string '\\N' value will be replaced by the node name. +The string '\\G' value will be replaced by the graph name. +Record labels may contain recursive box lists delimited by { | }. +Port identifiers in labels are set off by angle brackets < >. +In the graph file, use colon (such as, \fBnode0:port28\fR). +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBcolor=\fIcolorvalue\fR sets the outline color, and the default fill color +if style=filled and \fBfillcolor\fR is not specified. +.PP +\fBfillcolor=\fIcolorvalue\fR sets the fill color +when style=filled. If not specified, the fillcolor when style=filled defaults +to be the same as the outline color. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +A \fIcolorvalue\fP may be "\fIh,s,v\fB"\fR (hue, saturation, brightness) +floating point numbers between 0 and 1, or an X11 color name such as +\fBwhite black red green blue yellow magenta cyan\fR or \fBburlywood\fR, +or a "\fI#rrggbb" (red, green, blue, 2 hex characters each) value. +.PP +\fBstyle=filled solid dashed dotted bold invis\fP or any Postscript code. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the node's active layers. +The empty string means no layers (invisible). +.PP +The following attributes apply only to polygon shape nodes: +.PP +\fBregular=\fIn\fR if \fIn\fR is non\(hyzero then the polygon is made +regular, i.e. symmetric about the x and y axis, otherwise the +polygon takes on the aspect ratio of the label. +\fIbuiltin_polygons\fR that are not already regular are made regular +by this attribute. +\fIbuiltin_polygons\fR that are already regular are not affected (i.e. +they cannot be made asymmetric). +.PP +\fBperipheries=\fIn\fR sets the number of periphery lines drawn around +the polygon. This value supersedes the number of periphery lines +of \fIbuiltin_polygons\fR. +.PP +\fBsides=\fIn\fR sets the number of sides to the polygon. \fIn\fR<3 +results in an ellipse. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBorientation=\fIf\fR sets the orientation of the first apex of the +polygon counterclockwise from the vertical, in degrees. +\fIf\fR may be a floating point number. +The orientation of labels is not affected by this attribute. +This attribute is added to the initial orientation of \fIbuiltin_polygons.\fR +.PP +\fBdistortion=\fIf\fR sets the amount of broadening of the top and +narrowing of the bottom of the polygon (relative to its orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBskew=\fIf\fR sets the amount of right\(hydisplacement of the top and +left\(hydisplacement of the bottom of the polygon (relative to its +orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. +The target string is used to determine which window of the browser is used +for the URL. Setting it to "_graphviz" will open a new window if it doesn't +already exist, or reuse it if it does. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. The tooltip string defaults to be the +same as the label string, but this attribute permits nodes without +labels to still have tooltips thus permitting denser graphs. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fItrue/false\fR. This specifies that the block containing the given +node be treated as the root of the spanning tree in the layout. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBpin=\fIval\fR. If \fIval\fR is "true", the node will remain at +its initial position. + +.SH "EDGE ATTRIBUTES" +\fBminlen=\fIn\fR where \fIn\fP is an integer factor that applies +to the edge length (ranks for normal edges, or minimum node separation +for flat edges). +.PP +\fBweight=\fIn\fR where \fIn\fP is the integer cost of the edge. +Values greater than 1 tend to shorten the edge. Weight 0 flat +edges are ignored for ordering nodes. +.PP +\fBlabel=\fItext\fR where \fItext\fR may include escaped newlines +\\\|n, \\\|l, or \\\|r for centered, left, or right justified lines. +If the substring '\\T' is found in a label it will be replaced by the tail_node name. +If the substring '\\H' is found in a label it will be replaced by the head_node name. +If the substring '\\E' value is found in a label it will be replaced by: tail_node_name\->head_node_name +If the substring '\\G' is found in a label it will be replaced by the graph name. +or by: tail_node_name\-\-head_node_name for undirected graphs. +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +\fBstyle=solid dashed dotted bold invis\fP +.PP +\fBcolor=\fIcolorvalue\fR sets the line color for edges. +.PP +\fBcolor=\fIcolorvaluelist\fR a ':' separated list of \fIcolorvalue\fR creates +parallel edges, one edge for each color. +.PP +\fBdir=forward back both none\fP controls arrow direction. +.PP +\fBtailclip,headclip=false\fP disables endpoint shape clipping. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edges have a URL. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when edges have a URL. The tooltip string defaults to be the +same as the edge label string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBarrowhead,arrowtail=none, normal, inv, dot, odot, invdot, invodot, +tee, empty, invempty, open, halfopen, diamond, odiamond, box, obox, crow\fP. +.PP +\fBarrowsize\fP (norm_length=10,norm_width=5, +inv_length=6,inv_width=7,dot_radius=2) +.PP +\fBheadlabel,taillabel=string\fP for port labels. +\fBlabelfontcolor\fP,\fBlabelfontname\fP,\fBlabelfontsize\fP +for head and tail labels. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadhref=\fI"url"\fR sets the url for the head port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadURL=\fI"url"\fR ("headURL" is a synonym for "headhref".) +.PP +\fBheadtarget=\fI"headtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge heads have a URL. +The headtarget string is used to determine which window of the browser is used +for the URL. If the headtarget string is empty, the default, +then headtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when head ports have a URL. The tooltip string defaults to be the +same as the headlabel string. +The substrings '\\T', '\\H', and '\\E' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailhref=\fI"url"\fR sets the url for the tail port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailURL=\fI"url"\fR ("tailURL" is a synonym for "tailhref".) +.PP +\fBtailtarget=\fI"tailtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge tails have a URL. +The tailtarget string is used to determine which window of the browser is used +for the URL. If the tailtarget string is empty, the default, +then tailtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when tail ports have a URL. The tooltip string defaults to be the +same as the taillabel string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBlabeldistance\fP and \fPport_label_distance\fP set distance; also +\fBlabelangle\fP (in degrees CCW) +.PP +\fBdecorate\fP draws line from edge to label. +.PP +\fBsamehead,sametail\fP aim edges having the same value to the +same port, using the average landing point. +.PP +\fBconstraint=false\fP causes an edge to be ignored for rank assignment. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the edge's active layers. +The empty string means no layers (invisible). + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBw=\fIf\fR sets the weight (spring constant) of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.PP +\fBlen=\fIf\fR sets the optimal length of an edge. +The default is 1.0. +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBweight=\fIf\fR sets the weight of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.SH "COMMAND LINE OPTIONS" +\fB\-G\fP sets a default graph attribute. +.br +\fB\-N\fP sets a default node attribute. +.br +\fB\-E\fP sets a default edge attribute. +Example: \fB\-Gsize="7,8" \-Nshape=box \-Efontsize=8\fR +.PP +\fB\-l\fIfile\fR loads custom PostScript library files. +Usually these define custom shapes or styles. +If \fB\-l\fP is given by itself, the standard library is omitted. +.PP +\fB\-T\fIlang\fR sets the output language as described above. + +.PP +\fB\-n\fR[\fB1\fR|\fB2\fR] (no\(hyop) +If set, neato assumes nodes have already been positioned and all +nodes have a pos attribute giving the positions. +It then performs an optional adjustment to remove node\(hynode overlap, +depending on the value of the overlap attribute, +computes the edge layouts, depending on the value of the \fBsplines\fR attribute, +and emits the graph in the appropriate format. +If num is supplied, the following actions occur: +.nf + num = 1 +.fi +Equivalent to \-n. +.nf + num > 1 +.fi +Use node positions as specified, +with no adjustment to remove node\(hynode overlaps, +and use any edge layouts already specified by the pos attribute. +neato computes an edge layout for any edge that does not have a pos attribute. +As usual, edge layout is guided by the \fBsplines\fR attribute. +.PP +\fB\-K\fIlayout\fR override the default layout engine implied by the command name. +.PP +\fB\-O\fP automatically generate output filenames based on the input filename and the -T format. +.PP +\fB\-P\fP generate a graph of the currently available plugins. +.PP +\fB\-v\fP (verbose) prints various information useful for debugging. +.PP +\fB\-V\fP (version) prints version information and exits. +.PP +\fB\-?\fP prints the usage and exits. +.SH "EXAMPLES" +.nf +digraph test123 { + a \-> b \-> c; + a \-> {x y}; + b [shape=box]; + c [label="hello\\\|nworld",color=blue,fontsize=24, + fontname="Palatino\-Italic",fontcolor=red,style=filled]; + a \-> z [label="hi", weight=100]; + x \-> z [label="multi\-line\\\|nlabel"]; + edge [style=dashed,color=red]; + b \-> x; + {rank=same; b x} +} +.fi +.PP +.nf +graph test123 { + a \-\- b \-\- c; + a \-\- {x y}; + x \-\- c [w=10.0]; + x \-\- y [w=5.0,len=3]; +} +.fi +.SH "CAVEATS" +Edge splines can overlap unintentionally. +.PP +Flat edge labels are slightly broken. +Intercluster edge labels are totally broken. +.PP +Because unconstrained optimization is employed, node boxes can +possibly overlap or touch unrelated edges. All existing +spring embedders seem to have this limitation. +.PP +Apparently reasonable attempts to pin nodes or adjust edge lengths +and weights can cause instability. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.br +John C. Ellson <ellson@research.att.com> +.PP +The bitmap driver (PNG, GIF etc) is by Thomas Boutell, <http://www.boutell.com/gd> +.PP +The Truetype font renderer is from +the Freetype Project (David Turner, Robert Wilhelm, and Werner Lemberg) +(who can be contacted at freetype\-devel@lists.lrz\-muenchen.de). +.SH "SEE ALSO" +This man page contains only a small amount of the information related +to the Graphviz layout programs. The most complete information can be +found at http://www.graphviz.org/Documentation.php, especially in the +on\(hyline reference pages. Most of these documents are also available in the +\fIdoc\fP and \fIdoc/info\fP subtrees in the source and binary distributions. +.PP +dotty(1) +.br +tcldot(n) +.br +xcolors(1) +.br +libgraph(3) +.PP +E. R. Gansner, S. C. North, K. P. Vo, "DAG \(hy A Program to Draw Directed Graphs", Software \(hy Practice and Experience 17(1), 1988, pp. 1047\(hy1062. +.br +E. R. Gansner, E. Koutsofios, S. C. North, K. P. Vo, "A Technique for Drawing Directed Graphs," IEEE Trans. on Soft. Eng. 19(3), 1993, pp. 214\(hy230. +.br +S. North and E. Koutsofios, "Applications of graph visualization", +Graphics Interface 94, pp. 234\(hy245. +.br +E. Koutsofios and S. C. North, "Drawing Graphs with dot," +Available on research.att.com in dist/drawdag/dotguide.ps.Z. +.br +S. C. North, "NEATO User's Manual". +Available on research.att.com in dist/drawdag/neatodoc.ps.Z.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/gc.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,83 @@ +.TH GC 1 "21 March 2001" +.SH NAME +gc \- count graph components +.SH SYNOPSIS +.B gc +[ +.B \-necCaDUrs? +] +[ +.I files +] +.SH DESCRIPTION +.B gc +is a graph analogue to +.B wc +in that it prints to standard output +the number of nodes, edges, connected components or clusters contained +in the input files. +It also prints a total count for +all graphs if more than one graph is given. +.SH OPTIONS +The following options are supported: +.TP +.B \-n +Count nodes. +.TP +.B \-e +Count edges. +.TP +.B \-c +Count connected components. +.TP +.B \-C +Count clusters. By definition, a cluster is a graph or +subgraph whose name begins with "cluster". +.TP +.B \-a +Count all. Equivalent to +.B \-encC +.TP +.B \-r +Recursively analyze subgraphs. +.TP +.B \-s +Print no output. Only exit value is important. +.TP +.B \-D +Only analyze directed graphs. +.TP +.B \-U +Only analyze undirected graphs. +.TP +.B \-? +Print usage information. +.LP +By default, +.I gc +returns the number of nodes and edges. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in dot format. +If no +.I files +operand is specified, +the standard input will be used. +.SH "EXIT STATUS" +The following exit values are returned: +.TP 4 +.B 0 +Successful completion. +.TP +.B 1 +The +.B \-U +or +.B \-E +option was used, and a graph of the wrong type was encountered. +.SH AUTHOR +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +wc(1), acyclic(1), gvpr(1), gvcolor(1), ccomps(1), sccmap(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/gv2gxl.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +.TH GXL2GV,GV2GXL 1 "20 December 2002" +.SH NAME +gxl2gv,gv2gxl \- GXL-GV converters +.SH SYNOPSIS +.B gxl2gv +[ +.B \-gd? +] +[ +.BI -o outfile +] +[ +.I files +] +.br +.B gv2gxl +[ +.B \-gd? +] +[ +.BI -o outfile +] +[ +.I files +] +.SH DESCRIPTION +.B gxl2gv +converts between graphs represented in GXL and in the +GV language. Unless a conversion type is specified using +a flag, +.B gxl2gv +will deduce the type of conversion from the suffix of +the input file, a ".gv" (or a ".dot") suffix causing a conversion from GV +to GXL, and a ".gxl" suffix causing a conversion from GXL to GV. +If no suffix is available, e.g. when the input is from a pipe, +and no flags are used then +.B gxl2gv +assumes the type of the input file from its executable name +so that +.B gxl2gv +converts from GXL to GV, and +.B gv2gxl +converts from GV to GXL. +.PP +GXL supports a much richer graph model than GV. \fBgxl2gv\fP +will attempt to map GXL constructs into the analogous GV construct +when this is possible. If not, the GXL information is stored as +an attribute. The intention is that applying \fBgxl2gv|gv2gxl\fP +is semantically equivalent to the identity operator. +.SH OPTIONS +The following options are supported: +.TP +.B \-g +The command name and input file extensions are ignored, the +input is taken as a GV file and a GXL file is generated. +.TP +.B \-d +The command name and input file extensions are ignored, the +input is taken as a GXL file and a GV file is generated. +.TP +.B \-? +Prints usage information and exits. +.TP +.BI \-o " outfile" +If specified, the output will be written into the file +\fIoutfile\fP. Otherwise, output is written to standard out. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in GXL or GV. +If no +.I files +operand is specified, +the standard input will be used. +.SH RETURN CODES +Both \fBgxl2gv\fP and \fBgv2gxl\fP return \fB0\fP +if there were no problems during conversion; +and non-zero if any error occurred. +.SH "BUGS" +\fBgxl2gv\fP will only convert in one direction even if given multiple files +with varying suffixes. +.PP +The conversion can only handle one graph per GXL file. +.PP +There are some GXL constructs which \fBgxl2gv\fP cannot handle. +.SH AUTHORS +Krishnam Pericherla <kp@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +dot(1), libgraph(3), libagraph(3), neato(1), twopi(1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/gvcolor.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +.TH GVCOLOR 1 "21 March 2001" +.SH NAME +gvcolor \- flow colors through a ranked digraph +.br +( previously known as +.B colorize +) +.SH SYNOPSIS +.B gvcolor +[ +.I files +] +.SH "USAGE" +.ft 5 +dot file.dot | gvcolor | dot \-T\fI<format>\fP +.ft +.SH DESCRIPTION +.B gvcolor +is a filter that sets node colors from initial seed values. +Colors flow along edges from tail to head, and are averaged +(as HSB vectors) at nodes. +\fIThe graph must already have been processed by dot.\fP +Appropriate choice of initial colors yields drawings in which node +colors help to emphasize logical relationships between nodes, even +when they are spread far apart in the layout. +.PP +Initial colors must be set externally, using the \fBcolor\fP +attribute of a node. It is often effective to +assign colors to a few key source or sink nodes, manually setting +their colors by editing the graph file. +Color names are as in \fIdot(1)\fP: symbolic names or RGB triples. +It is best to choose some easily\(hydistinguished but related colors; +not necessarily spaced evenly around the color wheel. For example, +blue_green, green, and light_yellow looks better than red, green, blue. +.PP +Certain graph attributes control the \fIgvcolor\fP algorithm. +\fBflow=back\fP reverses the flow of colors from heads to tails. +\fBsaturation=.1,.9\fP (or any two numbers between 0 and 1) +adjusts the color saturation linearly from least to greatest rank. +If \fBDefcolor\fP is set, this color value is applied to any +node not otherwise colored. +.SH "EXIT STATUS" +The following exit values are returned: +.TP 4 +.B 0 +Successful completion. +.TP +.B 1 +If nodes of the graph do not possess a ``pos'' attribute. +.SH BUGS +It would be nice to make the program work without relying on +an initial pass through \fBdot\fP. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +gc(1), dot(1), gvpr(1), ccomps(1), sccmap(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/gvgen.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,138 @@ +.TH GC 1 "27 March 2008" +.SH NAME +gvgen \- generate graphs +.SH SYNOPSIS +.B gvgen +[ +.B \-d? +] +[ +.BI -c n +] +[ +.BI -C x,y +] +[ +.BI -g [\fBf\fP]x,y +] +[ +.BI -G [\fBf\fP]x,y +] +[ +.BI -h n +] +[ +.BI -k n +] +[ +.BI -b x,y +] +[ +.BI -p n +] +[ +.BI -s n +] +[ +.BI -S n +] +[ +.BI -t n +] +[ +.BI -T x,y +] +[ +.BI -w n +] +[ +.BI -o outfile +] +.SH DESCRIPTION +.B gvgen +generates a variety of simple, regularly-structured abstract +graphs. +.SH OPTIONS +The following options are supported: +.TP +.BI \-c " n" +Generate a cycle with \fIn\fP vertices and edges. +.TP +.BI \-C " x,y" +Generate an \fIx\fP by \fIy\fP cylinder. +This will have \fIx*y\fP vertices and +\fI2*x*y - y\fP edges. +.TP +.BI \-g " [\fBf\fP]x,y" +Generate an \fIx\fP by \fIy\fP grid. +If \fBf\fP is given, the grid is folded, with an edge +attaching each pair of opposing corner vertices. +This will have \fIx*y\fP vertices and +\fI2*x*y - y - x\fP edges if unfolded and +\fI2*x*y - y - x + 2\fP edges if folded. +.TP +.BI \-G " [\fBf\fP]x,y" +Generate an \fIx\fP by \fIy\fP partial grid. +If \fBf\fP is given, the grid is folded, with an edge +attaching each pair of opposing corner vertices. +This will have \fIx*y\fP vertices. +.TP +.BI \-h " n" +Generate a hypercube of degree \fIn\fP. +This will have \fI2^n\fP vertices and \fIn*2^(n-1)\fP edges. +.TP +.BI \-k " n" +Generate a complete graph on \fIn\fP vertices with +\fIn*(n-1)/2\fP edges. +.TP +.BI \-b " x,y" +Generate a complete \fIx\fP by \fIy\fP bipartite graph. +This will have \fIx+y\fP vertices and +\fIx*y\fP edges. +.TP +.BI \-p " n" +Generate a path on \fIn\fP vertices. +This will have \fIn-1\fP edges. +.TP +.BI \-s " n" +Generate a star on \fIn\fP vertices. +This will have \fIn-1\fP edges. +.TP +.BI \-S " n" +Generate a Sierpinski graph of order \fIn\fP. +This will have \fI3*(3^(n-1) - 1)/2\fP vertices and +\fI3^n\fP edges. +.TP +.BI \-t " n" +Generate a binary tree of height \fIn\fP. +This will have \fI2^n-1\fP vertices and +\fI2^n-2\fP edges. +.TP +.BI \-T " x,y" +Generate an \fIx\fP by \fIy\fP torus. +This will have \fIx*y\fP vertices and +\fI2*x*y\fP edges. +.TP +.BI \-w " n" +Generate a path on \fIn\fP vertices. +This will have \fIn-1\fP edges. +.TP +.BI \-o " outfile" +If specified, the generated graph is written into the file +.I outfile. +Otherwise, the graph is written to standard out. +.TP +.B \-d +Make the generated graph directed. +.TP +.B \-? +Print usage information. +.SH "EXIT STATUS" +.B gvgen +exits with 0 on successful completion, +and exits with 1 if given an ill-formed or incorrect flag, +or if the specified output file could not be opened. +.SH AUTHOR +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +gc(1), acyclic(1), gvpr(1), gvcolor(1), ccomps(1), sccmap(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/gvpack.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,101 @@ +.TH GVPACK 1 "8 April 2003" +.SH NAME +gvpack \- merge and pack disjoint graphs +.SH SYNOPSIS +.B gvpack +[ +.B \-nguv? +] +[ +.BI \-m margin +] +[ +.BI \-o outfile +] +[ +.BI \-G name\fB=\fPvalue +] +[ +.I files +] +.SH DESCRIPTION +.B gvpack +reads in a stream of graphs, combines the graphs into a single +layout, and produces a single graph serving as the union of the +input graphs. The input graphs must be in dot format, and must have +all necessary layout information. Acceptable input is produced +by applying a Graphviz layout program, such as \fBdot\fP or \fBneato\fP, +with no \fB\-T\fP flag. +.P +By default, the packing is done at the cluster level. Thus, parts of +one graph will not intrude into any top\(hylevel clusters or overlap +any nodes or edges of another. +.P +The output of \fBgvpack\fP can be used to produce concrete output +by applying \fBneato \-s \-n2\fP with the desired \fB\-T\fP flag. +.SH OPTIONS +The following options are supported: +.TP +.B \-g +Combines the graphs at the graph level. This uses more space, but prevents +parts of one graph from occurring between parts of another. +.TP +.BI \-G "name\fB=\fPvalue" +Specifies attributes to be added to the resulting union graph. For +example, this can be used to specify a graph label. +.TP +.BI \-m "margin" +Packs the graphs allowing a margin of \fIoutput\fP points around +the parts. +.TP +.B \-n +Combines the graphs at the node level. Clusters are ignored in the packing. +.TP +.BI \-o "output" +Prints output to the file \fIoutput\fP. If not given, \fBgvpack\fP +uses stdout. +.TP +.B \-u +Don't pack the graphs. Just combine them into a single graph. +.TP +.B \-v +Verbose mode. +.TP +.B \-? +Prints usage information and exit. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in dot format. +If no +.I files +operand is specified, +the standard input will be used. +.SH RETURN CODES +.B gvpack +returns +.B 0 +if there were no problems, and non\(hyzero otherwise. +.SH EXAMPLES +.EX +ccomps \-x abc.dot | dot | gvpack | neato \-s \-n2 \-Tps +.EE +This pipeline decomposes the graph in \fIabc.dot\fP into its +connected components, lays out each using \fBdot\fP, packs them all together +again, and produces the final drawing in PostScript. Of course, +there is nothing to prevent one from using different layouts for +each component. +.SH "BUGS" +All the input graphs must be directed or undirected. +.P +An input graph should not have a label, since this will be used in its +layout. Since \fBgvpack\fP ignores root graph labels, resulting layout +may contain some extra space. +.P +\fBgvpack\fP unsets the bounding box attribute of all non\(hycluster +subgraphs. +.SH AUTHORS +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +gvpr(1), dot(1), neato(1), twopi(1), ccomps(1), libpack(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/gvpr.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1041 @@ +.de TQ +. br +. ns +. TP \\$1 +.. +.TH GVPR 1 "24 April 2008" +.SH NAME +gvpr \- graph pattern scanning and processing language +.br +( previously known as +.B gpr +) +.SH SYNOPSIS +.B gvpr +[\fB\-icqV?\fP] +[ +.BI \-o +.I outfile +] +[ +.BI \-a +.I args +] +[ +.I 'prog' +| +.BI \-f +.I progfile +] +[ +.I files +] +.SH DESCRIPTION +.B gvpr +is a graph stream editor inspired by \fBawk\fP. +It copies input graphs to its +output, possibly transforming their structure and attributes, +creating new graphs, or printing arbitrary information. +The graph model is that provided by +.IR libcgraph (3). +In particular, \fBgvpr\fP reads and writes graphs using the +dot language. +.PP +Basically, +.B gvpr +traverses each input graph, denoted by \fB$G\fP, visiting each node and edge, +matching it with the predicate\(hyaction rules supplied in the input program. +The rules are evaluated in order. +For each predicate evaluating to true, the corresponding +action is performed. +During the traversal, the current node or edge being visited +is denoted by \fB$\fP. +.PP +For each input graph, there is a target subgraph, denoted by +\fB$T\fP, initially empty and used to accumulate +chosen entities, and an output graph, \fB$O\fP, used for final processing +and then written to output. +By default, the output graph is the target graph. +The output graph can be set in the program or, in a limited sense, +on the command line. +.SH OPTIONS +The following options are supported: +.TP +.BI \-a " args" +The string \fIargs\fP is split into whitespace\(hyseparated tokens, +with the individual tokens +available as strings in the \fBgvpr\fP program +as \fBARGV[\fI0\fP],...,ARGV[ARGC\-1]\fR. +Whitespace characters within single or double quoted substrings, or +preceded by a backslash, are ignored as separators. +In general, a backslash character turns off any special meaning of the +following character. +Note that the tokens derived from multiple \fB\-a\fP flags are concatenated. +.TP +.B \-c +Use the source graph as the output graph. +.TP +.B \-i +Derive the node\(hyinduced subgraph extension of the output graph in the context +of its root graph. +.TP +.BI \-o " outfile" +Causes the output stream to be written to the specified file; by default, +output is written to \fBstdout\fP. +.TP +.BI \-f " progfile" +Use the contents of the specified file as the program to execute +on the input. If \fIprogfile\fP contains a slash character, the name is taken +as the pathname of the file. Otherwise, \fBgvpr\fP will use the +directories specified in the environment variable \fBGPRPATH\fP to look +for the file. If +.B \-f +is not given, +.B gvpr +will use the first non\(hyoption argument as the program. +.TP +.B \-q +Turns off warning messages. +.TP +.B \-V +Causes the program to print version information and exit. +.TP +.B \-? +Causes the program to print usage information and exit. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in the dot language. +If no +.B \-f +option is given, the first name is removed from the list and used +as the input program. If the list of files is empty, \fBstdin\fP will be used. +.SH PROGRAMS +A +.B gvpr +program consists of a list of predicate\(hyaction clauses, having one +of the forms: +.IP +.BI "BEGIN { " action " }" +.IP +.BI "BEG_G { " action " }" +.IP +.BI "N [ " predicate " ] { " action " } +.IP +.BI "E [ " predicate " ] { " action " } +.IP +.BI "END_G { " action " }" +.IP +.BI "END { " action " }" +.PP +A program can contain at most one of each of the \fBBEGIN\fP, \fBBEG_G\fP, +\fBEND_G\fP and \fBEND\fP clauses. +There can be any number of \fBN\fP and \fBE\fP statements, +the first applied to nodes, the second to edges. +The top\(hylevel semantics of a \fBgvpr\fP program are: +.PP +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +Evaluate the \fBBEGIN\fP clause, if any. +For each input graph \fIG\fP { + Set \fIG\fP as the current graph and current object. + Evaluate the \fBBEG_G\fP clause, if any. + For each node and edge in \fIG\fP { + Set the node or edge as the current object. + Evaluate the \fBN\fP or \fBE\fP clauses, as appropriate. + } + Set \fIG\fP as the current object. + Evaluate the \fBEND_G\fP clause, if any. +} +Evaluate the \fBEND\fP clause, if any. +.fi +.RE +.DT +.PP +The actions of the \fBBEGIN\fP, \fBBEG_G\fP, \fBEND_G\fP and \fBEND\fP clauses +are performed when the clauses are evaluated. +For \fBN\fP or \fBE\fP clauses, +either the predicate or action may be omitted. +If there is no predicate with an action, the action is +performed on every node or edge, as appropriate. +If there is no action and the predicate evaluates to true, +the associated node or edge is added to the target graph. +.PP +Predicates and actions are sequences of statements in the C dialect +supported by the +.IR libexpr (3) +library. +The only difference between predicates and actions is that the former +must have a type that may interpreted as either true or false. +Here the usual C convention is followed, in which a non\(hyzero value is +considered true. This would include non\(hyempty strings and non\(hyempty +references to nodes, edges, etc. However, if a string can be +converted to an integer, this value is used. +.PP +In addition to the usual C base types +(\fBvoid\fP, \fBint\fP, \fBchar\fP, \fBfloat\fP, \fBlong\fP, +\fBunsigned\fP and \fBdouble\fP), +\fBgvpr\fP \fRprovides \fBstring\fP as a synonym for \fBchar*\fP, and +the graph\(hybased types \fBnode_t\fP, +\fBedge_t\fP, \fBgraph_t\fP and \fBobj_t\fP. +The \fBobj_t\fP type can be viewed as a supertype of the other 3 concrete types; +the correct base type is maintained dynamically. +Besides these base types, the only other supported type expressions +are (associative) arrays. +.PP +Constants follow C syntax, but strings may be quoted with either +\fB"..."\fP or \fB'...'\fP. In certain contexts, string values are +interpreted as patterns for the purpose of regular expression matching. +Patterns use +.IR ksh (1) +file match pattern syntax. +\fBgvpr\fP accepts C++ comments as well as cpp\(hytype comments. +For the latter, if a line begins with a '#' character, the rest of +the line is ignored. +.PP +A statement can be a declaration of a function, a variable +or an array, or an executable statement. For declarations, there +is a single scope. Array declarations have the form: +.PP +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +\fI type array \fB[\fP type0 \fB]\fR +.fi +.RE +.DT +.PP +where \fI type0 \fP is optional. If it is supplied, the parser will +enforce that all array subscripts have the specified type. If it is +not supplied, objects of all types can be used as subscripts. +As in C, variables and arrays must +be declared. In particular, an undeclared variable will be interpreted +as the name of an attribute of a node, edge or graph, depending on the +context. +.PP +Executable statements can be one of the following: +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +\fB{\fR [\fI statement ... \fR] \fB}\fR +\fIexpression\fP \fR// commonly\fP\fI var \fB=\fP expression\fR +\fBif(\fI expression \fP)\fI statement \fR[ \fBelse\fI statement \fR] +\fBfor(\fI expression \fP;\fI expression \fP;\fI expression \fP)\fI statement\fP +\fBfor(\fI array \fP[\fI var \fP])\fI statement\fP +\fBwhile(\fI expression \fP)\fI statement\fP +\fBswitch(\fI expression \fP)\fI case statements\fP +\fBbreak [\fI expression \fP] +\fBcontinue [\fI expression \fP] +\fBreturn [\fI expression \fP]\fR +.fi +.RE +.ST +Items in brackets are optional. +.PP +In the second form of the \fBfor\fP statement, the variable \fIvar\fP +is set to each value used as an index in the specified array and then +the associated \fIstatement\fP is evaluated. Function definitions can +only appear in the \fBBEGIN\fP clause. +.PP +Expressions include the usual C expressions. +String comparisons using \fB==\fP and \fB!=\fP +treat the right hand operand as a pattern. +\fBgvpr\fP will attempt to use an expression as a string or numeric value +as appropriate. +.PP +Expressions of graphical type (i.e., \fBgraph_t, node_t, +edge_t, obj_t\fP) may be followed by a field reference in the +form of \fB.\fP\fIname\fP. The resulting value is the value +of the attribute named \fIname\fP of the given object. +In addition, in certain contexts an undeclared, unmodified +identifier is taken to be an +attribute name. Specifically, such identifiers denote attributes +of the current node or edge, respectively, in \fBN\fP +and \fBE\fP clauses, and the current graph in \fBBEG_G\fP and \fBEND_G\fP +clauses. +.PP +As usual in the +.IR libcgraph (3) +model, attributes are string\(hyvalued. +In addition, +.B gvpr +supports certain pseudo\(hyattributes of graph objects, not necessarily +string\(hyvalued. These reflect intrinsic properties of the graph objects +and cannot be set by the user. +.TP +\fBhead\fR : \fBnode_t\fR +the head of an edge. +.TP +\fBtail\fR : \fBnode_t\fR +the tail of an edge. +.TP +\fBname\fR : \fBstring\fR +the name of an edge, node or graph. The name of an edge has the +form "\fI<tail\(hyname><edge\(hyop><head\(hyname>\fB[\fI<key>\fB]\fR", +where \fI<edge\(hyop>\fP is "\fB\->\fP" or "\fB\-\-\fP" depending on +whether the graph is directed or not. The bracket part \fB[\fI<key>\fB]\fR +only appears if the edge has a non\(hytrivial key. +.TP +\fBindegree\fR : \fBint\fR +the indegree of a node. +.TP +\fBoutdegree\fR : \fBint\fR +the outdegree of a node. +.TP +\fBdegree\fR : \fBint\fR +the degree of a node. +.TP +\fBroot\fR : \fBgraph_t\fR +the root graph of an object. The root of a root graph +is itself. +.TP +\fBparent\fR : \fBgraph_t\fR +the parent graph of a subgraph. The parent of a root graph +is \fBNULL\fP +.TP +\fBn_edges\fR : \fBint\fR +the number of edges in the graph +.TP +\fBn_nodes\fR : \fBint\fR +the number of nodes in the graph +.TP +\fBdirected\fR : \fBint\fR +true (non\(hyzero) if the graph is directed +.TP +\fBstrict\fR : \fBint\fR +true (non\(hyzero) if the graph is strict +.SH "BUILT\(hyIN FUNCTIONS" +.PP +The following functions are built into \fBgvpr\fP. Those functions +returning references to graph objects return \fBNULL\fP in case of failure. +.SS "Graphs and subgraph" +.TP +\fBgraph\fP(\fIs\fP : \fBstring\fP, \fIt\fP : \fBstring\fP) : \fBgraph_t\fP +creates a graph whose name is \fIs\fP and whose type is +specified by the string \fIt\fP. Ignoring case, the characters +\fBU, D, S, N\fR have the interpretation undirected, directed, +strict, and non\(hystrict, respectively. If \fIt\fP is empty, +a directed, non\(hystrict graph is generated. +.TP +\fBsubg\fP(\fIg\fP : \fBgraph_t\fP, \fIs\fP : \fBstring\fP) : \fBgraph_t\fP +creates a subgraph in graph \fIg\fP with name \fIs\fP. If the subgraph +already exists, it is returned. +.TP +\fBisSubg\fP(\fIg\fP : \fBgraph_t\fP, \fIs\fP : \fBstring\fP) : \fBgraph_t\fP +returns the subgraph in graph \fIg\fP with name \fIs\fP, if it exists, +or \fBNULL\fP otherwise. +.TP +\fBfstsubg\fP(\fIg\fP : \fBgraph_t\fP) : \fBgraph_t\fP +returns the first subgraph in graph \fIg\fP, or \fBNULL\fP if none exists. +.TP +\fBnxtsubg\fP(\fIsg\fP : \fBgraph_t\fP) : \fBgraph_t\fP +returns the next subgraph after \fIsg\fP, or \fBNULL\fP. +.TP +\fBisDirect\fP(\fIg\fP : \fBgraph_t\fP) : \fBint\fP +returns true if and only if \fIg\fP is directed. +.TP +\fBisStrict\fP(\fIg\fP : \fBgraph_t\fP) : \fBint\fP +returns true if and only if \fIg\fP is strict. +.TP +\fBnNodes\fP(\fIg\fP : \fBgraph_t\fP) : \fBint\fP +returns the number of nodes in \fIg\fP. +.TP +\fBnEdges\fP(\fIg\fP : \fBgraph_t\fP) : \fBint\fP +returns the number of edges in \fIg\fP. +.SS "Nodes" +.TP +\fBnode\fP(\fIsg\fP : \fBgraph_t\fP, \fIs\fP : \fBstring\fP) : \fBnode_t\fP +creates a node in graph \fIg\fP of name \fIs\fP. If such a node +already exists, it is returned. +.TP +\fBsubnode\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBnode_t\fP +inserts the node \fIn\fP into the subgraph \fIg\fP. Returns the node. +.TP +\fBfstnode\fP(\fIg\fP : \fBgraph_t\fP) : \fBnode_t\fP +returns the first node in graph \fIg\fP, or \fBNULL\fP if none exists. +.TP +\fBnxtnode\fP(\fIn\fP : \fBnode_t\fP) : \fBnode_t\fP +returns the next node after \fIn\fP in the root graph, or \fBNULL\fP. +.TP +\fBnxtnode_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBnode_t\fP +returns the next node after \fIn\fP in \fIsg\fP, or \fBNULL\fP. +.TP +\fBisNode\fP(\fIsg\fP : \fBgraph_t\fP, \fIs\fP : \fBstring\fP) : \fBnode_t\fP +looks for a node in (sub)graph \fIsg\fP of name \fIs\fP. If such a node +exists, it is returned. Otherwise, \fBNULL\fP is returned. +.TP +\fBisSubnode\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBint\fP +returns non-zero if node \fIn\fP is in (sub)graph \fIsg\fP, or zero +otherwise. +.TP +\fBindegreeOf\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBint\fP +returns the indegree of node \fIn\fP in (sub)graph \fIsg\fP. +.TP +\fBoutdegreeOf\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBint\fP +returns the outdegree of node \fIn\fP in (sub)graph \fIsg\fP. +.TP +\fBdegreeOf\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBint\fP +returns the degree of node \fIn\fP in (sub)graph \fIsg\fP. +.SS "Edges" +.TP +\fBedge\fP(\fIt\fP : \fBnode_t\fP, \fIh\fP : \fBnode_t\fP, \fIs\fP : \fBstring\fP) : \fBedge_t\fP +creates an edge with tail node \fIt\fP, head node \fIh\fP and +name \fIs\fP in the root graph. If the graph is undirected, the +distinction between head and tail nodes is unimportant. +If such an edge already exists, it is returned. +.TP +\fBedge_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIt\fP : \fBnode_t\fP, \fIh\fP : \fBnode_t\fP, \fIs\fP : \fBstring\fP) : \fBedge_t\fP +creates an edge with tail node \fIt\fP, head node \fIh\fP and name \fIs\fP +in (sub)graph \fIsg\fP (and all parent graphs). If the graph is undirected, the distinction between +head and tail nodes is unimportant. +If such an edge already exists, it is returned. +.TP +\fBsubedge\fP(\fIg\fP : \fBgraph_t\fP, \fIe\fP : \fBedge_t\fP) : \fBedge_t\fP +inserts the edge \fIe\fP into the subgraph \fIg\fP. Returns the edge. +.TP +\fBisEdge\fP(\fIt\fP : \fBnode_t\fP, \fIh\fP : \fBnode_t\fP, \fIs\fP : \fBstring\fP) : \fBedge_t\fP +looks for an edge with tail node \fIt\fP, head node \fIh\fP and +name \fIs\fP. If the graph is undirected, the distinction between +head and tail nodes is unimportant. +If such an edge exists, it is returned. Otherwise, \fBNULL\fP is returned. +.TP +\fBisEdge_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIt\fP : \fBnode_t\fP, \fIh\fP : \fBnode_t\fP, \fIs\fP : \fBstring\fP) : \fBedge_t\fP +looks for an edge with tail node \fIt\fP, head node \fIh\fP and +name \fIs\fP in (sub)graph \fIsg\fP. If the graph is undirected, the distinction between +head and tail nodes is unimportant. +If such an edge exists, it is returned. Otherwise, \fBNULL\fP is returned. +.TP +\fBisSubedge\fP(\fIg\fP : \fBgraph_t\fP, \fIe\fP : \fBedge_t\fP) : \fBint\fP +returns non-zero if edge \fIe\fP is in (sub)graph \fIsg\fP, or zero +otherwise. +.TP +\fBfstout\fP(\fIn\fP : \fBnode_t\fP) : \fBedge_t\fP +returns the first outedge of node \fIn\fP in the root graph. +.TP +\fBfstout_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBedge_t\fP +returns the first outedge of node \fIn\fP in (sub)graph \fIsg\fP. +.TP +\fBnxtout\fP(\fIe\fP : \fBedge_t\fP) : \fBedge_t\fP +returns the next outedge after \fIe\fP in the root graph. +.TP +\fBnxtout_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIe\fP : \fBedge_t\fP) : \fBedge_t\fP +returns the next outedge after \fIe\fP in graph \fIsg\fP. +.TP +\fBfstin\fP(\fIn\fP : \fBnode_t\fP) : \fBedge_t\fP +returns the first inedge of node \fIn\fP in the root graph. +.TP +\fBfstin_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBedge_t\fP +returns the first inedge of node \fIn\fP in graph \fIsg\fP. +.TP +\fBnxtin\fP(\fIe\fP : \fBedge_t\fP) : \fBedge_t\fP +returns the next inedge after \fIe\fP in the root graph. +.TP +\fBnxtin_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIe\fP : \fBedge_t\fP) : \fBedge_t\fP +returns the next inedge after \fIe\fP in graph \fIsg\fP. +.TP +\fBfstedge\fP(\fIn\fP : \fBnode_t\fP) : \fBedge_t\fP +returns the first edge of node \fIn\fP in the root graph. +.TP +\fBfstedge_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBedge_t\fP +returns the first edge of node \fIn\fP in graph \fIsg\fP. +.TP +\fBnxtedge\fP(\fIe\fP : \fBedge_t\fP, \fBnode_t\fP) : \fBedge_t\fP +returns the next edge after \fIe\fP in the root graph. +.TP +\fBnxtedge_sg\fP(\fIsg\fP : \fBgraph_t\fP, \fIe\fP : \fBedge_t\fP, \fBnode_t\fP) : \fBedge_t\fP +returns the next edge after \fIe\fP in the graph \fIsg\fP. +.SS "Graph I/O" +.TP +\fBwrite\fP(\fIg\fP : \fBgraph_t\fP) : \fBvoid\fP +prints \fIg\fP in dot format onto the output stream. +.TP +\fBwriteG\fP(\fIg\fP : \fBgraph_t\fP, \fIfname\fP : \fBstring\fP) : \fBvoid\fP +prints \fIg\fP in dot format into the file \fIfname\fP. +.TP +\fBfwriteG\fP(\fIg\fP : \fBgraph_t\fP, \fIfd\fP : \fBint\fP) : \fBvoid\fP +prints \fIg\fP in dot format onto the open stream denoted +by the integer \fIfd\fP. +.TP +\fBreadG\fP(\fIfname\fP : \fBstring\fP) : \fBgraph_t\fP +returns a graph read from the file \fIfname\fP. The graph should be +in dot format. If no graph can be read, \fBNULL\fP is returned. +.TP +\fBfreadG\fP(\fIfd\fP : \fBint\fP) : \fBgraph_t\fP +returns the next graph read from the open stream \fIfd\fP. +Returns \fBNULL\fP at end of file. +.SS "Graph miscellany" +.TP +\fBdelete\fP(\fIg\fP : \fBgraph_t\fP, \fIx\fP : \fBobj_t\fP) : \fBvoid\fP +deletes object \fIx\fP from graph \fIg\fP. +If \fIg\fP is \fBNULL\fP, the function uses the root graph of \fIx\fP. +If \fIx\fP is a graph or subgraph, it is closed unless \fIx\fP is locked. +.TP +\fBisIn\fP(\fIg\fP : \fBgraph_t\fP, \fIx\fP : \fBobj_t\fP) : \fBint\fP +returns true if \fIx\fP is in subgraph \fIg\fP. +.TP +\fBclone\fP(\fIg\fP : \fBgraph_t\fP, \fIx\fP : \fBobj_t\fP) : \fBobj_t\fP +creates a clone of object \fIx\fP in graph \fIg\fP. +In particular, the new object has the same name/value attributes +and structure as the original object. +If an object with the same key as \fIx\fP already exists, its attributes +are overlaid by those of \fIx\fP and the object is returned. +If an edge is cloned, both endpoints are implicitly cloned. +If a graph is cloned, all nodes, edges and subgraphs are implicitly +cloned. +If \fIx\fP is a graph, \fIg\fP may be \fBNULL\fP, in which case the cloned +object will be a new root graph. +.TP +\fBcopy\fP(\fIg\fP : \fBgraph_t\fP, \fIx\fP : \fBobj_t\fP) : \fBobj_t\fP +creates a copy of object \fIx\fP in graph \fIg\fP, +where the new object has the same name/value attributes +as the original object. +If an object with the same key as \fIx\fP already exists, its attributes +are overlaid by those of \fIx\fP and the object is returned. +Note that this is a shallow copy. If \fIx\fP is a graph, none of its nodes, +edges or subgraphs are copied into the new graph. If \fIx\fP is an edge, +the endpoints are created if necessary, but they are not cloned. +If \fIx\fP is a graph, \fIg\fP may be \fBNULL\fP, in which case the cloned +object will be a new root graph. +.TP +\fBcopyA\fP(\fIsrc\fP : \fBobj_t\fP, \fItgt\fP : \fBobj_t\fP) : \fBint\fP +copies the attributes of object \fIsrc\fP to object \fItgt\fP, overwriting +any attribute values \fItgt\fP may initially have. +.TP +\fBinduce\fP(\fIg\fP : \fBgraph_t\fP) : \fBvoid\fP +extends \fIg\fP to its node\(hyinduced subgraph extension in its root graph. +.TP +\fBhasAttr\fP(\fIsrc\fP : \fBobj_t\fP, \fIname\fP : \fBstring\fP) : \fBint\fP +returns non-zero if object \fIsrc\fP has an attribute whose name is +\fIname\fP. It returns 0 otherwise. +.TP +\fBisAttr\fP(\fIg\fP : \fBgraph_t\fP, \fIkind\fP : \fBstring\fP, \fIname\fP : \fBstring\fP) : \fBint\fP +returns non-zero if an attribute \fIname\fP has been defined in \fIg\fP +for objects of the given \fIkind\fP. For nodes, edges, and graphs, \fIkind\fP +should be "N", "E", and "G", respectively. +It returns 0 otherwise. +.TP +\fBaget\fP(\fIsrc\fP : \fBobj_t\fP, \fIname\fP : \fBstring\fP) : \fBstring\fP +returns the value of attribute \fIname\fP in object \fIsrc\fP. This is +useful for those cases when \fIname\fP conflicts with one of the keywords +such as "head" or "root". +If the attribute has not been declared in the graph, the function will +initialize it with a default value of "". To avoid this, one should use +the \fBhasAttr\fP or \fBisAttr\fP function to check that the attribute exists. +.TP +\fBaset\fP(\fIsrc\fP : \fBobj_t\fP, \fIname\fP : \fBstring\fP, \fIvalue\fP : \fBstring\fP) : \fBint\fP +sets the value of attribute \fIname\fP in object \fIsrc\fP to \fIvalue\fP. +Returns 0 on success, non\(hyzero on failure. See \fBaget\fP above. +.TP +\fBgetDflt\fP(\fIg\fP : \fBgraph_t\fP, \fIkind\fP : \fBstring\fP, \fIname\fP : \fBstring\fP) : \fBstring\fP +returns the default value of attribute \fIname\fP in objects in \fIg\fP of +the given \fIkind\fP. For nodes, edges, and graphs, \fIkind\fP +should be "N", "E", and "G", respectively. +If the attribute has not been declared in the graph, the function will +initialize it with a default value of "". To avoid this, one should use +the \fBisAttr\fP function to check that the attribute exists. +.TP +\fBsetDflt\fP(\fIg\fP : \fBgraph_t\fP, \fIkind\fP : \fBstring\fP, \fIname\fP : \fBstring\fP, \fIvalue\fP : \fBstring\fP) : \fBint\fP +sets the default value of attribute \fIname\fP to \fIvalue\fP in +objects in \fIg\fP of +the given \fIkind\fP. For nodes, edges, and graphs, \fIkind\fP +should be "N", "E", and "G", respectively. +Returns 0 on success, non\(hyzero on failure. See \fBgetDflt\fP above. +.TP +\fBfstAttr\fP(\fIg\fP : \fBgraph_t\fP, \fIkind\fP : \fBstring\fP) : \fBstring\fP +returns the name of the first attribute of objects in \fIg\fP of +the given \fIkind\fP. For nodes, edges, and graphs, \fIkind\fP +should be "N", "E", and "G", respectively. +If there are no attributes, the string "" is returned. +.TP +\fBnxtAttr\fP(\fIg\fP : \fBgraph_t\fP, \fIkind\fP : \fBstring\fP, \fIname\fP : \fBstring\fP) : \fBstring\fP +returns the name of the next attribute of objects in \fIg\fP of +the given \fIkind\fP after the attribute \fIname\fP. +The argument \fIname\fP must be the name of an existing attribute; it will +typically be the return value of an previous call to \fBfstAttr\fP or +\fBnxtAttr\fP. +For nodes, edges, and graphs, \fIkind\fP +should be "N", "E", and "G", respectively. +If there are no attributes left, the string "" is returned. +.TP +\fBcompOf\fP(\fIg\fP : \fBgraph_t\fP, \fIn\fP : \fBnode_t\fP) : \fBgraph_t\fP +returns the connected component of the graph \fIg\fP containing node \fIn\fP, +as a subgraph of \fIg\fP. The subgraph only contains the nodes. One can +use \fIinduce\fP to add the edges. The function fails and returns \fBNULL\fP +if \fIn\fP is not in \fIg\fP. Connectivity is based on the underlying +undirected graph of \fIg\fP. +.TP +\fBkindOf\fP(\fIobj\fP : \fBobj_t\fP) : \fBstring\fP +returns an indication of what kind of graph object is the argument. +For nodes, edges, and graphs, it returns +should be "N", "E", and "G", respectively. +.TP +\fBlock\fP(\fIg\fP : \fBgraph_t\fP, \fIv\fP : \fBint\fP) : \fBint\fP +implements graph locking on root graphs. If the integer \fIv\fP is positive, the +graph is set so that future calls to \fBdelete\fP have no immediate effect. +If \fIv\fP is zero, the graph is unlocked. If there has been a call +to delete the graph while it was locked, the graph is closed. +If \fIv\fP is negative, nothing is done. +In all cases, the previous lock value is returned. +.SS "Strings" +.TP +\fBsprintf\fP(\fIfmt\fP : \fBstring\fP, \fI...\fP) : \fBstring\fP +returns the string resulting from formatting +the values of the expressions occurring after \fIfmt\fP +according to the +.IR printf (3) +format +.I fmt +.TP +\fBgsub\fP(\fIstr\fP : \fBstring\fP, \fIpat\fP : \fBstring\fP) : \fBstring\fP +.TP +\fBgsub\fP(\fIstr\fP : \fBstring\fP, \fIpat\fP : \fBstring\fP, \fIrepl\fP : \fBstring\fP) : \fBstring\fP +returns \fIstr\fP with all substrings matching \fIpat\fP +deleted or replaced by \fIrepl\fP, respectively. +.TP +\fBsub\fP(\fIstr\fP : \fBstring\fP, \fIpat\fP : \fBstring\fP) : \fBstring\fP +.TP +\fBsub\fP(\fIstr\fP : \fBstring\fP, \fIpat\fP : \fBstring\fP, \fIrepl\fP : \fBstring\fP) : \fBstring\fP +returns \fIstr\fP with the leftmost substring matching \fIpat\fP +deleted or replaced by \fIrepl\fP, respectively. The +characters '^' and '$' +may be used at the beginning and end, respectively, +of \fIpat\fP to anchor the pattern to the beginning or end of \fIstr\fP. +.TP +\fBsubstr\fP(\fIstr\fP : \fBstring\fP, \fIidx\fP : \fBint\fP) : \fBstring\fP +.TP +\fBsubstr\fP(\fIstr\fP : \fBstring\fP, \fIidx\fP : \fBint\fP, \fIlen\fP : \fBint\fP) : \fBstring\fP +returns the substring of \fIstr\fP starting at position \fIidx\fP to +the end of the string or of length \fIlen\fP, respectively. +Indexing starts at 0. If \fIidx\fP is negative or \fIidx\fP is greater than +the length of \fIstr\fP, a fatal error occurs. Similarly, in the second +case, if \fIlen\fP is negative or \fIidx\fP + \fIlen\fP is greater than the +length of \fIstr\fP, a fatal error occurs. +.TP +\fBlength\fP(\fIs\fP : \fBstring\fP) : \fBint\fP +returns the length of the string \fIs\fP. +.TP +\fBindex\fP(\fIs\fP : \fBstring\fP, \fIt\fP : \fBstring\fP) : \fBint\fP +.TP +\fBrindex\fP(\fIs\fP : \fBstring\fP, \fIt\fP : \fBstring\fP) : \fBint\fP +returns the index of the character in string \fIs\fP where the leftmost +(rightmost) copy of string \fIt\fP can be found, or \-1 if \fIt\fP is not a +substring of \fIs\fP. +.TP +\fBmatch\fP(\fIs\fP : \fBstring\fP, \fIp\fP : \fBstring\fP) : \fBint\fP +returns the index of the character in string \fIs\fP where the leftmost +match of pattern \fIp\fP can be found, or \-1 if no substring of \fIs\fP +matches \fIp\fP. +.TP +\fBcanon\fP(\fIs\fP : \fBstring\fP) : \fBstring\fP +returns a version of \fIs\fP appropriate to be used as an identifier +in a dot file. +.TP +\fBxOf\fP(\fIs\fP : \fBstring\fP) : \fBstring\fP +returns the string "\fIx\fP" if \fIs\fP has the form "\fIx\fP,\fIy\fP", +where both \fIx\fP and \fIy\fP are numeric. +.TP +\fByOf\fP(\fIs\fP : \fBstring\fP) : \fBstring\fP +returns the string "\fIy\fP" if \fIs\fP has the form "\fIx\fP,\fIy\fP", +where both \fIx\fP and \fIy\fP are numeric. +.TP +\fBllOf\fP(\fIs\fP : \fBstring\fP) : \fBstring\fP +returns the string "\fIllx\fP,\fIlly\fP" if \fIs\fP has the form +"\fIllx\fP,\fIlly\fP,\fIurx\fP,\fIury\fP", +where all of \fIllx\fP, \fIlly\fP, \fIurx\fP, and \fIury\fP are numeric. +.TP +.BI urOf( s ) +\fBurOf\fP(\fIs\fP : \fBstring\fP) : \fBstring\fP +returns the string "\fIurx\fP,\fIury\fP" if \fIs\fP has the form +"\fIllx\fP,\fIlly\fP,\fIurx\fP,\fIury\fP", +where all of \fIllx\fP, \fIlly\fP, \fIurx\fP, and \fIury\fP are numeric. +.TP +\fBsscanf\fP(\fIs\fP : \fBstring\fP, \fIfmt\fP : \fBstring\fP, \fI...\fP) : \fBint\fP +scans the string \fIs\fP, extracting values +according to the +.IR sscanf (3) +format +.IR fmt . +The values are stored in the addresses following \fIfmt\fP, +addresses having the form \fB&\fP\fIv\fP, where \fIv\fP is some declared +variable of the correct type. +Returns the number of items successfully scanned. +.SS "I/O" +.TP +\fBprint\fP(\fI...\fP) : \fBvoid\fP +.BI print( " expr" , " ...\fB ) +prints a string representation of each argument in turn onto +\fBstdout\fP, followed by a newline. +.TP +\fBprintf\fP(\fIfmt\fP : \fBstring\fP, \fI...\fP) : \fBint\fP +.TP +\fBprintf\fP(\fIfd\fP : \fBint\fP, \fIfmt\fP : \fBstring\fP, \fI...\fP) : \fBint\fP +prints the string resulting from formatting +the values of the expressions following \fIfmt\fP +according to the +.IR printf (3) +format +.IR fmt . +Returns 0 on success. +By default, it prints on \fBstdout\fP. +If the optional integer \fIfd\fP is given, output is written on the open +stream associated with \fIfd\fP. +.TP +\fBscanf\fP(\fIfmt\fP : \fBstring\fP, \fI...\fP) : \fBint\fP +.TP +\fBscanf\fP(\fIfd\fP : \fBint\fP, \fIfmt\fP : \fBstring\fP, \fI...\fP) : \fBint\fP +scans in values from an input stream according to the +.IR scanf (3) +format +.IR fmt . +The values are stored in the addresses following \fIfmt\fP, +addresses having the form \fB&\fP\fIv\fP, where \fIv\fP is some declared +variable of the correct type. +By default, it reads from \fBstdin\fP. +If the optional integer \fIfd\fP is given, input is read from the open +stream associated with \fIfd\fP. +Returns the number of items successfully scanned. +.TP +\fBopenF\fP(\fIs\fP : \fBstring\fP, \fIt\fP : \fBstring\fP) : \fBint\fP +opens the file \fIs\fP as an I/O stream. The string argument \fIt\fP +specifies how the file is opened. The arguments are the same as for +the C function +.IR fopen (3). +It returns an integer denoting the stream, or \-1 on error. +.sp +As usual, streams 0, 1 and 2 are already open as \fBstdin\fP, \fBstdout\fP, +and \fBstderr\fP, respectively. Since \fBgvpr\fP may use \fBstdin\fP to +read the input graphs, the user should avoid using this stream. +.TP +\fBcloseF\fP(\fIfd\fP : \fBint\fP) : \fBint\fP +closes the open stream denoted by the integer \fIfd\fP. +Streams 0, 1 and 2 cannot be closed. +Returns 0 on success. +.TP +\fBreadL\fP(\fIfd\fP : \fBint\fP) : \fBstring\fP +returns the next line read from the input stream \fIfd\fP. It returns +the empty string "" on end of file. Note that the newline character is +left in the returned string. +.SS "Math" +.TP +\fBexp\fP(\fId\fP : \fBdouble\fP) : \fBdouble\fP +returns e to the \fId\fPth power. +.TP +\fBlog\fP(\fId\fP : \fBdouble\fP) : \fBdouble\fP +returns the natural log of \fId\fP. +.TP +\fBsqrt\fP(\fId\fP : \fBdouble\fP) : \fBdouble\fP +returns the square root of the double \fId\fP. +.TP +\fBpow\fP(\fId\fP : \fBdouble\fP, \fIx\fP : \fBdouble\fP) : \fBdouble\fP +returns \fId\fP raised to the \fIx\fPth power. +.TP +\fBcos\fP(\fId\fP : \fBdouble\fP) : \fBdouble\fP +returns the cosine of \fId\fP. +.TP +\fBsin\fP(\fId\fP : \fBdouble\fP) : \fBdouble\fP +returns the sine of \fId\fP. +.TP +\fBatan2\fP(\fIy\fP : \fBdouble\fP, \fIx\fP : \fBdouble\fP) : \fBdouble\fP +returns the arctangent of \fIy/x\fP in the range \-pi to pi. +.TP +\fBMIN\fP(\fIy\fP : \fBdouble\fP, \fIx\fP : \fBdouble\fP) : \fBdouble\fP +returns the minimum of \fIy\fP and \fIx\fP. +.TP +\fBMAX\fP(\fIy\fP : \fBdouble\fP, \fIx\fP : \fBdouble\fP) : \fBdouble\fP +returns the maximum of \fIy\fP and \fIx\fP. +.SS "Miscellaneous" +.TP +\fBexit\fP(\fIv\fP : \fBint\fP) : \fBvoid\fP +causes +.B gvpr +to exit with the exit code +.IR v . +.TP +\fBsystem\fP(\fIcmd\fP : \fBstring\fP) : \fBint\fP +provides the standard C function +.IR system (3). +It executes \fIcmd\fP if the user's shell environment, and +returns the exit status of the shell. +.TP +\fBrand\fP() : \fBdouble\fP +returns a pseudo\(hyrandom double between 0 and 1. +.TP +\fBsrand\fP() : \fBint\fP +.TP +\fBsrand\fP(\fIv\fP : \fBint\fP) : \fBint\fP +sets a seed for the random number generator. The optional argument gives +the seed; if it is omitted, the current time is used. The previous seed +value is returned. \fBsrand\fP should be called before any calls to +\fBrand\fP. +.SH "BUILT\(hyIN VARIABLES" +.PP +.B gvpr +provides certain special, built\(hyin variables, whose values are set +automatically by \fBgvpr\fP depending on the context. Except as noted, +the user cannot modify their values. +.TP +\fB$\fP : \fBobj_t\fP +denotes the current object (node, edge, graph) depending on the +context. It is not available in \fBBEGIN\fP or \fBEND\fP clauses. +.TP +\fB$F\fP : \fBstring\fP +is the name of the current input file. +.TP +\fB$G\fP : \fBgraph_t\fP +denotes the current graph being processed. It is not available +in \fBBEGIN\fP or \fBEND\fP clauses. +.TP +\fB$O\fP : \fBgraph_t\fP +denotes the output graph. Before graph traversal, it is initialized +to the target graph. After traversal and any \fBEND_G\fP actions, +if it refers to a non\(hyempty graph, that graph is printed onto the output stream. +It is only valid in \fBN\fP, \fBE\fP and \fBEND_G\fP clauses. +The output graph may be set by the user. +.TP +\fB$T\fP : \fBgraph_t\fP +denotes the current target graph. It is a subgraph of \fB$G\fP +and is available only in \fBN\fP, \fBE\fP and \fBEND_G\fP clauses. +.TP +\fB$tgtname\fP : \fBstring\fP +denotes the name of the target graph. +By default, it is set to \fB"gvpr_result"\fP. +If used multiple times during the execution of +.BR gvpr , +the name will be appended with an integer. +This variable may be set by the user. +.TP +\fB$tvroot\fP : \fBnode_t\fP +indicates the starting node for a (directed or undirected) +depth\(hyfirst traversal of the +graph (cf. \fB$tvtype\fP below). +The default value is \fBNULL\fP for each input graph. +.TP +\fB$tvtype\fP : \fBtvtype_t\fP +indicates how \fBgvpr\fP traverses a graph. It can only take +one of the constant values with the previx "TV_" described below. +\fBTV_flat\fP is the default. +.IP +In the underlying graph library +.IR cgraph (3), +edges in undirected graphs are given an arbitrary direction. This is +used for traversals, such as \fBTV_fwd\fR, requiring directed edges. +. +.TP +\fBARGC\fP : \fBint\fP +denotes the number of arguments specified by the +\fB\-a\fP \fIargs\fP command\(hyline argument. +.TP +\fBARGV\fP : \fBstring array\fP +denotes the array of arguments specified by the +\fB\-a\fP \fIargs\fP +command\(hyline argument. The \fIi\fPth argument is given +by \fBARGV[\fIi\fP]\fR. +.SH "BUILT\(hyIN CONSTANTS" +.PP +There are several symbolic constants defined by \fBgvpr\fP. +.TP +\fBNULL\fR : \fIobj_t\fR +a null object reference, equivalent to 0. +.TP +\fBTV_flat\fR : \fItvtype_t\fR +a simple, flat traversal, with graph objects visited in +seemingly arbitrary order. +.TP +\fBTV_ne\fR : \fItvtype_t\fR +a traversal which first visits all of the nodes, then all +of the edges. +.TP +\fBTV_en\fR : \fItvtype_t\fR +a traversal which first visits all of the edges, then all +of the nodes. +.TP +\fBTV_dfs\fR : \fItvtype_t\fR +.TQ +\fBTV_postdfs\fR : \fItvtype_t\fR +.TQ +\fBTV_prepostdfs\fR : \fItvtype_t\fR +a traversal of the graph using a depth\(hyfirst search on the +underlying undirected graph. +To do the traversal, \fBgvpr\fP will check the value of +\fB$tvroot\fP. If this has the same value that it had previously +(at the start, the previous value is initialized to \fBNULL\fP.), \fBgvpr\fP +will simply look for some unvisited node and traverse its connected +component. On the other hand, if \fB$tvroot\fP has changed, its connected +component will be toured, assuming it has not been previously visited or, +if \fB$tvroot\fP is \fBNULL\fP, the traversal will stop. Note that using +\fBTV_dfs\fP and \fB$tvroot\fP, it is possible to create an infinite loop. +. +.IP +By default, the traversal is done in pre-order. That is, a node is +visited before all of its unvisited edges. For \fBTV_postdfs\fR, +all of a node's unvisited edges are visited before the node. For +\fBTV_prepostdfs\fR, a node is visited twice, before and after all of +its unvisited edges. +. +.TP +\fBTV_fwd\fR : \fItvtype_t\fR +.TQ +\fBTV_postfwd\fR : \fItvtype_t\fR +.TQ +\fBTV_prepostfwd\fR : \fItvtype_t\fR +A traversal of the graph using a depth\(hyfirst search on the +graph following only forward arcs. +The choice of roots for the traversal is the +same as described for \fBTV_dfs\fR above. +The different order of visitation specified by \fBTV_fwd\fR, +\fBTV_postfwd\fR and \fBTV_prepostfwd\fR are the same as those +specified by the analogous traversals \fBTV_dfs\fR, +\fBTV_postdfs\fR and \fBTV_prepostdfs\fR. +.TP +\fBTV_rev\fR : \fItvtype_t\fR +.TQ +\fBTV_postrev\fR : \fItvtype_t\fR +.TQ +\fBTV_prepostrev\fR : \fItvtype_t\fR +A traversal of the graph using a depth\(hyfirst search on the +graph following only reverse arcs. +The choice of roots for the traversal is the +same as described for \fBTV_dfs\fR above. +The different order of visitation specified by \fBTV_rev\fR, +\fBTV_postrev\fR and \fBTV_prepostrev\fR are the same as those +specified by the analogous traversals \fBTV_dfs\fR, +\fBTV_postdfs\fR and \fBTV_prepostdfs\fR. +.TP +\fBTV_bfs\fR : \fItvtype_t\fR +A traversal of the graph using a bread\(hyfirst search on the +graph ignoring edge directions. See the item on \fBTV_dfs\fR above +for the role of \fB$tvroot\fP. +.SH EXAMPLES +.PP +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +\fBgvpr \-i 'N[color=="blue"]' file.dot\fP +.fi +.RE +.DT +.PP +Generate the node\(hyinduced subgraph of all nodes with color blue. +.PP +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +\fBgvpr \-c 'N[color=="blue"]{color = "red"}' file.dot\fP +.fi +.RE +.DT +.PP +Make all blue nodes red. +.PP +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +\fBBEGIN { int n, e; int tot_n = 0; int tot_e = 0; } +BEG_G { + n = nNodes($G); + e = nEdges($G); + printf ("%d nodes %d edges %s\n", n, e, $G.name); + tot_n += n; + tot_e += e; +} +END { printf ("%d nodes %d edges total\n", tot_n, tot_e) }\fP +.fi +.RE +.DT +.PP +Version of the program \fBgc\fP. +.PP +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +\fBgvpr \-c ""\fP +.fi +.RE +.DT +.PP +Equivalent to \fBnop\fP. +.PP +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +\fBBEG_G { graph_t g = graph ("merge", "S"); } +E { + node_t h = clone(g,$.head); + node_t t = clone(g,$.tail); + edge_t e = edge(t,h,""); + e.weight = e.weight + 1; +} +END_G { $O = g; }\fP +.fi +.RE +.DT +.PP +Produces a strict version of the input graph, where the weight attribute +of an edge indicates how many edges from the input graph the edge represents. +.PP +.ta \w'\f(CWdelete array[expression]'u +.RS +.nf +\fBBEGIN {node_t n; int deg[]} +E{deg[head]++; deg[tail]++; } +END_G { + for (deg[n]) { + printf ("deg[%s] = %d\n", n.name, deg[n]); + } +}\fP +.fi +.RE +.DT +.PP +Computes the degrees of nodes with edges. +.SH ENVIRONMENT +.TP +.B GPRPATH +Colon\(hyseparated list of directories to be searched to find +the file specified by the \-f option. +.SH BUGS AND WARNINGS +When the program is given as a command line argument, the usual +shell interpretation takes place, which may affect some of the +special names in \fBgvpr\fP. To avoid this, it is best to wrap the +program in single quotes. +.PP +As of 24 April 2008, \fBgvpr\fP switched to using a new, underlying +graph library, which uses the simpler model that there is only one +copy of a node, not one copy for each subgraph logically containing +it. This means that iterators such as \Inxtnode\P cannot traverse +a subgraph using just a node argument. For this reason, subgraph +traversal requires new functions ending in "_sg", which also take +a subgraph argument. The versions without that suffix will always +traverse the root graph. +.PP +There is a single global scope, except for formal function parameters, +and even these can interfere with the type system. Also, the +extent of all variables is the entire life of the program. +It might be preferable for scope +to reflect the natural nesting of the clauses, or for the program +to at least reset locally declared variables. +For now, it is advisable to use distinct names for all variables. +.PP +If a function ends with a complex statement, such as an +IF statement, with each branch doing a return, type checking may fail. +Functions should use a return at the end. +.PP +The expr library does not support string values of (char*)0. +This means we can't distinguish between "" and (char*)0 edge keys. +For the purposes of looking up and creating edges, we translate "" +to be (char*)0, since this latter value is +necessary in order to look up any edge with a matching head and tail. +.PP +Related to this, strings converted to integers act like char pointers, +getting the value 0 or 1 depending on whether the string consists +solely of zeroes or not. Thus, the ((int)"2") evaluates to 1. +.PP +The language inherits the usual C problems such as dangling references +and the confusion between '=' and '=='. +.SH AUTHOR +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +.PP +awk(1), gc(1), dot(1), nop(1), libexpr(3), libcgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/gxl2gv.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,92 @@ +.TH GXL2GV,GV2GXL 1 "20 December 2002" +.SH NAME +gxl2gv,gv2gxl \- GXL-GV converters +.SH SYNOPSIS +.B gxl2gv +[ +.B \-gd? +] +[ +.BI -o outfile +] +[ +.I files +] +.br +.B gv2gxl +[ +.B \-gd? +] +[ +.BI -o outfile +] +[ +.I files +] +.SH DESCRIPTION +.B gxl2gv +converts between graphs represented in GXL and in the +GV language. Unless a conversion type is specified using +a flag, +.B gxl2gv +will deduce the type of conversion from the suffix of +the input file, a ".gv" (or a ".dot") suffix causing a conversion from GV +to GXL, and a ".gxl" suffix causing a conversion from GXL to GV. +If no suffix is available, e.g. when the input is from a pipe, +and no flags are used then +.B gxl2gv +assumes the type of the input file from its executable name +so that +.B gxl2gv +converts from GXL to GV, and +.B gv2gxl +converts from GV to GXL. +.PP +GXL supports a much richer graph model than GV. \fBgxl2gv\fP +will attempt to map GXL constructs into the analogous GV construct +when this is possible. If not, the GXL information is stored as +an attribute. The intention is that applying \fBgxl2gv|gv2gxl\fP +is semantically equivalent to the identity operator. +.SH OPTIONS +The following options are supported: +.TP +.B \-g +The command name and input file extensions are ignored, the +input is taken as a GV file and a GXL file is generated. +.TP +.B \-d +The command name and input file extensions are ignored, the +input is taken as a GXL file and a GV file is generated. +.TP +.B \-? +Prints usage information and exits. +.TP +.BI \-o " outfile" +If specified, the output will be written into the file +\fIoutfile\fP. Otherwise, output is written to standard out. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in GXL or GV. +If no +.I files +operand is specified, +the standard input will be used. +.SH RETURN CODES +Both \fBgxl2gv\fP and \fBgv2gxl\fP return \fB0\fP +if there were no problems during conversion; +and non-zero if any error occurred. +.SH "BUGS" +\fBgxl2gv\fP will only convert in one direction even if given multiple files +with varying suffixes. +.PP +The conversion can only handle one graph per GXL file. +.PP +There are some GXL constructs which \fBgxl2gv\fP cannot handle. +.SH AUTHORS +Krishnam Pericherla <kp@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +dot(1), libgraph(3), libagraph(3), neato(1), twopi(1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/lefty.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,120 @@ +.TH LEFTY 1 +.SH NAME +lefty \- A Programmable Graphics Editor +.SH SYNOPSIS +.B lefty +[ +.I options +] [ +.I file +] +.SH DESCRIPTION +.B lefty +is a two-view graphics editor for technical pictures. This editor has +no hardwired knowledge about specific picture layouts or editing operations. +Each picture is described by a program that contains functions to draw the +picture and functions to perform editing operations that are appropriate for +the specific picture. Primitive user actions, like mouse and keyboard events, +are also bound to functions in this program. Besides the graphical view of the +picture itself, the editor presents a textual view of the program that +describes the picture. Programmability and the two-view interface allow the +editor to handle a variety of pictures, but are particularly useful for +pictures used in technical contexts, e.g., graphs and trees. Also, +.B lefty +can communicate with other processes. This feature allows it to use existing +tools to compute specific picture layouts and allows external processes to use +the editor as a front end to display their data structures graphically. +.SH USAGE +The file name is optional. It may be +.BR - , +for reading from standard input. +.B lefty +uses two environment variables, +.B LEFTYPATH +and +.BR LEFTYOPTIONS . +.B LEFTYPATH +is a colon separated list of directories. When +.B lefty +tries to open a file, it searches that path for the file. When +.B lefty +tries to start up another process, it searches +.B LEFTYPATH +first, then the standard +.B PATH +variable. +.B LEFTYOPTIONS +can be used to set specific options. Options specified on the command line +override options set through this variable. +.SH OPTIONS +.TP +.B -x +Instructs the editor to exit after processing +.BR file . +.TP +.B "-e <expression>" +.B <expression> +is parsed and executed. +.TP +.B -el <num> +Set error reporting level. The default value is +.BR 0 . +.B 0 +never prints any messages. +.B 1 +prints severe errors, such as trying to return from a non function. +.B 2 +is the most useful: it reports function calls that cannot be executed, either +because there is no function, or because of argument mismatches. +.B 3 +also warns about bad variable names. +.B 4,5 +warn about expressions that do not return a value. Only level +.B 1 +messages are real errors. The rest arise from legal +.B lefty +statements, but may be cased by some logic errors. +.TP +.B "-sd <num>" +Specifies how much of the stack to show, when an error message is to be +printed. The default value is +.BR 2 . +With +.BR 0 , +no part of the stack is shown. With +.BR 1 , +only the top stack frame is printed. With +.BR 2 , +the full stack is printed. +.TP +.B "-sb <num>" +Specifies how much of each function in the stack to show, when an error message +is to be printed. The default value is +.BR 2 . +With +.BR 0 , +no part of the function is shown. With +.BR 1 , +only the line around the error is printed. With +.BR 2 , +the full function body is printed. +.TP +.B "-df <string>" +Sets the default font. This font is used whenever a requested font cannot be +found. The string must be a legal X font. If string is +.BR "''" , +.B lefty +will draw small boxes instead of text. +.TP +.B "-ps <file>" +Specifies a default file name for postscript files. This name is used when no +name is specified in the +.B createwidget +call. The default file name is +.BR out.ps . +.TP +.B -V +Prints the version. +.SH SEE ALSO +.I lefty +user guide.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/lneato.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,49 @@ +.TH LNEATO 1 +.SH NAME +lneato \- A Customizable Graph Editor +.SH SYNOPSIS +.B lneato +[ +.B -V +] [ +.I file +] +.SH DESCRIPTION +.B lneato +is a graph editor for the X Window System. It may be run as a standalone +editor, or as a front end for applications that use graphs. It can control +multiple windows viewing different graphs. + +.B lneato +is written on top of +.B neato +and +.BR lefty . +.B lefty +is a general-purpose programmable editor for technical pictures. It has an +interpretive programming language similar to AWK and C. The user interface and +graph editing operations of +.B lneato +are written as +.B lefty +functions. +Programmer-defined graph operations may be loaded as well. Graph layouts are +made by +.BR neato , +which runs as a separate process that communicates with +.B lefty +through pipes. +.SH USAGE +The file name is optional. If present, the graph contained in that file is +displayed in the +.B lneato +window. +.SH OPTIONS +.TP +.B -V +Prints the version. +.SH SEE ALSO +neato(1), lefty(1), dotty(1), +.br +.I dotty +user guide.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/mm2gv.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,47 @@ +.TH MM2GV 1 "31 July 2008" +.SH NAME +mm2gv \- Matrix Market-DOT converters +.SH SYNOPSIS +.B mm2gv +[ +.B \-u? +] +[ +.BI -o outfile +] +[ +.I file +] +.br +.SH DESCRIPTION +.B mm2gv +converts a sparse matrix of the Matrix Market format to a graph in the GV (formerly DOT) format. +.SH OPTIONS +The following options are supported: +.TP +.B \-u +If specified, the graph is assumed to be undirected. By default the graph generated is directed. +.TP +.BI \-o "outfile" +Prints output to the file \fIoutfile\fP. If not given, \fBmm2gv\fP +uses stdout. +.TP +.SH OPERANDS +The following operand is supported: +.TP 8 +.I file +Name of the file in MatrixMarket format. +If no +.I file +operand is specified, +the standard input will be used. +.SH RETURN CODES +Return \fB0\fP +if there were no problems during conversion; +and non-zero if any error occurred. +.SH "LIMITATIONS" +Currently the matrix entry values are not used. +.SH AUTHORS +Yifan Hu <yifanhu@research.att.com> +.SH ADDITIONAL INFO +See http://math.nist.gov/MatrixMarket/ for description of the format and http://www.cise.ufl.edu/research/sparse/matrices/ for a large collection of sparse matrices in this format.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/neato.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,725 @@ +.TH DOT 1 "23 August 2004" \*(eX +.SH NAME +dot \- filter for drawing directed graphs +.br +neato \- filter for drawing undirected graphs +.br +twopi \- filter for radial layouts of graphs +.br +circo \- filter for circular layout of graphs +.br +fdp \- filter for drawing undirected graphs +.br +sfdp \- filter for drawing large undirected graphs +.SH SYNOPSIS +\fBdot\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBneato\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-n\fR[\fB1\fR|\fB2\fR]] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBtwopi\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBcirco\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBsfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.SH DESCRIPTION +.I dot +draws directed graphs. It works well on DAGs and other graphs +that can be drawn as hierarchies. +It reads attributed graph files and writes drawings. +By default, the output format +.I dot +is the input file with layout coordinates appended. +.PP +.I neato +draws undirected graphs using ``spring'' models (see Kamada and Kawai, +Information Processing Letters 31:1, April 1989). Input files must be +formatted in the +.I dot +attributed graph language. +By default, the output of +.I neato +is the input graph with layout coordinates appended. +.PP +.I twopi +draws graphs using a radial layout (see G. Wills, +Symposium on Graph Drawing GD'97, September, 1997). +Basically, one node is chosen as the center and put at the origin. +The remaining nodes are placed on a sequence of concentric circles +centered about the origin, each a fixed radial distance from the +previous circle. +All nodes distance 1 from the center are placed on the first circle; +all nodes distance 1 from a node on the first circle are placed on +the second circle; and so forth. +.PP +.I circo +draws graphs using a circular layout (see +Six and Tollis, GD '99 and ALENEX '99, and +Kaufmann and Wiese, GD '02.) +The tool identifies biconnected components and draws the nodes of +the component on a circle. The block\(hycutpoint tree +is then laid out using a recursive radial algorithm. Edge +crossings within a circle are minimized by placing as many edges on +the circle's perimeter as possible. +In particular, if the component is outerplanar, the component will +have a planar layout. +.PP +If a node belongs to multiple non\(hytrivial biconnected components, +the layout puts the node in one of them. By default, this is the first +non\(hytrivial component found in the search from the root component. +.PP +.I fdp +draws undirected graphs using a ``spring'' model. It relies on a +force\(hydirected approach in the spirit of Fruchterman and Reingold +(cf. Software\(hyPractice & Experience 21(11), 1991, pp. 1129\(hy1164). +.PP +.I sfdp +also draws undirected graphs using the ``spring'' model described +above, but it uses a multi-scale approach to produce layouts +of large graphs in a reasonably short time. +.SH OUTPUT FORMATS +Dot uses an extensible plugin mechanism for its output renderers, +so to see what output formats your installation of dot supports +you can use ``dot \-Txxx'' (where xxx is an unlikely format) +and check the warning message. +Also, The plugin mechanism supports multiple implementations +of the output formats. +To see what variants are available, use, for example: ``dot \-Tpng:'' +and to force a particular variant, use, for example: ``dot \-Tpng:gd'' +.P +Traditionally, dot supports the following: +\fB\-Tps\fP (PostScript), +\fB\-Tsvg\fP \fB\-Tsvgz\fP (Structured Vector Graphics), +\fB\-Tfig\fP (XFIG graphics), +\fB\-Tmif\fP (FrameMaker graphics), +\fB\-Thpgl\fP (HP pen plotters), and \fB\-Tpcl\fP (Laserjet printers), +\fB\-Tpng\fP \fB\-Tgif\fP (bitmap graphics), +\fB\-Tdia\fP (GTK+ based diagrams), +\fB\-Timap\fP (imagemap files for httpd servers for each node or edge +that has a non\(hynull "href" attribute.), +\fB\-Tcmapx\fP (client\(hyside imagemap for use in html and xhtml). +Additional less common or more special\(hypurpose output formats +can be found at http://www.graphviz.org/cvs/doc/info/output.html.) +.P +Alternative plugins providing support for a given output format +can be found from the error message resulting from appending a ':' to the format. e.g. \fB-Tpng:\fP +The first plugin listed is always the default. +.P +The \fB\-P\fP switch can be used to produce a graph of all output variants supported by plugins in the local installation of graphviz. +.SH GRAPH FILE LANGUAGE +Here is a synopsis of the graph file language, traditionally using the extension \fB.dot\fR, for graphs: +.PP +[\fBstrict\fR] (\fBgraph\fR|\fBdigraph\fR) \fIname\fP { \fIstatement\(hylist\fP }\fR +.br +Is the top level graph. If the graph is \fBstrict\fR then multiple edges are +not allowed between the same pairs of nodes. +If it is a directed graph, indicated by \fBdigraph\fR, +then the \fIedgeop\fR must be "\->". If it is an undirected \fBgraph\fR +then the \fIedgeop\fR must be "\-\-". +Statements may be: +.PP +\fIname\fB=\fIval\fB;\fR +.br +\fBnode [\fIname\fB=\fIval\fB];\fR +.br +\fBedge [\fIname\fB=\fIval\fB];\fR +.br +Set default graph, node, or edge attribute \fIname\fP to \fIval\fP. +Any subgraph, node, or edge appearing after this inherits the new +default attributes. +.PP +\fBn0 [\fIname0=val0,name1=val1,...\fB];\fR +Creates node \fBn0\fP (if it does not already exist) +and sets its attributes according to the optional list. +.PP +\fBn0 \fIedgeop\fR n1 \fIedgeop\fR \fI...\fB \fIedgeop\fR nn [\fIname0=val0,name1=val1,...\fB];\fR +.br +Creates edges between nodes \fBn0\fP, \fBn1\fP, ..., \fBnn\fP and sets +their attributes according to the optional list. +Creates nodes as necessary. +.PP +[\fBsubgraph \fIname\fB] { \fIstatement\(hylist \fB}\fR +.br +Creates a subgraph. Subgraphs may be used in place +of \fBn0\fP, ..., \fBnn\fP in the above statements to create edges. +[\fBsubgraph \fIname\fR] is optional; +if missing, the subgraph is assigned an internal name. +.PP +Comments may be /*C\(hylike*/ or //C++\(hylike. + +.PP +Attribute names and values are ordinary (C\(hystyle) strings. +The following sections describe attributes that control graph layout. + +.SH "GRAPH ATTRIBUTES" +.PP +\fBsize="\fIx,y\fP"\fR sets bounding box of drawing in inches. +.PP +\fBpage="\fIx,y\fP"\fR sets the PostScript pagination unit. +.PP +\fBratio=\fIf\fR sets the aspect ratio to \fIf\fP which may be +a floating point number, or one of the keywords \fBfill\fP, +\fBcompress\fP, or \fBauto\fP. +.PP +\fBlayout=\fIengine\fR indicates the preferred layout engine ("dot", "neato", fdp" etc) overriding the default from the basename of the command or the -K commandline option. +.PP +\fBmargin=\fIf\fR sets the page margin (included in the page size). +.PP +\fBnodesep=\fIf\fR sets the minimum separation between nodes. +.PP +\fBranksep=\fIf\fR sets the minimum separation between ranks. +.PP +\fBordering=out\fR constrains order of out\(hyedges in a subgraph +according to their file sequence. +.PP +\fBrankdir=LR|RL|BT\fR requests a left\(hyto\(hyright, right\(hyto\(hyleft, or bottom\(hyto\(hytop, drawing. +.PP +\fBpagedir=\fR[TBLR][TBLR] sets the major and minor order of pagination. +.PP +\fBrank=same\fR (or \fBmin\fP or \fBmax\fP) in a subgraph +constrains the rank assignment of its nodes. If a subgraph's +name has the prefix \fBcluster\fP, its nodes are drawn in +a distinct rectangle of the layout. Clusters may be nested. +.PP +\fBrotate=90\fR sets landscape mode. +(\fBorientation=land\fR is backward compatible but obsolete.) +.PP +\fBcenter=\fIn\fR a non\(hyzero value centers the drawing on the page. +.PP +\fBnslimit=\fIf\fR or \fBmclimit=\fIf\fR adjusts the bound on the +number of network simplex or mincross iterations by the given ratio. +For example, \fBmclimit=2.0\fP runs twice as long. +.PP +\fBlayers="\fIid:id:id:id\fR" is a sequence of layer identifiers for +overlay diagrams. The PostScript array variable \fIlayercolorseq\fR +sets the assignment of colors to layers. The least index is 1 and +each element must be a 3\(hyelement array to be interpreted as a color coordinate. +.PP +\fBcolor=\fIcolorvalue\fR sets foreground color (\fBbgcolor\fP for background). +.PP +\fBhref=\fI"url"\fR the default url for image map files; in PostScript files, +the base URL for all relative URLs, as recognized by Acrobat Distiller +3.0 and up. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBstylesheet=\fI"file.css"\fR includes a reference to a stylesheet +in \-Tsvg and \-Tsvgz outputs. Ignored by other formats. +.PP +\fBsplines\fR. If set to \fItrue\fR, edges are +drawn as splines. +If set to \fIpolyline\fR, edges are +drawn as polylines. +If set to \fIortho\fR, edges are +drawn as orthogonal polylines. +In all of these cases, the nodes may not overlap. +If \fBsplines=\fIfalse\fR or \fBsplines=\fIline\fR, edges are +drawn as line segments. +The default is \fItrue\fR for dot, and \fIfalse\fR for all other layouts. + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBstart=\fIval\fR. Requests random initial placement and seeds +the random number generator. If \fIval\fP is not an integer, +the process ID or current time is used as the seed. +.PP +\fBepsilon=\fIn\fR. Sets the cutoff for the solver. +The default is 0.1. + +.PP +\fB(twopi\(hyspecific attributes)\fR +.br +\fBroot=\fIctr\fR. This specifies the node to be used as the center of +the layout. If not specified, \fItwopi\fP will randomly pick one of the +nodes that are furthest from a leaf node, where a leaf node is a node +of degree 1. If no leaf nodes exists, an arbitrary node is picked as center. +.PP +\fBranksep=\fIval\fR. Specifies the radial distance in inches between +the sequence of rings. The default is 0.75. +.PP +\fBoverlap=\fImode\fR. This specifies what \fItwopi\fP should do if +any nodes overlap. If mode is \fI"false"\fP, the program uses Voronoi +diagrams to adjust the nodes to eliminate overlaps. If mode is \fI"scale"\fP, +the layout is uniformly scaled up, preserving node sizes, until nodes no +longer overlap. The latter technique removes overlaps while preserving +symmetry and structure, while the former removes overlaps more compactly +but destroys symmetries. +If mode is \fI"true"\fP (the default), no repositioning is done. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fInodename\fR. Specifies the name of a node occurring in the +root block. If the graph is disconnected, the \fBroot\fP node attribute +can be used to specify additional root blocks. +.PP +\fBmindist=\fIvalue\fR. Sets the minimum separation between all nodes. If not +specified then \fIcirco\fP uses a default value of 1.0. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBK=\fIval\fR. Sets the default ideal node separation +in the layout. +.PP +\fBmaxiter=\fIval\fR. Sets the maximum number of iterations used to +layout the graph. +.PP +\fBstart=\fIval\fR. Adjusts the random initial placement of nodes +with no specified position. If \fIval\fP is is an integer, +it is used as the seed for the random number generator. +If \fIval\fP is not an integer, a random system\(hygenerated integer, +such as the process ID or current time, is used as the seed. +.PP + +.SH "NODE ATTRIBUTES" +.PP +\fBheight=\fId\fR or \fBwidth=\fId\fR sets minimum height or width. +Adding \fBfixedsize=true\fP forces these to be the actual size +(text labels are ignored). +.PP +\fBshape=record polygon epsf \fIbuiltin_polygon\fR +.br +\fIbuiltin_polygon\fR is one of: \fBplaintext ellipse oval circle egg +triangle box diamond trapezium parallelogram house hexagon octagon +note tab box3d component.\fR +(Polygons are defined or modified by the following node attributes: +\fBregular\fR, \fBperipheries\fR, \fBsides\fR, \fBorientation\fR, +\fBdistortion\fR and \fBskew\fR.) \fBepsf\fR uses the node's +\fBshapefile\fR attribute as the path name of an external +EPSF file to be automatically loaded for the node shape. +.PP +\fBlabel=\fItext\fR where \fItext\fP may include escaped newlines +\\\|n, \\\|l, or \\\|r for center, left, and right justified lines. +The string '\\N' value will be replaced by the node name. +The string '\\G' value will be replaced by the graph name. +Record labels may contain recursive box lists delimited by { | }. +Port identifiers in labels are set off by angle brackets < >. +In the graph file, use colon (such as, \fBnode0:port28\fR). +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBcolor=\fIcolorvalue\fR sets the outline color, and the default fill color +if style=filled and \fBfillcolor\fR is not specified. +.PP +\fBfillcolor=\fIcolorvalue\fR sets the fill color +when style=filled. If not specified, the fillcolor when style=filled defaults +to be the same as the outline color. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +A \fIcolorvalue\fP may be "\fIh,s,v\fB"\fR (hue, saturation, brightness) +floating point numbers between 0 and 1, or an X11 color name such as +\fBwhite black red green blue yellow magenta cyan\fR or \fBburlywood\fR, +or a "\fI#rrggbb" (red, green, blue, 2 hex characters each) value. +.PP +\fBstyle=filled solid dashed dotted bold invis\fP or any Postscript code. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the node's active layers. +The empty string means no layers (invisible). +.PP +The following attributes apply only to polygon shape nodes: +.PP +\fBregular=\fIn\fR if \fIn\fR is non\(hyzero then the polygon is made +regular, i.e. symmetric about the x and y axis, otherwise the +polygon takes on the aspect ratio of the label. +\fIbuiltin_polygons\fR that are not already regular are made regular +by this attribute. +\fIbuiltin_polygons\fR that are already regular are not affected (i.e. +they cannot be made asymmetric). +.PP +\fBperipheries=\fIn\fR sets the number of periphery lines drawn around +the polygon. This value supersedes the number of periphery lines +of \fIbuiltin_polygons\fR. +.PP +\fBsides=\fIn\fR sets the number of sides to the polygon. \fIn\fR<3 +results in an ellipse. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBorientation=\fIf\fR sets the orientation of the first apex of the +polygon counterclockwise from the vertical, in degrees. +\fIf\fR may be a floating point number. +The orientation of labels is not affected by this attribute. +This attribute is added to the initial orientation of \fIbuiltin_polygons.\fR +.PP +\fBdistortion=\fIf\fR sets the amount of broadening of the top and +narrowing of the bottom of the polygon (relative to its orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBskew=\fIf\fR sets the amount of right\(hydisplacement of the top and +left\(hydisplacement of the bottom of the polygon (relative to its +orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. +The target string is used to determine which window of the browser is used +for the URL. Setting it to "_graphviz" will open a new window if it doesn't +already exist, or reuse it if it does. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. The tooltip string defaults to be the +same as the label string, but this attribute permits nodes without +labels to still have tooltips thus permitting denser graphs. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fItrue/false\fR. This specifies that the block containing the given +node be treated as the root of the spanning tree in the layout. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBpin=\fIval\fR. If \fIval\fR is "true", the node will remain at +its initial position. + +.SH "EDGE ATTRIBUTES" +\fBminlen=\fIn\fR where \fIn\fP is an integer factor that applies +to the edge length (ranks for normal edges, or minimum node separation +for flat edges). +.PP +\fBweight=\fIn\fR where \fIn\fP is the integer cost of the edge. +Values greater than 1 tend to shorten the edge. Weight 0 flat +edges are ignored for ordering nodes. +.PP +\fBlabel=\fItext\fR where \fItext\fR may include escaped newlines +\\\|n, \\\|l, or \\\|r for centered, left, or right justified lines. +If the substring '\\T' is found in a label it will be replaced by the tail_node name. +If the substring '\\H' is found in a label it will be replaced by the head_node name. +If the substring '\\E' value is found in a label it will be replaced by: tail_node_name\->head_node_name +If the substring '\\G' is found in a label it will be replaced by the graph name. +or by: tail_node_name\-\-head_node_name for undirected graphs. +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +\fBstyle=solid dashed dotted bold invis\fP +.PP +\fBcolor=\fIcolorvalue\fR sets the line color for edges. +.PP +\fBcolor=\fIcolorvaluelist\fR a ':' separated list of \fIcolorvalue\fR creates +parallel edges, one edge for each color. +.PP +\fBdir=forward back both none\fP controls arrow direction. +.PP +\fBtailclip,headclip=false\fP disables endpoint shape clipping. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edges have a URL. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when edges have a URL. The tooltip string defaults to be the +same as the edge label string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBarrowhead,arrowtail=none, normal, inv, dot, odot, invdot, invodot, +tee, empty, invempty, open, halfopen, diamond, odiamond, box, obox, crow\fP. +.PP +\fBarrowsize\fP (norm_length=10,norm_width=5, +inv_length=6,inv_width=7,dot_radius=2) +.PP +\fBheadlabel,taillabel=string\fP for port labels. +\fBlabelfontcolor\fP,\fBlabelfontname\fP,\fBlabelfontsize\fP +for head and tail labels. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadhref=\fI"url"\fR sets the url for the head port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadURL=\fI"url"\fR ("headURL" is a synonym for "headhref".) +.PP +\fBheadtarget=\fI"headtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge heads have a URL. +The headtarget string is used to determine which window of the browser is used +for the URL. If the headtarget string is empty, the default, +then headtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when head ports have a URL. The tooltip string defaults to be the +same as the headlabel string. +The substrings '\\T', '\\H', and '\\E' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailhref=\fI"url"\fR sets the url for the tail port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailURL=\fI"url"\fR ("tailURL" is a synonym for "tailhref".) +.PP +\fBtailtarget=\fI"tailtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge tails have a URL. +The tailtarget string is used to determine which window of the browser is used +for the URL. If the tailtarget string is empty, the default, +then tailtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when tail ports have a URL. The tooltip string defaults to be the +same as the taillabel string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBlabeldistance\fP and \fPport_label_distance\fP set distance; also +\fBlabelangle\fP (in degrees CCW) +.PP +\fBdecorate\fP draws line from edge to label. +.PP +\fBsamehead,sametail\fP aim edges having the same value to the +same port, using the average landing point. +.PP +\fBconstraint=false\fP causes an edge to be ignored for rank assignment. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the edge's active layers. +The empty string means no layers (invisible). + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBw=\fIf\fR sets the weight (spring constant) of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.PP +\fBlen=\fIf\fR sets the optimal length of an edge. +The default is 1.0. +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBweight=\fIf\fR sets the weight of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.SH "COMMAND LINE OPTIONS" +\fB\-G\fP sets a default graph attribute. +.br +\fB\-N\fP sets a default node attribute. +.br +\fB\-E\fP sets a default edge attribute. +Example: \fB\-Gsize="7,8" \-Nshape=box \-Efontsize=8\fR +.PP +\fB\-l\fIfile\fR loads custom PostScript library files. +Usually these define custom shapes or styles. +If \fB\-l\fP is given by itself, the standard library is omitted. +.PP +\fB\-T\fIlang\fR sets the output language as described above. + +.PP +\fB\-n\fR[\fB1\fR|\fB2\fR] (no\(hyop) +If set, neato assumes nodes have already been positioned and all +nodes have a pos attribute giving the positions. +It then performs an optional adjustment to remove node\(hynode overlap, +depending on the value of the overlap attribute, +computes the edge layouts, depending on the value of the \fBsplines\fR attribute, +and emits the graph in the appropriate format. +If num is supplied, the following actions occur: +.nf + num = 1 +.fi +Equivalent to \-n. +.nf + num > 1 +.fi +Use node positions as specified, +with no adjustment to remove node\(hynode overlaps, +and use any edge layouts already specified by the pos attribute. +neato computes an edge layout for any edge that does not have a pos attribute. +As usual, edge layout is guided by the \fBsplines\fR attribute. +.PP +\fB\-K\fIlayout\fR override the default layout engine implied by the command name. +.PP +\fB\-O\fP automatically generate output filenames based on the input filename and the -T format. +.PP +\fB\-P\fP generate a graph of the currently available plugins. +.PP +\fB\-v\fP (verbose) prints various information useful for debugging. +.PP +\fB\-V\fP (version) prints version information and exits. +.PP +\fB\-?\fP prints the usage and exits. +.SH "EXAMPLES" +.nf +digraph test123 { + a \-> b \-> c; + a \-> {x y}; + b [shape=box]; + c [label="hello\\\|nworld",color=blue,fontsize=24, + fontname="Palatino\-Italic",fontcolor=red,style=filled]; + a \-> z [label="hi", weight=100]; + x \-> z [label="multi\-line\\\|nlabel"]; + edge [style=dashed,color=red]; + b \-> x; + {rank=same; b x} +} +.fi +.PP +.nf +graph test123 { + a \-\- b \-\- c; + a \-\- {x y}; + x \-\- c [w=10.0]; + x \-\- y [w=5.0,len=3]; +} +.fi +.SH "CAVEATS" +Edge splines can overlap unintentionally. +.PP +Flat edge labels are slightly broken. +Intercluster edge labels are totally broken. +.PP +Because unconstrained optimization is employed, node boxes can +possibly overlap or touch unrelated edges. All existing +spring embedders seem to have this limitation. +.PP +Apparently reasonable attempts to pin nodes or adjust edge lengths +and weights can cause instability. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.br +John C. Ellson <ellson@research.att.com> +.PP +The bitmap driver (PNG, GIF etc) is by Thomas Boutell, <http://www.boutell.com/gd> +.PP +The Truetype font renderer is from +the Freetype Project (David Turner, Robert Wilhelm, and Werner Lemberg) +(who can be contacted at freetype\-devel@lists.lrz\-muenchen.de). +.SH "SEE ALSO" +This man page contains only a small amount of the information related +to the Graphviz layout programs. The most complete information can be +found at http://www.graphviz.org/Documentation.php, especially in the +on\(hyline reference pages. Most of these documents are also available in the +\fIdoc\fP and \fIdoc/info\fP subtrees in the source and binary distributions. +.PP +dotty(1) +.br +tcldot(n) +.br +xcolors(1) +.br +libgraph(3) +.PP +E. R. Gansner, S. C. North, K. P. Vo, "DAG \(hy A Program to Draw Directed Graphs", Software \(hy Practice and Experience 17(1), 1988, pp. 1047\(hy1062. +.br +E. R. Gansner, E. Koutsofios, S. C. North, K. P. Vo, "A Technique for Drawing Directed Graphs," IEEE Trans. on Soft. Eng. 19(3), 1993, pp. 214\(hy230. +.br +S. North and E. Koutsofios, "Applications of graph visualization", +Graphics Interface 94, pp. 234\(hy245. +.br +E. Koutsofios and S. C. North, "Drawing Graphs with dot," +Available on research.att.com in dist/drawdag/dotguide.ps.Z. +.br +S. C. North, "NEATO User's Manual". +Available on research.att.com in dist/drawdag/neatodoc.ps.Z.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/nop.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,31 @@ +.TH NOP 1 "21 March 2001" +.SH NAME +nop \- pretty-print graph file +.SH SYNOPSIS +.B nop +[ +.B \-p? +] +[ +.I files +] +.SH DESCRIPTION +.B nop +reads a stream of graphs and prints each in pretty-printed (canonical) format +on stdout. If no +.I files +are given, it reads from stdin. +.SH OPTIONS +The following options are supported: +.TP +.B \-p +Produce no output - just check the input for valid DOT. +.TP +.B \-? +Print usage information. +.SH "EXIT STATUS" +If any errors occurred while processing any input, such as a file +not found or a file containing illegal DOT, a non-zero exit value +is returned. Otherwise, zero is returned. +.SH "SEE ALSO" +wc(1), acyclic(1), gvpr(1), gvcolor(1), ccomps(1), sccmap(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/osage.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,443 @@ +.TH OSAGE 1 "27 May 2009" \*(eX +.SH NAME +osage \- filter for drawing clustered graphs +.SH SYNOPSIS +\fBosage\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.SH DESCRIPTION +.I osage +draws clustered graphs. As input, it takes any graph in the +.I dot +format. It draws the graph recursively. At each level, there will +be a collection of nodes and a collection of cluster subgraphs. +The internals of each cluster subgraph are laid out, then the +cluster subgraphs and nodes at the current level are positioned +relative to each other, treating each cluster subgraph as a node. +.P +At each level, the nodes and cluster subgraphs are viewed as rectangles +to be packed together. At present, edges are ignored during packing. +Packing is done using the standard packing functions. In particular, +the graph attributes \fBpack\fP and \fBpackmode\fP control the layout. +Each graph and cluster can specify its own values for these attributes. +Remember also that a cluster inherits its attribute values from +its parent graph. +.P +After all nodes and clusters, edges are routed based on the value of +the \fBsplines\fP attribute. +.SH OUTPUT FORMATS +Osage uses an extensible plugin mechanism for its output renderers, +so to see what output formats your installation of osage supports +you can use ``osage \-Txxx'' (where xxx is an unlikely format) +and check the warning message. +Also, The plugin mechanism supports multiple implementations +of the output formats. +To see what variants are available, use, for example: ``osage \-Tpng:'' +and to force a particular variant, use, for example: ``osage \-Tpng:gd'' +.P +Traditionally, osage supports the following: +\fB\-Tps\fP (PostScript), +\fB\-Tsvg\fP \fB\-Tsvgz\fP (Structured Vector Graphics), +\fB\-Tfig\fP (XFIG graphics), +\fB\-Tmif\fP (FrameMaker graphics), +\fB\-Thpgl\fP (HP pen plotters), and \fB\-Tpcl\fP (Laserjet printers), +\fB\-Tpng\fP \fB\-Tgif\fP (bitmap graphics), +\fB\-Tdia\fP (GTK+ based diagrams), +\fB\-Timap\fP (imagemap files for httpd servers for each node or edge +that has a non\(hynull "href" attribute.), +\fB\-Tcmapx\fP (client\(hyside imagemap for use in html and xhtml). +Additional less common or more special\(hypurpose output formats +can be found at http://www.graphviz.org/cvs/doc/info/output.html.) +.P +Alternative plugins providing support for a given output format +can be found from the error message resulting from appending a ':' to the format. e.g. \fB-Tpng:\fP +The first plugin listed is always the default. +.SH GRAPH FILE LANGUAGE +Here is a synopsis of the graph file language, traditionally using the extension \fB.gv\fR or \fB.dot\fR, for graphs: +.PP +[\fBstrict\fR] (\fBgraph\fR|\fBdigraph\fR) \fIname\fP { \fIstatement\(hylist\fP }\fR +.br +Is the top level graph. If the graph is \fBstrict\fR then multiple edges are +not allowed between the same pairs of nodes. +If it is a directed graph, indicated by \fBdigraph\fR, +then the \fIedgeop\fR must be "\->". If it is an undirected \fBgraph\fR +then the \fIedgeop\fR must be "\-\-". +Statements may be: +.PP +\fIname\fB=\fIval\fB;\fR +.br +\fBnode [\fIname\fB=\fIval\fB];\fR +.br +\fBedge [\fIname\fB=\fIval\fB];\fR +.br +Set default graph, node, or edge attribute \fIname\fP to \fIval\fP. +Any subgraph, node, or edge appearing after this inherits the new +default attributes. +.PP +\fBn0 [\fIname0=val0,name1=val1,...\fB];\fR +Creates node \fBn0\fP (if it does not already exist) +and sets its attributes according to the optional list. +.PP +\fBn0 \fIedgeop\fR n1 \fIedgeop\fR \fI...\fB \fIedgeop\fR nn [\fIname0=val0,name1=val1,...\fB];\fR +.br +Creates edges between nodes \fBn0\fP, \fBn1\fP, ..., \fBnn\fP and sets +their attributes according to the optional list. +Creates nodes as necessary. +.PP +[\fBsubgraph \fIname\fB] { \fIstatement\(hylist \fB}\fR +.br +Creates a subgraph. Subgraphs may be used in place +of \fBn0\fP, ..., \fBnn\fP in the above statements to create edges. +[\fBsubgraph \fIname\fR] is optional; +if missing, the subgraph is assigned an internal name. +.PP +Comments may be /*C\(hylike*/ or //C++\(hylike. + +.PP +Attribute names and values are ordinary (C\(hystyle) strings. +The following sections describe attributes that control graph layout. + +.SH "GRAPH ATTRIBUTES" +.PP +\fBsize="\fIx,y\fP"\fR sets bounding box of drawing in inches. +.PP +\fBpage="\fIx,y\fP"\fR sets the PostScript pagination unit. +.PP +\fBratio=\fIf\fR sets the aspect ratio to \fIf\fP which may be +a floating point number, or one of the keywords \fBfill\fP, +\fBcompress\fP, or \fBauto\fP. +.PP +\fBpagedir=\fR[TBLR][TBLR] sets the major and minor order of pagination. +.PP +\fBrotate=90\fR sets landscape mode. +(\fBorientation=land\fR is backward compatible but obsolete.) +.PP +\fBcenter=\fIn\fR a non\(hyzero value centers the drawing on the page. +.PP +\fBlayers="\fIid:id:id:id\fR" is a sequence of layer identifiers for +overlay diagrams. The PostScript array variable \fIlayercolorseq\fR +sets the assignment of colors to layers. The least index is 1 and +each element must be a 3\(hyelement array to be interpreted as a color coordinate. +.PP +\fBcolor=\fIcolorvalue\fR sets foreground color (\fBbgcolor\fP for background). +.PP +\fBhref=\fI"url"\fR the default url for image map files; in PostScript files, +the base URL for all relative URLs, as recognized by Acrobat Distiller +3.0 and up. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBstylesheet=\fI"file.css"\fR includes a reference to a stylesheet +in \-Tsvg and \-Tsvgz outputs. Ignored by other formats. +.PP +\fBsplines\fR. If set to \fItrue\fR, edges are +drawn as splines. +If set to \fIpolyline\fR, edges are +drawn as polylines. +If set to \fIortho\fR, edges are +drawn as orthogonal polylines. +In all of these cases, the nodes may not overlap. +If \fBsplines=\fIfalse\fR or \fBsplines=\fIline\fR, edges are +drawn as line segments. +The default is \fIfalse\fR. + +.SH "NODE ATTRIBUTES" +.PP +\fBheight=\fId\fR or \fBwidth=\fId\fR sets minimum height or width. +Adding \fBfixedsize=true\fP forces these to be the actual size +(text labels are ignored). +.PP +\fBshape=record polygon epsf \fIbuiltin_polygon\fR +.br +\fIbuiltin_polygon\fR is one of: \fBplaintext ellipse oval circle egg +triangle box diamond trapezium parallelogram house hexagon octagon +note tab box3d component.\fR +(Polygons are defined or modified by the following node attributes: +\fBregular\fR, \fBperipheries\fR, \fBsides\fR, \fBorientation\fR, +\fBdistortion\fR and \fBskew\fR.) \fBepsf\fR uses the node's +\fBshapefile\fR attribute as the path name of an external +EPSF file to be automatically loaded for the node shape. +.PP +\fBlabel=\fItext\fR where \fItext\fP may include escaped newlines +\\\|n, \\\|l, or \\\|r for center, left, and right justified lines. +The string '\\N' value will be replaced by the node name. +The string '\\G' value will be replaced by the graph name. +Record labels may contain recursive box lists delimited by { | }. +Port identifiers in labels are set off by angle brackets < >. +In the graph file, use colon (such as, \fBnode0:port28\fR). +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBcolor=\fIcolorvalue\fR sets the outline color, and the default fill color +if style=filled and \fBfillcolor\fR is not specified. +.PP +\fBfillcolor=\fIcolorvalue\fR sets the fill color +when style=filled. If not specified, the fillcolor when style=filled defaults +to be the same as the outline color. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +A \fIcolorvalue\fP may be "\fIh,s,v\fB"\fR (hue, saturation, brightness) +floating point numbers between 0 and 1, or an X11 color name such as +\fBwhite black red green blue yellow magenta cyan\fR or \fBburlywood\fR, +or a "\fI#rrggbb" (red, green, blue, 2 hex characters each) value. +.PP +\fBstyle=filled solid dashed dotted bold invis\fP or any Postscript code. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the node's active layers. +The empty string means no layers (invisible). +.PP +The following attributes apply only to polygon shape nodes: +.PP +\fBregular=\fIn\fR if \fIn\fR is non\(hyzero then the polygon is made +regular, i.e. symmetric about the x and y axis, otherwise the +polygon takes on the aspect ratio of the label. +\fIbuiltin_polygons\fR that are not already regular are made regular +by this attribute. +\fIbuiltin_polygons\fR that are already regular are not affected (i.e. +they cannot be made asymmetric). +.PP +\fBperipheries=\fIn\fR sets the number of periphery lines drawn around +the polygon. This value supersedes the number of periphery lines +of \fIbuiltin_polygons\fR. +.PP +\fBsides=\fIn\fR sets the number of sides to the polygon. \fIn\fR<3 +results in an ellipse. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBorientation=\fIf\fR sets the orientation of the first apex of the +polygon counterclockwise from the vertical, in degrees. +\fIf\fR may be a floating point number. +The orientation of labels is not affected by this attribute. +This attribute is added to the initial orientation of \fIbuiltin_polygons.\fR +.PP +\fBdistortion=\fIf\fR sets the amount of broadening of the top and +narrowing of the bottom of the polygon (relative to its orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBskew=\fIf\fR sets the amount of right\(hydisplacement of the top and +left\(hydisplacement of the bottom of the polygon (relative to its +orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. +The target string is used to determine which window of the browser is used +for the URL. Setting it to "_graphviz" will open a new window if it doesn't +already exist, or reuse it if it does. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. The tooltip string defaults to be the +same as the label string, but this attribute permits nodes without +labels to still have tooltips thus permitting denser graphs. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. + + +.SH "EDGE ATTRIBUTES" +.PP +\fBlabel=\fItext\fR where \fItext\fR may include escaped newlines +\\\|n, \\\|l, or \\\|r for centered, left, or right justified lines. +If the substring '\\T' is found in a label it will be replaced by the tail_node name. +If the substring '\\H' is found in a label it will be replaced by the head_node name. +If the substring '\\E' value is found in a label it will be replaced by: tail_node_name\->head_node_name +If the substring '\\G' is found in a label it will be replaced by the graph name. +or by: tail_node_name\-\-head_node_name for undirected graphs. +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +\fBstyle=solid dashed dotted bold invis\fP +.PP +\fBcolor=\fIcolorvalue\fR sets the line color for edges. +.PP +\fBcolor=\fIcolorvaluelist\fR a ':' separated list of \fIcolorvalue\fR creates +parallel edges, one edge for each color. +.PP +\fBdir=forward back both none\fP controls arrow direction. +.PP +\fBtailclip,headclip=false\fP disables endpoint shape clipping. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edges have a URL. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when edges have a URL. The tooltip string defaults to be the +same as the edge label string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBarrowhead,arrowtail=none, normal, inv, dot, odot, invdot, invodot, +tee, empty, invempty, open, halfopen, diamond, odiamond, box, obox, crow\fP. +.PP +\fBarrowsize\fP (norm_length=10,norm_width=5, +inv_length=6,inv_width=7,dot_radius=2) +.PP +\fBheadlabel,taillabel=string\fP for port labels. +\fBlabelfontcolor\fP,\fBlabelfontname\fP,\fBlabelfontsize\fP +for head and tail labels. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadhref=\fI"url"\fR sets the url for the head port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadURL=\fI"url"\fR ("headURL" is a synonym for "headhref".) +.PP +\fBheadtarget=\fI"headtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge heads have a URL. +The headtarget string is used to determine which window of the browser is used +for the URL. If the headtarget string is empty, the default, +then headtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when head ports have a URL. The tooltip string defaults to be the +same as the headlabel string. +The substrings '\\T', '\\H', and '\\E' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailhref=\fI"url"\fR sets the url for the tail port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailURL=\fI"url"\fR ("tailURL" is a synonym for "tailhref".) +.PP +\fBtailtarget=\fI"tailtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge tails have a URL. +The tailtarget string is used to determine which window of the browser is used +for the URL. If the tailtarget string is empty, the default, +then tailtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when tail ports have a URL. The tooltip string defaults to be the +same as the taillabel string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBlabeldistance\fP and \fPport_label_distance\fP set distance; also +\fBlabelangle\fP (in degrees CCW) +.PP +\fBdecorate\fP draws line from edge to label. +.PP +\fBsamehead,sametail\fP aim edges having the same value to the +same port, using the average landing point. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the edge's active layers. +The empty string means no layers (invisible). + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBw=\fIf\fR sets the weight (spring constant) of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.PP +\fBlen=\fIf\fR sets the optimal length of an edge. +The default is 1.0. +.SH "COMMAND LINE OPTIONS" +\fB\-G\fP sets a default graph attribute. +.br +\fB\-N\fP sets a default node attribute. +.br +\fB\-E\fP sets a default edge attribute. +Example: \fB\-Gsize="7,8" \-Nshape=box \-Efontsize=8\fR +.PP +\fB\-l\fIfile\fR loads custom PostScript library files. +Usually these define custom shapes or styles. +If \fB\-l\fP is given by itself, the standard library is omitted. +.PP +\fB\-T\fIlang\fR sets the output language as described above. +.PP +\fB\-O\fP automatically generate output filenames based on the input filename and the -T format. +.PP +\fB\-v\fP (verbose) prints various information useful for debugging. +.PP +\fB\-V\fP (version) prints version information and exits. +.PP +\fB\-?\fP prints the usage and exits. +.SH "EXAMPLES" +.nf +digraph test123 { + pack=8 + subgraph cluster0 { + packmode=array + x y x0 y0 x1 + subgraph cluster1 { + m n + } + } + b [shape=box]; + c [label="hello\\\nworld",color=blue,fontsize=24, + fontname="Palatino\-Italic",fontcolor=red,style=filled]; + a \-> z + x \-> z + a \-> b \-> c; + a \-> {x y}; + edge [style=dashed,color=red]; + b \-> x; +} +.fi +.SH AUTHORS +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +This man page contains only a small amount of the information related +to the Graphviz layout programs. The most complete information can be +found at http://www.graphviz.org/Documentation.php, especially in the +on\(hyline reference pages. Most of these documents are also available in the +\fIdoc\fP and \fIdoc/info\fP subtrees in the source and binary distributions. +.PP +dot(1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/prune.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,155 @@ +.\" Copyright (c) 2002 Marcus Harnisch <marcus.harnisch@gmx.net> +.\" +.\" This is free documenation. It is provided to you without any +.\" warranty that it is useful or that you can understand it. +.\" +.\" You are granted the right to use and redistribute the source code +.\" or parts of it (even single words and letters), provided that the +.\" copyright notice and the license terms will not be removed. +.\" +.TH prune 1 + +.SH NAME +prune \- Prune directed graphs +.SH SYNOPSIS +.B prune +[ +.BI \-n " node" +] +[ +.BI \-N " attrspec" +] +[ +.I files ... +] +.SH DESCRIPTION +.B prune +reads directed graphs in the same format used by +.B dot(1) +and removes subgraphs rooted at nodes specified on the +command line via options. These nodes themselves will not be removed, +but can be given attributes so that they can be easily located by a +graph stream editor such as +.B gpr(1). +.B prune +correctly handles cycles, loops and multi\(hyedges. + +Both options can appear multiple times on the command line. All +subgraphs rooted at the respective nodes given will then be +processed. If a node does not exist, +.B prune +will skip it and print a warning message to stderr. +If multiple attributes are given, they will be applied to +all nodes that have been processed. +.B prune +writes the result to the stdout. +.SH OPTIONS +.TP +.BI \-n " name" +Specifies name of node to prune. +.TP +.BI \-N " attrspec" +Specifies attribute that will be set (or changed if it exists) for any +pruned node. +.I attrspec +is a string of the form +.IR attr "=" value. + +.SH EXAMPLES +An input graph +.I test.dot +of the form +.PP + digraph DG { +.br + A \-> B; +.br + A \-> C; +.br + +.br + B \-> D; +.br + B \-> E; +.br + } +.br + +, processed by the command +.PP + prune \-n B test.dot +.PP +would produce the following output (the actual code might be formatted +in a slightly different way). +.PP + digraph DG { +.br + A \-> B; +.br + A \-> C; +.br + } +.br + +Another input graph +.I test.dot +of the form +.PP + digraph DG { +.br + A \-> B; +.br + A \-> C; +.br + +.br + B \-> D; +.br + B \-> E; +.br + +.br + C \-> E; +.br + } +.br + +(note the additional edge from +.I C +to +.I E +), processed by the command +.PP + prune \-n B \-N color=red test.dot +.PP +results in +.PP + digraph DG { +.br + B [color=red]; +.br + A \-> B; +.br + A \-> C; +.br + C \-> E; +.br + } +.br + +Node +.I E +has not been removed since its second parent +.I C +is not being pruned. + +.SH "EXIT STATUS" +.B prune +returns 0 on successful completion. +It returns 1 if an error occurs. +.SH SEE ALSO +.BR dot (1), +.BR gpr (1) + +.SH AUTHOR +Marcus Harnisch <marcus.harnisch@gmx.net>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/sccmap.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +.TH SCCMAP 1 "21 March 2001" \*(eX +.SH NAME +sccmap \- extract strongly connected components of directed graphs +.SH SYNOPSIS +\fBsccmap\fR +[\fB\-dsv\fR] +[ +.I files +] +.SH DESCRIPTION +.I sccmap +decomposes digraphs into strongly connected components +and an auxiliary map of the relationship between components. +In this map, each component is collapsed into a node. +The resulting graphs are printed to standard out. +The number of nodes, edges and strongly connected components +are printed to standard error. +.B sccmap +is a way of partitioning large graphs into more manageable pieces. +.SH OPTIONS +The following options are supported: +.TP +.B \-d +Preserve degenerate components of only one node. +.TP +.B \-s +Do not print the resulting graphs. Only the statistics are +important. +.TP +.B \-S +Just print the resulting graphs. No statistics are printed. +.TP +.B \-v +Generate additional statistics. In particular, +.B sccmap +prints the number of nodes, edges, connected components, +and strongly connected components, followed by the fraction of +nodes in a non-trivial strongly connected components, +the maximum degree of the graph, and fraction of non-tree edges +in the graph. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in dot format. +If no +.I files +operand is specified, +the standard input will be used. +.SH "DIAGNOSTICS" +\fBsccmap\fP emits a warning if it encounters an undirected graph, +and ignores it. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +gc(1), dot(1), acyclic(1), gvpr(1), gvcolor(1), ccomps(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/tred.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,42 @@ +.TH TRED 1 "21 March 2001" +.SH NAME +tred \- transitive reduction filter for directed graphs +.SH SYNOPSIS +.B tred +[ +.I files +] +.SH DESCRIPTION +.B tred +computes the transitive reduction of directed graphs, +and prints the resulting graphs to standard output. +This removes edges implied by transitivity. +Nodes and subgraphs are not otherwise affected. +The ``meaning'' and validity of the reduced graphs +is application dependent. +.B tred +is particularly useful as a preprocessor to +.I dot +to reduce clutter in dense layouts. +.PP +Undirected graphs are silently ignored. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in dot format. +If no +.I files +operand is specified, +the standard input will be used. +.SH "BUGS" +Using bitmaps internally would substantially decrease running time. +.SH "DIAGNOSTICS" +If a graph has cycles, its transitive reduction is not uniquely defined. +In this case \fItred\fP emits a warning. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +gc(1), dot(1), acyclic(1), gvpr(1), gvcolor(1), ccomps(1), sccmap(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/twopi.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,725 @@ +.TH DOT 1 "23 August 2004" \*(eX +.SH NAME +dot \- filter for drawing directed graphs +.br +neato \- filter for drawing undirected graphs +.br +twopi \- filter for radial layouts of graphs +.br +circo \- filter for circular layout of graphs +.br +fdp \- filter for drawing undirected graphs +.br +sfdp \- filter for drawing large undirected graphs +.SH SYNOPSIS +\fBdot\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBneato\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-n\fR[\fB1\fR|\fB2\fR]] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBtwopi\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBcirco\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.br +\fBsfdp\fR +[\fB\-\fR(\fBG\fR|\fBN\fR|\fBE\fR)\fIname=value\fR] +[\fB\-T\fIlang\fR] +[\fB\-l\fIlibfile\fR] +[\fB\-o\fIoutfile\fR] +[\fB\-K\fIlayout\fR] +[\fB\-O\fR] +[\fB\-P\fR] +[\fB\-v\fR] +[\fB\-V\fR] +[files] +.SH DESCRIPTION +.I dot +draws directed graphs. It works well on DAGs and other graphs +that can be drawn as hierarchies. +It reads attributed graph files and writes drawings. +By default, the output format +.I dot +is the input file with layout coordinates appended. +.PP +.I neato +draws undirected graphs using ``spring'' models (see Kamada and Kawai, +Information Processing Letters 31:1, April 1989). Input files must be +formatted in the +.I dot +attributed graph language. +By default, the output of +.I neato +is the input graph with layout coordinates appended. +.PP +.I twopi +draws graphs using a radial layout (see G. Wills, +Symposium on Graph Drawing GD'97, September, 1997). +Basically, one node is chosen as the center and put at the origin. +The remaining nodes are placed on a sequence of concentric circles +centered about the origin, each a fixed radial distance from the +previous circle. +All nodes distance 1 from the center are placed on the first circle; +all nodes distance 1 from a node on the first circle are placed on +the second circle; and so forth. +.PP +.I circo +draws graphs using a circular layout (see +Six and Tollis, GD '99 and ALENEX '99, and +Kaufmann and Wiese, GD '02.) +The tool identifies biconnected components and draws the nodes of +the component on a circle. The block\(hycutpoint tree +is then laid out using a recursive radial algorithm. Edge +crossings within a circle are minimized by placing as many edges on +the circle's perimeter as possible. +In particular, if the component is outerplanar, the component will +have a planar layout. +.PP +If a node belongs to multiple non\(hytrivial biconnected components, +the layout puts the node in one of them. By default, this is the first +non\(hytrivial component found in the search from the root component. +.PP +.I fdp +draws undirected graphs using a ``spring'' model. It relies on a +force\(hydirected approach in the spirit of Fruchterman and Reingold +(cf. Software\(hyPractice & Experience 21(11), 1991, pp. 1129\(hy1164). +.PP +.I sfdp +also draws undirected graphs using the ``spring'' model described +above, but it uses a multi-scale approach to produce layouts +of large graphs in a reasonably short time. +.SH OUTPUT FORMATS +Dot uses an extensible plugin mechanism for its output renderers, +so to see what output formats your installation of dot supports +you can use ``dot \-Txxx'' (where xxx is an unlikely format) +and check the warning message. +Also, The plugin mechanism supports multiple implementations +of the output formats. +To see what variants are available, use, for example: ``dot \-Tpng:'' +and to force a particular variant, use, for example: ``dot \-Tpng:gd'' +.P +Traditionally, dot supports the following: +\fB\-Tps\fP (PostScript), +\fB\-Tsvg\fP \fB\-Tsvgz\fP (Structured Vector Graphics), +\fB\-Tfig\fP (XFIG graphics), +\fB\-Tmif\fP (FrameMaker graphics), +\fB\-Thpgl\fP (HP pen plotters), and \fB\-Tpcl\fP (Laserjet printers), +\fB\-Tpng\fP \fB\-Tgif\fP (bitmap graphics), +\fB\-Tdia\fP (GTK+ based diagrams), +\fB\-Timap\fP (imagemap files for httpd servers for each node or edge +that has a non\(hynull "href" attribute.), +\fB\-Tcmapx\fP (client\(hyside imagemap for use in html and xhtml). +Additional less common or more special\(hypurpose output formats +can be found at http://www.graphviz.org/cvs/doc/info/output.html.) +.P +Alternative plugins providing support for a given output format +can be found from the error message resulting from appending a ':' to the format. e.g. \fB-Tpng:\fP +The first plugin listed is always the default. +.P +The \fB\-P\fP switch can be used to produce a graph of all output variants supported by plugins in the local installation of graphviz. +.SH GRAPH FILE LANGUAGE +Here is a synopsis of the graph file language, traditionally using the extension \fB.dot\fR, for graphs: +.PP +[\fBstrict\fR] (\fBgraph\fR|\fBdigraph\fR) \fIname\fP { \fIstatement\(hylist\fP }\fR +.br +Is the top level graph. If the graph is \fBstrict\fR then multiple edges are +not allowed between the same pairs of nodes. +If it is a directed graph, indicated by \fBdigraph\fR, +then the \fIedgeop\fR must be "\->". If it is an undirected \fBgraph\fR +then the \fIedgeop\fR must be "\-\-". +Statements may be: +.PP +\fIname\fB=\fIval\fB;\fR +.br +\fBnode [\fIname\fB=\fIval\fB];\fR +.br +\fBedge [\fIname\fB=\fIval\fB];\fR +.br +Set default graph, node, or edge attribute \fIname\fP to \fIval\fP. +Any subgraph, node, or edge appearing after this inherits the new +default attributes. +.PP +\fBn0 [\fIname0=val0,name1=val1,...\fB];\fR +Creates node \fBn0\fP (if it does not already exist) +and sets its attributes according to the optional list. +.PP +\fBn0 \fIedgeop\fR n1 \fIedgeop\fR \fI...\fB \fIedgeop\fR nn [\fIname0=val0,name1=val1,...\fB];\fR +.br +Creates edges between nodes \fBn0\fP, \fBn1\fP, ..., \fBnn\fP and sets +their attributes according to the optional list. +Creates nodes as necessary. +.PP +[\fBsubgraph \fIname\fB] { \fIstatement\(hylist \fB}\fR +.br +Creates a subgraph. Subgraphs may be used in place +of \fBn0\fP, ..., \fBnn\fP in the above statements to create edges. +[\fBsubgraph \fIname\fR] is optional; +if missing, the subgraph is assigned an internal name. +.PP +Comments may be /*C\(hylike*/ or //C++\(hylike. + +.PP +Attribute names and values are ordinary (C\(hystyle) strings. +The following sections describe attributes that control graph layout. + +.SH "GRAPH ATTRIBUTES" +.PP +\fBsize="\fIx,y\fP"\fR sets bounding box of drawing in inches. +.PP +\fBpage="\fIx,y\fP"\fR sets the PostScript pagination unit. +.PP +\fBratio=\fIf\fR sets the aspect ratio to \fIf\fP which may be +a floating point number, or one of the keywords \fBfill\fP, +\fBcompress\fP, or \fBauto\fP. +.PP +\fBlayout=\fIengine\fR indicates the preferred layout engine ("dot", "neato", fdp" etc) overriding the default from the basename of the command or the -K commandline option. +.PP +\fBmargin=\fIf\fR sets the page margin (included in the page size). +.PP +\fBnodesep=\fIf\fR sets the minimum separation between nodes. +.PP +\fBranksep=\fIf\fR sets the minimum separation between ranks. +.PP +\fBordering=out\fR constrains order of out\(hyedges in a subgraph +according to their file sequence. +.PP +\fBrankdir=LR|RL|BT\fR requests a left\(hyto\(hyright, right\(hyto\(hyleft, or bottom\(hyto\(hytop, drawing. +.PP +\fBpagedir=\fR[TBLR][TBLR] sets the major and minor order of pagination. +.PP +\fBrank=same\fR (or \fBmin\fP or \fBmax\fP) in a subgraph +constrains the rank assignment of its nodes. If a subgraph's +name has the prefix \fBcluster\fP, its nodes are drawn in +a distinct rectangle of the layout. Clusters may be nested. +.PP +\fBrotate=90\fR sets landscape mode. +(\fBorientation=land\fR is backward compatible but obsolete.) +.PP +\fBcenter=\fIn\fR a non\(hyzero value centers the drawing on the page. +.PP +\fBnslimit=\fIf\fR or \fBmclimit=\fIf\fR adjusts the bound on the +number of network simplex or mincross iterations by the given ratio. +For example, \fBmclimit=2.0\fP runs twice as long. +.PP +\fBlayers="\fIid:id:id:id\fR" is a sequence of layer identifiers for +overlay diagrams. The PostScript array variable \fIlayercolorseq\fR +sets the assignment of colors to layers. The least index is 1 and +each element must be a 3\(hyelement array to be interpreted as a color coordinate. +.PP +\fBcolor=\fIcolorvalue\fR sets foreground color (\fBbgcolor\fP for background). +.PP +\fBhref=\fI"url"\fR the default url for image map files; in PostScript files, +the base URL for all relative URLs, as recognized by Acrobat Distiller +3.0 and up. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBstylesheet=\fI"file.css"\fR includes a reference to a stylesheet +in \-Tsvg and \-Tsvgz outputs. Ignored by other formats. +.PP +\fBsplines\fR. If set to \fItrue\fR, edges are +drawn as splines. +If set to \fIpolyline\fR, edges are +drawn as polylines. +If set to \fIortho\fR, edges are +drawn as orthogonal polylines. +In all of these cases, the nodes may not overlap. +If \fBsplines=\fIfalse\fR or \fBsplines=\fIline\fR, edges are +drawn as line segments. +The default is \fItrue\fR for dot, and \fIfalse\fR for all other layouts. + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBstart=\fIval\fR. Requests random initial placement and seeds +the random number generator. If \fIval\fP is not an integer, +the process ID or current time is used as the seed. +.PP +\fBepsilon=\fIn\fR. Sets the cutoff for the solver. +The default is 0.1. + +.PP +\fB(twopi\(hyspecific attributes)\fR +.br +\fBroot=\fIctr\fR. This specifies the node to be used as the center of +the layout. If not specified, \fItwopi\fP will randomly pick one of the +nodes that are furthest from a leaf node, where a leaf node is a node +of degree 1. If no leaf nodes exists, an arbitrary node is picked as center. +.PP +\fBranksep=\fIval\fR. Specifies the radial distance in inches between +the sequence of rings. The default is 0.75. +.PP +\fBoverlap=\fImode\fR. This specifies what \fItwopi\fP should do if +any nodes overlap. If mode is \fI"false"\fP, the program uses Voronoi +diagrams to adjust the nodes to eliminate overlaps. If mode is \fI"scale"\fP, +the layout is uniformly scaled up, preserving node sizes, until nodes no +longer overlap. The latter technique removes overlaps while preserving +symmetry and structure, while the former removes overlaps more compactly +but destroys symmetries. +If mode is \fI"true"\fP (the default), no repositioning is done. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fInodename\fR. Specifies the name of a node occurring in the +root block. If the graph is disconnected, the \fBroot\fP node attribute +can be used to specify additional root blocks. +.PP +\fBmindist=\fIvalue\fR. Sets the minimum separation between all nodes. If not +specified then \fIcirco\fP uses a default value of 1.0. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBK=\fIval\fR. Sets the default ideal node separation +in the layout. +.PP +\fBmaxiter=\fIval\fR. Sets the maximum number of iterations used to +layout the graph. +.PP +\fBstart=\fIval\fR. Adjusts the random initial placement of nodes +with no specified position. If \fIval\fP is is an integer, +it is used as the seed for the random number generator. +If \fIval\fP is not an integer, a random system\(hygenerated integer, +such as the process ID or current time, is used as the seed. +.PP + +.SH "NODE ATTRIBUTES" +.PP +\fBheight=\fId\fR or \fBwidth=\fId\fR sets minimum height or width. +Adding \fBfixedsize=true\fP forces these to be the actual size +(text labels are ignored). +.PP +\fBshape=record polygon epsf \fIbuiltin_polygon\fR +.br +\fIbuiltin_polygon\fR is one of: \fBplaintext ellipse oval circle egg +triangle box diamond trapezium parallelogram house hexagon octagon +note tab box3d component.\fR +(Polygons are defined or modified by the following node attributes: +\fBregular\fR, \fBperipheries\fR, \fBsides\fR, \fBorientation\fR, +\fBdistortion\fR and \fBskew\fR.) \fBepsf\fR uses the node's +\fBshapefile\fR attribute as the path name of an external +EPSF file to be automatically loaded for the node shape. +.PP +\fBlabel=\fItext\fR where \fItext\fP may include escaped newlines +\\\|n, \\\|l, or \\\|r for center, left, and right justified lines. +The string '\\N' value will be replaced by the node name. +The string '\\G' value will be replaced by the graph name. +Record labels may contain recursive box lists delimited by { | }. +Port identifiers in labels are set off by angle brackets < >. +In the graph file, use colon (such as, \fBnode0:port28\fR). +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBcolor=\fIcolorvalue\fR sets the outline color, and the default fill color +if style=filled and \fBfillcolor\fR is not specified. +.PP +\fBfillcolor=\fIcolorvalue\fR sets the fill color +when style=filled. If not specified, the fillcolor when style=filled defaults +to be the same as the outline color. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +A \fIcolorvalue\fP may be "\fIh,s,v\fB"\fR (hue, saturation, brightness) +floating point numbers between 0 and 1, or an X11 color name such as +\fBwhite black red green blue yellow magenta cyan\fR or \fBburlywood\fR, +or a "\fI#rrggbb" (red, green, blue, 2 hex characters each) value. +.PP +\fBstyle=filled solid dashed dotted bold invis\fP or any Postscript code. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the node's active layers. +The empty string means no layers (invisible). +.PP +The following attributes apply only to polygon shape nodes: +.PP +\fBregular=\fIn\fR if \fIn\fR is non\(hyzero then the polygon is made +regular, i.e. symmetric about the x and y axis, otherwise the +polygon takes on the aspect ratio of the label. +\fIbuiltin_polygons\fR that are not already regular are made regular +by this attribute. +\fIbuiltin_polygons\fR that are already regular are not affected (i.e. +they cannot be made asymmetric). +.PP +\fBperipheries=\fIn\fR sets the number of periphery lines drawn around +the polygon. This value supersedes the number of periphery lines +of \fIbuiltin_polygons\fR. +.PP +\fBsides=\fIn\fR sets the number of sides to the polygon. \fIn\fR<3 +results in an ellipse. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBorientation=\fIf\fR sets the orientation of the first apex of the +polygon counterclockwise from the vertical, in degrees. +\fIf\fR may be a floating point number. +The orientation of labels is not affected by this attribute. +This attribute is added to the initial orientation of \fIbuiltin_polygons.\fR +.PP +\fBdistortion=\fIf\fR sets the amount of broadening of the top and +narrowing of the bottom of the polygon (relative to its orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBskew=\fIf\fR sets the amount of right\(hydisplacement of the top and +left\(hydisplacement of the bottom of the polygon (relative to its +orientation). +Floating point values between \-1 and +1 are suggested. +This attribute is ignored by \fIbuiltin_polygons\fR. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. +The target string is used to determine which window of the browser is used +for the URL. Setting it to "_graphviz" will open a new window if it doesn't +already exist, or reuse it if it does. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +and SVG, effective when nodes have a URL. The tooltip string defaults to be the +same as the label string, but this attribute permits nodes without +labels to still have tooltips thus permitting denser graphs. +The substrings '\\N' and '\\G' are substituted in the same manner as +for the node label attribute. +Additionally the substring '\\L' is substituted with the node label string. + +.PP +\fB(circo\(hyspecific attributes)\fR +.br +\fBroot=\fItrue/false\fR. This specifies that the block containing the given +node be treated as the root of the spanning tree in the layout. + +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBpin=\fIval\fR. If \fIval\fR is "true", the node will remain at +its initial position. + +.SH "EDGE ATTRIBUTES" +\fBminlen=\fIn\fR where \fIn\fP is an integer factor that applies +to the edge length (ranks for normal edges, or minimum node separation +for flat edges). +.PP +\fBweight=\fIn\fR where \fIn\fP is the integer cost of the edge. +Values greater than 1 tend to shorten the edge. Weight 0 flat +edges are ignored for ordering nodes. +.PP +\fBlabel=\fItext\fR where \fItext\fR may include escaped newlines +\\\|n, \\\|l, or \\\|r for centered, left, or right justified lines. +If the substring '\\T' is found in a label it will be replaced by the tail_node name. +If the substring '\\H' is found in a label it will be replaced by the head_node name. +If the substring '\\E' value is found in a label it will be replaced by: tail_node_name\->head_node_name +If the substring '\\G' is found in a label it will be replaced by the graph name. +or by: tail_node_name\-\-head_node_name for undirected graphs. +.PP +\fBfontsize=\fIn\fR sets the label type size to \fIn\fP points. +.PP +\fBfontname=\fIname\fR sets the label font family name. +.PP +\fBfontcolor=\fIcolorvalue\fR sets the label text color. +.PP +\fBstyle=solid dashed dotted bold invis\fP +.PP +\fBcolor=\fIcolorvalue\fR sets the line color for edges. +.PP +\fBcolor=\fIcolorvaluelist\fR a ':' separated list of \fIcolorvalue\fR creates +parallel edges, one edge for each color. +.PP +\fBdir=forward back both none\fP controls arrow direction. +.PP +\fBtailclip,headclip=false\fP disables endpoint shape clipping. +.PP +\fBhref=\fI"url"\fR sets the url for the node in imagemap, PostScript and SVG +files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBURL=\fI"url"\fR ("URL" is a synonym for "href".) +.PP +\fBtarget=\fI"target"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edges have a URL. +If the target string is empty, the default, +then no target attribute is included in the output. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when edges have a URL. The tooltip string defaults to be the +same as the edge label string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBarrowhead,arrowtail=none, normal, inv, dot, odot, invdot, invodot, +tee, empty, invempty, open, halfopen, diamond, odiamond, box, obox, crow\fP. +.PP +\fBarrowsize\fP (norm_length=10,norm_width=5, +inv_length=6,inv_width=7,dot_radius=2) +.PP +\fBheadlabel,taillabel=string\fP for port labels. +\fBlabelfontcolor\fP,\fBlabelfontname\fP,\fBlabelfontsize\fP +for head and tail labels. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadhref=\fI"url"\fR sets the url for the head port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadURL=\fI"url"\fR ("headURL" is a synonym for "headhref".) +.PP +\fBheadtarget=\fI"headtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge heads have a URL. +The headtarget string is used to determine which window of the browser is used +for the URL. If the headtarget string is empty, the default, +then headtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBheadtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when head ports have a URL. The tooltip string defaults to be the +same as the headlabel string. +The substrings '\\T', '\\H', and '\\E' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailhref=\fI"url"\fR sets the url for the tail port in imagemap, PostScript and SVG files. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailURL=\fI"url"\fR ("tailURL" is a synonym for "tailhref".) +.PP +\fBtailtarget=\fI"tailtarget"\fR is a target string for client\(hyside imagemaps +and SVG, effective when edge tails have a URL. +The tailtarget string is used to determine which window of the browser is used +for the URL. If the tailtarget string is empty, the default, +then tailtarget defaults to the same value as target for the edge. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBtailtooltip=\fI"tooltip"\fR is a tooltip string for client\(hyside imagemaps +effective when tail ports have a URL. The tooltip string defaults to be the +same as the taillabel string. +The substrings '\\T', '\\H', '\\E' and '\\G' are substituted in the same manner as +for the edge label attribute. +Additionally the substring '\\L' is substituted with the edge label string. +.PP +\fBlabeldistance\fP and \fPport_label_distance\fP set distance; also +\fBlabelangle\fP (in degrees CCW) +.PP +\fBdecorate\fP draws line from edge to label. +.PP +\fBsamehead,sametail\fP aim edges having the same value to the +same port, using the average landing point. +.PP +\fBconstraint=false\fP causes an edge to be ignored for rank assignment. +.PP +\fBlayer=\fIid\fR or \fIid:id\fR or "all" sets the edge's active layers. +The empty string means no layers (invisible). + +.PP +\fB(neato\(hyspecific attributes)\fR +.br +\fBw=\fIf\fR sets the weight (spring constant) of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.PP +\fBlen=\fIf\fR sets the optimal length of an edge. +The default is 1.0. +.PP +\fB(fdp\(hyspecific attributes)\fR +.br +\fBweight=\fIf\fR sets the weight of an edge +to the given floating point value. The default is 1.0; +greater values make the edge tend more toward its optimal length. +.SH "COMMAND LINE OPTIONS" +\fB\-G\fP sets a default graph attribute. +.br +\fB\-N\fP sets a default node attribute. +.br +\fB\-E\fP sets a default edge attribute. +Example: \fB\-Gsize="7,8" \-Nshape=box \-Efontsize=8\fR +.PP +\fB\-l\fIfile\fR loads custom PostScript library files. +Usually these define custom shapes or styles. +If \fB\-l\fP is given by itself, the standard library is omitted. +.PP +\fB\-T\fIlang\fR sets the output language as described above. + +.PP +\fB\-n\fR[\fB1\fR|\fB2\fR] (no\(hyop) +If set, neato assumes nodes have already been positioned and all +nodes have a pos attribute giving the positions. +It then performs an optional adjustment to remove node\(hynode overlap, +depending on the value of the overlap attribute, +computes the edge layouts, depending on the value of the \fBsplines\fR attribute, +and emits the graph in the appropriate format. +If num is supplied, the following actions occur: +.nf + num = 1 +.fi +Equivalent to \-n. +.nf + num > 1 +.fi +Use node positions as specified, +with no adjustment to remove node\(hynode overlaps, +and use any edge layouts already specified by the pos attribute. +neato computes an edge layout for any edge that does not have a pos attribute. +As usual, edge layout is guided by the \fBsplines\fR attribute. +.PP +\fB\-K\fIlayout\fR override the default layout engine implied by the command name. +.PP +\fB\-O\fP automatically generate output filenames based on the input filename and the -T format. +.PP +\fB\-P\fP generate a graph of the currently available plugins. +.PP +\fB\-v\fP (verbose) prints various information useful for debugging. +.PP +\fB\-V\fP (version) prints version information and exits. +.PP +\fB\-?\fP prints the usage and exits. +.SH "EXAMPLES" +.nf +digraph test123 { + a \-> b \-> c; + a \-> {x y}; + b [shape=box]; + c [label="hello\\\|nworld",color=blue,fontsize=24, + fontname="Palatino\-Italic",fontcolor=red,style=filled]; + a \-> z [label="hi", weight=100]; + x \-> z [label="multi\-line\\\|nlabel"]; + edge [style=dashed,color=red]; + b \-> x; + {rank=same; b x} +} +.fi +.PP +.nf +graph test123 { + a \-\- b \-\- c; + a \-\- {x y}; + x \-\- c [w=10.0]; + x \-\- y [w=5.0,len=3]; +} +.fi +.SH "CAVEATS" +Edge splines can overlap unintentionally. +.PP +Flat edge labels are slightly broken. +Intercluster edge labels are totally broken. +.PP +Because unconstrained optimization is employed, node boxes can +possibly overlap or touch unrelated edges. All existing +spring embedders seem to have this limitation. +.PP +Apparently reasonable attempts to pin nodes or adjust edge lengths +and weights can cause instability. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.br +John C. Ellson <ellson@research.att.com> +.PP +The bitmap driver (PNG, GIF etc) is by Thomas Boutell, <http://www.boutell.com/gd> +.PP +The Truetype font renderer is from +the Freetype Project (David Turner, Robert Wilhelm, and Werner Lemberg) +(who can be contacted at freetype\-devel@lists.lrz\-muenchen.de). +.SH "SEE ALSO" +This man page contains only a small amount of the information related +to the Graphviz layout programs. The most complete information can be +found at http://www.graphviz.org/Documentation.php, especially in the +on\(hyline reference pages. Most of these documents are also available in the +\fIdoc\fP and \fIdoc/info\fP subtrees in the source and binary distributions. +.PP +dotty(1) +.br +tcldot(n) +.br +xcolors(1) +.br +libgraph(3) +.PP +E. R. Gansner, S. C. North, K. P. Vo, "DAG \(hy A Program to Draw Directed Graphs", Software \(hy Practice and Experience 17(1), 1988, pp. 1047\(hy1062. +.br +E. R. Gansner, E. Koutsofios, S. C. North, K. P. Vo, "A Technique for Drawing Directed Graphs," IEEE Trans. on Soft. Eng. 19(3), 1993, pp. 214\(hy230. +.br +S. North and E. Koutsofios, "Applications of graph visualization", +Graphics Interface 94, pp. 234\(hy245. +.br +E. Koutsofios and S. C. North, "Drawing Graphs with dot," +Available on research.att.com in dist/drawdag/dotguide.ps.Z. +.br +S. C. North, "NEATO User's Manual". +Available on research.att.com in dist/drawdag/neatodoc.ps.Z.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man1/unflatten.1 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,58 @@ +.TH UNFLATTEN 1 "21 January 2001" +.SH NAME +unflatten \- adjust directed graphs to improve layout aspect ratio +.SH SYNOPSIS +.B unflatten +[\fB\-f\fR] +[\fB\-l\fIlen\fR] +[\fB\-c\fIlen\fR +] [ +.B \-o +.I outfile +] +[ files ] +.SH DESCRIPTION +.B unflatten +is a preprocessor to +.B dot +that is +used to improve the aspect ratio of graphs having many leaves +or disconnected nodes. +The usual layout for such a graph is generally very wide or tall. +.B unflatten +inserts invisible edges or adjusts the \fBminlen\fP on edges +to improve layout compaction. +.SH OPTIONS +The following options are supported: +.TP +.BI \-l " len" +The minimum length of leaf edges is staggered +between 1 and \fIlen\fP (a small integer). +.TP +.B \-f +Enables the staggering of the \fB-l\fP option to fanout nodes whose +indegree and outdegree are both 1. This helps with structures such +as \fIa -> {w x y z} -> b\fP. +This option only works if the \fB-l\fP flag is set. +.TP +.BI \-c " len" +Form disconnected nodes into chains of up to \fIlen\fP nodes. +.TP +.BI \-o " outfile" +causes the output to be written to the specified file; by default, +output is written to \fBstdout\fP. +.SH OPERANDS +The following operand is supported: +.TP 8 +.I files +Names of files containing 1 or more graphs in dot format. +If no +.I files +operand is specified, +the standard input will be used. +.SH AUTHORS +Stephen C. North <north@research.att.com> +.br +Emden R. Gansner <erg@research.att.com> +.SH "SEE ALSO" +gc(1), dot(1), acyclic(1), gvpr(1), gvcolor(1), ccomps(1), tred(1), libgraph(3)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man3/cdt.3 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,483 @@ +.TH LIBCDT 3 +.SH NAME +\fBCdt\fR \- container data types +.SH SYNOPSIS +.de Tp +.fl +.ne 2 +.TP +.. +.de Ss +.fl +.ne 2 +.SS "\\$1" +.. +.de Cs +.nf +.ft 5 +.. +.de Ce +.ft 1 +.fi +.. +.ta 1.0i 2.0i 3.0i 4.0i 5.0i +.Cs +#include <graphviz/cdt.h> +.Ce +.Ss "DICTIONARY TYPES" +.Cs +Void_t*; +Dt_t; +Dtdisc_t; +Dtmethod_t; +Dtlink_t; +Dtstat_t; +.Ce +.Ss "DICTIONARY CONTROL" +.Cs +Dt_t* dtopen(Dtdisc_t* disc, Dtmethod_t* meth); +int dtclose(Dt_t* dt); +void dtclear(dt); +Dtmethod_t* dtmethod(Dt_t* dt, Dtmethod_t* meth); +Dtdisc_t* dtdisc(Dt_t* dt, Dtdisc_t* disc, int type); +Dt_t* dtview(Dt_t* dt, Dt_t* view); +.Ce +.Ss "STORAGE METHODS" +.Cs +Dtmethod_t* Dtset; +Dtmethod_t* Dtbag; +Dtmethod_t* Dtoset; +Dtmethod_t* Dtobag; +Dtmethod_t* Dtlist; +Dtmethod_t* Dtstack; +Dtmethod_t* Dtqueue; +.Ce +.Ss "DISCIPLINE" +.Cs +typedef Void_t* (*Dtmake_f)(Dt_t*, Void_t*, Dtdisc_t*); +typedef void (*Dtfree_f)(Dt_t*, Void_t*, Dtdisc_t*); +typedef int (*Dtcompar_f)(Dt_t*, Void_t*, Void_t*, Dtdisc_t*); +typedef unsigned int (*Dthash_f)(Dt_t*, Void_t*, Dtdisc_t*); +typedef Void_t* (*Dtmemory_f)(Dt_t*, Void_t*, size_t, Dtdisc_t*); +typedef int (*Dtevent_f)(Dt_t*, int, Void_t*, Dtdisc_t*); +.Ce +.Ss "OBJECT OPERATIONS" +.Cs +Void_t* dtinsert(Dt_t* dt, Void_t* obj); +Void_t* dtdelete(Dt_t* dt, Void_t* obj); +Void_t* dtsearch(Dt_t* dt, Void_t* obj); +Void_t* dtmatch(Dt_t* dt, Void_t* key); +Void_t* dtfirst(Dt_t* dt); +Void_t* dtnext(Dt_t* dt, Void_t* obj); +Void_t* dtlast(Dt_t* dt); +Void_t* dtprev(Dt_t* dt, Void_t* obj); +Void_t* dtfinger(Dt_t* dt); +Void_t* dtrenew(Dt_t* dt, Void_t* obj); +int dtwalk(Dt_t* dt, int (*userf)(Dt_t*, Void_t*, Void_t*), Void_t*); +Dtlink_t* dtflatten(Dt_t* dt); +Dtlink_t* dtlink(Dt_t*, Dtlink_t* link); +Void_t* dtobj(Dt_t* dt, Dtlink_t* link); +Dtlink_t* dtextract(Dt_t* dt); +int dtrestore(Dt_t* dt, Dtlink_t* link); +.Ce +.Ss "DICTIONARY STATUS" +.Cs +Dt_t* dtvnext(Dt_t* dt); +int dtvcount(Dt_t* dt); +Dt_t* dtvhere(Dt_t* dt); +int dtsize(Dt_t* dt); +int dtstat(Dt_t* dt, Dtstat_t*, int all); +.Ce +.Ss "HASH FUNCTIONS" +.Cs +unsigned int dtstrhash(unsigned int h, char* str, int n); +unsigned int dtcharhash(unsigned int h, unsigned char c); +.Ce +.SH DESCRIPTION +.PP +\fICdt\fP manages run-time dictionaries using standard container data types: +unordered set/multiset, ordered set/multiset, list, stack, and queue. +.PP +.Ss "DICTIONARY TYPES" +.PP +.Ss " Void_t*" +This type is used to pass objects between \fICdt\fP and application code. +\f5Void_t\fP is defined as \f5void\fP for ANSI-C and C++ +and \f5char\fP for other compilation environments. +.PP +.Ss " Dt_t" +This is the type of a dictionary handle. +.PP +.Ss " Dtdisc_t" +This defines the type of a discipline structure which describes +object lay-out and manipulation functions. +.PP +.Ss " Dtmethod_t" +This defines the type of a container method. +.PP +.Ss " Dtlink_t" +This is the type of a dictionary object holder (see \f5dtdisc()\fP.) +.PP +.Ss " Dtstat_t" +This is the type of a structure to return dictionary statistics (see \f5dtstat()\fP.) +.PP +.Ss "DICTIONARY CONTROL" +.PP +.Ss " Dt_t* dtopen(Dtdisc_t* disc, Dtmethod_t* meth)" +This creates a new dictionary. +\f5disc\fP is a discipline structure to describe object format. +\f5meth\fP specifies a manipulation method. +\f5dtopen()\fP returns the new dictionary or \f5NULL\fP on error. +.PP +.Ss " int dtclose(Dt_t* dt)" +This deletes \f5dt\fP and its objects. +Note that \f5dtclose()\fP fails if \f5dt\fP is being viewed by +some other dictionaries (see \f5dtview()\fP). +\f5dtclose()\fP returns \f50\fP on success and \f5-1\fP on error. +.PP +.Ss " void dtclear(Dt_t* dt)" +This deletes all objects in \f5dt\fP without closing \f5dt\fP. +.PP +.Ss " Dtmethod_t dtmethod(Dt_t* dt, Dtmethod_t* meth)" +If \f5meth\fP is \f5NULL\fP, \f5dtmethod()\fP returns the current method. +Otherwise, it changes the storage method of \f5dt\fP to \f5meth\fP. +Object order remains the same during a +method switch among \f5Dtlist\fP, \f5Dtstack\fP and \f5Dtqueue\fP. +Switching to and from \f5Dtset/Dtbag\fP and \f5Dtoset/Dtobag\fP may cause +objects to be rehashed, reordered, or removed as the case requires. +\f5dtmethod()\fP returns the previous method or \f5NULL\fP on error. +.PP +.Ss " Dtdisc_t* dtdisc(Dt_t* dt, Dtdisc_t* disc, int type)" +If \f5disc\fP is \f5NULL\fP, \f5dtdisc()\fP returns the current discipline. +Otherwise, it changes the discipline of \f5dt\fP to \f5disc\fP. +Objects may be rehashed, reordered, or removed as appropriate. +\f5type\fP can be any bit combination of \f5DT_SAMECMP\fP and \f5DT_SAMEHASH\fP. +\f5DT_SAMECMP\fP means that objects will compare exactly the same as before +thus obviating the need for reordering or removing new duplicates. +\f5DT_SAMEHASH\fP means that hash values of objects remain the same +thus obviating the need to rehash. +\f5dtdisc()\fP returns the previous discipline on success +and \f5NULL\fP on error. +.PP +.Ss " Dt_t* dtview(Dt_t* dt, Dt_t* view)" +A viewpath allows a search or walk starting from a dictionary to continue to another. +\f5dtview()\fP first terminates any current view from \f5dt\fP to another dictionary. +Then, if \f5view\fP is \f5NULL\fP, \f5dtview\fP returns the terminated view dictionary. +If \f5view\fP is not \f5NULL\fP, a viewpath from \f5dt\fP to \f5view\fP is established. +\f5dtview()\fP returns \f5dt\fP on success and \f5NULL\fP on error. +.PP +If two dictionaries on the same viewpath have the same values for the discipline fields +\f5Dtdisc_t.link\fP, \f5Dtdisc_t.key\fP, \f5Dtdisc_t.size\fP, and \f5Dtdisc_t.hashf\fP, +it is expected that key hashing will be the same. +If not, undefined behaviors may result during a search or a walk. +.PP +.Ss "STORAGE METHODS" +.PP +Storage methods are of type \f5Dtmethod_t*\fP. +\fICdt\fP supports the following methods: +.PP +.Ss " Dtoset" +.Ss " Dtobag" +Objects are ordered by comparisons. +\f5Dtoset\fP keeps unique objects. +\f5Dtobag\fP allows repeatable objects. +.PP +.Ss " Dtset" +.Ss " Dtbag" +Objects are unordered. +\f5Dtset\fP keeps unique objects. +\f5Dtbag\fP allows repeatable objects and always keeps them together +(note the effect on dictionary walking.) +.PP +.Ss " Dtlist" +Objects are kept in a list. +New objects are inserted either +in front of \fIcurrent object\fP (see \f5dtfinger()\fP) if this is defined +or at list front if there is no current object. +.PP +.Ss " Dtstack" +Objects are kept in a stack, i.e., in reverse order of insertion. +Thus, the last object inserted is at stack top +and will be the first to be deleted. +.PP +.Ss " Dtqueue" +Objects are kept in a queue, i.e., in order of insertion. +Thus, the first object inserted is at queue head +and will be the first to be deleted. +.PP +.Ss "DISCIPLINE" +.PP +Object format and associated management functions are +defined in the type \f5Dtdisc_t\fP: +.Cs + typedef struct + { int key, size; + int link; + Dtmake_f makef; + Dtfree_f freef; + Dtcompar_f comparf; + Dthash_f hashf; + Dtmemory_f memoryf; + Dtevent_f eventf; + } Dtdisc_t; +.Ce +.Ss " int key, size" +Each object \f5obj\fP is identified by a key used for object comparison or hashing. +\f5key\fP should be non-negative and defines an offset into \f5obj\fP. +If \f5size\fP is negative, the key is a null-terminated +string with starting address \f5*(Void_t**)((char*)obj+key)\fP. +If \f5size\fP is zero, the key is a null-terminated string with starting address +\f5(Void_t*)((char*)obj+key)\fP. +Finally, if \f5size\fP is positive, the key is a byte array of length \f5size\fP +starting at \f5(Void_t*)((char*)obj+key)\fP. +.PP +.Ss " int link" +Let \f5obj\fP be an object to be inserted into \f5dt\fP as discussed below. +If \f5link\fP is negative, an internally allocated object holder is used +to hold \f5obj\fP. Otherwise, \f5obj\fP should have +a \f5Dtlink_t\fP structure embedded \f5link\fP bytes into it, +i.e., at address \f5(Dtlink_t*)((char*)obj+link)\fP. +.PP +.Ss " Void_t* (*makef)(Dt_t* dt, Void_t* obj, Dtdisc_t* disc)" +If \f5makef\fP is not \f5NULL\fP, +\f5dtinsert(dt,obj)\fP will call it +to make a copy of \f5obj\fP suitable for insertion into \f5dt\fP. +If \f5makef\fP is \f5NULL\fP, \f5obj\fP itself will be inserted into \f5dt\fP. +.PP +.Ss " void (*freef)(Dt_t* dt, Void_t* obj, Dtdisc_t* disc)" +If not \f5NULL\fP, +\f5freef\fP is used to destroy data associated with \f5obj\fP. +.PP +.Ss "int (*comparf)(Dt_t* dt, Void_t* key1, Void_t* key2, Dtdisc_t* disc)" +If not \f5NULL\fP, \f5comparf\fP is used to compare two keys. +Its return value should be \f5<0\fP, \f5=0\fP, or \f5>0\fP to indicate +whether \f5key1\fP is smaller, equal to, or larger than \f5key2\fP. +All three values are significant for method \f5Dtoset\fP and \f5Dtobag\fP. +For other methods, a zero value +indicates equality and a non-zero value indicates inequality. +If \f5(*comparf)()\fP is \f5NULL\fP, an internal function is used +to compare the keys as defined by the \f5Dtdisc_t.size\fP field. +.PP +.Ss " unsigned int (*hashf)(Dt_t* dt, Void_t* key, Dtdisc_t* disc)" +If not \f5NULL\fP, +\f5hashf\fP is used to compute the hash value of \f5key\fP. +It is required that keys compared equal will also have same hash values. +If \f5hashf\fP is \f5NULL\fP, an internal function is used to hash +the key as defined by the \f5Dtdisc_t.size\fP field. +.PP +.Ss " Void_t* (*memoryf)(Dt_t* dt, Void_t* addr, size_t size, Dtdisc_t* disc)" +If not \f5NULL\fP, \f5memoryf\fP is used to allocate and free memory. +When \f5addr\fP is \f5NULL\fP, a memory segment of size \f5size\fP is requested. +If \f5addr\fP is not \f5NULL\fP and \f5size\fP is zero, \f5addr\fP is to be freed. +If \f5addr\fP is not \f5NULL\fP and \f5size\fP is positive, +\f5addr\fP is to be resized to the given size. +If \f5memoryf\fP is \f5NULL\fP, \fImalloc(3)\fP is used. +When dictionaries share memory, +a record of the first allocated memory segment should be kept +so that it can be used to initialize new dictionaries (see below.) +.PP +.Ss " int (*eventf)(Dt_t* dt, int type, Void_t* data, Dtdisc_t* disc)" +If not \f5NULL\fP, \f5eventf\fP announces various events. +If it returns a negative value, the calling operation will terminate with failure. +Unless noted otherwise, a non-negative return value let the +calling function proceed normally. Following are the events: +.Tp +\f5DT_OPEN\fP: +\f5dt\fP is being opened. +If \f5eventf\fP returns zero, the opening process proceeds normally. +A positive return value indicates that \f5dt\fP +uses memory already initialized by a different dictionary. +In that case, \f5*(Void_t**)data\fP should be set to +the first allocated memory segment as discussed in \f5memoryf\fP. +\f5dtopen()\fP may fail if this segment is not returned or +if it has not been properly initialized. +.Tp +\f5DT_CLOSE\fP: +\f5dt\fP is being closed. +.Tp +\f5DT_DISC\fP: +The discipline of \f5dt\fP is being changed to a new one given in +\f5(Dtdisc_t*)data\fP. +.Tp +\f5DT_METH\fP: +The method of \f5dt\fP is being changed to a new one given in +\f5(Dtmethod_t*)data\fP. +.PP +.Ss "OBJECT OPERATIONS" +.PP +.Ss " Void_t* dtinsert(Dt_t* dt, Void_t* obj)" +This inserts an object prototyped by \f5obj\fP into \f5dt\fP. +If there is an existing object in \f5dt\fP matching \f5obj\fP +and the storage method is \f5Dtset\fP or \f5Dtoset\fP, +\f5dtinsert()\fP will simply return the matching object. +Otherwise, a new object is inserted according to the method in use. +See \f5Dtdisc_t.makef\fP for object construction. +\f5dtinsert()\fP returns the new object, a matching object as noted, +or \f5NULL\fP on error. +.PP +.Ss " Void_t* dtdelete(Dt_t* dt, Void_t* obj)" +If \f5obj\fP is not \f5NULL\fP, the first object matching it is deleted. +If \f5obj\fP is \f5NULL\fP, methods \f5Dtstack\fP and \f5Dtqueue\fP +delete respectively stack top or queue head while other methods do nothing. +See \f5Dtdisc_t.freef\fP for object destruction. +\f5dtdelete()\fP returns the deleted object (even if it was deallocated) +or \f5NULL\fP on error. +.PP +.Ss " Void_t* dtsearch(Dt_t* dt, Void_t* obj)" +.Ss " Void_t* dtmatch(Dt_t* dt, Void_t* key)" +These functions find an object matching \f5obj\fP or \f5key\fP either from \f5dt\fP or +from some dictionary accessible from \f5dt\fP via a viewpath (see \f5dtview()\fP.) +\f5dtsearch()\fP and \f5dtmatch()\fP return the matching object or +\f5NULL\fP on failure. +.PP +.Ss " Void_t* dtfirst(Dt_t* dt)" +.Ss " Void_t* dtnext(Dt_t* dt, Void_t* obj)" +\f5dtfirst()\fP returns the first object in \f5dt\fP. +\f5dtnext()\fP returns the object following \f5obj\fP. +Objects are ordered based on the storage method in use. +For \f5Dtoset\fP and \f5Dtobag\fP, objects are ordered by object comparisons. +For \f5Dtstack\fP, objects are ordered in reverse order of insertion. +For \f5Dtqueue\fP, objects are ordered in order of insertion. +For \f5Dtlist\fP, objects are ordered by list position. +For \f5Dtset\fP and \f5Dtbag\fP, +objects use some internal ordering which +may change on any search, insert, or delete operations. +Therefore, these operations should not be used +during a walk on a dictionary using either \f5Dtset\fP or \f5Dtbag\fP. +.PP +Objects in a dictionary or a viewpath can be walked using +a \f5for(;;)\fP loop as below. +Note that only one loop can be used at a time per dictionary. +Concurrent or nested loops may result in unexpected behaviors. +.Cs + for(obj = dtfirst(dt); obj; obj = dtnext(dt,obj)) +.Ce +.Ss " Void_t* dtlast(Dt_t* dt)" +.Ss " Void_t* dtprev(Dt_t* dt, Void_t* obj)" +\f5dtlast()\fP and \f5dtprev()\fP are like \f5dtfirst()\fP and \f5dtnext()\fP +but work in reverse order. +Note that dictionaries on a viewpath are still walked in order +but objects in each dictionary are walked in reverse order. +.PP +.Ss " Void_t* dtfinger(Dt_t* dt)" +This function returns the \fIcurrent object\fP of \f5dt\fP, if any. +The current object is defined after a successful call to one of +\f5dtsearch()\fP, \f5dtmatch()\fP, \f5dtinsert()\fP, +\f5dtfirst()\fP, \f5dtnext()\fP, \f5dtlast()\fP, or \f5dtprev()\fP. +As a side effect of this implementation of \fICdt\fP, +when a dictionary is based on \f5Dtoset\fP and \f5Dtobag\fP, +the current object is always defined and is the root of the tree. +.PP +.Ss " Void_t* dtrenew(Dt_t* dt, Void_t* obj)" +This function repositions and perhaps rehashes +an object \f5obj\fP after its key has been changed. +\f5dtrenew()\fP only works if \f5obj\fP is the current object (see \f5dtfinger()\fP). +.PP +.Ss " dtwalk(Dt_t* dt, int (*userf)(Dt_t*, Void_t*, Void_t*), Void_t* data)" +This function calls \f5(*userf)(walk,obj,data)\fP on each object in \f5dt\fP and +other dictionaries viewable from it. +\f5walk\fP is the dictionary containing \f5obj\fP. +If \f5userf()\fP returns a \f5<0\fP value, +\f5dtwalk()\fP terminates and returns the same value. +\f5dtwalk()\fP returns \f50\fP on completion. +.PP +.Ss " Dtlink_t* dtflatten(Dt_t* dt)" +.Ss " Dtlink_t* dtlink(Dt_t* dt, Dtlink_t* link)" +.Ss " Void_t* dtobj(Dt_t* dt, Dtlink_t* link)" +Using \f5dtfirst()/dtnext()\fP or \f5dtlast()/dtprev()\fP +to walk a single dictionary can incur significant cost due to function calls. +For efficient walking of a single directory (i.e., no viewpathing), +\f5dtflatten()\fP and \f5dtlink()\fP can be used. +Objects in \f5dt\fP are made into a linked list and walked as follows: +.Cs + for(link = dtflatten(dt); link; link = dtlink(dt,link) ) +.Ce +.PP +Note that \f5dtflatten()\fP returns a list of type \f5Dtlink_t*\fP, +not \f5Void_t*\fP. That is, it returns a dictionary holder pointer, +not a user object pointer +(although both are the same if the discipline field \f5link\fP is non-negative.) +The macro function \f5dtlink()\fP +returns the dictionary holder object following \f5link\fP. +The macro function \f5dtobj(dt,link)\fP +returns the user object associated with \f5link\fP, +Beware that the flattened object list is unflattened on any +dictionary operations other than \f5dtlink()\fP. +.PP +.Ss " Dtlink_t* dtextract(Dt_t* dt)" +.Ss " int dtrestore(Dt_t* dt, Dtlink_t* link)" +\f5dtextract()\fP extracts all objects from \f5dt\fP and makes it appear empty. +\f5dtrestore()\fP repopulates \f5dt\fP with +objects previously obtained via \f5dtextract()\fP. +\f5dtrestore()\fP will fail if \f5dt\fP is not empty. +These functions can be used +to share a same \f5dt\fP handle among many sets of objects. +They are useful to reduce dictionary overhead +in an application that creates concurrently many dictionaries. +It is important that the same discipline and method are in use at both +extraction and restoration. Otherwise, undefined behaviors may result. +.PP +.Ss "DICTIONARY INFORMATION" +.PP +.Ss " Dt_t* dtvnext(Dt_t* dt)" +This returns the dictionary that \f5dt\fP is viewing, if any. +.Ss " int dtvcount(Dt_t* dt)" +This returns the number of dictionaries that view \f5dt\fP. +.Ss " Dt_t* dtvhere(Dt_t* dt)" +This returns the dictionary \f5v\fP viewable from \f5dt\fP +where an object was found from the most recent search or walk operation. +.Ss " int dtsize(Dt_t* dt)" +This function returns the number of objects stored in \f5dt\fP. +.PP +.Ss " int dtstat(Dt_t *dt, Dtstat_t* st, int all)" +This function reports dictionary statistics. +If \f5all\fP is non-zero, all fields of \f5st\fP are filled. +Otherwise, only the \f5dt_type\fP and \f5dt_size\fP fields are filled. +It returns \f50\fP on success and \f5-1\fP on error. +.PP +\f5Dtstat_t\fP contains the below fields: +.Tp +\f5int dt_type\fP: +This is one of \f5DT_SET\fP, \f5DT_BAG\fP, \f5DT_OSET\fP, \f5DT_OBAG\fP, +\f5DT_LIST\fP, \f5DT_STACK\fP, and \f5DT_QUEUE\fP. +.Tp +\f5int dt_size\fP: +This contains the number of objects in the dictionary. +.Tp +\f5int dt_n\fP: +For \f5Dtset\fP and \f5Dtbag\fP, +this is the number of non-empty chains in the hash table. +For \f5Dtoset\fP and \f5Dtobag\fP, +this is the deepest level in the tree (counting from zero.) +Each level in the tree contains all nodes of equal distance from the root node. +\f5dt_n\fP and the below two fields are undefined for other methods. +.Tp +\f5int dt_max\fP: +For \f5Dtbag\fP and \f5Dtset\fP, this is the size of a largest chain. +For \f5Dtoset\fP and \f5Dtobag\fP, this is the size of a largest level. +.Tp +\f5int* dt_count\fP: +For \f5Dtset\fP and \f5Dtbag\fP, +this is the list of counts for chains of particular sizes. +For example, \f5dt_count[1]\fP is the number of chains of size \f51\fP. +For \f5Dtoset\fP and \f5Dtobag\fP, this is the list of sizes of the levels. +For example, \f5dt_count[1]\fP is the size of level \f51\fP. +.PP +.Ss "HASH FUNCTIONS" +.PP +.Ss " unsigned int dtcharhash(unsigned int h, char c)" +.Ss " unsigned int dtstrhash(unsigned int h, char* str, int n)" +These functions compute hash values from bytes or strings. +\f5dtcharhash()\fP computes a new hash value from byte \f5c\fP and seed value \f5h\fP. +\f5dtstrhash()\fP computes a new hash value from string \f5str\fP and seed value \f5h\fP. +If \f5n\fP is positive, \f5str\fP is a byte array of length \f5n\fP; +otherwise, \f5str\fP is a null-terminated string. +.PP +.SH IMPLEMENTATION NOTES +\f5Dtset\fP and \f5Dtbag\fP are based on hash tables with +move-to-front collision chains. +\f5Dtoset\fP and \f5Dtobag\fP are based on top-down splay trees. +\f5Dtlist\fP, \f5Dtstack\fP and \f5Dtqueue\fP are based on doubly linked list. +.PP +.SH AUTHOR +Kiem-Phong Vo, kpv@research.att.com
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man3/cgraph.3 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,522 @@ +.de P0 +.nf +\f5 +.. +.de P1 +\fP +.fi +.. +.de Ss +.fl +.ne 2 +.SS "\\$1" +.. +.TH LIBCGRAPH 3 "30 JULY 2007" +.SH "NAME" +\fBlibcgraph\fR \- abstract graph library +.SH "SYNOPSIS" +."ta .75i 1.5i 2.25i 3i 3.75i 4.5i 5.25i 6i +.PP +.nf +.P0 +#include <graphviz/cgraph.h> +.P1 +.SS "TYPES" +.P0 +Agraph_t; +Agnode_t; +Agedge_t; +Agdesc_t; +Agdisc_t; +Agsym_t; +.P1 +.SS "GRAPHS" +.P0 +Agraph_t *agopen(char *name, Agdesc_t kind, Agdisc_t *disc); +int agclose(Agraph_t *g); +Agraph_t *agread(void *channel, Agdisc_t *); +void agreadline(int line_no); +void agsetfile(char *file_name); +Agraph_t *agconcat(Agraph_t *g, void *channel, Agdisc_t *disc) +int agwrite(Agraph_t *g, void *channel); +int agnnodes(Agraph_t *g),agnedges(Agraph_t *g); +int agisdirected(Agraph_t * g),agisundirected(Agraph_t * g),agisstrict(Agraph_t * g), agissimple(Agraph_t * g); +.SS "SUBGRAPHS" +.P0 +Agraph_t *agsubg(Agraph_t *g, char *name, int createflag); +Agraph_t *agidsubg(Agraph_t * g, unsigned long id, int cflag); +Agraph_t *agfstsubg(Agraph_t *g), agnxtsubg(Agraph_t *); +Agraph_t *agparent(Agraph_t *g); +int agdelsubg(Agraph_t * g, Agraph_t * sub); /* same as agclose() */ +.P1 +.SS "NODES" +.P0 +Agnode_t *agnode(Agraph_t *g, char *name, int createflag); +Agnode_t *agidnode(Agraph_t *g, ulong id, int createflag); +Agnode_t *agsubnode(Agraph_t *g, Agnode_t *n, int createflag); +Agnode_t *agfstnode(Agraph_t *g); +Agnode_t *agnxtnode(Agraph_t *g, Agnode_t *n); +Agnode_t *agprvnode(Agraph_t *g, Agnode_t *n); +Agnode_t *aglstnode(Agraph_t *g); +int agdelnode(Agraph_t *g, Agnode_t *n); +int agdegree(Agnode_t *n, int use_inedges, int use_outedges); +.P1 +.SS "EDGES" +.P0 +Agedge_t *agedge(Agraph_t* g, Agnode_t *t, Agnode_t *h, char *name, int createflag); +Agedge_t *agidedge(Agraph_t * g, Agnode_t * t, Agnode_t * h, unsigned long id, int createflag); +Agedge_t *agsubedge(Agraph_t *g, Agedge_t *e, int createflag); +Agnode_t *aghead(Agedge_t *e), *agtail(Agedge_t *e); +Agedge_t *agfstedge(Agraph_t* g, Agnode_t *n); +Agedge_t *agnxtedge(Agraph_t* g, Agedge_t *e, Agnode_t *n); +Agedge_t *agfstin(Agraph_t* g, Agnode_t *n); +Agedge_t *agnxtin(Agraph_t* g, Agedge_t *e); +Agedge_t *agfstout(Agraph_t* g, Agnode_t *n); +Agedge_t *agnxtout(Agraph_t* g, Agedge_t *e); +int agdeledge(Agraph_t *g, Agedge_t *e); +.SS "STRING ATTRIBUTES" +.P0 +Agsym_t *agattr(Agraph_t *g, int kind, char *name, char *value); +Agsym_t *agattrsym(void *obj, char *name); +Agsym_t *agnxtattr(Agraph_t *g, int kind, Agsym_t *attr); +char *agget(void *obj, char *name); +char *agxget(void *obj, Agsym_t *sym); +int agset(void *obj, char *name, char *value); +int agxset(void *obj, Agsym_t *sym, char *value); +int agsafeset(void *obj, char *name, char *value, char *def); +.P1 +.SS "RECORDS" +.P0 +void *agbindrec(void *obj, char *name, unsigned int size, move_to_front); +Agrec_t *aggetrec(void *obj, char *name, int move_to_front); +int agdelrec(Agraph_t *g, void *obj, char *name); +int agcopyattr(void *, void *); +void aginit(Agraph_t * g, int kind, char *rec_name, int rec_size, int move_to_front); +void agclean(Agraph_t * g, int kind, char *rec_name); +.P1 +.SS "CALLBACKS" +.P0 +Agcbdisc_t *agpopdisc(Agraph_t *g); +void agpushdisc(Agraph_t *g, Agcbdisc_t *disc); +void agmethod(Agraph_t *g, void *obj, Agcbdisc_t *disc, int initflag); +.P1 +.SS "MEMORY" +.P0 +void *agalloc(Agraph_t *g, size_t request); +void *agrealloc(Agraph_t *g, void *ptr, size_t oldsize, size_t newsize); +void agfree(Agraph_t *g, void *ptr); +.P1 +.SS "STRINGS" +.P0 +char *agstrdup(Agraph_t *, char *); +char *agstrdup_html(Agraph_t *, char *); +int aghtmlstr(char *); +char *agstrbind(Agraph_t * g, char *); +int strfree(Agraph_t *, char *); +char *agcanonStr(char *); +char *agstrcanon(char *, char *); +.P1 +.SS "GENERIC OBJECTS" +.P0 +Agraph_t *agraphof(void*); +Agraph_t *agroot(void*); +int agcontains(Agraph_t*, void*); +char *agnameof(void*); +void agdelete(Agraph_t *g, void *obj); +int agobjkind(void *obj); +Agrec_t *AGDATA(void *obj); +ulong AGID(void *obj); +int AGTYPE(void *obj); +.P1 +.SH "DESCRIPTION" +Libcgraph supports graph programming by maintaining graphs in memory +and reading and writing graph files. +Graphs are composed of nodes, edges, and nested subgraphs. +These graph objects may be attributed with string name-value pairs +and programmer-defined records (see Attributes). +.PP +All of Libcgraph's global symbols have the prefix \fBag\fR (case varying). +.SH "GRAPH AND SUBGRAPHS" +.PP +A ``main'' or ``root'' graph defines a namespace for a collection of +graph objects (subgraphs, nodes, edges) and their attributes. +Objects may be named by unique strings or by 32-bit IDs. +.PP +\fBagopen\fP creates a new graph with the given name and kind. +(Graph kinds are \fBAgdirected\fP, \fBAgundirected\fP, +\fBAgstrictdirected\fP, and \fBAgstrictundirected\fP. +A strict graph cannot have multi-edges or self-arcs.) +\fBagclose\fP deletes a graph, freeing its associated storage. +\fBagread\fP, \fBagwrite\fP, and \fBagconcat\fP perform file I/O +using the graph file language described below. \fBagread\fP +constructs a new graph while \fBagconcat\fP merges the file +contents with a pre-existing graph. Though I/O methods may +be overridden, the default is that the channel argument is +a stdio FILE pointer. \fBagsetfile\fP and \fBagreadline\fP +are helper functions that simply set the current file name +and input line number for subsequent error reporting. +.PP +\fBagsubg\fP finds or creates +a subgraph by name. A new subgraph is is initially empty and +is of the same kind as its parent. Nested subgraph trees may be created. +A subgraph's name is only interpreted relative to its parent. +A program can scan subgraphs under a given graph +using \fBagfstsubg\fP and \fRagnxtsubg\fP. A subgraph is +deleted with \fBagdelsubg\fP (or \fBagclose\fP). +.PP +By default, nodes are stored in ordered sets for efficient random +access to insert, find, and delete nodes. +The edges of a node are also stored in ordered sets. +The sets are maintained internally as splay tree dictionaries +using Phong Vo's cdt library. +.PP +\fBagnnodes\fP, \fBagnedges\fP, and \fBagdegree\fP return the +sizes of node and edge sets of a graph. The \fBagdegree\fP returns +the size of the edge set of a nodes, and takes flags +to select in-edges, out-edges, or both. +.PP +An \fBAgdisc_t\fP defines callbacks to be invoked by libcgraph when +initializing, modifying, or finalizing graph objects. (Casual users can ignore +the following.) Disciplines are kept on a stack. Libcgraph automatically +calls the methods on the stack, top-down. Callbacks are installed +with \fBagpushdisc\fP, uninstalled with \fBagpopdisc\fP, and +can be held pending or released via \fBagcallbacks\fP. +.PP +(Casual users may ignore the following. +When Libcgraph is compiled with Vmalloc (which is not the default), +each graph has its own heap. +Programmers may allocate application-dependent data within the +same heap as the rest of the graph. The advantage is that +a graph can be deleted by atomically freeing its entire heap +without scanning each individual node and edge. +.SH "NODES" +A node is created by giving a unique string name or +programmer defined 32-bit ID, and is represented by a +unique internal object. (Node equality can checked +by pointer comparison.) +.PP +\fBagnode\fP searches in a graph or subgraph for a node +with the given name, and returns it if found. +If not found, if \fBcreateflag\fP is boolean true +a new node is created and returned, otherwise a nil +pointer is returned. +\fBagidnode\fP allows a programmer to specify the node +by a unique 32-bit ID. +\fBagsubnode\fP performs a similar operation on +an existing node and a subgraph. +.Pp +\fBagfstnode\fP and \fBagnxtnode\fP scan node lists. +\fBagprvnode\fP and \fPaglstnode\fP are symmetric but scan backward. +The default sequence is order of creation (object timestamp.) +\fBagdelnode\fP removes a node from a graph or subgraph. +.SH "EDGES" +.PP +An abstract edge has two endpoint nodes called tail and head +where the all outedges of the same node have it as the tail +value and similarly all inedges have it as the head. +In an undirected graph, head and tail are interchangable. +If a graph has multi-edges between the same pair of nodes, +the edge's string name behaves as a secondary key. +.Pp +\fBagedge\fP searches in a graph of subgraph for an +edge between the given endpoints (with an optional +multi-edge selector name) and returns it if found. +Otherwise, if \fBcreateflag\fP is boolean true, +a new edge is created and returned: otherwise +a nil pointer is returned. If the \fBname\fP +is NULL, then an anonymous internal +value is generated. \fBagidedge\fP allows a programmer +to create an edge by giving its unique 32-bit ID. +\fBagfstin\fP, \fBagnxtint\fP, \fBagfstout\fP, and +\fBagnxtout\fP visit directed in- and out- edge lists, +and ordinarily apply only in directed graphs. +\fBagfstedge\fP and \fBagnxtedge\fP visit all edges +incident to a node. \fBagtail\fP and \fBaghead\fP +get the endpoint of an edge. +.SH "INTERNAL ATTRIBUTES" +Programmer-defined values may be dynamically +attached to graphs, subgraphs, nodes, and edges. +Such values are either uninterpreted binary records +(for implementing efficient algorithms) +or character string data (for I/O). +.SH "STRING ATTRIBUTES" +String attributes are handled automatically in reading +and writing graph files. +A string attribute is identified by name and by +an internal symbol table entry (\fBAgsym_t\fP) created by Libcgraph. +Attributes of nodes, edges, and graphs (with their subgraphs) +have separate namespaces. The contents of an \fBAgsym_t\fP +is listed below, followed by primitives to operate on string +attributes. +.P0 +typedef struct Agsym_s { /* symbol in one of the above dictionaries */ + Dtlink_t link; + char *name; /* attribute's name */ + char *defval; /* its default value for initialization */ + int id; /* its index in attr[] */ + unsigned char kind; /* referent object type */ + unsigned char fixed; /* immutable value */ +} Agsym_t; +.P1 +.PP +\fBagattr\fP creates or looks up attributes. +\fBkind\fP may be \fBAGRAPH\fP, \fBAGNODE\fP, or \fBAGEDGE\fP. +If \fBvalue\fP is \fB(char*)0)\fP, the request is to search +for an existing attribute of the given kind and name. +Otherwise, if the attribute already exists, its default +for creating new objects is set to the given value; +if it does not exist, a new attribute is created with the +given default, and the default is applied to all pre-existing +objects of the given kind. If \fBg\fP is NIL, the default is +set for all graphs created subsequently. +\fBagattrsym\fP is a helper function +that looks up an attribute for a graph object given as an argument. +\fBagnxtattr\fP permits traversing the list of attributes of +a given type. If \fBNIL\fP is passed as an argument it gets +the first attribute, otherwise it returns the next one in +succession or returns \fBNIL\fP at the end of the list. +\fBagget\fP and \fPagset\fP allow fetching and updating a +string attribute for an object taking the attribute name as +an argument. \fBagxget\fP and \fBagxset\fP do this but with +an attribute symbol table entry as an argument (to avoid +the cost of the string lookup). \fBagsafeset\fP is a +convenience function that ensures the given attribute is +declared before setting it locally on an object. + +.SH "STRINGS" +Libcgraph performs its own storage management of strings as +reference-counted strings. +The caller does not need to dynamically allocate storage. +.PP +\fBagstrdup\fP returns a pointer to a reference-counted copy of +the argument string, creating one if necessary. \fBagstrbind\fP +returns a pointer to a reference-counted string if it exists, or NULL if not. +All uses of cgraph strings need to be freed using \fBagstrfree\fP +in order to correctly maintain the reference count. +.PP +\fBagcanonStr\fP returns a pointer to a version of the input string +canonicalized for output for later re-parsing. This includes quoting +special characters and keywords. It uses its own internal buffer, so +the value will be lost on the next call to \fBagcanonStr\fP. +\fBagstrcanon\fP is an unsafe version of \fBagcanonStr\fP, in which +the application passes in a buffer as the second argument. Note that +the buffer may not be used; if the input string is in canonical form, +the function will just return a pointer to it. +.PP +The cgraph parser handles HTML-like strings. These should be +indistinguishable from other strings for most purposes. To create +an HTML-like string, use \fBagstrdup_html\fP. The \fBaghtmlstr\fP +function can be used to query if a string is an ordinary string or +an HTML-like string. +.SH "RECORDS" +Uninterpreted records may be attached to graphs, subgraphs, nodes, +and edges for efficient operations on values such as marks, weights, +counts, and pointers needed by algorithms. Application programmers +define the fields of these records, but they must be declared with +a common header as shown below. +.P0 +typedef struct Agrec_s { + Agrec_t header; + /* programmer-defined fields follow */ +} Agrec_t; +.P1 +Records are created and managed by Libcgraph. A programmer must +explicitly attach them to the objects in a graph, either to +individual objects one at a time via \fBagbindrec\fP, or to +all the objects of the same class in a graph via \fBaginit\fP. +The \fBname\fP argument a record distinguishes various types of records, +and is programmer defined (Libcgraph reserves the prefix \fB_ag\fR). +If size is 0, the call to \fBagbindrec\fP is simply a lookup. +\fBagdelrec\fP is the deletes records one at a time. +\fBagclean\fP does the same for all objects of the same +class in an entire graph. + +Internally, records are maintained in circular linked lists +attached to graph objects. +To allow referencing application-dependent data without function +calls or search, Libcgraph allows setting and locking the list +pointer of a graph, node, or edge on a particular record. +This pointer can be obtained with the macro \fBAGDATA(obj)\fP. +A cast, generally within a macro or inline function, +is usually applied to convert the list pointer to +an appropriate programmer-defined type. + +To control the setting of this pointer, +the \fBmove_to_front\fP flag may be \fBAG_MTF_FALSE\fP, +\fBAG_MTF_SOFT\fP, or \fBAG_MTF_HARD\fP accordingly. +The \fBAG_MTF_SOFT\fP field is only a hint that decreases +overhead in subsequent calls of \fBaggetrec\fP; +\fBAG_MTF_HARD\fP guarantees that a lock was obtained. +To release locks, use \fBAG_MTF_SOFT\fP or \fBAG_MTF_FALSE\fP. +Use of this feature implies cooperation or at least isolation +from other functions also using the move-to-front convention. + +.SH "DISCIPLINES" +(The following is not intended for casual users.) +Programmer-defined disciplines customize certain resources- +ID namespace, memory, and I/O - needed by Libcgraph. +A discipline struct (or NIL) is passed at graph creation time. +.P0 +struct Agdisc_s { /* user's discipline */ + Agmemdisc_t *mem; + Agiddisc_t *id; + Agiodisc_t *io; +} ; +.P1 +A default discipline is supplied when NIL is given for +any of these fields. + +An ID allocator discipline allows a client to control assignment +of IDs (uninterpreted 32-bit values) to objects, and possibly how +they are mapped to and from strings. + +.P0 +struct Agiddisc_s { /* object ID allocator */ + void *(*open)(Agraph_t *g); /* associated with a graph */ + int (*map)(void *state, int objtype, char *str, ulong *id, int createflag); + int (*alloc)(void *state, int objtype, ulong id); + void (*free)(void *state, int objtype, ulong id); + char *(*print)(void *state, int objtype, ulong id); + void (*close)(void *state); +} ; +.P1 + +\f5open\fP permits the ID discipline to initialize any data +structures that maintains per individual graph. +Its return value is then passed as the first argument to +all subsequent ID manager calls. + +\f5alloc\fP informs the ID manager that Libcgraph is attempting +to create an object with a specific ID that was given by a client. +The ID manager should return TRUE (nonzero) if the ID can be +allocated, or FALSE (which aborts the operation). + +\f5free\fP is called to inform the ID manager that the +object labeled with the given ID is about to go out of existence. + +\f5map\fP is called to create or look-up IDs by string name +(if supported by the ID manager). Returning TRUE (nonzero) +in all cases means that the request succeeded (with a valid +ID stored through \f5result\fP. There are four cases: +.PP +\f5name != NULL\fP and \f5createflag == 1\fP: +This requests mapping a string (e.g. a name in a graph file) into a new ID. +If the ID manager can comply, then it stores the result and returns TRUE. +It is then also responsible for being able to \f5print\fP the ID again +as a string. Otherwise the ID manager may return FALSE but it must +implement the following (at least for graph file reading and writing to work): +.PP +\f5name == NULL\fP and \f5createflag == 1\fP: +The ID manager creates a unique new ID of its own choosing. +Although it may return FALSE if it does not support anonymous objects, +but this is strongly discouraged (to support "local names" in graph files.) +.PP +\f5name != NULL\fP and \f5createflag == 0\fP: +This is a namespace probe. If the name was previously mapped into +an allocated ID by the ID manager, then the manager must return this ID. +Otherwise, the ID manager may either return FALSE, or may store +any unallocated ID into result. (This is convenient, for example, +if names are known to be digit strings that are directly converted into 32 bit values.) +.PP +\f5name == NULL\fP and \f5createflag == 0\fP: forbidden. +.PP +\f5print\fP is allowed to return a pointer to a static buffer; +a caller must copy its value if needed past subsequent calls. +\f5NULL\fP should be returned by ID managers that do not map names. +.PP +The \f5map\fP and \f5alloc\fP calls do not pass a pointer to the +newly allocated object. If a client needs to install object +pointers in a handle table, it can obtain them via +new object callbacks. +.P0 +struct Agiodisc_s { + int (*fread)(void *chan, char *buf, int bufsize); + int (*putstr)(void *chan, char *str); + int (*flush)(void *chan); /* sync */ + /* error messages? */ +} ; + +struct Agmemdisc_s { /* memory allocator */ + void *(*open)(void); /* independent of other resources */ + void *(*alloc)(void *state, size_t req); + void *(*resize)(void *state, void *ptr, size_t old, size_t req); + void (*free)(void *state, void *ptr); + void (*close)(void *state); +} ; +.P1 + +.SH "EXAMPLE PROGRAM" +.P0 +#include <graphviz/cgraph.h> +typedef struct mydata_s {Agrec_t hdr; int x,y,z;} mydata; + +main(int argc, char **argv) +{ + Agraph_t *g; + Agnode_t *v; + Agedge_t *e; + Agsym_t *attr; + Dict_t *d + int cnt; + mydata *p; + + if (g = agread(stdin,NIL(Agdisc_t*))) { + cnt = 0; attr = 0; + while (attr = agnxtattr(g, AGNODE, attr)) cnt++; + printf("The graph %s has %d attributes\n",agnameof(g),cnt); + + /* make the graph have a node color attribute, default is blue */ + attr = agattr(g,AGNODE,"color","blue"); + + /* create a new graph of the same kind as g */ + h = agopen("tmp",g->desc); + + /* this is a way of counting all the edges of the graph */ + cnt = 0; + for (v = agfstnode(g); v; v = agnxtnode(g,v)) + for (e = agfstout(g,v); e; e = agnxtout(g,e)) + cnt++; + + /* attach records to edges */ + for (v = agfstnode(g); v; v = agnxtnode(g,v)) + for (e = agfstout(g,v); e; e; = agnxtout(g,e)) { + p = (mydata*) agbindrec(g,e,"mydata",sizeof(mydata),TRUE); + p->x = 27; /* meaningless data access example */ + ((mydata*)(AGDATA(e)))->y = 999; /* another example */ + } + } +} +.P1 +.SH "EXAMPLE GRAPH FILES" +.P0 +digraph G { + a -> b; + c [shape=box]; + a -> c [weight=29,label="some text]; + subgraph anything { + /* the following affects only x,y,z */ + node [shape=circle]; + a; x; y -> z; y -> z; /* multiple edges */ + } +} + +strict graph H { + n0 -- n1 -- n2 -- n0; /* a cycle */ + n0 -- {a b c d}; /* a star */ + n0 -- n3; + n0 -- n3 [weight=1]; /* same edge because graph is strict */ +} +.P1 +.SH "SEE ALSO" +Libcdt(3) + +.SH "BUGS" +It is difficult to change endpoints of edges, delete string attributes or +modify edge keys. The work-around is to create a new object and copy the +contents of an old one (but new object obviously has a different ID, +internal address, and object creation timestamp). + +The API lacks convenient functions to substitute programmer-defined ordering of +nodes and edges but in principle this can be supported. +.SH "AUTHOR" +Stephen North, north@research.att.com, AT&T Research.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man3/graph.3 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,270 @@ +.TH LIBGRAPH 3 "01 MARCH 1993" +.SH NAME +\fBlibgraph\fR \- abstract graph library +.SH SYNOPSIS +.ta .75i 1.5i 2.25i 3i 3.75i 4.5i 5.25i 6i +.PP +.nf +\f5 +#include <graphviz/graph.h> +void aginit(); +Agraph_t *agread(FILE*); +int agwrite(Agraph_t*, FILE*); +int agerrors(); +Agraph_t *agopen(char *name, int kind); +void agclose(Agraph_t *g); +Agraph_t *agsubg(Agraph_t *g, char *name); +Agraph_t *agfindsubg(Agraph_t *g, char *name); +Agnode_t *agmetanode(Agraph_t *g); +Agraph_t *agusergraph(Agnode_t *metanode); +int agnnodes(Agraph_t *g), agnedges(Agraph_t *g); +.sp .i1 +int agcontains(Agraph_t *g, void *obj); +int aginsert(Agraph_t *g, void *obj); +int agdelete(Agraph_t *g, void *obj); +.sp .1i +Agnode_t *agnode(Agraph_t *g, char *name); +Agnode_t *agfindnode(Agraph_t *g, char *name); +Agnode_t *agfstnode(Agraph_t *g); +Agnode_t *agnxtnode(Agraph_t *g, Agnode_t *n); +Agnode_t *aglstnode(Agraph_t *g); +Agnode_t *agprvnode(Agraph_t *g, Agnode_t *n); +.sp .1i +Agedge_t *agedge(Agraph_t *g, Agnode_t *tail, Agnode_t *head); +Agedge_t *agfindedge(Agraph_t *g, Agnode_t *tail, Agnode_t *head); +Agedge_t *agfstedge(Agraph_t *g, Agnode_t *n); +Agedge_t *agnxtedge(Agraph_t *g, Agedge_t *e, Agnode_t *n); +Agedge_t *agfstin(Agraph_t *g, Agnode_t *n); +Agedge_t *agnxtin(Agraph_t *g, Agedge_t *e); +Agedge_t *agfstout(Agraph_t *g, Agnode_t *n); +Agedge_t *agnxtout(Agraph_t *g, Agedge_t *e); +.sp .1i +char *agget(void *obj, char *name); +char *agxget(void *obj, int index); +void agset(void *obj, char *name, char *value); +void agxset(void *obj, int index, char *value); +int agindex(void *obj, char *name); +.sp .1i +Agsym_t* agraphattr(Agraph_t *g,char *name,char *value); +Agsym_t* agnodeattr(Agraph_t *g,char *name,char *value); +Agsym_t* agedgeattr(Agraph_t *g,char *name,char *value); +Agsym_t* agfindattr(void *obj,char *name); +\fP +.fi +.SH DESCRIPTION +\fIlibgraph\fP maintains directed and undirected attributed graphs +in memory and reads and writes graph files. Graphs are composed of +nodes, edges, and nested subgraphs. A subgraph may contain any +nodes and edges of its parents, and may be passed to any +\fIlibgraph\fP function taking a graph pointer, except the three +that create new attributes (where a main graph is required). + +Attributes are internal or external. +Internal attributes are fields in the graph, node and edge structs +defined at compile time. +These allow efficient representation and direct access to values +such as marks, weights, and pointers for writing graph algorithms. +External attributes, on the other hand, are character strings +(name\(hyvalue pairs) dynamically allocated at runtime and accessed +through \fIlibgraph\fP calls. External attributes are used in +graph file I/O; internal attributes are not. Conversion between +internal and external attributes must be explicitly programmed. + +The subgraphs in a main graph are represented by an auxiliary directed +graph (a meta\(hygraph). Meta\(hynodes correspond to subgraphs, and meta\(hyedges +signify containment of one subgraph in another. +\f5agmetanode\fP and \f5agusergraph\fP map between +subgraphs and meta\(hynodes. The nodes and edges of the meta\(hygraph may +be traversed by the usual \fIlibgraph\fP functions for this purpose. + +.SH USE +1. Define types \f5Agraphinfo_t\fP, \f5Agnodeinfo_t\fP, +and \f5Agedgeinfo_t\fP (usually in a header file) before +including \f5<graphviz/graph.h>\fP. + +2. Call \f5aginit()\fP before any other \fIlibgraph\fP functions. +(This is a macro that calls \f5aginitlib()\fP to define the sizes +of Agraphinfo_t, Agnodeinfo_t, and Agedgeinfo_t.) + +3. Compile with \-lgraph \-lcdt. + +Except for the \fBu\fP fields, \fIlibgraph\fP +data structures must be considered read\(hyonly. +Corrupting their contents by direct updates can cause +catastrophic errors. + +.SH "GRAPHS" +.nf +\f5 +typedef struct Agraph_t { + char kind; + char *name; + Agraph_t *root; + char **attr; + graphdata_t *univ; + Dict_t *nodes,*inedges,*outedges; + proto_t *proto; + Agraphinfo_t u; +} Agraph_t; + +typedef struct graphdata_t { + Dict_t *node_dict; + attrdict_t *nodeattr, *edgeattr, *globattr; +} graphdata_t; + +typedef struct proto_t { + Agnode_t *n; + Agedge_t *e; + proto_t *prev; +} proto_t; +\fP +.fi +A graph \fIkind\fP is one of: +AGRAPH, AGRAPHSTRICT, AGDIGRAPH, or AGDIGRAPHSTRICT. +There are related macros for testing the properties of a graph: +AG_IS_DIRECTED(g) and AG_IS_STRICT(g). +Strict graphs cannot have self\(hyarcs or multi\(hyedges. +\fBattr\fP is the array of external attribute values. +\fBuniv\fP points to values shared by all subgraphs of a main graph. +\fBnodes\fP, \fBinedges\fP, and \fBoutedges\fP are sets maintained +by \fBcdt(3)\fP. Normally you don't access these dictionaries +directly, though the edge dictionaries may be re\(hyordered to support +programmer\(hydefined ordered edges (see \f5dtreorder\fP in \fIcdt(3)\fP). +\fBproto\fP is a stack of templates for node and edge initialization. +The attributes of these nodes and edges are set in the usual way (\f5agget\fP, +\f5agset\fP, etc.) to set defaults. +.PP +\f5agread\fP reads a file and returns a new graph if one +was succesfully parsed, otherwise returns NULL if +\f5EOF\fP or a syntax error was encountered. +Errors are reported on stderr and a count is returned from +\g5agerrors()\fP. +\f5write_graph\fP prints a graph on a file. +\f5agopen\fP and \f5agsubg\fP create new empty graph and subgraphs. +\f5agfindsubg\fP searches for a subgraph by name, returning NULL +when the search fails. + +.SH ALL OBJECTS +\f5agcontains\fP, \f5aginsert\fP, \f5agdelete\fP are generic functions +for nodes, edges, and graphs. \f5gcontains\fP is a predicate that tests +if an object belongs to the given graph. \f5aginsert\fP inserts an +object in a graph and \f5agdelete\fP undoes this operation. +A node or edge is destroyed (and its storage freed) at the time it +is deleted from the main graph. Likewise a subgraph is destroyed +when it is deleted from its last parent or when its last parent is deleted. + +.SH NODES +.nf +\f5 +typedef struct Agnode_t { + char *name; + Agraph_t *graph; + char **attr; + Agnodeinfo_t u; +} Agnode_t; +\fP +.fi + +\f5agnode\fP attempts to create a node. +If one with the requested name already exists, the old node +is returned unmodified. +Otherwise a new node is created, with attributed copied from g\->proto\->n. +\f5agfstnode\fP (\f5agnxtnode\fP) return the first (next) element +in the node set of a graph, respectively, or NULL. +\f5aglstnode\fP (\f5agprvnode\fP) return the last (previous) element +in the node set of a graph, respectively, or NULL. + +.SH EDGES +.nf +\f5 +typedef struct Agedge_t { + Agnode_t *head,*tail; + char **attr; + Agedgeinfo_t u; +} Agedge_t; +\fP +.fi +\f5agedge\fP creates a new edge with the attributes of g\->proto\->e +including its key if not empty. +\f5agfindedge\fP finds the first (u,v) edge in \f5g\fP. +\f5agfstedge\fP (\f5agnxtedge\fP) return the first (next) element +in the edge set of a graph, respectively, or NULL. +\f5agfstin\fP, \f5agnxtin\fP, \f5agfstout\fP, \f5agnxtout\fP +refer to in\(hy or out\(hyedge sets. +The idiomatic usage in a directed graph is: +.sp +\f5 for (e = agfstout(g,n); e; e = agnextout(g,e)) your_fun(e);\fP +.P +An edge is uniquely identified by its endpoints and its \f5key\fP +attribute (if there are multiple edges). +If the \f5key\fP of \f5g\->proto\->e\fP is empty, +new edges are assigned an internal value. +Edges also have \f5tailport\fP and \f5headport\fP values. +These have special syntax in the graph file language but are +not otherwise interpreted. +.PP +.SH ATTRIBUTES +.nf +\f5 +typedef struct attrsym_t { + char *name,*value; + int index; + unsigned char printed; +} attrsym_t; +.bp +typedef struct attrdict_t { + char *name; + Dict_t *dict; + attrsym_t **list; +} attrdict_t; +\fP +.fi +\f5agraphattr\fP, \f5agnodeattr\fP, and \f5agedgeattr\fP make new attributes. +\f5g\fP should be a main graph, or \f5NULL\fP for declarations +applying to all graphs subsequently read or created. +\f5agfindattr\fP searches for an existing attribute. +.PP +External attributes are accessed by \f5agget\fP and \f5agset\fP +These take a pointer to any graph, node, or edge, and an attribute name. +Also, each attribute has an integer index. For efficiency this index +may be passed instead of the name, by calling \f5agxget\fP and \f5agxset\fP. +The \f5printed\fP flag of an attribute may be set to 0 to skip it +when writing a graph file. +.PP +The \f5list\fP in an attribute dictionary is maintained in order of creation +and is NULL terminated. +Here is a program fragment to print node attribute names: +.nf + \f5attrsym_t *aptr; + for (i = 0; aptr = g\->univ\->nodedict\->list[i]; i++) puts(aptr\->name);\fP +.fi +.SH EXAMPLE GRAPH FILES +.nf +graph any_name { /* an undirected graph */ + a \-\- b; /* a simple edge */ + a \-\- x1 \-\- x2 \-\- x3; /* a chain of edges */ + "x3.a!" \-\- a; /* quotes protect special characters */ + b \-\- {q r s t}; /* edges that fan out */ + b [color="red",size=".5,.5"]; /* set various node attributes */ + node [color=blue]; /* set default attributes */ + b \-\- c [weight=25]; /* set edge attributes */ + subgraph sink_nodes {a b c}; /* make a subgraph */ +} + +digraph G { + size="8.5,11"; /* sets a graph attribute */ + a \-> b; /* makes a directed edge */ + chip12.pin1 \-> chip28.pin3; /* uses named node "ports" */ +} +.fi + +.SH SEE ALSO +.BR dot (1), +.BR neato (1), +.BR libdict (3) +.br +S. C. North and K. P. Vo, "Dictionary and Graph Libraries'' +1993 Winter USENIX Conference Proceedings, pp. 1\(hy11. + +.SH AUTHOR +Stephen North (north@ulysses.att.com), AT&T Bell Laboratories.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man3/gvc.3 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +.TH LIBGVC 3 +.SH NAME +\fBlibgvc\fR \- Graphviz context library +.SH SYNOPSIS +.ta .75i 1.5i 2.25i 3i 3.75i 4.5i 5.25i 6i +.PP +.nf +\f5 +#include <graphviz/gvc.h> + +/* set up a graphviz context */ +extern GVC_t *gvNEWcontext(char **info, char *user); +extern char *gvUsername(void); + +/* set up a graphviz context \(hy alternative */ +/* (wraps the above two functions using info built into libgvc) */ +extern GVC_t *gvContext(void); + +/* parse command line args \(hy minimally argv[0] sets layout engine */ +extern int gvParseArgs(GVC_t *gvc, int argc, char **argv); +extern graph_t *gvNextInputGraph(GVC_t *gvc); + +/* Compute a layout using a specified engine */ +extern int gvLayout(GVC_t *gvc, graph_t *g, char *engine); + +/* Compute a layout using layout engine from command line args */ +extern int gvLayoutJobs(GVC_t *gvc, graph_t *g); + +/* Render layout into string attributes of the graph */ +extern void attach_attrs(graph_t *g); + +/* Parse an html string */ +extern char *agstrdup_html(char *s); +extern int aghtmlstr(char *s); + +/* Render layout in a specified format to an open FILE */ +extern int gvRender(GVC_t *gvc, graph_t *g, char *format, FILE *out); + +/* Render layout in a specified format to an open FILE */ +extern int gvRenderFilename(GVC_t *gvc, graph_t *g, char *format, char *filename); + +/* Render layout according to \-T and \-o options found by gvParseArgs */ +extern int gvRenderJobs(GVC_t *gvc, graph_t *g); + +/* Clean up layout data structures \(hy layouts are not nestable (yet) */ +extern int gvFreeLayout(GVC_t *gvc, graph_t *g); + +/* Clean up graphviz context */ +extern int gvFreeContext(GVC_t *gvc); + +\fP +.fi +.SH DESCRIPTION +\fIlibgvc\fP provides a context for applications wishing to manipulate +and render graphs. It provides a command line parsing, common rendering code, +and a plugin mechanism for renderers. + +.SH SEE ALSO +.BR dot (1), +.BR neato (1), +.BR libcdt (3) +.BR libgraph (3) +.br + +.SH AUTHOR +John Ellson (ellson@research.att.com), AT&T
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man3/pathplan.3 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,97 @@ +.TH LIBPATH 3 "01 APRIL 1997" +.SH NAME +\fBlibpathplan\fR \- finds and smooths shortest paths +.SH SYNOPSIS +.ta .75i 1.5i 2.25i 3i 3.75i 4.5i 5.25i 6i +.PP +.nf +\f5 +#include <graphviz/pathplan.h> + +typedef struct Pxy_t { + double x, y; +} Pxy_t; + +typedef struct Pxy_t Ppoint_t; +typedef struct Pxy_t Pvector_t; + +typedef struct Ppoly_t { + Ppoint_t *ps; + int pn; +} Ppoly_t; + +typedef Ppoly_t Ppolyline_t; + +typedef struct Pedge_t { + Ppoint_t a, b; +} Pedge_t; + +typedef struct vconfig_s vconfig_t; + +#define POLYID_NONE +#define POLYID_UNKNOWN + +\fP +.fi +.SH FUNCTIONS + +.nf +\f5 +int Pshortestpath(Ppoly_t *boundary, Ppoint_t endpoints[2], Ppolyline_t *output_route); +\fP +.fi +Finds a shortest path between two points in a simple polygon. +You pass endpoints interior to the polygon boundary. +A shortest path connecting the points that remains in the polygon +is returned in output_route. If either endpoint does not lie in +the polygon, an error code is returned. (what code!!) + +.nf +\f5 +vconfig_t *Pobsopen(Ppoly_t **obstacles, int n_obstacles); +.br +int Pobspath(vconfig_t *config, Ppoint_t p0, int poly0, Ppoint_t p1, int poly1, Ppolyline_t *output_route); +.br +void Pobsclose(vconfig_t *config); +\fP +.fi +These functions find a shortest path between two points in a +simple polygon that possibly contains polygonal obstacles (holes). +\f5Pobsopen\fP creates a configuration (an opaque struct of type +\f5vconfig_t\fP) on a set of obstacles. \f5Pobspath\fP finds +a shortest path between the endpoints that remains outside the +obstacles. If the endpoints are known to lie inside obstacles, +\f5poly0\fP or \f5poly1\fP should be set to the index in the +\f5obstacles\fP array. If an endpoint is definitely not inside +an obstacle, then \f5POLYID_NONE\fP should be passed. If the +caller has not checked, then \f5POLYID_UNKNOWN\fP should be passed, +and the path library will perform the test. + +(!! there is no boundary polygon in this model?!!!) + +.nf +\f5 +int Proutespline (Pedge_t *barriers, int n_barriers, Ppolyline_t input_route, Pvector_t endpoint_slopes[2], + Ppolyline_t *output_route); +\fP +.fi + +This function fits a Bezier curve to a polyline path. +The curve must avoid a set of barrier segments. The polyline +is usually the \f5output_route\fP of one of the shortest path +finders, but it can be any simple path that doesn't cross +any obstacles. The input also includes endpoint slopes and +0,0 means unconstrained slope. + +Finally, this utility function converts an input list of polygons +into an output list of barrier segments: +.nf +\f5 +int Ppolybarriers(Ppoly_t **polys, int n_polys, Pedge_t **barriers, int *n_barriers); +\fP +.fi + +.SH AUTHORS +David Dobkin (dpd@cs.princeton.edu), +Eleftherios Koutsofios (ek@research.att.com), +Emden Gansner (erg@research.att.com).
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/graph_visualisation/share/man/man7/graphviz.7 Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,106 @@ +.\" -*- nroff -*- +.\" This manual is for graphviz, a rich set of graph drawing tools, and it +.\" can be distributed under the same terms as the graphviz package. +.\" +.\" Copyright (C) 2006 Cyril Brulebois <cyril.brulebois@enst-bretagne.fr> +.\" +.TH GRAPHVIZ 7 "November 19, 2006" +.SH "NAME" +graphviz \- rich set of graph drawing tools + +.SH "SYNOPSIS" +This manpage has been written to fulfil the need of a centralized documentation +presenting all available tools in the graphviz package. + +.SH "AVAILABLE TOOLS" + +.SS "Graph layout programs" +.TP +.B dot +filter for hierarchical layouts of graphs +.TP +.B neato +filter for symmetric layouts of graphs +.TP +.B twopi +filter for radial layouts of graphs +.TP +.B circo +filter for circular layout of graphs +.TP +.B fdp +filter for symmetric layouts of graphs +.PP +All of the filters work with either directed or undirected graphs, though +.B dot +is typically used for directed graphs and +.B neato +for undirected graphs. +Note also that \fBneato \-n[2]\fP can be used to render layouts produced +by the other filters. +.SS "Graph drawing programs" +.TP +.B lefty +A Programmable Graphics Editor +.TP +.B lneato +lefty + neato +.TP +.B dotty +lefty + dot + + +.SS "Graph layout enhancement" +.TP +.B gvcolor +flow colors through a ranked digraph +.TP +.B unflatten +adjust directed graphs to improve layout aspect ratio +.TP +.B gvpack +merge and pack disjoint graphs + +.SS "Graph information and transformation" +.TP +.B gc +count graph components +.TP +.B acyclic +make directed graph acyclic +.TP +.B nop +pretty-print graph file +.TP +.B ccomps +connected components filter for graphs +.TP +.B sccmap +extract strongly connected components of directed graphs +.TP +.B tred +transitive reduction filter for directed graphs +.TP +.B dijkstra +single-source distance filter +.TP +.B bcomps +biconnected components filter for graphs +.TP +.B gvpr +graph pattern scanning and processing language +.TP +.B prune +prune directed graphs + +.SS "Other" +.TP +.B "gxl2dot, dot2gxl" +GXL-DOT converters + +.SH "AUTHOR" +This manual page was written by Cyril Brulebois +<cyril.brulebois@enst\-bretagne.fr> in november 2006, based on an initial +documentation effort by Joachim Berdal Haga <jbh@lupus.ig3.net>. It can be +distributed under the same terms as the graphviz package. +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/mp3readwrite/demo_mp3readwrite.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,133 @@ +%% MP3 reading and writing +% +% These function, mp3read and mp3write, aim to exactly duplicate +% the operation of wavread and wavwrite for accessing soundfiles, +% except the soundfiles are in Mpeg-Audio layer 3 (MP3) compressed +% format. All the hard work is done by external binaries written +% by others: mp3info to query the format of existing mp3 files, +% mpg123 to decode mp3 files, and lame to encode audio files. +% Binaries for these files are widely available (and may be +% included in this distribution). +% +% These functions were originally developed for access to very +% large mp3 files (i.e. many hours long), and so avoid creating +% the entire uncompressed audio stream if possible. mp3read +% allows you to specify the range of frames you want to read +% (as a second argument), and mp3read will construct an mpg123 +% command that skips blocks to decode only the part of the file +% that is required. This can be much quicker (and require less +% memory/temporary disk) than decoding the whole file. +% +% mpg123 also provides for "on the fly" downsampling at conversion +% to mono, which are supported as extra options in mp3read. +% +% mpg123 can read MP3s across the network. This is supported +% if the FILE argument is a URL (e.g. beginning 'http://...'). +% +% mp3info sometimes gets the file size wrong (as returned by the +% mp3read(...'size') syntax). I'm not sure when this happens +% exactly, but it's probably a result of VBR files. In the worst +% case, figuring the number of samples in such a file requires +% scanning through the whole file, and mp3info doesn't usually do +% this. +% +% For more information, including advice on handling MP4 files, +% see http://labrosa.ee.columbia.edu/matlab/mp3read.html + +%% Example usage +% Here, we read a wav file in, then write it out as an MP3, then +% read the resulting MP3 back in, and compare it to the original +% file. + +% Read an audio waveform +[d,sr] = wavread('piano.wav'); +% Save to mp3 (default settings) +mp3write(d,sr,'piano.mp3'); +% Read it back again +[d2,sr] = mp3read('piano.mp3'); +% mp3 encoding involves some extra padding at each end; we attempt +% to cut it off at the start, but can't do that at the end, because +% mp3read doesn't know how long the original was. But we do, so.. +% Chop it down to be the same length as the original +d2 = d2(1:length(d),:); +% What is the SNR (distortion)? +ddiff = d - d2; +disp(['SNR is ',num2str(10*log10(sum(d(:).^2)/sum(ddiff(:).^2))),' dB']); +% Do they look similar? +subplot(211) +specgram(d(:,1),1024,sr); +subplot(212) +plot(1:5000,d(10000+(1:5000),1),1:5000,d2(10000+(1:5000))); +% Yes, pretty close +% +% NB: lame followed by mpg123 causes a little attenuation; you +% can get a better match by scaling up the read-back waveform: +ddiff = d - 1.052*d2; +disp(['SNR is ',num2str(10*log10(sum(d(:).^2)/sum(ddiff(:).^2))),' dB']); + +%% Delay, size, and alignment +% +% In mid-2006 I noticed that mp3read followed by mp3write followed by +% mp3read effectively delayed the waveform by 2257 samples (at 44 +% kHz). So I introduced code to discard the first 2257 samples to ensure +% that the waveforms remained time aligned. As best I could understand, +% mpg123 (v 0.5.9) was including the "warm-up" samples from the +% synthesis filterbank which are more properly discarded. +% +% Then in late 2009 I noticed that some chord recognition code, which +% used mp3read to read files which were then segmented on the basis of +% some hand-marked timings, suddenly started getting much poorer +% results. It turned out that I had upgraded my version of mpg123 to v +% 1.9.0, and the warm-up samples had been fixed in this version. So my +% code was discarding 2257 *good* samples, and the data was skewed 51ms +% early relative to the hand labels. +% +% Hence, the current version of mp3read does not +% discard any samples by default -- appropriate for the recent versions +% of mpg123 included here. But if you know you're running an old, v +% 0.5.9, mpg123, you should edit the mp3read.m source to set the flag +% MPG123059 = 1. +% +% Note also that the 'size' function relies on the number of +% blocks reported by mp3info. However, many mp3 files include +% additional information about the size of the file in the +% so-called Xing header, embedded in the first frame, which can +% specify that a certain number of samples from start and end +% should additionally be dropped. mp3info doesn't read that, +% and there's no way for my code to probe it except by running +% mpg123. Hence, the results of mp3read(fn,'size') may sometimes +% overestimate the length of the actual vector you'll get if +% you read the whole file. + +%% External binaries +% The m files rely on three external binaries, each of which is +% available for Linux, Mac OS X, or Windows: +% +% *mpg123* is a high-performance mp3 decoder. Its home page is +% http://www.mpg123.de/ . +% +% *mp3info* is a utility to read technical information on an mp3 +% file. Its home page is http://www.ibiblio.org/mp3info/ . +% +% *lame* is an open-source MP3 encoder. Its homepage is +% http://lame.sourceforge.net/ . +% +% The various authors of these packages are gratefully acknowledged +% for doing all the hard work to make these Matlab functions possible. + +%% Installation +% The two routines, mp3read.m and mp3write.m, will look for their +% binaries (mpg123 and mp3info for mp3read; lame for mp3write) in +% the same directory where they are installed. Binaries for +% different architectures are distinguished by their extension, +% which is the standard Matlab computer code e.g. ".mac" for Mac +% PPC OS X, ".glnx86" for i386-linux. The exception is Windows, +% where the binaries have the extension ".exe". +% +% Temporary files +% will be written to (a) a directory taken from the environment +% variable TMPDIR (b) /tmp if it exists, or (c) the current +% directory. This can easily be changed by editing the m files. + +% Last updated: $Date: 2009/03/15 18:29:58 $ +% Dan Ellis <dpwe@ee.columbia.edu>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/mp3readwrite/html/demo_mp3readwrite.html Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,233 @@ + +<!DOCTYPE html + PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> +<html><head> + <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> + <!-- +This HTML is auto-generated from an M-file. +To make changes, update the M-file and republish this document. + --><title>MP3 reading and writing</title><meta name="generator" content="MATLAB 7.10"><meta name="date" content="2010-04-09"><meta name="m-file" content="demo_mp3readwrite"><style type="text/css"> + +body { + background-color: white; + margin:10px; +} + +h1 { + color: #990000; + font-size: x-large; +} + +h2 { + color: #990000; + font-size: medium; +} + +/* Make the text shrink to fit narrow windows, but not stretch too far in +wide windows. */ +p,h1,h2,div.content div { + max-width: 600px; + /* Hack for IE6 */ + width: auto !important; width: 600px; +} + +pre.codeinput { + background: #EEEEEE; + padding: 10px; +} +@media print { + pre.codeinput {word-wrap:break-word; width:100%;} +} + +span.keyword {color: #0000FF} +span.comment {color: #228B22} +span.string {color: #A020F0} +span.untermstring {color: #B20000} +span.syscmd {color: #B28C00} + +pre.codeoutput { + color: #666666; + padding: 10px; +} + +pre.error { + color: red; +} + +p.footer { + text-align: right; + font-size: xx-small; + font-weight: lighter; + font-style: italic; + color: gray; +} + + </style></head><body><div class="content"><h1>MP3 reading and writing</h1><!--introduction--><p>These function, mp3read and mp3write, aim to exactly duplicate the operation of wavread and wavwrite for accessing soundfiles, except the soundfiles are in Mpeg-Audio layer 3 (MP3) compressed format. All the hard work is done by external binaries written by others: mp3info to query the format of existing mp3 files, mpg123 to decode mp3 files, and lame to encode audio files. Binaries for these files are widely available (and may be included in this distribution).</p><p>These functions were originally developed for access to very large mp3 files (i.e. many hours long), and so avoid creating the entire uncompressed audio stream if possible. mp3read allows you to specify the range of frames you want to read (as a second argument), and mp3read will construct an mpg123 command that skips blocks to decode only the part of the file that is required. This can be much quicker (and require less memory/temporary disk) than decoding the whole file.</p><p>mpg123 also provides for "on the fly" downsampling at conversion to mono, which are supported as extra options in mp3read.</p><p>mpg123 can read MP3s across the network. This is supported if the FILE argument is a URL (e.g. beginning 'http://...').</p><p>mp3info sometimes gets the file size wrong (as returned by the mp3read(...'size') syntax). I'm not sure when this happens exactly, but it's probably a result of VBR files. In the worst case, figuring the number of samples in such a file requires scanning through the whole file, and mp3info doesn't usually do this.</p><p>For more information, including advice on handling MP4 files, see <a href="http://labrosa.ee.columbia.edu/matlab/mp3read.html">http://labrosa.ee.columbia.edu/matlab/mp3read.html</a></p><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Example usage</a></li><li><a href="#2">Delay, size, and alignment</a></li><li><a href="#3">External binaries</a></li><li><a href="#4">Installation</a></li></ul></div><h2>Example usage<a name="1"></a></h2><p>Here, we read a wav file in, then write it out as an MP3, then read the resulting MP3 back in, and compare it to the original file.</p><pre class="codeinput"><span class="comment">% Read an audio waveform</span> +[d,sr] = wavread(<span class="string">'piano.wav'</span>); +<span class="comment">% Save to mp3 (default settings)</span> +mp3write(d,sr,<span class="string">'piano.mp3'</span>); +<span class="comment">% Read it back again</span> +[d2,sr] = mp3read(<span class="string">'piano.mp3'</span>); +<span class="comment">% mp3 encoding involves some extra padding at each end; we attempt</span> +<span class="comment">% to cut it off at the start, but can't do that at the end, because</span> +<span class="comment">% mp3read doesn't know how long the original was. But we do, so..</span> +<span class="comment">% Chop it down to be the same length as the original</span> +d2 = d2(1:length(d),:); +<span class="comment">% What is the SNR (distortion)?</span> +ddiff = d - d2; +disp([<span class="string">'SNR is '</span>,num2str(10*log10(sum(d(:).^2)/sum(ddiff(:).^2))),<span class="string">' dB'</span>]); +<span class="comment">% Do they look similar?</span> +subplot(211) +specgram(d(:,1),1024,sr); +subplot(212) +plot(1:5000,d(10000+(1:5000),1),1:5000,d2(10000+(1:5000))); +<span class="comment">% Yes, pretty close</span> +<span class="comment">%</span> +<span class="comment">% NB: lame followed by mpg123 causes a little attenuation; you</span> +<span class="comment">% can get a better match by scaling up the read-back waveform:</span> +ddiff = d - 1.052*d2; +disp([<span class="string">'SNR is '</span>,num2str(10*log10(sum(d(:).^2)/sum(ddiff(:).^2))),<span class="string">' dB'</span>]); +</pre><pre class="codeoutput">Warning: popenw not available, writing temporary file +SNR is 22.632 dB +SNR is 24.8699 dB +</pre><img vspace="5" hspace="5" src="demo_mp3readwrite_01.png" alt=""> <h2>Delay, size, and alignment<a name="2"></a></h2><p>In mid-2006 I noticed that mp3read followed by mp3write followed by mp3read effectively delayed the waveform by 2257 samples (at 44 kHz). So I introduced code to discard the first 2257 samples to ensure that the waveforms remained time aligned. As best I could understand, mpg123 (v 0.5.9) was including the "warm-up" samples from the synthesis filterbank which are more properly discarded.</p><p>Then in late 2009 I noticed that some chord recognition code, which used mp3read to read files which were then segmented on the basis of some hand-marked timings, suddenly started getting much poorer results. It turned out that I had upgraded my version of mpg123 to v 1.9.0, and the warm-up samples had been fixed in this version. So my code was discarding 2257 <b>good</b> samples, and the data was skewed 51ms early relative to the hand labels.</p><p>Hence, the current version of mp3read does not discard any samples by default -- appropriate for the recent versions of mpg123 included here. But if you know you're running an old, v 0.5.9, mpg123, you should edit the mp3read.m source to set the flag MPG123059 = 1.</p><p>Note also that the 'size' function relies on the number of blocks reported by mp3info. However, many mp3 files include additional information about the size of the file in the so-called Xing header, embedded in the first frame, which can specify that a certain number of samples from start and end should additionally be dropped. mp3info doesn't read that, and there's no way for my code to probe it except by running mpg123. Hence, the results of mp3read(fn,'size') may sometimes overestimate the length of the actual vector you'll get if you read the whole file.</p><h2>External binaries<a name="3"></a></h2><p>The m files rely on three external binaries, each of which is available for Linux, Mac OS X, or Windows:</p><p><b>mpg123</b> is a high-performance mp3 decoder. Its home page is <a href="http://www.mpg123.de/">http://www.mpg123.de/</a> .</p><p><b>mp3info</b> is a utility to read technical information on an mp3 file. Its home page is <a href="http://www.ibiblio.org/mp3info/">http://www.ibiblio.org/mp3info/</a> .</p><p><b>lame</b> is an open-source MP3 encoder. Its homepage is <a href="http://lame.sourceforge.net/">http://lame.sourceforge.net/</a> .</p><p>The various authors of these packages are gratefully acknowledged for doing all the hard work to make these Matlab functions possible.</p><h2>Installation<a name="4"></a></h2><p>The two routines, mp3read.m and mp3write.m, will look for their binaries (mpg123 and mp3info for mp3read; lame for mp3write) in the same directory where they are installed. Binaries for different architectures are distinguished by their extension, which is the standard Matlab computer code e.g. ".mac" for Mac PPC OS X, ".glnx86" for i386-linux. The exception is Windows, where the binaries have the extension ".exe".</p><p>Temporary files will be written to (a) a directory taken from the environment variable TMPDIR (b) /tmp if it exists, or (c) the current directory. This can easily be changed by editing the m files.</p><pre class="codeinput"><span class="comment">% Last updated: $Date: 2009/03/15 18:29:58 $</span> +<span class="comment">% Dan Ellis <dpwe@ee.columbia.edu></span> +</pre><p class="footer"><br> + Published with MATLAB® 7.10<br></p></div><!-- +##### SOURCE BEGIN ##### +%% MP3 reading and writing +% +% These function, mp3read and mp3write, aim to exactly duplicate +% the operation of wavread and wavwrite for accessing soundfiles, +% except the soundfiles are in Mpeg-Audio layer 3 (MP3) compressed +% format. All the hard work is done by external binaries written +% by others: mp3info to query the format of existing mp3 files, +% mpg123 to decode mp3 files, and lame to encode audio files. +% Binaries for these files are widely available (and may be +% included in this distribution). +% +% These functions were originally developed for access to very +% large mp3 files (i.e. many hours long), and so avoid creating +% the entire uncompressed audio stream if possible. mp3read +% allows you to specify the range of frames you want to read +% (as a second argument), and mp3read will construct an mpg123 +% command that skips blocks to decode only the part of the file +% that is required. This can be much quicker (and require less +% memory/temporary disk) than decoding the whole file. +% +% mpg123 also provides for "on the fly" downsampling at conversion +% to mono, which are supported as extra options in mp3read. +% +% mpg123 can read MP3s across the network. This is supported +% if the FILE argument is a URL (e.g. beginning 'http://...'). +% +% mp3info sometimes gets the file size wrong (as returned by the +% mp3read(...'size') syntax). I'm not sure when this happens +% exactly, but it's probably a result of VBR files. In the worst +% case, figuring the number of samples in such a file requires +% scanning through the whole file, and mp3info doesn't usually do +% this. +% +% For more information, including advice on handling MP4 files, +% see http://labrosa.ee.columbia.edu/matlab/mp3read.html + +%% Example usage +% Here, we read a wav file in, then write it out as an MP3, then +% read the resulting MP3 back in, and compare it to the original +% file. + +% Read an audio waveform +[d,sr] = wavread('piano.wav'); +% Save to mp3 (default settings) +mp3write(d,sr,'piano.mp3'); +% Read it back again +[d2,sr] = mp3read('piano.mp3'); +% mp3 encoding involves some extra padding at each end; we attempt +% to cut it off at the start, but can't do that at the end, because +% mp3read doesn't know how long the original was. But we do, so.. +% Chop it down to be the same length as the original +d2 = d2(1:length(d),:); +% What is the SNR (distortion)? +ddiff = d - d2; +disp(['SNR is ',num2str(10*log10(sum(d(:).^2)/sum(ddiff(:).^2))),' dB']); +% Do they look similar? +subplot(211) +specgram(d(:,1),1024,sr); +subplot(212) +plot(1:5000,d(10000+(1:5000),1),1:5000,d2(10000+(1:5000))); +% Yes, pretty close +% +% NB: lame followed by mpg123 causes a little attenuation; you +% can get a better match by scaling up the read-back waveform: +ddiff = d - 1.052*d2; +disp(['SNR is ',num2str(10*log10(sum(d(:).^2)/sum(ddiff(:).^2))),' dB']); + +%% Delay, size, and alignment +% +% In mid-2006 I noticed that mp3read followed by mp3write followed by +% mp3read effectively delayed the waveform by 2257 samples (at 44 +% kHz). So I introduced code to discard the first 2257 samples to ensure +% that the waveforms remained time aligned. As best I could understand, +% mpg123 (v 0.5.9) was including the "warm-up" samples from the +% synthesis filterbank which are more properly discarded. +% +% Then in late 2009 I noticed that some chord recognition code, which +% used mp3read to read files which were then segmented on the basis of +% some hand-marked timings, suddenly started getting much poorer +% results. It turned out that I had upgraded my version of mpg123 to v +% 1.9.0, and the warm-up samples had been fixed in this version. So my +% code was discarding 2257 *good* samples, and the data was skewed 51ms +% early relative to the hand labels. +% +% Hence, the current version of mp3read does not +% discard any samples by default REPLACE_WITH_DASH_DASH appropriate for the recent versions +% of mpg123 included here. But if you know you're running an old, v +% 0.5.9, mpg123, you should edit the mp3read.m source to set the flag +% MPG123059 = 1. +% +% Note also that the 'size' function relies on the number of +% blocks reported by mp3info. However, many mp3 files include +% additional information about the size of the file in the +% so-called Xing header, embedded in the first frame, which can +% specify that a certain number of samples from start and end +% should additionally be dropped. mp3info doesn't read that, +% and there's no way for my code to probe it except by running +% mpg123. Hence, the results of mp3read(fn,'size') may sometimes +% overestimate the length of the actual vector you'll get if +% you read the whole file. + +%% External binaries +% The m files rely on three external binaries, each of which is +% available for Linux, Mac OS X, or Windows: +% +% *mpg123* is a high-performance mp3 decoder. Its home page is +% http://www.mpg123.de/ . +% +% *mp3info* is a utility to read technical information on an mp3 +% file. Its home page is http://www.ibiblio.org/mp3info/ . +% +% *lame* is an open-source MP3 encoder. Its homepage is +% http://lame.sourceforge.net/ . +% +% The various authors of these packages are gratefully acknowledged +% for doing all the hard work to make these Matlab functions possible. + +%% Installation +% The two routines, mp3read.m and mp3write.m, will look for their +% binaries (mpg123 and mp3info for mp3read; lame for mp3write) in +% the same directory where they are installed. Binaries for +% different architectures are distinguished by their extension, +% which is the standard Matlab computer code e.g. ".mac" for Mac +% PPC OS X, ".glnx86" for i386-linux. The exception is Windows, +% where the binaries have the extension ".exe". +% +% Temporary files +% will be written to (a) a directory taken from the environment +% variable TMPDIR (b) /tmp if it exists, or (c) the current +% directory. This can easily be changed by editing the m files. + +% Last updated: $Date: 2009/03/15 18:29:58 $ +% Dan Ellis <dpwe@ee.columbia.edu> + +##### SOURCE END ##### +--></body></html> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/mp3readwrite/license.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +Copyright (c) 2010, Daniel Ellis +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the distribution + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/mp3readwrite/mp3read.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,403 @@ +function [Y,FS,NBITS,OPTS] = mp3read(FILE,N,MONO,DOWNSAMP,DELAY) +% MP3READ Read MP3 audio file via use of external binaries. +% Y = MP3READ(FILE) reads an mp3-encoded audio file into the +% vector Y just like wavread reads a wav-encoded file (one channel +% per column). Extension ".mp3" is added if FILE has none. +% Also accepts other formats of wavread, such as +% Y = MP3READ(FILE,N) to read just the first N sample frames (N +% scalar), or the frames from N(1) to N(2) if N is a two-element vector. +% Y = MP3READ(FILE,FMT) or Y = mp3read(FILE,N,FMT) +% with FMT as 'native' returns int16 samples instead of doubles; +% FMT can be 'double' for default behavior (to exactly mirror the +% syntax of wavread). +% +% [Y,FS,NBITS,OPTS] = MP3READ(FILE...) returns extra information: +% FS is the sampling rate, NBITS is the bit depth (always 16), +% OPTS.fmt is a format info string; OPTS has multiple other +% fields, see WAVREAD. +% +% SIZ = MP3READ(FILE,'size') returns the size of the audio data contained +% in the file in place of the actual audio data, returning the +% 2-element vector SIZ=[samples channels]. +% +% [Y...] = MP3READ(FILE,N,MONO,DOWNSAMP,DELAY) extends the +% WAVREAD syntax to allow access to special features of the +% mpg123 engine: MONO = 1 forces output to be mono (by +% averaging stereo channels); DOWNSAMP = 2 or 4 downsamples by +% a factor of 2 or 4 (thus FS returns as 22050 or 11025 +% respectively for a 44 kHz mp3 file); +% To accommodate a bug in mpg123-0.59, DELAY controls how many +% "warm up" samples to drop at the start of the file; the +% default value of 2257 makes an mp3write/mp3read loop for a 44 +% kHz mp3 file be as close as possible to being temporally +% aligned; specify as 0 to prevent discard of initial samples. +% For later versions of mpg123 (e.g. 1.9.0) this is not needed; +% a flag in mp3read.m makes the default DELAY zero in this case. +% +% [Y...] = MP3READ(URL...) uses the built-in network +% functionality of mpg123 to read an MP3 file across the +% network. URL must be of the form 'http://...' or +% 'ftp://...'. 'size' and OPTS are not available in this mode. +% +% Example: +% To read an mp3 file as doubles at its original width and sampling rate: +% [Y,FS] = mp3read('piano.mp3'); +% To read the first 1 second of the same file, downsampled by a +% factor of 4, cast to mono, using the default filename +% extension: +% [Y,FS4] = mp3read('piano', FS/4, 1, 4); +% +% Note: Because the mp3 format encodes samples in blocks of 26 ms (at +% 44 kHz), and because of the "warm up" period of the encoder, +% the file length may not be exactly what you expect, depending +% on your version of mpg123 (recent versions fix warmup). +% +% Note: requires external binaries mpg123 and mp3info; you +% can find binaries for several platforms at: +% http://labrosa.ee.columbia.edu/matlab/mp3read.html +% +% See also mp3write, wavread. + +% $Header: /Users/dpwe/matlab/columbiafns/RCS/mp3read.m,v 1.7 2010/04/09 18:13:00 dpwe Exp dpwe $ + +% 2003-07-20 dpwe@ee.columbia.edu This version calls mpg123. +% 2004-08-31 Fixed to read whole files correctly +% 2004-09-08 Uses mp3info to get info about mp3 files too +% 2004-09-18 Reports all mp3info fields in OPTS.fmt; handles MPG2LSF sizes +% + added MONO, DOWNSAMP flags, changed default behavior. +% 2005-09-28 Fixed bug reading full-rate stereo as 1ch (thx bjoerns@vjk.dk) +% 2006-09-17 Chop off initial 2257 sample delay (for 44.1 kHz mp3) +% so read-write loop doesn't get progressively delayed. +% You can suppress this with a 5th argument of 0. +% 2007-02-04 Added support for FMT argument to match wavread +% Added automatic selection of binary etc. to allow it +% to work cross-platform without editing prior to +% submitting to Matlab File Exchange +% 2007-07-23 Tweaks to 'size' mode so it exactly agrees with read data. +% 2009-03-15 Added fixes so 'http://...' file URLs will work. +% 2009-03-26 Added filename length check to http: test (thx fabricio guzman) + +% find our baseline directory +path = fileparts(which('mp3read')); + +% %%%%% Directory for temporary file (if needed) +% % Try to read from environment, or use /tmp if it exists, or use CWD +tmpdir = getenv('TMPDIR'); +if isempty(tmpdir) || exist(tmpdir,'file')==0 + tmpdir = '/tmp'; +end +if exist(tmpdir,'file')==0 + tmpdir = ''; +end +% ensure it exists +%if length(tmpdir) > 0 && exist(tmpdir,'file')==0 +% mkdir(tmpdir); +%end + +%%%%%% Command to delete temporary file (if needed) +rmcmd = 'rm'; + +%%%%%% Location of the binaries - attempt to choose automatically +%%%%%% (or edit to be hard-coded for your installation) +ext = lower(computer); +if ispc + ext = 'exe'; + rmcmd = 'del'; +end +% mpg123-0.59 inserts silence at the start of decoded files, which +% we compensate. However, this is fixed in mpg123-1.9.0, so +% make this flag 1 only if you have mpg123-0.5.9 +MPG123059 = 0; +mpg123 = fullfile(path,['mpg123.',ext]); +mp3info = fullfile(path,['mp3info.',ext]); + +%%%%% Check for network mode +if length(FILE) > 6 && (strcmp(lower(FILE(1:7)),'http://') == 1 ... + || strcmp(lower(FILE(1:6)),'ftp://')) + % mp3info not available over network + OVERNET = 1; +else + OVERNET = 0; +end + + +%%%%% Process input arguments +if nargin < 2 + N = 0; +end + +% Check for FMT spec (per wavread) +FMT = 'double'; +if ischar(N) + FMT = lower(N); + N = 0; +end + +if length(N) == 1 + % Specified N was upper limit + N = [1 N]; +end +if nargin < 3 + forcemono = 0; +else + % Check for 3rd arg as FMT + if ischar(MONO) + FMT = lower(MONO); + MONO = 0; + end + forcemono = (MONO ~= 0); +end +if nargin < 4 + downsamp = 1; +else + downsamp = DOWNSAMP; +end +if downsamp ~= 1 && downsamp ~= 2 && downsamp ~= 4 + error('DOWNSAMP can only be 1, 2, or 4'); +end + +% process DELAY option (nargin 5) after we've read the SR + +if strcmp(FMT,'native') == 0 && strcmp(FMT,'double') == 0 && ... + strcmp(FMT,'size') == 0 + error(['FMT must be ''native'' or ''double'' (or ''size''), not ''',FMT,'''']); +end + + +%%%%%% Constants +NBITS=16; + +%%%%% add extension if none (like wavread) +[path,file,ext] = fileparts(FILE); +if isempty(ext) + FILE = [FILE, '.mp3']; +end + +%%%%% maybe expand ~ %%%%%% +if FILE(1) == '~' + FILE = [getenv('HOME'),FILE(2:end)]; +end + + +if ~OVERNET + %%%%%% Probe file to find format, size, etc. using "mp3info" utility + cmd = ['"',mp3info, '" -r m -p "%Q %u %b %r %v * %C %e %E %L %O %o %p" "', FILE,'"']; + % Q = samprate, u = #frames, b = #badframes (needed to get right answer from %u) + % r = bitrate, v = mpeg version (1/2/2.5) + % C = Copyright, e = emph, E = CRC, L = layer, O = orig, o = mono, p = pad + w = mysystem(cmd); + % Break into numerical and ascii parts by finding the delimiter we put in + starpos = findstr(w,'*'); + nums = str2num(w(1:(starpos - 2))); + strs = tokenize(w((starpos+2):end)); + + SR = nums(1); + nframes = nums(2); + nchans = 2 - strcmp(strs{6}, 'mono'); + layer = length(strs{4}); + bitrate = nums(4)*1000; + mpgv = nums(5); + % Figure samples per frame, after + % http://board.mp3-tech.org/view.php3?bn=agora_mp3techorg&key=1019510889 + if layer == 1 + smpspfrm = 384; + elseif SR < 32000 && layer ==3 + smpspfrm = 576; + if mpgv == 1 + error('SR < 32000 but mpeg version = 1'); + end + else + smpspfrm = 1152; + end + + OPTS.fmt.mpgBitrate = bitrate; + OPTS.fmt.mpgVersion = mpgv; + % fields from wavread's OPTS + OPTS.fmt.nAvgBytesPerSec = bitrate/8; + OPTS.fmt.nSamplesPerSec = SR; + OPTS.fmt.nChannels = nchans; + OPTS.fmt.nBlockAlign = smpspfrm/SR*bitrate/8; + OPTS.fmt.nBitsPerSample = NBITS; + OPTS.fmt.mpgNFrames = nframes; + OPTS.fmt.mpgCopyright = strs{1}; + OPTS.fmt.mpgEmphasis = strs{2}; + OPTS.fmt.mpgCRC = strs{3}; + OPTS.fmt.mpgLayer = strs{4}; + OPTS.fmt.mpgOriginal = strs{5}; + OPTS.fmt.mpgChanmode = strs{6}; + OPTS.fmt.mpgPad = strs{7}; + OPTS.fmt.mpgSampsPerFrame = smpspfrm; +else + % OVERNET mode + OPTS = []; + % guesses + smpspfrm = 1152; + SR = 44100; + nframes = 0; +end + +if SR == 16000 && downsamp == 4 + error('mpg123 will not downsample 16 kHz files by 4 (only 2)'); +end + +% from libmpg123/frame.h +GAPLESS_DELAY = 529; + +% process or set delay +if nargin < 5 + + if MPG123059 + mpg123delay44kHz = 2257; % empirical delay of lame/mpg123 loop + mpg123delay16kHz = 1105; % empirical delay of lame/mpg123 loop + % for 16 kHz sampling - one 1152 + % sample frame less?? + if SR == 16000 + rawdelay = mpg123delay16kHz; + else + rawdelay = mpg123delay44kHz; % until we know better + end + delay = round(rawdelay/downsamp); + else + % seems like predelay is fixed in mpg123-1.9.0 + delay = 0; + end +else + delay = DELAY; +end + +if downsamp == 1 + downsampstr = ''; +else + downsampstr = [' -',num2str(downsamp)]; +end +FS = SR/downsamp; + +if forcemono == 1 + nchans = 1; + chansstr = ' -m'; +else + chansstr = ''; +end + +% Size-reading version +if strcmp(FMT,'size') == 1 + if MPG123059 + Y = [floor(smpspfrm*nframes/downsamp)-delay, nchans]; + else + Y = [floor(smpspfrm*nframes/downsamp)-GAPLESS_DELAY, nchans]; + end +else + + % Temporary file to use + tmpfile = fullfile(tmpdir, ['tmp',num2str(round(1000*rand(1))),'.wav']); + + skipx = 0; + skipblks = 0; + skipstr = ''; + sttfrm = N(1)-1; + + % chop off transcoding delay? + %sttfrm = sttfrm + delay; % empirically measured + % no, we want to *decode* those samples, then drop them + % so delay gets added to skipx instead + + if sttfrm > 0 + skipblks = floor(sttfrm*downsamp/smpspfrm); + skipx = sttfrm - (skipblks*smpspfrm/downsamp); + skipstr = [' -k ', num2str(skipblks)]; + end + skipx = skipx + delay; + + lenstr = ''; + endfrm = -1; + decblk = 0; + if length(N) > 1 + endfrm = N(2); + if endfrm > sttfrm + decblk = ceil((endfrm+delay)*downsamp/smpspfrm) - skipblks + 10; + % we read 10 extra blks (+10) to cover the case where up to 10 bad + % blocks are included in the part we are trying to read (it happened) + lenstr = [' -n ', num2str(decblk)]; + % This generates a spurious "Warn: requested..." if reading right + % to the last sample by index (or bad blks), but no matter. + end + end + + % Run the decode + cmd=['"',mpg123,'"', downsampstr, chansstr, skipstr, lenstr, ... + ' -q -w "', tmpfile,'" "',FILE,'"']; + %w = + mysystem(cmd); + + % Load the data (may update FS if it was based on a guess previously) + [Y,FS] = wavread(tmpfile); + +% % pad delay on to end, just in case +% Y = [Y; zeros(delay,size(Y,2))]; +% % no, the saved file is just longer + + if decblk > 0 && length(Y) < decblk*smpspfrm/downsamp + % This will happen if the selected block range includes >1 bad block + disp(['Warn: requested ', num2str(decblk*smpspfrm/downsamp),' frames, returned ',num2str(length(Y))]); + end + + % Delete tmp file + mysystem([rmcmd,' "', tmpfile,'"']); + + % debug +% disp(['sttfrm=',num2str(sttfrm),' endfrm=',num2str(endfrm),' skipx=',num2str(skipx),' delay=',num2str(delay),' len=',num2str(length(Y))]); + + % Select the desired part + if skipx+endfrm-sttfrm > length(Y) + endfrm = length(Y)+sttfrm-skipx; + end + + if endfrm > sttfrm + Y = Y(skipx+(1:(endfrm-sttfrm)),:); + elseif skipx > 0 + Y = Y((skipx+1):end,:); + end + + % Convert to int if format = 'native' + if strcmp(FMT,'native') + Y = int16((2^15)*Y); + end + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function w = mysystem(cmd) +% Run system command; report error; strip all but last line +[s,w] = system(cmd); +if s ~= 0 + error(['unable to execute ',cmd,' (',w,')']); +end +% Keep just final line +w = w((1+max([0,findstr(w,10)])):end); +% Debug +%disp([cmd,' -> ','*',w,'*']); + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function a = tokenize(s,t) +% Break space-separated string into cell array of strings. +% Optional second arg gives alternate separator (default ' ') +% 2004-09-18 dpwe@ee.columbia.edu +if nargin < 2; t = ' '; end +a = []; +p = 1; +n = 1; +l = length(s); +nss = findstr([s(p:end),t],t); +for ns = nss + % Skip initial spaces (separators) + if ns == p + p = p+1; + else + if p <= l + a{n} = s(p:(ns-1)); + n = n+1; + p = ns+1; + end + end +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/mp3readwrite/mp3write.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,173 @@ +function mp3write(D,SR,NBITS,FILE,OPTIONS) +% MP3WRITE Write MP3 file by use of external binary +% MP3WRITE(Y,FS,NBITS,FILE) writes waveform data Y to mp3-encoded +% file FILE at sampling rate FS using bitdepth NBITS. +% The syntax exactly mirrors WAVWRITE. NBITS must be 16. +% MP3WRITE(Y,FS,FILE) assumes NBITS is 16 +% MP3WRITE(Y,FILE) further assumes FS = 8000. +% +% MP3WRITE(..., OPTIONS) specifies additional compression control +% options as a string passed directly to the lame encoder +% program; default is '--quiet -h' for high-quality model. +% +% Example: +% To convert a wav file to mp3 (assuming the sample rate is +% supported): +% [Y,FS] = wavread('piano.wav'); +% mp3write(Y,FS,'piano.mp3'); +% To force lame to use 160 kbps (instead of default 128 kbps) +% with the default filename extension (mp3): +% mp3write(Y,FS,'piano','--quiet -h -b 160'); +% +% Note: The actual mp3 encoding is done by an external binary, +% lame, which is available for multiple platforms. Usable +% binaries are available from: +% http://labrosa.ee.columbia.edu/matlab/mp3read.html +% +% Note: MP3WRITE will use the mex file popenw, if available, to +% open a pipe to the lame encoder. Otherwise, it will have to +% write a large temporary file, then execute lame on that file. +% popenw is available at: +% http://labrosa.ee.columbia.edu/matlab/popenrw.html +% This is a nice way to save large audio files as the +% incremental output of your code, but you'll have to adapt the +% central loop of this function (rather than using it directly). +% +% See also: mp3read, wavwrite, popenw. + +% 2005-11-10 Original version +% 2007-02-04 Modified to exactly match wavwrite syntax, and to +% automatically find architecture-dependent binaries. +% 2007-07-26 Writing of stereo files via tmp file fixed (thx Yu-ching Lin) +% +% $Header: /Users/dpwe/matlab/columbiafns/RCS/mp3write.m,v 1.2 2007/07/26 15:09:16 dpwe Exp $ + +% find our baseline directory +[path] = fileparts(which('mp3write')); + +% %%%%% Directory for temporary file (if needed) +% % Try to read from environment, or use /tmp if it exists, or use CWD +tmpdir = getenv('TMPDIR'); +if isempty(tmpdir) || exist(tmpdir,'file')==0 + tmpdir = '/tmp'; +end +if exist(tmpdir,'file')==0 + tmpdir = ''; +end +% ensure it exists +%if length(tmpdir) > 0 && exist(tmpdir,'file')==0 +% mkdir(tmpdir); +%end + +%%%%%% Command to delete temporary file (if needed) +rmcmd = 'rm'; + +%%%%%% Location of the binary - attempt to choose automatically +%%%%%% (or edit to be hard-coded for your installation) +ext = lower(computer); +if ispc + ext = 'exe'; + rmcmd = 'del'; +end +lame = fullfile(path,['lame.',ext]); + +%%%% Process input arguments +% Do we have NBITS? +mynargin = nargin; +if ischar(NBITS) + % NBITS is a string i.e. it's actually the filename + if mynargin > 3 + OPTIONS = FILE; + end + FILE = NBITS; + NBITS = 16; + % it's as if NBITS had been specified... + mynargin = mynargin + 1; +end + +if mynargin < 5 + OPTIONS = '--quiet -h'; % -h means high-quality psych model +end + +[nr, nc] = size(D); +if nc < nr + D = D'; + [nr, nc] = size(D); +end +% Now rows are channels, cols are time frames (so interleaving is right) + +%%%%% add extension if none (like wavread) +[path,file,ext] = fileparts(FILE); +if isempty(ext) + FILE = [FILE, '.mp3']; +end + +nchan = nr; +nfrm = nc; + +if nchan == 1 + monostring = ' -m m'; +else + monostring = ''; +end + +lameopts = [' ', OPTIONS, monostring, ' ']; + +%if exist('popenw') == 3 +if length(which('popenw')) > 0 + + % We have the writable stream process extensions + cmd = ['"',lame,'"', lameopts, '-r -s ',num2str(SR),' - "',FILE,'"']; + + p = popenw(cmd); + if p < 0 + error(['Error running popen(',cmd,')']); + end + + % We feed the audio to the encoder in blocks of <blksize> frames. + % By adapting this loop, you can create your own code to + % write a single, large, MP3 file one part at a time. + + blksiz = 10000; + + nrem = nfrm; + base = 0; + + while nrem > 0 + thistime = min(nrem, blksiz); + done = popenw(p,32767*D(:,base+(1:thistime)),'int16be'); + nrem = nrem - thistime; + base = base + thistime; + %disp(['done=',num2str(done)]); + end + + % Close pipe + popenw(p,[]); + +else + disp('Warning: popenw not available, writing temporary file'); + + tmpfile = fullfile(tmpdir,['tmp',num2str(round(1000*rand(1))),'.wav']); + + wavwrite(D',SR,tmpfile); + + cmd = ['"',lame,'"', lameopts, '"',tmpfile, '" "', FILE, '"']; + + mysystem(cmd); + + % Delete tmp file + mysystem([rmcmd, ' "', tmpfile,'"']); + +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function w = mysystem(cmd) +% Run system command; report error; strip all but last line +[s,w] = system(cmd); +if s ~= 0 + error(['unable to execute ',cmd,' (',w,')']); +end +% Keep just final line +w = w((1+max([0,findstr(w,10)])):end); +% Debug +%disp([cmd,' -> ','*',w,'*']);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/wget/man/cat1/wget.1.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,1733 @@ +WGET(1) GNU Wget WGET(1) + + + +NAME + Wget - The non-interactive network downloader. + +SYNOPSIS + wget [option]... [URL]... + +DESCRIPTION + GNU Wget is a free utility for non-interactive download + of files from the Web. It supports HTTP, HTTPS, and FTP + protocols, as well as retrieval through HTTP proxies. + + Wget is non-interactive, meaning that it can work in the + background, while the user is not logged on. This + allows you to start a retrieval and disconnect from the + system, letting Wget finish the work. By contrast, most + of the Web browsers require constant user's presence, + which can be a great hindrance when transferring a lot + of data. + + Wget can follow links in HTML and XHTML pages and create + local versions of remote web sites, fully recreating the + directory structure of the original site. This is some- + times referred to as "recursive downloading." While + doing that, Wget respects the Robot Exclusion Standard + (/robots.txt). Wget can be instructed to convert the + links in downloaded HTML files to the local files for + offline viewing. + + Wget has been designed for robustness over slow or + unstable network connections; if a download fails due to + a network problem, it will keep retrying until the whole + file has been retrieved. If the server supports reget- + ting, it will instruct the server to continue the down- + load from where it left off. + +OPTIONS + Option Syntax + + Since Wget uses GNU getopt to process command-line argu- + ments, every option has a long form along with the short + one. Long options are more convenient to remember, but + take time to type. You may freely mix different option + styles, or specify options after the command-line argu- + ments. Thus you may write: + + wget -r --tries=10 http://fly.srk.fer.hr/ -o log + + The space between the option accepting an argument and + the argument may be omitted. Instead of -o log you can + write -olog. + + You may put several options that do not require argu- + ments together, like: + + wget -drc <URL> + + This is a complete equivalent of: + + wget -d -r -c <URL> + + Since the options can be specified after the arguments, + you may terminate them with --. So the following will + try to download URL -x, reporting failure to log: + + wget -o log -- -x + + The options that accept comma-separated lists all + respect the convention that specifying an empty list + clears its value. This can be useful to clear the + .wgetrc settings. For instance, if your .wgetrc sets + "exclude_directories" to /cgi-bin, the following example + will first reset it, and then set it to exclude /~nobody + and /~somebody. You can also clear the lists in + .wgetrc. + + wget -X " -X /~nobody,/~somebody + + Most options that do not accept arguments are boolean + options, so named because their state can be captured + with a yes-or-no ("boolean") variable. For example, + --follow-ftp tells Wget to follow FTP links from HTML + files and, on the other hand, --no-glob tells it not to + perform file globbing on FTP URLs. A boolean option is + either affirmative or negative (beginning with --no). + All such options share several properties. + + Unless stated otherwise, it is assumed that the default + behavior is the opposite of what the option accom- + plishes. For example, the documented existence of + --follow-ftp assumes that the default is to not follow + FTP links from HTML pages. + + Affirmative options can be negated by prepending the + --no- to the option name; negative options can be + negated by omitting the --no- prefix. This might seem + superfluous---if the default for an affirmative option + is to not do something, then why provide a way to + explicitly turn it off? But the startup file may in + fact change the default. For instance, using "fol- + low_ftp = off" in .wgetrc makes Wget not follow FTP + links by default, and using --no-follow-ftp is the only + way to restore the factory default from the command + line. + + Basic Startup Options + + + -V + --version + Display the version of Wget. + + -h + --help + Print a help message describing all of Wget's com- + mand-line options. + + -b + --background + Go to background immediately after startup. If no + output file is specified via the -o, output is redi- + rected to wget-log. + + -e command + --execute command + Execute command as if it were a part of .wgetrc. A + command thus invoked will be executed after the com- + mands in .wgetrc, thus taking precedence over them. + If you need to specify more than one wgetrc command, + use multiple instances of -e. + + Logging and Input File Options + + + -o logfile + --output-file=logfile + Log all messages to logfile. The messages are nor- + mally reported to standard error. + + -a logfile + --append-output=logfile + Append to logfile. This is the same as -o, only it + appends to logfile instead of overwriting the old + log file. If logfile does not exist, a new file is + created. + + -d + --debug + Turn on debug output, meaning various information + important to the developers of Wget if it does not + work properly. Your system administrator may have + chosen to compile Wget without debug support, in + which case -d will not work. Please note that com- + piling with debug support is always safe---Wget com- + piled with the debug support will not print any + debug info unless requested with -d. + + -q + --quiet + Turn off Wget's output. + + -v + --verbose + Turn on verbose output, with all the available data. + The default output is verbose. + + -nv + --no-verbose + Turn off verbose without being completely quiet (use + -q for that), which means that error messages and + basic information still get printed. + + -i file + --input-file=file + Read URLs from file. If - is specified as file, + URLs are read from the standard input. (Use ./- to + read from a file literally named -.) + + If this function is used, no URLs need be present on + the command line. If there are URLs both on the + command line and in an input file, those on the com- + mand lines will be the first ones to be retrieved. + The file need not be an HTML document (but no harm + if it is)---it is enough if the URLs are just listed + sequentially. + + However, if you specify --force-html, the document + will be regarded as html. In that case you may have + problems with relative links, which you can solve + either by adding "<base href="url">" to the docu- + ments or by specifying --base=url on the command + line. + + -F + --force-html + When input is read from a file, force it to be + treated as an HTML file. This enables you to + retrieve relative links from existing HTML files on + your local disk, by adding "<base href="url">" to + HTML, or using the --base command-line option. + + -B URL + --base=URL + Prepends URL to relative links read from the file + specified with the -i option. + + Download Options + + + --bind-address=ADDRESS + When making client TCP/IP connections, bind to + ADDRESS on the local machine. ADDRESS may be speci- + fied as a hostname or IP address. This option can + be useful if your machine is bound to multiple IPs. + + -t number + --tries=number + Set number of retries to number. Specify 0 or inf + for infinite retrying. The default is to retry 20 + times, with the exception of fatal errors like "con- + nection refused" or "not found" (404), which are not + retried. + + -O file + --output-document=file + The documents will not be written to the appropriate + files, but all will be concatenated together and + written to file. If - is used as file, documents + will be printed to standard output, disabling link + conversion. (Use ./- to print to a file literally + named -.) + + Use of -O is not intended to mean simply "use the + name file instead of the one in the URL;" rather, it + is analogous to shell redirection: wget -O file + http://foo is intended to work like wget -O - + http://foo > file; file will be truncated immedi- + ately, and all downloaded content will be written + there. + + For this reason, -N (for timestamp-checking) is not + supported in combination with -O: since file is + always newly created, it will always have a very new + timestamp. A warning will be issued if this combina- + tion is used. + + Similarly, using -r or -p with -O may not work as + you expect: Wget won't just download the first file + to file and then download the rest to their normal + names: all downloaded content will be placed in + file. This was disabled in version 1.11, but has + been reinstated (with a warning) in 1.11.2, as there + are some cases where this behavior can actually have + some use. + + Note that a combination with -k is only permitted + when downloading a single document, as in that case + it will just convert all relative URIs to external + ones; -k makes no sense for multiple URIs when + they're all being downloaded to a single file. + + -nc + --no-clobber + If a file is downloaded more than once in the same + directory, Wget's behavior depends on a few options, + including -nc. In certain cases, the local file + will be clobbered, or overwritten, upon repeated + download. In other cases it will be preserved. + + When running Wget without -N, -nc, -r, or p, down- + loading the same file in the same directory will + result in the original copy of file being preserved + and the second copy being named file.1. If that + file is downloaded yet again, the third copy will be + named file.2, and so on. When -nc is specified, + this behavior is suppressed, and Wget will refuse to + download newer copies of file. Therefore, + ""no-clobber"" is actually a misnomer in this + mode---it's not clobbering that's prevented (as the + numeric suffixes were already preventing clobber- + ing), but rather the multiple version saving that's + prevented. + + When running Wget with -r or -p, but without -N or + -nc, re-downloading a file will result in the new + copy simply overwriting the old. Adding -nc will + prevent this behavior, instead causing the original + version to be preserved and any newer copies on the + server to be ignored. + + When running Wget with -N, with or without -r or -p, + the decision as to whether or not to download a + newer copy of a file depends on the local and remote + timestamp and size of the file. -nc may not be + specified at the same time as -N. + + Note that when -nc is specified, files with the suf- + fixes .html or .htm will be loaded from the local + disk and parsed as if they had been retrieved from + the Web. + + -c + --continue + Continue getting a partially-downloaded file. This + is useful when you want to finish up a download + started by a previous instance of Wget, or by + another program. For instance: + + wget -c ftp://sunsite.doc.ic.ac.uk/ls-lR.Z + + If there is a file named ls-lR.Z in the current + directory, Wget will assume that it is the first + portion of the remote file, and will ask the server + to continue the retrieval from an offset equal to + the length of the local file. + + Note that you don't need to specify this option if + you just want the current invocation of Wget to + retry downloading a file should the connection be + lost midway through. This is the default behavior. + -c only affects resumption of downloads started + prior to this invocation of Wget, and whose local + files are still sitting around. + + Without -c, the previous example would just download + the remote file to ls-lR.Z.1, leaving the truncated + ls-lR.Z file alone. + + Beginning with Wget 1.7, if you use -c on a non- + empty file, and it turns out that the server does + not support continued downloading, Wget will refuse + to start the download from scratch, which would + effectively ruin existing contents. If you really + want the download to start from scratch, remove the + file. + + Also beginning with Wget 1.7, if you use -c on a + file which is of equal size as the one on the + server, Wget will refuse to download the file and + print an explanatory message. The same happens when + the file is smaller on the server than locally (pre- + sumably because it was changed on the server since + your last download attempt)---because "continuing" + is not meaningful, no download occurs. + + On the other side of the coin, while using -c, any + file that's bigger on the server than locally will + be considered an incomplete download and only + "(length(remote) - length(local))" bytes will be + downloaded and tacked onto the end of the local + file. This behavior can be desirable in certain + cases---for instance, you can use wget -c to down- + load just the new portion that's been appended to a + data collection or log file. + + However, if the file is bigger on the server because + it's been changed, as opposed to just appended to, + you'll end up with a garbled file. Wget has no way + of verifying that the local file is really a valid + prefix of the remote file. You need to be espe- + cially careful of this when using -c in conjunction + with -r, since every file will be considered as an + "incomplete download" candidate. + + Another instance where you'll get a garbled file if + you try to use -c is if you have a lame HTTP proxy + that inserts a "transfer interrupted" string into + the local file. In the future a "rollback" option + may be added to deal with this case. + + Note that -c only works with FTP servers and with + HTTP servers that support the "Range" header. + + --progress=type + Select the type of the progress indicator you wish + to use. Legal indicators are "dot" and "bar". + + The "bar" indicator is used by default. It draws an + ASCII progress bar graphics (a.k.a "thermometer" + display) indicating the status of retrieval. If the + output is not a TTY, the "dot" bar will be used by + default. + + Use --progress=dot to switch to the "dot" display. + It traces the retrieval by printing dots on the + screen, each dot representing a fixed amount of + downloaded data. + + When using the dotted retrieval, you may also set + the style by specifying the type as dot:style. Dif- + ferent styles assign different meaning to one dot. + With the "default" style each dot represents 1K, + there are ten dots in a cluster and 50 dots in a + line. The "binary" style has a more "computer"-like + orientation---8K dots, 16-dots clusters and 48 dots + per line (which makes for 384K lines). The "mega" + style is suitable for downloading very large + files---each dot represents 64K retrieved, there are + eight dots in a cluster, and 48 dots on each line + (so each line contains 3M). + + Note that you can set the default style using the + "progress" command in .wgetrc. That setting may be + overridden from the command line. The exception is + that, when the output is not a TTY, the "dot" + progress will be favored over "bar". To force the + bar output, use --progress=bar:force. + + -N + --timestamping + Turn on time-stamping. + + -S + --server-response + Print the headers sent by HTTP servers and responses + sent by FTP servers. + + --spider + When invoked with this option, Wget will behave as a + Web spider, which means that it will not download + the pages, just check that they are there. For + example, you can use Wget to check your bookmarks: + + wget --spider --force-html -i bookmarks.html + + This feature needs much more work for Wget to get + close to the functionality of real web spiders. + + -T seconds + --timeout=seconds + Set the network timeout to seconds seconds. This is + equivalent to specifying --dns-timeout, --con- + nect-timeout, and --read-timeout, all at the same + time. + + When interacting with the network, Wget can check + for timeout and abort the operation if it takes too + long. This prevents anomalies like hanging reads + and infinite connects. The only timeout enabled by + default is a 900-second read timeout. Setting a + timeout to 0 disables it altogether. Unless you + know what you are doing, it is best not to change + the default timeout settings. + + All timeout-related options accept decimal values, + as well as subsecond values. For example, 0.1 sec- + onds is a legal (though unwise) choice of timeout. + Subsecond timeouts are useful for checking server + response times or for testing network latency. + + --dns-timeout=seconds + Set the DNS lookup timeout to seconds seconds. DNS + lookups that don't complete within the specified + time will fail. By default, there is no timeout on + DNS lookups, other than that implemented by system + libraries. + + --connect-timeout=seconds + Set the connect timeout to seconds seconds. TCP + connections that take longer to establish will be + aborted. By default, there is no connect timeout, + other than that implemented by system libraries. + + --read-timeout=seconds + Set the read (and write) timeout to seconds seconds. + The "time" of this timeout refers to idle time: if, + at any point in the download, no data is received + for more than the specified number of seconds, read- + ing fails and the download is restarted. This + option does not directly affect the duration of the + entire download. + + Of course, the remote server may choose to terminate + the connection sooner than this option requires. + The default read timeout is 900 seconds. + + --limit-rate=amount + Limit the download speed to amount bytes per second. + Amount may be expressed in bytes, kilobytes with the + k suffix, or megabytes with the m suffix. For exam- + ple, --limit-rate=20k will limit the retrieval rate + to 20KB/s. This is useful when, for whatever rea- + son, you don't want Wget to consume the entire + available bandwidth. + + This option allows the use of decimal numbers, usu- + ally in conjunction with power suffixes; for exam- + ple, --limit-rate=2.5k is a legal value. + + Note that Wget implements the limiting by sleeping + the appropriate amount of time after a network read + that took less time than specified by the rate. + Eventually this strategy causes the TCP transfer to + slow down to approximately the specified rate. How- + ever, it may take some time for this balance to be + achieved, so don't be surprised if limiting the rate + doesn't work well with very small files. + + -w seconds + --wait=seconds + Wait the specified number of seconds between the + retrievals. Use of this option is recommended, as + it lightens the server load by making the requests + less frequent. Instead of in seconds, the time can + be specified in minutes using the "m" suffix, in + hours using "h" suffix, or in days using "d" suffix. + + Specifying a large value for this option is useful + if the network or the destination host is down, so + that Wget can wait long enough to reasonably expect + the network error to be fixed before the retry. The + waiting interval specified by this function is + influenced by "--random-wait", which see. + + --waitretry=seconds + If you don't want Wget to wait between every + retrieval, but only between retries of failed down- + loads, you can use this option. Wget will use lin- + ear backoff, waiting 1 second after the first fail- + ure on a given file, then waiting 2 seconds after + the second failure on that file, up to the maximum + number of seconds you specify. Therefore, a value + of 10 will actually make Wget wait up to (1 + 2 + + ... + 10) = 55 seconds per file. + + Note that this option is turned on by default in the + global wgetrc file. + + --random-wait + Some web sites may perform log analysis to identify + retrieval programs such as Wget by looking for sta- + tistically significant similarities in the time + between requests. This option causes the time + between requests to vary between 0.5 and 1.5 * wait + seconds, where wait was specified using the --wait + option, in order to mask Wget's presence from such + analysis. + + A 2001 article in a publication devoted to develop- + ment on a popular consumer platform provided code to + perform this analysis on the fly. Its author sug- + gested blocking at the class C address level to + ensure automated retrieval programs were blocked + despite changing DHCP-supplied addresses. + + The --random-wait option was inspired by this ill- + advised recommendation to block many unrelated users + from a web site due to the actions of one. + + --no-proxy + Don't use proxies, even if the appropriate *_proxy + environment variable is defined. + + -Q quota + --quota=quota + Specify download quota for automatic retrievals. + The value can be specified in bytes (default), kilo- + bytes (with k suffix), or megabytes (with m suffix). + + Note that quota will never affect downloading a sin- + gle file. So if you specify wget -Q10k + ftp://wuarchive.wustl.edu/ls-lR.gz, all of the + ls-lR.gz will be downloaded. The same goes even + when several URLs are specified on the command-line. + However, quota is respected when retrieving either + recursively, or from an input file. Thus you may + safely type wget -Q2m -i sites---download will be + aborted when the quota is exceeded. + + Setting quota to 0 or to inf unlimits the download + quota. + + --no-dns-cache + Turn off caching of DNS lookups. Normally, Wget + remembers the IP addresses it looked up from DNS so + it doesn't have to repeatedly contact the DNS server + for the same (typically small) set of hosts it + retrieves from. This cache exists in memory only; a + new Wget run will contact DNS again. + + However, it has been reported that in some situa- + tions it is not desirable to cache host names, even + for the duration of a short-running application like + Wget. With this option Wget issues a new DNS lookup + (more precisely, a new call to "gethostbyname" or + "getaddrinfo") each time it makes a new connection. + Please note that this option will not affect caching + that might be performed by the resolving library or + by an external caching layer, such as NSCD. + + If you don't understand exactly what this option + does, you probably won't need it. + + --restrict-file-names=mode + Change which characters found in remote URLs may + show up in local file names generated from those + URLs. Characters that are restricted by this option + are escaped, i.e. replaced with %HH, where HH is the + hexadecimal number that corresponds to the + restricted character. + + By default, Wget escapes the characters that are not + valid as part of file names on your operating sys- + tem, as well as control characters that are typi- + cally unprintable. This option is useful for chang- + ing these defaults, either because you are download- + ing to a non-native partition, or because you want + to disable escaping of the control characters. + + When mode is set to "unix", Wget escapes the charac- + ter / and the control characters in the ranges 0--31 + and 128--159. This is the default on Unix-like + OS'es. + + When mode is set to "windows", Wget escapes the + characters \, |, /, :, ?, ", *, <, >, and the con- + trol characters in the ranges 0--31 and 128--159. + In addition to this, Wget in Windows mode uses + + instead of : to separate host and port in local file + names, and uses @ instead of ? to separate the query + portion of the file name from the rest. Therefore, + a URL that would be saved as + www.xemacs.org:4300/search.pl?input=blah in Unix + mode would be saved as + www.xemacs.org+4300/search.pl@input=blah in Windows + mode. This mode is the default on Windows. + + If you append ,nocontrol to the mode, as in + unix,nocontrol, escaping of the control characters + is also switched off. You can use + --restrict-file-names=nocontrol to turn off escaping + of control characters without affecting the choice + of the OS to use as file name restriction mode. + + -4 + --inet4-only + -6 + --inet6-only + Force connecting to IPv4 or IPv6 addresses. With + --inet4-only or -4, Wget will only connect to IPv4 + hosts, ignoring AAAA records in DNS, and refusing to + connect to IPv6 addresses specified in URLs. Con- + versely, with --inet6-only or -6, Wget will only + connect to IPv6 hosts and ignore A records and IPv4 + addresses. + + Neither options should be needed normally. By + default, an IPv6-aware Wget will use the address + family specified by the host's DNS record. If the + DNS responds with both IPv4 and IPv6 addresses, Wget + will try them in sequence until it finds one it can + connect to. (Also see "--prefer-family" option + described below.) + + These options can be used to deliberately force the + use of IPv4 or IPv6 address families on dual family + systems, usually to aid debugging or to deal with + broken network configuration. Only one of + --inet6-only and --inet4-only may be specified at + the same time. Neither option is available in Wget + compiled without IPv6 support. + + --prefer-family=IPv4/IPv6/none + When given a choice of several addresses, connect to + the addresses with specified address family first. + IPv4 addresses are preferred by default. + + This avoids spurious errors and connect attempts + when accessing hosts that resolve to both IPv6 and + IPv4 addresses from IPv4 networks. For example, + www.kame.net resolves to + 2001:200:0:8002:203:47ff:fea5:3085 and to + 203.178.141.194. When the preferred family is + "IPv4", the IPv4 address is used first; when the + preferred family is "IPv6", the IPv6 address is used + first; if the specified value is "none", the address + order returned by DNS is used without change. + + Unlike -4 and -6, this option doesn't inhibit access + to any address family, it only changes the order in + which the addresses are accessed. Also note that + the reordering performed by this option is sta- + ble---it doesn't affect order of addresses of the + same family. That is, the relative order of all + IPv4 addresses and of all IPv6 addresses remains + intact in all cases. + + --retry-connrefused + Consider "connection refused" a transient error and + try again. Normally Wget gives up on a URL when it + is unable to connect to the site because failure to + connect is taken as a sign that the server is not + running at all and that retries would not help. + This option is for mirroring unreliable sites whose + servers tend to disappear for short periods of time. + + --user=user + --password=password + Specify the username user and password password for + both FTP and HTTP file retrieval. These parameters + can be overridden using the --ftp-user and + --ftp-password options for FTP connections and the + --http-user and --http-password options for HTTP + connections. + + Directory Options + + + -nd + --no-directories + Do not create a hierarchy of directories when + retrieving recursively. With this option turned on, + all files will get saved to the current directory, + without clobbering (if a name shows up more than + once, the filenames will get extensions .n). + + -x + --force-directories + The opposite of -nd---create a hierarchy of directo- + ries, even if one would not have been created other- + wise. E.g. wget -x http://fly.srk.fer.hr/robots.txt + will save the downloaded file to fly.srk.fer.hr/ro- + bots.txt. + + -nH + --no-host-directories + Disable generation of host-prefixed directories. By + default, invoking Wget with -r + http://fly.srk.fer.hr/ will create a structure of + directories beginning with fly.srk.fer.hr/. This + option disables such behavior. + + --protocol-directories + Use the protocol name as a directory component of + local file names. For example, with this option, + wget -r http://host will save to http/host/... + rather than just to host/.... + + --cut-dirs=number + Ignore number directory components. This is useful + for getting a fine-grained control over the direc- + tory where recursive retrieval will be saved. + + Take, for example, the directory at + ftp://ftp.xemacs.org/pub/xemacs/. If you retrieve + it with -r, it will be saved locally under + ftp.xemacs.org/pub/xemacs/. While the -nH option + can remove the ftp.xemacs.org/ part, you are still + stuck with pub/xemacs. This is where --cut-dirs + comes in handy; it makes Wget not "see" number + remote directory components. Here are several exam- + ples of how --cut-dirs option works. + + No options -> ftp.xemacs.org/pub/xemacs/ + -nH -> pub/xemacs/ + -nH --cut-dirs=1 -> xemacs/ + -nH --cut-dirs=2 -> . + + --cut-dirs=1 -> ftp.xemacs.org/xemacs/ + ... + + If you just want to get rid of the directory struc- + ture, this option is similar to a combination of -nd + and -P. However, unlike -nd, --cut-dirs does not + lose with subdirectories---for instance, with -nH + --cut-dirs=1, a beta/ subdirectory will be placed to + xemacs/beta, as one would expect. + + -P prefix + --directory-prefix=prefix + Set directory prefix to prefix. The directory pre- + fix is the directory where all other files and sub- + directories will be saved to, i.e. the top of the + retrieval tree. The default is . (the current + directory). + + HTTP Options + + + -E + --html-extension + If a file of type application/xhtml+xml or text/html + is downloaded and the URL does not end with the reg- + exp \.[Hh][Tt][Mm][Ll]?, this option will cause the + suffix .html to be appended to the local filename. + This is useful, for instance, when you're mirroring + a remote site that uses .asp pages, but you want the + mirrored pages to be viewable on your stock Apache + server. Another good use for this is when you're + downloading CGI-generated materials. A URL like + http://site.com/article.cgi?25 will be saved as + article.cgi?25.html. + + Note that filenames changed in this way will be re- + downloaded every time you re-mirror a site, because + Wget can't tell that the local X.html file corre- + sponds to remote URL X (since it doesn't yet know + that the URL produces output of type text/html or + application/xhtml+xml. To prevent this re-download- + ing, you must use -k and -K so that the original + version of the file will be saved as X.orig. + + --http-user=user + --http-password=password + Specify the username user and password password on + an HTTP server. According to the type of the chal- + lenge, Wget will encode them using either the + "basic" (insecure), the "digest", or the Windows + "NTLM" authentication scheme. + + Another way to specify username and password is in + the URL itself. Either method reveals your password + to anyone who bothers to run "ps". To prevent the + passwords from being seen, store them in .wgetrc or + .netrc, and make sure to protect those files from + other users with "chmod". If the passwords are + really important, do not leave them lying in those + files either---edit the files and delete them after + Wget has started the download. + + --no-cache + Disable server-side cache. In this case, Wget will + send the remote server an appropriate directive + (Pragma: no-cache) to get the file from the remote + service, rather than returning the cached version. + This is especially useful for retrieving and flush- + ing out-of-date documents on proxy servers. + + Caching is allowed by default. + + --no-cookies + Disable the use of cookies. Cookies are a mechanism + for maintaining server-side state. The server sends + the client a cookie using the "Set-Cookie" header, + and the client responds with the same cookie upon + further requests. Since cookies allow the server + owners to keep track of visitors and for sites to + exchange this information, some consider them a + breach of privacy. The default is to use cookies; + however, storing cookies is not on by default. + + --load-cookies file + Load cookies from file before the first HTTP + retrieval. file is a textual file in the format + originally used by Netscape's cookies.txt file. + + You will typically use this option when mirroring + sites that require that you be logged in to access + some or all of their content. The login process + typically works by the web server issuing an HTTP + cookie upon receiving and verifying your creden- + tials. The cookie is then resent by the browser + when accessing that part of the site, and so proves + your identity. + + Mirroring such a site requires Wget to send the same + cookies your browser sends when communicating with + the site. This is achieved by --load-cookies---sim- + ply point Wget to the location of the cookies.txt + file, and it will send the same cookies your browser + would send in the same situation. Different + browsers keep textual cookie files in different + locations: + + @asis<Netscape 4.x.> + The cookies are in ~/.netscape/cookies.txt. + + @asis<Mozilla and Netscape 6.x.> + Mozilla's cookie file is also named cookies.txt, + located somewhere under ~/.mozilla, in the + directory of your profile. The full path usu- + ally ends up looking somewhat like + ~/.mozilla/default/some-weird-string/cook- + ies.txt. + + @asis<Internet Explorer.> + You can produce a cookie file Wget can use by + using the File menu, Import and Export, Export + Cookies. This has been tested with Internet + Explorer 5; it is not guaranteed to work with + earlier versions. + + @asis<Other browsers.> + If you are using a different browser to create + your cookies, --load-cookies will only work if + you can locate or produce a cookie file in the + Netscape format that Wget expects. + + If you cannot use --load-cookies, there might still + be an alternative. If your browser supports a + "cookie manager", you can use it to view the cookies + used when accessing the site you're mirroring. + Write down the name and value of the cookie, and + manually instruct Wget to send those cookies, + bypassing the "official" cookie support: + + wget --no-cookies --header "Cookie: <name>=<value>" + + --save-cookies file + Save cookies to file before exiting. This will not + save cookies that have expired or that have no + expiry time (so-called "session cookies"), but also + see --keep-session-cookies. + + --keep-session-cookies + When specified, causes --save-cookies to also save + session cookies. Session cookies are normally not + saved because they are meant to be kept in memory + and forgotten when you exit the browser. Saving + them is useful on sites that require you to log in + or to visit the home page before you can access some + pages. With this option, multiple Wget runs are + considered a single browser session as far as the + site is concerned. + + Since the cookie file format does not normally carry + session cookies, Wget marks them with an expiry + timestamp of 0. Wget's --load-cookies recognizes + those as session cookies, but it might confuse other + browsers. Also note that cookies so loaded will be + treated as other session cookies, which means that + if you want --save-cookies to preserve them again, + you must use --keep-session-cookies again. + + --ignore-length + Unfortunately, some HTTP servers (CGI programs, to + be more precise) send out bogus "Content-Length" + headers, which makes Wget go wild, as it thinks not + all the document was retrieved. You can spot this + syndrome if Wget retries getting the same document + again and again, each time claiming that the (other- + wise normal) connection has closed on the very same + byte. + + With this option, Wget will ignore the "Con- + tent-Length" header---as if it never existed. + + --header=header-line + Send header-line along with the rest of the headers + in each HTTP request. The supplied header is sent + as-is, which means it must contain name and value + separated by colon, and must not contain newlines. + + You may define more than one additional header by + specifying --header more than once. + + wget --header='Accept-Charset: iso-8859-2' \ + --header='Accept-Language: hr' \ + http://fly.srk.fer.hr/ + + Specification of an empty string as the header value + will clear all previous user-defined headers. + + As of Wget 1.10, this option can be used to override + headers otherwise generated automatically. This + example instructs Wget to connect to localhost, but + to specify foo.bar in the "Host" header: + + wget --header="Host: foo.bar" http://localhost/ + + In versions of Wget prior to 1.10 such use of + --header caused sending of duplicate headers. + + --max-redirect=number + Specifies the maximum number of redirections to fol- + low for a resource. The default is 20, which is + usually far more than necessary. However, on those + occasions where you want to allow more (or fewer), + this is the option to use. + + --proxy-user=user + --proxy-password=password + Specify the username user and password password for + authentication on a proxy server. Wget will encode + them using the "basic" authentication scheme. + + Security considerations similar to those with + --http-password pertain here as well. + + --referer=url + Include `Referer: url' header in HTTP request. Use- + ful for retrieving documents with server-side pro- + cessing that assume they are always being retrieved + by interactive web browsers and only come out prop- + erly when Referer is set to one of the pages that + point to them. + + --save-headers + Save the headers sent by the HTTP server to the + file, preceding the actual contents, with an empty + line as the separator. + + -U agent-string + --user-agent=agent-string + Identify as agent-string to the HTTP server. + + The HTTP protocol allows the clients to identify + themselves using a "User-Agent" header field. This + enables distinguishing the WWW software, usually for + statistical purposes or for tracing of protocol vio- + lations. Wget normally identifies as Wget/version, + version being the current version number of Wget. + + However, some sites have been known to impose the + policy of tailoring the output according to the + "User-Agent"-supplied information. While this is + not such a bad idea in theory, it has been abused by + servers denying information to clients other than + (historically) Netscape or, more frequently, + Microsoft Internet Explorer. This option allows you + to change the "User-Agent" line issued by Wget. Use + of this option is discouraged, unless you really + know what you are doing. + + Specifying empty user agent with --user-agent="" + instructs Wget not to send the "User-Agent" header + in HTTP requests. + + --post-data=string + --post-file=file + Use POST as the method for all HTTP requests and + send the specified data in the request body. + "--post-data" sends string as data, whereas + "--post-file" sends the contents of file. Other + than that, they work in exactly the same way. + + Please be aware that Wget needs to know the size of + the POST data in advance. Therefore the argument to + "--post-file" must be a regular file; specifying a + FIFO or something like /dev/stdin won't work. It's + not quite clear how to work around this limitation + inherent in HTTP/1.0. Although HTTP/1.1 introduces + chunked transfer that doesn't require knowing the + request length in advance, a client can't use chun- + ked unless it knows it's talking to an HTTP/1.1 + server. And it can't know that until it receives a + response, which in turn requires the request to have + been completed -- a chicken-and-egg problem. + + Note: if Wget is redirected after the POST request + is completed, it will not send the POST data to the + redirected URL. This is because URLs that process + POST often respond with a redirection to a regular + page, which does not desire or accept POST. It is + not completely clear that this behavior is optimal; + if it doesn't work out, it might be changed in the + future. + + This example shows how to log to a server using POST + and then proceed to download the desired pages, pre- + sumably only accessible to authorized users: + + # Log in to the server. This can be done only once. + wget --save-cookies cookies.txt \ + --post-data 'user=foo&password=bar' \ + http://server.com/auth.php + + # Now grab the page or pages we care about. + wget --load-cookies cookies.txt \ + -p http://server.com/interesting/article.php + + If the server is using session cookies to track user + authentication, the above will not work because + --save-cookies will not save them (and neither will + browsers) and the cookies.txt file will be empty. + In that case use --keep-session-cookies along with + --save-cookies to force saving of session cookies. + + --content-disposition + If this is set to on, experimental (not fully-func- + tional) support for "Content-Disposition" headers is + enabled. This can currently result in extra round- + trips to the server for a "HEAD" request, and is + known to suffer from a few bugs, which is why it is + not currently enabled by default. + + This option is useful for some file-downloading CGI + programs that use "Content-Disposition" headers to + describe what the name of a downloaded file should + be. + + --auth-no-challenge + If this option is given, Wget will send Basic HTTP + authentication information (plaintext username and + password) for all requests, just like Wget 1.10.2 + and prior did by default. + + Use of this option is not recommended, and is + intended only to support some few obscure servers, + which never send HTTP authentication challenges, but + accept unsolicited auth info, say, in addition to + form-based authentication. + + HTTPS (SSL/TLS) Options + + To support encrypted HTTP (HTTPS) downloads, Wget must + be compiled with an external SSL library, currently + OpenSSL. If Wget is compiled without SSL support, none + of these options are available. + + --secure-protocol=protocol + Choose the secure protocol to be used. Legal values + are auto, SSLv2, SSLv3, and TLSv1. If auto is used, + the SSL library is given the liberty of choosing the + appropriate protocol automatically, which is + achieved by sending an SSLv2 greeting and announcing + support for SSLv3 and TLSv1. This is the default. + + Specifying SSLv2, SSLv3, or TLSv1 forces the use of + the corresponding protocol. This is useful when + talking to old and buggy SSL server implementations + that make it hard for OpenSSL to choose the correct + protocol version. Fortunately, such servers are + quite rare. + + --no-check-certificate + Don't check the server certificate against the + available certificate authorities. Also don't + require the URL host name to match the common name + presented by the certificate. + + As of Wget 1.10, the default is to verify the + server's certificate against the recognized certifi- + cate authorities, breaking the SSL handshake and + aborting the download if the verification fails. + Although this provides more secure downloads, it + does break interoperability with some sites that + worked with previous Wget versions, particularly + those using self-signed, expired, or otherwise + invalid certificates. This option forces an "inse- + cure" mode of operation that turns the certificate + verification errors into warnings and allows you to + proceed. + + If you encounter "certificate verification" errors + or ones saying that "common name doesn't match + requested host name", you can use this option to + bypass the verification and proceed with the down- + load. Only use this option if you are otherwise + convinced of the site's authenticity, or if you + really don't care about the validity of its certifi- + cate. It is almost always a bad idea not to check + the certificates when transmitting confidential or + important data. + + --certificate=file + Use the client certificate stored in file. This is + needed for servers that are configured to require + certificates from the clients that connect to them. + Normally a certificate is not required and this + switch is optional. + + --certificate-type=type + Specify the type of the client certificate. Legal + values are PEM (assumed by default) and DER, also + known as ASN1. + + --private-key=file + Read the private key from file. This allows you to + provide the private key in a file separate from the + certificate. + + --private-key-type=type + Specify the type of the private key. Accepted val- + ues are PEM (the default) and DER. + + --ca-certificate=file + Use file as the file with the bundle of certificate + authorities ("CA") to verify the peers. The cer- + tificates must be in PEM format. + + Without this option Wget looks for CA certificates + at the system-specified locations, chosen at OpenSSL + installation time. + + --ca-directory=directory + Specifies directory containing CA certificates in + PEM format. Each file contains one CA certificate, + and the file name is based on a hash value derived + from the certificate. This is achieved by process- + ing a certificate directory with the "c_rehash" + utility supplied with OpenSSL. Using --ca-directory + is more efficient than --ca-certificate when many + certificates are installed because it allows Wget to + fetch certificates on demand. + + Without this option Wget looks for CA certificates + at the system-specified locations, chosen at OpenSSL + installation time. + + --random-file=file + Use file as the source of random data for seeding + the pseudo-random number generator on systems with- + out /dev/random. + + On such systems the SSL library needs an external + source of randomness to initialize. Randomness may + be provided by EGD (see --egd-file below) or read + from an external source specified by the user. If + this option is not specified, Wget looks for random + data in $RANDFILE or, if that is unset, in + $HOME/.rnd. If none of those are available, it is + likely that SSL encryption will not be usable. + + If you're getting the "Could not seed OpenSSL PRNG; + disabling SSL." error, you should provide random + data using some of the methods described above. + + --egd-file=file + Use file as the EGD socket. EGD stands for Entropy + Gathering Daemon, a user-space program that collects + data from various unpredictable system sources and + makes it available to other programs that might need + it. Encryption software, such as the SSL library, + needs sources of non-repeating randomness to seed + the random number generator used to produce crypto- + graphically strong keys. + + OpenSSL allows the user to specify his own source of + entropy using the "RAND_FILE" environment variable. + If this variable is unset, or if the specified file + does not produce enough randomness, OpenSSL will + read random data from EGD socket specified using + this option. + + If this option is not specified (and the equivalent + startup command is not used), EGD is never con- + tacted. EGD is not needed on modern Unix systems + that support /dev/random. + + FTP Options + + + --ftp-user=user + --ftp-password=password + Specify the username user and password password on + an FTP server. Without this, or the corresponding + startup option, the password defaults to -wget@, + normally used for anonymous FTP. + + Another way to specify username and password is in + the URL itself. Either method reveals your password + to anyone who bothers to run "ps". To prevent the + passwords from being seen, store them in .wgetrc or + .netrc, and make sure to protect those files from + other users with "chmod". If the passwords are + really important, do not leave them lying in those + files either---edit the files and delete them after + Wget has started the download. + + --no-remove-listing + Don't remove the temporary .listing files generated + by FTP retrievals. Normally, these files contain + the raw directory listings received from FTP + servers. Not removing them can be useful for debug- + ging purposes, or when you want to be able to easily + check on the contents of remote server directories + (e.g. to verify that a mirror you're running is com- + plete). + + Note that even though Wget writes to a known file- + name for this file, this is not a security hole in + the scenario of a user making .listing a symbolic + link to /etc/passwd or something and asking "root" + to run Wget in his or her directory. Depending on + the options used, either Wget will refuse to write + to .listing, making the globbing/recur- + sion/time-stamping operation fail, or the symbolic + link will be deleted and replaced with the actual + .listing file, or the listing will be written to a + .listing.number file. + + Even though this situation isn't a problem, though, + "root" should never run Wget in a non-trusted user's + directory. A user could do something as simple as + linking index.html to /etc/passwd and asking "root" + to run Wget with -N or -r so the file will be over- + written. + + --no-glob + Turn off FTP globbing. Globbing refers to the use + of shell-like special characters (wildcards), like + *, ?, [ and ] to retrieve more than one file from + the same directory at once, like: + + wget ftp://gnjilux.srk.fer.hr/*.msg + + By default, globbing will be turned on if the URL + contains a globbing character. This option may be + used to turn globbing on or off permanently. + + You may have to quote the URL to protect it from + being expanded by your shell. Globbing makes Wget + look for a directory listing, which is system-spe- + cific. This is why it currently works only with + Unix FTP servers (and the ones emulating Unix "ls" + output). + + --no-passive-ftp + Disable the use of the passive FTP transfer mode. + Passive FTP mandates that the client connect to the + server to establish the data connection rather than + the other way around. + + If the machine is connected to the Internet + directly, both passive and active FTP should work + equally well. Behind most firewall and NAT configu- + rations passive FTP has a better chance of working. + However, in some rare firewall configurations, + active FTP actually works when passive FTP doesn't. + If you suspect this to be the case, use this option, + or set "passive_ftp=off" in your init file. + + --retr-symlinks + Usually, when retrieving FTP directories recursively + and a symbolic link is encountered, the linked-to + file is not downloaded. Instead, a matching sym- + bolic link is created on the local filesystem. The + pointed-to file will not be downloaded unless this + recursive retrieval would have encountered it sepa- + rately and downloaded it anyway. + + When --retr-symlinks is specified, however, symbolic + links are traversed and the pointed-to files are + retrieved. At this time, this option does not cause + Wget to traverse symlinks to directories and recurse + through them, but in the future it should be + enhanced to do this. + + Note that when retrieving a file (not a directory) + because it was specified on the command-line, rather + than because it was recursed to, this option has no + effect. Symbolic links are always traversed in this + case. + + --no-http-keep-alive + Turn off the "keep-alive" feature for HTTP down- + loads. Normally, Wget asks the server to keep the + connection open so that, when you download more than + one document from the same server, they get trans- + ferred over the same TCP connection. This saves + time and at the same time reduces the load on the + server. + + This option is useful when, for some reason, persis- + tent (keep-alive) connections don't work for you, + for example due to a server bug or due to the + inability of server-side scripts to cope with the + connections. + + Recursive Retrieval Options + + + -r + --recursive + Turn on recursive retrieving. + + -l depth + --level=depth + Specify recursion maximum depth level depth. The + default maximum depth is 5. + + --delete-after + This option tells Wget to delete every single file + it downloads, after having done so. It is useful + for pre-fetching popular pages through a proxy, + e.g.: + + wget -r -nd --delete-after http://whatever.com/~popular/page/ + + The -r option is to retrieve recursively, and -nd to + not create directories. + + Note that --delete-after deletes files on the local + machine. It does not issue the DELE command to + remote FTP sites, for instance. Also note that when + --delete-after is specified, --convert-links is + ignored, so .orig files are simply not created in + the first place. + + -k + --convert-links + After the download is complete, convert the links in + the document to make them suitable for local view- + ing. This affects not only the visible hyperlinks, + but any part of the document that links to external + content, such as embedded images, links to style + sheets, hyperlinks to non-HTML content, etc. + + Each link will be changed in one of the two ways: + + * The links to files that have been downloaded by + Wget will be changed to refer to the file they + point to as a relative link. + + Example: if the downloaded file /foo/doc.html + links to /bar/img.gif, also downloaded, then the + link in doc.html will be modified to point to + ../bar/img.gif. This kind of transformation + works reliably for arbitrary combinations of + directories. + + * The links to files that have not been downloaded + by Wget will be changed to include host name and + absolute path of the location they point to. + + Example: if the downloaded file /foo/doc.html + links to /bar/img.gif (or to ../bar/img.gif), + then the link in doc.html will be modified to + point to http://hostname/bar/img.gif. + + Because of this, local browsing works reliably: if a + linked file was downloaded, the link will refer to + its local name; if it was not downloaded, the link + will refer to its full Internet address rather than + presenting a broken link. The fact that the former + links are converted to relative links ensures that + you can move the downloaded hierarchy to another + directory. + + Note that only at the end of the download can Wget + know which links have been downloaded. Because of + that, the work done by -k will be performed at the + end of all the downloads. + + -K + --backup-converted + When converting a file, back up the original version + with a .orig suffix. Affects the behavior of -N. + + -m + --mirror + Turn on options suitable for mirroring. This option + turns on recursion and time-stamping, sets infinite + recursion depth and keeps FTP directory listings. + It is currently equivalent to -r -N -l inf + --no-remove-listing. + + -p + --page-requisites + This option causes Wget to download all the files + that are necessary to properly display a given HTML + page. This includes such things as inlined images, + sounds, and referenced stylesheets. + + Ordinarily, when downloading a single HTML page, any + requisite documents that may be needed to display it + properly are not downloaded. Using -r together with + -l can help, but since Wget does not ordinarily dis- + tinguish between external and inlined documents, one + is generally left with "leaf documents" that are + missing their requisites. + + For instance, say document 1.html contains an + "<IMG>" tag referencing 1.gif and an "<A>" tag + pointing to external document 2.html. Say that + 2.html is similar but that its image is 2.gif and it + links to 3.html. Say this continues up to some + arbitrarily high number. + + If one executes the command: + + wget -r -l 2 http://<site>/1.html + + then 1.html, 1.gif, 2.html, 2.gif, and 3.html will + be downloaded. As you can see, 3.html is without + its requisite 3.gif because Wget is simply counting + the number of hops (up to 2) away from 1.html in + order to determine where to stop the recursion. + However, with this command: + + wget -r -l 2 -p http://<site>/1.html + + all the above files and 3.html's requisite 3.gif + will be downloaded. Similarly, + + wget -r -l 1 -p http://<site>/1.html + + will cause 1.html, 1.gif, 2.html, and 2.gif to be + downloaded. One might think that: + + wget -r -l 0 -p http://<site>/1.html + + would download just 1.html and 1.gif, but unfortu- + nately this is not the case, because -l 0 is equiva- + lent to -l inf---that is, infinite recursion. To + download a single HTML page (or a handful of them, + all specified on the command-line or in a -i URL + input file) and its (or their) requisites, simply + leave off -r and -l: + + wget -p http://<site>/1.html + + Note that Wget will behave as if -r had been speci- + fied, but only that single page and its requisites + will be downloaded. Links from that page to exter- + nal documents will not be followed. Actually, to + download a single page and all its requisites (even + if they exist on separate websites), and make sure + the lot displays properly locally, this author likes + to use a few options in addition to -p: + + wget -E -H -k -K -p http://<site>/<document> + + To finish off this topic, it's worth knowing that + Wget's idea of an external document link is any URL + specified in an "<A>" tag, an "<AREA>" tag, or a + "<LINK>" tag other than "<LINK REL="stylesheet">". + + --strict-comments + Turn on strict parsing of HTML comments. The + default is to terminate comments at the first occur- + rence of -->. + + According to specifications, HTML comments are + expressed as SGML declarations. Declaration is spe- + cial markup that begins with <! and ends with >, + such as <!DOCTYPE ...>, that may contain comments + between a pair of -- delimiters. HTML comments are + "empty declarations", SGML declarations without any + non-comment text. Therefore, <!--foo--> is a valid + comment, and so is <!--one-- --two-->, but + <!--1--2--> is not. + + On the other hand, most HTML writers don't perceive + comments as anything other than text delimited with + <!-- and -->, which is not quite the same. For + example, something like <!------------> works as a + valid comment as long as the number of dashes is a + multiple of four (!). If not, the comment techni- + cally lasts until the next --, which may be at the + other end of the document. Because of this, many + popular browsers completely ignore the specification + and implement what users have come to expect: com- + ments delimited with <!-- and -->. + + Until version 1.9, Wget interpreted comments + strictly, which resulted in missing links in many + web pages that displayed fine in browsers, but had + the misfortune of containing non-compliant comments. + Beginning with version 1.9, Wget has joined the + ranks of clients that implements "naive" comments, + terminating each comment at the first occurrence of + -->. + + If, for whatever reason, you want strict comment + parsing, use this option to turn it on. + + Recursive Accept/Reject Options + + + -A acclist --accept acclist + -R rejlist --reject rejlist + Specify comma-separated lists of file name suffixes + or patterns to accept or reject. Note that if any of + the wildcard characters, *, ?, [ or ], appear in an + element of acclist or rejlist, it will be treated as + a pattern, rather than a suffix. + + -D domain-list + --domains=domain-list + Set domains to be followed. domain-list is a comma- + separated list of domains. Note that it does not + turn on -H. + + --exclude-domains domain-list + Specify the domains that are not to be followed.. + + --follow-ftp + Follow FTP links from HTML documents. Without this + option, Wget will ignore all the FTP links. + + --follow-tags=list + Wget has an internal table of HTML tag / attribute + pairs that it considers when looking for linked doc- + uments during a recursive retrieval. If a user + wants only a subset of those tags to be considered, + however, he or she should be specify such tags in a + comma-separated list with this option. + + --ignore-tags=list + This is the opposite of the --follow-tags option. + To skip certain HTML tags when recursively looking + for documents to download, specify them in a comma- + separated list. + + In the past, this option was the best bet for down- + loading a single page and its requisites, using a + command-line like: + + wget --ignore-tags=a,area -H -k -K -r http://<site>/<document> + + However, the author of this option came across a + page with tags like "<LINK REL="home" HREF="/">" and + came to the realization that specifying tags to + ignore was not enough. One can't just tell Wget to + ignore "<LINK>", because then stylesheets will not + be downloaded. Now the best bet for downloading a + single page and its requisites is the dedicated + --page-requisites option. + + --ignore-case + Ignore case when matching files and directories. + This influences the behavior of -R, -A, -I, and -X + options, as well as globbing implemented when down- + loading from FTP sites. For example, with this + option, -A *.txt will match file1.txt, but also + file2.TXT, file3.TxT, and so on. + + -H + --span-hosts + Enable spanning across hosts when doing recursive + retrieving. + + -L + --relative + Follow relative links only. Useful for retrieving a + specific home page without any distractions, not + even those from the same hosts. + + -I list + --include-directories=list + Specify a comma-separated list of directories you + wish to follow when downloading. Elements of list + may contain wildcards. + + -X list + --exclude-directories=list + Specify a comma-separated list of directories you + wish to exclude from download. Elements of list may + contain wildcards. + + -np + --no-parent + Do not ever ascend to the parent directory when + retrieving recursively. This is a useful option, + since it guarantees that only the files below a cer- + tain hierarchy will be downloaded. + +FILES + /usr/local/etc/wgetrc + Default location of the global startup file. + + .wgetrc + User startup file. + +BUGS + You are welcome to submit bug reports via the GNU Wget + bug tracker (see <http://wget.addictivecode.org/Bug- + Tracker>). + + Before actually submitting a bug report, please try to + follow a few simple guidelines. + + 1. Please try to ascertain that the behavior you see + really is a bug. If Wget crashes, it's a bug. If + Wget does not behave as documented, it's a bug. If + things work strange, but you are not sure about the + way they are supposed to work, it might well be a + bug, but you might want to double-check the documen- + tation and the mailing lists. + + 2. Try to repeat the bug in as simple circumstances as + possible. E.g. if Wget crashes while downloading + wget -rl0 -kKE -t5 --no-proxy http://yoyodyne.com -o + /tmp/log, you should try to see if the crash is + repeatable, and if will occur with a simpler set of + options. You might even try to start the download + at the page where the crash occurred to see if that + page somehow triggered the crash. + + Also, while I will probably be interested to know + the contents of your .wgetrc file, just dumping it + into the debug message is probably a bad idea. + Instead, you should first try to see if the bug + repeats with .wgetrc moved out of the way. Only if + it turns out that .wgetrc settings affect the bug, + mail me the relevant parts of the file. + + 3. Please start Wget with -d option and send us the + resulting output (or relevant parts thereof). If + Wget was compiled without debug support, recompile + it---it is much easier to trace bugs with debug sup- + port on. + + Note: please make sure to remove any potentially + sensitive information from the debug log before + sending it to the bug address. The "-d" won't go + out of its way to collect sensitive information, but + the log will contain a fairly complete transcript of + Wget's communication with the server, which may + include passwords and pieces of downloaded data. + Since the bug address is publically archived, you + may assume that all bug reports are visible to the + public. + + 4. If Wget has crashed, try to run it in a debugger, + e.g. "gdb `which wget` core" and type "where" to get + the backtrace. This may not work if the system + administrator has disabled core files, but it is + safe to try. + +SEE ALSO + This is not the complete manual for GNU Wget. For more + complete information, including more detailed explana- + tions of some of the options, and a number of commands + available for use with .wgetrc files and the -e option, + see the GNU Info entry for wget. + +AUTHOR + Originally written by Hrvoje Niksic + <hniksic@xemacs.org>. Currently maintained by Micah + Cowan <micah@cowan.name>. + +COPYRIGHT + Copyright (c) 1996, 1997, 1998, 1999, 2000, 2001, 2002, + 2003, 2004, 2005, 2006, 2007, 2008 Free Software Founda- + tion, Inc. + + Permission is granted to copy, distribute and/or modify + this document under the terms of the GNU Free Documenta- + tion License, Version 1.2 or any later version published + by the Free Software Foundation; with no Invariant Sec- + tions, no Front-Cover Texts, and no Back-Cover Texts. A + copy of the license is included in the section entitled + "GNU Free Documentation License". + + + +GNU Wget 1.11.4 2008-06-29 WGET(1)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/wget/wget.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,141 @@ +function [ out ] = wget(url, options) +%WGET Summary of this function goes here +% Detailed explanation goes here +% +% GNU Wget 1.11.4, a non-interactive network retriever. +% Usage: wget [OPTION]... [URL]... +% +% Mandatory arguments to long options are mandatory for short options too. +% +% Startup: +% -V, --version display the version of Wget and exit. +% -h, --help print this help. +% -b, --background go to background after startup. +% -e, --execute=COMMAND execute a `.wgetrc'-style command. +% +% Logging and input file: +% -o, --output-file=FILE log messages to FILE. +% -a, --append-output=FILE append messages to FILE. +% -d, --debug print lots of debugging information. +% -q, --quiet quiet (no output). +% -v, --verbose be verbose (this is the default). +% -nv, --no-verbose turn off verboseness, without being quiet. +% -i, --input-file=FILE download URLs found in FILE. +% -F, --force-html treat input file as HTML. +% -B, --base=URL prepends URL to relative links in -F -i file. +% +% Download: +% -t, --tries=NUMBER set number of retries to NUMBER (0 unlimits). +% --retry-connrefused retry even if connection is refused. +% -O, --output-document=FILE write documents to FILE. +% -nc, --no-clobber skip downloads that would download to +% existing files. +% -c, --continue resume getting a partially-downloaded file. +% --progress=TYPE select progress gauge type. +% -N, --timestamping don't re-retrieve files unless newer than +% local. +% -S, --server-response print server response. +% --spider don't download anything. +% -T, --timeout=SECONDS set all timeout values to SECONDS. +% --dns-timeout=SECS set the DNS lookup timeout to SECS. +% --connect-timeout=SECS set the connect timeout to SECS. +% --read-timeout=SECS set the read timeout to SECS. +% -w, --wait=SECONDS wait SECONDS between retrievals. +% --waitretry=SECONDS wait 1..SECONDS between retries of a retrieval. +% --random-wait wait from 0...2*WAIT secs between retrievals. +% --no-proxy explicitly turn off proxy. +% -Q, --quota=NUMBER set retrieval quota to NUMBER. +% --bind-address=ADDRESS bind to ADDRESS (hostname or IP) on local host. +% --limit-rate=RATE limit download rate to RATE. +% --no-dns-cache disable caching DNS lookups. +% --restrict-file-names=OS restrict chars in file names to ones OS allows. +% --ignore-case ignore case when matching files/directories. +% --user=USER set both ftp and http user to USER. +% --password=PASS set both ftp and http password to PASS. +% +% Directories: +% -nd, --no-directories don't create directories. +% -x, --force-directories force creation of directories. +% -nH, --no-host-directories don't create host directories. +% --protocol-directories use protocol name in directories. +% -P, --directory-prefix=PREFIX save files to PREFIX/... +% --cut-dirs=NUMBER ignore NUMBER remote directory components. +% +% HTTP options: +% --http-user=USER set http user to USER. +% --http-password=PASS set http password to PASS. +% --no-cache disallow server-cached data. +% -E, --html-extension save HTML documents with `.html' extension. +% --ignore-length ignore `Content-Length' header field. +% --header=STRING insert STRING among the headers. +% --max-redirect maximum redirections allowed per page. +% --proxy-user=USER set USER as proxy username. +% --proxy-password=PASS set PASS as proxy password. +% --referer=URL include `Referer: URL' header in HTTP request. +% --save-headers save the HTTP headers to file. +% -U, --user-agent=AGENT identify as AGENT instead of Wget/VERSION. +% --no-http-keep-alive disable HTTP keep-alive (persistent connections). +% --no-cookies don't use cookies. +% --load-cookies=FILE load cookies from FILE before session. +% --save-cookies=FILE save cookies to FILE after session. +% --keep-session-cookies load and save session (non-permanent) cookies. +% --post-data=STRING use the POST method; send STRING as the data. +% --post-file=FILE use the POST method; send contents of FILE. +% --content-disposition honor the Content-Disposition header when +% choosing local file names (EXPERIMENTAL). +% --auth-no-challenge Send Basic HTTP authentication information +% without first waiting for the server's +% challenge. +% +% HTTPS (SSL/TLS) options: +% --secure-protocol=PR choose secure protocol, one of auto, SSLv2, +% SSLv3, and TLSv1. +% --no-check-certificate don't validate the server's certificate. +% --certificate=FILE client certificate file. +% --certificate-type=TYPE client certificate type, PEM or DER. +% --private-key=FILE private key file. +% --private-key-type=TYPE private key type, PEM or DER. +% --ca-certificate=FILE file with the bundle of CA's. +% --ca-directory=DIR directory where hash list of CA's is stored. +% --random-file=FILE file with random data for seeding the SSL PRNG. +% --egd-file=FILE file naming the EGD socket with random data. +% +% FTP options: +% --ftp-user=USER set ftp user to USER. +% --ftp-password=PASS set ftp password to PASS. +% --no-remove-listing don't remove `.listing' files. +% --no-glob turn off FTP file name globbing. +% --no-passive-ftp disable the "passive" transfer mode. +% --retr-symlinks when recursing, get linked-to files (not dir). +% --preserve-permissions preserve remote file permissions. +% +% Recursive download: +% -r, --recursive specify recursive download. +% -l, --level=NUMBER maximum recursion depth (inf or 0 for infinite). +% --delete-after delete files locally after downloading them. +% -k, --convert-links make links in downloaded HTML point to local files. +% -K, --backup-converted before converting file X, back up as X.orig. +% -m, --mirror shortcut for -N -r -l inf --no-remove-listing. +% -p, --page-requisites get all images, etc. needed to display HTML page. +% --strict-comments turn on strict (SGML) handling of HTML comments. +% +% Recursive accept/reject: +% -A, --accept=LIST comma-separated list of accepted extensions. +% -R, --reject=LIST comma-separated list of rejected extensions. +% -D, --domains=LIST comma-separated list of accepted domains. +% --exclude-domains=LIST comma-separated list of rejected domains. +% --follow-ftp follow FTP links from HTML documents. +% --follow-tags=LIST comma-separated list of followed HTML tags. +% --ignore-tags=LIST comma-separated list of ignored HTML tags. +% -H, --span-hosts go to foreign hosts when recursive. +% -L, --relative follow relative links only. +% -I, --include-directories=LIST list of allowed directories. +% -X, --exclude-directories=LIST list of excluded directories. +% -np, --no-parent don't ascend to the parent directory. +% +% Mail bug reports and suggestions to <bug-wget@gnu.org>. + + cmd=sprintf('%s %s %s','wget', options, url); + out = system(cmd); +end +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/INSTALL.TXT Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,61 @@ +-------------------------------------------------------------------- +XML Toolbox for Matlab +http://www.geodise.org +Author: Marc Molinari <m.molinari@soton.ac.uk> +$Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ +-------------------------------------------------------------------- + +The XML Toolbox for Matlab provides easy-to-use XML functionality +to the Matlab environment. It has been tested with Matlab versions +6.5 (R13) and 7 (R14). It can be used standalone and does not +require Java. + +--------------------------- + Installation Instructions +--------------------------- + +Unzip the files from the XMLToolbox.zip file. This creates a +subfolder named "xml_toolbox". + +This new directory containing .m and .p files should be added +to the Matlab search path. This can be done by either of... + +(1) If using the Matlab desktop, navigate to the 'Set Path' dialog + ('File' > 'Set Path'). Click the 'Add Folder' button and browse + to the directory containing the XML Toolbox, select 'OK' to + confirm. You may wish to click the 'Save' button to preserve + the configuration between sessions. Click 'Close' to dismiss + the dialog. + +(2) If you are using Matlab via the Unix terminal you can instead + use the 'addpath' and 'savepath' functions at the Matlab command + line. + >> addpath /home/USER/GeodiseLab/XMLToolbox + +(3) System administrators configuring a multi-user Matlab installation + may find it preferable to edit $MATLABROOT/toolbox/local/pathdef.m + to make changes to the Matlab search path available to all users. + +To confirm that the Matlab search path has been successfully +configured type the following (without the >>) and press Return: + >> str = which('xml_help') +The variable str should contain the path of the XML Toolbox. + +--------------------------- + Test the Installation +--------------------------- + +To test the installation, type "xml_help" at the Matlab prompt. +This should display a short description of the functions in the +XML Toolbox. Each function is easy to use and contains its own +help (available with ">> help xml_xxxx"). +Try now to execute the following at the Matlab command prompt: + +>> xml_help +>> xml_format( 'Hello World!' ) +>> +>> str = xml_format(17.29) +>> a = xml_parse(str) + +As an alternative test and example, try the xml_tests script in the +tests subdirectory.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/LICENSE.TXT Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +Copyright (c) 2004, Geodise Project, University of Southampton +All rights reserved. + +Redistribution and use in source and binary forms, with +or without modification, are permitted provided that the +following conditions are met: + + * Redistributions of source code must retain the above + copyright notice, this list of conditions and the + following disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials + provided with the distribution. + + * Neither the name of the University of Southampton nor the + names of its contributors may be used to endorse or promote + products derived from this software without specific prior + written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/matlab.xsd Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,146 @@ +<!-- Copyright 2002 Geodise Project, University of Southampton --> +<!-- matlab.xsd - Jasmin Wason - 01/11/02 --> +<!-- Schema to validate XML documents generated by the xml_save function --> +<!-- of the XML Toolbox for Matlab written by Marc Molinari. --> +<!-- Modification 14/11/02 Jasmin Wason --> +<!-- Included a definition for the complex element --> +<!-- Modification 27/11/02 Jasmin Wason --> +<!-- Added complex element as a child of struct, sparse and cell --> +<xs:schema targetNamespace="http://www.geodise.org/matlab.xsd" xmlns="http://www.geodise.org/matlab.xsd" xmlns:xs="http://www.w3.org/2001/XMLSchema" elementFormDefault="qualified"> + <!-- struct element definition --> + <!-- A struct element contains 0 or more struct, double, char, sparse, cell or complex elements --> + <!-- These child elements may appear in any order --> + <!-- A struct element contains a list of attributes as described in the structAttributes attributeGroup --> + <xs:element name="struct"> + <xs:complexType> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="struct" /> + <xs:element ref="double" /> + <xs:element ref="char" /> + <xs:element ref="sparse" /> + <xs:element ref="cell" /> + <xs:element ref="complex" /> + </xs:choice> + <xs:attributeGroup ref="structAttributes" /> + </xs:complexType> + </xs:element> + <!-- double element definition --> + <!-- double may contain numerical entities of type integer, double, long etc --> + <!-- The content is a list of XSD doubles --> + <!-- and a list of attributes as described in the commonAttributes attributeGroup --> + <xs:element name="double"> + <xs:complexType> + <xs:simpleContent> + <xs:extension base="listOfXsdDoubles"> + <xs:attributeGroup ref="commonAttributes" /> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + </xs:element> + <!-- char element definition --> + <!-- char contains alphanumerical entities (characters, strings, etc.) --> + <!-- The content is an XSD string --> + <!-- and a list of attributes as described in the commonAttributes attributeGroup --> + <xs:element name="char"> + <xs:complexType> + <xs:simpleContent> + <xs:extension base="xs:string"> + <xs:attributeGroup ref="commonAttributes" /> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + </xs:element> + <!-- sparse element definition --> + <!-- sparse contains sparse matrices (2 arrays of type double and 1 array of type double or complex) --> + <!-- ([i], [j], [k]), S(i, j)=k) --> + <!-- A sparse element is either empty or contains --> + <!-- a list of 2 double + 1 double or complex element --> + <!-- and a list of attributes as described in the commonAttributes attributeGroup --> + <xs:element name="sparse"> + <xs:complexType> + <xs:sequence minOccurs="0"> + <xs:element ref="double" minOccurs="2" maxOccurs="2" /> + <xs:choice> + <xs:element ref="double" minOccurs="1" maxOccurs="1" /> + <xs:element ref="complex" minOccurs="1" maxOccurs="1" /> + </xs:choice> + </xs:sequence> + <xs:attributeGroup ref="commonAttributes" /> + </xs:complexType> + </xs:element> + <!-- cell element definition --> + <!-- cell contains cell entities --> + <!-- A cell element contains 0 or more struct, double, char, sparse, cell or complex elements --> + <!-- These child elements may appear in any order --> + <!-- A cell element contains a list of attributes as described in the commonAttributes attributeGroup --> + <xs:element name="cell"> + <xs:complexType> + <xs:choice minOccurs="0" maxOccurs="unbounded"> + <xs:element ref="struct" /> + <xs:element ref="double" /> + <xs:element ref="char" /> + <xs:element ref="sparse" /> + <xs:element ref="cell" /> + <xs:element ref="complex" /> + </xs:choice> + <xs:attributeGroup ref="commonAttributes" /> + </xs:complexType> + </xs:element> + <!-- complex element definition --> + <!-- complex contains complex numbers a+ib with real part a and imaginary part b, which are both represented by entries of type double --> + <!-- A complex element contains a list of either 2 or 0 double elements --> + <!-- and a list of attributes as described in the commonAttributes attributeGroup --> + <xs:element name="complex"> + <xs:complexType> + <xs:sequence> + <xs:element name="double" minOccurs="2" maxOccurs="2"> + <xs:complexType> + <xs:simpleContent> + <xs:extension base="nonEmptyListOfXsdDoubles"> + <xs:attributeGroup ref="commonAttributes" /> + </xs:extension> + </xs:simpleContent> + </xs:complexType> + </xs:element> + </xs:sequence> + <xs:attributeGroup ref="commonAttributes" /> + </xs:complexType> + </xs:element> + <!-- commonAttributes is a list of attributes that all struct, double, char, sparse and cell elements have --> + <xs:attributeGroup name="commonAttributes"> + <xs:attribute name="idx" type="xs:integer" use="required" /> + <xs:attribute name="name" type="xs:string" use="optional" /> + <xs:attribute name="size" type="listOfXsdInts" use="optional" default="1 1" /> + <!-- idx indicates what position in the parent array this element belongs. For root element i="0" --> + <!-- name represents the name of the current element inside a structure --> + <!-- name only occurs in elements inside a structure, although this schema does not enforce this rule --> + <!-- size represents the size of the entry. It allows Matlab to form matrices from vectors with reshape() --> + </xs:attributeGroup> + <!-- structAttributes is a list of attributes that all struct elements have --> + <xs:attributeGroup name="structAttributes"> + <xs:attributeGroup ref="commonAttributes" /> + <xs:attribute name="fields" type="listOfXsdStrings" use="optional" /> + <!-- Include all the attributes in commonAttributes --> + <!-- fields contains the field names of a structure. --> + </xs:attributeGroup> + <!-- listOfXsdDoubles is a type describing a list of xs:doubles --> + <!-- i.e. a double as defined by the XML Schema specification, not matlab.xsd --> + <xs:simpleType name="listOfXsdDoubles"> + <xs:list itemType="xs:double" /> + </xs:simpleType> + <!-- nonEmptyListOfXsdDoubles is a type describing a list of xs:doubles --> + <!-- with length at least 1 --> + <xs:simpleType name="nonEmptyListOfXsdDoubles"> + <xs:restriction base="listOfXsdDoubles"> + <xs:minLength value="1" /> + </xs:restriction> + </xs:simpleType> + <!-- listOfXsdInts is a type describing a list of integers --> + <xs:simpleType name="listOfXsdInts"> + <xs:list itemType="xs:int" /> + </xs:simpleType> + <!-- listOfXsdStrings is a type describing a list of strings --> + <xs:simpleType name="listOfXsdStrings"> + <xs:list itemType="xs:string" /> + </xs:simpleType> +</xs:schema>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/strsplit.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +%function cstr = strsplit(strarr, ch) +% +% FUNCTIONALITY +% Splits string <strarr> at positions where substring <ch> +% occurs. If <ch> is not given, then ascii character 10 +% (newline) is the splitting position. +% This would split for example a read-in file into its lines. +% +% INPUT +% strarr string to split +% ch optional, character or substring at which to split, +% default is character ascii 10 = 'newline' +% +% OUTPUT +% cstr cell array of strings, all leading and trailing +% spaces are deleted. Substring ch is removed from string. +% +% EXAMPLES +% a = strsplit('aaa bbb ccc ', ' ') +% Result: a{1}='aaa', a{2}='bbb', a{3}='ccc' +% a = strsplit('user@server', '@') +% Result: a{1}='user', a{3}='server' +% a = strsplit('aabbccddeeffgg', 'dd') +% Result: a{1}='aabbcc', a{2} = 'eeffgg' +% +% RELATED +% strfun, depreciated stringify.m + +% Copyright (C) 2002 Marc Molinari, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/tests/xml_tests.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,322 @@ +function toolbox_test +% function toolbox_test +% +% This function is used to test the XML Toolbox for Matlab. +% + +% Copyright (C) 2002-2005, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ + +INFO = ver('Matlab'); +VER = str2num(INFO.Version); + +N=1; % test index (gets increased with every run) + +% ========================================= +comm = 'double'; +c = -5.789; +dotest(c, N, comm); N=N+1; clear c; + +comm='empty double'; +c = []; +dotest(c, N, comm); N=N+1; clear c; + +comm='double array'; +c = [-10:0.75:10]; +dotest(c, N, comm); N=N+1; clear c; + +comm='double array 2 dim'; +c = [-5:5; 10:20]; +dotest(c, N, comm); N=N+1; clear c; + +comm='double array 3 dim'; +c(1,1,:) = [-10:10]; +c(2,2,:) = [100:120]; +c(3,3,:) = [-1.0:0.1:1]; +dotest(c, N, comm); N=N+1; clear c; + +comm='large double'; +c = 999999999999999; +dotest(c, N, comm); N=N+1; clear c; + +comm='small negative double'; +c = -0.0000000000001; +dotest(c, N, comm); N=N+1; clear c; + +% ========================================= +comm='char'; +c = 'z'; +dotest(c, N, comm); N=N+1; clear c; + +comm='empty char'; +c = ['']; +dotest(c, N, comm); N=N+1; clear c; + +comm='single space'; +c = [' ']; +dotest(c, N, comm); N=N+1; clear c; + +comm='several spaces'; +c = [' ']; +dotest(c, N, comm); N=N+1; clear c; + +comm='non-xml characters, <&''"> with leading and trailing spaces'; +c = [' <&''"> ']; +dotest(c, N, comm); N=N+1; clear c; + +comm='char array / string'; +c = 'Hello World! Look out of the window'; +dotest(c, N, comm); N=N+1; clear c; + +comm='char array / string with leading+trailing space'; +c = ' This has a leading and trailing space. '; +dotest(c, N, comm); N=N+1; clear c; + +comm='funny ascii characters'; +c = 'Funny chars: !$^&*()_+=-@#{}[];:,./\?'; +dotest(c, N, comm); N=N+1; clear c; + +comm='char array'; +c = ['abcdefg'; ... + 'hijklmn']; +dotest(c, N, comm); N=N+1; clear c; + +% ========================================= +comm='complex'; +c = -7.14 + 2.03i; +dotest(c, N, comm); N=N+1; clear c; + +comm='pure imaginary'; +c = 8i; +dotest(c, N, comm); N=N+1; clear c; + +comm='complex array'; +a = [-10:0.75:10]; +b = [10:-0.75:-10]; +c = a+b*i; +dotest(c, N, comm); N=N+1; clear a b c; + +comm='complex array 2 dim'; +a = [-5:5; 10:20]; +b = [-5:5; 10:20]; +c = a+b*i; +dotest(c, N, comm); N=N+1; clear a b c; + +% ========================================= +comm='sparse'; +c = sparse(4,5,1); +dotest(c, N, comm); N=N+1; clear c; + +comm='empty sparse'; +c = sparse(10,7,0); +dotest(c, N, comm); N=N+1; clear c; + +comm='complex sparse'; +c = sparse(20,40,4+2i); +dotest(c, N, comm); N=N+1; clear c; + +% ========================================= +comm='empty struct'; +if (VER <= 5.3) + % Matlab up to V. 5.3 does not like empty structs + c = []; +else + c = struct([]); +end +dotest(c, N, comm); N=N+1; clear c; + +comm='struct'; +c.A = 1; +c.B = 2; +dotest(c, N, comm); N=N+1; clear c; + +comm='struct with arrays'; +c.A = [1 2 3 4 5 6]; +c.B = [10 20; 30 40; 50 60; 70 80; 90 100]; +c.C = [9 8 7 6 5 4 3 2 1]'; % transposed +dotest(c, N, comm); N=N+1; clear c; + +comm='struct with chars'; +c.A = 'a b c d e f g'; +c.B = 'zz yy xx ww vv uu'; +c.C = ['hippopotamus']'; % transposed +dotest(c, N, comm); N=N+1; clear c; + +comm='struct with sparse'; +c.A = sparse(100,100,42); +c.B = sparse(1,1,0); +c.C.s = sparse(eye(4)); +dotest(c, N, comm); N=N+1; clear c; + +comm='substructures'; +c.A.a.b = 1; +c.A.b.c = 'cAbc'; +c.B = [5 5 5 5]; +if (VER <= 5.3) + % Matlab up to V. 5.3 does not like empty structs + c.C.elongated_name = []; +else + c.C.elongated_name = struct([]); +end +c.D.complex = [1+i 2+2i 3+3i]; +dotest(c, N, comm); N=N+1; clear c; + + +% ========================================= +comm='cell - empty double'; +c = {[]}; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell - empty char'; +c = {''}; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell - char with spaces only'; +c = {' '}; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell - empty double, char, double'; +c = {[], '', []}; +dotest(c, N, comm); N=N+1; clear c; + +if (VER>5.3) + comm='cell - empty struct, double'; + c = {struct([]), []}; + dotest(c, N, comm); N=N+1; clear c; +end + +comm='cell with 3 empty cells'; +c = { {} {} {} }; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell numeric'; +c = {177}; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell complex'; +c = {101-99i}; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell alphanumeric'; +c = {'aabbccdd'}; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell structure'; +a.b.c = [1 2 3]; +a.b.d = 'Hello World!'; +c = {a}; +dotest(c, N, comm); N=N+1; clear c a; + +comm='cell containing empty char field'; +c = {'the next one is empty', '', 'the previous one is empty'}; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell containing empty double field'; +c = {'the next one is empty', [], 'the previous one is empty'}; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell mixed'; +c = { 'abc', 987, 'def', 654, 'ghijklmno', 10000, 9999-i }; +dotest(c, N, comm); N=N+1; clear c; + +comm='cell of cells'; +c = { {'abc', 987}, {'def', 654}, {'ghijklmno', 10000}, {-1-i} }; +dotest(c, N, comm); N=N+1; clear c; + +comm='array of cells'; +c{1,1} = { {'abc', 987}, {'def', 654}, {'ghijklmno', 10000} }; +c{2,1} = { 'second row', 22222222222, 0.9222i }; +dotest(c, N, comm); N=N+1; clear c; + +% ========================================= +comm='combination of all types'; +c(1,1).a = 9e-9; +c(1,1).b = 'aaa'; +c(1,1).c = {'bbb', [10 20 30], 'ccccccccccc'}; +c(1,1).d.e.f.g.h = [10 20 30; 40 50 60; 70 80 90; 100 110 120]; +c(1,1).e = sparse([1 2 4 5], [1 2 4 5], [1 1 1 1]); +c(1,2).c = [22+33i; 44-55i; -66+77i; -88+99i]; +c(2,2).a.x(2).y(3).z = 'this is cool'; +c(2,2).b = 7e-7; +c(2,2).d.hello.world.hitchhiker.galaxy = 42; +c(2,2).e = { sparse(4,7,1), ' check this out with spaces ', pi }; +dotest(c, N, comm); N=N+1; clear c; + +return + + + +% ======================================== +% ======================================== +function dotest(c, N, comm) +% c is variable, N is id number and comm is comment. + +if nargin<3, comm=''; end + +% name based routines: +tsave_n(N, c); +str_c = xml_format(c); +%x = tload_n(N); +% % %str_x = xml_var2xml(x); +% % str_x = xml_format(x); +% % +% % if (~strcmp( class(c), class(x)) | ... +% % ~strcmp( str_c, str_x ) ) +% % disp(['Test ', num2str(N), ' (V.1.x) ***FAILED*** ("', comm, '") <=====================']); +% % return +% % else +% % disp(['Test ', num2str(N), ' (V.1.x) passed ("', comm, '")']); +% % end + +% test format from previous versions: +% save in type-based (1.x) version +%tsave_t(N, c); +str_c = xml_format(c); +% load with name-based (2.0) version +x = tload_n(N); +str_x = xml_format(x); + +if (~strcmp( class(c), class(x)) | ... + ~strcmp( str_c, str_x ) ) + disp(['Test ', num2str(N), ' (V.2.x,3.x) ***FAILED*** ("', comm, '") <=====================']); + return +else + disp(['Test ', num2str(N), ' (V.2.x,3.x) passed ("', comm, '")']); +end + +return + + +% ======================================== +% ======================================== +function tsave_t(N, c) +% saves variable c in file test_t_N.xml in old format of V.1.x +%xml_oldsave( ['test_t_', num2str(N), '.xml'], c ); +str = xml_format_old(c); +fid = fopen(['test_t_', num2str(N), '.xml'], 'w'); +fprintf( fid, '%s', str); +fclose(fid); + +% ======================================== +% ======================================== +function tsave_n(N, c) +% saves variable c in file test_n_N.xml +% xml_save( ['test_n_', num2str(N), '.xml'], c ); +str = xml_format(c); +fid = fopen(['test_n_', num2str(N), '.xml'], 'w'); +fprintf( fid, '%s', str); +fclose(fid); + +% % ======================================== +% % ======================================== +% function c = tload_t(N) +% % loads variable c in file test_t_N.xml +% c = xml_load( ['test_t_', num2str(N), '.xml'] ); + +% ======================================== +% ======================================== +function c = tload_n(N) +% loads variable c in file test_n_N.xml +str = fileread(['test_n_', num2str(N), '.xml']); +c = xml_parse(str);
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/unblank.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,5 @@ +% +% UNBLANK(S) removes leading and trailing blanks from string S. +% + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/xml_format.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +%XML_FORMAT Convert a Matlab variable or struct into an XML string. +% +% SYNTAX: xmlstr = xml_format(v, [attswitch], [name] ) +% +% INPUT +% v Matlab variable type "struct", "char", "double"(numeric), +% "complex", "sparse", "cell", or "logical"(boolean) +% attswitch optional, default='on': +% 'on' writes header attributes idx,size,type for +% identification by Matlab when parsing in XML later +% 'off' writes "plain" XML without header attributes +% name optional, give root element a specific name, eg. 'project' +% +% OUTPUT +% xmlstr string, containing XML description of variable V +% +% SEE ALSO +% xml_help, xml_formatany, xml_parse, xml_load, xml_save, (xmlread, xmlwrite) + +% Copyright (C) 2002-2005, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/xml_formatany.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +%XML_FORMATANY Formats a matlab variable into an XML string. +% +% SYNTAX +% xmlstr = xml_formatany(V) +% xmlstr = xml_formatany(V, rootname ) +% +% INPUT +% V Matlab variable or structure. +% The data types we can deal with are: +% char, numeric, complex, struct, sparse, cell, logical/boolean +% -> struct fields named ATTRIBUTE (user-definable) get converted into XML attribute +% Not handled are data types: +% function_handle, single, intxx, uintxx, java objects +% +% rootname optional, give root element a specific name, eg. 'books' +% +% OUTPUT +% xmlstr string, containing XML description of variable V +% +% SPECIAL FIELDNAMES +% .ATTRIBUTE. define additional attributes by using subfields, eg V.ATTRIBUTE.type='mydbtype' +% .CONTENT define content if attribute field given (all capitals) +% .ATTRIBUTE.NAMESPACE define namespace (all capitals) +% .ATTRIBUTE.TAGNAME define element tag name (if not an allowed Matlab fieldname in struct) e.g.: v.any.ATTRIBUTE.TAGNAME = 'xml-gherkin' +% +% SEE ALSO +% xml_help, xml_parse, xml_parseany, xml_load, xml_save, (xmlread, xmlwrite) + +% Copyright (C) 2002-2005, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/xml_help.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,32 @@ +% -------------------------- +% XML TOOLBOX FOR MATLAB 3.1 +% -------------------------- +% +% FUNCTIONS: +% xml_format converts a Matlab variable into XML string +% xml_formatany converts a Matlab variable into XML string with attributes +% xml_parse parses & converts XML string into Matlab structure +% xml_parseany parses & converts XML string into Matlab structure with attributes +% xml_save saves a Matlab variable/structure in XML format in a file +% xml_load loads the .xml file written with xml_save back into a variable +% xml_help this file, displays info about available xml_* commands +% tests/xml_tests tests the xml toolbox by writing/reading a number of xml test files +% strsplit utility function which splits a string at specified characters +% unblank utility function which removes leading and trailing spaces +% +% FILES: +% doc/xml_matlab documentation containing info on installation, usage, implementation, etc. +% matlab.xsd contains the Schema to validate xml files for the toolbox +% (if not present, look at http://www.geodise.org/matlab.xsd) +% +% RELATED: +% xmlread, xmlwrite (shipped with Matlab from version 6.5) +% +% Further information can be obtained by using the help command on +% a specific function, e.g. help xml_format or by visiting +% http://www.geodise.org + +% Copyright (C) 2002-2005, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/xml_load.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,26 @@ +%XML_LOAD Loads XML file and return contents as Matlab structure or variable +% +% SYNTAX V = xml_load( filename ) +% V = xml_load( filename, attswitch ) +% +% INPUT +% filename filename of xml file to load (if extension .xml is omitted, +% xml_load tries to append it if the file cannot be found). +% This file should have been saved with xml_save(). +% If it's a non-Matlab XML file, please use e.g. +% xmlstr = fileread(filename); V = xml_parseany(xmlstr); +% attswitch optional, default='on': +% 'on' takes attributes idx,size,type into account for +% creating corresponding Matlab data types +% 'off' ignores attributes in XML element headers +% +% OUTPUT +% V Matlab structure or variable +% +% SEE ALSO +% xml_save, xml_format, xml_formatany, xml_parse, xml_parseany, xml_help, (xmlread, xmlwrite) + +% Copyright (C) 2002-2005, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/xml_parse.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,27 @@ +%XML_PARSE parses XML string and returns corresponding Matlab structure. +% +% SYNTAX +% V = xml_parse(xmlstr) +% V = xml_parse(xmlstr, attswitch) +% V = xml_parse(xmlstr, attswitch, S) +% +% Parses XML string str and returns matlab variable/structure. +% This is a non-validating parser! +% +% INPUT +% str xml string, possibly from file with function xml_load.m +% att_switch 'on'- reads attributes, 'off'- ignores attributes +% S Optional. Variable which gets extended or whose +% substructure parameters get overridden by entries in +% the string. (may be removed in future versions) +% +% OUTPUT +% V Matlab variable or structure +% +% RELATED +% xml_format, xml_formatany, xml_parseany, xml_load, xml_save, (xmlread, xmlwrite) + +% Copyright (C) 2002-2005, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/xml_parseany.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,20 @@ +%XML_PARSEANY parses XML strings and convert content into Matlab structure +% +% SYNTAX +% V = xml_parseany( xmlstr ) +% +% INPUT +% xmlstr xml string, possibly from file with function xml_load.m +% +% OUTPUT +% v Matlab variable or cell array. +% +% RELATED +% xml_format, xml_formatany, xml_parse, xml_load, xml_save, (xmlread, xmlwrite) + +% Copyright (C) 2002-2005, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/xml_save.m Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,24 @@ +%XML_SAVE saves Matlab variables and data structures as XML +% +% SYNTAX +% xml_save( filename, var ) +% xml_save( filename, var, attswitch ) +% INPUT +% filename filename +% var Matlab variable or structure to store in file. +% attswitch optional, 'on' stores XML type attributes (default), +% 'off' doesn't store XML type attributes +% +% NOTE: Matlab data stored with the 'on' attribute (default) can be reloaded +% in its original form into Matlab with the XML_LOAD command. +% +% OUTPUT +% none +% +% RELATED +% xml_load, xml_format, xml_formatany, xml_parse, xml_parseany, xml_help, (xmlread, xmlwrite) + +% Copyright (C) 2002-2005, University of Southampton +% Author: Dr Marc Molinari <m.molinari@soton.ac.uk> +% $Revision: 1.1 $ $Date: 2005/04/15 17:12:14 $ $Tag$ +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/xml_toolbox/xml_toolbox-version.txt Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,23 @@ +This folder contains the XML Toolbox for Matlab. +Version: Release 3.1.2 + +Function\Version 1.0 1.1 2.0 2.1 2.2 3.0 3.1 3.1.2 +---------------------------------------------------- + xml_format x x x x x x x x + xml_format_att - - - - - x - - + xml_formatany - - - - - - x x + xml_parse x x x x x x x x + xml_parse_att - - - - - x - - + xml_parseany - - - - - - x x + xml_help x x x x x x x x + xml_load x x x x x x x x + xml_save x x x x x x x x + tests/xml_tests - x x x x x x x + matlab.xsd x x - - - - - - + strsplit - x x x x x x x + unblank - - - - x x x x +-------------------------------------------------- +Developer version: + xml_test_[func] - - - - - - x x + tests/@struct/ - - - - - x x x + tests/@cell/ - - - - - x x x